diff --git "a/llmeval-env/lib/python3.10/site-packages/datasets/builder.py" "b/llmeval-env/lib/python3.10/site-packages/datasets/builder.py"
new file mode 100644--- /dev/null
+++ "b/llmeval-env/lib/python3.10/site-packages/datasets/builder.py"
@@ -0,0 +1,2293 @@
+# 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 posixpath
+import shutil
+import textwrap
+import time
+import urllib
+import warnings
+from dataclasses import dataclass
+from functools import partial
+from pathlib import Path
+from typing import TYPE_CHECKING, Dict, Iterable, Mapping, Optional, Tuple, Union
+from unittest.mock import patch
+
+import fsspec
+import pyarrow as pa
+from fsspec.core import url_to_fs
+from multiprocess import Pool
+from tqdm.contrib.concurrent import thread_map
+
+from . import config, utils
+from .arrow_dataset import Dataset
+from .arrow_reader import (
+ HF_GCP_BASE_URL,
+ ArrowReader,
+ DatasetNotOnHfGcsError,
+ MissingFilesOnHfGcsError,
+ ReadInstruction,
+)
+from .arrow_writer import ArrowWriter, BeamWriter, ParquetWriter, SchemaInferenceError
+from .data_files import DataFilesDict, DataFilesPatternsDict, sanitize_patterns
+from .dataset_dict import DatasetDict, IterableDatasetDict
+from .download.download_config import DownloadConfig
+from .download.download_manager import DownloadManager, DownloadMode
+from .download.mock_download_manager import MockDownloadManager
+from .download.streaming_download_manager import StreamingDownloadManager, xjoin, xopen
+from .exceptions import DatasetGenerationCastError, DatasetGenerationError, FileFormatError, ManualDownloadError
+from .features import Features
+from .filesystems import (
+ is_remote_filesystem,
+ rename,
+)
+from .fingerprint import Hasher
+from .info import DatasetInfo, DatasetInfosDict, PostProcessedInfo
+from .iterable_dataset import ArrowExamplesIterable, ExamplesIterable, IterableDataset
+from .keyhash import DuplicatedKeysError
+from .naming import INVALID_WINDOWS_CHARACTERS_IN_PATH, camelcase_to_snakecase
+from .splits import Split, SplitDict, SplitGenerator, SplitInfo
+from .streaming import extend_dataset_builder_for_streaming
+from .table import CastError
+from .utils import logging
+from .utils import tqdm as hf_tqdm
+from .utils._filelock import FileLock
+from .utils.deprecation_utils import deprecated
+from .utils.file_utils import cached_path, is_remote_url
+from .utils.info_utils import VerificationMode, get_size_checksum_dict, verify_checksums, verify_splits
+from .utils.py_utils import (
+ classproperty,
+ convert_file_size_to_int,
+ has_sufficient_disk_space,
+ iflatmap_unordered,
+ map_nested,
+ memoize,
+ size_str,
+ temporary_assignment,
+)
+from .utils.sharding import _number_of_shards_in_gen_kwargs, _split_gen_kwargs
+from .utils.track import tracked_list
+
+
+if TYPE_CHECKING:
+ from .load import DatasetModule
+
+
+logger = logging.get_logger(__name__)
+
+
+class InvalidConfigName(ValueError):
+ pass
+
+
+@dataclass
+class BuilderConfig:
+ """Base class for `DatasetBuilder` data configuration.
+
+ `DatasetBuilder` subclasses with data configuration options should subclass
+ `BuilderConfig` and add their own properties.
+
+ Attributes:
+ name (`str`, defaults to `default`):
+ The name of the configuration.
+ version (`Version` or `str`, defaults to `0.0.0`):
+ The version of the configuration.
+ data_dir (`str`, *optional*):
+ Path to the directory containing the source data.
+ data_files (`str` or `Sequence` or `Mapping`, *optional*):
+ Path(s) to source data file(s).
+ description (`str`, *optional*):
+ A human description of the configuration.
+ """
+
+ name: str = "default"
+ version: Optional[Union[utils.Version, str]] = utils.Version("0.0.0")
+ data_dir: Optional[str] = None
+ data_files: Optional[Union[DataFilesDict, DataFilesPatternsDict]] = None
+ description: Optional[str] = None
+
+ def __post_init__(self):
+ # The config name is used to name the cache directory.
+ for invalid_char in INVALID_WINDOWS_CHARACTERS_IN_PATH:
+ if invalid_char in self.name:
+ raise InvalidConfigName(
+ f"Bad characters from black list '{INVALID_WINDOWS_CHARACTERS_IN_PATH}' found in '{self.name}'. "
+ 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, DataFilesPatternsDict)):
+ 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:
+ if config_kwargs_to_add_to_suffix["data_dir"] is None:
+ config_kwargs_to_add_to_suffix.pop("data_dir", None)
+ else:
+ # canonicalize the data dir to avoid two paths to the same location having different
+ # hashes
+ data_dir = config_kwargs_to_add_to_suffix["data_dir"]
+ data_dir = os.path.normpath(data_dir)
+ config_kwargs_to_add_to_suffix["data_dir"] = 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 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
+
+ def _resolve_data_files(self, base_path: str, download_config: DownloadConfig) -> None:
+ if isinstance(self.data_files, DataFilesPatternsDict):
+ base_path = xjoin(base_path, self.data_dir) if self.data_dir else base_path
+ self.data_files = self.data_files.resolve(base_path, download_config)
+
+
+class DatasetBuilder:
+ """Abstract base class for all datasets.
+
+ `DatasetBuilder` has 3 key methods:
+
+ - [`DatasetBuilder.info`]: Documents the dataset, including feature
+ names, types, shapes, version, splits, citation, etc.
+ - [`DatasetBuilder.download_and_prepare`]: Downloads the source data
+ and writes it to disk.
+ - [`DatasetBuilder.as_dataset`]: Generates a [`Dataset`].
+
+ Some `DatasetBuilder`s expose multiple variants of the
+ dataset by defining a [`BuilderConfig`] subclass and accepting a
+ config object (or name) on construction. Configurable datasets expose a
+ pre-defined set of configurations in [`DatasetBuilder.builder_configs`].
+
+ Args:
+ cache_dir (`str`, *optional*):
+ Directory to cache data. Defaults to `"~/.cache/huggingface/datasets"`.
+ dataset_name (`str`, *optional*):
+ Name of the dataset, if different from the builder name. Useful for packaged builders
+ like csv, imagefolder, audiofolder, etc. to reflect the difference between datasets
+ that use the same packaged builder.
+ config_name (`str`, *optional*):
+ Name of the dataset configuration.
+ It affects the data generated on disk. Different configurations will have their own subdirectories and
+ versions.
+ If not provided, the default configuration is used (if it exists).
+
+
+
+ Parameter `name` was renamed to `config_name`.
+
+
+ hash (`str`, *optional*):
+ 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`, *optional*):
+ Base path for relative paths that are used to download files.
+ This can be a remote URL.
+ features ([`Features`], *optional*):
+ Features types to use with this dataset.
+ It can be used to change the [`Features`] types of a dataset, for example.
+ token (`str` or `bool`, *optional*):
+ String or boolean to use as Bearer token for remote files on the
+ Datasets Hub. If `True`, will get token from `"~/.huggingface"`.
+ repo_id (`str`, *optional*):
+ ID of the dataset repository.
+ Used to distinguish builders with the same name but not coming from the same namespace, for example "squad"
+ and "lhoestq/squad" repo IDs. In the latter, the builder name would be "lhoestq___squad".
+ data_files (`str` or `Sequence` or `Mapping`, *optional*):
+ Path(s) to source data file(s).
+ 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`, *optional*):
+ Path to directory containing source data file(s).
+ Use only if `data_files` is not passed, in which case it is equivalent to passing
+ `os.path.join(data_dir, "**")` as `data_files`.
+ For builders that require manual download, it must be the path to the local directory containing the
+ manually downloaded data.
+ storage_options (`dict`, *optional*):
+ Key/value pairs to be passed on to the dataset file-system backend, if any.
+ writer_batch_size (`int`, *optional*):
+ 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.
+ name (`str`): Configuration name for the dataset.
+
+
+
+ Use `config_name` instead.
+
+
+
+ **config_kwargs (additional keyword arguments): Keyword arguments to be passed to the corresponding builder
+ configuration class, set on the class attribute [`DatasetBuilder.BUILDER_CONFIG_CLASS`]. The builder
+ configuration class is [`BuilderConfig`] or a subclass of it.
+ """
+
+ # Default version
+ VERSION = None # Default version set in BuilderConfig
+
+ # 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 when name is None
+ DEFAULT_CONFIG_NAME = None
+
+ # 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,
+ cache_dir: Optional[str] = None,
+ dataset_name: Optional[str] = None,
+ config_name: Optional[str] = None,
+ hash: Optional[str] = None,
+ base_path: Optional[str] = None,
+ info: Optional[DatasetInfo] = None,
+ features: Optional[Features] = None,
+ token: Optional[Union[bool, str]] = None,
+ use_auth_token="deprecated",
+ repo_id: Optional[str] = None,
+ data_files: Optional[Union[str, list, dict, DataFilesDict]] = None,
+ data_dir: Optional[str] = None,
+ storage_options: Optional[dict] = None,
+ writer_batch_size: Optional[int] = None,
+ name="deprecated",
+ **config_kwargs,
+ ):
+ if use_auth_token != "deprecated":
+ warnings.warn(
+ "'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\n"
+ f"You can remove this warning by passing 'token={use_auth_token}' instead.",
+ FutureWarning,
+ )
+ token = use_auth_token
+ if name != "deprecated":
+ warnings.warn(
+ "Parameter 'name' was renamed to 'config_name' in version 2.3.0 and will be removed in 3.0.0.",
+ category=FutureWarning,
+ )
+ config_name = name
+ # DatasetBuilder name
+ self.name: str = camelcase_to_snakecase(self.__module__.split(".")[-1])
+ self.hash: Optional[str] = hash
+ self.base_path = base_path
+ self.token = token
+ # For backwards compatibility (e.g. if accessed in a dataset script)
+ self.use_auth_token = token
+ self.repo_id = repo_id
+ self.storage_options = storage_options or {}
+ self.dataset_name = camelcase_to_snakecase(dataset_name) if dataset_name else self.name
+ self._writer_batch_size = writer_batch_size or self.DEFAULT_WRITER_BATCH_SIZE
+
+ if data_files is not None and not isinstance(data_files, DataFilesDict):
+ data_files = DataFilesDict.from_patterns(
+ sanitize_patterns(data_files),
+ base_path=base_path,
+ download_config=DownloadConfig(token=token, storage_options=self.storage_options),
+ )
+
+ # 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_kwargs = config_kwargs
+ self.config, self.config_id = self._create_builder_config(
+ config_name=config_name,
+ custom_features=features,
+ **config_kwargs,
+ )
+
+ # prepare info: DatasetInfo are a standardized dataclass across all datasets
+ # Prefill datasetinfo
+ if info is None:
+ # TODO FOR PACKAGED MODULES IT IMPORTS DATA FROM src/packaged_modules which doesn't make sense
+ info = self.get_exported_dataset_info()
+ info.update(self._info())
+ info.builder_name = self.name
+ info.dataset_name = self.dataset_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:
+ # cache_dir can be a remote bucket on GCS or S3 (when using BeamBasedBuilder for distributed data processing)
+ self._cache_dir_root = str(cache_dir or config.HF_DATASETS_CACHE)
+ self._cache_dir_root = (
+ self._cache_dir_root if is_remote_url(self._cache_dir_root) else os.path.expanduser(self._cache_dir_root)
+ )
+ self._cache_downloaded_dir = (
+ posixpath.join(self._cache_dir_root, config.DOWNLOADED_DATASETS_DIR)
+ if cache_dir
+ else str(config.DOWNLOADED_DATASETS_PATH)
+ )
+ self._cache_downloaded_dir = (
+ self._cache_downloaded_dir
+ if is_remote_url(self._cache_downloaded_dir)
+ else os.path.expanduser(self._cache_downloaded_dir)
+ )
+
+ # In case there exists a legacy cache directory
+ self._legacy_relative_data_dir = None
+
+ 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, Path(self._cache_dir).as_posix().replace("/", "_") + ".lock"
+ )
+ with FileLock(lock_path):
+ if os.path.exists(self._cache_dir): # check if data exist
+ if len(os.listdir(self._cache_dir)) > 0:
+ if os.path.exists(os.path.join(self._cache_dir, config.DATASET_INFO_FILENAME)):
+ logger.info("Overwrite dataset info from restored data version if exists.")
+ 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(
+ f"Old caching folder {self._cache_dir} for dataset {self.dataset_name} exists but no data were found. Removing it. "
+ )
+ os.rmdir(self._cache_dir)
+
+ # Store in the cache by default unless the user specifies a custom output_dir to download_and_prepare
+ self._output_dir = self._cache_dir
+ self._fs: fsspec.AbstractFileSystem = fsspec.filesystem("file")
+
+ # Set download manager
+ self.dl_manager = None
+
+ # Set to True by "datasets-cli test" to generate file checksums for (deprecated) dataset_infos.json independently of verification_mode value.
+ self._record_infos = False
+
+ # Set in `.download_and_prepare` once the format of the generated dataset is known
+ self._file_format = None
+
+ # Enable streaming (e.g. it patches "open" to work with remote files)
+ extend_dataset_builder_for_streaming(self)
+
+ def __getstate__(self):
+ return self.__dict__
+
+ def __setstate__(self, d):
+ self.__dict__ = d
+ # Re-enable streaming, since patched functions are not kept when pickling
+ extend_dataset_builder_for_streaming(self)
+
+ # 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
+
+ def _check_legacy_cache(self) -> Optional[str]:
+ """Check for the old cache directory template {cache_dir}/{namespace}___{builder_name} from 2.13"""
+ if (
+ self.__module__.startswith("datasets.")
+ and not is_remote_url(self._cache_dir_root)
+ and self.config.name == "default"
+ ):
+ from .packaged_modules import _PACKAGED_DATASETS_MODULES
+
+ namespace = self.repo_id.split("/")[0] if self.repo_id and self.repo_id.count("/") > 0 else None
+ config_name = self.repo_id.replace("/", "--") if self.repo_id is not None else self.dataset_name
+ config_id = config_name + self.config_id[len(self.config.name) :]
+ hash = _PACKAGED_DATASETS_MODULES.get(self.name, "missing")[1]
+ legacy_relative_data_dir = posixpath.join(
+ self.dataset_name if namespace is None else f"{namespace}___{self.dataset_name}",
+ config_id,
+ "0.0.0",
+ hash,
+ )
+ legacy_cache_dir = posixpath.join(self._cache_dir_root, legacy_relative_data_dir)
+ if os.path.isdir(legacy_cache_dir):
+ return legacy_relative_data_dir
+
+ def _check_legacy_cache2(self, dataset_module: "DatasetModule") -> Optional[str]:
+ """Check for the old cache directory template {cache_dir}/{namespace}___{dataset_name}/{config_name}-xxx from 2.14 and 2.15"""
+ if (
+ self.__module__.startswith("datasets.")
+ and not is_remote_url(self._cache_dir_root)
+ and not (set(self.config_kwargs) - {"data_files", "data_dir"})
+ ):
+ from .packaged_modules import _PACKAGED_DATASETS_MODULES
+ from .utils._dill import Pickler
+
+ def update_hash_with_config_parameters(hash: str, config_parameters: dict) -> str:
+ """
+ Used to update hash of packaged modules which is used for creating unique cache directories to reflect
+ different config parameters which are passed in metadata from readme.
+ """
+ params_to_exclude = {"config_name", "version", "description"}
+ params_to_add_to_hash = {
+ param: value
+ for param, value in sorted(config_parameters.items())
+ if param not in params_to_exclude
+ }
+ m = Hasher()
+ m.update(hash)
+ m.update(params_to_add_to_hash)
+ return m.hexdigest()
+
+ namespace = self.repo_id.split("/")[0] if self.repo_id and self.repo_id.count("/") > 0 else None
+ with patch.object(Pickler, "_legacy_no_dict_keys_sorting", True):
+ config_id = self.config.name + "-" + Hasher.hash({"data_files": self.config.data_files})
+ hash = _PACKAGED_DATASETS_MODULES.get(self.name, "missing")[1]
+ if (
+ dataset_module.builder_configs_parameters.metadata_configs
+ and self.config.name in dataset_module.builder_configs_parameters.metadata_configs
+ ):
+ hash = update_hash_with_config_parameters(
+ hash, dataset_module.builder_configs_parameters.metadata_configs[self.config.name]
+ )
+ legacy_relative_data_dir = posixpath.join(
+ self.dataset_name if namespace is None else f"{namespace}___{self.dataset_name}",
+ config_id,
+ "0.0.0",
+ hash,
+ )
+ legacy_cache_dir = posixpath.join(self._cache_dir_root, legacy_relative_data_dir)
+ if os.path.isdir(legacy_cache_dir):
+ return legacy_relative_data_dir
+
+ @classmethod
+ def get_all_exported_dataset_infos(cls) -> DatasetInfosDict:
+ """Empty dict if doesn't exist
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset_builder
+ >>> ds_builder = load_dataset_builder('rotten_tomatoes')
+ >>> ds_builder.get_all_exported_dataset_infos()
+ {'default': DatasetInfo(description="Movie Review Dataset.\nThis is a dataset of containing 5,331 positive and 5,331 negative processed\nsentences from Rotten Tomatoes movie reviews. This data was first used in Bo\nPang and Lillian Lee, ``Seeing stars: Exploiting class relationships for\nsentiment categorization with respect to rating scales.'', Proceedings of the\nACL, 2005.\n", citation='@InProceedings{Pang+Lee:05a,\n author = {Bo Pang and Lillian Lee},\n title = {Seeing stars: Exploiting class relationships for sentiment\n categorization with respect to rating scales},\n booktitle = {Proceedings of the ACL},\n year = 2005\n}\n', homepage='http://www.cs.cornell.edu/people/pabo/movie-review-data/', license='', features={'text': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['neg', 'pos'], id=None)}, post_processed=None, supervised_keys=SupervisedKeysData(input='', output=''), task_templates=[TextClassification(task='text-classification', text_column='text', label_column='label')], builder_name='rotten_tomatoes_movie_review', config_name='default', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=1074810, num_examples=8530, dataset_name='rotten_tomatoes_movie_review'), 'validation': SplitInfo(name='validation', num_bytes=134679, num_examples=1066, dataset_name='rotten_tomatoes_movie_review'), 'test': SplitInfo(name='test', num_bytes=135972, num_examples=1066, dataset_name='rotten_tomatoes_movie_review')}, download_checksums={'https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz': {'num_bytes': 487770, 'checksum': 'a05befe52aafda71d458d188a1c54506a998b1308613ba76bbda2e5029409ce9'}}, download_size=487770, post_processing_size=None, dataset_size=1345461, size_in_bytes=1833231)}
+ ```
+ """
+ return DatasetInfosDict.from_directory(cls.get_imported_module_dir())
+
+ def get_exported_dataset_info(self) -> DatasetInfo:
+ """Empty `DatasetInfo` if doesn't exist
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset_builder
+ >>> ds_builder = load_dataset_builder('rotten_tomatoes')
+ >>> ds_builder.get_exported_dataset_info()
+ DatasetInfo(description="Movie Review Dataset.\nThis is a dataset of containing 5,331 positive and 5,331 negative processed\nsentences from Rotten Tomatoes movie reviews. This data was first used in Bo\nPang and Lillian Lee, ``Seeing stars: Exploiting class relationships for\nsentiment categorization with respect to rating scales.'', Proceedings of the\nACL, 2005.\n", citation='@InProceedings{Pang+Lee:05a,\n author = {Bo Pang and Lillian Lee},\n title = {Seeing stars: Exploiting class relationships for sentiment\n categorization with respect to rating scales},\n booktitle = {Proceedings of the ACL},\n year = 2005\n}\n', homepage='http://www.cs.cornell.edu/people/pabo/movie-review-data/', license='', features={'text': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['neg', 'pos'], id=None)}, post_processed=None, supervised_keys=SupervisedKeysData(input='', output=''), task_templates=[TextClassification(task='text-classification', text_column='text', label_column='label')], builder_name='rotten_tomatoes_movie_review', config_name='default', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=1074810, num_examples=8530, dataset_name='rotten_tomatoes_movie_review'), 'validation': SplitInfo(name='validation', num_bytes=134679, num_examples=1066, dataset_name='rotten_tomatoes_movie_review'), 'test': SplitInfo(name='test', num_bytes=135972, num_examples=1066, dataset_name='rotten_tomatoes_movie_review')}, download_checksums={'https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz': {'num_bytes': 487770, 'checksum': 'a05befe52aafda71d458d188a1c54506a998b1308613ba76bbda2e5029409ce9'}}, download_size=487770, post_processing_size=None, dataset_size=1345461, size_in_bytes=1833231)
+ ```
+ """
+ return self.get_all_exported_dataset_infos().get(self.config.name, DatasetInfo())
+
+ def _create_builder_config(
+ self, config_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 config_name and DEFAULT_CONFIG_NAME are None.
+ config_kwargs override the defaults kwargs in config
+ """
+ builder_config = None
+
+ # try default config
+ if config_name is None and self.BUILDER_CONFIGS:
+ if self.DEFAULT_CONFIG_NAME is not None:
+ builder_config = self.builder_configs.get(self.DEFAULT_CONFIG_NAME)
+ logger.info(f"No config specified, defaulting to: {self.dataset_name}/{builder_config.name}")
+ else:
+ if len(self.BUILDER_CONFIGS) > 1:
+ if not config_kwargs:
+ example_of_usage = f"load_dataset('{self.dataset_name}', '{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}`"
+ )
+ else:
+ builder_config = self.BUILDER_CONFIGS[0]
+ logger.info(
+ f"No config specified, defaulting to the single config: {self.dataset_name}/{builder_config.name}"
+ )
+
+ # try to get config by name
+ if isinstance(config_name, str):
+ builder_config = self.builder_configs.get(config_name)
+ if builder_config is None and self.BUILDER_CONFIGS:
+ raise ValueError(
+ f"BuilderConfig '{config_name}' not found. Available: {list(self.builder_configs.keys())}"
+ )
+
+ # if not using an existing config, then create a new config on the fly
+ if not builder_config:
+ if config_name is not None:
+ config_kwargs["name"] = config_name
+ elif self.DEFAULT_CONFIG_NAME and not config_kwargs:
+ # Use DEFAULT_CONFIG_NAME only if no config_kwargs are passed
+ config_kwargs["name"] = self.DEFAULT_CONFIG_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) if config_kwargs else 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(f"BuilderConfig must have a name, got {builder_config.name}")
+
+ # resolve data files if needed
+ builder_config._resolve_data_files(
+ base_path=self.base_path,
+ download_config=DownloadConfig(token=self.token, storage_options=self.storage_options),
+ )
+
+ # 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) and config_id != "default"
+ if is_custom:
+ logger.info(f"Using custom data configuration {config_id}")
+ else:
+ if (
+ builder_config.name in self.builder_configs
+ and builder_config != self.builder_configs[builder_config.name]
+ ):
+ 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 {builder_config.name} must have a version")
+
+ return builder_config, config_id
+
+ @classproperty
+ @classmethod
+ @memoize()
+ def builder_configs(cls) -> Dict[str, BuilderConfig]:
+ """Dictionary of pre-defined configurations for this builder class."""
+ configs = {config.name: config for config in cls.BUILDER_CONFIGS}
+ if len(configs) != len(cls.BUILDER_CONFIGS):
+ names = [config.name 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 _use_legacy_cache_dir_if_possible(self, dataset_module: "DatasetModule"):
+ # Check for the legacy cache directory template (datasets<3.0.0)
+ self._legacy_relative_data_dir = (
+ self._check_legacy_cache2(dataset_module) or self._check_legacy_cache() or None
+ )
+ self._cache_dir = self._build_cache_dir()
+ self._output_dir = 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.dataset_name/self.config.version/self.hash/
+ or if a repo_id with a namespace has been specified:
+ self.namespace___self.dataset_name/self.config.version/self.hash/
+ If any of these element is missing or if ``with_version=False`` the corresponding subfolders are dropped.
+ """
+ if self._legacy_relative_data_dir is not None and with_version and with_hash:
+ return self._legacy_relative_data_dir
+
+ namespace = self.repo_id.split("/")[0] if self.repo_id and self.repo_id.count("/") > 0 else None
+ builder_data_dir = self.dataset_name if namespace is None else f"{namespace}___{self.dataset_name}"
+ builder_data_dir = posixpath.join(builder_data_dir, self.config_id)
+ if with_version:
+ builder_data_dir = posixpath.join(builder_data_dir, str(self.config.version))
+ if with_hash and self.hash and isinstance(self.hash, str):
+ builder_data_dir = posixpath.join(builder_data_dir, self.hash)
+ return builder_data_dir
+
+ def _build_cache_dir(self):
+ """Return the data directory for the current version."""
+ builder_data_dir = posixpath.join(self._cache_dir_root, self._relative_data_dir(with_version=False))
+ version_data_dir = posixpath.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
+ if not is_remote_url(builder_data_dir):
+ 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 {self.dataset_name} 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 _rename(self, src: str, dst: str):
+ rename(self._fs, src, dst)
+
+ def download_and_prepare(
+ self,
+ output_dir: Optional[str] = None,
+ download_config: Optional[DownloadConfig] = None,
+ download_mode: Optional[Union[DownloadMode, str]] = None,
+ verification_mode: Optional[Union[VerificationMode, str]] = None,
+ ignore_verifications="deprecated",
+ try_from_hf_gcs="deprecated",
+ dl_manager: Optional[DownloadManager] = None,
+ base_path: Optional[str] = None,
+ use_auth_token="deprecated",
+ file_format: str = "arrow",
+ max_shard_size: Optional[Union[int, str]] = None,
+ num_proc: Optional[int] = None,
+ storage_options: Optional[dict] = None,
+ **download_and_prepare_kwargs,
+ ):
+ """Downloads and prepares dataset for reading.
+
+ Args:
+ output_dir (`str`, *optional*):
+ Output directory for the dataset.
+ Default to this builder's `cache_dir`, which is inside `~/.cache/huggingface/datasets` by default.
+
+
+ download_config (`DownloadConfig`, *optional*):
+ Specific download configuration parameters.
+ download_mode ([`DownloadMode`] or `str`, *optional*):
+ Select the download/generate mode, default to `REUSE_DATASET_IF_EXISTS`.
+ verification_mode ([`VerificationMode`] or `str`, defaults to `BASIC_CHECKS`):
+ Verification mode determining the checks to run on the downloaded/processed dataset information (checksums/size/splits/...).
+
+
+ ignore_verifications (`bool`, defaults to `False`):
+ Ignore the verifications of the downloaded/processed dataset information (checksums/size/splits/...).
+
+
+
+ `ignore_verifications` was deprecated in version 2.9.1 and will be removed in 3.0.0.
+ Please use `verification_mode` instead.
+
+
+ try_from_hf_gcs (`bool`):
+ If `True`, it will try to download the already prepared dataset from the HF Google cloud storage.
+
+
+
+ `try_from_hf_gcs` was deprecated in version 2.16.0 and will be removed in 3.0.0.
+ Host the processed files on the Hugging Face Hub instead.
+
+
+ dl_manager (`DownloadManager`, *optional*):
+ Specific `DownloadManger` to use.
+ base_path (`str`, *optional*):
+ 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 (`Union[str, bool]`, *optional*):
+ Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.
+ If True, or not specified, will get token from ~/.huggingface.
+
+
+
+ Pass `use_auth_token` to `load_dataset_builder` instead.
+
+
+ file_format (`str`, *optional*):
+ Format of the data files in which the dataset will be written.
+ Supported formats: "arrow", "parquet". Default to "arrow" format.
+ If the format is "parquet", then image and audio data are embedded into the Parquet files instead of pointing to local files.
+
+
+ max_shard_size (`Union[str, int]`, *optional*):
+ Maximum number of bytes written per shard, default is "500MB".
+ The size is based on uncompressed data size, so in practice your shard files may be smaller than
+ `max_shard_size` thanks to Parquet compression for example.
+
+
+ num_proc (`int`, *optional*, defaults to `None`):
+ Number of processes when downloading and generating the dataset locally.
+ Multiprocessing is disabled by default.
+
+
+ storage_options (`dict`, *optional*):
+ Key/value pairs to be passed on to the caching file-system backend, if any.
+
+
+ **download_and_prepare_kwargs (additional keyword arguments): Keyword arguments.
+
+ Example:
+
+ Download and prepare the dataset as Arrow files that can be loaded as a Dataset using `builder.as_dataset()`:
+
+ ```py
+ >>> from datasets import load_dataset_builder
+ >>> builder = load_dataset_builder("rotten_tomatoes")
+ >>> builder.download_and_prepare()
+ ```
+
+ Download and prepare the dataset as sharded Parquet files locally:
+
+ ```py
+ >>> from datasets import load_dataset_builder
+ >>> builder = load_dataset_builder("rotten_tomatoes")
+ >>> builder.download_and_prepare("./output_dir", file_format="parquet")
+ ```
+
+ Download and prepare the dataset as sharded Parquet files in a cloud storage:
+
+ ```py
+ >>> from datasets import load_dataset_builder
+ >>> storage_options = {"key": aws_access_key_id, "secret": aws_secret_access_key}
+ >>> builder = load_dataset_builder("rotten_tomatoes")
+ >>> builder.download_and_prepare("s3://my-bucket/my_rotten_tomatoes", storage_options=storage_options, file_format="parquet")
+ ```
+ """
+ if ignore_verifications != "deprecated":
+ verification_mode = VerificationMode.NO_CHECKS if ignore_verifications else VerificationMode.ALL_CHECKS
+ warnings.warn(
+ "'ignore_verifications' was deprecated in favor of 'verification_mode' in version 2.9.1 and will be removed in 3.0.0.\n"
+ f"You can remove this warning by passing 'verification_mode={verification_mode.value}' instead.",
+ FutureWarning,
+ )
+ if use_auth_token != "deprecated":
+ warnings.warn(
+ "'use_auth_token' was deprecated in version 2.7.1 and will be removed in 3.0.0. Pass `token` to `load_dataset_builder` instead.",
+ FutureWarning,
+ )
+ token = use_auth_token
+ else:
+ token = self.token
+
+ if try_from_hf_gcs != "deprecated":
+ warnings.warn(
+ "'try_from_hf_gcs' was deprecated in version 2.16.0 and will be removed in 3.0.0.",
+ FutureWarning,
+ )
+ else:
+ try_from_hf_gcs = False
+
+ output_dir = output_dir if output_dir is not None else self._cache_dir
+ # output_dir can be a remote bucket on GCS or S3 (when using BeamBasedBuilder for distributed data processing)
+ fs, output_dir = url_to_fs(output_dir, **(storage_options or {}))
+ self._fs = fs
+ self._output_dir = output_dir if not is_remote_filesystem(self._fs) else self._fs.unstrip_protocol(output_dir)
+
+ download_mode = DownloadMode(download_mode or DownloadMode.REUSE_DATASET_IF_EXISTS)
+ verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS)
+ base_path = base_path if base_path is not None else self.base_path
+
+ if file_format is not None and file_format not in ["arrow", "parquet"]:
+ raise ValueError(f"Unsupported file_format: {file_format}. Expected 'arrow' or 'parquet'")
+ self._file_format = file_format
+
+ if self._fs._strip_protocol(self._output_dir) == "":
+ # We don't support the root directory, because it has no dirname,
+ # and we need a dirname to use a .incomplete directory
+ # when the dataset is being written
+ raise RuntimeError(
+ f"Unable to download and prepare the dataset at the root {self._output_dir}. "
+ f"Please specify a subdirectory, e.g. '{self._output_dir + self.dataset_name}'"
+ )
+
+ if dl_manager is None:
+ if download_config is None:
+ download_config = DownloadConfig(
+ cache_dir=self._cache_downloaded_dir,
+ force_download=download_mode == DownloadMode.FORCE_REDOWNLOAD,
+ force_extract=download_mode == DownloadMode.FORCE_REDOWNLOAD,
+ use_etag=False,
+ num_proc=num_proc,
+ token=token,
+ storage_options=self.storage_options,
+ ) # We don't use etag for data files to speed up the process
+
+ dl_manager = DownloadManager(
+ dataset_name=self.dataset_name,
+ download_config=download_config,
+ data_dir=self.config.data_dir,
+ base_path=base_path,
+ record_checksums=(self._record_infos or verification_mode == VerificationMode.ALL_CHECKS),
+ )
+
+ is_local = not is_remote_filesystem(self._fs)
+
+ if (
+ isinstance(dl_manager, MockDownloadManager)
+ or not is_local
+ or file_format != "arrow"
+ or max_shard_size is not None
+ ):
+ try_from_hf_gcs = False
+ self.dl_manager = dl_manager
+
+ # Prevent parallel local disk operations
+ if is_local:
+ # Create parent directory of the output_dir to put the lock file in there
+ Path(self._output_dir).parent.mkdir(parents=True, exist_ok=True)
+ lock_path = self._output_dir + "_builder.lock"
+
+ # File locking only with local paths; no file locking on GCS or S3
+ with FileLock(lock_path) if is_local else contextlib.nullcontext():
+ # Check if the data already exists
+ data_exists = self._fs.exists(posixpath.join(self._output_dir, config.DATASET_INFO_FILENAME))
+ if data_exists and download_mode == DownloadMode.REUSE_DATASET_IF_EXISTS:
+ logger.info(f"Found cached dataset {self.dataset_name} ({self._output_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(f"Generating dataset {self.dataset_name} ({self._output_dir})")
+ if is_local: # if cache dir is local, check for available space
+ if not has_sufficient_disk_space(
+ self.info.size_in_bytes or 0, directory=Path(self._output_dir).parent
+ ):
+ raise OSError(
+ f"Not enough disk space. Needed: {size_str(self.info.size_in_bytes or 0)} (download: {size_str(self.info.download_size or 0)}, generated: {size_str(self.info.dataset_size or 0)}, post-processed: {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 not is_local:
+ self._fs.makedirs(dirname, exist_ok=True)
+ 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)
+ # LocalFileSystem.mv does copy + rm, it is more efficient to simply rename a local directory
+ shutil.move(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:
+ logger.info(
+ f"Downloading and preparing dataset {self.dataset_name}/{self.config.name} "
+ f"(download: {size_str(self.info.download_size)}, generated: {size_str(self.info.dataset_size)}, "
+ f"post-processed: {size_str(self.info.post_processing_size)}, "
+ f"total: {size_str(self.info.size_in_bytes)}) to {self._output_dir}..."
+ )
+ else:
+ _dest = self._fs._strip_protocol(self._output_dir) if is_local else self._output_dir
+ logger.info(f"Downloading and preparing dataset {self.dataset_name}/{self.config.name} to {_dest}...")
+
+ self._check_manual_download(dl_manager)
+
+ # Create a tmp dir and rename to self._output_dir on successful exit.
+ with incomplete_dir(self._output_dir) as tmp_output_dir:
+ # Temporarily assign _output_dir to tmp_data_dir to avoid having to forward
+ # it to every sub function.
+ with temporary_assignment(self, "_output_dir", tmp_output_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):
+ 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:
+ prepare_split_kwargs = {"file_format": file_format}
+ if max_shard_size is not None:
+ prepare_split_kwargs["max_shard_size"] = max_shard_size
+ if num_proc is not None:
+ prepare_split_kwargs["num_proc"] = num_proc
+ self._download_and_prepare(
+ dl_manager=dl_manager,
+ verification_mode=verification_mode,
+ **prepare_split_kwargs,
+ **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)
+
+ logger.info(
+ f"Dataset {self.dataset_name} downloaded and prepared to {self._output_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.dataset_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.dataset_name}", data_dir="")"""
+ )
+ )
+
+ 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._output_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._output_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(f"Resources shouldn't be in a sub-directory: {resource_file_name}")
+ try:
+ resource_path = cached_path(remote_cache_dir + "/" + resource_file_name)
+ shutil.move(resource_path, os.path.join(self._output_dir, resource_file_name))
+ except ConnectionError:
+ logger.info(f"Couldn't download resourse file {resource_file_name} from Hf google storage.")
+ logger.info("Dataset downloaded from Hf google storage.")
+
+ def _download_and_prepare(self, dl_manager, verification_mode, **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.
+ verification_mode ([`VerificationMode`]):
+ if `ALL_CHECKS`, perform all the verifications including checksums.
+ if `BASIC_CHECKS`, do not perform checksums, only perform split tests.
+ if `NO_CHECKS`, do not perform any verification.
+ prepare_split_kwargs: Additional options, such as `file_format`, `max_shard_size`
+ """
+ # Generating data for all splits
+ split_dict = SplitDict(dataset_name=self.dataset_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 verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums:
+ 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(f"Generating {split_generator.split_info.name} 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
+ # If check_duplicates is set to True , then except DuplicatedKeysError
+ except DuplicatedKeysError as e:
+ raise DuplicatedKeysError(
+ e.key,
+ e.duplicate_key_indices,
+ fix_msg=f"To avoid duplicate keys, please fix the dataset script {self.name}.py",
+ ) from None
+ dl_manager.manage_extracted_files()
+
+ if verification_mode == VerificationMode.BASIC_CHECKS or verification_mode == VerificationMode.ALL_CHECKS:
+ 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 or []:
+ for resource_name, resource_file_name in self._post_processing_resources(split).items():
+ if not not is_remote_filesystem(self._fs):
+ raise NotImplementedError(f"Post processing is not supported on filesystem {self._fs}")
+ 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._output_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(f"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._output_dir, storage_options=self._fs.storage_options)
+
+ def _save_info(self):
+ file_lock = (
+ FileLock(self._output_dir + "_info.lock")
+ if not is_remote_filesystem(self._fs)
+ else contextlib.nullcontext()
+ )
+ with file_lock:
+ self.info.write_to_directory(self._output_dir, storage_options=self._fs.storage_options)
+
+ def _save_infos(self):
+ file_lock = (
+ FileLock(self._output_dir + "_infos.lock")
+ if not is_remote_filesystem(self._fs)
+ else contextlib.nullcontext()
+ )
+ with file_lock:
+ 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,
+ verification_mode: Optional[Union[VerificationMode, str]] = None,
+ ignore_verifications="deprecated",
+ 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`, defaults to `True`):
+ Whether to run post-processing dataset transforms and/or add
+ indexes.
+ verification_mode ([`VerificationMode`] or `str`, defaults to `BASIC_CHECKS`):
+ Verification mode determining the checks to run on the
+ downloaded/processed dataset information (checksums/size/splits/...).
+
+
+ ignore_verifications (`bool`, defaults to `False`):
+ Whether to ignore the verifications of the
+ downloaded/processed dataset information (checksums/size/splits/...).
+
+
+
+ `ignore_verifications` was deprecated in version 2.9.1 and will be removed in 3.0.0.
+ Please use `verification_mode` instead.
+
+
+ in_memory (`bool`, defaults to `False`):
+ Whether to copy the data in-memory.
+
+ Returns:
+ datasets.Dataset
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset_builder
+ >>> builder = load_dataset_builder('rotten_tomatoes')
+ >>> builder.download_and_prepare()
+ >>> ds = builder.as_dataset(split='train')
+ >>> ds
+ Dataset({
+ features: ['text', 'label'],
+ num_rows: 8530
+ })
+ ```
+ """
+ if ignore_verifications != "deprecated":
+ verification_mode = verification_mode.NO_CHECKS if ignore_verifications else VerificationMode.ALL_CHECKS
+ warnings.warn(
+ "'ignore_verifications' was deprecated in favor of 'verification' in version 2.9.1 and will be removed in 3.0.0.\n"
+ f"You can remove this warning by passing 'verification_mode={verification_mode.value}' instead.",
+ FutureWarning,
+ )
+ if self._file_format is not None and self._file_format != "arrow":
+ raise FileFormatError('Loading a dataset not written in the "arrow" format is not supported.')
+ if is_remote_filesystem(self._fs):
+ raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.")
+ if not os.path.exists(self._output_dir):
+ raise FileNotFoundError(
+ f"Dataset {self.dataset_name}: could not find data in {self._output_dir}. Please make sure to call "
+ "builder.download_and_prepare(), or use "
+ "datasets.load_dataset() before trying to access the Dataset object."
+ )
+
+ logger.debug(f'Constructing Dataset for split {split or ", ".join(self.info.splits)}, from {self._output_dir}')
+
+ # By default, return all splits
+ if split is None:
+ split = {s: s for s in self.info.splits}
+
+ verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS)
+
+ # Create a dataset for each of the given splits
+ datasets = map_nested(
+ partial(
+ self._build_single_dataset,
+ run_post_process=run_post_process,
+ verification_mode=verification_mode,
+ in_memory=in_memory,
+ ),
+ split,
+ map_tuple=True,
+ disable_tqdm=True,
+ )
+ if isinstance(datasets, dict):
+ datasets = DatasetDict(datasets)
+ return datasets
+
+ def _build_single_dataset(
+ self,
+ split: Union[str, ReadInstruction, Split],
+ run_post_process: bool,
+ verification_mode: VerificationMode,
+ in_memory: bool = False,
+ ):
+ """as_dataset for a single split."""
+ 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(f"Resources shouldn't be in a sub-directory: {resource_file_name}")
+ resources_paths = {
+ resource_name: os.path.join(self._output_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 = {}
+ record_checksums = False
+ for resource_name, resource_path in resources_paths.items():
+ size_checksum = get_size_checksum_dict(resource_path)
+ recorded_checksums[resource_name] = size_checksum
+ if verification_mode == VerificationMode.ALL_CHECKS and record_checksums:
+ 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(
+ f"Post-processed features info don't match the dataset:\nGot\n{self.info.post_processed.features}\nbut expected something like\n{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`, defaults to `False`):
+ Whether to copy the data in-memory.
+
+ Returns:
+ `Dataset`
+ """
+ cache_dir = self._fs._strip_protocol(self._output_dir)
+ dataset_name = self.dataset_name
+ if self._check_legacy_cache():
+ dataset_name = self.name
+ dataset_kwargs = ArrowReader(cache_dir, self.info).read(
+ name=dataset_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(Path(self._relative_data_dir()).as_posix())
+ 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,
+ ) -> Union[Dict[str, IterableDataset], IterableDataset]:
+ if is_remote_filesystem(self._fs):
+ raise NotImplementedError(
+ f"Loading a streaming dataset cached in a {type(self._fs).__name__} is not supported yet."
+ )
+
+ dl_manager = StreamingDownloadManager(
+ base_path=base_path or self.base_path,
+ download_config=DownloadConfig(token=self.token, storage_options=self.storage_options),
+ dataset_name=self.dataset_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 = 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)
+ # add auth to be able to access and decode audio/image files from private repositories.
+ token_per_repo_id = {self.repo_id: self.token} if self.repo_id else {}
+ return IterableDataset(
+ ex_iterable, info=self.info, split=splits_generator.name, token_per_repo_id=token_per_repo_id
+ )
+
+ 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: Union[DownloadManager, StreamingDownloadManager]):
+ """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 (`Union[DownloadManager, StreamingDownloadManager]`):
+ Download manager to download the data
+
+ Returns:
+ `list`.
+ """
+ raise NotImplementedError()
+
+ @abc.abstractmethod
+ def _prepare_split(
+ self,
+ split_generator: SplitGenerator,
+ file_format: str = "arrow",
+ max_shard_size: Optional[Union[str, int]] = None,
+ num_proc: Optional[int] = None,
+ **kwargs,
+ ):
+ """Generate the examples and record them on disk.
+
+ Args:
+ split_generator (`SplitGenerator`):
+ Split generator to process
+ file_format (`str`, *optional*):
+ format of the data files in which the dataset will be written.
+ Supported formats: "arrow", "parquet". Default to "arrow" format.
+ max_shard_size (`Union[str, int]`, *optional*):
+ Maximum number of bytes written per shard, default is "500MB".
+ The size is based on uncompressed data size, so in practice your shard files may be smaller than
+ `max_shard_size` thanks to Parquet compression for example.
+ num_proc (`int`, *optional*, defaults to `None`):
+ Number of processes when downloading and generating the dataset locally.
+ Multiprocessing is disabled by default.
+
+
+ **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()
+
+
+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.
+ """
+
+ @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 (additional keyword arguments):
+ 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`, 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: SplitGenerator,
+ check_duplicate_keys: bool,
+ file_format="arrow",
+ num_proc: Optional[int] = None,
+ max_shard_size: Optional[Union[int, str]] = None,
+ ):
+ max_shard_size = convert_file_size_to_int(max_shard_size or config.MAX_SHARD_SIZE)
+
+ if self.info.splits is not None:
+ split_info = self.info.splits[split_generator.name]
+ else:
+ split_info = split_generator.split_info
+
+ SUFFIX = "-JJJJJ-SSSSS-of-NNNNN"
+ fname = f"{self.dataset_name}-{split_generator.name}{SUFFIX}.{file_format}"
+ fpath = posixpath.join(self._output_dir, fname)
+
+ if num_proc and num_proc > 1:
+ num_input_shards = _number_of_shards_in_gen_kwargs(split_generator.gen_kwargs)
+ if num_input_shards <= 1:
+ logger.warning(
+ f"Setting num_proc from {num_proc} back to 1 for the {split_info.name} split to disable multiprocessing as it only contains one shard."
+ )
+ num_proc = 1
+ elif num_input_shards < num_proc:
+ logger.warning(
+ f"Setting num_proc from {num_proc} to {num_input_shards} for the {split_info.name} split as it only contains {num_input_shards} shards."
+ )
+ num_proc = num_input_shards
+
+ pbar = hf_tqdm(
+ unit=" examples",
+ total=split_info.num_examples,
+ desc=f"Generating {split_info.name} split",
+ )
+
+ _prepare_split_args = {
+ "fpath": fpath,
+ "file_format": file_format,
+ "max_shard_size": max_shard_size,
+ "split_info": split_info,
+ "check_duplicate_keys": check_duplicate_keys,
+ }
+
+ if num_proc is None or num_proc == 1:
+ result = None
+ gen_kwargs = split_generator.gen_kwargs
+ job_id = 0
+ with pbar:
+ for job_id, done, content in self._prepare_split_single(
+ gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
+ ):
+ if done:
+ result = content
+ else:
+ pbar.update(content)
+ # wrapping everything into lists for consistency with the multiprocessed code path
+ assert result is not None, "Failed to retrieve results from prepare_split"
+ examples_per_job, bytes_per_job, features_per_job, shards_per_job, shard_lengths_per_job = [
+ [item] for item in result
+ ]
+ else:
+ kwargs_per_job = [
+ {"gen_kwargs": gen_kwargs, "job_id": job_id, **_prepare_split_args}
+ for job_id, gen_kwargs in enumerate(
+ _split_gen_kwargs(split_generator.gen_kwargs, max_num_jobs=num_proc)
+ )
+ ]
+ num_jobs = len(kwargs_per_job)
+
+ examples_per_job = [None] * num_jobs
+ bytes_per_job = [None] * num_jobs
+ features_per_job = [None] * num_jobs
+ shards_per_job = [None] * num_jobs
+ shard_lengths_per_job = [None] * num_jobs
+
+ with Pool(num_proc) as pool:
+ with pbar:
+ for job_id, done, content in iflatmap_unordered(
+ pool, self._prepare_split_single, kwargs_iterable=kwargs_per_job
+ ):
+ if done:
+ # the content is the result of the job
+ (
+ examples_per_job[job_id],
+ bytes_per_job[job_id],
+ features_per_job[job_id],
+ shards_per_job[job_id],
+ shard_lengths_per_job[job_id],
+ ) = content
+ else:
+ # the content is the number of examples progress update
+ pbar.update(content)
+
+ assert (
+ None not in examples_per_job
+ ), f"Failed to retrieve results from prepare_split: result list {examples_per_job} still contains None - at least one worker failed to return its results"
+
+ total_shards = sum(shards_per_job)
+ total_num_examples = sum(examples_per_job)
+ total_num_bytes = sum(bytes_per_job)
+ features = features_per_job[0]
+
+ split_generator.split_info.num_examples = total_num_examples
+ split_generator.split_info.num_bytes = total_num_bytes
+
+ # should rename everything at the end
+ logger.debug(f"Renaming {total_shards} shards.")
+ if total_shards > 1:
+ # use the -SSSSS-of-NNNNN pattern
+
+ def _rename_shard(shard_and_job: Tuple[int]):
+ shard_id, job_id = shard_and_job
+ global_shard_id = sum(shards_per_job[:job_id]) + shard_id
+ self._rename(
+ fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
+ fpath.replace("JJJJJ-SSSSS", f"{global_shard_id:05d}").replace("NNNNN", f"{total_shards:05d}"),
+ )
+
+ shards_and_jobs = [
+ (shard_id, job_id)
+ for job_id, num_shards in enumerate(shards_per_job)
+ for shard_id in range(num_shards)
+ ]
+ thread_map(_rename_shard, shards_and_jobs, disable=True, max_workers=64)
+
+ split_generator.split_info.shard_lengths = [
+ shard_length for shard_lengths in shard_lengths_per_job for shard_length in shard_lengths
+ ]
+ else:
+ # don't use any pattern
+ shard_id, job_id = 0, 0
+ self._rename(
+ fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
+ fpath.replace(SUFFIX, ""),
+ )
+
+ if self.info.features is None:
+ self.info.features = features
+
+ def _prepare_split_single(
+ self,
+ gen_kwargs: dict,
+ fpath: str,
+ file_format: str,
+ max_shard_size: int,
+ split_info: SplitInfo,
+ check_duplicate_keys: bool,
+ job_id: int,
+ ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
+ generator = self._generate_examples(**gen_kwargs)
+ writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
+ embed_local_files = file_format == "parquet"
+ shard_lengths = []
+ total_num_examples, total_num_bytes = 0, 0
+
+ shard_id = 0
+ num_examples_progress_update = 0
+ try:
+ writer = writer_class(
+ features=self.info.features,
+ path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
+ writer_batch_size=self._writer_batch_size,
+ hash_salt=split_info.name,
+ check_duplicates=check_duplicate_keys,
+ storage_options=self._fs.storage_options,
+ embed_local_files=embed_local_files,
+ )
+ try:
+ _time = time.time()
+ for key, record in generator:
+ if max_shard_size is not None and writer._num_bytes > max_shard_size:
+ num_examples, num_bytes = writer.finalize()
+ writer.close()
+ shard_lengths.append(num_examples)
+ total_num_examples += num_examples
+ total_num_bytes += num_bytes
+ shard_id += 1
+ writer = writer_class(
+ features=writer._features,
+ path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
+ writer_batch_size=self._writer_batch_size,
+ hash_salt=split_info.name,
+ check_duplicates=check_duplicate_keys,
+ storage_options=self._fs.storage_options,
+ embed_local_files=embed_local_files,
+ )
+ example = self.info.features.encode_example(record) if self.info.features is not None else record
+ writer.write(example, key)
+ num_examples_progress_update += 1
+ if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
+ _time = time.time()
+ yield job_id, False, num_examples_progress_update
+ num_examples_progress_update = 0
+ finally:
+ yield job_id, False, num_examples_progress_update
+ num_shards = shard_id + 1
+ num_examples, num_bytes = writer.finalize()
+ writer.close()
+ shard_lengths.append(num_examples)
+ total_num_examples += num_examples
+ total_num_bytes += num_bytes
+ except Exception as e:
+ # Ignore the writer's error for no examples written to the file if this error was caused by the error in _generate_examples before the first example was yielded
+ if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
+ e = e.__context__
+ raise DatasetGenerationError("An error occurred while generating the dataset") from e
+
+ yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
+
+ def _download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs):
+ super()._download_and_prepare(
+ dl_manager,
+ verification_mode,
+ check_duplicate_keys=verification_mode == VerificationMode.BASIC_CHECKS
+ or verification_mode == VerificationMode.ALL_CHECKS,
+ **prepare_splits_kwargs,
+ )
+
+ def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
+ return ExamplesIterable(self._generate_examples, split_generator.gen_kwargs)
+
+
+class ArrowBasedBuilder(DatasetBuilder):
+ """Base class for datasets with data generation based on Arrow loading functions (CSV/JSON/Parquet)."""
+
+ @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 (additional keyword arguments):
+ 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: SplitGenerator,
+ file_format: str = "arrow",
+ num_proc: Optional[int] = None,
+ max_shard_size: Optional[Union[str, int]] = None,
+ ):
+ max_shard_size = convert_file_size_to_int(max_shard_size or config.MAX_SHARD_SIZE)
+
+ try:
+ split_info = self.info.splits[split_generator.name]
+ except Exception:
+ split_info = split_generator.split_info
+
+ SUFFIX = "-JJJJJ-SSSSS-of-NNNNN"
+ fname = f"{self.dataset_name}-{split_generator.name}{SUFFIX}.{file_format}"
+ fpath = posixpath.join(self._output_dir, fname)
+
+ if num_proc and num_proc > 1:
+ num_input_shards = _number_of_shards_in_gen_kwargs(split_generator.gen_kwargs)
+ if num_input_shards <= 1:
+ logger.warning(
+ f"Setting num_proc from {num_proc} back to 1 for the {split_info.name} split to disable multiprocessing as it only contains one shard."
+ )
+ num_proc = 1
+ elif num_input_shards < num_proc:
+ logger.warning(
+ f"Setting num_proc from {num_proc} to {num_input_shards} for the {split_info.name} split as it only contains {num_input_shards} shards."
+ )
+ num_proc = num_input_shards
+
+ pbar = hf_tqdm(
+ unit=" examples",
+ total=split_info.num_examples,
+ desc=f"Generating {split_info.name} split",
+ )
+
+ _prepare_split_args = {
+ "fpath": fpath,
+ "file_format": file_format,
+ "max_shard_size": max_shard_size,
+ }
+
+ if num_proc is None or num_proc == 1:
+ result = None
+ gen_kwargs = split_generator.gen_kwargs
+ job_id = 0
+ with pbar:
+ for job_id, done, content in self._prepare_split_single(
+ gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
+ ):
+ if done:
+ result = content
+ else:
+ pbar.update(content)
+ # wrapping everything into lists for consistency with the multiprocessed code path
+ assert result is not None, "Failed to retrieve results from prepare_split"
+ examples_per_job, bytes_per_job, features_per_job, shards_per_job, shard_lengths_per_job = [
+ [item] for item in result
+ ]
+ else:
+ kwargs_per_job = [
+ {"gen_kwargs": gen_kwargs, "job_id": job_id, **_prepare_split_args}
+ for job_id, gen_kwargs in enumerate(
+ _split_gen_kwargs(split_generator.gen_kwargs, max_num_jobs=num_proc)
+ )
+ ]
+ num_jobs = len(kwargs_per_job)
+
+ examples_per_job = [None] * num_jobs
+ bytes_per_job = [None] * num_jobs
+ features_per_job = [None] * num_jobs
+ shards_per_job = [None] * num_jobs
+ shard_lengths_per_job = [None] * num_jobs
+
+ with Pool(num_proc) as pool:
+ with pbar:
+ for job_id, done, content in iflatmap_unordered(
+ pool, self._prepare_split_single, kwargs_iterable=kwargs_per_job
+ ):
+ if done:
+ # the content is the result of the job
+ (
+ examples_per_job[job_id],
+ bytes_per_job[job_id],
+ features_per_job[job_id],
+ shards_per_job[job_id],
+ shard_lengths_per_job[job_id],
+ ) = content
+ else:
+ # the content is the number of examples progress update
+ pbar.update(content)
+
+ assert (
+ None not in examples_per_job
+ ), f"Failed to retrieve results from prepare_split: result list {examples_per_job} still contains None - at least one worker failed to return its results"
+
+ total_shards = sum(shards_per_job)
+ total_num_examples = sum(examples_per_job)
+ total_num_bytes = sum(bytes_per_job)
+ features = features_per_job[0]
+
+ split_generator.split_info.num_examples = total_num_examples
+ split_generator.split_info.num_bytes = total_num_bytes
+
+ # should rename everything at the end
+ logger.debug(f"Renaming {total_shards} shards.")
+ if total_shards > 1:
+ # use the -SSSSS-of-NNNNN pattern
+
+ def _rename_shard(shard_id_and_job: Tuple[int]):
+ shard_id, job_id = shard_id_and_job
+ global_shard_id = sum(shards_per_job[:job_id]) + shard_id
+ self._rename(
+ fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
+ fpath.replace("JJJJJ-SSSSS", f"{global_shard_id:05d}").replace("NNNNN", f"{total_shards:05d}"),
+ )
+
+ shard_ids_and_jobs = [
+ (shard_id, job_id)
+ for job_id, num_shards in enumerate(shards_per_job)
+ for shard_id in range(num_shards)
+ ]
+ thread_map(_rename_shard, shard_ids_and_jobs, disable=True, max_workers=64)
+
+ split_generator.split_info.shard_lengths = [
+ shard_length for shard_lengths in shard_lengths_per_job for shard_length in shard_lengths
+ ]
+ else:
+ # don't use any pattern
+ shard_id, job_id = 0, 0
+ self._rename(
+ fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
+ fpath.replace(SUFFIX, ""),
+ )
+
+ if self.info.features is None:
+ self.info.features = features
+
+ def _prepare_split_single(
+ self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
+ ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
+ gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
+ generator = self._generate_tables(**gen_kwargs)
+ writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
+ embed_local_files = file_format == "parquet"
+ shard_lengths = []
+ total_num_examples, total_num_bytes = 0, 0
+
+ shard_id = 0
+ num_examples_progress_update = 0
+ try:
+ writer = writer_class(
+ features=self.info.features,
+ path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
+ writer_batch_size=self._writer_batch_size,
+ storage_options=self._fs.storage_options,
+ embed_local_files=embed_local_files,
+ )
+ try:
+ _time = time.time()
+ for _, table in generator:
+ if max_shard_size is not None and writer._num_bytes > max_shard_size:
+ num_examples, num_bytes = writer.finalize()
+ writer.close()
+ shard_lengths.append(num_examples)
+ total_num_examples += num_examples
+ total_num_bytes += num_bytes
+ shard_id += 1
+ writer = writer_class(
+ features=writer._features,
+ path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
+ writer_batch_size=self._writer_batch_size,
+ storage_options=self._fs.storage_options,
+ embed_local_files=embed_local_files,
+ )
+ try:
+ writer.write_table(table)
+ except CastError as cast_error:
+ raise DatasetGenerationCastError.from_cast_error(
+ cast_error=cast_error,
+ builder_name=self.info.builder_name,
+ gen_kwargs=gen_kwargs,
+ token=self.token,
+ )
+ num_examples_progress_update += len(table)
+ if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
+ _time = time.time()
+ yield job_id, False, num_examples_progress_update
+ num_examples_progress_update = 0
+ finally:
+ yield job_id, False, num_examples_progress_update
+ num_shards = shard_id + 1
+ num_examples, num_bytes = writer.finalize()
+ writer.close()
+ shard_lengths.append(num_examples)
+ total_num_examples += num_examples
+ total_num_bytes += num_bytes
+ except Exception as e:
+ # Ignore the writer's error for no examples written to the file if this error was caused by the error in _generate_examples before the first example was yielded
+ if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
+ e = e.__context__
+ if isinstance(e, DatasetGenerationError):
+ raise
+ raise DatasetGenerationError("An error occurred while generating the dataset") from e
+
+ yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
+
+ def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
+ return ArrowExamplesIterable(self._generate_tables, kwargs=split_generator.gen_kwargs)
+
+
+class MissingBeamOptions(ValueError):
+ pass
+
+
+@deprecated("Use `GeneratorBasedBuilder` or `ArrowBasedBuilder` instead.")
+class BeamBasedBuilder(DatasetBuilder):
+ """Beam-based Builder."""
+
+ def __init__(self, *args, beam_runner=None, beam_options=None, **kwargs):
+ self._beam_runner = beam_runner
+ self._beam_options = beam_options
+ self._beam_writers = {} # {split: beam_writer} mapping.
+ super().__init__(*args, **kwargs)
+
+ 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.
+
+
+ Args:
+ pipeline ([`utils.beam_utils.BeamPipeline`]):
+ Apache Beam pipeline.
+ **kwargs (additional keyword arguments):
+ Arguments forwarded from the SplitGenerator.gen_kwargs.
+
+ Returns:
+ `beam.PCollection`: Apache Beam PCollection containing the
+ example to send to `self.info.features.encode_example(...)`.
+
+ Example:
+
+ ```
+ def _build_pcollection(pipeline, extracted_dir=None):
+ return (
+ pipeline
+ | beam.Create(gfile.io.listdir(extracted_dir))
+ | beam.Map(_process_file)
+ )
+ ```
+ """
+ raise NotImplementedError()
+
+ def _download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs):
+ # 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: "
+ f"\n\t`{usage_example}`"
+ )
+ if self._writer_batch_size is not None:
+ logger.warning(
+ "`writer_batch_size` is not supported for beam pipelines yet. Using the default chunk size for writing."
+ )
+
+ # 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.
+ pipeline_options = {"pipeline_type_check": False}
+ if "num_proc" in prepare_splits_kwargs:
+ num_workers = prepare_splits_kwargs.pop("num_proc")
+ pipeline_options["direct_num_workers"] = num_workers
+ pipeline_options["num_workers"] = num_workers
+ pipeline_options["direct_running_mode"] = "multi_processing"
+ # TODO: Fix ModuleNotFoundError: No module named 'datasets_modules' when running multiprocessed DirectRunner
+ raise NotImplementedError("Using a DirectRunner with `num_proc` for multiprocessing it not supported yet.")
+ beam_options = beam_options or beam.options.pipeline_options.PipelineOptions.from_dictionary(pipeline_options)
+ # Use a single pipeline for all splits
+ pipeline = beam_utils.BeamPipeline(
+ runner=beam_runner,
+ options=beam_options,
+ )
+ super()._download_and_prepare(
+ dl_manager, verification_mode=VerificationMode.NO_CHECKS, pipeline=pipeline, **prepare_splits_kwargs
+ ) # TODO handle verification_mode 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
+ if hasattr(beam_writer, "_shard_lengths") and len(beam_writer._shard_lengths) > 1:
+ # keep the -SSSSS-of-NNNNN pattern
+ split_info.shard_lengths = beam_writer._shard_lengths
+ else:
+ # don't use any pattern
+ file_format = prepare_splits_kwargs.get("file_format", "arrow")
+ src_fname = f"{self.dataset_name}-{split_name}-00000-of-00001.{file_format}"
+ dst_fname = f"{self.dataset_name}-{split_name}.{file_format}"
+ src_fpath = posixpath.join(self._output_dir, src_fname)
+ dst_fpath = posixpath.join(self._output_dir, dst_fname)
+ self._rename(src_fpath, dst_fpath)
+
+ def _save_info(self):
+ download_config = (
+ self.dl_manager.download_config
+ if self.dl_manager
+ else DownloadConfig(token=self.token, storage_options=self._fs.storage_options)
+ )
+ with xopen(f"{self._output_dir}/{config.DATASET_INFO_FILENAME}", "wb", download_config=download_config) as f:
+ self.info._dump_info(f)
+ if self.info.license:
+ with xopen(f"{self._output_dir}/{config.LICENSE_FILENAME}", "wb", download_config=download_config) as f:
+ self.info._dump_license(f)
+
+ def _prepare_split(
+ self, split_generator, pipeline, file_format="arrow", max_shard_size: Optional[Union[str, int]] = None
+ ):
+ import apache_beam as beam
+
+ if max_shard_size is not None:
+ raise NotImplementedError(
+ "max_shard_size is not supported for Beam datasets."
+ "Please set it to None to use the default Apache Beam sharding and get the best performance."
+ )
+
+ # To write examples in filesystem:
+ split_name = split_generator.split_info.name
+ fname = f"{self.dataset_name}-{split_name}.{file_format}"
+ fpath = posixpath.join(self._output_dir, fname)
+ beam_writer = BeamWriter(
+ features=self.info.features, path=fpath, namespace=split_name, cache_dir=self._output_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 max_bytes_per_shard
+
+ def as_streaming_dataset(
+ self,
+ split: Optional[str] = None,
+ ) -> Union[Dict[str, IterableDataset], IterableDataset]:
+ self._request_info_from_hf_gcs()
+ datasets = {
+ split.name: IterableDataset(self._get_examples_iterable_for_split(split), info=self.info, split=split.name)
+ for split in self.info.splits.values()
+ }
+ if split:
+ try:
+ datasets = datasets[split]
+ except KeyError:
+ raise ValueError(f"Bad split: {split}. Available splits: {list(datasets)}")
+ if isinstance(datasets, dict):
+ datasets = IterableDatasetDict(datasets)
+ return datasets
+
+ def _get_examples_iterable_for_split(self, split: SplitInfo) -> ExamplesIterable:
+ return ExamplesIterable(self._generate_examples_from_hf_gcs, {"split": split})
+
+ def _generate_examples_from_hf_gcs(self, split: SplitInfo):
+ if split.shard_lengths:
+ num_shards = len(split.shard_lengths)
+ remote_prepared_urls = [
+ f"{self._remote_cache_dir_from_hf_gcs}/{self.name}-{split.name}-{shard_id:05d}-of-{num_shards:05d}.arrow"
+ for shard_id in range(num_shards)
+ ]
+ else:
+ remote_prepared_urls = [f"{self._remote_cache_dir_from_hf_gcs}/{self.name}-{split.name}.arrow"]
+ key = 0
+ download_config = (
+ self.dl_manager.download_config
+ if self.dl_manager
+ else DownloadConfig(token=self.token, storage_options=self._fs.storage_options)
+ )
+ for remote_prepared_url in remote_prepared_urls:
+ with xopen(remote_prepared_url, "rb", download_config=download_config) as f:
+ with pa.ipc.open_stream(f) as reader:
+ for record_batch in reader:
+ for record in record_batch.to_pylist():
+ yield key, record
+ key += 1
+
+ def _request_info_from_hf_gcs(self):
+ from .download.streaming_download_manager import xopen
+
+ remote_dataset_info = f"{self._remote_cache_dir_from_hf_gcs}/{config.DATASET_INFO_FILENAME}"
+ try:
+ download_config = download_config = (
+ self.dl_manager.download_config
+ if self.dl_manager
+ else DownloadConfig(token=self.token, storage_options=self._fs.storage_options)
+ )
+ with xopen(remote_dataset_info, download_config=download_config) as f:
+ import json
+
+ _info = json.load(f)
+ except FileNotFoundError as err:
+ raise DatasetNotOnHfGcsError(err) from None
+ self.info.update(DatasetInfo.from_dict(_info))
+
+ @property
+ def _remote_cache_dir_from_hf_gcs(self):
+ relative_data_dir = self._relative_data_dir(with_hash=False)
+ return HF_GCP_BASE_URL + "/" + Path(relative_data_dir).as_posix()