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.
  
  import numpy as np
  import unittest
  from op_test import OpTest
  import paddle.fluid.core as core
  from paddle.fluid import compiler, Program, program_guard
  import paddle
  import paddle.nn.functional as F
  import paddle.fluid as fluid
  
  
  def adaptive_start_index(index, input_size, output_size):
      return int(np.floor(index * input_size / output_size))
  
  
  def adaptive_end_index(index, input_size, output_size):
      return int(np.ceil((index + 1) * input_size / output_size))
  
  
  def max_pool1D_forward_naive(x,
                               ksize,
                               strides,
                               paddings,
                               global_pool=0,
                               ceil_mode=False,
                               exclusive=False,
                               adaptive=False,
                               data_type=np.float64):
      N, C, L = x.shape
      if global_pool == 1:
          ksize = [L]
      if adaptive:
          L_out = ksize[0]
      else:
          L_out = (L - ksize[0] + 2 * paddings[0] + strides[0] - 1
                   ) // strides[0] + 1 if ceil_mode else (
                       L - ksize[0] + 2 * paddings[0]) // strides[0] + 1
  
      out = np.zeros((N, C, L_out))
      for i in range(L_out):
          if adaptive:
              r_start = adaptive_start_index(i, L, ksize[0])
              r_end = adaptive_end_index(i, L, ksize[0])
          else:
              r_start = np.max((i * strides[0] - paddings[0], 0))
              r_end = np.min((i * strides[0] + ksize[0] - paddings[0], L))
          x_masked = x[:, :, r_start:r_end]
  
          out[:, :, i] = np.max(x_masked, axis=(2))
      return out
  
  
  class TestPool1D_API(unittest.TestCase):
      def setUp(self):
          np.random.seed(123)
          self.places = [fluid.CPUPlace()]
          if core.is_compiled_with_cuda():
              self.places.append(fluid.CUDAPlace(0))
  
      def check_adaptive_max_dygraph_results(self, place):
          with fluid.dygraph.guard(place):
              input_np = np.random.random([2, 3, 32]).astype("float32")
              input = fluid.dygraph.to_variable(input_np)
              result = F.adaptive_max_pool1d(input, output_size=16)
  
              result_np = max_pool1D_forward_naive(
                  input_np, ksize=[16], strides=[0], paddings=[0], adaptive=True)
              self.assertTrue(np.allclose(result.numpy(), result_np))
  
              ada_max_pool1d_dg = paddle.nn.layer.AdaptiveMaxPool1D(
                  output_size=16)
              result = ada_max_pool1d_dg(input)
              self.assertTrue(np.allclose(result.numpy(), result_np))
  
      def check_adaptive_max_static_results(self, place):
          with fluid.program_guard(fluid.Program(), fluid.Program()):
              input = fluid.data(name="input", shape=[2, 3, 32], dtype="float32")
              result = F.adaptive_max_pool1d(input, output_size=16)
  
              input_np = np.random.random([2, 3, 32]).astype("float32")
              result_np = max_pool1D_forward_naive(
                  input_np, ksize=[16], strides=[2], paddings=[0], adaptive=True)
  
              exe = fluid.Executor(place)
              fetches = exe.run(fluid.default_main_program(),
                                feed={"input": input_np},
                                fetch_list=[result])
              self.assertTrue(np.allclose(fetches[0], result_np))
  
      def test_adaptive_max_pool1d(self):
          for place in self.places:
              self.check_adaptive_max_dygraph_results(place)
              self.check_adaptive_max_static_results(place)
  
  
  if __name__ == '__main__':
      unittest.main()
  