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1118181 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 | """Dataset loading — flat, config-per-model, PR-based. OCR column discovery."""
from __future__ import annotations
import json
import structlog
from datasets import Dataset, get_dataset_config_names, load_dataset
from huggingface_hub import HfApi
logger = structlog.get_logger()
class DatasetError(Exception):
"""Raised when dataset loading or column discovery fails."""
# ---------------------------------------------------------------------------
# OCR column discovery
# ---------------------------------------------------------------------------
def discover_ocr_columns(dataset: Dataset) -> dict[str, str]:
"""Discover OCR output columns and their model names from a dataset.
Strategy:
1. Parse ``inference_info`` JSON from the first row (list or single entry).
2. Fallback: heuristic column-name matching (``markdown``, ``ocr``, ``text``).
3. Disambiguate duplicate model names by appending the column name.
Returns:
Mapping of ``column_name → model_name``.
Raises:
DatasetError: If no OCR columns can be found.
"""
columns: dict[str, str] = {}
try:
if "inference_info" not in dataset.column_names:
raise KeyError("no inference_info column")
info_raw = dataset["inference_info"][0] # column access avoids image decode
if info_raw:
info = json.loads(info_raw)
if not isinstance(info, list):
info = [info]
for entry in info:
col = entry.get("column_name", "")
model = entry.get("model_id", entry.get("model_name", "unknown"))
if col and col in dataset.column_names:
columns[col] = model
except (json.JSONDecodeError, TypeError, KeyError) as exc:
logger.warning("could_not_parse_inference_info", error=str(exc))
# Fallback: heuristic
if not columns:
for col in dataset.column_names:
lower = col.lower()
if "markdown" in lower or "ocr" in lower or col == "text":
columns[col] = col
if not columns:
raise DatasetError(f"No OCR columns found. Available columns: {dataset.column_names}")
# Disambiguate duplicates
model_counts: dict[str, int] = {}
for model in columns.values():
model_counts[model] = model_counts.get(model, 0) + 1
disambiguated: dict[str, str] = {}
for col, model in columns.items():
if model_counts[model] > 1:
short = model.split("/")[-1] if "/" in model else model
disambiguated[col] = f"{short} ({col})"
else:
disambiguated[col] = model
return disambiguated
# ---------------------------------------------------------------------------
# PR-based config discovery
# ---------------------------------------------------------------------------
def discover_pr_configs(
repo_id: str,
merge: bool = False,
api: HfApi | None = None,
) -> tuple[list[str], dict[str, str]]:
"""Discover dataset configs from open PRs on a Hub dataset repo.
PR titles must end with ``[config_name]`` to be detected.
Args:
repo_id: HF dataset repo id.
merge: If True, merge each discovered PR before loading.
api: Optional pre-configured HfApi instance.
Returns:
Tuple of (config_names, {config_name: pr_revision}).
"""
if api is None:
api = HfApi()
config_names: list[str] = []
revisions: dict[str, str] = {}
discussions = api.get_repo_discussions(repo_id, repo_type="dataset")
for disc in discussions:
if not disc.is_pull_request or disc.status != "open":
continue
title = disc.title
if "[" in title and title.endswith("]"):
config = title[title.rindex("[") + 1 : -1].strip()
if config:
if merge:
api.merge_pull_request(repo_id, disc.num, repo_type="dataset")
logger.info("merged_pr", pr=disc.num, config=config)
else:
revisions[config] = f"refs/pr/{disc.num}"
config_names.append(config)
return config_names, revisions
def discover_configs(repo_id: str) -> list[str]:
"""List non-default configs from the main branch of a Hub dataset.
Returns:
Config names excluding "default", or empty list if none found.
"""
try:
configs = get_dataset_config_names(repo_id)
except Exception as exc:
logger.info("no_configs_on_main", repo=repo_id, reason=str(exc))
return []
return [c for c in configs if c != "default"]
# ---------------------------------------------------------------------------
# Config-per-model loading
# ---------------------------------------------------------------------------
def load_config_dataset(
repo_id: str,
config_names: list[str],
split: str = "train",
pr_revisions: dict[str, str] | None = None,
) -> tuple[Dataset, dict[str, str]]:
"""Load multiple configs from a Hub dataset and merge into one.
Each config becomes a column whose name is the config name and whose value
is the OCR text (from the first column matching heuristics, or ``markdown``).
Args:
repo_id: HF dataset repo id.
config_names: List of config names to load.
split: Dataset split to load.
pr_revisions: Optional mapping of config_name → revision for PR-based loading.
Returns:
Tuple of (unified Dataset, {column_name: model_id}).
"""
if not config_names:
raise DatasetError("No config names provided")
pr_revisions = pr_revisions or {}
unified: Dataset | None = None
ocr_columns: dict[str, str] = {}
for config in config_names:
revision = pr_revisions.get(config)
kwargs: dict = {"path": repo_id, "name": config, "split": split}
if revision:
kwargs["revision"] = revision
ds = load_dataset(**kwargs)
# Find the OCR text column in this config
text_col = _find_text_column(ds)
if text_col is None:
logger.warning("no_text_column_in_config", config=config)
continue
# Extract model_id from inference_info if available
model_id = _extract_model_id(ds, config)
ocr_columns[config] = model_id
# Build unified dataset using Arrow-level ops (no per-row image decode)
text_values = ds[text_col] # column access — no image decoding
if unified is None:
# First config: keep all columns except text_col, add text as config name
drop = [text_col] if text_col != config else []
unified = ds.remove_columns(drop) if drop else ds
if config != text_col:
unified = unified.add_column(config, text_values)
# Also rename text_col to config if they differ and text_col was kept
else:
if len(ds) != len(unified):
logger.warning(
"config_length_mismatch",
config=config,
expected=len(unified),
got=len(ds),
)
text_values = text_values[: len(unified)]
unified = unified.add_column(config, text_values)
if unified is None:
raise DatasetError("No configs loaded successfully")
return unified, ocr_columns
def _extract_model_id(ds: Dataset, config: str) -> str:
"""Extract model_id from inference_info in first row, falling back to config name."""
if "inference_info" not in ds.column_names:
return config
try:
info_raw = ds["inference_info"][0] # column access avoids image decode
if info_raw:
info = json.loads(info_raw)
if isinstance(info, list):
info = info[0]
return info.get("model_id", info.get("model_name", config))
except (json.JSONDecodeError, TypeError, KeyError, IndexError):
pass
return config
def _find_text_column(ds: Dataset) -> str | None:
"""Find the likely OCR text column in a dataset.
Priority:
1. ``inference_info[0]["column_name"]`` if present and exists in dataset.
2. First column matching ``markdown`` (case-insensitive).
3. First column matching ``ocr`` (case-insensitive).
4. Column named exactly ``text``.
"""
# Try inference_info first (column access avoids image decoding)
if "inference_info" in ds.column_names:
try:
info_raw = ds["inference_info"][0]
if info_raw:
info = json.loads(info_raw)
if isinstance(info, list):
info = info[0]
col_name = info.get("column_name", "")
if col_name and col_name in ds.column_names:
return col_name
except (json.JSONDecodeError, TypeError, KeyError, IndexError):
pass
# Prioritized heuristic: markdown > ocr > text
for pattern in ["markdown", "ocr"]:
for col in ds.column_names:
if pattern in col.lower():
return col
if "text" in ds.column_names:
return "text"
return None
# ---------------------------------------------------------------------------
# Flat dataset loading
# ---------------------------------------------------------------------------
def load_flat_dataset(
repo_id: str,
split: str = "train",
columns: list[str] | None = None,
) -> tuple[Dataset, dict[str, str]]:
"""Load a flat dataset from Hub and discover OCR columns.
Args:
repo_id: HF dataset repo id.
split: Dataset split.
columns: If given, use these as OCR columns (maps col→col).
Returns:
Tuple of (Dataset, {column_name: model_name}).
"""
ds = load_dataset(repo_id, split=split)
if columns:
# Validate columns exist
for col in columns:
if col not in ds.column_names:
raise DatasetError(f"Column '{col}' not found. Available: {ds.column_names}")
ocr_columns = {col: col for col in columns}
else:
ocr_columns = discover_ocr_columns(ds)
return ds, ocr_columns
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