text: # Copyright 2020 - 2021 MONAI Consortium
  # 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
  from unittest import skipUnless
  
  import numpy as np
  import torch
  
  from monai.transforms import ToNumpy
  from monai.utils import optional_import
  from tests.utils import assert_allclose, skip_if_no_cuda
  
  cp, has_cp = optional_import("cupy")
  
  
  class TestToNumpy(unittest.TestCase):
      @skipUnless(has_cp, "CuPy is required.")
      def test_cupy_input(self):
          test_data = cp.array([[1, 2], [3, 4]])
          test_data = cp.rot90(test_data)
          self.assertFalse(test_data.flags["C_CONTIGUOUS"])
          result = ToNumpy()(test_data)
          self.assertTrue(isinstance(result, np.ndarray))
          self.assertTrue(result.flags["C_CONTIGUOUS"])
          assert_allclose(result, test_data.get(), type_test=False)
  
      def test_numpy_input(self):
          test_data = np.array([[1, 2], [3, 4]])
          test_data = np.rot90(test_data)
          self.assertFalse(test_data.flags["C_CONTIGUOUS"])
          result = ToNumpy(dtype="float32")(test_data)
          self.assertTrue(isinstance(result, np.ndarray))
          self.assertTrue(result.dtype == np.float32)
          self.assertTrue(result.flags["C_CONTIGUOUS"])
          assert_allclose(result, test_data, type_test=False)
  
      def test_tensor_input(self):
          test_data = torch.tensor([[1, 2], [3, 4]])
          test_data = test_data.rot90()
          self.assertFalse(test_data.is_contiguous())
          result = ToNumpy(dtype=torch.uint8)(test_data)
          self.assertTrue(isinstance(result, np.ndarray))
          self.assertTrue(result.flags["C_CONTIGUOUS"])
          assert_allclose(result, test_data, type_test=False)
  
      @skip_if_no_cuda
      def test_tensor_cuda_input(self):
          test_data = torch.tensor([[1, 2], [3, 4]]).cuda()
          test_data = test_data.rot90()
          self.assertFalse(test_data.is_contiguous())
          result = ToNumpy()(test_data)
          self.assertTrue(isinstance(result, np.ndarray))
          self.assertTrue(result.flags["C_CONTIGUOUS"])
          assert_allclose(result, test_data, type_test=False)
  
      def test_list_tuple(self):
          test_data = [[1, 2], [3, 4]]
          result = ToNumpy()(test_data)
          assert_allclose(result, np.asarray(test_data), type_test=False)
          test_data = ((1, 2), (3, 4))
          result = ToNumpy(wrap_sequence=False)(test_data)
          self.assertTrue(type(result), tuple)
          assert_allclose(result, ((np.asarray(1), np.asarray(2)), (np.asarray(3), np.asarray(4))))
  
      def test_single_value(self):
          for test_data in [5, np.array(5), torch.tensor(5)]:
              result = ToNumpy(dtype=np.uint8)(test_data)
              self.assertTrue(isinstance(result, np.ndarray))
              assert_allclose(result, np.asarray(test_data), type_test=False)
              self.assertEqual(result.ndim, 0)
  
  
  if __name__ == "__main__":
      unittest.main()
  