Mailman 3 February 2021 - Python-Dev

Python 0.9.1
by Skip Montanaro Feb. 19, 2021

Feb. 19, 2021

A note to webmaster(a)python.org from an astute user named Hiromi in Japan* referred us to Guido's shell archives for the 0.9.1 release from 1991. As that wasn't listed in the historical releases README file: https://legacy.python.org/download/releases/src/README I pulled the shar files (and a patch), then made a few tweaks to get it to build: % ./python >>> print 'hello world!' hello world! >>> import sys >>> dir(sys) ['argv', 'exit', 'modules', 'path', 'ps1', '… [View More]

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Summary of Python tracker Issues
by Python tracker Feb. 19, 2021

Feb. 19, 2021

ACTIVITY SUMMARY (2021-02-12 - 2021-02-19) Python tracker at https://bugs.python.org/ To view or respond to any of the issues listed below, click on the issue. Do NOT respond to this message. Issues counts and deltas: open 7428 (+13) closed 47578 (+49) total 55006 (+62) Open issues with patches: 2943 Issues opened (43) ================== #37146: opcode cache for LOAD_GLOBAL emits false alarm in memory leak https://bugs.python.org/issue37146 reopened by vstinner #42990: … [View More]

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[RELEASE] Python 3.9.2 and 3.8.8 are now available
by Łukasz Langa Feb. 19, 2021

Feb. 19, 2021

Convinced of the wonders of free two-day deliveries, I’m pleased to present you Python 3.9.2 and 3.8.8. Get them from: https://www.python.org/downloads/release/python-392/ <https://www.python.org/downloads/release/python-392/> https://www.python.org/downloads/release/python-388/ <https://www.python.org/downloads/release/python-388/> Next up, the last full regular maintenance release of Python 3.8 is planned for May 3rd 2021, after which it will shift to source releases only for … [View More]security bug fixes only. Maintenance releases for the 3.9 series will continue at regular bi-monthly intervals, with 3.9.3 planned for early May 2021. Why the expedited final release? This release, just as the candidate before it, contains two security fixes: bpo-42938 <https://bugs.python.org/issue42938>: Avoid static buffers when computing the repr of ctypes.c_double and ctypes.c_longdouble values. This issue was assigned CVE-2021-3177 <https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-3177>. bpo-42967 <https://bugs.python.org/issue42967>: Fix web cache poisoning vulnerability by defaulting the query args separator to &, and allowing the user to choose a custom separator. This issue was assigned CVE-2021-23336 <https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-23336>. Since the announcement of the release candidates for 3.9.2 on 3.8.8, we received a number of inquiries from end users urging us to expedite the final releases due to the security content, especially CVE-2021-3177 <https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-3177>. This took us somewhat by surprise since we believed security content is cherry-picked by downstream distributors from source either way, and the RC releases provide installers for everybody else interested in upgrading in the meantime. It turns out that release candidates are mostly invisible to the community and in many cases cannot be used due to upgrade processes which users have in place. In turn, the other active release managers and I decided to stop providing release candidates for bugfix versions. Starting from now on after the initial 3.x.0 final release, all subsequent releases are going to be provided as is in bi-monthly intervals. The release calendar PEPs for 3.8 and 3.9 have been updated accordingly. On the severity of CVE-2021-3177 We recommend you upgrade your systems to Python 3.8.8 or 3.9.2. Our understanding is that while the CVE is listed as “remote code execution”, practical exploits of this vulnerability as such are very unlikely due the following conditions needing to be met for successful RCE: pass an untrusted floating point number from a remote party to ctypes.c_double.from_param (note: Python floating point numbers were not affected); have that object be passed to repr() (for instance through logging); have that float point number be valid machine code; have the buffer overflow overwrite the stack at exactly the right place for the code to get executed. In fact, Red Hat’s evaluation of the vulnerability was consistent with ours. They write: “the highest threat from this vulnerability is to system availability <https://access.redhat.com/security/cve/cve-2021-3177>.” To be sure, denial of service through malicious input is also a serious issue. Thus, to help the community members for whom the release candidate was insufficient, we are releasing the final versions of 3.9.2 and 3.8.8 today. What’s new? The Python 3.9 series contains many new features and optimizations over 3.8. See the “What’s New in Python 3.9 2 <https://docs.python.org/3.9/whatsnew/3.9.html>” document for more information about features included in the 3.9 series. We also have a detailed change log for 3.9.2rc1 <https://docs.python.org/release/3.9.2/whatsnew/changelog.html#python-3-9-2-…> specifically. The final release only contains a single bugfix <https://github.com/python/cpython/pull/24554> over the release candidate. Detailed information about all changes made in version 3.8.8rc1 specifically can be found in its respective changelog <https://docs.python.org/release/3.8.8/whatsnew/changelog.html#python-3-8-8-…>. The final version contains no changes over the release candidate. We hope you enjoy those new releases! Thanks to all of the many volunteers who help make Python Development and these releases possible! Please consider supporting our efforts by volunteering yourself or through organization contributions to the Python Software Foundation. Your friendly release team, Ned Deily @nad <https://discuss.python.org/u/nad> Steve Dower @steve.dower <https://discuss.python.org/u/steve.dower> Łukasz Langa @ambv <https://discuss.python.org/u/ambv> [View Less]

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[RELEASE] Python 3.9.2rc1 and 3.8.8rc1 are now available for testing
by Łukasz Langa Feb. 17, 2021

Feb. 17, 2021

I’m happy to announce two release candidates today: Python 3.9.2rc1, and Python 3.8.8rc1. Get them from: https://www.python.org/downloads/release/python-392rc1/ <https://www.python.org/downloads/release/python-392rc1/> https://www.python.org/downloads/release/python-388rc1/ <https://www.python.org/downloads/release/python-388rc1/> Unless critical issues are discovered, both release candidates will become their respective final versions on Monday, March 1st. Following that, the … [View More]

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PEP: Deferred Evaluation Of Annotations Using Descriptors
by Larry Hastings Feb. 16, 2021

Feb. 16, 2021

I've written a new PEP. Please find it below. Happy reading! //arry/ ---------- PEP: XXXX Title: Deferred Evaluation Of Annotations Using Descriptors Version: $Revision$ Last-Modified: $Date$ Author: Larry Hastings <larry(a)hastings.org> Discussions-To: Python-Dev <python-dev(a)python.org> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 11-Jan-2021 Abstract ======== As of Python 3.9, Python supports two different behaviors for annotations: * … [View More]original Python semantics, in which annotations are evaluated at the time they are bound, and * PEP 563 semantics, currently enabled per-module by ``from __future__ import annotations``, in which annotations are converted back into strings and must be parsed by ``eval()`` to be used. Original Python semantics created a circular references problem for static typing analysis. PEP 563 solved that problem, but its novel semantics introduced new problems. This PEP proposes a third way that embodies the best of both previous approaches. It solves the same circular reference problems solved by PEP 563, while preserving Python's original straightforward runtime semantics for annotations. In this new approach, the code to generate the annotations dict is written to its own callable, and ``__annotations__`` is a "data descriptor" which calls the callable once and preserves the result. If accepted, these new semantics for annotations would initially be gated behind ``from __future__ import co_annotations``. However, these semantics would eventually be promoted to be the default behavior. Thus this PEP would *supercede* PEP 563, and PEP 563's behavior would be deprecated and eventually removed. Overview ======== .. note:: The code presented in this section is highly simplified for clarity. The intention is to communicate the high-level concepts involved without getting lost in with the details. The actual details are often quite different. See the Implementation_ section later in this PEP for a much more accurate description of how this PEP works. Consider this example code:: def foo(x: int = 3, y: MyType = None) -> float: ... class MyType: ... foo_y_type = foo.__annotations__['y'] As we see here, annotations are available at runtime through an ``__annotations__`` attribute on functions, classes, and modules. When annotations are specified on one of these objects, ``__annotations__`` is a dictionary mapping the names of the fields to the value specified as that field's annotation. The default behavior in Python 3.9 is to evaluate the expressions for the annotations, and build the annotations dict, at the time the function, class, or module is bound. At runtime the above code actually works something like this:: annotations = {'x': int, 'y': MyType, 'return': float} def foo(x = 3, y = "abc"): ... foo.__annotations__ = annotations class MyType: ... foo_y_type = foo.__annotations__['y'] The crucial detail here is that the values ``int``, ``MyType``, and ``float`` are looked up at the time the function object is bound, and these values are stored in the annotations dict. But this code doesn't run—it throws a ``NameError`` on the first line, because ``MyType`` hasn't been defined yet. PEP 563's solution is to decompile the expressions back into strings, and store those *strings* in the annotations dict. The equivalent runtime code would look something like this:: annotations = {'x': 'int', 'y': 'MyType', 'return': 'float'} def foo(x = 3, y = "abc"): ... foo.__annotations__ = annotations class MyType: ... foo_y_type = foo.__annotations__['y'] This code now runs successfully. However, ``foo_y_type`` is no longer a reference to ``MyType``, it is the *string* ``'MyType'``. The code would have to be further modified to call ``eval()`` or ``typing.get_type_hints()`` to convert the string into a useful reference to the actual ``MyType`` object. This PEP proposes a third approach, delaying the evaluation of the annotations by computing them in their own function. If this PEP was active, the generated code would work something like this:: class function: @property # __annotations__ on a function object is already a # "data descriptor", we're just changing what it does def __annotations__(self): return self.__co_annotations__() # ... def foo_annotations_fn(): return {'x': int, 'y': MyType, 'return': float} def foo(x = 3, y = "abc"): ... foo.__co_annotations__ = foo_annotations_fn class MyType: ... foo_y_type = foo.__annotations__['y'] The important change is that the code constructing the annotations dict now lives in a function—here, called `` foo_annotations__fn()``. But this function isn't called until we ask for the value of ``foo.__annotations__``, and we don't do that until *after* the definition of ``MyType``. So this code also runs successfully, and ``foo_y_type`` now has the correct value, the class ``MyType``. Motivation ========== Python's original semantics for annotations made its use for static type analysis painful due to forward reference problems. This was the main justification for PEP 563, and we need not revisit those arguments here. However, PEP 563's solution was to de-compile code for Python annotations back into strings at compile time, requiring users of annotations to ``eval()`` those strings to turn them back into Python values. This has several drawbacks: * It requires Python implementations to stringize their annotations. This is surprising—unprecedented behavior for a language-level feature. Also, adding this feature to CPython was complicated, and this complicated code would need to be reimplemented independently by every other Python implementation. * It requires a code change every time existing code uses an annotation, to handle converting the stringized annotation back into a useful value. * ``eval()`` is slow. * ``eval()`` isn't always available; it's sometimes removed from Python for space reasons. * In order to evaluate the annotations stored with a class, it requires obtaining a reference to that class's globals, which PEP 563 suggests should be done by looking up that class by name in ``sys.modules``—another surprising requirement for a language-level feature. * It adds an ongoing maintenance burden to Python implementations. Every time the language adds a new feature available in expressions, the implementation's stringizing code must be updated in tandem to support decompiling it. This PEP also solves the forward reference problem outlined in PEP 563 while avoiding the problems listed above: * Python implementations would generate annotations as code objects. This is simpler than stringizing, and is something Python implementations are already quite good at. This means: * alternate implementations would need to write less code to implement this feature, and * the implementation would be simpler overall, which should reduce its ongoing maintenance cost. * Code examining annotations at runtime would no longer need to use ``eval()`` or anything else—it would automatically get the correct values. This is easier, almost certainly faster, and removes the dependency on ``eval()``. Backwards Compatibility ======================= PEP 563 changed the semantics of annotations. When its semantics are active, annotations must assume they will be evaluated in *module-level* scope. They may no longer refer directly to local variables or class attributes. This PEP retains that semantic change, also requiring that annotations be evaluated in *module-level* scope. Thus, code changed so its annotations are compatible with PEP 563 should *already* compatible with this aspect of this PEP and would not need further change. Modules still using stock semantics would have to be revised so its annotations evaluate properly in module-level scope, in the same way they would have to be to achieve compatibility with PEP 563. PEP 563 also requires using ``eval()`` or ``typing.get_type_hints()`` to examine annotations. Code updated to work with PEP 563 that calls ``eval()`` directly would have to be updated simply to remove the ``eval()`` call. Code using ``typing.get_type_hints()`` would continue to work unchanged, though future use of that function would become optional in most cases. Because this PEP makes the same backwards-compatible change to annotation scoping as PEP 563, this PEP will be initially gated with a per-module ``from __future__ import co_annotations`` before it eventually becomes the default behavior. Apart from these two changes already discussed: * the evaluation of values in annotation dicts will be delayed until the ``__annotations__`` attribute is evaluated, and * annotations are now evaluated in module-level scope, this PEP preserves nearly all existing behavior of annotations dicts. Specifically: * Annotations dicts are mutable, and any changes to them are preserved. * The ``__annotations__`` attribute can be explicitly set, and any value set this way will be preserved. * The ``__annotations__`` attribute can be deleted using the ``del`` statement. However, there are two uncommon interactions possible with class and module annotations that work today—both with stock semantics, and with PEP 563 semantics—that would no longer work when this PEP was active. These two interactions would have to be prohibited. The good news is, neither is common, and neither is considered good practice. In fact, they're rarely seen outside of Python's own regression test suite. They are: * *Code that sets annotations from inside any kind of flow control statement.* It's currently possible to set module and class attributes with annotations inside an ``if`` or ``try`` statement, and it works as one would expect. It's untenable to support this behavior when this PEP is active. * *Code in module or class scope that references or modifies the local* ``__annotations__`` *dict directly.* Currently, when setting annotations on module or class attributes, the generated code simply creates a local ``__annotations__`` dict, then sets mappings in it as needed. It's also possible for user code to directly modify this dict, though this doesn't seem like it's an intentional feature. Although it'd be possible to support this after a fashion when this PEP was active, the semantics would likely be surprising and wouldn't make anyone happy. Note that these are both also pain points for static type checkers, and are unsupported by those checkers. It seems reasonable to declare that both are at the very least unsupported, and their use results in undefined behavior. It might be worth making a small effort to explicitly prohibit them with compile-time checks. There's one more idiom that's actually somewhat common when dealing with class annotations, and which will become more problematic when this PEP is active: code often accesses class annotations via ``cls.__dict__.get("__annotations__", {})`` rather than simply ``cls.__annotations__``. It's due to a flaw in the original design of annotations themselves. This topic will be examined in a separate discussion; the outcome of that discussion will likely guide the future evolution of this PEP. Mistaken Rejection Of This Approach In November 2017 ==================================================== During the early days of discussion around PEP 563, using code to delay the evaluation of annotations was briefly discussed, in a November 2017 thread in ``comp.lang.python-dev``. At the time the technique was termed an "implicit lambda expression". Guido van Rossum—Python's BDFL at the time—replied, asserting that these "implicit lambda expression" wouldn't work, because they'd only be able to resolve symbols at module-level scope: IMO the inability of referencing class-level definitions from annotations on methods pretty much kills this idea. https://mail.python.org/pipermail/python-dev/2017-November/150109.html This led to a short discussion about extending lambda-ized annotations for methods to be able to refer to class-level definitions, by maintaining a reference to the class-level scope. This idea, too, was quickly rejected. PEP 563 summarizes the above discussion here: https://www.python.org/dev/peps/pep-0563/#keeping-the-ability-to-use-functi… What's puzzling is PEP 563's own changes to the scoping rules of annotations—it *also* doesn't permit annotations to reference class-level definitions. It's not immediately clear why an inability to reference class-level definitions was enough to reject using "implicit lambda expressions" for annotations, but was acceptable for stringized annotations. In retrospect there was probably a pivot during the development of PEP 563. It seems that, early on, there was a prevailing assumption that PEP 563 would support references to class-level definitions. But by the time PEP 563 was finalized, this assumption had apparently been abandoned. And it looks like "implicit lambda expressions" were never reconsidered in this new light. PEP 563 semantics have shipped in three major Python releases. These semantics are now widely used in organizations depending on static type analysis. Evaluating annotations at module-level scope is clearly acceptable to all interested parties. Therefore delayed evaluation of annotations with code using the same scoping rules is obviously also completely viable. .. _Implementation: Implementation ============== There's a prototype implementation of this PEP, here: https://github.com/larryhastings/co_annotations/ As of this writing, all features described in this PEP are implemented, and there are some rudimentary tests in the test suite. There are still some broken tests, and the repo is many months behind. from __future__ import co_annotations ------------------------------------- In the prototype, the semantics presented in this PEP are gated with: from __future__ import co_annotations __co_annotations__ ------------------ Python supports runtime metadata for annotations for three different types: function, classes, and modules. The basic approach to implement this PEP is much the same for all three with only minor variations. With this PEP, each of these types adds a new attribute, ``__co_annotations__``, with the following semantics: * ``__co_annotations__`` is always set, and may contain either ``None`` or a callable. * ``__co_annotations__`` cannot be deleted. * ``__annotations__`` and ``__co_annotations__`` can't both be set to a useful value simultaneously: * If you set ``__annotations__`` to a dict, this also sets ``__co_annotations__`` to None. * If you set ``__co_annotations__`` to a callable, this also deletes ``__annotations__`` Internally, ``__co_annotations__`` is a "data descriptor", where functions are called whenever user code gets, sets, or deletes the attribute. In all three cases, the object has a separate internal place to store the current value of the ``__co_annotations__`` attribute. ``__annotations__`` is also reimplemented as a data descriptor, with its own separate internal storage for its internal value. The code implementing the "get" for ``__annotations__`` works something like this:: if (the internal value is set) return the internal annotations dict if (__co_annotations__ is not None) call the __co_annotations__ function if the result is a dict: store the result as the internal value set __co_annotations__ to None return the internal value do whatever this object does when there are no annotations Unbound code objects -------------------- When Python code defines one of these three objects with annotations, the Python compiler generates a separate code object which builds and returns the appropriate annotations dict. The "annotation code object" is then stored *unbound* as the internal value of ``__co_annotations__``; it is then bound on demand when the user asks for ``__annotations__``. This is an important optimization, for both speed and memory consumption. Python processes rarely examine annotations at runtime. Therefore, pre-binding these code objects to function objects would be a waste of resources in nearly all cases. Note that user code isn't permitted to see these unbound code objects. If the user gets the value of ``__co_annotations__``, and the internal value of ``__co_annotations__`` is an unbound code object, it is bound, and the resulting function object is stored as the new value of ``__co_annotations__``. The annotations function ------------------------ Annotations functions take no arguments and must return a dict (or subclass of dict). The bytecode generated for annotations code objects always uses the ``BUILD_CONST_KEY_MAP`` opcode to build the dict. Stock and PEP 563 semantics only uses this bytecode for function annotations; for class and module annotations, they generate a longer and slightly-less-efficient stanza of bytecode. Also, when generating the bytecode for an annotations code object, all ``LOAD_*`` opcodes are forced to be ``LOAD_GLOBAL``. Function Annotations -------------------- When compiling a function, the CPython bytecode compiler visits the annotations for the function all in one place, starting with ``compiler_visit_annotations()``. If there are any annotations, they create the scope for the annotations function on demand, and ``compiler_visit_annotations()`` assembles it. The code object is passed in in place of the annotations dict for the ``MAKE_FUNCTION`` bytecode. ``MAKE_FUNCTION`` supports a new bit in its oparg bitfield, ``0x10``, which tells it to expect a ``co_annotations`` code object on the stack. The bitfields for ``annotations`` (``0x04``) and ``co_annotations`` (``0x10``) are mutually exclusive. When binding an unbound annotation code object, a function will use its own ``__globals__`` as the new function's globals. One quirk of Python: you can't actually remove the annotations from a function object. If you delete the ``__annotations__`` attribute of a function, then get its ``__annotations__`` member, it will create an empty dict and use that as its ``__annotations__``. Naturally the implementation of this PEP maintains this quirk. Class Annotations ----------------- When compiling a class body, the compiler maintains two scopes: one for the normal class body code, and one for annotations. (This is facilitated by four new functions: ``compiler.c`` adds ``compiler_push_scope()`` and ``compiler_pop_scope()``, and ``symtable.c`` adds ``symtable_push_scope()`` and ``symtable_pop_scope()``.) Once the code generator reaches the end of the class body, but before it generates the bytecode for the class body, it assembles the bytecode for ``__co_annotations__``, then assigns that to ``__co_annotations__`` using ``STORE_NAME``. It also sets a new ``__globals__`` attribute. Currently it does this by calling ``globals()`` and storing the result. (Surely there's a more elegant way to find the class's globals--but this was good enough for the prototype.) When binding an unbound annotation code object, a class will use the value of this ``__globals__`` attribute. When the class drops its reference to the unbound code object--either because it has bound it to a function, or because ``__annotations__`` has been explicitly set--it also deletes its ``__globals__`` attribute. As discussed above, examination / modification of ``__annotations__`` from within the class body is no longer supported. Also, any flow control (``if`` / ``try``) around declarations of members with annotations is unsupported. If you delete the ``__annotations__`` attribute of a class, then get its ``__annotations__`` member, it will return the annotations dict of the first base class with annotations set. If no base classes have annotations set, it will raise ``AttributeError``. Although it's an implementation-specific detail, currently classes store the internal value of ``__co_annotations__`` in their ``tp_dict`` under the same name. Module Annotations ------------------ Module annotations work much the same as class annotations. The main difference is, a module uses its own dict as the ``__globals__`` when binding the function. If you delete the ``__annotations__`` attribute of a class, then get its ``__annotations__`` member, the module will raise ``AttributeError``. Interactive REPL Shell ---------------------- Everything works the same inside Python's interactive REPL shell, except for module annotations in the interactive module (``__main__``) itself. Since that module is never "finished", there's no specific point where we can compile the ``__co_annotations__`` function. For the sake of simplicity, in this case we forego delayed evaluation. Module-level annotations in the REPL shell will continue to work exactly as they do today, evaluating immediately and setting the result directly inside the ``__annotations__`` dict. (It might be possible to support delayed evaluation here. But it gets complicated quickly, and for a nearly-non-existent use case.) Local Annotations Inside Functions ---------------------------------- Python supports syntax for local variable annotations inside functions. However, these annotations have no runtime effect. Thus this PEP doesn't need to do anything to support them. Performance ----------- Performance with this PEP should be favorable. In general, resources are only consumed on demand—"you only pay for what you use". There are three scenarios to consider: * the runtime cost when annotations aren't defined, * the runtime cost when annotations are defined but *not* referenced, and * the runtime cost when annotations are defined *and* referenced. We'll examine each of these scenarios in the context of all three semantics for annotations: stock, PEP 563, and this PEP. When there are no annotations, all three semantics have the same runtime cost: zero. No annotations dict is created and no code is generated for it. This requires no runtime processor time and consumes no memory. When annotations are defined but not referenced, the runtime cost of Python with this PEP should be slightly faster than either original Python semantics or PEP 563 semantics. With those, the annotations dicts are built but never examined; with this PEP, the annotations dicts won't even be built. All that happens at runtime is the loading of a single constant (a simple code object) which is then set as an attribute on an object. Since the annotations are never referenced, the code object is never bound to a function, the code to create the dict is never executed, and the dict is never constructed. When annotations are both defined and referenced, code using this PEP should be much faster than code using PEP 563 semantics, and roughly the same as original Python semantics. PEP 563 semantics requires invoking ``eval()`` for every value inside an annotations dict, which is much slower. And, as already mentioned, this PEP generates more efficient bytecode for class and module annotations than either stock or PEP 563 semantics. Memory use should also be comparable in all three scenarios across all three semantic contexts. In the first and third scenarios, memory usage should be roughly equivalent in all cases. In the second scenario, when annotations are defined but not referenced, using this PEP's semantics will mean the function/class/module will store one unused code object; with the other two semantics, they'll store one unused dictionary. For Future Discussion ===================== __globals__ ----------- Is it permissable to add the ``__globals__`` reference to class objects as proposed here? It's not clear why this hasn't already been done; PEP 563 could have made use of class globals, but instead makes do with looking up classes inside ``sys.modules``. Yet Python seems strangely allergic to adding a ``__globals__`` reference to class objects. If adding ``__globals__`` to class objects is indeed a bad idea (for reasons I don't know), here are two alternatives as to how classes could get a reference to their globals for the implementation of this PEP: * The generate code for a class could bind its annotations code object to a function at the time the class is bound, rather than waiting for ``__annotations__`` to be referenced, making them an exception to the rule (even though "special cases aren't special enough to break the rules"). This would result in a small additional runtime cost when annotations were defined but not referenced on class objects. Honestly I'm more worried about the lack of symmetry in semantics. (But I wouldn't want to pre-bind all annotations code objects, as that would become much more costly for function objects, even as annotations are rarely used at runtime.) * Use the class's ``__module__`` attribute to look up its module by name in ``sys.modules``. This is what PEP 563 advises. While this is passable for userspace or library code, it seems like a little bit of a code smell for this to be defined semantics baked into the language itself. Also, the prototype gets globals for class objects by calling ``globals()`` then storing the result. I'm sure there's a much faster way to do this, I just didn't know what it was when I was prototyping. I'm sure we can revise this to something much faster and much more sanitary. I'd prefer to make it completely internal anyway, and not make it visible to the user (via this new __globals__ attribute). There's possibly already a good place to put it anyway--``ht_module``. Bikeshedding the name --------------------- During most of the development of this PEP, user code actually could see the raw annotation code objects. ``__co_annotations__`` could only be set to a code object; functions and other callables weren't permitted. In that context the name ``co_annotations`` makes a lot of sense. But with this last-minute pivot where ``__co_annotations__`` now presents itself as a callable, perhaps the name of the attribute and the name of the ``from __future__ import`` needs a re-think. Acknowledgements ================ Thanks to Barry Warsaw, Eric V. Smith, and Mark Shannon for feedback and encouragement. Thanks in particular to Mark Shannon for two key suggestions—build the entire annotations dict inside a single code object, and only bind it to a function on demand—that quickly became among the best aspects of this proposal. Copyright ========= This document is placed in the public domain. .. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 coding: utf-8 End: [View Less]

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Re: It is really necessary to check that a object has a Py_TPFLAGS_HEAPTYPE flag?
by Андрей Казанцев Feb. 16, 2021

Feb. 16, 2021

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It is really necessary to check that a object has a Py_TPFLAGS_HEAPTYPE flag?
by Kazantcev Andrey Feb. 16, 2021

Feb. 16, 2021

Hi everywhere. I decided to figure out why it is impossible to change methods of built-in types and found ``` if (!(type->tp_flags & Py_TPFLAGS_HEAPTYPE)) { PyErr_Format( PyExc_TypeError, "can't set attributes of built-in/extension type '%s'", type->tp_name); return -1; } ``` in function type_setattro. If comment this line and try to patch method __eq__ on the list for example the code will work as expected Test: ``` with … [View More]

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[RELEASE] Python 3.7.10 and 3.6.13 security updates now available
by Ned Deily Feb. 16, 2021

Feb. 16, 2021

Python 3.7.10 and 3.6.13, the lastest security fix rollups for Python 3.7 and Python 3.6, are now available. You can find the release files, links to the changelogs, and more information here: https://www.python.org/downloads/release/python-3710/ https://www.python.org/downloads/release/python-3613/ These releases are source code only; Windows and macOS binary installers are not provided for security fix releases. Note that Python 3.9 is now the latest feature release series of … [View More]

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Python standardization
by Dan Stromberg Feb. 15, 2021

Feb. 15, 2021

What would it take to create an ANSI, ECMA and/or ISO standard for Python? It seems to have really helped C. It looks like Java isn't standardized, and it's done OK, though perhaps it was healthier in the past - before Oracle decided API's were ownable. I think standardizing Python might be really good for controlling its growth and avoiding featuritis.

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PR Review request - bpo-41928: Add support for Unicode Path Extra Field in ZipFile
by Andrea Giudiceandrea Feb. 13, 2021

Feb. 13, 2021

Hi Python Dev team, I submitted a PR https://github.com/python/cpython/pull/23736 two months ago. The PR, which fixes an issue https://bugs.python.org/issue41928 in ZipFile, is "awaiting core review". Best regards. Andrea Giudiceandrea

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