diff --git a/pytorch_transformers/tests/tokenization_tests_commons.py b/pytorch_transformers/tests/tokenization_tests_commons.py
index 5da19bb660..8a3b56a058 100644
--- a/pytorch_transformers/tests/tokenization_tests_commons.py
+++ b/pytorch_transformers/tests/tokenization_tests_commons.py
@@ -263,3 +263,10 @@ class CommonTestCases:
             assert overflowing_tokens_first_truncated == sequence_0_no_special_tokens[-(2 + stride):]
             assert len(truncated_sequence) == len(sequence) - 2
             assert truncated_sequence == truncated_second_sequence
+
+        def test_tokens_sent_to_encode(self):
+            tokenizer = self.get_tokenizer()
+
+            sequence = "Let's encode this sequence"
+            tokens = tokenizer.encode(sequence)
+            tokenizer.encode(tokens, add_special_tokens=True)
diff --git a/pytorch_transformers/tokenization_utils.py b/pytorch_transformers/tokenization_utils.py
index 0b23d00b8a..b32d6daeb6 100644
--- a/pytorch_transformers/tokenization_utils.py
+++ b/pytorch_transformers/tokenization_utils.py
@@ -707,14 +707,14 @@ class PreTrainedTokenizer(object):
         """
         if text_pair is None:
             if add_special_tokens:
-                sequence_tokens = self.convert_tokens_to_ids(self.tokenize(text, **kwargs))
+                sequence_tokens = self.convert_tokens_to_ids(self.tokenize(text, **kwargs)) if isinstance(text, str) else text
                 return self.add_special_tokens_single_sequence(sequence_tokens)
             else:
-                ids = self.convert_tokens_to_ids(self.tokenize(text, **kwargs))
+                ids = self.convert_tokens_to_ids(self.tokenize(text, **kwargs)) if isinstance(text, str) else text
                 return ids
 
-        first_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text, **kwargs)]
-        second_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text_pair, **kwargs)]
+        first_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text, **kwargs)] if isinstance(text, str) else text
+        second_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text_pair, **kwargs)] if isinstance(text_pair, str) else text_pair
 
         if add_special_tokens:
             return self.add_special_tokens_sequence_pair(first_sentence_tokens, second_sentence_tokens)
@@ -754,7 +754,7 @@ class PreTrainedTokenizer(object):
         information = {}
 
         if text_pair is None:
-            sequence_tokens = self.convert_tokens_to_ids(self.tokenize(text, **kwargs))
+            sequence_tokens = self.convert_tokens_to_ids(self.tokenize(text, **kwargs)) if isinstance(text, str) else text
             if add_special_tokens:
                 information = self.prepare_for_model(sequence_tokens, max_length, stride)
             else:
@@ -766,8 +766,8 @@ class PreTrainedTokenizer(object):
             if output_mask:
                 information["mask"] = [0] * len(information["sequence"])
         else:
-            first_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text, **kwargs)]
-            second_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text_pair, **kwargs)]
+            first_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text, **kwargs)] if isinstance(text, str) else text
+            second_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text_pair, **kwargs)] if isinstance(text_pair, str) else text_pair
 
             if add_special_tokens:
                 information = self.prepare_pair_for_model(