



import torch

from transformers import AutoTokenizer







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   def __init__(    self:     Dict  ,lowerCamelCase_:     Optional[Any]							)	->      Union[str, Any]:
       UpperCAmelCase_     :       int  		=					AutoTokenizer.from_pretrained(lowerCamelCase_							)


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     Dict							)	->      str:

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

           UpperCAmelCase_     :       Optional[Any]  		=					torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""							)							)
           UpperCAmelCase_     :       List[Any]  		=					torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""							)							)

       elif isinstance(lowerCamelCase_  ,lowerCamelCase_							) and all(isinstance(_x  ,lowerCamelCase_							) for _x in x							):
           UpperCAmelCase_     :       List[Any]  		=					self.tokenizer(
               lowerCamelCase_  ,padding="""max_length"""  ,max_length=384  ,truncation=lowerCamelCase_  ,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:     Union[str, Any]  ,lowerCamelCase_:     List[str]  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     str  ,lowerCamelCase_:     int=0.7_0							)	->      Optional[int]:
       UpperCAmelCase_     :       Union[str, Any]  		=					[]
       for idx in range(len(W_query["""input_ids"""]							)							):
           UpperCAmelCase_     :       List[Any]  		=					range(p_start.shape[1]							)

           UpperCAmelCase_     :       str  		=					[]
           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 lowerCamelCase_							: (tup[2] * tup[3])  ,reverse=lowerCamelCase_							)
           UpperCAmelCase_     :       str  		=					[]
           for k in range(len(lowerCamelCase_							)							):
               if output[k][2] * output[k][3] >= thresh:
                   UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					output[k][0], output[k][1]
                   UpperCAmelCase_     :       Dict  		=					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(lowerCamelCase_							)

       return final_outputs

def   a_					(_lowerCAmelCase  :   str       )->				int:
    snake_case:						List[str]         =   0
    for letter in data:
        snake_case:						int         =   (hash_ << 4) + ord(_lowerCAmelCase       )
        snake_case:						Optional[int]         =   hash_ & 0xf_0_0_0_0_0_0_0
        if x != 0:
            hash_ ^= x >> 24
        hash_ &= ~x
    return hash_


if __name__ == "__main__":
    import doctest

    doctest.testmod()


