




import torch

from transformers import AutoModel







class   A(						torch.nn.Module   ):



 '''simple docstring'''


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

       """simple docstring"""






       super(snake_case_						,							self  ).__init__()

       lowerCamelCase_		     =    AutoModel.from_pretrained(snake_case_						,							return_dict=snake_case_  )
       lowerCamelCase_		     =    torch.nn.CosineSimilarity(3						,							1E-08  )
       lowerCamelCase_		     =    torch.nn.Softmax(dim=1  )

 def 				a__ (				self					:  Optional[int]						,							**A_					:  str  )   ->	int:

       """simple docstring"""






       return self.bert(**snake_case_  ).last_hidden_state

 def 				a__ (				self					:  Any						,							A_					:  List[Any]  )   ->	str:

       """simple docstring"""






       return token_embeddings.sum(2						,							keepdim=snake_case_  )

 def 				a__ (				self					:  Optional[Any]						,							A_					:  int						,							A_					:  Union[str, Any]						,							A_					:  Union[str, Any]=1  )   ->	Tuple:

       """simple docstring"""






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





 def 				a__ (				self					:  Any						,							A_					:  Optional[Any]						,							A_					:  int  )   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    W_supports["""sizes"""].tolist()
       lowerCamelCase_		     =    W_supports["""start_token_id"""].item()
       lowerCamelCase_		     =    W_supports["""end_token_id"""].item()

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

       lowerCamelCase_		     =    self.BERT(**snake_case_  )
       lowerCamelCase_		     =    self.BERT(**snake_case_  )

       lowerCamelCase_		     =    None
       lowerCamelCase_		     =    None

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

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

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

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

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

       return p_starts, p_ends








'''simple docstring'''





def 						__snake_case(				_lowerCAmelCase = 1_000		)		-> int:
      return sum(e for e in range(3     ,   _lowerCAmelCase		) if e % 3 == 0 or e % 5 == 0		)


if __name__ == "__main__":
       print(F"{solution() = }")
