"""Re-render and re-score submitted scenes from the episode tables: an independent check of the recorded scores.

  uv run python scripts/rescore.py --setting medium_effort
  uv run python scripts/rescore.py --episode gpt-6-astra__nerf_lego__sculpt__eval

The stored Gaussians are rounded to 3 decimals, as the environment logs them, so a re-score can differ from the
recorded score by a little (medium effort: mean |difference| 0.02 points, largest 0.22).
"""
import argparse
import json
from pathlib import Path

import numpy as np
import pyarrow.parquet as pq
import torch

from splatbench.render import empty_gaussians
from splatbench.scene import Scene
from splatbench.score import evaluate, splat_score

ap = argparse.ArgumentParser()
ap.add_argument("--data", default="data")
ap.add_argument("--setting", default="medium_effort",
                choices=["medium_effort", "high_effort", "max_effort", "early_submit_refused", "one_shot"])
ap.add_argument("--episode", default=None)
ap.add_argument("--limit", type=int, default=None)
ap.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
a = ap.parse_args()

rows = pq.read_table(Path(a.data) / "episodes" / f"{a.setting}.parquet",
                     columns=["episode_id", "task_id", "score", "final_gaussians"]).to_pylist()
if a.episode:
    rows = [r for r in rows if r["episode_id"] == a.episode]
rows = rows[:a.limit] if a.limit else rows
scenes, diffs = {}, []
for r in rows:
    scene = r["task_id"].split("__")[0]
    if scene not in scenes:
        sd = Path(a.data) / "bench" / "scenes" / scene
        scenes[scene] = (Scene.load(sd), json.loads((sd / "norm.json").read_text()))
    sc, norm = scenes[scene]
    G = r["final_gaussians"]
    f = lambda k: torch.tensor([g[k] for g in G], dtype=torch.float32, device=a.device)
    gs = dict(means=f("pos"), scales=f("scale"), quats=f("quat"), colors=f("color"), opacities=f("opacity")) if G \
        else empty_gaussians(a.device)
    s = splat_score(evaluate(gs, sc, "test", a.device, with_lpips=True)["mean"], norm["test"])["score"]
    diffs.append(s - r["score"])
    print(f"{r['episode_id']:58s} recorded {r['score']:7.2f}  re-scored {s:7.2f}  difference {s - r['score']:+.3f}", flush=True)
d = np.abs(np.array(diffs))
print(f"{len(d)} episodes: mean |difference| {d.mean():.3f}, largest {d.max():.3f}")