import gym
import logging
from typing import Callable, Dict, List, Tuple, Optional, Union, Set, Type

from ray.rllib.env.base_env import BaseEnv
from ray.rllib.env.env_context import EnvContext
from ray.rllib.utils.annotations import (
    ExperimentalAPI,
    override,
    PublicAPI,
    DeveloperAPI,
)
from ray.rllib.utils.typing import (
    AgentID,
    EnvCreator,
    EnvID,
    EnvType,
    MultiAgentDict,
    MultiEnvDict,
)
from ray.util import log_once

# If the obs space is Dict type, look for the global state under this key.
ENV_STATE = "state"

logger = logging.getLogger(__name__)


@PublicAPI
class MultiAgentEnv(gym.Env):
    """An environment that hosts multiple independent agents.

    Agents are identified by (string) agent ids. Note that these "agents" here
    are not to be confused with RLlib Algorithms, which are also sometimes
    referred to as "agents" or "RL agents".
    """

    def __init__(self):
        if not hasattr(self, "observation_space"):
            self.observation_space = None
        if not hasattr(self, "action_space"):
            self.action_space = None
        if not hasattr(self, "_agent_ids"):
            self._agent_ids = set()

        # Do the action and observation spaces map from agent ids to spaces
        # for the individual agents?
        if not hasattr(self, "_spaces_in_preferred_format"):
            self._spaces_in_preferred_format = None

    @PublicAPI
    def reset(self) -> MultiAgentDict:
        """Resets the env and returns observations from ready agents.

        Returns:
            New observations for each ready agent.

        Examples:
            >>> from ray.rllib.env.multi_agent_env import MultiAgentEnv
            >>> class MyMultiAgentEnv(MultiAgentEnv): # doctest: +SKIP
            ...     # Define your env here. # doctest: +SKIP
            ...     ... # doctest: +SKIP
            >>> env = MyMultiAgentEnv() # doctest: +SKIP
            >>> obs = env.reset() # doctest: +SKIP
            >>> print(obs) # doctest: +SKIP
            {
                "car_0": [2.4, 1.6],
                "car_1": [3.4, -3.2],
                "traffic_light_1": [0, 3, 5, 1],
            }
        """
        raise NotImplementedError

    @PublicAPI
    def step(
        self, action_dict: MultiAgentDict
    ) -> Tuple[MultiAgentDict, MultiAgentDict, MultiAgentDict, MultiAgentDict]:
        """Returns observations from ready agents.

        The returns are dicts mapping from agent_id strings to values. The
        number of agents in the env can vary over time.

        Returns:
            Tuple containing 1) new observations for
            each ready agent, 2) reward values for each ready agent. If
            the episode is just started, the value will be None.
            3) Done values for each ready agent. The special key
            "__all__" (required) is used to indicate env termination.
            4) Optional info values for each agent id.

        Examples:
            >>> env = ... # doctest: +SKIP
            >>> obs, rewards, dones, infos = env.step( # doctest: +SKIP
            ...    action_dict={ # doctest: +SKIP
            ...        "car_0": 1, "car_1": 0, "traffic_light_1": 2, # doctest: +SKIP
            ...    }) # doctest: +SKIP
            >>> print(rewards) # doctest: +SKIP
            {
                "car_0": 3,
                "car_1": -1,
                "traffic_light_1": 0,
            }
            >>> print(dones) # doctest: +SKIP
            {
                "car_0": False,    # car_0 is still running
                "car_1": True,     # car_1 is done
                "__all__": False,  # the env is not done
            }
            >>> print(infos) # doctest: +SKIP
            {
                "car_0": {},  # info for car_0
                "car_1": {},  # info for car_1
            }
        """
        raise NotImplementedError

    @ExperimentalAPI
    def observation_space_contains(self, x: MultiAgentDict) -> bool:
        """Checks if the observation space contains the given key.

        Args:
            x: Observations to check.

        Returns:
            True if the observation space contains the given all observations
                in x.
        """
        if (
            not hasattr(self, "_spaces_in_preferred_format")
            or self._spaces_in_preferred_format is None
        ):
            self._spaces_in_preferred_format = (
                self._check_if_space_maps_agent_id_to_sub_space()
            )
        if self._spaces_in_preferred_format:
            for key, agent_obs in x.items():
                if not self.observation_space[key].contains(agent_obs):
                    return False
            if not all(k in self.observation_space for k in x):
                if log_once("possibly_bad_multi_agent_dict_missing_agent_observations"):
                    logger.warning(
                        "You environment returns observations that are "
                        "MultiAgentDicts with incomplete information. "
                        "Meaning that they only contain information on a subset of"
                        " participating agents. Ignore this warning if this is "
                        "intended, for example if your environment is a turn-based "
                        "simulation."
                    )
            return True

        logger.warning("observation_space_contains() has not been implemented")
        return True

    @ExperimentalAPI
    def action_space_contains(self, x: MultiAgentDict) -> bool:
        """Checks if the action space contains the given action.

        Args:
            x: Actions to check.

        Returns:
            True if the action space contains all actions in x.
        """
        if (
            not hasattr(self, "_spaces_in_preferred_format")
            or self._spaces_in_preferred_format is None
        ):
            self._spaces_in_preferred_format = (
                self._check_if_space_maps_agent_id_to_sub_space()
            )
        if self._spaces_in_preferred_format:
            return all([self.action_space[agent].contains(x[agent]) for agent in x])

        if log_once("action_space_contains"):
            logger.warning("action_space_contains() has not been implemented")
        return True

    @ExperimentalAPI
    def action_space_sample(self, agent_ids: list = None) -> MultiAgentDict:
        """Returns a random action for each environment, and potentially each
            agent in that environment.

        Args:
            agent_ids: List of agent ids to sample actions for. If None or
                empty list, sample actions for all agents in the
                environment.

        Returns:
            A random action for each environment.
        """
        if (
            not hasattr(self, "_spaces_in_preferred_format")
            or self._spaces_in_preferred_format is None
        ):
            self._spaces_in_preferred_format = (
                self._check_if_space_maps_agent_id_to_sub_space()
            )
        if self._spaces_in_preferred_format:
            if agent_ids is None:
                agent_ids = self.get_agent_ids()
            samples = self.action_space.sample()
            return {
                agent_id: samples[agent_id]
                for agent_id in agent_ids
                if agent_id != "__all__"
            }
        logger.warning("action_space_sample() has not been implemented")
        return {}

    @ExperimentalAPI
    def observation_space_sample(self, agent_ids: list = None) -> MultiEnvDict:
        """Returns a random observation from the observation space for each
        agent if agent_ids is None, otherwise returns a random observation for
        the agents in agent_ids.

        Args:
            agent_ids: List of agent ids to sample actions for. If None or
                empty list, sample actions for all agents in the
                environment.

        Returns:
            A random action for each environment.
        """

        if (
            not hasattr(self, "_spaces_in_preferred_format")
            or self._spaces_in_preferred_format is None
        ):
            self._spaces_in_preferred_format = (
                self._check_if_space_maps_agent_id_to_sub_space()
            )
        if self._spaces_in_preferred_format:
            if agent_ids is None:
                agent_ids = self.get_agent_ids()
            samples = self.observation_space.sample()
            samples = {agent_id: samples[agent_id] for agent_id in agent_ids}
            return samples
        if log_once("observation_space_sample"):
            logger.warning("observation_space_sample() has not been implemented")
        return {}

    @PublicAPI
    def get_agent_ids(self) -> Set[AgentID]:
        """Returns a set of agent ids in the environment.

        Returns:
            Set of agent ids.
        """
        if not isinstance(self._agent_ids, set):
            self._agent_ids = set(self._agent_ids)
        return self._agent_ids

    @PublicAPI
    def render(self, mode=None) -> None:
        """Tries to render the environment."""

        # By default, do nothing.
        pass

    # fmt: off
    # __grouping_doc_begin__
    def with_agent_groups(
        self,
        groups: Dict[str, List[AgentID]],
        obs_space: gym.Space = None,
            act_space: gym.Space = None) -> "MultiAgentEnv":
        """Convenience method for grouping together agents in this env.

        An agent group is a list of agent IDs that are mapped to a single
        logical agent. All agents of the group must act at the same time in the
        environment. The grouped agent exposes Tuple action and observation
        spaces that are the concatenated action and obs spaces of the
        individual agents.

        The rewards of all the agents in a group are summed. The individual
        agent rewards are available under the "individual_rewards" key of the
        group info return.

        Agent grouping is required to leverage algorithms such as Q-Mix.

        Args:
            groups: Mapping from group id to a list of the agent ids
                of group members. If an agent id is not present in any group
                value, it will be left ungrouped. The group id becomes a new agent ID
                in the final environment.
            obs_space: Optional observation space for the grouped
                env. Must be a tuple space. If not provided, will infer this to be a
                Tuple of n individual agents spaces (n=num agents in a group).
            act_space: Optional action space for the grouped env.
                Must be a tuple space. If not provided, will infer this to be a Tuple
                of n individual agents spaces (n=num agents in a group).

        Examples:
            >>> from ray.rllib.env.multi_agent_env import MultiAgentEnv
            >>> class MyMultiAgentEnv(MultiAgentEnv): # doctest: +SKIP
            ...     # define your env here
            ...     ... # doctest: +SKIP
            >>> env = MyMultiAgentEnv(...) # doctest: +SKIP
            >>> grouped_env = env.with_agent_groups(env, { # doctest: +SKIP
            ...   "group1": ["agent1", "agent2", "agent3"], # doctest: +SKIP
            ...   "group2": ["agent4", "agent5"], # doctest: +SKIP
            ... }) # doctest: +SKIP
        """

        from ray.rllib.env.wrappers.group_agents_wrapper import \
            GroupAgentsWrapper
        return GroupAgentsWrapper(self, groups, obs_space, act_space)

    # __grouping_doc_end__
    # fmt: on

    @PublicAPI
    def to_base_env(
        self,
        make_env: Optional[Callable[[int], EnvType]] = None,
        num_envs: int = 1,
        remote_envs: bool = False,
        remote_env_batch_wait_ms: int = 0,
        restart_failed_sub_environments: bool = False,
    ) -> "BaseEnv":
        """Converts an RLlib MultiAgentEnv into a BaseEnv object.

        The resulting BaseEnv is always vectorized (contains n
        sub-environments) to support batched forward passes, where n may
        also be 1. BaseEnv also supports async execution via the `poll` and
        `send_actions` methods and thus supports external simulators.

        Args:
            make_env: A callable taking an int as input (which indicates
                the number of individual sub-environments within the final
                vectorized BaseEnv) and returning one individual
                sub-environment.
            num_envs: The number of sub-environments to create in the
                resulting (vectorized) BaseEnv. The already existing `env`
                will be one of the `num_envs`.
            remote_envs: Whether each sub-env should be a @ray.remote
                actor. You can set this behavior in your config via the
                `remote_worker_envs=True` option.
            remote_env_batch_wait_ms: The wait time (in ms) to poll remote
                sub-environments for, if applicable. Only used if
                `remote_envs` is True.
            restart_failed_sub_environments: If True and any sub-environment (within
                a vectorized env) throws any error during env stepping, we will try to
                restart the faulty sub-environment. This is done
                without disturbing the other (still intact) sub-environments.

        Returns:
            The resulting BaseEnv object.
        """
        from ray.rllib.env.remote_base_env import RemoteBaseEnv

        if remote_envs:
            env = RemoteBaseEnv(
                make_env,
                num_envs,
                multiagent=True,
                remote_env_batch_wait_ms=remote_env_batch_wait_ms,
                restart_failed_sub_environments=restart_failed_sub_environments,
            )
        # Sub-environments are not ray.remote actors.
        else:
            env = MultiAgentEnvWrapper(
                make_env=make_env,
                existing_envs=[self],
                num_envs=num_envs,
                restart_failed_sub_environments=restart_failed_sub_environments,
            )

        return env

    @DeveloperAPI
    def _check_if_space_maps_agent_id_to_sub_space(self) -> bool:
        # do the action and observation spaces map from agent ids to spaces
        # for the individual agents?
        obs_space_check = (
            hasattr(self, "observation_space")
            and isinstance(self.observation_space, gym.spaces.Dict)
            and set(self.observation_space.spaces.keys()) == self.get_agent_ids()
        )
        action_space_check = (
            hasattr(self, "action_space")
            and isinstance(self.action_space, gym.spaces.Dict)
            and set(self.action_space.keys()) == self.get_agent_ids()
        )
        return obs_space_check and action_space_check


@PublicAPI
def make_multi_agent(
    env_name_or_creator: Union[str, EnvCreator],
) -> Type["MultiAgentEnv"]:
    """Convenience wrapper for any single-agent env to be converted into MA.

    Allows you to convert a simple (single-agent) `gym.Env` class
    into a `MultiAgentEnv` class. This function simply stacks n instances
    of the given ```gym.Env``` class into one unified ``MultiAgentEnv`` class
    and returns this class, thus pretending the agents act together in the
    same environment, whereas - under the hood - they live separately from
    each other in n parallel single-agent envs.

    Agent IDs in the resulting and are int numbers starting from 0
    (first agent).

    Args:
        env_name_or_creator: String specifier or env_maker function taking
            an EnvContext object as only arg and returning a gym.Env.

    Returns:
        New MultiAgentEnv class to be used as env.
        The constructor takes a config dict with `num_agents` key
        (default=1). The rest of the config dict will be passed on to the
        underlying single-agent env's constructor.

    Examples:
         >>> from ray.rllib.env.multi_agent_env import make_multi_agent
         >>> # By gym string:
         >>> ma_cartpole_cls = make_multi_agent("CartPole-v0") # doctest: +SKIP
         >>> # Create a 2 agent multi-agent cartpole.
         >>> ma_cartpole = ma_cartpole_cls({"num_agents": 2}) # doctest: +SKIP
         >>> obs = ma_cartpole.reset() # doctest: +SKIP
         >>> print(obs) # doctest: +SKIP
         {0: [...], 1: [...]}
         >>> # By env-maker callable:
         >>> from ray.rllib.examples.env.stateless_cartpole # doctest: +SKIP
         ...    import StatelessCartPole
         >>> ma_stateless_cartpole_cls = make_multi_agent( # doctest: +SKIP
         ...    lambda config: StatelessCartPole(config)) # doctest: +SKIP
         >>> # Create a 3 agent multi-agent stateless cartpole.
         >>> ma_stateless_cartpole = ma_stateless_cartpole_cls( # doctest: +SKIP
         ...    {"num_agents": 3}) # doctest: +SKIP
         >>> print(obs) # doctest: +SKIP
         {0: [...], 1: [...], 2: [...]}
    """

    class MultiEnv(MultiAgentEnv):
        def __init__(self, config: EnvContext = None):
            MultiAgentEnv.__init__(self)
            # Note(jungong) : explicitly check for None here, because config
            # can have an empty dict but meaningful data fields (worker_index,
            # vector_index) etc.
            # TODO(jungong) : clean this up, so we are not mixing up dict fields
            # with data fields.
            if config is None:
                config = {}
            num = config.pop("num_agents", 1)
            if isinstance(env_name_or_creator, str):
                self.agents = [gym.make(env_name_or_creator) for _ in range(num)]
            else:
                self.agents = [env_name_or_creator(config) for _ in range(num)]
            self.dones = set()
            self.observation_space = self.agents[0].observation_space
            self.action_space = self.agents[0].action_space
            self._agent_ids = set(range(num))

        @override(MultiAgentEnv)
        def observation_space_sample(self, agent_ids: list = None) -> MultiAgentDict:
            if agent_ids is None:
                agent_ids = list(range(len(self.agents)))
            obs = {agent_id: self.observation_space.sample() for agent_id in agent_ids}

            return obs

        @override(MultiAgentEnv)
        def action_space_sample(self, agent_ids: list = None) -> MultiAgentDict:
            if agent_ids is None:
                agent_ids = list(range(len(self.agents)))
            actions = {agent_id: self.action_space.sample() for agent_id in agent_ids}

            return actions

        @override(MultiAgentEnv)
        def action_space_contains(self, x: MultiAgentDict) -> bool:
            if not isinstance(x, dict):
                return False
            return all(self.action_space.contains(val) for val in x.values())

        @override(MultiAgentEnv)
        def observation_space_contains(self, x: MultiAgentDict) -> bool:
            if not isinstance(x, dict):
                return False
            return all(self.observation_space.contains(val) for val in x.values())

        @override(MultiAgentEnv)
        def reset(self):
            self.dones = set()
            return {i: a.reset() for i, a in enumerate(self.agents)}

        @override(MultiAgentEnv)
        def step(self, action_dict):
            obs, rew, done, info = {}, {}, {}, {}
            for i, action in action_dict.items():
                obs[i], rew[i], done[i], info[i] = self.agents[i].step(action)
                if done[i]:
                    self.dones.add(i)
            done["__all__"] = len(self.dones) == len(self.agents)
            return obs, rew, done, info

        @override(MultiAgentEnv)
        def render(self, mode=None):
            return self.agents[0].render(mode)

    return MultiEnv


@PublicAPI
class MultiAgentEnvWrapper(BaseEnv):
    """Internal adapter of MultiAgentEnv to BaseEnv.

    This also supports vectorization if num_envs > 1.
    """

    def __init__(
        self,
        make_env: Callable[[int], EnvType],
        existing_envs: List["MultiAgentEnv"],
        num_envs: int,
        restart_failed_sub_environments: bool = False,
    ):
        """Wraps MultiAgentEnv(s) into the BaseEnv API.

        Args:
            make_env: Factory that produces a new MultiAgentEnv instance taking the
                vector index as only call argument.
                Must be defined, if the number of existing envs is less than num_envs.
            existing_envs: List of already existing multi-agent envs.
            num_envs: Desired num multiagent envs to have at the end in
                total. This will include the given (already created)
                `existing_envs`.
            restart_failed_sub_environments: If True and any sub-environment (within
                this vectorized env) throws any error during env stepping, we will try
                to restart the faulty sub-environment. This is done
                without disturbing the other (still intact) sub-environments.
        """
        self.make_env = make_env
        self.envs = existing_envs
        self.num_envs = num_envs
        self.restart_failed_sub_environments = restart_failed_sub_environments

        self.dones = set()
        while len(self.envs) < self.num_envs:
            self.envs.append(self.make_env(len(self.envs)))
        for env in self.envs:
            assert isinstance(env, MultiAgentEnv)
        self._init_env_state(idx=None)
        self._unwrapped_env = self.envs[0].unwrapped

    @override(BaseEnv)
    def poll(
        self,
    ) -> Tuple[MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict]:
        obs, rewards, dones, infos = {}, {}, {}, {}
        for i, env_state in enumerate(self.env_states):
            obs[i], rewards[i], dones[i], infos[i] = env_state.poll()
        return obs, rewards, dones, infos, {}

    @override(BaseEnv)
    def send_actions(self, action_dict: MultiEnvDict) -> None:
        for env_id, agent_dict in action_dict.items():
            if env_id in self.dones:
                raise ValueError(
                    f"Env {env_id} is already done and cannot accept new actions"
                )
            env = self.envs[env_id]
            try:
                obs, rewards, dones, infos = env.step(agent_dict)
            except Exception as e:
                if self.restart_failed_sub_environments:
                    logger.exception(e.args[0])
                    self.try_restart(env_id=env_id)
                    obs, rewards, dones, infos = e, {}, {"__all__": True}, {}
                else:
                    raise e

            assert isinstance(
                obs, (dict, Exception)
            ), "Not a multi-agent obs dict or an Exception!"
            assert isinstance(rewards, dict), "Not a multi-agent reward dict!"
            assert isinstance(dones, dict), "Not a multi-agent done dict!"
            assert isinstance(infos, dict), "Not a multi-agent info dict!"
            if isinstance(obs, dict) and set(infos).difference(set(obs)):
                raise ValueError(
                    "Key set for infos must be a subset of obs: "
                    "{} vs {}".format(infos.keys(), obs.keys())
                )
            if "__all__" not in dones:
                raise ValueError(
                    "In multi-agent environments, '__all__': True|False must "
                    "be included in the 'done' dict: got {}.".format(dones)
                )

            if dones["__all__"]:
                self.dones.add(env_id)
            self.env_states[env_id].observe(obs, rewards, dones, infos)

    @override(BaseEnv)
    def try_reset(self, env_id: Optional[EnvID] = None) -> Optional[MultiEnvDict]:
        ret = {}
        if isinstance(env_id, int):
            env_id = [env_id]
        if env_id is None:
            env_id = list(range(len(self.envs)))
        for idx in env_id:
            obs = self.env_states[idx].reset()
            if isinstance(obs, Exception):
                if self.restart_failed_sub_environments:
                    self.env_states[idx].env = self.envs[idx] = self.make_env(idx)
                else:
                    raise obs
            else:
                assert isinstance(obs, dict), "Not a multi-agent obs dict!"
            if obs is not None and idx in self.dones:
                self.dones.remove(idx)
            ret[idx] = obs
        return ret

    @override(BaseEnv)
    def try_restart(self, env_id: Optional[EnvID] = None) -> None:
        if isinstance(env_id, int):
            env_id = [env_id]
        if env_id is None:
            env_id = list(range(len(self.envs)))
        for idx in env_id:
            # Recreate the sub-env.
            logger.warning(f"Trying to restart sub-environment at index {idx}.")
            self.env_states[idx].env = self.envs[idx] = self.make_env(idx)
            logger.warning(f"Sub-environment at index {idx} restarted successfully.")

    @override(BaseEnv)
    def get_sub_environments(
        self, as_dict: bool = False
    ) -> Union[Dict[str, EnvType], List[EnvType]]:
        if as_dict:
            return {_id: env_state.env for _id, env_state in enumerate(self.env_states)}
        return [state.env for state in self.env_states]

    @override(BaseEnv)
    def try_render(self, env_id: Optional[EnvID] = None) -> None:
        if env_id is None:
            env_id = 0
        assert isinstance(env_id, int)
        return self.envs[env_id].render()

    @property
    @override(BaseEnv)
    @PublicAPI
    def observation_space(self) -> gym.spaces.Dict:
        return self.envs[0].observation_space

    @property
    @override(BaseEnv)
    @PublicAPI
    def action_space(self) -> gym.Space:
        return self.envs[0].action_space

    @override(BaseEnv)
    def observation_space_contains(self, x: MultiEnvDict) -> bool:
        return all(self.envs[0].observation_space_contains(val) for val in x.values())

    @override(BaseEnv)
    def action_space_contains(self, x: MultiEnvDict) -> bool:
        return all(self.envs[0].action_space_contains(val) for val in x.values())

    @override(BaseEnv)
    def observation_space_sample(self, agent_ids: list = None) -> MultiEnvDict:
        return {0: self.envs[0].observation_space_sample(agent_ids)}

    @override(BaseEnv)
    def action_space_sample(self, agent_ids: list = None) -> MultiEnvDict:
        return {0: self.envs[0].action_space_sample(agent_ids)}

    @override(BaseEnv)
    def get_agent_ids(self) -> Set[AgentID]:
        return self.envs[0].get_agent_ids()

    def _init_env_state(self, idx: Optional[int] = None) -> None:
        """Resets all or one particular sub-environment's state (by index).

        Args:
            idx: The index to reset at. If None, reset all the sub-environments' states.
        """
        # If index is None, reset all sub-envs' states:
        if idx is None:
            self.env_states = [
                _MultiAgentEnvState(env, self.restart_failed_sub_environments)
                for env in self.envs
            ]
        # Index provided, reset only the sub-env's state at the given index.
        else:
            assert isinstance(idx, int)
            self.env_states[idx] = _MultiAgentEnvState(
                self.envs[idx], self.restart_failed_sub_environments
            )


class _MultiAgentEnvState:
    def __init__(self, env: MultiAgentEnv, return_error_as_obs: bool = False):
        assert isinstance(env, MultiAgentEnv)
        self.env = env
        self.return_error_as_obs = return_error_as_obs

        self.initialized = False
        self.last_obs = {}
        self.last_rewards = {}
        self.last_dones = {"__all__": False}
        self.last_infos = {}

    def poll(
        self,
    ) -> Tuple[MultiAgentDict, MultiAgentDict, MultiAgentDict, MultiAgentDict]:
        if not self.initialized:
            self.reset()
            self.initialized = True

        observations = self.last_obs
        rewards = {}
        dones = {"__all__": self.last_dones["__all__"]}
        infos = {}

        # If episode is done or we have an error, release everything we have.
        if dones["__all__"] or isinstance(observations, Exception):
            rewards = self.last_rewards
            self.last_rewards = {}
            dones = self.last_dones
            if isinstance(observations, Exception):
                dones["__all__"] = True
            self.last_dones = {}
            self.last_obs = {}
            infos = self.last_infos
            self.last_infos = {}
        # Only release those agents' rewards/dones/infos, whose
        # observations we have.
        else:
            for ag in observations.keys():
                if ag in self.last_rewards:
                    rewards[ag] = self.last_rewards[ag]
                    del self.last_rewards[ag]
                if ag in self.last_dones:
                    dones[ag] = self.last_dones[ag]
                    del self.last_dones[ag]
                if ag in self.last_infos:
                    infos[ag] = self.last_infos[ag]
                    del self.last_infos[ag]

        self.last_dones["__all__"] = False
        return observations, rewards, dones, infos

    def observe(
        self,
        obs: MultiAgentDict,
        rewards: MultiAgentDict,
        dones: MultiAgentDict,
        infos: MultiAgentDict,
    ):
        self.last_obs = obs
        for ag, r in rewards.items():
            if ag in self.last_rewards:
                self.last_rewards[ag] += r
            else:
                self.last_rewards[ag] = r
        for ag, d in dones.items():
            if ag in self.last_dones:
                self.last_dones[ag] = self.last_dones[ag] or d
            else:
                self.last_dones[ag] = d
        self.last_infos = infos

    def reset(self) -> MultiAgentDict:
        try:
            self.last_obs = self.env.reset()
        except Exception as e:
            if self.return_error_as_obs:
                logger.exception(e.args[0])
                self.last_obs = e
            else:
                raise e
        self.last_rewards = {}
        self.last_dones = {"__all__": False}
        self.last_infos = {}
        return self.last_obs