
def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      _validate_point(UpperCamelCase__		)
      _validate_point(UpperCamelCase__		)
      if len(UpperCamelCase__		) != len(UpperCamelCase__		):
            raise ValueError('''Both points must be in the same n-dimensional space'''		)

      return float(sum(abs(a - b		) for a, b in zip(UpperCamelCase__ ,		UpperCamelCase__		)		)		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      if point:
            if isinstance(UpperCamelCase__ ,		UpperCamelCase__		):
                  for item in point:
                        if not isinstance(UpperCamelCase__ ,		(int, float)		):
                              UpperCamelCase__    :   Optional[Any]					=				(
                                  '''Expected a list of numbers as input, found '''
                                  f'''{type(UpperCamelCase__		).__name__}'''
                              )
                              raise TypeError(UpperCamelCase__		)
            else:
                  UpperCamelCase__    :   List[str]					=				f'''Expected a list of numbers as input, found {type(UpperCamelCase__		).__name__}'''
                  raise TypeError(UpperCamelCase__		)
      else:
            raise ValueError('''Missing an input'''		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      _validate_point(UpperCamelCase__		)
      _validate_point(UpperCamelCase__		)
      if len(UpperCamelCase__		) != len(UpperCamelCase__		):
            raise ValueError('''Both points must be in the same n-dimensional space'''		)

      return float(sum(abs(x - y		) for x, y in zip(UpperCamelCase__ ,		UpperCamelCase__		)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




import torch

from transformers import AutoModel




class 				_lowerCamelCase (      torch.nn.Module ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"  )  ->   Optional[Any]:




           """simple docstring"""







           super(__SCREAMING_SNAKE_CASE						,						self  ).__init__()

           UpperCamelCase__    :   Tuple					=				AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE						,						return_dict=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				torch.nn.CosineSimilarity(3						,						1e-08  )
           UpperCamelCase__    :   List[Any]					=				torch.nn.Softmax(dim=1  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						**__SCREAMING_SNAKE_CASE  )  ->   Union[str, Any]:




           """simple docstring"""







           return self.bert(**__SCREAMING_SNAKE_CASE  ).last_hidden_state






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Dict:




           """simple docstring"""







           return token_embeddings.sum(2						,						keepdim=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=1  )  ->   str:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				W_supports['''sizes'''].tolist()
           UpperCamelCase__    :   Tuple					=				W_supports['''start_token_id'''].item()
           UpperCamelCase__    :   int					=				W_supports['''end_token_id'''].item()

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

           UpperCamelCase__    :   Optional[int]					=				self.BERT(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				self.BERT(**__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   int					=				None
           UpperCamelCase__    :   Dict					=				None

           UpperCamelCase__    :   Any					=				W_supports['''input_ids'''] == start_token_id
           UpperCamelCase__    :   Optional[int]					=				W_supports['''input_ids'''] == end_token_id

           for i, size in enumerate(__SCREAMING_SNAKE_CASE  ):
                 if i == 0:
                       UpperCamelCase__    :   int					=				0
                 else:
                       UpperCamelCase__    :   Optional[int]					=				support_sizes[i - 1]

                 UpperCamelCase__    :   List[Any]					=				S[s : s + size][start_token_masks[s : s + size]]
                 UpperCamelCase__    :   Optional[Any]					=				S[s : s + size][end_token_masks[s : s + size]]

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

                 if p_starts is not None:
                       UpperCamelCase__    :   Union[str, Any]					=				torch.vstack((p_starts, p_start)  )
                       UpperCamelCase__    :   str					=				torch.vstack((p_ends, p_end)  )
                 else:
                       UpperCamelCase__    :   List[Any]					=				p_start
                       UpperCamelCase__    :   List[str]					=				p_end

           return p_starts, p_ends


