# Copyright 2020 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.

import csv
import json
import os

import datasets


# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {K-pop girls groups dataset},
author={smwoo, Inc.
},
year={2023}
}
"""

_BASE_URL = "https://drive.google.com/u/0/uc?id=16_GUeJcC88LrB8zJni0-XdjdL14WXOVW&export=download"

_METADATA_URLS = {
    "train": "https://drive.google.com/u/0/uc?id=1mPwH_p1-QQtY0xsFjLIzQLkAXDNyB8sO&export=download",
    "test": "https://drive.google.com/u/0/uc?id=1mPwH_p1-QQtY0xsFjLIzQLkAXDNyB8sO&export=download"
}

# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to learn how to make custom dataset. 
"""

_HOMEPAGE = "https://cislab.cau.ac.kr/"

_LICENSE = ""

_IMAGES_DIR = "data/"

class GirlsGroupsConfig(datasets.BuilderConfig):
    """BuilderConfig for GirlsGroups dataset."""
    
    def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs):
        super(GirlsGroupsConfig, self).__init__(version=datasets.Version("1.1.0"), **kwargs)
        self.features = features
        self.label_classes = label_classes
        self.data_url = data_url
        self.citation = citation
        self.url = url


class GirlsGroups(datasets.GeneratorBasedBuilder):
    """GirlsGroups Images dataset."""

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

    def _split_generators(self, dl_manager):
        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS 
        archive_path = dl_manager.download(_BASE_URL)
        split_metadata_paths = dl_manager.download(_METADATA_URLS)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images": dl_manager.iter_archive(archive_path),
                    "metadata_path": split_metadata_paths["train"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "images": dl_manager.iter_archive(archive_path),
                    "metadata_path": split_metadata_paths["test"],
                },
            ),
        ]

    def _generate_examples(self, images, metadata_path):
        """Generate images and labels for splits."""
        save_list = {}
        with open(metadata_path, newline='') as csvfile:
            spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
            for row in spamreader:
                save_list[row[0]] = row[1]
        for file_path, file_obj in images:
            yield file_path, {
                "image": {"path": file_path, "bytes": file_obj.read()},
                "annotation" : save_list[file_path]
            }