"""
From gym==0.22.0

Use terminated/truncated instead of done.

Disable auto-reset after done. Reset in MultiStepWrapper instead.

Add reset_arg() that allows all environments with different options.
Add reset_one_arg() that allows resetting a single environment with options.
Add render().

"""

from typing import Optional, Union, List

import numpy as np
import multiprocessing as mp
import time
import sys
from enum import Enum
from copy import deepcopy
import time

from gym import logger

# from gym.vector.vector_env import VectorEnv
from .vector_env import VectorEnv
from gym.error import (
    AlreadyPendingCallError,
    NoAsyncCallError,
    ClosedEnvironmentError,
    CustomSpaceError,
)
from gym.vector.utils import (
    create_shared_memory,
    create_empty_array,
    write_to_shared_memory,
    read_from_shared_memory,
    concatenate,
    iterate,
    CloudpickleWrapper,
    clear_mpi_env_vars,
)


__all__ = ["AsyncVectorEnv"]


class AsyncState(Enum):
    DEFAULT = "default"
    WAITING_RESET = "reset"
    WAITING_STEP = "step"
    WAITING_CALL = "call"


class AsyncVectorEnv(VectorEnv):
    """Vectorized environment that runs multiple environments in parallel. It
    uses `multiprocessing`_ processes, and pipes for communication.

    Parameters
    ----------
    env_fns : iterable of callable
        Functions that create the environments.

    observation_space : :class:`gym.spaces.Space`, optional
        Observation space of a single environment. If ``None``, then the
        observation space of the first environment is taken.

    action_space : :class:`gym.spaces.Space`, optional
        Action space of a single environment. If ``None``, then the action space
        of the first environment is taken.

    shared_memory : bool
        If ``True``, then the observations from the worker processes are
        communicated back through shared variables. This can improve the
        efficiency if the observations are large (e.g. images).

    copy : bool
        If ``True``, then the :meth:`~AsyncVectorEnv.reset` and
        :meth:`~AsyncVectorEnv.step` methods return a copy of the observations.

    context : str, optional
        Context for `multiprocessing`_. If ``None``, then the default context is used.

    daemon : bool
        If ``True``, then subprocesses have ``daemon`` flag turned on; that is, they
        will quit if the head process quits. However, ``daemon=True`` prevents
        subprocesses to spawn children, so for some environments you may want
        to have it set to ``False``.

    worker : callable, optional
        If set, then use that worker in a subprocess instead of a default one.
        Can be useful to override some inner vector env logic, for instance,
        how resets on done are handled.

    Warning
    -------
    :attr:`worker` is an advanced mode option. It provides a high degree of
    flexibility and a high chance to shoot yourself in the foot; thus,
    if you are writing your own worker, it is recommended to start from the code
    for ``_worker`` (or ``_worker_shared_memory``) method, and add changes.

    Raises
    ------
    RuntimeError
        If the observation space of some sub-environment does not match
        :obj:`observation_space` (or, by default, the observation space of
        the first sub-environment).

    ValueError
        If :obj:`observation_space` is a custom space (i.e. not a default
        space in Gym, such as :class:`~gym.spaces.Box`, :class:`~gym.spaces.Discrete`,
        or :class:`~gym.spaces.Dict`) and :obj:`shared_memory` is ``True``.

    Example
    -------

    .. code-block::

        >>> env = gym.vector.AsyncVectorEnv([
        ...     lambda: gym.make("Pendulum-v0", g=9.81),
        ...     lambda: gym.make("Pendulum-v0", g=1.62)
        ... ])
        >>> env.reset()
        array([[-0.8286432 ,  0.5597771 ,  0.90249056],
               [-0.85009176,  0.5266346 ,  0.60007906]], dtype=float32)
    """

    def __init__(
        self,
        env_fns,
        dummy_env_fn=None,
        observation_space=None,
        action_space=None,
        shared_memory=True,
        copy=True,
        context=None,
        daemon=True,
        worker=None,
        delay_init=False,
    ):
        ctx = mp.get_context(context)
        self.env_fns = env_fns
        self.shared_memory = shared_memory
        self.copy = copy
        if dummy_env_fn is None:
            dummy_env_fn = env_fns[0]
        dummy_env = dummy_env_fn()
        self.metadata = dummy_env.metadata

        self.n_envs = len(env_fns)
        if (observation_space is None) or (action_space is None):
            observation_space = observation_space or dummy_env.observation_space
            action_space = action_space or dummy_env.action_space
        dummy_env.close()
        del dummy_env
        super().__init__(
            num_envs=len(env_fns),
            observation_space=observation_space,
            action_space=action_space,
        )

        if self.shared_memory:
            try:
                _obs_buffer = create_shared_memory(
                    self.single_observation_space, n=self.num_envs, ctx=ctx
                )
                self.observations = read_from_shared_memory(
                    self.single_observation_space, _obs_buffer, n=self.num_envs
                )
            except CustomSpaceError:
                raise ValueError(
                    "Using `shared_memory=True` in `AsyncVectorEnv` "
                    "is incompatible with non-standard Gym observation spaces "
                    "(i.e. custom spaces inheriting from `gym.Space`), and is "
                    "only compatible with default Gym spaces (e.g. `Box`, "
                    "`Tuple`, `Dict`) for batching. Set `shared_memory=False` "
                    "if you use custom observation spaces."
                )
        else:
            _obs_buffer = None
            self.observations = create_empty_array(
                self.single_observation_space, n=self.num_envs, fn=np.zeros
            )

        self.parent_pipes, self.processes = [], []
        self.error_queue = ctx.Queue()
        target = _worker_shared_memory if self.shared_memory else _worker
        target = worker or target
        with clear_mpi_env_vars():
            for idx, env_fn in enumerate(self.env_fns):
                parent_pipe, child_pipe = ctx.Pipe()
                process = ctx.Process(
                    target=target,
                    name=f"Worker<{type(self).__name__}>-{idx}",
                    args=(
                        idx,
                        CloudpickleWrapper(env_fn),
                        child_pipe,
                        parent_pipe,
                        _obs_buffer,
                        self.error_queue,
                    ),
                )

                self.parent_pipes.append(parent_pipe)
                self.processes.append(process)

                process.daemon = daemon
                process.start()
                child_pipe.close()
                if (
                    delay_init
                ):  # D3IL complains about temporary XML if n_envs is too large. Adding a delay avoids the error.
                    time.sleep(0.1)

        self._state = AsyncState.DEFAULT
        # self._check_spaces()

    def seed(self, seed=None):
        super().seed(seed=seed)
        self._assert_is_running()
        if seed is None:
            seed = [None for _ in range(self.num_envs)]
        if isinstance(seed, int):
            seed = [seed + i for i in range(self.num_envs)]
        assert len(seed) == self.num_envs

        if self._state != AsyncState.DEFAULT:
            raise AlreadyPendingCallError(
                f"Calling `seed` while waiting for a pending call to `{self._state.value}` to complete.",
                self._state.value,
            )

        for pipe, seed in zip(self.parent_pipes, seed):
            pipe.send(("seed", seed))
        _, successes = zip(*[pipe.recv() for pipe in self.parent_pipes])
        self._raise_if_errors(successes)

    def reset_async(
        self,
        seed: Optional[Union[int, List[int]]] = None,
        return_info: bool = False,
        options: Optional[dict] = None,
    ):
        """Send the calls to :obj:`reset` to each sub-environment.

        Raises
        ------
        ClosedEnvironmentError
            If the environment was closed (if :meth:`close` was previously called).

        AlreadyPendingCallError
            If the environment is already waiting for a pending call to another
            method (e.g. :meth:`step_async`). This can be caused by two consecutive
            calls to :meth:`reset_async`, with no call to :meth:`reset_wait` in
            between.
        """
        self._assert_is_running()

        if seed is None:
            seed = [None for _ in range(self.num_envs)]
        if isinstance(seed, int):
            seed = [seed + i for i in range(self.num_envs)]
        assert len(seed) == self.num_envs

        if self._state != AsyncState.DEFAULT:
            raise AlreadyPendingCallError(
                f"Calling `reset_async` while waiting for a pending call to `{self._state.value}` to complete",
                self._state.value,
            )

        for pipe, single_seed in zip(self.parent_pipes, seed):
            single_kwargs = {}
            if single_seed is not None:
                single_kwargs["seed"] = single_seed
            if return_info:
                single_kwargs["return_info"] = return_info
            if options is not None:
                single_kwargs["options"] = options

            pipe.send(("reset", single_kwargs))
        self._state = AsyncState.WAITING_RESET

    def reset_wait(
        self,
        timeout=None,
        seed: Optional[int] = None,
        return_info: bool = False,
        options: Optional[dict] = None,
    ):
        """
        Parameters
        ----------
        timeout : int or float, optional
            Number of seconds before the call to `reset_wait` times out. If
            `None`, the call to `reset_wait` never times out.
        seed: ignored
        options: ignored

        Returns
        -------
        element of :attr:`~VectorEnv.observation_space`
            A batch of observations from the vectorized environment.
        infos : list of dicts containing metadata

        Raises
        ------
        ClosedEnvironmentError
            If the environment was closed (if :meth:`close` was previously called).

        NoAsyncCallError
            If :meth:`reset_wait` was called without any prior call to
            :meth:`reset_async`.

        TimeoutError
            If :meth:`reset_wait` timed out.
        """
        self._assert_is_running()
        if self._state != AsyncState.WAITING_RESET:
            raise NoAsyncCallError(
                "Calling `reset_wait` without any prior " "call to `reset_async`.",
                AsyncState.WAITING_RESET.value,
            )

        if not self._poll(timeout):
            self._state = AsyncState.DEFAULT
            raise mp.TimeoutError(
                f"The call to `reset_wait` has timed out after {timeout} second(s)."
            )

        results, successes = zip(*[pipe.recv() for pipe in self.parent_pipes])
        self._raise_if_errors(successes)
        self._state = AsyncState.DEFAULT

        if return_info:
            results, infos = zip(*results)
            infos = list(infos)

            if not self.shared_memory:
                self.observations = concatenate(
                    self.single_observation_space, results, self.observations
                )

            return (
                deepcopy(self.observations) if self.copy else self.observations
            ), infos
        else:
            if not self.shared_memory:
                self.observations = concatenate(
                    self.single_observation_space, results, self.observations
                )

            return deepcopy(self.observations) if self.copy else self.observations

    def step_async(self, actions):
        """Send the calls to :obj:`step` to each sub-environment.

        Parameters
        ----------
        actions : element of :attr:`~VectorEnv.action_space`
            Batch of actions.

        Raises
        ------
        ClosedEnvironmentError
            If the environment was closed (if :meth:`close` was previously called).

        AlreadyPendingCallError
            If the environment is already waiting for a pending call to another
            method (e.g. :meth:`reset_async`). This can be caused by two consecutive
            calls to :meth:`step_async`, with no call to :meth:`step_wait` in
            between.
        """
        self._assert_is_running()
        if self._state != AsyncState.DEFAULT:
            raise AlreadyPendingCallError(
                f"Calling `step_async` while waiting for a pending call to `{self._state.value}` to complete.",
                self._state.value,
            )

        actions = iterate(self.action_space, actions)
        for pipe, action in zip(self.parent_pipes, actions):
            pipe.send(("step", action))
        self._state = AsyncState.WAITING_STEP

    def step_wait(self, timeout=None):
        """Wait for the calls to :obj:`step` in each sub-environment to finish.

        Parameters
        ----------
        timeout : int or float, optional
            Number of seconds before the call to :meth:`step_wait` times out. If
            ``None``, the call to :meth:`step_wait` never times out.

        Returns
        -------
        observations : element of :attr:`~VectorEnv.observation_space`
            A batch of observations from the vectorized environment.

        rewards : :obj:`np.ndarray`, dtype :obj:`np.float_`
            A vector of rewards from the vectorized environment.

        terminates : :obj:`np.ndarray`, dtype :obj:`np.bool_`
            A vector whose entries indicate whether the episode has terminated (failed).

        truncates : :obj:`np.ndarray`, dtype :obj:`np.bool_`
            A vector whose entries indicate whether the episode has been truncated (max episode length).

        infos : list of dict
            A list of auxiliary diagnostic information dicts from sub-environments.

        Raises
        ------
        ClosedEnvironmentError
            If the environment was closed (if :meth:`close` was previously called).

        NoAsyncCallError
            If :meth:`step_wait` was called without any prior call to
            :meth:`step_async`.

        TimeoutError
            If :meth:`step_wait` timed out.
        """
        self._assert_is_running()
        if self._state != AsyncState.WAITING_STEP:
            raise NoAsyncCallError(
                "Calling `step_wait` without any prior call " "to `step_async`.",
                AsyncState.WAITING_STEP.value,
            )

        if not self._poll(timeout):
            self._state = AsyncState.DEFAULT
            raise mp.TimeoutError(
                f"The call to `step_wait` has timed out after {timeout} second(s)."
            )

        results, successes = zip(*[pipe.recv() for pipe in self.parent_pipes])
        self._raise_if_errors(successes)
        self._state = AsyncState.DEFAULT
        observations_list, rewards, terminates, truncates, infos = zip(*results)

        if not self.shared_memory:
            self.observations = concatenate(
                self.single_observation_space,
                observations_list,
                self.observations,
            )

        return (
            deepcopy(self.observations) if self.copy else self.observations,
            np.array(rewards),
            np.array(terminates, dtype=np.bool_),
            np.array(truncates, dtype=np.bool_),
            infos,
        )

    def call_async(self, name, *args, **kwargs):
        """
        Parameters
        ----------
        name : string
            Name of the method or property to call.

        *args
            Arguments to apply to the method call.

        **kwargs
            Keywoard arguments to apply to the method call.
        """
        self._assert_is_running()
        if self._state != AsyncState.DEFAULT:
            raise AlreadyPendingCallError(
                "Calling `call_async` while waiting "
                f"for a pending call to `{self._state.value}` to complete.",
                self._state.value,
            )

        for pipe in self.parent_pipes:
            pipe.send(("_call", (name, args, kwargs)))
        self._state = AsyncState.WAITING_CALL

    def call_wait(self, timeout=None):
        """
        Parameters
        ----------
        timeout : int or float, optional
            Number of seconds before the call to `step_wait` times out. If
            `None` (default), the call to `step_wait` never times out.

        Returns
        -------
        results : list
            List of the results of the individual calls to the method or
            property for each environment.
        """
        self._assert_is_running()
        if self._state != AsyncState.WAITING_CALL:
            raise NoAsyncCallError(
                "Calling `call_wait` without any prior call to `call_async`.",
                AsyncState.WAITING_CALL.value,
            )

        if not self._poll(timeout):
            self._state = AsyncState.DEFAULT
            raise mp.TimeoutError(
                f"The call to `call_wait` has timed out after {timeout} second(s)."
            )

        results, successes = zip(*[pipe.recv() for pipe in self.parent_pipes])
        self._raise_if_errors(successes)
        self._state = AsyncState.DEFAULT

        return results

    def set_attr(self, name, values):
        """
        Parameters
        ----------
        name : string
            Name of the property to be set in each individual environment.

        values : list, tuple, or object
            Values of the property to be set to. If `values` is a list or
            tuple, then it corresponds to the values for each individual
            environment, otherwise a single value is set for all environments.
        """
        self._assert_is_running()
        if not isinstance(values, (list, tuple)):
            values = [values for _ in range(self.num_envs)]
        if len(values) != self.num_envs:
            raise ValueError(
                "Values must be a list or tuple with length equal to the "
                f"number of environments. Got `{len(values)}` values for "
                f"{self.num_envs} environments."
            )

        if self._state != AsyncState.DEFAULT:
            raise AlreadyPendingCallError(
                "Calling `set_attr` while waiting "
                f"for a pending call to `{self._state.value}` to complete.",
                self._state.value,
            )

        for pipe, value in zip(self.parent_pipes, values):
            pipe.send(("_setattr", (name, value)))
        _, successes = zip(*[pipe.recv() for pipe in self.parent_pipes])
        self._raise_if_errors(successes)

    def close_extras(self, timeout=None, terminate=False):
        """Close the environments & clean up the extra resources
        (processes and pipes).

        Parameters
        ----------
        timeout : int or float, optional
            Number of seconds before the call to :meth:`close` times out. If ``None``,
            the call to :meth:`close` never times out. If the call to :meth:`close`
            times out, then all processes are terminated.

        terminate : bool
            If ``True``, then the :meth:`close` operation is forced and all processes
            are terminated.

        Raises
        ------
        TimeoutError
            If :meth:`close` timed out.
        """
        timeout = 0 if terminate else timeout
        try:
            if self._state != AsyncState.DEFAULT:
                logger.warn(
                    f"Calling `close` while waiting for a pending call to `{self._state.value}` to complete."
                )
                function = getattr(self, f"{self._state.value}_wait")
                function(timeout)
        except mp.TimeoutError:
            terminate = True

        if terminate:
            for process in self.processes:
                if process.is_alive():
                    process.terminate()
        else:
            for pipe in self.parent_pipes:
                if (pipe is not None) and (not pipe.closed):
                    pipe.send(("close", None))
            for pipe in self.parent_pipes:
                if (pipe is not None) and (not pipe.closed):
                    pipe.recv()

        for pipe in self.parent_pipes:
            if pipe is not None:
                pipe.close()
        for process in self.processes:
            process.join()

    def _poll(self, timeout=None):
        self._assert_is_running()
        if timeout is None:
            return True
        end_time = time.perf_counter() + timeout
        delta = None
        for pipe in self.parent_pipes:
            delta = max(end_time - time.perf_counter(), 0)
            if pipe is None:
                return False
            if pipe.closed or (not pipe.poll(delta)):
                return False
        return True

    def _check_spaces(self):
        self._assert_is_running()
        spaces = (self.single_observation_space, self.single_action_space)
        for pipe in self.parent_pipes:
            pipe.send(("_check_spaces", spaces))
        results, successes = zip(*[pipe.recv() for pipe in self.parent_pipes])
        self._raise_if_errors(successes)
        same_observation_spaces, same_action_spaces = zip(*results)
        if not all(same_observation_spaces):
            raise RuntimeError(
                "Some environments have an observation space different from "
                f"`{self.single_observation_space}`. In order to batch observations, "
                "the observation spaces from all environments must be equal."
            )
        if not all(same_action_spaces):
            raise RuntimeError(
                "Some environments have an action space different from "
                f"`{self.single_action_space}`. In order to batch actions, the "
                "action spaces from all environments must be equal."
            )

    def _assert_is_running(self):
        if self.closed:
            raise ClosedEnvironmentError(
                f"Trying to operate on `{type(self).__name__}`, after a call to `close()`."
            )

    def _raise_if_errors(self, successes):
        if all(successes):
            return

        num_errors = self.num_envs - sum(successes)
        assert num_errors > 0
        for _ in range(num_errors):
            index, exctype, value = self.error_queue.get()
            logger.error(
                f"Received the following error from Worker-{index}: {exctype.__name__}: {value}"
            )
            logger.error(f"Shutting down Worker-{index}.")
            self.parent_pipes[index].close()
            self.parent_pipes[index] = None

        logger.error("Raising the last exception back to the main process.")
        raise exctype(value)

    def __del__(self):
        if not getattr(self, "closed", True):
            self.close(terminate=True)

    ######################### Added #########################
    def call_sync(self, method_name, indices=None, **method_kwargs):
        """Call instance methods of vectorized environments."""
        target_remotes = self._get_target_remotes(indices)
        for remote in target_remotes:
            remote.send(("_call_sync", (method_name, method_kwargs)))
        return [remote.recv() for remote in target_remotes]

    def call_sync_arg(
        self, method_name, method_arg_name, method_arg_list, indices=None
    ):
        """Call instance methods of vectorized environments with args."""
        target_remotes = self._get_target_remotes(indices)
        for method_arg, remote in zip(method_arg_list, target_remotes):
            method_kwargs = {method_arg_name: method_arg}
            remote.send(("_call_sync", (method_name, method_kwargs)))
        return [remote.recv() for remote in target_remotes]

    def _get_target_remotes(self, indices):
        """Get the connection object needed to communicate with the wanted
        envs that are in subprocesses."""
        if indices is None:
            indices = range(self.n_envs)
        return [self.parent_pipes[i] for i in indices]

    def reset_arg(self, options_list, **kwargs):
        results = self.call_sync_arg("reset", "options", options_list)
        obs = [result[0] for result in results]
        if isinstance(obs[0], np.ndarray):
            return np.stack(obs)
        else:
            assert isinstance(obs[0], dict)
            return obs

    def reset_one_arg(self, env_ind, options=None):
        """
        Reset one environment with options.
        """
        obs, success = self.call_sync(
            "reset",
            options=options,
            indices=[env_ind],
        )[0]
        return obs

    def render(self, *args, **kwargs):
        return self.call("render", *args, **kwargs)


def _worker(index, env_fn, pipe, parent_pipe, shared_memory, error_queue):
    assert shared_memory is None
    env = env_fn()
    parent_pipe.close()
    try:
        while True:
            command, data = pipe.recv()
            if command == "reset":
                if "return_info" in data and data["return_info"] == True:
                    observation, info = env.reset(**data)
                    pipe.send(((observation, info), True))
                else:
                    observation = env.reset(**data)
                    pipe.send((observation, True))

            elif command == "step":
                observation, reward, terminated, truncated, info = env.step(data)
                pipe.send(((observation, reward, terminated, truncated, info), True))
            elif command == "seed":
                env.seed(data)
                pipe.send((None, True))
            elif command == "close":
                pipe.send((None, True))
                break
            elif command == "_call_sync":
                function = getattr(env, data[0])
                pipe.send((function(**data[1]), True))
            elif command == "_call":
                name, args, kwargs = data
                if name in ["reset", "step", "seed", "close"]:
                    raise ValueError(
                        f"Trying to call function `{name}` with "
                        f"`_call`. Use `{name}` directly instead."
                    )
                function = getattr(env, name)
                if callable(function):
                    pipe.send((function(**kwargs), True))
                else:
                    pipe.send((function, True))
            elif command == "_setattr":
                name, value = data
                setattr(env, name, value)
                pipe.send((None, True))
            elif command == "_check_spaces":
                pipe.send(
                    (
                        (data[0] == env.observation_space, data[1] == env.action_space),
                        True,
                    )
                )
            else:
                raise RuntimeError(
                    f"Received unknown command `{command}`. Must "
                    "be one of {`reset`, `step`, `seed`, `close`, `_call`, "
                    "`_setattr`, `_check_spaces`}."
                )
    except (KeyboardInterrupt, Exception):
        error_queue.put((index,) + sys.exc_info()[:2])
        pipe.send((None, False))
    finally:
        env.close()


def _worker_shared_memory(index, env_fn, pipe, parent_pipe, shared_memory, error_queue):
    assert shared_memory is not None
    env = env_fn()
    observation_space = env.observation_space
    parent_pipe.close()
    try:
        while True:
            command, data = pipe.recv()
            if command == "reset":
                if "return_info" in data and data["return_info"] == True:
                    observation, info = env.reset(**data)
                    write_to_shared_memory(
                        observation_space, index, observation, shared_memory
                    )
                    pipe.send(((None, info), True))
                else:
                    observation = env.reset(**data)
                    write_to_shared_memory(
                        observation_space, index, observation, shared_memory
                    )
                    pipe.send((None, True))
            elif command == "step":
                observation, reward, terminated, truncated, info = env.step(data)
                write_to_shared_memory(
                    observation_space, index, observation, shared_memory
                )
                pipe.send(((None, reward, terminated, truncated, info), True))
            elif command == "seed":
                env.seed(data)
                pipe.send((None, True))
            elif command == "close":
                pipe.send((None, True))
                break
            elif command == "_call_sync":
                function = getattr(env, data[0])
                pipe.send((function(**data[1]), True))
            elif command == "_call":
                name, args, kwargs = data
                if name in ["reset", "step", "seed", "close"]:
                    raise ValueError(
                        f"Trying to call function `{name}` with "
                        f"`_call`. Use `{name}` directly instead."
                    )
                function = getattr(env, name)
                if callable(function):
                    pipe.send((function(*args, **kwargs), True))
                else:
                    pipe.send((function, True))
            elif command == "_setattr":
                name, value = data
                setattr(env, name, value)
                pipe.send((None, True))
            elif command == "_check_spaces":
                pipe.send(
                    ((data[0] == observation_space, data[1] == env.action_space), True)
                )
            else:
                raise RuntimeError(
                    f"Received unknown command `{command}`. Must "
                    "be one of {`reset`, `step`, `seed`, `close`, `_call`, "
                    "`_setattr`, `_check_spaces`}."
                )
    except (KeyboardInterrupt, Exception):
        error_queue.put((index,) + sys.exc_info()[:2])
        pipe.send((None, False))
    finally:
        env.close()
