Manual simplification of RUF005 fixes

Author: Armavica

pytensor/breakpoint.py

@@ -92,7 +92,7 @@ def make_node(self, condition, *monitored_vars):
             new_op.inp_types.append(monitored_vars[i].type)
 
         # Build the Apply node
-        inputs = [condition, *list(monitored_vars)]
+        inputs = [condition, *monitored_vars]
         outputs = [inp.type() for inp in monitored_vars]
         return Apply(op=new_op, inputs=inputs, outputs=outputs)
 

pytensor/graph/rewriting/basic.py

@@ -1139,7 +1139,7 @@ def decorator(f):
         if inplace:
             dh_handler = dh.DestroyHandler
             req = (
-                *tuple(requirements),
+                *requirements,
                 lambda fgraph: fgraph.attach_feature(dh_handler()),
             )
         rval = FromFunctionNodeRewriter(f, tracks, req)

pytensor/graph/rewriting/unify.py

@@ -283,7 +283,7 @@ def _convert(y):
             var_map[pattern] = v
             return v
         elif isinstance(y, tuple):
-            return etuple(*tuple(_convert(e) for e in y))
+            return etuple(*(_convert(e) for e in y))
         elif isinstance(y, (Number, np.ndarray)):
             from pytensor.tensor import as_tensor_variable
 

pytensor/ifelse.py

@@ -397,7 +397,7 @@ def ifelse(
 
     new_ifelse = IfElse(n_outs=len(then_branch), as_view=False, name=name)
 
-    ins = [condition, *list(then_branch), *list(else_branch)]
+    ins = [condition, *then_branch, *else_branch]
     rval = new_ifelse(*ins, return_list=True)
 
     if rval_type is None:

pytensor/link/c/type.py

@@ -508,8 +508,8 @@ def __hash__(self):
             (
                 type(self),
                 self.ctype,
-                *tuple((k, self[k]) for k in sorted(self.keys())),
-                *tuple((a, self.aliases[a]) for a in sorted(self.aliases.keys())),
+                *((k, self[k]) for k in sorted(self.keys())),
+                *((a, self.aliases[a]) for a in sorted(self.aliases.keys())),
             )
         )
 

pytensor/link/numba/dispatch/elemwise.py

@@ -447,7 +447,7 @@ def jit_compile_reducer(
 def create_axis_apply_fn(fn, axis, ndim, dtype):
     axis = normalize_axis_index(axis, ndim)
 
-    reaxis_first = (*tuple(i for i in range(ndim) if i != axis), axis)
+    reaxis_first = (*(i for i in range(ndim) if i != axis), axis)
 
     @numba_basic.numba_njit(boundscheck=False)
     def axis_apply_fn(x):

pytensor/link/numba/dispatch/extra_ops.py

@@ -44,7 +44,7 @@ def numba_funcify_CumOp(op: CumOp, node: Apply, **kwargs):
         if axis < 0 or axis >= ndim:
             raise ValueError(f"Invalid axis {axis} for array with ndim {ndim}")
 
-        reaxis_first = (axis, *tuple(i for i in range(ndim) if i != axis))
+        reaxis_first = (axis, *(i for i in range(ndim) if i != axis))
         reaxis_first_inv = tuple(np.argsort(reaxis_first))
 
     if mode == "add":

pytensor/link/numba/dispatch/random.py

@@ -240,7 +240,7 @@ def create_numba_random_fn(
     np_global_env["numba_vectorize"] = numba_basic.numba_vectorize
 
     unique_names = unique_name_generator(
-        [np_random_fn_name, *list(np_global_env.keys()), "rng", "size", "dtype"],
+        [np_random_fn_name, *np_global_env.keys(), "rng", "size", "dtype"],
         suffix_sep="_",
     )
 

pytensor/link/numba/dispatch/scalar.py

@@ -115,7 +115,7 @@ def {scalar_op_fn_name}({input_names}):
         global_env.update(input_tmp_dtype_names)
 
         unique_names = unique_name_generator(
-            [scalar_op_fn_name, "scalar_func_numba", *list(global_env.keys())],
+            [scalar_op_fn_name, "scalar_func_numba", *global_env.keys()],
             suffix_sep="_",
         )
 

pytensor/scalar/basic.py

@@ -416,7 +416,7 @@ def dtype_specs(self):
             )
 
     def upcast(self, *others):
-        return upcast(*[x.dtype for x in [self, *list(others)]])
+        return upcast(*[x.dtype for x in [self, *others]])
 
     def make_variable(self, name=None):
         return ScalarVariable(self, None, name=name)
@@ -1501,7 +1501,7 @@ def c_code(self, node, name, inputs, outputs, sub):
 
     def c_code_cache_version(self):
         scalarop_version = super().c_code_cache_version()
-        return (*tuple(scalarop_version), 3)
+        return (*scalarop_version, 3)
 
 
 isnan = IsNan()
@@ -1529,7 +1529,7 @@ def c_code(self, node, name, inputs, outputs, sub):
 
     def c_code_cache_version(self):
         scalarop_version = super().c_code_cache_version()
-        return (*tuple(scalarop_version), 3)
+        return (*scalarop_version, 3)
 
 
 isinf = IsInf()