# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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 pytest
import numpy as np

import mindspore
import mindspore.nn as nn
import mindspore.context as context

from mindspore import Tensor
from mindspore.ops.composite import GradOperation

@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_mirror_pad():
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")

    test1_arr_in = [[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]]
    test_1_paddings = ((0, 0), (0, 0), (1, 1), (2, 2))
    test1_arr_exp = [[[[6, 5, 4, 5, 6, 5, 4], [3, 2, 1, 2, 3, 2, 1], [6, 5, 4, 5, 6, 5, 4],
                       [9, 8, 7, 8, 9, 8, 7], [6, 5, 4, 5, 6, 5, 4]]]]

    test2_arr_in = [[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]]
    test_2_paddings = ((0, 0), (0, 0), (1, 1), (2, 2))
    test2_arr_exp = [[[[2, 1, 1, 2, 3, 3, 2], [2, 1, 1, 2, 3, 3, 2], [5, 4, 4, 5, 6, 6, 5],
                       [8, 7, 7, 8, 9, 9, 8], [8, 7, 7, 8, 9, 9, 8]]]]

    reflectOp = nn.Pad(mode='REFLECT', paddings=test_1_paddings)
    symmOp = nn.Pad(mode='SYMMETRIC', paddings=test_2_paddings)

    x_test_1 = Tensor(np.array(test1_arr_in), dtype=mindspore.float32)
    x_test_2 = Tensor(np.array(test2_arr_in), dtype=mindspore.float32)

    y_test_1 = reflectOp(x_test_1).asnumpy()
    y_test_2 = symmOp(x_test_2).asnumpy()

    print(np.array(test1_arr_in))
    print(y_test_1)

    np.testing.assert_equal(np.array(test1_arr_exp), y_test_1)
    np.testing.assert_equal(np.array(test2_arr_exp), y_test_2)


class Grad(nn.Cell):
    def __init__(self, network):
        super(Grad, self).__init__()
        self.grad = GradOperation(get_all=True, sens_param=True)
        self.network = network
    def construct(self, input_, output_grad):
        return self.grad(self.network)(input_, output_grad)

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.pad = nn.Pad(mode="REFLECT", paddings=((0, 0), (0, 0), (1, 0), (0, 2)))
    def construct(self, x):
        return self.pad(x)


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_mirror_pad_backprop():
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    test_arr_in = [[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]] # size -> 3*3
    test_arr_in = Tensor(test_arr_in, dtype=mindspore.float32)
    dy = (np.ones((1, 1, 4, 5)) * 0.1).astype(np.float32)
    expected_dx = np.array([[[[0.2, 0.2, 0.1],
                              [0.4, 0.4, 0.2],
                              [0.2, 0.2, 0.1]]]])
    net = Grad(Net())
    dx = net(test_arr_in, Tensor(dy))
    dx = dx[0].asnumpy()
    np.testing.assert_array_almost_equal(dx, expected_dx)