from __future__ import annotations

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


class CzechSoMeSentimentClassification(AbsTaskClassification):
    metadata = TaskMetadata(
        name="CzechSoMeSentimentClassification",
        description="User comments on Facebook",
        reference="https://aclanthology.org/W13-1609/",
        dataset={
            "path": "fewshot-goes-multilingual/cs_facebook-comments",
            "revision": "6ced1d87a030915822b087bf539e6d5c658f1988",
        },
        type="Classification",
        category="s2s",
        eval_splits=["test"],
        eval_langs=["ces-Latn"],
        main_score="accuracy",
        date=("2013-01-01", "2013-06-01"),
        form=["written"],
        dialect=[],
        domains=["Reviews"],
        task_subtypes=["Sentiment/Hate speech"],
        license="CC BY-NC-SA 4.0",
        socioeconomic_status="mixed",
        annotations_creators="derived",
        text_creation="found",
        bibtex_citation="""
        @inproceedings{habernal-etal-2013-sentiment,
            title = "Sentiment Analysis in {C}zech Social Media Using Supervised Machine Learning",
            author = "Habernal, Ivan  and
            Pt{\'a}{\v{c}}ek, Tom{\'a}{\v{s}}  and
            Steinberger, Josef",
            editor = "Balahur, Alexandra  and
            van der Goot, Erik  and
            Montoyo, Andres",
            booktitle = "Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
            month = jun,
            year = "2013",
            address = "Atlanta, Georgia",
            publisher = "Association for Computational Linguistics",
            url = "https://aclanthology.org/W13-1609",
            pages = "65--74",
        }
        """,
        n_samples={"test": 1000},
        avg_character_length={"test": 59.89},
    )

    @property
    def metadata_dict(self) -> dict[str, str]:
        metadata_dict = super().metadata_dict
        metadata_dict["n_experiments"] = 10
        metadata_dict["samples_per_label"] = 16
        return metadata_dict

    def dataset_transform(self) -> None:
        self.dataset = self.dataset.rename_columns(
            {"comment": "text", "sentiment_int": "label"}
        )