import abc
import pickle
import time
from collections import OrderedDict
from copy import deepcopy

import gtimer as gt
import numpy as np

import os

import torch
from rlkit.core import logger, eval_util
from rlkit.data_management.env_replay_buffer import EnvReplayBuffer
from rlkit.data_management.path_builder import PathBuilder
from rlkit.policies.base import ExplorationPolicy
from rlkit.torch.common.policies import MakeDeterministic
from rlkit.samplers import PathSampler

from gym.spaces import Dict

from tqdm import tqdm


class BaseAlgorithm(metaclass=abc.ABCMeta):
    """
    base algorithm for single task setting
    can be used for RL or Learning from Demonstrations
    """

    def __init__(
        self,
        env,
        exploration_policy: ExplorationPolicy,
        training_env=None,
        eval_policy=None,
        eval_sampler=None,
        num_epochs=100,
        num_steps_per_epoch=10000,
        num_steps_between_train_calls=1000,
        num_steps_per_eval=1000,
        max_path_length=1000,
        min_steps_before_training=0,
        replay_buffer=None,
        replay_buffer_size=10000,
        freq_saving=1,
        save_replay_buffer=False,
        save_environment=False,
        save_algorithm=False,
        save_best=False,
        save_best_starting_from_epoch=0,
        best_key="AverageReturn",  # higher is better
        no_terminal=False,
        eval_no_terminal=False,
        wrap_absorbing=False,
        render=False,
        render_kwargs={},
        freq_log_visuals=1,
        eval_deterministic=False,
        save_buffer_data=False,
        save_path=None,
        changing_dynamics=False,
        **kwargs
    ):
        self.env = env
        self.training_env = training_env or pickle.loads(pickle.dumps(env))
        self.exploration_policy = exploration_policy

        self.num_epochs = int(num_epochs)
        self.num_env_steps_per_epoch = num_steps_per_epoch
        self.num_steps_between_train_calls = num_steps_between_train_calls
        self.num_steps_per_eval = num_steps_per_eval
        self.max_path_length = max_path_length
        self.min_steps_before_training = min_steps_before_training

        self.render = render

        self.save_replay_buffer = save_replay_buffer
        self.save_algorithm = save_algorithm
        self.save_environment = save_environment
        self.save_best = save_best
        self.save_best_starting_from_epoch = save_best_starting_from_epoch
        self.best_key = best_key
        self.best_statistic_so_far = float("-Inf")

        self.changing_dynamics = changing_dynamics

        if eval_sampler is None:
            if eval_policy is None:
                eval_policy = exploration_policy
            eval_policy = MakeDeterministic(eval_policy)
            eval_sampler = PathSampler(
                env,
                eval_policy,
                num_steps_per_eval,
                max_path_length,
                no_terminal=eval_no_terminal,  # True, #False,
                render=render,
                render_kwargs=render_kwargs,
            )
        self.eval_policy = eval_policy
        self.eval_sampler = eval_sampler

        self.action_space = env.action_space
        self.obs_space = env.observation_space
        self.replay_buffer_size = replay_buffer_size
        if replay_buffer is None:
            assert max_path_length < replay_buffer_size
            replay_buffer = EnvReplayBuffer(
                self.replay_buffer_size, self.env, random_seed=np.random.randint(10000)
            )
        else:
            assert max_path_length < replay_buffer._max_replay_buffer_size
        self.replay_buffer = replay_buffer

        self._n_env_steps_total = 0
        self._n_train_steps_total = 0
        self._n_rollouts_total = 0
        self._do_train_time = 0
        self._epoch_start_time = None
        self._algo_start_time = None
        self._old_table_keys = None
        self._current_path_builder = PathBuilder()
        self._exploration_paths = []

        # if wrap_absorbing:
        #     # needs to be properly handled both here and in replay buffer
        #     raise NotImplementedError()
        self.wrap_absorbing = wrap_absorbing
        self.freq_saving = freq_saving
        self.no_terminal = no_terminal

        self.eval_statistics = None
        self.freq_log_visuals = freq_log_visuals

        self.save_buffer_data = save_buffer_data
        self.save_path = save_path

    def train(self, start_epoch=0, pred_obs=False):
        self.pretrain(pred_obs=pred_obs)
        if start_epoch == 0:
            params = self.get_epoch_snapshot(-1)
            logger.save_itr_params(-1, params)
        self.training_mode(False)
        self._n_env_steps_total = start_epoch * self.num_env_steps_per_epoch
        gt.reset()
        gt.set_def_unique(False)
        self.start_training(start_epoch=start_epoch, pred_obs=pred_obs)

    def pretrain(self, pred_obs=False):
        """
        Do anything before the main training phase.
        """
        pass

    def start_training(self, start_epoch=0, pred_obs=False):
        self.eval_statistics = None
        self._current_path_builder = PathBuilder()
        observation = self._start_new_rollout()
        pred_obs_prime = None

        for epoch in gt.timed_for(
            range(start_epoch, self.num_epochs),
            save_itrs=True,
        ):
            self._start_epoch(epoch)
            print("Training of epoch {}".format(epoch))
            for steps_this_epoch in tqdm(range(self.num_env_steps_per_epoch)):
                pred_obs_prime, action, agent_info = self._get_action_and_info(
                    observation, pred_obs
                )
                if self.render:
                    self.training_env.render()

                step_action = action.copy()
                if self.changing_dynamics:
                    step_action = -step_action
                next_ob, raw_reward, terminal, env_info = self.training_env.step(
                    step_action
                )
                add_inv_buf = terminal
                if self.no_terminal:
                    terminal = False
                self._n_env_steps_total += 1

                reward = np.array([raw_reward])
                terminal = np.array([terminal])

                timeout = False
                if len(self._current_path_builder) >= (self.max_path_length - 1):
                    timeout = True
                timeout = np.array([timeout])

                self._handle_step(
                    observation,
                    action,
                    reward,
                    next_ob,
                    np.array([False])
                    if (self.no_terminal or self.wrap_absorbing)
                    else terminal,
                    timeout,
                    pred_obs=pred_obs_prime,
                    absorbing=np.array([0.0, 0.0]),
                    agent_info=agent_info,
                    env_info=env_info,
                    # add_inv_buf=add_inv_buf,
                )
                if terminal[0]:
                    if self.wrap_absorbing:
                        # raise NotImplementedError()
                        """
                        If we wrap absorbing states, two additional
                        transitions must be added: (s_T, s_abs) and
                        (s_abs, s_abs). In Disc Actor Critic paper
                        they make s_abs be a vector of 0s with last
                        dim set to 1. Here we are going to add the following:
                        ([next_ob,0], random_action, [next_ob, 1]) and
                        ([next_ob,1], random_action, [next_ob, 1])
                        This way we can handle varying types of terminal states.
                        """
                        # next_ob is the absorbing state
                        # for now just taking the previous action
                        self._handle_step(
                            next_ob,
                            # action,
                            self.training_env.action_space.sample(),
                            # the reward doesn't matter
                            reward,
                            np.zeros_like(next_ob),
                            np.array([False]),
                            timeout,
                            pred_obs=pred_obs_prime,
                            absorbing=np.array([0.0, 1.0]),
                            agent_info=agent_info,
                            env_info=env_info,
                        )
                        self._handle_step(
                            np.zeros_like(next_ob),
                            # action,
                            self.training_env.action_space.sample(),
                            # the reward doesn't matter
                            reward,
                            # next_ob,
                            np.zeros_like(next_ob),
                            np.array([False]),
                            timeout,
                            pred_obs=pred_obs_prime,
                            absorbing=np.array([1.0, 1.0]),
                            agent_info=agent_info,
                            env_info=env_info,
                        )
                    self._handle_rollout_ending()
                    observation = self._start_new_rollout()
                elif len(self._current_path_builder) >= self.max_path_length:
                    self._handle_rollout_ending()
                    observation = self._start_new_rollout()
                else:
                    observation = next_ob

                if self._n_env_steps_total % self.num_steps_between_train_calls == 0:
                    gt.stamp("sample")
                    self._try_to_train(epoch)
                    gt.stamp("train")

            gt.stamp("sample")
            self._try_to_eval(epoch)
            gt.stamp("eval")
            self._end_epoch()

            if (self.save_buffer_data) and (self.num_epochs % 10 == 0):
                assert self.save_path != None
                if not os.path.exists(self.save_path):
                    os.makedirs(self.save_path)
                self.replay_buffer.save_data(
                    os.path.join(
                        self.save_path, "buffer_data_epoch_{}.pkl".format(epoch)
                    )
                )

    def _try_to_train(self, epoch):
        if self._can_train():
            self.training_mode(True)
            self._do_training(epoch)
            self._n_train_steps_total += 1
            self.training_mode(False)

    def _try_to_eval(self, epoch):

        if self._can_evaluate():
            # save if it's time to save
            if (epoch % self.freq_saving == 0) or (epoch + 1 >= self.num_epochs):
                # if epoch + 1 >= self.num_epochs:
                #     epoch = 'final'
                logger.save_extra_data(self.get_extra_data_to_save(epoch))
                params = self.get_epoch_snapshot(epoch)
                logger.save_itr_params(epoch, params)

            self.evaluate(epoch)

            logger.record_tabular(
                "Number of train calls total",
                self._n_train_steps_total,
            )
            logger.record_tabular(
                "Number of env steps total",
                self._n_env_steps_total,
            )
            logger.record_tabular(
                "Number of rollouts total",
                self._n_rollouts_total,
            )

            times_itrs = gt.get_times().stamps.itrs
            train_time = times_itrs["train"][-1]
            sample_time = times_itrs["sample"][-1]
            eval_time = times_itrs["eval"][-1] if epoch > 0 else 0
            epoch_time = train_time + sample_time + eval_time
            total_time = gt.get_times().total

            logger.record_tabular("Train Time (s)", train_time)
            logger.record_tabular("(Previous) Eval Time (s)", eval_time)
            logger.record_tabular("Sample Time (s)", sample_time)
            logger.record_tabular("Epoch Time (s)", epoch_time)
            logger.record_tabular("Total Train Time (s)", total_time)

            logger.record_tabular("Epoch", epoch)
            logger.dump_tabular(with_prefix=False, with_timestamp=False)
        else:
            logger.log("Skipping eval for now.")

    def _can_evaluate(self):
        """
        One annoying thing about the logger table is that the keys at each
        iteration need to be the exact same. So unless you can compute
        everything, skip evaluation.

        A common example for why you might want to skip evaluation is that at
        the beginning of training, you may not have enough data for a
        validation and training set.

        :return:
        """
        return (
            len(self._exploration_paths) > 0
            and self.replay_buffer.num_steps_can_sample()
            >= self.min_steps_before_training
        )

    def _can_train(self):
        return (
            self.replay_buffer.num_steps_can_sample() >= self.min_steps_before_training
        )

    def _get_action_and_info(self, observation, pred_obs=False):
        """
        Get an action to take in the environment.
        :param observation:
        :return:
        """
        self.exploration_policy.set_num_steps_total(self._n_env_steps_total)
        pred_obs_prime = None
        if pred_obs:
            pred_obs_prime, action, agent_info = self.exploration_policy.get_action(
                obs_np=observation, return_predicting_obs=pred_obs
            )
        else:
            action, agent_info = self.exploration_policy.get_action(obs_np=observation)

        return pred_obs_prime, action, agent_info

    def _start_epoch(self, epoch):
        self._epoch_start_time = time.time()
        self._exploration_paths = []
        self._do_train_time = 0
        logger.push_prefix("Iteration #%d | " % epoch)

    def _end_epoch(self):
        self.eval_statistics = None
        logger.log("Epoch Duration: {0}".format(time.time() - self._epoch_start_time))
        logger.log("Started Training: {0}".format(self._can_train()))
        logger.pop_prefix()

    def _start_new_rollout(self):
        self.exploration_policy.reset()
        return self.training_env.reset()

    def _handle_path(self, path, *args, **kwargs):
        """
        Naive implementation: just loop through each transition.
        :param path:
        :return:
        """
        for (ob, action, reward, next_ob, terminal, agent_info, env_info) in zip(
            path["observations"],
            path["actions"],
            path["rewards"],
            path["next_observations"],
            path["terminals"],
            path["agent_infos"],
            path["env_infos"],
        ):
            self._handle_step(
                ob,
                action,
                reward,
                next_ob,
                terminal,
                agent_info=agent_info,
                env_info=env_info,
            )
        self._handle_rollout_ending()

    def _handle_step(
        self,
        observation,
        action,
        reward,
        next_observation,
        terminal,
        timeout,
        absorbing,
        agent_info,
        env_info,
        *args,
        **kwargs
    ):
        """
        Implement anything that needs to happen after every step
        :return:
        """
        self._current_path_builder.add_all(
            observations=observation,
            actions=action,
            rewards=reward,
            next_observations=next_observation,
            terminals=terminal,
            absorbing=absorbing,
            agent_infos=agent_info,
            env_infos=env_info,
        )
        self.replay_buffer.add_sample(
            observation=observation,
            action=action,
            reward=reward,
            terminal=terminal,
            next_observation=next_observation,
            timeout=timeout,
            absorbing=absorbing,
            agent_info=agent_info,
            env_info=env_info,
        )

    def _handle_rollout_ending(self):
        """
        Implement anything that needs to happen after every rollout.
        """
        self.replay_buffer.terminate_episode()
        self._n_rollouts_total += 1
        if len(self._current_path_builder) > 0:
            self._exploration_paths.append(self._current_path_builder)
            self._current_path_builder = PathBuilder()

    def get_epoch_snapshot(self, epoch):
        """
        Probably will be overridden by each algorithm
        """
        data_to_save = dict(
            epoch=epoch,
            exploration_policy=[self.exploration_policy],
        )
        if self.save_environment:
            data_to_save["env"] = self.training_env
        return data_to_save

    # @abc.abstractmethod
    # def load_snapshot(self, snapshot):
    #     """
    #     Should be implemented on a per algorithm basis
    #     taking into consideration the particular
    #     get_epoch_snapshot implementation for the algorithm
    #     """
    #     pass

    def get_extra_data_to_save(self, epoch):
        """
        Save things that shouldn't be saved every snapshot but rather
        overwritten every time.
        :param epoch:
        :return:
        """
        if self.render:
            self.training_env.render(close=True)
        data_to_save = dict(
            epoch=epoch,
        )
        if self.save_environment:
            data_to_save["env"] = self.training_env
        if self.save_replay_buffer:
            data_to_save["replay_buffer"] = self.replay_buffer
        if self.save_algorithm:
            data_to_save["algorithm"] = self
        return data_to_save

    @abc.abstractmethod
    def training_mode(self, mode):
        """
        Set training mode to `mode`.
        :param mode: If True, training will happen (e.g. set the dropout
        probabilities to not all ones).
        """
        pass

    @abc.abstractmethod
    def _do_training(self):
        """
        Perform some update, e.g. perform one gradient step.
        :return:
        """
        pass

    def evaluate(self, epoch, pred_obs=False):
        """
        Evaluate the policy, e.g. save/print progress.
        :param epoch:
        :return:
        """
        statistics = OrderedDict()
        try:
            statistics.update(self.eval_statistics)
            self.eval_statistics = None
        except:
            print("No Stats to Eval")

        logger.log("Collecting samples for evaluation")
        test_paths = self.eval_sampler.obtain_samples(
            pred_obs=pred_obs, changing_dynamics=self.changing_dynamics
        )

        statistics.update(
            eval_util.get_generic_path_information(
                test_paths,
                stat_prefix="Test",
                pred_obs=pred_obs,
            )
        )
        statistics.update(
            eval_util.get_generic_path_information(
                self._exploration_paths,
                stat_prefix="Exploration",
            )
        )

        if hasattr(self.env, "log_diagnostics"):
            self.env.log_diagnostics(test_paths)
        if hasattr(self.env, "log_statistics"):
            statistics.update(self.env.log_statistics(test_paths))
        if int(epoch) % self.freq_log_visuals == 0:
            if hasattr(self.env, "log_visuals"):
                self.env.log_visuals(test_paths, epoch, logger.get_snapshot_dir())

        average_returns = eval_util.get_average_returns(test_paths)
        statistics["AverageReturn"] = average_returns
        for key, value in statistics.items():
            logger.record_tabular(key, value)

        best_statistic = statistics[self.best_key]
        if best_statistic > self.best_statistic_so_far:
            self.best_statistic_so_far = best_statistic
            if self.save_best and epoch >= self.save_best_starting_from_epoch:
                data_to_save = {"epoch": epoch, "statistics": statistics}
                data_to_save.update(self.get_epoch_snapshot(epoch))
                logger.save_extra_data(data_to_save, "best.pkl")
                print("\n\nSAVED BEST\n\n")
