Feat: update pre-commit rev (#432)

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
 -   repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 23.12.1 
+    rev: 24.1.1
     hooks:
     -   id: black
 -   repo: https://github.com/pycqa/isort

llms/mlx_lm/tuner/trainer.py

@@ -80,9 +80,9 @@ def iterate_batches(dataset, tokenizer, batch_size, max_seq_length, train=False)
             for j in range(batch_size):
                 truncated_length = min(lengths[j], max_seq_length)
                 batch_arr[j, :truncated_length] = batch[j][:truncated_length]
-                lengths[
-                    j
-                ] = truncated_length  # Update lengths to match truncated lengths
+                lengths[j] = (
+                    truncated_length  # Update lengths to match truncated lengths
+                )
             batch = mx.array(batch_arr)
 
             yield batch[:, :-1], batch[:, 1:], mx.array(lengths)

stable_diffusion/stable_diffusion/model_io.py

@@ -186,9 +186,11 @@ def load_unet(key: str = _DEFAULT_MODEL, float16: bool = False):
             out_channels=config["out_channels"],
             block_out_channels=config["block_out_channels"],
             layers_per_block=[config["layers_per_block"]] * n_blocks,
-            num_attention_heads=[config["attention_head_dim"]] * n_blocks
-            if isinstance(config["attention_head_dim"], int)
-            else config["attention_head_dim"],
+            num_attention_heads=(
+                [config["attention_head_dim"]] * n_blocks
+                if isinstance(config["attention_head_dim"], int)
+                else config["attention_head_dim"]
+            ),
             cross_attention_dim=[config["cross_attention_dim"]] * n_blocks,
             norm_num_groups=config["norm_num_groups"],
         )