'''simple docstring'''





def     __lowerCAmelCase						(       a_		)      ->	int:





     '''simple docstring'''





     if n == 1 or not isinstance(a_       ,      a_		):
          return 0
     elif n == 2:
          return 1
     else:
          SCREAMING_SNAKE_CASE					:			Dict    	=       [0, 1]
          for i in range(2       ,      n + 1		):
               sequence.append(sequence[i - 1] + sequence[i - 2]		)

          return sequence[n]


def     __lowerCAmelCase						(       a_		)      ->	int:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       0
     SCREAMING_SNAKE_CASE					:			List[str]    	=       2

     while digits < n:
          index += 1
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       len(str(fibonacci(a_		)		)		)

     return index


def     __lowerCAmelCase						(       a_ = 1000		)      ->	int:





     '''simple docstring'''





     return fibonacci_digits_index(a_		)


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




'''simple docstring'''





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


_lowerCAmelCase  :List[Any]  				=    False






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    pass






@nightly
@require_torch_gpu
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Any:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Dict    	=       VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
         # remove text_unet
         pipe.remove_unused_weights()
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'A painting of a squirrel eating a burger '
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy' ).images

         with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(lowercase__ )
              SCREAMING_SNAKE_CASE					:			int    	=       VersatileDiffusionTextToImagePipeline.from_pretrained(lowercase__ )
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       generator.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy' ).images

         assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       VersatileDiffusionTextToImagePipeline.from_pretrained(
             'shi-labs/versatile-diffusion'    ,							torch_dtype=torch.floataa )
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			int    	=       'A painting of a squirrel eating a burger '
         SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=50    ,							output_type='numpy' ).images

         SCREAMING_SNAKE_CASE					:			Dict    	=       image[0, 253:256, 253:256, -1]

         assert image.shape == (1, 512, 512, 3)
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

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



