import pickle

from joysim.annotations.config_class import configclass, field
from joysim.annotations.stereotype import stereotype
from joysim.controllers.motion_plan_controller import MotionPlanController
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
import numpy as np
import requests
import time

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

    robot_name: str = field(default="None", required=True, comment="The name of the robot")
    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.sensor_names = config.sensor_names
        self.server_url = config.server_url
        self.prompt = config.prompt

    def reset(self) -> None:
        self.current_ee_position_state = None
        self.current_ee_rot6d_state = None
        self.current_gripper_state = None
        self.current_chunk_id = 0
        self.current_chunk_result = None
        self.run_trunk_size = self.config.run_trunk_size

    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_base"]
        ee_position, ee_rot6d = ee_pose_base["position"],ee_pose_base["rot6d"]
        gripper = 0.0 if robot_obs["gripper_state"]["opened"] else 1.0
        state = np.concatenate([ee_position,ee_rot6d,np.array([gripper])])
        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_state = 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._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 postprocess_action(self, action: dict) -> BenchmarkAction:
        benchmark_action = BenchmarkAction()

        # 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)
        Log.debug(f"trunck_id: {self.current_chunk_id}, curr_state_ee_pose: {curr_state_ee_pose}")
        curr_action_ee_pose = curr_state_ee_pose * delta_ee_pose # action2base = state2base * action2state
        ik_result = MotionPlanController.solve_ik(
            robot_name=self.robot_name,
            base_frame_ee_pose=curr_action_ee_pose,
        ).unwrap()
        if not ik_result["success"]:
            Log.error(f"IK failed. Ignore this action.")
            return benchmark_action
        
        joint_names = ik_result["result"]["plannable_joint_names"]
        joint_positions = ik_result["result"]["plannable_joint_positions"][0]
        benchmark_action.add_robot_action(
            RobotAction(
                control_mode=ControlMode.POSITION,
                robot_name=self.robot_name,
                joint_names=joint_names,
                joint_positions=joint_positions
            )
        )
        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