from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")


DESCRIPTION = "Car dataset from the UCI repository."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/19/car+evaluation"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/19/car+evaluation")
_CITATION = """
@misc{misc_car_evaluation_19,
  author       = {Bohanec,Marko},
  title        = {{Car Evaluation}},
  year         = {1997},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C5JP48}}
}
"""

# Dataset info
_BASE_FEATURE_NAMES = [
	"buying",
	"maint",
	"doors",
	"persons",
	"lug_boot",
	"safety",
	"acceptability_level"
]
urls_per_split = {
	"train": "https://huggingface.co/datasets/mstz/car/raw/main/car.data"
}
features_types_per_config = {
	"car": {
		"buying": datasets.Value("int8"),
		"maint": datasets.Value("int8"),
		"doors": datasets.Value("int8"),
		"persons": datasets.Value("int8"),
		"lug_boot": datasets.Value("int8"),
		"safety": datasets.Value("int8"),
		"acceptability_level": datasets.ClassLabel(num_classes=4,
									 			   names=("unacceptable", "acceptable", "good", "very good"))
	},
	"car_binary": {
		"buying": datasets.Value("int8"),
		"maint": datasets.Value("int8"),
		"doors": datasets.Value("int8"),
		"persons": datasets.Value("int8"),
		"lug_boot": datasets.Value("int8"),
		"safety": datasets.Value("int8"),
		"acceptability_level": datasets.ClassLabel(num_classes=2,
												   names=("unacceptable", "acceptable"))
	},
	
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}

_ENCODING_DICS = {
	"buying": {
		"vhigh": 3,
		"high": 2,
		"med": 1,
		"low": 0
	},
	"maint": {
		"vhigh": 3,
		"high": 2,
		"med": 1,
		"low": 0
	},
	"doors": {
		"0": 0,
		"1": 1,
		"2": 2,
		"3": 3,
		"4": 4,
		"5more": 5
	},
	"persons": {
		"0": 0,
		"1": 1,
		"2": 2,
		"3": 3,
		"4": 4,
		"more": 5
	},
	"lug_boot": {
		"big": 2,
		"med": 1,
		"small": 0,
	},
	"safety": {
		"high": 2,
		"med": 1,
		"low": 0,
	},
	"acceptability_level": {
		"unacc": 0,
		"acc": 1,
		"good": 2,
		"vgood": 3
	}
	
}

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


class Car(datasets.GeneratorBasedBuilder):
	# dataset versions
	DEFAULT_CONFIG = "car"
	BUILDER_CONFIGS = [
		CarConfig(name="car",
					description="Car for 4-ary classification."),
		CarConfig(name="car_binary",
					description="Car 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, header=None)
		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, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
		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 == "car_binary":
			data.loc[:, "acceptability_level"] = data["acceptability_level"].apply(lambda x: x if x == 0 else 1)


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