# Copyright (c) 2022-2024, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

from dataclasses import MISSING

from omni.isaac.lab.scene import InteractiveSceneCfg
from omni.isaac.lab.sim import SimulationCfg
from omni.isaac.lab.utils import configclass
from omni.isaac.lab.utils.noise import NoiseModelCfg

from .common import AgentID, ViewerCfg
from .ui import BaseEnvWindow


@configclass
class DirectMARLEnvCfg:
    """Configuration for a MARL environment defined with the direct workflow.

    Please refer to the :class:`omni.isaac.lab.envs.direct_marl_env.DirectMARLEnv` class for more details.
    """

    # simulation settings
    viewer: ViewerCfg = ViewerCfg()
    """Viewer configuration. Default is ViewerCfg()."""

    sim: SimulationCfg = SimulationCfg()
    """Physics simulation configuration. Default is SimulationCfg()."""

    # ui settings
    ui_window_class_type: type | None = BaseEnvWindow
    """The class type of the UI window. Default is None.

    If None, then no UI window is created.

    Note:
        If you want to make your own UI window, you can create a class that inherits from
        from :class:`omni.isaac.lab.envs.ui.base_env_window.BaseEnvWindow`. Then, you can set
        this attribute to your class type.
    """

    # general settings
    seed: int | None = None
    """The seed for the random number generator. Defaults to None, in which case the seed is not set.

    Note:
      The seed is set at the beginning of the environment initialization. This ensures that the environment
      creation is deterministic and behaves similarly across different runs.
    """

    decimation: int = MISSING
    """Number of control action updates @ sim dt per policy dt.

    For instance, if the simulation dt is 0.01s and the policy dt is 0.1s, then the decimation is 10.
    This means that the control action is updated every 10 simulation steps.
    """

    is_finite_horizon: bool = False
    """Whether the learning task is treated as a finite or infinite horizon problem for the agent.
    Defaults to False, which means the task is treated as an infinite horizon problem.

    This flag handles the subtleties of finite and infinite horizon tasks:

    * **Finite horizon**: no penalty or bootstrapping value is required by the the agent for
      running out of time. However, the environment still needs to terminate the episode after the
      time limit is reached.
    * **Infinite horizon**: the agent needs to bootstrap the value of the state at the end of the episode.
      This is done by sending a time-limit (or truncated) done signal to the agent, which triggers this
      bootstrapping calculation.

    If True, then the environment is treated as a finite horizon problem and no time-out (or truncated) done signal
    is sent to the agent. If False, then the environment is treated as an infinite horizon problem and a time-out
    (or truncated) done signal is sent to the agent.

    Note:
        The base :class:`ManagerBasedRLEnv` class does not use this flag directly. It is used by the environment
        wrappers to determine what type of done signal to send to the corresponding learning agent.
    """

    episode_length_s: float = MISSING
    """Duration of an episode (in seconds).

    Based on the decimation rate and physics time step, the episode length is calculated as:

    .. code-block:: python

        episode_length_steps = ceil(episode_length_s / (decimation_rate * physics_time_step))

    For example, if the decimation rate is 10, the physics time step is 0.01, and the episode length is 10 seconds,
    then the episode length in steps is 100.
    """

    # environment settings
    scene: InteractiveSceneCfg = MISSING
    """Scene settings.

    Please refer to the :class:`omni.isaac.lab.scene.InteractiveSceneCfg` class for more details.
    """

    events: object = None
    """Event settings. Defaults to None, in which case no events are applied through the event manager.

    Please refer to the :class:`omni.isaac.lab.managers.EventManager` class for more details.
    """

    num_observations: dict[AgentID, int] = MISSING
    """The dimension of the observation space from each agent."""

    num_states: int = MISSING
    """The dimension of the state space from each environment instance.

    The following values are supported:

    * -1: All the observations from the different agents are automatically concatenated.
    * 0: No state-space will be constructed (`state_space` is None).
      This is useful to save computational resources when the algorithm to be trained does not need it.
    * greater than 0: Custom state-space dimension to be provided by the task implementation.
    """

    observation_noise_model: dict[AgentID, NoiseModelCfg | None] | None = None
    """The noise model to apply to the computed observations from the environment. Default is None, which means no noise is added.

    Please refer to the :class:`omni.isaac.lab.utils.noise.NoiseModel` class for more details.
    """

    num_actions: dict[AgentID, int] = MISSING
    """The dimension of the action space for each agent."""

    action_noise_model: dict[AgentID, NoiseModelCfg | None] | None = None
    """The noise model applied to the actions provided to the environment. Default is None, which means no noise is added.

    Please refer to the :class:`omni.isaac.lab.utils.noise.NoiseModel` class for more details.
    """

    possible_agents: list[AgentID] = MISSING
    """A list of all possible agents the environment could generate.

    The contents of the list cannot be modified during the entire training process.
    """
