Update the pre-commit hooks

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ exclude: |
     )$
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
       - id: debug-statements
         exclude: |
@@ -25,11 +25,11 @@ repos:
     rev: v1.0.0                                                                          
     hooks:                                                                               
     - id: sphinx-lint 
-      args: ["."]
+      args: ["-i", ".pixi", "."]
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.7.3
+    rev: v0.14.0
     hooks:
-      - id: ruff
+      - id: ruff-check
         types_or: [python, pyi, jupyter]
         args: ["--fix", "--output-format=full"]
       - id: ruff-format

pytensor/bin/pytensor_cache.py

@@ -1,6 +1,7 @@
 import logging
 import os
 import sys
+from pathlib import Path
 
 
 if sys.platform == "win32":
@@ -24,7 +25,7 @@
 
 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')
@@ -65,11 +66,7 @@ def main():
             # Print a warning if some cached modules were not removed, so that the
             # user knows he should manually delete them, or call
             # pytensor-cache purge, # to properly clear the cache.
-            items = [
-                item
-                for item in sorted(os.listdir(cache.dirname))
-                if item.startswith("tmp")
-            ]
+            items = list(Path(cache.dirname).glob("tmp*"))
             if items:
                 _logger.warning(
                     "There remain elements in the cache dir that you may "

pytensor/compile/builders.py

@@ -122,7 +122,7 @@ def construct_nominal_fgraph(
     (
         local_inputs,
         local_outputs,
-        (clone_d, update_d, update_expr, new_shared_inputs),
+        (_clone_d, update_d, update_expr, new_shared_inputs),
     ) = new
 
     assert len(local_inputs) == len(inputs) + len(implicit_shared_inputs)

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,
             )
 
@@ -592,7 +591,7 @@ def construct_pfunc_ins_and_outs(
             clone_inner_graphs=True,
         )
         input_variables, cloned_extended_outputs, other_stuff = output_vars
-        clone_d, update_d, update_expr, shared_inputs = other_stuff
+        clone_d, update_d, _update_expr, shared_inputs = other_stuff
 
         # Recover only the clones of the original outputs
         if outputs is None:

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/compile/mode.py

@@ -407,7 +407,7 @@ def register(self, *optimizations):
             optimizations.
         """
 
-        link, opt = self.get_linker_optimizer(
+        _link, opt = self.get_linker_optimizer(
             self.provided_linker, self.provided_optimizer
         )
         return self.clone(optimizer=opt.register(*optimizations))

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

@@ -514,14 +514,12 @@ def summary_class(self, file=sys.stderr, N=None):
             # While this carries over less information, it is arranged such
             # that it is way more readable than the previous output of the
             # profiler
+        nb_classes = max(0, len(otimes) - N)
+        percent_total = sum(f for f, t, a, ci, nb_call, nb_op in otimes[N:])
+        time_total = sum(t for f, t, a, ci, nb_call, nb_op in otimes[N:])
         print(
-            "   ... (remaining %i Classes account for %6.2f%%(%.2fs) of "
-            "the runtime)"
-            % (
-                max(0, len(otimes) - N),
-                sum(f for f, t, a, ci, nb_call, nb_op in otimes[N:]),
-                sum(t for f, t, a, ci, nb_call, nb_op in otimes[N:]),
-            ),
+            f"   ... (remaining {nb_classes} Classes account for "
+            f"{percent_total:6.2f}%%({time_total:.2f}s) of the runtime)",
             file=file,
         )
         print("", file=file)
@@ -607,14 +605,12 @@ def summary_ops(self, file=sys.stderr, N=None):
             # While this carries over less information, it is arranged such
             # that it is way more readable than the previous output of the
             # profiler
+        nb_ops = max(0, len(otimes) - N)
+        percent_total = sum(f for f, t, a, ci, nb_call, nb_op in otimes[N:])
+        time_total = sum(t for f, t, a, ci, nb_call, nb_op in otimes[N:])
         print(
-            "   ... (remaining %i Ops account for %6.2f%%(%.2fs) of "
-            "the runtime)"
-            % (
-                max(0, len(otimes) - N),
-                sum(f for f, t, a, ci, nb_call, nb_op in otimes[N:]),
-                sum(t for f, t, a, ci, nb_call, nb_op in otimes[N:]),
-            ),
+            f"   ... (remaining {nb_ops} Ops account for "
+            f"{percent_total:6.2f}%%({time_total:.2f}s) of the runtime)",
             file=file,
         )
         print("", file=file)
@@ -935,18 +931,14 @@ 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 +1085,7 @@ 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 +1294,22 @@ 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,
             )
@@ -1333,30 +1323,30 @@ def print_stats(stats1, stats2):
         )
         print_stats(stats[1], stats[3])
 
-        (max_node_memory_size, _, _, _) = stats[0]
+        (_max_node_memory_size, _, _, _) = stats[0]
         (new_max_node_memory_size, _, _, _) = stats[2]
         print(
             "    Max peak memory if allow_gc=False (linker don't make a difference)",
             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 +1395,7 @@ 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 +1535,7 @@ 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:
@@ -2199,9 +2198,9 @@ 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

@@ -427,19 +427,17 @@ class Variable(Node, Generic[_TypeType, OptionalApplyType]):
 
         c = a + b  # create a simple expression
 
-        f = pytensor.function(
-            [b], [c]
-        )  # this works because a has a value associated with it already
+        # this works because a has a value associated with it already
+        f = pytensor.function([b], [c])
 
-        assert 4.0 == f(2.5)  # bind 2.5 to an internal copy of b and evaluate an internal c
+        # bind 2.5 to an internal copy of b and evaluate an internal c
+        assert 4.0 == f(2.5)
 
-        pytensor.function(
-            [a], [c]
-        )  # compilation error because b (required by c) is undefined
+        # compilation error because b (required by c) is undefined
+        pytensor.function([a], [c])
 
-        pytensor.function(
-            [a, b], [c]
-        )  # compilation error because a is constant, it can't be an input
+        # compilation error because a is constant, it can't be an input
+        pytensor.function([a, b], [c])
 
 
     The python variables ``a, b, c`` all refer to instances of type

pytensor/graph/features.py

@@ -391,8 +391,7 @@ 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/fg.py

@@ -559,7 +559,7 @@ def remove_node(self, node: Apply, reason: str | None = None):
 
             out_clients = clients.get(out, ())
             while out_clients:
-                out_client, out_idx = out_clients.pop()
+                out_client, _out_idx = out_clients.pop()
 
                 if isinstance(out_client.op, Output):
                     self.remove_output(out_client.op.idx, remove_client=False)

pytensor/graph/rewriting/basic.py

@@ -2827,7 +2827,7 @@ def local_recursive_function(
     else:
         out_index = 0
 
-    final_outs, rewritten_nodes = local_recursive_function(rewrites, out, {}, 0)
+    final_outs, _rewritten_nodes = local_recursive_function(rewrites, out, {}, 0)
     return final_outs[out_index]
 
 
@@ -2975,10 +2975,7 @@ 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

@@ -464,7 +464,7 @@ 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/basic.py

@@ -509,7 +509,7 @@ def make_thunk(self, **kwargs):
         kwargs.pop("input_storage", None)
         make_all += [x.make_all(**kwargs) for x in self.linkers[1:]]
 
-        fns, input_lists, output_lists, thunk_lists, order_lists = zip(
+        _fns, input_lists, output_lists, thunk_lists, order_lists = zip(
             *make_all, strict=True
         )