"""simple docstring"""






import random




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase							)				-> List[str]:
				SCREAMING_SNAKE_CASE__			=					num - 1
				SCREAMING_SNAKE_CASE__			=					0

				while s % 2 == 0:
								SCREAMING_SNAKE_CASE__			=					s // 2
								t += 1

				for _ in range(5							):
								SCREAMING_SNAKE_CASE__			=					random.randrange(2	,       num - 1							)
								SCREAMING_SNAKE_CASE__			=					pow(lowerCamelCase_	,       lowerCamelCase_	,       lowerCamelCase_							)
								if v != 1:
												SCREAMING_SNAKE_CASE__			=					0
												while v != (num - 1):
																if i == t - 1:
																				return False
																else:
																				SCREAMING_SNAKE_CASE__			=					i + 1
																				SCREAMING_SNAKE_CASE__			=					(v**2) % num
				return True




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase							)				-> int:



				if num < 2:
								return False

				SCREAMING_SNAKE_CASE__			=					[
				    2,
				    3,
				    5,
				    7,
				    11,
				    13,
				    17,
				    19,
				    23,
				    29,
				    31,
				    37,
				    41,
				    43,
				    47,
				    53,
				    59,
				    61,
				    67,
				    71,
				    73,
				    79,
				    83,
				    89,
				    97,
				    101,
				    103,
				    107,
				    109,
				    113,
				    127,
				    131,
				    137,
				    139,
				    149,
				    151,
				    157,
				    163,
				    167,
				    173,
				    179,
				    181,
				    191,
				    193,
				    197,
				    199,
				    211,
				    223,
				    227,
				    229,
				    233,
				    239,
				    241,
				    251,
				    257,
				    263,
				    269,
				    271,
				    277,
				    281,
				    283,
				    293,
				    307,
				    311,
				    313,
				    317,
				    331,
				    337,
				    347,
				    349,
				    353,
				    359,
				    367,
				    373,
				    379,
				    383,
				    389,
				    397,
				    401,
				    409,
				    419,
				    421,
				    431,
				    433,
				    439,
				    443,
				    449,
				    457,
				    461,
				    463,
				    467,
				    479,
				    487,
				    491,
				    499,
				    503,
				    509,
				    521,
				    523,
				    541,
				    547,
				    557,
				    563,
				    569,
				    571,
				    577,
				    587,
				    593,
				    599,
				    601,
				    607,
				    613,
				    617,
				    619,
				    631,
				    641,
				    643,
				    647,
				    653,
				    659,
				    661,
				    673,
				    677,
				    683,
				    691,
				    701,
				    709,
				    719,
				    727,
				    733,
				    739,
				    743,
				    751,
				    757,
				    761,
				    769,
				    773,
				    787,
				    797,
				    809,
				    811,
				    821,
				    823,
				    827,
				    829,
				    839,
				    853,
				    857,
				    859,
				    863,
				    877,
				    881,
				    883,
				    887,
				    907,
				    911,
				    919,
				    929,
				    937,
				    941,
				    947,
				    953,
				    967,
				    971,
				    977,
				    983,
				    991,
				    997,
				]

				if num in low_primes:
								return True

				for prime in low_primes:
								if (num % prime) == 0:
												return False

				return rabin_miller(lowerCamelCase_							)




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase = 1_024							)				-> Optional[int]:



				while True:
								SCREAMING_SNAKE_CASE__			=					random.randrange(2 ** (keysize - 1)	,       2 ** (keysize)							)
								if is_prime_low_num(lowerCamelCase_							):
												return num


if __name__ == "__main__":
						_A        =     generate_large_prime()
						print(('Prime number:', num))
						print(('is_prime_low_num:', is_prime_low_num(num)))











'''simple docstring'''




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''Wav2Vec2FeatureExtractor'''
					SCREAMING_SNAKE_CASE__				=	'''AutoTokenizer'''


					def __init__(						self				:			Optional[int]      ,						lowerCamelCase_				:			str      ,						lowerCamelCase_				:			Dict						):

							'''simple docstring'''



							super().__init__(lowerCamelCase_      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			self.feature_extractor
							SCREAMING_SNAKE_CASE :				List[Any]		=			False


					@classmethod
					def lowerCamelCase_			(						cls				:			int      ,						lowerCamelCase_				:			List[str]      ,						**lowerCamelCase_				:			Any						):

							'''simple docstring'''



							try:
									return super().from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)
							except OSError:
									warnings.warn(
									    f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
									    """ include a `tokenizer_class` attribute is deprecated and will be """
									    """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
									    """ attribute to either your `config.json` or `tokenizer_config.json` """
									    """file to suppress this warning: """      ,						lowerCamelCase_      ,						)

									SCREAMING_SNAKE_CASE :				Optional[Any]		=			WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Dict		=			WavaVecaCTCTokenizer.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)

									return cls(feature_extractor=lowerCamelCase_      ,						tokenizer=lowerCamelCase_						)


					def __call__(						self				:			List[Any]      ,						*lowerCamelCase_				:			str      ,						**lowerCamelCase_				:			int						):

							'''simple docstring'''



							if self._in_target_context_manager:
									return self.current_processor(*lowerCamelCase_      ,						**lowerCamelCase_						)

							if "raw_speech" in kwargs:
									warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""						)
									SCREAMING_SNAKE_CASE :				Dict		=			kwargs.pop("""raw_speech"""						)
							else:
									SCREAMING_SNAKE_CASE :				Any		=			kwargs.pop("""audio"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			kwargs.pop("""sampling_rate"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			kwargs.pop("""text"""      ,						lowerCamelCase_						)
							if len(lowerCamelCase_						) > 0:
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			args[0]
									SCREAMING_SNAKE_CASE :				Dict		=			args[1:]

							if audio is None and text is None:
									raise ValueError("""You need to specify either an `audio` or `text` input to process."""						)

							if audio is not None:
									SCREAMING_SNAKE_CASE :				str		=			self.feature_extractor(lowerCamelCase_      ,						*lowerCamelCase_      ,						sampling_rate=lowerCamelCase_      ,						**lowerCamelCase_						)
							if text is not None:
									SCREAMING_SNAKE_CASE :				List[Any]		=			self.tokenizer(lowerCamelCase_      ,						**lowerCamelCase_						)

							if text is None:
									return inputs
							elif audio is None:
									return encodings
							else:
									SCREAMING_SNAKE_CASE :				List[str]		=			encodings["""input_ids"""]
									return inputs


					def lowerCamelCase_			(						self				:			Optional[Any]      ,						*lowerCamelCase_				:			Tuple      ,						**lowerCamelCase_				:			Dict						):

							'''simple docstring'''



							if self._in_target_context_manager:
									return self.current_processor.pad(*lowerCamelCase_      ,						**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				str		=			kwargs.pop("""input_features"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Dict		=			kwargs.pop("""labels"""      ,						lowerCamelCase_						)
							if len(lowerCamelCase_						) > 0:
									SCREAMING_SNAKE_CASE :				Optional[int]		=			args[0]
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			args[1:]

							if input_features is not None:
									SCREAMING_SNAKE_CASE :				Any		=			self.feature_extractor.pad(lowerCamelCase_      ,						*lowerCamelCase_      ,						**lowerCamelCase_						)
							if labels is not None:
									SCREAMING_SNAKE_CASE :				int		=			self.tokenizer.pad(lowerCamelCase_      ,						**lowerCamelCase_						)

							if labels is None:
									return input_features
							elif input_features is None:
									return labels
							else:
									SCREAMING_SNAKE_CASE :				Dict		=			labels["""input_ids"""]
									return input_features


					def lowerCamelCase_			(						self				:			Optional[int]      ,						*lowerCamelCase_				:			List[str]      ,						**lowerCamelCase_				:			Optional[Any]						):

							'''simple docstring'''



							return self.tokenizer.batch_decode(*lowerCamelCase_      ,						**lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[str]      ,						*lowerCamelCase_				:			Optional[int]      ,						**lowerCamelCase_				:			Optional[int]						):

							'''simple docstring'''



							return self.tokenizer.decode(*lowerCamelCase_      ,						**lowerCamelCase_						)



					@contextmanager
					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							warnings.warn(
							    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
							    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
							    """your audio inputs, or in a separate call."""						)
							SCREAMING_SNAKE_CASE :				int		=			True
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.tokenizer
							yield
							SCREAMING_SNAKE_CASE :				int		=			self.feature_extractor
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			False



