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>
Since PEP 393 in Python 3.3, this value is always 0x10ffff, the
maximum codepoint in Unicode; there's no longer such a thing as a
UCS-2 build of Python, which couldn't properly represent some
characters.
There are a couple of spots left where we still condition on the value
of this constant. Take them out.
weakref.WeakValueDictionary defines a local remove() function used as
callback for weak references. This function was created with a
closure. Modify the implementation to avoid the closure.
* bpo-26185: Fix repr() on empty ZipInfo object
It was failing on AttributeError due to inexistant
but required attributes file_size and compress_size.
They are now initialized to 0 in ZipInfo.__init__().
* Remove useless hasattr() in ZipInfo._open_to_write()
* Completely remove file_size setting in _open_to_write().
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.
Fixes a case in which email._header_value_parser.get_unstructured hangs the system for some invalid headers. This covers the cases in which the header contains either:
- a case without trailing whitespace
- an invalid encoded word
https://bugs.python.org/issue37764
This fix should also be backported to 3.7 and 3.8
https://bugs.python.org/issue37764
- drop TargetScopeError in favour of raising SyntaxError directly
as per the updated PEP 572
- comprehension iteration variables are explicitly local, but
named expression targets in comprehensions are nonlocal or
global. Raise SyntaxError as specified in PEP 572
- named expression targets in the outermost iterable of a
comprehension have an ambiguous target scope. Avoid resolving
that question now by raising SyntaxError. PEP 572
originally required this only for cases where the bound name
conflicts with the iteration variable in the comprehension,
but CPython can't easily restrict the exception to that case
(as it doesn't know the target variable names when visiting
the outermost iterator expression)
