Apply the ruff auto fixes

Author: Armavica

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/types.py

@@ -215,7 +215,7 @@ 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/link/c/type.py

@@ -481,7 +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))

pytensor/misc/ordered_set.py

@@ -9,7 +9,7 @@ def __init__(self, iterable: Iterable | None = None) -> None:
         if iterable is None:
             self.values = {}
         else:
-            self.values = {value: None for value in iterable}
+            self.values = dict.fromkeys(iterable)
 
     def __contains__(self, value) -> bool:
         return value in self.values

pytensor/printing.py

@@ -295,8 +295,10 @@ def debugprint(
         if hasattr(var.owner, "op"):
             if (
                 isinstance(var.owner.op, HasInnerGraph)
-                or hasattr(var.owner.op, "scalar_op")
-                and isinstance(var.owner.op.scalar_op, HasInnerGraph)
+                or (
+                    hasattr(var.owner.op, "scalar_op")
+                    and isinstance(var.owner.op.scalar_op, HasInnerGraph)
+                )
             ) and var not in inner_graph_vars:
                 inner_graph_vars.append(var)
             if print_op_info:
@@ -675,8 +677,10 @@ def get_id_str(
                 if hasattr(in_var, "owner") and hasattr(in_var.owner, "op"):
                     if (
                         isinstance(in_var.owner.op, HasInnerGraph)
-                        or hasattr(in_var.owner.op, "scalar_op")
-                        and isinstance(in_var.owner.op.scalar_op, HasInnerGraph)
+                        or (
+                            hasattr(in_var.owner.op, "scalar_op")
+                            and isinstance(in_var.owner.op.scalar_op, HasInnerGraph)
+                        )
                     ) and in_var not in inner_graph_ops:
                         inner_graph_ops.append(in_var)
 
@@ -882,7 +886,9 @@ def process(self, output, pstate):
         max_i = len(node.inputs) - 1
         for i, input in enumerate(node.inputs):
             new_precedence = self.precedence
-            if self.assoc == "left" and i != 0 or self.assoc == "right" and i != max_i:
+            if (self.assoc == "left" and i != 0) or (
+                self.assoc == "right" and i != max_i
+            ):
                 new_precedence += 1e-6
 
             with set_precedence(pstate, new_precedence):

pytensor/scalar/loop.py

@@ -219,7 +219,7 @@ def c_code_template(self):
             for i, c in enumerate(fgraph.inputs[n_update:], start=n_update + 1)
         }
         out_subd = {u: f"%(o{i})s" for i, u in enumerate(fgraph.outputs[:n_update])}
-        until_subd = {u: "until" for u in fgraph.outputs[n_update:]}
+        until_subd = dict.fromkeys(fgraph.outputs[n_update:], "until")
         subd = {**carry_subd, **constant_subd, **until_subd}
 
         for var in fgraph.variables:

pytensor/tensor/basic.py

@@ -4577,73 +4577,73 @@ def ix_(*args):
 
 
 __all__ = [
-    "take_along_axis",
-    "expand_dims",
-    "atleast_Nd",
+    "alloc",
+    "arange",
+    "as_tensor",
+    "as_tensor_variable",
     "atleast_1d",
     "atleast_2d",
     "atleast_3d",
+    "atleast_Nd",
+    "cast",
     "choose",
-    "swapaxes",
-    "moveaxis",
-    "stacklists",
+    "concatenate",
+    "constant",
+    "default",
     "diag",
     "diagonal",
-    "inverse_permutation",
-    "permute_row_elements",
-    "mgrid",
-    "ogrid",
-    "arange",
-    "tile",
+    "empty",
+    "empty_like",
+    "expand_dims",
+    "extract_diag",
+    "eye",
+    "fill",
+    "flatnonzero",
     "flatten",
-    "is_flat",
-    "vertical_stack",
-    "horizontal_stack",
+    "full",
+    "full_like",
+    "get_scalar_constant_value",
+    "get_underlying_scalar_constant_value",
     "get_vector_length",
-    "concatenate",
-    "stack",
-    "roll",
-    "join",
-    "split",
-    "transpose",
-    "matrix_transpose",
-    "default",
-    "tensor_copy",
+    "horizontal_stack",
     "identity",
-    "transfer",
-    "alloc",
     "identity_like",
-    "eye",
-    "triu",
-    "tril",
-    "tri",
-    "nonzero_values",
-    "flatnonzero",
+    "inverse_permutation",
+    "is_flat",
+    "join",
+    "matrix_transpose",
+    "mgrid",
+    "moveaxis",
     "nonzero",
+    "nonzero_values",
+    "ogrid",
     "ones",
-    "zeros",
-    "zeros_like",
     "ones_like",
-    "fill",
+    "permute_row_elements",
+    "roll",
+    "scalar_from_tensor",
     "second",
-    "where",
+    "split",
+    "stack",
+    "stacklists",
+    "swapaxes",
     "switch",
-    "cast",
-    "scalar_from_tensor",
+    "take_along_axis",
+    "tensor_copy",
     "tensor_from_scalar",
-    "get_scalar_constant_value",
-    "get_underlying_scalar_constant_value",
-    "constant",
-    "as_tensor_variable",
-    "as_tensor",
-    "extract_diag",
-    "full",
-    "full_like",
-    "empty",
-    "empty_like",
+    "tile",
     "trace",
+    "transfer",
+    "transpose",
+    "tri",
+    "tril",
     "tril_indices",
     "tril_indices_from",
+    "triu",
     "triu_indices",
     "triu_indices_from",
+    "vertical_stack",
+    "where",
+    "zeros",
+    "zeros_like",
 ]

pytensor/tensor/elemwise.py

@@ -821,7 +821,7 @@ def _c_all(self, node, nodename, inames, onames, sub):
         # for each input:
         # same as range(ndim), but with 'x' at all broadcastable positions
         orders = [
-            [s == 1 and "x" or i for i, s in enumerate(input.type.shape)]
+            [(s == 1 and "x") or i for i, s in enumerate(input.type.shape)]
             for input in inputs
         ]
 

pytensor/tensor/extra_ops.py

@@ -1558,7 +1558,7 @@ def broadcast_shape_iter(
             (one,) * (max_dims - len(a))
             + tuple(
                 one
-                if sh == 1 or isinstance(sh, Constant) and sh.value == 1
+                if sh == 1 or (isinstance(sh, Constant) and sh.value == 1)
                 else (ps.as_scalar(sh) if not isinstance(sh, Variable) else sh)
                 for sh in a
             )
@@ -2067,25 +2067,25 @@ def concat_with_broadcast(tensor_list, axis=0):
 
 
 __all__ = [
-    "searchsorted",
-    "cumsum",
-    "cumprod",
-    "diff",
-    "bincount",
-    "squeeze",
-    "compress",
-    "repeat",
     "bartlett",
-    "fill_diagonal",
-    "fill_diagonal_offset",
-    "unique",
-    "unravel_index",
-    "ravel_multi_index",
+    "bincount",
+    "broadcast_arrays",
     "broadcast_shape",
     "broadcast_to",
+    "compress",
     "concat_with_broadcast",
+    "cumprod",
+    "cumsum",
+    "diff",
+    "fill_diagonal",
+    "fill_diagonal_offset",
     "geomspace",
-    "logspace",
     "linspace",
-    "broadcast_arrays",
+    "logspace",
+    "ravel_multi_index",
+    "repeat",
+    "searchsorted",
+    "squeeze",
+    "unique",
+    "unravel_index",
 ]