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
  from op_test import OpTest
  import paddle.fluid as fluid
  
  
  class TestMatchMatrixTensorOp(OpTest):
      def setUp(self):
          self.init_op_type()
          self.set_data()
          self.compute()
  
      def init_op_type(self):
          self.op_type = "match_matrix_tensor"
  
      def set_data(self):
          ix, iy, h, dim_t = [5, 8, 20, 4]
          x_lod = [[1, 2, 2]]
          y_lod = [[3, 1, 4]]
          self.init_data(ix, x_lod, iy, y_lod, h, dim_t)
  
      def init_data(self, ix, x_lod, iy, y_lod, h, dim_t):
          x_data = np.random.random((ix, h)).astype('float32')
          y_data = np.random.random((iy, h)).astype('float32')
          w_data = np.random.random((h, dim_t, h)).astype('float32')
          self.inputs = {'X': (x_data, x_lod), 'Y': (y_data, y_lod), 'W': w_data}
          self.attrs = {'dim_t': dim_t}
  
      def compute(self):
          x_data, x_lod = self.inputs['X']
          y_data, y_lod = self.inputs['Y']
          # [k, dim_t, k] -> [dim_t, k, k]
          w_data = self.inputs['W'].transpose(1, 0, 2)
          out = np.zeros((0, 1), dtype=x_data.dtype)
          # for x*w
          tmp = np.zeros((0, 1), dtype=x_data.dtype)
          out_lod = [[]]
          tmp_lod = [[]]
  
          x_offset, y_offset = 0, 0
          for idx in range(len(x_lod[0])):
              x_len = x_lod[0][idx]
              y_len = y_lod[0][idx]
              x_sub = x_data[x_offset:(x_offset + x_len), :]
              y_sub = y_data[y_offset:(y_offset + y_len), :]
              tmp_sub = np.dot(x_sub, w_data)
              tmp = np.vstack((tmp, tmp_sub.reshape(tmp_sub.size, 1)))
  
              out_sub = np.dot(tmp_sub, y_sub.T).transpose(1, 0, 2)
              out_lod[0].append(out_sub.size)
              out = np.vstack((out, out_sub.reshape(out_sub.size, 1)))
  
              x_offset += x_len
              y_offset += y_len
          self.outputs = {'Out': (out, out_lod), 'Tmp': tmp}
  
      def test_check_output(self):
          self.check_output(check_dygraph=False)
  
      def test_check_grad(self):
          self.check_grad(['X', 'Y'], 'Out', check_dygraph=False)
  
  
  class TestMatchMatrixTensorOpCase1(TestMatchMatrixTensorOp):
      def set_data(self):
          ix, iy, h, dim_t = [5, 8, 25, 4]
          x_lod = [[5]]
          y_lod = [[8]]
          self.init_data(ix, x_lod, iy, y_lod, h, dim_t)
  
  
  class TestMatchMatrixTensorOpCase2(TestMatchMatrixTensorOp):
      def set_data(self):
          ix, iy, h, dim_t = [105, 120, 1, 4]
          x_lod = [[30, 45, 30]]
          y_lod = [[45, 15, 60]]
          self.init_data(ix, x_lod, iy, y_lod, h, dim_t)
  
  
  class TestMatchMatrixTensorOpCase3(TestMatchMatrixTensorOp):
      def set_data(self):
          ix, iy, h, dim_t = [5, 9, 32, 1]
          x_lod = [[1, 2, 2]]
          y_lod = [[3, 2, 4]]
          self.init_data(ix, x_lod, iy, y_lod, h, dim_t)
  
  
  class TestMatchMatrixTensorOpCase4(TestMatchMatrixTensorOp):
      def set_data(self):
          ix, iy, h, dim_t = [8, 12, 16, 5]
          x_lod = [[1, 2, 3, 1, 1]]
          y_lod = [[3, 2, 4, 1, 2]]
          self.init_data(ix, x_lod, iy, y_lod, h, dim_t)
  
      def test_api(self):
          x_lod_tensor = fluid.layers.data(name='x', shape=[10], lod_level=1)
          y_lod_tensor = fluid.layers.data(name='y', shape=[10], lod_level=1)
          out, out_tmp = fluid.contrib.match_matrix_tensor(
              x=x_lod_tensor, y=y_lod_tensor, channel_num=3)
  
          place = fluid.CPUPlace()
          x_data = np.random.rand(7, 10).astype('float32')
          y_data = np.random.rand(9, 10).astype('float32')
          x = fluid.create_lod_tensor(x_data, [[2, 5]], place)
          y = fluid.create_lod_tensor(y_data, [[3, 6]], place)
  
          exe = fluid.Executor(place=place)
          exe.run(fluid.default_startup_program())
          ret = exe.run(feed={'x': x,
                              'y': y},
                        fetch_list=[out],
                        return_numpy=False)
  
  
  if __name__ == '__main__':
      unittest.main()
  