import torch.nn.functional as F


class InputPadder:
    """ Pads images such that dimensions are divisible by divisor """

    def __init__(self, dims, divisor=16):
        self.ht, self.wd = dims[-2:]
        pad_ht = (((self.ht // divisor) + 1) * divisor - self.ht) % divisor
        pad_wd = (((self.wd // divisor) + 1) * divisor - self.wd) % divisor
        self._pad = [pad_wd // 2, pad_wd - pad_wd // 2, pad_ht // 2, pad_ht - pad_ht // 2]

    def pad(self, *inputs):
        if len(inputs) == 1:
            return F.pad(inputs[0], self._pad, mode='replicate')
        else:
            return [F.pad(x, self._pad, mode='replicate') for x in inputs]

    def unpad(self, *inputs):
        if len(inputs) == 1:
            return self._unpad(inputs[0])
        else:
            return [self._unpad(x) for x in inputs]

    def _unpad(self, x):
        ht, wd = x.shape[-2:]
        c = [self._pad[2], ht - self._pad[3], self._pad[0], wd - self._pad[1]]
        return x[..., c[0]:c[1], c[2]:c[3]]