# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""(SC)^2QA: Self-Contained Summary-Centric QA Dataset.
This dataset (https://huggingface.co/datasets/sc2qa/sc2q_commoncrawl_large) contains 529,039 question and article pairs.
If you want {Question, Article, Summary, Length Constraint} 4-tuples, please load sc2qa_commoncrawl (https://huggingface.co/datasets/sc2qa/sc2qa_commoncrawl)
"""

import csv

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@article{zhou2021generating,
       author = {Li Zhou, Kevin Small, Yong Zhang, Sandeep Atluri},
       title = "{Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning}",
       conference = {The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021)},
       year = 2021,
}
"""

_DESCRIPTION = """\
"""

_URLS = {
        "train":"https://huggingface.co/datasets/sc2qa/sc2q_commoncrawl_large/resolve/main/train.csv",
        "val":"https://huggingface.co/datasets/sc2qa/sc2q_commoncrawl_large/resolve/main/val.csv",
        "test":"https://huggingface.co/datasets/sc2qa/sc2q_commoncrawl_large/resolve/main/test.csv",
}

class SC2QAConfig(datasets.BuilderConfig):
    """BuilderConfig for (SC)^2QA."""

    def __init__(self, **kwargs):
        """BuilderConfig for (SC)^2QA.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(SC2QAConfig, self).__init__(**kwargs)


class SC2QA(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        SC2QAConfig(
            name="plain_text",
            version=datasets.Version("1.0.0", ""),
            description="Plain text",
        ),
    ]

    def _info(self):
        # Should return a datasets.DatasetInfo object
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "question": datasets.Value("string"),
                    "article": datasets.Value("string"),
                    "url": datasets.Value("string"),
                }
            ),
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract(_URLS)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["val"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)
        key = 0
        with open(filepath, encoding="ascii", errors='ignore') as f:
            csv_reader = csv.DictReader(f)
            for i, row in enumerate(csv_reader):
                yield i, row