## This code contains the base classess used in generating synthetic data

from linearmodels.iv import IV2SLS
from dowhy import CausalModel
from dowhy import datasets as dset
from sklearn.linear_model import LogisticRegression
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
import pandas as pd
from pathlib import Path
import matplotlib.pyplot as plt

class DataGenerator:
    """
    Base class for generating synthetic data

    Attributes:
        n_observations (int): Number of observations
        n_continuous_covars (int): Number of covariates
        n_covars (int): total number of covariates (continuous + binary)
        n_treatments (int): Number of treatments
        true_effect (float): True effect size
        seed (int): Random seed for reproducibility
        data (pd.DataFrame): Generated data
        info (dict): Dictionary to store additional information about the data
        method (str): the causal inference method assocated with the synthetic
        mean (np.ndarray): mean of the covariates
        covar (np.ndarray): covariance matrix for the covariates
        heterogeneity (bool): whether or not the treatment effects are heterogeneous
    """

    def __init__(self, n_observations, n_continuous_covars, n_binary_covars=2, mean=None,
                 covar = None, n_treatments=1, true_effect=0 ,seed=111, heterogeneity=0):

        np.random.seed(seed)
        self.n_observations = n_observations
        self.n_continuous_covars = n_continuous_covars
        self.n_covars = n_continuous_covars + n_binary_covars
        self.n_treatments = n_treatments
        self.n_binary_covars = n_binary_covars
        self.data = None
        self.seed = seed
        self.true_effect = true_effect
        self.method = None
        self.mean = mean
        self.covar = covar
        if mean is None:
            self.mean = np.random.randint(3, 20, size=self.n_continuous_covars)
        if self.covar is None:
            self.covar = np.identity(self.n_continuous_covars)
        self.heterogeneity = heterogeneity

    def generate_data(self):
        """
        Generates the synthetic data

        Returns:
            pd.DataFrame: The generated data
        """

        raise NotImplementedError("Invoke the method in the subclass")

    def save_data(self, folder, filename):
        """
        Saves the generated data as a CSV file

        Args:
            folder (str): path to the folder where the data is saved
            filename (str): name of the file
        """

        if self.data is None:
            raise ValueError("Data not generated yet. Please generate data first.")
        path = Path(folder)
        path.mkdir(parents=True, exist_ok=True)
        if not filename.endswith('.csv'):
            filename += '.csv'
        self.data.to_csv(path / filename, index=False)

    def test_data(self, print_=False):
        """
        Test the generated data, using the appropriate method.
        """

        raise NotImplementedError("This method should be overridden by subclasses")

    def generate_covariates(self):
        """
        Generate covariates. For continuous covariates, we use multivariate normal distribution, and for
        binary covars, we use binomial distribution. The non-binary covariates are discretized to their floor
        integer.
        """

        X_c = np.random.multivariate_normal(mean=self.mean, cov=self.covar,
                                                   size=self.n_observations)
        p = np.random.uniform(0.3, 0.7)
        X_b = np.random.binomial(1, p, size=(self.n_observations, self.n_binary_covars)).astype(int)
        covariates = np.hstack((X_c, X_b))
        covariates = covariates.astype(int)

        return covariates

class MultiTreatRCTGenerator(DataGenerator):
    """
    Base class for generating synthetic data for multi-treatment RCTs

    Additional Attributes:
        true_effect_vec (np.ndarray): the treatment effect for different treatments.
    """
    def __init__(self, n_observations, n_continuous_covars, n_treatments, n_binary_covars=2,
                 mean=None, covar=None, true_effect=1.0, true_effect_vec = None,
                 seed=111, heterogeneity=0):

        super().__init__(n_observations, n_continuous_covars, n_binary_covars=n_binary_covars,
                         mean=mean, covar=covar, true_effect=true_effect, seed=seed,
                         heterogeneity=heterogeneity, n_treatments=n_treatments)

        self.method = "MultiTreatRCT"
        self.true_effect_vec = true_effect_vec

        ## if true effect vec is None, we set the treatment effects to be the same for all treatments
        if true_effect_vec is None:
            self.true_effect_vec = np.zeros(n_treatments)
            for i in range(1, n_treatments):
                self.true_effect_vec[i] = self.true_effect

    def generate_data(self):

        X = self.generate_covariates()
        cols = [f"X{i+1}" for i in range(self.n_covars)]
        df = pd.DataFrame(X, columns=cols)


        df['D'] = np.random.randint(0, self.n_treatments+1, size=self.n_observations)
        vec = np.random.uniform(0, 1, size=self.n_covars)
        intercept = np.random.normal(50, 3)
        noise = np.random.normal(0, 1, size=self.n_observations)

        # Apply appropriate treatment effect per treatment arm
        treatment_effects = np.array(self.true_effect_vec)
        df['treat_effect'] = treatment_effects[df['D']]

        df['Y'] = intercept + X.dot(vec) + df['treat_effect'] + noise

        df.drop(columns='treat_effect', inplace=True)
        self.data = df

        return df


    def test_data(self, print_=False):

        if self.data is None:
            raise ValueError("Data not generated yet. Please generate data first.")

        model = smf.ols('Y ~ C(D)', data=self.data).fit()

        result = model.summary()
        if print_:
            print(result)
        return result


# Front-Door Criterion Generator
class FrontDoorGenerator(DataGenerator):
    """
    Generates synthetic data satisfying the front-door criterion.
    D → M → Y, D ← U → Y
    """
    def __init__(self, n_observations, n_continuous_covars=2, n_binary_covars=2,
                 mean=None, covar=None, seed=111, true_effect=2.0, heterogeneity=0):
        super().__init__(n_observations, n_continuous_covars, n_binary_covars=n_binary_covars,
                         mean=mean, covar=covar, seed=seed, true_effect=true_effect,
                         n_treatments=1, heterogeneity=heterogeneity)
        self.method = "FrontDoor"

    def generate_data(self):
        X = self.generate_covariates()
        cols = [f"X{i+1}" for i in range(self.n_covars)]
        df = pd.DataFrame(X, columns=cols)

        # Latent confounder
        U = np.random.normal(0, 1, self.n_observations)

        # Treatment depends on U and X
        vec_d = np.random.uniform(0.5, 1.5, size=self.n_covars)
        df['D'] = (X @ vec_d + 0.8 * U + np.random.normal(0, 1, self.n_observations)) > 0
        df['D'] = df['D'].astype(int)

        # Mediator depends on D and X
        vec_m = np.random.uniform(0.5, 1.5, size=self.n_covars)
        df['M'] = X @ vec_m + df['D'] * 1.5 + np.random.normal(0, 1, self.n_observations)

        # Outcome depends on M, U and X
        vec_y = np.random.uniform(0.5, 1.5, size=self.n_covars)
        df['Y'] = 50 + 2.0 * df['M'] + 1.0 * U + X @ vec_y + np.random.normal(0, 1, self.n_observations)

        self.data = df
        return df

    def test_data(self, print_=False):
        if self.data is None:
            raise ValueError("Data not generated yet. Please generate data first.")
        
        model_m = smf.ols("M ~ D", data=self.data).fit()
        model_y = smf.ols("Y ~ M + D", data=self.data).fit()

        if print_:
            print("Regression: M ~ D")
            print(model_m.summary())
            print("\nRegression: Y ~ M + D")
            print(model_y.summary())

        return {"M~D": model_m.summary(), "Y~M+D": model_y.summary()}

class ObservationalDataGenerator(DataGenerator):
    """
    Generate synthetic data for observational studies.

    Additional Attributes:
        self.weights (np.ndarray): the propoensity score weights for each observation
    """

    def __init__(self, n_observations, n_continuous_covars, n_binary_covars=2, mean=None, covar=None,
                 true_effect=1.0, seed=111, heterogeneity=0):

        super().__init__(n_observations, n_continuous_covars, n_binary_covars=n_binary_covars, mean=mean, covar=covar,
                         true_effect=true_effect, seed=seed, heterogeneity=heterogeneity)

    def generate_data(self):

        X = self.generate_covariates()

        cols = [f"X{i+1}" for i in range(self.n_covars)]
        df = pd.DataFrame(X, columns=cols)
        X_norm = (X - X.mean(axis=0)) / X.std(axis=0)

        vec1 = np.random.normal(0, 0.5, size=self.n_covars)
        lin = X_norm @ vec1 + np.random.normal(0, 1, self.n_observations)
        ## the propensity score
        ps = 1 / (1 + np.exp(-lin))
        ## we do this for stability reasons
        ps = np.clip(ps, 1e-3, 1 -1e-3)
        df['D'] = np.random.binomial(1, ps).astype(int)
        vec2 = np.random.normal(0, 0.5, size=self.n_covars)
        intercept = np.random.normal(50, 3)
        noise = np.random.normal(0, 1, size=self.n_observations)
        df['Y'] = intercept + X @ vec2 + self.true_effect * df['D'] + noise

        self.propensity = ps
        self.weights = np.where(df['D'] == 1, 1 / ps, 1 / (1 - ps))
        self.data = df

        return self.data

class PSMGenerator(ObservationalDataGenerator):
    """
    Generate synthetic data for Propensity Score Matching (PSM)
    """

    def __init__(self, n_observations, n_continuous_covars, n_binary_covars=2, mean=None, covar=None,
                 true_effect=1.0, seed=111, heterogeneity=0):
        super().__init__(n_observations, n_continuous_covars, n_binary_covars=n_binary_covars, mean=mean, covar=covar,
                         true_effect=true_effect, seed=seed, heterogeneity=heterogeneity)
        self.method = "PSM"

    def test_data(self, print_=False):
        """
        Test the generated data
        """
        if self.data is None:
            raise ValueError("Data not generated yet. Please generate data first.")

        lr = LogisticRegression(solver='lbfgs')
        X = self.data[[f"X{i+1}" for i in range(self.n_covars)]]
        lr.fit(X, self.data['D'])
        ps_hat = lr.predict_proba(X)[:, 1]
        treated = self.data[self.data['D'] == 1]
        control = self.data[self.data['D'] == 0]

        ## perform matching using the propensity scores
        match_idxs = [np.abs(ps_hat[control.index] - ps_hat[i]).argmin() for i in treated.index]
        matches = control.iloc[match_idxs]
        att = treated['Y'].mean() - matches['Y'].mean()

        result = f"Estimated ATT (matching): {att:.3f} | True: {self.true_effect}"
        if print_:
            print(result)
        return result

class PSWGenerator(ObservationalDataGenerator):
    """
    Generate synthetic data for Propensity Score Weighting (PSW)
    """

    def __init__(self, n_observations, n_continuous_covars, n_binary_covars=2, mean=None, covar=None,
                 true_effect=1.0, seed=111, heterogeneity=0):
        super().__init__(n_observations, n_continuous_covars, n_binary_covars=n_binary_covars, mean=mean, covar=covar,
                         true_effect=true_effect, seed=seed, heterogeneity=heterogeneity)
        self.method = "PSW"

    def test_data(self, print_=False):
        """
        Test the generated data
        """
        if self.data is None:
            raise ValueError("Data not generated yet. Please generate data first.")

        df = self.data.copy()
        D = df['D']
        Y = df['Y']

        treated = D == 1
        control = D == 0

        w = np.zeros(self.n_observations)
        w[control] = self.propensity[control] / (1 - self.propensity[control])
        w[treated] = 1

        Y1 = Y[treated].mean()
        Y0_weighted = np.average(Y[control], weights=w[control])

        att = Y1 - Y0_weighted
        ate = np.average(Y * D / self.propensity - (1 - D) * Y / (1 - self.propensity))
        result = f"Estimated ATT (IPW): {att:.3f} | True: {self.true_effect}\nEstimated ATE: {ate:.3f} | True:{self.true_effect}"
        if print_:
            print(result)

        return result


class RCTGenerator(DataGenerator):
    """
    Generate synthetic data for Randomized Controlled Trials (RCT)
    """

    def __init__(self, n_observations, n_continuous_covars, n_binary_covars=2, mean=None,
                covar=None, true_effect=1.0, seed=111, heterogeneity=0):
        super().__init__(n_observations, n_continuous_covars, n_binary_covars=n_binary_covars,
                             mean=mean, covar=covar, true_effect=true_effect, seed=seed,
                             heterogeneity=heterogeneity)
        self.method = "RCT"

    def generate_data(self):

        X = self.generate_covariates()
        cols = [f"X{i+1}" for i in range(self.n_covars)]
        df = pd.DataFrame(X, columns=cols)
        df['D'] = np.random.binomial(1, 0.5, size=self.n_observations)
        vec = np.random.uniform(0, 1, size=self.n_covars)
        intercept = np.random.normal(50, 3)
        noise = np.random.normal(0, 1, size=self.n_observations)
        df['Y'] = (intercept + X.dot(vec) + self.true_effect * df['D'] + noise)
        self.data = df

    def test_data(self, print=False):
        if self.data is None:
            raise ValueError("Data not generated yet. Please generate data first.")
        model = smf.ols('Y ~ D', data=self.data).fit()
        result = model.summary()
        if print:
            print(result)
        est = model.params['D']
        conf_int = model.conf_int().loc['D']
        result = f"TRUE ATE: {self.true_effect:.3f}, ESTIMATED ATE: {est:.3f}, \
            95% CI: [{conf_int[0]:.3f}, {conf_int[1]:.3f}]"

        return result

class IVGenerator(DataGenerator):
    """
    Generate synthetic data for Instrumental Variables (IV) analysis. We assume two forms:
        1. Encouragement Design:
             Z -> D -> Y
             In this setting, encouragements (Z) is randomized. For instance, consider the administering of  vaccines.
             We cannot force people to take vaccines, however we can encourage them to take the vaccine. We could run
             a vaccine awareness campaign, where we randomly pick participants, and inform them about the benefits of
             vaccine. The user can either comply (take the vaccine) or not comply (not take the vaccine). Likewise, in the control
             group, the user can comply (not take the vaccine) or defy (take the vaccine)
        2.
               U
              / \
        Z -> D -> Y
        This is the classical setting where we have an unobserved confounder affecting both treatment (D) and outcome (Y).


    Additional Attributes:
        alpha (float): the effect of the instrument on the treatment (Z on D)
        encouragement (bool): whether or not this is an encouragement design
        beta_d (float): effect of the unobserved confounder (U) on treatment (D)
        beta_y (float): effect of the unobserved confounders (U) on outcome (Y)

    """

    def __init__(self, n_observations, n_continuous_covars, n_binary_covars=2, mean=None, beta_d = 1.0,
                beta_y = 1.5, covar=None, true_effect=1.0, seed=111, heterogeneity=0, alpha=0.5,
                encouragement=False):
        super().__init__(n_observations, n_continuous_covars, n_binary_covars=n_binary_covars, mean=mean,
                         covar=covar, true_effect=true_effect, seed=seed, heterogeneity=heterogeneity)
        self.method = "IV"
        self.alpha = alpha
        self.encouragement = encouragement
        self.beta_d = beta_d
        self.beta_y = beta_y

    def generate_data(self):
        X = self.generate_covariates()

        mean = np.random.randint(8, 13)
        Z = np.random.normal(mean, 2, size=self.n_observations).astype(int)
        U = np.random.normal(0, 1, size=self.n_observations)
        vec1 = np.random.normal(0, 0.5, size=self.n_covars)
        intercept1 = np.random.normal(30, 2)
        D = self.alpha * Z + X @ vec1 + np.random.normal(size=self.n_observations) + intercept1
        if self.encouragement:
            D = (D > np.mean(D)).astype(int)
        else:
            D = D + self.beta_d * U
        D = D.astype(int)

        intercept2 = np.random.normal(50, 3)
        vec2 = np.random.normal(0, 0.5, size=self.n_covars)
        Y = self.true_effect * D + X @ vec2 + np.random.normal(size=self.n_observations) + intercept2
        if not self.encouragement:
            Y = Y + self.beta_y * U
        df = pd.DataFrame(X, columns=[f"X{i+1}" for i in range(self.n_covars)])
        df['Z'] = Z
        df['D'] = D
        df['Y'] = Y
        self.data = df

        return self.data


    def test_data(self, print_=False):

        if self.data is None:
            raise ValueError("Data not generated yet.")
        model = IV2SLS.from_formula('Y ~ 1 + [D ~ Z]', data=self.data).fit()
        est = model.params['D']
        conf_int = model.conf_int().loc['D']
        result = f"TRUE LATE: {self.true_effect:.3f}, ESTIMATED LATE: {est:.3f}, \
            95% CI: [{conf_int[0]:.3f}, {conf_int[1]:.3f}]"
        if print_:
            print(result)
        return result


class RDDGenerator(DataGenerator):
    """
    Generate synthetic data for (sharp) Regression Discontinuity Design (RDD).

    Additional Attributes:
        cutoff (float): the cutoff for treatment assignment
        bandwidth (float): the bandwidth for the running variable we consider when estimating the treatment effects
        plot (bool): whether we plot the data or not
    """

    def __init__(self, n_observations, n_continuous_covars, n_binary_covars=2, mean=None, plot=False,
                 covar=None, true_effect=1.0, seed=111, heterogeneity=0, cutoff=10, bandwidth=0.1):
        super().__init__(n_observations, n_continuous_covars, n_binary_covars=n_binary_covars,
                         mean=mean, covar=covar, true_effect=true_effect, seed=seed,
                         heterogeneity=heterogeneity)
        self.cutoff = cutoff
        self.bandwidth = bandwidth
        self.method = "RDD"
        self.plot=plot

        print("self.plot", self.plot)

    def generate_data(self):

        X = self.generate_covariates()
        cols = [f"X{i+1}" for i in range(self.n_covars)]
        df = pd.DataFrame(X, columns=cols)

        df['running_X'] = np.random.normal(0, 2, size=self.n_observations) + self.cutoff


        df['D'] = (df['running_X'] >= self.cutoff).astype(int)

        intercept = 10
        coeffs = np.random.normal(0, 0.1, size=self.n_covars)

        ## slope of the line below the threshold
        m_below = 1.5
        ## slope of the line above the threshold
        m_above = 0.8

        df['running_centered'] = df['running_X'] - self.cutoff
        # Use centered version for slope
        df["Y"] = (intercept + self.true_effect * df['D'] + m_below * df['running_centered'] * (1 - df['D']) +  \
                   m_above * df['running_centered'] * df['D'] +  X @ coeffs + np.random.normal(0, 0.5, size=self.n_observations))

        if self.plot:
            plt.figure(figsize=(10, 6))
            plt.scatter(df[df['D']==0]['running_X'], df[df['D']==0]['Y'],
                   alpha=0.5, label='Control', color='blue')
            plt.scatter(df[df['D']==1]['running_X'], df[df['D']==1]['Y'],
                   alpha=0.5, label='Treatment', color='red')
            plt.axvline(self.cutoff, color='black', linestyle='--', label='Cutoff')
            plt.show()

        self.data = df[[cols for cols in df.columns if cols != 'running_centered']]

        return self.data


    def test_data(self, print_=False):

        if self.data is None:
            raise ValueError("Data not generated yet.")
        df = self.data.copy()
        df['running_adj'] = df['running_X'].astype(float) - self.cutoff
        df = df[np.abs(df['running_adj']) <= self.bandwidth].copy()
        model = smf.ols('Y ~ D + running_adj + D:running_adj', data=df).fit()
        est = model.params['D']
        conf_int = model.conf_int().loc['D']

        result = f"TRUE LATE: {self.true_effect:.3f}, ESTIMATED LATE: {est:.3f}, \
              95% CI: [{conf_int[0]:.3f}, {conf_int[1]:.3f}]"
        if print_:
            print(result)
        return result


class DiDGenerator(DataGenerator):
    """
    Generate synthetic data for Difference-in-Differences (DiD) analysis

    Additional Attributes:
        1. n_periods (int): number of time-periods
    """

    def __init__(self, n_observations, n_continuous_covars, n_binary_covars=2, n_periods=2,
                 mean=None, covar=None, true_effect=1.0, seed=111, heterogeneity=0):
        super().__init__(n_observations, n_continuous_covars, n_binary_covars=n_binary_covars,
                         mean=mean, covar=covar, true_effect=true_effect,
                         seed=seed, heterogeneity=heterogeneity)

        self.method = "DiD"
        self.n_periods = n_periods

    def canonical_did_model(self):
        """
        This is the classical DiD setting with two periods (pre and post treatment) and two groups (treatment and control)
        """

        ## fraction of observations that receives the treatment
        frac_treated = np.random.uniform(0.35, 0.65)
        n_treated = int(frac_treated * self.n_observations)
        unit_ids = np.arange(self.n_observations)
        treatment_status = np.zeros(self.n_observations, dtype=int)
        treatment_status[:n_treated] = 1
        np.random.shuffle(treatment_status)

        X = self.generate_covariates()
        cols = [f"X{i+1}" for i in range(self.n_covars)]
        covar_df = pd.DataFrame(X, columns=cols)


        vec = np.random.normal(0, 0.1, size=self.n_covars)

        intercept = np.random.normal(50, 3)
        treat_effect = np.random.normal(0, 1)
        time_effect = np.random.normal(0, 1)

        covar_term = X @ vec
        pre_noise = np.random.normal(0, 1, self.n_observations)
        pre_outcome = intercept + covar_term + pre_noise + treat_effect * treatment_status
        pre_data = pd.DataFrame({'unit_id': unit_ids, 'post': 0, 'D': treatment_status,
                                 'Y': pre_outcome})
        post_noise = np.random.normal(0, 1, self.n_observations)
        post_outcome = (intercept + time_effect + covar_term + self.true_effect * treatment_status
                        + treat_effect * treatment_status + post_noise)

        post_data = pd.DataFrame({'unit_id': unit_ids, 'post': 1, 'D': treatment_status,
                                  'Y': post_outcome})

        df = pd.concat([pre_data, post_data], ignore_index=True)

        df = df.merge(covar_df, left_on="unit_id", right_index=True)

        return df[['unit_id', 'post', 'D', 'Y'] + cols]

    def twfe_model(self):
        """
        Generate panel data for Two-Way Fixed Effects DiD model. This is a generalization of 2-period DiD for multi-year treatments
        """

        ## fraction of observations that receives the treatment
        frac_treated = np.random.uniform(0.35, 0.65)
        unit_ids = np.arange(1, self.n_observations + 1)
        time_periods = np.arange(0, self.n_periods)

        df = pd.DataFrame([(i, t) for i in unit_ids for t in time_periods],
                      columns=["unit", "time"])

        X = self.generate_covariates()
        for j in range(self.n_covars):
            df[f"X{j+1}"] = np.repeat(X[:, j], self.n_periods)

        ## Assign treatment timing
        n_treated = int(frac_treated * self.n_observations)
        treated_units = np.random.choice(unit_ids, size=n_treated, replace=False)
        treatment_start = {unit: np.random.randint(1, self.n_periods) for unit in treated_units}

        df["treat_post"] = df.apply(lambda row: int(row["unit"] in treatment_start and
                                                    row["time"] >= treatment_start[row["unit"]]),axis=1)

        ## State fixed effects
        unit_effects = dict(zip(unit_ids, np.random.normal(0, 1.0, self.n_observations)))
        ## Time fixed effects
        time_effects = dict(zip(time_periods, np.random.normal(0, 1, len(time_periods))))
        df["unit_fe"] = df["unit"].map(unit_effects)
        df["time_fe"] = df["time"].map(time_effects)

        covar_effects = np.random.normal(0, 0.1, self.n_covars)
        X_matrix = df[[f"X{j+1}" for j in range(self.n_covars)]].values
        covar_term = X_matrix @ covar_effects
        intercept = np.random.normal(50, 3)
        noise = np.random.normal(0, 1, len(df))

        df["Y"] = intercept + covar_term + df["unit_fe"] + df["time_fe"] + self.true_effect * df["treat_post"] + noise

        final_df = df[["unit", "time", "treat_post", "Y"] + [f"X{j+1}" for j in range(self.n_covars)]]
        final_df = final_df.rename(columns={"time": "year", "treat_post": "D"})

        return final_df


    def generate_data(self):

        if self.n_periods == 2:
            self.data = self.canonical_did_model()
        else:
            self.data = self.twfe_model()

        return self.data


    def test_data(self, print_=False):

        estimated_att = None
        if self.data is None:
            raise ValueError("Data not generated yet.")
        if self.n_periods == 2:
            print("Testing canonical DiD model")
            model = smf.ols('Y ~ D * post', data=self.data).fit()
            estimated_att = model.params['D:post']
            conf_int = model.conf_int().loc['D:post']
        else:
            print("Testing TWFE model")
            model = smf.ols('Y ~ D + C(unit) + C(year)', data=self.data).fit()
            estimated_att = model.params['D']
            conf_int = model.conf_int().loc['D']

        result = "TRUE ATT: {:.3f}, EMPRICAL ATT:{:.3f}\nCONFIDENCE INTERVAL:{}".format(
                 self.true_effect, estimated_att, conf_int)
        if print_:
            print(result)
        return result