

'''simple docstring'''







import torch

from transformers import AutoModel


class    A__ ( torch.nn.Module     ):



    def __init__(   self						,			UpperCamelCase__="sayef/fsner-bert-base-uncased"    )  -> Any:





           '''simple docstring'''
           super(__a						,			self    ).__init__()

           A_		   = AutoModel.from_pretrained(__a						,			return_dict=__a    )
           A_		   = torch.nn.CosineSimilarity(3						,			1e-0_8    )
           A_		   = torch.nn.Softmax(dim=1    )



    def    snake_case_     (   self						,			**UpperCamelCase__    )  -> Dict:





           '''simple docstring'''
           return self.bert(**__a    ).last_hidden_state



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> str:





           '''simple docstring'''
           return token_embeddings.sum(2						,			keepdim=__a    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=1    )  -> Any:





           '''simple docstring'''
           return self.softmax(T * self.cos(__a						,			__a    )    )







    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__    )  -> str:





           '''simple docstring'''
           A_		   = W_supports["""sizes"""].tolist()
           A_		   = W_supports["""start_token_id"""].item()
           A_		   = W_supports["""end_token_id"""].item()

           del W_supports["sizes"]
           del W_supports["start_token_id"]
           del W_supports["end_token_id"]

           A_		   = self.BERT(**__a    )
           A_		   = self.BERT(**__a    )

           A_		   = None
           A_		   = None

           A_		   = W_supports["""input_ids"""] == start_token_id
           A_		   = W_supports["""input_ids"""] == end_token_id

           for i, size in enumerate(__a    ):
                  if i == 0:
                         A_		   = 0
                  else:
                         A_		   = support_sizes[i - 1]

                  A_		   = S[s : s + size][start_token_masks[s : s + size]]
                  A_		   = S[s : s + size][end_token_masks[s : s + size]]

                  A_		   = torch.matmul(q[i]						,			s_start.T    ).sum(1    ).softmax(0    )
                  A_		   = torch.matmul(q[i]						,			s_end.T    ).sum(1    ).softmax(0    )

                  if p_starts is not None:
                         A_		   = torch.vstack((p_starts, p_start)    )
                         A_		   = torch.vstack((p_ends, p_end)    )
                  else:
                         A_		   = p_start
                         A_		   = p_end

           return p_starts, p_ends



'''simple docstring'''







from __future__ import annotations

from typing import Any
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       if not postfix_notation:
              return 0

       A_		   = {"""+""", """-""", """*""", """/"""}
       A_		   = []

       for token in postfix_notation:
              if token in operations:
                     A_   ,     A_		   = stack.pop(), stack.pop()
                     if token == "+":
                            stack.append(a + b			)
                     elif token == "-":
                            stack.append(a - b			)
                     elif token == "*":
                            stack.append(a * b			)
                     else:
                            if a * b < 0 and a % b != 0:
                                   stack.append(a // b + 1			)
                            else:
                                   stack.append(a // b			)
              else:
                     stack.append(int(UpperCAmelCase__			)			)

       return stack.pop()


if __name__ == "__main__":
      import doctest

      doctest.testmod()
