# coding=utf-8
# 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.
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"""ImageNet-Sketch data set for evaluating model's ability in learning (out-of-domain) semantics at ImageNet scale"""

import os

import pandas as pd
import datasets
from datasets.tasks import ImageClassification

# from .classes import IMAGENET2012_CLASSES


_HOMEPAGE = "https://huggingface.co/datasets/AIPI540/test2/tree/main"

_CITATION = """\
@inproceedings{wang2019learning,
        title={Learning Robust Global Representations by Penalizing Local Predictive Power},
        author={Wang, Haohan and Ge, Songwei and Lipton, Zachary and Xing, Eric P},
        booktitle={Advances in Neural Information Processing Systems},
        pages={10506--10518},
        year={2019}
}
"""

_DESCRIPTION = """\
Artwork Images, to predict the year of the artwork created.
"""

_URL = "https://huggingface.co/datasets/AIPI540/Art2/resolve/main/final_art_data.parquet"

class Artwork(datasets.GeneratorBasedBuilder):
    """Artwork Images - a dataset of centuries of Images classes"""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "label": datasets.features.ClassLabel(names=classes),
                    "image_data": datasets.Value("binary"),
                }
            ),
            supervised_keys=("label","image_data"),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            task_templates=[ImageClassification(image_column="image_data", label_column="label")],
        )

    def _split_generators(self, dl_manager):
        data_files = dl_manager.download_and_extract(_URL)
        df = pd.read_parquet(data_files, engine='pyarrow')

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

    def _generate_examples(self, files):
        cnt=0
        for path in files.itertuples():
            print(cnt)
            cnt+=1
            print(path)
            print(path.label)
            print(type(path.label))
            print(path.image_data)
            print(type(path.image_data))
            yield {
              "label": classes[(path.label)],
              "image_data": path.image_data,
            }