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

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deepmd/dpmodel/atomic_model/linear_atomic_model.py

@@ -457,9 +457,9 @@ def _compute_weight(
         list[np.ndarray]
             the atomic ZBL weight for interpolation. (nframes, nloc, 1)
         """
-        assert (
-            self.sw_rmax > self.sw_rmin
-        ), "The upper boundary `sw_rmax` must be greater than the lower boundary `sw_rmin`."
+        assert self.sw_rmax > self.sw_rmin, (
+            "The upper boundary `sw_rmax` must be greater than the lower boundary `sw_rmin`."
+        )
 
         xp = array_api_compat.array_namespace(extended_coord, extended_atype)
         dp_nlist = nlists_[0]

deepmd/dpmodel/descriptor/dpa1.py

@@ -432,9 +432,9 @@ def change_type_map(
         """Change the type related params to new ones, according to `type_map` and the original one in the model.
         If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types.
         """
-        assert (
-            self.type_map is not None
-        ), "'type_map' must be defined when performing type changing!"
+        assert self.type_map is not None, (
+            "'type_map' must be defined when performing type changing!"
+        )
         remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map)
         obj = self.se_atten
         obj.ntypes = len(type_map)

deepmd/dpmodel/descriptor/dpa2.py

@@ -543,9 +543,9 @@ def init_subclass_params(sub_data, sub_class):
             )
         self.rcsl_list.sort()
         for ii in range(1, len(self.rcsl_list)):
-            assert (
-                self.rcsl_list[ii - 1][1] <= self.rcsl_list[ii][1]
-            ), "rcut and sel are not in the same order"
+            assert self.rcsl_list[ii - 1][1] <= self.rcsl_list[ii][1], (
+                "rcut and sel are not in the same order"
+            )
         self.rcut_list = [ii[0] for ii in self.rcsl_list]
         self.nsel_list = [ii[1] for ii in self.rcsl_list]
         self.use_econf_tebd = use_econf_tebd
@@ -678,9 +678,9 @@ def change_type_map(
         """Change the type related params to new ones, according to `type_map` and the original one in the model.
         If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types.
         """
-        assert (
-            self.type_map is not None
-        ), "'type_map' must be defined when performing type changing!"
+        assert self.type_map is not None, (
+            "'type_map' must be defined when performing type changing!"
+        )
         remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map)
         self.type_map = type_map
         self.type_embedding.change_type_map(type_map=type_map)

deepmd/dpmodel/descriptor/hybrid.py

@@ -71,7 +71,9 @@ def __init__(
         for ii in range(1, self.numb_descrpt):
             assert (
                 self.descrpt_list[ii].get_ntypes() == self.descrpt_list[0].get_ntypes()
-            ), f"number of atom types in {ii}th descriptor {self.descrpt_list[0].__class__.__name__} does not match others"
+            ), (
+                f"number of atom types in {ii}th descriptor {self.descrpt_list[0].__class__.__name__} does not match others"
+            )
         # if hybrid sel is larger than sub sel, the nlist needs to be cut for each type
         hybrid_sel = self.get_sel()
         nlist_cut_idx: list[np.ndarray] = []

deepmd/dpmodel/descriptor/se_t_tebd.py

@@ -270,9 +270,9 @@ def change_type_map(
         """Change the type related params to new ones, according to `type_map` and the original one in the model.
         If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types.
         """
-        assert (
-            self.type_map is not None
-        ), "'type_map' must be defined when performing type changing!"
+        assert self.type_map is not None, (
+            "'type_map' must be defined when performing type changing!"
+        )
         remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map)
         obj = self.se_ttebd
         obj.ntypes = len(type_map)

deepmd/dpmodel/fitting/general_fitting.py

@@ -242,9 +242,9 @@ def change_type_map(
         """Change the type related params to new ones, according to `type_map` and the original one in the model.
         If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types.
         """
-        assert (
-            self.type_map is not None
-        ), "'type_map' must be defined when performing type changing!"
+        assert self.type_map is not None, (
+            "'type_map' must be defined when performing type changing!"
+        )
         assert self.mixed_types, "Only models in mixed types can perform type changing!"
         remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map)
         self.type_map = type_map

deepmd/dpmodel/fitting/polarizability_fitting.py

@@ -224,9 +224,9 @@ def change_type_map(
         """Change the type related params to new ones, according to `type_map` and the original one in the model.
         If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types.
         """
-        assert (
-            self.type_map is not None
-        ), "'type_map' must be defined when performing type changing!"
+        assert self.type_map is not None, (
+            "'type_map' must be defined when performing type changing!"
+        )
         assert self.mixed_types, "Only models in mixed types can perform type changing!"
         remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map)
         super().change_type_map(type_map=type_map)
@@ -280,9 +280,9 @@ def call(
         """
         xp = array_api_compat.array_namespace(descriptor, atype)
         nframes, nloc, _ = descriptor.shape
-        assert (
-            gr is not None
-        ), "Must provide the rotation matrix for polarizability fitting."
+        assert gr is not None, (
+            "Must provide the rotation matrix for polarizability fitting."
+        )
         # (nframes, nloc, _net_out_dim)
         out = self._call_common(descriptor, atype, gr, g2, h2, fparam, aparam)[
             self.var_name

deepmd/dpmodel/utils/network.py

@@ -551,7 +551,7 @@ def check_shape_consistency(self) -> None:
                 if self.layers[ii].dim_out() != self.layers[ii + 1].dim_in():
                     raise ValueError(
                         f"the dim of layer {ii} output {self.layers[ii].dim_out} ",
-                        f"does not match the dim of layer {ii+1} ",
+                        f"does not match the dim of layer {ii + 1} ",
                         f"output {self.layers[ii].dim_out}",
                     )
 

deepmd/dpmodel/utils/type_embed.py

@@ -166,9 +166,9 @@ def change_type_map(
         """Change the type related params to new ones, according to `type_map` and the original one in the model.
         If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types.
         """
-        assert (
-            self.type_map is not None
-        ), "'type_map' must be defined when performing type changing!"
+        assert self.type_map is not None, (
+            "'type_map' must be defined when performing type changing!"
+        )
         remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map)
         if not self.use_econf_tebd:
             do_resnet = self.neuron[0] in [
@@ -177,9 +177,9 @@ def change_type_map(
                 len(type_map),
                 len(type_map) * 2,
             ]
-            assert (
-                not do_resnet or self.activation_function == "Linear"
-            ), "'activation_function' must be 'Linear' when performing type changing on resnet structure!"
+            assert not do_resnet or self.activation_function == "Linear", (
+                "'activation_function' must be 'Linear' when performing type changing on resnet structure!"
+            )
             first_layer_matrix = self.embedding_net.layers[0].w
             eye_vector = np.eye(self.ntypes, dtype=PRECISION_DICT[self.precision])
             # preprocess for resnet connection
@@ -227,9 +227,9 @@ def get_econf_tebd(type_map, precision: str = "default"):
     )
     from deepmd.utils.econf_embd import type_map as periodic_table
 
-    assert (
-        type_map is not None
-    ), "When using electronic configuration type embedding, type_map must be provided!"
+    assert type_map is not None, (
+        "When using electronic configuration type embedding, type_map must be provided!"
+    )
 
     missing_types = [t for t in type_map if t not in periodic_table]
     assert not missing_types, (

deepmd/infer/model_devi.py

@@ -211,7 +211,7 @@ def write_model_devi_out(
             f"min_devi_{item}",
             f"avg_devi_{item}",
         )
-    header += f'{"devi_e":19s}'
+    header += f"{'devi_e':19s}"
     if atomic:
         header += f"{'atm_devi_f(N)':19s}"
     with open(fname, "ab") as fp:

deepmd/loggers/training.py

@@ -9,7 +9,7 @@ def format_training_message(
     wall_time: float,
 ) -> str:
     """Format a training message."""
-    return f"batch {batch:7d}: " f"total wall time = {wall_time:.2f} s"
+    return f"batch {batch:7d}: total wall time = {wall_time:.2f} s"
 
 
 def format_training_message_per_task(

deepmd/main.py

@@ -72,7 +72,7 @@ class DeprecateAction(argparse.Action):
     def __init__(self, *args, **kwargs) -> None:
         self.call_count = 0
         if "help" in kwargs:
-            kwargs["help"] = f'[DEPRECATED] {kwargs["help"]}'
+            kwargs["help"] = f"[DEPRECATED] {kwargs['help']}"
         super().__init__(*args, **kwargs)
 
     def __call__(self, parser, namespace, values, option_string=None):

deepmd/pd/entrypoints/main.py

@@ -247,9 +247,9 @@ def train(
     if multi_task:
         config["model"], shared_links = preprocess_shared_params(config["model"])
         # handle the special key
-        assert (
-            "RANDOM" not in config["model"]["model_dict"]
-        ), "Model name can not be 'RANDOM' in multi-task mode!"
+        assert "RANDOM" not in config["model"]["model_dict"], (
+            "Model name can not be 'RANDOM' in multi-task mode!"
+        )
 
     # update fine-tuning config
     finetune_links = None
@@ -404,9 +404,9 @@ def change_bias(
     multi_task = "model_dict" in model_params
     bias_adjust_mode = "change-by-statistic" if mode == "change" else "set-by-statistic"
     if multi_task:
-        assert (
-            model_branch is not None
-        ), "For multitask model, the model branch must be set!"
+        assert model_branch is not None, (
+            "For multitask model, the model branch must be set!"
+        )
         assert model_branch in model_params["model_dict"], (
             f"For multitask model, the model branch must be in the 'model_dict'! "
             f"Available options are : {list(model_params['model_dict'].keys())}."
@@ -427,12 +427,12 @@ def change_bias(
 
     if bias_value is not None:
         # use user-defined bias
-        assert model_to_change.model_type in [
-            "ener"
-        ], "User-defined bias is only available for energy model!"
-        assert (
-            len(bias_value) == len(type_map)
-        ), f"The number of elements in the bias should be the same as that in the type_map: {type_map}."
+        assert model_to_change.model_type in ["ener"], (
+            "User-defined bias is only available for energy model!"
+        )
+        assert len(bias_value) == len(type_map), (
+            f"The number of elements in the bias should be the same as that in the type_map: {type_map}."
+        )
         old_bias = model_to_change.get_out_bias()
         bias_to_set = paddle.to_tensor(
             bias_value, dtype=old_bias.dtype, place=old_bias.place

deepmd/pd/infer/deep_eval.py

@@ -92,12 +92,12 @@ def __init__(
                 model_keys = list(self.input_param["model_dict"].keys())
                 if isinstance(head, int):
                     head = model_keys[0]
-                assert (
-                    head is not None
-                ), f"Head must be set for multitask model! Available heads are: {model_keys}"
-                assert (
-                    head in model_keys
-                ), f"No head named {head} in model! Available heads are: {model_keys}"
+                assert head is not None, (
+                    f"Head must be set for multitask model! Available heads are: {model_keys}"
+                )
+                assert head in model_keys, (
+                    f"No head named {head} in model! Available heads are: {model_keys}"
+                )
                 self.input_param = self.input_param["model_dict"][head]
                 state_dict_head = {"_extra_state": state_dict["_extra_state"]}
                 for item in state_dict:

deepmd/pd/model/descriptor/descriptor.py

@@ -128,9 +128,9 @@ def share_params(self, base_class, shared_level, resume=False):
         If not start from checkpoint (resume is False),
         some separated parameters (e.g. mean and stddev) will be re-calculated across different classes.
         """
-        assert (
-            self.__class__ == base_class.__class__
-        ), "Only descriptors of the same type can share params!"
+        assert self.__class__ == base_class.__class__, (
+            "Only descriptors of the same type can share params!"
+        )
         if shared_level == 0:
             # link buffers
             if hasattr(self, "mean"):

deepmd/pd/model/descriptor/dpa1.py

@@ -378,9 +378,9 @@ def share_params(self, base_class, shared_level, resume=False) -> None:
         If not start from checkpoint (resume is False),
         some separated parameters (e.g. mean and stddev) will be re-calculated across different classes.
         """
-        assert (
-            self.__class__ == base_class.__class__
-        ), "Only descriptors of the same type can share params!"
+        assert self.__class__ == base_class.__class__, (
+            "Only descriptors of the same type can share params!"
+        )
         # For DPA1 descriptors, the user-defined share-level
         # shared_level: 0
         # share all parameters in both type_embedding and se_atten
@@ -449,9 +449,9 @@ def change_type_map(
         """Change the type related params to new ones, according to `type_map` and the original one in the model.
         If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types.
         """
-        assert (
-            self.type_map is not None
-        ), "'type_map' must be defined when performing type changing!"
+        assert self.type_map is not None, (
+            "'type_map' must be defined when performing type changing!"
+        )
         remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map)
         obj = self.se_atten
         obj.ntypes = len(type_map)

deepmd/pd/model/descriptor/dpa2.py

@@ -254,9 +254,9 @@ def init_subclass_params(sub_data, sub_class):
             )
         self.rcsl_list.sort()
         for ii in range(1, len(self.rcsl_list)):
-            assert (
-                self.rcsl_list[ii - 1][1] <= self.rcsl_list[ii][1]
-            ), "rcut and sel are not in the same order"
+            assert self.rcsl_list[ii - 1][1] <= self.rcsl_list[ii][1], (
+                "rcut and sel are not in the same order"
+            )
         self.rcut_list = [ii[0] for ii in self.rcsl_list]
         self.nsel_list = [ii[1] for ii in self.rcsl_list]
         self.use_econf_tebd = use_econf_tebd
@@ -386,9 +386,9 @@ def share_params(self, base_class, shared_level, resume=False) -> None:
         If not start from checkpoint (resume is False),
         some separated parameters (e.g. mean and stddev) will be re-calculated across different classes.
         """
-        assert (
-            self.__class__ == base_class.__class__
-        ), "Only descriptors of the same type can share params!"
+        assert self.__class__ == base_class.__class__, (
+            "Only descriptors of the same type can share params!"
+        )
         # For DPA2 descriptors, the user-defined share-level
         # shared_level: 0
         # share all parameters in type_embedding, repinit and repformers
@@ -419,9 +419,9 @@ def change_type_map(
         """Change the type related params to new ones, according to `type_map` and the original one in the model.
         If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types.
         """
-        assert (
-            self.type_map is not None
-        ), "'type_map' must be defined when performing type changing!"
+        assert self.type_map is not None, (
+            "'type_map' must be defined when performing type changing!"
+        )
         remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map)
         self.type_map = type_map
         self.type_embedding.change_type_map(type_map=type_map)

deepmd/pd/model/descriptor/se_a.py

@@ -169,9 +169,9 @@ def share_params(self, base_class, shared_level, resume=False) -> None:
         If not start from checkpoint (resume is False),
         some separated parameters (e.g. mean and stddev) will be re-calculated across different classes.
         """
-        assert (
-            self.__class__ == base_class.__class__
-        ), "Only descriptors of the same type can share params!"
+        assert self.__class__ == base_class.__class__, (
+            "Only descriptors of the same type can share params!"
+        )
         # For SeA descriptors, the user-defined share-level
         # shared_level: 0
         # share all parameters in sea

deepmd/pd/model/descriptor/se_t_tebd.py

@@ -246,9 +246,9 @@ def share_params(self, base_class, shared_level, resume=False) -> None:
         If not start from checkpoint (resume is False),
         some separated parameters (e.g. mean and stddev) will be re-calculated across different classes.
         """
-        assert (
-            self.__class__ == base_class.__class__
-        ), "Only descriptors of the same type can share params!"
+        assert self.__class__ == base_class.__class__, (
+            "Only descriptors of the same type can share params!"
+        )
         # For DPA1 descriptors, the user-defined share-level
         # shared_level: 0
         # share all parameters in both type_embedding and se_ttebd
@@ -317,9 +317,9 @@ def change_type_map(
         """Change the type related params to new ones, according to `type_map` and the original one in the model.
         If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types.
         """
-        assert (
-            self.type_map is not None
-        ), "'type_map' must be defined when performing type changing!"
+        assert self.type_map is not None, (
+            "'type_map' must be defined when performing type changing!"
+        )
         remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map)
         obj = self.se_ttebd
         obj.ntypes = len(type_map)