Let BertTokenizer avoid the bad asset path after repeated save/load

reported in https://github.com/tensorflow/tensorflow/issues/46293.

A test case is added to export_tfhub_lib_test.

PiperOrigin-RevId: 351741572

official/nlp/modeling/layers/text_layers.py

@@ -133,7 +133,16 @@ class BertTokenizer(tf.keras.layers.Layer):
     _check_if_tf_text_installed()
 
     self.tokenize_with_offsets = tokenize_with_offsets
-    self._vocab_table = self._create_vocab_table(vocab_file)
+    # TODO(b/177326279): Stop storing the vocab table initializer as an
+    # attribute when https://github.com/tensorflow/tensorflow/issues/46293
+    # has been fixed in the TensorFlow versions of the TF Hub users that load
+    # a SavedModel created from this layer. Due to that issue, loading such a
+    # SavedModel forgets to add .vocab_table._initializer as a trackable
+    # dependency of .vocab_table, so that saving it again to a second SavedModel
+    # (e.g., the final model built using TF Hub) does not properly track
+    # the ._vocab_table._initializer._filename as an Asset.
+    self._vocab_table, self._vocab_initializer_donotuse = (
+        self._create_vocab_table_and_initializer(vocab_file))
     self._special_tokens_dict = self._create_special_tokens_dict(
         self._vocab_table, vocab_file)
     super().__init__(**kwargs)
@@ -144,12 +153,13 @@ class BertTokenizer(tf.keras.layers.Layer):
   def vocab_size(self):
     return self._vocab_table.size()
 
-  def _create_vocab_table(self, vocab_file):
+  def _create_vocab_table_and_initializer(self, vocab_file):
     vocab_initializer = tf.lookup.TextFileInitializer(
         vocab_file,
         key_dtype=tf.string, key_index=tf.lookup.TextFileIndex.WHOLE_LINE,
         value_dtype=tf.int64, value_index=tf.lookup.TextFileIndex.LINE_NUMBER)
-    return tf.lookup.StaticHashTable(vocab_initializer, default_value=-1)
+    vocab_table = tf.lookup.StaticHashTable(vocab_initializer, default_value=-1)
+    return vocab_table, vocab_initializer
 
   def call(self, inputs: tf.Tensor):
     """Calls text.BertTokenizer on inputs.
@@ -230,7 +240,8 @@ class BertTokenizer(tf.keras.layers.Layer):
             "Non-eager init context; computing "
             "BertTokenizer's special_tokens_dict in tf.compat.v1.Session")
         with tf.Graph().as_default():
-          local_vocab_table = self._create_vocab_table(vocab_file)
+          local_vocab_table, _ = self._create_vocab_table_and_initializer(
+              vocab_file)
           special_token_ids_tensor = local_vocab_table.lookup(
               tf.constant(list(special_tokens.values()), tf.string))
           init_ops = [tf.compat.v1.initialize_all_tables()]