diff --git a/tensorflow/python/keras/layers/preprocessing/text_vectorization.py b/tensorflow/python/keras/layers/preprocessing/text_vectorization.py
index 3f9c36eb4ce..6080d0b7ede 100644
--- a/tensorflow/python/keras/layers/preprocessing/text_vectorization.py
+++ b/tensorflow/python/keras/layers/preprocessing/text_vectorization.py
@@ -568,7 +568,7 @@ class TextVectorization(CombinerPreprocessingLayer):
       self.set_vocabulary(updates[_VOCAB_NAME])
 
   def _preprocess(self, inputs):
-    if self._standardize is LOWER_AND_STRIP_PUNCTUATION:
+    if self._standardize == LOWER_AND_STRIP_PUNCTUATION:
       lowercase_inputs = gen_string_ops.string_lower(inputs)
       inputs = string_ops.regex_replace(lowercase_inputs, DEFAULT_STRIP_REGEX,
                                         "")
@@ -586,7 +586,7 @@ class TextVectorization(CombinerPreprocessingLayer):
       # so can be squeezed out. We do this here instead of after splitting for
       # performance reasons - it's more expensive to squeeze a ragged tensor.
       inputs = array_ops.squeeze(inputs, axis=1)
-      if self._split is SPLIT_ON_WHITESPACE:
+      if self._split == SPLIT_ON_WHITESPACE:
         # This treats multiple whitespaces as one whitespace, and strips leading
         # and trailing whitespace.
         inputs = ragged_string_ops.string_split_v2(inputs)
diff --git a/tensorflow/python/keras/layers/preprocessing/text_vectorization_test.py b/tensorflow/python/keras/layers/preprocessing/text_vectorization_test.py
index 8f65c481400..b20b0164247 100644
--- a/tensorflow/python/keras/layers/preprocessing/text_vectorization_test.py
+++ b/tensorflow/python/keras/layers/preprocessing/text_vectorization_test.py
@@ -470,6 +470,34 @@ class TextVectorizationPreprocessingTest(
     with self.assertRaisesRegex(ValueError, ".*is not a supported splitting.*"):
       _ = layer(input_data)
 
+  def test_standardize_with_no_identical_argument(self):
+    input_array = np.array([["hello world"]])
+    expected_output = np.array([[1, 1]])
+
+    standardize = "".join(["lower", "_and_strip_punctuation"])
+    layer = get_layer_class()(standardize=standardize)
+
+    input_data = keras.Input(shape=(1,), dtype=dtypes.string)
+    output_data = layer(input_data)
+    model = keras.Model(inputs=input_data, outputs=output_data)
+    output = model.predict(input_array)
+
+    self.assertAllEqual(expected_output, output)
+
+  def test_splitting_with_no_identical_argument(self):
+    input_array = np.array([["hello world"]])
+    expected_output = np.array([[1, 1]])
+
+    split = "".join(["white", "space"])
+    layer = get_layer_class()(split=split)
+
+    input_data = keras.Input(shape=(1,), dtype=dtypes.string)
+    output_data = layer(input_data)
+    model = keras.Model(inputs=input_data, outputs=output_data)
+    output = model.predict(input_array)
+
+    self.assertAllEqual(expected_output, output)
+
 
 @keras_parameterized.run_all_keras_modes
 class TextVectorizationOutputTest(