# 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.

"""This dataset contains interior images created using DALL-E.
The dataset contains 512x512 images split into 5 classes:
* bathroom: 1000 images
* bedroom: 1000 images
* dining_room: 1000 images
* kitchen: 1000 images
* living_room: 1000 images
"""

import datasets
from datasets.download.download_manager import DownloadManager
from datasets.tasks import ImageClassification
from pathlib import Path
from typing import List, Iterator

_ALLOWED_IMG_EXT = {".png", ".jpg"}

_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Computer Generated interior images},
author={Padilla, Rafael},
year={2023}
}
"""

_DESCRIPTION = """\
This new dataset contains CG interior images representing interior of houses in 5 classes, with \
    1000 images per class.
"""

_HOMEPAGE = "https://huggingface.co/datasets/rafaelpadilla/interior-cgi/"

_LICENSE = ""

_URLS = {
    "test": "https://huggingface.co/datasets/rafaelpadilla/interior-cgi/resolve/main/data/test.zip",
    "train": "https://huggingface.co/datasets/rafaelpadilla/interior-cgi/resolve/main/data/train.zip",
}

_NAMES = ["bathroom", "bedroom", "dining_room", "kitchen", "living_room"]


class CGInteriorDataset(datasets.GeneratorBasedBuilder):
    """CGInterior: Computer Generated Interior images dataset"""

    VERSION = datasets.Version("1.1.0")

    def _info(self) -> datasets.DatasetInfo:
        """
        Returns the dataset metadata and features.

        Returns:
            DatasetInfo: Metadata and features of the dataset.
        """
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "label_id": datasets.features.ClassLabel(names=_NAMES),
                    "label_name": datasets.Value("string"),
                }
            ),
            supervised_keys=("image", "label_id"),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
            task_templates=[
                ImageClassification(image_column="image", label_column="label_id")
            ],
        )

    def _split_generators(
        self, dl_manager: DownloadManager
    ) -> List[datasets.SplitGenerator]:
        """
        Provides the split information and downloads the data.

        Args:
            dl_manager (DownloadManager): The DownloadManager to use for downloading and extracting data.

        Returns:
            List[SplitGenerator]: List of SplitGenerator objects representing the data splits.
        """
        data_files = dl_manager.download_and_extract(_URLS)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "files": dl_manager.iter_files(data_files["train"]),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "files": dl_manager.iter_files(data_files["test"]),
                },
            ),
        ]

    def _generate_examples(self, files: List[str]) -> Iterator:
        """
        Generates examples for the dataset.

        Args:
            files (List[str]): List of image paths.

        Yields:
            Dict[str, Union[str, Image]]: A dictionary containing the generated examples.
        """
        for idx, img_path in enumerate(files):
            path = Path(img_path)
            if path.suffix in _ALLOWED_IMG_EXT:
                yield idx, {
                    "image": img_path,
                    "label_id": path.parent.name,
                    "label_name": path.parent.name,
                }