#!/usr/bin/env python3
"""Regenerate GenScale Parquet/CSV files from the cleaned anonymized JSON.

Usage:
  python scripts/make_genscale_parquet.py --benchmark GenScale_Benchmark_anonymous_clean.json --out_dir data
"""
import argparse, json
from pathlib import Path
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq

def jd(x):
    return json.dumps(x, ensure_ascii=False) if x is not None else None
def flatten(d, prefix=""):
    out = {}
    if not isinstance(d, dict):
        return out
    for k, v in d.items():
        key = f"{prefix}{k}" if prefix else str(k)
        if isinstance(v, dict):
            out.update(flatten(v, key + "."))
        elif isinstance(v, list):
            out[key] = jd(v)
        else:
            out[key] = v
    return out
def parse_pair(k):
    return k.split("_to_", 1) if isinstance(k, str) and "_to_" in k else (None, None)
def write_parquet(df, path):
    pq.write_table(pa.Table.from_pandas(df, preserve_index=False), path)

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--benchmark", required=True)
    ap.add_argument("--out_dir", default="data")
    args = ap.parse_args()
    out_dir = Path(args.out_dir)
    out_dir.mkdir(parents=True, exist_ok=True)
    data = json.load(open(args.benchmark, encoding="utf-8"))

    entry_rows, pair_rows, edit_rows = [], [], []
    for entry in data:
        task_id = entry.get("task_id")
        task = task_id.split("_")[0] if task_id else None
        gt = entry.get("gt_ratios") or {}
        entry_row = {
            "entry_id": task_id, "task_id": task_id, "task": task, "scenario": entry.get("scenario"),
            "source_task_id": entry.get("source_task_id"), "source_task_type": entry.get("source_task_type"),
            "prompt_type": entry.get("prompt_type"), "num_objects": entry.get("num_objects", len(entry.get("objects_included", []) or [])),
            "num_pairs": len(gt) if isinstance(gt, dict) else 0, "objects_included": jd(entry.get("objects_included")),
            "prompt": entry.get("prompt"), "reference_image_path": entry.get("reference_image_path"),
            "image_path": entry.get("image_path"), "raw_listing_title": entry.get("raw_listing_title"),
            "source_size_score": entry.get("source_size_score"), "pair_key": entry.get("pair_key"),
            "generated_ratio": entry.get("generated_ratio"), "target_ratio": entry.get("target_ratio"),
        }
        for pk in ["product_scale", "refine_meta", "edit_plan"]:
            if isinstance(entry.get(pk), dict):
                entry_row.update(flatten(entry[pk], pk + "."))
            elif pk in entry:
                entry_row[pk] = jd(entry.get(pk))
        entry_rows.append(entry_row)

        if isinstance(gt, dict):
            for idx, (pair_key, pair_val) in enumerate(gt.items()):
                object_a, object_b = parse_pair(pair_key)
                pr = pair_val if isinstance(pair_val, dict) else {"target_ratio": pair_val}
                acc = pr.get("acceptable_range")
                row = {
                    "pair_id": f"{task_id}__pair_{idx:02d}", "entry_id": task_id, "task_id": task_id, "task": task,
                    "scenario": entry.get("scenario"), "source_task_id": entry.get("source_task_id"),
                    "source_task_type": entry.get("source_task_type"), "prompt_type": entry.get("prompt_type"),
                    "pair_key": pair_key, "object_a": object_a, "object_b": object_b,
                    "target_ratio_a_to_b": pr.get("target_ratio"),
                    "acceptable_ratio_min": acc[0] if isinstance(acc, list) and len(acc) > 0 else None,
                    "acceptable_ratio_max": acc[1] if isinstance(acc, list) and len(acc) > 1 else None,
                    "generated_ratio_a_to_b": entry.get("generated_ratio") if entry.get("pair_key") == pair_key else None,
                    "source_size_score": entry.get("source_size_score"), "objects_included": jd(entry.get("objects_included")),
                    "prompt": entry.get("prompt"), "reference_image_path": entry.get("reference_image_path"),
                    "image_path": entry.get("image_path"),
                }
                if isinstance(entry.get("product_scale"), dict):
                    row.update(flatten(entry["product_scale"], "product_scale."))
                if isinstance(entry.get("edit_plan"), dict):
                    row.update(flatten(entry["edit_plan"], "edit_plan."))
                pair_rows.append(row)

        if isinstance(entry.get("edit_plan"), dict):
            ep = entry["edit_plan"]
            object_a, object_b = parse_pair(entry.get("pair_key"))
            edit_rows.append({
                "entry_id": task_id, "task_id": task_id, "scenario": entry.get("scenario"),
                "source_task_id": entry.get("source_task_id"), "source_task_type": entry.get("source_task_type"),
                "prompt_type": entry.get("prompt_type"), "pair_key": entry.get("pair_key"),
                "object_a": object_a, "object_b": object_b, "generated_ratio_a_to_b": entry.get("generated_ratio"),
                "target_ratio_a_to_b": entry.get("target_ratio"), "source_size_score": entry.get("source_size_score"),
                "edit_target_object": ep.get("edit_target_object"), "reference_object": ep.get("reference_object"),
                "scale_factor": ep.get("scale_factor"), "direction": ep.get("direction"), "prompt": entry.get("prompt"),
                "image_path": entry.get("image_path"), "reference_image_path": entry.get("reference_image_path"),
            })

    for df, name in [(pd.DataFrame(entry_rows), "genscale_entries"), (pd.DataFrame(pair_rows), "genscale_pairs"), (pd.DataFrame(edit_rows), "genscale_task3_edit_plans")]:
        write_parquet(df, out_dir / f"{name}.parquet")
        df.to_csv(out_dir / f"{name}.csv", index=False)

if __name__ == "__main__":
    main()