wikipedia_html_enterprise.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 mwparserfromhell
from multiprocess import Process, Manager
from tqdm import tqdm
import multiprocessing
import datasets
from functools import partial
from pathlib import Path

logger = datasets.logging.get_logger(__name__)


_CITATION = """"""

_DESCRIPTION = """"""

_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."
)

_INFO_FILE = "dumpstatus.json"


_VERSION = datasets.Version("2.0.0", "")
_NUM_SPLITS = 68

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

    def __init__(self, shard=None, version=_VERSION, **kwargs):
        """BuilderConfig for Wikipedia.

        Args:
          split: int, split number.
          **kwargs: keyword arguments forwarded to super.
        """
        super().__init__(
            name=f"shard_{shard}",
            description=f"Wikipedia dataset for split {shard}",
            version=version,
            **kwargs,
        )
        self.shard = shard
        print(f"Split: {self.shard}")


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

    # Use mirror (your.org) to avoid download caps.
    BUILDER_CONFIG_CLASS = WikipediaConfig
    BUILDER_CONFIG = [WikipediaConfig(shard=str(id)) for id in range(_NUM_SPLITS)]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "identifier": datasets.Value("string"),
                    "name": datasets.Value("string"),
                    "namespace_name": datasets.Value("string"),
                    "namespace_identifier": datasets.Value("string"),
                    "categories": [
                        {
                            "name": datasets.Value("string"),
                            "url": datasets.Value("string"),
                        }
                    ],
                    "date_modified": datasets.Value("string"),
                    "url": datasets.Value("string"),
                    "html": datasets.Value("string"),
                    "wikitext": datasets.Value("string"),
                    "in_language": datasets.Value("string"),
                    "main_entity": {
                        "identifier": datasets.Value("string"),
                        "url": datasets.Value("string"),
                    },
                    "is_part_of"   : {
                        "name": datasets.Value("string"),
                        "identifier": datasets.Value("string"),
                    },
                    "license":[ {
                        "name": datasets.Value("string"),
                        "url": datasets.Value("string"),
                        "identifier": datasets.Value("string"),
                    }]
                }
            ),
            # No default supervised_keys.
            supervised_keys=None,
            homepage="https://dumps.wikimedia.org",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_paths = [ 
            Path(self.config.data_dir) / f"enwiki_{self.config.shard}.ndjson"
        ]
        return [
            datasets.SplitGenerator(  # pylint:disable=g-complex-comprehension
                name=datasets.Split.TRAIN, gen_kwargs={"filepaths": data_paths}
            )
        ]

    def _generate_examples(self, filepaths, ):
          

        print("Parsing and cleaning Wikipedia examples")

        for filepath in filepaths:
            with open(filepath, 'r') as f:
                for line in tqdm(f):
                    example = json.loads(line)
                    clean_example = {}
                    clean_example['name'] = example['name']
                    clean_example['identifier'] = example['identifier']
                    clean_example['date_modified'] = example['date_modified']
                    clean_example['namespace_name'] = example['namespace']["name"]
                    clean_example['namespace_identifier'] = example['namespace']["identifier"]
                    clean_example["categories"] = example.get("categories", None)
                    clean_example['url'] = example['url']
                    clean_example['html'] = f'{example["article_body"]["html"]}'
                    clean_example['wikitext'] = example['article_body']['wikitext']
                    clean_example['in_language'] = example['in_language']
                    clean_example['main_entity'] = example.get('main_entity', None)
                    clean_example['is_part_of'] = example['is_part_of']
                    clean_example['license'] = example['license']
                    yield clean_example['identifier'], clean_example
        # num_processes = 16
        # with multiprocessing.Pool(processes=num_processes) as pool:

        #     results = pool.imap_unordered(partial(parse_and_clean), filepaths)
        #     for result in results:
        #         for example in result:
        #             yield example

def parse_and_clean(filepath):
    examples = []
    with open(filepath, 'r') as f:
        for line in tqdm(f):
            example = json.loads(line)
            clean_example = {}
            clean_example['id'] = example['identifier']
            clean_example['date_modified'] = example['date_modified']
            clean_example['url'] = example['url']
            clean_example['html'] = f'{example["article_body"]["html"]}'
            clean_example['wikitext'] = example['article_body']['wikitext']
            
            examples.append(clean_example)
    return examples