task: metadata: install: pip install -e .[tests]
patch: diff --git a/keras_nlp/models/distil_bert/distil_bert_preprocessor.py b/keras_nlp/models/distil_bert/distil_bert_preprocessor.py
index 344067a7..75cc3cb8 100644
--- a/keras_nlp/models/distil_bert/distil_bert_preprocessor.py
+++ b/keras_nlp/models/distil_bert/distil_bert_preprocessor.py
@@ -22,6 +22,10 @@ from keras_nlp.models.distil_bert.distil_bert_presets import backbone_presets
 from keras_nlp.models.distil_bert.distil_bert_tokenizer import (
     DistilBertTokenizer,
 )
+from keras_nlp.utils.keras_utils import (
+    convert_inputs_to_list_of_tensor_segments,
+)
+from keras_nlp.utils.keras_utils import pack_x_y_sample_weight
 from keras_nlp.utils.python_utils import classproperty
 from keras_nlp.utils.python_utils import format_docstring
 
@@ -32,15 +36,28 @@ class DistilBertPreprocessor(keras.layers.Layer):
 
     This preprocessing layer will do three things:
 
-     - Tokenize any number of inputs using the `tokenizer`.
+     - Tokenize any number of input segments using the `tokenizer`.
      - Pack the inputs together using a `keras_nlp.layers.MultiSegmentPacker`.
        with the appropriate `"[CLS]"`, `"[SEP]"` and `"[PAD]"` tokens.
      - Construct a dictionary of with keys `"token_ids"` and `"padding_mask"`,
        that can be passed directly to a DistilBERT model.
 
-    This layer will accept either a tuple of (possibly batched) inputs, or a
-    single input tensor. If a single tensor is passed, it will be packed
-    equivalently to a tuple with a single element.
+    This layer can be used directly with `tf.data.Dataset.map` to preprocess
+    string data in the `(x, y, sample_weight)` format used by
+    `keras.Model.fit`.
+
+    The call method of this layer accepts three arguments, `x`, `y`, and
+    `sample_weight`. `x` can be a python string or tensor representing a single
+    segment, a list of python strings representing a batch of single segments,
+    or a list of tensors representing multiple segments to be packed together.
+    `y` and `sample_weight` are both optional, can have any format, and will be
+    passed through unaltered.
+
+    Special care should be taken when using `tf.data` to map over an unlabeled
+    tuple of string segments. `tf.data.Dataset.map` will unpack this tuple
+    directly into the call arguments of this layer, rather than forward all
+    argument to `x`. To handle this case, it is recommended to  explicitly call
+    the layer, e.g. `ds.map(lambda seg1, seg2: preprocessor(x=(seg1, seg2)))`.
 
     Args:
         tokenizer: A `keras_nlp.models.DistilBertTokenizer` instance.
@@ -66,32 +83,62 @@ class DistilBertPreprocessor(keras.layers.Layer):
     tokenizer = keras_nlp.models.DistilBertTokenizer(vocabulary=vocab)
     preprocessor = keras_nlp.models.DistilBertPreprocessor(tokenizer)
 
-    # Tokenize and pack a single sentence directly.
+    # Tokenize and pack a single sentence.
+    sentence = tf.constant("The quick brown fox jumped.")
+    preprocessor(sentence)
+    # Same output.
     preprocessor("The quick brown fox jumped.")
 
-    # Tokenize and pack a multiple sentence directly.
-    preprocessor(("The quick brown fox jumped.", "Call me Ishmael."))
+    # Tokenize and a batch of single sentences.
+    sentences = tf.constant(
+        ["The quick brown fox jumped.", "Call me Ishmael."]
+    )
+    preprocessor(sentences)
+    # Same output.
+    preprocessor(
+        ["The quick brown fox jumped.", "Call me Ishmael."]
+    )
+
+    # Tokenize and pack a sentence pair.
+    first_sentence = tf.constant("The quick brown fox jumped.")
+    second_sentence = tf.constant("The fox tripped.")
+    preprocessor((first_sentence, second_sentence))
 
     # Map a dataset to preprocess a single sentence.
-    features = ["The quick brown fox jumped.", "I forgot my homework."]
-    labels = [0, 1]
-    ds = tf.data.Dataset.from_tensor_slices((features, labels))
-    ds = ds.map(
-        lambda x, y: (preprocessor(x), y),
-        num_parallel_calls=tf.data.AUTOTUNE,
+    features = tf.constant(
+        ["The quick brown fox jumped.", "Call me Ishmael."]
     )
+    labels = tf.constant([0, 1])
+    ds = tf.data.Dataset.from_tensor_slices((features, labels))
+    ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)
 
-    # Map a dataset to preprocess a multiple sentences.
-    first_sentences = ["The quick brown fox jumped.", "Call me Ishmael."]
-    second_sentences = ["The fox tripped.", "Oh look, a whale."]
-    labels = [1, 1]
+    # Map a dataset to preprocess sentence pairs.
+    first_sentences = tf.constant(
+        ["The quick brown fox jumped.", "Call me Ishmael."]
+    )
+    second_sentences = tf.constant(
+        ["The fox tripped.", "Oh look, a whale."]
+    )
+    labels = tf.constant([1, 1])
     ds = tf.data.Dataset.from_tensor_slices(
         (
             (first_sentences, second_sentences), labels
         )
     )
+    ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)
+
+    # Map a dataset to preprocess unlabeled sentence pairs.
+    first_sentences = tf.constant(
+        ["The quick brown fox jumped.", "Call me Ishmael."]
+    )
+    second_sentences = tf.constant(
+        ["The fox tripped.", "Oh look, a whale."]
+    )
+    ds = tf.data.Dataset.from_tensor_slices((first_sentences, second_sentences))
+    # Watch out for tf.data's default unpacking of tuples here!
+    # Best to invoke the `preprocessor` directly in this case.
     ds = ds.map(
-        lambda x, y: (preprocessor(x), y),
+        lambda s1, s2: preprocessor(x=(s1, s2)),
         num_parallel_calls=tf.data.AUTOTUNE,
     )
     ```
@@ -136,16 +183,15 @@ class DistilBertPreprocessor(keras.layers.Layer):
             config["tokenizer"] = keras.layers.deserialize(config["tokenizer"])
         return cls(**config)
 
-    def call(self, inputs):
-        if not isinstance(inputs, (list, tuple)):
-            inputs = [inputs]
-
-        inputs = [self.tokenizer(x) for x in inputs]
-        token_ids, _ = self.packer(inputs)
-        return {
+    def call(self, x, y=None, sample_weight=None):
+        x = convert_inputs_to_list_of_tensor_segments(x)
+        x = [self.tokenizer(segment) for segment in x]
+        token_ids, _ = self.packer(x)
+        x = {
             "token_ids": token_ids,
             "padding_mask": token_ids != self.tokenizer.pad_token_id,
         }
+        return pack_x_y_sample_weight(x, y, sample_weight)
 
     @classproperty
     def presets(cls):

problem_statement: Allow DistilBertClassifier to operate directly on raw strings
We should mirror the change in https://github.com/keras-team/keras-nlp/pull/485 for DistilBertClassifier, and allow fit/predict/evaluate to take in raw strings.
DistilBertPreprocessor should handle lists of strings as a batch
Once https://github.com/keras-team/keras-nlp/pull/504 lands, we should mirror changes for DistilBert.
repo: keras-team/keras-nlp
test_cmd: pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
test_patch: diff --git a/keras_nlp/models/distil_bert/distil_bert_preprocessor_test.py b/keras_nlp/models/distil_bert/distil_bert_preprocessor_test.py
index e292c9d1..43a4ce73 100644
--- a/keras_nlp/models/distil_bert/distil_bert_preprocessor_test.py
+++ b/keras_nlp/models/distil_bert/distil_bert_preprocessor_test.py
@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""Tests for DistilBERT preprocessor layers."""
+"""Tests for DistilBERT preprocessor layer."""
 
 import os
 
@@ -22,7 +22,7 @@ from tensorflow import keras
 from keras_nlp.models.distil_bert.distil_bert_preprocessor import (
     DistilBertPreprocessor,
 )
-from keras_nlp.models.distil_bert.distil_bert_preprocessor import (
+from keras_nlp.models.distil_bert.distil_bert_tokenizer import (
     DistilBertTokenizer,
 )
 
@@ -32,89 +32,82 @@ class DistilBertPreprocessorTest(tf.test.TestCase, parameterized.TestCase):
         self.vocab = ["[PAD]", "[UNK]", "[CLS]", "[SEP]", "[MASK]"]
         self.vocab += ["THE", "QUICK", "BROWN", "FOX"]
         self.vocab += ["the", "quick", "brown", "fox"]
-
-    def test_tokenize(self):
-        input_data = ["THE QUICK BROWN FOX."]
-        preprocessor = DistilBertPreprocessor(
+        self.preprocessor = DistilBertPreprocessor(
             DistilBertTokenizer(vocabulary=self.vocab),
             sequence_length=8,
         )
-        output = preprocessor(input_data)
+
+    def test_tokenize_strings(self):
+        input_data = "THE QUICK BROWN FOX."
+        output = self.preprocessor(input_data)
         self.assertAllEqual(output["token_ids"], [2, 5, 6, 7, 8, 1, 3, 0])
         self.assertAllEqual(output["padding_mask"], [1, 1, 1, 1, 1, 1, 1, 0])
 
-    def test_tokenize_batch(self):
-        input_data = tf.constant(
-            [
-                "THE QUICK BROWN FOX.",
-                "THE QUICK BROWN FOX.",
-                "THE QUICK BROWN FOX.",
-                "THE QUICK BROWN FOX.",
-            ]
-        )
-        preprocessor = DistilBertPreprocessor(
-            DistilBertTokenizer(vocabulary=self.vocab),
-            sequence_length=8,
-        )
-        output = preprocessor(input_data)
+    def test_tokenize_list_of_strings(self):
+        # We should handle a list of strings as as batch.
+        input_data = ["THE QUICK BROWN FOX."] * 4
+        output = self.preprocessor(input_data)
         self.assertAllEqual(output["token_ids"], [[2, 5, 6, 7, 8, 1, 3, 0]] * 4)
         self.assertAllEqual(
             output["padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 0]] * 4
         )
 
-    def test_tokenize_multiple_sentences(self):
-        sentence_one = "THE QUICK"
-        sentence_two = "BROWN FOX."
-        preprocessor = DistilBertPreprocessor(
-            DistilBertTokenizer(vocabulary=self.vocab),
-            sequence_length=8,
+    def test_tokenize_labeled_batch(self):
+        x = tf.constant(["THE QUICK BROWN FOX."] * 4)
+        y = tf.constant([1] * 4)
+        sw = tf.constant([1.0] * 4)
+        x_out, y_out, sw_out = self.preprocessor(x, y, sw)
+        self.assertAllEqual(x_out["token_ids"], [[2, 5, 6, 7, 8, 1, 3, 0]] * 4)
+        self.assertAllEqual(
+            x_out["padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 0]] * 4
         )
-        # The first tuple or list is always interpreted as an enumeration of
-        # separate sequences to concatenate.
-        output = preprocessor((sentence_one, sentence_two))
+        self.assertAllEqual(y_out, y)
+        self.assertAllEqual(sw_out, sw)
+
+    def test_tokenize_labeled_dataset(self):
+        x = tf.constant(["THE QUICK BROWN FOX."] * 4)
+        y = tf.constant([1] * 4)
+        sw = tf.constant([1.0] * 4)
+        ds = tf.data.Dataset.from_tensor_slices((x, y, sw))
+        ds = ds.map(self.preprocessor)
+        x_out, y_out, sw_out = ds.batch(4).take(1).get_single_element()
+        self.assertAllEqual(x_out["token_ids"], [[2, 5, 6, 7, 8, 1, 3, 0]] * 4)
+        self.assertAllEqual(
+            x_out["padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 0]] * 4
+        )
+        self.assertAllEqual(y_out, y)
+        self.assertAllEqual(sw_out, sw)
+
+    def test_tokenize_multiple_sentences(self):
+        sentence_one = tf.constant("THE QUICK")
+        sentence_two = tf.constant("BROWN FOX.")
+        output = self.preprocessor((sentence_one, sentence_two))
         self.assertAllEqual(output["token_ids"], [2, 5, 6, 3, 7, 8, 1, 3])
         self.assertAllEqual(output["padding_mask"], [1, 1, 1, 1, 1, 1, 1, 1])
 
     def test_tokenize_multiple_batched_sentences(self):
-        sentence_one = tf.constant(
-            [
-                "THE QUICK",
-                "THE QUICK",
-                "THE QUICK",
-                "THE QUICK",
-            ]
-        )
-        sentence_two = tf.constant(
-            [
-                "BROWN FOX.",
-                "BROWN FOX.",
-                "BROWN FOX.",
-                "BROWN FOX.",
-            ]
-        )
-        preprocessor = DistilBertPreprocessor(
-            DistilBertTokenizer(vocabulary=self.vocab),
-            sequence_length=8,
-        )
+        sentence_one = tf.constant(["THE QUICK"] * 4)
+        sentence_two = tf.constant(["BROWN FOX."] * 4)
         # The first tuple or list is always interpreted as an enumeration of
         # separate sequences to concatenate.
-        output = preprocessor((sentence_one, sentence_two))
+        output = self.preprocessor((sentence_one, sentence_two))
         self.assertAllEqual(output["token_ids"], [[2, 5, 6, 3, 7, 8, 1, 3]] * 4)
         self.assertAllEqual(
             output["padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 1]] * 4
         )
 
+    def test_errors_for_2d_list_input(self):
+        ambiguous_input = [["one", "two"], ["three", "four"]]
+        with self.assertRaises(ValueError):
+            self.preprocessor(ambiguous_input)
+
     @parameterized.named_parameters(
         ("save_format_tf", "tf"), ("save_format_h5", "h5")
     )
     def test_saving_model(self, save_format):
         input_data = tf.constant(["THE QUICK BROWN FOX."])
-        preprocessor = DistilBertPreprocessor(
-            DistilBertTokenizer(vocabulary=self.vocab),
-            sequence_length=8,
-        )
         inputs = keras.Input(dtype="string", shape=())
-        outputs = preprocessor(inputs)
+        outputs = self.preprocessor(inputs)
         model = keras.Model(inputs, outputs)
         path = os.path.join(self.get_temp_dir(), "model")
         model.save(path, save_format=save_format)