


'''simple docstring'''





import functools
import operator

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


UpperCAmelCase__  			=				logging.get_logger(__name__)

UpperCAmelCase__  			=				{
    '''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 						a__					(					a_   ):





			'''simple docstring'''

			A						:				str					      =			'''sew'''
			def __init__(  self							:			List[Any]			,    lowerCAmelCase_							:			List[str]=32			,    lowerCAmelCase_							:			Any=768			,    lowerCAmelCase_							:			List[str]=12			,    lowerCAmelCase_							:			Any=12			,    lowerCAmelCase_							:			Optional[int]=3_072			,    lowerCAmelCase_							:			List[str]=2			,    lowerCAmelCase_							:			Optional[int]="gelu"			,    lowerCAmelCase_							:			Union[str, Any]=0.1			,    lowerCAmelCase_							:			str=0.1			,    lowerCAmelCase_							:			List[str]=0.1			,    lowerCAmelCase_							:			Optional[Any]=0.0			,    lowerCAmelCase_							:			Optional[int]=0.1			,    lowerCAmelCase_							:			str=0.1			,    lowerCAmelCase_							:			Optional[Any]=0.02			,    lowerCAmelCase_							:			Union[str, Any]=1E-5			,    lowerCAmelCase_							:			List[Any]="group"			,    lowerCAmelCase_							:			Tuple="gelu"			,    lowerCAmelCase_							:			Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)			,    lowerCAmelCase_							:			str=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)			,    lowerCAmelCase_							:			Optional[Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)			,    lowerCAmelCase_							:			Any=False			,    lowerCAmelCase_							:			Any=128			,    lowerCAmelCase_							:			Dict=16			,    lowerCAmelCase_							:			Union[str, Any]=True			,    lowerCAmelCase_							:			Union[str, Any]=0.05			,    lowerCAmelCase_							:			int=10			,    lowerCAmelCase_							:			Optional[int]=2			,    lowerCAmelCase_							:			List[str]=0.0			,    lowerCAmelCase_							:			int=10			,    lowerCAmelCase_							:			Tuple=0			,    lowerCAmelCase_							:			Dict="mean"			,    lowerCAmelCase_							:			str=False			,    lowerCAmelCase_							:			Optional[Any]=False			,    lowerCAmelCase_							:			Optional[Any]=256			,    lowerCAmelCase_							:			str=0			,    lowerCAmelCase_							:			Tuple=1			,    lowerCAmelCase_							:			Optional[int]=2			,    **lowerCAmelCase_							:			Optional[int]			,    )      ->		Optional[Any]:
						super().__init__(**lowerCAmelCase_			,    pad_token_id=lowerCAmelCase_			,    bos_token_id=lowerCAmelCase_			,    eos_token_id=lowerCAmelCase_	)
						__A=	hidden_size
						__A=	feat_extract_norm
						__A=	feat_extract_activation
						__A=	list(lowerCAmelCase_	)
						__A=	list(lowerCAmelCase_	)
						__A=	list(lowerCAmelCase_	)
						__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 					lowerCAmelCase     (  self							:			List[Any]	)      ->		int:
						return functools.reduce(operator.mul			,    self.conv_stride			,    1	)







'''simple docstring'''





from __future__ import annotations

from functools import lru_cache
from math import ceil

UpperCAmelCase__  			=				1_0_0

UpperCAmelCase__  			=				set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCAmelCase__  			=				42

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
					if prime not in primes:
										continue
					primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))






@lru_cache(maxsize=100 )
def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			if number_to_partition < 0:
						return set()
			elif number_to_partition == 0:
						return {1}

			__A=	set()
			__A=	42
			__A=	42

			for prime in primes:
						if prime > number_to_partition:
									continue
						for sub in partition(number_to_partition - prime ):
									ret.add(sub * prime )

			return ret






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int = 5000 ):






			"""simple docstring"""




			for number_to_partition in range(1,_SCREAMING_SNAKE_CASE ):
						if len(partition(_SCREAMING_SNAKE_CASE ) ) > number_unique_partitions:
									return number_to_partition
			return None


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



