We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Deberta tokenizer.detokenize() errors out with mask token
When working on the Deberta masked language model, we had to do some special treatment for the mask token in the tokenizer.

We left one outstanding bug on the main PR, which is that detokenize will error out with a mask token. See:
https://github.com/keras-team/keras-nlp/pull/732#issuecomment-1449746110

Here's a colab:
https://colab.research.google.com/gist/mattdangerw/5164a7cad80e9f5fcbb9a495264f80e1/deberta-detokenize-error.ipynb

We should either strip or properly render the mask token during detokenize so the call does not error out.

--- 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/models/deberta_v3/deberta_v3_tokenizer.py`
Content:
```
1 # Copyright 2023 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 """DeBERTa tokenizer."""
16 
17 import copy
18 
19 from keras_nlp.api_export import keras_nlp_export
20 from keras_nlp.models.deberta_v3.deberta_v3_presets import backbone_presets
21 from keras_nlp.tokenizers.sentence_piece_tokenizer import SentencePieceTokenizer
22 from keras_nlp.utils.python_utils import classproperty
23 
24 
25 @keras_nlp_export("keras_nlp.models.DebertaV3Tokenizer")
26 class DebertaV3Tokenizer(SentencePieceTokenizer):
27     """DeBERTa tokenizer layer based on SentencePiece.
28 
29     This tokenizer class will tokenize raw strings into integer sequences and
30     is based on `keras_nlp.tokenizers.SentencePieceTokenizer`. Unlike the
31     underlying tokenizer, it will check for all special tokens needed by
32     DeBERTa models and provides a `from_preset()` method to automatically
33     download a matching vocabulary for a DeBERTa preset.
34 
35     This tokenizer does not provide truncation or padding of inputs. It can be
36     combined with a `keras_nlp.models.DebertaV3Preprocessor` layer for input
37     packing.
38 
39     If input is a batch of strings (rank > 0), the layer will output a
40     `tf.RaggedTensor` where the last dimension of the output is ragged.
41 
42     If input is a scalar string (rank == 0), the layer will output a dense
43     `tf.Tensor` with static shape `[None]`.
44 
45     Note: The mask token (`"[MASK]"`) is handled differently in this tokenizer.
46     If the token is not present in the provided SentencePiece vocabulary, the
47     token will be appended to the vocabulary. For example, if the vocabulary
48     size is 100, the mask token will be assigned the ID 100.
49 
50     Args:
51         proto: Either a `string` path to a SentencePiece proto file, or a
52             `bytes` object with a serialized SentencePiece proto. See the
53             [SentencePiece repository](https://github.com/google/sentencepiece)
54             for more details on the format.
55 
56     Examples:
57 
58     ```python
59     tokenizer = keras_nlp.models.DebertaV3Tokenizer(proto="model.spm")
60 
61     # Batched inputs.
62     tokenizer(["the quick brown fox", "the earth is round"])
63 
64     # Unbatched inputs.
65     tokenizer("the quick brown fox")
66 
67     # Detokenization.
68     tokenizer.detokenize(tf.constant([[1, 4, 9, 5, 7, 2]]))
69     ```
70     """
71 
72     def __init__(self, proto, **kwargs):
73         super().__init__(proto=proto, **kwargs)
74 
75         # Check for necessary special tokens.
76         cls_token = "[CLS]"
77         sep_token = "[SEP]"
78         pad_token = "[PAD]"
79         mask_token = "[MASK]"
80 
81         # We do not throw an error if `mask_token` is not present in the
82         # vocabulary.
83         for token in [cls_token, pad_token, sep_token]:
84             if token not in super().get_vocabulary():
85                 raise ValueError(
86                     f"Cannot find token `'{token}'` in the provided "
87                     f"`vocabulary`. Please provide `'{token}'` in your "
88                     "`vocabulary` or use a pretrained `vocabulary` name."
89                 )
90 
91         self.cls_token_id = self.token_to_id(cls_token)
92         self.sep_token_id = self.token_to_id(sep_token)
93         self.pad_token_id = self.token_to_id(pad_token)
94         # If the mask token is not in the vocabulary, add it to the end of the
95         # vocabulary.
96         if mask_token in super().get_vocabulary():
97             self.mask_token_id = super().token_to_id(mask_token)
98         else:
99             self.mask_token_id = super().vocabulary_size()
100 
101     def vocabulary_size(self):
102         sentence_piece_size = super().vocabulary_size()
103         if sentence_piece_size == self.mask_token_id:
104             return sentence_piece_size + 1
105         return sentence_piece_size
106 
107     def get_vocabulary(self):
108         sentence_piece_vocabulary = super().get_vocabulary()
109         if self.mask_token_id < super().vocabulary_size():
110             return sentence_piece_vocabulary
111         return sentence_piece_vocabulary + ["[MASK]"]
112 
113     def id_to_token(self, id):
114         if id == self.mask_token_id:
115             return "[MASK]"
116         return super().id_to_token(id)
117 
118     def token_to_id(self, token):
119         if token == "[MASK]":
120             return self.mask_token_id
121         return super().token_to_id(token)
122 
123     @classproperty
124     def presets(cls):
125         return copy.deepcopy(backbone_presets)
126
```
--- 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/models/deberta_v3/deberta_v3_tokenizer.py b/keras_nlp/models/deberta_v3/deberta_v3_tokenizer.py
--- a/keras_nlp/models/deberta_v3/deberta_v3_tokenizer.py
+++ b/keras_nlp/models/deberta_v3/deberta_v3_tokenizer.py
@@ -16,6 +16,8 @@
 
 import copy
 
+import tensorflow as tf
+
 from keras_nlp.api_export import keras_nlp_export
 from keras_nlp.models.deberta_v3.deberta_v3_presets import backbone_presets
 from keras_nlp.tokenizers.sentence_piece_tokenizer import SentencePieceTokenizer
@@ -120,6 +122,10 @@
             return self.mask_token_id
         return super().token_to_id(token)
 
+    def detokenize(self, ids):
+        ids = tf.ragged.boolean_mask(ids, tf.not_equal(ids, self.mask_token_id))
+        return super().detokenize(ids)
+
     @classproperty
     def presets(cls):
         return copy.deepcopy(backbone_presets)

{"golden_diff": "diff --git a/keras_nlp/models/deberta_v3/deberta_v3_tokenizer.py b/keras_nlp/models/deberta_v3/deberta_v3_tokenizer.py\n--- a/keras_nlp/models/deberta_v3/deberta_v3_tokenizer.py\n+++ b/keras_nlp/models/deberta_v3/deberta_v3_tokenizer.py\n@@ -16,6 +16,8 @@\n \n import copy\n \n+import tensorflow as tf\n+\n from keras_nlp.api_export import keras_nlp_export\n from keras_nlp.models.deberta_v3.deberta_v3_presets import backbone_presets\n from keras_nlp.tokenizers.sentence_piece_tokenizer import SentencePieceTokenizer\n@@ -120,6 +122,10 @@\n             return self.mask_token_id\n         return super().token_to_id(token)\n \n+    def detokenize(self, ids):\n+        ids = tf.ragged.boolean_mask(ids, tf.not_equal(ids, self.mask_token_id))\n+        return super().detokenize(ids)\n+\n     @classproperty\n     def presets(cls):\n         return copy.deepcopy(backbone_presets)\n", "issue": "Deberta tokenizer.detokenize() errors out with mask token\nWhen working on the Deberta masked language model, we had to do some special treatment for the mask token in the tokenizer.\r\n\r\nWe left one outstanding bug on the main PR, which is that detokenize will error out with a mask token. See:\r\nhttps://github.com/keras-team/keras-nlp/pull/732#issuecomment-1449746110\r\n\r\nHere's a colab:\r\nhttps://colab.research.google.com/gist/mattdangerw/5164a7cad80e9f5fcbb9a495264f80e1/deberta-detokenize-error.ipynb\r\n\r\nWe should either strip or properly render the mask token during detokenize so the call does not error out.\n", "before_files": [{"content": "# Copyright 2023 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\"\"\"DeBERTa tokenizer.\"\"\"\n\nimport copy\n\nfrom keras_nlp.api_export import keras_nlp_export\nfrom keras_nlp.models.deberta_v3.deberta_v3_presets import backbone_presets\nfrom keras_nlp.tokenizers.sentence_piece_tokenizer import SentencePieceTokenizer\nfrom keras_nlp.utils.python_utils import classproperty\n\n\n@keras_nlp_export(\"keras_nlp.models.DebertaV3Tokenizer\")\nclass DebertaV3Tokenizer(SentencePieceTokenizer):\n    \"\"\"DeBERTa tokenizer layer based on SentencePiece.\n\n    This tokenizer class will tokenize raw strings into integer sequences and\n    is based on `keras_nlp.tokenizers.SentencePieceTokenizer`. Unlike the\n    underlying tokenizer, it will check for all special tokens needed by\n    DeBERTa models and provides a `from_preset()` method to automatically\n    download a matching vocabulary for a DeBERTa preset.\n\n    This tokenizer does not provide truncation or padding of inputs. It can be\n    combined with a `keras_nlp.models.DebertaV3Preprocessor` layer for input\n    packing.\n\n    If input is a batch of strings (rank > 0), the layer will output a\n    `tf.RaggedTensor` where the last dimension of the output is ragged.\n\n    If input is a scalar string (rank == 0), the layer will output a dense\n    `tf.Tensor` with static shape `[None]`.\n\n    Note: The mask token (`\"[MASK]\"`) is handled differently in this tokenizer.\n    If the token is not present in the provided SentencePiece vocabulary, the\n    token will be appended to the vocabulary. For example, if the vocabulary\n    size is 100, the mask token will be assigned the ID 100.\n\n    Args:\n        proto: Either a `string` path to a SentencePiece proto file, or a\n            `bytes` object with a serialized SentencePiece proto. See the\n            [SentencePiece repository](https://github.com/google/sentencepiece)\n            for more details on the format.\n\n    Examples:\n\n    ```python\n    tokenizer = keras_nlp.models.DebertaV3Tokenizer(proto=\"model.spm\")\n\n    # Batched inputs.\n    tokenizer([\"the quick brown fox\", \"the earth is round\"])\n\n    # Unbatched inputs.\n    tokenizer(\"the quick brown fox\")\n\n    # Detokenization.\n    tokenizer.detokenize(tf.constant([[1, 4, 9, 5, 7, 2]]))\n    ```\n    \"\"\"\n\n    def __init__(self, proto, **kwargs):\n        super().__init__(proto=proto, **kwargs)\n\n        # Check for necessary special tokens.\n        cls_token = \"[CLS]\"\n        sep_token = \"[SEP]\"\n        pad_token = \"[PAD]\"\n        mask_token = \"[MASK]\"\n\n        # We do not throw an error if `mask_token` is not present in the\n        # vocabulary.\n        for token in [cls_token, pad_token, sep_token]:\n            if token not in super().get_vocabulary():\n                raise ValueError(\n                    f\"Cannot find token `'{token}'` in the provided \"\n                    f\"`vocabulary`. Please provide `'{token}'` in your \"\n                    \"`vocabulary` or use a pretrained `vocabulary` name.\"\n                )\n\n        self.cls_token_id = self.token_to_id(cls_token)\n        self.sep_token_id = self.token_to_id(sep_token)\n        self.pad_token_id = self.token_to_id(pad_token)\n        # If the mask token is not in the vocabulary, add it to the end of the\n        # vocabulary.\n        if mask_token in super().get_vocabulary():\n            self.mask_token_id = super().token_to_id(mask_token)\n        else:\n            self.mask_token_id = super().vocabulary_size()\n\n    def vocabulary_size(self):\n        sentence_piece_size = super().vocabulary_size()\n        if sentence_piece_size == self.mask_token_id:\n            return sentence_piece_size + 1\n        return sentence_piece_size\n\n    def get_vocabulary(self):\n        sentence_piece_vocabulary = super().get_vocabulary()\n        if self.mask_token_id < super().vocabulary_size():\n            return sentence_piece_vocabulary\n        return sentence_piece_vocabulary + [\"[MASK]\"]\n\n    def id_to_token(self, id):\n        if id == self.mask_token_id:\n            return \"[MASK]\"\n        return super().id_to_token(id)\n\n    def token_to_id(self, token):\n        if token == \"[MASK]\":\n            return self.mask_token_id\n        return super().token_to_id(token)\n\n    @classproperty\n    def presets(cls):\n        return copy.deepcopy(backbone_presets)\n", "path": "keras_nlp/models/deberta_v3/deberta_v3_tokenizer.py"}], "after_files": [{"content": "# Copyright 2023 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\"\"\"DeBERTa tokenizer.\"\"\"\n\nimport copy\n\nimport tensorflow as tf\n\nfrom keras_nlp.api_export import keras_nlp_export\nfrom keras_nlp.models.deberta_v3.deberta_v3_presets import backbone_presets\nfrom keras_nlp.tokenizers.sentence_piece_tokenizer import SentencePieceTokenizer\nfrom keras_nlp.utils.python_utils import classproperty\n\n\n@keras_nlp_export(\"keras_nlp.models.DebertaV3Tokenizer\")\nclass DebertaV3Tokenizer(SentencePieceTokenizer):\n    \"\"\"DeBERTa tokenizer layer based on SentencePiece.\n\n    This tokenizer class will tokenize raw strings into integer sequences and\n    is based on `keras_nlp.tokenizers.SentencePieceTokenizer`. Unlike the\n    underlying tokenizer, it will check for all special tokens needed by\n    DeBERTa models and provides a `from_preset()` method to automatically\n    download a matching vocabulary for a DeBERTa preset.\n\n    This tokenizer does not provide truncation or padding of inputs. It can be\n    combined with a `keras_nlp.models.DebertaV3Preprocessor` layer for input\n    packing.\n\n    If input is a batch of strings (rank > 0), the layer will output a\n    `tf.RaggedTensor` where the last dimension of the output is ragged.\n\n    If input is a scalar string (rank == 0), the layer will output a dense\n    `tf.Tensor` with static shape `[None]`.\n\n    Note: The mask token (`\"[MASK]\"`) is handled differently in this tokenizer.\n    If the token is not present in the provided SentencePiece vocabulary, the\n    token will be appended to the vocabulary. For example, if the vocabulary\n    size is 100, the mask token will be assigned the ID 100.\n\n    Args:\n        proto: Either a `string` path to a SentencePiece proto file, or a\n            `bytes` object with a serialized SentencePiece proto. See the\n            [SentencePiece repository](https://github.com/google/sentencepiece)\n            for more details on the format.\n\n    Examples:\n\n    ```python\n    tokenizer = keras_nlp.models.DebertaV3Tokenizer(proto=\"model.spm\")\n\n    # Batched inputs.\n    tokenizer([\"the quick brown fox\", \"the earth is round\"])\n\n    # Unbatched inputs.\n    tokenizer(\"the quick brown fox\")\n\n    # Detokenization.\n    tokenizer.detokenize(tf.constant([[1, 4, 9, 5, 7, 2]]))\n    ```\n    \"\"\"\n\n    def __init__(self, proto, **kwargs):\n        super().__init__(proto=proto, **kwargs)\n\n        # Check for necessary special tokens.\n        cls_token = \"[CLS]\"\n        sep_token = \"[SEP]\"\n        pad_token = \"[PAD]\"\n        mask_token = \"[MASK]\"\n\n        # We do not throw an error if `mask_token` is not present in the\n        # vocabulary.\n        for token in [cls_token, pad_token, sep_token]:\n            if token not in super().get_vocabulary():\n                raise ValueError(\n                    f\"Cannot find token `'{token}'` in the provided \"\n                    f\"`vocabulary`. Please provide `'{token}'` in your \"\n                    \"`vocabulary` or use a pretrained `vocabulary` name.\"\n                )\n\n        self.cls_token_id = self.token_to_id(cls_token)\n        self.sep_token_id = self.token_to_id(sep_token)\n        self.pad_token_id = self.token_to_id(pad_token)\n        # If the mask token is not in the vocabulary, add it to the end of the\n        # vocabulary.\n        if mask_token in super().get_vocabulary():\n            self.mask_token_id = super().token_to_id(mask_token)\n        else:\n            self.mask_token_id = super().vocabulary_size()\n\n    def vocabulary_size(self):\n        sentence_piece_size = super().vocabulary_size()\n        if sentence_piece_size == self.mask_token_id:\n            return sentence_piece_size + 1\n        return sentence_piece_size\n\n    def get_vocabulary(self):\n        sentence_piece_vocabulary = super().get_vocabulary()\n        if self.mask_token_id < super().vocabulary_size():\n            return sentence_piece_vocabulary\n        return sentence_piece_vocabulary + [\"[MASK]\"]\n\n    def id_to_token(self, id):\n        if id == self.mask_token_id:\n            return \"[MASK]\"\n        return super().id_to_token(id)\n\n    def token_to_id(self, token):\n        if token == \"[MASK]\":\n            return self.mask_token_id\n        return super().token_to_id(token)\n\n    def detokenize(self, ids):\n        ids = tf.ragged.boolean_mask(ids, tf.not_equal(ids, self.mask_token_id))\n        return super().detokenize(ids)\n\n    @classproperty\n    def presets(cls):\n        return copy.deepcopy(backbone_presets)\n", "path": "keras_nlp/models/deberta_v3/deberta_v3_tokenizer.py"}]}