from __future__ import annotations

from mteb.abstasks.AbsTaskClustering import AbsTaskClustering
from mteb.abstasks.AbsTaskClusteringFast import AbsTaskClusteringFast
from mteb.abstasks.TaskMetadata import TaskMetadata

NUM_SAMPLES = 2048


class BigPatentClustering(AbsTaskClustering):
    superseeded_by = "BigPatentClustering.v2"

    metadata = TaskMetadata(
        name="BigPatentClustering",
        description="Clustering of documents from the Big Patent dataset. Test set only includes documents"
        "belonging to a single category, with a total of 9 categories.",
        reference="https://www.kaggle.com/datasets/big_patent",
        dataset={
            "path": "jinaai/big-patent-clustering",
            "revision": "62d5330920bca426ce9d3c76ea914f15fc83e891",
        },
        type="Clustering",
        category="s2s",
        eval_splits=["test"],
        eval_langs=["eng-Latn"],
        main_score="v_measure",
        date=None,
        form=None,
        domains=None,
        task_subtypes=None,
        license=None,
        socioeconomic_status=None,
        annotations_creators=None,
        dialect=None,
        text_creation=None,
        bibtex_citation=None,
        n_samples=None,
        avg_character_length=None,
    )


class BigPatentClusteringFast(AbsTaskClusteringFast):
    max_depth = 1
    metadata = TaskMetadata(
        name="BigPatentClustering.v2",
        description="Clustering of documents from the Big Patent dataset. Test set only includes documents"
        "belonging to a single category, with a total of 9 categories.",
        reference="https://huggingface.co/datasets/NortheasternUniversity/big_patent",
        dataset={
            "path": "mteb/big-patent",
            "revision": "58a863a958586a5d6ba51088b94ac74a46aa864f",
        },
        type="Clustering",
        category="p2p",
        eval_splits=["test"],
        eval_langs=["eng-Latn"],
        main_score="v_measure",
        date=(
            "1971-01-01",
            "2019-06-10",
        ),  # start date from paper, end date - paper publication
        form=["written"],
        domains=["Legal"],
        task_subtypes=["Thematic clustering"],
        license="cc-by-4.0",
        socioeconomic_status="high",
        annotations_creators="derived",
        dialect=[],
        text_creation="found",
        bibtex_citation="""@article{DBLP:journals/corr/abs-1906-03741,
  author    = {Eva Sharma and
               Chen Li and
               Lu Wang},
  title     = {{BIGPATENT:} {A} Large-Scale Dataset for Abstractive and Coherent
               Summarization},
  journal   = {CoRR},
  volume    = {abs/1906.03741},
  year      = {2019},
  url       = {http://arxiv.org/abs/1906.03741},
  eprinttype = {arXiv},
  eprint    = {1906.03741},
  timestamp = {Wed, 26 Jun 2019 07:14:58 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}""",
        n_samples={"test": NUM_SAMPLES},
        avg_character_length={"test": 30995.5},
    )

    def dataset_transform(self):
        self.dataset = self.stratified_subsampling(
            self.dataset,
            self.seed,
            self.metadata.eval_splits,
            label="labels",
            n_samples=NUM_SAMPLES,
        )