text: #   Copyright (c) 2018 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
  import paddle.fluid as fluid
  from op_test import OpTest
  import paddle.fluid.core as core
  
  
  class TestScatterOp(OpTest):
      def setUp(self):
          self.op_type = "scatter"
          ref_np = np.ones((3, 50)).astype("float32")
          index_np = np.array([1, 2]).astype("int32")
          updates_np = np.random.random((2, 50)).astype("float32")
          output_np = np.copy(ref_np)
          output_np[index_np] = updates_np
          self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
          self.outputs = {'Out': output_np}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          self.check_grad(['Updates'], 'Out', in_place=True)
  
  
  class TestScatterOp0(OpTest):
      def setUp(self):
          self.op_type = "scatter"
          ref_np = np.ones((3, 3)).astype("float32")
          index_np = np.array([1, 2]).astype("int32")
          updates_np = np.random.random((2, 3)).astype("float32")
          output_np = np.copy(ref_np)
          output_np[index_np] = updates_np
          self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
          self.attrs = {'overwrite': True}
          self.outputs = {'Out': output_np}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          self.check_grad(['Updates'], 'Out', in_place=True)
  
  
  class TestScatterOp1(OpTest):
      def setUp(self):
          self.op_type = "scatter"
          ref_np = np.ones((3, 3)).astype("float32")
          zeros_np = np.zeros([2, 3]).astype('float32')
          index_np = np.array([1, 1]).astype("int32")
          updates_np = np.random.random((2, 3)).astype("float32")
          output_np = np.copy(ref_np)
          output_np[index_np] = zeros_np
          for i in range(0, len(index_np)):
              output_np[index_np[i]] += updates_np[i]
          self.attrs = {'overwrite': False}
          self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
          self.outputs = {'Out': output_np}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          self.check_grad(['Updates'], 'Out', in_place=True)
  
  
  @unittest.skipIf(not core.is_compiled_with_cuda(),
                   "core is not compiled with CUDA")
  class TestScatterOp2(OpTest):
      def setUp(self):
          self.op_type = "scatter"
          ref_np = np.ones((3, 3)).astype("float32")
          index_np = np.array([1, 2]).astype("int32")
          updates_np = np.random.random((2, 3)).astype("float32")
          output_np = np.copy(ref_np)
          output_np[index_np] = updates_np
          self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
          self.outputs = {'Out': output_np}
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              self.check_output_with_place(place, atol=1e-3)
  
      def test_check_grad(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              self.check_grad_with_place(place, ['Updates'], 'Out', in_place=True)
  
  
  @unittest.skipIf(not core.is_compiled_with_cuda(),
                   "core is not compiled with CUDA")
  class TestScatterOp3(OpTest):
      def setUp(self):
          self.op_type = "scatter"
          ref_np = np.ones((3, 3)).astype("float32")
          zeros_np = np.zeros([2, 3]).astype('float32')
          index_np = np.array([1, 1]).astype("int32")
          updates_np = np.random.random((2, 3)).astype("float32")
          output_np = np.copy(ref_np)
          output_np[index_np] = zeros_np
          for i in range(0, len(index_np)):
              output_np[index_np[i]] += updates_np[i]
          self.attrs = {'overwrite': False}
          self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
          self.outputs = {'Out': output_np}
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              self.check_output_with_place(place, atol=1e-3)
  
      def test_check_grad(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              self.check_grad_with_place(place, ['Updates'], 'Out', in_place=True)
  
  
  class TestScatterOp4(OpTest):
      def setUp(self):
          self.op_type = "scatter"
          ref_np = np.ones((3, 3)).astype("float32")
          index_np = np.array([1, 2]).astype("int64")
          updates_np = np.random.random((2, 3)).astype("float32")
          output_np = np.copy(ref_np)
          output_np[index_np] = updates_np
          self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
          self.outputs = {'Out': output_np}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          self.check_grad(['Updates'], 'Out', in_place=True)
  
  
  @unittest.skipIf(not core.is_compiled_with_cuda(),
                   "core is not compiled with CUDA")
  class TestScatterOp5(OpTest):
      def setUp(self):
          self.op_type = "scatter"
          ref_np = np.ones((3, 3)).astype("float32")
          index_np = np.array([1, 2]).astype("int64")
          updates_np = np.random.random((2, 3)).astype("float32")
          output_np = np.copy(ref_np)
          output_np[index_np] = updates_np
          self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
          self.outputs = {'Out': output_np}
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              self.check_output_with_place(place, atol=1e-3)
  
      def test_check_grad(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              self.check_grad_with_place(place, ['Updates'], 'Out', in_place=True)
  
  
  class TestScatterAPI(unittest.TestCase):
      def setUp(self):
          self.places = [fluid.CPUPlace()]
          if core.is_compiled_with_cuda():
              self.places.append(fluid.CUDAPlace(0))
  
      def check_static_result(self, place):
          with fluid.program_guard(fluid.Program(), fluid.Program()):
              input = fluid.data(name="input", shape=[3, 2], dtype="float64")
              index = fluid.data(name="index", shape=[4], dtype="int64")
              updates = fluid.data(name="updates", shape=[4, 2], dtype="float64")
              result = paddle.scatter(input, index, updates, False)
  
              input_data = np.array([[1, 1], [2, 2], [3, 3]]).astype(np.float64)
              index_data = np.array([2, 1, 0, 1]).astype(np.int64)
              updates_data = np.array(
                  [[1, 1], [2, 2], [3, 3], [4, 4]]).astype(np.float64)
  
              exe = fluid.Executor(place)
              fetches = exe.run(fluid.default_main_program(),
                                feed={
                                    "input": input_data,
                                    "index": index_data,
                                    "updates": updates_data
                                },
                                fetch_list=[result])
              self.assertEqual((fetches[0] == \
                                np.array([[3., 3.],[6., 6.],[1., 1.]])).all(), True)
  
      def test_static(self):
          for place in self.places:
              self.check_static_result(place=place)
  
      def test_dygraph(self):
          for place in self.places:
              with fluid.dygraph.guard(place):
                  x_data = np.array([[1, 1], [2, 2], [3, 3]]).astype(np.float64)
                  index_data = np.array([2, 1, 0, 1]).astype(np.int64)
                  updates_data = np.array(
                      [[1, 1], [2, 2], [3, 3], [4, 4]]).astype(np.float64)
  
                  x = fluid.dygraph.to_variable(x_data)
                  index = fluid.dygraph.to_variable(index_data)
                  updates = fluid.dygraph.to_variable(updates_data)
  
                  output1 = paddle.scatter(x, index, updates, overwrite=False)
                  self.assertEqual((output1.numpy() == \
                                    np.array([[3., 3.],[6., 6.],[1., 1.]])).all(), True)
  
  
  if __name__ == "__main__":
      unittest.main()
  