import datasets
from datasets import Sequence, ClassLabel, Value

_TRAIN = "MACCROBAT2020-V2.json"


class MACCROBAT_biomedical_ner(datasets.GeneratorBasedBuilder):
    def _info(self):
        return datasets.DatasetInfo(
            description="",
            features=datasets.Features(
                {
                    "full_text": Value(dtype="string"),
                    "ner_info": [
                        {
                            "text": Value(dtype="string"),
                            "label": Value(dtype="string"),
                            "start": Value(dtype="int64"),
                            "end": Value(dtype="int64"),
                        }
                    ],
                    "tokens": Sequence(Value(dtype="string")),
                    "ner_labels": Sequence(
                        ClassLabel(
                            names=[
                                "O",
                                "B-ACTIVITY",
                                "I-ACTIVITY",
                                "I-ADMINISTRATION",
                                "B-ADMINISTRATION",
                                "B-AGE",
                                "I-AGE",
                                "I-AREA",
                                "B-AREA",
                                "B-BIOLOGICAL_ATTRIBUTE",
                                "I-BIOLOGICAL_ATTRIBUTE",
                                "I-BIOLOGICAL_STRUCTURE",
                                "B-BIOLOGICAL_STRUCTURE",
                                "B-CLINICAL_EVENT",
                                "I-CLINICAL_EVENT",
                                "B-COLOR",
                                "I-COLOR",
                                "I-COREFERENCE",
                                "B-COREFERENCE",
                                "B-DATE",
                                "I-DATE",
                                "I-DETAILED_DESCRIPTION",
                                "B-DETAILED_DESCRIPTION",
                                "I-DIAGNOSTIC_PROCEDURE",
                                "B-DIAGNOSTIC_PROCEDURE",
                                "I-DISEASE_DISORDER",
                                "B-DISEASE_DISORDER",
                                "B-DISTANCE",
                                "I-DISTANCE",
                                "B-DOSAGE",
                                "I-DOSAGE",
                                "I-DURATION",
                                "B-DURATION",
                                "I-FAMILY_HISTORY",
                                "B-FAMILY_HISTORY",
                                "B-FREQUENCY",
                                "I-FREQUENCY",
                                "I-HEIGHT",
                                "B-HEIGHT",
                                "B-HISTORY",
                                "I-HISTORY",
                                "I-LAB_VALUE",
                                "B-LAB_VALUE",
                                "I-MASS",
                                "B-MASS",
                                "I-MEDICATION",
                                "B-MEDICATION",
                                "I-NONBIOLOGICAL_LOCATION",
                                "B-NONBIOLOGICAL_LOCATION",
                                "I-OCCUPATION",
                                "B-OCCUPATION",
                                "B-OTHER_ENTITY",
                                "I-OTHER_ENTITY",
                                "B-OTHER_EVENT",
                                "I-OTHER_EVENT",
                                "I-OUTCOME",
                                "B-OUTCOME",
                                "I-PERSONAL_BACKGROUND",
                                "B-PERSONAL_BACKGROUND",
                                "B-QUALITATIVE_CONCEPT",
                                "I-QUALITATIVE_CONCEPT",
                                "I-QUANTITATIVE_CONCEPT",
                                "B-QUANTITATIVE_CONCEPT",
                                "B-SEVERITY",
                                "I-SEVERITY",
                                "B-SEX",
                                "I-SEX",
                                "B-SHAPE",
                                "I-SHAPE",
                                "B-SIGN_SYMPTOM",
                                "I-SIGN_SYMPTOM",
                                "B-SUBJECT",
                                "I-SUBJECT",
                                "B-TEXTURE",
                                "I-TEXTURE",
                                "B-THERAPEUTIC_PROCEDURE",
                                "I-THERAPEUTIC_PROCEDURE",
                                "I-TIME",
                                "B-TIME",
                                "B-VOLUME",
                                "I-VOLUME",
                                "I-WEIGHT",
                                "B-WEIGHT",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="",
            citation="",
        )

    def _split_generators(self, a):
        """Returns SplitGenerators."""

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": _TRAIN}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, encoding="utf-8") as f:
            datas = json.load(f)
            datas = datas["data"]
            guid = 0
            for data in datas:
                yield guid, data
                guid += 1