Fix E721: do not compare types, for exact checks use is / is not

pytensor/compile/debugmode.py

@@ -687,7 +687,7 @@ def _lessbroken_deepcopy(a):
     else:
         rval = copy.deepcopy(a)
 
-    assert type(rval) == type(a), (type(rval), type(a))
+    assert type(rval) is type(a), (type(rval), type(a))
 
     if isinstance(rval, np.ndarray):
         assert rval.dtype == a.dtype
@@ -1154,7 +1154,7 @@ class _FunctionGraphEvent:
             return str(self.__dict__)
 
     def __eq__(self, other):
-        rval = type(self) == type(other)
+        rval = type(self) is type(other)
         if rval:
             # nodes are not compared because this comparison is
             # supposed to be true for corresponding events that happen

pytensor/compile/ops.py

@@ -246,7 +246,7 @@ class FromFunctionOp(Op):
             self.infer_shape = self._infer_shape
 
     def __eq__(self, other):
-        return type(self) == type(other) and self.__fn == other.__fn
+        return type(self) is type(other) and self.__fn == other.__fn
 
     def __hash__(self):
         return hash(type(self)) ^ hash(self.__fn)

pytensor/graph/basic.py

@@ -748,7 +748,7 @@ class NominalVariable(AtomicVariable[_TypeType]):
             return True
 
         return (
-            type(self) == type(other)
+            type(self) is type(other)
             and self.id == other.id
             and self.type == other.type
         )

pytensor/graph/null_type.py

@@ -33,7 +33,7 @@ class NullType(Type):
         raise ValueError("NullType has no values to compare")
 
     def __eq__(self, other):
-        return type(self) == type(other)
+        return type(self) is type(other)
 
     def __hash__(self):
         return hash(type(self))

pytensor/graph/rewriting/unify.py

@@ -57,8 +57,8 @@ class ConstrainedVar(Var):
         return obj
 
     def __eq__(self, other):
-        if type(self) == type(other):
-            return self.token == other.token and self.constraint == other.constraint
+        if type(self) is type(other):
+            return self.token is other.token and self.constraint == other.constraint
         return NotImplemented
 
     def __hash__(self):

pytensor/graph/utils.py

@@ -229,7 +229,7 @@ class MetaType(ABCMeta):
             if "__eq__" not in dct:
 
                 def __eq__(self, other):
-                    return type(self) == type(other) and tuple(
+                    return type(self) is type(other) and tuple(
                         getattr(self, a) for a in props
                     ) == tuple(getattr(other, a) for a in props)
 

pytensor/ifelse.py

@@ -78,7 +78,7 @@ class IfElse(_NoPythonOp):
         self.name = name
 
     def __eq__(self, other):
-        if type(self) != type(other):
+        if type(self) is not type(other):
             return False
         if self.as_view != other.as_view:
             return False

pytensor/link/c/params_type.py

@@ -301,7 +301,7 @@ class Params(dict):
 
     def __eq__(self, other):
         return (
-            type(self) == type(other)
+            type(self) is type(other)
             and self.__params_type__ == other.__params_type__
             and all(
                 # NB: Params object should have been already filtered.
@@ -435,7 +435,7 @@ class ParamsType(CType):
 
     def __eq__(self, other):
         return (
-            type(self) == type(other)
+            type(self) is type(other)
             and self.fields == other.fields
             and self.types == other.types
         )

pytensor/link/c/type.py

@@ -515,7 +515,7 @@ class EnumType(CType, dict):
 
     def __eq__(self, other):
         return (
-            type(self) == type(other)
+            type(self) is type(other)
             and self.ctype == other.ctype
             and len(self) == len(other)
             and len(self.aliases) == len(other.aliases)

pytensor/raise_op.py

@@ -16,7 +16,7 @@ from pytensor.tensor.type import DenseTensorType
 
 class ExceptionType(Generic):
     def __eq__(self, other):
-        return type(self) == type(other)
+        return type(self) is type(other)
 
     def __hash__(self):
         return hash(type(self))
@@ -51,7 +51,7 @@ class CheckAndRaise(COp):
         return f"CheckAndRaise{{{self.exc_type}({self.msg})}}"
 
     def __eq__(self, other):
-        if type(self) != type(other):
+        if type(self) is not type(other):
             return False
 
         if self.msg == other.msg and self.exc_type == other.exc_type:

pytensor/scalar/basic.py

@@ -1074,7 +1074,7 @@ class unary_out_lookup(MetaObject):
             return [rval]
 
     def __eq__(self, other):
-        return type(self) == type(other) and self.tbl == other.tbl
+        return type(self) is type(other) and self.tbl == other.tbl
 
     def __hash__(self):
         return hash(type(self))  # ignore hash of table
@@ -1160,7 +1160,7 @@ class ScalarOp(COp):
         return self.grad(inputs, output_gradients)
 
     def __eq__(self, other):
-        test = type(self) == type(other) and getattr(
+        test = type(self) is type(other) and getattr(
             self, "output_types_preference", None
         ) == getattr(other, "output_types_preference", None)
         return test
@@ -4133,7 +4133,7 @@ class ScalarInnerGraphOp(ScalarOp, HasInnerGraph):
         if self is other:
             return True
         if (
-            type(self) != type(other)
+            type(self) is not type(other)
             or self.nin != other.nin
             or self.nout != other.nout
         ):

pytensor/scalar/math.py

@@ -626,7 +626,7 @@ class Chi2SF(BinaryScalarOp):
         raise NotImplementedError("only floatingpoint is implemented")
 
     def __eq__(self, other):
-        return type(self) == type(other)
+        return type(self) is type(other)
 
     def __hash__(self):
         return hash(type(self))
@@ -679,7 +679,7 @@ class GammaInc(BinaryScalarOp):
         raise NotImplementedError("only floatingpoint is implemented")
 
     def __eq__(self, other):
-        return type(self) == type(other)
+        return type(self) is type(other)
 
     def __hash__(self):
         return hash(type(self))
@@ -732,7 +732,7 @@ class GammaIncC(BinaryScalarOp):
         raise NotImplementedError("only floatingpoint is implemented")
 
     def __eq__(self, other):
-        return type(self) == type(other)
+        return type(self) is type(other)
 
     def __hash__(self):
         return hash(type(self))
@@ -1045,7 +1045,7 @@ class GammaU(BinaryScalarOp):
         raise NotImplementedError("only floatingpoint is implemented")
 
     def __eq__(self, other):
-        return type(self) == type(other)
+        return type(self) is type(other)
 
     def __hash__(self):
         return hash(type(self))
@@ -1083,7 +1083,7 @@ class GammaL(BinaryScalarOp):
         raise NotImplementedError("only floatingpoint is implemented")
 
     def __eq__(self, other):
-        return type(self) == type(other)
+        return type(self) is type(other)
 
     def __hash__(self):
         return hash(type(self))

pytensor/scan/op.py

@@ -1246,7 +1246,7 @@ class Scan(Op, ScanMethodsMixin, HasInnerGraph):
         return apply_node
 
     def __eq__(self, other):
-        if type(self) != type(other):
+        if type(self) is not type(other):
             return False
 
         if self.info != other.info:

pytensor/sparse/basic.py

@@ -462,7 +462,7 @@ class SparseConstantSignature(tuple):
         return (
             a == x
             and (b.dtype == y.dtype)
-            and (type(b) == type(y))
+            and (type(b) is type(y))
             and (b.shape == y.shape)
             and (abs(b - y).sum() < 1e-6 * b.nnz)
         )

pytensor/tensor/random/type.py

@@ -107,7 +107,7 @@ class RandomGeneratorType(RandomType[np.random.Generator]):
         return _eq(sa, sb)
 
     def __eq__(self, other):
-        return type(self) == type(other)
+        return type(self) is type(other)
 
     def __hash__(self):
         return hash(type(self))

pytensor/tensor/rewriting/math.py

@@ -1742,7 +1742,7 @@ def local_reduce_broadcastable(fgraph, node):
                     ii += 1
             new_reduced = reduced.dimshuffle(*pattern)
             if new_axis:
-                if type(node.op) == CAReduce:
+                if type(node.op) is CAReduce:
                     # This case handles `CAReduce` instances
                     # (e.g. generated by `scalar_elemwise`), and not the
                     # scalar `Op`-specific subclasses

pytensor/tensor/type.py

@@ -370,7 +370,7 @@ class TensorType(CType[np.ndarray], HasDataType, HasShape):
         return values_eq_approx(a, b, allow_remove_inf, allow_remove_nan, rtol, atol)
 
     def __eq__(self, other):
-        if type(self) != type(other):
+        if type(self) is not type(other):
             return NotImplemented
 
         return other.dtype == self.dtype and other.shape == self.shape
@@ -624,7 +624,7 @@ class TensorType(CType[np.ndarray], HasDataType, HasShape):
 
 class DenseTypeMeta(MetaType):
     def __instancecheck__(self, o):
-        if type(o) == TensorType or isinstance(o, DenseTypeMeta):
+        if type(o) is TensorType or isinstance(o, DenseTypeMeta):
             return True
         return False
 

pytensor/tensor/type_other.py

@@ -64,7 +64,7 @@ class SliceType(Type[slice]):
         return "slice"
 
     def __eq__(self, other):
-        return type(self) == type(other)
+        return type(self) is type(other)
 
     def __hash__(self):
         return hash(type(self))

pytensor/tensor/variable.py

@@ -945,7 +945,7 @@ class TensorConstantSignature(tuple):
     """
 
     def __eq__(self, other):
-        if type(self) != type(other):
+        if type(self) is not type(other):
             return False
         try:
             (t0, d0), (t1, d1) = self, other
@@ -1105,7 +1105,7 @@ TensorType.constant_type = TensorConstant
 
 class DenseVariableMeta(MetaType):
     def __instancecheck__(self, o):
-        if type(o) == TensorVariable or isinstance(o, DenseVariableMeta):
+        if type(o) is TensorVariable or isinstance(o, DenseVariableMeta):
             return True
         return False
 
@@ -1120,7 +1120,7 @@ class DenseTensorVariable(TensorType, metaclass=DenseVariableMeta):
 
 class DenseConstantMeta(MetaType):
     def __instancecheck__(self, o):
-        if type(o) == TensorConstant or isinstance(o, DenseConstantMeta):
+        if type(o) is TensorConstant or isinstance(o, DenseConstantMeta):
             return True
         return False
 

pytensor/typed_list/type.py

@@ -55,7 +55,7 @@ class TypedListType(CType):
         Two lists are equal if they contain the same type.
 
         """
-        return type(self) == type(other) and self.ttype == other.ttype
+        return type(self) is type(other) and self.ttype == other.ttype
 
     def __hash__(self):
         return hash((type(self), self.ttype))

tests/graph/rewriting/test_unify.py

@@ -42,7 +42,7 @@ class CustomOpNoPropsNoEq(Op):
 
 class CustomOpNoProps(CustomOpNoPropsNoEq):
     def __eq__(self, other):
-        return type(self) == type(other) and self.a == other.a
+        return type(self) is type(other) and self.a == other.a
 
     def __hash__(self):
         return hash((type(self), self.a))

tests/graph/test_fg.py

@@ -31,8 +31,8 @@ class TestFunctionGraph:
         s = pickle.dumps(func)
         new_func = pickle.loads(s)
 
-        assert all(type(a) == type(b) for a, b in zip(func.inputs, new_func.inputs))
-        assert all(type(a) == type(b) for a, b in zip(func.outputs, new_func.outputs))
+        assert all(type(a) is type(b) for a, b in zip(func.inputs, new_func.inputs))
+        assert all(type(a) is type(b) for a, b in zip(func.outputs, new_func.outputs))
         assert all(
             type(a.op) is type(b.op)  # noqa: E721
             for a, b in zip(func.apply_nodes, new_func.apply_nodes)

tests/graph/test_op.py

@@ -25,7 +25,7 @@ class MyType(Type):
         self.thingy = thingy
 
     def __eq__(self, other):
-        return type(other) == type(self) and other.thingy == self.thingy
+        return type(other) is type(self) and other.thingy == self.thingy
 
     def __str__(self):
         return str(self.thingy)

tests/link/c/test_basic.py

@@ -71,7 +71,7 @@ class TDouble(CType):
         return (1,)
 
     def __eq__(self, other):
-        return type(self) == type(other)
+        return type(self) is type(other)
 
     def __hash__(self):
         return hash(type(self))

tests/sparse/test_basic.py

@@ -348,7 +348,7 @@ class TestVerifyGradSparse:
             self.structured = structured
 
         def __eq__(self, other):
-            return (type(self) == type(other)) and self.structured == other.structured
+            return (type(self) is type(other)) and self.structured == other.structured
 
         def __hash__(self):
             return hash(type(self)) ^ hash(self.structured)

tests/tensor/test_basic.py

@@ -3163,7 +3163,7 @@ def test_stack():
     sx, sy = dscalar(), dscalar()
 
     rval = inplace_func([sx, sy], stack([sx, sy]))(-4.0, -2.0)
-    assert type(rval) == np.ndarray
+    assert type(rval) is np.ndarray
     assert [-4, -2] == list(rval)
 
 

tests/tensor/test_subtensor.py

@@ -819,7 +819,7 @@ class TestSubtensor(utt.OptimizationTestMixin):
             assert np.allclose(val, good), (val, good)
 
             # Test reuse of output memory
-            if type(AdvancedSubtensor1) == AdvancedSubtensor1:
+            if type(AdvancedSubtensor1) is AdvancedSubtensor1:
                 op = AdvancedSubtensor1()
                 # When idx is a TensorConstant.
                 if hasattr(idx, "data"):