import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__lowerCamelCase			: List[str]							   =		logging.get_logger(__name__)

__lowerCamelCase			: Optional[int]							   =		OrderedDict(
    [
        ('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
        ('''beit''', '''BeitFeatureExtractor'''),
        ('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
        ('''clap''', '''ClapFeatureExtractor'''),
        ('''clip''', '''CLIPFeatureExtractor'''),
        ('''clipseg''', '''ViTFeatureExtractor'''),
        ('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
        ('''convnext''', '''ConvNextFeatureExtractor'''),
        ('''cvt''', '''ConvNextFeatureExtractor'''),
        ('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
        ('''data2vec-vision''', '''BeitFeatureExtractor'''),
        ('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
        ('''deit''', '''DeiTFeatureExtractor'''),
        ('''detr''', '''DetrFeatureExtractor'''),
        ('''dinat''', '''ViTFeatureExtractor'''),
        ('''donut-swin''', '''DonutFeatureExtractor'''),
        ('''dpt''', '''DPTFeatureExtractor'''),
        ('''encodec''', '''EncodecFeatureExtractor'''),
        ('''flava''', '''FlavaFeatureExtractor'''),
        ('''glpn''', '''GLPNFeatureExtractor'''),
        ('''groupvit''', '''CLIPFeatureExtractor'''),
        ('''hubert''', '''Wav2Vec2FeatureExtractor'''),
        ('''imagegpt''', '''ImageGPTFeatureExtractor'''),
        ('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
        ('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
        ('''levit''', '''LevitFeatureExtractor'''),
        ('''maskformer''', '''MaskFormerFeatureExtractor'''),
        ('''mctct''', '''MCTCTFeatureExtractor'''),
        ('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
        ('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
        ('''mobilevit''', '''MobileViTFeatureExtractor'''),
        ('''nat''', '''ViTFeatureExtractor'''),
        ('''owlvit''', '''OwlViTFeatureExtractor'''),
        ('''perceiver''', '''PerceiverFeatureExtractor'''),
        ('''poolformer''', '''PoolFormerFeatureExtractor'''),
        ('''regnet''', '''ConvNextFeatureExtractor'''),
        ('''resnet''', '''ConvNextFeatureExtractor'''),
        ('''segformer''', '''SegformerFeatureExtractor'''),
        ('''sew''', '''Wav2Vec2FeatureExtractor'''),
        ('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
        ('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
        ('''speecht5''', '''SpeechT5FeatureExtractor'''),
        ('''swiftformer''', '''ViTFeatureExtractor'''),
        ('''swin''', '''ViTFeatureExtractor'''),
        ('''swinv2''', '''ViTFeatureExtractor'''),
        ('''table-transformer''', '''DetrFeatureExtractor'''),
        ('''timesformer''', '''VideoMAEFeatureExtractor'''),
        ('''tvlt''', '''TvltFeatureExtractor'''),
        ('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
        ('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
        ('''van''', '''ConvNextFeatureExtractor'''),
        ('''videomae''', '''VideoMAEFeatureExtractor'''),
        ('''vilt''', '''ViltFeatureExtractor'''),
        ('''vit''', '''ViTFeatureExtractor'''),
        ('''vit_mae''', '''ViTFeatureExtractor'''),
        ('''vit_msn''', '''ViTFeatureExtractor'''),
        ('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
        ('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
        ('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
        ('''whisper''', '''WhisperFeatureExtractor'''),
        ('''xclip''', '''CLIPFeatureExtractor'''),
        ('''yolos''', '''YolosFeatureExtractor'''),
    ]
)

__lowerCamelCase			: List[str]							   =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str	)							->					List[Any]:







  """simple docstring"""





  for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
    if class_name in extractors:
      SCREAMING_SNAKE_CASE__										=      model_type_to_module_name(__UpperCamelCase	)

      SCREAMING_SNAKE_CASE__										=      importlib.import_module(f""".{module_name}""" ,     """transformers.models"""	)
      try:
        return getattr(__UpperCamelCase ,     __UpperCamelCase	)
      except AttributeError:
        continue

  for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
    if getattr(__UpperCamelCase ,     """__name__""" ,     __UpperCamelCase	) == class_name:
      return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
  SCREAMING_SNAKE_CASE__										=      importlib.import_module("""transformers"""	)
  if hasattr(__UpperCamelCase ,     __UpperCamelCase	):
    return getattr(__UpperCamelCase ,     __UpperCamelCase	)

  return None



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, os.PathLike] ,     __UpperCamelCase    :       Optional[Union[str, os.PathLike]] = None ,     __UpperCamelCase    :       bool = False ,     __UpperCamelCase    :       bool = False ,     __UpperCamelCase    :       Optional[Dict[str, str]] = None ,     __UpperCamelCase    :       Optional[Union[bool, str]] = None ,     __UpperCamelCase    :       Optional[str] = None ,     __UpperCamelCase    :       bool = False ,     **__UpperCamelCase    :       List[str] ,     )							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      get_file_from_repo(
      __UpperCamelCase ,     __UpperCamelCase ,     cache_dir=__UpperCamelCase ,     force_download=__UpperCamelCase ,     resume_download=__UpperCamelCase ,     proxies=__UpperCamelCase ,     use_auth_token=__UpperCamelCase ,     revision=__UpperCamelCase ,     local_files_only=__UpperCamelCase ,     )
  if resolved_config_file is None:
    logger.info(
        """Could not locate the feature extractor configuration file, will try to use the model config instead."""	)
    return {}

  with open(__UpperCamelCase ,     encoding="""utf-8"""	) as reader:
    return json.load(__UpperCamelCase	)







class __snake_case				:




   def __init__(    self			:				List[str]      ):



     """simple docstring"""






     raise EnvironmentError(
         """AutoFeatureExtractor is designed to be instantiated """
         """using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."""      )




   @classmethod
   @replace_list_option_in_docstrings(_lowercase      )
   def 	__a						(    cls			:				List[Any]       ,	_lowercase			:				Union[str, Any]       ,	**_lowercase			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      kwargs.pop("""config"""       ,	_lowercase      )
     SCREAMING_SNAKE_CASE__										=      kwargs.pop("""trust_remote_code"""       ,	_lowercase      )
     SCREAMING_SNAKE_CASE__										=      True

     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      FeatureExtractionMixin.get_feature_extractor_dict(_lowercase       ,	**_lowercase      )
     SCREAMING_SNAKE_CASE__										=      config_dict.get("""feature_extractor_type"""       ,	_lowercase      )
     SCREAMING_SNAKE_CASE__										=      None
     if "AutoFeatureExtractor" in config_dict.get("""auto_map"""       ,	{}      ):
       SCREAMING_SNAKE_CASE__										=      config_dict["""auto_map"""]["""AutoFeatureExtractor"""]

     # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
     if feature_extractor_class is None and feature_extractor_auto_map is None:
       if not isinstance(_lowercase       ,	_lowercase      ):
         SCREAMING_SNAKE_CASE__										=      AutoConfig.from_pretrained(_lowercase       ,	**_lowercase      )
       # It could be in `config.feature_extractor_type``
       SCREAMING_SNAKE_CASE__										=      getattr(_lowercase       ,	"""feature_extractor_type"""       ,	_lowercase      )
       if hasattr(_lowercase       ,	"""auto_map"""      ) and "AutoFeatureExtractor" in config.auto_map:
         SCREAMING_SNAKE_CASE__										=      config.auto_map["""AutoFeatureExtractor"""]

     if feature_extractor_class is not None:
       SCREAMING_SNAKE_CASE__										=      feature_extractor_class_from_name(_lowercase      )

     SCREAMING_SNAKE_CASE__										=      feature_extractor_auto_map is not None
     SCREAMING_SNAKE_CASE__										=      feature_extractor_class is not None or type(_lowercase      ) in FEATURE_EXTRACTOR_MAPPING
     SCREAMING_SNAKE_CASE__										=      resolve_trust_remote_code(
         _lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase      )

     if has_remote_code and trust_remote_code:
       SCREAMING_SNAKE_CASE__										=      get_class_from_dynamic_module(
           _lowercase       ,	_lowercase       ,	**_lowercase      )
       SCREAMING_SNAKE_CASE__										=      kwargs.pop("""code_revision"""       ,	_lowercase      )
       if os.path.isdir(_lowercase      ):
         feature_extractor_class.register_for_auto_class()
       return feature_extractor_class.from_dict(_lowercase       ,	**_lowercase      )
     elif feature_extractor_class is not None:
       return feature_extractor_class.from_dict(_lowercase       ,	**_lowercase      )
     # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
     elif type(_lowercase      ) in FEATURE_EXTRACTOR_MAPPING:
       SCREAMING_SNAKE_CASE__										=      FEATURE_EXTRACTOR_MAPPING[type(_lowercase      )]
       return feature_extractor_class.from_dict(_lowercase       ,	**_lowercase      )

     raise ValueError(
         f"""Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a """
         f"""`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following """
         f"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()      )}"""      )




   @staticmethod
   def 	__a						(    _lowercase			:				Dict       ,	_lowercase			:				str      ):



     """simple docstring"""






     FEATURE_EXTRACTOR_MAPPING.register(_lowercase       ,	_lowercase      )

from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
   __lowerCamelCase			: str							   =		input('''Enter image url: ''').strip()
   print(F"""Downloading image from {url} ...""")
   __lowerCamelCase			: Any							   =		BeautifulSoup(requests.get(url).content, '''html.parser''')
   # The image URL is in the content field of the first meta tag with property og:image
   __lowerCamelCase			: List[Any]							   =		soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
   __lowerCamelCase			: Tuple							   =		requests.get(image_url).content
   __lowerCamelCase			: Union[str, Any]							   =		F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
   with open(file_name, '''wb''') as fp:
      fp.write(image_data)
   print(F"""Done. Image saved to disk as {file_name}.""")
