import numpy as np
from gym.spaces import Box

from metaworld.envs.asset_path_utils import full_v2_path_for
from metaworld.envs.mujoco.sawyer_xyz.sawyer_xyz_env import SawyerXYZEnv, _assert_task_is_set


class SawyerPlateSlideBackEnvV2(SawyerXYZEnv):

    def __init__(self):

        goal_low = (-0.1, 0.6, 0.015)
        goal_high = (0.1, 0.6, 0.015)
        hand_low = (-0.5, 0.40, 0.05)
        hand_high = (0.5, 1, 0.5)
        obj_low = (0., 0.85, 0.)
        obj_high = (0., 0.85, 0.)

        super().__init__(
            self.model_name,
            hand_low=hand_low,
            hand_high=hand_high,
        )

        self.init_config = {
            'obj_init_angle': 0.3,
            'obj_init_pos': np.array([0., 0.85, 0.], dtype=np.float32),
            'hand_init_pos': np.array((0, 0.6, 0.2), dtype=np.float32),
        }
        self.goal = np.array([0., 0.6, 0.015])
        self.obj_init_pos = self.init_config['obj_init_pos']
        self.obj_init_angle = self.init_config['obj_init_angle']
        self.hand_init_pos = self.init_config['hand_init_pos']

        self.max_path_length = 150

        self._random_reset_space = Box(
            np.hstack((obj_low, goal_low)),
            np.hstack((obj_high, goal_high)),
        )
        self.goal_space = Box(np.array(goal_low), np.array(goal_high))

    @property
    def model_name(self):
        return full_v2_path_for('sawyer_xyz/sawyer_plate_slide.xml')

    @_assert_task_is_set
    def step(self, action):
        ob = super().step(action)
        reward, reachDist, pullDist = self.compute_reward(action, ob)
        self.curr_path_length +=1

        info = {
            'reachDist': reachDist,
            'goalDist': pullDist,
            'epRew': reward,
            'pickRew': None,
            'success': float(pullDist <= 0.07)
        }

        return ob, reward, False, info

    def _get_pos_objects(self):
        return self.data.get_geom_xpos('puck')

    def _set_obj_xyz(self, pos):
        qpos = self.data.qpos.flat.copy()
        qvel = self.data.qvel.flat.copy()
        qpos[9:11] = pos
        self.set_state(qpos, qvel)

    def reset_model(self):
        self._reset_hand()

        self.obj_init_pos = self.init_config['obj_init_pos']
        self._target_pos = self.goal.copy()

        if self.random_init:
            rand_vec = self._get_state_rand_vec()
            self.obj_init_pos = rand_vec[:3]
            self._target_pos = rand_vec[3:]

        self.sim.model.body_pos[self.model.body_name2id('puck_goal')] = self.obj_init_pos
        self._set_obj_xyz(np.array([0, 0.15]))

        self.objHeight = self.data.get_geom_xpos('puck')[2]
        self.maxDist = np.linalg.norm(self.data.get_geom_xpos('puck')[:-1] - self._target_pos[:-1])
        self.target_reward = 1000*self.maxDist + 1000*2

        return self._get_obs()

    def _reset_hand(self):
        super()._reset_hand()

    def compute_reward(self, actions, obs):
        del actions

        objPos = obs[3:6]

        rightFinger, leftFinger = self._get_site_pos('rightEndEffector'), self._get_site_pos('leftEndEffector')
        fingerCOM  =  (rightFinger + leftFinger)/2

        pullGoal = self._target_pos

        reachDist = np.linalg.norm(objPos - fingerCOM)

        pullDist = np.linalg.norm(objPos[:-1] - pullGoal[:-1])

        c1 = 1000
        c2 = 0.01
        c3 = 0.001
        if reachDist < 0.05:
            pullRew = 1000*(self.maxDist - pullDist) + c1*(np.exp(-(pullDist**2)/c2) + np.exp(-(pullDist**2)/c3))
            pullRew = max(pullRew, 0)
        else:
            pullRew = 0

        reward = -reachDist + pullRew

        return [reward, reachDist, pullDist]
