Apply rule RUF005

pytensor/link/jax/dispatch/random.py

@@ -324,7 +324,7 @@ def jax_funcify_choice(op: ptr.ChoiceWithoutReplacement, node):
             batch_sampling_keys = jax.random.split(rng_key, np.prod(size))
 
             # Ravel the batch dimensions because vmap only works along a single axis
-            raveled_batch_a = a.reshape((-1,) + a.shape[batch_ndim:])
+            raveled_batch_a = a.reshape((-1, *a.shape[batch_ndim:]))
             if p is None:
                 raveled_sample = jax.vmap(
                     lambda key, a: jax.random.choice(
@@ -332,7 +332,7 @@ def jax_funcify_choice(op: ptr.ChoiceWithoutReplacement, node):
                     )
                 )(batch_sampling_keys, raveled_batch_a)
             else:
-                raveled_batch_p = p.reshape((-1,) + p.shape[batch_ndim:])
+                raveled_batch_p = p.reshape((-1, *p.shape[batch_ndim:]))
                 raveled_sample = jax.vmap(
                     lambda key, a, p: jax.random.choice(
                         key, a, shape=core_shape, replace=False, p=p
@@ -363,7 +363,7 @@ def jax_sample_fn_permutation(op, node):
                 x = jax.numpy.broadcast_to(x, size + x.shape[batch_ndim:])
 
             batch_sampling_keys = jax.random.split(rng_key, np.prod(size))
-            raveled_batch_x = x.reshape((-1,) + x.shape[batch_ndim:])
+            raveled_batch_x = x.reshape((-1, *x.shape[batch_ndim:]))
             raveled_sample = jax.vmap(lambda key, x: jax.random.permutation(key, x))(
                 batch_sampling_keys, raveled_batch_x
             )

pytensor/scan/op.py

@@ -2104,7 +2104,7 @@ class Scan(Op, ScanMethodsMixin, HasInnerGraph):
                     # are read and written.
                     # This way, there will be no information overwritten
                     # before it is read (as it used to happen).
-                    shape = (pdx,) + output_storage[idx][0].shape[1:]
+                    shape = (pdx, *output_storage[idx][0].shape[1:])
                     tmp = np.empty(shape, dtype=node.outputs[idx].type.dtype)
                     tmp[:] = output_storage[idx][0][:pdx]
                     output_storage[idx][0][: store_steps[idx] - pdx] = output_storage[
@@ -2113,7 +2113,7 @@ class Scan(Op, ScanMethodsMixin, HasInnerGraph):
                     output_storage[idx][0][store_steps[idx] - pdx :] = tmp
                     del tmp
                 else:
-                    shape = (store_steps[idx] - pdx,) + output_storage[idx][0].shape[1:]
+                    shape = (store_steps[idx] - pdx, *output_storage[idx][0].shape[1:])
                     tmp = np.empty(shape, dtype=node.outputs[idx].type.dtype)
                     tmp[:] = output_storage[idx][0][pdx:]
                     output_storage[idx][0][store_steps[idx] - pdx :] = output_storage[
@@ -2304,7 +2304,7 @@ class Scan(Op, ScanMethodsMixin, HasInnerGraph):
                 if x is None:
                     scan_outs.append(None)
                 else:
-                    scan_outs.append((Shape_i(0)(o),) + x[1:])
+                    scan_outs.append((Shape_i(0)(o), *x[1:]))
         return scan_outs
 
     def connection_pattern(self, node):

pytensor/tensor/basic.py

@@ -4051,8 +4051,8 @@ def alloc_diag(diag, offset=0, axis1=0, axis2=1):
         # Re-order axes so they correspond to diagonals at axis1, axis2
         axes = list(range(diag.type.ndim - 1))
         last_idx = axes[-1]
-        axes = axes[:axis1] + [last_idx + 1] + axes[axis1:]
-        axes = axes[:axis2] + [last_idx + 2] + axes[axis2:]
+        axes = [*axes[:axis1], last_idx + 1, *axes[axis1:]]
+        axes = [*axes[:axis2], last_idx + 2, *axes[axis2:]]
         result = result.transpose(axes)
 
     return AllocDiag(
@@ -4525,7 +4525,7 @@ def _make_along_axis_idx(arr_shape, indices, axis):
         if dim is None:
             fancy_index.append(indices)
         else:
-            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim + 1 :]
+            ind_shape = (*shape_ones[:dim], -1, *shape_ones[dim + 1 :])
             fancy_index.append(arange(n).reshape(ind_shape))
 
     return tuple(fancy_index)

pytensor/tensor/conv/abstract_conv.py

@@ -244,7 +244,7 @@ def get_conv_gradweights_shape(
             for i in range(len(subsample))
         )
     if unshared:
-        return (nchan,) + top_shape[2:] + (nkern,) + out_shp
+        return (nchan, *top_shape[2:], nkern, *out_shp)
     else:
         return (nchan, nkern, *out_shp)
 
@@ -2906,9 +2906,9 @@ class AbstractConv_gradWeights(BaseAbstractConv):
         def correct_for_groups(mat):
             mshp0 = mat.shape[0] // self.num_groups
             mshp1 = mat.shape[1] * self.num_groups
-            mat = mat.reshape((self.num_groups, mshp0) + mat.shape[1:])
+            mat = mat.reshape((self.num_groups, mshp0, *mat.shape[1:]))
             mat = mat.transpose((1, 0, 2, *range(3, 3 + self.convdim)))
-            mat = mat.reshape((mshp0, mshp1) + mat.shape[-self.convdim :])
+            mat = mat.reshape((mshp0, mshp1, *mat.shape[-self.convdim :]))
             return mat
 
         if self.num_groups > 1:
@@ -3283,7 +3283,7 @@ class AbstractConv_gradInputs(BaseAbstractConv):
         def correct_for_groups(mat):
             mshp0 = mat.shape[0] // self.num_groups
             mshp1 = mat.shape[-self.convdim - 1] * self.num_groups
-            mat = mat.reshape((self.num_groups, mshp0) + mat.shape[1:])
+            mat = mat.reshape((self.num_groups, mshp0, *mat.shape[1:]))
             if self.unshared:
                 # for 2D -> (1, 2, 3, 0, 4, 5, 6)
                 mat = mat.transpose(
@@ -3294,14 +3294,16 @@ class AbstractConv_gradInputs(BaseAbstractConv):
                     )
                 )
                 mat = mat.reshape(
-                    (mshp0,)
-                    + mat.shape[1 : 1 + self.convdim]
-                    + (mshp1,)
-                    + mat.shape[-self.convdim :]
+                    (
+                        mshp0,
+                        *mat.shape[1 : 1 + self.convdim],
+                        mshp1,
+                        *mat.shape[-self.convdim :],
+                    )
                 )
             else:
                 mat = mat.transpose((1, 0, 2, *range(3, 3 + self.convdim)))
-                mat = mat.reshape((mshp0, mshp1) + mat.shape[-self.convdim :])
+                mat = mat.reshape((mshp0, mshp1, *mat.shape[-self.convdim :]))
             return mat
 
         kern = correct_for_groups(kern)

tests/compile/test_builders.py

@@ -563,7 +563,7 @@ class TestOpFromGraph(unittest_tools.InferShapeTester):
         assert y_clone != y
         y_clone.name = "y_clone"
 
-        out_new = test_ofg.make_node(*(out.owner.inputs[:1] + [y_clone])).outputs[0]
+        out_new = test_ofg.make_node(*([*out.owner.inputs[:1], y_clone])).outputs[0]
 
         assert "on_unused_input" in out_new.owner.op.kwargs
         assert out_new.owner.op.shared_inputs == [y_clone]

tests/scan/test_basic.py

@@ -144,12 +144,12 @@ class multiple_outputs_numeric_grad:
                         t = t.flatten()
                         t[pos] += _eps
                         t = t.reshape(pt[i].shape)
-                        f_eps = f(*(pt[:i] + [t] + pt[i + 1 :]))
+                        f_eps = f(*([*pt[:i], t, *pt[i + 1 :]]))
                         _g.append(np.asarray((f_eps - f_x) / _eps))
                     gx.append(np.asarray(_g).reshape(pt[i].shape))
                 else:
                     t = np.array(pt[i] + _eps)
-                    f_eps = f(*(pt[:i] + [t] + pt[i + 1 :]))
+                    f_eps = f(*([*pt[:i], t, *pt[i + 1 :]]))
                     gx.append(np.asarray((f_eps - f_x) / _eps))
         self.gx = gx
 

tests/tensor/conv/test_abstract_conv.py

@@ -266,7 +266,7 @@ class TestConvGradInputsShape:
                         computed_image_shape = get_conv_gradinputs_shape(
                             kernel_shape, output_shape, b, (2, 3), (d, d)
                         )
-                        image_shape_with_None = image_shape[:2] + (None, None)
+                        image_shape_with_None = (*image_shape[:2], None, None)
                         assert computed_image_shape == image_shape_with_None
 
                         # compute the kernel_shape given this output_shape
@@ -276,7 +276,7 @@ class TestConvGradInputsShape:
 
                         # if border_mode == 'half', the shape should be None
                         if b == "half":
-                            kernel_shape_with_None = kernel_shape[:2] + (None, None)
+                            kernel_shape_with_None = (*kernel_shape[:2], None, None)
                             assert computed_kernel_shape == kernel_shape_with_None
                         else:
                             assert computed_kernel_shape == kernel_shape
@@ -285,7 +285,7 @@ class TestConvGradInputsShape:
                         computed_kernel_shape = get_conv_gradweights_shape(
                             kernel_shape, output_shape, b, (2, 3), (d, d)
                         )
-                        kernel_shape_with_None = kernel_shape[:2] + (None, None)
+                        kernel_shape_with_None = (*kernel_shape[:2], None, None)
                         assert computed_kernel_shape == kernel_shape_with_None
 
 

tests/tensor/test_extra_ops.py

@@ -1019,7 +1019,7 @@ class TestRavelMultiIndex(utt.InferShapeTester):
             )
             # create some invalid indices to test the mode
             if mode in ("wrap", "clip"):
-                multi_index = (multi_index[0] - 1,) + multi_index[1:]
+                multi_index = (multi_index[0] - 1, *multi_index[1:])
             # test with scalars and higher-dimensional indices
             if index_ndim == 0:
                 multi_index = tuple(i[-1] for i in multi_index)

tests/tensor/test_subtensor.py

@@ -1272,7 +1272,7 @@ class TestSubtensor(utt.OptimizationTestMixin):
                         if len(inc_shape) == len(data_shape) and (
                             len(inc_shapes) == 0 or inc_shape[0] != 1
                         ):
-                            inc_shape = (n_to_inc,) + inc_shape[1:]
+                            inc_shape = (n_to_inc, *inc_shape[1:])
 
                         # Symbolic variable with increment value.
                         inc_var_static_shape = tuple(
@@ -2822,15 +2822,15 @@ test_idx = np.ix_(np.array([True, True]), np.array([True]), np.array([True, True
         (np.arange(np.prod((5, 6, 7, 8))).reshape((5, 6, 7, 8)), test_idx[:2]),
         (
             np.arange(np.prod((5, 6, 7, 8))).reshape((5, 6, 7, 8)),
-            test_idx[:2] + (slice(None, None),),
+            (*test_idx[:2], slice(None, None)),
         ),
         (
             np.arange(np.prod((5, 6, 7, 8))).reshape((5, 6, 7, 8)),
-            (slice(None, None),) + test_idx[:1],
+            (slice(None, None), *test_idx[:1]),
         ),
         (
             np.arange(np.prod((5, 6, 7, 8))).reshape((5, 6, 7, 8)),
-            (slice(None, None), None) + test_idx[1:2],
+            (slice(None, None), None, *test_idx[1:2]),
         ),
         (
             np.arange(np.prod((5, 6, 7, 8))).reshape((5, 6, 7, 8)),
@@ -2842,15 +2842,15 @@ test_idx = np.ix_(np.array([True, True]), np.array([True]), np.array([True, True
         ),
         (
             np.arange(np.prod((5, 6, 7, 8))).reshape((5, 6, 7, 8)),
-            test_idx[:1] + (slice(None, None),) + test_idx[1:2],
+            (*test_idx[:1], slice(None, None), *test_idx[1:2]),
         ),
         (
             np.arange(np.prod((5, 6, 7, 8))).reshape((5, 6, 7, 8)),
-            test_idx[:1] + (slice(None, None),) + test_idx[1:2] + (slice(None, None),),
+            (*test_idx[:1], slice(None, None), *test_idx[1:2], slice(None, None)),
         ),
         (
             np.arange(np.prod((5, 6, 7, 8))).reshape((5, 6, 7, 8)),
-            test_idx[:1] + (None,) + test_idx[1:2],
+            (*test_idx[:1], None, *test_idx[1:2]),
         ),
         (np.arange(np.prod((5, 4))).reshape((5, 4)), ([1, 3, 2], slice(1, 3))),
         (np.arange(np.prod((5, 4))).reshape((5, 4)), (slice(1, 3), [1, 3, 2])),