import os
import gym
import torch
from tensorboardX import SummaryWriter
from easydict import EasyDict

from ding.config import compile_config
from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer
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 dizoo.classic_control.cartpole.config.cartpole_dqn_config import cartpole_dqn_config


# Get DI-engine form env class
def wrapped_cartpole_env():
    return DingEnvWrapper(
        gym.make('CartPole-v0'),
        EasyDict(env_wrapper='default'),
    )
    # from dizoo.classic_control.cartpole.envs.cartpole_env import CartPoleEnv
    # return CartPoleEnv({})


def main(cfg, seed=0):
    cfg = compile_config(
        cfg,
        BaseEnvManager,
        DQNPolicy,
        BaseLearner,
        SampleSerialCollector,
        InteractionSerialEvaluator,
        AdvancedReplayBuffer,
        save_cfg=True
    )
    evaluator_env_num = cfg.env.evaluator_env_num
    evaluator_env = BaseEnvManager(env_fn=[wrapped_cartpole_env for _ in range(evaluator_env_num)], cfg=cfg.env.manager)
    evaluator_env.enable_save_replay(cfg.env.replay_path)  # switch save replay interface

    # Set random seed for all package and instance
    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)
    policy.eval_mode.load_state_dict(torch.load(cfg.policy.load_path, map_location='cpu'))

    # evaluate
    tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial'))
    evaluator = InteractionSerialEvaluator(
        cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name
    )
    evaluator.eval()


if __name__ == "__main__":
    main(cartpole_dqn_config)