



"""simple docstring"""





def 						__lowerCAmelCase    (		__UpperCamelCase   :      int     ):


						'''simple docstring'''






						if n == 1 or not isinstance(__UpperCamelCase       ,			__UpperCamelCase     ):
												return 0
						elif n == 2:
												return 1
						else:
												snake_case_ :			Union[str, Any]            =      [0, 1]
												for i in range(2       ,			n + 1     ):
																		sequence.append(sequence[i - 1] + sequence[i - 2]     )

												return sequence[n]
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int     ):


						'''simple docstring'''






						snake_case_ :			Dict            =      0
						snake_case_ :			Optional[Any]            =      2

						while digits < n:
												index += 1
												snake_case_ :			Any            =      len(str(fibonacci(__UpperCamelCase     )     )     )

						return index
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int = 1_0_0_0     ):


						'''simple docstring'''






						return fibonacci_digits_index(__UpperCamelCase     )


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







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a    :							Tuple										=    {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a    :							str										=    [
							    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'EncodecModel',
							    'EncodecPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_encodec import (
							    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    EncodecConfig,
							)
							from .feature_extraction_encodec import EncodecFeatureExtractor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_encodec import (
														    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EncodecModel,
														    EncodecPreTrainedModel,
														)

else:
							import sys

							a    :							List[Any]										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

