"typing" --- 类型标注支持
*************************

3.5 新版功能.

**源码：** Lib/typing.py

注解:

  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**。

注解:

  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 新版功能.


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)

在 3.7 版更改: "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 新版功能.

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 新版功能.

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 新版功能.

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 新版功能.

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 新版功能.

   3.6.1 新版功能.

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 新版功能.

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 新版功能.

class typing.AsyncIterable(Generic[T_co])

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

   3.5.2 新版功能.

class typing.AsyncIterator(AsyncIterable[T_co])

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

   3.5.2 新版功能.

class typing.ContextManager(Generic[T_co])

   "contextlib.AbstractContextManager" 的泛型版本。

   3.5.4 新版功能.

   3.6.0 新版功能.

class typing.AsyncContextManager(Generic[T_co])

   "contextlib.AbstractAsyncContextManager" 的泛型版本。

   3.5.4 新版功能.

   3.6.2 新版功能.

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 新版功能.

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

   "collections.OrderedDict" 的泛型版本。

   3.7.2 新版功能.

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

   "collections.Counter" 的泛型版本。

   3.5.4 新版功能.

   3.6.1 新版功能.

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

   "collections.ChainMap" 的泛型版本。

   3.5.4 新版功能.

   3.6.1 新版功能.

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 新版功能.

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 新版功能.

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)])

   在 3.6 版更改: Added support for **PEP 526** variable annotation
   syntax.

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

   在 3.8 版更改: The "_field_types" and "__annotations__" attributes
   are now regular dictionaries instead of instances of "OrderedDict".

   在 3.9 版更改: 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 新版功能.

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(name, tp)

   A helper function to indicate a distinct type 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 新版功能.

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']
      }

   在 3.9 版更改: 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 新版功能.

@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 新版功能.

@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 新版功能.

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 新版功能.

   3.6.2 新版功能.

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。

   在 3.7 版更改: 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 新版功能.

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 新版功能.

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 新版功能.

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 新版功能.

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 新版功能.
