import warnings

from torchmeta_local.datasets import (
    Omniglot, MiniImagenet, TieredImagenet, CIFARFS, FC100, CUB, DoubleMNIST,
    TripleMNIST, Pascal5i, MiniImagenet32)
from torchmeta_local.transforms import (
    Categorical, ClassSplitter, Rotation, SegmentationPairTransform)
from torchvision.transforms import (
    Compose, Resize, CenterCrop, ToTensor, Lambda, Normalize)


__all__ = [
    'omniglot',
    'omniglot_norm',
    'omniglot_rgb84x84',
    'omniglot_rgb84x84_norm',
    'miniimagenet',
    'miniimagenet_norm',
    'miniimagenet_32_norm_cache',
    'tieredimagenet',
    'cifar_fs',
    'cifar_fs_norm',
    'cifar_fs_rfs',
    'fc100',
    'fc100_norm',
    'fc100_rfs',
    'cub',
    'doublemnist',
    'triplemnist'
]


def helper_with_default(klass, folder, shots, ways, shuffle=True,
                        test_shots=None, seed=None, defaults={}, num_samples_per_class=None, **kwargs):
    if 'num_classes_per_task' in kwargs:
        warnings.warn('Both arguments `ways` and `num_classes_per_task` were '
            'set in the helper function for the number of classes per task. '
            'Ignoring the argument `ways`.', stacklevel=2)
        ways = kwargs['num_classes_per_task']
    if 'transform' not in kwargs:
        kwargs['transform'] = defaults.get('transform', ToTensor())
    if 'target_transform' not in kwargs:
        kwargs['target_transform'] = defaults.get('target_transform',
                                                  Categorical(ways))
    if 'class_augmentations' not in kwargs:
        kwargs['class_augmentations'] = defaults.get('class_augmentations', None)
    if test_shots is None:
        test_shots = shots
    dataset = klass(folder, num_classes_per_task=ways, **kwargs)
    if num_samples_per_class is not None:
        dataset = ClassSplitter(
            dataset, shuffle=shuffle,
            num_samples_per_class=num_samples_per_class)
    else:
        dataset = ClassSplitter(dataset, shuffle=shuffle,
            num_train_per_class=shots, num_test_per_class=test_shots)
    dataset.seed(seed)

    return dataset


def omniglot(folder, shots, ways, shuffle=True, test_shots=None,
             seed=None, **kwargs):
    """Helper function to create a meta-dataset for the Omniglot dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `omniglot` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `Omniglot` class.

    See also
    --------
    `datasets.Omniglot` : Meta-dataset for the Omniglot dataset.
    """
    defaults = {
        'transform': Compose([Resize(28), ToTensor()]),
        'class_augmentations': [Rotation([90, 180, 270])]
    }

    return helper_with_default(Omniglot, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)


def omniglot_norm(folder, shots, ways, shuffle=True, test_shots=None,
                  seed=None, **kwargs):
    """Helper function to create a meta-dataset for the Omniglot dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `omniglot` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `Omniglot` class.

    See also
    --------
    `datasets.Omniglot` : Meta-dataset for the Omniglot dataset.
    """
    norm_params = {'mean': [0.922], 'std': [0.084]}
    defaults = {
        'transform': Compose(
            [Resize(28), ToTensor(), Normalize(**norm_params)]),
        'class_augmentations': [Rotation([90, 180, 270])]
    }

    return helper_with_default(Omniglot, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)


def omniglot_32_norm(folder, shots, ways, shuffle=True, test_shots=None,
                  seed=None, **kwargs):
    """Helper function to create a meta-dataset for the Omniglot dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `omniglot` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `Omniglot` class.

    See also
    --------
    `datasets.Omniglot` : Meta-dataset for the Omniglot dataset.
    """
    norm_params = {'mean': [0.922], 'std': [0.084]}
    defaults = {
        'transform': Compose(
            [Resize(32), ToTensor(), Normalize(**norm_params), Lambda(lambda x: x.repeat(3, 1, 1))]),
        'class_augmentations': [Rotation([90, 180, 270])]
    }

    return helper_with_default(Omniglot, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)


def omniglot_rgb84x84(folder, shots, ways, shuffle=True, test_shots=None,
                      seed=None, **kwargs):
    """Helper function to create a meta-dataset for the Omniglot dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `omniglot` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `Omniglot` class.

    See also
    --------
    `datasets.Omniglot` : Meta-dataset for the Omniglot dataset.
    """
    defaults = {
        'transform': Compose(
            [Resize(84), ToTensor(), Lambda(lambda x: x.repeat(3, 1, 1))]),
        'class_augmentations': [Rotation([90, 180, 270])]
    }

    return helper_with_default(Omniglot, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)


def omniglot_rgb84x84_norm(folder, shots, ways, shuffle=True, test_shots=None,
                           seed=None, **kwargs):
    """Helper function to create a meta-dataset for the Omniglot dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `omniglot` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `Omniglot` class.

    See also
    --------
    `datasets.Omniglot` : Meta-dataset for the Omniglot dataset.
    """
    norm_params = {'mean': [0.922, 0.922, 0.922], 'std': [0.084, 0.084, 0.084]}
    defaults = {
        'transform': Compose(
            [Resize(84), ToTensor(),
             Lambda(lambda x: x.repeat(3, 1, 1)), Normalize(**norm_params)]),
        'class_augmentations': [Rotation([90, 180, 270])]
    }

    return helper_with_default(Omniglot, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)


def omniglot_rgb32x32_norm(folder, shots, ways, shuffle=True, test_shots=None,
                           seed=None, **kwargs):
    """Helper function to create a meta-dataset for the Omniglot dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `omniglot` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `Omniglot` class.

    See also
    --------
    `datasets.Omniglot` : Meta-dataset for the Omniglot dataset.
    """
    norm_params = {'mean': [0.922, 0.922, 0.922], 'std': [0.084, 0.084, 0.084]}
    defaults = {
        'transform': Compose(
            [Resize(32), ToTensor(),
             Lambda(lambda x: x.repeat(3, 1, 1)), Normalize(**norm_params)]),
        'class_augmentations': [Rotation([90, 180, 270])]
    }

    return helper_with_default(Omniglot, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)


def miniimagenet(folder, shots, ways, shuffle=True, test_shots=None,
                 seed=None, **kwargs):
    """Helper function to create a meta-dataset for the Mini-Imagenet dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `miniimagenet` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `MiniImagenet` class.

    See also
    --------
    `datasets.MiniImagenet` : Meta-dataset for the Mini-Imagenet dataset.
    """
    defaults = {
        'transform': Compose([Resize(84), ToTensor()])
    }

    return helper_with_default(MiniImagenet, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)


def miniimagenet_norm(folder, shots, ways, shuffle=True, test_shots=None,
                      seed=None, **kwargs):
    """Helper function to create a meta-dataset for the Mini-Imagenet dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `miniimagenet` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `MiniImagenet` class.

    See also
    --------
    `datasets.MiniImagenet` : Meta-dataset for the Mini-Imagenet dataset.
    """
    # params taken from https://github.com/yinboc/few-shot-meta-baseline/blob/master/datasets/mini_imagenet.py
    norm_params = {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}
    defaults = {
        'transform': Compose(
            [Resize(84), ToTensor(),
             Normalize(**norm_params)])
    }

    return helper_with_default(MiniImagenet, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)


def miniimagenet_32_norm(folder, shots, ways, shuffle=True, test_shots=None,
                         seed=None, **kwargs):
    """Helper function to create a meta-dataset for the Mini-Imagenet dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `miniimagenet` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `MiniImagenet` class.

    See also
    --------
    `datasets.MiniImagenet` : Meta-dataset for the Mini-Imagenet dataset.
    """
    # params taken from https://github.com/yinboc/few-shot-meta-baseline/blob/master/datasets/mini_imagenet.py
    norm_params = {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}
    defaults = {
        'transform': Compose(
            [Resize(32), ToTensor(),
             Normalize(**norm_params)])
    }

    return helper_with_default(MiniImagenet, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)


def miniimagenet_32_norm_cache(folder, shots, ways, shuffle=True, test_shots=None,
                               seed=None, **kwargs):
    """Helper function to create a meta-dataset for the Mini-Imagenet dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `miniimagenet` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `MiniImagenet` class.

    See also
    --------
    `datasets.MiniImagenet` : Meta-dataset for the Mini-Imagenet dataset.
    """
    # params taken from https://github.com/yinboc/few-shot-meta-baseline/blob/master/datasets/mini_imagenet.py
    norm_params = {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}
    defaults = {
        'transform': None
    }

    return helper_with_default(MiniImagenet32, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)


def tieredimagenet(folder, shots, ways, shuffle=True, test_shots=None,
                   seed=None, **kwargs):
    """Helper function to create a meta-dataset for the Tiered-Imagenet dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `tieredimagenet` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `TieredImagenet` class.

    See also
    --------
    `datasets.TieredImagenet` : Meta-dataset for the Tiered-Imagenet dataset.
    """
    defaults = {
        'transform': Compose([Resize(84), ToTensor()])
    }

    return helper_with_default(TieredImagenet, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)


def cifar_fs(folder, shots, ways, shuffle=True, test_shots=None,
             seed=None, **kwargs):
    """Helper function to create a meta-dataset for the CIFAR-FS dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `cifar100` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `CIFARFS` class.

    See also
    --------
    `datasets.cifar100.CIFARFS` : Meta-dataset for the CIFAR-FS dataset.
    """
    return helper_with_default(CIFARFS, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults={}, **kwargs)


def cifar_fs_norm(folder, shots, ways, shuffle=True, test_shots=None,
               seed=None, **kwargs):
    """Helper function to create a meta-dataset for the FC100 dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `cifar100` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `FC100` class.

    See also
    --------
    `datasets.cifar100.FC100` : Meta-dataset for the FC100 dataset.
    """
    norm_params = {'mean': [0.507, 0.487, 0.441], 'std': [0.267, 0.256, 0.276]}
    defaults = {
        'transform': Compose(
            [ToTensor(), Normalize(**norm_params)])
    }
    return helper_with_default(CIFARFS, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)


def cifar_fs_rfs(folder, shots, ways, shuffle=True, test_shots=None,
              seed=None, **kwargs):
    """Helper function to create a meta-dataset for the FC100 dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `cifar100` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `FC100` class.

    See also
    --------
    `datasets.cifar100.FC100` : Meta-dataset for the FC100 dataset.
    """
    norm_params = {'mean': [0.507, 0.487, 0.441], 'std': [0.267, 0.256, 0.276]}
    from torchvision.transforms import (
        RandomCrop, ColorJitter, RandomHorizontalFlip)
    rfs_transform = Compose([
        RandomCrop(32, padding=4),
        ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
        RandomHorizontalFlip(),
        ToTensor(),
        Normalize(**norm_params),
    ])
    defaults = {'transform': rfs_transform}

    return helper_with_default(CIFARFS, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)


def fc100(folder, shots, ways, shuffle=True, test_shots=None,
          seed=None, **kwargs):
    """Helper function to create a meta-dataset for the FC100 dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `cifar100` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `FC100` class.

    See also
    --------
    `datasets.cifar100.FC100` : Meta-dataset for the FC100 dataset.
    """
    return helper_with_default(FC100, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults={}, **kwargs)


def fc100_norm(folder, shots, ways, shuffle=True, test_shots=None,
               seed=None, **kwargs):
    """Helper function to create a meta-dataset for the FC100 dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `cifar100` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `FC100` class.

    See also
    --------
    `datasets.cifar100.FC100` : Meta-dataset for the FC100 dataset.
    """
    norm_params = {'mean': [0.507, 0.487, 0.441], 'std': [0.267, 0.256, 0.276]}
    defaults = {
        'transform': Compose(
            [ToTensor(), Normalize(**norm_params)])
    }
    return helper_with_default(FC100, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)


def fc100_rfs(folder, shots, ways, shuffle=True, test_shots=None,
              seed=None, **kwargs):
    """Helper function to create a meta-dataset for the FC100 dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `cifar100` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `FC100` class.

    See also
    --------
    `datasets.cifar100.FC100` : Meta-dataset for the FC100 dataset.
    """
    norm_params = {'mean': [0.507, 0.487, 0.441], 'std': [0.267, 0.256, 0.276]}
    from torchvision.transforms import (
        RandomCrop, ColorJitter, RandomHorizontalFlip)
    rfs_transform = Compose([
        RandomCrop(32, padding=4),
        ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
        RandomHorizontalFlip(),
        ToTensor(),
        Normalize(**norm_params),
    ])
    defaults = {'transform': rfs_transform}

    return helper_with_default(FC100, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)


def cub(folder, shots, ways, shuffle=True, test_shots=None,
        seed=None, **kwargs):
    """Helper function to create a meta-dataset for the Caltech-UCSD Birds dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `cub` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `CUB` class.

    See also
    --------
    `datasets.cub.CUB` : Meta-dataset for the Caltech-UCSD Birds dataset.
    """
    image_size = 84
    defaults = {
        'transform': Compose([
                        Resize(int(image_size * 1.5)),
                        CenterCrop(image_size),
                        ToTensor()
                    ])
    }

    return helper_with_default(CUB, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)


def doublemnist(folder, shots, ways, shuffle=True, test_shots=None,
                seed=None, **kwargs):
    """Helper function to create a meta-dataset for the Double MNIST dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `doublemnist` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `DoubleMNIST` class.

    See also
    --------
    `datasets.doublemnist.DoubleMNIST` : Meta-dataset for the Double MNIST dataset.
    """
    return helper_with_default(DoubleMNIST, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults={}, **kwargs)


def triplemnist(folder, shots, ways, shuffle=True, test_shots=None,
                seed=None, **kwargs):
    """Helper function to create a meta-dataset for the Triple MNIST dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `triplemnist` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds 
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way` 
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the 
        number of test examples is equal to the number of training examples per 
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `TripleMNIST` class.

    See also
    --------
    `datasets.triplemnist.TripleMNIST` : Meta-dataset for the Triple MNIST dataset.
    """
    return helper_with_default(TripleMNIST, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults={}, **kwargs)


def pascal5i(folder, shots, ways=1, shuffle=True, test_shots=None,
             seed=None, **kwargs):
    """Helper function to create a meta-dataset for the PASCAL-VOC dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `omniglot` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification. Only supports 1-way currently

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `Omniglot` class.

    """
    defaults = {
        'transform': SegmentationPairTransform(500),
        'class_augmentations': []
    }
    return helper_with_default(Pascal5i, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)
