"""Korean Balanced Evaluation of Significant Tasks"""


import csv

import pandas as pd

import datasets


_CITATAION = """\
TBD
"""

_DESCRIPTION = """\
    The dataset contains data for KoBEST dataset
"""

_URL = "https://github.com/SKT-LSL/KoBEST_datarepo"

_DATA_URLS = {
    "boolq": {
        "train": _URL + "/v1.0/BoolQ/train.tsv",
        "dev": _URL + "/v1.0/BoolQ/dev.tsv",
        "test": _URL + "/v1.0/BoolQ/test.tsv",
    },
    "copa": {
        "train": _URL + "/v1.0/COPA/train.tsv",
        "dev": _URL + "/v1.0/COPA/dev.tsv",
        "test": _URL + "/v1.0/COPA/test.tsv",
    },
    "sentineg": {
        "train": _URL + "/v1.0/SentiNeg/train.tsv",
        "dev": _URL + "/v1.0/SentiNeg/dev.tsv",
        "test": _URL + "/v1.0/SentiNeg/test.tsv",
    },
    "hellaswag": {
        "train": _URL + "/v1.0/HellaSwag/train.tsv",
        "dev": _URL + "/v1.0/HellaSwag/dev.tsv",
        "test": _URL + "/v1.0/HellaSwag/test.tsv",
    },
    "wic": {
        "train": _URL + "/v1.0/WiC/train.tsv",
        "dev": _URL + "/v1.0/WiC/dev.tsv",
        "test": _URL + "/v1.0/WiC/test.tsv",
    },
}


class KoBESTConfig(datasets.BuilderConfig):
    """Config for building KoBEST"""

    def __init__(self, description, data_url, citation, url, **kwargs):
        """
        Args:
            description: `string`, brief description of the dataset
            data_url: `dictionary`, dict with url for each split of data.
            citation: `string`, citation for the dataset.
            url: `string`, url for information about the dataset.
            **kwrags: keyword arguments frowarded to super
        """
        super(KoBESTConfig, self).__init__(version=datasets.Version("1.0", ""), **kwargs)
        self.description = description
        self.data_url = data_url
        self.citation = citation
        self.url = url


class KoBEST(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        KoBESTConfig(name=name, description=_DESCRIPTION, data_url=_DATA_URLS[name], citation=_CITATAION, url=_URL)
        for name in ["boolq", "copa", 'sentineg', 'hellaswag', 'wic']
    ]
    BUILDER_CONFIG_CLASS = KoBESTConfig

    def _info(self):
        features = {}
        if self.config.name == "boolq":
            labels = ["True", "False"]
            features["paragraph"] = datasets.Value("string")
            features["question"] = datasets.Value("string")
            features["label"] = datasets.features.ClassLabel(names=labels)

        if self.config.name == "copa":
            labels = ["alternative_1", "alternative_2"]
            features["premise"] = datasets.Value("string")
            features["question"] = datasets.Value("string")
            features["alternative_1"] = datasets.Value("string")
            features["alternative_2"] = datasets.Value("string")
            features["label"] = datasets.features.ClassLabel(names=labels)

        if self.config.name == "wic":
            labels = ["True", "False"]
            features["word"] = datasets.Value("string")
            features["context_1"] = datasets.Value("string")
            features["context_2"] = datasets.Value("string")
            features["label"] = datasets.features.ClassLabel(names=labels)

        if self.config.name == "hellaswag":
            labels = ["ending_1", "ending_2", "ending_3", "ending_4"]

            features["context"] = datasets.Value("string")
            features["ending_1"] = datasets.Value("string")
            features["ending_2"] = datasets.Value("string")
            features["ending_3"] = datasets.Value("string")
            features["ending_4"] = datasets.Value("string")
            features["label"] = datasets.features.ClassLabel(names=labels)

        if self.config.name == "sentineg":
            labels = ["negative", "positive"]
            features["sentence"] = datasets.Value("string")
            features["label"] = datasets.features.ClassLabel(names=labels)

        return datasets.DatasetInfo(
            description=_DESCRIPTION, features=datasets.Features(features), homepage=_URL, citation=_CITATAION
        )

    def _split_generators(self, dl_manager):

        train = dl_manager.download_and_extract(self.config.data_url["train"])
        dev = dl_manager.download_and_extract(self.config.data_url["dev"])
        test = dl_manager.download_and_extract(self.config.data_url["test"])

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train, "split": "train"}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dev, "split": "dev"}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test, "split": "test"}),
        ]

        # if self.config.name == "boolq":
        #     train = dl_manager.download_and_extract(self.config.data_url["train"])
        #     dev = dl_manager.download_and_extract(self.config.data_url["dev"])
        #     test = dl_manager.download_and_extract(self.config.data_url["test"])
        #
        #     return [
        #         datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train, "split": "train"}),
        #         datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dev, "split": "dev"}),
        #         datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test, "split": "test"}),
        #     ]
        #

    def _generate_examples(self, filepath, split):
        if self.config.name == "boolq":
            df = pd.read_csv(filepath, sep="\t")
            df = df.dropna()

            for id_, row in df.iterrows():
                yield id_, {
                    "paragraph": str(row["Text"]),
                    "question": str(row["Question"]),
                    "label": str(int(row["Answer"])),
                }

        if self.config.name == "copa":
            df = pd.read_csv(filepath, sep="\t")
            df = df.dropna()

            for id_, row in df.iterrows():
                yield id_, {
                    "premise": str(row["sentence"]),
                    "question": str(row["question"]),
                    "alternative_1": str(int(row["1"])),
                    "alternative_2": str(int(row["2"])),
                    "label": str(row["Answer"]-1),
                }

        if self.config.name == "wic":
            df = pd.read_csv(filepath, sep="\t")
            df = df.dropna()

            for id_, row in df.iterrows():
                yield id_, {
                    "word": str(row["Target"]),
                    "context_1": str(row["SENTENCE1"]),
                    "context_2": str(int(row["SENTENCE2"])),
                    "label": str(int(row["Answer"])),
                }

        if self.config.name == "hellaswag":
            df = pd.read_csv(filepath, sep="\t")
            df = df.dropna()

            for id_, row in df.iterrows():
                yield id_, {
                    "context": str(row["context"]),
                    "ending_1": str(row["choice1"]),
                    "ending_2": str(int(row["choice2"])),
                    "ending_3": str(int(row["choice3"])),
                    "ending_4": str(int(row["choice4"])),
                    "label": str(row["label"]),
                }

        if self.config.name == "sentineg":
            df = pd.read_csv(filepath, sep="\t")
            df = df.dropna()

            for id_, row in df.iterrows():
                yield id_, {
                    "sentence": str(row["Text"]),
                    "label": str(int(row["Label"])),
                }