import gzip
import json
import datasets


logger = datasets.logging.get_logger(__name__)


_URL = "https://huggingface.co/datasets/allenai/pes2o"

_VARIANTS = ["v1", "v2"]

_N_SHARDS_PER_SPLIT = {
    "v1": {"train": {'s2orc': 10, 's2ag': 10}, "valid": {'s2orc': 1, 's2ag': 1}},
    "v2": {"train": {'s2orc': 10, 's2ag': 10}, "valid": {'s2orc': 1, 's2ag': 1}},
}

_DATA_URL = "\
https://huggingface.co/datasets/allenai/pes2o/resolve/main/\
{name}/{subset}/{split}/{shard:05d}.json.gz\
"

_DESCRIPTION = "\
The PES2O dataset is a collection of ~40M creative commmon licensed academic \
papers, cleaned, filtered, and formatted for pre-training of language models. \
It is derived from the Semantic Scholar Open Research Corpus(Lo et al, 2020), \
or S2ORC.\
"

_CITATION = ""


class pes2o(datasets.GeneratorBasedBuilder):
    """Pretraining Efficiently on S2ORC!"""

    BUILDER_CONFIGS = [datasets.BuilderConfig(name) for name in _VARIANTS]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "added": datasets.Value("string"),
                    "created": datasets.Value("string"),
                    "id": datasets.Value("string"),
                    "source": datasets.Value("string"),
                    "text": datasets.Value("string"),
                    "version": datasets.Value("string")
                }
            ),
            supervised_keys=None,
            homepage=_URL,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_urls = {}
        for split in ["train", "validation"]:
            n_shards = _N_SHARDS_PER_SPLIT[self.config.name][split]
            data_urls[split] = [
                _DATA_URL.format(
                    name=self.config.name,
                    split=split,
                    subset=subset,
                    index=index
                )
                for subset, n_shards in n_shards.items()
                for index in range(n_shards)
            ]
        train_downloaded_files = dl_manager.download(
            data_urls["train"]
        )
        validation_downloaded_files = dl_manager.download(
            data_urls["validation"]
        )
        return [
            datasets.SplitGenerator(
                name=str(datasets.Split.TRAIN), gen_kwargs={
                    "filepaths": train_downloaded_files
                }),
            datasets.SplitGenerator(
                name=str(datasets.Split.VALIDATION), gen_kwargs={
                    "filepaths": validation_downloaded_files
                }
            ),
        ]

    def _generate_examples(self, filepaths):
        """This function returns the examples in the raw (text) form by
        iterating on all the files."""
        id_ = 0
        for filepath in filepaths:
            logger.info("generating examples from = %s", filepath)
            with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
                for line in f:
                    if line:
                        example = json.loads(line)
                        yield id_, example
                        id_ += 1