import argparse
import glob
import math
import os
import re
from collections.abc import Sized
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path
from typing import Dict, List, Optional, Sequence, Tuple

import pyarrow.parquet as pq
from datasets import (
    Audio as HFAudio,
    Dataset,
    DatasetDict,
    Features,
    Image as HFImage,
    Video as HFVideo,
    load_dataset,
)
from tqdm import tqdm


_MEDIA_FEATURES = Features(
    {
        "image": HFImage(),
        "video": HFVideo(),
        "audio": HFAudio(),
    }
)
MEDIA_TYPE_COLUMNS = ("image", "video", "audio")
MEDIA_ROLES = ("media", "query", "candidate")
DEFAULT_ROW_GROUP_SIZE = 100
DEFAULT_MEDIA_ROWS_PER_SHARD = 5000
KNOWN_DATA_SPLITS = ("train", "test", "validation", "valid", "dev")


def resolve_split_output_dir(output_root, split_name: str, subset_name: str) -> Path:
    """Resolve the output dir for one subset of a split.

    Default layout mirrors the Hub repos: ``{output_root}/MVEB-{split}/{subset}``.
    Override the split root with env ``MVEB_TRAIN_DIR`` / ``MVEB_TEST_DIR``
    (e.g. point them at locally downloaded MVEB-train / MVEB-test repos).
    """
    override = os.environ.get(f"MVEB_{split_name.upper()}_DIR")
    base = Path(override) if override else Path(output_root) / f"MVEB-{split_name}"
    return base / subset_name


def _infer_collection_name(dir_path: str) -> str:
    """Infer collection label from path basename (e.g. ``test`` / ``train`` for Hub repo name)."""
    return os.path.basename(os.path.normpath(dir_path))


def _infer_data_split(path_like: str, default: str = "train") -> str:
    """Infer split name from path components, fallback to ``default``."""
    parts = [p.lower() for p in os.path.normpath(path_like).split(os.sep) if p]
    for part in reversed(parts):
        if part in KNOWN_DATA_SPLITS:
            return part
    return default


def infer_media_type(media_row: dict) -> Optional[str]:
    """Return which media column is set (``image`` / ``video`` / ``audio``)."""
    for column in MEDIA_TYPE_COLUMNS:
        if media_row.get(column) is not None:
            return column
    return None


def get_media_payload(media_row: dict):
    """Return the non-null media payload; raises if the row is empty."""
    media_type = infer_media_type(media_row)
    if media_type is None:
        raise ValueError("media row has no populated image/video/audio column")
    return media_row[media_type]


def _make_media_row(
    *,
    image=None,
    video=None,
    audio=None,
) -> dict:
    populated = [
        name
        for name, value in (("image", image), ("video", video), ("audio", audio))
        if value is not None
    ]
    if len(populated) != 1:
        raise ValueError(f"exactly one media column must be set, got {populated}")
    return {"image": image, "video": video, "audio": audio}


def _read_media_bytes(media_path: str) -> bytes:
    with open(media_path, "rb") as f:
        return f.read()


def _parse_size(size: str) -> int:
    size = size.strip().upper()
    m = re.fullmatch(r"(\d+(?:\.\d+)?)\s*([KMGT]?B)", size)
    if not m:
        raise ValueError(f"Invalid size string: {size!r}, e.g. 500MB or 1GB")
    value = float(m.group(1))
    unit = m.group(2)
    scale = {"B": 1, "KB": 1024, "MB": 1024**2, "GB": 1024**3, "TB": 1024**4}
    return int(value * scale[unit])


def _resolve_image_path(image_path: str, image_dir: str) -> str:
    if not image_path:
        raise ValueError("empty image_path")
    if os.path.isabs(image_path) and os.path.exists(image_path):
        return image_path
    candidate = os.path.join(image_dir, image_path)
    if os.path.exists(candidate):
        return candidate
    raise FileNotFoundError(
        f"Image not found: {image_path!r} (image_dir={image_dir!r})"
    )


def _discover_subsets(split_dir: str) -> List[str]:
    subsets: List[str] = []
    for name in sorted(os.listdir(split_dir)):
        if name == "images":
            continue
        sub_path = os.path.join(split_dir, name)
        if not os.path.isdir(sub_path):
            continue
        query_path = os.path.join(sub_path, "query.parquet")
        candidate_path = os.path.join(sub_path, "candidate.parquet")
        if os.path.exists(query_path) and os.path.exists(candidate_path):
            subsets.append(name)
    return subsets


def _collect_unique_paths(
    query_rows: Sequence[dict],
    candidate_rows: Sequence[dict],
) -> Tuple[List[str], Dict[str, int]]:
    path_to_index: Dict[str, int] = {}
    ordered_paths: List[str] = []

    def add_path(path: Optional[str]) -> None:
        if not path:
            return
        if path not in path_to_index:
            path_to_index[path] = len(ordered_paths)
            ordered_paths.append(path)

    for row in query_rows:
        add_path(row.get("image_path"))
    for row in candidate_rows:
        add_path(row.get("image_path"))

    return ordered_paths, path_to_index


def _rows_with_media_index(
    rows: Sequence[dict],
    path_to_index: Dict[str, int],
) -> List[dict]:
    converted: List[dict] = []
    for row in rows:
        new_row = {
            k: v
            for k, v in row.items()
            if k not in ("image_path", "media_type")
        }
        image_path = row.get("image_path")
        if image_path:
            new_row["media_index"] = path_to_index[image_path]
        elif "media_index" not in new_row:
            raise ValueError(f"row id={row.get('id')}: missing image_path and media_index")
        converted.append(new_row)
    return converted


def _load_media_rows(
    ordered_paths: Sequence[str],
    image_dir: str,
    desc: str = "load images",
    show_progress: bool = True,
) -> List[dict]:
    iterator = ordered_paths
    if show_progress:
        iterator = tqdm(ordered_paths, desc=desc)
    media_rows: List[dict] = []
    for rel_path in iterator:
        abs_path = _resolve_image_path(rel_path, image_dir)
        media_rows.append(
            _make_media_row(image={"bytes": _read_media_bytes(abs_path), "path": None})
        )
    return media_rows


def _estimate_num_shards_by_rows(
    media_items: Sized,
    media_rows_per_shard: int,
) -> int:
    """Shard count for ``media_items`` (media rows or their source paths)."""
    if media_rows_per_shard < 1:
        raise ValueError(
            f"media_rows_per_shard must be >= 1, got {media_rows_per_shard}"
        )
    num_items = len(media_items)
    if num_items == 0:
        return 1
    return max(1, math.ceil(num_items / media_rows_per_shard))


def _write_parquet(
    rows_or_ds: Sequence[dict] | Dataset,
    path: str,
    row_group_size: int,
    *,
    features: Optional[Features] = None,
) -> None:
    """Write parquet with explicit row groups (HF ``batch_size`` = rows per group)."""
    if row_group_size < 1:
        raise ValueError(f"row_group_size must be >= 1, got {row_group_size}")
    if isinstance(rows_or_ds, Dataset):
        ds = rows_or_ds
    elif features is not None:
        ds = Dataset.from_list(list(rows_or_ds), features=features)
    else:
        ds = Dataset.from_list(list(rows_or_ds))
    ds.to_parquet(path, batch_size=row_group_size)


def _media_shard_path(output_dir: str, shard_idx: int, num_shards: int) -> str:
    return os.path.join(
        output_dir,
        f"media-{shard_idx:05d}-of-{num_shards:05d}.parquet",
    )


def _write_one_media_shard(
    shard_idx: int,
    num_shards: int,
    shard_paths: Sequence[str],
    image_dir: str,
    output_dir: str,
    row_group_size: int,
) -> Tuple[int, int, str]:
    shard_rows = _load_media_rows(
        shard_paths,
        image_dir,
        show_progress=False,
    )
    shard = Dataset.from_list(shard_rows, features=_MEDIA_FEATURES)
    out_path = _media_shard_path(output_dir, shard_idx, num_shards)
    _write_parquet(shard, out_path, row_group_size)
    return shard_idx, len(shard_rows), out_path


def _write_media_shards(
    ordered_paths: Sequence[str],
    image_dir: str,
    output_dir: str,
    media_rows_per_shard: int,
    row_group_size: int,
    num_workers: int,
    *,
    desc: str = "media",
    show_progress: bool = True,
) -> int:
    os.makedirs(output_dir, exist_ok=True)
    num_shards = _estimate_num_shards_by_rows(ordered_paths, media_rows_per_shard)
    for old_path in glob.glob(os.path.join(output_dir, "media-*.parquet")):
        os.remove(old_path)

    shard_specs = []
    for shard_idx in range(num_shards):
        start = shard_idx * media_rows_per_shard
        end = min(start + media_rows_per_shard, len(ordered_paths))
        shard_specs.append((shard_idx, ordered_paths[start:end]))

    if num_workers < 1:
        raise ValueError(f"num_workers must be >= 1, got {num_workers}")

    if num_workers == 1 or num_shards == 1:
        for shard_idx, shard_paths in shard_specs:
            shard_rows = _load_media_rows(
                shard_paths,
                image_dir,
                desc=f"{desc} shard {shard_idx + 1}/{num_shards}",
                show_progress=show_progress,
            )
            shard = Dataset.from_list(shard_rows, features=_MEDIA_FEATURES)
            out_path = _media_shard_path(output_dir, shard_idx, num_shards)
            _write_parquet(shard, out_path, row_group_size)
        return num_shards

    workers = min(num_workers, num_shards)
    with ProcessPoolExecutor(max_workers=workers) as executor:
        futures = [
            executor.submit(
                _write_one_media_shard,
                shard_idx,
                num_shards,
                shard_paths,
                image_dir,
                output_dir,
                row_group_size,
            )
            for shard_idx, shard_paths in shard_specs
        ]
        iterator = as_completed(futures)
        if show_progress:
            iterator = tqdm(
                iterator,
                total=len(futures),
                desc=f"{desc} shards",
            )
        for future in iterator:
            future.result()

    return num_shards


def _write_readme(
    output_dir: str,
    dataset_name: str,
    data_split: str,
    num_media: int,
    num_query: int,
    num_candidate: int,
    query_feature_names: Sequence[str],
    candidate_feature_names: Sequence[str],
) -> None:
    readme = _render_subset_readme_yaml(
        config_name=dataset_name,
        path_prefix="",
        data_split=data_split,
        num_media=num_media,
        num_query=num_query,
        num_candidate=num_candidate,
        query_feature_names=query_feature_names,
        candidate_feature_names=candidate_feature_names,
        pretty_name=dataset_name,
    )
    with open(os.path.join(output_dir, "README.md"), "w", encoding="utf-8") as f:
        f.write(readme)


def _discover_packed_subsets(output_dir: str) -> List[str]:
    """Find packed subset folders under a split output root."""
    subsets: List[str] = []
    for name in sorted(os.listdir(output_dir)):
        sub_path = os.path.join(output_dir, name)
        if not os.path.isdir(sub_path):
            continue
        if not os.path.exists(os.path.join(sub_path, "query.parquet")):
            continue
        if not glob.glob(os.path.join(sub_path, "media-*.parquet")):
            continue
        subsets.append(name)
    return subsets


def _parquet_num_rows(parquet_path: str) -> int:
    return pq.read_metadata(parquet_path).num_rows


def _parquet_column_names(parquet_path: str) -> List[str]:
    return pq.read_schema(parquet_path).names


def _media_num_rows(subset_dir: str) -> int:
    media_files = sorted(glob.glob(os.path.join(subset_dir, "media-*.parquet")))
    if not media_files:
        raise FileNotFoundError(f"No media shards under {subset_dir}")
    return sum(_parquet_num_rows(path) for path in media_files)


def _hub_feature_yaml_lines(name: str, indent: str = "  ") -> str:
    inner = indent + "  "
    if name in ("pos_ids", "neg_ids", "pool_ids"):
        return f"{inner}- name: {name}\n{inner}  sequence: string"
    if name == "scores":
        return f"{inner}- name: {name}\n{inner}  sequence: float64"
    if name == "media_index":
        return f"{inner}- name: {name}\n{inner}  dtype: int64"
    return f"{inner}- name: {name}\n{inner}  dtype: string"


def _features_yaml_block(feature_names: Sequence[str], indent: str = "  ") -> str:
    return "\n".join(_hub_feature_yaml_lines(name, indent) for name in feature_names)


def _media_features_yaml() -> str:
    inner = "  "
    return (
        f"{inner}- name: image\n{inner}  dtype: image\n"
        f"{inner}- name: video\n{inner}  dtype: video\n"
        f"{inner}- name: audio\n{inner}  dtype: audio"
    )


def _role_config_name(subset_name: str, role: str) -> str:
    if role not in MEDIA_ROLES:
        raise ValueError(f"role must be one of {MEDIA_ROLES}, got {role!r}")
    return f"{subset_name}_{role}"


def _render_subset_readme_yaml(
    *,
    config_name: str,
    path_prefix: str,
    data_split: str,
    num_media: int,
    num_query: int,
    num_candidate: int,
    query_feature_names: Sequence[str],
    candidate_feature_names: Sequence[str],
    pretty_name: Optional[str] = None,
) -> str:
    media_path = f"{path_prefix}media-*.parquet" if path_prefix else "media-*.parquet"
    query_path = f"{path_prefix}query.parquet" if path_prefix else "query.parquet"
    candidate_path = f"{path_prefix}candidate.parquet" if path_prefix else "candidate.parquet"
    title_line = f"pretty_name: {pretty_name}\n" if pretty_name else ""
    media_config = _role_config_name(config_name, "media")
    query_config = _role_config_name(config_name, "query")
    candidate_config = _role_config_name(config_name, "candidate")
    return (
        "---\n"
        f"{title_line}"
        "configs:\n"
        f"- config_name: {media_config}\n"
        "  data_files:\n"
        f"  - split: {data_split}\n"
        f"    path: {media_path}\n"
        f"- config_name: {query_config}\n"
        "  data_files:\n"
        f"  - split: {data_split}\n"
        f"    path: {query_path}\n"
        f"- config_name: {candidate_config}\n"
        "  data_files:\n"
        f"  - split: {data_split}\n"
        f"    path: {candidate_path}\n"
        "---\n"
    )


def _collect_subset_hub_metadata(
    subset_name: str,
    subset_dir: str,
) -> dict:
    """Build hub README metadata for one subset config (splits: media/query/candidate)."""
    query_path = os.path.join(subset_dir, "query.parquet")
    candidate_path = os.path.join(subset_dir, "candidate.parquet")
    return {
        "config_name": subset_name,
        "num_media": _media_num_rows(subset_dir),
        "num_query": _parquet_num_rows(query_path),
        "num_candidate": _parquet_num_rows(candidate_path),
        "query_feature_names": _parquet_column_names(query_path),
        "candidate_feature_names": _parquet_column_names(candidate_path),
    }


def build_hub_readme(
    output_dir: str,
    *,
    data_split: Optional[str] = None,
    dataset_title: Optional[str] = None,
    subsets: Optional[Sequence[str]] = None,
) -> str:
    """
    Write a top-level HuggingFace Hub README for all packed subsets under ``output_dir``.

    One config per subset; HF splits are ``media`` / ``query`` / ``candidate``.
    Use separate repos or directories (e.g. ``.../test``, ``.../train``) for collections.
    """
    output_dir = os.path.abspath(output_dir)
    subset_names = list(subsets) if subsets else _discover_packed_subsets(output_dir)
    if not subset_names:
        raise ValueError(f"No packed subsets found under {output_dir}")

    title = dataset_title or _infer_collection_name(output_dir)
    split_name = data_split or _infer_data_split(output_dir)
    config_blocks: List[str] = []
    for subset_name in subset_names:
        _ = _collect_subset_hub_metadata(subset_name, os.path.join(output_dir, subset_name))
        config_blocks.append(
            f"- config_name: {_role_config_name(subset_name, 'media')}\n"
            "  data_files:\n"
            f"  - split: {split_name}\n"
            f"    path: {subset_name}/media-*.parquet\n"
            f"- config_name: {_role_config_name(subset_name, 'query')}\n"
            "  data_files:\n"
            f"  - split: {split_name}\n"
            f"    path: {subset_name}/query.parquet\n"
            f"- config_name: {_role_config_name(subset_name, 'candidate')}\n"
            "  data_files:\n"
            f"  - split: {split_name}\n"
            f"    path: {subset_name}/candidate.parquet"
        )

    readme = (
        "---\n"
        f"pretty_name: {title}\n"
        "configs:\n"
        + "\n".join(config_blocks)
        + "\n---\n"
    )
    readme_path = os.path.join(output_dir, "README.md")
    with open(readme_path, "w", encoding="utf-8") as f:
        f.write(readme)
    return readme_path


def load_hub_subset(
    repo_or_path: str,
    subset_name: str,
    role: str,
    data_split: Optional[str] = None,
) -> Dataset:
    """Load one role dataset for a subset (config: ``{subset}_{role}``, split inferred from path)."""
    if role not in MEDIA_ROLES:
        raise ValueError(f"role must be one of {MEDIA_ROLES}, got {role!r}")
    config_name = _role_config_name(subset_name, role)
    split_name = data_split or _infer_data_split(repo_or_path)
    return load_dataset(repo_or_path, config_name, split=split_name)


def load_hub_subset_dict(
    repo_or_path: str,
    subset_name: str,
    data_split: Optional[str] = None,
) -> DatasetDict:
    """Load media / query / candidate datasets for one subset."""
    return DatasetDict(
        {
            "media": load_hub_subset(repo_or_path, subset_name, "media", data_split=data_split),
            "query": load_hub_subset(repo_or_path, subset_name, "query", data_split=data_split),
            "candidate": load_hub_subset(
                repo_or_path, subset_name, "candidate", data_split=data_split
            ),
        }
    )


def pack_dataset_with_media(
    query_annotations: Sequence[dict],
    candidate_annotations: Sequence[dict],
    image_dir: str,
    output_dir: str,
    *,
    max_shard_size: str = "500MB",
    media_rows_per_shard: int = DEFAULT_MEDIA_ROWS_PER_SHARD,
    num_workers: int = 1,
    row_group_size: int = DEFAULT_ROW_GROUP_SIZE,
    dataset_name: Optional[str] = None,
    data_split: str = "train",
    write_subset_readme: bool = True,
    show_progress: bool = True,
) -> Dict[str, int]:
    query_rows = list(query_annotations)
    candidate_rows = list(candidate_annotations)

    if not query_rows and not candidate_rows:
        raise ValueError("query_annotations and candidate_annotations are both empty")

    ordered_paths, path_to_index = _collect_unique_paths(query_rows, candidate_rows)
    if not ordered_paths:
        raise ValueError("No image_path found in query/candidate annotations")

    name = dataset_name or os.path.basename(os.path.normpath(output_dir))
    # Deprecated in favor of fixed rows-per-shard; kept for CLI compatibility.
    _ = max_shard_size

    os.makedirs(output_dir, exist_ok=True)
    num_shards = _write_media_shards(
        ordered_paths,
        image_dir,
        output_dir,
        media_rows_per_shard,
        row_group_size,
        num_workers,
        desc=f"{name} images",
        show_progress=show_progress,
    )

    query_out = _rows_with_media_index(query_rows, path_to_index)
    candidate_out = _rows_with_media_index(candidate_rows, path_to_index)

    _write_parquet(query_out, os.path.join(output_dir, "query.parquet"), row_group_size)
    _write_parquet(
        candidate_out, os.path.join(output_dir, "candidate.parquet"), row_group_size
    )

    if write_subset_readme:
        _write_readme(
            output_dir=output_dir,
            dataset_name=name,
            data_split=data_split,
            num_media=len(ordered_paths),
            num_query=len(query_out),
            num_candidate=len(candidate_out),
            query_feature_names=list(query_out[0].keys()) if query_out else [],
            candidate_feature_names=list(candidate_out[0].keys()) if candidate_out else [],
        )

    return {
        "num_media": len(ordered_paths),
        "num_query": len(query_out),
        "num_candidate": len(candidate_out),
        "num_shards": num_shards,
    }


def pack_subset_dir(
    subset_input_dir: str,
    subset_output_dir: str,
    image_dir: str,
    *,
    subset_name: Optional[str] = None,
    max_shard_size: str = "500MB",
    media_rows_per_shard: int = DEFAULT_MEDIA_ROWS_PER_SHARD,
    num_workers: int = 1,
    row_group_size: int = DEFAULT_ROW_GROUP_SIZE,
    data_split: str = "train",
    write_subset_readme: bool = True,
    show_progress: bool = True,
) -> Dict[str, int]:
    query_path = os.path.join(subset_input_dir, "query.parquet")
    candidate_path = os.path.join(subset_input_dir, "candidate.parquet")
    if not os.path.exists(query_path):
        raise FileNotFoundError(f"Missing {query_path}")
    if not os.path.exists(candidate_path):
        raise FileNotFoundError(f"Missing {candidate_path}")

    query_rows = pq.read_table(query_path).to_pylist()
    candidate_rows = pq.read_table(candidate_path).to_pylist()

    return pack_dataset_with_media(
        query_rows,
        candidate_rows,
        image_dir=image_dir,
        output_dir=subset_output_dir,
        max_shard_size=max_shard_size,
        media_rows_per_shard=media_rows_per_shard,
        num_workers=num_workers,
        row_group_size=row_group_size,
        dataset_name=subset_name or os.path.basename(subset_input_dir),
        data_split=data_split,
        write_subset_readme=write_subset_readme,
        show_progress=show_progress,
    )


def pack_split_dir(
    input_dir: str,
    output_dir: str,
    *,
    image_dir: Optional[str] = None,
    max_shard_size: str = "500MB",
    media_rows_per_shard: int = DEFAULT_MEDIA_ROWS_PER_SHARD,
    num_workers: int = 1,
    row_group_size: int = DEFAULT_ROW_GROUP_SIZE,
    subsets: Optional[Sequence[str]] = None,
    write_subset_readme: bool = True,
    write_hub_readme: bool = True,
    hub_dataset_title: Optional[str] = None,
    show_progress: bool = True,
) -> Dict[str, Dict[str, int]]:
    """
    Iterate all subsets under a split directory and pack each to
    ``{output_dir}/{subset_name}/``.
    """
    input_dir = os.path.abspath(input_dir)
    output_dir = os.path.abspath(output_dir)
    image_root = os.path.abspath(image_dir or input_dir)
    split_name = _infer_data_split(input_dir)

    subset_names = list(subsets) if subsets else _discover_subsets(input_dir)
    if not subset_names:
        raise ValueError(f"No subsets with query/candidate parquet found under {input_dir}")

    os.makedirs(output_dir, exist_ok=True)
    all_stats: Dict[str, Dict[str, int]] = {}

    subset_iter = subset_names
    if show_progress:
        subset_iter = tqdm(subset_names, desc="subsets")

    for subset_name in subset_iter:
        subset_input = os.path.join(input_dir, subset_name)
        subset_output = os.path.join(output_dir, subset_name)
        stats = pack_subset_dir(
            subset_input,
            subset_output,
            image_dir=image_root,
            subset_name=subset_name,
            max_shard_size=max_shard_size,
            media_rows_per_shard=media_rows_per_shard,
            num_workers=num_workers,
            row_group_size=row_group_size,
            data_split=split_name,
            write_subset_readme=write_subset_readme,
            show_progress=show_progress,
        )
        all_stats[subset_name] = stats
        if show_progress and not isinstance(subset_iter, tqdm):
            print(
                f"[{subset_name}] media={stats['num_media']} "
                f"({stats['num_shards']} shards), "
                f"query={stats['num_query']}, candidate={stats['num_candidate']}"
            )

    if write_hub_readme and all_stats:
        hub_path = build_hub_readme(
            output_dir,
            data_split=split_name,
            dataset_title=hub_dataset_title,
            subsets=list(all_stats.keys()),
        )
        if show_progress:
            print(f"Hub README: {hub_path}")
    return all_stats


def _build_argparser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(
        description="Pack all subsets under a split dir into media_index format.",
    )
    parser.add_argument(
        "--input-dir",
        help="Split directory, e.g. data/preprocess/Identity/test",
    )
    parser.add_argument(
        "--output-dir",
        required=True,
        help="Output root; each subset is written to {output_dir}/{subset_name}/",
    )
    parser.add_argument(
        "--image-dir",
        default=None,
        help="Root for relative image_path values (defaults to input-dir)",
    )
    parser.add_argument(
        "--max-shard-size",
        default="500MB",
        help=(
            "Deprecated. Previously used for byte-based media shard sizing; "
            "kept for compatibility."
        ),
    )
    parser.add_argument(
        "--media-rows-per-shard",
        type=int,
        default=DEFAULT_MEDIA_ROWS_PER_SHARD,
        help=f"Fixed number of media rows per shard (default: {DEFAULT_MEDIA_ROWS_PER_SHARD})",
    )
    parser.add_argument(
        "--num-workers",
        type=int,
        default=4,
        help="Number of worker processes for media shard writing (default: 1)",
    )
    parser.add_argument(
        "--row-group-size",
        type=int,
        default=DEFAULT_ROW_GROUP_SIZE,
        help=(
            "Rows per parquet row group for media/query/candidate "
            f"(default: {DEFAULT_ROW_GROUP_SIZE}, same as colpali_train_set)"
        ),
    )
    parser.add_argument(
        "--no-subset-readme",
        action="store_true",
        help="Do not write per-subset README.md (Hub root README only)",
    )
    parser.add_argument(
        "--no-hub-readme",
        action="store_true",
        help="Do not write top-level Hub README.md under output-dir",
    )
    parser.add_argument(
        "--hub-readme-only",
        action="store_true",
        help="Only (re)generate top-level Hub README from existing packed subsets",
    )
    parser.add_argument(
        "--hub-dataset-title",
        default=None,
        help="pretty_name for top-level Hub README (default: output-dir basename)",
    )
    parser.add_argument(
        "--subsets",
        nargs="*",
        default=None,
        help="Process only these subset names (default: all under input-dir)",
    )
    parser.add_argument(
        "--no-progress",
        action="store_true",
        help="Disable progress bars",
    )
    return parser


def main() -> None:
    args = _build_argparser().parse_args()
    if args.hub_readme_only:
        if not args.output_dir:
            raise SystemExit("--output-dir is required with --hub-readme-only")
        hub_path = build_hub_readme(
            args.output_dir,
            data_split=_infer_data_split(args.output_dir),
            dataset_title=args.hub_dataset_title,
            subsets=args.subsets,
        )
        print(f"Hub README written: {hub_path}")
        return

    if not args.input_dir:
        raise SystemExit("--input-dir is required unless --hub-readme-only is set")
    if not args.output_dir:
        raise SystemExit("--output-dir is required")
    all_stats = pack_split_dir(
        input_dir=args.input_dir,
        output_dir=args.output_dir,
        image_dir=args.image_dir,
        max_shard_size=args.max_shard_size,
        media_rows_per_shard=args.media_rows_per_shard,
        num_workers=args.num_workers,
        row_group_size=args.row_group_size,
        subsets=args.subsets,
        write_subset_readme=not args.no_subset_readme,
        write_hub_readme=not args.no_hub_readme,
        hub_dataset_title=args.hub_dataset_title,
        show_progress=not args.no_progress,
    )
    print(f"Done: {len(all_stats)} subsets -> {args.output_dir}")
    for subset_name, stats in all_stats.items():
        print(
            f"  {subset_name}: media={stats['num_media']} "
            f"({stats['num_shards']} shards), "
            f"query={stats['num_query']}, candidate={stats['num_candidate']}"
        )


if __name__ == "__main__":
    main()