Fix UP038 (isinstance(..., X | Y))

Author: Armavica

pymc/backends/arviz.py

@@ -78,7 +78,7 @@ def is_data(name, var, model) -> bool:
             and var not in model.potentials
             and var not in model.value_vars
             and name not in observations
-            and isinstance(var, (Constant, SharedVariable))
+            and isinstance(var, Constant | SharedVariable)
         )
 
     # The assumption is that constants (like pm.Data) are named

pymc/distributions/censored.py

@@ -89,7 +89,7 @@ class Censored(Distribution):
     @classmethod
     def dist(cls, dist, lower, upper, **kwargs):
         if not isinstance(dist, TensorVariable) or not isinstance(
-            dist.owner.op, (RandomVariable, SymbolicRandomVariable)
+            dist.owner.op, RandomVariable | SymbolicRandomVariable
         ):
             raise ValueError(
                 f"Censoring dist must be a distribution created via the `.dist()` API, got {type(dist)}"

pymc/distributions/distribution.py

@@ -102,7 +102,7 @@ def rewrite_support_point_scan_node(self, node):
 
         for nd in local_fgraph_topo:
             if nd not in to_replace_set and isinstance(
-                nd.op, (RandomVariable, SymbolicRandomVariable)
+                nd.op, RandomVariable | SymbolicRandomVariable
             ):
                 replace_with_support_point.append(nd.out)
                 to_replace_set.add(nd)
@@ -132,7 +132,7 @@ def add_requirements(self, fgraph):
 
     def apply(self, fgraph):
         for node in fgraph.toposort():
-            if isinstance(node.op, (RandomVariable, SymbolicRandomVariable)):
+            if isinstance(node.op, RandomVariable | SymbolicRandomVariable):
                 fgraph.replace(node.out, support_point(node.out))
             elif isinstance(node.op, Scan):
                 new_node = self.rewrite_support_point_scan_node(node)
@@ -837,7 +837,7 @@ def custom_dist_get_support_point(op, rv, size, *params):
                     *[
                         p
                         for p in params
-                        if not isinstance(p.type, (RandomType, RandomGeneratorType))
+                        if not isinstance(p.type, RandomType | RandomGeneratorType)
                     ],
                 )
 

pymc/distributions/mixture.py

@@ -178,7 +178,7 @@ class Mixture(Distribution):
 
     @classmethod
     def dist(cls, w, comp_dists, **kwargs):
-        if not isinstance(comp_dists, (tuple, list)):
+        if not isinstance(comp_dists, tuple | list):
             # comp_dists is a single component
             comp_dists = [comp_dists]
         elif len(comp_dists) == 1:
@@ -204,7 +204,7 @@ def dist(cls, w, comp_dists, **kwargs):
             # TODO: Allow these to not be a RandomVariable as long as we can call `ndim_supp` on them
             #  and resize them
             if not isinstance(dist, TensorVariable) or not isinstance(
-                dist.owner.op, (RandomVariable, SymbolicRandomVariable)
+                dist.owner.op, RandomVariable | SymbolicRandomVariable
             ):
                 raise ValueError(
                     f"Component dist must be a distribution created via the `.dist()` API, got {type(dist)}"

pymc/distributions/multivariate.py

@@ -1083,7 +1083,7 @@ def WishartBartlett(name, S, nu, is_cholesky=False, return_cholesky=False, initv
 
 def _lkj_normalizing_constant(eta, n):
     # TODO: This is mixing python branching with the potentially symbolic n and eta variables
-    if not isinstance(eta, (int, float)):
+    if not isinstance(eta, int | float):
         raise NotImplementedError("eta must be an int or float")
     if not isinstance(n, int):
         raise NotImplementedError("n must be an integer")
@@ -1185,7 +1185,7 @@ def dist(cls, n, eta, sd_dist, **kwargs):
         if not (
             isinstance(sd_dist, Variable)
             and sd_dist.owner is not None
-            and isinstance(sd_dist.owner.op, (RandomVariable, SymbolicRandomVariable))
+            and isinstance(sd_dist.owner.op, RandomVariable | SymbolicRandomVariable)
             and sd_dist.owner.op.ndim_supp < 2
         ):
             raise TypeError("sd_dist must be a scalar or vector distribution variable")
@@ -2262,7 +2262,7 @@ def logp(value, mu, W, alpha, tau):
         TensorVariable
         """
 
-        sparse = isinstance(W, (pytensor.sparse.SparseConstant, pytensor.sparse.SparseVariable))
+        sparse = isinstance(W, pytensor.sparse.SparseConstant | pytensor.sparse.SparseVariable)
 
         if sparse:
             D = sp_sum(W, axis=0)

pymc/distributions/shape_utils.py

@@ -193,7 +193,7 @@ def convert_dims(dims: Dims | None) -> StrongDims | None:
 
     if isinstance(dims, str):
         dims = (dims,)
-    elif isinstance(dims, (list, tuple)):
+    elif isinstance(dims, list | tuple):
         dims = tuple(dims)
     else:
         raise ValueError(f"The `dims` parameter must be a tuple, str or list. Actual: {type(dims)}")
@@ -209,7 +209,7 @@ def convert_shape(shape: Shape) -> StrongShape | None:
         shape = (shape,)
     elif isinstance(shape, TensorVariable) and shape.ndim == 1:
         shape = tuple(shape)
-    elif isinstance(shape, (list, tuple)):
+    elif isinstance(shape, list | tuple):
         shape = tuple(shape)
     else:
         raise ValueError(
@@ -227,7 +227,7 @@ def convert_size(size: Size) -> StrongSize | None:
         size = (size,)
     elif isinstance(size, TensorVariable) and size.ndim == 1:
         size = tuple(size)
-    elif isinstance(size, (list, tuple)):
+    elif isinstance(size, list | tuple):
         size = tuple(size)
     else:
         raise ValueError(

pymc/distributions/timeseries.py

@@ -88,15 +88,15 @@ def dist(cls, init_dist, innovation_dist, steps=None, **kwargs) -> pt.TensorVari
         if not (
             isinstance(init_dist, pt.TensorVariable)
             and init_dist.owner is not None
-            and isinstance(init_dist.owner.op, (RandomVariable, SymbolicRandomVariable))
+            and isinstance(init_dist.owner.op, RandomVariable | SymbolicRandomVariable)
         ):
             raise TypeError("init_dist must be a distribution variable")
         check_dist_not_registered(init_dist)
 
         if not (
             isinstance(innovation_dist, pt.TensorVariable)
             and innovation_dist.owner is not None
-            and isinstance(innovation_dist.owner.op, (RandomVariable, SymbolicRandomVariable))
+            and isinstance(innovation_dist.owner.op, RandomVariable | SymbolicRandomVariable)
         ):
             raise TypeError("innovation_dist must be a distribution variable")
         check_dist_not_registered(innovation_dist)
@@ -129,7 +129,7 @@ def get_steps(cls, innovation_dist, steps, shape, dims, observed):
         if not (
             isinstance(innovation_dist, pt.TensorVariable)
             and innovation_dist.owner is not None
-            and isinstance(innovation_dist.owner.op, (RandomVariable, SymbolicRandomVariable))
+            and isinstance(innovation_dist.owner.op, RandomVariable | SymbolicRandomVariable)
         ):
             raise TypeError("innovation_dist must be a distribution variable")
 
@@ -549,7 +549,7 @@ def dist(
 
         if init_dist is not None:
             if not isinstance(init_dist, TensorVariable) or not isinstance(
-                init_dist.owner.op, (RandomVariable, SymbolicRandomVariable)
+                init_dist.owner.op, RandomVariable | SymbolicRandomVariable
             ):
                 raise ValueError(
                     f"Init dist must be a distribution created via the `.dist()` API, "
@@ -948,7 +948,7 @@ def dist(cls, dt, sde_fn, sde_pars, *, init_dist=None, steps=None, **kwargs):
 
         if init_dist is not None:
             if not isinstance(init_dist, TensorVariable) or not isinstance(
-                init_dist.owner.op, (RandomVariable, SymbolicRandomVariable)
+                init_dist.owner.op, RandomVariable | SymbolicRandomVariable
             ):
                 raise ValueError(
                     f"Init dist must be a distribution created via the `.dist()` API, "

pymc/gp/cov.py

@@ -256,11 +256,7 @@ def _merge_factors_cov(self, X, Xs=None, diag=False):
 
             elif isinstance(
                 factor,
-                (
-                    TensorConstant,
-                    TensorVariable,
-                    TensorSharedVariable,
-                ),
+                TensorConstant | TensorVariable | TensorSharedVariable,
             ):
                 if factor.ndim == 2 and diag:
                     factor_list.append(pt.diag(factor))
@@ -524,7 +520,7 @@ def __init__(
         if (ls is None and ls_inv is None) or (ls is not None and ls_inv is not None):
             raise ValueError("Only one of 'ls' or 'ls_inv' must be provided")
         elif ls_inv is not None:
-            if isinstance(ls_inv, (list, tuple)):
+            if isinstance(ls_inv, list | tuple):
                 ls = 1.0 / np.asarray(ls_inv)
             else:
                 ls = 1.0 / ls_inv

pymc/gp/hsgp_approx.py

@@ -328,7 +328,7 @@ def prior_linearized(self, Xs: TensorLike):
 
         # If not provided, use Xs and c to set L
         if self._L is None:
-            assert isinstance(self._c, (numbers.Real, np.ndarray, pt.TensorVariable))
+            assert isinstance(self._c, numbers.Real | np.ndarray | pt.TensorVariable)
             self.L = pt.as_tensor(set_boundary(Xs, self._c))
         else:
             self.L = self._L

pymc/gp/util.py

@@ -113,7 +113,7 @@ def kmeans_inducing_points(n_inducing, X, **kmeans_kwargs):
     # first whiten X
     if isinstance(X, TensorConstant):
         X = X.value
-    elif isinstance(X, (np.ndarray, tuple, list)):
+    elif isinstance(X, np.ndarray | tuple | list):
         X = np.asarray(X)
     else:
         raise TypeError(