[pre-commit.ci] auto fixes from pre-commit.com hooks

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deepmd/pt/model/task/polarizability.py

@@ -119,7 +119,9 @@ def __init__(
             self.scale, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE
         ).view(ntypes, 1)
         self.shift_diag = shift_diag
-        self.constant_matrix = torch.zeros(ntypes, dtype = env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE)
+        self.constant_matrix = torch.zeros(
+            ntypes, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE
+        )
         super().__init__(
             var_name=kwargs.pop("var_name", "polar"),
             ntypes=ntypes,
@@ -215,7 +217,9 @@ def compute_output_stats(
                     else:
                         if not sampled[sys]["find_polarizability"] > 0.0:
                             continue
-                        sys_type_count = np.zeros((nframs, self.ntypes), dtype=env.GLOBAL_NP_FLOAT_PRECISION)
+                        sys_type_count = np.zeros(
+                            (nframs, self.ntypes), dtype=env.GLOBAL_NP_FLOAT_PRECISION
+                        )
                         for itype in range(self.ntypes):
                             type_mask = sampled[sys]["type"] == itype
                             sys_type_count[:, itype] = type_mask.sum(dim=1).numpy(
@@ -227,8 +231,10 @@ def compute_output_stats(
                         sys_atom_polar = compute_stats_from_redu(
                             sys_bias_redu, sys_type_count, rcond=self.rcond
                         )[0]
-                    cur_constant_matrix = np.zeros(self.ntypes, dtype= env.GLOBAL_NP_FLOAT_PRECISION)
-
+                    cur_constant_matrix = np.zeros(
+                        self.ntypes, dtype=env.GLOBAL_NP_FLOAT_PRECISION
+                    )
+
                     for itype in range(self.ntypes):
                         cur_constant_matrix[itype] = np.mean(
                             np.diagonal(sys_atom_polar[itype].reshape(3, 3))
@@ -242,7 +248,6 @@ def compute_output_stats(
                 stat_file_path.save_numpy(self.constant_matrix)
             self.constant_matrix = torch.tensor(constant_matrix, device=env.DEVICE)
 
-
     def forward(
         self,
         descriptor: torch.Tensor,

source/tests/pt/model/test_polar_stat.py

@@ -22,7 +22,7 @@ class TestConsistency(unittest.TestCase):
     def setUp(self) -> None:
         types = torch.randint(0, 4, (1, 5), device=env.DEVICE)
         types = torch.cat((types, types, types), dim=0)
-        types[:,-1] = 3
+        types[:, -1] = 3
         ntypes = 4
         atomic_polarizability = torch.rand((3, 5, 9), device=env.DEVICE)
         polarizability = torch.rand((3, 9), device=env.DEVICE)