# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

# needed to import for allowing type-hinting: torch.Tensor | None
from __future__ import annotations

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

from isaaclab.app import AppLauncher

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

"""Rest everything follows."""

import torch
from collections import namedtuple
from typing import TYPE_CHECKING

import pytest

import isaaclab.sim as sim_utils
from isaaclab.managers import (
    ManagerTermBase,
    ObservationGroupCfg,
    ObservationManager,
    ObservationTermCfg,
    RewardTermCfg,
)
from isaaclab.utils import configclass, modifiers

if TYPE_CHECKING:
    from isaaclab.envs import ManagerBasedEnv


def grilled_chicken(env):
    return torch.ones(env.num_envs, 4, device=env.device)


def grilled_chicken_with_bbq(env, bbq: bool):
    return bbq * torch.ones(env.num_envs, 1, device=env.device)


def grilled_chicken_with_curry(env, hot: bool):
    return hot * 2 * torch.ones(env.num_envs, 1, device=env.device)


def grilled_chicken_with_yoghurt(env, hot: bool, bland: float):
    return hot * bland * torch.ones(env.num_envs, 5, device=env.device)


def grilled_chicken_with_yoghurt_and_bbq(env, hot: bool, bland: float, bbq: bool = False):
    return hot * bland * bbq * torch.ones(env.num_envs, 3, device=env.device)


def grilled_chicken_image(env, bland: float, channel: int = 1):
    return bland * torch.ones(env.num_envs, 128, 256, channel, device=env.device)


class complex_function_class(ManagerTermBase):
    def __init__(self, cfg: ObservationTermCfg, env: object):
        self.cfg = cfg
        self.env = env
        # define some variables
        self._time_passed = torch.zeros(env.num_envs, device=env.device)

    def reset(self, env_ids: torch.Tensor | None = None):
        if env_ids is None:
            env_ids = slice(None)
        self._time_passed[env_ids] = 0.0

    def __call__(self, env: object, interval: float) -> torch.Tensor:
        self._time_passed += interval
        return self._time_passed.clone().unsqueeze(-1)


class non_callable_complex_function_class(ManagerTermBase):
    def __init__(self, cfg: ObservationTermCfg, env: object):
        self.cfg = cfg
        self.env = env
        # define some variables
        self._cost = 2 * self.env.num_envs

    def call_me(self, env: object) -> torch.Tensor:
        return torch.ones(env.num_envs, 2, device=env.device) * self._cost


class MyDataClass:

    def __init__(self, num_envs: int, device: str):
        self.pos_w = torch.rand((num_envs, 3), device=device)
        self.lin_vel_w = torch.rand((num_envs, 3), device=device)


def pos_w_data(env) -> torch.Tensor:
    return env.data.pos_w


def lin_vel_w_data(env) -> torch.Tensor:
    return env.data.lin_vel_w


@pytest.fixture(autouse=True)
def setup_env():
    dt = 0.01
    num_envs = 20
    device = "cuda:0"
    # set up sim
    sim_cfg = sim_utils.SimulationCfg(dt=dt, device=device)
    sim = sim_utils.SimulationContext(sim_cfg)
    # create dummy environment
    env = namedtuple("ManagerBasedEnv", ["num_envs", "device", "data", "dt", "sim"])(
        num_envs, device, MyDataClass(num_envs, device), dt, sim
    )
    # let the simulation play (we need this for observation manager to compute obs dims)
    env.sim._app_control_on_stop_handle = None
    env.sim.reset()
    return env


def test_str(setup_env):
    env = setup_env
    """Test the string representation of the observation manager."""

    @configclass
    class MyObservationManagerCfg:
        """Test config class for observation manager."""

        @configclass
        class SampleGroupCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            term_1 = ObservationTermCfg(func=grilled_chicken, scale=10)
            term_2 = ObservationTermCfg(func=grilled_chicken, scale=2)
            term_3 = ObservationTermCfg(func=grilled_chicken_with_bbq, scale=5, params={"bbq": True})
            term_4 = ObservationTermCfg(
                func=grilled_chicken_with_yoghurt, scale=1.0, params={"hot": False, "bland": 2.0}
            )
            term_5 = ObservationTermCfg(
                func=grilled_chicken_with_yoghurt_and_bbq, scale=1.0, params={"hot": False, "bland": 2.0}
            )

        policy: ObservationGroupCfg = SampleGroupCfg()

    # create observation manager
    cfg = MyObservationManagerCfg()
    obs_man = ObservationManager(cfg, env)
    assert len(obs_man.active_terms["policy"]) == 5
    # print the expected string
    obs_man_str = str(obs_man)
    print()
    print(obs_man_str)
    obs_man_str_split = obs_man_str.split("|")
    term_1_str_index = obs_man_str_split.index(" term_1           ")
    term_1_str_shape = obs_man_str_split[term_1_str_index + 1].strip()
    assert term_1_str_shape == "(4,)"


def test_str_with_history(setup_env):
    env = setup_env
    """Test the string representation of the observation manager with history terms."""

    TERM_1_HISTORY = 5

    @configclass
    class MyObservationManagerCfg:
        """Test config class for observation manager."""

        @configclass
        class SampleGroupCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            term_1 = ObservationTermCfg(func=grilled_chicken, scale=10, history_length=TERM_1_HISTORY)
            term_2 = ObservationTermCfg(func=grilled_chicken, scale=2)
            term_3 = ObservationTermCfg(func=grilled_chicken_with_bbq, scale=5, params={"bbq": True})
            term_4 = ObservationTermCfg(
                func=grilled_chicken_with_yoghurt, scale=1.0, params={"hot": False, "bland": 2.0}
            )
            term_5 = ObservationTermCfg(
                func=grilled_chicken_with_yoghurt_and_bbq, scale=1.0, params={"hot": False, "bland": 2.0}
            )

        policy: ObservationGroupCfg = SampleGroupCfg()

    # create observation manager
    cfg = MyObservationManagerCfg()
    obs_man = ObservationManager(cfg, env)
    assert len(obs_man.active_terms["policy"]) == 5
    # print the expected string
    obs_man_str = str(obs_man)
    print()
    print(obs_man_str)
    obs_man_str_split = obs_man_str.split("|")
    term_1_str_index = obs_man_str_split.index(" term_1           ")
    term_1_str_shape = obs_man_str_split[term_1_str_index + 1].strip()
    assert term_1_str_shape == "(20,)"


def test_config_equivalence(setup_env):
    env = setup_env
    """Test the equivalence of observation manager created from different config types."""

    # create from config class
    @configclass
    class MyObservationManagerCfg:
        """Test config class for observation manager."""

        @configclass
        class SampleGroupCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            your_term = ObservationTermCfg(func=grilled_chicken, scale=10)
            his_term = ObservationTermCfg(func=grilled_chicken, scale=2)
            my_term = ObservationTermCfg(func=grilled_chicken_with_bbq, scale=5, params={"bbq": True})
            her_term = ObservationTermCfg(
                func=grilled_chicken_with_yoghurt, scale=1.0, params={"hot": False, "bland": 2.0}
            )

        policy = SampleGroupCfg()
        critic = SampleGroupCfg(concatenate_terms=False, her_term=None)

    cfg = MyObservationManagerCfg()
    obs_man_from_cfg = ObservationManager(cfg, env)

    # create from config class
    @configclass
    class MyObservationManagerAnnotatedCfg:
        """Test config class for observation manager with annotations on terms."""

        @configclass
        class SampleGroupCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            your_term: ObservationTermCfg = ObservationTermCfg(func=grilled_chicken, scale=10)
            his_term: ObservationTermCfg = ObservationTermCfg(func=grilled_chicken, scale=2)
            my_term: ObservationTermCfg = ObservationTermCfg(
                func=grilled_chicken_with_bbq, scale=5, params={"bbq": True}
            )
            her_term: ObservationTermCfg = ObservationTermCfg(
                func=grilled_chicken_with_yoghurt, scale=1.0, params={"hot": False, "bland": 2.0}
            )

        policy: ObservationGroupCfg = SampleGroupCfg()
        critic: ObservationGroupCfg = SampleGroupCfg(concatenate_terms=False, her_term=None)

    cfg = MyObservationManagerAnnotatedCfg()
    obs_man_from_annotated_cfg = ObservationManager(cfg, env)

    # check equivalence
    # parsed terms
    assert obs_man_from_cfg.active_terms == obs_man_from_annotated_cfg.active_terms
    assert obs_man_from_cfg.group_obs_term_dim == obs_man_from_annotated_cfg.group_obs_term_dim
    assert obs_man_from_cfg.group_obs_dim == obs_man_from_annotated_cfg.group_obs_dim
    # parsed term configs
    assert obs_man_from_cfg._group_obs_term_cfgs == obs_man_from_annotated_cfg._group_obs_term_cfgs
    assert obs_man_from_cfg._group_obs_concatenate == obs_man_from_annotated_cfg._group_obs_concatenate


def test_config_terms(setup_env):
    env = setup_env
    """Test the number of terms in the observation manager."""

    @configclass
    class MyObservationManagerCfg:
        """Test config class for observation manager."""

        @configclass
        class SampleGroupCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            term_1 = ObservationTermCfg(func=grilled_chicken, scale=10)
            term_2 = ObservationTermCfg(func=grilled_chicken_with_curry, scale=0.0, params={"hot": False})

        @configclass
        class SampleMixedGroupCfg(ObservationGroupCfg):
            """Test config class for policy observation group with a mix of vector and matrix terms."""

            concatenate_terms = False
            term_1 = ObservationTermCfg(func=grilled_chicken, scale=2.0)
            term_2 = ObservationTermCfg(func=grilled_chicken_image, scale=1.5, params={"bland": 0.5})

        @configclass
        class SampleImageGroupCfg(ObservationGroupCfg):

            term_1 = ObservationTermCfg(func=grilled_chicken_image, scale=1.5, params={"bland": 0.5, "channel": 1})
            term_2 = ObservationTermCfg(func=grilled_chicken_image, scale=0.5, params={"bland": 0.1, "channel": 3})

        policy: ObservationGroupCfg = SampleGroupCfg()
        critic: ObservationGroupCfg = SampleGroupCfg(term_2=None)
        mixed: ObservationGroupCfg = SampleMixedGroupCfg()
        image: ObservationGroupCfg = SampleImageGroupCfg()

    # create observation manager
    cfg = MyObservationManagerCfg()
    obs_man = ObservationManager(cfg, env)

    assert len(obs_man.active_terms["policy"]) == 2
    assert len(obs_man.active_terms["critic"]) == 1
    assert len(obs_man.active_terms["mixed"]) == 2
    assert len(obs_man.active_terms["image"]) == 2

    # create a new obs manager but where mixed group has invalid config
    cfg = MyObservationManagerCfg()
    cfg.mixed.concatenate_terms = True

    with pytest.raises(RuntimeError):
        ObservationManager(cfg, env)


def test_compute(setup_env):
    env = setup_env
    """Test the observation computation."""

    pos_scale_tuple = (2.0, 3.0, 1.0)

    @configclass
    class MyObservationManagerCfg:
        """Test config class for observation manager."""

        @configclass
        class PolicyCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            term_1 = ObservationTermCfg(func=grilled_chicken, scale=10)
            term_2 = ObservationTermCfg(func=grilled_chicken_with_curry, scale=0.0, params={"hot": False})
            term_3 = ObservationTermCfg(func=pos_w_data, scale=pos_scale_tuple)
            term_4 = ObservationTermCfg(func=lin_vel_w_data, scale=1.5)

        @configclass
        class CriticCfg(ObservationGroupCfg):
            term_1 = ObservationTermCfg(func=pos_w_data, scale=pos_scale_tuple)
            term_2 = ObservationTermCfg(func=lin_vel_w_data, scale=1.5)
            term_3 = ObservationTermCfg(func=pos_w_data, scale=pos_scale_tuple)
            term_4 = ObservationTermCfg(func=lin_vel_w_data, scale=1.5)

        @configclass
        class ImageCfg(ObservationGroupCfg):

            term_1 = ObservationTermCfg(func=grilled_chicken_image, scale=1.5, params={"bland": 0.5, "channel": 1})
            term_2 = ObservationTermCfg(func=grilled_chicken_image, scale=0.5, params={"bland": 0.1, "channel": 3})

        policy: ObservationGroupCfg = PolicyCfg()
        critic: ObservationGroupCfg = CriticCfg()
        image: ObservationGroupCfg = ImageCfg()

    # create observation manager
    cfg = MyObservationManagerCfg()
    obs_man = ObservationManager(cfg, env)
    # compute observation using manager
    observations = obs_man.compute()

    # obtain the group observations
    obs_policy: torch.Tensor = observations["policy"]
    obs_critic: torch.Tensor = observations["critic"]
    obs_image: torch.Tensor = observations["image"]

    # check the observation shape
    assert obs_policy.shape == (env.num_envs, 11)
    assert obs_critic.shape == (env.num_envs, 12)
    assert obs_image.shape == (env.num_envs, 128, 256, 4)
    # check that the scales are applied correctly
    assert torch.equal(env.data.pos_w * torch.tensor(pos_scale_tuple, device=env.device), obs_critic[:, :3])
    assert torch.equal(env.data.lin_vel_w * 1.5, obs_critic[:, 3:6])
    # make sure that the data are the same for same terms
    # -- within group
    assert torch.equal(obs_critic[:, 0:3], obs_critic[:, 6:9])
    assert torch.equal(obs_critic[:, 3:6], obs_critic[:, 9:12])
    # -- between groups
    assert torch.equal(obs_policy[:, 5:8], obs_critic[:, 0:3])
    assert torch.equal(obs_policy[:, 8:11], obs_critic[:, 3:6])


def test_compute_with_history(setup_env):
    env = setup_env
    """Test the observation computation with history buffers."""
    HISTORY_LENGTH = 5

    @configclass
    class MyObservationManagerCfg:
        """Test config class for observation manager."""

        @configclass
        class PolicyCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            term_1 = ObservationTermCfg(func=grilled_chicken, history_length=HISTORY_LENGTH)
            # total observation size: term_dim (4) * history_len (5) = 20
            term_2 = ObservationTermCfg(func=lin_vel_w_data)
            # total observation size: term_dim (3) = 3

        policy: ObservationGroupCfg = PolicyCfg()

    # create observation manager
    cfg = MyObservationManagerCfg()
    obs_man = ObservationManager(cfg, env)
    # compute observation using manager
    observations = obs_man.compute()
    # obtain the group observations
    obs_policy: torch.Tensor = observations["policy"]
    # check the observation shape
    assert obs_policy.shape == (env.num_envs, 23)
    # check the observation data
    expected_obs_term_1_data = torch.ones(env.num_envs, 4 * HISTORY_LENGTH, device=env.device)
    expected_obs_term_2_data = lin_vel_w_data(env)
    expected_obs_data_t0 = torch.concat((expected_obs_term_1_data, expected_obs_term_2_data), dim=-1)
    torch.testing.assert_close(expected_obs_data_t0, obs_policy)
    # test that the history buffer holds previous data
    for _ in range(HISTORY_LENGTH):
        observations = obs_man.compute()
        obs_policy = observations["policy"]
    expected_obs_term_1_data = torch.ones(env.num_envs, 4 * HISTORY_LENGTH, device=env.device)
    expected_obs_data_t5 = torch.concat((expected_obs_term_1_data, expected_obs_term_2_data), dim=-1)
    assert torch.equal(expected_obs_data_t5, obs_policy)
    # test reset
    obs_man.reset()
    observations = obs_man.compute()
    obs_policy = observations["policy"]
    torch.testing.assert_close(expected_obs_data_t0, obs_policy)
    # test reset of specific env ids
    reset_env_ids = [2, 4, 16]
    obs_man.reset(reset_env_ids)
    torch.testing.assert_close(expected_obs_data_t0[reset_env_ids], obs_policy[reset_env_ids])


def test_compute_with_2d_history(setup_env):
    env = setup_env
    """Test the observation computation with history buffers for 2D observations."""
    HISTORY_LENGTH = 5

    @configclass
    class MyObservationManagerCfg:
        """Test config class for observation manager."""

        @configclass
        class FlattenedPolicyCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            term_1 = ObservationTermCfg(
                func=grilled_chicken_image, params={"bland": 1.0, "channel": 1}, history_length=HISTORY_LENGTH
            )
            # total observation size: term_dim (128, 256) * history_len (5) = 163840

        @configclass
        class PolicyCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            term_1 = ObservationTermCfg(
                func=grilled_chicken_image,
                params={"bland": 1.0, "channel": 1},
                history_length=HISTORY_LENGTH,
                flatten_history_dim=False,
            )
            # total observation size: (5, 128, 256, 1)

        flat_obs_policy: ObservationGroupCfg = FlattenedPolicyCfg()
        policy: ObservationGroupCfg = PolicyCfg()

    # create observation manager
    cfg = MyObservationManagerCfg()
    obs_man = ObservationManager(cfg, env)
    # compute observation using manager
    observations = obs_man.compute()
    # obtain the group observations
    obs_policy_flat: torch.Tensor = observations["flat_obs_policy"]
    obs_policy: torch.Tensor = observations["policy"]
    # check the observation shapes
    assert obs_policy_flat.shape == (env.num_envs, 163840)
    assert obs_policy.shape == (env.num_envs, HISTORY_LENGTH, 128, 256, 1)


def test_compute_with_group_history(setup_env):
    env = setup_env
    """Test the observation computation with group level history buffer configuration."""
    TERM_HISTORY_LENGTH = 5
    GROUP_HISTORY_LENGTH = 10

    @configclass
    class MyObservationManagerCfg:
        """Test config class for observation manager."""

        @configclass
        class PolicyCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            history_length = GROUP_HISTORY_LENGTH
            # group level history length will override all terms
            term_1 = ObservationTermCfg(func=grilled_chicken, history_length=TERM_HISTORY_LENGTH)
            # total observation size: term_dim (4) * history_len (5) = 20
            # with override total obs size: term_dim (4) * history_len (10) = 40
            term_2 = ObservationTermCfg(func=lin_vel_w_data)
            # total observation size: term_dim (3) = 3
            # with override total obs size: term_dim (3) * history_len (10) = 30

        policy: ObservationGroupCfg = PolicyCfg()

    # create observation manager
    cfg = MyObservationManagerCfg()
    obs_man = ObservationManager(cfg, env)
    # compute observation using manager
    observations = obs_man.compute()
    # obtain the group observations
    obs_policy: torch.Tensor = observations["policy"]
    # check the total observation shape
    assert obs_policy.shape == (env.num_envs, 70)
    # check the observation data is initialized properly
    expected_obs_term_1_data = torch.ones(env.num_envs, 4 * GROUP_HISTORY_LENGTH, device=env.device)
    expected_obs_term_2_data = lin_vel_w_data(env).repeat(1, GROUP_HISTORY_LENGTH)
    expected_obs_data_t0 = torch.concat((expected_obs_term_1_data, expected_obs_term_2_data), dim=-1)
    torch.testing.assert_close(expected_obs_data_t0, obs_policy)
    # test that the history buffer holds previous data
    for _ in range(GROUP_HISTORY_LENGTH):
        observations = obs_man.compute()
        obs_policy = observations["policy"]
    expected_obs_term_1_data = torch.ones(env.num_envs, 4 * GROUP_HISTORY_LENGTH, device=env.device)
    expected_obs_term_2_data = lin_vel_w_data(env).repeat(1, GROUP_HISTORY_LENGTH)
    expected_obs_data_t10 = torch.concat((expected_obs_term_1_data, expected_obs_term_2_data), dim=-1)
    torch.testing.assert_close(expected_obs_data_t10, obs_policy)
    # test reset
    obs_man.reset()
    observations = obs_man.compute()
    obs_policy = observations["policy"]
    torch.testing.assert_close(expected_obs_data_t0, obs_policy)
    # test reset of specific env ids
    reset_env_ids = [2, 4, 16]
    obs_man.reset(reset_env_ids)
    torch.testing.assert_close(expected_obs_data_t0[reset_env_ids], obs_policy[reset_env_ids])


def test_invalid_observation_config(setup_env):
    env = setup_env
    """Test the invalid observation config."""

    @configclass
    class MyObservationManagerCfg:
        """Test config class for observation manager."""

        @configclass
        class PolicyCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            term_1 = ObservationTermCfg(func=grilled_chicken_with_bbq, scale=0.1, params={"hot": False})
            term_2 = ObservationTermCfg(func=grilled_chicken_with_yoghurt, scale=2.0, params={"hot": False})

        policy: ObservationGroupCfg = PolicyCfg()

    # create observation manager
    cfg = MyObservationManagerCfg()
    # check the invalid config
    with pytest.raises(ValueError):
        ObservationManager(cfg, env)


def test_callable_class_term(setup_env):
    env = setup_env
    """Test the observation computation with callable class term."""

    @configclass
    class MyObservationManagerCfg:
        """Test config class for observation manager."""

        @configclass
        class PolicyCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            term_1 = ObservationTermCfg(func=grilled_chicken, scale=10)
            term_2 = ObservationTermCfg(func=complex_function_class, scale=0.2, params={"interval": 0.5})

        policy: ObservationGroupCfg = PolicyCfg()

    # create observation manager
    cfg = MyObservationManagerCfg()
    obs_man = ObservationManager(cfg, env)
    # compute observation using manager
    observations = obs_man.compute()
    # check the observation
    assert observations["policy"].shape == (env.num_envs, 5)
    assert observations["policy"][0, -1].item() == pytest.approx(0.2 * 0.5)

    # check memory in term
    num_exec_count = 10
    for _ in range(num_exec_count):
        observations = obs_man.compute()
    assert observations["policy"][0, -1].item() == pytest.approx(0.2 * 0.5 * (num_exec_count + 1))

    # check reset works
    obs_man.reset(env_ids=[0, 4, 9, 14, 19])
    observations = obs_man.compute()
    assert observations["policy"][0, -1].item() == pytest.approx(0.2 * 0.5)
    assert observations["policy"][1, -1].item() == pytest.approx(0.2 * 0.5 * (num_exec_count + 2))


def test_non_callable_class_term(setup_env):
    env = setup_env
    """Test the observation computation with non-callable class term."""

    @configclass
    class MyObservationManagerCfg:
        """Test config class for observation manager."""

        @configclass
        class PolicyCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            term_1 = ObservationTermCfg(func=grilled_chicken, scale=10)
            term_2 = ObservationTermCfg(func=non_callable_complex_function_class, scale=0.2)

        policy: ObservationGroupCfg = PolicyCfg()

    # create observation manager config
    cfg = MyObservationManagerCfg()
    # create observation manager
    with pytest.raises(NotImplementedError):
        ObservationManager(cfg, env)


def test_modifier_compute(setup_env):
    env = setup_env
    """Test the observation computation with modifiers."""

    modifier_1 = modifiers.ModifierCfg(func=modifiers.bias, params={"value": 1.0})
    modifier_2 = modifiers.ModifierCfg(func=modifiers.scale, params={"multiplier": 2.0})
    modifier_3 = modifiers.ModifierCfg(func=modifiers.clip, params={"bounds": (-0.5, 0.5)})
    modifier_4 = modifiers.IntegratorCfg(dt=env.dt)

    @configclass
    class MyObservationManagerCfg:
        """Test config class for observation manager."""

        @configclass
        class PolicyCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            concatenate_terms = False
            term_1 = ObservationTermCfg(func=pos_w_data, modifiers=[])
            term_2 = ObservationTermCfg(func=pos_w_data, modifiers=[modifier_1])
            term_3 = ObservationTermCfg(func=pos_w_data, modifiers=[modifier_1, modifier_4])

        @configclass
        class CriticCfg(ObservationGroupCfg):
            """Test config class for critic observation group"""

            concatenate_terms = False
            term_1 = ObservationTermCfg(func=pos_w_data, modifiers=[])
            term_2 = ObservationTermCfg(func=pos_w_data, modifiers=[modifier_1])
            term_3 = ObservationTermCfg(func=pos_w_data, modifiers=[modifier_1, modifier_2])
            term_4 = ObservationTermCfg(func=pos_w_data, modifiers=[modifier_1, modifier_2, modifier_3])

        policy: ObservationGroupCfg = PolicyCfg()
        critic: ObservationGroupCfg = CriticCfg()

    # create observation manager
    cfg = MyObservationManagerCfg()
    obs_man = ObservationManager(cfg, env)
    # compute observation using manager
    observations = obs_man.compute()

    # obtain the group observations
    obs_policy: dict[str, torch.Tensor] = observations["policy"]
    obs_critic: dict[str, torch.Tensor] = observations["critic"]

    # check correct application of modifications
    assert torch.equal(obs_policy["term_1"] + 1.0, obs_policy["term_2"])
    assert torch.equal(obs_critic["term_1"] + 1.0, obs_critic["term_2"])
    assert torch.equal(2.0 * (obs_critic["term_1"] + 1.0), obs_critic["term_3"])
    assert torch.min(obs_critic["term_4"]) >= -0.5
    assert torch.max(obs_critic["term_4"]) <= 0.5


def test_serialize(setup_env):
    """Test serialize call for ManagerTermBase terms."""
    env = setup_env

    serialize_data = {"test": 0}

    class test_serialize_term(ManagerTermBase):

        def __init__(self, cfg: RewardTermCfg, env: ManagerBasedEnv):
            super().__init__(cfg, env)

        def __call__(self, env: ManagerBasedEnv) -> torch.Tensor:
            return grilled_chicken(env)

        def serialize(self) -> dict:
            return serialize_data

    @configclass
    class MyObservationManagerCfg:
        """Test config class for observation manager."""

        @configclass
        class PolicyCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            concatenate_terms = False
            term_1 = ObservationTermCfg(func=test_serialize_term)

        policy: ObservationGroupCfg = PolicyCfg()

    # create observation manager
    cfg = MyObservationManagerCfg()
    obs_man = ObservationManager(cfg, env)

    # check expected output
    assert obs_man.serialize() == {"policy": {"term_1": serialize_data}}


def test_modifier_invalid_config(setup_env):
    env = setup_env
    """Test modifier initialization with invalid config."""

    modifier = modifiers.ModifierCfg(func=modifiers.clip, params={"min": -0.5, "max": 0.5})

    @configclass
    class MyObservationManagerCfg:
        """Test config class for observation manager."""

        @configclass
        class PolicyCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            concatenate_terms = False
            term_1 = ObservationTermCfg(func=pos_w_data, modifiers=[modifier])

        policy: ObservationGroupCfg = PolicyCfg()

    # create observation manager
    cfg = MyObservationManagerCfg()

    with pytest.raises(ValueError):
        ObservationManager(cfg, env)


def test_concatenate_dim(setup_env):
    """Test concatenation of observations along different dimensions."""
    env = setup_env

    @configclass
    class MyObservationManagerCfg:
        """Test config class for observation manager."""

        @configclass
        class PolicyCfg(ObservationGroupCfg):
            """Test config class for policy observation group."""

            concatenate_terms = True
            concatenate_dim = 1  # Concatenate along dimension 1
            term_1 = ObservationTermCfg(func=grilled_chicken_image, scale=1.0, params={"bland": 1.0, "channel": 1})
            term_2 = ObservationTermCfg(func=grilled_chicken_image, scale=1.0, params={"bland": 1.0, "channel": 1})

        @configclass
        class CriticCfg(ObservationGroupCfg):
            """Test config class for critic observation group."""

            concatenate_terms = True
            concatenate_dim = 2  # Concatenate along dimension 2
            term_1 = ObservationTermCfg(func=grilled_chicken_image, scale=1.0, params={"bland": 1.0, "channel": 1})
            term_2 = ObservationTermCfg(func=grilled_chicken_image, scale=1.0, params={"bland": 1.0, "channel": 1})

        @configclass
        class CriticCfg_neg_dim(ObservationGroupCfg):
            """Test config class for critic observation group."""

            concatenate_terms = True
            concatenate_dim = -1  # Concatenate along last dimension
            term_1 = ObservationTermCfg(func=grilled_chicken_image, scale=1.0, params={"bland": 1.0, "channel": 1})
            term_2 = ObservationTermCfg(func=grilled_chicken_image, scale=1.0, params={"bland": 1.0, "channel": 1})

        policy: ObservationGroupCfg = PolicyCfg()
        critic: ObservationGroupCfg = CriticCfg()
        critic_neg_dim: ObservationGroupCfg = CriticCfg_neg_dim()

    # create observation manager
    cfg = MyObservationManagerCfg()
    obs_man = ObservationManager(cfg, env)
    # compute observation using manager
    observations = obs_man.compute()

    # obtain the group observations
    obs_policy: torch.Tensor = observations["policy"]
    obs_critic: torch.Tensor = observations["critic"]
    obs_critic_neg_dim: torch.Tensor = observations["critic_neg_dim"]

    # check the observation shapes
    # For policy: concatenated along dim 1, so width should be doubled
    assert obs_policy.shape == (env.num_envs, 128, 512, 1)
    # For critic: concatenated along last dim, so channels should be doubled
    assert obs_critic.shape == (env.num_envs, 128, 256, 2)
    # For critic_neg_dim: concatenated along last dim, so channels should be doubled
    assert obs_critic_neg_dim.shape == (env.num_envs, 128, 256, 2)

    # verify the data is concatenated correctly
    # For policy: check that the second half matches the first half
    torch.testing.assert_close(obs_policy[:, :, :256, :], obs_policy[:, :, 256:, :])
    # For critic: check that the second channel matches the first channel
    torch.testing.assert_close(obs_critic[:, :, :, 0], obs_critic[:, :, :, 1])

    # For critic_neg_dim: check that it is the same as critic
    torch.testing.assert_close(obs_critic_neg_dim, obs_critic)
