



'''simple docstring'''





import torch

from transformers import AutoModel






class 	lowerCAmelCase_(    torch.nn.Module					):
  '''simple docstring'''




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

      lowerCAmelCase__			:  Tuple											= AutoModel.from_pretrained(__UpperCAmelCase  ,return_dict=__UpperCAmelCase				)
      lowerCAmelCase__			:  Tuple											= torch.nn.CosineSimilarity(3  ,1E-08				)
      lowerCAmelCase__			:  Optional[Any]											= torch.nn.Softmax(dim=1				)




  def 							UpperCAmelCase_     (   self  ,**__UpperCAmelCase				)   ->		str:
      return self.bert(**__UpperCAmelCase				).last_hidden_state




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		List[Any]:
      return token_embeddings.sum(2  ,keepdim=__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=1				)   ->		Optional[int]:
      return self.softmax(T * self.cos(__UpperCAmelCase  ,__UpperCAmelCase				)				)






  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Tuple:
      lowerCAmelCase__			:  Any											= W_supports["""sizes"""].tolist()
      lowerCAmelCase__			:  Dict											= W_supports["""start_token_id"""].item()
      lowerCAmelCase__			:  Optional[Any]											= 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(**__UpperCAmelCase				)
      lowerCAmelCase__			:  List[Any]											= self.BERT(**__UpperCAmelCase				)

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

      lowerCAmelCase__			:  Any											= W_supports["""input_ids"""] == start_token_id
      lowerCAmelCase__			:  List[Any]											= W_supports["""input_ids"""] == end_token_id

      for i, size in enumerate(__UpperCAmelCase				):
          if i == 0:
              lowerCAmelCase__			:  Tuple											= 0
          else:
              lowerCAmelCase__			:  Optional[Any]											= support_sizes[i - 1]

          lowerCAmelCase__			:  List[str]											= S[s : s + size][start_token_masks[s : s + size]]
          lowerCAmelCase__			:  List[Any]											= S[s : s + size][end_token_masks[s : s + size]]

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

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

      return p_starts, p_ends







'''simple docstring'''





def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    def count_of_possible_combinations(UpperCamelCase      ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item      ) for item in array      )

    return count_of_possible_combinations(UpperCamelCase      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase							,   UpperCamelCase      ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        lowerCAmelCase__			:  Any											= sum(
            count_of_possible_combinations_with_dp_array(target - item							,   UpperCamelCase      )
            for item in array      )
        lowerCAmelCase__			:  Tuple											= answer
        return answer

    lowerCAmelCase__			:  Optional[int]											= [-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase							,   UpperCamelCase      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  str											= [0] * (target + 1)
    lowerCAmelCase__			:  List[Any]											= 1

    for i in range(1							,   target + 1      ):
        for j in range(UpperCamelCase      ):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _lowerCAmelCase										=     3
 _lowerCAmelCase										=     5
 _lowerCAmelCase										=     [1, 2, 5]
 print(combination_sum_iv(n, array, target))


