From 99c0a2fbb092f5ceff92851457bc37e98e7de6ab Mon Sep 17 00:00:00 2001
From: Tim Miller <timothy.miller@childrens.harvard.edu>
Date: Fri, 27 Sep 2024 18:59:17 -0400
Subject: [PATCH] Updated black version and ran black.

---
 .pre-commit-config.yaml   |  2 +-
 src/cnlpt/train_system.py | 18 +++++++++---------
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 6841d79c..5c2701e6 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos:
 -   repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 23.7.0
+    rev: 24.8.0
     hooks:
     -   id: black
 -   repo: https://github.com/pycqa/flake8
diff --git a/src/cnlpt/train_system.py b/src/cnlpt/train_system.py
index 2c12a444..b8dc34f9 100644
--- a/src/cnlpt/train_system.py
+++ b/src/cnlpt/train_system.py
@@ -545,16 +545,16 @@ def main(
             # steps per epoch factors in gradient accumulation steps (as compared to batches_per_epoch above which doesn't)
             steps_per_epoch = int(total_steps // training_args.num_train_epochs)
             training_args.eval_steps = steps_per_epoch // training_args.evals_per_epoch
-            training_args.evaluation_strategy = (
-                training_args.eval_strategy
-            ) = IntervalStrategy.STEPS
+            training_args.evaluation_strategy = training_args.eval_strategy = (
+                IntervalStrategy.STEPS
+            )
             # This will save model per epoch
             # training_args.save_strategy = IntervalStrategy.EPOCH
         elif training_args.do_eval:
             logger.info("Evaluation strategy not specified so evaluating every epoch")
-            training_args.evaluation_strategy = (
-                training_args.eval_strategy
-            ) = IntervalStrategy.EPOCH
+            training_args.evaluation_strategy = training_args.eval_strategy = (
+                IntervalStrategy.EPOCH
+            )
 
     current_prediction_packet = deque()
 
@@ -662,9 +662,9 @@ def compute_metrics_fn(p: EvalPrediction):
                                     "w",
                                 ) as f:
                                     config_dict = model_args.to_dict()
-                                    config_dict[
-                                        "label_dictionary"
-                                    ] = dataset.get_labels()
+                                    config_dict["label_dictionary"] = (
+                                        dataset.get_labels()
+                                    )
                                     config_dict["task_names"] = task_names
                                     json.dump(config_dict, f)
                         for task_ind, task_name in enumerate(metrics):