# Loading script for the CaSum dataset.
import json
import datasets

logger = datasets.logging.get_logger(__name__)

_CITATION = """@misc{degibert2022sequencetosequence,
      title={Sequence-to-Sequence Resources for Catalan}, 
      author={Ona de Gibert and Ksenia Kharitonova and Blanca Calvo Figueras and Jordi Armengol-Estapé and Maite Melero},
      year={2022},
      eprint={2202.06871},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}"""

_DESCRIPTION = """CaSum is a summarization dataset. It is extracted from a newswire corpus crawled from the Catalan News Agency. The corpus consists of 217,735 instances that are composed by the headline and the body.
"""

_HOMEPAGE = """https://github.com/TeMU-BSC/seq-to-seq-catalan"""

_URL = "https://huggingface.co/datasets/projecte-aina/casum/resolve/main/"
_TRAIN_FILE = "train.jsonl"
_VALID_FILE = "valid.jsonl"
_TEST_FILE = "test.jsonl"

class CaSumConfig(datasets.BuilderConfig):
    """ Builder config for the CaSum dataset """

    def __init__(self, **kwargs):
        """BuilderConfig for CaSum.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(CaSumConfig, self).__init__(**kwargs)


class CaSum(datasets.GeneratorBasedBuilder):
    """CaSum Dataset."""

    BUILDER_CONFIGS = [
        CaSumConfig(
            name="CaSum",
            version=datasets.Version("1.0.0"),
            description="CaSum dataset"
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "summary": datasets.Value("string"),
                    "text": datasets.Value("string")
                }
       
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION
        )
        
    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{_TRAIN_FILE}",
            "valid": f"{_URL}{_VALID_FILE}",
            "test": f"{_URL}{_TEST_FILE}"
        }
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["valid"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)
        with open(filepath) as f:
            for id_, row in enumerate(f):
                article = json.loads(row)
                text = article['text']
                summary = article['summary']
                yield id_, { "summary": summary,"text": text}