text: # Copyright (c) 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
  
  import torch
  from parameterized import parameterized
  
  from monai.transforms import Randomizable, RandTorchVisiond
  from monai.utils import set_determinism
  from tests.utils import SkipIfBeforePyTorchVersion
  
  TEST_CASE_1 = [
      {"keys": "img", "name": "ColorJitter"},
      {"img": torch.tensor([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]])},
      torch.tensor([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]]),
  ]
  
  TEST_CASE_2 = [
      {"keys": "img", "name": "ColorJitter", "brightness": 0.5, "contrast": 0.5, "saturation": [0.1, 0.8], "hue": 0.5},
      {"img": torch.tensor([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]])},
      torch.tensor(
          [
              [[0.1090, 0.6193], [0.6193, 0.9164]],
              [[0.1090, 0.6193], [0.6193, 0.9164]],
              [[0.1090, 0.6193], [0.6193, 0.9164]],
          ]
      ),
  ]
  
  TEST_CASE_3 = [
      {"keys": "img", "name": "Pad", "padding": [1, 1, 1, 1]},
      {"img": torch.tensor([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]])},
      torch.tensor(
          [
              [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 2.0, 0.0], [0.0, 0.0, 0.0, 0.0]],
              [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 2.0, 0.0], [0.0, 0.0, 0.0, 0.0]],
              [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 2.0, 0.0], [0.0, 0.0, 0.0, 0.0]],
          ]
      ),
  ]
  
  
  @SkipIfBeforePyTorchVersion((1, 7))
  class TestRandTorchVisiond(unittest.TestCase):
      @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
      def test_value(self, input_param, input_data, expected_value):
          set_determinism(seed=0)
          transform = RandTorchVisiond(**input_param)
          result = transform(input_data)
          self.assertTrue(isinstance(transform, Randomizable))
          torch.testing.assert_allclose(result["img"], expected_value)
  
  
  if __name__ == "__main__":
      unittest.main()
  