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Edit File : typing_extensions.py \| Size : 100.97 KB
import abc import collections import collections.abc import functools import inspect import operator import sys import types as _types import typing import warnings __all__ = [ # Super-special typing primitives. 'Any', 'ClassVar', 'Concatenate', 'Final', 'LiteralString', 'ParamSpec', 'ParamSpecArgs', 'ParamSpecKwargs', 'Self', 'Type', 'TypeVar', 'TypeVarTuple', 'Unpack', # ABCs (from collections.abc). 'Awaitable', 'AsyncIterator', 'AsyncIterable', 'Coroutine', 'AsyncGenerator', 'AsyncContextManager', 'Buffer', 'ChainMap', # Concrete collection types. 'ContextManager', 'Counter', 'Deque', 'DefaultDict', 'NamedTuple', 'OrderedDict', 'TypedDict', # Structural checks, a.k.a. protocols. 'SupportsAbs', 'SupportsBytes', 'SupportsComplex', 'SupportsFloat', 'SupportsIndex', 'SupportsInt', 'SupportsRound', # One-off things. 'Annotated', 'assert_never', 'assert_type', 'clear_overloads', 'dataclass_transform', 'deprecated', 'Doc', 'get_overloads', 'final', 'get_args', 'get_origin', 'get_original_bases', 'get_protocol_members', 'get_type_hints', 'IntVar', 'is_protocol', 'is_typeddict', 'Literal', 'NewType', 'overload', 'override', 'Protocol', 'reveal_type', 'runtime', 'runtime_checkable', 'Text', 'TypeAlias', 'TypeAliasType', 'TypeGuard', 'TYPE_CHECKING', 'Never', 'NoReturn', 'Required', 'NotRequired', # Pure aliases, have always been in typing 'AbstractSet', 'AnyStr', 'BinaryIO', 'Callable', 'Collection', 'Container', 'Dict', 'ForwardRef', 'FrozenSet', 'Generator', 'Generic', 'Hashable', 'IO', 'ItemsView', 'Iterable', 'Iterator', 'KeysView', 'List', 'Mapping', 'MappingView', 'Match', 'MutableMapping', 'MutableSequence', 'MutableSet', 'Optional', 'Pattern', 'Reversible', 'Sequence', 'Set', 'Sized', 'TextIO', 'Tuple', 'Union', 'ValuesView', 'cast', 'no_type_check', 'no_type_check_decorator', ] # for backward compatibility PEP_560 = True GenericMeta = type # The functions below are modified copies of typing internal helpers. # They are needed by _ProtocolMeta and they provide support for PEP 646. class _Sentinel: def __repr__(self): return "<sentinel>" _marker = _Sentinel() def _check_generic(cls, parameters, elen=_marker): """Check correct count for parameters of a generic cls (internal helper). This gives a nice error message in case of count mismatch. """ if not elen: raise TypeError(f"{cls} is not a generic class") if elen is _marker: if not hasattr(cls, "__parameters__") or not cls.__parameters__: raise TypeError(f"{cls} is not a generic class") elen = len(cls.__parameters__) alen = len(parameters) if alen != elen: if hasattr(cls, "__parameters__"): parameters = [p for p in cls.__parameters__ if not _is_unpack(p)] num_tv_tuples = sum(isinstance(p, TypeVarTuple) for p in parameters) if (num_tv_tuples > 0) and (alen >= elen - num_tv_tuples): return raise TypeError(f"Too {'many' if alen > elen else 'few'} parameters for {cls};" f" actual {alen}, expected {elen}") if sys.version_info >= (3, 10): def _should_collect_from_parameters(t): return isinstance( t, (typing._GenericAlias, _types.GenericAlias, _types.UnionType) ) elif sys.version_info >= (3, 9): def _should_collect_from_parameters(t): return isinstance(t, (typing._GenericAlias, _types.GenericAlias)) else: def _should_collect_from_parameters(t): return isinstance(t, typing._GenericAlias) and not t._special def _collect_type_vars(types, typevar_types=None): """Collect all type variable contained in types in order of first appearance (lexicographic order). For example:: _collect_type_vars((T, List[S, T])) == (T, S) """ if typevar_types is None: typevar_types = typing.TypeVar tvars = [] for t in types: if ( isinstance(t, typevar_types) and t not in tvars and not _is_unpack(t) ): tvars.append(t) if _should_collect_from_parameters(t): tvars.extend([t for t in t.__parameters__ if t not in tvars]) return tuple(tvars) NoReturn = typing.NoReturn # Some unconstrained type variables. These are used by the container types. # (These are not for export.) T = typing.TypeVar('T') # Any type. KT = typing.TypeVar('KT') # Key type. VT = typing.TypeVar('VT') # Value type. T_co = typing.TypeVar('T_co', covariant=True) # Any type covariant containers. T_contra = typing.TypeVar('T_contra', contravariant=True) # Ditto contravariant. if sys.version_info >= (3, 11): from typing import Any else: class _AnyMeta(type): def __instancecheck__(self, obj): if self is Any: raise TypeError("typing_extensions.Any cannot be used with isinstance()") return super().__instancecheck__(obj) def __repr__(self): if self is Any: return "typing_extensions.Any" return super().__repr__() class Any(metaclass=_AnyMeta): """Special type indicating an unconstrained type. - Any is compatible with every type. - Any assumed to have all methods. - All values assumed to be instances of Any. Note that all the above statements are true from the point of view of static type checkers. At runtime, Any should not be used with instance checks. """ def __new__(cls, args, kwargs): if cls is Any: raise TypeError("Any cannot be instantiated") return super().__new__(cls, args, *kwargs) ClassVar = typing.ClassVar class _ExtensionsSpecialForm(typing._SpecialForm, _root=True): def __repr__(self): return 'typing_extensions.' + self._name Final = typing.Final if sys.version_info >= (3, 11): final = typing.final else: # @final exists in 3.8+, but we backport it for all versions # before 3.11 to keep support for the __final__ attribute. # See https://bugs.python.org/issue46342 def final(f): """This decorator can be used to indicate to type checkers that the decorated method cannot be overridden, and 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. The decorator sets the ``__final__`` attribute to ``True`` on the decorated object to allow runtime introspection. """ try: f.__final__ = True except (AttributeError, TypeError): # Skip the attribute silently if it is not writable. # AttributeError happens if the object has __slots__ or a # read-only property, TypeError if it's a builtin class. pass return f def IntVar(name): return typing.TypeVar(name) # A Literal bug was fixed in 3.11.0, 3.10.1 and 3.9.8 if sys.version_info >= (3, 10, 1): Literal = typing.Literal else: def _flatten_literal_params(parameters): """An internal helper for Literal creation: flatten Literals among parameters""" params = [] for p in parameters: if isinstance(p, _LiteralGenericAlias): params.extend(p.__args__) else: params.append(p) return tuple(params) def _value_and_type_iter(params): for p in params: yield p, type(p) class _LiteralGenericAlias(typing._GenericAlias, _root=True): def __eq__(self, other): if not isinstance(other, _LiteralGenericAlias): return NotImplemented these_args_deduped = set(_value_and_type_iter(self.__args__)) other_args_deduped = set(_value_and_type_iter(other.__args__)) return these_args_deduped == other_args_deduped def __hash__(self): return hash(frozenset(_value_and_type_iter(self.__args__))) class _LiteralForm(_ExtensionsSpecialForm, _root=True): def __init__(self, doc: str): self._name = 'Literal' self._doc = self.__doc__ = doc def __getitem__(self, parameters): if not isinstance(parameters, tuple): parameters = (parameters,) parameters = _flatten_literal_params(parameters) val_type_pairs = list(_value_and_type_iter(parameters)) try: deduped_pairs = set(val_type_pairs) except TypeError: # unhashable parameters pass else: # similar logic to typing._deduplicate on Python 3.9+ if len(deduped_pairs) < len(val_type_pairs): new_parameters = [] for pair in val_type_pairs: if pair in deduped_pairs: new_parameters.append(pair[0]) deduped_pairs.remove(pair) assert not deduped_pairs, deduped_pairs parameters = tuple(new_parameters) return _LiteralGenericAlias(self, parameters) Literal = _LiteralForm(doc="""\ A type that can be used to indicate to type checkers that the corresponding value has a value literally equivalent to the provided parameter. For example: var: Literal[4] = 4 The type checker understands that 'var' is literally equal to the value 4 and no other value. Literal[...] cannot be subclassed. There is no runtime checking verifying that the parameter is actually a value instead of a type.""") _overload_dummy = typing._overload_dummy if hasattr(typing, "get_overloads"): # 3.11+ overload = typing.overload get_overloads = typing.get_overloads clear_overloads = typing.clear_overloads else: # {module: {qualname: {firstlineno: func}}} _overload_registry = collections.defaultdict( functools.partial(collections.defaultdict, dict) ) def overload(func): """Decorator for overloaded functions/methods. In a stub file, place two or more stub definitions for the same function in a row, each decorated with @overload. For example: @overload def utf8(value: None) -> None: ... @overload def utf8(value: bytes) -> bytes: ... @overload def utf8(value: str) -> bytes: ... In a non-stub file (i.e. a regular .py file), do the same but follow it with an implementation. The implementation should not* be decorated with @overload. For example: @overload def utf8(value: None) -> None: ... @overload def utf8(value: bytes) -> bytes: ... @overload def utf8(value: str) -> bytes: ... def utf8(value): # implementation goes here The overloads for a function can be retrieved at runtime using the get_overloads() function. """ # classmethod and staticmethod f = getattr(func, "__func__", func) try: _overload_registry[f.__module__][f.__qualname__][ f.__code__.co_firstlineno ] = func except AttributeError: # Not a normal function; ignore. pass return _overload_dummy def get_overloads(func): """Return all defined overloads for func as a sequence.""" # classmethod and staticmethod f = getattr(func, "__func__", func) if f.__module__ not in _overload_registry: return [] mod_dict = _overload_registry[f.__module__] if f.__qualname__ not in mod_dict: return [] return list(mod_dict[f.__qualname__].values()) def clear_overloads(): """Clear all overloads in the registry.""" _overload_registry.clear() # This is not a real generic class. Don't use outside annotations. Type = typing.Type # Various ABCs mimicking those in collections.abc. # A few are simply re-exported for completeness. Awaitable = typing.Awaitable Coroutine = typing.Coroutine AsyncIterable = typing.AsyncIterable AsyncIterator = typing.AsyncIterator Deque = typing.Deque ContextManager = typing.ContextManager AsyncContextManager = typing.AsyncContextManager DefaultDict = typing.DefaultDict OrderedDict = typing.OrderedDict Counter = typing.Counter ChainMap = typing.ChainMap AsyncGenerator = typing.AsyncGenerator Text = typing.Text TYPE_CHECKING = typing.TYPE_CHECKING _PROTO_ALLOWLIST = { 'collections.abc': [ 'Callable', 'Awaitable', 'Iterable', 'Iterator', 'AsyncIterable', 'Hashable', 'Sized', 'Container', 'Collection', 'Reversible', 'Buffer', ], 'contextlib': ['AbstractContextManager', 'AbstractAsyncContextManager'], 'typing_extensions': ['Buffer'], } _EXCLUDED_ATTRS = { "__abstractmethods__", "__annotations__", "__weakref__", "_is_protocol", "_is_runtime_protocol", "__dict__", "__slots__", "__parameters__", "__orig_bases__", "__module__", "_MutableMapping__marker", "__doc__", "__subclasshook__", "__orig_class__", "__init__", "__new__", "__protocol_attrs__", "__callable_proto_members_only__", } if sys.version_info >= (3, 9): _EXCLUDED_ATTRS.add("__class_getitem__") if sys.version_info >= (3, 12): _EXCLUDED_ATTRS.add("__type_params__") _EXCLUDED_ATTRS = frozenset(_EXCLUDED_ATTRS) def _get_protocol_attrs(cls): attrs = set() for base in cls.__mro__[:-1]: # without object if base.__name__ in {'Protocol', 'Generic'}: continue annotations = getattr(base, '__annotations__', {}) for attr in (base.__dict__, annotations): if (not attr.startswith('_abc_') and attr not in _EXCLUDED_ATTRS): attrs.add(attr) return attrs def _caller(depth=2): try: return sys._getframe(depth).f_globals.get('__name__', '__main__') except (AttributeError, ValueError): # For platforms without _getframe() return None # The performance of runtime-checkable protocols is significantly improved on Python 3.12, # so we backport the 3.12 version of Protocol to Python <=3.11 if sys.version_info >= (3, 12): Protocol = typing.Protocol else: def _allow_reckless_class_checks(depth=3): """Allow instance and class checks for special stdlib modules. The abc and functools modules indiscriminately call isinstance() and issubclass() on the whole MRO of a user class, which may contain protocols. """ return _caller(depth) in {'abc', 'functools', None} def _no_init(self, args, kwargs): if type(self)._is_protocol: raise TypeError('Protocols cannot be instantiated') # Inheriting from typing._ProtocolMeta isn't actually desirable, # but is necessary to allow typing.Protocol and typing_extensions.Protocol # to mix without getting TypeErrors about "metaclass conflict" class _ProtocolMeta(type(typing.Protocol)): # This metaclass is somewhat unfortunate, # but is necessary for several reasons... # # NOTE: DO NOT call super() in any methods in this class # That would call the methods on typing._ProtocolMeta on Python 3.8-3.11 # and those are slow def __new__(mcls, name, bases, namespace, kwargs): if name == "Protocol" and len(bases) < 2: pass elif {Protocol, typing.Protocol} & set(bases): for base in bases: if not ( base in {object, typing.Generic, Protocol, typing.Protocol} or base.__name__ in _PROTO_ALLOWLIST.get(base.__module__, []) or is_protocol(base) ): raise TypeError( f"Protocols can only inherit from other protocols, " f"got {base!r}" ) return abc.ABCMeta.__new__(mcls, name, bases, namespace, kwargs) def __init__(cls, args, *kwargs): abc.ABCMeta.__init__(cls, args, *kwargs) if getattr(cls, "_is_protocol", False): cls.__protocol_attrs__ = _get_protocol_attrs(cls) # PEP 544 prohibits using issubclass() # with protocols that have non-method members. cls.__callable_proto_members_only__ = all( callable(getattr(cls, attr, None)) for attr in cls.__protocol_attrs__ ) def __subclasscheck__(cls, other): if cls is Protocol: return type.__subclasscheck__(cls, other) if ( getattr(cls, '_is_protocol', False) and not _allow_reckless_class_checks() ): if not isinstance(other, type): # Same error message as for issubclass(1, int). raise TypeError('issubclass() arg 1 must be a class') if ( not cls.__callable_proto_members_only__ and cls.__dict__.get("__subclasshook__") is _proto_hook ): raise TypeError( "Protocols with non-method members don't support issubclass()" ) if not getattr(cls, '_is_runtime_protocol', False): raise TypeError( "Instance and class checks can only be used with " "@runtime_checkable protocols" ) return abc.ABCMeta.__subclasscheck__(cls, other) def __instancecheck__(cls, instance): # We need this method for situations where attributes are # assigned in __init__. if cls is Protocol: return type.__instancecheck__(cls, instance) if not getattr(cls, "_is_protocol", False): # i.e., it's a concrete subclass of a protocol return abc.ABCMeta.__instancecheck__(cls, instance) if ( not getattr(cls, '_is_runtime_protocol', False) and not _allow_reckless_class_checks() ): raise TypeError("Instance and class checks can only be used with" " @runtime_checkable protocols") if abc.ABCMeta.__instancecheck__(cls, instance): return True for attr in cls.__protocol_attrs__: try: val = inspect.getattr_static(instance, attr) except AttributeError: break if val is None and callable(getattr(cls, attr, None)): break else: return True return False def __eq__(cls, other): # Hack so that typing.Generic.__class_getitem__ # treats typing_extensions.Protocol # as equivalent to typing.Protocol if abc.ABCMeta.__eq__(cls, other) is True: return True return cls is Protocol and other is typing.Protocol # This has to be defined, or the abc-module cache # complains about classes with this metaclass being unhashable, # if we define only __eq__! def __hash__(cls) -> int: return type.__hash__(cls) @classmethod def _proto_hook(cls, other): if not cls.__dict__.get('_is_protocol', False): return NotImplemented for attr in cls.__protocol_attrs__: for base in other.__mro__: # Check if the members appears in the class dictionary... if attr in base.__dict__: if base.__dict__[attr] is None: return NotImplemented break # ...or in annotations, if it is a sub-protocol. annotations = getattr(base, '__annotations__', {}) if ( isinstance(annotations, collections.abc.Mapping) and attr in annotations and is_protocol(other) ): break else: return NotImplemented return True class Protocol(typing.Generic, metaclass=_ProtocolMeta): __doc__ = typing.Protocol.__doc__ __slots__ = () _is_protocol = True _is_runtime_protocol = False def __init_subclass__(cls, args, *kwargs): super().__init_subclass__(args, *kwargs) # Determine if this is a protocol or a concrete subclass. if not cls.__dict__.get('_is_protocol', False): cls._is_protocol = any(b is Protocol for b in cls.__bases__) # Set (or override) the protocol subclass hook. if '__subclasshook__' not in cls.__dict__: cls.__subclasshook__ = _proto_hook # Prohibit instantiation for protocol classes if cls._is_protocol and cls.__init__ is Protocol.__init__: cls.__init__ = _no_init # The "runtime" alias exists for backwards compatibility. runtime = runtime_checkable = typing.runtime_checkable # Our version of runtime-checkable protocols is faster on Python 3.8-3.11 if sys.version_info >= (3, 12): SupportsInt = typing.SupportsInt SupportsFloat = typing.SupportsFloat SupportsComplex = typing.SupportsComplex SupportsBytes = typing.SupportsBytes SupportsIndex = typing.SupportsIndex SupportsAbs = typing.SupportsAbs SupportsRound = typing.SupportsRound else: @runtime_checkable class SupportsInt(Protocol): """An ABC with one abstract method __int__.""" __slots__ = () @abc.abstractmethod def __int__(self) -> int: pass @runtime_checkable class SupportsFloat(Protocol): """An ABC with one abstract method __float__.""" __slots__ = () @abc.abstractmethod def __float__(self) -> float: pass @runtime_checkable class SupportsComplex(Protocol): """An ABC with one abstract method __complex__.""" __slots__ = () @abc.abstractmethod def __complex__(self) -> complex: pass @runtime_checkable class SupportsBytes(Protocol): """An ABC with one abstract method __bytes__.""" __slots__ = () @abc.abstractmethod def __bytes__(self) -> bytes: pass @runtime_checkable class SupportsIndex(Protocol): __slots__ = () @abc.abstractmethod def __index__(self) -> int: pass @runtime_checkable class SupportsAbs(Protocol[T_co]): """ An ABC with one abstract method __abs__ that is covariant in its return type. """ __slots__ = () @abc.abstractmethod def __abs__(self) -> T_co: pass @runtime_checkable class SupportsRound(Protocol[T_co]): """ An ABC with one abstract method __round__ that is covariant in its return type. """ __slots__ = () @abc.abstractmethod def __round__(self, ndigits: int = 0) -> T_co: pass def _ensure_subclassable(mro_entries): def inner(func): if sys.implementation.name == "pypy" and sys.version_info < (3, 9): cls_dict = { "__call__": staticmethod(func), "__mro_entries__": staticmethod(mro_entries) } t = type(func.__name__, (), cls_dict) return functools.update_wrapper(t(), func) else: func.__mro_entries__ = mro_entries return func return inner if sys.version_info >= (3, 13): # The standard library TypedDict in Python 3.8 does not store runtime information # about which (if any) keys are optional. See https://bugs.python.org/issue38834 # The standard library TypedDict in Python 3.9.0/1 does not honour the "total" # keyword with old-style TypedDict(). See https://bugs.python.org/issue42059 # The standard library TypedDict below Python 3.11 does not store runtime # information about optional and required keys when using Required or NotRequired. # Generic TypedDicts are also impossible using typing.TypedDict on Python <3.11. # Aaaand on 3.12 we add __orig_bases__ to TypedDict # to enable better runtime introspection. # On 3.13 we deprecate some odd ways of creating TypedDicts. TypedDict = typing.TypedDict _TypedDictMeta = typing._TypedDictMeta is_typeddict = typing.is_typeddict else: # 3.10.0 and later _TAKES_MODULE = "module" in inspect.signature(typing._type_check).parameters class _TypedDictMeta(type): def __new__(cls, name, bases, ns, total=True): """Create new typed dict class object. This method is called when TypedDict is subclassed, or when TypedDict is instantiated. This way TypedDict supports all three syntax forms described in its docstring. Subclasses and instances of TypedDict return actual dictionaries. """ for base in bases: if type(base) is not _TypedDictMeta and base is not typing.Generic: raise TypeError('cannot inherit from both a TypedDict type ' 'and a non-TypedDict base class') if any(issubclass(b, typing.Generic) for b in bases): generic_base = (typing.Generic,) else: generic_base = () # typing.py generally doesn't let you inherit from plain Generic, unless # the name of the class happens to be "Protocol" tp_dict = type.__new__(_TypedDictMeta, "Protocol", (generic_base, dict), ns) tp_dict.__name__ = name if tp_dict.__qualname__ == "Protocol": tp_dict.__qualname__ = name if not hasattr(tp_dict, '__orig_bases__'): tp_dict.__orig_bases__ = bases annotations = {} own_annotations = ns.get('__annotations__', {}) msg = "TypedDict('Name', {f0: t0, f1: t1, ...}); each t must be a type" if _TAKES_MODULE: own_annotations = { n: typing._type_check(tp, msg, module=tp_dict.__module__) for n, tp in own_annotations.items() } else: own_annotations = { n: typing._type_check(tp, msg) for n, tp in own_annotations.items() } required_keys = set() optional_keys = set() for base in bases: annotations.update(base.__dict__.get('__annotations__', {})) required_keys.update(base.__dict__.get('__required_keys__', ())) optional_keys.update(base.__dict__.get('__optional_keys__', ())) annotations.update(own_annotations) for annotation_key, annotation_type in own_annotations.items(): annotation_origin = get_origin(annotation_type) if annotation_origin is Annotated: annotation_args = get_args(annotation_type) if annotation_args: annotation_type = annotation_args[0] annotation_origin = get_origin(annotation_type) if annotation_origin is Required: required_keys.add(annotation_key) elif annotation_origin is NotRequired: optional_keys.add(annotation_key) elif total: required_keys.add(annotation_key) else: optional_keys.add(annotation_key) tp_dict.__annotations__ = annotations tp_dict.__required_keys__ = frozenset(required_keys) tp_dict.__optional_keys__ = frozenset(optional_keys) if not hasattr(tp_dict, '__total__'): tp_dict.__total__ = total return tp_dict __call__ = dict # static method def __subclasscheck__(cls, other): # Typed dicts are only for static structural subtyping. raise TypeError('TypedDict does not support instance and class checks') __instancecheck__ = __subclasscheck__ _TypedDict = type.__new__(_TypedDictMeta, 'TypedDict', (), {}) @_ensure_subclassable(lambda bases: (_TypedDict,)) def TypedDict(typename, fields=_marker, /, , total=True, kwargs): """A simple typed namespace. At runtime it is equivalent to a plain dict. TypedDict creates a dictionary type such that a type checker will expect all 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. 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 can be accessed via the Point2D.__annotations__ dict, and the Point2D.__required_keys__ and Point2D.__optional_keys__ frozensets. TypedDict supports an additional equivalent form:: 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:: 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. The Required and NotRequired special forms can also be used to mark individual keys as being required or not required:: class Point2D(TypedDict): x: int # the "x" key must always be present (Required is the default) y: NotRequired[int] # the "y" key can be omitted See PEP 655 for more details on Required and NotRequired. """ if fields is _marker or fields is None: if fields is _marker: deprecated_thing = "Failing to pass a value for the 'fields' parameter" else: deprecated_thing = "Passing `None` as the 'fields' parameter" example = f"`{typename} = TypedDict({typename!r}, {{}})`" deprecation_msg = ( f"{deprecated_thing} is deprecated and will be disallowed in " "Python 3.15. To create a TypedDict class with 0 fields " "using the functional syntax, pass an empty dictionary, e.g. " ) + example + "." warnings.warn(deprecation_msg, DeprecationWarning, stacklevel=2) fields = kwargs elif kwargs: raise TypeError("TypedDict takes either a dict or keyword arguments," " but not both") if kwargs: warnings.warn( "The kwargs-based syntax for TypedDict definitions is deprecated " "in Python 3.11, will be removed in Python 3.13, and may not be " "understood by third-party type checkers.", DeprecationWarning, stacklevel=2, ) ns = {'__annotations__': dict(fields)} module = _caller() if module is not None: # Setting correct module is necessary to make typed dict classes pickleable. ns['__module__'] = module td = _TypedDictMeta(typename, (), ns, total=total) td.__orig_bases__ = (TypedDict,) return td if hasattr(typing, "_TypedDictMeta"): _TYPEDDICT_TYPES = (typing._TypedDictMeta, _TypedDictMeta) else: _TYPEDDICT_TYPES = (_TypedDictMeta,) def is_typeddict(tp): """Check if an annotation is a TypedDict class For example:: class Film(TypedDict): title: str year: int is_typeddict(Film) # => True is_typeddict(Union[list, str]) # => False """ # On 3.8, this would otherwise return True if hasattr(typing, "TypedDict") and tp is typing.TypedDict: return False return isinstance(tp, _TYPEDDICT_TYPES) if hasattr(typing, "assert_type"): assert_type = typing.assert_type else: def assert_type(val, typ, /): """Assert (to the type checker) that the value is of the given type. When the type checker encounters a call to assert_type(), it emits an error if the value is not of the specified type:: def greet(name: str) -> None: assert_type(name, str) # ok assert_type(name, int) # type checker error At runtime this returns the first argument unchanged and otherwise does nothing. """ return val if hasattr(typing, "Required"): # 3.11+ get_type_hints = typing.get_type_hints else: # <=3.10 # replaces _strip_annotations() def _strip_extras(t): """Strips Annotated, Required and NotRequired from a given type.""" if isinstance(t, _AnnotatedAlias): return _strip_extras(t.__origin__) if hasattr(t, "__origin__") and t.__origin__ in (Required, NotRequired): return _strip_extras(t.__args__[0]) if isinstance(t, typing._GenericAlias): stripped_args = tuple(_strip_extras(a) for a in t.__args__) if stripped_args == t.__args__: return t return t.copy_with(stripped_args) if hasattr(_types, "GenericAlias") and isinstance(t, _types.GenericAlias): stripped_args = tuple(_strip_extras(a) for a in t.__args__) if stripped_args == t.__args__: return t return _types.GenericAlias(t.__origin__, stripped_args) if hasattr(_types, "UnionType") and isinstance(t, _types.UnionType): stripped_args = tuple(_strip_extras(a) for a in t.__args__) if stripped_args == t.__args__: return t return functools.reduce(operator.or_, stripped_args) return t def get_type_hints(obj, globalns=None, localns=None, include_extras=False): """Return type hints for an object. This is often the same as obj.__annotations__, but it handles forward references encoded as string literals, adds Optional[t] if a default value equal to None is set and recursively replaces all 'Annotated[T, ...]', 'Required[T]' or 'NotRequired[T]' with 'T' (unless 'include_extras=True'). The argument may be a module, class, method, or function. The annotations are returned as a dictionary. For classes, annotations include also inherited members. TypeError is raised if the argument is not of a type that can contain annotations, and an empty dictionary is returned if no annotations are present. BEWARE -- the behavior of globalns and localns is counterintuitive (unless you are familiar with how eval() and exec() work). The search order is locals first, then globals. - If no dict arguments are passed, an attempt is made to use the globals from obj (or the respective module's globals for classes), and these are also used as the locals. If the object does not appear to have globals, an empty dictionary is used. - If one dict argument is passed, it is used for both globals and locals. - If two dict arguments are passed, they specify globals and locals, respectively. """ if hasattr(typing, "Annotated"): # 3.9+ hint = typing.get_type_hints( obj, globalns=globalns, localns=localns, include_extras=True ) else: # 3.8 hint = typing.get_type_hints(obj, globalns=globalns, localns=localns) if include_extras: return hint return {k: _strip_extras(t) for k, t in hint.items()} # Python 3.9+ has PEP 593 (Annotated) if hasattr(typing, 'Annotated'): Annotated = typing.Annotated # Not exported and not a public API, but needed for get_origin() and get_args() # to work. _AnnotatedAlias = typing._AnnotatedAlias # 3.8 else: class _AnnotatedAlias(typing._GenericAlias, _root=True): """Runtime representation of an annotated type. At its core 'Annotated[t, dec1, dec2, ...]' is an alias for the type 't' with extra annotations. The alias behaves like a normal typing alias, instantiating is the same as instantiating the underlying type, binding it to types is also the same. """ def __init__(self, origin, metadata): if isinstance(origin, _AnnotatedAlias): metadata = origin.__metadata__ + metadata origin = origin.__origin__ super().__init__(origin, origin) self.__metadata__ = metadata def copy_with(self, params): assert len(params) == 1 new_type = params[0] return _AnnotatedAlias(new_type, self.__metadata__) def __repr__(self): return (f"typing_extensions.Annotated[{typing._type_repr(self.__origin__)}, " f"{', '.join(repr(a) for a in self.__metadata__)}]") def __reduce__(self): return operator.getitem, ( Annotated, (self.__origin__,) + self.__metadata__ ) def __eq__(self, other): if not isinstance(other, _AnnotatedAlias): return NotImplemented if self.__origin__ != other.__origin__: return False return self.__metadata__ == other.__metadata__ def __hash__(self): return hash((self.__origin__, self.__metadata__)) class Annotated: """Add context specific metadata to a type. Example: Annotated[int, runtime_check.Unsigned] indicates to the hypothetical runtime_check module that this type is an unsigned int. Every other consumer of this type can ignore this metadata and treat this type as int. The first argument to Annotated must be a valid type (and will be in the __origin__ field), the remaining arguments are kept as a tuple in the __extra__ field. Details: - It's an error to call `Annotated` with less than two arguments. - Nested Annotated are flattened:: Annotated[Annotated[T, Ann1, Ann2], Ann3] == Annotated[T, Ann1, Ann2, Ann3] - Instantiating an annotated type is equivalent to instantiating the underlying type:: Annotated[C, Ann1](5) == C(5) - Annotated can be used as a generic type alias:: Optimized = Annotated[T, runtime.Optimize()] Optimized[int] == Annotated[int, runtime.Optimize()] OptimizedList = Annotated[List[T], runtime.Optimize()] OptimizedList[int] == Annotated[List[int], runtime.Optimize()] """ __slots__ = () def __new__(cls, args, *kwargs): raise TypeError("Type Annotated cannot be instantiated.") @typing._tp_cache def __class_getitem__(cls, params): if not isinstance(params, tuple) or len(params) < 2: raise TypeError("Annotated[...] should be used " "with at least two arguments (a type and an " "annotation).") allowed_special_forms = (ClassVar, Final) if get_origin(params[0]) in allowed_special_forms: origin = params[0] else: msg = "Annotated[t, ...]: t must be a type." origin = typing._type_check(params[0], msg) metadata = tuple(params[1:]) return _AnnotatedAlias(origin, metadata) def __init_subclass__(cls, args, *kwargs): raise TypeError( f"Cannot subclass {cls.__module__}.Annotated" ) # Python 3.8 has get_origin() and get_args() but those implementations aren't # Annotated-aware, so we can't use those. Python 3.9's versions don't support # ParamSpecArgs and ParamSpecKwargs, so only Python 3.10's versions will do. if sys.version_info[:2] >= (3, 10): get_origin = typing.get_origin get_args = typing.get_args # 3.8-3.9 else: try: # 3.9+ from typing import _BaseGenericAlias except ImportError: _BaseGenericAlias = typing._GenericAlias try: # 3.9+ from typing import GenericAlias as _typing_GenericAlias except ImportError: _typing_GenericAlias = typing._GenericAlias def get_origin(tp): """Get the unsubscripted version of a type. This supports generic types, Callable, Tuple, Union, Literal, Final, ClassVar and Annotated. Return None for unsupported types. Examples:: get_origin(Literal[42]) is Literal get_origin(int) is None get_origin(ClassVar[int]) is ClassVar get_origin(Generic) is Generic get_origin(Generic[T]) is Generic get_origin(Union[T, int]) is Union get_origin(List[Tuple[T, T]][int]) == list get_origin(P.args) is P """ if isinstance(tp, _AnnotatedAlias): return Annotated if isinstance(tp, (typing._GenericAlias, _typing_GenericAlias, _BaseGenericAlias, ParamSpecArgs, ParamSpecKwargs)): return tp.__origin__ if tp is typing.Generic: return typing.Generic return None def get_args(tp): """Get type arguments with all substitutions performed. For unions, basic simplifications used by Union constructor are performed. Examples:: get_args(Dict[str, int]) == (str, int) get_args(int) == () get_args(Union[int, Union[T, int], str][int]) == (int, str) get_args(Union[int, Tuple[T, int]][str]) == (int, Tuple[str, int]) get_args(Callable[[], T][int]) == ([], int) """ if isinstance(tp, _AnnotatedAlias): return (tp.__origin__,) + tp.__metadata__ if isinstance(tp, (typing._GenericAlias, _typing_GenericAlias)): if getattr(tp, "_special", False): return () res = tp.__args__ if get_origin(tp) is collections.abc.Callable and res[0] is not Ellipsis: res = (list(res[:-1]), res[-1]) return res return () # 3.10+ if hasattr(typing, 'TypeAlias'): TypeAlias = typing.TypeAlias # 3.9 elif sys.version_info[:2] >= (3, 9): @_ExtensionsSpecialForm def TypeAlias(self, parameters): """Special marker indicating that an assignment should be recognized as a proper type alias definition by type checkers. For example:: Predicate: TypeAlias = Callable[..., bool] It's invalid when used anywhere except as in the example above. """ raise TypeError(f"{self} is not subscriptable") # 3.8 else: TypeAlias = _ExtensionsSpecialForm( 'TypeAlias', doc="""Special marker indicating that an assignment should be recognized as a proper type alias definition by type checkers. For example:: Predicate: TypeAlias = Callable[..., bool] It's invalid when used anywhere except as in the example above.""" ) def _set_default(type_param, default): if isinstance(default, (tuple, list)): type_param.__default__ = tuple((typing._type_check(d, "Default must be a type") for d in default)) elif default != _marker: if isinstance(type_param, ParamSpec) and default is ...: # ... not valid <3.11 type_param.__default__ = default else: type_param.__default__ = typing._type_check(default, "Default must be a type") else: type_param.__default__ = None def _set_module(typevarlike): # for pickling: def_mod = _caller(depth=3) if def_mod != 'typing_extensions': typevarlike.__module__ = def_mod class _DefaultMixin: """Mixin for TypeVarLike defaults.""" __slots__ = () __init__ = _set_default # Classes using this metaclass must provide a _backported_typevarlike ClassVar class _TypeVarLikeMeta(type): def __instancecheck__(cls, __instance: Any) -> bool: return isinstance(__instance, cls._backported_typevarlike) # Add default and infer_variance parameters from PEP 696 and 695 class TypeVar(metaclass=_TypeVarLikeMeta): """Type variable.""" _backported_typevarlike = typing.TypeVar def __new__(cls, name, constraints, bound=None, covariant=False, contravariant=False, default=_marker, infer_variance=False): if hasattr(typing, "TypeAliasType"): # PEP 695 implemented (3.12+), can pass infer_variance to typing.TypeVar typevar = typing.TypeVar(name, constraints, bound=bound, covariant=covariant, contravariant=contravariant, infer_variance=infer_variance) else: typevar = typing.TypeVar(name, constraints, bound=bound, covariant=covariant, contravariant=contravariant) if infer_variance and (covariant or contravariant): raise ValueError("Variance cannot be specified with infer_variance.") typevar.__infer_variance__ = infer_variance _set_default(typevar, default) _set_module(typevar) return typevar def __init_subclass__(cls) -> None: raise TypeError(f"type '{__name__}.TypeVar' is not an acceptable base type") # Python 3.10+ has PEP 612 if hasattr(typing, 'ParamSpecArgs'): ParamSpecArgs = typing.ParamSpecArgs ParamSpecKwargs = typing.ParamSpecKwargs # 3.8-3.9 else: class _Immutable: """Mixin to indicate that object should not be copied.""" __slots__ = () def __copy__(self): return self def __deepcopy__(self, memo): return self class ParamSpecArgs(_Immutable): """The args for a ParamSpec object. Given a ParamSpec object P, P.args is an instance of ParamSpecArgs. ParamSpecArgs objects have a reference back to their ParamSpec: P.args.__origin__ is P This type is meant for runtime introspection and has no special meaning to static type checkers. """ def __init__(self, origin): self.__origin__ = origin def __repr__(self): return f"{self.__origin__.__name__}.args" def __eq__(self, other): if not isinstance(other, ParamSpecArgs): return NotImplemented return self.__origin__ == other.__origin__ class ParamSpecKwargs(_Immutable): """The kwargs for a ParamSpec object. Given a ParamSpec object P, P.kwargs is an instance of ParamSpecKwargs. ParamSpecKwargs objects have a reference back to their ParamSpec: P.kwargs.__origin__ is P This type is meant for runtime introspection and has no special meaning to static type checkers. """ def __init__(self, origin): self.__origin__ = origin def __repr__(self): return f"{self.__origin__.__name__}.kwargs" def __eq__(self, other): if not isinstance(other, ParamSpecKwargs): return NotImplemented return self.__origin__ == other.__origin__ # 3.10+ if hasattr(typing, 'ParamSpec'): # Add default parameter - PEP 696 class ParamSpec(metaclass=_TypeVarLikeMeta): """Parameter specification.""" _backported_typevarlike = typing.ParamSpec def __new__(cls, name, , bound=None, covariant=False, contravariant=False, infer_variance=False, default=_marker): if hasattr(typing, "TypeAliasType"): # PEP 695 implemented, can pass infer_variance to typing.TypeVar paramspec = typing.ParamSpec(name, bound=bound, covariant=covariant, contravariant=contravariant, infer_variance=infer_variance) else: paramspec = typing.ParamSpec(name, bound=bound, covariant=covariant, contravariant=contravariant) paramspec.__infer_variance__ = infer_variance _set_default(paramspec, default) _set_module(paramspec) return paramspec def __init_subclass__(cls) -> None: raise TypeError(f"type '{__name__}.ParamSpec' is not an acceptable base type") # 3.8-3.9 else: # Inherits from list as a workaround for Callable checks in Python < 3.9.2. class ParamSpec(list, _DefaultMixin): """Parameter specification variable. Usage:: P = ParamSpec('P') Parameter specification variables exist primarily for the benefit of static type checkers. They are used to forward the parameter types of one callable to another callable, a pattern commonly found in higher order functions and decorators. They are only valid when used in ``Concatenate``, or s the first argument to ``Callable``. In Python 3.10 and higher, they are also supported in user-defined Generics at runtime. See class Generic for more information on generic types. An example for annotating a decorator:: T = TypeVar('T') P = ParamSpec('P') def add_logging(f: Callable[P, T]) -> Callable[P, T]: '''A type-safe decorator to add logging to a function.''' def inner(args: P.args, *kwargs: P.kwargs) -> T: logging.info(f'{f.__name__} was called') return f(args, *kwargs) return inner @add_logging def add_two(x: float, y: float) -> float: '''Add two numbers together.''' return x + y Parameter specification variables defined with covariant=True or contravariant=True can be used to declare covariant or contravariant generic types. These keyword arguments are valid, but their actual semantics are yet to be decided. See PEP 612 for details. Parameter specification variables can be introspected. e.g.: P.__name__ == 'T' P.__bound__ == None P.__covariant__ == False P.__contravariant__ == False Note that only parameter specification variables defined in global scope can be pickled. """ # Trick Generic __parameters__. __class__ = typing.TypeVar @property def args(self): return ParamSpecArgs(self) @property def kwargs(self): return ParamSpecKwargs(self) def __init__(self, name, , bound=None, covariant=False, contravariant=False, infer_variance=False, default=_marker): super().__init__([self]) self.__name__ = name self.__covariant__ = bool(covariant) self.__contravariant__ = bool(contravariant) self.__infer_variance__ = bool(infer_variance) if bound: self.__bound__ = typing._type_check(bound, 'Bound must be a type.') else: self.__bound__ = None _DefaultMixin.__init__(self, default) # for pickling: def_mod = _caller() if def_mod != 'typing_extensions': self.__module__ = def_mod def __repr__(self): if self.__infer_variance__: prefix = '' elif self.__covariant__: prefix = '+' elif self.__contravariant__: prefix = '-' else: prefix = '~' return prefix + self.__name__ def __hash__(self): return object.__hash__(self) def __eq__(self, other): return self is other def __reduce__(self): return self.__name__ # Hack to get typing._type_check to pass. def __call__(self, args, kwargs): pass # 3.8-3.9 if not hasattr(typing, 'Concatenate'): # Inherits from list as a workaround for Callable checks in Python < 3.9.2. class _ConcatenateGenericAlias(list): # Trick Generic into looking into this for __parameters__. __class__ = typing._GenericAlias # Flag in 3.8. _special = False def __init__(self, origin, args): super().__init__(args) self.__origin__ = origin self.__args__ = args def __repr__(self): _type_repr = typing._type_repr return (f'{_type_repr(self.__origin__)}' f'[{", ".join(_type_repr(arg) for arg in self.__args__)}]') def __hash__(self): return hash((self.__origin__, self.__args__)) # Hack to get typing._type_check to pass in Generic. def __call__(self, args, *kwargs): pass @property def __parameters__(self): return tuple( tp for tp in self.__args__ if isinstance(tp, (typing.TypeVar, ParamSpec)) ) # 3.8-3.9 @typing._tp_cache def _concatenate_getitem(self, parameters): if parameters == (): raise TypeError("Cannot take a Concatenate of no types.") if not isinstance(parameters, tuple): parameters = (parameters,) if not isinstance(parameters[-1], ParamSpec): raise TypeError("The last parameter to Concatenate should be a " "ParamSpec variable.") msg = "Concatenate[arg, ...]: each arg must be a type." parameters = tuple(typing._type_check(p, msg) for p in parameters) return _ConcatenateGenericAlias(self, parameters) # 3.10+ if hasattr(typing, 'Concatenate'): Concatenate = typing.Concatenate _ConcatenateGenericAlias = typing._ConcatenateGenericAlias # noqa: F811 # 3.9 elif sys.version_info[:2] >= (3, 9): @_ExtensionsSpecialForm def Concatenate(self, parameters): """Used in conjunction with ``ParamSpec`` and ``Callable`` to represent a higher order function which adds, removes or transforms parameters of a callable. For example:: Callable[Concatenate[int, P], int] See PEP 612 for detailed information. """ return _concatenate_getitem(self, parameters) # 3.8 else: class _ConcatenateForm(_ExtensionsSpecialForm, _root=True): def __getitem__(self, parameters): return _concatenate_getitem(self, parameters) Concatenate = _ConcatenateForm( 'Concatenate', doc="""Used in conjunction with ``ParamSpec`` and ``Callable`` to represent a higher order function which adds, removes or transforms parameters of a callable. For example:: Callable[Concatenate[int, P], int] See PEP 612 for detailed information. """) # 3.10+ if hasattr(typing, 'TypeGuard'): TypeGuard = typing.TypeGuard # 3.9 elif sys.version_info[:2] >= (3, 9): @_ExtensionsSpecialForm def TypeGuard(self, parameters): """Special typing form used to annotate the return type of a user-defined type guard function. ``TypeGuard`` only accepts a single type argument. At runtime, functions marked this way should return a boolean. ``TypeGuard`` aims to benefit type narrowing* -- a technique used by static type checkers to determine a more precise type of an expression within a program's code flow. Usually type narrowing is done by analyzing conditional code flow and applying the narrowing to a block of code. The conditional expression here is sometimes referred to as a "type guard". Sometimes it would be convenient to use a user-defined boolean function as a type guard. Such a function should use ``TypeGuard[...]`` as its return type to alert static type checkers to this intention. Using ``-> TypeGuard`` tells the static type checker that for a given function: 1. The return value is a boolean. 2. If the return value is ``True``, the type of its argument is the type inside ``TypeGuard``. For example:: def is_str(val: Union[str, float]): # "isinstance" type guard if isinstance(val, str): # Type of ``val`` is narrowed to ``str`` ... else: # Else, type of ``val`` is narrowed to ``float``. ... Strict type narrowing is not enforced -- ``TypeB`` need not be a narrower form of ``TypeA`` (it can even be a wider form) and this may lead to type-unsafe results. The main reason is to allow for things like narrowing ``List[object]`` to ``List[str]`` even though the latter is not a subtype of the former, since ``List`` is invariant. The responsibility of writing type-safe type guards is left to the user. ``TypeGuard`` also works with type variables. For more information, see PEP 647 (User-Defined Type Guards). """ item = typing._type_check(parameters, f'{self} accepts only a single type.') return typing._GenericAlias(self, (item,)) # 3.8 else: class _TypeGuardForm(_ExtensionsSpecialForm, _root=True): def __getitem__(self, parameters): item = typing._type_check(parameters, f'{self._name} accepts only a single type') return typing._GenericAlias(self, (item,)) TypeGuard = _TypeGuardForm( 'TypeGuard', doc="""Special typing form used to annotate the return type of a user-defined type guard function. ``TypeGuard`` only accepts a single type argument. At runtime, functions marked this way should return a boolean. ``TypeGuard`` aims to benefit type narrowing -- a technique used by static type checkers to determine a more precise type of an expression within a program's code flow. Usually type narrowing is done by analyzing conditional code flow and applying the narrowing to a block of code. The conditional expression here is sometimes referred to as a "type guard". Sometimes it would be convenient to use a user-defined boolean function as a type guard. Such a function should use ``TypeGuard[...]`` as its return type to alert static type checkers to this intention. Using ``-> TypeGuard`` tells the static type checker that for a given function: 1. The return value is a boolean. 2. If the return value is ``True``, the type of its argument is the type inside ``TypeGuard``. For example:: def is_str(val: Union[str, float]): # "isinstance" type guard if isinstance(val, str): # Type of ``val`` is narrowed to ``str`` ... else: # Else, type of ``val`` is narrowed to ``float``. ... Strict type narrowing is not enforced -- ``TypeB`` need not be a narrower form of ``TypeA`` (it can even be a wider form) and this may lead to type-unsafe results. The main reason is to allow for things like narrowing ``List[object]`` to ``List[str]`` even though the latter is not a subtype of the former, since ``List`` is invariant. The responsibility of writing type-safe type guards is left to the user. ``TypeGuard`` also works with type variables. For more information, see PEP 647 (User-Defined Type Guards). """) # Vendored from cpython typing._SpecialFrom class _SpecialForm(typing._Final, _root=True): __slots__ = ('_name', '__doc__', '_getitem') def __init__(self, getitem): self._getitem = getitem self._name = getitem.__name__ self.__doc__ = getitem.__doc__ def __getattr__(self, item): if item in {'__name__', '__qualname__'}: return self._name raise AttributeError(item) def __mro_entries__(self, bases): raise TypeError(f"Cannot subclass {self!r}") def __repr__(self): return f'typing_extensions.{self._name}' def __reduce__(self): return self._name def __call__(self, args, kwds): raise TypeError(f"Cannot instantiate {self!r}") def __or__(self, other): return typing.Union[self, other] def __ror__(self, other): return typing.Union[other, self] def __instancecheck__(self, obj): raise TypeError(f"{self} cannot be used with isinstance()") def __subclasscheck__(self, cls): raise TypeError(f"{self} cannot be used with issubclass()") @typing._tp_cache def __getitem__(self, parameters): return self._getitem(self, parameters) if hasattr(typing, "LiteralString"): # 3.11+ LiteralString = typing.LiteralString else: @_SpecialForm def LiteralString(self, params): """Represents an arbitrary literal string. Example:: from typing_extensions import LiteralString def query(sql: LiteralString) -> ...: ... query("SELECT FROM table") # ok query(f"SELECT * FROM {input()}") # not ok See PEP 675 for details. """ raise TypeError(f"{self} is not subscriptable") if hasattr(typing, "Self"): # 3.11+ Self = typing.Self else: @_SpecialForm def Self(self, params): """Used to spell the type of "self" in classes. Example:: from typing import Self class ReturnsSelf: def parse(self, data: bytes) -> Self: ... return self """ raise TypeError(f"{self} is not subscriptable") if hasattr(typing, "Never"): # 3.11+ Never = typing.Never else: @_SpecialForm def Never(self, params): """The bottom type, a type that has no members. This can be used to define a function that should never be called, or a function that never returns:: from typing_extensions import Never def never_call_me(arg: Never) -> None: pass def int_or_str(arg: int \| str) -> None: never_call_me(arg) # type checker error match arg: case int(): print("It's an int") case str(): print("It's a str") case _: never_call_me(arg) # ok, arg is of type Never """ raise TypeError(f"{self} is not subscriptable") if hasattr(typing, 'Required'): # 3.11+ Required = typing.Required NotRequired = typing.NotRequired elif sys.version_info[:2] >= (3, 9): # 3.9-3.10 @_ExtensionsSpecialForm def Required(self, parameters): """A special typing construct to mark a key of a total=False TypedDict as required. For example: class Movie(TypedDict, total=False): title: Required[str] year: int m = Movie( title='The Matrix', # typechecker error if key is omitted year=1999, ) There is no runtime checking that a required key is actually provided when instantiating a related TypedDict. """ item = typing._type_check(parameters, f'{self._name} accepts only a single type.') return typing._GenericAlias(self, (item,)) @_ExtensionsSpecialForm def NotRequired(self, parameters): """A special typing construct to mark a key of a TypedDict as potentially missing. For example: class Movie(TypedDict): title: str year: NotRequired[int] m = Movie( title='The Matrix', # typechecker error if key is omitted year=1999, ) """ item = typing._type_check(parameters, f'{self._name} accepts only a single type.') return typing._GenericAlias(self, (item,)) else: # 3.8 class _RequiredForm(_ExtensionsSpecialForm, _root=True): def __getitem__(self, parameters): item = typing._type_check(parameters, f'{self._name} accepts only a single type.') return typing._GenericAlias(self, (item,)) Required = _RequiredForm( 'Required', doc="""A special typing construct to mark a key of a total=False TypedDict as required. For example: class Movie(TypedDict, total=False): title: Required[str] year: int m = Movie( title='The Matrix', # typechecker error if key is omitted year=1999, ) There is no runtime checking that a required key is actually provided when instantiating a related TypedDict. """) NotRequired = _RequiredForm( 'NotRequired', doc="""A special typing construct to mark a key of a TypedDict as potentially missing. For example: class Movie(TypedDict): title: str year: NotRequired[int] m = Movie( title='The Matrix', # typechecker error if key is omitted year=1999, ) """) _UNPACK_DOC = """\ Type unpack operator. The type unpack operator takes the child types from some container type, such as `tuple[int, str]` or a `TypeVarTuple`, and 'pulls them out'. For example: # For some generic class `Foo`: Foo[Unpack[tuple[int, str]]] # Equivalent to Foo[int, str] Ts = TypeVarTuple('Ts') # Specifies that `Bar` is generic in an arbitrary number of types. # (Think of `Ts` as a tuple of an arbitrary number of individual # `TypeVar`s, which the `Unpack` is 'pulling out' directly into the # `Generic[]`.) class Bar(Generic[Unpack[Ts]]): ... Bar[int] # Valid Bar[int, str] # Also valid From Python 3.11, this can also be done using the `` operator: Foo[tuple[int, str]] class Bar(Generic[Ts]): ... The operator can also be used along with a `TypedDict` to annotate `kwargs` in a function signature. For instance: class Movie(TypedDict): name: str year: int # This function expects two keyword arguments - name* of type `str` and # year of type `int`. def foo(*kwargs: Unpack[Movie]): ... Note that there is only some runtime checking of this operator. Not everything the runtime allows may be accepted by static type checkers. For more information, see PEP 646 and PEP 692. """ if sys.version_info >= (3, 12): # PEP 692 changed the repr of Unpack[] Unpack = typing.Unpack def _is_unpack(obj): return get_origin(obj) is Unpack elif sys.version_info[:2] >= (3, 9): # 3.9+ class _UnpackSpecialForm(_ExtensionsSpecialForm, _root=True): def __init__(self, getitem): super().__init__(getitem) self.__doc__ = _UNPACK_DOC class _UnpackAlias(typing._GenericAlias, _root=True): __class__ = typing.TypeVar @_UnpackSpecialForm def Unpack(self, parameters): item = typing._type_check(parameters, f'{self._name} accepts only a single type.') return _UnpackAlias(self, (item,)) def _is_unpack(obj): return isinstance(obj, _UnpackAlias) else: # 3.8 class _UnpackAlias(typing._GenericAlias, _root=True): __class__ = typing.TypeVar class _UnpackForm(_ExtensionsSpecialForm, _root=True): def __getitem__(self, parameters): item = typing._type_check(parameters, f'{self._name} accepts only a single type.') return _UnpackAlias(self, (item,)) Unpack = _UnpackForm('Unpack', doc=_UNPACK_DOC) def _is_unpack(obj): return isinstance(obj, _UnpackAlias) if hasattr(typing, "TypeVarTuple"): # 3.11+ # Add default parameter - PEP 696 class TypeVarTuple(metaclass=_TypeVarLikeMeta): """Type variable tuple.""" _backported_typevarlike = typing.TypeVarTuple def __new__(cls, name, , default=_marker): tvt = typing.TypeVarTuple(name) _set_default(tvt, default) _set_module(tvt) return tvt def __init_subclass__(self, args, kwds): raise TypeError("Cannot subclass special typing classes") else: # <=3.10 class TypeVarTuple(_DefaultMixin): """Type variable tuple. Usage:: Ts = TypeVarTuple('Ts') In the same way that a normal type variable is a stand-in for a single type such as ``int``, a type variable tuple* is a stand-in for a tuple type such as ``Tuple[int, str]``. Type variable tuples can be used in ``Generic`` declarations. Consider the following example:: class Array(Generic[Ts]): ... The ``Ts`` type variable tuple here behaves like ``tuple[T1, T2]``, where ``T1`` and ``T2`` are type variables. To use these type variables as type parameters of ``Array``, we must unpack* the type variable tuple using the star operator: ``Ts``. The signature of ``Array`` then behaves as if we had simply written ``class Array(Generic[T1, T2]): ...``. In contrast to ``Generic[T1, T2]``, however, ``Generic[Shape]`` allows us to parameterise the class with an arbitrary number of type parameters. Type variable tuples can be used anywhere a normal ``TypeVar`` can. This includes class definitions, as shown above, as well as function signatures and variable annotations:: class Array(Generic[Ts]): def __init__(self, shape: Tuple[Ts]): self._shape: Tuple[Ts] = shape def get_shape(self) -> Tuple[Ts]: return self._shape shape = (Height(480), Width(640)) x: Array[Height, Width] = Array(shape) y = abs(x) # Inferred type is Array[Height, Width] z = x + x # ... is Array[Height, Width] x.get_shape() # ... is tuple[Height, Width] """ # Trick Generic __parameters__. __class__ = typing.TypeVar def __iter__(self): yield self.__unpacked__ def __init__(self, name, , default=_marker): self.__name__ = name _DefaultMixin.__init__(self, default) # for pickling: def_mod = _caller() if def_mod != 'typing_extensions': self.__module__ = def_mod self.__unpacked__ = Unpack[self] def __repr__(self): return self.__name__ def __hash__(self): return object.__hash__(self) def __eq__(self, other): return self is other def __reduce__(self): return self.__name__ def __init_subclass__(self, args, *kwds): if '_root' not in kwds: raise TypeError("Cannot subclass special typing classes") if hasattr(typing, "reveal_type"): # 3.11+ reveal_type = typing.reveal_type else: # <=3.10 def reveal_type(obj: T, /) -> T: """Reveal the inferred type of a variable. When a static type checker encounters a call to ``reveal_type()``, it will emit the inferred type of the argument:: x: int = 1 reveal_type(x) Running a static type checker (e.g., ``mypy``) on this example will produce output similar to 'Revealed type is "builtins.int"'. At runtime, the function prints the runtime type of the argument and returns it unchanged. """ print(f"Runtime type is {type(obj).__name__!r}", file=sys.stderr) return obj if hasattr(typing, "assert_never"): # 3.11+ assert_never = typing.assert_never else: # <=3.10 def assert_never(arg: Never, /) -> Never: """Assert to the type checker that a line of code is unreachable. Example:: def int_or_str(arg: int \| str) -> None: match arg: case int(): print("It's an int") case str(): print("It's a str") case _: assert_never(arg) If a type checker finds that a call to assert_never() is reachable, it will emit an error. At runtime, this throws an exception when called. """ raise AssertionError("Expected code to be unreachable") if sys.version_info >= (3, 12): # 3.12+ # dataclass_transform exists in 3.11 but lacks the frozen_default parameter dataclass_transform = typing.dataclass_transform else: # <=3.11 def dataclass_transform( , eq_default: bool = True, order_default: bool = False, kw_only_default: bool = False, frozen_default: bool = False, field_specifiers: typing.Tuple[ typing.Union[typing.Type[typing.Any], typing.Callable[..., typing.Any]], ... ] = (), *kwargs: typing.Any, ) -> typing.Callable[[T], T]: """Decorator that marks a function, class, or metaclass as providing dataclass-like behavior. Example: from typing_extensions import dataclass_transform _T = TypeVar("_T") # Used on a decorator function @dataclass_transform() def create_model(cls: type[_T]) -> type[_T]: ... return cls @create_model class CustomerModel: id: int name: str # Used on a base class @dataclass_transform() class ModelBase: ... class CustomerModel(ModelBase): id: int name: str # Used on a metaclass @dataclass_transform() class ModelMeta(type): ... class ModelBase(metaclass=ModelMeta): ... class CustomerModel(ModelBase): id: int name: str Each of the ``CustomerModel`` classes defined in this example will now behave similarly to a dataclass created with the ``@dataclasses.dataclass`` decorator. For example, the type checker will synthesize an ``__init__`` method. The arguments to this decorator can be used to customize this behavior: - ``eq_default`` indicates whether the ``eq`` parameter is assumed to be True or False if it is omitted by the caller. - ``order_default`` indicates whether the ``order`` parameter is assumed to be True or False if it is omitted by the caller. - ``kw_only_default`` indicates whether the ``kw_only`` parameter is assumed to be True or False if it is omitted by the caller. - ``frozen_default`` indicates whether the ``frozen`` parameter is assumed to be True or False if it is omitted by the caller. - ``field_specifiers`` specifies a static list of supported classes or functions that describe fields, similar to ``dataclasses.field()``. At runtime, this decorator records its arguments in the ``__dataclass_transform__`` attribute on the decorated object. See PEP 681 for details. """ def decorator(cls_or_fn): cls_or_fn.__dataclass_transform__ = { "eq_default": eq_default, "order_default": order_default, "kw_only_default": kw_only_default, "frozen_default": frozen_default, "field_specifiers": field_specifiers, "kwargs": kwargs, } return cls_or_fn return decorator if hasattr(typing, "override"): # 3.12+ override = typing.override else: # <=3.11 _F = typing.TypeVar("_F", bound=typing.Callable[..., typing.Any]) def override(arg: _F, /) -> _F: """Indicate that a method is intended to override a method in a base class. Usage: class Base: def method(self) -> None: ... pass class Child(Base): @override def method(self) -> None: super().method() When this decorator is applied to a method, the type checker will validate that it overrides a method with the same name on a base class. This helps prevent bugs that may occur when a base class is changed without an equivalent change to a child class. There is no runtime checking of these properties. The decorator sets the ``__override__`` attribute to ``True`` on the decorated object to allow runtime introspection. See PEP 698 for details. """ try: arg.__override__ = True except (AttributeError, TypeError): # Skip the attribute silently if it is not writable. # AttributeError happens if the object has __slots__ or a # read-only property, TypeError if it's a builtin class. pass return arg if hasattr(typing, "deprecated"): deprecated = typing.deprecated else: _T = typing.TypeVar("_T") def deprecated( msg: str, /, , category: typing.Optional[typing.Type[Warning]] = DeprecationWarning, stacklevel: int = 1, ) -> typing.Callable[[_T], _T]: """Indicate that a class, function or overload is deprecated. Usage: @deprecated("Use B instead") class A: pass @deprecated("Use g instead") def f(): pass @overload @deprecated("int support is deprecated") def g(x: int) -> int: ... @overload def g(x: str) -> int: ... When this decorator is applied to an object, the type checker will generate a diagnostic on usage of the deprecated object. The warning specified by ``category`` will be emitted on use of deprecated objects. For functions, that happens on calls; for classes, on instantiation. If the ``category`` is ``None``, no warning is emitted. The ``stacklevel`` determines where the warning is emitted. If it is ``1`` (the default), the warning is emitted at the direct caller of the deprecated object; if it is higher, it is emitted further up the stack. The decorator sets the ``__deprecated__`` attribute on the decorated object to the deprecation message passed to the decorator. If applied to an overload, the decorator must be after the ``@overload`` decorator for the attribute to exist on the overload as returned by ``get_overloads()``. See PEP 702 for details. """ def decorator(arg: _T, /) -> _T: if category is None: arg.__deprecated__ = msg return arg elif isinstance(arg, type): original_new = arg.__new__ has_init = arg.__init__ is not object.__init__ @functools.wraps(original_new) def __new__(cls, args, kwargs): warnings.warn(msg, category=category, stacklevel=stacklevel + 1) if original_new is not object.__new__: return original_new(cls, args, *kwargs) # Mirrors a similar check in object.__new__. elif not has_init and (args or kwargs): raise TypeError(f"{cls.__name__}() takes no arguments") else: return original_new(cls) arg.__new__ = staticmethod(__new__) arg.__deprecated__ = __new__.__deprecated__ = msg return arg elif callable(arg): @functools.wraps(arg) def wrapper(args, *kwargs): warnings.warn(msg, category=category, stacklevel=stacklevel + 1) return arg(args, *kwargs) arg.__deprecated__ = wrapper.__deprecated__ = msg return wrapper else: raise TypeError( "@deprecated decorator with non-None category must be applied to " f"a class or callable, not {arg!r}" ) return decorator # We have to do some monkey patching to deal with the dual nature of # Unpack/TypeVarTuple: # - We want Unpack to be a kind of TypeVar so it gets accepted in # Generic[Unpack[Ts]] # - We want it to not* be treated as a TypeVar for the purposes of # counting generic parameters, so that when we subscript a generic, # the runtime doesn't try to substitute the Unpack with the subscripted type. if not hasattr(typing, "TypeVarTuple"): typing._collect_type_vars = _collect_type_vars typing._check_generic = _check_generic # Backport typing.NamedTuple as it exists in Python 3.13. # In 3.11, the ability to define generic `NamedTuple`s was supported. # This was explicitly disallowed in 3.9-3.10, and only half-worked in <=3.8. # On 3.12, we added __orig_bases__ to call-based NamedTuples # On 3.13, we deprecated kwargs-based NamedTuples if sys.version_info >= (3, 13): NamedTuple = typing.NamedTuple else: def _make_nmtuple(name, types, module, defaults=()): fields = [n for n, t in types] annotations = {n: typing._type_check(t, f"field {n} annotation must be a type") for n, t in types} nm_tpl = collections.namedtuple(name, fields, defaults=defaults, module=module) nm_tpl.__annotations__ = nm_tpl.__new__.__annotations__ = annotations # The `_field_types` attribute was removed in 3.9; # in earlier versions, it is the same as the `__annotations__` attribute if sys.version_info < (3, 9): nm_tpl._field_types = annotations return nm_tpl _prohibited_namedtuple_fields = typing._prohibited _special_namedtuple_fields = frozenset({'__module__', '__name__', '__annotations__'}) class _NamedTupleMeta(type): def __new__(cls, typename, bases, ns): assert _NamedTuple in bases for base in bases: if base is not _NamedTuple and base is not typing.Generic: raise TypeError( 'can only inherit from a NamedTuple type and Generic') bases = tuple(tuple if base is _NamedTuple else base for base in bases) types = ns.get('__annotations__', {}) default_names = [] for field_name in types: if field_name in ns: default_names.append(field_name) elif default_names: raise TypeError(f"Non-default namedtuple field {field_name} " f"cannot follow default field" f"{'s' if len(default_names) > 1 else ''} " f"{', '.join(default_names)}") nm_tpl = _make_nmtuple( typename, types.items(), defaults=[ns[n] for n in default_names], module=ns['__module__'] ) nm_tpl.__bases__ = bases if typing.Generic in bases: if hasattr(typing, '_generic_class_getitem'): # 3.12+ nm_tpl.__class_getitem__ = classmethod(typing._generic_class_getitem) else: class_getitem = typing.Generic.__class_getitem__.__func__ nm_tpl.__class_getitem__ = classmethod(class_getitem) # update from user namespace without overriding special namedtuple attributes for key in ns: if key in _prohibited_namedtuple_fields: raise AttributeError("Cannot overwrite NamedTuple attribute " + key) elif key not in _special_namedtuple_fields and key not in nm_tpl._fields: setattr(nm_tpl, key, ns[key]) if typing.Generic in bases: nm_tpl.__init_subclass__() return nm_tpl _NamedTuple = type.__new__(_NamedTupleMeta, 'NamedTuple', (), {}) def _namedtuple_mro_entries(bases): assert NamedTuple in bases return (_NamedTuple,) @_ensure_subclassable(_namedtuple_mro_entries) def NamedTuple(typename, fields=_marker, /, *kwargs): """Typed version of namedtuple. Usage:: class Employee(NamedTuple): name: str id: int This is equivalent to:: Employee = collections.namedtuple('Employee', ['name', 'id']) The resulting class has an extra __annotations__ attribute, giving a dict that maps field names to types. (The field names are also in the _fields attribute, which is part of the namedtuple API.) An alternative equivalent functional syntax is also accepted:: Employee = NamedTuple('Employee', [('name', str), ('id', int)]) """ if fields is _marker: if kwargs: deprecated_thing = "Creating NamedTuple classes using keyword arguments" deprecation_msg = ( "{name} is deprecated and will be disallowed in Python {remove}. " "Use the class-based or functional syntax instead." ) else: deprecated_thing = "Failing to pass a value for the 'fields' parameter" example = f"`{typename} = NamedTuple({typename!r}, [])`" deprecation_msg = ( "{name} is deprecated and will be disallowed in Python {remove}. " "To create a NamedTuple class with 0 fields " "using the functional syntax, " "pass an empty list, e.g. " ) + example + "." elif fields is None: if kwargs: raise TypeError( "Cannot pass `None` as the 'fields' parameter " "and also specify fields using keyword arguments" ) else: deprecated_thing = "Passing `None` as the 'fields' parameter" example = f"`{typename} = NamedTuple({typename!r}, [])`" deprecation_msg = ( "{name} is deprecated and will be disallowed in Python {remove}. " "To create a NamedTuple class with 0 fields " "using the functional syntax, " "pass an empty list, e.g. " ) + example + "." elif kwargs: raise TypeError("Either list of fields or keywords" " can be provided to NamedTuple, not both") if fields is _marker or fields is None: warnings.warn( deprecation_msg.format(name=deprecated_thing, remove="3.15"), DeprecationWarning, stacklevel=2, ) fields = kwargs.items() nt = _make_nmtuple(typename, fields, module=_caller()) nt.__orig_bases__ = (NamedTuple,) return nt if hasattr(collections.abc, "Buffer"): Buffer = collections.abc.Buffer else: class Buffer(abc.ABC): """Base class for classes that implement the buffer protocol. The buffer protocol allows Python objects to expose a low-level memory buffer interface. Before Python 3.12, it is not possible to implement the buffer protocol in pure Python code, or even to check whether a class implements the buffer protocol. In Python 3.12 and higher, the ``__buffer__`` method allows access to the buffer protocol from Python code, and the ``collections.abc.Buffer`` ABC allows checking whether a class implements the buffer protocol. To indicate support for the buffer protocol in earlier versions, inherit from this ABC, either in a stub file or at runtime, or use ABC registration. This ABC provides no methods, because there is no Python-accessible methods shared by pre-3.12 buffer classes. It is useful primarily for static checks. """ # As a courtesy, register the most common stdlib buffer classes. Buffer.register(memoryview) Buffer.register(bytearray) Buffer.register(bytes) # Backport of types.get_original_bases, available on 3.12+ in CPython if hasattr(_types, "get_original_bases"): get_original_bases = _types.get_original_bases else: def get_original_bases(cls, /): """Return the class's "original" bases prior to modification by `__mro_entries__`. Examples:: from typing import TypeVar, Generic from typing_extensions import NamedTuple, TypedDict T = TypeVar("T") class Foo(Generic[T]): ... class Bar(Foo[int], float): ... class Baz(list[str]): ... Eggs = NamedTuple("Eggs", [("a", int), ("b", str)]) Spam = TypedDict("Spam", {"a": int, "b": str}) assert get_original_bases(Bar) == (Foo[int], float) assert get_original_bases(Baz) == (list[str],) assert get_original_bases(Eggs) == (NamedTuple,) assert get_original_bases(Spam) == (TypedDict,) assert get_original_bases(int) == (object,) """ try: return cls.__dict__.get("__orig_bases__", cls.__bases__) except AttributeError: raise TypeError( f'Expected an instance of type, not {type(cls).__name__!r}' ) from None # NewType is a class on Python 3.10+, making it pickleable # The error message for subclassing instances of NewType was improved on 3.11+ if sys.version_info >= (3, 11): NewType = typing.NewType else: class NewType: """NewType creates simple unique types with almost zero runtime overhead. NewType(name, tp) is considered a subtype of tp by static type checkers. At runtime, NewType(name, tp) returns a dummy callable that simply returns its argument. Usage:: UserId = NewType('UserId', int) def name_by_id(user_id: UserId) -> str: ... UserId('user') # Fails type check name_by_id(42) # Fails type check name_by_id(UserId(42)) # OK num = UserId(5) + 1 # type: int """ def __call__(self, obj): return obj def __init__(self, name, tp): self.__qualname__ = name if '.' in name: name = name.rpartition('.')[-1] self.__name__ = name self.__supertype__ = tp def_mod = _caller() if def_mod != 'typing_extensions': self.__module__ = def_mod def __mro_entries__(self, bases): # We defined __mro_entries__ to get a better error message # if a user attempts to subclass a NewType instance. bpo-46170 supercls_name = self.__name__ class Dummy: def __init_subclass__(cls): subcls_name = cls.__name__ raise TypeError( f"Cannot subclass an instance of NewType. " f"Perhaps you were looking for: " f"`{subcls_name} = NewType({subcls_name!r}, {supercls_name})`" ) return (Dummy,) def __repr__(self): return f'{self.__module__}.{self.__qualname__}' def __reduce__(self): return self.__qualname__ if sys.version_info >= (3, 10): # PEP 604 methods # It doesn't make sense to have these methods on Python <3.10 def __or__(self, other): return typing.Union[self, other] def __ror__(self, other): return typing.Union[other, self] if hasattr(typing, "TypeAliasType"): TypeAliasType = typing.TypeAliasType else: def _is_unionable(obj): """Corresponds to is_unionable() in unionobject.c in CPython.""" return obj is None or isinstance(obj, ( type, _types.GenericAlias, _types.UnionType, TypeAliasType, )) class TypeAliasType: """Create named, parameterized type aliases. This provides a backport of the new `type` statement in Python 3.12: type ListOrSet[T] = list[T] \| set[T] is equivalent to: T = TypeVar("T") ListOrSet = TypeAliasType("ListOrSet", list[T] \| set[T], type_params=(T,)) The name ListOrSet can then be used as an alias for the type it refers to. The type_params argument should contain all the type parameters used in the value of the type alias. If the alias is not generic, this argument is omitted. Static type checkers should only support type aliases declared using TypeAliasType that follow these rules: - The first argument (the name) must be a string literal. - The TypeAliasType instance must be immediately assigned to a variable of the same name. (For example, 'X = TypeAliasType("Y", int)' is invalid, as is 'X, Y = TypeAliasType("X", int), TypeAliasType("Y", int)'). """ def __init__(self, name: str, value, , type_params=()): if not isinstance(name, str): raise TypeError("TypeAliasType name must be a string") self.__value__ = value self.__type_params__ = type_params parameters = [] for type_param in type_params: if isinstance(type_param, TypeVarTuple): parameters.extend(type_param) else: parameters.append(type_param) self.__parameters__ = tuple(parameters) def_mod = _caller() if def_mod != 'typing_extensions': self.__module__ = def_mod # Setting this attribute closes the TypeAliasType from further modification self.__name__ = name def __setattr__(self, name: str, value: object, /) -> None: if hasattr(self, "__name__"): self._raise_attribute_error(name) super().__setattr__(name, value) def __delattr__(self, name: str, /) -> Never: self._raise_attribute_error(name) def _raise_attribute_error(self, name: str) -> Never: # Match the Python 3.12 error messages exactly if name == "__name__": raise AttributeError("readonly attribute") elif name in {"__value__", "__type_params__", "__parameters__", "__module__"}: raise AttributeError( f"attribute '{name}' of 'typing.TypeAliasType' objects " "is not writable" ) else: raise AttributeError( f"'typing.TypeAliasType' object has no attribute '{name}'" ) def __repr__(self) -> str: return self.__name__ def __getitem__(self, parameters): if not isinstance(parameters, tuple): parameters = (parameters,) parameters = [ typing._type_check( item, f'Subscripting {self.__name__} requires a type.' ) for item in parameters ] return typing._GenericAlias(self, tuple(parameters)) def __reduce__(self): return self.__name__ def __init_subclass__(cls, args, kwargs): raise TypeError( "type 'typing_extensions.TypeAliasType' is not an acceptable base type" ) # The presence of this method convinces typing._type_check # that TypeAliasTypes are types. def __call__(self): raise TypeError("Type alias is not callable") if sys.version_info >= (3, 10): def __or__(self, right): # For forward compatibility with 3.12, reject Unions # that are not accepted by the built-in Union. if not _is_unionable(right): return NotImplemented return typing.Union[self, right] def __ror__(self, left): if not _is_unionable(left): return NotImplemented return typing.Union[left, self] if hasattr(typing, "is_protocol"): is_protocol = typing.is_protocol get_protocol_members = typing.get_protocol_members else: def is_protocol(tp: type, /) -> bool: """Return True if the given type is a Protocol. Example:: >>> from typing_extensions import Protocol, is_protocol >>> class P(Protocol): ... def a(self) -> str: ... ... b: int >>> is_protocol(P) True >>> is_protocol(int) False """ return ( isinstance(tp, type) and getattr(tp, '_is_protocol', False) and tp is not Protocol and tp is not typing.Protocol ) def get_protocol_members(tp: type, /) -> typing.FrozenSet[str]: """Return the set of members defined in a Protocol. Example:: >>> from typing_extensions import Protocol, get_protocol_members >>> class P(Protocol): ... def a(self) -> str: ... ... b: int >>> get_protocol_members(P) frozenset({'a', 'b'}) Raise a TypeError for arguments that are not Protocols. """ if not is_protocol(tp): raise TypeError(f'{tp!r} is not a Protocol') if hasattr(tp, '__protocol_attrs__'): return frozenset(tp.__protocol_attrs__) return frozenset(_get_protocol_attrs(tp)) if hasattr(typing, "Doc"): Doc = typing.Doc else: class Doc: """Define the documentation of a type annotation using ``Annotated``, to be used in class attributes, function and method parameters, return values, and variables. The value should be a positional-only string literal to allow static tools like editors and documentation generators to use it. This complements docstrings. The string value passed is available in the attribute ``documentation``. Example:: >>> from typing_extensions import Annotated, Doc >>> def hi(to: Annotated[str, Doc("Who to say hi to")]) -> None: ... """ def __init__(self, documentation: str, /) -> None: self.documentation = documentation def __repr__(self) -> str: return f"Doc({self.documentation!r})" def __hash__(self) -> int: return hash(self.documentation) def __eq__(self, other: object) -> bool: if not isinstance(other, Doc): return NotImplemented return self.documentation == other.documentation # Aliases for items that have always been in typing. # Explicitly assign these (rather than using `from typing import ` at the top), # so that we get a CI error if one of these is deleted from typing.py # in a future version of Python AbstractSet = typing.AbstractSet AnyStr = typing.AnyStr BinaryIO = typing.BinaryIO Callable = typing.Callable Collection = typing.Collection Container = typing.Container Dict = typing.Dict ForwardRef = typing.ForwardRef FrozenSet = typing.FrozenSet Generator = typing.Generator Generic = typing.Generic Hashable = typing.Hashable IO = typing.IO ItemsView = typing.ItemsView Iterable = typing.Iterable Iterator = typing.Iterator KeysView = typing.KeysView List = typing.List Mapping = typing.Mapping MappingView = typing.MappingView Match = typing.Match MutableMapping = typing.MutableMapping MutableSequence = typing.MutableSequence MutableSet = typing.MutableSet Optional = typing.Optional Pattern = typing.Pattern Reversible = typing.Reversible Sequence = typing.Sequence Set = typing.Set Sized = typing.Sized TextIO = typing.TextIO Tuple = typing.Tuple Union = typing.Union ValuesView = typing.ValuesView cast = typing.cast no_type_check = typing.no_type_check no_type_check_decorator = typing.no_type_check_decorator Back

Edit File : typing_extensions.py | Size : 100.97 KB

import abc
import collections
import collections.abc
import functools
import inspect
import operator
import sys
import types as _types
import typing
import warnings

__all__ = [
    # Super-special typing primitives.
    'Any',
    'ClassVar',
    'Concatenate',
    'Final',
    'LiteralString',
    'ParamSpec',
    'ParamSpecArgs',
    'ParamSpecKwargs',
    'Self',
    'Type',
    'TypeVar',
    'TypeVarTuple',
    'Unpack',

# ABCs (from collections.abc).
    'Awaitable',
    'AsyncIterator',
    'AsyncIterable',
    'Coroutine',
    'AsyncGenerator',
    'AsyncContextManager',
    'Buffer',
    'ChainMap',

# Concrete collection types.
    'ContextManager',
    'Counter',
    'Deque',
    'DefaultDict',
    'NamedTuple',
    'OrderedDict',
    'TypedDict',

# Structural checks, a.k.a. protocols.
    'SupportsAbs',
    'SupportsBytes',
    'SupportsComplex',
    'SupportsFloat',
    'SupportsIndex',
    'SupportsInt',
    'SupportsRound',

# One-off things.
    'Annotated',
    'assert_never',
    'assert_type',
    'clear_overloads',
    'dataclass_transform',
    'deprecated',
    'Doc',
    'get_overloads',
    'final',
    'get_args',
    'get_origin',
    'get_original_bases',
    'get_protocol_members',
    'get_type_hints',
    'IntVar',
    'is_protocol',
    'is_typeddict',
    'Literal',
    'NewType',
    'overload',
    'override',
    'Protocol',
    'reveal_type',
    'runtime',
    'runtime_checkable',
    'Text',
    'TypeAlias',
    'TypeAliasType',
    'TypeGuard',
    'TYPE_CHECKING',
    'Never',
    'NoReturn',
    'Required',
    'NotRequired',

# Pure aliases, have always been in typing
    'AbstractSet',
    'AnyStr',
    'BinaryIO',
    'Callable',
    'Collection',
    'Container',
    'Dict',
    'ForwardRef',
    'FrozenSet',
    'Generator',
    'Generic',
    'Hashable',
    'IO',
    'ItemsView',
    'Iterable',
    'Iterator',
    'KeysView',
    'List',
    'Mapping',
    'MappingView',
    'Match',
    'MutableMapping',
    'MutableSequence',
    'MutableSet',
    'Optional',
    'Pattern',
    'Reversible',
    'Sequence',
    'Set',
    'Sized',
    'TextIO',
    'Tuple',
    'Union',
    'ValuesView',
    'cast',
    'no_type_check',
    'no_type_check_decorator',
]

# for backward compatibility
PEP_560 = True
GenericMeta = type

# The functions below are modified copies of typing internal helpers.
# They are needed by _ProtocolMeta and they provide support for PEP 646.

class _Sentinel:
    def __repr__(self):
        return "<sentinel>"

_marker = _Sentinel()

def _check_generic(cls, parameters, elen=_marker):
    """Check correct count for parameters of a generic cls (internal helper).
    This gives a nice error message in case of count mismatch.
    """
    if not elen:
        raise TypeError(f"{cls} is not a generic class")
    if elen is _marker:
        if not hasattr(cls, "__parameters__") or not cls.__parameters__:
            raise TypeError(f"{cls} is not a generic class")
        elen = len(cls.__parameters__)
    alen = len(parameters)
    if alen != elen:
        if hasattr(cls, "__parameters__"):
            parameters = [p for p in cls.__parameters__ if not _is_unpack(p)]
            num_tv_tuples = sum(isinstance(p, TypeVarTuple) for p in parameters)
            if (num_tv_tuples > 0) and (alen >= elen - num_tv_tuples):
                return
        raise TypeError(f"Too {'many' if alen > elen else 'few'} parameters for {cls};"
                        f" actual {alen}, expected {elen}")

if sys.version_info >= (3, 10):
    def _should_collect_from_parameters(t):
        return isinstance(
            t, (typing._GenericAlias, _types.GenericAlias, _types.UnionType)
        )
elif sys.version_info >= (3, 9):
    def _should_collect_from_parameters(t):
        return isinstance(t, (typing._GenericAlias, _types.GenericAlias))
else:
    def _should_collect_from_parameters(t):
        return isinstance(t, typing._GenericAlias) and not t._special

def _collect_type_vars(types, typevar_types=None):
    """Collect all type variable contained in types in order of
    first appearance (lexicographic order). For example::

_collect_type_vars((T, List[S, T])) == (T, S)
    """
    if typevar_types is None:
        typevar_types = typing.TypeVar
    tvars = []
    for t in types:
        if (
            isinstance(t, typevar_types) and
            t not in tvars and
            not _is_unpack(t)
        ):
            tvars.append(t)
        if _should_collect_from_parameters(t):
            tvars.extend([t for t in t.__parameters__ if t not in tvars])
    return tuple(tvars)

NoReturn = typing.NoReturn

# Some unconstrained type variables.  These are used by the container types.
# (These are not for export.)
T = typing.TypeVar('T')  # Any type.
KT = typing.TypeVar('KT')  # Key type.
VT = typing.TypeVar('VT')  # Value type.
T_co = typing.TypeVar('T_co', covariant=True)  # Any type covariant containers.
T_contra = typing.TypeVar('T_contra', contravariant=True)  # Ditto contravariant.

if sys.version_info >= (3, 11):
    from typing import Any
else:

class _AnyMeta(type):
        def __instancecheck__(self, obj):
            if self is Any:
                raise TypeError("typing_extensions.Any cannot be used with isinstance()")
            return super().__instancecheck__(obj)

def __repr__(self):
            if self is Any:
                return "typing_extensions.Any"
            return super().__repr__()

class Any(metaclass=_AnyMeta):
        """Special type indicating an unconstrained type.
        - Any is compatible with every type.
        - Any assumed to have all methods.
        - All values assumed to be instances of Any.
        Note that all the above statements are true from the point of view of
        static type checkers. At runtime, Any should not be used with instance
        checks.
        """
        def __new__(cls, *args, **kwargs):
            if cls is Any:
                raise TypeError("Any cannot be instantiated")
            return super().__new__(cls, *args, **kwargs)

ClassVar = typing.ClassVar

class _ExtensionsSpecialForm(typing._SpecialForm, _root=True):
    def __repr__(self):
        return 'typing_extensions.' + self._name

Final = typing.Final

if sys.version_info >= (3, 11):
    final = typing.final
else:
    # @final exists in 3.8+, but we backport it for all versions
    # before 3.11 to keep support for the __final__ attribute.
    # See https://bugs.python.org/issue46342
    def final(f):
        """This decorator can be used to indicate to type checkers that
        the decorated method cannot be overridden, and 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. The decorator
        sets the ``__final__`` attribute to ``True`` on the decorated object
        to allow runtime introspection.
        """
        try:
            f.__final__ = True
        except (AttributeError, TypeError):
            # Skip the attribute silently if it is not writable.
            # AttributeError happens if the object has __slots__ or a
            # read-only property, TypeError if it's a builtin class.
            pass
        return f

def IntVar(name):
    return typing.TypeVar(name)

# A Literal bug was fixed in 3.11.0, 3.10.1 and 3.9.8
if sys.version_info >= (3, 10, 1):
    Literal = typing.Literal
else:
    def _flatten_literal_params(parameters):
        """An internal helper for Literal creation: flatten Literals among parameters"""
        params = []
        for p in parameters:
            if isinstance(p, _LiteralGenericAlias):
                params.extend(p.__args__)
            else:
                params.append(p)
        return tuple(params)

def _value_and_type_iter(params):
        for p in params:
            yield p, type(p)

class _LiteralGenericAlias(typing._GenericAlias, _root=True):
        def __eq__(self, other):
            if not isinstance(other, _LiteralGenericAlias):
                return NotImplemented
            these_args_deduped = set(_value_and_type_iter(self.__args__))
            other_args_deduped = set(_value_and_type_iter(other.__args__))
            return these_args_deduped == other_args_deduped

def __hash__(self):
            return hash(frozenset(_value_and_type_iter(self.__args__)))

class _LiteralForm(_ExtensionsSpecialForm, _root=True):
        def __init__(self, doc: str):
            self._name = 'Literal'
            self._doc = self.__doc__ = doc

def __getitem__(self, parameters):
            if not isinstance(parameters, tuple):
                parameters = (parameters,)

parameters = _flatten_literal_params(parameters)

val_type_pairs = list(_value_and_type_iter(parameters))
            try:
                deduped_pairs = set(val_type_pairs)
            except TypeError:
                # unhashable parameters
                pass
            else:
                # similar logic to typing._deduplicate on Python 3.9+
                if len(deduped_pairs) < len(val_type_pairs):
                    new_parameters = []
                    for pair in val_type_pairs:
                        if pair in deduped_pairs:
                            new_parameters.append(pair[0])
                            deduped_pairs.remove(pair)
                    assert not deduped_pairs, deduped_pairs
                    parameters = tuple(new_parameters)

return _LiteralGenericAlias(self, parameters)

Literal = _LiteralForm(doc="""\
                           A type that can be used to indicate to type checkers
                           that the corresponding value has a value literally equivalent
                           to the provided parameter. For example:

var: Literal[4] = 4

The type checker understands that 'var' is literally equal to
                           the value 4 and no other value.

Literal[...] cannot be subclassed. There is no runtime
                           checking verifying that the parameter is actually a value
                           instead of a type.""")

_overload_dummy = typing._overload_dummy

if hasattr(typing, "get_overloads"):  # 3.11+
    overload = typing.overload
    get_overloads = typing.get_overloads
    clear_overloads = typing.clear_overloads
else:
    # {module: {qualname: {firstlineno: func}}}
    _overload_registry = collections.defaultdict(
        functools.partial(collections.defaultdict, dict)
    )

def overload(func):
        """Decorator for overloaded functions/methods.

In a stub file, place two or more stub definitions for the same
        function in a row, each decorated with @overload.  For example:

@overload
        def utf8(value: None) -> None: ...
        @overload
        def utf8(value: bytes) -> bytes: ...
        @overload
        def utf8(value: str) -> bytes: ...

In a non-stub file (i.e. a regular .py file), do the same but
        follow it with an implementation.  The implementation should *not*
        be decorated with @overload.  For example:

@overload
        def utf8(value: None) -> None: ...
        @overload
        def utf8(value: bytes) -> bytes: ...
        @overload
        def utf8(value: str) -> bytes: ...
        def utf8(value):
            # implementation goes here

The overloads for a function can be retrieved at runtime using the
        get_overloads() function.
        """
        # classmethod and staticmethod
        f = getattr(func, "__func__", func)
        try:
            _overload_registry[f.__module__][f.__qualname__][
                f.__code__.co_firstlineno
            ] = func
        except AttributeError:
            # Not a normal function; ignore.
            pass
        return _overload_dummy

def get_overloads(func):
        """Return all defined overloads for *func* as a sequence."""
        # classmethod and staticmethod
        f = getattr(func, "__func__", func)
        if f.__module__ not in _overload_registry:
            return []
        mod_dict = _overload_registry[f.__module__]
        if f.__qualname__ not in mod_dict:
            return []
        return list(mod_dict[f.__qualname__].values())

def clear_overloads():
        """Clear all overloads in the registry."""
        _overload_registry.clear()

# This is not a real generic class.  Don't use outside annotations.
Type = typing.Type

# Various ABCs mimicking those in collections.abc.
# A few are simply re-exported for completeness.
Awaitable = typing.Awaitable
Coroutine = typing.Coroutine
AsyncIterable = typing.AsyncIterable
AsyncIterator = typing.AsyncIterator
Deque = typing.Deque
ContextManager = typing.ContextManager
AsyncContextManager = typing.AsyncContextManager
DefaultDict = typing.DefaultDict
OrderedDict = typing.OrderedDict
Counter = typing.Counter
ChainMap = typing.ChainMap
AsyncGenerator = typing.AsyncGenerator
Text = typing.Text
TYPE_CHECKING = typing.TYPE_CHECKING

_PROTO_ALLOWLIST = {
    'collections.abc': [
        'Callable', 'Awaitable', 'Iterable', 'Iterator', 'AsyncIterable',
        'Hashable', 'Sized', 'Container', 'Collection', 'Reversible', 'Buffer',
    ],
    'contextlib': ['AbstractContextManager', 'AbstractAsyncContextManager'],
    'typing_extensions': ['Buffer'],
}

_EXCLUDED_ATTRS = {
    "__abstractmethods__", "__annotations__", "__weakref__", "_is_protocol",
    "_is_runtime_protocol", "__dict__", "__slots__", "__parameters__",
    "__orig_bases__", "__module__", "_MutableMapping__marker", "__doc__",
    "__subclasshook__", "__orig_class__", "__init__", "__new__",
    "__protocol_attrs__", "__callable_proto_members_only__",
}

if sys.version_info >= (3, 9):
    _EXCLUDED_ATTRS.add("__class_getitem__")

if sys.version_info >= (3, 12):
    _EXCLUDED_ATTRS.add("__type_params__")

_EXCLUDED_ATTRS = frozenset(_EXCLUDED_ATTRS)

def _get_protocol_attrs(cls):
    attrs = set()
    for base in cls.__mro__[:-1]:  # without object
        if base.__name__ in {'Protocol', 'Generic'}:
            continue
        annotations = getattr(base, '__annotations__', {})
        for attr in (*base.__dict__, *annotations):
            if (not attr.startswith('_abc_') and attr not in _EXCLUDED_ATTRS):
                attrs.add(attr)
    return attrs

def _caller(depth=2):
    try:
        return sys._getframe(depth).f_globals.get('__name__', '__main__')
    except (AttributeError, ValueError):  # For platforms without _getframe()
        return None

# The performance of runtime-checkable protocols is significantly improved on Python 3.12,
# so we backport the 3.12 version of Protocol to Python <=3.11
if sys.version_info >= (3, 12):
    Protocol = typing.Protocol
else:
    def _allow_reckless_class_checks(depth=3):
        """Allow instance and class checks for special stdlib modules.
        The abc and functools modules indiscriminately call isinstance() and
        issubclass() on the whole MRO of a user class, which may contain protocols.
        """
        return _caller(depth) in {'abc', 'functools', None}

def _no_init(self, *args, **kwargs):
        if type(self)._is_protocol:
            raise TypeError('Protocols cannot be instantiated')

# Inheriting from typing._ProtocolMeta isn't actually desirable,
    # but is necessary to allow typing.Protocol and typing_extensions.Protocol
    # to mix without getting TypeErrors about "metaclass conflict"
    class _ProtocolMeta(type(typing.Protocol)):
        # This metaclass is somewhat unfortunate,
        # but is necessary for several reasons...
        #
        # NOTE: DO NOT call super() in any methods in this class
        # That would call the methods on typing._ProtocolMeta on Python 3.8-3.11
        # and those are slow
        def __new__(mcls, name, bases, namespace, **kwargs):
            if name == "Protocol" and len(bases) < 2:
                pass
            elif {Protocol, typing.Protocol} & set(bases):
                for base in bases:
                    if not (
                        base in {object, typing.Generic, Protocol, typing.Protocol}
                        or base.__name__ in _PROTO_ALLOWLIST.get(base.__module__, [])
                        or is_protocol(base)
                    ):
                        raise TypeError(
                            f"Protocols can only inherit from other protocols, "
                            f"got {base!r}"
                        )
            return abc.ABCMeta.__new__(mcls, name, bases, namespace, **kwargs)

def __init__(cls, *args, **kwargs):
            abc.ABCMeta.__init__(cls, *args, **kwargs)
            if getattr(cls, "_is_protocol", False):
                cls.__protocol_attrs__ = _get_protocol_attrs(cls)
                # PEP 544 prohibits using issubclass()
                # with protocols that have non-method members.
                cls.__callable_proto_members_only__ = all(
                    callable(getattr(cls, attr, None)) for attr in cls.__protocol_attrs__
                )

def __subclasscheck__(cls, other):
            if cls is Protocol:
                return type.__subclasscheck__(cls, other)
            if (
                getattr(cls, '_is_protocol', False)
                and not _allow_reckless_class_checks()
            ):
                if not isinstance(other, type):
                    # Same error message as for issubclass(1, int).
                    raise TypeError('issubclass() arg 1 must be a class')
                if (
                    not cls.__callable_proto_members_only__
                    and cls.__dict__.get("__subclasshook__") is _proto_hook
                ):
                    raise TypeError(
                        "Protocols with non-method members don't support issubclass()"
                    )
                if not getattr(cls, '_is_runtime_protocol', False):
                    raise TypeError(
                        "Instance and class checks can only be used with "
                        "@runtime_checkable protocols"
                    )
            return abc.ABCMeta.__subclasscheck__(cls, other)

def __instancecheck__(cls, instance):
            # We need this method for situations where attributes are
            # assigned in __init__.
            if cls is Protocol:
                return type.__instancecheck__(cls, instance)
            if not getattr(cls, "_is_protocol", False):
                # i.e., it's a concrete subclass of a protocol
                return abc.ABCMeta.__instancecheck__(cls, instance)

if (
                not getattr(cls, '_is_runtime_protocol', False) and
                not _allow_reckless_class_checks()
            ):
                raise TypeError("Instance and class checks can only be used with"
                                " @runtime_checkable protocols")

if abc.ABCMeta.__instancecheck__(cls, instance):
                return True

for attr in cls.__protocol_attrs__:
                try:
                    val = inspect.getattr_static(instance, attr)
                except AttributeError:
                    break
                if val is None and callable(getattr(cls, attr, None)):
                    break
            else:
                return True

return False

def __eq__(cls, other):
            # Hack so that typing.Generic.__class_getitem__
            # treats typing_extensions.Protocol
            # as equivalent to typing.Protocol
            if abc.ABCMeta.__eq__(cls, other) is True:
                return True
            return cls is Protocol and other is typing.Protocol

# This has to be defined, or the abc-module cache
        # complains about classes with this metaclass being unhashable,
        # if we define only __eq__!
        def __hash__(cls) -> int:
            return type.__hash__(cls)

@classmethod
    def _proto_hook(cls, other):
        if not cls.__dict__.get('_is_protocol', False):
            return NotImplemented

for attr in cls.__protocol_attrs__:
            for base in other.__mro__:
                # Check if the members appears in the class dictionary...
                if attr in base.__dict__:
                    if base.__dict__[attr] is None:
                        return NotImplemented
                    break

# ...or in annotations, if it is a sub-protocol.
                annotations = getattr(base, '__annotations__', {})
                if (
                    isinstance(annotations, collections.abc.Mapping)
                    and attr in annotations
                    and is_protocol(other)
                ):
                    break
            else:
                return NotImplemented
        return True

class Protocol(typing.Generic, metaclass=_ProtocolMeta):
        __doc__ = typing.Protocol.__doc__
        __slots__ = ()
        _is_protocol = True
        _is_runtime_protocol = False

def __init_subclass__(cls, *args, **kwargs):
            super().__init_subclass__(*args, **kwargs)

# Determine if this is a protocol or a concrete subclass.
            if not cls.__dict__.get('_is_protocol', False):
                cls._is_protocol = any(b is Protocol for b in cls.__bases__)

# Set (or override) the protocol subclass hook.
            if '__subclasshook__' not in cls.__dict__:
                cls.__subclasshook__ = _proto_hook

# Prohibit instantiation for protocol classes
            if cls._is_protocol and cls.__init__ is Protocol.__init__:
                cls.__init__ = _no_init

# The "runtime" alias exists for backwards compatibility.
runtime = runtime_checkable = typing.runtime_checkable

# Our version of runtime-checkable protocols is faster on Python 3.8-3.11
if sys.version_info >= (3, 12):
    SupportsInt = typing.SupportsInt
    SupportsFloat = typing.SupportsFloat
    SupportsComplex = typing.SupportsComplex
    SupportsBytes = typing.SupportsBytes
    SupportsIndex = typing.SupportsIndex
    SupportsAbs = typing.SupportsAbs
    SupportsRound = typing.SupportsRound
else:
    @runtime_checkable
    class SupportsInt(Protocol):
        """An ABC with one abstract method __int__."""
        __slots__ = ()

@abc.abstractmethod
        def __int__(self) -> int:
            pass

@runtime_checkable
    class SupportsFloat(Protocol):
        """An ABC with one abstract method __float__."""
        __slots__ = ()

@abc.abstractmethod
        def __float__(self) -> float:
            pass

@runtime_checkable
    class SupportsComplex(Protocol):
        """An ABC with one abstract method __complex__."""
        __slots__ = ()

@abc.abstractmethod
        def __complex__(self) -> complex:
            pass

@runtime_checkable
    class SupportsBytes(Protocol):
        """An ABC with one abstract method __bytes__."""
        __slots__ = ()

@abc.abstractmethod
        def __bytes__(self) -> bytes:
            pass

@runtime_checkable
    class SupportsIndex(Protocol):
        __slots__ = ()

@abc.abstractmethod
        def __index__(self) -> int:
            pass

@runtime_checkable
    class SupportsAbs(Protocol[T_co]):
        """
        An ABC with one abstract method __abs__ that is covariant in its return type.
        """
        __slots__ = ()

@abc.abstractmethod
        def __abs__(self) -> T_co:
            pass

@runtime_checkable
    class SupportsRound(Protocol[T_co]):
        """
        An ABC with one abstract method __round__ that is covariant in its return type.
        """
        __slots__ = ()

@abc.abstractmethod
        def __round__(self, ndigits: int = 0) -> T_co:
            pass

def _ensure_subclassable(mro_entries):
    def inner(func):
        if sys.implementation.name == "pypy" and sys.version_info < (3, 9):
            cls_dict = {
                "__call__": staticmethod(func),
                "__mro_entries__": staticmethod(mro_entries)
            }
            t = type(func.__name__, (), cls_dict)
            return functools.update_wrapper(t(), func)
        else:
            func.__mro_entries__ = mro_entries
            return func
    return inner

if sys.version_info >= (3, 13):
    # The standard library TypedDict in Python 3.8 does not store runtime information
    # about which (if any) keys are optional.  See https://bugs.python.org/issue38834
    # The standard library TypedDict in Python 3.9.0/1 does not honour the "total"
    # keyword with old-style TypedDict().  See https://bugs.python.org/issue42059
    # The standard library TypedDict below Python 3.11 does not store runtime
    # information about optional and required keys when using Required or NotRequired.
    # Generic TypedDicts are also impossible using typing.TypedDict on Python <3.11.
    # Aaaand on 3.12 we add __orig_bases__ to TypedDict
    # to enable better runtime introspection.
    # On 3.13 we deprecate some odd ways of creating TypedDicts.
    TypedDict = typing.TypedDict
    _TypedDictMeta = typing._TypedDictMeta
    is_typeddict = typing.is_typeddict
else:
    # 3.10.0 and later
    _TAKES_MODULE = "module" in inspect.signature(typing._type_check).parameters

class _TypedDictMeta(type):
        def __new__(cls, name, bases, ns, total=True):
            """Create new typed dict class object.

This method is called when TypedDict is subclassed,
            or when TypedDict is instantiated. This way
            TypedDict supports all three syntax forms described in its docstring.
            Subclasses and instances of TypedDict return actual dictionaries.
            """
            for base in bases:
                if type(base) is not _TypedDictMeta and base is not typing.Generic:
                    raise TypeError('cannot inherit from both a TypedDict type '
                                    'and a non-TypedDict base class')

if any(issubclass(b, typing.Generic) for b in bases):
                generic_base = (typing.Generic,)
            else:
                generic_base = ()

# typing.py generally doesn't let you inherit from plain Generic, unless
            # the name of the class happens to be "Protocol"
            tp_dict = type.__new__(_TypedDictMeta, "Protocol", (*generic_base, dict), ns)
            tp_dict.__name__ = name
            if tp_dict.__qualname__ == "Protocol":
                tp_dict.__qualname__ = name

if not hasattr(tp_dict, '__orig_bases__'):
                tp_dict.__orig_bases__ = bases

annotations = {}
            own_annotations = ns.get('__annotations__', {})
            msg = "TypedDict('Name', {f0: t0, f1: t1, ...}); each t must be a type"
            if _TAKES_MODULE:
                own_annotations = {
                    n: typing._type_check(tp, msg, module=tp_dict.__module__)
                    for n, tp in own_annotations.items()
                }
            else:
                own_annotations = {
                    n: typing._type_check(tp, msg)
                    for n, tp in own_annotations.items()
                }
            required_keys = set()
            optional_keys = set()

for base in bases:
                annotations.update(base.__dict__.get('__annotations__', {}))
                required_keys.update(base.__dict__.get('__required_keys__', ()))
                optional_keys.update(base.__dict__.get('__optional_keys__', ()))

annotations.update(own_annotations)
            for annotation_key, annotation_type in own_annotations.items():
                annotation_origin = get_origin(annotation_type)
                if annotation_origin is Annotated:
                    annotation_args = get_args(annotation_type)
                    if annotation_args:
                        annotation_type = annotation_args[0]
                        annotation_origin = get_origin(annotation_type)

if annotation_origin is Required:
                    required_keys.add(annotation_key)
                elif annotation_origin is NotRequired:
                    optional_keys.add(annotation_key)
                elif total:
                    required_keys.add(annotation_key)
                else:
                    optional_keys.add(annotation_key)

tp_dict.__annotations__ = annotations
            tp_dict.__required_keys__ = frozenset(required_keys)
            tp_dict.__optional_keys__ = frozenset(optional_keys)
            if not hasattr(tp_dict, '__total__'):
                tp_dict.__total__ = total
            return tp_dict

__call__ = dict  # static method

def __subclasscheck__(cls, other):
            # Typed dicts are only for static structural subtyping.
            raise TypeError('TypedDict does not support instance and class checks')

__instancecheck__ = __subclasscheck__

_TypedDict = type.__new__(_TypedDictMeta, 'TypedDict', (), {})

@_ensure_subclassable(lambda bases: (_TypedDict,))
    def TypedDict(typename, fields=_marker, /, *, total=True, **kwargs):
        """A simple typed namespace. At runtime it is equivalent to a plain dict.

TypedDict creates a dictionary type such that a type checker will expect all
        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.

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 can be accessed via the Point2D.__annotations__ dict, and
        the Point2D.__required_keys__ and Point2D.__optional_keys__ frozensets.
        TypedDict supports an additional equivalent form::

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::

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.

The Required and NotRequired special forms can also be used to mark
        individual keys as being required or not required::

class Point2D(TypedDict):
                x: int  # the "x" key must always be present (Required is the default)
                y: NotRequired[int]  # the "y" key can be omitted

See PEP 655 for more details on Required and NotRequired.
        """
        if fields is _marker or fields is None:
            if fields is _marker:
                deprecated_thing = "Failing to pass a value for the 'fields' parameter"
            else:
                deprecated_thing = "Passing `None` as the 'fields' parameter"

example = f"`{typename} = TypedDict({typename!r}, {{}})`"
            deprecation_msg = (
                f"{deprecated_thing} is deprecated and will be disallowed in "
                "Python 3.15. To create a TypedDict class with 0 fields "
                "using the functional syntax, pass an empty dictionary, e.g. "
            ) + example + "."
            warnings.warn(deprecation_msg, DeprecationWarning, stacklevel=2)
            fields = kwargs
        elif kwargs:
            raise TypeError("TypedDict takes either a dict or keyword arguments,"
                            " but not both")
        if kwargs:
            warnings.warn(
                "The kwargs-based syntax for TypedDict definitions is deprecated "
                "in Python 3.11, will be removed in Python 3.13, and may not be "
                "understood by third-party type checkers.",
                DeprecationWarning,
                stacklevel=2,
            )

ns = {'__annotations__': dict(fields)}
        module = _caller()
        if module is not None:
            # Setting correct module is necessary to make typed dict classes pickleable.
            ns['__module__'] = module

td = _TypedDictMeta(typename, (), ns, total=total)
        td.__orig_bases__ = (TypedDict,)
        return td

if hasattr(typing, "_TypedDictMeta"):
        _TYPEDDICT_TYPES = (typing._TypedDictMeta, _TypedDictMeta)
    else:
        _TYPEDDICT_TYPES = (_TypedDictMeta,)

def is_typeddict(tp):
        """Check if an annotation is a TypedDict class

For example::
            class Film(TypedDict):
                title: str
                year: int

is_typeddict(Film)  # => True
            is_typeddict(Union[list, str])  # => False
        """
        # On 3.8, this would otherwise return True
        if hasattr(typing, "TypedDict") and tp is typing.TypedDict:
            return False
        return isinstance(tp, _TYPEDDICT_TYPES)

if hasattr(typing, "assert_type"):
    assert_type = typing.assert_type

else:
    def assert_type(val, typ, /):
        """Assert (to the type checker) that the value is of the given type.

When the type checker encounters a call to assert_type(), it
        emits an error if the value is not of the specified type::

def greet(name: str) -> None:
                assert_type(name, str)  # ok
                assert_type(name, int)  # type checker error

At runtime this returns the first argument unchanged and otherwise
        does nothing.
        """
        return val

if hasattr(typing, "Required"):  # 3.11+
    get_type_hints = typing.get_type_hints
else:  # <=3.10
    # replaces _strip_annotations()
    def _strip_extras(t):
        """Strips Annotated, Required and NotRequired from a given type."""
        if isinstance(t, _AnnotatedAlias):
            return _strip_extras(t.__origin__)
        if hasattr(t, "__origin__") and t.__origin__ in (Required, NotRequired):
            return _strip_extras(t.__args__[0])
        if isinstance(t, typing._GenericAlias):
            stripped_args = tuple(_strip_extras(a) for a in t.__args__)
            if stripped_args == t.__args__:
                return t
            return t.copy_with(stripped_args)
        if hasattr(_types, "GenericAlias") and isinstance(t, _types.GenericAlias):
            stripped_args = tuple(_strip_extras(a) for a in t.__args__)
            if stripped_args == t.__args__:
                return t
            return _types.GenericAlias(t.__origin__, stripped_args)
        if hasattr(_types, "UnionType") and isinstance(t, _types.UnionType):
            stripped_args = tuple(_strip_extras(a) for a in t.__args__)
            if stripped_args == t.__args__:
                return t
            return functools.reduce(operator.or_, stripped_args)

return t

def get_type_hints(obj, globalns=None, localns=None, include_extras=False):
        """Return type hints for an object.

This is often the same as obj.__annotations__, but it handles
        forward references encoded as string literals, adds Optional[t] if a
        default value equal to None is set and recursively replaces all
        'Annotated[T, ...]', 'Required[T]' or 'NotRequired[T]' with 'T'
        (unless 'include_extras=True').

The argument may be a module, class, method, or function. The annotations
        are returned as a dictionary. For classes, annotations include also
        inherited members.

TypeError is raised if the argument is not of a type that can contain
        annotations, and an empty dictionary is returned if no annotations are
        present.

BEWARE -- the behavior of globalns and localns is counterintuitive
        (unless you are familiar with how eval() and exec() work).  The
        search order is locals first, then globals.

- If no dict arguments are passed, an attempt is made to use the
          globals from obj (or the respective module's globals for classes),
          and these are also used as the locals.  If the object does not appear
          to have globals, an empty dictionary is used.

- If one dict argument is passed, it is used for both globals and
          locals.

- If two dict arguments are passed, they specify globals and
          locals, respectively.
        """
        if hasattr(typing, "Annotated"):  # 3.9+
            hint = typing.get_type_hints(
                obj, globalns=globalns, localns=localns, include_extras=True
            )
        else:  # 3.8
            hint = typing.get_type_hints(obj, globalns=globalns, localns=localns)
        if include_extras:
            return hint
        return {k: _strip_extras(t) for k, t in hint.items()}

# Python 3.9+ has PEP 593 (Annotated)
if hasattr(typing, 'Annotated'):
    Annotated = typing.Annotated
    # Not exported and not a public API, but needed for get_origin() and get_args()
    # to work.
    _AnnotatedAlias = typing._AnnotatedAlias
# 3.8
else:
    class _AnnotatedAlias(typing._GenericAlias, _root=True):
        """Runtime representation of an annotated type.

At its core 'Annotated[t, dec1, dec2, ...]' is an alias for the type 't'
        with extra annotations. The alias behaves like a normal typing alias,
        instantiating is the same as instantiating the underlying type, binding
        it to types is also the same.
        """
        def __init__(self, origin, metadata):
            if isinstance(origin, _AnnotatedAlias):
                metadata = origin.__metadata__ + metadata
                origin = origin.__origin__
            super().__init__(origin, origin)
            self.__metadata__ = metadata

def copy_with(self, params):
            assert len(params) == 1
            new_type = params[0]
            return _AnnotatedAlias(new_type, self.__metadata__)

def __repr__(self):
            return (f"typing_extensions.Annotated[{typing._type_repr(self.__origin__)}, "
                    f"{', '.join(repr(a) for a in self.__metadata__)}]")

def __reduce__(self):
            return operator.getitem, (
                Annotated, (self.__origin__,) + self.__metadata__
            )

def __eq__(self, other):
            if not isinstance(other, _AnnotatedAlias):
                return NotImplemented
            if self.__origin__ != other.__origin__:
                return False
            return self.__metadata__ == other.__metadata__

def __hash__(self):
            return hash((self.__origin__, self.__metadata__))

class Annotated:
        """Add context specific metadata to a type.

Example: Annotated[int, runtime_check.Unsigned] indicates to the
        hypothetical runtime_check module that this type is an unsigned int.
        Every other consumer of this type can ignore this metadata and treat
        this type as int.

The first argument to Annotated must be a valid type (and will be in
        the __origin__ field), the remaining arguments are kept as a tuple in
        the __extra__ field.

Details:

- It's an error to call `Annotated` with less than two arguments.
        - Nested Annotated are flattened::

Annotated[Annotated[T, Ann1, Ann2], Ann3] == Annotated[T, Ann1, Ann2, Ann3]

- Instantiating an annotated type is equivalent to instantiating the
        underlying type::

Annotated[C, Ann1](5) == C(5)

- Annotated can be used as a generic type alias::

Optimized = Annotated[T, runtime.Optimize()]
            Optimized[int] == Annotated[int, runtime.Optimize()]

OptimizedList = Annotated[List[T], runtime.Optimize()]
            OptimizedList[int] == Annotated[List[int], runtime.Optimize()]
        """

__slots__ = ()

def __new__(cls, *args, **kwargs):
            raise TypeError("Type Annotated cannot be instantiated.")

@typing._tp_cache
        def __class_getitem__(cls, params):
            if not isinstance(params, tuple) or len(params) < 2:
                raise TypeError("Annotated[...] should be used "
                                "with at least two arguments (a type and an "
                                "annotation).")
            allowed_special_forms = (ClassVar, Final)
            if get_origin(params[0]) in allowed_special_forms:
                origin = params[0]
            else:
                msg = "Annotated[t, ...]: t must be a type."
                origin = typing._type_check(params[0], msg)
            metadata = tuple(params[1:])
            return _AnnotatedAlias(origin, metadata)

def __init_subclass__(cls, *args, **kwargs):
            raise TypeError(
                f"Cannot subclass {cls.__module__}.Annotated"
            )

# Python 3.8 has get_origin() and get_args() but those implementations aren't
# Annotated-aware, so we can't use those. Python 3.9's versions don't support
# ParamSpecArgs and ParamSpecKwargs, so only Python 3.10's versions will do.
if sys.version_info[:2] >= (3, 10):
    get_origin = typing.get_origin
    get_args = typing.get_args
# 3.8-3.9
else:
    try:
        # 3.9+
        from typing import _BaseGenericAlias
    except ImportError:
        _BaseGenericAlias = typing._GenericAlias
    try:
        # 3.9+
        from typing import GenericAlias as _typing_GenericAlias
    except ImportError:
        _typing_GenericAlias = typing._GenericAlias

def get_origin(tp):
        """Get the unsubscripted version of a type.

This supports generic types, Callable, Tuple, Union, Literal, Final, ClassVar
        and Annotated. Return None for unsupported types. Examples::

get_origin(Literal[42]) is Literal
            get_origin(int) is None
            get_origin(ClassVar[int]) is ClassVar
            get_origin(Generic) is Generic
            get_origin(Generic[T]) is Generic
            get_origin(Union[T, int]) is Union
            get_origin(List[Tuple[T, T]][int]) == list
            get_origin(P.args) is P
        """
        if isinstance(tp, _AnnotatedAlias):
            return Annotated
        if isinstance(tp, (typing._GenericAlias, _typing_GenericAlias, _BaseGenericAlias,
                           ParamSpecArgs, ParamSpecKwargs)):
            return tp.__origin__
        if tp is typing.Generic:
            return typing.Generic
        return None

def get_args(tp):
        """Get type arguments with all substitutions performed.

For unions, basic simplifications used by Union constructor are performed.
        Examples::
            get_args(Dict[str, int]) == (str, int)
            get_args(int) == ()
            get_args(Union[int, Union[T, int], str][int]) == (int, str)
            get_args(Union[int, Tuple[T, int]][str]) == (int, Tuple[str, int])
            get_args(Callable[[], T][int]) == ([], int)
        """
        if isinstance(tp, _AnnotatedAlias):
            return (tp.__origin__,) + tp.__metadata__
        if isinstance(tp, (typing._GenericAlias, _typing_GenericAlias)):
            if getattr(tp, "_special", False):
                return ()
            res = tp.__args__
            if get_origin(tp) is collections.abc.Callable and res[0] is not Ellipsis:
                res = (list(res[:-1]), res[-1])
            return res
        return ()

# 3.10+
if hasattr(typing, 'TypeAlias'):
    TypeAlias = typing.TypeAlias
# 3.9
elif sys.version_info[:2] >= (3, 9):
    @_ExtensionsSpecialForm
    def TypeAlias(self, parameters):
        """Special marker indicating that an assignment should
        be recognized as a proper type alias definition by type
        checkers.

For example::

Predicate: TypeAlias = Callable[..., bool]

It's invalid when used anywhere except as in the example above.
        """
        raise TypeError(f"{self} is not subscriptable")
# 3.8
else:
    TypeAlias = _ExtensionsSpecialForm(
        'TypeAlias',
        doc="""Special marker indicating that an assignment should
        be recognized as a proper type alias definition by type
        checkers.

For example::

Predicate: TypeAlias = Callable[..., bool]

It's invalid when used anywhere except as in the example
        above."""
    )

def _set_default(type_param, default):
    if isinstance(default, (tuple, list)):
        type_param.__default__ = tuple((typing._type_check(d, "Default must be a type")
                                        for d in default))
    elif default != _marker:
        if isinstance(type_param, ParamSpec) and default is ...:  # ... not valid <3.11
            type_param.__default__ = default
        else:
            type_param.__default__ = typing._type_check(default, "Default must be a type")
    else:
        type_param.__default__ = None

def _set_module(typevarlike):
    # for pickling:
    def_mod = _caller(depth=3)
    if def_mod != 'typing_extensions':
        typevarlike.__module__ = def_mod

class _DefaultMixin:
    """Mixin for TypeVarLike defaults."""

__slots__ = ()
    __init__ = _set_default

# Classes using this metaclass must provide a _backported_typevarlike ClassVar
class _TypeVarLikeMeta(type):
    def __instancecheck__(cls, __instance: Any) -> bool:
        return isinstance(__instance, cls._backported_typevarlike)

# Add default and infer_variance parameters from PEP 696 and 695
class TypeVar(metaclass=_TypeVarLikeMeta):
    """Type variable."""

_backported_typevarlike = typing.TypeVar

def __new__(cls, name, *constraints, bound=None,
                covariant=False, contravariant=False,
                default=_marker, infer_variance=False):
        if hasattr(typing, "TypeAliasType"):
            # PEP 695 implemented (3.12+), can pass infer_variance to typing.TypeVar
            typevar = typing.TypeVar(name, *constraints, bound=bound,
                                     covariant=covariant, contravariant=contravariant,
                                     infer_variance=infer_variance)
        else:
            typevar = typing.TypeVar(name, *constraints, bound=bound,
                                     covariant=covariant, contravariant=contravariant)
            if infer_variance and (covariant or contravariant):
                raise ValueError("Variance cannot be specified with infer_variance.")
            typevar.__infer_variance__ = infer_variance
        _set_default(typevar, default)
        _set_module(typevar)
        return typevar

def __init_subclass__(cls) -> None:
        raise TypeError(f"type '{__name__}.TypeVar' is not an acceptable base type")

# Python 3.10+ has PEP 612
if hasattr(typing, 'ParamSpecArgs'):
    ParamSpecArgs = typing.ParamSpecArgs
    ParamSpecKwargs = typing.ParamSpecKwargs
# 3.8-3.9
else:
    class _Immutable:
        """Mixin to indicate that object should not be copied."""
        __slots__ = ()

def __copy__(self):
            return self

def __deepcopy__(self, memo):
            return self

class ParamSpecArgs(_Immutable):
        """The args for a ParamSpec object.

Given a ParamSpec object P, P.args is an instance of ParamSpecArgs.

ParamSpecArgs objects have a reference back to their ParamSpec:

P.args.__origin__ is P

This type is meant for runtime introspection and has no special meaning to
        static type checkers.
        """
        def __init__(self, origin):
            self.__origin__ = origin

def __repr__(self):
            return f"{self.__origin__.__name__}.args"

def __eq__(self, other):
            if not isinstance(other, ParamSpecArgs):
                return NotImplemented
            return self.__origin__ == other.__origin__

class ParamSpecKwargs(_Immutable):
        """The kwargs for a ParamSpec object.

Given a ParamSpec object P, P.kwargs is an instance of ParamSpecKwargs.

ParamSpecKwargs objects have a reference back to their ParamSpec:

P.kwargs.__origin__ is P

This type is meant for runtime introspection and has no special meaning to
        static type checkers.
        """
        def __init__(self, origin):
            self.__origin__ = origin

def __repr__(self):
            return f"{self.__origin__.__name__}.kwargs"

def __eq__(self, other):
            if not isinstance(other, ParamSpecKwargs):
                return NotImplemented
            return self.__origin__ == other.__origin__

# 3.10+
if hasattr(typing, 'ParamSpec'):

# Add default parameter - PEP 696
    class ParamSpec(metaclass=_TypeVarLikeMeta):
        """Parameter specification."""

_backported_typevarlike = typing.ParamSpec

def __new__(cls, name, *, bound=None,
                    covariant=False, contravariant=False,
                    infer_variance=False, default=_marker):
            if hasattr(typing, "TypeAliasType"):
                # PEP 695 implemented, can pass infer_variance to typing.TypeVar
                paramspec = typing.ParamSpec(name, bound=bound,
                                             covariant=covariant,
                                             contravariant=contravariant,
                                             infer_variance=infer_variance)
            else:
                paramspec = typing.ParamSpec(name, bound=bound,
                                             covariant=covariant,
                                             contravariant=contravariant)
                paramspec.__infer_variance__ = infer_variance

_set_default(paramspec, default)
            _set_module(paramspec)
            return paramspec

def __init_subclass__(cls) -> None:
            raise TypeError(f"type '{__name__}.ParamSpec' is not an acceptable base type")

# 3.8-3.9
else:

# Inherits from list as a workaround for Callable checks in Python < 3.9.2.
    class ParamSpec(list, _DefaultMixin):
        """Parameter specification variable.

Usage::

P = ParamSpec('P')

Parameter specification variables exist primarily for the benefit of static
        type checkers.  They are used to forward the parameter types of one
        callable to another callable, a pattern commonly found in higher order
        functions and decorators.  They are only valid when used in ``Concatenate``,
        or s the first argument to ``Callable``. In Python 3.10 and higher,
        they are also supported in user-defined Generics at runtime.
        See class Generic for more information on generic types.  An
        example for annotating a decorator::

T = TypeVar('T')
           P = ParamSpec('P')

def add_logging(f: Callable[P, T]) -> Callable[P, T]:
               '''A type-safe decorator to add logging to a function.'''
               def inner(*args: P.args, **kwargs: P.kwargs) -> T:
                   logging.info(f'{f.__name__} was called')
                   return f(*args, **kwargs)
               return inner

@add_logging
           def add_two(x: float, y: float) -> float:
               '''Add two numbers together.'''
               return x + y

Parameter specification variables defined with covariant=True or
        contravariant=True can be used to declare covariant or contravariant
        generic types.  These keyword arguments are valid, but their actual semantics
        are yet to be decided.  See PEP 612 for details.

Parameter specification variables can be introspected. e.g.:

P.__name__ == 'T'
           P.__bound__ == None
           P.__covariant__ == False
           P.__contravariant__ == False

Note that only parameter specification variables defined in global scope can
        be pickled.
        """

# Trick Generic __parameters__.
        __class__ = typing.TypeVar

@property
        def args(self):
            return ParamSpecArgs(self)

@property
        def kwargs(self):
            return ParamSpecKwargs(self)

def __init__(self, name, *, bound=None, covariant=False, contravariant=False,
                     infer_variance=False, default=_marker):
            super().__init__([self])
            self.__name__ = name
            self.__covariant__ = bool(covariant)
            self.__contravariant__ = bool(contravariant)
            self.__infer_variance__ = bool(infer_variance)
            if bound:
                self.__bound__ = typing._type_check(bound, 'Bound must be a type.')
            else:
                self.__bound__ = None
            _DefaultMixin.__init__(self, default)

# for pickling:
            def_mod = _caller()
            if def_mod != 'typing_extensions':
                self.__module__ = def_mod

def __repr__(self):
            if self.__infer_variance__:
                prefix = ''
            elif self.__covariant__:
                prefix = '+'
            elif self.__contravariant__:
                prefix = '-'
            else:
                prefix = '~'
            return prefix + self.__name__

def __hash__(self):
            return object.__hash__(self)

def __eq__(self, other):
            return self is other

def __reduce__(self):
            return self.__name__

# Hack to get typing._type_check to pass.
        def __call__(self, *args, **kwargs):
            pass

# 3.8-3.9
if not hasattr(typing, 'Concatenate'):
    # Inherits from list as a workaround for Callable checks in Python < 3.9.2.
    class _ConcatenateGenericAlias(list):

# Trick Generic into looking into this for __parameters__.
        __class__ = typing._GenericAlias

# Flag in 3.8.
        _special = False

def __init__(self, origin, args):
            super().__init__(args)
            self.__origin__ = origin
            self.__args__ = args

def __repr__(self):
            _type_repr = typing._type_repr
            return (f'{_type_repr(self.__origin__)}'
                    f'[{", ".join(_type_repr(arg) for arg in self.__args__)}]')

def __hash__(self):
            return hash((self.__origin__, self.__args__))

# Hack to get typing._type_check to pass in Generic.
        def __call__(self, *args, **kwargs):
            pass

@property
        def __parameters__(self):
            return tuple(
                tp for tp in self.__args__ if isinstance(tp, (typing.TypeVar, ParamSpec))
            )

# 3.8-3.9
@typing._tp_cache
def _concatenate_getitem(self, parameters):
    if parameters == ():
        raise TypeError("Cannot take a Concatenate of no types.")
    if not isinstance(parameters, tuple):
        parameters = (parameters,)
    if not isinstance(parameters[-1], ParamSpec):
        raise TypeError("The last parameter to Concatenate should be a "
                        "ParamSpec variable.")
    msg = "Concatenate[arg, ...]: each arg must be a type."
    parameters = tuple(typing._type_check(p, msg) for p in parameters)
    return _ConcatenateGenericAlias(self, parameters)

# 3.10+
if hasattr(typing, 'Concatenate'):
    Concatenate = typing.Concatenate
    _ConcatenateGenericAlias = typing._ConcatenateGenericAlias  # noqa: F811
# 3.9
elif sys.version_info[:2] >= (3, 9):
    @_ExtensionsSpecialForm
    def Concatenate(self, parameters):
        """Used in conjunction with ``ParamSpec`` and ``Callable`` to represent a
        higher order function which adds, removes or transforms parameters of a
        callable.

For example::

Callable[Concatenate[int, P], int]

See PEP 612 for detailed information.
        """
        return _concatenate_getitem(self, parameters)
# 3.8
else:
    class _ConcatenateForm(_ExtensionsSpecialForm, _root=True):
        def __getitem__(self, parameters):
            return _concatenate_getitem(self, parameters)

Concatenate = _ConcatenateForm(
        'Concatenate',
        doc="""Used in conjunction with ``ParamSpec`` and ``Callable`` to represent a
        higher order function which adds, removes or transforms parameters of a
        callable.

For example::

Callable[Concatenate[int, P], int]

See PEP 612 for detailed information.
        """)

# 3.10+
if hasattr(typing, 'TypeGuard'):
    TypeGuard = typing.TypeGuard
# 3.9
elif sys.version_info[:2] >= (3, 9):
    @_ExtensionsSpecialForm
    def TypeGuard(self, parameters):
        """Special typing form used to annotate the return type of a user-defined
        type guard function.  ``TypeGuard`` only accepts a single type argument.
        At runtime, functions marked this way should return a boolean.

``TypeGuard`` aims to benefit *type narrowing* -- a technique used by static
        type checkers to determine a more precise type of an expression within a
        program's code flow.  Usually type narrowing is done by analyzing
        conditional code flow and applying the narrowing to a block of code.  The
        conditional expression here is sometimes referred to as a "type guard".

Sometimes it would be convenient to use a user-defined boolean function
        as a type guard.  Such a function should use ``TypeGuard[...]`` as its
        return type to alert static type checkers to this intention.

Using  ``-> TypeGuard`` tells the static type checker that for a given
        function:

1. The return value is a boolean.
        2. If the return value is ``True``, the type of its argument
        is the type inside ``TypeGuard``.

For example::

def is_str(val: Union[str, float]):
                # "isinstance" type guard
                if isinstance(val, str):
                    # Type of ``val`` is narrowed to ``str``
                    ...
                else:
                    # Else, type of ``val`` is narrowed to ``float``.
                    ...

Strict type narrowing is not enforced -- ``TypeB`` need not be a narrower
        form of ``TypeA`` (it can even be a wider form) and this may lead to
        type-unsafe results.  The main reason is to allow for things like
        narrowing ``List[object]`` to ``List[str]`` even though the latter is not
        a subtype of the former, since ``List`` is invariant.  The responsibility of
        writing type-safe type guards is left to the user.

``TypeGuard`` also works with type variables.  For more information, see
        PEP 647 (User-Defined Type Guards).
        """
        item = typing._type_check(parameters, f'{self} accepts only a single type.')
        return typing._GenericAlias(self, (item,))
# 3.8
else:
    class _TypeGuardForm(_ExtensionsSpecialForm, _root=True):
        def __getitem__(self, parameters):
            item = typing._type_check(parameters,
                                      f'{self._name} accepts only a single type')
            return typing._GenericAlias(self, (item,))

TypeGuard = _TypeGuardForm(
        'TypeGuard',
        doc="""Special typing form used to annotate the return type of a user-defined
        type guard function.  ``TypeGuard`` only accepts a single type argument.
        At runtime, functions marked this way should return a boolean.

Using  ``-> TypeGuard`` tells the static type checker that for a given
        function:

1. The return value is a boolean.
        2. If the return value is ``True``, the type of its argument
        is the type inside ``TypeGuard``.

For example::

``TypeGuard`` also works with type variables.  For more information, see
        PEP 647 (User-Defined Type Guards).
        """)

# Vendored from cpython typing._SpecialFrom
class _SpecialForm(typing._Final, _root=True):
    __slots__ = ('_name', '__doc__', '_getitem')

def __init__(self, getitem):
        self._getitem = getitem
        self._name = getitem.__name__
        self.__doc__ = getitem.__doc__

def __getattr__(self, item):
        if item in {'__name__', '__qualname__'}:
            return self._name

raise AttributeError(item)

def __mro_entries__(self, bases):
        raise TypeError(f"Cannot subclass {self!r}")

def __repr__(self):
        return f'typing_extensions.{self._name}'

def __reduce__(self):
        return self._name

def __call__(self, *args, **kwds):
        raise TypeError(f"Cannot instantiate {self!r}")

def __or__(self, other):
        return typing.Union[self, other]

def __ror__(self, other):
        return typing.Union[other, self]

def __instancecheck__(self, obj):
        raise TypeError(f"{self} cannot be used with isinstance()")

def __subclasscheck__(self, cls):
        raise TypeError(f"{self} cannot be used with issubclass()")

@typing._tp_cache
    def __getitem__(self, parameters):
        return self._getitem(self, parameters)

if hasattr(typing, "LiteralString"):  # 3.11+
    LiteralString = typing.LiteralString
else:
    @_SpecialForm
    def LiteralString(self, params):
        """Represents an arbitrary literal string.

Example::

from typing_extensions import LiteralString

def query(sql: LiteralString) -> ...:
              ...

query("SELECT * FROM table")  # ok
          query(f"SELECT * FROM {input()}")  # not ok

See PEP 675 for details.

"""
        raise TypeError(f"{self} is not subscriptable")

if hasattr(typing, "Self"):  # 3.11+
    Self = typing.Self
else:
    @_SpecialForm
    def Self(self, params):
        """Used to spell the type of "self" in classes.

Example::

from typing import Self

class ReturnsSelf:
              def parse(self, data: bytes) -> Self:
                  ...
                  return self

"""

raise TypeError(f"{self} is not subscriptable")

if hasattr(typing, "Never"):  # 3.11+
    Never = typing.Never
else:
    @_SpecialForm
    def Never(self, params):
        """The bottom type, a type that has no members.

This can be used to define a function that should never be
        called, or a function that never returns::

from typing_extensions import Never

def never_call_me(arg: Never) -> None:
                pass

def int_or_str(arg: int | str) -> None:
                never_call_me(arg)  # type checker error
                match arg:
                    case int():
                        print("It's an int")
                    case str():
                        print("It's a str")
                    case _:
                        never_call_me(arg)  # ok, arg is of type Never

"""

raise TypeError(f"{self} is not subscriptable")

if hasattr(typing, 'Required'):  # 3.11+
    Required = typing.Required
    NotRequired = typing.NotRequired
elif sys.version_info[:2] >= (3, 9):  # 3.9-3.10
    @_ExtensionsSpecialForm
    def Required(self, parameters):
        """A special typing construct to mark a key of a total=False TypedDict
        as required. For example:

class Movie(TypedDict, total=False):
                title: Required[str]
                year: int

m = Movie(
                title='The Matrix',  # typechecker error if key is omitted
                year=1999,
            )

There is no runtime checking that a required key is actually provided
        when instantiating a related TypedDict.
        """
        item = typing._type_check(parameters, f'{self._name} accepts only a single type.')
        return typing._GenericAlias(self, (item,))

@_ExtensionsSpecialForm
    def NotRequired(self, parameters):
        """A special typing construct to mark a key of a TypedDict as
        potentially missing. For example:

class Movie(TypedDict):
                title: str
                year: NotRequired[int]

m = Movie(
                title='The Matrix',  # typechecker error if key is omitted
                year=1999,
            )
        """
        item = typing._type_check(parameters, f'{self._name} accepts only a single type.')
        return typing._GenericAlias(self, (item,))

else:  # 3.8
    class _RequiredForm(_ExtensionsSpecialForm, _root=True):
        def __getitem__(self, parameters):
            item = typing._type_check(parameters,
                                      f'{self._name} accepts only a single type.')
            return typing._GenericAlias(self, (item,))

Required = _RequiredForm(
        'Required',
        doc="""A special typing construct to mark a key of a total=False TypedDict
        as required. For example:

class Movie(TypedDict, total=False):
                title: Required[str]
                year: int

m = Movie(
                title='The Matrix',  # typechecker error if key is omitted
                year=1999,
            )

There is no runtime checking that a required key is actually provided
        when instantiating a related TypedDict.
        """)
    NotRequired = _RequiredForm(
        'NotRequired',
        doc="""A special typing construct to mark a key of a TypedDict as
        potentially missing. For example:

class Movie(TypedDict):
                title: str
                year: NotRequired[int]

m = Movie(
                title='The Matrix',  # typechecker error if key is omitted
                year=1999,
            )
        """)

_UNPACK_DOC = """\
Type unpack operator.

The type unpack operator takes the child types from some container type,
such as `tuple[int, str]` or a `TypeVarTuple`, and 'pulls them out'. For
example:

# For some generic class `Foo`:
  Foo[Unpack[tuple[int, str]]]  # Equivalent to Foo[int, str]

Ts = TypeVarTuple('Ts')
  # Specifies that `Bar` is generic in an arbitrary number of types.
  # (Think of `Ts` as a tuple of an arbitrary number of individual
  #  `TypeVar`s, which the `Unpack` is 'pulling out' directly into the
  #  `Generic[]`.)
  class Bar(Generic[Unpack[Ts]]): ...
  Bar[int]  # Valid
  Bar[int, str]  # Also valid

From Python 3.11, this can also be done using the `*` operator:

Foo[*tuple[int, str]]
    class Bar(Generic[*Ts]): ...

The operator can also be used along with a `TypedDict` to annotate
`**kwargs` in a function signature. For instance:

class Movie(TypedDict):
    name: str
    year: int

# This function expects two keyword arguments - *name* of type `str` and
  # *year* of type `int`.
  def foo(**kwargs: Unpack[Movie]): ...

Note that there is only some runtime checking of this operator. Not
everything the runtime allows may be accepted by static type checkers.

For more information, see PEP 646 and PEP 692.
"""

if sys.version_info >= (3, 12):  # PEP 692 changed the repr of Unpack[]
    Unpack = typing.Unpack

def _is_unpack(obj):
        return get_origin(obj) is Unpack

elif sys.version_info[:2] >= (3, 9):  # 3.9+
    class _UnpackSpecialForm(_ExtensionsSpecialForm, _root=True):
        def __init__(self, getitem):
            super().__init__(getitem)
            self.__doc__ = _UNPACK_DOC

class _UnpackAlias(typing._GenericAlias, _root=True):
        __class__ = typing.TypeVar

@_UnpackSpecialForm
    def Unpack(self, parameters):
        item = typing._type_check(parameters, f'{self._name} accepts only a single type.')
        return _UnpackAlias(self, (item,))

def _is_unpack(obj):
        return isinstance(obj, _UnpackAlias)

else:  # 3.8
    class _UnpackAlias(typing._GenericAlias, _root=True):
        __class__ = typing.TypeVar

class _UnpackForm(_ExtensionsSpecialForm, _root=True):
        def __getitem__(self, parameters):
            item = typing._type_check(parameters,
                                      f'{self._name} accepts only a single type.')
            return _UnpackAlias(self, (item,))

Unpack = _UnpackForm('Unpack', doc=_UNPACK_DOC)

def _is_unpack(obj):
        return isinstance(obj, _UnpackAlias)

if hasattr(typing, "TypeVarTuple"):  # 3.11+

# Add default parameter - PEP 696
    class TypeVarTuple(metaclass=_TypeVarLikeMeta):
        """Type variable tuple."""

_backported_typevarlike = typing.TypeVarTuple

def __new__(cls, name, *, default=_marker):
            tvt = typing.TypeVarTuple(name)
            _set_default(tvt, default)
            _set_module(tvt)
            return tvt

def __init_subclass__(self, *args, **kwds):
            raise TypeError("Cannot subclass special typing classes")

else:  # <=3.10
    class TypeVarTuple(_DefaultMixin):
        """Type variable tuple.

Usage::

Ts = TypeVarTuple('Ts')

In the same way that a normal type variable is a stand-in for a single
        type such as ``int``, a type variable *tuple* is a stand-in for a *tuple*
        type such as ``Tuple[int, str]``.

Type variable tuples can be used in ``Generic`` declarations.
        Consider the following example::

class Array(Generic[*Ts]): ...

The ``Ts`` type variable tuple here behaves like ``tuple[T1, T2]``,
        where ``T1`` and ``T2`` are type variables. To use these type variables
        as type parameters of ``Array``, we must *unpack* the type variable tuple using
        the star operator: ``*Ts``. The signature of ``Array`` then behaves
        as if we had simply written ``class Array(Generic[T1, T2]): ...``.
        In contrast to ``Generic[T1, T2]``, however, ``Generic[*Shape]`` allows
        us to parameterise the class with an *arbitrary* number of type parameters.

Type variable tuples can be used anywhere a normal ``TypeVar`` can.
        This includes class definitions, as shown above, as well as function
        signatures and variable annotations::

class Array(Generic[*Ts]):

def __init__(self, shape: Tuple[*Ts]):
                    self._shape: Tuple[*Ts] = shape

def get_shape(self) -> Tuple[*Ts]:
                    return self._shape

shape = (Height(480), Width(640))
            x: Array[Height, Width] = Array(shape)
            y = abs(x)  # Inferred type is Array[Height, Width]
            z = x + x   #        ...    is Array[Height, Width]
            x.get_shape()  #     ...    is tuple[Height, Width]

"""

# Trick Generic __parameters__.
        __class__ = typing.TypeVar

def __iter__(self):
            yield self.__unpacked__

def __init__(self, name, *, default=_marker):
            self.__name__ = name
            _DefaultMixin.__init__(self, default)

# for pickling:
            def_mod = _caller()
            if def_mod != 'typing_extensions':
                self.__module__ = def_mod

self.__unpacked__ = Unpack[self]

def __repr__(self):
            return self.__name__

def __hash__(self):
            return object.__hash__(self)

def __eq__(self, other):
            return self is other

def __reduce__(self):
            return self.__name__

def __init_subclass__(self, *args, **kwds):
            if '_root' not in kwds:
                raise TypeError("Cannot subclass special typing classes")

if hasattr(typing, "reveal_type"):  # 3.11+
    reveal_type = typing.reveal_type
else:  # <=3.10
    def reveal_type(obj: T, /) -> T:
        """Reveal the inferred type of a variable.

When a static type checker encounters a call to ``reveal_type()``,
        it will emit the inferred type of the argument::

x: int = 1
            reveal_type(x)

Running a static type checker (e.g., ``mypy``) on this example
        will produce output similar to 'Revealed type is "builtins.int"'.

At runtime, the function prints the runtime type of the
        argument and returns it unchanged.

"""
        print(f"Runtime type is {type(obj).__name__!r}", file=sys.stderr)
        return obj

if hasattr(typing, "assert_never"):  # 3.11+
    assert_never = typing.assert_never
else:  # <=3.10
    def assert_never(arg: Never, /) -> Never:
        """Assert to the type checker that a line of code is unreachable.

Example::

def int_or_str(arg: int | str) -> None:
                match arg:
                    case int():
                        print("It's an int")
                    case str():
                        print("It's a str")
                    case _:
                        assert_never(arg)

If a type checker finds that a call to assert_never() is
        reachable, it will emit an error.

At runtime, this throws an exception when called.

"""
        raise AssertionError("Expected code to be unreachable")

if sys.version_info >= (3, 12):  # 3.12+
    # dataclass_transform exists in 3.11 but lacks the frozen_default parameter
    dataclass_transform = typing.dataclass_transform
else:  # <=3.11
    def dataclass_transform(
        *,
        eq_default: bool = True,
        order_default: bool = False,
        kw_only_default: bool = False,
        frozen_default: bool = False,
        field_specifiers: typing.Tuple[
            typing.Union[typing.Type[typing.Any], typing.Callable[..., typing.Any]],
            ...
        ] = (),
        **kwargs: typing.Any,
    ) -> typing.Callable[[T], T]:
        """Decorator that marks a function, class, or metaclass as providing
        dataclass-like behavior.

Example:

from typing_extensions import dataclass_transform

_T = TypeVar("_T")

# Used on a decorator function
            @dataclass_transform()
            def create_model(cls: type[_T]) -> type[_T]:
                ...
                return cls

@create_model
            class CustomerModel:
                id: int
                name: str

# Used on a base class
            @dataclass_transform()
            class ModelBase: ...

class CustomerModel(ModelBase):
                id: int
                name: str

# Used on a metaclass
            @dataclass_transform()
            class ModelMeta(type): ...

class ModelBase(metaclass=ModelMeta): ...

class CustomerModel(ModelBase):
                id: int
                name: str

Each of the ``CustomerModel`` classes defined in this example will now
        behave similarly to a dataclass created with the ``@dataclasses.dataclass``
        decorator. For example, the type checker will synthesize an ``__init__``
        method.

The arguments to this decorator can be used to customize this behavior:
        - ``eq_default`` indicates whether the ``eq`` parameter is assumed to be
          True or False if it is omitted by the caller.
        - ``order_default`` indicates whether the ``order`` parameter is
          assumed to be True or False if it is omitted by the caller.
        - ``kw_only_default`` indicates whether the ``kw_only`` parameter is
          assumed to be True or False if it is omitted by the caller.
        - ``frozen_default`` indicates whether the ``frozen`` parameter is
          assumed to be True or False if it is omitted by the caller.
        - ``field_specifiers`` specifies a static list of supported classes
          or functions that describe fields, similar to ``dataclasses.field()``.

At runtime, this decorator records its arguments in the
        ``__dataclass_transform__`` attribute on the decorated object.

See PEP 681 for details.

"""
        def decorator(cls_or_fn):
            cls_or_fn.__dataclass_transform__ = {
                "eq_default": eq_default,
                "order_default": order_default,
                "kw_only_default": kw_only_default,
                "frozen_default": frozen_default,
                "field_specifiers": field_specifiers,
                "kwargs": kwargs,
            }
            return cls_or_fn
        return decorator

if hasattr(typing, "override"):  # 3.12+
    override = typing.override
else:  # <=3.11
    _F = typing.TypeVar("_F", bound=typing.Callable[..., typing.Any])

def override(arg: _F, /) -> _F:
        """Indicate that a method is intended to override a method in a base class.

Usage:

class Base:
                def method(self) -> None: ...
                    pass

class Child(Base):
                @override
                def method(self) -> None:
                    super().method()

When this decorator is applied to a method, the type checker will
        validate that it overrides a method with the same name on a base class.
        This helps prevent bugs that may occur when a base class is changed
        without an equivalent change to a child class.

There is no runtime checking of these properties. The decorator
        sets the ``__override__`` attribute to ``True`` on the decorated object
        to allow runtime introspection.

See PEP 698 for details.

"""
        try:
            arg.__override__ = True
        except (AttributeError, TypeError):
            # Skip the attribute silently if it is not writable.
            # AttributeError happens if the object has __slots__ or a
            # read-only property, TypeError if it's a builtin class.
            pass
        return arg

if hasattr(typing, "deprecated"):
    deprecated = typing.deprecated
else:
    _T = typing.TypeVar("_T")

def deprecated(
        msg: str,
        /,
        *,
        category: typing.Optional[typing.Type[Warning]] = DeprecationWarning,
        stacklevel: int = 1,
    ) -> typing.Callable[[_T], _T]:
        """Indicate that a class, function or overload is deprecated.

Usage:

@deprecated("Use B instead")
            class A:
                pass

@deprecated("Use g instead")
            def f():
                pass

@overload
            @deprecated("int support is deprecated")
            def g(x: int) -> int: ...
            @overload
            def g(x: str) -> int: ...

When this decorator is applied to an object, the type checker
        will generate a diagnostic on usage of the deprecated object.

The warning specified by ``category`` will be emitted on use
        of deprecated objects. For functions, that happens on calls;
        for classes, on instantiation. If the ``category`` is ``None``,
        no warning is emitted. The ``stacklevel`` determines where the
        warning is emitted. If it is ``1`` (the default), the warning
        is emitted at the direct caller of the deprecated object; if it
        is higher, it is emitted further up the stack.

The decorator sets the ``__deprecated__``
        attribute on the decorated object to the deprecation message
        passed to the decorator. If applied to an overload, the decorator
        must be after the ``@overload`` decorator for the attribute to
        exist on the overload as returned by ``get_overloads()``.

See PEP 702 for details.

"""
        def decorator(arg: _T, /) -> _T:
            if category is None:
                arg.__deprecated__ = msg
                return arg
            elif isinstance(arg, type):
                original_new = arg.__new__
                has_init = arg.__init__ is not object.__init__

@functools.wraps(original_new)
                def __new__(cls, *args, **kwargs):
                    warnings.warn(msg, category=category, stacklevel=stacklevel + 1)
                    if original_new is not object.__new__:
                        return original_new(cls, *args, **kwargs)
                    # Mirrors a similar check in object.__new__.
                    elif not has_init and (args or kwargs):
                        raise TypeError(f"{cls.__name__}() takes no arguments")
                    else:
                        return original_new(cls)

arg.__new__ = staticmethod(__new__)
                arg.__deprecated__ = __new__.__deprecated__ = msg
                return arg
            elif callable(arg):
                @functools.wraps(arg)
                def wrapper(*args, **kwargs):
                    warnings.warn(msg, category=category, stacklevel=stacklevel + 1)
                    return arg(*args, **kwargs)

arg.__deprecated__ = wrapper.__deprecated__ = msg
                return wrapper
            else:
                raise TypeError(
                    "@deprecated decorator with non-None category must be applied to "
                    f"a class or callable, not {arg!r}"
                )

return decorator

# We have to do some monkey patching to deal with the dual nature of
# Unpack/TypeVarTuple:
# - We want Unpack to be a kind of TypeVar so it gets accepted in
#   Generic[Unpack[Ts]]
# - We want it to *not* be treated as a TypeVar for the purposes of
#   counting generic parameters, so that when we subscript a generic,
#   the runtime doesn't try to substitute the Unpack with the subscripted type.
if not hasattr(typing, "TypeVarTuple"):
    typing._collect_type_vars = _collect_type_vars
    typing._check_generic = _check_generic

# Backport typing.NamedTuple as it exists in Python 3.13.
# In 3.11, the ability to define generic `NamedTuple`s was supported.
# This was explicitly disallowed in 3.9-3.10, and only half-worked in <=3.8.
# On 3.12, we added __orig_bases__ to call-based NamedTuples
# On 3.13, we deprecated kwargs-based NamedTuples
if sys.version_info >= (3, 13):
    NamedTuple = typing.NamedTuple
else:
    def _make_nmtuple(name, types, module, defaults=()):
        fields = [n for n, t in types]
        annotations = {n: typing._type_check(t, f"field {n} annotation must be a type")
                       for n, t in types}
        nm_tpl = collections.namedtuple(name, fields,
                                        defaults=defaults, module=module)
        nm_tpl.__annotations__ = nm_tpl.__new__.__annotations__ = annotations
        # The `_field_types` attribute was removed in 3.9;
        # in earlier versions, it is the same as the `__annotations__` attribute
        if sys.version_info < (3, 9):
            nm_tpl._field_types = annotations
        return nm_tpl

_prohibited_namedtuple_fields = typing._prohibited
    _special_namedtuple_fields = frozenset({'__module__', '__name__', '__annotations__'})

class _NamedTupleMeta(type):
        def __new__(cls, typename, bases, ns):
            assert _NamedTuple in bases
            for base in bases:
                if base is not _NamedTuple and base is not typing.Generic:
                    raise TypeError(
                        'can only inherit from a NamedTuple type and Generic')
            bases = tuple(tuple if base is _NamedTuple else base for base in bases)
            types = ns.get('__annotations__', {})
            default_names = []
            for field_name in types:
                if field_name in ns:
                    default_names.append(field_name)
                elif default_names:
                    raise TypeError(f"Non-default namedtuple field {field_name} "
                                    f"cannot follow default field"
                                    f"{'s' if len(default_names) > 1 else ''} "
                                    f"{', '.join(default_names)}")
            nm_tpl = _make_nmtuple(
                typename, types.items(),
                defaults=[ns[n] for n in default_names],
                module=ns['__module__']
            )
            nm_tpl.__bases__ = bases
            if typing.Generic in bases:
                if hasattr(typing, '_generic_class_getitem'):  # 3.12+
                    nm_tpl.__class_getitem__ = classmethod(typing._generic_class_getitem)
                else:
                    class_getitem = typing.Generic.__class_getitem__.__func__
                    nm_tpl.__class_getitem__ = classmethod(class_getitem)
            # update from user namespace without overriding special namedtuple attributes
            for key in ns:
                if key in _prohibited_namedtuple_fields:
                    raise AttributeError("Cannot overwrite NamedTuple attribute " + key)
                elif key not in _special_namedtuple_fields and key not in nm_tpl._fields:
                    setattr(nm_tpl, key, ns[key])
            if typing.Generic in bases:
                nm_tpl.__init_subclass__()
            return nm_tpl

_NamedTuple = type.__new__(_NamedTupleMeta, 'NamedTuple', (), {})

def _namedtuple_mro_entries(bases):
        assert NamedTuple in bases
        return (_NamedTuple,)

@_ensure_subclassable(_namedtuple_mro_entries)
    def NamedTuple(typename, fields=_marker, /, **kwargs):
        """Typed version of namedtuple.

Usage::

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

This is equivalent to::

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

The resulting class has an extra __annotations__ attribute, giving a
        dict that maps field names to types.  (The field names are also in
        the _fields attribute, which is part of the namedtuple API.)
        An alternative equivalent functional syntax is also accepted::

Employee = NamedTuple('Employee', [('name', str), ('id', int)])
        """
        if fields is _marker:
            if kwargs:
                deprecated_thing = "Creating NamedTuple classes using keyword arguments"
                deprecation_msg = (
                    "{name} is deprecated and will be disallowed in Python {remove}. "
                    "Use the class-based or functional syntax instead."
                )
            else:
                deprecated_thing = "Failing to pass a value for the 'fields' parameter"
                example = f"`{typename} = NamedTuple({typename!r}, [])`"
                deprecation_msg = (
                    "{name} is deprecated and will be disallowed in Python {remove}. "
                    "To create a NamedTuple class with 0 fields "
                    "using the functional syntax, "
                    "pass an empty list, e.g. "
                ) + example + "."
        elif fields is None:
            if kwargs:
                raise TypeError(
                    "Cannot pass `None` as the 'fields' parameter "
                    "and also specify fields using keyword arguments"
                )
            else:
                deprecated_thing = "Passing `None` as the 'fields' parameter"
                example = f"`{typename} = NamedTuple({typename!r}, [])`"
                deprecation_msg = (
                    "{name} is deprecated and will be disallowed in Python {remove}. "
                    "To create a NamedTuple class with 0 fields "
                    "using the functional syntax, "
                    "pass an empty list, e.g. "
                ) + example + "."
        elif kwargs:
            raise TypeError("Either list of fields or keywords"
                            " can be provided to NamedTuple, not both")
        if fields is _marker or fields is None:
            warnings.warn(
                deprecation_msg.format(name=deprecated_thing, remove="3.15"),
                DeprecationWarning,
                stacklevel=2,
            )
            fields = kwargs.items()
        nt = _make_nmtuple(typename, fields, module=_caller())
        nt.__orig_bases__ = (NamedTuple,)
        return nt

if hasattr(collections.abc, "Buffer"):
    Buffer = collections.abc.Buffer
else:
    class Buffer(abc.ABC):
        """Base class for classes that implement the buffer protocol.

The buffer protocol allows Python objects to expose a low-level
        memory buffer interface. Before Python 3.12, it is not possible
        to implement the buffer protocol in pure Python code, or even
        to check whether a class implements the buffer protocol. In
        Python 3.12 and higher, the ``__buffer__`` method allows access
        to the buffer protocol from Python code, and the
        ``collections.abc.Buffer`` ABC allows checking whether a class
        implements the buffer protocol.

To indicate support for the buffer protocol in earlier versions,
        inherit from this ABC, either in a stub file or at runtime,
        or use ABC registration. This ABC provides no methods, because
        there is no Python-accessible methods shared by pre-3.12 buffer
        classes. It is useful primarily for static checks.

"""

# As a courtesy, register the most common stdlib buffer classes.
    Buffer.register(memoryview)
    Buffer.register(bytearray)
    Buffer.register(bytes)

# Backport of types.get_original_bases, available on 3.12+ in CPython
if hasattr(_types, "get_original_bases"):
    get_original_bases = _types.get_original_bases
else:
    def get_original_bases(cls, /):
        """Return the class's "original" bases prior to modification by `__mro_entries__`.

Examples::

from typing import TypeVar, Generic
            from typing_extensions import NamedTuple, TypedDict

T = TypeVar("T")
            class Foo(Generic[T]): ...
            class Bar(Foo[int], float): ...
            class Baz(list[str]): ...
            Eggs = NamedTuple("Eggs", [("a", int), ("b", str)])
            Spam = TypedDict("Spam", {"a": int, "b": str})

assert get_original_bases(Bar) == (Foo[int], float)
            assert get_original_bases(Baz) == (list[str],)
            assert get_original_bases(Eggs) == (NamedTuple,)
            assert get_original_bases(Spam) == (TypedDict,)
            assert get_original_bases(int) == (object,)
        """
        try:
            return cls.__dict__.get("__orig_bases__", cls.__bases__)
        except AttributeError:
            raise TypeError(
                f'Expected an instance of type, not {type(cls).__name__!r}'
            ) from None

# NewType is a class on Python 3.10+, making it pickleable
# The error message for subclassing instances of NewType was improved on 3.11+
if sys.version_info >= (3, 11):
    NewType = typing.NewType
else:
    class NewType:
        """NewType creates simple unique types with almost zero
        runtime overhead. NewType(name, tp) is considered a subtype of tp
        by static type checkers. At runtime, NewType(name, tp) returns
        a dummy callable that simply returns its argument. Usage::
            UserId = NewType('UserId', int)
            def name_by_id(user_id: UserId) -> str:
                ...
            UserId('user')          # Fails type check
            name_by_id(42)          # Fails type check
            name_by_id(UserId(42))  # OK
            num = UserId(5) + 1     # type: int
        """

def __call__(self, obj):
            return obj

def __init__(self, name, tp):
            self.__qualname__ = name
            if '.' in name:
                name = name.rpartition('.')[-1]
            self.__name__ = name
            self.__supertype__ = tp
            def_mod = _caller()
            if def_mod != 'typing_extensions':
                self.__module__ = def_mod

def __mro_entries__(self, bases):
            # We defined __mro_entries__ to get a better error message
            # if a user attempts to subclass a NewType instance. bpo-46170
            supercls_name = self.__name__

class Dummy:
                def __init_subclass__(cls):
                    subcls_name = cls.__name__
                    raise TypeError(
                        f"Cannot subclass an instance of NewType. "
                        f"Perhaps you were looking for: "
                        f"`{subcls_name} = NewType({subcls_name!r}, {supercls_name})`"
                    )

return (Dummy,)

def __repr__(self):
            return f'{self.__module__}.{self.__qualname__}'

def __reduce__(self):
            return self.__qualname__

if sys.version_info >= (3, 10):
            # PEP 604 methods
            # It doesn't make sense to have these methods on Python <3.10

def __or__(self, other):
                return typing.Union[self, other]

def __ror__(self, other):
                return typing.Union[other, self]

if hasattr(typing, "TypeAliasType"):
    TypeAliasType = typing.TypeAliasType
else:
    def _is_unionable(obj):
        """Corresponds to is_unionable() in unionobject.c in CPython."""
        return obj is None or isinstance(obj, (
            type,
            _types.GenericAlias,
            _types.UnionType,
            TypeAliasType,
        ))

class TypeAliasType:
        """Create named, parameterized type aliases.

This provides a backport of the new `type` statement in Python 3.12:

type ListOrSet[T] = list[T] | set[T]

is equivalent to:

T = TypeVar("T")
            ListOrSet = TypeAliasType("ListOrSet", list[T] | set[T], type_params=(T,))

The name ListOrSet can then be used as an alias for the type it refers to.

The type_params argument should contain all the type parameters used
        in the value of the type alias. If the alias is not generic, this
        argument is omitted.

Static type checkers should only support type aliases declared using
        TypeAliasType that follow these rules:

- The first argument (the name) must be a string literal.
        - The TypeAliasType instance must be immediately assigned to a variable
          of the same name. (For example, 'X = TypeAliasType("Y", int)' is invalid,
          as is 'X, Y = TypeAliasType("X", int), TypeAliasType("Y", int)').

"""

def __init__(self, name: str, value, *, type_params=()):
            if not isinstance(name, str):
                raise TypeError("TypeAliasType name must be a string")
            self.__value__ = value
            self.__type_params__ = type_params

parameters = []
            for type_param in type_params:
                if isinstance(type_param, TypeVarTuple):
                    parameters.extend(type_param)
                else:
                    parameters.append(type_param)
            self.__parameters__ = tuple(parameters)
            def_mod = _caller()
            if def_mod != 'typing_extensions':
                self.__module__ = def_mod
            # Setting this attribute closes the TypeAliasType from further modification
            self.__name__ = name

def __setattr__(self, name: str, value: object, /) -> None:
            if hasattr(self, "__name__"):
                self._raise_attribute_error(name)
            super().__setattr__(name, value)

def __delattr__(self, name: str, /) -> Never:
            self._raise_attribute_error(name)

def _raise_attribute_error(self, name: str) -> Never:
            # Match the Python 3.12 error messages exactly
            if name == "__name__":
                raise AttributeError("readonly attribute")
            elif name in {"__value__", "__type_params__", "__parameters__", "__module__"}:
                raise AttributeError(
                    f"attribute '{name}' of 'typing.TypeAliasType' objects "
                    "is not writable"
                )
            else:
                raise AttributeError(
                    f"'typing.TypeAliasType' object has no attribute '{name}'"
                )

def __repr__(self) -> str:
            return self.__name__

def __getitem__(self, parameters):
            if not isinstance(parameters, tuple):
                parameters = (parameters,)
            parameters = [
                typing._type_check(
                    item, f'Subscripting {self.__name__} requires a type.'
                )
                for item in parameters
            ]
            return typing._GenericAlias(self, tuple(parameters))

def __reduce__(self):
            return self.__name__

def __init_subclass__(cls, *args, **kwargs):
            raise TypeError(
                "type 'typing_extensions.TypeAliasType' is not an acceptable base type"
            )

# The presence of this method convinces typing._type_check
        # that TypeAliasTypes are types.
        def __call__(self):
            raise TypeError("Type alias is not callable")

if sys.version_info >= (3, 10):
            def __or__(self, right):
                # For forward compatibility with 3.12, reject Unions
                # that are not accepted by the built-in Union.
                if not _is_unionable(right):
                    return NotImplemented
                return typing.Union[self, right]

def __ror__(self, left):
                if not _is_unionable(left):
                    return NotImplemented
                return typing.Union[left, self]

if hasattr(typing, "is_protocol"):
    is_protocol = typing.is_protocol
    get_protocol_members = typing.get_protocol_members
else:
    def is_protocol(tp: type, /) -> bool:
        """Return True if the given type is a Protocol.

Example::

>>> from typing_extensions import Protocol, is_protocol
            >>> class P(Protocol):
            ...     def a(self) -> str: ...
            ...     b: int
            >>> is_protocol(P)
            True
            >>> is_protocol(int)
            False
        """
        return (
            isinstance(tp, type)
            and getattr(tp, '_is_protocol', False)
            and tp is not Protocol
            and tp is not typing.Protocol
        )

def get_protocol_members(tp: type, /) -> typing.FrozenSet[str]:
        """Return the set of members defined in a Protocol.

Example::

>>> from typing_extensions import Protocol, get_protocol_members
            >>> class P(Protocol):
            ...     def a(self) -> str: ...
            ...     b: int
            >>> get_protocol_members(P)
            frozenset({'a', 'b'})

Raise a TypeError for arguments that are not Protocols.
        """
        if not is_protocol(tp):
            raise TypeError(f'{tp!r} is not a Protocol')
        if hasattr(tp, '__protocol_attrs__'):
            return frozenset(tp.__protocol_attrs__)
        return frozenset(_get_protocol_attrs(tp))

if hasattr(typing, "Doc"):
    Doc = typing.Doc
else:
    class Doc:
        """Define the documentation of a type annotation using ``Annotated``, to be
         used in class attributes, function and method parameters, return values,
         and variables.

The value should be a positional-only string literal to allow static tools
        like editors and documentation generators to use it.

This complements docstrings.

The string value passed is available in the attribute ``documentation``.

Example::

>>> from typing_extensions import Annotated, Doc
            >>> def hi(to: Annotated[str, Doc("Who to say hi to")]) -> None: ...
        """
        def __init__(self, documentation: str, /) -> None:
            self.documentation = documentation

def __repr__(self) -> str:
            return f"Doc({self.documentation!r})"

def __hash__(self) -> int:
            return hash(self.documentation)

def __eq__(self, other: object) -> bool:
            if not isinstance(other, Doc):
                return NotImplemented
            return self.documentation == other.documentation

# Aliases for items that have always been in typing.
# Explicitly assign these (rather than using `from typing import *` at the top),
# so that we get a CI error if one of these is deleted from typing.py
# in a future version of Python
AbstractSet = typing.AbstractSet
AnyStr = typing.AnyStr
BinaryIO = typing.BinaryIO
Callable = typing.Callable
Collection = typing.Collection
Container = typing.Container
Dict = typing.Dict
ForwardRef = typing.ForwardRef
FrozenSet = typing.FrozenSet
Generator = typing.Generator
Generic = typing.Generic
Hashable = typing.Hashable
IO = typing.IO
ItemsView = typing.ItemsView
Iterable = typing.Iterable
Iterator = typing.Iterator
KeysView = typing.KeysView
List = typing.List
Mapping = typing.Mapping
MappingView = typing.MappingView
Match = typing.Match
MutableMapping = typing.MutableMapping
MutableSequence = typing.MutableSequence
MutableSet = typing.MutableSet
Optional = typing.Optional
Pattern = typing.Pattern
Reversible = typing.Reversible
Sequence = typing.Sequence
Set = typing.Set
Sized = typing.Sized
TextIO = typing.TextIO
Tuple = typing.Tuple
Union = typing.Union
ValuesView = typing.ValuesView
cast = typing.cast
no_type_check = typing.no_type_check
no_type_check_decorator = typing.no_type_check_decorator

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