# # Unity ML-Agents Toolkit
from typing import Dict, List
from collections import defaultdict
import abc

from mlagents.trainers.optimizer.tf_optimizer import TFOptimizer
from mlagents.trainers.buffer import AgentBuffer
from mlagents.trainers.trainer import Trainer
from mlagents.trainers.exception import UnityTrainerException
from mlagents.trainers.components.reward_signals import RewardSignalResult
from mlagents_envs.timers import hierarchical_timer
from mlagents.trainers.agent_processor import AgentManagerQueue
from mlagents.trainers.trajectory import Trajectory
from mlagents.trainers.stats import StatsPropertyType

RewardSignalResults = Dict[str, RewardSignalResult]


class RLTrainer(Trainer):  # pylint: disable=abstract-method
    """
    This class is the base class for trainers that use Reward Signals.
    """

    def __init__(self, *args, **kwargs):
        super(RLTrainer, self).__init__(*args, **kwargs)
        # Make sure we have at least one reward_signal
        if not self.trainer_parameters["reward_signals"]:
            raise UnityTrainerException(
                "No reward signals were defined. At least one must be used with {}.".format(
                    self.__class__.__name__
                )
            )
        # collected_rewards is a dictionary from name of reward signal to a dictionary of agent_id to cumulative reward
        # used for reporting only. We always want to report the environment reward to Tensorboard, regardless
        # of what reward signals are actually present.
        self.cumulative_returns_since_policy_update: List[float] = []
        self.collected_rewards: Dict[str, Dict[str, int]] = {
            "environment": defaultdict(lambda: 0)
        }
        self.update_buffer: AgentBuffer = AgentBuffer()
        self._stats_reporter.add_property(
            StatsPropertyType.HYPERPARAMETERS, self.trainer_parameters
        )

    def end_episode(self) -> None:
        """
        A signal that the Episode has ended. The buffer must be reset.
        Get only called when the academy resets.
        """
        for rewards in self.collected_rewards.values():
            for agent_id in rewards:
                rewards[agent_id] = 0

    def _update_end_episode_stats(self, agent_id: str, optimizer: TFOptimizer) -> None:
        for name, rewards in self.collected_rewards.items():
            if name == "environment":
                self.stats_reporter.add_stat(
                    "Environment/Cumulative Reward", rewards.get(agent_id, 0)
                )
                self.cumulative_returns_since_policy_update.append(
                    rewards.get(agent_id, 0)
                )
                self.reward_buffer.appendleft(rewards.get(agent_id, 0))
                rewards[agent_id] = 0
            else:
                self.stats_reporter.add_stat(
                    optimizer.reward_signals[name].stat_name, rewards.get(agent_id, 0)
                )
                rewards[agent_id] = 0

    def _clear_update_buffer(self) -> None:
        """
        Clear the buffers that have been built up during inference.
        """
        self.update_buffer.reset_agent()

    @abc.abstractmethod
    def _is_ready_update(self):
        """
        Returns whether or not the trainer has enough elements to run update model
        :return: A boolean corresponding to wether or not update_model() can be run
        """
        return False

    @abc.abstractmethod
    def _update_policy(self):
        """
        Uses demonstration_buffer to update model.
        """
        pass

    def _increment_step(self, n_steps: int, name_behavior_id: str) -> None:
        """
        Increment the step count of the trainer
        :param n_steps: number of steps to increment the step count by
        """
        self.step += n_steps
        self.next_summary_step = self._get_next_summary_step()
        p = self.get_policy(name_behavior_id)
        if p:
            p.increment_step(n_steps)

    def _get_next_summary_step(self) -> int:
        """
        Get the next step count that should result in a summary write.
        """
        return self.step + (self.summary_freq - self.step % self.summary_freq)

    def _write_summary(self, step: int) -> None:
        """
        Saves training statistics to Tensorboard.
        """
        self.stats_reporter.add_stat("Is Training", float(self.should_still_train))
        self.stats_reporter.write_stats(int(step))

    @abc.abstractmethod
    def _process_trajectory(self, trajectory: Trajectory) -> None:
        """
        Takes a trajectory and processes it, putting it into the update buffer.
        :param trajectory: The Trajectory tuple containing the steps to be processed.
        """
        self._maybe_write_summary(self.get_step + len(trajectory.steps))
        self._increment_step(len(trajectory.steps), trajectory.behavior_id)

    def _maybe_write_summary(self, step_after_process: int) -> None:
        """
        If processing the trajectory will make the step exceed the next summary write,
        write the summary. This logic ensures summaries are written on the update step and not in between.
        :param step_after_process: the step count after processing the next trajectory.
        """
        if step_after_process >= self.next_summary_step and self.get_step != 0:
            self._write_summary(self.next_summary_step)

    def advance(self) -> None:
        """
        Steps the trainer, taking in trajectories and updates if ready.
        """
        with hierarchical_timer("process_trajectory"):
            for traj_queue in self.trajectory_queues:
                # We grab at most the maximum length of the queue.
                # This ensures that even if the queue is being filled faster than it is
                # being emptied, the trajectories in the queue are on-policy.
                for _ in range(traj_queue.maxlen):
                    try:
                        t = traj_queue.get_nowait()
                        self._process_trajectory(t)
                    except AgentManagerQueue.Empty:
                        break
        if self.should_still_train:
            if self._is_ready_update():
                with hierarchical_timer("_update_policy"):
                    self._update_policy()
                    for q in self.policy_queues:
                        # Get policies that correspond to the policy queue in question
                        q.put(self.get_policy(q.behavior_id))
        else:
            self._clear_update_buffer()
