# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Brill Iconclass AI Test Set data."""


import json
import os
from PIL import Image
import datasets

_CITATION = """\
@MISC{iconclass,
title = {Brill Iconclass AI Test Set},
author={Etienne Posthumus},
year={2020}
}
"""


_DESCRIPTION = """\
A dataset for applying machine learning to collections described with the Iconclass classification system.
"""

_HOMEPAGE = "https://iconclass.org/testset/"

_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"

_URL = "https://iconclass.org/testset/779ba2ca9e977c58d818e3823a676973.zip"

class BrillIconclass(datasets.GeneratorBasedBuilder):
    """Brill IconClass AI dataset"""

    VERSION = datasets.Version("1.1.0")


    def _info(self):
        features = datasets.Features(
            {
                "image": datasets.Image(),
                "label": [datasets.Value("string")]
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"data_json": os.path.join(data_dir, "data.json"), "data_dir": data_dir},
            ),
        ]

    def _generate_examples(self, data_json, data_dir):
        with open(data_json, encoding="utf-8") as f:
            data = json.load(f)
            for row, item in enumerate(data.items()):
                filepath, labels = item
                yield row, {"image": os.path.join(data_dir, filepath), "label": labels}