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/text-classification/run_glue_no_trainer.py

@@ -165,7 +165,7 @@ 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()
@@ -192,7 +192,7 @@ 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",
@@ -430,7 +430,10 @@ def main():
 
     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
-        accelerator.init_trackers("glue_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("glue_no_trainer", experiment_config)
 
     # Get the metric function
     if args.task_name is not None:
@@ -520,8 +523,8 @@ def main():
                     "accuracy" if args.task_name is not None else "glue": eval_metric,
                     "train_loss": total_loss,
                     "epoch": epoch,
+                    "step": completed_steps,
                 },
-                step=completed_steps,
             )
 
         if args.push_to_hub and epoch < args.num_train_epochs - 1: