# # Unity ML-Agents Toolkit
# ## ML-Agent Learning (PPO)
# Contains an implementation of PPO as described in: https://arxiv.org/abs/1707.06347

import logging
from collections import defaultdict

import numpy as np

from mlagents.trainers.ppo.policy import PPOPolicy
from mlagents.trainers.ppo.multi_gpu_policy import MultiGpuPPOPolicy, get_devices
from mlagents.trainers.rl_trainer import RLTrainer
from mlagents.trainers.brain import BrainParameters
from mlagents.trainers.tf_policy import TFPolicy
from mlagents.trainers.trajectory import Trajectory

logger = logging.getLogger("mlagents.trainers")


class PPOTrainer(RLTrainer):
    """The PPOTrainer is an implementation of the PPO algorithm."""

    def __init__(
        self,
        brain_name: str,
        reward_buff_cap: int,
        trainer_parameters: dict,
        training: bool,
        load: bool,
        seed: int,
        run_id: str,
        multi_gpu: bool,
    ):
        """
        Responsible for collecting experiences and training PPO model.
        :param brain_name: The name of the brain associated with trainer config
        :param reward_buff_cap: Max reward history to track in the reward buffer
        :param trainer_parameters: The parameters for the trainer (dictionary).
        :param training: Whether the trainer is set for training.
        :param load: Whether the model should be loaded.
        :param seed: The seed the model will be initialized with
        :param run_id: The identifier of the current run
        :param multi_gpu: Boolean for multi-gpu policy model
        """
        super(PPOTrainer, self).__init__(
            brain_name, trainer_parameters, training, run_id, reward_buff_cap
        )
        self.param_keys = [
            "batch_size",
            "beta",
            "buffer_size",
            "epsilon",
            "hidden_units",
            "lambd",
            "learning_rate",
            "max_steps",
            "normalize",
            "num_epoch",
            "num_layers",
            "time_horizon",
            "sequence_length",
            "summary_freq",
            "use_recurrent",
            "summary_path",
            "memory_size",
            "model_path",
            "reward_signals",
        ]
        self.check_param_keys()
        self.load = load
        self.multi_gpu = multi_gpu
        self.seed = seed
        self.policy: TFPolicy = None

    def process_trajectory(self, trajectory: Trajectory) -> None:
        """
        Takes a trajectory and processes it, putting it into the update buffer.
        Processing involves calculating value and advantage targets for model updating step.
        :param trajectory: The Trajectory tuple containing the steps to be processed.
        """
        agent_id = trajectory.agent_id  # All the agents should have the same ID

        # Add to episode_steps
        self.episode_steps[agent_id] += len(trajectory.steps)

        agent_buffer_trajectory = trajectory.to_agentbuffer()
        # Update the normalization
        if self.is_training:
            self.policy.update_normalization(agent_buffer_trajectory["vector_obs"])

        # Get all value estimates
        value_estimates = self.policy.get_batched_value_estimates(
            agent_buffer_trajectory
        )
        for name, v in value_estimates.items():
            agent_buffer_trajectory["{}_value_estimates".format(name)].extend(v)
            self.stats_reporter.add_stat(
                self.policy.reward_signals[name].value_name, np.mean(v)
            )

        value_next = self.policy.get_value_estimates(
            trajectory.next_obs,
            agent_id,
            trajectory.done_reached and not trajectory.max_step_reached,
        )

        # Evaluate all reward functions
        self.collected_rewards["environment"][agent_id] += np.sum(
            agent_buffer_trajectory["environment_rewards"]
        )
        for name, reward_signal in self.policy.reward_signals.items():
            evaluate_result = reward_signal.evaluate_batch(
                agent_buffer_trajectory
            ).scaled_reward
            agent_buffer_trajectory["{}_rewards".format(name)].extend(evaluate_result)
            # Report the reward signals
            self.collected_rewards[name][agent_id] += np.sum(evaluate_result)

        # Compute GAE and returns
        tmp_advantages = []
        tmp_returns = []
        for name in self.policy.reward_signals:
            bootstrap_value = value_next[name]

            local_rewards = agent_buffer_trajectory[
                "{}_rewards".format(name)
            ].get_batch()
            local_value_estimates = agent_buffer_trajectory[
                "{}_value_estimates".format(name)
            ].get_batch()
            local_advantage = get_gae(
                rewards=local_rewards,
                value_estimates=local_value_estimates,
                value_next=bootstrap_value,
                gamma=self.policy.reward_signals[name].gamma,
                lambd=self.trainer_parameters["lambd"],
            )
            local_return = local_advantage + local_value_estimates
            # This is later use as target for the different value estimates
            agent_buffer_trajectory["{}_returns".format(name)].set(local_return)
            agent_buffer_trajectory["{}_advantage".format(name)].set(local_advantage)
            tmp_advantages.append(local_advantage)
            tmp_returns.append(local_return)

        # Get global advantages
        global_advantages = list(
            np.mean(np.array(tmp_advantages, dtype=np.float32), axis=0)
        )
        global_returns = list(np.mean(np.array(tmp_returns, dtype=np.float32), axis=0))
        agent_buffer_trajectory["advantages"].set(global_advantages)
        agent_buffer_trajectory["discounted_returns"].set(global_returns)
        # Append to update buffer
        agent_buffer_trajectory.resequence_and_append(
            self.update_buffer, training_length=self.policy.sequence_length
        )

        # If this was a terminal trajectory, append stats and reset reward collection
        if trajectory.done_reached:
            self._update_end_episode_stats(
                agent_id, self.get_policy(trajectory.behavior_id)
            )

    def is_ready_update(self):
        """
        Returns whether or not the trainer has enough elements to run update model
        :return: A boolean corresponding to whether or not update_model() can be run
        """
        size_of_buffer = self.update_buffer.num_experiences
        return size_of_buffer > self.trainer_parameters["buffer_size"]

    def update_policy(self):
        """
        Uses demonstration_buffer to update the policy.
        The reward signal generators must be updated in this method at their own pace.
        """
        buffer_length = self.update_buffer.num_experiences
        self.cumulative_returns_since_policy_update.clear()

        # Make sure batch_size is a multiple of sequence length. During training, we
        # will need to reshape the data into a batch_size x sequence_length tensor.
        batch_size = (
            self.trainer_parameters["batch_size"]
            - self.trainer_parameters["batch_size"] % self.policy.sequence_length
        )
        # Make sure there is at least one sequence
        batch_size = max(batch_size, self.policy.sequence_length)

        n_sequences = max(
            int(self.trainer_parameters["batch_size"] / self.policy.sequence_length), 1
        )

        advantages = self.update_buffer["advantages"].get_batch()
        self.update_buffer["advantages"].set(
            (advantages - advantages.mean()) / (advantages.std() + 1e-10)
        )
        num_epoch = self.trainer_parameters["num_epoch"]
        batch_update_stats = defaultdict(list)
        for _ in range(num_epoch):
            self.update_buffer.shuffle(sequence_length=self.policy.sequence_length)
            buffer = self.update_buffer
            max_num_batch = buffer_length // batch_size
            for l in range(0, max_num_batch * batch_size, batch_size):
                update_stats = self.policy.update(
                    buffer.make_mini_batch(l, l + batch_size), n_sequences
                )
                for stat_name, value in update_stats.items():
                    batch_update_stats[stat_name].append(value)

        for stat, stat_list in batch_update_stats.items():
            self.stats_reporter.add_stat(stat, np.mean(stat_list))

        if self.policy.bc_module:
            update_stats = self.policy.bc_module.update()
            for stat, val in update_stats.items():
                self.stats_reporter.add_stat(stat, val)
        self.clear_update_buffer()

    def create_policy(self, brain_parameters: BrainParameters) -> TFPolicy:
        """
        Creates a PPO policy to trainers list of policies.
        :param brain_parameters: specifications for policy construction
        :return policy
        """

        if self.multi_gpu and len(get_devices()) > 1:
            policy: PPOPolicy = MultiGpuPPOPolicy(
                self.seed,
                brain_parameters,
                self.trainer_parameters,
                self.is_training,
                self.load,
            )
        else:
            policy = PPOPolicy(
                self.seed,
                brain_parameters,
                self.trainer_parameters,
                self.is_training,
                self.load,
            )

        for _reward_signal in policy.reward_signals.keys():
            self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)

        return policy

    def add_policy(self, name_behavior_id: str, policy: TFPolicy) -> None:
        """
        Adds policy to trainer.
        :param brain_parameters: specifications for policy construction
        """
        if self.policy:
            logger.warning(
                "add_policy has been called twice. {} is not a multi-agent trainer".format(
                    self.__class__.__name__
                )
            )
        self.policy = policy

    def get_policy(self, name_behavior_id: str) -> TFPolicy:
        """
        Gets policy from trainer associated with name_behavior_id
        :param name_behavior_id: full identifier of policy
        """

        return self.policy


def discount_rewards(r, gamma=0.99, value_next=0.0):
    """
    Computes discounted sum of future rewards for use in updating value estimate.
    :param r: List of rewards.
    :param gamma: Discount factor.
    :param value_next: T+1 value estimate for returns calculation.
    :return: discounted sum of future rewards as list.
    """
    discounted_r = np.zeros_like(r)
    running_add = value_next
    for t in reversed(range(0, r.size)):
        running_add = running_add * gamma + r[t]
        discounted_r[t] = running_add
    return discounted_r


def get_gae(rewards, value_estimates, value_next=0.0, gamma=0.99, lambd=0.95):
    """
    Computes generalized advantage estimate for use in updating policy.
    :param rewards: list of rewards for time-steps t to T.
    :param value_next: Value estimate for time-step T+1.
    :param value_estimates: list of value estimates for time-steps t to T.
    :param gamma: Discount factor.
    :param lambd: GAE weighing factor.
    :return: list of advantage estimates for time-steps t to T.
    """
    value_estimates = np.append(value_estimates, value_next)
    delta_t = rewards + gamma * value_estimates[1:] - value_estimates[:-1]
    advantage = discount_rewards(r=delta_t, gamma=gamma * lambd)
    return advantage
