Apply isort and black reformatting

Signed-off-by: rhmukundan <[email protected]>

Author: rhmukundan

scripts/performance/argument_parser.py

@@ -425,7 +425,6 @@ def list_of_strings(arg):
         default=[],
     )
 
-
     parser.add_argument(
         "-ev",
         "--custom_env_vars",

scripts/performance/llm/finetune_llama4_e128.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
 from os.path import basename, splitext
 
 import nemo_run as run
@@ -20,7 +21,7 @@
 from nemo.collections.llm.recipes.llama4_e128 import finetune_recipe, model
 from nemo.collections.llm.recipes.precision.mixed_precision import bf16_with_fp8_mixed
 from nemo.lightning.run.plugins import NsysPlugin, PerfEnvPlugin
-import os
+
 from ..argument_parser import parse_cli_args
 from ..utils import (
     args_sanity_check,
@@ -225,7 +226,7 @@ def override_recipe_configs(
             args.partition,
             args.log_dir,
             1,  # Single node for setup tasks
-            1,  # Single GPU for setup tasks  
+            1,  # Single GPU for setup tasks
             args.time_limit,
             args.container_image,
             custom_mounts=args.custom_mounts,
@@ -256,7 +257,7 @@ def override_recipe_configs(
                 name=exp_name,
                 plugins=plugins,
             )
-        
+
         if not args.dryrun:
             exp.run(sequential=True, detach=True)
         else:

scripts/performance/utils.py

@@ -549,9 +549,7 @@ def get_nemo_home(nemo_home=None):
     raise ValueError("Neither nemo_home argument nor NEMO_HOME environment variable is set")
 
 
-def prepare_squad_dataset(
-    model_name: str, seq_length: int = 2048, nemo_home=None
-):
+def prepare_squad_dataset(model_name: str, seq_length: int = 2048, nemo_home=None):
     """Prepare the SQuAD dataset for fine-tuning.
 
     Args:
@@ -623,6 +621,7 @@ def prepare_squad_dataset_experiment(
         "prepare_squad_dataset_exp",
     )
 
+
 def isfile_train_pack_metadata(hf_model_uri: str, data_config: run.Config[SquadDataModule]) -> bool:
     """
     This method is used for fine-tuning. It checks if packed train data for a partiular