# coding=utf-8
"""MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition"""

import datasets

logger = datasets.logging.get_logger(__name__)

_CITATION = """\
@misc{malmasi2022multiconer,
    title={MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition}, 
    author={Shervin Malmasi and Anjie Fang and Besnik Fetahu and Sudipta Kar and Oleg Rokhlenko},
    year={2022},
    eprint={2208.14536},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
"""

_DESCRIPTION = """\
We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki \
sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. \
This dataset is designed to represent contemporary challenges in NER, including low-context scenarios (short \
and uncased text), syntactically complex entities like movie titles, and long-tail entity distributions. The \
26M token dataset is compiled from public resources using techniques such as heuristic-based sentence sampling, \
template extraction and slotting, and machine translation. We applied two NER models on our dataset: a baseline \
XLM-RoBERTa model, and a state-of-the-art GEMNET model that leverages gazetteers. The baseline achieves moderate \
performance (macro-F1=54%), highlighting the difficulty of our data. GEMNET, which uses gazetteers, improvement \
significantly (average improvement of macro-F1=+30%). MultiCoNER poses challenges even for large pre-trained \
language models, and we believe that it can help further research in building robust NER systems. MultiCoNER \
is publicly available at https://registry.opendata.aws/multiconer/ and we hope that this resource will help \
advance research in various aspects of NER. 
"""

subset_to_dir = {
    "bn": "BN-Bangla",
    "de": "DE-German",
    "en": "EN-English",
    "es": "ES-Spanish",
    "fa": "FA-Farsi",
    "hi": "HI-Hindi",
    "ko": "KO-Korean",
    "nl": "NL-Dutch",
    "ru": "RU-Russian",
    "tr": "TR-Turkish",
    "zh": "ZH-Chinese",
    "multi": "MULTI_Multilingual",
    "mix": "MIX_Code_mixed",
}


class MultiCoNERConfig(datasets.BuilderConfig):
    """BuilderConfig for MultiCoNER"""

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


class MultiCoNER(datasets.GeneratorBasedBuilder):
    """MultiCoNER dataset."""

    BUILDER_CONFIGS = [
        MultiCoNERConfig(
            name="bn",
            version=datasets.Version("1.0.0"),
            description="MultiCoNER Bangla dataset",
        ),
        MultiCoNERConfig(
            name="de",
            version=datasets.Version("1.0.0"),
            description="MultiCoNER German dataset",
        ),
        MultiCoNERConfig(
            name="en",
            version=datasets.Version("1.0.0"),
            description="MultiCoNER English dataset",
        ),
        MultiCoNERConfig(
            name="es",
            version=datasets.Version("1.0.0"),
            description="MultiCoNER Spanish dataset",
        ),
        MultiCoNERConfig(
            name="fa",
            version=datasets.Version("1.0.0"),
            description="MultiCoNER Farsi dataset",
        ),
        MultiCoNERConfig(
            name="hi",
            version=datasets.Version("1.0.0"),
            description="MultiCoNER Hindi dataset",
        ),
        MultiCoNERConfig(
            name="ko",
            version=datasets.Version("1.0.0"),
            description="MultiCoNER Korean dataset",
        ),
        MultiCoNERConfig(
            name="nl",
            version=datasets.Version("1.0.0"),
            description="MultiCoNER Dutch dataset",
        ),
        MultiCoNERConfig(
            name="ru",
            version=datasets.Version("1.0.0"),
            description="MultiCoNER Russian dataset",
        ),
        MultiCoNERConfig(
            name="tr",
            version=datasets.Version("1.0.0"),
            description="MultiCoNER Turkish dataset",
        ),
        MultiCoNERConfig(
            name="zh",
            version=datasets.Version("1.0.0"),
            description="MultiCoNER Chinese dataset",
        ),
        MultiCoNERConfig(
            name="multi",
            version=datasets.Version("1.0.0"),
            description="MultiCoNER Multilingual dataset",
        ),
        MultiCoNERConfig(
            name="mix",
            version=datasets.Version("1.0.0"),
            description="MultiCoNER Mixed dataset",
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("int32"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-PER",
                                "I-PER",
                                "B-LOC",
                                "I-LOC",
                                "B-CORP",
                                "I-CORP",
                                "B-GRP",
                                "I-GRP",
                                "B-PROD",
                                "I-PROD",
                                "B-CW",
                                "I-CW",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{subset_to_dir[self.config.name]}/{self.config.name}_train.conll",
            "dev": f"{subset_to_dir[self.config.name]}/{self.config.name}_dev.conll",
            "test": f"{subset_to_dir[self.config.name]}/{self.config.name}_test.conll",
        }
        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):
        logger.info("⏳ Generating examples from = %s", filepath)

        with open(filepath, "r", encoding="utf8") as f:
            guid = -1
            tokens = []
            ner_tags = []

            for line in f:
                if line.strip().startswith("# id"):
                    guid += 1
                    tokens = []
                    ner_tags = []
                elif " _ _ " in line:
                    # Separator is " _ _ "
                    splits = line.split(" _ _ ")
                    tokens.append(splits[0].strip())
                    ner_tags.append(splits[1].strip())
                elif len(line.strip()) == 0:
                    if len(tokens) >= 1 and len(tokens) == len(ner_tags):
                        yield guid, {
                            "id": guid,
                            "tokens": tokens,
                            "ner_tags": ner_tags,
                        }
                        tokens = []
                        ner_tags = []
                    else:
                        continue

            if len(tokens) >= 1 and len(tokens) == len(ner_tags):
                yield guid, {
                    "id": guid,
                    "tokens": tokens,
                    "ner_tags": ner_tags,
                }