


import functools
import operator

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


snake_case							:	int					      =				logging.get_logger(__name__)

snake_case							:	str					      =				{
    '''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 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   str   				=      '''sew'''
					def __init__(				self       :List[str]      ,__snake_case       :Dict=32      ,__snake_case       :Optional[Any]=7_68      ,__snake_case       :int=12      ,__snake_case       :Any=12      ,__snake_case       :str=30_72      ,__snake_case       :Any=2      ,__snake_case       :Dict="gelu"      ,__snake_case       :Union[str, Any]=0.1      ,__snake_case       :Dict=0.1      ,__snake_case       :Tuple=0.1      ,__snake_case       :int=0.0      ,__snake_case       :str=0.1      ,__snake_case       :Optional[Any]=0.1      ,__snake_case       :Optional[Any]=0.02      ,__snake_case       :Dict=1E-5      ,__snake_case       :Tuple="group"      ,__snake_case       :Optional[int]="gelu"      ,__snake_case       :Dict=(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)      ,__snake_case       :Optional[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)      ,__snake_case       :Any=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)      ,__snake_case       :Dict=False      ,__snake_case       :List[Any]=1_28      ,__snake_case       :Optional[Any]=16      ,__snake_case       :Union[str, Any]=True      ,__snake_case       :List[str]=0.05      ,__snake_case       :Optional[int]=10      ,__snake_case       :List[Any]=2      ,__snake_case       :str=0.0      ,__snake_case       :List[Any]=10      ,__snake_case       :Optional[int]=0      ,__snake_case       :Dict="mean"      ,__snake_case       :List[str]=False      ,__snake_case       :str=False      ,__snake_case       :Dict=2_56      ,__snake_case       :Optional[int]=0      ,__snake_case       :str=1      ,__snake_case       :Any=2      ,**__snake_case       :List[str]      ,) ->   Any:
												super().__init__(**__snake_case      ,pad_token_id=__snake_case      ,bos_token_id=__snake_case      ,eos_token_id=__snake_case				)
												a__					=					hidden_size
												a__					=					feat_extract_norm
												a__					=					feat_extract_activation
												a__					=					list(__snake_case				)
												a__					=					list(__snake_case				)
												a__					=					list(__snake_case				)
												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       :Optional[Any]				) ->   Optional[int]:
												return functools.reduce(operator.mul      ,self.conv_stride      ,1				)










from __future__ import annotations






def 							__lowercase       (			__lowerCAmelCase							:    float ,   __lowerCAmelCase							:    float ,   __lowerCAmelCase							:    float       ):
							if (voltage, current, resistance).count(0       ) != 1:
														raise ValueError('One and only one argument must be 0'       )
							if resistance < 0:
														raise ValueError('Resistance cannot be negative'       )
							if voltage == 0:
														return {"voltage": float(current * resistance       )}
							elif current == 0:
														return {"current": voltage / resistance}
							elif resistance == 0:
														return {"resistance": voltage / current}
							else:
														raise ValueError('Exactly one argument must be 0'       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




