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234 | """Load custom dataset configs.
This module provides the main entry point for loading dataset configurations from
Hugging Face repositories, including Python-based configs. YAML-specific loading
logic lives in the `yaml_config` module.
"""
import importlib.util
import logging
import sys
from pathlib import Path
from types import ModuleType
from huggingface_hub import HfApi
from .data_models import DatasetConfig
from .logging_utils import log_once
from .split_utils import get_repo_splits
from .utils import get_hf_token
from .yaml_config import load_yaml_config
def load_custom_datasets_module(custom_datasets_file: Path) -> ModuleType | None:
"""Load the custom datasets module if it exists.
Args:
custom_datasets_file:
The path to the custom datasets module.
Returns:
The custom datasets module, or None if it does not exist.
"""
if custom_datasets_file.exists():
spec = importlib.util.spec_from_file_location(
name="custom_datasets_module", location=str(custom_datasets_file.resolve())
)
if spec is None:
log_once(
message=(
"Could not load the spec for the custom datasets file from "
f"{custom_datasets_file.resolve()}."
),
level=logging.ERROR,
)
return None
module = importlib.util.module_from_spec(spec=spec)
if spec.loader is None:
log_once(
message=(
"Could not load the module for the custom datasets file from "
f"{custom_datasets_file.resolve()}."
),
level=logging.ERROR,
)
return None
spec.loader.exec_module(module)
return module
return None
def try_get_dataset_config_from_repo(
dataset_id: str,
api_key: str | None,
cache_dir: Path,
trust_remote_code: bool,
run_with_cli: bool,
) -> DatasetConfig | None:
"""Try to get a dataset config from a Hugging Face dataset repository.
The function first looks for a YAML config file (`eval.yaml`) which can be
loaded without executing any remote code. If no YAML file is present the
function falls back to `euroeval_config.py`, which requires
`trust_remote_code=True`.
Args:
dataset_id:
The ID of the dataset to get the config for.
api_key:
The Hugging Face API key to use to check if the repositories have
custom dataset configs.
cache_dir:
The directory to store the cache in.
trust_remote_code:
Whether to trust remote code. Only required when loading a Python
config (`euroeval_config.py`). YAML configs never require this flag.
run_with_cli:
Whether the code is being run with the CLI.
Returns:
The dataset config if it exists, otherwise None.
"""
token = get_hf_token(api_key=api_key)
hf_api = HfApi(token=token)
if not hf_api.repo_exists(repo_id=dataset_id, repo_type="dataset"):
return None
repo_files = list(
hf_api.list_repo_files(repo_id=dataset_id, repo_type="dataset", revision="main")
)
if "eval.yaml" in repo_files:
return load_yaml_config(
hf_api=hf_api, dataset_id=dataset_id, cache_dir=cache_dir
)
return load_python_config(
hf_api=hf_api,
dataset_id=dataset_id,
cache_dir=cache_dir,
trust_remote_code=trust_remote_code,
run_with_cli=run_with_cli,
)
def load_python_config(
hf_api: HfApi,
dataset_id: str,
cache_dir: Path,
trust_remote_code: bool,
run_with_cli: bool,
) -> DatasetConfig | None:
"""Load a dataset config from a euroeval_config.py file in a Hugging Face repo.
Args:
hf_api:
The Hugging Face API object.
dataset_id:
The ID of the dataset to get the config for.
cache_dir:
The directory to store the cache in.
trust_remote_code:
Whether to trust remote code.
run_with_cli:
Whether the code is being run with the CLI.
Returns:
The dataset config if it exists, otherwise None.
"""
repo_files = list(
hf_api.list_repo_files(repo_id=dataset_id, repo_type="dataset", revision="main")
)
if "euroeval_config.py" not in repo_files:
log_once(
message=(
f"Dataset {dataset_id} does not have a euroeval_config.py or a YAML "
"config file (eval.yaml), so we cannot load it. Skipping."
),
level=logging.WARNING,
)
return None
if not trust_remote_code:
rerunning_msg = (
"the --trust-remote-code flag"
if run_with_cli
else "`trust_remote_code=True`"
)
log_once(
message=(
f"The dataset {dataset_id} exists on the Hugging Face Hub and has a "
"euroeval_config.py file, but remote code is not allowed. Please "
f"rerun this with {rerunning_msg} if you trust the code in this "
"repository."
),
level=logging.ERROR,
)
sys.exit(1)
external_config_path = cache_dir / "external_dataset_configs" / dataset_id
external_config_path.mkdir(parents=True, exist_ok=True)
hf_api.hf_hub_download(
repo_id=dataset_id,
repo_type="dataset",
filename="euroeval_config.py",
local_dir=external_config_path,
local_dir_use_symlinks=False,
)
module = load_custom_datasets_module(
custom_datasets_file=external_config_path / "euroeval_config.py"
)
if module is None:
return None
repo_dataset_configs = [
cfg for cfg in vars(module).values() if isinstance(cfg, DatasetConfig)
]
if not repo_dataset_configs:
return None
if len(repo_dataset_configs) > 1:
log_once(
message=(
f"Dataset {dataset_id} has multiple dataset configurations. Please "
"ensure that only a single DatasetConfig is defined in the "
"`euroeval_config.py` file."
),
level=logging.WARNING,
)
return None
train_split, val_split, test_split = get_repo_splits(
hf_api=hf_api, dataset_id=dataset_id
)
if test_split is None:
log_once(
message=(
f"Dataset {dataset_id} does not have a test split, so we cannot load "
"it. Please ensure that the dataset has a test split."
),
level=logging.ERROR,
)
return None
if train_split is None and val_split is not None:
log_once(
message=(
f"Dataset {dataset_id!r} has no training split. Using the validation "
f"split {val_split!r} as the training split instead."
),
level=logging.DEBUG,
)
train_split = val_split
val_split = None
repo_dataset_config = repo_dataset_configs[0]
repo_dataset_config.name = dataset_id
repo_dataset_config.pretty_name = dataset_id
repo_dataset_config.source = dataset_id
repo_dataset_config.train_split = train_split
repo_dataset_config.val_split = val_split
repo_dataset_config.test_split = test_split
return repo_dataset_config
|