"""PageBlocks Dataset"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")

_ENCODING_DICS = {
}

DESCRIPTION = "PageBlocks dataset."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/78/page+blocks+classification"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/78/page+blocks+classification")
_CITATION = """
@misc{misc_page_blocks_classification_78,
  author       = {Malerba,Donato},
  title        = {{Page Blocks Classification}},
  year         = {1995},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C5J590}}
}
"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co./datasets/mstz/page_blocks/raw/main/page_blocks.data"
}
features_types_per_config = {
    "page_blocks": {
		"height": datasets.Value("float64"),
        "lenght": datasets.Value("float64"),
        "area": datasets.Value("float64"),
        "eccentricity": datasets.Value("float64"),
        "percentage_black_pixels": datasets.Value("float64"),
        "percentage_black_pixels_after_rlsa_and": datasets.Value("float64"),
        "mean_numer_of_transitions": datasets.Value("float64"),
        "number_of_black_pixels": datasets.Value("float64"),
        "number_of_black_pixels_after_rlsa": datasets.Value("float64"),
        "number_of_transitions": datasets.Value("int8")
    },
    "page_blocks_binary": {
		"height": datasets.Value("float64"),
        "lenght": datasets.Value("float64"),
        "area": datasets.Value("float64"),
        "eccentricity": datasets.Value("float64"),
        "percentage_black_pixels": datasets.Value("float64"),
        "percentage_black_pixels_after_rlsa_and": datasets.Value("float64"),
        "mean_numer_of_transitions": datasets.Value("float64"),
        "number_of_black_pixels": datasets.Value("float64"),
        "number_of_black_pixels_after_rlsa": datasets.Value("float64"),
        "has_multiple_transitions": datasets.ClassLabel(num_classes=2)
    }
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


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


class PageBlocks(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "page_blocks"
    BUILDER_CONFIGS = [
        PageBlocksConfig(name="page_blocks",
                   description="PageBlocks for regression."),
        PageBlocksConfig(name="page_blocks_binary",
                   description="PageBlocks 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)
        data = self.preprocess(data)

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

            yield row_id, data_row

    def preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame:
        if self.config.name == "page_blocks_binary":
            data["number_of_transitions"] = data["number_of_transitions"].apply(lambda x: 1 if x > 1 else 0)
            data = data.rename(columns={"number_of_transitions": "has_multiple_transitions"})

        for feature in _ENCODING_DICS:
            encoding_function = partial(self.encode, feature)
            data.loc[:, feature] = data[feature].apply(encoding_function)
        
        data = data.reset_index()
        data.drop("index", axis="columns", inplace=True)
                
        return data[list(features_types_per_config[self.config.name].keys())]

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