


import random


def 				lowerCamelCase__	(      a	:   int				)    ->	bool:





			"""simple docstring"""
			a__ :int						  =    num - 1
			a__ :List[Any]						  =    0

			while s % 2 == 0:
						a__ :Union[str, Any]						  =    s // 2
						t += 1

			for _ in range(5				):
						a__ :List[str]						  =    random.randrange(2	,  num - 1				)
						a__ :Optional[int]						  =    pow(a	,  a	,  a				)
						if v != 1:
									a__ :Dict						  =    0
									while v != (num - 1):
												if i == t - 1:
															return False
												else:
															a__ :List[str]						  =    i + 1
															a__ :Any						  =    (v**2) % num
			return True


def 				lowerCamelCase__	(      a	:   int				)    ->	bool:





			"""simple docstring"""
			if num < 2:
						return False

			a__ :Optional[int]						  =    [
			    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(a				)


def 				lowerCamelCase__	(      a	:   int = 1_024				)    ->	int:





			"""simple docstring"""
			while True:
						a__ :Any						  =    random.randrange(2 ** (keysize - 1)	,  2 ** (keysize)				)
						if is_prime_low_num(a				):
									return num


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






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'sew'




						def __init__(	self						:   Any   ,     __A						:   str=32   ,     __A						:   Dict=768   ,     __A						:   int=12   ,     __A						:   Dict=12   ,     __A						:   Dict=3072   ,     __A						:   int=2   ,     __A						:   Union[str, Any]="gelu"   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   Optional[Any]=0.1   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   str=0.0   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   Optional[Any]=0.1   ,     __A						:   Tuple=0.02   ,     __A						:   Any=1E-5   ,     __A						:   Optional[Any]="group"   ,     __A						:   str="gelu"   ,     __A						:   Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,     __A						:   Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,     __A						:   int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,     __A						:   List[str]=False   ,     __A						:   Tuple=128   ,     __A						:   Tuple=16   ,     __A						:   Optional[int]=True   ,     __A						:   Union[str, Any]=0.05   ,     __A						:   List[str]=10   ,     __A						:   Optional[int]=2   ,     __A						:   List[Any]=0.0   ,     __A						:   Optional[Any]=10   ,     __A						:   Tuple=0   ,     __A						:   Tuple="mean"   ,     __A						:   Any=False   ,     __A						:   str=False   ,     __A						:   Dict=256   ,     __A						:   Union[str, Any]=0   ,     __A						:   Optional[int]=1   ,     __A						:   Optional[Any]=2   ,     **__A						:   Tuple   ,     )      ->Tuple:






									"""simple docstring"""






									super().__init__(**__A   ,     pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A						)
									a__ :List[Any]						  =    hidden_size
									a__ :List[Any]						  =    feat_extract_norm
									a__ :List[str]						  =    feat_extract_activation
									a__ :Any						  =    list(__A						)
									a__ :Dict						  =    list(__A						)
									a__ :Optional[int]						  =    list(__A						)
									a__ :Any						  =    conv_bias
									a__ :List[str]						  =    num_conv_pos_embeddings
									a__ :str						  =    num_conv_pos_embedding_groups
									a__ :Optional[int]						  =    len(self.conv_dim						)
									a__ :List[Any]						  =    num_hidden_layers
									a__ :str						  =    intermediate_size
									a__ :Dict						  =    squeeze_factor
									a__ :List[Any]						  =    hidden_act
									a__ :Optional[Any]						  =    num_attention_heads
									a__ :Tuple						  =    hidden_dropout
									a__ :Tuple						  =    attention_dropout
									a__ :List[Any]						  =    activation_dropout
									a__ :str						  =    feat_proj_dropout
									a__ :Any						  =    final_dropout
									a__ :Dict						  =    layerdrop
									a__ :List[str]						  =    layer_norm_eps
									a__ :Tuple						  =    initializer_range
									a__ :Dict						  =    vocab_size

									if (
									    (len(self.conv_stride						) != self.num_feat_extract_layers)
									    or (len(self.conv_kernel						) != self.num_feat_extract_layers)
									    or (len(self.conv_dim						) != self.num_feat_extract_layers)
									):
												raise ValueError(
												    "Configuration for convolutional layers is incorrect."
												    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
												    F'''but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)'''
												    F'''= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'''						)

									# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
									a__ :int						  =    apply_spec_augment
									a__ :Optional[Any]						  =    mask_time_prob
									a__ :List[Any]						  =    mask_time_length
									a__ :Any						  =    mask_time_min_masks
									a__ :Any						  =    mask_feature_prob
									a__ :Dict						  =    mask_feature_length
									a__ :Union[str, Any]						  =    mask_feature_min_masks

									# ctc loss
									a__ :Dict						  =    ctc_loss_reduction
									a__ :Any						  =    ctc_zero_infinity

									# sequence classification
									a__ :Optional[int]						  =    use_weighted_layer_sum
									a__ :Tuple						  =    classifier_proj_size






						@property
						def  _snake_case					(	self						:   Dict						)      ->Union[str, Any]:






									"""simple docstring"""






									return functools.reduce(operator.mul   ,     self.conv_stride   ,     1						)


