#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
#    Licensed under the Apache License, Version 2.0 (the "License");
#    you may not use this file except in compliance with the License.
#    You may obtain a copy of the License at
#
#        http://www.apache.org/licenses/LICENSE-2.0
#
#    Unless required by applicable law or agreed to in writing, software
#    distributed under the License is distributed on an "AS IS" BASIS,
#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#    See the License for the specific language governing permissions and
#    limitations under the License.

import numpy as np
import torch
from torch import nn


def sum_tensor(inp, axes, keepdim=False):
    axes = np.unique(axes).astype(int)
    if keepdim:
        for ax in axes:
            inp = inp.sum(int(ax), keepdim=True)
    else:
        for ax in sorted(axes, reverse=True):
            inp = inp.sum(int(ax))
    return inp


def mean_tensor(inp, axes, keepdim=False):
    axes = np.unique(axes).astype(int)
    if keepdim:
        for ax in axes:
            inp = inp.mean(int(ax), keepdim=True)
    else:
        for ax in sorted(axes, reverse=True):
            inp = inp.mean(int(ax))
    return inp


def flip(x, dim):
    """
    flips the tensor at dimension dim (mirroring!)
    :param x:
    :param dim:
    :return:
    """
    indices = [slice(None)] * x.dim()
    indices[dim] = torch.arange(x.size(dim) - 1, -1, -1,
                                dtype=torch.long, device=x.device)
    return x[tuple(indices)]