如何检查值是否与python中的类型匹配？

Question

假设我有一个python函数，其单个参数是一个非平凡的类型：

from typing import List, Dict
ArgType = List[Dict[str, int]]  # this could be any non-trivial type
def myfun(a: ArgType) -> None:
    ...

...然后我有一个我从JSON源解压缩的数据结构：

import json
data = json.loads(...)

我的问题是：如何在运行时检查data是否具有正确的类型，以便在将myfun()用作myfun()的参数之前用作

if not isCorrectType(data, ArgType):
    raise TypeError("data is not correct type")
else:
    myfun(data)

的参数？

typing

Answer 1

验证类型注释是一项非常重要的任务。 Python不会自动执行，编写自己的验证器很困难，因为typing模块没有提供太多有用的接口。 （事实上​​，

import inspect
import typing

__all__ = ['is_instance', 'is_subtype', 'python_type', 'is_generic', 'is_base_generic', 'is_qualified_generic']


if hasattr(typing, '_GenericAlias'):
    # python 3.7
    def _is_generic(cls):
        if isinstance(cls, typing._GenericAlias):
            return True

        if isinstance(cls, typing._SpecialForm):
            return cls not in {typing.Any}

        return False


    def _is_base_generic(cls):
        if isinstance(cls, typing._GenericAlias):
            if cls.__origin__ in {typing.Generic, typing._Protocol}:
                return False

            if isinstance(cls, typing._VariadicGenericAlias):
                return True

            return len(cls.__parameters__) > 0

        if isinstance(cls, typing._SpecialForm):
            return cls._name in {'ClassVar', 'Union', 'Optional'}

        return False


    def _get_base_generic(cls):
        # subclasses of Generic will have their _name set to None, but
        # their __origin__ will point to the base generic
        if cls._name is None:
            return cls.__origin__
        else:
            return getattr(typing, cls._name)


    def _get_python_type(cls):
        """
        Like `python_type`, but only works with `typing` classes.
        """
        return cls.__origin__


    def _get_name(cls):
        return cls._name
else:
    # python <3.7
    if hasattr(typing, '_Union'):
        # python 3.6
        def _is_generic(cls):
            if isinstance(cls, (typing.GenericMeta, typing._Union, typing._Optional, typing._ClassVar)):
                return True

            return False


        def _is_base_generic(cls):
            if isinstance(cls, (typing.GenericMeta, typing._Union)):
                return cls.__args__ in {None, ()}

            if isinstance(cls, typing._Optional):
                return True

            return False
    else:
        # python 3.5
        def _is_generic(cls):
            if isinstance(cls, (typing.GenericMeta, typing.UnionMeta, typing.OptionalMeta, typing.CallableMeta, typing.TupleMeta)):
                return True

            return False


        def _is_base_generic(cls):
            if isinstance(cls, typing.GenericMeta):
                return all(isinstance(arg, typing.TypeVar) for arg in cls.__parameters__)

            if isinstance(cls, typing.UnionMeta):
                return cls.__union_params__ is None

            if isinstance(cls, typing.TupleMeta):
                return cls.__tuple_params__ is None

            if isinstance(cls, typing.CallableMeta):
                return cls.__args__ is None

            if isinstance(cls, typing.OptionalMeta):
                return True

            return False


    def _get_base_generic(cls):
        try:
            return cls.__origin__
        except AttributeError:
            pass

        name = type(cls).__name__
        if not name.endswith('Meta'):
            raise NotImplementedError("Cannot determine base of {}".format(cls))

        name = name[:-4]
        return getattr(typing, name)


    def _get_python_type(cls):
        """
        Like `python_type`, but only works with `typing` classes.
        """
        # Many classes actually reference their corresponding abstract base class from the abc module
        # instead of their builtin variant (i.e. typing.List references MutableSequence instead of list).
        # We're interested in the builtin class (if any), so we'll traverse the MRO and look for it there.
        for typ in cls.mro():
            if typ.__module__ == 'builtins' and typ is not object:
                return typ

        try:
            return cls.__extra__
        except AttributeError:
            pass

        if is_qualified_generic(cls):
            cls = get_base_generic(cls)

        if cls is typing.Tuple:
            return tuple

        raise NotImplementedError("Cannot determine python type of {}".format(cls))


    def _get_name(cls):
        try:
            return cls.__name__
        except AttributeError:
            return type(cls).__name__[1:]


if hasattr(typing.List, '__args__'):
    # python 3.6+
    def _get_subtypes(cls):
        subtypes = cls.__args__

        if get_base_generic(cls) is typing.Callable:
            if len(subtypes) != 2 or subtypes[0] is not ...:
                subtypes = (subtypes[:-1], subtypes[-1])

        return subtypes
else:
    # python 3.5
    def _get_subtypes(cls):
        if isinstance(cls, typing.CallableMeta):
            if cls.__args__ is None:
                return ()

            return cls.__args__, cls.__result__

        for name in ['__parameters__', '__union_params__', '__tuple_params__']:
            try:
                subtypes = getattr(cls, name)
                break
            except AttributeError:
                pass
        else:
            raise NotImplementedError("Cannot extract subtypes from {}".format(cls))

        subtypes = [typ for typ in subtypes if not isinstance(typ, typing.TypeVar)]
        return subtypes


def is_generic(cls):
    """
    Detects any kind of generic, for example `List` or `List[int]`. This includes "special" types like
    Union and Tuple - anything that's subscriptable, basically.
    """
    return _is_generic(cls)


def is_base_generic(cls):
    """
    Detects generic base classes, for example `List` (but not `List[int]`)
    """
    return _is_base_generic(cls)


def is_qualified_generic(cls):
    """
    Detects generics with arguments, for example `List[int]` (but not `List`)
    """
    return is_generic(cls) and not is_base_generic(cls)


def get_base_generic(cls):
    if not is_qualified_generic(cls):
        raise TypeError('{} is not a qualified Generic and thus has no base'.format(cls))

    return _get_base_generic(cls)


def get_subtypes(cls):
    return _get_subtypes(cls)


def _instancecheck_iterable(iterable, type_args):
    if len(type_args) != 1:
        raise TypeError("Generic iterables must have exactly 1 type argument; found {}".format(type_args))

    type_ = type_args[0]
    return all(is_instance(val, type_) for val in iterable)


def _instancecheck_mapping(mapping, type_args):
    return _instancecheck_itemsview(mapping.items(), type_args)


def _instancecheck_itemsview(itemsview, type_args):
    if len(type_args) != 2:
        raise TypeError("Generic mappings must have exactly 2 type arguments; found {}".format(type_args))

    key_type, value_type = type_args
    return all(is_instance(key, key_type) and is_instance(val, value_type) for key, val in itemsview)


def _instancecheck_tuple(tup, type_args):
    if len(tup) != len(type_args):
        return False

    return all(is_instance(val, type_) for val, type_ in zip(tup, type_args))


_ORIGIN_TYPE_CHECKERS = {}
for class_path, check_func in {
                        # iterables
                        'typing.Container': _instancecheck_iterable,
                        'typing.Collection': _instancecheck_iterable,
                        'typing.AbstractSet': _instancecheck_iterable,
                        'typing.MutableSet': _instancecheck_iterable,
                        'typing.Sequence': _instancecheck_iterable,
                        'typing.MutableSequence': _instancecheck_iterable,
                        'typing.ByteString': _instancecheck_iterable,
                        'typing.Deque': _instancecheck_iterable,
                        'typing.List': _instancecheck_iterable,
                        'typing.Set': _instancecheck_iterable,
                        'typing.FrozenSet': _instancecheck_iterable,
                        'typing.KeysView': _instancecheck_iterable,
                        'typing.ValuesView': _instancecheck_iterable,
                        'typing.AsyncIterable': _instancecheck_iterable,

                        # mappings
                        'typing.Mapping': _instancecheck_mapping,
                        'typing.MutableMapping': _instancecheck_mapping,
                        'typing.MappingView': _instancecheck_mapping,
                        'typing.ItemsView': _instancecheck_itemsview,
                        'typing.Dict': _instancecheck_mapping,
                        'typing.DefaultDict': _instancecheck_mapping,
                        'typing.Counter': _instancecheck_mapping,
                        'typing.ChainMap': _instancecheck_mapping,

                        # other
                        'typing.Tuple': _instancecheck_tuple,
                    }.items():
    try:
        cls = eval(class_path)
    except AttributeError:
        continue

    _ORIGIN_TYPE_CHECKERS[cls] = check_func


def _instancecheck_callable(value, type_):
    if not callable(value):
        return False

    if is_base_generic(type_):
        return True

    param_types, ret_type = get_subtypes(type_)
    sig = inspect.signature(value)

    missing_annotations = []

    if param_types is not ...:
        if len(param_types) != len(sig.parameters):
            return False

        # FIXME: add support for TypeVars

        # if any of the existing annotations don't match the type, we'll return False.
        # Then, if any annotations are missing, we'll throw an exception.
        for param, expected_type in zip(sig.parameters.values(), param_types):
            param_type = param.annotation
            if param_type is inspect.Parameter.empty:
                missing_annotations.append(param)
                continue

            if not is_subtype(param_type, expected_type):
                return False

    if sig.return_annotation is inspect.Signature.empty:
        missing_annotations.append('return')
    else:
        if not is_subtype(sig.return_annotation, ret_type):
            return False

    if missing_annotations:
        raise ValueError("Missing annotations: {}".format(missing_annotations))

    return True


def _instancecheck_union(value, type_):
    types = get_subtypes(type_)
    return any(is_instance(value, typ) for typ in types)


def _instancecheck_type(value, type_):
    # if it's not a class, return False
    if not isinstance(value, type):
        return False

    if is_base_generic(type_):
        return True

    type_args = get_subtypes(type_)
    if len(type_args) != 1:
        raise TypeError("Type must have exactly 1 type argument; found {}".format(type_args))

    return is_subtype(value, type_args[0])


_SPECIAL_INSTANCE_CHECKERS = {
    'Union': _instancecheck_union,
    'Callable': _instancecheck_callable,
    'Type': _instancecheck_type,
    'Any': lambda v, t: True,
}


def is_instance(obj, type_):
    if type_.__module__ == 'typing':
        if is_qualified_generic(type_):
            base_generic = get_base_generic(type_)
        else:
            base_generic = type_
        name = _get_name(base_generic)

        try:
            validator = _SPECIAL_INSTANCE_CHECKERS[name]
        except KeyError:
            pass
        else:
            return validator(obj, type_)

    if is_base_generic(type_):
        python_type = _get_python_type(type_)
        return isinstance(obj, python_type)

    if is_qualified_generic(type_):
        python_type = _get_python_type(type_)
        if not isinstance(obj, python_type):
            return False

        base = get_base_generic(type_)
        try:
            validator = _ORIGIN_TYPE_CHECKERS[base]
        except KeyError:
            raise NotImplementedError("Cannot perform isinstance check for type {}".format(type_))

        type_args = get_subtypes(type_)
        return validator(obj, type_args)

    return isinstance(obj, type_)


def is_subtype(sub_type, super_type):
    if not is_generic(sub_type):
        python_super = python_type(super_type)
        return issubclass(sub_type, python_super)

    # at this point we know `sub_type` is a generic
    python_sub = python_type(sub_type)
    python_super = python_type(super_type)
    if not issubclass(python_sub, python_super):
        return False

    # at this point we know that `sub_type`'s base type is a subtype of `super_type`'s base type.
    # If `super_type` isn't qualified, then there's nothing more to do.
    if not is_generic(super_type) or is_base_generic(super_type):
        return True

    # at this point we know that `super_type` is a qualified generic... so if `sub_type` isn't
    # qualified, it can't be a subtype.
    if is_base_generic(sub_type):
        return False

    # at this point we know that both types are qualified generics, so we just have to
    # compare their sub-types.
    sub_args = get_subtypes(sub_type)
    super_args = get_subtypes(super_type)
    return all(is_subtype(sub_arg, super_arg) for sub_arg, super_arg in zip(sub_args, super_args))


def python_type(annotation):
    """
    Given a type annotation or a class as input, returns the corresponding python class.

    Examples:

    ::
        >>> python_type(typing.Dict)
        <class 'dict'>
        >>> python_type(typing.List[int])
        <class 'list'>
        >>> python_type(int)
        <class 'int'>
    """
    try:
        mro = annotation.mro()
    except AttributeError:
        # if it doesn't have an mro method, it must be a weird typing object
        return _get_python_type(annotation)

    if Type in mro:
        return annotation.python_type
    elif annotation.__module__ == 'typing':
        return _get_python_type(annotation)
    else:
        return annotation

模块的内部结构自从在python 3.5中引入以来已经发生了很大的变化，它实际上是一个与之合作的噩梦。）

这是一个类型验证器功能，取自我的一个个人项目（代码墙警告）：

>>> is_instance([{'x': 3}], List[Dict[str, int]])
True
>>> is_instance([{'x': 3}, {'y': 7.5}], List[Dict[str, int]])
False

示范：

typing module backport

（据我所知，这支持所有python版本，甚至是使用isinstance的<3.5版本。）

Answer 2

首先，即使我认为你知道但是为了完整性，输入库包含类型提示的类型。 IDE使用这些类型提示来检查您的代码是否有点理智，并且还可以作为开发人员期望的类型的文档。

要检查变量是否是某种类型的东西，我们必须使用

from typing import List

value = []
isinstance(value, List)

函数。令人惊讶的是，我们可以使用直接类型的输入库函数，例如。

List[Dict[str, int]]

但是，对于像myfun这样的嵌套结构，我们不能直接使用它，因为你足够有趣得到一个TypeError。你要做的是：

1. 检查初始值是否为列表
2. 检查列表中的每个项目是否为dict类型
3. 检查每个dict的每个键实际上是否为字符串，以及每个值实际上是否为int

不幸的是，对于严格检查python有点麻烦。但是，要注意python使用鸭子打字：如果它像鸭子一样，表现得像鸭子，那么它绝对是一只鸭子。

Answer 3

处理这个问题的常用方法是利用这样一个事实：如果传递给TypeError的任何对象没有所需的功能，则会引发相应的异常（通常是AttributeError或

try:
    myfun(data)
except (TypeError, AttributeError) as err:
    # Fallback for invalid types here.

）。所以你会做以下事情：

TypeError

你在问题中指出如果传递的对象没有适当的结构你会提出一个try / except但是Python已经为你做了这个。关键问题是如何处理这种情况。如果合适的话，你也可以将myfun块移动到duck typing。当你在Python中输入时，你通常依赖于AttributeError：如果对象具有所需的功能，那么你就不关心它是什么类型，只要它能达到目的。

请考虑以下示例。我们只是将数据传递给函数，然后免费获得

>>> def myfun(data):
...     for x in data:
...             print(x.items())
... 
>>> data = json.loads('[[["a", 1], ["b", 2]], [["c", 3], ["d", 4]]]')
>>> myfun(data)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 3, in myfun
AttributeError: 'list' object has no attribute 'items'

（我们可以除外）;无需手动类型检查：

try:
    myfun(data)
except (TypeError, AttributeError) as err:
    raise TypeError('Data has incorrect structure') from err

try:
    myfun(data)
except (TypeError, AttributeError) as err:
    err.args = ('Data has incorrect structure',)
    raise

如果您担心产生的错误的有用性，您仍然可以除了然后重新引发自定义异常（甚至更改异常的消息）：

numpy.inner

使用第三方代码时，应始终检查文档以了解将引发的异常。例如，ValueError报告说它会在某些情况下提高typing。使用该功能时，我们不需要自己执行任何检查，但依赖于在需要时会引发错误的事实。当使用第三方代码时，不清楚它在某些角落情况下的表现如何，i.m.o。只需硬编码相应的类型检查器（见下文），而不是使用适用于任何类型的通用解决方案，这样更容易，更清晰。无论如何这些情况应该是罕见的，并留下相应的评论使您的开发人员了解情况。

def type_checker(data):
    return (
        isinstance(data, list)
        and all(isinstance(x, dict) for x in list)
        and all(isinstance(k, str) and isinstance(v, int) for x in list for k, v in x.items())
    )

库用于类型提示，因此它不会在运行时检查类型。当然你可以手动完成，但它相当麻烦：

import collections.abc 

def isCorrectType(data):
    if isinstance(data, collections.abc.Collection): 
        for d in data:
            if isinstance(d,collections.abc.MutableMapping): 
                for key in d:
                    if isinstance(key,str) and isinstance(d[key],int):
                        pass
                    else:
                        return False
            else: 
                return False
    else:
        return False
    return True

这与适当的评论一起仍然是可接受的解决方案，并且在需要类似数据结构的情况下可重复使用。意图很明确，代码很容易验证。

Answer 4

您必须手动检查嵌套类型结构 - 不强制执行类型提示。

像这样检查最好使用ABC（抽象元类） - 因此用户可以提供支持与默认dict / lists相同访问的派生类：

print ( isCorrectType( [ {"a":2} ] ))       # True
print ( isCorrectType( [ {2:2} ] ))         # False   
print ( isCorrectType( [ {"a":"a"} ] ))     # False   
print ( isCorrectType( [ {"a":2},1 ] ))     # False   

输出：

ABC - abstract meta classes

质地：

• What is duck typing?

有关：

• "Ask forgiveness not permission" - explain

---

另一种方式是遵循try:/except:范式，并以你想要的形式使用你的数据，并且如果它不符合你想要的那样，就可以使用What is duck typing?。这更适合qazxswpoi - 并且允许（类似于ABC检查）消费者为你提供list / dict的派生类，同时它仍然可以工作......