import datasets
from datasets.tasks import TextClassification

_DESCRIPTION = """
Movie Review Dataset.

This is a dataset containing 4,265 positive and 4,265 negative processed
sentences from Rotten Tomatoes movie reviews.
"""

_CITATION = """
@InProceedings{Pang+Lee:05a,
  author =       {Bo Pang and Lillian Lee},
  title =        {Seeing stars: Exploiting class relationships for sentiment
                  categorization with respect to rating scales},
  booktitle =    {Proceedings of the ACL},
  year =         2005
}
"""

_DOWNLOAD_URL = "https://testerstories.com/files/ai_learn/rt-polaritydata.tar.gz"


class RottenTomatoesReviews(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "label": datasets.features.ClassLabel(names=["neg", "pos"]),
                }
            ),
            supervised_keys=[""],
            homepage="http://www.cs.cornell.edu/people/pabo/movie-review-data/",
            citation=_CITATION,
            task_templates=[
                TextClassification(text_column="text", label_column="label")
            ],
        )

    def _split_generators(self, dl_manager):
        archive = dl_manager.download(_DOWNLOAD_URL)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "split_key": "train",
                    "files": dl_manager.iter_archive(archive),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "split_key": "validation",
                    "files": dl_manager.iter_archive(archive),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "split_key": "test",
                    "files": dl_manager.iter_archive(archive),
                },
            ),
        ]

    def _get_examples_from_split(self, split_key, files):
        data_dir = "rt-polaritydata/"
        pos_samples, neg_samples = None, None

        for path, f in files:
            if path == data_dir + "rt-polarity.pos":
                pos_samples = [line.decode("latin-1").strip() for line in f]
            elif path == data_dir + "rt-polarity.neg":
                neg_samples = [line.decode("latin-1").strip() for line in f]

            if pos_samples is not None and neg_samples is not None:
                break

        i1 = int(len(pos_samples) * 0.8 + 0.5)
        i2 = int(len(pos_samples) * 0.9 + 0.5)

        train_samples = pos_samples[:i1] + neg_samples[:i1]
        train_labels = (["pos"] * i1) + (["neg"] * i1)

        validation_samples = pos_samples[i1:i2] + neg_samples[i1:i2]
        validation_labels = (["pos"] * (i2 - i1)) + (["neg"] * (i2 - i1))

        test_samples = pos_samples[i2:] + neg_samples[i2:]
        test_labels = (["pos"] * (len(pos_samples) - i2)) + (
            ["neg"] * (len(pos_samples) - i2)
        )

        if split_key == "train":
            return (train_samples, train_labels)
        if split_key == "validation":
            return (validation_samples, validation_labels)
        if split_key == "test":
            return (test_samples, test_labels)
        else:
            raise ValueError(f"Invalid split key {split_key}")

    def _generate_examples(self, split_key, files):
        split_text, split_labels = self._get_examples_from_split(split_key, files)

        for text, label in zip(split_text, split_labels):
            data_key = split_key + "_" + text
            feature_dict = {"text": text, "label": label}

            yield data_key, feature_dict