diff --git a/src/transformers/tokenization_bart.py b/src/transformers/tokenization_bart.py
index 7378da968..2b6f7240c 100644
--- a/src/transformers/tokenization_bart.py
+++ b/src/transformers/tokenization_bart.py
@@ -198,6 +198,7 @@ class MBartTokenizer(XLMRobertaTokenizer):
         max_length: Optional[int] = None,
         padding: str = "longest",
         return_tensors: str = "pt",
+        **kwargs,
     ) -> BatchEncoding:
         """Prepare a batch that can be passed directly to an instance of MBartModel.
         Arguments:
@@ -207,6 +208,7 @@ class MBartTokenizer(XLMRobertaTokenizer):
             tgt_lang: default ro_RO (romanian), the language we are translating to
             max_length: (default=None, which defers to the config value of 1024 for facebook/mbart-large*
             padding: strategy for padding input_ids and decoder_input_ids. Should be max_length or longest.
+            **kwargs: passed to self.__call__
 
         Returns:
             :obj:`BatchEncoding`: with keys input_ids, attention_mask, decoder_input_ids, decoder_attention_mask.
@@ -221,6 +223,7 @@ class MBartTokenizer(XLMRobertaTokenizer):
             max_length=max_length,
             padding=padding,
             truncation=True,
+            **kwargs,
         )
         if tgt_texts is None:
             return model_inputs
@@ -232,6 +235,7 @@ class MBartTokenizer(XLMRobertaTokenizer):
             padding=padding,
             max_length=max_length,
             truncation=True,
+            **kwargs,
         )
         for k, v in decoder_inputs.items():
             model_inputs[f"decoder_{k}"] = v