StringLookup & IntegerLookup now save vocabulary loaded from file

keras/src/layers/preprocessing/index_lookup.py

@@ -4,6 +4,7 @@
 
 from keras.src import backend
 from keras.src.layers.layer import Layer
+from keras.src.saving import serialization_lib
 from keras.src.utils import argument_validation
 from keras.src.utils import numerical_utils
 from keras.src.utils import tf_utils
@@ -178,7 +179,12 @@ def __init__(
         self.vocabulary_dtype = tf.as_dtype(vocabulary_dtype).name
         self._frozen_vocab_size = kwargs.pop("vocabulary_size", None)
 
-        self.input_vocabulary = vocabulary
+        # Remember original `vocabulary` as `input_vocabulary` for serialization
+        # via `get_config`. However, if `vocabulary` is a file path or a URL, we
+        # serialize the vocabulary as an asset and clear the original path/URL.
+        self.input_vocabulary = (
+            vocabulary if not isinstance(vocabulary, str) else None
+        )
         self.input_idf_weights = idf_weights
 
         # We set this hidden attr to
@@ -382,6 +388,18 @@ def set_vocabulary(self, vocabulary, idf_weights=None):
             )
 
         if isinstance(vocabulary, str):
+            if serialization_lib.in_safe_mode():
+                raise ValueError(
+                    "Requested the loading of a vocabulary file outside of the "
+                    "model archive. This carries a potential risk of loading "
+                    "arbitrary and sensitive files and thus it is disallowed "
+                    "by default. If you trust the source of the artifact, you "
+                    "can override this error by passing `safe_mode=False` to "
+                    "the loading function, or calling "
+                    "`keras.config.enable_unsafe_deserialization(). "
+                    f"Vocabulary file: '{vocabulary}'"
+                )
+
             if not tf.io.gfile.exists(vocabulary):
                 raise ValueError(
                     f"Vocabulary file {vocabulary} does not exist."

keras/src/layers/preprocessing/string_lookup_test.py

@@ -1,9 +1,13 @@
+import os
+
 import numpy as np
 import pytest
 from tensorflow import data as tf_data
 
 from keras.src import backend
 from keras.src import layers
+from keras.src import models
+from keras.src import saving
 from keras.src import testing
 from keras.src.ops import convert_to_tensor
 
@@ -19,6 +23,40 @@ def test_config(self):
             mask_token="[MASK]",
         )
         self.run_class_serialization_test(layer)
+        self.assertEqual(layer.get_config()["vocabulary"], ["a", "b", "c"])
+
+    def test_vocabulary_file(self):
+        temp_dir = self.get_temp_dir()
+        vocab_path = os.path.join(temp_dir, "vocab.txt")
+        with open(vocab_path, "w") as file:
+            file.write("a\nb\nc\n")
+
+        layer = layers.StringLookup(
+            output_mode="int",
+            vocabulary=vocab_path,
+            oov_token="[OOV]",
+            mask_token="[MASK]",
+            name="index",
+        )
+        self.assertEqual(
+            [str(v) for v in layer.get_vocabulary()],
+            ["[MASK]", "[OOV]", "a", "b", "c"],
+        )
+        self.assertIsNone(layer.get_config().get("vocabulary", None))
+
+        # Make sure vocabulary comes from the archive, not the original file.
+        os.remove(vocab_path)
+
+        model = models.Sequential([layer])
+        model_path = os.path.join(temp_dir, "test_model.keras")
+        model.save(model_path)
+
+        reloaded_model = saving.load_model(model_path)
+        reloaded_layer = reloaded_model.get_layer("index")
+        self.assertEqual(
+            [str(v) for v in reloaded_layer.get_vocabulary()],
+            ["[MASK]", "[OOV]", "a", "b", "c"],
+        )
 
     def test_adapt_flow(self):
         layer = layers.StringLookup(