"""CrossSum cross-lingual abstractive summarization dataset."""


import json
import os

import datasets


_CITATION = """\
@article{hasan2021crosssum,
  author    = {Tahmid Hasan and Abhik Bhattacharjee and Wasi Uddin Ahmad and Yuan-Fang Li and Yong-bin Kang and Rifat Shahriyar},
  title     = {CrossSum: Beyond English-Centric Cross-Lingual Abstractive Text Summarization for 1500+ Language Pairs},
  journal   = {CoRR},
  volume    = {abs/2112.08804},
  year      = {2021},
  url       = {https://arxiv.org/abs/2112.08804},
  eprinttype = {arXiv},
  eprint    = {2112.08804}
}
"""


_DESCRIPTION = """\
We present CrossSum, a large-scale dataset
comprising 1.70 million cross-lingual article summary samples in 1500+ language-pairs
constituting 45 languages. We use the multilingual XL-Sum dataset and align identical 
articles written in different languages via crosslingual retrieval using a language-agnostic 
representation model. 
"""

_HOMEPAGE = "https://github.com/csebuetnlp/CrossSum"

_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)"

_URL = "https://huggingface.co/datasets/csebuetnlp/CrossSum/resolve/main/data/{}-{}_CrossSum.tar.bz2"

_LANGUAGES = [
    "oromo",
    "french",
    "amharic",
    "arabic",
    "azerbaijani",
    "bengali",
    "burmese",
    "chinese_simplified",
    "chinese_traditional",
    "welsh",
    "english",
    "kirundi",
    "gujarati",
    "hausa",
    "hindi",
    "igbo",
    "indonesian",
    "japanese",
    "korean",
    "kyrgyz",
    "marathi",
    "spanish",
    "scottish_gaelic",
    "nepali",
    "pashto",
    "persian",
    "pidgin",
    "portuguese",
    "punjabi",
    "russian",
    "serbian_cyrillic",
    "serbian_latin",
    "sinhala",
    "somali",
    "swahili",
    "tamil",
    "telugu",
    "thai",
    "tigrinya",
    "turkish",
    "ukrainian",
    "urdu",
    "uzbek",
    "vietnamese",
    "yoruba",
]


class Crosssum(datasets.GeneratorBasedBuilder):
    
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="{}-{}".format(src_lang, tgt_lang),
            version=datasets.Version("1.0.0")
        )
        for src_lang in _LANGUAGES 
        for tgt_lang in _LANGUAGES
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "source_url": datasets.Value("string"),
                    "target_url": 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, self.VERSION.version_str[:-2])

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

    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_, {
                    "source_url": data["source_url"],
                    "target_url": data["target_url"],
                    "summary": data["summary"],
                    "text": data["text"],
                }