import numpy as np
from pyrep.objects.shape import Shape
from pyrep.objects.proximity_sensor import ProximitySensor

from env import setup_environment, shutdown_environment

from skill_code import *

from video import init_video_writers, recording_step, recording_get_observation

from object_positions import get_object_positions

def run_skeleton_task():
    '''Generic skeleton for running any task in your simulation.'''
    print("===== Starting Skeleton Task =====")
    
    # === Environment Setup ===
    env, task = setup_environment()
    try:
        # Reset the task to its initial state
        descriptions, obs = task.reset()

        # (Optional) Initialize video writers for capturing your simulation
        init_video_writers(obs)

        # Wrap the task steps for recording (if needed)
        original_step = task.step
        task.step = recording_step(original_step)
        original_get_obs = task.get_observation
        task.get_observation = recording_get_observation(original_get_obs)

        # === Retrieve Object Positions ===
        positions = get_object_positions()

        # Define the object to pick and the trash can
        obj_name = 'rubbish'
        trash_can_name = 'bin'
        obj_pos = positions.get(obj_name)
        if obj_pos is None:
            raise RuntimeError(f"Could not find position for object '{obj_name}'")
        trash_can_pos = positions.get(trash_can_name)
        if trash_can_pos is None:
            raise RuntimeError(f"Could not find position for trash can '{trash_can_name}'")

        # Step 1: pick up the rubbish
        print(f"[Task] Picking up '{obj_name}' at: {obj_pos}")
        obs, reward, done = pick(
                env,
                task,
                target_pos=obj_pos,
                approach_distance=0.15,
                max_steps=100,
                threshold=0.01,
                approach_axis='z',
                timeout=10.0
            )
        if done:
            print(f"[Task] Task ended after pick. Reward: {reward}")
            return

        # Step 2: place the rubbish into the trash can
        print(f"[Task] Placing '{obj_name}' into trash can at: {trash_can_pos}")
        obs, reward, done = place(
                env,
                task,
                target_pos=trash_can_pos,
                approach_distance=0.15,
                max_steps=100,
                threshold=0.01,
                approach_axis='z',
                timeout=10.0
            )
        if done:
            print(f"[Task] Task completed after placing '{obj_name}'. Reward: {reward}")
            return

        print("[Task] Task completed successfully. Reward:", reward)
    finally:
        # Always ensure the environment is properly shutdown
        shutdown_environment(env)

    print("===== End of Skeleton Task =====")


if __name__ == "__main__":
    run_skeleton_task()