




'''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_      :		List[Any]							      =       logging.get_logger(__name__)

a_      :		Tuple							      =       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      a_							(      __snake_case							:			str   )							->						Any:



       """simple docstring"""
       for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
              if class_name in extractors:
                     lowerCamelCase_							=model_type_to_module_name(__snake_case   )

                     lowerCamelCase_							=importlib.import_module(F'''.{module_name}'''   ,       '''transformers.models'''   )
                     try:
                            return getattr(__snake_case   ,       __snake_case   )
                     except AttributeError:
                            continue

       for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
              if getattr(__snake_case   ,       '''__name__'''   ,       __snake_case   ) == 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_							=importlib.import_module('''transformers'''   )
       if hasattr(__snake_case   ,       __snake_case   ):
              return getattr(__snake_case   ,       __snake_case   )

       return None





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



       """simple docstring"""
       lowerCamelCase_							=get_file_from_repo(
           __snake_case   ,       __snake_case   ,       cache_dir=__snake_case   ,       force_download=__snake_case   ,       resume_download=__snake_case   ,       proxies=__snake_case   ,       use_auth_token=__snake_case   ,       revision=__snake_case   ,       local_files_only=__snake_case   ,       )
       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(__snake_case   ,       encoding='''utf-8'''   ) as reader:
              return json.load(__snake_case   )


class   __UpperCamelCase       :






       def __init__(							self  ):



              """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      lowercase__     (							cls,		lowerCAmelCase,		**lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=kwargs.pop('''config''',		lowerCAmelCase  )
              lowerCamelCase_							=kwargs.pop('''trust_remote_code''',		lowerCAmelCase  )
              lowerCamelCase_							=True

              lowerCamelCase_,      lowerCamelCase_							=FeatureExtractionMixin.get_feature_extractor_dict(lowerCAmelCase,		**lowerCAmelCase  )
              lowerCamelCase_							=config_dict.get('''feature_extractor_type''',		lowerCAmelCase  )
              lowerCamelCase_							=None
              if "AutoFeatureExtractor" in config_dict.get('''auto_map''',		{}  ):
                     lowerCamelCase_							=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_							=AutoConfig.from_pretrained(lowerCAmelCase,		**lowerCAmelCase  )
                     # It could be in `config.feature_extractor_type``
                     lowerCamelCase_							=getattr(lowerCAmelCase,		'''feature_extractor_type''',		lowerCAmelCase  )
                     if hasattr(lowerCAmelCase,		'''auto_map'''  ) and "AutoFeatureExtractor" in config.auto_map:
                            lowerCamelCase_							=config.auto_map['''AutoFeatureExtractor''']

              if feature_extractor_class is not None:
                     lowerCamelCase_							=feature_extractor_class_from_name(lowerCAmelCase  )

              lowerCamelCase_							=feature_extractor_auto_map is not None
              lowerCamelCase_							=feature_extractor_class is not None or type(lowerCAmelCase  ) in FEATURE_EXTRACTOR_MAPPING
              lowerCamelCase_							=resolve_trust_remote_code(
                  lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  )

              if has_remote_code and trust_remote_code:
                     lowerCamelCase_							=get_class_from_dynamic_module(
                         lowerCAmelCase,		lowerCAmelCase,		**lowerCAmelCase  )
                     lowerCamelCase_							=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_							=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,		lowerCAmelCase  ):



              """simple docstring"""







              FEATURE_EXTRACTOR_MAPPING.register(lowerCAmelCase,		lowerCAmelCase  )








'''simple docstring'''
import math




class 			lowerCamelCase_   :






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    list[list[float]]						,						_A				:    list[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				0.0
          UpperCAmelCase__			:		Union[str, Any]													=				0.0
          for i in range(len(_A ) ):
              da += math.pow((sample[i] - weights[0][i])						,						2 )
              da += math.pow((sample[i] - weights[1][i])						,						2 )
              return 0 if da > da else 1
          return 0






      def lowercase_							(					self				:    Dict						,						_A				:    list[list[int | float]]						,						_A				:    list[int]						,						_A				:    int						,						_A				:    float ):


          '''simple docstring'''


          for i in range(len(_A ) ):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
          return weights





def 					a__       (					)    ->	None:
    # Training Examples ( m, n )
    UpperCAmelCase__			:		Optional[Any]													=				[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

    # weight initialization ( n, C )
    UpperCAmelCase__			:		str													=				[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

    # training
    UpperCAmelCase__			:		Optional[Any]													=				SelfOrganizingMap()
    UpperCAmelCase__			:		Tuple													=				3
    UpperCAmelCase__			:		Dict													=				0.5

    for _ in range(lowerCAmelCase__						):
        for j in range(len(lowerCAmelCase__						)						):
            # training sample
            UpperCAmelCase__			:		Optional[int]													=				training_samples[j]

            # Compute the winning vector
            UpperCAmelCase__			:		str													=				self_organizing_map.get_winner(lowerCAmelCase__  ,					lowerCAmelCase__						)

            # Update the winning vector
            UpperCAmelCase__			:		int													=				self_organizing_map.update(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

    # classify test sample
    UpperCAmelCase__			:		str													=				[0, 0, 0, 1]
    UpperCAmelCase__			:		int													=				self_organizing_map.get_winner(lowerCAmelCase__  ,					lowerCAmelCase__						)

    # results
    print(F"""Clusters that the test sample belongs to : {winner}"""						)
    print(F"""Weights that have been trained : {weights}"""						)


# running the main() function
if __name__ == "__main__":
  main()
