"""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):
        if self.config.name == "encoding":
            data = self.encodings()

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

                yield row_id, data_row

        elif self.config.name in ["magic", "magic-no race", "race"]:               
            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

        else:
            raise ValueError(f"Unknown config: {self.config.name}")

    def encodings(self):
        data = [pandas.DataFrame([(feature, original_value, encoded_value)
                                   for original_value, encoded_value in d.items()],
                                 columns=["feature", "original_value", "encoded_value"])
                for feature, d in _ENCODING_DICS.items()]
        data.append(pandas.DataFrame([("race", original_value, encoded_value)
                                       for original_value, encoded_value in _RACE_ENCODING.items()],
                    columns=["feature", "original_value", "encoded_value"]))
        data.append(pandas.DataFrame([("education", original_value, encoded_value)
                                       for original_value, encoded_value in _EDUCATION_ENCODING.items()],
                    columns=["feature", "original_value", "encoded_value"]))
        data = pandas.concat(data, axis="rows").reset_index()
        data.drop("index", axis="columns", inplace=True)

        return data


    def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
        data.drop("education", axis="columns", inplace=True)
        data = data.rename(columns={"threshold": "over_threshold", "sex": "is_male"})

        data = data[["age", "capital_gain", "capital_loss", "education-num", "final_weight",
                     "hours_per_week", "marital_status", "native_country", "occupation",
                     "race", "relationship", "is_male", "workclass", "over_threshold"]]
        data.columns = _BASE_FEATURE_NAMES

        for feature in _ENCODING_DICS:
            encoding_function = partial(self.encode, feature)
            data.loc[:, feature] = data[feature].apply(encoding_function)
        

        if config == "magic":
            return data[list(features_types_per_config["magic"].keys())]
        elif config == "magic-no race":
            return data[list(features_types_per_config["magic-no race"].keys())]
        elif config =="race":
            data.loc[:, "race"] = data.race.apply(self.encode_race)
            data = data[list(features_types_per_config["race"].keys())]

            return data
        else:
            raise ValueError(f"Unknown config: {config}")
    
    def encode(self, feature, value):
        if feature in _ENCODING_DICS:
            return _ENCODING_DICS[feature][value]
        raise ValueError(f"Unknown feature: {feature}")
    
    def encode_race(self, race):
        return _RACE_ENCODING[race]