




'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case   :   str     							=	logging.get_logger(__name__)

_snake_case   :   Optional[int]     							=	{
    'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}

class     A					(	a__					):
							lowercase_           =       "vit_mae"







							def __init__(						self :						str							,    lowerCAmelCase_ :						Optional[Any]=7_68							,    lowerCAmelCase_ :						Union[str, Any]=12							,    lowerCAmelCase_ :						Tuple=12							,    lowerCAmelCase_ :						str=30_72							,    lowerCAmelCase_ :						str="gelu"							,    lowerCAmelCase_ :						List[str]=0.0							,    lowerCAmelCase_ :						Optional[Any]=0.0							,    lowerCAmelCase_ :						Union[str, Any]=0.0_2							,    lowerCAmelCase_ :						List[Any]=1e-12							,    lowerCAmelCase_ :						List[str]=2_24							,    lowerCAmelCase_ :						Optional[Any]=16							,    lowerCAmelCase_ :						Optional[Any]=3							,    lowerCAmelCase_ :						Optional[Any]=True							,    lowerCAmelCase_ :						Union[str, Any]=16							,    lowerCAmelCase_ :						Dict=5_12							,    lowerCAmelCase_ :						Tuple=8							,    lowerCAmelCase_ :						int=20_48							,    lowerCAmelCase_ :						List[Any]=0.7_5							,    lowerCAmelCase_ :						Optional[int]=False							,    **lowerCAmelCase_ :						int							,    )    ->							List[str]:


														"""simple docstring"""


														super().__init__(**lowerCamelCase_      )

														_a			  =       hidden_size
														_a			  =       num_hidden_layers
														_a			  =       num_attention_heads
														_a			  =       intermediate_size
														_a			  =       hidden_act
														_a			  =       hidden_dropout_prob
														_a			  =       attention_probs_dropout_prob
														_a			  =       initializer_range
														_a			  =       layer_norm_eps
														_a			  =       image_size
														_a			  =       patch_size
														_a			  =       num_channels
														_a			  =       qkv_bias
														_a			  =       decoder_num_attention_heads
														_a			  =       decoder_hidden_size
														_a			  =       decoder_num_hidden_layers
														_a			  =       decoder_intermediate_size
														_a			  =       mask_ratio
														_a			  =       norm_pix_loss







'''simple docstring'''


def 	snake_case_					(UpperCamelCase :						int   ):





							'''simple docstring'''


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

														return sequence[n]
def 	snake_case_					(UpperCamelCase :						int   ):





							'''simple docstring'''


							_a			  =       0
							_a			  =       2

							while digits < n:
														index += 1
														_a			  =       len(str(fibonacci(UpperCamelCase   )   )   )

							return index
def 	snake_case_					(UpperCamelCase :						int = 1000   ):





							'''simple docstring'''


							return fibonacci_digits_index(UpperCamelCase   )


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

