Fix code quality: remove whitespace from blank lines and format test file

Author: jackiehimel

tests/models/layoutlmv3/test_modeling_layoutlmv3.py

@@ -370,7 +370,7 @@ def test_batching_equivalence(self, atol=1e-5, rtol=1e-5):
         LayoutLMv3's relative position bias is incompatible with SDPA/FlashAttention.
         """
         from transformers.testing_utils import set_config_for_less_flaky_test
-        
+
         def recursive_check(batched_object, single_row_object, model_name, key):
             if isinstance(batched_object, (list, tuple)):
                 for batched_object_value, single_row_object_value in zip(batched_object, single_row_object):
@@ -407,12 +407,12 @@ def recursive_check(batched_object, single_row_object, model_name, key):
                     msg = f"Batched and Single row outputs are not equal in {model_name} for key={key}.\n\n"
                     msg += str(e)
                     raise AssertionError(msg)
-        
+
         config, batched_input = self.model_tester.prepare_config_and_inputs_for_common()
         set_config_for_less_flaky_test(config)
         # Force eager attention since LayoutLMv3 has relative position bias enabled by default
         config._attn_implementation = "eager"
-        
+
         for model_class in self.all_model_classes:
             config.output_hidden_states = True
             model_name = model_class.__name__
@@ -423,8 +423,9 @@ def recursive_check(batched_object, single_row_object, model_name, key):
             batched_input_prepared = self._prepare_for_class(batched_input, model_class)
             model = model_class(copy.deepcopy(config)).to(torch_device).eval()
             from transformers.testing_utils import set_model_for_less_flaky_test
+
             set_model_for_less_flaky_test(model)
-            
+
             batch_size = self.model_tester.batch_size
             single_row_input = {}
             for key, value in batched_input_prepared.items():
@@ -433,15 +434,15 @@ def recursive_check(batched_object, single_row_object, model_name, key):
                     single_row_input[key] = value[:single_batch_shape]
                 else:
                     single_row_input[key] = value
-            
+
             with torch.no_grad():
                 model_batched_output = model(**batched_input_prepared)
                 model_row_output = model(**single_row_input)
-            
+
             if isinstance(model_batched_output, torch.Tensor):
                 model_batched_output = {"model_output": model_batched_output}
                 model_row_output = {"model_output": model_row_output}
-            
+
             recursive_check(model_batched_output, model_row_output, model_name, "")
 
     @slow