from typing import Optional, List

from torch.nn import Module
from torch.optim import Optimizer
from torch.utils.data import ConcatDataset

from avalanche.benchmarks.utils import AvalancheConcatDataset
from avalanche.training.plugins.evaluation import default_logger
from avalanche.training.plugins import StrategyPlugin, EvaluationPlugin
from avalanche.training.strategies import BaseStrategy


class Cumulative(BaseStrategy):
    """ Cumulative training strategy.

    At each experience, train model with data from all previous experiences
        and current experience.
        """

    def __init__(self, model: Module, optimizer: Optimizer, criterion,
                 train_mb_size: int = 1, train_epochs: int = 1,
                 eval_mb_size: int = None, device=None,
                 plugins: Optional[List[StrategyPlugin]] = None,
                 evaluator: EvaluationPlugin = default_logger, eval_every=-1):
        """ Init.

        :param model: The model.
        :param optimizer: The optimizer to use.
        :param criterion: The loss criterion to use.
        :param train_mb_size: The train minibatch size. Defaults to 1.
        :param train_epochs: The number of training epochs. Defaults to 1.
        :param eval_mb_size: The eval minibatch size. Defaults to 1.
        :param device: The device to use. Defaults to None (cpu).
        :param plugins: Plugins to be added. Defaults to None.
        :param evaluator: (optional) instance of EvaluationPlugin for logging
            and metric computations.
        :param eval_every: the frequency of the calls to `eval` inside the
            training loop. -1 disables the evaluation. 0 means `eval` is called
            only at the end of the learning experience. Values >0 mean that 
            `eval` is called every `eval_every` epochs and at the end of the 
            learning experience.
        """

        super().__init__(
            model, optimizer, criterion,
            train_mb_size=train_mb_size, train_epochs=train_epochs,
            eval_mb_size=eval_mb_size, device=device, plugins=plugins,
            evaluator=evaluator, eval_every=eval_every)

        self.dataset = None  # cumulative dataset

    def train_dataset_adaptation(self, **kwargs):
        """
            Concatenates all the previous experiences.
        """
        if self.dataset is None:
            self.dataset = self.experience.dataset
        else:
            self.dataset = AvalancheConcatDataset(
                [self.dataset, self.experience.dataset])
        self.adapted_dataset = self.dataset
