



import functools
import operator

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


lowerCamelCase__			= logging.get_logger(__name__)

lowerCamelCase__			= {
    """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    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	str    ='sew'





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

											'''simple docstring'''
											super().__init__(**__lowercase      ,				pad_token_id=__lowercase      ,				bos_token_id=__lowercase      ,				eos_token_id=__lowercase	)
											__a       =		hidden_size
											__a       =		feat_extract_norm
											__a       =		feat_extract_activation
											__a       =		list(__lowercase	)
											__a       =		list(__lowercase	)
											__a       =		list(__lowercase	)
											__a       =		conv_bias
											__a       =		num_conv_pos_embeddings
											__a       =		num_conv_pos_embedding_groups
											__a       =		len(self.conv_dim	)
											__a       =		num_hidden_layers
											__a       =		intermediate_size
											__a       =		squeeze_factor
											__a       =		hidden_act
											__a       =		num_attention_heads
											__a       =		hidden_dropout
											__a       =		attention_dropout
											__a       =		activation_dropout
											__a       =		feat_proj_dropout
											__a       =		final_dropout
											__a       =		layerdrop
											__a       =		layer_norm_eps
											__a       =		initializer_range
											__a       =		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       =		apply_spec_augment
											__a       =		mask_time_prob
											__a       =		mask_time_length
											__a       =		mask_time_min_masks
											__a       =		mask_feature_prob
											__a       =		mask_feature_length
											__a       =		mask_feature_min_masks

											# ctc loss
											__a       =		ctc_loss_reduction
											__a       =		ctc_zero_infinity

											# sequence classification
											__a       =		use_weighted_layer_sum
											__a       =		classifier_proj_size







							@property
							def 						UpperCamelCase_					(   self		:		int	):

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





from math import sqrt

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5				,     int(sqrt(_SCREAMING_SNAKE_CASE    ) + 1    )				,     6    ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int = 1_0001    ):
				"""simple docstring"""







				__a       =		0
				__a       =		1
				while count != nth and number < 3:
								number += 1
								if is_prime(_SCREAMING_SNAKE_CASE    ):
												count += 1
				while count != nth:
								number += 2
								if is_prime(_SCREAMING_SNAKE_CASE    ):
												count += 1
				return number


if __name__ == "__main__":
						print(F"""{solution() = }""")
