Conditionally import tf text

keras_nlp/tokenizers/byte_pair_tokenizer.py

@@ -24,10 +24,15 @@
 from typing import List
 
 import tensorflow as tf
-import tensorflow_text as tf_text
 from tensorflow import keras
 
 from keras_nlp.tokenizers import tokenizer
+from keras_nlp.utils.tf_utils import assert_tf_text_installed
+
+try:
+    import tensorflow_text as tf_text
+except ImportError:
+    tf_text = None
 
 # As python and TF handles special spaces differently, we need to
 # manually handle special spaces during string split.
@@ -220,6 +225,8 @@ def __init__(
         sequence_length=None,
         **kwargs,
     ) -> None:
+        assert_tf_text_installed(self.__class__.__name__)
+
         # Check dtype and provide a default.
         if "dtype" not in kwargs or kwargs["dtype"] is None:
             kwargs["dtype"] = tf.int32
@@ -241,7 +248,8 @@ def __init__(
         else:
             raise ValueError(
                 "Vocabulary must be an file path or dictionary mapping string "
-                f"token to int ids. Received: `type(vocabulary)={type(vocabulary)}`."
+                "token to int ids. Received: "
+                f"`type(vocabulary)={type(vocabulary)}`."
             )
         if isinstance(merges, str):
             self.merges = [bp.rstrip() for bp in tf.io.gfile.GFile(merges)]