Replace specific tokenizer in log message by AutoTokenizer (#12745)

src/transformers/models/deberta_v2/tokenization_deberta_v2.py

@@ -137,7 +137,7 @@ class DebertaV2Tokenizer(PreTrainedTokenizer):
         if not os.path.isfile(vocab_file):
             raise ValueError(
                 f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained "
-                "model use `tokenizer = DebertaV2Tokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+                "model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
             )
         self.do_lower_case = do_lower_case
         self.split_by_punct = split_by_punct

src/transformers/models/mpnet/tokenization_mpnet.py

@@ -170,7 +170,7 @@ class MPNetTokenizer(PreTrainedTokenizer):
         if not os.path.isfile(vocab_file):
             raise ValueError(
                 f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained "
-                "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+                "model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
             )
         self.vocab = load_vocab(vocab_file)
         self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])

src/transformers/models/prophetnet/tokenization_prophetnet.py

@@ -136,7 +136,7 @@ class ProphetNetTokenizer(PreTrainedTokenizer):
         if not os.path.isfile(vocab_file):
             raise ValueError(
                 f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained "
-                "model use `tokenizer = ProphetNetTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+                "model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
             )
         self.vocab = load_vocab(vocab_file)
         self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])

src/transformers/models/roformer/tokenization_roformer.py

@@ -144,7 +144,7 @@ class RoFormerTokenizer(PreTrainedTokenizer):
         if not os.path.isfile(vocab_file):
             raise ValueError(
                 f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained "
-                "model use `tokenizer = RoFormerTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+                "model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
             )
         self.vocab = load_vocab(vocab_file)
         self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])