The instance destructor for a type is responsible for preparing
an instance for deallocation by decrementing the reference counts
of its referents.
If an instance belongs to a heap type, the type object of an instance
has its reference count decremented while for static types, which
are permanently allocated, the type object is unaffected by the
instance destructor.
Previously, the default instance destructor searched the class
hierarchy for an inherited instance destructor and, if present,
would invoke it.
Then, if the instance type is a heap type, it would decrement the
reference count of that heap type. However, this could result in the
premature destruction of a type because the inherited instance
destructor should have already decremented the reference count
of the type object.
This change avoids the premature destruction of the type object
by suppressing the decrement of its reference count when an
inherited, non-default instance destructor has been invoked.
Finally, an assertion on the Py_SIZE of a type was deleted. Heap
types have a non zero size, making this into an incorrect assertion.
https://github.com/python/cpython/pull/15323
Add functions with various calling conventions to `_testcapi`, expose them as module-level functions, bound methods, class methods, and static methods, and test calling them and introspecting them through GDB.
https://bugs.python.org/issue37499
Co-authored-by: Jeroen Demeyer <J.Demeyer@UGent.be>
Automerge-Triggered-By: @pganssle
Summary:
Eliminate uses of `_Py_IDENTIFIER` from `_posixsubprocess`, replacing them with interned strings.
Also tries to find an existing version of the module, which will allow subinterpreters.
https://bugs.python.org/issue38069
* PEP-384 _struct
* More PEP-384 fixes for _struct
Summary: Add a couple of more fixes for `_struct` that were previously missed such as removing `tp_*` accessors and using `PyBytesWriter` instead of calling `PyBytes_FromStringAndSize` with `NULL`. Also added a test to confirm that `iter_unpack` type is still uninstantiable.
* 📜🤖 Added by blurb_it.
Accumulate certificates in a set instead of doing a costly list contain
operation. A Windows cert store can easily contain over hundred
certificates. The old code would result in way over 5,000 comparison
operations
Signed-off-by: Christian Heimes <christian@python.org>
In debug mode, visit_decref() now calls _PyObject_IsFreed() to ensure
that the object is not freed. If it's freed, the program fails with
an assertion error and Python dumps informations about the freed
object.
ssl_collect_certificates function in _ssl.c has a memory leak.
Calling CertOpenStore() and CertAddStoreToCollection(), a store's refcnt gets incremented by 2.
But CertCloseStore() is called only once and the refcnt leaves 1.
If FormatMessageW() is passed the FORMAT_MESSAGE_FROM_SYSTEM flag without FORMAT_MESSAGE_IGNORE_INSERTS, it will fail if there are insert sequences in the message definition.
* Rename PyThreadState_DeleteCurrent()
to _PyThreadState_DeleteCurrent()
* Move it to the internal C API
Co-Authored-By: Carol Willing <carolcode@willingconsulting.com>
The purpose of the `unicodedata.is_normalized` function is to answer
the question `str == unicodedata.normalized(form, str)` more
efficiently than writing just that, by using the "quick check"
optimization described in the Unicode standard in UAX #15.
However, it turns out the code doesn't implement the full algorithm
from the standard, and as a result we often miss the optimization and
end up having to compute the whole normalized string after all.
Implement the standard's algorithm. This greatly speeds up
`unicodedata.is_normalized` in many cases where our partial variant
of quick-check had been returning MAYBE and the standard algorithm
returns NO.
At a quick test on my desktop, the existing code takes about 4.4 ms/MB
(so 4.4 ns per byte) when the partial quick-check returns MAYBE and it
has to do the slow normalize-and-compare:
$ build.base/python -m timeit -s 'import unicodedata; s = "\uf900"*500000' \
-- 'unicodedata.is_normalized("NFD", s)'
50 loops, best of 5: 4.39 msec per loop
With this patch, it gets the answer instantly (58 ns) on the same 1 MB
string:
$ build.dev/python -m timeit -s 'import unicodedata; s = "\uf900"*500000' \
-- 'unicodedata.is_normalized("NFD", s)'
5000000 loops, best of 5: 58.2 nsec per loop
This restores a small optimization that the original version of this
code had for the `unicodedata.normalize` use case.
With this, that case is actually faster than in master!
$ build.base/python -m timeit -s 'import unicodedata; s = "\u0338"*500000' \
-- 'unicodedata.normalize("NFD", s)'
500 loops, best of 5: 561 usec per loop
$ build.dev/python -m timeit -s 'import unicodedata; s = "\u0338"*500000' \
-- 'unicodedata.normalize("NFD", s)'
500 loops, best of 5: 512 usec per loop
* Use the 'p' format unit instead of manually called PyObject_IsTrue().
* Pass boolean value instead 0/1 integers to functions that needs boolean.
* Convert some arguments to boolean only once.
Fix a ctypes regression of Python 3.8. When a ctypes.Structure is
passed by copy to a function, ctypes internals created a temporary
object which had the side effect of calling the structure finalizer
(__del__) twice. The Python semantics requires a finalizer to be
called exactly once. Fix ctypes internals to no longer call the
finalizer twice.
Create a new internal StructParam_Type which is only used by
_ctypes_callproc() to call PyMem_Free(ptr) on Py_DECREF(argument).
StructUnionType_paramfunc() creates such object.
Steve Dower
Jakub Kulík
Eddie Elizondo
Jordon Xu
Dino Viehland
Petr Viktorin
Dino Viehland
Kenta Murata
Sergey Fedoseev
Christian Heimes
Orivej Desh
Zackery Spytz
Victor Stinner
animalize
neonene
Greg Price
Serhiy Storchaka
HongWeipeng
Joannah Nanjekye
Dong-hee Na
Inada Naoki
Min ho Kim
Thomas A Caswell
Rémi Lapeyre
Justin Blanchard
HongWeipeng
Vinay Sharma
vrajivk
Raymond Hettinger