Source code for datasets.builder

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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 typing import Dict, Mapping, Optional, Tuple, Union

from datasets.features import Features
from datasets.utils.mock_download_manager import MockDownloadManager

from . import config, utils
from .arrow_dataset import Dataset
from .arrow_reader import (
from .arrow_writer import ArrowWriter, BeamWriter
from .data_files import DataFilesDict, sanitize_patterns
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_remote_url
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):

class DatasetBuildError(Exception):

class ManualDownloadError(DatasetBuildError):

[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[DataFilesDict] = 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 raise InvalidConfigName( f"Bad characters from black list '{invalid_windows_characters}' found in '{}'. " f"They could create issues when creating a directory for this config on Windows filesystem." ) if self.data_files is not None and not isinstance(self.data_files, DataFilesDict): raise ValueError(f"Expected a DataFilesDict in data_files but got {self.data_files}") 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, ) -> 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 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: config_kwargs_to_add_to_suffix.pop("data_dir", None) 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 custom_features is not None: m = Hasher() if suffix: m.update(suffix) m.update(custom_features) suffix = m.hexdigest() if suffix: config_id = + "-" + suffix if len(config_id) > config.MAX_DATASET_CONFIG_ID_READABLE_LENGTH: config_id = + "-" + Hasher.hash(suffix) return config_id else: return
[docs]class DatasetBuilder: """Abstract base class for all datasets. `DatasetBuilder` has 3 key methods: - :meth:``: 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, namespace: Optional[str] = None, data_files: Optional[Union[str, list, dict, DataFilesDict]] = None, data_dir: Optional[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"``. namespace: `str`, used to separate builders with the same name but not coming from the same namespace. For example to separate "squad" from "lhoestq/squad" (the builder name would be "lhoestq___squad"). data_files: for builders like "csv" or "json" that need the user to specify data files. They can be either local or remote files. For convenience you can use a DataFilesDict. data_dir: `str`, for builders that require manual download. It must be the path to the local directory containing the manually downloaded data. config_kwargs: will override the defaults kwargs in config """ # DatasetBuilder 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 self.namespace = namespace if data_files is not None and not isinstance(data_files, DataFilesDict): data_files = DataFilesDict.from_local_or_remote( sanitize_patterns(data_files), base_path=base_path, 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 if data_files is not None: config_kwargs["data_files"] = data_files if data_dir is not None: config_kwargs["data_dir"] = 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 = info.config_name = info.version = self.config.version = info # update info with user specified infos if features is not None: = 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:"Overwrite dataset info from restored data version.") = DatasetInfo.from_directory(self._cache_dir) else: # dir exists but no data, remove the empty dir as data aren't available anymore logger.warning( f"Old caching folder {self._cache_dir} for dataset {} exists but not data were found. Removing it. " ) 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(, 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(f"No config specified, defaulting to: {}/{}") else: if len(self.BUILDER_CONFIGS) > 1: example_of_usage = f"load_dataset('{}', '{self.BUILDER_CONFIGS[0].name}')" raise ValueError( "Config name is missing." f"\nPlease pick one among the available configs: {list(self.builder_configs.keys())}" + f"\nExample of usage:\n\t`{example_of_usage}`" ) builder_config = self.BUILDER_CONFIGS[0]"No config specified, defaulting to first: {}/{}") # 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(f"BuilderConfig {name} not found. Available: {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 raise ValueError(f"BuilderConfig must have a name, got {}") # 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, ) is_custom = config_id not in self.builder_configs if is_custom: logger.warning(f"Using custom data configuration {config_id}") else: if builder_config != self.builder_configs[]: raise ValueError( "Cannot name a custom BuilderConfig the same as an available " f"BuilderConfig. Change the name. Available BuilderConfigs: {list(self.builder_configs.keys())}" ) if not builder_config.version: raise ValueError(f"BuilderConfig {} must have a version") # if not builder_config.description: # raise ValueError(f"BuilderConfig {} must have a description" ) return builder_config, config_id @utils.classproperty @classmethod @utils.memoize() def builder_configs(cls): """Pre-defined list of configurations for this builder class.""" configs = { config for config in cls.BUILDER_CONFIGS} if len(configs) != len(cls.BUILDER_CONFIGS): names = [ for config in cls.BUILDER_CONFIGS] raise ValueError(f"Names in BUILDER_CONFIGS must not be duplicated. Got {names}") return configs @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: or if a namespace has been specified: If any of these element is missing or if ``with_version=False`` the corresponding subfolders are dropped. """ builder_data_dir = if self.namespace is None else f"{self.namespace}___{}" 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 = ( f"Found a different version {str(other_version)} of dataset {} in " f"cache_dir {self._cache_dir_root}. Using currently defined version " f"{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), force_extract=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(, 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(f"Reusing dataset {} ({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._load_info() self.download_post_processing_resources(dl_manager) return"Generating dataset {} ({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( or 0, directory=self._cache_dir_root): raise IOError( f"Not enough disk space. Needed: {utils.size_str( or 0)} (download: {utils.size_str( or 0)}, generated: {utils.size_str( or 0)}, post-processed: {utils.size_str( 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 print( f"Downloading and preparing dataset {}/{} " f"(download: {utils.size_str(}, generated: {utils.size_str(}, " f"post-processed: {utils.size_str(}, " f"total: {utils.size_str(}) to {self._cache_dir}..." ) else: print( f"Downloading and preparing dataset {}/{} 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 (DatasetNotOnHfGcsError, MissingFilesOnHfGcsError):"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 = sum(split.num_bytes for split in = dl_manager.get_recorded_sizes_checksums() = + # Save info self._save_info() # Download post processing resources self.download_post_processing_resources(dl_manager) print( f"Dataset {} 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 {} with config {} requires manual data. Please follow the manual download instructions: {self.manual_download_instructions} Manual data can be loaded with: datasets.load_dataset({}, 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, # 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) # download post processing resources remote_cache_dir = HF_GCP_BASE_URL + "/" + relative_data_dir.replace(os.sep, "/") for split in for resource_file_name in self._post_processing_resources(split).values(): if os.sep in resource_file_name: raise ValueError(f"Resources shouldn't be in a sub-directory: {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:"Couldn't download resourse file {resource_file_name} from Hf google storage.")"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( 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(, dl_manager.get_recorded_sizes_checksums(), "dataset source files" ) # Build splits for split_generator in split_generators: if str( == "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()." )"Generating split {}") 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) ) from None dl_manager.manage_extracted_files() if verify_infos: verify_splits(, split_dict) # Update the info object with the splits. = split_dict = dl_manager.downloaded_size def download_post_processing_resources(self, dl_manager): for split in for resource_name, resource_file_name in self._post_processing_resources(split).items(): if os.sep in resource_file_name: raise ValueError(f"Resources shouldn't be in a sub-directory: {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:"Downloaded post-processing resource {resource_name} as {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): 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(**{}).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( ( f"Dataset {}: could not find data in {self._cache_dir_root}. Please make sure to call " "builder.download_and_prepare(), or pass download=True to " "datasets.load_dataset() before trying to access the Dataset object." ) ) logger.debug(f'Constructing Dataset for split {split or ", ".join(}, from {self._cache_dir}') # By default, return all splits if split is None: split = {s: s for s in} # 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, disable_tqdm=False, ) 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( 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(f"Resources shouldn't be in a sub-directory: {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 is None or is None: expected_checksums = None else: expected_checksums = verify_checksums(expected_checksums, recorded_checksums, "post processing resources") if is None: = PostProcessedInfo() if is None: = {}[str(split)] = recorded_checksums = sum( checksums_dict["num_bytes"] for split_checksums_dicts in for checksums_dict in split_checksums_dicts.values() ) if is not None and is not None: = ( + + ) self._save_info() ds._info.post_processed = ds._info.post_processing_size = ds._info.size_in_bytes = if is not None: if != ds.features.type: raise ValueError( f"Post-processed features info don't match the dataset:\nGot\n{}\nbut expected something like\n{ds.features}" ) else: = 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,, instructions=split,, 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 {} is not streamable.") dl_manager = StreamingDownloadManager( base_path=base_path or self.base_path, download_config=DownloadConfig(use_auth_token=use_auth_token),, data_dir=self.config.data_dir, ) self._check_manual_download(dl_manager) splits_generators = { 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,, 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': ''}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={'file': ''}, ), ] The above code will first call `_generate_examples(file='')` to write the train data, then `_generate_examples(file='')` 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 `{...})`. """ raise NotImplementedError() def _prepare_split(self, split_generator): split_info = split_generator.split_info fname = f"{}-{}.arrow" fpath = os.path.join(self._cache_dir, fname) generator = self._generate_examples(**split_generator.gen_kwargs) with ArrowWriter(, path=fpath, writer_batch_size=self._writer_batch_size,, 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 = 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 = f"{}-{}.arrow" fpath = os.path.join(self._cache_dir, fname) generator = self._generate_tables(**split_generator.gen_kwargs) with ArrowWriter(, path=fpath) as writer: for key, table in utils.tqdm( generator, unit=" tables", leave=False, disable=True # 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 is None: = 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( | 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 ``. """ 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('{}', '{}', 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 " "" "\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: " f"\n\t`{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_results.wait_until_finish() metrics = pipeline_results.metrics() # Update `info.splits`. split_dict = 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 = with fs.create(os.path.join(self._cache_dir, config.DATASET_INFO_FILENAME)) as f: with fs.create(os.path.join(self._cache_dir, config.LICENSE_FILENAME)) as f: def _prepare_split(self, split_generator, pipeline): import apache_beam as beam split_name = output_prefix = filename_prefix_for_split(, split_name) output_prefix = os.path.join(self._cache_dir, output_prefix) # To write examples to disk: fname = f"{}-{split_name}.arrow" fpath = os.path.join(self._cache_dir, fname) beam_writer = BeamWriter(, path=fpath, namespace=split_name, cache_dir=self._cache_dir ) self._beam_writers[split_name] = beam_writer 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