prep/ds_script.py

# 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
from urllib.parse import quote

import datasets

from .category_aliases import CATEGORY_ALIASES
from .lang_def import WIKIPEDIA_LANGUAGES
from .media_aliases import MEDIA_ALIASES

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."
)

# List of mirrors at https://dumps.wikimedia.org/mirrors.html
# - default mirror: https://dumps.wikimedia.org
#   yields https://dumps.wikimedia.org/enwiki/20220301/
# - example: https://ftp.acc.umu.se/mirror/wikimedia.org/dumps/
#   yields https://ftp.acc.umu.se/mirror/wikimedia.org/dumps/enwiki/20220301/
_BASE_URL_TMPL = "{mirror_url}/{lang}wiki/{date}/"
_INFO_FILE = "dumpstatus.json"


_VERSION = datasets.Version("2.0.0", "")


class WikipediaConfig(datasets.BuilderConfig):
    """BuilderConfig for Wikipedia."""

    def __init__(
        self,
        language=None,
        date=None,
        mirror_url="https://dumps.wikimedia.org",
        version=_VERSION,
        **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().__init__(
            name=f"{date}.{language}",
            description=f"Wikipedia dataset for {language}, parsed from {date} dump.",
            version=version,
            **kwargs,
        )
        self.date = date
        self.language = language
        self.mirror_url = mirror_url.rstrip("/")


# _DATE = "20220301"


class Wikipedia(datasets.BeamBasedBuilder):
    """Wikipedia dataset."""

    # Use mirror (your.org) to avoid download caps.
    BUILDER_CONFIG_CLASS = WikipediaConfig
    BUILDER_CONFIGS = [
        WikipediaConfig(
            language=lang,
            date="some future date",
        )  # pylint:disable=g-complex-comprehension
        for lang in WIKIPEDIA_LANGUAGES
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "url": datasets.Value("string"),
                    "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,
                mirror_url=self.config.mirror_url,
            )

        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
                    red_ = elem.find(f"./{namespace}redirect")

                    # 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 red_ is not None:
                        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, language):
            """Cleans raw wikicode to extract text."""
            id_, title, raw_content = inputs
            try:
                text = _parse_and_clean_wikicode(
                    raw_content, parser=mwparserfromhell, language=language
                )
            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

            url = _construct_url(title, language)

            beam.metrics.Metrics.counter(language, "cleaned-examples").inc()

            yield id_, {"id": id_, "url": url, "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, language=language)
        )


def _parse_and_clean_wikicode(raw_content, parser, language):
    """Strips formatting and unwanted sections from raw page content."""
    wikicode = parser.parse(raw_content)

    # Filters for magic words that are parser instructions -- e.g., __NOTOC__
    re_rm_magic = re.compile("__[A-Z]*__", flags=re.UNICODE)

    # Filters for file/image links.
    media_prefixes = "|".join(
        ["File", "Image", "Media"] + MEDIA_ALIASES.get(language, [])
    )
    re_rm_wikilink = re.compile(
        f"^(?:{media_prefixes}):", flags=re.IGNORECASE | re.UNICODE
    )

    def rm_wikilink(obj):
        return bool(re_rm_wikilink.match(str(obj.title)))

    # Filters for references and tables
    def rm_tag(obj):
        return str(obj.tag) in {"ref", "table"}

    # Leave category links in-place but remove the category prefixes
    cat_prefixes = "|".join(["Category"] + CATEGORY_ALIASES.get(language, []))
    re_clean_wikilink = re.compile(
        f"^(?:{cat_prefixes}):", flags=re.IGNORECASE | re.UNICODE
    )

    def is_category(obj):
        return bool(re_clean_wikilink.match(str(obj.title)))

    def clean_wikilink(obj):
        text = obj.__strip__()
        text = re.sub(re_clean_wikilink, "", text)
        obj.text = text

    def try_replace_obj(obj):
        try:
            clean_wikilink(obj)
        except ValueError:
            # For unknown reasons, objects are sometimes not found.
            pass

    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(recursive=True):
            if rm_wikilink(obj):
                try_remove_obj(obj, section)
            elif is_category(obj):
                try_replace_obj(obj)
        for obj in section.ifilter_tags(matches=rm_tag, recursive=True):
            try_remove_obj(obj, section)

        section_text.append(re.sub(re_rm_magic, "", section.strip_code().strip()))
    return "\n\n".join(section_text)


def _construct_url(title, language):
    # See: https://meta.wikimedia.org/wiki/Help:URL
    return f"https://{language}.wikipedia.org/wiki/{quote(title)}"