# coding=utf-8
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# Lint as: python3
"""IMDB movie reviews dataset."""
import gzip
import json

import datasets
from datasets.tasks import TextClassification


_DESCRIPTION = """\
Large Movie Review Dataset translated to Dutch.

This is a dataset for binary sentiment classification containing substantially \
more data than previous benchmark datasets. We provide a set of 24,992 highly \
polar movie reviews for training, and 24,992 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 = "https://huggingface.co/datasets/yhavinga/imdb_dutch/resolve/main/dataset/{split}.jsonl.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"),
                    "text_en": datasets.Value("string"),
                    "label": datasets.features.ClassLabel(names=["neg", "pos"]),
                }
            ),
            supervised_keys=None,
            homepage="http://ai.stanford.edu/~amaas/data/sentiment/",
            citation=_CITATION,
            task_templates=[
                TextClassification(text_column="text", label_column="label")
            ],
        )

    def _split_generators(self, dl_manager):
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "files": dl_manager.download([_DOWNLOAD_URL.format(split="train")]),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "files": dl_manager.download([_DOWNLOAD_URL.format(split="test")]),
                    "split": "test",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split("unsupervised"),
                gen_kwargs={
                    "files": dl_manager.download(
                        [_DOWNLOAD_URL.format(split="unsupervised")]
                    ),
                    "split": "unsupervised",
                    "labeled": False,
                },
            ),
        ]

    def _generate_examples(self, files, split, labeled=True):
        """Generate aclImdb examples."""
        for filepath in files:
            with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
                for _, line in enumerate(f):
                    example = json.loads(line)
                    yield _, example