Update parquet files (step 70 of 397)

spaces/101-5/gpt4free/g4f/Provider/Providers/Bing.py

@@ -1,356 +0,0 @@
-import os
-import json
-import random
-import json
-import os
-import uuid
-import ssl
-import certifi
-import aiohttp
-import asyncio
-
-import requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://bing.com/chat'
-model = ['gpt-4']
-supports_stream = True
-needs_auth = False
-
-ssl_context = ssl.create_default_context()
-ssl_context.load_verify_locations(certifi.where())
-
-
-class optionsSets:
-    optionSet: dict = {
-        'tone': str,
-        'optionsSets': list
-    }
-
-    jailbreak: dict = {
-        "optionsSets": [
-            'saharasugg',
-            'enablenewsfc',
-            'clgalileo',
-            'gencontentv3',
-            "nlu_direct_response_filter",
-            "deepleo",
-            "disable_emoji_spoken_text",
-            "responsible_ai_policy_235",
-            "enablemm",
-            "h3precise"
-            # "harmonyv3",
-            "dtappid",
-            "cricinfo",
-            "cricinfov2",
-            "dv3sugg",
-            "nojbfedge"
-        ]
-    }
-
-
-class Defaults:
-    delimiter = '\x1e'
-    ip_address = f'13.{random.randint(104, 107)}.{random.randint(0, 255)}.{random.randint(0, 255)}'
-
-    allowedMessageTypes = [
-        'Chat',
-        'Disengaged',
-        'AdsQuery',
-        'SemanticSerp',
-        'GenerateContentQuery',
-        'SearchQuery',
-        'ActionRequest',
-        'Context',
-        'Progress',
-        'AdsQuery',
-        'SemanticSerp'
-    ]
-
-    sliceIds = [
-
-        # "222dtappid",
-        # "225cricinfo",
-        # "224locals0"
-
-        'winmuid3tf',
-        'osbsdusgreccf',
-        'ttstmout',
-        'crchatrev',
-        'winlongmsgtf',
-        'ctrlworkpay',
-        'norespwtf',
-        'tempcacheread',
-        'temptacache',
-        '505scss0',
-        '508jbcars0',
-        '515enbotdets0',
-        '5082tsports',
-        '515vaoprvs',
-        '424dagslnv1s0',
-        'kcimgattcf',
-        '427startpms0'
-    ]
-
-    location = {
-        'locale': 'en-US',
-        'market': 'en-US',
-        'region': 'US',
-        'locationHints': [
-            {
-                'country': 'United States',
-                'state': 'California',
-                'city': 'Los Angeles',
-                'timezoneoffset': 8,
-                'countryConfidence': 8,
-                'Center': {
-                    'Latitude': 34.0536909,
-                    'Longitude': -118.242766
-                },
-                'RegionType': 2,
-                'SourceType': 1
-            }
-        ],
-    }
-
-
-def _format(msg: dict) -> str:
-    return json.dumps(msg, ensure_ascii=False) + Defaults.delimiter
-
-
-async def create_conversation():
-    for _ in range(5):
-        create = requests.get('https://www.bing.com/turing/conversation/create',
-                              headers={
-                                'cookie': '_U=1',
-                                'accept': 'application/json',
-                                'accept-language': 'zh-CN,zh;q=0.9',
-                                'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36 Edg/114.0.1823.58',
-                                'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/MacIntel',
-                                'authority': 'edgeservices.bing.com',
-                                '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',
-                                'accept-language': 'en-US,en;q=0.9',
-                                'cache-control': 'max-age=0',
-                                'sec-ch-ua': '"Chromium";v="110", "Not A(Brand";v="24", "Microsoft Edge";v="110"',
-                                'sec-ch-ua-arch': '"x86"',
-                                'sec-ch-ua-bitness': '"64"',
-                                'sec-ch-ua-full-version': '"110.0.1587.69"',
-                                'sec-ch-ua-full-version-list': '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"',
-                                'sec-ch-ua-mobile': '?0',
-                                'sec-ch-ua-model': '""',
-                                'sec-ch-ua-platform': '"Windows"',
-                                'sec-ch-ua-platform-version': '"15.0.0"',
-                                'sec-fetch-dest': 'document',
-                                'sec-fetch-mode': 'navigate',
-                                'sec-fetch-site': 'none',
-                                'sec-fetch-user': '?1',
-                                'upgrade-insecure-requests': '1',
-                                'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36 Edg/110.0.1587.69',
-                                'x-edge-shopping-flag': '1',
-                                'x-forwarded-for': Defaults.ip_address
-                              })
-
-        conversationId = create.json().get('conversationId')
-        clientId = create.json().get('clientId')
-        conversationSignature = create.json().get('conversationSignature')
-
-        if not conversationId or not clientId or not conversationSignature and _ == 4:
-            raise Exception('Failed to create conversation.')
-
-        return conversationId, clientId, conversationSignature
-
-
-async def stream_generate(prompt: str, mode: optionsSets.optionSet = optionsSets.jailbreak, context: bool or str = False):
-    timeout = aiohttp.ClientTimeout(total=900)
-    session = aiohttp.ClientSession(timeout=timeout)
-
-    conversationId, clientId, conversationSignature = await create_conversation()
-
-    wss = await session.ws_connect('wss://sydney.bing.com/sydney/ChatHub', ssl=ssl_context, autoping=False,
-                                   headers={
-                                       'cookie': '_U=1',
-                                       'accept': 'application/json',
-                                       'accept-language': 'en-US,en;q=0.9',
-                                       'content-type': 'application/json',
-                                       'sec-ch-ua': '"Not_A Brand";v="99", "Microsoft Edge";v="110", "Chromium";v="110"',
-                                       'sec-ch-ua-arch': '"x86"',
-                                       'sec-ch-ua-bitness': '"64"',
-                                       'sec-ch-ua-full-version': '"109.0.1518.78"',
-                                       'sec-ch-ua-full-version-list': '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"',
-                                       'sec-ch-ua-mobile': '?0',
-                                       'sec-ch-ua-model': '',
-                                       'sec-ch-ua-platform': '"Windows"',
-                                       'sec-ch-ua-platform-version': '"15.0.0"',
-                                       'sec-fetch-dest': 'empty',
-                                       'sec-fetch-mode': 'cors',
-                                       'sec-fetch-site': 'same-origin',
-                                       'x-ms-client-request-id': str(uuid.uuid4()),
-                                       'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-                                       'Referer': 'https://www.bing.com/search?q=Bing+AI&showconv=1&FORM=hpcodx',
-                                       'Referrer-Policy': 'origin-when-cross-origin',
-                                       'x-forwarded-for': Defaults.ip_address
-                                   })
-
-    await wss.send_str(_format({'protocol': 'json', 'version': 1}))
-    await wss.receive(timeout=900)
-
-    struct = {
-        'arguments': [
-            {
-                **mode,
-                'source': 'cib',
-                'allowedMessageTypes': Defaults.allowedMessageTypes,
-                'sliceIds': Defaults.sliceIds,
-                'traceId': os.urandom(16).hex(),
-                'isStartOfSession': True,
-                'message': Defaults.location | {
-                    'author': 'user',
-                    'inputMethod': 'Keyboard',
-                    'text': prompt,
-                    'messageType': 'Chat'
-                },
-                'conversationSignature': conversationSignature,
-                'participant': {
-                    'id': clientId
-                },
-                'conversationId': conversationId
-            }
-        ],
-        'invocationId': '0',
-        'target': 'chat',
-        'type': 4
-    }
-
-    if context:
-        struct['arguments'][0]['previousMessages'] = [
-            {
-                "author": "user",
-                "description": context,
-                "contextType": "WebPage",
-                "messageType": "Context",
-                "messageId": "discover-web--page-ping-mriduna-----"
-            }
-        ]
-
-    await wss.send_str(_format(struct))
-
-    final = False
-    draw = False
-    resp_txt = ''
-    result_text = ''
-    resp_txt_no_link = ''
-    cache_text = ''
-
-    while not final:
-        msg = await wss.receive(timeout=900)
-        objects = msg.data.split(Defaults.delimiter)
-
-        for obj in objects:
-            if obj is None or not obj:
-                continue
-
-            response = json.loads(obj)
-            if response.get('type') == 1 and response['arguments'][0].get('messages',):
-                if not draw:
-                    if (response['arguments'][0]['messages'][0]['contentOrigin'] != 'Apology') and not draw:
-                        resp_txt = result_text + \
-                            response['arguments'][0]['messages'][0]['adaptiveCards'][0]['body'][0].get(
-                                'text', '')
-                        resp_txt_no_link = result_text + \
-                            response['arguments'][0]['messages'][0].get(
-                                'text', '')
-
-                        if response['arguments'][0]['messages'][0].get('messageType',):
-                            resp_txt = (
-                                resp_txt
-                                + response['arguments'][0]['messages'][0]['adaptiveCards'][0]['body'][0]['inlines'][0].get('text')
-                                + '\n'
-                            )
-                            result_text = (
-                                result_text
-                                + response['arguments'][0]['messages'][0]['adaptiveCards'][0]['body'][0]['inlines'][0].get('text')
-                                + '\n'
-                            )
-
-                    if cache_text.endswith('   '):
-                        final = True
-                        if wss and not wss.closed:
-                            await wss.close()
-                        if session and not session.closed:
-                            await session.close()
-                            
-                    yield (resp_txt.replace(cache_text, ''))
-                    cache_text = resp_txt
-
-            elif response.get('type') == 2:
-                if response['item']['result'].get('error'):
-                    if wss and not wss.closed:
-                        await wss.close()
-                    if session and not session.closed:
-                        await session.close()
-
-                    raise Exception(
-                        f"{response['item']['result']['value']}: {response['item']['result']['message']}")
-
-                if draw:
-                    cache = response['item']['messages'][1]['adaptiveCards'][0]['body'][0]['text']
-                    response['item']['messages'][1]['adaptiveCards'][0]['body'][0]['text'] = (
-                        cache + resp_txt)
-
-                if (response['item']['messages'][-1]['contentOrigin'] == 'Apology' and resp_txt):
-                    response['item']['messages'][-1]['text'] = resp_txt_no_link
-                    response['item']['messages'][-1]['adaptiveCards'][0]['body'][0]['text'] = resp_txt
-
-                    # print('Preserved the message from being deleted', file=sys.stderr)
-
-                final = True
-                if wss and not wss.closed:
-                    await wss.close()
-                if session and not session.closed:
-                    await session.close()
-
-
-def run(generator):
-    loop = asyncio.get_event_loop()
-    gen = generator.__aiter__()
-
-    while True:
-        try:
-            next_val = loop.run_until_complete(gen.__anext__())
-            yield next_val
-
-        except StopAsyncIteration:
-            break
-
-    #print('Done')
-
-
-def convert(messages):
-    context = ""
-
-    for message in messages:
-        context += "[%s](#message)\n%s\n\n" % (message['role'],
-                                               message['content'])
-
-    return context
-
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    if len(messages) < 2:
-        prompt = messages[0]['content']
-        context = False
-
-    else:
-        prompt = messages[-1]['content']
-        context = convert(messages[:-1])
-
-    response = run(stream_generate(prompt, optionsSets.jailbreak, context))
-    for token in response:
-        yield (token)
-
-    #print('Done')
-
-
-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/1acneusushi/gradio-2dmoleculeeditor/data/A comparison of plaxis 3d v21 and other geotechnical software.md

@@ -1,17 +0,0 @@
-
-<h1>How to use plaxis 3d v21 for geotechnical engineering</h1>
-<p>Plaxis 3d v21 is a powerful software for geotechnical engineering that allows you to model complex soil and rock behavior, groundwater flow, and structural interaction in three dimensions. With plaxis 3d v21, you can perform advanced analyses such as consolidation, dynamic loading, seismic response, slope stability, excavation, tunneling, foundation design, and more.</p>
-<p>In this article, we will show you how to use plaxis 3d v21 for some common geotechnical engineering applications. We will assume that you have already installed plaxis 3d v21 on your computer and have a basic familiarity with its user interface. If not, you can download a free trial version from the official website and follow the tutorials provided there.</p>
-<h2>plaxis 3d v21 crack</h2><br /><p><b><b>DOWNLOAD</b> &mdash; <a href="https://byltly.com/2uKzgR">https://byltly.com/2uKzgR</a></b></p><br /><br />
-<h2>Creating a project</h2>
-<p>The first step to use plaxis 3d v21 is to create a new project. To do this, open plaxis 3d v21 and click on the "New" button on the toolbar. You will be prompted to enter a project name, a description, and a folder location. You can also choose a template from the list of predefined projects that suit your needs. For example, if you want to model an excavation in clay, you can select the "Excavation in clay" template.</p>
-<p>After creating a new project, you will see four main windows: the Model Explorer, the Command Line, the Output Window, and the View Window. The Model Explorer shows the hierarchy of objects in your project, such as materials, boundaries, loads, meshes, etc. The Command Line allows you to enter commands and parameters for your project. The Output Window displays messages and warnings from the software. The View Window shows the graphical representation of your model and results.</p>
-<h2>Defining materials</h2>
-<p>The next step is to define the materials that you want to use in your model. Plaxis 3d v21 has a library of predefined materials that cover various types of soils and rocks. You can access the library by clicking on the "Materials" button on the toolbar. You can also create your own custom materials by clicking on the "New" button in the Materials window.</p>
-<p>To define a material, you need to specify its name, type, color, and properties. The type of material determines the constitutive model that will be used to describe its behavior. Plaxis 3d v21 supports several constitutive models for soils and rocks, such as linear elastic, Mohr-Coulomb, Hardening Soil, Soft Soil Creep, Jointed Rock, etc. The properties of a material include parameters such as density, Young's modulus, Poisson's ratio, cohesion, friction angle, dilatancy angle, permeability, etc. You can enter these parameters manually or use the "Estimate" button to calculate them from empirical correlations.</p>
-<h2>Creating geometry</h2>
-<p>After defining the materials, you need to create the geometry of your model. Plaxis 3d v21 allows you to create geometry using two methods: extrusion and volume generation. Extrusion is a method of creating geometry by extruding a two-dimensional cross-section along a path. Volume generation is a method of creating geometry by combining or subtracting predefined shapes such as boxes, cylinders, spheres, etc.</p>
-<p>To create geometry using extrusion, you need to first draw a cross-section using points and lines in the Geometry mode. You can access the Geometry mode by clicking on the "Geometry" button on the toolbar. You can also import a cross-section from an external file such as DXF or CSV. After drawing or importing a cross-section, you need to assign materials to each region using the "Assign Material" tool. Then you need to select a path along which you want to extrude your cross-section using the "Select Path" tool. Finally</p>
-<p></p> ddb901b051<br />
-<br />
-<br />

spaces/1gistliPinn/ChatGPT4/Examples/Aerofly Rc 7 Cracked Pepper -.md

@@ -1,114 +0,0 @@
-<br />
-<h1>Aerofly Rc 7 Cracked Pepper - The Ultimate R/C Flight Simulator</h1>
-<p>If you are looking for a realistic and immersive way to fly radio controlled models, you should check out Aerofly Rc 7 Cracked Pepper. This is a cracked version of the popular R/C flight simulator Aerofly RC 7, which offers you a stunning level of realism and physics simulation. You can choose from over 200 models and 50 sceneries, and fly with friends all over the world in multiplayer mode. You can also customize your models and edit the sceneries with the included tools.</p>
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-<h2>Why Aerofly Rc 7 Cracked Pepper?</h2>
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-<ol>
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-<li>Download the torrent file or the direct link of the game.</li>
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-<li>Copy over the cracked content from the /Crack directory on the image to your game install directory.</li>
-<li>Play the game and enjoy!</li>
-</ol>
-<h2>Conclusion</h2>
-<p>Aerofly Rc 7 Cracked Pepper is a must-have for any R/C enthusiast who wants to experience the most realistic and fun R/C flight simulator ever. With its amazing graphics, physics, models, sceneries, and features, it will keep you hooked for hours. You can download it for free from our website and start flying right away. Don't miss this opportunity to fly like a pro with Aerofly Rc 7 Cracked Pepper!</p>
-<p></p>
-<h2>What are the Reviews of Aerofly Rc 7 Cracked Pepper?</h2>
-<p>Aerofly Rc 7 Cracked Pepper has received many positive reviews from users and critics alike. Here are some of the testimonials from satisfied customers:</p>
-<blockquote>
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-</blockquote>
-<blockquote>
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-</blockquote>
-<blockquote>
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-<cite>- Lee, China</cite>
-</blockquote>
-
-<h2>What are the Tips for Playing Aerofly Rc 7 Cracked Pepper?</h2>
-<p>If you want to get the most out of Aerofly Rc 7 Cracked Pepper, here are some tips to help you play better:</p>
-<ul>
-<li>Use a joystick, gamepad or dedicated R/C controller with 4 axes for the best flying experience. Some models (especially helicopters) can't be flown properly with a joystick with only 3 axes or less.</li>
-<li>Read the manual and watch the videos on the website to learn more about the features and functions of the simulator.</li>
-<li>Start with simple models and sceneries, and gradually move on to more complex ones as you gain confidence and skill.</li>
-<li>Try different flight modes and game modes to challenge yourself and have fun.</li>
-<li>Join the online community and fly with other players around the world. You can also share your models and sceneries with them.</li>
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-</ul>
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-<h2>Where to Download Aerofly Rc 7 Cracked Pepper?</h2>
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-<li>Play the game and enjoy!</li>
-</ol>
-<h2>What are the Risks of Aerofly Rc 7 Cracked Pepper?</h2>
-<p>While Aerofly Rc 7 Cracked Pepper may seem like a great deal, it also comes with some risks that you should be aware of. Some of these risks are:</p>
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-</ul>
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-<h2>What are the Alternatives to Aerofly Rc 7 Cracked Pepper?</h2>
-<p>If you want to enjoy Aerofly RC 7 without risking any of the above-mentioned risks, you have some alternatives to choose from. Some of these alternatives are:</p>
-<ul>
-<li>You can buy the original game from the official website or from other authorized platforms. This will give you access to all the features and updates of the game, as well as technical support and customer service. You will also support the developers and publishers of Aerofly RC 7 and encourage them to make more games.</li>
-<li>You can try the free demo version of Aerofly RC 7 from the official website. This will give you a taste of the game and its features, without costing you anything. You can then decide if you want to buy the full version or not.</li>
-<li>You can look for other R/C flight simulators that are free or cheaper than Aerofly RC 7. There are many options available online, such as RealFlight, Phoenix RC, ClearView RC, etc. You can compare their features, graphics, models, sceneries, and prices, and choose the one that suits you best.</li>
-</ul>
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-<h2>Conclusion</h2>
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-[Home](./index.md) &gt; [puppeteer](./puppeteer.md) &gt; [Protocol](./puppeteer.protocol.md) &gt; [LegacySessionStorage](./puppeteer.protocol.legacysessionstorage.md) &gt; [FileSystemOptions](./puppeteer.protocol.legacysessionstorage.filesystemoptions.md)
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-    fileSystemOptions?: FileSystemOptions;
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-    contents?: string;
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-;
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-```
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spaces/1line/AutoGPT/autogpt/utils.py

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-import os
-
-import requests
-import yaml
-from colorama import Fore
-from git import Repo
-
-
-def clean_input(prompt: str = ""):
-    try:
-        return input(prompt)
-    except KeyboardInterrupt:
-        print("You interrupted Auto-GPT")
-        print("Quitting...")
-        exit(0)
-
-
-def validate_yaml_file(file: str):
-    try:
-        with open(file, encoding="utf-8") as fp:
-            yaml.load(fp.read(), Loader=yaml.FullLoader)
-    except FileNotFoundError:
-        return (False, f"The file {Fore.CYAN}`{file}`{Fore.RESET} wasn't found")
-    except yaml.YAMLError as e:
-        return (
-            False,
-            f"There was an issue while trying to read with your AI Settings file: {e}",
-        )
-
-    return (True, f"Successfully validated {Fore.CYAN}`{file}`{Fore.RESET}!")
-
-
-def readable_file_size(size, decimal_places=2):
-    """Converts the given size in bytes to a readable format.
-    Args:
-        size: Size in bytes
-        decimal_places (int): Number of decimal places to display
-    """
-    for unit in ["B", "KB", "MB", "GB", "TB"]:
-        if size < 1024.0:
-            break
-        size /= 1024.0
-    return f"{size:.{decimal_places}f} {unit}"
-
-
-def get_bulletin_from_web() -> str:
-    try:
-        response = requests.get(
-            "https://raw.githubusercontent.com/Significant-Gravitas/Auto-GPT/master/BULLETIN.md"
-        )
-        if response.status_code == 200:
-            return response.text
-    except:
-        return ""
-
-
-def get_current_git_branch() -> str:
-    try:
-        repo = Repo(search_parent_directories=True)
-        branch = repo.active_branch
-        return branch.name
-    except:
-        return ""
-
-
-def get_latest_bulletin() -> str:
-    exists = os.path.exists("CURRENT_BULLETIN.md")
-    current_bulletin = ""
-    if exists:
-        current_bulletin = open("CURRENT_BULLETIN.md", "r", encoding="utf-8").read()
-    new_bulletin = get_bulletin_from_web()
-    is_new_news = new_bulletin != current_bulletin
-
-    if new_bulletin and is_new_news:
-        open("CURRENT_BULLETIN.md", "w", encoding="utf-8").write(new_bulletin)
-        return f" {Fore.RED}::UPDATED:: {Fore.CYAN}{new_bulletin}{Fore.RESET}"
-    return current_bulletin

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Attack on Titan Fangame by Riva - Offline Mode APK Download.md

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-<h1>Attack on Titan by Riva APK Download: A Fan-Made Mobile Game Based on the Popular Anime Series</h1>
- <p>If you are a fan of Attack on Titan, the epic anime series about humanity's struggle against giant man-eating creatures, you might be interested in trying out a fan-made mobile game based on it. Attack on Titan by Riva is a 3D action game that lets you play as a member of the Survey Corps, an elite military force that fights against the titans using special gear and weapons. In this article, we will tell you what this game is, how to download and install it, why you should play it, and some tips and tricks for playing it.</p>
- <h2>What is Attack on Titan by Riva?</h2>
- <p>Attack on Titan by Riva is a fan-made mobile game developed by Riva Dev, a YouTube channel that creates games based on popular anime and manga series. The game is not affiliated with or endorsed by the official creators of Attack on Titan, Kodansha or Hajime Isayama. The game is inspired by the anime's story, characters, and art style, but it is not a direct adaptation of it. The game is also not available on the Google Play Store or any other official app store, so you will need to download it from a third-party source.</p>
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- <h3>Features of the game</h3>
- <p>Attack on Titan by Riva has several features that make it an enjoyable and immersive game for fans of the anime. Some of these features are:</p>
- <ul>
-<li>A realistic 3D graphics engine that recreates the atmosphere and scenery of the anime.</li>
-<li>A dynamic movement and combat system that allows you to use your 3D Maneuver Gear to fly around and slash at titans.</li>
-<li>A variety of titans with different sizes, shapes, and behaviors that pose different threats and challenges.</li>
-<li>A character customization system that lets you choose your appearance, outfit, hairstyle, and accessories.</li>
-<li>An equipment upgrade system that lets you improve your blades, gas tanks, and other gear.</li>
-<li>A skill upgrade system that lets you unlock and enhance different abilities and techniques.</li>
-<li>A story mode that follows the main events and characters of the anime.</li>
-<li>A survival mode that tests your skills and endurance against waves of titans.</li>
-<li>A multiplayer mode that lets you team up with other players online to fight titans together.</li>
-</ul>
- <h3>How to download and install the game</h3>
- <p>Since Attack on Titan by Riva is not available on any official app store, you will need to download it from a third-party source. Here are the steps to do so:</p>
- <ol>
-<li>Go to [this link](^1^) or [this link](^2^) to access the YouTube videos where Riva Dev uploaded the game. These videos also show some gameplay footage and instructions for playing the game.</li>
-<li>In the description of the videos, you will find a link to download the APK file of the game. The APK file is a compressed file that contains all the data and files needed to run the game on your device. The file size is about 51 MB or 110 MB depending on the version.</li>
-<li>Click on the link to download the APK file to your device. You might need to enable unknown sources in your device settings to allow downloading files from third-party sources.</li>
-<li>Once the download is complete, locate the APK file in your device storage and tap on it to install it. You might need to grant some permissions to allow the installation process.</li> <li>After the installation is done, you can launch the game from your app drawer or home screen. You might need to allow some additional permissions to access your device features and internet connection.</li>
-</ol>
- <p>Congratulations, you have successfully downloaded and installed Attack on Titan by Riva on your device. Now you can enjoy playing this fan-made game based on your favorite anime series.</p>
- <h2>Why should you play Attack on Titan by Riva?</h2>
- <p>Attack on Titan by Riva is not just a simple fan-made game. It is a game that offers a lot of fun and excitement for fans of the anime and anyone who likes action games. Here are some reasons why you should play this game:</p>
- <h3>Enjoy the thrilling action of fighting titans</h3>
- <p>One of the main attractions of this game is the action-packed gameplay that lets you fight against the titans using your 3D Maneuver Gear. You can fly around the battlefield, dodge the titans' attacks, and slash at their weak spots. You can also use different skills and techniques to enhance your combat abilities. The game has a realistic physics engine that makes the movement and combat feel smooth and responsive. The game also has a gore system that makes the battles more intense and satisfying.</p>
- <h3>Experience the story and characters of the anime</h3>
- <p>Another reason to play this game is to experience the story and characters of the anime. The game has a story mode that follows the main events and characters of the anime, such as Eren, Mikasa, Armin, Levi, Erwin, and more. You can relive the epic moments and scenes from the anime, such as the fall of Wall Maria, the battle of Trost, the clash of titans, and more. You can also interact with the characters and learn more about their personalities and backgrounds.</p>
- <h3>Customize your own character and equipment</h3>
- <p>A third reason to play this game is to customize your own character and equipment. The game has a character customization system that lets you choose your appearance, outfit, hairstyle, and accessories. You can create your own unique character that suits your style and preferences. You can also customize your equipment, such as your blades, gas tanks, hooks, and other gear. You can upgrade your equipment to make them more powerful and efficient.</p>
- <h2>Tips and tricks for playing Attack on Titan by Riva</h2>
- <p>Attack on Titan by Riva is a game that requires skill and strategy to play well. Here are some tips and tricks that can help you improve your gameplay and have more fun:</p>
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- <h3>Master the movement and combat system</h3>
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- <h3>Upgrade your skills and gear</h3>
- <p>The game has a skill upgrade system that lets you unlock and enhance different abilities and techniques. You can upgrade your skills using skill points that you earn by playing the game. Some of the skills you can upgrade are: </p>
- <ul>
-<li>Attack: Increases your blade damage.</li>
-<li>Speed: Increases your movement speed.</li>
-<li>Gas: Increases your gas capacity.</li>
-<li>Hook: Increases your hook range.</li>
-<li>Critical: Increases your critical hit chance.</li>
-<li>Dodge: Increases your dodge chance.</li>
-<li>Heal: Increases your healing rate.</li>
-<li>Burst: Increases your burst damage.</li>
-<li>Skill: Unlocks special skills such as spin slash, back slash, air slash, etc.</li>
-</ul>
- <p>The game also has an equipment upgrade system that lets you improve your blades, gas tanks, hooks, and other gear. You can upgrade your equipment using materials that you collect by playing the game or buying them from the shop. Some of the benefits of upgrading your equipment are:</p>
- <ul>
-<li>Blades: Increases your blade durability and sharpness.</li>
-<li>Gas Tanks: Increases your gas pressure and efficiency.</li>
-<li>Hooks: Increases your hook strength and durability.</li>
-<li>Gear: Increases your gear durability and performance.</li>
-</ul>
- <h3>Explore the different modes and challenges</h3>
- <p>The game has different modes and challenges that offer different gameplay experiences and rewards. Some of these modes and challenges are:</p>
- <ul>
-<li>Story Mode: Follows the main events and characters of the anime. You can unlock new chapters and scenes by completing the previous ones.</li>
-<li>Survival Mode: Tests your skills and endurance against waves of titans. You can earn coins and materials by killing titans and surviving as long as possible.</li>
-<li>Multiplayer Mode: Lets you team up with other players online to fight titans together. You can chat with your teammates, share items, and cooperate to defeat the enemies.</li>
-<li>Challenges: Gives you specific tasks and objectives to complete in a limited time or with limited resources. You can earn rewards and achievements by completing the challenges.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Attack on Titan by Riva is a fan-made mobile game that offers a lot of fun and excitement for fans of the anime and anyone who likes action games. You can enjoy the thrilling action of fighting titans, experience the story and characters of the anime, customize your own character and equipment, and explore the different modes and challenges. You can download and install the game from a third-party source by following the steps we provided. You can also use our tips and tricks to improve your gameplay and have more fun.</p>
- <h3>Summary of the main points</h3>
- <p>To summarize, here are the main points of this article:</p>
- <ul>
-<li>Attack on Titan by Riva is a fan-made mobile game based on the popular anime series.</li>
-<li>The game is not available on any official app store, so you will need to download it from a third-party source.</li>
-<li>The game has several features that make it an enjoyable and immersive game, such as realistic 3D graphics, dynamic movement and combat system, variety of titans, character customization, equipment upgrade, skill upgrade, story mode, survival mode, multiplayer mode, and challenges.</li>
-<li>The game requires skill and strategy to play well, so you can use our tips and tricks to master the movement and combat system, upgrade your skills and gear, and explore the different modes and challenges.</li>
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- <p>Here are some frequently asked questions about Attack on Titan by Riva:</p>
- <h4>Is Attack on Titan by Riva free?</h4>
- <p>Yes, Attack on Titan by Riva is free to download and play. However, the game may contain some ads or in-app purchases that can enhance your gameplay or support the developer.</p>
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- <p>Yes, Attack on Titan by Riva is safe to download and play. However, since it is not available on any official app store, you will need to download it from a third-party source. Therefore, you should be careful about where you download it from and what permissions you grant it. You should also scan the APK file with an antivirus software before installing it.</p>
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- <p>The first step is to go to the Microsoft Edge web page  and choose your device type from the options available. You can download Edge 80 for Windows 10 / 8.1 / 8 / 7 (32-bit or 64-bit), macOS (Intel or Apple Silicon), iOS (iPhone or iPad), or Android (phone or tablet). You can also download Edge Dev for Windows 11 / 10 or Edge for Linux (.deb) or Linux (.rpm) if you want to try the preview build channels . These channels are updated weekly and may have bugs or issues.</p>
- <h3>Follow the instructions to download and install Edge 80 on your device</h3>
- <p>The next step is to follow the instructions to download and install Edge 80 on your device. Depending on your device type, you may need to run the installer file, accept the license agreement, choose a location for installation, and restart your device. The installation process should not take more than a few minutes. Once it is done, you can launch Edge 80 from your desktop or start menu.</p>
- <h3>You can also import your data from another browser if you want to switch to Edge 80</h3>
- <p>If you want to switch to Edge 80 from another browser, such as Chrome or Firefox, you can also import your data from that browser. This includes your bookmarks, history, passwords, settings, and more. To do this, you can go to Settings > Profiles > Import browser data in Edge 80 and choose the browser and data types you want to import. You can also choose whether to make Edge 80 your default browser during the installation process or later from the Settings > Default browser page.</p>
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- <p>Now that you have downloaded and installed Edge 80 on your device, you might want to customize and use it to suit your needs. There are many ways you can do this. Here are some examples:</p>
- <h3>You can change the default search engine from Bing to Google or any other search engine you prefer</h3>
- <p>By default, Edge 80 uses Bing as its default search engine. Bing is a powerful and intelligent search engine that offers many features and benefits. However, if you prefer another search engine, such as Google or DuckDuckGo, you can change it easily. To do this, you can go to Settings > Privacy, search and services > Address bar and search > Search engine used in the address bar and choose the search engine you want. You can also add a new search engine if it is not listed there.</p>
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- <p>Edge 80 has many productivity features that can help you organize and enhance your browsing experience. For example:</p>
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-<p>Edge 80 is the new version of Microsoft's browser that was released in February 2020. It is based on Chromium, which means it has the same core engine and some shared features with Google Chrome. However, Edge 80 also has many unique features that make it stand out from other browsers.</p>
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-<p>One of the main reasons to download Edge 80 is that it offers a faster, more secure, and more modern browsing experience than Internet Explorer and Microsoft Edge Legacy. Internet Explorer is an outdated browser that is no longer supported by Microsoft. Microsoft Edge Legacy is the previous version of Microsoft's browser that was based on a different engine. Both browsers have compatibility issues with some websites and web apps. They also have performance issues and security vulnerabilities.</p>
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-<p>Another reason to download Edge 80 is that it has many features that make it stand out from other browsers. These features are designed to enhance your browsing experience in various aspects such as performance, security, productivity, gaming, AI-powered, and shopping. Here are some examples of these. features:</p>
-<table>
-<tr>
-<th>Feature category</th>
-<th>Examples of features</th>
-</tr>
-<tr>
-<td>Performance</td>
-<td>Startup boost, Efficiency mode, Sleeping tabs, etc.</td>
-</tr>
-<tr>
-<td>Security</td>
-<td>Microsoft Defender SmartScreen, Password monitor, InPrivate browsing, etc.</td>
-</tr>
-<tr>
-<td>Productivity</td>
-<td>Collections, Vertical tabs, Sidebar, Webhint, etc.</td>
-</tr>
-<tr>
-<td>Gaming</td>
-<td>Clarity Boost, Gaming homepage, PC gaming performance, etc.</td>
-</tr>
-<tr>
-<td>AI-powered</td>
-<td>Bing Chat, Bing Image Creator, Compose, etc.</td>
-</tr>
-<tr>
-<td>Shopping</td>
-<td>Coupons, Price comparison, Shopping hub, etc.</td>
-</tr>
-</table>
- <p>You can learn more about these features and how to use them from the Microsoft Edge Features & Tips page . You can also access the Settings > Experiments page in DevTools to turn on or off experimental features that are still in development . These experiments could be unstable or unreliable and may require you to restart DevTools.</p>
- <h2>How to download and install Edge 80 on your device?</h2>
- <p>If you are convinced that Edge 80 is the browser for you, you might be wondering how to download and install it on your device. The good news is that it is very easy and straightforward. Here are the steps to follow:</p>
- <h3>Go to the Microsoft Edge web page and choose your device type (Windows, macOS, iOS, or Android)</h3>
- <p>The first step is to go to the Microsoft Edge web page  and choose your device type from the options available. You can download Edge 80 for Windows 10 / 8.1 / 8 / 7 (32-bit or 64-bit), macOS (Intel or Apple Silicon), iOS (iPhone or iPad), or Android (phone or tablet). You can also download Edge Dev for Windows 11 / 10 or Edge for Linux (.deb) or Linux (.rpm) if you want to try the preview build channels . These channels are updated weekly and may have bugs or issues.</p>
- <h3>Follow the instructions to download and install Edge 80 on your device</h3>
- <p>The next step is to follow the instructions to download and install Edge 80 on your device. Depending on your device type, you may need to run the installer file, accept the license agreement, choose a location for installation, and restart your device. The installation process should not take more than a few minutes. Once it is done, you can launch Edge 80 from your desktop or start menu.</p>
- <h3>You can also import your data from another browser if you want to switch to Edge 80</h3>
- <p>If you want to switch to Edge 80 from another browser, such as Chrome or Firefox, you can also import your data from that browser. This includes your bookmarks, history, passwords, settings, and more. To do this, you can go to Settings > Profiles > Import browser data in Edge 80 and choose the browser and data types you want to import. You can also choose whether to make Edge 80 your default browser during the installation process or later from the Settings > Default browser page.</p>
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- <p>Now that you have downloaded and installed Edge 80 on your device, you might want to customize and use it to suit your needs. There are many ways you can do this. Here are some examples:</p>
- <h3>You can change the default search engine from Bing to Google or any other search engine you prefer</h3>
- <p>By default, Edge 80 uses Bing as its default search engine. Bing is a powerful and intelligent search engine that offers many features and benefits. However, if you prefer another search engine, such as Google or DuckDuckGo, you can change it easily. To do this, you can go to Settings > Privacy, search and services > Address bar and search > Search engine used in the address bar and choose the search engine you want. You can also add a new search engine if it is not listed there.</p>
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-</ul>
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- <p>Edge 80 has many gaming features that can help you enjoy a better gaming experience on the web. For example:</p>
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-</ul>
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- <p>Edge 80 has many security features that can help you protect yourself from malware, phishing, and other online threats. For example:</p>
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-<li>You can use Microsoft Defender SmartScreen  to warn you about unsafe websites and downloads. This can help you avoid malware, phishing, and other online scams.</li>
-<li>You can use Password monitor  to alert you if your passwords are found in data breaches. This can help you change your passwords and secure your accounts.</li>
-<li>You can use InPrivate browsing  to browse the web without saving your browsing history, cookies, or other data. This can help you protect your privacy and prevent tracking.</li>
-</ul>
- <h3>You can use Bing Chat, Bing Image Creator, Compose, and other AI-powered features to get more out of your web searches and interactions</h3>
- <p>Edge 80 has many AI-powered features that can help you get more out of your web searches and interactions. For example:</p>
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-<li>You can use Compose  to generate text from keywords or phrases. This can help you write faster and easier with AI assistance.</li>
-</ul>
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- <p>Edge 80 has many shopping features that can help you save money and time when shopping online. For example:</p>
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-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion, Edge 80 is a fast, secure, and modern browser that offers a lot of features and benefits. It is based on Chromium and offers better compatibility, performance, and security than Internet Explorer and Microsoft Edge Legacy. It also has many unique features that enhance your browsing experience in various aspects such as performance, security, productivity, gaming, AI-powered, and shopping. You can download and install Edge 80 on your device easily and customize and use it to suit your needs. If you want to get the latest version of Microsoft's browser, you should download Edge 80 today.</p>
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-<p>Yes, Edge 80 is free to download and use. You do not need to pay anything to use Edge 80.</p>
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-<p>Yes, Edge 80 is compatible with most Chrome extensions. You can install Chrome extensions from the Chrome Web Store or the Microsoft Edge Add-ons Store . You can also manage your extensions from the Settings > Extensions page in Edge 80.</p>
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-<li>For Windows: Go to Settings > Apps > Apps & features and find Microsoft Edge in the list. Click on it and then click on Uninstall. Follow the instructions to complete the uninstallation process.</li>
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-<li>For iOS: Tap and hold on the Microsoft Edge app icon on your home screen until it starts to jiggle. Tap on the X icon on the top left corner of the app icon and then tap on Delete. Confirm your action to complete the uninstallation process.</li>
-<li>For Android: Go to Settings > Apps & notifications > See all apps and find Microsoft Edge in the list. Tap on it and then tap on Uninstall. Confirm your action to complete the uninstallation process.</li>
-</ul></p> 197e85843d<br />
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- <h2>UPSC Syllabus for Mains 2023 in Hindi</h2>
-<p>UPSC Mains 2023 के लिए पाठ्यक्रम में भी कोई बदलाव नहीं हुआ है। UPSC Mains 2023 में कुल 9 पेपर होंगे, जिनमें से 2 पेपर Qualifying Nature के होंगे और 7 पेपर Merit Ranking के लिए Count होंगे। Qualifying Papers में Indian Language और English होंगे, जिनके लिए 300-300 अंक होंगे। Merit Papers में Essay, General Studies 1, General Studies 2, General Studies 3, General Studies 4, Optional Paper 1 और Optional Paper 2 होंगे, जिनके लिए 250-250 अंक होंगे। प्रत्येक पेपर का समय 3 घंटे होगा।</p>
- <h3>Qualifying Papers</h3>
-<p>Qualifying Papers में Indian Language और English होंगे। Indian Language का पेपर UPSC की सूची में से किसी भी भारतीय भाषा में हो सकता है। English का पेपर English Language में होगा। Qualifying Papers का मार्क्स Merit Ranking में Count नहीं होगा, लेकिन Qualify करना अनिवार्य होगा। Qualifying Papers में कम से कम 25% (75) अंक प्राप्त करना होगा। Qualifying Papers में Comprehension, Precis Writing, Translation, Grammar, Vocabulary, etc. Cover होंगे।</p>
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- <h3>Papers to be counted for Merit</h3>
-<p>Papers to be counted for Merit में Essay, General Studies 1, General Studies 2, General Studies 3, General Studies 4, Optional Paper 1 और Optional Paper 2 होंगे। Essay का पेपर Hindi या English में हो सकता है। General Studies के पेपर English में ही होंगे। Optional Paper का पेपर UPSC की सूची में से किसी भी Subject में हो सकता है। Papers to be counted for Merit का मार्क्स Merit Ranking में Count होगा। Papers to be counted for Merit में Essay Writing, Analytical and Critical Writing, Current Affairs and General Knowledge, Conceptual and Theoretical Understanding, Subject Knowledge and Application, etc. Cover होंगे।</p>
- <h2>UPSC Syllabus for Interview 2023 in Hindi</h2>
-<p>UPSC Interview 2023 के लिए पाठ्यक्रम में भी कोई बदलाव नहीं हुआ है। UPSC Interview 2023 में Personality Test होगा, जिसके लिए 275 अंक होंगे। Personality Test में UPSC की Board Members के सामने साक्षात्कार होगा, जिसमें उम्मीदवार की Personality, Mental Alertness, Critical Powers of Assimilation, Clear and Logical Exposition, Balance of Judgement, Variety and Depth of Interest, Ability for Social Cohesion and Leadership, Intellectual and Moral Integrity, etc. का मूल्यांकन किया जाएगा। Personality Test की तैयारी के लिए उम्मीदवारों को अपने DAF (Detailed Application Form) को अच्छी तरह से पढ़ना, Current Affairs और General Knowledge को Update रखना, Mock Interviews लेना, Confidence और Communication Skills को Improve करना, Honesty और Integrity को Demonstrate करना, etc. होगा।</p>
- <h2>How to Download UPSC Syllabus PDF in Hindi 2023</h2>
-<p>UPSC Syllabus PDF in Hindi 2023 को Download करने के लिए निम्नलिखित Steps Follow करें:</p>
-<ol>
-<li>UPSC की Official Website <a href="">www.upsc.gov.in</a> पर Visit करें।</li>
-<li>Home Page पर Examinations Tab पर Click करें।</li>
-<li>Examinations Tab में Active Examinations Link पर Click करें।</li>
-<li>Active Examinations List में Civil Services (Preliminary) Examination 2023 Link पर Click करें।</li>
-<li>Civil Services (Preliminary) Examination 2023 Page पर Notice Link पर Click करें।</li>
-<li>Notice Page पर Hindi Version Link पर Click करें।</li>
-<li>Hindi Version Page पर UPSC Syllabus PDF in Hindi 2023 Download हो जाएगा।</li>
-</ol>
-<p>UPSC Syllabus PDF in Hindi 2023 को Download करने के लिए Other Sources भी Available हैं, जैसे Online Coaching Websites, Online Study Portals, Online Booksellers, etc. लेकिन UPSC Syllabus PDF in Hindi 2023 Download करते समय सत्यापन और सुरक्षा का ध्यान रखें।</p>
- <h2>How to Prepare for UPSC Exam 2023 in Hindi</h2>
-<p>UPSC Exam 2023 in Hindi की तैयारी के लिए Books and Resources, Tips and Strategies <p>UPSC Exam 2023 in Hindi की तैयारी के लिए Books and Resources, Tips and Strategies का उपयोग करना चाहिए। Books and Resources में निम्नलिखित सुझाए गए हैं:</p>
- <h3>Books and Resources</h3>
-<table>
-<tr>
-<th>Subject</th>
-<th>Books and Resources</th>
-</tr>
-<tr>
-<td>History</td>
-<td>NCERT Books (Class 6 to 12), Spectrum's A Brief History of Modern India, Bipin Chandra's India's Struggle for Independence, etc.</td>
-</tr>
-<tr>
-<td>Geography</td>
-<td>NCERT Books (Class 6 to 12), G C Leong's Certificate Physical and Human Geography, Majid Hussain's Geography of India, etc.</td>
-</tr>
-<tr>
-<td>Polity</td>
-<td>M Laxmikanth's Indian Polity, D D Basu's Introduction to the Constitution of India, Subhash Kashyap's Our Constitution, etc.</td>
-</tr>
-<tr>
-<td>Economy</td>
-<td>NCERT Books (Class 9 to 12), Ramesh Singh's Indian Economy, Sanjiv Verma's The Indian Economy, Economic Survey, Budget, etc.</td>
-</tr>
-<tr>
-<td>Environment</td>
-<td>NCERT Books (Class 12 Biology), Shankar IAS Academy's Environment, Ministry of Environment and Forests' Reports, etc.</td>
-</tr>
-<tr>
-<td>Science and Technology</td>
-<td>NCERT Books (Class 6 to 10), Science Reporter Magazine, The Hindu Science and Technology Page, etc.</td>
-</tr>
-<tr>
-<td>Current Affairs</td>
-<td>The Hindu Newspaper, Yojana Magazine, Kurukshetra Magazine, Rajya Sabha TV Debates, All India Radio News Analysis, etc.</td>
-</tr>
-<tr>
-<td>CSAT</td <td>CSAT</td>
-<td>R S Aggarwal's A Modern Approach to Verbal and Non-Verbal Reasoning, R S Aggarwal's Quantitative Aptitude for Competitive Examinations, Arihant's CSAT Paper 2 Practice Sets, etc.</td>
-</tr>
-<tr>
-<td>Essay</td>
-<td>Yojana Magazine, The Hindu Editorial Page, Rajya Sabha TV Big Picture, etc.</td>
-</tr>
-<tr>
-<td>Optional Subject</td>
-<td>Depends on the Choice of the Candidate. Refer to the UPSC Syllabus PDF in Hindi 2023 for the Detailed Syllabus of Each Optional Subject.</td>
-</tr>
-</table>
- <h3>Tips and Strategies</h3>
-<p>Tips and Strategies में निम्नलिखित सुझाए गए हैं:</p>
-<ul>
-<li>UPSC Syllabus PDF in Hindi 2023 को अच्छी तरह से पढ़ें और उसके हिसाब से Study Plan बनाएं।</li>
-<li>Newspaper पढ़ना रोजाना की आदत बनाएं। Newspaper से Current Affairs, General Knowledge, Vocabulary, etc. Improve होंगे।</li>
-<li>Notes बनाना और Revise करना न भूलें। Notes से Concepts Clear होंगे, Memory Boost होगी, और Revision Easy होगा।</li>
-<li>Previous Year Papers Solve करें। Previous Year Papers से Exam Pattern, Question Trend, Difficulty Level, Time Management, etc. का पता चलेगा।</li>
-<li>Mock Tests Attempt करें। Mock Tests से Preparation Level, Strengths and Weaknesses, Accuracy and Speed, etc. का पता चलेगा।</li>
-<li>Regularly Update करें। Regularly Update से Current Affairs, General Knowledge, New Developments, etc. का पता चलेगा।</li>
-<li>Self Study करें। Self Study से Confidence, Self Reliance, Self Motivation, etc. Increase होंगे।</li>
-<li>Guidance लें। Guidance से Doubts Clear होंगे, Tips and Tricks मिलेंगे, Feedback मिलेगा, etc.</li>
-<li>Health and Fitness का ध्यान रखें। Health and Fitness से Stress Relief होगा, Concentration Improve होगा, Energy Level Maintain होगा, etc.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>UPSC Exam 2023 in Hindi की तैयारी करने के लिए UPSC Syllabus PDF in Hindi 2023 Download करना बहुत जरूरी है। UPSC Syllabus PDF in Hindi 2023 Download करने के बाद UPSC Syllabus PDF in Hindi 2023 को समझना और <p>UPSC Exam 2023 in Hindi की तैयारी करने के लिए UPSC Syllabus PDF in Hindi 2023 Download करना बहुत जरूरी है। UPSC Syllabus PDF in Hindi 2023 Download करने के बाद UPSC Syllabus PDF in Hindi 2023 को समझना और उसके हिसाब से Study Plan बनाना होगा। UPSC Syllabus PDF in Hindi 2023 में Prelims, Mains और Interview के लिए पूरे पाठ्यक्रम को विस्तार से दिया गया है। UPSC Syllabus PDF in Hindi 2023 को Download करने के लिए Official Website या Other Sources से Link मिल सकता है। UPSC Exam 2023 in Hindi की तैयारी के लिए Books and Resources, Tips and Strategies का भी प्रयोग करना चाहिए। UPSC Exam 2023 in Hindi में सफल होने के लिए Hard Work, Dedication, Consistency, Revision, Practice, etc. की जरूरत होती है।</p>
- <h2>FAQs</h2>
-<p>Q1. UPSC Syllabus PDF in Hindi 2023 कब Release होगा?</p>
-<p>A1. UPSC Syllabus PDF in Hindi 2023 Civil Services (Preliminary) Examination 2023 के साथ ही Release होगा, जो Expected है कि June 2023 में होगा।</p>
-<p>Q2. UPSC Syllabus PDF in Hindi 2023 में Prelims, Mains और Interview के लिए Same पाठ्यक्रम होगा?</p>
-<p>A2. No, UPSC Syllabus PDF in Hindi 2023 में Prelims, Mains और Interview के लिए Different पाठ्यक्रम होंगे। Prelims में General Studies Paper 1 और General Studies Paper 2 (CSAT) होंगे। Mains में Essay, General Studies 1, General Studies 2, General Studies 3, General Studies 4, Optional Paper 1 और Optional Paper 2 होंगे। Interview में Personality Test होगा।</p>
-<p>Q3. UPSC Syllabus PDF in Hindi 2023 में Optional Subject कैसे Choose करें?</p>
-<p>A3. UPSC Syllabus PDF in Hindi 2023 में Optional Subject Choose करने के लिए निम्नलिखित Factors Consider करें:</p>
-<ul>
-<li>Interest and Aptitude: Optional Subject में Interest and Aptitude होना चाहिए, जिससे Study Enjoyable and Easy हो।</li>
-<li>Syllabus and Difficulty Level: Optional Subject का Syllabus and Difficulty Level Manageable होना <p>चाहिए, जिससे Study Overwhelming and Difficult न हो।</li>
-<li>Availability and Quality of Books and Resources: Optional Subject के लिए Books and Resources Easily Available and Good Quality के होने चाहिए, जिससे Study Effective and Efficient हो।</li>
-<li>Scoring Potential and Success Rate: Optional Subject का Scoring Potential and Success Rate High होना चाहिए, जिससे Merit Ranking Improve हो।</li>
-<li>Overlap with General Studies: Optional Subject का General Studies के साथ Overlap होना चाहिए, जिससे Syllabus Coverage and Revision Easy हो।</li>
-</ul>
-<p>Q4. UPSC Syllabus PDF in Hindi 2023 में Negative Marking होगी?</p>
-<p>A4. Yes, UPSC Syllabus PDF in Hindi 2023 में Negative Marking होगी। Prelims में प्रत्येक Wrong Answer के लिए 1/3 Marks Deduct होंगे। Mains में प्रत्येक Irrelevant or Misleading Answer के लिए Marks Deduct हो सकते हैं। Interview में Negative Marking नहीं होगी।</p>
-<p>Q5. UPSC Syllabus PDF in Hindi 2023 में Language Papers का महत्व क्या है?</p>
-<p>A5. UPSC Syllabus PDF in Hindi 2023 में Language Papers का महत्व बहुत है। Language Papers में Indian Language और English होंगे, जो Qualifying Nature के होंगे। Language Papers में Comprehension, Precis Writing, Translation, Grammar, Vocabulary, etc. Cover होंगे। Language Papers में कम से कम 25% (75) अंक प्राप्त करना होगा, वरना Mains के Other Papers का Evaluation नहीं होगा। Language Papers से Candidates की Language Proficiency, Communication Skills, Expression Power, etc. का पता चलता है।</p> 401be4b1e0<br />
-<br />
-<br />

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-<br />
-<h1>How to Fix Slow Download Speed and Enjoy Faster Internet</h1>
-<p>Have you ever experienced the frustration of waiting for a file, a video, or a website to load, only to see the progress bar crawling at a snail's pace? If so, you are not alone. Slow download speed is a common problem that can affect your online activities, productivity, and entertainment. But don't worry, there are ways to fix it and enjoy faster internet.</p>
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-<h2>What is download speed and why does it matter?</h2>
-<p>Download speed is the rate at which data is transferred from the internet to your device. It is measured in megabits per second (Mbps). The higher the download speed, the faster you can access online content, such as images, videos, audio, files, and text. Download speed is important for various online activities, such as browsing the web, streaming media, downloading files, gaming, video conferencing, and more. A slow download speed can result in poor quality, buffering, interruptions, errors, and long waiting times.</p>
-<h3>How to measure your download speed</h3>
-<p>The easiest way to check your download speed is to use an online tool that can test your internet connection. You can simply type "internet speed test" into Google and click the "run speed test" button. This will show you how many Mbps your device is receiving from your internet service provider (ISP). You can also use other websites or apps that offer similar services, such as Speedtest.net or Fast.com.</p>
-<p>According to the Federal Communication Commission (FCC), a good download speed is at least 25 Mbps for most online activities. However, this may vary depending on how many devices and users are on your network at the same time, and what kind of content you are accessing. For example, if you want to stream HD video or play online games, you may need a higher download speed than if you just want to check your email or social media.</p>
-<h3>What causes slow download speed?</h3>
-<p>There are many factors that can affect your download speed, some of which are related to your device, some to your network, and some to your ISP. Here are some of the most common causes of slow download speed:</p>
-<ul>
-<li>Your device is overloaded with too many apps, programs, or browser tabs running at the same time.</li>
-<li>Your device is infected with malware or viruses that are slowing it down or stealing your bandwidth.</li>
-<li>Your device is outdated or has low memory or storage space.</li>
-<li>Your router or modem is faulty, old, or poorly configured.</li>
-<li>Your Wi-Fi signal is weak, unstable, or interfered by other devices or objects.</li>
-<li>Your internet plan is not suitable for your needs or usage habits.</li>
-<li>Your ISP is experiencing congestion, outages, or throttling.</li>
-<li>Your DNS server is slow or unreliable.</li>
-<li>You are downloading large files or multiple files at the same time.</li>
-<li>You are downloading from a slow or distant server.</li>
-</ul>
-<h3>How to increase your download speed: 15 tips and tricks</h3>
-<p>The good news is that there are many ways to improve your download speed and enjoy faster internet. Some of them are simple and easy to do yourself, while others may require more technical skills or professional assistance. Here are 15 tips and tricks that can help you increase your download speed:</p>
-<h4>Restart your device</h4>
-<p>This is one of the simplest and most effective ways to fix slow download speed. Restarting your device can clear any temporary files, cache data, or background processes that may be slowing it down. It can also refresh <h4>Test your internet speed</h4>
-<p>Another simple way to diagnose slow download speed is to test your internet speed using an online tool, as mentioned above. This can help you determine if the problem is with your device, your network, or your ISP. You can also compare your actual download speed with the speed that your ISP promises you in your internet plan. If there is a significant difference, you may want to contact your ISP and ask for an explanation or a solution.</p>
-<h4>Upgrade your internet plan</h4>
-<p>If your download speed is consistently lower than what you need or expect, it may be time to upgrade your internet plan. You can check with your ISP if they offer faster or more reliable plans that suit your needs and budget. You can also shop around and compare different ISPs and their plans to find the best deal. However, keep in mind that upgrading your internet plan may not guarantee faster download speed if there are other issues with your device or network.</p>
-<h4>Disable other devices connected to your router</h4>
-<p>One of the main factors that can affect your download speed is the number of devices and users that are connected to your router at the same time. The more devices and users, the more bandwidth is consumed and the slower the download speed. Therefore, you can try to disable or disconnect any devices that are not in use or that are not essential for your online activity. For example, you can turn off your smart TV, gaming console, printer, or other smart devices that may be using your Wi-Fi.</p>
-<p>How to boost your download speed<br />
-Why are my downloads so slow in Windows 10<br />
-How to increase download speed: 15 tips and tricks<br />
-How to fix slow download speed on PS4<br />
-How to speed up downloads on Xbox One<br />
-How to troubleshoot slow download speed on Steam<br />
-How to optimize your internet connection for faster downloads<br />
-How to use a download manager to improve download speed<br />
-How to change your DNS settings to increase download speed<br />
-How to disable background apps that slow down your downloads<br />
-How to clear your browser cache and cookies to boost download speed<br />
-How to update your network drivers for faster downloads<br />
-How to limit bandwidth usage of other devices on your network<br />
-How to check your download speed and compare it with your internet plan<br />
-How to use a VPN to bypass ISP throttling and increase download speed<br />
-How to enable parallel downloading in Chrome<br />
-How to adjust your router settings for optimal download speed<br />
-How to use Ethernet instead of Wi-Fi for faster downloads<br />
-How to move closer to your router or use a Wi-Fi extender<br />
-How to switch from 2.4 GHz to 5 GHz Wi-Fi band for faster downloads<br />
-How to scan your computer for malware that may slow down your downloads<br />
-How to defragment your hard drive for better download performance<br />
-How to disable Windows Update Delivery Optimization (WUDO)<br />
-How to change the download location or folder on your computer<br />
-How to pause and resume downloads in different browsers and apps<br />
-How to avoid seeding or uploading while downloading torrents<br />
-How to close other open apps and tabs while downloading<br />
-How to use a download accelerator or booster software<br />
-How to schedule your downloads for off-peak hours<br />
-How to contact your ISP or service provider for help with slow downloads<br />
-How to upgrade your internet speed or switch to a better plan<br />
-How to test different servers or mirrors for faster downloads<br />
-How to use a proxy server or a CDN service for faster downloads<br />
-How to disable firewalls or antivirus software that may interfere with downloads<br />
-How to enable QoS (Quality of Service) on your router for prioritizing downloads<br />
-How to flush your DNS cache and renew your IP address<br />
-How to check the health or availability of the files you are downloading<br />
-How to verify the integrity of the downloaded files<br />
-How to use a download splitter or joiner tool for large files<br />
-How to compress or zip files before downloading them</p>
-<h4>Clear your browser's cache and cookies</h4>
-<p>Your browser's cache and cookies are files that store information about the websites you visit and the data you enter. They can help improve your browsing experience by loading web pages faster and remembering your preferences. However, they can also accumulate over time and take up space on your device, which can slow down your download speed. Therefore, you can try to clear your browser's cache and cookies regularly to free up some space and speed up your downloads. You can do this by going to your browser's settings and finding the option to clear browsing data.</p>
-<h4>Use a wired connection instead of Wi-Fi</h4>
-<p>Wi-Fi is convenient and wireless, but it is also prone to interference, instability, and signal loss. These factors can reduce your download speed and affect your online experience. Therefore, if possible, you can try to use a wired connection instead of Wi-Fi for faster and more reliable downloads. You can do this by connecting your device directly to your router or modem using an Ethernet cable. This can eliminate any Wi-Fi issues and ensure a stable and secure connection.</p>
-<h4>Scan your device for malware and viruses</h4>
-<p>Malware and viruses are malicious software that can infect your device and cause various problems, such as slowing it down, stealing your data, or displaying unwanted ads. They can also affect your download speed by consuming your bandwidth or redirecting your traffic to malicious servers. Therefore, you should scan your device regularly for any malware or viruses and remove them as soon as possible. You can use a reputable antivirus software or app to do this, and make sure to keep it updated and active.</p>
-<h4>Pause or cancel other downloads and updates</h4>
-<p>Another factor that can affect your download speed is the number of downloads and updates that are running on your device or network at the same time. The more downloads and updates, the more bandwidth is used and the slower the download speed. Therefore, you can try to pause or cancel any downloads and updates that are not urgent or important for your online activity. For example, you can pause or cancel any software updates, app downloads, file transfers, or cloud backups that are not essential.</p>
-<h4>Change your DNS server</h4>
-<p>A DNS server is a service that translates domain names (such as www.google.com) into IP addresses (such as 172.217.14.206) that your device can understand and connect to. Your ISP usually assigns you a default DNS server, but it may not be the fastest or most reliable one. Therefore, you can try to change your DNS server to a different one that may offer better performance and security. You can do this by going to your device's network settings and finding the option to change your DNS server. You can choose from various free or paid DNS servers, such as Google Public DNS, Cloudflare DNS, or OpenDNS.</p>
-<h4>Use a download manager or accelerator</h4>
-<p>A download manager or accelerator is a software or app that can help you speed up your downloads by using various techniques, such as splitting files into smaller parts, resuming interrupted downloads, optimizing connections, or finding faster servers. You can use a download manager or accelerator to download large files or multiple files at the same time more efficiently and quickly. You can find various download managers or accelerators online, such as Internet Download Manager, Free Download Manager, or Download Accelerator Plus.</p>
-<h2>Conclusion</h2>
-<p>Slow download speed can be a frustrating and annoying problem that can affect your online experience and productivity. However, there are many ways to fix it and enjoy faster internet. In this article, we have shared 15 tips and tricks that can help you increase your download speed, such as restarting your device, testing your internet speed, upgrading your internet plan, disabling other devices connected to your router, clearing your browser's cache and cookies, using a wired connection instead of Wi-Fi, scanning your device for malware and viruses, pausing or canceling other downloads and updates, changing your DNS server, and using a download manager or accelerator. We hope that these tips and tricks will help you solve your slow download speed problem and improve your online experience.</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about slow download speed and how to fix it:</p>
-<ul>
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-<li><b>How do I fix slow download speed on Mac?</b><br>There are many ways to fix slow download speed on Mac, such as restarting your device, testing your internet speed, upgrading your internet plan, disabling other devices connected to your router, clearing your browser's cache and cookies, using a wired connection instead of Wi-Fi, scanning your device for malware and viruses, pausing or canceling other downloads and updates, changing your DNS server, and using a download manager or accelerator. You can also try some Mac-specific tips, such as updating your software, deleting unused apps or files, optimizing your storage space, or using the Mac network utility.</li>
-<li><b>How do I fix slow download speed on Android?</b><br>There are many ways to fix slow download speed on Android, such as restarting your device, testing your internet speed, upgrading your internet plan, disabling other devices connected to your router, clearing your browser's cache and cookies, using a wired connection instead of Wi-Fi, scanning your device for malware and viruses, pausing or canceling other downloads and updates, changing your DNS server, and using a download manager or accelerator. You can also try some Android-specific tips, such as updating your apps, deleting unwanted apps or files, freeing up memory space, or using the Android data saver mode.</li>
-</ul></p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Download YouTube Premium APK Mod and Access Exclusive Content Background Play and More.md

@@ -1,107 +0,0 @@
-<br />
-<h1>How to Download YouTube Premium APK Mod for Android</h1>
-<p>YouTube is one of the most popular social media platforms for videos, where you can watch, share, like, comment, and upload your own content. However, if you want to enjoy some extra features and benefits, such as ad-free viewing, offline downloads, background play, and access to YouTube Music and YouTube Originals, you need to subscribe to YouTube Premium, which costs $11.99 per month.</p>
-<p>But what if you don't want to pay for YouTube Premium? Is there a way to get all the premium features for free? The answer is yes, with YouTube Premium APK Mod. In this article, we will show you what YouTube Premium APK Mod is, how to download and install it on your Android device, how to use it, and what are the pros and cons of using it. Let's get started!</p>
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- <h2>What is YouTube Premium APK Mod?</h2>
-<p>YouTube Premium APK Mod is a modified version of the official YouTube app that unlocks all the premium features without requiring a subscription. With YouTube Premium APK Mod, you can enjoy the following features and benefits:</p>
-<ul>
-<li><strong>Ad-free viewing:</strong> You can watch any video without any annoying ads interrupting your experience.</li>
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-<p>YouTube Premium APK Mod is not available on the Google Play Store, so you need to download it from a third-party source. However, you need to be careful when downloading any modded app, as some of them may contain malware or viruses that can harm your device. We recommend you to download YouTube Premium APK Mod from a trusted source, such as [APKdone](^1^), which offers safe and secure downloads of various modded apps and games.</p>
- <h2>How to Download and Install YouTube Premium APK Mod?</h2>
-<p>To download and install YouTube Premium APK Mod on your Android device, follow these simple steps:</p>
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- <h2>Pros and Cons of YouTube Premium APK Mod</h2>
-<p>YouTube Premium APK Mod is a great way to enjoy all the premium features of YouTube for free, but it also has some drawbacks that you should be aware of. Here are some of the pros and cons of using YouTube Premium APK Mod:</p>
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-<tr>
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-<li>You can play any video in the background.</li>
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- <h2>Conclusion</h2>
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- <h2>FAQs</h2>
-<h3>Is YouTube Premium APK Mod safe to use?</h3>
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- <h3>Is YouTube Premium APK Mod legal to use?</h3>
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- <h3>Will YouTube Premium APK Mod work on iOS devices?</h3>
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spaces/2ndelement/voicevox/test/test_synthesis_engine_base.py

@@ -1,411 +0,0 @@
-from typing import List, Union
-from unittest import TestCase
-from unittest.mock import Mock
-
-import numpy
-
-from voicevox_engine.model import AccentPhrase, AudioQuery, Mora
-from voicevox_engine.synthesis_engine import SynthesisEngine
-
-
-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(round(float(phoneme_list[i] * 0.0625 + speaker_id), 2))
-    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(
-            round(
-                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]
-                    )
-                    * 0.0625
-                    + speaker_id
-                ),
-                2,
-            )
-        )
-    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)
-
-
-def koreha_arimasuka_base_expected():
-    return [
-        AccentPhrase(
-            moras=[
-                Mora(
-                    text="コ",
-                    consonant="k",
-                    consonant_length=2.44,
-                    vowel="o",
-                    vowel_length=2.88,
-                    pitch=4.38,
-                ),
-                Mora(
-                    text="レ",
-                    consonant="r",
-                    consonant_length=3.06,
-                    vowel="e",
-                    vowel_length=1.88,
-                    pitch=4.0,
-                ),
-                Mora(
-                    text="ワ",
-                    consonant="w",
-                    consonant_length=3.62,
-                    vowel="a",
-                    vowel_length=1.44,
-                    pitch=4.19,
-                ),
-            ],
-            accent=3,
-            pause_mora=None,
-            is_interrogative=False,
-        ),
-        AccentPhrase(
-            moras=[
-                Mora(
-                    text="ア",
-                    consonant=None,
-                    consonant_length=None,
-                    vowel="a",
-                    vowel_length=1.44,
-                    pitch=1.44,
-                ),
-                Mora(
-                    text="リ",
-                    consonant="r",
-                    consonant_length=3.06,
-                    vowel="i",
-                    vowel_length=2.31,
-                    pitch=4.44,
-                ),
-                Mora(
-                    text="マ",
-                    consonant="m",
-                    consonant_length=2.62,
-                    vowel="a",
-                    vowel_length=1.44,
-                    pitch=3.12,
-                ),
-                Mora(
-                    text="ス",
-                    consonant="s",
-                    consonant_length=3.19,
-                    vowel="U",
-                    vowel_length=1.38,
-                    pitch=0.0,
-                ),
-                Mora(
-                    text="カ",
-                    consonant="k",
-                    consonant_length=2.44,
-                    vowel="a",
-                    vowel_length=1.44,
-                    pitch=2.94,
-                ),
-            ],
-            accent=3,
-            pause_mora=None,
-            is_interrogative=False,
-        ),
-    ]
-
-
-def create_mock_query(accent_phrases):
-    return AudioQuery(
-        accent_phrases=accent_phrases,
-        speedScale=1,
-        pitchScale=0,
-        intonationScale=1,
-        volumeScale=1,
-        prePhonemeLength=0.1,
-        postPhonemeLength=0.1,
-        outputSamplingRate=24000,
-        outputStereo=False,
-        kana="",
-    )
-
-
-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 TestSynthesisEngineBase(TestCase):
-    def setUp(self):
-        super().setUp()
-        self.synthesis_engine = SynthesisEngine(
-            core=MockCore(),
-        )
-        self.synthesis_engine._synthesis_impl = Mock()
-
-    def create_accent_phrases_test_base(self, text: str, expected: List[AccentPhrase]):
-        actual = self.synthesis_engine.create_accent_phrases(text, 1)
-        self.assertEqual(
-            expected,
-            actual,
-            "case(text:" + text + ")",
-        )
-
-    def create_synthesis_test_base(
-        self,
-        text: str,
-        expected: List[AccentPhrase],
-        enable_interrogative_upspeak: bool,
-    ):
-        """音声合成時に疑問文モーラ処理を行っているかどうかを検証
-        (https://github.com/VOICEVOX/voicevox_engine/issues/272#issuecomment-1022610866)
-        """
-        accent_phrases = self.synthesis_engine.create_accent_phrases(text, 1)
-        query = create_mock_query(accent_phrases=accent_phrases)
-        self.synthesis_engine.synthesis(
-            query, 0, enable_interrogative_upspeak=enable_interrogative_upspeak
-        )
-        # _synthesis_implの第一引数に与えられたqueryを検証
-        actual = self.synthesis_engine._synthesis_impl.call_args[0][0].accent_phrases
-
-        self.assertEqual(
-            expected,
-            actual,
-            "case(text:" + text + ")",
-        )
-
-    def test_create_accent_phrases(self):
-        """accent_phrasesの作成時では疑問文モーラ処理を行わない
-        (https://github.com/VOICEVOX/voicevox_engine/issues/272#issuecomment-1022610866)
-        """
-        expected = koreha_arimasuka_base_expected()
-        expected[-1].is_interrogative = True
-        self.create_accent_phrases_test_base(text="これはありますか？", expected=expected)
-
-    def test_synthesis_interrogative(self):
-        expected = koreha_arimasuka_base_expected()
-        expected[-1].is_interrogative = True
-        expected[-1].moras += [
-            Mora(
-                text="ア",
-                consonant=None,
-                consonant_length=None,
-                vowel="a",
-                vowel_length=0.15,
-                pitch=expected[-1].moras[-1].pitch + 0.3,
-            )
-        ]
-        self.create_synthesis_test_base(
-            text="これはありますか？",
-            expected=expected,
-            enable_interrogative_upspeak=True,
-        )
-
-        expected = koreha_arimasuka_base_expected()
-        expected[-1].is_interrogative = True
-        self.create_synthesis_test_base(
-            text="これはありますか？",
-            expected=expected,
-            enable_interrogative_upspeak=False,
-        )
-
-        expected = koreha_arimasuka_base_expected()
-        self.create_synthesis_test_base(
-            text="これはありますか",
-            expected=expected,
-            enable_interrogative_upspeak=True,
-        )
-
-        def nn_base_expected():
-            return [
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ン",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="N",
-                            vowel_length=1.25,
-                            pitch=1.44,
-                        )
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                    is_interrogative=False,
-                )
-            ]
-
-        expected = nn_base_expected()
-        self.create_synthesis_test_base(
-            text="ん",
-            expected=expected,
-            enable_interrogative_upspeak=True,
-        )
-
-        expected = nn_base_expected()
-        expected[-1].is_interrogative = True
-        expected[-1].moras += [
-            Mora(
-                text="ン",
-                consonant=None,
-                consonant_length=None,
-                vowel="N",
-                vowel_length=0.15,
-                pitch=expected[-1].moras[-1].pitch + 0.3,
-            )
-        ]
-        self.create_synthesis_test_base(
-            text="ん？",
-            expected=expected,
-            enable_interrogative_upspeak=True,
-        )
-
-        expected = nn_base_expected()
-        expected[-1].is_interrogative = True
-        self.create_synthesis_test_base(
-            text="ん？",
-            expected=expected,
-            enable_interrogative_upspeak=False,
-        )
-
-        def ltu_base_expected():
-            return [
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ッ",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="cl",
-                            vowel_length=1.69,
-                            pitch=0.0,
-                        )
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                    is_interrogative=False,
-                )
-            ]
-
-        expected = ltu_base_expected()
-        self.create_synthesis_test_base(
-            text="っ",
-            expected=expected,
-            enable_interrogative_upspeak=True,
-        )
-
-        expected = ltu_base_expected()
-        expected[-1].is_interrogative = True
-        self.create_synthesis_test_base(
-            text="っ？",
-            expected=expected,
-            enable_interrogative_upspeak=True,
-        )
-
-        expected = ltu_base_expected()
-        expected[-1].is_interrogative = True
-        self.create_synthesis_test_base(
-            text="っ？",
-            expected=expected,
-            enable_interrogative_upspeak=False,
-        )
-
-        def su_base_expected():
-            return [
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ス",
-                            consonant="s",
-                            consonant_length=3.19,
-                            vowel="u",
-                            vowel_length=3.5,
-                            pitch=5.94,
-                        )
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                    is_interrogative=False,
-                )
-            ]
-
-        expected = su_base_expected()
-        self.create_synthesis_test_base(
-            text="す",
-            expected=expected,
-            enable_interrogative_upspeak=True,
-        )
-
-        expected = su_base_expected()
-        expected[-1].is_interrogative = True
-        expected[-1].moras += [
-            Mora(
-                text="ウ",
-                consonant=None,
-                consonant_length=None,
-                vowel="u",
-                vowel_length=0.15,
-                pitch=expected[-1].moras[-1].pitch + 0.3,
-            )
-        ]
-        self.create_synthesis_test_base(
-            text="す？",
-            expected=expected,
-            enable_interrogative_upspeak=True,
-        )
-
-        expected = su_base_expected()
-        expected[-1].is_interrogative = True
-        self.create_synthesis_test_base(
-            text="す？",
-            expected=expected,
-            enable_interrogative_upspeak=False,
-        )

spaces/4Taps/SadTalker/src/audio2pose_models/audio2pose.py

@@ -1,94 +0,0 @@
-import torch
-from torch import nn
-from src.audio2pose_models.cvae import CVAE
-from src.audio2pose_models.discriminator import PoseSequenceDiscriminator
-from src.audio2pose_models.audio_encoder import AudioEncoder
-
-class Audio2Pose(nn.Module):
-    def __init__(self, cfg, wav2lip_checkpoint, device='cuda'):
-        super().__init__()
-        self.cfg = cfg
-        self.seq_len = cfg.MODEL.CVAE.SEQ_LEN
-        self.latent_dim = cfg.MODEL.CVAE.LATENT_SIZE
-        self.device = device
-
-        self.audio_encoder = AudioEncoder(wav2lip_checkpoint)
-        self.audio_encoder.eval()
-        for param in self.audio_encoder.parameters():
-            param.requires_grad = False
-
-        self.netG = CVAE(cfg)
-        self.netD_motion = PoseSequenceDiscriminator(cfg)
-        
-        self.gan_criterion = nn.MSELoss()
-        self.reg_criterion = nn.L1Loss(reduction='none')
-        self.pair_criterion = nn.PairwiseDistance()
-        self.cosine_loss = nn.CosineSimilarity(dim=1)
-        
-    def forward(self, x):
-
-        batch = {}
-        coeff_gt = x['gt'].cuda().squeeze(0)           #bs frame_len+1 73
-        batch['pose_motion_gt'] = coeff_gt[:, 1:, -9:-3] - coeff_gt[:, :1, -9:-3] #bs frame_len 6
-        batch['ref'] = coeff_gt[:, 0, -9:-3]  #bs  6
-        batch['class'] = x['class'].squeeze(0).cuda() # bs
-        indiv_mels= x['indiv_mels'].cuda().squeeze(0) # bs seq_len+1 80 16
-
-        # forward
-        audio_emb_list = []
-        audio_emb = self.audio_encoder(indiv_mels[:, 1:, :, :].unsqueeze(2)) #bs seq_len 512
-        batch['audio_emb'] = audio_emb
-        batch = self.netG(batch)
-
-        pose_motion_pred = batch['pose_motion_pred']           # bs frame_len 6
-        pose_gt = coeff_gt[:, 1:, -9:-3].clone()               # bs frame_len 6
-        pose_pred = coeff_gt[:, :1, -9:-3] + pose_motion_pred  # bs frame_len 6
-
-        batch['pose_pred'] = pose_pred
-        batch['pose_gt'] = pose_gt
-
-        return batch
-
-    def test(self, x):
-
-        batch = {}
-        ref = x['ref']                            #bs 1 70
-        batch['ref'] = x['ref'][:,0,-6:]  
-        batch['class'] = x['class']  
-        bs = ref.shape[0]
-        
-        indiv_mels= x['indiv_mels']               # bs T 1 80 16
-        indiv_mels_use = indiv_mels[:, 1:]        # we regard the ref as the first frame
-        num_frames = x['num_frames']
-        num_frames = int(num_frames) - 1
-
-        #  
-        div = num_frames//self.seq_len
-        re = num_frames%self.seq_len
-        audio_emb_list = []
-        pose_motion_pred_list = [torch.zeros(batch['ref'].unsqueeze(1).shape, dtype=batch['ref'].dtype, 
-                                                device=batch['ref'].device)]
-
-        for i in range(div):
-            z = torch.randn(bs, self.latent_dim).to(ref.device)
-            batch['z'] = z
-            audio_emb = self.audio_encoder(indiv_mels_use[:, i*self.seq_len:(i+1)*self.seq_len,:,:,:]) #bs seq_len 512
-            batch['audio_emb'] = audio_emb
-            batch = self.netG.test(batch)
-            pose_motion_pred_list.append(batch['pose_motion_pred'])  #list of bs seq_len 6
-        
-        if re != 0:
-            z = torch.randn(bs, self.latent_dim).to(ref.device)
-            batch['z'] = z
-            audio_emb = self.audio_encoder(indiv_mels_use[:, -1*self.seq_len:,:,:,:]) #bs seq_len  512
-            batch['audio_emb'] = audio_emb
-            batch = self.netG.test(batch)
-            pose_motion_pred_list.append(batch['pose_motion_pred'][:,-1*re:,:])   
-        
-        pose_motion_pred = torch.cat(pose_motion_pred_list, dim = 1)
-        batch['pose_motion_pred'] = pose_motion_pred
-
-        pose_pred = ref[:, :1, -6:] + pose_motion_pred  # bs T 6
-
-        batch['pose_pred'] = pose_pred
-        return batch

spaces/AIWaves/Debate/src/agents/Environment/__init__.py

@@ -1 +0,0 @@
-from .base_environment import Environment

spaces/Ababababababbababa/poetry2023/README.md

@@ -1,13 +0,0 @@
----
-title: Poetry2023
-emoji: 👁
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.16.0
-app_file: app.py
-pinned: false
-duplicated_from: Aaaaaaaabdualh/poetry2023
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/arcadetcrp.js

@@ -1,11 +0,0 @@
-import Recorder from './logic/runcommands/arcadetcrp/Recorder.js';
-import Player from './logic/runcommands/arcadetcrp/Player.js';
-import StepRunner from './logic/runcommands/arcadetcrp/StepRunner.js';
-import RunCommands from './logic/runcommands/RunCommands.js';
-
-export default {
-    Recorder: Recorder,
-    Player: Player,
-    StepRunner: StepRunner,
-    RunCommands: RunCommands
-};

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

@@ -1,2 +0,0 @@
-import MouseWheelToUpDown from './input/mousewheeltoupdown/MouseWheelToUpDown';
-export default MouseWheelToUpDown;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/holygrail/methods/Build.js

@@ -1,40 +0,0 @@
-import LayoutMode0 from './LayoutMode0.js';
-import LayoutMode1 from './LayoutMode1.js';
-import LayoutMode2 from './LayoutMode2.js';
-import LayoutMode3 from './LayoutMode3.js';
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-const LayoutCallbacks = [LayoutMode0, LayoutMode1, LayoutMode2, LayoutMode3];
-
-var Build = function (config) {
-    this.clear(true);
-
-    // Add Background
-    var background = GetValue(config, 'background', undefined);
-    if (background) {
-        this.addBackground(background);
-    }
-
-    var layoutMode = GetValue(config, 'layoutMode', 0);
-    if (typeof (layoutMode) === 'string') {
-        layoutMode = LayoutModesMap[layoutMode.toUpperCase()];
-    }
-    var layoutCallback = LayoutCallbacks[layoutMode] || LayoutCallbacks[0];
-    layoutCallback.call(this, config);
-
-    this.addChildrenMap('background', config.background);
-    this.addChildrenMap('header', config.header);
-    this.addChildrenMap('leftSide', config.leftSide);
-    this.addChildrenMap('content', config.content);
-    this.addChildrenMap('rightSide', config.rightSide);
-    this.addChildrenMap('footer', config.footer);
-}
-
-const LayoutModesMap = {
-    'FFF': 0,
-    'LFF': 1,
-    'FFR': 2,
-    'LFR': 3
-}
-
-export default Build;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/slider/Slider.js

@@ -1,194 +0,0 @@
-import Sizer from '../sizer/Sizer.js';
-import CreateBackground from '../utils/build/CreateBackground.js';
-import ProgressBase from '../../../plugins/utils/progressbase/ProgressBase.js';
-import OnDragThumb from './OnDragThumb.js';
-import OnTouchTrack from './OnTouchTrack.js';
-import GetStartPoint from './GetStartPoint.js';
-import GetEndPoint from './GetEndPoint.js';
-import UpdateThumb from './UpdateThumb.js';
-import UpdateIndicator from './UpdateIndicator.js';
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-const IsPlainObject = Phaser.Utils.Objects.IsPlainObject;
-const Clamp = Phaser.Math.Clamp;
-const SnapTo = Phaser.Math.Snap.To;
-
-class Slider extends ProgressBase(Sizer) {
-    constructor(scene, config) {
-        // Create sizer
-        super(scene, config);
-        this.type = 'rexSlider';
-
-        this.bootProgressBase(config);
-
-        this.reverseAxis = GetValue(config, 'reverseAxis', false);
-
-        // Add elements
-        var background = GetValue(config, 'background', undefined);
-        var track = GetValue(config, 'track', undefined);
-        var indicator = GetValue(config, 'indicator', undefined);
-        var thumb = GetValue(config, 'thumb', undefined);
-
-        if (background) {
-            if (IsPlainObject(background)) {
-                background = CreateBackground(scene, background);
-            }
-            this.addBackground(background);
-        }
-
-        if (track) {
-            if (IsPlainObject(track)) {
-                track = CreateBackground(scene, track);
-            }
-            this.add(track,
-                {
-                    proportion: 1,
-                    expand: true,
-                    minWidth: ((this.orientation === 0) ? 0 : undefined),
-                    minHeight: ((this.orientation === 1) ? 0 : undefined)
-                }
-            )
-        }
-
-        if (indicator) {
-            if (IsPlainObject(indicator)) {
-                indicator = CreateBackground(scene, indicator);
-            }
-            this.pin(indicator); // Put into container but not layout it
-        }
-
-        if (thumb) {
-            if (IsPlainObject(thumb)) {
-                thumb = CreateBackground(scene, thumb);
-            }
-            this.pin(thumb); // Put into container but not layout it
-
-        }
-
-        // Input
-        var inputMode = GetValue(config, 'input', 0);
-        if (typeof (inputMode) === 'string') {
-            inputMode = INPUTMODE[inputMode];
-        }
-        switch (inputMode) {
-            case 0: // 'drag'
-                if (thumb) {
-                    thumb.setInteractive();
-                    this.scene.input.setDraggable(thumb);
-                    thumb
-                        .on('drag', OnDragThumb, this)
-                        .on('dragstart', function (pointer) {
-                            this.eventEmitter.emit('inputstart', pointer);
-                        }, this)
-                        .on('dragend', function (pointer) {
-                            this.eventEmitter.emit('inputend', pointer);
-                        }, this)
-
-                }
-                break;
-            case 1: // 'click'
-                this
-                    .on('pointerdown', OnTouchTrack, this)
-                    .on('pointermove', OnTouchTrack, this)
-                    .on('pointerdown', function (pointer) {
-                        this.eventEmitter.emit('inputstart', pointer);
-                    }, this)
-                    .on('pointerup', function (pointer) {
-                        this.eventEmitter.emit('inputend', pointer);
-                    }, this)
-                    .on('pointerover', function (pointer) {
-                        if (pointer.isDown) {
-                            this.eventEmitter.emit('inputstart', pointer);
-                        }
-                    }, this)
-                    .on('pointerout', function (pointer) {
-                        if (pointer.isDown) {
-                            this.eventEmitter.emit('inputend', pointer);
-                        }
-                    }, this)
-                    .setInteractive()
-
-                break;
-        }
-
-        this.addChildrenMap('background', background);
-        this.addChildrenMap('track', track);
-        this.addChildrenMap('indicator', indicator);
-        this.addChildrenMap('thumb', thumb);
-
-        this.setEnable(GetValue(config, 'enable', undefined));
-        this.setGap(GetValue(config, 'gap', undefined));
-        this.setValue(GetValue(config, 'value', 0), GetValue(config, 'min', undefined), GetValue(config, 'max', undefined));
-
-    }
-
-    setEnable(enable) {
-        if (enable === undefined) {
-            enable = true;
-        }
-        this.enable = enable;
-        return this;
-    }
-
-    setGap(gap, min, max) {
-        if (gap && (min !== undefined)) {
-            gap = gap / (max - min);
-        }
-
-        this.gap = gap;
-        return this;
-    }
-
-    // Override
-    get value() {
-        return this._value;
-    }
-
-    // Override
-    set value(value) {
-        if (this.gap !== undefined) {
-            value = SnapTo(value, this.gap);
-        }
-        var oldValue = this._value;
-        this._value = Clamp(value, 0, 1);
-
-        if (oldValue !== this._value) {
-            this.updateThumb(this._value);
-            this.updateIndicator(this._value);
-            this.eventEmitter.emit('valuechange', this._value, oldValue, this.eventEmitter);
-        }
-    }
-
-    runLayout(parent, newWidth, newHeight) {
-        // Skip hidden or !dirty sizer
-        if (this.ignoreLayout) {
-            return this;
-        }
-
-        super.runLayout(parent, newWidth, newHeight);
-        this.updateThumb();
-        this.updateIndicator();
-        return this;
-    }
-}
-
-const INPUTMODE = {
-    pan: 0,
-    drag: 0,
-    click: 1,
-    none: -1,
-}
-
-var methods = {
-    getStartPoint: GetStartPoint,
-    getEndPoint: GetEndPoint,
-    updateThumb: UpdateThumb,
-    updateIndicator: UpdateIndicator,
-}
-
-Object.assign(
-    Slider.prototype,
-    methods,
-);
-
-export default Slider;

spaces/AlekseyKorshuk/instagram-filter-removal/README.md

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

spaces/AlexWang/lama/bin/extract_masks.py

@@ -1,63 +0,0 @@
-import PIL.Image as Image
-import numpy as np
-import os
-
-
-def main(args):
-    if not args.indir.endswith('/'):
-        args.indir += '/'
-    os.makedirs(args.outdir, exist_ok=True)
-
-    src_images = [
-        args.indir+fname for fname in  os.listdir(args.indir)]
-
-    tgt_masks = [
-        args.outdir+fname[:-4] + f'_mask000.png' 
-            for fname in  os.listdir(args.indir)]
-
-    for img_name, msk_name in zip(src_images, tgt_masks):
-        #print(img)
-        #print(msk)
-
-        image = Image.open(img_name).convert('RGB')
-        image = np.transpose(np.array(image), (2, 0, 1))
-
-        mask = (image == 255).astype(int)
-
-        print(mask.dtype, mask.shape)
-
-
-        Image.fromarray(
-            np.clip(mask[0,:,:] * 255, 0, 255).astype('uint8'),mode='L'
-        ).save(msk_name)
-
-
-
-
-    '''
-    for infile in src_images:
-        try:
-            file_relpath = infile[len(indir):]
-            img_outpath = os.path.join(outdir, file_relpath)
-            os.makedirs(os.path.dirname(img_outpath), exist_ok=True)
-
-            image = Image.open(infile).convert('RGB')
-
-            mask = 
-
-            Image.fromarray(
-                np.clip(
-                    cur_mask * 255, 0, 255).astype('uint8'),
-                    mode='L'
-                ).save(cur_basename + f'_mask{i:03d}.png')
-    '''
-
-
-
-if __name__ == '__main__':
-    import argparse
-    aparser = argparse.ArgumentParser()
-    aparser.add_argument('--indir', type=str, help='Path to folder with images')
-    aparser.add_argument('--outdir', type=str, help='Path to folder to store aligned images and masks to')
-
-    main(aparser.parse_args())

spaces/Alycer/VITS-Umamusume-voice-synthesizer/text/cleaners.py

@@ -1,146 +0,0 @@
-import re
-
-
-def japanese_cleaners(text):
-    from text.japanese import japanese_to_romaji_with_accent
-    text = japanese_to_romaji_with_accent(text)
-    text = re.sub(r'([A-Za-z])$', r'\1.', text)
-    return text
-
-
-def japanese_cleaners2(text):
-    return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…')
-
-
-def korean_cleaners(text):
-    '''Pipeline for Korean text'''
-    from text.korean import latin_to_hangul, number_to_hangul, divide_hangul
-    text = latin_to_hangul(text)
-    text = number_to_hangul(text)
-    text = divide_hangul(text)
-    text = re.sub(r'([\u3131-\u3163])$', r'\1.', text)
-    return text
-
-
-def chinese_cleaners(text):
-    '''Pipeline for Chinese text'''
-    from text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = re.sub(r'([ˉˊˇˋ˙])$', r'\1。', text)
-    return text
-
-
-def zh_ja_mixture_cleaners(text):
-    from text.mandarin import chinese_to_romaji
-    from text.japanese import japanese_to_romaji_with_accent
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_romaji(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_romaji_with_accent(
-        x.group(1)).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…')+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def sanskrit_cleaners(text):
-    text = text.replace('॥', '।').replace('ॐ', 'ओम्')
-    if text[-1] != '।':
-        text += ' ।'
-    return text
-
-
-def cjks_cleaners(text):
-    from text.mandarin import chinese_to_lazy_ipa
-    from text.japanese import japanese_to_ipa
-    from text.korean import korean_to_lazy_ipa
-    from text.sanskrit import devanagari_to_ipa
-    from text.english import english_to_lazy_ipa
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_lazy_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_lazy_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[SA\](.*?)\[SA\]',
-                  lambda x: devanagari_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_lazy_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def cjke_cleaners(text):
-    from text.mandarin import chinese_to_lazy_ipa
-    from text.japanese import japanese_to_ipa
-    from text.korean import korean_to_ipa
-    from text.english import english_to_ipa2
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]', lambda x: chinese_to_lazy_ipa(x.group(1)).replace(
-        'ʧ', 'tʃ').replace('ʦ', 'ts').replace('ɥan', 'ɥæn')+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_ipa(x.group(1)).replace('ʧ', 'tʃ').replace(
-        'ʦ', 'ts').replace('ɥan', 'ɥæn').replace('ʥ', 'dz')+' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]', lambda x: english_to_ipa2(x.group(1)).replace('ɑ', 'a').replace(
-        'ɔ', 'o').replace('ɛ', 'e').replace('ɪ', 'i').replace('ʊ', 'u')+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def cjke_cleaners2(text):
-    from text.mandarin import chinese_to_ipa
-    from text.japanese import japanese_to_ipa2
-    from text.korean import korean_to_ipa
-    from text.english import english_to_ipa2
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def thai_cleaners(text):
-    from text.thai import num_to_thai, latin_to_thai
-    text = num_to_thai(text)
-    text = latin_to_thai(text)
-    return text
-
-
-def shanghainese_cleaners(text):
-    from text.shanghainese import shanghainese_to_ipa
-    text = shanghainese_to_ipa(text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def chinese_dialect_cleaners(text):
-    from text.mandarin import chinese_to_ipa2
-    from text.japanese import japanese_to_ipa3
-    from text.shanghainese import shanghainese_to_ipa
-    from text.cantonese import cantonese_to_ipa
-    from text.english import english_to_lazy_ipa2
-    from text.ngu_dialect import ngu_dialect_to_ipa
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa3(x.group(1)).replace('Q', 'ʔ')+' ', text)
-    text = re.sub(r'\[SH\](.*?)\[SH\]', lambda x: shanghainese_to_ipa(x.group(1)).replace('1', '˥˧').replace('5',
-                  '˧˧˦').replace('6', '˩˩˧').replace('7', '˥').replace('8', '˩˨').replace('ᴀ', 'ɐ').replace('ᴇ', 'e')+' ', text)
-    text = re.sub(r'\[GD\](.*?)\[GD\]',
-                  lambda x: cantonese_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_lazy_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\[([A-Z]{2})\](.*?)\[\1\]', lambda x: ngu_dialect_to_ipa(x.group(2), x.group(
-        1)).replace('ʣ', 'dz').replace('ʥ', 'dʑ').replace('ʦ', 'ts').replace('ʨ', 'tɕ')+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/controlnet/train_controlnet.py

@@ -1,1127 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-
-import argparse
-import logging
-import math
-import os
-import random
-import shutil
-from pathlib import Path
-
-import accelerate
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import ProjectConfiguration, set_seed
-from datasets import load_dataset
-from huggingface_hub import create_repo, upload_folder
-from packaging import version
-from PIL import Image
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import AutoTokenizer, PretrainedConfig
-
-import diffusers
-from diffusers import (
-    AutoencoderKL,
-    ControlNetModel,
-    DDPMScheduler,
-    StableDiffusionControlNetPipeline,
-    UNet2DConditionModel,
-    UniPCMultistepScheduler,
-)
-from diffusers.optimization import get_scheduler
-from diffusers.utils import check_min_version, is_wandb_available
-from diffusers.utils.import_utils import is_xformers_available
-
-
-if is_wandb_available():
-    import wandb
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.19.0")
-
-logger = get_logger(__name__)
-
-
-def image_grid(imgs, rows, cols):
-    assert len(imgs) == rows * cols
-
-    w, h = imgs[0].size
-    grid = Image.new("RGB", size=(cols * w, rows * h))
-
-    for i, img in enumerate(imgs):
-        grid.paste(img, box=(i % cols * w, i // cols * h))
-    return grid
-
-
-def log_validation(vae, text_encoder, tokenizer, unet, controlnet, args, accelerator, weight_dtype, step):
-    logger.info("Running validation... ")
-
-    controlnet = accelerator.unwrap_model(controlnet)
-
-    pipeline = StableDiffusionControlNetPipeline.from_pretrained(
-        args.pretrained_model_name_or_path,
-        vae=vae,
-        text_encoder=text_encoder,
-        tokenizer=tokenizer,
-        unet=unet,
-        controlnet=controlnet,
-        safety_checker=None,
-        revision=args.revision,
-        torch_dtype=weight_dtype,
-    )
-    pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
-    pipeline = pipeline.to(accelerator.device)
-    pipeline.set_progress_bar_config(disable=True)
-
-    if args.enable_xformers_memory_efficient_attention:
-        pipeline.enable_xformers_memory_efficient_attention()
-
-    if args.seed is None:
-        generator = None
-    else:
-        generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
-
-    if len(args.validation_image) == len(args.validation_prompt):
-        validation_images = args.validation_image
-        validation_prompts = args.validation_prompt
-    elif len(args.validation_image) == 1:
-        validation_images = args.validation_image * len(args.validation_prompt)
-        validation_prompts = args.validation_prompt
-    elif len(args.validation_prompt) == 1:
-        validation_images = args.validation_image
-        validation_prompts = args.validation_prompt * len(args.validation_image)
-    else:
-        raise ValueError(
-            "number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`"
-        )
-
-    image_logs = []
-
-    for validation_prompt, validation_image in zip(validation_prompts, validation_images):
-        validation_image = Image.open(validation_image).convert("RGB")
-
-        images = []
-
-        for _ in range(args.num_validation_images):
-            with torch.autocast("cuda"):
-                image = pipeline(
-                    validation_prompt, validation_image, num_inference_steps=20, generator=generator
-                ).images[0]
-
-            images.append(image)
-
-        image_logs.append(
-            {"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt}
-        )
-
-    for tracker in accelerator.trackers:
-        if tracker.name == "tensorboard":
-            for log in image_logs:
-                images = log["images"]
-                validation_prompt = log["validation_prompt"]
-                validation_image = log["validation_image"]
-
-                formatted_images = []
-
-                formatted_images.append(np.asarray(validation_image))
-
-                for image in images:
-                    formatted_images.append(np.asarray(image))
-
-                formatted_images = np.stack(formatted_images)
-
-                tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats="NHWC")
-        elif tracker.name == "wandb":
-            formatted_images = []
-
-            for log in image_logs:
-                images = log["images"]
-                validation_prompt = log["validation_prompt"]
-                validation_image = log["validation_image"]
-
-                formatted_images.append(wandb.Image(validation_image, caption="Controlnet conditioning"))
-
-                for image in images:
-                    image = wandb.Image(image, caption=validation_prompt)
-                    formatted_images.append(image)
-
-            tracker.log({"validation": formatted_images})
-        else:
-            logger.warn(f"image logging not implemented for {tracker.name}")
-
-        return image_logs
-
-
-def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
-    text_encoder_config = PretrainedConfig.from_pretrained(
-        pretrained_model_name_or_path,
-        subfolder="text_encoder",
-        revision=revision,
-    )
-    model_class = text_encoder_config.architectures[0]
-
-    if model_class == "CLIPTextModel":
-        from transformers import CLIPTextModel
-
-        return CLIPTextModel
-    elif model_class == "RobertaSeriesModelWithTransformation":
-        from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
-
-        return RobertaSeriesModelWithTransformation
-    else:
-        raise ValueError(f"{model_class} is not supported.")
-
-
-def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):
-    img_str = ""
-    if image_logs is not None:
-        img_str = "You can find some example images below.\n"
-        for i, log in enumerate(image_logs):
-            images = log["images"]
-            validation_prompt = log["validation_prompt"]
-            validation_image = log["validation_image"]
-            validation_image.save(os.path.join(repo_folder, "image_control.png"))
-            img_str += f"prompt: {validation_prompt}\n"
-            images = [validation_image] + images
-            image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png"))
-            img_str += f"![images_{i})](./images_{i}.png)\n"
-
-    yaml = f"""
----
-license: creativeml-openrail-m
-base_model: {base_model}
-tags:
-- stable-diffusion
-- stable-diffusion-diffusers
-- text-to-image
-- diffusers
-- controlnet
-inference: true
----
-    """
-    model_card = f"""
-# controlnet-{repo_id}
-
-These are controlnet weights trained on {base_model} with new type of conditioning.
-{img_str}
-"""
-    with open(os.path.join(repo_folder, "README.md"), "w") as f:
-        f.write(yaml + model_card)
-
-
-def parse_args(input_args=None):
-    parser = argparse.ArgumentParser(description="Simple example of a ControlNet training script.")
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--controlnet_model_name_or_path",
-        type=str,
-        default=None,
-        help="Path to pretrained controlnet model or model identifier from huggingface.co/models."
-        " If not specified controlnet weights are initialized from unet.",
-    )
-    parser.add_argument(
-        "--revision",
-        type=str,
-        default=None,
-        required=False,
-        help=(
-            "Revision of pretrained model identifier from huggingface.co/models. Trainable model components should be"
-            " float32 precision."
-        ),
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        type=str,
-        default=None,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="controlnet-model",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument(
-        "--cache_dir",
-        type=str,
-        default=None,
-        help="The directory where the downloaded models and datasets will be stored.",
-    )
-    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=1)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=None,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--checkpointing_steps",
-        type=int,
-        default=500,
-        help=(
-            "Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
-            "In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
-            "Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
-            "See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
-            "instructions."
-        ),
-    )
-    parser.add_argument(
-        "--checkpoints_total_limit",
-        type=int,
-        default=None,
-        help=("Max number of checkpoints to store."),
-    )
-    parser.add_argument(
-        "--resume_from_checkpoint",
-        type=str,
-        default=None,
-        help=(
-            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
-            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
-        ),
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument(
-        "--gradient_checkpointing",
-        action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=5e-6,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument(
-        "--lr_num_cycles",
-        type=int,
-        default=1,
-        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
-    )
-    parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
-    parser.add_argument(
-        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
-    )
-    parser.add_argument(
-        "--dataloader_num_workers",
-        type=int,
-        default=0,
-        help=(
-            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
-        ),
-    )
-    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--hub_model_id",
-        type=str,
-        default=None,
-        help="The name of the repository to keep in sync with the local `output_dir`.",
-    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
-    )
-    parser.add_argument(
-        "--allow_tf32",
-        action="store_true",
-        help=(
-            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
-            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
-        ),
-    )
-    parser.add_argument(
-        "--report_to",
-        type=str,
-        default="tensorboard",
-        help=(
-            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
-            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
-        ),
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default=None,
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
-            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
-            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
-        ),
-    )
-    parser.add_argument(
-        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
-    )
-    parser.add_argument(
-        "--set_grads_to_none",
-        action="store_true",
-        help=(
-            "Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
-            " behaviors, so disable this argument if it causes any problems. More info:"
-            " https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
-        ),
-    )
-    parser.add_argument(
-        "--dataset_name",
-        type=str,
-        default=None,
-        help=(
-            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
-            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
-            " or to a folder containing files that 🤗 Datasets can understand."
-        ),
-    )
-    parser.add_argument(
-        "--dataset_config_name",
-        type=str,
-        default=None,
-        help="The config of the Dataset, leave as None if there's only one config.",
-    )
-    parser.add_argument(
-        "--train_data_dir",
-        type=str,
-        default=None,
-        help=(
-            "A folder containing the training data. Folder contents must follow the structure described in"
-            " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
-            " must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
-        ),
-    )
-    parser.add_argument(
-        "--image_column", type=str, default="image", help="The column of the dataset containing the target image."
-    )
-    parser.add_argument(
-        "--conditioning_image_column",
-        type=str,
-        default="conditioning_image",
-        help="The column of the dataset containing the controlnet conditioning image.",
-    )
-    parser.add_argument(
-        "--caption_column",
-        type=str,
-        default="text",
-        help="The column of the dataset containing a caption or a list of captions.",
-    )
-    parser.add_argument(
-        "--max_train_samples",
-        type=int,
-        default=None,
-        help=(
-            "For debugging purposes or quicker training, truncate the number of training examples to this "
-            "value if set."
-        ),
-    )
-    parser.add_argument(
-        "--proportion_empty_prompts",
-        type=float,
-        default=0,
-        help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
-    )
-    parser.add_argument(
-        "--validation_prompt",
-        type=str,
-        default=None,
-        nargs="+",
-        help=(
-            "A set of prompts evaluated every `--validation_steps` and logged to `--report_to`."
-            " Provide either a matching number of `--validation_image`s, a single `--validation_image`"
-            " to be used with all prompts, or a single prompt that will be used with all `--validation_image`s."
-        ),
-    )
-    parser.add_argument(
-        "--validation_image",
-        type=str,
-        default=None,
-        nargs="+",
-        help=(
-            "A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`"
-            " and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a"
-            " a single `--validation_prompt` to be used with all `--validation_image`s, or a single"
-            " `--validation_image` that will be used with all `--validation_prompt`s."
-        ),
-    )
-    parser.add_argument(
-        "--num_validation_images",
-        type=int,
-        default=4,
-        help="Number of images to be generated for each `--validation_image`, `--validation_prompt` pair",
-    )
-    parser.add_argument(
-        "--validation_steps",
-        type=int,
-        default=100,
-        help=(
-            "Run validation every X steps. Validation consists of running the prompt"
-            " `args.validation_prompt` multiple times: `args.num_validation_images`"
-            " and logging the images."
-        ),
-    )
-    parser.add_argument(
-        "--tracker_project_name",
-        type=str,
-        default="train_controlnet",
-        help=(
-            "The `project_name` argument passed to Accelerator.init_trackers for"
-            " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
-        ),
-    )
-
-    if input_args is not None:
-        args = parser.parse_args(input_args)
-    else:
-        args = parser.parse_args()
-
-    if args.dataset_name is None and args.train_data_dir is None:
-        raise ValueError("Specify either `--dataset_name` or `--train_data_dir`")
-
-    if args.dataset_name is not None and args.train_data_dir is not None:
-        raise ValueError("Specify only one of `--dataset_name` or `--train_data_dir`")
-
-    if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
-        raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
-
-    if args.validation_prompt is not None and args.validation_image is None:
-        raise ValueError("`--validation_image` must be set if `--validation_prompt` is set")
-
-    if args.validation_prompt is None and args.validation_image is not None:
-        raise ValueError("`--validation_prompt` must be set if `--validation_image` is set")
-
-    if (
-        args.validation_image is not None
-        and args.validation_prompt is not None
-        and len(args.validation_image) != 1
-        and len(args.validation_prompt) != 1
-        and len(args.validation_image) != len(args.validation_prompt)
-    ):
-        raise ValueError(
-            "Must provide either 1 `--validation_image`, 1 `--validation_prompt`,"
-            " or the same number of `--validation_prompt`s and `--validation_image`s"
-        )
-
-    if args.resolution % 8 != 0:
-        raise ValueError(
-            "`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the controlnet encoder."
-        )
-
-    return args
-
-
-def make_train_dataset(args, tokenizer, accelerator):
-    # Get the datasets: you can either provide your own training and evaluation files (see below)
-    # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
-
-    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
-    # download the dataset.
-    if args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        dataset = load_dataset(
-            args.dataset_name,
-            args.dataset_config_name,
-            cache_dir=args.cache_dir,
-        )
-    else:
-        if args.train_data_dir is not None:
-            dataset = load_dataset(
-                args.train_data_dir,
-                cache_dir=args.cache_dir,
-            )
-        # See more about loading custom images at
-        # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script
-
-    # Preprocessing the datasets.
-    # We need to tokenize inputs and targets.
-    column_names = dataset["train"].column_names
-
-    # 6. Get the column names for input/target.
-    if args.image_column is None:
-        image_column = column_names[0]
-        logger.info(f"image column defaulting to {image_column}")
-    else:
-        image_column = args.image_column
-        if image_column not in column_names:
-            raise ValueError(
-                f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
-            )
-
-    if args.caption_column is None:
-        caption_column = column_names[1]
-        logger.info(f"caption column defaulting to {caption_column}")
-    else:
-        caption_column = args.caption_column
-        if caption_column not in column_names:
-            raise ValueError(
-                f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
-            )
-
-    if args.conditioning_image_column is None:
-        conditioning_image_column = column_names[2]
-        logger.info(f"conditioning image column defaulting to {conditioning_image_column}")
-    else:
-        conditioning_image_column = args.conditioning_image_column
-        if conditioning_image_column not in column_names:
-            raise ValueError(
-                f"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
-            )
-
-    def tokenize_captions(examples, is_train=True):
-        captions = []
-        for caption in examples[caption_column]:
-            if random.random() < args.proportion_empty_prompts:
-                captions.append("")
-            elif isinstance(caption, str):
-                captions.append(caption)
-            elif isinstance(caption, (list, np.ndarray)):
-                # take a random caption if there are multiple
-                captions.append(random.choice(caption) if is_train else caption[0])
-            else:
-                raise ValueError(
-                    f"Caption column `{caption_column}` should contain either strings or lists of strings."
-                )
-        inputs = tokenizer(
-            captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
-        )
-        return inputs.input_ids
-
-    image_transforms = transforms.Compose(
-        [
-            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
-            transforms.CenterCrop(args.resolution),
-            transforms.ToTensor(),
-            transforms.Normalize([0.5], [0.5]),
-        ]
-    )
-
-    conditioning_image_transforms = transforms.Compose(
-        [
-            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
-            transforms.CenterCrop(args.resolution),
-            transforms.ToTensor(),
-        ]
-    )
-
-    def preprocess_train(examples):
-        images = [image.convert("RGB") for image in examples[image_column]]
-        images = [image_transforms(image) for image in images]
-
-        conditioning_images = [image.convert("RGB") for image in examples[conditioning_image_column]]
-        conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images]
-
-        examples["pixel_values"] = images
-        examples["conditioning_pixel_values"] = conditioning_images
-        examples["input_ids"] = tokenize_captions(examples)
-
-        return examples
-
-    with accelerator.main_process_first():
-        if args.max_train_samples is not None:
-            dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
-        # Set the training transforms
-        train_dataset = dataset["train"].with_transform(preprocess_train)
-
-    return train_dataset
-
-
-def collate_fn(examples):
-    pixel_values = torch.stack([example["pixel_values"] for example in examples])
-    pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-
-    conditioning_pixel_values = torch.stack([example["conditioning_pixel_values"] for example in examples])
-    conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float()
-
-    input_ids = torch.stack([example["input_ids"] for example in examples])
-
-    return {
-        "pixel_values": pixel_values,
-        "conditioning_pixel_values": conditioning_pixel_values,
-        "input_ids": input_ids,
-    }
-
-
-def main(args):
-    logging_dir = Path(args.output_dir, args.logging_dir)
-
-    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
-
-    accelerator = Accelerator(
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        mixed_precision=args.mixed_precision,
-        log_with=args.report_to,
-        project_config=accelerator_project_config,
-    )
-
-    # Make one log on every process with the configuration for debugging.
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    logger.info(accelerator.state, main_process_only=False)
-    if accelerator.is_local_main_process:
-        transformers.utils.logging.set_verbosity_warning()
-        diffusers.utils.logging.set_verbosity_info()
-    else:
-        transformers.utils.logging.set_verbosity_error()
-        diffusers.utils.logging.set_verbosity_error()
-
-    # If passed along, set the training seed now.
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    # Handle the repository creation
-    if accelerator.is_main_process:
-        if args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-        if args.push_to_hub:
-            repo_id = create_repo(
-                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
-            ).repo_id
-
-    # Load the tokenizer
-    if args.tokenizer_name:
-        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
-    elif args.pretrained_model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(
-            args.pretrained_model_name_or_path,
-            subfolder="tokenizer",
-            revision=args.revision,
-            use_fast=False,
-        )
-
-    # import correct text encoder class
-    text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
-
-    # Load scheduler and models
-    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
-    text_encoder = text_encoder_cls.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
-    )
-    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
-    unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
-    )
-
-    if args.controlnet_model_name_or_path:
-        logger.info("Loading existing controlnet weights")
-        controlnet = ControlNetModel.from_pretrained(args.controlnet_model_name_or_path)
-    else:
-        logger.info("Initializing controlnet weights from unet")
-        controlnet = ControlNetModel.from_unet(unet)
-
-    # `accelerate` 0.16.0 will have better support for customized saving
-    if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
-        # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
-        def save_model_hook(models, weights, output_dir):
-            i = len(weights) - 1
-
-            while len(weights) > 0:
-                weights.pop()
-                model = models[i]
-
-                sub_dir = "controlnet"
-                model.save_pretrained(os.path.join(output_dir, sub_dir))
-
-                i -= 1
-
-        def load_model_hook(models, input_dir):
-            while len(models) > 0:
-                # pop models so that they are not loaded again
-                model = models.pop()
-
-                # load diffusers style into model
-                load_model = ControlNetModel.from_pretrained(input_dir, subfolder="controlnet")
-                model.register_to_config(**load_model.config)
-
-                model.load_state_dict(load_model.state_dict())
-                del load_model
-
-        accelerator.register_save_state_pre_hook(save_model_hook)
-        accelerator.register_load_state_pre_hook(load_model_hook)
-
-    vae.requires_grad_(False)
-    unet.requires_grad_(False)
-    text_encoder.requires_grad_(False)
-    controlnet.train()
-
-    if args.enable_xformers_memory_efficient_attention:
-        if is_xformers_available():
-            import xformers
-
-            xformers_version = version.parse(xformers.__version__)
-            if xformers_version == version.parse("0.0.16"):
-                logger.warn(
-                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
-                )
-            unet.enable_xformers_memory_efficient_attention()
-            controlnet.enable_xformers_memory_efficient_attention()
-        else:
-            raise ValueError("xformers is not available. Make sure it is installed correctly")
-
-    if args.gradient_checkpointing:
-        controlnet.enable_gradient_checkpointing()
-
-    # Check that all trainable models are in full precision
-    low_precision_error_string = (
-        " Please make sure to always have all model weights in full float32 precision when starting training - even if"
-        " doing mixed precision training, copy of the weights should still be float32."
-    )
-
-    if accelerator.unwrap_model(controlnet).dtype != torch.float32:
-        raise ValueError(
-            f"Controlnet loaded as datatype {accelerator.unwrap_model(controlnet).dtype}. {low_precision_error_string}"
-        )
-
-    # Enable TF32 for faster training on Ampere GPUs,
-    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
-    if args.allow_tf32:
-        torch.backends.cuda.matmul.allow_tf32 = True
-
-    if args.scale_lr:
-        args.learning_rate = (
-            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
-        )
-
-    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
-    if args.use_8bit_adam:
-        try:
-            import bitsandbytes as bnb
-        except ImportError:
-            raise ImportError(
-                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
-            )
-
-        optimizer_class = bnb.optim.AdamW8bit
-    else:
-        optimizer_class = torch.optim.AdamW
-
-    # Optimizer creation
-    params_to_optimize = controlnet.parameters()
-    optimizer = optimizer_class(
-        params_to_optimize,
-        lr=args.learning_rate,
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
-    )
-
-    train_dataset = make_train_dataset(args, tokenizer, accelerator)
-
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset,
-        shuffle=True,
-        collate_fn=collate_fn,
-        batch_size=args.train_batch_size,
-        num_workers=args.dataloader_num_workers,
-    )
-
-    # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
-
-    lr_scheduler = get_scheduler(
-        args.lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
-        num_training_steps=args.max_train_steps * accelerator.num_processes,
-        num_cycles=args.lr_num_cycles,
-        power=args.lr_power,
-    )
-
-    # Prepare everything with our `accelerator`.
-    controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        controlnet, optimizer, train_dataloader, lr_scheduler
-    )
-
-    # For mixed precision training we cast the text_encoder and vae weights to half-precision
-    # as these models are only used for inference, keeping weights in full precision is not required.
-    weight_dtype = torch.float32
-    if accelerator.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif accelerator.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    # Move vae, unet and text_encoder to device and cast to weight_dtype
-    vae.to(accelerator.device, dtype=weight_dtype)
-    unet.to(accelerator.device, dtype=weight_dtype)
-    text_encoder.to(accelerator.device, dtype=weight_dtype)
-
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if overrode_max_train_steps:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-    # Afterwards we recalculate our number of training epochs
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    # We need to initialize the trackers we use, and also store our configuration.
-    # The trackers initializes automatically on the main process.
-    if accelerator.is_main_process:
-        tracker_config = dict(vars(args))
-
-        # tensorboard cannot handle list types for config
-        tracker_config.pop("validation_prompt")
-        tracker_config.pop("validation_image")
-
-        accelerator.init_trackers(args.tracker_project_name, config=tracker_config)
-
-    # Train!
-    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num batches each epoch = {len(train_dataloader)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
-    global_step = 0
-    first_epoch = 0
-
-    # Potentially load in the weights and states from a previous save
-    if args.resume_from_checkpoint:
-        if args.resume_from_checkpoint != "latest":
-            path = os.path.basename(args.resume_from_checkpoint)
-        else:
-            # Get the most recent checkpoint
-            dirs = os.listdir(args.output_dir)
-            dirs = [d for d in dirs if d.startswith("checkpoint")]
-            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
-            path = dirs[-1] if len(dirs) > 0 else None
-
-        if path is None:
-            accelerator.print(
-                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
-            )
-            args.resume_from_checkpoint = None
-            initial_global_step = 0
-        else:
-            accelerator.print(f"Resuming from checkpoint {path}")
-            accelerator.load_state(os.path.join(args.output_dir, path))
-            global_step = int(path.split("-")[1])
-
-            initial_global_step = global_step
-            first_epoch = global_step // num_update_steps_per_epoch
-    else:
-        initial_global_step = 0
-
-    progress_bar = tqdm(
-        range(0, args.max_train_steps),
-        initial=initial_global_step,
-        desc="Steps",
-        # Only show the progress bar once on each machine.
-        disable=not accelerator.is_local_main_process,
-    )
-
-    image_logs = None
-    for epoch in range(first_epoch, args.num_train_epochs):
-        for step, batch in enumerate(train_dataloader):
-            with accelerator.accumulate(controlnet):
-                # Convert images to latent space
-                latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
-                latents = latents * vae.config.scaling_factor
-
-                # Sample noise that we'll add to the latents
-                noise = torch.randn_like(latents)
-                bsz = latents.shape[0]
-                # Sample a random timestep for each image
-                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
-                timesteps = timesteps.long()
-
-                # Add noise to the latents according to the noise magnitude at each timestep
-                # (this is the forward diffusion process)
-                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
-                # Get the text embedding for conditioning
-                encoder_hidden_states = text_encoder(batch["input_ids"])[0]
-
-                controlnet_image = batch["conditioning_pixel_values"].to(dtype=weight_dtype)
-
-                down_block_res_samples, mid_block_res_sample = controlnet(
-                    noisy_latents,
-                    timesteps,
-                    encoder_hidden_states=encoder_hidden_states,
-                    controlnet_cond=controlnet_image,
-                    return_dict=False,
-                )
-
-                # Predict the noise residual
-                model_pred = unet(
-                    noisy_latents,
-                    timesteps,
-                    encoder_hidden_states=encoder_hidden_states,
-                    down_block_additional_residuals=[
-                        sample.to(dtype=weight_dtype) for sample in down_block_res_samples
-                    ],
-                    mid_block_additional_residual=mid_block_res_sample.to(dtype=weight_dtype),
-                ).sample
-
-                # Get the target for loss depending on the prediction type
-                if noise_scheduler.config.prediction_type == "epsilon":
-                    target = noise
-                elif noise_scheduler.config.prediction_type == "v_prediction":
-                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
-                else:
-                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
-                accelerator.backward(loss)
-                if accelerator.sync_gradients:
-                    params_to_clip = controlnet.parameters()
-                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad(set_to_none=args.set_grads_to_none)
-
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-
-                if accelerator.is_main_process:
-                    if global_step % args.checkpointing_steps == 0:
-                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
-                        if args.checkpoints_total_limit is not None:
-                            checkpoints = os.listdir(args.output_dir)
-                            checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
-                            checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
-
-                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
-                            if len(checkpoints) >= args.checkpoints_total_limit:
-                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
-                                removing_checkpoints = checkpoints[0:num_to_remove]
-
-                                logger.info(
-                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
-                                )
-                                logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
-
-                                for removing_checkpoint in removing_checkpoints:
-                                    removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
-                                    shutil.rmtree(removing_checkpoint)
-
-                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
-                        accelerator.save_state(save_path)
-                        logger.info(f"Saved state to {save_path}")
-
-                    if args.validation_prompt is not None and global_step % args.validation_steps == 0:
-                        image_logs = log_validation(
-                            vae,
-                            text_encoder,
-                            tokenizer,
-                            unet,
-                            controlnet,
-                            args,
-                            accelerator,
-                            weight_dtype,
-                            global_step,
-                        )
-
-            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-            accelerator.log(logs, step=global_step)
-
-            if global_step >= args.max_train_steps:
-                break
-
-    # Create the pipeline using using the trained modules and save it.
-    accelerator.wait_for_everyone()
-    if accelerator.is_main_process:
-        controlnet = accelerator.unwrap_model(controlnet)
-        controlnet.save_pretrained(args.output_dir)
-
-        if args.push_to_hub:
-            save_model_card(
-                repo_id,
-                image_logs=image_logs,
-                base_model=args.pretrained_model_name_or_path,
-                repo_folder=args.output_dir,
-            )
-            upload_folder(
-                repo_id=repo_id,
-                folder_path=args.output_dir,
-                commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*"],
-            )
-
-    accelerator.end_training()
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_original_controlnet_to_diffusers.py

@@ -1,109 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Conversion script for stable diffusion checkpoints which _only_ contain a contrlnet. """
-
-import argparse
-
-from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
-    )
-    parser.add_argument(
-        "--original_config_file",
-        type=str,
-        required=True,
-        help="The YAML config file corresponding to the original architecture.",
-    )
-    parser.add_argument(
-        "--num_in_channels",
-        default=None,
-        type=int,
-        help="The number of input channels. If `None` number of input channels will be automatically inferred.",
-    )
-    parser.add_argument(
-        "--image_size",
-        default=512,
-        type=int,
-        help=(
-            "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
-            " Base. Use 768 for Stable Diffusion v2."
-        ),
-    )
-    parser.add_argument(
-        "--extract_ema",
-        action="store_true",
-        help=(
-            "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
-            " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
-            " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
-        ),
-    )
-    parser.add_argument(
-        "--upcast_attention",
-        action="store_true",
-        help=(
-            "Whether the attention computation should always be upcasted. This is necessary when running stable"
-            " diffusion 2.1."
-        ),
-    )
-    parser.add_argument(
-        "--from_safetensors",
-        action="store_true",
-        help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
-    )
-    parser.add_argument(
-        "--to_safetensors",
-        action="store_true",
-        help="Whether to store pipeline in safetensors format or not.",
-    )
-    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
-    parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
-
-    # small workaround to get argparser to parse a boolean input as either true _or_ false
-    def parse_bool(string):
-        if string == "True":
-            return True
-        elif string == "False":
-            return False
-        else:
-            raise ValueError(f"could not parse string as bool {string}")
-
-    parser.add_argument(
-        "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
-    )
-
-    parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)
-
-    args = parser.parse_args()
-
-    controlnet = download_controlnet_from_original_ckpt(
-        checkpoint_path=args.checkpoint_path,
-        original_config_file=args.original_config_file,
-        image_size=args.image_size,
-        extract_ema=args.extract_ema,
-        num_in_channels=args.num_in_channels,
-        upcast_attention=args.upcast_attention,
-        from_safetensors=args.from_safetensors,
-        device=args.device,
-        use_linear_projection=args.use_linear_projection,
-        cross_attention_dim=args.cross_attention_dim,
-    )
-
-    controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

spaces/Andy1621/uniformer_image_detection/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py

@@ -1,2 +0,0 @@
-_base_ = './fast_rcnn_r50_fpn_2x_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_detection/mmdet/core/evaluation/bbox_overlaps.py

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

spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/bbox_heads/__init__.py

@@ -1,13 +0,0 @@
-from .bbox_head import BBoxHead
-from .convfc_bbox_head import (ConvFCBBoxHead, Shared2FCBBoxHead,
-                               Shared4Conv1FCBBoxHead)
-from .dii_head import DIIHead
-from .double_bbox_head import DoubleConvFCBBoxHead
-from .sabl_head import SABLHead
-from .scnet_bbox_head import SCNetBBoxHead
-
-__all__ = [
-    'BBoxHead', 'ConvFCBBoxHead', 'Shared2FCBBoxHead',
-    'Shared4Conv1FCBBoxHead', 'DoubleConvFCBBoxHead', 'SABLHead', 'DIIHead',
-    'SCNetBBoxHead'
-]

spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py

@@ -1,9 +0,0 @@
-_base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(
-    pretrained='torchvision://resnet18',
-    backbone=dict(type='ResNet', depth=18),
-    decode_head=dict(
-        in_channels=512,
-        channels=128,
-    ),
-    auxiliary_head=dict(in_channels=256, channels=64))

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/__init__.py

@@ -1,47 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .base_module import BaseModule, ModuleList, Sequential
-from .base_runner import BaseRunner
-from .builder import RUNNERS, build_runner
-from .checkpoint import (CheckpointLoader, _load_checkpoint,
-                         _load_checkpoint_with_prefix, load_checkpoint,
-                         load_state_dict, save_checkpoint, weights_to_cpu)
-from .default_constructor import DefaultRunnerConstructor
-from .dist_utils import (allreduce_grads, allreduce_params, get_dist_info,
-                         init_dist, master_only)
-from .epoch_based_runner import EpochBasedRunner, Runner
-from .fp16_utils import LossScaler, auto_fp16, force_fp32, wrap_fp16_model
-from .hooks import (HOOKS, CheckpointHook, ClosureHook, DistEvalHook,
-                    DistSamplerSeedHook, DvcliveLoggerHook, EMAHook, EvalHook,
-                    Fp16OptimizerHook, GradientCumulativeFp16OptimizerHook,
-                    GradientCumulativeOptimizerHook, Hook, IterTimerHook,
-                    LoggerHook, LrUpdaterHook, MlflowLoggerHook,
-                    NeptuneLoggerHook, OptimizerHook, PaviLoggerHook,
-                    SyncBuffersHook, TensorboardLoggerHook, TextLoggerHook,
-                    WandbLoggerHook)
-from .iter_based_runner import IterBasedRunner, IterLoader
-from .log_buffer import LogBuffer
-from .optimizer import (OPTIMIZER_BUILDERS, OPTIMIZERS,
-                        DefaultOptimizerConstructor, build_optimizer,
-                        build_optimizer_constructor)
-from .priority import Priority, get_priority
-from .utils import get_host_info, get_time_str, obj_from_dict, set_random_seed
-
-__all__ = [
-    'BaseRunner', 'Runner', 'EpochBasedRunner', 'IterBasedRunner', 'LogBuffer',
-    'HOOKS', 'Hook', 'CheckpointHook', 'ClosureHook', 'LrUpdaterHook',
-    'OptimizerHook', 'IterTimerHook', 'DistSamplerSeedHook', 'LoggerHook',
-    'PaviLoggerHook', 'TextLoggerHook', 'TensorboardLoggerHook',
-    'NeptuneLoggerHook', 'WandbLoggerHook', 'MlflowLoggerHook',
-    'DvcliveLoggerHook', '_load_checkpoint', 'load_state_dict',
-    'load_checkpoint', 'weights_to_cpu', 'save_checkpoint', 'Priority',
-    'get_priority', 'get_host_info', 'get_time_str', 'obj_from_dict',
-    'init_dist', 'get_dist_info', 'master_only', 'OPTIMIZER_BUILDERS',
-    'OPTIMIZERS', 'DefaultOptimizerConstructor', 'build_optimizer',
-    'build_optimizer_constructor', 'IterLoader', 'set_random_seed',
-    'auto_fp16', 'force_fp32', 'wrap_fp16_model', 'Fp16OptimizerHook',
-    'SyncBuffersHook', 'EMAHook', 'build_runner', 'RUNNERS', 'allreduce_grads',
-    'allreduce_params', 'LossScaler', 'CheckpointLoader', 'BaseModule',
-    '_load_checkpoint_with_prefix', 'EvalHook', 'DistEvalHook', 'Sequential',
-    'ModuleList', 'GradientCumulativeOptimizerHook',
-    'GradientCumulativeFp16OptimizerHook', 'DefaultRunnerConstructor'
-]

spaces/AnthonyTruchetPoC/persistent-docker/doc/conf.py

@@ -1,52 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# For the full list of built-in configuration values, see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Project information -----------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
-
-project = "Template Python project"
-copyright = "2023, Polyconseil"
-author = "Anthony Truchet"
-release = "0.1"
-
-# -- General configuration ---------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
-
-templates_path = ["_templates"]
-exclude_patterns = []
-
-
-extensions = [
-    "sphinx.ext.autodoc",
-    "sphinx.ext.autosummary",
-    "sphinx.ext.napoleon",
-    "sphinx.ext.todo",
-    "myst_parser",
-    "sphinx_rtd_theme",
-]
-
-autodoc_typehints = "signature"
-autodoc_default_options = {
-    "members": True,
-    "member-order": "bysource",
-    "special-members": "__call__",
-    "undoc-members": True,
-    "exclude-members": "__weakref__",
-    "show-inheritance": True,
-    "autodoc_typehints": "signature",
-}
-
-autosummary_generate = True
-
-# https://www.sphinx-doc.org/en/master/usage/extensions/todo.html
-todo_include_todos = True
-
-# -- Options for HTML output -------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
-
-# html_theme = 'alabaster'
-html_theme = "sphinx_rtd_theme"
-
-html_static_path = ["_static"]

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

@@ -1,12 +0,0 @@
-from typing import List, Optional
-
-
-def main(args: Optional[List[str]] = None) -> int:
-    """This is preserved for old console scripts that may still be referencing
-    it.
-
-    For additional details, see https://github.com/pypa/pip/issues/7498.
-    """
-    from pip._internal.utils.entrypoints import _wrapper
-
-    return _wrapper(args)

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

@@ -1,270 +0,0 @@
-import email.message
-import email.parser
-import logging
-import os
-import zipfile
-from typing import Collection, Iterable, Iterator, List, Mapping, NamedTuple, Optional
-
-from pip._vendor import pkg_resources
-from pip._vendor.packaging.requirements import Requirement
-from pip._vendor.packaging.utils import NormalizedName, canonicalize_name
-from pip._vendor.packaging.version import parse as parse_version
-
-from pip._internal.exceptions import InvalidWheel, NoneMetadataError, UnsupportedWheel
-from pip._internal.utils.egg_link import egg_link_path_from_location
-from pip._internal.utils.misc import display_path, normalize_path
-from pip._internal.utils.wheel import parse_wheel, read_wheel_metadata_file
-
-from .base import (
-    BaseDistribution,
-    BaseEntryPoint,
-    BaseEnvironment,
-    DistributionVersion,
-    InfoPath,
-    Wheel,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class EntryPoint(NamedTuple):
-    name: str
-    value: str
-    group: str
-
-
-class InMemoryMetadata:
-    """IMetadataProvider that reads metadata files from a dictionary.
-
-    This also maps metadata decoding exceptions to our internal exception type.
-    """
-
-    def __init__(self, metadata: Mapping[str, bytes], wheel_name: str) -> None:
-        self._metadata = metadata
-        self._wheel_name = wheel_name
-
-    def has_metadata(self, name: str) -> bool:
-        return name in self._metadata
-
-    def get_metadata(self, name: str) -> str:
-        try:
-            return self._metadata[name].decode()
-        except UnicodeDecodeError as e:
-            # Augment the default error with the origin of the file.
-            raise UnsupportedWheel(
-                f"Error decoding metadata for {self._wheel_name}: {e} in {name} file"
-            )
-
-    def get_metadata_lines(self, name: str) -> Iterable[str]:
-        return pkg_resources.yield_lines(self.get_metadata(name))
-
-    def metadata_isdir(self, name: str) -> bool:
-        return False
-
-    def metadata_listdir(self, name: str) -> List[str]:
-        return []
-
-    def run_script(self, script_name: str, namespace: str) -> None:
-        pass
-
-
-class Distribution(BaseDistribution):
-    def __init__(self, dist: pkg_resources.Distribution) -> None:
-        self._dist = dist
-
-    @classmethod
-    def from_directory(cls, directory: str) -> BaseDistribution:
-        dist_dir = directory.rstrip(os.sep)
-
-        # Build a PathMetadata object, from path to metadata. :wink:
-        base_dir, dist_dir_name = os.path.split(dist_dir)
-        metadata = pkg_resources.PathMetadata(base_dir, dist_dir)
-
-        # Determine the correct Distribution object type.
-        if dist_dir.endswith(".egg-info"):
-            dist_cls = pkg_resources.Distribution
-            dist_name = os.path.splitext(dist_dir_name)[0]
-        else:
-            assert dist_dir.endswith(".dist-info")
-            dist_cls = pkg_resources.DistInfoDistribution
-            dist_name = os.path.splitext(dist_dir_name)[0].split("-")[0]
-
-        dist = dist_cls(base_dir, project_name=dist_name, metadata=metadata)
-        return cls(dist)
-
-    @classmethod
-    def from_metadata_file_contents(
-        cls,
-        metadata_contents: bytes,
-        filename: str,
-        project_name: str,
-    ) -> BaseDistribution:
-        metadata_dict = {
-            "METADATA": metadata_contents,
-        }
-        dist = pkg_resources.DistInfoDistribution(
-            location=filename,
-            metadata=InMemoryMetadata(metadata_dict, filename),
-            project_name=project_name,
-        )
-        return cls(dist)
-
-    @classmethod
-    def from_wheel(cls, wheel: Wheel, name: str) -> BaseDistribution:
-        try:
-            with wheel.as_zipfile() as zf:
-                info_dir, _ = parse_wheel(zf, name)
-                metadata_dict = {
-                    path.split("/", 1)[-1]: read_wheel_metadata_file(zf, path)
-                    for path in zf.namelist()
-                    if path.startswith(f"{info_dir}/")
-                }
-        except zipfile.BadZipFile as e:
-            raise InvalidWheel(wheel.location, name) from e
-        except UnsupportedWheel as e:
-            raise UnsupportedWheel(f"{name} has an invalid wheel, {e}")
-        dist = pkg_resources.DistInfoDistribution(
-            location=wheel.location,
-            metadata=InMemoryMetadata(metadata_dict, wheel.location),
-            project_name=name,
-        )
-        return cls(dist)
-
-    @property
-    def location(self) -> Optional[str]:
-        return self._dist.location
-
-    @property
-    def installed_location(self) -> Optional[str]:
-        egg_link = egg_link_path_from_location(self.raw_name)
-        if egg_link:
-            location = egg_link
-        elif self.location:
-            location = self.location
-        else:
-            return None
-        return normalize_path(location)
-
-    @property
-    def info_location(self) -> Optional[str]:
-        return self._dist.egg_info
-
-    @property
-    def installed_by_distutils(self) -> bool:
-        # A distutils-installed distribution is provided by FileMetadata. This
-        # provider has a "path" attribute not present anywhere else. Not the
-        # best introspection logic, but pip has been doing this for a long time.
-        try:
-            return bool(self._dist._provider.path)
-        except AttributeError:
-            return False
-
-    @property
-    def canonical_name(self) -> NormalizedName:
-        return canonicalize_name(self._dist.project_name)
-
-    @property
-    def version(self) -> DistributionVersion:
-        return parse_version(self._dist.version)
-
-    def is_file(self, path: InfoPath) -> bool:
-        return self._dist.has_metadata(str(path))
-
-    def iter_distutils_script_names(self) -> Iterator[str]:
-        yield from self._dist.metadata_listdir("scripts")
-
-    def read_text(self, path: InfoPath) -> str:
-        name = str(path)
-        if not self._dist.has_metadata(name):
-            raise FileNotFoundError(name)
-        content = self._dist.get_metadata(name)
-        if content is None:
-            raise NoneMetadataError(self, name)
-        return content
-
-    def iter_entry_points(self) -> Iterable[BaseEntryPoint]:
-        for group, entries in self._dist.get_entry_map().items():
-            for name, entry_point in entries.items():
-                name, _, value = str(entry_point).partition("=")
-                yield EntryPoint(name=name.strip(), value=value.strip(), group=group)
-
-    def _metadata_impl(self) -> email.message.Message:
-        """
-        :raises NoneMetadataError: if the distribution reports `has_metadata()`
-            True but `get_metadata()` returns None.
-        """
-        if isinstance(self._dist, pkg_resources.DistInfoDistribution):
-            metadata_name = "METADATA"
-        else:
-            metadata_name = "PKG-INFO"
-        try:
-            metadata = self.read_text(metadata_name)
-        except FileNotFoundError:
-            if self.location:
-                displaying_path = display_path(self.location)
-            else:
-                displaying_path = repr(self.location)
-            logger.warning("No metadata found in %s", displaying_path)
-            metadata = ""
-        feed_parser = email.parser.FeedParser()
-        feed_parser.feed(metadata)
-        return feed_parser.close()
-
-    def iter_dependencies(self, extras: Collection[str] = ()) -> Iterable[Requirement]:
-        if extras:  # pkg_resources raises on invalid extras, so we sanitize.
-            extras = frozenset(extras).intersection(self._dist.extras)
-        return self._dist.requires(extras)
-
-    def iter_provided_extras(self) -> Iterable[str]:
-        return self._dist.extras
-
-
-class Environment(BaseEnvironment):
-    def __init__(self, ws: pkg_resources.WorkingSet) -> None:
-        self._ws = ws
-
-    @classmethod
-    def default(cls) -> BaseEnvironment:
-        return cls(pkg_resources.working_set)
-
-    @classmethod
-    def from_paths(cls, paths: Optional[List[str]]) -> BaseEnvironment:
-        return cls(pkg_resources.WorkingSet(paths))
-
-    def _iter_distributions(self) -> Iterator[BaseDistribution]:
-        for dist in self._ws:
-            yield Distribution(dist)
-
-    def _search_distribution(self, name: str) -> Optional[BaseDistribution]:
-        """Find a distribution matching the ``name`` in the environment.
-
-        This searches from *all* distributions available in the environment, to
-        match the behavior of ``pkg_resources.get_distribution()``.
-        """
-        canonical_name = canonicalize_name(name)
-        for dist in self.iter_all_distributions():
-            if dist.canonical_name == canonical_name:
-                return dist
-        return None
-
-    def get_distribution(self, name: str) -> Optional[BaseDistribution]:
-        # Search the distribution by looking through the working set.
-        dist = self._search_distribution(name)
-        if dist:
-            return dist
-
-        # If distribution could not be found, call working_set.require to
-        # update the working set, and try to find the distribution again.
-        # This might happen for e.g. when you install a package twice, once
-        # using setup.py develop and again using setup.py install. Now when
-        # running pip uninstall twice, the package gets removed from the
-        # working set in the first uninstall, so we have to populate the
-        # working set again so that pip knows about it and the packages gets
-        # picked up and is successfully uninstalled the second time too.
-        try:
-            # We didn't pass in any version specifiers, so this can never
-            # raise pkg_resources.VersionConflict.
-            self._ws.require(name)
-        except pkg_resources.DistributionNotFound:
-            return None
-        return self._search_distribution(name)

spaces/Atualli/yoloxTeste/yoloxdetect2/configs/yolov3.py

@@ -1,33 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) Megvii, Inc. and its affiliates.
-
-import os
-
-import torch.nn as nn
-
-from yolox.exp import Exp as MyExp
-
-
-class Exp(MyExp):
-    def __init__(self):
-        super(Exp, self).__init__()
-        self.depth = 1.0
-        self.width = 1.0
-        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
-
-    def get_model(self, sublinear=False):
-        def init_yolo(M):
-            for m in M.modules():
-                if isinstance(m, nn.BatchNorm2d):
-                    m.eps = 1e-3
-                    m.momentum = 0.03
-        if "model" not in self.__dict__:
-            from yolox.models import YOLOX, YOLOFPN, YOLOXHead
-            backbone = YOLOFPN()
-            head = YOLOXHead(self.num_classes, self.width, in_channels=[128, 256, 512], act="lrelu")
-            self.model = YOLOX(backbone, head)
-        self.model.apply(init_yolo)
-        self.model.head.initialize_biases(1e-2)
-
-        return self.model

spaces/Audio-AGI/WavJourney/VoiceParser/pre_kmeans_hubert.py

@@ -1,106 +0,0 @@
-"""
-Modified HuBERT model without kmeans.
-Original author: https://github.com/lucidrains/
-Modified by: https://www.github.com/gitmylo/
-License: MIT
-"""
-
-# Modified code from https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch/hubert_kmeans.py
-
-from pathlib import Path
-
-import torch
-from torch import nn
-from einops import pack, unpack
-
-import fairseq
-
-from torchaudio.functional import resample
-
-from audiolm_pytorch.utils import curtail_to_multiple
-
-import logging
-logging.root.setLevel(logging.ERROR)
-
-
-def exists(val):
-    return val is not None
-
-
-def default(val, d):
-    return val if exists(val) else d
-
-
-class CustomHubert(nn.Module):
-    """
-    checkpoint and kmeans can be downloaded at https://github.com/facebookresearch/fairseq/tree/main/examples/hubert
-    or you can train your own
-    """
-
-    def __init__(
-        self,
-        checkpoint_path,
-        target_sample_hz=16000,
-        seq_len_multiple_of=None,
-        output_layer=9,
-        device=None
-    ):
-        super().__init__()
-        self.target_sample_hz = target_sample_hz
-        self.seq_len_multiple_of = seq_len_multiple_of
-        self.output_layer = output_layer
-
-        if device is not None:
-            self.to(device)
-
-        model_path = Path(checkpoint_path)
-
-        assert model_path.exists(), f'path {checkpoint_path} does not exist'
-
-        checkpoint = torch.load(checkpoint_path, map_location=device)
-        load_model_input = {checkpoint_path: checkpoint}
-        model, *_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(load_model_input)
-
-        if device is not None:
-            model[0].to(device)
-
-        self.model = model[0]
-        self.model.eval()
-
-    @property
-    def groups(self):
-        return 1
-
-    @torch.no_grad()
-    def forward(
-        self,
-        wav_input,
-        flatten=True,
-        input_sample_hz=None
-    ):
-        device = wav_input.device
-
-        if exists(input_sample_hz):
-            wav_input = resample(wav_input, input_sample_hz, self.target_sample_hz)
-
-        if exists(self.seq_len_multiple_of):
-            wav_input = curtail_to_multiple(wav_input, self.seq_len_multiple_of)
-
-        embed = self.model(
-            wav_input,
-            features_only=True,
-            mask=False,  # thanks to @maitycyrus for noticing that mask is defaulted to True in the fairseq code
-            output_layer=self.output_layer
-        )
-
-        embed, packed_shape = pack([embed['x']], '* d')
-
-        # codebook_indices = self.kmeans.predict(embed.cpu().detach().numpy())
-
-        codebook_indices = torch.from_numpy(embed.cpu().detach().numpy()).to(device)  # .long()
-
-        if flatten:
-            return codebook_indices
-
-        codebook_indices, = unpack(codebook_indices, packed_shape, '*')
-        return codebook_indices

spaces/Bart92/RVC_HF/diffq/base.py

@@ -1,262 +0,0 @@
-# Copyright (c) Facebook, Inc. and its 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.
-
-from dataclasses import dataclass
-from concurrent import futures
-from fnmatch import fnmatch
-from functools import partial
-import io
-import math
-from multiprocessing import cpu_count
-import typing as tp
-import zlib
-
-import torch
-
-
-class BaseQuantizer:
-    @dataclass
-    class _QuantizedParam:
-        name: str
-        param: torch.nn.Parameter
-        module: torch.nn.Module
-        # If a Parameter is used multiple times, `other` can be used
-        # to share state between the different Quantizers
-        other: tp.Optional[tp.Any]
-
-    def __init__(self, model: torch.nn.Module, min_size: float = 0.01, float16: bool = False,
-                 exclude: tp.Optional[tp.List[str]] = [], detect_bound: bool = True):
-        self.model = model
-        self.min_size = min_size
-        self.float16 = float16
-        self.exclude = exclude
-        self.detect_bound = detect_bound
-        self._quantized = False
-        self._pre_handle = self.model.register_forward_pre_hook(self._forward_pre_hook)
-        self._post_handle = self.model.register_forward_hook(self._forward_hook)
-
-        self._quantized_state = None
-        self._qparams = []
-        self._float16 = []
-        self._others = []
-        self._rnns = []
-
-        self._saved = []
-
-        self._find_params()
-
-    def _find_params(self):
-        min_params = self.min_size * 2**20 // 4
-        previous = {}
-        for module_name, module in self.model.named_modules():
-            if isinstance(module, torch.nn.RNNBase):
-                self._rnns.append(module)
-            for name, param in list(module.named_parameters(recurse=False)):
-                full_name = f"{module_name}.{name}"
-                matched = False
-                for pattern in self.exclude:
-                    if fnmatch(full_name, pattern) or fnmatch(name, pattern):
-                        matched = True
-                        break
-
-                if param.numel() <= min_params or matched:
-                    if id(param) in previous:
-                        continue
-                    if self.detect_bound:
-                        previous[id(param)] = None
-                    if self.float16:
-                        self._float16.append(param)
-                    else:
-                        self._others.append(param)
-                else:
-                    qparam = self._register_param(name, param, module, previous.get(id(param)))
-                    if self.detect_bound:
-                        previous[id(param)] = qparam
-                    self._qparams.append(qparam)
-
-    def _register_param(self, name, param, module, other):
-        return self.__class__._QuantizedParam(name, param, module, other)
-
-    def _forward_pre_hook(self, module, input):
-        if self.model.training:
-            self._quantized_state = None
-            if self._quantized:
-                self.unquantize()
-            if self._pre_forward_train():
-                self._fix_rnns()
-        else:
-            self.quantize()
-
-    def _forward_hook(self, module, input, output):
-        if self.model.training:
-            if self._post_forward_train():
-                self._fix_rnns(flatten=False)  # Hacky, next forward will flatten
-
-    def quantize(self, save=True):
-        """
-        Immediately apply quantization to the model parameters.
-        If `save` is True, save a copy of the unquantized parameters, that can be
-        restored with `unquantize()`.
-        """
-        if self._quantized:
-            return
-        if save:
-            self._saved = [qp.param.data.to('cpu', copy=True)
-                           for qp in self._qparams if qp.other is None]
-        self.restore_quantized_state(self.get_quantized_state())
-        self._quantized = True
-        self._fix_rnns()
-
-    def unquantize(self):
-        """
-        Revert a previous call to `quantize()`.
-        """
-        if not self._quantized:
-            raise RuntimeError("Can only be called on a quantized model.")
-        if not self._saved:
-            raise RuntimeError("Nothing to restore.")
-        for qparam in self._qparams:
-            if qparam.other is None:
-                qparam.param.data[:] = self._saved.pop(0)
-        assert len(self._saved) == 0
-        self._quantized = False
-        self._fix_rnns()
-
-    def _pre_forward_train(self) -> bool:
-        """
-        Called once before each forward for continuous quantization.
-        Should return  True if parameters were changed.
-        """
-        return False
-
-    def _post_forward_train(self) -> bool:
-        """
-        Called once after each forward (to restore state for instance).
-        Should return True if parameters were changed.
-        """
-        return False
-
-    def _fix_rnns(self, flatten=True):
-        """
-        To be called after quantization happened to fix RNNs.
-        """
-        for rnn in self._rnns:
-            rnn._flat_weights = [
-                (lambda wn: getattr(rnn, wn) if hasattr(rnn, wn) else None)(wn)
-                for wn in rnn._flat_weights_names]
-            if flatten:
-                rnn.flatten_parameters()
-
-    def get_quantized_state(self):
-        """
-        Returns sufficient quantized information to rebuild the model state.
-
-        ..Note::
-            To achieve maximum compression, you should compress this with
-            gzip or other, as quantized weights are not optimally coded!
-        """
-        if self._quantized_state is None:
-            self._quantized_state = self._get_quantized_state()
-        return self._quantized_state
-
-    def _get_quantized_state(self):
-        """
-        Actual implementation for `get_quantized_state`.
-        """
-        float16_params = []
-        for p in self._float16:
-            q = p.data.half()
-            float16_params.append(q)
-
-        return {
-            "quantized": [self._quantize_param(qparam) for qparam in self._qparams
-                          if qparam.other is None],
-            "float16": float16_params,
-            "others": [p.data.clone() for p in self._others],
-        }
-
-    def _quantize_param(self, qparam: _QuantizedParam) -> tp.Any:
-        """
-        To be overriden.
-        """
-        raise NotImplementedError()
-
-    def _unquantize_param(self, qparam: _QuantizedParam, quantized: tp.Any) -> torch.Tensor:
-        """
-        To be overriden.
-        """
-        raise NotImplementedError()
-
-    def restore_quantized_state(self, state) -> None:
-        """
-        Restore the state of the model from the quantized state.
-        """
-        for p, q in zip(self._float16, state["float16"]):
-            p.data[:] = q.to(p)
-
-        for p, q in zip(self._others, state["others"]):
-            p.data[:] = q
-
-        remaining = list(state["quantized"])
-        for qparam in self._qparams:
-            if qparam.other is not None:
-                # Only unquantize first appearance of nn.Parameter.
-                continue
-            quantized = remaining.pop(0)
-            qparam.param.data[:] = self._unquantize_param(qparam, quantized)
-        self._fix_rnns()
-
-    def detach(self) -> None:
-        """
-        Detach from the model, removes hooks and anything else.
-        """
-        self._pre_handle.remove()
-        self._post_handle.remove()
-
-    def model_size(self) -> torch.Tensor:
-        """
-        Returns an estimate of the quantized model size.
-        """
-        total = torch.tensor(0.)
-        for p in self._float16:
-            total += 16 * p.numel()
-        for p in self._others:
-            total += 32 * p.numel()
-        return total / 2**20 / 8  # bits to MegaBytes
-
-    def true_model_size(self) -> float:
-        """
-        Return the true quantized model size, in MB, without extra
-        compression.
-        """
-        return self.model_size().item()
-
-    def compressed_model_size(self, compress_level=-1, num_workers=8) -> float:
-        """
-        Return the compressed quantized model size, in MB.
-
-        Args:
-            compress_level (int): compression level used with zlib,
-                see `zlib.compress` for details.
-            num_workers (int): will split the final big byte representation in that
-                many chunks processed in parallels.
-        """
-        out = io.BytesIO()
-        torch.save(self.get_quantized_state(), out)
-        ms = _parallel_compress_len(out.getvalue(), compress_level, num_workers)
-        return ms / 2 ** 20
-
-
-def _compress_len(data, compress_level):
-    return len(zlib.compress(data, level=compress_level))
-
-
-def _parallel_compress_len(data, compress_level, num_workers):
-    num_workers = min(cpu_count(), num_workers)
-    chunk_size = int(math.ceil(len(data) / num_workers))
-    chunks = [data[offset:offset + chunk_size] for offset in range(0, len(data), chunk_size)]
-    with futures.ProcessPoolExecutor(num_workers) as pool:
-        return sum(pool.map(partial(_compress_len, compress_level=compress_level), chunks))

spaces/Benson/text-generation/Examples/Barikad Crew Album Goumen Pou Saw Kwe Mp3 Download.md

@@ -1,29 +0,0 @@
-
-<h1>Álbum de la tripulación de Barikad Goumen Pou Saw Kwe Mp3 Descargar</h1>
- <p>Si usted está buscando un poco de auténtica y poderosa música rap haitiana, es posible que desee echar un vistazo a barikad crew, uno de los grupos de rap kreyòl más populares e influyentes en Haití. Su álbum goumen pou vio kwe, que significa "luchar por lo que crees", es una obra maestra de comentario social, expresión cultural y creatividad musical. En este artículo, exploraremos la historia, la música y las opciones de descarga de este increíble álbum. </p>
-<h2>barikad crew album goumen pou saw kwe mp3 download</h2><br /><p><b><b>Download File</b> &#9913; <a href="https://bltlly.com/2v6L7I">https://bltlly.com/2v6L7I</a></b></p><br /><br />
- <h2>Historia de Barikad Crew</h2>
- <p>Barikad crew fue fundada en 2002 por tres raperos del barrio de Bas Peu d'Chose en Puerto Príncipe: Papa K-tafalk, Deja-Voo y Kondagana. Invitaron a varios otros raperos de diferentes grupos underground a unirse a ellos y formar un colectivo que representaría la voz de los barrios marginales. Su nombre proviene de las barricadas que solían bloquear las calles durante las protestas y manifestaciones. </p>
- <p>El grupo saltó a la fama en 2005 cuando ganaron el tercer lugar en un concurso de canciones de Navidad en Telemax con su canción "Nwel Pa’m". También lanzaron su primer single "Bay Hip Hop Bourad" en 2004, seguido por "Kijan'l Te Ye" en 2005, y "Trip N'ap Trip" en 2006. Su álbum debut goumen pou vio kwe fue lanzado en 2007 y recibió la aclamación de la crítica y el éxito comercial. El álbum cuenta con 21 temas que cubren varios temas como la política, cuestiones sociales, religión, amor e identidad. </p>
-
- <h2>Música de Barikad Crew</h2>
- <p>La música de la tripulación barikad se caracteriza por su fusión de ritmos africanos, melodías caribeñas y ritmos de hip hop. El grupo utiliza varios instrumentos como tambores, trompas, guitarras, teclados y tocadiscos para crear un sonido rico y diverso. El grupo también incorpora elementos de otros géneros musicales haitianos como kompa, rara, twoubadou, merengue y vodou drumming. </p>
- <p>Las letras de los barikad son principalmente en criollo haitiano, el idioma más hablado en Haití. El grupo usa sus palabras para expresar sus opiniones, emociones, experiencias y aspiraciones. También usan metáforas, humor, sarcasmo y juegos de palabras para transmitir sus mensajes. El grupo aborda temas como pobreza, corrupción, violencia, injusticia, opresión, resistencia, esperanza, fe, orgullo, solidaridad y patriotismo. </p>
-<p></p>
- <p>La música de la tripulación barikad no solo es entretenida, sino también educativa e inspiradora. El objetivo del grupo es crear conciencia sobre las realidades y los desafíos del pueblo haitiano y alentarlo a luchar por sus derechos y su dignidad. El grupo también celebra la belleza y diversidad de la cultura y la historia de Haití y rinde homenaje a sus antepasados y héroes. </p>
- <h2>Opciones de descarga</h2>
- <p>Si quieres escuchar el álbum de la banda de barikad goumen pou saw kwe o cualquiera de sus otros álbumes, tienes varias opciones para descargarlos en línea. Puedes comprarlos desde plataformas como iTunes , Amazon , o CD Baby . También puede transmitirlos en plataformas como Spotify , YouTube , o TIDAL . También puede descargar sus canciones de forma gratuita desde su sitio web oficial o desde otros sitios web que ofrecen descargas de mp3 gratis  . Sin embargo, te recomendamos que apoyes a los artistas comprando sus álbumes o transmitiendo su música legalmente. </p>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
- <h3>¿Qué significa tripulación barikad? </h3>
- <p>Tripulación Barikad significa equipo de barricadas. El nombre proviene de las barricadas que el grupo solía bloquear las calles durante las protestas y manifestaciones. El nombre también simboliza su resistencia a la opresión y la injusticia y su defensa de sus derechos y dignidad. </p>
- <h3>¿Cuántos miembros hay en la tripulación de Barikad? </h3>
- <p>El equipo barikad original tenía 13 miembros: Papa K-tafalk, Deja-Voo, Kondagana, Dade, Young Cliff, Fantom, Bricks, Izolan, Marco, Brital, Manno Beats, Master Sun y Bafon Plim. Sin embargo, cuatro de ellos murieron en trágicos incidentes: K-tafalk, Deja-Voo, Dade y Young Cliff. El equipo barikad actual tiene nueve miembros: Fantom, Bricks, Izolan, Marco, Brital, Manno Beats, Master Sun, Bafon Plim y Drz.</p>
- <h3>¿Cuáles son algunas de las mejores canciones de barikad crew? </h3>
- <p>Some of the best songs by barikad crew are: "Goumen Pou Saw Kwè", "Toup Pou Yo", "Trip N'ap Trip", "Kijan'l Te Ye", "Bay Hip Hop Bourad", "Tòf", "Travay", "Tann Jou Pa'w", "Jiskobou", and "Men Flow". </p>
- <h3>¿Cuáles son algunos de los premios y honores que la tripulación barikad ha recibido? </h3>
- <p>Algunos de los premios y honores que el equipo barikad ha recibido son: Mejor Grupo de Rap en los Haiti Music Awards en 2007 y 2008; Mejor Álbum de Rap para goumen pou vio kwe en los Haiti Music Awards en 2008; Mejor Álbum de Rap para RED en los Haiti Music Awards en 2013; Mejor álbum de rap para Toutouni en los Haiti Music Awards en 2016; Mejor álbum de rap para Travay en los Haiti Music Awards en 2017; Mejor álbum de rap para Tann Jou Pa'w en los Haiti Music Awards en 2018; Mejor álbum de rap para TOF en los Haiti Music Awards en 2019; y Mejor Grupo de Rap en los Caribbean Music Awards en 2019. </p>
- <h3>¿Dónde puedo encontrar más información sobre la tripulación de barikad? </h3> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Cmo Descargar Sims En Sims 3.md

@@ -1,154 +0,0 @@
-<br />
-<h1>Cómo descargar Sims en Sims 3</h1>
-<p>Si eres fan de la serie Los Sims, probablemente sepas que uno de los aspectos más divertidos del juego es crear y personalizar tus propios Sims. Pero, ¿sabías que también puedes descargar Sims que otros jugadores han hecho y usarlos en tu juego? En este artículo, te mostraremos cómo descargar Sims en Sims 3 desde diferentes fuentes, como el sitio web oficial, videos de YouTube y otros sitios web. También explicaremos cómo instalarlos y cuáles son los requisitos y características del sistema de Sims 3.</p>
- <h2>Introducción</h2>
-<h3>¿Qué es Sims 3 y por qué descargar Sims? </h3>
-<p>Los Sims 3 es el tercer título principal de la serie Los Sims de juegos de simulación de vida. Fue lanzado en 2009 para Windows, Mac y varias consolas y dispositivos móviles. El juego te permite crear y controlar personas virtuales llamadas "Sims" en un entorno de mundo abierto. Puedes personalizar su apariencia, personalidad, habilidades, relaciones, carreras, pasatiempos y más. También puede construir y decorar sus casas, lotes comunitarios y vecindarios. </p>
-<h2>cómo descargar sims en sims 3</h2><br /><p><b><b>DOWNLOAD</b> &#9734;&#9734;&#9734;&#9734;&#9734; <a href="https://bltlly.com/2v6Mx2">https://bltlly.com/2v6Mx2</a></b></p><br /><br />
-<p>Una de las razones por las que quieres descargar Sims en Sims 3 es para añadir más variedad y diversidad a tu juego. Puedes encontrar Sims que tienen diferentes looks, estilos, rasgos, historias y orígenes que los que creas tú mismo. También puedes encontrar Sims inspirados en celebridades, personajes de ficción o personas reales. Descargar Sims puede ayudarte a llenar tu mundo con personajes interesantes y únicos con los que puedes interactuar, entablar amistad o incluso romance. </p>
- <h3>¿Cuáles son los requisitos y características del sistema de los Sims 3?</h3>
-<p>Antes de descargar Sims en Sims 3, debes asegurarte de que tu computadora o dispositivo cumple con los requisitos mínimos del sistema para el juego. Según [EA Help]( 9 ), estos son:</p>
-<ul>
-<li>OS: Windows XP (Service Pack 2) o Windows Vista (Service Pack 1)</li>
-
-<li>Memoria: (XP) 1 GB RAM; (Vista) 1.5 GB RAM</li>
-<li>Gráficos: Tarjeta de video de 128 MB con soporte para Pixel Shader 2.0</li>
-<li>DirectX: Versión 9.0c</li>
-<li>Almacenamiento: Al menos 6.5 GB de espacio en el disco duro con al menos 1 GB de espacio adicional para contenido personalizado</li>
-</ul>
-<p>Si tienes un Mac, necesitas:</p>
-<ul>
-<li>OS: Mac OS X 10.5.7 Leopardo o superior</li>
-<li>Procesador: Procesador Intel Core Duo</li>
-<li>Memoria: 2 GB de RAM</li>
-<li>Gráficos: ATI X1600 o Nvidia 7300 GT con 128 MB de RAM de vídeo, o Intel integrado GMA X3100</li>
-<li>Almacenamiento: Al menos 6.1 GB de espacio en el disco duro con al menos 1 GB de espacio adicional para contenido personalizado</li>
-</ul>
-<p>Los Sims 3 tiene muchas características que lo convierten en un gran juego para descargar Sims. Algunas de estas características son:</p>
-<ul>
-<li>Un concepto de mundo abierto que te permite explorar toda una ciudad sin necesidad de cargar pantallas</li>
-<li>A <li>A Crea un modo Sim que te permite personalizar cada detalle de tus Sims, desde sus características faciales, forma del cuerpo, tono de piel, color de pelo, ropa, accesorios, tatuajes y más</li>
-<li>Una herramienta para crear un estilo que te permite cambiar el color y el patrón de cualquier objeto en el juego</li>
-<li>Un sistema de rasgos que da a tus Sims personalidades y comportamientos únicos</li>
-<li>Un sistema de deseos y felicidad de por vida que motiva a tus Sims a perseguir sus metas y sueños</li>
-<li>Un sistema de habilidades que permite a tus Sims aprender y mejorar varias habilidades, como cocinar, pintar, cultivar un huerto, escribir y más</li>
-<li>Un sistema de carrera que ofrece a tus Sims diferentes oportunidades y trayectorias de trabajo, como periodismo, aplicación de la ley, medicina, música y más</li>
-<li>Un sistema social que permite a tus Sims interactuar con otros Sims de varias maneras, como chatear, flirtear, pelear, bromear y más</li>
-<li>Un sistema familiar que permite a tus Sims formar relaciones, casarse, tener hijos y envejecer</li>
-
-</ul>
- <h2>Cómo descargar Sims de diferentes fuentes</h2>
-<p>Ahora que sabes lo que es Sims 3 y lo que puede ofrecerte, te estarás preguntando cómo descargar Sims en Sims 3. Hay muchas fuentes donde puedes encontrar Sims para descargar tu juego. Algunos de los más populares son:</p>
-<ul>
-<li>El sitio web oficial de Los Sims 3</li>
-<li>videos de YouTube</li>
-<li>Otros sitios web</li>
-</ul>
-<p>Explicaremos cómo descargar Sims de cada una de estas fuentes en las siguientes secciones. </p>
- <h3>Cómo descargar Sims desde el sitio web oficial</h3>
-<p>El sitio web oficial de Los Sims 3 es [thesims3.com]. Aquí es donde puede comprar el juego y sus expansiones, registrar su juego y canjear su código, acceder a su cuenta y perfil, navegar por la tienda en línea y el intercambio, y descargar mundos y contenido de bonificación. También puedes encontrar Sims predefinidos creados por los desarrolladores u otros jugadores. Para descargar Sims desde el sitio web oficial, debes seguir estos pasos:</p>
-<p></p>
- <h4>Cómo registrar tu juego y canjear tu código</h4>
-<p>Si compraste el juego desde un disco físico o un servicio de descarga digital distinto de Origin (como Steam), necesitas registrar tu juego y canjear tu código en el sitio web oficial. Esto te permitirá acceder a las funciones online del juego y descargar contenido de la tienda e intercambiar. Para hacer esto, necesitas:</p>
-<ol>
-<li>Crear una cuenta EA o iniciar sesión con una existente</li>
-<li>Ir a [thesims3.com/registeragame] e introduzca su código de serie de 20 dígitos que vino con su juego o fue enviado por correo electrónico a usted</li>
-<li>Haga clic en "Registrarse" y confirme su registro</li>
-<li>Ir a [thesims3.com/myaccount] y haga clic en "Canjear un código"</li>
-<li>Ingrese su código de 16 dígitos que vino con su juego o fue enviado por correo electrónico a usted</li>
-<li>Haga clic en "Canjear" y confirme su redención</li>
-<li>Tu juego se añadirá a tu biblioteca de Origin y podrás descargarlo desde allí</li>
-</ol>
-
-<p>Si compraste el juego en Origin o lo registraste en el sitio web oficial, puedes instalarlo desde Origin. Origin es una plataforma de distribución digital que te permite comprar, descargar y jugar juegos de EA. Para instalar tu juego y expansiones en Origin, necesitas:</p>
-<ol>
-<li>Crear una cuenta de Origin o iniciar sesión con una existente</li>
-<li>Descargar e instalar Origin desde [origin.com]</li>
-<li> Origen de lanzamiento y vaya a "Mi biblioteca de juegos"</li>
-<li>Encuentra los Sims 3 y haz clic en "Descargar"</li>
-<li>Espere a que finalicen la descarga y la instalación</li>
-<li>Repita los pasos 4-5 para cualquier expansión o paquete de cosas que haya comprado o canjeado</li>
-<li>Puedes iniciar el juego desde Origin o desde tu acceso directo de escritorio</li>
-</ol>
- <h4>Cómo descargar e instalar mundos y contenido adicional de thesims3.com</h4>
-<p>El sitio web oficial de Los Sims 3 también ofrece algunos mundos libres y contenido extra que puedes descargar para tu juego. Estos incluyen Riverview, Barnacle Bay, Hidden Springs, Lunar Lakes, Lucky Palms, Sunlit Tides, Monte Vista, Dragon Valley, Midnight Hollow, Roaring Heights y algunos artículos y conjuntos adicionales. Para descargar e instalar estos mundos y contenido adicional, necesitas:</p>
-<ol>
-<li>Ir a [thesims3.com/worlds] y elegir el mundo que desea descargar</li>
-<li>Haga clic en "Descargar" y confirme su compra (algunos mundos son gratuitos y algunos requieren SimPoints, que puede comprar con dinero real)</li>
-<li>Vaya a [thesims3.com/myaccount] y haga clic en "Historial de compras"</li>
-<li>Encuentra el mundo que has descargado y haz clic en "Descargar"</li>
-<li>El lanzador se abrirá e instalará el mundo automáticamente</li>
-<li>Repita los pasos 1-5 para cualquier contenido de bonificación que desee descargar</li>
-<li>Puedes encontrar los mundos y el contenido de bonificación en tu juego bajo "New Game" o "Edit Town"</li>
-</ol>
- <h3>Cómo descargar Sims de vídeos de YouTube</h3>
-
- <h4>Cómo encontrar y descargar Sims pre-hechos de los canales de YouTube</h4>
-<p>Para encontrar y descargar Sims pre-hechos de los canales de YouTube, es necesario:</p>
-<ol>
-<li>Buscar "descarga de Sims 3" o palabras clave similares en YouTube</li>
-<li>Navegar por los resultados y encontrar un video que muestra un Sim que te gusta</li>
-<li>Mira el video y presta atención a la descripción y los comentarios</li>
-<li>Busque un enlace para descargar el Sim o el contenido personalizado utilizado para el Sim</li>
-<li>Haga clic en el enlace y siga las instrucciones para descargar el archivo (generalmente un .sim o un archivo .zip)</li>
-<li>Guarde el archivo en su computadora</li>
-</ol>
- <h4>Cómo instalar Sims pre-hechos usando el lanzador o la carpeta mods</h4>
-<p>Para instalar Sims pre-hechos usando el lanzador o la carpeta mods, necesitas:</p>
-<ol>
-<li>Si el archivo es un archivo . sim, cópialo a Documents Electronic Arts Los Sims 3 SavedSims</li>
-<li> Si el archivo es un archivo . zip, extraerlo usando un programa como WinRAR o 7-Zip</li>
-<li>Si el archivo extraído es un archivo . sim, copiarlo a Documents Electronic Arts Los Sims 3 SavedSims</li>
-<li>Si el archivo extraído es un . archivo de paquete, copiarlo a Documents Electronic Arts The Sims 3 Mods Packages (si no tiene una carpeta Mods, puede crear una siguiente [esta guía])</li>
-<li>Inicie el juego y vaya a Crear un modo Sim</li>
-<li>Haga clic en "Pre-made Sims" y encontrar el Sim que ha descargado</li>
-<li>Personaliza el Sim como quieras y agrégalo a tu hogar</li>
-</ol>
- <h3>Cómo descargar Sims desde otros sitios web</h3>
-<p>La última fuente donde puedes encontrar Sims para descargar tu juego son otros sitios web. Hay muchos sitios de fans y foros que crean y comparten Sims personalizados para Sims 3. Estos Sims suelen tener contenido personalizado, como peinados, ropa, maquillaje, accesorios, etc. Para descargar Sims de otros sitios web, debe seguir estos pasos:</p>
- <h4>Cómo encontrar y descargar Sims personalizados desde sitios de fans y foros</h4>
-
-<ol>
-<li>Buscar "Sims 3 sims personalizados" o palabras clave similares en Google o su motor de búsqueda preferido</li>
-<li>Busca los resultados y encuentra un sitio web que ofrezca Sims personalizados para descargar (algunos ejemplos son [Mod The Sims], [The Sims Resource], [Sims 3 Updates], etc.)</li>
-<li>Navegar por el sitio web y encontrar un Sim que te gusta</li>
-<li>Haga clic en el nombre o la imagen del Sim y lea la descripción y los comentarios</li>
-<li>Busque un enlace o un botón para descargar el Sim o el contenido personalizado utilizado para el Sim (generalmente un .sim o un archivo .zip)</li>
-<li>Haga clic en el enlace o botón y siga las instrucciones para descargar el archivo (es posible que tenga que registrarse o iniciar sesión en algunos sitios web)</li>
-<li>Guarde el archivo en su computadora</li>
-</ol>
- <h4>Cómo instalar Sims personalizados usando el lanzador o la carpeta mods</h4>
-<p>Para instalar Sims personalizados usando el lanzador o la carpeta mods, debe seguir los mismos pasos que en la sección anterior. </ <p>Ahora ya sabes cómo descargar Sims en Sims 3 desde diferentes fuentes y cómo instalarlos en tu juego. Puedes disfrutar jugando con los Sims que descargaste o crear tus propios Sims usando las herramientas y características del juego. También puedes compartir tus Sims con otros jugadores en el sitio web oficial, YouTube u otros sitios web. </p>
- <h2>Conclusión</h2>
-<h3>Resumen de los principales puntos y consejos</h3>
-<p>En este artículo, hemos cubierto los siguientes temas:</p>
-<ul>
-<li>¿Qué es Sims 3 y por qué descargar Sims? </li>
-<li> ¿Cuáles son los requisitos y características del sistema de Sims 3?</li>
-<li>Cómo descargar Sims desde el sitio web oficial, vídeos de YouTube y otros sitios web</li>
-<li>Cómo instalar Sims usando el lanzador o la carpeta mods</li>
-</ul>
-<p>Aquí hay algunos consejos para recordar al descargar Sims en Sims 3:</p>
-<ul>
-<li> Compruebe siempre la descripción y los comentarios del Sim que desea descargar para obtener instrucciones, advertencias o recomendaciones</li>
-<li>Siempre copia de seguridad de los archivos del juego antes de instalar cualquier contenido personalizado</li>
-
-<li>Siempre da crédito a los creadores originales de los Sims o al contenido personalizado que usas o compartes</li>
-<li>Siempre diviértete y sé creativo con tus Sims</li>
-</ul>
- <h3>Preguntas frecuentes</h3>
-<p>Aquí hay algunas preguntas frecuentes sobre la descarga de Sims en Sims 3:</p>
-<ol>
-<li><b>¿Cómo puedo desinstalar un Sim o un contenido personalizado que he descargado? </b></li>
-<p>Puede desinstalar un Sim o un contenido personalizado que descargó borrando el archivo de su carpeta SavedSims o Mods Packages. También puede usar el lanzador para desinstalarlos yendo a "Contenido instalado" y haciendo clic en "Desinstalar". </p>
-<li><b>¿Cómo actualizo mi juego y mi contenido personalizado? </b></li>
-<p>Puedes actualizar tu juego y tu contenido personalizado usando Origin o el lanzador. Origin actualizará automáticamente tu juego y tus expansiones cuando lo lances. El lanzador comprobará si hay actualizaciones cuando lo inicie y le pedirá que las descargue. También puede actualizar su contenido personalizado visitando los sitios web donde los descargó y buscando nuevas versiones. </p>
-<li><b>¿Cómo puedo arreglar un Sim roto o dañado o contenido personalizado? </b></li>
-<p>Puedes arreglar un Sim roto o dañado o contenido personalizado usando una herramienta como [Delphy’s Dashboard] o [Custard]. Estas herramientas pueden escanear tus archivos de juego e identificar cualquier problema o conflicto con tu contenido personalizado. A continuación, puede eliminar o corregir los archivos problemáticos. </p>
-<li><b>¿Cómo puedo encontrar más Sims o contenido personalizado para descargar? </b></li>
-<p>Puedes encontrar más Sims o contenido personalizado para descargar buscando en línea o navegando por diferentes sitios web. Algunos de los sitios web más populares para descargar Sims o contenido personalizado son [Mod The Sims], [The Sims Resource], [Sims 3 Updates], [Around The Sims 3], [Lorandia Sims 3], [Parsimonious], [Anubis Under The Sun], [All About Style], [Liana Sims 3], [XM Sims 3, etc/p.
-<li><b>¿Cómo hago mis propios Sims o contenido personalizado? </b></li>
-
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/packaging/_structures.py

@@ -1,61 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-
-class InfinityType:
-    def __repr__(self) -> str:
-        return "Infinity"
-
-    def __hash__(self) -> int:
-        return hash(repr(self))
-
-    def __lt__(self, other: object) -> bool:
-        return False
-
-    def __le__(self, other: object) -> bool:
-        return False
-
-    def __eq__(self, other: object) -> bool:
-        return isinstance(other, self.__class__)
-
-    def __gt__(self, other: object) -> bool:
-        return True
-
-    def __ge__(self, other: object) -> bool:
-        return True
-
-    def __neg__(self: object) -> "NegativeInfinityType":
-        return NegativeInfinity
-
-
-Infinity = InfinityType()
-
-
-class NegativeInfinityType:
-    def __repr__(self) -> str:
-        return "-Infinity"
-
-    def __hash__(self) -> int:
-        return hash(repr(self))
-
-    def __lt__(self, other: object) -> bool:
-        return True
-
-    def __le__(self, other: object) -> bool:
-        return True
-
-    def __eq__(self, other: object) -> bool:
-        return isinstance(other, self.__class__)
-
-    def __gt__(self, other: object) -> bool:
-        return False
-
-    def __ge__(self, other: object) -> bool:
-        return False
-
-    def __neg__(self: object) -> InfinityType:
-        return Infinity
-
-
-NegativeInfinity = NegativeInfinityType()

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

@@ -1,763 +0,0 @@
-"""setuptools.command.egg_info
-
-Create a distribution's .egg-info directory and contents"""
-
-from distutils.filelist import FileList as _FileList
-from distutils.errors import DistutilsInternalError
-from distutils.util import convert_path
-from distutils import log
-import distutils.errors
-import distutils.filelist
-import functools
-import os
-import re
-import sys
-import io
-import warnings
-import time
-import collections
-
-from .._importlib import metadata
-from .. import _entry_points
-
-from setuptools import Command
-from setuptools.command.sdist import sdist
-from setuptools.command.sdist import walk_revctrl
-from setuptools.command.setopt import edit_config
-from setuptools.command import bdist_egg
-from pkg_resources import (
-    Requirement, safe_name, parse_version,
-    safe_version, to_filename)
-import setuptools.unicode_utils as unicode_utils
-from setuptools.glob import glob
-
-from setuptools.extern import packaging
-from setuptools.extern.jaraco.text import yield_lines
-from setuptools import SetuptoolsDeprecationWarning
-
-
-def translate_pattern(glob):  # noqa: C901  # is too complex (14)  # FIXME
-    """
-    Translate a file path glob like '*.txt' in to a regular expression.
-    This differs from fnmatch.translate which allows wildcards to match
-    directory separators. It also knows about '**/' which matches any number of
-    directories.
-    """
-    pat = ''
-
-    # This will split on '/' within [character classes]. This is deliberate.
-    chunks = glob.split(os.path.sep)
-
-    sep = re.escape(os.sep)
-    valid_char = '[^%s]' % (sep,)
-
-    for c, chunk in enumerate(chunks):
-        last_chunk = c == len(chunks) - 1
-
-        # Chunks that are a literal ** are globstars. They match anything.
-        if chunk == '**':
-            if last_chunk:
-                # Match anything if this is the last component
-                pat += '.*'
-            else:
-                # Match '(name/)*'
-                pat += '(?:%s+%s)*' % (valid_char, sep)
-            continue  # Break here as the whole path component has been handled
-
-        # Find any special characters in the remainder
-        i = 0
-        chunk_len = len(chunk)
-        while i < chunk_len:
-            char = chunk[i]
-            if char == '*':
-                # Match any number of name characters
-                pat += valid_char + '*'
-            elif char == '?':
-                # Match a name character
-                pat += valid_char
-            elif char == '[':
-                # Character class
-                inner_i = i + 1
-                # Skip initial !/] chars
-                if inner_i < chunk_len and chunk[inner_i] == '!':
-                    inner_i = inner_i + 1
-                if inner_i < chunk_len and chunk[inner_i] == ']':
-                    inner_i = inner_i + 1
-
-                # Loop till the closing ] is found
-                while inner_i < chunk_len and chunk[inner_i] != ']':
-                    inner_i = inner_i + 1
-
-                if inner_i >= chunk_len:
-                    # Got to the end of the string without finding a closing ]
-                    # Do not treat this as a matching group, but as a literal [
-                    pat += re.escape(char)
-                else:
-                    # Grab the insides of the [brackets]
-                    inner = chunk[i + 1:inner_i]
-                    char_class = ''
-
-                    # Class negation
-                    if inner[0] == '!':
-                        char_class = '^'
-                        inner = inner[1:]
-
-                    char_class += re.escape(inner)
-                    pat += '[%s]' % (char_class,)
-
-                    # Skip to the end ]
-                    i = inner_i
-            else:
-                pat += re.escape(char)
-            i += 1
-
-        # Join each chunk with the dir separator
-        if not last_chunk:
-            pat += sep
-
-    pat += r'\Z'
-    return re.compile(pat, flags=re.MULTILINE | re.DOTALL)
-
-
-class InfoCommon:
-    tag_build = None
-    tag_date = None
-
-    @property
-    def name(self):
-        return safe_name(self.distribution.get_name())
-
-    def tagged_version(self):
-        return safe_version(self._maybe_tag(self.distribution.get_version()))
-
-    def _maybe_tag(self, version):
-        """
-        egg_info may be called more than once for a distribution,
-        in which case the version string already contains all tags.
-        """
-        return (
-            version if self.vtags and self._already_tagged(version)
-            else version + self.vtags
-        )
-
-    def _already_tagged(self, version: str) -> bool:
-        # Depending on their format, tags may change with version normalization.
-        # So in addition the regular tags, we have to search for the normalized ones.
-        return version.endswith(self.vtags) or version.endswith(self._safe_tags())
-
-    def _safe_tags(self) -> str:
-        # To implement this we can rely on `safe_version` pretending to be version 0
-        # followed by tags. Then we simply discard the starting 0 (fake version number)
-        return safe_version(f"0{self.vtags}")[1:]
-
-    def tags(self) -> str:
-        version = ''
-        if self.tag_build:
-            version += self.tag_build
-        if self.tag_date:
-            version += time.strftime("-%Y%m%d")
-        return version
-    vtags = property(tags)
-
-
-class egg_info(InfoCommon, Command):
-    description = "create a distribution's .egg-info directory"
-
-    user_options = [
-        ('egg-base=', 'e', "directory containing .egg-info directories"
-                           " (default: top of the source tree)"),
-        ('tag-date', 'd', "Add date stamp (e.g. 20050528) to version number"),
-        ('tag-build=', 'b', "Specify explicit tag to add to version number"),
-        ('no-date', 'D', "Don't include date stamp [default]"),
-    ]
-
-    boolean_options = ['tag-date']
-    negative_opt = {
-        'no-date': 'tag-date',
-    }
-
-    def initialize_options(self):
-        self.egg_base = None
-        self.egg_name = None
-        self.egg_info = None
-        self.egg_version = None
-        self.broken_egg_info = False
-        self.ignore_egg_info_in_manifest = False
-
-    ####################################
-    # allow the 'tag_svn_revision' to be detected and
-    # set, supporting sdists built on older Setuptools.
-    @property
-    def tag_svn_revision(self):
-        pass
-
-    @tag_svn_revision.setter
-    def tag_svn_revision(self, value):
-        pass
-    ####################################
-
-    def save_version_info(self, filename):
-        """
-        Materialize the value of date into the
-        build tag. Install build keys in a deterministic order
-        to avoid arbitrary reordering on subsequent builds.
-        """
-        egg_info = collections.OrderedDict()
-        # follow the order these keys would have been added
-        # when PYTHONHASHSEED=0
-        egg_info['tag_build'] = self.tags()
-        egg_info['tag_date'] = 0
-        edit_config(filename, dict(egg_info=egg_info))
-
-    def finalize_options(self):
-        # Note: we need to capture the current value returned
-        # by `self.tagged_version()`, so we can later update
-        # `self.distribution.metadata.version` without
-        # repercussions.
-        self.egg_name = self.name
-        self.egg_version = self.tagged_version()
-        parsed_version = parse_version(self.egg_version)
-
-        try:
-            is_version = isinstance(parsed_version, packaging.version.Version)
-            spec = "%s==%s" if is_version else "%s===%s"
-            Requirement(spec % (self.egg_name, self.egg_version))
-        except ValueError as e:
-            raise distutils.errors.DistutilsOptionError(
-                "Invalid distribution name or version syntax: %s-%s" %
-                (self.egg_name, self.egg_version)
-            ) from e
-
-        if self.egg_base is None:
-            dirs = self.distribution.package_dir
-            self.egg_base = (dirs or {}).get('', os.curdir)
-
-        self.ensure_dirname('egg_base')
-        self.egg_info = to_filename(self.egg_name) + '.egg-info'
-        if self.egg_base != os.curdir:
-            self.egg_info = os.path.join(self.egg_base, self.egg_info)
-        if '-' in self.egg_name:
-            self.check_broken_egg_info()
-
-        # Set package version for the benefit of dumber commands
-        # (e.g. sdist, bdist_wininst, etc.)
-        #
-        self.distribution.metadata.version = self.egg_version
-
-        # If we bootstrapped around the lack of a PKG-INFO, as might be the
-        # case in a fresh checkout, make sure that any special tags get added
-        # to the version info
-        #
-        pd = self.distribution._patched_dist
-        if pd is not None and pd.key == self.egg_name.lower():
-            pd._version = self.egg_version
-            pd._parsed_version = parse_version(self.egg_version)
-            self.distribution._patched_dist = None
-
-    def write_or_delete_file(self, what, filename, data, force=False):
-        """Write `data` to `filename` or delete if empty
-
-        If `data` is non-empty, this routine is the same as ``write_file()``.
-        If `data` is empty but not ``None``, this is the same as calling
-        ``delete_file(filename)`.  If `data` is ``None``, then this is a no-op
-        unless `filename` exists, in which case a warning is issued about the
-        orphaned file (if `force` is false), or deleted (if `force` is true).
-        """
-        if data:
-            self.write_file(what, filename, data)
-        elif os.path.exists(filename):
-            if data is None and not force:
-                log.warn(
-                    "%s not set in setup(), but %s exists", what, filename
-                )
-                return
-            else:
-                self.delete_file(filename)
-
-    def write_file(self, what, filename, data):
-        """Write `data` to `filename` (if not a dry run) after announcing it
-
-        `what` is used in a log message to identify what is being written
-        to the file.
-        """
-        log.info("writing %s to %s", what, filename)
-        data = data.encode("utf-8")
-        if not self.dry_run:
-            f = open(filename, 'wb')
-            f.write(data)
-            f.close()
-
-    def delete_file(self, filename):
-        """Delete `filename` (if not a dry run) after announcing it"""
-        log.info("deleting %s", filename)
-        if not self.dry_run:
-            os.unlink(filename)
-
-    def run(self):
-        self.mkpath(self.egg_info)
-        os.utime(self.egg_info, None)
-        for ep in metadata.entry_points(group='egg_info.writers'):
-            writer = ep.load()
-            writer(self, ep.name, os.path.join(self.egg_info, ep.name))
-
-        # Get rid of native_libs.txt if it was put there by older bdist_egg
-        nl = os.path.join(self.egg_info, "native_libs.txt")
-        if os.path.exists(nl):
-            self.delete_file(nl)
-
-        self.find_sources()
-
-    def find_sources(self):
-        """Generate SOURCES.txt manifest file"""
-        manifest_filename = os.path.join(self.egg_info, "SOURCES.txt")
-        mm = manifest_maker(self.distribution)
-        mm.ignore_egg_info_dir = self.ignore_egg_info_in_manifest
-        mm.manifest = manifest_filename
-        mm.run()
-        self.filelist = mm.filelist
-
-    def check_broken_egg_info(self):
-        bei = self.egg_name + '.egg-info'
-        if self.egg_base != os.curdir:
-            bei = os.path.join(self.egg_base, bei)
-        if os.path.exists(bei):
-            log.warn(
-                "-" * 78 + '\n'
-                "Note: Your current .egg-info directory has a '-' in its name;"
-                '\nthis will not work correctly with "setup.py develop".\n\n'
-                'Please rename %s to %s to correct this problem.\n' + '-' * 78,
-                bei, self.egg_info
-            )
-            self.broken_egg_info = self.egg_info
-            self.egg_info = bei  # make it work for now
-
-
-class FileList(_FileList):
-    # Implementations of the various MANIFEST.in commands
-
-    def __init__(self, warn=None, debug_print=None, ignore_egg_info_dir=False):
-        super().__init__(warn, debug_print)
-        self.ignore_egg_info_dir = ignore_egg_info_dir
-
-    def process_template_line(self, line):
-        # Parse the line: split it up, make sure the right number of words
-        # is there, and return the relevant words.  'action' is always
-        # defined: it's the first word of the line.  Which of the other
-        # three are defined depends on the action; it'll be either
-        # patterns, (dir and patterns), or (dir_pattern).
-        (action, patterns, dir, dir_pattern) = self._parse_template_line(line)
-
-        action_map = {
-            'include': self.include,
-            'exclude': self.exclude,
-            'global-include': self.global_include,
-            'global-exclude': self.global_exclude,
-            'recursive-include': functools.partial(
-                self.recursive_include, dir,
-            ),
-            'recursive-exclude': functools.partial(
-                self.recursive_exclude, dir,
-            ),
-            'graft': self.graft,
-            'prune': self.prune,
-        }
-        log_map = {
-            'include': "warning: no files found matching '%s'",
-            'exclude': (
-                "warning: no previously-included files found "
-                "matching '%s'"
-            ),
-            'global-include': (
-                "warning: no files found matching '%s' "
-                "anywhere in distribution"
-            ),
-            'global-exclude': (
-                "warning: no previously-included files matching "
-                "'%s' found anywhere in distribution"
-            ),
-            'recursive-include': (
-                "warning: no files found matching '%s' "
-                "under directory '%s'"
-            ),
-            'recursive-exclude': (
-                "warning: no previously-included files matching "
-                "'%s' found under directory '%s'"
-            ),
-            'graft': "warning: no directories found matching '%s'",
-            'prune': "no previously-included directories found matching '%s'",
-        }
-
-        try:
-            process_action = action_map[action]
-        except KeyError:
-            raise DistutilsInternalError(
-                "this cannot happen: invalid action '{action!s}'".
-                format(action=action),
-            )
-
-        # OK, now we know that the action is valid and we have the
-        # right number of words on the line for that action -- so we
-        # can proceed with minimal error-checking.
-
-        action_is_recursive = action.startswith('recursive-')
-        if action in {'graft', 'prune'}:
-            patterns = [dir_pattern]
-        extra_log_args = (dir, ) if action_is_recursive else ()
-        log_tmpl = log_map[action]
-
-        self.debug_print(
-            ' '.join(
-                [action] +
-                ([dir] if action_is_recursive else []) +
-                patterns,
-            )
-        )
-        for pattern in patterns:
-            if not process_action(pattern):
-                log.warn(log_tmpl, pattern, *extra_log_args)
-
-    def _remove_files(self, predicate):
-        """
-        Remove all files from the file list that match the predicate.
-        Return True if any matching files were removed
-        """
-        found = False
-        for i in range(len(self.files) - 1, -1, -1):
-            if predicate(self.files[i]):
-                self.debug_print(" removing " + self.files[i])
-                del self.files[i]
-                found = True
-        return found
-
-    def include(self, pattern):
-        """Include files that match 'pattern'."""
-        found = [f for f in glob(pattern) if not os.path.isdir(f)]
-        self.extend(found)
-        return bool(found)
-
-    def exclude(self, pattern):
-        """Exclude files that match 'pattern'."""
-        match = translate_pattern(pattern)
-        return self._remove_files(match.match)
-
-    def recursive_include(self, dir, pattern):
-        """
-        Include all files anywhere in 'dir/' that match the pattern.
-        """
-        full_pattern = os.path.join(dir, '**', pattern)
-        found = [f for f in glob(full_pattern, recursive=True)
-                 if not os.path.isdir(f)]
-        self.extend(found)
-        return bool(found)
-
-    def recursive_exclude(self, dir, pattern):
-        """
-        Exclude any file anywhere in 'dir/' that match the pattern.
-        """
-        match = translate_pattern(os.path.join(dir, '**', pattern))
-        return self._remove_files(match.match)
-
-    def graft(self, dir):
-        """Include all files from 'dir/'."""
-        found = [
-            item
-            for match_dir in glob(dir)
-            for item in distutils.filelist.findall(match_dir)
-        ]
-        self.extend(found)
-        return bool(found)
-
-    def prune(self, dir):
-        """Filter out files from 'dir/'."""
-        match = translate_pattern(os.path.join(dir, '**'))
-        return self._remove_files(match.match)
-
-    def global_include(self, pattern):
-        """
-        Include all files anywhere in the current directory that match the
-        pattern. This is very inefficient on large file trees.
-        """
-        if self.allfiles is None:
-            self.findall()
-        match = translate_pattern(os.path.join('**', pattern))
-        found = [f for f in self.allfiles if match.match(f)]
-        self.extend(found)
-        return bool(found)
-
-    def global_exclude(self, pattern):
-        """
-        Exclude all files anywhere that match the pattern.
-        """
-        match = translate_pattern(os.path.join('**', pattern))
-        return self._remove_files(match.match)
-
-    def append(self, item):
-        if item.endswith('\r'):  # Fix older sdists built on Windows
-            item = item[:-1]
-        path = convert_path(item)
-
-        if self._safe_path(path):
-            self.files.append(path)
-
-    def extend(self, paths):
-        self.files.extend(filter(self._safe_path, paths))
-
-    def _repair(self):
-        """
-        Replace self.files with only safe paths
-
-        Because some owners of FileList manipulate the underlying
-        ``files`` attribute directly, this method must be called to
-        repair those paths.
-        """
-        self.files = list(filter(self._safe_path, self.files))
-
-    def _safe_path(self, path):
-        enc_warn = "'%s' not %s encodable -- skipping"
-
-        # To avoid accidental trans-codings errors, first to unicode
-        u_path = unicode_utils.filesys_decode(path)
-        if u_path is None:
-            log.warn("'%s' in unexpected encoding -- skipping" % path)
-            return False
-
-        # Must ensure utf-8 encodability
-        utf8_path = unicode_utils.try_encode(u_path, "utf-8")
-        if utf8_path is None:
-            log.warn(enc_warn, path, 'utf-8')
-            return False
-
-        try:
-            # ignore egg-info paths
-            is_egg_info = ".egg-info" in u_path or b".egg-info" in utf8_path
-            if self.ignore_egg_info_dir and is_egg_info:
-                return False
-            # accept is either way checks out
-            if os.path.exists(u_path) or os.path.exists(utf8_path):
-                return True
-        # this will catch any encode errors decoding u_path
-        except UnicodeEncodeError:
-            log.warn(enc_warn, path, sys.getfilesystemencoding())
-
-
-class manifest_maker(sdist):
-    template = "MANIFEST.in"
-
-    def initialize_options(self):
-        self.use_defaults = 1
-        self.prune = 1
-        self.manifest_only = 1
-        self.force_manifest = 1
-        self.ignore_egg_info_dir = False
-
-    def finalize_options(self):
-        pass
-
-    def run(self):
-        self.filelist = FileList(ignore_egg_info_dir=self.ignore_egg_info_dir)
-        if not os.path.exists(self.manifest):
-            self.write_manifest()  # it must exist so it'll get in the list
-        self.add_defaults()
-        if os.path.exists(self.template):
-            self.read_template()
-        self.add_license_files()
-        self.prune_file_list()
-        self.filelist.sort()
-        self.filelist.remove_duplicates()
-        self.write_manifest()
-
-    def _manifest_normalize(self, path):
-        path = unicode_utils.filesys_decode(path)
-        return path.replace(os.sep, '/')
-
-    def write_manifest(self):
-        """
-        Write the file list in 'self.filelist' to the manifest file
-        named by 'self.manifest'.
-        """
-        self.filelist._repair()
-
-        # Now _repairs should encodability, but not unicode
-        files = [self._manifest_normalize(f) for f in self.filelist.files]
-        msg = "writing manifest file '%s'" % self.manifest
-        self.execute(write_file, (self.manifest, files), msg)
-
-    def warn(self, msg):
-        if not self._should_suppress_warning(msg):
-            sdist.warn(self, msg)
-
-    @staticmethod
-    def _should_suppress_warning(msg):
-        """
-        suppress missing-file warnings from sdist
-        """
-        return re.match(r"standard file .*not found", msg)
-
-    def add_defaults(self):
-        sdist.add_defaults(self)
-        self.filelist.append(self.template)
-        self.filelist.append(self.manifest)
-        rcfiles = list(walk_revctrl())
-        if rcfiles:
-            self.filelist.extend(rcfiles)
-        elif os.path.exists(self.manifest):
-            self.read_manifest()
-
-        if os.path.exists("setup.py"):
-            # setup.py should be included by default, even if it's not
-            # the script called to create the sdist
-            self.filelist.append("setup.py")
-
-        ei_cmd = self.get_finalized_command('egg_info')
-        self.filelist.graft(ei_cmd.egg_info)
-
-    def add_license_files(self):
-        license_files = self.distribution.metadata.license_files or []
-        for lf in license_files:
-            log.info("adding license file '%s'", lf)
-            pass
-        self.filelist.extend(license_files)
-
-    def prune_file_list(self):
-        build = self.get_finalized_command('build')
-        base_dir = self.distribution.get_fullname()
-        self.filelist.prune(build.build_base)
-        self.filelist.prune(base_dir)
-        sep = re.escape(os.sep)
-        self.filelist.exclude_pattern(r'(^|' + sep + r')(RCS|CVS|\.svn)' + sep,
-                                      is_regex=1)
-
-    def _safe_data_files(self, build_py):
-        """
-        The parent class implementation of this method
-        (``sdist``) will try to include data files, which
-        might cause recursion problems when
-        ``include_package_data=True``.
-
-        Therefore, avoid triggering any attempt of
-        analyzing/building the manifest again.
-        """
-        if hasattr(build_py, 'get_data_files_without_manifest'):
-            return build_py.get_data_files_without_manifest()
-
-        warnings.warn(
-            "Custom 'build_py' does not implement "
-            "'get_data_files_without_manifest'.\nPlease extend command classes"
-            " from setuptools instead of distutils.",
-            SetuptoolsDeprecationWarning
-        )
-        return build_py.get_data_files()
-
-
-def write_file(filename, contents):
-    """Create a file with the specified name and write 'contents' (a
-    sequence of strings without line terminators) to it.
-    """
-    contents = "\n".join(contents)
-
-    # assuming the contents has been vetted for utf-8 encoding
-    contents = contents.encode("utf-8")
-
-    with open(filename, "wb") as f:  # always write POSIX-style manifest
-        f.write(contents)
-
-
-def write_pkg_info(cmd, basename, filename):
-    log.info("writing %s", filename)
-    if not cmd.dry_run:
-        metadata = cmd.distribution.metadata
-        metadata.version, oldver = cmd.egg_version, metadata.version
-        metadata.name, oldname = cmd.egg_name, metadata.name
-
-        try:
-            # write unescaped data to PKG-INFO, so older pkg_resources
-            # can still parse it
-            metadata.write_pkg_info(cmd.egg_info)
-        finally:
-            metadata.name, metadata.version = oldname, oldver
-
-        safe = getattr(cmd.distribution, 'zip_safe', None)
-
-        bdist_egg.write_safety_flag(cmd.egg_info, safe)
-
-
-def warn_depends_obsolete(cmd, basename, filename):
-    if os.path.exists(filename):
-        log.warn(
-            "WARNING: 'depends.txt' is not used by setuptools 0.6!\n"
-            "Use the install_requires/extras_require setup() args instead."
-        )
-
-
-def _write_requirements(stream, reqs):
-    lines = yield_lines(reqs or ())
-
-    def append_cr(line):
-        return line + '\n'
-    lines = map(append_cr, lines)
-    stream.writelines(lines)
-
-
-def write_requirements(cmd, basename, filename):
-    dist = cmd.distribution
-    data = io.StringIO()
-    _write_requirements(data, dist.install_requires)
-    extras_require = dist.extras_require or {}
-    for extra in sorted(extras_require):
-        data.write('\n[{extra}]\n'.format(**vars()))
-        _write_requirements(data, extras_require[extra])
-    cmd.write_or_delete_file("requirements", filename, data.getvalue())
-
-
-def write_setup_requirements(cmd, basename, filename):
-    data = io.StringIO()
-    _write_requirements(data, cmd.distribution.setup_requires)
-    cmd.write_or_delete_file("setup-requirements", filename, data.getvalue())
-
-
-def write_toplevel_names(cmd, basename, filename):
-    pkgs = dict.fromkeys(
-        [
-            k.split('.', 1)[0]
-            for k in cmd.distribution.iter_distribution_names()
-        ]
-    )
-    cmd.write_file("top-level names", filename, '\n'.join(sorted(pkgs)) + '\n')
-
-
-def overwrite_arg(cmd, basename, filename):
-    write_arg(cmd, basename, filename, True)
-
-
-def write_arg(cmd, basename, filename, force=False):
-    argname = os.path.splitext(basename)[0]
-    value = getattr(cmd.distribution, argname, None)
-    if value is not None:
-        value = '\n'.join(value) + '\n'
-    cmd.write_or_delete_file(argname, filename, value, force)
-
-
-def write_entries(cmd, basename, filename):
-    eps = _entry_points.load(cmd.distribution.entry_points)
-    defn = _entry_points.render(eps)
-    cmd.write_or_delete_file('entry points', filename, defn, True)
-
-
-def get_pkg_info_revision():
-    """
-    Get a -r### off of PKG-INFO Version in case this is an sdist of
-    a subversion revision.
-    """
-    warnings.warn(
-        "get_pkg_info_revision is deprecated.", EggInfoDeprecationWarning)
-    if os.path.exists('PKG-INFO'):
-        with io.open('PKG-INFO') as f:
-            for line in f:
-                match = re.match(r"Version:.*-r(\d+)\s*$", line)
-                if match:
-                    return int(match.group(1))
-    return 0
-
-
-class EggInfoDeprecationWarning(SetuptoolsDeprecationWarning):
-    """Deprecated behavior warning for EggInfo, bypassing suppression."""

spaces/CVH-vn1210/make_hair/minigpt4/models/blip2.py

@@ -1,221 +0,0 @@
-"""
- Copyright (c) 2023, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-import contextlib
-import logging
-import os
-import time
-import datetime
-
-import torch
-import torch.nn as nn
-import torch.distributed as dist
-import torch.nn.functional as F
-
-import minigpt4.common.dist_utils as dist_utils
-from minigpt4.common.dist_utils import download_cached_file
-from minigpt4.common.utils import is_url
-from minigpt4.common.logger import MetricLogger
-from minigpt4.models.base_model import BaseModel
-from minigpt4.models.Qformer import BertConfig, BertLMHeadModel
-from minigpt4.models.eva_vit import create_eva_vit_g
-from transformers import BertTokenizer
-
-
-class Blip2Base(BaseModel):
-    @classmethod
-    def init_tokenizer(cls):
-        tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-        tokenizer.add_special_tokens({"bos_token": "[DEC]"})
-        return tokenizer
-
-    def maybe_autocast(self, dtype=torch.float16):
-        # if on cpu, don't use autocast
-        # if on gpu, use autocast with dtype if provided, otherwise use torch.float16
-        enable_autocast = self.device != torch.device("cpu")
-
-        if enable_autocast:
-            return torch.cuda.amp.autocast(dtype=dtype)
-        else:
-            return contextlib.nullcontext()
-
-    @classmethod
-    def init_Qformer(cls, num_query_token, vision_width, cross_attention_freq=2):
-        encoder_config = BertConfig.from_pretrained("bert-base-uncased")
-        encoder_config.encoder_width = vision_width
-        # insert cross-attention layer every other block
-        encoder_config.add_cross_attention = True
-        encoder_config.cross_attention_freq = cross_attention_freq
-        encoder_config.query_length = num_query_token
-        Qformer = BertLMHeadModel(config=encoder_config)
-        query_tokens = nn.Parameter(
-            torch.zeros(1, num_query_token, encoder_config.hidden_size)
-        )
-        query_tokens.data.normal_(mean=0.0, std=encoder_config.initializer_range)
-        return Qformer, query_tokens
-
-    @classmethod
-    def init_vision_encoder(
-        cls, model_name, img_size, drop_path_rate, use_grad_checkpoint, precision
-    ):
-        assert model_name == "eva_clip_g", "vit model must be eva_clip_g for current version of MiniGPT-4"
-        visual_encoder = create_eva_vit_g(
-            img_size, drop_path_rate, use_grad_checkpoint, precision
-        )
-
-        ln_vision = LayerNorm(visual_encoder.num_features)
-        return visual_encoder, ln_vision
-
-    def load_from_pretrained(self, url_or_filename):
-        if is_url(url_or_filename):
-            cached_file = download_cached_file(
-                url_or_filename, check_hash=False, progress=True
-            )
-            checkpoint = torch.load(cached_file, map_location="cpu")
-        elif os.path.isfile(url_or_filename):
-            checkpoint = torch.load(url_or_filename, map_location="cpu")
-        else:
-            raise RuntimeError("checkpoint url or path is invalid")
-
-        state_dict = checkpoint["model"]
-
-        msg = self.load_state_dict(state_dict, strict=False)
-
-        # logging.info("Missing keys {}".format(msg.missing_keys))
-        logging.info("load checkpoint from %s" % url_or_filename)
-
-        return msg
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        ret = super().forward(x.type(torch.float32))
-        return ret.type(orig_type)
-
-
-def compute_sim_matrix(model, data_loader, **kwargs):
-    k_test = kwargs.pop("k_test")
-
-    metric_logger = MetricLogger(delimiter="  ")
-    header = "Evaluation:"
-
-    logging.info("Computing features for evaluation...")
-    start_time = time.time()
-
-    texts = data_loader.dataset.text
-    num_text = len(texts)
-    text_bs = 256
-    text_ids = []
-    text_embeds = []
-    text_atts = []
-    for i in range(0, num_text, text_bs):
-        text = texts[i : min(num_text, i + text_bs)]
-        text_input = model.tokenizer(
-            text,
-            padding="max_length",
-            truncation=True,
-            max_length=35,
-            return_tensors="pt",
-        ).to(model.device)
-        text_feat = model.forward_text(text_input)
-        text_embed = F.normalize(model.text_proj(text_feat))
-        text_embeds.append(text_embed)
-        text_ids.append(text_input.input_ids)
-        text_atts.append(text_input.attention_mask)
-
-    text_embeds = torch.cat(text_embeds, dim=0)
-    text_ids = torch.cat(text_ids, dim=0)
-    text_atts = torch.cat(text_atts, dim=0)
-
-    vit_feats = []
-    image_embeds = []
-    for samples in data_loader:
-        image = samples["image"]
-
-        image = image.to(model.device)
-        image_feat, vit_feat = model.forward_image(image)
-        image_embed = model.vision_proj(image_feat)
-        image_embed = F.normalize(image_embed, dim=-1)
-
-        vit_feats.append(vit_feat.cpu())
-        image_embeds.append(image_embed)
-
-    vit_feats = torch.cat(vit_feats, dim=0)
-    image_embeds = torch.cat(image_embeds, dim=0)
-
-    sims_matrix = []
-    for image_embed in image_embeds:
-        sim_q2t = image_embed @ text_embeds.t()
-        sim_i2t, _ = sim_q2t.max(0)
-        sims_matrix.append(sim_i2t)
-    sims_matrix = torch.stack(sims_matrix, dim=0)
-
-    score_matrix_i2t = torch.full(
-        (len(data_loader.dataset.image), len(texts)), -100.0
-    ).to(model.device)
-
-    num_tasks = dist_utils.get_world_size()
-    rank = dist_utils.get_rank()
-    step = sims_matrix.size(0) // num_tasks + 1
-    start = rank * step
-    end = min(sims_matrix.size(0), start + step)
-
-    for i, sims in enumerate(
-        metric_logger.log_every(sims_matrix[start:end], 50, header)
-    ):
-        topk_sim, topk_idx = sims.topk(k=k_test, dim=0)
-        image_inputs = vit_feats[start + i].repeat(k_test, 1, 1).to(model.device)
-        score = model.compute_itm(
-            image_inputs=image_inputs,
-            text_ids=text_ids[topk_idx],
-            text_atts=text_atts[topk_idx],
-        ).float()
-        score_matrix_i2t[start + i, topk_idx] = score + topk_sim
-
-    sims_matrix = sims_matrix.t()
-    score_matrix_t2i = torch.full(
-        (len(texts), len(data_loader.dataset.image)), -100.0
-    ).to(model.device)
-
-    step = sims_matrix.size(0) // num_tasks + 1
-    start = rank * step
-    end = min(sims_matrix.size(0), start + step)
-
-    for i, sims in enumerate(
-        metric_logger.log_every(sims_matrix[start:end], 50, header)
-    ):
-        topk_sim, topk_idx = sims.topk(k=k_test, dim=0)
-        image_inputs = vit_feats[topk_idx.cpu()].to(model.device)
-        score = model.compute_itm(
-            image_inputs=image_inputs,
-            text_ids=text_ids[start + i].repeat(k_test, 1),
-            text_atts=text_atts[start + i].repeat(k_test, 1),
-        ).float()
-        score_matrix_t2i[start + i, topk_idx] = score + topk_sim
-
-    if dist_utils.is_dist_avail_and_initialized():
-        dist.barrier()
-        torch.distributed.all_reduce(
-            score_matrix_i2t, op=torch.distributed.ReduceOp.SUM
-        )
-        torch.distributed.all_reduce(
-            score_matrix_t2i, op=torch.distributed.ReduceOp.SUM
-        )
-
-    total_time = time.time() - start_time
-    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-    logging.info("Evaluation time {}".format(total_time_str))
-
-    return score_matrix_i2t.cpu().numpy(), score_matrix_t2i.cpu().numpy()

spaces/CVH-vn1210/make_hair/minigpt4/models/blip2_outputs.py

@@ -1,110 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-from dataclasses import dataclass
-from typing import Optional
-
-import torch
-from transformers.modeling_outputs import (
-    ModelOutput,
-    BaseModelOutputWithPoolingAndCrossAttentions,
-    CausalLMOutputWithCrossAttentions,
-)
-
-
-@dataclass
-class BlipSimilarity(ModelOutput):
-    sim_i2t: torch.FloatTensor = None
-    sim_t2i: torch.FloatTensor = None
-
-    sim_i2t_m: Optional[torch.FloatTensor] = None
-    sim_t2i_m: Optional[torch.FloatTensor] = None
-
-    sim_i2t_targets: Optional[torch.FloatTensor] = None
-    sim_t2i_targets: Optional[torch.FloatTensor] = None
-
-
-@dataclass
-class BlipIntermediateOutput(ModelOutput):
-    """
-    Data class for intermediate outputs of BLIP models.
-
-    image_embeds (torch.FloatTensor): Image embeddings, shape (batch_size, num_patches, embed_dim).
-    text_embeds (torch.FloatTensor): Text embeddings, shape (batch_size, seq_len, embed_dim).
-
-    image_embeds_m (torch.FloatTensor): Image embeddings from momentum visual encoder, shape (batch_size, num_patches, embed_dim).
-    text_embeds_m (torch.FloatTensor): Text embeddings from momentum text encoder, shape (batch_size, seq_len, embed_dim).
-
-    encoder_output (BaseModelOutputWithPoolingAndCrossAttentions): output from the image-grounded text encoder.
-    encoder_output_neg (BaseModelOutputWithPoolingAndCrossAttentions): output from the image-grounded text encoder for negative pairs.
-
-    decoder_output (CausalLMOutputWithCrossAttentions): output from the image-grounded text decoder.
-    decoder_labels (torch.LongTensor): labels for the captioning loss.
-
-    itm_logits (torch.FloatTensor): logits for the image-text matching loss, shape (batch_size * 3, 2).
-    itm_labels (torch.LongTensor): labels for the image-text matching loss, shape (batch_size * 3,)
-
-    """
-
-    # uni-modal features
-    image_embeds: torch.FloatTensor = None
-    text_embeds: Optional[torch.FloatTensor] = None
-
-    image_embeds_m: Optional[torch.FloatTensor] = None
-    text_embeds_m: Optional[torch.FloatTensor] = None
-
-    # intermediate outputs of multimodal encoder
-    encoder_output: Optional[BaseModelOutputWithPoolingAndCrossAttentions] = None
-    encoder_output_neg: Optional[BaseModelOutputWithPoolingAndCrossAttentions] = None
-
-    itm_logits: Optional[torch.FloatTensor] = None
-    itm_labels: Optional[torch.LongTensor] = None
-
-    # intermediate outputs of multimodal decoder
-    decoder_output: Optional[CausalLMOutputWithCrossAttentions] = None
-    decoder_labels: Optional[torch.LongTensor] = None
-
-
-@dataclass
-class BlipOutput(ModelOutput):
-    # some finetuned models (e.g. BlipVQA) do not compute similarity, thus optional.
-    sims: Optional[BlipSimilarity] = None
-
-    intermediate_output: BlipIntermediateOutput = None
-
-    loss: Optional[torch.FloatTensor] = None
-
-    loss_itc: Optional[torch.FloatTensor] = None
-
-    loss_itm: Optional[torch.FloatTensor] = None
-
-    loss_lm: Optional[torch.FloatTensor] = None
-
-
-@dataclass
-class BlipOutputFeatures(ModelOutput):
-    """
-    Data class of features from BlipFeatureExtractor.
-
-    Args:
-        image_embeds: (torch.FloatTensor) of shape (batch_size, num_patches+1, embed_dim), optional
-        image_features: (torch.FloatTensor) of shape (batch_size, num_patches+1, feature_dim), optional
-        text_embeds: (torch.FloatTensor) of shape (batch_size, sequence_length+1, embed_dim), optional
-        text_features: (torch.FloatTensor) of shape (batch_size, sequence_length+1, feature_dim), optional
-
-        The first embedding or feature is for the [CLS] token.
-
-        Features are obtained by projecting the corresponding embedding into a normalized low-dimensional space.
-    """
-
-    image_embeds: Optional[torch.FloatTensor] = None
-    image_embeds_proj: Optional[torch.FloatTensor] = None
-
-    text_embeds: Optional[torch.FloatTensor] = None
-    text_embeds_proj: Optional[torch.FloatTensor] = None
-
-    multimodal_embeds: Optional[torch.FloatTensor] = None

spaces/CVPR/LIVE/thrust/thrust/functional.h

@@ -1,1719 +0,0 @@
-/*
- *  Copyright 2008-2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file functional.h
- *  \brief Function objects and tools for manipulating them
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <functional>
-#include <thrust/detail/functional/placeholder.h>
-
-namespace thrust
-{
-
-/*! \addtogroup function_objects Function Objects
- */
-
-template<typename Operation> struct unary_traits;
-
-template<typename Operation> struct binary_traits;
-
-/*! \addtogroup function_object_adaptors Function Object Adaptors
- *  \ingroup function_objects
- *  \{
- */
-
-/*! \p unary_function is an empty base class: it contains no member functions
- *  or member variables, but only type information. The only reason it exists
- *  is to make it more convenient to define types that are models of the
- *  concept Adaptable Unary Function. Specifically, any model of Adaptable
- *  Unary Function must define nested \c typedefs. Those \c typedefs are
- *  provided by the base class \p unary_function.
- *
- *  The following code snippet demonstrates how to construct an 
- *  Adaptable Unary Function using \p unary_function.
- *
- *  \code
- *  struct sine : public thrust::unary_function<float,float>
- *  {
- *    __host__ __device__
- *    float operator()(float x) { return sinf(x); }
- *  };
- *  \endcode
- *
- *  \note Because C++11 language support makes the functionality of
- *        \c unary_function obsolete, its use is optional if C++11 language
- *        features are enabled.
- *
- *  \see http://www.sgi.com/tech/stl/unary_function.html
- *  \see binary_function
- */
-template<typename Argument,
-         typename Result>
-struct unary_function
-{
-  /*! \typedef argument_type
-   *  \brief The type of the function object's argument.
-   */
-  typedef Argument argument_type;
-
-  /*! \typedef result_type;
-   *  \brief The type of the function object's result.
-   */
-  typedef Result   result_type;
-}; // end unary_function
-
-/*! \p binary_function is an empty base class: it contains no member functions
- *  or member variables, but only type information. The only reason it exists
- *  is to make it more convenient to define types that are models of the
- *  concept Adaptable Binary Function. Specifically, any model of Adaptable
- *  Binary Function must define nested \c typedefs. Those \c typedefs are
- *  provided by the base class \p binary_function.
- *
- *  The following code snippet demonstrates how to construct an 
- *  Adaptable Binary Function using \p binary_function.
- *
- *  \code
- *  struct exponentiate : public thrust::binary_function<float,float,float>
- *  {
- *    __host__ __device__
- *    float operator()(float x, float y) { return powf(x,y); }
- *  };
- *  \endcode
- *
- *  \note Because C++11 language support makes the functionality of
- *        \c binary_function obsolete, its use is optional if C++11 language
- *        features are enabled.
- *
- *  \see http://www.sgi.com/tech/stl/binary_function.html
- *  \see unary_function
- */
-template<typename Argument1,
-         typename Argument2,
-         typename Result>
-struct binary_function
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef Argument1 first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef Argument2 second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef Result    result_type;
-}; // end binary_function
-
-/*! \}
- */
-
-
-/*! \addtogroup predefined_function_objects Predefined Function Objects
- *  \ingroup function_objects
- */
-
-/*! \addtogroup arithmetic_operations Arithmetic Operations
- *  \ingroup predefined_function_objects
- *  \{
- */
-
-#define THRUST_UNARY_FUNCTOR_VOID_SPECIALIZATION(func, impl)                   \
-  template <>                                                                  \
-  struct func<void>                                                            \
-  {                                                                            \
-    using is_transparent = void;                                               \
-    __thrust_exec_check_disable__                                              \
-    template <typename T>                                                      \
-    __host__ __device__                                                        \
-    constexpr auto operator()(T&& x) const                                     \
-      noexcept(noexcept(impl)) -> decltype(impl)                               \
-    {                                                                          \
-      return impl;                                                             \
-    }                                                                          \
-  }
-
-#define THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION(func, impl)                  \
-  template <>                                                                  \
-  struct func<void>                                                            \
-  {                                                                            \
-    using is_transparent = void;                                               \
-    __thrust_exec_check_disable__                                              \
-    template <typename T1, typename T2>                                        \
-    __host__ __device__                                                        \
-    constexpr auto operator()(T1&& t1, T2&& t2) const                          \
-      noexcept(noexcept(impl)) -> decltype(impl)                               \
-    {                                                                          \
-      return impl;                                                             \
-    }                                                                          \
-  }
-
-#define THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(func, op)                 \
-  THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION(                                   \
-    func, THRUST_FWD(t1) op THRUST_FWD(t2))
-
-
-/*! \p plus is a function object. Specifically, it is an Adaptable Binary Function.
- *  If \c f is an object of class <tt>plus<T></tt>, and \c x and \c y are objects
- *  of class \c T, then <tt>f(x,y)</tt> returns <tt>x+y</tt>.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
- *          and if \c x and \c y are objects of type \p T, then <tt>x+y</tt> must be defined and must have a return type that is convertible to \c T.
- *
- *  The following code snippet demonstrates how to use <tt>plus</tt> to sum two
- *  device_vectors of \c floats.
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/sequence.h>
- *  #include <thrust/fill.h>
- *  #include <thrust/transform.h>
- *  ...
- *  const int N = 1000;
- *  thrust::device_vector<float> V1(N);
- *  thrust::device_vector<float> V2(N);
- *  thrust::device_vector<float> V3(N);
- *
- *  thrust::sequence(V1.begin(), V1.end(), 1);
- *  thrust::fill(V2.begin(), V2.end(), 75);
- *
- *  thrust::transform(V1.begin(), V1.end(), V2.begin(), V3.begin(),
- *                    thrust::plus<float>());
- *  // V3 is now {76, 77, 78, ..., 1075}
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/plus.html
- *  \see binary_function
- */
-template<typename T = void>
-struct plus
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef T result_type;
-
-  /*! Function call operator. The return value is <tt>lhs + rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr T operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs + rhs;
-  }
-}; // end plus
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(plus, +);
-
-/*! \p minus is a function object. Specifically, it is an Adaptable Binary Function.
- *  If \c f is an object of class <tt>minus<T></tt>, and \c x and \c y are objects
- *  of class \c T, then <tt>f(x,y)</tt> returns <tt>x-y</tt>.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
- *          and if \c x and \c y are objects of type \p T, then <tt>x-y</tt> must be defined and must have a return type that is convertible to \c T.
- *
- *  The following code snippet demonstrates how to use <tt>minus</tt> to subtract
- *  a device_vector of \c floats from another.
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/sequence.h>
- *  #include <thrust/fill.h>
- *  #include <thrust/transform.h>
- *  ...
- *  const int N = 1000;
- *  thrust::device_vector<float> V1(N);
- *  thrust::device_vector<float> V2(N);
- *  thrust::device_vector<float> V3(N);
- *
- *  thrust::sequence(V1.begin(), V1.end(), 1);
- *  thrust::fill(V2.begin(), V2.end(), 75);
- *
- *  thrust::transform(V1.begin(), V1.end(), V2.begin(), V3.begin(),
- *                    thrust::minus<float>());
- *  // V3 is now {-74, -73, -72, ..., 925}
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/minus.html
- *  \see binary_function
- */
-template<typename T = void>
-struct minus
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef T result_type;
-
-  /*! Function call operator. The return value is <tt>lhs - rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr T operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs - rhs;
-  }
-}; // end minus
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(minus, -);
-
-/*! \p multiplies is a function object. Specifically, it is an Adaptable Binary Function.
- *  If \c f is an object of class <tt>multiplies<T></tt>, and \c x and \c y are objects
- *  of class \c T, then <tt>f(x,y)</tt> returns <tt>x*y</tt>.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
- *          and if \c x and \c y are objects of type \p T, then <tt>x*y</tt> must be defined and must have a return type that is convertible to \c T.
- *
- *  The following code snippet demonstrates how to use <tt>multiplies</tt> to multiply
- *  two device_vectors of \c floats.
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/sequence.h>
- *  #include <thrust/fill.h>
- *  #include <thrust/transform.h>
- *  ...
- *  const int N = 1000;
- *  thrust::device_vector<float> V1(N);
- *  thrust::device_vector<float> V2(N);
- *  thrust::device_vector<float> V3(N);
- *
- *  thrust::sequence(V1.begin(), V1.end(), 1);
- *  thrust::fill(V2.begin(), V2.end(), 75);
- *
- *  thrust::transform(V1.begin(), V1.end(), V2.begin(), V3.begin(),
- *                    thrust::multiplies<float>());
- *  // V3 is now {75, 150, 225, ..., 75000}
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/multiplies.html
- *  \see binary_function
- */
-template<typename T = void>
-struct multiplies
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef T result_type;
-
-  /*! Function call operator. The return value is <tt>lhs * rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr T operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs * rhs;
-  }
-}; // end multiplies
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(multiplies, *);
-
-/*! \p divides is a function object. Specifically, it is an Adaptable Binary Function.
- *  If \c f is an object of class <tt>divides<T></tt>, and \c x and \c y are objects
- *  of class \c T, then <tt>f(x,y)</tt> returns <tt>x/y</tt>.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
- *          and if \c x and \c y are objects of type \p T, then <tt>x/y</tt> must be defined and must have a return type that is convertible to \c T.
- *
- *  The following code snippet demonstrates how to use <tt>divides</tt> to divide
- *  one device_vectors of \c floats by another.
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/sequence.h>
- *  #include <thrust/fill.h>
- *  #include <thrust/transform.h>
- *  ...
- *  const int N = 1000;
- *  thrust::device_vector<float> V1(N);
- *  thrust::device_vector<float> V2(N);
- *  thrust::device_vector<float> V3(N);
- *
- *  thrust::sequence(V1.begin(), V1.end(), 1);
- *  thrust::fill(V2.begin(), V2.end(), 75);
- *
- *  thrust::transform(V1.begin(), V1.end(), V2.begin(), V3.begin(),
- *                    thrust::divides<float>());
- *  // V3 is now {1/75, 2/75, 3/75, ..., 1000/75}
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/divides.html
- *  \see binary_function
- */
-template<typename T = void>
-struct divides
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef T result_type;
-
-  /*! Function call operator. The return value is <tt>lhs / rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr T operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs / rhs;
-  }
-}; // end divides
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(divides, /);
-
-/*! \p modulus is a function object. Specifically, it is an Adaptable Binary Function.
- *  If \c f is an object of class <tt>modulus<T></tt>, and \c x and \c y are objects
- *  of class \c T, then <tt>f(x,y)</tt> returns <tt>x \% y</tt>.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
- *          and if \c x and \c y are objects of type \p T, then <tt>x \% y</tt> must be defined and must have a return type that is convertible to \c T.
- *
- *  The following code snippet demonstrates how to use <tt>modulus</tt> to take
- *  the modulus of one device_vectors of \c floats by another.
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/sequence.h>
- *  #include <thrust/fill.h>
- *  #include <thrust/transform.h>
- *  ...
- *  const int N = 1000;
- *  thrust::device_vector<float> V1(N);
- *  thrust::device_vector<float> V2(N);
- *  thrust::device_vector<float> V3(N);
- *
- *  thrust::sequence(V1.begin(), V1.end(), 1);
- *  thrust::fill(V2.begin(), V2.end(), 75);
- *
- *  thrust::transform(V1.begin(), V1.end(), V2.begin(), V3.begin(),
- *                    thrust::modulus<int>());
- *  // V3 is now {1%75, 2%75, 3%75, ..., 1000%75}
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/modulus.html
- *  \see binary_function
- */
-template<typename T = void>
-struct modulus
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef T result_type;
-
-  /*! Function call operator. The return value is <tt>lhs % rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr T operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs % rhs;
-  }
-}; // end modulus
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(modulus, %);
-
-/*! \p negate is a function object. Specifically, it is an Adaptable Unary Function.
- *  If \c f is an object of class <tt>negate<T></tt>, and \c x is an object
- *  of class \c T, then <tt>f(x)</tt> returns <tt>-x</tt>.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
- *          and if \c x is an object of type \p T, then <tt>-x</tt> must be defined and must have a return type that is convertible to \c T.
- *
- *  The following code snippet demonstrates how to use <tt>negate</tt> to negate
- *  the elements of a device_vector of \c floats.
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/sequence.h>
- *  #include <thrust/transform.h>
- *  ...
- *  const int N = 1000;
- *  thrust::device_vector<float> V1(N);
- *  thrust::device_vector<float> V2(N);
- *
- *  thrust::sequence(V1.begin(), V1.end(), 1);
- *
- *  thrust::transform(V1.begin(), V1.end(), V2.begin(),
- *                    thrust::negate<float>());
- *  // V2 is now {-1, -2, -3, ..., -1000}
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/negate.html
- *  \see unary_function
- */
-template<typename T = void>
-struct negate
-{
-  /*! \typedef argument_type
-   *  \brief The type of the function object's argument.
-   */
-  typedef T argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef T result_type;
-
-  /*! Function call operator. The return value is <tt>-x</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr T operator()(const T &x) const
-  {
-    return -x;
-  }
-}; // end negate
-
-THRUST_UNARY_FUNCTOR_VOID_SPECIALIZATION(negate, -THRUST_FWD(x));
-
-/*! \p square is a function object. Specifically, it is an Adaptable Unary Function.
- *  If \c f is an object of class <tt>square<T></tt>, and \c x is an object
- *  of class \c T, then <tt>f(x)</tt> returns <tt>x*x</tt>.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
- *          and if \c x is an object of type \p T, then <tt>x*x</tt> must be defined and must have a return type that is convertible to \c T.
- *
- *  The following code snippet demonstrates how to use <tt>square</tt> to square
- *  the elements of a device_vector of \c floats.
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/sequence.h>
- *  #include <thrust/transform.h>
- *  ...
- *  const int N = 1000;
- *  thrust::device_vector<float> V1(N);
- *  thrust::device_vector<float> V2(N);
- *
- *  thrust::sequence(V1.begin(), V1.end(), 1);
- *
- *  thrust::transform(V1.begin(), V1.end(), V2.begin(),
- *                    thrust::square<float>());
- *  // V2 is now {1, 4, 9, ..., 1000000}
- *  \endcode
- *
- *  \see unary_function
- */
-template<typename T = void>
-struct square
-{
-  /*! \typedef argument_type
-   *  \brief The type of the function object's argument.
-   */
-  typedef T argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef T result_type;
-
-  /*! Function call operator. The return value is <tt>x*x</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr T operator()(const T &x) const
-  {
-    return x*x;
-  }
-}; // end square
-
-THRUST_UNARY_FUNCTOR_VOID_SPECIALIZATION(square, x*x);
-
-/*! \}
- */
-
-/*! \addtogroup comparison_operations Comparison Operations
- *  \ingroup predefined_function_objects
- *  \{
- */
-
-/*! \p equal_to is a function object. Specifically, it is an Adaptable Binary
- *  Predicate, which means it is a function object that tests the truth or falsehood
- *  of some condition. If \c f is an object of class <tt>equal_to<T></tt> and \c x
- *  and \c y are objects of class \c T, then <tt>f(x,y)</tt> returns \c true if
- *  <tt>x == y</tt> and \c false otherwise.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- *
- *  \see http://www.sgi.com/tech/stl/equal_to.html
- *  \see binary_function
- */
-template<typename T = void>
-struct equal_to
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef bool result_type;
-
-  /*! Function call operator. The return value is <tt>lhs == rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr bool operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs == rhs;
-  }
-}; // end equal_to
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(equal_to, ==);
-
-/*! \p not_equal_to is a function object. Specifically, it is an Adaptable Binary
- *  Predicate, which means it is a function object that tests the truth or falsehood
- *  of some condition. If \c f is an object of class <tt>not_equal_to<T></tt> and \c x
- *  and \c y are objects of class \c T, then <tt>f(x,y)</tt> returns \c true if
- *  <tt>x != y</tt> and \c false otherwise.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- *
- *  \see http://www.sgi.com/tech/stl/not_equal_to.html
- *  \see binary_function
- */
-template<typename T = void>
-struct not_equal_to
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef bool result_type;
-
-  /*! Function call operator. The return value is <tt>lhs != rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr bool operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs != rhs;
-  }
-}; // end not_equal_to
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(not_equal_to, !=);
-
-/*! \p greater is a function object. Specifically, it is an Adaptable Binary
- *  Predicate, which means it is a function object that tests the truth or falsehood
- *  of some condition. If \c f is an object of class <tt>greater<T></tt> and \c x
- *  and \c y are objects of class \c T, then <tt>f(x,y)</tt> returns \c true if
- *  <tt>x > y</tt> and \c false otherwise.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *
- *  \see http://www.sgi.com/tech/stl/greater.html
- *  \see binary_function
- */
-template<typename T = void>
-struct greater
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef bool result_type;
-
-  /*! Function call operator. The return value is <tt>lhs > rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr bool operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs > rhs;
-  }
-}; // end greater
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(greater, >);
-
-/*! \p less is a function object. Specifically, it is an Adaptable Binary
- *  Predicate, which means it is a function object that tests the truth or falsehood
- *  of some condition. If \c f is an object of class <tt>less<T></tt> and \c x
- *  and \c y are objects of class \c T, then <tt>f(x,y)</tt> returns \c true if
- *  <tt>x < y</tt> and \c false otherwise.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *
- *  \see http://www.sgi.com/tech/stl/less.html
- *  \see binary_function
- */
-template<typename T = void>
-struct less
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef bool result_type;
-
-  /*! Function call operator. The return value is <tt>lhs < rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr bool operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs < rhs;
-  }
-}; // end less
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(less, <);
-
-/*! \p greater_equal is a function object. Specifically, it is an Adaptable Binary
- *  Predicate, which means it is a function object that tests the truth or falsehood
- *  of some condition. If \c f is an object of class <tt>greater_equal<T></tt> and \c x
- *  and \c y are objects of class \c T, then <tt>f(x,y)</tt> returns \c true if
- *  <tt>x >= y</tt> and \c false otherwise.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *
- *  \see http://www.sgi.com/tech/stl/greater_equal.html
- *  \see binary_function
- */
-template<typename T = void>
-struct greater_equal
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef bool result_type;
-
-  /*! Function call operator. The return value is <tt>lhs >= rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr bool operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs >= rhs;
-  }
-}; // end greater_equal
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(greater_equal, >=);
-
-/*! \p less_equal is a function object. Specifically, it is an Adaptable Binary
- *  Predicate, which means it is a function object that tests the truth or falsehood
- *  of some condition. If \c f is an object of class <tt>less_equal<T></tt> and \c x
- *  and \c y are objects of class \c T, then <tt>f(x,y)</tt> returns \c true if
- *  <tt>x <= y</tt> and \c false otherwise.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *
- *  \see http://www.sgi.com/tech/stl/less_equal.html
- *  \see binary_function
- */
-template<typename T = void>
-struct less_equal
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef bool result_type;
-
-  /*! Function call operator. The return value is <tt>lhs <= rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr bool operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs <= rhs;
-  }
-}; // end less_equal
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(less_equal, <=);
-
-/*! \}
- */
-
-
-/*! \addtogroup logical_operations Logical Operations
- *  \ingroup predefined_function_objects
- *  \{
- */
-
-/*! \p logical_and is a function object. Specifically, it is an Adaptable Binary Predicate,
- *  which means it is a function object that tests the truth or falsehood of some condition.
- *  If \c f is an object of class <tt>logical_and<T></tt> and \c x and \c y are objects of
- *  class \c T (where \c T is convertible to \c bool) then <tt>f(x,y)</tt> returns \c true
- *  if and only if both \c x and \c y are \c true.
- *
- *  \tparam T must be convertible to \c bool.
- *
- *  \see http://www.sgi.com/tech/stl/logical_and.html
- *  \see binary_function
- */
-template<typename T = void>
-struct logical_and
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef bool result_type;
-
-  /*! Function call operator. The return value is <tt>lhs && rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr bool operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs && rhs;
-  }
-}; // end logical_and
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(logical_and, &&);
-
-/*! \p logical_or is a function object. Specifically, it is an Adaptable Binary Predicate,
- *  which means it is a function object that tests the truth or falsehood of some condition.
- *  If \c f is an object of class <tt>logical_or<T></tt> and \c x and \c y are objects of
- *  class \c T (where \c T is convertible to \c bool) then <tt>f(x,y)</tt> returns \c true
- *  if and only if either \c x or \c y are \c true.
- *
- *  \tparam T must be convertible to \c bool.
- *
- *  \see http://www.sgi.com/tech/stl/logical_or.html
- *  \see binary_function
- */
-template<typename T = void>
-struct logical_or
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef bool result_type;
-
-  /*! Function call operator. The return value is <tt>lhs || rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr bool operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs || rhs;
-  }
-}; // end logical_or
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(logical_or, ||);
-
-/*! \p logical_not is a function object. Specifically, it is an Adaptable Predicate,
- *  which means it is a function object that tests the truth or falsehood of some condition.
- *  If \c f is an object of class <tt>logical_not<T></tt> and \c x is an object of
- *  class \c T (where \c T is convertible to \c bool) then <tt>f(x)</tt> returns \c true
- *  if and only if \c x is \c false.
- *
- *  \tparam T must be convertible to \c bool.
- *
- *  The following code snippet demonstrates how to use \p logical_not to transform
- *  a device_vector of \c bools into its logical complement.
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *  #include <thrust/transform.h>
- *  #include <thrust/functional.h>
- *  ...
- *  thrust::device_vector<bool> V;
- *  ...
- *  thrust::transform(V.begin(), V.end(), V.begin(), thrust::logical_not<bool>());
- *  // The elements of V are now the logical complement of what they were prior
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/logical_not.html
- *  \see unary_function
- */
-template<typename T = void>
-struct logical_not
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef bool result_type;
-
-  /*! Function call operator. The return value is <tt>!x</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr bool operator()(const T &x) const
-  {
-    return !x;
-  }
-}; // end logical_not
-
-THRUST_UNARY_FUNCTOR_VOID_SPECIALIZATION(logical_not, !THRUST_FWD(x));
-
-/*! \}
- */
-
-/*! \addtogroup bitwise_operations Bitwise Operations
- *  \ingroup predefined_function_objects
- *  \{
- */
-
-/*! \p bit_and is a function object. Specifically, it is an Adaptable Binary Function.
- *  If \c f is an object of class <tt>bit_and<T></tt>, and \c x and \c y are objects
- *  of class \c T, then <tt>f(x,y)</tt> returns <tt>x&y</tt>.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
- *          and if \c x and \c y are objects of type \p T, then <tt>x&y</tt> must be defined and must have a return type that is convertible to \c T.
- *
- *  The following code snippet demonstrates how to use <tt>bit_and</tt> to take
- *  the bitwise AND of one device_vector of \c ints by another.
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/sequence.h>
- *  #include <thrust/fill.h>
- *  #include <thrust/transform.h>
- *  ...
- *  const int N = 1000;
- *  thrust::device_vector<int> V1(N);
- *  thrust::device_vector<int> V2(N);
- *  thrust::device_vector<int> V3(N);
- *
- *  thrust::sequence(V1.begin(), V1.end(), 1);
- *  thrust::fill(V2.begin(), V2.end(), 13);
- *
- *  thrust::transform(V1.begin(), V1.end(), V2.begin(), V3.begin(),
- *                    thrust::bit_and<int>());
- *  // V3 is now {1&13, 2&13, 3&13, ..., 1000%13}
- *  \endcode
- *
- *  \see binary_function
- */
-template<typename T = void>
-struct bit_and
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef T result_type;
-
-  /*! Function call operator. The return value is <tt>lhs & rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr T operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs & rhs;
-  }
-}; // end bit_and
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(bit_and, &);
-
-/*! \p bit_or is a function object. Specifically, it is an Adaptable Binary Function.
- *  If \c f is an object of class <tt>bit_and<T></tt>, and \c x and \c y are objects
- *  of class \c T, then <tt>f(x,y)</tt> returns <tt>x|y</tt>.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
- *          and if \c x and \c y are objects of type \p T, then <tt>x|y</tt> must be defined and must have a return type that is convertible to \c T.
- *
- *  The following code snippet demonstrates how to use <tt>bit_or</tt> to take
- *  the bitwise OR of one device_vector of \c ints by another.
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/sequence.h>
- *  #include <thrust/fill.h>
- *  #include <thrust/transform.h>
- *  ...
- *  const int N = 1000;
- *  thrust::device_vector<int> V1(N);
- *  thrust::device_vector<int> V2(N);
- *  thrust::device_vector<int> V3(N);
- *
- *  thrust::sequence(V1.begin(), V1.end(), 1);
- *  thrust::fill(V2.begin(), V2.end(), 13);
- *
- *  thrust::transform(V1.begin(), V1.end(), V2.begin(), V3.begin(),
- *                    thrust::bit_or<int>());
- *  // V3 is now {1|13, 2|13, 3|13, ..., 1000|13}
- *  \endcode
- *
- *  \see binary_function
- */
-template<typename T = void>
-struct bit_or
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef T result_type;
-
-  /*! Function call operator. The return value is <tt>lhs | rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr T operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs | rhs;
-  }
-}; // end bit_or
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(bit_or, |);
-
-/*! \p bit_xor is a function object. Specifically, it is an Adaptable Binary Function.
- *  If \c f is an object of class <tt>bit_and<T></tt>, and \c x and \c y are objects
- *  of class \c T, then <tt>f(x,y)</tt> returns <tt>x^y</tt>.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
- *          and if \c x and \c y are objects of type \p T, then <tt>x^y</tt> must be defined and must have a return type that is convertible to \c T.
- *
- *  The following code snippet demonstrates how to use <tt>bit_xor</tt> to take
- *  the bitwise XOR of one device_vector of \c ints by another.
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/sequence.h>
- *  #include <thrust/fill.h>
- *  #include <thrust/transform.h>
- *  ...
- *  const int N = 1000;
- *  thrust::device_vector<int> V1(N);
- *  thrust::device_vector<int> V2(N);
- *  thrust::device_vector<int> V3(N);
- *
- *  thrust::sequence(V1.begin(), V1.end(), 1);
- *  thrust::fill(V2.begin(), V2.end(), 13);
- *
- *  thrust::transform(V1.begin(), V1.end(), V2.begin(), V3.begin(),
- *                    thrust::bit_xor<int>());
- *  // V3 is now {1^13, 2^13, 3^13, ..., 1000^13}
- *  \endcode
- *
- *  \see binary_function
- */
-template<typename T = void>
-struct bit_xor
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef T result_type;
-
-  /*! Function call operator. The return value is <tt>lhs ^ rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr T operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs ^ rhs;
-  }
-}; // end bit_xor
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP(bit_xor, ^);
-
-/*! \}
- */
-
-/*! \addtogroup generalized_identity_operations Generalized Identity Operations
- *  \ingroup predefined_function_objects
- *  \{
- */
-
-/*! \p identity is a Unary Function that represents the identity function: it takes
- *  a single argument \c x, and returns \c x.
- *
- *  \tparam T No requirements on \p T.
- *
- *  The following code snippet demonstrates that \p identity returns its
- *  argument.
- *
- *  \code
- *  #include <thrust/functional.h>
- *  #include <assert.h>
- *  ...
- *  int x = 137;
- *  thrust::identity<int> id;
- *  assert(x == id(x));
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/identity.html
- *  \see unary_function
- */
-template<typename T = void>
-struct identity
-{
-  /*! \typedef argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef T result_type;
-
-  /*! Function call operator. The return value is <tt>x</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr const T &operator()(const T &x) const
-  {
-    return x;
-  }
-}; // end identity
-
-THRUST_UNARY_FUNCTOR_VOID_SPECIALIZATION(identity, THRUST_FWD(x));
-
-/*! \p maximum is a function object that takes two arguments and returns the greater
- *  of the two. Specifically, it is an Adaptable Binary Function. If \c f is an
- *  object of class <tt>maximum<T></tt> and \c x and \c y are objects of class \c T
- *  <tt>f(x,y)</tt> returns \c x if <tt>x > y</tt> and \c y, otherwise.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *
- *  The following code snippet demonstrates that \p maximum returns its
- *  greater argument.
- *
- *  \code
- *  #include <thrust/functional.h>
- *  #include <assert.h>
- *  ...
- *  int x =  137;
- *  int y = -137;
- *  thrust::maximum<int> mx;
- *  assert(x == mx(x,y));
- *  \endcode
- *
- *  \see minimum
- *  \see min
- *  \see binary_function
- */
-template<typename T = void>
-struct maximum
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef T result_type;
-
-  /*! Function call operator. The return value is <tt>rhs < lhs ? lhs : rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr T operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs < rhs ? rhs : lhs;
-  }
-}; // end maximum
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION(maximum,
-                                          t1 < t2 ? THRUST_FWD(t2)
-                                                  : THRUST_FWD(t1));
-
-/*! \p minimum is a function object that takes two arguments and returns the lesser
- *  of the two. Specifically, it is an Adaptable Binary Function. If \c f is an
- *  object of class <tt>minimum<T></tt> and \c x and \c y are objects of class \c T
- *  <tt>f(x,y)</tt> returns \c x if <tt>x < y</tt> and \c y, otherwise.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *
- *  The following code snippet demonstrates that \p minimum returns its
- *  lesser argument.
- *
- *  \code
- *  #include <thrust/functional.h>
- *  #include <assert.h>
- *  ...
- *  int x =  137;
- *  int y = -137;
- *  thrust::minimum<int> mn;
- *  assert(y == mn(x,y));
- *  \endcode
- *
- *  \see maximum
- *  \see max
- *  \see binary_function
- */
-template<typename T = void>
-struct minimum
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef T result_type;
-
-  /*! Function call operator. The return value is <tt>lhs < rhs ? lhs : rhs</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  constexpr T operator()(const T &lhs, const T &rhs) const
-  {
-    return lhs < rhs ? lhs : rhs;
-  }
-}; // end minimum
-
-THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION(minimum,
-                                          t1 < t2 ? THRUST_FWD(t1)
-                                                  : THRUST_FWD(t2));
-
-/*! \p project1st is a function object that takes two arguments and returns
- *  its first argument; the second argument is unused. It is essentially a
- *  generalization of identity to the case of a Binary Function.
- *
- *  \code
- *  #include <thrust/functional.h>
- *  #include <assert.h>
- *  ...
- *  int x =  137;
- *  int y = -137;
- *  thrust::project1st<int> pj1;
- *  assert(x == pj1(x,y));
- *  \endcode
- *
- *  \see identity
- *  \see project2nd
- *  \see binary_function
- */
-template<typename T1 = void, typename T2 = void>
-struct project1st
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T1 first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T2 second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef T1 result_type;
-
-  /*! Function call operator. The return value is <tt>lhs</tt>.
-   */
-  __host__ __device__
-  constexpr const T1 &operator()(const T1 &lhs, const T2 & /*rhs*/) const
-  {
-    return lhs;
-  }
-}; // end project1st
-
-template <>
-struct project1st<void, void>
-{
-  using is_transparent = void;
-  __thrust_exec_check_disable__
-  template <typename T1, typename T2>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1, T2&&) const
-    noexcept(noexcept(THRUST_FWD(t1))) -> decltype(THRUST_FWD(t1))
-  {
-    return THRUST_FWD(t1);
-  }
-};
-
-/*! \p project2nd is a function object that takes two arguments and returns
- *  its second argument; the first argument is unused. It is essentially a
- *  generalization of identity to the case of a Binary Function.
- *
- *  \code
- *  #include <thrust/functional.h>
- *  #include <assert.h>
- *  ...
- *  int x =  137;
- *  int y = -137;
- *  thrust::project2nd<int> pj2;
- *  assert(y == pj2(x,y));
- *  \endcode
- *
- *  \see identity
- *  \see project1st
- *  \see binary_function
- */
-template<typename T1 = void, typename T2 = void>
-struct project2nd
-{
-  /*! \typedef first_argument_type
-   *  \brief The type of the function object's first argument.
-   */
-  typedef T1 first_argument_type;
-
-  /*! \typedef second_argument_type
-   *  \brief The type of the function object's second argument.
-   */
-  typedef T2 second_argument_type;
-
-  /*! \typedef result_type
-   *  \brief The type of the function object's result;
-   */
-  typedef T2 result_type;
-
-  /*! Function call operator. The return value is <tt>rhs</tt>.
-   */
-  __host__ __device__
-  constexpr const T2 &operator()(const T1 &/*lhs*/, const T2 &rhs) const
-  {
-    return rhs;
-  }
-}; // end project2nd
-
-template <>
-struct project2nd<void, void>
-{
-  using is_transparent = void;
-  __thrust_exec_check_disable__
-  template <typename T1, typename T2>
-  __host__ __device__
-  constexpr auto operator()(T1&&, T2&& t2) const
-  noexcept(noexcept(THRUST_FWD(t2))) -> decltype(THRUST_FWD(t2))
-  {
-    return THRUST_FWD(t2);
-  }
-};
-
-/*! \}
- */
-
-// odds and ends
-
-/*! \addtogroup function_object_adaptors
- *  \{
- */
-
-/*! \p unary_negate is a function object adaptor: it is an Adaptable Predicate
- *  that represents the logical negation of some other Adaptable Predicate.
- *  That is: if \c f is an object of class <tt>unary_negate<AdaptablePredicate></tt>,
- *  then there exists an object \c pred of class \c AdaptablePredicate such
- *  that <tt>f(x)</tt> always returns the same value as <tt>!pred(x)</tt>.
- *  There is rarely any reason to construct a <tt>unary_negate</tt> directly;
- *  it is almost always easier to use the helper function not1.
- *
- *  \see http://www.sgi.com/tech/stl/unary_negate.html
- *  \see not1
- */
-template<typename Predicate>
-struct unary_negate 
-    : public thrust::unary_function<typename Predicate::argument_type, bool>
-{
-  /*! Constructor takes a \p Predicate object to negate.
-   *  \param p The \p Predicate object to negate.
-   */
-  __host__ __device__
-  explicit unary_negate(Predicate p) : pred(p){}
-
-  /*! Function call operator. The return value is <tt>!pred(x)</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  bool operator()(const typename Predicate::argument_type& x) { return !pred(x); }
-
-  /*! \cond
-   */
-  Predicate pred;
-  /*! \endcond
-   */
-}; // end unary_negate
-
-/*! \p not1 is a helper function to simplify the creation of Adaptable Predicates:
- *  it takes an Adaptable Predicate \p pred as an argument and returns a new Adaptable
- *  Predicate that represents the negation of \p pred. That is: if \c pred is an object
- *  of a type which models Adaptable Predicate, then the the type of the result
- *  \c npred of <tt>not1(pred)</tt> is also a model of Adaptable Predicate and
- *  <tt>npred(x)</tt> always returns the same value as <tt>!pred(x)</tt>.
- *
- *  \param pred The Adaptable Predicate to negate.
- *  \return A new object, <tt>npred</tt> such that <tt>npred(x)</tt> always returns
- *          the same value as <tt>!pred(x)</tt>.
- *
- *  \tparam Predicate is a model of <a href="http://www.sgi.com/tech/stl/AdaptablePredicate.html">Adaptable Predicate</a>.
- *
- *  \see unary_negate
- *  \see not2
- */
-template<typename Predicate>
-  __host__ __device__
-  unary_negate<Predicate> not1(const Predicate &pred);
-
-/*! \p binary_negate is a function object adaptor: it is an Adaptable Binary 
- *  Predicate that represents the logical negation of some other Adaptable
- *  Binary Predicate. That is: if \c f is an object of class <tt>binary_negate<AdaptablePredicate></tt>,
- *  then there exists an object \c pred of class \c AdaptableBinaryPredicate
- *  such that <tt>f(x,y)</tt> always returns the same value as <tt>!pred(x,y)</tt>.
- *  There is rarely any reason to construct a <tt>binary_negate</tt> directly;
- *  it is almost always easier to use the helper function not2.
- *
- *  \see http://www.sgi.com/tech/stl/binary_negate.html
- */
-template<typename Predicate>
-struct binary_negate
-    : public thrust::binary_function<typename Predicate::first_argument_type,
-                                     typename Predicate::second_argument_type,
-                                     bool>
-{
-  /*! Constructor takes a \p Predicate object to negate.
-   *  \param p The \p Predicate object to negate.
-   */
-  __host__ __device__
-  explicit binary_negate(Predicate p) : pred(p){}
-
-  /*! Function call operator. The return value is <tt>!pred(x,y)</tt>.
-   */
-  __thrust_exec_check_disable__
-  __host__ __device__
-  bool operator()(const typename Predicate::first_argument_type& x, const typename Predicate::second_argument_type& y)
-  { 
-      return !pred(x,y); 
-  }
-
-  /*! \cond
-   */
-  Predicate pred;
-  /*! \endcond
-   */
-}; // end binary_negate
-
-/*! \p not2 is a helper function to simplify the creation of Adaptable Binary Predicates:
- *  it takes an Adaptable Binary Predicate \p pred as an argument and returns a new Adaptable
- *  Binary Predicate that represents the negation of \p pred. That is: if \c pred is an object
- *  of a type which models Adaptable Binary Predicate, then the the type of the result
- *  \c npred of <tt>not2(pred)</tt> is also a model of Adaptable Binary Predicate and
- *  <tt>npred(x,y)</tt> always returns the same value as <tt>!pred(x,y)</tt>.
- *
- *  \param pred The Adaptable Binary Predicate to negate.
- *  \return A new object, <tt>npred</tt> such that <tt>npred(x,y)</tt> always returns
- *          the same value as <tt>!pred(x,y)</tt>.
- *
- *  \tparam Binary Predicate is a model of <a href="http://www.sgi.com/tech/stl/AdaptableBinaryPredicate.html">Adaptable Binary Predicate</a>.
- *
- *  \see binary_negate
- *  \see not1
- */
-template<typename BinaryPredicate>
-  __host__ __device__
-  binary_negate<BinaryPredicate> not2(const BinaryPredicate &pred);
-
-/*! \}
- */
-
-
-/*! \addtogroup placeholder_objects Placeholder Objects
- *  \ingroup function_objects
- *  \{
- */
-
-
-/*! \namespace thrust::placeholders
- *  \brief Facilities for constructing simple functions inline.
- *
- *  Objects in the \p thrust::placeholders namespace may be used to create simple arithmetic functions inline
- *  in an algorithm invocation. Combining placeholders such as \p _1 and \p _2 with arithmetic operations such as \c +
- *  creates an unnamed function object which applies the operation to their arguments.
- *
- *  The type of placeholder objects is implementation-defined.
- *
- *  The following code snippet demonstrates how to use the placeholders \p _1 and \p _2 with \p thrust::transform
- *  to implement the SAXPY computation:
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *  #include <thrust/transform.h>
- *  #include <thrust/functional.h>
- *
- *  int main()
- *  {
- *    thrust::device_vector<float> x(4), y(4);
- *    x[0] = 1;
- *    x[1] = 2;
- *    x[2] = 3;
- *    x[3] = 4;
- *
- *    y[0] = 1;
- *    y[1] = 1;
- *    y[2] = 1;
- *    y[3] = 1;
- *
- *    float a = 2.0f;
- *
- *    using namespace thrust::placeholders;
- *
- *    thrust::transform(x.begin(), x.end(), y.begin(), y.begin(),
- *      a * _1 + _2
- *    );
- *
- *    // y is now {3, 5, 7, 9}
- *  }
- *  \endcode
- */
-namespace placeholders
-{
-
-
-/*! \p thrust::placeholders::_1 is the placeholder for the first function parameter.
- */
-THRUST_INLINE_CONSTANT thrust::detail::functional::placeholder<0>::type _1;
-
-
-/*! \p thrust::placeholders::_2 is the placeholder for the second function parameter.
- */
-THRUST_INLINE_CONSTANT thrust::detail::functional::placeholder<1>::type _2;
-
-
-/*! \p thrust::placeholders::_3 is the placeholder for the third function parameter.
- */
-THRUST_INLINE_CONSTANT thrust::detail::functional::placeholder<2>::type _3;
-
-
-/*! \p thrust::placeholders::_4 is the placeholder for the fourth function parameter.
- */
-THRUST_INLINE_CONSTANT thrust::detail::functional::placeholder<3>::type _4;
-
-
-/*! \p thrust::placeholders::_5 is the placeholder for the fifth function parameter.
- */
-THRUST_INLINE_CONSTANT thrust::detail::functional::placeholder<4>::type _5;
-
-
-/*! \p thrust::placeholders::_6 is the placeholder for the sixth function parameter.
- */
-THRUST_INLINE_CONSTANT thrust::detail::functional::placeholder<5>::type _6;
-
-
-/*! \p thrust::placeholders::_7 is the placeholder for the seventh function parameter.
- */
-THRUST_INLINE_CONSTANT thrust::detail::functional::placeholder<6>::type _7;
-
-
-/*! \p thrust::placeholders::_8 is the placeholder for the eighth function parameter.
- */
-THRUST_INLINE_CONSTANT thrust::detail::functional::placeholder<7>::type _8;
-
-
-/*! \p thrust::placeholders::_9 is the placeholder for the ninth function parameter.
- */
-THRUST_INLINE_CONSTANT thrust::detail::functional::placeholder<8>::type _9;
-
-
-/*! \p thrust::placeholders::_10 is the placeholder for the tenth function parameter.
- */
-THRUST_INLINE_CONSTANT thrust::detail::functional::placeholder<9>::type _10;
-
-
-} // end placeholders
-
-
-/*! \} // placeholder_objects
- */
-
-#undef THRUST_UNARY_FUNCTOR_VOID_SPECIALIZATION
-#undef THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION
-#undef THRUST_BINARY_FUNCTOR_VOID_SPECIALIZATION_OP
-
-} // end thrust
-
-#include <thrust/detail/functional.inl>
-#include <thrust/detail/functional/operators.h>
-

spaces/CVPR/WALT/mmdet/models/losses/ae_loss.py

@@ -1,102 +0,0 @@
-import mmcv
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-
-
-@mmcv.jit(derivate=True, coderize=True)
-def ae_loss_per_image(tl_preds, br_preds, match):
-    """Associative Embedding Loss in one image.
-
-    Associative Embedding Loss including two parts: pull loss and push loss.
-    Pull loss makes embedding vectors from same object closer to each other.
-    Push loss distinguish embedding vector from different objects, and makes
-        the gap between them is large enough.
-
-    During computing, usually there are 3 cases:
-        - no object in image: both pull loss and push loss will be 0.
-        - one object in image: push loss will be 0 and pull loss is computed
-            by the two corner of the only object.
-        - more than one objects in image: pull loss is computed by corner pairs
-            from each object, push loss is computed by each object with all
-            other objects. We use confusion matrix with 0 in diagonal to
-            compute the push loss.
-
-    Args:
-        tl_preds (tensor): Embedding feature map of left-top corner.
-        br_preds (tensor): Embedding feature map of bottim-right corner.
-        match (list): Downsampled coordinates pair of each ground truth box.
-    """
-
-    tl_list, br_list, me_list = [], [], []
-    if len(match) == 0:  # no object in image
-        pull_loss = tl_preds.sum() * 0.
-        push_loss = tl_preds.sum() * 0.
-    else:
-        for m in match:
-            [tl_y, tl_x], [br_y, br_x] = m
-            tl_e = tl_preds[:, tl_y, tl_x].view(-1, 1)
-            br_e = br_preds[:, br_y, br_x].view(-1, 1)
-            tl_list.append(tl_e)
-            br_list.append(br_e)
-            me_list.append((tl_e + br_e) / 2.0)
-
-        tl_list = torch.cat(tl_list)
-        br_list = torch.cat(br_list)
-        me_list = torch.cat(me_list)
-
-        assert tl_list.size() == br_list.size()
-
-        # N is object number in image, M is dimension of embedding vector
-        N, M = tl_list.size()
-
-        pull_loss = (tl_list - me_list).pow(2) + (br_list - me_list).pow(2)
-        pull_loss = pull_loss.sum() / N
-
-        margin = 1  # exp setting of CornerNet, details in section 3.3 of paper
-
-        # confusion matrix of push loss
-        conf_mat = me_list.expand((N, N, M)).permute(1, 0, 2) - me_list
-        conf_weight = 1 - torch.eye(N).type_as(me_list)
-        conf_mat = conf_weight * (margin - conf_mat.sum(-1).abs())
-
-        if N > 1:  # more than one object in current image
-            push_loss = F.relu(conf_mat).sum() / (N * (N - 1))
-        else:
-            push_loss = tl_preds.sum() * 0.
-
-    return pull_loss, push_loss
-
-
-@LOSSES.register_module()
-class AssociativeEmbeddingLoss(nn.Module):
-    """Associative Embedding Loss.
-
-    More details can be found in
-    `Associative Embedding <https://arxiv.org/abs/1611.05424>`_ and
-    `CornerNet <https://arxiv.org/abs/1808.01244>`_ .
-    Code is modified from `kp_utils.py <https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/kp_utils.py#L180>`_  # noqa: E501
-
-    Args:
-        pull_weight (float): Loss weight for corners from same object.
-        push_weight (float): Loss weight for corners from different object.
-    """
-
-    def __init__(self, pull_weight=0.25, push_weight=0.25):
-        super(AssociativeEmbeddingLoss, self).__init__()
-        self.pull_weight = pull_weight
-        self.push_weight = push_weight
-
-    def forward(self, pred, target, match):
-        """Forward function."""
-        batch = pred.size(0)
-        pull_all, push_all = 0.0, 0.0
-        for i in range(batch):
-            pull, push = ae_loss_per_image(pred[i], target[i], match[i])
-
-            pull_all += self.pull_weight * pull
-            push_all += self.push_weight * push
-
-        return pull_all, push_all

spaces/ChihChiu29/mychatbot/README.md

@@ -1,11 +0,0 @@
----
-title: Mychatbot
-emoji: 📉
-colorFrom: blue
-colorTo: blue
-sdk: docker
-pinned: false
-license: gpl-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/ChrisPreston/diff-svc_minato_aqua/modules/nsf_hifigan/utils.py

@@ -1,69 +0,0 @@
-import glob
-import os
-
-import matplotlib
-import torch
-from torch.nn.utils import weight_norm
-
-matplotlib.use("Agg")
-import matplotlib.pylab as plt
-
-
-def plot_spectrogram(spectrogram):
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-
-    fig.canvas.draw()
-    plt.close()
-
-    return fig
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def apply_weight_norm(m):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print("Loading '{}'".format(filepath))
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-def save_checkpoint(filepath, obj):
-    print("Saving checkpoint to {}".format(filepath))
-    torch.save(obj, filepath)
-    print("Complete.")
-
-
-def del_old_checkpoints(cp_dir, prefix, n_models=2):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern)  # get checkpoint paths
-    cp_list = sorted(cp_list)  # sort by iter
-    if len(cp_list) > n_models:  # if more than n_models models are found
-        for cp in cp_list[:-n_models]:  # delete the oldest models other than lastest n_models
-            open(cp, 'w').close()  # empty file contents
-            os.unlink(cp)  # delete file (move to trash when using Colab)
-
-
-def scan_checkpoint(cp_dir, prefix):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern)
-    if len(cp_list) == 0:
-        return None
-    return sorted(cp_list)[-1]

spaces/DGSpitzer/DGS-Diffusion-Space/share_btn.py

@@ -1,72 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-	async function getOutputImgFile(imgEl){
-        const res = await fetch(imgEl.src);
-        const blob = await res.blob();
-        const imgId = Date.now() % 200;
-        const isPng = imgEl.src.startsWith(`data:image/png`);
-        if(isPng){
-            const fileName = `sd-perception-${{imgId}}.png`;
-            return new File([blob], fileName, { type: 'image/png' });
-        }else{
-            const fileName = `sd-perception-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        }
-	}
-    const gradioEl = document.querySelector('body > gradio-app');
-    const outputImgEl = gradioEl.querySelector('#output-img img');
-    
-    //const outputImg_src = outputImgEl.src;
-    //const outputImg_name = outputImg_src.split('/').pop();
-    //let titleTxt = outputImg_name;
-    
-    let titleTxt = gradioEl.querySelector('#input-prompt > label > textarea').value;
-    if(titleTxt.length > 100){
-        titleTxt = titleTxt.slice(0, 100) + ' ...';
-    }
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    const outputFile= await getOutputImgFile(outputImgEl);
-    const urlOutputImg = await uploadFile(outputFile);
-    
-    const descriptionMd = `#### Output Image:
-<img src='${urlOutputImg}' style='min-height: 704px;'>
-This is my cool character! Made with Cyberpunk Anime Diffusion AI!
-`;
-    const params = new URLSearchParams({
-        title: titleTxt,
-        description: descriptionMd,
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/DGSpitzer/DGS-Diffusion-Space/discussions/new?${paramsStr}`, '_blank');
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-    console.log(gradioEl.querySelector('#input-prompt'));
-}"""

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/_core/_synchronization.py

@@ -1,596 +0,0 @@
-from __future__ import annotations
-
-from collections import deque
-from dataclasses import dataclass
-from types import TracebackType
-from warnings import warn
-
-from ..lowlevel import cancel_shielded_checkpoint, checkpoint, checkpoint_if_cancelled
-from ._compat import DeprecatedAwaitable
-from ._eventloop import get_asynclib
-from ._exceptions import BusyResourceError, WouldBlock
-from ._tasks import CancelScope
-from ._testing import TaskInfo, get_current_task
-
-
-@dataclass(frozen=True)
-class EventStatistics:
-    """
-    :ivar int tasks_waiting: number of tasks waiting on :meth:`~.Event.wait`
-    """
-
-    tasks_waiting: int
-
-
-@dataclass(frozen=True)
-class CapacityLimiterStatistics:
-    """
-    :ivar int borrowed_tokens: number of tokens currently borrowed by tasks
-    :ivar float total_tokens: total number of available tokens
-    :ivar tuple borrowers: tasks or other objects currently holding tokens borrowed from this
-        limiter
-    :ivar int tasks_waiting: number of tasks waiting on :meth:`~.CapacityLimiter.acquire` or
-        :meth:`~.CapacityLimiter.acquire_on_behalf_of`
-    """
-
-    borrowed_tokens: int
-    total_tokens: float
-    borrowers: tuple[object, ...]
-    tasks_waiting: int
-
-
-@dataclass(frozen=True)
-class LockStatistics:
-    """
-    :ivar bool locked: flag indicating if this lock is locked or not
-    :ivar ~anyio.TaskInfo owner: task currently holding the lock (or ``None`` if the lock is not
-        held by any task)
-    :ivar int tasks_waiting: number of tasks waiting on :meth:`~.Lock.acquire`
-    """
-
-    locked: bool
-    owner: TaskInfo | None
-    tasks_waiting: int
-
-
-@dataclass(frozen=True)
-class ConditionStatistics:
-    """
-    :ivar int tasks_waiting: number of tasks blocked on :meth:`~.Condition.wait`
-    :ivar ~anyio.LockStatistics lock_statistics: statistics of the underlying :class:`~.Lock`
-    """
-
-    tasks_waiting: int
-    lock_statistics: LockStatistics
-
-
-@dataclass(frozen=True)
-class SemaphoreStatistics:
-    """
-    :ivar int tasks_waiting: number of tasks waiting on :meth:`~.Semaphore.acquire`
-
-    """
-
-    tasks_waiting: int
-
-
-class Event:
-    def __new__(cls) -> Event:
-        return get_asynclib().Event()
-
-    def set(self) -> DeprecatedAwaitable:
-        """Set the flag, notifying all listeners."""
-        raise NotImplementedError
-
-    def is_set(self) -> bool:
-        """Return ``True`` if the flag is set, ``False`` if not."""
-        raise NotImplementedError
-
-    async def wait(self) -> None:
-        """
-        Wait until the flag has been set.
-
-        If the flag has already been set when this method is called, it returns immediately.
-
-        """
-        raise NotImplementedError
-
-    def statistics(self) -> EventStatistics:
-        """Return statistics about the current state of this event."""
-        raise NotImplementedError
-
-
-class Lock:
-    _owner_task: TaskInfo | None = None
-
-    def __init__(self) -> None:
-        self._waiters: deque[tuple[TaskInfo, Event]] = deque()
-
-    async def __aenter__(self) -> None:
-        await self.acquire()
-
-    async def __aexit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> None:
-        self.release()
-
-    async def acquire(self) -> None:
-        """Acquire the lock."""
-        await checkpoint_if_cancelled()
-        try:
-            self.acquire_nowait()
-        except WouldBlock:
-            task = get_current_task()
-            event = Event()
-            token = task, event
-            self._waiters.append(token)
-            try:
-                await event.wait()
-            except BaseException:
-                if not event.is_set():
-                    self._waiters.remove(token)
-                elif self._owner_task == task:
-                    self.release()
-
-                raise
-
-            assert self._owner_task == task
-        else:
-            try:
-                await cancel_shielded_checkpoint()
-            except BaseException:
-                self.release()
-                raise
-
-    def acquire_nowait(self) -> None:
-        """
-        Acquire the lock, without blocking.
-
-        :raises ~anyio.WouldBlock: if the operation would block
-
-        """
-        task = get_current_task()
-        if self._owner_task == task:
-            raise RuntimeError("Attempted to acquire an already held Lock")
-
-        if self._owner_task is not None:
-            raise WouldBlock
-
-        self._owner_task = task
-
-    def release(self) -> DeprecatedAwaitable:
-        """Release the lock."""
-        if self._owner_task != get_current_task():
-            raise RuntimeError("The current task is not holding this lock")
-
-        if self._waiters:
-            self._owner_task, event = self._waiters.popleft()
-            event.set()
-        else:
-            del self._owner_task
-
-        return DeprecatedAwaitable(self.release)
-
-    def locked(self) -> bool:
-        """Return True if the lock is currently held."""
-        return self._owner_task is not None
-
-    def statistics(self) -> LockStatistics:
-        """
-        Return statistics about the current state of this lock.
-
-        .. versionadded:: 3.0
-        """
-        return LockStatistics(self.locked(), self._owner_task, len(self._waiters))
-
-
-class Condition:
-    _owner_task: TaskInfo | None = None
-
-    def __init__(self, lock: Lock | None = None):
-        self._lock = lock or Lock()
-        self._waiters: deque[Event] = deque()
-
-    async def __aenter__(self) -> None:
-        await self.acquire()
-
-    async def __aexit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> None:
-        self.release()
-
-    def _check_acquired(self) -> None:
-        if self._owner_task != get_current_task():
-            raise RuntimeError("The current task is not holding the underlying lock")
-
-    async def acquire(self) -> None:
-        """Acquire the underlying lock."""
-        await self._lock.acquire()
-        self._owner_task = get_current_task()
-
-    def acquire_nowait(self) -> None:
-        """
-        Acquire the underlying lock, without blocking.
-
-        :raises ~anyio.WouldBlock: if the operation would block
-
-        """
-        self._lock.acquire_nowait()
-        self._owner_task = get_current_task()
-
-    def release(self) -> DeprecatedAwaitable:
-        """Release the underlying lock."""
-        self._lock.release()
-        return DeprecatedAwaitable(self.release)
-
-    def locked(self) -> bool:
-        """Return True if the lock is set."""
-        return self._lock.locked()
-
-    def notify(self, n: int = 1) -> None:
-        """Notify exactly n listeners."""
-        self._check_acquired()
-        for _ in range(n):
-            try:
-                event = self._waiters.popleft()
-            except IndexError:
-                break
-
-            event.set()
-
-    def notify_all(self) -> None:
-        """Notify all the listeners."""
-        self._check_acquired()
-        for event in self._waiters:
-            event.set()
-
-        self._waiters.clear()
-
-    async def wait(self) -> None:
-        """Wait for a notification."""
-        await checkpoint()
-        event = Event()
-        self._waiters.append(event)
-        self.release()
-        try:
-            await event.wait()
-        except BaseException:
-            if not event.is_set():
-                self._waiters.remove(event)
-
-            raise
-        finally:
-            with CancelScope(shield=True):
-                await self.acquire()
-
-    def statistics(self) -> ConditionStatistics:
-        """
-        Return statistics about the current state of this condition.
-
-        .. versionadded:: 3.0
-        """
-        return ConditionStatistics(len(self._waiters), self._lock.statistics())
-
-
-class Semaphore:
-    def __init__(self, initial_value: int, *, max_value: int | None = None):
-        if not isinstance(initial_value, int):
-            raise TypeError("initial_value must be an integer")
-        if initial_value < 0:
-            raise ValueError("initial_value must be >= 0")
-        if max_value is not None:
-            if not isinstance(max_value, int):
-                raise TypeError("max_value must be an integer or None")
-            if max_value < initial_value:
-                raise ValueError(
-                    "max_value must be equal to or higher than initial_value"
-                )
-
-        self._value = initial_value
-        self._max_value = max_value
-        self._waiters: deque[Event] = deque()
-
-    async def __aenter__(self) -> Semaphore:
-        await self.acquire()
-        return self
-
-    async def __aexit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> None:
-        self.release()
-
-    async def acquire(self) -> None:
-        """Decrement the semaphore value, blocking if necessary."""
-        await checkpoint_if_cancelled()
-        try:
-            self.acquire_nowait()
-        except WouldBlock:
-            event = Event()
-            self._waiters.append(event)
-            try:
-                await event.wait()
-            except BaseException:
-                if not event.is_set():
-                    self._waiters.remove(event)
-                else:
-                    self.release()
-
-                raise
-        else:
-            try:
-                await cancel_shielded_checkpoint()
-            except BaseException:
-                self.release()
-                raise
-
-    def acquire_nowait(self) -> None:
-        """
-        Acquire the underlying lock, without blocking.
-
-        :raises ~anyio.WouldBlock: if the operation would block
-
-        """
-        if self._value == 0:
-            raise WouldBlock
-
-        self._value -= 1
-
-    def release(self) -> DeprecatedAwaitable:
-        """Increment the semaphore value."""
-        if self._max_value is not None and self._value == self._max_value:
-            raise ValueError("semaphore released too many times")
-
-        if self._waiters:
-            self._waiters.popleft().set()
-        else:
-            self._value += 1
-
-        return DeprecatedAwaitable(self.release)
-
-    @property
-    def value(self) -> int:
-        """The current value of the semaphore."""
-        return self._value
-
-    @property
-    def max_value(self) -> int | None:
-        """The maximum value of the semaphore."""
-        return self._max_value
-
-    def statistics(self) -> SemaphoreStatistics:
-        """
-        Return statistics about the current state of this semaphore.
-
-        .. versionadded:: 3.0
-        """
-        return SemaphoreStatistics(len(self._waiters))
-
-
-class CapacityLimiter:
-    def __new__(cls, total_tokens: float) -> CapacityLimiter:
-        return get_asynclib().CapacityLimiter(total_tokens)
-
-    async def __aenter__(self) -> None:
-        raise NotImplementedError
-
-    async def __aexit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> bool | None:
-        raise NotImplementedError
-
-    @property
-    def total_tokens(self) -> float:
-        """
-        The total number of tokens available for borrowing.
-
-        This is a read-write property. If the total number of tokens is increased, the
-        proportionate number of tasks waiting on this limiter will be granted their tokens.
-
-        .. versionchanged:: 3.0
-            The property is now writable.
-
-        """
-        raise NotImplementedError
-
-    @total_tokens.setter
-    def total_tokens(self, value: float) -> None:
-        raise NotImplementedError
-
-    async def set_total_tokens(self, value: float) -> None:
-        warn(
-            "CapacityLimiter.set_total_tokens has been deprecated. Set the value of the"
-            '"total_tokens" attribute directly.',
-            DeprecationWarning,
-        )
-        self.total_tokens = value
-
-    @property
-    def borrowed_tokens(self) -> int:
-        """The number of tokens that have currently been borrowed."""
-        raise NotImplementedError
-
-    @property
-    def available_tokens(self) -> float:
-        """The number of tokens currently available to be borrowed"""
-        raise NotImplementedError
-
-    def acquire_nowait(self) -> DeprecatedAwaitable:
-        """
-        Acquire a token for the current task without waiting for one to become available.
-
-        :raises ~anyio.WouldBlock: if there are no tokens available for borrowing
-
-        """
-        raise NotImplementedError
-
-    def acquire_on_behalf_of_nowait(self, borrower: object) -> DeprecatedAwaitable:
-        """
-        Acquire a token without waiting for one to become available.
-
-        :param borrower: the entity borrowing a token
-        :raises ~anyio.WouldBlock: if there are no tokens available for borrowing
-
-        """
-        raise NotImplementedError
-
-    async def acquire(self) -> None:
-        """
-        Acquire a token for the current task, waiting if necessary for one to become available.
-
-        """
-        raise NotImplementedError
-
-    async def acquire_on_behalf_of(self, borrower: object) -> None:
-        """
-        Acquire a token, waiting if necessary for one to become available.
-
-        :param borrower: the entity borrowing a token
-
-        """
-        raise NotImplementedError
-
-    def release(self) -> None:
-        """
-        Release the token held by the current task.
-        :raises RuntimeError: if the current task has not borrowed a token from this limiter.
-
-        """
-        raise NotImplementedError
-
-    def release_on_behalf_of(self, borrower: object) -> None:
-        """
-        Release the token held by the given borrower.
-
-        :raises RuntimeError: if the borrower has not borrowed a token from this limiter.
-
-        """
-        raise NotImplementedError
-
-    def statistics(self) -> CapacityLimiterStatistics:
-        """
-        Return statistics about the current state of this limiter.
-
-        .. versionadded:: 3.0
-
-        """
-        raise NotImplementedError
-
-
-def create_lock() -> Lock:
-    """
-    Create an asynchronous lock.
-
-    :return: a lock object
-
-    .. deprecated:: 3.0
-       Use :class:`~Lock` directly.
-
-    """
-    warn("create_lock() is deprecated -- use Lock() directly", DeprecationWarning)
-    return Lock()
-
-
-def create_condition(lock: Lock | None = None) -> Condition:
-    """
-    Create an asynchronous condition.
-
-    :param lock: the lock to base the condition object on
-    :return: a condition object
-
-    .. deprecated:: 3.0
-       Use :class:`~Condition` directly.
-
-    """
-    warn(
-        "create_condition() is deprecated -- use Condition() directly",
-        DeprecationWarning,
-    )
-    return Condition(lock=lock)
-
-
-def create_event() -> Event:
-    """
-    Create an asynchronous event object.
-
-    :return: an event object
-
-    .. deprecated:: 3.0
-       Use :class:`~Event` directly.
-
-    """
-    warn("create_event() is deprecated -- use Event() directly", DeprecationWarning)
-    return get_asynclib().Event()
-
-
-def create_semaphore(value: int, *, max_value: int | None = None) -> Semaphore:
-    """
-    Create an asynchronous semaphore.
-
-    :param value: the semaphore's initial value
-    :param max_value: if set, makes this a "bounded" semaphore that raises :exc:`ValueError` if the
-        semaphore's value would exceed this number
-    :return: a semaphore object
-
-    .. deprecated:: 3.0
-       Use :class:`~Semaphore` directly.
-
-    """
-    warn(
-        "create_semaphore() is deprecated -- use Semaphore() directly",
-        DeprecationWarning,
-    )
-    return Semaphore(value, max_value=max_value)
-
-
-def create_capacity_limiter(total_tokens: float) -> CapacityLimiter:
-    """
-    Create a capacity limiter.
-
-    :param total_tokens: the total number of tokens available for borrowing (can be an integer or
-        :data:`math.inf`)
-    :return: a capacity limiter object
-
-    .. deprecated:: 3.0
-       Use :class:`~CapacityLimiter` directly.
-
-    """
-    warn(
-        "create_capacity_limiter() is deprecated -- use CapacityLimiter() directly",
-        DeprecationWarning,
-    )
-    return get_asynclib().CapacityLimiter(total_tokens)
-
-
-class ResourceGuard:
-    __slots__ = "action", "_guarded"
-
-    def __init__(self, action: str):
-        self.action = action
-        self._guarded = False
-
-    def __enter__(self) -> None:
-        if self._guarded:
-            raise BusyResourceError(self.action)
-
-        self._guarded = True
-
-    def __exit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> bool | None:
-        self._guarded = False
-        return None

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/tz/_factories.py

@@ -1,80 +0,0 @@
-from datetime import timedelta
-import weakref
-from collections import OrderedDict
-
-from six.moves import _thread
-
-
-class _TzSingleton(type):
-    def __init__(cls, *args, **kwargs):
-        cls.__instance = None
-        super(_TzSingleton, cls).__init__(*args, **kwargs)
-
-    def __call__(cls):
-        if cls.__instance is None:
-            cls.__instance = super(_TzSingleton, cls).__call__()
-        return cls.__instance
-
-
-class _TzFactory(type):
-    def instance(cls, *args, **kwargs):
-        """Alternate constructor that returns a fresh instance"""
-        return type.__call__(cls, *args, **kwargs)
-
-
-class _TzOffsetFactory(_TzFactory):
-    def __init__(cls, *args, **kwargs):
-        cls.__instances = weakref.WeakValueDictionary()
-        cls.__strong_cache = OrderedDict()
-        cls.__strong_cache_size = 8
-
-        cls._cache_lock = _thread.allocate_lock()
-
-    def __call__(cls, name, offset):
-        if isinstance(offset, timedelta):
-            key = (name, offset.total_seconds())
-        else:
-            key = (name, offset)
-
-        instance = cls.__instances.get(key, None)
-        if instance is None:
-            instance = cls.__instances.setdefault(key,
-                                                  cls.instance(name, offset))
-
-        # This lock may not be necessary in Python 3. See GH issue #901
-        with cls._cache_lock:
-            cls.__strong_cache[key] = cls.__strong_cache.pop(key, instance)
-
-            # Remove an item if the strong cache is overpopulated
-            if len(cls.__strong_cache) > cls.__strong_cache_size:
-                cls.__strong_cache.popitem(last=False)
-
-        return instance
-
-
-class _TzStrFactory(_TzFactory):
-    def __init__(cls, *args, **kwargs):
-        cls.__instances = weakref.WeakValueDictionary()
-        cls.__strong_cache = OrderedDict()
-        cls.__strong_cache_size = 8
-
-        cls.__cache_lock = _thread.allocate_lock()
-
-    def __call__(cls, s, posix_offset=False):
-        key = (s, posix_offset)
-        instance = cls.__instances.get(key, None)
-
-        if instance is None:
-            instance = cls.__instances.setdefault(key,
-                cls.instance(s, posix_offset))
-
-        # This lock may not be necessary in Python 3. See GH issue #901
-        with cls.__cache_lock:
-            cls.__strong_cache[key] = cls.__strong_cache.pop(key, instance)
-
-            # Remove an item if the strong cache is overpopulated
-            if len(cls.__strong_cache) > cls.__strong_cache_size:
-                cls.__strong_cache.popitem(last=False)
-
-        return instance
-

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/feaLib/error.py

@@ -1,22 +0,0 @@
-class FeatureLibError(Exception):
-    def __init__(self, message, location):
-        Exception.__init__(self, message)
-        self.location = location
-
-    def __str__(self):
-        message = Exception.__str__(self)
-        if self.location:
-            return f"{self.location}: {message}"
-        else:
-            return message
-
-
-class IncludedFeaNotFound(FeatureLibError):
-    def __str__(self):
-        assert self.location is not None
-
-        message = (
-            "The following feature file should be included but cannot be found: "
-            f"{Exception.__str__(self)}"
-        )
-        return f"{self.location}: {message}"

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-ecdf43f2.js

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