text: # Copyright (c) 2022 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 unittest
  import numpy as np
  import paddle
  import paddle.fluid as fluid
  
  
  class TestGraphKhopSampler(unittest.TestCase):
  
      def setUp(self):
          num_nodes = 20
          edges = np.random.randint(num_nodes, size=(100, 2))
          edges = np.unique(edges, axis=0)
          edges_id = np.arange(0, len(edges))
          sorted_edges = edges[np.argsort(edges[:, 1])]
          sorted_eid = edges_id[np.argsort(edges[:, 1])]
  
          # Calculate dst index cumsum counts.
          dst_count = np.zeros(num_nodes)
          dst_src_dict = {}
          for dst in range(0, num_nodes):
              true_index = sorted_edges[:, 1] == dst
              dst_count[dst] = np.sum(true_index)
              dst_src_dict[dst] = sorted_edges[:, 0][true_index]
          dst_count = dst_count.astype("int64")
          colptr = np.cumsum(dst_count)
          colptr = np.insert(colptr, 0, 0)
  
          self.row = sorted_edges[:, 0].astype("int64")
          self.colptr = colptr.astype("int64")
          self.sorted_eid = sorted_eid.astype("int64")
          self.nodes = np.unique(np.random.randint(num_nodes,
                                                   size=5)).astype("int64")
          self.sample_sizes = [5, 5]
          self.dst_src_dict = dst_src_dict
  
      def func_sample_result(self):
          paddle.disable_static()
          row = paddle.to_tensor(self.row)
          colptr = paddle.to_tensor(self.colptr)
          nodes = paddle.to_tensor(self.nodes)
  
          edge_src, edge_dst, sample_index, reindex_nodes = \
              paddle.incubate.graph_khop_sampler(row, colptr,
                                                     nodes, self.sample_sizes,
                                                     return_eids=False)
          # Reindex edge_src and edge_dst to original index.
          edge_src = edge_src.reshape([-1])
          edge_dst = edge_dst.reshape([-1])
          sample_index = sample_index.reshape([-1])
  
          for i in range(len(edge_src)):
              edge_src[i] = sample_index[edge_src[i]]
              edge_dst[i] = sample_index[edge_dst[i]]
  
          for n in self.nodes:
              edge_src_n = edge_src[edge_dst == n]
              if edge_src_n.shape[0] == 0:
                  continue
              # Ensure no repetitive sample neighbors.
              self.assertTrue(
                  edge_src_n.shape[0] == paddle.unique(edge_src_n).shape[0])
              # Ensure the correct sample size.
              self.assertTrue(edge_src_n.shape[0] == self.sample_sizes[0]
                              or edge_src_n.shape[0] == len(self.dst_src_dict[n]))
              in_neighbors = np.isin(edge_src_n.numpy(), self.dst_src_dict[n])
              # Ensure the correct sample neighbors.
              self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
  
      def test_sample_result(self):
          with fluid.framework._test_eager_guard():
              self.func_sample_result()
          self.func_sample_result()
  
      def func_uva_sample_result(self):
          paddle.disable_static()
          if paddle.fluid.core.is_compiled_with_cuda():
              row = None
              if fluid.framework.in_dygraph_mode():
                  row = paddle.fluid.core.eager.to_uva_tensor(
                      self.row.astype(self.row.dtype), 0)
                  sorted_eid = paddle.fluid.core.eager.to_uva_tensor(
                      self.sorted_eid.astype(self.sorted_eid.dtype), 0)
              else:
                  row = paddle.fluid.core.to_uva_tensor(
                      self.row.astype(self.row.dtype))
                  sorted_eid = paddle.fluid.core.to_uva_tensor(
                      self.sorted_eid.astype(self.sorted_eid.dtype))
              colptr = paddle.to_tensor(self.colptr)
              nodes = paddle.to_tensor(self.nodes)
  
              edge_src, edge_dst, sample_index, reindex_nodes, edge_eids = \
                  paddle.incubate.graph_khop_sampler(row, colptr,
                                                         nodes, self.sample_sizes,
                                                         sorted_eids=sorted_eid,
                                                         return_eids=True)
              edge_src = edge_src.reshape([-1])
              edge_dst = edge_dst.reshape([-1])
              sample_index = sample_index.reshape([-1])
  
              for i in range(len(edge_src)):
                  edge_src[i] = sample_index[edge_src[i]]
                  edge_dst[i] = sample_index[edge_dst[i]]
  
              for n in self.nodes:
                  edge_src_n = edge_src[edge_dst == n]
                  if edge_src_n.shape[0] == 0:
                      continue
                  self.assertTrue(
                      edge_src_n.shape[0] == paddle.unique(edge_src_n).shape[0])
                  self.assertTrue(
                      edge_src_n.shape[0] == self.sample_sizes[0]
                      or edge_src_n.shape[0] == len(self.dst_src_dict[n]))
                  in_neighbors = np.isin(edge_src_n.numpy(), self.dst_src_dict[n])
                  self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
  
      def test_uva_sample_result(self):
          with fluid.framework._test_eager_guard():
              self.func_uva_sample_result()
          self.func_uva_sample_result()
  
      def test_sample_result_static_with_eids(self):
          paddle.enable_static()
          with paddle.static.program_guard(paddle.static.Program()):
              row = paddle.static.data(name="row",
                                       shape=self.row.shape,
                                       dtype=self.row.dtype)
              sorted_eids = paddle.static.data(name="eids",
                                               shape=self.sorted_eid.shape,
                                               dtype=self.sorted_eid.dtype)
              colptr = paddle.static.data(name="colptr",
                                          shape=self.colptr.shape,
                                          dtype=self.colptr.dtype)
              nodes = paddle.static.data(name="nodes",
                                         shape=self.nodes.shape,
                                         dtype=self.nodes.dtype)
  
              edge_src, edge_dst, sample_index, reindex_nodes, edge_eids = \
                  paddle.incubate.graph_khop_sampler(row, colptr,
                                                         nodes, self.sample_sizes,
                                                         sorted_eids, True)
              exe = paddle.static.Executor(paddle.CPUPlace())
              ret = exe.run(feed={
                  'row': self.row,
                  'eids': self.sorted_eid,
                  'colptr': self.colptr,
                  'nodes': self.nodes
              },
                            fetch_list=[edge_src, edge_dst, sample_index])
  
              edge_src, edge_dst, sample_index = ret
              edge_src = edge_src.reshape([-1])
              edge_dst = edge_dst.reshape([-1])
              sample_index = sample_index.reshape([-1])
  
              for i in range(len(edge_src)):
                  edge_src[i] = sample_index[edge_src[i]]
                  edge_dst[i] = sample_index[edge_dst[i]]
  
              for n in self.nodes:
                  edge_src_n = edge_src[edge_dst == n]
                  if edge_src_n.shape[0] == 0:
                      continue
                  self.assertTrue(
                      edge_src_n.shape[0] == np.unique(edge_src_n).shape[0])
                  self.assertTrue(
                      edge_src_n.shape[0] == self.sample_sizes[0]
                      or edge_src_n.shape[0] == len(self.dst_src_dict[n]))
                  in_neighbors = np.isin(edge_src_n, self.dst_src_dict[n])
                  self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
  
      def test_sample_result_static_without_eids(self):
          paddle.enable_static()
          with paddle.static.program_guard(paddle.static.Program()):
              row = paddle.static.data(name="row",
                                       shape=self.row.shape,
                                       dtype=self.row.dtype)
              colptr = paddle.static.data(name="colptr",
                                          shape=self.colptr.shape,
                                          dtype=self.colptr.dtype)
              nodes = paddle.static.data(name="nodes",
                                         shape=self.nodes.shape,
                                         dtype=self.nodes.dtype)
              edge_src, edge_dst, sample_index, reindex_nodes = \
                  paddle.incubate.graph_khop_sampler(row, colptr,
                                                         nodes, self.sample_sizes)
              exe = paddle.static.Executor(paddle.CPUPlace())
              ret = exe.run(feed={
                  'row': self.row,
                  'colptr': self.colptr,
                  'nodes': self.nodes
              },
                            fetch_list=[edge_src, edge_dst, sample_index])
              edge_src, edge_dst, sample_index = ret
              edge_src = edge_src.reshape([-1])
              edge_dst = edge_dst.reshape([-1])
              sample_index = sample_index.reshape([-1])
  
              for i in range(len(edge_src)):
                  edge_src[i] = sample_index[edge_src[i]]
                  edge_dst[i] = sample_index[edge_dst[i]]
  
              for n in self.nodes:
                  edge_src_n = edge_src[edge_dst == n]
                  if edge_src_n.shape[0] == 0:
                      continue
                  self.assertTrue(
                      edge_src_n.shape[0] == np.unique(edge_src_n).shape[0])
                  self.assertTrue(
                      edge_src_n.shape[0] == self.sample_sizes[0]
                      or edge_src_n.shape[0] == len(self.dst_src_dict[n]))
                  in_neighbors = np.isin(edge_src_n, self.dst_src_dict[n])
                  self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
  
  
  if __name__ == "__main__":
      unittest.main()
  