chore: update pre-commit hooks (#672)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.pre-commit-config.yaml

@@ -17,7 +17,7 @@ repos:
       - id: trailing-whitespace
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.8.6"
+    rev: "v0.9.1"
     hooks:
       - id: ruff
         args: ["--fix", "--show-fixes"]

autograd/core.py

@@ -293,7 +293,7 @@ def vspace(value):
             return vspace(getval(value))
         else:
             raise TypeError(
-                "Can't find vector space for value {} of type {}. " "Valid types are {}".format(
+                "Can't find vector space for value {} of type {}. Valid types are {}".format(
                     value, type(value), VSpace.mappings.keys()
                 )
             )

autograd/numpy/linalg.py

@@ -81,11 +81,9 @@ def check_implemented():
 
         if matrix_norm:
             if not (ord is None or ord == "fro" or ord == "nuc"):
-                raise NotImplementedError(
-                    "Gradient of matrix norm not " "implemented for ord={}".format(ord)
-                )
+                raise NotImplementedError("Gradient of matrix norm not implemented for ord={}".format(ord))
         elif not (ord is None or ord > 1):
-            raise NotImplementedError("Gradient of norm not " "implemented for ord={}".format(ord))
+            raise NotImplementedError("Gradient of norm not implemented for ord={}".format(ord))
 
     if axis is None:
         expand = lambda a: a
@@ -139,11 +137,9 @@ def check_implemented():
 
         if matrix_norm:
             if not (ord is None or ord == "fro" or ord == "nuc"):
-                raise NotImplementedError(
-                    "Gradient of matrix norm not " "implemented for ord={}".format(ord)
-                )
+                raise NotImplementedError("Gradient of matrix norm not implemented for ord={}".format(ord))
         elif not (ord is None or ord > 1):
-            raise NotImplementedError("Gradient of norm not " "implemented for ord={}".format(ord))
+            raise NotImplementedError("Gradient of norm not implemented for ord={}".format(ord))
 
     if axis is None:
         contract = lambda a: anp.sum(a)

autograd/numpy/numpy_vjps.py

@@ -251,11 +251,11 @@
 def grad_rollaxis(ans, a, axis, start=0):
     if axis < 0:
         raise NotImplementedError(
-            "Gradient of rollaxis not implemented for axis < 0. " "Please use moveaxis instead."
+            "Gradient of rollaxis not implemented for axis < 0. Please use moveaxis instead."
         )
     elif start < 0:
         raise NotImplementedError(
-            "Gradient of rollaxis not implemented for start < 0. " "Please use moveaxis instead."
+            "Gradient of rollaxis not implemented for start < 0. Please use moveaxis instead."
         )
     return lambda g: anp.rollaxis(g, start - 1, axis) if start > axis else anp.rollaxis(g, start, axis + 1)
 
@@ -293,9 +293,7 @@ def helper(g, n):
 def grad_gradient(ans, x, *vargs, **kwargs):
     axis = kwargs.pop("axis", None)
     if vargs or kwargs:
-        raise NotImplementedError(
-            "The only optional argument currently supported for np.gradient " "is axis."
-        )
+        raise NotImplementedError("The only optional argument currently supported for np.gradient is axis.")
     if axis is None:
         axis = range(x.ndim)
     elif type(axis) is int:

autograd/numpy/numpy_wrapper.py

@@ -76,7 +76,7 @@ def array(A, *args, **kwargs):
 def wrap_if_boxes_inside(raw_array, slow_op_name=None):
     if raw_array.dtype is _np.dtype("O"):
         if slow_op_name:
-            warnings.warn("{} is slow for array inputs. " "np.concatenate() is faster.".format(slow_op_name))
+            warnings.warn("{} is slow for array inputs. np.concatenate() is faster.".format(slow_op_name))
         return array_from_args((), {}, *raw_array.ravel()).reshape(raw_array.shape)
     else:
         return raw_array

autograd/scipy/stats/multivariate_normal.py

@@ -25,7 +25,7 @@ def generalized_outer_product(x):
 def covgrad(x, mean, cov, allow_singular=False):
     if allow_singular:
         raise NotImplementedError(
-            "The multivariate normal pdf is not " "differentiable w.r.t. a singular covariance matix"
+            "The multivariate normal pdf is not differentiable w.r.t. a singular covariance matix"
         )
     J = np.linalg.inv(cov)
     solved = np.matmul(J, np.expand_dims(x - mean, -1))

autograd/test_util.py

@@ -39,7 +39,7 @@ def check_vjp(f, x):
     vjv_exact = x_vs.inner_prod(x_v, vjp_y)
     vjv_numeric = y_vs.inner_prod(y_v, jvp(x_v))
     assert scalar_close(vjv_numeric, vjv_exact), (
-        "Derivative (VJP) check of {} failed with arg {}:\n" "analytic: {}\nnumeric:  {}".format(
+        "Derivative (VJP) check of {} failed with arg {}:\nanalytic: {}\nnumeric:  {}".format(
             get_name(f), x, vjv_exact, vjv_numeric
         )
     )

examples/fluidsim/fluidsim.py

@@ -143,7 +143,5 @@ def callback(params):
     simulate(init_vx, init_vy, init_smoke, simulation_timesteps, ax, render=True)
 
     print("Converting frames to an animated GIF...")
-    os.system(
-        "convert -delay 5 -loop 0 step*.png" " -delay 250 step100.png surprise.gif"
-    )  # Using imagemagick.
+    os.system("convert -delay 5 -loop 0 step*.png -delay 250 step100.png surprise.gif")  # Using imagemagick.
     os.system("rm step*.png")

examples/fluidsim/wing.py

@@ -183,8 +183,6 @@ def callback(weights):
 
     print("Converting frames to an animated GIF...")  # Using imagemagick.
     os.system(
-        "convert -delay 5 -loop 0 step*.png " "-delay 250 step{:03d}.png wing.gif".format(
-            simulation_timesteps
-        )
+        "convert -delay 5 -loop 0 step*.png -delay 250 step{:03d}.png wing.gif".format(simulation_timesteps)
     )
     os.system("rm step*.png")