"""LR-Sum summarization dataset"""

import json
import os

import datasets

_CITATION = """\
@inproceedings{palen-michel-lignos-2023-lr,
    title = "{LR}-Sum: Summarization for Less-Resourced Languages",
    author = "Palen-Michel, Chester  and
      Lignos, Constantine",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.427",
    doi = "10.18653/v1/2023.findings-acl.427",
    pages = "6829--6844",
    abstract = "We introduce LR-Sum, a new permissively-licensed dataset created with the goal of enabling further research in automatic summarization for less-resourced languages.LR-Sum contains human-written summaries for 40 languages, many of which are less-resourced. We describe our process for extracting and filtering the dataset from the Multilingual Open Text corpus (Palen-Michel et al., 2022).The source data is public domain newswire collected from from Voice of America websites, and LR-Sum is released under a Creative Commons license (CC BY 4.0), making it one of the most openly-licensed multilingual summarization datasets. We describe abstractive and extractive summarization experiments to establish baselines and discuss the limitations of this dataset.",
}
"""

_DESCRIPTION = """\
We introduce LR-Sum, a new permissively-licensed dataset created with the goal of enabling further research in automatic summarization for less-resourced languages. 
LR-Sum contains human-written summaries for 40 languages, many of which are less-resourced. 
We describe our process for extracting and filtering the dataset from the Multilingual Open Text corpus (Palen-Michel et al., 2022).
The source data is public domain newswire collected from from Voice of America websites, and LR-Sum is released under a Creative Commons license (CC BY 4.0), making it one of the most openly-licensed multilingual summarization datasets. 
We describe abstractive and extractive summarization experiments to establish baselines and discuss the limitations of this dataset.
"""

_HOMEPAGE = "https://github.com/bltlab"

_LICENSE = "Creative Commons Attribution 4.0 International (CC-BY 4.0)"

_URL = "https://huggingface.co/datasets/bltlab/lr-sum/resolve/main/data/{}.tar.bz2"

_LANGUAGES = [
    "amh",
    "aze",
    "ben",
    "bod",
    "bos",
    "ckb",
    "cmn_t",
    "cmn_s",
    "ell",
    "eng",
    "fas",
    "fra",
    "hat",
    "hau",
    "hye",
    "ind",
    "kat",
    "khm",
    "kin",
    "kor",
    "kmr",
    "lao",
    "mkd",
    "mya",
    "nde",
    "por",
    "prs",
    "pus",
    "rus",
    "sna",
    "som",
    "spa",
    "sqi",
    "srp",
    "swh",
    "tha",
    "tir",
    "tur",
    "ukr",
    "urd",
    "uzb",
    "vie",
]


class Lrsum(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="{}".format(lang),
            version=datasets.Version("1.0.0")
        )
        for lang in _LANGUAGES
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "url": datasets.Value("string"),
                    "title": datasets.Value("string"),
                    "summary": datasets.Value("string"),
                    "text": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
            version=self.VERSION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        lang = str(self.config.name)
        url = _URL.format(lang)

        data_dir = dl_manager.download_and_extract(url)
        ret = [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, lang + "_test.jsonl"),
                },
            )
        ]
        if os.path.exists(os.path.join(data_dir, lang + "_train.jsonl")):
            ret.append(datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, lang + "_train.jsonl"),
                },
            )
            )
        if os.path.exists(os.path.join(data_dir, lang + "_val.jsonl")):
            ret.append(
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    gen_kwargs={
                        "filepath": os.path.join(data_dir, lang + "_val.jsonl"),
                    },
                )
            )

        return ret

    def _generate_examples(self, filepath):
        """Yields examples as (key, example) tuples."""
        with open(filepath, encoding="utf-8") as f:
            for idx_, row in enumerate(f):
                data = json.loads(row)
                yield idx_, {
                    "id": data["id"],
                    "url": data["url"],
                    "title": data["title"],
                    "summary": data["summary"],
                    "text": data["text"],
                }