"""Cartoonset-10k Data Set"""


import pickle

import numpy as np
import PIL.Image


import datasets
from datasets.tasks import ImageClassification


_CITATION = r"""
@article{DBLP:journals/corr/abs-1711-05139,
  author    = {Amelie Royer and
               Konstantinos Bousmalis and
               Stephan Gouws and
               Fred Bertsch and
               Inbar Mosseri and
               Forrester Cole and
               Kevin Murphy},
  title     = {{XGAN:} Unsupervised Image-to-Image Translation for many-to-many Mappings},
  journal   = {CoRR},
  volume    = {abs/1711.05139},
  year      = {2017},
  url       = {http://arxiv.org/abs/1711.05139},
  eprinttype = {arXiv},
  eprint    = {1711.05139},
  timestamp = {Mon, 13 Aug 2018 16:47:38 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1711-05139.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""

_DESCRIPTION = """\
Cartoon Set is a collection of random, 2D cartoon avatar images. The cartoons vary in 10 artwork 
categories, 4 color categories, and 4 proportion categories, with a total of ~1013 possible 
combinations. We provide sets of 10k and 100k randomly chosen cartoons and labeled attributes. 
"""

_DATA_URLS = {
    "10k": "https://huggingface.co/datasets/cgarciae/cartoonset/resolve/1.0.0/data/cartoonset10k.tgz",
    "100k": "https://huggingface.co/datasets/cgarciae/cartoonset/resolve/1.0.0/data/cartoonset100k.tgz",
}


class Cartoonset(datasets.GeneratorBasedBuilder):
    """Cartoonset-10k Data Set"""

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="10k",
            version=datasets.Version("1.0.0", ""),
            description="Loads the Cartoonset-10k Data Set",
        ),
        datasets.BuilderConfig(
            name="100k",
            version=datasets.Version("1.0.0", ""),
            description="Loads the Cartoonset-100k Data Set",
        ),
    ]

    DEFAULT_CONFIG_NAME = "10k"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "img": datasets.Image(),
                }
            ),
            supervised_keys=("img",),
            homepage="https://www.cs.toronto.edu/~kriz/cifar.html",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager):
        url = _DATA_URLS[self.config.name]
        archive = dl_manager.download(url)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "files": dl_manager.iter_archive(archive),
                    "split": "train",
                },
            ),
        ]

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

        path: str
        for path, file_obj in files:

            if path.endswith(".png"):
                image = PIL.Image.open(file_obj)

                yield path, {
                    "img": image,
                }