Simplify some type checking

pytensor/compile/function/pfunc.py

@@ -515,9 +515,7 @@ def construct_pfunc_ins_and_outs(
     if not isinstance(params, list | tuple):
         raise TypeError("The `params` argument must be a list or a tuple")
 
-    if not isinstance(no_default_updates, bool) and not isinstance(
-        no_default_updates, list
-    ):
+    if not isinstance(no_default_updates, bool | list):
         raise TypeError("The `no_default_update` argument must be a boolean or list")
 
     if len(updates) > 0 and not all(

pytensor/compile/io.py

@@ -207,7 +207,7 @@ class In(SymbolicInput):
         if implicit is None:
             from pytensor.compile.sharedvalue import SharedVariable
 
-            implicit = isinstance(value, Container) or isinstance(value, SharedVariable)
+            implicit = isinstance(value, Container | SharedVariable)
         super().__init__(
             variable=variable,
             name=name,

pytensor/gradient.py

@@ -1788,7 +1788,7 @@ def verify_grad(
     o_fn = fn_maker(tensor_pt, o_output, name="gradient.py fwd")
     o_fn_out = o_fn(*[p.copy() for p in pt])
 
-    if isinstance(o_fn_out, tuple) or isinstance(o_fn_out, list):
+    if isinstance(o_fn_out, tuple | list):
         raise TypeError(
             "It seems like you are trying to use verify_grad "
             "on an Op or a function which outputs a list: there should"

pytensor/scan/basic.py

@@ -68,14 +68,14 @@ def get_updates_and_outputs(ls):
         """
         # Is `x` a container we can iterate on?
         iter_on = None
-        if isinstance(x, list) or isinstance(x, tuple):
+        if isinstance(x, list | tuple):
             iter_on = x
         elif isinstance(x, dict):
             iter_on = x.items()
         if iter_on is not None:
             return all(_filter(y) for y in iter_on)
         else:
-            return isinstance(x, Variable) or isinstance(x, until)
+            return isinstance(x, Variable | until)
 
     if not _filter(ls):
         raise ValueError(
@@ -840,11 +840,7 @@ def scan(
     # add only the non-shared variables and non-constants to the arguments of
     # the dummy function [ a function should not get shared variables or
     # constants as input ]
-    dummy_args = [
-        arg
-        for arg in args
-        if (not isinstance(arg, SharedVariable) and not isinstance(arg, Constant))
-    ]
+    dummy_args = [arg for arg in args if not isinstance(arg, SharedVariable | Constant)]
     # when we apply the lambda expression we get a mixture of update rules
     # and outputs that needs to be separated
 
@@ -1043,16 +1039,14 @@ def scan(
     other_inner_args = []
 
     other_scan_args += [
-        arg
-        for arg in non_seqs
-        if (not isinstance(arg, SharedVariable) and not isinstance(arg, Constant))
+        arg for arg in non_seqs if not isinstance(arg, SharedVariable | Constant)
     ]
 
     # Step 5.6 all shared variables with no update rules
     other_inner_args += [
         safe_new(arg, "_copy")
         for arg in non_seqs
-        if (not isinstance(arg, SharedVariable) and not isinstance(arg, Constant))
+        if not isinstance(arg, SharedVariable | Constant)
     ]
 
     inner_replacements.update(dict(zip(other_scan_args, other_inner_args)))

pytensor/tensor/basic.py

@@ -1956,9 +1956,7 @@ def extract_constant(x, elemwise=True, only_process_constants=False):
         x = get_underlying_scalar_constant_value(x, elemwise, only_process_constants)
     except NotScalarConstantError:
         pass
-    if isinstance(x, ps.ScalarVariable) or isinstance(
-        x, ps.sharedvar.ScalarSharedVariable
-    ):
+    if isinstance(x, ps.ScalarVariable | ps.sharedvar.ScalarSharedVariable):
         if x.owner and isinstance(x.owner.op, ScalarFromTensor):
             x = x.owner.inputs[0]
         else:

tests/tensor/rewriting/test_math.py

@@ -2204,9 +2204,7 @@ class TestLocalErf:
         assert len(topo) == 2
         assert topo[0].op == erf
         assert isinstance(topo[1].op, Elemwise)
-        assert isinstance(topo[1].op.scalar_op, ps.Add) or isinstance(
-            topo[1].op.scalar_op, ps.Sub
-        )
+        assert isinstance(topo[1].op.scalar_op, ps.Add | ps.Sub)
 
     def test_local_erf_minus_one(self):
         val = np.asarray([-30, -3, -2, -1, 0, 1, 2, 3, 30], dtype=config.floatX)
@@ -2227,9 +2225,7 @@ class TestLocalErf:
         assert len(topo) == 2
         assert topo[0].op == erf
         assert isinstance(topo[1].op, Elemwise)
-        assert isinstance(topo[1].op.scalar_op, ps.Add) or isinstance(
-            topo[1].op.scalar_op, ps.Sub
-        )
+        assert isinstance(topo[1].op.scalar_op, ps.Add | ps.Sub)
 
 
 @pytest.mark.skipif(