



"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      List[str]:
  if n == 1 or not isinstance(_lowerCAmelCase, _lowerCAmelCase	):
    return 0
  elif n == 2:
    return 1
  else:
    a_     :      str 						=     [0, 1]
    for i in range(2, n + 1	):
      sequence.append(sequence[i - 1] + sequence[i - 2]	)

    return sequence[n]



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
  a_     :      List[Any] 						=     0
  a_     :      Any 						=     2

  while digits < n:
    index += 1
    a_     :      List[Any] 						=     len(str(fibonacci(_lowerCAmelCase	)	)	)

  return index



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__ = 1_000	)			->      List[Any]:
  return fibonacci_digits_index(_lowerCAmelCase	)


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




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


SCREAMING_SNAKE_CASE_				= False




class 			snake_case_						(						unittest.TestCase				):
							pass




@slow
@require_torch_gpu
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"      )
									pipe.to(a_      )
									pipe.set_progress_bar_config(disable=a_      )

									a_     :      Optional[Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"      )
									a_     :      Optional[Any] 						=     torch.manual_seed(0      )
									a_     :      Dict 						=     pipe(
									    image=a_			,     generator=a_			,     guidance_scale=7.5			,     num_inference_steps=5_0			,     output_type="numpy"			,     ).images

									a_     :      List[Any] 						=     image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									a_     :      Any 						=     np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2