






'''simple docstring'''



import functools
import operator

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


SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						=	{
    '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 UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			'''sew-d'''






      def __init__( self			:		Optional[Any]     ,      UpperCAmelCase			:		Any=32     ,      UpperCAmelCase			:		str=768     ,      UpperCAmelCase			:		List[Any]=12     ,      UpperCAmelCase			:		List[str]=12     ,      UpperCAmelCase			:		List[Any]=3072     ,      UpperCAmelCase			:		Optional[int]=2     ,      UpperCAmelCase			:		List[str]=512     ,      UpperCAmelCase			:		str=256     ,      UpperCAmelCase			:		Any=True     ,      UpperCAmelCase			:		int=True     ,      UpperCAmelCase			:		str=("p2c", "c2p")     ,      UpperCAmelCase			:		Optional[Any]="layer_norm"     ,      UpperCAmelCase			:		Dict="gelu_python"     ,      UpperCAmelCase			:		Optional[Any]=0.1     ,      UpperCAmelCase			:		Optional[Any]=0.1     ,      UpperCAmelCase			:		List[Any]=0.1     ,      UpperCAmelCase			:		List[Any]=0.0     ,      UpperCAmelCase			:		Union[str, Any]=0.1     ,      UpperCAmelCase			:		str=0.0_2     ,      UpperCAmelCase			:		List[str]=1e-7     ,      UpperCAmelCase			:		Dict=1e-5     ,      UpperCAmelCase			:		Optional[Any]="group"     ,      UpperCAmelCase			:		str="gelu"     ,      UpperCAmelCase			:		str=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)     ,      UpperCAmelCase			:		Optional[int]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)     ,      UpperCAmelCase			:		int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)     ,      UpperCAmelCase			:		Union[str, Any]=False     ,      UpperCAmelCase			:		Dict=128     ,      UpperCAmelCase			:		Union[str, Any]=16     ,      UpperCAmelCase			:		int=True     ,      UpperCAmelCase			:		List[str]=0.0_5     ,      UpperCAmelCase			:		Tuple=10     ,      UpperCAmelCase			:		Any=2     ,      UpperCAmelCase			:		Dict=0.0     ,      UpperCAmelCase			:		Tuple=10     ,      UpperCAmelCase			:		List[str]=0     ,      UpperCAmelCase			:		Optional[Any]="mean"     ,      UpperCAmelCase			:		Any=False     ,      UpperCAmelCase			:		int=False     ,      UpperCAmelCase			:		List[str]=256     ,      UpperCAmelCase			:		str=0     ,      UpperCAmelCase			:		List[str]=1     ,      UpperCAmelCase			:		Tuple=2     ,      **UpperCAmelCase			:		int     ,      )  ->  str:


       '''simple docstring'''

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

       # ctc loss
       lowercase					:   List[str]				  =ctc_loss_reduction
       lowercase					:   Dict				  =ctc_zero_infinity

       # sequence classification
       lowercase					:   int				  =use_weighted_layer_sum
       lowercase					:   Dict				  =classifier_proj_size






      @property
      def 				A__      ( self			:		int		)  ->  Any:


       '''simple docstring'''

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











'''simple docstring'''



SCREAMING_SNAKE_CASE						=	'Alexander Joslin'

import operator as op

from .stack import Stack


def 							lowercase_				(    __A :       str			)       ->      int:



 """simple docstring"""




 lowercase					:   int				  ={'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

 lowercase					:   Stack[int]				  =Stack()
 lowercase					:   Stack[str]				  =Stack()

 for i in equation:
  if i.isdigit():
   # RULE 1
   operand_stack.push(int(__A			)			)
  elif i in operators:
   # RULE 2
   operator_stack.push(__A			)
  elif i == ")":
   # RULE 4
   lowercase					:   Optional[Any]				  =operator_stack.peek()
   operator_stack.pop()
   lowercase					:   Optional[Any]				  =operand_stack.peek()
   operand_stack.pop()
   lowercase					:   Optional[Any]				  =operand_stack.peek()
   operand_stack.pop()

   lowercase					:   List[str]				  =operators[opr](__A					,      __A			)
   operand_stack.push(__A			)

    # RULE 5
 return operand_stack.peek()


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	'(5 + ((4 * 2) * (2 + 3)))'
      # answer = 45
      print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



