Datasets:
wikipedia

	# 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."""

	from __future__ import absolute_import, division, print_function

	import codecs
	import json
	import logging
	import re
	import xml.etree.cElementTree as etree

	import six

	import datasets


	if six.PY3:
	import bz2 # pylint:disable=g-import-not-at-top
	else:
	# py2's built-in bz2 package does not support reading from file objects.
	import bz2file as bz2 # pylint:disable=g-import-not-at-top

	_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="{0}.{1}".format(date, language),
	description="Wikipedia dataset for {0}, parsed from {1} dump.".format(language, date),
	**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_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."""
	logging.info("generating examples from = %s", filepath)
	with beam.io.filesystems.FileSystems.open(filepath) as f:
	f = bz2.BZ2File(filename=f)
	if six.PY3:
	# Workaround due to:
	# https://github.com/tensorflow/tensorflow/issues/33563
	utf_f = codecs.getreader("utf-8")(f)
	else:
	utf_f = f

	# To clear root, to free-up more memory than just `elem.clear()`.
	context = etree.iterparse(utf_f, events=("end",))
	context = iter(context)
	unused_event, root = next(context)
	for unused_event, elem in context:
	if not elem.tag.endswith("page"):
	continue
	namespace = elem.tag[:-4]
	title = elem.find("./{0}title".format(namespace)).text
	ns = elem.find("./{0}ns".format(namespace)).text
	id_ = elem.find("./{0}id".format(namespace)).text

	# Filter pages that are not in the "main" namespace.
	if ns != "0":
	root.clear()
	continue

	raw_content = elem.find("./{0}revision/{0}text".format(namespace)).text
	root.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()
	logging.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(six.text_type(obj.title)))

	def rm_tag(obj):
	return six.text_type(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)