[pre-commit.ci] pre-commit autoupdate (#2743)

<!--pre-commit.ci start-->
updates:
- [github.com/astral-sh/ruff-pre-commit: v0.0.282 →
v0.0.284](astral-sh/ruff-pre-commit@v0.0.282...v0.0.284)
- [github.com/Lucas-C/pre-commit-hooks: v1.5.3 →
v1.5.4](Lucas-C/pre-commit-hooks@v1.5.3...v1.5.4)
<!--pre-commit.ci end-->

---------

Signed-off-by: Jinzhe Zeng <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jinzhe Zeng <[email protected]>

Author: pre-commit-ci[bot]

.pre-commit-config.yaml

@@ -33,7 +33,7 @@ repos:
       files: \.py$
 -   repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.0.282
+    rev: v0.0.284
     hooks:
     - id: ruff
       args: ["--fix"]
@@ -72,7 +72,7 @@ repos:
     #- id: cmake-lint
 # license header
 - repo: https://github.com/Lucas-C/pre-commit-hooks
-  rev: v1.5.3
+  rev: v1.5.4
   hooks:
     # C++, js
     -   id: insert-license

deepmd/entrypoints/train.py

@@ -445,7 +445,7 @@ def get_min_nbor_dist(jdata, rcut):
 
 
 def parse_auto_sel(sel):
-    if type(sel) is not str:
+    if not isinstance(sel, str):
         return False
     words = sel.split(":")
     if words[0] == "auto":

deepmd/env.py

@@ -418,9 +418,9 @@ def get_module(module_name: str) -> "ModuleType":
                 ) from e
             error_message = (
                 "This deepmd-kit package is inconsitent with TensorFlow "
-                "Runtime, thus an error is raised when loading {}. "
+                f"Runtime, thus an error is raised when loading {module_name}. "
                 "You need to rebuild deepmd-kit against this TensorFlow "
-                "runtime.".format(module_name)
+                "runtime."
             )
             if TF_CXX11_ABI_FLAG == 1:
                 # #1791

deepmd/utils/argcheck.py

@@ -1861,8 +1861,8 @@ def normalize_fitting_weight(fitting_keys, data_keys, fitting_weight=None):
                 else:
                     valid_fitting_keys.remove(item)
                     log.warning(
-                        "Fitting net '{}' has zero or invalid weight "
-                        "and will not be used in training.".format(item)
+                        f"Fitting net '{item}' has zero or invalid weight "
+                        "and will not be used in training."
                     )
                     new_weight[item] = 0.0
             else:

deepmd/utils/finetune.py

@@ -55,8 +55,8 @@ def replace_model_params_with_pretrained_model(
         if i not in pretrained_type_map:
             out_line_type.append(i)
     assert not out_line_type, (
-        "{} type(s) not contained in the pretrained model! "
-        "Please choose another suitable one.".format(str(out_line_type))
+        f"{str(out_line_type)} type(s) not contained in the pretrained model! "
+        "Please choose another suitable one."
     )
     if cur_type_map != pretrained_type_map:
         log.info(

deepmd/utils/multi_init.py

@@ -54,8 +54,8 @@ def replace_model_params_with_frz_multi_model(
         if i not in pretrained_type_map:
             out_line_type.append(i)
     assert not out_line_type, (
-        "{} type(s) not contained in the pretrained model! "
-        "Please choose another suitable one.".format(str(out_line_type))
+        f"{str(out_line_type)} type(s) not contained in the pretrained model! "
+        "Please choose another suitable one."
     )
     if cur_type_map != pretrained_type_map:
         log.info(

source/install/build_tf.py

@@ -150,9 +150,9 @@ def __call__(self):
             self.download()
             if not self.exists:
                 raise RuntimeError(
-                    "Download {} from {} failed! "
-                    "You can manually download it to {} and "
-                    "retry the script.".format(self.filename, self.url, str(self.path))
+                    f"Download {self.filename} from {self.url} failed! "
+                    f"You can manually download it to {str(self.path)} and "
+                    "retry the script."
                 )
         self.post_process()