code: 
  
  
  
  '''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
style_context: 
  
  
  
  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''']					)