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Source code for torchtext.datasets.iwslt2017

import os
from torchtext.utils import (download_from_url, extract_archive)
from torchtext.data.datasets_utils import (
    _RawTextIterableDataset,
    _wrap_split_argument,
    _clean_xml_file,
    _clean_tags_file,
    _read_text_iterator,
)
from torchtext.data.datasets_utils import _create_dataset_directory

SUPPORTED_DATASETS = {

    'URL': 'https://drive.google.com/u/0/uc?id=12ycYSzLIG253AFN35Y6qoyf9wtkOjakp',
    '_PATH': '2017-01-trnmted.tgz',
    'MD5': 'aca701032b1c4411afc4d9fa367796ba',
    'valid_test': ['dev2010', 'tst2010'],
    'language_pair': {
        'en': ['nl', 'de', 'it', 'ro'],
        'ro': ['de', 'en', 'nl', 'it'],
        'de': ['ro', 'en', 'nl', 'it'],
        'it': ['en', 'nl', 'de', 'ro'],
        'nl': ['de', 'en', 'it', 'ro'],
    },
    'year': 17,
}

URL = SUPPORTED_DATASETS['URL']
MD5 = SUPPORTED_DATASETS['MD5']

NUM_LINES = {
    'train': {
        'train': {
            ('en', 'nl'): 237240,
            ('de', 'en'): 206112,
            ('en', 'it'): 231619,
            ('en', 'ro'): 220538,
            ('de', 'ro'): 201455,
            ('nl', 'ro'): 206920,
            ('it', 'ro'): 217551,
            ('de', 'nl'): 213628,
            ('de', 'it'): 205465,
            ('it', 'nl'): 233415
        }
    },
    'valid': {
        'dev2010': {
            ('en', 'nl'): 1003,
            ('de', 'en'): 888,
            ('en', 'it'): 929,
            ('en', 'ro'): 914,
            ('de', 'ro'): 912,
            ('nl', 'ro'): 913,
            ('it', 'ro'): 914,
            ('de', 'nl'): 1001,
            ('de', 'it'): 923,
            ('it', 'nl'): 1001
        },
        'tst2010': {
            ('en', 'nl'): 1777,
            ('de', 'en'): 1568,
            ('en', 'it'): 1566,
            ('en', 'ro'): 1678,
            ('de', 'ro'): 1677,
            ('nl', 'ro'): 1680,
            ('it', 'ro'): 1643,
            ('de', 'nl'): 1779,
            ('de', 'it'): 1567,
            ('it', 'nl'): 1669
        }
    },
    'test': {
        'dev2010': {
            ('en', 'nl'): 1003,
            ('de', 'en'): 888,
            ('en', 'it'): 929,
            ('en', 'ro'): 914,
            ('de', 'ro'): 912,
            ('nl', 'ro'): 913,
            ('it', 'ro'): 914,
            ('de', 'nl'): 1001,
            ('de', 'it'): 923,
            ('it', 'nl'): 1001
        },
        'tst2010': {
            ('en', 'nl'): 1777,
            ('de', 'en'): 1568,
            ('en', 'it'): 1566,
            ('en', 'ro'): 1678,
            ('de', 'ro'): 1677,
            ('nl', 'ro'): 1680,
            ('it', 'ro'): 1643,
            ('de', 'nl'): 1779,
            ('de', 'it'): 1567,
            ('it', 'nl'): 1669
        }
    }
}


def _construct_filenames(filename, languages):
    filenames = []
    for lang in languages:
        filenames.append(filename + "." + lang)
    return filenames


def _construct_filepaths(paths, src_filename, tgt_filename):
    src_path = None
    tgt_path = None
    for p in paths:
        src_path = p if src_filename in p else src_path
        tgt_path = p if tgt_filename in p else tgt_path
    return (src_path, tgt_path)


DATASET_NAME = "IWSLT2017"


[docs]@_create_dataset_directory(dataset_name=DATASET_NAME) @_wrap_split_argument(('train', 'valid', 'test')) def IWSLT2017(root='.data', split=('train', 'valid', 'test'), language_pair=('de', 'en')): """IWSLT2017 dataset The available datasets include following: **Language pairs**: +-----+-----+-----+-----+-----+-----+ | |'en' |'nl' |'de' |'it' |'ro' | +-----+-----+-----+-----+-----+-----+ |'en' | | x | x | x | x | +-----+-----+-----+-----+-----+-----+ |'nl' | x | | x | x | x | +-----+-----+-----+-----+-----+-----+ |'de' | x | x | | x | x | +-----+-----+-----+-----+-----+-----+ |'it' | x | x | x | | x | +-----+-----+-----+-----+-----+-----+ |'ro' | x | x | x | x | | +-----+-----+-----+-----+-----+-----+ For additional details refer to source website: https://wit3.fbk.eu/2017-01 Args: root: Directory where the datasets are saved. Default: ".data" 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 Examples: >>> from torchtext.datasets import IWSLT2017 >>> train_iter, valid_iter, test_iter = IWSLT2017() >>> src_sentence, tgt_sentence = next(train_iter) """ valid_set = 'dev2010' test_set = 'tst2010' num_lines_set_identifier = { 'train': 'train', 'valid': valid_set, 'test': test_set } if not isinstance(language_pair, list) and not isinstance(language_pair, tuple): raise ValueError("language_pair must be list or tuple but got {} instead".format(type(language_pair))) assert (len(language_pair) == 2), 'language_pair must contain only 2 elements: src and tgt language respectively' src_language, tgt_language = language_pair[0], language_pair[1] if src_language not in SUPPORTED_DATASETS['language_pair']: raise ValueError("src_language '{}' is not valid. Supported source languages are {}". format(src_language, list(SUPPORTED_DATASETS['language_pair']))) if tgt_language not in SUPPORTED_DATASETS['language_pair'][src_language]: raise ValueError("tgt_language '{}' is not valid for give src_language '{}'. Supported target language are {}". format(tgt_language, src_language, SUPPORTED_DATASETS['language_pair'][src_language])) train_filenames = ('train.{}-{}.{}'.format(src_language, tgt_language, src_language), 'train.{}-{}.{}'.format(src_language, tgt_language, tgt_language)) valid_filenames = ('IWSLT{}.TED.{}.{}-{}.{}'.format(SUPPORTED_DATASETS['year'], valid_set, src_language, tgt_language, src_language), 'IWSLT{}.TED.{}.{}-{}.{}'.format(SUPPORTED_DATASETS['year'], valid_set, src_language, tgt_language, tgt_language)) test_filenames = ('IWSLT{}.TED.{}.{}-{}.{}'.format(SUPPORTED_DATASETS['year'], test_set, src_language, tgt_language, src_language), 'IWSLT{}.TED.{}.{}-{}.{}'.format(SUPPORTED_DATASETS['year'], test_set, src_language, tgt_language, tgt_language)) src_train, tgt_train = train_filenames src_eval, tgt_eval = valid_filenames src_test, tgt_test = test_filenames extracted_files = [] # list of paths to the extracted files dataset_tar = download_from_url(SUPPORTED_DATASETS['URL'], root=root, hash_value=SUPPORTED_DATASETS['MD5'], path=os.path.join(root, SUPPORTED_DATASETS['_PATH']), hash_type='md5') extracted_dataset_tar = extract_archive(dataset_tar) # IWSLT dataset's url downloads a multilingual tgz. # We need to take an extra step to pick out the specific language pair from it. src_language = train_filenames[0].split(".")[-1] tgt_language = train_filenames[1].split(".")[-1] iwslt_tar = os.path.join(root, SUPPORTED_DATASETS['_PATH'].split(".")[0], 'texts/DeEnItNlRo/DeEnItNlRo', 'DeEnItNlRo-DeEnItNlRo.tgz') extracted_dataset_tar = extract_archive(iwslt_tar) extracted_files.extend(extracted_dataset_tar) # Clean the xml and tag file in the archives file_archives = [] for fname in extracted_files: if 'xml' in fname: _clean_xml_file(fname) file_archives.append(os.path.splitext(fname)[0]) elif "tags" in fname: _clean_tags_file(fname) file_archives.append(fname.replace('.tags', '')) else: file_archives.append(fname) data_filenames = { "train": _construct_filepaths(file_archives, src_train, tgt_train), "valid": _construct_filepaths(file_archives, src_eval, tgt_eval), "test": _construct_filepaths(file_archives, src_test, tgt_test) } for key in data_filenames: if len(data_filenames[key]) == 0 or data_filenames[key] is None: raise FileNotFoundError( "Files are not found for data type {}".format(key)) src_data_iter = _read_text_iterator(data_filenames[split][0]) tgt_data_iter = _read_text_iterator(data_filenames[split][1]) def _iter(src_data_iter, tgt_data_iter): for item in zip(src_data_iter, tgt_data_iter): yield item return _RawTextIterableDataset(DATASET_NAME, NUM_LINES[split][num_lines_set_identifier[split]][tuple(sorted(language_pair))], _iter(src_data_iter, tgt_data_iter))

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