text: #   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
  #
  # 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.
  
  from __future__ import print_function
  
  import unittest
  import numpy as np
  from op_test import OpTest
  
  
  class TestProximalAdagradOp(OpTest):
      def setUp(self):
          self.op_type = "proximal_adagrad"
          w = np.random.random((102, 105)).astype("float32")
          m = np.random.random((102, 105)).astype("float32")
          g = np.random.random((102, 105)).astype("float32")
          lr = np.array([0.1]).astype("float32")
          l1 = 0.1
          l2 = 0.2
  
          self.inputs = {'Param': w, 'Grad': g, 'Moment': m, 'LearningRate': lr}
          self.attrs = {'l1': l1, 'l2': l2}
          param_out = 0.0
  
          moment_out = m + g * g
          prox_param = w - lr * g / np.sqrt(moment_out)
          if l1 > 0.0:
              x = np.abs(prox_param) - lr * l1
              x[x < 0] = 0
              param_out = np.sign(prox_param) * (x / (1.0 + lr * l2))
          else:
              param_out = prox_param / (1.0 + lr * l2)
  
          self.outputs = {'ParamOut': param_out, 'MomentOut': moment_out}
  
      def test_check_output(self):
          self.check_output()
  
  
  if __name__ == "__main__":
      unittest.main()
  