from typing import List

import datasets

import pandas


VERSION = datasets.Version("1.0.0")
_BASE_FEATURE_NAMES = [
    "family",
    "product_type",
    "steel_type",
    "carbon",
    "hardness",
    "temper_rolling",
    "condition",
    "formability",
    "strength",
    "non_ageing",
    "surface_finish",
    "surface_quality",
    "enamelability",
    "bc",
    "bf",
    "bt",
    "bw_time",
    "bl",
    "m",
    "chrom",
    "phos",
    "cbond",
    "marvi",
    "exptl",
    "ferro",
    "corr",
    "blue",
    "lustre",
    "jurofm",
    "s",
    "p",
    "is_coil",
    "thick",
    "width",
    "len",
    "oil",
    "bore",
    "packing",
    "class"
]


DESCRIPTION = "Anneal dataset from the UCI ML repository."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/3/annealing"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/3/annealing")
_CITATION = """"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/anneal/raw/main/anneal.data",
    "test": "https://huggingface.co/datasets/mstz/anneal/raw/main/anneal.test"
}
features_types_per_config = {
    "annealing": {
        "family": datasets.Value("string"),
        "steel_type": datasets.Value("string"),
        "carbon": datasets.Value("float64"),
        "hardness": datasets.Value("float64"),
        "temper_rolling": datasets.Value("bool"),
        "condition": datasets.Value("string"),
        "formability": datasets.Value("int8"),
        "strength": datasets.Value("float64"),
        "surface_finish": datasets.Value("string"),
        "surface_quality": datasets.Value("string"),
        "enamelability": datasets.Value("int8"),
        "bc": datasets.Value("bool"),
        "bf": datasets.Value("bool"),
        "bt": datasets.Value("bool"),
        "bw_time": datasets.Value("string"),
        "bl": datasets.Value("bool"),
        "chrom": datasets.Value("bool"),
        "phos": datasets.Value("bool"),
        "cbond": datasets.Value("bool"),
        "marvi": datasets.Value("bool"),
        "exptl": datasets.Value("bool"),
        "ferro": datasets.Value("bool"),
        "corr": datasets.Value("bool"),
        "blue": datasets.Value("string"),
        "lustre": datasets.Value("bool"),
        "jurofm": datasets.Value("bool"),
        "s": datasets.Value("bool"),
        "p": datasets.Value("bool"),
        "is_coil": datasets.Value("bool"),
        "thick": datasets.Value("float64"),
        "width": datasets.Value("float64"),
        "len": datasets.Value("float64"),
        "oil": datasets.Value("string"),
        "bore": datasets.Value("string"),
        "packing": datasets.Value("string"),
        "class": datasets.ClassLabel(num_classes=6, names=["1", "2", "3", "4", "5", "U"])
    }
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


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


class Anneal(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "annealing"
    BUILDER_CONFIGS = [
        AnnealConfig(name="annealing",
                    description="Anneal for multiclass 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"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloads["test"]})
        ]
    
    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.columns = _BASE_FEATURE_NAMES
        # data = data[data.formability != "?"]
        # data = data[data.enamelability != "?"]

        data.drop("product_type", axis="columns", inplace=True)
        data.drop("non_ageing", axis="columns", inplace=True)
        data.drop("m", axis="columns", inplace=True)
        
        data.loc[:, "formability"] = data.formability.apply(lambda x: int(x) if x not in ("?", "-") else 0)
        data.loc[:, "enamelability"] = data.enamelability.apply(lambda x: int(x) if x not in ("?", "-") else 0)
        # data.loc[:, "non_ageing"] = data.formability.apply(lambda x: True if x == "-" else False)
        data.loc[:, "bc"] = data.bc.apply(lambda x: True if x == "Y" else False)
        data.loc[:, "bf"] = data.bf.apply(lambda x: True if x == "Y" else False)
        data.loc[:, "bt"] = data.bt.apply(lambda x: True if x == "Y" else False)
        data.loc[:, "bl"] = data.bl.apply(lambda x: True if x == "Y" else False)
        # data.loc[:, "m"] = data.m.apply(lambda x: True if x == "Y" else False)
        data.loc[:, "chrom"] = data.chrom.apply(lambda x: True if x == "C" else False)
        data.loc[:, "phos"] = data.phos.apply(lambda x: True if x == "P" else False)
        data.loc[:, "cbond"] = data.cbond.apply(lambda x: True if x == "Y" else False)
        data.loc[:, "marvi"] = data.marvi.apply(lambda x: True if x == "Y" else False)
        data.loc[:, "exptl"] = data.exptl.apply(lambda x: True if x == "Y" else False)
        data.loc[:, "ferro"] = data.ferro.apply(lambda x: True if x == "Y" else False)
        data.loc[:, "corr"] = data["corr"].apply(lambda x: True if x == "Y" else False)
        data.loc[:, "lustre"] = data.lustre.apply(lambda x: True if x == "Y" else False)
        data.loc[:, "jurofm"] = data.jurofm.apply(lambda x: True if x == "Y" else False)
        data.loc[:, "s"] = data.s.apply(lambda x: True if x == "Y" else False)
        data.loc[:, "p"] = data.p.apply(lambda x: True if x == "Y" else False)
        data.loc[:, "class"] = data.p.apply(lambda x: int(x) if x != "U" else 0)

        return data