from __future__ import annotations

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

N_SAMPLES = 2048


class TweetEmotionClassification(AbsTaskClassification):
    metadata = TaskMetadata(
        name="TweetEmotionClassification",
        dataset={
            "path": "emotone_ar",
            "revision": "0ded8ff72cc68cbb7bb5c01b0a9157982b73ddaf",
        },
        description="A dataset of 10,000 tweets that was created with the aim of covering the most frequently used emotion categories in Arabic tweets.",
        reference="https://link.springer.com/chapter/10.1007/978-3-319-77116-8_8",
        type="Classification",
        category="s2s",
        eval_splits=["train"],
        eval_langs=["ara-Arab"],
        main_score="accuracy",
        date=("2014-01-01", "2016-08-31"),
        form=["written"],
        domains=["Social"],
        task_subtypes=["Sentiment/Hate speech"],
        license="Not specified",
        socioeconomic_status="mixed",
        annotations_creators="human-annotated",
        dialect=["ara-arab-EG", "ara-arab-LB", "ara-arab-JO", "ara-arab-SA"],
        text_creation="found",
        bibtex_citation="""
@inproceedings{al2018emotional,
  title={Emotional tone detection in arabic tweets},
  author={Al-Khatib, Amr and El-Beltagy, Samhaa R},
  booktitle={Computational Linguistics and Intelligent Text Processing: 18th International Conference, CICLing 2017, Budapest, Hungary, April 17--23, 2017, Revised Selected Papers, Part II 18},
  pages={105--114},
  year={2018},
  organization={Springer}
}
""",
        n_samples={"train": N_SAMPLES},
        avg_character_length={"train": 78.8},
    )

    def dataset_transform(self):
        self.dataset = self.dataset.rename_column("tweet", "text")
        self.dataset = self.stratified_subsampling(
            self.dataset, seed=self.seed, splits=["train"]
        )