"""Partially observed variant of the CartPole gym environment.

https://github.com/openai/gym/blob/master/gym/envs/classic_control/cartpole.py

We delete the velocity component of the state, so that it can only be solved
by a LSTM policy."""

import argparse
import math
import gym
from gym import spaces
from gym.utils import seeding
import numpy as np

parser = argparse.ArgumentParser()
parser.add_argument("--stop", type=int, default=200)
parser.add_argument("--use-prev-action-reward", action="store_true")
parser.add_argument("--run", type=str, default="PPO")


class CartPoleStatelessEnv(gym.Env):
    metadata = {
        'render.modes': ['human', 'rgb_array'],
        'video.frames_per_second': 60
    }

    def __init__(self):
        self.gravity = 9.8
        self.masscart = 1.0
        self.masspole = 0.1
        self.total_mass = (self.masspole + self.masscart)
        self.length = 0.5  # actually half the pole's length
        self.polemass_length = (self.masspole * self.length)
        self.force_mag = 10.0
        self.tau = 0.02  # seconds between state updates

        # Angle at which to fail the episode
        self.theta_threshold_radians = 12 * 2 * math.pi / 360
        self.x_threshold = 2.4

        high = np.array([
            self.x_threshold * 2,
            self.theta_threshold_radians * 2,
        ])

        self.action_space = spaces.Discrete(2)
        self.observation_space = spaces.Box(-high, high)

        self.seed()
        self.viewer = None
        self.state = None

        self.steps_beyond_done = None

    def seed(self, seed=None):
        self.np_random, seed = seeding.np_random(seed)
        return [seed]

    def step(self, action):
        assert self.action_space.contains(
            action), "%r (%s) invalid" % (action, type(action))
        state = self.state
        x, x_dot, theta, theta_dot = state
        force = self.force_mag if action == 1 else -self.force_mag
        costheta = math.cos(theta)
        sintheta = math.sin(theta)
        temp = (force + self.polemass_length * theta_dot * theta_dot * sintheta
                ) / self.total_mass
        thetaacc = (self.gravity * sintheta - costheta * temp) / (
            self.length *
            (4.0 / 3.0 - self.masspole * costheta * costheta / self.total_mass)
        )
        xacc = (temp -
                self.polemass_length * thetaacc * costheta / self.total_mass)
        x = x + self.tau * x_dot
        x_dot = x_dot + self.tau * xacc
        theta = theta + self.tau * theta_dot
        theta_dot = theta_dot + self.tau * thetaacc
        self.state = (x, x_dot, theta, theta_dot)
        done = (x < -self.x_threshold or x > self.x_threshold
                or theta < -self.theta_threshold_radians
                or theta > self.theta_threshold_radians)
        done = bool(done)

        if not done:
            reward = 1.0
        elif self.steps_beyond_done is None:
            # Pole just fell!
            self.steps_beyond_done = 0
            reward = 1.0
        else:
            self.steps_beyond_done += 1
            reward = 0.0

        rv = np.r_[self.state[0], self.state[2]]
        return rv, reward, done, {}

    def reset(self):
        self.state = self.np_random.uniform(low=-0.05, high=0.05, size=(4, ))
        self.steps_beyond_done = None

        rv = np.r_[self.state[0], self.state[2]]
        return rv

    def render(self, mode='human'):
        screen_width = 600
        screen_height = 400

        world_width = self.x_threshold * 2
        scale = screen_width / world_width
        carty = 100  # TOP OF CART
        polewidth = 10.0
        polelen = scale * 1.0
        cartwidth = 50.0
        cartheight = 30.0

        if self.viewer is None:
            from gym.envs.classic_control import rendering
            self.viewer = rendering.Viewer(screen_width, screen_height)
            l, r, t, b = (-cartwidth / 2, cartwidth / 2, cartheight / 2,
                          -cartheight / 2)
            axleoffset = cartheight / 4.0
            cart = rendering.FilledPolygon([(l, b), (l, t), (r, t), (r, b)])
            self.carttrans = rendering.Transform()
            cart.add_attr(self.carttrans)
            self.viewer.add_geom(cart)
            l, r, t, b = (-polewidth / 2, polewidth / 2,
                          polelen - polewidth / 2, -polewidth / 2)
            pole = rendering.FilledPolygon([(l, b), (l, t), (r, t), (r, b)])
            pole.set_color(.8, .6, .4)
            self.poletrans = rendering.Transform(translation=(0, axleoffset))
            pole.add_attr(self.poletrans)
            pole.add_attr(self.carttrans)
            self.viewer.add_geom(pole)
            self.axle = rendering.make_circle(polewidth / 2)
            self.axle.add_attr(self.poletrans)
            self.axle.add_attr(self.carttrans)
            self.axle.set_color(.5, .5, .8)
            self.viewer.add_geom(self.axle)
            self.track = rendering.Line((0, carty), (screen_width, carty))
            self.track.set_color(0, 0, 0)
            self.viewer.add_geom(self.track)

        if self.state is None:
            return None

        x = self.state
        cartx = x[0] * scale + screen_width / 2.0  # MIDDLE OF CART
        self.carttrans.set_translation(cartx, carty)
        self.poletrans.set_rotation(-x[2])

        return self.viewer.render(return_rgb_array=mode == 'rgb_array')

    def close(self):
        if self.viewer:
            self.viewer.close()


if __name__ == "__main__":
    import ray
    from ray import tune

    args = parser.parse_args()

    tune.register_env("cartpole_stateless", lambda _: CartPoleStatelessEnv())

    ray.init()

    configs = {
        "PPO": {
            "num_sgd_iter": 5,
        },
        "IMPALA": {
            "num_workers": 2,
            "num_gpus": 0,
            "vf_loss_coeff": 0.01,
        },
    }

    tune.run_experiments({
        "test": {
            "env": "cartpole_stateless",
            "run": args.run,
            "stop": {
                "episode_reward_mean": args.stop
            },
            "config": dict(
                configs[args.run], **{
                    "model": {
                        "use_lstm": True,
                        "lstm_use_prev_action_reward": args.
                        use_prev_action_reward,
                    },
                }),
        }
    })
