Apply isort and black reformatting

Signed-off-by: DrownFish19 <[email protected]>

nemo/utils/exp_manager.py

@@ -352,6 +352,7 @@ def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
     def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
         self._on_batch_end("validation_step_timing in s", trainer, pl_module)
 
+
 class TokenPerformanceCallback(Callback):
     """
     Logs performance in token-level of train steps using nemo logger. Calculates
@@ -394,7 +395,7 @@ def on_train_epoch_start(self, trainer, pl_module):
 
     def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
         self._on_batch_end("train_step_token_performance", pl_module)
-    
+
         elapsed_time = self.timer["train_step_token_performance"]
 
         # sum local tokens
@@ -404,14 +405,16 @@ def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
         torch.distributed.all_reduce(total_tokens, group=parallel_state.get_data_parallel_group())
         total_tokens_per_second = float(total_tokens) / elapsed_time
         total_tokens_per_second_per_device = int(total_tokens_per_second / float(torch.distributed.get_world_size()))
-        
+
         # sum local tokens
         effective_tokens = sum(batch["token_count"])
         # sum tokens cross all data_parallel_group
         effective_tokens = torch.tensor([effective_tokens]).cuda()
         torch.distributed.all_reduce(effective_tokens, group=parallel_state.get_data_parallel_group())
         effective_tokens_per_second = float(effective_tokens) / elapsed_time
-        effective_tokens_per_second_per_device = int(effective_tokens_per_second / float(torch.distributed.get_world_size()))
+        effective_tokens_per_second_per_device = int(
+            effective_tokens_per_second / float(torch.distributed.get_world_size())
+        )
 
         pl_module.log(
             'effective_tokens_per_second_per_device',