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# Lint as: python3
"""IMDB movie reviews dataset."""


import os

import datasets


_DESCRIPTION = """\
Large Movie Review Dataset.
This is a dataset for binary sentiment classification containing substantially \
more data than previous benchmark datasets. We provide a set of 25,000 highly \
polar movie reviews for training, and 25,000 for testing. There is additional \
unlabeled data for use as well.\
"""

_CITATION = """\
@InProceedings{maas-EtAl:2011:ACL-HLT2011,
  author    = {Maas, Andrew L.  and  Daly, Raymond E.  and  Pham, Peter T.  and  Huang, Dan  and  Ng, Andrew Y.  and  Potts, Christopher},
  title     = {Learning Word Vectors for Sentiment Analysis},
  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
  month     = {June},
  year      = {2011},
  address   = {Portland, Oregon, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {142--150},
  url       = {http://www.aclweb.org/anthology/P11-1015}
}
"""

_DOWNLOAD_URL = "http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"


class IMDBReviewsConfig(datasets.BuilderConfig):
    """BuilderConfig for IMDBReviews."""

    def __init__(self, **kwargs):
        """BuilderConfig for IMDBReviews.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(IMDBReviewsConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


class Imdb(datasets.GeneratorBasedBuilder):
    """IMDB movie reviews dataset."""

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

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["neg", "pos"])}
            ),
            supervised_keys=None,
            homepage="http://ai.stanford.edu/~amaas/data/sentiment/",
            citation=_CITATION,
        )

    def _vocab_text_gen(self, archive):
        for _, ex in self._generate_examples(archive, os.path.join("aclImdb", "train")):
            yield ex["text"]

    def _split_generators(self, dl_manager):
        arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
        data_dir = os.path.join(arch_path, "aclImdb")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"directory": os.path.join(data_dir, "train")}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"directory": os.path.join(data_dir, "test")}
            ),
            datasets.SplitGenerator(
                name=datasets.Split("unsupervised"),
                gen_kwargs={"directory": os.path.join(data_dir, "train"), "labeled": False},
            ),
        ]

    def _generate_examples(self, directory, labeled=True):
        """Generate IMDB examples."""
        # For labeled examples, extract the label from the path.
        if labeled:
            files = {
                "pos": sorted(os.listdir(os.path.join(directory, "pos"))),
                "neg": sorted(os.listdir(os.path.join(directory, "neg"))),
            }
            for key in files:
                for id_, file in enumerate(files[key]):
                    filepath = os.path.join(directory, key, file)
                    with open(filepath, encoding="UTF-8") as f:
                        yield key + "_" + str(id_), {"text": f.read(), "label": key}
        else:
            unsup_files = sorted(os.listdir(os.path.join(directory, "unsup")))
            for id_, file in enumerate(unsup_files):
                filepath = os.path.join(directory, "unsup", file)
                with open(filepath, encoding="UTF-8") as f:
                    yield id_, {"text": f.read(), "label": -1}