

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,
)


__UpperCAmelCase								=			logging.get_logger(__name__)

__UpperCAmelCase								=			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"""),
    ]
)

__UpperCAmelCase								=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)





def      snake_case_				(__A					:						str					)      ->     Union[str, Any]:
   for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
      if class_name in extractors:
         __lowerCAmelCase  :      str    =						model_type_to_module_name(__A					)

         __lowerCAmelCase  :      Dict    =						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  :      Tuple    =						importlib.import_module("""transformers"""					)
   if hasattr(__A ,       __A					):
      return getattr(__A ,       __A					)

   return None





def      snake_case_				(__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					:						int ,       )      ->     List[Any]:
   __lowerCAmelCase  :      Union[str, Any]    =						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 SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   int   )							->     Optional[Any]:






     """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(lowerCAmelCase   )
  def 	SCREAMING_SNAKE_CASE   (      cls			:   Optional[Any]	,							lowerCAmelCase			:   List[str]	,							**lowerCAmelCase			:   Optional[Any]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						kwargs.pop("""config"""	,							lowerCAmelCase   )
     __lowerCAmelCase  :      Any    =						kwargs.pop("""trust_remote_code"""	,							lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						True

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

     if feature_extractor_class is not None:
        __lowerCAmelCase  :      List[Any]    =						feature_extractor_class_from_name(lowerCAmelCase   )

     __lowerCAmelCase  :      Union[str, Any]    =						feature_extractor_auto_map is not None
     __lowerCAmelCase  :      List[str]    =						feature_extractor_class is not None or type(lowerCAmelCase   ) in FEATURE_EXTRACTOR_MAPPING
     __lowerCAmelCase  :      List[str]    =						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  :      Dict    =						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  :      str    =						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 	SCREAMING_SNAKE_CASE   (      lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   int   )							->     Union[str, Any]:






     """simple docstring"""







     FEATURE_EXTRACTOR_MAPPING.register(lowerCAmelCase	,							lowerCAmelCase   )





from __future__ import annotations





def      snake_case_				(__A					:						list[float] ,       __A					:						list[float]					)      ->     float:
   __lowerCAmelCase  :      Union[str, Any]    =						sorted(numsa + numsa					)
   __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[int]    =						divmod(len(__A					) ,       2					)
   if mod == 1:
      return all_numbers[div]
   else:
      return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   __UpperCAmelCase								=			[float(x) for x in input("""Enter the elements of first array: """).split()]
   __UpperCAmelCase								=			[float(x) for x in input("""Enter the elements of second array: """).split()]
   print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')


