# coding=utf-8
import datasets
logger = datasets.logging.get_logger(__name__)
_URL = "https://huggingface.co/datasets/adalbertojunior/punctuation-ptbr/resolve/main/"
_TRAIN_FILE = "train.txt"
_TEST_FILE = "test.txt"
_DEV_FILE = "dev.txt"


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

    VERSION = datasets.Version("1.0.0")
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name='punctuation-ptbr', version=VERSION, description="punctuation-ptbr dataset"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "pos_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                            ]
                        )
                    ),
                    "chunk_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                            ]
                        )
                    ),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-Virgula",
                                "B-Ponto",
                                "B-Interrogacao",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
        )

    def _split_generators(self, dl_manager):
        #
        urls_to_download = {
            "train": f"{_URL}{_TRAIN_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"], "split": "train"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": downloaded_files["dev"], "split": "dev"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": downloaded_files["test"], "split": "test"},
            ),
        ]

    def _generate_examples(self, filepath, split):
        """This function returns the examples in the raw (text) form by iterating on all the files."""

        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 == "" 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:
                    splits = line.split(" ")
                    tokens.append(splits[0])
                    pos_tags.append(splits[1])
                    chunk_tags.append(splits[2])
                    ner_tags.append(splits[-1].rstrip())

            # last example
            yield guid, {
                "id": str(guid),
                "tokens": tokens,
                "pos_tags": pos_tags,
                "chunk_tags": chunk_tags,
                "ner_tags": ner_tags,
            }