import abc

import gtimer as gt
from forks.rlkit.rlkit.core.rl_algorithm import BaseRLAlgorithm
from forks.rlkit.rlkit.data_management.replay_buffer import ReplayBuffer
from forks.rlkit.rlkit.samplers.data_collector import PathCollector, StepCollector


class OnlineRLAlgorithm(BaseRLAlgorithm, metaclass=abc.ABCMeta):
    def __init__(
        self,
        trainer,
        exploration_env,
        evaluation_env,
        exploration_data_collector: StepCollector,
        evaluation_data_collector: PathCollector,
        replay_buffer: ReplayBuffer,
        batch_size,
        max_path_length,
        num_epochs,
        num_eval_steps_per_epoch,
        num_expl_steps_per_train_loop,
        num_trains_per_train_loop,
        num_train_loops_per_epoch=1,
        min_num_steps_before_training=0,
    ):
        super().__init__(
            trainer,
            exploration_env,
            evaluation_env,
            exploration_data_collector,
            evaluation_data_collector,
            replay_buffer,
        )
        self.batch_size = batch_size
        self.max_path_length = max_path_length
        self.num_epochs = num_epochs
        self.num_eval_steps_per_epoch = num_eval_steps_per_epoch
        self.num_trains_per_train_loop = num_trains_per_train_loop
        self.num_train_loops_per_epoch = num_train_loops_per_epoch
        self.num_expl_steps_per_train_loop = num_expl_steps_per_train_loop
        self.min_num_steps_before_training = min_num_steps_before_training

        assert (
            self.num_trains_per_train_loop >= self.num_expl_steps_per_train_loop
        ), "Online training presumes num_trains_per_train_loop >= num_expl_steps_per_train_loop"

    def _train(self):
        self.training_mode(False)
        if self.min_num_steps_before_training > 0:
            self.expl_data_collector.collect_new_steps(
                self.max_path_length,
                self.min_num_steps_before_training,
                discard_incomplete_paths=False,
            )
            init_expl_paths = self.expl_data_collector.get_epoch_paths()
            self.replay_buffer.add_paths(init_expl_paths)
            self.expl_data_collector.end_epoch(-1)

            gt.stamp("initial exploration", unique=True)

        num_trains_per_expl_step = (
            self.num_trains_per_train_loop // self.num_expl_steps_per_train_loop
        )
        for epoch in gt.timed_for(
            range(self._start_epoch, self.num_epochs), save_itrs=True
        ):
            self.eval_data_collector.collect_new_paths(
                self.max_path_length,
                self.num_eval_steps_per_epoch,
                discard_incomplete_paths=True,
            )
            gt.stamp("evaluation sampling")

            for _ in range(self.num_train_loops_per_epoch):
                for _ in range(self.num_expl_steps_per_train_loop):
                    self.expl_data_collector.collect_new_steps(
                        self.max_path_length,
                        1,  # num steps
                        discard_incomplete_paths=False,
                    )
                    gt.stamp("exploration sampling", unique=False)

                    self.training_mode(True)
                    for _ in range(num_trains_per_expl_step):
                        train_data = self.replay_buffer.random_batch(self.batch_size)
                        self.trainer.train(train_data)
                    gt.stamp("training", unique=False)
                    self.training_mode(False)

            new_expl_paths = self.expl_data_collector.get_epoch_paths()
            self.replay_buffer.add_paths(new_expl_paths)
            gt.stamp("data storing", unique=False)

            self._end_epoch(epoch)
