Apply isort and black reformatting

Signed-off-by: akoumpa <akoumpa@users.noreply.github.com>

examples/nlp/language_modeling/upcyle_dense_to_moe.py

@@ -28,10 +28,6 @@
 import torch.nn
 from pytorch_lightning.trainer.trainer import Trainer
 
-from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
-from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy
-from nemo.utils import logging
-
 from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
 from nemo.collections.nlp.parts.nlp_overrides import (
     GradScaler,
@@ -44,13 +40,13 @@
 
 def get_args():
     parser = ArgumentParser()
+    parser.add_argument("--model", type=str, default=None, required=True, help="Path to NeMo checkpoint")
     parser.add_argument(
-        "--model", type=str, default=None, required=True, help="Path to NeMo checkpoint"
+        "--output-path", type=str, default='', required=False, help="Path to NeMo save upcycled checkpoint"
     )
     parser.add_argument(
-        "--output-path", type=str, default='', required=False, help="Path to NeMo save upcycled checkpoint"
+        "--num-experts", type=int, default=8, required=True, help="Number of experts to use in upcycled model."
     )
-    parser.add_argument("--num-experts", type=int, default=8, required=True, help="Number of experts to use in upcycled model.")
     args = parser.parse_args()
     assert isinstance(args.num_experts, int)
     assert args.num_experts > 1, "Expected --num-experts to be greater-than 1."
@@ -61,10 +57,12 @@ def get_args():
 
 def make_moe_config_from_dense(config, num_experts=8):
     from copy import deepcopy
+
     moe_config = deepcopy(config)
     moe_config['num_moe_experts'] = num_experts
     return moe_config
 
+
 def upcycle(in_file, num_experts, cpu_only=True) -> None:
     """
     Upcycle dense checkpoint to MoE.
@@ -98,15 +96,14 @@ def upcycle(in_file, num_experts, cpu_only=True) -> None:
 
     # convert state dict dense -> MoE
     from megatron.core.transformer.moe.upcycling_utils import upcycle_state_dict
+
     moe_state_dict = upcycle_state_dict([moe_model.model.module], [dense_model.model.module])
     moe_model.model.module.load_state_dict(moe_state_dict['model'])
     moe_model._save_restore_connector = NLPSaveRestoreConnector()
 
     moe_model.save_to(args.output_path)
 
 
-
-
 if __name__ == '__main__':
     args = get_args()
     upcycle(args.model, args.num_experts)