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# Lint as: python3
"""WebQuestions Benchmark for Question Answering."""


import json
import re

import datasets


_CITATION = """
@inproceedings{berant-etal-2013-semantic,
    title = "Semantic Parsing on {F}reebase from Question-Answer Pairs",
    author = "Berant, Jonathan  and
      Chou, Andrew  and
      Frostig, Roy  and
      Liang, Percy",
    booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
    month = oct,
    year = "2013",
    address = "Seattle, Washington, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D13-1160",
    pages = "1533--1544",
}
"""
_SPLIT_DOWNLOAD_URL = {
    "train": "https://worksheets.codalab.org/rest/bundles/0x4a763f8cde224c2da592b75f29e2f5c2/contents/blob/",
    "test": "https://worksheets.codalab.org/rest/bundles/0xe7bac352fce7448c9ef238fb0a297ec2/contents/blob/",
}

_DESCRIPTION = """\
This dataset consists of 6,642 question/answer pairs.
The questions are supposed to be answerable by Freebase, a large knowledge graph.
The questions are mostly centered around a single named entity.
The questions are popular ones asked on the web (at least in 2013).
"""


class WebQuestions(datasets.GeneratorBasedBuilder):
    """WebQuestions Benchmark for Question Answering."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "url": datasets.Value("string"),
                    "question": datasets.Value("string"),
                    "answers": datasets.features.Sequence(datasets.Value("string")),
                }
            ),
            supervised_keys=None,
            homepage="https://worksheets.codalab.org/worksheets/0xba659fe363cb46e7a505c5b6a774dc8a",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        file_paths = dl_manager.download(_SPLIT_DOWNLOAD_URL)

        return [
            datasets.SplitGenerator(name=split, gen_kwargs={"file_path": file_path})
            for split, file_path in file_paths.items()
        ]

    def _generate_examples(self, file_path):
        """Parses split file and yields examples."""

        def _target_to_answers(target):
            target = re.sub(r"^\(list |\)$", "", target)
            return ["".join(ans) for ans in re.findall(r'\(description (?:"([^"]+?)"|([^)]+?))\)\w*', target)]

        with open(file_path, encoding="utf-8") as f:
            examples = json.load(f)
            for i, ex in enumerate(examples):
                yield i, {
                    "url": ex["url"],
                    "question": ex["utterance"],
                    "answers": _target_to_answers(ex["targetValue"]),
                }