


from sklearn.metrics import mean_absolute_error

import datasets


__SCREAMING_SNAKE_CASE	:							List[str]           =  '''\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
'''

__SCREAMING_SNAKE_CASE	:							Tuple           =  '''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


__SCREAMING_SNAKE_CASE	:							Any           =  '''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 "raw_values" : Returns a full set of errors in case of multioutput input.

                 "uniform_average" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric("mae")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.5}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric("mae", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results)
    {\'mae\': array([0.5, 1. ])}
'''


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,		_KWARGS_DESCRIPTION )
class        __lowerCamelCase      (				datasets.Metric ):


				"""simple docstring"""







				def        lowerCAmelCase__			(				self     :    List[str]					):
								return datasets.MetricInfo(
								    description=_DESCRIPTION	,							citation=_CITATION	,							inputs_description=_KWARGS_DESCRIPTION	,							features=datasets.Features(self._get_feature_types()					)	,							reference_urls=[
								        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"""
								    ]	,							)






				def        lowerCAmelCase__			(				self     :    Union[str, Any]					):
								if self.config_name == "multilist":
												return {
												    "predictions": datasets.Sequence(datasets.Value("""float"""					)					),
												    "references": datasets.Sequence(datasets.Value("""float"""					)					),
												}
								else:
												return {
												    "predictions": datasets.Value("""float"""					),
												    "references": datasets.Value("""float"""					),
												}






				def        lowerCAmelCase__			(				self     :    List[str]	,							lowerCamelCase_     :    Optional[int]	,							lowerCamelCase_     :    Optional[int]	,							lowerCamelCase_     :    str=None	,							lowerCamelCase_     :    Union[str, Any]="uniform_average"					):
								_lowerCAmelCase  						=mean_absolute_error(lowerCamelCase_	,							lowerCamelCase_	,							sample_weight=lowerCamelCase_	,							multioutput=lowerCamelCase_					)

								return {"mae": mae_score}





from math import floor, sqrt

def 		snake_case_   (				lowercase__     :    int     ):






				'''simple docstring'''






				_lowerCAmelCase  						=0.0
				_lowerCAmelCase  						=1.0
				_lowerCAmelCase  						=int(sqrt(lowercase__     )     )
				_lowerCAmelCase  						=root
				_lowerCAmelCase  						=0
				while integer_part != 2 * root:
								_lowerCAmelCase  						=denominator * integer_part - numerator
								_lowerCAmelCase  						=(n - numerator**2) / denominator
								_lowerCAmelCase  						=int((root + numerator) / denominator     )
								period += 1
				return period

def 		snake_case_   (				lowercase__     :    int = 1_00_00     ):






				'''simple docstring'''






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


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

