Source code for torchtext.datasets.wikitext103

import os
from functools import partial
from typing import Union, Tuple

from torchdata.datapipes.iter import FileOpener, IterableWrapper
from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
    _wrap_split_argument,
    _create_dataset_directory,
)

URL = "https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip"

MD5 = "9ddaacaf6af0710eda8c456decff7832"

NUM_LINES = {
    "train": 1801350,
    "valid": 3760,
    "test": 4358,
}

DATASET_NAME = "WikiText103"

_EXTRACTED_FILES = {
    "train": os.path.join("wikitext-103", "wiki.train.tokens"),
    "test": os.path.join("wikitext-103", "wiki.test.tokens"),
    "valid": os.path.join("wikitext-103", "wiki.valid.tokens"),
}


def _filepath_fn(root, _=None):
    return os.path.join(root, os.path.basename(URL))


def _extracted_filepath_fn(root, split, _=None):
    return os.path.join(root, _EXTRACTED_FILES[split])


def _filter_fn(split, x):
    return _EXTRACTED_FILES[split] in x[0]


@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(("train", "valid", "test"))
def WikiText103(root: str, split: Union[Tuple[str], str]):
    """WikiText103 Dataset

    .. warning::

        using datapipes is still currently subject to a few caveats. if you wish
        to use this dataset with shuffling, multi-processing, or distributed
        learning, please see :ref:`this note <datapipes_warnings>` for further
        instructions.

    For additional details refer to https://blog.salesforceairesearch.com/the-wikitext-long-term-dependency-language-modeling-dataset/

    Number of lines per split:
        - train: 1801350
        - valid: 3760
        - test: 4358

    Args:
        root: Directory where the datasets are saved. Default: os.path.expanduser('~/.torchtext/cache')
        split: split or splits to be returned. Can be a string or tuple of strings. Default: (`train`, `valid`, `test`)

    :returns: DataPipe that yields text from Wikipedia articles
    :rtype: str
    """
    if not is_module_available("torchdata"):
        raise ModuleNotFoundError(
            "Package `torchdata` not found. Please install following instructions at https://github.com/pytorch/data"
        )

    url_dp = IterableWrapper([URL])
    # cache data on-disk
    cache_compressed_dp = url_dp.on_disk_cache(
        filepath_fn=partial(_filepath_fn, root),
        hash_dict={_filepath_fn(root): MD5},
        hash_type="md5",
    )
    cache_compressed_dp = HttpReader(cache_compressed_dp).end_caching(mode="wb", same_filepath_fn=True)
    cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=partial(_extracted_filepath_fn, root, split))
    # Extract zip and filter the appropriate split file
    cache_decompressed_dp = (
        FileOpener(cache_decompressed_dp, mode="b").load_from_zip().filter(partial(_filter_fn, split))
    )
    cache_decompressed_dp = cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True)
    data_dp = FileOpener(cache_decompressed_dp, encoding="utf-8")
    return data_dp.readlines(strip_newline=False, return_path=False).shuffle().set_shuffle(False).sharding_filter()