import numpy as np
import pandas as pd
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score


def mse(real_data: pd.Series, predictions: pd.Series) -> float:
    """
    Calculates the mean squared error between the real data and the predictions.
    """
    return round(
        mean_squared_error(real_data.astype(np.float64), predictions.astype(np.float64)), 2
    )


def mae(real_data: pd.Series, predictions: pd.Series) -> float:
    """
    Calculates the mean absolute error between the real data and the predictions.
    """
    return round(
        mean_absolute_error(real_data.astype(np.float64), predictions.astype(np.float64)), 2
    )


def r2(real_data: pd.Series, predictions: pd.Series) -> float:
    """
    Calculates the R2 score between the real data and the predictions.
    """
    return round(r2_score(real_data.astype(np.float64), predictions.astype(np.float64)), 2)