diff --git a/tests/test_modeling_tf_common.py b/tests/test_modeling_tf_common.py
index 2cf272f4aac1..eb328d83e9e7 100644
--- a/tests/test_modeling_tf_common.py
+++ b/tests/test_modeling_tf_common.py
@@ -1715,10 +1715,9 @@ def test_dataset_conversion(self):
                 model.train_on_batch(test_batch, test_batch_labels)
 
     def _test_xla_generate(self, **generate_kwargs):
-        def _generate_and_check_results(model, inputs_dict):
-            if "input_ids" in inputs_dict:
-                inputs = inputs_dict["input_ids"]
-                # make sure there are no pad tokens in prompt, which may trigger unwanted behavior
+        def _generate_and_check_results(model, inputs, is_input_ids):
+            # make sure there are no pad tokens in prompt, which may trigger unwanted behavior
+            if is_input_ids:
                 if model.generation_config.pad_token_id is not None:
                     if config.pad_token_id == 0:
                         new_pad_token = model.generation_config.pad_token_id + 1
@@ -1727,10 +1726,6 @@ def _generate_and_check_results(model, inputs_dict):
                 else:
                     new_pad_token = None
                 inputs = tf.where(inputs != model.generation_config.pad_token_id, inputs, new_pad_token)
-            elif "input_features" in inputs_dict:
-                inputs = inputs_dict["input_features"]
-            else:
-                raise ValueError("No valid generate input found in inputs_dict")
 
             generated = model.generate(inputs, **generate_kwargs).numpy()
             generate_xla = tf.function(model.generate, jit_compile=True)
@@ -1753,12 +1748,20 @@ def _generate_and_check_results(model, inputs_dict):
             config.eos_token_id = None  # Generate until max length
             config.do_sample = False
 
+            # extract the input to the model
+            is_input_ids = "input_ids" in inputs_dict
+            is_input_features = "input_features" in inputs_dict
+            if not (is_input_ids or is_input_features):
+                raise ValueError("No valid generate input found in inputs_dict")
+            inputs = inputs_dict["input_ids"] if is_input_ids else inputs_dict["input_features"]
+
             # fix config for models with additional sequence-length limiting settings
+            seq_len = inputs.get_shape()[1]
             for var_name in ["max_position_embeddings", "max_target_positions"]:
                 attr = getattr(config, var_name, None)
-                if attr is not None and attr < generate_kwargs["max_new_tokens"]:
+                if attr is not None and attr < seq_len + generate_kwargs["max_new_tokens"]:
                     try:
-                        setattr(config, var_name, generate_kwargs["max_new_tokens"])
+                        setattr(config, var_name, seq_len + generate_kwargs["max_new_tokens"])
                     except NotImplementedError:
                         # xlnet will raise an exception when trying to set
                         # max_position_embeddings.
@@ -1767,10 +1770,10 @@ def _generate_and_check_results(model, inputs_dict):
             model = model_class(config)
 
             if model.supports_xla_generation:
-                _generate_and_check_results(model, inputs_dict)
+                _generate_and_check_results(model, inputs, is_input_ids)
             else:
                 with self.assertRaises(ValueError):
-                    _generate_and_check_results(model, inputs_dict)
+                    _generate_and_check_results(model, inputs, is_input_ids)
 
     def test_xla_generate_fast(self):
         """