A. Holidays

On the planet Mars a year lasts exactly n days (there are no leap years on Mars). But Martians have the same weeks as earthlings — 5 work days and then 2 days off. Your task is to determine the minimum possible and the maximum possible number of days off per year on Mars.

Input
The first line of the input contains a positive integer n (1 ≤ n ≤ 1 000 000) — the number of days in a year on Mars.

Output
Print two integers — the minimum possible and the maximum possible number of days off per year on Mars.

Examples
input
14
output
4 4
input
2
output
0 2

My Answer Code:

/*
	AUthor:Albert Tesla Wizard
	TIme:2021/4/14 18:38
*/
#include<bits/stdc++.h>
using namespace std;
int main()
{
    ios::sync_with_stdio(false);
    cin.tie(0);
    int n,Min,Max;
    cin>>n;
    if(n<6)Min=0;
    else Min=(n-6)/7+n/7+1;
    if(n<2)Max=n;
    else if(n<=9)Max=(n-2)/6+(n-2)/7+2;
    else Max=(n-2)/7+(n-8)/7+3;
    cout<<Min<<" "<<Max<<'\n';
    return 0;
}

"""Prophet forecaster. Parameters ---------- growth: String 'linear', 'logistic' or 'flat' to specify a linear, logistic or flat trend. changepoints: List of dates at which to include potential changepoints. If not specified, potential changepoints are selected automatically. n_changepoints: Number of potential changepoints to include. Not used if input `changepoints` is supplied. If `changepoints` is not supplied, then n_changepoints potential changepoints are selected uniformly from the first `changepoint_range` proportion of the history. changepoint_range: Proportion of history in which trend changepoints will be estimated. Defaults to 0.8 for the first 80%. Not used if `changepoints` is specified. yearly_seasonality: Fit yearly seasonality. Can be 'auto', True, False, or a number of Fourier terms to generate. weekly_seasonality: Fit weekly seasonality. Can be 'auto', True, False, or a number of Fourier terms to generate. daily_seasonality: Fit daily seasonality. Can be 'auto', True, False, or a number of Fourier terms to generate. holidays: pd.DataFrame with columns holiday (string) and ds (date type) and optionally columns lower_window and upper_window which specify a range of days around the date to be included as holidays. lower_window=-2 will include 2 days prior to the date as holidays. Also optionally can have a column prior_scale specifying the prior scale for that holiday. seasonality_mode: 'additive' (default) or 'multiplicative'. seasonality_prior_scale: Parameter modulating the strength of the seasonality model. Larger values allow the model to fit larger seasonal fluctuations, smaller values dampen the seasonality. Can be specified for individual seasonalities using add_seasonality. holidays_prior_scale: Parameter modulating the strength of the holiday components model, unless overridden in the holidays input. changepoint_prior_scale: Parameter modulating the flexibility of the automatic changepoint selection. Large values will allow many changepoints, small values will allow few changepoints. mcmc_samples: Integer, if greater than 0, will do full Bayesian inference with the specified number of MCMC samples. If 0, will do MAP estimation. interval_width: Float, width of the uncertainty intervals provided for the forecast. If mcmc_samples=0, this will be only the uncertainty in the trend using the MAP estimate of the extrapolated generative model. If mcmc.samples>0, this will be integrated over all model parameters, which will include uncertainty in seasonality. uncertainty_samples: Number of simulated draws used to estimate uncertainty intervals. Settings this value to 0 or False will disable uncertainty estimation and speed up the calculation. stan_backend: str as defined in StanBackendEnum default: None - will try to iterate over all available backends and find the working one. scaling: 'absmax' (default) or 'minmax'. holidays_mode: 'additive' or 'multiplicative'. Defaults to seasonality_mode. """ 翻译一下
12-06
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