




'''simple docstring'''



def 			SCREAMING_SNAKE_CASE_			(							__A    :			str  )			->			Any:
   return " ".join(input_str.split()[::-1]  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()







'''simple docstring'''



import torch

from transformers import AutoModel







class 		lowercase_   (			torch.nn.Module       ):


   """simple docstring"""

   def __init__(   self :     List[Any]  ,				__lowerCamelCase :     Union[str, Any]="sayef/fsner-bert-base-uncased"   ):

      """simple docstring"""
      super(__lowerCamelCase  ,				self   ).__init__()

      _SCREAMING_SNAKE_CASE	=				AutoModel.from_pretrained(__lowerCamelCase  ,				return_dict=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				torch.nn.CosineSimilarity(3  ,				1e-08   )
      _SCREAMING_SNAKE_CASE	=				torch.nn.Softmax(dim=1   )



   def 							lowerCAmelCase_      (   self :     Dict  ,				**__lowerCamelCase :     Any   ):

      """simple docstring"""
      return self.bert(**__lowerCamelCase   ).last_hidden_state



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      return token_embeddings.sum(2  ,				keepdim=__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Tuple=1   ):

      """simple docstring"""
      return self.softmax(T * self.cos(__lowerCamelCase  ,				__lowerCamelCase   )   )



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				W_supports["sizes"].tolist()
      _SCREAMING_SNAKE_CASE	=				W_supports["start_token_id"].item()
      _SCREAMING_SNAKE_CASE	=				W_supports["end_token_id"].item()

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

      _SCREAMING_SNAKE_CASE	=				self.BERT(**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.BERT(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				None
      _SCREAMING_SNAKE_CASE	=				None

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

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

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

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

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

      return p_starts, p_ends

