




"""simple docstring"""




import functools
import operator

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


a_        =       logging.get_logger(__name__)

a_        =       {
    '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   UpperCAmelCase_    (			snake_case		):
		UpperCamelCase							="sew"
		def __init__(						self				,						UpperCamelCase_=32				,						UpperCamelCase_=7_68				,						UpperCamelCase_=12				,						UpperCamelCase_=12				,						UpperCamelCase_=30_72				,						UpperCamelCase_=2				,						UpperCamelCase_="gelu"				,						UpperCamelCase_=0.1				,						UpperCamelCase_=0.1				,						UpperCamelCase_=0.1				,						UpperCamelCase_=0.0				,						UpperCamelCase_=0.1				,						UpperCamelCase_=0.1				,						UpperCamelCase_=0.0_2				,						UpperCamelCase_=1E-5				,						UpperCamelCase_="group"				,						UpperCamelCase_="gelu"				,						UpperCamelCase_=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12)				,						UpperCamelCase_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,						UpperCamelCase_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,						UpperCamelCase_=False				,						UpperCamelCase_=1_28				,						UpperCamelCase_=16				,						UpperCamelCase_=True				,						UpperCamelCase_=0.0_5				,						UpperCamelCase_=10				,						UpperCamelCase_=2				,						UpperCamelCase_=0.0				,						UpperCamelCase_=10				,						UpperCamelCase_=0				,						UpperCamelCase_="mean"				,						UpperCamelCase_=False				,						UpperCamelCase_=False				,						UpperCamelCase_=2_56				,						UpperCamelCase_=0				,						UpperCamelCase_=1				,						UpperCamelCase_=2				,						**UpperCamelCase_				,						)						->       Dict:
					super().__init__(**UpperCamelCase_				,						pad_token_id=UpperCamelCase_				,						bos_token_id=UpperCamelCase_				,						eos_token_id=UpperCamelCase_       )
					__lowercase			:  Any								=       hidden_size
					__lowercase			:  str								=       feat_extract_norm
					__lowercase			:  List[str]								=       feat_extract_activation
					__lowercase			:  Optional[int]								=       list(UpperCamelCase_       )
					__lowercase			:  Optional[Any]								=       list(UpperCamelCase_       )
					__lowercase			:  Tuple								=       list(UpperCamelCase_       )
					__lowercase			:  List[Any]								=       conv_bias
					__lowercase			:  Dict								=       num_conv_pos_embeddings
					__lowercase			:  Optional[int]								=       num_conv_pos_embedding_groups
					__lowercase			:  str								=       len(self.conv_dim       )
					__lowercase			:  Union[str, Any]								=       num_hidden_layers
					__lowercase			:  List[Any]								=       intermediate_size
					__lowercase			:  List[Any]								=       squeeze_factor
					__lowercase			:  int								=       hidden_act
					__lowercase			:  Optional[Any]								=       num_attention_heads
					__lowercase			:  List[Any]								=       hidden_dropout
					__lowercase			:  int								=       attention_dropout
					__lowercase			:  str								=       activation_dropout
					__lowercase			:  Union[str, Any]								=       feat_proj_dropout
					__lowercase			:  Tuple								=       final_dropout
					__lowercase			:  Union[str, Any]								=       layerdrop
					__lowercase			:  Dict								=       layer_norm_eps
					__lowercase			:  Tuple								=       initializer_range
					__lowercase			:  Union[str, Any]								=       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
					__lowercase			:  Any								=       apply_spec_augment
					__lowercase			:  Optional[int]								=       mask_time_prob
					__lowercase			:  int								=       mask_time_length
					__lowercase			:  Dict								=       mask_time_min_masks
					__lowercase			:  Any								=       mask_feature_prob
					__lowercase			:  Tuple								=       mask_feature_length
					__lowercase			:  List[str]								=       mask_feature_min_masks

					# ctc loss
					__lowercase			:  int								=       ctc_loss_reduction
					__lowercase			:  str								=       ctc_zero_infinity

					# sequence classification
					__lowercase			:  str								=       use_weighted_layer_sum
					__lowercase			:  Tuple								=       classifier_proj_size
		@property
		def 	_lowerCamelCase							(						self       )						->       str:
					return functools.reduce(operator.mul				,						self.conv_stride				,						1       )







def     lowercase	(    a = 50 ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						[1] * (length + 1)

		for row_length in range(3     , length + 1 ):
				for block_length in range(3     , row_length + 1 ):
						for block_start in range(row_length - block_length ):
								ways_number[row_length] += ways_number[
								    row_length - block_start - block_length - 1
								]

						ways_number[row_length] += 1

		return ways_number[length]


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






