Source code for torchtext.datasets.multi30k

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

from torchtext._internal.module_utils import is_module_available
from import (

if is_module_available("torchdata"):
    from torchdata.datapipes.iter import FileOpener, IterableWrapper
    from torchtext._download_hooks import HttpReader

URL = {
    "train": r"",
    "valid": r"",
    "test": r"",

MD5 = {
    "train": "20140d013d05dd9a72dfde46478663ba05737ce983f478f960c1123c6671be5e",
    "valid": "a7aa20e9ebd5ba5adce7909498b94410996040857154dab029851af3a866da8c",
    "test": "0681be16a532912288a91ddd573594fbdd57c0fbb81486eff7c55247e35326c2",

    "train": "train",
    "valid": "val",
    "test": "test",

    "train": 29000,
    "valid": 1014,
    "test": 1000,

DATASET_NAME = "Multi30k"

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

def _decompressed_filepath_fn(root, split, language_pair, i, _):
    return os.path.join(root, f"{_PREFIX[split]}.{language_pair[i]}")

def _filter_fn(split, language_pair, i, x):
    return f"{_PREFIX[split]}.{language_pair[i]}" in x[0]

[docs]@_create_dataset_directory(dataset_name=DATASET_NAME) @_wrap_split_argument(("train", "valid", "test")) def Multi30k(root: str, split: Union[Tuple[str], str], language_pair: Tuple[str] = ("de", "en")): """Multi30k 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 Number of lines per split: - train: 29000 - valid: 1014 - test: 1000 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') language_pair: tuple or list containing src and tgt language. Available options are ('de','en') and ('en', 'de') :return: DataPipe that yields tuple of source and target sentences :rtype: (str, str) """ assert len(language_pair) == 2, "language_pair must contain only 2 elements: src and tgt language respectively" assert tuple(sorted(language_pair)) == ( "de", "en", ), "language_pair must be either ('de','en') or ('en', 'de')" if not is_module_available("torchdata"): raise ModuleNotFoundError( "Package `torchdata` not found. Please install following instructions at" ) url_dp = IterableWrapper([URL[split]]) cache_compressed_dp = url_dp.on_disk_cache( filepath_fn=partial(_filepath_fn, root, split), hash_dict={_filepath_fn(root, split): MD5[split]}, hash_type="sha256", ) cache_compressed_dp = HttpReader(cache_compressed_dp).end_caching(mode="wb", same_filepath_fn=True) cache_compressed_dp_1, cache_compressed_dp_2 = cache_compressed_dp.fork(num_instances=2) src_cache_decompressed_dp = cache_compressed_dp_1.on_disk_cache( filepath_fn=partial(_decompressed_filepath_fn, root, split, language_pair, 0) ) src_cache_decompressed_dp = ( FileOpener(src_cache_decompressed_dp, mode="b") .load_from_tar() .filter(partial(_filter_fn, split, language_pair, 0)) ) src_cache_decompressed_dp = src_cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True) tgt_cache_decompressed_dp = cache_compressed_dp_2.on_disk_cache( filepath_fn=partial(_decompressed_filepath_fn, root, split, language_pair, 1) ) tgt_cache_decompressed_dp = ( FileOpener(tgt_cache_decompressed_dp, mode="b") .load_from_tar() .filter(partial(_filter_fn, split, language_pair, 1)) ) tgt_cache_decompressed_dp = tgt_cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True) src_data_dp = FileOpener(src_cache_decompressed_dp, encoding="utf-8").readlines( return_path=False, strip_newline=True ) tgt_data_dp = FileOpener(tgt_cache_decompressed_dp, encoding="utf-8").readlines( return_path=False, strip_newline=True ) return


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