ruff fix

tests/test_guider_math.py

@@ -13,7 +13,7 @@ class MockGuider:
         self.model_patcher.model_dtype = lambda: torch.float32
         self.original_conds = {}
         self.model_options = {}
-        
+
     def __call__(self, x, sigma, model_options={}, seed=None):
         return torch.full_like(x, self.value)
 
@@ -24,34 +24,34 @@ class TestMathGuider(unittest.TestCase):
         g1 = MockGuider(2.0)
         G = {"G0": g0, "G1": g1}
         F = {"F0": 0.5}
-        
+
         # Expression: Average G0 and G1
         expr = "G0 * 0.5 + G1 * 0.5"
-        
+
         math_guider = MathGuider(G, F, expr)
-        
+
         # Pseudo input
         x = torch.zeros((1, 4, 16, 16))
         sigma = torch.tensor(1.0)
-        
+
         # Call
         result = math_guider(x, sigma)
-        
+
         # Expected: 1.0 * 0.5 + 2.0 * 0.5 = 1.5
         self.assertTrue(torch.allclose(result, torch.tensor(1.5)))
-        
+
     def test_math_guider_aliases(self):
         g0 = MockGuider(10.0)
         G = {"G0": g0}
         F = {"F0": 2.0}
-        
+
         # a = G0, w = F0
         expr = "a + w"
-        
+
         math_guider = MathGuider(G, F, expr)
         x = torch.zeros((1, 4, 8, 8))
         sigma = torch.tensor(1.0)
-        
+
         result = math_guider(x, sigma)
         self.assertTrue(torch.allclose(result, torch.tensor(12.0)))
 
@@ -61,11 +61,11 @@ class TestMathGuider(unittest.TestCase):
         G = {"G0": g0}
         F = {}
         math_guider = MathGuider(G, F, "G0")
-        
+
         # Check if the property exists and matches g0's patcher
         self.assertIsNotNone(math_guider.model_patcher)
         self.assertEqual(math_guider.model_patcher, g0.model_patcher)
-        
+
     def test_math_guider_model_patcher_missing(self):
         # Verify behavior when input guiders don't have model_patcher (e.g. None or broken)
         g0 = MockGuider(1.0)
@@ -80,19 +80,19 @@ class TestMathGuider(unittest.TestCase):
         sigmas = torch.tensor([10.0, 5.0, 0.0])
         g0 = MockGuider(1.0)
         G = {"G0": g0}
-        
+
         math_guider = MathGuider(G, {}, "current_step / steps")
         math_guider.sigmas = sigmas # sets sigmas directly for testing
-        
+
         # Step 0: sigma = 10.0
         x = torch.zeros((1, 1, 1, 1))
         res0 = math_guider(x, torch.tensor(10.0))
         self.assertTrue(torch.allclose(res0, torch.tensor(0.0 / 2.0)))
-        
+
         # Step 1: sigma = 5.0
         res1 = math_guider(x, torch.tensor(5.0))
         self.assertTrue(torch.allclose(res1, torch.tensor(1.0 / 2.0)))
-        
+
         # Intermediate sigma should find closest
         res_near = math_guider(x, torch.tensor(4.8))
         self.assertTrue(torch.allclose(res_near, torch.tensor(1.0 / 2.0)))