import numpy as np

from gym import utils
from gym.envs.mujoco import mujoco_env
from gym.spaces import Box


class InvertedPendulumEnv(mujoco_env.MujocoEnv, utils.EzPickle):
    metadata = {
        "render_modes": [
            "human",
            "rgb_array",
            "depth_array",
            "single_rgb_array",
            "single_depth_array",
        ],
        "render_fps": 25,
    }

    def __init__(self, **kwargs):
        utils.EzPickle.__init__(self)
        observation_space = Box(low=-np.inf, high=np.inf, shape=(4,), dtype=np.float64)
        mujoco_env.MujocoEnv.__init__(
            self,
            "inverted_pendulum.xml",
            2,
            mujoco_bindings="mujoco_py",
            observation_space=observation_space,
            **kwargs
        )

    def step(self, a):
        reward = 1.0
        self.do_simulation(a, self.frame_skip)

        self.renderer.render_step()

        ob = self._get_obs()
        notdone = np.isfinite(ob).all() and (np.abs(ob[1]) <= 0.2)
        done = not notdone
        return ob, reward, done, {}

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

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

    def viewer_setup(self):
        v = self.viewer
        v.cam.trackbodyid = 0
        v.cam.distance = self.model.stat.extent