"""Magic"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")
_BASE_FEATURE_NAMES = [
    "major_axis_length",
    "minor_axis_length",
    "log_of_sum_of_content",
    "ratio_of_sum_of_highest_pixels_and_size",
    "ratio_of_highest_pixel_and_size",
    "projected_distance_highest_to_center_pixel",
    "third_root_of_third_moment_along_major_axis",
    "third_root_of_third_moment_along_minor_axis",
    "angle_major_axis_to_origin",
    "distance_origin_to_center",
    "class"
]


DESCRIPTION = "Magic dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Magic"
_URLS = ("https://archive.ics.uci.edu/ml/datasets/Magic")
_CITATION = """
@misc{misc_magic_gamma_telescope_159,
  author       = {Bock,R.},
  title        = {{MAGIC Gamma Telescope}},
  year         = {2007},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C52C8B}}
}"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/magic/raw/main/magic04.data"
}
features_types_per_config = {
    "magic": {
        "major_axis_length": datasets.Value("float64"),
        "minor_axis_length": datasets.Value("float64"),
        "log_of_sum_of_content": datasets.Value("float64"),
        "ratio_of_sum_of_highest_pixels_and_size": datasets.Value("float64"),
        "ratio_of_highest_pixel_and_size": datasets.Value("float64"),
        "projected_distance_highest_to_center_pixel": datasets.Value("float64"),
        "third_root_of_third_moment_along_major_axis": datasets.Value("float64"),
        "third_root_of_third_moment_along_minor_axis": datasets.Value("float64"),
        "angle_major_axis_to_origin": datasets.Value("float64"),
        "distance_origin_to_center": datasets.Value("float64"),
        "class": 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 MagicConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(MagicConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class Magic(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "magic"
    BUILDER_CONFIGS = [
        MagicConfig(name="magic",
                    description="Magic 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, header=None)
        data.columns = _BASE_FEATURE_NAMES
        data.loc[:, "class"] = data["class"].apply(lambda x: 1 if x == "g" else 0)

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

            yield row_id, data_row