Update Ruff 11.13

.pre-commit-config.yaml

@@ -27,7 +27,7 @@ repos:
     - id: sphinx-lint 
       args: ["."]
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.7.3
+    rev: v0.11.13
     hooks:
       - id: ruff
         args: ["--fix", "--output-format=full"]

pytensor/bin/pytensor_cache.py

@@ -24,7 +24,7 @@
 
 def print_help(exit_status):
     if exit_status:
-        print(f"command \"{' '.join(sys.argv)}\" not recognized")
+        print(f'command "{" ".join(sys.argv)}" not recognized')
     print('Type "pytensor-cache" to print the cache location')
     print('Type "pytensor-cache help" to print this help')
     print('Type "pytensor-cache clear" to erase the cache')

pytensor/compile/function/__init__.py

@@ -12,7 +12,7 @@
 from pytensor.graph import Variable
 
 
-__all__ = ["types", "pfunc"]
+__all__ = ["pfunc", "types"]
 
 __docformat__ = "restructuredtext en"
 _logger = logging.getLogger("pytensor.compile.function")

pytensor/compile/function/pfunc.py

@@ -328,8 +328,7 @@ def clone_inputs(i):
             cloned_outputs = []  # TODO: get Function.__call__ to return None
         else:
             raise TypeError(
-                "output must be an PyTensor Variable or Out "
-                "instance (or list of them)",
+                "output must be an PyTensor Variable or Out instance (or list of them)",
                 outputs,
             )
 

pytensor/compile/function/types.py

@@ -216,7 +216,7 @@ def add_supervisor_to_fgraph(
             input
             for spec, input in zip(input_specs, fgraph.inputs, strict=True)
             if not (
-                spec.mutable or has_destroy_handler and fgraph.has_destroyers([input])
+                spec.mutable or (has_destroy_handler and fgraph.has_destroyers([input]))
             )
         )
     )

pytensor/compile/monitormode.py

@@ -45,8 +45,7 @@ def __init__(
             optimizer = config.optimizer
         if linker is not None and not isinstance(linker.mode, MonitorMode):
             raise Exception(
-                "MonitorMode can only use its own linker! You "
-                "should not provide one.",
+                "MonitorMode can only use its own linker! You should not provide one.",
                 linker,
             )
 

pytensor/compile/profiling.py

@@ -935,18 +935,14 @@ def count_running_memory(order, fgraph, nodes_mem, ignore_dmap=False):
                     if dmap and idx2 in dmap:
                         vidx = dmap[idx2]
                         assert len(vidx) == 1, (
-                            "Here we only support the "
-                            "possibility to destroy one "
-                            "input"
+                            "Here we only support the possibility to destroy one input"
                         )
                         ins = node.inputs[vidx[0]]
                     if vmap and idx2 in vmap:
                         assert ins is None
                         vidx = vmap[idx2]
                         assert len(vidx) == 1, (
-                            "Here we only support the "
-                            "possibility to view one "
-                            "input"
+                            "Here we only support the possibility to view one input"
                         )
                         ins = node.inputs[vidx[0]]
                     if ins is not None:
@@ -1093,9 +1089,7 @@ def min_memory_generator(executable_nodes, viewed_by, view_of):
                             assert ins is None
                             vidx = vmap[idx]
                             assert len(vidx) == 1, (
-                                "Here we only support "
-                                "the possibility to "
-                                "view one input"
+                                "Here we only support the possibility to view one input"
                             )
                             ins = node.inputs[vidx[0]]
                         if ins is not None:
@@ -1304,22 +1298,22 @@ def print_stats(stats1, stats2):
 
             print(
                 (
-                    f"        CPU: {int(round(new_max_running_max_memory_size[1] / 1024.0))}KB "
-                    f"({int(round(max_running_max_memory_size[1] / 1024.0))}KB)"
+                    f"        CPU: {round(new_max_running_max_memory_size[1] / 1024.0)}KB "
+                    f"({round(max_running_max_memory_size[1] / 1024.0)}KB)"
                 ),
                 file=file,
             )
             print(
                 (
-                    f"        GPU: {int(round(new_max_running_max_memory_size[2] / 1024.0))}KB "
-                    f"({int(round(max_running_max_memory_size[2] / 1024.0))}KB)"
+                    f"        GPU: {round(new_max_running_max_memory_size[2] / 1024.0)}KB "
+                    f"({round(max_running_max_memory_size[2] / 1024.0)}KB)"
                 ),
                 file=file,
             )
             print(
                 (
-                    f"        CPU + GPU: {int(round(new_max_running_max_memory_size[0] / 1024.0))}KB "
-                    f"({int(round(max_running_max_memory_size[0] / 1024.0))}KB)"
+                    f"        CPU + GPU: {round(new_max_running_max_memory_size[0] / 1024.0)}KB "
+                    f"({round(max_running_max_memory_size[0] / 1024.0)}KB)"
                 ),
                 file=file,
             )
@@ -1340,23 +1334,23 @@ def print_stats(stats1, stats2):
             file=file,
         )
         print(
-            f"        CPU: {int(round(new_max_node_memory_size[1] / 1024.0))}KB",
+            f"        CPU: {round(new_max_node_memory_size[1] / 1024.0)}KB",
             file=file,
         )
         print(
-            f"        GPU: {int(round(new_max_node_memory_size[2] / 1024.0))}KB",
+            f"        GPU: {round(new_max_node_memory_size[2] / 1024.0)}KB",
             file=file,
         )
         print(
-            f"        CPU + GPU: {int(round(new_max_node_memory_size[0] / 1024.0))}KB",
+            f"        CPU + GPU: {round(new_max_node_memory_size[0] / 1024.0)}KB",
             file=file,
         )
         print("---", file=file)
 
         if min_max_peak:
             print(
                 "    Minimum peak from all valid apply node order is "
-                f"{int(round(min_max_peak / 1024.0))}KB(took {min_peak_time:3f}s to compute)",
+                f"{round(min_max_peak / 1024.0)}KB(took {min_peak_time:3f}s to compute)",
                 file=file,
             )
 
@@ -1405,7 +1399,7 @@ def print_stats(stats1, stats2):
         print(
             (
                 f"   ... (remaining {max(0, len(node_mem) - N)} Apply account for "
-                f"{sum_remaining:4d}B/{size_sum_dense :d}B ({p}) of the"
+                f"{sum_remaining:4d}B/{size_sum_dense:d}B ({p}) of the"
                 " Apply with dense outputs sizes)"
             ),
             file=file,
@@ -1545,8 +1539,7 @@ def amdlibm_speed_up(op):
                         return True
                     elif s_op.__class__ not in scalar_op_amdlibm_no_speed_up:
                         print(
-                            "We don't know if amdlibm will accelerate "
-                            "this scalar op.",
+                            "We don't know if amdlibm will accelerate this scalar op.",
                             s_op,
                             file=file,
                         )

pytensor/configdefaults.py

@@ -67,8 +67,7 @@ def _warn_cxx(val):
     """We only support clang++ as otherwise we hit strange g++/OSX bugs."""
     if sys.platform == "darwin" and val and "clang++" not in val:
         _logger.warning(
-            "Only clang++ is supported. With g++,"
-            " we end up with strange g++/OSX bugs."
+            "Only clang++ is supported. With g++, we end up with strange g++/OSX bugs."
         )
     return True
 

pytensor/gradient.py

@@ -624,8 +624,7 @@ def grad(
 
     if cost is not None and isinstance(cost.type, NullType):
         raise ValueError(
-            "Can't differentiate a NaN cost. "
-            f"Cost is NaN because {cost.type.why_null}"
+            f"Can't differentiate a NaN cost. Cost is NaN because {cost.type.why_null}"
         )
 
     if cost is not None and cost.type.ndim != 0:
@@ -2181,9 +2180,9 @@ def hessian(cost, wrt, consider_constant=None, disconnected_inputs="raise"):
             sequences=pytensor.tensor.arange(expr.shape[0]),
             non_sequences=[expr, input],
         )
-        assert (
-            not updates
-        ), "Scan has returned a list of updates; this should not happen."
+        assert not updates, (
+            "Scan has returned a list of updates; this should not happen."
+        )
         hessians.append(hess)
     return as_list_or_tuple(using_list, using_tuple, hessians)
 

pytensor/graph/basic.py

@@ -408,7 +408,9 @@ class Variable(Node, Generic[_TypeType, OptionalApplyType]):
             [b], [c]
         )  # this works because a has a value associated with it already
 
-        assert 4.0 == f(2.5)  # bind 2.5 to an internal copy of b and evaluate an internal c
+        assert 4.0 == f(
+            2.5
+        )  # bind 2.5 to an internal copy of b and evaluate an internal c
 
         pytensor.function(
             [a], [c]

pytensor/graph/features.py

@@ -390,8 +390,7 @@ def __init__(self):
     def on_attach(self, fgraph):
         if hasattr(fgraph, "checkpoint") or hasattr(fgraph, "revert"):
             raise AlreadyThere(
-                "History feature is already present or in"
-                " conflict with another plugin."
+                "History feature is already present or in conflict with another plugin."
             )
         self.history[fgraph] = []
         # Don't call unpickle here, as ReplaceValidate.on_attach()

pytensor/graph/rewriting/basic.py

@@ -3044,10 +3044,7 @@ def check_stack_trace(f_or_fgraph, ops_to_check="last", bug_print="raise"):
         raise ValueError("ops_to_check does not have the right type")
 
     if not apply_nodes_to_check:
-        msg = (
-            "Provided op instances/classes are not in the graph or the "
-            "graph is empty"
-        )
+        msg = "Provided op instances/classes are not in the graph or the graph is empty"
         if bug_print == "warn":
             warnings.warn(msg)
         elif bug_print == "raise":

pytensor/graph/rewriting/db.py

@@ -460,7 +460,7 @@ def query(self, *tags, position_cutoff: int | float | None = None, **kwtags):
         return ret
 
     def print_summary(self, stream=sys.stdout):
-        print(f"{self.__class__.__name__ } (id {id(self)})", file=stream)
+        print(f"{self.__class__.__name__} (id {id(self)})", file=stream)
         positions = list(self.__position__.items())
 
         def c(a, b):

pytensor/link/c/basic.py

@@ -821,9 +821,9 @@ def code_gen(self):
 
             behavior = op.c_code(node, name, isyms, osyms, sub)
 
-            assert isinstance(
-                behavior, str
-            ), f"{node.op} didn't return a string for c_code"
+            assert isinstance(behavior, str), (
+                f"{node.op} didn't return a string for c_code"
+            )
             # To help understand what is following. It help read the c code.
             # This prevent different op that generate the same c code
             # to be merged, I suppose this won't happen...
@@ -1752,10 +1752,7 @@ def __call__(self):
                 exc_value.__thunk_trace__ = trace
             except Exception:
                 print(  # noqa: T201
-                    (
-                        "ERROR retrieving error_storage."
-                        "Was the error set in the c code?"
-                    ),
+                    ("ERROR retrieving error_storage.Was the error set in the c code?"),
                     end=" ",
                     file=sys.stderr,
                 )

pytensor/link/c/op.py

@@ -588,7 +588,7 @@ def c_code(self, node, name, inp, out, sub):
                 {define_macros}
                 {{
                   if ({self.func_name}({self.format_c_function_args(inp, out)}{params}) != 0) {{
-                    {sub['fail']}
+                    {sub["fail"]}
                   }}
                 }}
                 {undef_macros}

pytensor/link/c/params_type.py

@@ -116,7 +116,8 @@ def __init__(value_attr1, value_attr2):
 .. code-block:: python
 
     wrapper = ParamsType(
-        enum1=EnumList("A", ("B", "beta"), "C"), enum2=EnumList(("D", "delta"), "E", "F")
+        enum1=EnumList("A", ("B", "beta"), "C"),
+        enum2=EnumList(("D", "delta"), "E", "F"),
     )
     b1 = wrapper.B
     b2 = wrapper.get_enum("B")
@@ -540,7 +541,9 @@ def enum_from_alias(self, alias):
             wrapper = ParamsType(
                 scalar=ScalarType("int32"),
                 letters=EnumType(A=(1, "alpha"), B=(2, "beta"), C=3),
-                digits=EnumList(("ZERO", "nothing"), ("ONE", "unit"), ("TWO", "couple")),
+                digits=EnumList(
+                    ("ZERO", "nothing"), ("ONE", "unit"), ("TWO", "couple")
+                ),
             )
             print(wrapper.get_enum("C"))  # 3
             print(wrapper.get_enum("TWO"))  # 2
@@ -593,7 +596,9 @@ def __init__(self):
                     self.b = numpy.asarray([[1, 2, 3], [4, 5, 6]])
 
 
-            params_type = ParamsType(a=ScalarType("int32"), b=dmatrix, c=ScalarType("bool"))
+            params_type = ParamsType(
+                a=ScalarType("int32"), b=dmatrix, c=ScalarType("bool")
+            )
 
             o = MyObject()
             value_for_c = False
@@ -852,20 +857,20 @@ def c_extract(self, name, sub, check_input=True, **kwargs):
         const char* fields[] = {{{fields_list}}};
         if (py_{name} == Py_None) {{
             PyErr_SetString(PyExc_ValueError, "ParamsType: expected an object, not None.");
-            {sub['fail']}
+            {sub["fail"]}
         }}
         for (int i = 0; i < {self.length}; ++i) {{
             PyObject* o = PyDict_GetItemString(py_{name}, fields[i]);
             if (o == NULL) {{
                 PyErr_Format(PyExc_TypeError, "ParamsType: missing expected attribute \\"%s\\" in object.", fields[i]);
-                {sub['fail']}
+                {sub["fail"]}
             }}
             {name}->extract(o, i);
             if ({name}->errorOccurred()) {{
                 /* The extract code from attribute type should have already raised a Python exception,
                  * so we just print the attribute name in stderr. */
                 fprintf(stderr, "\\nParamsType: error when extracting value for attribute \\"%s\\".\\n", fields[i]);
-                {sub['fail']}
+                {sub["fail"]}
             }}
         }}
         }}

pytensor/link/c/type.py

@@ -350,7 +350,11 @@ class EnumType(CType, dict):
         .. code-block:: python
 
             enum = EnumType(
-                CONSTANT_1=0, CONSTANT_2=1, CONSTANT_3=2, ctype="size_t", cname="MyEnumName"
+                CONSTANT_1=0,
+                CONSTANT_2=1,
+                CONSTANT_3=2,
+                ctype="size_t",
+                cname="MyEnumName",
             )
 
     **Example with aliases**
@@ -477,7 +481,7 @@ def get_aliases(self):
         return tuple(sorted(self.aliases))
 
     def __repr__(self):
-        names_to_aliases = {constant_name: "" for constant_name in self}
+        names_to_aliases = dict.fromkeys(self, "")
         for alias in self.aliases:
             names_to_aliases[self.aliases[alias]] = f"({alias})"
         args = ", ".join(f"{k}{names_to_aliases[k]}:{self[k]}" for k in sorted(self))
@@ -674,7 +678,9 @@ class EnumList(EnumType):
 
     .. code-block:: python
 
-        enum = EnumList(("A", "alpha"), ("B", "beta"), "C", "D", "E", "F", ("G", "gamma"))
+        enum = EnumList(
+            ("A", "alpha"), ("B", "beta"), "C", "D", "E", "F", ("G", "gamma")
+        )
 
     See test class :class:`tests.graph.test_types.TestOpEnumList` for a working example.
 

pytensor/link/numba/dispatch/scalar.py

@@ -119,12 +119,12 @@ def {scalar_op_fn_name}({input_names}):
         )
         if not has_pyx_skip_dispatch:
             scalar_op_src = f"""
-def {scalar_op_fn_name}({', '.join(input_names)}):
+def {scalar_op_fn_name}({", ".join(input_names)}):
     return direct_cast(scalar_func_numba({converted_call_args}), output_dtype)
             """
         else:
             scalar_op_src = f"""
-def {scalar_op_fn_name}({', '.join(input_names)}):
+def {scalar_op_fn_name}({", ".join(input_names)}):
     return direct_cast(scalar_func_numba({converted_call_args}, np.intc(1)), output_dtype)
             """
 

pytensor/link/vm.py

@@ -83,16 +83,16 @@ def calculate_reallocate_info(
             ins = None
             if dmap and idx_o in dmap:
                 idx_v = dmap[idx_o]
-                assert (
-                    len(idx_v) == 1
-                ), "Here we only support the possibility to destroy one input"
+                assert len(idx_v) == 1, (
+                    "Here we only support the possibility to destroy one input"
+                )
                 ins = node.inputs[idx_v[0]]
             if vmap and idx_o in vmap:
                 assert ins is None
                 idx_v = vmap[idx_o]
-                assert (
-                    len(idx_v) == 1
-                ), "Here we only support the possibility to view one input"
+                assert len(idx_v) == 1, (
+                    "Here we only support the possibility to view one input"
+                )
                 ins = node.inputs[idx_v[0]]
             if ins is not None:
                 assert isinstance(ins, Variable)