🚨 🚨 🚨 Fix Issue 15003: SentencePiece Tokenizers Not Adding Special Tokens in convert_tokens_to_string (#15775)

* Add test for SentencePiece not adding special tokens to strings

* Add SentencePieceStringConversionMixin to fix issue 15003

* Fix conversion from tokens to string for most SentencePiece tokenizers

Tokenizers fixed:
- AlbertTokenizer
- BarthezTokenizer
- CamembertTokenizer
- FNetTokenizer
- M2M100Tokenizer
- MBart50Tokenizer
- PegasusTokenizer
- Speech2TextTokenizer

* Fix MarianTokenizer, adjust SentencePiece test to accomodate vocab

* Fix DebertaV2Tokenizer

* Ignore LayoutXLMTokenizer in SentencePiece string conversion test

* Run 'make style' and 'make quality'

* Clean convert_tokens_to_string test

Instead of explicitly ignoring LayoutXLMTokenizer in the test,
override the test in LayoutLMTokenizationTest and do nothing in it.

* Remove commented out code

* Improve robustness of convert_tokens_to_string test

Instead of comparing lengths of re-tokenized text and input_ids,
check that converting all special tokens to string yields a string
with all special tokens.

* Inline and remove SentencePieceStringConversionMixin

The convert_tokens_to_string method is now implemented
in each relevant SentencePiece tokenizer.

* Run 'make style' and 'make quality'

* Revert removal of space in convert_tokens_to_string

* Remove redundant import

* Revert test text to original

* Uncomment the lowercasing of the reverse_text variable

* Mimic Rust tokenizer behavior for tokenizers

- Albert
- Barthez
- Camembert
- MBart50
- T5

* Fix accidentally skipping test in wrong tokenizer

* Add test for equivalent Rust and slow tokenizer behavior

* Override _decode in BigBirdTokenizer to mimic Rust behavior

* Override _decode in FNetTokenizer to mimic Rust behavior

* Override _decode in XLNetTokenizer to mimic Rust behavior

* Remove unused 're' import

* Update DebertaV2Tokenizer to mimic Rust tokenizer

* Deberta tokenizer now behaves like Albert and its `convert_tokens_to_string` is not tested.

* Ignore problematic tests in Deberta V2

* Add comment on why the Deberta V2 tests are skipped

src/transformers/models/albert/tokenization_albert.py

@@ -250,7 +250,23 @@ class AlbertTokenizer(PreTrainedTokenizer):
         return self.sp_model.IdToPiece(index)
 
     def convert_tokens_to_string(self, tokens):
-        return self.sp_model.decode(tokens)
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string.strip()
 
     def build_inputs_with_special_tokens(
         self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None

src/transformers/models/barthez/tokenization_barthez.py

@@ -263,6 +263,25 @@ class BarthezTokenizer(PreTrainedTokenizer):
             return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index)
 
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string.strip()
+
     def __getstate__(self):
         state = self.__dict__.copy()
         state["sp_model"] = None
@@ -278,10 +297,6 @@ class BarthezTokenizer(PreTrainedTokenizer):
         self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(self.vocab_file)
 
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (strings for sub-words) in a single string."""
-        return self.sp_model.decode(tokens)
-
     def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
         if not os.path.isdir(save_directory):
             logger.error(f"Vocabulary path ({save_directory}) should be a directory")

src/transformers/models/bert_generation/tokenization_bert_generation.py

@@ -151,8 +151,17 @@ class BertGenerationTokenizer(PreTrainedTokenizer):
 
     def convert_tokens_to_string(self, tokens):
         """Converts a sequence of tokens (string) in a single string."""
-        out_string = self.sp_model.decode_pieces(tokens)
-        return out_string
+        current_sub_tokens = []
+        out_string = ""
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string.strip()
 
     def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
         if not os.path.isdir(save_directory):

src/transformers/models/big_bird/tokenization_big_bird.py

@@ -16,6 +16,7 @@
 
 
 import os
+import re
 from shutil import copyfile
 from typing import Any, Dict, List, Optional, Tuple
 
@@ -182,8 +183,65 @@ class BigBirdTokenizer(PreTrainedTokenizer):
 
     def convert_tokens_to_string(self, tokens):
         """Converts a sequence of tokens (string) in a single string."""
-        out_string = self.sp_model.decode_pieces(tokens)
-        return out_string
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string.strip()
+
+    def _decode(
+        self,
+        token_ids: List[int],
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: bool = True,
+        spaces_between_special_tokens: bool = True,
+        **kwargs
+    ) -> str:
+        self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False)
+
+        filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
+
+        # To avoid mixing byte-level and unicode for byte-level BPT
+        # we need to build string separately for added tokens and byte-level tokens
+        # cf. https://github.com/huggingface/transformers/issues/1133
+        sub_texts = []
+        current_sub_text = []
+        for token in filtered_tokens:
+            if skip_special_tokens and token in self.all_special_ids:
+                continue
+            if token in self.added_tokens_encoder:
+                if current_sub_text:
+                    sub_texts.append(self.convert_tokens_to_string(current_sub_text))
+                    current_sub_text = []
+                sub_texts.append(token)
+            else:
+                current_sub_text.append(token)
+        if current_sub_text:
+            sub_texts.append(self.convert_tokens_to_string(current_sub_text))
+
+        # Mimic the behavior of the Rust tokenizer:
+        # No space before [MASK] and [SEP]
+        if spaces_between_special_tokens:
+            text = re.sub(r" (\[(MASK|SEP)\])", r"\1", " ".join(sub_texts))
+        else:
+            text = "".join(sub_texts)
+
+        if clean_up_tokenization_spaces:
+            clean_text = self.clean_up_tokenization(text)
+            return clean_text
+        else:
+            return text
 
     def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
         if not os.path.isdir(save_directory):

src/transformers/models/camembert/tokenization_camembert.py

@@ -261,6 +261,25 @@ class CamembertTokenizer(PreTrainedTokenizer):
             return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset)
 
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string.strip()
+
     def __getstate__(self):
         state = self.__dict__.copy()
         state["sp_model"] = None
@@ -276,10 +295,6 @@ class CamembertTokenizer(PreTrainedTokenizer):
         self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(self.vocab_file)
 
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (strings for sub-words) in a single string."""
-        return self.sp_model.decode(tokens)
-
     def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
         if not os.path.isdir(save_directory):
             logger.error(f"Vocabulary path ({save_directory}) should be a directory")

src/transformers/models/deberta_v2/tokenization_deberta_v2.py

@@ -146,7 +146,9 @@ class DebertaV2Tokenizer(PreTrainedTokenizer):
         self.do_lower_case = do_lower_case
         self.split_by_punct = split_by_punct
         self.vocab_file = vocab_file
-        self._tokenizer = SPMTokenizer(vocab_file, split_by_punct=split_by_punct, sp_model_kwargs=self.sp_model_kwargs)
+        self._tokenizer = SPMTokenizer(
+            vocab_file, self.all_special_tokens, split_by_punct=split_by_punct, sp_model_kwargs=self.sp_model_kwargs
+        )
 
     @property
     def vocab_size(self):
@@ -291,7 +293,9 @@ class SPMTokenizer:
               BPE-dropout.
     """
 
-    def __init__(self, vocab_file, split_by_punct=False, sp_model_kwargs: Optional[Dict[str, Any]] = None):
+    def __init__(
+        self, vocab_file, special_tokens, split_by_punct=False, sp_model_kwargs: Optional[Dict[str, Any]] = None
+    ):
         self.split_by_punct = split_by_punct
         self.vocab_file = vocab_file
         self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
@@ -312,6 +316,7 @@ class SPMTokenizer:
         # self.vocab['[UNK]'] = 3
 
         self.spm = spm
+        self.special_tokens = special_tokens
 
     def __getstate__(self):
         state = self.__dict__.copy()
@@ -339,7 +344,22 @@ class SPMTokenizer:
 
     def decode(self, tokens, start=-1, end=-1, raw_text=None):
         if raw_text is None:
-            return self.spm.decode_pieces([t for t in tokens])
+            current_sub_tokens = []
+            out_string = ""
+            prev_is_special = False
+            for token in tokens:
+                # make sure that special tokens are not decoded using sentencepiece model
+                if token in self.special_tokens:
+                    if not prev_is_special:
+                        out_string += " "
+                    out_string += self.spm.decode_pieces(current_sub_tokens) + token
+                    prev_is_special = True
+                    current_sub_tokens = []
+                else:
+                    current_sub_tokens.append(token)
+                    prev_is_special = False
+            out_string += self.spm.decode_pieces(current_sub_tokens)
+            return out_string.strip()
         else:
             words = self.split_to_words(raw_text)
             word_tokens = [self.tokenize(w) for w in words]

src/transformers/models/fnet/tokenization_fnet.py

@@ -15,6 +15,7 @@
 """ Tokenization classes for FNet model."""
 
 import os
+import re
 import unicodedata
 from shutil import copyfile
 from typing import Any, Dict, List, Optional, Tuple
@@ -213,7 +214,66 @@ class FNetTokenizer(PreTrainedTokenizer):
         return self.sp_model.IdToPiece(index)
 
     def convert_tokens_to_string(self, tokens):
-        return self.sp_model.decode(tokens)
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string.strip()
+
+    def _decode(
+        self,
+        token_ids: List[int],
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: bool = True,
+        spaces_between_special_tokens: bool = True,
+        **kwargs
+    ) -> str:
+        self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False)
+
+        filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
+
+        # To avoid mixing byte-level and unicode for byte-level BPT
+        # we need to build string separately for added tokens and byte-level tokens
+        # cf. https://github.com/huggingface/transformers/issues/1133
+        sub_texts = []
+        current_sub_text = []
+        for token in filtered_tokens:
+            if skip_special_tokens and token in self.all_special_ids:
+                continue
+            if token in self.added_tokens_encoder:
+                if current_sub_text:
+                    sub_texts.append(self.convert_tokens_to_string(current_sub_text))
+                    current_sub_text = []
+                sub_texts.append(token)
+            else:
+                current_sub_text.append(token)
+        if current_sub_text:
+            sub_texts.append(self.convert_tokens_to_string(current_sub_text))
+
+        # Mimic the behavior of the Rust tokenizer:
+        # No space after <unk>
+        if spaces_between_special_tokens:
+            text = re.sub(r"(<unk>) ", r"\1", " ".join(sub_texts))
+        else:
+            text = "".join(sub_texts)
+
+        if clean_up_tokenization_spaces:
+            clean_text = self.clean_up_tokenization(text)
+            return clean_text
+        else:
+            return text
 
     def build_inputs_with_special_tokens(
         self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None

src/transformers/models/m2m_100/tokenization_m2m_100.py

@@ -218,9 +218,19 @@ class M2M100Tokenizer(PreTrainedTokenizer):
             return self.id_to_lang_token[index]
         return self.decoder.get(index, self.unk_token)
 
-    def convert_tokens_to_string(self, tokens: List[str]) -> str:
-        """Converts a sequence of tokens (strings for sub-words) in a single string."""
-        return self.sp_model.decode(tokens)
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string.strip()
 
     def get_special_tokens_mask(
         self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False

src/transformers/models/marian/tokenization_marian.py

@@ -265,10 +265,18 @@ class MarianTokenizer(PreTrainedTokenizer):
 
     def convert_tokens_to_string(self, tokens: List[str]) -> str:
         """Uses source spm if _decode_use_source_tokenizer is True, and target spm otherwise"""
-        if self._decode_use_source_tokenizer:
-            return self.spm_source.DecodePieces(tokens)
-        else:
-            return self.spm_target.DecodePieces(tokens)
+        sp_model = self.spm_source if self._decode_use_source_tokenizer else self.spm_target
+        current_sub_tokens = []
+        out_string = ""
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                out_string += sp_model.decode_pieces(current_sub_tokens) + token + " "
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+        out_string += sp_model.decode_pieces(current_sub_tokens)
+        return out_string.strip()
 
     def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
         """Build model inputs from a sequence by appending eos_token_id."""

src/transformers/models/mbart50/tokenization_mbart50.py

@@ -232,9 +232,24 @@ class MBart50Tokenizer(PreTrainedTokenizer):
             return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset)
 
-    def convert_tokens_to_string(self, tokens: List[str]) -> str:
-        """Converts a sequence of tokens (strings for sub-words) in a single string."""
-        return self.sp_model.decode(tokens)
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string.strip()
 
     def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
         if not os.path.isdir(save_directory):

src/transformers/models/pegasus/tokenization_pegasus.py

@@ -231,8 +231,17 @@ class PegasusTokenizer(PreTrainedTokenizer):
 
     def convert_tokens_to_string(self, tokens):
         """Converts a sequence of tokens (string) in a single string."""
-        out_string = self.sp_model.decode_pieces(tokens)
-        return out_string
+        current_sub_tokens = []
+        out_string = ""
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string.strip()
 
     def num_special_tokens_to_add(self, pair=False):
         """Just EOS"""

src/transformers/models/reformer/tokenization_reformer.py

@@ -158,8 +158,17 @@ class ReformerTokenizer(PreTrainedTokenizer):
 
     def convert_tokens_to_string(self, tokens):
         """Converts a sequence of tokens (string) in a single string."""
-        out_string = self.sp_model.decode_pieces(tokens)
-        return out_string
+        current_sub_tokens = []
+        out_string = ""
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string.strip()
 
     def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
         if not os.path.isdir(save_directory):

src/transformers/models/speech_to_text/tokenization_speech_to_text.py

@@ -190,11 +190,19 @@ class Speech2TextTokenizer(PreTrainedTokenizer):
 
     def convert_tokens_to_string(self, tokens: List[str]) -> str:
         """Converts a sequence of tokens (strings for sub-words) in a single string."""
-        out_string = self.sp_model.decode(tokens)
-
-        if self.do_upper_case:
-            out_string = out_string.upper()
-        return out_string
+        current_sub_tokens = []
+        out_string = ""
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                decoded = self.sp_model.decode(current_sub_tokens)
+                out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+        decoded = self.sp_model.decode(current_sub_tokens)
+        out_string += decoded.upper() if self.do_upper_case else decoded
+        return out_string.strip()
 
     def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
         """Build model inputs from a sequence by appending eos_token_id."""

src/transformers/models/t5/tokenization_t5.py

@@ -311,14 +311,19 @@ class T5Tokenizer(PreTrainedTokenizer):
         """Converts a sequence of tokens (string) in a single string."""
         current_sub_tokens = []
         out_string = ""
+        prev_is_special = False
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
-                out_string += self.sp_model.decode_pieces(current_sub_tokens) + token + " "
+                if not prev_is_special:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
                 current_sub_tokens = []
             else:
                 current_sub_tokens.append(token)
-        out_string += self.sp_model.decode_pieces(current_sub_tokens)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
         return out_string.strip()
 
     def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:

src/transformers/models/xlnet/tokenization_xlnet.py

@@ -250,6 +250,46 @@ class XLNetTokenizer(PreTrainedTokenizer):
         out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
         return out_string
 
+    def _decode(
+        self,
+        token_ids: List[int],
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: bool = True,
+        spaces_between_special_tokens: bool = True,
+        **kwargs
+    ) -> str:
+        self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False)
+
+        filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
+
+        # To avoid mixing byte-level and unicode for byte-level BPT
+        # we need to build string separately for added tokens and byte-level tokens
+        # cf. https://github.com/huggingface/transformers/issues/1133
+        sub_texts = []
+        current_sub_text = []
+        for token in filtered_tokens:
+            if skip_special_tokens and token in self.all_special_ids:
+                continue
+            if token in self.added_tokens_encoder:
+                if current_sub_text:
+                    sub_texts.append(self.convert_tokens_to_string(current_sub_text))
+                    current_sub_text = []
+                sub_texts.append(token)
+            else:
+                current_sub_text.append(token)
+        if current_sub_text:
+            sub_texts.append(self.convert_tokens_to_string(current_sub_text))
+
+        # Mimic the behavior of the Rust tokenizer:
+        # By default, there are no spaces between special tokens
+        text = "".join(sub_texts)
+
+        if clean_up_tokenization_spaces:
+            clean_text = self.clean_up_tokenization(text)
+            return clean_text
+        else:
+            return text
+
     def build_inputs_with_special_tokens(
         self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
     ) -> List[int]:

tests/models/deberta_v2/test_tokenization_deberta_v2.py

@@ -37,7 +37,7 @@ class DebertaV2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         super().setUp()
 
         # We have a SentencePiece fixture for testing
-        tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB)
+        tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, unk_token="<unk>")
         tokenizer.save_pretrained(self.tmpdirname)
 
     def get_input_output_texts(self, tokenizer):
@@ -55,7 +55,6 @@ class DebertaV2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
 
     def test_get_vocab(self):
         vocab_keys = list(self.get_tokenizer().get_vocab().keys())
-
         self.assertEqual(vocab_keys[0], "<pad>")
         self.assertEqual(vocab_keys[1], "<unk>")
         self.assertEqual(vocab_keys[-1], "[PAD]")
@@ -80,6 +79,14 @@ class DebertaV2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
 
         self.assertListEqual(rust_tokens, tokens_target)
 
+    @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.")
+    def test_sentencepiece_tokenize_and_convert_tokens_to_string(self):
+        pass
+
+    @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.")
+    def test_sentencepiece_tokenize_and_decode(self):
+        pass
+
     def test_split_by_punct(self):
         # fmt: off
         sequence = "I was born in 92000, and this is falsé."

tests/models/layoutxlm/test_tokenization_layoutxlm.py

@@ -1946,3 +1946,11 @@ class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     @unittest.skip("Doesn't support another framework than PyTorch")
     def test_np_encode_plus_sent_to_model(self):
         pass
+
+    @unittest.skip("Doesn't use SentencePiece")
+    def test_sentencepiece_tokenize_and_convert_tokens_to_string(self):
+        pass
+
+    @unittest.skip("Doesn't use SentencePiece")
+    def test_sentencepiece_tokenize_and_decode(self):
+        pass

tests/test_tokenization_common.py

@@ -385,6 +385,33 @@ class TokenizerTesterMixin:
 
         self.assertEqual(reverse_text, text)
 
+        special_tokens = tokenizer.all_special_tokens
+        special_tokens_string = tokenizer.convert_tokens_to_string(special_tokens)
+        for special_token in special_tokens:
+            self.assertIn(special_token, special_tokens_string)
+
+        if self.test_rust_tokenizer:
+            rust_tokenizer = self.get_rust_tokenizer()
+            special_tokens_string_rust = rust_tokenizer.convert_tokens_to_string(special_tokens)
+            self.assertEqual(special_tokens_string, special_tokens_string_rust)
+
+    def test_sentencepiece_tokenize_and_decode(self):
+        if not self.test_sentencepiece:
+            return
+
+        text = "This is text to test the tokenizer."
+        if self.test_rust_tokenizer:
+            tokenizer = self.get_tokenizer()
+            rust_tokenizer = self.get_rust_tokenizer()
+
+            slow_ids = tokenizer(text).input_ids
+            fast_ids = rust_tokenizer(text).input_ids
+            self.assertEqual(slow_ids, fast_ids)
+
+            slow_decoded = tokenizer.decode(slow_ids)
+            fast_decoded = rust_tokenizer.decode(slow_ids)
+            self.assertEqual(slow_decoded, fast_decoded)
+
     def test_subword_regularization_tokenizer(self) -> None:
         if not self.test_sentencepiece:
             return