from typing import List

import datasets

import pandas
import gzip


VERSION = datasets.Version("1.0.0")


DESCRIPTION = "Sonar dataset from the UCI ML repository."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/31/sonar"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/31/sonar")
_CITATION = """"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/sonar/raw/main/sonar.all-data"
}
features_types_per_config = {
    "sonar": {str(i): datasets.Value("float32") for i in range(60)}
}
features_types_per_config["sonar"]["is_rock"] = datasets.ClassLabel(num_classes=2)
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


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


class Sonar(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "sonar"
    BUILDER_CONFIGS = [
        SonarConfig(name="sonar",
                    description="Sonar for binary classification.")
    ]


    def _info(self):
        if self.config.name not in features_per_config:
            raise ValueError(f"Unknown configuration: {self.config.name}")
        
        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 = [str(i) for i in range(60)] + ["is_rock"]
        data = self.preprocess(data, config=self.config.name)

        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.loc[:, "is_rock"] = data["is_rock"].apply(lambda x: 1 if x == "R" else 0)
        
        return data