






"""simple docstring"""


import torch

from transformers import AutoTokenizer


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def __init__(      self					:	Dict,			_lowerCamelCase					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					AutoTokenizer.from_pretrained(_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str,			_lowerCamelCase					:	Optional[Any]    ):

     '''simple docstring'''

     if isinstance(_lowerCamelCase,			_lowerCamelCase    ) and all(isinstance(_x,			_lowerCamelCase    ) for _x in x    ):
       __A		     =					None
       for l in x:
         __A		     =					self.tokenizer(
             _lowerCamelCase,			padding='''max_length''',			max_length=3_84,			truncation=_lowerCamelCase,			return_tensors='''pt''',			)
         __A		     =					torch.tensor([len(_lowerCamelCase    )]    )
         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(_lowerCamelCase,			_lowerCamelCase    ) and all(isinstance(_x,			_lowerCamelCase    ) for _x in x    ):
       __A		     =					self.tokenizer(
           _lowerCamelCase,			padding='''max_length''',			max_length=3_84,			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 							_SCREAMING_SNAKE_CASE							(      self					:	int,			_lowerCamelCase					:	Union[str, Any],			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	str,			_lowerCamelCase					:	Optional[int],			_lowerCamelCase					:	Any=0.70    ):

     '''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 _lowerCamelCase    : (tup[2] * tup[3]),			reverse=_lowerCamelCase    )
       __A		     =					[]
       for k in range(len(_lowerCamelCase    )    ):
         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(_lowerCamelCase    )

     return final_outputs










from __future__ import annotations

import sys
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    if n < 0:
        raise ValueError("""Negative arguments are not supported"""		)
    return _fib(UpperCAmelCase_		)[0]
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    if n == 0:  # (F(0), F(1))
        return (0, 1)

    # F(2n) = F(n)[2F(n+1) − F(n)]
    # F(2n+1) = F(n+1)^2+F(n)^2
    a__			,		a__  			=							_fib(n // 2		)
    a__  			=							a * (b * 2 - a)
    a__  			=							a * a + b * b
    return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_    :       Dict       =							int(sys.argv[1])
 print(F"fibonacci({n}) is {fibonacci(n)}")






