



"""simple docstring"""






def 					snake_case__       (		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			if n == 1 or not isinstance(__lowerCamelCase							,		__lowerCamelCase     ):
						return 0
			elif n == 2:
						return 1
			else:
						lowerCamelCase__		:		Optional[Any]		     =[0, 1]
						for i in range(2							,		n + 1     ):
									sequence.append(sequence[i - 1] + sequence[i - 2]     )

						return sequence[n]






def 					snake_case__       (		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]		     =0
			lowerCamelCase__		:		Any		     =2

			while digits < n:
						index += 1
						lowerCamelCase__		:		Any		     =len(str(fibonacci(__lowerCamelCase     )     )     )

			return index






def 					snake_case__       (		__lowerCamelCase    :   int = 1000     ):
			"""simple docstring"""
			return fibonacci_digits_index(__lowerCamelCase     )


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









"""simple docstring"""






import math

import flax.linen as nn
import jax.numpy as jnp






def 					snake_case__       (		__lowerCamelCase    :   jnp.ndarray							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   float = 1							,		__lowerCamelCase    :   float = 1							,		__lowerCamelCase    :   float = 1.0e4							,		__lowerCamelCase    :   bool = False							,		__lowerCamelCase    :   float = 1.0							,		):
			"""simple docstring"""
			assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
			assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
			lowerCamelCase__		:		Any		     =float(embedding_dim // 2     )
			lowerCamelCase__		:		List[str]		     =math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
			lowerCamelCase__		:		int		     =min_timescale * jnp.exp(jnp.arange(__lowerCamelCase							,		dtype=jnp.floataa     ) * -log_timescale_increment     )
			lowerCamelCase__		:		Tuple		     =jnp.expand_dims(__lowerCamelCase							,		1     ) * jnp.expand_dims(__lowerCamelCase							,		0     )

			# scale embeddings
			lowerCamelCase__		:		List[str]		     =scale * emb

			if flip_sin_to_cos:
						lowerCamelCase__		:		int		     =jnp.concatenate([jnp.cos(__lowerCamelCase     ), jnp.sin(__lowerCamelCase     )]							,		axis=1     )
			else:
						lowerCamelCase__		:		List[str]		     =jnp.concatenate([jnp.sin(__lowerCamelCase     ), jnp.cos(__lowerCamelCase     )]							,		axis=1     )
			lowerCamelCase__		:		str		     =jnp.reshape(__lowerCamelCase							,		[jnp.shape(__lowerCamelCase     )[0], embedding_dim]     )
			return signal

class 	__SCREAMING_SNAKE_CASE     (				nn.Module       ):


					'''simple docstring'''





					_a             =							3_2
					_a             =							jnp.floataa



					@nn.compact
					def __call__(	self							:  Optional[Any],  lowerCamelCase							:  int					)-> Any:
								lowerCamelCase__		:		Optional[Any]		     =nn.Dense(self.time_embed_dim,  dtype=self.dtype,  name='''linear_1'''					)(lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =nn.silu(lowerCamelCase					)
								lowerCamelCase__		:		Any		     =nn.Dense(self.time_embed_dim,  dtype=self.dtype,  name='''linear_2'''					)(lowerCamelCase					)
								return temb





class 	__SCREAMING_SNAKE_CASE     (				nn.Module       ):


					'''simple docstring'''





					_a             =							3_2
					_a             =							False
					_a             =							1



					@nn.compact
					def __call__(	self							:  Any,  lowerCamelCase							:  int					)-> int:
								return get_sinusoidal_embeddings(
								    lowerCamelCase,  embedding_dim=self.dim,  flip_sin_to_cos=self.flip_sin_to_cos,  freq_shift=self.freq_shift					)




