text: # Copyright 2021 The TensorFlow 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.
  # ==============================================================================
  """Tests for tf.Mean JIT compilation."""
  
  import numpy as np
  
  from tensorflow.compiler.mlir.tfrt.jit.python_binding import tf_jitrt
  from tensorflow.python.platform import test
  
  jitrt = tf_jitrt.TfJitRtExecutor()
  
  
  class TfMeanTest(test.TestCase):
  
    def test_mean_2d(self):
      mlir_function = """
        func @mean(%arg0: tensor<?x?xf32>) -> tensor<?x1xf32> {
          %dim = "tf.Const"() {value = dense<1> : tensor<1xi32>}
                 : () -> tensor<1xi32>
          %0 = "tf.Mean"(%arg0, %dim) { keep_dims = true }
               : (tensor<?x?xf32>, tensor<1xi32>) -> tensor<?x1xf32>
          return %0 : tensor<?x1xf32>
        }"""
      compiled = jitrt.compile(mlir_function, 'mean')
      arg0 = np.random.uniform(0.0, 1.0, size=(100, 200)).astype(np.float32)
      [res] = jitrt.execute(compiled, [arg0])
      mean = np.mean(arg0, axis=1, keepdims=True)
      np.testing.assert_allclose(res, mean, rtol=1e-05)
  
  
  if __name__ == '__main__':
    np.random.seed(0)
    test.main()
  