import gym
import time
import ctypes
import numpy as np
from collections import OrderedDict
from multiprocessing.context import Process
from multiprocessing import Array, Pipe, connection
from typing import Any, List, Tuple, Union, Callable, Optional

from rlkit.envs.worker import EnvWorker
from rlkit.envs.worker.utils import CloudpickleWrapper


_NP_TO_CT = {
    np.bool_: ctypes.c_bool,
    np.uint8: ctypes.c_uint8,
    np.uint16: ctypes.c_uint16,
    np.uint32: ctypes.c_uint32,
    np.uint64: ctypes.c_uint64,
    np.int8: ctypes.c_int8,
    np.int16: ctypes.c_int16,
    np.int32: ctypes.c_int32,
    np.int64: ctypes.c_int64,
    np.float32: ctypes.c_float,
    np.float64: ctypes.c_double,
}


class ShArray:
    """Wrapper of multiprocessing Array."""

    def __init__(self, dtype: np.generic, shape: Tuple[int]) -> None:
        self.arr = Array(_NP_TO_CT[dtype.type], int(np.prod(shape)))  # type: ignore
        self.dtype = dtype
        self.shape = shape

    def save(self, ndarray: np.ndarray) -> None:
        assert isinstance(ndarray, np.ndarray)
        dst = self.arr.get_obj()
        dst_np = np.frombuffer(dst, dtype=self.dtype).reshape(self.shape)
        np.copyto(dst_np, ndarray)

    def get(self) -> np.ndarray:
        obj = self.arr.get_obj()
        return np.frombuffer(obj, dtype=self.dtype).reshape(self.shape)


def _setup_buf(space: gym.Space) -> Union[dict, tuple, ShArray]:
    if isinstance(space, gym.spaces.Dict):
        assert isinstance(space.spaces, OrderedDict)
        return {k: _setup_buf(v) for k, v in space.spaces.items()}
    elif isinstance(space, gym.spaces.Tuple):
        assert isinstance(space.spaces, tuple)
        return tuple([_setup_buf(t) for t in space.spaces])
    else:
        return ShArray(space.dtype, space.shape)


def _worker(
    parent: connection.Connection,
    p: connection.Connection,
    env_fn_wrapper: CloudpickleWrapper,
    obs_bufs: Optional[Union[dict, tuple, ShArray]] = None,
) -> None:
    def _encode_obs(
        obs: Union[dict, tuple, np.ndarray], buffer: Union[dict, tuple, ShArray]
    ) -> None:
        if isinstance(obs, np.ndarray) and isinstance(buffer, ShArray):
            buffer.save(obs)
        elif isinstance(obs, tuple) and isinstance(buffer, tuple):
            for o, b in zip(obs, buffer):
                _encode_obs(o, b)
        elif isinstance(obs, dict) and isinstance(buffer, dict):
            for k in obs.keys():
                _encode_obs(obs[k], buffer[k])
        return None

    parent.close()
    env = env_fn_wrapper.data()
    try:
        while True:
            try:
                cmd, data = p.recv()
            except EOFError:  # the pipe has been closed
                p.close()
                break
            if cmd == "step":
                obs, reward, done, info = env.step(data)
                if obs_bufs is not None:
                    _encode_obs(obs, obs_bufs)
                    obs = None
                p.send((obs, reward, done, info))
            elif cmd == "reset":
                obs = env.reset()
                if obs_bufs is not None:
                    _encode_obs(obs, obs_bufs)
                    obs = None
                p.send(obs)
            elif cmd == "close":
                p.send(env.close())
                p.close()
                break
            elif cmd == "render":
                p.send(env.render(**data) if hasattr(env, "render") else None)
            elif cmd == "seed":
                p.send(env.seed(data) if hasattr(env, "seed") else None)
            elif cmd == "getattr":
                p.send(getattr(env, data) if hasattr(env, data) else None)
            else:
                p.close()
                raise NotImplementedError
    except KeyboardInterrupt:
        p.close()


class SubprocEnvWorker(EnvWorker):
    """Subprocess worker used in SubprocVectorEnv and ShmemVectorEnv."""

    def __init__(
        self, env_fn: Callable[[], gym.Env], share_memory: bool = False
    ) -> None:
        self.parent_remote, self.child_remote = Pipe()
        self.share_memory = share_memory
        self.buffer: Optional[Union[dict, tuple, ShArray]] = None
        if self.share_memory:
            dummy = env_fn()
            obs_space = dummy.observation_space
            dummy.close()
            del dummy
            self.buffer = _setup_buf(obs_space)
        args = (
            self.parent_remote,
            self.child_remote,
            CloudpickleWrapper(env_fn),
            self.buffer,
        )
        self.process = Process(target=_worker, args=args, daemon=True)
        self.process.start()
        self.child_remote.close()
        super().__init__(env_fn)

    def __getattr__(self, key: str) -> Any:
        self.parent_remote.send(["getattr", key])
        return self.parent_remote.recv()

    def _decode_obs(self) -> Union[dict, tuple, np.ndarray]:
        def decode_obs(
            buffer: Optional[Union[dict, tuple, ShArray]]
        ) -> Union[dict, tuple, np.ndarray]:
            if isinstance(buffer, ShArray):
                return buffer.get()
            elif isinstance(buffer, tuple):
                return tuple([decode_obs(b) for b in buffer])
            elif isinstance(buffer, dict):
                return {k: decode_obs(v) for k, v in buffer.items()}
            else:
                raise NotImplementedError

        return decode_obs(self.buffer)

    def reset(self) -> Any:
        self.parent_remote.send(["reset", None])
        obs = self.parent_remote.recv()
        if self.share_memory:
            obs = self._decode_obs()
        return obs

    @staticmethod
    def wait(  # type: ignore
        workers: List["SubprocEnvWorker"],
        wait_num: int,
        timeout: Optional[float] = None,
    ) -> List["SubprocEnvWorker"]:
        remain_conns = conns = [x.parent_remote for x in workers]
        ready_conns: List[connection.Connection] = []
        remain_time, t1 = timeout, time.time()
        while len(remain_conns) > 0 and len(ready_conns) < wait_num:
            if timeout:
                remain_time = timeout - (time.time() - t1)
                if remain_time <= 0:
                    break
            # connection.wait hangs if the list is empty
            new_ready_conns = connection.wait(remain_conns, timeout=remain_time)
            ready_conns.extend(new_ready_conns)  # type: ignore
            remain_conns = [conn for conn in remain_conns if conn not in ready_conns]
        return [workers[conns.index(con)] for con in ready_conns]

    def send_action(self, action: np.ndarray) -> None:
        self.parent_remote.send(["step", action])

    def get_result(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
        obs, rew, done, info = self.parent_remote.recv()
        if self.share_memory:
            obs = self._decode_obs()
        return obs, rew, done, info

    def seed(self, seed: Optional[int] = None) -> Optional[List[int]]:
        super().seed(seed)
        self.parent_remote.send(["seed", seed])
        return self.parent_remote.recv()

    def render(self, **kwargs: Any) -> Any:
        self.parent_remote.send(["render", kwargs])
        return self.parent_remote.recv()

    def close_env(self) -> None:
        try:
            self.parent_remote.send(["close", None])
            # mp may be deleted so it may raise AttributeError
            self.parent_remote.recv()
            self.process.join()
        except (BrokenPipeError, EOFError, AttributeError):
            pass
        # ensure the subproc is terminated
        self.process.terminate()
