# coding=utf-8

import datasets


logger = datasets.logging.get_logger(__name__)

_URL = "https://huggingface.co/datasets/adalbertojunior/segmentacao2/resolve/main/"
_TRAIN_FILE = "train.conll"
_TEST_FILE = "test.conll"


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

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name='segmentacao',version=VERSION,description="segmentacao 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=[
                                "B-Segmento",
                                "I-Segmento",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
        )

    def _split_generators(self, dl_manager):
        #
        urls_to_download = {
            "train": f"{_URL}{_TRAIN_FILE}",
            "dev": f"{_URL}{_TEST_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,
            }