"""Dataset class for Individuality Of Handwriting dataset."""

import pathlib

from datasets.tasks import ImageClassification

import datasets

_BASE_URL = "https://cedar.buffalo.edu/NIJ/data/signatures.rar"

_HOMEPAGE = "https://cedar.buffalo.edu/NIJ/projectinfo.html"

_DESCRIPTION = """
This dataset consists of handwriting samples of 1500 individuals, representative of the US population with respect to gender, age, ethnic groups...
"""

_NAMES = [
    "original",
    "forgeries",
]

class IndividualityOfHandwriting(datasets.GeneratorBasedBuilder):
    """Food-101 Images dataset."""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "label": datasets.ClassLabel(names=_NAMES),
                    "individual": datasets.Value("uint8"),
                    "figure": datasets.Value("uint8"),
                }
            ),
            supervised_keys=("image", "label"),
            homepage=_HOMEPAGE,
            task_templates=[ImageClassification(image_column="image", label_column="label")],
        )

    def _split_generators(self, dl_manager):
        archive_path = dl_manager.download_and_extract(_BASE_URL)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "data_dir": archive_path,
                },
            ),
        ]

    def _generate_examples(self, data_dir):
        """Generate images and labels for splits."""
        rglob = pathlib.Path(data_dir).rglob("*.png")
        for index, filepath in enumerate(rglob):
            filename = filepath.with_suffix("").name
            label, individual, figure = filename.split("_")
            
            yield index, {
                "image": str(filepath),
                "label": label,
                "individual": individual,
                "figure": figure,
            }