"""Monk Dataset"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")
_ORIGINAL_FEATURE_NAMES = [
    "is_monk",
    "head_shape",
    "body_shape",
    "is_smiling",
    "holding",
    "jacket_color",
    "has_tie",
    "ID"
]
_BASE_FEATURE_NAMES = [
    "head_shape",
    "body_shape",
    "is_smiling",
    "holding",
    "jacket_color",
    "has_tie",
    "is_monk"
]

_ENCODING_DICS = {
    "head_shape": {
        0: "round",
        1: "square",
        2: "octagon",
    },
    "body_shape": {
        0: "round",
        1: "square",
        2: "octagon",
    },
    "holding": {
        0: "sword",
        1: "baloon",
        2: "flag",
    },
    "jacket_color": {
        0: "red",
        1: "yellow",
        2: "green",
        3: "blue"
    },
    "is_smiling": {
        1: True,
        0: False
    },
    "has_tie": {
        1: True,
        0: False
    }    
}

DESCRIPTION = "Monk quality dataset."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/70/monk+s+problems"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/70/monk+s+problems")
_CITATION = """
@misc{misc_monk's_problems_70,
  author       = {Wnek,J.},
  title        = {{MONK's Problems}},
  year         = {1992},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C5R30R}}
}"""

# Dataset info
urls_per_split = {
    "monks1": {
        "train": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-1.train",
        "test": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-1.test"
    },
    "monks2": {
        "train": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-2.train",
        "test": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-2.test"
    },
    "monks3": {
        "train": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-3.train",
        "test": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-3.test"
    }
}
features_types_per_config = {
    "monks1": {
        "head_shape": datasets.Value("string"),
        "body_shape": datasets.Value("string"),
        "is_smiling": datasets.Value("bool"),
        "holding": datasets.Value("string"),
        "jacket_color": datasets.Value("string"),
        "has_tie": datasets.Value("bool"),
        "is_monk": datasets.ClassLabel(num_classes=2)
    }
    
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


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


class Monk(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "monks1"
    BUILDER_CONFIGS = [
        MonkConfig(name="monks1",
                   description="Monk 1 problem."),
        MonkConfig(name="monks2",
                   description="Monk 2 problem."),
        MonkConfig(name="monks3",
                   description="Monk 3 problem.")
    ]


    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, sep=" ")
        data = self.preprocess(data, config=self.config.name)
        print(data.head())

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

            yield row_id, data_row

    def preprocess(self, data: pandas.DataFrame, config: str = "monks1") -> pandas.DataFrame:
        data.columns = _BASE_FEATURE_NAMES
        data.drop("ID", axis="columns", inplace=True)
        data.loc[:, "has_tie"] = data.has_tie.apply(bool)
        data.loc[:, "is_smiling"] = data.is_smiling.apply(bool)

        for feature in _ENCODING_DICS:
            encoding_function = partial(self.encode, feature)
            data.loc[:, feature] = data[feature].apply(encoding_function)
                
        return data[list(features_types_per_config[config].keys())]

    def encode(self, feature, value):
        if feature in _ENCODING_DICS:
            return _ENCODING_DICS[feature][value]
        raise ValueError(f"Unknown feature: {feature}")