"""Abalone."""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")
_ORIGINAL_FEATURE_NAMES = [
    "Sex",
    "Length",
    "Diameter",
    "Height",
    "Whole_weight",
    "Shucked_weight",
    "Viscera_weight",
    "Shell_weight",
    "Ring",
]
_BASE_FEATURE_NAMES = [
    "sex",
    "length",
    "diameter",
    "height",
    "whole_weight",
    "shucked_weight",
    "viscera_weight",
    "shell_weight",
    "number_of_rings",
]

DESCRIPTION = "Abalone dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Abalone"
_URLS = ("https://huggingface.co/datasets/mstz/abalone/raw/abalone.data")
_CITATION = """
@misc{misc_abalone_1,
  title        = {{Abalone}},
  year         = {1995},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C55C7W}}
}"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/abalone/raw/main/abalone.data",
}
features_types_per_config = {
    "abalone": {
        "sex": datasets.Value("string"),
        "length": datasets.Value("float64"),
        "diameter": datasets.Value("float64"),
        "height": datasets.Value("float64"),
        "whole_weight": datasets.Value("float64"),
        "shucked_weight": datasets.Value("float64"),
        "viscera_weight": datasets.Value("float64"),
        "shell_weight": datasets.Value("float64"),
        "number_of_rings": datasets.Value("int8")
    },
    "binary": {
        "sex": datasets.Value("string"),
        "length": datasets.Value("float64"),
        "diameter": datasets.Value("float64"),
        "height": datasets.Value("float64"),
        "whole_weight": datasets.Value("float64"),
        "shucked_weight": datasets.Value("float64"),
        "viscera_weight": datasets.Value("float64"),
        "shell_weight": datasets.Value("float64"),
        "is_old": datasets.ClassLabel(num_classes=2)
    }
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


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


class Abalone(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "abalone"
    BUILDER_CONFIGS = [
        AbaloneConfig(name="abalone", description="Abalone for regression."),
        AbaloneConfig(name="binary", description="Abalone 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.columns = _BASE_FEATURE_NAMES

        if self.config.name == "binary":
            data = data.rename(columns={"number_of_rings": "is_old"})
            data["is_old"] = data["is_old"].apply(lambda x: 1 if x > 9 else 0)

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

            yield row_id, data_row