# 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 numpy as np

import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.ops import operations as P
from mindspore.common import dtype as mstype

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


class Net(nn.Cell):
    def __init__(self, shape):
        super(Net, self).__init__()
        self.poisson = P.Poisson()
        self.shape = shape

    def construct(self, mean):
        return self.poisson(self.shape, mean)


def test_net_1():
    shape = (2, 16)
    mean = np.array([5.0]).astype(np.float32)
    net = Net(shape)
    tmean = Tensor(mean)
    output = net(tmean)
    print(output.asnumpy())
    assert output.shape == (2, 16)


def test_net_2():
    shape = (4, 1)
    mean = np.array([5.0, 10.0]).astype(np.float32)
    net = Net(shape)
    tmean = Tensor(mean)
    output = net(tmean)
    print(output.asnumpy())
    assert output.shape == (4, 2)
