"""Hill"""

from typing import List

import datasets

import pandas


VERSION = datasets.Version("1.0.0")

DESCRIPTION = "Hill dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Hill"
_URLS = ("https://archive.ics.uci.edu/ml/datasets/Hill")
_CITATION = """
@misc{misc_hill-valley_166,
  author       = {Graham,Lee & Oppacher,Franz},
  title        = {{Hill-Valley}},
  year         = {2008},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C5JC8P}}
}"""

# Dataset info
urls_per_split = {
    "train": "Hill_Valley_without_noise_Training.data", 
    "test": "Hill_Valley_without_noise_Testing.data"
}
features_types_per_config = {
    "hill": {
        "X1": datasets.Value("float64"),
        "X2": datasets.Value("float64"),
        "X3": datasets.Value("float64"),
        "X4": datasets.Value("float64"),
        "X5": datasets.Value("float64"),
        "X6": datasets.Value("float64"),
        "X7": datasets.Value("float64"),
        "X8": datasets.Value("float64"),
        "X9": datasets.Value("float64"),
        "X10": datasets.Value("float64"),
        "X11": datasets.Value("float64"),
        "X12": datasets.Value("float64"),
        "X13": datasets.Value("float64"),
        "X14": datasets.Value("float64"),
        "X15": datasets.Value("float64"),
        "X16": datasets.Value("float64"),
        "X17": datasets.Value("float64"),
        "X18": datasets.Value("float64"),
        "X19": datasets.Value("float64"),
        "X20": datasets.Value("float64"),
        "X21": datasets.Value("float64"),
        "X22": datasets.Value("float64"),
        "X23": datasets.Value("float64"),
        "X24": datasets.Value("float64"),
        "X25": datasets.Value("float64"),
        "X26": datasets.Value("float64"),
        "X27": datasets.Value("float64"),
        "X28": datasets.Value("float64"),
        "X29": datasets.Value("float64"),
        "X30": datasets.Value("float64"),
        "X31": datasets.Value("float64"),
        "X32": datasets.Value("float64"),
        "X33": datasets.Value("float64"),
        "X34": datasets.Value("float64"),
        "X35": datasets.Value("float64"),
        "X36": datasets.Value("float64"),
        "X37": datasets.Value("float64"),
        "X38": datasets.Value("float64"),
        "X39": datasets.Value("float64"),
        "X40": datasets.Value("float64"),
        "X41": datasets.Value("float64"),
        "X42": datasets.Value("float64"),
        "X43": datasets.Value("float64"),
        "X44": datasets.Value("float64"),
        "X45": datasets.Value("float64"),
        "X46": datasets.Value("float64"),
        "X47": datasets.Value("float64"),
        "X48": datasets.Value("float64"),
        "X49": datasets.Value("float64"),
        "X50": datasets.Value("float64"),
        "X51": datasets.Value("float64"),
        "X52": datasets.Value("float64"),
        "X53": datasets.Value("float64"),
        "X54": datasets.Value("float64"),
        "X55": datasets.Value("float64"),
        "X56": datasets.Value("float64"),
        "X57": datasets.Value("float64"),
        "X58": datasets.Value("float64"),
        "X59": datasets.Value("float64"),
        "X60": datasets.Value("float64"),
        "X61": datasets.Value("float64"),
        "X62": datasets.Value("float64"),
        "X63": datasets.Value("float64"),
        "X64": datasets.Value("float64"),
        "X65": datasets.Value("float64"),
        "X66": datasets.Value("float64"),
        "X67": datasets.Value("float64"),
        "X68": datasets.Value("float64"),
        "X69": datasets.Value("float64"),
        "X70": datasets.Value("float64"),
        "X71": datasets.Value("float64"),
        "X72": datasets.Value("float64"),
        "X73": datasets.Value("float64"),
        "X74": datasets.Value("float64"),
        "X75": datasets.Value("float64"),
        "X76": datasets.Value("float64"),
        "X77": datasets.Value("float64"),
        "X78": datasets.Value("float64"),
        "X79": datasets.Value("float64"),
        "X80": datasets.Value("float64"),
        "X81": datasets.Value("float64"),
        "X82": datasets.Value("float64"),
        "X83": datasets.Value("float64"),
        "X84": datasets.Value("float64"),
        "X85": datasets.Value("float64"),
        "X86": datasets.Value("float64"),
        "X87": datasets.Value("float64"),
        "X88": datasets.Value("float64"),
        "X89": datasets.Value("float64"),
        "X90": datasets.Value("float64"),
        "X91": datasets.Value("float64"),
        "X92": datasets.Value("float64"),
        "X93": datasets.Value("float64"),
        "X94": datasets.Value("float64"),
        "X95": datasets.Value("float64"),
        "X96": datasets.Value("float64"),
        "X97": datasets.Value("float64"),
        "X98": datasets.Value("float64"),
        "X99": datasets.Value("float64"),
        "X100": datasets.Value("float64"),
        "class": datasets.ClassLabel(num_classes=2)
    }
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


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


class Hill(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "hill"
    BUILDER_CONFIGS = [
        HillConfig(name="hill",
                    description="Hill 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"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloads["test"]}),
        ]
    
    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