



"""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,
)


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        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Optional[Any]:
		for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
				if class_name in extractors:
						a_     :      Union[str, Any] 						=     model_type_to_module_name(SCREAMING_SNAKE_CASE__	)

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

		for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
				if getattr(SCREAMING_SNAKE_CASE__, "__name__", SCREAMING_SNAKE_CASE__	) == 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_     :      List[str] 						=     importlib.import_module("transformers"	)
		if hasattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
				return getattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

		return None



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




class 			snake_case_						:






							def __init__(   self      ):
									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 snake_case_  (   cls			,     a_			,     **a_      ):
									a_     :      List[Any] 						=     kwargs.pop("config"			,     a_      )
									a_     :      str 						=     kwargs.pop("trust_remote_code"			,     a_      )
									a_     :      Tuple 						=     True

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

									if feature_extractor_class is not None:
											a_     :      Any 						=     feature_extractor_class_from_name(a_      )

									a_     :      str 						=     feature_extractor_auto_map is not None
									a_     :      int 						=     feature_extractor_class is not None or type(a_      ) in FEATURE_EXTRACTOR_MAPPING
									a_     :      Any 						=     resolve_trust_remote_code(
									    a_			,     a_			,     a_			,     a_      )

									if has_remote_code and trust_remote_code:
											a_     :      int 						=     get_class_from_dynamic_module(
											    a_			,     a_			,     **a_      )
											a_     :      Dict 						=     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:
											a_     :      List[str] 						=     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 snake_case_  (   a_			,     a_      ):
									FEATURE_EXTRACTOR_MAPPING.register(a_			,     a_      )




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = False	)			->      bool:
		if n == 2:
				return True
		if not n % 2 or n < 2:
				return False
		if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
				return False
		if n > 3_317_044_064_679_887_385_961_981 and not allow_probable:
				raise ValueError(
				    "Warning: upper bound of deterministic test is exceeded. "
				    "Pass allow_probable=True to allow probabilistic test. "
				    "A return value of True indicates a probable prime."	)
		# array bounds provided by analysis
		a_     :      Optional[Any] 						=     [
		    2_047,
		    1_373_653,
		    25_326_001,
		    3_215_031_751,
		    2_152_302_898_747,
		    3_474_749_660_383,
		    341_550_071_728_321,
		    1,
		    3_825_123_056_546_413_051,
		    1,
		    1,
		    318_665_857_834_031_151_167_461,
		    3_317_044_064_679_887_385_961_981,
		]

		a_     :      Optional[int] 						=     [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
		for idx, _p in enumerate(SCREAMING_SNAKE_CASE__, 1	):
				if n < _p:
						# then we have our last prime to check
						a_     :      List[Any] 						=     primes[:idx]
						break
		a_						,				a_     :      Tuple 						=     n - 1, 0
		# break up n -1 into a power of 2 (s) and
		# remaining odd component
		# essentially, solve for d * 2 ** s == n - 1
		while d % 2 == 0:
				d //= 2
				s += 1
		for prime in plist:
				a_     :      str 						=     False
				for r in range(SCREAMING_SNAKE_CASE__	):
						a_     :      List[str] 						=     pow(SCREAMING_SNAKE_CASE__, d * 2**r, SCREAMING_SNAKE_CASE__	)
						# see article for analysis explanation for m
						if (r == 0 and m == 1) or ((m + 1) % n == 0):
								a_     :      List[Any] 						=     True
								# this loop will not determine compositeness
								break
				if pr:
						continue
				# if pr is False, then the above loop never evaluated to true,
				# and the n MUST be composite
				return False
		return True



def        lowerCAmelCase_						(							)			->      None:
		assert not miller_rabin(561	)
		assert miller_rabin(563	)
		# 2047

		assert not miller_rabin(838_201	)
		assert miller_rabin(838_207	)
		# 1_373_653

		assert not miller_rabin(17_316_001	)
		assert miller_rabin(17_316_017	)
		# 25_326_001

		assert not miller_rabin(3_078_386_641	)
		assert miller_rabin(3_078_386_653	)
		# 3_215_031_751

		assert not miller_rabin(1_713_045_574_801	)
		assert miller_rabin(1_713_045_574_819	)
		# 2_152_302_898_747

		assert not miller_rabin(2_779_799_728_307	)
		assert miller_rabin(2_779_799_728_327	)
		# 3_474_749_660_383

		assert not miller_rabin(113_850_023_909_441	)
		assert miller_rabin(113_850_023_909_527	)
		# 341_550_071_728_321

		assert not miller_rabin(1_275_041_018_848_804_351	)
		assert miller_rabin(1_275_041_018_848_804_391	)
		# 3_825_123_056_546_413_051

		assert not miller_rabin(79_666_464_458_507_787_791_867	)
		assert miller_rabin(79_666_464_458_507_787_791_951	)
		# 318_665_857_834_031_151_167_461

		assert not miller_rabin(552_840_677_446_647_897_660_333	)
		assert miller_rabin(552_840_677_446_647_897_660_359	)
		# 3_317_044_064_679_887_385_961_981
		# upper limit for probabilistic test


if __name__ == "__main__":
					test_miller_rabin()