import numpy as np

from metaworld.policies.action import Action
from metaworld.policies.policy import Policy, assert_fully_parsed, move


class SawyerStickPullV2Policy(Policy):

    @staticmethod
    @assert_fully_parsed
    def _parse_obs(obs):
        return {
            'hand_pos': obs[:3],
            'unused_1': obs[3],
            'stick_pos': obs[4:7],
            'unused_2': obs[7:11],
            'obj_pos': obs[11:14],
            'unused_3': obs[14:-3],
            'goal_pos': obs[-3:],
        }

    def get_action(self, obs):
        o_d = self._parse_obs(obs)

        action = Action({
            'delta_pos': np.arange(3),
            'grab_pow': 3
        })

        action['delta_pos'] = move(o_d['hand_pos'], to_xyz=self._desired_xyz(o_d), p=25.)
        action['grab_pow'] = self._grab_pow(o_d)

        return action.array

    @staticmethod
    def _desired_xyz(o_d):
        hand_pos = o_d['hand_pos']
        stick_pos = o_d['stick_pos'] + np.array([-.015, .0, .03])
        thermos_pos = o_d['obj_pos'] + np.array([-.015, .0, .03])
        goal_pos = o_d['goal_pos'] + np.array([-.05, .0, .0])

        if abs(stick_pos[0] - thermos_pos[0]) > 0.04:
            if np.linalg.norm(hand_pos[:2] - stick_pos[:2]) > 0.1: # modification: change the tolerance
                print('mode 1')
                return stick_pos + np.array([0., 0., 0.1])
            elif abs(hand_pos[2] - stick_pos[2]) > 0.02:
                print('mode 2')
                return stick_pos
            elif abs(stick_pos[1] - thermos_pos[1]) > 0.02:
                print('mode 3')
                return np.array([stick_pos[0], thermos_pos[1], stick_pos[2]])
            elif abs(stick_pos[2] - thermos_pos[2]) > 0.02:
                print('mode 4')
                return np.array([stick_pos[0], *thermos_pos[1:]])
            else:
                print('mode 5')
                return thermos_pos
        else:
            print('mode 6')
            # return o_d['goal_pos'] + np.array([-.05, .05, .1])
            target_pos = np.array([*o_d['goal_pos'][:2], thermos_pos[2]])
            target_pos += np.array([0.0, -0.05,0.0]) 
            return target_pos # modification: change the target pos

    @staticmethod
    def _grab_pow(o_d):
        hand_pos = o_d['hand_pos']
        stick_pos = o_d['stick_pos'] + np.array([-.015, .0, .03])

        if np.linalg.norm(hand_pos[:2] - stick_pos[:2]) > 0.04 or abs(hand_pos[2] - stick_pos[2]) > 0.1:
            return -1.0
        else:
            return +0.7
