"""Chess"""

from typing import List

import datasets

import pandas


VERSION = datasets.Version("1.0.0")
_BASE_FEATURE_NAMES = [
    "bkblk",
	"bknwy",
	"bkon8",
	"bkona",
	"bkspr",
	"bkxbq",
	"bkxcr",
	"bkxwp",
	"blxwp",
	"bxqsq",
	"cntxt",
	"dsopp",
	"dwipd",
	"hdchk",
	"katri",
	"mulch",
	"qxmsq",
	"r2ar8",
	"reskd",
	"reskr",
	"rimmx",
	"rkxwp",
	"rxmsq",
	"simpl",
	"skach",
	"skewr",
	"skrxp",
	"spcop",
	"stlmt",
	"thrsk",
	"wkcti",
	"wkna8",
	"wknck",
	"wkovl",
	"wkpos",
	"white_wins"
]

DESCRIPTION = "Chess dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Chess"
_URLS = ("https://huggingface.co/datasets/mstz/chess/raw/chess.csv")
_CITATION = """
@misc{misc_chess_(king-rook_vs._king-pawn)_22,
  title        = {{Chess (King-Rook vs. King-Pawn)}},
  year         = {1989},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C5DK5C}}
}"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/chess/raw/main/kr-vs-kp.data"
}
features_types_per_config = {
    "chess": {
        "bkblk": datasets.Value("string"),
        "bknwy": datasets.Value("string"),
        "bkon8": datasets.Value("string"),
        "bkona": datasets.Value("string"),
        "bkspr": datasets.Value("string"),
        "bkxbq": datasets.Value("string"),
        "bkxcr": datasets.Value("string"),
        "bkxwp": datasets.Value("string"),
        "blxwp": datasets.Value("string"),
        "bxqsq": datasets.Value("string"),
        "cntxt": datasets.Value("string"),
        "dsopp": datasets.Value("string"),
        "dwipd": datasets.Value("string"),
        "hdchk": datasets.Value("string"),
        "katri": datasets.Value("string"),
        "mulch": datasets.Value("string"),
        "qxmsq": datasets.Value("string"),
        "r2ar8": datasets.Value("string"),
        "reskd": datasets.Value("string"),
        "reskr": datasets.Value("string"),
        "rimmx": datasets.Value("string"),
        "rkxwp": datasets.Value("string"),
        "rxmsq": datasets.Value("string"),
        "simpl": datasets.Value("string"),
        "skach": datasets.Value("string"),
        "skewr": datasets.Value("string"),
        "skrxp": datasets.Value("string"),
        "spcop": datasets.Value("string"),
        "stlmt": datasets.Value("string"),
        "thrsk": datasets.Value("string"),
        "wkcti": datasets.Value("string"),
        "wkna8": datasets.Value("string"),
        "wknck": datasets.Value("string"),
        "wkovl": datasets.Value("string"),
        "wkpos": datasets.Value("string"),
        "wtoeg": datasets.Value("string"),
        "white_wins": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
    }
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


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


class Chess(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "chess"
    BUILDER_CONFIGS = [
        ChessConfig(name="chess",
                    description="Chess for 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)

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

            yield row_id, data_row