"""Javanese IMDB movie reviews dataset."""

from __future__ import absolute_import, division, print_function

import csv
import os

import datasets


_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}
}
"""

_DESCRIPTION = """\
Large Movie Review Dataset translated to Javanese.
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. We translated the original IMDB Dataset to \
Javanese using the multi-lingual MarianMT Transformer model from \
`Helsinki-NLP/opus-mt-en-mul`. 
"""

_URL = "https://github.com/w11wo/javanese-nlp/blob/main/imdb-javanese/javanese_imdb_csv.zip?raw=true"

_HOMEPAGE = "https://github.com/w11wo/javanese-nlp"


class JavaneseImdbReviews(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.ClassLabel(names=["0", "1", "-1"]),
                }
            ),
            citation=_CITATION,
            homepage=_HOMEPAGE,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        dl_path = dl_manager.download_and_extract(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(dl_path, "javanese_imdb_train.csv")
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(dl_path, "javanese_imdb_test.csv")
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split("unsupervised"),
                gen_kwargs={
                    "filepath": os.path.join(dl_path, "javanese_imdb_unsup.csv")
                },
            ),
        ]

    def _generate_examples(self, filepath):
        """Yields examples."""
        with open(filepath, encoding="utf-8") as f:
            reader = csv.reader(f, delimiter=",")
            for id_, row in enumerate(reader):
                if id_ == 0:
                    continue
                yield id_, {"label": row[0], "text": row[1]}