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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()
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