from typing import Any, List, Union, Optional
import time
import gym
import numpy as np
from ding.envs import BaseEnv, BaseEnvTimestep, FrameStackWrapper
from ding.torch_utils import to_ndarray, to_list
from ding.envs.common.common_function import affine_transform
from ding.utils import ENV_REGISTRY


@ENV_REGISTRY.register('bipedalwalker')
class BipedalWalkerEnv(BaseEnv):

    def __init__(self, cfg: dict) -> None:
        self._cfg = cfg
        self._init_flag = False
        self._act_scale = cfg.act_scale
        self._rew_clip = cfg.rew_clip
        if "replay_path" in cfg:
            self._replay_path = cfg.replay_path
        else:
            self._replay_path = None

    def reset(self) -> np.ndarray:
        if not self._init_flag:
            self._env = gym.make('BipedalWalker-v3')
            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)
        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))
            )
        self._eval_episode_return = 0
        obs = self._env.reset()
        obs = to_ndarray(obs).astype(np.float32)
        return obs

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

    def render(self) -> None:
        self._env.render()

    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)
        if action.shape == (1, ):
            action = action.squeeze()  # 0-dim array
        if self._act_scale:
            action = affine_transform(action, min_val=self.action_space.low, max_val=self.action_space.high)

        obs, rew, done, info = self._env.step(action)
        self._eval_episode_return += rew
        if self._rew_clip:
            rew = max(-10, rew)
        rew = np.float32(rew)

        if done:
            info['eval_episode_return'] = self._eval_episode_return
        obs = to_ndarray(obs).astype(np.float32)
        rew = to_ndarray([rew])  # wrapped to be transfered to a array with shape (1,)
        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()
        if isinstance(random_action, np.ndarray):
            pass
        elif isinstance(random_action, int):
            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 __repr__(self) -> str:
        return "DI-engine BipedalWalker Env"