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1# Copyright 2020 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15import numpy as np
16import pytest
17import mindspore.context as context
18import mindspore.nn as nn
19from mindspore import Tensor
20import mindspore.common.dtype as mstype
21from mindspore.ops import operations as P
22
23context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
24
25
26class Net(nn.Cell):
27    def __init__(self):
28        super(Net, self).__init__()
29        self.unique = P.Unique()
30
31    def construct(self, x):
32        return self.unique(x)
33
34
35class UniqueSquare(nn.Cell):
36    def __init__(self):
37        super(UniqueSquare, self).__init__()
38        self.unique = P.Unique()
39        self.square = P.Square()
40
41    def construct(self, x):
42        x, _ = self.unique(x)
43        return self.square(x)
44
45
46@pytest.mark.level0
47@pytest.mark.platform_arm_ascend_training
48@pytest.mark.platform_x86_ascend_training
49@pytest.mark.env_onecard
50def test_unique_cpu():
51    x = Tensor(np.array([1, 1, 2, 2, 3, 3]), mstype.int32)
52    unique = Net()
53    output = unique(x)
54    expect1 = np.array([1, 2, 3])
55    expect2 = np.array([0, 0, 1, 1, 2, 2])
56    assert (output[0].asnumpy() == expect1).all()
57    assert (output[1].asnumpy() == expect2).all()
58
59
60@pytest.mark.level0
61@pytest.mark.platform_arm_ascend_training
62@pytest.mark.platform_x86_ascend_training
63@pytest.mark.env_onecard
64def test_unique_square():
65    x = Tensor(np.array([1, 1, 2, 2, 3, 3]), mstype.int32)
66    net = UniqueSquare()
67    output = net(x)
68    expect1 = np.array([1, 4, 9])
69    assert (output.asnumpy() == expect1).all()
70