apply new formatting config

Summary:
pyfmt now specifies a target Python version of 3.8 when formatting
with black. With this new config, black adds trailing commas to all
multiline function calls. This applies the new formatting as part
of rolling out the linttool-integration for pyfmt.

paintitblack

Reviewed By: zertosh, lisroach

Differential Revision: D37084377

fbshipit-source-id: 781a1b883a381a172e54d6e447137657977876b4

Author: amyreese

botorch/models/kernels/categorical.py

@@ -28,7 +28,7 @@ def forward(
         x2: Tensor,
         diag: bool = False,
         last_dim_is_batch: bool = False,
-        **kwargs
+        **kwargs,
     ) -> Tensor:
         delta = x1.unsqueeze(-2) != x2.unsqueeze(-3)
         dists = delta / self.lengthscale.unsqueeze(-2)

test/acquisition/multi_objective/test_multi_output_risk_measures.py

@@ -285,7 +285,7 @@ def set_equals(t1: Tensor, t2: Tensor) -> bool:
                     [4, 1],
                     [3.5, 3.5],
                 ],
-                **tkwargs
+                **tkwargs,
             )
             cpu_mvar = mvar.get_mvar_set_cpu(Y)
             gpu_mvar = mvar.get_mvar_set_gpu(Y)[0]
@@ -299,7 +299,7 @@ def set_equals(t1: Tensor, t2: Tensor) -> bool:
                     [3, 3.5],
                     [4, 2],
                 ],
-                **tkwargs
+                **tkwargs,
             )
             self.assertTrue(set_equals(cpu_mvar, expected_w_dominated))
             expected_non_dominated = expected_w_dominated[

test/acquisition/multi_objective/test_objective.py

@@ -76,7 +76,7 @@ def test_feasibility_weighted_mc_multi_output_objective(self):
                     [4.0, 1.0],
                     [5.0, 1.0],
                 ],
-                **tkwargs
+                **tkwargs,
             )
             variances = torch.zeros(5, 2, **tkwargs)
             mm = MockModel(MockPosterior(mean=means, variance=variances))

test/acquisition/test_objective.py

@@ -133,7 +133,7 @@ def test_expectation_posterior_transform(self):
                 [0.2, 0.15, 0.2, 0.7, 1.0, 0.7],
                 [0.1, 0.1, 0.05, 0.6, 0.7, 1.0],
             ],
-            **tkwargs
+            **tkwargs,
         )
         org_mvn = MultivariateNormal(org_loc, lazify(org_covar))
         org_post = GPyTorchPosterior(mvn=org_mvn)
@@ -168,7 +168,7 @@ def test_expectation_posterior_transform(self):
                 [0.0, 0.0, 0.0, 0.0, 0.4, 0.3, 1.4, 0.5],
                 [0.0, 0.0, 0.0, 0.0, 0.3, 0.2, 0.5, 1.2],
             ],
-            **tkwargs
+            **tkwargs,
         )
         # Making it batched by adding two more batches, mostly the same.
         org_loc = org_loc.repeat(3, 1)
@@ -217,7 +217,7 @@ def test_expectation_posterior_transform(self):
                 [0.0, 0.0, 0.875, 0.35],
                 [0.0, 0.0, 0.35, 1.05],
             ],
-            **tkwargs
+            **tkwargs,
         ).repeat(3, 1, 1)
         self.assertTrue(torch.allclose(tf_mvn.loc, expected_loc, atol=1e-3))
         self.assertTrue(

test/models/test_pairwise_gp.py

@@ -60,7 +60,7 @@ def test_pairwise_gp(self):
                 batch_shape=batch_shape,
                 X_dim=X_dim,
                 likelihood_cls=likelihood_cls,
-                **tkwargs
+                **tkwargs,
             )
             train_X = model_kwargs["datapoints"]
             train_comp = model_kwargs["comparisons"]
@@ -201,7 +201,7 @@ def test_condition_on_observations(self):
                 batch_shape=batch_shape,
                 X_dim=X_dim,
                 likelihood_cls=likelihood_cls,
-                **tkwargs
+                **tkwargs,
             )
             train_X = model_kwargs["datapoints"]
             train_comp = model_kwargs["comparisons"]
@@ -304,7 +304,7 @@ def test_fantasize(self):
                 batch_shape=batch_shape,
                 X_dim=X_dim,
                 likelihood_cls=likelihood_cls,
-                **tkwargs
+                **tkwargs,
             )
 
             # fantasize