






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       :       Optional[int]        =				logging.get_logger(__name__)

lowerCamelCase       :       Dict        =				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       :       Any        =				_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def 					snake_case_						(   lowerCAmelCase_				:  str					):
							for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
														if class_name in extractors:
																					__lowercase					:							str			=   model_type_to_module_name(lowerCAmelCase_					)

																					__lowercase					:							Union[str, Any]			=   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.
							__lowercase					:							int			=   importlib.import_module("""transformers"""					)
							if hasattr(lowerCAmelCase_				,				lowerCAmelCase_					):
														return getattr(lowerCAmelCase_				,				lowerCAmelCase_					)

							return None
def 					snake_case_						(   lowerCAmelCase_				:  Union[str, os.PathLike]				,				lowerCAmelCase_				:  Optional[Union[str, os.PathLike]] = None				,				lowerCAmelCase_				:  bool = False				,				lowerCAmelCase_				:  bool = False				,				lowerCAmelCase_				:  Optional[Dict[str, str]] = None				,				lowerCAmelCase_				:  Optional[Union[bool, str]] = None				,				lowerCAmelCase_				:  Optional[str] = None				,				lowerCAmelCase_				:  bool = False				,				**lowerCAmelCase_				:  List[Any]				,				):
							__lowercase					:							Tuple			=   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 						lowerCAmelCase   :

					'''simple docstring'''

					def __init__(     self :		int							)	->     Dict:


												"""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(__a							)
					def 					lowerCAmelCase (     cls :		str						,   __a :		Tuple						,   **__a :		Optional[int]							)	->     Any:


												"""simple docstring"""

												__lowercase					:							Tuple			=   kwargs.pop("""config"""						,   __a							)
												__lowercase					:							Optional[int]			=   kwargs.pop("""trust_remote_code"""						,   __a							)
												__lowercase					:							Optional[int]			=   True

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

												if feature_extractor_class is not None:
																			__lowercase					:							List[Any]			=   feature_extractor_class_from_name(__a							)

												__lowercase					:							List[Any]			=   feature_extractor_auto_map is not None
												__lowercase					:							Union[str, Any]			=   feature_extractor_class is not None or type(__a							) in FEATURE_EXTRACTOR_MAPPING
												__lowercase					:							Dict			=   resolve_trust_remote_code(
												    __a						,   __a						,   __a						,   __a							)

												if has_remote_code and trust_remote_code:
																			__lowercase					:							Any			=   get_class_from_dynamic_module(
																			    __a						,   __a						,   **__a							)
																			__lowercase					:							Tuple			=   kwargs.pop("""code_revision"""						,   __a							)
																			if os.path.isdir(__a							):
																										feature_extractor_class.register_for_auto_class()
																			return feature_extractor_class.from_dict(__a						,   **__a							)
												elif feature_extractor_class is not None:
																			return feature_extractor_class.from_dict(__a						,   **__a							)
												# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
												elif type(__a							) in FEATURE_EXTRACTOR_MAPPING:
																			__lowercase					:							List[Any]			=   FEATURE_EXTRACTOR_MAPPING[type(__a							)]
																			return feature_extractor_class.from_dict(__a						,   **__a							)

												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 					lowerCAmelCase (     __a :		Any						,   __a :		Union[str, Any]							)	->     Dict:


												"""simple docstring"""

												FEATURE_EXTRACTOR_MAPPING.register(__a						,   __a							)







from maths.prime_check import is_prime
def 					snake_case_						(   lowerCAmelCase_				:  int					):
							if not isinstance(lowerCAmelCase_				,				lowerCAmelCase_					):
														__lowercase					:							Dict			=   F"Input value of [number={number}] must be an integer"
														raise TypeError(lowerCAmelCase_					)
							if is_prime(lowerCAmelCase_					) and is_prime(number + 2					):
														return number + 2
							else:
														return -1


if __name__ == "__main__":
	import doctest

	doctest.testmod()