text: #
  # Copyright (c) 2021, NVIDIA CORPORATION. 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.
  #
  
  
  """tests of supportive functions"""
  
  import pytest
  import numpy as np
  
  import torch
  
  import pytorch_quantization.nn.functional as QF
  
  np.random.seed(1234)
  torch.manual_seed(1234)
  
  # pylint:disable=missing-docstring, no-self-use
  
  torch.set_default_tensor_type('torch.cuda.FloatTensor')
  
  
  class TestClip():
  
      def test_simple_run(self):
          x_np = np.random.rand(1023).astype(np.float32)
          x_torch = torch.Tensor(x_np)
          clip_x_np = np.clip(x_np, 0.3, 0.7)
          clip_x_torch = QF.clip(x_torch, torch.tensor(0.3), torch.tensor(0.7))
          np.testing.assert_array_equal(clip_x_torch.cpu().numpy(), clip_x_np)
  
      def test_raise(self):
          x = torch.randn(3, 7, requires_grad=True)
  
          min_value = torch.Tensor(3, 7)
          max_value = torch.Tensor(3, 7)
          min_value.requires_grad = True
          max_value.requires_grad = True
          clip_x = QF.clip(x, min_value, max_value)
  
          labels = torch.randint(6, (3,)).type(torch.LongTensor).cuda()
          criterion = torch.nn.CrossEntropyLoss()
          loss = criterion(clip_x, labels)
          with pytest.raises(RuntimeError, match="can only be scalar"):
              loss.backward()
  
      def test_broadcast(self):
          """Test broadcast behavior by randomly picked shuffling of np.random.rand"""
          x_np = np.random.rand(1023, 4, 5, 6).astype(np.float32) - 0.5
          x_torch = torch.Tensor(x_np)
          min_value = np.random.rand(1, 4, 1, 1).astype(np.float32) * 0.1 - 0.2
          max_value = np.random.rand(1, 4, 1, 1).astype(np.float32) * 10 + 0.5
          clip_x_np = np.clip(x_np, min_value, max_value)
          clip_x_torch = QF.clip(x_torch, torch.tensor(min_value), torch.tensor(max_value))
          np.testing.assert_array_equal(clip_x_torch.cpu().numpy(), clip_x_np)
  
      def test_backward(self):
          x = torch.randn(3, 1025, requires_grad=True)
          x.retain_grad()
  
          min_value = torch.tensor(0.3)
          max_value = torch.tensor(0.7)
          min_value.requires_grad = True
          max_value.requires_grad = True
          min_value.retain_grad()
          max_value.retain_grad()
          clip_x = QF.clip(x, min_value, max_value)
          clip_x.retain_grad()
  
          labels = torch.randint(6, (3,)).type(torch.LongTensor).cuda()
          criterion = torch.nn.CrossEntropyLoss()
          loss = criterion(clip_x, labels)
          loss.backward()
  
          np.testing.assert_array_almost_equal(
              clip_x.grad[x < min_value].sum().cpu().numpy(), min_value.grad.cpu().numpy(), decimal=6)
          np.testing.assert_array_almost_equal(
              clip_x.grad[x > max_value].sum().cpu().numpy(), max_value.grad.cpu().numpy(), decimal=6)
          assert x.grad.cpu()[x < min_value].sum() == 0
          assert x.grad.cpu()[x > max_value].sum() == 0
          assert torch.equal(clip_x.grad[(x > min_value) & (x < max_value)], x.grad[(x > min_value) & (x < max_value)])
  