import json
import random
import string
import warnings
from typing import Dict, List, Optional, Union

import datasets as ds
import pandas as pd

_CITATION = """
@InProceedings{Kurihara_nlp2020,
  author =  "鈴木正敏 and 鈴木潤 and 松田耕史 and ⻄田京介 and 井之上直也",
  title =   "JAQKET: クイズを題材にした日本語 QA データセットの構築",
  booktitle =   "言語処理学会第26回年次大会",
  year =    "2020",
  url = "https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf"
  note= "in Japanese"
"""

_DESCRIPTION = """\
JAQKET: JApanese Questions on Knowledge of EnTitie
"""

_HOMEPAGE = "https://sites.google.com/view/project-aio/dataset"

_LICENSE = """\
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
"""

_DESCRIPTION_CONFIGS = {
    "v2.0": "v2.0",
}

_URLS = {
    "v2.0": {
        "train": "https://huggingface.co/datasets/kumapo/JAQKET/resolve/main/train_jaqket_59.350.json",
        "valid": "https://huggingface.co/datasets/kumapo/JAQKET/resolve/main/dev_jaqket_59.350.json",
    },
}

def dataset_info_v2() -> ds.Features:
    features = ds.Features(
        {
            "qid": ds.Value("string"),
            "question": ds.Value("string"),
            "answers": ds.Sequence({
                "text": ds.Value("string"),
                "answer_start": ds.Value("int32"),
            }),
            "ctxs": ds.Sequence({
                "id": ds.Value("string"),
                "title": ds.Value("string"),
                "text": ds.Value("string"),
                "score": ds.Value("float32"),
                "has_answer": ds.Value("bool"),
            })
        }
    )
    return ds.DatasetInfo(
        description=_DESCRIPTION,
        citation=_CITATION,
        homepage=_HOMEPAGE,
        license=_LICENSE,
        features=features,
    )


class JAQKET(ds.GeneratorBasedBuilder):
    VERSION = ds.Version("0.1.0")
    BUILDER_CONFIGS = [
        ds.BuilderConfig(
            name="v2.0",
            version=VERSION,
            description=_DESCRIPTION_CONFIGS["v2.0"],
        ),
    ]

    def _info(self) -> ds.DatasetInfo:
        if self.config.name == "v2.0":
            return dataset_info_v2()
        else:
            raise ValueError(f"Invalid config name: {self.config.name}")

    def _split_generators(self, dl_manager: ds.DownloadManager):
        file_paths = dl_manager.download_and_extract(_URLS[self.config.name])
        return [
            ds.SplitGenerator(
                name=ds.Split.TRAIN,
                gen_kwargs={"file_path": file_paths["train"]},
            ),
            ds.SplitGenerator(
                name=ds.Split.VALIDATION,
                gen_kwargs={"file_path": file_paths["valid"]},
            ),
        ]

    def _generate_examples(
        self,
        file_path: Optional[str] = None,
        split_df: Optional[pd.DataFrame] = None,
    ):
        if file_path is None:
            raise ValueError(f"Invalid argument for {self.config.name}")

        with open(file_path, "r") as rf:
            json_data = json.load(rf)

        for json_dict in json_data:
            q_id = json_dict["qid"]
            question = json_dict["question"]
            answers = [
                {"text": answer, "answer_start": -1 } # -1: dummy
                for answer in json_dict["answers"]
            ]
            ctxs = [
                {
                    "id": ctx["id"],
                    "title": ctx["title"],
                    "text": ctx["text"],
                    "score": float(ctx["score"]),
                    "has_answer": ctx["has_answer"]

                }
                for ctx in json_dict["ctxs"]
            ]
            example_dict = {
                "qid": q_id,
                "question": question,
                "answers": answers,
                "ctxs": ctxs
            }
            yield q_id, example_dict