# Copyright (c) 2024-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: Apache-2.0

import torch
from collections.abc import Sequence

import isaaclab.utils.math as PoseUtils
from isaaclab.envs import ManagerBasedRLMimicEnv


class FrankaCubeStackIKRelMimicEnv(ManagerBasedRLMimicEnv):
    """
    Isaac Lab Mimic environment wrapper class for Franka Cube Stack IK Rel env.
    """

    def get_robot_eef_pose(self, eef_name: str, env_ids: Sequence[int] | None = None) -> torch.Tensor:
        """
        Get current robot end effector pose. Should be the same frame as used by the robot end-effector controller.

        Args:
            eef_name: Name of the end effector.
            env_ids: Environment indices to get the pose for. If None, all envs are considered.

        Returns:
            A torch.Tensor eef pose matrix. Shape is (len(env_ids), 4, 4)
        """
        if env_ids is None:
            env_ids = slice(None)

        # Retrieve end effector pose from the observation buffer
        eef_pos = self.obs_buf["policy"]["eef_pos"][env_ids]
        eef_quat = self.obs_buf["policy"]["eef_quat"][env_ids]
        # Quaternion format is w,x,y,z
        return PoseUtils.make_pose(eef_pos, PoseUtils.matrix_from_quat(eef_quat))

    def target_eef_pose_to_action(
        self,
        target_eef_pose_dict: dict,
        gripper_action_dict: dict,
        action_noise_dict: dict | None = None,
        env_id: int = 0,
    ) -> torch.Tensor:
        """
        Takes a target pose and gripper action for the end effector controller and returns an action
        (usually a normalized delta pose action) to try and achieve that target pose.
        Noise is added to the target pose action if specified.

        Args:
            target_eef_pose_dict: Dictionary of 4x4 target eef pose for each end-effector.
            gripper_action_dict: Dictionary of gripper actions for each end-effector.
            noise: Noise to add to the action. If None, no noise is added.
            env_id: Environment index to get the action for.

        Returns:
            An action torch.Tensor that's compatible with env.step().
        """
        eef_name = list(self.cfg.subtask_configs.keys())[0]

        # target position and rotation
        (target_eef_pose,) = target_eef_pose_dict.values()
        target_pos, target_rot = PoseUtils.unmake_pose(target_eef_pose)

        # current position and rotation
        curr_pose = self.get_robot_eef_pose(eef_name, env_ids=[env_id])[0]
        curr_pos, curr_rot = PoseUtils.unmake_pose(curr_pose)

        # normalized delta position action
        delta_position = target_pos - curr_pos

        # normalized delta rotation action
        delta_rot_mat = target_rot.matmul(curr_rot.transpose(-1, -2))
        delta_quat = PoseUtils.quat_from_matrix(delta_rot_mat)
        delta_rotation = PoseUtils.axis_angle_from_quat(delta_quat)

        # get gripper action for single eef
        (gripper_action,) = gripper_action_dict.values()

        # add noise to action
        pose_action = torch.cat([delta_position, delta_rotation], dim=0)
        if action_noise_dict is not None:
            noise = action_noise_dict[eef_name] * torch.randn_like(pose_action)
            pose_action += noise
            pose_action = torch.clamp(pose_action, -1.0, 1.0)

        return torch.cat([pose_action, gripper_action], dim=0)

    def action_to_target_eef_pose(self, action: torch.Tensor) -> dict[str, torch.Tensor]:
        """
        Converts action (compatible with env.step) to a target pose for the end effector controller.
        Inverse of @target_eef_pose_to_action. Usually used to infer a sequence of target controller poses
        from a demonstration trajectory using the recorded actions.

        Args:
            action: Environment action. Shape is (num_envs, action_dim)

        Returns:
            A dictionary of eef pose torch.Tensor that @action corresponds to
        """
        eef_name = list(self.cfg.subtask_configs.keys())[0]

        delta_position = action[:, :3]
        delta_rotation = action[:, 3:6]

        # current position and rotation
        curr_pose = self.get_robot_eef_pose(eef_name, env_ids=None)
        curr_pos, curr_rot = PoseUtils.unmake_pose(curr_pose)

        # get pose target
        target_pos = curr_pos + delta_position

        # Convert delta_rotation to axis angle form
        delta_rotation_angle = torch.linalg.norm(delta_rotation, dim=-1, keepdim=True)
        delta_rotation_axis = delta_rotation / delta_rotation_angle

        # Handle invalid division for the case when delta_rotation_angle is close to zero
        is_close_to_zero_angle = torch.isclose(delta_rotation_angle, torch.zeros_like(delta_rotation_angle)).squeeze(1)
        delta_rotation_axis[is_close_to_zero_angle] = torch.zeros_like(delta_rotation_axis)[is_close_to_zero_angle]

        delta_quat = PoseUtils.quat_from_angle_axis(delta_rotation_angle.squeeze(1), delta_rotation_axis).squeeze(0)
        delta_rot_mat = PoseUtils.matrix_from_quat(delta_quat)
        target_rot = torch.matmul(delta_rot_mat, curr_rot)

        target_poses = PoseUtils.make_pose(target_pos, target_rot).clone()

        return {eef_name: target_poses}

    def actions_to_gripper_actions(self, actions: torch.Tensor) -> dict[str, torch.Tensor]:
        """
        Extracts the gripper actuation part from a sequence of env actions (compatible with env.step).

        Args:
            actions: environment actions. The shape is (num_envs, num steps in a demo, action_dim).

        Returns:
            A dictionary of torch.Tensor gripper actions. Key to each dict is an eef_name.
        """
        # last dimension is gripper action
        return {list(self.cfg.subtask_configs.keys())[0]: actions[:, -1:]}

    def get_subtask_term_signals(self, env_ids: Sequence[int] | None = None) -> dict[str, torch.Tensor]:
        """
        Gets a dictionary of termination signal flags for each subtask in a task. The flag is 1
        when the subtask has been completed and 0 otherwise. The implementation of this method is
        required if intending to enable automatic subtask term signal annotation when running the
        dataset annotation tool. This method can be kept unimplemented if intending to use manual
        subtask term signal annotation.

        Args:
            env_ids: Environment indices to get the termination signals for. If None, all envs are considered.

        Returns:
            A dictionary termination signal flags (False or True) for each subtask.
        """
        if env_ids is None:
            env_ids = slice(None)

        signals = dict()
        subtask_terms = self.obs_buf["subtask_terms"]
        signals["grasp_1"] = subtask_terms["grasp_1"][env_ids]
        signals["grasp_2"] = subtask_terms["grasp_2"][env_ids]
        signals["stack_1"] = subtask_terms["stack_1"][env_ids]
        # final subtask is placing cubeC on cubeA (motion relative to cubeA) - but final subtask signal is not needed
        return signals

    def get_expected_attached_object(self, eef_name: str, subtask_index: int, env_cfg) -> str | None:
        """
        (SkillGen) Return the expected attached object for the given EEF/subtask.

        Assumes 'stack' subtasks place the object grasped in the preceding 'grasp' subtask.
        Returns None for 'grasp' (or others) at subtask start.
        """
        if eef_name not in env_cfg.subtask_configs:
            return None

        subtask_configs = env_cfg.subtask_configs[eef_name]
        if not (0 <= subtask_index < len(subtask_configs)):
            return None

        current_cfg = subtask_configs[subtask_index]
        # If stacking, expect we are holding the object grasped in the prior subtask
        if "stack" in str(current_cfg.subtask_term_signal).lower():
            if subtask_index > 0:
                prev_cfg = subtask_configs[subtask_index - 1]
                if "grasp" in str(prev_cfg.subtask_term_signal).lower():
                    return prev_cfg.object_ref
        return None
