from __future__ import annotations

import random
from pathlib import Path
from typing import Generator

import datasets

_CITATION = ""
_DESCRIPTION = "This is a dataset of livedoor news articles."
_HOMEPAGE = "https://www.rondhuit.com/download.html#news%20corpus"
_LICENSE = "This work is license under CC BY-ND 2.1 JP"
_URL = "https://www.rondhuit.com/download/ldcc-20140209.tar.gz"


class LivedoorNewsCorpusConfig(datasets.BuilderConfig):
    def __init__(
        self,
        name: str = "default",
        version: datasets.Version | str | None = datasets.Version("0.0.0"),
        data_dir: str | None = None,
        data_files: datasets.data_files.DataFilesDict | None = None,
        description: str | None = _DESCRIPTION,
        shuffle: bool = True,
        seed: int = 42,
        train_ratio: float = 0.8,
        validation_ratio: float = 0.1,
    ) -> None:
        super().__init__(
            name=name,
            version=version,
            data_dir=data_dir,
            data_files=data_files,
            description=description,
        )
        self.shuffle = shuffle
        self.seed = seed
        self.train_ratio = train_ratio
        self.validation_ratio = validation_ratio


class LivedoorNewsCorpus(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIG_CLASS = LivedoorNewsCorpusConfig

    def _info(self) -> datasets.DatasetInfo:
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            citation=_CITATION,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            features=datasets.Features(
                {
                    "url": datasets.Value("string"),
                    "date": datasets.Value("string"),
                    "title": datasets.Value("string"),
                    "content": datasets.Value("string"),
                    "category": datasets.Value("string"),
                }
            ),
        )

    def _split_generators(
        self, dl_manager: datasets.DownloadManager
    ) -> list[datasets.SplitGenerator]:
        dataset_dir = Path(dl_manager.download_and_extract(_URL))

        data = []
        for file_name in sorted(dataset_dir.glob("*/*/*")):
            if "LICENSE.txt" in str(file_name):
                continue
            with open(file_name, "r", encoding="utf-8") as f:
                d = [line.strip() for line in f]
                data.append(
                    {
                        "url": d[0],
                        "date": d[1],
                        "title": d[2],
                        "content": " ".join(d[3:]),
                        "category": file_name.parent.name,
                    }
                )

        if self.config.shuffle:
            random.seed(self.config.seed)
            random.shuffle(data)

        num_data = len(data)
        num_train_data = int(num_data * self.config.train_ratio)
        num_validation_data = int(num_data * self.config.validation_ratio)
        train_data = data[:num_train_data]
        validation_data = data[num_train_data : num_train_data + num_validation_data]
        test_data = data[num_train_data + num_validation_data :]
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"data": train_data}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, gen_kwargs={"data": validation_data}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"data": test_data}
            ),
        ]

    def _generate_examples(self, data: list[dict[str, str]]) -> Generator:
        for i, d in enumerate(data):
            yield i, d