




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,
)


__lowerCAmelCase	:       Union[str, Any]     =	logging.get_logger(__name__)

__lowerCAmelCase	:       int     =	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'),
    ]
)

__lowerCAmelCase	:       str     =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)




def     __magic_name__       (				A	:      str     ):






			'''simple docstring'''







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

									a					=    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.
			a					=    importlib.import_module("transformers"     )
			if hasattr(A,						A     ):
						return getattr(A,						A     )

			return None




def     __magic_name__       (				A	:      Union[str, os.PathLike],						A	:      Optional[Union[str, os.PathLike]] = None,						A	:      bool = False,						A	:      bool = False,						A	:      Optional[Dict[str, str]] = None,						A	:      Optional[Union[bool, str]] = None,						A	:      Optional[str] = None,						A	:      bool = False,						**A	:      str,						):






			'''simple docstring'''







			a					=    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 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  Optional[int]							)      ->  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 							__UpperCAmelCase							(   cls       :  str      ,   __lowerCamelCase       :  Dict      ,   **__lowerCamelCase       :  Optional[Any]							)      ->  Union[str, Any]:
							a					=    kwargs.pop("config"      ,   __lowerCamelCase							)
							a					=    kwargs.pop("trust_remote_code"      ,   __lowerCamelCase							)
							a					=    True

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

							if feature_extractor_class is not None:
										a					=    feature_extractor_class_from_name(__lowerCamelCase							)

							a					=    feature_extractor_auto_map is not None
							a					=    feature_extractor_class is not None or type(__lowerCamelCase							) in FEATURE_EXTRACTOR_MAPPING
							a					=    resolve_trust_remote_code(
							    __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase							)

							if has_remote_code and trust_remote_code:
										a					=    get_class_from_dynamic_module(
										    __lowerCamelCase      ,   __lowerCamelCase      ,   **__lowerCamelCase							)
										a					=    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:
										a					=    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 							__UpperCAmelCase							(   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  List[str]							)      ->  Optional[int]:
							FEATURE_EXTRACTOR_MAPPING.register(__lowerCamelCase      ,   __lowerCamelCase							)












def     __magic_name__       (				A	:      str,						A	:      str     ):






			'''simple docstring'''







			def get_matched_characters(A	:      str,						A	:      str     ) -> str:
						a					=    []
						a					=    min(len(_stra     ),						len(_stra     )     ) // 2
						for i, l in enumerate(_stra     ):
									a					=    int(max(0,						i - limit     )     )
									a					=    int(min(i + limit + 1,						len(_stra     )     )     )
									if l in _stra[left:right]:
												matched.append(A     )
												a					=    F"""{_stra[0:_stra.index(A     )]} {_stra[_stra.index(A     ) + 1:]}"""

						return "".join(A     )

			# matching characters
			a					=    get_matched_characters(A,						A     )
			a					=    get_matched_characters(A,						A     )
			a					=    len(A     )

			# transposition
			a					=    (
			    len([(ca, ca) for ca, ca in zip(A,						A     ) if ca != ca]     ) // 2
			)

			if not match_count:
						a					=    0.0
			else:
						a					=    (
						    1
						    / 3
						    * (
						        match_count / len(A     )
						        + match_count / len(A     )
						        + (match_count - transpositions) / match_count
						    )
						)

			# common prefix up to 4 characters
			a					=    0
			for ca, ca in zip(stra[:4],						stra[:4]     ):
						if ca == ca:
									prefix_len += 1
						else:
									break

			return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(jaro_winkler('hello', 'world'))






