"""WRENCH classification dataset."""

import json

import datasets


class WrenchConfig(datasets.BuilderConfig):
    """BuilderConfig for WRENCH."""

    def __init__(
        self,
        dataset_path,
        **kwargs,
    ):
        super(WrenchConfig, self).__init__(
            version=datasets.Version("1.0.0", ""), **kwargs
        )
        self.dataset_path = dataset_path


class Wrench(datasets.GeneratorBasedBuilder):
    """WRENCH classification dataset."""

    BUILDER_DICT = [
        {
            "name": "imdb",
            "dataset_path": "./imdb",
        },
        {
            "name": "yelp",
            "dataset_path": "./yelp",
        },
        {
            "name": "youtube",
            "dataset_path": "./youtube",
        },
        {
            "name": "sms",
            "dataset_path": "./sms",
        },
        {
            "name": "trec",
            "dataset_path": "./trec",
        },
        {
            "name": "cdr",
            "dataset_path": "./cdr",
        },
        {
            "name": "semeval",
            "dataset_path": "./semeval",
        },
        {
            "name": "Bioresponse",
            "dataset_path": "./Bioresponse",
        },
        {
            "name": "wikigold",
            "dataset_path": "./wikigold",
        },
        {
            "name": "PhishingWebsites",
            "dataset_path": "./PhishingWebsites",
        },
        {
            "name": "bank-marketing",
            "dataset_path": "./bank-marketing",
        },
        {
            "name": "spambase",
            "dataset_path": "./spambase",
        },
        {
            "name": "mit-movie",
            "dataset_path": "./mit-movie",
        },
        {
            "name": "basketball",
            "dataset_path": "./basketball",
        },
        {
            "name": "agnews",
            "dataset_path": "./agnews",
        },
        {
            "name": "commercial",
            "dataset_path": "./commercial",
        },
        {
            "name": "spouse",
            "dataset_path": "./spouse",
        },
        {
            "name": "mit-restaurant",
            "dataset_path": "./mit-restaurant",
        },
        {
            "name": "conll",
            "dataset_path": "./conll",
        },
        {
            "name": "chemprot",
            "dataset_path": "./chemprot",
        },
        {
            "name": "ncbi-diseas",
            "dataset_path": "./ncbi-diseas",
        },
        {
            "name": "bc5cdr",
            "dataset_path": "./bc5cdr",
        },
        {
            "name": "mushroom",
            "dataset_path": "./mushroom",
        },
        {
            "name": "laptopreview",
            "dataset_path": "./laptopreview",
        },
        {
            "name": "census",
            "dataset_path": "./census",
        },
        {
            "name": "tennis",
            "dataset_path": "./tennis",
        },
    ]
    BUILDER_CONFIGS = [
        WrenchConfig(name=i["name"], dataset_path=i["dataset_path"])
        for i in BUILDER_DICT
    ]

    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "label": datasets.Value("int8"),
                    "weak_labels": datasets.Sequence(datasets.Value("int8")),
                }
            )
        )

    def _split_generators(self, dl_manager):
        dataset_path = self.config.dataset_path
        train_path = dl_manager.download_and_extract(f"{dataset_path}/train.json")
        valid_path = dl_manager.download_and_extract(f"{dataset_path}/valid.json")
        test_path = dl_manager.download_and_extract(f"{dataset_path}/test.json")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}
            ),
        ]

    def _generate_examples(self, filepath):
        """Generate Custom examples."""
        print(f"\n\n\n{filepath}\n\n\n\n\n\n\n")

        with open(filepath, encoding="utf-8") as f:
            json_data = json.load(f)
        print("\n\n", json_data, "\n\n")

        list_of_dicts = [dict(key=key, **value) for key, value in json_data.items()]
        print("\n\n", list_of_dicts, "\n\n")
        for idx in list_of_dicts:
            data = json_data[idx]

            text = data["data"]["text"]
            weak_labels = data["weak_labels"]
            label = data["label"]

            yield int(idx), {
                "text": text,
                "label": label,
                "weak_labels": weak_labels,
            }