# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.

# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


import torch

torch.manual_seed(41)
a = torch.tensor(
    [
        [[[1, 2, 3], [4, 5, 6], [7, 8, 9.0], [10, 11, 12]]],
        [[[13, 14, 15], [16, 17, 18], [19, 20, 21.0], [22, 23, 24]]],
    ],
    requires_grad=True,
)
print("Input: ", a)
shifts = [1, 2, 3, 4]
for i in range(4):
    a = torch.tensor(
        [
            [[[1, 2, 3], [4, 5, 6], [7, 8, 9.0], [10, 11, 12]]],
            [[[13, 14, 15], [16, 17, 18], [19, 20, 21.0], [22, 23, 24]]],
        ],
        requires_grad=True,
    )
    b = torch.roll(a, shifts[i], i)
    print("Result:", b)
    b.sum().backward()
    print("Gradient for input:", a.grad)
