import datasets
from datasets.download.download_manager import DownloadManager
import pyarrow.parquet as pq
import json

_DESCRIPTION = """\
The Weibo NER dataset is a Chinese Named Entity Recognition dataset 
drawn from the social media website Sina Weibo.
"""

_CITATION = """\
@inproceedings{peng-dredze-2015-named,
    title = "Named Entity Recognition for {C}hinese 
        Social Media with Jointly Trained Embeddings",
    author = "Peng, Nanyun  and Dredze, Mark",
    booktitle = "Proceedings of the 2015 Conference on 
        Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D15-1064",
    doi = "10.18653/v1/D15-1064",
    pages = "548--554",
}
"""

_URL = "https://huggingface.co/datasets/minskiter/weibo/resolve/main/"
_URLS = {
    "train": _URL + "data/train.parquet",
    "validation": _URL + "data/validation.parquet",
    "test": _URL + "data/test.parquet",
}

class WeiboNamedEntities(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Sequence(datasets.Value("string")),
                    "labels": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                'O',
                                'B-PER.NAM',
                                'I-PER.NAM',
                                'E-PER.NAM',
                                'S-PER.NAM',
                                'B-ORG.NAM',
                                'I-ORG.NAM',
                                'E-ORG.NAM',
                                'S-ORG.NAM',
                                'B-LOC.NAM',
                                'I-LOC.NAM',
                                'E-LOC.NAM',
                                'S-LOC.NAM',
                                'B-GPE.NAM',
                                'I-GPE.NAM',
                                'E-GPE.NAM',
                                'S-GPE.NAM',
                                'B-PER.NOM',
                                'I-PER.NOM',
                                'E-PER.NOM',
                                'S-PER.NOM',
                                'B-ORG.NOM',
                                'I-ORG.NOM',
                                'E-ORG.NOM',
                                'S-ORG.NOM',
                                'B-LOC.NOM',
                                'I-LOC.NOM',
                                'E-LOC.NOM',
                                'S-LOC.NOM',
                                'B-GPE.NOM',
                                'I-GPE.NOM',
                                'E-GPE.NOM',
                                'S-GPE.NOM',
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://aclanthology.org/D15-1064/",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager: DownloadManager):
        urls_to_download = _URLS
        download_files = dl_manager.download_and_extract(urls_to_download)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": download_files["train"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepath": download_files["validation"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"filepath": download_files["test"]},
            ),
        ]

    def _generate_examples(self, filepath):
        # fix: https://discuss.huggingface.co/t/dataset-preview-error-with-a-dataset-script-and-parquet-files/43160
        with open(filepath, "rb") as f:
            with pq.ParquetFile(f) as file:
                _id = -1
                for i in file.iter_batches(batch_size=64):
                    rows = i.to_pylist()
                    for row in rows:
                        _id+=1
                        yield _id, row