import numpy as np

from utils1.trainer import Trainer


class EarlyStopping:
    """Early stops the training if validation loss doesn't improve after a given patience."""

    def __init__(self, patience=1, verbose=False, delta=0):
        """
        Args:
            patience (int): How long to wait after last time validation loss improved.
                            Default: 7
            verbose (bool): If True, prints a message for each validation loss improvement.
                            Default: False
            delta (float): Minimum change in the monitored quantity to qualify as an improvement.
                            Default: 0
        """
        self.patience = patience
        self.verbose = verbose
        self.counter = 0
        self.best_score = None
        self.early_stop = False
        self.score_max = -np.Inf
        self.delta = delta

    def __call__(self, score: float, trainer: Trainer):
        if self.best_score is None:
            self.best_score = score
            self.save_checkpoint(score, trainer)
        elif score < self.best_score - self.delta:
            self.counter += 1
            print(f"EarlyStopping counter: {self.counter} out of {self.patience}")
            if self.counter >= self.patience:
                self.early_stop = True
        else:
            self.best_score = score
            self.save_checkpoint(score, trainer)
            self.counter = 0

    def save_checkpoint(self, score: float, trainer: Trainer):
        """Saves model when validation loss decrease."""
        if self.verbose:
            print(f"Validation accuracy increased ({self.score_max:.6f} --> {score:.6f}).  Saving model ...")
        trainer.save_networks("best")
        self.score_max = score