import json
import pandas as pd
import datasets

logger = datasets.logging.get_logger(__name__)

_DESCRIPTION = """\
Klue Relation Extraction Data
"""

_URL = "https://huggingface.co/datasets/LeverageX/klue-re/resolve/main/"
_URLS = {
    "train_data": _URL + "klue-re-v1.1_train.json",
    "validation_data": _URL + "klue-re-v1.1_dev.json",
}

class KoreanNewspaper(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="KLUE Relation Extraction",
            version=datasets.Version("1.0.0", ""),
            description="For LeverageX Project",
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "guid": datasets.Value("string"),
                    "label": datasets.Value("string"),
                    "object_entity": 
                        {
                            "word": datasets.Value("string"),
                            "start_idx": datasets.Value("int32"),
                            "end_idx": datasets.Value("int32"),
                            "type": datasets.Value("string"),
                        },
                    "sentence": datasets.Value("string"),
                    "source": datasets.Value("string"),
                    "subject_entity": 
                        {
                            "word": datasets.Value("string"),
                            "start_idx": datasets.Value("int32"),
                            "end_idx": datasets.Value("int32"),
                            "type": datasets.Value("string"),
                        }
                }
            ),
            # No default supervised_keys (as we have to pass both question
            # and context as input).
            supervised_keys=None,
            homepage="https://klue-benchmark.com/tasks/70/overview/description",
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract(_URLS)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train_data"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation_data"]}),
        ]

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)
        key = 0
        with open(filepath, encoding="utf-8") as f :
            data = json.load(f)

        for info in data :
            guid = info['guid']  
            label = info['label']
            object_entity = info['object_entity']
            subject_entity = info['subject_entity']
            source = info['source']
            sentence = info['sentence']

            yield key, {
                "guid" : guid,
                "label" : label,
                "object_entity" : object_entity,
                "subject_entity" : subject_entity,
                "source" : source,
                "sentence" : sentence,
            }
            key += 1