




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,
)


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE 						=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"""),
    ]
)

__SCREAMING_SNAKE_CASE 						=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)

def 			a   (_lowerCAmelCase     ):
							for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
														if class_name in extractors:
																					SCREAMING_SNAKE_CASE_   	=    model_type_to_module_name(_lowerCAmelCase     )

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

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

							return None

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase = None  ,    _lowerCAmelCase = False  ,    _lowerCAmelCase = False  ,    _lowerCAmelCase = None  ,    _lowerCAmelCase = None  ,    _lowerCAmelCase = None  ,    _lowerCAmelCase = False  ,    **_lowerCAmelCase  ,    ):
							SCREAMING_SNAKE_CASE_   	=    get_file_from_repo(
							    _lowerCAmelCase  ,    _lowerCAmelCase  ,    cache_dir=_lowerCAmelCase  ,    force_download=_lowerCAmelCase  ,    resume_download=_lowerCAmelCase  ,    proxies=_lowerCAmelCase  ,    use_auth_token=_lowerCAmelCase  ,    revision=_lowerCAmelCase  ,    local_files_only=_lowerCAmelCase  ,    )
							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(_lowerCAmelCase  ,    encoding='''utf-8'''     ) as reader:
														return json.load(_lowerCAmelCase     )






class  __magic_name__						:
				'''simple docstring'''







				def __init__(							self:   Optional[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     _A							(							cls:   Any		,					_lowerCamelCase:   Tuple		,					**_lowerCamelCase:   int		):
											SCREAMING_SNAKE_CASE_   	=    kwargs.pop('''config'''		,					_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    kwargs.pop('''trust_remote_code'''		,					_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    True

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

											if feature_extractor_class is not None:
																		SCREAMING_SNAKE_CASE_   	=    feature_extractor_class_from_name(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    feature_extractor_auto_map is not None
											SCREAMING_SNAKE_CASE_   	=    feature_extractor_class is not None or type(_lowerCamelCase		) in FEATURE_EXTRACTOR_MAPPING
											SCREAMING_SNAKE_CASE_   	=    resolve_trust_remote_code(
											    _lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		)

											if has_remote_code and trust_remote_code:
																		SCREAMING_SNAKE_CASE_   	=    get_class_from_dynamic_module(
																		    _lowerCamelCase		,					_lowerCamelCase		,					**_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    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:
																		SCREAMING_SNAKE_CASE_   	=    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     _A							(							_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Tuple		):
											FEATURE_EXTRACTOR_MAPPING.register(_lowerCamelCase		,					_lowerCamelCase		)








def 			a   (_lowerCAmelCase     ):
							if number > 0:
														raise ValueError('''input must be a negative integer'''     )
							SCREAMING_SNAKE_CASE_   	=    len(bin(_lowerCAmelCase     )[3:]     )
							SCREAMING_SNAKE_CASE_   	=    bin(abs(_lowerCAmelCase     ) - (1 << binary_number_length)     )[3:]
							SCREAMING_SNAKE_CASE_   	=    (
							    (
							        '''1'''
							        + '''0''' * (binary_number_length - len(_lowerCAmelCase     ))
							        + twos_complement_number
							    )
							    if number < 0
							    else '''0'''
							)
							return "0b" + twos_complement_number


if __name__ == "__main__":
			import doctest

			doctest.testmod()


