# coding=utf-8
# 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
"""DatasetBuilder base class."""
import abc
import contextlib
import copy
import inspect
import os
import shutil
import textwrap
import urllib
from dataclasses import dataclass
from functools import partial
from pathlib import PurePath
from typing import Dict, List, Mapping, Optional, Sequence, Tuple, Union
from datasets.features import Features
from datasets.utils.mock_download_manager import MockDownloadManager
from datasets.utils.py_utils import map_nested
from . import config, utils
from .arrow_dataset import Dataset
from .arrow_reader import HF_GCP_BASE_URL, ArrowReader, DatasetNotOnHfGcs, MissingFilesOnHfGcs, ReadInstruction
from .arrow_writer import ArrowWriter, BeamWriter
from .dataset_dict import DatasetDict, IterableDatasetDict
from .fingerprint import Hasher
from .info import DatasetInfo, DatasetInfosDict, PostProcessedInfo
from .iterable_dataset import ExamplesIterable, IterableDataset, _generate_examples_from_tables_wrapper
from .naming import camelcase_to_snakecase, filename_prefix_for_split
from .splits import Split, SplitDict, SplitGenerator
from .utils import logging
from .utils.download_manager import DownloadManager, GenerateMode
from .utils.file_utils import DownloadConfig, is_relative_path, is_remote_url, request_etags, url_or_path_join
from .utils.filelock import FileLock
from .utils.info_utils import get_size_checksum_dict, verify_checksums, verify_splits
from .utils.streaming_download_manager import StreamingDownloadManager
logger = logging.get_logger(__name__)
class InvalidConfigName(ValueError):
pass
class DatasetBuildError(Exception):
pass
class ManualDownloadError(DatasetBuildError):
pass
[docs]@dataclass
class BuilderConfig:
"""Base class for :class:`DatasetBuilder` data configuration.
DatasetBuilder subclasses with data configuration options should subclass
:class:`BuilderConfig` and add their own properties.
Attributes:
name (:obj:`str`, default ``"default"``):
version (:class:`Version` or :obj:`str`, optional):
data_dir (:obj:`str`, optional):
data_files (:obj:`str` or :obj:`Sequence` or :obj:`Mapping`, optional): Path(s) to source data file(s).
description (:obj:`str`, optional):
"""
name: str = "default"
version: Optional[Union[str, utils.Version]] = "0.0.0"
data_dir: Optional[str] = None
data_files: Optional[Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]]] = None
description: Optional[str] = None
def __post_init__(self):
# The config name is used to name the cache directory.
invalid_windows_characters = r"<>:/\|?*"
for invalid_char in invalid_windows_characters:
if invalid_char in self.name:
raise InvalidConfigName(
(
"Bad characters from black list '{}' found in '{}'. "
"They could create issues when creating a directory "
"for this config on Windows filesystem."
).format(invalid_windows_characters, self.name)
)
def __eq__(self, o):
# we need to override the default dataclass __eq__ since it doesn't check for
# other attributes that the ones of the signature.
if set(self.__dict__.keys()) != set(o.__dict__.keys()):
return False
return all((k, getattr(self, k)) == (k, getattr(o, k)) for k in self.__dict__.keys())
def create_config_id(
self,
config_kwargs: dict,
custom_features: Optional[Features] = None,
use_auth_token: Optional[Union[bool, str]] = None,
base_path: Optional[Union[bool, str]] = None,
) -> str:
"""
The config id is used to build the cache directory.
By default it is equal to the config name.
However the name of a config is not sufficient to have a unique identifier for the dataset being generated
since it doesn't take into account:
- the config kwargs that can be used to overwrite attributes
- the custom features used to write the dataset
- the data_files for json/text/csv/pandas datasets
Therefore the config id is just the config name with an optional suffix based on these.
"""
# Possibly add a suffix to the name to handle custom features/data_files/config_kwargs
suffix: Optional[str] = None
config_kwargs_to_add_to_suffix = config_kwargs.copy()
# name and version are already used to build the cache directory
config_kwargs_to_add_to_suffix.pop("name", None)
config_kwargs_to_add_to_suffix.pop("version", None)
# data files are handled differently
config_kwargs_to_add_to_suffix.pop("data_files", None)
# data dir handling (when specified it points to the manually downloaded data):
# it was previously ignored before the introduction of config id because we didn't want
# to change the config name. Now it's fine to take it into account for the config id.
# config_kwargs_to_add_to_suffix.pop("data_dir", None)
if "data_dir" in config_kwargs_to_add_to_suffix and config_kwargs_to_add_to_suffix["data_dir"] is None:
del config_kwargs_to_add_to_suffix["data_dir"]
if config_kwargs_to_add_to_suffix:
# we don't care about the order of the kwargs
config_kwargs_to_add_to_suffix = {
k: config_kwargs_to_add_to_suffix[k] for k in sorted(config_kwargs_to_add_to_suffix)
}
if all(isinstance(v, (str, bool, int, float)) for v in config_kwargs_to_add_to_suffix.values()):
suffix = ",".join(
str(k) + "=" + urllib.parse.quote_plus(str(v)) for k, v in config_kwargs_to_add_to_suffix.items()
)
if len(suffix) > 32: # hash if too long
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
else:
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
if self.data_files is not None:
m = Hasher()
if suffix:
m.update(suffix)
if isinstance(self.data_files, str):
data_files = {"train": [self.data_files]}
elif isinstance(self.data_files, (tuple, list)):
data_files = {"train": self.data_files}
elif isinstance(self.data_files, dict):
data_files = {
str(key): files if isinstance(files, (tuple, list)) else [files]
for key, files in self.data_files.items()
}
else:
raise ValueError("Please provide a valid `data_files` in `DatasetBuilder`")
def abspath(data_file) -> str:
data_file = data_file.as_posix() if isinstance(data_file, PurePath) else str(data_file)
return url_or_path_join(base_path, data_file) if is_relative_path(data_file) else data_file
data_files: Dict[str, List[str]] = map_nested(abspath, data_files)
remote_urls = [
data_file for key in data_files for data_file in data_files[key] if is_remote_url(data_file)
]
etags = dict(
zip(
remote_urls,
request_etags(
remote_urls, use_auth_token=use_auth_token, tqdm_kwargs={"desc": "Check remote data files"}
),
)
)
for key in sorted(data_files.keys()):
m.update(key)
for data_file in data_files[key]:
if is_remote_url(data_file):
m.update(data_file)
m.update(etags[data_file])
else:
m.update(os.path.abspath(data_file))
m.update(str(os.path.getmtime(data_file)))
suffix = m.hexdigest()
if custom_features is not None:
m = Hasher()
if suffix:
m.update(suffix)
m.update(custom_features)
suffix = m.hexdigest()
if suffix:
config_id = self.name + "-" + suffix
if len(config_id) > config.MAX_DATASET_CONFIG_ID_READABLE_LENGTH:
config_id = self.name + "-" + Hasher.hash(suffix)
return config_id
else:
return self.name
[docs]class DatasetBuilder:
"""Abstract base class for all datasets.
`DatasetBuilder` has 3 key methods:
- :meth:`datasets.DatasetBuilder.info`: Documents the dataset, including feature
names, types, and shapes, version, splits, citation, etc.
- :meth:`datasets.DatasetBuilder.download_and_prepare`: Downloads the source data
and writes it to disk.
- :meth:`datasets.DatasetBuilder.as_dataset`: Generates a `Dataset`.
**Configuration**: Some `DatasetBuilder`s expose multiple variants of the
dataset by defining a `datasets.BuilderConfig` subclass and accepting a
config object (or name) on construction. Configurable datasets expose a
pre-defined set of configurations in :meth:`datasets.DatasetBuilder.builder_configs`.
"""
# Default version.
VERSION = utils.Version("0.0.0")
# Class for the builder config.
BUILDER_CONFIG_CLASS = BuilderConfig
# Named configurations that modify the data generated by download_and_prepare.
BUILDER_CONFIGS = []
# Optional default config name to be used used when name is None
DEFAULT_CONFIG_NAME = None
def __init__(
self,
cache_dir: Optional[str] = None,
name: Optional[str] = None,
hash: Optional[str] = None,
base_path: Optional[str] = None,
features: Optional[Features] = None,
use_auth_token: Optional[Union[bool, str]] = None,
**config_kwargs,
):
"""Constructs a DatasetBuilder.
Callers must pass arguments as keyword arguments.
Args:
cache_dir: `str`, directory to read/write data. Defaults to "~/datasets".
name: `str` name, optional configuration for the dataset that affects the data generated on disk. Different
`builder_config`s will have their own subdirectories and versions.
If not provided, uses the first configuration in self.BUILDER_CONFIGS
hash: a hash specific to the dataset code. Used to update the caching directory when the dataset loading
script code is updated (to avoid reusing old data).
The typical caching directory (defined in ``self._relative_data_dir``) is: ``name/version/hash/``
base_path: `str`, base path for relative paths that are used to download files. This can be a remote url.
features: `Features`, optional features that will be used to read/write the dataset
It can be used to changed the :obj:`datasets.Features` description of a dataset for example.
use_auth_token (:obj:`str` or :obj:`bool`, optional): Optional string or boolean to use as Bearer token
for remote files on the Datasets Hub. If True, will get token from ``"~/.huggingface"``.
config_kwargs: will override the defaults kwargs in config
"""
# DatasetBuilder name
self.name: str = camelcase_to_snakecase(self.__class__.__name__)
self.hash: Optional[str] = hash
self.base_path = base_path
self.use_auth_token = use_auth_token
# Prepare config: DatasetConfig contains name, version and description but can be extended by each dataset
if "features" in inspect.signature(self.BUILDER_CONFIG_CLASS.__init__).parameters and features is not None:
config_kwargs["features"] = features
# Discard default config parameters
if "data_files" in config_kwargs and config_kwargs["data_files"] is None:
del config_kwargs["data_files"]
if "data_dir" in config_kwargs and config_kwargs["data_dir"] is None:
del config_kwargs["data_dir"]
self.config, self.config_id = self._create_builder_config(
name,
custom_features=features,
**config_kwargs,
)
# prepare info: DatasetInfo are a standardized dataclass across all datasets
# Prefill datasetinfo
info = self.get_exported_dataset_info()
info.update(self._info())
info.builder_name = self.name
info.config_name = self.config.name
info.version = self.config.version
self.info = info
# update info with user specified infos
if features is not None:
self.info.features = features
# prepare data dirs
self._cache_dir_root = os.path.expanduser(cache_dir or config.HF_DATASETS_CACHE)
self._cache_downloaded_dir = (
os.path.join(cache_dir, config.DOWNLOADED_DATASETS_DIR) if cache_dir else config.DOWNLOADED_DATASETS_PATH
)
self._cache_dir = self._build_cache_dir()
if not is_remote_url(self._cache_dir_root):
os.makedirs(self._cache_dir_root, exist_ok=True)
lock_path = os.path.join(self._cache_dir_root, self._cache_dir.replace(os.sep, "_") + ".lock")
with FileLock(lock_path):
if os.path.exists(self._cache_dir): # check if data exist
if len(os.listdir(self._cache_dir)) > 0:
logger.info("Overwrite dataset info from restored data version.")
self.info = DatasetInfo.from_directory(self._cache_dir)
else: # dir exists but no data, remove the empty dir as data aren't available anymore
logger.warning(
"Old caching folder {} for dataset {} exists but not data were found. Removing it. ".format(
self._cache_dir, self.name
)
)
os.rmdir(self._cache_dir)
# Set download manager
self.dl_manager = None
# Must be set for datasets that use 'data_dir' functionality - the ones
# that require users to do additional steps to download the data
# (this is usually due to some external regulations / rules).
# This field should contain a string with user instructions, including
# the list of files that should be present. It will be
# displayed in the dataset documentation.
@property
def manual_download_instructions(self) -> Optional[str]:
return None
@classmethod
def get_all_exported_dataset_infos(cls) -> dict:
"""Empty dict if doesn't exist"""
dset_infos_file_path = os.path.join(cls.get_imported_module_dir(), config.DATASETDICT_INFOS_FILENAME)
if os.path.exists(dset_infos_file_path):
return DatasetInfosDict.from_directory(cls.get_imported_module_dir())
return {}
def get_exported_dataset_info(self) -> DatasetInfo:
"""Empty DatasetInfo if doesn't exist"""
return self.get_all_exported_dataset_infos().get(self.config.name, DatasetInfo())
def _create_builder_config(self, name=None, custom_features=None, **config_kwargs) -> Tuple[BuilderConfig, str]:
"""Create and validate BuilderConfig object as well as a unique config id for this config.
Raises ValueError if there are multiple builder configs and name and DEFAULT_CONFIG_NAME are None.
config_kwargs override the defaults kwargs in config
"""
builder_config = None
# try default config
if name is None and self.BUILDER_CONFIGS and not config_kwargs:
if self.DEFAULT_CONFIG_NAME is not None:
builder_config = self.builder_configs.get(self.DEFAULT_CONFIG_NAME)
logger.warning("No config specified, defaulting to: %s/%s", self.name, builder_config.name)
else:
if len(self.BUILDER_CONFIGS) > 1:
example_of_usage = "load_dataset('{}', '{}')".format(self.name, self.BUILDER_CONFIGS[0].name)
raise ValueError(
"Config name is missing."
"\nPlease pick one among the available configs: %s" % list(self.builder_configs.keys())
+ "\nExample of usage:\n\t`{}`".format(example_of_usage)
)
builder_config = self.BUILDER_CONFIGS[0]
logger.info("No config specified, defaulting to first: %s/%s", self.name, builder_config.name)
# try get config by name
if isinstance(name, str):
builder_config = self.builder_configs.get(name)
if builder_config is None and self.BUILDER_CONFIGS:
raise ValueError(
"BuilderConfig %s not found. Available: %s" % (name, list(self.builder_configs.keys()))
)
# if not using an existing config, then create a new config on the fly with config_kwargs
if not builder_config:
if name is not None:
config_kwargs["name"] = name
if "version" not in config_kwargs and hasattr(self, "VERSION") and self.VERSION:
config_kwargs["version"] = self.VERSION
builder_config = self.BUILDER_CONFIG_CLASS(**config_kwargs)
# otherwise use the config_kwargs to overwrite the attributes
else:
builder_config = copy.deepcopy(builder_config)
for key, value in config_kwargs.items():
if value is not None:
if not hasattr(builder_config, key):
raise ValueError(f"BuilderConfig {builder_config} doesn't have a '{key}' key.")
setattr(builder_config, key, value)
if not builder_config.name:
raise ValueError("BuilderConfig must have a name, got %s" % builder_config.name)
# compute the config id that is going to be used for caching
config_id = builder_config.create_config_id(
config_kwargs,
custom_features=custom_features,
use_auth_token=self.use_auth_token,
base_path=self.base_path if self.base_path is not None else "",
)
is_custom = config_id not in self.builder_configs
if is_custom:
logger.warning("Using custom data configuration %s", config_id)
else:
if builder_config != self.builder_configs[builder_config.name]:
raise ValueError(
"Cannot name a custom BuilderConfig the same as an available "
"BuilderConfig. Change the name. Available BuilderConfigs: %s"
% (list(self.builder_configs.keys()))
)
if not builder_config.version:
raise ValueError("BuilderConfig %s must have a version" % builder_config.name)
# if not builder_config.description:
# raise ValueError("BuilderConfig %s must have a description" % builder_config.name)
return builder_config, config_id
@utils.classproperty
@classmethod
@utils.memoize()
def builder_configs(cls):
"""Pre-defined list of configurations for this builder class."""
config_dict = {config.name: config for config in cls.BUILDER_CONFIGS}
if len(config_dict) != len(cls.BUILDER_CONFIGS):
names = [config.name for config in cls.BUILDER_CONFIGS]
raise ValueError("Names in BUILDER_CONFIGS must not be duplicated. Got %s" % names)
return config_dict
@property
def cache_dir(self):
return self._cache_dir
def _relative_data_dir(self, with_version=True, with_hash=True) -> str:
"""Relative path of this dataset in cache_dir:
Will be:
self.name/self.config.version/self.hash/
If any of these element is missing or if ``with_version=False`` the corresponding subfolders are dropped.
"""
builder_data_dir = self.name
builder_config = self.config
hash = self.hash
if builder_config:
# use the enriched name instead of the name to make it unique
builder_data_dir = os.path.join(builder_data_dir, self.config_id)
if with_version:
builder_data_dir = os.path.join(builder_data_dir, str(self.config.version))
if with_hash and hash and isinstance(hash, str):
builder_data_dir = os.path.join(builder_data_dir, hash)
return builder_data_dir
def _build_cache_dir(self):
"""Return the data directory for the current version."""
builder_data_dir = os.path.join(self._cache_dir_root, self._relative_data_dir(with_version=False))
version_data_dir = os.path.join(self._cache_dir_root, self._relative_data_dir(with_version=True))
def _other_versions_on_disk():
"""Returns previous versions on disk."""
if not os.path.exists(builder_data_dir):
return []
version_dirnames = []
for dir_name in os.listdir(builder_data_dir):
try:
version_dirnames.append((utils.Version(dir_name), dir_name))
except ValueError: # Invalid version (ex: incomplete data dir)
pass
version_dirnames.sort(reverse=True)
return version_dirnames
# Check and warn if other versions exist on disk
version_dirs = _other_versions_on_disk()
if version_dirs:
other_version = version_dirs[0][0]
if other_version != self.config.version:
warn_msg = (
"Found a different version {other_version} of dataset {name} in "
"cache_dir {cache_dir}. Using currently defined version "
"{cur_version}.".format(
other_version=str(other_version),
name=self.name,
cache_dir=self._cache_dir_root,
cur_version=str(self.config.version),
)
)
logger.warning(warn_msg)
return version_data_dir
@abc.abstractmethod
def _info(self) -> DatasetInfo:
"""Construct the DatasetInfo object. See `DatasetInfo` for details.
Warning: This function is only called once and the result is cached for all
following .info() calls.
Returns:
info: (DatasetInfo) The dataset information
"""
raise NotImplementedError
@classmethod
def get_imported_module_dir(cls):
"""Return the path of the module of this class or subclass."""
return os.path.dirname(inspect.getfile(inspect.getmodule(cls)))
def download_and_prepare(
self,
download_config: Optional[DownloadConfig] = None,
download_mode: Optional[GenerateMode] = None,
ignore_verifications: bool = False,
try_from_hf_gcs: bool = True,
dl_manager: Optional[DownloadManager] = None,
base_path: Optional[str] = None,
use_auth_token: Optional[Union[bool, str]] = None,
**download_and_prepare_kwargs,
):
"""Downloads and prepares dataset for reading.
Args:
download_config (Optional ``datasets.DownloadConfig``: specific download configuration parameters.
download_mode (Optional `datasets.GenerateMode`): select the download/generate mode - Default to REUSE_DATASET_IF_EXISTS
ignore_verifications (bool): Ignore the verifications of the downloaded/processed dataset information (checksums/size/splits/...)
save_infos (bool): Save the dataset information (checksums/size/splits/...)
try_from_hf_gcs (bool): If True, it will try to download the already prepared dataset from the Hf google cloud storage
dl_manager (Optional ``datasets.DownloadManager``): specific Download Manger to use
base_path ( Optional ``str``): base path for relative paths that are used to download files. This can be a remote url.
If not specified, the value of the ``base_path`` attribute (``self.base_path``) will be used instead.
use_auth_token (Optional ``Union[str, bool]``): Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.
If True, will get token from ~/.huggingface.
"""
download_mode = GenerateMode(download_mode or GenerateMode.REUSE_DATASET_IF_EXISTS)
verify_infos = not ignore_verifications
base_path = base_path if base_path is not None else self.base_path
if dl_manager is None:
if download_config is None:
download_config = DownloadConfig(
cache_dir=self._cache_downloaded_dir,
force_download=bool(download_mode == GenerateMode.FORCE_REDOWNLOAD),
use_etag=False,
use_auth_token=use_auth_token,
) # We don't use etag for data files to speed up the process
dl_manager = DownloadManager(
dataset_name=self.name,
download_config=download_config,
data_dir=self.config.data_dir,
base_path=base_path,
)
elif isinstance(dl_manager, MockDownloadManager):
try_from_hf_gcs = False
self.dl_manager = dl_manager
# Prevent parallel disk operations
lock_path = os.path.join(self._cache_dir_root, self._cache_dir.replace(os.sep, "_") + ".lock")
with FileLock(lock_path):
data_exists = os.path.exists(self._cache_dir)
if data_exists and download_mode == GenerateMode.REUSE_DATASET_IF_EXISTS:
logger.warning("Reusing dataset %s (%s)", self.name, self._cache_dir)
# We need to update the info in case some splits were added in the meantime
# for example when calling load_dataset from multiple workers.
self.info = self._load_info()
self.download_post_processing_resources(dl_manager)
return
logger.info("Generating dataset %s (%s)", self.name, self._cache_dir)
if not is_remote_url(self._cache_dir_root): # if cache dir is local, check for available space
if not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root):
raise IOError(
"Not enough disk space. Needed: {} (download: {}, generated: {}, post-processed: {})".format(
utils.size_str(self.info.size_in_bytes or 0),
utils.size_str(self.info.download_size or 0),
utils.size_str(self.info.dataset_size or 0),
utils.size_str(self.info.post_processing_size or 0),
)
)
@contextlib.contextmanager
def incomplete_dir(dirname):
"""Create temporary dir for dirname and rename on exit."""
if is_remote_url(dirname):
yield dirname
else:
tmp_dir = dirname + ".incomplete"
os.makedirs(tmp_dir, exist_ok=True)
try:
yield tmp_dir
if os.path.isdir(dirname):
shutil.rmtree(dirname)
os.rename(tmp_dir, dirname)
finally:
if os.path.exists(tmp_dir):
shutil.rmtree(tmp_dir)
# Print is intentional: we want this to always go to stdout so user has
# information needed to cancel download/preparation if needed.
# This comes right before the progress bar.
if self.info.size_in_bytes:
print(
f"Downloading and preparing dataset {self.info.builder_name}/{self.info.config_name} "
f"(download: {utils.size_str(self.info.download_size)}, generated: {utils.size_str(self.info.dataset_size)}, "
f"post-processed: {utils.size_str(self.info.post_processing_size)}, "
f"total: {utils.size_str(self.info.size_in_bytes)}) to {self._cache_dir}..."
)
else:
print(
f"Downloading and preparing dataset {self.info.builder_name}/{self.info.config_name} to {self._cache_dir}..."
)
self._check_manual_download(dl_manager)
# Create a tmp dir and rename to self._cache_dir on successful exit.
with incomplete_dir(self._cache_dir) as tmp_data_dir:
# Temporarily assign _cache_dir to tmp_data_dir to avoid having to forward
# it to every sub function.
with utils.temporary_assignment(self, "_cache_dir", tmp_data_dir):
# Try to download the already prepared dataset files
downloaded_from_gcs = False
if try_from_hf_gcs:
try:
self._download_prepared_from_hf_gcs(dl_manager._download_config)
downloaded_from_gcs = True
except (DatasetNotOnHfGcs, MissingFilesOnHfGcs):
logger.info("Dataset not on Hf google storage. Downloading and preparing it from source")
except ConnectionError:
logger.warning("HF google storage unreachable. Downloading and preparing it from source")
if not downloaded_from_gcs:
self._download_and_prepare(
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
)
# Sync info
self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
self.info.download_checksums = dl_manager.get_recorded_sizes_checksums()
self.info.size_in_bytes = self.info.dataset_size + self.info.download_size
# Save info
self._save_info()
# Download post processing resources
self.download_post_processing_resources(dl_manager)
print(
f"Dataset {self.name} downloaded and prepared to {self._cache_dir}. "
f"Subsequent calls will reuse this data."
)
def _check_manual_download(self, dl_manager):
if self.manual_download_instructions is not None and dl_manager.manual_dir is None:
raise ManualDownloadError(
textwrap.dedent(
f"""The dataset {self.name} with config {self.config.name} requires manual data.
Please follow the manual download instructions:
{self.manual_download_instructions}
Manual data can be loaded with:
datasets.load_dataset({self.name}, data_dir='<path/to/manual/data>')"""
)
)
def _download_prepared_from_hf_gcs(self, download_config: DownloadConfig):
relative_data_dir = self._relative_data_dir(with_version=True, with_hash=False)
reader = ArrowReader(self._cache_dir, self.info)
# use reader instructions to download the right files
reader.download_from_hf_gcs(download_config, relative_data_dir)
downloaded_info = DatasetInfo.from_directory(self._cache_dir)
self.info.update(downloaded_info)
# download post processing resources
remote_cache_dir = HF_GCP_BASE_URL + "/" + relative_data_dir.replace(os.sep, "/")
for split in self.info.splits:
for resource_file_name in self._post_processing_resources(split).values():
if os.sep in resource_file_name:
raise ValueError("Resources shouldn't be in a sub-directory: {}".format(resource_file_name))
try:
resource_path = utils.cached_path(remote_cache_dir + "/" + resource_file_name)
shutil.move(resource_path, os.path.join(self._cache_dir, resource_file_name))
except ConnectionError:
logger.info(
"Couldn't download resourse file {} from Hf google storage.".format(resource_file_name)
)
logger.info("Dataset downloaded from Hf google storage.")
def _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs):
"""Downloads and prepares dataset for reading.
This is the internal implementation to overwrite called when user calls
`download_and_prepare`. It should download all required data and generate
the pre-processed datasets files.
Args:
dl_manager: (DownloadManager) `DownloadManager` used to download and cache
data.
verify_infos: bool, if False, do not perform checksums and size tests.
prepare_split_kwargs: Additional options.
"""
# Generating data for all splits
split_dict = SplitDict(dataset_name=self.name)
split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
# Checksums verification
if verify_infos:
verify_checksums(
self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
)
# Build splits
for split_generator in split_generators:
if str(split_generator.split_info.name).lower() == "all":
raise ValueError(
"`all` is a special split keyword corresponding to the "
"union of all splits, so cannot be used as key in "
"._split_generator()."
)
logger.info("Generating split %s", split_generator.split_info.name)
split_dict.add(split_generator.split_info)
try:
# Prepare split will record examples associated to the split
self._prepare_split(split_generator, **prepare_split_kwargs)
except OSError as e:
raise OSError(
"Cannot find data file. "
+ (self.manual_download_instructions or "")
+ "\nOriginal error:\n"
+ str(e)
)
dl_manager.manage_extracted_files()
if verify_infos:
verify_splits(self.info.splits, split_dict)
# Update the info object with the splits.
self.info.splits = split_dict
self.info.download_size = dl_manager.downloaded_size
def download_post_processing_resources(self, dl_manager):
for split in self.info.splits:
for resource_name, resource_file_name in self._post_processing_resources(split).items():
if os.sep in resource_file_name:
raise ValueError("Resources shouldn't be in a sub-directory: {}".format(resource_file_name))
resource_path = os.path.join(self._cache_dir, resource_file_name)
if not os.path.exists(resource_path):
downloaded_resource_path = self._download_post_processing_resources(
split, resource_name, dl_manager
)
if downloaded_resource_path:
logger.info(
"Downloaded post-processing resource {} as {}".format(resource_name, resource_file_name)
)
shutil.move(downloaded_resource_path, resource_path)
def _load_info(self) -> DatasetInfo:
return DatasetInfo.from_directory(self._cache_dir)
def _save_info(self):
lock_path = os.path.join(self._cache_dir_root, self._cache_dir.replace(os.sep, "_") + ".lock")
with FileLock(lock_path):
self.info.write_to_directory(self._cache_dir)
def _save_infos(self):
lock_path = os.path.join(self._cache_dir_root, self._cache_dir.replace(os.sep, "_") + ".lock")
with FileLock(lock_path):
DatasetInfosDict(**{self.config.name: self.info}).write_to_directory(self.get_imported_module_dir())
def _make_split_generators_kwargs(self, prepare_split_kwargs):
"""Get kwargs for `self._split_generators()` from `prepare_split_kwargs`."""
del prepare_split_kwargs
return {}
def as_dataset(
self, split: Optional[Split] = None, run_post_process=True, ignore_verifications=False, in_memory=False
) -> Union[Dataset, DatasetDict]:
"""Return a Dataset for the specified split.
Args:
split (`datasets.Split`): Which subset of the data to return.
run_post_process (bool, default=True): Whether to run post-processing dataset transforms and/or add
indexes.
ignore_verifications (bool, default=False): Whether to ignore the verifications of the
downloaded/processed dataset information (checksums/size/splits/...).
in_memory (bool, default=False): Whether to copy the data in-memory.
Returns:
datasets.Dataset
"""
if not os.path.exists(self._cache_dir):
raise AssertionError(
(
"Dataset %s: could not find data in %s. Please make sure to call "
"builder.download_and_prepare(), or pass download=True to "
"datasets.load_dataset() before trying to access the Dataset object."
)
% (self.name, self._cache_dir_root)
)
logger.debug(
"Constructing Dataset for split %s, from %s", split or ", ".join(self.info.splits), self._cache_dir
)
# By default, return all splits
if split is None:
split = {s: s for s in self.info.splits}
# Create a dataset for each of the given splits
datasets = utils.map_nested(
partial(
self._build_single_dataset,
run_post_process=run_post_process,
ignore_verifications=ignore_verifications,
in_memory=in_memory,
),
split,
map_tuple=True,
)
if isinstance(datasets, dict):
datasets = DatasetDict(datasets)
return datasets
def _build_single_dataset(
self,
split: Union[str, ReadInstruction, Split],
run_post_process: bool,
ignore_verifications: bool,
in_memory: bool = False,
):
"""as_dataset for a single split."""
verify_infos = not ignore_verifications
if not isinstance(split, ReadInstruction):
split = str(split)
if split == "all":
split = "+".join(self.info.splits.keys())
split = Split(split)
# Build base dataset
ds = self._as_dataset(
split=split,
in_memory=in_memory,
)
if run_post_process:
for resource_file_name in self._post_processing_resources(split).values():
if os.sep in resource_file_name:
raise ValueError("Resources shouldn't be in a sub-directory: {}".format(resource_file_name))
resources_paths = {
resource_name: os.path.join(self._cache_dir, resource_file_name)
for resource_name, resource_file_name in self._post_processing_resources(split).items()
}
post_processed = self._post_process(ds, resources_paths)
if post_processed is not None:
ds = post_processed
recorded_checksums = {}
for resource_name, resource_path in resources_paths.items():
size_checksum = get_size_checksum_dict(resource_path)
recorded_checksums[resource_name] = size_checksum
if verify_infos:
if self.info.post_processed is None or self.info.post_processed.resources_checksums is None:
expected_checksums = None
else:
expected_checksums = self.info.post_processed.resources_checksums.get(split)
verify_checksums(expected_checksums, recorded_checksums, "post processing resources")
if self.info.post_processed is None:
self.info.post_processed = PostProcessedInfo()
if self.info.post_processed.resources_checksums is None:
self.info.post_processed.resources_checksums = {}
self.info.post_processed.resources_checksums[str(split)] = recorded_checksums
self.info.post_processing_size = sum(
checksums_dict["num_bytes"]
for split_checksums_dicts in self.info.post_processed.resources_checksums.values()
for checksums_dict in split_checksums_dicts.values()
)
if self.info.dataset_size is not None and self.info.download_size is not None:
self.info.size_in_bytes = (
self.info.dataset_size + self.info.download_size + self.info.post_processing_size
)
self._save_info()
ds._info.post_processed = self.info.post_processed
ds._info.post_processing_size = self.info.post_processing_size
ds._info.size_in_bytes = self.info.size_in_bytes
if self.info.post_processed.features is not None:
if self.info.post_processed.features.type != ds.features.type:
raise ValueError(
"Post-processed features info don't match the dataset:\nGot\n{}\nbut expected something like\n{}".format(
self.info.post_processed.features, ds.features
)
)
else:
ds.info.features = self.info.post_processed.features
return ds
def _as_dataset(self, split: Union[ReadInstruction, Split] = Split.TRAIN, in_memory: bool = False) -> Dataset:
"""Constructs a `Dataset`.
This is the internal implementation to overwrite called when user calls
`as_dataset`. It should read the pre-processed datasets files and generate
the `Dataset` object.
Args:
split: `datasets.Split` which subset of the data to read.
in_memory (bool, default False): Whether to copy the data in-memory.
Returns:
`Dataset`
"""
dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(
name=self.name,
instructions=split,
split_infos=self.info.splits.values(),
in_memory=in_memory,
)
fingerprint = self._get_dataset_fingerprint(split)
return Dataset(fingerprint=fingerprint, **dataset_kwargs)
def _get_dataset_fingerprint(self, split: Union[ReadInstruction, Split]) -> str:
"""The dataset fingerprint is the hash of the relative directory dataset_name/config_name/version/hash, as well as the split specs."""
hasher = Hasher()
hasher.update(self._relative_data_dir().replace(os.sep, "/"))
hasher.update(str(split)) # for example: train, train+test, train[:10%], test[:33%](pct1_dropremainder)
fingerprint = hasher.hexdigest()
return fingerprint
def as_streaming_dataset(
self,
split: Optional[str] = None,
base_path: Optional[str] = None,
use_auth_token: Optional[str] = None,
) -> Union[Dict[str, IterableDataset], IterableDataset]:
if not isinstance(self, (GeneratorBasedBuilder, ArrowBasedBuilder)):
raise ValueError(f"Builder {self.name} is not streamable.")
dl_manager = StreamingDownloadManager(
base_path=base_path or self.base_path,
download_config=DownloadConfig(use_auth_token=use_auth_token),
dataset_name=self.name,
data_dir=self.config.data_dir,
)
self._check_manual_download(dl_manager)
splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
# By default, return all splits
if split is None:
splits_generator = splits_generators
elif split in splits_generators:
splits_generator = splits_generators[split]
else:
raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
# Create a dataset for each of the given splits
datasets = utils.map_nested(
self._as_streaming_dataset_single,
splits_generator,
map_tuple=True,
)
if isinstance(datasets, dict):
datasets = IterableDatasetDict(datasets)
return datasets
def _as_streaming_dataset_single(
self,
splits_generator,
) -> IterableDataset:
ex_iterable = self._get_examples_iterable_for_split(splits_generator)
return IterableDataset(ex_iterable, info=self.info, split=splits_generator.name)
def _post_process(self, dataset: Dataset, resources_paths: Mapping[str, str]) -> Optional[Dataset]:
"""Run dataset transforms or add indexes"""
return None
def _post_processing_resources(self, split: str) -> Dict[str, str]:
"""Mapping resource_name -> resource_file_name"""
return {}
def _download_post_processing_resources(
self, split: str, resource_name: str, dl_manager: DownloadManager
) -> Optional[str]:
"""Download the resource using the download manager and return the downloaded path."""
return None
@abc.abstractmethod
def _split_generators(self, dl_manager: DownloadManager):
"""Specify feature dictionary generators and dataset splits.
This function returns a list of `SplitGenerator`s defining how to generate
data and what splits to use.
Example:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={'file': 'train_data.zip'},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={'file': 'test_data.zip'},
),
]
The above code will first call `_generate_examples(file='train_data.zip')`
to write the train data, then `_generate_examples(file='test_data.zip')` to
write the test data.
Datasets are typically split into different subsets to be used at various
stages of training and evaluation.
Note that for datasets without a `VALIDATION` split, you can use a
fraction of the `TRAIN` data for evaluation as you iterate on your model
so as not to overfit to the `TEST` data.
For downloads and extractions, use the given `download_manager`.
Note that the `DownloadManager` caches downloads, so it is fine to have each
generator attempt to download the source data.
A good practice is to download all data in this function, and then
distribute the relevant parts to each split with the `gen_kwargs` argument
Args:
dl_manager: (DownloadManager) Download manager to download the data
Returns:
`list<SplitGenerator>`.
"""
raise NotImplementedError()
@abc.abstractmethod
def _prepare_split(self, split_generator: SplitGenerator, **kwargs):
"""Generate the examples and record them on disk.
Args:
split_generator: `SplitGenerator`, Split generator to process
**kwargs: Additional kwargs forwarded from _download_and_prepare (ex:
beam pipeline)
"""
raise NotImplementedError()
def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
"""Generate the examples on the fly.
Args:
split_generator: `SplitGenerator`, Split generator to process
"""
raise NotImplementedError()
[docs]class GeneratorBasedBuilder(DatasetBuilder):
"""Base class for datasets with data generation based on dict generators.
`GeneratorBasedBuilder` is a convenience class that abstracts away much
of the data writing and reading of `DatasetBuilder`. It expects subclasses to
implement generators of feature dictionaries across the dataset splits
(`_split_generators`). See the method docstrings for details.
"""
# GeneratorBasedBuilder should have dummy data for tests by default
test_dummy_data = True
# Default batch size used by the ArrowWriter
# It defines the number of samples that are kept in memory before writing them
# and also the length of the arrow chunks
# None means that the ArrowWriter will use its default value
DEFAULT_WRITER_BATCH_SIZE = None
def __init__(self, *args, writer_batch_size=None, **kwargs):
super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
# Batch size used by the ArrowWriter
# It defines the number of samples that are kept in memory before writing them
# and also the length of the arrow chunks
# None means that the ArrowWriter will use its default value
self._writer_batch_size = writer_batch_size or self.DEFAULT_WRITER_BATCH_SIZE
@abc.abstractmethod
def _generate_examples(self, **kwargs):
"""Default function generating examples for each `SplitGenerator`.
This function preprocess the examples from the raw data to the preprocessed
dataset files.
This function is called once for each `SplitGenerator` defined in
`_split_generators`. The examples yielded here will be written on
disk.
Args:
**kwargs: `dict`, Arguments forwarded from the SplitGenerator.gen_kwargs
Yields:
key: `str` or `int`, a unique deterministic example identification key.
* Unique: An error will be raised if two examples are yield with the
same key.
* Deterministic: When generating the dataset twice, the same example
should have the same key.
Good keys can be the image id, or line number if examples are extracted
from a text file.
The key will be hashed and sorted to shuffle examples deterministically,
such as generating the dataset multiple times keep examples in the
same order.
example: `dict<str feature_name, feature_value>`, a feature dictionary
ready to be encoded and written to disk. The example will be
encoded with `self.info.features.encode_example({...})`.
"""
raise NotImplementedError()
def _prepare_split(self, split_generator):
split_info = split_generator.split_info
fname = "{}-{}.arrow".format(self.name, split_generator.name)
fpath = os.path.join(self._cache_dir, fname)
generator = self._generate_examples(**split_generator.gen_kwargs)
with ArrowWriter(
features=self.info.features,
path=fpath,
writer_batch_size=self._writer_batch_size,
hash_salt=split_info.name,
check_duplicates=True,
) as writer:
try:
for key, record in utils.tqdm(
generator,
unit=" examples",
total=split_info.num_examples,
leave=False,
disable=bool(logging.get_verbosity() == logging.NOTSET),
):
example = self.info.features.encode_example(record)
writer.write(example, key)
finally:
num_examples, num_bytes = writer.finalize()
split_generator.split_info.num_examples = num_examples
split_generator.split_info.num_bytes = num_bytes
def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
return ExamplesIterable(self._generate_examples, split_generator.gen_kwargs)
[docs]class ArrowBasedBuilder(DatasetBuilder):
"""Base class for datasets with data generation based on Arrow loading functions (CSV/JSON/Parquet)."""
# ArrowBasedBuilder should have dummy data for tests by default
test_dummy_data = True
@abc.abstractmethod
def _generate_tables(self, **kwargs):
"""Default function generating examples for each `SplitGenerator`.
This function preprocess the examples from the raw data to the preprocessed
dataset files.
This function is called once for each `SplitGenerator` defined in
`_split_generators`. The examples yielded here will be written on
disk.
Args:
**kwargs: `dict`, Arguments forwarded from the SplitGenerator.gen_kwargs
Yields:
key: `str` or `int`, a unique deterministic example identification key.
* Unique: An error will be raised if two examples are yield with the
same key.
* Deterministic: When generating the dataset twice, the same example
should have the same key.
Good keys can be the image id, or line number if examples are extracted
from a text file.
The key will be hashed and sorted to shuffle examples deterministically,
such as generating the dataset multiple times keep examples in the
same order.
example: `pyarrow.Table`, a feature table
ready to be encoded and written to disk.
"""
raise NotImplementedError()
def _prepare_split(self, split_generator):
fname = "{}-{}.arrow".format(self.name, split_generator.name)
fpath = os.path.join(self._cache_dir, fname)
generator = self._generate_tables(**split_generator.gen_kwargs)
with ArrowWriter(features=self.info.features, path=fpath) as writer:
for key, table in utils.tqdm(
generator, unit=" tables", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET)
):
writer.write_table(table)
num_examples, num_bytes = writer.finalize()
split_generator.split_info.num_examples = num_examples
split_generator.split_info.num_bytes = num_bytes
if self.info.features is None:
self.info.features = writer._features
def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
return ExamplesIterable(
_generate_examples_from_tables_wrapper(self._generate_tables), kwargs=split_generator.gen_kwargs
)
class MissingBeamOptions(ValueError):
pass
[docs]class BeamBasedBuilder(DatasetBuilder):
"""Beam based Builder."""
# BeamBasedBuilder does not have dummy data for tests yet
test_dummy_data = False
def __init__(self, *args, **kwargs):
self._beam_runner = kwargs.pop("beam_runner", None)
self._beam_options = kwargs.pop("beam_options", None)
super(BeamBasedBuilder, self).__init__(*args, **kwargs)
self._beam_writers = {} # {split: beam_writer} mapping.
def _make_split_generators_kwargs(self, prepare_split_kwargs):
# Pass `pipeline` into `_split_generators()` from `prepare_split_kwargs` if
# it's in the call signature of `_split_generators()`.
# This allows for global preprocessing in beam.
split_generators_kwargs = {}
split_generators_arg_names = inspect.signature(self._split_generators).parameters.keys()
if "pipeline" in split_generators_arg_names:
split_generators_kwargs["pipeline"] = prepare_split_kwargs["pipeline"]
return split_generators_kwargs
@abc.abstractmethod
def _build_pcollection(self, pipeline, **kwargs):
"""Build the beam pipeline examples for each `SplitGenerator`.
This function extracts examples from the raw data with parallel transforms
in a Beam pipeline. It is called once for each `SplitGenerator` defined in
`_split_generators`. The examples from the PCollection will be
encoded and written to disk.
Warning: When running in a distributed setup, make sure that the data
which will be read (download_dir, manual_dir,...) and written (cache_dir)
can be accessed by the workers jobs. The data should be located in a
shared filesystem, like GCS.
Example:
```
def _build_pcollection(pipeline, extracted_dir):
return (
pipeline
| beam.Create(gfile.io.listdir(extracted_dir))
| beam.Map(_process_file)
)
```
Args:
pipeline: `beam.Pipeline`, root Beam pipeline
**kwargs: Arguments forwarded from the SplitGenerator.gen_kwargs
Returns:
pcollection: `PCollection`, an Apache Beam PCollection containing the
example to send to `self.info.features.encode_example(...)`.
"""
raise NotImplementedError()
def _download_and_prepare(self, dl_manager, verify_infos):
# Create the Beam pipeline and forward it to _prepare_split
import apache_beam as beam
import datasets.utils.beam_utils as beam_utils
beam_runner = self._beam_runner
beam_options = self._beam_options
if not beam_runner and not beam_options:
usage_example = f"load_dataset('{self.name}', '{self.config.name}', beam_runner='DirectRunner')"
raise MissingBeamOptions(
"Trying to generate a dataset using Apache Beam, yet no Beam Runner "
"or PipelineOptions() has been provided in `load_dataset` or in the "
"builder arguments. For big datasets it has to run on large-scale data "
"processing tools like Dataflow, Spark, etc. More information about "
"Apache Beam runners at "
"https://beam.apache.org/documentation/runners/capability-matrix/"
"\nIf you really want to run it locally because you feel like the "
"Dataset is small enough, you can use the local beam runner called "
"`DirectRunner` (you may run out of memory). \nExample of usage: "
"\n\t`{}`".format(usage_example)
)
beam_options = beam_options or beam.options.pipeline_options.PipelineOptions()
# Beam type checking assumes transforms multiple outputs are of same type,
# which is not our case. Plus it doesn't handle correctly all types, so we
# are better without it.
beam_options.view_as(beam.options.pipeline_options.TypeOptions).pipeline_type_check = False
# Use a single pipeline for all splits
pipeline = beam_utils.BeamPipeline(
runner=beam_runner,
options=beam_options,
)
super(BeamBasedBuilder, self)._download_and_prepare(
dl_manager,
verify_infos=False,
pipeline=pipeline,
) # TODO handle verify_infos in beam datasets
# Run pipeline
pipeline_results = pipeline.run()
pipeline_results.wait_until_finish()
metrics = pipeline_results.metrics()
# Update `info.splits`.
split_dict = self.info.splits
for split_name, beam_writer in self._beam_writers.items():
m_filter = beam.metrics.MetricsFilter().with_namespace(namespace=split_name)
num_examples, num_bytes = beam_writer.finalize(metrics.query(m_filter))
split_info = split_dict[split_name]
split_info.num_examples = num_examples
split_info.num_bytes = num_bytes
def _save_info(self):
if os.path.exists(self._cache_dir):
super()._save_info()
else:
import apache_beam as beam
fs = beam.io.filesystems.FileSystems
with fs.create(os.path.join(self._cache_dir, config.DATASET_INFO_FILENAME)) as f:
self.info._dump_info(f)
with fs.create(os.path.join(self._cache_dir, config.LICENSE_FILENAME)) as f:
self.info._dump_license(f)
def _prepare_split(self, split_generator, pipeline):
import apache_beam as beam
split_name = split_generator.split_info.name
output_prefix = filename_prefix_for_split(self.name, split_name)
output_prefix = os.path.join(self._cache_dir, output_prefix)
# To write examples to disk:
fname = "{}-{}.arrow".format(self.name, split_name)
fpath = os.path.join(self._cache_dir, fname)
beam_writer = BeamWriter(
features=self.info.features, path=fpath, namespace=split_name, cache_dir=self._cache_dir
)
self._beam_writers[split_name] = beam_writer
encode_example = self.info.features.encode_example
# Note: We need to wrap the pipeline in a PTransform to avoid re-using the
# same label names for each split
@beam.ptransform_fn
def _build_pcollection(pipeline):
"""PTransformation which build a single split."""
# Encode the PCollection
pcoll_examples = self._build_pcollection(pipeline, **split_generator.gen_kwargs)
pcoll_examples |= "Encode" >> beam.Map(lambda key_ex: (key_ex[0], encode_example(key_ex[1])))
return beam_writer.write_from_pcollection(pcoll_examples)
# Add the PCollection to the pipeline
_ = pipeline | split_name >> _build_pcollection() # pylint: disable=no-value-for-parameter