Fix RUF005

Automated fixes by RUF, and update of the TensorConstructorType
type in scan/op.py because mypy didn't like something there.

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, *list(monitored_vars)]
         outputs = [inp.type() for inp in monitored_vars]
         return Apply(op=new_op, inputs=inputs, outputs=outputs)
 
@@ -142,7 +142,7 @@ def perform(self, node, inputs, output_storage):
                 output_storage[i][0] = inputs[i + 1]
 
     def grad(self, inputs, output_gradients):
-        return [DisconnectedType()()] + output_gradients
+        return [DisconnectedType()(), *output_gradients]
 
     def infer_shape(self, fgraph, inputs, input_shapes):
         # Return the shape of every input but the condition (first input)

pytensor/compile/debugmode.py

@@ -892,9 +892,9 @@ def _get_preallocated_maps(
 
         # Use the same step on all dimensions before the last check_ndim.
         if all(s == 1 for s in out_shape[:-check_ndim]):
-            step_signs_list = [(1,)] + step_signs_list
+            step_signs_list = [(1,), *step_signs_list]
         else:
-            step_signs_list = [(-1, 1)] + step_signs_list
+            step_signs_list = [(-1, 1), *step_signs_list]
 
         for step_signs in itertools_product(*step_signs_list):
             for step_size in (1, 2):

pytensor/compile/sharedvalue.py

@@ -209,7 +209,7 @@ def shared(value, name=None, strict=False, allow_downcast=None, **kwargs):
         add_tag_trace(var)
         return var
     except MemoryError as e:
-        e.args = e.args + ("Consider using `pytensor.shared(..., borrow=True)`",)
+        e.args = (*e.args, "Consider using `pytensor.shared(..., borrow=True)`")
         raise
 
 

pytensor/configdefaults.py

@@ -1382,7 +1382,8 @@ def add_caching_dir_configvars():
     "fft_tiling",
     "winograd",
     "winograd_non_fused",
-) + SUPPORTED_DNN_CONV_ALGO_RUNTIME
+    *SUPPORTED_DNN_CONV_ALGO_RUNTIME,
+)
 
 SUPPORTED_DNN_CONV_ALGO_BWD_DATA = (
     "none",
@@ -1391,7 +1392,8 @@ def add_caching_dir_configvars():
     "fft_tiling",
     "winograd",
     "winograd_non_fused",
-) + SUPPORTED_DNN_CONV_ALGO_RUNTIME
+    *SUPPORTED_DNN_CONV_ALGO_RUNTIME,
+)
 
 SUPPORTED_DNN_CONV_ALGO_BWD_FILTER = (
     "none",
@@ -1400,7 +1402,8 @@ def add_caching_dir_configvars():
     "small",
     "winograd_non_fused",
     "fft_tiling",
-) + SUPPORTED_DNN_CONV_ALGO_RUNTIME
+    *SUPPORTED_DNN_CONV_ALGO_RUNTIME,
+)
 
 SUPPORTED_DNN_CONV_PRECISION = (
     "as_input_f32",

pytensor/gradient.py

@@ -1972,7 +1972,7 @@ def inner_function(*args):
     jacobs, updates = pytensor.scan(
         inner_function,
         sequences=pytensor.tensor.arange(expression.shape[0]),
-        non_sequences=[expression] + wrt,
+        non_sequences=[expression, *wrt],
     )
     assert not updates, "Scan has returned a list of updates; this should not happen."
     return as_list_or_tuple(using_list, using_tuple, jacobs)

pytensor/graph/features.py

@@ -508,11 +508,13 @@ def consistent_(self, fgraph):
 
 
 class ReplaceValidate(History, Validator):
-    pickle_rm_attr = (
-        ["replace_validate", "replace_all_validate", "replace_all_validate_remove"]
-        + History.pickle_rm_attr
-        + Validator.pickle_rm_attr
-    )
+    pickle_rm_attr = [
+        "replace_validate",
+        "replace_all_validate",
+        "replace_all_validate_remove",
+        *History.pickle_rm_attr,
+        *Validator.pickle_rm_attr,
+    ]
 
     def on_attach(self, fgraph):
         for attr in (

pytensor/graph/rewriting/basic.py

@@ -405,7 +405,7 @@ def print_profile(cls, stream, prof, level=0):
             else:
                 name = rewrite.name
             idx = rewrites.index(rewrite)
-            ll.append((name, rewrite.__class__.__name__, idx) + nb_n)
+            ll.append((name, rewrite.__class__.__name__, idx, *nb_n))
         lll = sorted(zip(prof, ll), key=lambda a: a[0])
 
         for t, rewrite in lll[::-1]:
@@ -1138,7 +1138,8 @@ def decorator(f):
         req = requirements
         if inplace:
             dh_handler = dh.DestroyHandler
-            req = tuple(requirements) + (
+            req = (
+                *tuple(requirements),
                 lambda fgraph: fgraph.attach_feature(dh_handler()),
             )
         rval = FromFunctionNodeRewriter(f, tracks, req)

pytensor/graph/rewriting/db.py

@@ -73,7 +73,7 @@ def register(
 
         if use_db_name_as_tag:
             if self.name is not None:
-                tags = tags + (self.name,)
+                tags = (*tags, self.name)
 
         rewriter.name = name
         # This restriction is there because in many place we suppose that

pytensor/graph/utils.py

@@ -171,7 +171,7 @@ def __init__(self, *args, **kwargs):
             assert list(kwargs.keys()) == ["variable"]
             error_msg = get_variable_trace_string(kwargs["variable"])
             if error_msg:
-                args = args + (error_msg,)
+                args = (*args, error_msg)
         s = "\n".join(args)  # Needed to have the new line print correctly
         super().__init__(s)
 

pytensor/ifelse.py

@@ -227,7 +227,7 @@ def make_node(self, condition: "TensorLike", *true_false_branches: Any):
 
         return Apply(
             self,
-            [condition] + new_inputs_true_branch + new_inputs_false_branch,
+            [condition, *new_inputs_true_branch, *new_inputs_false_branch],
             output_vars,
         )
 
@@ -275,11 +275,11 @@ def grad(self, ins, grads):
         # condition + epsilon always triggers the same branch as condition
         condition_grad = condition.zeros_like().astype(config.floatX)
 
-        return (
-            [condition_grad]
-            + if_true_op(*inputs_true_grad, return_list=True)
-            + if_false_op(*inputs_false_grad, return_list=True)
-        )
+        return [
+            condition_grad,
+            *if_true_op(*inputs_true_grad, return_list=True),
+            *if_false_op(*inputs_false_grad, return_list=True),
+        ]
 
     def make_thunk(self, node, storage_map, compute_map, no_recycling, impl=None):
         cond = node.inputs[0]
@@ -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, *list(then_branch), *list(else_branch)]
     rval = new_ifelse(*ins, return_list=True)
 
     if rval_type is None:
@@ -611,7 +611,7 @@ def apply(self, fgraph):
                 mn_fs = merging_node.inputs[1:][merging_node.op.n_outs :]
                 pl_ts = proposal.inputs[1:][: proposal.op.n_outs]
                 pl_fs = proposal.inputs[1:][proposal.op.n_outs :]
-                new_ins = [merging_node.inputs[0]] + mn_ts + pl_ts + mn_fs + pl_fs
+                new_ins = [merging_node.inputs[0], *mn_ts, *pl_ts, *mn_fs, *pl_fs]
                 mn_name = "?"
                 if merging_node.op.name:
                     mn_name = merging_node.op.name
@@ -673,7 +673,7 @@ def cond_remove_identical(fgraph, node):
 
     new_ifelse = IfElse(n_outs=len(nw_ts), as_view=op.as_view, name=op.name)
 
-    new_ins = [node.inputs[0]] + nw_ts + nw_fs
+    new_ins = [node.inputs[0], *nw_ts, *nw_fs]
     new_outs = new_ifelse(*new_ins, return_list=True)
 
     rval = []
@@ -711,7 +711,7 @@ def cond_merge_random_op(fgraph, main_node):
             mn_fs = merging_node.inputs[1:][merging_node.op.n_outs :]
             pl_ts = proposal.inputs[1:][: proposal.op.n_outs]
             pl_fs = proposal.inputs[1:][proposal.op.n_outs :]
-            new_ins = [merging_node.inputs[0]] + mn_ts + pl_ts + mn_fs + pl_fs
+            new_ins = [merging_node.inputs[0], *mn_ts, *pl_ts, *mn_fs, *pl_fs]
             mn_name = "?"
             if merging_node.op.name:
                 mn_name = merging_node.op.name

pytensor/link/basic.py

@@ -106,7 +106,7 @@ def __set__(self, value: Any) -> None:
                 self.storage[0] = self.type.filter(value, **kwargs)
 
         except Exception as e:
-            e.args = e.args + (f'Container name "{self.name}"',)
+            e.args = (*e.args, f'Container name "{self.name}"')
             raise
 
     data = property(__get__, __set__)

pytensor/link/c/cmodule.py

@@ -1890,7 +1890,7 @@ def _try_compile_tmp(
                 os.close(fd)
                 fd = None
                 out, err, p_ret = output_subprocess_Popen(
-                    [compiler] + args + [path, "-o", exe_path] + flags
+                    [compiler, *args, path, "-o", exe_path, *flags]
                 )
                 if p_ret != 0:
                     compilation_ok = False

pytensor/link/c/params_type.py

@@ -293,7 +293,7 @@ def __hash__(self):
                 .signature()
                 for i in range(self.__params_type__.length)
             )
-        return hash((type(self), self.__params_type__) + self.__signatures__)
+        return hash((type(self), self.__params_type__, *self.__signatures__))
 
     def __eq__(self, other):
         return (
@@ -437,7 +437,7 @@ def __eq__(self, other):
         )
 
     def __hash__(self):
-        return hash((type(self),) + self.fields + self.types)
+        return hash((type(self), *self.fields, *self.types))
 
     def generate_struct_name(self):
         # This method tries to generate an unique name for the current instance.
@@ -807,7 +807,7 @@ def c_support_code(self, **kwargs):
             )
         )
 
-        return sorted(c_support_code_set) + [final_struct_code]
+        return [*sorted(c_support_code_set), final_struct_code]
 
     def c_code_cache_version(self):
         return ((3,), tuple(t.c_code_cache_version() for t in self.types))

pytensor/link/c/type.py

@@ -267,7 +267,7 @@ def c_compile_args(self, **kwargs):
     def c_code_cache_version(self):
         v = (3,)
         if self.version is not None:
-            v = v + (self.version,)
+            v = (*v, self.version)
         return v
 
     def __str__(self):
@@ -505,9 +505,12 @@ def __delitem__(self, key):
     def __hash__(self):
         # All values are Python basic types, then easy to hash.
         return hash(
-            (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()))
+            (
+                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())),
+            )
         )
 
     def __eq__(self, other):

pytensor/link/jax/dispatch/nlinalg.py

@@ -130,7 +130,8 @@ def maxandargmax(x, axis=axis):
 
         # Numpy.prod returns 1.0 when arg is empty, so we cast it to int64
         # Otherwise reshape would complain citing float arg
-        new_shape = kept_shape + (
+        new_shape = (
+            *kept_shape,
             jnp.prod(jnp.array(reduced_shape, dtype="int64"), dtype="int64"),
         )
         reshaped_x = transposed_x.reshape(new_shape)

pytensor/link/jax/dispatch/scalar.py

@@ -95,7 +95,7 @@ def jax_funcify_ScalarOp(op, node, **kwargs):
 
     func_name = nfunc_spec[0]
     if "." in func_name:
-        jax_func = functools.reduce(getattr, [jax] + func_name.split("."))
+        jax_func = functools.reduce(getattr, [jax, *func_name.split(".")])
     else:
         jax_func = getattr(jnp, func_name)
 

pytensor/link/numba/dispatch/basic.py

@@ -330,7 +330,7 @@ def creator(args):
 
         @numba_njit
         def creator(args, creator=creator, i=i):
-            return creator(args) + (f(i, *args),)
+            return (*creator(args), f(i, *args))
 
     return numba_njit(lambda *args: creator(args))
 

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 = (*tuple(i for i in range(ndim) if i != axis), axis)
 
     @numba_basic.numba_njit(boundscheck=False)
     def axis_apply_fn(x):
@@ -1042,7 +1042,7 @@ def maxandargmax(x):
 
             # Numpy.prod returns 1.0 when arg is empty, so we cast it to int64
             # Otherwise reshape would complain citing float arg
-            new_shape = kept_shape + (reduced_size,)
+            new_shape = (*kept_shape, reduced_size)
             reshaped_x = transposed_x.reshape(new_shape)
 
             max_idx_res = argmax_axis(reshaped_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, *tuple(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

@@ -240,15 +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, *list(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", *list(global_env.keys())],
             suffix_sep="_",
         )
 

pytensor/link/numba/dispatch/slinalg.py

@@ -144,8 +144,8 @@ def _check_scipy_linalg_matrix(a, func_name):
         msg = "{}.{}() only supported for array types".format(*interp)
         raise numba.TypingError(msg, highlighting=False)
     if a.ndim not in [1, 2]:
-        msg = "%s.%s() only supported on 1d or 2d arrays, found %s." % (
-            interp + (a.ndim,)
+        msg = "{}.{}() only supported on 1d or 2d arrays, found {}.".format(
+            *interp, a.ndim
         )
         raise numba.TypingError(msg, highlighting=False)
     if not isinstance(a.dtype, (types.Float, types.Complex)):

pytensor/link/numba/dispatch/tensor_basic.py

@@ -174,14 +174,14 @@ def extract_diag(x):
             else:
                 diag_len = min(x.shape[axis2], max(0, x.shape[axis1] + offset))
             base_shape = x.shape[:axis1] + x.shape[axis1p1:axis2] + x.shape[axis2p1:]
-            out_shape = base_shape + (diag_len,)
+            out_shape = (*base_shape, diag_len)
             out = np.empty(out_shape)
 
             for i in range(diag_len):
                 if offset >= 0:
-                    new_entry = x[leading_dims + (i,) + middle_dims + (i + offset,)]
+                    new_entry = x[(*leading_dims, i, *middle_dims, i + offset)]
                 else:
-                    new_entry = x[leading_dims + (i - offset,) + middle_dims + (i,)]
+                    new_entry = x[(*leading_dims, i - offset, *middle_dims, i)]
                 out[..., i] = new_entry
             return out
 

pytensor/raise_op.py

@@ -87,7 +87,7 @@ def make_node(self, value: Variable, *conds: Variable):
 
         return Apply(
             self,
-            [value] + conds,
+            [value, *conds],
             [value.type()],
         )