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load.py
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/
load.py
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# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Access datasets."""
import filecmp
import importlib
import inspect
import json
import os
import posixpath
import shutil
import time
import warnings
from collections import Counter
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List, Mapping, Optional, Sequence, Tuple, Type, Union
import fsspec
import requests
from huggingface_hub import HfApi
from . import config
from .arrow_dataset import Dataset
from .builder import DatasetBuilder
from .data_files import (
DEFAULT_PATTERNS_ALL,
DataFilesDict,
DataFilesList,
EmptyDatasetError,
get_data_patterns_in_dataset_repository,
get_data_patterns_locally,
get_metadata_patterns_in_dataset_repository,
get_metadata_patterns_locally,
sanitize_patterns,
)
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download.download_config import DownloadConfig
from .download.download_manager import DownloadMode
from .download.streaming_download_manager import StreamingDownloadManager, xglob, xjoin
from .features import Features
from .filesystems import extract_path_from_uri, is_remote_filesystem
from .info import DatasetInfo, DatasetInfosDict
from .iterable_dataset import IterableDataset
from .metric import Metric
from .packaged_modules import (
_EXTENSION_TO_MODULE,
_MODULE_SUPPORTS_METADATA,
_PACKAGED_DATASETS_MODULES,
_hash_python_lines,
)
from .splits import Split
from .tasks import TaskTemplate
from .utils.deprecation_utils import deprecated
from .utils.file_utils import (
OfflineModeIsEnabled,
_raise_if_offline_mode_is_enabled,
cached_path,
head_hf_s3,
hf_github_url,
init_hf_modules,
is_relative_path,
relative_to_absolute_path,
url_or_path_join,
)
from .utils.filelock import FileLock
from .utils.hub import hf_hub_url
from .utils.info_utils import VerificationMode, is_small_dataset
from .utils.logging import get_logger
from .utils.metadata import DatasetMetadata
from .utils.py_utils import get_imports
from .utils.version import Version
logger = get_logger(__name__)
ALL_ALLOWED_EXTENSIONS = list(_EXTENSION_TO_MODULE.keys()) + ["zip"]
def init_dynamic_modules(
name: str = config.MODULE_NAME_FOR_DYNAMIC_MODULES, hf_modules_cache: Optional[Union[Path, str]] = None
):
"""
Create a module with name `name` in which you can add dynamic modules
such as metrics or datasets. The module can be imported using its name.
The module is created in the HF_MODULE_CACHE directory by default (~/.cache/huggingface/modules) but it can
be overridden by specifying a path to another directory in `hf_modules_cache`.
"""
hf_modules_cache = init_hf_modules(hf_modules_cache)
dynamic_modules_path = os.path.join(hf_modules_cache, name)
os.makedirs(dynamic_modules_path, exist_ok=True)
if not os.path.exists(os.path.join(dynamic_modules_path, "__init__.py")):
with open(os.path.join(dynamic_modules_path, "__init__.py"), "w"):
pass
return dynamic_modules_path
def import_main_class(module_path, dataset=True) -> Optional[Union[Type[DatasetBuilder], Type[Metric]]]:
"""Import a module at module_path and return its main class:
- a DatasetBuilder if dataset is True
- a Metric if dataset is False
"""
module = importlib.import_module(module_path)
if dataset:
main_cls_type = DatasetBuilder
else:
main_cls_type = Metric
# Find the main class in our imported module
module_main_cls = None
for name, obj in module.__dict__.items():
if inspect.isclass(obj) and issubclass(obj, main_cls_type):
if inspect.isabstract(obj):
continue
module_main_cls = obj
obj_module = inspect.getmodule(obj)
if obj_module is not None and module == obj_module:
break
return module_main_cls
def files_to_hash(file_paths: List[str]) -> str:
"""
Convert a list of scripts or text files provided in file_paths into a hashed filename in a repeatable way.
"""
# List all python files in directories if directories are supplied as part of external imports
to_use_files: List[Union[Path, str]] = []
for file_path in file_paths:
if os.path.isdir(file_path):
to_use_files.extend(list(Path(file_path).rglob("*.[pP][yY]")))
else:
to_use_files.append(file_path)
# Get the code from all these files
lines = []
for file_path in to_use_files:
with open(file_path, encoding="utf-8") as f:
lines.extend(f.readlines())
return _hash_python_lines(lines)
def increase_load_count(name: str, resource_type: str):
"""Update the download count of a dataset or metric."""
if not config.HF_DATASETS_OFFLINE and config.HF_UPDATE_DOWNLOAD_COUNTS:
try:
head_hf_s3(name, filename=name + ".py", dataset=(resource_type == "dataset"))
except Exception:
pass
def _download_additional_modules(
name: str, base_path: str, imports: Tuple[str, str, str, str], download_config: Optional[DownloadConfig]
) -> List[Tuple[str, str]]:
"""
Download additional module for a module <name>.py at URL (or local path) <base_path>/<name>.py
The imports must have been parsed first using ``get_imports``.
If some modules need to be installed with pip, an error is raised showing how to install them.
This function return the list of downloaded modules as tuples (import_name, module_file_path).
The downloaded modules can then be moved into an importable directory with ``_copy_script_and_other_resources_in_importable_dir``.
"""
local_imports = []
library_imports = []
download_config = download_config.copy()
if download_config.download_desc is None:
download_config.download_desc = "Downloading extra modules"
for import_type, import_name, import_path, sub_directory in imports:
if import_type == "library":
library_imports.append((import_name, import_path)) # Import from a library
continue
if import_name == name:
raise ValueError(
f"Error in the {name} script, importing relative {import_name} module "
f"but {import_name} is the name of the script. "
f"Please change relative import {import_name} to another name and add a '# From: URL_OR_PATH' "
f"comment pointing to the original relative import file path."
)
if import_type == "internal":
url_or_filename = url_or_path_join(base_path, import_path + ".py")
elif import_type == "external":
url_or_filename = import_path
else:
raise ValueError("Wrong import_type")
local_import_path = cached_path(
url_or_filename,
download_config=download_config,
)
if sub_directory is not None:
local_import_path = os.path.join(local_import_path, sub_directory)
local_imports.append((import_name, local_import_path))
# Check library imports
needs_to_be_installed = {}
for library_import_name, library_import_path in library_imports:
try:
lib = importlib.import_module(library_import_name) # noqa F841
except ImportError:
if library_import_name not in needs_to_be_installed or library_import_path != library_import_name:
needs_to_be_installed[library_import_name] = library_import_path
if needs_to_be_installed:
_dependencies_str = "dependencies" if len(needs_to_be_installed) > 1 else "dependency"
_them_str = "them" if len(needs_to_be_installed) > 1 else "it"
if "sklearn" in needs_to_be_installed.keys():
needs_to_be_installed["sklearn"] = "scikit-learn"
raise ImportError(
f"To be able to use {name}, you need to install the following {_dependencies_str}: "
f"{', '.join(needs_to_be_installed)}.\nPlease install {_them_str} using 'pip install "
f"{' '.join(needs_to_be_installed.values())}' for instance."
)
return local_imports
def _copy_script_and_other_resources_in_importable_dir(
name: str,
importable_directory_path: str,
subdirectory_name: str,
original_local_path: str,
local_imports: List[Tuple[str, str]],
additional_files: List[Tuple[str, str]],
download_mode: Optional[Union[DownloadMode, str]],
) -> str:
"""Copy a script and its required imports to an importable directory
Args:
name (str): name of the resource to load
importable_directory_path (str): path to the loadable folder in the dynamic modules directory
subdirectory_name (str): name of the subdirectory in importable_directory_path in which to place the script
original_local_path (str): local path to the resource script
local_imports (List[Tuple[str, str]]): list of (destination_filename, import_file_to_copy)
additional_files (List[Tuple[str, str]]): list of (destination_filename, additional_file_to_copy)
download_mode (Optional[Union[DownloadMode, str]]): download mode
Return:
importable_local_file: path to an importable module with importlib.import_module
"""
# Define a directory with a unique name in our dataset or metric folder
# path is: ./datasets|metrics/dataset|metric_name/hash_from_code/script.py
# we use a hash as subdirectory_name to be able to have multiple versions of a dataset/metric processing file together
importable_subdirectory = os.path.join(importable_directory_path, subdirectory_name)
importable_local_file = os.path.join(importable_subdirectory, name + ".py")
# Prevent parallel disk operations
lock_path = importable_directory_path + ".lock"
with FileLock(lock_path):
# Create main dataset/metrics folder if needed
if download_mode == DownloadMode.FORCE_REDOWNLOAD and os.path.exists(importable_directory_path):
shutil.rmtree(importable_directory_path)
os.makedirs(importable_directory_path, exist_ok=True)
# add an __init__ file to the main dataset folder if needed
init_file_path = os.path.join(importable_directory_path, "__init__.py")
if not os.path.exists(init_file_path):
with open(init_file_path, "w"):
pass
# Create hash dataset folder if needed
os.makedirs(importable_subdirectory, exist_ok=True)
# add an __init__ file to the hash dataset folder if needed
init_file_path = os.path.join(importable_subdirectory, "__init__.py")
if not os.path.exists(init_file_path):
with open(init_file_path, "w"):
pass
# Copy dataset.py file in hash folder if needed
if not os.path.exists(importable_local_file):
shutil.copyfile(original_local_path, importable_local_file)
# Record metadata associating original dataset path with local unique folder
# Use os.path.splitext to split extension from importable_local_file
meta_path = os.path.splitext(importable_local_file)[0] + ".json"
if not os.path.exists(meta_path):
meta = {"original file path": original_local_path, "local file path": importable_local_file}
# the filename is *.py in our case, so better rename to filename.json instead of filename.py.json
with open(meta_path, "w", encoding="utf-8") as meta_file:
json.dump(meta, meta_file)
# Copy all the additional imports
for import_name, import_path in local_imports:
if os.path.isfile(import_path):
full_path_local_import = os.path.join(importable_subdirectory, import_name + ".py")
if not os.path.exists(full_path_local_import):
shutil.copyfile(import_path, full_path_local_import)
elif os.path.isdir(import_path):
full_path_local_import = os.path.join(importable_subdirectory, import_name)
if not os.path.exists(full_path_local_import):
shutil.copytree(import_path, full_path_local_import)
else:
raise ImportError(f"Error with local import at {import_path}")
# Copy additional files like dataset_infos.json file if needed
for file_name, original_path in additional_files:
destination_additional_path = os.path.join(importable_subdirectory, file_name)
if not os.path.exists(destination_additional_path) or not filecmp.cmp(
original_path, destination_additional_path
):
shutil.copyfile(original_path, destination_additional_path)
return importable_local_file
def _create_importable_file(
local_path: str,
local_imports: List[Tuple[str, str]],
additional_files: List[Tuple[str, str]],
dynamic_modules_path: str,
module_namespace: str,
name: str,
download_mode: DownloadMode,
) -> Tuple[str, str]:
importable_directory_path = os.path.join(dynamic_modules_path, module_namespace, name.replace("/", "--"))
Path(importable_directory_path).mkdir(parents=True, exist_ok=True)
(Path(importable_directory_path).parent / "__init__.py").touch(exist_ok=True)
hash = files_to_hash([local_path] + [loc[1] for loc in local_imports])
importable_local_file = _copy_script_and_other_resources_in_importable_dir(
name=name.split("/")[-1],
importable_directory_path=importable_directory_path,
subdirectory_name=hash,
original_local_path=local_path,
local_imports=local_imports,
additional_files=additional_files,
download_mode=download_mode,
)
logger.debug(f"Created importable dataset file at {importable_local_file}")
module_path = ".".join(
[os.path.basename(dynamic_modules_path), module_namespace, name.replace("/", "--"), hash, name.split("/")[-1]]
)
return module_path, hash
def infer_module_for_data_files(
data_files_list: DataFilesList, use_auth_token: Optional[Union[bool, str]] = None
) -> Optional[Tuple[str, str]]:
"""Infer module (and builder kwargs) from list of data files.
Args:
data_files_list (DataFilesList): List of data files.
use_auth_token (bool or str, optional): Whether to use token or token to authenticate on the Hugging Face Hub
for private remote files.
Returns:
tuple[str, str]: Tuple with
- inferred module name
- builder kwargs
"""
extensions_counter = Counter(
suffix[1:]
for filepath in data_files_list[: config.DATA_FILES_MAX_NUMBER_FOR_MODULE_INFERENCE]
for suffix in Path(filepath).suffixes
)
if extensions_counter:
for ext, _ in extensions_counter.most_common():
if ext in _EXTENSION_TO_MODULE:
return _EXTENSION_TO_MODULE[ext]
elif ext == "zip":
return infer_module_for_data_files_in_archives(data_files_list, use_auth_token=use_auth_token)
return None, {}
def infer_module_for_data_files_in_archives(
data_files_list: DataFilesList, use_auth_token: Optional[Union[bool, str]]
) -> Optional[Tuple[str, str]]:
"""Infer module (and builder kwargs) from list of archive data files.
Args:
data_files_list (DataFilesList): List of data files.
use_auth_token (bool or str, optional): Whether to use token or token to authenticate on the Hugging Face Hub
for private remote files.
Returns:
tuple[str, str]: Tuple with
- inferred module name
- builder kwargs
"""
archived_files = []
archive_files_counter = 0
for filepath in data_files_list:
if str(filepath).endswith(".zip"):
archive_files_counter += 1
if archive_files_counter > config.GLOBBED_DATA_FILES_MAX_NUMBER_FOR_MODULE_INFERENCE:
break
extracted = xjoin(StreamingDownloadManager().extract(filepath), "**")
archived_files += [
f.split("::")[0]
for f in xglob(extracted, recursive=True, use_auth_token=use_auth_token)[
: config.ARCHIVED_DATA_FILES_MAX_NUMBER_FOR_MODULE_INFERENCE
]
]
extensions_counter = Counter(suffix[1:] for filepath in archived_files for suffix in Path(filepath).suffixes)
if extensions_counter:
most_common = extensions_counter.most_common(1)[0][0]
if most_common in _EXTENSION_TO_MODULE:
return _EXTENSION_TO_MODULE[most_common]
return None, {}
@dataclass
class DatasetModule:
module_path: str
hash: str
builder_kwargs: dict
@dataclass
class MetricModule:
module_path: str
hash: str
class _DatasetModuleFactory:
def get_module(self) -> DatasetModule:
raise NotImplementedError
class _MetricModuleFactory:
def get_module(self) -> MetricModule:
raise NotImplementedError
class GithubMetricModuleFactory(_MetricModuleFactory):
"""Get the module of a metric. The metric script is downloaded from GitHub.
<Deprecated version="2.5.0">
Use the new library 馃 Evaluate instead: https://huggingface.co/docs/evaluate
</Deprecated>
"""
@deprecated("Use the new library 馃 Evaluate instead: https://huggingface.co/docs/evaluate")
def __init__(
self,
name: str,
revision: Optional[Union[str, Version]] = None,
download_config: Optional[DownloadConfig] = None,
download_mode: Optional[Union[DownloadMode, str]] = None,
dynamic_modules_path: Optional[str] = None,
):
self.name = name
self.revision = revision
self.download_config = download_config.copy() if download_config else DownloadConfig()
if self.download_config.max_retries < 3:
self.download_config.max_retries = 3
self.download_mode = download_mode
self.dynamic_modules_path = dynamic_modules_path
assert self.name.count("/") == 0
increase_load_count(name, resource_type="metric")
def download_loading_script(self, revision: Optional[str]) -> str:
file_path = hf_github_url(path=self.name, name=self.name + ".py", revision=revision, dataset=False)
download_config = self.download_config.copy()
if download_config.download_desc is None:
download_config.download_desc = "Downloading builder script"
return cached_path(file_path, download_config=download_config)
def get_module(self) -> MetricModule:
# get script and other files
revision = self.revision
try:
local_path = self.download_loading_script(revision)
revision = self.revision
except FileNotFoundError:
if revision is not None:
raise
else:
revision = "main"
local_path = self.download_loading_script(revision)
logger.warning(
f"Couldn't find a directory or a metric named '{self.name}' in this version. "
f"It was picked from the main branch on github instead."
)
imports = get_imports(local_path)
local_imports = _download_additional_modules(
name=self.name,
base_path=hf_github_url(path=self.name, name="", revision=revision, dataset=False),
imports=imports,
download_config=self.download_config,
)
# copy the script and the files in an importable directory
dynamic_modules_path = self.dynamic_modules_path if self.dynamic_modules_path else init_dynamic_modules()
module_path, hash = _create_importable_file(
local_path=local_path,
local_imports=local_imports,
additional_files=[],
dynamic_modules_path=dynamic_modules_path,
module_namespace="metrics",
name=self.name,
download_mode=self.download_mode,
)
# make the new module to be noticed by the import system
importlib.invalidate_caches()
return MetricModule(module_path, hash)
class LocalMetricModuleFactory(_MetricModuleFactory):
"""Get the module of a local metric. The metric script is loaded from a local script.
<Deprecated version="2.5.0">
Use the new library 馃 Evaluate instead: https://huggingface.co/docs/evaluate
</Deprecated>
"""
@deprecated("Use the new library 馃 Evaluate instead: https://huggingface.co/docs/evaluate")
def __init__(
self,
path: str,
download_config: Optional[DownloadConfig] = None,
download_mode: Optional[Union[DownloadMode, str]] = None,
dynamic_modules_path: Optional[str] = None,
):
self.path = path
self.name = Path(path).stem
self.download_config = download_config or DownloadConfig()
self.download_mode = download_mode
self.dynamic_modules_path = dynamic_modules_path
def get_module(self) -> MetricModule:
# get script and other files
imports = get_imports(self.path)
local_imports = _download_additional_modules(
name=self.name,
base_path=str(Path(self.path).parent),
imports=imports,
download_config=self.download_config,
)
# copy the script and the files in an importable directory
dynamic_modules_path = self.dynamic_modules_path if self.dynamic_modules_path else init_dynamic_modules()
module_path, hash = _create_importable_file(
local_path=self.path,
local_imports=local_imports,
additional_files=[],
dynamic_modules_path=dynamic_modules_path,
module_namespace="metrics",
name=self.name,
download_mode=self.download_mode,
)
# make the new module to be noticed by the import system
importlib.invalidate_caches()
return MetricModule(module_path, hash)
class LocalDatasetModuleFactoryWithScript(_DatasetModuleFactory):
"""Get the module of a local dataset. The dataset script is loaded from a local script."""
def __init__(
self,
path: str,
download_config: Optional[DownloadConfig] = None,
download_mode: Optional[Union[DownloadMode, str]] = None,
dynamic_modules_path: Optional[str] = None,
):
self.path = path
self.name = Path(path).stem
self.download_config = download_config or DownloadConfig()
self.download_mode = download_mode
self.dynamic_modules_path = dynamic_modules_path
def get_module(self) -> DatasetModule:
# get script and other files
dataset_infos_path = Path(self.path).parent / config.DATASETDICT_INFOS_FILENAME
dataset_readme_path = Path(self.path).parent / "README.md"
imports = get_imports(self.path)
local_imports = _download_additional_modules(
name=self.name,
base_path=str(Path(self.path).parent),
imports=imports,
download_config=self.download_config,
)
additional_files = []
if dataset_infos_path.is_file():
additional_files.append((config.DATASETDICT_INFOS_FILENAME, str(dataset_infos_path)))
if dataset_readme_path.is_file():
additional_files.append(("README.md", dataset_readme_path))
# copy the script and the files in an importable directory
dynamic_modules_path = self.dynamic_modules_path if self.dynamic_modules_path else init_dynamic_modules()
module_path, hash = _create_importable_file(
local_path=self.path,
local_imports=local_imports,
additional_files=additional_files,
dynamic_modules_path=dynamic_modules_path,
module_namespace="datasets",
name=self.name,
download_mode=self.download_mode,
)
# make the new module to be noticed by the import system
importlib.invalidate_caches()
builder_kwargs = {"hash": hash, "base_path": str(Path(self.path).parent)}
return DatasetModule(module_path, hash, builder_kwargs)
class LocalDatasetModuleFactoryWithoutScript(_DatasetModuleFactory):
"""Get the module of a dataset loaded from the user's data files. The dataset builder module to use is inferred
from the data files extensions."""
def __init__(
self,
path: str,
data_dir: Optional[str] = None,
data_files: Optional[Union[str, List, Dict]] = None,
download_mode: Optional[Union[DownloadMode, str]] = None,
):
if data_dir and os.path.isabs(data_dir):
raise ValueError(f"`data_dir` must be relative to a dataset directory's root: {path}")
self.path = path
self.name = Path(path).stem
self.data_files = data_files
self.data_dir = data_dir
self.download_mode = download_mode
def get_module(self) -> DatasetModule:
base_path = os.path.join(self.path, self.data_dir) if self.data_dir else self.path
patterns = (
sanitize_patterns(self.data_files) if self.data_files is not None else get_data_patterns_locally(base_path)
)
data_files = DataFilesDict.from_local_or_remote(
patterns,
base_path=base_path,
allowed_extensions=ALL_ALLOWED_EXTENSIONS,
)
split_modules = {
split: infer_module_for_data_files(data_files_list) for split, data_files_list in data_files.items()
}
module_name, builder_kwargs = next(iter(split_modules.values()))
if any((module_name, builder_kwargs) != split_module for split_module in split_modules.values()):
raise ValueError(f"Couldn't infer the same data file format for all splits. Got {split_modules}")
if not module_name:
raise FileNotFoundError(f"No (supported) data files or dataset script found in {self.path}")
# Collect metadata files if the module supports them
if self.data_files is None and module_name in _MODULE_SUPPORTS_METADATA and patterns != DEFAULT_PATTERNS_ALL:
try:
metadata_patterns = get_metadata_patterns_locally(base_path)
except FileNotFoundError:
metadata_patterns = None
if metadata_patterns is not None:
metadata_files = DataFilesList.from_local_or_remote(metadata_patterns, base_path=base_path)
for key in data_files:
data_files[key] = DataFilesList(
data_files[key] + metadata_files,
data_files[key].origin_metadata + metadata_files.origin_metadata,
)
module_path, hash = _PACKAGED_DATASETS_MODULES[module_name]
builder_kwargs = {
"hash": hash,
"data_files": data_files,
"config_name": os.path.basename(self.path.rstrip("/")),
"base_path": self.path,
**builder_kwargs,
}
if os.path.isfile(os.path.join(self.path, config.DATASETDICT_INFOS_FILENAME)):
with open(os.path.join(self.path, config.DATASETDICT_INFOS_FILENAME), encoding="utf-8") as f:
dataset_infos: DatasetInfosDict = json.load(f)
if dataset_infos:
builder_kwargs["config_name"] = next(iter(dataset_infos))
builder_kwargs["info"] = DatasetInfo.from_dict(next(iter(dataset_infos.values())))
if os.path.isfile(os.path.join(self.path, "README.md")):
dataset_metadata = DatasetMetadata.from_readme(Path(self.path) / "README.md")
if isinstance(dataset_metadata.get("dataset_info"), list) and dataset_metadata["dataset_info"]:
dataset_info_dict = dataset_metadata["dataset_info"][0]
builder_kwargs["info"] = DatasetInfo._from_yaml_dict(dataset_info_dict)
if "config_name" in dataset_info_dict:
builder_kwargs["config_name"] = dataset_info_dict["config_name"]
elif isinstance(dataset_metadata.get("dataset_info"), dict) and dataset_metadata["dataset_info"]:
dataset_info_dict = dataset_metadata["dataset_info"]
builder_kwargs["info"] = DatasetInfo._from_yaml_dict(dataset_info_dict)
if "config_name" in dataset_info_dict:
builder_kwargs["config_name"] = dataset_info_dict["config_name"]
return DatasetModule(module_path, hash, builder_kwargs)
class PackagedDatasetModuleFactory(_DatasetModuleFactory):
"""Get the dataset builder module from the ones that are packaged with the library: csv, json, etc."""
def __init__(
self,
name: str,
data_dir: Optional[str] = None,
data_files: Optional[Union[str, List, Dict]] = None,
download_config: Optional[DownloadConfig] = None,
download_mode: Optional[Union[DownloadMode, str]] = None,
):
self.name = name
self.data_files = data_files
self.data_dir = data_dir
self.download_config = download_config
self.download_mode = download_mode
increase_load_count(name, resource_type="dataset")
def get_module(self) -> DatasetModule:
base_path = (
str(Path(self.data_dir).expanduser().resolve()) if self.data_dir is not None else str(Path().resolve())
)
patterns = (
sanitize_patterns(self.data_files) if self.data_files is not None else get_data_patterns_locally(base_path)
)
data_files = DataFilesDict.from_local_or_remote(
patterns,
use_auth_token=self.download_config.use_auth_token,
base_path=base_path,
)
if self.data_files is None and self.name in _MODULE_SUPPORTS_METADATA and patterns != DEFAULT_PATTERNS_ALL:
try:
metadata_patterns = get_metadata_patterns_locally(base_path)
except FileNotFoundError:
metadata_patterns = None
if metadata_patterns is not None:
metadata_files = DataFilesList.from_local_or_remote(
metadata_patterns, use_auth_token=self.download_config.use_auth_token, base_path=base_path
)
for key in data_files:
data_files[key] = DataFilesList(
data_files[key] + metadata_files,
data_files[key].origin_metadata + metadata_files.origin_metadata,
)
module_path, hash = _PACKAGED_DATASETS_MODULES[self.name]
builder_kwargs = {"hash": hash, "data_files": data_files}
return DatasetModule(module_path, hash, builder_kwargs)
class HubDatasetModuleFactoryWithoutScript(_DatasetModuleFactory):
"""
Get the module of a dataset loaded from data files of a dataset repository.
The dataset builder module to use is inferred from the data files extensions.
"""
def __init__(
self,
name: str,
revision: Optional[Union[str, Version]] = None,
data_dir: Optional[str] = None,
data_files: Optional[Union[str, List, Dict]] = None,
download_config: Optional[DownloadConfig] = None,
download_mode: Optional[Union[DownloadMode, str]] = None,
):
self.name = name
self.revision = revision
self.data_files = data_files
self.data_dir = data_dir
self.download_config = download_config or DownloadConfig()
self.download_mode = download_mode
increase_load_count(name, resource_type="dataset")
def get_module(self) -> DatasetModule:
hfh_dataset_info = HfApi(config.HF_ENDPOINT).dataset_info(
self.name,
revision=self.revision,
token=self.download_config.use_auth_token,
timeout=100.0,
)
patterns = (
sanitize_patterns(self.data_files)
if self.data_files is not None
else get_data_patterns_in_dataset_repository(hfh_dataset_info, self.data_dir)
)
data_files = DataFilesDict.from_hf_repo(
patterns,
dataset_info=hfh_dataset_info,
base_path=self.data_dir,
allowed_extensions=ALL_ALLOWED_EXTENSIONS,
)
split_modules = {
split: infer_module_for_data_files(data_files_list, use_auth_token=self.download_config.use_auth_token)
for split, data_files_list in data_files.items()
}
module_name, builder_kwargs = next(iter(split_modules.values()))
if any((module_name, builder_kwargs) != split_module for split_module in split_modules.values()):
raise ValueError(f"Couldn't infer the same data file format for all splits. Got {split_modules}")
if not module_name:
raise FileNotFoundError(f"No (supported) data files or dataset script found in {self.name}")
# Collect metadata files if the module supports them
if self.data_files is None and module_name in _MODULE_SUPPORTS_METADATA and patterns != DEFAULT_PATTERNS_ALL:
try:
metadata_patterns = get_metadata_patterns_in_dataset_repository(hfh_dataset_info, self.data_dir)
except FileNotFoundError:
metadata_patterns = None
if metadata_patterns is not None:
metadata_files = DataFilesList.from_hf_repo(
metadata_patterns, dataset_info=hfh_dataset_info, base_path=self.data_dir
)
for key in data_files:
data_files[key] = DataFilesList(
data_files[key] + metadata_files,
data_files[key].origin_metadata + metadata_files.origin_metadata,
)
module_path, hash = _PACKAGED_DATASETS_MODULES[module_name]
builder_kwargs = {
"hash": hash,
"data_files": data_files,
"config_name": self.name.replace("/", "--"),
"base_path": hf_hub_url(self.name, "", revision=self.revision),
"repo_id": self.name,
**builder_kwargs,
}
download_config = self.download_config.copy()
if download_config.download_desc is None:
download_config.download_desc = "Downloading metadata"
try:
dataset_infos_path = cached_path(
hf_hub_url(self.name, config.DATASETDICT_INFOS_FILENAME, revision=self.revision),
download_config=download_config,
)
with open(dataset_infos_path, encoding="utf-8") as f:
dataset_infos: DatasetInfosDict = json.load(f)
if dataset_infos:
builder_kwargs["config_name"] = next(iter(dataset_infos))
builder_kwargs["info"] = DatasetInfo.from_dict(next(iter(dataset_infos.values())))
except FileNotFoundError:
pass
download_config = self.download_config.copy()
if download_config.download_desc is None:
download_config.download_desc = "Downloading readme"
try:
dataset_readme_path = cached_path(
hf_hub_url(self.name, "README.md", revision=self.revision),
download_config=download_config,
)
dataset_metadata = DatasetMetadata.from_readme(Path(dataset_readme_path))
if isinstance(dataset_metadata.get("dataset_info"), list) and dataset_metadata["dataset_info"]:
dataset_info_dict = dataset_metadata["dataset_info"][0]
builder_kwargs["info"] = DatasetInfo._from_yaml_dict(dataset_info_dict)
if "config_name" in dataset_info_dict:
builder_kwargs["config_name"] = dataset_info_dict["config_name"]
elif isinstance(dataset_metadata.get("dataset_info"), dict) and dataset_metadata["dataset_info"]:
dataset_info_dict = dataset_metadata["dataset_info"]
builder_kwargs["info"] = DatasetInfo._from_yaml_dict(dataset_info_dict)
if "config_name" in dataset_info_dict:
builder_kwargs["config_name"] = dataset_info_dict["config_name"]
except FileNotFoundError:
pass
return DatasetModule(module_path, hash, builder_kwargs)
class HubDatasetModuleFactoryWithScript(_DatasetModuleFactory):
"""Get the module of a dataset from a dataset repository. The dataset script comes from the script inside the dataset repository."""
def __init__(
self,
name: str,
revision: Optional[Union[str, Version]] = None,
download_config: Optional[DownloadConfig] = None,
download_mode: Optional[Union[DownloadMode, str]] = None,
dynamic_modules_path: Optional[str] = None,
):
self.name = name
self.revision = revision
self.download_config = download_config or DownloadConfig()
self.download_mode = download_mode
self.dynamic_modules_path = dynamic_modules_path
increase_load_count(name, resource_type="dataset")
def download_loading_script(self) -> str:
file_path = hf_hub_url(repo_id=self.name, path=self.name.split("/")[-1] + ".py", revision=self.revision)
download_config = self.download_config.copy()
if download_config.download_desc is None:
download_config.download_desc = "Downloading builder script"
return cached_path(file_path, download_config=download_config)
def download_dataset_infos_file(self) -> str:
dataset_infos = hf_hub_url(repo_id=self.name, path=config.DATASETDICT_INFOS_FILENAME, revision=self.revision)
# Download the dataset infos file if available
download_config = self.download_config.copy()
if download_config.download_desc is None:
download_config.download_desc = "Downloading metadata"
try:
return cached_path(
dataset_infos,
download_config=download_config,
)
except (FileNotFoundError, ConnectionError):
return None
def download_dataset_readme_file(self) -> str:
readme_url = hf_hub_url(repo_id=self.name, path="README.md", revision=self.revision)
# Download the dataset infos file if available
download_config = self.download_config.copy()
if download_config.download_desc is None:
download_config.download_desc = "Downloading readme"
try:
return cached_path(
readme_url,
download_config=download_config,
)
except (FileNotFoundError, ConnectionError):
return None
def get_module(self) -> DatasetModule:
# get script and other files
local_path = self.download_loading_script()
dataset_infos_path = self.download_dataset_infos_file()
dataset_readme_path = self.download_dataset_readme_file()
imports = get_imports(local_path)
local_imports = _download_additional_modules(
name=self.name,
base_path=hf_hub_url(repo_id=self.name, path="", revision=self.revision),
imports=imports,
download_config=self.download_config,
)
additional_files = []
if dataset_infos_path:
additional_files.append((config.DATASETDICT_INFOS_FILENAME, dataset_infos_path))
if dataset_readme_path:
additional_files.append(("README.md", dataset_readme_path))
# copy the script and the files in an importable directory
dynamic_modules_path = self.dynamic_modules_path if self.dynamic_modules_path else init_dynamic_modules()
module_path, hash = _create_importable_file(
local_path=local_path,
local_imports=local_imports,
additional_files=additional_files,
dynamic_modules_path=dynamic_modules_path,
module_namespace="datasets",
name=self.name,
download_mode=self.download_mode,
)
# make the new module to be noticed by the import system
importlib.invalidate_caches()
builder_kwargs = {
"hash": hash,
"base_path": hf_hub_url(self.name, "", revision=self.revision),
"repo_id": self.name,
}
return DatasetModule(module_path, hash, builder_kwargs)
class CachedDatasetModuleFactory(_DatasetModuleFactory):
"""
Get the module of a dataset that has been loaded once already and cached.
The script that is loaded from the cache is the most recent one with a matching name.
"""
def __init__(
self,
name: str,
dynamic_modules_path: Optional[str] = None,
):
self.name = name
self.dynamic_modules_path = dynamic_modules_path
assert self.name.count("/") <= 1
def get_module(self) -> DatasetModule:
dynamic_modules_path = self.dynamic_modules_path if self.dynamic_modules_path else init_dynamic_modules()
importable_directory_path = os.path.join(dynamic_modules_path, "datasets", self.name.replace("/", "--"))
hashes = (
[h for h in os.listdir(importable_directory_path) if len(h) == 64]
if os.path.isdir(importable_directory_path)
else None
)
if not hashes:
raise FileNotFoundError(f"Dataset {self.name} is not cached in {dynamic_modules_path}")
# get most recent
def _get_modification_time(module_hash):
return (Path(importable_directory_path) / module_hash / (self.name.split("/")[-1] + ".py")).stat().st_mtime
hash = sorted(hashes, key=_get_modification_time)[-1]
warning_msg = (
f"Using the latest cached version of the module from {os.path.join(importable_directory_path, hash)} "
f"(last modified on {time.ctime(_get_modification_time(hash))}) since it "
f"couldn't be found locally at {self.name}."
)
if not config.HF_DATASETS_OFFLINE:
warning_msg += ", or remotely on the Hugging Face Hub."
logger.warning(warning_msg)
# make the new module to be noticed by the import system
module_path = ".".join(
[
os.path.basename(dynamic_modules_path),
"datasets",
self.name.replace("/", "--"),
hash,
self.name.split("/")[-1],
]
)
importlib.invalidate_caches()
builder_kwargs = {
"hash": hash,
"repo_id": self.name,
}
return DatasetModule(module_path, hash, builder_kwargs)
class CachedMetricModuleFactory(_MetricModuleFactory):
"""
Get the module of a metric that has been loaded once already and cached.