We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Why does the docstring say vocab size should be no larger than 999?
https://github.com/keras-team/keras-nlp/blob/e3adddaa98bbe1aee071117c01678fe3017dae80/keras_nlp/layers/token_and_position_embedding.py#L30

Seems like a very small vocab to me

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `keras_nlp/layers/token_and_position_embedding.py`
Content:
```
1 # Copyright 2022 The KerasNLP Authors
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
6 #
7 #     https://www.apache.org/licenses/LICENSE-2.0
8 #
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 
15 """Creates an Embedding Layer and adds Positional Embeddings"""
16 
17 from tensorflow import keras
18 
19 import keras_nlp.layers
20 
21 
22 class TokenAndPositionEmbedding(keras.layers.Layer):
23     """A layer which sums a token and position embedding.
24 
25     This layer assumes that the last dimension in the input corresponds
26     to the sequence dimension.
27 
28     Args:
29         vocabulary_size: The size of the vocabulary (should be no larger
30             than 999)
31         sequence_length: The maximum length of input sequence
32         embedding_dim: The output dimension of the embedding layer
33         embeddings_initializer: The initializer to use for the Embedding
34             Layers
35         mask_zero: Boolean, whether or not the input value 0 is a special
36             "padding" value that should be masked out.
37             This is useful when using recurrent layers which may take variable
38             length input. If this is True, then all subsequent layers in the
39             model need to support masking or an exception will be raised.
40             If mask_zero` is set to True, as a consequence, index 0 cannot be
41             used in the vocabulary
42             (input_dim should equal size of vocabulary + 1).
43 
44     Examples:
45     ```python
46     seq_length = 50
47     vocab_size = 5000
48     embed_dim = 128
49     inputs = keras.Input(shape=(seq_length,))
50     embedding_layer = keras_nlp.layers.TokenAndPositionEmbedding(
51         vocabulary_size=vocab_size,
52         sequence_length=seq_length,
53         embedding_dim=embed_dim,
54     )
55     outputs = embedding_layer(inputs)
56     ```
57     """
58 
59     def __init__(
60         self,
61         vocabulary_size,
62         sequence_length,
63         embedding_dim,
64         embeddings_initializer="glorot_uniform",
65         mask_zero=False,
66         **kwargs
67     ):
68         super().__init__(**kwargs)
69         if vocabulary_size is None:
70             raise ValueError(
71                 "`vocabulary_size` must be an Integer, received `None`."
72             )
73         if sequence_length is None:
74             raise ValueError(
75                 "`sequence_length` must be an Integer, received `None`."
76             )
77         if embedding_dim is None:
78             raise ValueError(
79                 "`embedding_dim` must be an Integer, received `None`."
80             )
81         self.vocabulary_size = int(vocabulary_size)
82         self.sequence_length = int(sequence_length)
83         self.embedding_dim = int(embedding_dim)
84         self.token_embedding = keras.layers.Embedding(
85             vocabulary_size,
86             embedding_dim,
87             embeddings_initializer=embeddings_initializer,
88             mask_zero=mask_zero,
89         )
90         self.position_embedding = keras_nlp.layers.PositionEmbedding(
91             sequence_length=sequence_length,
92             initializer=embeddings_initializer,
93         )
94         self.supports_masking = self.token_embedding.supports_masking
95 
96     def get_config(self):
97         config = super().get_config()
98         config.update(
99             {
100                 "vocabulary_size": self.vocabulary_size,
101                 "sequence_length": self.sequence_length,
102                 "embedding_dim": self.embedding_dim,
103                 "embeddings_initializer": keras.initializers.serialize(
104                     self.token_embedding.embeddings_initializer
105                 ),
106                 "mask_zero": self.token_embedding.mask_zero,
107             },
108         )
109         return config
110 
111     def call(self, inputs):
112         embedded_tokens = self.token_embedding(inputs)
113         embedded_positions = self.position_embedding(embedded_tokens)
114         outputs = embedded_tokens + embedded_positions
115         return outputs
116 
117     def compute_mask(self, inputs, mask=None):
118         return self.token_embedding.compute_mask(inputs, mask=mask)
119
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/keras_nlp/layers/token_and_position_embedding.py b/keras_nlp/layers/token_and_position_embedding.py
--- a/keras_nlp/layers/token_and_position_embedding.py
+++ b/keras_nlp/layers/token_and_position_embedding.py
@@ -26,8 +26,7 @@
     to the sequence dimension.
 
     Args:
-        vocabulary_size: The size of the vocabulary (should be no larger
-            than 999)
+        vocabulary_size: The size of the vocabulary.
         sequence_length: The maximum length of input sequence
         embedding_dim: The output dimension of the embedding layer
         embeddings_initializer: The initializer to use for the Embedding

{"golden_diff": "diff --git a/keras_nlp/layers/token_and_position_embedding.py b/keras_nlp/layers/token_and_position_embedding.py\n--- a/keras_nlp/layers/token_and_position_embedding.py\n+++ b/keras_nlp/layers/token_and_position_embedding.py\n@@ -26,8 +26,7 @@\n     to the sequence dimension.\n \n     Args:\n-        vocabulary_size: The size of the vocabulary (should be no larger\n-            than 999)\n+        vocabulary_size: The size of the vocabulary.\n         sequence_length: The maximum length of input sequence\n         embedding_dim: The output dimension of the embedding layer\n         embeddings_initializer: The initializer to use for the Embedding\n", "issue": "Why does the docstring say vocab size should be no larger than 999?\nhttps://github.com/keras-team/keras-nlp/blob/e3adddaa98bbe1aee071117c01678fe3017dae80/keras_nlp/layers/token_and_position_embedding.py#L30\r\n\r\nSeems like a very small vocab to me\n", "before_files": [{"content": "# Copyright 2022 The KerasNLP Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Creates an Embedding Layer and adds Positional Embeddings\"\"\"\n\nfrom tensorflow import keras\n\nimport keras_nlp.layers\n\n\nclass TokenAndPositionEmbedding(keras.layers.Layer):\n    \"\"\"A layer which sums a token and position embedding.\n\n    This layer assumes that the last dimension in the input corresponds\n    to the sequence dimension.\n\n    Args:\n        vocabulary_size: The size of the vocabulary (should be no larger\n            than 999)\n        sequence_length: The maximum length of input sequence\n        embedding_dim: The output dimension of the embedding layer\n        embeddings_initializer: The initializer to use for the Embedding\n            Layers\n        mask_zero: Boolean, whether or not the input value 0 is a special\n            \"padding\" value that should be masked out.\n            This is useful when using recurrent layers which may take variable\n            length input. If this is True, then all subsequent layers in the\n            model need to support masking or an exception will be raised.\n            If mask_zero` is set to True, as a consequence, index 0 cannot be\n            used in the vocabulary\n            (input_dim should equal size of vocabulary + 1).\n\n    Examples:\n    ```python\n    seq_length = 50\n    vocab_size = 5000\n    embed_dim = 128\n    inputs = keras.Input(shape=(seq_length,))\n    embedding_layer = keras_nlp.layers.TokenAndPositionEmbedding(\n        vocabulary_size=vocab_size,\n        sequence_length=seq_length,\n        embedding_dim=embed_dim,\n    )\n    outputs = embedding_layer(inputs)\n    ```\n    \"\"\"\n\n    def __init__(\n        self,\n        vocabulary_size,\n        sequence_length,\n        embedding_dim,\n        embeddings_initializer=\"glorot_uniform\",\n        mask_zero=False,\n        **kwargs\n    ):\n        super().__init__(**kwargs)\n        if vocabulary_size is None:\n            raise ValueError(\n                \"`vocabulary_size` must be an Integer, received `None`.\"\n            )\n        if sequence_length is None:\n            raise ValueError(\n                \"`sequence_length` must be an Integer, received `None`.\"\n            )\n        if embedding_dim is None:\n            raise ValueError(\n                \"`embedding_dim` must be an Integer, received `None`.\"\n            )\n        self.vocabulary_size = int(vocabulary_size)\n        self.sequence_length = int(sequence_length)\n        self.embedding_dim = int(embedding_dim)\n        self.token_embedding = keras.layers.Embedding(\n            vocabulary_size,\n            embedding_dim,\n            embeddings_initializer=embeddings_initializer,\n            mask_zero=mask_zero,\n        )\n        self.position_embedding = keras_nlp.layers.PositionEmbedding(\n            sequence_length=sequence_length,\n            initializer=embeddings_initializer,\n        )\n        self.supports_masking = self.token_embedding.supports_masking\n\n    def get_config(self):\n        config = super().get_config()\n        config.update(\n            {\n                \"vocabulary_size\": self.vocabulary_size,\n                \"sequence_length\": self.sequence_length,\n                \"embedding_dim\": self.embedding_dim,\n                \"embeddings_initializer\": keras.initializers.serialize(\n                    self.token_embedding.embeddings_initializer\n                ),\n                \"mask_zero\": self.token_embedding.mask_zero,\n            },\n        )\n        return config\n\n    def call(self, inputs):\n        embedded_tokens = self.token_embedding(inputs)\n        embedded_positions = self.position_embedding(embedded_tokens)\n        outputs = embedded_tokens + embedded_positions\n        return outputs\n\n    def compute_mask(self, inputs, mask=None):\n        return self.token_embedding.compute_mask(inputs, mask=mask)\n", "path": "keras_nlp/layers/token_and_position_embedding.py"}], "after_files": [{"content": "# Copyright 2022 The KerasNLP Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Creates an Embedding Layer and adds Positional Embeddings\"\"\"\n\nfrom tensorflow import keras\n\nimport keras_nlp.layers\n\n\nclass TokenAndPositionEmbedding(keras.layers.Layer):\n    \"\"\"A layer which sums a token and position embedding.\n\n    This layer assumes that the last dimension in the input corresponds\n    to the sequence dimension.\n\n    Args:\n        vocabulary_size: The size of the vocabulary.\n        sequence_length: The maximum length of input sequence\n        embedding_dim: The output dimension of the embedding layer\n        embeddings_initializer: The initializer to use for the Embedding\n            Layers\n        mask_zero: Boolean, whether or not the input value 0 is a special\n            \"padding\" value that should be masked out.\n            This is useful when using recurrent layers which may take variable\n            length input. If this is True, then all subsequent layers in the\n            model need to support masking or an exception will be raised.\n            If mask_zero` is set to True, as a consequence, index 0 cannot be\n            used in the vocabulary\n            (input_dim should equal size of vocabulary + 1).\n\n    Examples:\n    ```python\n    seq_length = 50\n    vocab_size = 5000\n    embed_dim = 128\n    inputs = keras.Input(shape=(seq_length,))\n    embedding_layer = keras_nlp.layers.TokenAndPositionEmbedding(\n        vocabulary_size=vocab_size,\n        sequence_length=seq_length,\n        embedding_dim=embed_dim,\n    )\n    outputs = embedding_layer(inputs)\n    ```\n    \"\"\"\n\n    def __init__(\n        self,\n        vocabulary_size,\n        sequence_length,\n        embedding_dim,\n        embeddings_initializer=\"glorot_uniform\",\n        mask_zero=False,\n        **kwargs\n    ):\n        super().__init__(**kwargs)\n        if vocabulary_size is None:\n            raise ValueError(\n                \"`vocabulary_size` must be an Integer, received `None`.\"\n            )\n        if sequence_length is None:\n            raise ValueError(\n                \"`sequence_length` must be an Integer, received `None`.\"\n            )\n        if embedding_dim is None:\n            raise ValueError(\n                \"`embedding_dim` must be an Integer, received `None`.\"\n            )\n        self.vocabulary_size = int(vocabulary_size)\n        self.sequence_length = int(sequence_length)\n        self.embedding_dim = int(embedding_dim)\n        self.token_embedding = keras.layers.Embedding(\n            vocabulary_size,\n            embedding_dim,\n            embeddings_initializer=embeddings_initializer,\n            mask_zero=mask_zero,\n        )\n        self.position_embedding = keras_nlp.layers.PositionEmbedding(\n            sequence_length=sequence_length,\n            initializer=embeddings_initializer,\n        )\n        self.supports_masking = self.token_embedding.supports_masking\n\n    def get_config(self):\n        config = super().get_config()\n        config.update(\n            {\n                \"vocabulary_size\": self.vocabulary_size,\n                \"sequence_length\": self.sequence_length,\n                \"embedding_dim\": self.embedding_dim,\n                \"embeddings_initializer\": keras.initializers.serialize(\n                    self.token_embedding.embeddings_initializer\n                ),\n                \"mask_zero\": self.token_embedding.mask_zero,\n            },\n        )\n        return config\n\n    def call(self, inputs):\n        embedded_tokens = self.token_embedding(inputs)\n        embedded_positions = self.position_embedding(embedded_tokens)\n        outputs = embedded_tokens + embedded_positions\n        return outputs\n\n    def compute_mask(self, inputs, mask=None):\n        return self.token_embedding.compute_mask(inputs, mask=mask)\n", "path": "keras_nlp/layers/token_and_position_embedding.py"}]}