from __future__ import annotations

from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata


class WisesightSentimentClassification(AbsTaskClassification):
    metadata = TaskMetadata(
        name="WisesightSentimentClassification",
        description="Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment label (positive, neutral, negative, question)",
        reference="https://github.com/PyThaiNLP/wisesight-sentiment",
        dataset={
            "path": "pythainlp/wisesight_sentiment",
            "revision": "14aa5773afa135ba835cc5179bbc4a63657a42ae",
        },
        type="Classification",
        category="s2s",
        eval_splits=["test"],
        eval_langs=["tha-Thai"],
        main_score="f1",
        date=("2019-05-24", "2021-09-16"),
        form=["written"],
        dialect=[],
        domains=["Social", "News"],
        task_subtypes=["Sentiment/Hate speech"],
        license="cc0-1.0",
        socioeconomic_status="mixed",
        annotations_creators="expert-annotated",
        text_creation="found",
        bibtex_citation="""@software{bact_2019_3457447,
  author       = {Suriyawongkul, Arthit and
                  Chuangsuwanich, Ekapol and
                  Chormai, Pattarawat and
                  Polpanumas, Charin},
  title        = {PyThaiNLP/wisesight-sentiment: First release},
  month        = sep,
  year         = 2019,
  publisher    = {Zenodo},
  version      = {v1.0},
  doi          = {10.5281/zenodo.3457447},
  url          = {https://doi.org/10.5281/zenodo.3457447}
}

""",
        n_samples={"train": 2048},
        avg_character_length={"train": 103.42},
    )

    def dataset_transform(self):
        for split in self.dataset.keys():
            self.dataset[split] = self.dataset[split].rename_column("texts", "text")
            self.dataset[split] = self.dataset[split].rename_column("category", "label")

        self.dataset = self.stratified_subsampling(
            self.dataset,
            seed=self.seed,
            splits=["test"],
        )