



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,
)


lowercase_:  Dict     						=					logging.get_logger(__name__)

lowercase_:  List[str]     						=					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'),
    ]
)

lowercase_:  Optional[Any]     						=					_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def 	_lowercase	(     UpperCAmelCase_):




 """simple docstring"""




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

   snake_case__				:							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.
 snake_case__				:							Optional[Any]      							= importlib.import_module("""transformers""")
 if hasattr(__A						,   __A):
  return getattr(__A						,   __A)

 return None
def 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_ = None						,   UpperCAmelCase_ = False						,   UpperCAmelCase_ = False						,   UpperCAmelCase_ = None						,   UpperCAmelCase_ = None						,   UpperCAmelCase_ = None						,   UpperCAmelCase_ = False						,   **UpperCAmelCase_						,   ):




 """simple docstring"""




 snake_case__				:							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 						lowercase__      :

     """simple docstring"""






     def __init__( self :	Optional[int] ):
      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 		lowercase   ( cls :	Dict   ,			__a :	List[str]   ,			**__a :	int ):
      snake_case__				:							Optional[Any]      							= kwargs.pop("""config"""   ,			lowercase__ )
      snake_case__				:							str      							= kwargs.pop("""trust_remote_code"""   ,			lowercase__ )
      snake_case__				:							List[Any]      							= True

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

      if feature_extractor_class is not None:
       snake_case__				:							List[str]      							= feature_extractor_class_from_name(lowercase__ )

      snake_case__				:							Optional[Any]      							= feature_extractor_auto_map is not None
      snake_case__				:							Union[str, Any]      							= feature_extractor_class is not None or type(lowercase__ ) in FEATURE_EXTRACTOR_MAPPING
      snake_case__				:							str      							= resolve_trust_remote_code(
          lowercase__   ,			lowercase__   ,			lowercase__   ,			lowercase__ )

      if has_remote_code and trust_remote_code:
       snake_case__				:							Union[str, Any]      							= get_class_from_dynamic_module(
           lowercase__   ,			lowercase__   ,			**lowercase__ )
       snake_case__				:							Optional[Any]      							= 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:
       snake_case__				:							Union[str, Any]      							= 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 		lowercase   ( __a :	str   ,			__a :	Optional[Any] ):
      FEATURE_EXTRACTOR_MAPPING.register(lowercase__   ,			lowercase__ )







def      A__					(			__A  :						str     ,				__A  :						str			)					->str:

      if not (isinstance(__A     ,				__A			) and isinstance(__A     ,				__A			)):
            raise ValueError('''longest_common_substring() takes two strings for inputs'''			)

      __A    =len(__A			)
      __A    =len(__A			)

      __A    =[[0] * (texta_length + 1) for _ in range(texta_length + 1			)]
      __A    =0
      __A    =0

      for i in range(1     ,				texta_length + 1			):
            for j in range(1     ,				texta_length + 1			):
                  if texta[i - 1] == texta[j - 1]:
                        __A    =1 + dp[i - 1][j - 1]
                        if dp[i][j] > ans_length:
                              __A    =i
                              __A    =dp[i][j]

      return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


