Update parquet files (step 99 of 249)

spaces/101-5/gpt4free/app.py

@@ -1,172 +0,0 @@
-import g4f
-import gradio as gr
-from g4f.Provider import (
-    Ails,
-    You,
-    Bing,
-    Yqcloud,
-    Theb,
-    Aichat,
-    Bard,
-    Vercel,
-    Forefront,
-    Lockchat,
-    Liaobots,
-    H2o,
-    ChatgptLogin,
-    DeepAi,
-    GetGpt
-)
-import os
-import json
-import pandas as pd
-
-from models_for_langchain.model import CustomLLM
-from langchain.memory import ConversationBufferWindowMemory, ConversationTokenBufferMemory
-from langchain import LLMChain, PromptTemplate
-from langchain.prompts import (
-    ChatPromptTemplate,
-    PromptTemplate,
-    SystemMessagePromptTemplate,
-    AIMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-
-provider_dict = {
-    'Ails': Ails,
-    'You': You,
-    'Bing': Bing,
-    'Yqcloud': Yqcloud,
-    'Theb': Theb,
-    'Aichat': Aichat,
-    'Bard': Bard,
-    'Vercel': Vercel,
-    'Forefront': Forefront,
-    'Lockchat': Lockchat,
-    'Liaobots': Liaobots,
-    'H2o': H2o,
-    'ChatgptLogin': ChatgptLogin,
-    'DeepAi': DeepAi,
-    'GetGpt': GetGpt
-}
-
-prompt_set_list = {}
-for prompt_file in os.listdir("prompt_set"):
-    key = prompt_file
-    if '.csv' in key:
-        df = pd.read_csv("prompt_set/" + prompt_file)
-        prompt_dict = dict(zip(df['act'], df['prompt']))
-    else:
-        with open("prompt_set/" + prompt_file, encoding='utf-8') as f:
-            ds = json.load(f)
-        prompt_dict = {item["act"]: item["prompt"] for item in ds}
-    prompt_set_list[key] = prompt_dict
-
-with gr.Blocks() as demo:
-    llm = CustomLLM()
-
-    template = """
-    Chat with human based on following instructions:
-    ```
-    {system_instruction}
-    ```
-    The following is a conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
-    {{chat_history}}
-    Human: {{human_input}}
-    Chatbot:"""
-
-    memory = ConversationBufferWindowMemory(k=10, memory_key="chat_history")
-
-    chatbot = gr.Chatbot([], label='AI')
-    msg = gr.Textbox(value="", label='请输入：')
-    with gr.Row():
-        clear = gr.Button("清空对话", scale=2)
-        chat_mode = gr.Checkbox(value=True, label='聊天模式', interactive=True, scale=1)
-    system_msg = gr.Textbox(value="你是一名助手，可以解答问题。", label='系统提示')
-    with gr.Row():
-        default_prompt_set = "1 中文提示词.json"
-        prompt_set_name = gr.Dropdown(prompt_set_list.keys(), value=default_prompt_set, label='提示词集合')
-        prompt_name = gr.Dropdown(prompt_set_list[default_prompt_set].keys(), label='提示词', min_width=20)
-    with gr.Row():
-        model_name = gr.Dropdown(['gpt-3.5-turbo', 'gpt-4'], value='gpt-3.5-turbo', label='模型')
-        provider_name  =  gr.Dropdown(provider_dict.keys(), value='GetGpt', label='提供者', min_width=20)
-
-    def change_prompt_set(prompt_set_name):
-        return gr.Dropdown.update(choices=list(prompt_set_list[prompt_set_name].keys()))
-
-    def change_prompt(prompt_set_name, prompt_name):
-        return gr.update(value=prompt_set_list[prompt_set_name][prompt_name])
-
-    def user(user_message, history = []):
-        return gr.update(value="", interactive=False), history + [[user_message, None]]
-
-    def bot(history, model_name, provider_name, system_msg, chat_mode):
-        history[-1][1] = ''
-        if len(system_msg)>3000:
-            system_msg = system_msg[:2000] + system_msg[-1000:]
-
-        if not chat_mode:
-            global template, memory
-            llm.model_name = model_name
-            llm.provider_name = provider_name
-            prompt = PromptTemplate(
-                                input_variables=["chat_history", "human_input"], template=template.format(system_instruction=system_msg)
-                            )
-            llm_chain = LLMChain(
-                                llm=llm,
-                                prompt=prompt,
-                                verbose=False,
-                                memory=memory,
-                            )
-            bot_msg = llm_chain.run(history[-1][0])
-            for c in bot_msg:
-                history[-1][1] += c
-                yield history
-        else:
-            prompt = """
-            请你仔细阅读以下提示，然后针对用户的话进行回答。
-            提示：
-            ```
-            {}
-            ```
-            用户最新的话：
-            ```
-            {}
-            ```
-            请回答：
-            """
-
-            # print(history)
-            messages = []         
-            for user_message, assistant_message in history[:-1]:
-                messages.append({"role": "user", "content": user_message})
-                messages.append({"role": "assistant", "content": assistant_message})
-            messages.append({"role": "user", "content": history[-1][0]})
-            # print(messages)
-
-            bot_msg = g4f.ChatCompletion.create(
-                                            model=model_name, 
-                                            provider=provider_dict[provider_name], 
-                                            messages=messages,
-                                            stream=True)
-            for c in bot_msg:
-                history[-1][1] += c
-                print(c, flush=True, end='')
-                yield history
-
-    def empty_chat():
-        global memory
-        memory = ConversationBufferWindowMemory(k=10, memory_key="chat_history")
-        return None
-    response = msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
-        bot, [chatbot, model_name, provider_name, system_msg, chat_mode], chatbot
-    )
-    prompt_set_name.select(change_prompt_set, prompt_set_name, prompt_name)
-    prompt_name.select(change_prompt, [prompt_set_name, prompt_name], system_msg)
-
-    response.then(lambda: gr.update(interactive=True), None, [msg], queue=False)
-    clear.click(empty_chat, None, [chatbot], queue=False)
-
-demo.title = "AI Chat"
-demo.queue()
-demo.launch()

spaces/1acneusushi/gradio-2dmoleculeeditor/data/ArcGIS 10.8 Full Crack Kuyhaa - A Powerful and Easy-to-Use GIS Software for Your PC.md

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-<h2>How to Download ArcGIS 10.8 Full Crack Kuyhaa</h2>
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-<ul>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Neighbours From Hell 6 Free and Make Your Neighbours Lives Miserable.md

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-<br />
-<h1>Download Neighbours From Hell 6 Free</h1>
-<p>Have you ever wanted to get back at your annoying neighbour for making your life miserable? If so, you might enjoy playing <strong>Neighbours From Hell 6</strong>, a comedy strategy game that lets you prank your neighbour in various ways. In this article, we will show you how to download Neighbours From Hell 6 for free, give you some tips and tricks for playing it, and review its features, pros and cons.</p>
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- <h2>How to download Neighbours From Hell 6 for free</h2>
-<p>Neighbours From Hell 6 is the sixth installment of the popular Neighbours From Hell series, which was developed by Orion Games and released in 2009. The game is not available on any official platforms, such as Steam or GOG, but you can still download it for free from some unofficial sources. Here are the steps you need to follow:</p>
-<ol>
-<li><p>Find a reliable source for the game file. You can search online for websites that offer free downloads of Neighbours From Hell 6, but be careful not to click on any suspicious links or ads that might contain malware or viruses. One of the websites that we recommend is <a href="https://www.youtube.com/watch?v=G4HwB9vXReM">this one</a>, which has a video tutorial on how to get the game file.</p></li>
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-</ol>
- <h2>Tips and tricks for playing Neighbours From Hell 6</h2>
-<p>Neighbours From Hell 6 is a fun and easy game to play, but it can also be challenging if you want to achieve a high score and unlock all the achievements. Here are some tips and tricks that might help you:</p>
-<ul>
-<li><p>Plan your pranks carefully. The game consists of 14 episodes, each with a different setting and a number of pranks that you can perform on your neighbour. You have a limited time to complete each episode, so you need to plan your pranks ahead and execute them in the right order. You can use the eye icon to spy on your neighbour's movements and actions, and use the pause button to think about your next move.</p></li>
-<li><p>Use the environment to your advantage. The game offers a variety of pranks and items that you can use to annoy your neighbour, such as glue, pepper, soap, scissors, etc. You can also interact with some objects in the environment, such as doors, windows, switches, faucets, etc., to create more chaos. For example, you can turn on the water while your neighbour is showering, or cut off the electricity while he is watching TV.</p></li>
-<li><p>Watch out for the dog and other obstacles. Your neighbour is not the only one who can ruin your plans. There are also some obstacles that you need to avoid or overcome, such as his dog Chilli, who will chase you if he sees you; his girlfriend Olga, who will slap you if she catches you; or his mother Rottweiler, who will scold him if she finds out what he is doing. You can use some items or tricks to distract them or get rid of them temporarily.</p>
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-<p>Neighbours From Hell 6 is a game that offers a lot of features that make it enjoyable and entertaining. Here are some of them:</p>
-<ul>
-<li><p>14 hilarious episodes with different settings. The game takes you to various locations where you can prank your neighbour, such as his house, his office, his hotel room, his cruise ship cabin, his ski resort chalet, etc. Each episode has its own theme and atmosphere, as well as unique pranks and items that you can use.</p></li>
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- <h2>Pros and cons of Neighbours From Hell 6</h2>
-<p>Neighbours From Hell 6 is a game that has its pros and cons. Here are some of them:</p>
- <table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td><p>A fun and addictive gameplay. The game is easy to play but hard to master. It requires strategy, timing, creativity, and patience. It also has a replay value because you can try different pranks or aim for higher scores.</p></td>
-<td><p>A repetitive and predictable pattern. The game follows a similar pattern in each episode. You have to spy on your neighbour's routine, find items in hidden places, set up pranks in specific spots, etc. The neighbour's behaviour is also predictable after a while.</p></td>
-</tr>
-<tr>
-<td><p>A humorous and original concept. The game has a unique concept that makes it stand out from other games. It is based on a TV show with the same name that aired in Germany in 2003-2005. It is also inspired by real-life situations that many people can relate to.</p></td>
-<td><p>A lack of difficulty and challenge. The game is not very challenging because it does not have any penalties or consequences for failing an episode or getting caught by your neighbour. You can simply restart or retry until you succeed. The game also does not have any difficulty levels or modes that could make it more challenging.</p></td>
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-<tr>
-<td><p>A low system requirement and easy installation. The game does not require a high-end computer or device to run smoothly. It has low graphics quality and size that make it compatible with most systems. It also has an easy installation process that does not require any additional programs or files.</p></td>
-<td><p>A dated graphics and animation. The game has poor graphics quality and animation that make it look outdated compared to other games released in the same year or later. The characters are pixelated and stiff; the backgrounds are bland and blurry; the movements are slow and unnatural.</p></td>
-</tr>
-</table>
- <h2>Conclusion</h2>
-<p>Neighbours From Hell 6 is a comedy strategy game that lets you prank your neighbour in various ways. You can download it for free from some unofficial sources and enjoy playing it on your computer. The game has a fun and addictive gameplay, a humorous and original concept, and a catchy soundtrack and sound effects. However, it also has some drawbacks, such as a repetitive and predictable pattern, a lack of difficulty and challenge, and a dated graphics and animation. Overall, Neighbours From Hell 6 is a game that can make you laugh and have a good time, but it might not appeal to everyone.</p>
-<p>If you are interested in playing Neighbours From Hell 6, you can follow the steps we provided in this article and start pranking your neighbour today. You can also check out some tips and tricks we shared to help you improve your performance and score. And if you want to learn more about the features, pros and cons of Neighbours From Hell 6, you can read our review and see if it suits your taste.</p>
-<p>We hope you enjoyed reading this article and found it useful. If you did, please share it with your friends and family who might also like to play Neighbours From Hell 6. And if you have any questions or feedback, please leave them in the comments section below. We would love to hear from you.</p>
-<p>Thank you for reading and happy pranking!</p>
- <h3>FAQs</h3>
-<ul>
-<li><p><strong>Q: What is the difference between Neighbours From Hell 6 and other Neighbours From Hell games?</strong></p>
-<p>A: Neighbours From Hell 6 is the sixth installment of the series, which was released in 2009. It has 14 episodes with different settings, such as a cruise ship, a ski resort, a casino, etc. It also has some new pranks and items that were not available in previous games.</p></li>
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-<p>A: It depends on your skill level and how much time you spend on each episode. Each episode has a time limit that ranges from 5 to 15 minutes. If you complete all the episodes with 100% score, it might take you around 3 to 4 hours to finish the game.</p></li>
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spaces/1gistliPinn/ChatGPT4/Examples/AIRAC Cycle 1210 (complete) [FSX FS9 X-Plane] Demol.md

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-<h1>AIRAC Cycle 1210 (complete) [FSX, FS9, X-Plane] Demol: How to Update Your Flight Simulator with the Latest Navigation Data</h1>
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-<p>If you are a flight simulator enthusiast, you probably know how important it is to have the most accurate and up-to-date navigation data for your flights. Navigation data includes information such as waypoints, airways, navaids, procedures, and more. Without it, you might end up flying to the wrong destination, missing an approach, or violating airspace restrictions.</p>
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-<p>But how do you update your flight simulator with the new AIRAC cycle? That's where AIRAC Cycle 1210 (complete) [FSX, FS9, X-Plane] Demol comes in. This is a package that contains all the navigation data files for three popular flight simulators: Microsoft Flight Simulator X (FSX), Microsoft Flight Simulator 2004 (FS9), and X-Plane. It also includes a tool called Demol that allows you to easily install the files into your simulator.</p>
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-<p>In this article, we will show you how to use AIRAC Cycle 1210 (complete) [FSX, FS9, X-Plane] Demol to update your flight simulator with the latest navigation data. Follow these simple steps and enjoy more realistic and accurate flights.</p>
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-<h2>Step 1: Download AIRAC Cycle 1210 (complete) [FSX, FS9, X-Plane] Demol</h2>
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-<p>The first step is to download the package from the link below. The package is a ZIP file that contains all the navigation data files for FSX, FS9, and X-Plane, as well as the Demol tool. The file size is about 1.2 GB, so make sure you have enough space on your hard drive and a stable internet connection.</p>
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-<h2>Step 2: Extract the ZIP file</h2>
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-<p>The next step is to extract the ZIP file to a folder on your computer. You can use any software that can handle ZIP files, such as WinZip, WinRAR, or 7-Zip. To extract the file, right-click on it and select "Extract All" or "Extract Here". Choose a destination folder where you want to save the extracted files.</p>
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-<h2>Step 3: Run Demol</h2>
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-<p>The third step is to run Demol.exe from the extracted folder. This is a tool that will help you install the navigation data files into your flight simulator. When you run Demol.exe, you will see a window like this:</p>
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-<img src="https://example.com/images/demol-window.png" alt="Demol window">
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-<p>As you can see, Demol has four tabs: FSX/P3D, FS9/FS2004, X-Plane 10/11/12/13/14/15/16/17/18/19/20/21/22/23/24/25/26/27/28/29/30
-
-<h2>Step 4: Select your flight simulator and install the files</h2>
-
-<p>The fourth step is to select your flight simulator and install the navigation data files. To do this, click on the tab that corresponds to your simulator: FSX/P3D, FS9/FS2004, or X-Plane. You will see a window like this:</p>
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-<img src="https://example.com/images/demol-fsx.png" alt="Demol FSX tab">
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spaces/1gistliPinn/ChatGPT4/Examples/Enter Macbeth Movie Download In Hd Watch the Shakespearean Tragedy on Your Screen.md

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spaces/1phancelerku/anime-remove-background/Car Stunt 3D Extreme City - MOD APK with Unlimited Money and Features.md

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-<p>One of the benefits of Car Stunt 3D Extreme City Mod Apk is that it gives you unlimited money and gems in the game. Money and gems are the main currencies in the game that you can use to buy new cars, upgrade them, customize them, and unlock new tracks. However, earning money and gems in the game can be time-consuming and tedious. You have to complete missions, win races, watch ads, or spend real money to get them. With Car Stunt 3D Extreme City Mod Apk, you don't have to worry about that anymore. You can get unlimited money and gems for free without any effort. You can use them to buy anything you want in the game without any restrictions.</p>
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spaces/1phancelerku/anime-remove-background/Download Candy Crush Saga and Discover Thousands of Levels and Challenges on Your iPhone or iPad.md

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- <h2>What is Candy Crush Saga and why is it popular?</h2>
- <p>Candy Crush Saga is a match-three puzzle game developed by King, a leading company in casual gaming. The game was released in 2012 for Facebook, and later for iOS, Android, Windows Phone, and Windows 10. The game has over a trillion levels, each with a different objective and layout. The basic gameplay involves swapping adjacent candies to make matches of three or more of the same color, which will clear them from the board and make way for new ones. Matching more than three candies will create special candies that have various effects, such as clearing a whole row or column, or exploding all candies of a certain color. The game also has different game modes, such as target score, clear the jelly, collect the ingredients, and order mode.</p>
-<h2>candy crush saga ios download</h2><br /><p><b><b>DOWNLOAD</b> ::: <a href="https://jinyurl.com/2uNUtG">https://jinyurl.com/2uNUtG</a></b></p><br /><br />
- <p>Candy Crush Saga is popular because it is simple to play but challenging to master. It appeals to a wide range of players, from casual gamers who want to kill some time, to hardcore gamers who want to compete with their friends and other players around the world. The game also has a colorful and cute design, with catchy music and sound effects. The game is constantly updated with new levels and features, keeping the players engaged and entertained.</p>
- <h2>How to download and play Candy Crush Saga on iOS devices?</h2>
- <p>Downloading and playing Candy Crush Saga on your iPhone or iPad is easy and free. Here are the steps you need to follow:</p>
- <ol>
-<li>Go to the App Store on your device and search for Candy Crush Saga.</li>
-<li>Tap on the Get button to download the game. You may need to enter your Apple ID password or use Touch ID or Face ID to confirm.</li>
-<li>Once the download is complete, tap on the Open button to launch the game.</li>
-<li>You will see a tutorial that will teach you the basics of the game. You can also tap on the Level Type button at the bottom-left corner of the screen to learn more about the different objectives and game modes.</li>
-<li>To start playing, tap on a level on the map screen. You can also swipe left or right to see more levels.</li>
-<li>To swap candies, simply drag one candy over another adjacent one. You can also tap on a candy and then tap on another one next to it.</li>
-<li>To use a special candy, match it with other candies of the same color or type.</li>
-<li>To use a booster, tap on it before starting a level or during a level. Boosters can help you clear difficult levels by giving you extra moves, lives, or power-ups.</li>
-<li>To pause or exit a level, tap on the Menu button at the bottom-right corner of the screen.</li>
-</ol>
- <p>You can also connect your game to your Facebook account to sync your progress, get more lives, and play with your friends. To do this, tap on the Connect button on the main screen and follow the instructions. You can also invite your friends to play Candy Crush Saga and send or receive lives and boosters from them.</p>
- <h2>Features of Candy Crush Saga</h2>
- <p>Candy Crush Saga is not just a simple match-three game. It has many features that make it more fun and exciting. Here are some of them:</p>
- <h3>The game that keeps you craving more</h3>
- <p>Candy Crush Saga has over a trillion levels, and new ones are added every week. You will never run out of challenges and surprises. The game also has different episodes, each with a unique theme and story. You will meet various characters along the way, such as Tiffi, Mr. Toffee, Odus the Owl, and many more. You will also encounter different types of candies, such as striped, wrapped, color bomb, jelly fish, coconut wheel, and more. Each candy has a different effect when matched or activated.</p>
- <h3>Many ways to win rewards</h3>
- <p>Candy Crush Saga rewards you for playing well and being loyal. You can earn stars by completing levels with high scores. You can also collect sugar drops by matching certain candies. These stars and sugar drops can be used to unlock special features and boosters. You can also spin the Daily Booster Wheel every day to get a free booster. You can also participate in events and quests to win more prizes and bonuses. Some of the events and quests are Daily Quests, Sugar Track, Build-a-Bot, Fantastic Five, Sweet Streak, and more.</p>
- <h3>Variety of sugar-coated challenges</h3>
- <p>Candy Crush Saga has different game modes that test your skills and strategy. Some of the game modes are:</p>
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- <ul>
-<li>Target Score: You need to reach a certain score within a limited number of moves or time.</li>
-<li>Clear the Jelly: You need to clear all the jelly tiles from the board by matching candies on them.</li>
-<li>Collect the Ingredients: You need to bring down all the ingredients (cherries or hazelnuts) to the bottom of the board.</li>
-<li>Order Mode: You need to collect a specific number or type of candies or combinations.</li>
-<li>Mixed Mode: You need to complete two or more objectives in one level.</li>
-</ul>
- <p>Each game mode has its own challenges and strategies. You need to adapt your moves according to the objective and the layout of the board.</p>
- <h3>Play alone or with friends</h3>
- <p>Candy Crush Saga is a game that you can enjoy alone or with your friends. You can play offline or online, depending on your preference. You can also connect your game to your Facebook account to see how your friends are doing, compare scores, send or receive lives and boosters, and compete in leaderboards and tournaments. You can also join a team with other players and work together to achieve common goals and rewards.</p>
- <h2>Tips and tricks for Candy Crush Saga</h2>
- <p>Candy Crush Saga is a game that requires skill, strategy, and luck. Sometimes, you may get stuck on a level or run out of lives or boosters. Don't worry, we have some tips and tricks that can help you overcome these challenges and have more fun playing the game. Here are some of them:</p>
- <h3>Know the best and worst combos</h3>
- <p>One of the keys to success in Candy Crush Saga is to make good use of the special candies and their combinations. Some of the best combos are:</p>
- <ul>
-<li>Striped + Wrapped: This creates a giant candy that clears three rows and three columns.</li>
-<li>Striped + Color Bomb: This turns all the candies of the same color as the striped candy into striped candies and activates them.</li>
-<li>Wrapped + Color Bomb: This clears all the candies of two colors from the board.</li>
-<li>Color Bomb + Color Bomb: This clears all the candies from the board.</li>
-</ul>
- <p>Some of the worst combos are:</p>
- <ul>
-<li>Jelly Fish + Jelly Fish: This only creates three more jelly fish that may not be very helpful.</li>
-<li>Coconut Wheel + Coconut Wheel: This only creates two striped candies that may not be in a good position.</li>
-<li>Lucky Candy + Lucky Candy: This only creates two random special candies that may not match your objective.</li>
-</ul>
- <h3>Use special candies wisely</h3>
- <p>Special candies are very powerful, but they are also limited. You should use them wisely and strategically. Here are some tips on how to use them:</p>
- <ul>
-<li>Don't waste them on easy matches or low-value candies. Save them for hard-to-reach areas, obstacles, or objectives.</li>
-<li>Don't activate them right away. Wait for the best opportunity to make a bigger impact.</li>
-<li>Don't combine them randomly. Think about the best combo for your situation and objective.</li>
-<li>Don't rely on them too much. Sometimes, a simple match can be more effective than a special candy.</li>
-</ul>
- <h3>Plan your moves ahead</h3>
- <p>Candy Crush Saga is a game that requires you to think ahead and plan your moves carefully. You should not just match candies randomly or impulsively. Here are some tips on how to plan your moves:</p>
- <ul>
-<li>Look at the whole board and the objective before making a move.</li>
-<li>Try to create matches at the bottom of the board first, as this will cause more cascades and create more opportunities for matches and special candies.</li>
-<li>Try to create matches near the edges or corners of the board, as these are harder to clear and may contain obstacles or ingredients.</li>
-<li>Try to create matches that will help you clear the jelly, collect the ingredients, or fulfill the order, depending on the game mode.</li>
-<li>Try to avoid moves that will disrupt your existing combos or potential matches.</li>
-</ul>
- <h3>Save your boosters for hard levels</h3>
- <p>Boosters are very helpful in Candy Crush Saga, but they are also scarce and expensive. You should not use them on easy levels or when you are not sure if they will help you. Here are some tips on how to save your boosters:</p>
- <ul>
-<li>Earn boosters by playing well, spinning the Daily Booster Wheel, completing events and quests, or watching ads.</li>
-<li>Use boosters only when you are stuck on a level or when you are close to completing it.</li>
-<li>Use boosters that match your objective and situation. For example, use a Lollipop Hammer to clear a single candy or obstacle, use a Free Switch to swap two candies that are not adjacent, use a Striped and Wrapped to clear a large area of the board, etc.</li>
-<li>Use boosters in combination with special candies or other boosters to maximize their effect.</li>
-</ul>
- <h3>Connect to Facebook for extra benefits</h3>
- <p>Candy Crush Saga is more fun when you play with your friends. You can connect your game to your Facebook account to enjoy some extra benefits. Here are some of them:</p>
- <ul>
-<li>You can sync your progress across different devices and platforms.</li>
-<li>You can see how your friends are doing on the map screen and compare scores with them.</li>
-<li>You can send and receive lives and boosters from your friends.</li>
-<li>You can compete with your friends and other players in leaderboards and tournaments.</li>
-<li>You can join a team with other players and work together to achieve common goals and rewards.</li>
-</ul>
- <h2>Alternatives to Candy Crush Saga</h2>
- <p>Candy Crush Saga is a great game, but it is not the only one of its kind. There are many other games that offer similar or different gameplay and features. If you want to try something new, here are some alternatives to Candy Crush Saga that you might like:</p>
- <h3>Zookeeper Battle</h3>
- <p>Zookeeper Battle is a match-three puzzle game that pits you against other players in real-time battles. You need to match animal tiles to attack your opponent and defend yourself. You can also use items and skills to gain an edge in the battle. The game has cute graphics and sound effects, and a simple but addictive gameplay. You can play with your friends or with random players from around the world.</p>
- <h3>Bejeweled Blitz</h3>
- <p>Bejeweled Blitz is a fast-paced match-three puzzle game that challenges you to score as high as possible in 60 seconds. You need to match gems of the same color to clear them from the board and create cascades and combos. You can also use special gems and boosters to increase your score and unleash powerful effects. The game has stunning graphics and sound effects, and a competitive gameplay. You can play with your friends or with millions of players from around the world.</p>
- <h3>Two Dots</h3>
- <p>Two Dots is a minimalist match-three puzzle game that requires you to connect dots of the same color to clear them from the board. You need to complete different objectives in each level, such as breaking ice, dropping anchors, or collecting fireflies. The game has a beautiful design and music, and a relaxing but challenging gameplay. You can play alone or with your friends in co-op mode.</p>
- <h3>Futurama: Game of Drones</h3>
- <p>Futurama: Game of Drones is a match-four puzzle game that features the characters and humor of the popular animated series Futurama. You need to match delivery drones of the same color to clear them from the board and deliver packages. You can also use special drones and power-ups to create explosions and combos. The game has a hilarious story and dialogue, and a fun and addictive gameplay. You can play with your friends or with other players from around the world.</p>
- <h2>Conclusion</h2>
- <p>Candy Crush Saga is a game that you can download and play on your iOS device for free. It is a match-three puzzle game that has over a trillion levels, each with a different objective and game mode. It also has many features that make it more fun and exciting, such as special candies, boosters, events, quests, and more. You can also play with your friends or with other players from around the world. Candy Crush Saga is a game that will keep you craving more.</p>
- <p>If you are ready to join the sweet adventure, download Candy Crush Saga today and start matching candies. You will not regret it.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Candy Crush Saga:</p>
- <h3>How do I get more lives in Candy Crush Saga?</h3>
- <p>You can get more lives in Candy Crush Saga by doing one of the following:</p>
- <ul>
-<li>Wait for 30 minutes for each life to regenerate.</li>
-<li>Ask your friends to send you lives.</li>
-<li>Buy lives with gold bars.</li>
-<li>Use the Infinite Lives booster when it is available.</li>
-</ul>
- <h3>How do I sync my progress across devices?</h3>
- <p>You can sync your progress across devices by connecting your game to your Facebook account. This will allow you to access your game data on any device or platform that supports Candy Crush Saga.</p>
- <h3>How do I clear jelly and other obstacles?</h3>
- <p>You can clear jelly and other obstacles by matching candies on them or near them. Some obstacles may require more than one match to clear, such as double jelly, licorice locks, chocolate, or cake bombs. You can also use special candies or boosters to clear them faster or easier.</p>
- <h3>How do I get free gold bars in Candy Crush Saga?</h3>
- <p>You can get free gold bars in Candy Crush Saga by doing one of the following:</p>
- <ul>
-<li>Complete achievements and milestones.</li>
-<li>Participate in events and quests that offer gold bars as rewards.</li>
-<li>Watch ads when they are available.</li>
-<li>Use the Piggy Bank feature when it is full.</li>
-</ul>
- <h3>How do I contact the support team for Candy Crush Saga?</h3>
- <p>You can contact the support team for Candy Crush Saga by doing one of the following:</p>
- <ul>
-<li>Tapping on the Settings button on the main screen and then tapping on Help Center.</li>
-<li>Visiting the official website of King and clicking on Support.</li>
-<li>Sending an email to [email protected].</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />

spaces/1toTree/lora_test/ppdiffusers/schedulers/scheduling_ddpm.py

@@ -1,360 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 UC Berkeley Team and 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.
-
-# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
-
-import math
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import paddle
-import paddle.nn.functional as F
-
-from ..configuration_utils import ConfigMixin, FrozenDict, register_to_config
-from ..utils import _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS, BaseOutput, deprecate
-from .scheduling_utils import SchedulerMixin
-
-
-@dataclass
-class DDPMSchedulerOutput(BaseOutput):
-    """
-    Output class for the scheduler's step function output.
-
-    Args:
-        prev_sample (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        pred_original_sample (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
-            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
-            `pred_original_sample` can be used to preview progress or for guidance.
-    """
-
-    prev_sample: paddle.Tensor
-    pred_original_sample: Optional[paddle.Tensor] = None
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
-    (1-beta) over time from t = [0,1].
-
-    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
-    to that part of the diffusion process.
-
-
-    Args:
-        num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-
-    Returns:
-        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
-    """
-
-    def alpha_bar(time_step):
-        return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
-
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return paddle.to_tensor(betas, dtype="float32")
-
-
-class DDPMScheduler(SchedulerMixin, ConfigMixin):
-    """
-    Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and
-    Langevin dynamics sampling.
-
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-
-    For more details, see the original paper: https://arxiv.org/abs/2006.11239
-
-    Args:
-        num_train_timesteps (`int`): number of diffusion steps used to train the model.
-        beta_start (`float`): the starting `beta` value of inference.
-        beta_end (`float`): the final `beta` value.
-        beta_schedule (`str`):
-            the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
-        trained_betas (`np.ndarray`, optional):
-            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
-        variance_type (`str`):
-            options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`,
-            `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.
-        clip_sample (`bool`, default `True`):
-            option to clip predicted sample between -1 and 1 for numerical stability.
-        prediction_type (`str`, default `epsilon`, optional):
-            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
-            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
-            https://imagen.research.google/video/paper.pdf)
-    """
-
-    _compatibles = _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS.copy()
-    _deprecated_kwargs = ["predict_epsilon"]
-    order = 1
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.0001,
-        beta_end: float = 0.02,
-        beta_schedule: str = "linear",
-        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
-        variance_type: str = "fixed_small",
-        clip_sample: bool = True,
-        prediction_type: str = "epsilon",
-        **kwargs,
-    ):
-        message = (
-            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
-            " DDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
-        )
-        predict_epsilon = deprecate("predict_epsilon", "0.13.0", message, take_from=kwargs)
-        if predict_epsilon is not None:
-            self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
-        if trained_betas is not None:
-            self.betas = paddle.to_tensor(trained_betas, dtype="float32")
-        elif beta_schedule == "linear":
-            self.betas = paddle.linspace(beta_start, beta_end, num_train_timesteps, dtype="float32")
-        elif beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            self.betas = paddle.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype="float32") ** 2
-        elif beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            self.betas = betas_for_alpha_bar(num_train_timesteps)
-        elif beta_schedule == "sigmoid":
-            # GeoDiff sigmoid schedule
-            betas = paddle.linspace(-6, 6, num_train_timesteps)
-            self.betas = F.sigmoid(betas) * (beta_end - beta_start) + beta_start
-        else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
-
-        self.alphas = 1.0 - self.betas
-        self.alphas_cumprod = paddle.cumprod(self.alphas, 0)
-        self.one = paddle.to_tensor(1.0)
-
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = 1.0
-
-        # setable values
-        self.num_inference_steps = None
-        self.timesteps = paddle.to_tensor(np.arange(0, num_train_timesteps)[::-1].copy())
-
-        self.variance_type = variance_type
-
-    def scale_model_input(self, sample: paddle.Tensor, timestep: Optional[int] = None) -> paddle.Tensor:
-        """
-        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
-        current timestep.
-
-        Args:
-            sample (`paddle.Tensor`): input sample
-            timestep (`int`, optional): current timestep
-
-        Returns:
-            `paddle.Tensor`: scaled input sample
-        """
-        return sample
-
-    def set_timesteps(self, num_inference_steps: int):
-        """
-        Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
-
-        Args:
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-        """
-        num_inference_steps = min(self.config.num_train_timesteps, num_inference_steps)
-        self.num_inference_steps = num_inference_steps
-        timesteps = np.arange(
-            0, self.config.num_train_timesteps, self.config.num_train_timesteps // self.num_inference_steps
-        )[::-1].copy()
-        self.timesteps = paddle.to_tensor(timesteps)
-
-    def _get_variance(self, t, predicted_variance=None, variance_type=None):
-        alpha_prod_t = self.alphas_cumprod[t]
-        alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one
-
-        # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
-        # and sample from it to get previous sample
-        # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
-        variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
-
-        if variance_type is None:
-            variance_type = self.config.variance_type
-
-        # hacks - were probably added for training stability
-        if variance_type == "fixed_small":
-            variance = paddle.clip(variance, min=1e-20)
-        # for rl-diffuser https://arxiv.org/abs/2205.09991
-        elif variance_type == "fixed_small_log":
-            variance = paddle.log(paddle.clip(variance, min=1e-20))
-            variance = paddle.exp(0.5 * variance)
-        elif variance_type == "fixed_large":
-            variance = self.betas[t]
-        elif variance_type == "fixed_large_log":
-            # Glide max_log
-            variance = paddle.log(self.betas[t])
-        elif variance_type == "learned":
-            return predicted_variance
-        elif variance_type == "learned_range":
-            min_log = variance
-            max_log = self.betas[t]
-            frac = (predicted_variance + 1) / 2
-            variance = frac * max_log + (1 - frac) * min_log
-
-        return variance
-
-    def step(
-        self,
-        model_output: paddle.Tensor,
-        timestep: int,
-        sample: paddle.Tensor,
-        generator=None,
-        return_dict: bool = True,
-        **kwargs,
-    ) -> Union[DDPMSchedulerOutput, Tuple]:
-        """
-        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        Args:
-            model_output (`paddle.Tensor`): direct output from learned diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`paddle.Tensor`):
-                current instance of sample being created by diffusion process.
-            generator: random number generator.
-            return_dict (`bool`): option for returning tuple rather than DDPMSchedulerOutput class
-
-        Returns:
-            [`~schedulers.scheduling_utils.DDPMSchedulerOutput`] or `tuple`:
-            [`~schedulers.scheduling_utils.DDPMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
-            returning a tuple, the first element is the sample tensor.
-
-        """
-        message = (
-            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
-            " DDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
-        )
-        predict_epsilon = deprecate("predict_epsilon", "0.13.0", message, take_from=kwargs)
-        if predict_epsilon is not None:
-            new_config = dict(self.config)
-            new_config["prediction_type"] = "epsilon" if predict_epsilon else "sample"
-            self._internal_dict = FrozenDict(new_config)
-
-        t = timestep
-
-        if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
-            model_output, predicted_variance = paddle.split(model_output, sample.shape[1], axis=1)
-        else:
-            predicted_variance = None
-
-        # 1. compute alphas, betas
-        alpha_prod_t = self.alphas_cumprod[t]
-        alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one
-        beta_prod_t = 1 - alpha_prod_t
-        beta_prod_t_prev = 1 - alpha_prod_t_prev
-
-        # 2. compute predicted original sample from predicted noise also called
-        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
-        if self.config.prediction_type == "epsilon":
-            pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
-        elif self.config.prediction_type == "sample":
-            pred_original_sample = model_output
-        elif self.config.prediction_type == "v_prediction":
-            pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
-        else:
-            raise ValueError(
-                f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or"
-                " `v_prediction`  for the DDPMScheduler."
-            )
-
-        # 3. Clip "predicted x_0"
-        if self.config.clip_sample:
-            pred_original_sample = paddle.clip(pred_original_sample, -1, 1)
-
-        # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
-        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
-        pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * self.betas[t]) / beta_prod_t
-        current_sample_coeff = self.alphas[t] ** (0.5) * beta_prod_t_prev / beta_prod_t
-
-        # 5. Compute predicted previous sample µ_t
-        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
-        pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
-
-        # 6. Add noise
-        variance = 0
-        if t > 0:
-            variance_noise = paddle.randn(model_output.shape, generator=generator, dtype=model_output.dtype)
-            if self.variance_type == "fixed_small_log":
-                variance = self._get_variance(t, predicted_variance=predicted_variance) * variance_noise
-            else:
-                variance = (self._get_variance(t, predicted_variance=predicted_variance) ** 0.5) * variance_noise
-
-        pred_prev_sample = pred_prev_sample + variance
-
-        if not return_dict:
-            return (pred_prev_sample,)
-
-        return DDPMSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)
-
-    def add_noise(
-        self,
-        original_samples: paddle.Tensor,
-        noise: paddle.Tensor,
-        timesteps: paddle.Tensor,
-    ) -> paddle.Tensor:
-        # Make sure alphas_cumprod and timestep have same dtype as original_samples
-        self.alphas_cumprod = self.alphas_cumprod.cast(original_samples.dtype)
-
-        sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
-        return noisy_samples
-
-    def get_velocity(self, sample: paddle.Tensor, noise: paddle.Tensor, timesteps: paddle.Tensor) -> paddle.Tensor:
-        # Make sure alphas_cumprod and timestep have same dtype as original_samples
-        self.alphas_cumprod = self.alphas_cumprod.cast(sample.dtype)
-
-        sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(sample.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
-        return velocity
-
-    def __len__(self):
-        return self.config.num_train_timesteps

spaces/2ndelement/voicevox/test/test_synthesis_engine.py

@@ -1,654 +0,0 @@
-import math
-from copy import deepcopy
-from random import random
-from typing import Union
-from unittest import TestCase
-from unittest.mock import Mock
-
-import numpy
-
-from voicevox_engine.acoustic_feature_extractor import OjtPhoneme
-from voicevox_engine.model import AccentPhrase, AudioQuery, Mora
-from voicevox_engine.synthesis_engine import SynthesisEngine
-
-# TODO: import from voicevox_engine.synthesis_engine.mora
-from voicevox_engine.synthesis_engine.synthesis_engine import (
-    mora_phoneme_list,
-    pre_process,
-    split_mora,
-    to_flatten_moras,
-    to_phoneme_data_list,
-    unvoiced_mora_phoneme_list,
-)
-
-
-def yukarin_s_mock(length: int, phoneme_list: numpy.ndarray, speaker_id: numpy.ndarray):
-    result = []
-    # mockとしての適当な処理、特に意味はない
-    for i in range(length):
-        result.append(float(phoneme_list[i] * 0.5 + speaker_id))
-    return numpy.array(result)
-
-
-def yukarin_sa_mock(
-    length: int,
-    vowel_phoneme_list: numpy.ndarray,
-    consonant_phoneme_list: numpy.ndarray,
-    start_accent_list: numpy.ndarray,
-    end_accent_list: numpy.ndarray,
-    start_accent_phrase_list: numpy.ndarray,
-    end_accent_phrase_list: numpy.ndarray,
-    speaker_id: numpy.ndarray,
-):
-    result = []
-    # mockとしての適当な処理、特に意味はない
-    for i in range(length):
-        result.append(
-            float(
-                (
-                    vowel_phoneme_list[0][i]
-                    + consonant_phoneme_list[0][i]
-                    + start_accent_list[0][i]
-                    + end_accent_list[0][i]
-                    + start_accent_phrase_list[0][i]
-                    + end_accent_phrase_list[0][i]
-                )
-                * 0.5
-                + speaker_id
-            )
-        )
-    return numpy.array(result)[numpy.newaxis]
-
-
-def decode_mock(
-    length: int,
-    phoneme_size: int,
-    f0: numpy.ndarray,
-    phoneme: numpy.ndarray,
-    speaker_id: Union[numpy.ndarray, int],
-):
-    result = []
-    # mockとしての適当な処理、特に意味はない
-    for i in range(length):
-        # decode forwardはデータサイズがlengthの256倍になるのでとりあえず256回データをresultに入れる
-        for _ in range(256):
-            result.append(
-                float(
-                    f0[i][0] * (numpy.where(phoneme[i] == 1)[0] / phoneme_size)
-                    + speaker_id
-                )
-            )
-    return numpy.array(result)
-
-
-class MockCore:
-    yukarin_s_forward = Mock(side_effect=yukarin_s_mock)
-    yukarin_sa_forward = Mock(side_effect=yukarin_sa_mock)
-    decode_forward = Mock(side_effect=decode_mock)
-
-    def metas(self):
-        return ""
-
-    def supported_devices(self):
-        return ""
-
-    def is_model_loaded(self, speaker_id):
-        return True
-
-
-class TestSynthesisEngine(TestCase):
-    def setUp(self):
-        super().setUp()
-        self.str_list_hello_hiho = (
-            "sil k o N n i ch i w a pau h i h o d e s U sil".split()
-        )
-        self.phoneme_data_list_hello_hiho = [
-            OjtPhoneme(phoneme=p, start=i, end=i + 1)
-            for i, p in enumerate(
-                "pau k o N n i ch i w a pau h i h o d e s U pau".split()
-            )
-        ]
-        self.accent_phrases_hello_hiho = [
-            AccentPhrase(
-                moras=[
-                    Mora(
-                        text="コ",
-                        consonant="k",
-                        consonant_length=0.0,
-                        vowel="o",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                    Mora(
-                        text="ン",
-                        consonant=None,
-                        consonant_length=None,
-                        vowel="N",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                    Mora(
-                        text="ニ",
-                        consonant="n",
-                        consonant_length=0.0,
-                        vowel="i",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                    Mora(
-                        text="チ",
-                        consonant="ch",
-                        consonant_length=0.0,
-                        vowel="i",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                    Mora(
-                        text="ワ",
-                        consonant="w",
-                        consonant_length=0.0,
-                        vowel="a",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                ],
-                accent=5,
-                pause_mora=Mora(
-                    text="、",
-                    consonant=None,
-                    consonant_length=None,
-                    vowel="pau",
-                    vowel_length=0.0,
-                    pitch=0.0,
-                ),
-            ),
-            AccentPhrase(
-                moras=[
-                    Mora(
-                        text="ヒ",
-                        consonant="h",
-                        consonant_length=0.0,
-                        vowel="i",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                    Mora(
-                        text="ホ",
-                        consonant="h",
-                        consonant_length=0.0,
-                        vowel="o",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                    Mora(
-                        text="デ",
-                        consonant="d",
-                        consonant_length=0.0,
-                        vowel="e",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                    Mora(
-                        text="ス",
-                        consonant="s",
-                        consonant_length=0.0,
-                        vowel="U",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                ],
-                accent=1,
-                pause_mora=None,
-            ),
-        ]
-        core = MockCore()
-        self.yukarin_s_mock = core.yukarin_s_forward
-        self.yukarin_sa_mock = core.yukarin_sa_forward
-        self.decode_mock = core.decode_forward
-        self.synthesis_engine = SynthesisEngine(
-            core=core,
-        )
-
-    def test_to_flatten_moras(self):
-        flatten_moras = to_flatten_moras(self.accent_phrases_hello_hiho)
-        self.assertEqual(
-            flatten_moras,
-            self.accent_phrases_hello_hiho[0].moras
-            + [self.accent_phrases_hello_hiho[0].pause_mora]
-            + self.accent_phrases_hello_hiho[1].moras,
-        )
-
-    def test_to_phoneme_data_list(self):
-        phoneme_data_list = to_phoneme_data_list(self.str_list_hello_hiho)
-        self.assertEqual(phoneme_data_list, self.phoneme_data_list_hello_hiho)
-
-    def test_split_mora(self):
-        consonant_phoneme_list, vowel_phoneme_list, vowel_indexes = split_mora(
-            self.phoneme_data_list_hello_hiho
-        )
-
-        self.assertEqual(vowel_indexes, [0, 2, 3, 5, 7, 9, 10, 12, 14, 16, 18, 19])
-        self.assertEqual(
-            vowel_phoneme_list,
-            [
-                OjtPhoneme(phoneme="pau", start=0, end=1),
-                OjtPhoneme(phoneme="o", start=2, end=3),
-                OjtPhoneme(phoneme="N", start=3, end=4),
-                OjtPhoneme(phoneme="i", start=5, end=6),
-                OjtPhoneme(phoneme="i", start=7, end=8),
-                OjtPhoneme(phoneme="a", start=9, end=10),
-                OjtPhoneme(phoneme="pau", start=10, end=11),
-                OjtPhoneme(phoneme="i", start=12, end=13),
-                OjtPhoneme(phoneme="o", start=14, end=15),
-                OjtPhoneme(phoneme="e", start=16, end=17),
-                OjtPhoneme(phoneme="U", start=18, end=19),
-                OjtPhoneme(phoneme="pau", start=19, end=20),
-            ],
-        )
-        self.assertEqual(
-            consonant_phoneme_list,
-            [
-                None,
-                OjtPhoneme(phoneme="k", start=1, end=2),
-                None,
-                OjtPhoneme(phoneme="n", start=4, end=5),
-                OjtPhoneme(phoneme="ch", start=6, end=7),
-                OjtPhoneme(phoneme="w", start=8, end=9),
-                None,
-                OjtPhoneme(phoneme="h", start=11, end=12),
-                OjtPhoneme(phoneme="h", start=13, end=14),
-                OjtPhoneme(phoneme="d", start=15, end=16),
-                OjtPhoneme(phoneme="s", start=17, end=18),
-                None,
-            ],
-        )
-
-    def test_pre_process(self):
-        flatten_moras, phoneme_data_list = pre_process(
-            deepcopy(self.accent_phrases_hello_hiho)
-        )
-
-        mora_index = 0
-        phoneme_index = 1
-
-        self.assertEqual(phoneme_data_list[0], OjtPhoneme("pau", 0, 1))
-        for accent_phrase in self.accent_phrases_hello_hiho:
-            moras = accent_phrase.moras
-            for mora in moras:
-                self.assertEqual(flatten_moras[mora_index], mora)
-                mora_index += 1
-                if mora.consonant is not None:
-                    self.assertEqual(
-                        phoneme_data_list[phoneme_index],
-                        OjtPhoneme(mora.consonant, phoneme_index, phoneme_index + 1),
-                    )
-                    phoneme_index += 1
-                self.assertEqual(
-                    phoneme_data_list[phoneme_index],
-                    OjtPhoneme(mora.vowel, phoneme_index, phoneme_index + 1),
-                )
-                phoneme_index += 1
-            if accent_phrase.pause_mora:
-                self.assertEqual(flatten_moras[mora_index], accent_phrase.pause_mora)
-                mora_index += 1
-                self.assertEqual(
-                    phoneme_data_list[phoneme_index],
-                    OjtPhoneme("pau", phoneme_index, phoneme_index + 1),
-                )
-                phoneme_index += 1
-        self.assertEqual(
-            phoneme_data_list[phoneme_index],
-            OjtPhoneme("pau", phoneme_index, phoneme_index + 1),
-        )
-
-    def test_replace_phoneme_length(self):
-        result = self.synthesis_engine.replace_phoneme_length(
-            accent_phrases=deepcopy(self.accent_phrases_hello_hiho), speaker_id=1
-        )
-
-        # yukarin_sに渡される値の検証
-        yukarin_s_args = self.yukarin_s_mock.call_args[1]
-        list_length = yukarin_s_args["length"]
-        phoneme_list = yukarin_s_args["phoneme_list"]
-        self.assertEqual(list_length, 20)
-        self.assertEqual(list_length, len(phoneme_list))
-        numpy.testing.assert_array_equal(
-            phoneme_list,
-            numpy.array(
-                [
-                    0,
-                    23,
-                    30,
-                    4,
-                    28,
-                    21,
-                    10,
-                    21,
-                    42,
-                    7,
-                    0,
-                    19,
-                    21,
-                    19,
-                    30,
-                    12,
-                    14,
-                    35,
-                    6,
-                    0,
-                ],
-                dtype=numpy.int64,
-            ),
-        )
-        self.assertEqual(yukarin_s_args["speaker_id"], 1)
-
-        # flatten_morasを使わずに愚直にaccent_phrasesにデータを反映させてみる
-        true_result = deepcopy(self.accent_phrases_hello_hiho)
-        index = 1
-
-        def result_value(i: int):
-            return float(phoneme_list[i] * 0.5 + 1)
-
-        for accent_phrase in true_result:
-            moras = accent_phrase.moras
-            for mora in moras:
-                if mora.consonant is not None:
-                    mora.consonant_length = result_value(index)
-                    index += 1
-                mora.vowel_length = result_value(index)
-                index += 1
-            if accent_phrase.pause_mora is not None:
-                accent_phrase.pause_mora.vowel_length = result_value(index)
-                index += 1
-
-        self.assertEqual(result, true_result)
-
-    def test_replace_mora_pitch(self):
-        # 空のリストでエラーを吐かないか
-        empty_accent_phrases = []
-        self.assertEqual(
-            self.synthesis_engine.replace_mora_pitch(
-                accent_phrases=empty_accent_phrases, speaker_id=1
-            ),
-            [],
-        )
-
-        result = self.synthesis_engine.replace_mora_pitch(
-            accent_phrases=deepcopy(self.accent_phrases_hello_hiho), speaker_id=1
-        )
-
-        # yukarin_saに渡される値の検証
-        yukarin_sa_args = self.yukarin_sa_mock.call_args[1]
-        list_length = yukarin_sa_args["length"]
-        vowel_phoneme_list = yukarin_sa_args["vowel_phoneme_list"][0]
-        consonant_phoneme_list = yukarin_sa_args["consonant_phoneme_list"][0]
-        start_accent_list = yukarin_sa_args["start_accent_list"][0]
-        end_accent_list = yukarin_sa_args["end_accent_list"][0]
-        start_accent_phrase_list = yukarin_sa_args["start_accent_phrase_list"][0]
-        end_accent_phrase_list = yukarin_sa_args["end_accent_phrase_list"][0]
-        self.assertEqual(list_length, 12)
-        self.assertEqual(list_length, len(vowel_phoneme_list))
-        self.assertEqual(list_length, len(consonant_phoneme_list))
-        self.assertEqual(list_length, len(start_accent_list))
-        self.assertEqual(list_length, len(end_accent_list))
-        self.assertEqual(list_length, len(start_accent_phrase_list))
-        self.assertEqual(list_length, len(end_accent_phrase_list))
-        self.assertEqual(yukarin_sa_args["speaker_id"], 1)
-
-        numpy.testing.assert_array_equal(
-            vowel_phoneme_list,
-            numpy.array(
-                [
-                    0,
-                    30,
-                    4,
-                    21,
-                    21,
-                    7,
-                    0,
-                    21,
-                    30,
-                    14,
-                    6,
-                    0,
-                ]
-            ),
-        )
-        numpy.testing.assert_array_equal(
-            consonant_phoneme_list,
-            numpy.array(
-                [
-                    -1,
-                    23,
-                    -1,
-                    28,
-                    10,
-                    42,
-                    -1,
-                    19,
-                    19,
-                    12,
-                    35,
-                    -1,
-                ]
-            ),
-        )
-        numpy.testing.assert_array_equal(
-            start_accent_list, numpy.array([0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0])
-        )
-        numpy.testing.assert_array_equal(
-            end_accent_list, numpy.array([0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0])
-        )
-        numpy.testing.assert_array_equal(
-            start_accent_phrase_list, numpy.array([0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0])
-        )
-        numpy.testing.assert_array_equal(
-            end_accent_phrase_list, numpy.array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0])
-        )
-
-        # flatten_morasを使わずに愚直にaccent_phrasesにデータを反映させてみる
-        true_result = deepcopy(self.accent_phrases_hello_hiho)
-        index = 1
-
-        def result_value(i: int):
-            # unvoiced_mora_phoneme_listのPhoneme ID版
-            unvoiced_mora_phoneme_id_list = [
-                OjtPhoneme(p, 0, 0).phoneme_id for p in unvoiced_mora_phoneme_list
-            ]
-            if vowel_phoneme_list[i] in unvoiced_mora_phoneme_id_list:
-                return 0
-            return (
-                vowel_phoneme_list[i]
-                + consonant_phoneme_list[i]
-                + start_accent_list[i]
-                + end_accent_list[i]
-                + start_accent_phrase_list[i]
-                + end_accent_phrase_list[i]
-            ) * 0.5 + 1
-
-        for accent_phrase in true_result:
-            moras = accent_phrase.moras
-            for mora in moras:
-                mora.pitch = result_value(index)
-                index += 1
-            if accent_phrase.pause_mora is not None:
-                accent_phrase.pause_mora.pitch = result_value(index)
-                index += 1
-
-        self.assertEqual(result, true_result)
-
-    def synthesis_test_base(self, audio_query: AudioQuery):
-        accent_phrases = audio_query.accent_phrases
-
-        # decode forwardのために適当にpitchとlengthを設定し、リストで持っておく
-        phoneme_length_list = [0.0]
-        phoneme_id_list = [0]
-        f0_list = [0.0]
-        for accent_phrase in accent_phrases:
-            moras = accent_phrase.moras
-            for mora in moras:
-                if mora.consonant is not None:
-                    mora.consonant_length = 0.1
-                    phoneme_length_list.append(0.1)
-                    phoneme_id_list.append(OjtPhoneme(mora.consonant, 0, 0).phoneme_id)
-                mora.vowel_length = 0.2
-                phoneme_length_list.append(0.2)
-                phoneme_id_list.append(OjtPhoneme(mora.vowel, 0, 0).phoneme_id)
-                if mora.vowel not in unvoiced_mora_phoneme_list:
-                    mora.pitch = 5.0 + random()
-                f0_list.append(mora.pitch)
-            if accent_phrase.pause_mora is not None:
-                accent_phrase.pause_mora.vowel_length = 0.2
-                phoneme_length_list.append(0.2)
-                phoneme_id_list.append(OjtPhoneme("pau", 0, 0).phoneme_id)
-                f0_list.append(0.0)
-        phoneme_length_list.append(0.0)
-        phoneme_id_list.append(0)
-        f0_list.append(0.0)
-
-        phoneme_length_list[0] = audio_query.prePhonemeLength
-        phoneme_length_list[-1] = audio_query.postPhonemeLength
-
-        for i in range(len(phoneme_length_list)):
-            phoneme_length_list[i] /= audio_query.speedScale
-
-        result = self.synthesis_engine.synthesis(query=audio_query, speaker_id=1)
-
-        # decodeに渡される値の検証
-        decode_args = self.decode_mock.call_args[1]
-        list_length = decode_args["length"]
-        self.assertEqual(
-            list_length,
-            int(sum([round(p * 24000 / 256) for p in phoneme_length_list])),
-        )
-
-        num_phoneme = OjtPhoneme.num_phoneme
-        # mora_phoneme_listのPhoneme ID版
-        mora_phoneme_id_list = [
-            OjtPhoneme(p, 0, 0).phoneme_id for p in mora_phoneme_list
-        ]
-
-        # numpy.repeatをfor文でやる
-        f0 = []
-        phoneme = []
-        f0_index = 0
-        mean_f0 = []
-        for i, phoneme_length in enumerate(phoneme_length_list):
-            f0_single = numpy.array(f0_list[f0_index], dtype=numpy.float32) * (
-                2**audio_query.pitchScale
-            )
-            for _ in range(int(round(phoneme_length * (24000 / 256)))):
-                f0.append([f0_single])
-                phoneme_s = []
-                for _ in range(num_phoneme):
-                    phoneme_s.append(0)
-                # one hot
-                phoneme_s[phoneme_id_list[i]] = 1
-                phoneme.append(phoneme_s)
-            # consonantとvowelを判別し、vowelであればf0_indexを一つ進める
-            if phoneme_id_list[i] in mora_phoneme_id_list:
-                if f0_single > 0:
-                    mean_f0.append(f0_single)
-                f0_index += 1
-
-        mean_f0 = numpy.array(mean_f0, dtype=numpy.float32).mean()
-        f0 = numpy.array(f0, dtype=numpy.float32)
-        for i in range(len(f0)):
-            if f0[i][0] != 0.0:
-                f0[i][0] = (f0[i][0] - mean_f0) * audio_query.intonationScale + mean_f0
-
-        phoneme = numpy.array(phoneme, dtype=numpy.float32)
-
-        # 乱数の影響で数値の位置がずれが生じるので、大半(4/5)があっていればよしとする
-        # また、上の部分のint(round(phoneme_length * (24000 / 256)))の影響で
-        # 本来のf0/phonemeとテスト生成したf0/phonemeの長さが変わることがあり、
-        # テスト生成したものが若干長くなることがあるので、本来のものの長さを基準にassertする
-        assert_f0_count = 0
-        decode_f0 = decode_args["f0"]
-        for i in range(len(decode_f0)):
-            # 乱数の影響等で数値にずれが生じるので、10の-5乗までの近似値であれば許容する
-            assert_f0_count += math.isclose(f0[i][0], decode_f0[i][0], rel_tol=10e-5)
-        self.assertTrue(assert_f0_count >= int(len(decode_f0) / 5) * 4)
-        assert_phoneme_count = 0
-        decode_phoneme = decode_args["phoneme"]
-        for i in range(len(decode_phoneme)):
-            assert_true_count = 0
-            for j in range(len(decode_phoneme[i])):
-                assert_true_count += bool(phoneme[i][j] == decode_phoneme[i][j])
-            assert_phoneme_count += assert_true_count == num_phoneme
-        self.assertTrue(assert_phoneme_count >= int(len(decode_phoneme) / 5) * 4)
-        self.assertEqual(decode_args["speaker_id"], 1)
-
-        # decode forwarderのmockを使う
-        true_result = decode_mock(list_length, num_phoneme, f0, phoneme, 1)
-
-        true_result *= audio_query.volumeScale
-
-        # TODO: resampyの部分は値の検証しようがないので、パスする
-        if audio_query.outputSamplingRate != 24000:
-            return
-
-        assert_result_count = 0
-        for i in range(len(true_result)):
-            if audio_query.outputStereo:
-                assert_result_count += math.isclose(
-                    true_result[i], result[i][0], rel_tol=10e-5
-                ) and math.isclose(true_result[i], result[i][1], rel_tol=10e-5)
-            else:
-                assert_result_count += math.isclose(
-                    true_result[i], result[i], rel_tol=10e-5
-                )
-        self.assertTrue(assert_result_count >= int(len(true_result) / 5) * 4)
-
-    def test_synthesis(self):
-        audio_query = AudioQuery(
-            accent_phrases=deepcopy(self.accent_phrases_hello_hiho),
-            speedScale=1.0,
-            pitchScale=1.0,
-            intonationScale=1.0,
-            volumeScale=1.0,
-            prePhonemeLength=0.1,
-            postPhonemeLength=0.1,
-            outputSamplingRate=24000,
-            outputStereo=False,
-            # このテスト内では使わないので生成不要
-            kana="",
-        )
-
-        self.synthesis_test_base(audio_query)
-
-        # speed scaleのテスト
-        audio_query.speedScale = 1.2
-        self.synthesis_test_base(audio_query)
-
-        # pitch scaleのテスト
-        audio_query.pitchScale = 1.5
-        audio_query.speedScale = 1.0
-        self.synthesis_test_base(audio_query)
-
-        # intonation scaleのテスト
-        audio_query.pitchScale = 1.0
-        audio_query.intonationScale = 1.4
-        self.synthesis_test_base(audio_query)
-
-        # volume scaleのテスト
-        audio_query.intonationScale = 1.0
-        audio_query.volumeScale = 2.0
-        self.synthesis_test_base(audio_query)
-
-        # pre/post phoneme lengthのテスト
-        audio_query.volumeScale = 1.0
-        audio_query.prePhonemeLength = 0.5
-        audio_query.postPhonemeLength = 0.5
-        self.synthesis_test_base(audio_query)
-
-        # output sampling rateのテスト
-        audio_query.prePhonemeLength = 0.1
-        audio_query.postPhonemeLength = 0.1
-        audio_query.outputSamplingRate = 48000
-        self.synthesis_test_base(audio_query)
-
-        # output stereoのテスト
-        audio_query.outputSamplingRate = 24000
-        audio_query.outputStereo = True
-        self.synthesis_test_base(audio_query)

spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/utils/utils_callbacks.py

@@ -1,117 +0,0 @@
-import logging
-import os
-import time
-from typing import List
-
-import torch
-
-from eval import verification
-from utils.utils_logging import AverageMeter
-
-
-class CallBackVerification(object):
-    def __init__(self, frequent, rank, val_targets, rec_prefix, image_size=(112, 112)):
-        self.frequent: int = frequent
-        self.rank: int = rank
-        self.highest_acc: float = 0.0
-        self.highest_acc_list: List[float] = [0.0] * len(val_targets)
-        self.ver_list: List[object] = []
-        self.ver_name_list: List[str] = []
-        if self.rank is 0:
-            self.init_dataset(val_targets=val_targets, data_dir=rec_prefix, image_size=image_size)
-
-    def ver_test(self, backbone: torch.nn.Module, global_step: int):
-        results = []
-        for i in range(len(self.ver_list)):
-            acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test(
-                self.ver_list[i], backbone, 10, 10)
-            logging.info('[%s][%d]XNorm: %f' % (self.ver_name_list[i], global_step, xnorm))
-            logging.info('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (self.ver_name_list[i], global_step, acc2, std2))
-            if acc2 > self.highest_acc_list[i]:
-                self.highest_acc_list[i] = acc2
-            logging.info(
-                '[%s][%d]Accuracy-Highest: %1.5f' % (self.ver_name_list[i], global_step, self.highest_acc_list[i]))
-            results.append(acc2)
-
-    def init_dataset(self, val_targets, data_dir, image_size):
-        for name in val_targets:
-            path = os.path.join(data_dir, name + ".bin")
-            if os.path.exists(path):
-                data_set = verification.load_bin(path, image_size)
-                self.ver_list.append(data_set)
-                self.ver_name_list.append(name)
-
-    def __call__(self, num_update, backbone: torch.nn.Module):
-        if self.rank is 0 and num_update > 0 and num_update % self.frequent == 0:
-            backbone.eval()
-            self.ver_test(backbone, num_update)
-            backbone.train()
-
-
-class CallBackLogging(object):
-    def __init__(self, frequent, rank, total_step, batch_size, world_size, writer=None):
-        self.frequent: int = frequent
-        self.rank: int = rank
-        self.time_start = time.time()
-        self.total_step: int = total_step
-        self.batch_size: int = batch_size
-        self.world_size: int = world_size
-        self.writer = writer
-
-        self.init = False
-        self.tic = 0
-
-    def __call__(self,
-                 global_step: int,
-                 loss: AverageMeter,
-                 epoch: int,
-                 fp16: bool,
-                 learning_rate: float,
-                 grad_scaler: torch.cuda.amp.GradScaler):
-        if self.rank == 0 and global_step > 0 and global_step % self.frequent == 0:
-            if self.init:
-                try:
-                    speed: float = self.frequent * self.batch_size / (time.time() - self.tic)
-                    speed_total = speed * self.world_size
-                except ZeroDivisionError:
-                    speed_total = float('inf')
-
-                time_now = (time.time() - self.time_start) / 3600
-                time_total = time_now / ((global_step + 1) / self.total_step)
-                time_for_end = time_total - time_now
-                if self.writer is not None:
-                    self.writer.add_scalar('time_for_end', time_for_end, global_step)
-                    self.writer.add_scalar('learning_rate', learning_rate, global_step)
-                    self.writer.add_scalar('loss', loss.avg, global_step)
-                if fp16:
-                    msg = "Speed %.2f samples/sec   Loss %.4f   LearningRate %.4f   Epoch: %d   Global Step: %d   " \
-                          "Fp16 Grad Scale: %2.f   Required: %1.f hours" % (
-                              speed_total, loss.avg, learning_rate, epoch, global_step,
-                              grad_scaler.get_scale(), time_for_end
-                          )
-                else:
-                    msg = "Speed %.2f samples/sec   Loss %.4f   LearningRate %.4f   Epoch: %d   Global Step: %d   " \
-                          "Required: %1.f hours" % (
-                              speed_total, loss.avg, learning_rate, epoch, global_step, time_for_end
-                          )
-                logging.info(msg)
-                loss.reset()
-                self.tic = time.time()
-            else:
-                self.init = True
-                self.tic = time.time()
-
-
-class CallBackModelCheckpoint(object):
-    def __init__(self, rank, output="./"):
-        self.rank: int = rank
-        self.output: str = output
-
-    def __call__(self, global_step, backbone, partial_fc, ):
-        if global_step > 100 and self.rank == 0:
-            path_module = os.path.join(self.output, "backbone.pth")
-            torch.save(backbone.module.state_dict(), path_module)
-            logging.info("Pytorch Model Saved in '{}'".format(path_module))
-
-        if global_step > 100 and partial_fc is not None:
-            partial_fc.save_params()

spaces/7hao/bingo/README.md

@@ -1,195 +0,0 @@
----
-title: bingo
-emoji: 📉
-colorFrom: red
-colorTo: red
-sdk: docker
-pinned: true
-license: mit
-duplicated_from: hf4all/bingo
----
-
-<div align="center">
-
-# Bingo 
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-![Github stars](https://badgen.net/github/stars/weaigc/bingo?icon=github&label=stars)
-![Gthub issues](https://img.shields.io/github/issues/weaigc/bingo)
-[![docker build](https://github.com/weaigc/bingo/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![docker hub](https://badgen.net/docker/size/weaigc/bingo?icon=docker&label=image%20size)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![MIT License](https://img.shields.io/badge/license-MIT-97c50f)](https://github.com/weaigc/bingo/blob/main/license)
-
-</div>
-
-## 演示站点
-
-https://bing.github1s.tk
-
-
-
-[![img](./docs/images/demo.png)](https://bing.github1s.tk)
-
-## 功能和特点
-
-- 完全基于 Next.js 重写，高度还原 New Bing Web 版 UI，使用体验和 Bing AI 基本一致。
-- 支持 Docker 构建，方便快捷地部署和访问。
-- Cookie 可全局配置，全局共享。
-- 支持持续语音对话
-
-## RoadMap
-
- - [x] 支持 wss 转发
- - [x] 支持一键部署
- - [x] 优化移动端展示
- - [x] 支持画图
- - [x] 支持语音输入(支持语音指令，目前仅支持 PC 版 Edge 及 Chrome 浏览器)
- - [x] 支持语音输出(需要手动开启)
- - [x] 支持图片输入
- - [x] 支持自定义域名
- - [ ] 支持历史记录
- - [ ] 适配深色模式
- - [ ] 支持内置提示词
- - [ ] 支持离线访问
- - [ ] 国际化翻译
-
-## 一键部署
-你也可以一键部署自己的 New Bing AI 到 🤗 HuggingFace 。
-
-### 部署到 Huggingface
-1. 点击此图标
-[![Deploy to HuggingFace](https://img.shields.io/badge/%E7%82%B9%E5%87%BB%E9%83%A8%E7%BD%B2-%F0%9F%A4%97-fff)](https://huggingface.co/login?next=%2Fspaces%2Fhf4all%2Fbingo%3Fduplicate%3Dtrue%26visibility%3Dpublic)，配置可以不改。
-
-2. 部署署完成后，点击“设置” 》“站点域名”，点一下，复制一下 HF 域名信息，然后分享给别人即可。
-
-> Huggingface 不支持绑定自己的域名，不过我们可以使用曲线救国的方式来达到这个目的
-> 1. 方式二，借助 Cloudflare Workers [部署Cloudflare Workers](#使用Cloudflare-Workers自定义域名)
-> 2. 方式一，借助 Github Pages 及 iframe [如何绑定域名](https://github.com/weaigc/bingo/issues/4)
-
-### 使用Cloudflare Workers自定义域名
-
-> 核心代码 [worker.js](./cloudflare/worker.js)
-
-- [注册 Cloudflare 账号](https://dash.cloudflare.com/sign-up)
-
-- 添加一个新的网站，需要你有自己的域名并且将域名`Name Server`托管给 Cloudflare 才行（更多信息可自行 Google)
-
-- 通过左侧菜单进入「Workers」，并点击「Create a Worker」。
-
-- 创建 Worker 服务，复制 [worker.js](./cloudflare/worker.js) 全部代码，粘贴至创建的服务中，根据注释进行改动，保存并部署。
-
-- 触发器 中自定义访问域名。
-
-### 部署其它平台
-<details>
-<summary>
-由于其他平台目前遭到 New Bing 封杀，会遇到很多问题，不再做推荐，有需要的可以自行查看
-</summary>
-
-#### 部署到 Netlify
-[![Deploy to Netlify Button](https://www.netlify.com/img/deploy/button.svg)](https://app.netlify.com/start/deploy?repository=https://github.com/weaigc/bingo)
-
-#### 部署到 Vercel
-如果你是 Vercel 付费用户，可以点以下链接一键部署到 Vercel。免费版本有[接口超时限制](https://vercel.com/docs/concepts/limits/overview)，不推荐使用
-
-[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?demo-title=bingo&demo-description=bingo&demo-url=https%3A%2F%2Fbing.github1s.tk%2F&project-name=bingo&repository-name=bingo&repository-url=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo&from=templates&skippable-integrations=1&env=BING_HEADER&envDescription=%E5%A6%82%E6%9E%9C%E4%B8%8D%E7%9F%A5%E9%81%93%E6%80%8E%E4%B9%88%E9%85%8D%E7%BD%AE%E8%AF%B7%E7%82%B9%E5%8F%B3%E4%BE%A7Learn+More&envLink=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo%2Fblob%2Fmain%2F.env.example)
-
-#### 部署到 Render
-
-[![Deploy to Render](https://render.com/images/deploy-to-render-button.svg)](https://render.com/deploy?repo=https://github.com/weaigc/bingo)
-</details>
-
-## 环境和依赖
-
-- Node.js >= 18
-- Bing AI 的[身份信息](#如何获取-BING_HEADER))
-
-## 安装和使用
-
-* 使用 Node 启动
-
-```bash
-git clone https://github.com/weaigc/bingo.git
-npm i # 推荐使用 pnpm i
-npm run build
-npm run start
-```
-
-* 使用 Docker 启动
-```bash
-docker pull weaigc/bingo
-docker run --rm -it -p 7860:7860 weaigc/bingo
-# 或者
-docker run --rm -it -e BING_HEADER=xxxx -p 7860:7860 weaigc/bingo
-```
-
-## 如何获取 BING_HEADER
-> 配置了 BING_HEADER 意味着你将自己的账号共享给所有���用此服务的人，如果不需要免登录画图的功能，不建议设置此变量
-
-打开 https://www.bing.com 并登录，然后访问 https://www.bing.com/turing/captcha/challenge，通过人机校验，然后
-
-![BING HEADER](./docs/images/curl.png)
-
-> 复制出来的内容应该如下所示。确认格式无误后，打开 https://effulgent-bubblegum-e2f5df.netlify.app/#dialog=%22settings%22 ，粘贴进去，点击“转成 BING_HEADER 并复制”，然后从剪切板粘贴即可得到。（你也可以先在网页上进行验证）
-
-以下是格式参考，需要注意的是，网页端保存的格式是以`curl`开头, 而服务端配置的 `BING_HEADER` 是 `base64` 格式，两者不能互通。
-<details>
-<summary>正常格式/网页端保存的格式(格式仅供参考)</summary>
-
-```
-curl 'https://www.bing.com/turing/captcha/challenge' \
-  -H 'authority: www.bing.com' \
-  -H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7' \
-  -H 'accept-language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6' \
-  -H 'cache-control: max-age=0' \
-  -H 'cookie: MicrosoftApplicationsTelemetryDeviceId=3399c004-fd0e-48ec-bb92-d82a27b2bbd4; _EDGE_V=1; SRCHD=AF=NOFORM; SRCHUID=V=2&GUID=29EBDDA4E6674329ACCF1A0A423C3E98&dmnchg=1; _UR=QS=0&TQS=0; _HPVN=CS=eyJQbiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiUCJ9LCJTYyI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiSCJ9LCJReiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiVCJ9LCJBcCI6dHJ1ZSwiTXV0ZSI6dHJ1ZSwiTGFkIjoiMjAyMy0wNy0yNVQwMDowMDowMFoiLCJJb3RkIjowLCJHd2IiOjAsIkRmdCI6bnVsbCwiTXZzIjowLCJGbHQiOjAsIkltcCI6Mn0=; _RwBf=ilt=1&ihpd=1&ispd=0&rc=0&rb=0&gb=0&rg=200&pc=0&mtu=0&rbb=0&g=0&cid=&clo=0&v=1&l=2023-07-25T07:00:00.0000000Z&lft=0001-01-01T00:00:00.0000000&aof=0&o=2&p=&c=&t=0&s=0001-01-01T00:00:00.0000000+00:00&ts=2023-07-25T11:00:31.7111548+00:00&rwred=0&wls=&lka=0&lkt=0&TH=&dci=0; ANON=A=0043C6590EA808ED6E395059FFFFFFFF&E=1c8b&W=1; NAP=V=1.9&E=1c31&C=DnaMSbDN_4efZ_xXqBF3Daorjr53kYqYoaP8YHsupjmiXnysX7a37A&W=1; PPLState=1; KievRPSSecAuth=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; WLS=C=9df3f9d8518fae19&N=wen; WLID=pGY8HgWCu4p5XYCOk2oa0+DBdftkMUfmNIn8XtSjSTKsgv/Il7GUlYs0Jpjf/E12jZMgV7x44Dy3fXOgjjUoJx7Y/ClLrLhsk20THksJJoI=; _EDGE_S=F=1&SID=17CF6EE006426448213C7DB907436588&mkt=zh-CN; MUID=225621093D8A6C27301632413C0E6D08; MUIDB=225621093D8A6C27301632413C0E6D08; SUID=A; SNRHOP=I=&TS=; _U=nGyzKQruEsDwLiu65fZFIG6e12hf2lwTJmroW__k8joUJIKmG3OIjayXKGW9dCVR3sNhF76mEVxyW6yjUGPodOfjtSa3s3J_DxMOrEK1BqXCOBI9bC66spAIASV7prsYFlVAJz73jVNENp_tBubLHJy6EbT0BKRe4AjrYkH-9uMnmCKB8Zmyg; _SS=SID=17CF6EE006426448213C7DB907436588&R=0&RB=0&GB=0&RG=200&RP=0&PC=U531; SRCHS=PC=U531; USRLOC=HS=1&ELOC=LAT=22.501529693603516|LON=113.9263687133789|N=%E5%8D%97%E5%B1%B1%E5%8C%BA%EF%BC%8C%E5%B9%BF%E4%B8%9C%E7%9C%81|ELT=2|&CLOC=LAT=22.50153029046461|LON=113.92637070632928|A=733.4464586120832|TS=230726151034|SRC=W; SRCHUSR=DOB=20230725&T=1690384908000&POEX=W; ipv6=hit=1690388509974&t=6; SRCHHPGUSR=HV=1690384945&SRCHLANG=zh-Hans&PV=15.0.0&BRW=MW&BRH=MT&CW=410&CH=794&SCW=410&SCH=794&DPR=1.5&UTC=480&DM=0&WTS=63825879627&PRVCW=410&PRVCH=794&PR=1.5; cct=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpny6Y_CVyi_MSyM94VyMWnjdYkkccVtm3czoIAtXUGQA; GC=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpR3Y_D9Ytcks4Ht6XhadXk75dvhzP4YOUS0UmoEyqyxw' \
-  -H 'dnt: 1' \
-  -H 'sec-ch-ua: "Chromium";v="116", "Not)A;Brand";v="24", "Microsoft Edge";v="116"' \
-  -H 'sec-ch-ua-arch: "x86"' \
-  -H 'sec-ch-ua-bitness: "64"' \
-  -H 'sec-ch-ua-full-version: "116.0.1938.29"' \
-  -H 'sec-ch-ua-full-version-list: "Chromium";v="116.0.5845.42", "Not)A;Brand";v="24.0.0.0", "Microsoft Edge";v="116.0.1938.29"' \
-  -H 'sec-ch-ua-mobile: ?0' \
-  -H 'sec-ch-ua-model: ""' \
-  -H 'sec-ch-ua-platform: "Windows"' \
-  -H 'sec-ch-ua-platform-version: "15.0.0"' \
-  -H 'sec-fetch-dest: document' \
-  -H 'sec-fetch-mode: navigate' \
-  -H 'sec-fetch-site: none' \
-  -H 'sec-fetch-user: ?1' \
-  -H 'sec-ms-gec: B3F47AD4A283CAB374C0451C46AAFD147C6A4DACAFF6A1C13F34B2C72B024494' \
-  -H 'sec-ms-gec-version: 1-116.0.1938.29' \
-  -H 'upgrade-insecure-requests: 1' \
-  -H 'user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36 Edg/116.0.0.0' \
-  -H 'x-client-data: eyIxIjoiMiIsIjEwIjoiXCJTMGg3R05HOTF2aDQ1TUZSUnZ5NHN2akRmMWdlaVJKenNxNlA3aU1WbnF3PVwiIiwiMiI6IjEiLCIzIjoiMSIsIjQiOiIyMTU4ODQ5NTM4MjY4OTM5NTA3IiwiNSI6IlwiSm9GUWpPTDk3OS9MbkRRZnlCd2N1M2FsOUN3eTZTQmdaMGNYMXBtOWVMZz1cIiIsIjYiOiJiZXRhIiwiNyI6IjE4MDM4ODYyNjQzNSIsIjkiOiJkZXNrdG9wIn0=' \
-  -H 'x-edge-shopping-flag: 1' \
-  --compressed
-```
-</details>
-
-<details>
-<summary>转成base64之后的格式(BING_HEADER只能使用 base64 之后的格式)</summary>
-
-```
-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
-```
-</details>
-
-
-## 鸣谢
- - 感谢 [EdgeGPT](https://github.com/acheong08/EdgeGPT) 提供的代理 API 的方法。
- - 感谢 [Vercel AI](https://github.com/vercel-labs/ai-chatbot) 提供的基础脚手架和 [ChatHub](https://github.com/chathub-dev/chathub) [go-proxy-bingai](https://github.com/adams549659584/go-proxy-bingai) 提供的部分代码。 
-
-
-## 答疑及交流
-
-<image src="./docs/images/wechat.png" width=240 />
-
-## License
-
-MIT © [LICENSE](https://github.com/weaigc/bingo/blob/main/LICENSE).
-
-

spaces/AIConsultant/MusicGen/audiocraft/losses/specloss.py

@@ -1,149 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import typing as tp
-
-import numpy as np
-from torchaudio.transforms import MelSpectrogram
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from ..modules import pad_for_conv1d
-
-
-class MelSpectrogramWrapper(nn.Module):
-    """Wrapper around MelSpectrogram torchaudio transform providing proper padding
-    and additional post-processing including log scaling.
-
-    Args:
-        n_mels (int): Number of mel bins.
-        n_fft (int): Number of fft.
-        hop_length (int): Hop size.
-        win_length (int): Window length.
-        n_mels (int): Number of mel bins.
-        sample_rate (int): Sample rate.
-        f_min (float or None): Minimum frequency.
-        f_max (float or None): Maximum frequency.
-        log (bool): Whether to scale with log.
-        normalized (bool): Whether to normalize the melspectrogram.
-        floor_level (float): Floor level based on human perception (default=1e-5).
-    """
-    def __init__(self, n_fft: int = 1024, hop_length: int = 256, win_length: tp.Optional[int] = None,
-                 n_mels: int = 80, sample_rate: float = 22050, f_min: float = 0.0, f_max: tp.Optional[float] = None,
-                 log: bool = True, normalized: bool = False, floor_level: float = 1e-5):
-        super().__init__()
-        self.n_fft = n_fft
-        hop_length = int(hop_length)
-        self.hop_length = hop_length
-        self.mel_transform = MelSpectrogram(n_mels=n_mels, sample_rate=sample_rate, n_fft=n_fft, hop_length=hop_length,
-                                            win_length=win_length, f_min=f_min, f_max=f_max, normalized=normalized,
-                                            window_fn=torch.hann_window, center=False)
-        self.floor_level = floor_level
-        self.log = log
-
-    def forward(self, x):
-        p = int((self.n_fft - self.hop_length) // 2)
-        if len(x.shape) == 2:
-            x = x.unsqueeze(1)
-        x = F.pad(x, (p, p), "reflect")
-        # Make sure that all the frames are full.
-        # The combination of `pad_for_conv1d` and the above padding
-        # will make the output of size ceil(T / hop).
-        x = pad_for_conv1d(x, self.n_fft, self.hop_length)
-        self.mel_transform.to(x.device)
-        mel_spec = self.mel_transform(x)
-        B, C, freqs, frame = mel_spec.shape
-        if self.log:
-            mel_spec = torch.log10(self.floor_level + mel_spec)
-        return mel_spec.reshape(B, C * freqs, frame)
-
-
-class MelSpectrogramL1Loss(torch.nn.Module):
-    """L1 Loss on MelSpectrogram.
-
-    Args:
-        sample_rate (int): Sample rate.
-        n_fft (int): Number of fft.
-        hop_length (int): Hop size.
-        win_length (int): Window length.
-        n_mels (int): Number of mel bins.
-        f_min (float or None): Minimum frequency.
-        f_max (float or None): Maximum frequency.
-        log (bool): Whether to scale with log.
-        normalized (bool): Whether to normalize the melspectrogram.
-        floor_level (float): Floor level value based on human perception (default=1e-5).
-    """
-    def __init__(self, sample_rate: int, n_fft: int = 1024, hop_length: int = 256, win_length: int = 1024,
-                 n_mels: int = 80, f_min: float = 0.0, f_max: tp.Optional[float] = None,
-                 log: bool = True, normalized: bool = False, floor_level: float = 1e-5):
-        super().__init__()
-        self.l1 = torch.nn.L1Loss()
-        self.melspec = MelSpectrogramWrapper(n_fft=n_fft, hop_length=hop_length, win_length=win_length,
-                                             n_mels=n_mels, sample_rate=sample_rate, f_min=f_min, f_max=f_max,
-                                             log=log, normalized=normalized, floor_level=floor_level)
-
-    def forward(self, x, y):
-        self.melspec.to(x.device)
-        s_x = self.melspec(x)
-        s_y = self.melspec(y)
-        return self.l1(s_x, s_y)
-
-
-class MultiScaleMelSpectrogramLoss(nn.Module):
-    """Multi-Scale spectrogram loss (msspec).
-
-    Args:
-        sample_rate (int): Sample rate.
-        range_start (int): Power of 2 to use for the first scale.
-        range_stop (int): Power of 2 to use for the last scale.
-        n_mels (int): Number of mel bins.
-        f_min (float): Minimum frequency.
-        f_max (float or None): Maximum frequency.
-        normalized (bool): Whether to normalize the melspectrogram.
-        alphas (bool): Whether to use alphas as coefficients or not.
-        floor_level (float): Floor level value based on human perception (default=1e-5).
-    """
-    def __init__(self, sample_rate: int, range_start: int = 6, range_end: int = 11,
-                 n_mels: int = 64, f_min: float = 0.0, f_max: tp.Optional[float] = None,
-                 normalized: bool = False, alphas: bool = True, floor_level: float = 1e-5):
-        super().__init__()
-        l1s = list()
-        l2s = list()
-        self.alphas = list()
-        self.total = 0
-        self.normalized = normalized
-        for i in range(range_start, range_end):
-            l1s.append(
-                MelSpectrogramWrapper(n_fft=2 ** i, hop_length=(2 ** i) / 4, win_length=2 ** i,
-                                      n_mels=n_mels, sample_rate=sample_rate, f_min=f_min, f_max=f_max,
-                                      log=False, normalized=normalized, floor_level=floor_level))
-            l2s.append(
-                MelSpectrogramWrapper(n_fft=2 ** i, hop_length=(2 ** i) / 4, win_length=2 ** i,
-                                      n_mels=n_mels, sample_rate=sample_rate, f_min=f_min, f_max=f_max,
-                                      log=True, normalized=normalized, floor_level=floor_level))
-            if alphas:
-                self.alphas.append(np.sqrt(2 ** i - 1))
-            else:
-                self.alphas.append(1)
-            self.total += self.alphas[-1] + 1
-
-        self.l1s = nn.ModuleList(l1s)
-        self.l2s = nn.ModuleList(l2s)
-
-    def forward(self, x, y):
-        loss = 0.0
-        self.l1s.to(x.device)
-        self.l2s.to(x.device)
-        for i in range(len(self.alphas)):
-            s_x_1 = self.l1s[i](x)
-            s_y_1 = self.l1s[i](y)
-            s_x_2 = self.l2s[i](x)
-            s_y_2 = self.l2s[i](y)
-            loss += F.l1_loss(s_x_1, s_y_1) + self.alphas[i] * F.mse_loss(s_x_2, s_y_2)
-        if self.normalized:
-            loss = loss / self.total
-        return loss

spaces/AIGC-Audio/Make_An_Audio/vocoder/bigvgan/alias_free_torch/resample.py

@@ -1,49 +0,0 @@
-# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
-#   LICENSE is in incl_licenses directory.
-
-import torch.nn as nn
-from torch.nn import functional as F
-from .filter import LowPassFilter1d
-from .filter import kaiser_sinc_filter1d
-
-
-class UpSample1d(nn.Module):
-    def __init__(self, ratio=2, kernel_size=None):
-        super().__init__()
-        self.ratio = ratio
-        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
-        self.stride = ratio
-        self.pad = self.kernel_size // ratio - 1
-        self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
-        self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
-        filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio,
-                                      half_width=0.6 / ratio,
-                                      kernel_size=self.kernel_size)
-        self.register_buffer("filter", filter)
-
-    # x: [B, C, T]
-    def forward(self, x):
-        _, C, _ = x.shape
-
-        x = F.pad(x, (self.pad, self.pad), mode='replicate')
-        x = self.ratio * F.conv_transpose1d(
-            x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
-        x = x[..., self.pad_left:-self.pad_right]
-
-        return x
-
-
-class DownSample1d(nn.Module):
-    def __init__(self, ratio=2, kernel_size=None):
-        super().__init__()
-        self.ratio = ratio
-        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
-        self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio,
-                                       half_width=0.6 / ratio,
-                                       stride=ratio,
-                                       kernel_size=self.kernel_size)
-
-    def forward(self, x):
-        xx = self.lowpass(x)
-
-        return xx

spaces/AIGText/GlyphControl/ldm/modules/midas/midas/dpt_depth.py

@@ -1,109 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .base_model import BaseModel
-from .blocks import (
-    FeatureFusionBlock,
-    FeatureFusionBlock_custom,
-    Interpolate,
-    _make_encoder,
-    forward_vit,
-)
-
-
-def _make_fusion_block(features, use_bn):
-    return FeatureFusionBlock_custom(
-        features,
-        nn.ReLU(False),
-        deconv=False,
-        bn=use_bn,
-        expand=False,
-        align_corners=True,
-    )
-
-
-class DPT(BaseModel):
-    def __init__(
-        self,
-        head,
-        features=256,
-        backbone="vitb_rn50_384",
-        readout="project",
-        channels_last=False,
-        use_bn=False,
-    ):
-
-        super(DPT, self).__init__()
-
-        self.channels_last = channels_last
-
-        hooks = {
-            "vitb_rn50_384": [0, 1, 8, 11],
-            "vitb16_384": [2, 5, 8, 11],
-            "vitl16_384": [5, 11, 17, 23],
-        }
-
-        # Instantiate backbone and reassemble blocks
-        self.pretrained, self.scratch = _make_encoder(
-            backbone,
-            features,
-            False, # Set to true of you want to train from scratch, uses ImageNet weights
-            groups=1,
-            expand=False,
-            exportable=False,
-            hooks=hooks[backbone],
-            use_readout=readout,
-        )
-
-        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
-
-        self.scratch.output_conv = head
-
-
-    def forward(self, x):
-        if self.channels_last == True:
-            x.contiguous(memory_format=torch.channels_last)
-
-        layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
-
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
-        out = self.scratch.output_conv(path_1)
-
-        return out
-
-
-class DPTDepthModel(DPT):
-    def __init__(self, path=None, non_negative=True, **kwargs):
-        features = kwargs["features"] if "features" in kwargs else 256
-
-        head = nn.Sequential(
-            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
-            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
-            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
-            nn.ReLU(True),
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-            nn.Identity(),
-        )
-
-        super().__init__(head, **kwargs)
-
-        if path is not None:
-           self.load(path)
-
-    def forward(self, x):
-        return super().forward(x).squeeze(dim=1)
-

spaces/AIWaves/Debate/src/agents/Component/ExtraComponent.py

@@ -1,128 +0,0 @@
-from .ToolComponent import ToolComponent
-import json
-from utils import flatten_dict,get_embedding,matching_category,search_with_api,limit_keys,limit_values
-import os
-
-
-class CategoryRequirementsComponent(ToolComponent):
-    def __init__(self, information_path):
-        super().__init__()
-        self.information_dataset = []
-        self.leaf_name = []
-        for toy_path in information_path:
-            with open(toy_path, encoding="utf-8") as json_file:
-                data = json.load(json_file)
-            for d in data:
-                if "/" in d["cat_leaf_name"]:
-                    leaf_names = d["cat_leaf_name"].split("/") + [d["cat_leaf_name"]]
-                else:
-                    leaf_names = [d["cat_leaf_name"]]
-                for name in leaf_names:
-                    self.leaf_name.append(name)
-                    new_d = d.copy()
-                    new_d["cat_leaf_name"] = name
-                    new_d["information"] = flatten_dict(new_d["information"])
-                    self.information_dataset.append(new_d)
-
-        self.target_embbeding = get_embedding(
-            self.leaf_name
-        )
-
-    def search_information(self, category, information_dataset):
-        knowledge = {}
-        for d in information_dataset:
-            if category == d["cat_leaf_name"]:
-                knowledge = d["information"]
-                knowledge = {
-                    key: value
-                    for key, value in knowledge.items()
-                    if (value and key != "相关分类")
-                }
-                break
-        return knowledge
-
-    def func(self, agent):
-        prompt = ""
-        messages = agent.long_term_memory
-        outputdict = {}
-        functions = [
-            {
-                "name": "search_information",
-                "description": "根据用户所需要购买商品的种类跟用户的需求去寻找用户所需要的商品",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "category": {
-                            "type": "string",
-                            "description": "用户现在所需要的商品类别，比如纸尿布，笔记本电脑等，注意，只能有一个",
-                        },
-                        "requirements": {
-                            "type": "string",
-                            "description": "用户现在的需求，比如说便宜，安踏品牌等等，可以有多个需求，中间以“ ”分隔",
-                        },
-                    },
-                    "required": ["category", "requirements"],
-                },
-            }
-        ]
-        
-        response = agent.LLM.get_response(
-            messages,
-            None,
-            None,
-            functions=functions,
-            stream=False,
-            function_call={"name": "search_information"},
-        )
-        response_message = json.loads(response["function_call"]["arguments"])
-        category = (
-            response_message["category"] if response_message["category"] else None
-        )
-        requirements = (
-            response_message["requirements"]
-            if response_message["requirements"]
-            else category
-        )
-        if not (category or requirements):
-            return {}
-
-        topk_result = matching_category(
-            category, self.leaf_name, None, self.target_embbeding, top_k=3
-        )
-
-        top1_score = topk_result[1][0]
-        request_items, top_category = search_with_api(requirements, category)
-        
-
-        MIN_CATEGORY_SIM = eval(os.environ["MIN_CATEGORY_SIM"]
-                                ) if "MIN_CATEGORY_SIM" in os.environ else 0.7
-
-        if top1_score > MIN_CATEGORY_SIM:
-            agent.environment.shared_memory["category"] = topk_result[0][0]
-            category = topk_result[0][0]
-            information = self.search_information(
-                topk_result[0][0], self.information_dataset
-            )
-            information = limit_keys(information, 3)
-            information = limit_values(information, 2)
-            prompt += f"""你需要知道的是：用户目前选择的商品是{category}，该商品信息为{information}。你需要根据这些商品信息来详细介绍商品，比如详细介绍商品有哪些品牌，有哪些分类等等，并且询问用户是否有更多的需求。"""
-            if category in top_category:
-                top_category.remove(category)
-
-            recommend = "\n经过搜索后，推荐商品如下：\n"
-            prompt += "筛选出的商品如下：\n"
-            
-            for i, request_item in enumerate(request_items):
-                
-                itemTitle = request_item["itemTitle"]
-                itemPrice = request_item["itemPrice"]
-                itemPicUrl = request_item["itemPicUrl"]
-                recommend += f"[{i}.商品名称：{itemTitle},商品价格:{float(itemPrice)/100}]({itemPicUrl})\n"
-                prompt += f"[{i}.商品名称：{itemTitle},商品价格:{float(itemPrice)/100}]\n"
-            outputdict["recommend"] = recommend
-            print(recommend)
-        else:
-            prompt += f"""你需要知道的是：用户目前选择的商品是{category}，而我们店里没有这类商品，但是我们店里有一些近似商品，如{top_category},{topk_result[0][0]}，你需要对这些近似商品进行介绍，并引导用户购买"""
-        outputdict["prompt"] = prompt
-        return outputdict
-

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/custom_cfg.py

@@ -1,10 +0,0 @@
-_base_ = [                                    # 此配置文件将继承所有 `_base_` 中的配置
-    '../configs/_base_/models/custom_model.py',           # 模型配置
-    '../configs/_base_/datasets/custom_ds.py',    # 数据配置
-    '../configs/_base_/schedules/custom_schedule.py',  # 训练策略配置
-    '../configs/_base_/default_runtime.py'            # 默认运行设置
-]
-
-visualizer = dict(
-    vis_backends=[dict(type='LocalVisBackend'),
-                  dict(type='WandbVisBackend')])

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Bard.py

@@ -1,74 +0,0 @@
-import os, requests, json, browser_cookie3, re, random
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://bard.google.com'
-model = ['Palm2']
-supports_stream = False
-needs_auth = True
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    psid = {cookie.name: cookie.value for cookie in browser_cookie3.chrome(
-        domain_name='.google.com')}['__Secure-1PSID']
-    
-    formatted = '\n'.join([
-        '%s: %s' % (message['role'], message['content']) for message in messages
-    ])
-    prompt = f'{formatted}\nAssistant:'
-
-    proxy = kwargs.get('proxy', False)
-    if proxy == False:
-        print('warning!, you did not give a proxy, a lot of countries are banned from Google Bard, so it may not work')
-    
-    snlm0e = None
-    conversation_id = None
-    response_id = None
-    choice_id = None
-
-    client = requests.Session()
-    client.proxies = {
-        'http': f'http://{proxy}',
-        'https': f'http://{proxy}'} if proxy else None
-
-    client.headers = {
-        'authority': 'bard.google.com',
-        'content-type': 'application/x-www-form-urlencoded;charset=UTF-8',
-        'origin': 'https://bard.google.com',
-        'referer': 'https://bard.google.com/',
-        'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
-        'x-same-domain': '1',
-        'cookie': f'__Secure-1PSID={psid}'
-    }
-
-    snlm0e = re.search(r'SNlM0e\":\"(.*?)\"',
-                    client.get('https://bard.google.com/').text).group(1) if not snlm0e else snlm0e
-
-    params = {
-        'bl': 'boq_assistant-bard-web-server_20230326.21_p0',
-        '_reqid': random.randint(1111, 9999),
-        'rt': 'c'
-    }
-
-    data = {
-        'at': snlm0e,
-        'f.req': json.dumps([None, json.dumps([[prompt], None, [conversation_id, response_id, choice_id]])])}
-
-    intents = '.'.join([
-        'assistant',
-        'lamda',
-        'BardFrontendService'
-    ])
-
-    response = client.post(f'https://bard.google.com/_/BardChatUi/data/{intents}/StreamGenerate',
-                        data=data, params=params)
-
-    chat_data = json.loads(response.content.splitlines()[3])[0][2]
-    if chat_data:
-        json_chat_data = json.loads(chat_data)
-
-        yield json_chat_data[0][0]
-        
-    else:
-        yield 'error'
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/Adapting/YouTube-Downloader/tube/var.py

@@ -1,4 +0,0 @@
-from pathlib import Path
-
-PROJECT_ROOT = Path(__package__).absolute().parent
-OUTPUT_DIR = Path(PROJECT_ROOT).joinpath('downloads')

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/radio/Factory.d.ts

@@ -1,6 +0,0 @@
-import Radio from './Radio';
-import Base from '../base/Base';
-
-export default function Factory(
-    config?: Base.IConfig
-): Radio;

spaces/Amrrs/DragGan-Inversion/PTI/dnnlib/util.py

@@ -1,477 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Miscellaneous utility classes and functions."""
-
-import ctypes
-import fnmatch
-import importlib
-import inspect
-import numpy as np
-import os
-import shutil
-import sys
-import types
-import io
-import pickle
-import re
-import requests
-import html
-import hashlib
-import glob
-import tempfile
-import urllib
-import urllib.request
-import uuid
-
-from distutils.util import strtobool
-from typing import Any, List, Tuple, Union
-
-
-# Util classes
-# ------------------------------------------------------------------------------------------
-
-
-class EasyDict(dict):
-    """Convenience class that behaves like a dict but allows access with the attribute syntax."""
-
-    def __getattr__(self, name: str) -> Any:
-        try:
-            return self[name]
-        except KeyError:
-            raise AttributeError(name)
-
-    def __setattr__(self, name: str, value: Any) -> None:
-        self[name] = value
-
-    def __delattr__(self, name: str) -> None:
-        del self[name]
-
-
-class Logger(object):
-    """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file."""
-
-    def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True):
-        self.file = None
-
-        if file_name is not None:
-            self.file = open(file_name, file_mode)
-
-        self.should_flush = should_flush
-        self.stdout = sys.stdout
-        self.stderr = sys.stderr
-
-        sys.stdout = self
-        sys.stderr = self
-
-    def __enter__(self) -> "Logger":
-        return self
-
-    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
-        self.close()
-
-    def write(self, text: Union[str, bytes]) -> None:
-        """Write text to stdout (and a file) and optionally flush."""
-        if isinstance(text, bytes):
-            text = text.decode()
-        if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
-            return
-
-        if self.file is not None:
-            self.file.write(text)
-
-        self.stdout.write(text)
-
-        if self.should_flush:
-            self.flush()
-
-    def flush(self) -> None:
-        """Flush written text to both stdout and a file, if open."""
-        if self.file is not None:
-            self.file.flush()
-
-        self.stdout.flush()
-
-    def close(self) -> None:
-        """Flush, close possible files, and remove stdout/stderr mirroring."""
-        self.flush()
-
-        # if using multiple loggers, prevent closing in wrong order
-        if sys.stdout is self:
-            sys.stdout = self.stdout
-        if sys.stderr is self:
-            sys.stderr = self.stderr
-
-        if self.file is not None:
-            self.file.close()
-            self.file = None
-
-
-# Cache directories
-# ------------------------------------------------------------------------------------------
-
-_dnnlib_cache_dir = None
-
-def set_cache_dir(path: str) -> None:
-    global _dnnlib_cache_dir
-    _dnnlib_cache_dir = path
-
-def make_cache_dir_path(*paths: str) -> str:
-    if _dnnlib_cache_dir is not None:
-        return os.path.join(_dnnlib_cache_dir, *paths)
-    if 'DNNLIB_CACHE_DIR' in os.environ:
-        return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths)
-    if 'HOME' in os.environ:
-        return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths)
-    if 'USERPROFILE' in os.environ:
-        return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths)
-    return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths)
-
-# Small util functions
-# ------------------------------------------------------------------------------------------
-
-
-def format_time(seconds: Union[int, float]) -> str:
-    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
-    s = int(np.rint(seconds))
-
-    if s < 60:
-        return "{0}s".format(s)
-    elif s < 60 * 60:
-        return "{0}m {1:02}s".format(s // 60, s % 60)
-    elif s < 24 * 60 * 60:
-        return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60)
-    else:
-        return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60)
-
-
-def ask_yes_no(question: str) -> bool:
-    """Ask the user the question until the user inputs a valid answer."""
-    while True:
-        try:
-            print("{0} [y/n]".format(question))
-            return strtobool(input().lower())
-        except ValueError:
-            pass
-
-
-def tuple_product(t: Tuple) -> Any:
-    """Calculate the product of the tuple elements."""
-    result = 1
-
-    for v in t:
-        result *= v
-
-    return result
-
-
-_str_to_ctype = {
-    "uint8": ctypes.c_ubyte,
-    "uint16": ctypes.c_uint16,
-    "uint32": ctypes.c_uint32,
-    "uint64": ctypes.c_uint64,
-    "int8": ctypes.c_byte,
-    "int16": ctypes.c_int16,
-    "int32": ctypes.c_int32,
-    "int64": ctypes.c_int64,
-    "float32": ctypes.c_float,
-    "float64": ctypes.c_double
-}
-
-
-def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
-    """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes."""
-    type_str = None
-
-    if isinstance(type_obj, str):
-        type_str = type_obj
-    elif hasattr(type_obj, "__name__"):
-        type_str = type_obj.__name__
-    elif hasattr(type_obj, "name"):
-        type_str = type_obj.name
-    else:
-        raise RuntimeError("Cannot infer type name from input")
-
-    assert type_str in _str_to_ctype.keys()
-
-    my_dtype = np.dtype(type_str)
-    my_ctype = _str_to_ctype[type_str]
-
-    assert my_dtype.itemsize == ctypes.sizeof(my_ctype)
-
-    return my_dtype, my_ctype
-
-
-def is_pickleable(obj: Any) -> bool:
-    try:
-        with io.BytesIO() as stream:
-            pickle.dump(obj, stream)
-        return True
-    except:
-        return False
-
-
-# Functionality to import modules/objects by name, and call functions by name
-# ------------------------------------------------------------------------------------------
-
-def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]:
-    """Searches for the underlying module behind the name to some python object.
-    Returns the module and the object name (original name with module part removed)."""
-
-    # allow convenience shorthands, substitute them by full names
-    obj_name = re.sub("^np.", "numpy.", obj_name)
-    obj_name = re.sub("^tf.", "tensorflow.", obj_name)
-
-    # list alternatives for (module_name, local_obj_name)
-    parts = obj_name.split(".")
-    name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)]
-
-    # try each alternative in turn
-    for module_name, local_obj_name in name_pairs:
-        try:
-            module = importlib.import_module(module_name) # may raise ImportError
-            get_obj_from_module(module, local_obj_name) # may raise AttributeError
-            return module, local_obj_name
-        except:
-            pass
-
-    # maybe some of the modules themselves contain errors?
-    for module_name, _local_obj_name in name_pairs:
-        try:
-            importlib.import_module(module_name) # may raise ImportError
-        except ImportError:
-            if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"):
-                raise
-
-    # maybe the requested attribute is missing?
-    for module_name, local_obj_name in name_pairs:
-        try:
-            module = importlib.import_module(module_name) # may raise ImportError
-            get_obj_from_module(module, local_obj_name) # may raise AttributeError
-        except ImportError:
-            pass
-
-    # we are out of luck, but we have no idea why
-    raise ImportError(obj_name)
-
-
-def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
-    """Traverses the object name and returns the last (rightmost) python object."""
-    if obj_name == '':
-        return module
-    obj = module
-    for part in obj_name.split("."):
-        obj = getattr(obj, part)
-    return obj
-
-
-def get_obj_by_name(name: str) -> Any:
-    """Finds the python object with the given name."""
-    module, obj_name = get_module_from_obj_name(name)
-    return get_obj_from_module(module, obj_name)
-
-
-def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
-    """Finds the python object with the given name and calls it as a function."""
-    assert func_name is not None
-    func_obj = get_obj_by_name(func_name)
-    assert callable(func_obj)
-    return func_obj(*args, **kwargs)
-
-
-def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any:
-    """Finds the python class with the given name and constructs it with the given arguments."""
-    return call_func_by_name(*args, func_name=class_name, **kwargs)
-
-
-def get_module_dir_by_obj_name(obj_name: str) -> str:
-    """Get the directory path of the module containing the given object name."""
-    module, _ = get_module_from_obj_name(obj_name)
-    return os.path.dirname(inspect.getfile(module))
-
-
-def is_top_level_function(obj: Any) -> bool:
-    """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'."""
-    return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__
-
-
-def get_top_level_function_name(obj: Any) -> str:
-    """Return the fully-qualified name of a top-level function."""
-    assert is_top_level_function(obj)
-    module = obj.__module__
-    if module == '__main__':
-        module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0]
-    return module + "." + obj.__name__
-
-
-# File system helpers
-# ------------------------------------------------------------------------------------------
-
-def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]:
-    """List all files recursively in a given directory while ignoring given file and directory names.
-    Returns list of tuples containing both absolute and relative paths."""
-    assert os.path.isdir(dir_path)
-    base_name = os.path.basename(os.path.normpath(dir_path))
-
-    if ignores is None:
-        ignores = []
-
-    result = []
-
-    for root, dirs, files in os.walk(dir_path, topdown=True):
-        for ignore_ in ignores:
-            dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)]
-
-            # dirs need to be edited in-place
-            for d in dirs_to_remove:
-                dirs.remove(d)
-
-            files = [f for f in files if not fnmatch.fnmatch(f, ignore_)]
-
-        absolute_paths = [os.path.join(root, f) for f in files]
-        relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths]
-
-        if add_base_to_relative:
-            relative_paths = [os.path.join(base_name, p) for p in relative_paths]
-
-        assert len(absolute_paths) == len(relative_paths)
-        result += zip(absolute_paths, relative_paths)
-
-    return result
-
-
-def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
-    """Takes in a list of tuples of (src, dst) paths and copies files.
-    Will create all necessary directories."""
-    for file in files:
-        target_dir_name = os.path.dirname(file[1])
-
-        # will create all intermediate-level directories
-        if not os.path.exists(target_dir_name):
-            os.makedirs(target_dir_name)
-
-        shutil.copyfile(file[0], file[1])
-
-
-# URL helpers
-# ------------------------------------------------------------------------------------------
-
-def is_url(obj: Any, allow_file_urls: bool = False) -> bool:
-    """Determine whether the given object is a valid URL string."""
-    if not isinstance(obj, str) or not "://" in obj:
-        return False
-    if allow_file_urls and obj.startswith('file://'):
-        return True
-    try:
-        res = requests.compat.urlparse(obj)
-        if not res.scheme or not res.netloc or not "." in res.netloc:
-            return False
-        res = requests.compat.urlparse(requests.compat.urljoin(obj, "/"))
-        if not res.scheme or not res.netloc or not "." in res.netloc:
-            return False
-    except:
-        return False
-    return True
-
-
-def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any:
-    """Download the given URL and return a binary-mode file object to access the data."""
-    assert num_attempts >= 1
-    assert not (return_filename and (not cache))
-
-    # Doesn't look like an URL scheme so interpret it as a local filename.
-    if not re.match('^[a-z]+://', url):
-        return url if return_filename else open(url, "rb")
-
-    # Handle file URLs.  This code handles unusual file:// patterns that
-    # arise on Windows:
-    #
-    # file:///c:/foo.txt
-    #
-    # which would translate to a local '/c:/foo.txt' filename that's
-    # invalid.  Drop the forward slash for such pathnames.
-    #
-    # If you touch this code path, you should test it on both Linux and
-    # Windows.
-    #
-    # Some internet resources suggest using urllib.request.url2pathname() but
-    # but that converts forward slashes to backslashes and this causes
-    # its own set of problems.
-    if url.startswith('file://'):
-        filename = urllib.parse.urlparse(url).path
-        if re.match(r'^/[a-zA-Z]:', filename):
-            filename = filename[1:]
-        return filename if return_filename else open(filename, "rb")
-
-    assert is_url(url)
-
-    # Lookup from cache.
-    if cache_dir is None:
-        cache_dir = make_cache_dir_path('downloads')
-
-    url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest()
-    if cache:
-        cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*"))
-        if len(cache_files) == 1:
-            filename = cache_files[0]
-            return filename if return_filename else open(filename, "rb")
-
-    # Download.
-    url_name = None
-    url_data = None
-    with requests.Session() as session:
-        if verbose:
-            print("Downloading %s ..." % url, end="", flush=True)
-        for attempts_left in reversed(range(num_attempts)):
-            try:
-                with session.get(url) as res:
-                    res.raise_for_status()
-                    if len(res.content) == 0:
-                        raise IOError("No data received")
-
-                    if len(res.content) < 8192:
-                        content_str = res.content.decode("utf-8")
-                        if "download_warning" in res.headers.get("Set-Cookie", ""):
-                            links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link]
-                            if len(links) == 1:
-                                url = requests.compat.urljoin(url, links[0])
-                                raise IOError("Google Drive virus checker nag")
-                        if "Google Drive - Quota exceeded" in content_str:
-                            raise IOError("Google Drive download quota exceeded -- please try again later")
-
-                    match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", ""))
-                    url_name = match[1] if match else url
-                    url_data = res.content
-                    if verbose:
-                        print(" done")
-                    break
-            except KeyboardInterrupt:
-                raise
-            except:
-                if not attempts_left:
-                    if verbose:
-                        print(" failed")
-                    raise
-                if verbose:
-                    print(".", end="", flush=True)
-
-    # Save to cache.
-    if cache:
-        safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name)
-        cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name)
-        temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name)
-        os.makedirs(cache_dir, exist_ok=True)
-        with open(temp_file, "wb") as f:
-            f.write(url_data)
-        os.replace(temp_file, cache_file) # atomic
-        if return_filename:
-            return cache_file
-
-    # Return data as file object.
-    assert not return_filename
-    return io.BytesIO(url_data)

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

@@ -1,17 +0,0 @@
-from ...utils import (
-    OptionalDependencyNotAvailable,
-    is_torch_available,
-    is_transformers_available,
-    is_transformers_version,
-)
-
-
-try:
-    if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.27.0")):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from ...utils.dummy_torch_and_transformers_objects import (
-        AudioLDMPipeline,
-    )
-else:
-    from .pipeline_audioldm import AudioLDMPipeline

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py

@@ -1,128 +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.
-
-import inspect
-from typing import List, Optional, Tuple, Union
-
-import torch
-
-from ...models import UNet2DModel, VQModel
-from ...schedulers import DDIMScheduler
-from ...utils import randn_tensor
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-class LDMPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for unconditional image generation using latent diffusion.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Parameters:
-        vqvae ([`VQModel`]):
-            Vector-quantized (VQ) model to encode and decode images to and from latent representations.
-        unet ([`UNet2DModel`]):
-            A `UNet2DModel` to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            [`DDIMScheduler`] is used in combination with `unet` to denoise the encoded image latents.
-    """
-
-    def __init__(self, vqvae: VQModel, unet: UNet2DModel, scheduler: DDIMScheduler):
-        super().__init__()
-        self.register_modules(vqvae=vqvae, unet=unet, scheduler=scheduler)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        batch_size: int = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        eta: float = 0.0,
-        num_inference_steps: int = 50,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        **kwargs,
-    ) -> Union[Tuple, ImagePipelineOutput]:
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            batch_size (`int`, *optional*, defaults to 1):
-                Number of images to generate.
-            generator (`torch.Generator`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-
-        Example:
-
-        ```py
-        >>> from diffusers import LDMPipeline
-
-        >>> # load model and scheduler
-        >>> pipe = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
-
-        >>> # run pipeline in inference (sample random noise and denoise)
-        >>> image = pipe().images[0]
-        ```
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
-                returned where the first element is a list with the generated images
-        """
-
-        latents = randn_tensor(
-            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),
-            generator=generator,
-        )
-        latents = latents.to(self.device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-
-        self.scheduler.set_timesteps(num_inference_steps)
-
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-
-        extra_kwargs = {}
-        if accepts_eta:
-            extra_kwargs["eta"] = eta
-
-        for t in self.progress_bar(self.scheduler.timesteps):
-            latent_model_input = self.scheduler.scale_model_input(latents, t)
-            # predict the noise residual
-            noise_prediction = self.unet(latent_model_input, t).sample
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_prediction, t, latents, **extra_kwargs).prev_sample
-
-        # decode the image latents with the VAE
-        image = self.vqvae.decode(latents).sample
-
-        image = (image / 2 + 0.5).clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).numpy()
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)

spaces/Andy1621/uniformer_image_detection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py

@@ -1,13 +0,0 @@
-_base_ = './ms_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))

spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/nasfcos.py

@@ -1,20 +0,0 @@
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class NASFCOS(SingleStageDetector):
-    """NAS-FCOS: Fast Neural Architecture Search for Object Detection.
-
-    https://arxiv.org/abs/1906.0442
-    """
-
-    def __init__(self,
-                 backbone,
-                 neck,
-                 bbox_head,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super(NASFCOS, self).__init__(backbone, neck, bbox_head, train_cfg,
-                                      test_cfg, pretrained)

spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/single_stage.py

@@ -1,154 +0,0 @@
-import torch
-import torch.nn as nn
-
-from mmdet.core import bbox2result
-from ..builder import DETECTORS, build_backbone, build_head, build_neck
-from .base import BaseDetector
-
-
-@DETECTORS.register_module()
-class SingleStageDetector(BaseDetector):
-    """Base class for single-stage detectors.
-
-    Single-stage detectors directly and densely predict bounding boxes on the
-    output features of the backbone+neck.
-    """
-
-    def __init__(self,
-                 backbone,
-                 neck=None,
-                 bbox_head=None,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super(SingleStageDetector, self).__init__()
-        self.backbone = build_backbone(backbone)
-        if neck is not None:
-            self.neck = build_neck(neck)
-        bbox_head.update(train_cfg=train_cfg)
-        bbox_head.update(test_cfg=test_cfg)
-        self.bbox_head = build_head(bbox_head)
-        self.train_cfg = train_cfg
-        self.test_cfg = test_cfg
-        self.init_weights(pretrained=pretrained)
-
-    def init_weights(self, pretrained=None):
-        """Initialize the weights in detector.
-
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-        super(SingleStageDetector, self).init_weights(pretrained)
-        self.backbone.init_weights(pretrained=pretrained)
-        if self.with_neck:
-            if isinstance(self.neck, nn.Sequential):
-                for m in self.neck:
-                    m.init_weights()
-            else:
-                self.neck.init_weights()
-        self.bbox_head.init_weights()
-
-    def extract_feat(self, img):
-        """Directly extract features from the backbone+neck."""
-        x = self.backbone(img)
-        if self.with_neck:
-            x = self.neck(x)
-        return x
-
-    def forward_dummy(self, img):
-        """Used for computing network flops.
-
-        See `mmdetection/tools/analysis_tools/get_flops.py`
-        """
-        x = self.extract_feat(img)
-        outs = self.bbox_head(x)
-        return outs
-
-    def forward_train(self,
-                      img,
-                      img_metas,
-                      gt_bboxes,
-                      gt_labels,
-                      gt_bboxes_ignore=None):
-        """
-        Args:
-            img (Tensor): Input images of shape (N, C, H, W).
-                Typically these should be mean centered and std scaled.
-            img_metas (list[dict]): A List of image info dict where each dict
-                has: 'img_shape', 'scale_factor', 'flip', and may also contain
-                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
-                For details on the values of these keys see
-                :class:`mmdet.datasets.pipelines.Collect`.
-            gt_bboxes (list[Tensor]): Each item are the truth boxes for each
-                image in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): Class indices corresponding to each box
-            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
-                boxes can be ignored when computing the loss.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        super(SingleStageDetector, self).forward_train(img, img_metas)
-        x = self.extract_feat(img)
-        losses = self.bbox_head.forward_train(x, img_metas, gt_bboxes,
-                                              gt_labels, gt_bboxes_ignore)
-        return losses
-
-    def simple_test(self, img, img_metas, rescale=False):
-        """Test function without test time augmentation.
-
-        Args:
-            imgs (list[torch.Tensor]): List of multiple images
-            img_metas (list[dict]): List of image information.
-            rescale (bool, optional): Whether to rescale the results.
-                Defaults to False.
-
-        Returns:
-            list[list[np.ndarray]]: BBox results of each image and classes.
-                The outer list corresponds to each image. The inner list
-                corresponds to each class.
-        """
-        x = self.extract_feat(img)
-        outs = self.bbox_head(x)
-        # get origin input shape to support onnx dynamic shape
-        if torch.onnx.is_in_onnx_export():
-            # get shape as tensor
-            img_shape = torch._shape_as_tensor(img)[2:]
-            img_metas[0]['img_shape_for_onnx'] = img_shape
-        bbox_list = self.bbox_head.get_bboxes(
-            *outs, img_metas, rescale=rescale)
-        # skip post-processing when exporting to ONNX
-        if torch.onnx.is_in_onnx_export():
-            return bbox_list
-
-        bbox_results = [
-            bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes)
-            for det_bboxes, det_labels in bbox_list
-        ]
-        return bbox_results
-
-    def aug_test(self, imgs, img_metas, rescale=False):
-        """Test function with test time augmentation.
-
-        Args:
-            imgs (list[Tensor]): the outer list indicates test-time
-                augmentations and inner Tensor should have a shape NxCxHxW,
-                which contains all images in the batch.
-            img_metas (list[list[dict]]): the outer list indicates test-time
-                augs (multiscale, flip, etc.) and the inner list indicates
-                images in a batch. each dict has image information.
-            rescale (bool, optional): Whether to rescale the results.
-                Defaults to False.
-
-        Returns:
-            list[list[np.ndarray]]: BBox results of each image and classes.
-                The outer list corresponds to each image. The inner list
-                corresponds to each class.
-        """
-        assert hasattr(self.bbox_head, 'aug_test'), \
-            f'{self.bbox_head.__class__.__name__}' \
-            ' does not support test-time augmentation'
-
-        feats = self.extract_feats(imgs)
-        return [self.bbox_head.aug_test(feats, img_metas, rescale=rescale)]

spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/two_stage.py

@@ -1,215 +0,0 @@
-import torch
-import torch.nn as nn
-
-# from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
-from ..builder import DETECTORS, build_backbone, build_head, build_neck
-from .base import BaseDetector
-
-
-@DETECTORS.register_module()
-class TwoStageDetector(BaseDetector):
-    """Base class for two-stage detectors.
-
-    Two-stage detectors typically consisting of a region proposal network and a
-    task-specific regression head.
-    """
-
-    def __init__(self,
-                 backbone,
-                 neck=None,
-                 rpn_head=None,
-                 roi_head=None,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super(TwoStageDetector, self).__init__()
-        self.backbone = build_backbone(backbone)
-
-        if neck is not None:
-            self.neck = build_neck(neck)
-
-        if rpn_head is not None:
-            rpn_train_cfg = train_cfg.rpn if train_cfg is not None else None
-            rpn_head_ = rpn_head.copy()
-            rpn_head_.update(train_cfg=rpn_train_cfg, test_cfg=test_cfg.rpn)
-            self.rpn_head = build_head(rpn_head_)
-
-        if roi_head is not None:
-            # update train and test cfg here for now
-            # TODO: refactor assigner & sampler
-            rcnn_train_cfg = train_cfg.rcnn if train_cfg is not None else None
-            roi_head.update(train_cfg=rcnn_train_cfg)
-            roi_head.update(test_cfg=test_cfg.rcnn)
-            self.roi_head = build_head(roi_head)
-
-        self.train_cfg = train_cfg
-        self.test_cfg = test_cfg
-
-        self.init_weights(pretrained=pretrained)
-
-    @property
-    def with_rpn(self):
-        """bool: whether the detector has RPN"""
-        return hasattr(self, 'rpn_head') and self.rpn_head is not None
-
-    @property
-    def with_roi_head(self):
-        """bool: whether the detector has a RoI head"""
-        return hasattr(self, 'roi_head') and self.roi_head is not None
-
-    def init_weights(self, pretrained=None):
-        """Initialize the weights in detector.
-
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-        super(TwoStageDetector, self).init_weights(pretrained)
-        self.backbone.init_weights(pretrained=pretrained)
-        if self.with_neck:
-            if isinstance(self.neck, nn.Sequential):
-                for m in self.neck:
-                    m.init_weights()
-            else:
-                self.neck.init_weights()
-        if self.with_rpn:
-            self.rpn_head.init_weights()
-        if self.with_roi_head:
-            self.roi_head.init_weights(pretrained)
-
-    def extract_feat(self, img):
-        """Directly extract features from the backbone+neck."""
-        x = self.backbone(img)
-        if self.with_neck:
-            x = self.neck(x)
-        return x
-
-    def forward_dummy(self, img):
-        """Used for computing network flops.
-
-        See `mmdetection/tools/analysis_tools/get_flops.py`
-        """
-        outs = ()
-        # backbone
-        x = self.extract_feat(img)
-        # rpn
-        if self.with_rpn:
-            rpn_outs = self.rpn_head(x)
-            outs = outs + (rpn_outs, )
-        proposals = torch.randn(1000, 4).to(img.device)
-        # roi_head
-        roi_outs = self.roi_head.forward_dummy(x, proposals)
-        outs = outs + (roi_outs, )
-        return outs
-
-    def forward_train(self,
-                      img,
-                      img_metas,
-                      gt_bboxes,
-                      gt_labels,
-                      gt_bboxes_ignore=None,
-                      gt_masks=None,
-                      proposals=None,
-                      **kwargs):
-        """
-        Args:
-            img (Tensor): of shape (N, C, H, W) encoding input images.
-                Typically these should be mean centered and std scaled.
-
-            img_metas (list[dict]): list of image info dict where each dict
-                has: 'img_shape', 'scale_factor', 'flip', and may also contain
-                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
-                For details on the values of these keys see
-                `mmdet/datasets/pipelines/formatting.py:Collect`.
-
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-
-            gt_labels (list[Tensor]): class indices corresponding to each box
-
-            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
-                boxes can be ignored when computing the loss.
-
-            gt_masks (None | Tensor) : true segmentation masks for each box
-                used if the architecture supports a segmentation task.
-
-            proposals : override rpn proposals with custom proposals. Use when
-                `with_rpn` is False.
-
-        Returns:
-            dict[str, Tensor]: a dictionary of loss components
-        """
-        x = self.extract_feat(img)
-
-        losses = dict()
-
-        # RPN forward and loss
-        if self.with_rpn:
-            proposal_cfg = self.train_cfg.get('rpn_proposal',
-                                              self.test_cfg.rpn)
-            rpn_losses, proposal_list = self.rpn_head.forward_train(
-                x,
-                img_metas,
-                gt_bboxes,
-                gt_labels=None,
-                gt_bboxes_ignore=gt_bboxes_ignore,
-                proposal_cfg=proposal_cfg)
-            losses.update(rpn_losses)
-        else:
-            proposal_list = proposals
-
-        roi_losses = self.roi_head.forward_train(x, img_metas, proposal_list,
-                                                 gt_bboxes, gt_labels,
-                                                 gt_bboxes_ignore, gt_masks,
-                                                 **kwargs)
-        losses.update(roi_losses)
-
-        return losses
-
-    async def async_simple_test(self,
-                                img,
-                                img_meta,
-                                proposals=None,
-                                rescale=False):
-        """Async test without augmentation."""
-        assert self.with_bbox, 'Bbox head must be implemented.'
-        x = self.extract_feat(img)
-
-        if proposals is None:
-            proposal_list = await self.rpn_head.async_simple_test_rpn(
-                x, img_meta)
-        else:
-            proposal_list = proposals
-
-        return await self.roi_head.async_simple_test(
-            x, proposal_list, img_meta, rescale=rescale)
-
-    def simple_test(self, img, img_metas, proposals=None, rescale=False):
-        """Test without augmentation."""
-        assert self.with_bbox, 'Bbox head must be implemented.'
-
-        x = self.extract_feat(img)
-
-        # get origin input shape to onnx dynamic input shape
-        if torch.onnx.is_in_onnx_export():
-            img_shape = torch._shape_as_tensor(img)[2:]
-            img_metas[0]['img_shape_for_onnx'] = img_shape
-
-        if proposals is None:
-            proposal_list = self.rpn_head.simple_test_rpn(x, img_metas)
-        else:
-            proposal_list = proposals
-
-        return self.roi_head.simple_test(
-            x, proposal_list, img_metas, rescale=rescale)
-
-    def aug_test(self, imgs, img_metas, rescale=False):
-        """Test with augmentations.
-
-        If rescale is False, then returned bboxes and masks will fit the scale
-        of imgs[0].
-        """
-        x = self.extract_feats(imgs)
-        proposal_list = self.rpn_head.aug_test_rpn(x, img_metas)
-        return self.roi_head.aug_test(
-            x, proposal_list, img_metas, rescale=rescale)

spaces/Andyrasika/xlm-roberta-base-finetuned-panx-de/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Andyrasika/xlm-roberta-base-finetuned-panx-de").launch()

spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/guided_diffusion/guided_diffusion/resample.py

@@ -1,154 +0,0 @@
-from abc import ABC, abstractmethod
-
-import numpy as np
-import torch as th
-import torch.distributed as dist
-
-
-def create_named_schedule_sampler(name, diffusion):
-    """
-    Create a ScheduleSampler from a library of pre-defined samplers.
-
-    :param name: the name of the sampler.
-    :param diffusion: the diffusion object to sample for.
-    """
-    if name == "uniform":
-        return UniformSampler(diffusion)
-    elif name == "loss-second-moment":
-        return LossSecondMomentResampler(diffusion)
-    else:
-        raise NotImplementedError(f"unknown schedule sampler: {name}")
-
-
-class ScheduleSampler(ABC):
-    """
-    A distribution over timesteps in the diffusion process, intended to reduce
-    variance of the objective.
-
-    By default, samplers perform unbiased importance sampling, in which the
-    objective's mean is unchanged.
-    However, subclasses may override sample() to change how the resampled
-    terms are reweighted, allowing for actual changes in the objective.
-    """
-
-    @abstractmethod
-    def weights(self):
-        """
-        Get a numpy array of weights, one per diffusion step.
-
-        The weights needn't be normalized, but must be positive.
-        """
-
-    def sample(self, batch_size, device):
-        """
-        Importance-sample timesteps for a batch.
-
-        :param batch_size: the number of timesteps.
-        :param device: the torch device to save to.
-        :return: a tuple (timesteps, weights):
-                 - timesteps: a tensor of timestep indices.
-                 - weights: a tensor of weights to scale the resulting losses.
-        """
-        w = self.weights()
-        p = w / np.sum(w)
-        indices_np = np.random.choice(len(p), size=(batch_size,), p=p)
-        indices = th.from_numpy(indices_np).long().to(device)
-        weights_np = 1 / (len(p) * p[indices_np])
-        weights = th.from_numpy(weights_np).float().to(device)
-        return indices, weights
-
-
-class UniformSampler(ScheduleSampler):
-    def __init__(self, diffusion):
-        self.diffusion = diffusion
-        self._weights = np.ones([diffusion.num_timesteps])
-
-    def weights(self):
-        return self._weights
-
-
-class LossAwareSampler(ScheduleSampler):
-    def update_with_local_losses(self, local_ts, local_losses):
-        """
-        Update the reweighting using losses from a model.
-
-        Call this method from each rank with a batch of timesteps and the
-        corresponding losses for each of those timesteps.
-        This method will perform synchronization to make sure all of the ranks
-        maintain the exact same reweighting.
-
-        :param local_ts: an integer Tensor of timesteps.
-        :param local_losses: a 1D Tensor of losses.
-        """
-        batch_sizes = [
-            th.tensor([0], dtype=th.int32, device=local_ts.device)
-            for _ in range(dist.get_world_size())
-        ]
-        dist.all_gather(
-            batch_sizes,
-            th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device),
-        )
-
-        # Pad all_gather batches to be the maximum batch size.
-        batch_sizes = [x.item() for x in batch_sizes]
-        max_bs = max(batch_sizes)
-
-        timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes]
-        loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes]
-        dist.all_gather(timestep_batches, local_ts)
-        dist.all_gather(loss_batches, local_losses)
-        timesteps = [
-            x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs]
-        ]
-        losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]]
-        self.update_with_all_losses(timesteps, losses)
-
-    @abstractmethod
-    def update_with_all_losses(self, ts, losses):
-        """
-        Update the reweighting using losses from a model.
-
-        Sub-classes should override this method to update the reweighting
-        using losses from the model.
-
-        This method directly updates the reweighting without synchronizing
-        between workers. It is called by update_with_local_losses from all
-        ranks with identical arguments. Thus, it should have deterministic
-        behavior to maintain state across workers.
-
-        :param ts: a list of int timesteps.
-        :param losses: a list of float losses, one per timestep.
-        """
-
-
-class LossSecondMomentResampler(LossAwareSampler):
-    def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
-        self.diffusion = diffusion
-        self.history_per_term = history_per_term
-        self.uniform_prob = uniform_prob
-        self._loss_history = np.zeros(
-            [diffusion.num_timesteps, history_per_term], dtype=np.float64
-        )
-        self._loss_counts = np.zeros([diffusion.num_timesteps], dtype=np.int)
-
-    def weights(self):
-        if not self._warmed_up():
-            return np.ones([self.diffusion.num_timesteps], dtype=np.float64)
-        weights = np.sqrt(np.mean(self._loss_history ** 2, axis=-1))
-        weights /= np.sum(weights)
-        weights *= 1 - self.uniform_prob
-        weights += self.uniform_prob / len(weights)
-        return weights
-
-    def update_with_all_losses(self, ts, losses):
-        for t, loss in zip(ts, losses):
-            if self._loss_counts[t] == self.history_per_term:
-                # Shift out the oldest loss term.
-                self._loss_history[t, :-1] = self._loss_history[t, 1:]
-                self._loss_history[t, -1] = loss
-            else:
-                self._loss_history[t, self._loss_counts[t]] = loss
-                self._loss_counts[t] += 1
-
-    def _warmed_up(self):
-        return (self._loss_counts == self.history_per_term).all()

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py

@@ -1,96 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch.nn as nn
-
-from .conv_module import ConvModule
-
-
-class DepthwiseSeparableConvModule(nn.Module):
-    """Depthwise separable convolution module.
-
-    See https://arxiv.org/pdf/1704.04861.pdf for details.
-
-    This module can replace a ConvModule with the conv block replaced by two
-    conv block: depthwise conv block and pointwise conv block. The depthwise
-    conv block contains depthwise-conv/norm/activation layers. The pointwise
-    conv block contains pointwise-conv/norm/activation layers. It should be
-    noted that there will be norm/activation layer in the depthwise conv block
-    if `norm_cfg` and `act_cfg` are specified.
-
-    Args:
-        in_channels (int): Number of channels in the input feature map.
-            Same as that in ``nn._ConvNd``.
-        out_channels (int): Number of channels produced by the convolution.
-            Same as that in ``nn._ConvNd``.
-        kernel_size (int | tuple[int]): Size of the convolving kernel.
-            Same as that in ``nn._ConvNd``.
-        stride (int | tuple[int]): Stride of the convolution.
-            Same as that in ``nn._ConvNd``. Default: 1.
-        padding (int | tuple[int]): Zero-padding added to both sides of
-            the input. Same as that in ``nn._ConvNd``. Default: 0.
-        dilation (int | tuple[int]): Spacing between kernel elements.
-            Same as that in ``nn._ConvNd``. Default: 1.
-        norm_cfg (dict): Default norm config for both depthwise ConvModule and
-            pointwise ConvModule. Default: None.
-        act_cfg (dict): Default activation config for both depthwise ConvModule
-            and pointwise ConvModule. Default: dict(type='ReLU').
-        dw_norm_cfg (dict): Norm config of depthwise ConvModule. If it is
-            'default', it will be the same as `norm_cfg`. Default: 'default'.
-        dw_act_cfg (dict): Activation config of depthwise ConvModule. If it is
-            'default', it will be the same as `act_cfg`. Default: 'default'.
-        pw_norm_cfg (dict): Norm config of pointwise ConvModule. If it is
-            'default', it will be the same as `norm_cfg`. Default: 'default'.
-        pw_act_cfg (dict): Activation config of pointwise ConvModule. If it is
-            'default', it will be the same as `act_cfg`. Default: 'default'.
-        kwargs (optional): Other shared arguments for depthwise and pointwise
-            ConvModule. See ConvModule for ref.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 dilation=1,
-                 norm_cfg=None,
-                 act_cfg=dict(type='ReLU'),
-                 dw_norm_cfg='default',
-                 dw_act_cfg='default',
-                 pw_norm_cfg='default',
-                 pw_act_cfg='default',
-                 **kwargs):
-        super(DepthwiseSeparableConvModule, self).__init__()
-        assert 'groups' not in kwargs, 'groups should not be specified'
-
-        # if norm/activation config of depthwise/pointwise ConvModule is not
-        # specified, use default config.
-        dw_norm_cfg = dw_norm_cfg if dw_norm_cfg != 'default' else norm_cfg
-        dw_act_cfg = dw_act_cfg if dw_act_cfg != 'default' else act_cfg
-        pw_norm_cfg = pw_norm_cfg if pw_norm_cfg != 'default' else norm_cfg
-        pw_act_cfg = pw_act_cfg if pw_act_cfg != 'default' else act_cfg
-
-        # depthwise convolution
-        self.depthwise_conv = ConvModule(
-            in_channels,
-            in_channels,
-            kernel_size,
-            stride=stride,
-            padding=padding,
-            dilation=dilation,
-            groups=in_channels,
-            norm_cfg=dw_norm_cfg,
-            act_cfg=dw_act_cfg,
-            **kwargs)
-
-        self.pointwise_conv = ConvModule(
-            in_channels,
-            out_channels,
-            1,
-            norm_cfg=pw_norm_cfg,
-            act_cfg=pw_act_cfg,
-            **kwargs)
-
-    def forward(self, x):
-        x = self.depthwise_conv(x)
-        x = self.pointwise_conv(x)
-        return x

spaces/Artrajz/vits-simple-api/static/js/bootstrap.bundle.min.js

@@ -1,7 +0,0 @@
-/*!
-  * Bootstrap v4.6.2 (https://getbootstrap.com/)
-  * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
-  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
-  */
-!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?e(exports,require("jquery")):"function"==typeof define&&define.amd?define(["exports","jquery"],e):e((t="undefined"!=typeof globalThis?globalThis:t||self).bootstrap={},t.jQuery)}(this,(function(t,e){"use strict";function n(t){return t&&"object"==typeof t&&"default"in t?t:{default:t}}var i=n(e);function o(t,e){for(var n=0;n<e.length;n++){var i=e[n];i.enumerable=i.enumerable||!1,i.configurable=!0,"value"in i&&(i.writable=!0),Object.defineProperty(t,i.key,i)}}function r(t,e,n){return e&&o(t.prototype,e),n&&o(t,n),Object.defineProperty(t,"prototype",{writable:!1}),t}function a(){return a=Object.assign?Object.assign.bind():function(t){for(var e=1;e<arguments.length;e++){var n=arguments[e];for(var i in n)Object.prototype.hasOwnProperty.call(n,i)&&(t[i]=n[i])}return t},a.apply(this,arguments)}function s(t,e){return s=Object.setPrototypeOf?Object.setPrototypeOf.bind():function(t,e){return t.__proto__=e,t},s(t,e)}var 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this.state.isDestroyed=!0,kt(this.modifiers,"applyStyle")&&(this.popper.removeAttribute("x-placement"),this.popper.style.position="",this.popper.style.top="",this.popper.style.left="",this.popper.style.right="",this.popper.style.bottom="",this.popper.style.willChange="",this.popper.style[It("transform")]=""),this.disableEventListeners(),this.options.removeOnDestroy&&this.popper.parentNode.removeChild(this.popper),this}function xt(t){var e=t.ownerDocument;return e?e.defaultView:window}function jt(t,e,n,i){var o="BODY"===t.nodeName,r=o?t.ownerDocument.defaultView:t;r.addEventListener(e,n,{passive:!0}),o||jt(G(r.parentNode),e,n,i),i.push(r)}function Lt(t,e,n,i){n.updateBound=i,xt(t).addEventListener("resize",n.updateBound,{passive:!0});var o=G(t);return jt(o,"scroll",n.updateBound,n.scrollParents),n.scrollElement=o,n.eventsEnabled=!0,n}function Pt(){this.state.eventsEnabled||(this.state=Lt(this.reference,this.options,this.state,this.scheduleUpdate))}function Ft(){var t,e;this.state.eventsEnabled&&(cancelAnimationFrame(this.scheduleUpdate),this.state=(t=this.reference,e=this.state,xt(t).removeEventListener("resize",e.updateBound),e.scrollParents.forEach((function(t){t.removeEventListener("scroll",e.updateBound)})),e.updateBound=null,e.scrollParents=[],e.scrollElement=null,e.eventsEnabled=!1,e))}function Rt(t){return""!==t&&!isNaN(parseFloat(t))&&isFinite(t)}function Bt(t,e){Object.keys(e).forEach((function(n){var i="";-1!==["width","height","top","right","bottom","left"].indexOf(n)&&Rt(e[n])&&(i="px"),t.style[n]=e[n]+i}))}var Ht=U&&/Firefox/i.test(navigator.userAgent);function Mt(t,e,n){var i=Nt(t,(function(t){return t.name===e})),o=!!i&&t.some((function(t){return t.name===n&&t.enabled&&t.order<i.order}));if(!o){var r="`"+e+"`",a="`"+n+"`";console.warn(a+" modifier is required by "+r+" modifier in order to work, be sure to include it before "+r+"!")}return o}var qt=["auto-start","auto","auto-end","top-start","top","top-end","right-start","right","right-end","bottom-end","bottom","bottom-start","left-end","left","left-start"],Qt=qt.slice(3);function Wt(t){var e=arguments.length>1&&void 0!==arguments[1]&&arguments[1],n=Qt.indexOf(t),i=Qt.slice(n+1).concat(Qt.slice(0,n));return e?i.reverse():i}var Ut={placement:"bottom",positionFixed:!1,eventsEnabled:!0,removeOnDestroy:!1,onCreate:function(){},onUpdate:function(){},modifiers:{shift:{order:100,enabled:!0,fn:function(t){var e=t.placement,n=e.split("-")[0],i=e.split("-")[1];if(i){var o=t.offsets,r=o.reference,a=o.popper,s=-1!==["bottom","top"].indexOf(n),l=s?"left":"top",u=s?"width":"height",f={start:dt({},l,r[l]),end:dt({},l,r[l]+r[u]-a[u])};t.offsets.popper=ct({},a,f[i])}return t}},offset:{order:200,enabled:!0,fn:function(t,e){var n,i=e.offset,o=t.placement,r=t.offsets,a=r.popper,s=r.reference,l=o.split("-")[0];return n=Rt(+i)?[+i,0]:function(t,e,n,i){var o=[0,0],r=-1!==["right","left"].indexOf(i),a=t.split(/(\+|\-)/).map((function(t){return t.trim()})),s=a.indexOf(Nt(a,(function(t){return-1!==t.search(/,|\s/)})));a[s]&&-1===a[s].indexOf(",")&&console.warn("Offsets separated by white space(s) are deprecated, use a comma (,) instead.");var l=/\s*,\s*|\s+/,u=-1!==s?[a.slice(0,s).concat([a[s].split(l)[0]]),[a[s].split(l)[1]].concat(a.slice(s+1))]:[a];return u=u.map((function(t,i){var o=(1===i?!r:r)?"height":"width",a=!1;return t.reduce((function(t,e){return""===t[t.length-1]&&-1!==["+","-"].indexOf(e)?(t[t.length-1]=e,a=!0,t):a?(t[t.length-1]+=e,a=!1,t):t.concat(e)}),[]).map((function(t){return function(t,e,n,i){var o=t.match(/((?:\-|\+)?\d*\.?\d*)(.*)/),r=+o[1],a=o[2];return 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f[t]<l[t]&&!e.escapeWithReference&&(n=Math.max(f[t],l[t])),dt({},t,n)},secondary:function(t){var n="right"===t?"left":"top",i=f[n];return f[t]>l[t]&&!e.escapeWithReference&&(i=Math.min(f[n],l[t]-("right"===t?f.width:f.height))),dt({},n,i)}};return u.forEach((function(t){var e=-1!==["left","top"].indexOf(t)?"primary":"secondary";f=ct({},f,d[e](t))})),t.offsets.popper=f,t},priority:["left","right","top","bottom"],padding:5,boundariesElement:"scrollParent"},keepTogether:{order:400,enabled:!0,fn:function(t){var e=t.offsets,n=e.popper,i=e.reference,o=t.placement.split("-")[0],r=Math.floor,a=-1!==["top","bottom"].indexOf(o),s=a?"right":"bottom",l=a?"left":"top",u=a?"width":"height";return n[s]<r(i[l])&&(t.offsets.popper[l]=r(i[l])-n[u]),n[l]>r(i[s])&&(t.offsets.popper[l]=r(i[s])),t}},arrow:{order:500,enabled:!0,fn:function(t,e){var n;if(!Mt(t.instance.modifiers,"arrow","keepTogether"))return t;var i=e.element;if("string"==typeof i){if(!(i=t.instance.popper.querySelector(i)))return t}else if(!t.instance.popper.contains(i))return console.warn("WARNING: `arrow.element` must be child of its popper element!"),t;var o=t.placement.split("-")[0],r=t.offsets,a=r.popper,s=r.reference,l=-1!==["left","right"].indexOf(o),u=l?"height":"width",f=l?"Top":"Left",d=f.toLowerCase(),c=l?"left":"top",h=l?"bottom":"right",p=Tt(i)[u];s[h]-p<a[d]&&(t.offsets.popper[d]-=a[d]-(s[h]-p)),s[d]+p>a[h]&&(t.offsets.popper[d]+=s[d]+p-a[h]),t.offsets.popper=ht(t.offsets.popper);var m=s[d]+s[u]/2-p/2,g=K(t.instance.popper),_=parseFloat(g["margin"+f]),v=parseFloat(g["border"+f+"Width"]),b=m-t.offsets.popper[d]-_-v;return b=Math.max(Math.min(a[u]-p,b),0),t.arrowElement=i,t.offsets.arrow=(dt(n={},d,Math.round(b)),dt(n,c,""),n),t},element:"[x-arrow]"},flip:{order:600,enabled:!0,fn:function(t,e){if(kt(t.instance.modifiers,"inner"))return t;if(t.flipped&&t.placement===t.originalPlacement)return t;var n=bt(t.instance.popper,t.instance.reference,e.padding,e.boundariesElement,t.positionFixed),i=t.placement.split("-")[0],o=Ct(i),r=t.placement.split("-")[1]||"",a=[];switch(e.behavior){case"flip":a=[i,o];break;case"clockwise":a=Wt(i);break;case"counterclockwise":a=Wt(i,!0);break;default:a=e.behavior}return a.forEach((function(s,l){if(i!==s||a.length===l+1)return t;i=t.placement.split("-")[0],o=Ct(i);var u=t.offsets.popper,f=t.offsets.reference,d=Math.floor,c="left"===i&&d(u.right)>d(f.left)||"right"===i&&d(u.left)<d(f.right)||"top"===i&&d(u.bottom)>d(f.top)||"bottom"===i&&d(u.top)<d(f.bottom),h=d(u.left)<d(n.left),p=d(u.right)>d(n.right),m=d(u.top)<d(n.top),g=d(u.bottom)>d(n.bottom),_="left"===i&&h||"right"===i&&p||"top"===i&&m||"bottom"===i&&g,v=-1!==["top","bottom"].indexOf(i),b=!!e.flipVariations&&(v&&"start"===r&&h||v&&"end"===r&&p||!v&&"start"===r&&m||!v&&"end"===r&&g),y=!!e.flipVariationsByContent&&(v&&"start"===r&&p||v&&"end"===r&&h||!v&&"start"===r&&g||!v&&"end"===r&&m),E=b||y;(c||_||E)&&(t.flipped=!0,(c||_)&&(i=a[l+1]),E&&(r=function(t){return"end"===t?"start":"start"===t?"end":t}(r)),t.placement=i+(r?"-"+r:""),t.offsets.popper=ct({},t.offsets.popper,St(t.instance.popper,t.offsets.reference,t.placement)),t=Dt(t.instance.modifiers,t,"flip"))})),t},behavior:"flip",padding:5,boundariesElement:"viewport",flipVariations:!1,flipVariationsByContent:!1},inner:{order:700,enabled:!1,fn:function(t){var e=t.placement,n=e.split("-")[0],i=t.offsets,o=i.popper,r=i.reference,a=-1!==["left","right"].indexOf(n),s=-1===["top","left"].indexOf(n);return o[a?"left":"top"]=r[n]-(s?o[a?"width":"height"]:0),t.placement=Ct(e),t.offsets.popper=ht(o),t}},hide:{order:800,enabled:!0,fn:function(t){if(!Mt(t.instance.modifiers,"hide","preventOverflow"))return t;var e=t.offsets.reference,n=Nt(t.instance.modifiers,(function(t){return"preventOverflow"===t.name})).boundaries;if(e.bottom<n.top||e.left>n.right||e.top>n.bottom||e.right<n.left){if(!0===t.hide)return t;t.hide=!0,t.attributes["x-out-of-boundaries"]=""}else{if(!1===t.hide)return t;t.hide=!1,t.attributes["x-out-of-boundaries"]=!1}return t}},computeStyle:{order:850,enabled:!0,fn:function(t,e){var n=e.x,i=e.y,o=t.offsets.popper,r=Nt(t.instance.modifiers,(function(t){return"applyStyle"===t.name})).gpuAcceleration;void 0!==r&&console.warn("WARNING: `gpuAcceleration` option moved to `computeStyle` modifier and will not be supported in future versions of 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t.attributes=ct({},v,t.attributes),t.styles=ct({},d,t.styles),t.arrowStyles=ct({},t.offsets.arrow,t.arrowStyles),t},gpuAcceleration:!0,x:"bottom",y:"right"},applyStyle:{order:900,enabled:!0,fn:function(t){var e,n;return Bt(t.instance.popper,t.styles),e=t.instance.popper,n=t.attributes,Object.keys(n).forEach((function(t){!1!==n[t]?e.setAttribute(t,n[t]):e.removeAttribute(t)})),t.arrowElement&&Object.keys(t.arrowStyles).length&&Bt(t.arrowElement,t.arrowStyles),t},onLoad:function(t,e,n,i,o){var r=wt(o,e,t,n.positionFixed),a=Et(n.placement,r,e,t,n.modifiers.flip.boundariesElement,n.modifiers.flip.padding);return e.setAttribute("x-placement",a),Bt(e,{position:n.positionFixed?"fixed":"absolute"}),n},gpuAcceleration:void 0}}},Vt=function(){function t(e,n){var i=this,o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};ut(this,t),this.scheduleUpdate=function(){return 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e=this,n=i.default(this._element).hasClass(ce),o=this._dialog?this._dialog.querySelector(".modal-body"):null;this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.appendChild(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),i.default(this._dialog).hasClass("modal-dialog-scrollable")&&o?o.scrollTop=0:this._element.scrollTop=0,n&&u.reflow(this._element),i.default(this._element).addClass(he),this._config.focus&&this._enforceFocus();var r=i.default.Event("shown.bs.modal",{relatedTarget:t}),a=function(){e._config.focus&&e._element.focus(),e._isTransitioning=!1,i.default(e._element).trigger(r)};if(n){var s=u.getTransitionDurationFromElement(this._dialog);i.default(this._dialog).one(u.TRANSITION_END,a).emulateTransitionEnd(s)}else a()},e._enforceFocus=function(){var t=this;i.default(document).off(_e).on(_e,(function(e){document!==e.target&&t._element!==e.target&&0===i.default(t._element).has(e.target).length&&t._element.focus()}))},e._setEscapeEvent=function(){var t=this;this._isShown?i.default(this._element).on(ye,(function(e){t._config.keyboard&&27===e.which?(e.preventDefault(),t.hide()):t._config.keyboard||27!==e.which||t._triggerBackdropTransition()})):this._isShown||i.default(this._element).off(ye)},e._setResizeEvent=function(){var t=this;this._isShown?i.default(window).on(ve,(function(e){return t.handleUpdate(e)})):i.default(window).off(ve)},e._hideModal=function(){var 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-//# sourceMappingURL=bootstrap.bundle.min.js.map

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

@@ -1,437 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2013-2015 Vinay Sajip.
-# Licensed to the Python Software Foundation under a contributor agreement.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-from io import BytesIO
-import logging
-import os
-import re
-import struct
-import sys
-import time
-from zipfile import ZipInfo
-
-from .compat import sysconfig, detect_encoding, ZipFile
-from .resources import finder
-from .util import (FileOperator, get_export_entry, convert_path,
-                   get_executable, get_platform, in_venv)
-
-logger = logging.getLogger(__name__)
-
-_DEFAULT_MANIFEST = '''
-<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<assembly xmlns="urn:schemas-microsoft-com:asm.v1" manifestVersion="1.0">
- <assemblyIdentity version="1.0.0.0"
- processorArchitecture="X86"
- name="%s"
- type="win32"/>
-
- <!-- Identify the application security requirements. -->
- <trustInfo xmlns="urn:schemas-microsoft-com:asm.v3">
- <security>
- <requestedPrivileges>
- <requestedExecutionLevel level="asInvoker" uiAccess="false"/>
- </requestedPrivileges>
- </security>
- </trustInfo>
-</assembly>'''.strip()
-
-# check if Python is called on the first line with this expression
-FIRST_LINE_RE = re.compile(b'^#!.*pythonw?[0-9.]*([ \t].*)?$')
-SCRIPT_TEMPLATE = r'''# -*- coding: utf-8 -*-
-import re
-import sys
-from %(module)s import %(import_name)s
-if __name__ == '__main__':
-    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
-    sys.exit(%(func)s())
-'''
-
-
-def enquote_executable(executable):
-    if ' ' in executable:
-        # make sure we quote only the executable in case of env
-        # for example /usr/bin/env "/dir with spaces/bin/jython"
-        # instead of "/usr/bin/env /dir with spaces/bin/jython"
-        # otherwise whole
-        if executable.startswith('/usr/bin/env '):
-            env, _executable = executable.split(' ', 1)
-            if ' ' in _executable and not _executable.startswith('"'):
-                executable = '%s "%s"' % (env, _executable)
-        else:
-            if not executable.startswith('"'):
-                executable = '"%s"' % executable
-    return executable
-
-# Keep the old name around (for now), as there is at least one project using it!
-_enquote_executable = enquote_executable
-
-class ScriptMaker(object):
-    """
-    A class to copy or create scripts from source scripts or callable
-    specifications.
-    """
-    script_template = SCRIPT_TEMPLATE
-
-    executable = None  # for shebangs
-
-    def __init__(self, source_dir, target_dir, add_launchers=True,
-                 dry_run=False, fileop=None):
-        self.source_dir = source_dir
-        self.target_dir = target_dir
-        self.add_launchers = add_launchers
-        self.force = False
-        self.clobber = False
-        # It only makes sense to set mode bits on POSIX.
-        self.set_mode = (os.name == 'posix') or (os.name == 'java' and
-                                                 os._name == 'posix')
-        self.variants = set(('', 'X.Y'))
-        self._fileop = fileop or FileOperator(dry_run)
-
-        self._is_nt = os.name == 'nt' or (
-            os.name == 'java' and os._name == 'nt')
-        self.version_info = sys.version_info
-
-    def _get_alternate_executable(self, executable, options):
-        if options.get('gui', False) and self._is_nt:  # pragma: no cover
-            dn, fn = os.path.split(executable)
-            fn = fn.replace('python', 'pythonw')
-            executable = os.path.join(dn, fn)
-        return executable
-
-    if sys.platform.startswith('java'):  # pragma: no cover
-        def _is_shell(self, executable):
-            """
-            Determine if the specified executable is a script
-            (contains a #! line)
-            """
-            try:
-                with open(executable) as fp:
-                    return fp.read(2) == '#!'
-            except (OSError, IOError):
-                logger.warning('Failed to open %s', executable)
-                return False
-
-        def _fix_jython_executable(self, executable):
-            if self._is_shell(executable):
-                # Workaround for Jython is not needed on Linux systems.
-                import java
-
-                if java.lang.System.getProperty('os.name') == 'Linux':
-                    return executable
-            elif executable.lower().endswith('jython.exe'):
-                # Use wrapper exe for Jython on Windows
-                return executable
-            return '/usr/bin/env %s' % executable
-
-    def _build_shebang(self, executable, post_interp):
-        """
-        Build a shebang line. In the simple case (on Windows, or a shebang line
-        which is not too long or contains spaces) use a simple formulation for
-        the shebang. Otherwise, use /bin/sh as the executable, with a contrived
-        shebang which allows the script to run either under Python or sh, using
-        suitable quoting. Thanks to Harald Nordgren for his input.
-
-        See also: http://www.in-ulm.de/~mascheck/various/shebang/#length
-                  https://hg.mozilla.org/mozilla-central/file/tip/mach
-        """
-        if os.name != 'posix':
-            simple_shebang = True
-        else:
-            # Add 3 for '#!' prefix and newline suffix.
-            shebang_length = len(executable) + len(post_interp) + 3
-            if sys.platform == 'darwin':
-                max_shebang_length = 512
-            else:
-                max_shebang_length = 127
-            simple_shebang = ((b' ' not in executable) and
-                              (shebang_length <= max_shebang_length))
-
-        if simple_shebang:
-            result = b'#!' + executable + post_interp + b'\n'
-        else:
-            result = b'#!/bin/sh\n'
-            result += b"'''exec' " + executable + post_interp + b' "$0" "$@"\n'
-            result += b"' '''"
-        return result
-
-    def _get_shebang(self, encoding, post_interp=b'', options=None):
-        enquote = True
-        if self.executable:
-            executable = self.executable
-            enquote = False     # assume this will be taken care of
-        elif not sysconfig.is_python_build():
-            executable = get_executable()
-        elif in_venv():  # pragma: no cover
-            executable = os.path.join(sysconfig.get_path('scripts'),
-                            'python%s' % sysconfig.get_config_var('EXE'))
-        else:  # pragma: no cover
-            executable = os.path.join(
-                sysconfig.get_config_var('BINDIR'),
-               'python%s%s' % (sysconfig.get_config_var('VERSION'),
-                               sysconfig.get_config_var('EXE')))
-            if not os.path.isfile(executable):
-                # for Python builds from source on Windows, no Python executables with
-                # a version suffix are created, so we use python.exe
-                executable = os.path.join(sysconfig.get_config_var('BINDIR'),
-                                'python%s' % (sysconfig.get_config_var('EXE')))
-        if options:
-            executable = self._get_alternate_executable(executable, options)
-
-        if sys.platform.startswith('java'):  # pragma: no cover
-            executable = self._fix_jython_executable(executable)
-
-        # Normalise case for Windows - COMMENTED OUT
-        # executable = os.path.normcase(executable)
-        # N.B. The normalising operation above has been commented out: See
-        # issue #124. Although paths in Windows are generally case-insensitive,
-        # they aren't always. For example, a path containing a ẞ (which is a
-        # LATIN CAPITAL LETTER SHARP S - U+1E9E) is normcased to ß (which is a
-        # LATIN SMALL LETTER SHARP S' - U+00DF). The two are not considered by
-        # Windows as equivalent in path names.
-
-        # If the user didn't specify an executable, it may be necessary to
-        # cater for executable paths with spaces (not uncommon on Windows)
-        if enquote:
-            executable = enquote_executable(executable)
-        # Issue #51: don't use fsencode, since we later try to
-        # check that the shebang is decodable using utf-8.
-        executable = executable.encode('utf-8')
-        # in case of IronPython, play safe and enable frames support
-        if (sys.platform == 'cli' and '-X:Frames' not in post_interp
-            and '-X:FullFrames' not in post_interp):  # pragma: no cover
-            post_interp += b' -X:Frames'
-        shebang = self._build_shebang(executable, post_interp)
-        # Python parser starts to read a script using UTF-8 until
-        # it gets a #coding:xxx cookie. The shebang has to be the
-        # first line of a file, the #coding:xxx cookie cannot be
-        # written before. So the shebang has to be decodable from
-        # UTF-8.
-        try:
-            shebang.decode('utf-8')
-        except UnicodeDecodeError:  # pragma: no cover
-            raise ValueError(
-                'The shebang (%r) is not decodable from utf-8' % shebang)
-        # If the script is encoded to a custom encoding (use a
-        # #coding:xxx cookie), the shebang has to be decodable from
-        # the script encoding too.
-        if encoding != 'utf-8':
-            try:
-                shebang.decode(encoding)
-            except UnicodeDecodeError:  # pragma: no cover
-                raise ValueError(
-                    'The shebang (%r) is not decodable '
-                    'from the script encoding (%r)' % (shebang, encoding))
-        return shebang
-
-    def _get_script_text(self, entry):
-        return self.script_template % dict(module=entry.prefix,
-                                           import_name=entry.suffix.split('.')[0],
-                                           func=entry.suffix)
-
-    manifest = _DEFAULT_MANIFEST
-
-    def get_manifest(self, exename):
-        base = os.path.basename(exename)
-        return self.manifest % base
-
-    def _write_script(self, names, shebang, script_bytes, filenames, ext):
-        use_launcher = self.add_launchers and self._is_nt
-        linesep = os.linesep.encode('utf-8')
-        if not shebang.endswith(linesep):
-            shebang += linesep
-        if not use_launcher:
-            script_bytes = shebang + script_bytes
-        else:  # pragma: no cover
-            if ext == 'py':
-                launcher = self._get_launcher('t')
-            else:
-                launcher = self._get_launcher('w')
-            stream = BytesIO()
-            with ZipFile(stream, 'w') as zf:
-                source_date_epoch = os.environ.get('SOURCE_DATE_EPOCH')
-                if source_date_epoch:
-                    date_time = time.gmtime(int(source_date_epoch))[:6]
-                    zinfo = ZipInfo(filename='__main__.py', date_time=date_time)
-                    zf.writestr(zinfo, script_bytes)
-                else:
-                    zf.writestr('__main__.py', script_bytes)
-            zip_data = stream.getvalue()
-            script_bytes = launcher + shebang + zip_data
-        for name in names:
-            outname = os.path.join(self.target_dir, name)
-            if use_launcher:  # pragma: no cover
-                n, e = os.path.splitext(outname)
-                if e.startswith('.py'):
-                    outname = n
-                outname = '%s.exe' % outname
-                try:
-                    self._fileop.write_binary_file(outname, script_bytes)
-                except Exception:
-                    # Failed writing an executable - it might be in use.
-                    logger.warning('Failed to write executable - trying to '
-                                   'use .deleteme logic')
-                    dfname = '%s.deleteme' % outname
-                    if os.path.exists(dfname):
-                        os.remove(dfname)       # Not allowed to fail here
-                    os.rename(outname, dfname)  # nor here
-                    self._fileop.write_binary_file(outname, script_bytes)
-                    logger.debug('Able to replace executable using '
-                                 '.deleteme logic')
-                    try:
-                        os.remove(dfname)
-                    except Exception:
-                        pass    # still in use - ignore error
-            else:
-                if self._is_nt and not outname.endswith('.' + ext):  # pragma: no cover
-                    outname = '%s.%s' % (outname, ext)
-                if os.path.exists(outname) and not self.clobber:
-                    logger.warning('Skipping existing file %s', outname)
-                    continue
-                self._fileop.write_binary_file(outname, script_bytes)
-                if self.set_mode:
-                    self._fileop.set_executable_mode([outname])
-            filenames.append(outname)
-
-    variant_separator = '-'
-
-    def get_script_filenames(self, name):
-        result = set()
-        if '' in self.variants:
-            result.add(name)
-        if 'X' in self.variants:
-            result.add('%s%s' % (name, self.version_info[0]))
-        if 'X.Y' in self.variants:
-            result.add('%s%s%s.%s' % (name, self.variant_separator,
-                                      self.version_info[0], self.version_info[1]))
-        return result
-
-    def _make_script(self, entry, filenames, options=None):
-        post_interp = b''
-        if options:
-            args = options.get('interpreter_args', [])
-            if args:
-                args = ' %s' % ' '.join(args)
-                post_interp = args.encode('utf-8')
-        shebang = self._get_shebang('utf-8', post_interp, options=options)
-        script = self._get_script_text(entry).encode('utf-8')
-        scriptnames = self.get_script_filenames(entry.name)
-        if options and options.get('gui', False):
-            ext = 'pyw'
-        else:
-            ext = 'py'
-        self._write_script(scriptnames, shebang, script, filenames, ext)
-
-    def _copy_script(self, script, filenames):
-        adjust = False
-        script = os.path.join(self.source_dir, convert_path(script))
-        outname = os.path.join(self.target_dir, os.path.basename(script))
-        if not self.force and not self._fileop.newer(script, outname):
-            logger.debug('not copying %s (up-to-date)', script)
-            return
-
-        # Always open the file, but ignore failures in dry-run mode --
-        # that way, we'll get accurate feedback if we can read the
-        # script.
-        try:
-            f = open(script, 'rb')
-        except IOError:  # pragma: no cover
-            if not self.dry_run:
-                raise
-            f = None
-        else:
-            first_line = f.readline()
-            if not first_line:  # pragma: no cover
-                logger.warning('%s is an empty file (skipping)', script)
-                return
-
-            match = FIRST_LINE_RE.match(first_line.replace(b'\r\n', b'\n'))
-            if match:
-                adjust = True
-                post_interp = match.group(1) or b''
-
-        if not adjust:
-            if f:
-                f.close()
-            self._fileop.copy_file(script, outname)
-            if self.set_mode:
-                self._fileop.set_executable_mode([outname])
-            filenames.append(outname)
-        else:
-            logger.info('copying and adjusting %s -> %s', script,
-                        self.target_dir)
-            if not self._fileop.dry_run:
-                encoding, lines = detect_encoding(f.readline)
-                f.seek(0)
-                shebang = self._get_shebang(encoding, post_interp)
-                if b'pythonw' in first_line:  # pragma: no cover
-                    ext = 'pyw'
-                else:
-                    ext = 'py'
-                n = os.path.basename(outname)
-                self._write_script([n], shebang, f.read(), filenames, ext)
-            if f:
-                f.close()
-
-    @property
-    def dry_run(self):
-        return self._fileop.dry_run
-
-    @dry_run.setter
-    def dry_run(self, value):
-        self._fileop.dry_run = value
-
-    if os.name == 'nt' or (os.name == 'java' and os._name == 'nt'):  # pragma: no cover
-        # Executable launcher support.
-        # Launchers are from https://bitbucket.org/vinay.sajip/simple_launcher/
-
-        def _get_launcher(self, kind):
-            if struct.calcsize('P') == 8:   # 64-bit
-                bits = '64'
-            else:
-                bits = '32'
-            platform_suffix = '-arm' if get_platform() == 'win-arm64' else ''
-            name = '%s%s%s.exe' % (kind, bits, platform_suffix)
-            # Issue 31: don't hardcode an absolute package name, but
-            # determine it relative to the current package
-            distlib_package = __name__.rsplit('.', 1)[0]
-            resource = finder(distlib_package).find(name)
-            if not resource:
-                msg = ('Unable to find resource %s in package %s' % (name,
-                       distlib_package))
-                raise ValueError(msg)
-            return resource.bytes
-
-    # Public API follows
-
-    def make(self, specification, options=None):
-        """
-        Make a script.
-
-        :param specification: The specification, which is either a valid export
-                              entry specification (to make a script from a
-                              callable) or a filename (to make a script by
-                              copying from a source location).
-        :param options: A dictionary of options controlling script generation.
-        :return: A list of all absolute pathnames written to.
-        """
-        filenames = []
-        entry = get_export_entry(specification)
-        if entry is None:
-            self._copy_script(specification, filenames)
-        else:
-            self._make_script(entry, filenames, options=options)
-        return filenames
-
-    def make_multiple(self, specifications, options=None):
-        """
-        Take a list of specifications and make scripts from them,
-        :param specifications: A list of specifications.
-        :return: A list of all absolute pathnames written to,
-        """
-        filenames = []
-        for specification in specifications:
-            filenames.extend(self.make(specification, options))
-        return filenames

spaces/AyameYODAYO/xijinpingx/index.html

@@ -1,24 +0,0 @@
-<!DOCTYPE html>
-<html>
-	<head>
-		<meta charset="utf-8" />
-		<meta name="viewport" content="width=device-width" />
-		<title>My static Space</title>
-		<link rel="stylesheet" href="style.css" />
-	</head>
-	<body>
-		<div class="card">
-			<h1>Welcome to your static Space!</h1>
-			<p>
-				You can modify this app directly by editing <i>index.html</i> in the
-				Files and versions tab.
-			</p>
-			<p>
-				Also don't forget to check the
-				<a href="https://huggingface.co/docs/hub/spaces" target="_blank"
-					>Spaces documentation</a
-				>.
-			</p>
-		</div>
-	</body>
-</html>

spaces/Benson/text-generation/Examples/Descargar Caramelo Crush Saga Para Windows Pc 7.md

@@ -1,45 +0,0 @@
-
-<h1>Cómo descargar Candy Crush Saga para PC Windows 7</h1>
-<p>Si eres un fan de los juegos de puzzle, probablemente hayas oído hablar de Candy Crush Saga, uno de los juegos más populares y adictivos de todos los tiempos. ¿Pero sabías que también puedes jugar a este juego en tu PC Windows 7? En este artículo, te mostraremos cómo descargar e instalar Candy Crush Saga para PC Windows 7 usando dos métodos diferentes: desde la tienda de Microsoft y usando un emulador. ¡Sigue leyendo para saber más! </p>
-<h2>Introducción</h2>
-<h3>¿Qué es Candy Crush Saga? </h3>
-<p>Candy Crush Saga es un juego de puzzle desarrollado por King, una empresa líder en la industria de los juegos casuales. El juego fue lanzado en 2012 y desde entonces se ha convertido en un fenómeno mundial, con más de mil millones de descargas y millones de jugadores activos cada día. El juego es simple pero desafiante: tienes que cambiar y combinar dulces del mismo color para limpiar el tablero y completar los niveles. Hay miles de niveles para jugar, cada uno con diferentes objetivos y obstáculos. También puedes usar potenciadores y dulces especiales para ayudarte. El juego es gratis, pero también puedes comprar movimientos adicionales, vidas y otros artículos con dinero real. </p>
-<h2>descargar caramelo crush saga para windows pc 7</h2><br /><p><b><b>Download File</b> &#9675; <a href="https://bltlly.com/2v6K90">https://bltlly.com/2v6K90</a></b></p><br /><br />
-<h3> ¿Por qué jugar Candy Crush Saga en PC Windows 7?</h3>
-<p>Mientras que Candy Crush Saga está diseñado principalmente para dispositivos móviles, hay muchas razones por las que es posible que desee jugar en su PC Windows 7. Aquí están algunos de ellos:</p>
-<ul>
-<li>Puedes disfrutar de una pantalla más grande y mejores gráficos. </li>
-<li> Puede utilizar el teclado y el ratón para un control más preciso. </li>
-<li> Puede ahorrar la vida de la batería y el uso de datos. </li>
-<li>Puedes jugar offline sin interrupciones. </li>
-<li>Puede sincronizar su progreso a través de múltiples dispositivos. </li>
-</ul>
-<p>Entonces, ¿cómo se descarga Candy Crush Saga para PC Windows 7? Hay dos maneras de hacerlo: desde la tienda de Microsoft o usando un emulador. Veamos cómo funciona cada método. </p>
-<h2>Cómo descargar Candy Crush Saga de Microsoft Store</h2>
-
-<h3>Paso 1: Ir al sitio web de Microsoft Store</h3>
-<p>Abra su navegador web y vaya a [el sitio web de Microsoft Store]( 1 ). También puede acceder a la tienda desde su menú de inicio o barra de tareas. </p>
-<h3>Paso 2: Búsqueda de Candy Crush Saga</h3>
-<p>En la barra de búsqueda en la esquina superior derecha, escriba "Candy Crush Saga" y pulse Enter. Verá una lista de resultados relacionados con su consulta. Haga clic en el que dice "Candy Crush Saga" por king.com. </p>
-<h3>Paso 3: Haga clic en el botón Obtener</h3>
-<p>En la página del juego, verá un botón que dice "Obtener". Haga clic en él para comenzar a descargar el juego. Es posible que tengas que iniciar sesión con tu cuenta de Microsoft si aún no lo has hecho. El juego es gratuito, pero puede ofrecer compras en la aplicación. </p>
-<h3>Paso 4: Iniciar el juego y disfrutar de</h3>
-<p>Una vez completada la descarga, puedes iniciar el juego desde tu menú de inicio o barra de tareas. Verás una pantalla de bienvenida que te pide que conectes tu cuenta de Facebook o que juegues como invitado. También puede elegir su idioma preferido en el menú de configuración. Ahora está listo para jugar Candy Crush Saga en su PC Windows 7!</p>
-<h2>Cómo descargar Candy Crush Saga usando el emulador de BlueStacks</h2>
-<p>Un emulador es un software que le permite ejecutar aplicaciones y juegos para Android en su PC. Hay muchos emuladores disponibles, pero uno de los más populares y fiables es BlueStacks. BlueStacks es gratuito y fácil de usar, y tiene una gran biblioteca de aplicaciones y juegos que puedes descargar y jugar. Estos son los pasos para descargar Candy Crush Saga usando el emulador de BlueStacks:</p>
-<h3>Paso 1: Descargar e instalar BlueStacks en su PC</h3>
-<p>Vaya a [el sitio web de BlueStacks] y haga clic en el botón "Descargar BlueStacks". Esto comenzará a descargar el archivo de instalación en su PC. Una vez finalizada la descarga, ejecute el archivo y siga las instrucciones para instalar BlueStacks en su PC. Es posible que necesite conceder algunos permisos y reiniciar su PC durante el proceso. </p>
-<p></p>
-<h3>Paso 2: Inicia sesión con tu cuenta de Google</h3>
-
-<h3>Paso 3: Búsqueda de Candy Crush Saga en la Play Store</h3>
-<p>Una vez que haya iniciado sesión, verá la pantalla de inicio de BlueStacks, que parece una tableta Android. En la esquina superior derecha, verá un icono de búsqueda. Haga clic en él y escriba "Candy Crush Saga" en la barra de búsqueda. Verá una lista de resultados relacionados con su consulta. Haga clic en el que dice "Candy Crush Saga" por King.</p>
-<h3>Paso 4: Instalar y jugar el juego en BlueStacks</h3>
-<p>En la página del juego, verá un botón que dice "Instalar". Haga clic en él para comenzar a descargar e instalar el juego en BlueStacks. El juego es gratuito, pero puede ofrecer compras en la aplicación. Una vez completada la instalación, puede iniciar el juego desde la pantalla de inicio o el cajón de aplicaciones de BlueStacks. Verás una pantalla de bienvenida que te pide que conectes tu cuenta de Facebook o que juegues como invitado. También puede elegir su idioma preferido en el menú de configuración. Ahora está listo para jugar Candy Crush Saga en su PC Windows 7!</p>
-<h2>Conclusión</h2>
-<h3>Resumen de los puntos principales</h3>
-<p>En este artículo, le hemos mostrado cómo descargar e instalar Candy Crush Saga para PC Windows 7 usando dos métodos diferentes: desde la Tienda de Microsoft y usando un emulador. Ambos métodos son fáciles y seguros, y te permiten disfrutar de este divertido y adictivo juego de puzzle en tu PC Windows 7. También puedes sincronizar tu progreso a través de múltiples dispositivos y jugar sin conexión sin interrupciones. </p>
-<h3>Llamada a la acción</h3>
-<p>Así que, ¿qué estás esperando? Descargar Candy Crush Saga para PC Windows 7 hoy y empezar a emparejar caramelos y niveles de limpieza! Usted tendrá una explosión jugando a este juego, si usted es un principiante o un experto. ¡Y no olvides compartir tu experiencia con nosotros en los comentarios de abajo! </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/resources/action.py

@@ -1,257 +0,0 @@
-# Copyright 2014 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# https://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-
-import logging
-
-from botocore import xform_name
-
-from boto3.docs.docstring import ActionDocstring
-from boto3.utils import inject_attribute
-
-from .model import Action
-from .params import create_request_parameters
-from .response import RawHandler, ResourceHandler
-
-logger = logging.getLogger(__name__)
-
-
-class ServiceAction:
-    """
-    A class representing a callable action on a resource, for example
-    ``sqs.get_queue_by_name(...)`` or ``s3.Bucket('foo').delete()``.
-    The action may construct parameters from existing resource identifiers
-    and may return either a raw response or a new resource instance.
-
-    :type action_model: :py:class`~boto3.resources.model.Action`
-    :param action_model: The action model.
-
-    :type factory: ResourceFactory
-    :param factory: The factory that created the resource class to which
-                    this action is attached.
-
-    :type service_context: :py:class:`~boto3.utils.ServiceContext`
-    :param service_context: Context about the AWS service
-    """
-
-    def __init__(self, action_model, factory=None, service_context=None):
-        self._action_model = action_model
-
-        # In the simplest case we just return the response, but if a
-        # resource is defined, then we must create these before returning.
-        resource_response_model = action_model.resource
-        if resource_response_model:
-            self._response_handler = ResourceHandler(
-                search_path=resource_response_model.path,
-                factory=factory,
-                resource_model=resource_response_model,
-                service_context=service_context,
-                operation_name=action_model.request.operation,
-            )
-        else:
-            self._response_handler = RawHandler(action_model.path)
-
-    def __call__(self, parent, *args, **kwargs):
-        """
-        Perform the action's request operation after building operation
-        parameters and build any defined resources from the response.
-
-        :type parent: :py:class:`~boto3.resources.base.ServiceResource`
-        :param parent: The resource instance to which this action is attached.
-        :rtype: dict or ServiceResource or list(ServiceResource)
-        :return: The response, either as a raw dict or resource instance(s).
-        """
-        operation_name = xform_name(self._action_model.request.operation)
-
-        # First, build predefined params and then update with the
-        # user-supplied kwargs, which allows overriding the pre-built
-        # params if needed.
-        params = create_request_parameters(parent, self._action_model.request)
-        params.update(kwargs)
-
-        logger.debug(
-            'Calling %s:%s with %r',
-            parent.meta.service_name,
-            operation_name,
-            params,
-        )
-
-        response = getattr(parent.meta.client, operation_name)(*args, **params)
-
-        logger.debug('Response: %r', response)
-
-        return self._response_handler(parent, params, response)
-
-
-class BatchAction(ServiceAction):
-    """
-    An action which operates on a batch of items in a collection, typically
-    a single page of results from the collection's underlying service
-    operation call. For example, this allows you to delete up to 999
-    S3 objects in a single operation rather than calling ``.delete()`` on
-    each one individually.
-
-    :type action_model: :py:class`~boto3.resources.model.Action`
-    :param action_model: The action model.
-
-    :type factory: ResourceFactory
-    :param factory: The factory that created the resource class to which
-                    this action is attached.
-
-    :type service_context: :py:class:`~boto3.utils.ServiceContext`
-    :param service_context: Context about the AWS service
-    """
-
-    def __call__(self, parent, *args, **kwargs):
-        """
-        Perform the batch action's operation on every page of results
-        from the collection.
-
-        :type parent:
-            :py:class:`~boto3.resources.collection.ResourceCollection`
-        :param parent: The collection iterator to which this action
-                       is attached.
-        :rtype: list(dict)
-        :return: A list of low-level response dicts from each call.
-        """
-        service_name = None
-        client = None
-        responses = []
-        operation_name = xform_name(self._action_model.request.operation)
-
-        # Unlike the simple action above, a batch action must operate
-        # on batches (or pages) of items. So we get each page, construct
-        # the necessary parameters and call the batch operation.
-        for page in parent.pages():
-            params = {}
-            for index, resource in enumerate(page):
-                # There is no public interface to get a service name
-                # or low-level client from a collection, so we get
-                # these from the first resource in the collection.
-                if service_name is None:
-                    service_name = resource.meta.service_name
-                if client is None:
-                    client = resource.meta.client
-
-                create_request_parameters(
-                    resource,
-                    self._action_model.request,
-                    params=params,
-                    index=index,
-                )
-
-            if not params:
-                # There are no items, no need to make a call.
-                break
-
-            params.update(kwargs)
-
-            logger.debug(
-                'Calling %s:%s with %r', service_name, operation_name, params
-            )
-
-            response = getattr(client, operation_name)(*args, **params)
-
-            logger.debug('Response: %r', response)
-
-            responses.append(self._response_handler(parent, params, response))
-
-        return responses
-
-
-class WaiterAction:
-    """
-    A class representing a callable waiter action on a resource, for example
-    ``s3.Bucket('foo').wait_until_bucket_exists()``.
-    The waiter action may construct parameters from existing resource
-    identifiers.
-
-    :type waiter_model: :py:class`~boto3.resources.model.Waiter`
-    :param waiter_model: The action waiter.
-    :type waiter_resource_name: string
-    :param waiter_resource_name: The name of the waiter action for the
-                                 resource. It usually begins with a
-                                 ``wait_until_``
-    """
-
-    def __init__(self, waiter_model, waiter_resource_name):
-        self._waiter_model = waiter_model
-        self._waiter_resource_name = waiter_resource_name
-
-    def __call__(self, parent, *args, **kwargs):
-        """
-        Perform the wait operation after building operation
-        parameters.
-
-        :type parent: :py:class:`~boto3.resources.base.ServiceResource`
-        :param parent: The resource instance to which this action is attached.
-        """
-        client_waiter_name = xform_name(self._waiter_model.waiter_name)
-
-        # First, build predefined params and then update with the
-        # user-supplied kwargs, which allows overriding the pre-built
-        # params if needed.
-        params = create_request_parameters(parent, self._waiter_model)
-        params.update(kwargs)
-
-        logger.debug(
-            'Calling %s:%s with %r',
-            parent.meta.service_name,
-            self._waiter_resource_name,
-            params,
-        )
-
-        client = parent.meta.client
-        waiter = client.get_waiter(client_waiter_name)
-        response = waiter.wait(**params)
-
-        logger.debug('Response: %r', response)
-
-
-class CustomModeledAction:
-    """A custom, modeled action to inject into a resource."""
-
-    def __init__(self, action_name, action_model, function, event_emitter):
-        """
-        :type action_name: str
-        :param action_name: The name of the action to inject, e.g.
-            'delete_tags'
-
-        :type action_model: dict
-        :param action_model: A JSON definition of the action, as if it were
-            part of the resource model.
-
-        :type function: function
-        :param function: The function to perform when the action is called.
-            The first argument should be 'self', which will be the resource
-            the function is to be called on.
-
-        :type event_emitter: :py:class:`botocore.hooks.BaseEventHooks`
-        :param event_emitter: The session event emitter.
-        """
-        self.name = action_name
-        self.model = action_model
-        self.function = function
-        self.emitter = event_emitter
-
-    def inject(self, class_attributes, service_context, event_name, **kwargs):
-        resource_name = event_name.rsplit(".")[-1]
-        action = Action(self.name, self.model, {})
-        self.function.__name__ = self.name
-        self.function.__doc__ = ActionDocstring(
-            resource_name=resource_name,
-            event_emitter=self.emitter,
-            action_model=action,
-            service_model=service_context.service_model,
-            include_signature=False,
-        )
-        inject_attribute(class_attributes, self.name, self.function)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/req/req_set.py

@@ -1,82 +0,0 @@
-import logging
-from collections import OrderedDict
-from typing import Dict, List
-
-from pip._vendor.packaging.utils import canonicalize_name
-
-from pip._internal.req.req_install import InstallRequirement
-
-logger = logging.getLogger(__name__)
-
-
-class RequirementSet:
-    def __init__(self, check_supported_wheels: bool = True) -> None:
-        """Create a RequirementSet."""
-
-        self.requirements: Dict[str, InstallRequirement] = OrderedDict()
-        self.check_supported_wheels = check_supported_wheels
-
-        self.unnamed_requirements: List[InstallRequirement] = []
-
-    def __str__(self) -> str:
-        requirements = sorted(
-            (req for req in self.requirements.values() if not req.comes_from),
-            key=lambda req: canonicalize_name(req.name or ""),
-        )
-        return " ".join(str(req.req) for req in requirements)
-
-    def __repr__(self) -> str:
-        requirements = sorted(
-            self.requirements.values(),
-            key=lambda req: canonicalize_name(req.name or ""),
-        )
-
-        format_string = "<{classname} object; {count} requirement(s): {reqs}>"
-        return format_string.format(
-            classname=self.__class__.__name__,
-            count=len(requirements),
-            reqs=", ".join(str(req.req) for req in requirements),
-        )
-
-    def add_unnamed_requirement(self, install_req: InstallRequirement) -> None:
-        assert not install_req.name
-        self.unnamed_requirements.append(install_req)
-
-    def add_named_requirement(self, install_req: InstallRequirement) -> None:
-        assert install_req.name
-
-        project_name = canonicalize_name(install_req.name)
-        self.requirements[project_name] = install_req
-
-    def has_requirement(self, name: str) -> bool:
-        project_name = canonicalize_name(name)
-
-        return (
-            project_name in self.requirements
-            and not self.requirements[project_name].constraint
-        )
-
-    def get_requirement(self, name: str) -> InstallRequirement:
-        project_name = canonicalize_name(name)
-
-        if project_name in self.requirements:
-            return self.requirements[project_name]
-
-        raise KeyError(f"No project with the name {name!r}")
-
-    @property
-    def all_requirements(self) -> List[InstallRequirement]:
-        return self.unnamed_requirements + list(self.requirements.values())
-
-    @property
-    def requirements_to_install(self) -> List[InstallRequirement]:
-        """Return the list of requirements that need to be installed.
-
-        TODO remove this property together with the legacy resolver, since the new
-             resolver only returns requirements that need to be installed.
-        """
-        return [
-            install_req
-            for install_req in self.all_requirements
-            if not install_req.constraint and not install_req.satisfied_by
-        ]

spaces/Boadiwaa/Recipes/openai/api_resources/abstract/api_resource.py

@@ -1,117 +0,0 @@
-from urllib.parse import quote_plus
-
-import openai
-from openai import api_requestor, error, util
-from openai.openai_object import OpenAIObject
-from openai.util import ApiType
-
-
-class APIResource(OpenAIObject):
-    api_prefix = ""
-    azure_api_prefix = "openai"
-    azure_deployments_prefix = "deployments"
-
-    @classmethod
-    def retrieve(cls, id, api_key=None, request_id=None, **params):
-        instance = cls(id, api_key, **params)
-        instance.refresh(request_id=request_id)
-        return instance
-
-    def refresh(self, request_id=None):
-        self.refresh_from(
-            self.request("get", self.instance_url(), request_id=request_id)
-        )
-        return self
-
-    @classmethod
-    def class_url(cls):
-        if cls == APIResource:
-            raise NotImplementedError(
-                "APIResource is an abstract class. You should perform actions on its subclasses."
-            )
-        # Namespaces are separated in object names with periods (.) and in URLs
-        # with forward slashes (/), so replace the former with the latter.
-        base = cls.OBJECT_NAME.replace(".", "/")  # type: ignore
-        if cls.api_prefix:
-            return "/%s/%s" % (cls.api_prefix, base)
-        return "/%s" % (base)
-
-    def instance_url(self, operation=None):
-        id = self.get("id")
-
-        if not isinstance(id, str):
-            raise error.InvalidRequestError(
-                "Could not determine which URL to request: %s instance "
-                "has invalid ID: %r, %s. ID should be of type `str` (or"
-                " `unicode`)" % (type(self).__name__, id, type(id)),
-                "id",
-            )
-        api_version = self.api_version or openai.api_version
-        extn = quote_plus(id)
-
-        if self.typed_api_type == ApiType.AZURE:
-            if not api_version:
-                raise error.InvalidRequestError(
-                    "An API version is required for the Azure API type."
-                )
-            
-            if not operation:
-                base = self.class_url()
-                return "/%s%s/%s?api-version=%s" % (
-                    self.azure_api_prefix,
-                    base,
-                    extn,
-                    api_version
-                )
-
-            return "/%s/%s/%s/%s?api-version=%s" % (
-                self.azure_api_prefix,
-                self.azure_deployments_prefix,
-                extn,
-                operation,
-                api_version
-            )
-
-
-        elif self.typed_api_type == ApiType.OPEN_AI:
-            base = self.class_url()
-            return "%s/%s" % (base, extn)
-
-        else:
-            raise error.InvalidAPIType("Unsupported API type %s" % self.api_type)
-
-    # The `method_` and `url_` arguments are suffixed with an underscore to
-    # avoid conflicting with actual request parameters in `params`.
-    @classmethod
-    def _static_request(
-        cls,
-        method_,
-        url_,
-        api_key=None,
-        api_base=None,
-        api_type=None,
-        request_id=None,
-        api_version=None,
-        organization=None,
-        **params,
-    ):
-        requestor = api_requestor.APIRequestor(
-            api_key,
-            api_version=api_version,
-            organization=organization,
-            api_base=api_base,
-            api_type=api_type
-        )
-        response, _, api_key = requestor.request(
-            method_, url_, params, request_id=request_id
-        )
-        return util.convert_to_openai_object(
-            response, api_key, api_version, organization
-        )
-
-    @classmethod
-    def _get_api_type_and_version(cls, api_type: str, api_version: str):
-        typed_api_type = ApiType.from_str(api_type) if api_type else ApiType.from_str(openai.api_type)
-        typed_api_version = api_version or openai.api_version
-        return (typed_api_type, typed_api_version)
-

spaces/Bonosa2/movies/app3.py

@@ -1,82 +0,0 @@
-import pandas as pd
-import numpy as np
-from transformers import AutoTokenizer, AutoModel
-import torch
-import scipy.spatial
-import gradio as gr
-
-# Load the movies dataset
-url = 'https://storage.googleapis.com/movves123/movies.csv'
-df_movies = pd.read_csv(url)
-
-# Load the ratings dataset
-df_ratings = pd.read_csv('ratings.csv')
-
-# Calculate the average rating for each movie
-average_ratings = df_ratings.groupby('movieId')['rating'].mean()
-
-# Join the average ratings with the movies DataFrame
-df = df_movies.join(average_ratings, on='movieId')
-
-# Load DistilBERT model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
-model = AutoModel.from_pretrained('distilbert-base-uncased')
-
-# Precompute movie title embeddings
-titles = df['title'].tolist()
-genres = df['genres'].tolist()
-ratings = df['rating'].tolist()  # Use the average rating
-
-# Combine title, genre, and ratings into a single string and compute embeddings
-combined = [f"{title} {genre} {rating}" for title, genre, rating in zip(titles, genres, ratings)]
-inputs = tokenizer(combined, return_tensors='pt', padding=True, truncation=True, max_length=512)
-outputs = model(**inputs)
-embeddings = outputs.last_hidden_state.mean(1).detach().numpy()
-
-# Convert embeddings to a tensor for similarity calculation
-embeddings = torch.tensor(embeddings)
-
-# List of movie genres
-genre_keywords = ['Action', 'Adventure', 'Animation', 'Children', 'Comedy', 'Crime', 
-                  'Documentary', 'Drama', 'Fantasy', 'Film-Noir', 'Horror', 'Musical', 
-                  'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western']
-
-def recommend_movies(user_input):
-    # Detect genre from user's input
-    user_genre = [genre for genre in genre_keywords if genre.lower() in user_input.lower()]
-    
-    # If a genre is detected, recommend movies from that genre
-    if user_genre:
-        inputs = tokenizer(user_genre[0], return_tensors='pt')
-        query_embedding = model(**inputs).last_hidden_state.mean(1).detach().numpy()
-    else:
-        inputs = tokenizer(user_input, return_tensors='pt')
-        query_embedding = model(**inputs).last_hidden_state.mean(1).detach().numpy()
-
-    # Compute cosine similarity scores
-    cosine_scores = scipy.spatial.distance.cdist(query_embedding, embeddings, "cosine")[0]
-
-    # Get top 5 matches
-    top_results = np.argpartition(-cosine_scores, range(5))[:5]
-    
-    # Generate a list of numbered recommendations
-    final_recommendations = [f"{i+1}. {df.iloc[idx]['title']} (Rating: {df.iloc[idx]['rating']})" for i, idx in enumerate(top_results)]
-    
-    return "\n".join(final_recommendations)
-
-
-examples = [
-    ['I\'m in the mood for a comedy.'], 
-    ['How about some action?'], 
-    ['I want to watch a romance movie.']
-]
-
-iface = gr.Interface(fn=recommend_movies, 
-                     inputs=gr.inputs.Textbox(lines=2, placeholder='Type something...'), 
-                     outputs=gr.outputs.Textbox(),
-                     examples=examples)  # Include examples
-iface.launch()
-
-
-
-

spaces/BorisovMaksim/denoising/Dockerfile

@@ -1,14 +0,0 @@
-# syntax=docker/dockerfile:1
-
-FROM python:3.8-slim-buster
-LABEL maintainer="Borisov Maksim"
-
-WORKDIR /app
-
-COPY requirements.txt requirements.txt
-RUN pip3 install -r requirements.txt
-
-COPY . .
-
-WORKDIR /app
-CMD ["python3", "app.py"]

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/meta_arch/rcnn.py

@@ -1,282 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import logging
-import numpy as np
-import torch
-from torch import nn
-
-from detectron2.structures import ImageList
-from detectron2.utils.events import get_event_storage
-from detectron2.utils.logger import log_first_n
-
-from ..backbone import build_backbone
-from ..postprocessing import detector_postprocess
-from ..proposal_generator import build_proposal_generator
-from ..roi_heads import build_roi_heads
-from .build import META_ARCH_REGISTRY
-
-__all__ = ["GeneralizedRCNN", "ProposalNetwork"]
-
-
-@META_ARCH_REGISTRY.register()
-class GeneralizedRCNN(nn.Module):
-    """
-    Generalized R-CNN. Any models that contains the following three components:
-    1. Per-image feature extraction (aka backbone)
-    2. Region proposal generation
-    3. Per-region feature extraction and prediction
-    """
-
-    def __init__(self, cfg):
-        super().__init__()
-
-        self.device = torch.device(cfg.MODEL.DEVICE)
-        self.backbone = build_backbone(cfg)
-        self.proposal_generator = build_proposal_generator(
-            cfg, self.backbone.output_shape()
-        )
-        self.roi_heads = build_roi_heads(cfg, self.backbone.output_shape())
-        self.vis_period = cfg.VIS_PERIOD
-        self.input_format = cfg.INPUT.FORMAT
-
-        assert len(cfg.MODEL.PIXEL_MEAN) == len(cfg.MODEL.PIXEL_STD)
-        num_channels = len(cfg.MODEL.PIXEL_MEAN)
-        pixel_mean = (
-            torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(num_channels, 1, 1)
-        )
-        pixel_std = (
-            torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(num_channels, 1, 1)
-        )
-        self.normalizer = lambda x: (x - pixel_mean) / pixel_std
-        self.to(self.device)
-
-    def visualize_training(self, batched_inputs, proposals):
-        """
-        A function used to visualize images and proposals. It shows ground truth
-        bounding boxes on the original image and up to 20 predicted object
-        proposals on the original image. Users can implement different
-        visualization functions for different models.
-
-        Args:
-            batched_inputs (list): a list that contains input to the model.
-            proposals (list): a list that contains predicted proposals. Both
-                batched_inputs and proposals should have the same length.
-        """
-        from detectron2.utils.visualizer import Visualizer
-
-        storage = get_event_storage()
-        max_vis_prop = 20
-
-        for input, prop in zip(batched_inputs, proposals):
-            img = input["image"].cpu().numpy()
-            assert img.shape[0] == 3, "Images should have 3 channels."
-            if self.input_format == "BGR":
-                img = img[::-1, :, :]
-            img = img.transpose(1, 2, 0)
-            v_gt = Visualizer(img, None)
-            v_gt = v_gt.overlay_instances(boxes=input["instances"].gt_boxes)
-            anno_img = v_gt.get_image()
-            box_size = min(len(prop.proposal_boxes), max_vis_prop)
-            v_pred = Visualizer(img, None)
-            v_pred = v_pred.overlay_instances(
-                boxes=prop.proposal_boxes[0:box_size].tensor.cpu().numpy()
-            )
-            prop_img = v_pred.get_image()
-            vis_img = np.concatenate((anno_img, prop_img), axis=1)
-            vis_img = vis_img.transpose(2, 0, 1)
-            vis_name = "Left: GT bounding boxes;  Right: Predicted proposals"
-            storage.put_image(vis_name, vis_img)
-            break  # only visualize one image in a batch
-
-    def forward(self, batched_inputs):
-        """
-        Args:
-            batched_inputs: a list, batched outputs of :class:`DatasetMapper` .
-                Each item in the list contains the inputs for one image.
-                For now, each item in the list is a dict that contains:
-
-                * image: Tensor, image in (C, H, W) format.
-                * instances (optional): groundtruth :class:`Instances`
-                * proposals (optional): :class:`Instances`, precomputed proposals.
-
-                Other information that's included in the original dicts, such as:
-
-                * "height", "width" (int): the output resolution of the model, used in inference.
-                  See :meth:`postprocess` for details.
-
-        Returns:
-            list[dict]:
-                Each dict is the output for one input image.
-                The dict contains one key "instances" whose value is a :class:`Instances`.
-                The :class:`Instances` object has the following keys:
-                "pred_boxes", "pred_classes", "scores", "pred_masks", "pred_keypoints"
-        """
-        if not self.training:
-            return self.inference(batched_inputs)
-
-        images = self.preprocess_image(batched_inputs)
-        if "instances" in batched_inputs[0]:
-            gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
-        elif "targets" in batched_inputs[0]:
-            log_first_n(
-                logging.WARN,
-                "'targets' in the model inputs is now renamed to 'instances'!",
-                n=10,
-            )
-            gt_instances = [x["targets"].to(self.device) for x in batched_inputs]
-        else:
-            gt_instances = None
-
-        features = self.backbone(images.tensor)
-
-        if self.proposal_generator:
-            proposals, proposal_losses = self.proposal_generator(
-                images, features, gt_instances
-            )
-        else:
-            assert "proposals" in batched_inputs[0]
-            proposals = [x["proposals"].to(self.device) for x in batched_inputs]
-            proposal_losses = {}
-
-        _, detector_losses = self.roi_heads(images, features, proposals, gt_instances)
-        if self.vis_period > 0:
-            storage = get_event_storage()
-            if storage.iter % self.vis_period == 0:
-                self.visualize_training(batched_inputs, proposals)
-
-        losses = {}
-        losses.update(detector_losses)
-        losses.update(proposal_losses)
-        return losses
-
-    def inference(self, batched_inputs, detected_instances=None, do_postprocess=True):
-        """
-        Run inference on the given inputs.
-
-        Args:
-            batched_inputs (list[dict]): same as in :meth:`forward`
-            detected_instances (None or list[Instances]): if not None, it
-                contains an `Instances` object per image. The `Instances`
-                object contains "pred_boxes" and "pred_classes" which are
-                known boxes in the image.
-                The inference will then skip the detection of bounding boxes,
-                and only predict other per-ROI outputs.
-            do_postprocess (bool): whether to apply post-processing on the outputs.
-
-        Returns:
-            same as in :meth:`forward`.
-        """
-        assert not self.training
-
-        images = self.preprocess_image(batched_inputs)
-        features = self.backbone(images.tensor)
-
-        if detected_instances is None:
-            if self.proposal_generator:
-                proposals, _ = self.proposal_generator(images, features, None)
-            else:
-                assert "proposals" in batched_inputs[0]
-                proposals = [x["proposals"].to(self.device) for x in batched_inputs]
-
-            results, box_features = self.roi_heads(images, features, proposals, None)
-        else:
-            detected_instances = [x.to(self.device) for x in detected_instances]
-            results = self.roi_heads.forward_with_given_boxes(
-                features, detected_instances
-            )
-
-        if do_postprocess:
-            return (
-                GeneralizedRCNN._postprocess(
-                    results, batched_inputs, images.image_sizes
-                ),
-                box_features,
-            )
-        else:
-            return results
-
-    def preprocess_image(self, batched_inputs):
-        """
-        Normalize, pad and batch the input images.
-        """
-        images = [x["image"].to(self.device) for x in batched_inputs]
-        images = [self.normalizer(x) for x in images]
-        images = ImageList.from_tensors(images, self.backbone.size_divisibility)
-        return images
-
-    @staticmethod
-    def _postprocess(instances, batched_inputs, image_sizes):
-        """
-        Rescale the output instances to the target size.
-        """
-        # note: private function; subject to changes
-        processed_results = []
-        for results_per_image, input_per_image, image_size in zip(
-            instances, batched_inputs, image_sizes
-        ):
-            height = input_per_image.get("height", image_size[0])
-            width = input_per_image.get("width", image_size[1])
-            r = detector_postprocess(results_per_image, height, width)
-            processed_results.append({"instances": r})
-        return processed_results
-
-
-@META_ARCH_REGISTRY.register()
-class ProposalNetwork(nn.Module):
-    def __init__(self, cfg):
-        super().__init__()
-        self.device = torch.device(cfg.MODEL.DEVICE)
-
-        self.backbone = build_backbone(cfg)
-        self.proposal_generator = build_proposal_generator(
-            cfg, self.backbone.output_shape()
-        )
-
-        pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(-1, 1, 1)
-        pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(-1, 1, 1)
-        self.normalizer = lambda x: (x - pixel_mean) / pixel_std
-        self.to(self.device)
-
-    def forward(self, batched_inputs):
-        """
-        Args:
-            Same as in :class:`GeneralizedRCNN.forward`
-
-        Returns:
-            list[dict]:
-                Each dict is the output for one input image.
-                The dict contains one key "proposals" whose value is a
-                :class:`Instances` with keys "proposal_boxes" and "objectness_logits".
-        """
-        images = [x["image"].to(self.device) for x in batched_inputs]
-        images = [self.normalizer(x) for x in images]
-        images = ImageList.from_tensors(images, self.backbone.size_divisibility)
-        features = self.backbone(images.tensor)
-
-        if "instances" in batched_inputs[0]:
-            gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
-        elif "targets" in batched_inputs[0]:
-            log_first_n(
-                logging.WARN,
-                "'targets' in the model inputs is now renamed to 'instances'!",
-                n=10,
-            )
-            gt_instances = [x["targets"].to(self.device) for x in batched_inputs]
-        else:
-            gt_instances = None
-        proposals, proposal_losses = self.proposal_generator(
-            images, features, gt_instances
-        )
-        # In training, the proposals are not useful at all but we generate them anyway.
-        # This makes RPN-only models about 5% slower.
-        if self.training:
-            return proposal_losses
-
-        processed_results = []
-        for results_per_image, input_per_image, image_size in zip(
-            proposals, batched_inputs, images.image_sizes
-        ):
-            height = input_per_image.get("height", image_size[0])
-            width = input_per_image.get("width", image_size[1])
-            r = detector_postprocess(results_per_image, height, width)
-            processed_results.append({"proposals": r})
-        return processed_results

spaces/CVPR/Text2Human/Text2Human/data/mask_dataset.py

@@ -1,59 +0,0 @@
-import os
-import os.path
-import random
-
-import numpy as np
-import torch
-import torch.utils.data as data
-from PIL import Image
-
-
-class MaskDataset(data.Dataset):
-
-    def __init__(self, segm_dir, ann_dir, downsample_factor=2, xflip=False):
-
-        self._segm_path = segm_dir
-        self._image_fnames = []
-
-        self.downsample_factor = downsample_factor
-        self.xflip = xflip
-
-        # load attributes
-        assert os.path.exists(f'{ann_dir}/upper_fused.txt')
-        for idx, row in enumerate(
-                open(os.path.join(f'{ann_dir}/upper_fused.txt'), 'r')):
-            annotations = row.split()
-            self._image_fnames.append(annotations[0])
-
-    def _open_file(self, path_prefix, fname):
-        return open(os.path.join(path_prefix, fname), 'rb')
-
-    def _load_segm(self, raw_idx):
-        fname = self._image_fnames[raw_idx]
-        fname = f'{fname[:-4]}_segm.png'
-        with self._open_file(self._segm_path, fname) as f:
-            segm = Image.open(f)
-            if self.downsample_factor != 1:
-                width, height = segm.size
-                width = width // self.downsample_factor
-                height = height // self.downsample_factor
-                segm = segm.resize(
-                    size=(width, height), resample=Image.NEAREST)
-            segm = np.array(segm)
-        # segm = segm[:, :, np.newaxis].transpose(2, 0, 1)
-        return segm.astype(np.float32)
-
-    def __getitem__(self, index):
-        segm = self._load_segm(index)
-
-        if self.xflip and random.random() > 0.5:
-            segm = segm[:, ::-1].copy()
-
-        segm = torch.from_numpy(segm).long()
-
-        return_dict = {'segm': segm, 'img_name': self._image_fnames[index]}
-
-        return return_dict
-
-    def __len__(self):
-        return len(self._image_fnames)

spaces/ChallengeHub/Chinese-LangChain/clc/gpt_service.py

@@ -1,62 +0,0 @@
-#!/usr/bin/env python
-# -*- coding:utf-8 _*-
-"""
-@author:quincy qiang
-@license: Apache Licence
-@file: generate.py
-@time: 2023/04/17
-@contact: [email protected]
-@software: PyCharm
-@description: coding..
-"""
-
-from typing import List, Optional
-
-from langchain.llms.base import LLM
-from langchain.llms.utils import enforce_stop_tokens
-from transformers import AutoModel, AutoTokenizer
-
-
-class ChatGLMService(LLM):
-    max_token: int = 10000
-    temperature: float = 0.1
-    top_p = 0.9
-    history = []
-    tokenizer: object = None
-    model: object = None
-
-    def __init__(self):
-        super().__init__()
-
-    @property
-    def _llm_type(self) -> str:
-        return "ChatGLM"
-
-    def _call(self,
-              prompt: str,
-              stop: Optional[List[str]] = None) -> str:
-        response, _ = self.model.chat(
-            self.tokenizer,
-            prompt,
-            history=self.history,
-            max_length=self.max_token,
-            temperature=self.temperature,
-        )
-        if stop is not None:
-            response = enforce_stop_tokens(response, stop)
-        self.history = self.history + [[None, response]]
-        return response
-
-    def load_model(self,
-                   model_name_or_path: str = "THUDM/chatglm-6b"):
-        self.tokenizer = AutoTokenizer.from_pretrained(
-            model_name_or_path,
-            trust_remote_code=True
-        )
-        self.model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True).half().cuda()
-        self.model=self.model.eval()
-# if __name__ == '__main__':
-#     config=LangChainCFG()
-#     chatLLM = ChatGLMService()
-#     chatLLM.load_model(model_name_or_path=config.llm_model_name)
-

spaces/Codecooker/rvcapi/src/vc_infer_pipeline.py

@@ -1,653 +0,0 @@
-from functools import lru_cache
-from time import time as ttime
-
-import faiss
-import librosa
-import numpy as np
-import os
-import parselmouth
-import pyworld
-import sys
-import torch
-import torch.nn.functional as F
-import torchcrepe
-import traceback
-from scipy import signal
-from torch import Tensor
-
-BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-now_dir = os.path.join(BASE_DIR, 'src')
-sys.path.append(now_dir)
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-input_audio_path2wav = {}
-
-
-@lru_cache
-def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
-    audio = input_audio_path2wav[input_audio_path]
-    f0, t = pyworld.harvest(
-        audio,
-        fs=fs,
-        f0_ceil=f0max,
-        f0_floor=f0min,
-        frame_period=frame_period,
-    )
-    f0 = pyworld.stonemask(audio, f0, t, fs)
-    return f0
-
-
-def change_rms(data1, sr1, data2, sr2, rate):  # 1是输入音频，2是输出音频,rate是2的占比
-    # print(data1.max(),data2.max())
-    rms1 = librosa.feature.rms(
-        y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
-    )  # 每半秒一个点
-    rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
-    rms1 = torch.from_numpy(rms1)
-    rms1 = F.interpolate(
-        rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
-    ).squeeze()
-    rms2 = torch.from_numpy(rms2)
-    rms2 = F.interpolate(
-        rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
-    ).squeeze()
-    rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
-    data2 *= (
-        torch.pow(rms1, torch.tensor(1 - rate))
-        * torch.pow(rms2, torch.tensor(rate - 1))
-    ).numpy()
-    return data2
-
-
-class VC(object):
-    def __init__(self, tgt_sr, config):
-        self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
-            config.x_pad,
-            config.x_query,
-            config.x_center,
-            config.x_max,
-            config.is_half,
-        )
-        self.sr = 16000  # hubert输入采样率
-        self.window = 160  # 每帧点数
-        self.t_pad = self.sr * self.x_pad  # 每条前后pad时间
-        self.t_pad_tgt = tgt_sr * self.x_pad
-        self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * self.x_query  # 查询切点前后查询时间
-        self.t_center = self.sr * self.x_center  # 查询切点位置
-        self.t_max = self.sr * self.x_max  # 免查询时长阈值
-        self.device = config.device
-
-    # Fork Feature: Get the best torch device to use for f0 algorithms that require a torch device. Will return the type (torch.device)
-    def get_optimal_torch_device(self, index: int = 0) -> torch.device:
-        # Get cuda device
-        if torch.cuda.is_available():
-            return torch.device(
-                f"cuda:{index % torch.cuda.device_count()}"
-            )  # Very fast
-        elif torch.backends.mps.is_available():
-            return torch.device("mps")
-        # Insert an else here to grab "xla" devices if available. TO DO later. Requires the torch_xla.core.xla_model library
-        # Else wise return the "cpu" as a torch device,
-        return torch.device("cpu")
-
-    # Fork Feature: Compute f0 with the crepe method
-    def get_f0_crepe_computation(
-        self,
-        x,
-        f0_min,
-        f0_max,
-        p_len,
-        hop_length=160,  # 512 before. Hop length changes the speed that the voice jumps to a different dramatic pitch. Lower hop lengths means more pitch accuracy but longer inference time.
-        model="full",  # Either use crepe-tiny "tiny" or crepe "full". Default is full
-    ):
-        x = x.astype(
-            np.float32
-        )  # fixes the F.conv2D exception. We needed to convert double to float.
-        x /= np.quantile(np.abs(x), 0.999)
-        torch_device = self.get_optimal_torch_device()
-        audio = torch.from_numpy(x).to(torch_device, copy=True)
-        audio = torch.unsqueeze(audio, dim=0)
-        if audio.ndim == 2 and audio.shape[0] > 1:
-            audio = torch.mean(audio, dim=0, keepdim=True).detach()
-        audio = audio.detach()
-        print("Initiating prediction with a crepe_hop_length of: " + str(hop_length))
-        pitch: Tensor = torchcrepe.predict(
-            audio,
-            self.sr,
-            hop_length,
-            f0_min,
-            f0_max,
-            model,
-            batch_size=hop_length * 2,
-            device=torch_device,
-            pad=True,
-        )
-        p_len = p_len or x.shape[0] // hop_length
-        # Resize the pitch for final f0
-        source = np.array(pitch.squeeze(0).cpu().float().numpy())
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * p_len, len(source)) / p_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        f0 = np.nan_to_num(target)
-        return f0  # Resized f0
-
-    def get_f0_official_crepe_computation(
-        self,
-        x,
-        f0_min,
-        f0_max,
-        model="full",
-    ):
-        # Pick a batch size that doesn't cause memory errors on your gpu
-        batch_size = 512
-        # Compute pitch using first gpu
-        audio = torch.tensor(np.copy(x))[None].float()
-        f0, pd = torchcrepe.predict(
-            audio,
-            self.sr,
-            self.window,
-            f0_min,
-            f0_max,
-            model,
-            batch_size=batch_size,
-            device=self.device,
-            return_periodicity=True,
-        )
-        pd = torchcrepe.filter.median(pd, 3)
-        f0 = torchcrepe.filter.mean(f0, 3)
-        f0[pd < 0.1] = 0
-        f0 = f0[0].cpu().numpy()
-        return f0
-
-    # Fork Feature: Compute pYIN f0 method
-    def get_f0_pyin_computation(self, x, f0_min, f0_max):
-        y, sr = librosa.load("saudio/Sidney.wav", self.sr, mono=True)
-        f0, _, _ = librosa.pyin(y, sr=self.sr, fmin=f0_min, fmax=f0_max)
-        f0 = f0[1:]  # Get rid of extra first frame
-        return f0
-
-    # Fork Feature: Acquire median hybrid f0 estimation calculation
-    def get_f0_hybrid_computation(
-        self,
-        methods_str,
-        input_audio_path,
-        x,
-        f0_min,
-        f0_max,
-        p_len,
-        filter_radius,
-        crepe_hop_length,
-        time_step,
-    ):
-        # Get various f0 methods from input to use in the computation stack
-        s = methods_str
-        s = s.split("hybrid")[1]
-        s = s.replace("[", "").replace("]", "")
-        methods = s.split("+")
-        f0_computation_stack = []
-
-        print("Calculating f0 pitch estimations for methods: %s" % str(methods))
-        x = x.astype(np.float32)
-        x /= np.quantile(np.abs(x), 0.999)
-        # Get f0 calculations for all methods specified
-        for method in methods:
-            f0 = None
-            if method == "pm":
-                f0 = (
-                    parselmouth.Sound(x, self.sr)
-                    .to_pitch_ac(
-                        time_step=time_step / 1000,
-                        voicing_threshold=0.6,
-                        pitch_floor=f0_min,
-                        pitch_ceiling=f0_max,
-                    )
-                    .selected_array["frequency"]
-                )
-                pad_size = (p_len - len(f0) + 1) // 2
-                if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                    f0 = np.pad(
-                        f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
-                    )
-            elif method == "crepe":
-                f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
-                f0 = f0[1:]  # Get rid of extra first frame
-            elif method == "crepe-tiny":
-                f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny")
-                f0 = f0[1:]  # Get rid of extra first frame
-            elif method == "mangio-crepe":
-                f0 = self.get_f0_crepe_computation(
-                    x, f0_min, f0_max, p_len, crepe_hop_length
-                )
-            elif method == "mangio-crepe-tiny":
-                f0 = self.get_f0_crepe_computation(
-                    x, f0_min, f0_max, p_len, crepe_hop_length, "tiny"
-                )
-            elif method == "harvest":
-                f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
-                if filter_radius > 2:
-                    f0 = signal.medfilt(f0, 3)
-                f0 = f0[1:]  # Get rid of first frame.
-            elif method == "dio":  # Potentially buggy?
-                f0, t = pyworld.dio(
-                    x.astype(np.double),
-                    fs=self.sr,
-                    f0_ceil=f0_max,
-                    f0_floor=f0_min,
-                    frame_period=10,
-                )
-                f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-                f0 = signal.medfilt(f0, 3)
-                f0 = f0[1:]
-            # elif method == "pyin": Not Working just yet
-            #    f0 = self.get_f0_pyin_computation(x, f0_min, f0_max)
-            # Push method to the stack
-            f0_computation_stack.append(f0)
-
-        for fc in f0_computation_stack:
-            print(len(fc))
-
-        print("Calculating hybrid median f0 from the stack of: %s" % str(methods))
-        f0_median_hybrid = None
-        if len(f0_computation_stack) == 1:
-            f0_median_hybrid = f0_computation_stack[0]
-        else:
-            f0_median_hybrid = np.nanmedian(f0_computation_stack, axis=0)
-        return f0_median_hybrid
-
-    def get_f0(
-        self,
-        input_audio_path,
-        x,
-        p_len,
-        f0_up_key,
-        f0_method,
-        filter_radius,
-        crepe_hop_length,
-        inp_f0=None,
-    ):
-        global input_audio_path2wav
-        time_step = self.window / self.sr * 1000
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        if f0_method == "pm":
-            f0 = (
-                parselmouth.Sound(x, self.sr)
-                .to_pitch_ac(
-                    time_step=time_step / 1000,
-                    voicing_threshold=0.6,
-                    pitch_floor=f0_min,
-                    pitch_ceiling=f0_max,
-                )
-                .selected_array["frequency"]
-            )
-            pad_size = (p_len - len(f0) + 1) // 2
-            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                f0 = np.pad(
-                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
-                )
-        elif f0_method == "harvest":
-            input_audio_path2wav[input_audio_path] = x.astype(np.double)
-            f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
-            if filter_radius > 2:
-                f0 = signal.medfilt(f0, 3)
-        elif f0_method == "dio":  # Potentially Buggy?
-            f0, t = pyworld.dio(
-                x.astype(np.double),
-                fs=self.sr,
-                f0_ceil=f0_max,
-                f0_floor=f0_min,
-                frame_period=10,
-            )
-            f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-            f0 = signal.medfilt(f0, 3)
-        elif f0_method == "crepe":
-            f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
-        elif f0_method == "crepe-tiny":
-            f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny")
-        elif f0_method == "mangio-crepe":
-            f0 = self.get_f0_crepe_computation(
-                x, f0_min, f0_max, p_len, crepe_hop_length
-            )
-        elif f0_method == "mangio-crepe-tiny":
-            f0 = self.get_f0_crepe_computation(
-                x, f0_min, f0_max, p_len, crepe_hop_length, "tiny"
-            )
-        elif f0_method == "rmvpe":
-            if hasattr(self, "model_rmvpe") == False:
-                from rmvpe import RMVPE
-
-                self.model_rmvpe = RMVPE(
-                    os.path.join(BASE_DIR, 'rvc_models', 'rmvpe.pt'), is_half=self.is_half, device=self.device
-                )
-            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
-
-        elif "hybrid" in f0_method:
-            # Perform hybrid median pitch estimation
-            input_audio_path2wav[input_audio_path] = x.astype(np.double)
-            f0 = self.get_f0_hybrid_computation(
-                f0_method,
-                input_audio_path,
-                x,
-                f0_min,
-                f0_max,
-                p_len,
-                filter_radius,
-                crepe_hop_length,
-                time_step,
-            )
-
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
-                :shape
-            ]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-
-        return f0_coarse, f0bak  # 1-0
-
-    def vc(
-        self,
-        model,
-        net_g,
-        sid,
-        audio0,
-        pitch,
-        pitchf,
-        times,
-        index,
-        big_npy,
-        index_rate,
-        version,
-        protect,
-    ):  # ,file_index,file_big_npy
-        feats = torch.from_numpy(audio0)
-        if self.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        if feats.dim() == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
-        inputs = {
-            "source": feats.to(self.device),
-            "padding_mask": padding_mask,
-            "output_layer": 9 if version == "v1" else 12,
-        }
-        t0 = ttime()
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
-        if protect < 0.5 and pitch != None and pitchf != None:
-            feats0 = feats.clone()
-        if (
-            isinstance(index, type(None)) == False
-            and isinstance(big_npy, type(None)) == False
-            and index_rate != 0
-        ):
-            npy = feats[0].cpu().numpy()
-            if self.is_half:
-                npy = npy.astype("float32")
-
-            # _, I = index.search(npy, 1)
-            # npy = big_npy[I.squeeze()]
-
-            score, ix = index.search(npy, k=8)
-            weight = np.square(1 / score)
-            weight /= weight.sum(axis=1, keepdims=True)
-            npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-
-            if self.is_half:
-                npy = npy.astype("float16")
-            feats = (
-                torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
-                + (1 - index_rate) * feats
-            )
-
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        if protect < 0.5 and pitch != None and pitchf != None:
-            feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
-                0, 2, 1
-            )
-        t1 = ttime()
-        p_len = audio0.shape[0] // self.window
-        if feats.shape[1] < p_len:
-            p_len = feats.shape[1]
-            if pitch != None and pitchf != None:
-                pitch = pitch[:, :p_len]
-                pitchf = pitchf[:, :p_len]
-
-        if protect < 0.5 and pitch != None and pitchf != None:
-            pitchff = pitchf.clone()
-            pitchff[pitchf > 0] = 1
-            pitchff[pitchf < 1] = protect
-            pitchff = pitchff.unsqueeze(-1)
-            feats = feats * pitchff + feats0 * (1 - pitchff)
-            feats = feats.to(feats0.dtype)
-        p_len = torch.tensor([p_len], device=self.device).long()
-        with torch.no_grad():
-            if pitch != None and pitchf != None:
-                audio1 = (
-                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                )
-            else:
-                audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
-                )
-        del feats, p_len, padding_mask
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        t2 = ttime()
-        times[0] += t1 - t0
-        times[2] += t2 - t1
-        return audio1
-
-    def pipeline(
-        self,
-        model,
-        net_g,
-        sid,
-        audio,
-        input_audio_path,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        # file_big_npy,
-        index_rate,
-        if_f0,
-        filter_radius,
-        tgt_sr,
-        resample_sr,
-        rms_mix_rate,
-        version,
-        protect,
-        crepe_hop_length,
-        f0_file=None,
-    ):
-        if (
-            file_index != ""
-            # and file_big_npy != ""
-            # and os.path.exists(file_big_npy) == True
-            and os.path.exists(file_index) == True
-            and index_rate != 0
-        ):
-            try:
-                index = faiss.read_index(file_index)
-                # big_npy = np.load(file_big_npy)
-                big_npy = index.reconstruct_n(0, index.ntotal)
-            except:
-                traceback.print_exc()
-                index = big_npy = None
-        else:
-            index = big_npy = None
-        audio = signal.filtfilt(bh, ah, audio)
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
-        opt_ts = []
-        if audio_pad.shape[0] > self.t_max:
-            audio_sum = np.zeros_like(audio)
-            for i in range(self.window):
-                audio_sum += audio_pad[i : i - self.window]
-            for t in range(self.t_center, audio.shape[0], self.t_center):
-                opt_ts.append(
-                    t
-                    - self.t_query
-                    + np.where(
-                        np.abs(audio_sum[t - self.t_query : t + self.t_query])
-                        == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
-                    )[0][0]
-                )
-        s = 0
-        audio_opt = []
-        t = None
-        t1 = ttime()
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
-        p_len = audio_pad.shape[0] // self.window
-        inp_f0 = None
-        if hasattr(f0_file, "name") == True:
-            try:
-                with open(f0_file.name, "r") as f:
-                    lines = f.read().strip("\n").split("\n")
-                inp_f0 = []
-                for line in lines:
-                    inp_f0.append([float(i) for i in line.split(",")])
-                inp_f0 = np.array(inp_f0, dtype="float32")
-            except:
-                traceback.print_exc()
-        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
-        pitch, pitchf = None, None
-        if if_f0 == 1:
-            pitch, pitchf = self.get_f0(
-                input_audio_path,
-                audio_pad,
-                p_len,
-                f0_up_key,
-                f0_method,
-                filter_radius,
-                crepe_hop_length,
-                inp_f0,
-            )
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            if self.device == "mps":
-                pitchf = pitchf.astype(np.float32)
-            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
-            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
-        t2 = ttime()
-        times[1] += t2 - t1
-        for t in opt_ts:
-            t = t // self.window * self.window
-            if if_f0 == 1:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        pitch[:, s // self.window : (t + self.t_pad2) // self.window],
-                        pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                        version,
-                        protect,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            else:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        None,
-                        None,
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                        version,
-                        protect,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            s = t
-        if if_f0 == 1:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    pitch[:, t // self.window :] if t is not None else pitch,
-                    pitchf[:, t // self.window :] if t is not None else pitchf,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                    version,
-                    protect,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        else:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    None,
-                    None,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                    version,
-                    protect,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        audio_opt = np.concatenate(audio_opt)
-        if rms_mix_rate != 1:
-            audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
-        if resample_sr >= 16000 and tgt_sr != resample_sr:
-            audio_opt = librosa.resample(
-                audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
-            )
-        audio_max = np.abs(audio_opt).max() / 0.99
-        max_int16 = 32768
-        if audio_max > 1:
-            max_int16 /= audio_max
-        audio_opt = (audio_opt * max_int16).astype(np.int16)
-        del pitch, pitchf, sid
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio_opt