Replace more "if not a or not b" with "if not (a and b)"

pytensor/sparse/__init__.py

@@ -15,8 +15,8 @@ def sparse_grad(var):
     """
     from pytensor.tensor.subtensor import AdvancedSubtensor, AdvancedSubtensor1
 
-    if var.owner is None or not isinstance(
-        var.owner.op, AdvancedSubtensor | AdvancedSubtensor1
+    if not (
+        var.owner and isinstance(var.owner.op, AdvancedSubtensor | AdvancedSubtensor1)
     ):
         raise TypeError(
             "Sparse gradient is only implemented for AdvancedSubtensor and AdvancedSubtensor1"

pytensor/tensor/math.py

@@ -2134,8 +2134,8 @@ def dense_dot(a, b):
     """
     a, b = as_tensor_variable(a), as_tensor_variable(b)
 
-    if not isinstance(a.type, DenseTensorType) or not isinstance(
-        b.type, DenseTensorType
+    if not (
+        isinstance(a.type, DenseTensorType) and isinstance(b.type, DenseTensorType)
     ):
         raise TypeError("The dense dot product is only supported for dense types")
 

pytensor/tensor/rewriting/basic.py

@@ -658,13 +658,13 @@ def local_cast_cast(fgraph, node):
           and the first cast cause an upcast.
 
     """
-    if not isinstance(node.op, Elemwise) or not isinstance(node.op.scalar_op, ps.Cast):
+    if not (isinstance(node.op, Elemwise) and isinstance(node.op.scalar_op, ps.Cast)):
         return
     x = node.inputs[0]
-    if (
-        x.owner is None
-        or not isinstance(x.owner.op, Elemwise)
-        or not isinstance(x.owner.op.scalar_op, ps.Cast)
+    if not (
+        x.owner
+        and isinstance(x.owner.op, Elemwise)
+        and isinstance(x.owner.op.scalar_op, ps.Cast)
     ):
         return
 
@@ -1053,8 +1053,9 @@ def local_merge_switch_same_cond(fgraph, node):
     Example: switch(c, a, b) + switch(c, x, y) -> switch(c, a+x, b+y)
     """
     # node must be binary elemwise or add or mul
-    if not isinstance(node.op, Elemwise) or not isinstance(
-        node.op.scalar_op, ps.BinaryScalarOp | ps.Add | ps.Mul
+    if not (
+        isinstance(node.op, Elemwise)
+        and isinstance(node.op.scalar_op, ps.BinaryScalarOp | ps.Add | ps.Mul)
     ):
         return
     # all inputs must be switch

pytensor/tensor/rewriting/elemwise.py

@@ -473,10 +473,10 @@ def local_useless_dimshuffle_makevector(fgraph, node):
 
     makevector_out = node.inputs[0]
 
-    if (
-        not makevector_out.owner
-        or not isinstance(makevector_out.owner.op, MakeVector)
-        or not makevector_out.broadcastable == (True,)
+    if not (
+        makevector_out.owner
+        and isinstance(makevector_out.owner.op, MakeVector)
+        and makevector_out.broadcastable == (True,)
     ):
         return
 
@@ -570,8 +570,8 @@ def local_add_mul_fusion(fgraph, node):
     This rewrite is almost useless after the AlgebraicCanonizer is used,
     but it catches a few edge cases that are not canonicalized by it
     """
-    if not isinstance(node.op, Elemwise) or not isinstance(
-        node.op.scalar_op, ps.Add | ps.Mul
+    if not (
+        isinstance(node.op, Elemwise) and isinstance(node.op.scalar_op, ps.Add | ps.Mul)
     ):
         return False
 
@@ -1094,8 +1094,8 @@ class FusionOptimizer(GraphRewriter):
 @node_rewriter([Elemwise])
 def local_useless_composite_outputs(fgraph, node):
     """Remove inputs and outputs of Composite Ops that are not used anywhere."""
-    if not isinstance(node.op, Elemwise) or not isinstance(
-        node.op.scalar_op, ps.Composite
+    if not (
+        isinstance(node.op, Elemwise) and isinstance(node.op.scalar_op, ps.Composite)
     ):
         return
     comp = node.op.scalar_op
@@ -1135,7 +1135,7 @@ def local_careduce_fusion(fgraph, node):
 
     elm_node = car_input.owner
 
-    if elm_node is None or not isinstance(elm_node.op, Elemwise):
+    if not (elm_node and isinstance(elm_node.op, Elemwise)):
         return False
 
     elm_scalar_op = elm_node.op.scalar_op

pytensor/tensor/rewriting/math.py

@@ -2343,12 +2343,14 @@ def local_log_sum_exp(fgraph, node):
     else:
         dimshuffle_op = None
 
-    if not sum_node or not isinstance(sum_node.op, Sum):
+    if not (sum_node and isinstance(sum_node.op, Sum)):
         return
 
     exp_node, axis = sum_node.inputs[0].owner, sum_node.op.axis
-    if not exp_node or not (
-        isinstance(exp_node.op, Elemwise) and isinstance(exp_node.op.scalar_op, ps.Exp)
+    if not (
+        exp_node
+        and isinstance(exp_node.op, Elemwise)
+        and isinstance(exp_node.op.scalar_op, ps.Exp)
     ):
         return
 
@@ -2660,7 +2662,7 @@ def local_log_erfc(fgraph, node):
             10.0541948,10.0541951,.0000001)]
     """
 
-    if not node.inputs[0].owner or node.inputs[0].owner.op != erfc:
+    if not (node.inputs[0].owner and node.inputs[0].owner.op == erfc):
         return False
 
     if hasattr(node.tag, "local_log_erfc_applied"):
@@ -2725,7 +2727,7 @@ def local_grad_log_erfc_neg(fgraph, node):
     if node.inputs[0].owner.op != mul:
         mul_in = None
         y = []
-        if not node.inputs[0].owner or node.inputs[0].owner.op != exp:
+        if not (node.inputs[0].owner and node.inputs[0].owner.op == exp):
             return False
         exp_in = node.inputs[0]
     else:
@@ -2749,7 +2751,9 @@ def local_grad_log_erfc_neg(fgraph, node):
 
     if exp_in.owner.inputs[0].owner.op == neg:
         neg_in = exp_in.owner.inputs[0]
-        if not neg_in.owner.inputs[0].owner or neg_in.owner.inputs[0].owner.op != sqr:
+        if not (
+            neg_in.owner.inputs[0].owner and neg_in.owner.inputs[0].owner.op == sqr
+        ):
             return False
         sqr_in = neg_in.owner.inputs[0]
         x = sqr_in.owner.inputs[0]
@@ -2794,9 +2798,9 @@ def local_grad_log_erfc_neg(fgraph, node):
             return False
 
         if len(mul_neg.owner.inputs) == 2:
-            if (
-                not mul_neg.owner.inputs[1].owner
-                or mul_neg.owner.inputs[1].owner.op != sqr
+            if not (
+                mul_neg.owner.inputs[1].owner
+                and mul_neg.owner.inputs[1].owner.op == sqr
             ):
                 return False
             sqr_in = mul_neg.owner.inputs[1]
@@ -2809,10 +2813,10 @@ def local_grad_log_erfc_neg(fgraph, node):
             return False
 
         if cst2 != -1:
-            if (
-                not erfc_x.owner
-                or erfc_x.owner.op != mul
-                or len(erfc_x.owner.inputs) != 2
+            if not (
+                erfc_x.owner
+                and erfc_x.owner.op == mul
+                and len(erfc_x.owner.inputs) == 2
             ):
                 # todo implement that case
                 return False

pytensor/utils.py

@@ -360,7 +360,7 @@ class Singleton:
         # don't want that, so we check the class.  When we add one, we
         # add one only to the current class, so all is working
         # correctly.
-        if cls.__instance is None or not isinstance(cls.__instance, cls):
+        if not (cls.__instance and isinstance(cls.__instance, cls)):
             cls.__instance = super().__new__(cls)
         return cls.__instance