# 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)