"""Solution for q_learning.py — remove before publishing to students."""

import random
import babysnake
from retro.input import ProgrammaticInput
from retro.views.headless import HeadlessView

ACTIONS = ["KEY_RIGHT", "KEY_DOWN", "KEY_LEFT", "KEY_UP"]


class BabySnakeEnv:
    def reset(self):
        self._inp = ProgrammaticInput()
        self.game = babysnake.create_game()
        self.game.input_source = self._inp
        self.game.view = HeadlessView()
        self.game.start()
        self._prev_reward = 0.0
        return self._get_state()

    def step(self, action):
        self._inp.press(action)
        self.game.step()
        next_state = self._get_state()
        reward = self.game.state['reward'] - self._prev_reward
        self._prev_reward = self.game.state['reward']
        done = not self.game.playing
        return next_state, reward, done

    def _get_state(self):
        s = self.game.state
        return (int(s['agent_x']), int(s['agent_y']),
                int(s['food_x']),  int(s['food_y']))


def choose_action(q_table, state, epsilon):
    if random.random() < epsilon:
        return random.choice(ACTIONS)
    q_values = [q_table.get((state, a), 0.0) for a in ACTIONS]
    return ACTIONS[q_values.index(max(q_values))]


def update_q(q_table, state, action, reward, next_state, alpha, gamma):
    old_q = q_table.get((state, action), 0.0)
    next_q_values = [q_table.get((next_state, a), 0.0) for a in ACTIONS]
    best_next_q = max(next_q_values)
    new_q = old_q + alpha * (reward + gamma * best_next_q - old_q)
    q_table[(state, action)] = new_q


def train(
    episodes=1000,
    alpha=0.1,
    gamma=0.95,
    epsilon=1.0,
    epsilon_decay=0.995,
    epsilon_min=0.05,
):
    q_table = {}
    env = BabySnakeEnv()

    for episode in range(episodes):
        state = env.reset()
        total_reward = 0.0

        while env.game.playing:
            action = choose_action(q_table, state, epsilon)
            next_state, reward, done = env.step(action)
            update_q(q_table, state, action, reward, next_state, alpha, gamma)
            state = next_state
            total_reward += reward

        epsilon = max(epsilon_min, epsilon * epsilon_decay)

        if (episode + 1) % 100 == 0:
            print(
                f"Episode {episode + 1:5d}  "
                f"reward={total_reward:6.1f}  "
                f"score={env.game.state['score']}  "
                f"epsilon={epsilon:.3f}  "
                f"q_entries={len(q_table)}"
            )

    return q_table


def watch(q_table=None):
    if q_table is None:
        print("Training first...")
        q_table = train()

    _inp = ProgrammaticInput()

    class PolicyInput:
        def collect(self):
            s = game.state
            state = (int(s['agent_x']), int(s['agent_y']),
                     int(s['food_x']),  int(s['food_y']))
            q_values = [q_table.get((state, a), 0.0) for a in ACTIONS]
            best = ACTIONS[q_values.index(max(q_values))]
            _inp.press(best)
            return _inp.collect()

    game = babysnake.create_game()
    game.play(input_source=PolicyInput())


if __name__ == '__main__':
    print("Training Q-learning agent on BabySnake...")
    q_table = train()
    print(f"\nDone. Q-table has {len(q_table)} entries.")
    print("\nWatching trained agent (press Enter or Escape to quit)...")
    watch(q_table)