text: # Copyright (c) 2020 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, skip_check_grad_ci
  import paddle
  import paddle.fluid.core as core
  
  
  class ApiMaximumTest(unittest.TestCase):
      def setUp(self):
          if core.is_compiled_with_cuda():
              self.place = core.CUDAPlace(0)
          else:
              self.place = core.CPUPlace()
  
          self.input_x = np.random.rand(10, 15).astype("float32")
          self.input_y = np.random.rand(10, 15).astype("float32")
          self.input_z = np.random.rand(15).astype("float32")
  
      def test_static_api(self):
          paddle.enable_static()
          with paddle.static.program_guard(paddle.static.Program(),
                                           paddle.static.Program()):
              data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
              data_y = paddle.static.data("y", shape=[10, 15], dtype="float32")
              result_max = paddle.maximum(data_x, data_y)
              exe = paddle.static.Executor(self.place)
              res, = exe.run(feed={"x": self.input_x,
                                   "y": self.input_y},
                             fetch_list=[result_max])
          self.assertEqual((res == np.maximum(self.input_x, self.input_y)).all(),
                           True)
  
          with paddle.static.program_guard(paddle.static.Program(),
                                           paddle.static.Program()):
              data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
              data_z = paddle.static.data("z", shape=[15], dtype="float32")
              result_max = paddle.maximum(data_x, data_z, axis=1)
              exe = paddle.static.Executor(self.place)
              res, = exe.run(feed={"x": self.input_x,
                                   "z": self.input_z},
                             fetch_list=[result_max])
          self.assertEqual((res == np.maximum(self.input_x, self.input_z)).all(),
                           True)
  
      def test_dynamic_api(self):
          paddle.disable_static()
          np_x = np.array([10, 10]).astype('float64')
          x = paddle.to_variable(self.input_x)
          y = paddle.to_variable(self.input_y)
          z = paddle.maximum(x, y)
          np_z = z.numpy()
          z_expected = np.array(np.maximum(self.input_x, self.input_y))
          self.assertEqual((np_z == z_expected).all(), True)
  
      def test_broadcast_axis(self):
          paddle.disable_static()
          np_x = np.random.rand(5, 4, 3, 2).astype("float64")
          np_y = np.random.rand(4, 3).astype("float64")
  
          x = paddle.to_variable(self.input_x)
          y = paddle.to_variable(self.input_y)
          result_1 = paddle.maximum(x, y, axis=1)
          result_2 = paddle.maximum(x, y, axis=-2)
          self.assertEqual((result_1.numpy() == result_2.numpy()).all(), True)
  