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

Author: Armavica

pytensor/compile/builders.py

@@ -275,7 +275,7 @@ def _filter_grad_var(grad, inp):
         #
         # For now, this converts NullType or DisconnectedType into zeros_like.
         # other types are unmodified: overrider_var -> None
-        if isinstance(grad.type, (NullType, DisconnectedType)):
+        if isinstance(grad.type, NullType | DisconnectedType):
             if hasattr(inp, "zeros_like"):
                 return inp.zeros_like(), grad
             else:
@@ -535,7 +535,7 @@ def lop_op(inps, grads):
             all_grads_l = list(all_grads_l)
             all_grads_ov_l = list(all_grads_ov_l)
         elif isinstance(lop_op, Variable):
-            if isinstance(lop_op.type, (DisconnectedType, NullType)):
+            if isinstance(lop_op.type, DisconnectedType | NullType):
                 all_grads_l = [inp.zeros_like() for inp in local_inputs]
                 all_grads_ov_l = [lop_op.type() for _ in range(inp_len)]
             else:
@@ -562,7 +562,7 @@ def lop_op(inps, grads):
                     all_grads_l.append(gnext)
                     all_grads_ov_l.append(gnext_ov)
                 elif isinstance(fn_gov, Variable):
-                    if isinstance(fn_gov.type, (DisconnectedType, NullType)):
+                    if isinstance(fn_gov.type, DisconnectedType | NullType):
                         all_grads_l.append(inp.zeros_like())
                         all_grads_ov_l.append(fn_gov.type())
                     else:

pytensor/compile/compiledir.py

@@ -55,7 +55,7 @@ def cleanup():
                                     have_npy_abi_version = True
                                 elif obj.startswith("c_compiler_str="):
                                     have_c_compiler = True
-                            elif isinstance(obj, (Op, CType)) and hasattr(
+                            elif isinstance(obj, Op | CType) and hasattr(
                                 obj, "c_code_cache_version"
                             ):
                                 v = obj.c_code_cache_version()

pytensor/compile/debugmode.py

@@ -679,7 +679,7 @@ def _lessbroken_deepcopy(a):
     # This logic is also in link.py
     from pytensor.link.c.type import _cdata_type
 
-    if isinstance(a, (np.ndarray, np.memmap)):
+    if isinstance(a, np.ndarray | np.memmap):
         rval = a.copy(order="K")
     elif isinstance(a, _cdata_type):
         # This is not copyable (and should be used for constant data).
@@ -1889,7 +1889,7 @@ def thunk():
                         # HACK TO LOOK LIKE A REAL DESTRUCTIVE ACTION
                         # TOOK PLACE
                         if (
-                            isinstance(dr_vals[r][0], (np.ndarray, np.memmap))
+                            isinstance(dr_vals[r][0], np.ndarray | np.memmap)
                             and (dr_vals[r][0].dtype == storage_map[r][0].dtype)
                             and (dr_vals[r][0].shape == storage_map[r][0].shape)
                         ):
@@ -2019,10 +2019,10 @@ def __init__(
         if outputs is None:
             return_none = True
             outputs = []
-        if not isinstance(outputs, (list, tuple)):
+        if not isinstance(outputs, list | tuple):
             unpack_single = True
             outputs = [outputs]
-        if not isinstance(inputs, (list, tuple)):
+        if not isinstance(inputs, list | tuple):
             inputs = [inputs]
 
         # Wrap them in In or Out instances if needed.

pytensor/compile/function/__init__.py

@@ -285,15 +285,15 @@ def opt_log1p(node):
 
     if givens is None:
         givens = []
-    if not isinstance(inputs, (list, tuple)):
+    if not isinstance(inputs, list | tuple):
         raise Exception(
             "Input variables of an PyTensor function should be "
             "contained in a list, even when there is a single "
             "input."
         )
 
     # compute some features of the arguments:
-    uses_tuple = any(isinstance(i, (list, tuple)) for i in inputs)
+    uses_tuple = any(isinstance(i, list | tuple) for i in inputs)
     uses_updates = bool(updates)
     uses_givens = bool(givens)
 

pytensor/compile/function/pfunc.py

@@ -512,7 +512,7 @@ def construct_pfunc_ins_and_outs(
     if givens is None:
         givens = []
 
-    if not isinstance(params, (list, tuple)):
+    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(
@@ -521,7 +521,7 @@ def construct_pfunc_ins_and_outs(
         raise TypeError("The `no_default_update` argument must be a boolean or list")
 
     if len(updates) > 0 and not all(
-        isinstance(pair, (tuple, list))
+        isinstance(pair, tuple | list)
         and len(pair) == 2
         and isinstance(pair[0], Variable)
         for pair in iter_over_pairs(updates)
@@ -575,7 +575,7 @@ def construct_pfunc_ins_and_outs(
         if outputs is None:
             out_list = []
         else:
-            if isinstance(outputs, (list, tuple)):
+            if isinstance(outputs, list | tuple):
                 out_list = list(outputs)
             else:
                 out_list = [outputs]
@@ -598,7 +598,7 @@ def construct_pfunc_ins_and_outs(
         if outputs is None:
             new_outputs = []
         else:
-            if isinstance(outputs, (list, tuple)):
+            if isinstance(outputs, list | tuple):
                 new_outputs = cloned_extended_outputs[: len(outputs)]
             else:
                 new_outputs = cloned_extended_outputs[0]

pytensor/compile/function/types.py

@@ -1310,7 +1310,7 @@ def wrap_in(input):
         elif isinstance(input, Variable):
             # r -> SymbolicInput(variable=r)
             return SymbolicInput(input)
-        elif isinstance(input, (list, tuple)):
+        elif isinstance(input, list | tuple):
             # (r, u) -> SymbolicInput(variable=r, update=u)
             if len(input) == 2:
                 return SymbolicInput(input[0], update=input[1])
@@ -1495,10 +1495,10 @@ def __init__(
         if outputs is None:
             return_none = True
             outputs = []
-        if not isinstance(outputs, (list, tuple)):
+        if not isinstance(outputs, list | tuple):
             unpack_single = True
             outputs = [outputs]
-        if not isinstance(inputs, (list, tuple)):
+        if not isinstance(inputs, list | tuple):
             inputs = [inputs]
 
         # Wrap them in In or Out instances if needed.
@@ -1734,14 +1734,14 @@ def orig_function(
     inputs = list(map(convert_function_input, inputs))
 
     if outputs is not None:
-        if isinstance(outputs, (list, tuple)):
+        if isinstance(outputs, list | tuple):
             outputs = list(map(FunctionMaker.wrap_out, outputs))
         else:
             outputs = FunctionMaker.wrap_out(outputs)
 
     defaults = [getattr(input, "value", None) for input in inputs]
 
-    if isinstance(mode, (list, tuple)):
+    if isinstance(mode, list | tuple):
         raise ValueError("We do not support the passing of multiple modes")
 
     fn = None
@@ -1797,7 +1797,7 @@ def convert_function_input(input):
         raise TypeError(f"A Constant instance is not a legal function input: {input}")
     elif isinstance(input, Variable):
         return In(input)
-    elif isinstance(input, (list, tuple)):
+    elif isinstance(input, list | tuple):
         orig = input
         if not input:
             raise TypeError(f"Nonsensical input specification: {input}")
@@ -1806,7 +1806,7 @@ def convert_function_input(input):
             input = input[1:]
         else:
             name = None
-        if isinstance(input[0], (list, tuple)):
+        if isinstance(input[0], list | tuple):
             if len(input[0]) != 2 or len(input) != 2:
                 raise TypeError(
                     f"Invalid input syntax: {orig} (check "
@@ -1843,7 +1843,7 @@ def convert_function_input(input):
             raise TypeError(
                 f"Unknown update type: {type(update)}, expected Variable instance"
             )
-        if value is not None and isinstance(value, (Variable, SymbolicInput)):
+        if value is not None and isinstance(value, Variable | SymbolicInput):
             raise TypeError(
                 f"The value for input {variable} should not be a Variable "
                 f"or SymbolicInput instance (got: {value})"

pytensor/compile/mode.py

@@ -565,7 +565,7 @@ def get_target_language(mode=None) -> tuple[Literal["py", "c", "numba", "jax"],
     if isinstance(linker, CLinker):
         return ("c",)
 
-    if isinstance(linker, (VMLinker, OpWiseCLinker)):
+    if isinstance(linker, VMLinker | OpWiseCLinker):
         return ("c", "py") if config.cxx else ("py",)
 
     raise Exception(f"Unsupported Linker: {linker}")

pytensor/compile/monitormode.py

@@ -105,7 +105,7 @@ def detect_nan(fgraph, i, node, fn):
 
     for output in fn.outputs:
         if (
-            not isinstance(output[0], (np.random.RandomState, np.random.Generator))
+            not isinstance(output[0], np.random.RandomState | np.random.Generator)
             and np.isnan(output[0]).any()
         ):
             print("*** NaN detected ***")

pytensor/compile/nanguardmode.py

@@ -34,7 +34,7 @@ def _is_numeric_value(arr, var):
 
     if isinstance(arr, _cdata_type):
         return False
-    elif isinstance(arr, (np.random.mtrand.RandomState, np.random.Generator)):
+    elif isinstance(arr, np.random.mtrand.RandomState | np.random.Generator):
         return False
     elif var and isinstance(var.type, RandomType):
         return False
@@ -62,10 +62,10 @@ def flatten(l):
         A flattened list of objects.
 
     """
-    if isinstance(l, (list, tuple, ValuesView)):
+    if isinstance(l, list | tuple | ValuesView):
         rval = []
         for elem in l:
-            if isinstance(elem, (list, tuple)):
+            if isinstance(elem, list | tuple):
                 rval.extend(flatten(elem))
             else:
                 rval.append(elem)

pytensor/compile/ops.py

@@ -256,7 +256,7 @@ def __str__(self):
 
     def perform(self, node, inputs, outputs):
         outs = self.__fn(*inputs)
-        if not isinstance(outs, (list, tuple)):
+        if not isinstance(outs, list | tuple):
             outs = (outs,)
         assert len(outs) == len(outputs)
         for i in range(len(outs)):
@@ -308,11 +308,11 @@ def numpy_dot(a, b):
         return numpy.dot(a, b)
 
     """
-    if not isinstance(itypes, (list, tuple)):
+    if not isinstance(itypes, list | tuple):
         itypes = [itypes]
     if not all(isinstance(t, CType) for t in itypes):
         raise TypeError("itypes has to be a list of PyTensor types")
-    if not isinstance(otypes, (list, tuple)):
+    if not isinstance(otypes, list | tuple):
         otypes = [otypes]
     if not all(isinstance(t, CType) for t in otypes):
         raise TypeError("otypes has to be a list of PyTensor types")

pytensor/d3viz/formatting.py

@@ -132,7 +132,7 @@ def __call__(self, fct, graph=None):
                 fct = [fct]
             elif isinstance(fct, Apply):
                 fct = fct.outputs
-            assert isinstance(fct, (list, tuple))
+            assert isinstance(fct, list | tuple)
             assert all(isinstance(v, Variable) for v in fct)
             fgraph = FunctionGraph(inputs=graph_inputs(fct), outputs=fct)
 
@@ -281,7 +281,7 @@ def var_tag(var):
     """Parse tag attribute of variable node."""
     tag = var.tag
     if hasattr(tag, "trace") and len(tag.trace) and len(tag.trace[0]) == 4:
-        if isinstance(tag.trace[0][0], (tuple, list)):
+        if isinstance(tag.trace[0][0], tuple | list):
             path, line, _, src = tag.trace[0][-1]
         else:
             path, line, _, src = tag.trace[0]

pytensor/gradient.py

@@ -193,17 +193,17 @@ def Rop(
         If `f` is a list/tuple, then return a list/tuple with the results.
     """
 
-    if not isinstance(wrt, (list, tuple)):
+    if not isinstance(wrt, list | tuple):
         _wrt: list[Variable] = [pytensor.tensor.as_tensor_variable(wrt)]
     else:
         _wrt = [pytensor.tensor.as_tensor_variable(x) for x in wrt]
 
-    if not isinstance(eval_points, (list, tuple)):
+    if not isinstance(eval_points, list | tuple):
         _eval_points: list[Variable] = [pytensor.tensor.as_tensor_variable(eval_points)]
     else:
         _eval_points = [pytensor.tensor.as_tensor_variable(x) for x in eval_points]
 
-    if not isinstance(f, (list, tuple)):
+    if not isinstance(f, list | tuple):
         _f: list[Variable] = [pytensor.tensor.as_tensor_variable(f)]
     else:
         _f = [pytensor.tensor.as_tensor_variable(x) for x in f]
@@ -381,19 +381,19 @@ def Lop(
         coordinates of the tensor elements.
         If `f` is a list/tuple, then return a list/tuple with the results.
     """
-    if not isinstance(eval_points, (list, tuple)):
+    if not isinstance(eval_points, list | tuple):
         _eval_points: list[Variable] = [pytensor.tensor.as_tensor_variable(eval_points)]
     else:
         _eval_points = [pytensor.tensor.as_tensor_variable(x) for x in eval_points]
 
-    if not isinstance(f, (list, tuple)):
+    if not isinstance(f, list | tuple):
         _f: list[Variable] = [pytensor.tensor.as_tensor_variable(f)]
     else:
         _f = [pytensor.tensor.as_tensor_variable(x) for x in f]
 
     grads = list(_eval_points)
 
-    if not isinstance(wrt, (list, tuple)):
+    if not isinstance(wrt, list | tuple):
         _wrt: list[Variable] = [pytensor.tensor.as_tensor_variable(wrt)]
     else:
         _wrt = [pytensor.tensor.as_tensor_variable(x) for x in wrt]
@@ -547,7 +547,7 @@ def grad(
             )
 
         if not isinstance(
-            g_var.type, (NullType, DisconnectedType)
+            g_var.type, NullType | DisconnectedType
         ) and "float" not in str(g_var.type.dtype):
             raise TypeError(
                 "Gradients must always be NullType, "
@@ -1276,7 +1276,7 @@ def try_to_copy_if_needed(var):
                                     f"of shape {i_shape}"
                                 )
 
-                if not isinstance(term.type, (NullType, DisconnectedType)):
+                if not isinstance(term.type, NullType | DisconnectedType):
                     if term.type.dtype not in pytensor.tensor.type.float_dtypes:
                         raise TypeError(
                             str(node.op) + ".grad illegally "
@@ -1500,7 +1500,7 @@ def prod(inputs):
             return rval
 
         packed_pt = False
-        if not isinstance(pt, (list, tuple)):
+        if not isinstance(pt, list | tuple):
             pt = [pt]
             packed_pt = True
 
@@ -1732,7 +1732,7 @@ def verify_grad(
     from pytensor.compile.function import function
     from pytensor.compile.sharedvalue import shared
 
-    if not isinstance(pt, (list, tuple)):
+    if not isinstance(pt, list | tuple):
         raise TypeError("`pt` should be a list or tuple")
 
     pt = [np.array(p) for p in pt]
@@ -1933,7 +1933,7 @@ def jacobian(expression, wrt, consider_constant=None, disconnected_inputs="raise
     using_list = isinstance(wrt, list)
     using_tuple = isinstance(wrt, tuple)
 
-    if isinstance(wrt, (list, tuple)):
+    if isinstance(wrt, list | tuple):
         wrt = list(wrt)
     else:
         wrt = [wrt]
@@ -2015,7 +2015,7 @@ def hessian(cost, wrt, consider_constant=None, disconnected_inputs="raise"):
     using_list = isinstance(wrt, list)
     using_tuple = isinstance(wrt, tuple)
 
-    if isinstance(wrt, (list, tuple)):
+    if isinstance(wrt, list | tuple):
         wrt = list(wrt)
     else:
         wrt = [wrt]

pytensor/graph/basic.py

@@ -252,7 +252,7 @@ def clone_with_new_inputs(
         """
         from pytensor.graph.op import HasInnerGraph
 
-        assert isinstance(inputs, (list, tuple))
+        assert isinstance(inputs, list | tuple)
         remake_node = False
         new_inputs: list["Variable"] = list(inputs)
 
@@ -1368,7 +1368,7 @@ def _compute_deps_cache_(io):
                 d = deps(io)
 
                 if d:
-                    if not isinstance(d, (list, OrderedSet)):
+                    if not isinstance(d, list | OrderedSet):
                         raise TypeError(
                             "Non-deterministic collections found; make"
                             " toposort non-deterministic."

pytensor/graph/destroyhandler.py

@@ -63,7 +63,7 @@ def _contains_cycle(fgraph, orderings):
 
     # this is performance-critical code. it is the largest single-function
     # bottleneck when compiling large graphs.
-    assert isinstance(outputs, (tuple, list, deque))
+    assert isinstance(outputs, tuple | list | deque)
 
     # TODO: For more speed - use a defaultdict for the orderings
     # (defaultdict runs faster than dict in the case where the key