#!/usr/bin/env python3
"""
regen_captions_v2.py — regenerate captions (v2, disease/lesion-centric) from an
existing *_images_v1.parquet, WITHOUT re-running extraction.

Works uniformly for the private / public-fundus / public-OCT manifests because
caption_v2.caption_rows_for() is a pure function of the manifest row.

Writes:
  {out_dir}/{out_name}                     unified captions_v2 parquet
  {out_dir}/{cohort}_captions_v2.parquet   per-cohort (HF staging) if --per-cohort
"""
import argparse
import time
from pathlib import Path

import pandas as pd

import caption_v2


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--manifest", required=True, help="path to *_images_v1.parquet")
    ap.add_argument("--out-dir", required=True)
    ap.add_argument("--out-name", default="captions_v2.parquet")
    ap.add_argument("--per-cohort", action="store_true",
                    help="also write one {cohort}_captions_v2.parquet per cohort")
    ap.add_argument("--lesion-sidecar", default=None,
                    help="optional parquet (image_id, lesion_codes) to merge into "
                         "lesion_tags before captioning (from enrich_lesion_tags.py)")
    args = ap.parse_args()

    out_dir = Path(args.out_dir)
    out_dir.mkdir(parents=True, exist_ok=True)

    print(f"loading manifest: {args.manifest}")
    df = pd.read_parquet(args.manifest)
    print(f"  {len(df)} image rows, {df.cohort.nunique()} cohort(s)")

    sidecar = {}
    if args.lesion_sidecar:
        sc = pd.read_parquet(args.lesion_sidecar)
        sidecar = {r["image_id"]: list(r["lesion_codes"]) for _, r in sc.iterrows()}
        print(f"  merged lesion sidecar: {len(sidecar)} images with extra lesion tags")

    t0 = time.time()
    records = df.to_dict("records")
    all_caps, per_cohort = [], {}
    for i, row in enumerate(records):
        if sidecar:
            extra = sidecar.get(row.get("image_id"))
            if extra:
                cur = caption_v2._as_list(row.get("lesion_tags"))
                row["lesion_tags"] = list(dict.fromkeys(cur + extra))
        caps = caption_v2.caption_rows_for(row)
        all_caps.extend(caps)
        if args.per_cohort:
            per_cohort.setdefault(row.get("cohort"), []).extend(caps)
        if (i + 1) % 100000 == 0:
            dt = time.time() - t0
            print(f"  ... {i+1}/{len(records)} ({dt:.0f}s, {(i+1)/dt:.0f} img/s)")
    dt = time.time() - t0
    print(f"  done in {dt:.0f}s: {len(all_caps)} caption rows "
          f"({len(all_caps)/max(len(records),1):.2f} per image)")

    cap_df = pd.DataFrame(all_caps, columns=caption_v2.CAP_COLS)
    uni = out_dir / args.out_name
    cap_df.to_parquet(uni, index=False)
    print(f"\n  wrote unified: {uni} ({len(cap_df)} rows)")
    print("  level distribution:")
    print(cap_df["level"].value_counts().to_string())

    if args.per_cohort:
        for cohort, caps in per_cohort.items():
            cp = out_dir / f"{cohort}_captions_v2.parquet"
            pd.DataFrame(caps, columns=caption_v2.CAP_COLS).to_parquet(cp, index=False)
        print(f"  wrote {len(per_cohort)} per-cohort caption parquets")

    # peek a few
    print("\n  sample captions:")
    for t in cap_df["prompt_text"].drop_duplicates().head(12):
        print(f"    {t}")


if __name__ == "__main__":
    main()