Add clean_up_tokenization_spaces to config (#22341)

* add draft changes

* fix failing wav2vec

* style

* make sure that the argument is saved + add tests

* style

* fixup

* update test

* default clean_up_tokenization_spaces to False for Bloom and Llama

* Update code based on review
Co-authored-by: Nicolas Patry <patry.nicolas@gmail.com>

* style

* quality

---------
Co-authored-by: Nicolas Patry <patry.nicolas@gmail.com>

src/transformers/models/big_bird/tokenization_big_bird.py

@@ -204,7 +204,7 @@ class BigBirdTokenizer(PreTrainedTokenizer):
         self,
         token_ids: List[int],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         spaces_between_special_tokens: bool = True,
         **kwargs,
     ) -> str:
@@ -237,6 +237,11 @@ class BigBirdTokenizer(PreTrainedTokenizer):
         else:
             text = "".join(sub_texts)
 
+        clean_up_tokenization_spaces = (
+            clean_up_tokenization_spaces
+            if clean_up_tokenization_spaces is not None
+            else self.clean_up_tokenization_spaces
+        )
         if clean_up_tokenization_spaces:
             clean_text = self.clean_up_tokenization(text)
             return clean_text

src/transformers/models/bloom/tokenization_bloom_fast.py

@@ -115,6 +115,7 @@ class BloomTokenizerFast(PreTrainedTokenizerFast):
         eos_token="</s>",
         pad_token="<pad>",
         add_prefix_space=False,
+        clean_up_tokenization_spaces=False,
         **kwargs,
     ):
         super().__init__(
@@ -126,6 +127,7 @@ class BloomTokenizerFast(PreTrainedTokenizerFast):
             eos_token=eos_token,
             pad_token=pad_token,
             add_prefix_space=add_prefix_space,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
             **kwargs,
         )
         pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())

src/transformers/models/codegen/tokenization_codegen.py

@@ -320,7 +320,7 @@ class CodeGenTokenizer(PreTrainedTokenizer):
         self,
         token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         truncate_before_pattern: Optional[List[str]] = None,
         **kwargs,
     ) -> str:
@@ -335,8 +335,9 @@ class CodeGenTokenizer(PreTrainedTokenizer):
                 List of tokenized input ids. Can be obtained using the `__call__` method.
             skip_special_tokens (`bool`, *optional*, defaults to `False`):
                 Whether or not to remove special tokens in the decoding.
-            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
-                Whether or not to clean up the tokenization spaces.
+            clean_up_tokenization_spaces (`bool`, *optional*):
+                Whether or not to clean up the tokenization spaces. If `None`, will default to
+                `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
             truncate_before_pattern (`List[str]`, *optional*, defaults to `None`):
                 A list of regular expression strings that will be used to truncate the returned string. This can be
                 used to remove extra pieces of code (e.g. truncate if observing a comment symbol "#" at the beginning

src/transformers/models/codegen/tokenization_codegen_fast.py

@@ -187,7 +187,7 @@ class CodeGenTokenizerFast(PreTrainedTokenizerFast):
         self,
         token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         truncate_before_pattern: Optional[List[str]] = None,
         **kwargs,
     ) -> str:
@@ -202,8 +202,9 @@ class CodeGenTokenizerFast(PreTrainedTokenizerFast):
                 List of tokenized input ids. Can be obtained using the `__call__` method.
             skip_special_tokens (`bool`, *optional*, defaults to `False`):
                 Whether or not to remove special tokens in the decoding.
-            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
-                Whether or not to clean up the tokenization spaces.
+            clean_up_tokenization_spaces (`bool`, *optional*):
+                Whether or not to clean up the tokenization spaces. If `None`, will default to
+                `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
             truncate_before_pattern (`List[str]`, *optional*, defaults to `None`):
                 A list of regular expression strings that will be used to truncate the returned string. This can be
                 used to remove extra pieces of code (e.g. truncate if observing a comment symbol "#" at the beginning

src/transformers/models/fnet/tokenization_fnet.py

@@ -236,7 +236,7 @@ class FNetTokenizer(PreTrainedTokenizer):
         self,
         token_ids: List[int],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         spaces_between_special_tokens: bool = True,
         **kwargs,
     ) -> str:
@@ -269,6 +269,11 @@ class FNetTokenizer(PreTrainedTokenizer):
         else:
             text = "".join(sub_texts)
 
+        clean_up_tokenization_spaces = (
+            clean_up_tokenization_spaces
+            if clean_up_tokenization_spaces is not None
+            else self.clean_up_tokenization_spaces
+        )
         if clean_up_tokenization_spaces:
             clean_text = self.clean_up_tokenization(text)
             return clean_text

src/transformers/models/llama/tokenization_llama.py

@@ -59,10 +59,17 @@ class LlamaTokenizer(PreTrainedTokenizer):
         add_bos_token=True,
         add_eos_token=False,
         decode_with_prefix_space=False,
+        clean_up_tokenization_spaces=False,
         **kwargs,
     ):
         self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
-        super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
         self.vocab_file = vocab_file
         self.add_bos_token = add_bos_token
         self.add_eos_token = add_eos_token

src/transformers/models/marian/tokenization_marian.py

@@ -225,8 +225,9 @@ class MarianTokenizer(PreTrainedTokenizer):
                 List of tokenized input ids. Can be obtained using the `__call__` method.
             skip_special_tokens (`bool`, *optional*, defaults to `False`):
                 Whether or not to remove special tokens in the decoding.
-            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
-                Whether or not to clean up the tokenization spaces.
+            clean_up_tokenization_spaces (`bool`, *optional*):
+                Whether or not to clean up the tokenization spaces. If `None`, will default to
+                `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
             use_source_tokenizer (`bool`, *optional*, defaults to `False`):
                 Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence
                 problems).
@@ -250,8 +251,9 @@ class MarianTokenizer(PreTrainedTokenizer):
                 List of tokenized input ids. Can be obtained using the `__call__` method.
             skip_special_tokens (`bool`, *optional*, defaults to `False`):
                 Whether or not to remove special tokens in the decoding.
-            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
-                Whether or not to clean up the tokenization spaces.
+            clean_up_tokenization_spaces (`bool`, *optional*):
+                Whether or not to clean up the tokenization spaces. If `None`, will default to
+                `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
             use_source_tokenizer (`bool`, *optional*, defaults to `False`):
                 Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence
                 problems).

src/transformers/models/wav2vec2/tokenization_wav2vec2.py

@@ -373,7 +373,7 @@ class Wav2Vec2CTCTokenizer(PreTrainedTokenizer):
         self,
         token_ids: List[int],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         group_tokens: bool = True,
         spaces_between_special_tokens: bool = False,
         output_word_offsets: Optional[bool] = False,
@@ -402,6 +402,11 @@ class Wav2Vec2CTCTokenizer(PreTrainedTokenizer):
 
         text = string_output["text"]
 
+        clean_up_tokenization_spaces = (
+            clean_up_tokenization_spaces
+            if clean_up_tokenization_spaces is not None
+            else self.clean_up_tokenization_spaces
+        )
         if clean_up_tokenization_spaces:
             text = self.clean_up_tokenization(text)
 
@@ -421,7 +426,7 @@ class Wav2Vec2CTCTokenizer(PreTrainedTokenizer):
         self,
         sequences: Union[List[int], List[List[int]], "np.ndarray", "torch.Tensor", "tf.Tensor"],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         output_char_offsets: bool = False,
         output_word_offsets: bool = False,
         **kwargs,
@@ -434,7 +439,7 @@ class Wav2Vec2CTCTokenizer(PreTrainedTokenizer):
                 List of tokenized input ids. Can be obtained using the `__call__` method.
             skip_special_tokens (`bool`, *optional*, defaults to `False`):
                 Whether or not to remove special tokens in the decoding.
-            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
+            clean_up_tokenization_spaces (`bool`, *optional*):
                 Whether or not to clean up the tokenization spaces.
             output_char_offsets (`bool`, *optional*, defaults to `False`):
                 Whether or not to output character offsets. Character offsets can be used in combination with the
@@ -491,7 +496,7 @@ class Wav2Vec2CTCTokenizer(PreTrainedTokenizer):
         self,
         token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         output_char_offsets: bool = False,
         output_word_offsets: bool = False,
         **kwargs,
@@ -507,7 +512,7 @@ class Wav2Vec2CTCTokenizer(PreTrainedTokenizer):
                 List of tokenized input ids. Can be obtained using the `__call__` method.
             skip_special_tokens (`bool`, *optional*, defaults to `False`):
                 Whether or not to remove special tokens in the decoding.
-            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
+            clean_up_tokenization_spaces (`bool`, *optional*):
                 Whether or not to clean up the tokenization spaces.
             output_char_offsets (`bool`, *optional*, defaults to `False`):
                 Whether or not to output character offsets. Character offsets can be used in combination with the
@@ -887,7 +892,7 @@ class Wav2Vec2Tokenizer(PreTrainedTokenizer):
         self,
         token_ids: List[int],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         **kwargs,
     ) -> str:
         """
@@ -905,6 +910,11 @@ class Wav2Vec2Tokenizer(PreTrainedTokenizer):
 
         text = self.convert_tokens_to_string(result)
 
+        clean_up_tokenization_spaces = (
+            clean_up_tokenization_spaces
+            if clean_up_tokenization_spaces is not None
+            else self.clean_up_tokenization_spaces
+        )
         if clean_up_tokenization_spaces:
             clean_text = self.clean_up_tokenization(text)
             return clean_text

src/transformers/models/wav2vec2_phoneme/tokenization_wav2vec2_phoneme.py

@@ -409,7 +409,7 @@ class Wav2Vec2PhonemeCTCTokenizer(PreTrainedTokenizer):
         self,
         token_ids: List[int],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         group_tokens: bool = True,
         filter_word_delimiter_token: bool = True,
         spaces_between_special_tokens: bool = False,
@@ -438,6 +438,11 @@ class Wav2Vec2PhonemeCTCTokenizer(PreTrainedTokenizer):
 
         text = string_output["text"]
 
+        clean_up_tokenization_spaces = (
+            clean_up_tokenization_spaces
+            if clean_up_tokenization_spaces is not None
+            else self.clean_up_tokenization_spaces
+        )
         if clean_up_tokenization_spaces:
             text = self.clean_up_tokenization(text)
 
@@ -451,7 +456,7 @@ class Wav2Vec2PhonemeCTCTokenizer(PreTrainedTokenizer):
         self,
         token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         output_char_offsets: bool = False,
         **kwargs,
     ) -> str:
@@ -466,7 +471,7 @@ class Wav2Vec2PhonemeCTCTokenizer(PreTrainedTokenizer):
                 List of tokenized input ids. Can be obtained using the `__call__` method.
             skip_special_tokens (`bool`, *optional*, defaults to `False`):
                 Whether or not to remove special tokens in the decoding.
-            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
+            clean_up_tokenization_spaces (`bool`, *optional*):
                 Whether or not to clean up the tokenization spaces.
             output_char_offsets (`bool`, *optional*, defaults to `False`):
                 Whether or not to output character offsets. Character offsets can be used in combination with the
@@ -507,7 +512,7 @@ class Wav2Vec2PhonemeCTCTokenizer(PreTrainedTokenizer):
         self,
         sequences: Union[List[int], List[List[int]], "np.ndarray", "torch.Tensor", "tf.Tensor"],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         output_char_offsets: bool = False,
         **kwargs,
     ) -> List[str]:
@@ -519,7 +524,7 @@ class Wav2Vec2PhonemeCTCTokenizer(PreTrainedTokenizer):
                 List of tokenized input ids. Can be obtained using the `__call__` method.
             skip_special_tokens (`bool`, *optional*, defaults to `False`):
                 Whether or not to remove special tokens in the decoding.
-            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
+            clean_up_tokenization_spaces (`bool`, *optional*):
                 Whether or not to clean up the tokenization spaces.
             output_char_offsets (`bool`, *optional*, defaults to `False`):
                 Whether or not to output character offsets. Character offsets can be used in combination with the

src/transformers/models/whisper/tokenization_whisper.py

@@ -556,7 +556,7 @@ class WhisperTokenizer(PreTrainedTokenizer):
         self,
         token_ids,
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         output_offsets: bool = False,
         time_precision=0.02,
         decode_with_timestamps: bool = False,
@@ -573,8 +573,9 @@ class WhisperTokenizer(PreTrainedTokenizer):
                 List of tokenized input ids. Can be obtained using the `__call__` method.
             skip_special_tokens (`bool`, *optional*, defaults to `False`):
                 Whether or not to remove special tokens in the decoding.
-            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
-                Whether or not to clean up the tokenization spaces.
+            clean_up_tokenization_spaces (`bool`, *optional*):
+                Whether or not to clean up the tokenization spaces. If `None`, will default to
+                `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
             kwargs (additional keyword arguments, *optional*):
                 Will be passed to the underlying model specific decode method.
             output_offsets (`bool`, *optional*, defaults to `False`):

src/transformers/models/whisper/tokenization_whisper_fast.py

@@ -266,7 +266,7 @@ class WhisperTokenizerFast(PreTrainedTokenizerFast):
         self,
         token_ids,
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         output_offsets: bool = False,
         time_precision=0.02,
         decode_with_timestamps: bool = False,
@@ -283,8 +283,9 @@ class WhisperTokenizerFast(PreTrainedTokenizerFast):
                 List of tokenized input ids. Can be obtained using the `__call__` method.
             skip_special_tokens (`bool`, *optional*, defaults to `False`):
                 Whether or not to remove special tokens in the decoding.
-            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
-                Whether or not to clean up the tokenization spaces.
+            clean_up_tokenization_spaces (`bool`, *optional*):
+                Whether or not to clean up the tokenization spaces. If `None`, will default to
+                `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
             kwargs (additional keyword arguments, *optional*):
                 Will be passed to the underlying model specific decode method.
             output_offsets (`bool`, *optional*, defaults to `False`):

src/transformers/models/xlnet/tokenization_xlnet.py

@@ -254,7 +254,7 @@ class XLNetTokenizer(PreTrainedTokenizer):
         self,
         token_ids: List[int],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         spaces_between_special_tokens: bool = True,
         **kwargs,
     ) -> str:
@@ -284,6 +284,11 @@ class XLNetTokenizer(PreTrainedTokenizer):
         # By default, there are no spaces between special tokens
         text = "".join(sub_texts)
 
+        clean_up_tokenization_spaces = (
+            clean_up_tokenization_spaces
+            if clean_up_tokenization_spaces is not None
+            else self.clean_up_tokenization_spaces
+        )
         if clean_up_tokenization_spaces:
             clean_text = self.clean_up_tokenization(text)
             return clean_text

src/transformers/tokenization_utils.py

@@ -922,7 +922,7 @@ class PreTrainedTokenizer(PreTrainedTokenizerBase):
         self,
         token_ids: List[int],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         spaces_between_special_tokens: bool = True,
         **kwargs,
     ) -> str:
@@ -953,6 +953,11 @@ class PreTrainedTokenizer(PreTrainedTokenizerBase):
         else:
             text = "".join(sub_texts)
 
+        clean_up_tokenization_spaces = (
+            clean_up_tokenization_spaces
+            if clean_up_tokenization_spaces is not None
+            else self.clean_up_tokenization_spaces
+        )
         if clean_up_tokenization_spaces:
             clean_text = self.clean_up_tokenization(text)
             return clean_text

src/transformers/tokenization_utils_base.py

@@ -1470,6 +1470,9 @@ INIT_TOKENIZER_DOCSTRING = r"""
             A tuple or a list of additional special tokens. Add them here to ensure they won't be split by the
             tokenization process. Will be associated to `self.additional_special_tokens` and
             `self.additional_special_tokens_ids`.
+        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should cleanup the spaces that were added when splitting the input text during the
+            tokenization process.
 """
 
 
@@ -1521,6 +1524,9 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
 
         self.model_input_names = kwargs.pop("model_input_names", self.model_input_names)
 
+        # By default, cleaning tokenization spaces for both fast and slow tokenizers
+        self.clean_up_tokenization_spaces = kwargs.pop("clean_up_tokenization_spaces", True)
+
         self.deprecation_warnings = (
             {}
         )  # Use to store when we have already noticed a deprecation warning (avoid overlogging).
@@ -1576,7 +1582,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
             f"{self.__class__.__name__}(name_or_path='{self.name_or_path}',"
             f" vocab_size={self.vocab_size}, model_max_length={self.model_max_length}, is_fast={self.is_fast},"
             f" padding_side='{self.padding_side}', truncation_side='{self.truncation_side}',"
-            f" special_tokens={self.special_tokens_map_extended})"
+            f" special_tokens={self.special_tokens_map_extended}, clean_up_tokenization_spaces={self.clean_up_tokenization_spaces})"
         )
 
     def __len__(self) -> int:
@@ -2112,7 +2118,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
 
         # TODO: Ensure the modified attributes (those are also in the __init__ kwargs) will give identical tokenizers
         # target_keys = self.init_kwargs.keys()
-        target_keys = ["model_max_length"]
+        target_keys = ["model_max_length", "clean_up_tokenization_spaces"]
         for k in target_keys:
             if hasattr(self, k):
                 tokenizer_config[k] = getattr(self, k)
@@ -3416,7 +3422,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
         self,
         sequences: Union[List[int], List[List[int]], "np.ndarray", "torch.Tensor", "tf.Tensor"],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         **kwargs,
     ) -> List[str]:
         """
@@ -3427,8 +3433,9 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
                 List of tokenized input ids. Can be obtained using the `__call__` method.
             skip_special_tokens (`bool`, *optional*, defaults to `False`):
                 Whether or not to remove special tokens in the decoding.
-            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
-                Whether or not to clean up the tokenization spaces.
+            clean_up_tokenization_spaces (`bool`, *optional*):
+                Whether or not to clean up the tokenization spaces. If `None`, will default to
+                `self.clean_up_tokenization_spaces`.
             kwargs (additional keyword arguments, *optional*):
                 Will be passed to the underlying model specific decode method.
 
@@ -3449,7 +3456,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
         self,
         token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         **kwargs,
     ) -> str:
         """
@@ -3463,8 +3470,9 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
                 List of tokenized input ids. Can be obtained using the `__call__` method.
             skip_special_tokens (`bool`, *optional*, defaults to `False`):
                 Whether or not to remove special tokens in the decoding.
-            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
-                Whether or not to clean up the tokenization spaces.
+            clean_up_tokenization_spaces (`bool`, *optional*):
+                Whether or not to clean up the tokenization spaces. If `None`, will default to
+                `self.clean_up_tokenization_spaces`.
             kwargs (additional keyword arguments, *optional*):
                 Will be passed to the underlying model specific decode method.
 
@@ -3485,7 +3493,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
         self,
         token_ids: Union[int, List[int]],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         **kwargs,
     ) -> str:
         raise NotImplementedError

src/transformers/tokenization_utils_fast.py

@@ -539,7 +539,7 @@ class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
         self,
         token_ids: Union[int, List[int]],
         skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
+        clean_up_tokenization_spaces: bool = None,
         **kwargs,
     ) -> str:
         self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False)
@@ -548,6 +548,11 @@ class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
             token_ids = [token_ids]
         text = self._tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
 
+        clean_up_tokenization_spaces = (
+            clean_up_tokenization_spaces
+            if clean_up_tokenization_spaces is not None
+            else self.clean_up_tokenization_spaces
+        )
         if clean_up_tokenization_spaces:
             clean_text = self.clean_up_tokenization(text)
             return clean_text

tests/test_tokenization_common.py

@@ -3895,6 +3895,51 @@ class TokenizerTesterMixin:
                     # Should not raise an error
                     self.rust_tokenizer_class.from_pretrained(tmp_dir_2)
 
+    def test_clean_up_tokenization_spaces(self):
+        tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+        assert tokenizer.clean_up_tokenization_spaces is True
+
+        tokens = tokenizer.encode("This shouldn't be! He'll go.")
+        decoded = tokenizer.decode(tokens)
+        assert decoded == "[CLS] this shouldn't be! he'll go. [SEP]"
+
+        tokenizer.clean_up_tokenization_spaces = False
+        decoded = tokenizer.decode(tokens)
+        assert decoded == "[CLS] this shouldn ' t be ! he ' ll go . [SEP]"
+        assert decoded == tokenizer.decode(tokens, clean_up_tokenization_spaces=False)
+
+        # Fast from slow
+        with tempfile.TemporaryDirectory() as tmp_dir_2:
+            tokenizer.save_pretrained(tmp_dir_2)
+            tokenizer_fast = BertTokenizerFast.from_pretrained(tmp_dir_2)
+            del tokenizer
+
+        assert tokenizer_fast.clean_up_tokenization_spaces is False
+        decoded = tokenizer_fast.decode(tokens)
+        # fast and slow don't have the same output when we don't cleanup
+        # tokenization space. Here `be!` vs `be !` and `go.` vs `go .`
+        assert decoded == "[CLS] this shouldn ' t be! he ' ll go. [SEP]"
+
+        tokenizer_fast.clean_up_tokenization_spaces = True
+        assert tokenizer_fast.clean_up_tokenization_spaces is True
+
+        decoded = tokenizer_fast.decode(tokens)
+        assert decoded == "[CLS] this shouldn't be! he'll go. [SEP]"
+
+        # Slow from fast
+        with tempfile.TemporaryDirectory() as tmp_dir_2:
+            tokenizer_fast.clean_up_tokenization_spaces = False
+            tokenizer_fast.save_pretrained(tmp_dir_2)
+            tokenizer = BertTokenizer.from_pretrained(tmp_dir_2)
+
+        assert tokenizer_fast.clean_up_tokenization_spaces is False
+        decoded = tokenizer.decode(tokens)
+        assert decoded == "[CLS] this shouldn ' t be ! he ' ll go . [SEP]"
+
+        tokenizer.clean_up_tokenization_spaces = True
+        decoded = tokenizer.decode(tokens)
+        assert decoded == "[CLS] this shouldn't be! he'll go. [SEP]"
+
 
 class TokenizerUtilTester(unittest.TestCase):
     def test_cached_files_are_used_when_internet_is_down(self):