import os from functools import partial from pathlib import Path, PurePath from typing import Callable, Dict, List, Optional, Set, Tuple, Union import huggingface_hub from fsspec.implementations.local import LocalFileSystem from tqdm.contrib.concurrent import thread_map from .filesystems.hffilesystem import HfFileSystem from .splits import Split from .utils import logging from .utils.file_utils import is_relative_path, is_remote_url, request_etag from .utils.hub import hf_hub_url from .utils.py_utils import string_to_dict SANITIZED_DEFAULT_SPLIT = str(Split.TRAIN) logger = logging.get_logger(__name__) class Url(str): pass class EmptyDatasetError(FileNotFoundError): pass SPLIT_PATTERN_SHARDED = "data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*" SPLIT_KEYWORDS = { Split.TRAIN: ["train", "training"], Split.VALIDATION: ["validation", "valid", "dev", "val"], Split.TEST: ["test", "testing", "eval", "evaluation"], } NON_WORDS_CHARS = "-._ 0-9" KEYWORDS_IN_FILENAME_BASE_PATTERNS = ["**[{sep}/]{keyword}[{sep}]*", "{keyword}[{sep}]*"] KEYWORDS_IN_DIR_NAME_BASE_PATTERNS = ["{keyword}[{sep}/]**", "**[{sep}/]{keyword}[{sep}/]**"] DEFAULT_SPLITS = [Split.TRAIN, Split.VALIDATION, Split.TEST] DEFAULT_PATTERNS_SPLIT_IN_FILENAME = { split: [ pattern.format(keyword=keyword, sep=NON_WORDS_CHARS) for keyword in SPLIT_KEYWORDS[split] for pattern in KEYWORDS_IN_FILENAME_BASE_PATTERNS ] for split in DEFAULT_SPLITS } DEFAULT_PATTERNS_SPLIT_IN_DIR_NAME = { split: [ pattern.format(keyword=keyword, sep=NON_WORDS_CHARS) for keyword in SPLIT_KEYWORDS[split] for pattern in KEYWORDS_IN_DIR_NAME_BASE_PATTERNS ] for split in DEFAULT_SPLITS } DEFAULT_PATTERNS_ALL = { Split.TRAIN: ["**"], } ALL_SPLIT_PATTERNS = [SPLIT_PATTERN_SHARDED] ALL_DEFAULT_PATTERNS = [ DEFAULT_PATTERNS_SPLIT_IN_DIR_NAME, DEFAULT_PATTERNS_SPLIT_IN_FILENAME, DEFAULT_PATTERNS_ALL, ] METADATA_PATTERNS = [ "metadata.csv", "**/metadata.csv", "metadata.jsonl", "**/metadata.jsonl", ] # metadata file for ImageFolder and AudioFolder WILDCARD_CHARACTERS = "*[]" FILES_TO_IGNORE = ["README.md", "config.json", "dataset_infos.json", "dummy_data.zip", "dataset_dict.json"] def contains_wildcards(pattern: str) -> bool: return any(wilcard_character in pattern for wilcard_character in WILDCARD_CHARACTERS) def sanitize_patterns(patterns: Union[Dict, List, str]) -> Dict[str, Union[List[str], "DataFilesList"]]: """ Take the data_files patterns from the user, and format them into a dictionary. Each key is the name of the split, and each value is a list of data files patterns (paths or urls). The default split is "train". Returns: patterns: dictionary of split_name -> list_of _atterns """ if isinstance(patterns, dict): return {str(key): value if isinstance(value, list) else [value] for key, value in patterns.items()} elif isinstance(patterns, str): return {SANITIZED_DEFAULT_SPLIT: [patterns]} elif isinstance(patterns, list): return {SANITIZED_DEFAULT_SPLIT: patterns} else: return {SANITIZED_DEFAULT_SPLIT: list(patterns)} def _is_inside_unrequested_special_dir(matched_rel_path: str, pattern: str) -> bool: """ When a path matches a pattern, we additionnally check if it's inside a special directory we ignore by default (if it starts with a double underscore). Users can still explicitly request a filepath inside such a directory if "__pycache__" is mentioned explicitly in the requested pattern. Some examples: base directory: ./ └── __pycache__ └── b.txt >>> _is_inside_unrequested_special_dir("__pycache__/b.txt", "**") True >>> _is_inside_unrequested_special_dir("__pycache__/b.txt", "*/b.txt") True >>> _is_inside_unrequested_special_dir("__pycache__/b.txt", "__pycache__/*") False >>> _is_inside_unrequested_special_dir("__pycache__/b.txt", "__*/*") False """ # We just need to check if every special directories from the path is present explicly in the pattern. # Since we assume that the path matches the pattern, it's equivalent to counting that both # the parent path and the parent pattern have the same number of special directories. data_dirs_to_ignore_in_path = [part for part in PurePath(matched_rel_path).parent.parts if part.startswith("__")] data_dirs_to_ignore_in_pattern = [part for part in PurePath(pattern).parent.parts if part.startswith("__")] return len(data_dirs_to_ignore_in_path) != len(data_dirs_to_ignore_in_pattern) def _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(matched_rel_path: str, pattern: str) -> bool: """ When a path matches a pattern, we additionnally check if it's a hidden file or if it's inside a hidden directory we ignore by default, i.e. if the file name or a parent directory name starts with a dot. Users can still explicitly request a filepath that is hidden or is inside a hidden directory if the hidden part is mentioned explicitly in the requested pattern. Some examples: base directory: ./ └── .hidden_file.txt >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_file.txt", "**") True >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_file.txt", ".*") False base directory: ./ └── .hidden_dir └── a.txt >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_dir/a.txt", "**") True >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_dir/a.txt", ".*/*") False >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_dir/a.txt", ".hidden_dir/*") False base directory: ./ └── .hidden_dir └── .hidden_file.txt >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_dir/.hidden_file.txt", "**") True >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_dir/.hidden_file.txt", ".*/*") True >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_dir/.hidden_file.txt", ".*/.*") False >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_dir/.hidden_file.txt", ".hidden_dir/*") True >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_dir/.hidden_file.txt", ".hidden_dir/.*") False """ # We just need to check if every hidden part from the path is present explicly in the pattern. # Since we assume that the path matches the pattern, it's equivalent to counting that both # the path and the pattern have the same number of hidden parts. hidden_directories_in_path = [ part for part in PurePath(matched_rel_path).parts if part.startswith(".") and not set(part) == {"."} ] hidden_directories_in_pattern = [ part for part in PurePath(pattern).parts if part.startswith(".") and not set(part) == {"."} ] return len(hidden_directories_in_path) != len(hidden_directories_in_pattern) def _get_data_files_patterns(pattern_resolver: Callable[[str], List[PurePath]]) -> Dict[str, List[str]]: """ Get the default pattern from a directory or repository by testing all the supported patterns. The first patterns to return a non-empty list of data files is returned. In order, it first tests if SPLIT_PATTERN_SHARDED works, otherwise it tests the patterns in ALL_DEFAULT_PATTERNS. """ # first check the split patterns like data/{split}-00000-of-00001.parquet for split_pattern in ALL_SPLIT_PATTERNS: pattern = split_pattern.replace("{split}", "*") data_files = pattern_resolver(pattern) if len(data_files) > 0: data_files = [p.as_posix() for p in data_files] splits: Set[str] = {string_to_dict(p, split_pattern)["split"] for p in data_files} sorted_splits = [str(split) for split in DEFAULT_SPLITS if split in splits] + sorted( splits - set(DEFAULT_SPLITS) ) return {split: [split_pattern.format(split=split)] for split in sorted_splits} # then check the default patterns based on train/valid/test splits for patterns_dict in ALL_DEFAULT_PATTERNS: non_empty_splits = [] for split, patterns in patterns_dict.items(): try: for pattern in patterns: data_files = pattern_resolver(pattern) if len(data_files) > 0: non_empty_splits.append(split) break except FileNotFoundError: pass if non_empty_splits: return {split: patterns_dict[split] for split in non_empty_splits} raise FileNotFoundError(f"Couldn't resolve pattern {pattern} with resolver {pattern_resolver}") def _get_metadata_files_patterns(pattern_resolver: Callable[[str], List[PurePath]]) -> Dict[str, List[str]]: """ Get the supported metadata patterns from a directory or repository. """ non_empty_patterns = [] for pattern in METADATA_PATTERNS: try: metadata_files = pattern_resolver(pattern) if len(metadata_files) > 0: non_empty_patterns.append(pattern) except FileNotFoundError: pass if non_empty_patterns: return non_empty_patterns raise FileNotFoundError(f"Couldn't resolve pattern {pattern} with resolver {pattern_resolver}") def _resolve_single_pattern_locally( base_path: str, pattern: str, allowed_extensions: Optional[List[str]] = None ) -> List[Path]: """ Return the absolute paths to all the files that match the given patterns. It also supports absolute paths in patterns. If an URL is passed, it is returned as is. """ if is_relative_path(pattern): pattern = os.path.join(base_path, pattern) else: base_path = os.path.splitdrive(pattern)[0] + os.sep fs = LocalFileSystem() glob_iter = [PurePath(filepath) for filepath in fs.glob(pattern) if fs.isfile(filepath)] matched_paths = [ Path(os.path.abspath(filepath)) for filepath in glob_iter if (filepath.name not in FILES_TO_IGNORE or PurePath(pattern).name == filepath.name) and not _is_inside_unrequested_special_dir( os.path.relpath(filepath, base_path), os.path.relpath(pattern, base_path) ) and not _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir( os.path.relpath(filepath, base_path), os.path.relpath(pattern, base_path) ) ] # ignore .ipynb and __pycache__, but keep /../ if allowed_extensions is not None: out = [ filepath for filepath in matched_paths if any(suffix[1:] in allowed_extensions for suffix in filepath.suffixes) ] if len(out) < len(matched_paths): invalid_matched_files = list(set(matched_paths) - set(out)) logger.info( f"Some files matched the pattern '{pattern}' at {Path(base_path).resolve()} but don't have valid data file extensions: {invalid_matched_files}" ) else: out = matched_paths if not out and not contains_wildcards(pattern): error_msg = f"Unable to find '{pattern}' at {Path(base_path).resolve()}" if allowed_extensions is not None: error_msg += f" with any supported extension {list(allowed_extensions)}" raise FileNotFoundError(error_msg) return sorted(out) def resolve_patterns_locally_or_by_urls( base_path: str, patterns: List[str], allowed_extensions: Optional[List[str]] = None ) -> List[Union[Path, Url]]: """ Resolve the paths and URLs of the data files from the patterns passed by the user. URLs are just returned as is. You can use patterns to resolve multiple local files. Here are a few examples: - *.csv to match all the CSV files at the first level - **.csv to match all the CSV files at any level - data/* to match all the files inside "data" - data/** to match all the files inside "data" and its subdirectories The patterns are resolved using the fsspec glob. Here are some behaviors specific to fsspec glob that are different from glob.glob, Path.glob, Path.match or fnmatch: - '*' matches only first level items - '**' matches all items - '**/*' matches all at least second level items More generally: - '*' matches any character except a forward-slash (to match just the file or directory name) - '**' matches any character including a forward-slash / Hidden files and directories (i.e. whose names start with a dot) are ignored, unless they are explicitly requested. The same applies to special directories that start with a double underscore like "__pycache__". You can still include one if the pattern explicilty mentions it: - to include a hidden file: "*/.hidden.txt" or "*/.*" - to include a hidden directory: ".hidden/*" or ".*/*" - to include a special directory: "__special__/*" or "__*/*" Example:: >>> from datasets.data_files import resolve_patterns_locally_or_by_urls >>> base_path = "." >>> resolve_patterns_locally_or_by_urls(base_path, ["src/**/*.yaml"]) [PosixPath('/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/utils/resources/readme_structure.yaml')] Args: base_path (str): Base path to use when resolving relative paths. patterns (List[str]): Unix patterns or paths or URLs of the data files to resolve. The paths can be absolute or relative to base_path. allowed_extensions (Optional[list], optional): White-list of file extensions to use. Defaults to None (all extensions). For example: allowed_extensions=["csv", "json", "txt", "parquet"] Returns: List[Union[Path, Url]]: List of paths or URLs to the local or remote files that match the patterns. """ data_files = [] for pattern in patterns: if is_remote_url(pattern): data_files.append(Url(pattern)) else: for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions): data_files.append(path) if not data_files: error_msg = f"Unable to resolve any data file that matches '{patterns}' at {Path(base_path).resolve()}" if allowed_extensions is not None: error_msg += f" with any supported extension {list(allowed_extensions)}" raise FileNotFoundError(error_msg) return data_files def get_data_patterns_locally(base_path: str) -> Dict[str, List[str]]: """ Get the default pattern from a directory testing all the supported patterns. The first patterns to return a non-empty list of data files is returned. Some examples of supported patterns: Input: my_dataset_repository/ ├── README.md └── dataset.csv Output: {"train": ["**"]} Input: my_dataset_repository/ ├── README.md ├── train.csv └── test.csv my_dataset_repository/ ├── README.md └── data/ ├── train.csv └── test.csv my_dataset_repository/ ├── README.md ├── train_0.csv ├── train_1.csv ├── train_2.csv ├── train_3.csv ├── test_0.csv └── test_1.csv Output: {"train": [**train*], "test": ["**test*"]} Input: my_dataset_repository/ ├── README.md └── data/ ├── train/ │ ├── shard_0.csv │ ├── shard_1.csv │ ├── shard_2.csv │ └── shard_3.csv └── test/ ├── shard_0.csv └── shard_1.csv Output: {"train": ["**train*/**"], "test": ["**test*/**"]} Input: my_dataset_repository/ ├── README.md └── data/ ├── train-00000-of-00003.csv ├── train-00001-of-00003.csv ├── train-00002-of-00003.csv ├── test-00000-of-00001.csv ├── random-00000-of-00003.csv ├── random-00001-of-00003.csv └── random-00002-of-00003.csv Output: { "train": ["data/train-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"], "test": ["data/test-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"], "random": ["data/random-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"], } In order, it first tests if SPLIT_PATTERN_SHARDED works, otherwise it tests the patterns in ALL_DEFAULT_PATTERNS. """ resolver = partial(_resolve_single_pattern_locally, base_path) try: return _get_data_files_patterns(resolver) except FileNotFoundError: raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None def get_metadata_patterns_locally(base_path: str) -> List[str]: """ Get the supported metadata patterns from a local directory. """ resolver = partial(_resolve_single_pattern_locally, base_path) try: return _get_metadata_files_patterns(resolver) except FileNotFoundError: raise FileNotFoundError(f"The directory at {base_path} doesn't contain any metadata file") from None def _resolve_single_pattern_in_dataset_repository( dataset_info: huggingface_hub.hf_api.DatasetInfo, pattern: str, base_path: Optional[str] = None, allowed_extensions: Optional[list] = None, ) -> List[PurePath]: fs = HfFileSystem(repo_info=dataset_info) if base_path: pattern = f"{base_path}/{pattern}" else: base_path = "/" glob_iter = [PurePath(filepath) for filepath in fs.glob(PurePath(pattern).as_posix()) if fs.isfile(filepath)] matched_paths = [ filepath for filepath in glob_iter if (filepath.name not in FILES_TO_IGNORE or PurePath(pattern).name == filepath.name) and not _is_inside_unrequested_special_dir( os.path.relpath(filepath, base_path), os.path.relpath(pattern, base_path) ) and not _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir( os.path.relpath(filepath, base_path), os.path.relpath(pattern, base_path) ) ] # ignore .ipynb and __pycache__, but keep /../ if allowed_extensions is not None: out = [ filepath for filepath in matched_paths if any(suffix[1:] in allowed_extensions for suffix in filepath.suffixes) ] if len(out) < len(matched_paths): invalid_matched_files = list(set(matched_paths) - set(out)) logger.info( f"Some files matched the pattern {pattern} in dataset repository {dataset_info.id} but don't have valid data file extensions: {invalid_matched_files}" ) else: out = matched_paths if not out and not contains_wildcards(pattern): error_msg = f"Unable to find {pattern} in dataset repository {dataset_info.id}" if allowed_extensions is not None: error_msg += f" with any supported extension {list(allowed_extensions)}" raise FileNotFoundError(error_msg) return sorted(out) def resolve_patterns_in_dataset_repository( dataset_info: huggingface_hub.hf_api.DatasetInfo, patterns: List[str], base_path: Optional[str] = None, allowed_extensions: Optional[list] = None, ) -> List[Url]: """ Resolve the URLs of the data files from the patterns passed by the user. You can use patterns to resolve multiple files. Here are a few examples: - *.csv to match all the CSV files at the first level - **.csv to match all the CSV files at any level - data/* to match all the files inside "data" - data/** to match all the files inside "data" and its subdirectories The patterns are resolved using the fsspec glob. Here are some behaviors specific to fsspec glob that are different from glob.glob, Path.glob, Path.match or fnmatch: - '*' matches only first level items - '**' matches all items - '**/*' matches all at least second level items More generally: - '*' matches any character except a forward-slash (to match just the file or directory name) - '**' matches any character including a forward-slash / Hidden files and directories (i.e. whose names start with a dot) are ignored, unless they are explicitly requested. The same applies to special directories that start with a double underscore like "__pycache__". You can still include one if the pattern explicilty mentions it: - to include a hidden file: "*/.hidden.txt" or "*/.*" - to include a hidden directory: ".hidden/*" or ".*/*" - to include a special directory: "__special__/*" or "__*/*" Example:: >>> import huggingface_hub >>> from datasets.data_files import resolve_patterns_in_dataset_repository >>> dataset_info = huggingface_hub.HfApi().dataset_info("lhoestq/demo1") >>> resolve_patterns_in_dataset_repository(dataset_info, ["data/*.csv"]) ['https://huggingface.co/datasets/lhoestq/demo1/resolve/0ca0d9f35b390ad11516095aeb27fd30cfe72578/data/test.csv', 'https://huggingface.co/datasets/lhoestq/demo1/resolve/0ca0d9f35b390ad11516095aeb27fd30cfe72578/data/train.csv'] Args: dataset_info (huggingface_hub.hf_api.DatasetInfo): dataset info obtained using the hugginggace_hub.HfApi patterns (List[str]): Unix patterns or paths of the files in the dataset repository. The paths should be relative to the root of the repository. base_path (Optional[str], optional): Path inside a repo to use when resolving relative paths. Defaults to None (search from a repository's root). Used if files only from a specific directory should be resolved. allowed_extensions (Optional[list], optional): White-list of file extensions to use. Defaults to None (all extensions). For example: allowed_extensions=["csv", "json", "txt", "parquet"] Returns: List[Url]: List of URLs to the files in the dataset repository that match the patterns. """ data_files_urls: List[Url] = [] for pattern in patterns: for rel_path in _resolve_single_pattern_in_dataset_repository( dataset_info, pattern, base_path, allowed_extensions ): data_files_urls.append(Url(hf_hub_url(dataset_info.id, rel_path.as_posix(), revision=dataset_info.sha))) if not data_files_urls: error_msg = f"Unable to resolve any data file that matches {patterns} in dataset repository {dataset_info.id}" if allowed_extensions is not None: error_msg += f" with any supported extension {list(allowed_extensions)}" raise FileNotFoundError(error_msg) return data_files_urls def get_data_patterns_in_dataset_repository( dataset_info: huggingface_hub.hf_api.DatasetInfo, base_path: str ) -> Dict[str, List[str]]: """ Get the default pattern from a repository by testing all the supported patterns. The first patterns to return a non-empty list of data files is returned. Some examples of supported patterns: Input: my_dataset_repository/ ├── README.md └── dataset.csv Output: {"train": ["**"]} Input: my_dataset_repository/ ├── README.md ├── train.csv └── test.csv my_dataset_repository/ ├── README.md └── data/ ├── train.csv └── test.csv my_dataset_repository/ ├── README.md ├── train_0.csv ├── train_1.csv ├── train_2.csv ├── train_3.csv ├── test_0.csv └── test_1.csv Output: {"train": ["**train*"], "test": ["**test*"]} Input: my_dataset_repository/ ├── README.md └── data/ ├── train/ │ ├── shard_0.csv │ ├── shard_1.csv │ ├── shard_2.csv │ └── shard_3.csv └── test/ ├── shard_0.csv └── shard_1.csv Output: {"train": ["**train*/**"], "test": ["**test*/**"]} Input: my_dataset_repository/ ├── README.md └── data/ ├── train-00000-of-00003.csv ├── train-00001-of-00003.csv ├── train-00002-of-00003.csv ├── test-00000-of-00001.csv ├── random-00000-of-00003.csv ├── random-00001-of-00003.csv └── random-00002-of-00003.csv Output: { "train": ["data/train-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"], "test": ["data/test-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"], "random": ["data/random-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"], } In order, it first tests if SPLIT_PATTERN_SHARDED works, otherwise it tests the patterns in ALL_DEFAULT_PATTERNS. """ resolver = partial(_resolve_single_pattern_in_dataset_repository, dataset_info, base_path=base_path) try: return _get_data_files_patterns(resolver) except FileNotFoundError: raise EmptyDatasetError( f"The dataset repository at '{dataset_info.id}' doesn't contain any data files" ) from None def get_metadata_patterns_in_dataset_repository( dataset_info: huggingface_hub.hf_api.DatasetInfo, base_path: str ) -> List[str]: """ Get the supported metadata patterns from a remote repository. """ resolver = partial(_resolve_single_pattern_in_dataset_repository, dataset_info, base_path=base_path) try: return _get_metadata_files_patterns(resolver) except FileNotFoundError: raise FileNotFoundError( f"The dataset repository at '{dataset_info.id}' doesn't contain any metadata file." ) from None def _get_single_origin_metadata_locally_or_by_urls( data_file: Union[Path, Url], use_auth_token: Optional[Union[bool, str]] = None ) -> Tuple[str]: if isinstance(data_file, Url): data_file = str(data_file) return (request_etag(data_file, use_auth_token=use_auth_token),) else: data_file = str(data_file.resolve()) return (str(os.path.getmtime(data_file)),) def _get_origin_metadata_locally_or_by_urls( data_files: List[Union[Path, Url]], max_workers=64, use_auth_token: Optional[Union[bool, str]] = None ) -> Tuple[str]: return thread_map( partial(_get_single_origin_metadata_locally_or_by_urls, use_auth_token=use_auth_token), data_files, max_workers=max_workers, tqdm_class=logging.tqdm, desc="Resolving data files", disable=len(data_files) <= 16 or not logging.is_progress_bar_enabled(), ) class DataFilesList(List[Union[Path, Url]]): """ List of data files (absolute local paths or URLs). It has two construction methods given the user's data files patterns : - ``from_hf_repo``: resolve patterns inside a dataset repository - ``from_local_or_remote``: resolve patterns from a local path Moreover DataFilesList has an additional attribute ``origin_metadata``. It can store: - the last modified time of local files - ETag of remote files - commit sha of a dataset repository Thanks to this additional attribute, it is possible to hash the list and get a different hash if and only if at least one file changed. This is useful for caching Dataset objects that are obtained from a list of data files. """ def __init__(self, data_files: List[Union[Path, Url]], origin_metadata: List[Tuple[str]]): super().__init__(data_files) self.origin_metadata = origin_metadata @classmethod def from_hf_repo( cls, patterns: List[str], dataset_info: huggingface_hub.hf_api.DatasetInfo, base_path: Optional[str] = None, allowed_extensions: Optional[List[str]] = None, ) -> "DataFilesList": data_files = resolve_patterns_in_dataset_repository(dataset_info, patterns, base_path, allowed_extensions) origin_metadata = [(dataset_info.id, dataset_info.sha) for _ in patterns] return cls(data_files, origin_metadata) @classmethod def from_local_or_remote( cls, patterns: List[str], base_path: Optional[str] = None, allowed_extensions: Optional[List[str]] = None, use_auth_token: Optional[Union[bool, str]] = None, ) -> "DataFilesList": base_path = base_path if base_path is not None else str(Path().resolve()) data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token) return cls(data_files, origin_metadata) class DataFilesDict(Dict[str, DataFilesList]): """ Dict of split_name -> list of data files (absolute local paths or URLs). It has two construction methods given the user's data files patterns : - ``from_hf_repo``: resolve patterns inside a dataset repository - ``from_local_or_remote``: resolve patterns from a local path Moreover each list is a DataFilesList. It is possible to hash the dictionary and get a different hash if and only if at least one file changed. For more info, see ``DataFilesList``. This is useful for caching Dataset objects that are obtained from a list of data files. Changing the order of the keys of this dictionary also doesn't change its hash. """ @classmethod def from_local_or_remote( cls, patterns: Dict[str, Union[List[str], DataFilesList]], base_path: Optional[str] = None, allowed_extensions: Optional[List[str]] = None, use_auth_token: Optional[Union[bool, str]] = None, ) -> "DataFilesDict": out = cls() for key, patterns_for_key in patterns.items(): out[key] = ( DataFilesList.from_local_or_remote( patterns_for_key, base_path=base_path, allowed_extensions=allowed_extensions, use_auth_token=use_auth_token, ) if not isinstance(patterns_for_key, DataFilesList) else patterns_for_key ) return out @classmethod def from_hf_repo( cls, patterns: Dict[str, Union[List[str], DataFilesList]], dataset_info: huggingface_hub.hf_api.DatasetInfo, base_path: Optional[str] = None, allowed_extensions: Optional[List[str]] = None, ) -> "DataFilesDict": out = cls() for key, patterns_for_key in patterns.items(): out[key] = ( DataFilesList.from_hf_repo( patterns_for_key, dataset_info=dataset_info, base_path=base_path, allowed_extensions=allowed_extensions, ) if not isinstance(patterns_for_key, DataFilesList) else patterns_for_key ) return out def __reduce__(self): """ To make sure the order of the keys doesn't matter when pickling and hashing: >>> from datasets.data_files import DataFilesDict >>> from datasets.fingerprint import Hasher >>> assert Hasher.hash(DataFilesDict(a=[], b=[])) == Hasher.hash(DataFilesDict(b=[], a=[])) """ return DataFilesDict, (dict(sorted(self.items())),)