annotationlib.type_repr() returns "None.list.append" for bound built-in methods
还没有人认领这个 Issue。
- 主要语言
- Python
- 星标
- 77.2k
- 派生
- 35.9k
- PR 合并指标
- PR 指标待抓取
描述
Bug report
annotationlib.type_repr() (a public, documented helper, exported in
__all__) returns a string that names a nonexistent module None for bound
built-in methods:
>>> from annotationlib import type_repr
>>> type_repr([].append)
'None.list.append'
>>> type_repr(dict.fromkeys)
'None.dict.fromkeys'
>>> import random; type_repr(random.random)
'None.Random.random'
Bound built-in methods (and C-accelerator functions) are BuiltinFunctionType
with __module__ set to None, so f"{value.__module__}.{value.__qualname__}"
interpolates the literal None. Every other object in this family produces a
resolvable name -- len -> 'len', os.getpid -> 'posix.getpid';
this is the only one that emits a None. prefix. 'None.list.append' is
also an active hazard: a STRING-format consumer that resolves it gets
AttributeError on the literal None.
annotations_to_string() and get_annotations(obj, format=Format.STRING)
propagate it when an annotation value is such a method.
Note for completeness: this is reached when a live method object is passed to
type_repr (its documented purpose), not from natural annotation source
syntax -- def f(x: [].append) is stringified correctly via the AST path.
The defect is the public-API output of type_repr itself.
Fix
Return __qualname__ ('list.append') when __module__ is None,
exactly as already done for the "builtins" module. repr() is not a
usable fallback: it embeds a non-deterministic heap address and is not
re-parseable.
Linked PRs
- gh-152693
贡献指南
从这里开始
- 先读完整个 Issue,再读项目的贡献指南。
- 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
- Fork 仓库,在一个分支上完成修改。
- 提交 Pull Request,并在描述里引用这个 Issue 编号。
调研方向
从公开的 annotationlib.type_repr() 入口点开始,检查它如何格式化 module 为 None 的对象。验证文档中的示例以及 annotations_to_string() 和 get_annotations(..., format=Format.STRING) 路径;完成的标准是,绑定的内置方法生成其 qualname 时不带 None. 前缀,并且仍然可解析。
由索引模型根据 Issue 内容生成。
评估
- 技术栈
- python
- 领域
- api
- Issue 类型
- 缺陷
- 难度
- 2/5
- 预计耗时
- 1-3 小时
- 活跃度
- 停滞
- 描述清晰度
- 描述清楚
- 新手友好度
- 35/100