"""Wine Dataset"""

from typing import List

import datasets

import pandas


VERSION = datasets.Version("1.0.0")
_BASE_FEATURE_NAMES = [
    "fixed_acidity",
    "volatile_acidity",
    "citric_acid",
    "residual_sugar",
    "chlorides",
    "free_sulfur_dioxide",
    "total_sulfur_dioxide",
    "density",
    "pH",
    "sulphates",
    "alcohol",
    "quality",
    "is_red"
]


DESCRIPTION = "Wine quality dataset."
_HOMEPAGE = "https://www.kaggle.com/datasets/ghassenkhaled/wine-quality-data"
_URLS = ("https://www.kaggle.com/datasets/ghassenkhaled/wine-quality-data")
_CITATION = """"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/wine/raw/main/Wine_Quality_Data.csv",
}
features_types_per_config = {
    "wine": {
        "fixed_acidity": datasets.Value("float64"),
        "volatile_acidity": datasets.Value("float64"),
        "citric_acid": datasets.Value("float64"),
        "residual_sugar": datasets.Value("float64"),
        "chlorides": datasets.Value("float64"),
        "free_sulfur_dioxide": datasets.Value("float64"),
        "total_sulfur_dioxide": datasets.Value("float64"),
        "density": datasets.Value("float64"),
        "pH": datasets.Value("float64"),
        "sulphates": datasets.Value("float64"),
        "alcohol": datasets.Value("float64"),
        "quality": datasets.Value("int8"),
        "is_red": datasets.ClassLabel(num_classes=2, names=("red", "white"))
    }
    
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


class WineConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(WineConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class Wine(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "wine"
    BUILDER_CONFIGS = [
        WineConfig(name="wine",
                   description="Binary classification."),
    ]


    def _info(self):
        info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
                                    features=features_per_config[self.config.name])

        return info
    
    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        downloads = dl_manager.download_and_extract(urls_per_split)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]}),
        ]
    
    def _generate_examples(self, filepath: str):
        data = pandas.read_csv(filepath)
        data = self.preprocess(data, config=self.config.name)

        for row_id, row in data.iterrows():
            data_row = dict(row)

            yield row_id, data_row

    def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
        data = data.rename(columns={"color": "is_red"})       
        data.loc[data.is_red == "red", "is_red"] = 0
        data.loc[data.is_red == "white", "is_red"] = 1
        
        return data