import json
from itertools import chain
import datasets

logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """[PII NER]"""
_NAME = "pii_ner"
_VERSION = "1.0.1"

_URL = f'https://huggingface.co/datasets/acram/{_NAME}/raw/main/dataset'
_URLS = {
    str(datasets.Split.TEST): [f'{_URL}/test.json'],
    str(datasets.Split.TRAIN): [f'{_URL}/train.json'],
    str(datasets.Split.VALIDATION): [f'{_URL}/valid.json'],
}


class PiiNERConfig(datasets.BuilderConfig):
    """BuilderConfig"""

    def __init__(self, **kwargs):
        """BuilderConfig.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(PiiNERConfig, self).__init__(**kwargs)


class PiiNER(datasets.GeneratorBasedBuilder):
    """Dataset."""

    BUILDER_CONFIGS = [
        PiiNERConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
    ]

    def _split_generators(self, dl_manager):
        downloaded_file = dl_manager.download_and_extract(_URLS)
        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
                for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]

    def _generate_examples(self, filepaths):
        _key = 0
        for filepath in filepaths:
            logger.info(f"generating examples from = {filepath}")
            with open(filepath, encoding="utf-8") as f:
                _list = [i for i in f.read().split('\n') if len(i) > 0]
                for i in _list:
                    data = json.loads(i)
                    yield _key, data
                    _key += 1

    def _info(self):
        #names = ["B-LOC", "B-MISC", "B-ORG", "B-PER", "I-LOC", "I-MISC", "I-ORG", "I-PER", "O"]
        names=['O','B-NAME_STUDENT','I-NAME_STUDENT','B-STREET_ADDRESS','I-STREET_ADDRESS','B-USERNAME','B-EMAIL','B-URL_PERSONAL','B-PHONE_NUM','B-DRIVING_LICENSE',
 'B-PASSPORT','B-PAN_NUMBER','B-ID_NUM','B-AADHAR_ID']
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "tags": datasets.Sequence(datasets.features.ClassLabel(names=names)),
                }
            ),
            supervised_keys=None,
        )