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# Lint as: python3
"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
    title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
    author = "Tjong Kim Sang, Erik F.  and
      De Meulder, Fien",
    booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
    year = "2003",
    url = "https://www.aclweb.org/anthology/W03-0419",
    pages = "142--147",
}
"""

_DESCRIPTION = """\
The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on
four types of named entities: persons, locations, organizations and names of miscellaneous entities that do
not belong to the previous three groups.
The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on
a separate line and there is an empty line after each sentence. The first item on each line is a word, the second
a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags
and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only
if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag
B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2
tagging scheme, whereas the original dataset uses IOB1.
For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419
"""

_URL = "../../../data/CoNLL03/"
_TRAINING_FILE = "train.txt"
_DEV_FILE = "valid.txt"
_TEST_FILE = "test.txt"


class Conll2003Config(datasets.BuilderConfig):
    """BuilderConfig for Conll2003"""

    def __init__(self, **kwargs):
        """BuilderConfig forConll2003.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(Conll2003Config, self).__init__(**kwargs)


class Conll2003(datasets.GeneratorBasedBuilder):
    """Conll2003 dataset."""

    BUILDER_CONFIGS = [
        Conll2003Config(name="conll2003", version=datasets.Version("1.0.0"), description="Conll2003 dataset"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "pos_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                '"',
                                "''",
                                "#",
                                "$",
                                "(",
                                ")",
                                ",",
                                ".",
                                ":",
                                "``",
                                "CC",
                                "CD",
                                "DT",
                                "EX",
                                "FW",
                                "IN",
                                "JJ",
                                "JJR",
                                "JJS",
                                "LS",
                                "MD",
                                "NN",
                                "NNP",
                                "NNPS",
                                "NNS",
                                "NN|SYM",
                                "PDT",
                                "POS",
                                "PRP",
                                "PRP$",
                                "RB",
                                "RBR",
                                "RBS",
                                "RP",
                                "SYM",
                                "TO",
                                "UH",
                                "VB",
                                "VBD",
                                "VBG",
                                "VBN",
                                "VBP",
                                "VBZ",
                                "WDT",
                                "WP",
                                "WP$",
                                "WRB",
                            ]
                        )
                    ),
                    "chunk_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-ADJP",
                                "I-ADJP",
                                "B-ADVP",
                                "I-ADVP",
                                "B-CONJP",
                                "I-CONJP",
                                "B-INTJ",
                                "I-INTJ",
                                "B-LST",
                                "I-LST",
                                "B-NP",
                                "I-NP",
                                "B-PP",
                                "I-PP",
                                "B-PRT",
                                "I-PRT",
                                "B-SBAR",
                                "I-SBAR",
                                "B-UCP",
                                "I-UCP",
                                "B-VP",
                                "I-VP",
                            ]
                        )
                    ),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-PER",
                                "I-PER",
                                "B-ORG",
                                "I-ORG",
                                "B-LOC",
                                "I-LOC",
                                "B-MISC",
                                "I-MISC",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://www.aclweb.org/anthology/W03-0419/",
            citation=_CITATION,
        )

    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):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            tokens = []
            pos_tags = []
            chunk_tags = []
            ner_tags = []
            for line in f:
                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                    if tokens:
                        yield guid, {
                            "id": str(guid),
                            "tokens": tokens,
                            "pos_tags": pos_tags,
                            "chunk_tags": chunk_tags,
                            "ner_tags": ner_tags,
                        }
                        guid += 1
                        tokens = []
                        pos_tags = []
                        chunk_tags = []
                        ner_tags = []
                else:
                    # conll2003 tokens are space separated
                    splits = line.split(" ")
                    tokens.append(splits[0])
                    pos_tags.append(splits[1])
                    chunk_tags.append(splits[2])
                    ner_tags.append(splits[3].rstrip())
            # last example
            yield guid, {
                "id": str(guid),
                "tokens": tokens,
                "pos_tags": pos_tags,
                "chunk_tags": chunk_tags,
                "ner_tags": ner_tags,
            }