"""ACP Bench dataset."""

import json
import datasets
import itertools


_CITATION = """\
@article{kokel2024acp,
  title={ACPBench: Reasoning about Action, Change, and Planning},
  author={Kokel, Harsha and Katz, Michael and Srinivas, Kavitha and Sohrabi, Shirin},
  journal={arXiv},
  year={2024}
}
"""

_DESCRIPTION = """ACPBench consists of 7 reasoning tasks over 13 domains. The 13 domains include 11 classical planning domains, ALFWorld, and a novel Swap domain. The 7 tasks included in ACPBench are Action Applicability (app), Progression (prog), Atom Reachability (reach), Validation (val), Action Reachability (areach), Justification (just), and Landmarks (land)."""

_HOMEPAGE = "https://ibm.github.io/ACPBench/"

_LICENSE = "MIT"

_BASE_URL = "https://raw.github.com/ibm/ACPBench/main/dataset"

task_list = [
    "app",
    "areach",
    "just",
    "land",
    "prog",
    "reach",
    "val"
    ]

format_list = [
    "bool", "mcq"
]


class ACPConfig(datasets.BuilderConfig):
    def __init__(self, urls, **kwargs):
        """
        urls: *dict[string]*, the urls for each split of the ACPBench set.
        """
        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
        self.urls = urls


class ACP(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        ACPConfig(
            name=f"acp_{task_name}_{format_name}",
            urls={
                "test":  f"{_BASE_URL}/{task_name}/test.{format_name}.json.gz",
                "val":  f"{_BASE_URL}/{task_name}/dev.{format_name}.json",
            },
        )
        for task_name, format_name in itertools.product(task_list,format_list)
    ]

    def _info(self):
        features = {
                "context": datasets.Value("string"),
                "question": datasets.Value("string"),
                "answer": datasets.Value("string"),
                "group": datasets.Value("string"),
                "id": datasets.Value("string")
            }
        if 'mcq' in self.config.name:
            features["query"]= datasets.Value("string")
            features["choices"]= datasets.features.Sequence(feature={'text': datasets.Value(dtype='string', id=None), 'label': datasets.Value(dtype='string', id=None)}, length=-1, id=None)
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(features),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )



    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(self.config.urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": data_dir["test"],
                },
            ),datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": data_dir["val"],
                },
            )
        ]

    def _generate_examples(self, filepath):
        with open(filepath) as f:
            examples = json.load(f)
            for i, instance in enumerate(examples):
                yield i, instance