






"""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,
)


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

UpperCAmelCase_     :				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"""),
    ]
)

UpperCAmelCase_     :				List[Any]         =				_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)



def       _A    (__a							)				-> int:
    """simple docstring"""






    for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
        if class_name in extractors:
            SCREAMING_SNAKE_CASE_   :						Tuple							=	model_type_to_module_name(__a							)

            SCREAMING_SNAKE_CASE_   :						Tuple							=	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.
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	importlib.import_module('''transformers'''							)
    if hasattr(__a					,       __a							):
        return getattr(__a					,       __a							)

    return None



def       _A    (__a					,       __a = None					,       __a = False					,       __a = False					,       __a = None					,       __a = None					,       __a = None					,       __a = False					,       **__a					,       )				-> str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[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 							lowerCAmelCase__       :






      '''simple docstring'''

      def __init__(		self  :						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(lowercase_)
      def _SCREAMING_SNAKE_CASE				(		cls  :						Any		,			lowercase_  :						str		,			**lowercase_  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	kwargs.pop('''config'''		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	kwargs.pop('''trust_remote_code'''		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	True

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

          if feature_extractor_class is not None:
              SCREAMING_SNAKE_CASE_   :						List[str]							=	feature_extractor_class_from_name(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	feature_extractor_auto_map is not None
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	feature_extractor_class is not None or type(lowercase_) in FEATURE_EXTRACTOR_MAPPING
          SCREAMING_SNAKE_CASE_   :						Dict							=	resolve_trust_remote_code(
              lowercase_		,			lowercase_		,			lowercase_		,			lowercase_)

          if has_remote_code and trust_remote_code:
              SCREAMING_SNAKE_CASE_   :						int							=	get_class_from_dynamic_module(
                  lowercase_		,			lowercase_		,			**lowercase_)
              SCREAMING_SNAKE_CASE_   :						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:
              SCREAMING_SNAKE_CASE_   :						Optional[int]							=	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 _SCREAMING_SNAKE_CASE				(		lowercase_  :						str		,			lowercase_  :						Tuple):




          '''simple docstring'''


          FEATURE_EXTRACTOR_MAPPING.register(lowercase_		,			lowercase_)










"""simple docstring"""






def       _A    (__a = 1_00							)				-> int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						int							=	(n * (n + 1) // 2) ** 2
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	n * (n + 1) * (2 * n + 1) // 6
    return sum_cubes - sum_squares


if __name__ == "__main__":
  print(f'''{solution() = }''')


