diff --git "a/venv/lib/python3.10/site-packages/datasets/dataset_dict.py" "b/venv/lib/python3.10/site-packages/datasets/dataset_dict.py" new file mode 100644--- /dev/null +++ "b/venv/lib/python3.10/site-packages/datasets/dataset_dict.py" @@ -0,0 +1,2293 @@ +import contextlib +import copy +import fnmatch +import json +import math +import posixpath +import re +import warnings +from io import BytesIO +from pathlib import Path +from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union + +import fsspec +import numpy as np +from fsspec.core import url_to_fs +from huggingface_hub import ( + CommitInfo, + CommitOperationAdd, + CommitOperationDelete, + DatasetCard, + DatasetCardData, + HfApi, +) +from huggingface_hub.hf_api import RepoFile + +from . import config +from .arrow_dataset import PUSH_TO_HUB_WITHOUT_METADATA_CONFIGS_SPLIT_PATTERN_SHARDED, Dataset +from .features import Features +from .features.features import FeatureType +from .info import DatasetInfo, DatasetInfosDict +from .naming import _split_re +from .splits import NamedSplit, Split, SplitDict, SplitInfo +from .table import Table +from .tasks import TaskTemplate +from .utils import logging +from .utils.deprecation_utils import deprecated +from .utils.doc_utils import is_documented_by +from .utils.metadata import MetadataConfigs +from .utils.py_utils import asdict, glob_pattern_to_regex, string_to_dict +from .utils.typing import PathLike + + +logger = logging.get_logger(__name__) + + +class DatasetDict(dict): + """A dictionary (dict of str: datasets.Dataset) with dataset transforms methods (map, filter, etc.)""" + + def _check_values_type(self): + for dataset in self.values(): + if not isinstance(dataset, Dataset): + raise TypeError(f"Values in `DatasetDict` should be of type `Dataset` but got type '{type(dataset)}'") + + def _check_values_features(self): + items = list(self.items()) + for item_a, item_b in zip(items[:-1], items[1:]): + if item_a[1].features != item_b[1].features: + raise ValueError( + f"All datasets in `DatasetDict` should have the same features but features for '{item_a[0]}' and '{item_b[0]}' don't match: {item_a[1].features} != {item_b[1].features}" + ) + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + # Here `del` is used to del the pyarrow tables. This properly closes the files used for memory mapped tables + for dataset in self.values(): + if hasattr(dataset, "_data"): + del dataset._data + if hasattr(dataset, "_indices"): + del dataset._indices + + def __getitem__(self, k) -> Dataset: + if isinstance(k, (str, NamedSplit)) or len(self) == 0: + return super().__getitem__(k) + else: + available_suggested_splits = [ + split for split in (Split.TRAIN, Split.TEST, Split.VALIDATION) if split in self + ] + suggested_split = available_suggested_splits[0] if available_suggested_splits else list(self)[0] + raise KeyError( + f"Invalid key: {k}. Please first select a split. For example: " + f"`my_dataset_dictionary['{suggested_split}'][{k}]`. " + f"Available splits: {sorted(self)}" + ) + + @property + def data(self) -> Dict[str, Table]: + """The Apache Arrow tables backing each split. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> ds.data + ``` + """ + self._check_values_type() + return {k: dataset.data for k, dataset in self.items()} + + @property + def cache_files(self) -> Dict[str, Dict]: + """The cache files containing the Apache Arrow table backing each split. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> ds.cache_files + {'test': [{'filename': '/root/.cache/huggingface/datasets/rotten_tomatoes_movie_review/default/1.0.0/40d411e45a6ce3484deed7cc15b82a53dad9a72aafd9f86f8f227134bec5ca46/rotten_tomatoes_movie_review-test.arrow'}], + 'train': [{'filename': '/root/.cache/huggingface/datasets/rotten_tomatoes_movie_review/default/1.0.0/40d411e45a6ce3484deed7cc15b82a53dad9a72aafd9f86f8f227134bec5ca46/rotten_tomatoes_movie_review-train.arrow'}], + 'validation': [{'filename': '/root/.cache/huggingface/datasets/rotten_tomatoes_movie_review/default/1.0.0/40d411e45a6ce3484deed7cc15b82a53dad9a72aafd9f86f8f227134bec5ca46/rotten_tomatoes_movie_review-validation.arrow'}]} + ``` + """ + self._check_values_type() + return {k: dataset.cache_files for k, dataset in self.items()} + + @property + def num_columns(self) -> Dict[str, int]: + """Number of columns in each split of the dataset. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> ds.num_columns + {'test': 2, 'train': 2, 'validation': 2} + ``` + """ + self._check_values_type() + return {k: dataset.num_columns for k, dataset in self.items()} + + @property + def num_rows(self) -> Dict[str, int]: + """Number of rows in each split of the dataset. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> ds.num_rows + {'test': 1066, 'train': 8530, 'validation': 1066} + ``` + """ + self._check_values_type() + return {k: dataset.num_rows for k, dataset in self.items()} + + @property + def column_names(self) -> Dict[str, List[str]]: + """Names of the columns in each split of the dataset. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> ds.column_names + {'test': ['text', 'label'], + 'train': ['text', 'label'], + 'validation': ['text', 'label']} + ``` + """ + self._check_values_type() + return {k: dataset.column_names for k, dataset in self.items()} + + @property + def shape(self) -> Dict[str, Tuple[int]]: + """Shape of each split of the dataset (number of columns, number of rows). + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> ds.shape + {'test': (1066, 2), 'train': (8530, 2), 'validation': (1066, 2)} + ``` + """ + self._check_values_type() + return {k: dataset.shape for k, dataset in self.items()} + + def flatten(self, max_depth=16) -> "DatasetDict": + """Flatten the Apache Arrow Table of each split (nested features are flatten). + Each column with a struct type is flattened into one column per struct field. + Other columns are left unchanged. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("squad") + >>> ds["train"].features + {'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None), + 'context': Value(dtype='string', id=None), + 'id': Value(dtype='string', id=None), + 'question': Value(dtype='string', id=None), + 'title': Value(dtype='string', id=None)} + >>> ds.flatten() + DatasetDict({ + train: Dataset({ + features: ['id', 'title', 'context', 'question', 'answers.text', 'answers.answer_start'], + num_rows: 87599 + }) + validation: Dataset({ + features: ['id', 'title', 'context', 'question', 'answers.text', 'answers.answer_start'], + num_rows: 10570 + }) + }) + ``` + """ + self._check_values_type() + return DatasetDict({k: dataset.flatten(max_depth=max_depth) for k, dataset in self.items()}) + + def unique(self, column: str) -> Dict[str, List]: + """Return a list of the unique elements in a column for each split. + + This is implemented in the low-level backend and as such, very fast. + + Args: + column (`str`): + column name (list all the column names with [`~datasets.DatasetDict.column_names`]) + + Returns: + Dict[`str`, `list`]: Dictionary of unique elements in the given column. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> ds.unique("label") + {'test': [1, 0], 'train': [1, 0], 'validation': [1, 0]} + ``` + """ + self._check_values_type() + return {k: dataset.unique(column) for k, dataset in self.items()} + + def cleanup_cache_files(self) -> Dict[str, int]: + """Clean up all cache files in the dataset cache directory, excepted the currently used cache file if there is one. + Be careful when running this command that no other process is currently using other cache files. + + Return: + `Dict` with the number of removed files for each split + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> ds.cleanup_cache_files() + {'test': 0, 'train': 0, 'validation': 0} + ``` + """ + self._check_values_type() + return {k: dataset.cleanup_cache_files() for k, dataset in self.items()} + + def __repr__(self): + repr = "\n".join([f"{k}: {v}" for k, v in self.items()]) + repr = re.sub(r"^", " " * 4, repr, 0, re.M) + return f"DatasetDict({{\n{repr}\n}})" + + def cast(self, features: Features) -> "DatasetDict": + """ + Cast the dataset to a new set of features. + The transformation is applied to all the datasets of the dataset dictionary. + + Args: + features ([`Features`]): + New features to cast the dataset to. + The name and order of the fields in the features must match the current column names. + The type of the data must also be convertible from one type to the other. + For non-trivial conversion, e.g. `string` <-> `ClassLabel` you should use [`~DatasetDict.map`] to update the dataset. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> ds["train"].features + {'label': ClassLabel(num_classes=2, names=['neg', 'pos'], id=None), + 'text': Value(dtype='string', id=None)} + >>> new_features = ds["train"].features.copy() + >>> new_features['label'] = ClassLabel(names=['bad', 'good']) + >>> new_features['text'] = Value('large_string') + >>> ds = ds.cast(new_features) + >>> ds["train"].features + {'label': ClassLabel(num_classes=2, names=['bad', 'good'], id=None), + 'text': Value(dtype='large_string', id=None)} + ``` + """ + self._check_values_type() + return DatasetDict({k: dataset.cast(features=features) for k, dataset in self.items()}) + + def cast_column(self, column: str, feature) -> "DatasetDict": + """Cast column to feature for decoding. + + Args: + column (`str`): + Column name. + feature ([`Feature`]): + Target feature. + + Returns: + [`DatasetDict`] + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> ds["train"].features + {'label': ClassLabel(num_classes=2, names=['neg', 'pos'], id=None), + 'text': Value(dtype='string', id=None)} + >>> ds = ds.cast_column('label', ClassLabel(names=['bad', 'good'])) + >>> ds["train"].features + {'label': ClassLabel(num_classes=2, names=['bad', 'good'], id=None), + 'text': Value(dtype='string', id=None)} + ``` + """ + self._check_values_type() + return DatasetDict({k: dataset.cast_column(column=column, feature=feature) for k, dataset in self.items()}) + + def remove_columns(self, column_names: Union[str, List[str]]) -> "DatasetDict": + """ + Remove one or several column(s) from each split in the dataset + and the features associated to the column(s). + + The transformation is applied to all the splits of the dataset dictionary. + + You can also remove a column using [`~DatasetDict.map`] with `remove_columns` but the present method + doesn't copy the data of the remaining columns and is thus faster. + + Args: + column_names (`Union[str, List[str]]`): + Name of the column(s) to remove. + + Returns: + [`DatasetDict`]: A copy of the dataset object without the columns to remove. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> ds = ds.remove_columns("label") + DatasetDict({ + train: Dataset({ + features: ['text'], + num_rows: 8530 + }) + validation: Dataset({ + features: ['text'], + num_rows: 1066 + }) + test: Dataset({ + features: ['text'], + num_rows: 1066 + }) + }) + ``` + """ + self._check_values_type() + return DatasetDict({k: dataset.remove_columns(column_names=column_names) for k, dataset in self.items()}) + + def rename_column(self, original_column_name: str, new_column_name: str) -> "DatasetDict": + """ + Rename a column in the dataset and move the features associated to the original column under the new column name. + The transformation is applied to all the datasets of the dataset dictionary. + + You can also rename a column using [`~DatasetDict.map`] with `remove_columns` but the present method: + - takes care of moving the original features under the new column name. + - doesn't copy the data to a new dataset and is thus much faster. + + Args: + original_column_name (`str`): + Name of the column to rename. + new_column_name (`str`): + New name for the column. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> ds = ds.rename_column("label", "label_new") + DatasetDict({ + train: Dataset({ + features: ['text', 'label_new'], + num_rows: 8530 + }) + validation: Dataset({ + features: ['text', 'label_new'], + num_rows: 1066 + }) + test: Dataset({ + features: ['text', 'label_new'], + num_rows: 1066 + }) + }) + ``` + """ + self._check_values_type() + return DatasetDict( + { + k: dataset.rename_column(original_column_name=original_column_name, new_column_name=new_column_name) + for k, dataset in self.items() + } + ) + + def rename_columns(self, column_mapping: Dict[str, str]) -> "DatasetDict": + """ + Rename several columns in the dataset, and move the features associated to the original columns under + the new column names. + The transformation is applied to all the datasets of the dataset dictionary. + + Args: + column_mapping (`Dict[str, str]`): + A mapping of columns to rename to their new names. + + Returns: + [`DatasetDict`]: A copy of the dataset with renamed columns. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> ds.rename_columns({'text': 'text_new', 'label': 'label_new'}) + DatasetDict({ + train: Dataset({ + features: ['text_new', 'label_new'], + num_rows: 8530 + }) + validation: Dataset({ + features: ['text_new', 'label_new'], + num_rows: 1066 + }) + test: Dataset({ + features: ['text_new', 'label_new'], + num_rows: 1066 + }) + }) + ``` + """ + self._check_values_type() + return DatasetDict({k: dataset.rename_columns(column_mapping=column_mapping) for k, dataset in self.items()}) + + def select_columns(self, column_names: Union[str, List[str]]) -> "DatasetDict": + """Select one or several column(s) from each split in the dataset and + the features associated to the column(s). + + The transformation is applied to all the splits of the dataset + dictionary. + + Args: + column_names (`Union[str, List[str]]`): + Name of the column(s) to keep. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> ds.select_columns("text") + DatasetDict({ + train: Dataset({ + features: ['text'], + num_rows: 8530 + }) + validation: Dataset({ + features: ['text'], + num_rows: 1066 + }) + test: Dataset({ + features: ['text'], + num_rows: 1066 + }) + }) + ``` + """ + self._check_values_type() + return DatasetDict({k: dataset.select_columns(column_names=column_names) for k, dataset in self.items()}) + + def class_encode_column(self, column: str, include_nulls: bool = False) -> "DatasetDict": + """Casts the given column as [`~datasets.features.ClassLabel`] and updates the tables. + + Args: + column (`str`): + The name of the column to cast. + include_nulls (`bool`, defaults to `False`): + Whether to include null values in the class labels. If `True`, the null values will be encoded as the `"None"` class label. + + + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("boolq") + >>> ds["train"].features + {'answer': Value(dtype='bool', id=None), + 'passage': Value(dtype='string', id=None), + 'question': Value(dtype='string', id=None)} + >>> ds = ds.class_encode_column("answer") + >>> ds["train"].features + {'answer': ClassLabel(num_classes=2, names=['False', 'True'], id=None), + 'passage': Value(dtype='string', id=None), + 'question': Value(dtype='string', id=None)} + ``` + """ + self._check_values_type() + return DatasetDict( + {k: dataset.class_encode_column(column=column, include_nulls=include_nulls) for k, dataset in self.items()} + ) + + @contextlib.contextmanager + def formatted_as( + self, + type: Optional[str] = None, + columns: Optional[List] = None, + output_all_columns: bool = False, + **format_kwargs, + ): + """To be used in a `with` statement. Set `__getitem__` return format (type and columns). + The transformation is applied to all the datasets of the dataset dictionary. + + Args: + type (`str`, *optional*): + Output type selected in `[None, 'numpy', 'torch', 'tensorflow', 'pandas', 'arrow', 'jax']`. + `None` means `__getitem__` returns python objects (default). + columns (`List[str]`, *optional*): + Columns to format in the output. + `None` means `__getitem__` returns all columns (default). + output_all_columns (`bool`, defaults to False): + Keep un-formatted columns as well in the output (as python objects). + **format_kwargs (additional keyword arguments): + Keywords arguments passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`. + """ + self._check_values_type() + old_format_type = {k: dataset._format_type for k, dataset in self.items()} + old_format_kwargs = {k: dataset._format_kwargs for k, dataset in self.items()} + old_format_columns = {k: dataset._format_columns for k, dataset in self.items()} + old_output_all_columns = {k: dataset._output_all_columns for k, dataset in self.items()} + try: + self.set_format(type, columns, output_all_columns, **format_kwargs) + yield + finally: + for k, dataset in self.items(): + dataset.set_format( + old_format_type[k], old_format_columns[k], old_output_all_columns[k], **old_format_kwargs[k] + ) + + def set_format( + self, + type: Optional[str] = None, + columns: Optional[List] = None, + output_all_columns: bool = False, + **format_kwargs, + ): + """Set `__getitem__` return format (type and columns). + The format is set for every dataset in the dataset dictionary. + + Args: + type (`str`, *optional*): + Output type selected in `[None, 'numpy', 'torch', 'tensorflow', 'pandas', 'arrow', 'jax']`. + `None` means `__getitem__` returns python objects (default). + columns (`List[str]`, *optional*): + Columns to format in the output. + `None` means `__getitem__` returns all columns (default). + output_all_columns (`bool`, defaults to False): + Keep un-formatted columns as well in the output (as python objects), + **format_kwargs (additional keyword arguments): + Keywords arguments passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`. + + It is possible to call `map` after calling `set_format`. Since `map` may add new columns, then the list of formatted columns + gets updated. In this case, if you apply `map` on a dataset to add a new column, then this column will be formatted: + + `new formatted columns = (all columns - previously unformatted columns)` + + Example: + + ```py + >>> from datasets import load_dataset + >>> from transformers import AutoTokenizer + >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") + >>> ds = ds.map(lambda x: tokenizer(x["text"], truncation=True, padding=True), batched=True) + >>> ds.set_format(type="numpy", columns=['input_ids', 'token_type_ids', 'attention_mask', 'label']) + >>> ds["train"].format + {'columns': ['input_ids', 'token_type_ids', 'attention_mask', 'label'], + 'format_kwargs': {}, + 'output_all_columns': False, + 'type': 'numpy'} + ``` + """ + self._check_values_type() + for dataset in self.values(): + dataset.set_format(type=type, columns=columns, output_all_columns=output_all_columns, **format_kwargs) + + def reset_format(self): + """Reset `__getitem__` return format to python objects and all columns. + The transformation is applied to all the datasets of the dataset dictionary. + + Same as `self.set_format()` + + Example: + + ```py + >>> from datasets import load_dataset + >>> from transformers import AutoTokenizer + >>> ds = load_dataset("rotten_tomatoes") + >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") + >>> ds = ds.map(lambda x: tokenizer(x["text"], truncation=True, padding=True), batched=True) + >>> ds.set_format(type="numpy", columns=['input_ids', 'token_type_ids', 'attention_mask', 'label']) + >>> ds["train"].format + {'columns': ['input_ids', 'token_type_ids', 'attention_mask', 'label'], + 'format_kwargs': {}, + 'output_all_columns': False, + 'type': 'numpy'} + >>> ds.reset_format() + >>> ds["train"].format + {'columns': ['text', 'label', 'input_ids', 'token_type_ids', 'attention_mask'], + 'format_kwargs': {}, + 'output_all_columns': False, + 'type': None} + ``` + """ + self._check_values_type() + for dataset in self.values(): + dataset.set_format() + + def set_transform( + self, + transform: Optional[Callable], + columns: Optional[List] = None, + output_all_columns: bool = False, + ): + """Set ``__getitem__`` return format using this transform. The transform is applied on-the-fly on batches when ``__getitem__`` is called. + The transform is set for every dataset in the dataset dictionary + As :func:`datasets.Dataset.set_format`, this can be reset using :func:`datasets.Dataset.reset_format` + + Args: + transform (`Callable`, optional): user-defined formatting transform, replaces the format defined by :func:`datasets.Dataset.set_format` + A formatting function is a callable that takes a batch (as a dict) as input and returns a batch. + This function is applied right before returning the objects in ``__getitem__``. + columns (`List[str]`, optional): columns to format in the output + If specified, then the input batch of the transform only contains those columns. + output_all_columns (`bool`, default to False): keep un-formatted columns as well in the output (as python objects) + If set to True, then the other un-formatted columns are kept with the output of the transform. + + """ + self._check_values_type() + for dataset in self.values(): + dataset.set_format("custom", columns=columns, output_all_columns=output_all_columns, transform=transform) + + def with_format( + self, + type: Optional[str] = None, + columns: Optional[List] = None, + output_all_columns: bool = False, + **format_kwargs, + ) -> "DatasetDict": + """Set `__getitem__` return format (type and columns). The data formatting is applied on-the-fly. + The format `type` (for example "numpy") is used to format batches when using `__getitem__`. + The format is set for every dataset in the dataset dictionary. + + It's also possible to use custom transforms for formatting using [`~datasets.Dataset.with_transform`]. + + Contrary to [`~datasets.DatasetDict.set_format`], `with_format` returns a new [`DatasetDict`] object with new [`Dataset`] objects. + + Args: + type (`str`, *optional*): + Output type selected in `[None, 'numpy', 'torch', 'tensorflow', 'pandas', 'arrow', 'jax']`. + `None` means `__getitem__` returns python objects (default). + columns (`List[str]`, *optional*): + Columns to format in the output. + `None` means `__getitem__` returns all columns (default). + output_all_columns (`bool`, defaults to `False`): + Keep un-formatted columns as well in the output (as python objects). + **format_kwargs (additional keyword arguments): + Keywords arguments passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`. + + Example: + + ```py + >>> from datasets import load_dataset + >>> from transformers import AutoTokenizer + >>> ds = load_dataset("rotten_tomatoes") + >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") + >>> ds = ds.map(lambda x: tokenizer(x['text'], truncation=True, padding=True), batched=True) + >>> ds["train"].format + {'columns': ['text', 'label', 'input_ids', 'token_type_ids', 'attention_mask'], + 'format_kwargs': {}, + 'output_all_columns': False, + 'type': None} + >>> ds = ds.with_format(type='tensorflow', columns=['input_ids', 'token_type_ids', 'attention_mask', 'label']) + >>> ds["train"].format + {'columns': ['input_ids', 'token_type_ids', 'attention_mask', 'label'], + 'format_kwargs': {}, + 'output_all_columns': False, + 'type': 'tensorflow'} + ``` + """ + dataset = copy.deepcopy(self) + dataset.set_format(type=type, columns=columns, output_all_columns=output_all_columns, **format_kwargs) + return dataset + + def with_transform( + self, + transform: Optional[Callable], + columns: Optional[List] = None, + output_all_columns: bool = False, + ) -> "DatasetDict": + """Set `__getitem__` return format using this transform. The transform is applied on-the-fly on batches when `__getitem__` is called. + The transform is set for every dataset in the dataset dictionary + + As [`~datasets.Dataset.set_format`], this can be reset using [`~datasets.Dataset.reset_format`]. + + Contrary to [`~datasets.DatasetDict.set_transform`], `with_transform` returns a new [`DatasetDict`] object with new [`Dataset`] objects. + + Args: + transform (`Callable`, *optional*): + User-defined formatting transform, replaces the format defined by [`~datasets.Dataset.set_format`]. + A formatting function is a callable that takes a batch (as a dict) as input and returns a batch. + This function is applied right before returning the objects in `__getitem__`. + columns (`List[str]`, *optional*): + Columns to format in the output. + If specified, then the input batch of the transform only contains those columns. + output_all_columns (`bool`, defaults to False): + Keep un-formatted columns as well in the output (as python objects). + If set to `True`, then the other un-formatted columns are kept with the output of the transform. + + Example: + + ```py + >>> from datasets import load_dataset + >>> from transformers import AutoTokenizer + >>> ds = load_dataset("rotten_tomatoes") + >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") + >>> def encode(example): + ... return tokenizer(example['text'], truncation=True, padding=True, return_tensors="pt") + >>> ds = ds.with_transform(encode) + >>> ds["train"][0] + {'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1]), + 'input_ids': tensor([ 101, 1103, 2067, 1110, 17348, 1106, 1129, 1103, 6880, 1432, + 112, 188, 1207, 107, 14255, 1389, 107, 1105, 1115, 1119, + 112, 188, 1280, 1106, 1294, 170, 24194, 1256, 3407, 1190, + 170, 11791, 5253, 188, 1732, 7200, 10947, 12606, 2895, 117, + 179, 7766, 118, 172, 15554, 1181, 3498, 6961, 3263, 1137, + 188, 1566, 7912, 14516, 6997, 119, 102]), + 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0])} + ``` + """ + dataset = copy.deepcopy(self) + dataset.set_transform(transform=transform, columns=columns, output_all_columns=output_all_columns) + return dataset + + def map( + self, + function: Optional[Callable] = None, + with_indices: bool = False, + with_rank: bool = False, + input_columns: Optional[Union[str, List[str]]] = None, + batched: bool = False, + batch_size: Optional[int] = 1000, + drop_last_batch: bool = False, + remove_columns: Optional[Union[str, List[str]]] = None, + keep_in_memory: bool = False, + load_from_cache_file: Optional[bool] = None, + cache_file_names: Optional[Dict[str, Optional[str]]] = None, + writer_batch_size: Optional[int] = 1000, + features: Optional[Features] = None, + disable_nullable: bool = False, + fn_kwargs: Optional[dict] = None, + num_proc: Optional[int] = None, + desc: Optional[str] = None, + ) -> "DatasetDict": + """Apply a function to all the elements in the table (individually or in batches) + and update the table (if function does updated examples). + The transformation is applied to all the datasets of the dataset dictionary. + + Args: + function (`callable`): with one of the following signature: + - `function(example: Dict[str, Any]) -> Dict[str, Any]` if `batched=False` and `with_indices=False` + - `function(example: Dict[str, Any], indices: int) -> Dict[str, Any]` if `batched=False` and `with_indices=True` + - `function(batch: Dict[str, List]) -> Dict[str, List]` if `batched=True` and `with_indices=False` + - `function(batch: Dict[str, List], indices: List[int]) -> Dict[str, List]` if `batched=True` and `with_indices=True` + + For advanced usage, the function can also return a `pyarrow.Table`. + Moreover if your function returns nothing (`None`), then `map` will run your function and return the dataset unchanged. + + with_indices (`bool`, defaults to `False`): + Provide example indices to `function`. Note that in this case the signature of `function` should be `def function(example, idx): ...`. + with_rank (`bool`, defaults to `False`): + Provide process rank to `function`. Note that in this case the + signature of `function` should be `def function(example[, idx], rank): ...`. + input_columns (`[Union[str, List[str]]]`, *optional*, defaults to `None`): + The columns to be passed into `function` as + positional arguments. If `None`, a dict mapping to all formatted columns is passed as one argument. + batched (`bool`, defaults to `False`): + Provide batch of examples to `function`. + batch_size (`int`, *optional*, defaults to `1000`): + Number of examples per batch provided to `function` if `batched=True`, + `batch_size <= 0` or `batch_size == None` then provide the full dataset as a single batch to `function`. + drop_last_batch (`bool`, defaults to `False`): + Whether a last batch smaller than the batch_size should be + dropped instead of being processed by the function. + remove_columns (`[Union[str, List[str]]]`, *optional*, defaults to `None`): + Remove a selection of columns while doing the mapping. + Columns will be removed before updating the examples with the output of `function`, i.e. if `function` is adding + columns with names in `remove_columns`, these columns will be kept. + keep_in_memory (`bool`, defaults to `False`): + Keep the dataset in memory instead of writing it to a cache file. + load_from_cache_file (`Optional[bool]`, defaults to `True` if caching is enabled): + If a cache file storing the current computation from `function` + can be identified, use it instead of recomputing. + cache_file_names (`[Dict[str, str]]`, *optional*, defaults to `None`): + Provide the name of a path for the cache file. It is used to store the + results of the computation instead of the automatically generated cache file name. + You have to provide one `cache_file_name` per dataset in the dataset dictionary. + writer_batch_size (`int`, default `1000`): + Number of rows per write operation for the cache file writer. + This value is a good trade-off between memory usage during the processing, and processing speed. + Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `map`. + features (`[datasets.Features]`, *optional*, defaults to `None`): + Use a specific [`Features`] to store the cache file + instead of the automatically generated one. + disable_nullable (`bool`, defaults to `False`): + Disallow null values in the table. + fn_kwargs (`Dict`, *optional*, defaults to `None`): + Keyword arguments to be passed to `function` + num_proc (`int`, *optional*, defaults to `None`): + Number of processes for multiprocessing. By default it doesn't + use multiprocessing. + desc (`str`, *optional*, defaults to `None`): + Meaningful description to be displayed alongside with the progress bar while mapping examples. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> def add_prefix(example): + ... example["text"] = "Review: " + example["text"] + ... return example + >>> ds = ds.map(add_prefix) + >>> ds["train"][0:3]["text"] + ['Review: the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .', + 'Review: the gorgeously elaborate continuation of " the lord of the rings " trilogy is so huge that a column of words cannot adequately describe co-writer/director peter jackson\'s expanded vision of j . r . r . tolkien\'s middle-earth .', + 'Review: effective but too-tepid biopic'] + + # process a batch of examples + >>> ds = ds.map(lambda example: tokenizer(example["text"]), batched=True) + # set number of processors + >>> ds = ds.map(add_prefix, num_proc=4) + ``` + """ + self._check_values_type() + if cache_file_names is None: + cache_file_names = {k: None for k in self} + return DatasetDict( + { + k: dataset.map( + function=function, + with_indices=with_indices, + with_rank=with_rank, + input_columns=input_columns, + batched=batched, + batch_size=batch_size, + drop_last_batch=drop_last_batch, + remove_columns=remove_columns, + keep_in_memory=keep_in_memory, + load_from_cache_file=load_from_cache_file, + cache_file_name=cache_file_names[k], + writer_batch_size=writer_batch_size, + features=features, + disable_nullable=disable_nullable, + fn_kwargs=fn_kwargs, + num_proc=num_proc, + desc=desc, + ) + for k, dataset in self.items() + } + ) + + def filter( + self, + function: Optional[Callable] = None, + with_indices: bool = False, + with_rank: bool = False, + input_columns: Optional[Union[str, List[str]]] = None, + batched: bool = False, + batch_size: Optional[int] = 1000, + keep_in_memory: bool = False, + load_from_cache_file: Optional[bool] = None, + cache_file_names: Optional[Dict[str, Optional[str]]] = None, + writer_batch_size: Optional[int] = 1000, + fn_kwargs: Optional[dict] = None, + num_proc: Optional[int] = None, + desc: Optional[str] = None, + ) -> "DatasetDict": + """Apply a filter function to all the elements in the table in batches + and update the table so that the dataset only includes examples according to the filter function. + The transformation is applied to all the datasets of the dataset dictionary. + + Args: + function (`Callable`): Callable with one of the following signatures: + + - `function(example: Dict[str, Any]) -> bool` if `batched=False` and `with_indices=False` and `with_rank=False` + - `function(example: Dict[str, Any], *extra_args) -> bool` if `batched=False` and `with_indices=True` and/or `with_rank=True` (one extra arg for each) + - `function(batch: Dict[str, List]) -> List[bool]` if `batched=True` and `with_indices=False` and `with_rank=False` + - `function(batch: Dict[str, List], *extra_args) -> List[bool]` if `batched=True` and `with_indices=True` and/or `with_rank=True` (one extra arg for each) + + If no function is provided, defaults to an always `True` function: `lambda x: True`. + with_indices (`bool`, defaults to `False`): + Provide example indices to `function`. Note that in this case the + signature of `function` should be `def function(example, idx[, rank]): ...`. + with_rank (`bool`, defaults to `False`): + Provide process rank to `function`. Note that in this case the + signature of `function` should be `def function(example[, idx], rank): ...`. + input_columns (`[Union[str, List[str]]]`, *optional*, defaults to `None`): + The columns to be passed into `function` as + positional arguments. If `None`, a dict mapping to all formatted columns is passed as one argument. + batched (`bool`, defaults to `False`): + Provide batch of examples to `function`. + batch_size (`int`, *optional*, defaults to `1000`): + Number of examples per batch provided to `function` if `batched=True` + `batch_size <= 0` or `batch_size == None` then provide the full dataset as a single batch to `function`. + keep_in_memory (`bool`, defaults to `False`): + Keep the dataset in memory instead of writing it to a cache file. + load_from_cache_file (`Optional[bool]`, defaults to `True` if caching is enabled): + If a cache file storing the current computation from `function` + can be identified, use it instead of recomputing. + cache_file_names (`[Dict[str, str]]`, *optional*, defaults to `None`): + Provide the name of a path for the cache file. It is used to store the + results of the computation instead of the automatically generated cache file name. + You have to provide one `cache_file_name` per dataset in the dataset dictionary. + writer_batch_size (`int`, defaults to `1000`): + Number of rows per write operation for the cache file writer. + This value is a good trade-off between memory usage during the processing, and processing speed. + Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `map`. + fn_kwargs (`Dict`, *optional*, defaults to `None`): + Keyword arguments to be passed to `function` + num_proc (`int`, *optional*, defaults to `None`): + Number of processes for multiprocessing. By default it doesn't + use multiprocessing. + desc (`str`, *optional*, defaults to `None`): + Meaningful description to be displayed alongside with the progress bar while filtering examples. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> ds.filter(lambda x: x["label"] == 1) + DatasetDict({ + train: Dataset({ + features: ['text', 'label'], + num_rows: 4265 + }) + validation: Dataset({ + features: ['text', 'label'], + num_rows: 533 + }) + test: Dataset({ + features: ['text', 'label'], + num_rows: 533 + }) + }) + ``` + """ + self._check_values_type() + if cache_file_names is None: + cache_file_names = {k: None for k in self} + return DatasetDict( + { + k: dataset.filter( + function=function, + with_indices=with_indices, + with_rank=with_rank, + input_columns=input_columns, + batched=batched, + batch_size=batch_size, + keep_in_memory=keep_in_memory, + load_from_cache_file=load_from_cache_file, + cache_file_name=cache_file_names[k], + writer_batch_size=writer_batch_size, + fn_kwargs=fn_kwargs, + num_proc=num_proc, + desc=desc, + ) + for k, dataset in self.items() + } + ) + + def flatten_indices( + self, + keep_in_memory: bool = False, + cache_file_names: Optional[Dict[str, Optional[str]]] = None, + writer_batch_size: Optional[int] = 1000, + features: Optional[Features] = None, + disable_nullable: bool = False, + num_proc: Optional[int] = None, + new_fingerprint: Optional[str] = None, + ) -> "DatasetDict": + """Create and cache a new Dataset by flattening the indices mapping. + + Args: + keep_in_memory (`bool`, defaults to `False`): + Keep the dataset in memory instead of writing it to a cache file. + cache_file_names (`Dict[str, str]`, *optional*, default `None`): + Provide the name of a path for the cache file. It is used to store the + results of the computation instead of the automatically generated cache file name. + You have to provide one `cache_file_name` per dataset in the dataset dictionary. + writer_batch_size (`int`, defaults to `1000`): + Number of rows per write operation for the cache file writer. + This value is a good trade-off between memory usage during the processing, and processing speed. + Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `map`. + features (`Optional[datasets.Features]`, defaults to `None`): + Use a specific [`Features`] to store the cache file + instead of the automatically generated one. + disable_nullable (`bool`, defaults to `False`): + Allow null values in the table. + num_proc (`int`, optional, default `None`): + Max number of processes when generating cache. Already cached shards are loaded sequentially + new_fingerprint (`str`, *optional*, defaults to `None`): + The new fingerprint of the dataset after transform. + If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments + """ + self._check_values_type() + if cache_file_names is None: + cache_file_names = {k: None for k in self} + return DatasetDict( + { + k: dataset.flatten_indices( + keep_in_memory=keep_in_memory, + cache_file_name=cache_file_names[k], + writer_batch_size=writer_batch_size, + features=features, + disable_nullable=disable_nullable, + num_proc=num_proc, + new_fingerprint=new_fingerprint, + ) + for k, dataset in self.items() + } + ) + + def sort( + self, + column_names: Union[str, Sequence[str]], + reverse: Union[bool, Sequence[bool]] = False, + kind="deprecated", + null_placement: str = "at_end", + keep_in_memory: bool = False, + load_from_cache_file: Optional[bool] = None, + indices_cache_file_names: Optional[Dict[str, Optional[str]]] = None, + writer_batch_size: Optional[int] = 1000, + ) -> "DatasetDict": + """Create a new dataset sorted according to a single or multiple columns. + + Args: + column_names (`Union[str, Sequence[str]]`): + Column name(s) to sort by. + reverse (`Union[bool, Sequence[bool]]`, defaults to `False`): + If `True`, sort by descending order rather than ascending. If a single bool is provided, + the value is applied to the sorting of all column names. Otherwise a list of bools with the + same length and order as column_names must be provided. + kind (`str`, *optional*): + Pandas algorithm for sorting selected in `{quicksort, mergesort, heapsort, stable}`, + The default is `quicksort`. Note that both `stable` and `mergesort` use timsort under the covers and, in general, + the actual implementation will vary with data type. The `mergesort` option is retained for backwards compatibility. + + + `kind` was deprecated in version 2.10.0 and will be removed in 3.0.0. + + + null_placement (`str`, defaults to `at_end`): + Put `None` values at the beginning if `at_start` or `first` or at the end if `at_end` or `last` + keep_in_memory (`bool`, defaults to `False`): + Keep the sorted indices in memory instead of writing it to a cache file. + load_from_cache_file (`Optional[bool]`, defaults to `True` if caching is enabled): + If a cache file storing the sorted indices + can be identified, use it instead of recomputing. + indices_cache_file_names (`[Dict[str, str]]`, *optional*, defaults to `None`): + Provide the name of a path for the cache file. It is used to store the + indices mapping instead of the automatically generated cache file name. + You have to provide one `cache_file_name` per dataset in the dataset dictionary. + writer_batch_size (`int`, defaults to `1000`): + Number of rows per write operation for the cache file writer. + Higher value gives smaller cache files, lower value consume less temporary memory. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset('rotten_tomatoes') + >>> ds['train']['label'][:10] + [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] + >>> sorted_ds = ds.sort('label') + >>> sorted_ds['train']['label'][:10] + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + >>> another_sorted_ds = ds.sort(['label', 'text'], reverse=[True, False]) + >>> another_sorted_ds['train']['label'][:10] + [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] + ``` + """ + self._check_values_type() + if indices_cache_file_names is None: + indices_cache_file_names = {k: None for k in self} + return DatasetDict( + { + k: dataset.sort( + column_names=column_names, + reverse=reverse, + kind=kind, + null_placement=null_placement, + keep_in_memory=keep_in_memory, + load_from_cache_file=load_from_cache_file, + indices_cache_file_name=indices_cache_file_names[k], + writer_batch_size=writer_batch_size, + ) + for k, dataset in self.items() + } + ) + + def shuffle( + self, + seeds: Optional[Union[int, Dict[str, Optional[int]]]] = None, + seed: Optional[int] = None, + generators: Optional[Dict[str, np.random.Generator]] = None, + keep_in_memory: bool = False, + load_from_cache_file: Optional[bool] = None, + indices_cache_file_names: Optional[Dict[str, Optional[str]]] = None, + writer_batch_size: Optional[int] = 1000, + ) -> "DatasetDict": + """Create a new Dataset where the rows are shuffled. + + The transformation is applied to all the datasets of the dataset dictionary. + + Currently shuffling uses numpy random generators. + You can either supply a NumPy BitGenerator to use, or a seed to initiate NumPy's default random generator (PCG64). + + Args: + seeds (`Dict[str, int]` or `int`, *optional*): + A seed to initialize the default BitGenerator if `generator=None`. + If `None`, then fresh, unpredictable entropy will be pulled from the OS. + If an `int` or `array_like[ints]` is passed, then it will be passed to SeedSequence to derive the initial BitGenerator state. + You can provide one `seed` per dataset in the dataset dictionary. + seed (`int`, *optional*): + A seed to initialize the default BitGenerator if `generator=None`. Alias for seeds (a `ValueError` is raised if both are provided). + generators (`Dict[str, *optional*, np.random.Generator]`): + Numpy random Generator to use to compute the permutation of the dataset rows. + If `generator=None` (default), uses `np.random.default_rng` (the default BitGenerator (PCG64) of NumPy). + You have to provide one `generator` per dataset in the dataset dictionary. + keep_in_memory (`bool`, defaults to `False`): + Keep the dataset in memory instead of writing it to a cache file. + load_from_cache_file (`Optional[bool]`, defaults to `True` if caching is enabled): + If a cache file storing the current computation from `function` + can be identified, use it instead of recomputing. + indices_cache_file_names (`Dict[str, str]`, *optional*): + Provide the name of a path for the cache file. It is used to store the + indices mappings instead of the automatically generated cache file name. + You have to provide one `cache_file_name` per dataset in the dataset dictionary. + writer_batch_size (`int`, defaults to `1000`): + Number of rows per write operation for the cache file writer. + This value is a good trade-off between memory usage during the processing, and processing speed. + Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `map`. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes") + >>> ds["train"]["label"][:10] + [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] + + # set a seed + >>> shuffled_ds = ds.shuffle(seed=42) + >>> shuffled_ds["train"]["label"][:10] + [0, 1, 0, 1, 0, 0, 0, 0, 0, 0] + ``` + """ + self._check_values_type() + if seed is not None and seeds is not None: + raise ValueError("Please specify seed or seeds, but not both") + seeds = seed if seed is not None else seeds + if seeds is None: + seeds = {k: None for k in self} + elif not isinstance(seeds, dict): + seeds = {k: seeds for k in self} + if generators is None: + generators = {k: None for k in self} + if indices_cache_file_names is None: + indices_cache_file_names = {k: None for k in self} + return DatasetDict( + { + k: dataset.shuffle( + seed=seeds[k], + generator=generators[k], + keep_in_memory=keep_in_memory, + load_from_cache_file=load_from_cache_file, + indices_cache_file_name=indices_cache_file_names[k], + writer_batch_size=writer_batch_size, + ) + for k, dataset in self.items() + } + ) + + def save_to_disk( + self, + dataset_dict_path: PathLike, + fs="deprecated", + max_shard_size: Optional[Union[str, int]] = None, + num_shards: Optional[Dict[str, int]] = None, + num_proc: Optional[int] = None, + storage_options: Optional[dict] = None, + ): + """ + Saves a dataset dict to a filesystem using `fsspec.spec.AbstractFileSystem`. + + For [`Image`] and [`Audio`] data: + + All the Image() and Audio() data are stored in the arrow files. + If you want to store paths or urls, please use the Value("string") type. + + Args: + dataset_dict_path (`str`): + Path (e.g. `dataset/train`) or remote URI + (e.g. `s3://my-bucket/dataset/train`) of the dataset dict directory where the dataset dict will be + saved to. + fs (`fsspec.spec.AbstractFileSystem`, *optional*): + Instance of the remote filesystem where the dataset will be saved to. + + + + `fs` was deprecated in version 2.8.0 and will be removed in 3.0.0. + Please use `storage_options` instead, e.g. `storage_options=fs.storage_options` + + + + max_shard_size (`int` or `str`, *optional*, defaults to `"500MB"`): + The maximum size of the dataset shards to be uploaded to the hub. If expressed as a string, needs to be digits followed by a unit + (like `"50MB"`). + num_shards (`Dict[str, int]`, *optional*): + Number of shards to write. By default the number of shards depends on `max_shard_size` and `num_proc`. + You need to provide the number of shards for each dataset in the dataset dictionary. + Use a dictionary to define a different num_shards for each split. + + + num_proc (`int`, *optional*, default `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 file-system backend, if any. + + + + Example: + + ```python + >>> dataset_dict.save_to_disk("path/to/dataset/directory") + >>> dataset_dict.save_to_disk("path/to/dataset/directory", max_shard_size="1GB") + >>> dataset_dict.save_to_disk("path/to/dataset/directory", num_shards={"train": 1024, "test": 8}) + ``` + """ + if fs != "deprecated": + warnings.warn( + "'fs' was deprecated in favor of 'storage_options' in version 2.8.0 and will be removed in 3.0.0.\n" + "You can remove this warning by passing 'storage_options=fs.storage_options' instead.", + FutureWarning, + ) + storage_options = fs.storage_options + + fs: fsspec.AbstractFileSystem + fs, _ = url_to_fs(dataset_dict_path, **(storage_options or {})) + + if num_shards is None: + num_shards = {k: None for k in self} + elif not isinstance(num_shards, dict): + raise ValueError( + "Please provide one `num_shards` per dataset in the dataset dictionary, e.g. {{'train': 128, 'test': 4}}" + ) + + fs.makedirs(dataset_dict_path, exist_ok=True) + + with fs.open(posixpath.join(dataset_dict_path, config.DATASETDICT_JSON_FILENAME), "w", encoding="utf-8") as f: + json.dump({"splits": list(self)}, f) + for k, dataset in self.items(): + dataset.save_to_disk( + posixpath.join(dataset_dict_path, k), + num_shards=num_shards.get(k), + max_shard_size=max_shard_size, + num_proc=num_proc, + storage_options=storage_options, + ) + + @staticmethod + def load_from_disk( + dataset_dict_path: PathLike, + fs="deprecated", + keep_in_memory: Optional[bool] = None, + storage_options: Optional[dict] = None, + ) -> "DatasetDict": + """ + Load a dataset that was previously saved using [`save_to_disk`] from a filesystem using `fsspec.spec.AbstractFileSystem`. + + Args: + dataset_dict_path (`str`): + Path (e.g. `"dataset/train"`) or remote URI (e.g. `"s3//my-bucket/dataset/train"`) + of the dataset dict directory where the dataset dict will be loaded from. + fs (`fsspec.spec.AbstractFileSystem`, *optional*): + Instance of the remote filesystem where the dataset will be saved to. + + + + `fs` was deprecated in version 2.8.0 and will be removed in 3.0.0. + Please use `storage_options` instead, e.g. `storage_options=fs.storage_options` + + + + keep_in_memory (`bool`, defaults to `None`): + Whether to copy the dataset in-memory. If `None`, the + dataset will not be copied in-memory unless explicitly enabled by setting + `datasets.config.IN_MEMORY_MAX_SIZE` to nonzero. See more details in the + [improve performance](../cache#improve-performance) section. + storage_options (`dict`, *optional*): + Key/value pairs to be passed on to the file-system backend, if any. + + + + Returns: + [`DatasetDict`] + + Example: + + ```py + >>> ds = load_from_disk('path/to/dataset/directory') + ``` + """ + if fs != "deprecated": + warnings.warn( + "'fs' was deprecated in favor of 'storage_options' in version 2.8.0 and will be removed in 3.0.0.\n" + "You can remove this warning by passing 'storage_options=fs.storage_options' instead.", + FutureWarning, + ) + storage_options = fs.storage_options + + fs: fsspec.AbstractFileSystem + fs, dataset_dict_path = url_to_fs(dataset_dict_path, **(storage_options or {})) + + dataset_dict_json_path = posixpath.join(dataset_dict_path, config.DATASETDICT_JSON_FILENAME) + dataset_state_json_path = posixpath.join(dataset_dict_path, config.DATASET_STATE_JSON_FILENAME) + dataset_info_path = posixpath.join(dataset_dict_path, config.DATASET_INFO_FILENAME) + if not fs.isfile(dataset_dict_json_path): + if fs.isfile(dataset_info_path) and fs.isfile(dataset_state_json_path): + raise FileNotFoundError( + f"No such file: '{dataset_dict_json_path}'. Expected to load a `DatasetDict` object, but got a `Dataset`. Please use either `datasets.load_from_disk` or `Dataset.load_from_disk` instead." + ) + raise FileNotFoundError( + f"No such file: '{dataset_dict_json_path}'. Expected to load a `DatasetDict` object, but provided path is not a `DatasetDict`." + ) + + with fs.open(dataset_dict_json_path, "r", encoding="utf-8") as f: + splits = json.load(f)["splits"] + + dataset_dict = DatasetDict() + for k in splits: + dataset_dict_split_path = posixpath.join(fs.unstrip_protocol(dataset_dict_path), k) + dataset_dict[k] = Dataset.load_from_disk( + dataset_dict_split_path, keep_in_memory=keep_in_memory, storage_options=storage_options + ) + return dataset_dict + + @staticmethod + def from_csv( + path_or_paths: Dict[str, PathLike], + features: Optional[Features] = None, + cache_dir: str = None, + keep_in_memory: bool = False, + **kwargs, + ) -> "DatasetDict": + """Create [`DatasetDict`] from CSV file(s). + + Args: + path_or_paths (`dict` of path-like): + Path(s) of the CSV file(s). + features ([`Features`], *optional*): + Dataset features. + cache_dir (str, *optional*, defaults to `"~/.cache/huggingface/datasets"`): + Directory to cache data. + keep_in_memory (`bool`, defaults to `False`): + Whether to copy the data in-memory. + **kwargs (additional keyword arguments): + Keyword arguments to be passed to [`pandas.read_csv`]. + + Returns: + [`DatasetDict`] + + Example: + + ```py + >>> from datasets import DatasetDict + >>> ds = DatasetDict.from_csv({'train': 'path/to/dataset.csv'}) + ``` + """ + # Dynamic import to avoid circular dependency + from .io.csv import CsvDatasetReader + + return CsvDatasetReader( + path_or_paths, features=features, cache_dir=cache_dir, keep_in_memory=keep_in_memory, **kwargs + ).read() + + @staticmethod + def from_json( + path_or_paths: Dict[str, PathLike], + features: Optional[Features] = None, + cache_dir: str = None, + keep_in_memory: bool = False, + **kwargs, + ) -> "DatasetDict": + """Create [`DatasetDict`] from JSON Lines file(s). + + Args: + path_or_paths (`path-like` or list of `path-like`): + Path(s) of the JSON Lines file(s). + features ([`Features`], *optional*): + Dataset features. + cache_dir (str, *optional*, defaults to `"~/.cache/huggingface/datasets"`): + Directory to cache data. + keep_in_memory (`bool`, defaults to `False`): + Whether to copy the data in-memory. + **kwargs (additional keyword arguments): + Keyword arguments to be passed to [`JsonConfig`]. + + Returns: + [`DatasetDict`] + + Example: + + ```py + >>> from datasets import DatasetDict + >>> ds = DatasetDict.from_json({'train': 'path/to/dataset.json'}) + ``` + """ + # Dynamic import to avoid circular dependency + from .io.json import JsonDatasetReader + + return JsonDatasetReader( + path_or_paths, features=features, cache_dir=cache_dir, keep_in_memory=keep_in_memory, **kwargs + ).read() + + @staticmethod + def from_parquet( + path_or_paths: Dict[str, PathLike], + features: Optional[Features] = None, + cache_dir: str = None, + keep_in_memory: bool = False, + columns: Optional[List[str]] = None, + **kwargs, + ) -> "DatasetDict": + """Create [`DatasetDict`] from Parquet file(s). + + Args: + path_or_paths (`dict` of path-like): + Path(s) of the CSV file(s). + features ([`Features`], *optional*): + Dataset features. + cache_dir (`str`, *optional*, defaults to `"~/.cache/huggingface/datasets"`): + Directory to cache data. + keep_in_memory (`bool`, defaults to `False`): + Whether to copy the data in-memory. + columns (`List[str]`, *optional*): + If not `None`, only these columns will be read from the file. + A column name may be a prefix of a nested field, e.g. 'a' will select + 'a.b', 'a.c', and 'a.d.e'. + **kwargs (additional keyword arguments): + Keyword arguments to be passed to [`ParquetConfig`]. + + Returns: + [`DatasetDict`] + + Example: + + ```py + >>> from datasets import DatasetDict + >>> ds = DatasetDict.from_parquet({'train': 'path/to/dataset/parquet'}) + ``` + """ + # Dynamic import to avoid circular dependency + from .io.parquet import ParquetDatasetReader + + return ParquetDatasetReader( + path_or_paths, + features=features, + cache_dir=cache_dir, + keep_in_memory=keep_in_memory, + columns=columns, + **kwargs, + ).read() + + @staticmethod + def from_text( + path_or_paths: Dict[str, PathLike], + features: Optional[Features] = None, + cache_dir: str = None, + keep_in_memory: bool = False, + **kwargs, + ) -> "DatasetDict": + """Create [`DatasetDict`] from text file(s). + + Args: + path_or_paths (`dict` of path-like): + Path(s) of the text file(s). + features ([`Features`], *optional*): + Dataset features. + cache_dir (`str`, *optional*, defaults to `"~/.cache/huggingface/datasets"`): + Directory to cache data. + keep_in_memory (`bool`, defaults to `False`): + Whether to copy the data in-memory. + **kwargs (additional keyword arguments): + Keyword arguments to be passed to [`TextConfig`]. + + Returns: + [`DatasetDict`] + + Example: + + ```py + >>> from datasets import DatasetDict + >>> ds = DatasetDict.from_text({'train': 'path/to/dataset.txt'}) + ``` + """ + # Dynamic import to avoid circular dependency + from .io.text import TextDatasetReader + + return TextDatasetReader( + path_or_paths, features=features, cache_dir=cache_dir, keep_in_memory=keep_in_memory, **kwargs + ).read() + + @deprecated() + @is_documented_by(Dataset.prepare_for_task) + def prepare_for_task(self, task: Union[str, TaskTemplate], id: int = 0) -> "DatasetDict": + self._check_values_type() + return DatasetDict({k: dataset.prepare_for_task(task=task, id=id) for k, dataset in self.items()}) + + @is_documented_by(Dataset.align_labels_with_mapping) + def align_labels_with_mapping(self, label2id: Dict, label_column: str) -> "DatasetDict": + self._check_values_type() + return DatasetDict( + { + k: dataset.align_labels_with_mapping(label2id=label2id, label_column=label_column) + for k, dataset in self.items() + } + ) + + def push_to_hub( + self, + repo_id, + config_name: str = "default", + set_default: Optional[bool] = None, + data_dir: Optional[str] = None, + commit_message: Optional[str] = None, + commit_description: Optional[str] = None, + private: Optional[bool] = False, + token: Optional[str] = None, + revision: Optional[str] = None, + branch="deprecated", + create_pr: Optional[bool] = False, + max_shard_size: Optional[Union[int, str]] = None, + num_shards: Optional[Dict[str, int]] = None, + embed_external_files: bool = True, + ) -> CommitInfo: + """Pushes the [`DatasetDict`] to the hub as a Parquet dataset. + The [`DatasetDict`] is pushed using HTTP requests and does not need to have neither git or git-lfs installed. + + Each dataset split will be pushed independently. The pushed dataset will keep the original split names. + + The resulting Parquet files are self-contained by default: if your dataset contains [`Image`] or [`Audio`] + data, the Parquet files will store the bytes of your images or audio files. + You can disable this by setting `embed_external_files` to False. + + Args: + repo_id (`str`): + The ID of the repository to push to in the following format: `/` or + `/`. Also accepts ``, which will default to the namespace + of the logged-in user. + config_name (`str`): + Configuration name of a dataset. Defaults to "default". + set_default (`bool`, *optional*): + Whether to set this configuration as the default one. Otherwise, the default configuration is the one + named "default". + data_dir (`str`, *optional*): + Directory name that will contain the uploaded data files. Defaults to the `config_name` if different + from "default", else "data". + + + commit_message (`str`, *optional*): + Message to commit while pushing. Will default to `"Upload dataset"`. + commit_description (`str`, *optional*): + Description of the commit that will be created. + Additionally, description of the PR if a PR is created (`create_pr` is True). + + + private (`bool`, *optional*): + Whether the dataset repository should be set to private or not. Only affects repository creation: + a repository that already exists will not be affected by that parameter. + token (`str`, *optional*): + An optional authentication token for the Hugging Face Hub. If no token is passed, will default + to the token saved locally when logging in with `huggingface-cli login`. Will raise an error + if no token is passed and the user is not logged-in. + revision (`str`, *optional*): + Branch to push the uploaded files to. Defaults to the `"main"` branch. + + + branch (`str`, *optional*): + The git branch on which to push the dataset. This defaults to the default branch as specified + in your repository, which defaults to `"main"`. + + + + `branch` was deprecated in favor of `revision` in version 2.15.0 and will be removed in 3.0.0. + + + create_pr (`bool`, *optional*, defaults to `False`): + Whether to create a PR with the uploaded files or directly commit. + + + max_shard_size (`int` or `str`, *optional*, defaults to `"500MB"`): + The maximum size of the dataset shards to be uploaded to the hub. If expressed as a string, needs to be digits followed by a unit + (like `"500MB"` or `"1GB"`). + num_shards (`Dict[str, int]`, *optional*): + Number of shards to write. By default, the number of shards depends on `max_shard_size`. + Use a dictionary to define a different num_shards for each split. + + + embed_external_files (`bool`, defaults to `True`): + Whether to embed file bytes in the shards. + In particular, this will do the following before the push for the fields of type: + + - [`Audio`] and [`Image`] removes local path information and embed file content in the Parquet files. + + Return: + huggingface_hub.CommitInfo + + Example: + + ```python + >>> dataset_dict.push_to_hub("/") + >>> dataset_dict.push_to_hub("/", private=True) + >>> dataset_dict.push_to_hub("/", max_shard_size="1GB") + >>> dataset_dict.push_to_hub("/", num_shards={"train": 1024, "test": 8}) + ``` + + If you want to add a new configuration (or subset) to a dataset (e.g. if the dataset has multiple tasks/versions/languages): + + ```python + >>> english_dataset.push_to_hub("/", "en") + >>> french_dataset.push_to_hub("/", "fr") + >>> # later + >>> english_dataset = load_dataset("/", "en") + >>> french_dataset = load_dataset("/", "fr") + ``` + """ + + if num_shards is None: + num_shards = {k: None for k in self} + elif not isinstance(num_shards, dict): + raise ValueError( + "Please provide one `num_shards` per dataset in the dataset dictionary, e.g. {{'train': 128, 'test': 4}}" + ) + + if branch != "deprecated": + warnings.warn( + "'branch' was deprecated in favor of 'revision' in version 2.15.0 and will be removed in 3.0.0.\n" + f"You can remove this warning by passing 'revision={branch}' instead.", + FutureWarning, + ) + revision = branch + + self._check_values_type() + self._check_values_features() + total_uploaded_size = 0 + total_dataset_nbytes = 0 + info_to_dump: DatasetInfo = next(iter(self.values())).info.copy() + info_to_dump.config_name = config_name + info_to_dump.splits = SplitDict() + + for split in self.keys(): + if not re.match(_split_re, split): + raise ValueError(f"Split name should match '{_split_re}' but got '{split}'.") + + api = HfApi(endpoint=config.HF_ENDPOINT, token=token) + + repo_url = api.create_repo( + repo_id, + token=token, + repo_type="dataset", + private=private, + exist_ok=True, + ) + repo_id = repo_url.repo_id + + if revision is not None: + api.create_branch(repo_id, branch=revision, token=token, repo_type="dataset", exist_ok=True) + + if not data_dir: + data_dir = config_name if config_name != "default" else "data" # for backward compatibility + + additions = [] + for split in self.keys(): + logger.info(f"Pushing split {split} to the Hub.") + # The split=key needs to be removed before merging + split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub( + repo_id, + data_dir=data_dir, + split=split, + token=token, + revision=revision, + create_pr=create_pr, + max_shard_size=max_shard_size, + num_shards=num_shards.get(split), + embed_external_files=embed_external_files, + ) + additions += split_additions + total_uploaded_size += uploaded_size + total_dataset_nbytes += dataset_nbytes + info_to_dump.splits[split] = SplitInfo(str(split), num_bytes=dataset_nbytes, num_examples=len(self[split])) + info_to_dump.download_checksums = None + info_to_dump.download_size = total_uploaded_size + info_to_dump.dataset_size = total_dataset_nbytes + info_to_dump.size_in_bytes = total_uploaded_size + total_dataset_nbytes + + # Check if the repo already has a README.md and/or a dataset_infos.json to update them with the new split info (size and pattern) + # and delete old split shards (if they exist) + repo_with_dataset_card, repo_with_dataset_infos = False, False + repo_splits = [] # use a list to keep the order of the splits + deletions = [] + repo_files_to_add = [addition.path_in_repo for addition in additions] + for repo_file in api.list_repo_tree( + repo_id=repo_id, revision=revision, repo_type="dataset", token=token, recursive=True + ): + if not isinstance(repo_file, RepoFile): + continue + if repo_file.rfilename == config.REPOCARD_FILENAME: + repo_with_dataset_card = True + elif repo_file.rfilename == config.DATASETDICT_INFOS_FILENAME: + repo_with_dataset_infos = True + elif ( + repo_file.rfilename.startswith(tuple(f"{data_dir}/{split}-" for split in self.keys())) + and repo_file.rfilename not in repo_files_to_add + ): + deletions.append(CommitOperationDelete(path_in_repo=repo_file.rfilename)) + elif fnmatch.fnmatch( + repo_file.rfilename, PUSH_TO_HUB_WITHOUT_METADATA_CONFIGS_SPLIT_PATTERN_SHARDED.replace("{split}", "*") + ): + repo_split = string_to_dict( + repo_file.rfilename, + glob_pattern_to_regex(PUSH_TO_HUB_WITHOUT_METADATA_CONFIGS_SPLIT_PATTERN_SHARDED), + )["split"] + if repo_split not in repo_splits: + repo_splits.append(split) + + # get the info from the README to update them + if repo_with_dataset_card: + dataset_card_path = api.hf_hub_download( + repo_id, config.REPOCARD_FILENAME, repo_type="dataset", revision=revision + ) + dataset_card = DatasetCard.load(Path(dataset_card_path)) + dataset_card_data = dataset_card.data + metadata_configs = MetadataConfigs.from_dataset_card_data(dataset_card_data) + # get the deprecated dataset_infos.json to update them + elif repo_with_dataset_infos: + dataset_card = None + dataset_card_data = DatasetCardData() + metadata_configs = MetadataConfigs() + else: + dataset_card = None + dataset_card_data = DatasetCardData() + metadata_configs = MetadataConfigs() + # create the metadata configs if it was uploaded with push_to_hub before metadata configs existed + if not metadata_configs and repo_splits: + default_metadata_configs_to_dump = { + "data_files": [{"split": split, "path": f"data/{split}-*"} for split in repo_splits] + } + MetadataConfigs({"default": default_metadata_configs_to_dump}).to_dataset_card_data(dataset_card_data) + metadata_config_to_dump = { + "data_files": [{"split": split, "path": f"{data_dir}/{split}-*"} for split in self.keys()], + } + if set_default and config_name != "default": + if metadata_configs: + default_config_name = metadata_configs.get_default_config_name() + if default_config_name == "default": + raise ValueError( + "There exists a configuration named 'default'. To set a different configuration as default, " + "rename the 'default' one first." + ) + else: + _ = metadata_configs[default_config_name].pop("default") + metadata_config_to_dump["default"] = True + # push to the deprecated dataset_infos.json + if repo_with_dataset_infos: + dataset_infos_path = api.hf_hub_download( + repo_id, config.DATASETDICT_INFOS_FILENAME, repo_type="dataset", revision=revision + ) + with open(dataset_infos_path, encoding="utf-8") as f: + dataset_infos: dict = json.load(f) + dataset_infos[config_name] = asdict(info_to_dump) + buffer = BytesIO() + buffer.write(json.dumps(dataset_infos, indent=4).encode("utf-8")) + additions.append( + CommitOperationAdd(path_in_repo=config.DATASETDICT_INFOS_FILENAME, path_or_fileobj=buffer) + ) + # push to README + DatasetInfosDict({config_name: info_to_dump}).to_dataset_card_data(dataset_card_data) + MetadataConfigs({config_name: metadata_config_to_dump}).to_dataset_card_data(dataset_card_data) + dataset_card = DatasetCard(f"---\n{dataset_card_data}\n---\n") if dataset_card is None else dataset_card + additions.append( + CommitOperationAdd(path_in_repo=config.REPOCARD_FILENAME, path_or_fileobj=str(dataset_card).encode()) + ) + + commit_message = commit_message if commit_message is not None else "Upload dataset" + if len(additions) <= config.UPLOADS_MAX_NUMBER_PER_COMMIT: + commit_info = api.create_commit( + repo_id, + operations=additions + deletions, + commit_message=commit_message, + commit_description=commit_description, + token=token, + repo_type="dataset", + revision=revision, + create_pr=create_pr, + ) + else: + logger.info( + f"Number of files to upload is larger than {config.UPLOADS_MAX_NUMBER_PER_COMMIT}. Splitting the push into multiple commits." + ) + num_commits = math.ceil(len(additions) / config.UPLOADS_MAX_NUMBER_PER_COMMIT) + for i in range(0, num_commits): + operations = additions[ + i * config.UPLOADS_MAX_NUMBER_PER_COMMIT : (i + 1) * config.UPLOADS_MAX_NUMBER_PER_COMMIT + ] + (deletions if i == 0 else []) + commit_info = api.create_commit( + repo_id, + operations=operations, + commit_message=commit_message + f" (part {i:05d}-of-{num_commits:05d})", + commit_description=commit_description, + token=token, + repo_type="dataset", + revision=revision, + create_pr=create_pr, + ) + logger.info( + f"Commit #{i+1} completed" + + (f" (still {num_commits - i - 1} to go)" if num_commits - i - 1 else "") + + "." + ) + return commit_info + + +class IterableDatasetDict(dict): + def __repr__(self): + repr = "\n".join([f"{k}: {v}" for k, v in self.items()]) + repr = re.sub(r"^", " " * 4, repr, 0, re.M) + return f"IterableDatasetDict({{\n{repr}\n}})" + + def with_format( + self, + type: Optional[str] = None, + ) -> "IterableDatasetDict": + """ + Return a dataset with the specified format. + This method only supports the "torch" format for now. + The format is set to all the datasets of the dataset dictionary. + + Args: + type (`str`, *optional*, defaults to `None`): + If set to "torch", the returned dataset + will be a subclass of `torch.utils.data.IterableDataset` to be used in a `DataLoader`. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes", streaming=True) + >>> from transformers import AutoTokenizer + >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + >>> def encode(example): + ... return tokenizer(examples["text"], truncation=True, padding="max_length") + >>> ds = ds.map(encode, batched=True, remove_columns=["text"]) + >>> ds = ds.with_format("torch") + ``` + """ + return IterableDatasetDict({k: dataset.with_format(type=type) for k, dataset in self.items()}) + + def map( + self, + function: Optional[Callable] = None, + with_indices: bool = False, + input_columns: Optional[Union[str, List[str]]] = None, + batched: bool = False, + batch_size: int = 1000, + drop_last_batch: bool = False, + remove_columns: Optional[Union[str, List[str]]] = None, + fn_kwargs: Optional[dict] = None, + ) -> "IterableDatasetDict": + """ + Apply a function to all the examples in the iterable dataset (individually or in batches) and update them. + If your function returns a column that already exists, then it overwrites it. + The function is applied on-the-fly on the examples when iterating over the dataset. + The transformation is applied to all the datasets of the dataset dictionary. + + You can specify whether the function should be batched or not with the `batched` parameter: + + - If batched is `False`, then the function takes 1 example in and should return 1 example. + An example is a dictionary, e.g. `{"text": "Hello there !"}`. + - If batched is `True` and `batch_size` is 1, then the function takes a batch of 1 example as input and can return a batch with 1 or more examples. + A batch is a dictionary, e.g. a batch of 1 example is `{"text": ["Hello there !"]}`. + - If batched is `True` and `batch_size` is `n` > 1, then the function takes a batch of `n` examples as input and can return a batch with `n` examples, or with an arbitrary number of examples. + Note that the last batch may have less than `n` examples. + A batch is a dictionary, e.g. a batch of `n` examples is `{"text": ["Hello there !"] * n}`. + + Args: + function (`Callable`, *optional*, defaults to `None`): + Function applied on-the-fly on the examples when you iterate on the dataset. + It must have one of the following signatures: + + - `function(example: Dict[str, Any]) -> Dict[str, Any]` if `batched=False` and `with_indices=False` + - `function(example: Dict[str, Any], idx: int) -> Dict[str, Any]` if `batched=False` and `with_indices=True` + - `function(batch: Dict[str, List]) -> Dict[str, List]` if `batched=True` and `with_indices=False` + - `function(batch: Dict[str, List], indices: List[int]) -> Dict[str, List]` if `batched=True` and `with_indices=True` + + For advanced usage, the function can also return a `pyarrow.Table`. + Moreover if your function returns nothing (`None`), then `map` will run your function and return the dataset unchanged. + If no function is provided, default to identity function: `lambda x: x`. + with_indices (`bool`, defaults to `False`): + Provide example indices to `function`. Note that in this case the signature of `function` should be `def function(example, idx[, rank]): ...`. + input_columns (`[Union[str, List[str]]]`, *optional*, defaults to `None`): + The columns to be passed into `function` + as positional arguments. If `None`, a dict mapping to all formatted columns is passed as one argument. + batched (`bool`, defaults to `False`): + Provide batch of examples to `function`. + batch_size (`int`, *optional*, defaults to `1000`): + Number of examples per batch provided to `function` if `batched=True`. + drop_last_batch (`bool`, defaults to `False`): + Whether a last batch smaller than the `batch_size` should be + dropped instead of being processed by the function. + remove_columns (`[List[str]]`, *optional*, defaults to `None`): + Remove a selection of columns while doing the mapping. + Columns will be removed before updating the examples with the output of `function`, i.e. if `function` is adding + columns with names in `remove_columns`, these columns will be kept. + fn_kwargs (`Dict`, *optional*, defaults to `None`): + Keyword arguments to be passed to `function` + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes", streaming=True) + >>> def add_prefix(example): + ... example["text"] = "Review: " + example["text"] + ... return example + >>> ds = ds.map(add_prefix) + >>> next(iter(ds["train"])) + {'label': 1, + 'text': 'Review: the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .'} + ``` + """ + return IterableDatasetDict( + { + k: dataset.map( + function=function, + with_indices=with_indices, + input_columns=input_columns, + batched=batched, + batch_size=batch_size, + drop_last_batch=drop_last_batch, + remove_columns=remove_columns, + fn_kwargs=fn_kwargs, + ) + for k, dataset in self.items() + } + ) + + def filter( + self, + function: Optional[Callable] = None, + with_indices=False, + input_columns: Optional[Union[str, List[str]]] = None, + batched: bool = False, + batch_size: Optional[int] = 1000, + fn_kwargs: Optional[dict] = None, + ) -> "IterableDatasetDict": + """Apply a filter function to all the elements so that the dataset only includes examples according to the filter function. + The filtering is done on-the-fly when iterating over the dataset. + The filtering is applied to all the datasets of the dataset dictionary. + + Args: + function (`Callable`): + Callable with one of the following signatures: + + - `function(example: Dict[str, Any]) -> bool` if `with_indices=False, batched=False` + - `function(example: Dict[str, Any], indices: int) -> bool` if `with_indices=True, batched=False` + - `function(example: Dict[str, List]) -> List[bool]` if `with_indices=False, batched=True` + - `function(example: Dict[str, List], indices: List[int]) -> List[bool]` if `with_indices=True, batched=True` + + If no function is provided, defaults to an always True function: `lambda x: True`. + with_indices (`bool`, defaults to `False`): + Provide example indices to `function`. Note that in this case the signature of `function` should be `def function(example, idx): ...`. + input_columns (`str` or `List[str]`, *optional*): + The columns to be passed into `function` as + positional arguments. If `None`, a dict mapping to all formatted columns is passed as one argument. + batched (`bool`, defaults to `False`): + Provide batch of examples to `function` + batch_size (`int`, *optional*, defaults to `1000`): + Number of examples per batch provided to `function` if `batched=True`. + fn_kwargs (`Dict`, *optional*, defaults to `None`): + Keyword arguments to be passed to `function` + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes", streaming=True) + >>> ds = ds.filter(lambda x: x["label"] == 0) + >>> list(ds["train"].take(3)) + [{'label': 0, 'text': 'Review: simplistic , silly and tedious .'}, + {'label': 0, + 'text': "Review: it's so laddish and juvenile , only teenage boys could possibly find it funny ."}, + {'label': 0, + 'text': 'Review: exploitative and largely devoid of the depth or sophistication that would make watching such a graphic treatment of the crimes bearable .'}] + ``` + """ + return IterableDatasetDict( + { + k: dataset.filter( + function=function, + with_indices=with_indices, + input_columns=input_columns, + batched=batched, + batch_size=batch_size, + fn_kwargs=fn_kwargs, + ) + for k, dataset in self.items() + } + ) + + def shuffle( + self, seed=None, generator: Optional[np.random.Generator] = None, buffer_size: int = 1000 + ) -> "IterableDatasetDict": + """ + Randomly shuffles the elements of this dataset. + The shuffling is applied to all the datasets of the dataset dictionary. + + This dataset fills a buffer with buffer_size elements, then randomly samples elements from this buffer, + replacing the selected elements with new elements. For perfect shuffling, a buffer size greater than or + equal to the full size of the dataset is required. + + For instance, if your dataset contains 10,000 elements but `buffer_size` is set to 1000, then `shuffle` will + initially select a random element from only the first 1000 elements in the buffer. Once an element is + selected, its space in the buffer is replaced by the next (i.e. 1,001-st) element, + maintaining the 1000 element buffer. + + If the dataset is made of several shards, it also does `shuffle` the order of the shards. + However if the order has been fixed by using [`~datasets.IterableDataset.skip`] or [`~datasets.IterableDataset.take`] + then the order of the shards is kept unchanged. + + Args: + seed (`int`, *optional*, defaults to `None`): + Random seed that will be used to shuffle the dataset. + It is used to sample from the shuffle buffer and also to shuffle the data shards. + generator (`numpy.random.Generator`, *optional*): + Numpy random Generator to use to compute the permutation of the dataset rows. + If `generator=None` (default), uses `np.random.default_rng` (the default BitGenerator (PCG64) of NumPy). + buffer_size (`int`, defaults to `1000`): + Size of the buffer. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes", streaming=True) + >>> list(ds["train"].take(3)) + [{'label': 1, + 'text': 'the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .'}, + {'label': 1, + 'text': 'the gorgeously elaborate continuation of " the lord of the rings " trilogy is so huge that a column of words cannot adequately describe co-writer/director peter jackson\'s expanded vision of j . r . r . tolkien\'s middle-earth .'}, + {'label': 1, 'text': 'effective but too-tepid biopic'}] + >>> ds = ds.shuffle(seed=42) + >>> list(ds["train"].take(3)) + [{'label': 1, + 'text': "a sports movie with action that's exciting on the field and a story you care about off it ."}, + {'label': 1, + 'text': 'at its best , the good girl is a refreshingly adult take on adultery . . .'}, + {'label': 1, + 'text': "sam jones became a very lucky filmmaker the day wilco got dropped from their record label , proving that one man's ruin may be another's fortune ."}] + ``` + """ + return IterableDatasetDict( + { + k: dataset.shuffle(seed=seed, generator=generator, buffer_size=buffer_size) + for k, dataset in self.items() + } + ) + + def rename_column(self, original_column_name: str, new_column_name: str) -> "IterableDatasetDict": + """ + Rename a column in the dataset, and move the features associated to the original column under the new column + name. + The renaming is applied to all the datasets of the dataset dictionary. + + Args: + original_column_name (`str`): + Name of the column to rename. + new_column_name (`str`): + New name for the column. + + Returns: + [`IterableDatasetDict`]: A copy of the dataset with a renamed column. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes", streaming=True) + >>> ds = ds.rename_column("text", "movie_review") + >>> next(iter(ds["train"])) + {'label': 1, + 'movie_review': 'the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .'} + ``` + """ + return IterableDatasetDict( + { + k: dataset.rename_column(original_column_name=original_column_name, new_column_name=new_column_name) + for k, dataset in self.items() + } + ) + + def rename_columns(self, column_mapping: Dict[str, str]) -> "IterableDatasetDict": + """ + Rename several columns in the dataset, and move the features associated to the original columns under + the new column names. + The renaming is applied to all the datasets of the dataset dictionary. + + Args: + column_mapping (`Dict[str, str]`): + A mapping of columns to rename to their new names. + + Returns: + [`IterableDatasetDict`]: A copy of the dataset with renamed columns + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes", streaming=True) + >>> ds = ds.rename_columns({"text": "movie_review", "label": "rating"}) + >>> next(iter(ds["train"])) + {'movie_review': 'the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .', + 'rating': 1} + ``` + """ + return IterableDatasetDict( + {k: dataset.rename_columns(column_mapping=column_mapping) for k, dataset in self.items()} + ) + + def remove_columns(self, column_names: Union[str, List[str]]) -> "IterableDatasetDict": + """ + Remove one or several column(s) in the dataset and the features associated to them. + The removal is done on-the-fly on the examples when iterating over the dataset. + The removal is applied to all the datasets of the dataset dictionary. + + + Args: + column_names (`Union[str, List[str]]`): + Name of the column(s) to remove. + + Returns: + [`IterableDatasetDict`]: A copy of the dataset object without the columns to remove. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes", streaming=True) + >>> ds = ds.remove_columns("label") + >>> next(iter(ds["train"])) + {'text': 'the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .'} + ``` + """ + return IterableDatasetDict({k: dataset.remove_columns(column_names) for k, dataset in self.items()}) + + def select_columns(self, column_names: Union[str, List[str]]) -> "IterableDatasetDict": + """Select one or several column(s) in the dataset and the features + associated to them. The selection is done on-the-fly on the examples + when iterating over the dataset. The selection is applied to all the + datasets of the dataset dictionary. + + + Args: + column_names (`Union[str, List[str]]`): + Name of the column(s) to keep. + + Returns: + [`IterableDatasetDict`]: A copy of the dataset object with only selected columns. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes", streaming=True) + >>> ds = ds.select("text") + >>> next(iter(ds["train"])) + {'text': 'the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .'} + ``` + """ + return IterableDatasetDict({k: dataset.select_columns(column_names) for k, dataset in self.items()}) + + def cast_column(self, column: str, feature: FeatureType) -> "IterableDatasetDict": + """Cast column to feature for decoding. + The type casting is applied to all the datasets of the dataset dictionary. + + Args: + column (`str`): + Column name. + feature ([`Feature`]): + Target feature. + + Returns: + [`IterableDatasetDict`] + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes", streaming=True) + >>> ds["train"].features + {'label': ClassLabel(num_classes=2, names=['neg', 'pos'], id=None), + 'text': Value(dtype='string', id=None)} + >>> ds = ds.cast_column('label', ClassLabel(names=['bad', 'good'])) + >>> ds["train"].features + {'label': ClassLabel(num_classes=2, names=['bad', 'good'], id=None), + 'text': Value(dtype='string', id=None)} + ``` + """ + return IterableDatasetDict( + {k: dataset.cast_column(column=column, feature=feature) for k, dataset in self.items()} + ) + + def cast( + self, + features: Features, + ) -> "IterableDatasetDict": + """ + Cast the dataset to a new set of features. + The type casting is applied to all the datasets of the dataset dictionary. + + Args: + features (`Features`): + New features to cast the dataset to. + The name of the fields in the features must match the current column names. + The type of the data must also be convertible from one type to the other. + For non-trivial conversion, e.g. `string` <-> `ClassLabel` you should use [`map`] to update the Dataset. + + Returns: + [`IterableDatasetDict`]: A copy of the dataset with casted features. + + Example: + + ```py + >>> from datasets import load_dataset + >>> ds = load_dataset("rotten_tomatoes", streaming=True) + >>> ds["train"].features + {'label': ClassLabel(num_classes=2, names=['neg', 'pos'], id=None), + 'text': Value(dtype='string', id=None)} + >>> new_features = ds["train"].features.copy() + >>> new_features['label'] = ClassLabel(names=['bad', 'good']) + >>> new_features['text'] = Value('large_string') + >>> ds = ds.cast(new_features) + >>> ds["train"].features + {'label': ClassLabel(num_classes=2, names=['bad', 'good'], id=None), + 'text': Value(dtype='large_string', id=None)} + ``` + """ + return IterableDatasetDict({k: dataset.cast(features=features) for k, dataset in self.items()})