Make style

src/peft/tuners/adalora/config.py

@@ -38,10 +38,10 @@ class AdaLoraConfig(LoraConfig):
     The last phase, beginning once `total_step - tfinal` steps are reached, does not change the layer ranks anymore but
     fine-tunes the reduced-rank layers that resulted from the previous phase.
 
-    A practical example: `tinit` is 10, `tfinal` is 20, `total_step` is 100.  We spend 10 steps doing pre-training
+    A practical example: `tinit` is 10, `tfinal` is 20, `total_step` is 100. We spend 10 steps doing pre-training
     without rank reduction because our budget is constant (init phase), then we spend 80 (100-20) steps in the
-    reduction phase where our budget decreases step-wise and, finally, 20 steps in the final fine-tuning stage
-    without reduction.
+    reduction phase where our budget decreases step-wise and, finally, 20 steps in the final fine-tuning stage without
+    reduction.
 
     Args:
         target_r (`int`): The target average rank of incremental matrix.

tests/test_config.py

@@ -332,31 +332,37 @@ def test_adalora_config_r_warning(self):
     def test_adalora_config_correct_timing_still_works(self):
         pass
 
-    @pytest.mark.parametrize('timing_kwargs', [
-        {'total_step': 100, 'tinit': 0, 'tfinal': 0},
-        {'total_step': 100, 'tinit': 10, 'tfinal': 10},
-        {'total_step': 100, 'tinit': 79, 'tfinal': 20},
-        {'total_step': 100, 'tinit': 80, 'tfinal': 19},
-    ])
+    @pytest.mark.parametrize(
+        "timing_kwargs",
+        [
+            {"total_step": 100, "tinit": 0, "tfinal": 0},
+            {"total_step": 100, "tinit": 10, "tfinal": 10},
+            {"total_step": 100, "tinit": 79, "tfinal": 20},
+            {"total_step": 100, "tinit": 80, "tfinal": 19},
+        ],
+    )
     def test_adalora_config_valid_timing_works(self, timing_kwargs):
         # Make sure that passing correct timing values is not prevented by faulty config checks.
-        AdaLoraConfig(**timing_kwargs) # does not raise
+        AdaLoraConfig(**timing_kwargs)  # does not raise
 
     def test_adalora_config_invalid_total_step_raises(self):
         with pytest.raises(ValueError) as e:
             AdaLoraConfig(total_step=None)
         assert "AdaLoRA does not work when `total_step` is None, supply a value > 0." in str(e)
 
-    @pytest.mark.parametrize('timing_kwargs', [
-        {'total_step': 100, 'tinit': 20, 'tfinal': 80},
-        {'total_step': 100, 'tinit': 80, 'tfinal': 20},
-        {'total_step': 10, 'tinit': 20, 'tfinal': 0},
-        {'total_step': 10, 'tinit': 0, 'tfinal': 10},
-        {'total_step': 10, 'tinit': 10, 'tfinal': 0},
-        {'total_step': 10, 'tinit': 20, 'tfinal': 0},
-        {'total_step': 10, 'tinit': 20, 'tfinal': 20},
-        {'total_step': 10, 'tinit': 0, 'tfinal': 20},
-    ])
+    @pytest.mark.parametrize(
+        "timing_kwargs",
+        [
+            {"total_step": 100, "tinit": 20, "tfinal": 80},
+            {"total_step": 100, "tinit": 80, "tfinal": 20},
+            {"total_step": 10, "tinit": 20, "tfinal": 0},
+            {"total_step": 10, "tinit": 0, "tfinal": 10},
+            {"total_step": 10, "tinit": 10, "tfinal": 0},
+            {"total_step": 10, "tinit": 20, "tfinal": 0},
+            {"total_step": 10, "tinit": 20, "tfinal": 20},
+            {"total_step": 10, "tinit": 0, "tfinal": 20},
+        ],
+    )
     def test_adalora_config_timing_bounds_error(self, timing_kwargs):
         # Check if the user supplied timing values that will certainly fail because it breaks
         # AdaLoRA assumptions.