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
  import paddle.fluid.core as core
  import paddle.fluid as fluid
  
  
  class TestExecutor(unittest.TestCase):
      def net(self):
          lr = fluid.data(name="lr", shape=[1], dtype='float32')
          x = fluid.data(name="x", shape=[None, 1], dtype='float32')
          y = fluid.data(name="y", shape=[None, 1], dtype='float32')
          y_predict = fluid.layers.fc(input=x, size=1, act=None)
  
          cost = fluid.layers.square_error_cost(input=y_predict, label=y)
          avg_cost = fluid.layers.mean(cost)
  
          opt = fluid.optimizer.Adam(learning_rate=lr)
          opt.minimize(avg_cost)
  
          return lr, avg_cost
  
      def test_program_feed_float(self):
          main_program = fluid.Program()
          startup_program = fluid.Program()
          scope = fluid.Scope()
          with fluid.program_guard(main_program, startup_program):
              with fluid.scope_guard(scope):
                  cpu = fluid.CPUPlace()
                  exe = fluid.Executor(cpu)
                  lr, cost = self.net()
                  exe.run(startup_program)
                  train_data = numpy.array(
                      [[1.0], [2.0], [3.0], [4.0]]).astype('float32')
                  y_true = numpy.array(
                      [[2.0], [4.0], [6.0], [8.0]]).astype('float32')
                  a = 0.01
                  _lr, _ = exe.run(feed={'x': train_data,
                                         'y': y_true,
                                         'lr': a},
                                   fetch_list=[lr, cost],
                                   return_numpy=False)
              self.assertEqual(_lr._dtype(), lr.dtype)
              self.assertEqual(_lr._dtype(), fluid.core.VarDesc.VarType.FP32)
              self.assertEqual(type(a), float)
  
      def test_program_feed_int(self):
          main_program = fluid.Program()
          startup_program = fluid.Program()
          scope = fluid.Scope()
          with fluid.program_guard(main_program, startup_program):
              with fluid.scope_guard(scope):
                  cpu = fluid.CPUPlace()
                  exe = fluid.Executor(cpu)
                  lr, cost = self.net()
                  exe.run(startup_program)
                  train_data = numpy.array(
                      [[1.0], [2.0], [3.0], [4.0]]).astype('float32')
                  y_true = numpy.array(
                      [[2.0], [4.0], [6.0], [8.0]]).astype('float32')
                  a = 0
                  _lr, _ = exe.run(feed={'x': train_data,
                                         'y': y_true,
                                         'lr': a},
                                   fetch_list=[lr, cost],
                                   return_numpy=False)
              self.assertEqual(_lr._dtype(), lr.dtype)
              self.assertEqual(_lr._dtype(), fluid.core.VarDesc.VarType.FP32)
              self.assertEqual(type(a), int)
  
      def test_compiled_program_feed_scalar(self):
          main_program = fluid.Program()
          startup_program = fluid.Program()
          scope = fluid.Scope()
          with fluid.program_guard(main_program, startup_program):
              with fluid.scope_guard(scope):
                  lr, cost = self.net()
                  cpu = fluid.CPUPlace()
                  exe = fluid.Executor(cpu)
                  exe.run(startup_program)
                  compiled_prog = fluid.CompiledProgram(
                      main_program).with_data_parallel(loss_name=cost.name)
                  train_data = numpy.array(
                      [[1.0], [2.0], [3.0], [4.0]]).astype('float32')
                  y_true = numpy.array(
                      [[2.0], [4.0], [6.0], [8.0]]).astype('float32')
                  a = 0.01
                  _lr, _ = exe.run(compiled_prog,
                                   feed={'x': train_data,
                                         'y': y_true,
                                         'lr': a},
                                   fetch_list=[lr, cost],
                                   return_numpy=False)
                  self.assertEqual(_lr._dtype(), lr.dtype)
                  self.assertEqual(_lr._dtype(), fluid.core.VarDesc.VarType.FP32)
                  self.assertEqual(type(a), float)
  
  
  class TestAsLodTensor(unittest.TestCase):
      def test_as_lodtensor_int32(self):
          cpu = fluid.CPUPlace()
          tensor = fluid.executor._as_lodtensor(1.0, cpu,
                                                fluid.core.VarDesc.VarType.INT32)
          self.assertEqual(tensor._dtype(), fluid.core.VarDesc.VarType.INT32)
  
      def test_as_lodtensor_fp64(self):
          cpu = fluid.CPUPlace()
          tensor = fluid.executor._as_lodtensor(1, cpu,
                                                fluid.core.VarDesc.VarType.FP64)
          self.assertEqual(tensor._dtype(), fluid.core.VarDesc.VarType.FP64)
  
      def test_as_lodtensor_error(self):
          cpu = fluid.CPUPlace()
          self.assertRaises(AssertionError, fluid.executor._as_lodtensor, 1, cpu)
  
  
  if __name__ == '__main__':
      unittest.main()
  