# Copyright 2019 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
from mindspore import Tensor
from mindspore.ops import operations as P
import mindspore.nn as nn
import numpy as np
import mindspore.context as context
from mindspore.common import dtype as mstype
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter

context.set_context(mode=context.GRAPH_MODE, device_target="CPU")


class NetArgmax(nn.Cell):
    def __init__(self):
        super(NetArgmax, self).__init__()
        self.argmax = P.Argmax(output_type=mstype.int32)
        x = Tensor(np.array([[1., 20., 5.],
                             [67., 8., 9.],
                             [130., 24., 15.]]).astype(np.float32))
        self.x = Parameter(initializer(x, x.shape()), name='x')

    def construct(self):
        return self.argmax(self.x)


@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_argmax():
    Argmax = NetArgmax()
    output = Argmax()
    print("================================")
    expect = np.array([1, 0, 0]).astype(np.float32)
    print(output)
    assert (output.asnumpy() == expect).all()