


'''simple docstring'''


import functools
import operator

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


lowerCamelCase__    	=     logging.get_logger(__name__)

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






class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	List[str]          =     "sew-d"




			def __init__(		self  :     Union[str, Any]	,  lowerCamelCase__  :     Optional[int]=32	,  lowerCamelCase__  :     Any=7_68	,  lowerCamelCase__  :     int=12	,  lowerCamelCase__  :     str=12	,  lowerCamelCase__  :     List[str]=30_72	,  lowerCamelCase__  :     Any=2	,  lowerCamelCase__  :     List[str]=5_12	,  lowerCamelCase__  :     List[str]=2_56	,  lowerCamelCase__  :     List[str]=True	,  lowerCamelCase__  :     int=True	,  lowerCamelCase__  :     Tuple=("p2c", "c2p")	,  lowerCamelCase__  :     List[Any]="layer_norm"	,  lowerCamelCase__  :     Optional[int]="gelu_python"	,  lowerCamelCase__  :     List[Any]=0.1	,  lowerCamelCase__  :     Union[str, Any]=0.1	,  lowerCamelCase__  :     List[Any]=0.1	,  lowerCamelCase__  :     Union[str, Any]=0.0	,  lowerCamelCase__  :     Tuple=0.1	,  lowerCamelCase__  :     Tuple=0.0_2	,  lowerCamelCase__  :     Any=1E-7	,  lowerCamelCase__  :     Dict=1E-5	,  lowerCamelCase__  :     List[Any]="group"	,  lowerCamelCase__  :     Tuple="gelu"	,  lowerCamelCase__  :     List[Any]=(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)	,  lowerCamelCase__  :     List[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)	,  lowerCamelCase__  :     Tuple=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)	,  lowerCamelCase__  :     Tuple=False	,  lowerCamelCase__  :     Tuple=1_28	,  lowerCamelCase__  :     Dict=16	,  lowerCamelCase__  :     int=True	,  lowerCamelCase__  :     List[Any]=0.0_5	,  lowerCamelCase__  :     Union[str, Any]=10	,  lowerCamelCase__  :     Optional[int]=2	,  lowerCamelCase__  :     Tuple=0.0	,  lowerCamelCase__  :     Any=10	,  lowerCamelCase__  :     Optional[int]=0	,  lowerCamelCase__  :     Dict="mean"	,  lowerCamelCase__  :     List[Any]=False	,  lowerCamelCase__  :     int=False	,  lowerCamelCase__  :     Tuple=2_56	,  lowerCamelCase__  :     Dict=0	,  lowerCamelCase__  :     List[str]=1	,  lowerCamelCase__  :     List[str]=2	,  **lowerCamelCase__  :     Union[str, Any]	,  )   ->Optional[int]:



					'''simple docstring'''







					super().__init__(**lowerCamelCase__	,  pad_token_id=lowerCamelCase__	,  bos_token_id=lowerCamelCase__	,  eos_token_id=lowerCamelCase__ )
					_UpperCAmelCase		:       str								=      hidden_size
					_UpperCAmelCase		:       Any								=      feat_extract_norm
					_UpperCAmelCase		:       Optional[Any]								=      feat_extract_activation
					_UpperCAmelCase		:       Any								=      list(lowerCamelCase__ )
					_UpperCAmelCase		:       Tuple								=      list(lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[int]								=      list(lowerCamelCase__ )
					_UpperCAmelCase		:       List[str]								=      conv_bias
					_UpperCAmelCase		:       Tuple								=      num_conv_pos_embeddings
					_UpperCAmelCase		:       Tuple								=      num_conv_pos_embedding_groups
					_UpperCAmelCase		:       Tuple								=      len(self.conv_dim )
					_UpperCAmelCase		:       List[str]								=      num_hidden_layers
					_UpperCAmelCase		:       Optional[int]								=      intermediate_size
					_UpperCAmelCase		:       Tuple								=      squeeze_factor
					_UpperCAmelCase		:       Any								=      max_position_embeddings
					_UpperCAmelCase		:       Tuple								=      position_buckets
					_UpperCAmelCase		:       Any								=      share_att_key
					_UpperCAmelCase		:       str								=      relative_attention
					_UpperCAmelCase		:       Union[str, Any]								=      norm_rel_ebd
					_UpperCAmelCase		:       List[str]								=      list(lowerCamelCase__ )
					_UpperCAmelCase		:       Dict								=      hidden_act
					_UpperCAmelCase		:       List[str]								=      num_attention_heads
					_UpperCAmelCase		:       Any								=      hidden_dropout
					_UpperCAmelCase		:       int								=      attention_dropout
					_UpperCAmelCase		:       Union[str, Any]								=      activation_dropout
					_UpperCAmelCase		:       int								=      feat_proj_dropout
					_UpperCAmelCase		:       List[str]								=      final_dropout
					_UpperCAmelCase		:       Dict								=      layer_norm_eps
					_UpperCAmelCase		:       List[Any]								=      feature_layer_norm_eps
					_UpperCAmelCase		:       List[str]								=      initializer_range
					_UpperCAmelCase		:       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
					_UpperCAmelCase		:       List[str]								=      apply_spec_augment
					_UpperCAmelCase		:       List[str]								=      mask_time_prob
					_UpperCAmelCase		:       Tuple								=      mask_time_length
					_UpperCAmelCase		:       List[str]								=      mask_time_min_masks
					_UpperCAmelCase		:       Tuple								=      mask_feature_prob
					_UpperCAmelCase		:       Optional[Any]								=      mask_feature_length
					_UpperCAmelCase		:       List[str]								=      mask_feature_min_masks

					# ctc loss
					_UpperCAmelCase		:       Dict								=      ctc_loss_reduction
					_UpperCAmelCase		:       Tuple								=      ctc_zero_infinity

					# sequence classification
					_UpperCAmelCase		:       List[str]								=      use_weighted_layer_sum
					_UpperCAmelCase		:       Optional[Any]								=      classifier_proj_size






			@property
			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->List[Any]:



					'''simple docstring'''







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







'''simple docstring'''


import os






def  __lowerCAmelCase ():
		_UpperCAmelCase		:       List[Any]								=      os.path.join(os.path.dirname(__lowerCAmelCase   )      , "num.txt"   )
		with open(__lowerCAmelCase   ) as file_hand:
				return str(sum(int(__lowerCAmelCase   ) for line in file_hand   )   )[:10]


if __name__ == "__main__":
						print(solution())



