"""
Build Parquet files for HuggingFace dataset viewer.

Reads Lightning Pose CSVs and corresponding PNG frames, then writes
Parquet files with embedded images to data/ for the HF viewer.

Usage (from repo root):
    python scripts/build_parquet.py
"""

import csv
import io
from pathlib import Path

import datasets
from datasets import Dataset, Features, Image, Value, Sequence

REPO_ROOT = Path(__file__).parent.parent
DATA_OUT = REPO_ROOT / "data"

VIEWS = ["Cam-A", "Cam-B", "Cam-C", "Cam-D", "Cam-E", "Cam-F"]

KEYPOINTS = [
    "L1A", "L1B", "L1C", "L1D", "L1E",
    "L2A", "L2B", "L2C", "L2D", "L2E",
    "L3A", "L3B", "L3C", "L3D", "L3E",
    "R1A", "R1B", "R1C", "R1D", "R1E",
    "R2A", "R2B", "R2C", "R2D", "R2E",
    "R3A", "R3B", "R3C", "R3D", "R3E",
]

FEATURES = Features(
    {
        "image": Image(),
        "session": Value("string"),
        "view": Value("string"),
        "split": Value("string"),
        "frame": Value("string"),
        **{f"{kp}_x": Value("float32") for kp in KEYPOINTS},
        **{f"{kp}_y": Value("float32") for kp in KEYPOINTS},
    }
)


def parse_csv(csv_path: Path, view: str, split: str) -> list[dict]:
    rows = []
    with open(csv_path) as f:
        reader = csv.reader(f)
        # Skip 3-row header: scorer, bodyparts, coords
        next(reader)
        next(reader)
        next(reader)
        for row in reader:
            img_rel_path = row[0]
            img_path = REPO_ROOT / img_rel_path
            if not img_path.exists():
                print(f"  WARNING: missing {img_path}, skipping")
                continue

            coords = row[1:]  # 60 values: x0,y0,x1,y1,...

            record: dict = {
                "image": {"path": None, "bytes": img_path.read_bytes()},
                "session": "_".join(Path(img_rel_path).parent.name.split("_")[:-1]),
                "view": view,
                "split": split,
                "frame": Path(img_rel_path).name,
            }

            for i, kp in enumerate(KEYPOINTS):
                x_str = coords[i * 2]
                y_str = coords[i * 2 + 1]
                record[f"{kp}_x"] = float(x_str) if x_str else float("nan")
                record[f"{kp}_y"] = float(y_str) if y_str else float("nan")

            rows.append(record)
    return rows


def build_split(csv_suffix: str, split_name: str) -> list[dict]:
    all_rows = []
    for view in VIEWS:
        csv_path = REPO_ROOT / f"CollectedData_{view}{csv_suffix}.csv"
        if not csv_path.exists():
            print(f"Skipping missing {csv_path}")
            continue
        print(f"  Reading {csv_path.name} ...")
        rows = parse_csv(csv_path, view, split_name)
        print(f"    {len(rows)} rows")
        all_rows.extend(rows)
    return all_rows


def main():
    DATA_OUT.mkdir(exist_ok=True)

    print("Building InD split ...")
    ind_rows = build_split("", "ind")
    ind_ds = Dataset.from_list(ind_rows, features=FEATURES)
    out = DATA_OUT / "ind-train-00000-of-00001.parquet"
    ind_ds.to_parquet(str(out))
    print(f"Wrote {out} ({out.stat().st_size / 1e6:.1f} MB, {len(ind_rows)} rows)")

    print("Building OOD split ...")
    ood_rows = build_split("_new", "ood")
    ood_ds = Dataset.from_list(ood_rows, features=FEATURES)
    out = DATA_OUT / "ood-test-00000-of-00001.parquet"
    ood_ds.to_parquet(str(out))
    print(f"Wrote {out} ({out.stat().st_size / 1e6:.1f} MB, {len(ood_rows)} rows)")


if __name__ == "__main__":
    main()