import numpy as np

from forks.rlkit.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 = 0.5 * 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=-0.1, high=0.1
        )
        qvel = self.init_qvel + self.np_random.randn(self.model.nv) * 0.1
        self.set_state(qpos, qvel)
        return self._get_obs()

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