from gym.envs.mujoco import mujoco_env
from gym import utils
import numpy as np


class HumanoidStandupEnv(mujoco_env.MujocoEnv, utils.EzPickle):
    def __init__(self):
        mujoco_env.MujocoEnv.__init__(self, "humanoidstandup.xml", 5)
        utils.EzPickle.__init__(self)

    def _get_obs(self):
        data = self.sim.data
        return np.concatenate(
            [
                data.qpos.flat[2:],
                data.qvel.flat
            ]
        )

    def step(self, a):
        self.do_simulation(a, self.frame_skip)
        pos_after = self.sim.data.qpos[2]
        data = self.sim.data
        uph_cost = (pos_after - 0) / self.model.opt.timestep

        quad_ctrl_cost = 0.1 * np.square(data.ctrl).sum()
        reward = uph_cost - quad_ctrl_cost + 1

        done = bool(False)
        return (
            self._get_obs(),
            reward,
            done,
            dict(
                reward_linup=uph_cost,
                reward_quadctrl=-quad_ctrl_cost
            ),
        )

    def reset_model(self):
        c = 0.01
        self.set_state(
            self.init_qpos + self.np_random.uniform(low=-c, high=c, size=self.model.nq),
            self.init_qvel
            + self.np_random.uniform(
                low=-c,
                high=c,
                size=self.model.nv,
            ),
        )
        return self._get_obs()

    def viewer_setup(self):
        self.viewer.cam.trackbodyid = 1
        self.viewer.cam.distance = self.model.stat.extent * 1.0
        self.viewer.cam.lookat[2] = 0.8925
        self.viewer.cam.elevation = -20
