[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

intel_extension_for_transformers/transformers/trainer.py

@@ -387,7 +387,7 @@ def distill(
             assert False, "Please provide teacher model for DistillationConfig."
         self._eval_func = self.builtin_eval_func if eval_func is None else eval_func
         self._train_func = self.builtin_train_func if train_func is None else train_func
-        
+
         compression_manager = prepare_compression(self.model, distillation_config)
         self.compression_manager = compression_manager
         self.compression_manager.callbacks.on_train_begin()

tests/Nightly/test_distillation.py

@@ -28,7 +28,7 @@
 from neural_compressor.config import (
     DistillationConfig,
     KnowledgeDistillationLossConfig,
-) 
+)
 from intel_extension_for_transformers.transformers.trainer import NLPTrainer
 from transformers import (
     AutoModelForSequenceClassification,
@@ -76,7 +76,7 @@ def compute_metrics(p):
             preds = np.argmax(preds, axis=1)
             return metric.compute(predictions=preds, references=p.label_ids)
         origin_weight = copy.deepcopy(self.model.classifier.weight)
-  
+
         self.trainer = NLPTrainer(
             model=copy.deepcopy(self.model),
             train_dataset=self.dataset,

tests/Nightly/test_orchestrate_optimization.py

@@ -85,8 +85,8 @@ def compute_metrics(p):
             name="eval_accuracy", is_relative=True, criterion=0.5
         )
         self.trainer.metrics = tune_metric
-        pruning_conf = WeightPruningConfig([{"start_step": 0, "end_step": 2}], 
-                                           target_sparsity=0.64, 
+        pruning_conf = WeightPruningConfig([{"start_step": 0, "end_step": 2}],
+                                           target_sparsity=0.64,
                                            pruning_scope="local")
         distillation_criterion = KnowledgeDistillationLossConfig(loss_types=["CE", "KL"])
         distillation_conf = DistillationConfig(teacher_model=self.teacher_model, criterion=distillation_criterion)

workflows/compression_aware_training/src/itrex_opt.py

@@ -778,7 +778,7 @@ def _do_quantization_aware_training(self):
             quantization_config = QuantizationAwareTrainingConfig(
                 tuning_criterion=tuning_criterion,
                 accuracy_criterion=accuracy_criterion
-            )            
+            )
             early_stopping_patience = 2
             early_stopping_threshold = 0.001 # optional
             self.trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience, \