# `typing` --- 类型标注支持

3.5 版新加入.

源码： Lib/typing.py

備註

The Python runtime does not enforce function and variable type annotations. They can be used by third party tools such as type checkers, IDEs, linters, etc.

This module provides runtime support for type hints as specified by PEP 484, PEP 526, PEP 544, PEP 586, PEP 589, and PEP 591. The most fundamental support consists of the types `Any`, `Union`, `Tuple`, `Callable`, `TypeVar`, and `Generic`. For full specification please see PEP 484. For a simplified introduction to type hints see PEP 483.

函数接受并返回一个字符串，注释像下面这样:

    def greeting(name: str) -> str:
        return 'Hello ' + name

在函数 `greeting` 中，参数 `name` 预期是 `str` 类型，并且返回 `str` 类型。子类型允许作为参数。

## 类型别名

类型别名通过将类型分配给别名来定义。在这个例子中， `Vector` 和 `List[float]` 将被视为可互换的同义词:

    from typing import List
    Vector = List[float]

    def scale(scalar: float, vector: Vector) -> Vector:
        return [scalar * num for num in vector]

    # typechecks; a list of floats qualifies as a Vector.
    new_vector = scale(2.0, [1.0, -4.2, 5.4])

类型别名可用于简化复杂类型签名。例如:

    from typing import Dict, Tuple, Sequence

    ConnectionOptions = Dict[str, str]
    Address = Tuple[str, int]
    Server = Tuple[Address, ConnectionOptions]

    def broadcast_message(message: str, servers: Sequence[Server]) -> None:
        ...

    # The static type checker will treat the previous type signature as
    # being exactly equivalent to this one.
    def broadcast_message(
            message: str,
            servers: Sequence[Tuple[Tuple[str, int], Dict[str, str]]]) -> None:
        ...

请注意，`None` 作为类型提示是一种特殊情况，并且由 `type(None)` 取代。

## NewType

使用 `NewType()` 辅助函数创建不同的类型:

    from typing import NewType

    UserId = NewType('UserId', int)
    some_id = UserId(524313)

静态类型检查器会将新类型视为它是原始类型的子类。这对于帮助捕捉逻辑错误非常有用:

    def get_user_name(user_id: UserId) -> str:
        ...

    # typechecks
    user_a = get_user_name(UserId(42351))

    # does not typecheck; an int is not a UserId
    user_b = get_user_name(-1)

您仍然可以对 `UserId` 类型的变量执行所有的 `int` 支持的操作，但结果将始终为 `int` 类型。这可以让你在需要 `int` 的地方传入 `UserId`，但会阻止你以无效的方式无意中创建 `UserId`:

    # 'output' is of type 'int', not 'UserId'
    output = UserId(23413) + UserId(54341)

Note that these checks are enforced only by the static type checker. At runtime, the statement `Derived = NewType('Derived', Base)` will make `Derived` a function that immediately returns whatever parameter you pass it. That means the expression `Derived(some_value)` does not create a new class or introduce any overhead beyond that of a regular function call.

更确切地说，表达式 `some_value is Derived(some_value)` 在运行时总是为真。

这也意味着无法创建 `Derived` 的子类型，因为它是运行时的标识函数，而不是实际的类型:

    from typing import NewType

    UserId = NewType('UserId', int)

    # Fails at runtime and does not typecheck
    class AdminUserId(UserId): pass

但是，可以基于'derived' `NewType` 创建 `NewType()`

    from typing import NewType

    UserId = NewType('UserId', int)

    ProUserId = NewType('ProUserId', UserId)

并且 `ProUserId` 的类型检查将按预期工作。

有关更多详细信息，请参阅 PEP 484。

備註

回想一下，使用类型别名声明两种类型彼此 等效 。`Alias = Original` 将使静态类型检查对待所有情况下 `Alias` 完全等同于 `Original`。当您想简化复杂类型签名时，这很有用。

相反，`NewType` 声明一种类型是另一种类型的子类型。`Derived = NewType('Derived', Original)` 将使静态类型检查器将 `Derived` 当作 `Original` 的 子类 ，这意味着 `Original` 类型的值不能用于 `Derived` 类型的值需要的地方。当您想以最小的运行时间成本防止逻辑错误时，这非常有用。

3.5.2 版新加入.

## Callable

期望特定签名的回调函数的框架可以将类型标注为 `Callable[[Arg1Type, Arg2Type], ReturnType]`。

例如:

    from typing import Callable

    def feeder(get_next_item: Callable[[], str]) -> None:
        # Body

    def async_query(on_success: Callable[[int], None],
                    on_error: Callable[[int, Exception], None]) -> None:
        # Body

通过用文字省略号替换类型提示中的参数列表： `Callable[...，ReturnType]`，可以声明可调用的返回类型，而无需指定调用签名。

## 泛型(Generic)

由于无法以通用方式静态推断有关保存在容器中的对象的类型信息，因此抽象基类已扩展为支持订阅以表示容器元素的预期类型。

    from typing import Mapping, Sequence

    def notify_by_email(employees: Sequence[Employee],
                        overrides: Mapping[str, str]) -> None: ...

泛型可以通过使用typing模块中名为 `TypeVar` 的新工厂进行参数化。

    from typing import Sequence, TypeVar

    T = TypeVar('T')      # Declare type variable

    def first(l: Sequence[T]) -> T:   # Generic function
        return l[0]

## 用户定义的泛型类型

用户定义的类可以定义为泛型类。

    from typing import TypeVar, Generic
    from logging import Logger

    T = TypeVar('T')

    class LoggedVar(Generic[T]):
        def __init__(self, value: T, name: str, logger: Logger) -> None:
            self.name = name
            self.logger = logger
            self.value = value

        def set(self, new: T) -> None:
            self.log('Set ' + repr(self.value))
            self.value = new

        def get(self) -> T:
            self.log('Get ' + repr(self.value))
            return self.value

        def log(self, message: str) -> None:
            self.logger.info('%s: %s', self.name, message)

`Generic[T]` 作为基类定义了类 `LoggedVar` 采用单个类型参数 `T`。这也使得 `T` 作为类体内的一个类型有效。

The `Generic` base class defines `__class_getitem__()` so that `LoggedVar[t]` is valid as a type:

    from typing import Iterable

    def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
        for var in vars:
            var.set(0)

泛型类型可以有任意数量的类型变量，并且类型变量可能会受到限制:

    from typing import TypeVar, Generic
    ...

    T = TypeVar('T')
    S = TypeVar('S', int, str)

    class StrangePair(Generic[T, S]):
        ...

`Generic` 每个参数的类型变量必须是不同的。这是无效的:

    from typing import TypeVar, Generic
    ...

    T = TypeVar('T')

    class Pair(Generic[T, T]):   # INVALID
        ...

您可以对 `Generic` 使用多重继承:

    from typing import TypeVar, Generic, Sized

    T = TypeVar('T')

    class LinkedList(Sized, Generic[T]):
        ...

从泛型类继承时，某些类型变量可能是固定的:

    from typing import TypeVar, Mapping

    T = TypeVar('T')

    class MyDict(Mapping[str, T]):
        ...

在这种情况下，`MyDict` 只有一个参数，`T`。

在不指定类型参数的情况下使用泛型类别会为每个位置假设 `Any`。在下面的例子中，`MyIterable` 不是泛型，但是隐式继承自 `Iterable[Any]`:

    from typing import Iterable

    class MyIterable(Iterable): # Same as Iterable[Any]

用户定义的通用类型别名也受支持。例子:

    from typing import TypeVar, Iterable, Tuple, Union
    S = TypeVar('S')
    Response = Union[Iterable[S], int]

    # Return type here is same as Union[Iterable[str], int]
    def response(query: str) -> Response[str]:
        ...

    T = TypeVar('T', int, float, complex)
    Vec = Iterable[Tuple[T, T]]

    def inproduct(v: Vec[T]) -> T: # Same as Iterable[Tuple[T, T]]
        return sum(x*y for x, y in v)

3.7 版更變: `Generic` no longer has a custom metaclass.

A user-defined generic class can have ABCs as base classes without a metaclass conflict. Generic metaclasses are not supported. The outcome of parameterizing generics is cached, and most types in the typing module are hashable and comparable for equality.

## `Any` 类型

`Any` 是一种特殊的类型。静态类型检查器将所有类型视为与 `Any` 兼容，反之亦然， `Any` 也与所有类型相兼容。

这意味着可对类型为 `Any` 的值执行任何操作或方法调用，并将其赋值给任何变量:

    from typing import Any

    a = None    # type: Any
    a = []      # OK
    a = 2       # OK

    s = ''      # type: str
    s = a       # OK

    def foo(item: Any) -> int:
        # Typechecks; 'item' could be any type,
        # and that type might have a 'bar' method
        item.bar()
        ...

需要注意的是，将 `Any` 类型的值赋值给另一个更具体的类型时，Python不会执行类型检查。例如，当把 `a` 赋值给 `s` 时，即使 `s` 被声明为 `str` 类型，在运行时接收到的是 `int` 值，静态类型检查器也不会报错。

此外，所有返回值无类型或形参无类型的函数将隐式地默认使用 `Any` 类型:

    def legacy_parser(text):
        ...
        return data

    # A static type checker will treat the above
    # as having the same signature as:
    def legacy_parser(text: Any) -> Any:
        ...
        return data

当需要混用动态类型和静态类型的代码时，上述行为可以让 `Any` 被用作 应急出口 。

`Any` 和 `object` 的行为对比。与 `Any` 相似，所有的类型都是 `object` 的子类型。然而不同于 `Any`，反之并不成立： `object` 不是 其他所有类型的子类型。

这意味着当一个值的类型是 `object` 的时候，类型检查器会拒绝对它的几乎所有的操作。把它赋值给一个指定了类型的变量（或者当作返回值）是一个类型错误。比如说：

    def hash_a(item: object) -> int:
        # Fails; an object does not have a 'magic' method.
        item.magic()
        ...

    def hash_b(item: Any) -> int:
        # Typechecks
        item.magic()
        ...

    # Typechecks, since ints and strs are subclasses of object
    hash_a(42)
    hash_a("foo")

    # Typechecks, since Any is compatible with all types
    hash_b(42)
    hash_b("foo")

使用 `object` 示意一个值可以类型安全地兼容任何类型。使用 `Any` 示意一个值地类型是动态定义的。

## Nominal vs structural subtyping

Initially PEP 484 defined Python static type system as using nominal subtyping. This means that a class `A` is allowed where a class `B` is expected if and only if `A` is a subclass of `B`.

This requirement previously also applied to abstract base classes, such as `Iterable`. The problem with this approach is that a class had to be explicitly marked to support them, which is unpythonic and unlike what one would normally do in idiomatic dynamically typed Python code. For example, this conforms to the PEP 484:

    from typing import Sized, Iterable, Iterator

    class Bucket(Sized, Iterable[int]):
        ...
        def __len__(self) -> int: ...
        def __iter__(self) -> Iterator[int]: ...

PEP 544 allows to solve this problem by allowing users to write the above code without explicit base classes in the class definition, allowing `Bucket` to be implicitly considered a subtype of both `Sized` and `Iterable[int]` by static type checkers. This is known as structural subtyping (or static duck-typing):

    from typing import Iterator, Iterable

    class Bucket:  # Note: no base classes
        ...
        def __len__(self) -> int: ...
        def __iter__(self) -> Iterator[int]: ...

    def collect(items: Iterable[int]) -> int: ...
    result = collect(Bucket())  # Passes type check

Moreover, by subclassing a special class `Protocol`, a user can define new custom protocols to fully enjoy structural subtyping (see examples below).

## 类,函数和修饰器.

这个模块定义了如下的类,模块和修饰器.

class `typing.``TypeVar`

  类型变量

  用法:

      T = TypeVar('T')  # Can be anything
      A = TypeVar('A', str, bytes)  # Must be str or bytes

  Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function definitions. See class Generic for more information on generic types. Generic functions work as follows:

      def repeat(x: T, n: int) -> Sequence[T]:
          """Return a list containing n references to x."""
          return [x]*n

      def longest(x: A, y: A) -> A:
          """Return the longest of two strings."""
          return x if len(x) >= len(y) else y

  The latter example's signature is essentially the overloading of `(str, str) -> str` and `(bytes, bytes) -> bytes`. Also note that if the arguments are instances of some subclass of `str`, the return type is still plain `str`.

  `isinstance(x, T)` 会在运行时抛出 `TypeError` 异常。一般地说， `isinstance()` 和 `issubclass()` 不应该和类型一起使用。

  Type variables may be marked covariant or contravariant by passing `covariant=True` or `contravariant=True`. See PEP 484 for more details. By default type variables are invariant. Alternatively, a type variable may specify an upper bound using `bound=<type>`. This means that an actual type substituted (explicitly or implicitly) for the type variable must be a subclass of the boundary type, see PEP 484.

class `typing.``Generic`

  Abstract base class for generic types.

  A generic type is typically declared by inheriting from an instantiation of this class with one or more type variables. For example, a generic mapping type might be defined as:

      class Mapping(Generic[KT, VT]):
          def __getitem__(self, key: KT) -> VT:
              ...
              # Etc.

  这个类之后可以被这样用:

      X = TypeVar('X')
      Y = TypeVar('Y')

      def lookup_name(mapping: Mapping[X, Y], key: X, default: Y) -> Y:
          try:
              return mapping[key]
          except KeyError:
              return default

class `typing.``Protocol`(Generic)

  Base class for protocol classes. Protocol classes are defined like this:

      class Proto(Protocol):
          def meth(self) -> int:
              ...

  Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:

      class C:
          def meth(self) -> int:
              return 0

      def func(x: Proto) -> int:
          return x.meth()

      func(C())  # Passes static type check

  See PEP 544 for details. Protocol classes decorated with `runtime_checkable()` (described later) act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures.

  Protocol classes can be generic, for example:

      class GenProto(Protocol[T]):
          def meth(self) -> T:
              ...

  3.8 版新加入.

class `typing.``Type`(Generic[CT_co])

  A variable annotated with `C` may accept a value of type `C`. In contrast, a variable annotated with `Type[C]` may accept values that are classes themselves -- specifically, it will accept the class object of `C`. For example:

      a = 3         # Has type 'int'
      b = int       # Has type 'Type[int]'
      c = type(a)   # Also has type 'Type[int]'

  Note that `Type[C]` is covariant:

      class User: ...
      class BasicUser(User): ...
      class ProUser(User): ...
      class TeamUser(User): ...

      # Accepts User, BasicUser, ProUser, TeamUser, ...
      def make_new_user(user_class: Type[User]) -> User:
          # ...
          return user_class()

  The fact that `Type[C]` is covariant implies that all subclasses of `C` should implement the same constructor signature and class method signatures as `C`. The type checker should flag violations of this, but should also allow constructor calls in subclasses that match the constructor calls in the indicated base class. How the type checker is required to handle this particular case may change in future revisions of PEP 484.

  The only legal parameters for `Type` are classes, `Any`, type variables, and unions of any of these types. For example:

      def new_non_team_user(user_class: Type[Union[BaseUser, ProUser]]): ...

  `Type[Any]` is equivalent to `Type` which in turn is equivalent to `type`, which is the root of Python's metaclass hierarchy.

  3.5.2 版新加入.

class `typing.``Iterable`(Generic[T_co])

  `collections.abc.Iterable` 的泛型版本。

class `typing.``Iterator`(Iterable[T_co])

  `collections.abc.Iterator` 的泛型版本。

class `typing.``Reversible`(Iterable[T_co])

  `collections.abc.Reversible` 的泛型版本。

class `typing.``SupportsInt`

  An ABC with one abstract method `__int__`.

class `typing.``SupportsFloat`

  An ABC with one abstract method `__float__`.

class `typing.``SupportsComplex`

  An ABC with one abstract method `__complex__`.

class `typing.``SupportsBytes`

  An ABC with one abstract method `__bytes__`.

class `typing.``SupportsIndex`

  An ABC with one abstract method `__index__`.

  3.8 版新加入.

class `typing.``SupportsAbs`

  An ABC with one abstract method `__abs__` that is covariant in its return type.

class `typing.``SupportsRound`

  An ABC with one abstract method `__round__` that is covariant in its return type.

class `typing.``Container`(Generic[T_co])

  `collections.abc.Container` 的泛型版本。

class `typing.``Hashable`

  `collections.abc.Hashable` 的别名。

class `typing.``Sized`

  `collections.abc.Sized` 的别名。

class `typing.``Collection`(Sized, Iterable[T_co], Container[T_co])

  `collections.abc.Collection` 的泛型版本。

  3.6.0 版新加入.

class `typing.``AbstractSet`(Sized, Collection[T_co])

  `collections.abc.Set` 的泛型版本。

class `typing.``MutableSet`(AbstractSet[T])

  `collections.abc.MutableSet` 的泛型版本。

class `typing.``Mapping`(Sized, Collection[KT], Generic[VT_co])

  `collections.abc.Mapping` 的泛型版本。这个类型可以如下使用:

      def get_position_in_index(word_list: Mapping[str, int], word: str) -> int:
          return word_list[word]

class `typing.``MutableMapping`(Mapping[KT, VT])

  `collections.abc.MutableMapping` 的泛型版本。

class `typing.``Sequence`(Reversible[T_co], Collection[T_co])

  `collections.abc.Sequence` 的泛型版本。

class `typing.``MutableSequence`(Sequence[T])

  `collections.abc.MutableSequence` 的泛型版本。

class `typing.``ByteString`(Sequence[int])

  `collections.abc.ByteString` 的泛型版本。

  This type represents the types `bytes`, `bytearray`, and `memoryview`.

  As a shorthand for this type, `bytes` can be used to annotate arguments of any of the types mentioned above.

class `typing.``Deque`(deque, MutableSequence[T])

  `collections.deque` 的泛型版本。

  3.5.4 版新加入.

  3.6.1 版新加入.

class `typing.``List`(list, MutableSequence[T])

  Generic version of `list`. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as `Sequence` or `Iterable`.

  这个类型可以这样用:

      T = TypeVar('T', int, float)

      def vec2(x: T, y: T) -> List[T]:
          return [x, y]

      def keep_positives(vector: Sequence[T]) -> List[T]:
          return [item for item in vector if item > 0]

class `typing.``Set`(set, MutableSet[T])

  A generic version of `builtins.set`. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as `AbstractSet`.

class `typing.``FrozenSet`(frozenset, AbstractSet[T_co])

  A generic version of `builtins.frozenset`.

class `typing.``MappingView`(Sized, Iterable[T_co])

  `collections.abc.MappingView` 的泛型版本。

class `typing.``KeysView`(MappingView[KT_co], AbstractSet[KT_co])

  `collections.abc.KeysView` 的泛型版本。

class `typing.``ItemsView`(MappingView, Generic[KT_co, VT_co])

  `collections.abc.ItemsView` 的泛型版本。

class `typing.``ValuesView`(MappingView[VT_co])

  `collections.abc.ValuesView` 的泛型版本。

class `typing.``Awaitable`(Generic[T_co])

  `collections.abc.Awaitable` 的泛型版本。

  3.5.2 版新加入.

class `typing.``Coroutine`(Awaitable[V_co], Generic[T_co T_contra, V_co])

  A generic version of `collections.abc.Coroutine`. The variance and order of type variables correspond to those of `Generator`, for example:

      from typing import List, Coroutine
      c = None # type: Coroutine[List[str], str, int]
      ...
      x = c.send('hi') # type: List[str]
      async def bar() -> None:
          x = await c # type: int

  3.5.3 版新加入.

class `typing.``AsyncIterable`(Generic[T_co])

  `collections.abc.AsyncIterable` 的泛型版本。

  3.5.2 版新加入.

class `typing.``AsyncIterator`(AsyncIterable[T_co])

  `collections.abc.AsyncIterator` 的泛型版本。

  3.5.2 版新加入.

class `typing.``ContextManager`(Generic[T_co])

  `contextlib.AbstractContextManager` 的泛型版本。

  3.5.4 版新加入.

  3.6.0 版新加入.

class `typing.``AsyncContextManager`(Generic[T_co])

  `contextlib.AbstractAsyncContextManager` 的泛型版本。

  3.5.4 版新加入.

  3.6.2 版新加入.

class `typing.``Dict`(dict, MutableMapping[KT, VT])

  `dict` 的泛型版本。对标注返回类型比较有用。如果要标注参数的话，使用如 `Mapping` 的抽象容器类型是更好的选择。

  这个类型可以这样使用:

      def count_words(text: str) -> Dict[str, int]:
          ...

class `typing.``DefaultDict`(collections.defaultdict, MutableMapping[KT, VT])

  `collections.defaultdict` 的泛型版本。

  3.5.2 版新加入.

class `typing.``OrderedDict`(collections.OrderedDict, MutableMapping[KT, VT])

  `collections.OrderedDict` 的泛型版本。

  3.7.2 版新加入.

class `typing.``Counter`(collections.Counter, Dict[T, int])

  `collections.Counter` 的泛型版本。

  3.5.4 版新加入.

  3.6.1 版新加入.

class `typing.``ChainMap`(collections.ChainMap, MutableMapping[KT, VT])

  `collections.ChainMap` 的泛型版本。

  3.5.4 版新加入.

  3.6.1 版新加入.

class `typing.``Generator`(Iterator[T_co], Generic[T_co, T_contra, V_co])

  A generator can be annotated by the generic type `Generator[YieldType, SendType, ReturnType]`. For example:

      def echo_round() -> Generator[int, float, str]:
          sent = yield 0
          while sent >= 0:
              sent = yield round(sent)
          return 'Done'

  Note that unlike many other generics in the typing module, the `SendType` of `Generator` behaves contravariantly, not covariantly or invariantly.

  If your generator will only yield values, set the `SendType` and `ReturnType` to `None`:

      def infinite_stream(start: int) -> Generator[int, None, None]:
          while True:
              yield start
              start += 1

  Alternatively, annotate your generator as having a return type of either `Iterable[YieldType]` or `Iterator[YieldType]`:

      def infinite_stream(start: int) -> Iterator[int]:
          while True:
              yield start
              start += 1

class `typing.``AsyncGenerator`(AsyncIterator[T_co], Generic[T_co, T_contra])

  An async generator can be annotated by the generic type `AsyncGenerator[YieldType, SendType]`. For example:

      async def echo_round() -> AsyncGenerator[int, float]:
          sent = yield 0
          while sent >= 0.0:
              rounded = await round(sent)
              sent = yield rounded

  Unlike normal generators, async generators cannot return a value, so there is no `ReturnType` type parameter. As with `Generator`, the `SendType` behaves contravariantly.

  If your generator will only yield values, set the `SendType` to `None`:

      async def infinite_stream(start: int) -> AsyncGenerator[int, None]:
          while True:
              yield start
              start = await increment(start)

  Alternatively, annotate your generator as having a return type of either `AsyncIterable[YieldType]` or `AsyncIterator[YieldType]`:

      async def infinite_stream(start: int) -> AsyncIterator[int]:
          while True:
              yield start
              start = await increment(start)

  3.6.1 版新加入.

class `typing.``Text`

  `Text` is an alias for `str`. It is provided to supply a forward compatible path for Python 2 code: in Python 2, `Text` is an alias for `unicode`.

  Use `Text` to indicate that a value must contain a unicode string in a manner that is compatible with both Python 2 and Python 3:

      def add_unicode_checkmark(text: Text) -> Text:
          return text + u' \u2713'

  3.5.2 版新加入.

class `typing.``IO`
class `typing.``TextIO`
class `typing.``BinaryIO`

  Generic type `IO[AnyStr]` and its subclasses `TextIO(IO[str])` and `BinaryIO(IO[bytes])` represent the types of I/O streams such as returned by `open()`.

class `typing.``Pattern`
class `typing.``Match`

  These type aliases correspond to the return types from `re.compile()` and `re.match()`. These types (and the corresponding functions) are generic in `AnyStr` and can be made specific by writing `Pattern[str]`, `Pattern[bytes]`, `Match[str]`, or `Match[bytes]`.

class `typing.``NamedTuple`

  Typed version of `collections.namedtuple()`.

  用法:

      class Employee(NamedTuple):
          name: str
          id: int

  这相当于:

      Employee = collections.namedtuple('Employee', ['name', 'id'])

  To give a field a default value, you can assign to it in the class body:

      class Employee(NamedTuple):
          name: str
          id: int = 3

      employee = Employee('Guido')
      assert employee.id == 3

  Fields with a default value must come after any fields without a default.

  The resulting class has an extra attribute `__annotations__` giving a dict that maps the field names to the field types. (The field names are in the `_fields` attribute and the default values are in the `_field_defaults` attribute both of which are part of the namedtuple API.)

  `NamedTuple` subclasses can also have docstrings and methods:

      class Employee(NamedTuple):
          """Represents an employee."""
          name: str
          id: int = 3

          def __repr__(self) -> str:
              return f'<Employee {self.name}, id={self.id}>'

  Backward-compatible usage:

      Employee = NamedTuple('Employee', [('name', str), ('id', int)])

  3.6 版更變: Added support for PEP 526 variable annotation syntax.

  3.6.1 版更變: Added support for default values, methods, and docstrings.

  3.8 版更變: The `_field_types` and `__annotations__` attributes are now regular dictionaries instead of instances of `OrderedDict`.

  3.9 版更變: Removed the `_field_types` attribute in favor of the more standard `__annotations__` attribute which has the same information.

class `typing.``TypedDict`(dict)

  A simple typed namespace. At runtime it is equivalent to a plain `dict`.

  `TypedDict` creates a dictionary type that expects all of its instances to have a certain set of keys, where each key is associated with a value of a consistent type. This expectation is not checked at runtime but is only enforced by type checkers. Usage:

      class Point2D(TypedDict):
          x: int
          y: int
          label: str

      a: Point2D = {'x': 1, 'y': 2, 'label': 'good'}  # OK
      b: Point2D = {'z': 3, 'label': 'bad'}           # Fails type check

      assert Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')

  The type info for introspection can be accessed via `Point2D.__annotations__` and `Point2D.__total__`. To allow using this feature with older versions of Python that do not support PEP 526, `TypedDict` supports two additional equivalent syntactic forms:

      Point2D = TypedDict('Point2D', x=int, y=int, label=str)
      Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})

  By default, all keys must be present in a TypedDict. It is possible to override this by specifying totality. Usage:

      class point2D(TypedDict, total=False):
          x: int
          y: int

  This means that a point2D TypedDict can have any of the keys omitted. A type checker is only expected to support a literal False or True as the value of the total argument. True is the default, and makes all items defined in the class body be required.

  See PEP 589 for more examples and detailed rules of using `TypedDict`.

  3.8 版新加入.

class `typing.``ForwardRef`

  A class used for internal typing representation of string forward references. For example, `List["SomeClass"]` is implicitly transformed into `List[ForwardRef("SomeClass")]`. This class should not be instantiated by a user, but may be used by introspection tools.

`typing.``NewType`(typ)

  A helper function to indicate a distinct types to a typechecker, see NewType. At runtime it returns a function that returns its argument. Usage:

      UserId = NewType('UserId', int)
      first_user = UserId(1)

  3.5.2 版新加入.

`typing.``cast`(typ, val)

  Cast a value to a type.

  This returns the value unchanged. To the type checker this signals that the return value has the designated type, but at runtime we intentionally don't check anything (we want this to be as fast as possible).

`typing.``get_type_hints`(obj, globalns=None, localns=None, include_extras=False)

  返回一个字典，字典内含有函数、方法、模块或类对象的类型提示。

  This is often the same as `obj.__annotations__`. In addition, forward references encoded as string literals are handled by evaluating them in `globals` and `locals` namespaces. If necessary, `Optional[t]` is added for function and method annotations if a default value equal to `None` is set. For a class `C`, return a dictionary constructed by merging all the `__annotations__` along `C.__mro__` in reverse order.

  The function recursively replaces all `Annotated[T, ...]` with `T`, unless `include_extras` is set to `True` (see `Annotated` for more information). For example:

      class Student(NamedTuple):
          name: Annotated[str, 'some marker']

      get_type_hints(Student) == {'name': str}
      get_type_hints(Student, include_extras=False) == {'name': str}
      get_type_hints(Student, include_extras=True) == {
          'name': Annotated[str, 'some marker']
      }

  3.9 版更變: Added `include_extras` parameter as part of PEP 593.

`typing.``get_origin`(tp)

`typing.``get_args`(tp)

  Provide basic introspection for generic types and special typing forms.

  For a typing object of the form `X[Y, Z, ...]` these functions return `X` and `(Y, Z, ...)`. If `X` is a generic alias for a builtin or `collections` class, it gets normalized to the original class. For unsupported objects return `None` and `()` correspondingly. Examples:

      assert get_origin(Dict[str, int]) is dict
      assert get_args(Dict[int, str]) == (int, str)

      assert get_origin(Union[int, str]) is Union
      assert get_args(Union[int, str]) == (int, str)

  3.8 版新加入.

`@``typing.``overload`

  The `@overload` decorator allows describing functions and methods that support multiple different combinations of argument types. A series of `@overload`-decorated definitions must be followed by exactly one non-`@overload`-decorated definition (for the same function/method). The `@overload`-decorated definitions are for the benefit of the type checker only, since they will be overwritten by the non-`@overload`-decorated definition, while the latter is used at runtime but should be ignored by a type checker. At runtime, calling a `@overload`-decorated function directly will raise `NotImplementedError`. An example of overload that gives a more precise type than can be expressed using a union or a type variable:

      @overload
      def process(response: None) -> None:
          ...
      @overload
      def process(response: int) -> Tuple[int, str]:
          ...
      @overload
      def process(response: bytes) -> str:
          ...
      def process(response):
          <actual implementation>

  See PEP 484 for details and comparison with other typing semantics.

`@``typing.``final`

  A decorator to indicate to type checkers that the decorated method cannot be overridden, and the decorated class cannot be subclassed. For example:

      class Base:
          @final
          def done(self) -> None:
              ...
      class Sub(Base):
          def done(self) -> None:  # Error reported by type checker
                ...

      @final
      class Leaf:
          ...
      class Other(Leaf):  # Error reported by type checker
          ...

  There is no runtime checking of these properties. See PEP 591 for more details.

  3.8 版新加入.

`@``typing.``no_type_check`

  用于指明标注不是类型提示的装饰器。

  此 decorator 装饰器生效于类或函数上。如果作用于类上的话，它会递归地作用于这个类的所定义的所有方法上（但是对于超类或子类所定义的方法不会生效）。

  此方法会就地地修改函数。

`@``typing.``no_type_check_decorator`

  使其它装饰器起到 `no_type_check()` 效果的装饰器。

  This wraps the decorator with something that wraps the decorated function in `no_type_check()`.

`@``typing.``type_check_only`

  标记一个类或函数在运行时内不可用的装饰器。

  This decorator is itself not available at runtime. It is mainly intended to mark classes that are defined in type stub files if an implementation returns an instance of a private class:

      @type_check_only
      class Response:  # private or not available at runtime
          code: int
          def get_header(self, name: str) -> str: ...

      def fetch_response() -> Response: ...

  Note that returning instances of private classes is not recommended. It is usually preferable to make such classes public.

`@``typing.``runtime_checkable`

  Mark a protocol class as a runtime protocol.

  Such a protocol can be used with `isinstance()` and `issubclass()`. This raises `TypeError` when applied to a non-protocol class. This allows a simple-minded structural check, very similar to "one trick ponies" in `collections.abc` such as `Iterable`. For example:

      @runtime_checkable
      class Closable(Protocol):
          def close(self): ...

      assert isinstance(open('/some/file'), Closable)

  Warning: this will check only the presence of the required methods, not their type signatures!

  3.8 版新加入.

`typing.``Any`

  特殊类型，表明类型没有任何限制。

  - 每一个类型都对 `Any` 兼容。

  - `Any` 对每一个类型都兼容。

`typing.``NoReturn`

  标记一个函数没有返回值的特殊类型。比如说:

      from typing import NoReturn

      def stop() -> NoReturn:
          raise RuntimeError('no way')

  3.5.4 版新加入.

  3.6.2 版新加入.

`typing.``Union`

  联合类型； `Union[X, Y]` 意味着：要不是 X，要不是 Y。

  使用形如 `Union[int, str]` 的形式来定义一个联合类型。细节如下:

  - 参数必须是类型，而且必须至少有一个参数。

  - 联合类型的联合类型会被展开打平，比如:

        Union[Union[int, str], float] == Union[int, str, float]

  - 仅有一个参数的联合类型会坍缩成参数自身，比如:

        Union[int] == int  # The constructor actually returns int

  - 多余的参数会被跳过，比如:

        Union[int, str, int] == Union[int, str]

  - 在比较联合类型的时候，参数顺序会被忽略，比如:

        Union[int, str] == Union[str, int]

  - 你不能继承或者实例化一个联合类型。

  - 你不能写成 `Union[X][Y]` 。

  - 你可以使用 `Optional[X]` 作为 `Union[X, None]` 的缩写。

  3.7 版更變: 不要在运行时内从联合类型中移除显式说明的子类。

`typing.``Optional`

  Optional type.

  `Optional[X]` is equivalent to `Union[X, None]`.

  Note that this is not the same concept as an optional argument, which is one that has a default. An optional argument with a default does not require the `Optional` qualifier on its type annotation just because it is optional. For example:

      def foo(arg: int = 0) -> None:
          ...

  On the other hand, if an explicit value of `None` is allowed, the use of `Optional` is appropriate, whether the argument is optional or not. For example:

      def foo(arg: Optional[int] = None) -> None:
          ...

`typing.``Tuple`

  Tuple type; `Tuple[X, Y]` is the type of a tuple of two items with the first item of type X and the second of type Y. The type of the empty tuple can be written as `Tuple[()]`.

  Example: `Tuple[T1, T2]` is a tuple of two elements corresponding to type variables T1 and T2. `Tuple[int, float, str]` is a tuple of an int, a float and a string.

  To specify a variable-length tuple of homogeneous type, use literal ellipsis, e.g. `Tuple[int, ...]`. A plain `Tuple` is equivalent to `Tuple[Any, ...]`, and in turn to `tuple`.

`typing.``Callable`

  Callable type; `Callable[[int], str]` is a function of (int) -> str.

  The subscription syntax must always be used with exactly two values: the argument list and the return type. The argument list must be a list of types or an ellipsis; the return type must be a single type.

  There is no syntax to indicate optional or keyword arguments; such function types are rarely used as callback types. `Callable[..., ReturnType]` (literal ellipsis) can be used to type hint a callable taking any number of arguments and returning `ReturnType`. A plain `Callable` is equivalent to `Callable[..., Any]`, and in turn to `collections.abc.Callable`.

`typing.``Literal`

  A type that can be used to indicate to type checkers that the corresponding variable or function parameter has a value equivalent to the provided literal (or one of several literals). For example:

      def validate_simple(data: Any) -> Literal[True]:  # always returns True
          ...

      MODE = Literal['r', 'rb', 'w', 'wb']
      def open_helper(file: str, mode: MODE) -> str:
          ...

      open_helper('/some/path', 'r')  # Passes type check
      open_helper('/other/path', 'typo')  # Error in type checker

  `Literal[...]` cannot be subclassed. At runtime, an arbitrary value is allowed as type argument to `Literal[...]`, but type checkers may impose restrictions. See PEP 586 for more details about literal types.

  3.8 版新加入.

`typing.``ClassVar`

  Special type construct to mark class variables.

  As introduced in PEP 526, a variable annotation wrapped in ClassVar indicates that a given attribute is intended to be used as a class variable and should not be set on instances of that class. Usage:

      class Starship:
          stats: ClassVar[Dict[str, int]] = {} # class variable
          damage: int = 10                     # instance variable

  `ClassVar` accepts only types and cannot be further subscribed.

  `ClassVar` is not a class itself, and should not be used with `isinstance()` or `issubclass()`. `ClassVar` does not change Python runtime behavior, but it can be used by third-party type checkers. For example, a type checker might flag the following code as an error:

      enterprise_d = Starship(3000)
      enterprise_d.stats = {} # Error, setting class variable on instance
      Starship.stats = {}     # This is OK

  3.5.3 版新加入.

`typing.``Final`

  A special typing construct to indicate to type checkers that a name cannot be re-assigned or overridden in a subclass. For example:

      MAX_SIZE: Final = 9000
      MAX_SIZE += 1  # Error reported by type checker

      class Connection:
          TIMEOUT: Final[int] = 10

      class FastConnector(Connection):
          TIMEOUT = 1  # Error reported by type checker

  There is no runtime checking of these properties. See PEP 591 for more details.

  3.8 版新加入.

`typing.``AnyStr`

  `AnyStr` is a type variable defined as `AnyStr = TypeVar('AnyStr', str, bytes)`.

  It is meant to be used for functions that may accept any kind of string without allowing different kinds of strings to mix. For example:

      def concat(a: AnyStr, b: AnyStr) -> AnyStr:
          return a + b

      concat(u"foo", u"bar")  # Ok, output has type 'unicode'
      concat(b"foo", b"bar")  # Ok, output has type 'bytes'
      concat(u"foo", b"bar")  # Error, cannot mix unicode and bytes

`typing.``TYPE_CHECKING`

  A special constant that is assumed to be `True` by 3rd party static type checkers. It is `False` at runtime. Usage:

      if TYPE_CHECKING:
          import expensive_mod

      def fun(arg: 'expensive_mod.SomeType') -> None:
          local_var: expensive_mod.AnotherType = other_fun()

  Note that the first type annotation must be enclosed in quotes, making it a "forward reference", to hide the `expensive_mod` reference from the interpreter runtime. Type annotations for local variables are not evaluated, so the second annotation does not need to be enclosed in quotes.

  3.5.2 版新加入.

`typing.``Annotated`

  A type, introduced in PEP 593 (`Flexible function and variable annotations`), to decorate existing types with context-specific metadata (possibly multiple pieces of it, as `Annotated` is variadic). Specifically, a type `T` can be annotated with metadata `x` via the typehint `Annotated[T, x]`. This metadata can be used for either static analysis or at runtime. If a library (or tool) encounters a typehint `Annotated[T, x]` and has no special logic for metadata `x`, it should ignore it and simply treat the type as `T`. Unlike the `no_type_check` functionality that currently exists in the `typing` module which completely disables typechecking annotations on a function or a class, the `Annotated` type allows for both static typechecking of `T` (e.g., via mypy or Pyre, which can safely ignore `x`) together with runtime access to `x` within a specific application.

  Ultimately, the responsibility of how to interpret the annotations (if at all) is the responsibility of the tool or library encountering the `Annotated` type. A tool or library encountering an `Annotated` type can scan through the annotations to determine if they are of interest (e.g., using `isinstance()`).

  When a tool or a library does not support annotations or encounters an unknown annotation it should just ignore it and treat annotated type as the underlying type.

  It's up to the tool consuming the annotations to decide whether the client is allowed to have several annotations on one type and how to merge those annotations.

  Since the `Annotated` type allows you to put several annotations of the same (or different) type(s) on any node, the tools or libraries consuming those annotations are in charge of dealing with potential duplicates. For example, if you are doing value range analysis you might allow this:

      T1 = Annotated[int, ValueRange(-10, 5)]
      T2 = Annotated[T1, ValueRange(-20, 3)]

  Passing `include_extras=True` to `get_type_hints()` lets one access the extra annotations at runtime.

  The details of the syntax:

  - The first argument to `Annotated` must be a valid type

  - Multiple type annotations are supported (`Annotated` supports variadic arguments):

        Annotated[int, ValueRange(3, 10), ctype("char")]

  - `Annotated` must be called with at least two arguments ( `Annotated[int]` is not valid)

  - The order of the annotations is preserved and matters for equality checks:

        Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[
            int, ctype("char"), ValueRange(3, 10)
        ]

  - Nested `Annotated` types are flattened, with metadata ordered starting with the innermost annotation:

        Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[
            int, ValueRange(3, 10), ctype("char")
        ]

  - Duplicated annotations are not removed:

        Annotated[int, ValueRange(3, 10)] != Annotated[
            int, ValueRange(3, 10), ValueRange(3, 10)
        ]

  - `Annotated` can be used with nested and generic aliases:

        T = TypeVar('T')
        Vec = Annotated[List[Tuple[T, T]], MaxLen(10)]
        V = Vec[int]

        V == Annotated[List[Tuple[int, int]], MaxLen(10)]

  3.9 版新加入.