Update asserts to avoid deprecated methods

keras_hub/src/layers/preprocessing/masked_lm_mask_generator_test.py

@@ -148,7 +148,7 @@ def test_config(self):
             "vocabulary_size": self.vocabulary_size,
             "unselectable_token_ids": unselectable_token_ids,
         }
-        self.assertDictContainsSubset(expected_config, config)
+        self.assertEqual(config, {**config, **expected_config})
 
         # Test cloned masked_lm_masker can be run.
         cloned_masked_lm_masker = MaskedLMMaskGenerator.from_config(config)

keras_hub/src/models/efficientnet/cba_test.py

@@ -10,13 +10,13 @@ def test_same_input_output_shapes(self):
         layer = CBABlock(input_filters=32, output_filters=32)
 
         output = layer(inputs)
-        self.assertEquals(output.shape, (1, 64, 64, 32))
+        self.assertEqual(output.shape, (1, 64, 64, 32))
         self.assertLen(output, 1)
 
     def test_different_input_output_shapes(self):
         inputs = keras.random.normal(shape=(1, 64, 64, 32), dtype="float32")
         layer = CBABlock(input_filters=32, output_filters=48)
 
         output = layer(inputs)
-        self.assertEquals(output.shape, (1, 64, 64, 48))
+        self.assertEqual(output.shape, (1, 64, 64, 48))
         self.assertLen(output, 1)

keras_hub/src/models/efficientnet/efficientnet_backbone_test.py

@@ -87,24 +87,24 @@ def test_feature_pyramid_outputs(self):
         height = width = 256
         outputs = model(keras.ops.ones(shape=(batch_size, height, width, 3)))
         levels = ["P1", "P2", "P3", "P4", "P5"]
-        self.assertEquals(list(outputs.keys()), levels)
-        self.assertEquals(
+        self.assertEqual(list(outputs.keys()), levels)
+        self.assertEqual(
             outputs["P1"].shape,
             (batch_size, height // 2**1, width // 2**1, 24),
         )
-        self.assertEquals(
+        self.assertEqual(
             outputs["P2"].shape,
             (batch_size, height // 2**2, width // 2**2, 48),
         )
-        self.assertEquals(
+        self.assertEqual(
             outputs["P3"].shape,
             (batch_size, height // 2**3, width // 2**3, 64),
         )
-        self.assertEquals(
+        self.assertEqual(
             outputs["P4"].shape,
             (batch_size, height // 2**4, width // 2**4, 160),
         )
-        self.assertEquals(
+        self.assertEqual(
             outputs["P5"].shape,
             (batch_size, height // 2**5, width // 2**5, 1280),
         )

keras_hub/src/models/efficientnet/fusedmbconv_test.py

@@ -10,15 +10,15 @@ def test_same_input_output_shapes(self):
         layer = FusedMBConvBlock(input_filters=32, output_filters=32)
 
         output = layer(inputs)
-        self.assertEquals(output.shape, (1, 64, 64, 32))
+        self.assertEqual(output.shape, (1, 64, 64, 32))
         self.assertLen(output, 1)
 
     def test_different_input_output_shapes(self):
         inputs = keras.random.normal(shape=(1, 64, 64, 32), dtype="float32")
         layer = FusedMBConvBlock(input_filters=32, output_filters=48)
 
         output = layer(inputs)
-        self.assertEquals(output.shape, (1, 64, 64, 48))
+        self.assertEqual(output.shape, (1, 64, 64, 48))
         self.assertLen(output, 1)
 
     def test_squeeze_excitation_ratio(self):
@@ -28,5 +28,5 @@ def test_squeeze_excitation_ratio(self):
         )
 
         output = layer(inputs)
-        self.assertEquals(output.shape, (1, 64, 64, 48))
+        self.assertEqual(output.shape, (1, 64, 64, 48))
         self.assertLen(output, 1)

keras_hub/src/models/efficientnet/mbconv_test.py

@@ -10,21 +10,21 @@ def test_same_input_output_shapes(self):
         layer = MBConvBlock(input_filters=32, output_filters=32)
 
         output = layer(inputs)
-        self.assertEquals(output.shape, (1, 64, 64, 32))
+        self.assertEqual(output.shape, (1, 64, 64, 32))
         self.assertLen(output, 1)
 
     def test_different_input_output_shapes(self):
         inputs = keras.random.normal(shape=(1, 64, 64, 32), dtype="float32")
         layer = MBConvBlock(input_filters=32, output_filters=48)
 
         output = layer(inputs)
-        self.assertEquals(output.shape, (1, 64, 64, 48))
+        self.assertEqual(output.shape, (1, 64, 64, 48))
         self.assertLen(output, 1)
 
     def test_squeeze_excitation_ratio(self):
         inputs = keras.random.normal(shape=(1, 64, 64, 32), dtype="float32")
         layer = MBConvBlock(input_filters=32, output_filters=48, se_ratio=0.25)
 
         output = layer(inputs)
-        self.assertEquals(output.shape, (1, 64, 64, 48))
+        self.assertEqual(output.shape, (1, 64, 64, 48))
         self.assertLen(output, 1)

keras_hub/src/tests/test_case.py

@@ -479,7 +479,7 @@ def run_backbone_test(
         # Check name maps to classname.
         name = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", cls.__name__)
         name = re.sub("([a-z])([A-Z])", r"\1_\2", name).lower()
-        self.assertRegexpMatches(backbone.name, name)
+        self.assertRegex(backbone.name, name)
 
         # Check mixed precision.
         if run_mixed_precision_check: