                          get_backend().upper()) for v in _VALUES]
  
  
  @pytest.mark.parametrize(_PARAMS, _VALUES, ids=_IDS)
  def test_layer_normalization(center, scale):  # pylint:disable=unused-argument
      """ Basic test for layer normalization. """
      layer_test(normalization.LayerNormalization,
                 kwargs={"center": center, "scale": scale},
                 input_shape=(4, 512))
  
 
</s> remove         self.data_format = K.normalize_data_format(data_format)
 </s> add         self.data_format = normalize_data_format(data_format) </s> remove from .normalization_common import *
 </s> add from .normalization_common import AdaInstanceNormalization
from .normalization_common import GroupNormalization
from .normalization_common import InstanceNormalization
 </s> remove from keras import regularizers
 </s> add import numpy as np
def test_adain_normalization(center, scale):
    """ Basic test for Ada Instance Normalization. """
    norm = normalization.AdaInstanceNormalization(center=center, scale=scale)
    shapes = [(4, 8, 8, 1280), (4, 1, 1, 1280), (4, 1, 1, 1280)]
    norm.build(shapes)
    expected_output_shape = norm.compute_output_shape(shapes)
    inputs = [layers.Input(shape=shapes[0][1:]),
              layers.Input(shape=shapes[1][1:]),
              layers.Input(shape=shapes[2][1:])]
    model = models.Model(inputs, norm(inputs))
    data = [10 * np.random.random(shape) for shape in shapes]

    actual_output = model.predict(data)
    actual_output_shape = actual_output.shape

    for expected_dim, actual_dim in zip(expected_output_shape,
                                        actual_output_shape):
        if expected_dim is not None:
            assert expected_dim == actual_dim


@pytest.mark.parametrize(_PARAMS, _VALUES, ids=_IDS)
def test_layer_normalization(center, scale):
                          get_backend().upper()) for v in _VALUES]
  
  
  @pytest.mark.parametrize(_PARAMS, _VALUES, ids=_IDS)
def test_adain_normalization(center, scale):
    """ Basic test for Ada Instance Normalization. """
    norm = normalization.AdaInstanceNormalization(center=center, scale=scale)
    shapes = [(4, 8, 8, 1280), (4, 1, 1, 1280), (4, 1, 1, 1280)]
    norm.build(shapes)
    expected_output_shape = norm.compute_output_shape(shapes)
    inputs = [layers.Input(shape=shapes[0][1:]),
              layers.Input(shape=shapes[1][1:]),
              layers.Input(shape=shapes[2][1:])]
    model = models.Model(inputs, norm(inputs))
    data = [10 * np.random.random(shape) for shape in shapes]

    actual_output = model.predict(data)
    actual_output_shape = actual_output.shape

    for expected_dim, actual_dim in zip(expected_output_shape,
                                        actual_output_shape):
        if expected_dim is not None:
            assert expected_dim == actual_dim


@pytest.mark.parametrize(_PARAMS, _VALUES, ids=_IDS)
def test_layer_normalization(center, scale):
      """ Basic test for layer normalization. """
      layer_test(normalization.LayerNormalization,
                 kwargs={"center": center, "scale": scale},
                 input_shape=(4, 512))
  
keep keep keep keep replace keep keep keep keep keep
 Unit tests - lib.model.normalization
  - __init__.py - Explicit imports (to fix pytests)
  - Add tests for all existing normalization layers
  - fix GroupNormalization for tensorflow backend 
tests/lib/model/normalization_test.py