code: # Copyright 2022 The KerasCV Authors
  #
  # 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
  #
  #     https://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 tensorflow as tf
  
  from keras_cv.layers import preprocessing
  
  
  class RandomSharpnessTest(tf.test.TestCase):
      def test_random_sharpness_preserves_output_shape(self):
          img_shape = (50, 50, 3)
          xs = tf.stack(
              [2 * tf.ones(img_shape), tf.ones(img_shape)],
              axis=0,
          )
  
          layer = preprocessing.RandomSharpness(0.0, value_range=(0, 255))
          ys = layer(xs)
  
          self.assertEqual(xs.shape, ys.shape)
          self.assertAllClose(xs, ys)
  
      def test_random_sharpness_blur_effect_single_channel(self):
          xs = tf.expand_dims(
              tf.constant(
                  [
                      [0, 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, 0, 0, 0, 0, 0],
                      [0, 0, 0, 0, 0, 0, 0],
                      [0, 0, 0, 0, 0, 0, 0],
                  ]
              ),
              axis=-1,
          )
          xs = tf.expand_dims(xs, axis=0)
  
          layer = preprocessing.RandomSharpness((1.0, 1.0), value_range=(0, 255))
          ys = layer(xs)
  
          self.assertEqual(xs.shape, ys.shape)
  
          result = tf.expand_dims(
              tf.constant(
                  [
                      [0, 0, 0, 0, 0, 0, 0],
                      [0, 0, 0, 0, 0, 0, 0],
                      [0, 0, 1 / 13, 1 / 13, 1 / 13, 0, 0],
                      [0, 0, 1 / 13, 5 / 13, 1 / 13, 0, 0],
                      [0, 0, 1 / 13, 1 / 13, 1 / 13, 0, 0],
                      [0, 0, 0, 0, 0, 0, 0],
                      [0, 0, 0, 0, 0, 0, 0],
                  ]
              ),
              axis=-1,
          )
          result = tf.expand_dims(result, axis=0)
  
          self.assertAllClose(ys, result)
  
apis: tensorflow.constant tensorflow.ones tensorflow.expand_dims