#!/usr/bin/env python3
"""Phase 3: Rule-based curriculum training - 1M steps with progressive opponents."""
import os, sys, subprocess, numpy as np, torch, gymnasium
from gymnasium.spaces import Box, Discrete

# ── 1. Download TIL env via snapshot_download ──
print("[1/5] Downloading TIL repo...")
from huggingface_hub import snapshot_download
snapshot_download(repo_id="e-rong/til-26-ae", repo_type="space", local_dir="/app/til-26-ae-repo")
PKG_ROOT = None
for root, dirs, files in os.walk("/app/til-26-ae-repo"):
    if "pyproject.toml" in files:
        PKG_ROOT = root
        break
if PKG_ROOT is None:
    raise RuntimeError("pyproject.toml not found")
subprocess.run(["pip", "install", "-e", "."], cwd=PKG_ROOT, check=True)
sys.path.insert(0, PKG_ROOT)

from til_environment.bomberman_env import Bomberman
from til_environment.config import default_config
from pettingzoo.utils.conversions import aec_to_parallel
from sb3_contrib import MaskablePPO
from sb3_contrib.common.wrappers import ActionMasker
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.monitor import Monitor
from huggingface_hub import HfApi, hf_hub_download

HUB_REPO = "E-Rong/til-26-ae-agent"
DATA_DIR = "/app/data"
os.makedirs(DATA_DIR, exist_ok=True)


def hub_push(local_path, repo_path):
    try:
        HfApi().upload_file(path_or_fileobj=local_path, path_in_repo=repo_path,
                          repo_id=HUB_REPO, repo_type="model")
        print(f"  -> pushed {repo_path}")
    except Exception as e:
        print(f"  -> push failed: {e}")


# ── Opponent Policies ──
def static_opponent(obs):
    """Never moves, never places bombs."""
    return 4  # STAY


def random_valid_opponent(obs):
    """Random valid action (Phase 1 style)."""
    mask = np.array(obs.get("action_mask", [1]*6), dtype=bool)
    valid = np.where(mask)[0]
    return int(np.random.choice(valid)) if len(valid) > 0 else 4


def simple_bomb_opponent(obs):
    """Moves randomly but places bombs when enemies are visible."""
    mask = np.array(obs.get("action_mask", [1]*6), dtype=bool)
    # Check if enemies visible in viewcone
    view = np.array(obs.get("agent_viewcone", np.zeros((7,5,25))))
    if view.shape[-1] >= 11:  # ENEMY_AGENT channel exists
        enemy_present = np.any(view[..., 10] > 0)  # ENEMY_AGENT channel
        if enemy_present and mask[5]:  # PLACE_BOMB is valid
            return 5
    valid = np.where(mask)[0]
    # Prefer movement over stay
    move_actions = [v for v in valid if v < 4]
    if move_actions:
        return int(np.random.choice(move_actions))
    return int(np.random.choice(valid)) if len(valid) > 0 else 4


def evasive_opponent(obs):
    """Tries to move away from bombs, random otherwise."""
    mask = np.array(obs.get("action_mask", [1]*6), dtype=bool)
    view = np.array(obs.get("agent_viewcone", np.zeros((7,5,25))))
    # If enemy bomb visible, try to move away
    if view.shape[-1] >= 20:
        enemy_bombs = view[..., 18]  # ENEMY_BOMB channel
        if np.any(enemy_bombs > 0):
            # Find safest direction - away from bomb
            bomb_y, bomb_x = np.where(enemy_bombs > 0)
            if len(bomb_y) > 0:
                # Just pick any valid movement action
                move_actions = [v for v in np.where(mask)[0] if v < 4]
                if move_actions:
                    return int(np.random.choice(move_actions))
    valid = np.where(mask)[0]
    return int(np.random.choice(valid)) if len(valid) > 0 else 4


CURRICULUM_STAGES = [
    ("static", static_opponent, 150000),
    ("random", random_valid_opponent, 200000),
    ("simple_bomb", simple_bomb_opponent, 250000),
    ("evasive", evasive_opponent, 200000),
    ("mixed", None, 200000),  # cycles through all
]


class CurriculumEnv(gymnasium.Env):
    """Single-agent env with curriculum opponents."""
    def __init__(self, opponent_fn=None, cfg=None):
        super().__init__()
        self.cfg = cfg or default_config()
        self.cfg.env.render_mode = None
        raw = Bomberman(self.cfg)
        self._parallel_env = aec_to_parallel(raw)
        self.agent_id = "agent_0"
        self._episode_count = 0
        self.action_space = Discrete(6)
        self._last_action_mask = None
        self._obs_size = None
        self._last_obs_dict = None
        self.opponent_fn = opponent_fn or random_valid_opponent
        self._compute_obs_space()

    def _compute_obs_space(self):
        cfg = self.cfg
        vl = int(cfg.dynamics.vision.behind) + int(cfg.dynamics.vision.ahead) + 1
        vw = int(cfg.dynamics.vision.left) + int(cfg.dynamics.vision.right) + 1
        av = vl * vw * 25
        br = int(cfg.entities.base.vision_radius)
        bs = 2 * br + 1
        bv = bs * bs * 25
        self._obs_size = av + bv + 11
        self.observation_space = Box(low=-np.inf, high=np.inf, shape=(self._obs_size,), dtype=np.float32)

    def reset(self, seed=None, options=None):
        self._episode_count += 1
        obs_dict, info_dict = self._parallel_env.reset(seed=self._episode_count, options=options)
        self._last_obs_dict = obs_dict
        self._last_action_mask = obs_dict[self.agent_id]["action_mask"].astype(bool)
        return self._flatten(obs_dict[self.agent_id]), {}

    def step(self, action):
        actions = {self.agent_id: action}
        for aid, obs in self._last_obs_dict.items():
            if aid != self.agent_id:
                actions[aid] = self.opponent_fn(obs)
        obs_dict, rewards, terminations, truncations, infos = self._parallel_env.step(actions)
        self._last_obs_dict = obs_dict
        if self.agent_id not in obs_dict:
            return np.zeros(self._obs_size, dtype=np.float32), 0.0, True, False, {}
        self._last_action_mask = obs_dict[self.agent_id]["action_mask"].astype(bool)
        obs = self._flatten(obs_dict[self.agent_id])
        r = float(rewards.get(self.agent_id, 0.0))
        done = terminations.get(self.agent_id, False) or truncations.get(self.agent_id, False)
        return obs, r, done, False, infos.get(self.agent_id, {})

    def action_masks(self):
        return self._last_action_mask

    def _flatten(self, od):
        return np.concatenate([
            od["agent_viewcone"].flatten(), od["base_viewcone"].flatten(),
            np.array([od["direction"]], dtype=np.float32),
            od["location"].flatten().astype(np.float32),
            od["base_location"].flatten().astype(np.float32),
            od["health"].flatten().astype(np.float32),
            np.array([od["frozen_ticks"]], dtype=np.float32),
            od["base_health"].flatten().astype(np.float32),
            od["team_resources"].flatten().astype(np.float32),
            np.array([od["team_bombs"]], dtype=np.float32),
            np.array([od["step"]], dtype=np.float32),
        ], dtype=np.float32)

    def close(self):
        self._parallel_env.close()


class CurriculumCallback(BaseCallback):
    """Advances curriculum stage based on win rate + pushes checkpoints."""
    def __init__(self, eval_freq=50000, save_freq=50000):
        super().__init__()
        self.eval_freq = eval_freq
        self.save_freq = save_freq
        self.stage_idx = 0
        self.stage_steps = 0
        self.wins_history = []
        self.eval_episodes = 100

    def _on_step(self) -> bool:
        if self.num_timesteps % self.save_freq == 0:
            path = os.path.join(DATA_DIR, f"phase3_ckpt_{self.num_timesteps}.zip")
            self.model.save(path)
            hub_push(path, f"phase3_ckpt_{self.num_timesteps}.zip")

        if self.num_timesteps % self.eval_freq == 0:
            self._evaluate_and_maybe_advance()
        return True

    def _evaluate_and_maybe_advance(self):
        stage_name, opp_fn, stage_limit = CURRICULUM_STAGES[self.stage_idx]
        print(f"\n--- Evaluating at stage {stage_name} (step {self.num_timesteps}) ---")

        # Run eval episodes
        env = CurriculumEnv(opponent_fn=opp_fn, cfg=default_config())
        env = ActionMasker(env, lambda e: e.action_masks())
        wins = 0; total_r = 0
        for ep in range(self.eval_episodes):
            obs, _ = env.reset(seed=ep + 100000 + self.num_timesteps)
            ep_r = 0; done = False
            while not done:
                action, _ = self.model.predict(obs, action_masks=env.action_masks(), deterministic=True)
                obs, r, done, _, _ = env.step(int(action))
                ep_r += r
            total_r += ep_r
            if ep_r > 10:
                wins += 1
        env.close()

        win_rate = wins / self.eval_episodes
        avg_r = total_r / self.eval_episodes
        print(f"  Win rate: {win_rate:.1%}, Avg reward: {avg_r:.1f}")
        self.wins_history.append((self.num_timesteps, stage_name, win_rate, avg_r))

        # Save eval results
        eval_file = f"/app/phase3_eval_{self.num_timesteps}.txt"
        with open(eval_file, "w") as f:
            f.write(f"Stage: {stage_name}\nStep: {self.num_timesteps}\nWinRate: {win_rate:.1%}\nAvgReward: {avg_r:.1f}\n")
        hub_push(eval_file, f"phase3_eval_{self.num_timesteps}.txt")

        # Advance curriculum if win rate > 55% and we've spent enough steps
        if win_rate > 0.55 and self.stage_idx < len(CURRICULUM_STAGES) - 1:
            self.stage_idx += 1
            new_stage = CURRICULUM_STAGES[self.stage_idx][0]
            print(f"  >>> ADVANCING to stage: {new_stage} <<<")


def main():
    print("=" * 60)
    print("PHASE 3: Rule-Based Curriculum")
    print("=" * 60)

    # Download latest checkpoint (phase2_final or best available)
    latest = None
    for ckpt in ["phase2_final.zip", "phase2_ckpt_600352.zip", "phase1_final.zip"]:
        try:
            latest = hf_hub_download(repo_id=HUB_REPO, filename=ckpt, repo_type="model", local_dir=DATA_DIR)
            print(f"Downloaded checkpoint: {ckpt}")
            break
        except Exception:
            pass
    if latest is None:
        raise RuntimeError("No checkpoint found!")

    # Start with first curriculum stage
    stage_name, opp_fn, _ = CURRICULUM_STAGES[0]
    print(f"Starting curriculum stage: {stage_name}")

    cfg = default_config()
    cfg.env.render_mode = None
    base = CurriculumEnv(opponent_fn=opp_fn, cfg=cfg)
    env = ActionMasker(base, lambda e: e.action_masks())
    env = Monitor(env)

    model = MaskablePPO.load(latest, env=env)
    start_ts = model.num_timesteps
    print(f"Loaded model at timestep {start_ts}")

    cb = CurriculumCallback(eval_freq=50000, save_freq=50000)
    model.learn(total_timesteps=1000000, callback=cb, progress_bar=False, reset_num_timesteps=False)

    # Save final
    final = os.path.join(DATA_DIR, "phase3_final.zip")
    model.save(final)
    hub_push(final, "phase3_final.zip")

    # Final eval
    print("\n=== FINAL EVALUATION ===")
    raw = Bomberman(default_config())
    env = aec_to_parallel(raw)
    wins = 0; total_r = 0
    for ep in range(200):
        obs, _ = env.reset(seed=ep + 200000)
        ep_r = 0; done = False
        while not done:
            if "agent_0" not in obs:
                break
            ao = obs["agent_0"]
            mask = np.array(ao.get("action_mask", [1]*6), dtype=bool)
            vec = np.concatenate([
                np.array(ao["agent_viewcone"], np.float32).flatten(),
                np.array(ao["base_viewcone"], np.float32).flatten(),
                np.array([ao["direction"]], np.float32),
                np.array(ao["location"], np.float32).flatten(),
                np.array(ao["base_location"], np.float32).flatten(),
                np.array(ao["health"], np.float32).flatten(),
                np.array([ao["frozen_ticks"]], np.float32),
                np.array(ao["base_health"], np.float32).flatten(),
                np.array(ao["team_resources"], np.float32).flatten(),
                np.array([ao["team_bombs"]], np.float32),
                np.array([ao["step"]], np.float32),
            ], dtype=np.float32)
            action, _ = model.predict(vec, action_masks=mask, deterministic=True)
            acts = {"agent_0": int(action)}
            for aid, o in obs.items():
                if aid != "agent_0":
                    acts[aid] = random_valid_opponent(o)
            obs, rewards, terminations, truncations, _ = env.step(acts)
            ep_r += rewards.get("agent_0", 0)
            done = terminations.get("agent_0", False) or truncations.get("agent_0", False) or "agent_0" not in obs
        total_r += ep_r
        if ep_r > 10:
            wins += 1
    env.close()

    results = (
        f"=== Phase 3 Final Evaluation ===\n"
        f"Episodes: 200\n"
        f"Win Rate: {wins/200:.1%}\n"
        f"Avg Reward: {total_r/200:.1f}\n"
    )
    print(results)
    with open("/app/phase3_final_eval.txt", "w") as f:
        f.write(results)
    hub_push("/app/phase3_final_eval.txt", "phase3_final_eval.txt")
    print("\n✅ PHASE 3 COMPLETE!")


if __name__ == "__main__":
    main()