"""WallFollowing Dataset"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")

_ENCODING_DICS = {}

DESCRIPTION = "WallFollowing dataset."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/194/wall+following+robot+navigation+data"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/194/wall+following+robot+navigation+data")
_CITATION = """
@misc{misc_wall-following_robot_navigation_data_194,
  author       = {Freire,Ananda, Veloso,Marcus & Barreto,Guilherme},
  title        = {{Wall-Following Robot Navigation Data}},
  year         = {2010},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C57C8W}}
}
"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/wall_following/raw/main/wall_following.csv"
}
features_types_per_config = {
    "wall_following": {
        "US1": datasets.Value("float64"),
		"US2": datasets.Value("float64"),
		"US3": datasets.Value("float64"),
		"US4": datasets.Value("float64"),
		"US5": datasets.Value("float64"),
		"US6": datasets.Value("float64"),
		"US7": datasets.Value("float64"),
		"US8": datasets.Value("float64"),
		"US9": datasets.Value("float64"),
		"US10": datasets.Value("float64"),
		"US11": datasets.Value("float64"),
		"US12": datasets.Value("float64"),
		"US13": datasets.Value("float64"),
		"US14": datasets.Value("float64"),
		"US15": datasets.Value("float64"),
		"US16": datasets.Value("float64"),
		"US17": datasets.Value("float64"),
		"US18": datasets.Value("float64"),
		"US19": datasets.Value("float64"),
		"US20": datasets.Value("float64"),
		"US21": datasets.Value("float64"),
		"US22": datasets.Value("float64"),
		"US23": datasets.Value("float64"),
		"US24": datasets.Value("float64"),
		"class": datasets.ClassLabel(num_classes=4),
    },
    "wall_following_0": {
        "US1": datasets.Value("float64"),
		"US2": datasets.Value("float64"),
		"US3": datasets.Value("float64"),
		"US4": datasets.Value("float64"),
		"US5": datasets.Value("float64"),
		"US6": datasets.Value("float64"),
		"US7": datasets.Value("float64"),
		"US8": datasets.Value("float64"),
		"US9": datasets.Value("float64"),
		"US10": datasets.Value("float64"),
		"US11": datasets.Value("float64"),
		"US12": datasets.Value("float64"),
		"US13": datasets.Value("float64"),
		"US14": datasets.Value("float64"),
		"US15": datasets.Value("float64"),
		"US16": datasets.Value("float64"),
		"US17": datasets.Value("float64"),
		"US18": datasets.Value("float64"),
		"US19": datasets.Value("float64"),
		"US20": datasets.Value("float64"),
		"US21": datasets.Value("float64"),
		"US22": datasets.Value("float64"),
		"US23": datasets.Value("float64"),
		"US24": datasets.Value("float64"),
		"class": datasets.ClassLabel(num_classes=2),
    },
    "wall_following_1": {
                "US1": datasets.Value("float64"),
		"US2": datasets.Value("float64"),
		"US3": datasets.Value("float64"),
		"US4": datasets.Value("float64"),
		"US5": datasets.Value("float64"),
		"US6": datasets.Value("float64"),
		"US7": datasets.Value("float64"),
		"US8": datasets.Value("float64"),
		"US9": datasets.Value("float64"),
		"US10": datasets.Value("float64"),
		"US11": datasets.Value("float64"),
		"US12": datasets.Value("float64"),
		"US13": datasets.Value("float64"),
		"US14": datasets.Value("float64"),
		"US15": datasets.Value("float64"),
		"US16": datasets.Value("float64"),
		"US17": datasets.Value("float64"),
		"US18": datasets.Value("float64"),
		"US19": datasets.Value("float64"),
		"US20": datasets.Value("float64"),
		"US21": datasets.Value("float64"),
		"US22": datasets.Value("float64"),
		"US23": datasets.Value("float64"),
		"US24": datasets.Value("float64"),
		"class": datasets.ClassLabel(num_classes=2),
    },
    "wall_following_2": {
        "US1": datasets.Value("float64"),
		"US2": datasets.Value("float64"),
		"US3": datasets.Value("float64"),
		"US4": datasets.Value("float64"),
		"US5": datasets.Value("float64"),
		"US6": datasets.Value("float64"),
		"US7": datasets.Value("float64"),
		"US8": datasets.Value("float64"),
		"US9": datasets.Value("float64"),
		"US10": datasets.Value("float64"),
		"US11": datasets.Value("float64"),
		"US12": datasets.Value("float64"),
		"US13": datasets.Value("float64"),
		"US14": datasets.Value("float64"),
		"US15": datasets.Value("float64"),
		"US16": datasets.Value("float64"),
		"US17": datasets.Value("float64"),
		"US18": datasets.Value("float64"),
		"US19": datasets.Value("float64"),
		"US20": datasets.Value("float64"),
		"US21": datasets.Value("float64"),
		"US22": datasets.Value("float64"),
		"US23": datasets.Value("float64"),
		"US24": datasets.Value("float64"),
		"class": datasets.ClassLabel(num_classes=2),
    },
    "wall_following_3": {
        "US1": datasets.Value("float64"),
		"US2": datasets.Value("float64"),
		"US3": datasets.Value("float64"),
		"US4": datasets.Value("float64"),
		"US5": datasets.Value("float64"),
		"US6": datasets.Value("float64"),
		"US7": datasets.Value("float64"),
		"US8": datasets.Value("float64"),
		"US9": datasets.Value("float64"),
		"US10": datasets.Value("float64"),
		"US11": datasets.Value("float64"),
		"US12": datasets.Value("float64"),
		"US13": datasets.Value("float64"),
		"US14": datasets.Value("float64"),
		"US15": datasets.Value("float64"),
		"US16": datasets.Value("float64"),
		"US17": datasets.Value("float64"),
		"US18": datasets.Value("float64"),
		"US19": datasets.Value("float64"),
		"US20": datasets.Value("float64"),
		"US21": datasets.Value("float64"),
		"US22": datasets.Value("float64"),
		"US23": datasets.Value("float64"),
		"US24": datasets.Value("float64"),
		"class": datasets.ClassLabel(num_classes=2),
    }
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


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


class WallFollowing(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "wall_following"
    BUILDER_CONFIGS = [
        WallFollowingConfig(name="wall_following", description="WallFollowing for multiclass classification."),
        WallFollowingConfig(name="wall_following_0", description="WallFollowing for binary classification."),
        WallFollowingConfig(name="wall_following_1", description="WallFollowing for binary classification."),
        WallFollowingConfig(name="wall_following_2", description="WallFollowing for binary classification."),
        WallFollowingConfig(name="wall_following_3", description="WallFollowing 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 == "wall_following_0":
            data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
        elif self.config.name == "wall_following_1":
            data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
        elif self.config.name == "wall_following_2":
            data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
        elif self.config.name == "wall_following_3":
            data["class"] = data["class"].apply(lambda x: 1 if x == 3 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}")