






import torch

from transformers import AutoTokenizer




class      __SCREAMING_SNAKE_CASE					(__A					):


  """simple docstring"""


  def __init__( self      ,  UpperCamelCase__  ):






         """simple docstring"""


         a_	    = AutoTokenizer.from_pretrained(UpperCamelCase__  )







  def       _a					( self      ,  UpperCamelCase__  ):






         """simple docstring"""


         if isinstance(UpperCamelCase__      ,  UpperCamelCase__  ) and all(isinstance(_x      ,  UpperCamelCase__  ) for _x in x  ):
                a_	    = None
                for l in x:
                       a_	    = self.tokenizer(
                           UpperCamelCase__      ,  padding='max_length'      ,  max_length=384      ,  truncation=UpperCamelCase__      ,  return_tensors='pt'      ,  )
                       a_	    = torch.tensor([len(UpperCamelCase__  )]  )
                       if d is not None:
                              for k in d.keys():
                                     a_	    = torch.cat((d[k], t[k])      ,  0  )
                       else:
                              a_	    = t

                a_	    = torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'  )  )
                a_	    = torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'  )  )

         elif isinstance(UpperCamelCase__      ,  UpperCamelCase__  ) and all(isinstance(_x      ,  UpperCamelCase__  ) for _x in x  ):
                a_	    = self.tokenizer(
                    UpperCamelCase__      ,  padding='max_length'      ,  max_length=384      ,  truncation=UpperCamelCase__      ,  return_tensors='pt'      ,  )

         else:
                raise Exception(
                    'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
                    ' strings` for supports are supported.'  )

         return d







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__=0.70  ):






         """simple docstring"""


         a_	    = []
         for idx in range(len(W_query['input_ids']  )  ):
                a_	    = range(p_start.shape[1]  )

                a_	    = []
                for start_id in start_indexes:
                       for end_id in end_indexes:
                              if start_id < end_id:
                                     output.append(
                                         (
                                             start_id,
                                             end_id,
                                             p_start[idx][start_id].item(),
                                             p_end[idx][end_id].item(),
                                         )  )

                output.sort(key=lambda UpperCamelCase__  : (tup[2] * tup[3])      ,  reverse=UpperCamelCase__  )
                a_	    = []
                for k in range(len(UpperCamelCase__  )  ):
                       if output[k][2] * output[k][3] >= thresh:
                              a_	    = output[k][0], output[k][1]
                              a_	    = self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos]  )
                              temp.append((decoded, output[k][2] * output[k][3])  )

                final_outputs.append(UpperCamelCase__  )

         return final_outputs












'''simple docstring'''







def 			__UpperCamelCase				(			lowercase_					:     int      ):
       """simple docstring"""




       if not isinstance(lowercase_      ,					lowercase_      ):
              raise ValueError('double_factorial() only accepts integral values'      )
       if num < 0:
              raise ValueError('double_factorial() not defined for negative values'      )
       a_	    = 1
       for i in range(lowercase_      ,					0      ,					-2      ):
              value *= i
       return value


if __name__ == "__main__":
  import doctest

  doctest.testmod()




