


'''simple docstring'''






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,
)


__A   :    str											= logging.get_logger(__name__)

__A   :    Optional[Any]											= 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"),
    ]
)

__A   :    Optional[Any]											= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)







def 							UpperCamelCase_     (       A__   :  str   ):
  '''simple docstring'''





  for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
    if class_name in extractors:
      lowerCAmelCase_     : List[str]     =  model_type_to_module_name(A__   )

      lowerCAmelCase_     : Optional[Any]     =  importlib.import_module(f'.{module_name}'				,       """transformers.models"""   )
      try:
        return getattr(A__				,       A__   )
      except AttributeError:
        continue

  for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
    if getattr(A__				,       """__name__"""				,       A__   ) == 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.
  lowerCAmelCase_     : Optional[int]     =  importlib.import_module("""transformers"""   )
  if hasattr(A__				,       A__   ):
    return getattr(A__				,       A__   )

  return None







def 							UpperCamelCase_     (       A__   :  Union[str, os.PathLike]				,       A__   :  Optional[Union[str, os.PathLike]] = None				,       A__   :  bool = False				,       A__   :  bool = False				,       A__   :  Optional[Dict[str, str]] = None				,       A__   :  Optional[Union[bool, str]] = None				,       A__   :  Optional[str] = None				,       A__   :  bool = False				,       **A__   :  Any				,       ):
  '''simple docstring'''





  lowerCAmelCase_     : List[str]     =  get_file_from_repo(
      A__				,       A__				,       cache_dir=A__				,       force_download=A__				,       resume_download=A__				,       proxies=A__				,       use_auth_token=A__				,       revision=A__				,       local_files_only=A__				,       )
  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(A__				,       encoding="""utf-8"""   ) as reader:
    return json.load(A__   )






class   __snake_case			:







    """simple docstring"""



    def __init__(  self       :    Optional[int]						)  ->    List[Any]:
      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(lowerCamelCase						)
    def __lowercase  (  cls       :    int       ,		lowerCamelCase       :    Union[str, Any]       ,		**lowerCamelCase       :    Union[str, Any]						)  ->    Optional[Any]:
      lowerCAmelCase_     : List[Any]     =  kwargs.pop("""config"""       ,		lowerCamelCase						)
      lowerCAmelCase_     : int     =  kwargs.pop("""trust_remote_code"""       ,		lowerCamelCase						)
      lowerCAmelCase_     : Tuple     =  True

      lowerCAmelCase_,				lowerCAmelCase_     : Dict     =  FeatureExtractionMixin.get_feature_extractor_dict(lowerCamelCase       ,		**lowerCamelCase						)
      lowerCAmelCase_     : Tuple     =  config_dict.get("""feature_extractor_type"""       ,		lowerCamelCase						)
      lowerCAmelCase_     : Optional[Any]     =  None
      if "AutoFeatureExtractor" in config_dict.get("""auto_map"""       ,		{}						):
        lowerCAmelCase_     : Dict     =  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(lowerCamelCase       ,		lowerCamelCase						):
          lowerCAmelCase_     : Any     =  AutoConfig.from_pretrained(lowerCamelCase       ,		**lowerCamelCase						)
        # It could be in `config.feature_extractor_type``
        lowerCAmelCase_     : Tuple     =  getattr(lowerCamelCase       ,		"""feature_extractor_type"""       ,		lowerCamelCase						)
        if hasattr(lowerCamelCase       ,		"""auto_map"""						) and "AutoFeatureExtractor" in config.auto_map:
          lowerCAmelCase_     : int     =  config.auto_map["""AutoFeatureExtractor"""]

      if feature_extractor_class is not None:
        lowerCAmelCase_     : Dict     =  feature_extractor_class_from_name(lowerCamelCase						)

      lowerCAmelCase_     : Any     =  feature_extractor_auto_map is not None
      lowerCAmelCase_     : Dict     =  feature_extractor_class is not None or type(lowerCamelCase						) in FEATURE_EXTRACTOR_MAPPING
      lowerCAmelCase_     : Union[str, Any]     =  resolve_trust_remote_code(
          lowerCamelCase       ,		lowerCamelCase       ,		lowerCamelCase       ,		lowerCamelCase						)

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

      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 __lowercase  (  lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    Any						)  ->    Dict:
      FEATURE_EXTRACTOR_MAPPING.register(lowerCamelCase       ,		lowerCamelCase						)







'''simple docstring'''






import requests
from bsa import BeautifulSoup







def 							UpperCamelCase_     (       A__   :  str = "AAPL"   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
  lowerCAmelCase_     : Optional[Any]     =  BeautifulSoup(requests.get(A__   ).text				,       """html.parser"""   )
  lowerCAmelCase_     : Union[str, Any]     =  """My(6px) Pos(r) smartphone_Mt(6px)"""
  return soup.find("""div"""				,       class_=class_   ).find("""span"""   ).text


if __name__ == "__main__":
  for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
    print(F'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')



