import pickle
import time
import json
import numpy as np
import requests

from joysim.annotations.config_class import configclass, field
from joysim.annotations.stereotype import stereotype
from joysim.controllers.spawnable_controller import SpawnableController
from joysim.controllers.visualize_controller import VisualizeController
from joysim.unisim.robots.models.modular_robot import ModularRobot
from joysim.utils.namespace import PoseVisualType, SimulatorType
from joysim.unisim.robots.actuator_configs.grippers import GripperDriveJointConfig
from joysim.extensions.benchmark.action import RobotAction
from joysim.extensions.benchmark.benchmark import (
    BenchmarkAction,
    BenchmarkObservation,
    ControlMode,
)
from joysim.extensions.benchmark.policy import Policy, PolicyConfig
from joysim.utils.log import Log
from joysim.utils.pose import Pose

@configclass
@stereotype.register_config("starvla")
class StarvlaPolicyConfig(PolicyConfig):

    robot_name: str = field(default="None", required=True, comment="The name of the robot")
    arm_name: str = field(default="main_arm", required=True, comment="The name of the arm module to control")
    drive_name: str = field(default="robotiq_85_left_knuckle_joint", required=True, comment="The name of the drive module to control")
    gripper_width_mapper_file: str = field(default="", required=True, comment="The file path to the gripper width mapper")
    visualize_action_ee_pose: bool = field(default=False, required=True, comment="Whether to visualize the action end effector pose")
    visualize_state_ee_pose: bool = field(default=False, required=True, comment="Whether to visualize the state end effector pose")
    visualize_bounding_box_targets: list[str] = field(
        default_factory=list,
        required=False,
        comment="Spawnable object names to draw bounding boxes for (empty = off)",
    )
    sensor_names: list[str] = field(
        default=["Hand_Camera", "Left_Camera", "Right_Camera"],
        required=True,
        comment="The names of the sensors"
    )
    server_url: str = field(
        default="http://127.0.0.1:5000/policy",
        required=True,
        comment="StarVLA policy server url"
    )

    prompt: str = field(
        default="pick the object",
        required=True,
        comment="task instruction"
    )

    run_trunk_size: int = field(
        default=16,
        required=True,
        comment="The number of chunks to run in one inference step"
    )

    
@stereotype.register_model("starvla")
class StarvlaPolicy(Policy):

    def __init__(self, config: StarvlaPolicyConfig):
        super().__init__(config)

        self.robot_name = config.robot_name
        self.arm_name = config.arm_name
        self.drive_name = config.drive_name
        self.sensor_names = config.sensor_names
        self.server_url = config.server_url
        self.prompt = config.prompt
        self.gripper_width_mapper = json.load(open(config.gripper_width_mapper_file, "r"))
        self.visualize_action_ee_pose = config.visualize_action_ee_pose
        self.visualize_state_ee_pose = config.visualize_state_ee_pose
        self.visualize_bounding_box_targets = list(config.visualize_bounding_box_targets or [])

    def reset(self) -> None:
        self.current_ee_position_state = None
        self.current_ee_rot6d_state = None
        self.current_gripper_width = None
        self.current_chunk_id = 0
        self.current_chunk_result = None
        self.run_trunk_size = self.config.run_trunk_size
        self.robot: ModularRobot = SpawnableController.get_spawnable_data(self.robot_name).unwrap()
        self.drive_joints: dict[str, GripperDriveJointConfig] = self.robot.get_arm(self.arm_name).get_ee().get_drive_joints()
        self.robot_drive_name = list(self.drive_joints.keys())[0]
        
        for joint_name, joint_config in self.drive_joints.items():
            SpawnableController.control_robot(self.robot_name, "set_joint_stiffness", parameters={"joint_names": [joint_name], "stiffness": joint_config.position_control_stiffness}).unwrap()
            SpawnableController.control_robot(self.robot_name, "set_joint_damping", parameters={"joint_names": [joint_name], "damping": joint_config.position_control_damping}).unwrap()
            SpawnableController.control_robot(self.robot_name, "set_joint_effort_limit", parameters={"joint_names": [joint_name], "effort_limit": 5000}).unwrap()
            SpawnableController.control_robot(self.robot_name, "set_joint_effort_limit", parameters={"joint_names": [self.robot_drive_name], "effort_limit": 5000}).unwrap()
        self.max_width = float("-inf")
        self.min_width = float("inf")
        for entry in self.gripper_width_mapper:
            self.max_width = max(self.max_width, entry["width"])
            self.min_width = min(self.min_width, entry["width"])

    def warmup(self, benchmark_observation: BenchmarkObservation) -> None:
        Log.info(f"Waiting for StarVLA inference server to be ready...")
        while True:
            try:
                if requests.get(f"{self.server_url}/health", timeout=1.0).status_code == 200:
                    break
            except Exception:
                time.sleep(1)
        Log.success(f"StarVLA inference server is ready.")

    def needs_observation(self) -> bool:
        return self.current_chunk_id == 0
          
    def _handle_server_error(self, response: requests.Response) -> None:
        if response.status_code == 500:
            err_obj = pickle.loads(response.content)
            Log.error(f"StarVLA server error: {err_obj['error']}")
            Log.error(f"Traceback: {err_obj['traceback']}", exit=True)
        elif response.status_code != 200:
            Log.error(f"StarVLA server error with status code <{response.status_code}> : {response.text}", exit=True)

    def preprocess_observation(self, benchmark_observation: BenchmarkObservation) -> dict:
        robot_obs = benchmark_observation.get_robot_observations(self.robot_name)["robot_data"]
        ee_pose_base = robot_obs["ee_pose"][self.arm_name]["base_frame"]
        ee_position, ee_rot6d = ee_pose_base["position"],ee_pose_base["rot6d"]
        arm_joint_positions = robot_obs["joint_positions"][:7] # 临时多加了一个drive的位置，现在读的最后一个joint值是drive
        drive_joint_positions = robot_obs["joint_positions"][-1]
        normalized_gripper_width = self.__map_joint_position_to_normalized_width(drive_joint_positions)
        Log.debug(f"input normalized_gripper_width state: {round(normalized_gripper_width, 2)}")
        state = np.concatenate([ee_position,ee_rot6d,np.array([normalized_gripper_width]), [0]*10, np.array(arm_joint_positions)])
        rgb_data = {}
        for sensor_name in self.sensor_names:
            sensor_obs = benchmark_observation.get_sensor_observations(sensor_name)
            rgb_data[sensor_name] = sensor_obs["rgb"].data.cpu().numpy().astype(np.uint8)
        obs = {"state": state,"rgb": rgb_data,"prompt": self.prompt}

        return obs

    def compute_action(self, observation: dict) -> dict:
        if self.current_chunk_result is None:
            self.current_ee_position_state = np.array(observation["state"][:3]).astype(np.float64)
            self.current_ee_rot6d_state = np.array(observation["state"][3:9]).astype(np.float64)
            self.current_gripper_width = np.array([observation["state"][9]])
            payload = pickle.dumps(observation)
            response = requests.post(
                f"{self.server_url}/inference",
                data=payload,
                headers={"Content-Type": "application/octet-stream"}
            )
            self.test_obs = observation["state"] #TODO
            self._handle_server_error(response)
            result = pickle.loads(response.content)
            max_trunk_size = len(result["ee_delta_position_chunks"])
            if self.run_trunk_size > max_trunk_size:
                Log.warning(f"Run trunk size {self.run_trunk_size} is greater than the number of chunks {max_trunk_size}. Set run trunk size to {max_trunk_size}.")
                self.run_trunk_size = max_trunk_size
            self.run_trunk_size = max_trunk_size
            self.current_chunk_result = result
        else:
            result = self.current_chunk_result

        return result
    def __map_joint_position_to_normalized_width(self, joint_position: float) -> float:
        if joint_position < 0:
            joint_position = 0
        if joint_position > 0.8:
            joint_position = 0.8
        for entry in self.gripper_width_mapper:
            if round(entry["angel"], 2) == round(joint_position, 2):
                return 1-(entry["width"] - self.min_width) / (self.max_width - self.min_width)
            
        

    def __map_gripper_joint_position(self, normalized_gripper_width: float) -> float:
        
        joint_positions = []
        joint_names = []
        if normalized_gripper_width > 0.5:
            for joint_name, joint_config in self.drive_joints.items():
                joint_positions.append(joint_config.close_position)
                joint_names.append(joint_name)
        else:
            for joint_name, joint_config in self.drive_joints.items():
                joint_positions.append(joint_config.open_position)
                joint_names.append(joint_name)
        return joint_positions, joint_names

    def postprocess_action(self, action: dict) -> BenchmarkAction:
        benchmark_action = BenchmarkAction()
        Log.debug(f"observation: {self.test_obs}")
        # import ipdb;ipdb.set_trace()
        
        # get base frame end-effector pose
        delta_ee_pose = Pose(position=action["ee_delta_position_chunks"][self.current_chunk_id], rot6d=action["ee_delta_rot6d_chunks"][self.current_chunk_id])
        curr_state_ee_pose = Pose(position=self.current_ee_position_state, rot6d=self.current_ee_rot6d_state)
        curr_action_ee_pose = curr_state_ee_pose * delta_ee_pose # action2base = state2base * action2state
        curr_action_gripper_width = action["gripper_width_chunks"][self.current_chunk_id]
        
        gripper_joint_positions, gripper_joint_names = self.__map_gripper_joint_position(curr_action_gripper_width[0])
        Log.debug(f"action_gripper_joint_positions: {gripper_joint_positions}, action_normalized_gripper_width: {round(curr_action_gripper_width[0], 2)}")
        benchmark_action.add_robot_action(
            RobotAction(
                control_mode=ControlMode.POSITION,
                robot_name=self.robot_name,
                joint_names=gripper_joint_names,
                joint_positions=gripper_joint_positions
            )
        )
        benchmark_action.add_robot_action(
            RobotAction(
                control_mode=ControlMode.EE_POSE,
                robot_name=self.robot_name,
                ee_pose=curr_action_ee_pose
            )
        )
        self._visualize_base_frame_ee_poses(curr_state_ee_pose, curr_action_ee_pose)
        self._visualize_bounding_boxes()
        self.current_chunk_id += 1
        if self.current_chunk_id == self.run_trunk_size:
            self.current_chunk_id = 0
            self.current_chunk_result = None
        return benchmark_action

    # ------------------- Visualization -------------------
    def _visualize_base_frame_ee_poses(
        self, pose_state_base: Pose, pose_action_base: Pose
    ) -> None:
        if not self.visualize_action_ee_pose and not self.visualize_state_ee_pose:
            return
        robot_base_world = SpawnableController.control_robot(
            self.robot_name, "get_pose"
        ).unwrap()
        if self.visualize_state_ee_pose:
            VisualizeController.create_pose_visualization(
                robot_base_world * pose_state_base,
                name=f"{self.robot_name}/starvla_state_ee",
                simulator=SimulatorType.ISAACLAB,
                pose_type=PoseVisualType.COORDINATE,
                extra_params={"axis_length": 0.08, "thickness": 0.006},
            ).unwrap()
        if self.visualize_action_ee_pose:
            VisualizeController.create_pose_visualization(
                robot_base_world * pose_action_base,
                name=f"{self.robot_name}/starvla_action_ee",
                simulator=SimulatorType.ISAACLAB,
                pose_type=PoseVisualType.COORDINATE,
                extra_params={"axis_length": 0.1, "thickness": 0.006},
            ).unwrap()

    def _visualize_bounding_boxes(self) -> None:
        if not self.visualize_bounding_box_targets:
            return
        for target_name in self.visualize_bounding_box_targets:
            VisualizeController.visualize_target_bounding_box(
                target_name, simulator=SimulatorType.ISAACLAB
            ).unwrap()