import logging
import numpy as np
import torch

class EarlyStoppingCriterion:
    """Early stops the training if validation loss doesn't improve after a given patience."""
    def __init__(self, patience=5, verbose=False, delta=0, save_path='./model/checkpoint.pkl'):
        """
        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_loss = None
        self.early_stop = False
        self.val_loss_min = np.Inf
        self.delta = delta
        self.save_path = save_path

    def __call__(self, val_loss, model):

        if self.best_loss is None:
            self.best_loss = val_loss

            self.save_checkpoint(val_loss, model)

        elif val_loss < self.best_loss + self.delta:
            self.counter += 1
            logging.info(f'val acc: {val_loss}, EarlyStopping counter: {self.counter} out of {self.patience}')
            if self.counter >= self.patience:
                self.early_stop = True
        else:
            self.best_loss = val_loss
            logging.info(f'val acc: {self.best_loss}, Performance is better... saving the model')
            self.save_checkpoint(val_loss, model)
            self.counter = 0

    def save_checkpoint(self, val_loss, model):
        '''Saves model when validation loss decrease.'''
        if self.verbose:
            logging.info(f'Validation acc increased ({self.val_loss_min:.6f} --> {val_loss:.6f}).Saving model ...')
        torch.save(model.state_dict(), self.save_path)
        self.val_loss_min = val_loss
