"""Early stopping"""
# pylint: disable= no-member, arguments-differ, invalid-name
import datetime
import torch

__all__ = ['EarlyStopping']

# pylint: disable=C0103
class EarlyStopping(object):
    """Early stop tracker

    Save model checkpoint when observing a performance improvement on
    the validation set and early stop if improvement has not been
    observed for a particular number of epochs.

    Parameters
    ----------
    mode : str
        * 'higher': Higher metric suggests a better model
        * 'lower': Lower metric suggests a better model
    patience : int
        The early stopping will happen if we do not observe performance
        improvement for ``patience`` consecutive epochs.
    filename : str or None
        Filename for storing the model checkpoint
    """
    def __init__(self, mode='higher', patience=10, filename=None):
        if filename is None:
            dt = datetime.datetime.now()
            filename = 'early_stop_{}_{:02d}-{:02d}-{:02d}.pth'.format(
                dt.date(), dt.hour, dt.minute, dt.second)

        assert mode in ['higher', 'lower']
        self.mode = mode
        if self.mode == 'higher':
            self._check = self._check_higher
        else:
            self._check = self._check_lower

        self.patience = patience
        self.counter = 0
        self.filename = filename
        self.best_score = None
        self.early_stop = False

    def _check_higher(self, score, prev_best_score):
        """Check if the new score is higher than the previous best score.

        Parameters
        ----------
        score : float
            New score.
        prev_best_score : float
            Previous best score.

        Returns
        -------
        bool
            Whether the new score is higher than the previous best score.
        """
        return score > prev_best_score

    def _check_lower(self, score, prev_best_score):
        """Check if the new score is lower than the previous best score.

        Parameters
        ----------
        score : float
            New score.
        prev_best_score : float
            Previous best score.

        Returns
        -------
        bool
            Whether the new score is lower than the previous best score.
        """
        return score < prev_best_score

    def step(self, score, model):
        """Update based on a new score.

        The new score is typically model performance on the validation set
        for a new epoch.

        Parameters
        ----------
        score : float
            New score.
        model : nn.Module
            Model instance.

        Returns
        -------
        bool
            Whether an early stop should be performed.
        """
        if self.best_score is None:
            self.best_score = score
            self.save_checkpoint(model)
        elif self._check(score, self.best_score):
            self.best_score = score
            self.save_checkpoint(model)
            self.counter = 0
        else:
            self.counter += 1
            print(
                f'EarlyStopping counter: {self.counter} out of {self.patience}')
            if self.counter >= self.patience:
                self.early_stop = True
        return self.early_stop

    def save_checkpoint(self, model):
        '''Saves model when the metric on the validation set gets improved.

        Parameters
        ----------
        model : nn.Module
            Model instance.
        '''
        torch.save({'model_state_dict': model.state_dict()}, self.filename)

    def load_checkpoint(self, model):
        '''Load the latest checkpoint

        Parameters
        ----------
        model : nn.Module
            Model instance.
        '''
        model.load_state_dict(torch.load(self.filename)['model_state_dict'])
