# 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.
"""MyoQuant-SDH-Data: The MyoQuant SDH Model Data."""


import csv
import json
import os

import datasets


_CITATION = """\
@InProceedings{Meyer,
title = {MyoQuant SDH Data},
author={Corentin Meyer},
year={2022}
}
"""
_NAMES = ["control", "sick"]

_DESCRIPTION = """\
This dataset is used to train the SDH model of MyoQuant to detect and quantify anomaly in the mitochondria repartition in SDH stained muscle fiber with myopathy disorders.
"""

_HOMEPAGE = "https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data"

_LICENSE = "agpl-3.0"

_URLS = {
    "SDH_16k": "https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data/resolve/main/SDH_16k/SDH_16k.zip"
}
_METADATA_URL = {
    "SDH_16k_metadata": "https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data/resolve/main/SDH_16k/metadata.jsonl"
}


class SDH_16k(datasets.GeneratorBasedBuilder):
    """This dataset is used to train the SDH model of MyoQuant to detect and quantify anomaly in the mitochondria repartition in SDH stained muscle fiber with myopathy disorders."""

    VERSION = datasets.Version("1.0.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')

    DEFAULT_CONFIG_NAME = "SDH_16k"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "label": datasets.ClassLabel(num_classes=2, names=_NAMES),
                }
            ),
            supervised_keys=("image", "label"),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
            task_templates=[
                datasets.ImageClassification(image_column="image", label_column="label")
            ],
        )

    def _split_generators(self, dl_manager):
        archive_path = dl_manager.download(_URLS)
        split_metadata_path = dl_manager.download(_METADATA_URL)
        files_metadata = {}
        with open(split_metadata_path["SDH_16k_metadata"], encoding="utf-8") as f:
            for lines in f.read().splitlines():
                file_json_metdata = json.loads(lines)
                files_metadata.setdefault(file_json_metdata["split"], []).append(
                    file_json_metdata
                )
        downloaded_files = dl_manager.download_and_extract(archive_path)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "download_path": downloaded_files["SDH_16k"],
                    "metadata": files_metadata["train"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "download_path": downloaded_files["SDH_16k"],
                    "metadata": files_metadata["validation"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "download_path": downloaded_files["SDH_16k"],
                    "metadata": files_metadata["test"],
                },
            ),
        ]

    def _generate_examples(self, download_path, metadata):
        """Generate images and labels for splits."""
        for i, single_metdata in enumerate(metadata):
            img_path = os.path.join(
                download_path,
                single_metdata["split"],
                single_metdata["label"],
                single_metdata["file_name"],
            )
            yield i, {
                "image": img_path,
                "label": single_metdata["label"],
            }