



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  UpperCAmelCase__    (    lowercase__   ) ->      int:
						for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
												if class_name in extractors:
																		__lowercase					=				model_type_to_module_name(lowercase__   )

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

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

						return None






def  UpperCAmelCase__    (    lowercase__						,       lowercase__ = None						,       lowercase__ = False						,       lowercase__ = False						,       lowercase__ = None						,       lowercase__ = None						,       lowercase__ = None						,       lowercase__ = False						,       **lowercase__						,       ) ->      Union[str, Any]:
						__lowercase					=				get_file_from_repo(
						    lowercase__						,       lowercase__						,       cache_dir=lowercase__						,       force_download=lowercase__						,       resume_download=lowercase__						,       proxies=lowercase__						,       use_auth_token=lowercase__						,       revision=lowercase__						,       local_files_only=lowercase__						,       )
						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(lowercase__						,       encoding="""utf-8"""   ) as reader:
												return json.load(lowercase__   )







class 			_lowerCAmelCase   :






			"""simple docstring"""





			def __init__(				self	:       List[Any]   )						->					Optional[int]:
									"""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 		snake_case__			(				cls	:       str			,     lowercase	:       str			,     **lowercase	:       int   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				kwargs.pop("""config"""			,     lowercase   )
									__lowercase					=				kwargs.pop("""trust_remote_code"""			,     lowercase   )
									__lowercase					=				True

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

									if feature_extractor_class is not None:
															__lowercase					=				feature_extractor_class_from_name(lowercase   )

									__lowercase					=				feature_extractor_auto_map is not None
									__lowercase					=				feature_extractor_class is not None or type(lowercase   ) in FEATURE_EXTRACTOR_MAPPING
									__lowercase					=				resolve_trust_remote_code(
									    lowercase			,     lowercase			,     lowercase			,     lowercase   )

									if has_remote_code and trust_remote_code:
															__lowercase					=				get_class_from_dynamic_module(
															    lowercase			,     lowercase			,     **lowercase   )
															__lowercase					=				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:
															__lowercase					=				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 		snake_case__			(				lowercase	:       List[str]			,     lowercase	:       Any   )						->					Optional[Any]:
									"""simple docstring"""







									FEATURE_EXTRACTOR_MAPPING.register(lowercase			,     lowercase   )










def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      int:
						if len(lowercase__   ) != len(lowercase__   ):
												raise ValueError("""String lengths must match!"""   )

						__lowercase					=				0

						for chara, chara in zip(lowercase__						,       lowercase__   ):
												if chara != chara:
																		count += 1

						return count


if __name__ == "__main__":
			import doctest

			doctest.testmod()





