# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Wikipedia dataset containing cleaned articles of all languages."""
import bz2
import codecs
import json
import re
import xml.etree.cElementTree as etree
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@ONLINE {wikidump,
author = {Wikimedia Foundation},
title = {Wikimedia Downloads},
url = {https://dumps.wikimedia.org}
}
"""
_DESCRIPTION = """\
Wikipedia dataset containing cleaned articles of all languages.
The datasets are built from the Wikipedia dump
(https://dumps.wikimedia.org/) with one split per language. Each example
contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
"""
_LICENSE = (
"This work is licensed under the Creative Commons Attribution-ShareAlike "
"3.0 Unported License. To view a copy of this license, visit "
"http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to "
"Creative Commons, PO Box 1866, Mountain View, CA 94042, USA."
)
# Source: https://en.wikipedia.org/wiki/List_of_Wikipedias (accessed 3/1/2019)
# Removed because no articles: hz.
WIKIPEDIA_LANGUAGES = [
"aa",
"ab",
"ace",
"ady",
"af",
"ak",
"als",
"am",
"an",
"ang",
"ar",
"arc",
"arz",
"as",
"ast",
"atj",
"av",
"ay",
"az",
"azb",
"ba",
"bar",
"bat-smg",
"bcl",
"be",
"be-x-old",
"bg",
"bh",
"bi",
"bjn",
"bm",
"bn",
"bo",
"bpy",
"br",
"bs",
"bug",
"bxr",
"ca",
"cbk-zam",
"cdo",
"ce",
"ceb",
"ch",
"cho",
"chr",
"chy",
"ckb",
"co",
"cr",
"crh",
"cs",
"csb",
"cu",
"cv",
"cy",
"da",
"de",
"din",
"diq",
"dsb",
"dty",
"dv",
"dz",
"ee",
"el",
"eml",
"en",
"eo",
"es",
"et",
"eu",
"ext",
"fa",
"ff",
"fi",
"fiu-vro",
"fj",
"fo",
"fr",
"frp",
"frr",
"fur",
"fy",
"ga",
"gag",
"gan",
"gd",
"gl",
"glk",
"gn",
"gom",
"gor",
"got",
"gu",
"gv",
"ha",
"hak",
"haw",
"he",
"hi",
"hif",
"ho",
"hr",
"hsb",
"ht",
"hu",
"hy",
"ia",
"id",
"ie",
"ig",
"ii",
"ik",
"ilo",
"inh",
"io",
"is",
"it",
"iu",
"ja",
"jam",
"jbo",
"jv",
"ka",
"kaa",
"kab",
"kbd",
"kbp",
"kg",
"ki",
"kj",
"kk",
"kl",
"km",
"kn",
"ko",
"koi",
"krc",
"ks",
"ksh",
"ku",
"kv",
"kw",
"ky",
"la",
"lad",
"lb",
"lbe",
"lez",
"lfn",
"lg",
"li",
"lij",
"lmo",
"ln",
"lo",
"lrc",
"lt",
"ltg",
"lv",
"mai",
"map-bms",
"mdf",
"mg",
"mh",
"mhr",
"mi",
"min",
"mk",
"ml",
"mn",
"mr",
"mrj",
"ms",
"mt",
"mus",
"mwl",
"my",
"myv",
"mzn",
"na",
"nah",
"nap",
"nds",
"nds-nl",
"ne",
"new",
"ng",
"nl",
"nn",
"no",
"nov",
"nrm",
"nso",
"nv",
"ny",
"oc",
"olo",
"om",
"or",
"os",
"pa",
"pag",
"pam",
"pap",
"pcd",
"pdc",
"pfl",
"pi",
"pih",
"pl",
"pms",
"pnb",
"pnt",
"ps",
"pt",
"qu",
"rm",
"rmy",
"rn",
"ro",
"roa-rup",
"roa-tara",
"ru",
"rue",
"rw",
"sa",
"sah",
"sat",
"sc",
"scn",
"sco",
"sd",
"se",
"sg",
"sh",
"si",
"simple",
"sk",
"sl",
"sm",
"sn",
"so",
"sq",
"sr",
"srn",
"ss",
"st",
"stq",
"su",
"sv",
"sw",
"szl",
"ta",
"tcy",
"te",
"tet",
"tg",
"th",
"ti",
"tk",
"tl",
"tn",
"to",
"tpi",
"tr",
"ts",
"tt",
"tum",
"tw",
"ty",
"tyv",
"udm",
"ug",
"uk",
"ur",
"uz",
"ve",
"vec",
"vep",
"vi",
"vls",
"vo",
"wa",
"war",
"wo",
"wuu",
"xal",
"xh",
"xmf",
"yi",
"yo",
"za",
"zea",
"zh",
"zh-classical",
"zh-min-nan",
"zh-yue",
"zu",
]
_BASE_URL_TMPL = "https://dumps.wikimedia.org/{lang}wiki/{date}/"
_INFO_FILE = "dumpstatus.json"
class WikipediaConfig(datasets.BuilderConfig):
"""BuilderConfig for Wikipedia."""
def __init__(self, language=None, date=None, **kwargs):
"""BuilderConfig for Wikipedia.
Args:
language: string, the language code for the Wikipedia dump to use.
date: string, date of the Wikipedia dump in YYYYMMDD format. A list of
available dates can be found at https://dumps.wikimedia.org/enwiki/.
**kwargs: keyword arguments forwarded to super.
"""
super(WikipediaConfig, self).__init__(
name=f"{date}.{language}",
description=f"Wikipedia dataset for {language}, parsed from {date} dump.",
**kwargs,
)
self.date = date
self.language = language
_VERSION = datasets.Version("1.0.0", "")
class Wikipedia(datasets.BeamBasedBuilder):
"""Wikipedia dataset."""
# Use mirror (your.org) to avoid download caps.
BUILDER_CONFIG_CLASS = WikipediaConfig
BUILDER_CONFIGS = [
WikipediaConfig(
version=_VERSION,
language=lang,
date="20200501",
) # pylint:disable=g-complex-comprehension
for lang in WIKIPEDIA_LANGUAGES
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({"title": datasets.Value("string"), "text": datasets.Value("string")}),
# No default supervised_keys.
supervised_keys=None,
homepage="https://dumps.wikimedia.org",
citation=_CITATION,
)
def _split_generators(self, dl_manager, pipeline):
def _base_url(lang):
return _BASE_URL_TMPL.format(lang=lang.replace("-", "_"), date=self.config.date)
lang = self.config.language
info_url = _base_url(lang) + _INFO_FILE
# Use dictionary since testing mock always returns the same result.
downloaded_files = dl_manager.download_and_extract({"info": info_url})
xml_urls = []
total_bytes = 0
with open(downloaded_files["info"], encoding="utf-8") as f:
dump_info = json.load(f)
multistream_dump_info = dump_info["jobs"]["articlesmultistreamdump"]
assert (
multistream_dump_info["status"] == "done"
), "Specified dump (%s) multistream status is not 'done': %s" % (
_base_url(lang),
multistream_dump_info["status"],
)
for fname, info in multistream_dump_info["files"].items():
if ".xml" not in fname:
continue
total_bytes += info["size"]
xml_urls.append(_base_url(lang) + fname)
# Use dictionary since testing mock always returns the same result.
downloaded_files = dl_manager.download({"xml": xml_urls})
if not pipeline.is_local():
downloaded_files = dl_manager.ship_files_with_pipeline(downloaded_files, pipeline)
return [
datasets.SplitGenerator( # pylint:disable=g-complex-comprehension
name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files["xml"], "language": lang}
)
]
def _build_pcollection(self, pipeline, filepaths, language):
"""Build PCollection of examples in the raw (text) form."""
import apache_beam as beam
import mwparserfromhell
def _extract_content(filepath):
"""Extracts article content from a single WikiMedia XML file."""
logger.info("generating examples from = %s", filepath)
with beam.io.filesystems.FileSystems.open(filepath) as f:
f = bz2.BZ2File(filename=f)
# Workaround due to: https://github.com/tensorflow/tensorflow/issues/33563
utf_f = codecs.getreader("utf-8")(f)
context = etree.iterparse(utf_f, events=("end",))
for unused_event, elem in context:
if not elem.tag.endswith("page"):
continue
namespace = elem.tag[:-4]
title = elem.find(f"./{namespace}title").text
ns = elem.find(f"./{namespace}ns").text
id_ = elem.find(f"./{namespace}id").text
# Filter pages that are not in the "main" namespace.
if ns != "0":
elem.clear()
continue
raw_content = elem.find(f"./{namespace}revision/{namespace}text").text
elem.clear()
# Filter redirects.
if raw_content is None or raw_content.lower().startswith("#redirect"):
beam.metrics.Metrics.counter(language, "filtered-redirects").inc()
continue
beam.metrics.Metrics.counter(language, "extracted-examples").inc()
yield (id_, title, raw_content)
def _clean_content(inputs):
"""Cleans raw wikicode to extract text."""
id_, title, raw_content = inputs
try:
text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)
except (mwparserfromhell.parser.ParserError) as e:
beam.metrics.Metrics.counter(language, "parser-error").inc()
logger.error("mwparserfromhell ParseError: %s", e)
return
if not text:
beam.metrics.Metrics.counter(language, "empty-clean-examples").inc()
return
beam.metrics.Metrics.counter(language, "cleaned-examples").inc()
yield id_, {"title": title, "text": text}
return (
pipeline
| "Initialize" >> beam.Create(filepaths)
| "Extract content" >> beam.FlatMap(_extract_content)
| "Distribute" >> beam.transforms.Reshuffle()
| "Clean content" >> beam.FlatMap(_clean_content)
)
def _parse_and_clean_wikicode(raw_content, parser):
"""Strips formatting and unwanted sections from raw page content."""
wikicode = parser.parse(raw_content)
# Filters for references, tables, and file/image links.
re_rm_wikilink = re.compile("^(?:File|Image|Media):", flags=re.IGNORECASE | re.UNICODE)
def rm_wikilink(obj):
return bool(re_rm_wikilink.match(str(obj.title)))
def rm_tag(obj):
return str(obj.tag) in {"ref", "table"}
def rm_template(obj):
return obj.name.lower() in {"reflist", "notelist", "notelist-ua", "notelist-lr", "notelist-ur", "notelist-lg"}
def try_remove_obj(obj, section):
try:
section.remove(obj)
except ValueError:
# For unknown reasons, objects are sometimes not found.
pass
section_text = []
# Filter individual sections to clean.
for section in wikicode.get_sections(flat=True, include_lead=True, include_headings=True):
for obj in section.ifilter_wikilinks(matches=rm_wikilink, recursive=True):
try_remove_obj(obj, section)
for obj in section.ifilter_templates(matches=rm_template, recursive=True):
try_remove_obj(obj, section)
for obj in section.ifilter_tags(matches=rm_tag, recursive=True):
try_remove_obj(obj, section)
section_text.append(section.strip_code().strip())
return "\n\n".join(section_text)