"""Verify pi0.5's discrete-state prompt fits inside max_token_len for this dataset.

    uv run python scripts/check_token_len.py [DATASET_ROOT] [--action-dim 32] [--max-token-len 200]

Why this check exists: with `pi05=True`, openpi's `Pi0.embed_suffix()` skips the continuous state
token (`if not self.pi05:`) and `state_proj` is never constructed, so pi0.5's ONLY proprioception
path is the discretised state string that `PaligemmaTokenizer` packs into the prompt:

    "Task: <prompt>, State: <action_dim ints>;\nAction: "

The pi0 default of 48 truncates that after ~9 of 32 state values -- keeping the left arm and
dropping the entire right arm and hand -- while emitting only a `logging.warning`. pi0.5's own
default is 200.

Self-contained: pulls the same PaliGemma tokenizer openpi uses and reads the parquets directly,
so it runs before openpi is installed.
"""
import argparse
import glob
import json
import pathlib
import sys
import urllib.request

import numpy as np

TOKENIZER_URL = "https://storage.googleapis.com/big_vision/paligemma_tokenizer.model"
UPPER = slice(15, 43)  # the 28 trained dims of the 43-dim whole-body vector


def main() -> int:
    ap = argparse.ArgumentParser()
    ap.add_argument("root", nargs="?", default=str(pathlib.Path(__file__).resolve().parent.parent))
    ap.add_argument("--action-dim", type=int, default=32)
    ap.add_argument("--max-token-len", type=int, default=200)
    ap.add_argument("--sample-frames", type=int, default=4000)
    a = ap.parse_args()
    root = pathlib.Path(a.root)

    try:
        import pandas as pd
        import sentencepiece
    except ImportError as e:
        print(f"needs pandas + sentencepiece + pyarrow: {e}")
        return 2

    prompt = json.loads(next(open(root / "meta/tasks.jsonl")))["task"]
    cache = pathlib.Path.home() / ".cache" / "paligemma_tokenizer.model"
    if not cache.exists():
        cache.parent.mkdir(parents=True, exist_ok=True)
        print(f"fetching tokenizer -> {cache}")
        urllib.request.urlretrieve(TOKENIZER_URL, cache)  # noqa: S310 - fixed GCS URL
    sp = sentencepiece.SentencePieceProcessor(model_file=str(cache))

    files = sorted(glob.glob(str(root / "data/chunk-000/episode_*.parquet")))
    if not files:
        print(f"no parquet files under {root}/data/chunk-000")
        return 2
    # Sample across the whole dataset, not just the first episode: state range drives digit count.
    step = max(1, len(files) // 40)
    S = np.concatenate([
        np.stack([np.asarray(v, float) for v in pd.read_parquet(f, columns=["observation.state"])["observation.state"]])
        for f in files[::step]
    ])[:, UPPER]

    # pi0.5 normalises state with q01/q99 quantiles, then pads to action_dim, then discretises.
    q01, q99 = np.percentile(S, 1, axis=0), np.percentile(S, 99, axis=0)
    N = (S - q01) / (q99 - q01 + 1e-6) * 2.0 - 1.0
    N = np.pad(N, ((0, 0), (0, max(0, a.action_dim - N.shape[1]))))

    idx = np.linspace(0, len(N) - 1, min(a.sample_frames, len(N))).astype(int)
    lens = []
    for i in idx:
        d = np.digitize(N[i], bins=np.linspace(-1, 1, 257)[:-1]) - 1
        full = f"Task: {prompt.strip()}, State: {' '.join(map(str, d))};\nAction: "
        lens.append(len(sp.encode(full, add_bos=True)))
    lens = np.array(lens)

    print(f"task            {prompt!r}")
    print(f"action_dim      {a.action_dim}   frames sampled {len(lens)}")
    print(f"token length    min={lens.min()} median={int(np.median(lens))} "
          f"p99={int(np.percentile(lens,99))} max={lens.max()}")
    print()
    for L in (48, 64, 128, 200, 256):
        over = 100 * (lens > L).mean()
        mark = " <-- your setting" if L == a.max_token_len else ""
        print(f"  max_token_len={L:>4}: {'OK' if over == 0 else f'TRUNCATES {over:.1f}% of frames'}{mark}")

    ok = int(lens.max()) <= a.max_token_len
    print(f"\nmax_token_len={a.max_token_len}: {'PASS' if ok else 'FAIL'}"
          f" (needs >= {int(lens.max())})")
    if not ok:
        print("Leave max_token_len unset in Pi0Config so the pi05 default of 200 applies.")
    return 0 if ok else 1


if __name__ == "__main__":
    sys.exit(main())