"""Train a PPO policy on LanderCliEnv.

Usage:
    python train.py --steps 100000                 # short smoke run
    python train.py --steps 2000000 --n-envs 8     # full training
"""
from __future__ import annotations

import argparse
from pathlib import Path

from stable_baselines3 import PPO
from stable_baselines3.common.callbacks import CheckpointCallback
from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv

# sys.path shim so this script can be run from any CWD.
import sys
sys.path.insert(0, str(Path(__file__).resolve().parent))

from lander_cli_env import LanderCliEnv


REPO_ROOT = Path(__file__).resolve().parents[1]
CLI_BINARY = REPO_ROOT / "publish" / "GameCli" / "GameCli"


def make_env(seed: int, max_episode_steps: int):
    """Return a thunk that SubprocVecEnv can call to construct one env."""
    def _fn():
        env = LanderCliEnv(
            cli_binary=str(CLI_BINARY),
            max_episode_steps=max_episode_steps,
        )
        env.reset(seed=seed)
        return env
    return _fn


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--steps", type=int, default=2_000_000,
                        help="total env steps to train for")
    parser.add_argument("--n-envs", type=int, default=8,
                        help="parallel envs; each spawns one GameCli subprocess")
    parser.add_argument("--max-episode-steps", type=int, default=1000)
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--checkpoint-dir", type=Path,
                        default=REPO_ROOT / "checkpoints")
    parser.add_argument("--tb-dir", type=Path,
                        default=REPO_ROOT / "tensorboard")
    parser.add_argument("--use-dummy", action="store_true",
                        help="run envs in-process (slower, easier to debug)")
    args = parser.parse_args()

    if not CLI_BINARY.exists():
        raise SystemExit(
            f"CLI binary not found at {CLI_BINARY}. Run: "
            f"dotnet publish GameCli/GameCli.csproj -c Release -o publish/GameCli"
        )

    args.checkpoint_dir.mkdir(parents=True, exist_ok=True)
    args.tb_dir.mkdir(parents=True, exist_ok=True)

    env_fns = [
        make_env(seed=args.seed + i, max_episode_steps=args.max_episode_steps)
        for i in range(args.n_envs)
    ]
    vec_env_cls = DummyVecEnv if args.use_dummy else SubprocVecEnv
    vec_env = vec_env_cls(env_fns)

    model = PPO(
        "MlpPolicy",
        vec_env,
        verbose=1,
        seed=args.seed,
        tensorboard_log=str(args.tb_dir),
        policy_kwargs=dict(net_arch=[64, 64]),
    )

    checkpoint_cb = CheckpointCallback(
        save_freq=max(args.steps // 10, 1) // args.n_envs,
        save_path=str(args.checkpoint_dir),
        name_prefix="ppo_lander",
    )

    try:
        model.learn(total_timesteps=args.steps, callback=checkpoint_cb)
        final_path = args.checkpoint_dir / "ppo_lander_final.zip"
        model.save(str(final_path))
        print(f"saved final model to {final_path}")
    finally:
        vec_env.close()


if __name__ == "__main__":
    main()