import numpy as np

from bgp.rlkit.envs.mujoco_env import MujocoEnv


class AntEnv(MujocoEnv):
    def __init__(self, use_low_gear_ratio=True):
        self.init_serialization(locals())
        if use_low_gear_ratio:
            xml_path = 'low_gear_ratio_ant.xml'
        else:
            xml_path = 'normal_gear_ratio_ant.xml'
        super().__init__(
            xml_path,
            frame_skip=5,
            automatically_set_obs_and_action_space=True,
        )

    def step(self, a):
        torso_xyz_before = self.get_body_com("torso")
        self.do_simulation(a, self.frame_skip)
        torso_xyz_after = self.get_body_com("torso")
        torso_velocity = torso_xyz_after - torso_xyz_before
        forward_reward = torso_velocity[0]/self.dt
        ctrl_cost = .5 * np.square(a).sum()
        contact_cost = 0.5 * 1e-3 * np.sum(
            np.square(np.clip(self.sim.data.cfrc_ext, -1, 1)))
        survive_reward = 1.0
        reward = forward_reward - ctrl_cost - contact_cost + survive_reward
        state = self.state_vector()
        notdone = np.isfinite(state).all() \
                  and state[2] >= 0.2 and state[2] <= 1.0
        done = not notdone
        ob = self._get_obs()
        return ob, reward, done, dict(
            reward_forward=forward_reward,
            reward_ctrl=-ctrl_cost,
            reward_contact=-contact_cost,
            reward_survive=survive_reward,
            torso_velocity=torso_velocity,
        )

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

    def reset_model(self):
        qpos = self.init_qpos + self.np_random.uniform(size=self.model.nq, low=-.1, high=.1)
        qvel = self.init_qvel + self.np_random.randn(self.model.nv) * .1
        self.set_state(qpos, qvel)
        return self._get_obs()

    def viewer_setup(self):
        self.viewer.cam.distance = self.model.stat.extent * 0.5
