
"""simple docstring"""







def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = False )  ->     bool:







							"""simple docstring"""


							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_31_70_44_06_46_79_88_73_85_96_19_81 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
							__snake_case     			=       [
							    20_47,
							    1_37_36_53,
							    25_32_60_01,
							    32_15_03_17_51,
							    2_15_23_02_89_87_47,
							    3_47_47_49_66_03_83,
							    3_41_55_00_71_72_83_21,
							    1,
							    3_82_51_23_05_65_46_41_30_51,
							    1,
							    1,
							    31_86_65_85_78_34_03_11_51_16_74_61,
							    3_31_70_44_06_46_79_88_73_85_96_19_81,
							]

							__snake_case     			=       [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
																					__snake_case     			=       primes[:idx]
																					break
							__snake_case			,   __snake_case     			=       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:
														__snake_case     			=       False
														for r in range(SCREAMING_SNAKE_CASE ):
																					__snake_case     			=       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):
																												__snake_case     			=       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   __UpperCamelCase   (					)  ->     None:







							"""simple docstring"""


							assert not miller_rabin(5_61 )
							assert miller_rabin(5_63 )
							# 2047

							assert not miller_rabin(83_82_01 )
							assert miller_rabin(83_82_07 )
							# 1_373_653

							assert not miller_rabin(17_31_60_01 )
							assert miller_rabin(17_31_60_17 )
							# 25_326_001

							assert not miller_rabin(30_78_38_66_41 )
							assert miller_rabin(30_78_38_66_53 )
							# 3_215_031_751

							assert not miller_rabin(1_71_30_45_57_48_01 )
							assert miller_rabin(1_71_30_45_57_48_19 )
							# 2_152_302_898_747

							assert not miller_rabin(2_77_97_99_72_83_07 )
							assert miller_rabin(2_77_97_99_72_83_27 )
							# 3_474_749_660_383

							assert not miller_rabin(1_13_85_00_23_90_94_41 )
							assert miller_rabin(1_13_85_00_23_90_95_27 )
							# 341_550_071_728_321

							assert not miller_rabin(1_27_50_41_01_88_48_80_43_51 )
							assert miller_rabin(1_27_50_41_01_88_48_80_43_91 )
							# 3_825_123_056_546_413_051

							assert not miller_rabin(7_96_66_46_44_58_50_77_87_79_18_67 )
							assert miller_rabin(7_96_66_46_44_58_50_77_87_79_19_51 )
							# 318_665_857_834_031_151_167_461

							assert not miller_rabin(55_28_40_67_74_46_64_78_97_66_03_33 )
							assert miller_rabin(55_28_40_67_74_46_64_78_97_66_03_59 )
							# 3_317_044_064_679_887_385_961_981
							# upper limit for probabilistic test


if __name__ == "__main__":
		test_miller_rabin()


"""simple docstring"""







import math

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


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	       =      {
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Any     							=   'data2vec-audio'





					def __init__(				self   :							Any							,						snake_case_   :							Optional[Any]=32							,						snake_case_   :							Tuple=768							,						snake_case_   :							Optional[int]=12							,						snake_case_   :							str=12							,						snake_case_   :							int=3072							,						snake_case_   :							Tuple="gelu"							,						snake_case_   :							Optional[Any]=0.1							,						snake_case_   :							str=0.1							,						snake_case_   :							int=0.1							,						snake_case_   :							int=0.0							,						snake_case_   :							Optional[int]=0.1							,						snake_case_   :							Dict=0.1							,						snake_case_   :							Dict=0.02							,						snake_case_   :							Optional[int]=1e-5							,						snake_case_   :							Optional[Any]="gelu"							,						snake_case_   :							Tuple=(512, 512, 512, 512, 512, 512, 512)							,						snake_case_   :							int=(5, 2, 2, 2, 2, 2, 2)							,						snake_case_   :							Union[str, Any]=(10, 3, 3, 3, 3, 2, 2)							,						snake_case_   :							Optional[int]=False							,						snake_case_   :							int=16							,						snake_case_   :							List[Any]=19							,						snake_case_   :							str=5							,						snake_case_   :							List[Any]=0.05							,						snake_case_   :							Dict=10							,						snake_case_   :							Dict=2							,						snake_case_   :							Dict=0.0							,						snake_case_   :							Union[str, Any]=10							,						snake_case_   :							Optional[Any]=0							,						snake_case_   :							Optional[Any]="sum"							,						snake_case_   :							List[str]=False							,						snake_case_   :							List[Any]=False							,						snake_case_   :							Optional[int]=256							,						snake_case_   :							int=(512, 512, 512, 512, 1500)							,						snake_case_   :							List[Any]=(5, 3, 3, 1, 1)							,						snake_case_   :							Any=(1, 2, 3, 1, 1)							,						snake_case_   :							List[Any]=512							,						snake_case_   :							Optional[int]=0							,						snake_case_   :							Dict=1							,						snake_case_   :							List[str]=2							,						snake_case_   :							Optional[Any]=False							,						snake_case_   :							Optional[Any]=3							,						snake_case_   :							Tuple=2							,						snake_case_   :							Any=3							,						snake_case_   :							Union[str, Any]=None							,						**snake_case_   :							int							,						):
												super().__init__(**snake_case_							,						pad_token_id=snake_case_							,						bos_token_id=snake_case_							,						eos_token_id=snake_case_    )
												__snake_case     			=       hidden_size
												__snake_case     			=       feat_extract_activation
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       conv_bias
												__snake_case     			=       num_conv_pos_embeddings
												__snake_case     			=       num_conv_pos_embedding_groups
												__snake_case     			=       conv_pos_kernel_size
												__snake_case     			=       len(self.conv_dim    )
												__snake_case     			=       num_hidden_layers
												__snake_case     			=       intermediate_size
												__snake_case     			=       hidden_act
												__snake_case     			=       num_attention_heads
												__snake_case     			=       hidden_dropout
												__snake_case     			=       attention_dropout
												__snake_case     			=       activation_dropout
												__snake_case     			=       feat_proj_dropout
												__snake_case     			=       final_dropout
												__snake_case     			=       layerdrop
												__snake_case     			=       layer_norm_eps
												__snake_case     			=       initializer_range
												__snake_case     			=       vocab_size
												__snake_case     			=       use_weighted_layer_sum

												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)`, but is `len(config.conv_dim) ="
																			    F''' {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,'''
																			    F''' `len(config.conv_kernel) = {len(self.conv_kernel    )}`.'''    )

												# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
												__snake_case     			=       mask_time_prob
												__snake_case     			=       mask_time_length
												__snake_case     			=       mask_time_min_masks
												__snake_case     			=       mask_feature_prob
												__snake_case     			=       mask_feature_length
												__snake_case     			=       mask_feature_min_masks

												# ctc loss
												__snake_case     			=       ctc_loss_reduction
												__snake_case     			=       ctc_zero_infinity

												# adapter
												__snake_case     			=       add_adapter
												__snake_case     			=       adapter_kernel_size
												__snake_case     			=       adapter_stride
												__snake_case     			=       num_adapter_layers
												__snake_case     			=       output_hidden_size or hidden_size

												# SequenceClassification-specific parameter. Feel free to ignore for other classes.
												__snake_case     			=       classifier_proj_size

												# XVector-specific parameters. Feel free to ignore for other classes.
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       xvector_output_dim





					@property
					def 							lowerCAmelCase      (				self   :							List[str]    ):
												return math.prod(self.conv_stride    )
