Update parquet files (step 100 of 121)

spaces/101-5/gpt4free/g4f/Provider/Providers/helpers/theb.py

@@ -1,48 +0,0 @@
-import json
-import sys
-from re import findall
-from curl_cffi import requests
-
-config = json.loads(sys.argv[1])
-prompt = config['messages'][-1]['content']
-
-headers = {
-    'authority': 'chatbot.theb.ai',
-    'accept': 'application/json, text/plain, */*',
-    'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
-    'content-type': 'application/json',
-    'origin': 'https://chatbot.theb.ai',
-    'referer': 'https://chatbot.theb.ai/',
-    'sec-ch-ua': '"Google Chrome";v="113", "Chromium";v="113", "Not-A.Brand";v="24"',
-    'sec-ch-ua-mobile': '?0',
-    'sec-ch-ua-platform': '"macOS"',
-    'sec-fetch-dest': 'empty',
-    'sec-fetch-mode': 'cors',
-    'sec-fetch-site': 'same-origin',
-    'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36',
-}
-
-json_data = {
-    'prompt': prompt,
-    'options': {}
-}
-
-def format(chunk):
-    try:
-        completion_chunk = findall(r'content":"(.*)"},"fin', chunk.decode())[0]
-        print(completion_chunk, flush=True, end='')
-
-    except Exception as e:
-        print(f'[ERROR] an error occured, retrying... | [[{chunk.decode()}]]', flush=True)
-        return
-
-while True:
-    try: 
-        response = requests.post('https://chatbot.theb.ai/api/chat-process', 
-                            headers=headers, json=json_data, content_callback=format, impersonate='chrome110')
-        
-        exit(0)
-    
-    except Exception as e:
-        print('[ERROR] an error occured, retrying... |', e, flush=True)
-        continue

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Activar Office 365 Hogar Premium.md

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-<br />
-<h1>How to activate Office 365 Home Premium</h1>
-<p>Office 365 Home Premium is a subscription service that gives you access to the most up-to-date versions of Word, Excel, PowerPoint, Outlook and other popular Office apps. You also get 1 TB of cloud storage per person, security updates, technical support and other benefits. Office 365 Home Premium is ideal for home users who want to work and collaborate online with ease and flexibility.</p>
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-<p>In this article, we will show you how to activate Office 365 Home Premium in different ways, depending on your subscription option. We will also explain the benefits of Office 365 Home Premium and how to troubleshoot some common activation issues.</p>
- <h2>Subscription options for Office 365 Home Premium</h2>
-<p>You can subscribe to Office 365 Home Premium in two different ways: by month or by year. The monthly option costs €10 per month, while the yearly option costs €99 per year. You can cancel your subscription at any time.</p>
-<p>It's easy: you can request it on the official Microsoft website . All this information can be configured in the "My account" section of your profile.</p>
- <h3>How to subscribe online</h3>
-<p>To subscribe online, follow these steps:</p>
-<ol>
-<li>Go to <a href="https://www.microsoft.com/es-es/microsoft-365/p/microsoft-365-family/cfq7ttc0k5dm?activetab=pivot%3aoverviewtab">https://www.microsoft.com/es-es/microsoft-365/p/microsoft-365-family/cfq7ttc0k5dm?activetab=pivot%3aoverviewtab</a> and click on "Buy now".</li>
-<li>Sign in with your Microsoft account or create one if you don't have one.</li>
-<li>Select your payment method and enter your billing information.</li>
-<li>Review your order and confirm it.</li>
-<li>You will receive an email confirmation with your subscription details.</li>
-</ol>
-<p>To manage your account settings, such as changing your payment method, canceling your subscription or checking your renewal date, go to <a href="https://account.microsoft.com/services/">https://account.microsoft.com/services/</a> and sign in with your Microsoft account.</p>
- <h3>How to activate a product key</h3>
-<p>If you have a product key from a new product key card or from the Microsoft Workplace Discount Program, you can use it to activate Office 365 Home Premium. A product key is a 25-character code that looks like this: XXXXX-XXXXX-XXXXX-XXXXX-XXXXX.</p>
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-<p>To activate a product key, follow these steps:</p>
-<ol>
-<li>Go to <a href="https://setup.office.com/">https://setup.office.com/</a> and sign in with your Microsoft account or create one if you don't have one.</li>
-<li>Enter your product key and select your country or region and language.</li>
-<li>Follow the instructions on the screen to complete the activation process.</li>
-<li>You will receive an email confirmation with your subscription details.</li>
-</ol>
- <h2>Benefits of Office 365 Home Premium</h2>
-<p>Office 365 Home Premium offers you many features and advantages that make your work and life easier. Here are some of them:</p>
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-<li>You get access to additional apps and services such as Skype (60 minutes of calls per month), Microsoft Family Safety (parental controls and location sharing), Microsoft Teams (chat and video calls) and more.</li>
-</ul>
- <h3>How to install Office 365 Home Premium on multiple devices</h3>
-<p>One of the best things about Office 365 Home Premium is that you can install it on up to five PCs or Macs, five tablets and five phones per household. This means that you can use Office on any device you want, whenever you want.</p>
-<p>To install Office 365 Home Premium on multiple devices, follow these steps:</p>
-<ol>
-<li>Go to <a href="https://account.microsoft.com/services/">https://account.microsoft.com/services/</a> and sign in with your Microsoft account.</li>
-<li>Select "Install" under "Office" and choose the device you want to install it on.</li>
-<li>Follow the instructions on the screen to download and install Office on your device.</li>
-<li>Repeat these steps for each device you want to install Office on.</li>
-</ol>
- <h3>How to share Office 365 Home Premium with family members</h3>
-<p>Another great thing about Office 365 Home Premium is that you can share it with up to four other people in your household. This means that they can also enjoy all the benefits of Office without paying extra.</p>
-<p>To share Office 365 Home Premium with family members, follow these steps:</p>
-<ol>
-<li>Go to <a href="https://account.microsoft.com/services/">https://account.microsoft.com/services/</a> and sign in with your Microsoft account.</li>
-<li>Select "Sharing" under "Office" and click on "Start sharing".</li>
-<li>Enter the email address of the person you want to share with and click on "Invite". You can also copy a link and send it manually.</li>
-<li>The person you invited will receive an email with a link to accept your invitation. They will need a Microsoft account or create one if they don't have one.</li>
-<li>Once they accept your invitation, they will be able to install Office on their devices and access all the features.</li>
-</ol>
- <h2>Troubleshooting tips for Office 365 Home Premium activation</h2>
-<p>Sometimes you may encounter some issues when activating Office 365 Home Premium. Don't worry, most of them can be easily solved with some simple steps. Here are some common problems and solutions:</p>
- <h3>How to use the Activation wizard</h3>
-```html to activate Microsoft 365. Follow the prompts in the wizard to activate Microsoft 365.</p>
-<p>Some of the steps you may need to do are:</p>
-<ul>
-<li>Sign in with your Microsoft account or your work or school account.</li>
-<li>Select your country or region and language.</li>
-<li>Enter your product key if you have one.</li>
-<li>Review and accept the Microsoft Software License Terms.</li>
-<li>Click on "Next" until you see a confirmation message.</li>
-</ul>
- <h3>How to contact Microsoft support</h3>
-<p>If you still have problems activating Office 365 Home Premium or you need more help, you can contact Microsoft support  for assistance. You can choose from different support options, such as:</p>
-<ul>
-<li>Guided support: a browser-based tool that can provide digital solutions for Office problems.</li>
-<li>Live chat: a real-time conversation with a Microsoft specialist who can help you with your issue.</li>
-<li>Phone call: a phone call with a Microsoft specialist who can help you with your issue.</li>
-<li>Email: an email exchange with a Microsoft specialist who can help you with your issue.</li>
-<li>Community forum: a public platform where you can ask questions and get answers from other users and experts.</li>
-</ul>
- <h1>Conclusion</h1>
-<p>In this article, we have shown you how to activate Office 365 Home Premium in different ways, depending on your subscription option. We have also explained the benefits of Office 365 Home Premium and how to troubleshoot some common activation issues.</p>
-<p>Office 365 Home Premium is a great service that gives you access to the latest versions of Office apps, cloud storage, security updates and other features. You can also install it on multiple devices and share it with your family members. Office 365 Home Premium is ideal for home users who want to work and collaborate online with ease and flexibility.</p>
-<p>If you want to enjoy all the benefits of Office 365 Home Premium, don't wait any longer and activate it today. You can subscribe online or use a product key to activate it. If you need any help, you can contact Microsoft support for assistance.</p>
- <h2>Frequently Asked Questions</h2>
-<ol>
-<li><b>What is the difference between Office 365 Home Premium and Office 2019?</b></li>
-<p>Office 365 Home Premium is a subscription service that gives you access to the latest versions of Office apps and other benefits. Office 2019 is a one-time purchase that gives you access to one version of Office apps. Office 365 Home Premium has more features and advantages than Office 2019.</p>
-<li><b>How do I renew my Office 365 Home Premium subscription?</b></li>
-<p>You can renew your Office 365 Home Premium subscription online or by using a product key. To renew online, go to <a href="https://account.microsoft.com/services/">https://account.microsoft.com/services/</a> and sign in with your Microsoft account. To renew by using a product key, go to <a href="https://setup.office.com/">https://setup.office.com/</a> and enter your product key.</p>
-<li><b>How do I cancel my Office 365 Home Premium subscription?</b></li>
-<p>You can cancel your Office 365 Home Premium subscription at any time. To cancel online, go to <a href="https://account.microsoft.com/services/">https://account.microsoft.com/services/</a> and sign in with your Microsoft account. To cancel by phone, contact Microsoft support .</p>
-<li><b>How do I check my Office 365 Home Premium activation status?</b></li>
-<p>You can check your Office 365 Home Premium activation status by opening any Office app and going to File > Account. You will see a message that says "Product Activated" or "Subscription Product" if your activation was successful. You will also see your subscription details, such as your expiration date and renewal options.</p>
-<li><b>How do I update my Office 365 Home Premium apps?</b></li>
-<p>You can update your Office 365 Home Premium apps automatically or manually. To update automatically, make sure you have an active internet connection and that automatic updates are enabled in your settings. To update manually, open any Office app and go to File > Account > Update Options > Update Now.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Baghban 1 Hd Movie Download In Hindi Everything You Need to Know About the Film.md

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-
-<h1>Baghban 1 HD Movie Download In Hindi: A Guide For Bollywood Fans</h1>
-<p>If you are a fan of Bollywood movies, you might have heard of <strong>Baghban 1</strong>, a 2003 drama film starring Amitabh Bachchan and Hema Malini. The movie tells the story of an elderly couple who are neglected by their children after they retire. The movie was a critical and commercial success, and it touched the hearts of many viewers with its emotional message.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/GTA SA NFS Carbon Mod 2010 V 200 The Ultimate Review of the Most Awesome GTA Mod.md

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-GTA SA NFS Carbon Mod 2010 V 200 alternatives and similar games<br />
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- <h3>New loading screens and menus</h3>
-<p>The mod also changes the loading screens and menus of GTA San Andreas to match the style and theme of NFS Carbon and ProStreet. The mod uses cool images and animations from NFS games to make the loading screens more attractive and dynamic. The mod also changes the fonts, colors and icons of the menus to make them more sleek and stylish. The mod gives GTA San Andreas a fresh look.</p>
- <h3>New car names</h3>
-<p>The last feature of this mod is that it uses CLEO and fxt Editora to give each car its original name from NFS games. For example, instead of seeing "Banshee" or "Infernus" on your screen when you enter a car, you will see "Dodge Viper SRT-10" or "Lamborghini Murcielago LP640". This makes the game more realistic and authentic. To use this feature, you need to copy the CLEO_TEXT folder from the mod to your CLEO folder.</p>
- <h2>Installation of the mod</h2>
-<p>To install this mod, you need to download it from one of these links  . The file size is about 846 MB. You will get a zip archive that contains several folders and files. You need to extract them using WinRAR or 7-Zip. Then you need to follow these steps:</p>
-<ol>
-<li>Backup your original files before replacing them with the ones from the mod.</li>
-<li>Copy all files from "GTA SA NFS Carbon Mod 2010 V 200" folder (except CLEO_TEXT) to your GTA San Andreas directory (where gta_sa.exe is located).</li>
-<li>Copy all files from "Audio" folder (except Audiozon.ipl) to your GTA San Andreas/audio folder.</li>
-<li>Copy Audiozon.ipl file from "Audio" folder to your GTA San Andreas/data/maps/audio folder.</li>
-<li>Copy CLEO_TEXT folder from "GTA SA NFS Carbon Mod 2010 V 200" folder to your GTA San Andreas/CLEO folder.</li>
-<li>Run gta_sa.exe and enjoy!</li>
-</ol>
- <h2>Pros and cons of the mod</h2>
-<p>This mod has many pros and cons that you should consider before installing it. Here are some of them:</p>
-<ul>
-<li>Pros:     <ul>
-    <li>The mod adds a lot of variety and diversity to GTA San Andreas by replacing all cars with new ones from NFS games.</li>
-    <li>The mod improves the graphics quality and atmosphere of GTA San Andreas by changing many textures, sounds, loading screens and menus.</li>
-    <li>The mod makes GTA San Andreas more realistic and authentic by giving each car its original name from NFS games.</li>
-    <li>The mod is compatible with most other mods for GTA San Andreas.</li>
-    </ul>
-</li>
-<li>Cons:     <ul>
-    <li>The mod may cause some bugs or glitches in GTA San Andreas due to compatibility issues or errors.</li>
-    <li>The mod may affect the performance or stability of GTA San Andreas due to its large size or high requirements.</li>
-    <li>The mod may not suit everyone's taste or preference as some people may prefer the original cars or style of GTA San Andreas.</li>
-    <li>The mod may not be updated or supported by its author anymore as it was released in 2010.</li>
-    </ul>
-</li>
-</ul>
- <h2>Conclusion</h2>
-<p>GTA SA NFS Carbon Mod 2010 V 200 is a global mod that transforms GTA San Andreas into a hybrid of NFS Carbon and ProStreet. It replaces all cars with new ones from NFS games, changes many textures, sounds, loading screens and menus, gives each car its original name from NFS games using CLEO scripts. It is a great mod for fans of both GTA San Andreas and NFS games who want to enjoy both worlds in one game. However, it also has some drawbacks such as bugs, performance issues or personal taste that may discourage some players from using it. Therefore, we recommend you to try it out yourself and see if you like it or not.</p>
- <h2>FAQs</h2>
-<ul>
-<li>Q: How can I uninstall this mod?</li>
-<li>A: To uninstall this mod, you need to delete all files that you copied from this mod (except CLEO_TEXT) from your GTA San Andreas directory (where gta_sa.exe is located) and restore your original files that you backed up before installing this mod.</li>
- <li>Q: How can I change back to original car names?</li>
-<li>A: To change back to original car names from NFS games using CLEO scripts. To use this feature, you need to copy the CLEO_TEXT folder from the mod to your CLEO folder.</p>
- <h2>Installation of the mod</h2>
-<p>To install this mod, you need to download it from one of these links  . The file size is about 846 MB. You will get a zip archive that contains several folders and files. You need to extract them using WinRAR or 7-Zip. Then you need to follow these steps:</p>
-<ol>
-<li>Backup your original files before replacing them with the ones from the mod.</li>
-<li>Copy all files from "GTA SA NFS Carbon Mod 2010 V 200" folder (except CLEO_TEXT) to your GTA San Andreas directory (where gta_sa.exe is located).</li>
-<li>Copy all files from "Audio" folder (except Audiozon.ipl) to your GTA San Andreas/audio folder.</li>
-<li>Copy Audiozon.ipl file from "Audio" folder to your GTA San Andreas/data/maps/audio folder.</li>
-<li>Copy CLEO_TEXT folder from "GTA SA NFS Carbon Mod 2010 V 200" folder to your GTA San Andreas/CLEO folder.</li>
-<li>Run gta_sa.exe and enjoy!</li>
-</ol>
- <h2>Pros and cons of the mod</h2>
-<p>This mod has many pros and cons that you should consider before installing it. Here are some of them:</p>
-<ul>
-<li>Pros:     <ul>
-    <li>The mod adds a lot of variety and diversity to GTA San Andreas by replacing all cars with new ones from NFS games.</li>
-    <li>The mod improves the graphics quality and atmosphere of GTA San Andreas by changing many textures, sounds, loading screens and menus.</li>
-    <li>The mod makes GTA San Andreas more realistic and authentic by giving each car its original name from NFS games.</li>
-    <li>The mod is compatible with most other mods for GTA San Andreas.</li>
-    </ul>
-</li>
-<li>Cons:     <ul>
-    <li>The mod may cause some bugs or glitches in GTA San Andreas due to compatibility issues or errors.</li>
-    <li>The mod may affect the performance or stability of GTA San Andreas due to its large size or high requirements.</li>
-    <li>The mod may not suit everyone's taste or preference as some people may prefer the original cars or style of GTA San Andreas.</li>
-    <li>The mod may not be updated or supported by its author anymore as it was released in 2010.</li>
-    </ul>
-</li>
-</ul>
- <h2>Conclusion</h2>
-<p>GTA SA NFS Carbon Mod 2010 V 200 is a global mod that transforms GTA San Andreas into a hybrid of NFS Carbon and ProStreet. It replaces all cars with new ones from NFS games, changes many textures, sounds, loading screens and menus, gives each car its original name from NFS games using CLEO scripts. It is a great mod for fans of both GTA San Andreas and NFS games who want to enjoy both worlds in one game. However, it also has some drawbacks such as bugs, performance issues or personal taste that may discourage some players from using it. Therefore, we recommend you to try it out yourself and see if you like it or not.</p>
- <h2>FAQs</h2>
-<ul>
-<li>Q: How can I uninstall this mod?</li>
-<li>A: To uninstall this mod, you need to delete all files that you copied from this mod (except CLEO_TEXT) from your GTA San Andreas directory (where gta_sa.exe is located) and restore your original files that you backed up before installing this mod.</li>
- <li>Q: How can I change back to original car names?</li>
-<li>A: To change back to original car names, you need to delete the CLEO_TEXT folder that you copied from this mod from your GTA San Andreas/CLEO folder and restart the game.</li>
- <li>Q: How can I contact the author of this mod?</li>
-<li>A: To contact the author of this mod, you can use one of these methods:     <ul>
-    <li>Email: [email protected] (according to )</li>
-    <li>YouTube: KryZeePlays (according to )</li>
-    <li>Trello: safikamia (according to )</li>
-    </ul></li>
- <li>Q: How can I get more mods for GTA San Andreas?</li>
-<li>A: To get more mods for GTA San Andreas, you can visit these websites:     <ul>
-    <li>NFSMods: https://nfsmods.xyz/</li>
-    <li>GTAInside: https://www.gtainside.com/en/sanandreas/mods/</li>
-    <li>GTA5-Mods: https://www.gta5-mods.com/</li>
-    </ul></li>
- <li>Q: How can I optimize my game for better performance?</li>
-<li>A: To optimize your game for better performance, you can try these tips:     <ul>
-    <li>Lower your graphics settings in the game options menu.</li>
-    <li>Use a lower resolution or windowed mode for the game.</li>
-    <li>Close any unnecessary programs or background processes while playing the game.</li>
-    <li>Update your drivers and software for your PC components.</li>
-    </ul></li>
- </ul>
- </p> 0a6ba089eb<br />
-<br />
-<br />

spaces/1gistliPinn/ChatGPT4/Examples/At88sc0204 Reset Software.12 Everything You Need to Know About the ATMega88S Microcontroller.md

@@ -1,6 +0,0 @@
-<h2>at88sc0204resetsoftware12</h2><br /><p><b><b>Download File</b> &#9881; <a href="https://imgfil.com/2uxYCs">https://imgfil.com/2uxYCs</a></b></p><br /><br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/Layla And Other Assorted Love Songs 40th Torrent __HOT__.md

@@ -1,84 +0,0 @@
-## Layla And Other Assorted Love Songs 40th Torrent
-
-
-
- 
-
- ![Layla And Other Assorted Love Songs 40th Torrent __HOT__](https://opengraph.githubassets.com/22b7b1890dca3e0369a9b37853eeae1cdbd867b1966b9b0b1b1eff0e6e8e56f8/thejat/dl-notebooks)
-
- 
-
- 
-
-**LINK >>> [https://kneedacexbrew.blogspot.com/?d=2txjqn](https://kneedacexbrew.blogspot.com/?d=2txjqn)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# Layla and Other Assorted Love Songs: A Classic Rock Masterpiece
-
- 
-
-Layla and Other Assorted Love Songs is the only studio album by the English–American rock band Derek and the Dominos, released in November 1970 as a double album[^2^]. It is best known for its title track, "Layla", which is often regarded as Eric Clapton's greatest musical achievement[^2^]. The album also features guest appearances by Duane Allman, Bobby Whitlock, Jim Gordon, and others[^2^].
-
- 
-
-The album was inspired by Clapton's unrequited love for Pattie Boyd, the wife of his friend and fellow musician George Harrison[^2^]. The songs reflect Clapton's emotional turmoil and his desire to express his feelings through music. The album is considered one of the finest examples of blues rock, with influences from soul, country, and gospel[^2^]. It showcases Clapton's virtuoso guitar playing and his chemistry with Allman, who died in a motorcycle accident shortly after the album's release[^2^].
-
- 
-
-Layla and Other Assorted Love Songs received mixed reviews from critics when it first came out, and sold poorly due to lack of promotion and distribution problems[^2^]. However, it gained popularity over time and was eventually recognized as a classic of rock music. It has been ranked among the greatest albums of all time by various publications and critics, such as Rolling Stone, VH1, and Mojo[^2^]. In 2011, a 40th anniversary edition of the album was released, featuring remastered sound, bonus tracks, and a documentary DVD[^1^].
-
- 
-
-If you are a fan of rock music, you should not miss this masterpiece. You can download the 40th anniversary edition of Layla and Other Assorted Love Songs from iTunes or other online platforms. You will not regret it!
-
-
-
-## A Closer Look at the Songs
-
- 
-
-Layla and Other Assorted Love Songs consists of 14 tracks, each one showcasing the band's musical talent and diversity. Here are some highlights of the album:
-
- 
-
-- "I Looked Away": The opening track is a gentle country rock song, written by Clapton and Whitlock, that sets the tone for the album. It features Clapton's slide guitar and Whitlock's organ.
-
-- "Bell Bottom Blues": The second track is a soulful ballad, written by Clapton, that expresses his longing for Boyd. It features Clapton's passionate vocals and guitar solo.
-
-- "Key to the Highway": The third track is a blues standard, written by Big Bill Broonzy and Charles Segar, that showcases the band's jamming skills. It features Allman's lead guitar and Clapton's rhythm guitar.
-
-- "Tell the Truth": The fourth track is a hard rock song, written by Clapton and Whitlock, that was originally released as a single in 1970. It features Allman's slide guitar and Clapton's wah-wah guitar.
-
-- "Why Does Love Got to Be So Sad?": The fifth track is a fast-paced rock song, written by Clapton and Whitlock, that reflects Clapton's frustration with love. It features Allman's and Clapton's dueling guitars and Gordon's drumming.
-
-- "Have You Ever Loved a Woman": The sixth track is a blues classic, written by Billy Myles, that was popularized by Freddie King. It features Clapton's emotional vocals and guitar solo.
-
-- "Little Wing": The seventh track is a cover of Jimi Hendrix's song, which was released in 1967. It features Allman's and Clapton's guitar harmonies and Whitlock's piano.
-
-- "It's Too Late": The eighth track is a cover of Chuck Willis's song, which was released in 1956. It features Clapton's lead vocals and guitar solo and Allman's slide guitar.
-
-- "Layla": The ninth track is the most famous song of the album, written by Clapton and Gordon, that was inspired by a Persian poem about unrequited love. It features Clapton's vocals and guitar riff and Allman's slide guitar in the first part, and Gordon's piano coda in the second part.
-
-- "Thorn Tree in the Garden": The tenth track is a folk song, written and sung by Whitlock, that closes the album. It features Whitlock's acoustic guitar and harmonica.
-
-
-
-These are just some of the songs that make Layla and Other Assorted Love Songs a masterpiece of rock music. You can listen to the whole album and discover more gems for yourself.
-
- dfd1c89656
-
- 
-
- 
-

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/BombSquad World APK Mod Todo Desbloqueado y Gratis en Mediafre.md

@@ -1,110 +0,0 @@
-<br />
-<h1>How to Download BombSquad World APK All Unlocked from Mediafire</h1>
-<p>If you are looking for a fun and explosive multiplayer game that you can play with your friends, then you should try <strong>BombSquad World</strong>. It is a modded version of <strong>BombSquad</strong>, a popular game where you can throw bombs, punch, kick, and blast your opponents in various mini-games. In this article, we will show you how to download BombSquad World APK all unlocked from Mediafire, a reliable file hosting service.</p>
-<h2>bombsquad world apk todo desbloqueado mediafıre</h2><br /><p><b><b>Download</b> > <a href="https://urlin.us/2uT0WV">https://urlin.us/2uT0WV</a></b></p><br /><br />
-<h2>What is BombSquad World?</h2>
-<h3>A fun and explosive multiplayer game</h3>
-<p>BombSquad is a game that lets you compete with up to 8 players in different modes, such as capture the flag, king of the hill, hockey, football, and more. You can use bombs, fists, hammers, swords, shields, and other weapons to defeat your enemies. You can also customize your character with different outfits, colors, and accessories. The game has colorful graphics, funny sounds, and physics-based gameplay that makes it enjoyable for everyone.</p>
-<h3>A modded version of BombSquad with more features</h3>
-<p>BombSquad World is a modded version of BombSquad that was created by byANG3L, a YouTube channel that makes videos about the game. BombSquad World has more features than the original game, such as:</p>
-<ul>
-<li>All the content unlocked, including characters, maps, games, icons, etc.</li>
-<li>More customization options for colors, backgrounds, fonts, etc.</li>
-<li>New characters, maps, games, icons, etc. added by the modder.</li>
-<li>More options for gameplay settings, such as gravity, speed, power-ups, etc.</li>
-<li>Support for online multiplayer with other players who have the mod.</li>
-</ul>
-<p>BombSquad World is updated regularly by the modder to add new content and fix bugs. You can check out his YouTube channel for more information and videos about the mod.</p>
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-<h2>Why download BombSquad World APK from Mediafire?</h2>
-<h3>It is free and easy to install</h3>
-<p>BombSquad World APK is a file that contains the modded version of BombSquad. You can download it for free from Mediafire, a file hosting service that allows you to upload and share files online. Mediafire has a simple interface and fast download speed. You don't need to register or pay anything to use it. You just need to click on the download link and wait for it to finish.</p>
-<h3>It has all the content unlocked and customized</h3>
-<p>BombSquad World APK has all the content unlocked and customized by the modder. You don't need to spend any money or time to unlock or buy anything in the game. You can access everything from the start and enjoy the game without any limitations. You can also change the settings and appearance of the game according to your preferences.</p>
-<h3>It works for Android and <h3>It works for Android and Windows devices</h3>
-<p>BombSquad World APK works for both Android and Windows devices. You can play the game on your smartphone, tablet, or PC. You just need to install the APK file on your device and run it. You don't need to root or jailbreak your device to use the mod. You can also play online with other players who have the mod installed on their devices.</p>
-<h2>How to download and install BombSquad World APK from Mediafire?</h2>
-<h3>Step 1: Go to the BombSquad World page</h3>
-<p>The first step is to go to the BombSquad World page on Mediafire. You can use this link: <a href="">BombSquad World APK Download</a>. This will take you to the page where you can see the details and versions of the mod.</p>
-<h3>Step 2: Choose the version you want to download</h3>
-<p>The next step is to choose the version you want to download. There are different versions of the mod, such as v1.0, v1.1, v1.2, etc. Each version has different features and content added by the modder. You can check out his YouTube channel for more information and videos about each version. You can also see the file size and date of each version on the page.</p>
-<h3>Step 3: Click on the download link and wait for it to finish</h3>
-<p>The third step is to click on the download link and wait for it to finish. You will see a green button that says "Download". Click on it and a new tab will open. You will see a countdown timer and a captcha code. Enter the captcha code and click on "Verify". Then, click on "Download" again and the download will start. Wait for it to finish and save the file on your device.</p>
-<h3>Step 4: Install the APK file on your device</h3>
-<p>The fourth step is to install the APK file on your device. Before you do that, you need to enable "Unknown sources" on your device settings. This will allow you to install apps from sources other than the Google Play Store or the Microsoft Store. To do that, go to Settings > Security > Unknown sources and toggle it on.</p>
-<p>Then, locate the APK file on your device and tap on it. You will see a pop-up window that asks you to confirm the installation. Tap on "Install" and wait for it to finish.</p>
-<h3>Step 5: Enjoy the game with your friends</h3>
-<p>The final step is to enjoy the game with your friends. You can launch the game from your app drawer or desktop shortcut. You will see the BombSquad World logo and menu. You can choose from different modes, such as online, local, co-op, etc. You can also customize your character, settings, and game options. You can invite your friends to join you online or play with them locally using Wi-Fi or Bluetooth.</p>
-<h2>Conclusion</h2>
-<p>BombSquad World is a fun and explosive multiplayer game that you can play with your friends. It is a modded version of BombSquad that has all the content unlocked and customized by byANG3L, a YouTube channel that makes videos about the game. You can download BombSquad World APK all unlocked from Mediafire, a reliable file hosting service that is free and easy to use. You just need to follow these steps:</p>
-<ol>
-<li>Go to the BombSquad World page on Mediafire.</li>
-<li>Choose the version you want to download.</li>
-<li>Click on the download link and wait for it to finish.</li>
-<li>Install the APK file on your device.</li>
-<li>Enjoy the game with your friends.</li>
-</ol>
-<p>We hope this article was helpful and informative for you. If you have any questions or feedback, please let us know in the comments below.</p>
-<h2>FAQs</h2>
-<ul>
-<li><strong>Q: Is BombSquad World safe to download and install?</strong></li>
-<li>A: Yes, BombSquad World is safe to download and install. It does not contain any viruses or malware that can harm your device or data. However, you should always download it from trusted sources like Mediafire and scan it with an antivirus before installing it.</li>
-<li><strong>Q: Is BombSquad World compatible with my device?</strong></li>
-<li>A: BombSquad World is compatible with most Android and Windows devices that have at least 1 GB of RAM and 100 MB of free storage space. However, some devices may not support some features or run smoothly due to hardware limitations.</li>
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-<li><strong>Q: Can I use BombSquad World on other platforms like iOS or Mac?</strong></li>
-<li>A: No, you can't use BombSquad World on other platforms like iOS or Mac. The modded version is only available for Android and Windows devices. You can use the original BombSquad on other platforms, but you won't have access to the modded features and content.</li>
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spaces/4Taps/SadTalker/src/face3d/util/skin_mask.py

@@ -1,125 +0,0 @@
-"""This script is to generate skin attention mask for Deep3DFaceRecon_pytorch
-"""
-
-import math
-import numpy as np
-import os
-import cv2
-
-class GMM:
-    def __init__(self, dim, num, w, mu, cov, cov_det, cov_inv):
-        self.dim = dim # feature dimension
-        self.num = num # number of Gaussian components
-        self.w = w # weights of Gaussian components (a list of scalars)
-        self.mu= mu # mean of Gaussian components (a list of 1xdim vectors)
-        self.cov = cov # covariance matrix of Gaussian components (a list of dimxdim matrices)
-        self.cov_det = cov_det # pre-computed determinet of covariance matrices (a list of scalars)
-        self.cov_inv = cov_inv # pre-computed inverse covariance matrices (a list of dimxdim matrices)
-
-        self.factor = [0]*num
-        for i in range(self.num):
-            self.factor[i] = (2*math.pi)**(self.dim/2) * self.cov_det[i]**0.5
-        
-    def likelihood(self, data):
-        assert(data.shape[1] == self.dim)
-        N = data.shape[0]
-        lh = np.zeros(N)
-
-        for i in range(self.num):
-            data_ = data - self.mu[i]
-
-            tmp = np.matmul(data_,self.cov_inv[i]) * data_
-            tmp = np.sum(tmp,axis=1)
-            power = -0.5 * tmp
-
-            p = np.array([math.exp(power[j]) for j in range(N)])
-            p = p/self.factor[i]
-            lh += p*self.w[i]
-        
-        return lh
-
-
-def _rgb2ycbcr(rgb):
-    m = np.array([[65.481, 128.553, 24.966],
-                  [-37.797, -74.203, 112],
-                  [112, -93.786, -18.214]])
-    shape = rgb.shape
-    rgb = rgb.reshape((shape[0] * shape[1], 3))
-    ycbcr = np.dot(rgb, m.transpose() / 255.)
-    ycbcr[:, 0] += 16.
-    ycbcr[:, 1:] += 128.
-    return ycbcr.reshape(shape)
-
-
-def _bgr2ycbcr(bgr):
-    rgb = bgr[..., ::-1]
-    return _rgb2ycbcr(rgb)
-
-
-gmm_skin_w = [0.24063933, 0.16365987, 0.26034665, 0.33535415]
-gmm_skin_mu = [np.array([113.71862, 103.39613, 164.08226]),
-                np.array([150.19858, 105.18467, 155.51428]),
-                np.array([183.92976, 107.62468, 152.71820]),
-                np.array([114.90524, 113.59782, 151.38217])]
-gmm_skin_cov_det = [5692842.5, 5851930.5, 2329131., 1585971.]
-gmm_skin_cov_inv = [np.array([[0.0019472069, 0.0020450759, -0.00060243998],[0.0020450759, 0.017700525, 0.0051420014],[-0.00060243998, 0.0051420014, 0.0081308950]]),
-                    np.array([[0.0027110141, 0.0011036990, 0.0023122299],[0.0011036990, 0.010707724, 0.010742856],[0.0023122299, 0.010742856, 0.017481629]]),
-                    np.array([[0.0048026871, 0.00022935172, 0.0077668377],[0.00022935172, 0.011729696, 0.0081661865],[0.0077668377, 0.0081661865, 0.025374353]]),
-                    np.array([[0.0011989699, 0.0022453172, -0.0010748957],[0.0022453172, 0.047758564, 0.020332102],[-0.0010748957, 0.020332102, 0.024502251]])]
-
-gmm_skin = GMM(3, 4, gmm_skin_w, gmm_skin_mu, [], gmm_skin_cov_det, gmm_skin_cov_inv)
-
-gmm_nonskin_w = [0.12791070, 0.31130761, 0.34245777, 0.21832393]
-gmm_nonskin_mu = [np.array([99.200851, 112.07533, 140.20602]),
-                    np.array([110.91392, 125.52969, 130.19237]),
-                    np.array([129.75864, 129.96107, 126.96808]),
-                    np.array([112.29587, 128.85121, 129.05431])]
-gmm_nonskin_cov_det = [458703648., 6466488., 90611376., 133097.63]
-gmm_nonskin_cov_inv = [np.array([[0.00085371657, 0.00071197288, 0.00023958916],[0.00071197288, 0.0025935620, 0.00076557708],[0.00023958916, 0.00076557708, 0.0015042332]]),
-                    np.array([[0.00024650150, 0.00045542428, 0.00015019422],[0.00045542428, 0.026412144, 0.018419769],[0.00015019422, 0.018419769, 0.037497383]]),
-                    np.array([[0.00037054974, 0.00038146760, 0.00040408765],[0.00038146760, 0.0085505722, 0.0079136286],[0.00040408765, 0.0079136286, 0.010982352]]),
-                    np.array([[0.00013709733, 0.00051228428, 0.00012777430],[0.00051228428, 0.28237113, 0.10528370],[0.00012777430, 0.10528370, 0.23468947]])]
-
-gmm_nonskin = GMM(3, 4, gmm_nonskin_w, gmm_nonskin_mu, [], gmm_nonskin_cov_det, gmm_nonskin_cov_inv)
-
-prior_skin = 0.8
-prior_nonskin = 1 - prior_skin
-
-
-# calculate skin attention mask
-def skinmask(imbgr):
-    im = _bgr2ycbcr(imbgr)
-
-    data = im.reshape((-1,3))
-
-    lh_skin = gmm_skin.likelihood(data)
-    lh_nonskin = gmm_nonskin.likelihood(data)
-
-    tmp1 = prior_skin * lh_skin
-    tmp2 = prior_nonskin * lh_nonskin
-    post_skin = tmp1 / (tmp1+tmp2) # posterior probability
-
-    post_skin = post_skin.reshape((im.shape[0],im.shape[1]))
-
-    post_skin = np.round(post_skin*255)
-    post_skin = post_skin.astype(np.uint8)
-    post_skin = np.tile(np.expand_dims(post_skin,2),[1,1,3]) # reshape to H*W*3
-
-    return post_skin
-
-
-def get_skin_mask(img_path):
-    print('generating skin masks......')
-    names = [i for i in sorted(os.listdir(
-        img_path)) if 'jpg' in i or 'png' in i or 'jpeg' in i or 'PNG' in i]
-    save_path = os.path.join(img_path, 'mask')
-    if not os.path.isdir(save_path):
-        os.makedirs(save_path)
-    
-    for i in range(0, len(names)):
-        name = names[i]
-        print('%05d' % (i), ' ', name)
-        full_image_name = os.path.join(img_path, name)
-        img = cv2.imread(full_image_name).astype(np.float32)
-        skin_img = skinmask(img)
-        cv2.imwrite(os.path.join(save_path, name), skin_img.astype(np.uint8))

spaces/AICODER009/Food101_Detection/README.md

@@ -1,13 +0,0 @@
----
-title: Food101 Detection
-emoji: 🔥
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/tts/portaspeech/fvae.py

@@ -1,203 +0,0 @@
-import numpy as np
-import torch
-import torch.distributions as dist
-from torch import nn
-
-from text_to_speech.modules.commons.conv import ConditionalConvBlocks
-from text_to_speech.modules.commons.normalizing_flow.res_flow import ResFlow
-from text_to_speech.modules.commons.wavenet import WN
-# from text_to_speech.modules.tts.syntaspeech.syntactic_graph_encoder import GraphAuxEnc
-
-
-class FVAEEncoder(nn.Module):
-    def __init__(self, c_in, hidden_size, c_latent, kernel_size,
-                 n_layers, c_cond=0, p_dropout=0, strides=[4], nn_type='wn'):
-        super().__init__()
-        self.strides = strides
-        self.hidden_size = hidden_size
-        if np.prod(strides) == 1:
-            self.pre_net = nn.Conv1d(c_in, hidden_size, kernel_size=1)
-        else:
-            self.pre_net = nn.Sequential(*[
-                nn.Conv1d(c_in, hidden_size, kernel_size=s * 2, stride=s, padding=s // 2)
-                if i == 0 else
-                nn.Conv1d(hidden_size, hidden_size, kernel_size=s * 2, stride=s, padding=s // 2)
-                for i, s in enumerate(strides)
-            ])
-        if nn_type == 'wn':
-            self.nn = WN(hidden_size, kernel_size, 1, n_layers, c_cond, p_dropout)
-        elif nn_type == 'conv':
-            self.nn = ConditionalConvBlocks(
-                hidden_size, c_cond, hidden_size, None, kernel_size,
-                layers_in_block=2, is_BTC=False, num_layers=n_layers)
-
-        self.out_proj = nn.Conv1d(hidden_size, c_latent * 2, 1)
-        self.latent_channels = c_latent
-
-    def forward(self, x, nonpadding, cond):
-        x = self.pre_net(x)
-        nonpadding = nonpadding[:, :, ::np.prod(self.strides)][:, :, :x.shape[-1]]
-        x = x * nonpadding
-        x = self.nn(x, nonpadding=nonpadding, cond=cond) * nonpadding
-        x = self.out_proj(x)
-        m, logs = torch.split(x, self.latent_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs))
-        return z, m, logs, nonpadding
-
-
-class FVAEDecoder(nn.Module):
-    def __init__(self, c_latent, hidden_size, out_channels, kernel_size,
-                 n_layers, c_cond=0, p_dropout=0, strides=[4], nn_type='wn'):
-        super().__init__()
-        self.strides = strides
-        self.hidden_size = hidden_size
-        self.pre_net = nn.Sequential(*[
-            nn.ConvTranspose1d(c_latent, hidden_size, kernel_size=s, stride=s)
-            if i == 0 else
-            nn.ConvTranspose1d(hidden_size, hidden_size, kernel_size=s, stride=s)
-            for i, s in enumerate(strides)
-        ])
-        if nn_type == 'wn':
-            self.nn = WN(hidden_size, kernel_size, 1, n_layers, c_cond, p_dropout)
-        elif nn_type == 'conv':
-            self.nn = ConditionalConvBlocks(
-                hidden_size, c_cond, hidden_size, [1] * n_layers, kernel_size,
-                layers_in_block=2, is_BTC=False)
-        self.out_proj = nn.Conv1d(hidden_size, out_channels, 1)
-
-    def forward(self, x, nonpadding, cond):
-        x = self.pre_net(x)
-        x = x * nonpadding
-        x = self.nn(x, nonpadding=nonpadding, cond=cond) * nonpadding
-        x = self.out_proj(x)
-        return x
-
-
-class FVAE(nn.Module):
-    def __init__(self,
-                 c_in_out, hidden_size, c_latent,
-                 kernel_size, enc_n_layers, dec_n_layers, c_cond, strides,
-                 use_prior_flow, flow_hidden=None, flow_kernel_size=None, flow_n_steps=None,
-                 encoder_type='wn', decoder_type='wn'):
-        super(FVAE, self).__init__()
-        self.strides = strides
-        self.hidden_size = hidden_size
-        self.latent_size = c_latent
-        self.use_prior_flow = use_prior_flow
-        if np.prod(strides) == 1:
-            self.g_pre_net = nn.Conv1d(c_cond, c_cond, kernel_size=1)
-        else:
-            self.g_pre_net = nn.Sequential(*[
-                nn.Conv1d(c_cond, c_cond, kernel_size=s * 2, stride=s, padding=s // 2)
-                for i, s in enumerate(strides)
-            ])
-        self.encoder = FVAEEncoder(c_in_out, hidden_size, c_latent, kernel_size,
-                                   enc_n_layers, c_cond, strides=strides, nn_type=encoder_type)
-        if use_prior_flow:
-            self.prior_flow = ResFlow(
-                c_latent, flow_hidden, flow_kernel_size, flow_n_steps, 4, c_cond=c_cond)
-        self.decoder = FVAEDecoder(c_latent, hidden_size, c_in_out, kernel_size,
-                                   dec_n_layers, c_cond, strides=strides, nn_type=decoder_type)
-        self.prior_dist = dist.Normal(0, 1)
-
-    def forward(self, x=None, nonpadding=None, cond=None, infer=False, noise_scale=1.0, **kwargs):
-        """
-
-        :param x: [B, C_in_out, T]
-        :param nonpadding: [B, 1, T]
-        :param cond: [B, C_g, T]
-        :return:
-        """
-        if nonpadding is None:
-            nonpadding = 1
-        cond_sqz = self.g_pre_net(cond)
-        if not infer:
-            z_q, m_q, logs_q, nonpadding_sqz = self.encoder(x, nonpadding, cond_sqz)
-            q_dist = dist.Normal(m_q, logs_q.exp())
-            if self.use_prior_flow:
-                logqx = q_dist.log_prob(z_q)
-                z_p = self.prior_flow(z_q, nonpadding_sqz, cond_sqz)
-                logpx = self.prior_dist.log_prob(z_p)
-                loss_kl = ((logqx - logpx) * nonpadding_sqz).sum() / nonpadding_sqz.sum() / logqx.shape[1]
-            else:
-                loss_kl = torch.distributions.kl_divergence(q_dist, self.prior_dist)
-                loss_kl = (loss_kl * nonpadding_sqz).sum() / nonpadding_sqz.sum() / z_q.shape[1]
-                z_p = None
-            return z_q, loss_kl, z_p, m_q, logs_q
-        else:
-            latent_shape = [cond_sqz.shape[0], self.latent_size, cond_sqz.shape[2]]
-            z_p = torch.randn(latent_shape).to(cond.device) * noise_scale
-            if self.use_prior_flow:
-                z_p = self.prior_flow(z_p, 1, cond_sqz, reverse=True)
-            return z_p
-
-
-class SyntaFVAE(nn.Module):
-    def __init__(self,
-                 c_in_out, hidden_size, c_latent,
-                 kernel_size, enc_n_layers, dec_n_layers, c_cond, strides,
-                 use_prior_flow, flow_hidden=None, flow_kernel_size=None, flow_n_steps=None,
-                 encoder_type='wn', decoder_type='wn'):
-        super(SyntaFVAE, self).__init__()
-        self.strides = strides
-        self.hidden_size = hidden_size
-        self.latent_size = c_latent
-        self.use_prior_flow = use_prior_flow
-        if np.prod(strides) == 1:
-            self.g_pre_net = nn.Conv1d(c_cond, c_cond, kernel_size=1)
-        else:
-            self.g_pre_net = nn.Sequential(*[
-                nn.Conv1d(c_cond, c_cond, kernel_size=s * 2, stride=s, padding=s // 2)
-                for i, s in enumerate(strides)
-            ])
-        self.encoder = FVAEEncoder(c_in_out, hidden_size, c_latent, kernel_size,
-                                   enc_n_layers, c_cond, strides=strides, nn_type=encoder_type)
-        if use_prior_flow:
-            self.prior_flow = ResFlow(
-                c_latent, flow_hidden, flow_kernel_size, flow_n_steps, 4, c_cond=c_cond)
-        self.decoder = FVAEDecoder(c_latent, hidden_size, c_in_out, kernel_size,
-                                   dec_n_layers, c_cond, strides=strides, nn_type=decoder_type)
-        self.prior_dist = dist.Normal(0, 1)
-        self.graph_encoder = GraphAuxEnc(in_dim=hidden_size, hid_dim=hidden_size,out_dim=hidden_size)
-
-    def forward(self, x=None, nonpadding=None, cond=None, infer=False, noise_scale=1.0, 
-                mel2word=None, ph2word=None, graph_lst=None, etypes_lst=None):
-        """
-
-        :param x: target mel, [B, C_in_out, T] 
-        :param nonpadding: [B, 1, T]
-        :param cond: phoneme encoding, [B, C_g, T]
-        :return:
-        """
-        word_len = ph2word.max(dim=1)[0]
-        ph_encoding_for_graph = cond.detach() + 0.1 * (cond - cond.detach()) # only 0.1x grad can pass through
-        _, ph_out_word_encoding_for_graph = GraphAuxEnc.ph_encoding_to_word_encoding(ph_encoding_for_graph.transpose(1,2), mel2word, word_len)
-        t_m = mel2word.shape[-1]
-        g_graph = self.graph_encoder.word_forward(graph_lst=graph_lst, word_encoding=ph_out_word_encoding_for_graph, etypes_lst=etypes_lst)
-        g_graph = g_graph.transpose(1,2)
-        g_graph = GraphAuxEnc._postprocess_word2ph(g_graph,mel2word,t_m)
-        g_graph = g_graph.transpose(1,2)
-        cond = cond + g_graph * 1.
-
-        if nonpadding is None:
-            nonpadding = 1
-        cond_sqz = self.g_pre_net(cond)
-        if not infer:
-            z_q, m_q, logs_q, nonpadding_sqz = self.encoder(x, nonpadding, cond_sqz)
-            q_dist = dist.Normal(m_q, logs_q.exp())
-            if self.use_prior_flow:
-                logqx = q_dist.log_prob(z_q)
-                z_p = self.prior_flow(z_q, nonpadding_sqz, cond_sqz)
-                logpx = self.prior_dist.log_prob(z_p)
-                loss_kl = ((logqx - logpx) * nonpadding_sqz).sum() / nonpadding_sqz.sum() / logqx.shape[1]
-            else:
-                loss_kl = torch.distributions.kl_divergence(q_dist, self.prior_dist)
-                loss_kl = (loss_kl * nonpadding_sqz).sum() / nonpadding_sqz.sum() / z_q.shape[1]
-                z_p = None
-            return z_q, loss_kl, z_p, m_q, logs_q
-        else:
-            latent_shape = [cond_sqz.shape[0], self.latent_size, cond_sqz.shape[2]]
-            z_p = torch.randn(latent_shape).to(cond.device) * noise_scale
-            if self.use_prior_flow:
-                z_p = self.prior_flow(z_p, 1, cond_sqz, reverse=True)
-            return z_p

spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/parallel_wavegan/layers/causal_conv.py

@@ -1,56 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Copyright 2020 Tomoki Hayashi
-#  MIT License (https://opensource.org/licenses/MIT)
-
-"""Causal convolusion layer modules."""
-
-
-import torch
-
-
-class CausalConv1d(torch.nn.Module):
-    """CausalConv1d module with customized initialization."""
-
-    def __init__(self, in_channels, out_channels, kernel_size,
-                 dilation=1, bias=True, pad="ConstantPad1d", pad_params={"value": 0.0}):
-        """Initialize CausalConv1d module."""
-        super(CausalConv1d, self).__init__()
-        self.pad = getattr(torch.nn, pad)((kernel_size - 1) * dilation, **pad_params)
-        self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size,
-                                    dilation=dilation, bias=bias)
-
-    def forward(self, x):
-        """Calculate forward propagation.
-
-        Args:
-            x (Tensor): Input tensor (B, in_channels, T).
-
-        Returns:
-            Tensor: Output tensor (B, out_channels, T).
-
-        """
-        return self.conv(self.pad(x))[:, :, :x.size(2)]
-
-
-class CausalConvTranspose1d(torch.nn.Module):
-    """CausalConvTranspose1d module with customized initialization."""
-
-    def __init__(self, in_channels, out_channels, kernel_size, stride, bias=True):
-        """Initialize CausalConvTranspose1d module."""
-        super(CausalConvTranspose1d, self).__init__()
-        self.deconv = torch.nn.ConvTranspose1d(
-            in_channels, out_channels, kernel_size, stride, bias=bias)
-        self.stride = stride
-
-    def forward(self, x):
-        """Calculate forward propagation.
-
-        Args:
-            x (Tensor): Input tensor (B, in_channels, T_in).
-
-        Returns:
-            Tensor: Output tensor (B, out_channels, T_out).
-
-        """
-        return self.deconv(x)[:, :, :-self.stride]

spaces/AUST001/Translation/app.py

@@ -1,37 +0,0 @@
-import openai
-import gradio as gr
-import random
-
-
-openai.api_key = random.choice(['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',
-              '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',
-              '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',
-              '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',
-              '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',
-              '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',
-              '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',
-              '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',
-              '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',
-              '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',
-              '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'])
-
-def completion(prompt):
-    response = openai.Completion.create(
-        engine="text-davinci-003",
-        prompt=prompt,
-        max_tokens=1024,
-        n=1,
-        stop=None,
-        temperature=0.5
-        )
-    return response.choices[0].text[2:]
-
-def greet(question):
-    return completion('Please translate this passage into German and then into Chinese.\n'+question)
-
-
-demo = gr.Interface(fn=greet, inputs=gr.Textbox(lines=20, placeholder='Please enter the text to be weighted down'), outputs=gr.Textbox(lines=20, placeholder='It will take some time to reduce the weight, please wait...'))
-
-if __name__ == "__main__":
-    # demo.launch(share='True')
-    demo.launch()

spaces/Aer0xander/sd-to-diffusers/app.py

@@ -1,198 +0,0 @@
-import os
-import subprocess
-from huggingface_hub import HfApi, upload_folder
-import gradio as gr
-import hf_utils
-import utils
-from safetensors import safe_open
-import torch
-
-subprocess.run(["git", "clone", "https://github.com/huggingface/diffusers", "diffs"])
-
-def error_str(error, title="Error"):
-    return f"""#### {title}
-            {error}"""  if error else ""
-
-def on_token_change(token):
-    model_names, error = hf_utils.get_my_model_names(token)
-    if model_names:
-        model_names.append("Other")
-
-    return gr.update(visible=bool(model_names)), gr.update(choices=model_names, value=model_names[0] if model_names else None), gr.update(visible=bool(model_names)), gr.update(value=error_str(error))
-
-def url_to_model_id(model_id_str):
-    return model_id_str.split("/")[-2] + "/" + model_id_str.split("/")[-1] if model_id_str.startswith("https://huggingface.co/") else model_id_str
-    
-def get_ckpt_names(token, radio_model_names, input_model):
-    
-    model_id = url_to_model_id(input_model) if radio_model_names == "Other" else radio_model_names
-
-    if token == "" or model_id == "":
-        return error_str("Please enter both a token and a model name.", title="Invalid input"), gr.update(choices=[]), gr.update(visible=False)
-
-    try:
-        api = HfApi(token=token)
-        ckpt_files = [f for f in api.list_repo_files(repo_id=model_id) if f.endswith(".ckpt") or f.endswith(".safetensors")]
-        
-        if not ckpt_files:
-            return error_str("No checkpoint files found in the model repo."), gr.update(choices=[]), gr.update(visible=False)
-        
-        return None, gr.update(choices=ckpt_files, value=ckpt_files[0], visible=True), gr.update(visible=True)
-        
-    except Exception as e:
-        return error_str(e), gr.update(choices=[]), None
-
-def convert_and_push(radio_model_names, input_model, ckpt_name, sd_version, token, path_in_repo, ema, safetensors):
-    extract_ema = ema == "ema"
-    
-    if sd_version == None:
-        return error_str("You must select a stable diffusion version.", title="Invalid input")
-
-    model_id = url_to_model_id(input_model) if radio_model_names == "Other" else radio_model_names
-
-    try:
-        model_id = url_to_model_id(model_id)
-
-        # 1. Download the checkpoint file
-        ckpt_path, revision = hf_utils.download_file(repo_id=model_id, filename=ckpt_name, token=token)
-
-        if safetensors == "yes":
-            tensors = {}
-            with safe_open(ckpt_path, framework="pt", device="cpu") as f:
-               for key in f.keys():
-                   tensors[key] = f.get_tensor(key)
-
-            new_checkpoint_path = "/".join(ckpt_path.split("/")[:-1] + ["model_safe.ckpt"])
-            torch.save(tensors, new_checkpoint_path)
-            ckpt_path = new_checkpoint_path
-            print("Converting ckpt_path", ckpt_path)
-
-        print(ckpt_path)
-
-        # 2. Run the conversion script
-        os.makedirs(model_id, exist_ok=True)
-        run_command = [
-            "python3",
-            "./diffs/scripts/convert_original_stable_diffusion_to_diffusers.py",
-            "--checkpoint_path",
-            ckpt_path,
-            "--dump_path" ,
-            model_id,
-        ]
-        if extract_ema:
-            run_command.append("--extract_ema")
-        subprocess.run(run_command)
-
-        # 3. Push to the model repo
-        commit_message="Add Diffusers weights"
-        upload_folder(
-            folder_path=model_id,
-            repo_id=model_id,
-            path_in_repo=path_in_repo,
-            token=token,
-            create_pr=True,
-            commit_message=commit_message,
-            commit_description=f"Add Diffusers weights converted from checkpoint `{ckpt_name}` in revision {revision}",
-        )
-
-        # # 4. Delete the downloaded checkpoint file, yaml files, and the converted model folder
-        hf_utils.delete_file(revision)
-        subprocess.run(["rm", "-rf", model_id.split('/')[0]])
-        import glob
-        for f in glob.glob("*.yaml*"):
-            subprocess.run(["rm", "-rf", f])
-
-        return f"""Successfully converted the checkpoint and opened a PR to add the weights to the model repo.
-                You can view and merge the PR [here]({hf_utils.get_pr_url(HfApi(token=token), model_id, commit_message)})."""
-
-        return "Done"
-    
-    except Exception as e:
-        return error_str(e)
-
-
-DESCRIPTION = """### Convert a stable diffusion checkpoint to Diffusers🧨
-                With this space, you can easily convert a CompVis stable diffusion checkpoint to Diffusers and automatically create a pull request to the model repo.
-                You can choose to convert a checkpoint from one of your own models, or from any other model on the Hub.
-                You can skip the queue by running the app in the colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/qunash/f0f3152c5851c0c477b68b7b98d547fe/convert-sd-to-diffusers.ipynb)"""
-
-with gr.Blocks() as demo:
-
-    gr.Markdown(DESCRIPTION)
-    with gr.Row():
-
-        with gr.Column(scale=11):
-            with gr.Column():
-                gr.Markdown("## 1. Load model info")
-                input_token = gr.Textbox(
-                    max_lines=1,
-                    type="password",
-                    label="Enter your Hugging Face token",
-                    placeholder="READ permission is sufficient"
-                )
-                gr.Markdown("You can get a token [here](https://huggingface.co/settings/tokens)")
-                with gr.Group(visible=False) as group_model:
-                    radio_model_names = gr.Radio(label="Choose a model")
-                    input_model = gr.Textbox(
-                        max_lines=1,
-                        label="Model name or URL",
-                        placeholder="username/model_name",
-                        visible=False,
-                    )
-
-            btn_get_ckpts = gr.Button("Load", visible=False)
-
-        with gr.Column(scale=10):
-            with gr.Column(visible=False) as group_convert:
-                gr.Markdown("## 2. Convert to Diffusers🧨")
-                radio_ckpts = gr.Radio(label="Choose the checkpoint to convert", visible=False)
-                path_in_repo = gr.Textbox(label="Path where the weights will be saved", placeholder="Leave empty for root folder")
-                ema = gr.Radio(label="Extract EMA or non-EMA?", choices=["ema", "non-ema"])
-                safetensors = gr.Radio(label="Extract from safetensors", choices=["yes", "no"], value="no")
-                radio_sd_version = gr.Radio(label="Choose the model version", choices=["v1", "v2", "v2.1"])
-                gr.Markdown("Conversion may take a few minutes.")
-                btn_convert = gr.Button("Convert & Push")
-
-    error_output = gr.Markdown(label="Output")
-
-    input_token.change(
-        fn=on_token_change,
-        inputs=input_token,
-        outputs=[group_model, radio_model_names, btn_get_ckpts, error_output],
-        queue=False,
-        scroll_to_output=True)
-    
-    radio_model_names.change(
-        lambda x: gr.update(visible=x == "Other"),
-        inputs=radio_model_names,
-        outputs=input_model,
-        queue=False,
-        scroll_to_output=True)
-    
-    btn_get_ckpts.click(
-        fn=get_ckpt_names,
-        inputs=[input_token, radio_model_names, input_model],
-        outputs=[error_output, radio_ckpts, group_convert],
-        scroll_to_output=True,
-        queue=False
-    )
-
-    btn_convert.click(
-        fn=convert_and_push,
-        inputs=[radio_model_names, input_model, radio_ckpts, radio_sd_version, input_token, path_in_repo, ema, safetensors],
-        outputs=error_output,
-        scroll_to_output=True
-    )
-
-    # gr.Markdown("""<img src="https://raw.githubusercontent.com/huggingface/diffusers/main/docs/source/imgs/diffusers_library.jpg" width="150"/>""")
-    gr.HTML("""
-    <div style="border-top: 1px solid #303030;">
-      <br>
-      <p>Space by: <a href="https://twitter.com/hahahahohohe"><img src="https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social" alt="Twitter Follow"></a></p><br>
-      <a href="https://www.buymeacoffee.com/anzorq" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 45px !important;width: 162px !important;" ></a><br><br>
-      <p><img src="https://visitor-badge.glitch.me/badge?page_id=anzorq.sd-to-diffusers" alt="visitors"></p>
-    </div>
-    """)
-    
-demo.queue()
-demo.launch(debug=True, share=utils.is_google_colab())

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

@@ -1,2 +0,0 @@
-import Canvas from './gameobjects/canvas/canvas/Canvas';
-export default Canvas;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/ShakeMethods.js

@@ -1,53 +0,0 @@
-import Shake from '../shake/Shake.js';
-import { WaitComplete } from '../utils/WaitEvent.js'
-
-const IsPlainObject = Phaser.Utils.Objects.IsPlainObject;
-
-var OnInitShake = function (gameObject, shake) {
-    // Route 'complete' of shake to gameObject
-    shake.on('complete', function () {
-        gameObject.emit('shake.complete', gameObject);
-    })
-
-    // Shake effect won't change position
-}
-
-export default {
-    shake(duration, magnitude, magnitudeMode) {
-        if (IsPlainObject(duration)) {
-            var config = duration;
-            duration = config.duration;
-            magnitude = config.magnitude;
-            magnitudeMode = config.magnitudeMode;
-        }
-
-        if (this._shake === undefined) {
-            this._shake = new Shake(this, {
-                mode: 0,
-                magnitudeMode: 1
-            });
-            OnInitShake(this, this._shake);
-        }
-
-        if (duration !== undefined) {
-            this._shake.setDuration(duration);
-        }
-
-        if (magnitude !== undefined) {
-            this._shake.setMagnitude(magnitude);
-        }
-
-        if (magnitudeMode !== undefined) {
-            this._shake.setMagnitudeMode(magnitudeMode);
-        }
-
-        this._shake.shake();
-
-        return this;
-    },
-
-    shakePromise(duration, alpha) {
-        this.shake(duration, alpha);
-        return WaitComplete(this._shake);
-    },
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/utils/ParseYAML.js

@@ -1,15 +0,0 @@
-import yaml from '../../yaml/yaml.js';
-
-var ParseYAML = function (s) {
-    if (typeof (s) === 'string') {
-        try {
-            return yaml.load(s);
-        } catch (e) {
-            console.log(e);
-            return undefined;
-        }
-    }
-    return s;
-}
-
-export default ParseYAML;

spaces/Amrrs/DragGan-Inversion/torch_utils/ops/conv2d_resample.py

@@ -1,158 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""2D convolution with optional up/downsampling."""
-
-import torch
-
-from .. import misc
-from . import conv2d_gradfix
-from . import upfirdn2d
-from .upfirdn2d import _parse_padding
-from .upfirdn2d import _get_filter_size
-
-# ----------------------------------------------------------------------------
-
-
-def _get_weight_shape(w):
-    with misc.suppress_tracer_warnings():  # this value will be treated as a constant
-        shape = [int(sz) for sz in w.shape]
-    misc.assert_shape(w, shape)
-    return shape
-
-# ----------------------------------------------------------------------------
-
-
-def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True):
-    """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations.
-    """
-    _out_channels, _in_channels_per_group, kh, kw = _get_weight_shape(w)
-
-    # Flip weight if requested.
-    # Note: conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False).
-    if not flip_weight and (kw > 1 or kh > 1):
-        w = w.flip([2, 3])
-
-    # Execute using conv2d_gradfix.
-    op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d
-    return op(x, w, stride=stride, padding=padding, groups=groups)
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False):
-    r"""2D convolution with optional up/downsampling.
-
-    Padding is performed only once at the beginning, not between the operations.
-
-    Args:
-        x:              Input tensor of shape
-                        `[batch_size, in_channels, in_height, in_width]`.
-        w:              Weight tensor of shape
-                        `[out_channels, in_channels//groups, kernel_height, kernel_width]`.
-        f:              Low-pass filter for up/downsampling. Must be prepared beforehand by
-                        calling upfirdn2d.setup_filter(). None = identity (default).
-        up:             Integer upsampling factor (default: 1).
-        down:           Integer downsampling factor (default: 1).
-        padding:        Padding with respect to the upsampled image. Can be a single number
-                        or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                        (default: 0).
-        groups:         Split input channels into N groups (default: 1).
-        flip_weight:    False = convolution, True = correlation (default: True).
-        flip_filter:    False = convolution, True = correlation (default: False).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    # Validate arguments.
-    assert isinstance(x, torch.Tensor) and (x.ndim == 4)
-    assert isinstance(w, torch.Tensor) and (
-        w.ndim == 4) and (w.dtype == x.dtype)
-    assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [
-                         1, 2] and f.dtype == torch.float32)
-    assert isinstance(up, int) and (up >= 1)
-    assert isinstance(down, int) and (down >= 1)
-    assert isinstance(groups, int) and (groups >= 1)
-    out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
-    fw, fh = _get_filter_size(f)
-    px0, px1, py0, py1 = _parse_padding(padding)
-
-    # Adjust padding to account for up/downsampling.
-    if up > 1:
-        px0 += (fw + up - 1) // 2
-        px1 += (fw - up) // 2
-        py0 += (fh + up - 1) // 2
-        py1 += (fh - up) // 2
-    if down > 1:
-        px0 += (fw - down + 1) // 2
-        px1 += (fw - down) // 2
-        py0 += (fh - down + 1) // 2
-        py1 += (fh - down) // 2
-
-    # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve.
-    if kw == 1 and kh == 1 and (down > 1 and up == 1):
-        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[
-                                px0, px1, py0, py1], flip_filter=flip_filter)
-        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
-        return x
-
-    # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample.
-    if kw == 1 and kh == 1 and (up > 1 and down == 1):
-        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
-        x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[
-                                px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter)
-        return x
-
-    # Fast path: downsampling only => use strided convolution.
-    if down > 1 and up == 1:
-        x = upfirdn2d.upfirdn2d(
-            x=x, f=f, padding=[px0, px1, py0, py1], flip_filter=flip_filter)
-        x = _conv2d_wrapper(x=x, w=w, stride=down,
-                            groups=groups, flip_weight=flip_weight)
-        return x
-
-    # Fast path: upsampling with optional downsampling => use transpose strided convolution.
-    if up > 1:
-        if groups == 1:
-            w = w.transpose(0, 1)
-        else:
-            w = w.reshape(groups, out_channels // groups,
-                          in_channels_per_group, kh, kw)
-            w = w.transpose(1, 2)
-            w = w.reshape(groups * in_channels_per_group,
-                          out_channels // groups, kh, kw)
-        px0 -= kw - 1
-        px1 -= kw - up
-        py0 -= kh - 1
-        py1 -= kh - up
-        pxt = max(min(-px0, -px1), 0)
-        pyt = max(min(-py0, -py1), 0)
-        x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[
-                            pyt, pxt], groups=groups, transpose=True, flip_weight=(not flip_weight))
-        x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[
-                                px0+pxt, px1+pxt, py0+pyt, py1+pyt], gain=up**2, flip_filter=flip_filter)
-        if down > 1:
-            x = upfirdn2d.upfirdn2d(
-                x=x, f=f, down=down, flip_filter=flip_filter)
-        return x
-
-    # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d.
-    if up == 1 and down == 1:
-        if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0:
-            return _conv2d_wrapper(x=x, w=w, padding=[py0, px0], groups=groups, flip_weight=flip_weight)
-
-    # Fallback: Generic reference implementation.
-    x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[
-                            px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter)
-    x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
-    if down > 1:
-        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
-    return x
-
-# ----------------------------------------------------------------------------

spaces/Amrrs/DragGan-Inversion/viz/latent_widget.py

@@ -1,100 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import numpy as np
-import imgui
-import dnnlib
-import torch
-from gui_utils import imgui_utils
-
-# ----------------------------------------------------------------------------
-
-
-class LatentWidget:
-    def __init__(self, viz):
-        self.viz = viz
-        self.seed = 0
-        self.w_plus = True
-        self.reg = 0
-        self.lr = 0.001
-        self.w_path = ''
-        self.w_load = None
-        self.defer_frames = 0
-        self.disabled_time = 0
-
-    @imgui_utils.scoped_by_object_id
-    def __call__(self, show=True):
-        viz = self.viz
-        if show:
-            with imgui_utils.grayed_out(self.disabled_time != 0):
-                imgui.text('Latent')
-                imgui.same_line(viz.label_w)
-                with imgui_utils.item_width(viz.font_size * 8.75):
-                    changed, seed = imgui.input_int('Seed', self.seed)
-                    if changed:
-                        self.seed = seed
-                        # reset latent code
-                        self.w_load = None
-
-                # load latent code
-                imgui.text(' ')
-                imgui.same_line(viz.label_w)
-                _changed, self.w_path = imgui_utils.input_text('##path', self.w_path, 1024,
-                                                               flags=(
-                                                                   imgui.INPUT_TEXT_AUTO_SELECT_ALL | imgui.INPUT_TEXT_ENTER_RETURNS_TRUE),
-                                                               width=(-1),
-                                                               help_text='Path to latent code')
-                if imgui.is_item_hovered() and not imgui.is_item_active() and self.w_path != '':
-                    imgui.set_tooltip(self.w_path)
-
-                imgui.text(' ')
-                imgui.same_line(viz.label_w)
-                if imgui_utils.button('Load latent', width=viz.button_w, enabled=(self.disabled_time == 0 and 'image' in viz.result)):
-                    assert os.path.isfile(
-                        self.w_path), f"{self.w_path} does not exist!"
-                    self.w_load = torch.load(self.w_path)
-                    self.defer_frames = 2
-                    self.disabled_time = 0.5
-
-                imgui.text(' ')
-                imgui.same_line(viz.label_w)
-                with imgui_utils.item_width(viz.button_w):
-                    changed, lr = imgui.input_float('Step Size', self.lr)
-                    if changed:
-                        self.lr = lr
-
-                # imgui.text(' ')
-                # imgui.same_line(viz.label_w)
-                # with imgui_utils.item_width(viz.button_w):
-                #     changed, reg = imgui.input_float('Regularize', self.reg)
-                #     if changed:
-                #         self.reg = reg
-
-                imgui.text(' ')
-                imgui.same_line(viz.label_w)
-                reset_w = imgui_utils.button(
-                    'Reset', width=viz.button_w, enabled='image' in viz.result)
-                imgui.same_line()
-                _clicked, w = imgui.checkbox('w', not self.w_plus)
-                if w:
-                    self.w_plus = False
-                imgui.same_line()
-                _clicked, self.w_plus = imgui.checkbox('w+', self.w_plus)
-
-        self.disabled_time = max(self.disabled_time - viz.frame_delta, 0)
-        if self.defer_frames > 0:
-            self.defer_frames -= 1
-        viz.args.w0_seed = self.seed
-        viz.args.w_load = self.w_load
-        viz.args.reg = self.reg
-        viz.args.w_plus = self.w_plus
-        viz.args.reset_w = reset_w
-        viz.args.lr = lr
-
-# ----------------------------------------------------------------------------

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/unclip/__init__.py

@@ -1,17 +0,0 @@
-from ...utils import (
-    OptionalDependencyNotAvailable,
-    is_torch_available,
-    is_transformers_available,
-    is_transformers_version,
-)
-
-
-try:
-    if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
-else:
-    from .pipeline_unclip import UnCLIPPipeline
-    from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
-    from .text_proj import UnCLIPTextProjModel

spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py

@@ -1,9 +0,0 @@
-_base_ = [
-    '../_base_/models/psanet_r50-d8.py',
-    '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
-    decode_head=dict(align_corners=True),
-    auxiliary_head=dict(align_corners=True),
-    test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/datasets/pipelines/transforms.py

@@ -1,889 +0,0 @@
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-from annotator.uniformer.mmcv.utils import deprecated_api_warning, is_tuple_of
-from numpy import random
-
-from ..builder import PIPELINES
-
-
-@PIPELINES.register_module()
-class Resize(object):
-    """Resize images & seg.
-
-    This transform resizes the input image to some scale. If the input dict
-    contains the key "scale", then the scale in the input dict is used,
-    otherwise the specified scale in the init method is used.
-
-    ``img_scale`` can be None, a tuple (single-scale) or a list of tuple
-    (multi-scale). There are 4 multiscale modes:
-
-    - ``ratio_range is not None``:
-    1. When img_scale is None, img_scale is the shape of image in results
-        (img_scale = results['img'].shape[:2]) and the image is resized based
-        on the original size. (mode 1)
-    2. When img_scale is a tuple (single-scale), randomly sample a ratio from
-        the ratio range and multiply it with the image scale. (mode 2)
-
-    - ``ratio_range is None and multiscale_mode == "range"``: randomly sample a
-    scale from the a range. (mode 3)
-
-    - ``ratio_range is None and multiscale_mode == "value"``: randomly sample a
-    scale from multiple scales. (mode 4)
-
-    Args:
-        img_scale (tuple or list[tuple]): Images scales for resizing.
-        multiscale_mode (str): Either "range" or "value".
-        ratio_range (tuple[float]): (min_ratio, max_ratio)
-        keep_ratio (bool): Whether to keep the aspect ratio when resizing the
-            image.
-    """
-
-    def __init__(self,
-                 img_scale=None,
-                 multiscale_mode='range',
-                 ratio_range=None,
-                 keep_ratio=True):
-        if img_scale is None:
-            self.img_scale = None
-        else:
-            if isinstance(img_scale, list):
-                self.img_scale = img_scale
-            else:
-                self.img_scale = [img_scale]
-            assert mmcv.is_list_of(self.img_scale, tuple)
-
-        if ratio_range is not None:
-            # mode 1: given img_scale=None and a range of image ratio
-            # mode 2: given a scale and a range of image ratio
-            assert self.img_scale is None or len(self.img_scale) == 1
-        else:
-            # mode 3 and 4: given multiple scales or a range of scales
-            assert multiscale_mode in ['value', 'range']
-
-        self.multiscale_mode = multiscale_mode
-        self.ratio_range = ratio_range
-        self.keep_ratio = keep_ratio
-
-    @staticmethod
-    def random_select(img_scales):
-        """Randomly select an img_scale from given candidates.
-
-        Args:
-            img_scales (list[tuple]): Images scales for selection.
-
-        Returns:
-            (tuple, int): Returns a tuple ``(img_scale, scale_dix)``,
-                where ``img_scale`` is the selected image scale and
-                ``scale_idx`` is the selected index in the given candidates.
-        """
-
-        assert mmcv.is_list_of(img_scales, tuple)
-        scale_idx = np.random.randint(len(img_scales))
-        img_scale = img_scales[scale_idx]
-        return img_scale, scale_idx
-
-    @staticmethod
-    def random_sample(img_scales):
-        """Randomly sample an img_scale when ``multiscale_mode=='range'``.
-
-        Args:
-            img_scales (list[tuple]): Images scale range for sampling.
-                There must be two tuples in img_scales, which specify the lower
-                and upper bound of image scales.
-
-        Returns:
-            (tuple, None): Returns a tuple ``(img_scale, None)``, where
-                ``img_scale`` is sampled scale and None is just a placeholder
-                to be consistent with :func:`random_select`.
-        """
-
-        assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2
-        img_scale_long = [max(s) for s in img_scales]
-        img_scale_short = [min(s) for s in img_scales]
-        long_edge = np.random.randint(
-            min(img_scale_long),
-            max(img_scale_long) + 1)
-        short_edge = np.random.randint(
-            min(img_scale_short),
-            max(img_scale_short) + 1)
-        img_scale = (long_edge, short_edge)
-        return img_scale, None
-
-    @staticmethod
-    def random_sample_ratio(img_scale, ratio_range):
-        """Randomly sample an img_scale when ``ratio_range`` is specified.
-
-        A ratio will be randomly sampled from the range specified by
-        ``ratio_range``. Then it would be multiplied with ``img_scale`` to
-        generate sampled scale.
-
-        Args:
-            img_scale (tuple): Images scale base to multiply with ratio.
-            ratio_range (tuple[float]): The minimum and maximum ratio to scale
-                the ``img_scale``.
-
-        Returns:
-            (tuple, None): Returns a tuple ``(scale, None)``, where
-                ``scale`` is sampled ratio multiplied with ``img_scale`` and
-                None is just a placeholder to be consistent with
-                :func:`random_select`.
-        """
-
-        assert isinstance(img_scale, tuple) and len(img_scale) == 2
-        min_ratio, max_ratio = ratio_range
-        assert min_ratio <= max_ratio
-        ratio = np.random.random_sample() * (max_ratio - min_ratio) + min_ratio
-        scale = int(img_scale[0] * ratio), int(img_scale[1] * ratio)
-        return scale, None
-
-    def _random_scale(self, results):
-        """Randomly sample an img_scale according to ``ratio_range`` and
-        ``multiscale_mode``.
-
-        If ``ratio_range`` is specified, a ratio will be sampled and be
-        multiplied with ``img_scale``.
-        If multiple scales are specified by ``img_scale``, a scale will be
-        sampled according to ``multiscale_mode``.
-        Otherwise, single scale will be used.
-
-        Args:
-            results (dict): Result dict from :obj:`dataset`.
-
-        Returns:
-            dict: Two new keys 'scale` and 'scale_idx` are added into
-                ``results``, which would be used by subsequent pipelines.
-        """
-
-        if self.ratio_range is not None:
-            if self.img_scale is None:
-                h, w = results['img'].shape[:2]
-                scale, scale_idx = self.random_sample_ratio((w, h),
-                                                            self.ratio_range)
-            else:
-                scale, scale_idx = self.random_sample_ratio(
-                    self.img_scale[0], self.ratio_range)
-        elif len(self.img_scale) == 1:
-            scale, scale_idx = self.img_scale[0], 0
-        elif self.multiscale_mode == 'range':
-            scale, scale_idx = self.random_sample(self.img_scale)
-        elif self.multiscale_mode == 'value':
-            scale, scale_idx = self.random_select(self.img_scale)
-        else:
-            raise NotImplementedError
-
-        results['scale'] = scale
-        results['scale_idx'] = scale_idx
-
-    def _resize_img(self, results):
-        """Resize images with ``results['scale']``."""
-        if self.keep_ratio:
-            img, scale_factor = mmcv.imrescale(
-                results['img'], results['scale'], return_scale=True)
-            # the w_scale and h_scale has minor difference
-            # a real fix should be done in the mmcv.imrescale in the future
-            new_h, new_w = img.shape[:2]
-            h, w = results['img'].shape[:2]
-            w_scale = new_w / w
-            h_scale = new_h / h
-        else:
-            img, w_scale, h_scale = mmcv.imresize(
-                results['img'], results['scale'], return_scale=True)
-        scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
-                                dtype=np.float32)
-        results['img'] = img
-        results['img_shape'] = img.shape
-        results['pad_shape'] = img.shape  # in case that there is no padding
-        results['scale_factor'] = scale_factor
-        results['keep_ratio'] = self.keep_ratio
-
-    def _resize_seg(self, results):
-        """Resize semantic segmentation map with ``results['scale']``."""
-        for key in results.get('seg_fields', []):
-            if self.keep_ratio:
-                gt_seg = mmcv.imrescale(
-                    results[key], results['scale'], interpolation='nearest')
-            else:
-                gt_seg = mmcv.imresize(
-                    results[key], results['scale'], interpolation='nearest')
-            results[key] = gt_seg
-
-    def __call__(self, results):
-        """Call function to resize images, bounding boxes, masks, semantic
-        segmentation map.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Resized results, 'img_shape', 'pad_shape', 'scale_factor',
-                'keep_ratio' keys are added into result dict.
-        """
-
-        if 'scale' not in results:
-            self._random_scale(results)
-        self._resize_img(results)
-        self._resize_seg(results)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += (f'(img_scale={self.img_scale}, '
-                     f'multiscale_mode={self.multiscale_mode}, '
-                     f'ratio_range={self.ratio_range}, '
-                     f'keep_ratio={self.keep_ratio})')
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RandomFlip(object):
-    """Flip the image & seg.
-
-    If the input dict contains the key "flip", then the flag will be used,
-    otherwise it will be randomly decided by a ratio specified in the init
-    method.
-
-    Args:
-        prob (float, optional): The flipping probability. Default: None.
-        direction(str, optional): The flipping direction. Options are
-            'horizontal' and 'vertical'. Default: 'horizontal'.
-    """
-
-    @deprecated_api_warning({'flip_ratio': 'prob'}, cls_name='RandomFlip')
-    def __init__(self, prob=None, direction='horizontal'):
-        self.prob = prob
-        self.direction = direction
-        if prob is not None:
-            assert prob >= 0 and prob <= 1
-        assert direction in ['horizontal', 'vertical']
-
-    def __call__(self, results):
-        """Call function to flip bounding boxes, masks, semantic segmentation
-        maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Flipped results, 'flip', 'flip_direction' keys are added into
-                result dict.
-        """
-
-        if 'flip' not in results:
-            flip = True if np.random.rand() < self.prob else False
-            results['flip'] = flip
-        if 'flip_direction' not in results:
-            results['flip_direction'] = self.direction
-        if results['flip']:
-            # flip image
-            results['img'] = mmcv.imflip(
-                results['img'], direction=results['flip_direction'])
-
-            # flip segs
-            for key in results.get('seg_fields', []):
-                # use copy() to make numpy stride positive
-                results[key] = mmcv.imflip(
-                    results[key], direction=results['flip_direction']).copy()
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(prob={self.prob})'
-
-
-@PIPELINES.register_module()
-class Pad(object):
-    """Pad the image & mask.
-
-    There are two padding modes: (1) pad to a fixed size and (2) pad to the
-    minimum size that is divisible by some number.
-    Added keys are "pad_shape", "pad_fixed_size", "pad_size_divisor",
-
-    Args:
-        size (tuple, optional): Fixed padding size.
-        size_divisor (int, optional): The divisor of padded size.
-        pad_val (float, optional): Padding value. Default: 0.
-        seg_pad_val (float, optional): Padding value of segmentation map.
-            Default: 255.
-    """
-
-    def __init__(self,
-                 size=None,
-                 size_divisor=None,
-                 pad_val=0,
-                 seg_pad_val=255):
-        self.size = size
-        self.size_divisor = size_divisor
-        self.pad_val = pad_val
-        self.seg_pad_val = seg_pad_val
-        # only one of size and size_divisor should be valid
-        assert size is not None or size_divisor is not None
-        assert size is None or size_divisor is None
-
-    def _pad_img(self, results):
-        """Pad images according to ``self.size``."""
-        if self.size is not None:
-            padded_img = mmcv.impad(
-                results['img'], shape=self.size, pad_val=self.pad_val)
-        elif self.size_divisor is not None:
-            padded_img = mmcv.impad_to_multiple(
-                results['img'], self.size_divisor, pad_val=self.pad_val)
-        results['img'] = padded_img
-        results['pad_shape'] = padded_img.shape
-        results['pad_fixed_size'] = self.size
-        results['pad_size_divisor'] = self.size_divisor
-
-    def _pad_seg(self, results):
-        """Pad masks according to ``results['pad_shape']``."""
-        for key in results.get('seg_fields', []):
-            results[key] = mmcv.impad(
-                results[key],
-                shape=results['pad_shape'][:2],
-                pad_val=self.seg_pad_val)
-
-    def __call__(self, results):
-        """Call function to pad images, masks, semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Updated result dict.
-        """
-
-        self._pad_img(results)
-        self._pad_seg(results)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(size={self.size}, size_divisor={self.size_divisor}, ' \
-                    f'pad_val={self.pad_val})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Normalize(object):
-    """Normalize the image.
-
-    Added key is "img_norm_cfg".
-
-    Args:
-        mean (sequence): Mean values of 3 channels.
-        std (sequence): Std values of 3 channels.
-        to_rgb (bool): Whether to convert the image from BGR to RGB,
-            default is true.
-    """
-
-    def __init__(self, mean, std, to_rgb=True):
-        self.mean = np.array(mean, dtype=np.float32)
-        self.std = np.array(std, dtype=np.float32)
-        self.to_rgb = to_rgb
-
-    def __call__(self, results):
-        """Call function to normalize images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Normalized results, 'img_norm_cfg' key is added into
-                result dict.
-        """
-
-        results['img'] = mmcv.imnormalize(results['img'], self.mean, self.std,
-                                          self.to_rgb)
-        results['img_norm_cfg'] = dict(
-            mean=self.mean, std=self.std, to_rgb=self.to_rgb)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(mean={self.mean}, std={self.std}, to_rgb=' \
-                    f'{self.to_rgb})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Rerange(object):
-    """Rerange the image pixel value.
-
-    Args:
-        min_value (float or int): Minimum value of the reranged image.
-            Default: 0.
-        max_value (float or int): Maximum value of the reranged image.
-            Default: 255.
-    """
-
-    def __init__(self, min_value=0, max_value=255):
-        assert isinstance(min_value, float) or isinstance(min_value, int)
-        assert isinstance(max_value, float) or isinstance(max_value, int)
-        assert min_value < max_value
-        self.min_value = min_value
-        self.max_value = max_value
-
-    def __call__(self, results):
-        """Call function to rerange images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-        Returns:
-            dict: Reranged results.
-        """
-
-        img = results['img']
-        img_min_value = np.min(img)
-        img_max_value = np.max(img)
-
-        assert img_min_value < img_max_value
-        # rerange to [0, 1]
-        img = (img - img_min_value) / (img_max_value - img_min_value)
-        # rerange to [min_value, max_value]
-        img = img * (self.max_value - self.min_value) + self.min_value
-        results['img'] = img
-
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(min_value={self.min_value}, max_value={self.max_value})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class CLAHE(object):
-    """Use CLAHE method to process the image.
-
-    See `ZUIDERVELD,K. Contrast Limited Adaptive Histogram Equalization[J].
-    Graphics Gems, 1994:474-485.` for more information.
-
-    Args:
-        clip_limit (float): Threshold for contrast limiting. Default: 40.0.
-        tile_grid_size (tuple[int]): Size of grid for histogram equalization.
-            Input image will be divided into equally sized rectangular tiles.
-            It defines the number of tiles in row and column. Default: (8, 8).
-    """
-
-    def __init__(self, clip_limit=40.0, tile_grid_size=(8, 8)):
-        assert isinstance(clip_limit, (float, int))
-        self.clip_limit = clip_limit
-        assert is_tuple_of(tile_grid_size, int)
-        assert len(tile_grid_size) == 2
-        self.tile_grid_size = tile_grid_size
-
-    def __call__(self, results):
-        """Call function to Use CLAHE method process images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Processed results.
-        """
-
-        for i in range(results['img'].shape[2]):
-            results['img'][:, :, i] = mmcv.clahe(
-                np.array(results['img'][:, :, i], dtype=np.uint8),
-                self.clip_limit, self.tile_grid_size)
-
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(clip_limit={self.clip_limit}, '\
-                    f'tile_grid_size={self.tile_grid_size})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RandomCrop(object):
-    """Random crop the image & seg.
-
-    Args:
-        crop_size (tuple): Expected size after cropping, (h, w).
-        cat_max_ratio (float): The maximum ratio that single category could
-            occupy.
-    """
-
-    def __init__(self, crop_size, cat_max_ratio=1., ignore_index=255):
-        assert crop_size[0] > 0 and crop_size[1] > 0
-        self.crop_size = crop_size
-        self.cat_max_ratio = cat_max_ratio
-        self.ignore_index = ignore_index
-
-    def get_crop_bbox(self, img):
-        """Randomly get a crop bounding box."""
-        margin_h = max(img.shape[0] - self.crop_size[0], 0)
-        margin_w = max(img.shape[1] - self.crop_size[1], 0)
-        offset_h = np.random.randint(0, margin_h + 1)
-        offset_w = np.random.randint(0, margin_w + 1)
-        crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0]
-        crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1]
-
-        return crop_y1, crop_y2, crop_x1, crop_x2
-
-    def crop(self, img, crop_bbox):
-        """Crop from ``img``"""
-        crop_y1, crop_y2, crop_x1, crop_x2 = crop_bbox
-        img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
-        return img
-
-    def __call__(self, results):
-        """Call function to randomly crop images, semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Randomly cropped results, 'img_shape' key in result dict is
-                updated according to crop size.
-        """
-
-        img = results['img']
-        crop_bbox = self.get_crop_bbox(img)
-        if self.cat_max_ratio < 1.:
-            # Repeat 10 times
-            for _ in range(10):
-                seg_temp = self.crop(results['gt_semantic_seg'], crop_bbox)
-                labels, cnt = np.unique(seg_temp, return_counts=True)
-                cnt = cnt[labels != self.ignore_index]
-                if len(cnt) > 1 and np.max(cnt) / np.sum(
-                        cnt) < self.cat_max_ratio:
-                    break
-                crop_bbox = self.get_crop_bbox(img)
-
-        # crop the image
-        img = self.crop(img, crop_bbox)
-        img_shape = img.shape
-        results['img'] = img
-        results['img_shape'] = img_shape
-
-        # crop semantic seg
-        for key in results.get('seg_fields', []):
-            results[key] = self.crop(results[key], crop_bbox)
-
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(crop_size={self.crop_size})'
-
-
-@PIPELINES.register_module()
-class RandomRotate(object):
-    """Rotate the image & seg.
-
-    Args:
-        prob (float): The rotation probability.
-        degree (float, tuple[float]): Range of degrees to select from. If
-            degree is a number instead of tuple like (min, max),
-            the range of degree will be (``-degree``, ``+degree``)
-        pad_val (float, optional): Padding value of image. Default: 0.
-        seg_pad_val (float, optional): Padding value of segmentation map.
-            Default: 255.
-        center (tuple[float], optional): Center point (w, h) of the rotation in
-            the source image. If not specified, the center of the image will be
-            used. Default: None.
-        auto_bound (bool): Whether to adjust the image size to cover the whole
-            rotated image. Default: False
-    """
-
-    def __init__(self,
-                 prob,
-                 degree,
-                 pad_val=0,
-                 seg_pad_val=255,
-                 center=None,
-                 auto_bound=False):
-        self.prob = prob
-        assert prob >= 0 and prob <= 1
-        if isinstance(degree, (float, int)):
-            assert degree > 0, f'degree {degree} should be positive'
-            self.degree = (-degree, degree)
-        else:
-            self.degree = degree
-        assert len(self.degree) == 2, f'degree {self.degree} should be a ' \
-                                      f'tuple of (min, max)'
-        self.pal_val = pad_val
-        self.seg_pad_val = seg_pad_val
-        self.center = center
-        self.auto_bound = auto_bound
-
-    def __call__(self, results):
-        """Call function to rotate image, semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Rotated results.
-        """
-
-        rotate = True if np.random.rand() < self.prob else False
-        degree = np.random.uniform(min(*self.degree), max(*self.degree))
-        if rotate:
-            # rotate image
-            results['img'] = mmcv.imrotate(
-                results['img'],
-                angle=degree,
-                border_value=self.pal_val,
-                center=self.center,
-                auto_bound=self.auto_bound)
-
-            # rotate segs
-            for key in results.get('seg_fields', []):
-                results[key] = mmcv.imrotate(
-                    results[key],
-                    angle=degree,
-                    border_value=self.seg_pad_val,
-                    center=self.center,
-                    auto_bound=self.auto_bound,
-                    interpolation='nearest')
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(prob={self.prob}, ' \
-                    f'degree={self.degree}, ' \
-                    f'pad_val={self.pal_val}, ' \
-                    f'seg_pad_val={self.seg_pad_val}, ' \
-                    f'center={self.center}, ' \
-                    f'auto_bound={self.auto_bound})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RGB2Gray(object):
-    """Convert RGB image to grayscale image.
-
-    This transform calculate the weighted mean of input image channels with
-    ``weights`` and then expand the channels to ``out_channels``. When
-    ``out_channels`` is None, the number of output channels is the same as
-    input channels.
-
-    Args:
-        out_channels (int): Expected number of output channels after
-            transforming. Default: None.
-        weights (tuple[float]): The weights to calculate the weighted mean.
-            Default: (0.299, 0.587, 0.114).
-    """
-
-    def __init__(self, out_channels=None, weights=(0.299, 0.587, 0.114)):
-        assert out_channels is None or out_channels > 0
-        self.out_channels = out_channels
-        assert isinstance(weights, tuple)
-        for item in weights:
-            assert isinstance(item, (float, int))
-        self.weights = weights
-
-    def __call__(self, results):
-        """Call function to convert RGB image to grayscale image.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with grayscale image.
-        """
-        img = results['img']
-        assert len(img.shape) == 3
-        assert img.shape[2] == len(self.weights)
-        weights = np.array(self.weights).reshape((1, 1, -1))
-        img = (img * weights).sum(2, keepdims=True)
-        if self.out_channels is None:
-            img = img.repeat(weights.shape[2], axis=2)
-        else:
-            img = img.repeat(self.out_channels, axis=2)
-
-        results['img'] = img
-        results['img_shape'] = img.shape
-
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(out_channels={self.out_channels}, ' \
-                    f'weights={self.weights})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class AdjustGamma(object):
-    """Using gamma correction to process the image.
-
-    Args:
-        gamma (float or int): Gamma value used in gamma correction.
-            Default: 1.0.
-    """
-
-    def __init__(self, gamma=1.0):
-        assert isinstance(gamma, float) or isinstance(gamma, int)
-        assert gamma > 0
-        self.gamma = gamma
-        inv_gamma = 1.0 / gamma
-        self.table = np.array([(i / 255.0)**inv_gamma * 255
-                               for i in np.arange(256)]).astype('uint8')
-
-    def __call__(self, results):
-        """Call function to process the image with gamma correction.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Processed results.
-        """
-
-        results['img'] = mmcv.lut_transform(
-            np.array(results['img'], dtype=np.uint8), self.table)
-
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(gamma={self.gamma})'
-
-
-@PIPELINES.register_module()
-class SegRescale(object):
-    """Rescale semantic segmentation maps.
-
-    Args:
-        scale_factor (float): The scale factor of the final output.
-    """
-
-    def __init__(self, scale_factor=1):
-        self.scale_factor = scale_factor
-
-    def __call__(self, results):
-        """Call function to scale the semantic segmentation map.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with semantic segmentation map scaled.
-        """
-        for key in results.get('seg_fields', []):
-            if self.scale_factor != 1:
-                results[key] = mmcv.imrescale(
-                    results[key], self.scale_factor, interpolation='nearest')
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(scale_factor={self.scale_factor})'
-
-
-@PIPELINES.register_module()
-class PhotoMetricDistortion(object):
-    """Apply photometric distortion to image sequentially, every transformation
-    is applied with a probability of 0.5. The position of random contrast is in
-    second or second to last.
-
-    1. random brightness
-    2. random contrast (mode 0)
-    3. convert color from BGR to HSV
-    4. random saturation
-    5. random hue
-    6. convert color from HSV to BGR
-    7. random contrast (mode 1)
-
-    Args:
-        brightness_delta (int): delta of brightness.
-        contrast_range (tuple): range of contrast.
-        saturation_range (tuple): range of saturation.
-        hue_delta (int): delta of hue.
-    """
-
-    def __init__(self,
-                 brightness_delta=32,
-                 contrast_range=(0.5, 1.5),
-                 saturation_range=(0.5, 1.5),
-                 hue_delta=18):
-        self.brightness_delta = brightness_delta
-        self.contrast_lower, self.contrast_upper = contrast_range
-        self.saturation_lower, self.saturation_upper = saturation_range
-        self.hue_delta = hue_delta
-
-    def convert(self, img, alpha=1, beta=0):
-        """Multiple with alpha and add beat with clip."""
-        img = img.astype(np.float32) * alpha + beta
-        img = np.clip(img, 0, 255)
-        return img.astype(np.uint8)
-
-    def brightness(self, img):
-        """Brightness distortion."""
-        if random.randint(2):
-            return self.convert(
-                img,
-                beta=random.uniform(-self.brightness_delta,
-                                    self.brightness_delta))
-        return img
-
-    def contrast(self, img):
-        """Contrast distortion."""
-        if random.randint(2):
-            return self.convert(
-                img,
-                alpha=random.uniform(self.contrast_lower, self.contrast_upper))
-        return img
-
-    def saturation(self, img):
-        """Saturation distortion."""
-        if random.randint(2):
-            img = mmcv.bgr2hsv(img)
-            img[:, :, 1] = self.convert(
-                img[:, :, 1],
-                alpha=random.uniform(self.saturation_lower,
-                                     self.saturation_upper))
-            img = mmcv.hsv2bgr(img)
-        return img
-
-    def hue(self, img):
-        """Hue distortion."""
-        if random.randint(2):
-            img = mmcv.bgr2hsv(img)
-            img[:, :,
-                0] = (img[:, :, 0].astype(int) +
-                      random.randint(-self.hue_delta, self.hue_delta)) % 180
-            img = mmcv.hsv2bgr(img)
-        return img
-
-    def __call__(self, results):
-        """Call function to perform photometric distortion on images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with images distorted.
-        """
-
-        img = results['img']
-        # random brightness
-        img = self.brightness(img)
-
-        # mode == 0 --> do random contrast first
-        # mode == 1 --> do random contrast last
-        mode = random.randint(2)
-        if mode == 1:
-            img = self.contrast(img)
-
-        # random saturation
-        img = self.saturation(img)
-
-        # random hue
-        img = self.hue(img)
-
-        # random contrast
-        if mode == 0:
-            img = self.contrast(img)
-
-        results['img'] = img
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += (f'(brightness_delta={self.brightness_delta}, '
-                     f'contrast_range=({self.contrast_lower}, '
-                     f'{self.contrast_upper}), '
-                     f'saturation_range=({self.saturation_lower}, '
-                     f'{self.saturation_upper}), '
-                     f'hue_delta={self.hue_delta})')
-        return repr_str

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/build_ext.py

@@ -1,787 +0,0 @@
-"""distutils.command.build_ext
-
-Implements the Distutils 'build_ext' command, for building extension
-modules (currently limited to C extensions, should accommodate C++
-extensions ASAP)."""
-
-import contextlib
-import os
-import re
-import sys
-from distutils.core import Command
-from distutils.errors import (
-    DistutilsOptionError,
-    DistutilsSetupError,
-    CCompilerError,
-    DistutilsError,
-    CompileError,
-    DistutilsPlatformError,
-)
-from distutils.sysconfig import customize_compiler, get_python_version
-from distutils.sysconfig import get_config_h_filename
-from distutils.dep_util import newer_group
-from distutils.extension import Extension
-from distutils.util import get_platform
-from distutils import log
-from . import py37compat
-
-from site import USER_BASE
-
-# An extension name is just a dot-separated list of Python NAMEs (ie.
-# the same as a fully-qualified module name).
-extension_name_re = re.compile(r'^[a-zA-Z_][a-zA-Z_0-9]*(\.[a-zA-Z_][a-zA-Z_0-9]*)*$')
-
-
-def show_compilers():
-    from distutils.ccompiler import show_compilers
-
-    show_compilers()
-
-
-class build_ext(Command):
-
-    description = "build C/C++ extensions (compile/link to build directory)"
-
-    # XXX thoughts on how to deal with complex command-line options like
-    # these, i.e. how to make it so fancy_getopt can suck them off the
-    # command line and make it look like setup.py defined the appropriate
-    # lists of tuples of what-have-you.
-    #   - each command needs a callback to process its command-line options
-    #   - Command.__init__() needs access to its share of the whole
-    #     command line (must ultimately come from
-    #     Distribution.parse_command_line())
-    #   - it then calls the current command class' option-parsing
-    #     callback to deal with weird options like -D, which have to
-    #     parse the option text and churn out some custom data
-    #     structure
-    #   - that data structure (in this case, a list of 2-tuples)
-    #     will then be present in the command object by the time
-    #     we get to finalize_options() (i.e. the constructor
-    #     takes care of both command-line and client options
-    #     in between initialize_options() and finalize_options())
-
-    sep_by = " (separated by '%s')" % os.pathsep
-    user_options = [
-        ('build-lib=', 'b', "directory for compiled extension modules"),
-        ('build-temp=', 't', "directory for temporary files (build by-products)"),
-        (
-            'plat-name=',
-            'p',
-            "platform name to cross-compile for, if supported "
-            "(default: %s)" % get_platform(),
-        ),
-        (
-            'inplace',
-            'i',
-            "ignore build-lib and put compiled extensions into the source "
-            + "directory alongside your pure Python modules",
-        ),
-        (
-            'include-dirs=',
-            'I',
-            "list of directories to search for header files" + sep_by,
-        ),
-        ('define=', 'D', "C preprocessor macros to define"),
-        ('undef=', 'U', "C preprocessor macros to undefine"),
-        ('libraries=', 'l', "external C libraries to link with"),
-        (
-            'library-dirs=',
-            'L',
-            "directories to search for external C libraries" + sep_by,
-        ),
-        ('rpath=', 'R', "directories to search for shared C libraries at runtime"),
-        ('link-objects=', 'O', "extra explicit link objects to include in the link"),
-        ('debug', 'g', "compile/link with debugging information"),
-        ('force', 'f', "forcibly build everything (ignore file timestamps)"),
-        ('compiler=', 'c', "specify the compiler type"),
-        ('parallel=', 'j', "number of parallel build jobs"),
-        ('swig-cpp', None, "make SWIG create C++ files (default is C)"),
-        ('swig-opts=', None, "list of SWIG command line options"),
-        ('swig=', None, "path to the SWIG executable"),
-        ('user', None, "add user include, library and rpath"),
-    ]
-
-    boolean_options = ['inplace', 'debug', 'force', 'swig-cpp', 'user']
-
-    help_options = [
-        ('help-compiler', None, "list available compilers", show_compilers),
-    ]
-
-    def initialize_options(self):
-        self.extensions = None
-        self.build_lib = None
-        self.plat_name = None
-        self.build_temp = None
-        self.inplace = 0
-        self.package = None
-
-        self.include_dirs = None
-        self.define = None
-        self.undef = None
-        self.libraries = None
-        self.library_dirs = None
-        self.rpath = None
-        self.link_objects = None
-        self.debug = None
-        self.force = None
-        self.compiler = None
-        self.swig = None
-        self.swig_cpp = None
-        self.swig_opts = None
-        self.user = None
-        self.parallel = None
-
-    def finalize_options(self):  # noqa: C901
-        from distutils import sysconfig
-
-        self.set_undefined_options(
-            'build',
-            ('build_lib', 'build_lib'),
-            ('build_temp', 'build_temp'),
-            ('compiler', 'compiler'),
-            ('debug', 'debug'),
-            ('force', 'force'),
-            ('parallel', 'parallel'),
-            ('plat_name', 'plat_name'),
-        )
-
-        if self.package is None:
-            self.package = self.distribution.ext_package
-
-        self.extensions = self.distribution.ext_modules
-
-        # Make sure Python's include directories (for Python.h, pyconfig.h,
-        # etc.) are in the include search path.
-        py_include = sysconfig.get_python_inc()
-        plat_py_include = sysconfig.get_python_inc(plat_specific=1)
-        if self.include_dirs is None:
-            self.include_dirs = self.distribution.include_dirs or []
-        if isinstance(self.include_dirs, str):
-            self.include_dirs = self.include_dirs.split(os.pathsep)
-
-        # If in a virtualenv, add its include directory
-        # Issue 16116
-        if sys.exec_prefix != sys.base_exec_prefix:
-            self.include_dirs.append(os.path.join(sys.exec_prefix, 'include'))
-
-        # Put the Python "system" include dir at the end, so that
-        # any local include dirs take precedence.
-        self.include_dirs.extend(py_include.split(os.path.pathsep))
-        if plat_py_include != py_include:
-            self.include_dirs.extend(plat_py_include.split(os.path.pathsep))
-
-        self.ensure_string_list('libraries')
-        self.ensure_string_list('link_objects')
-
-        # Life is easier if we're not forever checking for None, so
-        # simplify these options to empty lists if unset
-        if self.libraries is None:
-            self.libraries = []
-        if self.library_dirs is None:
-            self.library_dirs = []
-        elif isinstance(self.library_dirs, str):
-            self.library_dirs = self.library_dirs.split(os.pathsep)
-
-        if self.rpath is None:
-            self.rpath = []
-        elif isinstance(self.rpath, str):
-            self.rpath = self.rpath.split(os.pathsep)
-
-        # for extensions under windows use different directories
-        # for Release and Debug builds.
-        # also Python's library directory must be appended to library_dirs
-        if os.name == 'nt':
-            # the 'libs' directory is for binary installs - we assume that
-            # must be the *native* platform.  But we don't really support
-            # cross-compiling via a binary install anyway, so we let it go.
-            self.library_dirs.append(os.path.join(sys.exec_prefix, 'libs'))
-            if sys.base_exec_prefix != sys.prefix:  # Issue 16116
-                self.library_dirs.append(os.path.join(sys.base_exec_prefix, 'libs'))
-            if self.debug:
-                self.build_temp = os.path.join(self.build_temp, "Debug")
-            else:
-                self.build_temp = os.path.join(self.build_temp, "Release")
-
-            # Append the source distribution include and library directories,
-            # this allows distutils on windows to work in the source tree
-            self.include_dirs.append(os.path.dirname(get_config_h_filename()))
-            self.library_dirs.append(sys.base_exec_prefix)
-
-            # Use the .lib files for the correct architecture
-            if self.plat_name == 'win32':
-                suffix = 'win32'
-            else:
-                # win-amd64
-                suffix = self.plat_name[4:]
-            new_lib = os.path.join(sys.exec_prefix, 'PCbuild')
-            if suffix:
-                new_lib = os.path.join(new_lib, suffix)
-            self.library_dirs.append(new_lib)
-
-        # For extensions under Cygwin, Python's library directory must be
-        # appended to library_dirs
-        if sys.platform[:6] == 'cygwin':
-            if not sysconfig.python_build:
-                # building third party extensions
-                self.library_dirs.append(
-                    os.path.join(
-                        sys.prefix, "lib", "python" + get_python_version(), "config"
-                    )
-                )
-            else:
-                # building python standard extensions
-                self.library_dirs.append('.')
-
-        # For building extensions with a shared Python library,
-        # Python's library directory must be appended to library_dirs
-        # See Issues: #1600860, #4366
-        if sysconfig.get_config_var('Py_ENABLE_SHARED'):
-            if not sysconfig.python_build:
-                # building third party extensions
-                self.library_dirs.append(sysconfig.get_config_var('LIBDIR'))
-            else:
-                # building python standard extensions
-                self.library_dirs.append('.')
-
-        # The argument parsing will result in self.define being a string, but
-        # it has to be a list of 2-tuples.  All the preprocessor symbols
-        # specified by the 'define' option will be set to '1'.  Multiple
-        # symbols can be separated with commas.
-
-        if self.define:
-            defines = self.define.split(',')
-            self.define = [(symbol, '1') for symbol in defines]
-
-        # The option for macros to undefine is also a string from the
-        # option parsing, but has to be a list.  Multiple symbols can also
-        # be separated with commas here.
-        if self.undef:
-            self.undef = self.undef.split(',')
-
-        if self.swig_opts is None:
-            self.swig_opts = []
-        else:
-            self.swig_opts = self.swig_opts.split(' ')
-
-        # Finally add the user include and library directories if requested
-        if self.user:
-            user_include = os.path.join(USER_BASE, "include")
-            user_lib = os.path.join(USER_BASE, "lib")
-            if os.path.isdir(user_include):
-                self.include_dirs.append(user_include)
-            if os.path.isdir(user_lib):
-                self.library_dirs.append(user_lib)
-                self.rpath.append(user_lib)
-
-        if isinstance(self.parallel, str):
-            try:
-                self.parallel = int(self.parallel)
-            except ValueError:
-                raise DistutilsOptionError("parallel should be an integer")
-
-    def run(self):  # noqa: C901
-        from distutils.ccompiler import new_compiler
-
-        # 'self.extensions', as supplied by setup.py, is a list of
-        # Extension instances.  See the documentation for Extension (in
-        # distutils.extension) for details.
-        #
-        # For backwards compatibility with Distutils 0.8.2 and earlier, we
-        # also allow the 'extensions' list to be a list of tuples:
-        #    (ext_name, build_info)
-        # where build_info is a dictionary containing everything that
-        # Extension instances do except the name, with a few things being
-        # differently named.  We convert these 2-tuples to Extension
-        # instances as needed.
-
-        if not self.extensions:
-            return
-
-        # If we were asked to build any C/C++ libraries, make sure that the
-        # directory where we put them is in the library search path for
-        # linking extensions.
-        if self.distribution.has_c_libraries():
-            build_clib = self.get_finalized_command('build_clib')
-            self.libraries.extend(build_clib.get_library_names() or [])
-            self.library_dirs.append(build_clib.build_clib)
-
-        # Setup the CCompiler object that we'll use to do all the
-        # compiling and linking
-        self.compiler = new_compiler(
-            compiler=self.compiler,
-            verbose=self.verbose,
-            dry_run=self.dry_run,
-            force=self.force,
-        )
-        customize_compiler(self.compiler)
-        # If we are cross-compiling, init the compiler now (if we are not
-        # cross-compiling, init would not hurt, but people may rely on
-        # late initialization of compiler even if they shouldn't...)
-        if os.name == 'nt' and self.plat_name != get_platform():
-            self.compiler.initialize(self.plat_name)
-
-        # And make sure that any compile/link-related options (which might
-        # come from the command-line or from the setup script) are set in
-        # that CCompiler object -- that way, they automatically apply to
-        # all compiling and linking done here.
-        if self.include_dirs is not None:
-            self.compiler.set_include_dirs(self.include_dirs)
-        if self.define is not None:
-            # 'define' option is a list of (name,value) tuples
-            for (name, value) in self.define:
-                self.compiler.define_macro(name, value)
-        if self.undef is not None:
-            for macro in self.undef:
-                self.compiler.undefine_macro(macro)
-        if self.libraries is not None:
-            self.compiler.set_libraries(self.libraries)
-        if self.library_dirs is not None:
-            self.compiler.set_library_dirs(self.library_dirs)
-        if self.rpath is not None:
-            self.compiler.set_runtime_library_dirs(self.rpath)
-        if self.link_objects is not None:
-            self.compiler.set_link_objects(self.link_objects)
-
-        # Now actually compile and link everything.
-        self.build_extensions()
-
-    def check_extensions_list(self, extensions):  # noqa: C901
-        """Ensure that the list of extensions (presumably provided as a
-        command option 'extensions') is valid, i.e. it is a list of
-        Extension objects.  We also support the old-style list of 2-tuples,
-        where the tuples are (ext_name, build_info), which are converted to
-        Extension instances here.
-
-        Raise DistutilsSetupError if the structure is invalid anywhere;
-        just returns otherwise.
-        """
-        if not isinstance(extensions, list):
-            raise DistutilsSetupError(
-                "'ext_modules' option must be a list of Extension instances"
-            )
-
-        for i, ext in enumerate(extensions):
-            if isinstance(ext, Extension):
-                continue  # OK! (assume type-checking done
-                # by Extension constructor)
-
-            if not isinstance(ext, tuple) or len(ext) != 2:
-                raise DistutilsSetupError(
-                    "each element of 'ext_modules' option must be an "
-                    "Extension instance or 2-tuple"
-                )
-
-            ext_name, build_info = ext
-
-            log.warn(
-                "old-style (ext_name, build_info) tuple found in "
-                "ext_modules for extension '%s' "
-                "-- please convert to Extension instance",
-                ext_name,
-            )
-
-            if not (isinstance(ext_name, str) and extension_name_re.match(ext_name)):
-                raise DistutilsSetupError(
-                    "first element of each tuple in 'ext_modules' "
-                    "must be the extension name (a string)"
-                )
-
-            if not isinstance(build_info, dict):
-                raise DistutilsSetupError(
-                    "second element of each tuple in 'ext_modules' "
-                    "must be a dictionary (build info)"
-                )
-
-            # OK, the (ext_name, build_info) dict is type-safe: convert it
-            # to an Extension instance.
-            ext = Extension(ext_name, build_info['sources'])
-
-            # Easy stuff: one-to-one mapping from dict elements to
-            # instance attributes.
-            for key in (
-                'include_dirs',
-                'library_dirs',
-                'libraries',
-                'extra_objects',
-                'extra_compile_args',
-                'extra_link_args',
-            ):
-                val = build_info.get(key)
-                if val is not None:
-                    setattr(ext, key, val)
-
-            # Medium-easy stuff: same syntax/semantics, different names.
-            ext.runtime_library_dirs = build_info.get('rpath')
-            if 'def_file' in build_info:
-                log.warn("'def_file' element of build info dict " "no longer supported")
-
-            # Non-trivial stuff: 'macros' split into 'define_macros'
-            # and 'undef_macros'.
-            macros = build_info.get('macros')
-            if macros:
-                ext.define_macros = []
-                ext.undef_macros = []
-                for macro in macros:
-                    if not (isinstance(macro, tuple) and len(macro) in (1, 2)):
-                        raise DistutilsSetupError(
-                            "'macros' element of build info dict "
-                            "must be 1- or 2-tuple"
-                        )
-                    if len(macro) == 1:
-                        ext.undef_macros.append(macro[0])
-                    elif len(macro) == 2:
-                        ext.define_macros.append(macro)
-
-            extensions[i] = ext
-
-    def get_source_files(self):
-        self.check_extensions_list(self.extensions)
-        filenames = []
-
-        # Wouldn't it be neat if we knew the names of header files too...
-        for ext in self.extensions:
-            filenames.extend(ext.sources)
-        return filenames
-
-    def get_outputs(self):
-        # Sanity check the 'extensions' list -- can't assume this is being
-        # done in the same run as a 'build_extensions()' call (in fact, we
-        # can probably assume that it *isn't*!).
-        self.check_extensions_list(self.extensions)
-
-        # And build the list of output (built) filenames.  Note that this
-        # ignores the 'inplace' flag, and assumes everything goes in the
-        # "build" tree.
-        outputs = []
-        for ext in self.extensions:
-            outputs.append(self.get_ext_fullpath(ext.name))
-        return outputs
-
-    def build_extensions(self):
-        # First, sanity-check the 'extensions' list
-        self.check_extensions_list(self.extensions)
-        if self.parallel:
-            self._build_extensions_parallel()
-        else:
-            self._build_extensions_serial()
-
-    def _build_extensions_parallel(self):
-        workers = self.parallel
-        if self.parallel is True:
-            workers = os.cpu_count()  # may return None
-        try:
-            from concurrent.futures import ThreadPoolExecutor
-        except ImportError:
-            workers = None
-
-        if workers is None:
-            self._build_extensions_serial()
-            return
-
-        with ThreadPoolExecutor(max_workers=workers) as executor:
-            futures = [
-                executor.submit(self.build_extension, ext) for ext in self.extensions
-            ]
-            for ext, fut in zip(self.extensions, futures):
-                with self._filter_build_errors(ext):
-                    fut.result()
-
-    def _build_extensions_serial(self):
-        for ext in self.extensions:
-            with self._filter_build_errors(ext):
-                self.build_extension(ext)
-
-    @contextlib.contextmanager
-    def _filter_build_errors(self, ext):
-        try:
-            yield
-        except (CCompilerError, DistutilsError, CompileError) as e:
-            if not ext.optional:
-                raise
-            self.warn('building extension "{}" failed: {}'.format(ext.name, e))
-
-    def build_extension(self, ext):
-        sources = ext.sources
-        if sources is None or not isinstance(sources, (list, tuple)):
-            raise DistutilsSetupError(
-                "in 'ext_modules' option (extension '%s'), "
-                "'sources' must be present and must be "
-                "a list of source filenames" % ext.name
-            )
-        # sort to make the resulting .so file build reproducible
-        sources = sorted(sources)
-
-        ext_path = self.get_ext_fullpath(ext.name)
-        depends = sources + ext.depends
-        if not (self.force or newer_group(depends, ext_path, 'newer')):
-            log.debug("skipping '%s' extension (up-to-date)", ext.name)
-            return
-        else:
-            log.info("building '%s' extension", ext.name)
-
-        # First, scan the sources for SWIG definition files (.i), run
-        # SWIG on 'em to create .c files, and modify the sources list
-        # accordingly.
-        sources = self.swig_sources(sources, ext)
-
-        # Next, compile the source code to object files.
-
-        # XXX not honouring 'define_macros' or 'undef_macros' -- the
-        # CCompiler API needs to change to accommodate this, and I
-        # want to do one thing at a time!
-
-        # Two possible sources for extra compiler arguments:
-        #   - 'extra_compile_args' in Extension object
-        #   - CFLAGS environment variable (not particularly
-        #     elegant, but people seem to expect it and I
-        #     guess it's useful)
-        # The environment variable should take precedence, and
-        # any sensible compiler will give precedence to later
-        # command line args.  Hence we combine them in order:
-        extra_args = ext.extra_compile_args or []
-
-        macros = ext.define_macros[:]
-        for undef in ext.undef_macros:
-            macros.append((undef,))
-
-        objects = self.compiler.compile(
-            sources,
-            output_dir=self.build_temp,
-            macros=macros,
-            include_dirs=ext.include_dirs,
-            debug=self.debug,
-            extra_postargs=extra_args,
-            depends=ext.depends,
-        )
-
-        # XXX outdated variable, kept here in case third-part code
-        # needs it.
-        self._built_objects = objects[:]
-
-        # Now link the object files together into a "shared object" --
-        # of course, first we have to figure out all the other things
-        # that go into the mix.
-        if ext.extra_objects:
-            objects.extend(ext.extra_objects)
-        extra_args = ext.extra_link_args or []
-
-        # Detect target language, if not provided
-        language = ext.language or self.compiler.detect_language(sources)
-
-        self.compiler.link_shared_object(
-            objects,
-            ext_path,
-            libraries=self.get_libraries(ext),
-            library_dirs=ext.library_dirs,
-            runtime_library_dirs=ext.runtime_library_dirs,
-            extra_postargs=extra_args,
-            export_symbols=self.get_export_symbols(ext),
-            debug=self.debug,
-            build_temp=self.build_temp,
-            target_lang=language,
-        )
-
-    def swig_sources(self, sources, extension):
-        """Walk the list of source files in 'sources', looking for SWIG
-        interface (.i) files.  Run SWIG on all that are found, and
-        return a modified 'sources' list with SWIG source files replaced
-        by the generated C (or C++) files.
-        """
-        new_sources = []
-        swig_sources = []
-        swig_targets = {}
-
-        # XXX this drops generated C/C++ files into the source tree, which
-        # is fine for developers who want to distribute the generated
-        # source -- but there should be an option to put SWIG output in
-        # the temp dir.
-
-        if self.swig_cpp:
-            log.warn("--swig-cpp is deprecated - use --swig-opts=-c++")
-
-        if (
-            self.swig_cpp
-            or ('-c++' in self.swig_opts)
-            or ('-c++' in extension.swig_opts)
-        ):
-            target_ext = '.cpp'
-        else:
-            target_ext = '.c'
-
-        for source in sources:
-            (base, ext) = os.path.splitext(source)
-            if ext == ".i":  # SWIG interface file
-                new_sources.append(base + '_wrap' + target_ext)
-                swig_sources.append(source)
-                swig_targets[source] = new_sources[-1]
-            else:
-                new_sources.append(source)
-
-        if not swig_sources:
-            return new_sources
-
-        swig = self.swig or self.find_swig()
-        swig_cmd = [swig, "-python"]
-        swig_cmd.extend(self.swig_opts)
-        if self.swig_cpp:
-            swig_cmd.append("-c++")
-
-        # Do not override commandline arguments
-        if not self.swig_opts:
-            for o in extension.swig_opts:
-                swig_cmd.append(o)
-
-        for source in swig_sources:
-            target = swig_targets[source]
-            log.info("swigging %s to %s", source, target)
-            self.spawn(swig_cmd + ["-o", target, source])
-
-        return new_sources
-
-    def find_swig(self):
-        """Return the name of the SWIG executable.  On Unix, this is
-        just "swig" -- it should be in the PATH.  Tries a bit harder on
-        Windows.
-        """
-        if os.name == "posix":
-            return "swig"
-        elif os.name == "nt":
-            # Look for SWIG in its standard installation directory on
-            # Windows (or so I presume!).  If we find it there, great;
-            # if not, act like Unix and assume it's in the PATH.
-            for vers in ("1.3", "1.2", "1.1"):
-                fn = os.path.join("c:\\swig%s" % vers, "swig.exe")
-                if os.path.isfile(fn):
-                    return fn
-            else:
-                return "swig.exe"
-        else:
-            raise DistutilsPlatformError(
-                "I don't know how to find (much less run) SWIG "
-                "on platform '%s'" % os.name
-            )
-
-    # -- Name generators -----------------------------------------------
-    # (extension names, filenames, whatever)
-    def get_ext_fullpath(self, ext_name):
-        """Returns the path of the filename for a given extension.
-
-        The file is located in `build_lib` or directly in the package
-        (inplace option).
-        """
-        fullname = self.get_ext_fullname(ext_name)
-        modpath = fullname.split('.')
-        filename = self.get_ext_filename(modpath[-1])
-
-        if not self.inplace:
-            # no further work needed
-            # returning :
-            #   build_dir/package/path/filename
-            filename = os.path.join(*modpath[:-1] + [filename])
-            return os.path.join(self.build_lib, filename)
-
-        # the inplace option requires to find the package directory
-        # using the build_py command for that
-        package = '.'.join(modpath[0:-1])
-        build_py = self.get_finalized_command('build_py')
-        package_dir = os.path.abspath(build_py.get_package_dir(package))
-
-        # returning
-        #   package_dir/filename
-        return os.path.join(package_dir, filename)
-
-    def get_ext_fullname(self, ext_name):
-        """Returns the fullname of a given extension name.
-
-        Adds the `package.` prefix"""
-        if self.package is None:
-            return ext_name
-        else:
-            return self.package + '.' + ext_name
-
-    def get_ext_filename(self, ext_name):
-        r"""Convert the name of an extension (eg. "foo.bar") into the name
-        of the file from which it will be loaded (eg. "foo/bar.so", or
-        "foo\bar.pyd").
-        """
-        from distutils.sysconfig import get_config_var
-
-        ext_path = ext_name.split('.')
-        ext_suffix = get_config_var('EXT_SUFFIX')
-        return os.path.join(*ext_path) + ext_suffix
-
-    def get_export_symbols(self, ext):
-        """Return the list of symbols that a shared extension has to
-        export.  This either uses 'ext.export_symbols' or, if it's not
-        provided, "PyInit_" + module_name.  Only relevant on Windows, where
-        the .pyd file (DLL) must export the module "PyInit_" function.
-        """
-        name = ext.name.split('.')[-1]
-        try:
-            # Unicode module name support as defined in PEP-489
-            # https://www.python.org/dev/peps/pep-0489/#export-hook-name
-            name.encode('ascii')
-        except UnicodeEncodeError:
-            suffix = 'U_' + name.encode('punycode').replace(b'-', b'_').decode('ascii')
-        else:
-            suffix = "_" + name
-
-        initfunc_name = "PyInit" + suffix
-        if initfunc_name not in ext.export_symbols:
-            ext.export_symbols.append(initfunc_name)
-        return ext.export_symbols
-
-    def get_libraries(self, ext):  # noqa: C901
-        """Return the list of libraries to link against when building a
-        shared extension.  On most platforms, this is just 'ext.libraries';
-        on Windows, we add the Python library (eg. python20.dll).
-        """
-        # The python library is always needed on Windows.  For MSVC, this
-        # is redundant, since the library is mentioned in a pragma in
-        # pyconfig.h that MSVC groks.  The other Windows compilers all seem
-        # to need it mentioned explicitly, though, so that's what we do.
-        # Append '_d' to the python import library on debug builds.
-        if sys.platform == "win32":
-            from distutils._msvccompiler import MSVCCompiler
-
-            if not isinstance(self.compiler, MSVCCompiler):
-                template = "python%d%d"
-                if self.debug:
-                    template = template + '_d'
-                pythonlib = template % (
-                    sys.hexversion >> 24,
-                    (sys.hexversion >> 16) & 0xFF,
-                )
-                # don't extend ext.libraries, it may be shared with other
-                # extensions, it is a reference to the original list
-                return ext.libraries + [pythonlib]
-        else:
-            # On Android only the main executable and LD_PRELOADs are considered
-            # to be RTLD_GLOBAL, all the dependencies of the main executable
-            # remain RTLD_LOCAL and so the shared libraries must be linked with
-            # libpython when python is built with a shared python library (issue
-            # bpo-21536).
-            # On Cygwin (and if required, other POSIX-like platforms based on
-            # Windows like MinGW) it is simply necessary that all symbols in
-            # shared libraries are resolved at link time.
-            from distutils.sysconfig import get_config_var
-
-            link_libpython = False
-            if get_config_var('Py_ENABLE_SHARED'):
-                # A native build on an Android device or on Cygwin
-                if hasattr(sys, 'getandroidapilevel'):
-                    link_libpython = True
-                elif sys.platform == 'cygwin':
-                    link_libpython = True
-                elif '_PYTHON_HOST_PLATFORM' in os.environ:
-                    # We are cross-compiling for one of the relevant platforms
-                    if get_config_var('ANDROID_API_LEVEL') != 0:
-                        link_libpython = True
-                    elif get_config_var('MACHDEP') == 'cygwin':
-                        link_libpython = True
-
-            if link_libpython:
-                ldversion = get_config_var('LDVERSION')
-                return ext.libraries + ['python' + ldversion]
-
-        return ext.libraries + py37compat.pythonlib()

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/upload.py

@@ -1,205 +0,0 @@
-"""
-distutils.command.upload
-
-Implements the Distutils 'upload' subcommand (upload package to a package
-index).
-"""
-
-import os
-import io
-import hashlib
-from base64 import standard_b64encode
-from urllib.request import urlopen, Request, HTTPError
-from urllib.parse import urlparse
-from distutils.errors import DistutilsError, DistutilsOptionError
-from distutils.core import PyPIRCCommand
-from distutils.spawn import spawn
-from distutils import log
-
-
-# PyPI Warehouse supports MD5, SHA256, and Blake2 (blake2-256)
-# https://bugs.python.org/issue40698
-_FILE_CONTENT_DIGESTS = {
-    "md5_digest": getattr(hashlib, "md5", None),
-    "sha256_digest": getattr(hashlib, "sha256", None),
-    "blake2_256_digest": getattr(hashlib, "blake2b", None),
-}
-
-
-class upload(PyPIRCCommand):
-
-    description = "upload binary package to PyPI"
-
-    user_options = PyPIRCCommand.user_options + [
-        ('sign', 's', 'sign files to upload using gpg'),
-        ('identity=', 'i', 'GPG identity used to sign files'),
-    ]
-
-    boolean_options = PyPIRCCommand.boolean_options + ['sign']
-
-    def initialize_options(self):
-        PyPIRCCommand.initialize_options(self)
-        self.username = ''
-        self.password = ''
-        self.show_response = 0
-        self.sign = False
-        self.identity = None
-
-    def finalize_options(self):
-        PyPIRCCommand.finalize_options(self)
-        if self.identity and not self.sign:
-            raise DistutilsOptionError("Must use --sign for --identity to have meaning")
-        config = self._read_pypirc()
-        if config != {}:
-            self.username = config['username']
-            self.password = config['password']
-            self.repository = config['repository']
-            self.realm = config['realm']
-
-        # getting the password from the distribution
-        # if previously set by the register command
-        if not self.password and self.distribution.password:
-            self.password = self.distribution.password
-
-    def run(self):
-        if not self.distribution.dist_files:
-            msg = (
-                "Must create and upload files in one command "
-                "(e.g. setup.py sdist upload)"
-            )
-            raise DistutilsOptionError(msg)
-        for command, pyversion, filename in self.distribution.dist_files:
-            self.upload_file(command, pyversion, filename)
-
-    def upload_file(self, command, pyversion, filename):  # noqa: C901
-        # Makes sure the repository URL is compliant
-        schema, netloc, url, params, query, fragments = urlparse(self.repository)
-        if params or query or fragments:
-            raise AssertionError("Incompatible url %s" % self.repository)
-
-        if schema not in ('http', 'https'):
-            raise AssertionError("unsupported schema " + schema)
-
-        # Sign if requested
-        if self.sign:
-            gpg_args = ["gpg", "--detach-sign", "-a", filename]
-            if self.identity:
-                gpg_args[2:2] = ["--local-user", self.identity]
-            spawn(gpg_args, dry_run=self.dry_run)
-
-        # Fill in the data - send all the meta-data in case we need to
-        # register a new release
-        f = open(filename, 'rb')
-        try:
-            content = f.read()
-        finally:
-            f.close()
-
-        meta = self.distribution.metadata
-        data = {
-            # action
-            ':action': 'file_upload',
-            'protocol_version': '1',
-            # identify release
-            'name': meta.get_name(),
-            'version': meta.get_version(),
-            # file content
-            'content': (os.path.basename(filename), content),
-            'filetype': command,
-            'pyversion': pyversion,
-            # additional meta-data
-            'metadata_version': '1.0',
-            'summary': meta.get_description(),
-            'home_page': meta.get_url(),
-            'author': meta.get_contact(),
-            'author_email': meta.get_contact_email(),
-            'license': meta.get_licence(),
-            'description': meta.get_long_description(),
-            'keywords': meta.get_keywords(),
-            'platform': meta.get_platforms(),
-            'classifiers': meta.get_classifiers(),
-            'download_url': meta.get_download_url(),
-            # PEP 314
-            'provides': meta.get_provides(),
-            'requires': meta.get_requires(),
-            'obsoletes': meta.get_obsoletes(),
-        }
-
-        data['comment'] = ''
-
-        # file content digests
-        for digest_name, digest_cons in _FILE_CONTENT_DIGESTS.items():
-            if digest_cons is None:
-                continue
-            try:
-                data[digest_name] = digest_cons(content).hexdigest()
-            except ValueError:
-                # hash digest not available or blocked by security policy
-                pass
-
-        if self.sign:
-            with open(filename + ".asc", "rb") as f:
-                data['gpg_signature'] = (os.path.basename(filename) + ".asc", f.read())
-
-        # set up the authentication
-        user_pass = (self.username + ":" + self.password).encode('ascii')
-        # The exact encoding of the authentication string is debated.
-        # Anyway PyPI only accepts ascii for both username or password.
-        auth = "Basic " + standard_b64encode(user_pass).decode('ascii')
-
-        # Build up the MIME payload for the POST data
-        boundary = '--------------GHSKFJDLGDS7543FJKLFHRE75642756743254'
-        sep_boundary = b'\r\n--' + boundary.encode('ascii')
-        end_boundary = sep_boundary + b'--\r\n'
-        body = io.BytesIO()
-        for key, value in data.items():
-            title = '\r\nContent-Disposition: form-data; name="%s"' % key
-            # handle multiple entries for the same name
-            if not isinstance(value, list):
-                value = [value]
-            for value in value:
-                if type(value) is tuple:
-                    title += '; filename="%s"' % value[0]
-                    value = value[1]
-                else:
-                    value = str(value).encode('utf-8')
-                body.write(sep_boundary)
-                body.write(title.encode('utf-8'))
-                body.write(b"\r\n\r\n")
-                body.write(value)
-        body.write(end_boundary)
-        body = body.getvalue()
-
-        msg = "Submitting {} to {}".format(filename, self.repository)
-        self.announce(msg, log.INFO)
-
-        # build the Request
-        headers = {
-            'Content-type': 'multipart/form-data; boundary=%s' % boundary,
-            'Content-length': str(len(body)),
-            'Authorization': auth,
-        }
-
-        request = Request(self.repository, data=body, headers=headers)
-        # send the data
-        try:
-            result = urlopen(request)
-            status = result.getcode()
-            reason = result.msg
-        except HTTPError as e:
-            status = e.code
-            reason = e.msg
-        except OSError as e:
-            self.announce(str(e), log.ERROR)
-            raise
-
-        if status == 200:
-            self.announce('Server response ({}): {}'.format(status, reason), log.INFO)
-            if self.show_response:
-                text = self._read_pypi_response(result)
-                msg = '\n'.join(('-' * 75, text, '-' * 75))
-                self.announce(msg, log.INFO)
-        else:
-            msg = 'Upload failed ({}): {}'.format(status, reason)
-            self.announce(msg, log.ERROR)
-            raise DistutilsError(msg)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/config/defaults.py

@@ -1,635 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .config import CfgNode as CN
-
-# NOTE: given the new config system
-# (https://detectron2.readthedocs.io/en/latest/tutorials/lazyconfigs.html),
-# we will stop adding new functionalities to default CfgNode.
-
-# -----------------------------------------------------------------------------
-# Convention about Training / Test specific parameters
-# -----------------------------------------------------------------------------
-# Whenever an argument can be either used for training or for testing, the
-# corresponding name will be post-fixed by a _TRAIN for a training parameter,
-# or _TEST for a test-specific parameter.
-# For example, the number of images during training will be
-# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be
-# IMAGES_PER_BATCH_TEST
-
-# -----------------------------------------------------------------------------
-# Config definition
-# -----------------------------------------------------------------------------
-
-_C = CN()
-
-# The version number, to upgrade from old configs to new ones if any
-# changes happen. It's recommended to keep a VERSION in your config file.
-_C.VERSION = 2
-
-_C.MODEL = CN()
-_C.MODEL.LOAD_PROPOSALS = False
-_C.MODEL.MASK_ON = False
-_C.MODEL.KEYPOINT_ON = False
-_C.MODEL.DEVICE = "cuda"
-_C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN"
-
-# Path (a file path, or URL like detectron2://.., https://..) to a checkpoint file
-# to be loaded to the model. You can find available models in the model zoo.
-_C.MODEL.WEIGHTS = ""
-
-# Values to be used for image normalization (BGR order, since INPUT.FORMAT defaults to BGR).
-# To train on images of different number of channels, just set different mean & std.
-# Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675]
-_C.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675]
-# When using pre-trained models in Detectron1 or any MSRA models,
-# std has been absorbed into its conv1 weights, so the std needs to be set 1.
-# Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std)
-_C.MODEL.PIXEL_STD = [1.0, 1.0, 1.0]
-
-
-# -----------------------------------------------------------------------------
-# INPUT
-# -----------------------------------------------------------------------------
-_C.INPUT = CN()
-# By default, {MIN,MAX}_SIZE options are used in transforms.ResizeShortestEdge.
-# Please refer to ResizeShortestEdge for detailed definition.
-# Size of the smallest side of the image during training
-_C.INPUT.MIN_SIZE_TRAIN = (800,)
-# Sample size of smallest side by choice or random selection from range give by
-# INPUT.MIN_SIZE_TRAIN
-_C.INPUT.MIN_SIZE_TRAIN_SAMPLING = "choice"
-# Maximum size of the side of the image during training
-_C.INPUT.MAX_SIZE_TRAIN = 1333
-# Size of the smallest side of the image during testing. Set to zero to disable resize in testing.
-_C.INPUT.MIN_SIZE_TEST = 800
-# Maximum size of the side of the image during testing
-_C.INPUT.MAX_SIZE_TEST = 1333
-# Mode for flipping images used in data augmentation during training
-# choose one of ["horizontal, "vertical", "none"]
-_C.INPUT.RANDOM_FLIP = "horizontal"
-
-# `True` if cropping is used for data augmentation during training
-_C.INPUT.CROP = CN({"ENABLED": False})
-# Cropping type. See documentation of `detectron2.data.transforms.RandomCrop` for explanation.
-_C.INPUT.CROP.TYPE = "relative_range"
-# Size of crop in range (0, 1] if CROP.TYPE is "relative" or "relative_range" and in number of
-# pixels if CROP.TYPE is "absolute"
-_C.INPUT.CROP.SIZE = [0.9, 0.9]
-
-
-# Whether the model needs RGB, YUV, HSV etc.
-# Should be one of the modes defined here, as we use PIL to read the image:
-# https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes
-# with BGR being the one exception. One can set image format to BGR, we will
-# internally use RGB for conversion and flip the channels over
-_C.INPUT.FORMAT = "BGR"
-# The ground truth mask format that the model will use.
-# Mask R-CNN supports either "polygon" or "bitmask" as ground truth.
-_C.INPUT.MASK_FORMAT = "polygon"  # alternative: "bitmask"
-
-
-# -----------------------------------------------------------------------------
-# Dataset
-# -----------------------------------------------------------------------------
-_C.DATASETS = CN()
-# List of the dataset names for training. Must be registered in DatasetCatalog
-# Samples from these datasets will be merged and used as one dataset.
-_C.DATASETS.TRAIN = ()
-# List of the pre-computed proposal files for training, which must be consistent
-# with datasets listed in DATASETS.TRAIN.
-_C.DATASETS.PROPOSAL_FILES_TRAIN = ()
-# Number of top scoring precomputed proposals to keep for training
-_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN = 2000
-# List of the dataset names for testing. Must be registered in DatasetCatalog
-_C.DATASETS.TEST = ()
-# List of the pre-computed proposal files for test, which must be consistent
-# with datasets listed in DATASETS.TEST.
-_C.DATASETS.PROPOSAL_FILES_TEST = ()
-# Number of top scoring precomputed proposals to keep for test
-_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST = 1000
-
-# -----------------------------------------------------------------------------
-# DataLoader
-# -----------------------------------------------------------------------------
-_C.DATALOADER = CN()
-# Number of data loading threads
-_C.DATALOADER.NUM_WORKERS = 4
-# If True, each batch should contain only images for which the aspect ratio
-# is compatible. This groups portrait images together, and landscape images
-# are not batched with portrait images.
-_C.DATALOADER.ASPECT_RATIO_GROUPING = True
-# Options: TrainingSampler, RepeatFactorTrainingSampler
-_C.DATALOADER.SAMPLER_TRAIN = "TrainingSampler"
-# Repeat threshold for RepeatFactorTrainingSampler
-_C.DATALOADER.REPEAT_THRESHOLD = 0.0
-# Tf True, when working on datasets that have instance annotations, the
-# training dataloader will filter out images without associated annotations
-_C.DATALOADER.FILTER_EMPTY_ANNOTATIONS = True
-
-# ---------------------------------------------------------------------------- #
-# Backbone options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.BACKBONE = CN()
-
-_C.MODEL.BACKBONE.NAME = "build_resnet_backbone"
-# Freeze the first several stages so they are not trained.
-# There are 5 stages in ResNet. The first is a convolution, and the following
-# stages are each group of residual blocks.
-_C.MODEL.BACKBONE.FREEZE_AT = 2
-
-
-# ---------------------------------------------------------------------------- #
-# FPN options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.FPN = CN()
-# Names of the input feature maps to be used by FPN
-# They must have contiguous power of 2 strides
-# e.g., ["res2", "res3", "res4", "res5"]
-_C.MODEL.FPN.IN_FEATURES = []
-_C.MODEL.FPN.OUT_CHANNELS = 256
-
-# Options: "" (no norm), "GN"
-_C.MODEL.FPN.NORM = ""
-
-# Types for fusing the FPN top-down and lateral features. Can be either "sum" or "avg"
-_C.MODEL.FPN.FUSE_TYPE = "sum"
-
-
-# ---------------------------------------------------------------------------- #
-# Proposal generator options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.PROPOSAL_GENERATOR = CN()
-# Current proposal generators include "RPN", "RRPN" and "PrecomputedProposals"
-_C.MODEL.PROPOSAL_GENERATOR.NAME = "RPN"
-# Proposal height and width both need to be greater than MIN_SIZE
-# (a the scale used during training or inference)
-_C.MODEL.PROPOSAL_GENERATOR.MIN_SIZE = 0
-
-
-# ---------------------------------------------------------------------------- #
-# Anchor generator options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ANCHOR_GENERATOR = CN()
-# The generator can be any name in the ANCHOR_GENERATOR registry
-_C.MODEL.ANCHOR_GENERATOR.NAME = "DefaultAnchorGenerator"
-# Anchor sizes (i.e. sqrt of area) in absolute pixels w.r.t. the network input.
-# Format: list[list[float]]. SIZES[i] specifies the list of sizes to use for
-# IN_FEATURES[i]; len(SIZES) must be equal to len(IN_FEATURES) or 1.
-# When len(SIZES) == 1, SIZES[0] is used for all IN_FEATURES.
-_C.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64, 128, 256, 512]]
-# Anchor aspect ratios. For each area given in `SIZES`, anchors with different aspect
-# ratios are generated by an anchor generator.
-# Format: list[list[float]]. ASPECT_RATIOS[i] specifies the list of aspect ratios (H/W)
-# to use for IN_FEATURES[i]; len(ASPECT_RATIOS) == len(IN_FEATURES) must be true,
-# or len(ASPECT_RATIOS) == 1 is true and aspect ratio list ASPECT_RATIOS[0] is used
-# for all IN_FEATURES.
-_C.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.5, 1.0, 2.0]]
-# Anchor angles.
-# list[list[float]], the angle in degrees, for each input feature map.
-# ANGLES[i] specifies the list of angles for IN_FEATURES[i].
-_C.MODEL.ANCHOR_GENERATOR.ANGLES = [[-90, 0, 90]]
-# Relative offset between the center of the first anchor and the top-left corner of the image
-# Value has to be in [0, 1). Recommend to use 0.5, which means half stride.
-# The value is not expected to affect model accuracy.
-_C.MODEL.ANCHOR_GENERATOR.OFFSET = 0.0
-
-# ---------------------------------------------------------------------------- #
-# RPN options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.RPN = CN()
-_C.MODEL.RPN.HEAD_NAME = "StandardRPNHead"  # used by RPN_HEAD_REGISTRY
-
-# Names of the input feature maps to be used by RPN
-# e.g., ["p2", "p3", "p4", "p5", "p6"] for FPN
-_C.MODEL.RPN.IN_FEATURES = ["res4"]
-# Remove RPN anchors that go outside the image by BOUNDARY_THRESH pixels
-# Set to -1 or a large value, e.g. 100000, to disable pruning anchors
-_C.MODEL.RPN.BOUNDARY_THRESH = -1
-# IOU overlap ratios [BG_IOU_THRESHOLD, FG_IOU_THRESHOLD]
-# Minimum overlap required between an anchor and ground-truth box for the
-# (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD
-# ==> positive RPN example: 1)
-# Maximum overlap allowed between an anchor and ground-truth box for the
-# (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD
-# ==> negative RPN example: 0)
-# Anchors with overlap in between (BG_IOU_THRESHOLD <= IoU < FG_IOU_THRESHOLD)
-# are ignored (-1)
-_C.MODEL.RPN.IOU_THRESHOLDS = [0.3, 0.7]
-_C.MODEL.RPN.IOU_LABELS = [0, -1, 1]
-# Number of regions per image used to train RPN
-_C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256
-# Target fraction of foreground (positive) examples per RPN minibatch
-_C.MODEL.RPN.POSITIVE_FRACTION = 0.5
-# Options are: "smooth_l1", "giou", "diou", "ciou"
-_C.MODEL.RPN.BBOX_REG_LOSS_TYPE = "smooth_l1"
-_C.MODEL.RPN.BBOX_REG_LOSS_WEIGHT = 1.0
-# Weights on (dx, dy, dw, dh) for normalizing RPN anchor regression targets
-_C.MODEL.RPN.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
-# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1.
-_C.MODEL.RPN.SMOOTH_L1_BETA = 0.0
-_C.MODEL.RPN.LOSS_WEIGHT = 1.0
-# Number of top scoring RPN proposals to keep before applying NMS
-# When FPN is used, this is *per FPN level* (not total)
-_C.MODEL.RPN.PRE_NMS_TOPK_TRAIN = 12000
-_C.MODEL.RPN.PRE_NMS_TOPK_TEST = 6000
-# Number of top scoring RPN proposals to keep after applying NMS
-# When FPN is used, this limit is applied per level and then again to the union
-# of proposals from all levels
-# NOTE: When FPN is used, the meaning of this config is different from Detectron1.
-# It means per-batch topk in Detectron1, but per-image topk here.
-# See the "find_top_rpn_proposals" function for details.
-_C.MODEL.RPN.POST_NMS_TOPK_TRAIN = 2000
-_C.MODEL.RPN.POST_NMS_TOPK_TEST = 1000
-# NMS threshold used on RPN proposals
-_C.MODEL.RPN.NMS_THRESH = 0.7
-# Set this to -1 to use the same number of output channels as input channels.
-_C.MODEL.RPN.CONV_DIMS = [-1]
-
-# ---------------------------------------------------------------------------- #
-# ROI HEADS options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_HEADS = CN()
-_C.MODEL.ROI_HEADS.NAME = "Res5ROIHeads"
-# Number of foreground classes
-_C.MODEL.ROI_HEADS.NUM_CLASSES = 80
-# Names of the input feature maps to be used by ROI heads
-# Currently all heads (box, mask, ...) use the same input feature map list
-# e.g., ["p2", "p3", "p4", "p5"] is commonly used for FPN
-_C.MODEL.ROI_HEADS.IN_FEATURES = ["res4"]
-# IOU overlap ratios [IOU_THRESHOLD]
-# Overlap threshold for an RoI to be considered background (if < IOU_THRESHOLD)
-# Overlap threshold for an RoI to be considered foreground (if >= IOU_THRESHOLD)
-_C.MODEL.ROI_HEADS.IOU_THRESHOLDS = [0.5]
-_C.MODEL.ROI_HEADS.IOU_LABELS = [0, 1]
-# RoI minibatch size *per image* (number of regions of interest [ROIs]) during training
-# Total number of RoIs per training minibatch =
-#   ROI_HEADS.BATCH_SIZE_PER_IMAGE * SOLVER.IMS_PER_BATCH
-# E.g., a common configuration is: 512 * 16 = 8192
-_C.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
-# Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0)
-_C.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25
-
-# Only used on test mode
-
-# Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to
-# balance obtaining high recall with not having too many low precision
-# detections that will slow down inference post processing steps (like NMS)
-# A default threshold of 0.0 increases AP by ~0.2-0.3 but significantly slows down
-# inference.
-_C.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.05
-# Overlap threshold used for non-maximum suppression (suppress boxes with
-# IoU >= this threshold)
-_C.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.5
-# If True, augment proposals with ground-truth boxes before sampling proposals to
-# train ROI heads.
-_C.MODEL.ROI_HEADS.PROPOSAL_APPEND_GT = True
-
-# ---------------------------------------------------------------------------- #
-# Box Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_BOX_HEAD = CN()
-# C4 don't use head name option
-# Options for non-C4 models: FastRCNNConvFCHead,
-_C.MODEL.ROI_BOX_HEAD.NAME = ""
-# Options are: "smooth_l1", "giou", "diou", "ciou"
-_C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE = "smooth_l1"
-# The final scaling coefficient on the box regression loss, used to balance the magnitude of its
-# gradients with other losses in the model. See also `MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT`.
-_C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT = 1.0
-# Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets
-# These are empirically chosen to approximately lead to unit variance targets
-_C.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10.0, 10.0, 5.0, 5.0)
-# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1.
-_C.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA = 0.0
-_C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14
-_C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0
-# Type of pooling operation applied to the incoming feature map for each RoI
-_C.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2"
-
-_C.MODEL.ROI_BOX_HEAD.NUM_FC = 0
-# Hidden layer dimension for FC layers in the RoI box head
-_C.MODEL.ROI_BOX_HEAD.FC_DIM = 1024
-_C.MODEL.ROI_BOX_HEAD.NUM_CONV = 0
-# Channel dimension for Conv layers in the RoI box head
-_C.MODEL.ROI_BOX_HEAD.CONV_DIM = 256
-# Normalization method for the convolution layers.
-# Options: "" (no norm), "GN", "SyncBN".
-_C.MODEL.ROI_BOX_HEAD.NORM = ""
-# Whether to use class agnostic for bbox regression
-_C.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG = False
-# If true, RoI heads use bounding boxes predicted by the box head rather than proposal boxes.
-_C.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES = False
-
-# ---------------------------------------------------------------------------- #
-# Cascaded Box Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_BOX_CASCADE_HEAD = CN()
-# The number of cascade stages is implicitly defined by the length of the following two configs.
-_C.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS = (
-    (10.0, 10.0, 5.0, 5.0),
-    (20.0, 20.0, 10.0, 10.0),
-    (30.0, 30.0, 15.0, 15.0),
-)
-_C.MODEL.ROI_BOX_CASCADE_HEAD.IOUS = (0.5, 0.6, 0.7)
-
-
-# ---------------------------------------------------------------------------- #
-# Mask Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_MASK_HEAD = CN()
-_C.MODEL.ROI_MASK_HEAD.NAME = "MaskRCNNConvUpsampleHead"
-_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 14
-_C.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO = 0
-_C.MODEL.ROI_MASK_HEAD.NUM_CONV = 0  # The number of convs in the mask head
-_C.MODEL.ROI_MASK_HEAD.CONV_DIM = 256
-# Normalization method for the convolution layers.
-# Options: "" (no norm), "GN", "SyncBN".
-_C.MODEL.ROI_MASK_HEAD.NORM = ""
-# Whether to use class agnostic for mask prediction
-_C.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK = False
-# Type of pooling operation applied to the incoming feature map for each RoI
-_C.MODEL.ROI_MASK_HEAD.POOLER_TYPE = "ROIAlignV2"
-
-
-# ---------------------------------------------------------------------------- #
-# Keypoint Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_KEYPOINT_HEAD = CN()
-_C.MODEL.ROI_KEYPOINT_HEAD.NAME = "KRCNNConvDeconvUpsampleHead"
-_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION = 14
-_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO = 0
-_C.MODEL.ROI_KEYPOINT_HEAD.CONV_DIMS = tuple(512 for _ in range(8))
-_C.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS = 17  # 17 is the number of keypoints in COCO.
-
-# Images with too few (or no) keypoints are excluded from training.
-_C.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE = 1
-# Normalize by the total number of visible keypoints in the minibatch if True.
-# Otherwise, normalize by the total number of keypoints that could ever exist
-# in the minibatch.
-# The keypoint softmax loss is only calculated on visible keypoints.
-# Since the number of visible keypoints can vary significantly between
-# minibatches, this has the effect of up-weighting the importance of
-# minibatches with few visible keypoints. (Imagine the extreme case of
-# only one visible keypoint versus N: in the case of N, each one
-# contributes 1/N to the gradient compared to the single keypoint
-# determining the gradient direction). Instead, we can normalize the
-# loss by the total number of keypoints, if it were the case that all
-# keypoints were visible in a full minibatch. (Returning to the example,
-# this means that the one visible keypoint contributes as much as each
-# of the N keypoints.)
-_C.MODEL.ROI_KEYPOINT_HEAD.NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS = True
-# Multi-task loss weight to use for keypoints
-# Recommended values:
-#   - use 1.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is True
-#   - use 4.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is False
-_C.MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT = 1.0
-# Type of pooling operation applied to the incoming feature map for each RoI
-_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_TYPE = "ROIAlignV2"
-
-# ---------------------------------------------------------------------------- #
-# Semantic Segmentation Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.SEM_SEG_HEAD = CN()
-_C.MODEL.SEM_SEG_HEAD.NAME = "SemSegFPNHead"
-_C.MODEL.SEM_SEG_HEAD.IN_FEATURES = ["p2", "p3", "p4", "p5"]
-# Label in the semantic segmentation ground truth that is ignored, i.e., no loss is calculated for
-# the correposnding pixel.
-_C.MODEL.SEM_SEG_HEAD.IGNORE_VALUE = 255
-# Number of classes in the semantic segmentation head
-_C.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 54
-# Number of channels in the 3x3 convs inside semantic-FPN heads.
-_C.MODEL.SEM_SEG_HEAD.CONVS_DIM = 128
-# Outputs from semantic-FPN heads are up-scaled to the COMMON_STRIDE stride.
-_C.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4
-# Normalization method for the convolution layers. Options: "" (no norm), "GN".
-_C.MODEL.SEM_SEG_HEAD.NORM = "GN"
-_C.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT = 1.0
-
-_C.MODEL.PANOPTIC_FPN = CN()
-# Scaling of all losses from instance detection / segmentation head.
-_C.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT = 1.0
-
-# options when combining instance & semantic segmentation outputs
-_C.MODEL.PANOPTIC_FPN.COMBINE = CN({"ENABLED": True})  # "COMBINE.ENABLED" is deprecated & not used
-_C.MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH = 0.5
-_C.MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT = 4096
-_C.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = 0.5
-
-
-# ---------------------------------------------------------------------------- #
-# RetinaNet Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.RETINANET = CN()
-
-# This is the number of foreground classes.
-_C.MODEL.RETINANET.NUM_CLASSES = 80
-
-_C.MODEL.RETINANET.IN_FEATURES = ["p3", "p4", "p5", "p6", "p7"]
-
-# Convolutions to use in the cls and bbox tower
-# NOTE: this doesn't include the last conv for logits
-_C.MODEL.RETINANET.NUM_CONVS = 4
-
-# IoU overlap ratio [bg, fg] for labeling anchors.
-# Anchors with < bg are labeled negative (0)
-# Anchors  with >= bg and < fg are ignored (-1)
-# Anchors with >= fg are labeled positive (1)
-_C.MODEL.RETINANET.IOU_THRESHOLDS = [0.4, 0.5]
-_C.MODEL.RETINANET.IOU_LABELS = [0, -1, 1]
-
-# Prior prob for rare case (i.e. foreground) at the beginning of training.
-# This is used to set the bias for the logits layer of the classifier subnet.
-# This improves training stability in the case of heavy class imbalance.
-_C.MODEL.RETINANET.PRIOR_PROB = 0.01
-
-# Inference cls score threshold, only anchors with score > INFERENCE_TH are
-# considered for inference (to improve speed)
-_C.MODEL.RETINANET.SCORE_THRESH_TEST = 0.05
-# Select topk candidates before NMS
-_C.MODEL.RETINANET.TOPK_CANDIDATES_TEST = 1000
-_C.MODEL.RETINANET.NMS_THRESH_TEST = 0.5
-
-# Weights on (dx, dy, dw, dh) for normalizing Retinanet anchor regression targets
-_C.MODEL.RETINANET.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
-
-# Loss parameters
-_C.MODEL.RETINANET.FOCAL_LOSS_GAMMA = 2.0
-_C.MODEL.RETINANET.FOCAL_LOSS_ALPHA = 0.25
-_C.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA = 0.1
-# Options are: "smooth_l1", "giou", "diou", "ciou"
-_C.MODEL.RETINANET.BBOX_REG_LOSS_TYPE = "smooth_l1"
-
-# One of BN, SyncBN, FrozenBN, GN
-# Only supports GN until unshared norm is implemented
-_C.MODEL.RETINANET.NORM = ""
-
-
-# ---------------------------------------------------------------------------- #
-# ResNe[X]t options (ResNets = {ResNet, ResNeXt}
-# Note that parts of a resnet may be used for both the backbone and the head
-# These options apply to both
-# ---------------------------------------------------------------------------- #
-_C.MODEL.RESNETS = CN()
-
-_C.MODEL.RESNETS.DEPTH = 50
-_C.MODEL.RESNETS.OUT_FEATURES = ["res4"]  # res4 for C4 backbone, res2..5 for FPN backbone
-
-# Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt
-_C.MODEL.RESNETS.NUM_GROUPS = 1
-
-# Options: FrozenBN, GN, "SyncBN", "BN"
-_C.MODEL.RESNETS.NORM = "FrozenBN"
-
-# Baseline width of each group.
-# Scaling this parameters will scale the width of all bottleneck layers.
-_C.MODEL.RESNETS.WIDTH_PER_GROUP = 64
-
-# Place the stride 2 conv on the 1x1 filter
-# Use True only for the original MSRA ResNet; use False for C2 and Torch models
-_C.MODEL.RESNETS.STRIDE_IN_1X1 = True
-
-# Apply dilation in stage "res5"
-_C.MODEL.RESNETS.RES5_DILATION = 1
-
-# Output width of res2. Scaling this parameters will scale the width of all 1x1 convs in ResNet
-# For R18 and R34, this needs to be set to 64
-_C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256
-_C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64
-
-# Apply Deformable Convolution in stages
-# Specify if apply deform_conv on Res2, Res3, Res4, Res5
-_C.MODEL.RESNETS.DEFORM_ON_PER_STAGE = [False, False, False, False]
-# Use True to use modulated deform_conv (DeformableV2, https://arxiv.org/abs/1811.11168);
-# Use False for DeformableV1.
-_C.MODEL.RESNETS.DEFORM_MODULATED = False
-# Number of groups in deformable conv.
-_C.MODEL.RESNETS.DEFORM_NUM_GROUPS = 1
-
-
-# ---------------------------------------------------------------------------- #
-# Solver
-# ---------------------------------------------------------------------------- #
-_C.SOLVER = CN()
-
-# Options: WarmupMultiStepLR, WarmupCosineLR.
-# See detectron2/solver/build.py for definition.
-_C.SOLVER.LR_SCHEDULER_NAME = "WarmupMultiStepLR"
-
-_C.SOLVER.MAX_ITER = 40000
-
-_C.SOLVER.BASE_LR = 0.001
-
-_C.SOLVER.MOMENTUM = 0.9
-
-_C.SOLVER.NESTEROV = False
-
-_C.SOLVER.WEIGHT_DECAY = 0.0001
-# The weight decay that's applied to parameters of normalization layers
-# (typically the affine transformation)
-_C.SOLVER.WEIGHT_DECAY_NORM = 0.0
-
-_C.SOLVER.GAMMA = 0.1
-# The iteration number to decrease learning rate by GAMMA.
-_C.SOLVER.STEPS = (30000,)
-
-_C.SOLVER.WARMUP_FACTOR = 1.0 / 1000
-_C.SOLVER.WARMUP_ITERS = 1000
-_C.SOLVER.WARMUP_METHOD = "linear"
-
-# Save a checkpoint after every this number of iterations
-_C.SOLVER.CHECKPOINT_PERIOD = 5000
-
-# Number of images per batch across all machines. This is also the number
-# of training images per step (i.e. per iteration). If we use 16 GPUs
-# and IMS_PER_BATCH = 32, each GPU will see 2 images per batch.
-# May be adjusted automatically if REFERENCE_WORLD_SIZE is set.
-_C.SOLVER.IMS_PER_BATCH = 16
-
-# The reference number of workers (GPUs) this config is meant to train with.
-# It takes no effect when set to 0.
-# With a non-zero value, it will be used by DefaultTrainer to compute a desired
-# per-worker batch size, and then scale the other related configs (total batch size,
-# learning rate, etc) to match the per-worker batch size.
-# See documentation of `DefaultTrainer.auto_scale_workers` for details:
-_C.SOLVER.REFERENCE_WORLD_SIZE = 0
-
-# Detectron v1 (and previous detection code) used a 2x higher LR and 0 WD for
-# biases. This is not useful (at least for recent models). You should avoid
-# changing these and they exist only to reproduce Detectron v1 training if
-# desired.
-_C.SOLVER.BIAS_LR_FACTOR = 1.0
-_C.SOLVER.WEIGHT_DECAY_BIAS = None  # None means following WEIGHT_DECAY
-
-# Gradient clipping
-_C.SOLVER.CLIP_GRADIENTS = CN({"ENABLED": False})
-# Type of gradient clipping, currently 2 values are supported:
-# - "value": the absolute values of elements of each gradients are clipped
-# - "norm": the norm of the gradient for each parameter is clipped thus
-#   affecting all elements in the parameter
-_C.SOLVER.CLIP_GRADIENTS.CLIP_TYPE = "value"
-# Maximum absolute value used for clipping gradients
-_C.SOLVER.CLIP_GRADIENTS.CLIP_VALUE = 1.0
-# Floating point number p for L-p norm to be used with the "norm"
-# gradient clipping type; for L-inf, please specify .inf
-_C.SOLVER.CLIP_GRADIENTS.NORM_TYPE = 2.0
-
-# Enable automatic mixed precision for training
-# Note that this does not change model's inference behavior.
-# To use AMP in inference, run inference under autocast()
-_C.SOLVER.AMP = CN({"ENABLED": False})
-
-# ---------------------------------------------------------------------------- #
-# Specific test options
-# ---------------------------------------------------------------------------- #
-_C.TEST = CN()
-# For end-to-end tests to verify the expected accuracy.
-# Each item is [task, metric, value, tolerance]
-# e.g.: [['bbox', 'AP', 38.5, 0.2]]
-_C.TEST.EXPECTED_RESULTS = []
-# The period (in terms of steps) to evaluate the model during training.
-# Set to 0 to disable.
-_C.TEST.EVAL_PERIOD = 0
-# The sigmas used to calculate keypoint OKS. See http://cocodataset.org/#keypoints-eval
-# When empty, it will use the defaults in COCO.
-# Otherwise it should be a list[float] with the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.
-_C.TEST.KEYPOINT_OKS_SIGMAS = []
-# Maximum number of detections to return per image during inference (100 is
-# based on the limit established for the COCO dataset).
-_C.TEST.DETECTIONS_PER_IMAGE = 100
-
-_C.TEST.AUG = CN({"ENABLED": False})
-_C.TEST.AUG.MIN_SIZES = (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)
-_C.TEST.AUG.MAX_SIZE = 4000
-_C.TEST.AUG.FLIP = True
-
-_C.TEST.PRECISE_BN = CN({"ENABLED": False})
-_C.TEST.PRECISE_BN.NUM_ITER = 200
-
-# ---------------------------------------------------------------------------- #
-# Misc options
-# ---------------------------------------------------------------------------- #
-# Directory where output files are written
-_C.OUTPUT_DIR = "./output"
-# Set seed to negative to fully randomize everything.
-# Set seed to positive to use a fixed seed. Note that a fixed seed increases
-# reproducibility but does not guarantee fully deterministic behavior.
-# Disabling all parallelism further increases reproducibility.
-_C.SEED = -1
-# Benchmark different cudnn algorithms.
-# If input images have very different sizes, this option will have large overhead
-# for about 10k iterations. It usually hurts total time, but can benefit for certain models.
-# If input images have the same or similar sizes, benchmark is often helpful.
-_C.CUDNN_BENCHMARK = False
-# The period (in terms of steps) for minibatch visualization at train time.
-# Set to 0 to disable.
-_C.VIS_PERIOD = 0
-
-# global config is for quick hack purposes.
-# You can set them in command line or config files,
-# and access it with:
-#
-# from detectron2.config import global_cfg
-# print(global_cfg.HACK)
-#
-# Do not commit any configs into it.
-_C.GLOBAL = CN()
-_C.GLOBAL.HACK = 1.0

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tools/visualize_data.py

@@ -1,94 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates.
-import argparse
-import os
-from itertools import chain
-import cv2
-import tqdm
-
-from detectron2.config import get_cfg
-from detectron2.data import DatasetCatalog, MetadataCatalog, build_detection_train_loader
-from detectron2.data import detection_utils as utils
-from detectron2.data.build import filter_images_with_few_keypoints
-from detectron2.utils.logger import setup_logger
-from detectron2.utils.visualizer import Visualizer
-
-
-def setup(args):
-    cfg = get_cfg()
-    if args.config_file:
-        cfg.merge_from_file(args.config_file)
-    cfg.merge_from_list(args.opts)
-    cfg.DATALOADER.NUM_WORKERS = 0
-    cfg.freeze()
-    return cfg
-
-
-def parse_args(in_args=None):
-    parser = argparse.ArgumentParser(description="Visualize ground-truth data")
-    parser.add_argument(
-        "--source",
-        choices=["annotation", "dataloader"],
-        required=True,
-        help="visualize the annotations or the data loader (with pre-processing)",
-    )
-    parser.add_argument("--config-file", metavar="FILE", help="path to config file")
-    parser.add_argument("--output-dir", default="./", help="path to output directory")
-    parser.add_argument("--show", action="store_true", help="show output in a window")
-    parser.add_argument(
-        "opts",
-        help="Modify config options using the command-line",
-        default=None,
-        nargs=argparse.REMAINDER,
-    )
-    return parser.parse_args(in_args)
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    logger = setup_logger()
-    logger.info("Arguments: " + str(args))
-    cfg = setup(args)
-
-    dirname = args.output_dir
-    os.makedirs(dirname, exist_ok=True)
-    metadata = MetadataCatalog.get(cfg.DATASETS.TRAIN[0])
-
-    def output(vis, fname):
-        if args.show:
-            print(fname)
-            cv2.imshow("window", vis.get_image()[:, :, ::-1])
-            cv2.waitKey()
-        else:
-            filepath = os.path.join(dirname, fname)
-            print("Saving to {} ...".format(filepath))
-            vis.save(filepath)
-
-    scale = 1.0
-    if args.source == "dataloader":
-        train_data_loader = build_detection_train_loader(cfg)
-        for batch in train_data_loader:
-            for per_image in batch:
-                # Pytorch tensor is in (C, H, W) format
-                img = per_image["image"].permute(1, 2, 0).cpu().detach().numpy()
-                img = utils.convert_image_to_rgb(img, cfg.INPUT.FORMAT)
-
-                visualizer = Visualizer(img, metadata=metadata, scale=scale)
-                target_fields = per_image["instances"].get_fields()
-                labels = [metadata.thing_classes[i] for i in target_fields["gt_classes"]]
-                vis = visualizer.overlay_instances(
-                    labels=labels,
-                    boxes=target_fields.get("gt_boxes", None),
-                    masks=target_fields.get("gt_masks", None),
-                    keypoints=target_fields.get("gt_keypoints", None),
-                )
-                output(vis, str(per_image["image_id"]) + ".jpg")
-    else:
-        dicts = list(chain.from_iterable([DatasetCatalog.get(k) for k in cfg.DATASETS.TRAIN]))
-        if cfg.MODEL.KEYPOINT_ON:
-            dicts = filter_images_with_few_keypoints(dicts, 1)
-        for dic in tqdm.tqdm(dicts):
-            img = utils.read_image(dic["file_name"], "RGB")
-            visualizer = Visualizer(img, metadata=metadata, scale=scale)
-            vis = visualizer.draw_dataset_dict(dic)
-            output(vis, os.path.basename(dic["file_name"]))

spaces/B2gan/LLM_Can_See/app.py

@@ -1,87 +0,0 @@
-import gradio as gr
-
-from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
-from transformers import CLIPProcessor, CLIPModel
-from transformers import pipeline
-
-import torch
-from PIL import Image
-
-import openai
-import ai_functions
-
-import re
-import os
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-
-clip = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
-processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
-
-clip = clip.to(device)
-
-llm = None
-
-model.to(device)
-
-max_length = 16
-num_beams = 4
-gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
-
-@staticmethod
-def extract_list_items(text):
-	pattern = r"\d+[.\)-](?=\D)"
-	return re.split(pattern, text)[1:]
-
-def process_image_and_text(image, text, model_choice, api_key=None):
-	global llm
-	if model_choice != "llama 7b":
-		openai.api_key = api_key
-	
-	pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
-	pixel_values = pixel_values.to(device)
-
-	output_ids = model.generate(pixel_values, **gen_kwargs)
-
-	preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
-	preds = [pred.strip() for pred in preds][0]
-
-	if model_choice == "llama 7b":
-		result = ai_functions.ai_function(text, preds, llm)
-	else:
-		result = ai_functions.ai_function(text, preds, model_choice)
-
-	prompt = extract_list_items(result)
-
-	inputs = processor(text=prompt, images=image, return_tensors="pt", padding=True)
-	inputs = {name: tensor.to(device) for name, tensor in inputs.items()}
-
-	outputs = clip(**inputs)
-	logits_per_image = outputs.logits_per_image
-	probs = logits_per_image.softmax(dim=1)
-
-	return prompt[torch.argmax(torch.FloatTensor(probs.cpu()))]
-
-model_choices = gr.Dropdown(choices=["gpt-4", "gpt-3.5-turbo"], label="Model")
-api_key_input = gr.Textbox(label="OpenAI API Key")
-
-with gr.Blocks() as demo:
-    gr.Markdown("# LLM Can See\n\nThis is an integration project using the Large Language Model(LLM), vit-gpt2, and CLIP, with the aim of enabling LLM to interpret images and answer related questions.")
-    
-    
-    with gr.Row():
-        gr.Interface(
-            fn=process_image_and_text, 
-            inputs=["image", "text", model_choices, api_key_input],
-            outputs=["text"],
-            allow_flagging="never"
-        )
-    gr.Markdown("## How to Use\n\n1. **Upload an image**: Choose an image you want the model to interpret.\n2. **Enter a question**: Input your question for the model in the text box.\n3. **Select a model**: Select the model you wish to use for the answer. The current supported models are \"gpt-4\" and \"gpt-3.5-turbo\". Due to memory limitations, the \"llama2\" model is not currently supported.\n\n## Notes\n\nThe current prompt has been preset to \"assist the visually impaired\", so the questions answered by the model will be particularly biased towards this setting. Please note that this may affect the style and content of the model's answers.\n\n")
-
-demo.launch()

spaces/Bart92/RVC_HF/julius/filters.py

@@ -1,258 +0,0 @@
-# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details.
-# Author: adefossez, 2021
-"""
-FIR windowed sinc highpass and bandpass filters.
-Those are convenience wrappers around the filters defined in `julius.lowpass`.
-"""
-
-from typing import Sequence, Optional
-
-import torch
-
-# Import all lowpass filters for consistency.
-from .lowpass import lowpass_filter, lowpass_filters, LowPassFilter,  LowPassFilters  # noqa
-from .utils import simple_repr
-
-
-class HighPassFilters(torch.nn.Module):
-    """
-    Bank of high pass filters. See `julius.lowpass.LowPassFilters` for more
-    details on the implementation.
-
-    Args:
-        cutoffs (list[float]): list of cutoff frequencies, in [0, 0.5] expressed as `f/f_s` where
-            f_s is the samplerate and `f` is the cutoff frequency.
-            The upper limit is 0.5, because a signal sampled at `f_s` contains only
-            frequencies under `f_s / 2`.
-        stride (int): how much to decimate the output. Probably not a good idea
-            to do so with a high pass filters though...
-        pad (bool): if True, appropriately pad the input with zero over the edge. If `stride=1`,
-            the output will have the same length as the input.
-        zeros (float): Number of zero crossings to keep.
-            Controls the receptive field of the Finite Impulse Response filter.
-            For filters with low cutoff frequency, e.g. 40Hz at 44.1kHz,
-            it is a bad idea to set this to a high value.
-            This is likely appropriate for most use. Lower values
-            will result in a faster filter, but with a slower attenuation around the
-            cutoff frequency.
-        fft (bool or None): if True, uses `julius.fftconv` rather than PyTorch convolutions.
-            If False, uses PyTorch convolutions. If None, either one will be chosen automatically
-            depending on the effective filter size.
-
-
-    ..warning::
-        All the filters will use the same filter size, aligned on the lowest
-        frequency provided. If you combine a lot of filters with very diverse frequencies, it might
-        be more efficient to split them over multiple modules with similar frequencies.
-
-    Shape:
-
-        - Input: `[*, T]`
-        - Output: `[F, *, T']`, with `T'=T` if `pad` is True and `stride` is 1, and
-            `F` is the numer of cutoff frequencies.
-
-    >>> highpass = HighPassFilters([1/4])
-    >>> x = torch.randn(4, 12, 21, 1024)
-    >>> list(highpass(x).shape)
-    [1, 4, 12, 21, 1024]
-    """
-
-    def __init__(self, cutoffs: Sequence[float], stride: int = 1, pad: bool = True,
-                 zeros: float = 8, fft: Optional[bool] = None):
-        super().__init__()
-        self._lowpasses = LowPassFilters(cutoffs, stride, pad, zeros, fft)
-
-    @property
-    def cutoffs(self):
-        return self._lowpasses.cutoffs
-
-    @property
-    def stride(self):
-        return self._lowpasses.stride
-
-    @property
-    def pad(self):
-        return self._lowpasses.pad
-
-    @property
-    def zeros(self):
-        return self._lowpasses.zeros
-
-    @property
-    def fft(self):
-        return self._lowpasses.fft
-
-    def forward(self, input):
-        lows = self._lowpasses(input)
-
-        # We need to extract the right portion of the input in case
-        # pad is False or stride > 1
-        if self.pad:
-            start, end = 0, input.shape[-1]
-        else:
-            start = self._lowpasses.half_size
-            end = -start
-        input = input[..., start:end:self.stride]
-        highs = input - lows
-        return highs
-
-    def __repr__(self):
-        return simple_repr(self)
-
-
-class HighPassFilter(torch.nn.Module):
-    """
-    Same as `HighPassFilters` but applies a single high pass filter.
-
-    Shape:
-
-        - Input: `[*, T]`
-        - Output: `[*, T']`, with `T'=T` if `pad` is True and `stride` is 1.
-
-    >>> highpass = HighPassFilter(1/4, stride=1)
-    >>> x = torch.randn(4, 124)
-    >>> list(highpass(x).shape)
-    [4, 124]
-    """
-
-    def __init__(self, cutoff: float, stride: int = 1, pad: bool = True,
-                 zeros: float = 8, fft: Optional[bool] = None):
-        super().__init__()
-        self._highpasses = HighPassFilters([cutoff], stride, pad, zeros, fft)
-
-    @property
-    def cutoff(self):
-        return self._highpasses.cutoffs[0]
-
-    @property
-    def stride(self):
-        return self._highpasses.stride
-
-    @property
-    def pad(self):
-        return self._highpasses.pad
-
-    @property
-    def zeros(self):
-        return self._highpasses.zeros
-
-    @property
-    def fft(self):
-        return self._highpasses.fft
-
-    def forward(self, input):
-        return self._highpasses(input)[0]
-
-    def __repr__(self):
-        return simple_repr(self)
-
-
-def highpass_filters(input: torch.Tensor,  cutoffs: Sequence[float],
-                     stride: int = 1, pad: bool = True,
-                     zeros: float = 8, fft: Optional[bool] = None):
-    """
-    Functional version of `HighPassFilters`, refer to this class for more information.
-    """
-    return HighPassFilters(cutoffs, stride, pad, zeros, fft).to(input)(input)
-
-
-def highpass_filter(input: torch.Tensor,  cutoff: float,
-                    stride: int = 1, pad: bool = True,
-                    zeros: float = 8, fft: Optional[bool] = None):
-    """
-    Functional version of `HighPassFilter`, refer to this class for more information.
-    Output will not have a dimension inserted in the front.
-    """
-    return highpass_filters(input, [cutoff], stride, pad, zeros, fft)[0]
-
-
-class BandPassFilter(torch.nn.Module):
-    """
-    Single band pass filter, implemented as a the difference of two lowpass filters.
-
-    Args:
-        cutoff_low (float): lower cutoff frequency, in [0, 0.5] expressed as `f/f_s` where
-            f_s is the samplerate and `f` is the cutoff frequency.
-            The upper limit is 0.5, because a signal sampled at `f_s` contains only
-            frequencies under `f_s / 2`.
-        cutoff_high (float): higher cutoff frequency, in [0, 0.5] expressed as `f/f_s`.
-            This must be higher than cutoff_high. Note that due to the fact
-            that filter are not perfect, the output will be non zero even if
-            cutoff_high == cutoff_low.
-        stride (int): how much to decimate the output.
-        pad (bool): if True, appropriately pad the input with zero over the edge. If `stride=1`,
-            the output will have the same length as the input.
-        zeros (float): Number of zero crossings to keep.
-            Controls the receptive field of the Finite Impulse Response filter.
-            For filters with low cutoff frequency, e.g. 40Hz at 44.1kHz,
-            it is a bad idea to set this to a high value.
-            This is likely appropriate for most use. Lower values
-            will result in a faster filter, but with a slower attenuation around the
-            cutoff frequency.
-        fft (bool or None): if True, uses `julius.fftconv` rather than PyTorch convolutions.
-            If False, uses PyTorch convolutions. If None, either one will be chosen automatically
-            depending on the effective filter size.
-
-
-    Shape:
-
-        - Input: `[*, T]`
-        - Output: `[*, T']`, with `T'=T` if `pad` is True and `stride` is 1.
-
-    ..Note:: There is no BandPassFilters (bank of bandpasses) because its
-        signification would be the same as `julius.bands.SplitBands`.
-
-    >>> bandpass = BandPassFilter(1/4, 1/3)
-    >>> x = torch.randn(4, 12, 21, 1024)
-    >>> list(bandpass(x).shape)
-    [4, 12, 21, 1024]
-    """
-
-    def __init__(self, cutoff_low: float, cutoff_high: float, stride: int = 1, pad: bool = True,
-                 zeros: float = 8, fft: Optional[bool] = None):
-        super().__init__()
-        if cutoff_low > cutoff_high:
-            raise ValueError(f"Lower cutoff {cutoff_low} should be less than "
-                             f"higher cutoff {cutoff_high}.")
-        self._lowpasses = LowPassFilters([cutoff_low, cutoff_high], stride, pad, zeros, fft)
-
-    @property
-    def cutoff_low(self):
-        return self._lowpasses.cutoffs[0]
-
-    @property
-    def cutoff_high(self):
-        return self._lowpasses.cutoffs[1]
-
-    @property
-    def stride(self):
-        return self._lowpasses.stride
-
-    @property
-    def pad(self):
-        return self._lowpasses.pad
-
-    @property
-    def zeros(self):
-        return self._lowpasses.zeros
-
-    @property
-    def fft(self):
-        return self._lowpasses.fft
-
-    def forward(self, input):
-        lows = self._lowpasses(input)
-        return lows[1] - lows[0]
-
-    def __repr__(self):
-        return simple_repr(self)
-
-
-def bandpass_filter(input: torch.Tensor,  cutoff_low: float, cutoff_high: float,
-                    stride: int = 1, pad: bool = True,
-                    zeros: float = 8, fft: Optional[bool] = None):
-    """
-    Functional version of `BandPassfilter`, refer to this class for more information.
-    Output will not have a dimension inserted in the front.
-    """
-    return BandPassFilter(cutoff_low, cutoff_high, stride, pad, zeros, fft).to(input)(input)

spaces/Benson/text-generation/Examples/Blitzkrieg 3.md

@@ -1,63 +0,0 @@
-
-<h1>Blitzkrieg 3: Un juego RTS de la Segunda Guerra Mundial con IA de red neuronal</h1>
-<p>Si eres un fanático de la maestría estratégica y la guerra histórica, es posible que desees echar un vistazo a Blitzkrieg 3, un juego RTS de la WW2 que cuenta con una IA de red neuronal única que puede desafiar tus habilidades tácticas. En este artículo, te daremos una visión general de lo que es Blitzkrieg 3, cómo jugarlo y qué lo hace destacar de otros juegos RTS. </p>
- <h2>¿Qué es Blitzkrieg 3?</h2>
-<p>Blitzkrieg 3 es un juego RTS de la Segunda Guerra Mundial desarrollado y publicado por Nival, los creadores de los juegos anteriores de Blitzkrieg. Fue lanzado el 2 de junio de 2017, después de estar en Steam Early Access desde mayo de 2015. Es el tercer y último título de la serie Blitzkrieg, que se basa en los acontecimientos de la Segunda Guerra Mundial. </p>
-<h2>Blitzkrieg 3</h2><br /><p><b><b>Download</b> &#128279; <a href="https://bltlly.com/2v6L3A">https://bltlly.com/2v6L3A</a></b></p><br /><br />
- <h3>Historia y desarrollo del juego</h3>
-<p>El primer tráiler de Blitzkrieg 3 fue publicado en YouTube el 13 de agosto de 2013, con imágenes de acción en vivo mezcladas con ilustraciones conceptuales y secuencias y renders reales en el juego.  La campaña de pre-orden se lanzó en noviembre de 2014, y el juego llegó a Steam Early Access en mayo de 2015.  Durante el proceso de desarrollo, Nival involucró a la comunidad de jugadores en la definición de la forma de desarrollo del proyecto.  El juego fue lanzado oficialmente el 2 de junio de 2017, y cerrado el 14 de diciembre de 2022. </p>
- <h3>Las principales características y modos del juego</h3>
-<p>Blitzkrieg 3 es un juego RTS de la Segunda Guerra Mundial que ofrece una variedad de características y modos para diferentes tipos de jugadores. Algunas de las principales características y modos son:</p>
-<ul>
-<li>Una campaña para un jugador que cubre el período principal de la Segunda Guerra Mundial, desde la invasión de Polonia en 1939 hasta la captura de Berlín en 1945. Cada una de las tres campañas ofrece una combinación única de misiones PvE, PvP y PvAI. En total, el juego cuenta con más de 60 misiones históricas con más de 200 unidades de combate auténticas. </li>
-
-<li>Una IA de red neuronal llamada Boris que juega en el nivel del jugador superior sin usar ninguna información oculta sobre el enemigo. Boris analiza regularmente la sesión de juego y hace predicciones basadas en la red neuronal del comportamiento del enemigo. Este enfoque le permite idear sofisticadas contraestrategias y llevarlas a la vida, lo que lo hace difícil e impredecible. </li>
-<li>Una exactitud y autenticidad histórica que recrea las características de rendimiento, tácticas y apariencia de unidades de combate reales y comandantes de la Segunda Guerra Mundial. El juego también cuenta con batallas legendarias como Stalingrado, Día D y El Alamein. </li>
-</ul>
- <h2>Cómo jugar Blitzkrieg 3?</h2>
-<p>Blitzkrieg 3 es un juego RTS de la Segunda Guerra Mundial que requiere pensamiento estratégico, planificación táctica y toma de decisiones rápidas. Dependiendo de tu preferencia, puedes elegir entre la campaña para un jugador o el modo multijugador. </p>
- <h3>La campaña para un jugador</h3>
-<p <p>La campaña para un jugador de Blitzkrieg 3 consta de tres campañas, cada una de las cuales representa una de las principales facciones de la Segunda Guerra Mundial: el Eje, los Aliados y la URSS. Cada campaña tiene su propia historia, misiones, objetivos y desafíos. Las campañas son:</p>
- <h4>La campaña Axis</h4>
-<p>La campaña del Eje sigue a la máquina de guerra alemana mientras invade y conquista Europa y el norte de África. Usted comandará la Wehrmacht y el Afrika Korps, dirigido por famosos generales como Erwin Rommel, Heinz Guderian y Erich von Manstein. Se enfrentará a la resistencia de los ejércitos polaco, francés, británico y soviético, así como a los movimientos partidistas en Yugoslavia y Grecia. También serás testigo del ascenso y caída del Tercer Reich, desde la Blitzkrieg hasta la Batalla de Berlín. </p>
- <h4>La campaña de los aliados</h4>
-
- <h4>La campaña de la URSS</h4>
-<p>La campaña de la URSS sigue al Ejército Rojo soviético mientras defiende su patria y empuja a los invasores nazis. Comandarás las tropas soviéticas, lideradas por mariscales legendarios como Georgy Zhukov, Konstantin Rokossovsky e Ivan Konev. Usted participará en las batallas brutales de Moscú, Stalingrado, Kursk, Leningrado y Sebastopol. Usted también será testigo del heroísmo y la resistencia del pueblo soviético, desde el sitio de Leningrado hasta el asalto del Reichstag. </p>
-<p></p>
- <h3>El modo multijugador</h3>
-<p>El modo multijugador de Blitzkrieg 3 te permite jugar con Neural Network AI, amigos y otros jugadores online. Puedes elegir entre dos modos: Escaramuza y Asalto.</p>
- <h4>El modo de escaramuza</h4>
-<p>El modo escaramuza es un modo RTS clásico donde puedes jugar partidas 1x1, 2x2 o 3x3 contra otros jugadores o oponentes de IA. Puede elegir entre diferentes mapas, configuraciones y facciones. El objetivo es destruir todas las unidades y edificios enemigos o capturar todos los puntos estratégicos en el mapa. También puede personalizar la composición de su ejército, tácticas y habilidades de comandante. </p>
- <h4>El modo de asalto</h4>
-<p>El modo asalto es un modo RTS único donde un jugador ataca y el otro defiende sus fortificaciones. El atacante tiene un tiempo limitado para romper las defensas del defensor y capturar su cuartel general. El defensor tiene que usar sus recursos sabiamente para construir y mejorar sus fortificaciones y repeler los ataques del atacante. El modo es asíncrono, lo que significa que puedes atacar o defender en cualquier momento sin esperar a que tu oponente esté en línea. </p> <h2>¿Qué hace que Blitzkrieg 3 sea único? </h2>
-<p>Blitzkrieg 3 es un juego RTS de la Segunda Guerra Mundial que se destaca de otros juegos RTS por dos razones principales: su IA de red neuronal llamada Boris y su exactitud y autenticidad históricas. </p>
- <h3>La IA de la red neuronal llamada Boris</h3>
-
- <h4>¿Cómo funciona Boris? </h4>
-<p>Boris trabaja utilizando una combinación de aprendizaje automático, aprendizaje profundo y técnicas de aprendizaje de refuerzo. Aprende de su propia experiencia, de los comentarios de otros jugadores y de los datos recopilados por Nival. También utiliza una red neuronal para generar y evaluar posibles acciones y resultados. Puede ajustar su nivel de dificultad, su composición del ejército, sus tácticas y sus habilidades de comandante de acuerdo a la situación y el oponente. También puede aprender de sus errores y mejorar con el tiempo. </p>
- <h4>¿Cómo desafiar a Boris? </h4>
-<p>Si quieres desafiar a Boris, puedes hacerlo de dos maneras: jugando contra él en modo Escaramuza o atacando sus fortificaciones en modo Asalto. En el modo Escaramuza, puedes elegir el mapa, la configuración y la facción tanto para ti como para Boris. También puedes establecer el nivel de dificultad de Boris, de fácil a imposible. En el modo Asalto, puede elegir una de las fortificaciones de Boris para atacar, que se generan aleatoriamente en función de su red neuronal. También puedes ver cómo les ha ido a otros jugadores contra Boris y comparar tus resultados con los de ellos. </p>
- <h3>La exactitud histórica y la autenticidad del juego</h3>
-<p>Otro aspecto que hace que Blitzkrieg 3 sea único es su exactitud y autenticidad histórica. El juego recrea las características de rendimiento, tácticas y apariencia de unidades de combate reales y comandantes de la Segunda Guerra Mundial. El juego también cuenta con batallas legendarias como Stalingrado, Día D y El Alamein. </p>
- <h4>Las misiones y batallas históricas</h4>
-
- <h4>Las unidades y comandantes de combate auténticos</h4>
-<p>El juego cuenta con más de 200 unidades de combate auténticas de la Segunda Guerra Mundial, incluyendo tanques, infantería, artillería, aviones y barcos. Cada unidad tiene sus propias características de rendimiento, como velocidad, blindaje, potencia de fuego, rango y precisión que se basan en datos históricos reales. El juego también cuenta con más de 20 comandantes reales de la Segunda Guerra Mundial, como Erwin Rommel, Bernard Montgomery y Georgy Zhukov. Cada comandante tiene su propio retrato, biografía, habilidades y habilidades que influyen en el desempeño y comportamiento de sus unidades en el campo de batalla. </p>
- <h2>Conclusión y preguntas frecuentes</h2>
-<p>Blitzkrieg 3 es un juego RTS de la Segunda Guerra Mundial que ofrece una IA de red neuronal única que puede desafiar tus habilidades tácticas, así como una precisión y autenticidad histórica que recrea los eventos y escenarios de la Segunda Guerra Mundial. Ya sea que prefieras jugar solo o con otros en línea, Blitzkrieg 3 te proporcionará horas de dominio estratégico y guerra histórica. </p>
- <p>Aquí hay algunas preguntas frecuentes sobre Blitzkrieg 3:</p>
-<ul>
-<li><b>Q: ¿Todavía se puede jugar a Blitzkrieg 3? </b></li>
-<li>A: Sí, Blitzkrieg 3 todavía se puede jugar en línea a través de Steam o sin conexión a través de GOG.com. Sin embargo, los servidores oficiales se cerraron el 14 de diciembre de 2022 debido a la baja base de jugadores y los altos costos de mantenimiento. </li>
-<li><b>Q: ¿Cuánto cuesta Blitzkrieg 3? </b></li>
-<li>A: Blitzkrieg 3 cuesta $19.99 USD en Steam o $14.99 USD en GOG.com. También tiene dos DLC: Deluxe Edition Upgrade ($9.99 USD) que agrega cuatro misiones exclusivas, cuatro comandantes legendarios y cuatro unidades de combate exclusivas; y Digital Deluxe Edition Upgrade ($19.99 USD) que agrega el DLC anterior más cuatro misiones exclusivas, cuatro comandantes más legendarios y cuatro unidades de combate más exclusivas. </li>
-<li><b>Q: ¿Cuáles son los requisitos del sistema para Blitzkrieg 3?</b></li>
-
-<li><b>Q: ¿Cómo puedo aprender más sobre Blitzkrieg 3?</b></li>
-<li>A: Puedes aprender más sobre Blitzkrieg 3 visitando su sitio web oficial , su página de Steam , su página GOG.com , su página de Facebook , su página de Twitter , o su canal de YouTube . También puede leer comentarios, guías y consejos de otros jugadores y críticos en línea. </li>
-<li><b>Q: ¿Cómo puedo contactar a los desarrolladores de Blitzkrieg 3?</b></li>
-<li>A: Puede ponerse en contacto con los desarrolladores de Blitzkrieg 3 enviando un correo electrónico a [email protected] o rellenando el formulario de contacto en su sitio web . También puedes unirte a su servidor Discord o a su comunidad de Steam para chatear con ellos y otros jugadores. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Colorear El Color Del Libro Por Nmero Mod Apk.md

@@ -1,50 +0,0 @@
-
-<h1>Colorear el color del libro por el número Mod APK: Una manera divertida y relajante para dar rienda suelta a su creatividad</h1>
- <p>¿Amas los libros para colorear pero odias la molestia de comprarlos, llevarlos alrededor, y encontrar un lugar para colorearlos? ¿Te gustaría tener acceso a miles de hermosas imágenes que puedes colorear en cualquier momento, en cualquier lugar y como quieras? Si respondiste sí a cualquiera de estas preguntas, entonces te encantará Colorear Color de Libro por Número Mod APK.</p>
-<h2>colorear el color del libro por número mod apk</h2><br /><p><b><b>Download File</b> &#9675; <a href="https://bltlly.com/2v6K1x">https://bltlly.com/2v6K1x</a></b></p><br /><br />
- <h2>¿Qué es el color del libro para colorear por número Mod APK? </h2>
- <p>Colorear el color del libro por número Mod APK es una versión modificada de la aplicación popular para colorear Colorear el color del libro por número. Esta aplicación le permite colorear cientos de imágenes increíbles siguiendo los números de cada píxel. Puede elegir entre varias categorías como animales, flores, mandalas, paisajes, dibujos animados y más. También puede personalizar su experiencia de colorear cambiando el tamaño, la forma y el color de los píxeles, así como utilizando diferentes modos y herramientas de colorear. </p>
- <h3>Características del color del libro para colorear por número Mod APK</h3>
- <h4>- Miles de imágenes para elegir</h4>
- <p>Una de las mejores características de Colorear Libro Color por Número Mod APK es que le ofrece una gran colección de imágenes que se puede colorear de forma gratuita. Puede navegar a través de diferentes categorías y temas, o utilizar la función de búsqueda para encontrar sus imágenes favoritas. También puede guardar su progreso y reanudar su coloración en cualquier momento. </p>
- <h4>- Interfaz para colorear fácil e intuitiva</h4>
- <p>Otra gran característica de Colorear Libro Color por Number Mod APK es que tiene una interfaz simple y fácil de usar que hace que colorear fácil y divertido. Solo tienes que tocar en los píxeles que coinciden con los números en la parte inferior de la pantalla, y ver su imagen cobran vida. También puedes acercar y alejar, mover la imagen, deshacer y rehacer tus acciones, y usar sugerencias si te quedas atascado. </p>
-<p></p>
- <h4>- Diferentes modos de colorear y herramientas</h4>
-
- <h4>- Comparte tus obras de arte con amigos y familiares</h4>
- <p>Una vez que termine de colorear una imagen, también puede compartirla con sus amigos y familiares a través de las redes sociales o aplicaciones de mensajería. También puede guardarlo en la galería de su dispositivo o configurarlo como fondo de pantalla. También puede ver las obras de otros usuarios en la galería de aplicaciones y calificarlas. </p>
- <h3>¿Cómo descargar e instalar el color del libro para colorear por número Mod APK? </h3>
- <h4>- Paso 1: Descargar el archivo APK de una fuente de confianza</h4>
- <p>El primer paso para disfrutar de Coloring Book Color by Number Mod APK es descargar el archivo APK de una fuente confiable. Puede utilizar este enlace para descargar la última versión de la aplicación. </p>
- <h4>- Paso 2: Habilitar fuentes desconocidas en el dispositivo</h4>
- <p>El siguiente paso <p>El siguiente paso es habilitar fuentes desconocidas en su dispositivo. Esto le permitirá instalar aplicaciones que no son de la tienda de aplicaciones oficial. Para hacer esto, vaya a la configuración del dispositivo, luego a la seguridad y luego active la opción para permitir fuentes desconocidas. Es posible que necesite confirmar esta acción con un mensaje emergente. </p>
- <h4>- Paso 3: Instalar el archivo APK y disfrutar de la aplicación</h4>
- <p>El paso final es instalar el archivo APK y disfrutar de la aplicación. Para ello, busque el archivo descargado en el almacenamiento del dispositivo y, a continuación, toque en él para iniciar el proceso de instalación. Es posible que deba conceder algunos permisos a la aplicación, como el acceso a su almacenamiento, cámara y micrófono. Una vez completada la instalación, puede abrir la aplicación y empezar a colorear. </p>
- <h3>Beneficios de usar el color del libro para colorear por número Mod APK</h3>
- <h4>- No hay anuncios o compras en la aplicación</h4>
- <p>Uno de los principales beneficios de usar Coloring Book Color by Number Mod APK es que elimina todos los anuncios molestos y compras en la aplicación que la aplicación original tiene. Esto significa que puede disfrutar de la coloración sin interrupciones ni distracciones. También puede ahorrar dinero y tiempo al no tener que comprar monedas o suscripciones para desbloquear más imágenes y características. </p>
-
- <p>Otro beneficio de usar Colorear Libro Color por Número Mod APK es que le da acceso ilimitado a todas las imágenes y características que ofrece la aplicación. Puede colorear cualquier imagen que desee, de cualquier categoría o tema, sin limitaciones o restricciones. También puedes usar todos los modos de colorear y las herramientas que ofrece la aplicación, y personalizar tu experiencia de colorear a tu gusto. </p>
- <h4>- Mejora tu concentración y estado de ánimo</h4>
- <p>Un tercer beneficio de usar Colorear Libro Color por Número Mod APK es que puede ayudar a mejorar su concentración y estado de ánimo. Colorear es una actividad relajante y terapéutica que puede reducir el estrés, la ansiedad y el aburrimiento. También puede aumentar su creatividad, confianza y autoestima. Al colorear con números, también puedes entrenar a tu cerebro para enfocar y prestar atención a los detalles. </p>
- <h4>- Expresa tu personalidad y estilo</h4>
- <p>Un cuarto beneficio de usar Colorear Libro Color por Número Mod APK es que puede ayudarle a expresar su personalidad y estilo. Colorear es una forma de arte que puede reflejar tus emociones, preferencias y gustos. Usted puede elegir los colores que se adapten a su estado de ánimo, o experimentar con diferentes combinaciones y contrastes. También puedes mostrar tus obras a otros y recibir comentarios y cumplidos. </p>
- <h2>Conclusión</h2>
-
- <h3>Preguntas frecuentes</h3>
- <p>Aquí hay algunas preguntas frecuentes sobre el color del libro para colorear por número Mod APK:</p>
- <ol>
-<li> Es colorear el color del libro por el número Mod APK seguro de usar? </li>
-<p>Sí, Colorear el color del libro por número Mod APK es seguro de usar, siempre y cuando se descarga de una fuente confiable. Sin embargo, siempre debe tener cuidado al instalar aplicaciones de fuentes desconocidas, ya que pueden contener virus o malware que pueden dañar su dispositivo. </p>
-<li> ¿Cuáles son los requisitos para usar Colorear el color del libro por número Mod APK? </li>
-<p>Para utilizar el color del libro para colorear por número Mod APK, necesita un dispositivo Android con Android 4.1 o una versión superior, y al menos 100 MB de espacio de almacenamiento gratuito. </p>
-<li> ¿Puedo usar el color del libro para colorear por número Mod APK sin conexión? </li>
-<p>Sí, puede usar Colorear el color del libro por número Mod APK sin conexión a Internet. Sin embargo, es posible que necesite una conexión a Internet para descargar nuevas imágenes o compartir sus obras de arte en línea. </p>
-<li> ¿Puedo actualizar el color del libro para colorear por número Mod APK? </li>
-<p>No, no puede actualizar el color del libro para colorear por número Mod APK a través de la tienda de aplicaciones oficial, ya que es una versión modificada de la aplicación original. Sin embargo, puede comprobar si hay nuevas versiones del mod APK desde la fuente donde lo descargó. </p>
-<li> ¿Puedo solicitar nuevas imágenes para colorear el color del libro por el número Mod APK? </li>
-<p>Sí, puede solicitar nuevas imágenes para colorear el color del libro por el número Mod APK poniéndose en contacto con los desarrolladores <p>Sí, puede solicitar nuevas imágenes para colorear el color del libro por el número Mod APK contactando con los desarrolladores de la aplicación. Puede encontrar su información de contacto en la configuración de la aplicación o en su sitio web. También puedes sugerir nuevas categorías o temas que te gustaría ver en la app. </p>
- <p>Espero que hayas disfrutado leyendo este artículo y hayas aprendido algo nuevo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. Gracias por su tiempo y atención. </p> 64aa2da5cf<br />
-<br />
-<br />

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

@@ -1,720 +0,0 @@
-# Copyright 2012-2014 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 base64
-import json
-import logging
-from itertools import tee
-
-import jmespath
-
-from botocore.exceptions import PaginationError
-from botocore.utils import merge_dicts, set_value_from_jmespath
-
-log = logging.getLogger(__name__)
-
-
-class TokenEncoder:
-    """Encodes dictionaries into opaque strings.
-
-    This for the most part json dumps + base64 encoding, but also supports
-    having bytes in the dictionary in addition to the types that json can
-    handle by default.
-
-    This is intended for use in encoding pagination tokens, which in some
-    cases can be complex structures and / or contain bytes.
-    """
-
-    def encode(self, token):
-        """Encodes a dictionary to an opaque string.
-
-        :type token: dict
-        :param token: A dictionary containing pagination information,
-            particularly the service pagination token(s) but also other boto
-            metadata.
-
-        :rtype: str
-        :returns: An opaque string
-        """
-        try:
-            # Try just using json dumps first to avoid having to traverse
-            # and encode the dict. In 99.9999% of cases this will work.
-            json_string = json.dumps(token)
-        except (TypeError, UnicodeDecodeError):
-            # If normal dumping failed, go through and base64 encode all bytes.
-            encoded_token, encoded_keys = self._encode(token, [])
-
-            # Save the list of all the encoded key paths. We can safely
-            # assume that no service will ever use this key.
-            encoded_token['boto_encoded_keys'] = encoded_keys
-
-            # Now that the bytes are all encoded, dump the json.
-            json_string = json.dumps(encoded_token)
-
-        # base64 encode the json string to produce an opaque token string.
-        return base64.b64encode(json_string.encode('utf-8')).decode('utf-8')
-
-    def _encode(self, data, path):
-        """Encode bytes in given data, keeping track of the path traversed."""
-        if isinstance(data, dict):
-            return self._encode_dict(data, path)
-        elif isinstance(data, list):
-            return self._encode_list(data, path)
-        elif isinstance(data, bytes):
-            return self._encode_bytes(data, path)
-        else:
-            return data, []
-
-    def _encode_list(self, data, path):
-        """Encode any bytes in a list, noting the index of what is encoded."""
-        new_data = []
-        encoded = []
-        for i, value in enumerate(data):
-            new_path = path + [i]
-            new_value, new_encoded = self._encode(value, new_path)
-            new_data.append(new_value)
-            encoded.extend(new_encoded)
-        return new_data, encoded
-
-    def _encode_dict(self, data, path):
-        """Encode any bytes in a dict, noting the index of what is encoded."""
-        new_data = {}
-        encoded = []
-        for key, value in data.items():
-            new_path = path + [key]
-            new_value, new_encoded = self._encode(value, new_path)
-            new_data[key] = new_value
-            encoded.extend(new_encoded)
-        return new_data, encoded
-
-    def _encode_bytes(self, data, path):
-        """Base64 encode a byte string."""
-        return base64.b64encode(data).decode('utf-8'), [path]
-
-
-class TokenDecoder:
-    """Decodes token strings back into dictionaries.
-
-    This performs the inverse operation to the TokenEncoder, accepting
-    opaque strings and decoding them into a useable form.
-    """
-
-    def decode(self, token):
-        """Decodes an opaque string to a dictionary.
-
-        :type token: str
-        :param token: A token string given by the botocore pagination
-            interface.
-
-        :rtype: dict
-        :returns: A dictionary containing pagination information,
-            particularly the service pagination token(s) but also other boto
-            metadata.
-        """
-        json_string = base64.b64decode(token.encode('utf-8')).decode('utf-8')
-        decoded_token = json.loads(json_string)
-
-        # Remove the encoding metadata as it is read since it will no longer
-        # be needed.
-        encoded_keys = decoded_token.pop('boto_encoded_keys', None)
-        if encoded_keys is None:
-            return decoded_token
-        else:
-            return self._decode(decoded_token, encoded_keys)
-
-    def _decode(self, token, encoded_keys):
-        """Find each encoded value and decode it."""
-        for key in encoded_keys:
-            encoded = self._path_get(token, key)
-            decoded = base64.b64decode(encoded.encode('utf-8'))
-            self._path_set(token, key, decoded)
-        return token
-
-    def _path_get(self, data, path):
-        """Return the nested data at the given path.
-
-        For instance:
-            data = {'foo': ['bar', 'baz']}
-            path = ['foo', 0]
-            ==> 'bar'
-        """
-        # jmespath isn't used here because it would be difficult to actually
-        # create the jmespath query when taking all of the unknowns of key
-        # structure into account. Gross though this is, it is simple and not
-        # very error prone.
-        d = data
-        for step in path:
-            d = d[step]
-        return d
-
-    def _path_set(self, data, path, value):
-        """Set the value of a key in the given data.
-
-        Example:
-            data = {'foo': ['bar', 'baz']}
-            path = ['foo', 1]
-            value = 'bin'
-            ==> data = {'foo': ['bar', 'bin']}
-        """
-        container = self._path_get(data, path[:-1])
-        container[path[-1]] = value
-
-
-class PaginatorModel:
-    def __init__(self, paginator_config):
-        self._paginator_config = paginator_config['pagination']
-
-    def get_paginator(self, operation_name):
-        try:
-            single_paginator_config = self._paginator_config[operation_name]
-        except KeyError:
-            raise ValueError(
-                "Paginator for operation does not exist: %s" % operation_name
-            )
-        return single_paginator_config
-
-
-class PageIterator:
-    """An iterable object to pagiante API results.
-    Please note it is NOT a python iterator.
-    Use ``iter`` to wrap this as a generator.
-    """
-
-    def __init__(
-        self,
-        method,
-        input_token,
-        output_token,
-        more_results,
-        result_keys,
-        non_aggregate_keys,
-        limit_key,
-        max_items,
-        starting_token,
-        page_size,
-        op_kwargs,
-    ):
-        self._method = method
-        self._input_token = input_token
-        self._output_token = output_token
-        self._more_results = more_results
-        self._result_keys = result_keys
-        self._max_items = max_items
-        self._limit_key = limit_key
-        self._starting_token = starting_token
-        self._page_size = page_size
-        self._op_kwargs = op_kwargs
-        self._resume_token = None
-        self._non_aggregate_key_exprs = non_aggregate_keys
-        self._non_aggregate_part = {}
-        self._token_encoder = TokenEncoder()
-        self._token_decoder = TokenDecoder()
-
-    @property
-    def result_keys(self):
-        return self._result_keys
-
-    @property
-    def resume_token(self):
-        """Token to specify to resume pagination."""
-        return self._resume_token
-
-    @resume_token.setter
-    def resume_token(self, value):
-        if not isinstance(value, dict):
-            raise ValueError("Bad starting token: %s" % value)
-
-        if 'boto_truncate_amount' in value:
-            token_keys = sorted(self._input_token + ['boto_truncate_amount'])
-        else:
-            token_keys = sorted(self._input_token)
-        dict_keys = sorted(value.keys())
-
-        if token_keys == dict_keys:
-            self._resume_token = self._token_encoder.encode(value)
-        else:
-            raise ValueError("Bad starting token: %s" % value)
-
-    @property
-    def non_aggregate_part(self):
-        return self._non_aggregate_part
-
-    def __iter__(self):
-        current_kwargs = self._op_kwargs
-        previous_next_token = None
-        next_token = {key: None for key in self._input_token}
-        if self._starting_token is not None:
-            # If the starting token exists, populate the next_token with the
-            # values inside it. This ensures that we have the service's
-            # pagination token on hand if we need to truncate after the
-            # first response.
-            next_token = self._parse_starting_token()[0]
-        # The number of items from result_key we've seen so far.
-        total_items = 0
-        first_request = True
-        primary_result_key = self.result_keys[0]
-        starting_truncation = 0
-        self._inject_starting_params(current_kwargs)
-        while True:
-            response = self._make_request(current_kwargs)
-            parsed = self._extract_parsed_response(response)
-            if first_request:
-                # The first request is handled differently.  We could
-                # possibly have a resume/starting token that tells us where
-                # to index into the retrieved page.
-                if self._starting_token is not None:
-                    starting_truncation = self._handle_first_request(
-                        parsed, primary_result_key, starting_truncation
-                    )
-                first_request = False
-                self._record_non_aggregate_key_values(parsed)
-            else:
-                # If this isn't the first request, we have already sliced into
-                # the first request and had to make additional requests after.
-                # We no longer need to add this to truncation.
-                starting_truncation = 0
-            current_response = primary_result_key.search(parsed)
-            if current_response is None:
-                current_response = []
-            num_current_response = len(current_response)
-            truncate_amount = 0
-            if self._max_items is not None:
-                truncate_amount = (
-                    total_items + num_current_response - self._max_items
-                )
-            if truncate_amount > 0:
-                self._truncate_response(
-                    parsed,
-                    primary_result_key,
-                    truncate_amount,
-                    starting_truncation,
-                    next_token,
-                )
-                yield response
-                break
-            else:
-                yield response
-                total_items += num_current_response
-                next_token = self._get_next_token(parsed)
-                if all(t is None for t in next_token.values()):
-                    break
-                if (
-                    self._max_items is not None
-                    and total_items == self._max_items
-                ):
-                    # We're on a page boundary so we can set the current
-                    # next token to be the resume token.
-                    self.resume_token = next_token
-                    break
-                if (
-                    previous_next_token is not None
-                    and previous_next_token == next_token
-                ):
-                    message = (
-                        f"The same next token was received "
-                        f"twice: {next_token}"
-                    )
-                    raise PaginationError(message=message)
-                self._inject_token_into_kwargs(current_kwargs, next_token)
-                previous_next_token = next_token
-
-    def search(self, expression):
-        """Applies a JMESPath expression to a paginator
-
-        Each page of results is searched using the provided JMESPath
-        expression. If the result is not a list, it is yielded
-        directly. If the result is a list, each element in the result
-        is yielded individually (essentially implementing a flatmap in
-        which the JMESPath search is the mapping function).
-
-        :type expression: str
-        :param expression: JMESPath expression to apply to each page.
-
-        :return: Returns an iterator that yields the individual
-            elements of applying a JMESPath expression to each page of
-            results.
-        """
-        compiled = jmespath.compile(expression)
-        for page in self:
-            results = compiled.search(page)
-            if isinstance(results, list):
-                yield from results
-            else:
-                # Yield result directly if it is not a list.
-                yield results
-
-    def _make_request(self, current_kwargs):
-        return self._method(**current_kwargs)
-
-    def _extract_parsed_response(self, response):
-        return response
-
-    def _record_non_aggregate_key_values(self, response):
-        non_aggregate_keys = {}
-        for expression in self._non_aggregate_key_exprs:
-            result = expression.search(response)
-            set_value_from_jmespath(
-                non_aggregate_keys, expression.expression, result
-            )
-        self._non_aggregate_part = non_aggregate_keys
-
-    def _inject_starting_params(self, op_kwargs):
-        # If the user has specified a starting token we need to
-        # inject that into the operation's kwargs.
-        if self._starting_token is not None:
-            # Don't need to do anything special if there is no starting
-            # token specified.
-            next_token = self._parse_starting_token()[0]
-            self._inject_token_into_kwargs(op_kwargs, next_token)
-        if self._page_size is not None:
-            # Pass the page size as the parameter name for limiting
-            # page size, also known as the limit_key.
-            op_kwargs[self._limit_key] = self._page_size
-
-    def _inject_token_into_kwargs(self, op_kwargs, next_token):
-        for name, token in next_token.items():
-            if (token is not None) and (token != 'None'):
-                op_kwargs[name] = token
-            elif name in op_kwargs:
-                del op_kwargs[name]
-
-    def _handle_first_request(
-        self, parsed, primary_result_key, starting_truncation
-    ):
-        # If the payload is an array or string, we need to slice into it
-        # and only return the truncated amount.
-        starting_truncation = self._parse_starting_token()[1]
-        all_data = primary_result_key.search(parsed)
-        if isinstance(all_data, (list, str)):
-            data = all_data[starting_truncation:]
-        else:
-            data = None
-        set_value_from_jmespath(parsed, primary_result_key.expression, data)
-        # We also need to truncate any secondary result keys
-        # because they were not truncated in the previous last
-        # response.
-        for token in self.result_keys:
-            if token == primary_result_key:
-                continue
-            sample = token.search(parsed)
-            if isinstance(sample, list):
-                empty_value = []
-            elif isinstance(sample, str):
-                empty_value = ''
-            elif isinstance(sample, (int, float)):
-                empty_value = 0
-            else:
-                empty_value = None
-            set_value_from_jmespath(parsed, token.expression, empty_value)
-        return starting_truncation
-
-    def _truncate_response(
-        self,
-        parsed,
-        primary_result_key,
-        truncate_amount,
-        starting_truncation,
-        next_token,
-    ):
-        original = primary_result_key.search(parsed)
-        if original is None:
-            original = []
-        amount_to_keep = len(original) - truncate_amount
-        truncated = original[:amount_to_keep]
-        set_value_from_jmespath(
-            parsed, primary_result_key.expression, truncated
-        )
-        # The issue here is that even though we know how much we've truncated
-        # we need to account for this globally including any starting
-        # left truncation. For example:
-        # Raw response: [0,1,2,3]
-        # Starting index: 1
-        # Max items: 1
-        # Starting left truncation: [1, 2, 3]
-        # End right truncation for max items: [1]
-        # However, even though we only kept 1, this is post
-        # left truncation so the next starting index should be 2, not 1
-        # (left_truncation + amount_to_keep).
-        next_token['boto_truncate_amount'] = (
-            amount_to_keep + starting_truncation
-        )
-        self.resume_token = next_token
-
-    def _get_next_token(self, parsed):
-        if self._more_results is not None:
-            if not self._more_results.search(parsed):
-                return {}
-        next_tokens = {}
-        for output_token, input_key in zip(
-            self._output_token, self._input_token
-        ):
-            next_token = output_token.search(parsed)
-            # We do not want to include any empty strings as actual tokens.
-            # Treat them as None.
-            if next_token:
-                next_tokens[input_key] = next_token
-            else:
-                next_tokens[input_key] = None
-        return next_tokens
-
-    def result_key_iters(self):
-        teed_results = tee(self, len(self.result_keys))
-        return [
-            ResultKeyIterator(i, result_key)
-            for i, result_key in zip(teed_results, self.result_keys)
-        ]
-
-    def build_full_result(self):
-        complete_result = {}
-        for response in self:
-            page = response
-            # We want to try to catch operation object pagination
-            # and format correctly for those. They come in the form
-            # of a tuple of two elements: (http_response, parsed_responsed).
-            # We want the parsed_response as that is what the page iterator
-            # uses. We can remove it though once operation objects are removed.
-            if isinstance(response, tuple) and len(response) == 2:
-                page = response[1]
-            # We're incrementally building the full response page
-            # by page.  For each page in the response we need to
-            # inject the necessary components from the page
-            # into the complete_result.
-            for result_expression in self.result_keys:
-                # In order to incrementally update a result key
-                # we need to search the existing value from complete_result,
-                # then we need to search the _current_ page for the
-                # current result key value.  Then we append the current
-                # value onto the existing value, and re-set that value
-                # as the new value.
-                result_value = result_expression.search(page)
-                if result_value is None:
-                    continue
-                existing_value = result_expression.search(complete_result)
-                if existing_value is None:
-                    # Set the initial result
-                    set_value_from_jmespath(
-                        complete_result,
-                        result_expression.expression,
-                        result_value,
-                    )
-                    continue
-                # Now both result_value and existing_value contain something
-                if isinstance(result_value, list):
-                    existing_value.extend(result_value)
-                elif isinstance(result_value, (int, float, str)):
-                    # Modify the existing result with the sum or concatenation
-                    set_value_from_jmespath(
-                        complete_result,
-                        result_expression.expression,
-                        existing_value + result_value,
-                    )
-        merge_dicts(complete_result, self.non_aggregate_part)
-        if self.resume_token is not None:
-            complete_result['NextToken'] = self.resume_token
-        return complete_result
-
-    def _parse_starting_token(self):
-        if self._starting_token is None:
-            return None
-
-        # The starting token is a dict passed as a base64 encoded string.
-        next_token = self._starting_token
-        try:
-            next_token = self._token_decoder.decode(next_token)
-            index = 0
-            if 'boto_truncate_amount' in next_token:
-                index = next_token.get('boto_truncate_amount')
-                del next_token['boto_truncate_amount']
-        except (ValueError, TypeError):
-            next_token, index = self._parse_starting_token_deprecated()
-        return next_token, index
-
-    def _parse_starting_token_deprecated(self):
-        """
-        This handles parsing of old style starting tokens, and attempts to
-        coerce them into the new style.
-        """
-        log.debug(
-            "Attempting to fall back to old starting token parser. For "
-            "token: %s" % self._starting_token
-        )
-        if self._starting_token is None:
-            return None
-
-        parts = self._starting_token.split('___')
-        next_token = []
-        index = 0
-        if len(parts) == len(self._input_token) + 1:
-            try:
-                index = int(parts.pop())
-            except ValueError:
-                # This doesn't look like a valid old-style token, so we're
-                # passing it along as an opaque service token.
-                parts = [self._starting_token]
-
-        for part in parts:
-            if part == 'None':
-                next_token.append(None)
-            else:
-                next_token.append(part)
-        return self._convert_deprecated_starting_token(next_token), index
-
-    def _convert_deprecated_starting_token(self, deprecated_token):
-        """
-        This attempts to convert a deprecated starting token into the new
-        style.
-        """
-        len_deprecated_token = len(deprecated_token)
-        len_input_token = len(self._input_token)
-        if len_deprecated_token > len_input_token:
-            raise ValueError("Bad starting token: %s" % self._starting_token)
-        elif len_deprecated_token < len_input_token:
-            log.debug(
-                "Old format starting token does not contain all input "
-                "tokens. Setting the rest, in order, as None."
-            )
-            for i in range(len_input_token - len_deprecated_token):
-                deprecated_token.append(None)
-        return dict(zip(self._input_token, deprecated_token))
-
-
-class Paginator:
-    PAGE_ITERATOR_CLS = PageIterator
-
-    def __init__(self, method, pagination_config, model):
-        self._model = model
-        self._method = method
-        self._pagination_cfg = pagination_config
-        self._output_token = self._get_output_tokens(self._pagination_cfg)
-        self._input_token = self._get_input_tokens(self._pagination_cfg)
-        self._more_results = self._get_more_results_token(self._pagination_cfg)
-        self._non_aggregate_keys = self._get_non_aggregate_keys(
-            self._pagination_cfg
-        )
-        self._result_keys = self._get_result_keys(self._pagination_cfg)
-        self._limit_key = self._get_limit_key(self._pagination_cfg)
-
-    @property
-    def result_keys(self):
-        return self._result_keys
-
-    def _get_non_aggregate_keys(self, config):
-        keys = []
-        for key in config.get('non_aggregate_keys', []):
-            keys.append(jmespath.compile(key))
-        return keys
-
-    def _get_output_tokens(self, config):
-        output = []
-        output_token = config['output_token']
-        if not isinstance(output_token, list):
-            output_token = [output_token]
-        for config in output_token:
-            output.append(jmespath.compile(config))
-        return output
-
-    def _get_input_tokens(self, config):
-        input_token = self._pagination_cfg['input_token']
-        if not isinstance(input_token, list):
-            input_token = [input_token]
-        return input_token
-
-    def _get_more_results_token(self, config):
-        more_results = config.get('more_results')
-        if more_results is not None:
-            return jmespath.compile(more_results)
-
-    def _get_result_keys(self, config):
-        result_key = config.get('result_key')
-        if result_key is not None:
-            if not isinstance(result_key, list):
-                result_key = [result_key]
-            result_key = [jmespath.compile(rk) for rk in result_key]
-            return result_key
-
-    def _get_limit_key(self, config):
-        return config.get('limit_key')
-
-    def paginate(self, **kwargs):
-        """Create paginator object for an operation.
-
-        This returns an iterable object.  Iterating over
-        this object will yield a single page of a response
-        at a time.
-
-        """
-        page_params = self._extract_paging_params(kwargs)
-        return self.PAGE_ITERATOR_CLS(
-            self._method,
-            self._input_token,
-            self._output_token,
-            self._more_results,
-            self._result_keys,
-            self._non_aggregate_keys,
-            self._limit_key,
-            page_params['MaxItems'],
-            page_params['StartingToken'],
-            page_params['PageSize'],
-            kwargs,
-        )
-
-    def _extract_paging_params(self, kwargs):
-        pagination_config = kwargs.pop('PaginationConfig', {})
-        max_items = pagination_config.get('MaxItems', None)
-        if max_items is not None:
-            max_items = int(max_items)
-        page_size = pagination_config.get('PageSize', None)
-        if page_size is not None:
-            if self._limit_key is None:
-                raise PaginationError(
-                    message="PageSize parameter is not supported for the "
-                    "pagination interface for this operation."
-                )
-            input_members = self._model.input_shape.members
-            limit_key_shape = input_members.get(self._limit_key)
-            if limit_key_shape.type_name == 'string':
-                if not isinstance(page_size, str):
-                    page_size = str(page_size)
-            else:
-                page_size = int(page_size)
-        return {
-            'MaxItems': max_items,
-            'StartingToken': pagination_config.get('StartingToken', None),
-            'PageSize': page_size,
-        }
-
-
-class ResultKeyIterator:
-    """Iterates over the results of paginated responses.
-
-    Each iterator is associated with a single result key.
-    Iterating over this object will give you each element in
-    the result key list.
-
-    :param pages_iterator: An iterator that will give you
-        pages of results (a ``PageIterator`` class).
-    :param result_key: The JMESPath expression representing
-        the result key.
-
-    """
-
-    def __init__(self, pages_iterator, result_key):
-        self._pages_iterator = pages_iterator
-        self.result_key = result_key
-
-    def __iter__(self):
-        for page in self._pages_iterator:
-            results = self.result_key.search(page)
-            if results is None:
-                results = []
-            yield from results

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/dir_util.py

@@ -1,243 +0,0 @@
-"""distutils.dir_util
-
-Utility functions for manipulating directories and directory trees."""
-
-import os
-import errno
-from distutils.errors import DistutilsInternalError, DistutilsFileError
-from distutils import log
-
-# cache for by mkpath() -- in addition to cheapening redundant calls,
-# eliminates redundant "creating /foo/bar/baz" messages in dry-run mode
-_path_created = {}
-
-
-def mkpath(name, mode=0o777, verbose=1, dry_run=0):  # noqa: C901
-    """Create a directory and any missing ancestor directories.
-
-    If the directory already exists (or if 'name' is the empty string, which
-    means the current directory, which of course exists), then do nothing.
-    Raise DistutilsFileError if unable to create some directory along the way
-    (eg. some sub-path exists, but is a file rather than a directory).
-    If 'verbose' is true, print a one-line summary of each mkdir to stdout.
-    Return the list of directories actually created.
-
-    os.makedirs is not used because:
-
-    a) It's new to Python 1.5.2, and
-    b) it blows up if the directory already exists (in which case it should
-       silently succeed).
-    """
-
-    global _path_created
-
-    # Detect a common bug -- name is None
-    if not isinstance(name, str):
-        raise DistutilsInternalError(
-            "mkpath: 'name' must be a string (got {!r})".format(name)
-        )
-
-    # XXX what's the better way to handle verbosity? print as we create
-    # each directory in the path (the current behaviour), or only announce
-    # the creation of the whole path? (quite easy to do the latter since
-    # we're not using a recursive algorithm)
-
-    name = os.path.normpath(name)
-    created_dirs = []
-    if os.path.isdir(name) or name == '':
-        return created_dirs
-    if _path_created.get(os.path.abspath(name)):
-        return created_dirs
-
-    (head, tail) = os.path.split(name)
-    tails = [tail]  # stack of lone dirs to create
-
-    while head and tail and not os.path.isdir(head):
-        (head, tail) = os.path.split(head)
-        tails.insert(0, tail)  # push next higher dir onto stack
-
-    # now 'head' contains the deepest directory that already exists
-    # (that is, the child of 'head' in 'name' is the highest directory
-    # that does *not* exist)
-    for d in tails:
-        # print "head = %s, d = %s: " % (head, d),
-        head = os.path.join(head, d)
-        abs_head = os.path.abspath(head)
-
-        if _path_created.get(abs_head):
-            continue
-
-        if verbose >= 1:
-            log.info("creating %s", head)
-
-        if not dry_run:
-            try:
-                os.mkdir(head, mode)
-            except OSError as exc:
-                if not (exc.errno == errno.EEXIST and os.path.isdir(head)):
-                    raise DistutilsFileError(
-                        "could not create '{}': {}".format(head, exc.args[-1])
-                    )
-            created_dirs.append(head)
-
-        _path_created[abs_head] = 1
-    return created_dirs
-
-
-def create_tree(base_dir, files, mode=0o777, verbose=1, dry_run=0):
-    """Create all the empty directories under 'base_dir' needed to put 'files'
-    there.
-
-    'base_dir' is just the name of a directory which doesn't necessarily
-    exist yet; 'files' is a list of filenames to be interpreted relative to
-    'base_dir'.  'base_dir' + the directory portion of every file in 'files'
-    will be created if it doesn't already exist.  'mode', 'verbose' and
-    'dry_run' flags are as for 'mkpath()'.
-    """
-    # First get the list of directories to create
-    need_dir = set()
-    for file in files:
-        need_dir.add(os.path.join(base_dir, os.path.dirname(file)))
-
-    # Now create them
-    for dir in sorted(need_dir):
-        mkpath(dir, mode, verbose=verbose, dry_run=dry_run)
-
-
-def copy_tree(  # noqa: C901
-    src,
-    dst,
-    preserve_mode=1,
-    preserve_times=1,
-    preserve_symlinks=0,
-    update=0,
-    verbose=1,
-    dry_run=0,
-):
-    """Copy an entire directory tree 'src' to a new location 'dst'.
-
-    Both 'src' and 'dst' must be directory names.  If 'src' is not a
-    directory, raise DistutilsFileError.  If 'dst' does not exist, it is
-    created with 'mkpath()'.  The end result of the copy is that every
-    file in 'src' is copied to 'dst', and directories under 'src' are
-    recursively copied to 'dst'.  Return the list of files that were
-    copied or might have been copied, using their output name.  The
-    return value is unaffected by 'update' or 'dry_run': it is simply
-    the list of all files under 'src', with the names changed to be
-    under 'dst'.
-
-    'preserve_mode' and 'preserve_times' are the same as for
-    'copy_file'; note that they only apply to regular files, not to
-    directories.  If 'preserve_symlinks' is true, symlinks will be
-    copied as symlinks (on platforms that support them!); otherwise
-    (the default), the destination of the symlink will be copied.
-    'update' and 'verbose' are the same as for 'copy_file'.
-    """
-    from distutils.file_util import copy_file
-
-    if not dry_run and not os.path.isdir(src):
-        raise DistutilsFileError("cannot copy tree '%s': not a directory" % src)
-    try:
-        names = os.listdir(src)
-    except OSError as e:
-        if dry_run:
-            names = []
-        else:
-            raise DistutilsFileError(
-                "error listing files in '{}': {}".format(src, e.strerror)
-            )
-
-    if not dry_run:
-        mkpath(dst, verbose=verbose)
-
-    outputs = []
-
-    for n in names:
-        src_name = os.path.join(src, n)
-        dst_name = os.path.join(dst, n)
-
-        if n.startswith('.nfs'):
-            # skip NFS rename files
-            continue
-
-        if preserve_symlinks and os.path.islink(src_name):
-            link_dest = os.readlink(src_name)
-            if verbose >= 1:
-                log.info("linking %s -> %s", dst_name, link_dest)
-            if not dry_run:
-                os.symlink(link_dest, dst_name)
-            outputs.append(dst_name)
-
-        elif os.path.isdir(src_name):
-            outputs.extend(
-                copy_tree(
-                    src_name,
-                    dst_name,
-                    preserve_mode,
-                    preserve_times,
-                    preserve_symlinks,
-                    update,
-                    verbose=verbose,
-                    dry_run=dry_run,
-                )
-            )
-        else:
-            copy_file(
-                src_name,
-                dst_name,
-                preserve_mode,
-                preserve_times,
-                update,
-                verbose=verbose,
-                dry_run=dry_run,
-            )
-            outputs.append(dst_name)
-
-    return outputs
-
-
-def _build_cmdtuple(path, cmdtuples):
-    """Helper for remove_tree()."""
-    for f in os.listdir(path):
-        real_f = os.path.join(path, f)
-        if os.path.isdir(real_f) and not os.path.islink(real_f):
-            _build_cmdtuple(real_f, cmdtuples)
-        else:
-            cmdtuples.append((os.remove, real_f))
-    cmdtuples.append((os.rmdir, path))
-
-
-def remove_tree(directory, verbose=1, dry_run=0):
-    """Recursively remove an entire directory tree.
-
-    Any errors are ignored (apart from being reported to stdout if 'verbose'
-    is true).
-    """
-    global _path_created
-
-    if verbose >= 1:
-        log.info("removing '%s' (and everything under it)", directory)
-    if dry_run:
-        return
-    cmdtuples = []
-    _build_cmdtuple(directory, cmdtuples)
-    for cmd in cmdtuples:
-        try:
-            cmd[0](cmd[1])
-            # remove dir from cache if it's already there
-            abspath = os.path.abspath(cmd[1])
-            if abspath in _path_created:
-                del _path_created[abspath]
-        except OSError as exc:
-            log.warn("error removing %s: %s", directory, exc)
-
-
-def ensure_relative(path):
-    """Take the full path 'path', and make it a relative path.
-
-    This is useful to make 'path' the second argument to os.path.join().
-    """
-    drive, path = os.path.splitdrive(path)
-    if path[0:1] == os.sep:
-        path = drive + path[1:]
-    return path

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/more_itertools/recipes.py

@@ -1,620 +0,0 @@
-"""Imported from the recipes section of the itertools documentation.
-
-All functions taken from the recipes section of the itertools library docs
-[1]_.
-Some backward-compatible usability improvements have been made.
-
-.. [1] http://docs.python.org/library/itertools.html#recipes
-
-"""
-import warnings
-from collections import deque
-from itertools import (
-    chain,
-    combinations,
-    count,
-    cycle,
-    groupby,
-    islice,
-    repeat,
-    starmap,
-    tee,
-    zip_longest,
-)
-import operator
-from random import randrange, sample, choice
-
-__all__ = [
-    'all_equal',
-    'consume',
-    'convolve',
-    'dotproduct',
-    'first_true',
-    'flatten',
-    'grouper',
-    'iter_except',
-    'ncycles',
-    'nth',
-    'nth_combination',
-    'padnone',
-    'pad_none',
-    'pairwise',
-    'partition',
-    'powerset',
-    'prepend',
-    'quantify',
-    'random_combination_with_replacement',
-    'random_combination',
-    'random_permutation',
-    'random_product',
-    'repeatfunc',
-    'roundrobin',
-    'tabulate',
-    'tail',
-    'take',
-    'unique_everseen',
-    'unique_justseen',
-]
-
-
-def take(n, iterable):
-    """Return first *n* items of the iterable as a list.
-
-        >>> take(3, range(10))
-        [0, 1, 2]
-
-    If there are fewer than *n* items in the iterable, all of them are
-    returned.
-
-        >>> take(10, range(3))
-        [0, 1, 2]
-
-    """
-    return list(islice(iterable, n))
-
-
-def tabulate(function, start=0):
-    """Return an iterator over the results of ``func(start)``,
-    ``func(start + 1)``, ``func(start + 2)``...
-
-    *func* should be a function that accepts one integer argument.
-
-    If *start* is not specified it defaults to 0. It will be incremented each
-    time the iterator is advanced.
-
-        >>> square = lambda x: x ** 2
-        >>> iterator = tabulate(square, -3)
-        >>> take(4, iterator)
-        [9, 4, 1, 0]
-
-    """
-    return map(function, count(start))
-
-
-def tail(n, iterable):
-    """Return an iterator over the last *n* items of *iterable*.
-
-    >>> t = tail(3, 'ABCDEFG')
-    >>> list(t)
-    ['E', 'F', 'G']
-
-    """
-    return iter(deque(iterable, maxlen=n))
-
-
-def consume(iterator, n=None):
-    """Advance *iterable* by *n* steps. If *n* is ``None``, consume it
-    entirely.
-
-    Efficiently exhausts an iterator without returning values. Defaults to
-    consuming the whole iterator, but an optional second argument may be
-    provided to limit consumption.
-
-        >>> i = (x for x in range(10))
-        >>> next(i)
-        0
-        >>> consume(i, 3)
-        >>> next(i)
-        4
-        >>> consume(i)
-        >>> next(i)
-        Traceback (most recent call last):
-          File "<stdin>", line 1, in <module>
-        StopIteration
-
-    If the iterator has fewer items remaining than the provided limit, the
-    whole iterator will be consumed.
-
-        >>> i = (x for x in range(3))
-        >>> consume(i, 5)
-        >>> next(i)
-        Traceback (most recent call last):
-          File "<stdin>", line 1, in <module>
-        StopIteration
-
-    """
-    # Use functions that consume iterators at C speed.
-    if n is None:
-        # feed the entire iterator into a zero-length deque
-        deque(iterator, maxlen=0)
-    else:
-        # advance to the empty slice starting at position n
-        next(islice(iterator, n, n), None)
-
-
-def nth(iterable, n, default=None):
-    """Returns the nth item or a default value.
-
-    >>> l = range(10)
-    >>> nth(l, 3)
-    3
-    >>> nth(l, 20, "zebra")
-    'zebra'
-
-    """
-    return next(islice(iterable, n, None), default)
-
-
-def all_equal(iterable):
-    """
-    Returns ``True`` if all the elements are equal to each other.
-
-        >>> all_equal('aaaa')
-        True
-        >>> all_equal('aaab')
-        False
-
-    """
-    g = groupby(iterable)
-    return next(g, True) and not next(g, False)
-
-
-def quantify(iterable, pred=bool):
-    """Return the how many times the predicate is true.
-
-    >>> quantify([True, False, True])
-    2
-
-    """
-    return sum(map(pred, iterable))
-
-
-def pad_none(iterable):
-    """Returns the sequence of elements and then returns ``None`` indefinitely.
-
-        >>> take(5, pad_none(range(3)))
-        [0, 1, 2, None, None]
-
-    Useful for emulating the behavior of the built-in :func:`map` function.
-
-    See also :func:`padded`.
-
-    """
-    return chain(iterable, repeat(None))
-
-
-padnone = pad_none
-
-
-def ncycles(iterable, n):
-    """Returns the sequence elements *n* times
-
-    >>> list(ncycles(["a", "b"], 3))
-    ['a', 'b', 'a', 'b', 'a', 'b']
-
-    """
-    return chain.from_iterable(repeat(tuple(iterable), n))
-
-
-def dotproduct(vec1, vec2):
-    """Returns the dot product of the two iterables.
-
-    >>> dotproduct([10, 10], [20, 20])
-    400
-
-    """
-    return sum(map(operator.mul, vec1, vec2))
-
-
-def flatten(listOfLists):
-    """Return an iterator flattening one level of nesting in a list of lists.
-
-        >>> list(flatten([[0, 1], [2, 3]]))
-        [0, 1, 2, 3]
-
-    See also :func:`collapse`, which can flatten multiple levels of nesting.
-
-    """
-    return chain.from_iterable(listOfLists)
-
-
-def repeatfunc(func, times=None, *args):
-    """Call *func* with *args* repeatedly, returning an iterable over the
-    results.
-
-    If *times* is specified, the iterable will terminate after that many
-    repetitions:
-
-        >>> from operator import add
-        >>> times = 4
-        >>> args = 3, 5
-        >>> list(repeatfunc(add, times, *args))
-        [8, 8, 8, 8]
-
-    If *times* is ``None`` the iterable will not terminate:
-
-        >>> from random import randrange
-        >>> times = None
-        >>> args = 1, 11
-        >>> take(6, repeatfunc(randrange, times, *args))  # doctest:+SKIP
-        [2, 4, 8, 1, 8, 4]
-
-    """
-    if times is None:
-        return starmap(func, repeat(args))
-    return starmap(func, repeat(args, times))
-
-
-def _pairwise(iterable):
-    """Returns an iterator of paired items, overlapping, from the original
-
-    >>> take(4, pairwise(count()))
-    [(0, 1), (1, 2), (2, 3), (3, 4)]
-
-    On Python 3.10 and above, this is an alias for :func:`itertools.pairwise`.
-
-    """
-    a, b = tee(iterable)
-    next(b, None)
-    yield from zip(a, b)
-
-
-try:
-    from itertools import pairwise as itertools_pairwise
-except ImportError:
-    pairwise = _pairwise
-else:
-
-    def pairwise(iterable):
-        yield from itertools_pairwise(iterable)
-
-    pairwise.__doc__ = _pairwise.__doc__
-
-
-def grouper(iterable, n, fillvalue=None):
-    """Collect data into fixed-length chunks or blocks.
-
-    >>> list(grouper('ABCDEFG', 3, 'x'))
-    [('A', 'B', 'C'), ('D', 'E', 'F'), ('G', 'x', 'x')]
-
-    """
-    if isinstance(iterable, int):
-        warnings.warn(
-            "grouper expects iterable as first parameter", DeprecationWarning
-        )
-        n, iterable = iterable, n
-    args = [iter(iterable)] * n
-    return zip_longest(fillvalue=fillvalue, *args)
-
-
-def roundrobin(*iterables):
-    """Yields an item from each iterable, alternating between them.
-
-        >>> list(roundrobin('ABC', 'D', 'EF'))
-        ['A', 'D', 'E', 'B', 'F', 'C']
-
-    This function produces the same output as :func:`interleave_longest`, but
-    may perform better for some inputs (in particular when the number of
-    iterables is small).
-
-    """
-    # Recipe credited to George Sakkis
-    pending = len(iterables)
-    nexts = cycle(iter(it).__next__ for it in iterables)
-    while pending:
-        try:
-            for next in nexts:
-                yield next()
-        except StopIteration:
-            pending -= 1
-            nexts = cycle(islice(nexts, pending))
-
-
-def partition(pred, iterable):
-    """
-    Returns a 2-tuple of iterables derived from the input iterable.
-    The first yields the items that have ``pred(item) == False``.
-    The second yields the items that have ``pred(item) == True``.
-
-        >>> is_odd = lambda x: x % 2 != 0
-        >>> iterable = range(10)
-        >>> even_items, odd_items = partition(is_odd, iterable)
-        >>> list(even_items), list(odd_items)
-        ([0, 2, 4, 6, 8], [1, 3, 5, 7, 9])
-
-    If *pred* is None, :func:`bool` is used.
-
-        >>> iterable = [0, 1, False, True, '', ' ']
-        >>> false_items, true_items = partition(None, iterable)
-        >>> list(false_items), list(true_items)
-        ([0, False, ''], [1, True, ' '])
-
-    """
-    if pred is None:
-        pred = bool
-
-    evaluations = ((pred(x), x) for x in iterable)
-    t1, t2 = tee(evaluations)
-    return (
-        (x for (cond, x) in t1 if not cond),
-        (x for (cond, x) in t2 if cond),
-    )
-
-
-def powerset(iterable):
-    """Yields all possible subsets of the iterable.
-
-        >>> list(powerset([1, 2, 3]))
-        [(), (1,), (2,), (3,), (1, 2), (1, 3), (2, 3), (1, 2, 3)]
-
-    :func:`powerset` will operate on iterables that aren't :class:`set`
-    instances, so repeated elements in the input will produce repeated elements
-    in the output. Use :func:`unique_everseen` on the input to avoid generating
-    duplicates:
-
-        >>> seq = [1, 1, 0]
-        >>> list(powerset(seq))
-        [(), (1,), (1,), (0,), (1, 1), (1, 0), (1, 0), (1, 1, 0)]
-        >>> from more_itertools import unique_everseen
-        >>> list(powerset(unique_everseen(seq)))
-        [(), (1,), (0,), (1, 0)]
-
-    """
-    s = list(iterable)
-    return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))
-
-
-def unique_everseen(iterable, key=None):
-    """
-    Yield unique elements, preserving order.
-
-        >>> list(unique_everseen('AAAABBBCCDAABBB'))
-        ['A', 'B', 'C', 'D']
-        >>> list(unique_everseen('ABBCcAD', str.lower))
-        ['A', 'B', 'C', 'D']
-
-    Sequences with a mix of hashable and unhashable items can be used.
-    The function will be slower (i.e., `O(n^2)`) for unhashable items.
-
-    Remember that ``list`` objects are unhashable - you can use the *key*
-    parameter to transform the list to a tuple (which is hashable) to
-    avoid a slowdown.
-
-        >>> iterable = ([1, 2], [2, 3], [1, 2])
-        >>> list(unique_everseen(iterable))  # Slow
-        [[1, 2], [2, 3]]
-        >>> list(unique_everseen(iterable, key=tuple))  # Faster
-        [[1, 2], [2, 3]]
-
-    Similary, you may want to convert unhashable ``set`` objects with
-    ``key=frozenset``. For ``dict`` objects,
-    ``key=lambda x: frozenset(x.items())`` can be used.
-
-    """
-    seenset = set()
-    seenset_add = seenset.add
-    seenlist = []
-    seenlist_add = seenlist.append
-    use_key = key is not None
-
-    for element in iterable:
-        k = key(element) if use_key else element
-        try:
-            if k not in seenset:
-                seenset_add(k)
-                yield element
-        except TypeError:
-            if k not in seenlist:
-                seenlist_add(k)
-                yield element
-
-
-def unique_justseen(iterable, key=None):
-    """Yields elements in order, ignoring serial duplicates
-
-    >>> list(unique_justseen('AAAABBBCCDAABBB'))
-    ['A', 'B', 'C', 'D', 'A', 'B']
-    >>> list(unique_justseen('ABBCcAD', str.lower))
-    ['A', 'B', 'C', 'A', 'D']
-
-    """
-    return map(next, map(operator.itemgetter(1), groupby(iterable, key)))
-
-
-def iter_except(func, exception, first=None):
-    """Yields results from a function repeatedly until an exception is raised.
-
-    Converts a call-until-exception interface to an iterator interface.
-    Like ``iter(func, sentinel)``, but uses an exception instead of a sentinel
-    to end the loop.
-
-        >>> l = [0, 1, 2]
-        >>> list(iter_except(l.pop, IndexError))
-        [2, 1, 0]
-
-    """
-    try:
-        if first is not None:
-            yield first()
-        while 1:
-            yield func()
-    except exception:
-        pass
-
-
-def first_true(iterable, default=None, pred=None):
-    """
-    Returns the first true value in the iterable.
-
-    If no true value is found, returns *default*
-
-    If *pred* is not None, returns the first item for which
-    ``pred(item) == True`` .
-
-        >>> first_true(range(10))
-        1
-        >>> first_true(range(10), pred=lambda x: x > 5)
-        6
-        >>> first_true(range(10), default='missing', pred=lambda x: x > 9)
-        'missing'
-
-    """
-    return next(filter(pred, iterable), default)
-
-
-def random_product(*args, repeat=1):
-    """Draw an item at random from each of the input iterables.
-
-        >>> random_product('abc', range(4), 'XYZ')  # doctest:+SKIP
-        ('c', 3, 'Z')
-
-    If *repeat* is provided as a keyword argument, that many items will be
-    drawn from each iterable.
-
-        >>> random_product('abcd', range(4), repeat=2)  # doctest:+SKIP
-        ('a', 2, 'd', 3)
-
-    This equivalent to taking a random selection from
-    ``itertools.product(*args, **kwarg)``.
-
-    """
-    pools = [tuple(pool) for pool in args] * repeat
-    return tuple(choice(pool) for pool in pools)
-
-
-def random_permutation(iterable, r=None):
-    """Return a random *r* length permutation of the elements in *iterable*.
-
-    If *r* is not specified or is ``None``, then *r* defaults to the length of
-    *iterable*.
-
-        >>> random_permutation(range(5))  # doctest:+SKIP
-        (3, 4, 0, 1, 2)
-
-    This equivalent to taking a random selection from
-    ``itertools.permutations(iterable, r)``.
-
-    """
-    pool = tuple(iterable)
-    r = len(pool) if r is None else r
-    return tuple(sample(pool, r))
-
-
-def random_combination(iterable, r):
-    """Return a random *r* length subsequence of the elements in *iterable*.
-
-        >>> random_combination(range(5), 3)  # doctest:+SKIP
-        (2, 3, 4)
-
-    This equivalent to taking a random selection from
-    ``itertools.combinations(iterable, r)``.
-
-    """
-    pool = tuple(iterable)
-    n = len(pool)
-    indices = sorted(sample(range(n), r))
-    return tuple(pool[i] for i in indices)
-
-
-def random_combination_with_replacement(iterable, r):
-    """Return a random *r* length subsequence of elements in *iterable*,
-    allowing individual elements to be repeated.
-
-        >>> random_combination_with_replacement(range(3), 5) # doctest:+SKIP
-        (0, 0, 1, 2, 2)
-
-    This equivalent to taking a random selection from
-    ``itertools.combinations_with_replacement(iterable, r)``.
-
-    """
-    pool = tuple(iterable)
-    n = len(pool)
-    indices = sorted(randrange(n) for i in range(r))
-    return tuple(pool[i] for i in indices)
-
-
-def nth_combination(iterable, r, index):
-    """Equivalent to ``list(combinations(iterable, r))[index]``.
-
-    The subsequences of *iterable* that are of length *r* can be ordered
-    lexicographically. :func:`nth_combination` computes the subsequence at
-    sort position *index* directly, without computing the previous
-    subsequences.
-
-        >>> nth_combination(range(5), 3, 5)
-        (0, 3, 4)
-
-    ``ValueError`` will be raised If *r* is negative or greater than the length
-    of *iterable*.
-    ``IndexError`` will be raised if the given *index* is invalid.
-    """
-    pool = tuple(iterable)
-    n = len(pool)
-    if (r < 0) or (r > n):
-        raise ValueError
-
-    c = 1
-    k = min(r, n - r)
-    for i in range(1, k + 1):
-        c = c * (n - k + i) // i
-
-    if index < 0:
-        index += c
-
-    if (index < 0) or (index >= c):
-        raise IndexError
-
-    result = []
-    while r:
-        c, n, r = c * r // n, n - 1, r - 1
-        while index >= c:
-            index -= c
-            c, n = c * (n - r) // n, n - 1
-        result.append(pool[-1 - n])
-
-    return tuple(result)
-
-
-def prepend(value, iterator):
-    """Yield *value*, followed by the elements in *iterator*.
-
-        >>> value = '0'
-        >>> iterator = ['1', '2', '3']
-        >>> list(prepend(value, iterator))
-        ['0', '1', '2', '3']
-
-    To prepend multiple values, see :func:`itertools.chain`
-    or :func:`value_chain`.
-
-    """
-    return chain([value], iterator)
-
-
-def convolve(signal, kernel):
-    """Convolve the iterable *signal* with the iterable *kernel*.
-
-        >>> signal = (1, 2, 3, 4, 5)
-        >>> kernel = [3, 2, 1]
-        >>> list(convolve(signal, kernel))
-        [3, 8, 14, 20, 26, 14, 5]
-
-    Note: the input arguments are not interchangeable, as the *kernel*
-    is immediately consumed and stored.
-
-    """
-    kernel = tuple(kernel)[::-1]
-    n = len(kernel)
-    window = deque([0], maxlen=n) * n
-    for x in chain(signal, repeat(0, n - 1)):
-        window.append(x)
-        yield sum(map(operator.mul, kernel, window))

spaces/Blessing/Asphalt-Pavement-Distresses-Detector/app.py

@@ -1,174 +0,0 @@
-import gradio as gr
-import cv2 
-import requests
-import os 
-import random
-
-from ultralytics import YOLO
-
-file_urls = [
-    'https://www.dropbox.com/scl/fi/5pavu4vvkprrtkwktvei7/DSC02373.JPG?rlkey=fpj636qtkf3vrqfxy45n2d9ii&dl=1',
-    'https://www.dropbox.com/scl/fi/56pbn4r3ohk85rchcvwdj/DSC02813.JPG?rlkey=jnbsidqtthk6p4ysld6o6kc4t&dl=1',
-    'https://www.dropbox.com/scl/fi/av9g5zbmrrzg9064zivat/image_2.jpg?rlkey=ldocvzz5lq98zffqf1lmhbhv1&dl=1',
-    'https://www.dropbox.com/scl/fi/izo2eqqnqzcsaxis1qrbx/IMG_7612.JPG?rlkey=6wfjaux44khtlx454ex0ng0hp&dl=1',
-    'https://www.dropbox.com/scl/fi/e6vgy1et6vjr61uypk5yu/VID-20230809-WA0021.mp4?rlkey=khv8rw074vezzlg8ob38bpmbx&dl=1'
-]
-
-def download_file(url, save_name):
-    url = url
-    if not os.path.exists(save_name):
-        file = requests.get(url)
-        open(save_name, 'wb').write(file.content)
- 
-for i, url in enumerate(file_urls):
-    if 'mp4' in file_urls[i]:
-        download_file(
-            file_urls[i],
-            f"video.mp4"
-        )
-    else:
-        download_file(
-            file_urls[i],
-            f"image_{i}.jpg"
-        )
-
-
-model = YOLO('best.pt')
-path  = [['image_0.jpg'], ['image_1.jpg'], ['image_2.jpg'], ['image_3.jpg']]
-
-# path = [['IMG_7612.JPG'], ['IMG_7678.JPG'], ['all_33.jpg'], ['all_80.jpg'], 
-#     ['DSC02813.JPG'], ['DSC02373.JPG']]
-
-
-# path  = [['sc_1_0 (1) (1).JPG'], ['sc_1_0 (16) (1).JPG'], 
-#         ['sc_1_0 (18) (1).JPG'], ['sc_1_0 (18).JPG']]
-
-video_path = [['video.mp4']]
-
-classes = ['alligator_cracking', 'longitudinal_cracking', 'potholes', 'ravelling']
-
-def show_preds_image(image_path):
-    image = cv2.imread(image_path)
-    outputs = model.predict(source=image_path, agnostic_nms=True, conf=0.25, iou=0.4, imgsz=640)
-    results = outputs[0].cpu().numpy()
-
-    re_boxes = results.boxes.data.tolist()
-
-    class_colors = {1 : (95, 255, 54), 2: (242, 210, 100), 3: (96, 7, 70), 4:(221, 59, 41)}
-    random.seed(42)
-    # class_colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) for _ in range(4)]
-
-    for i, det in enumerate(results.boxes.xyxy):
-        x1, y1, x2, y2 = int(det[0]),  int(det[1]),  int(det[2]),  int(det[3])
-
-        class_label = int(re_boxes[i][-1])
-        rectangle_color = class_colors.get(class_label)
-        # rectangle_color = class_colors[class_label]
-        text_color = rectangle_color
-        cv2.rectangle(
-            image,
-            (int(det[0]), int(det[1])),
-            (int(det[2]), int(det[3])),
-            color=rectangle_color,
-            thickness=3,
-            lineType=cv2.LINE_AA
-        )
-
-        text_position = (x1, y1+100)
-        conf = re_boxes[i][-2]
-        class_name = classes[class_label]
-        # class_label = class_name.split('_')[0] + '\n' + class_name.split('_')[1] if '_' in class_name else class_name
-        cv2.putText(image, classes[class_label] + f' = {round(conf, 2)}',
-            text_position, cv2.FONT_HERSHEY_SIMPLEX, 1.5, text_color, 3)
-        
-    
-    # print(class_ids)
-    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
- 
-inputs_image = [
-    gr.components.Image(type="filepath", label="Input Image"),
-]
-outputs_image = [
-    gr.components.Image(type="numpy", label="Output Image"),
-]
-interface_image = gr.Interface(
-    fn=show_preds_image,
-    inputs=inputs_image,
-    outputs=outputs_image,
-    title="Asphalt Road Pavement Distresses Detector",
-    examples=path,
-    cache_examples=False,
-        description= 'This is a demo app that takes in images or videos of Asphalt pavement surfaces and \
-         \n detects the following pavement distresses: \
-         \n    \
-         \n Alligator cracking \
-        \n Longitudinal cracking \
-        \n Potholes \
-        \n Ravelling \
-        \n \
-        \n This is specifically for Inference and educational purpose.\
-        \n \
-        \n The model might ocassionaly give false outputs'
-    )
-
-def show_preds_video(video_path):
-    cap = cv2.VideoCapture(video_path)
-    while(cap.isOpened()):
-        ret, frame = cap.read()
-        if ret:
-            frame_copy = frame.copy()
-            outputs = model.predict(source=frame, agnostic_nms=True, conf=0.25, iou=0.4, imgsz=640)
-            results = outputs[0].cpu().numpy()
-            re_boxes = results.boxes.data.tolist()
-
-            class_colors = {1 : (95, 255, 54), 2: (242, 210, 100), 3: (96, 7, 70), 4:(221, 59, 41)}
-            random.seed(42)
-            # class_colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) for _ in range(4)]
-            
-            for i, det in enumerate(results.boxes.xyxy):
-                x1, y1, x2, y2 = int(det[0]),  int(det[1]),  int(det[2]),  int(det[3])
-
-                class_label = int(re_boxes[i][-1])
-                rectangle_color = class_colors.get(class_label)
-                # rectangle_color = class_colors[class_label]
-                text_color = rectangle_color
-
-                cv2.rectangle(
-                    frame_copy,
-                    (int(det[0]), int(det[1])),
-                    (int(det[2]), int(det[3])),
-                    color=rectangle_color,
-                    thickness=2,
-                    lineType=cv2.LINE_AA
-                )
-
-                
-                text_position = (x1, y1+100)
-                conf = re_boxes[i][-2]
-                class_name = classes[class_label]
-                # class_label = class_name.split('_')[0] + '\n' + class_name.split('_')[1] if '_' in class_name else class_name
-                cv2.putText(frame_copy, classes[class_label] + f' = {round(conf, 2)}',
-                text_position, cv2.FONT_HERSHEY_SIMPLEX, 1.5, text_color, 3)
-
-            yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
- 
-inputs_video = [
-    gr.components.Video(type="filepath", label="Input Video"),
- 
-]
-outputs_video = [
-    gr.components.Image(type="numpy", label="Output Video"),
-]
-interface_video = gr.Interface(
-    fn=show_preds_video,
-    inputs=inputs_video,
-    outputs=outputs_video,
-    title="Asphalt Road Pavement Distresses Detector",
-    examples=video_path,
-    cache_examples=False,
-    # live=True
-)
-gr.TabbedInterface(
-    [interface_image, interface_video],
-    tab_names=['Image inference', 'Video inference'],
-).queue().launch()

spaces/CVH-vn1210/make_hair/minigpt4/common/optims.py

@@ -1,119 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import math
-
-from minigpt4.common.registry import registry
-
-
-@registry.register_lr_scheduler("linear_warmup_step_lr")
-class LinearWarmupStepLRScheduler:
-    def __init__(
-        self,
-        optimizer,
-        max_epoch,
-        min_lr,
-        init_lr,
-        decay_rate=1,
-        warmup_start_lr=-1,
-        warmup_steps=0,
-        **kwargs
-    ):
-        self.optimizer = optimizer
-
-        self.max_epoch = max_epoch
-        self.min_lr = min_lr
-
-        self.decay_rate = decay_rate
-
-        self.init_lr = init_lr
-        self.warmup_steps = warmup_steps
-        self.warmup_start_lr = warmup_start_lr if warmup_start_lr >= 0 else init_lr
-
-    def step(self, cur_epoch, cur_step):
-        if cur_epoch == 0:
-            warmup_lr_schedule(
-                step=cur_step,
-                optimizer=self.optimizer,
-                max_step=self.warmup_steps,
-                init_lr=self.warmup_start_lr,
-                max_lr=self.init_lr,
-            )
-        else:
-            step_lr_schedule(
-                epoch=cur_epoch,
-                optimizer=self.optimizer,
-                init_lr=self.init_lr,
-                min_lr=self.min_lr,
-                decay_rate=self.decay_rate,
-            )
-
-
-@registry.register_lr_scheduler("linear_warmup_cosine_lr")
-class LinearWarmupCosineLRScheduler:
-    def __init__(
-        self,
-        optimizer,
-        max_epoch,
-        iters_per_epoch,
-        min_lr,
-        init_lr,
-        warmup_steps=0,
-        warmup_start_lr=-1,
-        **kwargs
-    ):
-        self.optimizer = optimizer
-
-        self.max_epoch = max_epoch
-        self.iters_per_epoch = iters_per_epoch
-        self.min_lr = min_lr
-
-        self.init_lr = init_lr
-        self.warmup_steps = warmup_steps
-        self.warmup_start_lr = warmup_start_lr if warmup_start_lr >= 0 else init_lr
-
-    def step(self, cur_epoch, cur_step):
-        total_cur_step = cur_epoch * self.iters_per_epoch + cur_step
-        if total_cur_step < self.warmup_steps:
-            warmup_lr_schedule(
-                step=cur_step,
-                optimizer=self.optimizer,
-                max_step=self.warmup_steps,
-                init_lr=self.warmup_start_lr,
-                max_lr=self.init_lr,
-            )
-        else:
-            cosine_lr_schedule(
-                epoch=total_cur_step,
-                optimizer=self.optimizer,
-                max_epoch=self.max_epoch * self.iters_per_epoch,
-                init_lr=self.init_lr,
-                min_lr=self.min_lr,
-            )
-
-
-def cosine_lr_schedule(optimizer, epoch, max_epoch, init_lr, min_lr):
-    """Decay the learning rate"""
-    lr = (init_lr - min_lr) * 0.5 * (
-        1.0 + math.cos(math.pi * epoch / max_epoch)
-    ) + min_lr
-    for param_group in optimizer.param_groups:
-        param_group["lr"] = lr
-
-
-def warmup_lr_schedule(optimizer, step, max_step, init_lr, max_lr):
-    """Warmup the learning rate"""
-    lr = min(max_lr, init_lr + (max_lr - init_lr) * step / max(max_step, 1))
-    for param_group in optimizer.param_groups:
-        param_group["lr"] = lr
-
-
-def step_lr_schedule(optimizer, epoch, init_lr, min_lr, decay_rate):
-    """Decay the learning rate"""
-    lr = max(min_lr, init_lr * (decay_rate**epoch))
-    for param_group in optimizer.param_groups:
-        param_group["lr"] = lr

spaces/CVMX-jaca-tonos/Spanish-Audio-Transcription-to-Quechua-Translation/README.md

@@ -1,12 +0,0 @@
----
-title: Spanish Audio Transcription To Quechua Translation
-emoji: 📈
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

spaces/CVPR/LIVE/thrust/thrust/detail/type_traits/pointer_traits.h

@@ -1,371 +0,0 @@
-/*
- *  Copyright 2008-2018 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/detail/type_traits.h>
-#include <thrust/detail/type_traits/is_metafunction_defined.h>
-#include <thrust/detail/type_traits/has_nested_type.h>
-#include <thrust/iterator/iterator_traits.h>
-#include <cstddef>
-
-namespace thrust
-{
-namespace detail
-{
-
-template<typename Ptr> struct pointer_element;
-
-template<template<typename> class Ptr, typename Arg>
-  struct pointer_element<Ptr<Arg> >
-{
-  typedef Arg type;
-};
-
-template<template<typename,typename> class Ptr, typename Arg1, typename Arg2>
-  struct pointer_element<Ptr<Arg1,Arg2> >
-{
-  typedef Arg1 type;
-};
-
-template<template<typename,typename,typename> class Ptr, typename Arg1, typename Arg2, typename Arg3>
-  struct pointer_element<Ptr<Arg1,Arg2,Arg3> >
-{
-  typedef Arg1 type;
-};
-
-template<template<typename,typename,typename,typename> class Ptr, typename Arg1, typename Arg2, typename Arg3, typename Arg4>
-  struct pointer_element<Ptr<Arg1,Arg2,Arg3,Arg4> >
-{
-  typedef Arg1 type;
-};
-
-template<template<typename,typename,typename,typename,typename> class Ptr, typename Arg1, typename Arg2, typename Arg3, typename Arg4, typename Arg5>
-  struct pointer_element<Ptr<Arg1,Arg2,Arg3,Arg4,Arg5> >
-{
-  typedef Arg1 type;
-};
-
-template<typename T>
-  struct pointer_element<T*>
-{
-  typedef T type;
-};
-
-template<typename Ptr>
-  struct pointer_difference
-{
-  typedef typename Ptr::difference_type type;
-};
-
-template<typename T>
-  struct pointer_difference<T*>
-{
-  typedef std::ptrdiff_t type;
-};
-
-template<typename Ptr, typename T> struct rebind_pointer;
-
-template<typename T, typename U>
-  struct rebind_pointer<T*,U>
-{
-  typedef U* type;
-};
-
-template<template<typename> class Ptr, typename Arg, typename T>
-  struct rebind_pointer<Ptr<Arg>,T>
-{
-  typedef Ptr<T> type;
-};
-
-template<template<typename, typename> class Ptr, typename Arg1, typename Arg2, typename T>
-  struct rebind_pointer<Ptr<Arg1,Arg2>,T>
-{
-  typedef Ptr<T,Arg2> type;
-};
-
-template<template<typename, typename, typename> class Ptr, typename Arg1, typename Arg2, typename Arg3, typename T>
-  struct rebind_pointer<Ptr<Arg1,Arg2,Arg3>,T>
-{
-  typedef Ptr<T,Arg2,Arg3> type;
-};
-
-template<template<typename, typename, typename, typename> class Ptr, typename Arg1, typename Arg2, typename Arg3, typename Arg4, typename T>
-  struct rebind_pointer<Ptr<Arg1,Arg2,Arg3,Arg4>,T>
-{
-  typedef Ptr<T,Arg2,Arg3,Arg4> type;
-};
-
-// XXX this should probably be renamed native_type or similar
-__THRUST_DEFINE_HAS_NESTED_TYPE(has_raw_pointer, raw_pointer)
-
-namespace pointer_traits_detail
-{
-
-template<typename Ptr, typename Enable = void> struct pointer_raw_pointer_impl {};
-
-template<typename T>
-  struct pointer_raw_pointer_impl<T*>
-{
-  typedef T* type;
-};
-
-template<typename Ptr>
-  struct pointer_raw_pointer_impl<Ptr, typename enable_if<has_raw_pointer<Ptr>::value>::type>
-{
-  typedef typename Ptr::raw_pointer type;
-};
-
-} // end pointer_traits_detail
-
-template<typename T>
-  struct pointer_raw_pointer
-    : pointer_traits_detail::pointer_raw_pointer_impl<T>
-{};
-
-namespace pointer_traits_detail
-{
-
-template<typename Void>
-  struct capture_address
-{
-  template<typename T>
-  __host__ __device__
-  capture_address(T &r)
-    : m_addr(&r)
-  {}
-
-  inline __host__ __device__
-  Void *operator&() const
-  {
-    return m_addr;
-  }
-
-  Void *m_addr;
-};
-
-// metafunction to compute the type of pointer_to's parameter below
-template<typename T>
-  struct pointer_to_param
-    : thrust::detail::eval_if<
-        thrust::detail::is_void<T>::value,
-        thrust::detail::identity_<capture_address<T> >,
-        thrust::detail::add_reference<T>
-      >
-{};
-
-}
-
-template<typename Ptr>
-  struct pointer_traits
-{
-  typedef Ptr                                    pointer;
-  typedef typename Ptr::reference                reference;
-  typedef typename pointer_element<Ptr>::type    element_type;
-  typedef typename pointer_difference<Ptr>::type difference_type;
-
-  template<typename U>
-    struct rebind 
-  {
-    typedef typename rebind_pointer<Ptr,U>::type other;
-  };
-
-  __host__ __device__
-  inline static pointer pointer_to(typename pointer_traits_detail::pointer_to_param<element_type>::type r)
-  {
-    // XXX this is supposed to be pointer::pointer_to(&r); (i.e., call a static member function of pointer called pointer_to)
-    //     assume that pointer has a constructor from raw pointer instead
-    
-    return pointer(&r);
-  }
-
-  // thrust additions follow
-  typedef typename pointer_raw_pointer<Ptr>::type raw_pointer;
-
-  __host__ __device__
-  inline static raw_pointer get(pointer ptr)
-  {
-    return ptr.get();
-  }
-};
-
-template<typename T>
-  struct pointer_traits<T*>
-{
-  typedef T*                                    pointer;
-  typedef T&                                    reference;
-  typedef T                                     element_type;
-  typedef typename pointer_difference<T*>::type difference_type;
-
-  template<typename U>
-    struct rebind
-  {
-    typedef U* other;
-  };
-
-  __host__ __device__
-  inline static pointer pointer_to(typename pointer_traits_detail::pointer_to_param<element_type>::type r)
-  {
-    return &r;
-  }
-
-  // thrust additions follow
-  typedef typename pointer_raw_pointer<T*>::type raw_pointer;
-
-  __host__ __device__
-  inline static raw_pointer get(pointer ptr)
-  {
-    return ptr;
-  }
-};
-
-template<>
-  struct pointer_traits<void*>
-{
-  typedef void*                                    pointer;
-  typedef void                                     reference;
-  typedef void                                     element_type;
-  typedef pointer_difference<void*>::type          difference_type;
-
-  template<typename U>
-    struct rebind
-  {
-    typedef U* other;
-  };
-
-  __host__ __device__
-  inline static pointer pointer_to(pointer_traits_detail::pointer_to_param<element_type>::type r)
-  {
-    return &r;
-  }
-
-  // thrust additions follow
-  typedef pointer_raw_pointer<void*>::type raw_pointer;
-
-  __host__ __device__
-  inline static raw_pointer get(pointer ptr)
-  {
-    return ptr;
-  }
-};
-
-template<>
-  struct pointer_traits<const void*>
-{
-  typedef const void*                           pointer;
-  typedef const void                            reference;
-  typedef const void                            element_type;
-  typedef pointer_difference<const void*>::type difference_type;
-
-  template<typename U>
-    struct rebind
-  {
-    typedef U* other;
-  };
-
-  __host__ __device__
-  inline static pointer pointer_to(pointer_traits_detail::pointer_to_param<element_type>::type r)
-  {
-    return &r;
-  }
-
-  // thrust additions follow
-  typedef pointer_raw_pointer<const void*>::type raw_pointer;
-
-  __host__ __device__
-  inline static raw_pointer get(pointer ptr)
-  {
-    return ptr;
-  }
-};
-
-template<typename FromPtr, typename ToPtr>
-  struct is_pointer_system_convertible
-    : thrust::detail::is_convertible<
-        typename iterator_system<FromPtr>::type,
-        typename iterator_system<ToPtr>::type
-      >
-{};
-
-template<typename FromPtr, typename ToPtr>
-  struct is_pointer_convertible
-    : thrust::detail::and_<
-        thrust::detail::is_convertible<
-          typename pointer_element<FromPtr>::type *,
-          typename pointer_element<ToPtr>::type *
-        >,
-        is_pointer_system_convertible<FromPtr, ToPtr>
-      >
-{};
-
-template<typename FromPtr, typename ToPtr>
-  struct is_void_pointer_system_convertible
-    : thrust::detail::and_<
-        thrust::detail::is_same<
-          typename pointer_element<FromPtr>::type,
-          void
-        >,
-        is_pointer_system_convertible<FromPtr, ToPtr>
-      >
-{};
-
-// this could be a lot better, but for our purposes, it's probably
-// sufficient just to check if pointer_raw_pointer<T> has meaning
-template<typename T>
-  struct is_thrust_pointer
-    : is_metafunction_defined<pointer_raw_pointer<T> >
-{};
-
-// avoid inspecting traits of the arguments if they aren't known to be pointers
-template<typename FromPtr, typename ToPtr>
-  struct lazy_is_pointer_convertible
-    : thrust::detail::eval_if<
-        is_thrust_pointer<FromPtr>::value && is_thrust_pointer<ToPtr>::value,
-        is_pointer_convertible<FromPtr,ToPtr>,
-        thrust::detail::identity_<thrust::detail::false_type>
-      >
-{};
-
-template<typename FromPtr, typename ToPtr>
-  struct lazy_is_void_pointer_system_convertible
-    : thrust::detail::eval_if<
-        is_thrust_pointer<FromPtr>::value && is_thrust_pointer<ToPtr>::value,
-        is_void_pointer_system_convertible<FromPtr,ToPtr>,
-        thrust::detail::identity_<thrust::detail::false_type>
-      >
-{};
-
-template<typename FromPtr, typename ToPtr, typename T = void>
-  struct enable_if_pointer_is_convertible
-    : thrust::detail::enable_if<
-        lazy_is_pointer_convertible<FromPtr,ToPtr>::type::value,
-        T
-      >
-{};
-
-template<typename FromPtr, typename ToPtr, typename T = void>
-  struct enable_if_void_pointer_is_system_convertible
-    : thrust::detail::enable_if<
-        lazy_is_void_pointer_system_convertible<FromPtr,ToPtr>::type::value,
-        T
-      >
-{};
-
-
-} // end detail
-} // end thrust
-

spaces/CVPR/LIVE/thrust/thrust/find.h

@@ -1,385 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file find.h
- *  \brief Locating values in (unsorted) ranges
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-
-namespace thrust
-{
-
-
-/*! \addtogroup algorithms
- */
-
-/*! \addtogroup searching
- *  \ingroup algorithms
- *  \{
- */
-
-
-/*! \p find returns the first iterator \c i in the range 
- *  <tt>[first, last)</tt> such that <tt>*i == value</tt>
- *  or \c last if no such iterator exists.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first Beginning of the sequence to search.
- *  \param last End of the sequence to search.
- *  \param value The value to find.
- *  \return The first iterator \c i such that <tt>*i == value</tt> or \c last.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>
- *          and \p InputIterator's \c value_type is equality comparable to type \c T.
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">EqualityComparable</a>. 
- *
- *  \code
- *  #include <thrust/find.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> input(4);
- *
- *  input[0] = 0;
- *  input[1] = 5;
- *  input[2] = 3;
- *  input[3] = 7;
- *
- *  thrust::device_vector<int>::iterator iter;
- *
- *  iter = thrust::find(thrust::device, input.begin(), input.end(), 3); // returns input.first() + 2
- *  iter = thrust::find(thrust::device, input.begin(), input.end(), 5); // returns input.first() + 1
- *  iter = thrust::find(thrust::device, input.begin(), input.end(), 9); // returns input.end()
- *  \endcode
- *
- *  \see find_if
- *  \see mismatch
- */
-template<typename DerivedPolicy, typename InputIterator, typename T>
-__host__ __device__
-InputIterator find(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                   InputIterator first,
-                   InputIterator last,
-                   const T& value);
-
-
-/*! \p find returns the first iterator \c i in the range 
- *  <tt>[first, last)</tt> such that <tt>*i == value</tt>
- *  or \c last if no such iterator exists.
- *
- *  \param first Beginning of the sequence to search.
- *  \param last End of the sequence to search.
- *  \param value The value to find.
- *  \return The first iterator \c i such that <tt>*i == value</tt> or \c last.
- *
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>
- *          and \p InputIterator's \c value_type is equality comparable to type \c T.
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">EqualityComparable</a>. 
- *
- *  \code
- *  #include <thrust/find.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<int> input(4);
- *
- *  input[0] = 0;
- *  input[1] = 5;
- *  input[2] = 3;
- *  input[3] = 7;
- *
- *  thrust::device_vector<int>::iterator iter;
- *
- *  iter = thrust::find(input.begin(), input.end(), 3); // returns input.first() + 2
- *  iter = thrust::find(input.begin(), input.end(), 5); // returns input.first() + 1
- *  iter = thrust::find(input.begin(), input.end(), 9); // returns input.end()
- *  \endcode
- *
- *  \see find_if
- *  \see mismatch
- */
-template <typename InputIterator, typename T>
-InputIterator find(InputIterator first,
-                   InputIterator last,
-                   const T& value);
-
-
-/*! \p find_if returns the first iterator \c i in the range 
- *  <tt>[first, last)</tt> such that <tt>pred(*i)</tt> is \c true
- *  or \c last if no such iterator exists.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first Beginning of the sequence to search.
- *  \param last End of the sequence to search.
- *  \param pred A predicate used to test range elements.
- *  \return The first iterator \c i such that <tt>pred(*i)</tt> is \c true, or \c last.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *  \tparam Predicate is a model of <a href="http://www.sgi.com/tech/stl/Predicate.html">Predicate</a>.
- *
- *  \code
- *  #include <thrust/find.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *
- *  struct greater_than_four
- *  {
- *    __host__ __device__
- *    bool operator()(int x)
- *    {
- *      return x > 4;
- *    }
- *  };
- *
- *  struct greater_than_ten
- *  {
- *    __host__ __device__
- *    bool operator()(int x)
- *    {
- *      return x > 10;
- *    }
- *  };
- *
- *  ...
- *  thrust::device_vector<int> input(4);
- *
- *  input[0] = 0;
- *  input[1] = 5;
- *  input[2] = 3;
- *  input[3] = 7;
- *
- *  thrust::device_vector<int>::iterator iter;
- *
- *  iter = thrust::find_if(thrust::device, input.begin(), input.end(), greater_than_four()); // returns input.first() + 1
- *
- *  iter = thrust::find_if(thrust::device, input.begin(), input.end(), greater_than_ten());  // returns input.end()
- *  \endcode
- *
- *  \see find
- *  \see find_if_not
- *  \see mismatch
- */
-template<typename DerivedPolicy, typename InputIterator, typename Predicate>
-__host__ __device__
-InputIterator find_if(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                      InputIterator first,
-                      InputIterator last,
-                      Predicate pred);
-
-
-/*! \p find_if returns the first iterator \c i in the range 
- *  <tt>[first, last)</tt> such that <tt>pred(*i)</tt> is \c true
- *  or \c last if no such iterator exists.
- *
- *  \param first Beginning of the sequence to search.
- *  \param last End of the sequence to search.
- *  \param pred A predicate used to test range elements.
- *  \return The first iterator \c i such that <tt>pred(*i)</tt> is \c true, or \c last.
- *
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *  \tparam Predicate is a model of <a href="http://www.sgi.com/tech/stl/Predicate.html">Predicate</a>.
- *
- *  \code
- *  #include <thrust/find.h>
- *  #include <thrust/device_vector.h>
- *
- *  struct greater_than_four
- *  {
- *    __host__ __device__
- *    bool operator()(int x)
- *    {
- *      return x > 4;
- *    }
- *  };
- *
- *  struct greater_than_ten
- *  {
- *    __host__ __device__
- *    bool operator()(int x)
- *    {
- *      return x > 10;
- *    }
- *  };
- *
- *  ...
- *  thrust::device_vector<int> input(4);
- *
- *  input[0] = 0;
- *  input[1] = 5;
- *  input[2] = 3;
- *  input[3] = 7;
- *
- *  thrust::device_vector<int>::iterator iter;
- *
- *  iter = thrust::find_if(input.begin(), input.end(), greater_than_four()); // returns input.first() + 1
- *
- *  iter = thrust::find_if(input.begin(), input.end(), greater_than_ten());  // returns input.end()
- *  \endcode
- *
- *  \see find
- *  \see find_if_not
- *  \see mismatch
- */
-template <typename InputIterator, typename Predicate>
-InputIterator find_if(InputIterator first,
-                      InputIterator last,
-                      Predicate pred);
-
-
-/*! \p find_if_not returns the first iterator \c i in the range 
- *  <tt>[first, last)</tt> such that <tt>pred(*i)</tt> is \c false
- *  or \c last if no such iterator exists.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first Beginning of the sequence to search.
- *  \param last End of the sequence to search.
- *  \param pred A predicate used to test range elements.
- *  \return The first iterator \c i such that <tt>pred(*i)</tt> is \c false, or \c last.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *  \tparam Predicate is a model of <a href="http://www.sgi.com/tech/stl/Predicate.html">Predicate</a>.
- *
- *  \code
- *  #include <thrust/find.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *
- *  struct greater_than_four
- *  {
- *    __host__ __device__
- *    bool operator()(int x)
- *    {
- *      return x > 4;
- *    }
- *  };
- *
- *  struct greater_than_ten
- *  {
- *    __host__ __device__
- *    bool operator()(int x)
- *    {
- *      return x > 10;
- *    }
- *  };
- *
- *  ...
- *  thrust::device_vector<int> input(4);
- *
- *  input[0] = 0;
- *  input[1] = 5;
- *  input[2] = 3;
- *  input[3] = 7;
- *
- *  thrust::device_vector<int>::iterator iter;
- *
- *  iter = thrust::find_if_not(thrust::device, input.begin(), input.end(), greater_than_four()); // returns input.first()
- *
- *  iter = thrust::find_if_not(thrust::device, input.begin(), input.end(), greater_than_ten());  // returns input.first()
- *  \endcode
- *
- *  \see find
- *  \see find_if
- *  \see mismatch
- */
-template<typename DerivedPolicy, typename InputIterator, typename Predicate>
-__host__ __device__
-InputIterator find_if_not(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                          InputIterator first,
-                          InputIterator last,
-                          Predicate pred);
-
-
-/*! \p find_if_not returns the first iterator \c i in the range 
- *  <tt>[first, last)</tt> such that <tt>pred(*i)</tt> is \c false
- *  or \c last if no such iterator exists.
- *
- *  \param first Beginning of the sequence to search.
- *  \param last End of the sequence to search.
- *  \param pred A predicate used to test range elements.
- *  \return The first iterator \c i such that <tt>pred(*i)</tt> is \c false, or \c last.
- *
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *  \tparam Predicate is a model of <a href="http://www.sgi.com/tech/stl/Predicate.html">Predicate</a>.
- *
- *  \code
- *  #include <thrust/find.h>
- *  #include <thrust/device_vector.h>
- *
- *  struct greater_than_four
- *  {
- *    __host__ __device__
- *    bool operator()(int x)
- *    {
- *      return x > 4;
- *    }
- *  };
- *
- *  struct greater_than_ten
- *  {
- *    __host__ __device__
- *    bool operator()(int x)
- *    {
- *      return x > 10;
- *    }
- *  };
- *
- *  ...
- *  thrust::device_vector<int> input(4);
- *
- *  input[0] = 0;
- *  input[1] = 5;
- *  input[2] = 3;
- *  input[3] = 7;
- *
- *  thrust::device_vector<int>::iterator iter;
- *
- *  iter = thrust::find_if_not(input.begin(), input.end(), greater_than_four()); // returns input.first()
- *
- *  iter = thrust::find_if_not(input.begin(), input.end(), greater_than_ten());  // returns input.first()
- *  \endcode
- *
- *  \see find
- *  \see find_if
- *  \see mismatch
- */
-template <typename InputIterator, typename Predicate>
-InputIterator find_if_not(InputIterator first,
-                          InputIterator last,
-                          Predicate pred);
-
-/*! \} // end searching
- */
-
-
-} // end namespace thrust
-
-#include <thrust/detail/find.inl>
-

spaces/CVPR/Text2Human/Text2Human/models/parsing_gen_model.py

@@ -1,220 +0,0 @@
-import logging
-import math
-from collections import OrderedDict
-
-import mmcv
-import numpy as np
-import torch
-from torchvision.utils import save_image
-
-from models.archs.fcn_arch import FCNHead
-from models.archs.shape_attr_embedding_arch import ShapeAttrEmbedding
-from models.archs.unet_arch import ShapeUNet
-from models.losses.accuracy import accuracy
-from models.losses.cross_entropy_loss import CrossEntropyLoss
-
-logger = logging.getLogger('base')
-
-
-class ParsingGenModel():
-    """Paring Generation model.
-    """
-
-    def __init__(self, opt):
-        self.opt = opt
-        self.device = torch.device('cuda')
-        self.is_train = opt['is_train']
-
-        self.attr_embedder = ShapeAttrEmbedding(
-            dim=opt['embedder_dim'],
-            out_dim=opt['embedder_out_dim'],
-            cls_num_list=opt['attr_class_num']).to(self.device)
-        self.parsing_encoder = ShapeUNet(
-            in_channels=opt['encoder_in_channels']).to(self.device)
-        self.parsing_decoder = FCNHead(
-            in_channels=opt['fc_in_channels'],
-            in_index=opt['fc_in_index'],
-            channels=opt['fc_channels'],
-            num_convs=opt['fc_num_convs'],
-            concat_input=opt['fc_concat_input'],
-            dropout_ratio=opt['fc_dropout_ratio'],
-            num_classes=opt['fc_num_classes'],
-            align_corners=opt['fc_align_corners'],
-        ).to(self.device)
-
-        self.init_training_settings()
-
-        self.palette = [[0, 0, 0], [255, 250, 250], [220, 220, 220],
-                        [250, 235, 215], [255, 250, 205], [211, 211, 211],
-                        [70, 130, 180], [127, 255, 212], [0, 100, 0],
-                        [50, 205, 50], [255, 255, 0], [245, 222, 179],
-                        [255, 140, 0], [255, 0, 0], [16, 78, 139],
-                        [144, 238, 144], [50, 205, 174], [50, 155, 250],
-                        [160, 140, 88], [213, 140, 88], [90, 140, 90],
-                        [185, 210, 205], [130, 165, 180], [225, 141, 151]]
-
-    def init_training_settings(self):
-        optim_params = []
-        for v in self.attr_embedder.parameters():
-            if v.requires_grad:
-                optim_params.append(v)
-        for v in self.parsing_encoder.parameters():
-            if v.requires_grad:
-                optim_params.append(v)
-        for v in self.parsing_decoder.parameters():
-            if v.requires_grad:
-                optim_params.append(v)
-        # set up optimizers
-        self.optimizer = torch.optim.Adam(
-            optim_params,
-            self.opt['lr'],
-            weight_decay=self.opt['weight_decay'])
-        self.log_dict = OrderedDict()
-        self.entropy_loss = CrossEntropyLoss().to(self.device)
-
-    def feed_data(self, data):
-        self.pose = data['densepose'].to(self.device)
-        self.attr = data['attr'].to(self.device)
-        self.segm = data['segm'].to(self.device)
-
-    def optimize_parameters(self):
-        self.attr_embedder.train()
-        self.parsing_encoder.train()
-        self.parsing_decoder.train()
-
-        self.attr_embedding = self.attr_embedder(self.attr)
-        self.pose_enc = self.parsing_encoder(self.pose, self.attr_embedding)
-        self.seg_logits = self.parsing_decoder(self.pose_enc)
-
-        loss = self.entropy_loss(self.seg_logits, self.segm)
-
-        self.optimizer.zero_grad()
-        loss.backward()
-        self.optimizer.step()
-
-        self.log_dict['loss_total'] = loss
-
-    def get_vis(self, save_path):
-        img_cat = torch.cat([
-            self.pose,
-            self.segm,
-        ], dim=3).detach()
-        img_cat = ((img_cat + 1) / 2)
-
-        img_cat = img_cat.clamp_(0, 1)
-
-        save_image(img_cat, save_path, nrow=1, padding=4)
-
-    def inference(self, data_loader, save_dir):
-        self.attr_embedder.eval()
-        self.parsing_encoder.eval()
-        self.parsing_decoder.eval()
-
-        acc = 0
-        num = 0
-
-        for _, data in enumerate(data_loader):
-            pose = data['densepose'].to(self.device)
-            attr = data['attr'].to(self.device)
-            segm = data['segm'].to(self.device)
-            img_name = data['img_name']
-
-            num += pose.size(0)
-            with torch.no_grad():
-                attr_embedding = self.attr_embedder(attr)
-                pose_enc = self.parsing_encoder(pose, attr_embedding)
-                seg_logits = self.parsing_decoder(pose_enc)
-            seg_pred = seg_logits.argmax(dim=1)
-            acc += accuracy(seg_logits, segm)
-            palette_label = self.palette_result(segm.cpu().numpy())
-            palette_pred = self.palette_result(seg_pred.cpu().numpy())
-            pose_numpy = ((pose[0] + 1) / 2. * 255.).expand(
-                3,
-                pose[0].size(1),
-                pose[0].size(2),
-            ).cpu().numpy().clip(0, 255).astype(np.uint8).transpose(1, 2, 0)
-            concat_result = np.concatenate(
-                (pose_numpy, palette_pred, palette_label), axis=1)
-            mmcv.imwrite(concat_result, f'{save_dir}/{img_name[0]}')
-
-        self.attr_embedder.train()
-        self.parsing_encoder.train()
-        self.parsing_decoder.train()
-        return (acc / num).item()
-
-    def get_current_log(self):
-        return self.log_dict
-
-    def update_learning_rate(self, epoch):
-        """Update learning rate.
-
-        Args:
-            current_iter (int): Current iteration.
-            warmup_iter (int): Warmup iter numbers. -1 for no warmup.
-                Default: -1.
-        """
-        lr = self.optimizer.param_groups[0]['lr']
-
-        if self.opt['lr_decay'] == 'step':
-            lr = self.opt['lr'] * (
-                self.opt['gamma']**(epoch // self.opt['step']))
-        elif self.opt['lr_decay'] == 'cos':
-            lr = self.opt['lr'] * (
-                1 + math.cos(math.pi * epoch / self.opt['num_epochs'])) / 2
-        elif self.opt['lr_decay'] == 'linear':
-            lr = self.opt['lr'] * (1 - epoch / self.opt['num_epochs'])
-        elif self.opt['lr_decay'] == 'linear2exp':
-            if epoch < self.opt['turning_point'] + 1:
-                # learning rate decay as 95%
-                # at the turning point (1 / 95% = 1.0526)
-                lr = self.opt['lr'] * (
-                    1 - epoch / int(self.opt['turning_point'] * 1.0526))
-            else:
-                lr *= self.opt['gamma']
-        elif self.opt['lr_decay'] == 'schedule':
-            if epoch in self.opt['schedule']:
-                lr *= self.opt['gamma']
-        else:
-            raise ValueError('Unknown lr mode {}'.format(self.opt['lr_decay']))
-        # set learning rate
-        for param_group in self.optimizer.param_groups:
-            param_group['lr'] = lr
-
-        return lr
-
-    def save_network(self, save_path):
-        """Save networks.
-        """
-
-        save_dict = {}
-        save_dict['embedder'] = self.attr_embedder.state_dict()
-        save_dict['encoder'] = self.parsing_encoder.state_dict()
-        save_dict['decoder'] = self.parsing_decoder.state_dict()
-
-        torch.save(save_dict, save_path)
-
-    def load_network(self):
-        checkpoint = torch.load(self.opt['pretrained_parsing_gen'])
-
-        self.attr_embedder.load_state_dict(checkpoint['embedder'], strict=True)
-        self.attr_embedder.eval()
-
-        self.parsing_encoder.load_state_dict(
-            checkpoint['encoder'], strict=True)
-        self.parsing_encoder.eval()
-
-        self.parsing_decoder.load_state_dict(
-            checkpoint['decoder'], strict=True)
-        self.parsing_decoder.eval()
-
-    def palette_result(self, result):
-        seg = result[0]
-        palette = np.array(self.palette)
-        assert palette.shape[1] == 3
-        assert len(palette.shape) == 2
-        color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
-        for label, color in enumerate(palette):
-            color_seg[seg == label, :] = color
-        # convert to BGR
-        color_seg = color_seg[..., ::-1]
-        return color_seg

spaces/CVPR/lama-example/saicinpainting/training/losses/distance_weighting.py

@@ -1,126 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-
-from saicinpainting.training.losses.perceptual import IMAGENET_STD, IMAGENET_MEAN
-
-
-def dummy_distance_weighter(real_img, pred_img, mask):
-    return mask
-
-
-def get_gauss_kernel(kernel_size, width_factor=1):
-    coords = torch.stack(torch.meshgrid(torch.arange(kernel_size),
-                                        torch.arange(kernel_size)),
-                         dim=0).float()
-    diff = torch.exp(-((coords - kernel_size // 2) ** 2).sum(0) / kernel_size / width_factor)
-    diff /= diff.sum()
-    return diff
-
-
-class BlurMask(nn.Module):
-    def __init__(self, kernel_size=5, width_factor=1):
-        super().__init__()
-        self.filter = nn.Conv2d(1, 1, kernel_size, padding=kernel_size // 2, padding_mode='replicate', bias=False)
-        self.filter.weight.data.copy_(get_gauss_kernel(kernel_size, width_factor=width_factor))
-
-    def forward(self, real_img, pred_img, mask):
-        with torch.no_grad():
-            result = self.filter(mask) * mask
-            return result
-
-
-class EmulatedEDTMask(nn.Module):
-    def __init__(self, dilate_kernel_size=5, blur_kernel_size=5, width_factor=1):
-        super().__init__()
-        self.dilate_filter = nn.Conv2d(1, 1, dilate_kernel_size, padding=dilate_kernel_size// 2, padding_mode='replicate',
-                                       bias=False)
-        self.dilate_filter.weight.data.copy_(torch.ones(1, 1, dilate_kernel_size, dilate_kernel_size, dtype=torch.float))
-        self.blur_filter = nn.Conv2d(1, 1, blur_kernel_size, padding=blur_kernel_size // 2, padding_mode='replicate', bias=False)
-        self.blur_filter.weight.data.copy_(get_gauss_kernel(blur_kernel_size, width_factor=width_factor))
-
-    def forward(self, real_img, pred_img, mask):
-        with torch.no_grad():
-            known_mask = 1 - mask
-            dilated_known_mask = (self.dilate_filter(known_mask) > 1).float()
-            result = self.blur_filter(1 - dilated_known_mask) * mask
-            return result
-
-
-class PropagatePerceptualSim(nn.Module):
-    def __init__(self, level=2, max_iters=10, temperature=500, erode_mask_size=3):
-        super().__init__()
-        vgg = torchvision.models.vgg19(pretrained=True).features
-        vgg_avg_pooling = []
-
-        for weights in vgg.parameters():
-            weights.requires_grad = False
-
-        cur_level_i = 0
-        for module in vgg.modules():
-            if module.__class__.__name__ == 'Sequential':
-                continue
-            elif module.__class__.__name__ == 'MaxPool2d':
-                vgg_avg_pooling.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0))
-            else:
-                vgg_avg_pooling.append(module)
-                if module.__class__.__name__ == 'ReLU':
-                    cur_level_i += 1
-                if cur_level_i == level:
-                    break
-
-        self.features = nn.Sequential(*vgg_avg_pooling)
-
-        self.max_iters = max_iters
-        self.temperature = temperature
-        self.do_erode = erode_mask_size > 0
-        if self.do_erode:
-            self.erode_mask = nn.Conv2d(1, 1, erode_mask_size, padding=erode_mask_size // 2, bias=False)
-            self.erode_mask.weight.data.fill_(1)
-
-    def forward(self, real_img, pred_img, mask):
-        with torch.no_grad():
-            real_img = (real_img - IMAGENET_MEAN.to(real_img)) / IMAGENET_STD.to(real_img)
-            real_feats = self.features(real_img)
-
-            vertical_sim = torch.exp(-(real_feats[:, :, 1:] - real_feats[:, :, :-1]).pow(2).sum(1, keepdim=True)
-                                     / self.temperature)
-            horizontal_sim = torch.exp(-(real_feats[:, :, :, 1:] - real_feats[:, :, :, :-1]).pow(2).sum(1, keepdim=True)
-                                       / self.temperature)
-
-            mask_scaled = F.interpolate(mask, size=real_feats.shape[-2:], mode='bilinear', align_corners=False)
-            if self.do_erode:
-                mask_scaled = (self.erode_mask(mask_scaled) > 1).float()
-
-            cur_knowness = 1 - mask_scaled
-
-            for iter_i in range(self.max_iters):
-                new_top_knowness = F.pad(cur_knowness[:, :, :-1] * vertical_sim, (0, 0, 1, 0), mode='replicate')
-                new_bottom_knowness = F.pad(cur_knowness[:, :, 1:] * vertical_sim, (0, 0, 0, 1), mode='replicate')
-
-                new_left_knowness = F.pad(cur_knowness[:, :, :, :-1] * horizontal_sim, (1, 0, 0, 0), mode='replicate')
-                new_right_knowness = F.pad(cur_knowness[:, :, :, 1:] * horizontal_sim, (0, 1, 0, 0), mode='replicate')
-
-                new_knowness = torch.stack([new_top_knowness, new_bottom_knowness,
-                                            new_left_knowness, new_right_knowness],
-                                           dim=0).max(0).values
-
-                cur_knowness = torch.max(cur_knowness, new_knowness)
-
-            cur_knowness = F.interpolate(cur_knowness, size=mask.shape[-2:], mode='bilinear')
-            result = torch.min(mask, 1 - cur_knowness)
-
-            return result
-
-
-def make_mask_distance_weighter(kind='none', **kwargs):
-    if kind == 'none':
-        return dummy_distance_weighter
-    if kind == 'blur':
-        return BlurMask(**kwargs)
-    if kind == 'edt':
-        return EmulatedEDTMask(**kwargs)
-    if kind == 'pps':
-        return PropagatePerceptualSim(**kwargs)
-    raise ValueError(f'Unknown mask distance weighter kind {kind}')

spaces/CarlosMF/AI-ORUS-License-v1.0.0/README.md

@@ -1,13 +0,0 @@
----
-title: AI ORUS License V1.0.0
-emoji: 🤝
-colorFrom: purple
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: cc
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/DCandE/rvc-models/README.md

@@ -1,14 +0,0 @@
----
-title: Rvc Models
-emoji: 🎤
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ardha27/rvc-models
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Column-2853eb31.css

@@ -1 +0,0 @@
-div.svelte-vt1mxs{display:flex;position:relative;flex-direction:column}div.svelte-vt1mxs>*,div.svelte-vt1mxs>.form>*{width:var(--size-full)}.gap.svelte-vt1mxs{gap:var(--layout-gap)}.hide.svelte-vt1mxs{display:none}.compact.svelte-vt1mxs>*,.compact.svelte-vt1mxs .box{border-radius:0}.compact.svelte-vt1mxs,.panel.svelte-vt1mxs{border:solid var(--panel-border-width) var(--panel-border-color);border-radius:var(--container-radius);background:var(--panel-background-fill);padding:var(--spacing-lg)}

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/themes/glass.py

@@ -1,99 +0,0 @@
-from __future__ import annotations
-
-from typing import Iterable
-
-from gradio.themes.base import Base
-from gradio.themes.utils import colors, fonts, sizes
-
-
-class Glass(Base):
-    def __init__(
-        self,
-        *,
-        primary_hue: colors.Color | str = colors.stone,
-        secondary_hue: colors.Color | str = colors.stone,
-        neutral_hue: colors.Color | str = colors.stone,
-        spacing_size: sizes.Size | str = sizes.spacing_sm,
-        radius_size: sizes.Size | str = sizes.radius_sm,
-        text_size: sizes.Size | str = sizes.text_sm,
-        font: fonts.Font
-        | str
-        | Iterable[fonts.Font | str] = (
-            "Optima",
-            "Candara",
-            "Noto Sans",
-            "source-sans-pro",
-            "sans-serif",
-        ),
-        font_mono: fonts.Font
-        | str
-        | Iterable[fonts.Font | str] = (
-            fonts.GoogleFont("IBM Plex Mono"),
-            "ui-monospace",
-            "Consolas",
-            "monospace",
-        ),
-    ):
-        super().__init__(
-            primary_hue=primary_hue,
-            secondary_hue=secondary_hue,
-            neutral_hue=neutral_hue,
-            spacing_size=spacing_size,
-            radius_size=radius_size,
-            text_size=text_size,
-            font=font,
-            font_mono=font_mono,
-        )
-        self.name = "glass"
-        super().set(
-            body_background_fill_dark="*primary_800",
-            background_fill_secondary_dark="*primary_800",
-            block_background_fill_dark="*primary_800",
-            button_primary_background_fill="linear-gradient(180deg, *primary_50 0%, *primary_200 50%, *primary_300 50%, *primary_200 100%)",
-            button_primary_background_fill_hover="linear-gradient(180deg, *primary_100 0%, *primary_200 50%, *primary_300 50%, *primary_200 100%)",
-            button_primary_background_fill_dark="linear-gradient(180deg, *primary_400 0%, *primary_500 50%, *primary_600 50%, *primary_500 100%)",
-            button_primary_background_fill_hover_dark="linear-gradient(180deg, *primary_400 0%, *primary_500 50%, *primary_600 50%, *primary_500 100%)",
-            button_secondary_background_fill="*button_primary_background_fill",
-            button_secondary_background_fill_hover="*button_primary_background_fill_hover",
-            button_secondary_background_fill_dark="*button_primary_background_fill",
-            button_secondary_background_fill_hover_dark="*button_primary_background_fill_hover",
-            button_cancel_background_fill="*button_primary_background_fill",
-            button_cancel_background_fill_hover="*button_primary_background_fill_hover",
-            button_cancel_background_fill_dark="*button_primary_background_fill",
-            button_cancel_background_fill_hover_dark="*button_primary_background_fill_hover",
-            button_cancel_border_color="*button_secondary_border_color",
-            button_cancel_border_color_dark="*button_secondary_border_color",
-            button_cancel_text_color="*button_secondary_text_color",
-            checkbox_border_width="0px",
-            checkbox_label_background_fill="*button_secondary_background_fill",
-            checkbox_label_background_fill_dark="*button_secondary_background_fill",
-            checkbox_label_background_fill_hover="*button_secondary_background_fill_hover",
-            checkbox_label_background_fill_hover_dark="*button_secondary_background_fill_hover",
-            checkbox_label_border_width="1px",
-            checkbox_background_color_dark="*primary_600",
-            button_border_width="1px",
-            button_shadow_active="*shadow_inset",
-            input_background_fill="linear-gradient(0deg, *secondary_50 0%, white 100%)",
-            input_background_fill_dark="*secondary_600",
-            input_border_color_focus_dark="*primary_400",
-            input_border_width="1px",
-            slider_color="*primary_400",
-            block_label_text_color="*primary_500",
-            block_title_text_color="*primary_500",
-            block_label_text_weight="600",
-            block_title_text_weight="600",
-            block_label_text_size="*text_md",
-            block_title_text_size="*text_md",
-            block_label_background_fill="*primary_200",
-            block_label_background_fill_dark="*primary_700",
-            block_border_width="0px",
-            block_border_width_dark="1px",
-            panel_border_width="1px",
-            border_color_primary_dark="*primary_500",
-            background_fill_primary_dark="*neutral_700",
-            background_fill_secondary="*primary_100",
-            block_background_fill="*primary_50",
-            block_shadow="*primary_400 0px 0px 3px 0px",
-            table_even_background_fill_dark="*neutral_700",
-            table_odd_background_fill_dark="*neutral_700",
-        )

spaces/DaleChen/AutoGPT/tests/integration/memory_tests.py

@@ -1,49 +0,0 @@
-import random
-import string
-import sys
-import unittest
-from pathlib import Path
-
-from autogpt.config import Config
-from autogpt.memory.local import LocalCache
-
-
-class TestLocalCache(unittest.TestCase):
-    def random_string(self, length):
-        return "".join(random.choice(string.ascii_letters) for _ in range(length))
-
-    def setUp(self):
-        cfg = cfg = Config()
-        self.cache = LocalCache(cfg)
-        self.cache.clear()
-
-        # Add example texts to the cache
-        self.example_texts = [
-            "The quick brown fox jumps over the lazy dog",
-            "I love machine learning and natural language processing",
-            "The cake is a lie, but the pie is always true",
-            "ChatGPT is an advanced AI model for conversation",
-        ]
-
-        for text in self.example_texts:
-            self.cache.add(text)
-
-        # Add some random strings to test noise
-        for _ in range(5):
-            self.cache.add(self.random_string(10))
-
-    def test_get_relevant(self):
-        query = "I'm interested in artificial intelligence and NLP"
-        k = 3
-        relevant_texts = self.cache.get_relevant(query, k)
-
-        print(f"Top {k} relevant texts for the query '{query}':")
-        for i, text in enumerate(relevant_texts, start=1):
-            print(f"{i}. {text}")
-
-        self.assertEqual(len(relevant_texts), k)
-        self.assertIn(self.example_texts[1], relevant_texts)
-
-
-if __name__ == "__main__":
-    unittest.main()

spaces/Detomo/Lighten_dark_image/README.md

@@ -1,11 +0,0 @@
----
-title: Lighten Dark Image
-emoji: 💡
-colorFrom: blue
-colorTo: white
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

spaces/Deviliaan/sd_twist/app.py

@@ -1,162 +0,0 @@
-"""
-Stable Diffusion Webui Version 1.4.0
-https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.5.0
-
-"""
-commit_id=r"c5934fb6e3007795efcf9b71be6df1581f61e4cb" #Version 1.5.1
-import os
-from sys import executable 
-import subprocess
-import pathlib
-import gc
-
-def Gitclone(URI:str,ClonePath:pathlib.Path ) -> int :
-  if pathlib.Path.exists(ClonePath):
-    return 0
-  while True:
-    i=subprocess.run([r"git",r"clone",str(URI),str(ClonePath)])
-    if(i.returncode == 0 ): 
-     del i
-     gc.collect()
-     return 0
-    else :
-     del i
-
-def DownLoad(URI:str,DownloadPath:pathlib.Path,DownLoadFileName:str ) -> int:
-  if (DownloadPath / DownLoadFileName).is_file(): return 0
-  while (True):
-    i=subprocess.run([r"aria2c",r"-c",r"-x" ,r"16", r"-s",r"16", r"-k" ,r"1M" ,r"-m",r"0",r"--enable-mmap=false",r"--console-log-level=error",r"-d",str(DownloadPath),r"-o",DownLoadFileName,URI]);
-    if(i.returncode == 0 ): 
-      del i
-      gc.collect()
-      return 0
-    else :
-      del i
-
-user_home =pathlib.Path.home().resolve()
-os.chdir(str(user_home))
-#clone stable-diffusion-webui repo
-print("cloning stable-diffusion-webui repo")
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui.git",user_home / r"stable-diffusion-webui")
-os.chdir(str(user_home / r"stable-diffusion-webui"))
-os.system("git reset --hard "+commit_id)
-#install extensions
-print("installing extensions")
-Gitclone(r"https://huggingface.co/embed/negative",user_home / r"stable-diffusion-webui" / r"embeddings"  / r"negative")
-Gitclone(r"https://huggingface.co/embed/lora",user_home / r"stable-diffusion-webui" / r"models" / r"Lora" / r"positive")
-DownLoad(r"https://huggingface.co/embed/upscale/resolve/main/4x-UltraSharp.pth",user_home / r"stable-diffusion-webui" / r"models" / r"ESRGAN" ,r"4x-UltraSharp.pth")
-while (True):
-   i=subprocess.run([r"wget",r"https://raw.githubusercontent.com/camenduru/stable-diffusion-webui-scripts/main/run_n_times.py",r"-O",str(user_home / r"stable-diffusion-webui" / r"scripts" / r"run_n_times.py")])
-   if(i.returncode == 0 ): 
-    del i
-    gc.collect()
-    break
-   else :
-    del i
-Gitclone(r"https://github.com/deforum-art/deforum-for-automatic1111-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"deforum-for-automatic1111-webui" )
-Gitclone(r"https://github.com/AlUlkesh/stable-diffusion-webui-images-browser",user_home / r"stable-diffusion-webui" / r"extensions"/ r"stable-diffusion-webui-images-browser")
-Gitclone(r"https://github.com/camenduru/stable-diffusion-webui-huggingface",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-huggingface")
-Gitclone(r"https://github.com/camenduru/sd-civitai-browser",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-civitai-browser")
-Gitclone(r"https://github.com/kohya-ss/sd-webui-additional-networks",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks")
-Gitclone(r"https://github.com/Mikubill/sd-webui-controlnet",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-controlnet")
-Gitclone(r"https://github.com/fkunn1326/openpose-editor",user_home / r"stable-diffusion-webui" / r"extensions" / r"openpose-editor")
-Gitclone(r"https://github.com/jexom/sd-webui-depth-lib",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-depth-lib")
-Gitclone(r"https://github.com/hnmr293/posex",user_home / r"stable-diffusion-webui" / r"extensions" / r"posex")
-Gitclone(r"https://github.com/nonnonstop/sd-webui-3d-open-pose-editor",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-3d-open-pose-editor")
-#中文本地化的请解除下一行的注释
-#Gitclone(r"https://github.com/dtlnor/stable-diffusion-webui-localization-zh_CN.git",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-localization-zh_CN")
-Gitclone(r"https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git" , user_home / r"stable-diffusion-webui" / r"extensions" / r"a1111-sd-webui-tagcomplete")
-Gitclone(r"https://github.com/camenduru/sd-webui-tunnels",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-tunnels")
-Gitclone(r"https://github.com/etherealxx/batchlinks-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"batchlinks-webui")
-Gitclone(r"https://github.com/catppuccin/stable-diffusion-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-catppuccin")
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui-rembg",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-rembg")
-Gitclone(r"https://github.com/ashen-sensored/stable-diffusion-webui-two-shot",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-two-shot")
-#Gitclone(r"https://github.com/camenduru/sd_webui_stealth_pnginfo",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd_webui_stealth_pnginfo")
-os.chdir(user_home / r"stable-diffusion-webui")
-#download ControlNet models
-print("extensions dolwnload done .\ndownloading ControlNet models")
-dList =[r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_openpose_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_scribble_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_seg_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_ip2p_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_shuffle_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_canny_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1p_sd15_depth_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_inpaint_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_lineart_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_mlsd_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_normalbae_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_openpose_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_scribble_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_seg_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_softedge_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15s2_lineart_anime_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1e_sd15_tile_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_style_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_seg_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_openpose_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_keypose_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd15v2.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd15v2.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd15v2.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_zoedepth_sd15v1.pth"]
-for i in range(0,len(dList)): DownLoad(dList[i],user_home / r"stable-diffusion-webui" / r"extensions" / "sd-webui-controlnet" / r"models",pathlib.Path(dList[i]).name)
-del dList
-#download model    
-#you can change model download address here
-print("ControlNet models download done.\ndownloading model")
-#Stable Diffusion Checkpoint Model
-#anything version4.5
-DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.5-pruned.ckpt",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"anything-v4.5-pruned.ckpt")
-DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.0.vae.pt",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"anything-v4.0.vae.pt")
-#Counterfeit-V3.0
-DownLoad(r"https://huggingface.co/gsdf/Counterfeit-V3.0/resolve/main/Counterfeit-V3.0_fp16.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"Counterfeit-V3.0_fp16.safetensors")
-#AbyssOrangeMix2 sfw
-DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_sfw.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"AbyssOrangeMix2_sfw.safetensors")
-DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"orangemix.vae.pt")
-#MeinaPastelV5
-DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV5%20-%20Baked%20VAE.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"MeinaPastelV5_BakedVAE.safetensors")
-DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV5%20-%20Without%20VAE.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"MeinaPastelV5_WithoutVAE.safetensors")
-
-#Lora Model
-#Better Light
-DownLoad(r"https://civitai.com/api/download/models/39885",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"Better_light.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/39885",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"Better_light.safetensors")
-#LAS
-DownLoad(r"https://civitai.com/api/download/models/21065",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"LAS.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/21065",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"LAS.safetensors")
-#Backlighting
-DownLoad(r"https://civitai.com/api/download/models/39164",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"backlighting.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/39164",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"backlighting.safetensors")
-#GFPGAN Model
-#detection Resnet50
-DownLoad(r"https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"detection_Resnet50_Final.pth")
-#parsing_parsenet
-DownLoad(r"https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"parsing_parsenet.pth")
-#GFPGANv1.4
-DownLoad(r"https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"GFPGANv1.4.pth")
-#strt Stable Diffusion Webui
-print("Done\nStarting Webui...")
-os.chdir(user_home / r"stable-diffusion-webui")
-while True:
-  ret=subprocess.run([executable ,user_home / r"stable-diffusion-webui" / r"launch.py",r"--precision",r"full",r"--no-half",r"--no-half-vae",r"--enable-insecure-extension-access",r"--medvram",r"--skip-torch-cuda-test",r"--enable-console-prompts",r"--ui-settings-file="+str(pathlib.Path(__file__).parent /r"config.json")])
-  if(ret.returncode == 0 ): 
-    del ret
-    gc.collect()
-  else :
-    del ret
-del os ,user_home ,pyexecutable ,subprocess

spaces/Dinoking/Guccio-AI-Designer/netdissect/proggan.py

@@ -1,299 +0,0 @@
-import torch, numpy, itertools
-import torch.nn as nn
-from collections import OrderedDict
-
-
-def print_network(net, verbose=False):
-    num_params = 0
-    for param in net.parameters():
-        num_params += param.numel()
-    if verbose:
-        print(net)
-    print('Total number of parameters: {:3.3f} M'.format(num_params / 1e6))
-
-
-def from_pth_file(filename):
-    '''
-    Instantiate from a pth file.
-    '''
-    state_dict = torch.load(filename)
-    if 'state_dict' in state_dict:
-        state_dict = state_dict['state_dict']
-    # Convert old version of parameter names
-    if 'features.0.conv.weight' in state_dict:
-        state_dict = state_dict_from_old_pt_dict(state_dict)
-    sizes = sizes_from_state_dict(state_dict)
-    result = ProgressiveGenerator(sizes=sizes)
-    result.load_state_dict(state_dict)
-    return result
-
-###############################################################################
-# Modules
-###############################################################################
-
-class ProgressiveGenerator(nn.Sequential):
-    def __init__(self, resolution=None, sizes=None, modify_sequence=None,
-            output_tanh=False):
-        '''
-        A pytorch progessive GAN generator that can be converted directly
-        from either a tensorflow model or a theano model.  It consists of
-        a sequence of convolutional layers, organized in pairs, with an
-        upsampling and reduction of channels at every other layer; and
-        then finally followed by an output layer that reduces it to an
-        RGB [-1..1] image.
-
-        The network can be given more layers to increase the output
-        resolution.  The sizes argument indicates the fieature depth at
-        each upsampling, starting with the input z: [input-dim, 4x4-depth,
-        8x8-depth, 16x16-depth...].  The output dimension is 2 * 2**len(sizes)
-
-        Some default architectures can be selected by supplying the
-        resolution argument instead.
-
-        The optional modify_sequence function can be used to transform the
-        sequence of layers before the network is constructed.
-
-        If output_tanh is set to True, the network applies a tanh to clamp
-        the output to [-1,1] before output; otherwise the output is unclamped.
-        '''
-        assert (resolution is None) != (sizes is None)
-        if sizes is None:
-            sizes = {
-                    8: [512, 512, 512],
-                    16: [512, 512, 512, 512],
-                    32: [512, 512, 512, 512, 256],
-                    64: [512, 512, 512, 512, 256, 128],
-                    128: [512, 512, 512, 512, 256, 128, 64],
-                    256: [512, 512, 512, 512, 256, 128, 64, 32],
-                    1024: [512, 512, 512, 512, 512, 256, 128, 64, 32, 16]
-                }[resolution]
-        # Follow the schedule of upsampling given by sizes.
-        # layers are called: layer1, layer2, etc; then output_128x128
-        sequence = []
-        def add_d(layer, name=None):
-            if name is None:
-                name = 'layer%d' % (len(sequence) + 1)
-            sequence.append((name, layer))
-        add_d(NormConvBlock(sizes[0], sizes[1], kernel_size=4, padding=3))
-        add_d(NormConvBlock(sizes[1], sizes[1], kernel_size=3, padding=1))
-        for i, (si, so) in enumerate(zip(sizes[1:-1], sizes[2:])):
-            add_d(NormUpscaleConvBlock(si, so, kernel_size=3, padding=1))
-            add_d(NormConvBlock(so, so, kernel_size=3, padding=1))
-        # Create an output layer.  During training, the progressive GAN
-        # learns several such output layers for various resolutions; we
-        # just include the last (highest resolution) one.
-        dim = 4 * (2 ** (len(sequence) // 2 - 1))
-        add_d(OutputConvBlock(sizes[-1], tanh=output_tanh),
-                name='output_%dx%d' % (dim, dim))
-        # Allow the sequence to be modified
-        if modify_sequence is not None:
-            sequence = modify_sequence(sequence)
-        super().__init__(OrderedDict(sequence))
-
-    def forward(self, x):
-        # Convert vector input to 1x1 featuremap.
-        x = x.view(x.shape[0], x.shape[1], 1, 1)
-        return super().forward(x)
-
-class PixelNormLayer(nn.Module):
-    def __init__(self):
-        super(PixelNormLayer, self).__init__()
-
-    def forward(self, x):
-        return x / torch.sqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)
-
-class DoubleResolutionLayer(nn.Module):
-    def forward(self, x):
-        x = nn.functional.interpolate(x, scale_factor=2, mode='nearest')
-        return x
-
-class WScaleLayer(nn.Module):
-    def __init__(self, size, fan_in, gain=numpy.sqrt(2)):
-        super(WScaleLayer, self).__init__()
-        self.scale = gain / numpy.sqrt(fan_in) # No longer a parameter
-        self.b = nn.Parameter(torch.randn(size))
-        self.size = size
-
-    def forward(self, x):
-        x_size = x.size()
-        x = x * self.scale + self.b.view(1, -1, 1, 1).expand(
-            x_size[0], self.size, x_size[2], x_size[3])
-        return x
-
-class NormConvBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, padding):
-        super(NormConvBlock, self).__init__()
-        self.norm = PixelNormLayer()
-        self.conv = nn.Conv2d(
-            in_channels, out_channels, kernel_size, 1, padding, bias=False)
-        self.wscale = WScaleLayer(out_channels, in_channels,
-                gain=numpy.sqrt(2) / kernel_size)
-        self.relu = nn.LeakyReLU(inplace=True, negative_slope=0.2)
-
-    def forward(self, x):
-        x = self.norm(x)
-        x = self.conv(x)
-        x = self.relu(self.wscale(x))
-        return x
-
-class NormUpscaleConvBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, padding):
-        super(NormUpscaleConvBlock, self).__init__()
-        self.norm = PixelNormLayer()
-        self.up = DoubleResolutionLayer()
-        self.conv = nn.Conv2d(
-            in_channels, out_channels, kernel_size, 1, padding, bias=False)
-        self.wscale = WScaleLayer(out_channels, in_channels,
-                gain=numpy.sqrt(2) / kernel_size)
-        self.relu = nn.LeakyReLU(inplace=True, negative_slope=0.2)
-
-    def forward(self, x):
-        x = self.norm(x)
-        x = self.up(x)
-        x = self.conv(x)
-        x = self.relu(self.wscale(x))
-        return x
-
-class OutputConvBlock(nn.Module):
-    def __init__(self, in_channels, tanh=False):
-        super().__init__()
-        self.norm = PixelNormLayer()
-        self.conv = nn.Conv2d(
-                in_channels, 3, kernel_size=1, padding=0, bias=False)
-        self.wscale = WScaleLayer(3, in_channels, gain=1)
-        self.clamp = nn.Hardtanh() if tanh else (lambda x: x)
-
-    def forward(self, x):
-        x = self.norm(x)
-        x = self.conv(x)
-        x = self.wscale(x)
-        x = self.clamp(x)
-        return x
-
-###############################################################################
-# Conversion
-###############################################################################
-
-def from_tf_parameters(parameters):
-    '''
-    Instantiate from tensorflow variables.
-    '''
-    state_dict = state_dict_from_tf_parameters(parameters)
-    sizes = sizes_from_state_dict(state_dict)
-    result = ProgressiveGenerator(sizes=sizes)
-    result.load_state_dict(state_dict)
-    return result
-
-def from_old_pt_dict(parameters):
-    '''
-    Instantiate from old pytorch state dict.
-    '''
-    state_dict = state_dict_from_old_pt_dict(parameters)
-    sizes = sizes_from_state_dict(state_dict)
-    result = ProgressiveGenerator(sizes=sizes)
-    result.load_state_dict(state_dict)
-    return result
-
-def sizes_from_state_dict(params):
-    '''
-    In a progressive GAN, the number of channels can change after each
-    upsampling.  This function reads the state dict to figure the
-    number of upsamplings and the channel depth of each filter.
-    '''
-    sizes = []
-    for i in itertools.count():
-        pt_layername = 'layer%d' % (i + 1)
-        try:
-            weight = params['%s.conv.weight' % pt_layername]
-        except KeyError:
-            break
-        if i == 0:
-            sizes.append(weight.shape[1])
-        if i % 2 == 0:
-            sizes.append(weight.shape[0])
-    return sizes
-
-def state_dict_from_tf_parameters(parameters):
-    '''
-    Conversion from tensorflow parameters
-    '''
-    def torch_from_tf(data):
-        return torch.from_numpy(data.eval())
-
-    params = dict(parameters)
-    result = {}
-    sizes = []
-    for i in itertools.count():
-        resolution = 4 * (2 ** (i // 2))
-        # Translate parameter names.  For example:
-        # 4x4/Dense/weight -> layer1.conv.weight
-        # 32x32/Conv0_up/weight -> layer7.conv.weight
-        # 32x32/Conv1/weight -> layer8.conv.weight
-        tf_layername = '%dx%d/%s' % (resolution, resolution,
-                'Dense' if i == 0 else 'Conv' if i == 1 else
-                'Conv0_up' if i % 2 == 0 else 'Conv1')
-        pt_layername = 'layer%d' % (i + 1)
-        # Stop looping when we run out of parameters.
-        try:
-            weight = torch_from_tf(params['%s/weight' % tf_layername])
-        except KeyError:
-            break
-        # Transpose convolution weights into pytorch format.
-        if i == 0:
-            # Convert dense layer to 4x4 convolution
-            weight = weight.view(weight.shape[0], weight.shape[1] // 16,
-                   4, 4).permute(1, 0, 2, 3).flip(2, 3)
-            sizes.append(weight.shape[0])
-        elif i % 2 == 0:
-            # Convert inverse convolution to convolution
-            weight = weight.permute(2, 3, 0, 1).flip(2, 3)
-        else:
-            # Ordinary Conv2d conversion.
-            weight = weight.permute(3, 2, 0, 1)
-            sizes.append(weight.shape[1])
-        result['%s.conv.weight' % (pt_layername)] = weight
-        # Copy bias vector.
-        bias = torch_from_tf(params['%s/bias' % tf_layername])
-        result['%s.wscale.b' % (pt_layername)] = bias
-    # Copy just finest-grained ToRGB output layers.  For example:
-    # ToRGB_lod0/weight -> output.conv.weight
-    i -= 1
-    resolution = 4 * (2 ** (i // 2))
-    tf_layername = 'ToRGB_lod0'
-    pt_layername = 'output_%dx%d' % (resolution, resolution)
-    result['%s.conv.weight' % pt_layername] = torch_from_tf(
-            params['%s/weight' % tf_layername]).permute(3, 2, 0, 1)
-    result['%s.wscale.b' % pt_layername] = torch_from_tf(
-            params['%s/bias' % tf_layername])
-    # Return parameters
-    return result
-
-def state_dict_from_old_pt_dict(params):
-    '''
-    Conversion from the old pytorch model layer names.
-    '''
-    result = {}
-    sizes = []
-    for i in itertools.count():
-        old_layername = 'features.%d' % i
-        pt_layername = 'layer%d' % (i + 1)
-        try:
-            weight = params['%s.conv.weight' % (old_layername)]
-        except KeyError:
-            break
-        if i == 0:
-            sizes.append(weight.shape[0])
-        if i % 2 == 0:
-            sizes.append(weight.shape[1])
-        result['%s.conv.weight' % (pt_layername)] = weight
-        result['%s.wscale.b' % (pt_layername)] = params[
-                '%s.wscale.b' % (old_layername)]
-    # Copy the output layers.
-    i -= 1
-    resolution = 4 * (2 ** (i // 2))
-    pt_layername = 'output_%dx%d' % (resolution, resolution)
-    result['%s.conv.weight' % pt_layername] = params['output.conv.weight']
-    result['%s.wscale.b' % pt_layername] = params['output.wscale.b']
-    # Return parameters and also network architecture sizes.
-    return result
-

spaces/ECCV2022/bytetrack/tutorials/trades/byte_tracker.py

@@ -1,352 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-from sklearn.utils.linear_assignment_ import linear_assignment
-# from numba import jit
-import copy
-from .mot_online.kalman_filter import KalmanFilter
-from .mot_online.basetrack import BaseTrack, TrackState
-from .mot_online import matching
-
-
-class STrack(BaseTrack):
-    shared_kalman = KalmanFilter()
-    def __init__(self, tlwh, score):
-
-        # wait activate
-        self._tlwh = np.asarray(tlwh, dtype=np.float)
-        self.kalman_filter = None
-        self.mean, self.covariance = None, None
-        self.is_activated = False
-
-        self.score = score
-        self.tracklet_len = 0
-
-    def predict(self):
-        mean_state = self.mean.copy()
-        if self.state != TrackState.Tracked:
-            mean_state[7] = 0
-        self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)
-
-    @staticmethod
-    def multi_predict(stracks):
-        if len(stracks) > 0:
-            multi_mean = np.asarray([st.mean.copy() for st in stracks])
-            multi_covariance = np.asarray([st.covariance for st in stracks])
-            for i, st in enumerate(stracks):
-                if st.state != TrackState.Tracked:
-                    multi_mean[i][7] = 0
-            multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(multi_mean, multi_covariance)
-            for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
-                stracks[i].mean = mean
-                stracks[i].covariance = cov
-
-    def activate(self, kalman_filter, frame_id):
-        """Start a new tracklet"""
-        self.kalman_filter = kalman_filter
-        self.track_id = self.next_id()
-        self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))
-
-        self.tracklet_len = 0
-        self.state = TrackState.Tracked
-        if frame_id == 1:
-            self.is_activated = True
-        # self.is_activated = True
-        self.frame_id = frame_id
-        self.start_frame = frame_id
-
-    def re_activate(self, new_track, frame_id, new_id=False):
-        self.mean, self.covariance = self.kalman_filter.update(
-            self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
-        )
-        self.tracklet_len = 0
-        self.state = TrackState.Tracked
-        self.is_activated = True
-        self.frame_id = frame_id
-        if new_id:
-            self.track_id = self.next_id()
-        self.score = new_track.score
-
-    def update(self, new_track, frame_id):
-        """
-        Update a matched track
-        :type new_track: STrack
-        :type frame_id: int
-        :type update_feature: bool
-        :return:
-        """
-        self.frame_id = frame_id
-        self.tracklet_len += 1
-
-        new_tlwh = new_track.tlwh
-        self.mean, self.covariance = self.kalman_filter.update(
-            self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))
-        self.state = TrackState.Tracked
-        self.is_activated = True
-
-        self.score = new_track.score
-
-    @property
-    # @jit(nopython=True)
-    def tlwh(self):
-        """Get current position in bounding box format `(top left x, top left y,
-                width, height)`.
-        """
-        if self.mean is None:
-            return self._tlwh.copy()
-        ret = self.mean[:4].copy()
-        ret[2] *= ret[3]
-        ret[:2] -= ret[2:] / 2
-        return ret
-
-    @property
-    # @jit(nopython=True)
-    def tlbr(self):
-        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
-        `(top left, bottom right)`.
-        """
-        ret = self.tlwh.copy()
-        ret[2:] += ret[:2]
-        return ret
-
-    @staticmethod
-    # @jit(nopython=True)
-    def tlwh_to_xyah(tlwh):
-        """Convert bounding box to format `(center x, center y, aspect ratio,
-        height)`, where the aspect ratio is `width / height`.
-        """
-        ret = np.asarray(tlwh).copy()
-        ret[:2] += ret[2:] / 2
-        ret[2] /= ret[3]
-        return ret
-
-    def to_xyah(self):
-        return self.tlwh_to_xyah(self.tlwh)
-
-    @staticmethod
-    # @jit(nopython=True)
-    def tlbr_to_tlwh(tlbr):
-        ret = np.asarray(tlbr).copy()
-        ret[2:] -= ret[:2]
-        return ret
-
-    @staticmethod
-    # @jit(nopython=True)
-    def tlwh_to_tlbr(tlwh):
-        ret = np.asarray(tlwh).copy()
-        ret[2:] += ret[:2]
-        return ret
-
-    def __repr__(self):
-        return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)
-
-
-
-class BYTETracker(object):
-    def __init__(self, args, frame_rate=30):
-        self.args = args
-        self.det_thresh = args.new_thresh
-        self.buffer_size = int(frame_rate / 30.0 * args.track_buffer)
-        self.max_time_lost = self.buffer_size
-        self.reset()
-    
-    # below has no effect to final output, just to be compatible to codebase
-    def init_track(self, results):
-        for item in results:
-            if item['score'] > self.opt.new_thresh and item['class'] == 1:
-                self.id_count += 1
-                item['active'] = 1
-                item['age'] = 1
-                item['tracking_id'] = self.id_count
-                if not ('ct' in item):
-                    bbox = item['bbox']
-                    item['ct'] = [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2]
-                self.tracks.append(item)
-    
-    def reset(self):
-        self.frame_id = 0
-        self.kalman_filter = KalmanFilter()
-        self.tracked_stracks = []  # type: list[STrack]
-        self.lost_stracks = []  # type: list[STrack]
-        self.removed_stracks = []  # type: list[STrack]
-        self.tracks = []    
-        
-        # below has no effect to final output, just to be compatible to codebase               
-        self.id_count = 0
-        
-    def step(self, results, public_det=None):
-        self.frame_id += 1
-        activated_starcks = []
-        refind_stracks = []
-        lost_stracks = []
-        removed_stracks = []
-        detections = []
-        detections_second = []
-        
-        scores = np.array([item['score'] for item in results if item['class'] == 1], np.float32)
-        bboxes = np.vstack([item['bbox'] for item in results if item['class'] == 1])  # N x 4, x1y1x2y2
-        
-        remain_inds = scores >= self.args.track_thresh
-        dets = bboxes[remain_inds]
-        scores_keep = scores[remain_inds]
-        
-        
-        inds_low = scores > self.args.out_thresh
-        inds_high = scores < self.args.track_thresh
-        inds_second = np.logical_and(inds_low, inds_high)
-        dets_second = bboxes[inds_second]
-        scores_second = scores[inds_second]        
-        
-        if len(dets) > 0:
-            '''Detections'''
-            detections = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for
-                          (tlbr, s) in zip(dets, scores_keep)]
-        else:
-            detections = []
-
-        ''' Add newly detected tracklets to tracked_stracks'''
-        unconfirmed = []
-        tracked_stracks = []  # type: list[STrack]
-        for track in self.tracked_stracks:
-            if not track.is_activated:
-                unconfirmed.append(track)
-            else:
-                tracked_stracks.append(track)
-
-        ''' Step 2: First association, with Kalman and IOU'''
-        strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
-        # Predict the current location with KF
-        STrack.multi_predict(strack_pool)
-        dists = matching.iou_distance(strack_pool, detections)
-        #dists = matching.fuse_motion(self.kalman_filter, dists, strack_pool, detections)
-        matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.9)
-
-        for itracked, idet in matches:
-            track = strack_pool[itracked]
-            det = detections[idet]
-            if track.state == TrackState.Tracked:
-                track.update(detections[idet], self.frame_id)
-                activated_starcks.append(track)
-            else:
-                track.re_activate(det, self.frame_id, new_id=False)
-                refind_stracks.append(track)
-
-        ''' Step 3: Second association, association the untrack to the low score detections， with IOU'''
-        if len(dets_second) > 0:
-            '''Detections'''
-            detections_second = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for
-                          (tlbr, s) in zip(dets_second, scores_second)]
-        else:
-            detections_second = []
-        r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]
-        dists = matching.iou_distance(r_tracked_stracks, detections_second)
-        matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=0.4)
-        for itracked, idet in matches:
-            track = r_tracked_stracks[itracked]
-            det = detections_second[idet]
-            if track.state == TrackState.Tracked:
-                track.update(det, self.frame_id)
-                activated_starcks.append(track)
-            else:
-                track.re_activate(det, self.frame_id, new_id=False)
-                refind_stracks.append(track)
-
-        for it in u_track:
-            #track = r_tracked_stracks[it]
-            track = r_tracked_stracks[it]
-            if not track.state == TrackState.Lost:
-                track.mark_lost()
-                lost_stracks.append(track)
-
-        '''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''
-        detections = [detections[i] for i in u_detection]
-        dists = matching.iou_distance(unconfirmed, detections)
-        matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)
-        for itracked, idet in matches:
-            unconfirmed[itracked].update(detections[idet], self.frame_id)
-            activated_starcks.append(unconfirmed[itracked])
-        for it in u_unconfirmed:
-            track = unconfirmed[it]
-            track.mark_removed()
-            removed_stracks.append(track)
-
-        """ Step 4: Init new stracks"""
-        for inew in u_detection:
-            track = detections[inew]
-            if track.score < self.det_thresh:
-                continue
-            track.activate(self.kalman_filter, self.frame_id)
-            activated_starcks.append(track)
-        """ Step 5: Update state"""
-        for track in self.lost_stracks:
-            if self.frame_id - track.end_frame > self.max_time_lost:
-                track.mark_removed()
-                removed_stracks.append(track)
-
-        self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]
-        self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)
-        self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)
-        self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)
-        self.lost_stracks.extend(lost_stracks)
-        self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)
-        self.removed_stracks.extend(removed_stracks)
-        self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)        
-        output_stracks = [track for track in self.tracked_stracks if track.is_activated]
-        
-        ret = []
-        for track in output_stracks:
-            track_dict = {}
-            track_dict['score'] = track.score
-            track_dict['bbox'] = track.tlbr
-            bbox = track_dict['bbox']
-            track_dict['ct'] = [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2]
-            track_dict['active'] = 1 if track.is_activated else 0
-            track_dict['tracking_id'] = track.track_id
-            track_dict['class'] = 1
-            ret.append(track_dict)
-        
-        self.tracks = ret
-        return ret        
-
-
-def joint_stracks(tlista, tlistb):
-    exists = {}
-    res = []
-    for t in tlista:
-        exists[t.track_id] = 1
-        res.append(t)
-    for t in tlistb:
-        tid = t.track_id
-        if not exists.get(tid, 0):
-            exists[tid] = 1
-            res.append(t)
-    return res
-
-
-def sub_stracks(tlista, tlistb):
-    stracks = {}
-    for t in tlista:
-        stracks[t.track_id] = t
-    for t in tlistb:
-        tid = t.track_id
-        if stracks.get(tid, 0):
-            del stracks[tid]
-    return list(stracks.values())
-
-
-def remove_duplicate_stracks(stracksa, stracksb):
-    pdist = matching.iou_distance(stracksa, stracksb)
-    pairs = np.where(pdist < 0.15)
-    dupa, dupb = list(), list()
-    for p, q in zip(*pairs):
-        timep = stracksa[p].frame_id - stracksa[p].start_frame
-        timeq = stracksb[q].frame_id - stracksb[q].start_frame
-        if timep > timeq:
-            dupb.append(q)
-        else:
-            dupa.append(p)
-    resa = [t for i, t in enumerate(stracksa) if not i in dupa]
-    resb = [t for i, t in enumerate(stracksb) if not i in dupb]
-    return resa, resb

spaces/Edward-Ji/essentials-of-microeconomics/essentials_of_microeconomics/util.py

@@ -1,31 +0,0 @@
-from enum import Enum
-
-from sympy import N, latex, parse_expr
-
-
-class Approx(Enum):
-    HIDE = "Hide"
-    REPLACE = "Replace"
-    APPEND = "Append"
-
-
-def latex_approx(expr, perc: int = 15, approx: Approx = Approx.HIDE):
-    if approx == Approx.HIDE:
-        return latex(expr)
-    evalf = N(expr, perc)
-    if evalf == expr:
-        return latex(expr)
-    if approx == Approx.REPLACE:
-        return latex(evalf)
-    if approx == Approx.APPEND:
-        return latex(expr) + r"\approx " + latex(evalf)
-    assert False
-
-
-sympy_dict = {}
-exec("from sympy import *", sympy_dict)  # pylint: disable=exec-used
-
-
-def parse_expr_safer(*args, **kwargs):
-    kwargs.setdefault("global_dict", {}).update(sympy_dict)
-    return parse_expr(*args, **kwargs)