"""Segment Dataset"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")

_ENCODING_DICS = {}

DESCRIPTION = "Segment 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_statlog_(image_segmentation)_147,
  title        = {{Statlog (Image Segmentation)}},
  year         = {1990},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C5P01G}}
}
"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/segment/raw/main/segment.csv"
}
features_types_per_config = {
    "segment": {
        "region_centroid_col": datasets.Value("float64"),
        "region_centroid_row": datasets.Value("float64"),
        "region_centroid_pixel_count": datasets.Value("float64"),
        "short_line_density": datasets.Value("float64"),
        "vedge_mean": datasets.Value("float64"),
        "vedge_std": datasets.Value("float64"),
        "hedge_mean": datasets.Value("float64"),
        "hedge_std": datasets.Value("float64"),
        "intensity_mean": datasets.Value("float64"),
        "rawred_mean": datasets.Value("float64"),
        "rawblue_mean": datasets.Value("float64"),
        "rawgreen_mean": datasets.Value("float64"),
        "exred_mean": datasets.Value("float64"),
        "exblue_mean": datasets.Value("float64"),
        "exgreen_mean": datasets.Value("float64"),
        "value_mean": datasets.Value("float64"),
        "saturation_mean": datasets.Value("float64"),
        "hue_mean": datasets.Value("float64"),
        "class": datasets.ClassLabel(num_classes=7,
                                     names=("brickface", "sky", "foliage", "cement", "window", "path", "grass")),
    },
    "brickface": {
        "region_centroid_col": datasets.Value("float64"),
        "region_centroid_row": datasets.Value("float64"),
        "region_centroid_pixel_count": datasets.Value("float64"),
        "short_line_density": datasets.Value("float64"),
        "vedge_mean": datasets.Value("float64"),
        "vedge_std": datasets.Value("float64"),
        "hedge_mean": datasets.Value("float64"),
        "hedge_std": datasets.Value("float64"),
        "intensity_mean": datasets.Value("float64"),
        "rawred_mean": datasets.Value("float64"),
        "rawblue_mean": datasets.Value("float64"),
        "rawgreen_mean": datasets.Value("float64"),
        "exred_mean": datasets.Value("float64"),
        "exblue_mean": datasets.Value("float64"),
        "exgreen_mean": datasets.Value("float64"),
        "value_mean": datasets.Value("float64"),
        "saturation_mean": datasets.Value("float64"),
        "hue_mean": datasets.Value("float64"),
        "class": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
    },
    "sky": {
        "region_centroid_col": datasets.Value("float64"),
        "region_centroid_row": datasets.Value("float64"),
        "region_centroid_pixel_count": datasets.Value("float64"),
        "short_line_density": datasets.Value("float64"),
        "vedge_mean": datasets.Value("float64"),
        "vedge_std": datasets.Value("float64"),
        "hedge_mean": datasets.Value("float64"),
        "hedge_std": datasets.Value("float64"),
        "intensity_mean": datasets.Value("float64"),
        "rawred_mean": datasets.Value("float64"),
        "rawblue_mean": datasets.Value("float64"),
        "rawgreen_mean": datasets.Value("float64"),
        "exred_mean": datasets.Value("float64"),
        "exblue_mean": datasets.Value("float64"),
        "exgreen_mean": datasets.Value("float64"),
        "value_mean": datasets.Value("float64"),
        "saturation_mean": datasets.Value("float64"),
        "hue_mean": datasets.Value("float64"),
        "class": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
    },
    "foliage": {
        "region_centroid_col": datasets.Value("float64"),
        "region_centroid_row": datasets.Value("float64"),
        "region_centroid_pixel_count": datasets.Value("float64"),
        "short_line_density": datasets.Value("float64"),
        "vedge_mean": datasets.Value("float64"),
        "vedge_std": datasets.Value("float64"),
        "hedge_mean": datasets.Value("float64"),
        "hedge_std": datasets.Value("float64"),
        "intensity_mean": datasets.Value("float64"),
        "rawred_mean": datasets.Value("float64"),
        "rawblue_mean": datasets.Value("float64"),
        "rawgreen_mean": datasets.Value("float64"),
        "exred_mean": datasets.Value("float64"),
        "exblue_mean": datasets.Value("float64"),
        "exgreen_mean": datasets.Value("float64"),
        "value_mean": datasets.Value("float64"),
        "saturation_mean": datasets.Value("float64"),
        "hue_mean": datasets.Value("float64"),
        "class": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
    },
    "cement": {
        "region_centroid_col": datasets.Value("float64"),
        "region_centroid_row": datasets.Value("float64"),
        "region_centroid_pixel_count": datasets.Value("float64"),
        "short_line_density": datasets.Value("float64"),
        "vedge_mean": datasets.Value("float64"),
        "vedge_std": datasets.Value("float64"),
        "hedge_mean": datasets.Value("float64"),
        "hedge_std": datasets.Value("float64"),
        "intensity_mean": datasets.Value("float64"),
        "rawred_mean": datasets.Value("float64"),
        "rawblue_mean": datasets.Value("float64"),
        "rawgreen_mean": datasets.Value("float64"),
        "exred_mean": datasets.Value("float64"),
        "exblue_mean": datasets.Value("float64"),
        "exgreen_mean": datasets.Value("float64"),
        "value_mean": datasets.Value("float64"),
        "saturation_mean": datasets.Value("float64"),
        "hue_mean": datasets.Value("float64"),
        "class": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
    },
    "window": {
        "region_centroid_col": datasets.Value("float64"),
        "region_centroid_row": datasets.Value("float64"),
        "region_centroid_pixel_count": datasets.Value("float64"),
        "short_line_density": datasets.Value("float64"),
        "vedge_mean": datasets.Value("float64"),
        "vedge_std": datasets.Value("float64"),
        "hedge_mean": datasets.Value("float64"),
        "hedge_std": datasets.Value("float64"),
        "intensity_mean": datasets.Value("float64"),
        "rawred_mean": datasets.Value("float64"),
        "rawblue_mean": datasets.Value("float64"),
        "rawgreen_mean": datasets.Value("float64"),
        "exred_mean": datasets.Value("float64"),
        "exblue_mean": datasets.Value("float64"),
        "exgreen_mean": datasets.Value("float64"),
        "value_mean": datasets.Value("float64"),
        "saturation_mean": datasets.Value("float64"),
        "hue_mean": datasets.Value("float64"),
        "class": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
    },
    "path": {
        "region_centroid_col": datasets.Value("float64"),
        "region_centroid_row": datasets.Value("float64"),
        "region_centroid_pixel_count": datasets.Value("float64"),
        "short_line_density": datasets.Value("float64"),
        "vedge_mean": datasets.Value("float64"),
        "vedge_std": datasets.Value("float64"),
        "hedge_mean": datasets.Value("float64"),
        "hedge_std": datasets.Value("float64"),
        "intensity_mean": datasets.Value("float64"),
        "rawred_mean": datasets.Value("float64"),
        "rawblue_mean": datasets.Value("float64"),
        "rawgreen_mean": datasets.Value("float64"),
        "exred_mean": datasets.Value("float64"),
        "exblue_mean": datasets.Value("float64"),
        "exgreen_mean": datasets.Value("float64"),
        "value_mean": datasets.Value("float64"),
        "saturation_mean": datasets.Value("float64"),
        "hue_mean": datasets.Value("float64"),
        "class": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
    },
    "grass": {
        "region_centroid_col": datasets.Value("float64"),
        "region_centroid_row": datasets.Value("float64"),
        "region_centroid_pixel_count": datasets.Value("float64"),
        "short_line_density": datasets.Value("float64"),
        "vedge_mean": datasets.Value("float64"),
        "vedge_std": datasets.Value("float64"),
        "hedge_mean": datasets.Value("float64"),
        "hedge_std": datasets.Value("float64"),
        "intensity_mean": datasets.Value("float64"),
        "rawred_mean": datasets.Value("float64"),
        "rawblue_mean": datasets.Value("float64"),
        "rawgreen_mean": datasets.Value("float64"),
        "exred_mean": datasets.Value("float64"),
        "exblue_mean": datasets.Value("float64"),
        "exgreen_mean": datasets.Value("float64"),
        "value_mean": datasets.Value("float64"),
        "saturation_mean": datasets.Value("float64"),
        "hue_mean": 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 SegmentConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(SegmentConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class Segment(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "segment"
    BUILDER_CONFIGS = [
        SegmentConfig(name="segment", description="Segment for multiclass classification."),
        SegmentConfig(name="brickface", description="Segment for binary classification."),
        SegmentConfig(name="sky", description="Segment for binary classification."),
        SegmentConfig(name="foliage", description="Segment for binary classification."),
        SegmentConfig(name="cement", description="Segment for binary classification."),
        SegmentConfig(name="window", description="Segment for binary classification."),
        SegmentConfig(name="path", description="Segment for binary classification."),
        SegmentConfig(name="grass", description="Segment 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:
        data["class"] = data["class"].apply(lambda x: x - 1)
        data = data.reset_index()
        data.drop("index", axis="columns", inplace=True)

        if self.config.name == "brickface":
            data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
        if self.config.name == "sky":
            data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
        if self.config.name == "foliage":
            data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
        if self.config.name == "cement":
            data["class"] = data["class"].apply(lambda x: 1 if x == 3 else 0)
        if self.config.name == "window":
            data["class"] = data["class"].apply(lambda x: 1 if x == 4 else 0)
        if self.config.name == "path":
            data["class"] = data["class"].apply(lambda x: 1 if x == 5 else 0)
        if self.config.name == "grass":
            data["class"] = data["class"].apply(lambda x: 1 if x == 6 else 0)

        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[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}")