from typing import Any, List, Union, Sequence, Optional
import copy
import numpy as np
import gym

from ding.envs import BaseEnv, BaseEnvTimestep, update_shape
from ding.utils import ENV_REGISTRY
from ding.torch_utils import to_tensor, to_ndarray, to_list
from .atari_wrappers import wrap_deepmind, wrap_deepmind_mr
from ding.envs import ObsPlusPrevActRewWrapper


@ENV_REGISTRY.register("atari")
class AtariEnv(BaseEnv):

    def __init__(self, cfg: dict) -> None:
        self._cfg = cfg
        self._init_flag = False
        self._replay_path = None

    def reset(self) -> np.ndarray:
        if not self._init_flag:
            self._env = self._make_env()
            if self._replay_path is not None:
                self._env = gym.wrappers.RecordVideo(
                    self._env,
                    video_folder=self._replay_path,
                    episode_trigger=lambda episode_id: True,
                    name_prefix='rl-video-{}'.format(id(self))
                )
            if hasattr(self._cfg, 'obs_plus_prev_action_reward') and self._cfg.obs_plus_prev_action_reward:
                self._env = ObsPlusPrevActRewWrapper(self._env)
            self._observation_space = self._env.observation_space
            self._action_space = self._env.action_space
            self._reward_space = gym.spaces.Box(
                low=self._env.reward_range[0], high=self._env.reward_range[1], shape=(1, ), dtype=np.float32
            )
            self._init_flag = True
        if hasattr(self, '_seed') and hasattr(self, '_dynamic_seed') and self._dynamic_seed:
            np_seed = 100 * np.random.randint(1, 1000)
            self._env.seed(self._seed + np_seed)
        elif hasattr(self, '_seed'):
            self._env.seed(self._seed)
        obs = self._env.reset()
        obs = to_ndarray(obs)
        self._eval_episode_return = 0.
        return obs

    def close(self) -> None:
        if self._init_flag:
            self._env.close()
        self._init_flag = False

    def seed(self, seed: int, dynamic_seed: bool = True) -> None:
        self._seed = seed
        self._dynamic_seed = dynamic_seed
        np.random.seed(self._seed)

    def step(self, action: np.ndarray) -> BaseEnvTimestep:
        assert isinstance(action, np.ndarray), type(action)
        action = action.item()
        obs, rew, done, info = self._env.step(action)
        # self._env.render()
        self._eval_episode_return += rew
        obs = to_ndarray(obs)
        rew = to_ndarray([rew]).astype(np.float32)  # wrapped to be transferred to a Tensor with shape (1,)
        if done:
            info['eval_episode_return'] = self._eval_episode_return
        return BaseEnvTimestep(obs, rew, done, info)

    def enable_save_replay(self, replay_path: Optional[str] = None) -> None:
        if replay_path is None:
            replay_path = './video'
        self._replay_path = replay_path

    def random_action(self) -> np.ndarray:
        random_action = self.action_space.sample()
        random_action = to_ndarray([random_action], dtype=np.int64)
        return random_action

    @property
    def observation_space(self) -> gym.spaces.Space:
        return self._observation_space

    @property
    def action_space(self) -> gym.spaces.Space:
        return self._action_space

    @property
    def reward_space(self) -> gym.spaces.Space:
        return self._reward_space

    def _make_env(self):
        return wrap_deepmind(
            self._cfg.env_id,
            frame_stack=self._cfg.frame_stack,
            episode_life=self._cfg.is_train,
            clip_rewards=self._cfg.is_train
        )

    def __repr__(self) -> str:
        return "DI-engine Atari Env({})".format(self._cfg.env_id)

    @staticmethod
    def create_collector_env_cfg(cfg: dict) -> List[dict]:
        collector_env_num = cfg.pop('collector_env_num')
        cfg = copy.deepcopy(cfg)
        cfg.is_train = True
        return [cfg for _ in range(collector_env_num)]

    @staticmethod
    def create_evaluator_env_cfg(cfg: dict) -> List[dict]:
        evaluator_env_num = cfg.pop('evaluator_env_num')
        cfg = copy.deepcopy(cfg)
        cfg.is_train = False
        return [cfg for _ in range(evaluator_env_num)]


@ENV_REGISTRY.register('atari_mr')
class AtariEnvMR(AtariEnv):

    def reset(self) -> np.ndarray:
        if not self._init_flag:
            self._env = self._make_env()
            self._observation_space = self._env.observation_space
            self._action_space = self._env.action_space
            self._reward_space = gym.spaces.Box(
                low=self._env.reward_range[0], high=self._env.reward_range[1], shape=(1, ), dtype=np.float32
            )
            self._init_flag = True
        if hasattr(self, '_seed'):
            np_seed = 100 * np.random.randint(1, 1000)
            self._env.seed(self._seed + np_seed)
        obs = self._env.reset()
        obs = to_ndarray(obs)
        self._eval_episode_return = 0.
        return obs

    def _make_env(self):
        return wrap_deepmind_mr(
            self._cfg.env_id,
            frame_stack=self._cfg.frame_stack,
            episode_life=self._cfg.is_train,
            clip_rewards=self._cfg.is_train
        )