import os
import gym
from tensorboardX import SummaryWriter
from easydict import EasyDict
from functools import partial

from ding.config import compile_config
from ding.worker import BaseLearner, EpisodeSerialCollector, InteractionSerialEvaluator, EpisodeReplayBuffer
from ding.envs import BaseEnvManager, DingEnvWrapper
from ding.policy import DQNPolicy
from ding.model import DQN
from ding.utils import set_pkg_seed
from ding.rl_utils import get_epsilon_greedy_fn
from ding.reward_model import HerRewardModel
from dizoo.bitflip.envs import BitFlipEnv
from dizoo.bitflip.config import bitflip_pure_dqn_config, bitflip_her_dqn_config


def main(cfg, seed=0, max_train_iter=int(1e8), max_env_step=int(1e8)):
    cfg = compile_config(
        cfg,
        BaseEnvManager,
        DQNPolicy,
        BaseLearner,
        EpisodeSerialCollector,
        InteractionSerialEvaluator,
        EpisodeReplayBuffer,
        save_cfg=True
    )
    collector_env_num, evaluator_env_num = cfg.env.collector_env_num, cfg.env.evaluator_env_num
    collector_env = BaseEnvManager(
        env_fn=[partial(BitFlipEnv, cfg=cfg.env) for _ in range(collector_env_num)], cfg=cfg.env.manager
    )
    evaluator_env = BaseEnvManager(
        env_fn=[partial(BitFlipEnv, cfg=cfg.env) for _ in range(evaluator_env_num)], cfg=cfg.env.manager
    )

    # Set random seed for all package and instance
    collector_env.seed(seed)
    evaluator_env.seed(seed, dynamic_seed=False)
    set_pkg_seed(seed, use_cuda=cfg.policy.cuda)

    # Set up RL Policy
    model = DQN(**cfg.policy.model)
    policy = DQNPolicy(cfg.policy, model=model)

    # Set up collection, training and evaluation utilities
    tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial'))
    learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name)
    collector = EpisodeSerialCollector(
        cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name
    )
    evaluator = InteractionSerialEvaluator(
        cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name
    )
    replay_buffer = EpisodeReplayBuffer(
        cfg.policy.other.replay_buffer, exp_name=cfg.exp_name, instance_name='episode_buffer'
    )

    # Set up other modules, etc. epsilon greedy, hindsight experience replay
    eps_cfg = cfg.policy.other.eps
    epsilon_greedy = get_epsilon_greedy_fn(eps_cfg.start, eps_cfg.end, eps_cfg.decay, eps_cfg.type)
    her_cfg = cfg.policy.other.get('her', None)
    if her_cfg is not None:
        her_model = HerRewardModel(her_cfg, cfg.policy.cuda)

    # Training & Evaluation loop
    while True:
        # Evaluating at the beginning and with specific frequency
        if evaluator.should_eval(learner.train_iter):
            stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
            if stop:
                break
        # Update other modules
        eps = epsilon_greedy(collector.envstep)
        # Sampling data from environments
        new_episode = collector.collect(train_iter=learner.train_iter, policy_kwargs={'eps': eps})
        replay_buffer.push(new_episode, cur_collector_envstep=collector.envstep)
        # Training
        for i in range(cfg.policy.learn.update_per_collect):
            if her_cfg and her_model.episode_size is not None:
                sample_size = her_model.episode_size
            else:
                sample_size = learner.policy.get_attribute('batch_size')
            train_episode = replay_buffer.sample(sample_size, learner.train_iter)
            if train_episode is None:
                break
            train_data = []
            if her_cfg is not None:
                her_episodes = []
                for e in train_episode:
                    her_episodes.extend(her_model.estimate(e))
            # Only use samples modified by HER reward_model to train.
            for e in her_episodes:
                train_data.extend(policy.collect_mode.get_train_sample(e))
            learner.train(train_data, collector.envstep)
        if learner.train_iter >= max_train_iter or collector.envstep >= max_env_step:
            break


if __name__ == "__main__":
    # main(bitflip_pure_dqn_config)
    main(bitflip_her_dqn_config)