"""
Load a saved run and evaluate the policy at every n_sheep from 1..N.
Tells you exactly where the curriculum stopped working.

Usage:
    python eval_per_sheep.py --run-dir runs/ppo_v3
    python eval_per_sheep.py --run-dir runs/ppo_v3 --max-sheep 10 --episodes 20
    python eval_per_sheep.py --model runs/ppo_v3/final_model.zip \
                              --vecnorm runs/ppo_v3/vecnorm.pkl
"""
import argparse
import os
from copy import deepcopy

import numpy as np
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize

from herding_env import HerdingEnv
from train import _classify, COMPACT_RADIUS


def evaluate(model, vn_template, n_sheep, n_episodes, max_steps):
    raw = DummyVecEnv([lambda: HerdingEnv(n_sheep=n_sheep, max_steps=max_steps)])
    vn  = VecNormalize(raw, norm_obs=True, norm_reward=False, training=False)
    vn.obs_rms = deepcopy(vn_template.obs_rms)
    vn.ret_rms = deepcopy(vn_template.ret_rms)

    failure = {}
    successes = 0
    act_mags, min_radii, min_dog_com, min_pen = [], [], [], []

    for _ in range(n_episodes):
        obs = vn.reset()
        done = False
        ep_radius, ep_com_dist, ep_dog_com, ep_act = [], [], [], []
        while not done:
            action, _ = model.predict(obs, deterministic=True)
            obs, _, dones, infos = vn.step(action)
            done = dones[0]
            inner = vn.envs[0]
            com, radius, _ = inner._flock_stats()
            ep_radius.append(radius)
            ep_com_dist.append(float(np.linalg.norm(com - inner.PEN_CENTER)))
            ep_dog_com.append(float(np.linalg.norm(inner.dog_pos - com)))
            ep_act.append(float(np.linalg.norm(action[0])))
        npen = infos[0].get("n_penned", 0)
        success = npen == n_sheep
        successes += int(success)
        mode = _classify(ep_radius, ep_com_dist, npen, n_sheep, success)
        failure[mode] = failure.get(mode, 0) + 1
        act_mags.extend(ep_act)
        min_radii.append(min(ep_radius))
        min_dog_com.append(min(ep_dog_com))
        min_pen.append(min(ep_com_dist))
    vn.close()

    return {
        "n_sheep": n_sheep,
        "success_rate": successes / n_episodes,
        "failure": failure,
        "mean_action": float(np.mean(act_mags)),
        "mean_min_radius": float(np.mean(min_radii)),
        "mean_min_dog_com": float(np.mean(min_dog_com)),
        "mean_min_pen": float(np.mean(min_pen)),
    }


def main():
    p = argparse.ArgumentParser()
    p.add_argument("--run-dir", type=str, default=None)
    p.add_argument("--model", type=str, default=None)
    p.add_argument("--vecnorm", type=str, default=None)
    p.add_argument("--max-sheep", type=int, default=10)
    p.add_argument("--episodes", type=int, default=10)
    p.add_argument("--max-steps", type=int, default=2000)
    args = p.parse_args()

    if args.run_dir:
        model_path = os.path.join(args.run_dir, "final_model.zip")
        if not os.path.exists(model_path):
            model_path = os.path.join(args.run_dir, "best_model", "best_model.zip")
        vn_path = os.path.join(args.run_dir, "vecnorm.pkl")
    else:
        model_path = args.model
        vn_path    = args.vecnorm

    print(f"Loading model:   {model_path}")
    print(f"Loading vecnorm: {vn_path}\n")
    model = PPO.load(model_path, device="cpu")
    raw = DummyVecEnv([lambda: HerdingEnv(n_sheep=1, max_steps=args.max_steps)])
    vn_template = VecNormalize.load(vn_path, raw)

    print(f"{'n_sheep':>7} {'success':>8} {'act':>6} {'min_r':>7} "
          f"{'dog→com':>8} {'com→pen':>8}  failure breakdown")
    print("-" * 90)
    for n in range(1, args.max_sheep + 1):
        r = evaluate(model, vn_template, n, args.episodes, args.max_steps)
        fb = " ".join(f"{m}={c}" for m, c in
                      sorted(r["failure"].items(), key=lambda x: -x[1]))
        print(f"{n:>7d} {r['success_rate']*100:>6.0f}% "
              f"{r['mean_action']:>6.2f} "
              f"{r['mean_min_radius']:>6.2f}m "
              f"{r['mean_min_dog_com']:>7.2f}m "
              f"{r['mean_min_pen']:>7.2f}m  {fb}")


if __name__ == "__main__":
    main()