text: #  Copyright (c) 2021 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 numpy as np
  import unittest
  import sys
  
  sys.path.append("..")
  
  import paddle
  import paddle.fluid as fluid
  from op_test_xpu import XPUOpTest
  from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper
  
  paddle.enable_static()
  
  
  def np_masked_select(x, mask):
      result = np.empty(shape=(0), dtype=x.dtype)
      for ele, ma in zip(np.nditer(x), np.nditer(mask)):
          if ma:
              result = np.append(result, ele)
      return result.flatten()
  
  
  class XPUTestMaskedSelectOp(XPUOpTestWrapper):
  
      def __init__(self):
          self.op_name = 'masked_select'
  
      class TestMaskedSelectOp(XPUOpTest):
  
          def setUp(self):
              self.init()
              self.dtype = self.in_type
              self.place = paddle.XPUPlace(0)
              self.op_type = "masked_select"
              self.__class__.no_need_check_grad = True
  
              x = np.random.random(self.shape).astype(self.dtype)
              mask = np.array(np.random.randint(2, size=self.shape, dtype=bool))
              out = np_masked_select(x, mask)
              self.inputs = {'X': x, 'Mask': mask}
              self.outputs = {'Y': out}
  
          def test_check_output(self):
              self.check_output_with_place(self.place)
  
          def init(self):
              self.shape = (50, 3)
  
      class TestMaskedSelectOp1(TestMaskedSelectOp):
  
          def init(self):
              self.shape = (6, 8, 9, 18)
  
      class TestMaskedSelectOp2(TestMaskedSelectOp):
  
          def init(self):
              self.shape = (168, )
  
  
  support_types = get_xpu_op_support_types('masked_select')
  for stype in support_types:
      create_test_class(globals(), XPUTestMaskedSelectOp, stype)
  
  
  class TestMaskedSelectAPI(unittest.TestCase):
  
      def test_imperative_mode(self):
          paddle.disable_static(paddle.XPUPlace(0))
          shape = (88, 6, 8)
          np_x = np.random.random(shape).astype('float32')
          np_mask = np.array(np.random.randint(2, size=shape, dtype=bool))
          x = paddle.to_tensor(np_x)
          mask = paddle.to_tensor(np_mask)
          out = paddle.masked_select(x, mask)
          np_out = np_masked_select(np_x, np_mask)
          self.assertEqual(np.allclose(out.numpy(), np_out), True)
          paddle.enable_static()
  
      def test_static_mode(self):
          shape = [8, 9, 6]
          x = paddle.fluid.data(shape=shape, dtype='float32', name='x')
          mask = paddle.fluid.data(shape=shape, dtype='bool', name='mask')
          np_x = np.random.random(shape).astype('float32')
          np_mask = np.array(np.random.randint(2, size=shape, dtype=bool))
  
          out = paddle.masked_select(x, mask)
          np_out = np_masked_select(np_x, np_mask)
  
          exe = paddle.static.Executor(place=paddle.XPUPlace(0))
  
          res = exe.run(paddle.static.default_main_program(),
                        feed={
                            "x": np_x,
                            "mask": np_mask
                        },
                        fetch_list=[out])
          self.assertEqual(np.allclose(res, np_out), True)
  
  
  class TestMaskedSelectError(unittest.TestCase):
  
      def test_error(self):
          with paddle.static.program_guard(paddle.static.Program(),
                                           paddle.static.Program()):
  
              shape = [8, 9, 6]
              x = paddle.fluid.data(shape=shape, dtype='float32', name='x')
              mask = paddle.fluid.data(shape=shape, dtype='bool', name='mask')
              mask_float = paddle.fluid.data(shape=shape,
                                             dtype='float32',
                                             name='mask_float')
              np_x = np.random.random(shape).astype('float32')
              np_mask = np.array(np.random.randint(2, size=shape, dtype=bool))
  
              def test_x_type():
                  paddle.masked_select(np_x, mask)
  
              self.assertRaises(TypeError, test_x_type)
  
              def test_mask_type():
                  paddle.masked_select(x, np_mask)
  
              self.assertRaises(TypeError, test_mask_type)
  
              def test_mask_dtype():
                  paddle.masked_select(x, mask_float)
  
              self.assertRaises(TypeError, test_mask_dtype)
  
  
  if __name__ == '__main__':
      unittest.main()
  