Revert frozen training arguments (#25903)

* Revert frozen training arguments

* TODO

tests/trainer/test_trainer_distributed.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import dataclasses
 from typing import Dict
 
 import numpy as np
@@ -206,14 +205,7 @@ if __name__ == "__main__":
             logger.error(p.metrics)
             exit(1)
 
-        training_args = dataclasses.replace(training_args, eval_accumulation_steps=2)
-        trainer = Trainer(
-            model=DummyModel(),
-            args=training_args,
-            data_collator=DummyDataCollator(),
-            eval_dataset=dataset,
-            compute_metrics=compute_metrics,
-        )
+        trainer.args.eval_accumulation_steps = 2
 
         metrics = trainer.evaluate()
         logger.info(metrics)
@@ -227,22 +219,15 @@ if __name__ == "__main__":
             logger.error(p.metrics)
             exit(1)
 
-        training_args = dataclasses.replace(training_args, eval_accumulation_steps=None)
-        trainer = Trainer(
-            model=DummyModel(),
-            args=training_args,
-            data_collator=DummyDataCollator(),
-            eval_dataset=dataset,
-            compute_metrics=compute_metrics,
-        )
+        trainer.args.eval_accumulation_steps = None
 
     # Check that `dispatch_batches=False` will work on a finite iterable dataset
 
     train_dataset = FiniteIterableDataset(label_names=["labels", "extra"], length=1)
 
     model = RegressionModel()
-    training_args = dataclasses.replace(
-        training_args, per_device_train_batch_size=1, max_steps=1, dispatch_batches=False
-    )
+    training_args.per_device_train_batch_size = 1
+    training_args.max_steps = 1
+    training_args.dispatch_batches = False
     trainer = Trainer(model, training_args, train_dataset=train_dataset)
     trainer.train()