import datasets

logger = datasets.logging.get_logger(__name__)

_CITATION = """
@InProceedings{huggingface:dataset,
title = {Luganda, Kanuri, and Hausa NER Dataset},
author = {multiple authors},
year = {2022}
}
"""

_DESCRIPTION = """
LugandaPII is a dataset that includes named entities such as PERSON, ORG, LOCATION, NORP, USERID, and DATE. 
The dataset is available in Lum, Kanuri, and Hausa languages, distributed across train, validation, and test splits.
"""

_URL = "https://github.com/EricPeter/pii/raw/main/data"
_TRAINING_FILE = "train.txt"
_VAL_FILE = "val.txt"
_TEST_FILE = "test.txt"

class LugPIIConfig(datasets.BuilderConfig):
    """Configuration for LugandaPII dataset."""
    def __init__(self, **kwargs):
        super().__init__(**kwargs)

class Masakhaner(datasets.GeneratorBasedBuilder):
    """Generator for Masakhaner dataset."""
    BUILDER_CONFIGS = [
        LugPIIConfig(name="lug", version=datasets.Version("1.0.0"), description="PII NER dataset for Luganda."),
        LugPIIConfig(name="hau", version=datasets.Version("1.0.0"), description="PII NER dataset for Hausa."),
        LugPIIConfig(name="knr", version=datasets.Version("1.0.0"), description="PII NER dataset for Kanuri."),
        LugPIIConfig(name="lum", version=datasets.Version("1.0.0"), description="PII NER dataset for Lum."),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                "id": datasets.Value("string"),
                "tokens": datasets.Sequence(datasets.Value("string")),
                "ner_tags": datasets.Sequence(datasets.features.ClassLabel(names=['B-DATE',
                    'B-GOVT_ID',
                    'B-LOC',
                    'B-LOCATION',
                    'B-NORP',
                    'B-ORG',
                    'B-PERSON',
                    'B-USERID',
                    'B-USER_ID',
                    'I-DATE',
                    'I-GOVT_ID',
                    'I-LOC',
                    'I-LOCATION',
                    'I-NORP',
                    'I-ORG',
                    'I-PERSON',
                    'I-USERID',
                    'I-USER_ID',
                    'L-DATE',
                    'L-GOVT_ID',
                    'L-LOC',
                    'L-LOCATION',
                    'L-NORP',
                    'L-ORG',
                    'L-PERSON',
                    'L-USERID',
                    'L-USER_ID',
                    'O',
                    'U-DATE',
                    'U-GOVT_ID',
                    'U-LOCATION',
                    'U-NORP',
                    'U-ORG',
                    'U-PERSON',
                    'U-USERID'])),
            }),
            supervised_keys=None,
            homepage="",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        lang_code = self.config.name  # 'lug', 'hau', 'knr', 'lum'
        urls_to_download = {
            "train": f"{_URL}/{lang_code}/{_TRAINING_FILE}",
            "val": f"{_URL}/{lang_code}/{_VAL_FILE}",
            "test": f"{_URL}/{lang_code}/{_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["val"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]})
        ]

    def _generate_examples(self, filepath):
        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.strip() == "":
                    if tokens:
                        yield guid, {"id": str(guid), "tokens": tokens, "ner_tags": ner_tags}
                        guid += 1
                        tokens = []
                        ner_tags = []
                    continue
                splits = line.strip().split()
                tokens.append(splits[0])
                ner_tags.append(splits[1])
            if tokens:
                yield guid, {"id": str(guid), "tokens": tokens, "ner_tags": ner_tags}