# dataset loading script for huggingface
import datasets
import json
try:
    import lzma as xz
except ImportError:
    import pylzma as xz

datasets.logging.set_verbosity_info()
logger = datasets.logging.get_logger(__name__)

_DESCRIPTION = """\
    """

_HOMEPAGE = "https://skatinger.github.io/master_thesis/"

_LICENSE = ""

_CITATION = ""

_TYPES = ["original", "paraphrased"]

_SIZES = [4096, 512]

_URLS = {
    "original_4096": "data/original_4096.jsonl.xz",
    "original_512": "data/original_512.jsonl.xz",
    "paraphrased_4096": "data/paraphrased_4096.jsonl.xz",
    "paraphrased_512": "data/paraphrased_512.jsonl.xz"
}


class WikipediaForMaskFillingConfig(datasets.BuilderConfig):
    """BuilderConfig for WikipediaForMaskFilling.

    features: *list[string]*, list of the features that will appear in the
    feature dict. Should not include "label".
    **kwargs: keyword arguments forwarded to super
    """

    def __init__(self, type:str, size=4096, **kwargs):
        """BuilderConfig for WikipediaForMaskFilling.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """

        # Version history:
        # 1.0.0: first version
        super(WikipediaForMaskFillingConfig, self).__init__(**kwargs)
        self.size = size
        self.type = type


class WikipediaForMaskFilling(datasets.GeneratorBasedBuilder):
    """WikipediaForMaskFilling dataset."""

    BUILDER_CONFIGS = [
        WikipediaForMaskFillingConfig(
            name="original_4096",
            version=datasets.Version("1.0.0"),
            description="Part of the dataset with original texts and masks, with text chunks split into size of max 4096 tokens (Longformer).",
            size=4096,
            type="original"
        ),
        WikipediaForMaskFillingConfig(
            name="original_512",
            version=datasets.Version("1.0.0"),
            description="text chunks split into size of max 512 tokens (roberta).",
            size=512,
            type="original"
        ),
        WikipediaForMaskFillingConfig(
            name="paraphrased_4096",
            version=datasets.Version("1.0.0"),
            description="Part of the dataset with paraphrased texts and masks, with text chunks split into size of max 4096 tokens (Longformer).",
            size=4096,
            type="paraphrased"
        ),
        WikipediaForMaskFillingConfig(
            name="paraphrased_512",
            version=datasets.Version("1.0.0"),
            description="Paraphrased text chunks split into size of max 512 tokens (roberta).",
            size=512,
            type="paraphrased"
        )
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("int32"),
                    "sequence_number": datasets.Value("int32"),
                    "title": datasets.Value("string"),
                    "type": datasets.Value("string"),
                    "size": datasets.Value("int32"),
                    "texts": datasets.Value("string"),
                    "masks": datasets.Sequence(datasets.Value("string")),
                }
            ),
            # No default supervised_keys (as we have to pass both question
            # and context as input).
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        type = self.config.type
        size = self.config.size
        filepath = dl_manager.download(f"data/{type}_{size}.jsonl.xz")

        return [
            datasets.SplitGenerator(name='train', gen_kwargs={"filepath": filepath}),
        ]

    def _generate_examples(self, filepath):
        id_ = 0
        if filepath:
            logger.info("Generating examples from = %s", filepath)
            try:
                with xz.open(open(filepath,'rb'), 'rt', encoding='utf-8') as f:
                    json_list = list(f)
                
                for json_str in json_list:
                    data = json.loads(json_str)
                    if data is not None and isinstance(data, dict):
                        yield id_, {
                            "id": data["id"],
                            "sequence_number": data["sequence_number"],
                            "title": data["title"],
                            "type": data["type"],
                            "size": data["size"],
                            "texts": data["texts"],
                            "masks": data["masks"]
                        }
                        id_ +=1

            except Exception:
                logger.exception("Error while processing file %s", filepath)