



'''simple docstring'''





import torch

from transformers import AutoTokenizer



class 		_snake_case      (			lowerCamelCase				):





 """simple docstring"""



 def __init__(						self	,    a					)	->							Tuple:




   """simple docstring"""







   _A           =    AutoTokenizer.from_pretrained(a					)
 def lowercase_		(						self	,    a					)	->							Optional[Any]:




   """simple docstring"""







   if isinstance(a	,    a					) and all(isinstance(_x	,    a					) for _x in x					):
     _A           =    None
     for l in x:
       _A           =    self.tokenizer(
           a	,    padding='''max_length'''	,    max_length=3_8_4	,    truncation=a	,    return_tensors='''pt'''	,    )
       _A           =    torch.tensor([len(a					)]					)
       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(a	,    a					) and all(isinstance(_x	,    a					) for _x in x					):
     _A           =    self.tokenizer(
         a	,    padding='''max_length'''	,    max_length=3_8_4	,    truncation=a	,    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 lowercase_		(						self	,    a	,    a	,    a	,    a	,    a=0.70					)	->							List[str]:




   """simple docstring"""







   _A           =    []
   for idx in range(len(W_query['''input_ids''']					)					):
     _A           =    _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 a					: (tup[2] * tup[3])	,    reverse=a					)
     _A           =    []
     for k in range(len(a					)					):
       if output[k][2] * output[k][3] >= thresh:
         _A    ,   _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(a					)

   return final_outputs




from ..utils import requires_backends


__lowerCamelCase  				=					None



class 		_snake_case      :





 """simple docstring"""



 def __init__(						self	,    *a	,    **a					)	->							Tuple:




   """simple docstring"""







   requires_backends(self	,    ['''detectron2''']					)



 @classmethod
 def lowercase_		(						cls	,    *a	,    **a					)	->							Optional[int]:




   """simple docstring"""







   requires_backends(cls	,    ['''detectron2''']					)