"""
check_force_range.py

Scan ALL clips and report the RAW (std-normalized, pre-clip) force abs-max per
clip, so you know exactly how many clips have extreme forces (>10, >15, etc).
This does NOT depend on the loss ranking -- it reads the actual data magnitudes.

Reports per-clip absmax (normalized by active std, BEFORE force_clip), sorted,
plus a histogram of how many clips exceed each threshold.

Run:
  python examples/wanvideo/model_training/check_force_range.py \
      --clips ... --stats ... --source_root ...
"""
import argparse, os, sys
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
import numpy as np
from tqdm import tqdm

# load stats + clips directly, replicate the dataloader normalization WITHOUT clip
import json


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--clips", required=True)
    ap.add_argument("--stats", required=True)
    ap.add_argument("--source_root", required=True)
    args = ap.parse_args()

    stats = json.load(open(args.stats))
    # force per-channel active std (the normalization the dataloader uses)
    std = np.array(stats.get("force_ch_active_std",
                             stats.get("force_ch_std")))  # (6,)
    clips = json.load(open(args.clips))["clips"]

    rows = []
    for i, c in enumerate(tqdm(clips, desc="clips")):
        # resolve force npy paths for this clip
        ep = c["episode"] if isinstance(c, dict) and "episode" in c else ""
        fpaths = None
        for k in ("force_paths", "force", "force_npy"):
            if isinstance(c, dict) and k in c:
                fpaths = c[k]; break
        if fpaths is None and "frame_indices" in c:
            base = os.path.join("modalities", "force")
            fpaths = [os.path.join(base, f"{idx:06d}.npy") for idx in c["frame_indices"]]
        if fpaths is None:
            continue

        clip_absmax = 0.0
        per_ch_absmax = np.zeros(6)
        n_loaded = 0
        for fp in fpaths:
            # force_paths are relative to source_root/episode
            full = fp
            if not os.path.isabs(full):
                full = os.path.join(args.source_root, ep, fp)
            if not os.path.exists(full):
                continue
            fp = full
            n_loaded += 1
            f = np.load(fp).astype(np.float64)        # (6,H,W)
            fn = f / std[:, None, None]               # normalize, NO clip
            a = np.abs(fn)
            clip_absmax = max(clip_absmax, a.max())
            per_ch_absmax = np.maximum(per_ch_absmax, a.reshape(6, -1).max(1))
        if i == 0:
            print(f"[debug] clip 0: episode={ep!r}, "
                  f"{len(fpaths)} paths, {n_loaded} loaded, "
                  f"example full path:\n  "
                  f"{os.path.join(args.source_root, ep, fpaths[0])}")
        rows.append((i, clip_absmax, per_ch_absmax))

    rows.sort(key=lambda r: -r[1])
    print("\n" + "="*72)
    print("ALL clips sorted by normalized force abs-max (BEFORE clip):")
    print(f"{'clip':>4s} {'absmax':>8s}  per-channel absmax [Lfx Lfy Lfz Rfx Rfy Rfz]")
    print("-"*72)
    for i, am, pc in rows:
        pcs = " ".join(f"{v:4.1f}" for v in pc)
        print(f"{i:4d} {am:8.2f}  [{pcs}]")

    absmaxes = np.array([r[1] for r in rows])
    print("="*72)
    for thr in [5, 8, 10, 12, 15, 20]:
        n = (absmaxes > thr).sum()
        print(f"  clips with force absmax > {thr:2d}: {n}/{len(absmaxes)}")
    print(f"\n  overall max across all clips: {absmaxes.max():.2f}")
    print(f"  p50={np.percentile(absmaxes,50):.2f} "
          f"p90={np.percentile(absmaxes,90):.2f} "
          f"p99={np.percentile(absmaxes,99):.2f}")


if __name__ == "__main__":
    main()