import json
import datasets

_DESCRIPTION = """
SciFact

A dataset of expert-written scientific claims paired with evidence-containing
abstracts and annotated with labels and rationales.
"""

_CITATION = """
@InProceedings{Wadden2020FactOF,
  author =      {David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang,
                 Madeleine van Zuylen, Arman Cohan, Hannaneh Hajishirzi},
  title =       {Fact or Fiction: Verifying Scientific Claims},
  booktitle =   {EMNLP},
  year =        2020,
}
"""

_DOWNLOAD_URL = "https://testerstories.com/files/ai_learn/data.tar.gz"


class ScifactConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(ScifactConfig, self).__init__(
            version=datasets.Version("1.0.0", ""), **kwargs
        )


class Scifact(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("0.1.0")

    BUILDER_CONFIGS = [
        ScifactConfig(name="corpus", description="The corpus of evidence documents"),
        ScifactConfig(
            name="claims", description="The claims are split into train, test, dev"
        ),
    ]

    def _info(self):
        if self.config.name == "corpus":
            features = {
                "doc_id": datasets.Value("int32"),
                "title": datasets.Value("string"),
                "abstract": datasets.features.Sequence(datasets.Value("string")),
                "structured": datasets.Value("bool"),
            }
        else:
            features = {
                "id": datasets.Value("int32"),
                "claim": datasets.Value("string"),
                "evidence_doc_id": datasets.Value("string"),
                "evidence_label": datasets.Value("string"),
                "evidence_sentences": datasets.features.Sequence(
                    datasets.Value("int32")
                ),
                "cited_doc_ids": datasets.features.Sequence(datasets.Value("int32")),
            }

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(features),
            supervised_keys=None,
            homepage="https://scifact.apps.allenai.org/",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        archive = dl_manager.download(_DOWNLOAD_URL)

        if self.config.name == "corpus":
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "filepath": "data/corpus.jsonl",
                        "split": "train",
                        "files": dl_manager.iter_archive(archive),
                    },
                ),
            ]
        else:
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "filepath": "data/claims_train.jsonl",
                        "split": "train",
                        "files": dl_manager.iter_archive(archive),
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.TEST,
                    gen_kwargs={
                        "filepath": "data/claims_test.jsonl",
                        "split": "test",
                        "files": dl_manager.iter_archive(archive),
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    gen_kwargs={
                        "filepath": "data/claims_dev.jsonl",
                        "split": "dev",
                        "files": dl_manager.iter_archive(archive),
                    },
                ),
            ]

    def _generate_examples(self, filepath, split, files):
        for path, f in files:
            if path == filepath:
                for id_, row in enumerate(f):
                    data = json.loads(row.decode("utf-8"))

                    if self.config.name == "corpus":
                        yield id_, {
                            "doc_id": int(data["doc_id"]),
                            "title": data["title"],
                            "abstract": data["abstract"],
                            "structured": data["structured"],
                        }
                    else:
                        if split == "test":
                            yield id_, {
                                "id": data["id"],
                                "claim": data["claim"],
                                "evidence_doc_id": "",
                                "evidence_label": "",
                                "evidence_sentences": [],
                                "cited_doc_ids": [],
                            }
                        else:
                            evidences = data["evidence"]

                            if evidences:
                                for id1, doc_id in enumerate(evidences):
                                    for id2, evidence in enumerate(evidences[doc_id]):
                                        yield str(id_) + "_" + str(id1) + "_" + str(
                                            id2
                                        ), {
                                            "id": data["id"],
                                            "claim": data["claim"],
                                            "evidence_doc_id": doc_id,
                                            "evidence_label": evidence["label"],
                                            "evidence_sentences": evidence["sentences"],
                                            "cited_doc_ids": data.get(
                                                "cited_doc_ids", []
                                            ),
                                        }
                            else:
                                yield id_, {
                                    "id": data["id"],
                                    "claim": data["claim"],
                                    "evidence_doc_id": "",
                                    "evidence_label": "",
                                    "evidence_sentences": [],
                                    "cited_doc_ids": data.get("cited_doc_ids", []),
                                }
                break