Update parquet files (step 96 of 397)

spaces/101-5/gpt4free/g4f/.v1/gpt4free/usesless/test.py

@@ -1,10 +0,0 @@
-# Fix by @enganese
-# Import Account class from __init__.py file
-from gpt4free import usesless
-
-# Create Account and enable logging to see all the log messages (it's very interesting, try it!)
-# New account credentials will be automatically saved in account.json file in such template: {"email": "[email protected]", "token": "token here"}
-token = usesless.Account.create(logging=True)
-
-# Print the new token
-print(token)

spaces/1gistliPinn/ChatGPT4/Examples/Aces Of The Luftwaffe - Squadron Extended Edition Full Crack [portable].md

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spaces/1gistliPinn/ChatGPT4/Examples/Crack Ufs3 Hwksetup Without Hwk Hardware WORK.md

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-<h1>How to Crack UFS3 Hwksetup Without Hwk Hardware</h1>
-<p>UFS3 (Universal Flash Storage) is a flash storage standard for smartphones and digital cameras that offers faster and more reliable data transfer than eMMC (embedded MultiMediaCard). [^5^] However, some UFS3 devices require a HWK (Hardware Key) chip to access certain features and functions. If you don't have a HWK chip, you may want to crack your UFS3 hwksetup without hwk hardware. Here are some steps to do that:</p>
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-<li>Install the UFS3 hwksetup software on your computer. You can find it on the official website of your UFS3 device manufacturer or from other sources. Make sure you have the latest version of the software.</li>
-<li>Run the HWK Killer 2.1b software and browse for the UFS3 hwksetup.exe file on your computer. Select it and click on "Patch". This will modify your UFS3 hwksetup.exe file and remove the HWK verification.</li>
-<li>Restart your computer and run the UFS3 hwksetup.exe file again. You should be able to use all the features and functions of your UFS3 device without the HWK chip.</li>
-</ol>
-<p>Note: This method may not work for newer versions of UFS3 hwksetup software, as they may have improved security measures to prevent cracking. In that case, you may need to buy a HWK chip or use another method to crack your UFS3 hwksetup without hwk hardware.</p>
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-<p>Benefits of cracking UFS3 hwksetup without hwk hardware</p>
-<p>By cracking your UFS3 hwksetup without hwk hardware, you can enjoy some benefits such as:</p>
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-<li>Saving money: You don't have to buy a HWK chip, which can be expensive and hard to find.</li>
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-<p>Risks of cracking UFS3 hwksetup without hwk hardware</p>
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-<li>Bricking device: You may damage your device or make it unusable if you crack your UFS3 hwksetup without hwk hardware incorrectly or use a faulty software.</li>
-<li>Exposing to malware: You may expose your device to malware or viruses if you download the HWK Killer 2.1b software or the UFS3 hwksetup software from untrusted sources.</li>
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-<p>Cracking your UFS3 hwksetup without hwk hardware can be a useful way to access all the features and functions of your UFS3 device without buying a HWK chip. However, you should also be aware of the potential risks and consequences of doing so. You should always backup your data and follow the instructions carefully before attempting to crack your UFS3 hwksetup without hwk hardware.</p> d5da3c52bf<br />
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spaces/1gistliPinn/ChatGPT4/Examples/FLAC To MP3 Converter V4.0.4.0 Serial Serial Key.md

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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Bicycle Card Games for PC A Versatile and Accessible App for All Card Lovers.md

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-<h1>Bicycle Card Games PC Download: How to Enjoy the Classic Card Games on Your Computer</h1>
- <p>If you love playing card games, you might be familiar with Bicycle Playing Cards, one of the most recognized brands of playing cards in the world. Since 1885, Bicycle has been producing high-quality cards for various games, such as Hearts, Spades, Solitaire, Gin Rummy, and more. But did you know that you can also play these games on your PC?</p>
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- <p>In this article, we will show you the benefits of playing bicycle card games on PC, how to download and play them, and some FAQs that you might have. Let's get started!</p>
- <h2>Benefits of Playing Bicycle Card Games on PC</h2>
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-<li><strong>Convenience:</strong> You don't need to worry about shuffling, dealing, or keeping track of cards. You can play anytime and anywhere with your PC, as long as you have an internet connection.</li>
-<li><strong>Variety:</strong> You can choose from a wide range of card games, from classic ones like Hearts and Spades to new ones like Euchre and Six Card Golf. You can also customize your cards with different designs and colors.</li>
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- <h2>How to Download and Play Bicycle Card Games on PC</h2>
- <p>Downloading and playing bicycle card games on PC is easy and fun. Here are the steps and tips you need to follow:</p>
- <ol>
-<li><strong>Download the app:</strong> You can download the app from the official website  or from the Google Play Store  or the App Store . The app is free to download and play, but it offers in-app purchases for extra diamonds.</li>
-<li><strong>Create an account:</strong> You can create an account using your email address or your Facebook account. You can also play as a guest without an account, but you won't be able to save your progress or access some features.</li>
-<li><strong>Select a game:</strong> You can choose from five different card games: Hearts, Spades, Solitaire, Gin Rummy, and Euchre. Each game has its own rules and strategies, which you can learn from the app's tutorial or from the website .</li>
-<li><strong>Select a mode:</strong> You can play in three different modes: Practice, Private Lobby, or Public Lobby. In Practice mode, you can play against bots to improve your skills. In Private Lobby mode, you can create or join a room with up to four players and use voice chat to communicate. In Public Lobby mode, you can join a random room with other players and compete for leaderboard points.</li>
-<li><strong>Enjoy the game:</strong> Once you start a game, you will see your cards at the bottom of the screen and the other players' cards at the top. You can drag and drop your cards to play them or tap them to select them. You can also use the buttons at the bottom right corner to access the menu, chat, settings, etc.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>Bicycle card games are a <p>Bicycle card games are a great way to have fun and challenge yourself with classic card games. You can play them on your PC with ease and convenience, and enjoy the variety, social interaction, and rewards that they offer. Whether you are a beginner or a pro, you will find something to suit your taste and skill level.</p>
- <p>So what are you waiting for? Download the app today and start playing your favorite card games on your PC. You will be amazed by how much fun you can have with Bicycle Card Games by Bicycle!</p>
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- <p>Here are some frequently asked questions and answers about bicycle card games on PC:</p>
- <h3>Q: How can I play bicycle card games on PC without downloading the app?</h3>
-<p>A: You can play some of the bicycle card games on the website  without downloading the app. However, you will need to create an account and log in to access the games. You will also miss out on some of the features and benefits that the app provides, such as voice chat, leaderboards, events, etc.</p>
- <h3>Q: How can I get more diamonds in the app?</h3>
-<p>A: You can get more diamonds by playing games, completing daily quests, watching ads, or purchasing them with real money. Diamonds can be used to unlock new cards, tables, and avatars in the app.</p>
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spaces/1phancelerku/anime-remove-background/Cell to Singularity MOD APK - The Ultimate Evolution Simulator Game.md

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-<h1>Cell to Singularity - Evolution Never Ends Mod APK: A Breathtaking Evolution Game</h1>
-<p>Have you ever wondered how life on Earth began and evolved? Have you ever imagined what the future of humanity and technology will be like? If you are curious about these questions, then you should try <strong>Cell to Singularity - Evolution Never Ends</strong>, a clicker game that tells the epic story of evolution, technology, and humanity.</p>
-<p>In this article, we will tell you everything you need to know about this amazing game, including its features, tips and tricks, benefits of mod apk, and how to download and install it. Read on to find out more!</p>
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- <h2>What is Cell to Singularity - Evolution Never Ends?</h2>
-<p>Cell to Singularity - Evolution Never Ends is a simulation game that lets you tap into the extraordinary tale of evolution in this cosmic clicker game. You start from a single cell organism in the primordial soup of Earth and gradually evolve into multi-celled organisms, fish, reptiles, mammals, monkeys, humans, and beyond. You also witness the great milestones of evolution, such as the extinction of the dinosaurs, the discovery of fire, the Industrial Revolution, and more. You can even explore the future of evolution and the mystery of the technological singularity.</p>
-<p>Cell to Singularity - Evolution Never Ends is also a science game that simulates the development of life on Earth and beyond. You can view the fruits of evolution in beautiful 3D habitats, unlock animals like fish, lizards, mammals, monkeys, etc., climb civilizations' tech tree by spending ideas on countless scientific and technology upgrades, upgrade tech to survive on Mars and terraform Mars, discover and learn scientific facts about evolution of life and natural history as you play, enter a space odyssey into speculative science fiction as you click past modern civilization, and more.</p>
-<p>Cell to Singularity - Evolution Never Ends is a free-to-play game that is available on Steam and mobile devices. You can play it on your PC or laptop with Windows or Mac OS, or on your smartphone or tablet with Android or iOS. You can also sync your progress across devices and platforms with your Google Play or Game Center account. You can also enjoy the game offline without internet connection. The game is updated regularly with new content and features, so you will never run out of things to do and learn.</p>
- <h2>What are the features of Cell to Singularity - Evolution Never Ends?</h2>
-<p>Cell to Singularity - Evolution Never Ends is a game that has many features that make it fun, educational, and addictive. Here are some of the main features of the game:</p>
-<ul>
-<li><strong>Countless hours of addictive and informative gameplay</strong>: You can tap and swipe to create life, humans, and technology. You can watch the evolution of life from the first cell to the last human. You can learn about the history of life and civilization through the tech tree and the encyclopedia. You can also explore the future of evolution and the singularity in the space odyssey mode.</li>
-<li><strong>Simple, intuitive controls and beautiful 3D graphics</strong>: You can play the game with just one finger, tapping and swiping to generate entropy, ideas, metabits, and darwinium. You can also view the stunning 3D graphics of the habitats, animals, and tech that you unlock. You can zoom in and out, rotate, and interact with the elements on the screen.</li>
-<li><strong>Climb civilizations' tech tree and unlock the future of evolution</strong>: You can spend your ideas on hundreds of scientific and technological upgrades that will advance your civilization from the stone age to the space age. You can unlock inventions like fire, writing, agriculture, steam engine, electricity, internet, AI, nanotechnology, etc. You can also unlock traits that will enhance your evolution such as intelligence, creativity, curiosity, etc.</li>
-<li><strong>Discover and learn scientific facts and speculative science fiction</strong>: You can access the encyclopedia that will provide you with factual information about the evolution of life and natural history. You can learn about the origin of life, the major eras and events of evolution, the characteristics and behaviors of different animals, etc. You can also access the cards that will give you a glimpse of speculative science fiction scenarios that may happen in the future of evolution such as cyborgs, aliens, time travel, etc.</li>
-<li><strong>Upgrade tech to survive on Mars and terraform Mars</strong>: You can use your metabits and darwinium to upgrade your tech level and unlock new features in the space odyssey mode. You can build a colony on Mars and terraform it to make it habitable for life. You can also research new technologies that will help you survive on Mars such as solar panels, greenhouses, rovers, etc.</li>
-</ul>
- <h2>What are the tips and tricks for Cell to Singularity - Evolution Never Ends?</h2>
-<p>Cell to Singularity - Evolution Never Ends is a game that requires some strategy and planning to progress faster and easier. Here are some tips and tricks that will help you play the game more efficiently:</p>
-<ul>
-<li><strong>Focus on adding life or civilization units that boost your income by 10% or more</strong>: When you are choosing which life or civilization units to add to your habitats or tech tree, you should prioritize those that have a 10% or higher income boost over those that have a lower boost. This will help you increase your entropy or ideas income faster and unlock more upgrades sooner.</li>
-<li><strong>Save your achievements for after you unlock Singularity and use them when you hit a wall</strong>: Achievements are milestones that you can complete by reaching certain levels of entropy, ideas, metabits, darwinium, etc. When you complete an achievement, you can claim a reward that will boost your income by a certain percentage for a limited time. However, you should not claim these rewards until you unlock Singularity mode (which requires 1e1000 ideas), because they will be more useful then when you face harder challenges. You should also use them when you hit a wall or a slowdown in your progress.</li>
-<li><strong>Use your cubes wisely and prioritize the x2 income boost</strong>: Cubes are special items that you can obtain by watching ads or spending darwinium. You can use cubes to activate various boosts such as x2 income for 4 hours, x5 income for 15 minutes, x10 income for 5 minutes, etc. However, you should not waste your cubes on boosts that have a short duration or a low multiplier. Instead, you should save your cubes for the x2 income boost for 4 hours, which is the most cost-effective and beneficial boost in the game.</li>
-<li><strong>Restart simulation when you can afford at least one new Reality Engine upgrade</strong>: Restarting simulation is a feature that allows you to reset your entropy and ideas income to zero but keep your metabits and darwin ium income. You can also buy new Reality Engine upgrades with your metabits that will increase your income multiplier and unlock new features. However, you should not restart simulation too often or too early, because it will slow down your progress. Instead, you should restart simulation only when you can afford at least one new Reality Engine upgrade that will significantly boost your income and help you reach the next milestone faster.</li>
-<li><strong>Exploit the burst boosts to chain upgrades and progress faster</strong>: Burst boosts are temporary boosts that you can activate by tapping on the screen when a blue circle appears around your finger. Burst boosts will increase your entropy or ideas income by a certain percentage for a few seconds. You can exploit these boosts to chain upgrades and progress faster in the game. For example, you can use a burst boost to buy an upgrade that will increase your income by 10%, then use another burst boost to buy another upgrade that will increase your income by another 10%, and so on. This way, you can multiply your income exponentially and reach higher levels of evolution and technology in a shorter time.</li>
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spaces/1phancelerku/anime-remove-background/Download Pink Colour Art and Paintings for Your Inspiration.md

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-<h1>Download Pink Colour: How to Find and Use the Perfect Shade of Pink for Your Project</h1>
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-<p>Pink has many shades that range from light to dark, warm to cool, and bright to dull. Some of the most common shades of pink are:</p>
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-<tr><td>Baby Pink</td><td>#F4C2C2</td><td>A soft and delicate shade of pink that is often used for baby girls' items or nursery rooms.</td></tr>
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-<tr><td>Coral Pink</td><td>#F88379</td><td>A warm and vibrant shade of pink that is often used for beach or nautical themes.</td></tr>
-<tr><td>Hot Pink</td><td>#FF69B4</td><td>A bold and bright shade of pink that is often used for fun or funky themes.</td></tr>
-<tr><td>Magenta</td><td>#FF00FF</td><td>A deep and intense shade of pink that is often used for artistic or creative themes.</td></tr>
-<tr><td>Mauve</td><td>#E0B0FF</td><td>A cool and elegant shade of pink that is often used for romantic or vintage themes.</td></tr>
-<tr><td>Burgundy </td><td>#800020</td><td>A dark and rich shade of pink that is often used for elegant or sophisticated themes.</td></tr>
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spaces/1phancelerku/anime-remove-background/Epic Conquest 2 APK The Most Anticipated RPG Game for Android.md

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-<p>If you are looking for a classic single-player action/adventure RPG with a solid combat and a great story, you might want to check out Epic Conquest 2. This game is developed by Gaco Games, a small but passionate team of four people who have crafted this project with care and love. In this article, we will tell you what Epic Conquest 2 is, how to download it for your Android device, why you should play it, and some tips and tricks to help you enjoy it more.</p>
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-<p>Epic Conquest 2 is a game that will satisfy your craving for a classic RPG with an open world and a great story. It is a game that has a lot of content and features to explore and customize. It is a game that offers a fun and immersive gameplay experience. It is a game that you can download for free on your Android device and play offline without any ads or payments. If you are looking for a game like this, you should download Epic Conquest 2 APK today and start your epic adventure.</p>
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-<p>Here are some of the frequently asked questions about Epic Conquest 2:</p>
- <h3>Q: How long is the game?</h3>
-<p>A: The game has about 20 hours of main story content, plus more hours of side quests, exploration, and replay value.</p>
- <h3>Q: How many endings are there in the game?</h3>
-<p>A: The game has four main endings, plus several variations depending on your choices and actions.</p>
- <h3>Q: How do I get more gold in the game?</h3>
-<p>A: You can get more gold by selling items, completing quests, finding treasures, or watching ads (optional).</p>
- <h3>Q: How do I get more costumes in the game?</h3>
-<p>A: You can get more costumes by buying them from shops, finding them in chests, completing achievements, or watching ads (optional).</p>
- <h3>Q: How do I contact the developers of the game?</h3>
-<p>A: You can contact the developers of the game by visiting their website at <a href="">https://gacogames.com/</a>, or by following them on their social media accounts at <a href="">https://www.facebook.com/gacogames/</a>, <a href="">https://twitter.com/gacogames/</a>, or <a href="">https://www.instagram.com/gacogames/</a>.</p> 401be4b1e0<br />
-<br />
-<br />

spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/AWS b022fe0cb7084cc0b64624f7bc8cde2c.md

@@ -1,5 +0,0 @@
-# AWS
-
-Last edited time: March 31, 2023 1:49 PM
-Owner: Anonymous
-Tags: Infrastructure

spaces/ADOPLE/ResumeAnalyzer/app.py

@@ -1,144 +0,0 @@
-import gradio as gr
-import PyPDF2
-import os
-import openai
-import re
-import plotly.graph_objects as go
-
-class ResumeAnalyser:
-  def __init__(self):
-      pass
-  def extract_text_from_file(self,file_path):
-      # Get the file extension
-      file_extension = os.path.splitext(file_path)[1]
-
-      if file_extension == '.pdf':
-          with open(file_path, 'rb') as file:
-              # Create a PDF file reader object
-              reader = PyPDF2.PdfFileReader(file)
-
-              # Create an empty string to hold the extracted text
-              extracted_text = ""
-
-              # Loop through each page in the PDF and extract the text
-              for page_number in range(reader.getNumPages()):
-                  page = reader.getPage(page_number)
-                  extracted_text += page.extractText()
-          return extracted_text
-
-      elif file_extension == '.txt':
-          with open(file_path, 'r') as file:
-              # Just read the entire contents of the text file
-              return file.read()
-
-      else:
-          return "Unsupported file type"
-
-  def responce_from_ai(self,textjd, textcv):
-      resume = self.extract_text_from_file(textjd)
-      job_description = self.extract_text_from_file(textcv)
-
-      response = openai.Completion.create(
-          engine="text-davinci-003",
-          prompt=f"""
-  Given the job description and the resume, assess the matching percentage to 100 and if 100 percentage not matched mention the remaining percentage with reason. **Job Description:**{job_description}**Resume:**{resume}
-  **Detailed Analysis:**
-                  the result should be in this format:
-                  Matched Percentage: [matching percentage].
-                  Reason            : [Mention Reason and keys from job_description and resume get this matched percentage.].
-                  Skills To Improve : [Mention the skills How to improve and get 100 percentage job description matching].
-                  Keywords          : [matched key words from {job_description} and {resume}].
-                  """,
-          temperature=0,
-          max_tokens=100,
-          n=1,
-          stop=None,
-      )
-      generated_text = response.choices[0].text.strip()
-      print(generated_text)
-      return generated_text
-
-
-  def matching_percentage(self,job_description_path, resume_path):
-      job_description_path = job_description_path.name
-      resume_path = resume_path.name
-
-      generated_text = self.responce_from_ai(job_description_path, resume_path)
-
-      result = generated_text
-
-      lines = result.split('\n')
-        
-      matched_percentage = None
-      matched_percentage_txt = None
-      reason = None
-      skills_to_improve = None
-      keywords = None
-    
-      for line in lines:
-          if line.startswith('Matched Percentage:'):
-              match = re.search(r"Matched Percentage: (\d+)%", line)
-              if match:
-                  matched_percentage = int(match.group(1))
-                  matched_percentage_txt = (f"Matched Percentage: {matched_percentage}%")
-          elif line.startswith('Reason'):
-              reason = line.split(':')[1].strip()
-          elif line.startswith('Skills To Improve'):
-              skills_to_improve = line.split(':')[1].strip()
-          elif line.startswith('Keywords'):
-              keywords = line.split(':')[1].strip()
-
-
-      # Extract the matched percentage using regular expression
-      # match1 = re.search(r"Matched Percentage: (\d+)%", matched_percentage)
-      # matched_Percentage = int(match1.group(1))
-
-      # Creating a pie chart with plotly
-      labels = ['Matched', 'Remaining']
-      values = [matched_percentage, 100 - matched_percentage]
-
-      fig = go.Figure(data=[go.Pie(labels=labels, values=values)])
-      # fig.update_layout(title='Matched Percentage')
-
-
-      return matched_percentage_txt,reason, skills_to_improve, keywords,fig
-
-
-  def gradio_interface(self):
-      with gr.Blocks(css="style.css",theme=gr.themes.Soft()) as app:
-            #gr.HTML("""<img class="center" align="center" src="https://drive.google.com/file/d/1Suir2UMmryGveM8P0LFO768WpCtJ8jBg/view?usp=sharing" alt="Image" width="210" height="210">""")
-            gr.HTML("""<h1 style="color:#100C08;text-align:center;font-size:6vw;">ADOPLE AI</h1>""")
-            with gr.Row():
-              with gr.Column(elem_id="col-container"):
-                gr.HTML(
-                    """<br style="color:white;">"""
-                    )
-                gr.HTML(
-                    """<h2 style="text-align:center; color:"white">ADOPLE AI Resume Analyzer</h2> """
-                )
-                gr.HTML("<br>")
-            with gr.Row():
-              with gr.Column(scale=0.45, min_width=150, ):
-                jobDescription = gr.File(label="Job Description")
-              with gr.Column(scale=0.45, min_width=150):
-                resume = gr.File(label="Resume")
-              with gr.Column(scale=0.10, min_width=150):
-                analyse = gr.Button("Analyse")
-            with gr.Row():
-              with gr.Column(scale=1.0, min_width=150):
-                perncentage = gr.Textbox(label="Matching Percentage",lines=8)
-              with gr.Column(scale=1.0, min_width=150):
-                reason = gr.Textbox(label="Matching Reason",lines=8)
-              with gr.Column(scale=1.0, min_width=150):
-                skills = gr.Textbox(label="Skills To Improve",lines=8)
-              with gr.Column(scale=1.0, min_width=150):
-                keywords = gr.Textbox(label="Matched Keywords",lines=8)
-            with gr.Row():
-              with gr.Column(scale=1.0, min_width=150):
-                pychart = gr.Plot(label="Matching Percentage Chart")
-            analyse.click(self.matching_percentage, [jobDescription, resume], [perncentage,reason,skills,keywords,pychart])
-
-      app.launch()
-
-resume=ResumeAnalyser()
-resume.gradio_interface()

spaces/AHzizi/WaifuVoiceGen/README.md

@@ -1,14 +0,0 @@
----
-title: Vits Models
-emoji: 🏃
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: sayashi/vits-models
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIConsultant/MusicGen/model_cards/AUDIOGEN_MODEL_CARD.md

@@ -1,79 +0,0 @@
-# AudioGen Model Card
-
-## Model details
-**Organization developing the model:** The FAIR team of Meta AI.
-
-**Model date:** This version of AudioGen was trained between July 2023 and August 2023.
-
-**Model version:** This is version 2 of the model, not to be confused with the original AudioGen model published in ["AudioGen: Textually Guided Audio Generation"][audiogen].
-In this version (v2), AudioGen was trained on the same data, but with some other differences:
-1. This model was trained on 10 seconds (vs. 5 seconds in v1).
-2. The discrete representation used under the hood is extracted using a retrained EnCodec model on the environmental sound data, following the EnCodec setup detailed in the ["Simple and Controllable Music Generation" paper][musicgen].
-3. No audio mixing augmentations.
-
-**Model type:** AudioGen consists of an EnCodec model for audio tokenization, and an auto-regressive language model based on the transformer architecture for audio modeling. The released model has 1.5B parameters.
-
-**Paper or resource for more information:** More information can be found in the paper [AudioGen: Textually Guided Audio Generation](https://arxiv.org/abs/2209.15352).
-
-**Citation details:** See [AudioGen paper][audiogen]
-
-**License:** Code is released under MIT, model weights are released under CC-BY-NC 4.0.
-
-**Where to send questions or comments about the model:** Questions and comments about AudioGen can be sent via the [GitHub repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue.
-
-## Intended use
-**Primary intended use:** The primary use of AudioGen is research on AI-based audio generation, including:
-- Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science
-- Generation of sound guided by text to understand current abilities of generative AI models by machine learning amateurs
-
-**Primary intended users:** The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models.
-
-**Out-of-scope use cases** The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate audio pieces that create hostile or alienating environments for people. This includes generating audio that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
-
-## Metrics
-
-**Models performance measures:** We used the following objective measure to evaluate the model on a standard audio benchmark:
-- Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish)
-- Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST)
-
-Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes:
-- Overall quality of the audio samples;
-- Text relevance to the provided text input;
-
-More details on performance measures and human studies can be found in the paper.
-
-**Decision thresholds:** Not applicable.
-
-## Evaluation datasets
-
-The model was evaluated on the [AudioCaps benchmark](https://audiocaps.github.io/).
-
-## Training datasets
-
-The model was trained on the following data sources: a subset of AudioSet (Gemmeke et al., 2017), [BBC sound effects](https://sound-effects.bbcrewind.co.uk/), AudioCaps (Kim et al., 2019), Clotho v2 (Drossos et al., 2020), VGG-Sound (Chen et al., 2020), FSD50K (Fonseca et al., 2021), [Free To Use Sounds](https://www.freetousesounds.com/all-in-one-bundle/), [Sonniss Game Effects](https://sonniss.com/gameaudiogdc), [WeSoundEffects](https://wesoundeffects.com/we-sound-effects-bundle-2020/), [Paramount Motion - Odeon Cinematic Sound Effects](https://www.paramountmotion.com/odeon-sound-effects).
-
-## Evaluation results
-
-Below are the objective metrics obtained with the released model on AudioCaps (consisting of 10-second long samples). Note that the model differs from the original AudioGen model introduced in the paper, hence the difference in the metrics.
-
-| Model | Frechet Audio Distance | KLD | Text consistency |
-|---|---|---|---|
-| facebook/audiogen-medium | 1.77 | 1.41 | 0.299 |
-
-More information can be found in the paper [AudioGen: Textually Guided Audio Generation][audiogen], in the Experiments section.
-
-## Limitations and biases
-
-**Limitations:**
-- The model is not able to generate realistic vocals.
-- The model has been trained with English descriptions and will not perform as well in other languages.
-- It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results.
-
-**Biases:** The datasets used for training may be lacking of diversity and are not representative of all possible sound events. The generated samples from the model will reflect the biases from the training data.
-
-**Risks and harms:** Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data.
-
-**Use cases:** Users must be aware of the biases, limitations and risks of the model. AudioGen is a model developed for artificial intelligence research on audio generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.
-
-[musicgen]: https://arxiv.org/abs/2306.05284
-[audiogen]: https://arxiv.org/abs/2209.15352

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/modules.py

@@ -1,350 +0,0 @@
-import torch
-import torch.nn as nn
-from functools import partial
-
-from ldm.modules.x_transformer import Encoder, TransformerWrapper  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
-from torch.utils.checkpoint import checkpoint
-from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel, AutoTokenizer
-from importlib_resources import files
-from ldm.modules.encoders.CLAP.utils import read_config_as_args
-from ldm.modules.encoders.CLAP.clap import TextEncoder
-from ldm.util import default, count_params
-import open_clip
-
-class AbstractEncoder(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def encode(self, *args, **kwargs):
-        raise NotImplementedError
-
-
-class ClassEmbedder(nn.Module):
-    def __init__(self, embed_dim, n_classes=1000, key='class'):
-        super().__init__()
-        self.key = key
-        self.embedding = nn.Embedding(n_classes, embed_dim)
-
-    def forward(self, batch, key=None):
-        if key is None:
-            key = self.key
-        # this is for use in crossattn
-        c = batch[key][:, None]# (bsz,1)
-        c = self.embedding(c)
-        return c
-
-
-class TransformerEmbedder(AbstractEncoder):
-    """Some transformer encoder layers"""
-    def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
-        super().__init__()
-        self.device = device
-        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
-                                              attn_layers=Encoder(dim=n_embed, depth=n_layer))
-
-    def forward(self, tokens):
-        tokens = tokens.to(self.device)  # meh
-        z = self.transformer(tokens, return_embeddings=True)
-        return z
-
-    def encode(self, x):
-        return self(x)
-
-
-class BERTTokenizer(AbstractEncoder):
-    """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
-    def __init__(self, device="cuda", vq_interface=True, max_length=77):
-        super().__init__()
-        from transformers import BertTokenizerFast  # TODO: add to reuquirements
-        self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
-        self.device = device
-        self.vq_interface = vq_interface
-        self.max_length = max_length
-
-    def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-        return tokens
-
-    @torch.no_grad()
-    def encode(self, text):
-        tokens = self(text)
-        if not self.vq_interface:
-            return tokens
-        return None, None, [None, None, tokens]
-
-    def decode(self, text):
-        return text
-
-
-class BERTEmbedder(AbstractEncoder):# 这里不是用的pretrained bert,是用的transformers的BertTokenizer加自定义的TransformerWrapper
-    """Uses the BERT tokenizr model and add some transformer encoder layers"""
-    def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
-                 device="cuda",use_tokenizer=True, embedding_dropout=0.0):
-        super().__init__()
-        self.use_tknz_fn = use_tokenizer
-        if self.use_tknz_fn:
-            self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
-        self.device = device
-        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
-                                              attn_layers=Encoder(dim=n_embed, depth=n_layer),
-                                              emb_dropout=embedding_dropout)
-
-    def forward(self, text):
-        if self.use_tknz_fn:
-            tokens = self.tknz_fn(text)#.to(self.device)
-        else:
-            tokens = text
-        z = self.transformer(tokens, return_embeddings=True)
-        return z
-
-    def encode(self, text):
-        # output of length 77
-        return self(text)
-
-
-class SpatialRescaler(nn.Module):
-    def __init__(self,
-                 n_stages=1,
-                 method='bilinear',
-                 multiplier=0.5,
-                 in_channels=3,
-                 out_channels=None,
-                 bias=False):
-        super().__init__()
-        self.n_stages = n_stages
-        assert self.n_stages >= 0
-        assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']
-        self.multiplier = multiplier
-        self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
-        self.remap_output = out_channels is not None
-        if self.remap_output:
-            print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')
-            self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)
-
-    def forward(self,x):
-        for stage in range(self.n_stages):
-            x = self.interpolator(x, scale_factor=self.multiplier)
-
-
-        if self.remap_output:
-            x = self.channel_mapper(x)
-        return x
-
-    def encode(self, x):
-        return self(x)
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-class FrozenT5Embedder(AbstractEncoder):
-    """Uses the T5 transformer encoder for text"""
-    def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
-        super().__init__()
-        self.tokenizer = T5Tokenizer.from_pretrained(version)
-        self.transformer = T5EncoderModel.from_pretrained(version)
-        self.device = device
-        self.max_length = max_length   # TODO: typical value?
-        if freeze:
-            self.freeze()
-
-    def freeze(self):
-        self.transformer = self.transformer.eval()
-        #self.train = disabled_train
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-        outputs = self.transformer(input_ids=tokens)
-
-        z = outputs.last_hidden_state
-        return z
-
-    def encode(self, text):
-        return self(text)
-
-
-class FrozenCLAPEmbedder(AbstractEncoder):
-    """Uses the CLAP transformer encoder for text (from huggingface)"""
-    def __init__(self, weights_path, freeze=True, device="cuda", max_length=77):  # clip-vit-base-patch32
-        super().__init__()
-
-        model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model']
-        match_params = dict()
-        for key in list(model_state_dict.keys()):
-            if 'caption_encoder' in key:
-                match_params[key.replace('caption_encoder.', '')] = model_state_dict[key]
-
-        config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text()
-        args = read_config_as_args(config_as_str, is_config_str=True)
-
-        # To device
-        self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model
-        self.caption_encoder = TextEncoder(
-            args.d_proj, args.text_model, args.transformer_embed_dim
-        )
-
-        self.max_length = max_length
-        self.device = device
-        if freeze: self.freeze()
-
-        print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.")
-
-    def freeze(self):
-        self.caption_encoder.base = self.caption_encoder.base.eval()
-        for param in self.caption_encoder.base.parameters():
-            param.requires_grad = False
-
-
-    def encode(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-
-        outputs = self.caption_encoder.base(input_ids=tokens)
-        z = self.caption_encoder.projection(outputs.last_hidden_state)
-        return z
-
-class FrozenCLAPEmbedderNoLoad(AbstractEncoder):
-    def __init__(self, config, freeze=True, device="cpu", max_length=77):
-        super().__init__()
-        args = config
-
-        # To device
-        self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model
-        self.caption_encoder = TextEncoder(
-            args.d_proj, args.text_model, args.transformer_embed_dim
-        )
-
-        self.max_length = max_length
-        self.device = device
-        if freeze: self.freeze()
-
-        print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.")        
-
-    def freeze(self):
-        self.caption_encoder.base = self.caption_encoder.base.eval()
-        for param in self.caption_encoder.base.parameters():
-            param.requires_grad = False
-
-
-    def encode(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-
-        outputs = self.caption_encoder.base(input_ids=tokens)
-        z = self.caption_encoder.projection(outputs.last_hidden_state)
-        return z
-
-
-class NewFrozenCLAPEmbedder(AbstractEncoder):
-    """Uses the CLAP transformer encoder for text (from huggingface)"""
-    def __init__(self, weights_path, freeze=True, device="cuda", max_length=77):  # clip-vit-base-patch32
-        super().__init__()
-        # To device
-        from transformers import RobertaTokenizer
-        from ldm.modules.encoders.open_clap import create_model
-
-
-        model, model_cfg = create_model(
-            'HTSAT-tiny',
-            'roberta',
-            weights_path,
-            enable_fusion=True,
-            fusion_type='aff_2d'
-        )
-
-        del model.audio_branch, model.audio_transform, model.audio_projection
-        self.tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
-        self.model = model
-
-        self.max_length = max_length
-        self.device = device
-        if freeze: self.freeze()
-
-        param_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
-        print(f'{self.model.__class__.__name__} comes with: {param_num / 1e+6:.3f} M params.')
-
-    def freeze(self):
-        self.model = self.model.eval()
-        for param in self.model.parameters():
-            param.requires_grad = False
-
-    def encode(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        outputs = self.model.text_branch(input_ids=batch_encoding["input_ids"].to(self.device), attention_mask=batch_encoding["attention_mask"].to(self.device))
-        z = self.model.text_projection(outputs.last_hidden_state)
-        return z
-
-class FrozenFLANEmbedder(AbstractEncoder):
-    """Uses the T5 transformer encoder for text"""
-    def __init__(self, version="google/flan-t5-large", device="cuda", max_length=77, freeze=True):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
-        super().__init__()
-        self.tokenizer = T5Tokenizer.from_pretrained(version)
-        self.transformer = T5EncoderModel.from_pretrained(version)
-        self.device = device
-        self.max_length = max_length   # TODO: typical value?
-        if freeze:
-            self.freeze()
-
-    def freeze(self):
-        self.transformer = self.transformer.eval()
-        #self.train = disabled_train
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-        outputs = self.transformer(input_ids=tokens)
-
-        z = outputs.last_hidden_state
-        return z
-
-    def encode(self, text):
-        return self(text)
-class FrozenGlobalNormOpenCLIPEmbedder(AbstractEncoder):
-    """
-    Uses the OpenCLIP transformer encoder for text
-    """
-    def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", freeze=True, delvisual=True):
-        super().__init__()
-        model, _, preprocess = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
-        if delvisual:
-            del model.visual
-            del preprocess
-        else:
-            self.preprocess = preprocess
-        self.model = model
-
-        self.device = device
-        if freeze:
-            self.freeze()
-
-    def freeze(self):
-        self.model = self.model.eval()
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text):
-        tokens = open_clip.tokenize(text)
-        z = self.model.encode_text(tokens.to(self.device))
-        z /= z.norm(dim=-1, keepdim=True)
-        return z.unsqueeze(1)
-
-    def forward_img(self, image):
-        z = self.model.encode_image(image.to(self.device))
-        z /= z.norm(dim=-1, keepdim=True)
-        return z.unsqueeze(1)
-
-    def encode(self, text):
-        return self(text)

spaces/Ababababababbababa/poetry2023/app.py

@@ -1,53 +0,0 @@
-import gc 
-import gradio as gr
-from transformers import pipeline, set_seed
-
-pipe = pipeline('text-generation', framework='pt', model='akhooli/ap2023', tokenizer='akhooli/ap2023')
-#gc.collect()
-samples = [['أنت'
-           ,1.0, 50, 1.0, 1.0, 114],['هل غادر'
-            ,1.0, 50, 1.0, 1.0, 114 ],['ألا ليت'
-              ,1.0, 50, 1.0, 1.0, 114 ],['يا قدس'
-                 ,1.0, 50, 1.0, 1.0, 114],['عيد بأية حال'
-                   ,1.0, 50, 1.0, 1.0, 114],['لكل شيء إذا ما'
-                    ,1.0, 50, 1.0, 1.0, 114 ],['.'
-                       ,1.0, 50, 1.0, 1.0, 114]]
-                     
-notes = """
-- Enter a short prompt or select (click) one of the examples and click SEND 
-- Adjust parameters (temperture, top k, top p and penalty) through the slider (keep close to default values).
-- For the same seed (randomness), the same output is regenerated if other parameters are fixed 
-- Clear and enter new prompt or select another example and SEND to regenerate
-- The '.' means start a new line from no prompt (your prompt need not be long) 
-- Be patient: this runs on CPU (free tier)
-- Feedback (Twitter): @akhooli (https://twitter.com/akhooli/status/1611025232201977859) 
-- Note/Disclaimer: may generate unaccepted or inappropriate content. Use at your own risk.
-"""
-def sayPoetry(prompt, temp=1.0, topk = 50, topp = 1.0, penalty=1.0, seed=114):
-    if not int(seed) >= 0: seed=114
-    set_seed(seed)
-    gen = pipe(prompt, max_length=96, do_sample=True, temperature=temp, top_k=topk, top_p=topp, repetition_penalty=penalty,
-              min_length = 64, no_repeat_ngram_size = 3, return_full_text=True,
-               num_beams=5, num_return_sequences=1)[0]["generated_text"]
-    poetry =""
-    for line in gen.split('.')[:-1]:
-        poetry += line #+ "\n"
-    return poetry
-poetry = gr.Interface(fn=sayPoetry, 
-                    inputs=[
-                        gr.Textbox(label="Enter short prompt or select from examples:"), 
-                        gr.Slider(0.70, 1.2, step=0.01,value=1.0, label='control  temperature'),
-                        gr.Slider(25, 100, step=1,value=50, label='control  top k'),
-                        gr.Slider(0.80, 1.0, step=0.01,value=1.0, label='control  top p'),
-                        gr.Slider(0.90, 1.50, step=0.01,value=1.0, label='control  penalty'),
-                        gr.Number(value=139750, precision=0, label='Seed'),
-                    ], 
-                outputs=[gr.Textbox(label="Generated Poetry:")], 
-                    
-                allow_flagging='never',
-                title='Arabic Poetry Generation Demo (updated Jan. 2023)',
-                description = "A simple demo of AI generated poetry based on 1M poems fine-tuned using AraGPT2 (be patient, runs on cpu)",
-                examples=samples,
-                cache_examples=False,
-                article = notes)
-poetry.launch() # show_error = True, debug=True

spaces/Abhilashvj/planogram-compliance/export.py

@@ -1,1013 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
-
-Format                      | `export.py --include`         | Model
----                         | ---                           | ---
-PyTorch                     | -                             | yolov5s.pt
-TorchScript                 | `torchscript`                 | yolov5s.torchscript
-ONNX                        | `onnx`                        | yolov5s.onnx
-OpenVINO                    | `openvino`                    | yolov5s_openvino_model/
-TensorRT                    | `engine`                      | yolov5s.engine
-CoreML                      | `coreml`                      | yolov5s.mlmodel
-TensorFlow SavedModel       | `saved_model`                 | yolov5s_saved_model/
-TensorFlow GraphDef         | `pb`                          | yolov5s.pb
-TensorFlow Lite             | `tflite`                      | yolov5s.tflite
-TensorFlow Edge TPU         | `edgetpu`                     | yolov5s_edgetpu.tflite
-TensorFlow.js               | `tfjs`                        | yolov5s_web_model/
-PaddlePaddle                | `paddle`                      | yolov5s_paddle_model/
-
-Requirements:
-    $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu  # CPU
-    $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow  # GPU
-
-Usage:
-    $ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...
-
-Inference:
-    $ python detect.py --weights yolov5s.pt                 # PyTorch
-                                 yolov5s.torchscript        # TorchScript
-                                 yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
-                                 yolov5s_openvino_model     # OpenVINO
-                                 yolov5s.engine             # TensorRT
-                                 yolov5s.mlmodel            # CoreML (macOS-only)
-                                 yolov5s_saved_model        # TensorFlow SavedModel
-                                 yolov5s.pb                 # TensorFlow GraphDef
-                                 yolov5s.tflite             # TensorFlow Lite
-                                 yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
-                                 yolov5s_paddle_model       # PaddlePaddle
-
-TensorFlow.js:
-    $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
-    $ npm install
-    $ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model
-    $ npm start
-"""
-
-import argparse
-import contextlib
-import json
-import os
-import platform
-import re
-import subprocess
-import sys
-import time
-import warnings
-from pathlib import Path
-
-import pandas as pd
-import torch
-from torch.utils.mobile_optimizer import optimize_for_mobile
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-if platform.system() != "Windows":
-    ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from models.experimental import attempt_load
-from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel
-from utils.dataloaders import LoadImages
-from utils.general import (
-    LOGGER,
-    Profile,
-    check_dataset,
-    check_img_size,
-    check_requirements,
-    check_version,
-    check_yaml,
-    colorstr,
-    file_size,
-    get_default_args,
-    print_args,
-    url2file,
-    yaml_save,
-)
-from utils.torch_utils import select_device, smart_inference_mode
-
-MACOS = platform.system() == "Darwin"  # macOS environment
-
-
-def export_formats():
-    # YOLOv5 export formats
-    x = [
-        ["PyTorch", "-", ".pt", True, True],
-        ["TorchScript", "torchscript", ".torchscript", True, True],
-        ["ONNX", "onnx", ".onnx", True, True],
-        ["OpenVINO", "openvino", "_openvino_model", True, False],
-        ["TensorRT", "engine", ".engine", False, True],
-        ["CoreML", "coreml", ".mlmodel", True, False],
-        ["TensorFlow SavedModel", "saved_model", "_saved_model", True, True],
-        ["TensorFlow GraphDef", "pb", ".pb", True, True],
-        ["TensorFlow Lite", "tflite", ".tflite", True, False],
-        ["TensorFlow Edge TPU", "edgetpu", "_edgetpu.tflite", False, False],
-        ["TensorFlow.js", "tfjs", "_web_model", False, False],
-        ["PaddlePaddle", "paddle", "_paddle_model", True, True],
-    ]
-    return pd.DataFrame(
-        x, columns=["Format", "Argument", "Suffix", "CPU", "GPU"]
-    )
-
-
-def try_export(inner_func):
-    # YOLOv5 export decorator, i..e @try_export
-    inner_args = get_default_args(inner_func)
-
-    def outer_func(*args, **kwargs):
-        prefix = inner_args["prefix"]
-        try:
-            with Profile() as dt:
-                f, model = inner_func(*args, **kwargs)
-            LOGGER.info(
-                f"{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)"
-            )
-            return f, model
-        except Exception as e:
-            LOGGER.info(f"{prefix} export failure ❌ {dt.t:.1f}s: {e}")
-            return None, None
-
-    return outer_func
-
-
-@try_export
-def export_torchscript(
-    model, im, file, optimize, prefix=colorstr("TorchScript:")
-):
-    # YOLOv5 TorchScript model export
-    LOGGER.info(
-        f"\n{prefix} starting export with torch {torch.__version__}..."
-    )
-    f = file.with_suffix(".torchscript")
-
-    ts = torch.jit.trace(model, im, strict=False)
-    d = {
-        "shape": im.shape,
-        "stride": int(max(model.stride)),
-        "names": model.names,
-    }
-    extra_files = {"config.txt": json.dumps(d)}  # torch._C.ExtraFilesMap()
-    if (
-        optimize
-    ):  # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
-        optimize_for_mobile(ts)._save_for_lite_interpreter(
-            str(f), _extra_files=extra_files
-        )
-    else:
-        ts.save(str(f), _extra_files=extra_files)
-    return f, None
-
-
-@try_export
-def export_onnx(
-    model, im, file, opset, dynamic, simplify, prefix=colorstr("ONNX:")
-):
-    # YOLOv5 ONNX export
-    check_requirements("onnx>=1.12.0")
-    import onnx
-
-    LOGGER.info(f"\n{prefix} starting export with onnx {onnx.__version__}...")
-    f = file.with_suffix(".onnx")
-
-    output_names = (
-        ["output0", "output1"]
-        if isinstance(model, SegmentationModel)
-        else ["output0"]
-    )
-    if dynamic:
-        dynamic = {
-            "images": {0: "batch", 2: "height", 3: "width"}
-        }  # shape(1,3,640,640)
-        if isinstance(model, SegmentationModel):
-            dynamic["output0"] = {
-                0: "batch",
-                1: "anchors",
-            }  # shape(1,25200,85)
-            dynamic["output1"] = {
-                0: "batch",
-                2: "mask_height",
-                3: "mask_width",
-            }  # shape(1,32,160,160)
-        elif isinstance(model, DetectionModel):
-            dynamic["output0"] = {
-                0: "batch",
-                1: "anchors",
-            }  # shape(1,25200,85)
-
-    torch.onnx.export(
-        model.cpu()
-        if dynamic
-        else model,  # --dynamic only compatible with cpu
-        im.cpu() if dynamic else im,
-        f,
-        verbose=False,
-        opset_version=opset,
-        do_constant_folding=True,  # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False
-        input_names=["images"],
-        output_names=output_names,
-        dynamic_axes=dynamic or None,
-    )
-
-    # Checks
-    model_onnx = onnx.load(f)  # load onnx model
-    onnx.checker.check_model(model_onnx)  # check onnx model
-
-    # Metadata
-    d = {"stride": int(max(model.stride)), "names": model.names}
-    for k, v in d.items():
-        meta = model_onnx.metadata_props.add()
-        meta.key, meta.value = k, str(v)
-    onnx.save(model_onnx, f)
-
-    # Simplify
-    if simplify:
-        try:
-            cuda = torch.cuda.is_available()
-            check_requirements(
-                (
-                    "onnxruntime-gpu" if cuda else "onnxruntime",
-                    "onnx-simplifier>=0.4.1",
-                )
-            )
-            import onnxsim
-
-            LOGGER.info(
-                f"{prefix} simplifying with onnx-simplifier {onnxsim.__version__}..."
-            )
-            model_onnx, check = onnxsim.simplify(model_onnx)
-            assert check, "assert check failed"
-            onnx.save(model_onnx, f)
-        except Exception as e:
-            LOGGER.info(f"{prefix} simplifier failure: {e}")
-    return f, model_onnx
-
-
-@try_export
-def export_openvino(file, metadata, half, prefix=colorstr("OpenVINO:")):
-    # YOLOv5 OpenVINO export
-    check_requirements(
-        "openvino-dev"
-    )  # requires openvino-dev: https://pypi.org/project/openvino-dev/
-    import openvino.inference_engine as ie
-
-    LOGGER.info(
-        f"\n{prefix} starting export with openvino {ie.__version__}..."
-    )
-    f = str(file).replace(".pt", f"_openvino_model{os.sep}")
-
-    cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
-    subprocess.run(cmd.split(), check=True, env=os.environ)  # export
-    yaml_save(
-        Path(f) / file.with_suffix(".yaml").name, metadata
-    )  # add metadata.yaml
-    return f, None
-
-
-@try_export
-def export_paddle(model, im, file, metadata, prefix=colorstr("PaddlePaddle:")):
-    # YOLOv5 Paddle export
-    check_requirements(("paddlepaddle", "x2paddle"))
-    import x2paddle
-    from x2paddle.convert import pytorch2paddle
-
-    LOGGER.info(
-        f"\n{prefix} starting export with X2Paddle {x2paddle.__version__}..."
-    )
-    f = str(file).replace(".pt", f"_paddle_model{os.sep}")
-
-    pytorch2paddle(
-        module=model, save_dir=f, jit_type="trace", input_examples=[im]
-    )  # export
-    yaml_save(
-        Path(f) / file.with_suffix(".yaml").name, metadata
-    )  # add metadata.yaml
-    return f, None
-
-
-@try_export
-def export_coreml(model, im, file, int8, half, prefix=colorstr("CoreML:")):
-    # YOLOv5 CoreML export
-    check_requirements("coremltools")
-    import coremltools as ct
-
-    LOGGER.info(
-        f"\n{prefix} starting export with coremltools {ct.__version__}..."
-    )
-    f = file.with_suffix(".mlmodel")
-
-    ts = torch.jit.trace(model, im, strict=False)  # TorchScript model
-    ct_model = ct.convert(
-        ts,
-        inputs=[
-            ct.ImageType(
-                "image", shape=im.shape, scale=1 / 255, bias=[0, 0, 0]
-            )
-        ],
-    )
-    bits, mode = (
-        (8, "kmeans_lut") if int8 else (16, "linear") if half else (32, None)
-    )
-    if bits < 32:
-        if MACOS:  # quantization only supported on macOS
-            with warnings.catch_warnings():
-                warnings.filterwarnings(
-                    "ignore", category=DeprecationWarning
-                )  # suppress numpy==1.20 float warning
-                ct_model = ct.models.neural_network.quantization_utils.quantize_weights(
-                    ct_model, bits, mode
-                )
-        else:
-            print(
-                f"{prefix} quantization only supported on macOS, skipping..."
-            )
-    ct_model.save(f)
-    return f, ct_model
-
-
-@try_export
-def export_engine(
-    model,
-    im,
-    file,
-    half,
-    dynamic,
-    simplify,
-    workspace=4,
-    verbose=False,
-    prefix=colorstr("TensorRT:"),
-):
-    # YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
-    assert (
-        im.device.type != "cpu"
-    ), "export running on CPU but must be on GPU, i.e. `python export.py --device 0`"
-    try:
-        import tensorrt as trt
-    except Exception:
-        if platform.system() == "Linux":
-            check_requirements(
-                "nvidia-tensorrt",
-                cmds="-U --index-url https://pypi.ngc.nvidia.com",
-            )
-        import tensorrt as trt
-
-    if (
-        trt.__version__[0] == "7"
-    ):  # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
-        grid = model.model[-1].anchor_grid
-        model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
-        export_onnx(model, im, file, 12, dynamic, simplify)  # opset 12
-        model.model[-1].anchor_grid = grid
-    else:  # TensorRT >= 8
-        check_version(
-            trt.__version__, "8.0.0", hard=True
-        )  # require tensorrt>=8.0.0
-        export_onnx(model, im, file, 12, dynamic, simplify)  # opset 12
-    onnx = file.with_suffix(".onnx")
-
-    LOGGER.info(
-        f"\n{prefix} starting export with TensorRT {trt.__version__}..."
-    )
-    assert onnx.exists(), f"failed to export ONNX file: {onnx}"
-    f = file.with_suffix(".engine")  # TensorRT engine file
-    logger = trt.Logger(trt.Logger.INFO)
-    if verbose:
-        logger.min_severity = trt.Logger.Severity.VERBOSE
-
-    builder = trt.Builder(logger)
-    config = builder.create_builder_config()
-    config.max_workspace_size = workspace * 1 << 30
-    # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30)  # fix TRT 8.4 deprecation notice
-
-    flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
-    network = builder.create_network(flag)
-    parser = trt.OnnxParser(network, logger)
-    if not parser.parse_from_file(str(onnx)):
-        raise RuntimeError(f"failed to load ONNX file: {onnx}")
-
-    inputs = [network.get_input(i) for i in range(network.num_inputs)]
-    outputs = [network.get_output(i) for i in range(network.num_outputs)]
-    for inp in inputs:
-        LOGGER.info(
-            f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}'
-        )
-    for out in outputs:
-        LOGGER.info(
-            f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}'
-        )
-
-    if dynamic:
-        if im.shape[0] <= 1:
-            LOGGER.warning(
-                f"{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument"
-            )
-        profile = builder.create_optimization_profile()
-        for inp in inputs:
-            profile.set_shape(
-                inp.name,
-                (1, *im.shape[1:]),
-                (max(1, im.shape[0] // 2), *im.shape[1:]),
-                im.shape,
-            )
-        config.add_optimization_profile(profile)
-
-    LOGGER.info(
-        f"{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}"
-    )
-    if builder.platform_has_fast_fp16 and half:
-        config.set_flag(trt.BuilderFlag.FP16)
-    with builder.build_engine(network, config) as engine, open(f, "wb") as t:
-        t.write(engine.serialize())
-    return f, None
-
-
-@try_export
-def export_saved_model(
-    model,
-    im,
-    file,
-    dynamic,
-    tf_nms=False,
-    agnostic_nms=False,
-    topk_per_class=100,
-    topk_all=100,
-    iou_thres=0.45,
-    conf_thres=0.25,
-    keras=False,
-    prefix=colorstr("TensorFlow SavedModel:"),
-):
-    # YOLOv5 TensorFlow SavedModel export
-    try:
-        import tensorflow as tf
-    except Exception:
-        check_requirements(
-            f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}"
-        )
-        import tensorflow as tf
-    from tensorflow.python.framework.convert_to_constants import (
-        convert_variables_to_constants_v2,
-    )
-
-    from models.tf import TFModel
-
-    LOGGER.info(
-        f"\n{prefix} starting export with tensorflow {tf.__version__}..."
-    )
-    f = str(file).replace(".pt", "_saved_model")
-    batch_size, ch, *imgsz = list(im.shape)  # BCHW
-
-    tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
-    im = tf.zeros((batch_size, *imgsz, ch))  # BHWC order for TensorFlow
-    _ = tf_model.predict(
-        im,
-        tf_nms,
-        agnostic_nms,
-        topk_per_class,
-        topk_all,
-        iou_thres,
-        conf_thres,
-    )
-    inputs = tf.keras.Input(
-        shape=(*imgsz, ch), batch_size=None if dynamic else batch_size
-    )
-    outputs = tf_model.predict(
-        inputs,
-        tf_nms,
-        agnostic_nms,
-        topk_per_class,
-        topk_all,
-        iou_thres,
-        conf_thres,
-    )
-    keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
-    keras_model.trainable = False
-    keras_model.summary()
-    if keras:
-        keras_model.save(f, save_format="tf")
-    else:
-        spec = tf.TensorSpec(
-            keras_model.inputs[0].shape, keras_model.inputs[0].dtype
-        )
-        m = tf.function(lambda x: keras_model(x))  # full model
-        m = m.get_concrete_function(spec)
-        frozen_func = convert_variables_to_constants_v2(m)
-        tfm = tf.Module()
-        tfm.__call__ = tf.function(
-            lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec]
-        )
-        tfm.__call__(im)
-        tf.saved_model.save(
-            tfm,
-            f,
-            options=tf.saved_model.SaveOptions(
-                experimental_custom_gradients=False
-            )
-            if check_version(tf.__version__, "2.6")
-            else tf.saved_model.SaveOptions(),
-        )
-    return f, keras_model
-
-
-@try_export
-def export_pb(keras_model, file, prefix=colorstr("TensorFlow GraphDef:")):
-    # YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
-    import tensorflow as tf
-    from tensorflow.python.framework.convert_to_constants import (
-        convert_variables_to_constants_v2,
-    )
-
-    LOGGER.info(
-        f"\n{prefix} starting export with tensorflow {tf.__version__}..."
-    )
-    f = file.with_suffix(".pb")
-
-    m = tf.function(lambda x: keras_model(x))  # full model
-    m = m.get_concrete_function(
-        tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
-    )
-    frozen_func = convert_variables_to_constants_v2(m)
-    frozen_func.graph.as_graph_def()
-    tf.io.write_graph(
-        graph_or_graph_def=frozen_func.graph,
-        logdir=str(f.parent),
-        name=f.name,
-        as_text=False,
-    )
-    return f, None
-
-
-@try_export
-def export_tflite(
-    keras_model,
-    im,
-    file,
-    int8,
-    data,
-    nms,
-    agnostic_nms,
-    prefix=colorstr("TensorFlow Lite:"),
-):
-    # YOLOv5 TensorFlow Lite export
-    import tensorflow as tf
-
-    LOGGER.info(
-        f"\n{prefix} starting export with tensorflow {tf.__version__}..."
-    )
-    batch_size, ch, *imgsz = list(im.shape)  # BCHW
-    f = str(file).replace(".pt", "-fp16.tflite")
-
-    converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
-    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
-    converter.target_spec.supported_types = [tf.float16]
-    converter.optimizations = [tf.lite.Optimize.DEFAULT]
-    if int8:
-        from models.tf import representative_dataset_gen
-
-        dataset = LoadImages(
-            check_dataset(check_yaml(data))["train"],
-            img_size=imgsz,
-            auto=False,
-        )
-        converter.representative_dataset = lambda: representative_dataset_gen(
-            dataset, ncalib=100
-        )
-        converter.target_spec.supported_ops = [
-            tf.lite.OpsSet.TFLITE_BUILTINS_INT8
-        ]
-        converter.target_spec.supported_types = []
-        converter.inference_input_type = tf.uint8  # or tf.int8
-        converter.inference_output_type = tf.uint8  # or tf.int8
-        converter.experimental_new_quantizer = True
-        f = str(file).replace(".pt", "-int8.tflite")
-    if nms or agnostic_nms:
-        converter.target_spec.supported_ops.append(
-            tf.lite.OpsSet.SELECT_TF_OPS
-        )
-
-    tflite_model = converter.convert()
-    open(f, "wb").write(tflite_model)
-    return f, None
-
-
-@try_export
-def export_edgetpu(file, prefix=colorstr("Edge TPU:")):
-    # YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
-    cmd = "edgetpu_compiler --version"
-    help_url = "https://coral.ai/docs/edgetpu/compiler/"
-    assert (
-        platform.system() == "Linux"
-    ), f"export only supported on Linux. See {help_url}"
-    if subprocess.run(f"{cmd} >/dev/null", shell=True).returncode != 0:
-        LOGGER.info(
-            f"\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}"
-        )
-        sudo = (
-            subprocess.run("sudo --version >/dev/null", shell=True).returncode
-            == 0
-        )  # sudo installed on system
-        for c in (
-            "curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -",
-            'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
-            "sudo apt-get update",
-            "sudo apt-get install edgetpu-compiler",
-        ):
-            subprocess.run(
-                c if sudo else c.replace("sudo ", ""), shell=True, check=True
-            )
-    ver = (
-        subprocess.run(cmd, shell=True, capture_output=True, check=True)
-        .stdout.decode()
-        .split()[-1]
-    )
-
-    LOGGER.info(f"\n{prefix} starting export with Edge TPU compiler {ver}...")
-    f = str(file).replace(".pt", "-int8_edgetpu.tflite")  # Edge TPU model
-    f_tfl = str(file).replace(".pt", "-int8.tflite")  # TFLite model
-
-    cmd = f"edgetpu_compiler -s -d -k 10 --out_dir {file.parent} {f_tfl}"
-    subprocess.run(cmd.split(), check=True)
-    return f, None
-
-
-@try_export
-def export_tfjs(file, prefix=colorstr("TensorFlow.js:")):
-    # YOLOv5 TensorFlow.js export
-    check_requirements("tensorflowjs")
-    import tensorflowjs as tfjs
-
-    LOGGER.info(
-        f"\n{prefix} starting export with tensorflowjs {tfjs.__version__}..."
-    )
-    f = str(file).replace(".pt", "_web_model")  # js dir
-    f_pb = file.with_suffix(".pb")  # *.pb path
-    f_json = f"{f}/model.json"  # *.json path
-
-    cmd = (
-        f"tensorflowjs_converter --input_format=tf_frozen_model "
-        f"--output_node_names=Identity,Identity_1,Identity_2,Identity_3 {f_pb} {f}"
-    )
-    subprocess.run(cmd.split())
-
-    json = Path(f_json).read_text()
-    with open(f_json, "w") as j:  # sort JSON Identity_* in ascending order
-        subst = re.sub(
-            r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
-            r'"Identity.?.?": {"name": "Identity.?.?"}, '
-            r'"Identity.?.?": {"name": "Identity.?.?"}, '
-            r'"Identity.?.?": {"name": "Identity.?.?"}}}',
-            r'{"outputs": {"Identity": {"name": "Identity"}, '
-            r'"Identity_1": {"name": "Identity_1"}, '
-            r'"Identity_2": {"name": "Identity_2"}, '
-            r'"Identity_3": {"name": "Identity_3"}}}',
-            json,
-        )
-        j.write(subst)
-    return f, None
-
-
-def add_tflite_metadata(file, metadata, num_outputs):
-    # Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata
-    with contextlib.suppress(ImportError):
-        # check_requirements('tflite_support')
-        from tflite_support import flatbuffers
-        from tflite_support import metadata as _metadata
-        from tflite_support import metadata_schema_py_generated as _metadata_fb
-
-        tmp_file = Path("/tmp/meta.txt")
-        with open(tmp_file, "w") as meta_f:
-            meta_f.write(str(metadata))
-
-        model_meta = _metadata_fb.ModelMetadataT()
-        label_file = _metadata_fb.AssociatedFileT()
-        label_file.name = tmp_file.name
-        model_meta.associatedFiles = [label_file]
-
-        subgraph = _metadata_fb.SubGraphMetadataT()
-        subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()]
-        subgraph.outputTensorMetadata = [
-            _metadata_fb.TensorMetadataT()
-        ] * num_outputs
-        model_meta.subgraphMetadata = [subgraph]
-
-        b = flatbuffers.Builder(0)
-        b.Finish(
-            model_meta.Pack(b),
-            _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER,
-        )
-        metadata_buf = b.Output()
-
-        populator = _metadata.MetadataPopulator.with_model_file(file)
-        populator.load_metadata_buffer(metadata_buf)
-        populator.load_associated_files([str(tmp_file)])
-        populator.populate()
-        tmp_file.unlink()
-
-
-@smart_inference_mode()
-def run(
-    data=ROOT / "data/coco128.yaml",  # 'dataset.yaml path'
-    weights=ROOT / "yolov5s.pt",  # weights path
-    imgsz=(640, 640),  # image (height, width)
-    batch_size=1,  # batch size
-    device="cpu",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-    include=("torchscript", "onnx"),  # include formats
-    half=False,  # FP16 half-precision export
-    inplace=False,  # set YOLOv5 Detect() inplace=True
-    keras=False,  # use Keras
-    optimize=False,  # TorchScript: optimize for mobile
-    int8=False,  # CoreML/TF INT8 quantization
-    dynamic=False,  # ONNX/TF/TensorRT: dynamic axes
-    simplify=False,  # ONNX: simplify model
-    opset=12,  # ONNX: opset version
-    verbose=False,  # TensorRT: verbose log
-    workspace=4,  # TensorRT: workspace size (GB)
-    nms=False,  # TF: add NMS to model
-    agnostic_nms=False,  # TF: add agnostic NMS to model
-    topk_per_class=100,  # TF.js NMS: topk per class to keep
-    topk_all=100,  # TF.js NMS: topk for all classes to keep
-    iou_thres=0.45,  # TF.js NMS: IoU threshold
-    conf_thres=0.25,  # TF.js NMS: confidence threshold
-):
-    t = time.time()
-    include = [x.lower() for x in include]  # to lowercase
-    fmts = tuple(export_formats()["Argument"][1:])  # --include arguments
-    flags = [x in include for x in fmts]
-    assert sum(flags) == len(
-        include
-    ), f"ERROR: Invalid --include {include}, valid --include arguments are {fmts}"
-    (
-        jit,
-        onnx,
-        xml,
-        engine,
-        coreml,
-        saved_model,
-        pb,
-        tflite,
-        edgetpu,
-        tfjs,
-        paddle,
-    ) = flags  # export booleans
-    file = Path(
-        url2file(weights)
-        if str(weights).startswith(("http:/", "https:/"))
-        else weights
-    )  # PyTorch weights
-
-    # Load PyTorch model
-    device = select_device(device)
-    if half:
-        assert (
-            device.type != "cpu" or coreml
-        ), "--half only compatible with GPU export, i.e. use --device 0"
-        assert (
-            not dynamic
-        ), "--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both"
-    model = attempt_load(
-        weights, device=device, inplace=True, fuse=True
-    )  # load FP32 model
-
-    # Checks
-    imgsz *= 2 if len(imgsz) == 1 else 1  # expand
-    if optimize:
-        assert (
-            device.type == "cpu"
-        ), "--optimize not compatible with cuda devices, i.e. use --device cpu"
-
-    # Input
-    gs = int(max(model.stride))  # grid size (max stride)
-    imgsz = [
-        check_img_size(x, gs) for x in imgsz
-    ]  # verify img_size are gs-multiples
-    im = torch.zeros(batch_size, 3, *imgsz).to(
-        device
-    )  # image size(1,3,320,192) BCHW iDetection
-
-    # Update model
-    model.eval()
-    for k, m in model.named_modules():
-        if isinstance(m, Detect):
-            m.inplace = inplace
-            m.dynamic = dynamic
-            m.export = True
-
-    for _ in range(2):
-        y = model(im)  # dry runs
-    if half and not coreml:
-        im, model = im.half(), model.half()  # to FP16
-    shape = tuple(
-        (y[0] if isinstance(y, tuple) else y).shape
-    )  # model output shape
-    metadata = {
-        "stride": int(max(model.stride)),
-        "names": model.names,
-    }  # model metadata
-    LOGGER.info(
-        f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)"
-    )
-
-    # Exports
-    f = [""] * len(fmts)  # exported filenames
-    warnings.filterwarnings(
-        action="ignore", category=torch.jit.TracerWarning
-    )  # suppress TracerWarning
-    if jit:  # TorchScript
-        f[0], _ = export_torchscript(model, im, file, optimize)
-    if engine:  # TensorRT required before ONNX
-        f[1], _ = export_engine(
-            model, im, file, half, dynamic, simplify, workspace, verbose
-        )
-    if onnx or xml:  # OpenVINO requires ONNX
-        f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify)
-    if xml:  # OpenVINO
-        f[3], _ = export_openvino(file, metadata, half)
-    if coreml:  # CoreML
-        f[4], _ = export_coreml(model, im, file, int8, half)
-    if any((saved_model, pb, tflite, edgetpu, tfjs)):  # TensorFlow formats
-        assert (
-            not tflite or not tfjs
-        ), "TFLite and TF.js models must be exported separately, please pass only one type."
-        assert not isinstance(
-            model, ClassificationModel
-        ), "ClassificationModel export to TF formats not yet supported."
-        f[5], s_model = export_saved_model(
-            model.cpu(),
-            im,
-            file,
-            dynamic,
-            tf_nms=nms or agnostic_nms or tfjs,
-            agnostic_nms=agnostic_nms or tfjs,
-            topk_per_class=topk_per_class,
-            topk_all=topk_all,
-            iou_thres=iou_thres,
-            conf_thres=conf_thres,
-            keras=keras,
-        )
-        if pb or tfjs:  # pb prerequisite to tfjs
-            f[6], _ = export_pb(s_model, file)
-        if tflite or edgetpu:
-            f[7], _ = export_tflite(
-                s_model,
-                im,
-                file,
-                int8 or edgetpu,
-                data=data,
-                nms=nms,
-                agnostic_nms=agnostic_nms,
-            )
-            if edgetpu:
-                f[8], _ = export_edgetpu(file)
-            add_tflite_metadata(
-                f[8] or f[7], metadata, num_outputs=len(s_model.outputs)
-            )
-        if tfjs:
-            f[9], _ = export_tfjs(file)
-    if paddle:  # PaddlePaddle
-        f[10], _ = export_paddle(model, im, file, metadata)
-
-    # Finish
-    f = [str(x) for x in f if x]  # filter out '' and None
-    if any(f):
-        cls, det, seg = (
-            isinstance(model, x)
-            for x in (ClassificationModel, DetectionModel, SegmentationModel)
-        )  # type
-        det &= (
-            not seg
-        )  # segmentation models inherit from SegmentationModel(DetectionModel)
-        dir = Path("segment" if seg else "classify" if cls else "")
-        h = "--half" if half else ""  # --half FP16 inference arg
-        s = (
-            "# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference"
-            if cls
-            else "# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference"
-            if seg
-            else ""
-        )
-        LOGGER.info(
-            f"\nExport complete ({time.time() - t:.1f}s)"
-            f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
-            f"\nDetect:          python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}"
-            f"\nValidate:        python {dir / 'val.py'} --weights {f[-1]} {h}"
-            f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}')  {s}"
-            f"\nVisualize:       https://netron.app"
-        )
-    return f  # return list of exported files/dirs
-
-
-def parse_opt():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--data",
-        type=str,
-        default=ROOT / "data/coco128.yaml",
-        help="dataset.yaml path",
-    )
-    parser.add_argument(
-        "--weights",
-        nargs="+",
-        type=str,
-        default=ROOT / "yolov5s.pt",
-        help="model.pt path(s)",
-    )
-    parser.add_argument(
-        "--imgsz",
-        "--img",
-        "--img-size",
-        nargs="+",
-        type=int,
-        default=[640, 640],
-        help="image (h, w)",
-    )
-    parser.add_argument("--batch-size", type=int, default=1, help="batch size")
-    parser.add_argument(
-        "--device", default="cpu", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
-    )
-    parser.add_argument(
-        "--half", action="store_true", help="FP16 half-precision export"
-    )
-    parser.add_argument(
-        "--inplace",
-        action="store_true",
-        help="set YOLOv5 Detect() inplace=True",
-    )
-    parser.add_argument("--keras", action="store_true", help="TF: use Keras")
-    parser.add_argument(
-        "--optimize",
-        action="store_true",
-        help="TorchScript: optimize for mobile",
-    )
-    parser.add_argument(
-        "--int8", action="store_true", help="CoreML/TF INT8 quantization"
-    )
-    parser.add_argument(
-        "--dynamic", action="store_true", help="ONNX/TF/TensorRT: dynamic axes"
-    )
-    parser.add_argument(
-        "--simplify", action="store_true", help="ONNX: simplify model"
-    )
-    parser.add_argument(
-        "--opset", type=int, default=17, help="ONNX: opset version"
-    )
-    parser.add_argument(
-        "--verbose", action="store_true", help="TensorRT: verbose log"
-    )
-    parser.add_argument(
-        "--workspace",
-        type=int,
-        default=4,
-        help="TensorRT: workspace size (GB)",
-    )
-    parser.add_argument(
-        "--nms", action="store_true", help="TF: add NMS to model"
-    )
-    parser.add_argument(
-        "--agnostic-nms",
-        action="store_true",
-        help="TF: add agnostic NMS to model",
-    )
-    parser.add_argument(
-        "--topk-per-class",
-        type=int,
-        default=100,
-        help="TF.js NMS: topk per class to keep",
-    )
-    parser.add_argument(
-        "--topk-all",
-        type=int,
-        default=100,
-        help="TF.js NMS: topk for all classes to keep",
-    )
-    parser.add_argument(
-        "--iou-thres",
-        type=float,
-        default=0.45,
-        help="TF.js NMS: IoU threshold",
-    )
-    parser.add_argument(
-        "--conf-thres",
-        type=float,
-        default=0.25,
-        help="TF.js NMS: confidence threshold",
-    )
-    parser.add_argument(
-        "--include",
-        nargs="+",
-        default=["torchscript"],
-        help="torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle",
-    )
-    opt = parser.parse_args()
-    print_args(vars(opt))
-    return opt
-
-
-def main(opt):
-    for opt.weights in (
-        opt.weights if isinstance(opt.weights, list) else [opt.weights]
-    ):
-        run(**vars(opt))
-
-
-if __name__ == "__main__":
-    opt = parse_opt()
-    main(opt)

spaces/AgentVerse/agentVerse/ui/src/classes/npc.ts

@@ -1,246 +0,0 @@
-import { Actor } from "./actor";
-import { DIRECTION } from "../utils";
-import {
-  MoveTo,
-  PathFinder,
-  Board,
-} from "../phaser3-rex-plugins/plugins/board-components";
-import { Label } from "../phaser3-rex-plugins/templates/ui/ui-components";
-import { COLOR_DARK, COLOR_LIGHT, COLOR_PRIMARY } from "../constants";
-import { TownScene } from "../scenes";
-import eventsCenter from "./event_center";
-
-export class NPC extends Actor {
-  private moveTo: MoveTo;
-  private board: Board;
-  private canMove: boolean = true;
-  private talkWithPlayer: boolean = false;
-  private path: PathFinder.NodeType[] = [];
-  private finalDirection: number = undefined;
-  private targetLocation: string = undefined;
-  private targetNPC: NPC = undefined;
-  private textBox: Label = undefined;
-
-  public id: number;
-  public direction: number = DIRECTION.DOWN;
-
-  constructor(
-    scene: Phaser.Scene,
-    board: Board,
-    x: number,
-    y: number,
-    name: string,
-    id: number
-  ) {
-    super(scene, x, y, name);
-
-    this.setName(name);
-    this.board = board;
-    this.id = id;
-    // PHYSICS
-    this.getBody().setSize(14, 16);
-    this.getBody().setOffset(0, 4);
-    this.getBody().setImmovable(true);
-    this.setOrigin(0, 0.2);
-
-    this.initAnimations();
-    this.moveTo = this.scene.rexBoard.add.moveTo(this, {
-      speed: 55,
-      sneak: true,
-    });
-    this.listenToDirectionEvent();
-  }
-
-  update(): void {
-    if (this.path.length > 0 && !this.moveTo.isRunning && this.canMove) {
-      var tileXY = this.board.worldXYToTileXY(this.x, this.y);
-      if (tileXY.x == this.path[0].x) {
-        if (tileXY.y < this.path[0].y) this.changeDirection(DIRECTION.DOWN);
-        else if (tileXY.y > this.path[0].y) this.changeDirection(DIRECTION.UP);
-      } else if (tileXY.y == this.path[0].y) {
-        if (tileXY.x < this.path[0].x) this.changeDirection(DIRECTION.RIGHT);
-        else if (tileXY.x > this.path[0].x)
-          this.changeDirection(DIRECTION.LEFT);
-      }
-      var move = this.moveTo.moveTo(this.path.shift());
-      move.removeAllListeners("complete");
-      move.on("complete", () => {
-        if (this.path.length == 0) {
-          this.changeDirection(this.finalDirection);
-          this.emitTurnEvent();
-          if (this.targetLocation != undefined) {
-            fetch("http://127.0.0.1:10002/update_location", {
-              method: "POST",
-              headers: {
-                "Content-Type": "application/json",
-              },
-              credentials: "same-origin",
-              body: JSON.stringify({
-                agent_locations: {
-                  [this.name]: this.targetLocation,
-                },
-              }),
-            });
-          }
-        }
-      });
-    }
-
-    var text = "";
-    switch (this.direction) {
-      case DIRECTION.UP:
-        text = "up";
-        break;
-      case DIRECTION.DOWN:
-        text = "down";
-        break;
-      case DIRECTION.LEFT:
-        text = "left";
-        break;
-      case DIRECTION.RIGHT:
-        text = "right";
-        break;
-    }
-    this.anims.play(this.name + "-walk-" + text, true);
-    if (this.anims.isPlaying && !this.moveTo.isRunning)
-      this.anims.setCurrentFrame(this.anims.currentAnim!.frames[0]);
-    this.updateTextBox();
-    this.depth = this.y + this.height * 0.8;
-  }
-
-  listenToDirectionEvent(): void {
-    eventsCenter.on(this.name + "-up", () => {
-      this.changeDirection(DIRECTION.UP);
-    });
-    eventsCenter.on(this.name + "-down", () => {
-      this.changeDirection(DIRECTION.DOWN);
-    });
-    eventsCenter.on(this.name + "-left", () => {
-      this.changeDirection(DIRECTION.LEFT);
-    });
-    eventsCenter.on(this.name + "-right", () => {
-      this.changeDirection(DIRECTION.RIGHT);
-    });
-  }
-
-  emitTurnEvent(): void {
-    // Make the listener NPC turn to the speaker NPC.
-    if (this.targetNPC == undefined) return;
-    var direction = "";
-    switch (this.finalDirection) {
-      case DIRECTION.UP:
-        direction = "down";
-        break;
-      case DIRECTION.DOWN:
-        direction = "up";
-        break;
-      case DIRECTION.LEFT:
-        direction = "right";
-        break;
-      case DIRECTION.RIGHT:
-        direction = "left";
-        break;
-    }
-    eventsCenter.emit(this.targetNPC.name + "-" + direction);
-    this.setTargetNPC();
-  }
-
-  updateTextBox(): void {
-    if (this.textBox == undefined) return;
-    this.textBox.setOrigin(0.5, 1.0);
-    var scale = this.scene.cameras.main.zoom;
-    this.textBox.setX(this.x + this.width / 2);
-    this.textBox.setY(this.y - this.height * 0.2);
-    this.textBox.depth = this.y + this.height * 0.8;
-    this.textBox.getChildren().forEach((child) => {
-      child.setDepth(this.y + this.height * 0.8);
-    });
-  }
-
-  public setTextBox(text: string): void {
-    this.destroyTextBox();
-    var scale = this.scene.cameras.main.zoom;
-    var scene = this.scene as TownScene;
-    this.textBox = scene.rexUI.add
-      .label({
-        x: this.x + this.width / 2,
-        y: this.y - this.height * 0.2,
-        width: 24 * scale,
-        orientation: "x",
-        background: scene.rexUI.add.roundRectangle(
-          0,
-          0,
-          2,
-          2,
-          20,
-          COLOR_PRIMARY,
-          0.7
-        ),
-        text: scene.rexUI.wrapExpandText(
-          scene.add.text(0, 0, text, {
-            fontSize: 10,
-          })
-        ),
-        expandTextWidth: true,
-        space: {
-          left: 10,
-          right: 10,
-          top: 10,
-          bottom: 10,
-        },
-      })
-      .setOrigin(0.5, 1.0)
-      .setScale(1 / scale, 1 / scale)
-      .setDepth(this.y + this.height * 0.8)
-      .layout();
-  }
-
-  public destroyTextBox(): void {
-    if (this.textBox != undefined) this.textBox.destroy();
-    this.textBox = undefined;
-  }
-
-  public changeDirection(direction: number): void {
-    if (direction == undefined) return;
-    this.direction = direction;
-  }
-
-  public moveAlongPath(
-    path: PathFinder.NodeType[],
-    finalDirection: number = undefined,
-    targetLocation: string = undefined
-  ): void {
-    if (path.length == 0) return;
-    if (this.moveTo.isRunning) return;
-    if (this.path.length > 0) return;
-    this.path = path;
-    this.finalDirection = finalDirection;
-    this.targetLocation = targetLocation;
-  }
-
-  public pauseMoving(): void {
-    this.moveTo.stop();
-    this.canMove = false;
-  }
-
-  public resumeMoving(): void {
-    this.moveTo.resume();
-    this.canMove = true;
-  }
-
-  public isMoving(): boolean {
-    return this.moveTo.isRunning || this.path.length > 0;
-  }
-
-  public isTalking(): boolean {
-    return this.talkWithPlayer;
-  }
-
-  public setTalking(talking: boolean): void {
-    this.talkWithPlayer = talking;
-  }
-
-  public setTargetNPC(targetNPC: NPC = undefined): void {
-    this.targetNPC = targetNPC;
-  }
-}

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

@@ -1,8 +0,0 @@
-import FSM from './fsm';
-
-export default class FSMPlugin extends Phaser.Plugins.BasePlugin {
-    add(
-        config?: FSM.IConfig
-    ): FSM;
-
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/Sizer.js

@@ -1,79 +0,0 @@
-import BaseSizer from '../basesizer/BaseSizer.js';
-import Methods from './Methods.js';
-import GetChildrenProportion from './GetChildrenProportion.js';
-import GetOrientationMode from '../utils/GetOrientationMode.js';
-
-const IsPlainObject = Phaser.Utils.Objects.IsPlainObject;
-const GetValue = Phaser.Utils.Objects.GetValue;
-
-class Sizer extends BaseSizer {
-    constructor(scene, x, y, minWidth, minHeight, orientation, config) {
-        if (IsPlainObject(x)) {
-            config = x;
-            x = GetValue(config, 'x', 0);
-            y = GetValue(config, 'y', 0);
-            minWidth = GetValue(config, 'width', undefined);
-            minHeight = GetValue(config, 'height', undefined);
-            orientation = GetValue(config, 'orientation', 0);
-        } else if (IsPlainObject(minWidth)) {
-            config = minWidth;
-            minWidth = GetValue(config, 'width', undefined);
-            minHeight = GetValue(config, 'height', undefined);
-            orientation = GetValue(config, 'orientation', 0);
-        } else if (IsPlainObject(orientation)) {
-            config = orientation;
-            orientation = GetValue(config, 'orientation', 0);
-        }
-
-        if (orientation === undefined) {
-            orientation = 0;
-        }
-        super(scene, x, y, minWidth, minHeight, config);
-
-        this.type = 'rexSizer';
-        this.sizerChildren = [];
-        this.setOrientation(orientation);
-        this.setItemSpacing(GetValue(config, 'space.item', 0));
-        this.setStartChildIndex(GetValue(config, 'startChildIndex', 0));
-        this.setRTL(GetValue(config, 'rtl', false));
-
-        this.addChildrenMap('items', this.sizerChildren);
-    }
-
-    setOrientation(orientation) {
-        this.orientation = GetOrientationMode(orientation);
-        return this;
-    }
-
-    setItemSpacing(space) {
-        this.space.item = space;
-        return this;
-    }
-
-    setStartChildIndex(index) {
-        this.startChildIndex = index;
-        return this;
-    }
-
-    setRTL(enable) {
-        if (enable === undefined) {
-            enable = true;
-        }
-        this.rtl = enable;
-        return this;
-    }
-
-    get childrenProportion() {
-        if (this._childrenProportion === undefined) {
-            this._childrenProportion = GetChildrenProportion.call(this);
-        }
-        return this._childrenProportion;
-    }
-}
-
-Object.assign(
-    Sizer.prototype,
-    Methods
-);
-
-export default Sizer;

spaces/Aki004/herta-so-vits/vdecoder/hifigan/models.py

@@ -1,503 +0,0 @@
-import os
-import json
-from .env import AttrDict
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from .utils import init_weights, get_padding
-
-LRELU_SLOPE = 0.1
-
-
-def load_model(model_path, device='cuda'):
-    config_file = os.path.join(os.path.split(model_path)[0], 'config.json')
-    with open(config_file) as f:
-        data = f.read()
-
-    global h
-    json_config = json.loads(data)
-    h = AttrDict(json_config)
-
-    generator = Generator(h).to(device)
-
-    cp_dict = torch.load(model_path)
-    generator.load_state_dict(cp_dict['generator'])
-    generator.eval()
-    generator.remove_weight_norm()
-    del cp_dict
-    return generator, h
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.h = h
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            xt = c2(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.h = h
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-def padDiff(x):
-    return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0)
-
-class SineGen(torch.nn.Module):
-    """ Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(self, samp_rate, harmonic_num=0,
-                 sine_amp=0.1, noise_std=0.003,
-                 voiced_threshold=0,
-                 flag_for_pulse=False):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-        self.flag_for_pulse = flag_for_pulse
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = (f0 > self.voiced_threshold).type(torch.float32)
-        return uv
-
-    def _f02sine(self, f0_values):
-        """ f0_values: (batchsize, length, dim)
-            where dim indicates fundamental tone and overtones
-        """
-        # convert to F0 in rad. The interger part n can be ignored
-        # because 2 * np.pi * n doesn't affect phase
-        rad_values = (f0_values / self.sampling_rate) % 1
-
-        # initial phase noise (no noise for fundamental component)
-        rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
-                              device=f0_values.device)
-        rand_ini[:, 0] = 0
-        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-
-        # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
-        if not self.flag_for_pulse:
-            # for normal case
-
-            # To prevent torch.cumsum numerical overflow,
-            # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
-            # Buffer tmp_over_one_idx indicates the time step to add -1.
-            # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
-            tmp_over_one = torch.cumsum(rad_values, 1) % 1
-            tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-
-            sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1)
-                              * 2 * np.pi)
-        else:
-            # If necessary, make sure that the first time step of every
-            # voiced segments is sin(pi) or cos(0)
-            # This is used for pulse-train generation
-
-            # identify the last time step in unvoiced segments
-            uv = self._f02uv(f0_values)
-            uv_1 = torch.roll(uv, shifts=-1, dims=1)
-            uv_1[:, -1, :] = 1
-            u_loc = (uv < 1) * (uv_1 > 0)
-
-            # get the instantanouse phase
-            tmp_cumsum = torch.cumsum(rad_values, dim=1)
-            # different batch needs to be processed differently
-            for idx in range(f0_values.shape[0]):
-                temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
-                temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
-                # stores the accumulation of i.phase within
-                # each voiced segments
-                tmp_cumsum[idx, :, :] = 0
-                tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
-
-            # rad_values - tmp_cumsum: remove the accumulation of i.phase
-            # within the previous voiced segment.
-            i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
-
-            # get the sines
-            sines = torch.cos(i_phase * 2 * np.pi)
-        return sines
-
-    def forward(self, f0):
-        """ sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,
-                                 device=f0.device)
-            # fundamental component
-            fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device))
-
-            # generate sine waveforms
-            sine_waves = self._f02sine(fn) * self.sine_amp
-
-            # generate uv signal
-            # uv = torch.ones(f0.shape)
-            # uv = uv * (f0 > self.voiced_threshold)
-            uv = self._f02uv(f0)
-
-            # noise: for unvoiced should be similar to sine_amp
-            #        std = self.sine_amp/3 -> max value ~ self.sine_amp
-            # .       for voiced regions is self.noise_std
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-
-            # first: set the unvoiced part to 0 by uv
-            # then: additive noise
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """ SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
-                                 sine_amp, add_noise_std, voiced_threshod)
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x):
-        """
-        Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-        F0_sampled (batchsize, length, 1)
-        Sine_source (batchsize, length, 1)
-        noise_source (batchsize, length 1)
-        """
-        # source for harmonic branch
-        sine_wavs, uv, _ = self.l_sin_gen(x)
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-
-        # source for noise branch, in the same shape as uv
-        noise = torch.randn_like(uv) * self.sine_amp / 3
-        return sine_merge, noise, uv
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, h):
-        super(Generator, self).__init__()
-        self.h = h
-
-        self.num_kernels = len(h["resblock_kernel_sizes"])
-        self.num_upsamples = len(h["upsample_rates"])
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h["upsample_rates"]))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=h["sampling_rate"],
-            harmonic_num=8)
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = weight_norm(Conv1d(h["inter_channels"], h["upsample_initial_channel"], 7, 1, padding=3))
-        resblock = ResBlock1 if h["resblock"] == '1' else ResBlock2
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(h["upsample_rates"], h["upsample_kernel_sizes"])):
-            c_cur = h["upsample_initial_channel"] // (2 ** (i + 1))
-            self.ups.append(weight_norm(
-                ConvTranspose1d(h["upsample_initial_channel"] // (2 ** i), h["upsample_initial_channel"] // (2 ** (i + 1)),
-                                k, u, padding=(k - u) // 2)))
-            if i + 1 < len(h["upsample_rates"]):  #
-                stride_f0 = np.prod(h["upsample_rates"][i + 1:])
-                self.noise_convs.append(Conv1d(
-                    1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = h["upsample_initial_channel"] // (2 ** (i + 1))
-            for j, (k, d) in enumerate(zip(h["resblock_kernel_sizes"], h["resblock_dilation_sizes"])):
-                self.resblocks.append(resblock(h, ch, k, d))
-
-        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
-        self.ups.apply(init_weights)
-        self.conv_post.apply(init_weights)
-        self.cond = nn.Conv1d(h['gin_channels'], h['upsample_initial_channel'], 1)
-
-    def forward(self, x, f0, g=None):
-        # print(1,x.shape,f0.shape,f0[:, None].shape)
-        f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2)  # bs,n,t
-        # print(2,f0.shape)
-        har_source, noi_source, uv = self.m_source(f0)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        x = x + self.cond(g)
-        # print(124,x.shape,har_source.shape)
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            # print(3,x.shape)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            # print(4,x_source.shape,har_source.shape,x.shape)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-        remove_weight_norm(self.conv_pre)
-        remove_weight_norm(self.conv_post)
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, periods=None):
-        super(MultiPeriodDiscriminator, self).__init__()
-        self.periods = periods if periods is not None else [2, 3, 5, 7, 11]
-        self.discriminators = nn.ModuleList()
-        for period in self.periods:
-            self.discriminators.append(DiscriminatorP(period))
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 128, 15, 1, padding=7)),
-            norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
-            norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
-            norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiScaleDiscriminator(torch.nn.Module):
-    def __init__(self):
-        super(MultiScaleDiscriminator, self).__init__()
-        self.discriminators = nn.ModuleList([
-            DiscriminatorS(use_spectral_norm=True),
-            DiscriminatorS(),
-            DiscriminatorS(),
-        ])
-        self.meanpools = nn.ModuleList([
-            AvgPool1d(4, 2, padding=2),
-            AvgPool1d(4, 2, padding=2)
-        ])
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            if i != 0:
-                y = self.meanpools[i - 1](y)
-                y_hat = self.meanpools[i - 1](y_hat)
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        r_loss = torch.mean((1 - dr) ** 2)
-        g_loss = torch.mean(dg ** 2)
-        loss += (r_loss + g_loss)
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        l = torch.mean((1 - dg) ** 2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses

spaces/AkshayDev/Lazy-Film-Reviews/README.md

@@ -1,13 +0,0 @@
----
-title: Lazy Film Reviews
-emoji: 🌖
-colorFrom: purple
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
-license: cc-by-nc-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/decoder/sh.py

@@ -1,133 +0,0 @@
-import torch
-
-################## sh function ##################
-C0 = 0.28209479177387814
-C1 = 0.4886025119029199
-C2 = [
-    1.0925484305920792,
-    -1.0925484305920792,
-    0.31539156525252005,
-    -1.0925484305920792,
-    0.5462742152960396
-]
-C3 = [
-    -0.5900435899266435,
-    2.890611442640554,
-    -0.4570457994644658,
-    0.3731763325901154,
-    -0.4570457994644658,
-    1.445305721320277,
-    -0.5900435899266435
-]
-C4 = [
-    2.5033429417967046,
-    -1.7701307697799304,
-    0.9461746957575601,
-    -0.6690465435572892,
-    0.10578554691520431,
-    -0.6690465435572892,
-    0.47308734787878004,
-    -1.7701307697799304,
-    0.6258357354491761,
-]
-
-def eval_sh(deg, sh, dirs):
-    """
-    Evaluate spherical harmonics at unit directions
-    using hardcoded SH polynomials.
-    Works with torch/np/jnp.
-    ... Can be 0 or more batch dimensions.
-    :param deg: int SH max degree. Currently, 0-4 supported
-    :param sh: torch.Tensor SH coeffs (..., C, (max degree + 1) ** 2)
-    :param dirs: torch.Tensor unit directions (..., 3)
-    :return: (..., C)
-    """
-    assert deg <= 4 and deg >= 0
-    assert (deg + 1) ** 2 == sh.shape[-1]
-    C = sh.shape[-2]
-
-    result = C0 * sh[..., 0]
-    if deg > 0:
-        x, y, z = dirs[..., 0:1], dirs[..., 1:2], dirs[..., 2:3]
-        result = (result -
-                C1 * y * sh[..., 1] +
-                C1 * z * sh[..., 2] -
-                C1 * x * sh[..., 3])
-        if deg > 1:
-            xx, yy, zz = x * x, y * y, z * z
-            xy, yz, xz = x * y, y * z, x * z
-            result = (result +
-                    C2[0] * xy * sh[..., 4] +
-                    C2[1] * yz * sh[..., 5] +
-                    C2[2] * (2.0 * zz - xx - yy) * sh[..., 6] +
-                    C2[3] * xz * sh[..., 7] +
-                    C2[4] * (xx - yy) * sh[..., 8])
-
-            if deg > 2:
-                result = (result +
-                        C3[0] * y * (3 * xx - yy) * sh[..., 9] +
-                        C3[1] * xy * z * sh[..., 10] +
-                        C3[2] * y * (4 * zz - xx - yy)* sh[..., 11] +
-                        C3[3] * z * (2 * zz - 3 * xx - 3 * yy) * sh[..., 12] +
-                        C3[4] * x * (4 * zz - xx - yy) * sh[..., 13] +
-                        C3[5] * z * (xx - yy) * sh[..., 14] +
-                        C3[6] * x * (xx - 3 * yy) * sh[..., 15])
-                if deg > 3:
-                    result = (result + C4[0] * xy * (xx - yy) * sh[..., 16] +
-                            C4[1] * yz * (3 * xx - yy) * sh[..., 17] +
-                            C4[2] * xy * (7 * zz - 1) * sh[..., 18] +
-                            C4[3] * yz * (7 * zz - 3) * sh[..., 19] +
-                            C4[4] * (zz * (35 * zz - 30) + 3) * sh[..., 20] +
-                            C4[5] * xz * (7 * zz - 3) * sh[..., 21] +
-                            C4[6] * (xx - yy) * (7 * zz - 1) * sh[..., 22] +
-                            C4[7] * xz * (xx - 3 * yy) * sh[..., 23] +
-                            C4[8] * (xx * (xx - 3 * yy) - yy * (3 * xx - yy)) * sh[..., 24])
-    return result
-
-def eval_sh_bases(deg, dirs):
-    """
-    Evaluate spherical harmonics bases at unit directions,
-    without taking linear combination.
-    At each point, the final result may the be
-    obtained through simple multiplication.
-    :param deg: int SH max degree. Currently, 0-4 supported
-    :param dirs: torch.Tensor (..., 3) unit directions
-    :return: torch.Tensor (..., (deg+1) ** 2)
-    """
-    assert deg <= 4 and deg >= 0
-    result = torch.empty((*dirs.shape[:-1], (deg + 1) ** 2), dtype=dirs.dtype, device=dirs.device)
-    result[..., 0] = C0
-    if deg > 0:
-        x, y, z = dirs.unbind(-1)
-        result[..., 1] = -C1 * y;
-        result[..., 2] = C1 * z;
-        result[..., 3] = -C1 * x;
-        if deg > 1:
-            xx, yy, zz = x * x, y * y, z * z
-            xy, yz, xz = x * y, y * z, x * z
-            result[..., 4] = C2[0] * xy;
-            result[..., 5] = C2[1] * yz;
-            result[..., 6] = C2[2] * (2.0 * zz - xx - yy);
-            result[..., 7] = C2[3] * xz;
-            result[..., 8] = C2[4] * (xx - yy);
-
-            if deg > 2:
-                result[..., 9] = C3[0] * y * (3 * xx - yy);
-                result[..., 10] = C3[1] * xy * z;
-                result[..., 11] = C3[2] * y * (4 * zz - xx - yy);
-                result[..., 12] = C3[3] * z * (2 * zz - 3 * xx - 3 * yy);
-                result[..., 13] = C3[4] * x * (4 * zz - xx - yy);
-                result[..., 14] = C3[5] * z * (xx - yy);
-                result[..., 15] = C3[6] * x * (xx - 3 * yy);
-
-                if deg > 3:
-                    result[..., 16] = C4[0] * xy * (xx - yy);
-                    result[..., 17] = C4[1] * yz * (3 * xx - yy);
-                    result[..., 18] = C4[2] * xy * (7 * zz - 1);
-                    result[..., 19] = C4[3] * yz * (7 * zz - 3);
-                    result[..., 20] = C4[4] * (zz * (35 * zz - 30) + 3);
-                    result[..., 21] = C4[5] * xz * (7 * zz - 3);
-                    result[..., 22] = C4[6] * (xx - yy) * (7 * zz - 1);
-                    result[..., 23] = C4[7] * xz * (xx - 3 * yy);
-                    result[..., 24] = C4[8] * (xx * (xx - 3 * yy) - yy * (3 * xx - yy));
-    return result

spaces/AndreLie95/Diabetes_Risk_Prediction/README.md

@@ -1,12 +0,0 @@
----
-title: Deploy Milestone 2
-emoji: ⚡
-colorFrom: indigo
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/optimization/opt_overview.md

@@ -1,17 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Overview
-
-Generating high-quality outputs is computationally intensive, especially during each iterative step where you go from a noisy output to a less noisy output. One of 🧨 Diffuser's goal is to make this technology widely accessible to everyone, which includes enabling fast inference on consumer and specialized hardware. 
-
-This section will cover tips and tricks - like half-precision weights and sliced attention - for optimizing inference speed and reducing memory-consumption. You can also learn how to speed up your PyTorch code with [`torch.compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) or [ONNX Runtime](https://onnxruntime.ai/docs/), and enable memory-efficient attention with [xFormers](https://facebookresearch.github.io/xformers/). There are also guides for running inference on specific hardware like Apple Silicon, and Intel or Habana processors.

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/controlnet/README.md

@@ -1,465 +0,0 @@
-# ControlNet training example
-
-[Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) by Lvmin Zhang and Maneesh Agrawala.
-
-This example is based on the [training example in the original ControlNet repository](https://github.com/lllyasviel/ControlNet/blob/main/docs/train.md). It trains a ControlNet to fill circles using a [small synthetic dataset](https://huggingface.co/datasets/fusing/fill50k).
-
-## Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-**Important**
-
-To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install -e .
-```
-
-Then cd in the example folder and run
-```bash
-pip install -r requirements.txt
-```
-
-And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-Or for a default accelerate configuration without answering questions about your environment
-
-```bash
-accelerate config default
-```
-
-Or if your environment doesn't support an interactive shell e.g. a notebook
-
-```python
-from accelerate.utils import write_basic_config
-write_basic_config()
-```
-
-## Circle filling dataset
-
-The original dataset is hosted in the [ControlNet repo](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip). We re-uploaded it to be compatible with `datasets` [here](https://huggingface.co/datasets/fusing/fill50k). Note that `datasets` handles dataloading within the training script.
-
-Our training examples use [Stable Diffusion 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) as the original set of ControlNet models were trained from it. However, ControlNet can be trained to augment any Stable Diffusion compatible model (such as [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)) or [stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1).
-
-## Training
-
-Our training examples use two test conditioning images. They can be downloaded by running
-
-```sh
-wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png
-
-wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png
-```
-
-
-```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="path to save model"
-
-accelerate launch train_controlnet.py \
- --pretrained_model_name_or_path=$MODEL_DIR \
- --output_dir=$OUTPUT_DIR \
- --dataset_name=fusing/fill50k \
- --resolution=512 \
- --learning_rate=1e-5 \
- --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
- --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
- --train_batch_size=4
-```
-
-This default configuration requires ~38GB VRAM.
-
-By default, the training script logs outputs to tensorboard. Pass `--report_to wandb` to use weights and
-biases.
-
-Gradient accumulation with a smaller batch size can be used to reduce training requirements to ~20 GB VRAM.
-
-```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="path to save model"
-
-accelerate launch train_controlnet.py \
- --pretrained_model_name_or_path=$MODEL_DIR \
- --output_dir=$OUTPUT_DIR \
- --dataset_name=fusing/fill50k \
- --resolution=512 \
- --learning_rate=1e-5 \
- --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
- --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
- --train_batch_size=1 \
- --gradient_accumulation_steps=4
-```
-
-## Training with multiple GPUs
-
-`accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch)
-for running distributed training with `accelerate`. Here is an example command:
-
-```bash 
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="path to save model"
-
-accelerate launch --mixed_precision="fp16" --multi_gpu train_controlnet.py \
- --pretrained_model_name_or_path=$MODEL_DIR \
- --output_dir=$OUTPUT_DIR \
- --dataset_name=fusing/fill50k \
- --resolution=512 \
- --learning_rate=1e-5 \
- --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
- --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
- --train_batch_size=4 \
- --mixed_precision="fp16" \
- --tracker_project_name="controlnet-demo" \
- --report_to=wandb
-```
-
-## Example results
-
-#### After 300 steps with batch size 8
-
-| |  | 
-|-------------------|:-------------------------:|
-| | red circle with blue background  | 
-![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png) | ![red circle with blue background](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/red_circle_with_blue_background_300_steps.png) |
-| | cyan circle with brown floral background | 
-![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png) | ![cyan circle with brown floral background](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/cyan_circle_with_brown_floral_background_300_steps.png) |
-
-
-#### After 6000 steps with batch size 8:
-
-| |  | 
-|-------------------|:-------------------------:|
-| | red circle with blue background  | 
-![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png) | ![red circle with blue background](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/red_circle_with_blue_background_6000_steps.png) |
-| | cyan circle with brown floral background | 
-![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png) | ![cyan circle with brown floral background](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/cyan_circle_with_brown_floral_background_6000_steps.png) |
-
-## Training on a 16 GB GPU
-
-Optimizations:
-- Gradient checkpointing
-- bitsandbyte's 8-bit optimizer
-
-[bitandbytes install instructions](https://github.com/TimDettmers/bitsandbytes#requirements--installation).
-
-```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="path to save model"
-
-accelerate launch train_controlnet.py \
- --pretrained_model_name_or_path=$MODEL_DIR \
- --output_dir=$OUTPUT_DIR \
- --dataset_name=fusing/fill50k \
- --resolution=512 \
- --learning_rate=1e-5 \
- --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
- --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
- --train_batch_size=1 \
- --gradient_accumulation_steps=4 \
- --gradient_checkpointing \
- --use_8bit_adam
-```
-
-## Training on a 12 GB GPU
-
-Optimizations:
-- Gradient checkpointing
-- bitsandbyte's 8-bit optimizer
-- xformers
-- set grads to none
-
-```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="path to save model"
-
-accelerate launch train_controlnet.py \
- --pretrained_model_name_or_path=$MODEL_DIR \
- --output_dir=$OUTPUT_DIR \
- --dataset_name=fusing/fill50k \
- --resolution=512 \
- --learning_rate=1e-5 \
- --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
- --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
- --train_batch_size=1 \
- --gradient_accumulation_steps=4 \
- --gradient_checkpointing \
- --use_8bit_adam \
- --enable_xformers_memory_efficient_attention \
- --set_grads_to_none
-```
-
-When using `enable_xformers_memory_efficient_attention`, please make sure to install `xformers` by `pip install xformers`. 
-
-## Training on an 8 GB GPU
-
-We have not exhaustively tested DeepSpeed support for ControlNet. While the configuration does
-save memory, we have not confirmed the configuration to train successfully. You will very likely
-have to make changes to the config to have a successful training run.
-
-Optimizations:
-- Gradient checkpointing
-- xformers
-- set grads to none
-- DeepSpeed stage 2 with parameter and optimizer offloading
-- fp16 mixed precision
-
-[DeepSpeed](https://www.deepspeed.ai/) can offload tensors from VRAM to either 
-CPU or NVME. This requires significantly more RAM (about 25 GB).
-
-Use `accelerate config` to enable DeepSpeed stage 2.
-
-The relevant parts of the resulting accelerate config file are
-
-```yaml
-compute_environment: LOCAL_MACHINE
-deepspeed_config:
-  gradient_accumulation_steps: 4
-  offload_optimizer_device: cpu
-  offload_param_device: cpu
-  zero3_init_flag: false
-  zero_stage: 2
-distributed_type: DEEPSPEED
-```
-
-See [documentation](https://huggingface.co/docs/accelerate/usage_guides/deepspeed) for more DeepSpeed configuration options.
-
-Changing the default Adam optimizer to DeepSpeed's Adam
-`deepspeed.ops.adam.DeepSpeedCPUAdam` gives a substantial speedup but
-it requires CUDA toolchain with the same version as pytorch. 8-bit optimizer
-does not seem to be compatible with DeepSpeed at the moment.
-
-```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="path to save model"
-
-accelerate launch train_controlnet.py \
- --pretrained_model_name_or_path=$MODEL_DIR \
- --output_dir=$OUTPUT_DIR \
- --dataset_name=fusing/fill50k \
- --resolution=512 \
- --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
- --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
- --train_batch_size=1 \
- --gradient_accumulation_steps=4 \
- --gradient_checkpointing \
- --enable_xformers_memory_efficient_attention \
- --set_grads_to_none \
- --mixed_precision fp16
-```
-
-## Performing inference with the trained ControlNet
-
-The trained model can be run the same as the original ControlNet pipeline with the newly trained ControlNet.
-Set `base_model_path` and `controlnet_path` to the values `--pretrained_model_name_or_path` and 
-`--output_dir` were respectively set to in the training script.
-
-```py
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
-from diffusers.utils import load_image
-import torch
-
-base_model_path = "path to model"
-controlnet_path = "path to controlnet"
-
-controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    base_model_path, controlnet=controlnet, torch_dtype=torch.float16
-)
-
-# speed up diffusion process with faster scheduler and memory optimization
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-# remove following line if xformers is not installed or when using Torch 2.0.
-pipe.enable_xformers_memory_efficient_attention()
-# memory optimization.
-pipe.enable_model_cpu_offload()
-
-control_image = load_image("./conditioning_image_1.png")
-prompt = "pale golden rod circle with old lace background"
-
-# generate image
-generator = torch.manual_seed(0)
-image = pipe(
-    prompt, num_inference_steps=20, generator=generator, image=control_image
-).images[0]
-image.save("./output.png")
-```
-
-## Training with Flax/JAX
-
-For faster training on TPUs and GPUs you can leverage the flax training example. Follow the instructions above to get the model and dataset before running the script.
-
-### Running on Google Cloud TPU
-
-See below for commands to set up a TPU VM(`--accelerator-type v4-8`). For more details about how to set up and use TPUs, refer to [Cloud docs for single VM setup](https://cloud.google.com/tpu/docs/run-calculation-jax).
-
-First create a single TPUv4-8 VM and connect to it:
-
-```
-ZONE=us-central2-b
-TPU_TYPE=v4-8
-VM_NAME=hg_flax
-
-gcloud alpha compute tpus tpu-vm create $VM_NAME \
- --zone $ZONE \
- --accelerator-type $TPU_TYPE \
- --version  tpu-vm-v4-base
-
-gcloud alpha compute tpus tpu-vm ssh $VM_NAME --zone $ZONE -- \
-```
-
-When connected install JAX `0.4.5`:
-
-```
-pip install "jax[tpu]==0.4.5" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
-```
-
-To verify that JAX was correctly installed, you can run the following command:
-
-```
-import jax
-jax.device_count()
-```
-
-This should display the number of TPU cores, which should be 4 on a TPUv4-8 VM.
-
-Then install Diffusers and the library's training dependencies:
-
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install .
-```
-
-Then cd in the example folder and run
-
-```bash
-pip install -U -r requirements_flax.txt
-```
-
-If you want to use Weights and Biases logging, you should also install `wandb` now
-
-```bash
-pip install wandb
-```
-
-
-Now let's downloading two conditioning images that we will use to run validation during the training in order to track our progress
-
-```
-wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png
-wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png
-```
-
-We encourage you to store or share your model with the community. To use huggingface hub, please login to your Hugging Face account, or ([create one](https://huggingface.co/docs/diffusers/main/en/training/hf.co/join) if you don’t have one already):
-
-```
-huggingface-cli login
-```
-
-Make sure you have the `MODEL_DIR`,`OUTPUT_DIR` and `HUB_MODEL_ID` environment variables set. The `OUTPUT_DIR` and `HUB_MODEL_ID` variables specify where to save the model to on the Hub:
-
-```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="runs/fill-circle-{timestamp}"
-export HUB_MODEL_ID="controlnet-fill-circle"
-```
-
-And finally start the training
-
-```bash
-python3 train_controlnet_flax.py \
- --pretrained_model_name_or_path=$MODEL_DIR \
- --output_dir=$OUTPUT_DIR \
- --dataset_name=fusing/fill50k \
- --resolution=512 \
- --learning_rate=1e-5 \
- --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
- --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
- --validation_steps=1000 \
- --train_batch_size=2 \
- --revision="non-ema" \
- --from_pt \
- --report_to="wandb" \
- --tracker_project_name=$HUB_MODEL_ID \
- --num_train_epochs=11 \
- --push_to_hub \
- --hub_model_id=$HUB_MODEL_ID
- ```
-
-Since we passed the `--push_to_hub` flag, it will automatically create a model repo under your huggingface account based on `$HUB_MODEL_ID`. By the end of training, the final checkpoint will be automatically stored on the hub. You can find an example model repo [here](https://huggingface.co/YiYiXu/fill-circle-controlnet).
-
-Our training script also provides limited support for streaming large datasets from the Hugging Face Hub. In order to enable streaming, one must also set `--max_train_samples`.  Here is an example command (from [this blog article](https://huggingface.co/blog/train-your-controlnet)):
-
-```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="runs/uncanny-faces-{timestamp}"
-export HUB_MODEL_ID="controlnet-uncanny-faces"
-
-python3 train_controlnet_flax.py \
- --pretrained_model_name_or_path=$MODEL_DIR \
- --output_dir=$OUTPUT_DIR \
- --dataset_name=multimodalart/facesyntheticsspigacaptioned \
- --streaming \
- --conditioning_image_column=spiga_seg \
- --image_column=image \
- --caption_column=image_caption \
- --resolution=512 \
- --max_train_samples 100000 \
- --learning_rate=1e-5 \
- --train_batch_size=1 \
- --revision="flax" \
- --report_to="wandb" \
- --tracker_project_name=$HUB_MODEL_ID
-```
-
-Note, however, that the performance of the TPUs might get bottlenecked as streaming with `datasets` is not optimized for images. For ensuring maximum throughput, we encourage you to explore the following options:
-
-* [Webdataset](https://webdataset.github.io/webdataset/)
-* [TorchData](https://github.com/pytorch/data)
-* [TensorFlow Datasets](https://www.tensorflow.org/datasets/tfless_tfds)
-
-When work with a larger dataset, you may need to run training process for a long time and it’s useful to save regular checkpoints during the process. You can use the following argument to enable intermediate checkpointing:
-
-```bash
- --checkpointing_steps=500
-```
-This will save the trained model in subfolders of your output_dir. Subfolder names is the number of steps performed so far; for example: a checkpoint saved after 500 training steps would be saved in a subfolder named 500 
-
-You can then start your training from this saved checkpoint with 
-
-```bash
- --controlnet_model_name_or_path="./control_out/500" 
-```
-
-We support training with the Min-SNR weighting strategy proposed in [Efficient Diffusion Training via Min-SNR Weighting Strategy](https://arxiv.org/abs/2303.09556) which helps to achieve faster convergence by rebalancing the loss. To use it, one needs to set the `--snr_gamma` argument. The recommended value when using it is `5.0`.
-
-We also support gradient accumulation - it is a technique that lets you use a bigger batch size than your machine would normally be able to fit into memory. You can use `gradient_accumulation_steps` argument to set gradient accumulation steps. The ControlNet author recommends using gradient accumulation to achieve better convergence. Read more [here](https://github.com/lllyasviel/ControlNet/blob/main/docs/train.md#more-consideration-sudden-converge-phenomenon-and-gradient-accumulation).
-
-You can **profile your code** with:
-
-```bash
- --profile_steps==5
-```
-
-Refer to the [JAX documentation on profiling](https://jax.readthedocs.io/en/latest/profiling.html). To inspect the profile trace, you'll have to install and start Tensorboard with the profile plugin:
-
-```bash
-pip install tensorflow tensorboard-plugin-profile
-tensorboard --logdir runs/fill-circle-100steps-20230411_165612/
-```
-
-The profile can then be inspected at http://localhost:6006/#profile
-
-Sometimes you'll get version conflicts (error messages like `Duplicate plugins for name projector`), which means that you have to uninstall and reinstall all versions of Tensorflow/Tensorboard (e.g. with `pip uninstall tensorflow tf-nightly tensorboard tb-nightly tensorboard-plugin-profile && pip install tf-nightly tbp-nightly tensorboard-plugin-profile`).
-
-Note that the debugging functionality of the Tensorboard `profile` plugin is still under active development. Not all views are fully functional, and for example the `trace_viewer` cuts off events after 1M (which can result in all your device traces getting lost if you for example profile the compilation step by accident).
-
-## Support for Stable Diffusion XL
-
-We provide a training script for training a ControlNet with [Stable Diffusion XL](https://huggingface.co/papers/2307.01952). Please refer to [README_sdxl.md](./README_sdxl.md) for more details.

spaces/Andy1621/uniformer_image_detection/mmdet/models/builder.py

@@ -1,77 +0,0 @@
-import warnings
-
-from mmcv.utils import Registry, build_from_cfg
-from torch import nn
-
-BACKBONES = Registry('backbone')
-NECKS = Registry('neck')
-ROI_EXTRACTORS = Registry('roi_extractor')
-SHARED_HEADS = Registry('shared_head')
-HEADS = Registry('head')
-LOSSES = Registry('loss')
-DETECTORS = Registry('detector')
-
-
-def build(cfg, registry, default_args=None):
-    """Build a module.
-
-    Args:
-        cfg (dict, list[dict]): The config of modules, is is either a dict
-            or a list of configs.
-        registry (:obj:`Registry`): A registry the module belongs to.
-        default_args (dict, optional): Default arguments to build the module.
-            Defaults to None.
-
-    Returns:
-        nn.Module: A built nn module.
-    """
-    if isinstance(cfg, list):
-        modules = [
-            build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg
-        ]
-        return nn.Sequential(*modules)
-    else:
-        return build_from_cfg(cfg, registry, default_args)
-
-
-def build_backbone(cfg):
-    """Build backbone."""
-    return build(cfg, BACKBONES)
-
-
-def build_neck(cfg):
-    """Build neck."""
-    return build(cfg, NECKS)
-
-
-def build_roi_extractor(cfg):
-    """Build roi extractor."""
-    return build(cfg, ROI_EXTRACTORS)
-
-
-def build_shared_head(cfg):
-    """Build shared head."""
-    return build(cfg, SHARED_HEADS)
-
-
-def build_head(cfg):
-    """Build head."""
-    return build(cfg, HEADS)
-
-
-def build_loss(cfg):
-    """Build loss."""
-    return build(cfg, LOSSES)
-
-
-def build_detector(cfg, train_cfg=None, test_cfg=None):
-    """Build detector."""
-    if train_cfg is not None or test_cfg is not None:
-        warnings.warn(
-            'train_cfg and test_cfg is deprecated, '
-            'please specify them in model', UserWarning)
-    assert cfg.get('train_cfg') is None or train_cfg is None, \
-        'train_cfg specified in both outer field and model field '
-    assert cfg.get('test_cfg') is None or test_cfg is None, \
-        'test_cfg specified in both outer field and model field '
-    return build(cfg, DETECTORS, dict(train_cfg=train_cfg, test_cfg=test_cfg))

spaces/Andy1621/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py

@@ -1,9 +0,0 @@
-_base_ = './ocrnet_hr18_512x512_160k_ade20k.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w18_small',
-    backbone=dict(
-        extra=dict(
-            stage1=dict(num_blocks=(2, )),
-            stage2=dict(num_blocks=(2, 2)),
-            stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
-            stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))

spaces/AnimalEquality/chatbot/constants.py

@@ -1,3 +0,0 @@
-from pathlib import Path
-
-ROOT_DIR = Path(__file__).parent

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/deepspeed_parameters.py

@@ -1,74 +0,0 @@
-def generate_ds_config(ds_bf16, train_batch_size, nvme_offload_dir):
-    '''
-    DeepSpeed configuration
-    https://huggingface.co/docs/transformers/main_classes/deepspeed
-    '''
-
-    if nvme_offload_dir:
-        ds_config = {
-            "fp16": {
-                "enabled": not ds_bf16,
-            },
-            "bf16": {
-                "enabled": ds_bf16,
-            },
-            "zero_optimization": {
-                "stage": 3,
-                "offload_param": {
-                    "device": "nvme",
-                    "nvme_path": nvme_offload_dir,
-                    "pin_memory": True,
-                    "buffer_count": 5,
-                    "buffer_size": 1e9,
-                    "max_in_cpu": 1e9
-                },
-                "overlap_comm": True,
-                "reduce_bucket_size": "auto",
-                "contiguous_gradients": True,
-                "sub_group_size": 1e8,
-                "stage3_prefetch_bucket_size": "auto",
-                "stage3_param_persistence_threshold": "auto",
-                "stage3_max_live_parameters": "auto",
-                "stage3_max_reuse_distance": "auto",
-            },
-            "aio": {
-                "block_size": 262144,
-                "queue_depth": 32,
-                "thread_count": 1,
-                "single_submit": False,
-                "overlap_events": True
-            },
-            "steps_per_print": 2000,
-            "train_batch_size": train_batch_size,
-            "train_micro_batch_size_per_gpu": 1,
-            "wall_clock_breakdown": False
-        }
-    else:
-        ds_config = {
-            "fp16": {
-                "enabled": not ds_bf16,
-            },
-            "bf16": {
-                "enabled": ds_bf16,
-            },
-            "zero_optimization": {
-                "stage": 3,
-                "offload_param": {
-                    "device": "cpu",
-                    "pin_memory": True
-                },
-                "overlap_comm": True,
-                "contiguous_gradients": True,
-                "reduce_bucket_size": "auto",
-                "stage3_prefetch_bucket_size": "auto",
-                "stage3_param_persistence_threshold": "auto",
-                "stage3_max_live_parameters": "auto",
-                "stage3_max_reuse_distance": "auto",
-            },
-            "steps_per_print": 2000,
-            "train_batch_size": train_batch_size,
-            "train_micro_batch_size_per_gpu": 1,
-            "wall_clock_breakdown": False
-        }
-
-    return ds_config

spaces/Anonymous-sub/Rerender/gmflow_module/scripts/demo.sh

@@ -1,63 +0,0 @@
-#!/usr/bin/env bash
-
-# inference GMFlow without refinement
-
-# sintel
-
-# only predict forward flow
-CUDA_VISIBLE_DEVICES=0 python main.py \
---inference_dir demo/sintel_market_1 \
---output_path output/gmflow-norefine-sintel_market_1 \
---resume pretrained/gmflow_sintel-0c07dcb3.pth
-
-# predict forward & backward flow
-CUDA_VISIBLE_DEVICES=0 python main.py \
---inference_dir demo/sintel_market_1 \
---output_path output/gmflow-norefine-sintel_market_1 \
---pred_bidir_flow \
---resume pretrained/gmflow_sintel-0c07dcb3.pth
-
-
-# predict forward & backward flow with forward-backward consistency check
-CUDA_VISIBLE_DEVICES=0 python main.py \
---inference_dir demo/sintel_market_1 \
---output_path output/gmflow-norefine-sintel_market_1 \
---pred_bidir_flow \
---fwd_bwd_consistency_check \
---resume pretrained/gmflow_sintel-0c07dcb3.pth
-
-
-# davis
-
-CUDA_VISIBLE_DEVICES=0 python main.py \
---inference_dir demo/davis_breakdance-flare \
---output_path output/gmflow-norefine-davis_breakdance-flare \
---resume pretrained/gmflow_sintel-0c07dcb3.pth
-
-
-
-
-# inference GMFlow with refinement
-
-CUDA_VISIBLE_DEVICES=0 python main.py \
---inference_dir demo/davis_breakdance-flare \
---output_path output/gmflow-withrefine-davis_breakdance-flare \
---resume pretrained/gmflow_with_refine_sintel-3ed1cf48.pth \
---padding_factor 32 \
---upsample_factor 4 \
---num_scales 2 \
---attn_splits_list 2 8 \
---corr_radius_list -1 4 \
---prop_radius_list -1 1
-
-
-
-
-CUDA_VISIBLE_DEVICES=0 python main.py \
---inference_dir demo/sintel_test_clean_market_1 \
---output_path output/gmflow-norefine-sintel_test_clean_market_1 \
---pred_bidir_flow \
---fwd_bwd_consistency_check \
---resume pretrained/gmflow_sintel-0c07dcb3.pth
-
-

spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/config/GroundingDINO_SwinB_cfg.py

@@ -1,43 +0,0 @@
-batch_size = 1
-modelname = "groundingdino"
-backbone = "swin_B_384_22k"
-position_embedding = "sine"
-pe_temperatureH = 20
-pe_temperatureW = 20
-return_interm_indices = [1, 2, 3]
-backbone_freeze_keywords = None
-enc_layers = 6
-dec_layers = 6
-pre_norm = False
-dim_feedforward = 2048
-hidden_dim = 256
-dropout = 0.0
-nheads = 8
-num_queries = 900
-query_dim = 4
-num_patterns = 0
-num_feature_levels = 4
-enc_n_points = 4
-dec_n_points = 4
-two_stage_type = "standard"
-two_stage_bbox_embed_share = False
-two_stage_class_embed_share = False
-transformer_activation = "relu"
-dec_pred_bbox_embed_share = True
-dn_box_noise_scale = 1.0
-dn_label_noise_ratio = 0.5
-dn_label_coef = 1.0
-dn_bbox_coef = 1.0
-embed_init_tgt = True
-dn_labelbook_size = 2000
-max_text_len = 256
-text_encoder_type = "bert-base-uncased"
-use_text_enhancer = True
-use_fusion_layer = True
-use_checkpoint = True
-use_transformer_ckpt = True
-use_text_cross_attention = True
-text_dropout = 0.0
-fusion_dropout = 0.0
-fusion_droppath = 0.1
-sub_sentence_present = True

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/cli/req_command.py

@@ -1,505 +0,0 @@
-"""Contains the Command base classes that depend on PipSession.
-
-The classes in this module are in a separate module so the commands not
-needing download / PackageFinder capability don't unnecessarily import the
-PackageFinder machinery and all its vendored dependencies, etc.
-"""
-
-import logging
-import os
-import sys
-from functools import partial
-from optparse import Values
-from typing import TYPE_CHECKING, Any, List, Optional, Tuple
-
-from pip._internal.cache import WheelCache
-from pip._internal.cli import cmdoptions
-from pip._internal.cli.base_command import Command
-from pip._internal.cli.command_context import CommandContextMixIn
-from pip._internal.exceptions import CommandError, PreviousBuildDirError
-from pip._internal.index.collector import LinkCollector
-from pip._internal.index.package_finder import PackageFinder
-from pip._internal.models.selection_prefs import SelectionPreferences
-from pip._internal.models.target_python import TargetPython
-from pip._internal.network.session import PipSession
-from pip._internal.operations.build.build_tracker import BuildTracker
-from pip._internal.operations.prepare import RequirementPreparer
-from pip._internal.req.constructors import (
-    install_req_from_editable,
-    install_req_from_line,
-    install_req_from_parsed_requirement,
-    install_req_from_req_string,
-)
-from pip._internal.req.req_file import parse_requirements
-from pip._internal.req.req_install import InstallRequirement
-from pip._internal.resolution.base import BaseResolver
-from pip._internal.self_outdated_check import pip_self_version_check
-from pip._internal.utils.temp_dir import (
-    TempDirectory,
-    TempDirectoryTypeRegistry,
-    tempdir_kinds,
-)
-from pip._internal.utils.virtualenv import running_under_virtualenv
-
-if TYPE_CHECKING:
-    from ssl import SSLContext
-
-logger = logging.getLogger(__name__)
-
-
-def _create_truststore_ssl_context() -> Optional["SSLContext"]:
-    if sys.version_info < (3, 10):
-        raise CommandError("The truststore feature is only available for Python 3.10+")
-
-    try:
-        import ssl
-    except ImportError:
-        logger.warning("Disabling truststore since ssl support is missing")
-        return None
-
-    try:
-        import truststore
-    except ImportError:
-        raise CommandError(
-            "To use the truststore feature, 'truststore' must be installed into "
-            "pip's current environment."
-        )
-
-    return truststore.SSLContext(ssl.PROTOCOL_TLS_CLIENT)
-
-
-class SessionCommandMixin(CommandContextMixIn):
-
-    """
-    A class mixin for command classes needing _build_session().
-    """
-
-    def __init__(self) -> None:
-        super().__init__()
-        self._session: Optional[PipSession] = None
-
-    @classmethod
-    def _get_index_urls(cls, options: Values) -> Optional[List[str]]:
-        """Return a list of index urls from user-provided options."""
-        index_urls = []
-        if not getattr(options, "no_index", False):
-            url = getattr(options, "index_url", None)
-            if url:
-                index_urls.append(url)
-        urls = getattr(options, "extra_index_urls", None)
-        if urls:
-            index_urls.extend(urls)
-        # Return None rather than an empty list
-        return index_urls or None
-
-    def get_default_session(self, options: Values) -> PipSession:
-        """Get a default-managed session."""
-        if self._session is None:
-            self._session = self.enter_context(self._build_session(options))
-            # there's no type annotation on requests.Session, so it's
-            # automatically ContextManager[Any] and self._session becomes Any,
-            # then https://github.com/python/mypy/issues/7696 kicks in
-            assert self._session is not None
-        return self._session
-
-    def _build_session(
-        self,
-        options: Values,
-        retries: Optional[int] = None,
-        timeout: Optional[int] = None,
-        fallback_to_certifi: bool = False,
-    ) -> PipSession:
-        cache_dir = options.cache_dir
-        assert not cache_dir or os.path.isabs(cache_dir)
-
-        if "truststore" in options.features_enabled:
-            try:
-                ssl_context = _create_truststore_ssl_context()
-            except Exception:
-                if not fallback_to_certifi:
-                    raise
-                ssl_context = None
-        else:
-            ssl_context = None
-
-        session = PipSession(
-            cache=os.path.join(cache_dir, "http") if cache_dir else None,
-            retries=retries if retries is not None else options.retries,
-            trusted_hosts=options.trusted_hosts,
-            index_urls=self._get_index_urls(options),
-            ssl_context=ssl_context,
-        )
-
-        # Handle custom ca-bundles from the user
-        if options.cert:
-            session.verify = options.cert
-
-        # Handle SSL client certificate
-        if options.client_cert:
-            session.cert = options.client_cert
-
-        # Handle timeouts
-        if options.timeout or timeout:
-            session.timeout = timeout if timeout is not None else options.timeout
-
-        # Handle configured proxies
-        if options.proxy:
-            session.proxies = {
-                "http": options.proxy,
-                "https": options.proxy,
-            }
-
-        # Determine if we can prompt the user for authentication or not
-        session.auth.prompting = not options.no_input
-        session.auth.keyring_provider = options.keyring_provider
-
-        return session
-
-
-class IndexGroupCommand(Command, SessionCommandMixin):
-
-    """
-    Abstract base class for commands with the index_group options.
-
-    This also corresponds to the commands that permit the pip version check.
-    """
-
-    def handle_pip_version_check(self, options: Values) -> None:
-        """
-        Do the pip version check if not disabled.
-
-        This overrides the default behavior of not doing the check.
-        """
-        # Make sure the index_group options are present.
-        assert hasattr(options, "no_index")
-
-        if options.disable_pip_version_check or options.no_index:
-            return
-
-        # Otherwise, check if we're using the latest version of pip available.
-        session = self._build_session(
-            options,
-            retries=0,
-            timeout=min(5, options.timeout),
-            # This is set to ensure the function does not fail when truststore is
-            # specified in use-feature but cannot be loaded. This usually raises a
-            # CommandError and shows a nice user-facing error, but this function is not
-            # called in that try-except block.
-            fallback_to_certifi=True,
-        )
-        with session:
-            pip_self_version_check(session, options)
-
-
-KEEPABLE_TEMPDIR_TYPES = [
-    tempdir_kinds.BUILD_ENV,
-    tempdir_kinds.EPHEM_WHEEL_CACHE,
-    tempdir_kinds.REQ_BUILD,
-]
-
-
-def warn_if_run_as_root() -> None:
-    """Output a warning for sudo users on Unix.
-
-    In a virtual environment, sudo pip still writes to virtualenv.
-    On Windows, users may run pip as Administrator without issues.
-    This warning only applies to Unix root users outside of virtualenv.
-    """
-    if running_under_virtualenv():
-        return
-    if not hasattr(os, "getuid"):
-        return
-    # On Windows, there are no "system managed" Python packages. Installing as
-    # Administrator via pip is the correct way of updating system environments.
-    #
-    # We choose sys.platform over utils.compat.WINDOWS here to enable Mypy platform
-    # checks: https://mypy.readthedocs.io/en/stable/common_issues.html
-    if sys.platform == "win32" or sys.platform == "cygwin":
-        return
-
-    if os.getuid() != 0:
-        return
-
-    logger.warning(
-        "Running pip as the 'root' user can result in broken permissions and "
-        "conflicting behaviour with the system package manager. "
-        "It is recommended to use a virtual environment instead: "
-        "https://pip.pypa.io/warnings/venv"
-    )
-
-
-def with_cleanup(func: Any) -> Any:
-    """Decorator for common logic related to managing temporary
-    directories.
-    """
-
-    def configure_tempdir_registry(registry: TempDirectoryTypeRegistry) -> None:
-        for t in KEEPABLE_TEMPDIR_TYPES:
-            registry.set_delete(t, False)
-
-    def wrapper(
-        self: RequirementCommand, options: Values, args: List[Any]
-    ) -> Optional[int]:
-        assert self.tempdir_registry is not None
-        if options.no_clean:
-            configure_tempdir_registry(self.tempdir_registry)
-
-        try:
-            return func(self, options, args)
-        except PreviousBuildDirError:
-            # This kind of conflict can occur when the user passes an explicit
-            # build directory with a pre-existing folder. In that case we do
-            # not want to accidentally remove it.
-            configure_tempdir_registry(self.tempdir_registry)
-            raise
-
-    return wrapper
-
-
-class RequirementCommand(IndexGroupCommand):
-    def __init__(self, *args: Any, **kw: Any) -> None:
-        super().__init__(*args, **kw)
-
-        self.cmd_opts.add_option(cmdoptions.no_clean())
-
-    @staticmethod
-    def determine_resolver_variant(options: Values) -> str:
-        """Determines which resolver should be used, based on the given options."""
-        if "legacy-resolver" in options.deprecated_features_enabled:
-            return "legacy"
-
-        return "2020-resolver"
-
-    @classmethod
-    def make_requirement_preparer(
-        cls,
-        temp_build_dir: TempDirectory,
-        options: Values,
-        build_tracker: BuildTracker,
-        session: PipSession,
-        finder: PackageFinder,
-        use_user_site: bool,
-        download_dir: Optional[str] = None,
-        verbosity: int = 0,
-    ) -> RequirementPreparer:
-        """
-        Create a RequirementPreparer instance for the given parameters.
-        """
-        temp_build_dir_path = temp_build_dir.path
-        assert temp_build_dir_path is not None
-
-        resolver_variant = cls.determine_resolver_variant(options)
-        if resolver_variant == "2020-resolver":
-            lazy_wheel = "fast-deps" in options.features_enabled
-            if lazy_wheel:
-                logger.warning(
-                    "pip is using lazily downloaded wheels using HTTP "
-                    "range requests to obtain dependency information. "
-                    "This experimental feature is enabled through "
-                    "--use-feature=fast-deps and it is not ready for "
-                    "production."
-                )
-        else:
-            lazy_wheel = False
-            if "fast-deps" in options.features_enabled:
-                logger.warning(
-                    "fast-deps has no effect when used with the legacy resolver."
-                )
-
-        return RequirementPreparer(
-            build_dir=temp_build_dir_path,
-            src_dir=options.src_dir,
-            download_dir=download_dir,
-            build_isolation=options.build_isolation,
-            check_build_deps=options.check_build_deps,
-            build_tracker=build_tracker,
-            session=session,
-            progress_bar=options.progress_bar,
-            finder=finder,
-            require_hashes=options.require_hashes,
-            use_user_site=use_user_site,
-            lazy_wheel=lazy_wheel,
-            verbosity=verbosity,
-        )
-
-    @classmethod
-    def make_resolver(
-        cls,
-        preparer: RequirementPreparer,
-        finder: PackageFinder,
-        options: Values,
-        wheel_cache: Optional[WheelCache] = None,
-        use_user_site: bool = False,
-        ignore_installed: bool = True,
-        ignore_requires_python: bool = False,
-        force_reinstall: bool = False,
-        upgrade_strategy: str = "to-satisfy-only",
-        use_pep517: Optional[bool] = None,
-        py_version_info: Optional[Tuple[int, ...]] = None,
-    ) -> BaseResolver:
-        """
-        Create a Resolver instance for the given parameters.
-        """
-        make_install_req = partial(
-            install_req_from_req_string,
-            isolated=options.isolated_mode,
-            use_pep517=use_pep517,
-        )
-        resolver_variant = cls.determine_resolver_variant(options)
-        # The long import name and duplicated invocation is needed to convince
-        # Mypy into correctly typechecking. Otherwise it would complain the
-        # "Resolver" class being redefined.
-        if resolver_variant == "2020-resolver":
-            import pip._internal.resolution.resolvelib.resolver
-
-            return pip._internal.resolution.resolvelib.resolver.Resolver(
-                preparer=preparer,
-                finder=finder,
-                wheel_cache=wheel_cache,
-                make_install_req=make_install_req,
-                use_user_site=use_user_site,
-                ignore_dependencies=options.ignore_dependencies,
-                ignore_installed=ignore_installed,
-                ignore_requires_python=ignore_requires_python,
-                force_reinstall=force_reinstall,
-                upgrade_strategy=upgrade_strategy,
-                py_version_info=py_version_info,
-            )
-        import pip._internal.resolution.legacy.resolver
-
-        return pip._internal.resolution.legacy.resolver.Resolver(
-            preparer=preparer,
-            finder=finder,
-            wheel_cache=wheel_cache,
-            make_install_req=make_install_req,
-            use_user_site=use_user_site,
-            ignore_dependencies=options.ignore_dependencies,
-            ignore_installed=ignore_installed,
-            ignore_requires_python=ignore_requires_python,
-            force_reinstall=force_reinstall,
-            upgrade_strategy=upgrade_strategy,
-            py_version_info=py_version_info,
-        )
-
-    def get_requirements(
-        self,
-        args: List[str],
-        options: Values,
-        finder: PackageFinder,
-        session: PipSession,
-    ) -> List[InstallRequirement]:
-        """
-        Parse command-line arguments into the corresponding requirements.
-        """
-        requirements: List[InstallRequirement] = []
-        for filename in options.constraints:
-            for parsed_req in parse_requirements(
-                filename,
-                constraint=True,
-                finder=finder,
-                options=options,
-                session=session,
-            ):
-                req_to_add = install_req_from_parsed_requirement(
-                    parsed_req,
-                    isolated=options.isolated_mode,
-                    user_supplied=False,
-                )
-                requirements.append(req_to_add)
-
-        for req in args:
-            req_to_add = install_req_from_line(
-                req,
-                comes_from=None,
-                isolated=options.isolated_mode,
-                use_pep517=options.use_pep517,
-                user_supplied=True,
-                config_settings=getattr(options, "config_settings", None),
-            )
-            requirements.append(req_to_add)
-
-        for req in options.editables:
-            req_to_add = install_req_from_editable(
-                req,
-                user_supplied=True,
-                isolated=options.isolated_mode,
-                use_pep517=options.use_pep517,
-                config_settings=getattr(options, "config_settings", None),
-            )
-            requirements.append(req_to_add)
-
-        # NOTE: options.require_hashes may be set if --require-hashes is True
-        for filename in options.requirements:
-            for parsed_req in parse_requirements(
-                filename, finder=finder, options=options, session=session
-            ):
-                req_to_add = install_req_from_parsed_requirement(
-                    parsed_req,
-                    isolated=options.isolated_mode,
-                    use_pep517=options.use_pep517,
-                    user_supplied=True,
-                    config_settings=parsed_req.options.get("config_settings")
-                    if parsed_req.options
-                    else None,
-                )
-                requirements.append(req_to_add)
-
-        # If any requirement has hash options, enable hash checking.
-        if any(req.has_hash_options for req in requirements):
-            options.require_hashes = True
-
-        if not (args or options.editables or options.requirements):
-            opts = {"name": self.name}
-            if options.find_links:
-                raise CommandError(
-                    "You must give at least one requirement to {name} "
-                    '(maybe you meant "pip {name} {links}"?)'.format(
-                        **dict(opts, links=" ".join(options.find_links))
-                    )
-                )
-            else:
-                raise CommandError(
-                    "You must give at least one requirement to {name} "
-                    '(see "pip help {name}")'.format(**opts)
-                )
-
-        return requirements
-
-    @staticmethod
-    def trace_basic_info(finder: PackageFinder) -> None:
-        """
-        Trace basic information about the provided objects.
-        """
-        # Display where finder is looking for packages
-        search_scope = finder.search_scope
-        locations = search_scope.get_formatted_locations()
-        if locations:
-            logger.info(locations)
-
-    def _build_package_finder(
-        self,
-        options: Values,
-        session: PipSession,
-        target_python: Optional[TargetPython] = None,
-        ignore_requires_python: Optional[bool] = None,
-    ) -> PackageFinder:
-        """
-        Create a package finder appropriate to this requirement command.
-
-        :param ignore_requires_python: Whether to ignore incompatible
-            "Requires-Python" values in links. Defaults to False.
-        """
-        link_collector = LinkCollector.create(session, options=options)
-        selection_prefs = SelectionPreferences(
-            allow_yanked=True,
-            format_control=options.format_control,
-            allow_all_prereleases=options.pre,
-            prefer_binary=options.prefer_binary,
-            ignore_requires_python=ignore_requires_python,
-        )
-
-        return PackageFinder.create(
-            link_collector=link_collector,
-            selection_prefs=selection_prefs,
-            target_python=target_python,
-        )

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

@@ -1,37 +0,0 @@
-from typing import Optional, TYPE_CHECKING
-
-from .jupyter import JupyterMixin
-from .measure import Measurement
-
-if TYPE_CHECKING:
-    from .console import Console, ConsoleOptions, RenderableType, RenderResult
-
-
-class Constrain(JupyterMixin):
-    """Constrain the width of a renderable to a given number of characters.
-
-    Args:
-        renderable (RenderableType): A renderable object.
-        width (int, optional): The maximum width (in characters) to render. Defaults to 80.
-    """
-
-    def __init__(self, renderable: "RenderableType", width: Optional[int] = 80) -> None:
-        self.renderable = renderable
-        self.width = width
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-        if self.width is None:
-            yield self.renderable
-        else:
-            child_options = options.update_width(min(self.width, options.max_width))
-            yield from console.render(self.renderable, child_options)
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "Measurement":
-        if self.width is not None:
-            options = options.update_width(self.width)
-        measurement = Measurement.get(console, options, self.renderable)
-        return measurement

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

@@ -1,251 +0,0 @@
-from typing import Iterator, List, Optional, Tuple
-
-from ._loop import loop_first, loop_last
-from .console import Console, ConsoleOptions, RenderableType, RenderResult
-from .jupyter import JupyterMixin
-from .measure import Measurement
-from .segment import Segment
-from .style import Style, StyleStack, StyleType
-from .styled import Styled
-
-
-class Tree(JupyterMixin):
-    """A renderable for a tree structure.
-
-    Args:
-        label (RenderableType): The renderable or str for the tree label.
-        style (StyleType, optional): Style of this tree. Defaults to "tree".
-        guide_style (StyleType, optional): Style of the guide lines. Defaults to "tree.line".
-        expanded (bool, optional): Also display children. Defaults to True.
-        highlight (bool, optional): Highlight renderable (if str). Defaults to False.
-    """
-
-    def __init__(
-        self,
-        label: RenderableType,
-        *,
-        style: StyleType = "tree",
-        guide_style: StyleType = "tree.line",
-        expanded: bool = True,
-        highlight: bool = False,
-        hide_root: bool = False,
-    ) -> None:
-        self.label = label
-        self.style = style
-        self.guide_style = guide_style
-        self.children: List[Tree] = []
-        self.expanded = expanded
-        self.highlight = highlight
-        self.hide_root = hide_root
-
-    def add(
-        self,
-        label: RenderableType,
-        *,
-        style: Optional[StyleType] = None,
-        guide_style: Optional[StyleType] = None,
-        expanded: bool = True,
-        highlight: Optional[bool] = False,
-    ) -> "Tree":
-        """Add a child tree.
-
-        Args:
-            label (RenderableType): The renderable or str for the tree label.
-            style (StyleType, optional): Style of this tree. Defaults to "tree".
-            guide_style (StyleType, optional): Style of the guide lines. Defaults to "tree.line".
-            expanded (bool, optional): Also display children. Defaults to True.
-            highlight (Optional[bool], optional): Highlight renderable (if str). Defaults to False.
-
-        Returns:
-            Tree: A new child Tree, which may be further modified.
-        """
-        node = Tree(
-            label,
-            style=self.style if style is None else style,
-            guide_style=self.guide_style if guide_style is None else guide_style,
-            expanded=expanded,
-            highlight=self.highlight if highlight is None else highlight,
-        )
-        self.children.append(node)
-        return node
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-
-        stack: List[Iterator[Tuple[bool, Tree]]] = []
-        pop = stack.pop
-        push = stack.append
-        new_line = Segment.line()
-
-        get_style = console.get_style
-        null_style = Style.null()
-        guide_style = get_style(self.guide_style, default="") or null_style
-        SPACE, CONTINUE, FORK, END = range(4)
-
-        ASCII_GUIDES = ("    ", "|   ", "+-- ", "`-- ")
-        TREE_GUIDES = [
-            ("    ", "│   ", "├── ", "└── "),
-            ("    ", "┃   ", "┣━━ ", "┗━━ "),
-            ("    ", "║   ", "╠══ ", "╚══ "),
-        ]
-        _Segment = Segment
-
-        def make_guide(index: int, style: Style) -> Segment:
-            """Make a Segment for a level of the guide lines."""
-            if options.ascii_only:
-                line = ASCII_GUIDES[index]
-            else:
-                guide = 1 if style.bold else (2 if style.underline2 else 0)
-                line = TREE_GUIDES[0 if options.legacy_windows else guide][index]
-            return _Segment(line, style)
-
-        levels: List[Segment] = [make_guide(CONTINUE, guide_style)]
-        push(iter(loop_last([self])))
-
-        guide_style_stack = StyleStack(get_style(self.guide_style))
-        style_stack = StyleStack(get_style(self.style))
-        remove_guide_styles = Style(bold=False, underline2=False)
-
-        depth = 0
-
-        while stack:
-            stack_node = pop()
-            try:
-                last, node = next(stack_node)
-            except StopIteration:
-                levels.pop()
-                if levels:
-                    guide_style = levels[-1].style or null_style
-                    levels[-1] = make_guide(FORK, guide_style)
-                    guide_style_stack.pop()
-                    style_stack.pop()
-                continue
-            push(stack_node)
-            if last:
-                levels[-1] = make_guide(END, levels[-1].style or null_style)
-
-            guide_style = guide_style_stack.current + get_style(node.guide_style)
-            style = style_stack.current + get_style(node.style)
-            prefix = levels[(2 if self.hide_root else 1) :]
-            renderable_lines = console.render_lines(
-                Styled(node.label, style),
-                options.update(
-                    width=options.max_width
-                    - sum(level.cell_length for level in prefix),
-                    highlight=self.highlight,
-                    height=None,
-                ),
-                pad=options.justify is not None,
-            )
-
-            if not (depth == 0 and self.hide_root):
-                for first, line in loop_first(renderable_lines):
-                    if prefix:
-                        yield from _Segment.apply_style(
-                            prefix,
-                            style.background_style,
-                            post_style=remove_guide_styles,
-                        )
-                    yield from line
-                    yield new_line
-                    if first and prefix:
-                        prefix[-1] = make_guide(
-                            SPACE if last else CONTINUE, prefix[-1].style or null_style
-                        )
-
-            if node.expanded and node.children:
-                levels[-1] = make_guide(
-                    SPACE if last else CONTINUE, levels[-1].style or null_style
-                )
-                levels.append(
-                    make_guide(END if len(node.children) == 1 else FORK, guide_style)
-                )
-                style_stack.push(get_style(node.style))
-                guide_style_stack.push(get_style(node.guide_style))
-                push(iter(loop_last(node.children)))
-                depth += 1
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "Measurement":
-        stack: List[Iterator[Tree]] = [iter([self])]
-        pop = stack.pop
-        push = stack.append
-        minimum = 0
-        maximum = 0
-        measure = Measurement.get
-        level = 0
-        while stack:
-            iter_tree = pop()
-            try:
-                tree = next(iter_tree)
-            except StopIteration:
-                level -= 1
-                continue
-            push(iter_tree)
-            min_measure, max_measure = measure(console, options, tree.label)
-            indent = level * 4
-            minimum = max(min_measure + indent, minimum)
-            maximum = max(max_measure + indent, maximum)
-            if tree.expanded and tree.children:
-                push(iter(tree.children))
-                level += 1
-        return Measurement(minimum, maximum)
-
-
-if __name__ == "__main__":  # pragma: no cover
-
-    from pip._vendor.rich.console import Group
-    from pip._vendor.rich.markdown import Markdown
-    from pip._vendor.rich.panel import Panel
-    from pip._vendor.rich.syntax import Syntax
-    from pip._vendor.rich.table import Table
-
-    table = Table(row_styles=["", "dim"])
-
-    table.add_column("Released", style="cyan", no_wrap=True)
-    table.add_column("Title", style="magenta")
-    table.add_column("Box Office", justify="right", style="green")
-
-    table.add_row("Dec 20, 2019", "Star Wars: The Rise of Skywalker", "$952,110,690")
-    table.add_row("May 25, 2018", "Solo: A Star Wars Story", "$393,151,347")
-    table.add_row("Dec 15, 2017", "Star Wars Ep. V111: The Last Jedi", "$1,332,539,889")
-    table.add_row("Dec 16, 2016", "Rogue One: A Star Wars Story", "$1,332,439,889")
-
-    code = """\
-class Segment(NamedTuple):
-    text: str = ""
-    style: Optional[Style] = None
-    is_control: bool = False
-"""
-    syntax = Syntax(code, "python", theme="monokai", line_numbers=True)
-
-    markdown = Markdown(
-        """\
-### example.md
-> Hello, World!
->
-> Markdown _all_ the things
-"""
-    )
-
-    root = Tree("🌲 [b green]Rich Tree", highlight=True, hide_root=True)
-
-    node = root.add(":file_folder: Renderables", guide_style="red")
-    simple_node = node.add(":file_folder: [bold yellow]Atomic", guide_style="uu green")
-    simple_node.add(Group("📄 Syntax", syntax))
-    simple_node.add(Group("📄 Markdown", Panel(markdown, border_style="green")))
-
-    containers_node = node.add(
-        ":file_folder: [bold magenta]Containers", guide_style="bold magenta"
-    )
-    containers_node.expanded = True
-    panel = Panel.fit("Just a panel", border_style="red")
-    containers_node.add(Group("📄 Panels", panel))
-
-    containers_node.add(Group("📄 [b magenta]Table", table))
-
-    console = Console()
-
-    console.print(root)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/packaging/_musllinux.py

@@ -1,136 +0,0 @@
-"""PEP 656 support.
-
-This module implements logic to detect if the currently running Python is
-linked against musl, and what musl version is used.
-"""
-
-import contextlib
-import functools
-import operator
-import os
-import re
-import struct
-import subprocess
-import sys
-from typing import IO, Iterator, NamedTuple, Optional, Tuple
-
-
-def _read_unpacked(f: IO[bytes], fmt: str) -> Tuple[int, ...]:
-    return struct.unpack(fmt, f.read(struct.calcsize(fmt)))
-
-
-def _parse_ld_musl_from_elf(f: IO[bytes]) -> Optional[str]:
-    """Detect musl libc location by parsing the Python executable.
-
-    Based on: https://gist.github.com/lyssdod/f51579ae8d93c8657a5564aefc2ffbca
-    ELF header: https://refspecs.linuxfoundation.org/elf/gabi4+/ch4.eheader.html
-    """
-    f.seek(0)
-    try:
-        ident = _read_unpacked(f, "16B")
-    except struct.error:
-        return None
-    if ident[:4] != tuple(b"\x7fELF"):  # Invalid magic, not ELF.
-        return None
-    f.seek(struct.calcsize("HHI"), 1)  # Skip file type, machine, and version.
-
-    try:
-        # e_fmt: Format for program header.
-        # p_fmt: Format for section header.
-        # p_idx: Indexes to find p_type, p_offset, and p_filesz.
-        e_fmt, p_fmt, p_idx = {
-            1: ("IIIIHHH", "IIIIIIII", (0, 1, 4)),  # 32-bit.
-            2: ("QQQIHHH", "IIQQQQQQ", (0, 2, 5)),  # 64-bit.
-        }[ident[4]]
-    except KeyError:
-        return None
-    else:
-        p_get = operator.itemgetter(*p_idx)
-
-    # Find the interpreter section and return its content.
-    try:
-        _, e_phoff, _, _, _, e_phentsize, e_phnum = _read_unpacked(f, e_fmt)
-    except struct.error:
-        return None
-    for i in range(e_phnum + 1):
-        f.seek(e_phoff + e_phentsize * i)
-        try:
-            p_type, p_offset, p_filesz = p_get(_read_unpacked(f, p_fmt))
-        except struct.error:
-            return None
-        if p_type != 3:  # Not PT_INTERP.
-            continue
-        f.seek(p_offset)
-        interpreter = os.fsdecode(f.read(p_filesz)).strip("\0")
-        if "musl" not in interpreter:
-            return None
-        return interpreter
-    return None
-
-
-class _MuslVersion(NamedTuple):
-    major: int
-    minor: int
-
-
-def _parse_musl_version(output: str) -> Optional[_MuslVersion]:
-    lines = [n for n in (n.strip() for n in output.splitlines()) if n]
-    if len(lines) < 2 or lines[0][:4] != "musl":
-        return None
-    m = re.match(r"Version (\d+)\.(\d+)", lines[1])
-    if not m:
-        return None
-    return _MuslVersion(major=int(m.group(1)), minor=int(m.group(2)))
-
-
-@functools.lru_cache()
-def _get_musl_version(executable: str) -> Optional[_MuslVersion]:
-    """Detect currently-running musl runtime version.
-
-    This is done by checking the specified executable's dynamic linking
-    information, and invoking the loader to parse its output for a version
-    string. If the loader is musl, the output would be something like::
-
-        musl libc (x86_64)
-        Version 1.2.2
-        Dynamic Program Loader
-    """
-    with contextlib.ExitStack() as stack:
-        try:
-            f = stack.enter_context(open(executable, "rb"))
-        except OSError:
-            return None
-        ld = _parse_ld_musl_from_elf(f)
-    if not ld:
-        return None
-    proc = subprocess.run([ld], stderr=subprocess.PIPE, universal_newlines=True)
-    return _parse_musl_version(proc.stderr)
-
-
-def platform_tags(arch: str) -> Iterator[str]:
-    """Generate musllinux tags compatible to the current platform.
-
-    :param arch: Should be the part of platform tag after the ``linux_``
-        prefix, e.g. ``x86_64``. The ``linux_`` prefix is assumed as a
-        prerequisite for the current platform to be musllinux-compatible.
-
-    :returns: An iterator of compatible musllinux tags.
-    """
-    sys_musl = _get_musl_version(sys.executable)
-    if sys_musl is None:  # Python not dynamically linked against musl.
-        return
-    for minor in range(sys_musl.minor, -1, -1):
-        yield f"musllinux_{sys_musl.major}_{minor}_{arch}"
-
-
-if __name__ == "__main__":  # pragma: no cover
-    import sysconfig
-
-    plat = sysconfig.get_platform()
-    assert plat.startswith("linux-"), "not linux"
-
-    print("plat:", plat)
-    print("musl:", _get_musl_version(sys.executable))
-    print("tags:", end=" ")
-    for t in platform_tags(re.sub(r"[.-]", "_", plat.split("-", 1)[-1])):
-        print(t, end="\n      ")

spaces/AtomdffAI/wechatgpt4atom/channel/wechat/wechaty_channel.py

@@ -1,201 +0,0 @@
-# encoding:utf-8
-
-"""
-wechaty channel
-Python Wechaty - https://github.com/wechaty/python-wechaty
-"""
-import io
-import os
-import json
-import time
-import asyncio
-import requests
-from typing import Optional, Union
-from wechaty_puppet import MessageType, FileBox, ScanStatus  # type: ignore
-from wechaty import Wechaty, Contact
-from wechaty.user import Message, Room, MiniProgram, UrlLink
-from channel.channel import Channel
-from common.log import logger
-from config import conf
-
-
-class WechatyChannel(Channel):
-
-    def __init__(self):
-        pass
-
-    def startup(self):
-        asyncio.run(self.main())
-
-    async def main(self):
-        config = conf()
-        # 使用PadLocal协议 比较稳定(免费web协议 os.environ['WECHATY_PUPPET_SERVICE_ENDPOINT'] = '127.0.0.1:8080')
-        token = config.get('wechaty_puppet_service_token')
-        os.environ['WECHATY_PUPPET_SERVICE_TOKEN'] = token
-        global bot
-        bot = Wechaty()
-
-        bot.on('scan', self.on_scan)
-        bot.on('login', self.on_login)
-        bot.on('message', self.on_message)
-        await bot.start()
-
-    async def on_login(self, contact: Contact):
-        logger.info('[WX] login user={}'.format(contact))
-
-    async def on_scan(self, status: ScanStatus, qr_code: Optional[str] = None,
-                      data: Optional[str] = None):
-        contact = self.Contact.load(self.contact_id)
-        logger.info('[WX] scan user={}, scan status={}, scan qr_code={}'.format(contact, status.name, qr_code))
-        # print(f'user <{contact}> scan status: {status.name} , 'f'qr_code: {qr_code}')
-
-    async def on_message(self, msg: Message):
-        """
-        listen for message event
-        """
-        from_contact = msg.talker()  # 获取消息的发送者
-        to_contact = msg.to()  # 接收人
-        room = msg.room()  # 获取消息来自的群聊. 如果消息不是来自群聊, 则返回None
-        from_user_id = from_contact.contact_id
-        to_user_id = to_contact.contact_id  # 接收人id
-        # other_user_id = msg['User']['UserName']  # 对手方id
-        content = msg.text()
-        mention_content = await msg.mention_text()  # 返回过滤掉@name后的消息
-        match_prefix = self.check_prefix(content, conf().get('single_chat_prefix'))
-        conversation: Union[Room, Contact] = from_contact if room is None else room
-
-        if room is None and msg.type() == MessageType.MESSAGE_TYPE_TEXT:
-            if not msg.is_self() and match_prefix is not None:
-                # 好友向自己发送消息
-                if match_prefix != '':
-                    str_list = content.split(match_prefix, 1)
-                    if len(str_list) == 2:
-                        content = str_list[1].strip()
-
-                img_match_prefix = self.check_prefix(content, conf().get('image_create_prefix'))
-                if img_match_prefix:
-                    content = content.split(img_match_prefix, 1)[1].strip()
-                    await self._do_send_img(content, from_user_id)
-                else:
-                    await self._do_send(content, from_user_id)
-            elif msg.is_self() and match_prefix:
-                # 自己给好友发送消息
-                str_list = content.split(match_prefix, 1)
-                if len(str_list) == 2:
-                    content = str_list[1].strip()
-                img_match_prefix = self.check_prefix(content, conf().get('image_create_prefix'))
-                if img_match_prefix:
-                    content = content.split(img_match_prefix, 1)[1].strip()
-                    await self._do_send_img(content, to_user_id)
-                else:
-                    await self._do_send(content, to_user_id)
-        elif room and msg.type() == MessageType.MESSAGE_TYPE_TEXT:
-            # 群组&文本消息
-            room_id = room.room_id
-            room_name = await room.topic()
-            from_user_id = from_contact.contact_id
-            from_user_name = from_contact.name
-            is_at = await msg.mention_self()
-            content = mention_content
-            config = conf()
-            match_prefix = (is_at and not config.get("group_at_off", False)) \
-                           or self.check_prefix(content, config.get('group_chat_prefix')) \
-                           or self.check_contain(content, config.get('group_chat_keyword'))
-            if ('ALL_GROUP' in config.get('group_name_white_list') or room_name in config.get(
-                    'group_name_white_list') or self.check_contain(room_name, config.get(
-                'group_name_keyword_white_list'))) and match_prefix:
-                img_match_prefix = self.check_prefix(content, conf().get('image_create_prefix'))
-                if img_match_prefix:
-                    content = content.split(img_match_prefix, 1)[1].strip()
-                    await self._do_send_group_img(content, room_id)
-                else:
-                    await self._do_send_group(content, room_id, from_user_id, from_user_name)
-
-    async def send(self, message: Union[str, Message, FileBox, Contact, UrlLink, MiniProgram], receiver):
-        logger.info('[WX] sendMsg={}, receiver={}'.format(message, receiver))
-        if receiver:
-            contact = await bot.Contact.find(receiver)
-            await contact.say(message)
-
-    async def send_group(self, message: Union[str, Message, FileBox, Contact, UrlLink, MiniProgram], receiver):
-        logger.info('[WX] sendMsg={}, receiver={}'.format(message, receiver))
-        if receiver:
-            room = await bot.Room.find(receiver)
-            await room.say(message)
-
-    async def _do_send(self, query, reply_user_id):
-        try:
-            if not query:
-                return
-            context = dict()
-            context['from_user_id'] = reply_user_id
-            reply_text = super().build_reply_content(query, context)
-            if reply_text:
-                await self.send(conf().get("single_chat_reply_prefix") + reply_text, reply_user_id)
-        except Exception as e:
-            logger.exception(e)
-
-    async def _do_send_img(self, query, reply_user_id):
-        try:
-            if not query:
-                return
-            context = dict()
-            context['type'] = 'IMAGE_CREATE'
-            img_url = super().build_reply_content(query, context)
-            if not img_url:
-                return
-            # 图片下载
-            # pic_res = requests.get(img_url, stream=True)
-            # image_storage = io.BytesIO()
-            # for block in pic_res.iter_content(1024):
-            #     image_storage.write(block)
-            # image_storage.seek(0)
-
-            # 图片发送
-            logger.info('[WX] sendImage, receiver={}'.format(reply_user_id))
-            t = int(time.time())
-            file_box = FileBox.from_url(url=img_url, name=str(t) + '.png')
-            await self.send(file_box, reply_user_id)
-        except Exception as e:
-            logger.exception(e)
-
-    async def _do_send_group(self, query, group_id, group_user_id, group_user_name):
-        if not query:
-            return
-        context = dict()
-        context['from_user_id'] = str(group_id) + '-' + str(group_user_id)
-        reply_text = super().build_reply_content(query, context)
-        if reply_text:
-            reply_text = '@' + group_user_name + ' ' + reply_text.strip()
-            await self.send_group(conf().get("group_chat_reply_prefix", "") + reply_text, group_id)
-
-    async def _do_send_group_img(self, query, reply_room_id):
-        try:
-            if not query:
-                return
-            context = dict()
-            context['type'] = 'IMAGE_CREATE'
-            img_url = super().build_reply_content(query, context)
-            if not img_url:
-                return
-            # 图片发送
-            logger.info('[WX] sendImage, receiver={}'.format(reply_room_id))
-            t = int(time.time())
-            file_box = FileBox.from_url(url=img_url, name=str(t) + '.png')
-            await self.send_group(file_box, reply_room_id)
-        except Exception as e:
-            logger.exception(e)
-
-    def check_prefix(self, content, prefix_list):
-        for prefix in prefix_list:
-            if content.startswith(prefix):
-                return prefix
-        return None
-
-    def check_contain(self, content, keyword_list):
-        if not keyword_list:
-            return None
-        for ky in keyword_list:
-            if content.find(ky) != -1:
-                return True
-        return None

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

@@ -1,239 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Lightning Trainer should be considered beta at this point
-# We have confirmed that training and validation run correctly and produce correct results
-# Depending on how you launch the trainer, there are issues with processes terminating correctly
-# This module is still dependent on D2 logging, but could be transferred to use Lightning logging
-
-import logging
-import os
-import time
-import weakref
-from collections import OrderedDict
-from typing import Any, Dict, List
-
-import detectron2.utils.comm as comm
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import get_cfg
-from detectron2.data import build_detection_test_loader, build_detection_train_loader
-from detectron2.engine import (
-    DefaultTrainer,
-    SimpleTrainer,
-    default_argument_parser,
-    default_setup,
-    default_writers,
-    hooks,
-)
-from detectron2.evaluation import print_csv_format
-from detectron2.evaluation.testing import flatten_results_dict
-from detectron2.modeling import build_model
-from detectron2.solver import build_lr_scheduler, build_optimizer
-from detectron2.utils.events import EventStorage
-from detectron2.utils.logger import setup_logger
-
-import pytorch_lightning as pl  # type: ignore
-from pytorch_lightning import LightningDataModule, LightningModule
-from train_net import build_evaluator
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("detectron2")
-
-
-class TrainingModule(LightningModule):
-    def __init__(self, cfg):
-        super().__init__()
-        if not logger.isEnabledFor(logging.INFO):  # setup_logger is not called for d2
-            setup_logger()
-        self.cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size())
-        self.storage: EventStorage = None
-        self.model = build_model(self.cfg)
-
-        self.start_iter = 0
-        self.max_iter = cfg.SOLVER.MAX_ITER
-
-    def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
-        checkpoint["iteration"] = self.storage.iter
-
-    def on_load_checkpoint(self, checkpointed_state: Dict[str, Any]) -> None:
-        self.start_iter = checkpointed_state["iteration"]
-        self.storage.iter = self.start_iter
-
-    def setup(self, stage: str):
-        if self.cfg.MODEL.WEIGHTS:
-            self.checkpointer = DetectionCheckpointer(
-                # Assume you want to save checkpoints together with logs/statistics
-                self.model,
-                self.cfg.OUTPUT_DIR,
-            )
-            logger.info(f"Load model weights from checkpoint: {self.cfg.MODEL.WEIGHTS}.")
-            # Only load weights, use lightning checkpointing if you want to resume
-            self.checkpointer.load(self.cfg.MODEL.WEIGHTS)
-
-        self.iteration_timer = hooks.IterationTimer()
-        self.iteration_timer.before_train()
-        self.data_start = time.perf_counter()
-        self.writers = None
-
-    def training_step(self, batch, batch_idx):
-        data_time = time.perf_counter() - self.data_start
-        # Need to manually enter/exit since trainer may launch processes
-        # This ideally belongs in setup, but setup seems to run before processes are spawned
-        if self.storage is None:
-            self.storage = EventStorage(0)
-            self.storage.__enter__()
-            self.iteration_timer.trainer = weakref.proxy(self)
-            self.iteration_timer.before_step()
-            self.writers = (
-                default_writers(self.cfg.OUTPUT_DIR, self.max_iter)
-                if comm.is_main_process()
-                else {}
-            )
-
-        loss_dict = self.model(batch)
-        SimpleTrainer.write_metrics(loss_dict, data_time)
-
-        opt = self.optimizers()
-        self.storage.put_scalar(
-            "lr", opt.param_groups[self._best_param_group_id]["lr"], smoothing_hint=False
-        )
-        self.iteration_timer.after_step()
-        self.storage.step()
-        # A little odd to put before step here, but it's the best way to get a proper timing
-        self.iteration_timer.before_step()
-
-        if self.storage.iter % 20 == 0:
-            for writer in self.writers:
-                writer.write()
-        return sum(loss_dict.values())
-
-    def training_step_end(self, training_step_outpus):
-        self.data_start = time.perf_counter()
-        return training_step_outpus
-
-    def training_epoch_end(self, training_step_outputs):
-        self.iteration_timer.after_train()
-        if comm.is_main_process():
-            self.checkpointer.save("model_final")
-        for writer in self.writers:
-            writer.write()
-            writer.close()
-        self.storage.__exit__(None, None, None)
-
-    def _process_dataset_evaluation_results(self) -> OrderedDict:
-        results = OrderedDict()
-        for idx, dataset_name in enumerate(self.cfg.DATASETS.TEST):
-            results[dataset_name] = self._evaluators[idx].evaluate()
-            if comm.is_main_process():
-                print_csv_format(results[dataset_name])
-
-        if len(results) == 1:
-            results = list(results.values())[0]
-        return results
-
-    def _reset_dataset_evaluators(self):
-        self._evaluators = []
-        for dataset_name in self.cfg.DATASETS.TEST:
-            evaluator = build_evaluator(self.cfg, dataset_name)
-            evaluator.reset()
-            self._evaluators.append(evaluator)
-
-    def on_validation_epoch_start(self, _outputs):
-        self._reset_dataset_evaluators()
-
-    def validation_epoch_end(self, _outputs):
-        results = self._process_dataset_evaluation_results(_outputs)
-
-        flattened_results = flatten_results_dict(results)
-        for k, v in flattened_results.items():
-            try:
-                v = float(v)
-            except Exception as e:
-                raise ValueError(
-                    "[EvalHook] eval_function should return a nested dict of float. "
-                    "Got '{}: {}' instead.".format(k, v)
-                ) from e
-        self.storage.put_scalars(**flattened_results, smoothing_hint=False)
-
-    def validation_step(self, batch, batch_idx: int, dataloader_idx: int = 0) -> None:
-        if not isinstance(batch, List):
-            batch = [batch]
-        outputs = self.model(batch)
-        self._evaluators[dataloader_idx].process(batch, outputs)
-
-    def configure_optimizers(self):
-        optimizer = build_optimizer(self.cfg, self.model)
-        self._best_param_group_id = hooks.LRScheduler.get_best_param_group_id(optimizer)
-        scheduler = build_lr_scheduler(self.cfg, optimizer)
-        return [optimizer], [{"scheduler": scheduler, "interval": "step"}]
-
-
-class DataModule(LightningDataModule):
-    def __init__(self, cfg):
-        super().__init__()
-        self.cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size())
-
-    def train_dataloader(self):
-        return build_detection_train_loader(self.cfg)
-
-    def val_dataloader(self):
-        dataloaders = []
-        for dataset_name in self.cfg.DATASETS.TEST:
-            dataloaders.append(build_detection_test_loader(self.cfg, dataset_name))
-        return dataloaders
-
-
-def main(args):
-    cfg = setup(args)
-    train(cfg, args)
-
-
-def train(cfg, args):
-    trainer_params = {
-        # training loop is bounded by max steps, use a large max_epochs to make
-        # sure max_steps is met first
-        "max_epochs": 10 ** 8,
-        "max_steps": cfg.SOLVER.MAX_ITER,
-        "val_check_interval": cfg.TEST.EVAL_PERIOD if cfg.TEST.EVAL_PERIOD > 0 else 10 ** 8,
-        "num_nodes": args.num_machines,
-        "gpus": args.num_gpus,
-        "num_sanity_val_steps": 0,
-    }
-    if cfg.SOLVER.AMP.ENABLED:
-        trainer_params["precision"] = 16
-
-    last_checkpoint = os.path.join(cfg.OUTPUT_DIR, "last.ckpt")
-    if args.resume:
-        # resume training from checkpoint
-        trainer_params["resume_from_checkpoint"] = last_checkpoint
-        logger.info(f"Resuming training from checkpoint: {last_checkpoint}.")
-
-    trainer = pl.Trainer(**trainer_params)
-    logger.info(f"start to train with {args.num_machines} nodes and {args.num_gpus} GPUs")
-
-    module = TrainingModule(cfg)
-    data_module = DataModule(cfg)
-    if args.eval_only:
-        logger.info("Running inference")
-        trainer.validate(module, data_module)
-    else:
-        logger.info("Running training")
-        trainer.fit(module, data_module)
-
-
-def setup(args):
-    """
-    Create configs and perform basic setups.
-    """
-    cfg = get_cfg()
-    cfg.merge_from_file(args.config_file)
-    cfg.merge_from_list(args.opts)
-    cfg.freeze()
-    default_setup(cfg, args)
-    return cfg
-
-
-if __name__ == "__main__":
-    parser = default_argument_parser()
-    args = parser.parse_args()
-    logger.info("Command Line Args:", args)
-    main(args)

spaces/BartPoint/VoiceChange_Beta/app_multi.py

@@ -1,496 +0,0 @@
-from typing import Union
-
-from argparse import ArgumentParser
-
-import asyncio
-import json
-import hashlib
-from os import path, getenv
-
-import gradio as gr
-
-import torch
-
-import numpy as np
-import librosa
-
-import edge_tts
-
-import config
-import util
-from fairseq import checkpoint_utils
-from infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from vc_infer_pipeline import VC
-from config import Config
-config = Config()
-force_support = None
-if config.unsupported is False:
-    if config.device == "mps" or config.device == "cpu":
-        force_support = False
-else:
-    force_support = True
-    
-# Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21  # noqa
-in_hf_space = getenv('SYSTEM') == 'spaces'
-
-# Argument parsing
-arg_parser = ArgumentParser()
-arg_parser.add_argument(
-    '--hubert',
-    default=getenv('RVC_HUBERT', 'hubert_base.pt'),
-    help='path to hubert base model (default: hubert_base.pt)'
-)
-arg_parser.add_argument(
-    '--config',
-    default=getenv('RVC_MULTI_CFG', 'multi_config.json'),
-    help='path to config file (default: multi_config.json)'
-)
-arg_parser.add_argument(
-    '--api',
-    action='store_true',
-    help='enable api endpoint'
-)
-arg_parser.add_argument(
-    '--cache-examples',
-    action='store_true',
-    help='enable example caching, please remember delete gradio_cached_examples folder when example config has been modified'  # noqa
-)
-args = arg_parser.parse_args()
-
-app_css = '''
-#model_info img {
-    max-width: 100px;
-    max-height: 100px;
-    float: right;
-}
-
-#model_info p {
-    margin: unset;
-}
-'''
-
-app = gr.Blocks(
-    theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate"),
-    css=app_css,
-    analytics_enabled=False
-)
-
-# Load hubert model
-models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
-    ["hubert_base.pt"],
-    suffix="",
-)
-hubert_model = models[0]
-hubert_model = hubert_model.to(config.device)
-if config.is_half:
-    hubert_model = hubert_model.half()
-else:
-    hubert_model = hubert_model.float()
-hubert_model.eval()
-
-# Load models
-multi_cfg = json.load(open(args.config, 'r'))
-loaded_models = []
-
-for model_name in multi_cfg.get('models'):
-    print(f'Loading model: {model_name}')
-
-    # Load model info
-    model_info = json.load(
-        open(path.join('model', model_name, 'config.json'), 'r')
-    )
-
-    # Load RVC checkpoint
-    cpt = torch.load(
-        path.join('model', model_name, model_info['model']),
-        map_location='cpu'
-    )
-    tgt_sr = cpt['config'][-1]
-    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-    if_f0 = cpt.get("f0", 1)
-    version = cpt.get("version", "v1")
-    if version == "v1":
-        if if_f0 == 1:
-            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
-        else:
-            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-        model_version = "V1"
-    elif version == "v2":
-        if if_f0 == 1:
-            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
-        else:
-            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-        model_version = "V2"
-    del net_g.enc_q
-
-    print(net_g.load_state_dict(cpt["weight"], strict=False))
-    net_g.eval().to(config.device)
-    if config.is_half:
-        net_g = net_g.half()
-    else:
-        net_g = net_g.float()
-    vc = VC(tgt_sr, config)
-    
-    loaded_models.append(dict(
-        name=model_name,
-        metadata=model_info,
-        vc=vc,
-        net_g=net_g,
-        if_f0=if_f0,
-        target_sr=tgt_sr,
-        test=model_version
-    ))
-        
-print(f'Models loaded: {len(loaded_models)}')
-
-# Edge TTS speakers
-tts_speakers_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())  # noqa
-
-
-# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118  # noqa
-def vc_func(
-    input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
-    filter_radius, rms_mix_rate, resample_option
-):
-    if input_audio is None:
-        return (None, 'Please provide input audio.')
-
-    if model_index is None:
-        return (None, 'Please select a model.')
-
-    model = loaded_models[model_index]
-
-    # Reference: so-vits
-    (audio_samp, audio_npy) = input_audio
-
-    # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49
-    # Can be change well, we will see
-    if (audio_npy.shape[0] / audio_samp) > 320 and in_hf_space:
-        return (None, 'Input audio is longer than 60 secs.')
-
-    # Bloody hell: https://stackoverflow.com/questions/26921836/
-    if audio_npy.dtype != np.float32:  # :thonk:
-        audio_npy = (
-            audio_npy / np.iinfo(audio_npy.dtype).max
-        ).astype(np.float32)
-
-    if len(audio_npy.shape) > 1:
-        audio_npy = librosa.to_mono(audio_npy.transpose(1, 0))
-
-    if audio_samp != 16000:
-        audio_npy = librosa.resample(
-            audio_npy,
-            orig_sr=audio_samp,
-            target_sr=16000
-        )
-
-    pitch_int = int(pitch_adjust)
-
-    resample = (
-        0 if resample_option == 'Disable resampling'
-        else int(resample_option)
-    )
-
-    times = [0, 0, 0]
-
-    checksum = hashlib.sha512()
-    checksum.update(audio_npy.tobytes())
-
-    print(model['test'])
-    
-    output_audio = model['vc'].pipeline(
-        hubert_model,
-        model['net_g'],
-        model['metadata'].get('speaker_id', 0),
-        audio_npy,
-        checksum.hexdigest(),
-        times,
-        pitch_int,
-        f0_method,
-        path.join('model', model['name'], model['metadata']['feat_index']),
-        feat_ratio,
-        model['if_f0'],
-        filter_radius,
-        model['target_sr'],
-        resample,
-        rms_mix_rate,
-        model['test'],
-        0.5    
-    )
-
-    out_sr = (
-        resample if resample >= 16000 and model['target_sr'] != resample
-        else model['target_sr']
-    )
-
-    print(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s')
-    return ((out_sr, output_audio), 'Success')
-
-
-async def edge_tts_vc_func(
-    input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
-    filter_radius, rms_mix_rate, resample_option
-):
-    if input_text is None:
-        return (None, 'Please provide TTS text.')
-
-    if tts_speaker is None:
-        return (None, 'Please select TTS speaker.')
-
-    if model_index is None:
-        return (None, 'Please select a model.')
-
-    speaker = tts_speakers_list[tts_speaker]['ShortName']
-    (tts_np, tts_sr) = await util.call_edge_tts(speaker, input_text)
-    return vc_func(
-        (tts_sr, tts_np),
-        model_index,
-        pitch_adjust,
-        f0_method,
-        feat_ratio,
-        filter_radius,
-        rms_mix_rate,
-        resample_option
-    )
-
-
-def update_model_info(model_index):
-    if model_index is None:
-        return str(
-            '### Model info\n'
-            'Please select a model from dropdown above.'
-        )
-
-    model = loaded_models[model_index]
-    model_icon = model['metadata'].get('icon', '')
-
-    return str(
-        '### Model info\n'
-        '![model icon]({icon})'
-        '**{name}**\n\n'
-        'Author: {author}\n\n'
-        'Source: {source}\n\n'
-        '{note}'
-    ).format(
-        name=model['metadata'].get('name'),
-        author=model['metadata'].get('author', 'Anonymous'),
-        source=model['metadata'].get('source', 'Unknown'),
-        note=model['metadata'].get('note', ''),
-        icon=(
-            model_icon
-            if model_icon.startswith(('http://', 'https://'))
-            else '/file/model/%s/%s' % (model['name'], model_icon)
-        )
-    )
-
-
-def _example_vc(
-    input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
-    filter_radius, rms_mix_rate, resample_option
-):
-    (audio, message) = vc_func(
-        input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
-        filter_radius, rms_mix_rate, resample_option
-    )
-    return (
-        audio,
-        message,
-        update_model_info(model_index)
-    )
-
-
-async def _example_edge_tts(
-    input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
-    filter_radius, rms_mix_rate, resample_option
-):
-    (audio, message) = await edge_tts_vc_func(
-        input_text, model_index, tts_speaker, pitch_adjust, f0_method,
-        feat_ratio, filter_radius, rms_mix_rate, resample_option
-    )
-    return (
-        audio,
-        message,
-        update_model_info(model_index)
-    )
-
-
-with app:
-    gr.Markdown(
-        '## A simplistic Web interface\n'
-        'RVC interface, project based on [RVC-WebUI](https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI)'  # thx noqa
-        'A lot of inspiration from what\'s already out there, including [zomehwh/rvc-models](https://huggingface.co/spaces/zomehwh/rvc-models) & [DJQmUKV/rvc-inference](https://huggingface.co/spaces/DJQmUKV/rvc-inference).\n '  # thx noqa
-    )
-
-    with gr.Row():
-        with gr.Column():
-            with gr.Tab('Audio conversion'):
-                input_audio = gr.Audio(label='Input audio')
-
-                vc_convert_btn = gr.Button('Convert', variant='primary')
-
-            with gr.Tab('TTS conversion'):
-                tts_input = gr.TextArea(
-                    label='TTS input text'
-                )
-                tts_speaker = gr.Dropdown(
-                    [
-                        '%s (%s)' % (
-                            s['FriendlyName'],
-                            s['Gender']
-                        )
-                        for s in tts_speakers_list
-                    ],
-                    label='TTS speaker',
-                    type='index'
-                )
-
-                tts_convert_btn = gr.Button('Convert', variant='primary')
-
-            pitch_adjust = gr.Slider(
-                label='Pitch',
-                minimum=-24,
-                maximum=24,
-                step=1,
-                value=0
-            )
-            f0_method = gr.Radio(
-                label='f0 methods',
-                choices=['pm', 'harvest', 'crepe'],
-                value='pm',
-                interactive=True
-            )
-
-            with gr.Accordion('Advanced options', open=False):
-                feat_ratio = gr.Slider(
-                    label='Feature ratio',
-                    minimum=0,
-                    maximum=1,
-                    step=0.1,
-                    value=0.6
-                )
-                filter_radius = gr.Slider(
-                    label='Filter radius',
-                    minimum=0,
-                    maximum=7,
-                    step=1,
-                    value=3
-                )
-                rms_mix_rate = gr.Slider(
-                    label='Volume envelope mix rate',
-                    minimum=0,
-                    maximum=1,
-                    step=0.1,
-                    value=1
-                )
-                resample_rate = gr.Dropdown(
-                    [
-                        'Disable resampling',
-                        '16000',
-                        '22050',
-                        '44100',
-                        '48000'
-                    ],
-                    label='Resample rate',
-                    value='Disable resampling'
-                )
-
-        with gr.Column():
-            # Model select
-            model_index = gr.Dropdown(
-                [
-                    '%s - %s' % (
-                        m['metadata'].get('source', 'Unknown'),
-                        m['metadata'].get('name')
-                    )
-                    for m in loaded_models
-                ],
-                label='Model',
-                type='index'
-            )
-
-            # Model info
-            with gr.Box():
-                model_info = gr.Markdown(
-                    '### Model info\n'
-                    'Please select a model from dropdown above.',
-                    elem_id='model_info'
-                )
-
-            output_audio = gr.Audio(label='Output audio')
-            output_msg = gr.Textbox(label='Output message')
-
-    multi_examples = multi_cfg.get('examples')
-    if (
-        multi_examples and
-        multi_examples.get('vc') and multi_examples.get('tts_vc')
-    ):
-        with gr.Accordion('Sweet sweet examples', open=False):
-            with gr.Row():
-                # VC Example
-                if multi_examples.get('vc'):
-                    gr.Examples(
-                        label='Audio conversion examples',
-                        examples=multi_examples.get('vc'),
-                        inputs=[
-                            input_audio, model_index, pitch_adjust, f0_method,
-                            feat_ratio
-                        ],
-                        outputs=[output_audio, output_msg, model_info],
-                        fn=_example_vc,
-                        cache_examples=args.cache_examples,
-                        run_on_click=args.cache_examples
-                    )
-
-                # Edge TTS Example
-                if multi_examples.get('tts_vc'):
-                    gr.Examples(
-                        label='TTS conversion examples',
-                        examples=multi_examples.get('tts_vc'),
-                        inputs=[
-                            tts_input, model_index, tts_speaker, pitch_adjust,
-                            f0_method, feat_ratio
-                        ],
-                        outputs=[output_audio, output_msg, model_info],
-                        fn=_example_edge_tts,
-                        cache_examples=args.cache_examples,
-                        run_on_click=args.cache_examples
-                    )
-
-    vc_convert_btn.click(
-        vc_func,
-        [
-            input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
-            filter_radius, rms_mix_rate, resample_rate
-        ],
-        [output_audio, output_msg],
-        api_name='audio_conversion'
-    )
-
-    tts_convert_btn.click(
-        edge_tts_vc_func,
-        [
-            tts_input, model_index, tts_speaker, pitch_adjust, f0_method,
-            feat_ratio, filter_radius, rms_mix_rate, resample_rate
-        ],
-        [output_audio, output_msg],
-        api_name='tts_conversion'
-    )
-
-    model_index.change(
-        update_model_info,
-        inputs=[model_index],
-        outputs=[model_info],
-        show_progress=False,
-        queue=False
-    )
-
-app.queue(
-    concurrency_count=1,
-    max_size=20,
-    api_open=args.api
-).launch()

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/formatters/img.py

@@ -1,645 +0,0 @@
-"""
-    pygments.formatters.img
-    ~~~~~~~~~~~~~~~~~~~~~~~
-
-    Formatter for Pixmap output.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import os
-import sys
-
-from pip._vendor.pygments.formatter import Formatter
-from pip._vendor.pygments.util import get_bool_opt, get_int_opt, get_list_opt, \
-    get_choice_opt
-
-import subprocess
-
-# Import this carefully
-try:
-    from PIL import Image, ImageDraw, ImageFont
-    pil_available = True
-except ImportError:
-    pil_available = False
-
-try:
-    import _winreg
-except ImportError:
-    try:
-        import winreg as _winreg
-    except ImportError:
-        _winreg = None
-
-__all__ = ['ImageFormatter', 'GifImageFormatter', 'JpgImageFormatter',
-           'BmpImageFormatter']
-
-
-# For some unknown reason every font calls it something different
-STYLES = {
-    'NORMAL':     ['', 'Roman', 'Book', 'Normal', 'Regular', 'Medium'],
-    'ITALIC':     ['Oblique', 'Italic'],
-    'BOLD':       ['Bold'],
-    'BOLDITALIC': ['Bold Oblique', 'Bold Italic'],
-}
-
-# A sane default for modern systems
-DEFAULT_FONT_NAME_NIX = 'DejaVu Sans Mono'
-DEFAULT_FONT_NAME_WIN = 'Courier New'
-DEFAULT_FONT_NAME_MAC = 'Menlo'
-
-
-class PilNotAvailable(ImportError):
-    """When Python imaging library is not available"""
-
-
-class FontNotFound(Exception):
-    """When there are no usable fonts specified"""
-
-
-class FontManager:
-    """
-    Manages a set of fonts: normal, italic, bold, etc...
-    """
-
-    def __init__(self, font_name, font_size=14):
-        self.font_name = font_name
-        self.font_size = font_size
-        self.fonts = {}
-        self.encoding = None
-        if sys.platform.startswith('win'):
-            if not font_name:
-                self.font_name = DEFAULT_FONT_NAME_WIN
-            self._create_win()
-        elif sys.platform.startswith('darwin'):
-            if not font_name:
-                self.font_name = DEFAULT_FONT_NAME_MAC
-            self._create_mac()
-        else:
-            if not font_name:
-                self.font_name = DEFAULT_FONT_NAME_NIX
-            self._create_nix()
-
-    def _get_nix_font_path(self, name, style):
-        proc = subprocess.Popen(['fc-list', "%s:style=%s" % (name, style), 'file'],
-                                stdout=subprocess.PIPE, stderr=None)
-        stdout, _ = proc.communicate()
-        if proc.returncode == 0:
-            lines = stdout.splitlines()
-            for line in lines:
-                if line.startswith(b'Fontconfig warning:'):
-                    continue
-                path = line.decode().strip().strip(':')
-                if path:
-                    return path
-            return None
-
-    def _create_nix(self):
-        for name in STYLES['NORMAL']:
-            path = self._get_nix_font_path(self.font_name, name)
-            if path is not None:
-                self.fonts['NORMAL'] = ImageFont.truetype(path, self.font_size)
-                break
-        else:
-            raise FontNotFound('No usable fonts named: "%s"' %
-                               self.font_name)
-        for style in ('ITALIC', 'BOLD', 'BOLDITALIC'):
-            for stylename in STYLES[style]:
-                path = self._get_nix_font_path(self.font_name, stylename)
-                if path is not None:
-                    self.fonts[style] = ImageFont.truetype(path, self.font_size)
-                    break
-            else:
-                if style == 'BOLDITALIC':
-                    self.fonts[style] = self.fonts['BOLD']
-                else:
-                    self.fonts[style] = self.fonts['NORMAL']
-
-    def _get_mac_font_path(self, font_map, name, style):
-        return font_map.get((name + ' ' + style).strip().lower())
-
-    def _create_mac(self):
-        font_map = {}
-        for font_dir in (os.path.join(os.getenv("HOME"), 'Library/Fonts/'),
-                         '/Library/Fonts/', '/System/Library/Fonts/'):
-            font_map.update(
-                (os.path.splitext(f)[0].lower(), os.path.join(font_dir, f))
-                for f in os.listdir(font_dir)
-                if f.lower().endswith(('ttf', 'ttc')))
-
-        for name in STYLES['NORMAL']:
-            path = self._get_mac_font_path(font_map, self.font_name, name)
-            if path is not None:
-                self.fonts['NORMAL'] = ImageFont.truetype(path, self.font_size)
-                break
-        else:
-            raise FontNotFound('No usable fonts named: "%s"' %
-                               self.font_name)
-        for style in ('ITALIC', 'BOLD', 'BOLDITALIC'):
-            for stylename in STYLES[style]:
-                path = self._get_mac_font_path(font_map, self.font_name, stylename)
-                if path is not None:
-                    self.fonts[style] = ImageFont.truetype(path, self.font_size)
-                    break
-            else:
-                if style == 'BOLDITALIC':
-                    self.fonts[style] = self.fonts['BOLD']
-                else:
-                    self.fonts[style] = self.fonts['NORMAL']
-
-    def _lookup_win(self, key, basename, styles, fail=False):
-        for suffix in ('', ' (TrueType)'):
-            for style in styles:
-                try:
-                    valname = '%s%s%s' % (basename, style and ' '+style, suffix)
-                    val, _ = _winreg.QueryValueEx(key, valname)
-                    return val
-                except OSError:
-                    continue
-        else:
-            if fail:
-                raise FontNotFound('Font %s (%s) not found in registry' %
-                                   (basename, styles[0]))
-            return None
-
-    def _create_win(self):
-        lookuperror = None
-        keynames = [ (_winreg.HKEY_CURRENT_USER, r'Software\Microsoft\Windows NT\CurrentVersion\Fonts'),
-                     (_winreg.HKEY_CURRENT_USER, r'Software\Microsoft\Windows\CurrentVersion\Fonts'),
-                     (_winreg.HKEY_LOCAL_MACHINE, r'Software\Microsoft\Windows NT\CurrentVersion\Fonts'),
-                     (_winreg.HKEY_LOCAL_MACHINE, r'Software\Microsoft\Windows\CurrentVersion\Fonts') ]
-        for keyname in keynames:
-            try:
-                key = _winreg.OpenKey(*keyname)
-                try:
-                    path = self._lookup_win(key, self.font_name, STYLES['NORMAL'], True)
-                    self.fonts['NORMAL'] = ImageFont.truetype(path, self.font_size)
-                    for style in ('ITALIC', 'BOLD', 'BOLDITALIC'):
-                        path = self._lookup_win(key, self.font_name, STYLES[style])
-                        if path:
-                            self.fonts[style] = ImageFont.truetype(path, self.font_size)
-                        else:
-                            if style == 'BOLDITALIC':
-                                self.fonts[style] = self.fonts['BOLD']
-                            else:
-                                self.fonts[style] = self.fonts['NORMAL']
-                    return
-                except FontNotFound as err:
-                    lookuperror = err
-                finally:
-                    _winreg.CloseKey(key)
-            except OSError:
-                pass
-        else:
-            # If we get here, we checked all registry keys and had no luck
-            # We can be in one of two situations now:
-            # * All key lookups failed. In this case lookuperror is None and we
-            #   will raise a generic error
-            # * At least one lookup failed with a FontNotFound error. In this
-            #   case, we will raise that as a more specific error
-            if lookuperror:
-                raise lookuperror
-            raise FontNotFound('Can\'t open Windows font registry key')
-
-    def get_char_size(self):
-        """
-        Get the character size.
-        """
-        return self.get_text_size('M')
-
-    def get_text_size(self, text):
-        """
-        Get the text size (width, height).
-        """
-        font = self.fonts['NORMAL']
-        if hasattr(font, 'getbbox'):  # Pillow >= 9.2.0
-            return font.getbbox(text)[2:4]
-        else:
-            return font.getsize(text)
-
-    def get_font(self, bold, oblique):
-        """
-        Get the font based on bold and italic flags.
-        """
-        if bold and oblique:
-            return self.fonts['BOLDITALIC']
-        elif bold:
-            return self.fonts['BOLD']
-        elif oblique:
-            return self.fonts['ITALIC']
-        else:
-            return self.fonts['NORMAL']
-
-
-class ImageFormatter(Formatter):
-    """
-    Create a PNG image from source code. This uses the Python Imaging Library to
-    generate a pixmap from the source code.
-
-    .. versionadded:: 0.10
-
-    Additional options accepted:
-
-    `image_format`
-        An image format to output to that is recognised by PIL, these include:
-
-        * "PNG" (default)
-        * "JPEG"
-        * "BMP"
-        * "GIF"
-
-    `line_pad`
-        The extra spacing (in pixels) between each line of text.
-
-        Default: 2
-
-    `font_name`
-        The font name to be used as the base font from which others, such as
-        bold and italic fonts will be generated.  This really should be a
-        monospace font to look sane.
-
-        Default: "Courier New" on Windows, "Menlo" on Mac OS, and
-                 "DejaVu Sans Mono" on \\*nix
-
-    `font_size`
-        The font size in points to be used.
-
-        Default: 14
-
-    `image_pad`
-        The padding, in pixels to be used at each edge of the resulting image.
-
-        Default: 10
-
-    `line_numbers`
-        Whether line numbers should be shown: True/False
-
-        Default: True
-
-    `line_number_start`
-        The line number of the first line.
-
-        Default: 1
-
-    `line_number_step`
-        The step used when printing line numbers.
-
-        Default: 1
-
-    `line_number_bg`
-        The background colour (in "#123456" format) of the line number bar, or
-        None to use the style background color.
-
-        Default: "#eed"
-
-    `line_number_fg`
-        The text color of the line numbers (in "#123456"-like format).
-
-        Default: "#886"
-
-    `line_number_chars`
-        The number of columns of line numbers allowable in the line number
-        margin.
-
-        Default: 2
-
-    `line_number_bold`
-        Whether line numbers will be bold: True/False
-
-        Default: False
-
-    `line_number_italic`
-        Whether line numbers will be italicized: True/False
-
-        Default: False
-
-    `line_number_separator`
-        Whether a line will be drawn between the line number area and the
-        source code area: True/False
-
-        Default: True
-
-    `line_number_pad`
-        The horizontal padding (in pixels) between the line number margin, and
-        the source code area.
-
-        Default: 6
-
-    `hl_lines`
-        Specify a list of lines to be highlighted.
-
-        .. versionadded:: 1.2
-
-        Default: empty list
-
-    `hl_color`
-        Specify the color for highlighting lines.
-
-        .. versionadded:: 1.2
-
-        Default: highlight color of the selected style
-    """
-
-    # Required by the pygments mapper
-    name = 'img'
-    aliases = ['img', 'IMG', 'png']
-    filenames = ['*.png']
-
-    unicodeoutput = False
-
-    default_image_format = 'png'
-
-    def __init__(self, **options):
-        """
-        See the class docstring for explanation of options.
-        """
-        if not pil_available:
-            raise PilNotAvailable(
-                'Python Imaging Library is required for this formatter')
-        Formatter.__init__(self, **options)
-        self.encoding = 'latin1'  # let pygments.format() do the right thing
-        # Read the style
-        self.styles = dict(self.style)
-        if self.style.background_color is None:
-            self.background_color = '#fff'
-        else:
-            self.background_color = self.style.background_color
-        # Image options
-        self.image_format = get_choice_opt(
-            options, 'image_format', ['png', 'jpeg', 'gif', 'bmp'],
-            self.default_image_format, normcase=True)
-        self.image_pad = get_int_opt(options, 'image_pad', 10)
-        self.line_pad = get_int_opt(options, 'line_pad', 2)
-        # The fonts
-        fontsize = get_int_opt(options, 'font_size', 14)
-        self.fonts = FontManager(options.get('font_name', ''), fontsize)
-        self.fontw, self.fonth = self.fonts.get_char_size()
-        # Line number options
-        self.line_number_fg = options.get('line_number_fg', '#886')
-        self.line_number_bg = options.get('line_number_bg', '#eed')
-        self.line_number_chars = get_int_opt(options,
-                                             'line_number_chars', 2)
-        self.line_number_bold = get_bool_opt(options,
-                                             'line_number_bold', False)
-        self.line_number_italic = get_bool_opt(options,
-                                               'line_number_italic', False)
-        self.line_number_pad = get_int_opt(options, 'line_number_pad', 6)
-        self.line_numbers = get_bool_opt(options, 'line_numbers', True)
-        self.line_number_separator = get_bool_opt(options,
-                                                  'line_number_separator', True)
-        self.line_number_step = get_int_opt(options, 'line_number_step', 1)
-        self.line_number_start = get_int_opt(options, 'line_number_start', 1)
-        if self.line_numbers:
-            self.line_number_width = (self.fontw * self.line_number_chars +
-                                      self.line_number_pad * 2)
-        else:
-            self.line_number_width = 0
-        self.hl_lines = []
-        hl_lines_str = get_list_opt(options, 'hl_lines', [])
-        for line in hl_lines_str:
-            try:
-                self.hl_lines.append(int(line))
-            except ValueError:
-                pass
-        self.hl_color = options.get('hl_color',
-                                    self.style.highlight_color) or '#f90'
-        self.drawables = []
-
-    def get_style_defs(self, arg=''):
-        raise NotImplementedError('The -S option is meaningless for the image '
-                                  'formatter. Use -O style=<stylename> instead.')
-
-    def _get_line_height(self):
-        """
-        Get the height of a line.
-        """
-        return self.fonth + self.line_pad
-
-    def _get_line_y(self, lineno):
-        """
-        Get the Y coordinate of a line number.
-        """
-        return lineno * self._get_line_height() + self.image_pad
-
-    def _get_char_width(self):
-        """
-        Get the width of a character.
-        """
-        return self.fontw
-
-    def _get_char_x(self, linelength):
-        """
-        Get the X coordinate of a character position.
-        """
-        return linelength + self.image_pad + self.line_number_width
-
-    def _get_text_pos(self, linelength, lineno):
-        """
-        Get the actual position for a character and line position.
-        """
-        return self._get_char_x(linelength), self._get_line_y(lineno)
-
-    def _get_linenumber_pos(self, lineno):
-        """
-        Get the actual position for the start of a line number.
-        """
-        return (self.image_pad, self._get_line_y(lineno))
-
-    def _get_text_color(self, style):
-        """
-        Get the correct color for the token from the style.
-        """
-        if style['color'] is not None:
-            fill = '#' + style['color']
-        else:
-            fill = '#000'
-        return fill
-
-    def _get_text_bg_color(self, style):
-        """
-        Get the correct background color for the token from the style.
-        """
-        if style['bgcolor'] is not None:
-            bg_color = '#' + style['bgcolor']
-        else:
-            bg_color = None
-        return bg_color
-
-    def _get_style_font(self, style):
-        """
-        Get the correct font for the style.
-        """
-        return self.fonts.get_font(style['bold'], style['italic'])
-
-    def _get_image_size(self, maxlinelength, maxlineno):
-        """
-        Get the required image size.
-        """
-        return (self._get_char_x(maxlinelength) + self.image_pad,
-                self._get_line_y(maxlineno + 0) + self.image_pad)
-
-    def _draw_linenumber(self, posno, lineno):
-        """
-        Remember a line number drawable to paint later.
-        """
-        self._draw_text(
-            self._get_linenumber_pos(posno),
-            str(lineno).rjust(self.line_number_chars),
-            font=self.fonts.get_font(self.line_number_bold,
-                                     self.line_number_italic),
-            text_fg=self.line_number_fg,
-            text_bg=None,
-        )
-
-    def _draw_text(self, pos, text, font, text_fg, text_bg):
-        """
-        Remember a single drawable tuple to paint later.
-        """
-        self.drawables.append((pos, text, font, text_fg, text_bg))
-
-    def _create_drawables(self, tokensource):
-        """
-        Create drawables for the token content.
-        """
-        lineno = charno = maxcharno = 0
-        maxlinelength = linelength = 0
-        for ttype, value in tokensource:
-            while ttype not in self.styles:
-                ttype = ttype.parent
-            style = self.styles[ttype]
-            # TODO: make sure tab expansion happens earlier in the chain.  It
-            # really ought to be done on the input, as to do it right here is
-            # quite complex.
-            value = value.expandtabs(4)
-            lines = value.splitlines(True)
-            # print lines
-            for i, line in enumerate(lines):
-                temp = line.rstrip('\n')
-                if temp:
-                    self._draw_text(
-                        self._get_text_pos(linelength, lineno),
-                        temp,
-                        font = self._get_style_font(style),
-                        text_fg = self._get_text_color(style),
-                        text_bg = self._get_text_bg_color(style),
-                    )
-                    temp_width, _ = self.fonts.get_text_size(temp)
-                    linelength += temp_width
-                    maxlinelength = max(maxlinelength, linelength)
-                    charno += len(temp)
-                    maxcharno = max(maxcharno, charno)
-                if line.endswith('\n'):
-                    # add a line for each extra line in the value
-                    linelength = 0
-                    charno = 0
-                    lineno += 1
-        self.maxlinelength = maxlinelength
-        self.maxcharno = maxcharno
-        self.maxlineno = lineno
-
-    def _draw_line_numbers(self):
-        """
-        Create drawables for the line numbers.
-        """
-        if not self.line_numbers:
-            return
-        for p in range(self.maxlineno):
-            n = p + self.line_number_start
-            if (n % self.line_number_step) == 0:
-                self._draw_linenumber(p, n)
-
-    def _paint_line_number_bg(self, im):
-        """
-        Paint the line number background on the image.
-        """
-        if not self.line_numbers:
-            return
-        if self.line_number_fg is None:
-            return
-        draw = ImageDraw.Draw(im)
-        recth = im.size[-1]
-        rectw = self.image_pad + self.line_number_width - self.line_number_pad
-        draw.rectangle([(0, 0), (rectw, recth)],
-                       fill=self.line_number_bg)
-        if self.line_number_separator:
-            draw.line([(rectw, 0), (rectw, recth)], fill=self.line_number_fg)
-        del draw
-
-    def format(self, tokensource, outfile):
-        """
-        Format ``tokensource``, an iterable of ``(tokentype, tokenstring)``
-        tuples and write it into ``outfile``.
-
-        This implementation calculates where it should draw each token on the
-        pixmap, then calculates the required pixmap size and draws the items.
-        """
-        self._create_drawables(tokensource)
-        self._draw_line_numbers()
-        im = Image.new(
-            'RGB',
-            self._get_image_size(self.maxlinelength, self.maxlineno),
-            self.background_color
-        )
-        self._paint_line_number_bg(im)
-        draw = ImageDraw.Draw(im)
-        # Highlight
-        if self.hl_lines:
-            x = self.image_pad + self.line_number_width - self.line_number_pad + 1
-            recth = self._get_line_height()
-            rectw = im.size[0] - x
-            for linenumber in self.hl_lines:
-                y = self._get_line_y(linenumber - 1)
-                draw.rectangle([(x, y), (x + rectw, y + recth)],
-                               fill=self.hl_color)
-        for pos, value, font, text_fg, text_bg in self.drawables:
-            if text_bg:
-                text_size = draw.textsize(text=value, font=font)
-                draw.rectangle([pos[0], pos[1], pos[0] + text_size[0], pos[1] + text_size[1]], fill=text_bg)
-            draw.text(pos, value, font=font, fill=text_fg)
-        im.save(outfile, self.image_format.upper())
-
-
-# Add one formatter per format, so that the "-f gif" option gives the correct result
-# when used in pygmentize.
-
-class GifImageFormatter(ImageFormatter):
-    """
-    Create a GIF image from source code. This uses the Python Imaging Library to
-    generate a pixmap from the source code.
-
-    .. versionadded:: 1.0
-    """
-
-    name = 'img_gif'
-    aliases = ['gif']
-    filenames = ['*.gif']
-    default_image_format = 'gif'
-
-
-class JpgImageFormatter(ImageFormatter):
-    """
-    Create a JPEG image from source code. This uses the Python Imaging Library to
-    generate a pixmap from the source code.
-
-    .. versionadded:: 1.0
-    """
-
-    name = 'img_jpg'
-    aliases = ['jpg', 'jpeg']
-    filenames = ['*.jpg']
-    default_image_format = 'jpeg'
-
-
-class BmpImageFormatter(ImageFormatter):
-    """
-    Create a bitmap image from source code. This uses the Python Imaging Library to
-    generate a pixmap from the source code.
-
-    .. versionadded:: 1.0
-    """
-
-    name = 'img_bmp'
-    aliases = ['bmp', 'bitmap']
-    filenames = ['*.bmp']
-    default_image_format = 'bmp'

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

@@ -1,238 +0,0 @@
-"""distutils.command.install_lib
-
-Implements the Distutils 'install_lib' command
-(install all Python modules)."""
-
-import os
-import importlib.util
-import sys
-
-from distutils.core import Command
-from distutils.errors import DistutilsOptionError
-
-
-# Extension for Python source files.
-PYTHON_SOURCE_EXTENSION = ".py"
-
-
-class install_lib(Command):
-
-    description = "install all Python modules (extensions and pure Python)"
-
-    # The byte-compilation options are a tad confusing.  Here are the
-    # possible scenarios:
-    #   1) no compilation at all (--no-compile --no-optimize)
-    #   2) compile .pyc only (--compile --no-optimize; default)
-    #   3) compile .pyc and "opt-1" .pyc (--compile --optimize)
-    #   4) compile "opt-1" .pyc only (--no-compile --optimize)
-    #   5) compile .pyc and "opt-2" .pyc (--compile --optimize-more)
-    #   6) compile "opt-2" .pyc only (--no-compile --optimize-more)
-    #
-    # The UI for this is two options, 'compile' and 'optimize'.
-    # 'compile' is strictly boolean, and only decides whether to
-    # generate .pyc files.  'optimize' is three-way (0, 1, or 2), and
-    # decides both whether to generate .pyc files and what level of
-    # optimization to use.
-
-    user_options = [
-        ('install-dir=', 'd', "directory to install to"),
-        ('build-dir=', 'b', "build directory (where to install from)"),
-        ('force', 'f', "force installation (overwrite existing files)"),
-        ('compile', 'c', "compile .py to .pyc [default]"),
-        ('no-compile', None, "don't compile .py files"),
-        (
-            'optimize=',
-            'O',
-            "also compile with optimization: -O1 for \"python -O\", "
-            "-O2 for \"python -OO\", and -O0 to disable [default: -O0]",
-        ),
-        ('skip-build', None, "skip the build steps"),
-    ]
-
-    boolean_options = ['force', 'compile', 'skip-build']
-    negative_opt = {'no-compile': 'compile'}
-
-    def initialize_options(self):
-        # let the 'install' command dictate our installation directory
-        self.install_dir = None
-        self.build_dir = None
-        self.force = 0
-        self.compile = None
-        self.optimize = None
-        self.skip_build = None
-
-    def finalize_options(self):
-        # Get all the information we need to install pure Python modules
-        # from the umbrella 'install' command -- build (source) directory,
-        # install (target) directory, and whether to compile .py files.
-        self.set_undefined_options(
-            'install',
-            ('build_lib', 'build_dir'),
-            ('install_lib', 'install_dir'),
-            ('force', 'force'),
-            ('compile', 'compile'),
-            ('optimize', 'optimize'),
-            ('skip_build', 'skip_build'),
-        )
-
-        if self.compile is None:
-            self.compile = True
-        if self.optimize is None:
-            self.optimize = False
-
-        if not isinstance(self.optimize, int):
-            try:
-                self.optimize = int(self.optimize)
-                if self.optimize not in (0, 1, 2):
-                    raise AssertionError
-            except (ValueError, AssertionError):
-                raise DistutilsOptionError("optimize must be 0, 1, or 2")
-
-    def run(self):
-        # Make sure we have built everything we need first
-        self.build()
-
-        # Install everything: simply dump the entire contents of the build
-        # directory to the installation directory (that's the beauty of
-        # having a build directory!)
-        outfiles = self.install()
-
-        # (Optionally) compile .py to .pyc
-        if outfiles is not None and self.distribution.has_pure_modules():
-            self.byte_compile(outfiles)
-
-    # -- Top-level worker functions ------------------------------------
-    # (called from 'run()')
-
-    def build(self):
-        if not self.skip_build:
-            if self.distribution.has_pure_modules():
-                self.run_command('build_py')
-            if self.distribution.has_ext_modules():
-                self.run_command('build_ext')
-
-    def install(self):
-        if os.path.isdir(self.build_dir):
-            outfiles = self.copy_tree(self.build_dir, self.install_dir)
-        else:
-            self.warn(
-                "'%s' does not exist -- no Python modules to install" % self.build_dir
-            )
-            return
-        return outfiles
-
-    def byte_compile(self, files):
-        if sys.dont_write_bytecode:
-            self.warn('byte-compiling is disabled, skipping.')
-            return
-
-        from distutils.util import byte_compile
-
-        # Get the "--root" directory supplied to the "install" command,
-        # and use it as a prefix to strip off the purported filename
-        # encoded in bytecode files.  This is far from complete, but it
-        # should at least generate usable bytecode in RPM distributions.
-        install_root = self.get_finalized_command('install').root
-
-        if self.compile:
-            byte_compile(
-                files,
-                optimize=0,
-                force=self.force,
-                prefix=install_root,
-                dry_run=self.dry_run,
-            )
-        if self.optimize > 0:
-            byte_compile(
-                files,
-                optimize=self.optimize,
-                force=self.force,
-                prefix=install_root,
-                verbose=self.verbose,
-                dry_run=self.dry_run,
-            )
-
-    # -- Utility methods -----------------------------------------------
-
-    def _mutate_outputs(self, has_any, build_cmd, cmd_option, output_dir):
-        if not has_any:
-            return []
-
-        build_cmd = self.get_finalized_command(build_cmd)
-        build_files = build_cmd.get_outputs()
-        build_dir = getattr(build_cmd, cmd_option)
-
-        prefix_len = len(build_dir) + len(os.sep)
-        outputs = []
-        for file in build_files:
-            outputs.append(os.path.join(output_dir, file[prefix_len:]))
-
-        return outputs
-
-    def _bytecode_filenames(self, py_filenames):
-        bytecode_files = []
-        for py_file in py_filenames:
-            # Since build_py handles package data installation, the
-            # list of outputs can contain more than just .py files.
-            # Make sure we only report bytecode for the .py files.
-            ext = os.path.splitext(os.path.normcase(py_file))[1]
-            if ext != PYTHON_SOURCE_EXTENSION:
-                continue
-            if self.compile:
-                bytecode_files.append(
-                    importlib.util.cache_from_source(py_file, optimization='')
-                )
-            if self.optimize > 0:
-                bytecode_files.append(
-                    importlib.util.cache_from_source(
-                        py_file, optimization=self.optimize
-                    )
-                )
-
-        return bytecode_files
-
-    # -- External interface --------------------------------------------
-    # (called by outsiders)
-
-    def get_outputs(self):
-        """Return the list of files that would be installed if this command
-        were actually run.  Not affected by the "dry-run" flag or whether
-        modules have actually been built yet.
-        """
-        pure_outputs = self._mutate_outputs(
-            self.distribution.has_pure_modules(),
-            'build_py',
-            'build_lib',
-            self.install_dir,
-        )
-        if self.compile:
-            bytecode_outputs = self._bytecode_filenames(pure_outputs)
-        else:
-            bytecode_outputs = []
-
-        ext_outputs = self._mutate_outputs(
-            self.distribution.has_ext_modules(),
-            'build_ext',
-            'build_lib',
-            self.install_dir,
-        )
-
-        return pure_outputs + bytecode_outputs + ext_outputs
-
-    def get_inputs(self):
-        """Get the list of files that are input to this command, ie. the
-        files that get installed as they are named in the build tree.
-        The files in this list correspond one-to-one to the output
-        filenames returned by 'get_outputs()'.
-        """
-        inputs = []
-
-        if self.distribution.has_pure_modules():
-            build_py = self.get_finalized_command('build_py')
-            inputs.extend(build_py.get_outputs())
-
-        if self.distribution.has_ext_modules():
-            build_ext = self.get_finalized_command('build_ext')
-            inputs.extend(build_ext.get_outputs())
-
-        return inputs

spaces/Billet/WizardLM-WizardMath-70B-V1.033/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/WizardLM/WizardMath-70B-V1.0").launch()

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/export/caffe2_inference.py

@@ -1,136 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-import collections
-import logging
-import numpy as np
-import torch
-from caffe2.proto import caffe2_pb2
-from caffe2.python import core
-
-from .caffe2_modeling import META_ARCH_CAFFE2_EXPORT_TYPE_MAP, convert_batched_inputs_to_c2_format
-from .shared import ScopedWS, get_pb_arg_vali, get_pb_arg_vals, infer_device_type
-
-logger = logging.getLogger(__name__)
-
-
-class ProtobufModel(torch.nn.Module):
-    """
-    A class works just like nn.Module in terms of inference, but running
-    caffe2 model under the hood. Input/Output are Dict[str, tensor] whose keys
-    are in external_input/output.
-    """
-
-    def __init__(self, predict_net, init_net):
-        logger.info("Initializing ProtobufModel ...")
-        super().__init__()
-        assert isinstance(predict_net, caffe2_pb2.NetDef)
-        assert isinstance(init_net, caffe2_pb2.NetDef)
-        self.ws_name = "__ws_tmp__"
-        self.net = core.Net(predict_net)
-
-        with ScopedWS(self.ws_name, is_reset=True, is_cleanup=False) as ws:
-            ws.RunNetOnce(init_net)
-            for blob in self.net.Proto().external_input:
-                if blob not in ws.Blobs():
-                    ws.CreateBlob(blob)
-            ws.CreateNet(self.net)
-
-        self._error_msgs = set()
-
-    def forward(self, inputs_dict):
-        assert all(inp in self.net.Proto().external_input for inp in inputs_dict)
-        with ScopedWS(self.ws_name, is_reset=False, is_cleanup=False) as ws:
-            for b, tensor in inputs_dict.items():
-                ws.FeedBlob(b, tensor)
-            try:
-                ws.RunNet(self.net.Proto().name)
-            except RuntimeError as e:
-                if not str(e) in self._error_msgs:
-                    self._error_msgs.add(str(e))
-                    logger.warning("Encountered new RuntimeError: \n{}".format(str(e)))
-                logger.warning("Catch the error and use partial results.")
-
-            outputs_dict = collections.OrderedDict(
-                [(b, ws.FetchBlob(b)) for b in self.net.Proto().external_output]
-            )
-            # Remove outputs of current run, this is necessary in order to
-            # prevent fetching the result from previous run if the model fails
-            # in the middle.
-            for b in self.net.Proto().external_output:
-                # Needs to create uninitialized blob to make the net runable.
-                # This is "equivalent" to: ws.RemoveBlob(b) then ws.CreateBlob(b),
-                # but there'no such API.
-                ws.FeedBlob(b, "{}, a C++ native class of type nullptr (uninitialized).".format(b))
-
-        return outputs_dict
-
-
-class ProtobufDetectionModel(torch.nn.Module):
-    """
-    A class works just like a pytorch meta arch in terms of inference, but running
-    caffe2 model under the hood.
-    """
-
-    def __init__(self, predict_net, init_net, *, convert_outputs=None):
-        """
-        Args:
-            predict_net, init_net (core.Net): caffe2 nets
-            convert_outptus (callable): a function that converts caffe2
-                outputs to the same format of the original pytorch model.
-                By default, use the one defined in the caffe2 meta_arch.
-        """
-        super().__init__()
-        self.protobuf_model = ProtobufModel(predict_net, init_net)
-        self.size_divisibility = get_pb_arg_vali(predict_net, "size_divisibility", 0)
-        self.device = get_pb_arg_vals(predict_net, "device", b"cpu").decode("ascii")
-
-        if convert_outputs is None:
-            meta_arch = get_pb_arg_vals(predict_net, "meta_architecture", b"GeneralizedRCNN")
-            meta_arch = META_ARCH_CAFFE2_EXPORT_TYPE_MAP[meta_arch.decode("ascii")]
-            self._convert_outputs = meta_arch.get_outputs_converter(predict_net, init_net)
-        else:
-            self._convert_outputs = convert_outputs
-
-    def _infer_output_devices(self, inputs_dict):
-        def _get_device_type(torch_tensor):
-            assert torch_tensor.device.type in ["cpu", "cuda"]
-            assert torch_tensor.device.index == 0
-            return torch_tensor.device.type
-
-        predict_net = self.protobuf_model.net.Proto()
-        input_device_types = {
-            (name, 0): _get_device_type(tensor) for name, tensor in inputs_dict.items()
-        }
-        device_type_map = infer_device_type(
-            predict_net, known_status=input_device_types, device_name_style="pytorch"
-        )
-        ssa, versions = core.get_ssa(predict_net)
-        versioned_outputs = [(name, versions[name]) for name in predict_net.external_output]
-        output_devices = [device_type_map[outp] for outp in versioned_outputs]
-        return output_devices
-
-    def _convert_inputs(self, batched_inputs):
-        # currently all models convert inputs in the same way
-        data, im_info = convert_batched_inputs_to_c2_format(
-            batched_inputs, self.size_divisibility, self.device
-        )
-        return {"data": data, "im_info": im_info}
-
-    def forward(self, batched_inputs):
-        c2_inputs = self._convert_inputs(batched_inputs)
-        c2_results = self.protobuf_model(c2_inputs)
-
-        if any(t.device.type != "cpu" for _, t in c2_inputs.items()):
-            output_devices = self._infer_output_devices(c2_inputs)
-        else:
-            output_devices = ["cpu" for _ in self.protobuf_model.net.Proto().external_output]
-
-        def _cast_caffe2_blob_to_torch_tensor(blob, device):
-            return torch.Tensor(blob).to(device) if isinstance(blob, np.ndarray) else None
-
-        c2_results = {
-            name: _cast_caffe2_blob_to_torch_tensor(c2_results[name], device)
-            for name, device in zip(self.protobuf_model.net.Proto().external_output, output_devices)
-        }
-
-        return self._convert_outputs(batched_inputs, c2_inputs, c2_results)

spaces/CVPR/LIVE/thrust/thrust/mr/allocator.h

@@ -1,250 +0,0 @@
-/*
- *  Copyright 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.
- */
-
-/*! \file allocator.h
- *  \brief Allocator types usable with NPA-based memory resources.
- */
-
-#pragma once
-
-#include <limits>
-
-#include <thrust/detail/config/exec_check_disable.h>
-#include <thrust/detail/type_traits/pointer_traits.h>
-
-#include <thrust/mr/detail/config.h>
-#include <thrust/mr/validator.h>
-#include <thrust/mr/polymorphic_adaptor.h>
-
-namespace thrust
-{
-namespace mr
-{
-
-/*! \addtogroup memory_management Memory Management
- *  \addtogroup memory_management_classes Memory Management Classes
- *  \ingroup memory_management
- *  \{
- */
-
-/*! An \p mr::allocator is a template that fulfills the C++ requirements for Allocators,
- *  allowing to use the NPA-based memory resources where an Allocator is required. Unlike
- *  memory resources, but like other allocators, \p mr::allocator is typed and bound to
- *  allocate object of a specific type, however it can be freely rebound to other types.
- *
- *  \tparam T the type that will be allocated by this allocator.
- *  \tparam MR the upstream memory resource to use for memory allocation. Must derive from
- *      \p thrust::mr::memory_resource and must be \p final (in C++11 and beyond).
- */
-template<typename T, class MR>
-class allocator : private validator<MR>
-{
-public:
-    /*! The pointer to void type of this allocator. */
-    typedef typename MR::pointer void_pointer;
-
-    /*! The value type allocated by this allocator. Equivalent to \p T. */
-    typedef T value_type;
-    /*! The pointer type allocated by this allocator. Equivaled to the pointer type of \p MR rebound to \p T. */
-    typedef typename thrust::detail::pointer_traits<void_pointer>::template rebind<T>::other pointer;
-    /*! The pointer to const type. Equivalent to a pointer type of \p MR reboud to <tt>const T</tt>. */
-    typedef typename thrust::detail::pointer_traits<void_pointer>::template rebind<const T>::other const_pointer;
-    /*! The reference to the type allocated by this allocator. Supports smart references. */
-    typedef typename thrust::detail::pointer_traits<pointer>::reference reference;
-    /*! The const reference to the type allocated by this allocator. Supports smart references. */
-    typedef typename thrust::detail::pointer_traits<const_pointer>::reference const_reference;
-    /*! The size type of this allocator. Always \p std::size_t. */
-    typedef std::size_t size_type;
-    /*! The difference type between pointers allocated by this allocator. */
-    typedef typename thrust::detail::pointer_traits<pointer>::difference_type difference_type;
-
-    /*! Specifies that the allocator shall be propagated on container copy assignment. */
-    typedef detail::true_type propagate_on_container_copy_assignment;
-    /*! Specifies that the allocator shall be propagated on container move assignment. */
-    typedef detail::true_type propagate_on_container_move_assignment;
-    /*! Specifies that the allocator shall be propagated on container swap. */
-    typedef detail::true_type propagate_on_container_swap;
-
-    /*! The \p rebind metafunction provides the type of an \p allocator instantiated with another type.
-     *
-     *  \tparam U the other type to use for instantiation.
-     */
-    template<typename U>
-    struct rebind
-    {
-        /*! The typedef \p other gives the type of the rebound \p allocator.
-         */
-        typedef allocator<U, MR> other;
-    };
-
-    /*! Calculates the maximum number of elements allocated by this allocator.
-     *
-     *  \returns the maximum value of \p std::size_t, divided by the size of \p T.
-     */
-    __thrust_exec_check_disable__
-    __host__ __device__
-    size_type max_size() const
-    {
-        return std::numeric_limits<size_type>::max() / sizeof(T);
-    }
-
-    /*! Constructor.
-     *
-     *  \param resource the resource to be used to allocate raw memory.
-     */
-    __host__ __device__
-    allocator(MR * resource) : mem_res(resource)
-    {
-    }
-
-    /*! Copy constructor. Copies the resource pointer. */
-    template<typename U>
-    __host__ __device__
-    allocator(const allocator<U, MR> & other) : mem_res(other.resource())
-    {
-    }
-
-    /*! Allocates objects of type \p T.
-     *
-     *  \param n number of elements to allocate
-     *  \returns a pointer to the newly allocated storage.
-     */
-    THRUST_NODISCARD
-    __host__
-    pointer allocate(size_type n)
-    {
-        return static_cast<pointer>(mem_res->do_allocate(n * sizeof(T), THRUST_ALIGNOF(T)));
-    }
-
-    /*! Deallocates objects of type \p T.
-     *
-     *  \param p pointer returned by a previous call to \p allocate
-     *  \param n number of elements, passed as an argument to the \p allocate call that produced \p p
-     */
-    __host__
-    void deallocate(pointer p, size_type n)
-    {
-        return mem_res->do_deallocate(p, n * sizeof(T), THRUST_ALIGNOF(T));
-    }
-
-    /*! Extracts the memory resource used by this allocator.
-     *
-     *  \returns the memory resource used by this allocator.
-     */
-    __host__ __device__
-    MR * resource() const
-    {
-        return mem_res;
-    }
-
-private:
-    MR * mem_res;
-};
-
-/*! Compares the allocators for equality by comparing the underlying memory resources. */
-template<typename T, typename MR>
-__host__ __device__
-bool operator==(const allocator<T, MR> & lhs, const allocator<T, MR> & rhs) THRUST_NOEXCEPT
-{
-    return *lhs.resource() == *rhs.resource();
-}
-
-/*! Compares the allocators for inequality by comparing the underlying memory resources. */
-template<typename T, typename MR>
-__host__ __device__
-bool operator!=(const allocator<T, MR> & lhs, const allocator<T, MR> & rhs) THRUST_NOEXCEPT
-{
-    return !(lhs == rhs);
-}
-
-#if THRUST_CPP_DIALECT >= 2011
-
-template<typename T, typename Pointer>
-using polymorphic_allocator = allocator<T, polymorphic_adaptor_resource<Pointer> >;
-
-#else // C++11
-
-template<typename T, typename Pointer>
-class polymorphic_allocator : public allocator<T, polymorphic_adaptor_resource<Pointer> >
-{
-    typedef allocator<T, polymorphic_adaptor_resource<Pointer> > base;
-
-public:
-    /*! Initializes the base class with the parameter \p resource.
-     */
-    polymorphic_allocator(polymorphic_adaptor_resource<Pointer>  * resource) : base(resource)
-    {
-    }
-};
-
-#endif // C++11
-
-/*! A helper allocator class that uses global instances of a given upstream memory resource. Requires the memory resource
- *      to be default constructible.
- *
- *  \tparam T the type that will be allocated by this allocator.
- *  \tparam Upstream the upstream memory resource to use for memory allocation. Must derive from
- *      \p thrust::mr::memory_resource and must be \p final (in C++11 and beyond).
- */
-template<typename T, typename Upstream>
-class stateless_resource_allocator : public thrust::mr::allocator<T, Upstream>
-{
-    typedef thrust::mr::allocator<T, Upstream> base;
-
-public:
-    /*! The \p rebind metafunction provides the type of an \p stateless_resource_allocator instantiated with another type.
-     *
-     *  \tparam U the other type to use for instantiation.
-     */
-    template<typename U>
-    struct rebind
-    {
-        /*! The typedef \p other gives the type of the rebound \p stateless_resource_allocator.
-         */
-        typedef stateless_resource_allocator<U, Upstream> other;
-    };
-
-    /*! Default constructor. Uses \p get_global_resource to get the global instance of \p Upstream and initializes the
-     *      \p allocator base subobject with that resource.
-     */
-    __host__
-    stateless_resource_allocator() : base(get_global_resource<Upstream>())
-    {
-    }
-
-    /*! Copy constructor. Copies the memory resource pointer. */
-    __host__ __device__
-    stateless_resource_allocator(const stateless_resource_allocator & other)
-        : base(other) {}
-
-    /*! Conversion constructor from an allocator of a different type. Copies the memory resource pointer. */
-    template<typename U>
-    __host__ __device__
-    stateless_resource_allocator(const stateless_resource_allocator<U, Upstream> & other)
-        : base(other) {}
-
-#if THRUST_CPP_DIALECT >= 2011
-    stateless_resource_allocator & operator=(const stateless_resource_allocator &) = default;
-#endif
-
-    /*! Destructor. */
-    __host__ __device__
-    ~stateless_resource_allocator() {}
-};
-
-} // end mr
-} // end thrust
-

spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/scatter.h

@@ -1,106 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-
-
-#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-#include <thrust/system/cuda/detail/transform.h>
-#include <thrust/iterator/permutation_iterator.h>
-
-namespace thrust
-{
-namespace cuda_cub {
-
-template <class Derived,
-          class ItemsIt,
-          class MapIt,
-          class ResultIt>
-void __host__ __device__
-scatter(execution_policy<Derived>& policy,
-        ItemsIt                    first,
-        ItemsIt                    last,
-        MapIt                      map,
-        ResultIt                   result)
-{
-  cuda_cub::transform(policy,
-                   first,
-                   last,
-                   thrust::make_permutation_iterator(result, map),
-                   identity());
-}
-
-template <class Derived,
-          class ItemsIt,
-          class MapIt,
-          class StencilIt,
-          class ResultIt,
-          class Predicate>
-void __host__ __device__
-scatter_if(execution_policy<Derived>& policy,
-           ItemsIt                    first,
-           ItemsIt                    last,
-           MapIt                      map,
-           StencilIt                  stencil,
-           ResultIt                   result,
-           Predicate                  predicate)
-{
-  cuda_cub::transform_if(policy,
-                      first,
-                      last,
-                      stencil,
-                      thrust::make_permutation_iterator(result, map),
-                      identity(),
-                      predicate);
-}
-
-template <class Derived,
-          class ItemsIt,
-          class MapIt,
-          class StencilIt,
-          class ResultIt,
-          class Predicate>
-void __host__ __device__
-scatter_if(execution_policy<Derived>& policy,
-           ItemsIt                    first,
-           ItemsIt                    last,
-           MapIt                      map,
-           StencilIt                  stencil,
-           ResultIt                   result)
-{
-  cuda_cub::scatter_if(policy,
-                    first,
-                    last,
-                    map,
-                    stencil,
-                    result,
-                    identity());
-}
-
-
-} // namespace cuda_cub
-} // end namespace thrust
-#endif

spaces/CVPR/regionclip-demo/detectron2/__init__.py

@@ -1,10 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from .utils.env import setup_environment
-
-setup_environment()
-
-
-# This line will be programatically read/write by setup.py.
-# Leave them at the bottom of this file and don't touch them.
-__version__ = "0.4"

spaces/Cicooo/vits-uma-genshin-honkai/transforms.py

@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/ttGlyphSet.py

@@ -1,322 +0,0 @@
-"""GlyphSets returned by a TTFont."""
-
-from abc import ABC, abstractmethod
-from collections.abc import Mapping
-from contextlib import contextmanager
-from copy import copy
-from types import SimpleNamespace
-from fontTools.misc.fixedTools import otRound
-from fontTools.misc.loggingTools import deprecateFunction
-from fontTools.misc.transform import Transform
-from fontTools.pens.transformPen import TransformPen, TransformPointPen
-
-
-class _TTGlyphSet(Mapping):
-
-    """Generic dict-like GlyphSet class that pulls metrics from hmtx and
-    glyph shape from TrueType or CFF.
-    """
-
-    def __init__(self, font, location, glyphsMapping):
-        self.font = font
-        self.defaultLocationNormalized = (
-            {axis.axisTag: 0 for axis in self.font["fvar"].axes}
-            if "fvar" in self.font
-            else {}
-        )
-        self.location = location if location is not None else {}
-        self.rawLocation = {}  # VarComponent-only location
-        self.originalLocation = location if location is not None else {}
-        self.depth = 0
-        self.locationStack = []
-        self.rawLocationStack = []
-        self.glyphsMapping = glyphsMapping
-        self.hMetrics = font["hmtx"].metrics
-        self.vMetrics = getattr(font.get("vmtx"), "metrics", None)
-        self.hvarTable = None
-        if location:
-            from fontTools.varLib.varStore import VarStoreInstancer
-
-            self.hvarTable = getattr(font.get("HVAR"), "table", None)
-            if self.hvarTable is not None:
-                self.hvarInstancer = VarStoreInstancer(
-                    self.hvarTable.VarStore, font["fvar"].axes, location
-                )
-            # TODO VVAR, VORG
-
-    @contextmanager
-    def pushLocation(self, location, reset: bool):
-        self.locationStack.append(self.location)
-        self.rawLocationStack.append(self.rawLocation)
-        if reset:
-            self.location = self.originalLocation.copy()
-            self.rawLocation = self.defaultLocationNormalized.copy()
-        else:
-            self.location = self.location.copy()
-            self.rawLocation = {}
-        self.location.update(location)
-        self.rawLocation.update(location)
-
-        try:
-            yield None
-        finally:
-            self.location = self.locationStack.pop()
-            self.rawLocation = self.rawLocationStack.pop()
-
-    @contextmanager
-    def pushDepth(self):
-        try:
-            depth = self.depth
-            self.depth += 1
-            yield depth
-        finally:
-            self.depth -= 1
-
-    def __contains__(self, glyphName):
-        return glyphName in self.glyphsMapping
-
-    def __iter__(self):
-        return iter(self.glyphsMapping.keys())
-
-    def __len__(self):
-        return len(self.glyphsMapping)
-
-    @deprecateFunction(
-        "use 'glyphName in glyphSet' instead", category=DeprecationWarning
-    )
-    def has_key(self, glyphName):
-        return glyphName in self.glyphsMapping
-
-
-class _TTGlyphSetGlyf(_TTGlyphSet):
-    def __init__(self, font, location):
-        self.glyfTable = font["glyf"]
-        super().__init__(font, location, self.glyfTable)
-        self.gvarTable = font.get("gvar")
-
-    def __getitem__(self, glyphName):
-        return _TTGlyphGlyf(self, glyphName)
-
-
-class _TTGlyphSetCFF(_TTGlyphSet):
-    def __init__(self, font, location):
-        tableTag = "CFF2" if "CFF2" in font else "CFF "
-        self.charStrings = list(font[tableTag].cff.values())[0].CharStrings
-        super().__init__(font, location, self.charStrings)
-        self.blender = None
-        if location:
-            from fontTools.varLib.varStore import VarStoreInstancer
-
-            varStore = getattr(self.charStrings, "varStore", None)
-            if varStore is not None:
-                instancer = VarStoreInstancer(
-                    varStore.otVarStore, font["fvar"].axes, location
-                )
-                self.blender = instancer.interpolateFromDeltas
-
-    def __getitem__(self, glyphName):
-        return _TTGlyphCFF(self, glyphName)
-
-
-class _TTGlyph(ABC):
-
-    """Glyph object that supports the Pen protocol, meaning that it has
-    .draw() and .drawPoints() methods that take a pen object as their only
-    argument. Additionally there are 'width' and 'lsb' attributes, read from
-    the 'hmtx' table.
-
-    If the font contains a 'vmtx' table, there will also be 'height' and 'tsb'
-    attributes.
-    """
-
-    def __init__(self, glyphSet, glyphName):
-        self.glyphSet = glyphSet
-        self.name = glyphName
-        self.width, self.lsb = glyphSet.hMetrics[glyphName]
-        if glyphSet.vMetrics is not None:
-            self.height, self.tsb = glyphSet.vMetrics[glyphName]
-        else:
-            self.height, self.tsb = None, None
-        if glyphSet.location and glyphSet.hvarTable is not None:
-            varidx = (
-                glyphSet.font.getGlyphID(glyphName)
-                if glyphSet.hvarTable.AdvWidthMap is None
-                else glyphSet.hvarTable.AdvWidthMap.mapping[glyphName]
-            )
-            self.width += glyphSet.hvarInstancer[varidx]
-        # TODO: VVAR/VORG
-
-    @abstractmethod
-    def draw(self, pen):
-        """Draw the glyph onto ``pen``. See fontTools.pens.basePen for details
-        how that works.
-        """
-        raise NotImplementedError
-
-    def drawPoints(self, pen):
-        """Draw the glyph onto ``pen``. See fontTools.pens.pointPen for details
-        how that works.
-        """
-        from fontTools.pens.pointPen import SegmentToPointPen
-
-        self.draw(SegmentToPointPen(pen))
-
-
-class _TTGlyphGlyf(_TTGlyph):
-    def draw(self, pen):
-        """Draw the glyph onto ``pen``. See fontTools.pens.basePen for details
-        how that works.
-        """
-        glyph, offset = self._getGlyphAndOffset()
-
-        with self.glyphSet.pushDepth() as depth:
-
-            if depth:
-                offset = 0  # Offset should only apply at top-level
-
-            if glyph.isVarComposite():
-                self._drawVarComposite(glyph, pen, False)
-                return
-
-            glyph.draw(pen, self.glyphSet.glyfTable, offset)
-
-    def drawPoints(self, pen):
-        """Draw the glyph onto ``pen``. See fontTools.pens.pointPen for details
-        how that works.
-        """
-        glyph, offset = self._getGlyphAndOffset()
-
-        with self.glyphSet.pushDepth() as depth:
-
-            if depth:
-                offset = 0  # Offset should only apply at top-level
-
-            if glyph.isVarComposite():
-                self._drawVarComposite(glyph, pen, True)
-                return
-
-            glyph.drawPoints(pen, self.glyphSet.glyfTable, offset)
-
-    def _drawVarComposite(self, glyph, pen, isPointPen):
-
-        from fontTools.ttLib.tables._g_l_y_f import (
-            VarComponentFlags,
-            VAR_COMPONENT_TRANSFORM_MAPPING,
-        )
-
-        for comp in glyph.components:
-
-            with self.glyphSet.pushLocation(
-                comp.location, comp.flags & VarComponentFlags.RESET_UNSPECIFIED_AXES
-            ):
-                try:
-                    pen.addVarComponent(
-                        comp.glyphName, comp.transform, self.glyphSet.rawLocation
-                    )
-                except AttributeError:
-                    t = comp.transform.toTransform()
-                    if isPointPen:
-                        tPen = TransformPointPen(pen, t)
-                        self.glyphSet[comp.glyphName].drawPoints(tPen)
-                    else:
-                        tPen = TransformPen(pen, t)
-                        self.glyphSet[comp.glyphName].draw(tPen)
-
-    def _getGlyphAndOffset(self):
-        if self.glyphSet.location and self.glyphSet.gvarTable is not None:
-            glyph = self._getGlyphInstance()
-        else:
-            glyph = self.glyphSet.glyfTable[self.name]
-
-        offset = self.lsb - glyph.xMin if hasattr(glyph, "xMin") else 0
-        return glyph, offset
-
-    def _getGlyphInstance(self):
-        from fontTools.varLib.iup import iup_delta
-        from fontTools.ttLib.tables._g_l_y_f import GlyphCoordinates
-        from fontTools.varLib.models import supportScalar
-
-        glyphSet = self.glyphSet
-        glyfTable = glyphSet.glyfTable
-        variations = glyphSet.gvarTable.variations[self.name]
-        hMetrics = glyphSet.hMetrics
-        vMetrics = glyphSet.vMetrics
-        coordinates, _ = glyfTable._getCoordinatesAndControls(
-            self.name, hMetrics, vMetrics
-        )
-        origCoords, endPts = None, None
-        for var in variations:
-            scalar = supportScalar(glyphSet.location, var.axes)
-            if not scalar:
-                continue
-            delta = var.coordinates
-            if None in delta:
-                if origCoords is None:
-                    origCoords, control = glyfTable._getCoordinatesAndControls(
-                        self.name, hMetrics, vMetrics
-                    )
-                    endPts = (
-                        control[1] if control[0] >= 1 else list(range(len(control[1])))
-                    )
-                delta = iup_delta(delta, origCoords, endPts)
-            coordinates += GlyphCoordinates(delta) * scalar
-
-        glyph = copy(glyfTable[self.name])  # Shallow copy
-        width, lsb, height, tsb = _setCoordinates(glyph, coordinates, glyfTable)
-        self.lsb = lsb
-        self.tsb = tsb
-        if glyphSet.hvarTable is None:
-            # no HVAR: let's set metrics from the phantom points
-            self.width = width
-            self.height = height
-        return glyph
-
-
-class _TTGlyphCFF(_TTGlyph):
-    def draw(self, pen):
-        """Draw the glyph onto ``pen``. See fontTools.pens.basePen for details
-        how that works.
-        """
-        self.glyphSet.charStrings[self.name].draw(pen, self.glyphSet.blender)
-
-
-def _setCoordinates(glyph, coord, glyfTable):
-    # Handle phantom points for (left, right, top, bottom) positions.
-    assert len(coord) >= 4
-    leftSideX = coord[-4][0]
-    rightSideX = coord[-3][0]
-    topSideY = coord[-2][1]
-    bottomSideY = coord[-1][1]
-
-    for _ in range(4):
-        del coord[-1]
-
-    if glyph.isComposite():
-        assert len(coord) == len(glyph.components)
-        glyph.components = [copy(comp) for comp in glyph.components]  # Shallow copy
-        for p, comp in zip(coord, glyph.components):
-            if hasattr(comp, "x"):
-                comp.x, comp.y = p
-    elif glyph.isVarComposite():
-        glyph.components = [copy(comp) for comp in glyph.components]  # Shallow copy
-        for comp in glyph.components:
-            coord = comp.setCoordinates(coord)
-        assert not coord
-    elif glyph.numberOfContours == 0:
-        assert len(coord) == 0
-    else:
-        assert len(coord) == len(glyph.coordinates)
-        glyph.coordinates = coord
-
-    glyph.recalcBounds(glyfTable)
-
-    horizontalAdvanceWidth = otRound(rightSideX - leftSideX)
-    verticalAdvanceWidth = otRound(topSideY - bottomSideY)
-    leftSideBearing = otRound(glyph.xMin - leftSideX)
-    topSideBearing = otRound(topSideY - glyph.yMax)
-    return (
-        horizontalAdvanceWidth,
-        leftSideBearing,
-        verticalAdvanceWidth,
-        topSideBearing,
-    )

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/h11/_headers.py

@@ -1,278 +0,0 @@
-import re
-from typing import AnyStr, cast, List, overload, Sequence, Tuple, TYPE_CHECKING, Union
-
-from ._abnf import field_name, field_value
-from ._util import bytesify, LocalProtocolError, validate
-
-if TYPE_CHECKING:
-    from ._events import Request
-
-try:
-    from typing import Literal
-except ImportError:
-    from typing_extensions import Literal  # type: ignore
-
-
-# Facts
-# -----
-#
-# Headers are:
-#   keys: case-insensitive ascii
-#   values: mixture of ascii and raw bytes
-#
-# "Historically, HTTP has allowed field content with text in the ISO-8859-1
-# charset [ISO-8859-1], supporting other charsets only through use of
-# [RFC2047] encoding.  In practice, most HTTP header field values use only a
-# subset of the US-ASCII charset [USASCII]. Newly defined header fields SHOULD
-# limit their field values to US-ASCII octets.  A recipient SHOULD treat other
-# octets in field content (obs-text) as opaque data."
-# And it deprecates all non-ascii values
-#
-# Leading/trailing whitespace in header names is forbidden
-#
-# Values get leading/trailing whitespace stripped
-#
-# Content-Disposition actually needs to contain unicode semantically; to
-# accomplish this it has a terrifically weird way of encoding the filename
-# itself as ascii (and even this still has lots of cross-browser
-# incompatibilities)
-#
-# Order is important:
-# "a proxy MUST NOT change the order of these field values when forwarding a
-# message"
-# (and there are several headers where the order indicates a preference)
-#
-# Multiple occurences of the same header:
-# "A sender MUST NOT generate multiple header fields with the same field name
-# in a message unless either the entire field value for that header field is
-# defined as a comma-separated list [or the header is Set-Cookie which gets a
-# special exception]" - RFC 7230. (cookies are in RFC 6265)
-#
-# So every header aside from Set-Cookie can be merged by b", ".join if it
-# occurs repeatedly. But, of course, they can't necessarily be split by
-# .split(b","), because quoting.
-#
-# Given all this mess (case insensitive, duplicates allowed, order is
-# important, ...), there doesn't appear to be any standard way to handle
-# headers in Python -- they're almost like dicts, but... actually just
-# aren't. For now we punt and just use a super simple representation: headers
-# are a list of pairs
-#
-#   [(name1, value1), (name2, value2), ...]
-#
-# where all entries are bytestrings, names are lowercase and have no
-# leading/trailing whitespace, and values are bytestrings with no
-# leading/trailing whitespace. Searching and updating are done via naive O(n)
-# methods.
-#
-# Maybe a dict-of-lists would be better?
-
-_content_length_re = re.compile(rb"[0-9]+")
-_field_name_re = re.compile(field_name.encode("ascii"))
-_field_value_re = re.compile(field_value.encode("ascii"))
-
-
-class Headers(Sequence[Tuple[bytes, bytes]]):
-    """
-    A list-like interface that allows iterating over headers as byte-pairs
-    of (lowercased-name, value).
-
-    Internally we actually store the representation as three-tuples,
-    including both the raw original casing, in order to preserve casing
-    over-the-wire, and the lowercased name, for case-insensitive comparisions.
-
-    r = Request(
-        method="GET",
-        target="/",
-        headers=[("Host", "example.org"), ("Connection", "keep-alive")],
-        http_version="1.1",
-    )
-    assert r.headers == [
-        (b"host", b"example.org"),
-        (b"connection", b"keep-alive")
-    ]
-    assert r.headers.raw_items() == [
-        (b"Host", b"example.org"),
-        (b"Connection", b"keep-alive")
-    ]
-    """
-
-    __slots__ = "_full_items"
-
-    def __init__(self, full_items: List[Tuple[bytes, bytes, bytes]]) -> None:
-        self._full_items = full_items
-
-    def __bool__(self) -> bool:
-        return bool(self._full_items)
-
-    def __eq__(self, other: object) -> bool:
-        return list(self) == list(other)  # type: ignore
-
-    def __len__(self) -> int:
-        return len(self._full_items)
-
-    def __repr__(self) -> str:
-        return "<Headers(%s)>" % repr(list(self))
-
-    def __getitem__(self, idx: int) -> Tuple[bytes, bytes]:  # type: ignore[override]
-        _, name, value = self._full_items[idx]
-        return (name, value)
-
-    def raw_items(self) -> List[Tuple[bytes, bytes]]:
-        return [(raw_name, value) for raw_name, _, value in self._full_items]
-
-
-HeaderTypes = Union[
-    List[Tuple[bytes, bytes]],
-    List[Tuple[bytes, str]],
-    List[Tuple[str, bytes]],
-    List[Tuple[str, str]],
-]
-
-
-@overload
-def normalize_and_validate(headers: Headers, _parsed: Literal[True]) -> Headers:
-    ...
-
-
-@overload
-def normalize_and_validate(headers: HeaderTypes, _parsed: Literal[False]) -> Headers:
-    ...
-
-
-@overload
-def normalize_and_validate(
-    headers: Union[Headers, HeaderTypes], _parsed: bool = False
-) -> Headers:
-    ...
-
-
-def normalize_and_validate(
-    headers: Union[Headers, HeaderTypes], _parsed: bool = False
-) -> Headers:
-    new_headers = []
-    seen_content_length = None
-    saw_transfer_encoding = False
-    for name, value in headers:
-        # For headers coming out of the parser, we can safely skip some steps,
-        # because it always returns bytes and has already run these regexes
-        # over the data:
-        if not _parsed:
-            name = bytesify(name)
-            value = bytesify(value)
-            validate(_field_name_re, name, "Illegal header name {!r}", name)
-            validate(_field_value_re, value, "Illegal header value {!r}", value)
-        assert isinstance(name, bytes)
-        assert isinstance(value, bytes)
-
-        raw_name = name
-        name = name.lower()
-        if name == b"content-length":
-            lengths = {length.strip() for length in value.split(b",")}
-            if len(lengths) != 1:
-                raise LocalProtocolError("conflicting Content-Length headers")
-            value = lengths.pop()
-            validate(_content_length_re, value, "bad Content-Length")
-            if seen_content_length is None:
-                seen_content_length = value
-                new_headers.append((raw_name, name, value))
-            elif seen_content_length != value:
-                raise LocalProtocolError("conflicting Content-Length headers")
-        elif name == b"transfer-encoding":
-            # "A server that receives a request message with a transfer coding
-            # it does not understand SHOULD respond with 501 (Not
-            # Implemented)."
-            # https://tools.ietf.org/html/rfc7230#section-3.3.1
-            if saw_transfer_encoding:
-                raise LocalProtocolError(
-                    "multiple Transfer-Encoding headers", error_status_hint=501
-                )
-            # "All transfer-coding names are case-insensitive"
-            # -- https://tools.ietf.org/html/rfc7230#section-4
-            value = value.lower()
-            if value != b"chunked":
-                raise LocalProtocolError(
-                    "Only Transfer-Encoding: chunked is supported",
-                    error_status_hint=501,
-                )
-            saw_transfer_encoding = True
-            new_headers.append((raw_name, name, value))
-        else:
-            new_headers.append((raw_name, name, value))
-    return Headers(new_headers)
-
-
-def get_comma_header(headers: Headers, name: bytes) -> List[bytes]:
-    # Should only be used for headers whose value is a list of
-    # comma-separated, case-insensitive values.
-    #
-    # The header name `name` is expected to be lower-case bytes.
-    #
-    # Connection: meets these criteria (including cast insensitivity).
-    #
-    # Content-Length: technically is just a single value (1*DIGIT), but the
-    # standard makes reference to implementations that do multiple values, and
-    # using this doesn't hurt. Ditto, case insensitivity doesn't things either
-    # way.
-    #
-    # Transfer-Encoding: is more complex (allows for quoted strings), so
-    # splitting on , is actually wrong. For example, this is legal:
-    #
-    #    Transfer-Encoding: foo; options="1,2", chunked
-    #
-    # and should be parsed as
-    #
-    #    foo; options="1,2"
-    #    chunked
-    #
-    # but this naive function will parse it as
-    #
-    #    foo; options="1
-    #    2"
-    #    chunked
-    #
-    # However, this is okay because the only thing we are going to do with
-    # any Transfer-Encoding is reject ones that aren't just "chunked", so
-    # both of these will be treated the same anyway.
-    #
-    # Expect: the only legal value is the literal string
-    # "100-continue". Splitting on commas is harmless. Case insensitive.
-    #
-    out: List[bytes] = []
-    for _, found_name, found_raw_value in headers._full_items:
-        if found_name == name:
-            found_raw_value = found_raw_value.lower()
-            for found_split_value in found_raw_value.split(b","):
-                found_split_value = found_split_value.strip()
-                if found_split_value:
-                    out.append(found_split_value)
-    return out
-
-
-def set_comma_header(headers: Headers, name: bytes, new_values: List[bytes]) -> Headers:
-    # The header name `name` is expected to be lower-case bytes.
-    #
-    # Note that when we store the header we use title casing for the header
-    # names, in order to match the conventional HTTP header style.
-    #
-    # Simply calling `.title()` is a blunt approach, but it's correct
-    # here given the cases where we're using `set_comma_header`...
-    #
-    # Connection, Content-Length, Transfer-Encoding.
-    new_headers: List[Tuple[bytes, bytes]] = []
-    for found_raw_name, found_name, found_raw_value in headers._full_items:
-        if found_name != name:
-            new_headers.append((found_raw_name, found_raw_value))
-    for new_value in new_values:
-        new_headers.append((name.title(), new_value))
-    return normalize_and_validate(new_headers)
-
-
-def has_expect_100_continue(request: "Request") -> bool:
-    # https://tools.ietf.org/html/rfc7231#section-5.1.1
-    # "A server that receives a 100-continue expectation in an HTTP/1.0 request
-    # MUST ignore that expectation."
-    if request.http_version < b"1.1":
-        return False
-    expect = get_comma_header(request.headers, b"expect")
-    return b"100-continue" in expect

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/utils/_chunk_utils.py

@@ -1,64 +0,0 @@
-# coding=utf-8
-# Copyright 2022-present, the HuggingFace Inc. team.
-#
-# 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.
-"""Contains a utility to iterate by chunks over an iterator."""
-import itertools
-from typing import Iterable, TypeVar
-
-
-T = TypeVar("T")
-
-
-def chunk_iterable(iterable: Iterable[T], chunk_size: int) -> Iterable[Iterable[T]]:
-    """Iterates over an iterator chunk by chunk.
-
-    Taken from https://stackoverflow.com/a/8998040.
-    See also https://github.com/huggingface/huggingface_hub/pull/920#discussion_r938793088.
-
-    Args:
-        iterable (`Iterable`):
-            The iterable on which we want to iterate.
-        chunk_size (`int`):
-            Size of the chunks. Must be a strictly positive integer (e.g. >0).
-
-    Example:
-
-    ```python
-    >>> from huggingface_hub.utils import chunk_iterable
-
-    >>> for items in chunk_iterable(range(17), chunk_size=8):
-    ...     print(items)
-    # [0, 1, 2, 3, 4, 5, 6, 7]
-    # [8, 9, 10, 11, 12, 13, 14, 15]
-    # [16] # smaller last chunk
-    ```
-
-    Raises:
-        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
-            If `chunk_size` <= 0.
-
-    <Tip warning={true}>
-        The last chunk can be smaller than `chunk_size`.
-    </Tip>
-    """
-    if not isinstance(chunk_size, int) or chunk_size <= 0:
-        raise ValueError("`chunk_size` must be a strictly positive integer (>0).")
-
-    iterator = iter(iterable)
-    while True:
-        try:
-            next_item = next(iterator)
-        except StopIteration:
-            return
-        yield itertools.chain((next_item,), itertools.islice(iterator, chunk_size - 1))