from __future__ import annotations

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


class DdiscoCohesionClassification(AbsTaskClassification):
    metadata = TaskMetadata(
        name="Ddisco",
        dataset={
            "path": "DDSC/ddisco",
            "revision": "514ab557579fcfba538a4078d6d647248a0e6eb7",
        },
        description="A Danish Discourse dataset with values for coherence and source (Wikipedia or Reddit)",
        reference="https://aclanthology.org/2022.lrec-1.260/",
        type="Classification",
        category="s2s",
        eval_splits=["test"],
        eval_langs=["dan-Latn"],
        main_score="accuracy",
        date=("2021-01-01", "2022-06-25"),
        form=["written"],
        domains=["Non-fiction", "Social"],
        dialect=[],
        task_subtypes=["Discourse coherence"],
        license="cc-by-sa-3.0",
        socioeconomic_status="high",
        annotations_creators="expert-annotated",
        text_creation="found",
        bibtex_citation="""
        @inproceedings{flansmose-mikkelsen-etal-2022-ddisco,
    title = "{DD}is{C}o: A Discourse Coherence Dataset for {D}anish",
    author = "Flansmose Mikkelsen, Linea  and
      Kinch, Oliver  and
      Jess Pedersen, Anders  and
      Lacroix, Oph{\'e}lie",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.lrec-1.260",
    pages = "2440--2445",
    abstract = "To date, there has been no resource for studying discourse coherence on real-world Danish texts. Discourse coherence has mostly been approached with the assumption that incoherent texts can be represented by coherent texts in which sentences have been shuffled. However, incoherent real-world texts rarely resemble that. We thus present DDisCo, a dataset including text from the Danish Wikipedia and Reddit annotated for discourse coherence. We choose to annotate real-world texts instead of relying on artificially incoherent text for training and testing models. Then, we evaluate the performance of several methods, including neural networks, on the dataset.",
}
        """,
        n_samples=None,
        avg_character_length=None,
    )

    def dataset_transform(self):
        self.dataset = self.dataset.rename_columns({"rating": "label"}).remove_columns(
            ["domain"]
        )