Update style with ruff 0.2.2 (#1565)

This is necessary to add to main fast, or else all branches from main
will require these changes to pass the quality checks.

examples/causal_language_modeling/peft_lora_clm_accelerate_ds_zero3_offload.py

@@ -260,9 +260,7 @@ def main():
         accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
         accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
         accelerator.print(
-            "CPU Total Peak Memory consumed during the train (max): {}".format(
+            f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
-                tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)
-            )
         )
         train_epoch_loss = total_loss / len(train_dataloader)
         train_ppl = torch.exp(train_epoch_loss)
@@ -294,9 +292,7 @@ def main():
         accelerator.print(f"CPU Memory consumed at the end of the eval (end-begin): {tracemalloc.cpu_used}")
         accelerator.print(f"CPU Peak Memory consumed during the eval (max-begin): {tracemalloc.cpu_peaked}")
         accelerator.print(
-            "CPU Total Peak Memory consumed during the eval (max): {}".format(
+            f"CPU Total Peak Memory consumed during the eval (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
-                tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)
-            )
         )
 
         correct = 0

examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py

@@ -211,9 +211,7 @@ def main():
         accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
         accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
         accelerator.print(
-            "CPU Total Peak Memory consumed during the train (max): {}".format(
+            f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
-                tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)
-            )
         )
         train_epoch_loss = total_loss / len(train_dataloader)
         train_ppl = torch.exp(train_epoch_loss)
@@ -244,9 +242,7 @@ def main():
         accelerator.print(f"CPU Memory consumed at the end of the eval (end-begin): {tracemalloc.cpu_used}")
         accelerator.print(f"CPU Peak Memory consumed during the eval (max-begin): {tracemalloc.cpu_peaked}")
         accelerator.print(
-            "CPU Total Peak Memory consumed during the eval (max): {}".format(
+            f"CPU Total Peak Memory consumed during the eval (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
-                tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)
-            )
         )
 
         correct = 0

examples/lora_dreambooth/train_dreambooth.py

@@ -1052,18 +1052,14 @@ def main(args):
             accelerator.print(f"GPU Memory consumed at the end of the train (end-begin): {tracemalloc.used}")
             accelerator.print(f"GPU Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}")
             accelerator.print(
-                "GPU Total Peak Memory consumed during the train (max): {}".format(
+                f"GPU Total Peak Memory consumed during the train (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
-                    tracemalloc.peaked + b2mb(tracemalloc.begin)
-                )
             )
 
             accelerator.print(f"CPU Memory before entering the train : {b2mb(tracemalloc.cpu_begin)}")
             accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
             accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
             accelerator.print(
-                "CPU Total Peak Memory consumed during the train (max): {}".format(
+                f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
-                    tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)
-                )
             )
 
     # Create the pipeline using using the trained modules and save it.

examples/oft_dreambooth/train_dreambooth.py

@@ -1060,18 +1060,14 @@ def main(args):
             accelerator.print(f"GPU Memory consumed at the end of the train (end-begin): {tracemalloc.used}")
             accelerator.print(f"GPU Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}")
             accelerator.print(
-                "GPU Total Peak Memory consumed during the train (max): {}".format(
+                f"GPU Total Peak Memory consumed during the train (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
-                    tracemalloc.peaked + b2mb(tracemalloc.begin)
-                )
             )
 
             accelerator.print(f"CPU Memory before entering the train : {b2mb(tracemalloc.cpu_begin)}")
             accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
             accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
             accelerator.print(
-                "CPU Total Peak Memory consumed during the train (max): {}".format(
+                f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
-                    tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)
-                )
             )
 
     # Create the pipeline using using the trained modules and save it.

examples/stable_diffusion/train_dreambooth.py

@@ -1225,9 +1225,7 @@ def main(args):
         accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
         accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
         accelerator.print(
-            "CPU Total Peak Memory consumed during the train (max): {}".format(
+            f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
-                tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)
-            )
         )
 
     # Create the pipeline using using the trained modules and save it.

scripts/stale.py

@@ -15,6 +15,7 @@
 Script to close stale issue. Taken in part from the AllenNLP repository.
 https://github.com/allenai/allennlp.
 """
+
 import os
 from datetime import datetime as dt
 from datetime import timezone