


from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
    from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline










def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   if n == 1 or not isinstance(lowerCAmelCase_	,    lowerCAmelCase_ ):
      return 0
   elif n == 2:
      return 1
   else:
      UpperCAmelCase      					=  [0, 1]
      for i in range(2	,    n + 1 ):
         sequence.append(sequence[i - 1] + sequence[i - 2] )

      return sequence[n]
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  2

   while digits < n:
      index += 1
      UpperCAmelCase      					=  len(str(fibonacci(lowerCAmelCase_ ) ) )

   return index
def 	_UpperCamelCase					(    lowerCAmelCase_ = 1_0_0_0 )    ->int:
   return fibonacci_digits_index(lowerCAmelCase_ )


if __name__ == "__main__":
 print(solution(int(str(input()).strip())))



