import os
import datasets
import pandas as pd


class semiHomoConfig(datasets.BuilderConfig):
    def __init__(self, features, data_url, **kwargs):
        super(semiHomoConfig, self).__init__(**kwargs)
        self.features = features
        self.data_url = data_url


class semiHomo(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        semiHomoConfig(
            name="pairs",
            features={
                "ltable_id": datasets.Value("string"),
                "rtable_id": datasets.Value("string"),
                "label": datasets.Value("string"),
            },
            data_url="https://huggingface.co/datasets/matchbench/semi-Homo/resolve/main/",
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features(self.config.features)
        )

    def _split_generators(self, dl_manager):
        if self.config.name == "pairs":
            return [
                datasets.SplitGenerator(
                    name=split,
                    gen_kwargs={
                        "path_file": dl_manager.download_and_extract(
                            os.path.join(self.config.data_url, f"{split}.csv")),
                        "split": split,
                    }
                )
                for split in ["train", "valid", "test"]
            ]

    def _generate_examples(self, path_file, split):
        file = pd.read_csv(path_file)
        for i, row in file.iterrows():
            if split  in ['train', 'valid','test']:
                yield i, {
                    "ltable_id": row["ltable_id"],
                    "rtable_id": row["rtable_id"],
                    "label": row["label"],
                }