Remove all type annotations for consistency.

Some tools don't like the mix of code with and without type hints.

keras/src/backend/common/backend_utils.py

@@ -356,7 +356,7 @@ def _vectorize_parse_input_dimensions(
             f"expected {len(input_core_dims)}, got {len(args)}"
         )
     shapes = []
-    dim_sizes: dict[str, int] = {}
+    dim_sizes = {}
     for arg, core_dims in zip(args, input_core_dims):
         _vectorize_update_dim_sizes(
             dim_sizes, arg.shape, core_dims, is_input=True

keras/src/backend/common/dtypes_test.py

@@ -43,7 +43,7 @@ def setUp(self):
         self.jax_enable_x64.__enter__()
         return super().setUp()
 
-    def tearDown(self) -> None:
+    def tearDown(self):
         self.jax_enable_x64.__exit__(None, None, None)
         return super().tearDown()
 

keras/src/backend/common/variables_test.py

@@ -820,7 +820,7 @@ def setUp(self):
         self.jax_enable_x64.__enter__()
         return super().setUp()
 
-    def tearDown(self) -> None:
+    def tearDown(self):
         self.jax_enable_x64.__exit__(None, None, None)
         return super().tearDown()
 

keras/src/backend/openvino/core.py

@@ -243,9 +243,9 @@ def __getitem__(self, indices):
     def __len__(self):
         ov_output = self.output
         ov_shape = ov_output.get_partial_shape()
-        assert ov_shape.rank.is_static and ov_shape.rank.get_length() > 0, (
-            "rank must be static and greater than zero"
-        )
+        assert (
+            ov_shape.rank.is_static and ov_shape.rank.get_length() > 0
+        ), "rank must be static and greater than zero"
         assert ov_shape[0].is_static, "the first dimension must be static"
         return ov_shape[0].get_length()
 
@@ -428,10 +428,10 @@ def convert_to_numpy(x):
             x = x.value
         else:
             return x.value.data
-    assert isinstance(x, OpenVINOKerasTensor), (
-        "unsupported type {} for `convert_to_numpy` in openvino backend".format(
-            type(x)
-        )
+    assert isinstance(
+        x, OpenVINOKerasTensor
+    ), "unsupported type {} for `convert_to_numpy` in openvino backend".format(
+        type(x)
     )
     try:
         ov_result = x.output

keras/src/backend/openvino/nn.py

@@ -325,9 +325,9 @@ def depthwise_conv(
     data_format = backend.standardize_data_format(data_format)
     num_spatial_dims = inputs.get_partial_shape().rank.get_length() - 2
 
-    assert data_format == "channels_last", (
-        "`depthwise_conv` is supported only for channels_last data_format"
-    )
+    assert (
+        data_format == "channels_last"
+    ), "`depthwise_conv` is supported only for channels_last data_format"
 
     strides = _adjust_strides_dilation(strides, num_spatial_dims)
     dilation_rate = _adjust_strides_dilation(dilation_rate, num_spatial_dims)

keras/src/backend/openvino/numpy.py

@@ -81,9 +81,9 @@ def mean(x, axis=None, keepdims=False):
 
 
 def max(x, axis=None, keepdims=False, initial=None):
-    assert initial is None, (
-        "`max` with not None initial is not supported by openvino backend"
-    )
+    assert (
+        initial is None
+    ), "`max` with not None initial is not supported by openvino backend"
     x = get_ov_output(x)
     reduce_axis = ov_opset.constant(axis, Type.i32).output(0)
     return OpenVINOKerasTensor(
@@ -260,9 +260,9 @@ def bincount(x, weights=None, minlength=0, sparse=False):
 
 
 def broadcast_to(x, shape):
-    assert isinstance(shape, (tuple, list)), (
-        "`broadcast_to` is supported only for tuple and list `shape`"
-    )
+    assert isinstance(
+        shape, (tuple, list)
+    ), "`broadcast_to` is supported only for tuple and list `shape`"
     target_shape = ov_opset.constant(list(shape), Type.i32).output(0)
     x = get_ov_output(x)
     return OpenVINOKerasTensor(ov_opset.broadcast(x, target_shape).output(0))

keras/src/backend/torch/layer.py

@@ -1,6 +1,3 @@
-from typing import Iterator
-from typing import Tuple
-
 import torch
 
 from keras.src.backend.common.stateless_scope import in_stateless_scope
@@ -30,10 +27,10 @@ def _track_variables(self):
 
     def named_parameters(
         self,
-        prefix: str = "",
-        recurse: bool = True,
-        remove_duplicate: bool = True,
-    ) -> Iterator[Tuple[str, torch.nn.Parameter]]:
+        prefix="",
+        recurse=True,
+        remove_duplicate=True,
+    ):
         if not hasattr(self, "_torch_params"):
             self._track_variables()
         return torch.nn.Module.named_parameters(

keras/src/layers/preprocessing/image_preprocessing/bounding_boxes/bounding_box.py

@@ -22,8 +22,8 @@ def __init__(self):
     def convert_format(
         self,
         boxes,
-        source: str,
-        target: str,
+        source,
+        target,
         height=None,
         width=None,
         dtype="float32",

keras/src/ops/core_test.py

@@ -936,7 +936,7 @@ def setUp(self):
         self.jax_enable_x64.__enter__()
         return super().setUp()
 
-    def tearDown(self) -> None:
+    def tearDown(self):
         self.jax_enable_x64.__exit__(None, None, None)
         return super().tearDown()
 

keras/src/ops/image_test.py

@@ -20,7 +20,7 @@ def setUp(self):
         backend.set_image_data_format("channels_last")
         return super().setUp()
 
-    def tearDown(self) -> None:
+    def tearDown(self):
         backend.set_image_data_format(self.data_format)
         return super().tearDown()
 
@@ -171,7 +171,7 @@ def setUp(self):
         backend.set_image_data_format("channels_last")
         return super().setUp()
 
-    def tearDown(self) -> None:
+    def tearDown(self):
         backend.set_image_data_format(self.data_format)
         return super().tearDown()
 
@@ -396,7 +396,7 @@ def setUp(self):
         backend.set_image_data_format("channels_last")
         return super().setUp()
 
-    def tearDown(self) -> None:
+    def tearDown(self):
         backend.set_image_data_format(self.data_format)
         return super().tearDown()
 
@@ -1144,7 +1144,7 @@ def setUp(self):
         backend.set_image_data_format("channels_last")
         return super().setUp()
 
-    def tearDown(self) -> None:
+    def tearDown(self):
         backend.set_image_data_format(self.data_format)
         return super().tearDown()
 

keras/src/ops/math_test.py

@@ -1003,7 +1003,7 @@ def setUp(self):
         self.jax_enable_x64.__enter__()
         return super().setUp()
 
-    def tearDown(self) -> None:
+    def tearDown(self):
         self.jax_enable_x64.__exit__(None, None, None)
         return super().tearDown()
 

keras/src/ops/nn_test.py

@@ -2474,7 +2474,7 @@ def setUp(self):
         self.jax_enable_x64.__enter__()
         return super().setUp()
 
-    def tearDown(self) -> None:
+    def tearDown(self):
         self.jax_enable_x64.__exit__(None, None, None)
         return super().tearDown()
 

keras/src/ops/numpy_test.py

@@ -5357,7 +5357,7 @@ def setUp(self):
         self.jax_enable_x64.__enter__()
         return super().setUp()
 
-    def tearDown(self) -> None:
+    def tearDown(self):
         self.jax_enable_x64.__exit__(None, None, None)
         return super().tearDown()
 

keras/src/random/random_test.py

@@ -456,7 +456,7 @@ def setUp(self):
             self.jax_enable_x64.__enter__()
         return super().setUp()
 
-    def tearDown(self) -> None:
+    def tearDown(self):
         if backend.backend() == "jax":
             self.jax_enable_x64.__exit__(None, None, None)
         return super().tearDown()

keras/src/saving/saving_lib_test.py

@@ -253,7 +253,7 @@ def setUp(self):
         saving_lib._MEMORY_UPPER_BOUND = 0
         return super().setUp()
 
-    def tearDown(self) -> None:
+    def tearDown(self):
         saving_lib._MEMORY_UPPER_BOUND = self.original_value
         return super().tearDown()
 
@@ -621,7 +621,7 @@ def test_partial_load(self):
         )
 
     @pytest.mark.requires_trainable_backend
-    def test_save_to_fileobj(self) -> None:
+    def test_save_to_fileobj(self):
         model = keras.Sequential(
             [keras.layers.Dense(1, input_shape=(1,)), keras.layers.Dense(1)]
         )

keras/src/visualization/draw_segmentation_masks.py

@@ -104,6 +104,6 @@ def draw_segmentation_masks(
     return outputs
 
 
-def _generate_color_palette(num_classes: int):
+def _generate_color_palette(num_classes):
     palette = np.array([2**25 - 1, 2**15 - 1, 2**21 - 1])
     return [((i * palette) % 255).tolist() for i in range(num_classes)]

keras/src/wrappers/sklearn_test.py

@@ -21,7 +21,7 @@
 def wrapped_parametrize_with_checks(
     estimators,
     *,
-    legacy: bool = True,
+    legacy=True,
     expected_failed_checks=None,
 ):
     """Wrapped `parametrize_with_checks` handling backwards compat."""
@@ -77,7 +77,7 @@ def dynamic_model(X, y, loss, layers=[10]):
 
 
 @contextmanager
-def use_floatx(x: str):
+def use_floatx(x):
     """Context manager to temporarily
     set the keras backend precision.
     """

keras/src/wrappers/sklearn_wrapper.py

@@ -107,7 +107,7 @@ def __sklearn_clone__(self):
         )
 
     @property
-    def epoch_(self) -> int:
+    def epoch_(self):
         """The current training epoch."""
         return getattr(self, "history_", {}).get("epoch", 0)