# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause


"""
This script checks the functionality of scale randomization.
"""

from __future__ import annotations

"""Launch Isaac Sim Simulator first."""

from isaaclab.app import AppLauncher

# launch omniverse app
app_launcher = AppLauncher(headless=True, enable_cameras=True)
simulation_app = app_launcher.app

"""Rest everything follows."""

import pytest
import torch

import omni.usd
from pxr import Sdf

import isaaclab.envs.mdp as mdp
import isaaclab.sim as sim_utils
from isaaclab.assets import AssetBaseCfg, RigidObject, RigidObjectCfg
from isaaclab.envs import ManagerBasedEnv, ManagerBasedEnvCfg
from isaaclab.managers import ActionTerm, ActionTermCfg, SceneEntityCfg
from isaaclab.managers import EventTermCfg as EventTerm
from isaaclab.managers import ObservationGroupCfg as ObsGroup
from isaaclab.managers import ObservationTermCfg as ObsTerm
from isaaclab.scene import InteractiveSceneCfg
from isaaclab.terrains import TerrainImporterCfg
from isaaclab.utils import configclass

##
# Custom action term
##


class CubeActionTerm(ActionTerm):
    """Simple action term that implements a PD controller to track a target position.

    The action term is applied to the cube asset. It involves two steps:

    1. **Process the raw actions**: Typically, this includes any transformations of the raw actions
       that are required to map them to the desired space. This is called once per environment step.
    2. **Apply the processed actions**: This step applies the processed actions to the asset.
       It is called once per simulation step.

    In this case, the action term simply applies the raw actions to the cube asset. The raw actions
    are the desired target positions of the cube in the environment frame. The pre-processing step
    simply copies the raw actions to the processed actions as no additional processing is required.
    The processed actions are then applied to the cube asset by implementing a PD controller to
    track the target position.
    """

    _asset: RigidObject
    """The articulation asset on which the action term is applied."""

    def __init__(self, cfg: CubeActionTermCfg, env: ManagerBasedEnv):
        # call super constructor
        super().__init__(cfg, env)
        # create buffers
        self._raw_actions = torch.zeros(env.num_envs, 3, device=self.device)
        self._processed_actions = torch.zeros(env.num_envs, 3, device=self.device)
        self._vel_command = torch.zeros(self.num_envs, 6, device=self.device)
        # gains of controller
        self.p_gain = cfg.p_gain
        self.d_gain = cfg.d_gain

    """
    Properties.
    """

    @property
    def action_dim(self) -> int:
        return self._raw_actions.shape[1]

    @property
    def raw_actions(self) -> torch.Tensor:
        return self._raw_actions

    @property
    def processed_actions(self) -> torch.Tensor:
        return self._processed_actions

    """
    Operations
    """

    def process_actions(self, actions: torch.Tensor):
        # store the raw actions
        self._raw_actions[:] = actions
        # no-processing of actions
        self._processed_actions[:] = self._raw_actions[:]

    def apply_actions(self):
        # implement a PD controller to track the target position
        pos_error = self._processed_actions - (self._asset.data.root_pos_w - self._env.scene.env_origins)
        vel_error = -self._asset.data.root_lin_vel_w
        # set velocity targets
        self._vel_command[:, :3] = self.p_gain * pos_error + self.d_gain * vel_error
        self._asset.write_root_velocity_to_sim(self._vel_command)


@configclass
class CubeActionTermCfg(ActionTermCfg):
    """Configuration for the cube action term."""

    class_type: type = CubeActionTerm
    """The class corresponding to the action term."""

    p_gain: float = 5.0
    """Proportional gain of the PD controller."""
    d_gain: float = 0.5
    """Derivative gain of the PD controller."""


##
# Custom observation term
##


def base_position(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor:
    """Root linear velocity in the asset's root frame."""
    # extract the used quantities (to enable type-hinting)
    asset: RigidObject = env.scene[asset_cfg.name]
    return asset.data.root_pos_w - env.scene.env_origins


##
# Scene definition
##


@configclass
class MySceneCfg(InteractiveSceneCfg):
    """Example scene configuration.

    The scene comprises of a ground plane, light source and floating cubes (gravity disabled).
    """

    # add terrain
    terrain = TerrainImporterCfg(prim_path="/World/ground", terrain_type="plane", debug_vis=False)

    # add cube for scale randomization
    cube1: RigidObjectCfg = RigidObjectCfg(
        prim_path="{ENV_REGEX_NS}/cube1",
        spawn=sim_utils.CuboidCfg(
            size=(0.2, 0.2, 0.2),
            rigid_props=sim_utils.RigidBodyPropertiesCfg(max_depenetration_velocity=1.0, disable_gravity=True),
            mass_props=sim_utils.MassPropertiesCfg(mass=1.0),
            physics_material=sim_utils.RigidBodyMaterialCfg(),
            visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 0.0)),
        ),
        init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.0, 5)),
    )

    # add cube for static scale values
    cube2: RigidObjectCfg = RigidObjectCfg(
        prim_path="{ENV_REGEX_NS}/cube2",
        spawn=sim_utils.CuboidCfg(
            size=(0.2, 0.2, 0.2),
            rigid_props=sim_utils.RigidBodyPropertiesCfg(max_depenetration_velocity=1.0, disable_gravity=True),
            mass_props=sim_utils.MassPropertiesCfg(mass=1.0),
            physics_material=sim_utils.RigidBodyMaterialCfg(),
            visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 0.0)),
        ),
        init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.0, 5)),
    )

    # lights
    light = AssetBaseCfg(
        prim_path="/World/light",
        spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0),
    )


##
# Environment settings
##


@configclass
class ActionsCfg:
    """Action specifications for the MDP."""

    joint_pos = CubeActionTermCfg(asset_name="cube1")


@configclass
class ObservationsCfg:
    """Observation specifications for the MDP."""

    @configclass
    class PolicyCfg(ObsGroup):
        """Observations for policy group."""

        # cube velocity
        position = ObsTerm(func=base_position, params={"asset_cfg": SceneEntityCfg("cube1")})

        def __post_init__(self):
            self.enable_corruption = True
            self.concatenate_terms = True

    # observation groups
    policy: PolicyCfg = PolicyCfg()


@configclass
class EventCfg:
    """Configuration for events."""

    reset_base = EventTerm(
        func=mdp.reset_root_state_uniform,
        mode="reset",
        params={
            "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)},
            "velocity_range": {
                "x": (-0.5, 0.5),
                "y": (-0.5, 0.5),
                "z": (-0.5, 0.5),
            },
            "asset_cfg": SceneEntityCfg("cube1"),
        },
    )

    # Scale randomization as intended
    randomize_cube1__scale = EventTerm(
        func=mdp.randomize_rigid_body_scale,
        mode="prestartup",
        params={
            "scale_range": {"x": (0.5, 1.5), "y": (0.5, 1.5), "z": (0.5, 1.5)},
            "asset_cfg": SceneEntityCfg("cube1"),
        },
    )

    # Static scale values
    randomize_cube2__scale = EventTerm(
        func=mdp.randomize_rigid_body_scale,
        mode="prestartup",
        params={
            "scale_range": {"x": (1.0, 1.0), "y": (1.0, 1.0), "z": (1.0, 1.0)},
            "asset_cfg": SceneEntityCfg("cube2"),
        },
    )


##
# Environment configuration
##


@configclass
class CubeEnvCfg(ManagerBasedEnvCfg):
    """Configuration for the locomotion velocity-tracking environment."""

    # Scene settings
    scene: MySceneCfg = MySceneCfg(num_envs=10, env_spacing=2.5, replicate_physics=False)
    # Basic settings
    observations: ObservationsCfg = ObservationsCfg()
    actions: ActionsCfg = ActionsCfg()
    events: EventCfg = EventCfg()

    def __post_init__(self):
        """Post initialization."""
        # general settings
        self.decimation = 2
        # simulation settings
        self.sim.dt = 0.01
        self.sim.physics_material = self.scene.terrain.physics_material
        self.sim.render_interval = self.decimation


@pytest.mark.parametrize("device", ["cpu", "cuda"])
def test_scale_randomization(device):
    """Test scale randomization for cube environment."""
    # create a new stage
    omni.usd.get_context().new_stage()

    # set the device
    env_cfg = CubeEnvCfg()
    env_cfg.sim.device = device

    # setup base environment
    env = ManagerBasedEnv(cfg=env_cfg)
    # setup target position commands
    target_position = torch.rand(env.num_envs, 3, device=env.device) * 2
    target_position[:, 2] += 2.0
    # offset all targets so that they move to the world origin
    target_position -= env.scene.env_origins

    # test to make sure all assets in the scene are created
    all_prim_paths = sim_utils.find_matching_prim_paths("/World/envs/env_.*/cube.*/.*")
    assert len(all_prim_paths) == (env.num_envs * 2)

    # test to make sure randomized values are truly random
    applied_scaling_randomization = set()
    prim_paths = sim_utils.find_matching_prim_paths("/World/envs/env_.*/cube1")

    # get the stage
    stage = omni.usd.get_context().get_stage()

    # check if the scale values are truly random
    for i in range(3):
        prim_spec = Sdf.CreatePrimInLayer(stage.GetRootLayer(), prim_paths[i])
        scale_spec = prim_spec.GetAttributeAtPath(prim_paths[i] + ".xformOp:scale")
        if scale_spec.default in applied_scaling_randomization:
            raise ValueError(
                "Detected repeat in applied scale values - indication scaling randomization is not working."
            )
        applied_scaling_randomization.add(scale_spec.default)

    # test to make sure that fixed values are assigned correctly
    prim_paths = sim_utils.find_matching_prim_paths("/World/envs/env_.*/cube2")
    for i in range(3):
        prim_spec = Sdf.CreatePrimInLayer(stage.GetRootLayer(), prim_paths[i])
        scale_spec = prim_spec.GetAttributeAtPath(prim_paths[i] + ".xformOp:scale")
        assert tuple(scale_spec.default) == (1.0, 1.0, 1.0)

    # simulate physics
    with torch.inference_mode():
        for count in range(200):
            # reset every few steps to check nothing breaks
            if count % 100 == 0:
                env.reset()
            # step the environment
            env.step(target_position)

    env.close()


def test_scale_randomization_failure_replicate_physics():
    """Test scale randomization failure when replicate physics is set to True."""
    # create a new stage
    omni.usd.get_context().new_stage()
    # set the arguments
    cfg_failure = CubeEnvCfg()
    cfg_failure.scene.replicate_physics = True

    # run the test
    with pytest.raises(RuntimeError):
        env = ManagerBasedEnv(cfg_failure)
        env.close()