import os
from collections import defaultdict
import datasets
from .process import process_text, get_structured_data
from typing import List
from math import ceil
from .configs import SUB_DATASETS
from datasets import load_dataset
def processing(data, name):
    if name == "processed":
        data['text'] = process_text(data['text'])
    elif name == "structured":
        data['text'] = process_text(data['text'])
        data['structured_text'] = get_structured_data(data['text'], default_value={"item": [], "content": []})
    return data


def sliding(texts: List[str], window_size: int=5, stride:int=3) -> List[str]:
    n_iter = ceil((len(texts)-window_size)/stride)+1
    return [texts[i*stride:i*stride+window_size] for i in range(n_iter)]


class NamuWiki(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = SUB_DATASETS

    def _info(self):
        return datasets.DatasetInfo(
            description="",
            features=self.config.features,
            homepage=self.config.url,
            citation=self.config.citation + "\n" + "",
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        if self.config.name == "processed":
            data_file = dl_manager.download(self.config.data_url)
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "data_file": data_file,
                        "split": "train"
                    }
                ),
            ]

        elif self.config.name.startswith(("char", "word")):
            _, length = self.config.name.split("-")
            length = int(length)
            data_file = dl_manager.download(self.config.data_url)
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "data_file": data_file,
                        "split": "train",
                        "length": length
                    }
                ),
            ]

        elif self.config.name == "raw":
            data_files = dl_manager.download_and_extract(self.config.data_url)
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "data_file": data_files,
                        "split": "train"
                    }
                ),
            ]

    def _generate_examples(self, data_file, split, length=None):
        n = 0
        _dataset = load_dataset("parquet", data_files={"train": data_file}, split="train", use_auth_token=self.use_auth_token)
        for data in _dataset:
            yield n, processing(data, self.config.name)
            n += 1