
"""simple docstring"""




import torch

from transformers import AutoModel


class 			__magic_name__       (       torch.nn.Module ):
       def __init__(     self ,				A_="sayef/fsner-bert-base-uncased"       )     ->	str:





         """simple docstring"""




         super(A_ ,				self       ).__init__()

         _lowercase:			int       =     AutoModel.from_pretrained(A_ ,				return_dict=A_       )
         _lowercase:			Tuple       =     torch.nn.CosineSimilarity(3 ,				1E-08       )
         _lowercase:			Union[str, Any]       =     torch.nn.Softmax(dim=1       )
       def 							lowercase_							(     self ,				**A_       )     ->	Tuple:





         """simple docstring"""




         return self.bert(**A_       ).last_hidden_state
       def 							lowercase_							(     self ,				A_       )     ->	Any:





         """simple docstring"""




         return token_embeddings.sum(2 ,				keepdim=A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_=1       )     ->	Any:





         """simple docstring"""




         return self.softmax(T * self.cos(A_ ,				A_       )       )



       def 							lowercase_							(     self ,				A_ ,				A_       )     ->	int:





         """simple docstring"""




         _lowercase:			Tuple       =     W_supports['''sizes'''].tolist()
         _lowercase:			List[Any]       =     W_supports['''start_token_id'''].item()
         _lowercase:			str       =     W_supports['''end_token_id'''].item()

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

         _lowercase:			Optional[Any]       =     self.BERT(**A_       )
         _lowercase:			Optional[int]       =     self.BERT(**A_       )

         _lowercase:			Tuple       =     None
         _lowercase:			Dict       =     None

         _lowercase:			Tuple       =     W_supports['''input_ids'''] == start_token_id
         _lowercase:			Union[str, Any]       =     W_supports['''input_ids'''] == end_token_id

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

           _lowercase:			Union[str, Any]       =     S[s : s + size][start_token_masks[s : s + size]]
           _lowercase:			Union[str, Any]       =     S[s : s + size][end_token_masks[s : s + size]]

           _lowercase:			Tuple       =     torch.matmul(q[i] ,				s_start.T       ).sum(1       ).softmax(0       )
           _lowercase:			Optional[Any]       =     torch.matmul(q[i] ,				s_end.T       ).sum(1       ).softmax(0       )

           if p_starts is not None:
             _lowercase:			Tuple       =     torch.vstack((p_starts, p_start)       )
             _lowercase:			Tuple       =     torch.vstack((p_ends, p_end)       )
           else:
             _lowercase:			Optional[int]       =     p_start
             _lowercase:			Dict       =     p_end

         return p_starts, p_ends


"""simple docstring"""




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  return number | (1 << position)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  return number & ~(1 << position)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  return number ^ (1 << position)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  return ((number >> position) & 1) == 1




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  return int((number & (1 << position)) != 0						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
