import datasets
logger = datasets.logging.get_logger(__name__)


_HOMEPAGE = "https://www.google.com"
_URL = f'https://huggingface.co./datasets/chintagunta85/pv_dataset/raw/main/'
_TRAINING_FILE = "pv_train.tsv"
_DEV_FILE = "pv_val.tsv"
_TEST_FILE = "pv_test.tsv"


class PVDatasetConfig(datasets.BuilderConfig):
    """BuilderConfig for Bc2gmCorpus"""

    def __init__(self, **kwargs):
        """BuilderConfig for Bc2gmCorpus.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(PVDatasetConfig, self).__init__(**kwargs)

class PVDataset(datasets.GeneratorBasedBuilder):
    """Bc2gmCorpus dataset."""

    BUILDER_CONFIGS = [
        PVDatasetConfig(name="PVDatasetCorpus", version=datasets.Version("1.0.0"), description="PVDataset"),
    ]

    def _info(self):

        custom_names = ['O','B-GENE','I-GENE','B-CHEMICAL','I-CHEMICAL','B-DISEASE','I-DISEASE','B-SPECIES', 'I-SPECIES']

        return datasets.DatasetInfo(
            description='abhi',
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=custom_names
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation='cite me',
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{_TRAINING_FILE}",
            "dev": f"{_URL}{_DEV_FILE}",
            "test": f"{_URL}{_TEST_FILE}",
        }
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        shift = 0
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            tokens = []
            ner_tags = []
            for line in f:
                if line == "" or line == "\n":
                    if tokens:
                        yield guid, {
                            "id": str(guid),
                            "tokens": tokens,
                            "ner_tags": ner_tags,
                        }
                        guid += 1
                        tokens = []
                        ner_tags = []
                else:
                    # tokens are tab separated
                    splits = line.split("\t")
                    tokens.append(splits[0])
                    if(splits[1].rstrip()=="B"):
                        ner_tags.append("B-SPECIES")
                    elif(splits[1].rstrip()=="I"):
                        ner_tags.append("I-SPECIES")             
                    elif(splits[1].rstrip()=="B-Chemical"):
                        ner_tags.append("B-CHEMICAL")
                    elif(splits[1].rstrip()=="I-Chemical"):
                        ner_tags.append("I-CHEMICAL")
                    elif(splits[1].rstrip()=="B-Disease"):
                        ner_tags.append("B-DISEASE")
                    elif(splits[1].rstrip()=="I-Disease"):
                        ner_tags.append("I-DISEASE")
                    else:
                        ner_tags.append(splits[1].rstrip())                
            # last example
            yield guid, {
                "id": str(guid),
                "tokens": tokens,
                "ner_tags": ner_tags,
            }