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

import datasets as ds
import pandas as pd

_CITATION = """\
@inproceedings{kurihara-etal-2022-jglue,
    title = "{JGLUE}: {J}apanese General Language Understanding Evaluation",
    author = "Kurihara, Kentaro  and
      Kawahara, Daisuke  and
      Shibata, Tomohide",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.lrec-1.317",
    pages = "2957--2966",
    abstract = "To develop high-performance natural language understanding (NLU) models, it is necessary to have a benchmark to evaluate and analyze NLU ability from various perspectives. While the English NLU benchmark, GLUE, has been the forerunner, benchmarks are now being released for languages other than English, such as CLUE for Chinese and FLUE for French; but there is no such benchmark for Japanese. We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.",
}

@InProceedings{Kurihara_nlp2022,
  author = 	"栗原健太郎 and 河原大輔 and 柴田知秀",
  title = 	"JGLUE: 日本語言語理解ベンチマーク",
  booktitle = 	"言語処理学会第28回年次大会",
  year =	"2022",
  url = "https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/E8-4.pdf"
  note= "in Japanese"
}
"""

_DESCRIPTION = """\
JGLUE, Japanese General Language Understanding Evaluation, is built to measure the general NLU ability in Japanese. JGLUE has been constructed from scratch without translation. We hope that JGLUE will facilitate NLU research in Japanese.
"""

_HOMEPAGE = "https://github.com/yahoojapan/JGLUE"

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

_DESCRIPTION_CONFIGS = {
    "MARC-ja": "MARC-ja is a dataset of the text classification task. This dataset is based on the Japanese portion of Multilingual Amazon Reviews Corpus (MARC) (Keung+, 2020).",
    "JSTS": "JSTS is a Japanese version of the STS (Semantic Textual Similarity) dataset. STS is a task to estimate the semantic similarity of a sentence pair.",
    "JNLI": "JNLI is a Japanese version of the NLI (Natural Language Inference) dataset. NLI is a task to recognize the inference relation that a premise sentence has to a hypothesis sentence.",
    "JSQuAD": "JSQuAD is a Japanese version of SQuAD (Rajpurkar+, 2016), one of the datasets of reading comprehension.",
    "JCommonsenseQA": "JCommonsenseQA is a Japanese version of CommonsenseQA (Talmor+, 2019), which is a multiple-choice question answering dataset that requires commonsense reasoning ability.",
}

_URLS = {
    "MARC-ja": {
        "data": "https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_multilingual_JP_v1_00.tsv.gz",
        "filter_review_id_list": {
            "valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/preprocess/marc-ja/data/filter_review_id_list/valid.txt"
        },
        "label_conv_review_id_list": {
            "valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/preprocess/marc-ja/data/label_conv_review_id_list/valid.txt"
        },
    },
    "JSTS": {
        "train": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsts-v1.1/train-v1.1.json",
        "valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsts-v1.1/valid-v1.1.json",
    },
    "JNLI": {
        "train": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jnli-v1.1/train-v1.1.json",
        "valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jnli-v1.1/valid-v1.1.json",
    },
    "JSQuAD": {
        "train": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsquad-v1.1/train-v1.1.json",
        "valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsquad-v1.1/valid-v1.1.json",
    },
    "JCommonsenseQA": {
        "train": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jcommonsenseqa-v1.1/train-v1.1.json",
        "valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jcommonsenseqa-v1.1/valid-v1.1.json",
    },
}


def features_jsts() -> ds.Features:
    features = ds.Features(
        {
            "sentence_pair_id": ds.Value("string"),
            "yjcaptions_id": ds.Value("string"),
            "sentence1": ds.Value("string"),
            "sentence2": ds.Value("string"),
            "label": ds.Value("float"),
        }
    )
    return features


def features_jnli() -> ds.Features:
    features = ds.Features(
        {
            "sentence_pair_id": ds.Value("string"),
            "yjcaptions_id": ds.Value("string"),
            "sentence1": ds.Value("string"),
            "sentence2": ds.Value("string"),
            "label": ds.ClassLabel(
                num_classes=3, names=["entailment", "contradiction", "neutral"]
            ),
        }
    )
    return features


def features_jsquad() -> ds.Features:
    title = ds.Value("string")
    answers = ds.Sequence(
        {"text": ds.Value("string"), "answer_start": ds.Value("int64")}
    )
    qas = ds.Sequence(
        {
            "question": ds.Value("string"),
            "id": ds.Value("string"),
            "answers": answers,
            "is_impossible": ds.Value("bool"),
        }
    )
    paragraphs = ds.Sequence({"qas": qas, "context": ds.Value("string")})
    features = ds.Features(
        {"data": ds.Sequence({"title": title, "paragraphs": paragraphs})}
    )
    return features


def features_jcommonsenseqa() -> ds.Features:
    features = ds.Features(
        {
            "q_id": ds.Value("int64"),
            "question": ds.Value("string"),
            "choice0": ds.Value("string"),
            "choice1": ds.Value("string"),
            "choice2": ds.Value("string"),
            "choice3": ds.Value("string"),
            "choice4": ds.Value("string"),
            "label": ds.Value("int8"),
        }
    )
    return features


def features_marc_ja() -> ds.Features:
    features = ds.Features(
        {
            "sentence": ds.Value("string"),
            "label": ds.ClassLabel(
                num_classes=3, names=["positive", "negative", "neutral"]
            ),
            "review_id": ds.Value("string"),
        }
    )
    return features


class MarcJaConfig(ds.BuilderConfig):
    def __init__(
        self,
        name: str = "MARC-ja",
        is_han_to_zen: bool = False,
        max_instance_num: Optional[int] = None,
        max_char_length: int = 500,
        is_pos_neg: bool = True,
        train_ratio: float = 0.94,
        val_ratio: float = 0.03,
        test_ratio: float = 0.03,
        output_testset: bool = False,
        filter_review_id_list_valid: bool = True,
        label_conv_review_id_list_valid: bool = True,
        version: Optional[Union[ds.utils.Version, str]] = ds.utils.Version("0.0.0"),
        data_dir: Optional[str] = None,
        data_files: Optional[ds.data_files.DataFilesDict] = None,
        description: Optional[str] = None,
    ) -> None:
        super().__init__(
            name=name,
            version=version,
            data_dir=data_dir,
            data_files=data_files,
            description=description,
        )
        assert train_ratio + val_ratio + test_ratio == 1.0

        self.train_ratio = train_ratio
        self.val_ratio = val_ratio
        self.test_ratio = test_ratio

        self.is_han_to_zen = is_han_to_zen
        self.max_instance_num = max_instance_num
        self.max_char_length = max_char_length
        self.is_pos_neg = is_pos_neg
        self.output_testset = output_testset

        self.filter_review_id_list_valid = filter_review_id_list_valid
        self.label_conv_review_id_list_valid = label_conv_review_id_list_valid


def get_label(rating: int, is_pos_neg: bool = False) -> Optional[str]:
    if rating >= 4:
        return "positive"
    elif rating <= 2:
        return "negative"
    else:
        if is_pos_neg:
            return None
        else:
            return "neutral"


def is_filtered_by_ascii_rate(text: str, threshold: float = 0.9) -> bool:
    ascii_letters = set(string.printable)
    rate = sum(c in ascii_letters for c in text) / len(text)
    return rate >= threshold


def shuffle_dataframe(df: pd.DataFrame) -> pd.DataFrame:
    instances = df.to_dict(orient="records")
    random.seed(1)
    random.shuffle(instances)
    return pd.DataFrame(instances)


def get_filter_review_id_list(
    filter_review_id_list_paths: Dict[str, str],
) -> Dict[str, List[str]]:
    filter_review_id_list_valid = filter_review_id_list_paths.get("valid")
    filter_review_id_list_test = filter_review_id_list_paths.get("test")

    filter_review_id_list = {}

    if filter_review_id_list_valid is not None:
        with open(filter_review_id_list_valid, "r") as rf:
            filter_review_id_list["valid"] = [line.rstrip() for line in rf]

    if filter_review_id_list_test is not None:
        with open(filter_review_id_list_test, "r") as rf:
            filter_review_id_list["test"] = [line.rstrip() for line in rf]

    return filter_review_id_list


def get_label_conv_review_id_list(
    label_conv_review_id_list_paths: Dict[str, str],
) -> Dict[str, Dict[str, str]]:
    import csv

    label_conv_review_id_list_valid = label_conv_review_id_list_paths.get("valid")
    label_conv_review_id_list_test = label_conv_review_id_list_paths.get("test")

    label_conv_review_id_list: Dict[str, Dict[str, str]] = {}

    if label_conv_review_id_list_valid is not None:
        with open(label_conv_review_id_list_valid, "r") as rf:
            label_conv_review_id_list["valid"] = {
                row[0]: row[1] for row in csv.reader(rf)
            }

    if label_conv_review_id_list_test is not None:
        with open(label_conv_review_id_list_test, "r") as rf:
            label_conv_review_id_list["test"] = {
                row[0]: row[1] for row in csv.reader(rf)
            }

    return label_conv_review_id_list


def output_data(
    df: pd.DataFrame,
    train_ratio: float,
    val_ratio: float,
    test_ratio: float,
    output_testset: bool,
    filter_review_id_list_paths: Dict[str, str],
    label_conv_review_id_list_paths: Dict[str, str],
) -> Dict[str, pd.DataFrame]:
    instance_num = len(df)
    split_dfs: Dict[str, pd.DataFrame] = {}
    length1 = int(instance_num * train_ratio)
    split_dfs["train"] = df.iloc[:length1]

    length2 = int(instance_num * (train_ratio + val_ratio))
    split_dfs["valid"] = df.iloc[length1:length2]
    split_dfs["test"] = df.iloc[length2:]

    filter_review_id_list = get_filter_review_id_list(
        filter_review_id_list_paths=filter_review_id_list_paths,
    )
    label_conv_review_id_list = get_label_conv_review_id_list(
        label_conv_review_id_list_paths=label_conv_review_id_list_paths,
    )

    for eval_type in ("valid", "test"):
        if filter_review_id_list.get(eval_type):
            df = split_dfs[eval_type]
            df = df[~df["review_id"].isin(filter_review_id_list[eval_type])]
            split_dfs[eval_type] = df

    for eval_type in ("valid", "test"):
        if label_conv_review_id_list.get(eval_type):
            df = split_dfs[eval_type]
            df = df.assign(
                converted_label=df["review_id"].map(label_conv_review_id_list["valid"])
            )
            df = df.assign(
                label=df[["label", "converted_label"]].apply(
                    lambda xs: xs["label"]
                    if pd.isnull(xs["converted_label"])
                    else xs["converted_label"],
                    axis=1,
                )
            )
            df = df.drop(columns=["converted_label"])
            split_dfs[eval_type] = df

    return {
        "train": split_dfs["train"],
        "valid": split_dfs["valid"],
    }


def preprocess_for_marc_ja(
    config: MarcJaConfig,
    data_file_path: str,
    filter_review_id_list_paths: Dict[str, str],
    label_conv_review_id_list_paths: Dict[str, str],
) -> Dict[str, pd.DataFrame]:
    import mojimoji
    from bs4 import BeautifulSoup
    from tqdm import tqdm

    df = pd.read_csv(data_file_path, delimiter="\t")
    df = df[["review_body", "star_rating", "review_id"]]

    # rename columns
    df = df.rename(columns={"review_body": "text", "star_rating": "rating"})

    # convert the rating to label
    tqdm.pandas(dynamic_ncols=True, desc="Convert the rating to the label")
    df = df.assign(
        label=df["rating"].progress_apply(
            lambda rating: get_label(rating, config.is_pos_neg)
        )
    )

    # remove rows where the label is None
    df = df[~df["label"].isnull()]

    # remove html tags from the text
    tqdm.pandas(dynamic_ncols=True, desc="Remove html tags from the text")
    df = df.assign(
        text=df["text"].progress_apply(
            lambda text: BeautifulSoup(text, "html.parser").get_text()
        )
    )

    # filter by ascii rate
    tqdm.pandas(dynamic_ncols=True, desc="Filter by ascii rate")
    df = df[~df["text"].progress_apply(is_filtered_by_ascii_rate)]

    if config.max_char_length is not None:
        df = df[df["text"].str.len() <= config.max_char_length]

    if config.is_han_to_zen:
        df = df.assign(text=df["text"].apply(mojimoji.han_to_zen))

    df = df[["text", "label", "review_id"]]
    df = df.rename(columns={"text": "sentence"})

    # shuffle dataset
    df = shuffle_dataframe(df)

    split_dfs = output_data(
        df=df,
        train_ratio=config.train_ratio,
        val_ratio=config.val_ratio,
        test_ratio=config.test_ratio,
        output_testset=config.output_testset,
        filter_review_id_list_paths=filter_review_id_list_paths,
        label_conv_review_id_list_paths=label_conv_review_id_list_paths,
    )
    return split_dfs


class JGLUE(ds.GeneratorBasedBuilder):
    VERSION = ds.Version("1.1.0")
    BUILDER_CONFIGS = [
        MarcJaConfig(
            name="MARC-ja",
            version=VERSION,
            description=_DESCRIPTION_CONFIGS["MARC-ja"],
        ),
        ds.BuilderConfig(
            name="JSTS",
            version=VERSION,
            description=_DESCRIPTION_CONFIGS["JSTS"],
        ),
        ds.BuilderConfig(
            name="JNLI",
            version=VERSION,
            description=_DESCRIPTION_CONFIGS["JNLI"],
        ),
        ds.BuilderConfig(
            name="JSQuAD",
            version=VERSION,
            description=_DESCRIPTION_CONFIGS["JSQuAD"],
        ),
        ds.BuilderConfig(
            name="JCommonsenseQA",
            version=VERSION,
            description=_DESCRIPTION_CONFIGS["JCommonsenseQA"],
        ),
    ]

    def _info(self) -> ds.DatasetInfo:
        if self.config.name == "JSTS":
            features = features_jsts()
        elif self.config.name == "JNLI":
            features = features_jnli()
        elif self.config.name == "JSQuAD":
            features = features_jsquad()
        elif self.config.name == "JCommonsenseQA":
            features = features_jcommonsenseqa()
        elif self.config.name == "MARC-ja":
            features = features_marc_ja()
        else:
            raise ValueError(f"Invalid config name: {self.config.name}")

        return ds.DatasetInfo(
            description=_DESCRIPTION,
            citation=_CITATION,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            features=features,
        )

    def _split_generators(self, dl_manager: ds.DownloadManager):
        file_paths = dl_manager.download_and_extract(_URLS[self.config.name])

        if self.config.name == "MARC-ja":
            filter_review_id_list = file_paths["filter_review_id_list"]
            label_conv_review_id_list = file_paths["label_conv_review_id_list"]

            split_dfs = preprocess_for_marc_ja(
                config=self.config,
                data_file_path=file_paths["data"],
                filter_review_id_list_paths=filter_review_id_list,
                label_conv_review_id_list_paths=label_conv_review_id_list,
            )
            return [
                ds.SplitGenerator(
                    name=ds.Split.TRAIN,
                    gen_kwargs={"split_df": split_dfs["train"]},
                ),
                ds.SplitGenerator(
                    name=ds.Split.VALIDATION,
                    gen_kwargs={"split_df": split_dfs["valid"]},
                ),
            ]
        else:
            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 self.config.name == "MARC-ja":
            if split_df is None:
                raise ValueError(f"Invalid preprocessing for {self.config.name}")

            instances = split_df.to_dict(orient="records")
            for i, data_dict in enumerate(instances):
                yield i, data_dict

        else:
            if file_path is None:
                raise ValueError(f"Invalid argument for {self.config.name}")

            with open(file_path, "r") as rf:
                for i, line in enumerate(rf):
                    json_dict = json.loads(line)
                    yield i, json_dict