text: #  Copyright (c) 2019 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
  import paddle.fluid.core as core
  from op_test import OpTest
  
  import random
  
  
  class TestElementwiseModOp(OpTest):
      def init_kernel_type(self):
          self.use_mkldnn = False
  
      def setUp(self):
          self.op_type = "elementwise_mod"
          self.axis = -1
          self.init_dtype()
          self.init_input_output()
          self.init_kernel_type()
          self.init_axis()
  
          self.inputs = {
              'X': OpTest.np_dtype_to_fluid_dtype(self.x),
              'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
          }
          self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
          self.outputs = {'Out': self.out}
  
      def test_check_output(self):
          self.check_output()
  
      def init_input_output(self):
          self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype)
          self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype)
          self.out = np.mod(self.x, self.y)
  
      def init_dtype(self):
          self.dtype = np.int32
  
      def init_axis(self):
          pass
  
  
  class TestElementwiseModOp_scalar(TestElementwiseModOp):
      def init_input_output(self):
          scale_x = random.randint(0, 100000000)
          scale_y = random.randint(1, 100000000)
          self.x = (np.random.rand(2, 3, 4) * scale_x).astype(self.dtype)
          self.y = (np.random.rand(1) * scale_y + 1).astype(self.dtype)
          self.out = np.mod(self.x, self.y)
  
  
  class TestElementwiseModOpFloat(TestElementwiseModOp):
      def init_dtype(self):
          self.dtype = np.float32
  
      def init_input_output(self):
          self.x = np.random.uniform(-1000, 1000, [10, 10]).astype(self.dtype)
          self.y = np.random.uniform(-100, 100, [10, 10]).astype(self.dtype)
          self.out = np.fmod(self.y + np.fmod(self.x, self.y), self.y)
  
      def test_check_output(self):
          self.check_output()
  
  
  class TestElementwiseModOpDouble(TestElementwiseModOpFloat):
      def init_dtype(self):
          self.dtype = np.float64
  
  
  if __name__ == '__main__':
      unittest.main()
  