import os
import re
from functools import partial
from pathlib import Path, PurePath
from typing import Callable, Dict, List, Optional, Set, Tuple, Union

import huggingface_hub
from fsspec import get_fs_token_paths
from fsspec.implementations.http import HTTPFileSystem
from huggingface_hub import HfFileSystem
from tqdm.contrib.concurrent import thread_map

from . import config
from .download import DownloadConfig
from .download.streaming_download_manager import _prepare_path_and_storage_options, xbasename, xjoin
from .splits import Split
from .utils import logging
from .utils.file_utils import is_local_path, is_relative_path
from .utils.py_utils import glob_pattern_to_regex, 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 patterns
    """
    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):
        if any(isinstance(pattern, dict) for pattern in patterns):
            for pattern in patterns:
                if not (
                    isinstance(pattern, dict)
                    and len(pattern) == 2
                    and "split" in pattern
                    and isinstance(pattern.get("path"), (str, list))
                ):
                    raise ValueError(
                        f"Expected each split to have a 'path' key which can be a string or a list of strings, but got {pattern}"
                    )
            splits = [pattern["split"] for pattern in patterns]
            if len(set(splits)) != len(splits):
                raise ValueError(f"Some splits are duplicated in data_files: {splits}")
            return {
                str(pattern["split"]): pattern["path"] if isinstance(pattern["path"], list) else [pattern["path"]]
                for pattern in patterns
            }
        else:
            return {SANITIZED_DEFAULT_SPLIT: patterns}
    else:
        return sanitize_patterns(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[str]]) -> 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}", "*")
        try:
            data_files = pattern_resolver(pattern)
        except FileNotFoundError:
            continue
        if len(data_files) > 0:
            splits: Set[str] = {string_to_dict(p, glob_pattern_to_regex(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():
            for pattern in patterns:
                try:
                    data_files = pattern_resolver(pattern)
                except FileNotFoundError:
                    continue
                if len(data_files) > 0:
                    non_empty_splits.append(split)
                    break
        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[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_pattern(
    pattern: str,
    base_path: str,
    allowed_extensions: Optional[List[str]] = None,
    download_config: Optional[DownloadConfig] = None,
) -> List[str]:
    """
    Resolve the paths and URLs of the data files from the pattern passed by the user.

    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_pattern
        >>> base_path = "."
        >>> resolve_pattern("docs/**/*.py", base_path)
        [/Users/mariosasko/Desktop/projects/datasets/docs/source/_config.py']

    Args:
        pattern (str): Unix pattern or paths or URLs of the data files to resolve.
            The paths can be absolute or relative to base_path.
            Remote filesystems using fsspec are supported, e.g. with the hf:// protocol.
        base_path (str): Base path to use when resolving relative paths.
        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.
    """
    if is_relative_path(pattern):
        pattern = xjoin(base_path, pattern)
    elif is_local_path(pattern):
        base_path = os.path.splitdrive(pattern)[0] + os.sep
    else:
        base_path = ""
    pattern, storage_options = _prepare_path_and_storage_options(pattern, download_config=download_config)
    fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options)
    fs_base_path = base_path.split("::")[0].split("://")[-1] or fs.root_marker
    fs_pattern = pattern.split("::")[0].split("://")[-1]
    files_to_ignore = set(FILES_TO_IGNORE) - {xbasename(pattern)}
    protocol = fs.protocol if isinstance(fs.protocol, str) else fs.protocol[0]
    protocol_prefix = protocol + "://" if protocol != "file" else ""
    matched_paths = [
        filepath if filepath.startswith(protocol_prefix) else protocol_prefix + filepath
        for filepath, info in fs.glob(pattern, detail=True).items()
        if info["type"] == "file"
        and (xbasename(filepath) not in files_to_ignore)
        and not _is_inside_unrequested_special_dir(
            os.path.relpath(filepath, fs_base_path), os.path.relpath(fs_pattern, fs_base_path)
        )
        and not _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(
            os.path.relpath(filepath, fs_base_path), os.path.relpath(fs_pattern, fs_base_path)
        )
    ]  # ignore .ipynb and __pycache__, but keep /../
    if allowed_extensions is not None:
        out = [
            filepath
            for filepath in matched_paths
            if any("." + suffix in allowed_extensions for suffix in xbasename(filepath).split(".")[1:])
        ]
        if len(out) < len(matched_paths):
            invalid_matched_files = list(set(matched_paths) - set(out))
            logger.info(
                f"Some files matched the pattern '{pattern}' but don't have valid data file extensions: {invalid_matched_files}"
            )
    else:
        out = matched_paths
    if not out:
        error_msg = f"Unable to find '{pattern}'"
        if allowed_extensions is not None:
            error_msg += f" with any supported extension {list(allowed_extensions)}"
        raise FileNotFoundError(error_msg)
    return out


def get_data_patterns(base_path: str, download_config: Optional[DownloadConfig] = None) -> 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_pattern, base_path=base_path, download_config=download_config)
    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(
    base_path: str,
    download_config: Optional[DownloadConfig] = None,
) -> List[str]:
    """
    Get the supported metadata patterns from a local directory.
    """
    resolver = partial(resolve_pattern, base_path=base_path, download_config=download_config)
    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 _get_single_origin_metadata(
    data_file: str,
    download_config: Optional[DownloadConfig] = None,
) -> Tuple[str]:
    data_file, storage_options = _prepare_path_and_storage_options(data_file, download_config=download_config)
    fs, _, _ = get_fs_token_paths(data_file, storage_options=storage_options)
    if isinstance(fs, HfFileSystem):
        resolved_path = fs.resolve_path(data_file)
        return (resolved_path.repo_id, resolved_path.revision)
    elif isinstance(fs, HTTPFileSystem) and data_file.startswith(config.HF_ENDPOINT):
        hffs = HfFileSystem(endpoint=config.HF_ENDPOINT, token=download_config.token)
        data_file = "hf://" + data_file[len(config.HF_ENDPOINT) + 1 :].replace("/resolve/", "@", 1)
        resolved_path = hffs.resolve_path(data_file)
        return (resolved_path.repo_id, resolved_path.revision)
    info = fs.info(data_file)
    # s3fs uses "ETag", gcsfs uses "etag", and for local we simply check mtime
    for key in ["ETag", "etag", "mtime"]:
        if key in info:
            return (str(info[key]),)
    return ()


def _get_origin_metadata(
    data_files: List[str],
    max_workers=64,
    download_config: Optional[DownloadConfig] = None,
) -> Tuple[str]:
    return thread_map(
        partial(_get_single_origin_metadata, download_config=download_config),
        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[str]):
    """
    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[str], origin_metadata: List[Tuple[str]]):
        super().__init__(data_files)
        self.origin_metadata = origin_metadata

    def __add__(self, other):
        return DataFilesList([*self, *other], self.origin_metadata + other.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,
        download_config: Optional[DownloadConfig] = None,
    ) -> "DataFilesList":
        base_path = f"hf://datasets/{dataset_info.id}@{dataset_info.sha}/{base_path or ''}".rstrip("/")
        return cls.from_patterns(
            patterns, base_path=base_path, allowed_extensions=allowed_extensions, download_config=download_config
        )

    @classmethod
    def from_local_or_remote(
        cls,
        patterns: List[str],
        base_path: Optional[str] = None,
        allowed_extensions: Optional[List[str]] = None,
        download_config: Optional[DownloadConfig] = None,
    ) -> "DataFilesList":
        base_path = base_path if base_path is not None else Path().resolve().as_posix()
        return cls.from_patterns(
            patterns, base_path=base_path, allowed_extensions=allowed_extensions, download_config=download_config
        )

    @classmethod
    def from_patterns(
        cls,
        patterns: List[str],
        base_path: Optional[str] = None,
        allowed_extensions: Optional[List[str]] = None,
        download_config: Optional[DownloadConfig] = None,
    ) -> "DataFilesList":
        base_path = base_path if base_path is not None else Path().resolve().as_posix()
        data_files = []
        for pattern in patterns:
            try:
                data_files.extend(
                    resolve_pattern(
                        pattern,
                        base_path=base_path,
                        allowed_extensions=allowed_extensions,
                        download_config=download_config,
                    )
                )
            except FileNotFoundError:
                pass
        origin_metadata = _get_origin_metadata(data_files, download_config=download_config)
        return cls(data_files, origin_metadata)

    def filter_extensions(self, extensions: List[str]) -> "DataFilesList":
        pattern = "|".join("\\" + ext for ext in extensions)
        pattern = re.compile(f".*({pattern})(\\..+)?$")
        return DataFilesList(
            [data_file for data_file in self if pattern.match(data_file)],
            origin_metadata=self.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,
        download_config: Optional[DownloadConfig] = 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,
                    download_config=download_config,
                )
                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,
        download_config: Optional[DownloadConfig] = 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,
                    download_config=download_config,
                )
                if not isinstance(patterns_for_key, DataFilesList)
                else patterns_for_key
            )
        return out

    @classmethod
    def from_patterns(
        cls,
        patterns: Dict[str, Union[List[str], DataFilesList]],
        base_path: Optional[str] = None,
        allowed_extensions: Optional[List[str]] = None,
        download_config: Optional[DownloadConfig] = None,
    ) -> "DataFilesDict":
        out = cls()
        for key, patterns_for_key in patterns.items():
            out[key] = (
                DataFilesList.from_patterns(
                    patterns_for_key,
                    base_path=base_path,
                    allowed_extensions=allowed_extensions,
                    download_config=download_config,
                )
                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())),)

    def filter_extensions(self, extensions: List[str]) -> "DataFilesDict":
        out = type(self)()
        for key, data_files_list in self.items():
            out[key] = data_files_list.filter_extensions(extensions)
        return out
