"""materialize.py —— 把 parquet 展开成目录树（按字节原样写出，不重编码）。

目录形状（与原 GenImage 一致，方便直接喂 ImageFolder）：

    <out>/<generator>/<split>/ai/<class_name>/*.PNG      # 假图
    <out>/nature/<split>/<class_name>/*.JPEG             # 真图池，全局只有一份

用法：
    python scripts/materialize.py --root . --out ./data
    python scripts/materialize.py --root . --out ./data --config tiny
    python scripts/materialize.py --root . --out ./data --splits test --generators ADM nature
    python scripts/materialize.py --root . --out ./data --dry-run
"""
from __future__ import annotations

import argparse
import sys
from pathlib import Path

import pyarrow.parquet as pq


def image_bytes(cell):
    """兼容两种写法：struct{bytes,path} 或裸 binary。"""
    if isinstance(cell, dict):
        return cell.get("bytes")
    if isinstance(cell, (bytes, bytearray, memoryview)):
        return bytes(cell)
    return None


def guess_ext(orig_path: str, fmt: str) -> str:
    suf = Path(orig_path).suffix
    if suf:
        return suf.lower()
    return {"PNG": ".png", "JPEG": ".jpg"}.get(fmt, ".bin")


def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("--root", required=True, help="仓库根目录（含 data/ 与 meta/）")
    ap.add_argument("--out", required=True, help="输出目录树")
    ap.add_argument("--config", default="default", choices=["default", "tiny"])
    ap.add_argument("--splits", nargs="*", default=None)
    ap.add_argument("--generators", nargs="*", default=None)
    ap.add_argument("--limit", type=int, default=None, help="每个 (生成器, split) 最多导出多少张")
    ap.add_argument("--dry-run", action="store_true")
    args = ap.parse_args()

    root = Path(args.root)
    sub = "data" if args.config == "default" else "tiny"
    files = sorted((root / sub).glob("*.parquet"))
    if not files:
        sys.exit(f"[!] {root / sub} 下没有 parquet，检查 --root / --config")

    out = Path(args.out)
    total = written = 0
    counts: dict[tuple, int] = {}

    for f in files:
        pf = pq.ParquetFile(f)
        for batch in pf.iter_batches(batch_size=128):
            d = batch.to_pydict()
            for i in range(batch.num_rows):
                split = d["split"][i]
                gen = d["generator"][i]
                if args.splits and split not in args.splits:
                    continue
                if args.generators and gen not in args.generators:
                    continue
                key = (gen, split)
                if args.limit and counts.get(key, 0) >= args.limit:
                    continue

                label = int(d["label"][i])
                cls = str(d["class_name"][i] or f"class_{d['class_id'][i]}").replace("/", "_")
                orig = d["image_path"][i]
                stem = Path(orig).stem
                ext = guess_ext(orig, d["format"][i])
                branch = "ai" if label == 1 else "nature"
                dst = out / gen / split / branch / cls / f"{stem}{ext}"
                total += 1
                if args.dry_run:
                    continue
                dst.parent.mkdir(parents=True, exist_ok=True)
                b = image_bytes(d["image"][i])
                if b is None:
                    continue
                dst.write_bytes(b)
                written += 1
                counts[key] = counts.get(key, 0) + 1
        print(f"\r  处理 {f.name} … 已导出 {written:,}", end="", flush=True)

    print()
    if args.dry_run:
        print(f"[dry-run] 将导出 {total:,} 张到 {out}")
        return
    print(f"完成：{written:,} 张 -> {out.resolve()}")
    for (gen, split), n in sorted(counts.items()):
        print(f"  {gen:10s} {split:6s} {n:>7,}")


if __name__ == "__main__":
    main()