import tempfile
import xml.etree.ElementTree as ET

import numpy as np
from gymnasium.spaces import Box

from ogbench.locomaze.ant import AntEnv
from ogbench.locomaze.humanoid import HumanoidEnv
from ogbench.locomaze.point import PointEnv


def make_maze_env(loco_env_type, maze_env_type, *args, **kwargs):
    """Factory function for creating a maze environment.

    Args:
        loco_env_type: Locomotion environment type. One of 'point', 'ant', or 'humanoid'.
        maze_env_type: Maze environment type. Either 'maze' or 'ball'.
        *args: Additional arguments to pass to the target class.
        **kwargs: Additional keyword arguments to pass to the target class.
    """
    if loco_env_type == 'point':
        loco_env_class = PointEnv
    elif loco_env_type == 'ant':
        loco_env_class = AntEnv
    elif loco_env_type == 'humanoid':
        loco_env_class = HumanoidEnv
    else:
        raise ValueError(f'Unknown locomotion environment type: {loco_env_type}')

    class MazeEnv(loco_env_class):
        """Maze environment.

        It inherits from the locomotion environment and adds a maze to it.
        """

        def __init__(
            self,
            maze_type='large',
            maze_unit=4.0,
            maze_height=0.5,
            terminate_at_goal=True,
            ob_type='states',
            add_noise_to_goal=True,
            reward_task_id=None,
            use_oracle_rep=False,
            *args,
            **kwargs,
        ):
            """Initialize the maze environment.

            Args:
                maze_type: Maze type. One of 'arena', 'medium', 'large', 'giant', or 'teleport'.
                maze_unit: Size of a maze unit block.
                maze_height: Height of the maze walls.
                terminate_at_goal: Whether to terminate the episode when the goal is reached.
                ob_type: Observation type. Either 'states' or 'pixels'.
                add_noise_to_goal: Whether to add noise to the goal position.
                reward_task_id: Task ID for single-task RL. If this is not None, the environment operates in a
                    single-task mode with the specified task ID. The task ID must be either a valid task ID or 0, where
                    0 means using the default task.
                use_oracle_rep: Whether to use oracle goal representations.
                *args: Additional arguments to pass to the parent locomotion environment.
                **kwargs: Additional keyword arguments to pass to the parent locomotion environment.
            """
            self._maze_type = maze_type
            self._maze_unit = maze_unit
            self._maze_height = maze_height
            self._terminate_at_goal = terminate_at_goal
            self._ob_type = ob_type
            self._add_noise_to_goal = add_noise_to_goal
            self._reward_task_id = reward_task_id
            self._use_oracle_rep = use_oracle_rep
            assert ob_type in ['states', 'pixels']

            # Define constants.
            self._offset_x = 4
            self._offset_y = 4
            self._noise = 1
            self._goal_tol = 1.0 if loco_env_type == 'point' else 0.5

            # Define maze map.
            self._teleport_info = None
            if self._maze_type == 'arena':
                maze_map = [
                    [1, 1, 1, 1, 1, 1, 1, 1],
                    [1, 0, 0, 0, 0, 0, 0, 1],
                    [1, 0, 0, 0, 0, 0, 0, 1],
                    [1, 0, 0, 0, 0, 0, 0, 1],
                    [1, 0, 0, 0, 0, 0, 0, 1],
                    [1, 0, 0, 0, 0, 0, 0, 1],
                    [1, 0, 0, 0, 0, 0, 0, 1],
                    [1, 1, 1, 1, 1, 1, 1, 1],
                ]
            elif self._maze_type == 'medium':
                maze_map = [
                    [1, 1, 1, 1, 1, 1, 1, 1],
                    [1, 0, 0, 1, 1, 0, 0, 1],
                    [1, 0, 0, 1, 0, 0, 0, 1],
                    [1, 1, 0, 0, 0, 1, 1, 1],
                    [1, 0, 0, 1, 0, 0, 0, 1],
                    [1, 0, 1, 0, 0, 1, 0, 1],
                    [1, 0, 0, 0, 1, 0, 0, 1],
                    [1, 1, 1, 1, 1, 1, 1, 1],
                ]
            elif self._maze_type == 'large':
                maze_map = [
                    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                    [1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1],
                    [1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1],
                    [1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1],
                    [1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1],
                    [1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1],
                    [1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1],
                    [1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
                    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                ]
            elif self._maze_type == 'giant':
                maze_map = [
                    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                    [1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],
                    [1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1],
                    [1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
                    [1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1],
                    [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1],
                    [1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1],
                    [1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
                    [1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1],
                    [1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1],
                    [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1],
                    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                ]
            elif self._maze_type == 'teleport':
                maze_map = [
                    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                    [1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1],
                    [1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1],
                    [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1],
                    [1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1],
                    [1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1],
                    [1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1],
                    [1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1],
                    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                ]
                self._teleport_info = dict(
                    teleport_in_ijs=[(4, 6), (5, 1)],
                    teleport_out_ijs=[(1, 7), (6, 1), (6, 10)],
                    teleport_radius=1,
                )
                self._teleport_info['teleport_in_xys'] = [
                    self.ij_to_xy(ij) for ij in self._teleport_info['teleport_in_ijs']
                ]
                self._teleport_info['teleport_out_xys'] = [
                    self.ij_to_xy(ij) for ij in self._teleport_info['teleport_out_ijs']
                ]
            else:
                raise ValueError(f'Unknown maze type: {self._maze_type}')

            self.maze_map = np.array(maze_map)

            # Update XML file.
            xml_file = self.xml_file
            tree = ET.parse(xml_file)
            self.update_tree(tree)
            _, maze_xml_file = tempfile.mkstemp(text=True, suffix='.xml')
            tree.write(maze_xml_file)

            super().__init__(xml_file=maze_xml_file, *args, **kwargs)

            # Set task goals.
            self.task_infos = []
            self.cur_task_id = None
            self.cur_task_info = None
            self.set_tasks()
            self.num_tasks = len(self.task_infos)
            self.cur_goal_xy = np.zeros(2)

            if self._ob_type == 'pixels':
                self.observation_space = Box(low=0, high=255, shape=(64, 64, 3), dtype=np.uint8)

                # Manually color the floor to enable the agent to infer its position from the observation.
                tex_grid = self.model.tex('grid')
                tex_height = tex_grid.height[0]
                tex_width = tex_grid.width[0]
                # MuJoCo 3.2.1 changed the attribute name from 'tex_rgb' to 'tex_data'.
                attr_name = 'tex_rgb' if hasattr(self.model, 'tex_rgb') else 'tex_data'
                tex_rgb = getattr(self.model, attr_name)[tex_grid.adr[0] : tex_grid.adr[0] + 3 * tex_height * tex_width]
                tex_rgb = tex_rgb.reshape(tex_height, tex_width, 3)
                for x in range(tex_height):
                    for y in range(tex_width):
                        min_value = 0
                        max_value = 192
                        r = int(x / tex_height * (max_value - min_value) + min_value)
                        g = int(y / tex_width * (max_value - min_value) + min_value)
                        tex_rgb[x, y, :] = [r, g, 128]
            else:
                ex_ob = self.get_ob()
                self.observation_space = Box(low=-np.inf, high=np.inf, shape=ex_ob.shape, dtype=ex_ob.dtype)

            # Set camera.
            self.reset()
            #self.render()
            #self.mujoco_renderer.viewer.cam.lookat[0] = 2 * (self.maze_map.shape[1] - 3)
            #self.mujoco_renderer.viewer.cam.lookat[1] = 2 * (self.maze_map.shape[0] - 3)
            #self.mujoco_renderer.viewer.cam.distance = 5 * (self.maze_map.shape[1] - 2)
            #self.mujoco_renderer.viewer.cam.elevation = -90

        def update_tree(self, tree):
            """Update the XML tree to include the maze."""
            worldbody = tree.find('.//worldbody')

            # Add walls.
            for i in range(self.maze_map.shape[0]):
                for j in range(self.maze_map.shape[1]):
                    struct = self.maze_map[i, j]
                    if struct == 1:
                        ET.SubElement(
                            worldbody,
                            'geom',
                            name=f'block_{i}_{j}',
                            pos=f'{j * self._maze_unit - self._offset_x} {i * self._maze_unit - self._offset_y} {self._maze_height / 2 * self._maze_unit}',
                            size=f'{self._maze_unit / 2} {self._maze_unit / 2} {self._maze_height / 2 * self._maze_unit}',
                            type='box',
                            contype='1',
                            conaffinity='1',
                            material='wall',
                        )

            # Adjust floor size.
            center_x, center_y = 2 * (self.maze_map.shape[1] - 3), 2 * (self.maze_map.shape[0] - 3)
            size_x, size_y = 2 * self.maze_map.shape[1], 2 * self.maze_map.shape[0]
            floor = tree.find('.//geom[@name="floor"]')
            floor.set('pos', f'{center_x} {center_y} 0')
            floor.set('size', f'{size_x} {size_y} 0.2')

            if self._teleport_info is not None:
                # Add teleports.
                for i, (x, y) in enumerate(self._teleport_info['teleport_in_xys']):
                    ET.SubElement(
                        worldbody,
                        'geom',
                        name=f'teleport_in_{i}',
                        type='cylinder',
                        size=f'{self._teleport_info["teleport_radius"]} .05',
                        pos=f'{x} {y} .05',
                        material='teleport_in',
                        contype='0',
                        conaffinity='0',
                    )
                for i, (x, y) in enumerate(self._teleport_info['teleport_out_xys']):
                    ET.SubElement(
                        worldbody,
                        'geom',
                        name=f'teleport_out_{i}',
                        type='cylinder',
                        size=f'{self._teleport_info["teleport_radius"]} .05',
                        pos=f'{x} {y} .05',
                        material='teleport_out',
                        contype='0',
                        conaffinity='0',
                    )

            if self._ob_type == 'pixels':
                # Color wall.
                wall = tree.find('.//material[@name="wall"]')
                wall.set('rgba', '.6 .6 .6 1')
                # Remove ambient light.
                light = tree.find('.//light[@name="global"]')
                light.attrib.pop('ambient')
                # Remove torso light.
                torso_light = tree.find('.//light[@name="torso_light"]')
                torso_light_parent = tree.find('.//light[@name="torso_light"]/..')
                torso_light_parent.remove(torso_light)
                # Remove texture repeat.
                grid = tree.find('.//material[@name="grid"]')
                grid.set('texuniform', 'false')
                if loco_env_type == 'ant':
                    # Color one leg white to break symmetry.
                    tree.find('.//geom[@name="aux_1_geom"]').set('material', 'self_white')
                    tree.find('.//geom[@name="left_leg_geom"]').set('material', 'self_white')
                    tree.find('.//geom[@name="left_ankle_geom"]').set('material', 'self_white')
            else:
                # Only show the target for states-based observation.
                ET.SubElement(
                    worldbody,
                    'geom',
                    name='target',
                    type='cylinder',
                    size='.5 .05',
                    pos='0 0 .05',
                    material='target',
                    contype='0',
                    conaffinity='0',
                )

        def set_tasks(self):
            # `tasks` is a list of tasks, where each task is a list of two tuples: (init_ij, goal_ij).
            if self._maze_type == 'arena':
                tasks = [
                    [(1, 1), (6, 6)],
                ]
            elif self._maze_type == 'medium':
                tasks = [
                    [(1, 1), (6, 6)],
                    [(6, 1), (1, 6)],
                    [(5, 3), (4, 2)],
                    [(6, 5), (6, 1)],
                    [(2, 6), (1, 1)],
                ]
            elif self._maze_type == 'large':
                tasks = [
                    [(1, 1), (7, 10)],
                    [(5, 4), (7, 1)],
                    [(7, 4), (1, 10)],
                    [(3, 8), (5, 4)],
                    [(1, 1), (5, 4)],
                ]
            elif self._maze_type == 'giant':
                tasks = [
                    [(1, 1), (10, 14)],
                    [(1, 14), (10, 1)],
                    [(8, 14), (1, 1)],
                    [(8, 3), (5, 12)],
                    [(5, 9), (3, 8)],
                ]
            elif self._maze_type == 'teleport':
                tasks = [
                    [(1, 10), (7, 1)],
                    [(1, 1), (7, 10)],
                    [(5, 6), (7, 10)],
                    [(7, 1), (7, 10)],
                    [(5, 6), (7, 1)],
                ]
            else:
                raise ValueError(f'Unknown maze type: {self._maze_type}')

            self.task_infos = []
            for i, task in enumerate(tasks):
                self.task_infos.append(
                    dict(
                        task_name=f'task{i + 1}',
                        init_ij=task[0],
                        init_xy=self.ij_to_xy(task[0]),
                        goal_ij=task[1],
                        goal_xy=self.ij_to_xy(task[1]),
                    )
                )

            if self._reward_task_id == 0:
                self._reward_task_id = 1  # Default task.

        def reset(self, options=None, *args, **kwargs):
            if options is None:
                options = {}
            # Set the task goal.
            if self._reward_task_id is not None:
                # Use the pre-defined task.
                assert 1 <= self._reward_task_id <= self.num_tasks, f'Task ID must be in [1, {self.num_tasks}].'
                self.cur_task_id = self._reward_task_id
                self.cur_task_info = self.task_infos[self.cur_task_id - 1]
            elif 'task_id' in options:
                # Use the pre-defined task.
                assert 1 <= options['task_id'] <= self.num_tasks, f'Task ID must be in [1, {self.num_tasks}].'
                self.cur_task_id = options['task_id']
                self.cur_task_info = self.task_infos[self.cur_task_id - 1]
            elif 'task_info' in options:
                # Use the provided task information.
                self.cur_task_id = None
                self.cur_task_info = options['task_info']
            else:
                # Randomly sample a task.
                self.cur_task_id = np.random.randint(1, self.num_tasks + 1)
                self.cur_task_info = self.task_infos[self.cur_task_id - 1]

            # Whether to provide a rendering of the goal.
            render_goal = False
            if 'render_goal' in options:
                render_goal = options['render_goal']

            # Get initial and goal positions with noise.
            init_xy = self.add_noise(self.ij_to_xy(self.cur_task_info['init_ij']))
            goal_xy = self.ij_to_xy(self.cur_task_info['goal_ij'])
            if self._add_noise_to_goal:
                goal_xy = self.add_noise(goal_xy)

            # First, force set the position to the goal position to obtain the goal observation.
            super().reset(*args, **kwargs)

            # Do a few random steps to stabilize the environment.
            num_random_actions = 40 if loco_env_type == 'humanoid' else 5
            for _ in range(num_random_actions):
                super().step(self.action_space.sample())

            # Save the goal observation.
            self.set_goal(goal_xy=goal_xy)
            self.set_xy(goal_xy)
            goal_ob = self.get_oracle_rep() if self._use_oracle_rep else self.get_ob()
            if render_goal:
                goal_rendered = self.render()
            goal_rendered = None

            # Now, do the actual reset.
            ob, info = super().reset(*args, **kwargs)
            self.set_goal(goal_xy=goal_xy)
            self.set_xy(init_xy)
            ob = self.get_ob()
            info['goal'] = goal_ob
            if render_goal:
                info['goal_rendered'] = goal_rendered

            return ob, info

        def step(self, action):
            ob, reward, terminated, truncated, info = super().step(action)

            if self._teleport_info is not None:
                # Check if the agent is close to a inbound teleport.
                for x, y in self._teleport_info['teleport_in_xys']:
                    if np.linalg.norm(self.get_xy() - np.array([x, y])) <= self._teleport_info['teleport_radius'] * 1.5:
                        # Teleport the agent to a random outbound teleport.
                        teleport_out_xy = self._teleport_info['teleport_out_xys'][
                            np.random.randint(len(self._teleport_info['teleport_out_xys']))
                        ]
                        self.set_xy(np.array(teleport_out_xy))
                        break

            # Check if the agent has reached the goal.
            if np.linalg.norm(self.get_xy() - self.cur_goal_xy) <= self._goal_tol:
                if self._terminate_at_goal:
                    terminated = True
                info['success'] = 1.0
                reward = 1.0
            else:
                info['success'] = 0.0
                reward = 0.0

            # If the environment is in the single-task mode, modify the reward.
            if self._reward_task_id is not None:
                reward = reward - 1.0  # -1 (failure) or 0 (success).

            return ob, reward, terminated, truncated, info

        def get_ob(self, ob_type=None):
            ob_type = self._ob_type if ob_type is None else ob_type
            if ob_type == 'states':
                return super().get_ob()
            else:
                frame = self.render()
                return frame

        def get_oracle_rep(self):
            """Return the oracle goal representation (i.e., the goal position)."""
            return np.array(self.cur_goal_xy)

        def set_goal(self, goal_ij=None, goal_xy=None):
            """Set the goal position and update the target object."""
            if goal_xy is None:
                self.cur_goal_xy = self.ij_to_xy(goal_ij)
                if self._add_noise_to_goal:
                    self.cur_goal_xy = self.add_noise(self.cur_goal_xy)
            else:
                self.cur_goal_xy = goal_xy
            if self._ob_type == 'states':
                self.model.geom('target').pos[:2] = goal_xy

        def get_oracle_subgoal(self, start_xy, goal_xy):
            """Get the oracle subgoal for the agent.

            If the goal is unreachable, it returns the current position as the subgoal.

            Args:
                start_xy: Starting position of the agent.
                goal_xy: Goal position of the agent.
            Returns:
                A tuple of the oracle subgoal and the BFS map.
            """
            start_ij = self.xy_to_ij(start_xy)
            goal_ij = self.xy_to_ij(goal_xy)

            # Run BFS to find the next subgoal.
            bfs_map = self.maze_map.copy()
            for i in range(self.maze_map.shape[0]):
                for j in range(self.maze_map.shape[1]):
                    bfs_map[i][j] = -1

            bfs_map[goal_ij[0], goal_ij[1]] = 0
            queue = [goal_ij]
            while len(queue) > 0:
                i, j = queue.pop(0)
                for di, dj in [(-1, 0), (0, -1), (1, 0), (0, 1)]:
                    ni, nj = i + di, j + dj
                    if (
                        0 <= ni < self.maze_map.shape[0]
                        and 0 <= nj < self.maze_map.shape[1]
                        and self.maze_map[ni, nj] == 0
                        and bfs_map[ni, nj] == -1
                    ):
                        bfs_map[ni][nj] = bfs_map[i][j] + 1
                        queue.append((ni, nj))

            # Find the subgoal that attains the minimum BFS value.
            subgoal_ij = start_ij
            for di, dj in [(-1, 0), (0, -1), (1, 0), (0, 1)]:
                ni, nj = start_ij[0] + di, start_ij[1] + dj
                if (
                    0 <= ni < self.maze_map.shape[0]
                    and 0 <= nj < self.maze_map.shape[1]
                    and self.maze_map[ni, nj] == 0
                    and bfs_map[ni, nj] < bfs_map[subgoal_ij[0], subgoal_ij[1]]
                ):
                    subgoal_ij = (ni, nj)
            subgoal_xy = self.ij_to_xy(subgoal_ij)
            return np.array(subgoal_xy), bfs_map

        def xy_to_ij(self, xy):
            maze_unit = self._maze_unit
            i = int((xy[1] + self._offset_y + 0.5 * maze_unit) / maze_unit)
            j = int((xy[0] + self._offset_x + 0.5 * maze_unit) / maze_unit)
            return i, j

        def ij_to_xy(self, ij):
            i, j = ij
            x = j * self._maze_unit - self._offset_x
            y = i * self._maze_unit - self._offset_y
            return x, y

        def add_noise(self, xy):
            random_x = np.random.uniform(low=-self._noise, high=self._noise) * self._maze_unit / 4
            random_y = np.random.uniform(low=-self._noise, high=self._noise) * self._maze_unit / 4
            return xy[0] + random_x, xy[1] + random_y

    class BallEnv(MazeEnv):
        def update_tree(self, tree):
            super().update_tree(tree)

            # Add ball.
            worldbody = tree.find('.//worldbody')
            ball = ET.SubElement(worldbody, 'body', name='ball', pos='0 0 0.5')
            ET.SubElement(ball, 'freejoint', name='ball_root')
            ET.SubElement(
                ball,
                'geom',
                name='ball',
                size='.25',
                material='ball',
                priority='1',
                conaffinity='1',
                condim='6',
            )
            ET.SubElement(ball, 'light', name='ball_light', pos='0 0 4', mode='trackcom')

        def set_tasks(self):
            # `tasks` is a list of tasks, where each task is a list of three tuples: (agent_init_ij, ball_init_ij,
            # goal_ij).
            if self._maze_type == 'arena':
                tasks = [
                    [(1, 6), (2, 3), (5, 2)],
                    [(2, 2), (5, 5), (2, 2)],
                    [(6, 1), (2, 3), (6, 6)],
                    [(6, 6), (1, 1), (6, 1)],
                    [(4, 6), (6, 2), (1, 6)],
                ]
            elif self._maze_type == 'medium':
                tasks = [
                    [(1, 1), (3, 4), (6, 6)],
                    [(6, 1), (6, 5), (1, 1)],
                    [(5, 3), (4, 2), (6, 5)],
                    [(6, 5), (1, 1), (5, 3)],
                    [(1, 6), (6, 1), (1, 6)],
                ]
            else:
                raise ValueError(f'Unknown maze type: {self._maze_type}')

            self.task_infos = []
            for i, task in enumerate(tasks):
                self.task_infos.append(
                    dict(
                        task_name=f'task{i + 1}',
                        agent_init_ij=task[0],
                        agent_init_xy=self.ij_to_xy(task[0]),
                        ball_init_ij=task[1],
                        ball_init_xy=self.ij_to_xy(task[1]),
                        goal_ij=task[2],
                        goal_xy=self.ij_to_xy(task[2]),
                    )
                )

            if self._reward_task_id == 0:
                self._reward_task_id = 4  # Default task.

        def reset(self, options=None, *args, **kwargs):
            if options is None:
                options = {}
            # Set the task goal.
            if self._reward_task_id is not None:
                # Use the pre-defined task.
                assert 1 <= self._reward_task_id <= self.num_tasks, f'Task ID must be in [1, {self.num_tasks}].'
                self.cur_task_id = self._reward_task_id
                self.cur_task_info = self.task_infos[self.cur_task_id - 1]
            elif 'task_id' in options:
                # Use the pre-defined task.
                assert 1 <= options['task_id'] <= self.num_tasks, f'Task ID must be in [1, {self.num_tasks}].'
                self.cur_task_id = options['task_id']
                self.cur_task_info = self.task_infos[self.cur_task_id - 1]
            elif 'task_info' in options:
                # Use the provided task information.
                self.cur_task_id = None
                self.cur_task_info = options['task_info']
            else:
                # Randomly sample a task.
                self.cur_task_id = np.random.randint(1, self.num_tasks + 1)
                self.cur_task_info = self.task_infos[self.cur_task_id - 1]

            # Whether to provide a rendering of the goal.
            render_goal = False
            if 'render_goal' in options:
                render_goal = options['render_goal']

            # Get initial and goal positions with noise.
            agent_init_xy = self.add_noise(self.ij_to_xy(self.cur_task_info['agent_init_ij']))
            ball_init_xy = self.add_noise(self.ij_to_xy(self.cur_task_info['ball_init_ij']))
            goal_xy = self.ij_to_xy(self.cur_task_info['goal_ij'])
            if self._add_noise_to_goal:
                goal_xy = self.add_noise(goal_xy)

            # First, force set the position to the goal position to obtain the goal observation.
            super(MazeEnv, self).reset(*args, **kwargs)

            # Do a few random steps to stabilize the environment.
            for _ in range(10):
                super(MazeEnv, self).step(self.action_space.sample())

            # Save the goal observation.
            self.set_goal(goal_xy=goal_xy)
            self.set_agent_ball_xy(goal_xy, goal_xy)
            goal_ob = self.get_oracle_rep() if self._use_oracle_rep else self.get_ob()
            if render_goal:
                goal_rendered = self.render()

            # Now, do the actual reset.
            ob, info = super(MazeEnv, self).reset(*args, **kwargs)
            self.set_goal(goal_xy=goal_xy)
            self.set_agent_ball_xy(agent_init_xy, ball_init_xy)
            ob = self.get_ob()
            info['goal'] = goal_ob
            if render_goal:
                info['goal_rendered'] = goal_rendered

            return ob, info

        def step(self, action):
            ob, reward, terminated, truncated, info = super(MazeEnv, self).step(action)

            # Check if the ball has reached the goal.
            if np.linalg.norm(self.get_agent_ball_xy()[1] - self.cur_goal_xy) <= self._goal_tol:
                if self._terminate_at_goal:
                    terminated = True
                info['success'] = 1.0
                reward = 1.0
            else:
                info['success'] = 0.0
                reward = 0.0

            # If the environment is in the single-task mode, modify the reward.
            if self._reward_task_id is not None:
                reward = reward - 1.0  # -1 (failure) or 0 (success).

            return ob, reward, terminated, truncated, info

        def get_agent_ball_xy(self):
            agent_xy = self.data.qpos[:2].copy()
            ball_xy = self.data.qpos[-7:-5].copy()

            return agent_xy, ball_xy

        def set_agent_ball_xy(self, agent_xy, ball_xy):
            qpos = self.data.qpos.copy()
            qvel = self.data.qvel.copy()
            qpos[:2] = agent_xy
            qpos[-7:-5] = ball_xy
            self.set_state(qpos, qvel)

    if maze_env_type == 'maze':
        return MazeEnv(*args, **kwargs)
    elif maze_env_type == 'ball':
        return BallEnv(*args, **kwargs)
    else:
        raise ValueError(f'Unknown maze environment type: {maze_env_type}')
