import torch
import os

class EarlyStoppingCallback:
    def __init__(self, model_name, patience=5, verbose=False):
        self.patience = patience
        self.verbose = verbose
        self.counter = 0
        self.best_score = None
        self.early_stop = False
        self.model_name = model_name

    def __call__(self, val_loss, model):
        score = -val_loss
        if self.best_score is None:
            self.best_score = score
            self.save_checkpoint(val_loss, model)
        elif score < self.best_score:
            self.counter += 1
            if self.counter >= self.patience:
                self.early_stop = True
        else:
            self.best_score = score
            self.save_checkpoint(val_loss, model)
            self.counter = 0

    def save_checkpoint(self, val_loss, model):
        directory = "models/checkpoints"
        if not os.path.exists(directory):
            os.makedirs(directory)  # Create the directory if it does not exist
        if self.verbose:
            print(f'└ Validation loss decreased ({self.best_score:.6f} --> {val_loss:.6f}).  Saving model ...')
        torch.save(model.state_dict(), os.path.join(directory, self.model_name))