"""simple docstring"""




import torch

from transformers import AutoModel
class 						__A							(			torch.nn.Module		):






     def __init__(		self    ,					a__="sayef/fsner-bert-base-uncased"  ):
       super(a__    ,					self  ).__init__()

       _lowerCAmelCase						:	int        =					AutoModel.from_pretrained(a__    ,					return_dict=a__  )
       _lowerCAmelCase						:	List[Any]        =					torch.nn.CosineSimilarity(3    ,					1e-08  )
       _lowerCAmelCase						:	Dict        =					torch.nn.Softmax(dim=1  )






     def    __A  (		self    ,					**a__  ):
       return self.bert(**a__  ).last_hidden_state






     def    __A  (		self    ,					a__  ):
       return token_embeddings.sum(2    ,					keepdim=a__  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__=1  ):
       return self.softmax(T * self.cos(a__    ,					a__  )  )






     def    __A  (		self    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Tuple        =					W_supports["""sizes"""].tolist()
       _lowerCAmelCase						:	List[Any]        =					W_supports["""start_token_id"""].item()
       _lowerCAmelCase						:	int        =					W_supports["""end_token_id"""].item()

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

       _lowerCAmelCase						:	str        =					self.BERT(**a__  )
       _lowerCAmelCase						:	Dict        =					self.BERT(**a__  )

       _lowerCAmelCase						:	List[str]        =					None
       _lowerCAmelCase						:	List[Any]        =					None

       _lowerCAmelCase						:	List[str]        =					W_supports["""input_ids"""] == start_token_id
       _lowerCAmelCase						:	Union[str, Any]        =					W_supports["""input_ids"""] == end_token_id

       for i, size in enumerate(a__  ):
         if i == 0:
           _lowerCAmelCase						:	int        =					0
         else:
           _lowerCAmelCase						:	Tuple        =					support_sizes[i - 1]

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

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

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

       return p_starts, p_ends





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int = 100     )	->				int:
  _lowerCAmelCase						:	Optional[Any]        =					n * (n + 1) * (2 * n + 1) / 6
  _lowerCAmelCase						:	Tuple        =					(n * (n + 1) / 2) ** 2
  return int(square_of_sum - sum_of_squares     )


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




