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# Source code for torchvision.datasets.gtsrb

    import csv
    import pathlib
    from typing import Any, Callable, Optional, Tuple

    import PIL

    from .folder import make_dataset
    from .utils import download_and_extract_archive, verify_str_arg
    from .vision import VisionDataset

    class GTSRB(VisionDataset):
        """`German Traffic Sign Recognition Benchmark (GTSRB) <https://benchmark.ini.rub.de/>`_ Dataset.

        Args:
            root (string): Root directory of the dataset.
            split (string, optional): The dataset split, supports ``"train"`` (default), or ``"test"``.
            transform (callable, optional): A function/transform that  takes in an PIL image and returns a transformed
                version. E.g, ``transforms.RandomCrop``.
            target_transform (callable, optional): A function/transform that takes in the target and transforms it.
            download (bool, optional): If True, downloads the dataset from the internet and
                puts it in root directory. If dataset is already downloaded, it is not
                downloaded again.
        """

        def __init__(
            self,
            root: str,
            split: str = "train",
            transform: Optional[Callable] = None,
            target_transform: Optional[Callable] = None,
            download: bool = False,
        ) -> None:

            super().__init__(root, transform=transform, target_transform=target_transform)

            self._split = verify_str_arg(split, "split", ("train", "test"))
            self._base_folder = pathlib.Path(root) / "gtsrb"
            self._target_folder = (
                self._base_folder / "GTSRB" / ("Training" if self._split == "train" else "Final_Test/Images")
            )

            if download:
                self.download()

            if not self._check_exists():
                raise RuntimeError("Dataset not found. You can use download=True to download it")

            if self._split == "train":
                samples = make_dataset(str(self._target_folder), extensions=(".ppm",))
            else:
                with open(self._base_folder / "GT-final_test.csv") as csv_file:
                    samples = [
                        (str(self._target_folder / row["Filename"]), int(row["ClassId"]))
                        for row in csv.DictReader(csv_file, delimiter=";", skipinitialspace=True)
                    ]

            self._samples = samples
            self.transform = transform
            self.target_transform = target_transform

        def __len__(self) -> int:
            return len(self._samples)

        def __getitem__(self, index: int) -> Tuple[Any, Any]:

            path, target = self._samples[index]
            sample = PIL.Image.open(path).convert("RGB")

            if self.transform is not None:
                sample = self.transform(sample)

            if self.target_transform is not None:
                target = self.target_transform(target)

            return sample, target

        def _check_exists(self) -> bool:
            return self._target_folder.is_dir()

        def download(self) -> None:
            if self._check_exists():
                return

            base_url = "https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/"

            if self._split == "train":
                download_and_extract_archive(
                    f"{base_url}GTSRB-Training_fixed.zip",
                    download_root=str(self._base_folder),
                    md5="513f3c79a4c5141765e10e952eaa2478",
                )
            else:
                download_and_extract_archive(
                    f"{base_url}GTSRB_Final_Test_Images.zip",
                    download_root=str(self._base_folder),
                    md5="c7e4e6327067d32654124b0fe9e82185",
                )
                download_and_extract_archive(
                    f"{base_url}GTSRB_Final_Test_GT.zip",
                    download_root=str(self._base_folder),
                    md5="fe31e9c9270bbcd7b84b7f21a9d9d9e5",
                )

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