import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
     import torch

     lowercase_  							=			True
except ImportError:
     lowercase_  							=			False


try:
     from torch.hub import _get_torch_home

     lowercase_  							=			_get_torch_home()
except ImportError:
     lowercase_  							=			os.path.expanduser(
         os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
     )

lowercase_  							=			os.path.join(torch_cache_home, "transformers")

lowercase_  							=			"https://cdn.huggingface.co"
lowercase_  							=			"https://s3.amazonaws.com/models.huggingface.co/bert"
lowercase_  							=			"/".join(str(Path(__file__).resolve()).split("/")[:-1])
lowercase_  							=			os.path.join(PATH, "config.yaml")
lowercase_  							=			os.path.join(PATH, "attributes.txt")
lowercase_  							=			os.path.join(PATH, "objects.txt")
lowercase_  							=			os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
lowercase_  							=			os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
lowercase_  							=			os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
lowercase_  							=			"pytorch_model.bin"
lowercase_  							=			"config.yaml"







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Dict=OBJECTS		,							__SCREAMING_SNAKE_CASE       :					str=ATTRIBUTES   ):







    '''simple docstring'''
    __snake_case       :				Optional[Any] 			=    []
    with open(__SCREAMING_SNAKE_CASE   ) as f:
        for object in f.readlines():
            vg_classes.append(object.split(""","""   )[0].lower().strip()   )

    __snake_case       :				str 			=    []
    with open(__SCREAMING_SNAKE_CASE   ) as f:
        for object in f.readlines():
            vg_attrs.append(object.split(""","""   )[0].lower().strip()   )
    return vg_classes, vg_attrs







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Union[str, Any]   ):







    '''simple docstring'''
    __snake_case       :				List[str] 			=    OrderedDict()
    with open(__SCREAMING_SNAKE_CASE		,							"""rb"""   ) as f:
        __snake_case       :				Optional[int] 			=    pkl.load(__SCREAMING_SNAKE_CASE   )["""model"""]
    for k in copy.deepcopy(list(ckp.keys()   )   ):
        __snake_case       :				Any 			=    ckp.pop(__SCREAMING_SNAKE_CASE   )
        if isinstance(__SCREAMING_SNAKE_CASE		,							np.ndarray   ):
            __snake_case       :				Union[str, Any] 			=    torch.tensor(__SCREAMING_SNAKE_CASE   )
        else:
            assert isinstance(__SCREAMING_SNAKE_CASE		,							torch.tensor   ), type(__SCREAMING_SNAKE_CASE   )
        __snake_case       :				Optional[int] 			=    v
    return r







class      SCREAMING_SNAKE_CASE__					:
       A							:	List[str]          =	{}

       def __init__(							self					:     List[Any]			,			_lowerCAmelCase					:     dict			,			_lowerCAmelCase					:     str = "root"			,			_lowerCAmelCase					:     Any=0			):
           __snake_case       :				Optional[Any] 			=    name
           __snake_case       :				List[Any] 			=    level
           __snake_case       :				int 			=    {}
           for k, v in dictionary.items():
               if v is None:
                   raise ValueError()
               __snake_case       :				List[str] 			=    copy.deepcopy(_lowerCAmelCase			)
               __snake_case       :				Union[str, Any] 			=    copy.deepcopy(_lowerCAmelCase			)
               if isinstance(_lowerCAmelCase			,			_lowerCAmelCase			):
                   __snake_case       :				Tuple 			=    Config(_lowerCAmelCase			,			name=_lowerCAmelCase			,			level=level + 1			)
               __snake_case       :				Any 			=    v
               setattr(self			,			_lowerCAmelCase			,			_lowerCAmelCase			)

           __snake_case       :				Union[str, Any] 			=    d

       def __repr__(							self					:     Tuple			):
           return str(list((self._pointer.keys())			)			)

       def __setattr__(							self					:     List[Any]			,			_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     List[str]			):
           __snake_case       :				Any 			=    val
           __snake_case       :				Dict 			=    val
           __snake_case       :				Any 			=    key.split("""."""			)
           __snake_case       :				str 			=    len(_lowerCAmelCase			) - 1
           __snake_case       :				List[Any] 			=    self._pointer
           if len(_lowerCAmelCase			) > 1:
               for i, l in enumerate(_lowerCAmelCase			):
                   if hasattr(self			,			_lowerCAmelCase			) and isinstance(getattr(self			,			_lowerCAmelCase			)			,			_lowerCAmelCase			):
                       setattr(getattr(self			,			_lowerCAmelCase			)			,			""".""".join(levels[i:]			)			,			_lowerCAmelCase			)
                   if l == last_level:
                       __snake_case       :				Dict 			=    val
                   else:
                       __snake_case       :				int 			=    pointer[l]

       def 			snake_case__    (							self					:     str			):
           return self._pointer

       def 			snake_case__    (							self					:     Optional[Any]			,			_lowerCAmelCase					:     Union[str, Any]			,			_lowerCAmelCase					:     Union[str, Any]			):
           with open(f'''{file_name}'''			,			"""w"""			) as stream:
               dump(_lowerCAmelCase			,			_lowerCAmelCase			)

       def 			snake_case__    (							self					:     str			,			_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     Any			):
           with open(f'''{file_name}'''			,			"""w"""			) as stream:
               json.dump(_lowerCAmelCase			,			_lowerCAmelCase			)

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     str			):
           with open(_lowerCAmelCase			) as stream:
               __snake_case       :				Optional[int] 			=    load(_lowerCAmelCase			,			Loader=_lowerCAmelCase			)
           return data

       def __str__(							self					:     Dict			):
           __snake_case       :				List[Any] 			=    """    """
           if self._name != "root":
               __snake_case       :				Optional[int] 			=    f'''{t * (self._level-1)}{self._name}:\n'''
           else:
               __snake_case       :				List[Any] 			=    """"""
           __snake_case       :				Any 			=    self._level
           for i, (k, v) in enumerate(self._pointer.items()			):
               if isinstance(_lowerCAmelCase			,			_lowerCAmelCase			):
                   r += f'''{t * (self._level)}{v}\n'''
                   self._level += 1
               else:
                   r += f'''{t * (self._level)}{k}: {v} ({type(_lowerCAmelCase			).__name__})\n'''
               __snake_case       :				List[str] 			=    level
           return r[:-1]

       @classmethod
       def 			snake_case__    (							cls					:     Dict			,			_lowerCAmelCase					:     str			,			**_lowerCAmelCase					:     List[str]			):
           __snake_case			, __snake_case       :				int 			=    cls.get_config_dict(_lowerCAmelCase			,			**_lowerCAmelCase			)
           return cls(_lowerCAmelCase			)





       @classmethod
       def 			snake_case__    (							cls					:     Any			,			_lowerCAmelCase					:     str			,			**_lowerCAmelCase					:     Optional[int]			):
           __snake_case       :				Optional[Any] 			=    kwargs.pop("""cache_dir"""			,			_lowerCAmelCase			)
           __snake_case       :				str 			=    kwargs.pop("""force_download"""			,			_lowerCAmelCase			)
           __snake_case       :				Optional[int] 			=    kwargs.pop("""resume_download"""			,			_lowerCAmelCase			)
           __snake_case       :				List[str] 			=    kwargs.pop("""proxies"""			,			_lowerCAmelCase			)
           __snake_case       :				Tuple 			=    kwargs.pop("""local_files_only"""			,			_lowerCAmelCase			)

           if os.path.isdir(_lowerCAmelCase			):
               __snake_case       :				Union[str, Any] 			=    os.path.join(_lowerCAmelCase			,			_lowerCAmelCase			)
           elif os.path.isfile(_lowerCAmelCase			) or is_remote_url(_lowerCAmelCase			):
               __snake_case       :				str 			=    pretrained_model_name_or_path
           else:
               __snake_case       :				Optional[int] 			=    hf_bucket_url(_lowerCAmelCase			,			filename=_lowerCAmelCase			,			use_cdn=_lowerCAmelCase			)

           try:
               # Load from URL or cache if already cached
               __snake_case       :				List[Any] 			=    cached_path(
                   _lowerCAmelCase			,			cache_dir=_lowerCAmelCase			,			force_download=_lowerCAmelCase			,			proxies=_lowerCAmelCase			,			resume_download=_lowerCAmelCase			,			local_files_only=_lowerCAmelCase			,			)
               # Load config dict
               if resolved_config_file is None:
                   raise EnvironmentError

               __snake_case       :				List[str] 			=    Config.load_yaml(_lowerCAmelCase			)

           except EnvironmentError:
               __snake_case       :				Dict 			=    """Can't load config for"""
               raise EnvironmentError(_lowerCAmelCase			)

           if resolved_config_file == config_file:
               print("""loading configuration file from path"""			)
           else:
               print("""loading configuration file cache"""			)

           return Config.load_yaml(_lowerCAmelCase			), kwargs







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					str   ):







    '''simple docstring'''
    __snake_case       :				Union[str, Any] 			=    torch.load("""dump.pt"""		,							map_location=in_tensor.device   )
    __snake_case       :				Optional[Any] 			=    in_tensor.numpy()
    __snake_case       :				Tuple 			=    out_tensor.numpy()[0]
    print(na.shape		,							na[0, 0, :5]   )
    print(na.shape		,							na[0, 0, :5]   )
    assert np.allclose(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							rtol=0.01		,							atol=0.1   ), (
        F'''{sum([1 for x in np.isclose(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							rtol=0.01		,							atol=0.1   ).flatten() if x is False]   )/len(na.flatten()   )*1_0_0:.4f} %'''
        " element-wise mismatch"
    )
    raise Exception("""tensors are all good"""   )

    # Hugging face functions below







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					List[Any]   ):







    '''simple docstring'''
    __snake_case       :				Optional[int] 			=    urlparse(__SCREAMING_SNAKE_CASE   )
    return parsed.scheme in ("http", "https")







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					str		,							__SCREAMING_SNAKE_CASE       :					str		,							__SCREAMING_SNAKE_CASE       :					str=True   ):







    '''simple docstring'''
    __snake_case       :				List[str] 			=    CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
    __snake_case       :				int 			=    """/""" not in model_id
    if legacy_format:
        return F'''{endpoint}/{model_id}-{filename}'''
    else:
        return F'''{endpoint}/{model_id}/{filename}'''







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					List[str]		,							__SCREAMING_SNAKE_CASE       :					Optional[Any]		,							__SCREAMING_SNAKE_CASE       :					int=None		,							__SCREAMING_SNAKE_CASE       :					int=0		,							__SCREAMING_SNAKE_CASE       :					Tuple=None		,							):







    '''simple docstring'''
    __snake_case       :				Dict 			=    """python/{}""".format(sys.version.split()[0]   )
    if _torch_available:
        ua += "; torch/{}".format(torch.__version__   )
    if isinstance(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   ):
        ua += "; " + "; ".join("""{}/{}""".format(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   ) for k, v in user_agent.items()   )
    elif isinstance(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   ):
        ua += "; " + user_agent
    __snake_case       :				List[Any] 			=    {"""user-agent""": ua}
    if resume_size > 0:
        __snake_case       :				List[str] 			=    """bytes=%d-""" % (resume_size,)
    __snake_case       :				int 			=    requests.get(__SCREAMING_SNAKE_CASE		,							stream=__SCREAMING_SNAKE_CASE		,							proxies=__SCREAMING_SNAKE_CASE		,							headers=__SCREAMING_SNAKE_CASE   )
    if response.status_code == 4_1_6:  # Range not satisfiable
        return
    __snake_case       :				int 			=    response.headers.get("""Content-Length"""   )
    __snake_case       :				int 			=    resume_size + int(__SCREAMING_SNAKE_CASE   ) if content_length is not None else None
    __snake_case       :				Dict 			=    tqdm(
        unit="""B"""		,							unit_scale=__SCREAMING_SNAKE_CASE		,							total=__SCREAMING_SNAKE_CASE		,							initial=__SCREAMING_SNAKE_CASE		,							desc="""Downloading"""		,							)
    for chunk in response.iter_content(chunk_size=1_0_2_4   ):
        if chunk:  # filter out keep-alive new chunks
            progress.update(len(__SCREAMING_SNAKE_CASE   )   )
            temp_file.write(__SCREAMING_SNAKE_CASE   )
    progress.close()







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					List[Any]		,							__SCREAMING_SNAKE_CASE       :					Optional[Any]=None		,							__SCREAMING_SNAKE_CASE       :					Union[str, Any]=False		,							__SCREAMING_SNAKE_CASE       :					Optional[int]=None		,							__SCREAMING_SNAKE_CASE       :					Optional[int]=1_0		,							__SCREAMING_SNAKE_CASE       :					int=False		,							__SCREAMING_SNAKE_CASE       :					str=None		,							__SCREAMING_SNAKE_CASE       :					Dict=False		,							):







    '''simple docstring'''
    if cache_dir is None:
        __snake_case       :				Any 			=    TRANSFORMERS_CACHE
    if isinstance(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   ):
        __snake_case       :				Optional[int] 			=    str(__SCREAMING_SNAKE_CASE   )

    os.makedirs(__SCREAMING_SNAKE_CASE		,							exist_ok=__SCREAMING_SNAKE_CASE   )

    __snake_case       :				List[Any] 			=    None
    if not local_files_only:
        try:
            __snake_case       :				Union[str, Any] 			=    requests.head(__SCREAMING_SNAKE_CASE		,							allow_redirects=__SCREAMING_SNAKE_CASE		,							proxies=__SCREAMING_SNAKE_CASE		,							timeout=__SCREAMING_SNAKE_CASE   )
            if response.status_code == 2_0_0:
                __snake_case       :				Optional[Any] 			=    response.headers.get("""ETag"""   )
        except (EnvironmentError, requests.exceptions.Timeout):
            # etag is already None
            pass

    __snake_case       :				Optional[int] 			=    url_to_filename(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )

    # get cache path to put the file
    __snake_case       :				Optional[Any] 			=    os.path.join(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )

    # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
    # try to get the last downloaded one
    if etag is None:
        if os.path.exists(__SCREAMING_SNAKE_CASE   ):
            return cache_path
        else:
            __snake_case       :				Tuple 			=    [
                file
                for file in fnmatch.filter(os.listdir(__SCREAMING_SNAKE_CASE   )		,							filename + """.*"""   )
                if not file.endswith(""".json"""   ) and not file.endswith(""".lock"""   )
            ]
            if len(__SCREAMING_SNAKE_CASE   ) > 0:
                return os.path.join(__SCREAMING_SNAKE_CASE		,							matching_files[-1]   )
            else:
                # If files cannot be found and local_files_only=True,
                # the models might've been found if local_files_only=False
                # Notify the user about that
                if local_files_only:
                    raise ValueError(
                        """Cannot find the requested files in the cached path and outgoing traffic has been"""
                        """ disabled. To enable model look-ups and downloads online, set 'local_files_only'"""
                        """ to False."""   )
                return None

    # From now on, etag is not None.
    if os.path.exists(__SCREAMING_SNAKE_CASE   ) and not force_download:
        return cache_path

    # Prevent parallel downloads of the same file with a lock.
    __snake_case       :				Dict 			=    cache_path + """.lock"""
    with FileLock(__SCREAMING_SNAKE_CASE   ):
        # If the download just completed while the lock was activated.
        if os.path.exists(__SCREAMING_SNAKE_CASE   ) and not force_download:
            # Even if returning early like here, the lock will be released.
            return cache_path

        if resume_download:
            __snake_case       :				Any 			=    cache_path + """.incomplete"""

            @contextmanager
            def _resumable_file_manager():
                with open(__SCREAMING_SNAKE_CASE		,							"""a+b"""   ) as f:
                    yield f

            __snake_case       :				str 			=    _resumable_file_manager
            if os.path.exists(__SCREAMING_SNAKE_CASE   ):
                __snake_case       :				str 			=    os.stat(__SCREAMING_SNAKE_CASE   ).st_size
            else:
                __snake_case       :				List[Any] 			=    0
        else:
            __snake_case       :				List[str] 			=    partial(tempfile.NamedTemporaryFile		,							dir=__SCREAMING_SNAKE_CASE		,							delete=__SCREAMING_SNAKE_CASE   )
            __snake_case       :				Optional[Any] 			=    0

        # Download to temporary file, then copy to cache dir once finished.
        # Otherwise you get corrupt cache entries if the download gets interrupted.
        with temp_file_manager() as temp_file:
            print(
                """%s not found in cache or force_download set to True, downloading to %s"""		,							__SCREAMING_SNAKE_CASE		,							temp_file.name		,							)

            http_get(
                __SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							proxies=__SCREAMING_SNAKE_CASE		,							resume_size=__SCREAMING_SNAKE_CASE		,							user_agent=__SCREAMING_SNAKE_CASE		,							)

        os.replace(temp_file.name		,							__SCREAMING_SNAKE_CASE   )

        __snake_case       :				List[str] 			=    {"""url""": url, """etag""": etag}
        __snake_case       :				Tuple 			=    cache_path + """.json"""
        with open(__SCREAMING_SNAKE_CASE		,							"""w"""   ) as meta_file:
            json.dump(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )

    return cache_path







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					str		,							__SCREAMING_SNAKE_CASE       :					List[Any]=None   ):







    '''simple docstring'''
    __snake_case       :				List[str] 			=    url.encode("""utf-8"""   )
    __snake_case       :				Union[str, Any] 			=    shaaaa(__SCREAMING_SNAKE_CASE   )
    __snake_case       :				Optional[int] 			=    url_hash.hexdigest()

    if etag:
        __snake_case       :				Optional[Any] 			=    etag.encode("""utf-8"""   )
        __snake_case       :				Union[str, Any] 			=    shaaaa(__SCREAMING_SNAKE_CASE   )
        filename += "." + etag_hash.hexdigest()

    if url.endswith(""".h5"""   ):
        filename += ".h5"

    return filename







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					str		,							__SCREAMING_SNAKE_CASE       :					Optional[int]=None		,							__SCREAMING_SNAKE_CASE       :					int=False		,							__SCREAMING_SNAKE_CASE       :					Any=None		,							__SCREAMING_SNAKE_CASE       :					str=False		,							__SCREAMING_SNAKE_CASE       :					Optional[Any]=None		,							__SCREAMING_SNAKE_CASE       :					Tuple=False		,							__SCREAMING_SNAKE_CASE       :					int=False		,							__SCREAMING_SNAKE_CASE       :					Tuple=False		,							):







    '''simple docstring'''
    if cache_dir is None:
        __snake_case       :				List[Any] 			=    TRANSFORMERS_CACHE
    if isinstance(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   ):
        __snake_case       :				List[Any] 			=    str(__SCREAMING_SNAKE_CASE   )
    if isinstance(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   ):
        __snake_case       :				List[Any] 			=    str(__SCREAMING_SNAKE_CASE   )

    if is_remote_url(__SCREAMING_SNAKE_CASE   ):
        # URL, so get it from the cache (downloading if necessary)
        __snake_case       :				List[Any] 			=    get_from_cache(
            __SCREAMING_SNAKE_CASE		,							cache_dir=__SCREAMING_SNAKE_CASE		,							force_download=__SCREAMING_SNAKE_CASE		,							proxies=__SCREAMING_SNAKE_CASE		,							resume_download=__SCREAMING_SNAKE_CASE		,							user_agent=__SCREAMING_SNAKE_CASE		,							local_files_only=__SCREAMING_SNAKE_CASE		,							)
    elif os.path.exists(__SCREAMING_SNAKE_CASE   ):
        # File, and it exists.
        __snake_case       :				Optional[Any] 			=    url_or_filename
    elif urlparse(__SCREAMING_SNAKE_CASE   ).scheme == "":
        # File, but it doesn't exist.
        raise EnvironmentError("""file {} not found""".format(__SCREAMING_SNAKE_CASE   )   )
    else:
        # Something unknown
        raise ValueError("""unable to parse {} as a URL or as a local path""".format(__SCREAMING_SNAKE_CASE   )   )

    if extract_compressed_file:
        if not is_zipfile(__SCREAMING_SNAKE_CASE   ) and not tarfile.is_tarfile(__SCREAMING_SNAKE_CASE   ):
            return output_path

        # Path where we extract compressed archives
        # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
        __snake_case			, __snake_case       :				Optional[Any] 			=    os.path.split(__SCREAMING_SNAKE_CASE   )
        __snake_case       :				int 			=    output_file.replace("""."""		,							"""-"""   ) + """-extracted"""
        __snake_case       :				List[str] 			=    os.path.join(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )

        if os.path.isdir(__SCREAMING_SNAKE_CASE   ) and os.listdir(__SCREAMING_SNAKE_CASE   ) and not force_extract:
            return output_path_extracted

        # Prevent parallel extractions
        __snake_case       :				str 			=    output_path + """.lock"""
        with FileLock(__SCREAMING_SNAKE_CASE   ):
            shutil.rmtree(__SCREAMING_SNAKE_CASE		,							ignore_errors=__SCREAMING_SNAKE_CASE   )
            os.makedirs(__SCREAMING_SNAKE_CASE   )
            if is_zipfile(__SCREAMING_SNAKE_CASE   ):
                with ZipFile(__SCREAMING_SNAKE_CASE		,							"""r"""   ) as zip_file:
                    zip_file.extractall(__SCREAMING_SNAKE_CASE   )
                    zip_file.close()
            elif tarfile.is_tarfile(__SCREAMING_SNAKE_CASE   ):
                __snake_case       :				Optional[Any] 			=    tarfile.open(__SCREAMING_SNAKE_CASE   )
                tar_file.extractall(__SCREAMING_SNAKE_CASE   )
                tar_file.close()
            else:
                raise EnvironmentError("""Archive format of {} could not be identified""".format(__SCREAMING_SNAKE_CASE   )   )

        return output_path_extracted

    return output_path







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Tuple		,							__SCREAMING_SNAKE_CASE       :					Optional[int]=","   ):







    '''simple docstring'''
    assert isinstance(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
    if os.path.isfile(__SCREAMING_SNAKE_CASE   ):
        with open(__SCREAMING_SNAKE_CASE   ) as f:
            __snake_case       :				List[str] 			=    eval(f.read()   )
    else:
        __snake_case       :				Optional[Any] 			=    requests.get(__SCREAMING_SNAKE_CASE   )
        try:
            __snake_case       :				List[Any] 			=    requests.json()
        except Exception:
            __snake_case       :				Optional[int] 			=    req.content.decode()
            assert data is not None, "could not connect"
            try:
                __snake_case       :				Tuple 			=    eval(__SCREAMING_SNAKE_CASE   )
            except Exception:
                __snake_case       :				Optional[int] 			=    data.split("""\n"""   )
        req.close()
    return data







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Optional[Any]   ):







    '''simple docstring'''
    __snake_case       :				Optional[int] 			=    requests.get(__SCREAMING_SNAKE_CASE   )
    __snake_case       :				List[Any] 			=    np.array(Image.open(BytesIO(response.content   )   )   )
    return img







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					List[str]   ):







    '''simple docstring'''
    __snake_case       :				List[Any] 			=    url.split("""/"""   )[-1]
    if fn not in os.listdir(os.getcwd()   ):
        wget.download(__SCREAMING_SNAKE_CASE   )
    with open(__SCREAMING_SNAKE_CASE		,							"""rb"""   ) as stream:
        __snake_case       :				List[Any] 			=    pkl.load(__SCREAMING_SNAKE_CASE   )
    __snake_case       :				Tuple 			=    weights.pop("""model"""   )
    __snake_case       :				Optional[int] 			=    {}
    for k, v in model.items():
        __snake_case       :				List[Any] 			=    torch.from_numpy(__SCREAMING_SNAKE_CASE   )
        if "running_var" in k:
            __snake_case       :				str 			=    torch.tensor([0]   )
            __snake_case       :				int 			=    k.replace("""running_var"""		,							"""num_batches_tracked"""   )
            __snake_case       :				Optional[int] 			=    zero
    return new







def 					__lowerCAmelCase				(		):







    '''simple docstring'''
    print(F'''{os.path.abspath(os.path.join(__SCREAMING_SNAKE_CASE		,							os.pardir   )   )}/demo.ipynb'''   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Any		,							__SCREAMING_SNAKE_CASE       :					Optional[int]="RGB"   ):







    '''simple docstring'''
    assert isinstance(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
    if os.path.isfile(__SCREAMING_SNAKE_CASE   ):
        __snake_case       :				int 			=    cva.imread(__SCREAMING_SNAKE_CASE   )
    else:
        __snake_case       :				List[str] 			=    get_image_from_url(__SCREAMING_SNAKE_CASE   )
        assert img is not None, F'''could not connect to: {im}'''
    __snake_case       :				List[str] 			=    cva.cvtColor(__SCREAMING_SNAKE_CASE		,							cva.COLOR_BGR2RGB   )
    if input_format == "RGB":
        __snake_case       :				Dict 			=    img[:, :, ::-1]
    return img







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					str		,							__SCREAMING_SNAKE_CASE       :					List[Any]=1   ):







    '''simple docstring'''
    return (images[i : i + batch] for i in range(0		,							len(__SCREAMING_SNAKE_CASE   )		,							__SCREAMING_SNAKE_CASE   ))




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	Optional[Any]          =	["image_processor", "tokenizer"]
       A							:	Dict          =	"VideoMAEImageProcessor"
       A							:	Tuple          =	("CLIPTokenizer", "CLIPTokenizerFast")

       def __init__(							self					:     str			,			_lowerCAmelCase					:     Tuple=None			,			_lowerCAmelCase					:     Any=None			,			**_lowerCAmelCase					:     Union[str, Any]			):
           __snake_case       :				int 			=    None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                   """ instead."""			,			_lowerCAmelCase			,			)
               __snake_case       :				Optional[Any] 			=    kwargs.pop("""feature_extractor"""			)

           __snake_case       :				int 			=    image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError("""You need to specify an `image_processor`."""			)
           if tokenizer is None:
               raise ValueError("""You need to specify a `tokenizer`."""			)

           super().__init__(_lowerCAmelCase			,			_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    self.image_processor

       def __call__(							self					:     str			,			_lowerCAmelCase					:     int=None			,			_lowerCAmelCase					:     Dict=None			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     List[str]			):

           if text is None and videos is None:
               raise ValueError("""You have to specify either text or videos. Both cannot be none."""			)

           if text is not None:
               __snake_case       :				Optional[Any] 			=    self.tokenizer(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if videos is not None:
               __snake_case       :				Optional[int] 			=    self.image_processor(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if text is not None and videos is not None:
               __snake_case       :				Union[str, Any] 			=    image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)			,			tensor_type=_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     str			,			**_lowerCAmelCase					:     List[Any]			):
           return self.tokenizer.batch_decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     List[Any]			,			**_lowerCAmelCase					:     Dict			):
           return self.tokenizer.decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       @property
       def 			snake_case__    (							self					:     str			):
           return ["input_ids", "attention_mask", "position_ids", "pixel_values"]

       @property
       def 			snake_case__    (							self					:     str			):
           warnings.warn(
               """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor_class





       @property
       def 			snake_case__    (							self					:     Union[str, Any]			):
           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor



