# Copyright (C) 2022, François-Guillaume Fernandez.

# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0> for full license details.

"""Imagenette dataset."""

import os
import json

import datasets


_HOMEPAGE = "https://github.com/fastai/imagenette"

_LICENSE = "Apache License 2.0"

_CITATION = """\
@software{Howard_Imagenette_2019,
    title={Imagenette: A smaller subset of 10 easily classified classes from Imagenet},
    author={Jeremy Howard},
    year={2019},
    month={March},
    publisher = {GitHub},
    url = {https://github.com/fastai/imagenette}
}
"""

_DESCRIPTION = """\
Imagenette is a subset of 10 easily classified classes from Imagenet
(tench, English springer, cassette player, chain saw, church, French
horn, garbage truck, gas pump, golf ball, parachute).
"""

_LABEL_MAP = [
    'n01440764',
    'n02102040',
    'n02979186',
    'n03000684',
    'n03028079',
    'n03394916',
    'n03417042',
    'n03425413',
    'n03445777',
    'n03888257',
]



class OpenFireConfig(datasets.BuilderConfig):
    """BuilderConfig for OpenFire."""

    def __init__(self, data_url, **kwargs):
        """BuilderConfig for OpenFire.
        Args:
          data_url: `string`, url to download the zip file from.
          **kwargs: keyword arguments forwarded to super.
        """
        super(OpenFireConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
        self.data_url = data_url


class OpenFire(datasets.GeneratorBasedBuilder):
    """OpenFire dataset."""

    BUILDER_CONFIGS = [
        OpenFireConfig(
            name="full_size",
            description="All images are in their original size.",
            data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz",
        ),
        OpenFireConfig(
            name="320px",
            description="All images were resized on their shortest side to 320 pixels.",
            data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-320.tgz",
        ),
        OpenFireConfig(
            name="160px",
            description="All images were resized on their shortest side to 160 pixels.",
            data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-160.tgz",
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION + self.config.description,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "label": datasets.ClassLabel(
                        names=[
                            "tench",
                            "English springer",
                            "cassette player",
                            "chain saw",
                            "church",
                            "French horn",
                            "garbage truck",
                            "gas pump",
                            "golf ball",
                            "parachute",
                        ]
                    ),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        _path = dl_manager.download_and_extract(self.config.data_url)
        local_extracted_archive = os.path.join(_path, self.config.data_url.rpartition("/")[-1].split('.')[0])
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "split_folder": os.path.join(local_extracted_archive, "train"),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "split_folder": os.path.join(local_extracted_archive, "val"),
                },
            ),
        ]

    def _generate_examples(self, split_folder):
        idx = 0
        for class_idx, class_folder in enumerate(_LABEL_MAP):
            for path in os.listdir(os.path.join(split_folder, class_folder)):
                yield idx, {"image": os.path.join(split_folder, class_folder, path), "label": class_idx}
                idx += 1