diff --git a/src/transformers/tokenization_utils_base.py b/src/transformers/tokenization_utils_base.py
index 76c2c151fdf..0da576be742 100644
--- a/src/transformers/tokenization_utils_base.py
+++ b/src/transformers/tokenization_utils_base.py
@@ -2733,11 +2733,10 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
         first_element = required_input[0]
         if isinstance(first_element, (list, tuple)):
             # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
-            index = 0
-            while len(required_input[index]) == 0:
-                index += 1
-            if index < len(required_input):
-                first_element = required_input[index][0]
+            for item in required_input:
+                if len(item) != 0:
+                    first_element = item[0]
+                    break
         # At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do.
         if not isinstance(first_element, (int, list, tuple)):
             if is_tf_available() and _is_tensorflow(first_element):
diff --git a/tests/test_tokenization_common.py b/tests/test_tokenization_common.py
index 685ccf43af1..e11f0d7150b 100644
--- a/tests/test_tokenization_common.py
+++ b/tests/test_tokenization_common.py
@@ -1393,7 +1393,7 @@ class TokenizerTesterMixin:
                 assert encoded_sequence == padded_sequence_left
 
     def test_padding_to_max_length(self):
-        """We keep this test for backward compatibility but it should be remove when `pad_to_max_length` will e deprecated"""
+        """We keep this test for backward compatibility but it should be remove when `pad_to_max_length` is deprecated."""
         tokenizers = self.get_tokenizers(do_lower_case=False)
         for tokenizer in tokenizers:
             with self.subTest(f"{tokenizer.__class__.__name__}"):
@@ -2997,8 +2997,8 @@ class TokenizerTesterMixin:
                 input_r = tokenizer_r.encode_plus("This is a input 1")
                 input_r = tokenizer_r.pad(input_r)
 
-                input_p = tokenizer_r.encode_plus("This is a input 1")
-                input_p = tokenizer_r.pad(input_p)
+                input_p = tokenizer_p.encode_plus("This is a input 1")
+                input_p = tokenizer_p.pad(input_p)
 
                 self.assert_padded_input_match(
                     input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
@@ -3008,8 +3008,8 @@ class TokenizerTesterMixin:
                 input_r = tokenizer_r.encode_plus("This is a input 1")
                 input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")
 
-                input_p = tokenizer_r.encode_plus("This is a input 1")
-                input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")
+                input_p = tokenizer_p.encode_plus("This is a input 1")
+                input_p = tokenizer_p.pad(input_p, max_length=max_length, padding="max_length")
 
                 self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
 
@@ -3019,10 +3019,10 @@ class TokenizerTesterMixin:
                 )
                 input_r = tokenizer_r.pad(input_r)
 
-                input_p = tokenizer_r.batch_encode_plus(
+                input_p = tokenizer_p.batch_encode_plus(
                     ["This is a input 1", "This is a much longer input whilch should be padded"]
                 )
-                input_p = tokenizer_r.pad(input_p)
+                input_p = tokenizer_p.pad(input_p)
 
                 self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
 
@@ -3032,11 +3032,15 @@ class TokenizerTesterMixin:
                 )
                 input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")
 
-                input_p = tokenizer_r.batch_encode_plus(
+                input_p = tokenizer_p.batch_encode_plus(
                     ["This is a input 1", "This is a much longer input whilch should be padded"]
                 )
-                input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")
+                input_p = tokenizer_p.pad(input_p, max_length=max_length, padding="max_length")
+                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
 
+                # Test padding nested empty lists (in some use-cases, there is no any token id in the `input_ids` list).
+                input_r = tokenizer_r.pad({"input_ids": [[], []]}, max_length=max_length, padding="max_length")
+                input_p = tokenizer_p.pad({"input_ids": [[], []]}, max_length=max_length, padding="max_length")
                 self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
 
     def test_padding_different_model_input_name(self):