






"""simple docstring"""



from math import floor, sqrt
def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  int   ):





  '''simple docstring'''





  lowercase							   =	0.0
  lowercase							   =	1.0
  lowercase							   =	int(sqrt(__snake_case   )   )
  lowercase							   =	root
  lowercase							   =	0
  while integer_part != 2 * root:
    lowercase							   =	denominator * integer_part - numerator
    lowercase							   =	(n - numerator**2) / denominator
    lowercase							   =	int((root + numerator) / denominator   )
    period += 1
  return period
def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  int = 1_00_00   ):





  '''simple docstring'''





  lowercase							   =	0
  for i in range(2		, n + 1   ):
    lowercase							   =	sqrt(__snake_case   )
    if sr - floor(__snake_case   ) != 0 and continuous_fraction_period(__snake_case   ) % 2 == 1:
      count_odd_periods += 1
  return count_odd_periods


if __name__ == "__main__":
    print(F'''{solution(int(input().strip()))}''')










"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase		:  str						=		{
    'configuration_wav2vec2_conformer': [
        'WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Wav2Vec2ConformerConfig',
    ],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  str						=		[
        'WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'Wav2Vec2ConformerForAudioFrameClassification',
        'Wav2Vec2ConformerForCTC',
        'Wav2Vec2ConformerForPreTraining',
        'Wav2Vec2ConformerForSequenceClassification',
        'Wav2Vec2ConformerForXVector',
        'Wav2Vec2ConformerModel',
        'Wav2Vec2ConformerPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_wavaveca_conformer import (
        WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        WavaVecaConformerConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_wavaveca_conformer import (
            WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            WavaVecaConformerForAudioFrameClassification,
            WavaVecaConformerForCTC,
            WavaVecaConformerForPreTraining,
            WavaVecaConformerForSequenceClassification,
            WavaVecaConformerForXVector,
            WavaVecaConformerModel,
            WavaVecaConformerPreTrainedModel,
        )

else:
    import sys

    _UpperCamelCase		:  Union[str, Any]						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


