Bump black (#2135)

* Bump the black version. Fixes the ci.

* Removed trailing commas.

* Update tensorflow_addons/image/filters.py

* Update tensorflow_addons/image/cutout_ops.py

* Removed trailing commas.

* Removed commas.

* Removed some more commas.

* Removed some commas.

* Removed some commas.

* Update tensorflow_addons/optimizers/moving_average.py

* Update tensorflow_addons/losses/tests/kappa_loss_test.py

* Update tensorflow_addons/layers/tests/wrappers_test.py

* Update tensorflow_addons/layers/tests/wrappers_test.py

* Update tensorflow_addons/layers/tests/wrappers_test.py

* Update tensorflow_addons/layers/tests/spectral_normalization_test.py

* Update tensorflow_addons/layers/tests/spectral_normalization_test.py

* Update tensorflow_addons/losses/tests/triplet_test.py

* Update tensorflow_addons/metrics/tests/hamming_test.py

* Update tensorflow_addons/metrics/tests/multilabel_confusion_matrix_test.py

* Update tensorflow_addons/optimizers/lookahead.py

* Update tensorflow_addons/metrics/tests/multilabel_confusion_matrix_test.py

* Update tensorflow_addons/metrics/r_square.py

* Removed commas.

* Removed commas.

Author: gabrieldemarmiesse

build_deps/toolchains/gpu/find_cuda_config.py

@@ -277,12 +277,16 @@ def get_nvcc_version(path):
 
     nvcc_name = "nvcc.exe" if _is_windows() else "nvcc"
     nvcc_path, nvcc_version = _find_versioned_file(
-        base_paths, ["", "bin",], nvcc_name, cuda_version, get_nvcc_version
+        base_paths,
+        ["", "bin"],
+        nvcc_name,
+        cuda_version,
+        get_nvcc_version,
     )
 
     nvvm_path = _find_file(
         base_paths,
-        ["nvvm/libdevice", "share/cuda", "lib/nvidia-cuda-toolkit/libdevice",],
+        ["nvvm/libdevice", "share/cuda", "lib/nvidia-cuda-toolkit/libdevice"],
         "libdevice*.10.bc",
     )
 

tensorflow_addons/activations/tests/sparsemax_test.py

@@ -112,7 +112,7 @@ def test_sparsemax_against_numpy_high_rank(dtype):
 def test_sparsemax_of_nan(dtype):
     """check sparsemax transfers nan."""
     z_nan = np.asarray(
-        [[0, np.nan, 0], [0, np.nan, np.nan], [np.nan, np.nan, np.nan],]
+        [[0, np.nan, 0], [0, np.nan, np.nan], [np.nan, np.nan, np.nan]]
     ).astype(dtype)
 
     tf_sparsemax_nan = sparsemax(z_nan)
@@ -132,7 +132,7 @@ def test_sparsemax_of_nan(dtype):
 def test_sparsemax_of_inf(dtype):
     """check sparsemax is infinity safe."""
     z_neg = np.asarray(
-        [[0, -np.inf, 0], [0, -np.inf, -np.inf], [-np.inf, -np.inf, -np.inf],]
+        [[0, -np.inf, 0], [0, -np.inf, -np.inf], [-np.inf, -np.inf, -np.inf]]
     ).astype(dtype)
     z_pos = np.asarray(
         [[0, np.inf, 0], [0, np.inf, np.inf], [np.inf, np.inf, np.inf]]

tensorflow_addons/image/compose_ops.py

@@ -43,7 +43,7 @@ def blend(image1: TensorLike, image2: TensorLike, factor: Number) -> tf.Tensor:
     Returns:
       A blended image Tensor of `tf.float32`.
 
-  """
+    """
     with tf.name_scope("blend"):
 
         if factor == 0.0:

tensorflow_addons/image/cutout_ops.py

@@ -100,7 +100,7 @@ def random_cutout(
     )
 
     offset = tf.transpose([cutout_center_height, cutout_center_width], [1, 0])
-    return cutout(images, mask_size, offset, constant_values, data_format,)
+    return cutout(images, mask_size, offset, constant_values, data_format)
 
 
 def cutout(

tensorflow_addons/image/filters.py

@@ -304,9 +304,7 @@ def gaussian_filter2d(
             gaussian_kernel_2d, [filter_shape[0], filter_shape[1], channels, 1]
         )
 
-        image = _pad(
-            image, filter_shape, mode=padding, constant_values=constant_values,
-        )
+        image = _pad(image, filter_shape, mode=padding, constant_values=constant_values)
 
         output = tf.nn.depthwise_conv2d(
             input=image,

tensorflow_addons/image/interpolate_spline.py

@@ -296,7 +296,7 @@ def interpolate_spline(
       train_values to perform polyharmonic interpolation. The query values are
       the values of the interpolant evaluated at the locations specified in
       query_points.
-  """
+    """
     with tf.name_scope(name or "interpolate_spline"):
         train_points = tf.convert_to_tensor(train_points)
         train_values = tf.convert_to_tensor(train_values)

tensorflow_addons/image/sparse_image_warp.py

@@ -39,7 +39,7 @@ def _expand_to_minibatch(np_array: TensorLike, batch_size: TensorLike) -> Tensor
 
 
 def _get_boundary_locations(
-    image_height: TensorLike, image_width: TensorLike, num_points_per_edge: TensorLike,
+    image_height: TensorLike, image_width: TensorLike, num_points_per_edge: TensorLike
 ) -> TensorLike:
     """Compute evenly-spaced indices along edge of image."""
     y_range = np.linspace(0, image_height - 1, num_points_per_edge + 2)

tensorflow_addons/image/tests/connected_components_test.py

@@ -118,7 +118,7 @@ def test_multiple_images():
 @pytest.mark.usefixtures("maybe_run_functions_eagerly")
 def test_zeros():
     np.testing.assert_equal(
-        connected_components(tf.zeros((100, 20, 50), tf.bool)), np.zeros((100, 20, 50)),
+        connected_components(tf.zeros((100, 20, 50), tf.bool)), np.zeros((100, 20, 50))
     )
 
 
@@ -134,7 +134,7 @@ def test_ones():
 def test_ones_small():
 
     np.testing.assert_equal(
-        connected_components(tf.ones((3, 5), tf.bool)).numpy(), np.ones((3, 5)),
+        connected_components(tf.ones((3, 5), tf.bool)).numpy(), np.ones((3, 5))
     )
 
 

tensorflow_addons/image/tests/dense_image_warp_test.py

@@ -30,7 +30,7 @@ def test_interpolate_small_grid_ij():
         shape=[1, 4, 3, 1],
     )
     query_points = tf.constant(
-        [[0.0, 0.0], [1.0, 0.0], [2.0, 0.5], [1.5, 1.5], [3.0, 2.0]], shape=[1, 5, 2],
+        [[0.0, 0.0], [1.0, 0.0], [2.0, 0.5], [1.5, 1.5], [3.0, 2.0]], shape=[1, 5, 2]
     )
     expected_results = np.reshape(np.array([0.0, 3.0, 6.5, 6.0, 11.0]), [1, 5, 1])
 
@@ -45,7 +45,7 @@ def test_interpolate_small_grid_xy():
         shape=[1, 4, 3, 1],
     )
     query_points = tf.constant(
-        [[0.0, 0.0], [0.0, 1.0], [0.5, 2.0], [1.5, 1.5], [2.0, 3.0]], shape=[1, 5, 2],
+        [[0.0, 0.0], [0.0, 1.0], [0.5, 2.0], [1.5, 1.5], [2.0, 3.0]], shape=[1, 5, 2]
     )
     expected_results = np.reshape(np.array([0.0, 3.0, 6.5, 6.0, 11.0]), [1, 5, 1])
 
@@ -91,7 +91,8 @@ def _check_zero_flow_correctness(shape, image_type, flow_type):
     rand_flows *= 0
 
     interp = dense_image_warp(
-        image=tf.convert_to_tensor(rand_image), flow=tf.convert_to_tensor(rand_flows),
+        image=tf.convert_to_tensor(rand_image),
+        flow=tf.convert_to_tensor(rand_flows),
     )
 
     np.testing.assert_allclose(rand_image, interp, rtol=1e-6, atol=1e-6)

tensorflow_addons/image/tests/distort_image_ops_test.py

@@ -125,7 +125,7 @@ def test_adjust_hsv_in_yiq_unknown_shape():
         image_np = np.random.rand(*shape) * 255.0
         image_tf = tf.constant(image_np)
         np.testing.assert_allclose(
-            _adjust_hue_in_yiq_np(image_np, 0), fn(image_tf), rtol=2e-4, atol=1e-4,
+            _adjust_hue_in_yiq_np(image_np, 0), fn(image_tf), rtol=2e-4, atol=1e-4
         )