"""The Stanford Sentiment Treebank translated to Portuguese."""

import csv

import datasets
from datasets.tasks import TextClassification

_DESCRIPTION = """\
The Stanford Sentiment Treebank consists of sentences from movie reviews and
human annotations of their sentiment. The task is to predict the sentiment of a
given sentence. We use the two-way (positive/negative) class split, and use only
sentence-level labels.
"""

_CITATION = """\
@inproceedings{socher2013recursive,
  title={Recursive deep models for semantic compositionality over a sentiment treebank},
  author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},
  booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},
  pages={1631--1642},
  year={2013}
}
"""

_HOMEPAGE = "https://nlp.stanford.edu/sentiment/"

_DOWNLOAD_URL = "https://huggingface.co/datasets/maritaca-ai/sst2_pt/resolve/main"

class SST2(datasets.GeneratorBasedBuilder):
    """The Stanford Sentiment Treebank translated to Portuguese."""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["negativo", "positivo"])}
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
            task_templates=[TextClassification(text_column="text", label_column="label")],
        )

    def _split_generators(self, dl_manager):
        train_path = dl_manager.download_and_extract(f"{_DOWNLOAD_URL}/train.csv")
        validation_path = dl_manager.download_and_extract(f"{_DOWNLOAD_URL}/validation.csv")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path, "split": "train"}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_path, "split": "validation"}
            ),
        ]

    def _generate_examples(self, filepath, split):
        with open(filepath, encoding="utf-8") as csv_file:
            csv_reader = csv.reader(
                csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
            )
            next(csv_reader) # Skip header (first line)
            for (idx, row) in enumerate(csv_reader):
                text, label = row
                yield idx, {"text": text, "label": label}