#!/usr/bin/env python3
"""Prepare vocab and normalization stats for HAI 21.03."""

import json
from pathlib import Path
from typing import Optional

from data_utils import compute_cont_stats, build_vocab, load_split
from platform_utils import safe_path, ensure_dir

BASE_DIR = Path(__file__).resolve().parent
REPO_DIR = BASE_DIR.parent.parent
DATA_PATH = REPO_DIR / "dataset" / "hai" / "hai-21.03" / "train1.csv.gz"
SPLIT_PATH = BASE_DIR / "feature_split.json"
OUT_STATS = BASE_DIR / "results" / "cont_stats.json"
OUT_VOCAB = BASE_DIR / "results" / "disc_vocab.json"


def main(max_rows: Optional[int] = None):
    split = load_split(safe_path(SPLIT_PATH))
    time_col = split.get("time_column", "time")
    cont_cols = [c for c in split["continuous"] if c != time_col]
    disc_cols = [c for c in split["discrete"] if not c.startswith("attack") and c != time_col]

    mean, std = compute_cont_stats(safe_path(DATA_PATH), cont_cols, max_rows=max_rows)
    vocab = build_vocab(safe_path(DATA_PATH), disc_cols, max_rows=max_rows)

    ensure_dir(OUT_STATS.parent)
    with open(safe_path(OUT_STATS), "w", encoding="utf-8") as f:
        json.dump({"mean": mean, "std": std, "max_rows": max_rows}, f, indent=2)

    with open(safe_path(OUT_VOCAB), "w", encoding="utf-8") as f:
        json.dump({"vocab": vocab, "max_rows": max_rows}, f, indent=2)


if __name__ == "__main__":
    # Default: sample 50000 rows for speed. Set to None for full scan.
    main(max_rows=50000)