# coding=utf-8
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# 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
"""The Stories CC dataset."""

import datasets


_DESCRIPTION = """\
CC-Stories (or STORIES) is a dataset for common sense reasoning and language modeling. It was constructed by aggregating documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions in commonsense reasoning tasks. The top 1.0% of highest ranked documents is chosen as the new training corpus.
"""

_CITATION = """\
@article{Trinh2018ASM,
  title={A Simple Method for Commonsense Reasoning},
  author={Trieu H. Trinh and Quoc V. Le},
  journal={ArXiv},
  year={2018},
  volume={abs/1806.02847}
}
"""


URL = "https://huggingface.co/datasets/spacemanidol/cc-stories/resolve/main/cc-stories.txt.gz"

_DATASET_URLS = {
    'train':  "https://huggingface.co/datasets/spacemanidol/cc-stories/resolve/main/cc-stories.txt",
}

class CCStoriesConfig(datasets.BuilderConfig):
    """BuilderConfig for CC Stories."""

    def __init__(self, **kwargs):
        """BuilderConfig for CC Stories
        Args:
        **kwargs: keyword arguments forwarded to super.
        """
        super(CCStoriesConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


class Bookcorpus(datasets.GeneratorBasedBuilder):
    """CC Stories dataset."""

    BUILDER_CONFIGS = [
        CCStoriesConfig(
            name="plain_text",
            description="Plain text",
        )
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage="",
            citation=_CITATION,
        )

    def _vocab_text_gen(self, archive):
        for _, ex in self._generate_examples(archive):
            yield ex["text"]
        
    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
        splits = [
            datasets.SplitGenerator(
                name=split,
                gen_kwargs={
                    "files": [downloaded_files[split]] if isinstance(downloaded_files[split], str) else downloaded_files[split],
                },
            ) for split in downloaded_files
        ]
        return splits

    def _generate_examples(self, files):
        _id = 0
        for path, file in files:
            for line in file:
                yield _id, {"text": line.decode("utf-8").strip()}
                _id += 1