from __future__ import annotations

from mteb.abstasks.TaskMetadata import TaskMetadata

from ....abstasks import AbsTaskClassification

_LANGUAGES = {
    "ar": ["ara-Arab"],
    "bg": ["bul-Cyrl"],
    "de": ["deu-Latn"],
    "el": ["ell-Grek"],
    "en": ["eng-Latn"],
    "es": ["spa-Latn"],
    "fr": ["fra-Latn"],
    "hi": ["hin-Deva"],
    "it": ["ita-Latn"],
    "ja": ["jpn-Jpan"],
    "nl": ["nld-Latn"],
    "pl": ["pol-Latn"],
    "pt": ["por-Latn"],
    "ru": ["rus-Cyrl"],
    "sw": ["swa-Latn"],
    "th": ["tha-Thai"],
    "tr": ["tur-Latn"],
    "ur": ["urd-Arab"],
    "vi": ["vie-Latn"],
    "zh": ["cmn-Hans"],
}


class LanguageClassification(AbsTaskClassification):
    metadata = TaskMetadata(
        name="LanguageClassification",
        dataset={
            "path": "papluca/language-identification",
            "revision": "aa56583bf2bc52b0565770607d6fc3faebecf9e2",
        },
        description="A language identification dataset for 20 languages.",
        reference="https://huggingface.co/datasets/papluca/language-identification",
        category="s2s",
        type="Classification",
        eval_splits=["test"],
        eval_langs=_LANGUAGES,
        main_score="accuracy",
        date=("2021-11-01", "2021-11-30"),
        form=["written"],
        domains=["Reviews", "Web", "Non-fiction", "Fiction", "Government"],
        task_subtypes=["Language identification"],
        license="Not specified",
        socioeconomic_status="mixed",
        annotations_creators="derived",
        dialect=[],
        text_creation="found",
        bibtex_citation="",
        n_samples={"test": 2048},
        avg_character_length={"test": 107.8},
    )

    def dataset_transform(self) -> None:
        self.dataset = self.dataset.rename_columns({"labels": "label"})
        self.dataset = self.stratified_subsampling(
            self.dataset, seed=self.seed, splits=["test"]
        )