import datasets
import os
import json


_CITATION = ""
_DESCRIPTION = """
    Scenario for single document text summarization.
    Currently supports the following datasets:
    1. XSum (https://arxiv.org/pdf/1808.08745.pdf)
    2. CNN/DailyMail non-anonymized (https://arxiv.org/pdf/1704.04368.pdf)

    Task prompt structure

        Summarize the given document.
        Document: {tok_1 ... tok_n}
        Summary: {tok_1 ... tok_m}

    Example from XSum dataset

        Document: {Part of the Broad Road was closed to traffic on Sunday at about 18:00 GMT.
                   The three adults and three children have been taken to Altnagelvin Hospital
                   with non life-threatening injuries. The Fire Service, Northern Ireland Ambulance Service
                   and police attended the crash. The Broad Road has since been reopened.}
        Summary: {Three adults and three children have been taken to hospital following a crash involving
                  a tractor and a campervan in Limavady, County Londonderry}
"""

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

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name=name, version=datasets.Version("1.0.0"), description="")
            for name in ["xsum", "xsum-sampled", "cnn-dm"]
        ]

    def _info(self):
        features = datasets.Features(
            {
                "article": datasets.Value("string"),
                "summary": datasets.Value("string"),

            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage="",
            license="",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        train_json = dl_manager.download(os.path.join(self.config.name, "train.jsonl"))
        test_json = dl_manager.download(os.path.join(self.config.name, "test.jsonl"))
        val_json = dl_manager.download(os.path.join(self.config.name, "validation.jsonl"))

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"path": train_json},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"path": test_json},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"path": val_json},
            )
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, path):
        with open(path, encoding="utf-8") as f:
            for key, row in enumerate(f):
                yield key, json.loads(row)