




"""simple docstring"""





import torch

from transformers import AutoModel



class      a__				(	torch.nn.Module   ):


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


      """simple docstring"""




      super(UpperCamelCase_      ,  self).__init__()

      __UpperCAmelCase	: List[str]		=					AutoModel.from_pretrained(UpperCamelCase_      ,  return_dict=UpperCamelCase_)
      __UpperCAmelCase	: List[Any]		=					torch.nn.CosineSimilarity(3      ,  1e-08)
      __UpperCAmelCase	: List[str]		=					torch.nn.Softmax(dim=1)


   def       a_     (      self      :    Optional[Any]      ,  **UpperCamelCase_      :    Any):


      """simple docstring"""




      return self.bert(**UpperCamelCase_).last_hidden_state


   def       a_     (      self      :    List[Any]      ,  UpperCamelCase_      :    Tuple):


      """simple docstring"""




      return token_embeddings.sum(2      ,  keepdim=UpperCamelCase_)


   def       a_     (      self      :    int      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    Union[str, Any]      ,  UpperCamelCase_      :    List[str]=1):


      """simple docstring"""




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


   def       a_     (      self      :    List[Any]      ,  UpperCamelCase_      :    Any      ,  UpperCamelCase_      :    List[str]):


      """simple docstring"""




      __UpperCAmelCase	: str		=					W_supports["sizes"].tolist()
      __UpperCAmelCase	: Dict		=					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	: str		=					self.BERT(**UpperCamelCase_)
      __UpperCAmelCase	: List[str]		=					self.BERT(**UpperCamelCase_)

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

      __UpperCAmelCase	: List[str]		=					W_supports["input_ids"] == start_token_id
      __UpperCAmelCase	: int		=					W_supports["input_ids"] == end_token_id

      for i, size in enumerate(UpperCamelCase_):
         if i == 0:
            __UpperCAmelCase	: Tuple		=					0
         else:
            __UpperCAmelCase	: Optional[Any]		=					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	: Dict		=					torch.matmul(q[i]      ,  s_start.T).sum(1).softmax(0)
         __UpperCAmelCase	: Any		=					torch.matmul(q[i]      ,  s_end.T).sum(1).softmax(0)

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

      return p_starts, p_ends








"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase							)			->			int:


			"""simple docstring"""

			if not grid or not grid[0]:
						raise TypeError("The grid does not contain the appropriate information"							)

			for cell_n in range(1				,       len(grid[0]							)							):
						grid[0][cell_n] += grid[0][cell_n - 1]
			__UpperCAmelCase	: int		=					grid[0]

			for row_n in range(1				,       len(UpperCamelCase							)							):
						__UpperCAmelCase	: int		=					grid[row_n]
						__UpperCAmelCase	: str		=					fill_row(UpperCamelCase				,       UpperCamelCase							)
						__UpperCAmelCase	: List[str]		=					grid[row_n]

			return grid[-1][-1]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			list:


			"""simple docstring"""
			current_row[0] += row_above[0]
			for cell_n in range(1				,       len(UpperCamelCase							)							):
						current_row[cell_n] += min(current_row[cell_n - 1]				,       row_above[cell_n]							)

			return current_row


if __name__ == "__main__":
					import doctest

					doctest.testmod()


