""" Statistics for teacher-forcing HTML token lengths (same encoding as train_compressor). Uses Qwen3-VL tokenizer with encode(..., add_special_tokens=False) to match _rebuild_sequence (excluding the trailing eos added in training). Usage: PYTHONPATH=. python scripts/stats_html_token_lengths.py PYTHONPATH=. python scripts/stats_html_token_lengths.py --model_id Qwen/Qwen3-VL-8B-Instruct """ from __future__ import annotations import argparse import json import os import sys from pathlib import Path os.environ.setdefault("HF_ENDPOINT", "https://hf-mirror.com") os.environ.setdefault("HF_HOME", os.environ.get("HF_HOME", "/root/rivermind-data/huggingface")) PROJECT_ROOT = Path(__file__).resolve().parent.parent def collect_lengths(html_paths: list[Path], tokenizer) -> list[int]: lengths: list[int] = [] for p in html_paths: try: text = p.read_text(encoding="utf-8", errors="ignore") except OSError: continue ids = tokenizer.encode(text, add_special_tokens=False) lengths.append(len(ids)) return lengths def percentile(sorted_vals: list[int], q: float) -> float: """Linear interpolation, q in [0,1].""" if not sorted_vals: return 0.0 n = len(sorted_vals) if n == 1: return float(sorted_vals[0]) pos = (n - 1) * q lo = int(pos) hi = min(lo + 1, n - 1) w = pos - lo return sorted_vals[lo] * (1 - w) + sorted_vals[hi] * w def main() -> int: ap = argparse.ArgumentParser() ap.add_argument("--model_id", default="Qwen/Qwen3-VL-8B-Instruct") ap.add_argument("--websight_dir", type=Path, default=PROJECT_ROOT / "data" / "websight") ap.add_argument("--gt_html_dir", type=Path, default=PROJECT_ROOT / "data" / "gt_html") ap.add_argument("--json_out", type=Path, default=None, help="Optional path to write summary JSON.") args = ap.parse_args() from transformers import AutoTokenizer tok = AutoTokenizer.from_pretrained(args.model_id, trust_remote_code=True) ws = sorted(args.websight_dir.glob("*.html")) if args.websight_dir.is_dir() else [] gt = sorted(args.gt_html_dir.glob("*.html")) if args.gt_html_dir.is_dir() else [] len_ws = collect_lengths(ws, tok) len_gt = collect_lengths(gt, tok) merged = len_ws + len_gt def summarize(name: str, vals: list[int]) -> dict: if not vals: return {"name": name, "n": 0} s = sorted(vals) return { "name": name, "n": len(vals), "min": s[0], "max": s[-1], "mean": round(sum(s) / len(s), 1), "p50": int(percentile(s, 0.50)), "p90": int(percentile(s, 0.90)), "p95": int(percentile(s, 0.95)), "p99": int(percentile(s, 0.99)), } out = { "model_id": args.model_id, "websight": summarize("websight", len_ws), "gt_html": summarize("gt_html (Design2Code eval GT)", len_gt), "merged": summarize("merged (websight + gt_html)", merged), "recommend_max_html_tokens": {}, } if merged: s = sorted(merged) p95 = int(percentile(s, 0.95)) p99 = int(percentile(s, 0.99)) ws_p99 = int(percentile(sorted(len_ws), 0.99)) if len_ws else 0 out["recommend_max_html_tokens"] = { "websight_only_suggest": min(max(ws_p99 + 128, 1024), 2048), "design2code_gt_reality": ( "gt_html is very long (often 20k–80k+ tokens). Any fixed cap heavily truncates CE on eval-GT." ), "mixed_train_practical": { "8192": "Still truncates most Design2Code HTML but keeps speed reasonable", "16384": "Better tail for long pages if VRAM/time allow", "2048": "Fine for WebSight-dominated runs; largely drops Design2Code GT tail", }, "note": "See websight vs gt_html blocks; do not use merged p95 alone when mixing splits.", } print(json.dumps(out, indent=2, ensure_ascii=False)) if args.json_out: args.json_out.parent.mkdir(parents=True, exist_ok=True) args.json_out.write_text(json.dumps(out, indent=2, ensure_ascii=False), encoding="utf-8") print(f"Wrote {args.json_out}", file=sys.stderr) return 0 if __name__ == "__main__": raise SystemExit(main())