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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Bizagi Bpm Suite Full Crack A Complete Guide to the Features and Benefits of this Powerful Tool.md

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- <p>If you are looking for a powerful and easy-to-use software to design, automate, and optimize your business processes, you might want to check out Bizagi Bpm Suite Full Crack. This is a comprehensive solution that allows you to create, execute, and monitor your workflows in a graphical and intuitive way. In this article, we will show you how to download and install Bizagi Bpm Suite Full Crack, how to use it to create and manage your business processes, what benefits and features it offers, and some tips and tricks for using it effectively. By the end of this article, you will have a clear idea of how Bizagi Bpm Suite Full Crack can help you improve your business performance and efficiency.</p>
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-<li>Choose the destination folder and click Next.</li>
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- <p>Bizagi Bpm Suite Full Crack consists of three main components: Bizagi Modeler, Bizagi Studio, and Bizagi Engine. Each component has a specific function and purpose. Let's see how they work together.</p>
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- <p>Bizagi Modeler is a free tool that allows you to design your business processes using the Business Process Model and Notation (BPMN) standard. BPMN is a graphical notation that represents the flow of activities, events, gateways, roles, and data in a business process. With Bizagi Modeler, you can easily create diagrams that capture the logic and sequence of your business processes. You can also add documentation, attributes, rules, forms, and data models to enrich your diagrams. To use Bizagi Modeler, follow these steps:</p>
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- <p>You have just created your first business process diagram with Bizagi Modeler. You can now move on to the next component: Bizagi Studio.</p>
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- <p>Bizagi Studio is a tool that allows you to automate your business processes by transforming your diagrams into executable applications. With Bizagi Studio, you can configure the behavior, appearance, and integration of your processes. You can also test, debug, and deploy your applications to the Bizagi Engine. To use Bizagi Studio, follow these steps:</p>
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-<li>Select the project folder that contains your .bpm file and click Open.</li>
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-<li>Use the buttons on the top right corner to test, debug, or deploy your process. You can also generate documentation or reports for your process.</li>
-<li>Save your changes as a .bex file or export them as a .bar file.</li>
-</ol>
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- <li>Other BPM software: There are many other BPM software in the market that offer similar or different functionality and pricing. Some examples are Camunda, Bonita, ProcessMaker, Appian, etc. You can compare them and choose the one that suits your needs and budget.</li>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Guts And Goals for Windows 8.1 and Enjoy the Ultimate Soccer Brawl.md

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-<h1>Guts And Goals: A Hilarious Way to Play Soccer</h1>
-<p>If you are looking for a fun and funny game to play with your friends, you might want to check out Guts And Goals. This is not your standard game of soccer. This is Guts And Goals, where soccer balls can be spiky, and you use weapons instead of your feet to score goals. In this article, we will tell you what Guts And Goals is, what features it has, and how to download it for Windows 8.1.</p>
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-<p>Guts And Goals is an action-sports game developed by CodeManu and published by PM Studios, inc. It was released on August 31, 2021, and it has received positive reviews from players and critics. The game mixes arcade-style soccer with beat 'em up gameplay that results in a hilarious way to play soccer. You can choose from over 30 unique heroes and get ready to play the world's game like never before!</p>
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-<h2>How to download Guts And Goals for Windows 8.1?</h2>
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-<li>Additional Notes: 1+ Controllers needed for local multiplayer</li>
-</ul>
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-<p>You can download Guts And Goals for Windows 8.1 from different sources, depending on your preference and budget. Here are some of the most popular options:</p>
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-<p>The easiest and most official way to download Guts And Goals is through Steam, the leading digital distribution platform for PC games. You can buy the game for $14.99 USD and enjoy all the features and updates that come with it. You will also need a Steam account and the Steam client installed on your PC.</p>
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- <h2>Conclusion</h2>
- <p>Guts And Goals is a fun and funny game that mixes arcade-style soccer with beat 'em up gameplay. You can choose from over 30 unique heroes and play in different stadiums with random mutators that change the way you play. You can also play online or offline, singleplayer or multiplayer, with your friends or strangers. If you want to download Guts And Goals for Windows 8.1, you can choose from different options such as Steam, Skidrow Cracked, or Game3rb.</p>
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- <p>Soccer and football are two names for the same sport, depending on where you live. In most parts of the world, football refers to the game where two teams try to kick a ball into a goal using their feet or other body parts (except their hands). In some countries, such as the United States and Canada, soccer is used to distinguish this sport from another sport called football (or American football), where two teams try to carry or throw an oval-shaped ball across a field.</p>
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- <p>If you enjoy playing Guts And Goals, you might also like some other games that combine sports with humor and action, such as Rocket League (a game where you play soccer with rocket-powered cars), Golf With Your Friends (a game where you play mini-golf with crazy courses and obstacles), or Gang Beasts (a game where you fight with floppy ragdoll characters).</p>
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-<h1>Review: Frank Turner - Tape Deck Heart (iTunes Deluxe Edition)</h1>
-<p>Frank Turner is a British singer-songwriter who started his career as the frontman of the post-hardcore band Million Dead. After their breakup in 2005, he embarked on a solo career that has seen him release six studio albums, several EPs and live recordings, and tour extensively around the world. His music blends folk, punk, rock and acoustic elements, with lyrics that often deal with personal, political and social issues.</p>
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-<p>Downloading your Air India ticket online is a simple and convenient process that can save you time and money. By following the steps mentioned in this article, you can easily download your ticket from the Air India website or app. You can also use some tips and tricks to save your ticket as a PDF file or check your email for confirmation and ticket details. We hope this article has helped you understand how to download your Air India ticket and make your travel experience more enjoyable.</p>
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spaces/7hao/bingo/src/components/chat-suggestions.tsx

@@ -1,45 +0,0 @@
-import React, { useMemo } from 'react'
-import Image from 'next/image'
-import HelpIcon from '@/assets/images/help.svg'
-import { SuggestedResponse } from '@/lib/bots/bing/types'
-import { useBing } from '@/lib/hooks/use-bing'
-import { atom, useAtom } from 'jotai'
-
-type Suggestions = SuggestedResponse[]
-const helpSuggestions = ['为什么不回应某些主题', '告诉我更多关于必应的资迅', '必应如何使用 AI?'].map((text) => ({ text }))
-const suggestionsAtom = atom<Suggestions>([])
-
-type ChatSuggestionsProps = React.ComponentProps<'div'> & Pick<ReturnType<typeof useBing>, 'setInput'> & { suggestions?: Suggestions }
-
-export function ChatSuggestions({ setInput, suggestions = [] }: ChatSuggestionsProps) {
-  const [currentSuggestions, setSuggestions] = useAtom(suggestionsAtom)
-  const toggleSuggestions = (() => {
-    if (currentSuggestions === helpSuggestions) {
-      setSuggestions(suggestions)
-    } else {
-      setSuggestions(helpSuggestions)
-    }
-  })
-
-  useMemo(() => {
-    setSuggestions(suggestions)
-    window.scrollBy(0, 2000)
-  }, [suggestions.length])
-
-  return currentSuggestions?.length ? (
-    <div className="py-6">
-      <div className="suggestion-items">
-        <button className="rai-button" type="button" aria-label="这是什么?" onClick={toggleSuggestions}>
-          <Image alt="help" src={HelpIcon} width={24} />
-        </button>
-        {
-          currentSuggestions.map(suggestion => (
-            <button key={suggestion.text} className="body-1-strong suggestion-container" type="button" onClick={() => setInput(suggestion.text)}>
-              {suggestion.text}
-            </button>
-          ))
-        }
-      </div>
-    </div>
-  ) : null
-}

spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/VUE 9501304a2b03470cad0eea93992d65ae.md

@@ -1,20 +0,0 @@
-# VUE
-
-Last edited time: March 31, 2023 1:56 PM
-Owner: Anonymous
-Tags: Codebase
-
-<aside>
-💡 This template provides context/instructions for the languages you use.
-
-</aside>
-
-# TypeScript
-
-We use VUE3 with TypeScript for our frontend codebase. 
-
-# Code Style Guide
-
-We largely follow Airbnb's React/JSX Style Guide:
-
-[Style Guide | Vue.js](https://vuejs.org/style-guide/)

spaces/AI-Hobbyist/Hoyo-RVC/envfilescheck.bat

@@ -1,348 +0,0 @@
-@echo off && chcp 65001
-
-echo working dir is %cd%
-echo downloading requirement aria2 check.
-echo=
-dir /a:d/b | findstr "aria2" > flag.txt
-findstr "aria2" flag.txt >nul
-if %errorlevel% ==0 (
-    echo aria2 checked.
-    echo=
-) else (
-    echo failed. please downloading aria2 from webpage!
-    echo unzip it and put in this directory!
-    timeout /T 5
-    start https://github.com/aria2/aria2/releases/tag/release-1.36.0
-    echo=
-    goto end
-)
-
-echo envfiles checking start.
-echo=
-
-for /f %%x in ('findstr /i /c:"aria2" "flag.txt"') do (set aria2=%%x)&goto endSch
-:endSch
-
-set d32=f0D32k.pth
-set d40=f0D40k.pth
-set d48=f0D48k.pth
-set g32=f0G32k.pth
-set g40=f0G40k.pth
-set g48=f0G48k.pth
-
-set d40v2=f0D40k.pth
-set g40v2=f0G40k.pth
-
-set dld32=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D32k.pth
-set dld40=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D40k.pth
-set dld48=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D48k.pth
-set dlg32=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G32k.pth
-set dlg40=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G40k.pth
-set dlg48=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G48k.pth
-
-set dld40v2=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth
-set dlg40v2=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth
-
-set hp2_all=HP2_all_vocals.pth
-set hp3_all=HP3_all_vocals.pth
-set hp5_only=HP5_only_main_vocal.pth
-set VR_DeEchoAggressive=VR-DeEchoAggressive.pth
-set VR_DeEchoDeReverb=VR-DeEchoDeReverb.pth
-set VR_DeEchoNormal=VR-DeEchoNormal.pth
-set onnx_dereverb=vocals.onnx
-
-set dlhp2_all=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2_all_vocals.pth
-set dlhp3_all=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP3_all_vocals.pth
-set dlhp5_only=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5_only_main_vocal.pth
-set dlVR_DeEchoAggressive=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoAggressive.pth
-set dlVR_DeEchoDeReverb=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoDeReverb.pth
-set dlVR_DeEchoNormal=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoNormal.pth
-set dlonnx_dereverb=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx
-
-set hb=hubert_base.pt
-
-set dlhb=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt
-
-echo dir check start.
-echo=
-
-if exist "%~dp0pretrained" (
-        echo dir .\pretrained checked.
-    ) else (
-        echo failed. generating dir .\pretrained.
-        mkdir pretrained
-    )
-if exist "%~dp0pretrained_v2" (
-        echo dir .\pretrained_v2 checked.
-    ) else (
-        echo failed. generating dir .\pretrained_v2.
-        mkdir pretrained_v2
-    )    
-if exist "%~dp0uvr5_weights" (
-        echo dir .\uvr5_weights checked.
-    ) else (
-        echo failed. generating dir .\uvr5_weights.
-        mkdir uvr5_weights
-    )
-if exist "%~dp0uvr5_weights\onnx_dereverb_By_FoxJoy" (
-        echo dir .\uvr5_weights\onnx_dereverb_By_FoxJoy checked.
-    ) else (
-        echo failed. generating dir .\uvr5_weights\onnx_dereverb_By_FoxJoy.
-        mkdir uvr5_weights\onnx_dereverb_By_FoxJoy
-    )    
-
-echo=
-echo dir check finished.
-
-echo=
-echo required files check start.
-
-echo checking D32k.pth
-if exist "%~dp0pretrained\D32k.pth" (
-        echo D32k.pth in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D32k.pth -d %~dp0pretrained -o D32k.pth
-        if exist "%~dp0pretrained\D32k.pth" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking D40k.pth
-if exist "%~dp0pretrained\D40k.pth" (
-        echo D40k.pth in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D40k.pth -d %~dp0pretrained -o D40k.pth
-        if exist "%~dp0pretrained\D40k.pth" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking D40k.pth
-if exist "%~dp0pretrained_v2\D40k.pth" (
-        echo D40k.pth in .\pretrained_v2 checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d %~dp0pretrained_v2 -o D40k.pth
-        if exist "%~dp0pretrained_v2\D40k.pth" (echo download successful.) else (echo please try again!
-        echo=)
-    )    
-echo checking D48k.pth
-if exist "%~dp0pretrained\D48k.pth" (
-        echo D48k.pth in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D48k.pth -d %~dp0pretrained -o D48k.pth
-        if exist "%~dp0pretrained\D48k.pth" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking G32k.pth
-if exist "%~dp0pretrained\G32k.pth" (
-        echo G32k.pth in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G32k.pth -d %~dp0pretrained -o G32k.pth
-        if exist "%~dp0pretrained\G32k.pth" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking G40k.pth
-if exist "%~dp0pretrained\G40k.pth" (
-        echo G40k.pth in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G40k.pth -d %~dp0pretrained -o G40k.pth
-        if exist "%~dp0pretrained\G40k.pth" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking G40k.pth
-if exist "%~dp0pretrained_v2\G40k.pth" (
-        echo G40k.pth in .\pretrained_v2 checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d %~dp0pretrained_v2 -o G40k.pth
-        if exist "%~dp0pretrained_v2\G40k.pth" (echo download successful.) else (echo please try again!
-        echo=)
-    )    
-echo checking G48k.pth
-if exist "%~dp0pretrained\G48k.pth" (
-        echo G48k.pth in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G48k.pth -d %~dp0pretrained -o G48k.pth
-        if exist "%~dp0pretrained\G48k.pth" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-
-echo checking %d32%
-if exist "%~dp0pretrained\%d32%" (
-        echo %d32% in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dld32% -d %~dp0pretrained -o %d32%
-        if exist "%~dp0pretrained\%d32%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %d40%
-if exist "%~dp0pretrained\%d40%" (
-        echo %d40% in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dld40% -d %~dp0pretrained -o %d40%
-        if exist "%~dp0pretrained\%d40%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %d40v2%
-if exist "%~dp0pretrained_v2\%d40v2%" (
-        echo %d40v2% in .\pretrained_v2 checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dld40v2% -d %~dp0pretrained_v2 -o %d40v2%
-        if exist "%~dp0pretrained_v2\%d40v2%" (echo download successful.) else (echo please try again!
-        echo=)
-    )    
-echo checking %d48%
-if exist "%~dp0pretrained\%d48%" (
-        echo %d48% in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dld48% -d %~dp0pretrained -o %d48%
-        if exist "%~dp0pretrained\%d48%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %g32%
-if exist "%~dp0pretrained\%g32%" (
-        echo %g32% in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlg32% -d %~dp0pretrained -o %g32%
-        if exist "%~dp0pretrained\%g32%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %g40%
-if exist "%~dp0pretrained\%g40%" (
-        echo %g40% in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlg40% -d %~dp0pretrained -o %g40%
-        if exist "%~dp0pretrained\%g40%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %g40v2%
-if exist "%~dp0pretrained_v2\%g40v2%" (
-        echo %g40v2% in .\pretrained_v2 checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlg40v2% -d %~dp0pretrained_v2 -o %g40v2%
-        if exist "%~dp0pretrained_v2\%g40v2%" (echo download successful.) else (echo please try again!
-        echo=)
-    )    
-echo checking %g48%
-if exist "%~dp0pretrained\%g48%" (
-        echo %g48% in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlg48% -d %~dp0\pretrained -o %g48%
-        if exist "%~dp0pretrained\%g48%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-
-echo checking %hp2_all%
-if exist "%~dp0uvr5_weights\%hp2_all%" (
-        echo %hp2_all% in .\uvr5_weights checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlhp2_all% -d %~dp0\uvr5_weights -o %hp2_all%
-        if exist "%~dp0uvr5_weights\%hp2_all%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %hp3_all%
-if exist "%~dp0uvr5_weights\%hp3_all%" (
-        echo %hp3_all% in .\uvr5_weights checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlhp3_all% -d %~dp0\uvr5_weights -o %hp3_all%
-        if exist "%~dp0uvr5_weights\%hp3_all%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %hp5_only%
-if exist "%~dp0uvr5_weights\%hp5_only%" (
-        echo %hp5_only% in .\uvr5_weights checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlhp5_only% -d %~dp0\uvr5_weights -o %hp5_only%
-        if exist "%~dp0uvr5_weights\%hp5_only%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %VR_DeEchoAggressive%
-if exist "%~dp0uvr5_weights\%VR_DeEchoAggressive%" (
-        echo %VR_DeEchoAggressive% in .\uvr5_weights checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlVR_DeEchoAggressive% -d %~dp0\uvr5_weights -o %VR_DeEchoAggressive%
-        if exist "%~dp0uvr5_weights\%VR_DeEchoAggressive%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %VR_DeEchoDeReverb%
-if exist "%~dp0uvr5_weights\%VR_DeEchoDeReverb%" (
-        echo %VR_DeEchoDeReverb% in .\uvr5_weights checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlVR_DeEchoDeReverb% -d %~dp0\uvr5_weights -o %VR_DeEchoDeReverb%
-        if exist "%~dp0uvr5_weights\%VR_DeEchoDeReverb%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %VR_DeEchoNormal%
-if exist "%~dp0uvr5_weights\%VR_DeEchoNormal%" (
-        echo %VR_DeEchoNormal% in .\uvr5_weights checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlVR_DeEchoNormal% -d %~dp0\uvr5_weights -o %VR_DeEchoNormal%
-        if exist "%~dp0uvr5_weights\%VR_DeEchoNormal%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %onnx_dereverb%
-if exist "%~dp0uvr5_weights\onnx_dereverb_By_FoxJoy\%onnx_dereverb%" (
-        echo %onnx_dereverb% in .\uvr5_weights\onnx_dereverb_By_FoxJoy checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlonnx_dereverb% -d %~dp0\uvr5_weights\onnx_dereverb_By_FoxJoy -o %onnx_dereverb%
-        if exist "%~dp0uvr5_weights\onnx_dereverb_By_FoxJoy\%onnx_dereverb%" (echo download successful.) else (echo please try again!
-        echo=)
-    )    
-
-echo checking %hb%
-if exist "%~dp0%hb%" (
-        echo %hb% in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlhb% -d %~dp0 -o %hb%
-        if exist "%~dp0%hb%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-
-echo required files check finished.
-echo envfiles check complete.
-pause
-:end
-del flag.txt

spaces/AIConsultant/MusicGen/audiocraft/solvers/__init__.py

@@ -1,17 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-Solvers. A Solver is a training recipe, combining the dataloaders, models,
-optimizer, losses etc into a single convenient object.
-"""
-
-# flake8: noqa
-from .audiogen import AudioGenSolver
-from .builders import get_solver
-from .base import StandardSolver
-from .compression import CompressionSolver
-from .musicgen import MusicGenSolver
-from .diffusion import DiffusionSolver

spaces/AIGC-Audio/Make_An_Audio/ldm/modules/image_degradation/utils_image.py

@@ -1,916 +0,0 @@
-import os
-import math
-import random
-import numpy as np
-import torch
-import cv2
-from torchvision.utils import make_grid
-from datetime import datetime
-#import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
-
-
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-
-
-'''
-# --------------------------------------------
-# Kai Zhang (github: https://github.com/cszn)
-# 03/Mar/2019
-# --------------------------------------------
-# https://github.com/twhui/SRGAN-pyTorch
-# https://github.com/xinntao/BasicSR
-# --------------------------------------------
-'''
-
-
-IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def get_timestamp():
-    return datetime.now().strftime('%y%m%d-%H%M%S')
-
-
-def imshow(x, title=None, cbar=False, figsize=None):
-    plt.figure(figsize=figsize)
-    plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
-    if title:
-        plt.title(title)
-    if cbar:
-        plt.colorbar()
-    plt.show()
-
-
-def surf(Z, cmap='rainbow', figsize=None):
-    plt.figure(figsize=figsize)
-    ax3 = plt.axes(projection='3d')
-
-    w, h = Z.shape[:2]
-    xx = np.arange(0,w,1)
-    yy = np.arange(0,h,1)
-    X, Y = np.meshgrid(xx, yy)
-    ax3.plot_surface(X,Y,Z,cmap=cmap)
-    #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
-    plt.show()
-
-
-'''
-# --------------------------------------------
-# get image pathes
-# --------------------------------------------
-'''
-
-
-def get_image_paths(dataroot):
-    paths = None  # return None if dataroot is None
-    if dataroot is not None:
-        paths = sorted(_get_paths_from_images(dataroot))
-    return paths
-
-
-def _get_paths_from_images(path):
-    assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
-    images = []
-    for dirpath, _, fnames in sorted(os.walk(path)):
-        for fname in sorted(fnames):
-            if is_image_file(fname):
-                img_path = os.path.join(dirpath, fname)
-                images.append(img_path)
-    assert images, '{:s} has no valid image file'.format(path)
-    return images
-
-
-'''
-# --------------------------------------------
-# split large images into small images 
-# --------------------------------------------
-'''
-
-
-def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
-    w, h = img.shape[:2]
-    patches = []
-    if w > p_max and h > p_max:
-        w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
-        h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
-        w1.append(w-p_size)
-        h1.append(h-p_size)
-#        print(w1)
-#        print(h1)
-        for i in w1:
-            for j in h1:
-                patches.append(img[i:i+p_size, j:j+p_size,:])
-    else:
-        patches.append(img)
-
-    return patches
-
-
-def imssave(imgs, img_path):
-    """
-    imgs: list, N images of size WxHxC
-    """
-    img_name, ext = os.path.splitext(os.path.basename(img_path))
-
-    for i, img in enumerate(imgs):
-        if img.ndim == 3:
-            img = img[:, :, [2, 1, 0]]
-        new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
-        cv2.imwrite(new_path, img)
-
-
-def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
-    """
-    split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
-    and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
-    will be splitted.
-    Args:
-        original_dataroot:
-        taget_dataroot:
-        p_size: size of small images
-        p_overlap: patch size in training is a good choice
-        p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
-    """
-    paths = get_image_paths(original_dataroot)
-    for img_path in paths:
-        # img_name, ext = os.path.splitext(os.path.basename(img_path))
-        img = imread_uint(img_path, n_channels=n_channels)
-        patches = patches_from_image(img, p_size, p_overlap, p_max)
-        imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
-        #if original_dataroot == taget_dataroot:
-        #del img_path
-
-'''
-# --------------------------------------------
-# makedir
-# --------------------------------------------
-'''
-
-
-def mkdir(path):
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-
-def mkdirs(paths):
-    if isinstance(paths, str):
-        mkdir(paths)
-    else:
-        for path in paths:
-            mkdir(path)
-
-
-def mkdir_and_rename(path):
-    if os.path.exists(path):
-        new_name = path + '_archived_' + get_timestamp()
-        print('Path already exists. Rename it to [{:s}]'.format(new_name))
-        os.rename(path, new_name)
-    os.makedirs(path)
-
-
-'''
-# --------------------------------------------
-# read image from path
-# opencv is fast, but read BGR numpy image
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# get uint8 image of size HxWxn_channles (RGB)
-# --------------------------------------------
-def imread_uint(path, n_channels=3):
-    #  input: path
-    # output: HxWx3(RGB or GGG), or HxWx1 (G)
-    if n_channels == 1:
-        img = cv2.imread(path, 0)  # cv2.IMREAD_GRAYSCALE
-        img = np.expand_dims(img, axis=2)  # HxWx1
-    elif n_channels == 3:
-        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # BGR or G
-        if img.ndim == 2:
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # GGG
-        else:
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # RGB
-    return img
-
-
-# --------------------------------------------
-# matlab's imwrite
-# --------------------------------------------
-def imsave(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-def imwrite(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-
-
-# --------------------------------------------
-# get single image of size HxWxn_channles (BGR)
-# --------------------------------------------
-def read_img(path):
-    # read image by cv2
-    # return: Numpy float32, HWC, BGR, [0,1]
-    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # cv2.IMREAD_GRAYSCALE
-    img = img.astype(np.float32) / 255.
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    # some images have 4 channels
-    if img.shape[2] > 3:
-        img = img[:, :, :3]
-    return img
-
-
-'''
-# --------------------------------------------
-# image format conversion
-# --------------------------------------------
-# numpy(single) <--->  numpy(unit)
-# numpy(single) <--->  tensor
-# numpy(unit)   <--->  tensor
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# numpy(single) [0, 1] <--->  numpy(unit)
-# --------------------------------------------
-
-
-def uint2single(img):
-
-    return np.float32(img/255.)
-
-
-def single2uint(img):
-
-    return np.uint8((img.clip(0, 1)*255.).round())
-
-
-def uint162single(img):
-
-    return np.float32(img/65535.)
-
-
-def single2uint16(img):
-
-    return np.uint16((img.clip(0, 1)*65535.).round())
-
-
-# --------------------------------------------
-# numpy(unit) (HxWxC or HxW) <--->  tensor
-# --------------------------------------------
-
-
-# convert uint to 4-dimensional torch tensor
-def uint2tensor4(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
-
-
-# convert uint to 3-dimensional torch tensor
-def uint2tensor3(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
-
-
-# convert 2/3/4-dimensional torch tensor to uint
-def tensor2uint(img):
-    img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    return np.uint8((img*255.0).round())
-
-
-# --------------------------------------------
-# numpy(single) (HxWxC) <--->  tensor
-# --------------------------------------------
-
-
-# convert single (HxWxC) to 3-dimensional torch tensor
-def single2tensor3(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
-
-
-# convert single (HxWxC) to 4-dimensional torch tensor
-def single2tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
-
-
-# convert torch tensor to single
-def tensor2single(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-
-    return img
-
-# convert torch tensor to single
-def tensor2single3(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    elif img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return img
-
-
-def single2tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
-
-
-def single32tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
-
-
-def single42tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
-
-
-# from skimage.io import imread, imsave
-def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
-    '''
-    Converts a torch Tensor into an image Numpy array of BGR channel order
-    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
-    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
-    '''
-    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
-    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
-    n_dim = tensor.dim()
-    if n_dim == 4:
-        n_img = len(tensor)
-        img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 3:
-        img_np = tensor.numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 2:
-        img_np = tensor.numpy()
-    else:
-        raise TypeError(
-            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
-    if out_type == np.uint8:
-        img_np = (img_np * 255.0).round()
-        # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
-    return img_np.astype(out_type)
-
-
-'''
-# --------------------------------------------
-# Augmentation, flipe and/or rotate
-# --------------------------------------------
-# The following two are enough.
-# (1) augmet_img: numpy image of WxHxC or WxH
-# (2) augment_img_tensor4: tensor image 1xCxWxH
-# --------------------------------------------
-'''
-
-
-def augment_img(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return np.flipud(np.rot90(img))
-    elif mode == 2:
-        return np.flipud(img)
-    elif mode == 3:
-        return np.rot90(img, k=3)
-    elif mode == 4:
-        return np.flipud(np.rot90(img, k=2))
-    elif mode == 5:
-        return np.rot90(img)
-    elif mode == 6:
-        return np.rot90(img, k=2)
-    elif mode == 7:
-        return np.flipud(np.rot90(img, k=3))
-
-
-def augment_img_tensor4(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.rot90(1, [2, 3]).flip([2])
-    elif mode == 2:
-        return img.flip([2])
-    elif mode == 3:
-        return img.rot90(3, [2, 3])
-    elif mode == 4:
-        return img.rot90(2, [2, 3]).flip([2])
-    elif mode == 5:
-        return img.rot90(1, [2, 3])
-    elif mode == 6:
-        return img.rot90(2, [2, 3])
-    elif mode == 7:
-        return img.rot90(3, [2, 3]).flip([2])
-
-
-def augment_img_tensor(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    img_size = img.size()
-    img_np = img.data.cpu().numpy()
-    if len(img_size) == 3:
-        img_np = np.transpose(img_np, (1, 2, 0))
-    elif len(img_size) == 4:
-        img_np = np.transpose(img_np, (2, 3, 1, 0))
-    img_np = augment_img(img_np, mode=mode)
-    img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
-    if len(img_size) == 3:
-        img_tensor = img_tensor.permute(2, 0, 1)
-    elif len(img_size) == 4:
-        img_tensor = img_tensor.permute(3, 2, 0, 1)
-
-    return img_tensor.type_as(img)
-
-
-def augment_img_np3(img, mode=0):
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.transpose(1, 0, 2)
-    elif mode == 2:
-        return img[::-1, :, :]
-    elif mode == 3:
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 4:
-        return img[:, ::-1, :]
-    elif mode == 5:
-        img = img[:, ::-1, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 6:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        return img
-    elif mode == 7:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-
-
-def augment_imgs(img_list, hflip=True, rot=True):
-    # horizontal flip OR rotate
-    hflip = hflip and random.random() < 0.5
-    vflip = rot and random.random() < 0.5
-    rot90 = rot and random.random() < 0.5
-
-    def _augment(img):
-        if hflip:
-            img = img[:, ::-1, :]
-        if vflip:
-            img = img[::-1, :, :]
-        if rot90:
-            img = img.transpose(1, 0, 2)
-        return img
-
-    return [_augment(img) for img in img_list]
-
-
-'''
-# --------------------------------------------
-# modcrop and shave
-# --------------------------------------------
-'''
-
-
-def modcrop(img_in, scale):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    if img.ndim == 2:
-        H, W = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r]
-    elif img.ndim == 3:
-        H, W, C = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r, :]
-    else:
-        raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
-    return img
-
-
-def shave(img_in, border=0):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    h, w = img.shape[:2]
-    img = img[border:h-border, border:w-border]
-    return img
-
-
-'''
-# --------------------------------------------
-# image processing process on numpy image
-# channel_convert(in_c, tar_type, img_list):
-# rgb2ycbcr(img, only_y=True):
-# bgr2ycbcr(img, only_y=True):
-# ycbcr2rgb(img):
-# --------------------------------------------
-'''
-
-
-def rgb2ycbcr(img, only_y=True):
-    '''same as matlab rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
-                              [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def ycbcr2rgb(img):
-    '''same as matlab ycbcr2rgb
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
-                          [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def bgr2ycbcr(img, only_y=True):
-    '''bgr version of rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
-                              [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def channel_convert(in_c, tar_type, img_list):
-    # conversion among BGR, gray and y
-    if in_c == 3 and tar_type == 'gray':  # BGR to gray
-        gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in gray_list]
-    elif in_c == 3 and tar_type == 'y':  # BGR to y
-        y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in y_list]
-    elif in_c == 1 and tar_type == 'RGB':  # gray/y to BGR
-        return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
-    else:
-        return img_list
-
-
-'''
-# --------------------------------------------
-# metric, PSNR and SSIM
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# PSNR
-# --------------------------------------------
-def calculate_psnr(img1, img2, border=0):
-    # img1 and img2 have range [0, 255]
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    mse = np.mean((img1 - img2)**2)
-    if mse == 0:
-        return float('inf')
-    return 20 * math.log10(255.0 / math.sqrt(mse))
-
-
-# --------------------------------------------
-# SSIM
-# --------------------------------------------
-def calculate_ssim(img1, img2, border=0):
-    '''calculate SSIM
-    the same outputs as MATLAB's
-    img1, img2: [0, 255]
-    '''
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    if img1.ndim == 2:
-        return ssim(img1, img2)
-    elif img1.ndim == 3:
-        if img1.shape[2] == 3:
-            ssims = []
-            for i in range(3):
-                ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
-            return np.array(ssims).mean()
-        elif img1.shape[2] == 1:
-            return ssim(np.squeeze(img1), np.squeeze(img2))
-    else:
-        raise ValueError('Wrong input image dimensions.')
-
-
-def ssim(img1, img2):
-    C1 = (0.01 * 255)**2
-    C2 = (0.03 * 255)**2
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    kernel = cv2.getGaussianKernel(11, 1.5)
-    window = np.outer(kernel, kernel.transpose())
-
-    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
-    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
-    mu1_sq = mu1**2
-    mu2_sq = mu2**2
-    mu1_mu2 = mu1 * mu2
-    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
-    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
-    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
-
-    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
-                                                            (sigma1_sq + sigma2_sq + C2))
-    return ssim_map.mean()
-
-
-'''
-# --------------------------------------------
-# matlab's bicubic imresize (numpy and torch) [0, 1]
-# --------------------------------------------
-'''
-
-
-# matlab 'imresize' function, now only support 'bicubic'
-def cubic(x):
-    absx = torch.abs(x)
-    absx2 = absx**2
-    absx3 = absx**3
-    return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
-        (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
-
-
-def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
-    if (scale < 1) and (antialiasing):
-        # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
-        kernel_width = kernel_width / scale
-
-    # Output-space coordinates
-    x = torch.linspace(1, out_length, out_length)
-
-    # Input-space coordinates. Calculate the inverse mapping such that 0.5
-    # in output space maps to 0.5 in input space, and 0.5+scale in output
-    # space maps to 1.5 in input space.
-    u = x / scale + 0.5 * (1 - 1 / scale)
-
-    # What is the left-most pixel that can be involved in the computation?
-    left = torch.floor(u - kernel_width / 2)
-
-    # What is the maximum number of pixels that can be involved in the
-    # computation?  Note: it's OK to use an extra pixel here; if the
-    # corresponding weights are all zero, it will be eliminated at the end
-    # of this function.
-    P = math.ceil(kernel_width) + 2
-
-    # The indices of the input pixels involved in computing the k-th output
-    # pixel are in row k of the indices matrix.
-    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
-        1, P).expand(out_length, P)
-
-    # The weights used to compute the k-th output pixel are in row k of the
-    # weights matrix.
-    distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
-    # apply cubic kernel
-    if (scale < 1) and (antialiasing):
-        weights = scale * cubic(distance_to_center * scale)
-    else:
-        weights = cubic(distance_to_center)
-    # Normalize the weights matrix so that each row sums to 1.
-    weights_sum = torch.sum(weights, 1).view(out_length, 1)
-    weights = weights / weights_sum.expand(out_length, P)
-
-    # If a column in weights is all zero, get rid of it. only consider the first and last column.
-    weights_zero_tmp = torch.sum((weights == 0), 0)
-    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 1, P - 2)
-        weights = weights.narrow(1, 1, P - 2)
-    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 0, P - 2)
-        weights = weights.narrow(1, 0, P - 2)
-    weights = weights.contiguous()
-    indices = indices.contiguous()
-    sym_len_s = -indices.min() + 1
-    sym_len_e = indices.max() - in_length
-    indices = indices + sym_len_s - 1
-    return weights, indices, int(sym_len_s), int(sym_len_e)
-
-
-# --------------------------------------------
-# imresize for tensor image [0, 1]
-# --------------------------------------------
-def imresize(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: pytorch tensor, CHW or HW [0,1]
-    # output: CHW or HW [0,1] w/o round
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(0)
-    in_C, in_H, in_W = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
-    img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:, :sym_len_Hs, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[:, -sym_len_He:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(in_C, out_H, in_W)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
-    out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :, :sym_len_Ws]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, :, -sym_len_We:]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(in_C, out_H, out_W)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-    return out_2
-
-
-# --------------------------------------------
-# imresize for numpy image [0, 1]
-# --------------------------------------------
-def imresize_np(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: Numpy, HWC or HW [0,1]
-    # output: HWC or HW [0,1] w/o round
-    img = torch.from_numpy(img)
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(2)
-
-    in_H, in_W, in_C = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
-    img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:sym_len_Hs, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[-sym_len_He:, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(out_H, in_W, in_C)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
-    out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :sym_len_Ws, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, -sym_len_We:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(out_H, out_W, in_C)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-
-    return out_2.numpy()
-
-
-if __name__ == '__main__':
-    print('---')
-#    img = imread_uint('test.bmp', 3)
-#    img = uint2single(img)
-#    img_bicubic = imresize_np(img, 1/4)

spaces/ASJMO/freegpt/g4f/Provider/Providers/Aichat.py

@@ -1,35 +0,0 @@
-import requests
-import os
-import json
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://hteyun.com'
-model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0613']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs):
-    headers = {
-        'Content-Type': 'application/json',
-    }
-    data = {
-        'model': model,
-        'temperature': 0.7,
-        'presence_penalty': 0,
-        'messages': messages,
-    }
-    response = requests.post(url + '/api/chat-stream',
-                             json=data, stream=True)
-    
-    if stream:
-        for chunk in response.iter_content(chunk_size=None):
-            chunk = chunk.decode('utf-8')
-            if chunk.strip():
-                message = json.loads(chunk)['choices'][0]['message']['content']
-                yield message
-    else:
-        message = response.json()['choices'][0]['message']['content']
-        yield message
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/AdamGustavsson/AnimeganV2Webcam/README.md

@@ -1,37 +0,0 @@
----
-title: AnimeganV2Webcam
-emoji: 😻
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.

spaces/Adapter/CoAdapter/ldm/data/dataset_laion.py

@@ -1,130 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import numpy as np
-import os
-import pytorch_lightning as pl
-import torch
-import webdataset as wds
-from torchvision.transforms import transforms
-
-from ldm.util import instantiate_from_config
-
-
-def dict_collation_fn(samples, combine_tensors=True, combine_scalars=True):
-    """Take a list  of samples (as dictionary) and create a batch, preserving the keys.
-    If `tensors` is True, `ndarray` objects are combined into
-    tensor batches.
-    :param dict samples: list of samples
-    :param bool tensors: whether to turn lists of ndarrays into a single ndarray
-    :returns: single sample consisting of a batch
-    :rtype: dict
-    """
-    keys = set.intersection(*[set(sample.keys()) for sample in samples])
-    batched = {key: [] for key in keys}
-
-    for s in samples:
-        [batched[key].append(s[key]) for key in batched]
-
-    result = {}
-    for key in batched:
-        if isinstance(batched[key][0], (int, float)):
-            if combine_scalars:
-                result[key] = np.array(list(batched[key]))
-        elif isinstance(batched[key][0], torch.Tensor):
-            if combine_tensors:
-                result[key] = torch.stack(list(batched[key]))
-        elif isinstance(batched[key][0], np.ndarray):
-            if combine_tensors:
-                result[key] = np.array(list(batched[key]))
-        else:
-            result[key] = list(batched[key])
-    return result
-
-
-class WebDataModuleFromConfig(pl.LightningDataModule):
-
-    def __init__(self,
-                 tar_base,
-                 batch_size,
-                 train=None,
-                 validation=None,
-                 test=None,
-                 num_workers=4,
-                 multinode=True,
-                 min_size=None,
-                 max_pwatermark=1.0,
-                 **kwargs):
-        super().__init__()
-        print(f'Setting tar base to {tar_base}')
-        self.tar_base = tar_base
-        self.batch_size = batch_size
-        self.num_workers = num_workers
-        self.train = train
-        self.validation = validation
-        self.test = test
-        self.multinode = multinode
-        self.min_size = min_size  # filter out very small images
-        self.max_pwatermark = max_pwatermark  # filter out watermarked images
-
-    def make_loader(self, dataset_config):
-        image_transforms = [instantiate_from_config(tt) for tt in dataset_config.image_transforms]
-        image_transforms = transforms.Compose(image_transforms)
-
-        process = instantiate_from_config(dataset_config['process'])
-
-        shuffle = dataset_config.get('shuffle', 0)
-        shardshuffle = shuffle > 0
-
-        nodesplitter = wds.shardlists.split_by_node if self.multinode else wds.shardlists.single_node_only
-
-        tars = os.path.join(self.tar_base, dataset_config.shards)
-
-        dset = wds.WebDataset(
-            tars, nodesplitter=nodesplitter, shardshuffle=shardshuffle,
-            handler=wds.warn_and_continue).repeat().shuffle(shuffle)
-        print(f'Loading webdataset with {len(dset.pipeline[0].urls)} shards.')
-
-        dset = (
-            dset.select(self.filter_keys).decode('pil',
-                                                 handler=wds.warn_and_continue).select(self.filter_size).map_dict(
-                                                     jpg=image_transforms, handler=wds.warn_and_continue).map(process))
-        dset = (dset.batched(self.batch_size, partial=False, collation_fn=dict_collation_fn))
-
-        loader = wds.WebLoader(dset, batch_size=None, shuffle=False, num_workers=self.num_workers)
-
-        return loader
-
-    def filter_size(self, x):
-        if self.min_size is None:
-            return True
-        try:
-            return x['json']['original_width'] >= self.min_size and x['json']['original_height'] >= self.min_size and x[
-                'json']['pwatermark'] <= self.max_pwatermark
-        except Exception:
-            return False
-
-    def filter_keys(self, x):
-        try:
-            return ("jpg" in x) and ("txt" in x)
-        except Exception:
-            return False
-
-    def train_dataloader(self):
-        return self.make_loader(self.train)
-
-    def val_dataloader(self):
-        return None
-
-    def test_dataloader(self):
-        return None
-
-
-if __name__ == '__main__':
-    from omegaconf import OmegaConf
-    config = OmegaConf.load("configs/stable-diffusion/train_canny_sd_v1.yaml")
-    datamod = WebDataModuleFromConfig(**config["data"]["params"])
-    dataloader = datamod.train_dataloader()
-
-    for batch in dataloader:
-        print(batch.keys())
-        print(batch['jpg'].shape)

spaces/Adapter/T2I-Adapter/test_composable_adapters.py

@@ -1,101 +0,0 @@
-import cv2
-import os
-import torch
-from pytorch_lightning import seed_everything
-from torch import autocast
-
-from basicsr.utils import tensor2img
-from ldm.inference_base import diffusion_inference, get_adapters, get_base_argument_parser, get_sd_models
-from ldm.modules.extra_condition import api
-from ldm.modules.extra_condition.api import ExtraCondition, get_adapter_feature, get_cond_model
-
-torch.set_grad_enabled(False)
-
-
-def main():
-    supported_cond = [e.name for e in ExtraCondition]
-    parser = get_base_argument_parser()
-    for cond_name in supported_cond:
-        parser.add_argument(
-            f'--{cond_name}_path',
-            type=str,
-            default=None,
-            help=f'condition image path for {cond_name}',
-        )
-        parser.add_argument(
-            f'--{cond_name}_inp_type',
-            type=str,
-            default='image',
-            help=f'the type of the input condition image, can be image or {cond_name}',
-            choices=['image', cond_name],
-        )
-        parser.add_argument(
-            f'--{cond_name}_adapter_ckpt',
-            type=str,
-            default=None,
-            help=f'path to checkpoint of the {cond_name} adapter, '
-                 f'if {cond_name}_path is not None, this should not be None too',
-        )
-        parser.add_argument(
-            f'--{cond_name}_weight',
-            type=float,
-            default=1.0,
-            help=f'the {cond_name} adapter features are multiplied by the {cond_name}_weight and then summed up together',
-        )
-    opt = parser.parse_args()
-
-    # process argument
-    activated_conds = []
-    cond_paths = []
-    adapter_ckpts = []
-    for cond_name in supported_cond:
-        if getattr(opt, f'{cond_name}_path') is None:
-            continue
-        assert getattr(opt, f'{cond_name}_adapter_ckpt') is not None, f'you should specify the {cond_name}_adapter_ckpt'
-        activated_conds.append(cond_name)
-        cond_paths.append(getattr(opt, f'{cond_name}_path'))
-        adapter_ckpts.append(getattr(opt, f'{cond_name}_adapter_ckpt'))
-    assert len(activated_conds) != 0, 'you did not input any condition'
-
-    if opt.outdir is None:
-        opt.outdir = f'outputs/test-composable-adapters'
-    os.makedirs(opt.outdir, exist_ok=True)
-    if opt.resize_short_edge is None:
-        print(f"you don't specify the resize_shot_edge, so the maximum resolution is set to {opt.max_resolution}")
-    opt.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-
-    # prepare models
-    adapters = []
-    cond_models = []
-    cond_inp_types = []
-    process_cond_modules = []
-    for cond_name in activated_conds:
-        adapters.append(get_adapters(opt, getattr(ExtraCondition, cond_name)))
-        cond_inp_type = getattr(opt, f'{cond_name}_inp_type', 'image')
-        if cond_inp_type == 'image':
-            cond_models.append(get_cond_model(opt, getattr(ExtraCondition, cond_name)))
-        else:
-            cond_models.append(None)
-        cond_inp_types.append(cond_inp_type)
-        process_cond_modules.append(getattr(api, f'get_cond_{cond_name}'))
-    sd_model, sampler = get_sd_models(opt)
-
-    # inference
-    with torch.inference_mode(), \
-            sd_model.ema_scope(), \
-            autocast('cuda'):
-        seed_everything(opt.seed)
-        conds = []
-        for cond_idx, cond_name in enumerate(activated_conds):
-            conds.append(process_cond_modules[cond_idx](
-                opt, cond_paths[cond_idx], cond_inp_types[cond_idx], cond_models[cond_idx],
-            ))
-        adapter_features, append_to_context = get_adapter_feature(conds, adapters)
-        for v_idx in range(opt.n_samples):
-            result = diffusion_inference(opt, sd_model, sampler, adapter_features, append_to_context)
-            base_count = len(os.listdir(opt.outdir))
-            cv2.imwrite(os.path.join(opt.outdir, f'{base_count:05}_result.png'), tensor2img(result))
-
-
-if __name__ == '__main__':
-    main()

spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/__init__.py

@@ -1 +0,0 @@
-from .base import SimulationRule

spaces/Aki004/herta-so-vits/modules/modules.py

@@ -1,342 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import modules.commons as commons
-from modules.commons import init_weights, get_padding
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/latent_diffusion_uncond.md

@@ -1,35 +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.
--->
-
-# Unconditional Latent Diffusion
-
-Unconditional Latent Diffusion was proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2112.10752) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer.
-
-The abstract from the paper is:
-
-*By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs.*
-
-The original codebase can be found at [CompVis/latent-diffusion](https://github.com/CompVis/latent-diffusion).
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## LDMPipeline
-[[autodoc]] LDMPipeline
-	- all
-	- __call__
-
-## ImagePipelineOutput
-[[autodoc]] pipelines.ImagePipelineOutput

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py

@@ -1,940 +0,0 @@
-import html
-import inspect
-import re
-import urllib.parse as ul
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
-
-from ...loaders import LoraLoaderMixin
-from ...models import UNet2DConditionModel
-from ...schedulers import DDPMScheduler
-from ...utils import (
-    BACKENDS_MAPPING,
-    PIL_INTERPOLATION,
-    is_accelerate_available,
-    is_accelerate_version,
-    is_bs4_available,
-    is_ftfy_available,
-    logging,
-    randn_tensor,
-    replace_example_docstring,
-)
-from ..pipeline_utils import DiffusionPipeline
-from . import IFPipelineOutput
-from .safety_checker import IFSafetyChecker
-from .watermark import IFWatermarker
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-if is_bs4_available():
-    from bs4 import BeautifulSoup
-
-if is_ftfy_available():
-    import ftfy
-
-
-def resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image:
-    w, h = images.size
-
-    coef = w / h
-
-    w, h = img_size, img_size
-
-    if coef >= 1:
-        w = int(round(img_size / 8 * coef) * 8)
-    else:
-        h = int(round(img_size / 8 / coef) * 8)
-
-    images = images.resize((w, h), resample=PIL_INTERPOLATION["bicubic"], reducing_gap=None)
-
-    return images
-
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline
-        >>> from diffusers.utils import pt_to_pil
-        >>> import torch
-        >>> from PIL import Image
-        >>> import requests
-        >>> from io import BytesIO
-
-        >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
-        >>> response = requests.get(url)
-        >>> original_image = Image.open(BytesIO(response.content)).convert("RGB")
-        >>> original_image = original_image.resize((768, 512))
-
-        >>> pipe = IFImg2ImgPipeline.from_pretrained(
-        ...     "DeepFloyd/IF-I-XL-v1.0",
-        ...     variant="fp16",
-        ...     torch_dtype=torch.float16,
-        ... )
-        >>> pipe.enable_model_cpu_offload()
-
-        >>> prompt = "A fantasy landscape in style minecraft"
-        >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
-
-        >>> image = pipe(
-        ...     image=original_image,
-        ...     prompt_embeds=prompt_embeds,
-        ...     negative_prompt_embeds=negative_embeds,
-        ...     output_type="pt",
-        ... ).images
-
-        >>> # save intermediate image
-        >>> pil_image = pt_to_pil(image)
-        >>> pil_image[0].save("./if_stage_I.png")
-
-        >>> super_res_1_pipe = IFImg2ImgSuperResolutionPipeline.from_pretrained(
-        ...     "DeepFloyd/IF-II-L-v1.0",
-        ...     text_encoder=None,
-        ...     variant="fp16",
-        ...     torch_dtype=torch.float16,
-        ... )
-        >>> super_res_1_pipe.enable_model_cpu_offload()
-
-        >>> image = super_res_1_pipe(
-        ...     image=image,
-        ...     original_image=original_image,
-        ...     prompt_embeds=prompt_embeds,
-        ...     negative_prompt_embeds=negative_embeds,
-        ... ).images
-        >>> image[0].save("./if_stage_II.png")
-        ```
-"""
-
-
-class IFImg2ImgPipeline(DiffusionPipeline, LoraLoaderMixin):
-    tokenizer: T5Tokenizer
-    text_encoder: T5EncoderModel
-
-    unet: UNet2DConditionModel
-    scheduler: DDPMScheduler
-
-    feature_extractor: Optional[CLIPImageProcessor]
-    safety_checker: Optional[IFSafetyChecker]
-
-    watermarker: Optional[IFWatermarker]
-
-    bad_punct_regex = re.compile(
-        r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
-    )  # noqa
-
-    _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"]
-
-    def __init__(
-        self,
-        tokenizer: T5Tokenizer,
-        text_encoder: T5EncoderModel,
-        unet: UNet2DConditionModel,
-        scheduler: DDPMScheduler,
-        safety_checker: Optional[IFSafetyChecker],
-        feature_extractor: Optional[CLIPImageProcessor],
-        watermarker: Optional[IFWatermarker],
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the IF license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        self.register_modules(
-            tokenizer=tokenizer,
-            text_encoder=text_encoder,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-            watermarker=watermarker,
-        )
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_model_cpu_offload
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
-        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
-        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-
-        if self.text_encoder is not None:
-            _, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook)
-
-            # Accelerate will move the next model to the device _before_ calling the offload hook of the
-            # previous model. This will cause both models to be present on the device at the same time.
-            # IF uses T5 for its text encoder which is really large. We can manually call the offload
-            # hook for the text encoder to ensure it's moved to the cpu before the unet is moved to
-            # the GPU.
-            self.text_encoder_offload_hook = hook
-
-        _, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook)
-
-        # if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet
-        self.unet_offload_hook = hook
-
-        if self.safety_checker is not None:
-            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.remove_all_hooks
-    def remove_all_hooks(self):
-        if is_accelerate_available():
-            from accelerate.hooks import remove_hook_from_module
-        else:
-            raise ImportError("Please install accelerate via `pip install accelerate`")
-
-        for model in [self.text_encoder, self.unet, self.safety_checker]:
-            if model is not None:
-                remove_hook_from_module(model, recurse=True)
-
-        self.unet_offload_hook = None
-        self.text_encoder_offload_hook = None
-        self.final_offload_hook = None
-
-    @torch.no_grad()
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt
-    def encode_prompt(
-        self,
-        prompt,
-        do_classifier_free_guidance=True,
-        num_images_per_prompt=1,
-        device=None,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        clean_caption: bool = False,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-             prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`, *optional*):
-                torch device to place the resulting embeddings on
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
-                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-        """
-        if prompt is not None and negative_prompt is not None:
-            if type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-
-        if device is None:
-            device = self._execution_device
-
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF
-        max_length = 77
-
-        if prompt_embeds is None:
-            prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                add_special_tokens=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {max_length} tokens: {removed_text}"
-                )
-
-            attention_mask = text_inputs.attention_mask.to(device)
-
-            prompt_embeds = self.text_encoder(
-                text_input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            prompt_embeds = prompt_embeds[0]
-
-        if self.text_encoder is not None:
-            dtype = self.text_encoder.dtype
-        elif self.unet is not None:
-            dtype = self.unet.dtype
-        else:
-            dtype = None
-
-        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
-            max_length = prompt_embeds.shape[1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_attention_mask=True,
-                add_special_tokens=True,
-                return_tensors="pt",
-            )
-            attention_mask = uncond_input.attention_mask.to(device)
-
-            negative_prompt_embeds = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            negative_prompt_embeds = negative_prompt_embeds[0]
-
-        if do_classifier_free_guidance:
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-
-            negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
-
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-        else:
-            negative_prompt_embeds = None
-
-        return prompt_embeds, negative_prompt_embeds
-
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
-            image, nsfw_detected, watermark_detected = self.safety_checker(
-                images=image,
-                clip_input=safety_checker_input.pixel_values.to(dtype=dtype),
-            )
-        else:
-            nsfw_detected = None
-            watermark_detected = None
-
-            if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
-                self.unet_offload_hook.offload()
-
-        return image, nsfw_detected, watermark_detected
-
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    def check_inputs(
-        self,
-        prompt,
-        image,
-        batch_size,
-        callback_steps,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-    ):
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-        if isinstance(image, list):
-            check_image_type = image[0]
-        else:
-            check_image_type = image
-
-        if (
-            not isinstance(check_image_type, torch.Tensor)
-            and not isinstance(check_image_type, PIL.Image.Image)
-            and not isinstance(check_image_type, np.ndarray)
-        ):
-            raise ValueError(
-                "`image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
-                f" {type(check_image_type)}"
-            )
-
-        if isinstance(image, list):
-            image_batch_size = len(image)
-        elif isinstance(image, torch.Tensor):
-            image_batch_size = image.shape[0]
-        elif isinstance(image, PIL.Image.Image):
-            image_batch_size = 1
-        elif isinstance(image, np.ndarray):
-            image_batch_size = image.shape[0]
-        else:
-            assert False
-
-        if batch_size != image_batch_size:
-            raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}")
-
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
-    def _text_preprocessing(self, text, clean_caption=False):
-        if clean_caption and not is_bs4_available():
-            logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
-            clean_caption = False
-
-        if clean_caption and not is_ftfy_available():
-            logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
-            clean_caption = False
-
-        if not isinstance(text, (tuple, list)):
-            text = [text]
-
-        def process(text: str):
-            if clean_caption:
-                text = self._clean_caption(text)
-                text = self._clean_caption(text)
-            else:
-                text = text.lower().strip()
-            return text
-
-        return [process(t) for t in text]
-
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
-    def _clean_caption(self, caption):
-        caption = str(caption)
-        caption = ul.unquote_plus(caption)
-        caption = caption.strip().lower()
-        caption = re.sub("<person>", "person", caption)
-        # urls:
-        caption = re.sub(
-            r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
-            "",
-            caption,
-        )  # regex for urls
-        caption = re.sub(
-            r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
-            "",
-            caption,
-        )  # regex for urls
-        # html:
-        caption = BeautifulSoup(caption, features="html.parser").text
-
-        # @<nickname>
-        caption = re.sub(r"@[\w\d]+\b", "", caption)
-
-        # 31C0—31EF CJK Strokes
-        # 31F0—31FF Katakana Phonetic Extensions
-        # 3200—32FF Enclosed CJK Letters and Months
-        # 3300—33FF CJK Compatibility
-        # 3400—4DBF CJK Unified Ideographs Extension A
-        # 4DC0—4DFF Yijing Hexagram Symbols
-        # 4E00—9FFF CJK Unified Ideographs
-        caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
-        caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
-        caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
-        caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
-        caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
-        caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
-        caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
-        #######################################################
-
-        # все виды тире / all types of dash --> "-"
-        caption = re.sub(
-            r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+",  # noqa
-            "-",
-            caption,
-        )
-
-        # кавычки к одному стандарту
-        caption = re.sub(r"[`´«»“”¨]", '"', caption)
-        caption = re.sub(r"[‘’]", "'", caption)
-
-        # &quot;
-        caption = re.sub(r"&quot;?", "", caption)
-        # &amp
-        caption = re.sub(r"&amp", "", caption)
-
-        # ip adresses:
-        caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
-
-        # article ids:
-        caption = re.sub(r"\d:\d\d\s+$", "", caption)
-
-        # \n
-        caption = re.sub(r"\\n", " ", caption)
-
-        # "#123"
-        caption = re.sub(r"#\d{1,3}\b", "", caption)
-        # "#12345.."
-        caption = re.sub(r"#\d{5,}\b", "", caption)
-        # "123456.."
-        caption = re.sub(r"\b\d{6,}\b", "", caption)
-        # filenames:
-        caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
-
-        #
-        caption = re.sub(r"[\"\']{2,}", r'"', caption)  # """AUSVERKAUFT"""
-        caption = re.sub(r"[\.]{2,}", r" ", caption)  # """AUSVERKAUFT"""
-
-        caption = re.sub(self.bad_punct_regex, r" ", caption)  # ***AUSVERKAUFT***, #AUSVERKAUFT
-        caption = re.sub(r"\s+\.\s+", r" ", caption)  # " . "
-
-        # this-is-my-cute-cat / this_is_my_cute_cat
-        regex2 = re.compile(r"(?:\-|\_)")
-        if len(re.findall(regex2, caption)) > 3:
-            caption = re.sub(regex2, " ", caption)
-
-        caption = ftfy.fix_text(caption)
-        caption = html.unescape(html.unescape(caption))
-
-        caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption)  # jc6640
-        caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption)  # jc6640vc
-        caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption)  # 6640vc231
-
-        caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
-        caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
-        caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
-        caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
-        caption = re.sub(r"\bpage\s+\d+\b", "", caption)
-
-        caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption)  # j2d1a2a...
-
-        caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
-
-        caption = re.sub(r"\b\s+\:\s+", r": ", caption)
-        caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
-        caption = re.sub(r"\s+", " ", caption)
-
-        caption.strip()
-
-        caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
-        caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
-        caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
-        caption = re.sub(r"^\.\S+$", "", caption)
-
-        return caption.strip()
-
-    def preprocess_image(self, image: PIL.Image.Image) -> torch.Tensor:
-        if not isinstance(image, list):
-            image = [image]
-
-        def numpy_to_pt(images):
-            if images.ndim == 3:
-                images = images[..., None]
-
-            images = torch.from_numpy(images.transpose(0, 3, 1, 2))
-            return images
-
-        if isinstance(image[0], PIL.Image.Image):
-            new_image = []
-
-            for image_ in image:
-                image_ = image_.convert("RGB")
-                image_ = resize(image_, self.unet.sample_size)
-                image_ = np.array(image_)
-                image_ = image_.astype(np.float32)
-                image_ = image_ / 127.5 - 1
-                new_image.append(image_)
-
-            image = new_image
-
-            image = np.stack(image, axis=0)  # to np
-            image = numpy_to_pt(image)  # to pt
-
-        elif isinstance(image[0], np.ndarray):
-            image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)
-            image = numpy_to_pt(image)
-
-        elif isinstance(image[0], torch.Tensor):
-            image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)
-
-        return image
-
-    def get_timesteps(self, num_inference_steps, strength):
-        # get the original timestep using init_timestep
-        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
-
-        return timesteps, num_inference_steps - t_start
-
-    def prepare_intermediate_images(
-        self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None
-    ):
-        _, channels, height, width = image.shape
-
-        batch_size = batch_size * num_images_per_prompt
-
-        shape = (batch_size, channels, height, width)
-
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
-        image = image.repeat_interleave(num_images_per_prompt, dim=0)
-        image = self.scheduler.add_noise(image, noise, timestep)
-
-        return image
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        image: Union[
-            PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]
-        ] = None,
-        strength: float = 0.7,
-        num_inference_steps: int = 80,
-        timesteps: List[int] = None,
-        guidance_scale: float = 10.0,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        clean_caption: bool = True,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            image (`torch.FloatTensor` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, that will be used as the starting point for the
-                process.
-            strength (`float`, *optional*, defaults to 0.8):
-                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
-                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
-                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
-                be maximum and the denoising process will run for the full number of iterations specified in
-                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            timesteps (`List[int]`, *optional*):
-                Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
-                timesteps are used. Must be in descending order.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-            clean_caption (`bool`, *optional*, defaults to `True`):
-                Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
-                be installed. If the dependencies are not installed, the embeddings will be created from the raw
-                prompt.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
-            returning a tuple, the first element is a list with the generated images, and the second element is a list
-            of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw)
-            or watermarked content, according to the `safety_checker`.
-        """
-        # 1. Check inputs. Raise error if not correct
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        self.check_inputs(
-            prompt, image, batch_size, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
-        )
-
-        # 2. Define call parameters
-        device = self._execution_device
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
-            prompt,
-            do_classifier_free_guidance,
-            num_images_per_prompt=num_images_per_prompt,
-            device=device,
-            negative_prompt=negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            clean_caption=clean_caption,
-        )
-
-        if do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
-        dtype = prompt_embeds.dtype
-
-        # 4. Prepare timesteps
-        if timesteps is not None:
-            self.scheduler.set_timesteps(timesteps=timesteps, device=device)
-            timesteps = self.scheduler.timesteps
-            num_inference_steps = len(timesteps)
-        else:
-            self.scheduler.set_timesteps(num_inference_steps, device=device)
-            timesteps = self.scheduler.timesteps
-
-        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
-
-        # 5. Prepare intermediate images
-        image = self.preprocess_image(image)
-        image = image.to(device=device, dtype=dtype)
-
-        noise_timestep = timesteps[0:1]
-        noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt)
-
-        intermediate_images = self.prepare_intermediate_images(
-            image, noise_timestep, batch_size, num_images_per_prompt, dtype, device, generator
-        )
-
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # HACK: see comment in `enable_model_cpu_offload`
-        if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
-            self.text_encoder_offload_hook.offload()
-
-        # 7. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                model_input = (
-                    torch.cat([intermediate_images] * 2) if do_classifier_free_guidance else intermediate_images
-                )
-                model_input = self.scheduler.scale_model_input(model_input, t)
-
-                # predict the noise residual
-                noise_pred = self.unet(
-                    model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    return_dict=False,
-                )[0]
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1)
-                    noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], dim=1)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-                    noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
-
-                if self.scheduler.config.variance_type not in ["learned", "learned_range"]:
-                    noise_pred, _ = noise_pred.split(model_input.shape[1], dim=1)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                intermediate_images = self.scheduler.step(
-                    noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False
-                )[0]
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, intermediate_images)
-
-        image = intermediate_images
-
-        if output_type == "pil":
-            # 8. Post-processing
-            image = (image / 2 + 0.5).clamp(0, 1)
-            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-            # 9. Run safety checker
-            image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
-
-            # 10. Convert to PIL
-            image = self.numpy_to_pil(image)
-
-            # 11. Apply watermark
-            if self.watermarker is not None:
-                self.watermarker.apply_watermark(image, self.unet.config.sample_size)
-        elif output_type == "pt":
-            nsfw_detected = None
-            watermark_detected = None
-
-            if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
-                self.unet_offload_hook.offload()
-        else:
-            # 8. Post-processing
-            image = (image / 2 + 0.5).clamp(0, 1)
-            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-            # 9. Run safety checker
-            image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
-
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
-
-        if not return_dict:
-            return (image, nsfw_detected, watermark_detected)
-
-        return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)

spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py

@@ -1,9 +0,0 @@
-_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
-model = dict(roi_head=dict(bbox_head=dict(num_classes=3)))
-classes = ('person', 'bicycle', 'car')
-data = dict(
-    train=dict(classes=classes),
-    val=dict(classes=classes),
-    test=dict(classes=classes))
-
-load_from = 'http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth'  # noqa

spaces/Andy1621/uniformer_image_detection/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py

@@ -1,2 +0,0 @@
-_base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_detection/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py

@@ -1,116 +0,0 @@
-_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    pretrained='open-mmlab://resnest50',
-    backbone=dict(
-        type='ResNeSt',
-        stem_channels=64,
-        depth=50,
-        radix=2,
-        reduction_factor=4,
-        avg_down_stride=True,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch'),
-    roi_head=dict(
-        bbox_head=[
-            dict(
-                type='Shared4Conv1FCBBoxHead',
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                norm_cfg=norm_cfg,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.1, 0.1, 0.2, 0.2]),
-                reg_class_agnostic=True,
-                loss_cls=dict(
-                    type='CrossEntropyLoss',
-                    use_sigmoid=False,
-                    loss_weight=1.0),
-                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
-                               loss_weight=1.0)),
-            dict(
-                type='Shared4Conv1FCBBoxHead',
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                norm_cfg=norm_cfg,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.05, 0.05, 0.1, 0.1]),
-                reg_class_agnostic=True,
-                loss_cls=dict(
-                    type='CrossEntropyLoss',
-                    use_sigmoid=False,
-                    loss_weight=1.0),
-                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
-                               loss_weight=1.0)),
-            dict(
-                type='Shared4Conv1FCBBoxHead',
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                norm_cfg=norm_cfg,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.033, 0.033, 0.067, 0.067]),
-                reg_class_agnostic=True,
-                loss_cls=dict(
-                    type='CrossEntropyLoss',
-                    use_sigmoid=False,
-                    loss_weight=1.0),
-                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
-        ], ))
-# # use ResNeSt img_norm
-img_norm_cfg = dict(
-    mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='LoadAnnotations',
-        with_bbox=True,
-        with_mask=False,
-        poly2mask=False),
-    dict(
-        type='Resize',
-        img_scale=[(1333, 640), (1333, 800)],
-        multiscale_mode='range',
-        keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))

spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/fovea_head.py

@@ -1,341 +0,0 @@
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, normal_init
-from mmcv.ops import DeformConv2d
-
-from mmdet.core import multi_apply, multiclass_nms
-from ..builder import HEADS
-from .anchor_free_head import AnchorFreeHead
-
-INF = 1e8
-
-
-class FeatureAlign(nn.Module):
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size=3,
-                 deform_groups=4):
-        super(FeatureAlign, self).__init__()
-        offset_channels = kernel_size * kernel_size * 2
-        self.conv_offset = nn.Conv2d(
-            4, deform_groups * offset_channels, 1, bias=False)
-        self.conv_adaption = DeformConv2d(
-            in_channels,
-            out_channels,
-            kernel_size=kernel_size,
-            padding=(kernel_size - 1) // 2,
-            deform_groups=deform_groups)
-        self.relu = nn.ReLU(inplace=True)
-
-    def init_weights(self):
-        normal_init(self.conv_offset, std=0.1)
-        normal_init(self.conv_adaption, std=0.01)
-
-    def forward(self, x, shape):
-        offset = self.conv_offset(shape)
-        x = self.relu(self.conv_adaption(x, offset))
-        return x
-
-
-@HEADS.register_module()
-class FoveaHead(AnchorFreeHead):
-    """FoveaBox: Beyond Anchor-based Object Detector
-    https://arxiv.org/abs/1904.03797
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 base_edge_list=(16, 32, 64, 128, 256),
-                 scale_ranges=((8, 32), (16, 64), (32, 128), (64, 256), (128,
-                                                                         512)),
-                 sigma=0.4,
-                 with_deform=False,
-                 deform_groups=4,
-                 **kwargs):
-        self.base_edge_list = base_edge_list
-        self.scale_ranges = scale_ranges
-        self.sigma = sigma
-        self.with_deform = with_deform
-        self.deform_groups = deform_groups
-        super().__init__(num_classes, in_channels, **kwargs)
-
-    def _init_layers(self):
-        # box branch
-        super()._init_reg_convs()
-        self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
-
-        # cls branch
-        if not self.with_deform:
-            super()._init_cls_convs()
-            self.conv_cls = nn.Conv2d(
-                self.feat_channels, self.cls_out_channels, 3, padding=1)
-        else:
-            self.cls_convs = nn.ModuleList()
-            self.cls_convs.append(
-                ConvModule(
-                    self.feat_channels, (self.feat_channels * 4),
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg,
-                    bias=self.norm_cfg is None))
-            self.cls_convs.append(
-                ConvModule((self.feat_channels * 4), (self.feat_channels * 4),
-                           1,
-                           stride=1,
-                           padding=0,
-                           conv_cfg=self.conv_cfg,
-                           norm_cfg=self.norm_cfg,
-                           bias=self.norm_cfg is None))
-            self.feature_adaption = FeatureAlign(
-                self.feat_channels,
-                self.feat_channels,
-                kernel_size=3,
-                deform_groups=self.deform_groups)
-            self.conv_cls = nn.Conv2d(
-                int(self.feat_channels * 4),
-                self.cls_out_channels,
-                3,
-                padding=1)
-
-    def init_weights(self):
-        super().init_weights()
-        if self.with_deform:
-            self.feature_adaption.init_weights()
-
-    def forward_single(self, x):
-        cls_feat = x
-        reg_feat = x
-        for reg_layer in self.reg_convs:
-            reg_feat = reg_layer(reg_feat)
-        bbox_pred = self.conv_reg(reg_feat)
-        if self.with_deform:
-            cls_feat = self.feature_adaption(cls_feat, bbox_pred.exp())
-        for cls_layer in self.cls_convs:
-            cls_feat = cls_layer(cls_feat)
-        cls_score = self.conv_cls(cls_feat)
-        return cls_score, bbox_pred
-
-    def _get_points_single(self, *args, **kwargs):
-        y, x = super()._get_points_single(*args, **kwargs)
-        return y + 0.5, x + 0.5
-
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             gt_bbox_list,
-             gt_label_list,
-             img_metas,
-             gt_bboxes_ignore=None):
-        assert len(cls_scores) == len(bbox_preds)
-
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        points = self.get_points(featmap_sizes, bbox_preds[0].dtype,
-                                 bbox_preds[0].device)
-        num_imgs = cls_scores[0].size(0)
-        flatten_cls_scores = [
-            cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels)
-            for cls_score in cls_scores
-        ]
-        flatten_bbox_preds = [
-            bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
-            for bbox_pred in bbox_preds
-        ]
-        flatten_cls_scores = torch.cat(flatten_cls_scores)
-        flatten_bbox_preds = torch.cat(flatten_bbox_preds)
-        flatten_labels, flatten_bbox_targets = self.get_targets(
-            gt_bbox_list, gt_label_list, featmap_sizes, points)
-
-        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-        pos_inds = ((flatten_labels >= 0)
-                    & (flatten_labels < self.num_classes)).nonzero().view(-1)
-        num_pos = len(pos_inds)
-
-        loss_cls = self.loss_cls(
-            flatten_cls_scores, flatten_labels, avg_factor=num_pos + num_imgs)
-        if num_pos > 0:
-            pos_bbox_preds = flatten_bbox_preds[pos_inds]
-            pos_bbox_targets = flatten_bbox_targets[pos_inds]
-            pos_weights = pos_bbox_targets.new_zeros(
-                pos_bbox_targets.size()) + 1.0
-            loss_bbox = self.loss_bbox(
-                pos_bbox_preds,
-                pos_bbox_targets,
-                pos_weights,
-                avg_factor=num_pos)
-        else:
-            loss_bbox = torch.tensor(
-                0,
-                dtype=flatten_bbox_preds.dtype,
-                device=flatten_bbox_preds.device)
-        return dict(loss_cls=loss_cls, loss_bbox=loss_bbox)
-
-    def get_targets(self, gt_bbox_list, gt_label_list, featmap_sizes, points):
-        label_list, bbox_target_list = multi_apply(
-            self._get_target_single,
-            gt_bbox_list,
-            gt_label_list,
-            featmap_size_list=featmap_sizes,
-            point_list=points)
-        flatten_labels = [
-            torch.cat([
-                labels_level_img.flatten() for labels_level_img in labels_level
-            ]) for labels_level in zip(*label_list)
-        ]
-        flatten_bbox_targets = [
-            torch.cat([
-                bbox_targets_level_img.reshape(-1, 4)
-                for bbox_targets_level_img in bbox_targets_level
-            ]) for bbox_targets_level in zip(*bbox_target_list)
-        ]
-        flatten_labels = torch.cat(flatten_labels)
-        flatten_bbox_targets = torch.cat(flatten_bbox_targets)
-        return flatten_labels, flatten_bbox_targets
-
-    def _get_target_single(self,
-                           gt_bboxes_raw,
-                           gt_labels_raw,
-                           featmap_size_list=None,
-                           point_list=None):
-
-        gt_areas = torch.sqrt((gt_bboxes_raw[:, 2] - gt_bboxes_raw[:, 0]) *
-                              (gt_bboxes_raw[:, 3] - gt_bboxes_raw[:, 1]))
-        label_list = []
-        bbox_target_list = []
-        # for each pyramid, find the cls and box target
-        for base_len, (lower_bound, upper_bound), stride, featmap_size, \
-            (y, x) in zip(self.base_edge_list, self.scale_ranges,
-                          self.strides, featmap_size_list, point_list):
-            # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-            labels = gt_labels_raw.new_zeros(featmap_size) + self.num_classes
-            bbox_targets = gt_bboxes_raw.new(featmap_size[0], featmap_size[1],
-                                             4) + 1
-            # scale assignment
-            hit_indices = ((gt_areas >= lower_bound) &
-                           (gt_areas <= upper_bound)).nonzero().flatten()
-            if len(hit_indices) == 0:
-                label_list.append(labels)
-                bbox_target_list.append(torch.log(bbox_targets))
-                continue
-            _, hit_index_order = torch.sort(-gt_areas[hit_indices])
-            hit_indices = hit_indices[hit_index_order]
-            gt_bboxes = gt_bboxes_raw[hit_indices, :] / stride
-            gt_labels = gt_labels_raw[hit_indices]
-            half_w = 0.5 * (gt_bboxes[:, 2] - gt_bboxes[:, 0])
-            half_h = 0.5 * (gt_bboxes[:, 3] - gt_bboxes[:, 1])
-            # valid fovea area: left, right, top, down
-            pos_left = torch.ceil(
-                gt_bboxes[:, 0] + (1 - self.sigma) * half_w - 0.5).long().\
-                clamp(0, featmap_size[1] - 1)
-            pos_right = torch.floor(
-                gt_bboxes[:, 0] + (1 + self.sigma) * half_w - 0.5).long().\
-                clamp(0, featmap_size[1] - 1)
-            pos_top = torch.ceil(
-                gt_bboxes[:, 1] + (1 - self.sigma) * half_h - 0.5).long().\
-                clamp(0, featmap_size[0] - 1)
-            pos_down = torch.floor(
-                gt_bboxes[:, 1] + (1 + self.sigma) * half_h - 0.5).long().\
-                clamp(0, featmap_size[0] - 1)
-            for px1, py1, px2, py2, label, (gt_x1, gt_y1, gt_x2, gt_y2) in \
-                    zip(pos_left, pos_top, pos_right, pos_down, gt_labels,
-                        gt_bboxes_raw[hit_indices, :]):
-                labels[py1:py2 + 1, px1:px2 + 1] = label
-                bbox_targets[py1:py2 + 1, px1:px2 + 1, 0] = \
-                    (stride * x[py1:py2 + 1, px1:px2 + 1] - gt_x1) / base_len
-                bbox_targets[py1:py2 + 1, px1:px2 + 1, 1] = \
-                    (stride * y[py1:py2 + 1, px1:px2 + 1] - gt_y1) / base_len
-                bbox_targets[py1:py2 + 1, px1:px2 + 1, 2] = \
-                    (gt_x2 - stride * x[py1:py2 + 1, px1:px2 + 1]) / base_len
-                bbox_targets[py1:py2 + 1, px1:px2 + 1, 3] = \
-                    (gt_y2 - stride * y[py1:py2 + 1, px1:px2 + 1]) / base_len
-            bbox_targets = bbox_targets.clamp(min=1. / 16, max=16.)
-            label_list.append(labels)
-            bbox_target_list.append(torch.log(bbox_targets))
-        return label_list, bbox_target_list
-
-    def get_bboxes(self,
-                   cls_scores,
-                   bbox_preds,
-                   img_metas,
-                   cfg=None,
-                   rescale=None):
-        assert len(cls_scores) == len(bbox_preds)
-        num_levels = len(cls_scores)
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        points = self.get_points(
-            featmap_sizes,
-            bbox_preds[0].dtype,
-            bbox_preds[0].device,
-            flatten=True)
-        result_list = []
-        for img_id in range(len(img_metas)):
-            cls_score_list = [
-                cls_scores[i][img_id].detach() for i in range(num_levels)
-            ]
-            bbox_pred_list = [
-                bbox_preds[i][img_id].detach() for i in range(num_levels)
-            ]
-            img_shape = img_metas[img_id]['img_shape']
-            scale_factor = img_metas[img_id]['scale_factor']
-            det_bboxes = self._get_bboxes_single(cls_score_list,
-                                                 bbox_pred_list, featmap_sizes,
-                                                 points, img_shape,
-                                                 scale_factor, cfg, rescale)
-            result_list.append(det_bboxes)
-        return result_list
-
-    def _get_bboxes_single(self,
-                           cls_scores,
-                           bbox_preds,
-                           featmap_sizes,
-                           point_list,
-                           img_shape,
-                           scale_factor,
-                           cfg,
-                           rescale=False):
-        cfg = self.test_cfg if cfg is None else cfg
-        assert len(cls_scores) == len(bbox_preds) == len(point_list)
-        det_bboxes = []
-        det_scores = []
-        for cls_score, bbox_pred, featmap_size, stride, base_len, (y, x) \
-                in zip(cls_scores, bbox_preds, featmap_sizes, self.strides,
-                       self.base_edge_list, point_list):
-            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-            scores = cls_score.permute(1, 2, 0).reshape(
-                -1, self.cls_out_channels).sigmoid()
-            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4).exp()
-            nms_pre = cfg.get('nms_pre', -1)
-            if (nms_pre > 0) and (scores.shape[0] > nms_pre):
-                max_scores, _ = scores.max(dim=1)
-                _, topk_inds = max_scores.topk(nms_pre)
-                bbox_pred = bbox_pred[topk_inds, :]
-                scores = scores[topk_inds, :]
-                y = y[topk_inds]
-                x = x[topk_inds]
-            x1 = (stride * x - base_len * bbox_pred[:, 0]).\
-                clamp(min=0, max=img_shape[1] - 1)
-            y1 = (stride * y - base_len * bbox_pred[:, 1]).\
-                clamp(min=0, max=img_shape[0] - 1)
-            x2 = (stride * x + base_len * bbox_pred[:, 2]).\
-                clamp(min=0, max=img_shape[1] - 1)
-            y2 = (stride * y + base_len * bbox_pred[:, 3]).\
-                clamp(min=0, max=img_shape[0] - 1)
-            bboxes = torch.stack([x1, y1, x2, y2], -1)
-            det_bboxes.append(bboxes)
-            det_scores.append(scores)
-        det_bboxes = torch.cat(det_bboxes)
-        if rescale:
-            det_bboxes /= det_bboxes.new_tensor(scale_factor)
-        det_scores = torch.cat(det_scores)
-        padding = det_scores.new_zeros(det_scores.shape[0], 1)
-        # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
-        # BG cat_id: num_class
-        det_scores = torch.cat([det_scores, padding], dim=1)
-        det_bboxes, det_labels = multiclass_nms(det_bboxes, det_scores,
-                                                cfg.score_thr, cfg.nms,
-                                                cfg.max_per_img)
-        return det_bboxes, det_labels

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/js/show_controls.js

@@ -1,22 +0,0 @@
-const belowChatInput = document.querySelectorAll("#chat-tab > div > :nth-child(n+2), #extensions");
-const chatParent = document.querySelector(".chat-parent");
-
-function toggle_controls(value) {
-  if (value) {
-    belowChatInput.forEach(element => {
-      element.style.display = "inherit";
-    });
-
-    chatParent.classList.remove("bigchat");
-    document.getElementById("chat-input-row").classList.remove("bigchat");
-    document.getElementById("chat-col").classList.remove("bigchat");
-  } else {
-    belowChatInput.forEach(element => {
-      element.style.display = "none";
-    });
-
-    chatParent.classList.add("bigchat");
-    document.getElementById("chat-input-row").classList.add("bigchat");
-    document.getElementById("chat-col").classList.add("bigchat");
-  }
-}

spaces/AnnonSubmission/xai-cl/app.py

@@ -1,209 +0,0 @@
-import torch
-import numpy as np
-import torch.nn as nn
-import torchvision.transforms as transforms
-import matplotlib
-import matplotlib.pyplot as plt
-from PIL import Image 
-import cv2
-import gradio as gr
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-from data_transforms import normal_transforms, no_shift_transforms, ig_transforms, modify_transforms
-from utils import overlay_heatmap, viz_map, show_image, deprocess, get_ssl_model, fig2img
-from methods import occlusion, pairwise_occlusion
-from methods import create_mixed_images, averaged_transforms, sailency, smooth_grad 
-from methods import get_gradcam, get_interactioncam
-
-matplotlib.use('Agg')
-
-def load_model(model_name):
-  
-  global network, ssl_model, denorm
-  if model_name == "simclrv2 (1X)":
-    variant = '1x'
-    network = 'simclrv2'
-    denorm = False
-
-  elif model_name == "simclrv2 (2X)":
-    variant = '2x'
-    network = 'simclrv2'
-    denorm = False
-
-  elif model_name == "Barlow Twins":
-    network = 'barlow_twins'
-    variant = None
-    denorm = True
-
-  ssl_model = get_ssl_model(network, variant)  
-
-  if network != 'simclrv2':
-    global normal_transforms, no_shift_transforms, ig_transforms
-    normal_transforms, no_shift_transforms, ig_transforms = modify_transforms(normal_transforms, no_shift_transforms, ig_transforms)
-
-  return "Loaded Model Successfully"
-
-def load_or_augment_images(img1_input, img2_input, use_aug):   
-
-  global img_main, img1, img2
-
-  img_main = img1_input.convert('RGB')
-
-  if use_aug:
-    img1 = normal_transforms['pure'](img_main).unsqueeze(0).to(device)
-    img2 = normal_transforms['aug'](img_main).unsqueeze(0).to(device)
-  else:
-    img1 = normal_transforms['pure'](img_main).unsqueeze(0).to(device)
-    img2 = img2_input.convert('RGB')
-    img2 = normal_transforms['pure'](img2).unsqueeze(0).to(device)
-
-  similarity = "Similarity: {:.3f}".format(nn.CosineSimilarity(dim=-1)(ssl_model(img1), ssl_model(img2)).item())
-
-  fig, axs = plt.subplots(1, 2, figsize=(10,10))
-  np.vectorize(lambda ax:ax.axis('off'))(axs)
-
-  axs[0].imshow(show_image(img1, denormalize = denorm))  
-  axs[1].imshow(show_image(img2, denormalize = denorm))
-  plt.subplots_adjust(wspace=0.1, hspace = 0)
-  pil_output = fig2img(fig)
-  return pil_output, similarity
-
-def run_occlusion(w_size, stride):
-  heatmap1, heatmap2 = occlusion(img1, img2, ssl_model, w_size = 64, stride = 8, batch_size = 32)
-  heatmap1_po, heatmap2_po = pairwise_occlusion(img1, img2, ssl_model, batch_size = 32, erase_scale = (0.1, 0.3), erase_ratio = (1, 1.5), num_erases = 100)
-
-  added_image1 = overlay_heatmap(img1, heatmap1, denormalize = denorm)
-  added_image2 = overlay_heatmap(img2, heatmap2, denormalize = denorm)
-
-  fig, axs = plt.subplots(2, 3, figsize=(20,10))
-  np.vectorize(lambda ax:ax.axis('off'))(axs)
-
-  axs[0, 0].imshow(show_image(img1, denormalize = denorm))
-  axs[0, 1].imshow(added_image1)
-  axs[0, 1].set_title("Conditional Occlusion")
-  axs[0, 2].imshow((deprocess(img1, denormalize = denorm) * heatmap1_po[:,:,None]).astype('uint8'))
-  axs[0, 2].set_title("Pairwise Occlusion")
-  axs[1, 0].imshow(show_image(img2, denormalize = denorm))
-  axs[1, 1].imshow(added_image2)
-  axs[1, 2].imshow((deprocess(img2, denormalize = denorm) * heatmap2_po[:,:,None]).astype('uint8'))
-  plt.subplots_adjust(wspace=0, hspace = 0.01)
-  pil_output = fig2img(fig)
-  return pil_output
-
-def get_avg_trasforms(transform_type, add_noise, blur_output, guided):
-
-  mixed_images = create_mixed_images(transform_type = transform_type, 
-                                    ig_transforms = ig_transforms, 
-                                    step = 0.1, 
-                                    img_path = img_main, 
-                                    add_noise = add_noise)
-
-  # vanilla gradients (for comparison purposes)
-  sailency1_van, sailency2_van = sailency(guided = guided, ssl_model = ssl_model, 
-                                          img1 = mixed_images[0], img2 = mixed_images[-1], 
-                                          blur_output = blur_output)
-
-  # smooth gradients (for comparison purposes)
-  sailency1_s, sailency2_s = smooth_grad(guided = guided, ssl_model = ssl_model, 
-                                        img1 = mixed_images[0], img2 = mixed_images[-1], 
-                                        blur_output = blur_output, steps = 50)
-
-  # integrated transform
-  sailency1, sailency2 = averaged_transforms(guided = guided, ssl_model = ssl_model, 
-                                            mixed_images = mixed_images, 
-                                            blur_output = blur_output)
-
-  fig, axs = plt.subplots(2, 4, figsize=(20,10))
-  np.vectorize(lambda ax:ax.axis('off'))(axs)
-
-  axs[0,0].imshow(show_image(mixed_images[0], denormalize = denorm))
-  axs[0,1].imshow(show_image(sailency1_van.detach(), squeeze = False).squeeze(), cmap = plt.cm.jet)
-  axs[0,1].imshow(show_image(mixed_images[0], denormalize = denorm), alpha=0.5)
-  axs[0,1].set_title("Vanilla Gradients")
-  axs[0,2].imshow(show_image(sailency1_s.detach(), squeeze = False).squeeze(), cmap = plt.cm.jet)
-  axs[0,2].imshow(show_image(mixed_images[0], denormalize = denorm), alpha=0.5)
-  axs[0,2].set_title("Smooth Gradients")
-  axs[0,3].imshow(show_image(sailency1.detach(), squeeze = False).squeeze(), cmap = plt.cm.jet)
-  axs[0,3].imshow(show_image(mixed_images[0], denormalize = denorm), alpha=0.5)
-  axs[0,3].set_title("Integrated Transform")
-  axs[1,0].imshow(show_image(mixed_images[-1], denormalize = denorm))
-  axs[1,1].imshow(show_image(sailency2_van.detach(), squeeze = False).squeeze(), cmap = plt.cm.jet)
-  axs[1,1].imshow(show_image(mixed_images[-1], denormalize = denorm), alpha=0.5)
-  axs[1,2].imshow(show_image(sailency2_s.detach(), squeeze = False).squeeze(), cmap = plt.cm.jet)
-  axs[1,2].imshow(show_image(mixed_images[-1], denormalize = denorm), alpha=0.5)
-  axs[1,3].imshow(show_image(sailency2.detach(), squeeze = False).squeeze(), cmap = plt.cm.jet)
-  axs[1,3].imshow(show_image(mixed_images[-1], denormalize = denorm), alpha=0.5)
-
-  plt.subplots_adjust(wspace=0.02, hspace = 0.02)
-  pil_output = fig2img(fig)
-  return pil_output
-
-def get_cams():
-
-  gradcam1, gradcam2 = get_gradcam(ssl_model, img1, img2)
-  intcam1_mean, intcam2_mean = get_interactioncam(ssl_model, img1, img2, reduction = 'mean')
-
-  fig, axs = plt.subplots(2, 3, figsize=(20,8))
-  np.vectorize(lambda ax:ax.axis('off'))(axs)
-
-  axs[0,0].imshow(show_image(img1[0], squeeze = False, denormalize = denorm))
-  axs[0,1].imshow(overlay_heatmap(img1, gradcam1, denormalize = denorm))
-  axs[0,1].set_title("Grad-CAM")
-  axs[0,2].imshow(overlay_heatmap(img1, intcam1_mean, denormalize = denorm))
-  axs[0,2].set_title("IntCAM")
-
-  axs[1,0].imshow(show_image(img2[0], squeeze = False, denormalize = denorm))
-  axs[1,1].imshow(overlay_heatmap(img2, gradcam2, denormalize = denorm))
-  axs[1,2].imshow(overlay_heatmap(img2, intcam2_mean, denormalize = denorm))
-
-  plt.subplots_adjust(wspace=0.01, hspace = 0.01)
-  pil_output = fig2img(fig)
-  return pil_output
-
-xai = gr.Blocks()
-
-with xai:
-  gr.Markdown("<h1>Visualizing and Understanding Contrastive Learning, TIP Submission</h1>")
-  gr.Markdown("The interface is simplified as much as possible with only necessary options to select for each method")
-  gr.Markdown("<b>Due to the latency in Hugging Face machines (this demo is using the free CPU Basic plan with 2 CPUs), the methods are very slow. We advice to use a local machine or our Google Colab demo (link in the GitHub)</b>")
-  
-  with gr.Row():
-    model_name = gr.Dropdown(["simclrv2 (1X)", "simclrv2 (2X)", "Barlow Twins"], label="Choose Model and press \"Load Model\"")
-    load_model_button = gr.Button("Load Model")
-    status_or_similarity = gr.inputs.Textbox(label = "Status")
-  with gr.Row():
-    gr.Markdown("You can either load two images or load a single image and augment it to get the second image (in that case please check the \"Use Augmentations\" checkbox). After that, please press on \"Show Images\". The similarity will be shown in the \"Status\" bar.")
-    img1 = gr.Image(type='pil', label = "First Image")
-    img2 = gr.Image(type='pil', label = "Second Image")
-  with gr.Row():
-    use_aug = gr.Checkbox(value = False, label = "Use Augmentations")
-    load_images_button = gr.Button("Show Images")
-    
-  gr.Markdown("Choose a method from the different tabs. You may leave the default options as they are and press on \"Run\" ")
-  with gr.Row():
-    with gr.Column():
-      with gr.Tabs():
-        with gr.TabItem("Interaction-CAM"):
-          cams_button = gr.Button("Get Heatmaps")
-        with gr.TabItem("Perturbation Methods"):
-          w_size = gr.Number(value = 64, label = "Occlusion Window Size", precision = 0)
-          stride = gr.Number(value = 8, label = "Occlusion Stride", precision = 0)
-          occlusion_button = gr.Button("Get Heatmap")
-        with gr.TabItem("Averaged Transforms"):
-          transform_type = gr.inputs.Radio(label="Data Augment", choices=['color_jitter', 'blur', 'grayscale', 'solarize', 'combine'], default="combine")
-          add_noise = gr.Checkbox(value = True, label = "Add Noise")
-          blur_output = gr.Checkbox(value = True, label = "Blur Output")
-          guided = gr.Checkbox(value = True, label = "Guided Backprop")
-          avgtransform_button = gr.Button("Get Saliency")
-
-    with gr.Column():
-      output_image = gr.Image(type='pil', show_label = False)
-
-  load_model_button.click(load_model, inputs = model_name, outputs = status_or_similarity)
-  load_images_button.click(load_or_augment_images, inputs = [img1, img2, use_aug], outputs = [output_image, status_or_similarity])
-  occlusion_button.click(run_occlusion, inputs=[w_size,stride], outputs=output_image)
-  avgtransform_button.click(get_avg_trasforms, inputs = [transform_type, add_noise, blur_output, guided], outputs = output_image)
-  cams_button.click(get_cams, inputs = [], outputs = output_image) 
-
-xai.launch()
-

spaces/Apex-X/Tm/roop/core.py

@@ -1,215 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-import sys
-# single thread doubles cuda performance - needs to be set before torch import
-if any(arg.startswith('--execution-provider') for arg in sys.argv):
-    os.environ['OMP_NUM_THREADS'] = '1'
-# reduce tensorflow log level
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
-import warnings
-from typing import List
-import platform
-import signal
-import shutil
-import argparse
-import torch
-import onnxruntime
-import tensorflow
-
-import roop.globals
-import roop.metadata
-import roop.ui as ui
-from roop.predicter import predict_image, predict_video
-from roop.processors.frame.core import get_frame_processors_modules
-from roop.utilities import has_image_extension, is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clean_temp, normalize_output_path
-
-if 'ROCMExecutionProvider' in roop.globals.execution_providers:
-    del torch
-
-warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
-warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
-
-
-def parse_args() -> None:
-    signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
-    program = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=100))
-    program.add_argument('-s', '--source', help='select an source image', dest='source_path')
-    program.add_argument('-t', '--target', help='select an target image or video', dest='target_path')
-    program.add_argument('-o', '--output', help='select output file or directory', dest='output_path')
-    program.add_argument('--frame-processor', help='frame processors (choices: face_swapper, face_enhancer, ...)', dest='frame_processor', default=['face_swapper'], nargs='+')
-    program.add_argument('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=False)
-    program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True)
-    program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=False)
-    program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true', default=False)
-    program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9'])
-    program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=18, choices=range(52), metavar='[0-51]')
-    program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory())
-    program.add_argument('--execution-provider', help='available execution provider (choices: cpu, ...)', dest='execution_provider', default=['cpu'], choices=suggest_execution_providers(), nargs='+')
-    program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=suggest_execution_threads())
-    program.add_argument('-v', '--version', action='version', version=f'{roop.metadata.name} {roop.metadata.version}')
-
-    args = program.parse_args()
-
-    roop.globals.source_path = args.source_path
-    roop.globals.target_path = args.target_path
-    roop.globals.output_path = normalize_output_path(roop.globals.source_path, roop.globals.target_path, args.output_path)
-    roop.globals.frame_processors = args.frame_processor
-    roop.globals.headless = args.source_path or args.target_path or args.output_path
-    roop.globals.keep_fps = args.keep_fps
-    roop.globals.keep_audio = args.keep_audio
-    roop.globals.keep_frames = args.keep_frames
-    roop.globals.many_faces = args.many_faces
-    roop.globals.video_encoder = args.video_encoder
-    roop.globals.video_quality = args.video_quality
-    roop.globals.max_memory = args.max_memory
-    roop.globals.execution_providers = decode_execution_providers(args.execution_provider)
-    roop.globals.execution_threads = args.execution_threads
-
-
-def encode_execution_providers(execution_providers: List[str]) -> List[str]:
-    return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
-
-
-def decode_execution_providers(execution_providers: List[str]) -> List[str]:
-    return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
-            if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
-
-
-def suggest_max_memory() -> int:
-    if platform.system().lower() == 'darwin':
-        return 4
-    return 16
-
-
-def suggest_execution_providers() -> List[str]:
-    return encode_execution_providers(onnxruntime.get_available_providers())
-
-
-def suggest_execution_threads() -> int:
-    if 'DmlExecutionProvider' in roop.globals.execution_providers:
-        return 1
-    if 'ROCMExecutionProvider' in roop.globals.execution_providers:
-        return 1
-    return 8
-
-
-def limit_resources() -> None:
-    # prevent tensorflow memory leak
-    gpus = tensorflow.config.experimental.list_physical_devices('GPU')
-    for gpu in gpus:
-        tensorflow.config.experimental.set_virtual_device_configuration(gpu, [
-            tensorflow.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)
-        ])
-    # limit memory usage
-    if roop.globals.max_memory:
-        memory = roop.globals.max_memory * 1024 ** 3
-        if platform.system().lower() == 'darwin':
-            memory = roop.globals.max_memory * 1024 ** 6
-        if platform.system().lower() == 'windows':
-            import ctypes
-            kernel32 = ctypes.windll.kernel32
-            kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
-        else:
-            import resource
-            resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
-
-
-def release_resources() -> None:
-    if 'CUDAExecutionProvider' in roop.globals.execution_providers:
-        torch.cuda.empty_cache()
-
-
-def pre_check() -> bool:
-    if sys.version_info < (3, 9):
-        update_status('Python version is not supported - please upgrade to 3.9 or higher.')
-        return False
-    if not shutil.which('ffmpeg'):
-        update_status('ffmpeg is not installed.')
-        return False
-    return True
-
-
-def update_status(message: str, scope: str = 'ROOP.CORE') -> None:
-    print(f'[{scope}] {message}')
-    if not roop.globals.headless:
-        ui.update_status(message)
-
-
-def start() -> None:
-    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-        if not frame_processor.pre_start():
-            return
-    # process image to image
-    if has_image_extension(roop.globals.target_path):
-        if predict_image(roop.globals.target_path):
-            destroy()
-        shutil.copy2(roop.globals.target_path, roop.globals.output_path)
-        for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-            update_status('Progressing...', frame_processor.NAME)
-            frame_processor.process_image(roop.globals.source_path, roop.globals.output_path, roop.globals.output_path)
-            frame_processor.post_process()
-            release_resources()
-        if is_image(roop.globals.target_path):
-            update_status('Processing to image succeed!')
-        else:
-            update_status('Processing to image failed!')
-        return
-    # process image to videos
-    if predict_video(roop.globals.target_path):
-        destroy()
-    update_status('Creating temp resources...')
-    create_temp(roop.globals.target_path)
-    update_status('Extracting frames...')
-    extract_frames(roop.globals.target_path)
-    temp_frame_paths = get_temp_frame_paths(roop.globals.target_path)
-    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-        update_status('Progressing...', frame_processor.NAME)
-        frame_processor.process_video(roop.globals.source_path, temp_frame_paths)
-        frame_processor.post_process()
-        release_resources()
-    # handles fps
-    if roop.globals.keep_fps:
-        update_status('Detecting fps...')
-        fps = detect_fps(roop.globals.target_path)
-        update_status(f'Creating video with {fps} fps...')
-        create_video(roop.globals.target_path, fps)
-    else:
-        update_status('Creating video with 30.0 fps...')
-        create_video(roop.globals.target_path)
-    # handle audio
-    if roop.globals.keep_audio:
-        if roop.globals.keep_fps:
-            update_status('Restoring audio...')
-        else:
-            update_status('Restoring audio might cause issues as fps are not kept...')
-        restore_audio(roop.globals.target_path, roop.globals.output_path)
-    else:
-        move_temp(roop.globals.target_path, roop.globals.output_path)
-    # clean and validate
-    clean_temp(roop.globals.target_path)
-    if is_video(roop.globals.target_path):
-        update_status('Processing to video succeed!')
-    else:
-        update_status('Processing to video failed!')
-
-
-def destroy() -> None:
-    if roop.globals.target_path:
-        clean_temp(roop.globals.target_path)
-    quit()
-
-
-def run() -> None:
-    parse_args()
-    if not pre_check():
-        return
-    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-        if not frame_processor.pre_check():
-            return
-    limit_resources()
-    if roop.globals.headless:
-        start()
-    else:
-        window = ui.init(start, destroy)
-        window.mainloop()

spaces/ArkanDash/rvc-models/infer_pack/models_onnx.py

@@ -1,849 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        x = self.proj(x) * x_mask
-        return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
-    """
-    Synthesizer for Training
-    """
-
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        # hop_length,
-        gin_channels=0,
-        use_sdp=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256Sim(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            is_half=kwargs["is_half"],
-        )
-
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
-        return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/cachecontrol/heuristics.py

@@ -1,139 +0,0 @@
-# SPDX-FileCopyrightText: 2015 Eric Larson
-#
-# SPDX-License-Identifier: Apache-2.0
-
-import calendar
-import time
-
-from email.utils import formatdate, parsedate, parsedate_tz
-
-from datetime import datetime, timedelta
-
-TIME_FMT = "%a, %d %b %Y %H:%M:%S GMT"
-
-
-def expire_after(delta, date=None):
-    date = date or datetime.utcnow()
-    return date + delta
-
-
-def datetime_to_header(dt):
-    return formatdate(calendar.timegm(dt.timetuple()))
-
-
-class BaseHeuristic(object):
-
-    def warning(self, response):
-        """
-        Return a valid 1xx warning header value describing the cache
-        adjustments.
-
-        The response is provided too allow warnings like 113
-        http://tools.ietf.org/html/rfc7234#section-5.5.4 where we need
-        to explicitly say response is over 24 hours old.
-        """
-        return '110 - "Response is Stale"'
-
-    def update_headers(self, response):
-        """Update the response headers with any new headers.
-
-        NOTE: This SHOULD always include some Warning header to
-              signify that the response was cached by the client, not
-              by way of the provided headers.
-        """
-        return {}
-
-    def apply(self, response):
-        updated_headers = self.update_headers(response)
-
-        if updated_headers:
-            response.headers.update(updated_headers)
-            warning_header_value = self.warning(response)
-            if warning_header_value is not None:
-                response.headers.update({"Warning": warning_header_value})
-
-        return response
-
-
-class OneDayCache(BaseHeuristic):
-    """
-    Cache the response by providing an expires 1 day in the
-    future.
-    """
-
-    def update_headers(self, response):
-        headers = {}
-
-        if "expires" not in response.headers:
-            date = parsedate(response.headers["date"])
-            expires = expire_after(timedelta(days=1), date=datetime(*date[:6]))
-            headers["expires"] = datetime_to_header(expires)
-            headers["cache-control"] = "public"
-        return headers
-
-
-class ExpiresAfter(BaseHeuristic):
-    """
-    Cache **all** requests for a defined time period.
-    """
-
-    def __init__(self, **kw):
-        self.delta = timedelta(**kw)
-
-    def update_headers(self, response):
-        expires = expire_after(self.delta)
-        return {"expires": datetime_to_header(expires), "cache-control": "public"}
-
-    def warning(self, response):
-        tmpl = "110 - Automatically cached for %s. Response might be stale"
-        return tmpl % self.delta
-
-
-class LastModified(BaseHeuristic):
-    """
-    If there is no Expires header already, fall back on Last-Modified
-    using the heuristic from
-    http://tools.ietf.org/html/rfc7234#section-4.2.2
-    to calculate a reasonable value.
-
-    Firefox also does something like this per
-    https://developer.mozilla.org/en-US/docs/Web/HTTP/Caching_FAQ
-    http://lxr.mozilla.org/mozilla-release/source/netwerk/protocol/http/nsHttpResponseHead.cpp#397
-    Unlike mozilla we limit this to 24-hr.
-    """
-    cacheable_by_default_statuses = {
-        200, 203, 204, 206, 300, 301, 404, 405, 410, 414, 501
-    }
-
-    def update_headers(self, resp):
-        headers = resp.headers
-
-        if "expires" in headers:
-            return {}
-
-        if "cache-control" in headers and headers["cache-control"] != "public":
-            return {}
-
-        if resp.status not in self.cacheable_by_default_statuses:
-            return {}
-
-        if "date" not in headers or "last-modified" not in headers:
-            return {}
-
-        date = calendar.timegm(parsedate_tz(headers["date"]))
-        last_modified = parsedate(headers["last-modified"])
-        if date is None or last_modified is None:
-            return {}
-
-        now = time.time()
-        current_age = max(0, now - date)
-        delta = date - calendar.timegm(last_modified)
-        freshness_lifetime = max(0, min(delta / 10, 24 * 3600))
-        if freshness_lifetime <= current_age:
-            return {}
-
-        expires = date + freshness_lifetime
-        return {"expires": time.strftime(TIME_FMT, time.gmtime(expires))}
-
-    def warning(self, resp):
-        return None

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/escsm.py

@@ -1,261 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License,  or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not,  write to the Free Software
-# Foundation,  Inc.,  51 Franklin St,  Fifth Floor,  Boston,  MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from .codingstatemachinedict import CodingStateMachineDict
-from .enums import MachineState
-
-# fmt: off
-HZ_CLS = (
-    1, 0, 0, 0, 0, 0, 0, 0,  # 00 - 07
-    0, 0, 0, 0, 0, 0, 0, 0,  # 08 - 0f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 10 - 17
-    0, 0, 0, 1, 0, 0, 0, 0,  # 18 - 1f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 20 - 27
-    0, 0, 0, 0, 0, 0, 0, 0,  # 28 - 2f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 30 - 37
-    0, 0, 0, 0, 0, 0, 0, 0,  # 38 - 3f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 40 - 47
-    0, 0, 0, 0, 0, 0, 0, 0,  # 48 - 4f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 50 - 57
-    0, 0, 0, 0, 0, 0, 0, 0,  # 58 - 5f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 60 - 67
-    0, 0, 0, 0, 0, 0, 0, 0,  # 68 - 6f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 70 - 77
-    0, 0, 0, 4, 0, 5, 2, 0,  # 78 - 7f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 80 - 87
-    1, 1, 1, 1, 1, 1, 1, 1,  # 88 - 8f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 90 - 97
-    1, 1, 1, 1, 1, 1, 1, 1,  # 98 - 9f
-    1, 1, 1, 1, 1, 1, 1, 1,  # a0 - a7
-    1, 1, 1, 1, 1, 1, 1, 1,  # a8 - af
-    1, 1, 1, 1, 1, 1, 1, 1,  # b0 - b7
-    1, 1, 1, 1, 1, 1, 1, 1,  # b8 - bf
-    1, 1, 1, 1, 1, 1, 1, 1,  # c0 - c7
-    1, 1, 1, 1, 1, 1, 1, 1,  # c8 - cf
-    1, 1, 1, 1, 1, 1, 1, 1,  # d0 - d7
-    1, 1, 1, 1, 1, 1, 1, 1,  # d8 - df
-    1, 1, 1, 1, 1, 1, 1, 1,  # e0 - e7
-    1, 1, 1, 1, 1, 1, 1, 1,  # e8 - ef
-    1, 1, 1, 1, 1, 1, 1, 1,  # f0 - f7
-    1, 1, 1, 1, 1, 1, 1, 1,  # f8 - ff
-)
-
-HZ_ST = (
-MachineState.START, MachineState.ERROR,      3, MachineState.START, MachineState.START, MachineState.START, MachineState.ERROR, MachineState.ERROR, # 00-07
-MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, # 08-0f
-MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ERROR, MachineState.ERROR, MachineState.START, MachineState.START,      4, MachineState.ERROR, # 10-17
-     5, MachineState.ERROR,      6, MachineState.ERROR,      5,      5,      4, MachineState.ERROR, # 18-1f
-     4, MachineState.ERROR,      4,      4,      4, MachineState.ERROR,      4, MachineState.ERROR, # 20-27
-     4, MachineState.ITS_ME, MachineState.START, MachineState.START, MachineState.START, MachineState.START, MachineState.START, MachineState.START, # 28-2f
-)
-# fmt: on
-
-HZ_CHAR_LEN_TABLE = (0, 0, 0, 0, 0, 0)
-
-HZ_SM_MODEL: CodingStateMachineDict = {
-    "class_table": HZ_CLS,
-    "class_factor": 6,
-    "state_table": HZ_ST,
-    "char_len_table": HZ_CHAR_LEN_TABLE,
-    "name": "HZ-GB-2312",
-    "language": "Chinese",
-}
-
-# fmt: off
-ISO2022CN_CLS = (
-    2, 0, 0, 0, 0, 0, 0, 0,  # 00 - 07
-    0, 0, 0, 0, 0, 0, 0, 0,  # 08 - 0f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 10 - 17
-    0, 0, 0, 1, 0, 0, 0, 0,  # 18 - 1f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 20 - 27
-    0, 3, 0, 0, 0, 0, 0, 0,  # 28 - 2f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 30 - 37
-    0, 0, 0, 0, 0, 0, 0, 0,  # 38 - 3f
-    0, 0, 0, 4, 0, 0, 0, 0,  # 40 - 47
-    0, 0, 0, 0, 0, 0, 0, 0,  # 48 - 4f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 50 - 57
-    0, 0, 0, 0, 0, 0, 0, 0,  # 58 - 5f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 60 - 67
-    0, 0, 0, 0, 0, 0, 0, 0,  # 68 - 6f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 70 - 77
-    0, 0, 0, 0, 0, 0, 0, 0,  # 78 - 7f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 80 - 87
-    2, 2, 2, 2, 2, 2, 2, 2,  # 88 - 8f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 90 - 97
-    2, 2, 2, 2, 2, 2, 2, 2,  # 98 - 9f
-    2, 2, 2, 2, 2, 2, 2, 2,  # a0 - a7
-    2, 2, 2, 2, 2, 2, 2, 2,  # a8 - af
-    2, 2, 2, 2, 2, 2, 2, 2,  # b0 - b7
-    2, 2, 2, 2, 2, 2, 2, 2,  # b8 - bf
-    2, 2, 2, 2, 2, 2, 2, 2,  # c0 - c7
-    2, 2, 2, 2, 2, 2, 2, 2,  # c8 - cf
-    2, 2, 2, 2, 2, 2, 2, 2,  # d0 - d7
-    2, 2, 2, 2, 2, 2, 2, 2,  # d8 - df
-    2, 2, 2, 2, 2, 2, 2, 2,  # e0 - e7
-    2, 2, 2, 2, 2, 2, 2, 2,  # e8 - ef
-    2, 2, 2, 2, 2, 2, 2, 2,  # f0 - f7
-    2, 2, 2, 2, 2, 2, 2, 2,  # f8 - ff
-)
-
-ISO2022CN_ST = (
-    MachineState.START,      3, MachineState.ERROR, MachineState.START, MachineState.START, MachineState.START, MachineState.START, MachineState.START, # 00-07
-    MachineState.START, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, # 08-0f
-    MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, # 10-17
-    MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR,      4, MachineState.ERROR, # 18-1f
-    MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, # 20-27
-        5,      6, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, # 28-2f
-    MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, # 30-37
-    MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ERROR, MachineState.START, # 38-3f
-)
-# fmt: on
-
-ISO2022CN_CHAR_LEN_TABLE = (0, 0, 0, 0, 0, 0, 0, 0, 0)
-
-ISO2022CN_SM_MODEL: CodingStateMachineDict = {
-    "class_table": ISO2022CN_CLS,
-    "class_factor": 9,
-    "state_table": ISO2022CN_ST,
-    "char_len_table": ISO2022CN_CHAR_LEN_TABLE,
-    "name": "ISO-2022-CN",
-    "language": "Chinese",
-}
-
-# fmt: off
-ISO2022JP_CLS = (
-    2, 0, 0, 0, 0, 0, 0, 0,  # 00 - 07
-    0, 0, 0, 0, 0, 0, 2, 2,  # 08 - 0f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 10 - 17
-    0, 0, 0, 1, 0, 0, 0, 0,  # 18 - 1f
-    0, 0, 0, 0, 7, 0, 0, 0,  # 20 - 27
-    3, 0, 0, 0, 0, 0, 0, 0,  # 28 - 2f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 30 - 37
-    0, 0, 0, 0, 0, 0, 0, 0,  # 38 - 3f
-    6, 0, 4, 0, 8, 0, 0, 0,  # 40 - 47
-    0, 9, 5, 0, 0, 0, 0, 0,  # 48 - 4f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 50 - 57
-    0, 0, 0, 0, 0, 0, 0, 0,  # 58 - 5f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 60 - 67
-    0, 0, 0, 0, 0, 0, 0, 0,  # 68 - 6f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 70 - 77
-    0, 0, 0, 0, 0, 0, 0, 0,  # 78 - 7f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 80 - 87
-    2, 2, 2, 2, 2, 2, 2, 2,  # 88 - 8f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 90 - 97
-    2, 2, 2, 2, 2, 2, 2, 2,  # 98 - 9f
-    2, 2, 2, 2, 2, 2, 2, 2,  # a0 - a7
-    2, 2, 2, 2, 2, 2, 2, 2,  # a8 - af
-    2, 2, 2, 2, 2, 2, 2, 2,  # b0 - b7
-    2, 2, 2, 2, 2, 2, 2, 2,  # b8 - bf
-    2, 2, 2, 2, 2, 2, 2, 2,  # c0 - c7
-    2, 2, 2, 2, 2, 2, 2, 2,  # c8 - cf
-    2, 2, 2, 2, 2, 2, 2, 2,  # d0 - d7
-    2, 2, 2, 2, 2, 2, 2, 2,  # d8 - df
-    2, 2, 2, 2, 2, 2, 2, 2,  # e0 - e7
-    2, 2, 2, 2, 2, 2, 2, 2,  # e8 - ef
-    2, 2, 2, 2, 2, 2, 2, 2,  # f0 - f7
-    2, 2, 2, 2, 2, 2, 2, 2,  # f8 - ff
-)
-
-ISO2022JP_ST = (
-    MachineState.START,      3, MachineState.ERROR, MachineState.START, MachineState.START, MachineState.START, MachineState.START, MachineState.START, # 00-07
-    MachineState.START, MachineState.START, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, # 08-0f
-    MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, # 10-17
-    MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ERROR, MachineState.ERROR, # 18-1f
-    MachineState.ERROR,      5, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR,      4, MachineState.ERROR, MachineState.ERROR, # 20-27
-    MachineState.ERROR, MachineState.ERROR, MachineState.ERROR,      6, MachineState.ITS_ME, MachineState.ERROR, MachineState.ITS_ME, MachineState.ERROR, # 28-2f
-    MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ITS_ME, # 30-37
-    MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, # 38-3f
-    MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ERROR, MachineState.START, MachineState.START, # 40-47
-)
-# fmt: on
-
-ISO2022JP_CHAR_LEN_TABLE = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
-
-ISO2022JP_SM_MODEL: CodingStateMachineDict = {
-    "class_table": ISO2022JP_CLS,
-    "class_factor": 10,
-    "state_table": ISO2022JP_ST,
-    "char_len_table": ISO2022JP_CHAR_LEN_TABLE,
-    "name": "ISO-2022-JP",
-    "language": "Japanese",
-}
-
-# fmt: off
-ISO2022KR_CLS = (
-    2, 0, 0, 0, 0, 0, 0, 0,  # 00 - 07
-    0, 0, 0, 0, 0, 0, 0, 0,  # 08 - 0f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 10 - 17
-    0, 0, 0, 1, 0, 0, 0, 0,  # 18 - 1f
-    0, 0, 0, 0, 3, 0, 0, 0,  # 20 - 27
-    0, 4, 0, 0, 0, 0, 0, 0,  # 28 - 2f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 30 - 37
-    0, 0, 0, 0, 0, 0, 0, 0,  # 38 - 3f
-    0, 0, 0, 5, 0, 0, 0, 0,  # 40 - 47
-    0, 0, 0, 0, 0, 0, 0, 0,  # 48 - 4f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 50 - 57
-    0, 0, 0, 0, 0, 0, 0, 0,  # 58 - 5f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 60 - 67
-    0, 0, 0, 0, 0, 0, 0, 0,  # 68 - 6f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 70 - 77
-    0, 0, 0, 0, 0, 0, 0, 0,  # 78 - 7f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 80 - 87
-    2, 2, 2, 2, 2, 2, 2, 2,  # 88 - 8f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 90 - 97
-    2, 2, 2, 2, 2, 2, 2, 2,  # 98 - 9f
-    2, 2, 2, 2, 2, 2, 2, 2,  # a0 - a7
-    2, 2, 2, 2, 2, 2, 2, 2,  # a8 - af
-    2, 2, 2, 2, 2, 2, 2, 2,  # b0 - b7
-    2, 2, 2, 2, 2, 2, 2, 2,  # b8 - bf
-    2, 2, 2, 2, 2, 2, 2, 2,  # c0 - c7
-    2, 2, 2, 2, 2, 2, 2, 2,  # c8 - cf
-    2, 2, 2, 2, 2, 2, 2, 2,  # d0 - d7
-    2, 2, 2, 2, 2, 2, 2, 2,  # d8 - df
-    2, 2, 2, 2, 2, 2, 2, 2,  # e0 - e7
-    2, 2, 2, 2, 2, 2, 2, 2,  # e8 - ef
-    2, 2, 2, 2, 2, 2, 2, 2,  # f0 - f7
-    2, 2, 2, 2, 2, 2, 2, 2,  # f8 - ff
-)
-
-ISO2022KR_ST = (
-    MachineState.START,      3, MachineState.ERROR, MachineState.START, MachineState.START, MachineState.START, MachineState.ERROR, MachineState.ERROR, # 00-07
-    MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, # 08-0f
-    MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR,      4, MachineState.ERROR, MachineState.ERROR, # 10-17
-    MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR,      5, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, # 18-1f
-    MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.START, MachineState.START, MachineState.START, MachineState.START, # 20-27
-)
-# fmt: on
-
-ISO2022KR_CHAR_LEN_TABLE = (0, 0, 0, 0, 0, 0)
-
-ISO2022KR_SM_MODEL: CodingStateMachineDict = {
-    "class_table": ISO2022KR_CLS,
-    "class_factor": 6,
-    "state_table": ISO2022KR_ST,
-    "char_len_table": ISO2022KR_CHAR_LEN_TABLE,
-    "name": "ISO-2022-KR",
-    "language": "Korean",
-}

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/lexers/_mapping.py

@@ -1,553 +0,0 @@
-# Automatically generated by scripts/gen_mapfiles.py.
-# DO NOT EDIT BY HAND; run `make mapfiles` instead.
-
-LEXERS = {
-    'ABAPLexer': ('pip._vendor.pygments.lexers.business', 'ABAP', ('abap',), ('*.abap', '*.ABAP'), ('text/x-abap',)),
-    'AMDGPULexer': ('pip._vendor.pygments.lexers.amdgpu', 'AMDGPU', ('amdgpu',), ('*.isa',), ()),
-    'APLLexer': ('pip._vendor.pygments.lexers.apl', 'APL', ('apl',), ('*.apl', '*.aplf', '*.aplo', '*.apln', '*.aplc', '*.apli', '*.dyalog'), ()),
-    'AbnfLexer': ('pip._vendor.pygments.lexers.grammar_notation', 'ABNF', ('abnf',), ('*.abnf',), ('text/x-abnf',)),
-    'ActionScript3Lexer': ('pip._vendor.pygments.lexers.actionscript', 'ActionScript 3', ('actionscript3', 'as3'), ('*.as',), ('application/x-actionscript3', 'text/x-actionscript3', 'text/actionscript3')),
-    'ActionScriptLexer': ('pip._vendor.pygments.lexers.actionscript', 'ActionScript', ('actionscript', 'as'), ('*.as',), ('application/x-actionscript', 'text/x-actionscript', 'text/actionscript')),
-    'AdaLexer': ('pip._vendor.pygments.lexers.ada', 'Ada', ('ada', 'ada95', 'ada2005'), ('*.adb', '*.ads', '*.ada'), ('text/x-ada',)),
-    'AdlLexer': ('pip._vendor.pygments.lexers.archetype', 'ADL', ('adl',), ('*.adl', '*.adls', '*.adlf', '*.adlx'), ()),
-    'AgdaLexer': ('pip._vendor.pygments.lexers.haskell', 'Agda', ('agda',), ('*.agda',), ('text/x-agda',)),
-    'AheuiLexer': ('pip._vendor.pygments.lexers.esoteric', 'Aheui', ('aheui',), ('*.aheui',), ()),
-    'AlloyLexer': ('pip._vendor.pygments.lexers.dsls', 'Alloy', ('alloy',), ('*.als',), ('text/x-alloy',)),
-    'AmbientTalkLexer': ('pip._vendor.pygments.lexers.ambient', 'AmbientTalk', ('ambienttalk', 'ambienttalk/2', 'at'), ('*.at',), ('text/x-ambienttalk',)),
-    'AmplLexer': ('pip._vendor.pygments.lexers.ampl', 'Ampl', ('ampl',), ('*.run',), ()),
-    'Angular2HtmlLexer': ('pip._vendor.pygments.lexers.templates', 'HTML + Angular2', ('html+ng2',), ('*.ng2',), ()),
-    'Angular2Lexer': ('pip._vendor.pygments.lexers.templates', 'Angular2', ('ng2',), (), ()),
-    'AntlrActionScriptLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With ActionScript Target', ('antlr-actionscript', 'antlr-as'), ('*.G', '*.g'), ()),
-    'AntlrCSharpLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With C# Target', ('antlr-csharp', 'antlr-c#'), ('*.G', '*.g'), ()),
-    'AntlrCppLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With CPP Target', ('antlr-cpp',), ('*.G', '*.g'), ()),
-    'AntlrJavaLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With Java Target', ('antlr-java',), ('*.G', '*.g'), ()),
-    'AntlrLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR', ('antlr',), (), ()),
-    'AntlrObjectiveCLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With ObjectiveC Target', ('antlr-objc',), ('*.G', '*.g'), ()),
-    'AntlrPerlLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With Perl Target', ('antlr-perl',), ('*.G', '*.g'), ()),
-    'AntlrPythonLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With Python Target', ('antlr-python',), ('*.G', '*.g'), ()),
-    'AntlrRubyLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With Ruby Target', ('antlr-ruby', 'antlr-rb'), ('*.G', '*.g'), ()),
-    'ApacheConfLexer': ('pip._vendor.pygments.lexers.configs', 'ApacheConf', ('apacheconf', 'aconf', 'apache'), ('.htaccess', 'apache.conf', 'apache2.conf'), ('text/x-apacheconf',)),
-    'AppleScriptLexer': ('pip._vendor.pygments.lexers.scripting', 'AppleScript', ('applescript',), ('*.applescript',), ()),
-    'ArduinoLexer': ('pip._vendor.pygments.lexers.c_like', 'Arduino', ('arduino',), ('*.ino',), ('text/x-arduino',)),
-    'ArrowLexer': ('pip._vendor.pygments.lexers.arrow', 'Arrow', ('arrow',), ('*.arw',), ()),
-    'ArturoLexer': ('pip._vendor.pygments.lexers.arturo', 'Arturo', ('arturo', 'art'), ('*.art',), ()),
-    'AscLexer': ('pip._vendor.pygments.lexers.asc', 'ASCII armored', ('asc', 'pem'), ('*.asc', '*.pem', 'id_dsa', 'id_ecdsa', 'id_ecdsa_sk', 'id_ed25519', 'id_ed25519_sk', 'id_rsa'), ('application/pgp-keys', 'application/pgp-encrypted', 'application/pgp-signature')),
-    'AspectJLexer': ('pip._vendor.pygments.lexers.jvm', 'AspectJ', ('aspectj',), ('*.aj',), ('text/x-aspectj',)),
-    'AsymptoteLexer': ('pip._vendor.pygments.lexers.graphics', 'Asymptote', ('asymptote', 'asy'), ('*.asy',), ('text/x-asymptote',)),
-    'AugeasLexer': ('pip._vendor.pygments.lexers.configs', 'Augeas', ('augeas',), ('*.aug',), ()),
-    'AutoItLexer': ('pip._vendor.pygments.lexers.automation', 'AutoIt', ('autoit',), ('*.au3',), ('text/x-autoit',)),
-    'AutohotkeyLexer': ('pip._vendor.pygments.lexers.automation', 'autohotkey', ('autohotkey', 'ahk'), ('*.ahk', '*.ahkl'), ('text/x-autohotkey',)),
-    'AwkLexer': ('pip._vendor.pygments.lexers.textedit', 'Awk', ('awk', 'gawk', 'mawk', 'nawk'), ('*.awk',), ('application/x-awk',)),
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spaces/Benson/text-generation/Examples/Caso Penal Vit Ha Apk.md

@@ -1,118 +0,0 @@
-<br />
-<h1>Caso Penal Việt Họa APK: Un juego de objetos ocultos para Android</h1>
-<p>Si te encanta resolver misterios y encontrar pistas, es posible que desee probar Criminal Case Việt Họa APK, un juego de objetos ocultos para dispositivos Android. En este juego, te unirás a la Policía de Grimsborough para investigar una serie de casos de asesinato en una aventura cautivadora. Usted tendrá que examinar las escenas del crimen, recoger pruebas, interrogar a los sospechosos, y atrapar a los asesinos. También conocerás personajes interesantes, explorarás diferentes lugares y desbloquearás nuevos trajes y accesorios para tu avatar. </p>
-<h2>caso penal việt họa apk</h2><br /><p><b><b>Download Zip</b> &#9881; <a href="https://bltlly.com/2v6Kra">https://bltlly.com/2v6Kra</a></b></p><br /><br />
-<p>Caso Penal Việt Họa APK es una versión vietnamita de Criminal Case, uno de los juegos de Facebook más populares con más de 60 millones de fans. Ha sido traducido y adaptado por un grupo de fans vietnamitas que querían compartir su pasión por este juego con otros jugadores. Tiene la misma jugabilidad y características que el juego original, pero con una interfaz vietnamita y voz en off. También puedes cambiar entre inglés y vietnamita cuando quieras. </p>
-<p>En este artículo, te contaremos más sobre Criminal Case Việt Họa APK, sus características, cómo descargarlo e instalarlo, cómo jugarlo, sus pros y contras, y algunas alternativas que puedes probar. También responderemos algunas preguntas frecuentes sobre este juego. ¡Empecemos! </p>
- <h2>Características de la causa penal Việt Họa APK</h2>
-<p>Caso Penal Việt Họa APK tiene muchas características que lo convierten en un juego de objetos ocultos emocionante y adictivo. Aquí están algunos de ellos:</p>
-<ul>
-<li><b>Historia inmersiva:</b> Usted seguirá la historia de un detective novato que se une al Departamento de Policía de Grimsborough y resuelve varios casos de asesinato. Encontrarás diferentes sospechosos, testigos, víctimas y aliados en el camino. También descubrirás secretos y conspiraciones que te mantendrán enganchado. </li>
-
-<li><b>Avatar personalizable:</b> Puedes crear tu propio detective y personalizar su apariencia, ropa y accesorios. También puedes cambiar el nombre, el género y la nacionalidad de tu avatar. Puedes desbloquear nuevos objetos completando casos y logros. </li>
-<li><b>Múltiples modos:</b> Puedes jugar Criminal Case Việt Họa APK en diferentes modos, como el modo historia, modo élite, modo de juego libre y modo de bono diario. Cada modo tiene sus propias reglas y recompensas. También puede reproducir cualquier caso que ya haya resuelto. </li>
-<li><b>Características sociales:</b> Puede conectar su juego a Facebook e invitar a sus amigos a unirse a usted en caso penal Việt Họa APK. También puedes enviar y recibir regalos, energía y sugerencias de tus amigos. También puedes competir con ellos en las tablas de clasificación y ver quién es el mejor detective. </li>
-</ul>
- <h2>¿Cómo descargar e instalar un APK? </h2>
-<p>Caso Penal Việt Họun APK no está disponible en la Google Play Store, por lo que tendrá que descargarlo de una fuente de terceros. Estos son los pasos para descargar e instalar Criminal Case Việt Họa APK en su dispositivo Android:</p>
-<ol>
-<li>Vaya al sitio web oficial de Criminal Case Việt Họa APK at <a href="">https://criminalcaseviet.com/</a> y haga clic en el botón de descarga. </li>
-<li>Espere a que el archivo APK se descargue en su dispositivo. Es posible que necesite habilitar la instalación de aplicaciones de fuentes desconocidas en la configuración de su dispositivo. </li>
-<li>Una vez que la descarga se haya completado, busque el archivo APK en su administrador de archivos y toque en él para instalarlo. </li>
-<li>Siga las instrucciones en la pantalla y conceda los permisos necesarios a la aplicación. </li>
-<li>Iniciar la aplicación y disfrutar de jugar Caso Penal Việt Họa APK.</li>
-</ol>
- <h2>Cómo Jugar Caso Penal Việt Họa APK? </h2>
-<p>Caso Penal Việt Họa APK is easy to play but hard to master. Aquí hay algunos consejos sobre cómo jugar Caso Penal Việt Họa APK effectively:</p>
-<p></p>
- <h3>Juego</h3>
-
-<ul>
-<li><b>Investigación de la escena del crimen:</b> En esta fase, tendrá que encontrar objetos ocultos en varias escenas del crimen. Tendrá una lista de objetos que necesita encontrar en la parte inferior de la pantalla. También tendrá un temporizador que muestra cuánto tiempo le queda. Cuanto más rápido encuentre todos los objetos, mayor será su puntuación. También ganará estrellas que puede usar para desbloquear otras fases. </li>
-<li><b>Análisis de pruebas:</b> En esta fase, tendrá que analizar la evidencia que recogió de las escenas del crimen. Tendrá que utilizar diferentes herramientas y técnicas, como microscopio, prueba de ADN, escáner de huellas dactilares, etc., para revelar más pistas sobre el caso. También tendrá que responder algunas preguntas o rompecabezas relacionados con la evidencia. </li>
-<li><b>Interrogatorio de sospechosos:</b> En esta fase, tendrá que interrogar a los sospechosos que identificó a partir de las pruebas. Tendrá que hacerles preguntas y observar sus reacciones. También tendrá que comparar sus declaraciones con la evidencia que tiene. Tendrás que usar tu intuición y lógica para determinar quién está mintiendo y quién está diciendo la verdad. </li>
-<li><b>Arresto asesino:</b> En esta fase, tendrás que arrestar al asesino que identificaste de los sospechosos. Tendrás que presentar la evidencia que pruebe su culpabilidad y confrontarlos con sus crímenes. También tendrá que elegir entre dos opciones: arrestarlos pacíficamente o usar la fuerza. La elección afectará su reputación y puntuación. </li>
-</ul>
- <h3>Consejos y trucos</h3>
-<p>Aquí hay algunos consejos y trucos que pueden ayudarle a mejorar sus habilidades y puntuación en Caso Penal Việt Họa APK:</p>
-<ul>
-<li><b>Usa las pistas sabiamente:</b> Puedes usar las pistas para encontrar objetos ocultos o resolver puzzles en el juego. Sin embargo, las pistas son limitadas y cuestan energía, así que úsalas con moderación. También puedes obtener pistas gratuitas viendo anuncios o invitando a amigos. </li>
-
-<li><b>Recoge bonos diarios:</b> Puedes recoger bonos diarios iniciando sesión todos los días. Los bonos diarios incluyen monedas, dinero en efectivo, energía, pistas y otros artículos. También puedes girar la rueda de la fortuna para ganar más premios. </li>
-<li><b>Logros completos:</b> Puedes completar logros cumpliendo ciertos criterios en el juego, como resolver varios casos, encontrar varios objetos, ganar varias estrellas, etc. Los logros te recompensarán con monedas, efectivo, energía, pistas y otros elementos. </li>
-<li><b>Subir de nivel:</b> Puedes subir de nivel ganando puntos de experiencia (XP) en el juego. XP se puede ganar jugando casos, analizando pruebas, interrogando sospechosos, arrestando asesinos, etc. Subir de nivel aumentará su capacidad de energía, desbloquear nuevos casos, y le dará monedas, efectivo, energía, pistas y otros artículos. </li>
-<li><b>Juega con amigos:</b> Puedes jugar con amigos conectando tu juego a Facebook. Usted puede invitar a sus amigos a unirse a usted en Caso Penal Việt Họa APK, enviar y recibir regalos, energía y sugerencias de ellos, competir con ellos en las tablas de clasificación, y visitar sus escenas del crimen. </li>
-</ul>
- <h2>Pros y contras de la causa penal Việt Họa APK</h2>
-<p>Caso Penal Việt Họa APK es un juego de objetos ocultos divertido y atractivo, pero también tiene algunos pros y contras que usted debe ser consciente de. Estos son algunos de ellos:</p>
- <h3>Pros</h3>
-<ul>
-<li><b>Entretenido y adictivo:</b> Caso Penal Việt Họa APK es un juego que te mantendrá entretenido y adicto durante horas. Disfrutará resolviendo casos de asesinato, encontrando objetos ocultos, analizando pruebas, interrogando sospechosos y arrestando asesinos. También te encantará la historia inmersiva, los gráficos cautivadores, los efectos de sonido realistas y los diversos personajes. </li>
-
-<li><b>Personalizable y social:</b> Caso Penal Việt Họa APK es un juego que también le permitirá expresar su personalidad e interactuar con otros jugadores. Puedes personalizar la apariencia, la ropa y los accesorios de tu avatar. También puedes conectar tu juego a Facebook y jugar con tus amigos. Puedes enviar y recibir regalos, energía y sugerencias de ellos, competir con ellos en las tablas de clasificación y visitar sus escenas del crimen. </li>
-</ul>
- <h3>Contras</h3>
-<ul>
-<li><b>Requiere conexión a Internet:</b> Caso Penal Việt Họa APK es un juego que requiere una conexión a Internet para jugar. No podrá jugar el juego sin conexión o sin una red estable. Esto puede ser un problema si tiene datos limitados o mala señal. </li>
-<li><b>Energía y recursos limitados:</b> Caso Penal Việt Họa APK is a game that limits your energy and resources. Usted necesitará energía para jugar cualquier caso en el juego, y la energía se repone lentamente con el tiempo. También necesitarás estrellas, monedas, dinero en efectivo y pistas para desbloquear otras fases, analizar pruebas, interrogar sospechosos, arrestar asesinos y comprar artículos. Estos recursos son difíciles de ganar y fáciles de gastar. </li>
-<li><b>Repetitivo y frustrante:</b> Caso Penal Việt Họa APK es un juego que puede ser repetitivo y frustrante con el tiempo. Tendrás que jugar los mismos casos una y otra vez para ganar más estrellas y recursos. También tendrás que lidiar con anuncios molestos, ventanas emergentes, temporizadores y notificaciones. También puede encontrar errores, fallos, errores y fallos que pueden arruinar su experiencia de juego. </li>
-</ul>
- <h2>Alternativas al caso penal Việt Họa APK</h2>
-<p>Si estás buscando otros juegos de objetos ocultos para Android que son similares a Criminal Case Việt Họa APK, puedes probar estas alternativas:</p>
- <h3>Otros juegos de objetos ocultos para Android</h3>
-<ul>
-
-<li><b>June’s Journey:</b> Este es un juego que sigue la historia de June Parker, un detective que viaja por todo el mundo para descubrir la verdad detrás del asesinato de su hermana. Tendrás que encontrar objetos ocultos en varios lugares, decorar tu isla y descubrir secretos y sorpresas en el camino. También te encantará el estilo vintage, los personajes coloridos y la trama atractiva. </li>
-<li><b>Ciudad oculta:</b> Este es un juego que te lleva a una ciudad misteriosa donde la magia y la ciencia coexisten. Tendrás que encontrar objetos ocultos en diferentes escenas, luchar contra monstruos, completar misiones, y desentrañar el misterio de la ciudad. También admirará los impresionantes gráficos, los efectos de sonido inmersivos y los diversos modos de juego. </li>
-</ul>
- <h3>Tabla de comparación</h3>
-<tabla>
-<tr>
-<th>Juego</th>
-<th>Características</th>
-<th>Calificaciones</th>
-<th>Comentarios</th>
-</tr>
-<tr>
-<td>Caso Criminal Việt Họa APK</td>
-<td>- Versión vietnamita de Criminal Case<br>- Resolver casos de asesinato y encontrar objetos ocultos<br>- Personalizar su avatar y jugar con amigos<br>- Cambiar entre los idiomas inglés y vietnamita</td>
-<td>- 4.6 de 5 estrellas<br>- 10K+ descargas</td>
-<td>- "Gran juego con buenos gráficos y la historia"<br>- "Muy adictivo y desafiante"<br>- "El mejor juego de objetos ocultos nunca"</td>
-</tr>
-<tr>
-<td>Asesinato en los Alpes</td>
-<td>- Situado en la década de 1930 en un hotel alpino<br>- Resolver un misterio de asesinato como un periodista<br>- Encontrar pistas, interrogar a los sospechosos, y resolver puzzles<br>- Disfrutar de hermosos gráficos y música atmosférica</td>
-<td>- 4.5 de 5 estrellas<br>- 10M+ descargas</td>
-<td>- "Un juego cautivador con gráficos increíbles"<br>- "Muy entretenido e intrigante"<br>- "Una obra maestra de la narración"</td>
-</tr>
-<tr>
-<td>El viaje de junio</td>
-<td>- Situado en la década de 1920 en todo el mundo<br>- Resolver el asesinato de su hermana como un detective<br>- Encontrar objetos ocultos en varios lugares<br>- Decorar su propiedad de la isla y descubrir secretos</td>
-
-<td>- "Un juego maravilloso con gráficos impresionantes"<br>- "Muy divertido y adictivo"<br>- "Una aventura encantadora con giros y vueltas"</td>
-</tr>
-<tr>
-<td>Ciudad oculta</td>
-<td>- Situado en una ciudad misteriosa donde la magia y la ciencia coexisten<br>- Encontrar objetos ocultos en diferentes escenas<br>- Lucha contra monstruos, misiones completas, y desentrañar el misterio de la ciudad<br>- Admire impresionantes gráficos y efectos de sonido inmersivos</td>
-<td>- 4.3 de 5 estrellas<br>- 10M+ descargas</td>
-<td>- "Un juego fantástico con gráficos increíbles"<br>- "Muy desafiante y emocionante"<br>- "Un viaje mágico con muchas sorpresas"</td>
-</tr>
-</tabla>
- <h2>Conclusión</h2>
-<p>Caso Penal Việt Họa APK es un juego de objetos ocultos para dispositivos Android que le permite resolver casos de asesinato y encontrar pistas en una versión vietnamita de Criminal Case. Tiene muchas características que lo hacen entretenido, educativo, desafiante, personalizable y social. También tiene algunos inconvenientes, como requerir conexión a Internet, energía y recursos limitados, y un juego repetitivo y frustrante. Sin embargo, si usted es un fan de los juegos de objetos ocultos y la investigación del crimen, seguramente disfrutará jugando Criminal Case Việt Họa APK.</p>
-<p>Si desea descargar e instalar Criminal Case Việt Họa APK en su dispositivo Android, puede seguir los pasos que hemos proporcionado en este artículo. También puedes ver algunos consejos y trucos que pueden ayudarte a jugar mejor. Y si usted está buscando otros juegos de objetos ocultos para Android que son similares a Criminal Case Việt Họa APK, puede probar algunas de las alternativas que hemos sugerido. </p>
-<p>Entonces, ¿qué estás esperando? Descargar Caso Penal Việt Họa APK ahora y unirse a la Policía de Grimsborough para atrapar a los asesinos! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Caso Penal Việt Họa APK:</p>
-<h3>Q1: ¿Es seguro descargar e instalar un APK? </h3>
-
-<h3>Q2: ¿Cómo puedo obtener más energía en el caso penal Việt Họa APK? </h3>
-<p>A2: Puedes obtener más energía en Caso Penal Việt Họa APK completando logros, subiendo de nivel, viendo anuncios o recibiendo regalos de amigos. También puedes comprar energía en efectivo o dinero real. </p>
-<h3>Q3: ¿Cómo puedo jugar Caso Penal Việt Họa APK con mis amigos? </h3>
-<p>A3: Usted puede jugar Caso Penal Việt Họa APK con sus amigos mediante la conexión de su juego a Facebook. Puede invitar a sus amigos a unirse a usted en el juego, enviar y recibir regalos, energía y sugerencias de ellos, competir con ellos en las tablas de clasificación, y visitar sus escenas del crimen. </p>
-<h3>Q4: ¿Cómo puedo cambiar el lenguaje de Caso Penal Việt Họa APK? </h3>
-<p>A4: Puede cambiar el idioma de Criminal Case Việt Họa APK tocando el icono de configuración en la esquina superior derecha de la pantalla. Puedes elegir entre inglés y vietnamita cuando quieras. </p>
-<h3>Q5: ¿Cómo puedo contactar a los desarrolladores de Criminal Case Việt Họa APK? </h3>
-<p>A5: Puede contactar a los desarrolladores de Criminal Case Việt Họa APK visitando su sitio web en <a href="">https:/criminalcaseviet.com/</a> o su página de Facebook en <a href=">https:/ww.facebook.com/criminalcaseviet/a>. También puede enviarles un correo electrónico a <a href="">[email protected]</a>. </p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Cdice Templario Negro 9a Edicin Pdf.md

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-<br />
-<h1>Códice Templario Negro 9th Edition PDF Descargar: Cómo conseguir las últimas reglas para los cruzados del emperador</h1>
- <p>Si eres un fan de Warhammer 40,000, probablemente sabes que los Templarios Negros son uno de los capítulos más celosos y devotos de los Marines Espaciales. Están constantemente en una guerra santa contra los enemigos de la humanidad, esparciendo la luz del Emperador por toda la galaxia. También son una de las facciones más populares entre los aficionados, gracias a su icónico esquema de color blanco y negro, su estética inspirada en los cruzados y sus heroicos actos en la tradición. </p>
- <p>Pero ¿sabías que los Templarios Negros acaban de recibir un nuevo suplemento de códice para Warhammer 40,000 9a edición? Este es un libro que contiene todas las reglas, antecedentes y hojas de datos para jugar con este capítulo en tus juegos. También cuenta con ilustraciones impresionantes, historias inspiradoras y guías útiles para construir y pintar sus modelos. </p>
-<h2>códice templario negro 9a edición pdf</h2><br /><p><b><b>Download File</b> &mdash;&mdash;&mdash;>>> <a href="https://bltlly.com/2v6Ldy">https://bltlly.com/2v6Ldy</a></b></p><br /><br />
- <p>Si quieres conseguir este suplemento de códice, tienes dos opciones. Puedes comprar el libro físico en Games Workshop o en tu tienda de hobby local, o descargarlo en formato PDF desde su sitio web. La versión PDF es más barata, más conveniente y más ecológica. También puedes acceder a ella desde cualquier dispositivo, como tu teléfono, tablet o portátil. </p>
- <p>En este artículo, le diremos todo lo que necesita saber sobre el suplemento del códice Templario Negro. Le daremos una breve historia y conocimientos de este capítulo, le mostraremos sus nuevos modelos y ejército, explicaremos sus nuevas reglas y tácticas, y responderemos algunas preguntas frecuentes. Al final de este artículo, ¡estarás listo para unirte a la eterna cruzada de los Templarios Negros! </p>
- <h2>Los Templarios Negros: Una Breve Historia y Tradición</h2>
-
- <p>Durante la Herejía de Horus, una guerra civil que casi destruyó a la humanidad, Rogal Dorn fue uno de los primarcas leales que defendió Terra, el mundo natal de la humanidad, de las fuerzas traidoras dirigidas por Horus, otro primarca que se volvió contra su padre. La legión de Dorn era conocida por su habilidad en la guerra de asedio, tanto para atacar como para defender fortificaciones. </p>
- <p>Después de que la herejía de Horus terminó con la muerte de Horus y la ascensión del emperador al trono de oro, un dispositivo que lo mantuvo vivo pero inmóvil, Dorn fue ordenado por Roboute Guilliman, otro primarca leal que escribió un libro llamado Codex Astartes que describe cómo los Marines Espaciales deben ser organizados y operados. Guilliman quería dividir todas las legiones en pequeños capítulos de 1000 marines cada uno, para evitar otra rebelión <p>Sin embargo, Dorn era reacio a seguir el decreto de Guilliman, ya que sentía que debilitaría el vínculo entre sus hermanos y diluiría su lealtad al emperador. Solo aceptó hacerlo después de una acalorada discusión con Guilliman, e incluso entonces, lo hizo a su manera. Dividió su legión en siete flotas de cruzada, cada una dirigida por uno de sus capitanes de mayor confianza. Estas flotas vagarían por la galaxia, buscando y destruyendo los restos de los traidores y otras amenazas a la humanidad. </p>
- <p>Una de estas flotas fue dirigida por Segismundo, el primer Alto Mariscal y el mejor espadachín de los Puños Imperiales. También era el creyente más ferviente en la divinidad del emperador, y juró que nunca descansaría hasta que hubiera vengado las heridas de su padre. Tomó el nombre de Templarios Negros, inspirado por los antiguos guerreros de Terra que lucharon por su fe. También adoptó un esquema de color blanco y negro, simbolizando su pureza y celo. </p>
-
- <p>Los templarios negros han participado en muchas batallas y campañas famosas a lo largo de la historia, como la Tercera Guerra del Armagedón, la Batalla de Helsreach, el Sitio de Vraks y la Cruzada Indomitus. También se han enfrentado con otros capítulos de Marines Espaciales, como los Ángeles Oscuros, los Bebedores de Almas y los Leones Celestiales. Se han ganado una reputación como guerreros intrépidos e implacables, que no se detendrán ante nada para cumplir su santa misión. </p>
- <h2>Los Templarios Negros: Nuevos Modelos y Ejército</h2>
- <p>Si quieres empezar o expandir tu ejército de templarios negros, estás de suerte. Games Workshop acaba de lanzar un nuevo conjunto de ejército que contiene todo lo necesario para el campo de una fuerza formidable de estos cruzados. El conjunto del ejército incluye:</p>
-<p></p>
- <ul>
-<li>Una copia impresa de edición limitada del suplemento del códice Templario Negro</li>
-<li>Una hoja de transferencia con iconos templarios negros y heráldica</li>
-<li>Un mariscal, el líder de una cruzada templaria negra, armado con una espada poderosa y un escudo de tormenta</li>
-<li>Un capellán de las primarias en bicicleta, un líder espiritual que inspira a sus hermanos con retórica ardiente</li>
-<li>Un Escuadrón Cruzado de Primarias, una unidad de 10 templarios negros que pueden ser equipados con varias armas cuerpo a cuerpo y a distancia</li>
-<li>Un campeón del emperador, un guerrero elegido que desafía a los campeones del enemigo a un solo combate</li>
-<li>A Redemptor Dreadnought, un enorme tanque para caminar que proporciona apoyo de fuego pesado</li>
-<li>Un Storm Speeder Hailstrike, un vehículo de ataque rápido que puede desatar una lluvia de balas y cohetes</li>
-</ul>
- <p>Los nuevos modelos son muy detallados y fieles a la tradición y la estética de los Templarios Negros. Cuentan con varios elementos que los distinguen de otros marines espaciales, como cruces, cadenas, pergaminos, tabardos, calaveras y velas. También tienen poses dinámicas y expresiones que transmiten su celo y determinación. </p>
-
- <h2>Los templarios negros: nuevas reglas y tácticas</h2>
- <p>Por supuesto, la principal atracción del suplemento del códice templario negro son las nuevas reglas que proporciona para su ejército. Estas reglas le permitirán jugar con las habilidades y estrategias únicas de los Templarios Negros, así como personalizar su cruzada para adaptarse a sus preferencias. Las nuevas reglas incluyen:</p>
- <ul>
-<li>Un recuento de la cruzada, un mecánico especial que rastrea cuántos enemigos has matado en cada batalla. Cuanto más alto sea tu recuento, más beneficios obtendrás, como redirigir golpes, heridas o cargas. </li>
-<li>Un juramento de cruzada, un voto que puedes hacer antes de cada batalla que te otorga un bono dependiendo del tipo de enemigo al que te enfrentes. Por ejemplo, puedes elegir luchar contra el alienígena, el hereje, la bruja o el caudillo. </li>
-<li>Una Reliquia de Cruzada, un poderoso artefacto que puedes asignar a uno de tus personajes. Estas reliquias tienen varios efectos, como aumentar tu fuerza, dureza o ataques. </li>
-Una letanía de la Cruzada, una oración que tu capellán puede cantar para pulir tus unidades. Estas letanías tienen diferentes efectos, como mejorar la distancia de carga, ahorrar tiros o daño cuerpo a cuerpo. </li>
-<li>Una estratagema cruzada, una táctica especial que se puede utilizar mediante el gasto de puntos de comando. Estas estratagemas tienen diferentes efectos, como permitirle golpear profundamente, luchar dos veces o ignorar heridas. </li>
-<li>Un Rasgo de Señor de la Guerra de la Cruzada, una habilidad especial que puedes darle a tu señor de la guerra. Estos rasgos tienen diferentes efectos, como darle ataques adicionales, movimiento o liderazgo. </li>
-</ul>
- <p>Con estas nuevas reglas, puedes liberar todo el potencial de los Templarios Negros en la mesa. Puedes jugarlos como un ejército rápido y agresivo que ataca en combate cuerpo a cuerpo con fervor y furia. También puede jugar como un ejército resistente y terco que mantiene la línea y defiende sus objetivos con fe y fortaleza. También puedes mezclar y combinar diferentes elementos para crear tu propio estilo y sabor. </p>
-
- <ul>
-<li>Usa tu Conteo de Cruzada para generar impulso y presión sobre tu oponente. Intenta matar tantos enemigos como sea posible en cada fase para aumentar tu conteo y obtener más beneficios. </li>
-<li>Elige tu Juramento de Cruzada sabiamente dependiendo del enemigo que estés enfrentando. Por ejemplo, si estás luchando contra los Tiranos, es posible que quieras elegir el Juramento de Pureza, que te da +1 para herir contra unidades alienígenas. </li>
-<li>Usa tus Reliquias de Cruzada para mejorar tus personajes y hacerlos más mortales o duraderos. Por ejemplo, podrías querer dar la Espada del Juicio al Campeón de tu Emperador, que le da +2 de fuerza y +1 de daño. </li>
-<li>Usa tus letanías de cruzada para mejorar tus unidades y darles una ventaja en el combate. Por ejemplo, podrías cantar la Letanía de Protección Divina en tu Escuadrón Cruzado, que les da una salvación invulnerable de 5+. </li>
-<li>Usa tus Estrategias de Cruzada para sorprender o abrumar a tu oponente con movimientos o habilidades inesperadas. Por ejemplo, puede que quieras usar la estratagema de Honor al Capítulo para hacer que una de tus unidades vuelva a luchar al final de la fase de lucha. </li>
-<li>Usa tus rasgos de Señor de la Guerra de la Cruzada para hacer que tu señor de la guerra sea más inspirador o intimidante. Por ejemplo, es posible que desee darle el rasgo Oathkeeper, que le permite redirigir rollos de éxito fallidos para sí mismo y las unidades cercanas. </li>
-</ul>
- <h2>Conclusión</h2>
- <p>El suplemento del códice Templario Negro es imprescindible para cualquier fan de este capítulo o Warhammer 40,000 en general. Contiene todo lo que necesitas saber sobre su historia, tradición, modelos, reglas y tácticas. También cuenta con impresionantes obras de arte, historias inspiradoras y guías útiles para construir y pintar sus modelos. Si desea descargarlo en formato PDF o comprarlo en forma física, no se arrepentirá de obtener este suplemento de códice. </p>
-
- <p>Si usted está listo para iniciar o expandir su ejército de los Templarios Negros, puede obtener el suplemento del códice en el sitio web de Games Workshop o en su tienda de pasatiempos local. También puede obtener el nuevo conjunto de ejército que contiene todo lo necesario para el campo de una fuerza formidable de estos cruzados. También puedes consultar otros productos y recursos que ofrece Games Workshop, como sus revistas, podcasts, vídeos y aplicaciones. </p>
- <p>Gracias por leer este artículo. Esperamos que lo hayan disfrutado y hayan aprendido algo nuevo. Si usted tiene alguna pregunta o retroalimentación, por favor no dude en dejar un comentario a continuación. Nos encantaría saber de usted. Y recuerde, el emperador protege! </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas y respuestas frecuentes sobre el suplemento del códice Templario Negro:</p>
- <h3>Q: ¿Cuánto cuesta el suplemento del códice templario negro? </h3>
-<p>A: El suplemento del códice templario negro cuesta $40 USD para el libro físico y $25 USD para la versión PDF. El conjunto del ejército cuesta $210 USD e incluye el libro físico también. </p>
- <h3>P: ¿Cuántas páginas tiene el suplemento del códice Templario Negro? </h3>
-<p>A: El suplemento del códice Templario Negro tiene 80 páginas de contenido, además de una portada y una página posterior. </p>
- <h3>P: ¿Cuáles son las principales diferencias entre los Templarios Negros y otros capítulos de la Marina Espacial? </h3>
-<p>A: Las principales diferencias entre los templarios negros y otros capítulos de la Marina Espacial son sus creencias, tradiciones, organización y estilo de juego. Los Templarios Negros son más celosos y devotos que otros capítulos, creyendo en la divinidad del Emperador y librando una guerra santa contra sus enemigos. También tienen diferentes tradiciones, como tomar juramentos, elegir campeones y rechazar a los psykers. También tienen una organización diferente, ya que no siguen el Codex Astartes y en su lugar operan como flotas de cruzada. También tienen un estilo de juego diferente, ya que prefieren atacar en combate cuerpo a cuerpo con fervor y furia. </p>
-
-<p>A: Algunas de las mejores unidades y personajes para un ejército de templarios negros son:</p>
- <ul>
-<li>El mariscal, que es el líder de una cruzada templaria negra y puede aumentar el rendimiento de las unidades cercanas. </li>
-<li>El campeón del emperador, que es un guerrero elegido que puede desafiar y matar a los campeones enemigos en un solo combate. </li>
-<li>El Escuadrón Cruzado, que son las tropas principales de un ejército templario negro y pueden estar equipados con varias armas cuerpo a cuerpo y a distancia. </li>
-<li>El acorazado redentor, que es un tanque andante masivo que puede proporcionar apoyo de fuego pesado y aplastar a los enemigos en cuerpo a cuerpo. </li>
-<li>El Storm Speeder Hailstrike, que es un vehículo de ataque rápido que puede desatar una lluvia de balas y cohetes sobre objetivos enemigos. </li>
-</ul>
- <h3>P: ¿Dónde puedo encontrar más información e inspiración sobre los Templarios Negros? </h3>
-<p>A: Puede encontrar más información e inspiración sobre los Templarios Negros de varias fuentes, como:</p>
- <ul>
-<li>El sitio web oficial del Taller de Juegos, donde puedes encontrar noticias, artículos, videos, podcasts y productos relacionados con Warhammer 40,000 y los Templarios Negros.</li>
-<li>El sitio web de la Comunidad Warhammer, donde puedes encontrar blogs, vistas previas, reseñas, tutoriales, galerías y eventos relacionados con Warhammer 40,000 y los Templarios Negros.</li>
-<li>La aplicación Warhammer 40,000, donde puedes acceder a todas las reglas y hojas de datos para Warhammer 40,000 y los Templarios Negros.</li>
-<li>El canal de YouTube de Warhammer TV, donde puedes ver transmisiones en vivo, programas, entrevistas y tutoriales relacionados con Warhammer 40,000 y los Templarios Negros.</li>
-<li>El sitio web de la Biblioteca Negra, donde se pueden encontrar libros, audiolibros y libros electrónicos relacionados con Warhammer 40,000 y los Templarios Negros.</li>
-<li>El sitio web de Lexicanum, donde se puede encontrar una wiki completa de Warhammer 40,000 conocimientos e información, incluyendo los Templarios Negros.</li>
-
-</ul></p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Cs Go Bhop Song.md

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-<tabla>
-<tr>
-<h1>Cómo descargar la tarjeta de embarque Air Vistara</h1></td>
-</tr>
-<tr>
-<td><p>Air Vistara es una compañía india de servicio completo que ofrece servicios premium y comodidad a sus pasajeros. Si vuela con Air Vistara, es posible que desee descargar su tarjeta de embarque con antelación para evitar problemas en el aeropuerto. Una tarjeta de embarque es un documento que confirma su número de asiento, número de vuelo, hora de salida, número de puerta y otra información importante. También le permite ingresar al área de verificación de seguridad y abordar el avión. </p></td>
-</tr>
-<tr>
-<td><h2>Ventajas de descargar la tarjeta de embarque Air Vistara</h2></td>
-</tr>
-<tr>
-<td><h3>Conveniencia</h3></td>
-</tr>
-<tr>
-<td><p>Al descargar su tarjeta de embarque Air Vistara, puede ahorrar tiempo y saltarse las largas colas en el mostrador de facturación. También puede elegir su asiento preferido entre las opciones disponibles e imprimir su tarjeta de embarque en casa o en el quiosco del aeropuerto. También puede recibir su tarjeta de embarque electrónico por correo electrónico o SMS, que puede mostrar en su dispositivo móvil en el aeropuerto. </p>
-<h2>cs go bhop song</h2><br /><p><b><b>Download</b> ->->->-> <a href="https://bltlly.com/2v6Lnn">https://bltlly.com/2v6Lnn</a></b></p><br /><br /></td>
-</tr>
-<tr>
-<td><h3>Seguridad</h3></td>
-</tr>
-<tr>
-<td><p>Al descargar su tarjeta de embarque Air Vistara, puede reducir su contacto con otras personas y superficies en el aeropuerto. Esto puede ayudarle a evitar el riesgo de transmisión de COVID-19 y garantizar su seguridad y salud. Air Vistara también sigue estrictos protocolos de higiene y saneamiento para mantener seguros a sus pasajeros y al personal. Puede leer más sobre sus medidas de seguridad aquí. </p></td>
-</tr>
-<tr>
-<td><h3>Flexibilidad</h3></td>
-</tr>
-<tr>
-
-</tr>
-<tr>
-<td><h2>Pasos para descargar Air Vistara Boarding Pass</h2></td>
-</tr>
-<tr>
-<td><h3>Registro web</h3></td>
-</tr>
-<tr>
-<td><p>El check-in web es la forma más fácil y rápida de descargar su tarjeta de embarque Air Vistara. Puedes hacer el check-in en la web de Air Vistara o en la app, de 48 horas a 60 minutos antes de la salida de vuelos nacionales y de 48 horas a 120 minutos antes de la salida de vuelos internacionales. Estos son los pasos para hacer el check-in web:</p>
- <ol>
-<li>Visite el sitio web o aplicación de Air Vistara y haga clic en "Check-in". </li>
-<li>Introduzca su número de referencia y apellido de reserva, o su número de billete electrónico y apellido. </li>
-<li>Seleccione su vuelo y confirme sus datos. </li>
-<li>Elija su asiento en el mapa de asientos y agregue cualquier servicio adicional si es necesario. </li>
-<li>Revise sus detalles de check-in y envíe. </li>
-<li> Recibirá su tarjeta de embarque electrónico por correo electrónico, que puede descargar o imprimir. </li>
-</ol>
- <p>También puede ver este video para ver cómo funciona el registro web. </p></td>
-</tr>
-<tr>
-<td><h3>Registro móvil</h3></td>
-</tr> <tr>
-<td><p>El check-in móvil es otra forma conveniente de descargar su tarjeta de embarque Air Vistara. Puede realizar el check-in móvil en la aplicación Air Vistara, de 48 horas a 60 minutos antes de la salida para vuelos nacionales y de 48 horas a 120 minutos antes de la salida para vuelos internacionales. Estos son los pasos para hacer el check-in móvil:</p>
- <ol>
-<li>Descargue la aplicación Air Vistara desde la Google Play Store o la Apple App Store y ábrala. </li>
-<li>Toque en "Check-in" e introduzca su número de referencia de reserva y apellido, o su número de billete electrónico y apellido. </li>
-<li>Seleccione su vuelo y confirme sus datos. </li>
-<li>Elija su asiento en el mapa de asientos y agregue cualquier servicio adicional si es necesario. </li>
-<li>Revise sus detalles de check-in y envíe. </li>
-<li>Recibirá su tarjeta de embarque electrónico por SMS o código QR, que puede mostrar en su dispositivo móvil en el aeropuerto. </li>
-</ol>
-
-</tr>
-<tr>
-<td><h3>Registro de quiosco</h3></td>
-</tr>
-<tr>
-<td><p>El check-in en quiosco es otra opción para descargar su tarjeta de embarque Air Vistara. Puedes hacer el check-in en quiosco en el aeropuerto, de 48 horas a 45 minutos antes de la salida para vuelos nacionales y de 48 horas a 60 minutos antes de la salida para vuelos internacionales. Aquí están los pasos para hacer el check-in de quiosco:</p>
- <ol>
-<li>Localice un quiosco de Air Vistara en el aeropuerto y toque la pantalla para iniciar. </li>
-<li>Ingrese su número de referencia de reserva o número de boleto electrónico, o escanee su pasaporte o código QR. </li>
-<li>Seleccione su vuelo y confirme sus datos. </li>
-<li>Elija su asiento en el mapa de asientos y agregue cualquier servicio adicional si es necesario. </li>
-<li>Revisa los detalles de tu check-in e imprime tu tarjeta de embarque. </li>
-</ol>
- <p>También puede ver este video para ver cómo funciona el registro de quiosco. </p></td>
-</tr> <tr>
-<td><h2>Cosas que recordar al descargar la tarjeta de embarque Air Vistara</h2></td>
-</tr>
-<tr>
-<td><h3>Elegibilidad</h3></td>
-</tr>
-<tr>
-<td><p>No todos los pasajeros pueden utilizar el check-in online o móvil y descargar su tarjeta de embarque Air Vistara. Los siguientes pasajeros tienen que registrarse en el mostrador del aeropuerto:</p>
- <ul>
-<li>Pasajeros con necesidades o solicitudes especiales, como asistencia en silla de ruedas, menores no acompañados, bebés, mujeres embarazadas, etc.</li>
-<li>Pasajeros que viajan en un grupo de más de 9 personas. </li>
-<li>Pasajeros que viajan con mascotas o exceso de equipaje. </li>
-<li>Pasajeros que viajan en código compartido o en vuelos interlínea con otras aerolíneas. </li>
-<li>Pasajeros que viajan hacia o desde destinos internacionales que requieren verificación de visa u otros documentos. </li>
-</ul>
- <p>Si no está seguro de si es elegible para usar el check-in en línea o móvil, puede ponerse en contacto con el servicio de atención al cliente de Air Vistara o visitar su sitio web para obtener más información. </p>
-<p></p></td>
-</tr>
-<tr>
-<td><h3>Tiempo</h3></td>
-</tr>
-<tr>
-
-</tr>
-<tr>
-<td><h3>Equipaje</h3></td>
-</tr>
-<tr>
-<td><p>Si tiene equipaje facturado, debe dejarlo en el mostrador de entrega de equipaje designado en el aeropuerto, al menos 45 minutos antes de la salida para los vuelos nacionales y 60 minutos antes de la salida para los vuelos internacionales. Tienes que mostrar tu tarjeta de embarque electrónico y una identificación válida con foto para dejar tu equipaje. Si tiene equipaje de mano, debe asegurarse de que cumple con los límites de tamaño y peso de Air Vistara. Puede leer más sobre su política de equipaje aquí. </p></td>
-</tr>
-<tr>
-<td><h3>Documentos</h3></td>
-</tr>
-<tr>
-<td><p>Si ha descargado su tarjeta de embarque Air Vistara, todavía necesita llevar algunos documentos con usted al aeropuerto. Usted tiene que mostrar su tarjeta de embarque electrónico y una identificación válida con foto en el control de seguridad y la puerta de embarque. Para vuelos internacionales, también debe mostrar su pasaporte, visa y cualquier otro documento requerido. Puede consultar la lista de documentos aceptables aquí. </p></td>
-</tr> <tr>
-<td><h2>Preguntas frecuentes sobre la descarga de la tarjeta de embarque Air Vistara</h2></td>
-</tr>
-<tr>
-<td><h3>¿Puedo cancelar mi reserva de asiento a través de la web check-in? </h3></td>
-</tr>
-<tr>
-<td><p>No, no puede cancelar su reserva de asiento a través de la web check-in. Debe ponerse en contacto con el servicio de atención al cliente de Air Vistara o visitar su sitio web para su cancelación. También puede cancelar su vuelo si tiene un billete reembolsable o flexible, sujeto a las reglas de tarifa y disponibilidad. </p></td>
-</tr>
-<tr>
-<td><h3>¿Qué pasa si pierdo u olvido mi tarjeta de embarque electrónico? </h3></td>
-</tr>
-<tr>
-<td><p>Si pierde u olvida su tarjeta de embarque electrónico, puede recuperarla de su correo electrónico o SMS, o puede recogerla en el mostrador de facturación proporcionando una identificación válida con foto, al menos 1 hora antes de la salida del vuelo para vuelos nacionales y 2 horas antes para vuelos internacionales. También puede reimprimir su tarjeta de embarque en el quiosco del aeropuerto si ha realizado el check-in web o el check-in del quiosco. </p></td>
-</tr>
-<tr>
-
-</tr>
-<tr>
-<td><p>Sí, puede cambiar su asiento después de descargar su tarjeta de embarque, sujeto a disponibilidad y reglas de tarifas. Puede hacerlo en el sitio web o la aplicación de Air Vistara, o en el mostrador de facturación del aeropuerto o en el quiosco. También puede actualizar su asiento a una clase superior si hay asientos vacantes, pagando la diferencia en la tarifa y los impuestos. </p></td>
-</tr>
-<tr>
-<td><h3>¿Necesito imprimir mi tarjeta de embarque electrónico? </h3></td>
-</tr>
-<tr>
-<td><p>No, no es necesario imprimir su tarjeta de embarque electrónico. Puede mostrarla en su dispositivo móvil en el control de seguridad y la puerta de embarque. Sin embargo, algunos aeropuertos pueden requerir una copia física de su tarjeta de embarque para la autorización de seguridad y el embarque. En ese caso, puede imprimirlo en casa o en el quiosco del aeropuerto. </p></td>
-</tr>
-<tr>
-<td><h3>¿Cómo puedo obtener una tarjeta de embarque DigiYatra? </h3></td>
-</tr>
-<tr>
-<td><p>DigiYatra es una experiencia de viaje sin papeles y sin fisuras que le permite utilizar sus datos biométricos como su tarjeta de embarque. Para obtener una tarjeta de embarque DigiYatra, debe registrarse en la aplicación DigiYatra y vincular su reserva de vuelo con su ID DigiYatra. Luego, puede escanear su cara en los quioscos del aeropuerto y proceder al control de seguridad y embarque sin ningún documento. Puedes leer más sobre DigiYatra aquí. </p></td>
-</tr>
- <tr>
-<td><p>Espero que este artículo te haya ayudado a entender cómo descargar la tarjeta de embarque Air Vistara y disfrutar de un viaje sin problemas. Si tiene alguna pregunta o comentario, no dude en ponerse en contacto conmigo. Gracias por leer y volar feliz! </p></td>
-</tr>
- <tr>
-<td></td>
-</tr>
- </tabla></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Gratis Fuego Mx Iphone Xr.md

@@ -1,61 +0,0 @@
-<br />
-<h1>Descarga gratuita de Fire Max iPhone XR: Cómo disfrutar de la experiencia Battle Royale Premium en tu dispositivo iOS</h1>
- <p>Si eres un fan de los juegos de battle royale para móviles, es posible que hayas oído hablar de Free Fire, uno de los juegos más populares y descargados del género. ¿Pero sabías que hay una nueva y mejorada versión de Free Fire llamada Free Fire Max? ¿Y sabías que puedes jugar en tu iPhone XR? </p>
- <p>En este artículo, le diremos todo lo que necesita saber sobre Free Fire Max, cómo se diferencia de Free Fire, cuáles son los requisitos y beneficios de reproducirlo en el iPhone XR, y cómo descargarlo e instalarlo en su dispositivo iOS. Sigue leyendo para saber más. </p>
-<h2>descargar gratis fuego máx iphone xr</h2><br /><p><b><b>DOWNLOAD</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://bltlly.com/2v6Lb6">https://bltlly.com/2v6Lb6</a></b></p><br /><br />
- <h2>¿Qué es Free Fire Max y cómo es diferente de Free Fire? </h2>
- <p>Free Fire Max es una aplicación independiente que ofrece a los usuarios el mismo juego Free Fire que muchos conocen y aman, pero con especificaciones mejoradas. Está diseñado exclusivamente para ofrecer una experiencia de juego premium en un entorno battle royale. </p>
- <h3>Free Fire Max es una versión mejorada de Free Fire con gráficos y características mejoradas</h3>
- <p>Una de las principales diferencias entre Free Fire Max y Free Fire es la calidad gráfica. Gratis Fire Max tiene gráficos en HD, efectos especiales mejorados y un juego más suave que proporcionan una experiencia de supervivencia realista e inmersiva para todos los fans de battle royale. Puedes esperar ver más detalles, texturas, animaciones y efectos de iluminación en Free Fire Max.</p>
- <h3>Free Fire Max ofrece nuevos modos de juego, mapas y opciones de personalización</h3>
- <p>Otra diferencia entre Free Fire Max y Free Fire es el contenido. Gratis Fire Max introduce nuevos modos de juego y mapas que son exclusivos de la aplicación. Por ejemplo, puede crear y jugar en su propio mapa personalizado en el modo Craftland, o disfrutar de un vestíbulo de 360 grados donde puede mostrar sus armas, vehículos y pieles de pared gloo. También puedes acceder a más opciones de personalización para tus personajes y armas en Free Fire Max.</p>
-
- <p>Una tercera diferencia entre Free Fire Max y Free Fire es la compatibilidad. Gracias a la tecnología Firelink, puedes jugar todos los modos de juego con los jugadores de Free Fire y Free Fire Max juntos, sin importar qué aplicación usen. También puede iniciar sesión con su cuenta de Free Fire existente para jugar Free Fire Max sin ningún problema. El progreso y los elementos se mantienen en ambas aplicaciones en tiempo real. </p>
- <h2>¿Cuáles son los requisitos y beneficios de jugar Free Fire Max en el iPhone XR? </h2>
- <p>Si se está preguntando si su iPhone XR puede ejecutar Free Fire Max sin problemas, la respuesta es sí. De hecho, hay muchas ventajas de jugar Free Fire Max en el iPhone XR.</p>
- <h3>iPhone XR cumple con las especificaciones mínimas para Free Fire Max</h3>
- <p>Las especificaciones mínimas para jugar Free Fire Max en dispositivos iOS son las siguientes:</p>
-<ul>
-<li>versión de iOS: iOS 11 <li>RAM: 2 GB <li>Almacenamiento: 2.5 GB </ul>
-<p>Como puedes ver, tu iPhone XR cumple fácilmente con estos requisitos, ya que tiene iOS 14, 3 GB de RAM y 64 GB de almacenamiento. Esto significa que usted puede jugar Free Fire Max sin ningún retraso o se bloquea en su iPhone XR.</p>
-<p></p>
- <h3>iPhone XR ofrece una experiencia de juego suave e inmersiva con pantalla de retina líquida y chip biónico A12</h3>
- <p>No solo tu iPhone XR cumple con las especificaciones mínimas para Free Fire Max, sino que también las supera con sus características avanzadas. Una de ellas es la pantalla Liquid Retina, que es una pantalla LCD de 6,1 pulgadas con una resolución de 1792 x 828 píxeles y una densidad de píxeles de 326 ppi. Esta pantalla ofrece colores impresionantes, contraste y brillo que hacen que Free Fire Max parezca más vívido y realista en tu iPhone XR.</p>
-
- <h3>iPhone XR tiene una larga vida de la batería y resistencia al agua para juegos ininterrumpidos</h3>
- <p>Un beneficio final de jugar Free Fire Max en el iPhone XR es la durabilidad y fiabilidad de su dispositivo. El iPhone XR tiene una capacidad de batería de 2942 mAh, que puede durar hasta 15 horas de reproducción de video, 25 horas de tiempo de conversación o 65 horas de reproducción de audio. Esto significa que puedes jugar Free Fire Max durante horas sin preocuparte por quedarte sin jugo. </p>
- <p>El iPhone XR también tiene una clasificación IP67, lo que significa que puede soportar la inmersión en agua hasta 1 metro durante 30 minutos. Esto significa que puedes jugar Free Fire Max en cualquier condición meteorológica o ambiente sin dañar tu dispositivo. </p>
- <h2>¿Cómo descargar e instalar Free Fire Max en el iPhone XR? </h2>
- <p>Ahora que conoce los beneficios de jugar Free Fire Max en el iPhone XR, es posible que se pregunte cómo descargar e instalar en su dispositivo. Bueno, es muy fácil y simple. Solo sigue estos pasos:</p>
- <h3>Paso 1: Pre-registro para Free Fire Max en la App Store o el sitio web oficial</h3>
- <p>El primer paso es pre-registrarse para Free Fire Max, que le dará acceso a la aplicación cuando se lance el 28 de septiembre de 2021. Puede pre-registrarse en la App Store buscando Free Fire Max y pulsando el botón "Pre-Orden". Alternativamente, puede pre-registrarse en el sitio web oficial ingresando su dirección de correo electrónico y seleccionando su región. </p>
- <h3>Paso 2: Espera la fecha oficial de lanzamiento de Free Fire Max el 28 de septiembre de 2021</h3>
- <p>El segundo paso es esperar pacientemente la fecha oficial de lanzamiento de Free Fire Max, que es el 28 de septiembre de 2021. En este día, usted recibirá una notificación de la App Store o el sitio web oficial que Free Fire Max está disponible para su descarga. </p>
- <h3>Paso 3: Descargar e instalar Free Fire Max en tu iPhone XR</h3>
-
- <h3>Paso 4: Inicie sesión con su cuenta de Free Fire existente o cree una nueva</h3>
- <p>El cuarto paso es iniciar sesión con su cuenta de Free Fire existente o crear una nueva. Puede hacer esto pulsando en el botón "Iniciar sesión" en la pantalla principal de Free Fire Max y eligiendo su método de inicio de sesión preferido. Puedes usar las opciones de inicio de sesión de Facebook, Google, Apple ID, VK, Twitter o Invitado. Si aún no tienes una cuenta, puedes tocar el botón "Crear cuenta" y seguir las instrucciones. </p>
- <h3>Paso 5: Disfruta de la experiencia premium battle royale en tu dispositivo iOS</h3>
- <p>El quinto y último paso es disfrutar de la experiencia premium battle royale en su dispositivo iOS. Puede personalizar la configuración, elegir el modo de juego y el mapa, invitar a sus amigos, y empezar a jugar Free Fire Max en su iPhone XR.</p>
- <h2>Conclusión</h2>
- <p>En conclusión, Free Fire Max es una versión mejorada de Free Fire que ofrece gráficos y características mejoradas, nuevos modos de juego y mapas, y cross-play y cross-progression con Free Fire. Es compatible con el iPhone XR, que ofrece una experiencia de juego suave e inmersiva con su pantalla Liquid Retina, chip A12 Bionic, larga duración de la batería y resistencia al agua. Puede descargar e instalar Free Fire Max en su iPhone XR mediante el registro previo en la App Store o el sitio web oficial, e iniciar sesión con su cuenta de Free Fire existente o crear una nueva. Si estás buscando una experiencia de batalla royale premium en tu dispositivo iOS, definitivamente deberías probar Free Fire Max. </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre Free Fire Max descargar iPhone XR:</p>
- <h3>Q: ¿Es Free Fire Max libre para jugar? </h3>
-<p>A: Sí, Free Fire Max es libre de jugar, al igual que Free Fire. Sin embargo, puedes comprar artículos del juego y divisas con dinero real si quieres mejorar tu experiencia de juego. </p>
- <h3>P: ¿Puedo jugar Free Fire Max con mis amigos que usan Free Fire? </h3>
-
- <h3>Q: ¿Cuáles son las diferencias entre Free Fire Max y PUBG Mobile? </h3>
-<p>A: Tanto Free Fire Max como PUBG Mobile son juegos populares de battle royale, pero tienen algunas diferencias. Por ejemplo, Free Fire Max tiene un tamaño de mapa más pequeño y una duración de partido más corta que PUBG Mobile, lo que lo hace más rápido y lleno de acción. Free Fire Max también tiene más opciones de personalización de personajes y armas que PUBG Mobile, lo que lo hace más diverso y creativo. </p>
- <h3>Q: ¿Cómo puedo conseguir más diamantes en Free Fire Max? </h3>
-<p>A: Los diamantes son la moneda premium en Free Fire Max, que puedes usar para comprar artículos y pieles exclusivos. Puedes obtener más diamantes al comprarlos con dinero real, completar misiones y eventos, participar en sorteos y concursos o usar aplicaciones y sitios web de terceros. Sin embargo, ten cuidado con estafas y hacks que podrían dañar tu dispositivo o cuenta. </p>
- <h3>Q: ¿Cómo puedo contactar al servicio al cliente de Free Fire Max? </h3>
-<p>A: Si tiene algún problema o consulta con respecto a Free Fire Max, puede ponerse en contacto con el servicio al cliente de Free Fire Max siguiendo estos pasos:</p>
-<ol>
-<li> Abra la aplicación y toque en el icono "Configuración" en la esquina superior derecha de la pantalla. </li>
-<li>Toque en la opción "Servicio al cliente" en la esquina inferior izquierda de la pantalla. </li>
-<li>Serás redirigido a una página web donde podrás enviar tus comentarios o consultas. </li>
-<li> También puede consultar la sección de preguntas frecuentes o las páginas oficiales de redes sociales de Free Fire Max para obtener más información. </li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/configloader.py

@@ -1,282 +0,0 @@
-# Copyright (c) 2012-2013 Mitch Garnaat http://garnaat.org/
-# Copyright 2012-2016 Amazon.com, Inc. or its affiliates. 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. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-import configparser
-import copy
-import os
-import shlex
-import sys
-
-import botocore.exceptions
-
-
-def multi_file_load_config(*filenames):
-    """Load and combine multiple INI configs with profiles.
-
-    This function will take a list of filesnames and return
-    a single dictionary that represents the merging of the loaded
-    config files.
-
-    If any of the provided filenames does not exist, then that file
-    is ignored.  It is therefore ok to provide a list of filenames,
-    some of which may not exist.
-
-    Configuration files are **not** deep merged, only the top level
-    keys are merged.  The filenames should be passed in order of
-    precedence.  The first config file has precedence over the
-    second config file, which has precedence over the third config file,
-    etc.  The only exception to this is that the "profiles" key is
-    merged to combine profiles from multiple config files into a
-    single profiles mapping.  However, if a profile is defined in
-    multiple config files, then the config file with the highest
-    precedence is used.  Profile values themselves are not merged.
-    For example::
-
-        FileA              FileB                FileC
-        [foo]             [foo]                 [bar]
-        a=1               a=2                   a=3
-                          b=2
-
-        [bar]             [baz]                [profile a]
-        a=2               a=3                  region=e
-
-        [profile a]       [profile b]          [profile c]
-        region=c          region=d             region=f
-
-    The final result of ``multi_file_load_config(FileA, FileB, FileC)``
-    would be::
-
-        {"foo": {"a": 1}, "bar": {"a": 2}, "baz": {"a": 3},
-        "profiles": {"a": {"region": "c"}}, {"b": {"region": d"}},
-                    {"c": {"region": "f"}}}
-
-    Note that the "foo" key comes from A, even though it's defined in both
-    FileA and FileB.  Because "foo" was defined in FileA first, then the values
-    for "foo" from FileA are used and the values for "foo" from FileB are
-    ignored.  Also note where the profiles originate from.  Profile "a"
-    comes FileA, profile "b" comes from FileB, and profile "c" comes
-    from FileC.
-
-    """
-    configs = []
-    profiles = []
-    for filename in filenames:
-        try:
-            loaded = load_config(filename)
-        except botocore.exceptions.ConfigNotFound:
-            continue
-        profiles.append(loaded.pop('profiles'))
-        configs.append(loaded)
-    merged_config = _merge_list_of_dicts(configs)
-    merged_profiles = _merge_list_of_dicts(profiles)
-    merged_config['profiles'] = merged_profiles
-    return merged_config
-
-
-def _merge_list_of_dicts(list_of_dicts):
-    merged_dicts = {}
-    for single_dict in list_of_dicts:
-        for key, value in single_dict.items():
-            if key not in merged_dicts:
-                merged_dicts[key] = value
-    return merged_dicts
-
-
-def load_config(config_filename):
-    """Parse a INI config with profiles.
-
-    This will parse an INI config file and map top level profiles
-    into a top level "profile" key.
-
-    If you want to parse an INI file and map all section names to
-    top level keys, use ``raw_config_parse`` instead.
-
-    """
-    parsed = raw_config_parse(config_filename)
-    return build_profile_map(parsed)
-
-
-def raw_config_parse(config_filename, parse_subsections=True):
-    """Returns the parsed INI config contents.
-
-    Each section name is a top level key.
-
-    :param config_filename: The name of the INI file to parse
-
-    :param parse_subsections: If True, parse indented blocks as
-       subsections that represent their own configuration dictionary.
-       For example, if the config file had the contents::
-
-           s3 =
-              signature_version = s3v4
-              addressing_style = path
-
-        The resulting ``raw_config_parse`` would be::
-
-            {'s3': {'signature_version': 's3v4', 'addressing_style': 'path'}}
-
-       If False, do not try to parse subsections and return the indented
-       block as its literal value::
-
-            {'s3': '\nsignature_version = s3v4\naddressing_style = path'}
-
-    :returns: A dict with keys for each profile found in the config
-        file and the value of each key being a dict containing name
-        value pairs found in that profile.
-
-    :raises: ConfigNotFound, ConfigParseError
-    """
-    config = {}
-    path = config_filename
-    if path is not None:
-        path = os.path.expandvars(path)
-        path = os.path.expanduser(path)
-        if not os.path.isfile(path):
-            raise botocore.exceptions.ConfigNotFound(path=_unicode_path(path))
-        cp = configparser.RawConfigParser()
-        try:
-            cp.read([path])
-        except (configparser.Error, UnicodeDecodeError) as e:
-            raise botocore.exceptions.ConfigParseError(
-                path=_unicode_path(path), error=e
-            ) from None
-        else:
-            for section in cp.sections():
-                config[section] = {}
-                for option in cp.options(section):
-                    config_value = cp.get(section, option)
-                    if parse_subsections and config_value.startswith('\n'):
-                        # Then we need to parse the inner contents as
-                        # hierarchical.  We support a single level
-                        # of nesting for now.
-                        try:
-                            config_value = _parse_nested(config_value)
-                        except ValueError as e:
-                            raise botocore.exceptions.ConfigParseError(
-                                path=_unicode_path(path), error=e
-                            ) from None
-                    config[section][option] = config_value
-    return config
-
-
-def _unicode_path(path):
-    if isinstance(path, str):
-        return path
-    # According to the documentation getfilesystemencoding can return None
-    # on unix in which case the default encoding is used instead.
-    filesystem_encoding = sys.getfilesystemencoding()
-    if filesystem_encoding is None:
-        filesystem_encoding = sys.getdefaultencoding()
-    return path.decode(filesystem_encoding, 'replace')
-
-
-def _parse_nested(config_value):
-    # Given a value like this:
-    # \n
-    # foo = bar
-    # bar = baz
-    # We need to parse this into
-    # {'foo': 'bar', 'bar': 'baz}
-    parsed = {}
-    for line in config_value.splitlines():
-        line = line.strip()
-        if not line:
-            continue
-        # The caller will catch ValueError
-        # and raise an appropriate error
-        # if this fails.
-        key, value = line.split('=', 1)
-        parsed[key.strip()] = value.strip()
-    return parsed
-
-
-def build_profile_map(parsed_ini_config):
-    """Convert the parsed INI config into a profile map.
-
-    The config file format requires that every profile except the
-    default to be prepended with "profile", e.g.::
-
-        [profile test]
-        aws_... = foo
-        aws_... = bar
-
-        [profile bar]
-        aws_... = foo
-        aws_... = bar
-
-        # This is *not* a profile
-        [preview]
-        otherstuff = 1
-
-        # Neither is this
-        [foobar]
-        morestuff = 2
-
-    The build_profile_map will take a parsed INI config file where each top
-    level key represents a section name, and convert into a format where all
-    the profiles are under a single top level "profiles" key, and each key in
-    the sub dictionary is a profile name.  For example, the above config file
-    would be converted from::
-
-        {"profile test": {"aws_...": "foo", "aws...": "bar"},
-         "profile bar": {"aws...": "foo", "aws...": "bar"},
-         "preview": {"otherstuff": ...},
-         "foobar": {"morestuff": ...},
-         }
-
-    into::
-
-        {"profiles": {"test": {"aws_...": "foo", "aws...": "bar"},
-                      "bar": {"aws...": "foo", "aws...": "bar"},
-         "preview": {"otherstuff": ...},
-         "foobar": {"morestuff": ...},
-        }
-
-    If there are no profiles in the provided parsed INI contents, then
-    an empty dict will be the value associated with the ``profiles`` key.
-
-    .. note::
-
-        This will not mutate the passed in parsed_ini_config.  Instead it will
-        make a deepcopy and return that value.
-
-    """
-    parsed_config = copy.deepcopy(parsed_ini_config)
-    profiles = {}
-    sso_sessions = {}
-    final_config = {}
-    for key, values in parsed_config.items():
-        if key.startswith("profile"):
-            try:
-                parts = shlex.split(key)
-            except ValueError:
-                continue
-            if len(parts) == 2:
-                profiles[parts[1]] = values
-        elif key.startswith("sso-session"):
-            try:
-                parts = shlex.split(key)
-            except ValueError:
-                continue
-            if len(parts) == 2:
-                sso_sessions[parts[1]] = values
-        elif key == 'default':
-            # default section is special and is considered a profile
-            # name but we don't require you use 'profile "default"'
-            # as a section.
-            profiles[key] = values
-        else:
-            final_config[key] = values
-    final_config['profiles'] = profiles
-    final_config['sso_sessions'] = sso_sessions
-    return final_config

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/dist_info.py

@@ -1,142 +0,0 @@
-"""
-Create a dist_info directory
-As defined in the wheel specification
-"""
-
-import os
-import re
-import shutil
-import sys
-import warnings
-from contextlib import contextmanager
-from inspect import cleandoc
-from pathlib import Path
-
-from distutils.core import Command
-from distutils import log
-from setuptools.extern import packaging
-from setuptools._deprecation_warning import SetuptoolsDeprecationWarning
-
-
-class dist_info(Command):
-
-    description = 'create a .dist-info directory'
-
-    user_options = [
-        ('egg-base=', 'e', "directory containing .egg-info directories"
-                           " (default: top of the source tree)"
-                           " DEPRECATED: use --output-dir."),
-        ('output-dir=', 'o', "directory inside of which the .dist-info will be"
-                             "created (default: top of the source tree)"),
-        ('tag-date', 'd', "Add date stamp (e.g. 20050528) to version number"),
-        ('tag-build=', 'b', "Specify explicit tag to add to version number"),
-        ('no-date', 'D', "Don't include date stamp [default]"),
-        ('keep-egg-info', None, "*TRANSITIONAL* will be removed in the future"),
-    ]
-
-    boolean_options = ['tag-date', 'keep-egg-info']
-    negative_opt = {'no-date': 'tag-date'}
-
-    def initialize_options(self):
-        self.egg_base = None
-        self.output_dir = None
-        self.name = None
-        self.dist_info_dir = None
-        self.tag_date = None
-        self.tag_build = None
-        self.keep_egg_info = False
-
-    def finalize_options(self):
-        if self.egg_base:
-            msg = "--egg-base is deprecated for dist_info command. Use --output-dir."
-            warnings.warn(msg, SetuptoolsDeprecationWarning)
-            self.output_dir = self.egg_base or self.output_dir
-
-        dist = self.distribution
-        project_dir = dist.src_root or os.curdir
-        self.output_dir = Path(self.output_dir or project_dir)
-
-        egg_info = self.reinitialize_command("egg_info")
-        egg_info.egg_base = str(self.output_dir)
-
-        if self.tag_date:
-            egg_info.tag_date = self.tag_date
-        else:
-            self.tag_date = egg_info.tag_date
-
-        if self.tag_build:
-            egg_info.tag_build = self.tag_build
-        else:
-            self.tag_build = egg_info.tag_build
-
-        egg_info.finalize_options()
-        self.egg_info = egg_info
-
-        name = _safe(dist.get_name())
-        version = _version(dist.get_version())
-        self.name = f"{name}-{version}"
-        self.dist_info_dir = os.path.join(self.output_dir, f"{self.name}.dist-info")
-
-    @contextmanager
-    def _maybe_bkp_dir(self, dir_path: str, requires_bkp: bool):
-        if requires_bkp:
-            bkp_name = f"{dir_path}.__bkp__"
-            _rm(bkp_name, ignore_errors=True)
-            _copy(dir_path, bkp_name, dirs_exist_ok=True, symlinks=True)
-            try:
-                yield
-            finally:
-                _rm(dir_path, ignore_errors=True)
-                shutil.move(bkp_name, dir_path)
-        else:
-            yield
-
-    def run(self):
-        self.output_dir.mkdir(parents=True, exist_ok=True)
-        self.egg_info.run()
-        egg_info_dir = self.egg_info.egg_info
-        assert os.path.isdir(egg_info_dir), ".egg-info dir should have been created"
-
-        log.info("creating '{}'".format(os.path.abspath(self.dist_info_dir)))
-        bdist_wheel = self.get_finalized_command('bdist_wheel')
-
-        # TODO: if bdist_wheel if merged into setuptools, just add "keep_egg_info" there
-        with self._maybe_bkp_dir(egg_info_dir, self.keep_egg_info):
-            bdist_wheel.egg2dist(egg_info_dir, self.dist_info_dir)
-
-
-def _safe(component: str) -> str:
-    """Escape a component used to form a wheel name according to PEP 491"""
-    return re.sub(r"[^\w\d.]+", "_", component)
-
-
-def _version(version: str) -> str:
-    """Convert an arbitrary string to a version string."""
-    v = version.replace(' ', '.')
-    try:
-        return str(packaging.version.Version(v)).replace("-", "_")
-    except packaging.version.InvalidVersion:
-        msg = f"""Invalid version: {version!r}.
-        !!\n\n
-        ###################
-        # Invalid version #
-        ###################
-        {version!r} is not valid according to PEP 440.\n
-        Please make sure specify a valid version for your package.
-        Also note that future releases of setuptools may halt the build process
-        if an invalid version is given.
-        \n\n!!
-        """
-        warnings.warn(cleandoc(msg))
-        return _safe(v).strip("_")
-
-
-def _rm(dir_name, **opts):
-    if os.path.isdir(dir_name):
-        shutil.rmtree(dir_name, **opts)
-
-
-def _copy(src, dst, **opts):
-    if sys.version_info < (3, 8):
-        opts.pop("dirs_exist_ok", None)
-    shutil.copytree(src, dst, **opts)

spaces/BigSalmon/Paraphrase/README.md

@@ -1,37 +0,0 @@
----
-title: Paraphrase
-emoji: 👀
-colorFrom: indigo
-colorTo: purple
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.

spaces/CVPR/GFPGAN-example/gfpgan/archs/gfpganv1_arch.py

@@ -1,439 +0,0 @@
-import math
-import random
-import torch
-from basicsr.archs.stylegan2_arch import (ConvLayer, EqualConv2d, EqualLinear, ResBlock, ScaledLeakyReLU,
-                                          StyleGAN2Generator)
-from basicsr.ops.fused_act import FusedLeakyReLU
-from basicsr.utils.registry import ARCH_REGISTRY
-from torch import nn
-from torch.nn import functional as F
-
-
-class StyleGAN2GeneratorSFT(StyleGAN2Generator):
-    """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).
-
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be
-            applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1).
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(self,
-                 out_size,
-                 num_style_feat=512,
-                 num_mlp=8,
-                 channel_multiplier=2,
-                 resample_kernel=(1, 3, 3, 1),
-                 lr_mlp=0.01,
-                 narrow=1,
-                 sft_half=False):
-        super(StyleGAN2GeneratorSFT, self).__init__(
-            out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            resample_kernel=resample_kernel,
-            lr_mlp=lr_mlp,
-            narrow=narrow)
-        self.sft_half = sft_half
-
-    def forward(self,
-                styles,
-                conditions,
-                input_is_latent=False,
-                noise=None,
-                randomize_noise=True,
-                truncation=1,
-                truncation_latent=None,
-                inject_index=None,
-                return_latents=False):
-        """Forward function for StyleGAN2GeneratorSFT.
-
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            conditions (list[Tensor]): SFT conditions to generators.
-            input_is_latent (bool): Whether input is latent style. Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-            truncation (float): The truncation ratio. Default: 1.
-            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
-            inject_index (int | None): The injection index for mixing noise. Default: None.
-            return_latents (bool): Whether to return style latents. Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
-            styles = style_truncation
-        # get style latents with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2],
-                                                        noise[2::2], self.to_rgbs):
-            out = conv1(out, latent[:, i], noise=noise1)
-
-            # the conditions may have fewer levels
-            if i < len(conditions):
-                # SFT part to combine the conditions
-                if self.sft_half:  # only apply SFT to half of the channels
-                    out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
-                    out_sft = out_sft * conditions[i - 1] + conditions[i]
-                    out = torch.cat([out_same, out_sft], dim=1)
-                else:  # apply SFT to all the channels
-                    out = out * conditions[i - 1] + conditions[i]
-
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
-
-
-class ConvUpLayer(nn.Module):
-    """Convolutional upsampling layer. It uses bilinear upsampler + Conv.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        stride (int): Stride of the convolution. Default: 1
-        padding (int): Zero-padding added to both sides of the input. Default: 0.
-        bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``.
-        bias_init_val (float): Bias initialized value. Default: 0.
-        activate (bool): Whether use activateion. Default: True.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 bias=True,
-                 bias_init_val=0,
-                 activate=True):
-        super(ConvUpLayer, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.padding = padding
-        # self.scale is used to scale the convolution weights, which is related to the common initializations.
-        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
-
-        self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size))
-
-        if bias and not activate:
-            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
-        else:
-            self.register_parameter('bias', None)
-
-        # activation
-        if activate:
-            if bias:
-                self.activation = FusedLeakyReLU(out_channels)
-            else:
-                self.activation = ScaledLeakyReLU(0.2)
-        else:
-            self.activation = None
-
-    def forward(self, x):
-        # bilinear upsample
-        out = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
-        # conv
-        out = F.conv2d(
-            out,
-            self.weight * self.scale,
-            bias=self.bias,
-            stride=self.stride,
-            padding=self.padding,
-        )
-        # activation
-        if self.activation is not None:
-            out = self.activation(out)
-        return out
-
-
-class ResUpBlock(nn.Module):
-    """Residual block with upsampling.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-    """
-
-    def __init__(self, in_channels, out_channels):
-        super(ResUpBlock, self).__init__()
-
-        self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True)
-        self.conv2 = ConvUpLayer(in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True)
-        self.skip = ConvUpLayer(in_channels, out_channels, 1, bias=False, activate=False)
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = self.conv2(out)
-        skip = self.skip(x)
-        out = (out + skip) / math.sqrt(2)
-        return out
-
-
-@ARCH_REGISTRY.register()
-class GFPGANv1(nn.Module):
-    """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.
-
-    Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.
-
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be
-            applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1).
-        decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None.
-        fix_decoder (bool): Whether to fix the decoder. Default: True.
-
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        input_is_latent (bool): Whether input is latent style. Default: False.
-        different_w (bool): Whether to use different latent w for different layers. Default: False.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(
-            self,
-            out_size,
-            num_style_feat=512,
-            channel_multiplier=1,
-            resample_kernel=(1, 3, 3, 1),
-            decoder_load_path=None,
-            fix_decoder=True,
-            # for stylegan decoder
-            num_mlp=8,
-            lr_mlp=0.01,
-            input_is_latent=False,
-            different_w=False,
-            narrow=1,
-            sft_half=False):
-
-        super(GFPGANv1, self).__init__()
-        self.input_is_latent = input_is_latent
-        self.different_w = different_w
-        self.num_style_feat = num_style_feat
-
-        unet_narrow = narrow * 0.5  # by default, use a half of input channels
-        channels = {
-            '4': int(512 * unet_narrow),
-            '8': int(512 * unet_narrow),
-            '16': int(512 * unet_narrow),
-            '32': int(512 * unet_narrow),
-            '64': int(256 * channel_multiplier * unet_narrow),
-            '128': int(128 * channel_multiplier * unet_narrow),
-            '256': int(64 * channel_multiplier * unet_narrow),
-            '512': int(32 * channel_multiplier * unet_narrow),
-            '1024': int(16 * channel_multiplier * unet_narrow)
-        }
-
-        self.log_size = int(math.log(out_size, 2))
-        first_out_size = 2**(int(math.log(out_size, 2)))
-
-        self.conv_body_first = ConvLayer(3, channels[f'{first_out_size}'], 1, bias=True, activate=True)
-
-        # downsample
-        in_channels = channels[f'{first_out_size}']
-        self.conv_body_down = nn.ModuleList()
-        for i in range(self.log_size, 2, -1):
-            out_channels = channels[f'{2**(i - 1)}']
-            self.conv_body_down.append(ResBlock(in_channels, out_channels, resample_kernel))
-            in_channels = out_channels
-
-        self.final_conv = ConvLayer(in_channels, channels['4'], 3, bias=True, activate=True)
-
-        # upsample
-        in_channels = channels['4']
-        self.conv_body_up = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f'{2**i}']
-            self.conv_body_up.append(ResUpBlock(in_channels, out_channels))
-            in_channels = out_channels
-
-        # to RGB
-        self.toRGB = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            self.toRGB.append(EqualConv2d(channels[f'{2**i}'], 3, 1, stride=1, padding=0, bias=True, bias_init_val=0))
-
-        if different_w:
-            linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat
-        else:
-            linear_out_channel = num_style_feat
-
-        self.final_linear = EqualLinear(
-            channels['4'] * 4 * 4, linear_out_channel, bias=True, bias_init_val=0, lr_mul=1, activation=None)
-
-        # the decoder: stylegan2 generator with SFT modulations
-        self.stylegan_decoder = StyleGAN2GeneratorSFT(
-            out_size=out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            resample_kernel=resample_kernel,
-            lr_mlp=lr_mlp,
-            narrow=narrow,
-            sft_half=sft_half)
-
-        # load pre-trained stylegan2 model if necessary
-        if decoder_load_path:
-            self.stylegan_decoder.load_state_dict(
-                torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema'])
-        # fix decoder without updating params
-        if fix_decoder:
-            for _, param in self.stylegan_decoder.named_parameters():
-                param.requires_grad = False
-
-        # for SFT modulations (scale and shift)
-        self.condition_scale = nn.ModuleList()
-        self.condition_shift = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f'{2**i}']
-            if sft_half:
-                sft_out_channels = out_channels
-            else:
-                sft_out_channels = out_channels * 2
-            self.condition_scale.append(
-                nn.Sequential(
-                    EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0),
-                    ScaledLeakyReLU(0.2),
-                    EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=1)))
-            self.condition_shift.append(
-                nn.Sequential(
-                    EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0),
-                    ScaledLeakyReLU(0.2),
-                    EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0)))
-
-    def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True):
-        """Forward function for GFPGANv1.
-
-        Args:
-            x (Tensor): Input images.
-            return_latents (bool): Whether to return style latents. Default: False.
-            return_rgb (bool): Whether return intermediate rgb images. Default: True.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-        """
-        conditions = []
-        unet_skips = []
-        out_rgbs = []
-
-        # encoder
-        feat = self.conv_body_first(x)
-        for i in range(self.log_size - 2):
-            feat = self.conv_body_down[i](feat)
-            unet_skips.insert(0, feat)
-
-        feat = self.final_conv(feat)
-
-        # style code
-        style_code = self.final_linear(feat.view(feat.size(0), -1))
-        if self.different_w:
-            style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)
-
-        # decode
-        for i in range(self.log_size - 2):
-            # add unet skip
-            feat = feat + unet_skips[i]
-            # ResUpLayer
-            feat = self.conv_body_up[i](feat)
-            # generate scale and shift for SFT layers
-            scale = self.condition_scale[i](feat)
-            conditions.append(scale.clone())
-            shift = self.condition_shift[i](feat)
-            conditions.append(shift.clone())
-            # generate rgb images
-            if return_rgb:
-                out_rgbs.append(self.toRGB[i](feat))
-
-        # decoder
-        image, _ = self.stylegan_decoder([style_code],
-                                         conditions,
-                                         return_latents=return_latents,
-                                         input_is_latent=self.input_is_latent,
-                                         randomize_noise=randomize_noise)
-
-        return image, out_rgbs
-
-
-@ARCH_REGISTRY.register()
-class FacialComponentDiscriminator(nn.Module):
-    """Facial component (eyes, mouth, noise) discriminator used in GFPGAN.
-    """
-
-    def __init__(self):
-        super(FacialComponentDiscriminator, self).__init__()
-        # It now uses a VGG-style architectrue with fixed model size
-        self.conv1 = ConvLayer(3, 64, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
-        self.conv2 = ConvLayer(64, 128, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
-        self.conv3 = ConvLayer(128, 128, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
-        self.conv4 = ConvLayer(128, 256, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
-        self.conv5 = ConvLayer(256, 256, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
-        self.final_conv = ConvLayer(256, 1, 3, bias=True, activate=False)
-
-    def forward(self, x, return_feats=False):
-        """Forward function for FacialComponentDiscriminator.
-
-        Args:
-            x (Tensor): Input images.
-            return_feats (bool): Whether to return intermediate features. Default: False.
-        """
-        feat = self.conv1(x)
-        feat = self.conv3(self.conv2(feat))
-        rlt_feats = []
-        if return_feats:
-            rlt_feats.append(feat.clone())
-        feat = self.conv5(self.conv4(feat))
-        if return_feats:
-            rlt_feats.append(feat.clone())
-        out = self.final_conv(feat)
-
-        if return_feats:
-            return out, rlt_feats
-        else:
-            return out, None

spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/sequence.h

@@ -1,64 +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>
-#include <thrust/system/detail/generic/tag.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-template<typename DerivedPolicy,
-         typename ForwardIterator>
-__host__ __device__
-  void sequence(thrust::execution_policy<DerivedPolicy> &exec,
-                ForwardIterator first,
-                ForwardIterator last);
-
-
-template<typename DerivedPolicy, typename ForwardIterator, typename T>
-__host__ __device__
-  void sequence(thrust::execution_policy<DerivedPolicy> &exec,
-                ForwardIterator first,
-                ForwardIterator last,
-                T init);
-
-
-template<typename DerivedPolicy, typename ForwardIterator, typename T>
-__host__ __device__
-  void sequence(thrust::execution_policy<DerivedPolicy> &exec,
-                ForwardIterator first,
-                ForwardIterator last,
-                T init,
-                T step);
-
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/generic/sequence.inl>
-

spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/unique_by_key.h

@@ -1,95 +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>
-#include <thrust/system/detail/generic/tag.h>
-#include <thrust/pair.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-template<typename ExecutionPolicy,
-         typename ForwardIterator1,
-         typename ForwardIterator2>
-__host__ __device__
-  thrust::pair<ForwardIterator1,ForwardIterator2>
-    unique_by_key(thrust::execution_policy<ExecutionPolicy> &exec,
-                  ForwardIterator1 keys_first, 
-                  ForwardIterator1 keys_last,
-                  ForwardIterator2 values_first);
-
-
-template<typename ExecutionPolicy,
-         typename ForwardIterator1,
-         typename ForwardIterator2,
-         typename BinaryPredicate>
-__host__ __device__
-  thrust::pair<ForwardIterator1,ForwardIterator2>
-    unique_by_key(thrust::execution_policy<ExecutionPolicy> &exec,
-                  ForwardIterator1 keys_first, 
-                  ForwardIterator1 keys_last,
-                  ForwardIterator2 values_first,
-                  BinaryPredicate binary_pred);
-
-
-template<typename ExecutionPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator1,
-         typename OutputIterator2>
-__host__ __device__
-  thrust::pair<OutputIterator1,OutputIterator2>
-    unique_by_key_copy(thrust::execution_policy<ExecutionPolicy> &exec,
-                       InputIterator1 keys_first, 
-                       InputIterator1 keys_last,
-                       InputIterator2 values_first,
-                       OutputIterator1 keys_output,
-                       OutputIterator2 values_output);
-
-
-template<typename ExecutionPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator1,
-         typename OutputIterator2,
-         typename BinaryPredicate>
-__host__ __device__
-  thrust::pair<OutputIterator1,OutputIterator2>
-    unique_by_key_copy(thrust::execution_policy<ExecutionPolicy> &exec,
-                       InputIterator1 keys_first, 
-                       InputIterator1 keys_last,
-                       InputIterator2 values_first,
-                       OutputIterator1 keys_output,
-                       OutputIterator2 values_output,
-                       BinaryPredicate binary_pred);
-
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/generic/unique_by_key.inl>
-

spaces/CVPR/regionclip-demo/detectron2/modeling/backbone/clip_backbone.py

@@ -1,882 +0,0 @@
-from collections import OrderedDict
-from typing import Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from .backbone import Backbone
-from .build import BACKBONE_REGISTRY
-from detectron2.layers.blocks import FrozenBatchNorm2d
-from detectron2.layers import ShapeSpec
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1, norm_type='FronzenBN'):
-        super().__init__()
-
-        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
-        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
-        if norm_type == 'FronzenBN':
-            self.bn1 = FrozenBatchNorm2d(planes) # nn.BatchNorm2d(planes)
-        elif norm_type == 'SyncBN':
-            self.bn1 = nn.SyncBatchNorm(planes)
-
-        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
-        if norm_type == 'FronzenBN':
-            self.bn2 = FrozenBatchNorm2d(planes) # nn.BatchNorm2d(planes)
-        elif norm_type == 'SyncBN':
-            self.bn2 = nn.SyncBatchNorm(planes)
-
-        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
-
-        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
-        if norm_type == 'FronzenBN':
-            self.bn3 = FrozenBatchNorm2d(planes * self.expansion) # nn.BatchNorm2d(planes * self.expansion)
-        elif norm_type == 'SyncBN':
-            self.bn3 = nn.SyncBatchNorm(planes * self.expansion)
-
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = None
-        self.stride = stride
-
-        if stride > 1 or inplanes != planes * Bottleneck.expansion:
-            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
-            if norm_type == 'FronzenBN':
-                this_norm = FrozenBatchNorm2d(planes * self.expansion) #("1", nn.BatchNorm2d(planes * self.expansion))
-            elif norm_type == 'SyncBN':
-                this_norm = nn.SyncBatchNorm(planes * self.expansion)
-            self.downsample = nn.Sequential(OrderedDict([
-                ("-1", nn.AvgPool2d(stride)),
-                ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
-                ("1", this_norm), #("1", nn.BatchNorm2d(planes * self.expansion))
-            ]))
-
-    def forward(self, x: torch.Tensor):
-        identity = x
-
-        out = self.relu(self.bn1(self.conv1(x)))
-        out = self.relu(self.bn2(self.conv2(out)))
-        out = self.avgpool(out)
-        out = self.bn3(self.conv3(out))
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-        return out
-
-
-class AttentionPool2d(nn.Module):
-    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
-        self.k_proj = nn.Linear(embed_dim, embed_dim)
-        self.q_proj = nn.Linear(embed_dim, embed_dim)
-        self.v_proj = nn.Linear(embed_dim, embed_dim)
-        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
-        self.num_heads = num_heads
-
-    def forward(self, x):
-        x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1)  # NCHW -> (HW)NC
-        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
-        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
-        x, _ = F.multi_head_attention_forward(
-            query=x, key=x, value=x,
-            embed_dim_to_check=x.shape[-1],
-            num_heads=self.num_heads,
-            q_proj_weight=self.q_proj.weight,
-            k_proj_weight=self.k_proj.weight,
-            v_proj_weight=self.v_proj.weight,
-            in_proj_weight=None,
-            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
-            bias_k=None,
-            bias_v=None,
-            add_zero_attn=False,
-            dropout_p=0,
-            out_proj_weight=self.c_proj.weight,
-            out_proj_bias=self.c_proj.bias,
-            use_separate_proj_weight=True,
-            training=self.training,
-            need_weights=False
-        )
-
-        return x[0]
-
-
-class ModifiedResNet(Backbone):
-    """
-    Extended from CLIP implementation. It contains following changes:
-    1. change all nn.BatchNorm2d() to FrozenBatchNorm2d(), due to small batch size of detection training
-    2. add self._out_feature_strides according to standard ResNet
-    2. modify forward() to be compatible with Detectron2
-    3. add freeze() and output_shape() to be compatible with Detectron2
-    4. add build_clip_resnet_backbone() to build this ModifiedResNet
-
-    A ResNet class that is similar to torchvision's but contains the following changes:
-    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
-    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
-    - The final pooling layer is a QKV attention instead of an average pool
-    """
-
-    def __init__(self, layers, output_dim, heads, input_resolution=224, width=64, 
-        out_features=None, freeze_at=0, depth=None, pool_vec=True, create_att_pool=False, norm_type='FronzenBN'):
-        super().__init__()
-        self.output_dim = output_dim
-        self.input_resolution = input_resolution
-        self.norm_type = norm_type
-
-        # the 3-layer stem
-        self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
-        if norm_type == 'FronzenBN':
-            self.bn1 = FrozenBatchNorm2d(width // 2)  # nn.BatchNorm2d(width // 2)
-        elif norm_type == 'SyncBN':
-            self.bn1 = nn.SyncBatchNorm(width // 2)
-        self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
-        if norm_type == 'FronzenBN':
-            self.bn2 = FrozenBatchNorm2d(width // 2)  # nn.BatchNorm2d(width // 2)
-        elif norm_type == 'SyncBN':
-            self.bn2 = nn.SyncBatchNorm(width // 2)
-        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
-        if norm_type == 'FronzenBN':
-            self.bn3 = FrozenBatchNorm2d(width) # nn.BatchNorm2d(width)
-        elif norm_type == 'SyncBN':
-            self.bn3 = nn.SyncBatchNorm(width)
-        self.avgpool = nn.AvgPool2d(2)
-        self.relu = nn.ReLU(inplace=True)
-
-        # residual layers
-        self._inplanes = width  # this is a *mutable* variable used during construction
-        self.layer1 = self._make_layer(width, layers[0])
-        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
-        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
-        if 'res5' in out_features:  # FPN
-            self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
-        else:  # C4, layer4 created here won't be used in backbone, but used in roi_head
-            self.layer4 = self._make_layer(width * 8, layers[3], stride=2) # None
-        
-        self.pool_vec = pool_vec
-        if self.pool_vec or create_att_pool:  # pool a vector representation for an image
-            embed_dim = width * 32  # the ResNet feature dimension
-            self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)
-        # if create_att_pool:  # freeze attnpool layer
-        #     for p in self.attnpool.parameters(): p.requires_grad = False
-
-        self._out_features = out_features if out_features else []
-        if depth in [50,101]: # resnet50 or resnet 101
-            # FPN: ["res2", "res3", "res4", "res5"]; C4: ["res4"]
-            self._out_feature_channels = {'stem': 64, 'res2': 256, 'res3': 512, 'res4': 1024, 'res5': 2048} if 'res5' in self._out_features \
-                else {'stem': 64, 'res2': 256, 'res3': 512, 'res4': 1024}
-            self._out_feature_strides = {'stem': 4, 'res2': 4, 'res3': 8, 'res4': 16, 'res5': 32} if 'res5' in self._out_features \
-                else  {'stem': 4, 'res2': 4, 'res3': 8, 'res4': 16}  # anti-aliasing strided conv???        
-        elif depth in [200]: # resnet50x4
-            # FPN: ["res2", "res3", "res4", "res5"]; C4: ["res4"]
-            self._out_feature_channels = {'stem': 80, 'res2': 320, 'res3': 640, 'res4': 1280, 'res5': 2560} if 'res5' in self._out_features \
-                else {'stem': 80, 'res2': 320, 'res3': 640, 'res4': 1280}
-            self._out_feature_strides = {'stem': 4, 'res2': 4, 'res3': 8, 'res4': 16, 'res5': 32} if 'res5' in self._out_features \
-                else  {'stem': 4, 'res2': 4, 'res3': 8, 'res4': 16}  # anti-aliasing strided conv???        
-        self.freeze(freeze_at)
-
-
-    def _make_layer(self, planes, blocks, stride=1):
-        layers = [Bottleneck(self._inplanes, planes, stride, norm_type=self.norm_type)]
-
-        self._inplanes = planes * Bottleneck.expansion
-        for _ in range(1, blocks):
-            layers.append(Bottleneck(self._inplanes, planes, norm_type=self.norm_type))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        def stem(x):
-            for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]:
-                x = self.relu(bn(conv(x)))
-            x = self.avgpool(x)
-            return x
-        
-        assert x.dim() == 4, f"ResNet takes an input of shape (N, C, H, W). Got {x.shape} instead!"
-        outputs = {}
-        x = x.type(self.conv1.weight.dtype) # det2 resnet50: [3, 800, 1216]; CLIP resnet50: [3, 224, 224]
-        x = stem(x) # det2 resnet50: [64, 200, 304]; CLIP resnet50: [64, 56, 56]
-        if "stem" in self._out_features:
-            outputs["stem"] = x
-        x = self.layer1(x) # det2 resnet50: [256, 200, 304]; CLIP resnet50: [256, 56, 56]
-        outputs['res2'] = x if "res2" in self._out_features else None
-        x = self.layer2(x) # det2 resnet50: [512, 100, 152]; CLIP resnet50: [512, 28, 28]
-        outputs['res3'] = x if "res3" in self._out_features else None
-        x = self.layer3(x) # det2 resnet50: [1024, 50, 76]; CLIP resnet50: [1024, 14, 14]
-        outputs['res4'] = x if "res4" in self._out_features else None
-        x = self.layer4(x)  if "res5" in self._out_features else x # det2 resnet50: [2048, 25, 38]; CLIP resnet50: [2048, 7, 7]
-        outputs['res5'] = x if "res5" in self._out_features else None
-
-        if self.pool_vec:  # pool a vector representation for an image, for global image classification
-            x = self.attnpool(x) # CLIP resnet50: [1024]
-            return x
-        else:  # for FPN
-            return outputs
-
-    def freeze(self, freeze_at=0):
-        """
-        Freeze the first several stages of the ResNet. Commonly used in
-        fine-tuning.
-
-        Layers that produce the same feature map spatial size are defined as one
-        "stage" by :paper:`FPN`.
-
-        Args:
-            freeze_at (int): number of stages to freeze.
-                `1` means freezing the stem. `2` means freezing the stem and
-                one residual stage, etc.
-
-        Returns:
-            nn.Module: this ResNet itself
-        """
-        def cnnblockbase_freeze(nn_module):
-            """
-            Make this block not trainable.
-            This method sets all parameters to `requires_grad=False`,
-            and convert all BatchNorm layers to FrozenBatchNorm
-
-            Returns:
-                the block itself
-            """
-            for p in nn_module.parameters():
-                p.requires_grad = False
-            FrozenBatchNorm2d.convert_frozen_batchnorm(nn_module)
-        
-        if freeze_at >= 1: # stem
-            cnnblockbase_freeze(self.conv1)
-            cnnblockbase_freeze(self.bn1)
-            cnnblockbase_freeze(self.conv2)
-            cnnblockbase_freeze(self.bn2)
-            cnnblockbase_freeze(self.conv3)
-            cnnblockbase_freeze(self.bn3)
-        # each stage is a torch.nn.modules.container.Sequential
-        for idx, stage in enumerate([self.layer1, self.layer2, self.layer3, self.layer4], start=2): 
-            if freeze_at >= idx:
-                for block in stage.children():  # each block is a Bottleneck
-                    cnnblockbase_freeze(block)  
-        return self
-
-    def output_shape(self):
-        return {
-            name: ShapeSpec(
-                channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
-            )
-            for name in self._out_features
-        }
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        ret = super().forward(x.type(torch.float32))
-        return ret.type(orig_type)
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
-        super().__init__()
-
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ln_1 = LayerNorm(d_model)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, d_model * 4)),
-            ("gelu", QuickGELU()),
-            ("c_proj", nn.Linear(d_model * 4, d_model))
-        ]))
-        self.ln_2 = LayerNorm(d_model)
-        self.attn_mask = attn_mask
-
-    def attention(self, x: torch.Tensor):
-        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
-        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
-
-    def forward(self, x: torch.Tensor):
-        x = x + self.attention(self.ln_1(x))
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
-        super().__init__()
-        self.width = width
-        self.layers = layers
-        self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
-
-    def forward(self, x: torch.Tensor):
-        return self.resblocks(x)
-
-
-class VisualTransformer(nn.Module):
-    def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.output_dim = output_dim
-        self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
-
-        scale = width ** -0.5
-        self.class_embedding = nn.Parameter(scale * torch.randn(width))
-        self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
-        self.ln_pre = LayerNorm(width)
-
-        self.transformer = Transformer(width, layers, heads)
-
-        self.ln_post = LayerNorm(width)
-        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
-
-    def forward(self, x: torch.Tensor):
-        x = self.conv1(x)  # shape = [*, width, grid, grid]
-        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
-        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
-        x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
-        x = x + self.positional_embedding.to(x.dtype)
-        x = self.ln_pre(x)
-
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        x = self.ln_post(x[:, 0, :])
-
-        if self.proj is not None:
-            x = x @ self.proj
-
-        return x
-
-
-class CLIP(Backbone):
-    def __init__(self,
-                 embed_dim: int,
-                 # vision
-                 image_resolution: int,
-                 vision_layers: Union[Tuple[int, int, int, int], int],
-                 vision_width: int,
-                 vision_patch_size: int,
-                 # text
-                 context_length: int,
-                 vocab_size: int,
-                 transformer_width: int,
-                 transformer_heads: int,
-                 transformer_layers: int,
-                 out_features,
-                 freeze_at,
-                 ):
-        super().__init__()
-
-        self.context_length = context_length
-
-        if isinstance(vision_layers, (tuple, list)):
-            vision_heads = vision_width * 32 // 64
-            self.visual = ModifiedResNet(
-                layers=vision_layers,
-                output_dim=embed_dim,
-                heads=vision_heads,
-                input_resolution=image_resolution,
-                width=vision_width,
-                out_features=out_features,
-                freeze_at=freeze_at,
-            )
-        else:
-            vision_heads = vision_width // 64
-            self.visual = VisualTransformer(
-                input_resolution=image_resolution,
-                patch_size=vision_patch_size,
-                width=vision_width,
-                layers=vision_layers,
-                heads=vision_heads,
-                output_dim=embed_dim
-            )
-
-        self.transformer = Transformer(
-            width=transformer_width,
-            layers=transformer_layers,
-            heads=transformer_heads,
-            attn_mask=self.build_attention_mask()
-        )
-
-        self.vocab_size = vocab_size
-        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
-        self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
-        self.ln_final = LayerNorm(transformer_width)
-
-        self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
-        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-
-        self.initialize_parameters()
-
-    def initialize_parameters(self):
-        nn.init.normal_(self.token_embedding.weight, std=0.02)
-        nn.init.normal_(self.positional_embedding, std=0.01)
-
-        if isinstance(self.visual, ModifiedResNet):
-            if self.visual.attnpool is not None:
-                std = self.visual.attnpool.c_proj.in_features ** -0.5
-                nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
-
-            for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
-                for name, param in resnet_block.named_parameters():
-                    if name.endswith("bn3.weight"):
-                        nn.init.zeros_(param)
-
-        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
-        attn_std = self.transformer.width ** -0.5
-        fc_std = (2 * self.transformer.width) ** -0.5
-        for block in self.transformer.resblocks:
-            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
-        if self.text_projection is not None:
-            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.context_length, self.context_length)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    @property
-    def dtype(self):
-        return self.visual.conv1.weight.dtype
-
-    def encode_image(self, image):
-        return self.visual(image.type(self.dtype))
-
-    def encode_text(self, text, norm=True):
-        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]
-
-        x = x + self.positional_embedding.type(self.dtype)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-        x = self.ln_final(x).type(self.dtype)
-
-        # x.shape = [batch_size, n_ctx, transformer.width]
-        # take features from the eot embedding (eot_token is the highest number in each sequence)
-        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-        if norm:
-            x = x / x.norm(dim=-1, keepdim=True)     
-        return x
-
-    def forward(self, image, text):
-        image_features = self.encode_image(image)
-        text_features = self.encode_text(text)
-
-        # normalized features
-        image_features = image_features / image_features.norm(dim=-1, keepdim=True)
-        text_features = text_features / text_features.norm(dim=-1, keepdim=True)
-
-        # cosine similarity as logits
-        logit_scale = self.logit_scale.exp()
-        logits_per_image = logit_scale * image_features @ text_features.t()
-        logits_per_text = logit_scale * text_features @ image_features.t()
-
-        # shape = [global_batch_size, global_batch_size]
-        return logits_per_image, logits_per_text
-
-
-def convert_weights(model: nn.Module):
-    """Convert applicable model parameters to fp16"""
-
-    def _convert_weights_to_fp16(l):
-        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
-            l.weight.data = l.weight.data.half()
-            if l.bias is not None:
-                l.bias.data = l.bias.data.half()
-
-        if isinstance(l, nn.MultiheadAttention):
-            for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
-                tensor = getattr(l, attr)
-                if tensor is not None:
-                    tensor.data = tensor.data.half()
-
-        for name in ["text_projection", "proj"]:
-            if hasattr(l, name):
-                attr = getattr(l, name)
-                if attr is not None:
-                    attr.data = attr.data.half()
-
-    model.apply(_convert_weights_to_fp16)
-
-
-def build_model(state_dict: dict):
-    vit = "visual.proj" in state_dict
-
-    if vit:
-        vision_width = state_dict["visual.conv1.weight"].shape[0]
-        vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
-        vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
-        grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
-        image_resolution = vision_patch_size * grid_size
-    else:
-        counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
-        vision_layers = tuple(counts)
-        vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
-        output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
-        vision_patch_size = None
-        assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
-        image_resolution = output_width * 32
-
-    embed_dim = state_dict["text_projection"].shape[1]
-    context_length = state_dict["positional_embedding"].shape[0]
-    vocab_size = state_dict["token_embedding.weight"].shape[0]
-    transformer_width = state_dict["ln_final.weight"].shape[0]
-    transformer_heads = transformer_width // 64
-    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
-
-    model = CLIP(
-        embed_dim,
-        image_resolution, vision_layers, vision_width, vision_patch_size,
-        context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
-    )
-
-    for key in ["input_resolution", "context_length", "vocab_size"]:
-        if key in state_dict:
-            del state_dict[key]
-
-    convert_weights(model)
-    model.load_state_dict(state_dict)
-    return model.eval()
-
-
-@BACKBONE_REGISTRY.register()
-def build_vit_clip(cfg, input_shape):
-    """
-    Create the whole CLIP instance from config.
-
-    Returns:
-        CLIP: a :class:`CLIP` instance.
-    """
-    # port standard ResNet config to CLIP ModifiedResNet
-    freeze_at           = cfg.MODEL.BACKBONE.FREEZE_AT
-    out_features        = ['res5'] # includes the whole ResNet # cfg.MODEL.RESNETS.OUT_FEATURES
-    depth               = cfg.MODEL.RESNETS.DEPTH
-    
-    # num_blocks_per_stage = {
-    #     18: [2, 2, 2, 2],
-    #     34: [3, 4, 6, 3],
-    #     50: [3, 4, 6, 3],
-    #     101: [3, 4, 23, 3],
-    #     152: [3, 8, 36, 3],
-    # }[depth]
-    vision_layers = 12 # num_blocks_per_stage
-    vision_width = 768 # cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
-    
-    # default configs of CLIP
-    embed_dim = 512 # 1024
-    image_resolution = 224
-    vision_patch_size = 32 # None
-    context_length = 77
-    vocab_size = 49408
-    transformer_width = 512
-    transformer_heads = 8
-    transformer_layers = 12
-
-    model = CLIP(
-            embed_dim,
-            image_resolution, vision_layers, vision_width, vision_patch_size,
-            context_length, vocab_size, transformer_width, transformer_heads, transformer_layers,
-            out_features, freeze_at
-        )
-    return model
-
-@BACKBONE_REGISTRY.register()
-def build_resnet_clip(cfg, input_shape):
-    """
-    Create the whole CLIP instance from config.
-
-    Returns:
-        CLIP: a :class:`CLIP` instance.
-    """
-    # port standard ResNet config to CLIP ModifiedResNet
-    freeze_at           = cfg.MODEL.BACKBONE.FREEZE_AT
-    out_features        = ['res5'] # includes the whole ResNet # cfg.MODEL.RESNETS.OUT_FEATURES
-    depth               = cfg.MODEL.RESNETS.DEPTH
-    
-    num_blocks_per_stage = {
-        18: [2, 2, 2, 2],
-        34: [3, 4, 6, 3],
-        50: [3, 4, 6, 3],
-        101: [3, 4, 23, 3],
-        152: [3, 8, 36, 3],
-        200: [4, 6, 10, 6], # flag for ResNet50x4
-    }[depth]
-    vision_layers = num_blocks_per_stage
-    vision_width = {
-        50: 64,
-        101: 64,
-        200: 80, # flag for ResNet50x4
-    }[depth]  # cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
-    
-    # default configs of CLIP
-    embed_dim = {
-        50: 1024,
-        101: 512,
-        200: 640, # flag for ResNet50x4
-    }[depth] 
-    vision_heads = vision_width * 32 // 64
-    image_resolution = {
-        50: 224,
-        101: 224,
-        200: 288, # flag for ResNet50x4
-    }[depth] 
-    vision_patch_size = None
-    context_length = 77
-    vocab_size = 49408
-    transformer_width = {
-        50: 512,
-        101: 512,
-        200: 640, # flag for ResNet50x4
-    }[depth] 
-    transformer_heads = {
-        50: 8,
-        101: 8,
-        200: 10, # flag for ResNet50x4
-    }[depth] 
-    transformer_layers = 12
-
-    model = CLIP(
-            embed_dim,
-            image_resolution, vision_layers, vision_width, vision_patch_size,
-            context_length, vocab_size, transformer_width, transformer_heads, transformer_layers,
-            out_features, freeze_at
-        )
-    return model
-
-
-@BACKBONE_REGISTRY.register()
-def build_clip_resnet_backbone(cfg, input_shape):
-    """
-    Create a CLIP ResNet instance from config.
-
-    Returns:
-        ModifiedResNet: a :class:`ModifiedResNet` instance.
-    """
-    # port standard ResNet config to CLIP ModifiedResNet
-    freeze_at           = cfg.MODEL.BACKBONE.FREEZE_AT
-    out_features        = cfg.MODEL.RESNETS.OUT_FEATURES
-    depth               = cfg.MODEL.RESNETS.DEPTH
-    # num_groups          = cfg.MODEL.RESNETS.NUM_GROUPS
-    # width_per_group     = cfg.MODEL.RESNETS.WIDTH_PER_GROUP
-    # bottleneck_channels = num_groups * width_per_group
-    # in_channels         = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
-    # out_channels        = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
-    # stride_in_1x1       = cfg.MODEL.RESNETS.STRIDE_IN_1X1
-    # res5_dilation       = cfg.MODEL.RESNETS.RES5_DILATION
-    # deform_on_per_stage = cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE
-    # deform_modulated    = cfg.MODEL.RESNETS.DEFORM_MODULATED
-    # deform_num_groups   = cfg.MODEL.RESNETS.DEFORM_NUM_GROUPS
-    
-    num_blocks_per_stage = {
-        18: [2, 2, 2, 2],
-        34: [3, 4, 6, 3],
-        50: [3, 4, 6, 3],
-        101: [3, 4, 23, 3],
-        152: [3, 8, 36, 3],
-        200: [4, 6, 10, 6], # flag for ResNet50x4
-    }[depth]
-    vision_layers = num_blocks_per_stage
-    vision_width = {
-        50: 64,
-        101: 64,
-        200: 80, # flag for ResNet50x4
-    }[depth]  # cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
-    
-    # default configs of CLIP ModifiedResNet, but not used if only building ModifiedResNet as backbone
-    embed_dim = {
-        50: 1024,
-        101: 512,
-        200: 640, # flag for ResNet50x4
-    }[depth] 
-    vision_heads = vision_width * 32 // 64
-    image_resolution = {
-        50: 224,
-        101: 224,
-        200: 288, # flag for ResNet50x4
-    }[depth] 
-
-    # if combine {ModifiedResNet of CLIP, C4, text emb as classifier}, then has to use att_pool to match dimension
-    create_att_pool = True if (cfg.MODEL.ROI_HEADS.NAME in ['CLIPRes5ROIHeads', 'CLIPStandardROIHeads'] and cfg.MODEL.CLIP.USE_TEXT_EMB_CLASSIFIER)\
-                           or cfg.MODEL.ROI_HEADS.NAME == 'PretrainRes5ROIHeads' else False
-
-    return ModifiedResNet(layers=vision_layers, 
-                output_dim=embed_dim,
-                heads=vision_heads,
-                input_resolution=image_resolution,
-                width=vision_width,
-                out_features=out_features, 
-                freeze_at=freeze_at,
-                depth=depth,
-                pool_vec=False,
-                create_att_pool=create_att_pool,
-                )
-
-
-class CLIPLangEncoder(nn.Module):
-    def __init__(self,
-                 embed_dim: int,
-                 # vision
-                 image_resolution: int,
-                 vision_layers: Union[Tuple[int, int, int, int], int],
-                 vision_width: int,
-                 vision_patch_size: int,
-                 # text
-                 context_length: int,
-                 vocab_size: int,
-                 transformer_width: int,
-                 transformer_heads: int,
-                 transformer_layers: int,
-                 out_features,
-                 freeze_at,
-                 ):
-        super().__init__()
-
-        self.context_length = context_length
-
-        self.transformer = Transformer(
-            width=transformer_width,
-            layers=transformer_layers,
-            heads=transformer_heads,
-            attn_mask=self.build_attention_mask()
-        )
-
-        self.vocab_size = vocab_size
-        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
-        self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
-        self.ln_final = LayerNorm(transformer_width)
-
-        self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
-        #self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-
-        self.initialize_parameters()
-
-    def initialize_parameters(self):
-        nn.init.normal_(self.token_embedding.weight, std=0.02)
-        nn.init.normal_(self.positional_embedding, std=0.01)
-
-        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
-        attn_std = self.transformer.width ** -0.5
-        fc_std = (2 * self.transformer.width) ** -0.5
-        for block in self.transformer.resblocks:
-            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
-        if self.text_projection is not None:
-            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.context_length, self.context_length)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    @property
-    def dtype(self):
-        return self.transformer.resblocks[0].mlp[0].weight.dtype # torch.float32, not sure whether need to be fp16 in pretraining
-
-    def encode_text(self, text, only_eot=True, norm=True):
-        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]
-
-        x = x + self.positional_embedding.type(self.dtype)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-        x = self.ln_final(x).type(self.dtype)
-
-        if only_eot:
-            # x.shape = [batch_size, n_ctx, transformer.width]
-            # take features from the eot embedding (eot_token is the highest number in each sequence)
-            x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-            if norm:
-                x = x / x.norm(dim=-1, keepdim=True)            
-            return x
-        else:
-            # return embeddings for all tokens, instead of the eot embedding as CLIP implementation below
-            x = x @ self.text_projection
-            if norm:
-                x = x / x.norm(dim=-1, keepdim=True)     
-            return x
-
-def build_clip_language_encoder(cfg):
-    """
-    Create the CLIP language encoder instance from config.
-
-    Returns:
-        CLIP: a :class:`CLIP` instance.
-    """
-    # port standard ResNet config to CLIP ModifiedResNet
-    freeze_at           = cfg.MODEL.BACKBONE.FREEZE_AT
-    out_features        = ['res5'] # includes the whole ResNet # cfg.MODEL.RESNETS.OUT_FEATURES
-    depth               = cfg.MODEL.RESNETS.DEPTH
-    
-    num_blocks_per_stage = {
-        18: [2, 2, 2, 2],
-        34: [3, 4, 6, 3],
-        50: [3, 4, 6, 3],
-        101: [3, 4, 23, 3],
-        152: [3, 8, 36, 3],
-        200: [4, 6, 10, 6], # flag for ResNet50x4
-    }[depth]
-    vision_layers = num_blocks_per_stage
-    vision_width = {
-        50: 64,
-        101: 64,
-        200: 80, # flag for ResNet50x4
-    }[depth]  # cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
-    
-    # default configs of CLIP
-    embed_dim = {
-        50: 1024,
-        101: 512,
-        200: 640, # flag for ResNet50x4
-    }[depth] 
-    vision_heads = vision_width * 32 // 64
-    image_resolution = {
-        50: 224,
-        101: 224,
-        200: 288, # flag for ResNet50x4
-    }[depth] 
-    vision_patch_size = None
-    context_length = 77
-    vocab_size = 49408
-    transformer_width = {
-        50: 512,
-        101: 512,
-        200: 640, # flag for ResNet50x4
-    }[depth] 
-    transformer_heads = {
-        50: 8,
-        101: 8,
-        200: 10, # flag for ResNet50x4
-    }[depth] 
-    transformer_layers = 12
-
-    model = CLIPLangEncoder(
-            embed_dim,
-            image_resolution, vision_layers, vision_width, vision_patch_size,
-            context_length, vocab_size, transformer_width, transformer_heads, transformer_layers,
-            out_features, freeze_at
-        )
-    return model

spaces/ChrisPreston/diff-svc_minato_aqua/preprocessing/svc_binarizer.py

@@ -1,224 +0,0 @@
-import json
-import logging
-import os
-import random
-from copy import deepcopy
-
-import numpy as np
-import yaml
-from resemblyzer import VoiceEncoder
-from tqdm import tqdm
-
-from infer_tools.f0_static import static_f0_time
-from modules.vocoders.nsf_hifigan import NsfHifiGAN
-from preprocessing.hubertinfer import HubertEncoder
-from preprocessing.process_pipeline import File2Batch
-from preprocessing.process_pipeline import get_pitch_parselmouth, get_pitch_crepe
-from utils.hparams import hparams
-from utils.hparams import set_hparams
-from utils.indexed_datasets import IndexedDatasetBuilder
-
-os.environ["OMP_NUM_THREADS"] = "1"
-BASE_ITEM_ATTRIBUTES = ['wav_fn', 'spk_id']
-
-
-class SvcBinarizer:
-    '''
-        Base class for data processing.
-        1. *process* and *process_data_split*:
-            process entire data, generate the train-test split (support parallel processing);
-        2. *process_item*:
-            process singe piece of data;
-        3. *get_pitch*:
-            infer the pitch using some algorithm;
-        4. *get_align*:
-            get the alignment using 'mel2ph' format (see https://arxiv.org/abs/1905.09263).
-        5. phoneme encoder, voice encoder, etc.
-
-        Subclasses should define:
-        1. *load_metadata*:
-            how to read multiple datasets from files;
-        2. *train_item_names*, *valid_item_names*, *test_item_names*:
-            how to split the dataset;
-        3. load_ph_set:
-            the phoneme set.
-    '''
-
-    def __init__(self, data_dir=None, item_attributes=None):
-        self.spk_map = None
-        self.vocoder = NsfHifiGAN()
-        self.phone_encoder = HubertEncoder(pt_path=hparams['hubert_path'])
-        if item_attributes is None:
-            item_attributes = BASE_ITEM_ATTRIBUTES
-        if data_dir is None:
-            data_dir = hparams['raw_data_dir']
-        if 'speakers' not in hparams:
-            speakers = hparams['datasets']
-            hparams['speakers'] = hparams['datasets']
-        else:
-            speakers = hparams['speakers']
-        assert isinstance(speakers, list), 'Speakers must be a list'
-        assert len(speakers) == len(set(speakers)), 'Speakers cannot contain duplicate names'
-
-        self.raw_data_dirs = data_dir if isinstance(data_dir, list) else [data_dir]
-        assert len(speakers) == len(self.raw_data_dirs), \
-            'Number of raw data dirs must equal number of speaker names!'
-        self.speakers = speakers
-        self.binarization_args = hparams['binarization_args']
-
-        self.items = {}
-        # every item in self.items has some attributes
-        self.item_attributes = item_attributes
-
-        # load each dataset
-        for ds_id, data_dir in enumerate(self.raw_data_dirs):
-            self.load_meta_data(data_dir, ds_id)
-            if ds_id == 0:
-                # check program correctness
-                assert all([attr in self.item_attributes for attr in list(self.items.values())[0].keys()])
-        self.item_names = sorted(list(self.items.keys()))
-
-        if self.binarization_args['shuffle']:
-            random.seed(hparams['seed'])
-            random.shuffle(self.item_names)
-
-        # set default get_pitch algorithm
-        if hparams['use_crepe']:
-            self.get_pitch_algorithm = get_pitch_crepe
-        else:
-            self.get_pitch_algorithm = get_pitch_parselmouth
-        print('spkers: ', set(self.speakers))
-        self._train_item_names, self._test_item_names = self.split_train_test_set(self.item_names)
-
-    @staticmethod
-    def split_train_test_set(item_names):
-        auto_test = item_names[-5:]
-        item_names = set(deepcopy(item_names))
-        if hparams['choose_test_manually']:
-            prefixes = set([str(pr) for pr in hparams['test_prefixes']])
-            test_item_names = set()
-            # Add prefixes that specified speaker index and matches exactly item name to test set
-            for prefix in deepcopy(prefixes):
-                if prefix in item_names:
-                    test_item_names.add(prefix)
-                    prefixes.remove(prefix)
-            # Add prefixes that exactly matches item name without speaker id to test set
-            for prefix in deepcopy(prefixes):
-                for name in item_names:
-                    if name.split(':')[-1] == prefix:
-                        test_item_names.add(name)
-                        prefixes.remove(prefix)
-            # Add names with one of the remaining prefixes to test set
-            for prefix in deepcopy(prefixes):
-                for name in item_names:
-                    if name.startswith(prefix):
-                        test_item_names.add(name)
-                        prefixes.remove(prefix)
-            for prefix in prefixes:
-                for name in item_names:
-                    if name.split(':')[-1].startswith(prefix):
-                        test_item_names.add(name)
-            test_item_names = sorted(list(test_item_names))
-        else:
-            test_item_names = auto_test
-        train_item_names = [x for x in item_names if x not in set(test_item_names)]
-        logging.info("train {}".format(len(train_item_names)))
-        logging.info("test {}".format(len(test_item_names)))
-        return train_item_names, test_item_names
-
-    @property
-    def train_item_names(self):
-        return self._train_item_names
-
-    @property
-    def valid_item_names(self):
-        return self._test_item_names
-
-    @property
-    def test_item_names(self):
-        return self._test_item_names
-
-    def load_meta_data(self, raw_data_dir, ds_id):
-        self.items.update(File2Batch.file2temporary_dict(raw_data_dir, ds_id))
-
-    @staticmethod
-    def build_spk_map():
-        spk_map = {x: i for i, x in enumerate(hparams['speakers'])}
-        assert len(spk_map) <= hparams['num_spk'], 'Actual number of speakers should be smaller than num_spk!'
-        return spk_map
-
-    def item_name2spk_id(self, item_name):
-        return self.spk_map[self.items[item_name]['spk_id']]
-
-    def meta_data_iterator(self, prefix):
-        if prefix == 'valid':
-            item_names = self.valid_item_names
-        elif prefix == 'test':
-            item_names = self.test_item_names
-        else:
-            item_names = self.train_item_names
-        for item_name in item_names:
-            meta_data = self.items[item_name]
-            yield item_name, meta_data
-
-    def process(self):
-        os.makedirs(hparams['binary_data_dir'], exist_ok=True)
-        self.spk_map = self.build_spk_map()
-        print("| spk_map: ", self.spk_map)
-        spk_map_fn = f"{hparams['binary_data_dir']}/spk_map.json"
-        json.dump(self.spk_map, open(spk_map_fn, 'w', encoding='utf-8'))
-        self.process_data_split('valid')
-        self.process_data_split('test')
-        self.process_data_split('train')
-
-    def process_data_split(self, prefix):
-        data_dir = hparams['binary_data_dir']
-        args = []
-        builder = IndexedDatasetBuilder(f'{data_dir}/{prefix}')
-        lengths = []
-        total_sec = 0
-        if self.binarization_args['with_spk_embed']:
-            voice_encoder = VoiceEncoder().cuda()
-        for item_name, meta_data in self.meta_data_iterator(prefix):
-            args.append([item_name, meta_data, self.binarization_args])
-        spec_min = []
-        spec_max = []
-        f0_dict = {}
-        # code for single cpu processing
-        for i in tqdm(reversed(range(len(args))), total=len(args)):
-            a = args[i]
-            item = self.process_item(*a)
-            if item is None:
-                continue
-            item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) \
-                if self.binarization_args['with_spk_embed'] else None
-            spec_min.append(item['spec_min'])
-            spec_max.append(item['spec_max'])
-            f0_dict[item['wav_fn']] = item['f0']
-            builder.add_item(item)
-            lengths.append(item['len'])
-            total_sec += item['sec']
-        if prefix == 'train':
-            spec_max = np.max(spec_max, 0)
-            spec_min = np.min(spec_min, 0)
-            pitch_time = static_f0_time(f0_dict)
-            with open(hparams['config_path'], encoding='utf-8') as f:
-                _hparams = yaml.safe_load(f)
-                _hparams['spec_max'] = spec_max.tolist()
-                _hparams['spec_min'] = spec_min.tolist()
-                if self.speakers == 1:
-                    _hparams['f0_static'] = json.dumps(pitch_time)
-            with open(hparams['config_path'], 'w', encoding='utf-8') as f:
-                yaml.safe_dump(_hparams, f)
-        builder.finalize()
-        np.save(f'{data_dir}/{prefix}_lengths.npy', lengths)
-        print(f"| {prefix} total duration: {total_sec:.3f}s")
-
-    def process_item(self, item_name, meta_data, binarization_args):
-        from preprocessing.process_pipeline import File2Batch
-        return File2Batch.temporary_dict2processed_input(item_name, meta_data, self.phone_encoder)
-
-
-if __name__ == "__main__":
-    set_hparams()
-    SvcBinarizer().process()

spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/model/db/base.js

@@ -1,43 +0,0 @@
-import { dirname, resolve } from 'path';
-import { fileURLToPath } from 'url'
-
-let Sequelize, DataTypes, sequelize, Op, existSQL = true
-try {
-    const modules = await import('sequelize');
-    Sequelize = modules.Sequelize;
-    DataTypes = modules.DataTypes;
-    Op = modules.Op
-
-    const __filename = fileURLToPath(import.meta.url);
-    const __dirname = dirname(__filename);
-
-    sequelize = new Sequelize({
-        dialect: 'sqlite',
-        storage: resolve(__dirname, 'data.db'),
-        logging: false,
-    })
-
-    await sequelize.authenticate()
-} catch (error) {
-    logger.warn('[ws-plugin] Yunzai-Bot暂不支持sqlite3数据库,建议切换至Miao-Yunzai获得最佳体验')
-    existSQL = false
-    sequelize = new Proxy({}, {
-        get: () => {
-            return () => {
-                return new Promise((resolve, reject) => {
-                    resolve();
-                });
-            }
-        },
-    });
-    DataTypes = {};
-}
-
-
-
-export {
-    sequelize,
-    DataTypes,
-    Op,
-    existSQL
-}

spaces/CoPoBio/skin_cancer_risk_prediction/helpers.py

@@ -1,95 +0,0 @@
-# import the necessary packages
-from collections import OrderedDict
-import numpy as np
-import cv2
-
-# define a dictionary that maps the indexes of the facial
-# landmarks to specific face regions
-
-#For dlib’s 68-point facial landmark detector:
-FACIAL_LANDMARKS_68_IDXS = OrderedDict([
-	("mouth", (48, 68)),
-	("inner_mouth", (60, 68)),
-	("right_eyebrow", (17, 22)),
-	("left_eyebrow", (22, 27)),
-	("right_eye", (36, 42)),
-	("left_eye", (42, 48)),
-	("nose", (27, 36)),
-	("jaw", (0, 17))
-])
-
-#For dlib’s 5-point facial landmark detector:
-FACIAL_LANDMARKS_5_IDXS = OrderedDict([
-	("right_eye", (2, 3)),
-	("left_eye", (0, 1)),
-	("nose", (4))
-])
-
-# in order to support legacy code, we'll default the indexes to the
-# 68-point model
-FACIAL_LANDMARKS_IDXS = FACIAL_LANDMARKS_68_IDXS
-
-def rect_to_bb(rect):
-	# take a bounding predicted by dlib and convert it
-	# to the format (x, y, w, h) as we would normally do
-	# with OpenCV
-	x = rect.left()
-	y = rect.top()
-	w = rect.right() - x
-	h = rect.bottom() - y
-
-	# return a tuple of (x, y, w, h)
-	return (x, y, w, h)
-
-def shape_to_np(shape, dtype="int"):
-	# initialize the list of (x, y)-coordinates
-	coords = np.zeros((shape.num_parts, 2), dtype=dtype)
-
-	# loop over all facial landmarks and convert them
-	# to a 2-tuple of (x, y)-coordinates
-	for i in range(0, shape.num_parts):
-		coords[i] = (shape.part(i).x, shape.part(i).y)
-
-	# return the list of (x, y)-coordinates
-	return coords
-
-def visualize_facial_landmarks(image, shape, colors=None, alpha=0.75):
-	# create two copies of the input image -- one for the
-	# overlay and one for the final output image
-	overlay = image.copy()
-	output = image.copy()
-
-	# if the colors list is None, initialize it with a unique
-	# color for each facial landmark region
-	if colors is None:
-		colors = [(19, 199, 109), (79, 76, 240), (230, 159, 23),
-			(168, 100, 168), (158, 163, 32),
-			(163, 38, 32), (180, 42, 220), (0, 0, 255)]
-
-	# loop over the facial landmark regions individually
-	for (i, name) in enumerate(FACIAL_LANDMARKS_IDXS.keys()):
-		# grab the (x, y)-coordinates associated with the
-		# face landmark
-		(j, k) = FACIAL_LANDMARKS_IDXS[name]
-		pts = shape[j:k]
-
-		# check if are supposed to draw the jawline
-		if name == "jaw":
-			# since the jawline is a non-enclosed facial region,
-			# just draw lines between the (x, y)-coordinates
-			for l in range(1, len(pts)):
-				ptA = tuple(pts[l - 1])
-				ptB = tuple(pts[l])
-				cv2.line(overlay, ptA, ptB, colors[i], 2)
-
-		# otherwise, compute the convex hull of the facial
-		# landmark coordinates points and display it
-		else:
-			hull = cv2.convexHull(pts)
-			cv2.drawContours(overlay, [hull], -1, colors[i], -1)
-
-	# apply the transparent overlay
-	cv2.addWeighted(overlay, alpha, output, 1 - alpha, 0, output)
-
-	# return the output image
-	return output

spaces/CobaltZvc/Docs_Buddy/README.md

@@ -1,12 +0,0 @@
----
-title: Docs Buddy
-emoji: 🩺
-colorFrom: purple
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Cyril666/ContourNet-ABI/modules/attention.py

@@ -1,97 +0,0 @@
-import torch
-import torch.nn as nn
-from .transformer import PositionalEncoding
-
-class Attention(nn.Module):
-    def __init__(self, in_channels=512, max_length=25, n_feature=256):
-        super().__init__()
-        self.max_length = max_length
-
-        self.f0_embedding = nn.Embedding(max_length, in_channels)
-        self.w0 = nn.Linear(max_length, n_feature)
-        self.wv = nn.Linear(in_channels, in_channels)
-        self.we = nn.Linear(in_channels, max_length)
-
-        self.active = nn.Tanh()
-        self.softmax = nn.Softmax(dim=2)
-
-    def forward(self, enc_output):
-        enc_output = enc_output.permute(0, 2, 3, 1).flatten(1, 2)
-        reading_order = torch.arange(self.max_length, dtype=torch.long, device=enc_output.device)
-        reading_order = reading_order.unsqueeze(0).expand(enc_output.size(0), -1)  # (S,) -> (B, S)
-        reading_order_embed = self.f0_embedding(reading_order)  # b,25,512
-
-        t = self.w0(reading_order_embed.permute(0, 2, 1))  # b,512,256
-        t = self.active(t.permute(0, 2, 1) + self.wv(enc_output))  # b,256,512
-
-        attn = self.we(t)  # b,256,25
-        attn = self.softmax(attn.permute(0, 2, 1))  # b,25,256
-        g_output = torch.bmm(attn, enc_output)  # b,25,512
-        return g_output, attn.view(*attn.shape[:2], 8, 32)
-
-
-def encoder_layer(in_c, out_c, k=3, s=2, p=1):
-    return nn.Sequential(nn.Conv2d(in_c, out_c, k, s, p),
-                         nn.BatchNorm2d(out_c),
-                         nn.ReLU(True))
-
-def decoder_layer(in_c, out_c, k=3, s=1, p=1, mode='nearest', scale_factor=None, size=None):
-    align_corners = None if mode=='nearest' else True
-    return nn.Sequential(nn.Upsample(size=size, scale_factor=scale_factor, 
-                                     mode=mode, align_corners=align_corners),
-                         nn.Conv2d(in_c, out_c, k, s, p),
-                         nn.BatchNorm2d(out_c),
-                         nn.ReLU(True))
-
-
-class PositionAttention(nn.Module):
-    def __init__(self, max_length, in_channels=512, num_channels=64, 
-                 h=8, w=32, mode='nearest', **kwargs):
-        super().__init__()
-        self.max_length = max_length
-        self.k_encoder = nn.Sequential(
-            encoder_layer(in_channels, num_channels, s=(1, 2)),
-            encoder_layer(num_channels, num_channels, s=(2, 2)),
-            encoder_layer(num_channels, num_channels, s=(2, 2)),
-            encoder_layer(num_channels, num_channels, s=(2, 2))
-        )
-        self.k_decoder = nn.Sequential(
-            decoder_layer(num_channels, num_channels, scale_factor=2, mode=mode),
-            decoder_layer(num_channels, num_channels, scale_factor=2, mode=mode),
-            decoder_layer(num_channels, num_channels, scale_factor=2, mode=mode),
-            decoder_layer(num_channels, in_channels, size=(h, w), mode=mode)
-        )
-
-        self.pos_encoder = PositionalEncoding(in_channels, dropout=0, max_len=max_length)
-        self.project = nn.Linear(in_channels, in_channels)
-
-    def forward(self, x):
-        N, E, H, W = x.size()
-        k, v = x, x  # (N, E, H, W)
-
-        # calculate key vector
-        features = []
-        for i in range(0, len(self.k_encoder)):
-            k = self.k_encoder[i](k)
-            features.append(k)
-        for i in range(0, len(self.k_decoder) - 1):
-            k = self.k_decoder[i](k)
-            k = k + features[len(self.k_decoder) - 2 - i]
-        k = self.k_decoder[-1](k)
-
-        # calculate query vector
-        # TODO q=f(q,k)
-        zeros = x.new_zeros((self.max_length, N, E))  # (T, N, E)
-        q = self.pos_encoder(zeros)  # (T, N, E)
-        q = q.permute(1, 0, 2)  # (N, T, E)
-        q = self.project(q)  # (N, T, E)
-        
-        # calculate attention
-        attn_scores = torch.bmm(q, k.flatten(2, 3))  # (N, T, (H*W))
-        attn_scores = attn_scores / (E ** 0.5)
-        attn_scores = torch.softmax(attn_scores, dim=-1)
-
-        v = v.permute(0, 2, 3, 1).view(N, -1, E)  # (N, (H*W), E)
-        attn_vecs = torch.bmm(attn_scores, v)  # (N, T, E)
-
-        return attn_vecs, attn_scores.view(N, -1, H, W)