# Copyright 2022 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 numpy as np
import pytest
import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor


class Net(nn.Cell):
    def construct(self, x, fill_value, wrap):
        return x.fill_diagonal(fill_value, wrap)


@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_cpu
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
@pytest.mark.parametrize('wrap', [True, False])
def test_tensor_fill_diag(mode, wrap):
    """
    Feature: tensor.fill_diagonal
    Description: Verify the result of fill_diagonal
    Expectation: success
    """
    ms.set_context(mode=mode)
    a = Tensor(np.zeros((6, 3)), ms.float32)
    output = a.fill_diagonal(5.0, wrap=wrap)
    expect_output = np.array([[5.0, 0.0, 0.0], [0.0, 5.0, 0.0], [0.0, 0.0, 5.0],
                              [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
    if wrap:
        expect_output = np.array([[5.0, 0.0, 0.0], [0.0, 5.0, 0.0], [0.0, 0.0, 5.0],
                                  [0.0, 0.0, 0.0], [5.0, 0.0, 0.0], [0.0, 5.0, 0.0]])

    assert np.allclose(output.asnumpy(), expect_output)
