Replace Black with Ruff formatter (#20445)

Author: mwtoews

.devcontainer/devcontainer.json

@@ -10,16 +10,15 @@
                     "source.organizeImports": true
                 },
                 "[python]": {
-                    "editor.defaultFormatter": "ms-python.black-formatter"
+                    "editor.defaultFormatter": "charliermarsh.ruff"
                 },
                 "editor.rulers": [
                     80
                 ]
             },
             "extensions": [
                 "charliermarsh.ruff",
-                "ms-python.python",
-                "ms-python.black-formatter"
+                "ms-python.python"
             ]
         }
     },

CONTRIBUTING.md

@@ -109,8 +109,7 @@ section of the README.
 
 ## Code style
 
-Keras uses [Black](https://black.readthedocs.io/en/stable/) and
-[Ruff](https://docs.astral.sh/ruff/) to format the code. Please refer to
+Keras uses [Ruff](https://docs.astral.sh/ruff/) to format the code. Please refer to
 [requirements-common.txt](https://github.com/keras-team/keras/blob/master/requirements-common.txt)
 for the required versions. Run the following command **at the root directory of
 the repo** to format your code.

examples/demo_custom_layer_backend_agnostic.py

@@ -47,9 +47,7 @@ def __init__(self, rate, name=None):
 
     def call(self, inputs):
         # Use `keras.random` for random ops.
-        return keras.random.dropout(
-            inputs, self.rate, seed=self.seed_generator
-        )
+        return keras.random.dropout(inputs, self.rate, seed=self.seed_generator)
 
 
 class MyModel(Model):

examples/demo_jax_distributed.py

@@ -27,9 +27,9 @@
 
 BATCH_SIZE = 192
 
-(x_train, train_labels), (
-    x_eval,
-    eval_labels,
+(
+    (x_train, train_labels),
+    (x_eval, eval_labels),
 ) = keras.datasets.mnist.load_data()
 x_train = np.expand_dims(x_train, axis=-1).astype(
     np.float32

guides/making_new_layers_and_models_via_subclassing.py

@@ -643,7 +643,7 @@ def __init__(
         intermediate_dim=64,
         latent_dim=32,
         name="autoencoder",
-        **kwargs
+        **kwargs,
     ):
         super().__init__(name=name, **kwargs)
         self.original_dim = original_dim

integration_tests/dataset_tests/boston_housing_test.py

@@ -3,7 +3,6 @@
 
 
 class BostonHousingTest(testing.TestCase):
-
     def test_load_data(self):
         (x_train, y_train), (x_test, y_test) = boston_housing.load_data()
         self.assertEqual(x_train.shape[1], 13)

integration_tests/dataset_tests/california_housing_test.py

@@ -3,7 +3,6 @@
 
 
 class CaliforniaHousingTest(testing.TestCase):
-
     def test_load_data_large(self):
         (x_train, y_train), (x_test, y_test) = california_housing.load_data(
             version="large"

integration_tests/model_visualization_test.py

@@ -41,7 +41,6 @@ def get_edge_dict(dot):
 
 
 class ModelVisualizationTest(testing.TestCase):
-
     def test_plot_sequential_model(self):
         model = keras.Sequential(
             [

keras/src/backend/common/masking_test.py

@@ -6,7 +6,6 @@
 
 
 class MaskingTest(testing.TestCase):
-
     def test_mask_on_eager_tensor(self):
         x = ops.zeros((2, 3))
         self.assertIsNone(get_keras_mask(x))
@@ -25,7 +24,6 @@ def test_mask_on_eager_tensor(self):
         self.assertIsNone(get_keras_mask(x))
 
     def test_mask_on_tracer_tensor(self):
-
         def fn(x):
             self.assertIsNone(get_keras_mask(x))
 

keras/src/backend/common/symbolic_scope_test.py

@@ -8,7 +8,6 @@
 
 class TestSymbolicScope(testing.TestCase):
     def test_basic_flow(self):
-
         # Define a function that behaves differently according to
         # `in_symbolic_scope`.
         def compute_loss(y, y_pred):