import datasets
import pandas as pd

_CITATION = """\
@InProceedings{huggingface:dataset,
title = {FaceSegmentation},
author = {TrainingDataPro},
year = {2023}
}
"""

_DESCRIPTION = """\
An example of a dataset that we've collected for a photo edit App.
The dataset includes 20 selfies of people (man and women)
in segmentation masks and their visualisations.
"""
_NAME = 'FaceSegmentation'

_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"

_LICENSE = ""

_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"


class FaceSegmentation(datasets.GeneratorBasedBuilder):
    """Small sample of image-text pairs"""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                'image': datasets.Image(),
                'mask': datasets.Image(),
                'id': datasets.Value('string'),
                'gender': datasets.Value('string'),
                'age': datasets.Value('int8')
            }),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        images = dl_manager.download(f"{_DATA}images.tar.gz")
        masks = dl_manager.download(f"{_DATA}masks.tar.gz")
        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
        images = dl_manager.iter_archive(images)
        masks = dl_manager.iter_archive(masks)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                    gen_kwargs={
                                        "images": images,
                                        'masks': masks,
                                        'annotations': annotations
                                    }),
        ]

    def _generate_examples(self, images, masks, annotations):
        annotations_df = pd.read_csv(annotations, sep=';')

        for idx, ((image_path, image),
                  (mask_path, mask)) in enumerate(zip(images, masks)):
            yield idx, {
                "image": {
                    "path": image_path,
                    "bytes": image.read()
                },
                "mask": {
                    "path": mask_path,
                    "bytes": mask.read()
                },
                'id': annotations_df['id'].iloc[idx],
                'gender': annotations_df['gender'].iloc[idx],
                'age': annotations_df['age'].iloc[idx]
            }