#!/usr/bin/env python
"""Prepare ULVR_v2_clean scene_graph parquet for Monet step1 (precompute_teacher_reps).

Reads scene_graph(+val) parquet (cols: sample_id, category, source_dataset, question,
answer, input_image, intermediate_image_1..3, num_intermediate_steps, messages_json),
and produces:
  <out_root>/train.jsonl  (and val.jsonl)   -- {"metadata":{dataset_name,sample_id}, "data":[...]}
  <out_root>/images/input/scene_graph/*.png
  <out_root>/images/intermediate/scene_graph/*.png
Image relative paths are taken verbatim from messages_json so precompute can resolve them
against --dataset_root=<out_root>.
"""
import argparse, glob, json, os
import pyarrow.parquet as pq

def run(parquet_glob, out_root, split_name):
    files=sorted(glob.glob(parquet_glob))
    assert files, f"no parquet matched {parquet_glob}"
    os.makedirs(out_root, exist_ok=True)
    jsonl_path=os.path.join(out_root, f"{split_name}.jsonl")
    n=0; nimg=0
    IMGCOLS=["input_image","intermediate_image_1","intermediate_image_2","intermediate_image_3"]
    with open(jsonl_path,"w") as jf:
        for fp in files:
            t=pq.read_table(fp, columns=["sample_id","messages_json"]+IMGCOLS).to_pylist()
            for r in t:
                mj=json.loads(r["messages_json"])
                # normalize to {metadata, data}
                if "data" in mj and "metadata" in mj:
                    rec=mj
                else:
                    rec={"metadata":{"dataset_name":"scene_graph","sample_id":r["sample_id"]},"data":mj if isinstance(mj,list) else mj.get("data",mj)}
                rec["metadata"].setdefault("dataset_name","scene_graph")
                rec["metadata"]["sample_id"]=r["sample_id"]
                jf.write(json.dumps(rec,ensure_ascii=False)+"\n"); n+=1
                # collect image rel-paths in order: user image -> input_image ; assistant images -> intermediate_*
                inpath=None; interpaths=[]
                for msg in rec["data"]:
                    for c in msg.get("content",[]):
                        if c.get("type")=="image":
                            if msg["role"]=="user": inpath=c["image"]
                            elif msg["role"]=="assistant": interpaths.append(c["image"])
                def dump(relpath, cell):
                    nonlocal nimg
                    if not relpath or not cell or not isinstance(cell,dict): return
                    b=cell.get("bytes")
                    if not b: return
                    out=os.path.join(out_root, relpath)
                    os.makedirs(os.path.dirname(out), exist_ok=True)
                    if not os.path.exists(out) or os.path.getsize(out)!=len(b):
                        with open(out,"wb") as fp2: fp2.write(b)
                    nimg+=1
                dump(inpath, r.get("input_image"))
                for i,ip in enumerate(interpaths, start=1):
                    dump(ip, r.get(f"intermediate_image_{i}"))
    print(f"[{split_name}] wrote {n} samples -> {jsonl_path} ; images written={nimg}")

if __name__=="__main__":
    ap=argparse.ArgumentParser()
    ap.add_argument("--train-glob", required=True)
    ap.add_argument("--val-glob", default="")
    ap.add_argument("--out-root", required=True)
    a=ap.parse_args()
    run(a.train_glob, a.out_root, "train")
    if a.val_glob: run(a.val_glob, a.out_root, "val")