# 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.initializer import initializer
from mindspore.common.parameter import Parameter

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

class NetSoftmaxWithCrossEntropy(nn.Cell):
    def __init__( self):
        super(NetSoftmaxWithCrossEntropy, self).__init__()
        logits = Tensor(np.array([[1,1,10],
                                  [1,10,1],
                                  [10,1,1]]).astype(np.float32))
        self.logits = Parameter(initializer(logits, logits.shape()), name ='logits')
        labels = Tensor(np.array([2,1,0]).astype(np.int32))
        self.labels = Parameter(initializer(labels, labels.shape()), name ='labels')
        self.SoftmaxWithCrossEntropy = P.SparseSoftmaxCrossEntropyWithLogits(True)

    def construct(self):
        return self.SoftmaxWithCrossEntropy(self.logits, self.labels)


@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_net():
    SoftmaxWithCrossEntropy = NetSoftmaxWithCrossEntropy()
    output = SoftmaxWithCrossEntropy()
    expect = np.array([[ 4.1126452e-05,  4.1126452e-05, -8.2234539e-05],
                       [ 4.1126452e-05, -8.2234539e-05,  4.1126452e-05],
                       [-8.2234539e-05,  4.1126452e-05,  4.1126452e-05]]).astype(np.float32)
    print(output)
    error = np.ones(shape=[3, 3]) * 1.0e-6
    diff = output.asnumpy() - expect
    print(diff)
    assert np.all(diff < error)
    assert np.all(-diff < error)
