Run ruff-format

Author: Armavica

pytensor/bin/pytensor_cache.py

@@ -24,7 +24,7 @@
 
 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')

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,
             )
 

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

@@ -935,18 +935,14 @@ 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 +1089,7 @@ 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:
@@ -1405,7 +1399,7 @@ 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 +1539,7 @@ 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/rewriting/basic.py

@@ -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):