"""Optdigits Dataset"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")

_ENCODING_DICS = {}
_BASE_FEATURE_NAMES = [
	"att1",
	"att2",
	"att3",
	"att4",
	"att5",
	"att6",
	"att7",
	"att8",
	"att9",
	"att10",
	"att11",
	"att12",
	"att13",
	"att14",
	"att15",
	"att16",
	"att17",
	"att18",
	"att19",
	"att20",
	"att21",
	"att22",
	"att23",
	"att24",
	"att25",
	"att26",
	"att27",
	"att28",
	"att29",
	"att30",
	"att31",
	"att32",
	"att33",
	"att34",
	"att35",
	"att36",
	"att37",
	"att38",
	"att39",
	"att40",
	"att41",
	"att42",
	"att43",
	"att44",
	"att45",
	"att46",
	"att47",
	"att48",
	"att49",
	"att50",
	"att51",
	"att52",
	"att53",
	"att54",
	"att55",
	"att56",
	"att57",
	"att58",
	"att59",
	"att60",
	"att61",
	"att62",
	"att63",
	"att64",
	"class",
]

DESCRIPTION = "Optdigits dataset."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/80/optical+recognition+of+handwritten+digits"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/80/optical+recognition+of+handwritten+digits")
_CITATION = """
@misc{misc_optical_recognition_of_handwritten_digits_80,
  author       = {Alpaydin,E. & Kaynak,C.},
  title        = {{Optical Recognition of Handwritten Digits}},
  year         = {1998},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C50P49}}
}
"""

# Dataset info
urls_per_split = {
	"train": "https://huggingface.co/datasets/mstz/optdigits/resolve/main/optdigits.data"
}
features_types_per_config = {
	"optdigits": {
		"att1": datasets.Value("int64"),
		"att2": datasets.Value("int64"),
		"att3": datasets.Value("int64"),
		"att4": datasets.Value("int64"),
		"att5": datasets.Value("int64"),
		"att6": datasets.Value("int64"),
		"att7": datasets.Value("int64"),
		"att8": datasets.Value("int64"),
		"att9": datasets.Value("int64"),
		"att10": datasets.Value("int64"),
		"att11": datasets.Value("int64"),
		"att12": datasets.Value("int64"),
		"att13": datasets.Value("int64"),
		"att14": datasets.Value("int64"),
		"att15": datasets.Value("int64"),
		"att16": datasets.Value("int64"),
		"att17": datasets.Value("int64"),
		"att18": datasets.Value("int64"),
		"att19": datasets.Value("int64"),
		"att20": datasets.Value("int64"),
		"att21": datasets.Value("int64"),
		"att22": datasets.Value("int64"),
		"att23": datasets.Value("int64"),
		"att24": datasets.Value("int64"),
		"att25": datasets.Value("int64"),
		"att26": datasets.Value("int64"),
		"att27": datasets.Value("int64"),
		"att28": datasets.Value("int64"),
		"att29": datasets.Value("int64"),
		"att30": datasets.Value("int64"),
		"att31": datasets.Value("int64"),
		"att32": datasets.Value("int64"),
		"att33": datasets.Value("int64"),
		"att34": datasets.Value("int64"),
		"att35": datasets.Value("int64"),
		"att36": datasets.Value("int64"),
		"att37": datasets.Value("int64"),
		"att38": datasets.Value("int64"),
		"att39": datasets.Value("int64"),
		"att40": datasets.Value("int64"),
		"att41": datasets.Value("int64"),
		"att42": datasets.Value("int64"),
		"att43": datasets.Value("int64"),
		"att44": datasets.Value("int64"),
		"att45": datasets.Value("int64"),
		"att46": datasets.Value("int64"),
		"att47": datasets.Value("int64"),
		"att48": datasets.Value("int64"),
		"att49": datasets.Value("int64"),
		"att50": datasets.Value("int64"),
		"att51": datasets.Value("int64"),
		"att52": datasets.Value("int64"),
		"att53": datasets.Value("int64"),
		"att54": datasets.Value("int64"),
		"att55": datasets.Value("int64"),
		"att56": datasets.Value("int64"),
		"att57": datasets.Value("int64"),
		"att58": datasets.Value("int64"),
		"att59": datasets.Value("int64"),
		"att60": datasets.Value("int64"),
		"att61": datasets.Value("int64"),
		"att62": datasets.Value("int64"),
		"att63": datasets.Value("int64"),
		"att64": datasets.Value("int64"),
		"class": datasets.ClassLabel(num_classes=10)
	}
}
for i in range(10):
	features_types_per_config[str(i)] = {
		"att1": datasets.Value("int64"),
		"att2": datasets.Value("int64"),
		"att3": datasets.Value("int64"),
		"att4": datasets.Value("int64"),
		"att5": datasets.Value("int64"),
		"att6": datasets.Value("int64"),
		"att7": datasets.Value("int64"),
		"att8": datasets.Value("int64"),
		"att9": datasets.Value("int64"),
		"att10": datasets.Value("int64"),
		"att11": datasets.Value("int64"),
		"att12": datasets.Value("int64"),
		"att13": datasets.Value("int64"),
		"att14": datasets.Value("int64"),
		"att15": datasets.Value("int64"),
		"att16": datasets.Value("int64"),
		"att17": datasets.Value("int64"),
		"att18": datasets.Value("int64"),
		"att19": datasets.Value("int64"),
		"att20": datasets.Value("int64"),
		"att21": datasets.Value("int64"),
		"att22": datasets.Value("int64"),
		"att23": datasets.Value("int64"),
		"att24": datasets.Value("int64"),
		"att25": datasets.Value("int64"),
		"att26": datasets.Value("int64"),
		"att27": datasets.Value("int64"),
		"att28": datasets.Value("int64"),
		"att29": datasets.Value("int64"),
		"att30": datasets.Value("int64"),
		"att31": datasets.Value("int64"),
		"att32": datasets.Value("int64"),
		"att33": datasets.Value("int64"),
		"att34": datasets.Value("int64"),
		"att35": datasets.Value("int64"),
		"att36": datasets.Value("int64"),
		"att37": datasets.Value("int64"),
		"att38": datasets.Value("int64"),
		"att39": datasets.Value("int64"),
		"att40": datasets.Value("int64"),
		"att41": datasets.Value("int64"),
		"att42": datasets.Value("int64"),
		"att43": datasets.Value("int64"),
		"att44": datasets.Value("int64"),
		"att45": datasets.Value("int64"),
		"att46": datasets.Value("int64"),
		"att47": datasets.Value("int64"),
		"att48": datasets.Value("int64"),
		"att49": datasets.Value("int64"),
		"att50": datasets.Value("int64"),
		"att51": datasets.Value("int64"),
		"att52": datasets.Value("int64"),
		"att53": datasets.Value("int64"),
		"att54": datasets.Value("int64"),
		"att55": datasets.Value("int64"),
		"att56": datasets.Value("int64"),
		"att57": datasets.Value("int64"),
		"att58": datasets.Value("int64"),
		"att59": datasets.Value("int64"),
		"att60": datasets.Value("int64"),
		"att61": datasets.Value("int64"),
		"att62": datasets.Value("int64"),
		"att63": datasets.Value("int64"),
		"att64": datasets.Value("int64"),
		"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 OptdigitsConfig(datasets.BuilderConfig):
	def __init__(self, **kwargs):
		super(OptdigitsConfig, self).__init__(version=VERSION, **kwargs)
		self.features = features_per_config[kwargs["name"]]


class Optdigits(datasets.GeneratorBasedBuilder):
	# dataset versions
	DEFAULT_CONFIG = "optdigits"
	BUILDER_CONFIGS = [
		OptdigitsConfig(name="optdigits", description="Optdigits for multiclass classification."),
		OptdigitsConfig(name="0", description="Optdigits for binary classification: is this a 0?."),
		OptdigitsConfig(name="1", description="Optdigits for binary classification: is this a 1?."),
		OptdigitsConfig(name="2", description="Optdigits for binary classification: is this a 2?."),
		OptdigitsConfig(name="3", description="Optdigits for binary classification: is this a 3?."),
		OptdigitsConfig(name="4", description="Optdigits for binary classification: is this a 4?."),
		OptdigitsConfig(name="5", description="Optdigits for binary classification: is this a 5?."),
		OptdigitsConfig(name="6", description="Optdigits for binary classification: is this a 6?."),
		OptdigitsConfig(name="7", description="Optdigits for binary classification: is this a 7?."),
		OptdigitsConfig(name="8", description="Optdigits for binary classification: is this a 8?."),
		OptdigitsConfig(name="9", description="Optdigits for binary classification: is this a 9?.")
	]


	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.columns = _BASE_FEATURE_NAMES
		
		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:
		for feature in _ENCODING_DICS:
			encoding_function = partial(self.encode, feature)
			data.loc[:, feature] = data[feature].apply(encoding_function)

		if self.config.name == "0":
			data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
		if self.config.name == "1":
			data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
		if self.config.name == "2":
			data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
		if self.config.name == "3":
			data["class"] = data["class"].apply(lambda x: 1 if x == 3 else 0)
		if self.config.name == "4":
			data["class"] = data["class"].apply(lambda x: 1 if x == 4 else 0)
		if self.config.name == "5":
			data["class"] = data["class"].apply(lambda x: 1 if x == 5 else 0)
		if self.config.name == "6":
			data["class"] = data["class"].apply(lambda x: 1 if x == 6 else 0)
		if self.config.name == "7":
			data["class"] = data["class"].apply(lambda x: 1 if x == 7 else 0)
		if self.config.name == "8":
			data["class"] = data["class"].apply(lambda x: 1 if x == 8 else 0)
		if self.config.name == "9":
			data["class"] = data["class"].apply(lambda x: 1 if x == 9 else 0)
				
		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}")