Make `get_special_tokens_mask` consider all tokens (#11163)

src/transformers/models/mpnet/tokenization_mpnet.py

@@ -266,12 +266,9 @@ class MPNetTokenizer(PreTrainedTokenizer):
             :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
         """
         if already_has_special_tokens:
-            if token_ids_1 is not None:
-                raise ValueError(
-                    "You should not supply a second sequence if the provided sequence of "
-                    "ids is already formated with special tokens for the model."
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
             )
-            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
 
         if token_ids_1 is None:
             return [1] + ([0] * len(token_ids_0)) + [1]

src/transformers/models/phobert/tokenization_phobert.py

@@ -201,12 +201,9 @@ class PhobertTokenizer(PreTrainedTokenizer):
         """
 
         if already_has_special_tokens:
-            if token_ids_1 is not None:
-                raise ValueError(
-                    "You should not supply a second sequence if the provided sequence of "
-                    "ids is already formatted with special tokens for the model."
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
             )
-            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
 
         if token_ids_1 is None:
             return [1] + ([0] * len(token_ids_0)) + [1]

src/transformers/models/prophetnet/tokenization_prophetnet.py

@@ -203,12 +203,9 @@ class ProphetNetTokenizer(PreTrainedTokenizer):
             :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
         """
         if already_has_special_tokens:
-            if token_ids_1 is not None:
-                raise ValueError(
-                    "You should not supply a second sequence if the provided sequence of "
-                    "ids is already formatted with special tokens for the model."
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
             )
-            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
 
         if token_ids_1 is None:
             return ([0] * len(token_ids_0)) + [1]

src/transformers/models/roberta/tokenization_roberta.py

@@ -215,12 +215,9 @@ class RobertaTokenizer(GPT2Tokenizer):
             :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
         """
         if already_has_special_tokens:
-            if token_ids_1 is not None:
-                raise ValueError(
-                    "You should not supply a second sequence if the provided sequence of "
-                    "ids is already formatted with special tokens for the model."
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
             )
-            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
 
         if token_ids_1 is None:
             return [1] + ([0] * len(token_ids_0)) + [1]

src/transformers/models/speech_to_text/tokenization_speech_to_text.py

@@ -199,12 +199,10 @@ class Speech2TextTokenizer(PreTrainedTokenizer):
         """
 
         if already_has_special_tokens:
-            if token_ids_1 is not None:
-                raise ValueError(
-                    "You should not supply a second sequence if the provided sequence of "
-                    "ids is already formatted with special tokens for the model."
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
             )
-            return list(map(lambda x: 1 if x in [self.bos_token_id, self.eos_token_id] else 0, token_ids_0))
+
         prefix_ones = [1] * len(self.prefix_tokens)
         suffix_ones = [1]
         if token_ids_1 is None:

src/transformers/models/t5/tokenization_t5.py

@@ -157,12 +157,10 @@ class T5Tokenizer(PreTrainedTokenizer):
             :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
         """
         if already_has_special_tokens:
-            if token_ids_1 is not None:
-                raise ValueError(
-                    "You should not supply a second sequence if the provided sequence of "
-                    "ids is already formatted with special tokens for the model."
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
             )
-            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
+
         # normal case: some special tokens
         if token_ids_1 is None:
             return ([0] * len(token_ids_0)) + [1]

src/transformers/models/tapas/tokenization_tapas.py

@@ -510,12 +510,9 @@ class TapasTokenizer(PreTrainedTokenizer):
         """
 
         if already_has_special_tokens:
-            if token_ids_1 is not None:
-                raise ValueError(
-                    "You should not supply a second sequence if the provided sequence of "
-                    "ids is already formatted with special tokens for the model."
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
             )
-            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
 
         if token_ids_1 is not None:
             return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))

src/transformers/models/xlm/tokenization_xlm.py

@@ -906,16 +906,8 @@ class XLMTokenizer(PreTrainedTokenizer):
         """
 
         if already_has_special_tokens:
-            if token_ids_1 is not None:
-                raise ValueError(
-                    "You should not supply a second sequence if the provided sequence of "
-                    "ids is already formatted with special tokens for the model."
-                )
-            return list(
-                map(
-                    lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0,
-                    token_ids_0,
-                )
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
             )
 
         if token_ids_1 is not None:

src/transformers/models/xlm_prophetnet/tokenization_xlm_prophetnet.py

@@ -200,12 +200,9 @@ class XLMProphetNetTokenizer(PreTrainedTokenizer):
         """
 
         if already_has_special_tokens:
-            if token_ids_1 is not None:
-                raise ValueError(
-                    "You should not supply a second sequence if the provided sequence of "
-                    "ids is already formatted with special tokens for the model."
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
             )
-            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
 
         if token_ids_1 is None:
             return ([0] * len(token_ids_0)) + [1]

src/transformers/models/xlm_roberta/tokenization_xlm_roberta.py

@@ -206,12 +206,9 @@ class XLMRobertaTokenizer(PreTrainedTokenizer):
         """
 
         if already_has_special_tokens:
-            if token_ids_1 is not None:
-                raise ValueError(
-                    "You should not supply a second sequence if the provided sequence of "
-                    "ids is already formatted with special tokens for the model."
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
             )
-            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
 
         if token_ids_1 is None:
             return [1] + ([0] * len(token_ids_0)) + [1]

src/transformers/models/xlm_roberta/tokenization_xlm_roberta_fast.py

@@ -172,37 +172,6 @@ class XLMRobertaTokenizerFast(PreTrainedTokenizerFast):
         sep = [self.sep_token_id]
         return cls + token_ids_0 + sep + sep + token_ids_1 + sep
 
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer ``prepare_for_model`` method.
-
-        Args:
-            token_ids_0 (:obj:`List[int]`):
-                List of IDs.
-            token_ids_1 (:obj:`List[int]`, `optional`):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-
-        Returns:
-            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-
-        if already_has_special_tokens:
-            if token_ids_1 is not None:
-                raise ValueError(
-                    "You should not supply a second sequence if the provided sequence of "
-                    "ids is already formatted with special tokens for the model."
-                )
-            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
-
-        if token_ids_1 is None:
-            return [1] + ([0] * len(token_ids_0)) + [1]
-        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
-
     def create_token_type_ids_from_sequences(
         self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
     ) -> List[int]:

src/transformers/models/xlnet/tokenization_xlnet.py

@@ -270,12 +270,9 @@ class XLNetTokenizer(PreTrainedTokenizer):
         """
 
         if already_has_special_tokens:
-            if token_ids_1 is not None:
-                raise ValueError(
-                    "You should not supply a second sequence if the provided sequence of "
-                    "ids is already formatted with special tokens for the model."
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
             )
-            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
 
         if token_ids_1 is not None:
             return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1, 1]

src/transformers/models/xlnet/tokenization_xlnet_fast.py

@@ -190,37 +190,6 @@ class XLNetTokenizerFast(PreTrainedTokenizerFast):
             return token_ids_0 + sep + cls
         return token_ids_0 + sep + token_ids_1 + sep + cls
 
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer ``prepare_for_model`` method.
-
-        Args:
-            token_ids_0 (:obj:`List[int]`):
-                List of IDs.
-            token_ids_1 (:obj:`List[int]`, `optional`):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-
-        Returns:
-            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-
-        if already_has_special_tokens:
-            if token_ids_1 is not None:
-                raise ValueError(
-                    "You should not supply a second sequence if the provided sequence of "
-                    "ids is already formatted with special tokens for the model."
-                )
-            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
-
-        if token_ids_1 is not None:
-            return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1, 1]
-        return ([0] * len(token_ids_0)) + [1, 1]
-
     def create_token_type_ids_from_sequences(
         self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
     ) -> List[int]:

src/transformers/tokenization_utils.py

@@ -670,6 +670,16 @@ class PreTrainedTokenizer(PreTrainedTokenizerBase):
         Returns:
             A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
         """
+        if already_has_special_tokens:
+            if token_ids_1 is not None:
+                raise ValueError(
+                    "You should not supply a second sequence if the provided sequence of "
+                    "ids is already formatted with special tokens for the model."
+                )
+
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
         return [0] * ((len(token_ids_1) if token_ids_1 else 0) + len(token_ids_0))
 
     @overload

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/tokenization_{{cookiecutter.lowercase_modelname}}.py

@@ -225,12 +225,9 @@ class {{cookiecutter.camelcase_modelname}}Tokenizer(PreTrainedTokenizer):
             :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
         """
         if already_has_special_tokens:
-            if token_ids_1 is not None:
-                raise ValueError(
-                    "You should not supply a second sequence if the provided sequence of "
-                    "ids is already formatted with special tokens for the model."
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
             )
-            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
 
         if token_ids_1 is None:
             return [1] + ([0] * len(token_ids_0)) + [1]

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/{{cookiecutter.lowercase_modelname}}.rst

@@ -46,8 +46,7 @@ Tips:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.{{cookiecutter.camelcase_modelname}}TokenizerFast
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
+    :members:
 
 
 {% if "PyTorch" in cookiecutter.generate_tensorflow_and_pytorch -%}