from chainer.datasets import tuple_dataset
import numpy as np


def preprocess_mnist(raw, withlabel, ndim, scale, image_dtype, label_dtype, rgb_format):
    images = raw['x']
    if ndim == 2:
        images = images.reshape(-1, 28, 28)
    elif ndim == 3:
        images = images.reshape(-1, 1, 28, 28)
        if rgb_format:
            images = np.broadcast_to(images, (len(images), 3) + images.shape[2:])
    elif ndim != 1:
        raise ValueError('invalid ndim for MNIST dataset')
    images = images.astype(image_dtype)
    images *= scale / 255.

    if withlabel:
        labels = raw['y'].astype(label_dtype)
        return tuple_dataset.TupleDataset(images, labels)
    return images