"""NPSC dataset."""
import gzip
import json
import datasets

logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\\nNorwegian Colossal Corpus v2. Short sequences of maximum 100k characters."""
_CITATION = """
TO BE DONE
"""
_URL = "https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-58/"
_DATA_URL = "https://huggingface.co/datasets/NbAiLab/NPSC/resolve/main/data/{split_suffix}-shard-{index:04d}-of-{n_shards:04d}.json.gz"
_N_SHARDS_PER_SPLIT = {
        "train": 1, "dev": 1, "test": 1
}


class NPSCConfig(datasets.BuilderConfig):
    """BuilderConfig for NbNn."""

    def __init__(self, *args, **kwargs):
        """BuilderConfig for NbNn.
        Args:
            **kwargs: keyword arguments forwarded to super.
        """
        super().__init__(
            *args,
            name="NPSC",
            **kwargs,
        )


class NPSC(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [NPSCConfig()]
    BUILDER_CONFIG_CLASS = NPSCConfig

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "sentence_order":  datasets.Value("int32"),
                    "speaker_id" : datasets.Value("int32"),
                    "speaker_name": datasets.Value("string"),
                    "sentence_text": datasets.Value("string"),
                    "sentence_language_code": datasets.Value("string"),
                    "text": datasets.Value("string"),
                    "start_time": datasets.Value("int32"),
                    "end_time": datasets.Value("int32"),
                    "normsentence_text": datasets.Value("string"),
                    "transsentence_text": datasets.Value("string"),
                    "translated": datasets.Value("int32"),
                    "audio": datasets.features.Audio(sampling_rate=48000),

                }
            ),
            supervised_keys=None,
            homepage=_URL,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_urls = {}
        for split in ["train", "dev", "test"]:
            data_urls[split] = [
                _DATA_URL.format(
                    language=self.config.name,
                    split_suffix=split,
                    index=index,
                    n_shards=_N_SHARDS_PER_SPLIT[split],
                )
                for index in range(1, _N_SHARDS_PER_SPLIT[split] + 1)
            ]
        train_downloaded_files = dl_manager.download(data_urls["train"])
        dev_downloaded_files = dl_manager.download(data_urls["dev"])
        test_downloaded_files = dl_manager.download(data_urls["test"])

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
            datasets.SplitGenerator(
                name=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