text: # Copyright 2021 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.
  # ============================================================================
  """ test ASGD """
  import numpy as np
  import pytest
  
  import mindspore.nn as nn
  from mindspore import Tensor, Parameter
  from mindspore.nn.optim import ASGD
  from mindspore.ops import operations as P
  
  class Net(nn.Cell):
      """ Net definition """
  
      def __init__(self):
          super(Net, self).__init__()
          self.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)), name="weight")
          self.bias = Parameter(Tensor(np.ones([10]).astype((np.float32))), name="bias")
          self.matmul = P.MatMul()
          self.biasAdd = P.BiasAdd()
  
      def construct(self, x):
          x = self.biasAdd(self.matmul(x, self.weight), self.bias)
          return x
  
  
  class NetWithoutWeight(nn.Cell):
      def __init__(self):
          super(NetWithoutWeight, self).__init__()
          self.matmul = P.MatMul()
  
      def construct(self, x):
          x = self.matmul(x, x)
          return x
  
  
  def test_asgdwithoutparam():
      """
      Feature: Test ASGD optimizer.
      Description: Test if error is raised when trainable_params is empty.
      Expectation: ValueError is raised.
      """
      net = NetWithoutWeight()
      net.set_train()
      with pytest.raises(ValueError, match=r"For 'Optimizer', the argument parameters must not be empty"):
          ASGD(net.trainable_params(), learning_rate=0.1)
  
  
  def test_asgd_lambd():
      """
      Feature: Test ASGD optimizer.
      Description: Test if error is raised when the type of lambd  is not correct.
      Expectation: ValueError is raised.
      """
      net = Net()
      with pytest.raises(TypeError):
          ASGD(net.get_parameters(), lambd=1, learning_rate=0.1)
  
  
  def test_asgd_alpha():
      """
      Feature: Test ASGD optimizer.
      Description: Test if error is raised when the type of alpha  is not correct.
      Expectation: ValueError is raised.
      """
      net = Net()
      with pytest.raises(TypeError):
          ASGD(net.get_parameters(), alpha=1, learning_rate=0.1)
  
  
  def test_asgd_t0():
      """
      Feature: Test ASGD optimizer.
      Description: Test if error is raised when the type of t0  is not correct.
      Expectation: ValueError is raised.
      """
      net = Net()
      with pytest.raises(TypeError):
          ASGD(net.get_parameters(), t0=1, learning_rate=0.1)
  
  
  def test_asgd_mindspore_with_empty_params():
      """
      Feature: Test ASGD optimizer.
      Description: Test if error is raised when there is no trainable_params.
      Expectation: ValueError is raised.
      """
      net = nn.Flatten()
      with pytest.raises(ValueError, match=r"For 'Optimizer', the argument parameters must not be empty"):
          ASGD(net.get_parameters())
  