import json
import datasets
import os


_CITATION = """\
@article{huggingface:dataset,
    title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
    authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
    year={2020}
    journal = {arXiv e-prints},
    eprint = {2012.06154},    
}
"""

# You can copy an official description
_DESCRIPTION = """A Persian multiple choice task."""

_HOMEPAGE = "https://github.com/persiannlp/parsinlu/"

_LICENSE = "CC BY-NC-SA 4.0"

_URL = "https://raw.githubusercontent.com/persiannlp/parsinlu/master/data/multiple-choice/"

_URLs = {
    "train": _URL + "train.jsonl",
    "val": _URL + "valid.jsonl",
    "test": _URL + "test.jsonl",
}


class ParsinluMultipleChoice(datasets.GeneratorBasedBuilder):
    """ParsiNLU Persian multiple choice task."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="parsinlu-repo", version=VERSION, description="Here the task is to pick a correct answer among 3-5 given candidate answers"
        ),]

    def _info(self):
        features = datasets.Features(
            {
                "answer": datasets.Value("int32"),
                "candidates": datasets.features.Sequence(feature=datasets.Value(dtype='string', id=None), length=-1),
                "category": datasets.Value("string"),
                "question": datasets.Value("string"),
                "id": datasets.Value("string")
            }
        )

        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )


    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_URLs)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["train"],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["test"], 
                    "split": "test"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["val"],
                    "split": "validation",
                },
            ),
        ]


    def _generate_examples(self, filepath, split):
        def get_answer_index(passage, answer):
            return passage.index(answer) if answer in passage else -1

        with open(filepath, encoding="utf-8") as f:
            for id_, row in enumerate(f):
                data = json.loads(row)
                yield id_, {
                    "answer": int(data["answer"]),
                    "candidates": data["candidates"],
                    "category": data["category"],
                    "question": data["question"],
                    "id": data['id']
                }