Fixup no_trainer examples scripts and add more tests (#16765)

* Change tracking to store_true

* Remove step param and use it in the log dictionary directly

* use vars(args) when passing args to init_trackers

* Include tracking tests since tensorboard is already a dep

examples/pytorch/summarization/run_summarization_no_trainer.py

@@ -277,7 +277,7 @@ def parse_args():
     )
     parser.add_argument(
         "--with_tracking",
-        required=False,
+        action="store_true",
         help="Whether to load in all available experiment trackers from the environment and use them for logging.",
     )
     args = parser.parse_args()
@@ -315,7 +315,7 @@ def main():
         )
     # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
     # If we're using tracking, we also need to initialize it here and it will pick up all supported trackers in the environment
-    accelerator = Accelerator(log_with="all") if args.with_tracking else Accelerator()
+    accelerator = Accelerator(log_with="all", logging_dir=args.output_dir) if args.with_tracking else Accelerator()
     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
@@ -548,7 +548,10 @@ def main():
 
     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
-        accelerator.init_trackers("summarization_no_trainer", args)
+        experiment_config = vars(args)
+        # TensorBoard cannot log Enums, need the raw value
+        experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
+        accelerator.init_trackers("summarization_no_trainer", experiment_config)
 
     # Metric
     metric = load_metric("rouge")
@@ -666,7 +669,8 @@ def main():
         if args.with_tracking:
             result["train_loss"] = total_loss
             result["epoch"] = epoch
-            accelerator.log(result, step=completed_steps)
+            result["step"] = completed_steps
+            accelerator.log(result)
 
         if args.push_to_hub and epoch < args.num_train_epochs - 1:
             accelerator.wait_for_everyone()