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Traktor/myenv/Lib/site-packages/typing_extensions.py
Pmik13 4090c9f502 dodanie neuralnetwork
2024-05-23 01:57:24 +02:00

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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',
'TypeIs',
'TYPE_CHECKING',
'Never',
'NoReturn',
'ReadOnly',
'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()
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
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__", "__non_callable_proto_members__",
"__match_args__",
}
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
# `__match_args__` attribute was removed from protocol members in 3.13,
# we want to backport this change to older Python versions.
if sys.version_info >= (3, 13):
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')
def _type_check_issubclass_arg_1(arg):
"""Raise TypeError if `arg` is not an instance of `type`
in `issubclass(arg, <protocol>)`.
In most cases, this is verified by type.__subclasscheck__.
Checking it again unnecessarily would slow down issubclass() checks,
so, we don't perform this check unless we absolutely have to.
For various error paths, however,
we want to ensure that *this* error message is shown to the user
where relevant, rather than a typing.py-specific error message.
"""
if not isinstance(arg, type):
# Same error message as for issubclass(1, int).
raise TypeError('issubclass() arg 1 must be a class')
# 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)
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 getattr(cls, '_is_runtime_protocol', False):
_type_check_issubclass_arg_1(other)
raise TypeError(
"Instance and class checks can only be used with "
"@runtime_checkable protocols"
)
if (
# this attribute is set by @runtime_checkable:
cls.__non_callable_proto_members__
and cls.__dict__.get("__subclasshook__") is _proto_hook
):
_type_check_issubclass_arg_1(other)
non_method_attrs = sorted(cls.__non_callable_proto_members__)
raise TypeError(
"Protocols with non-method members don't support issubclass()."
f" Non-method members: {str(non_method_attrs)[1:-1]}."
)
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
# this attribute is set by @runtime_checkable:
if val is None and attr not in cls.__non_callable_proto_members__:
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
if sys.version_info >= (3, 13):
runtime_checkable = typing.runtime_checkable
else:
def runtime_checkable(cls):
"""Mark a protocol class as a runtime protocol.
Such protocol can be used with isinstance() and issubclass().
Raise TypeError if applied to a non-protocol class.
This allows a simple-minded structural check very similar to
one trick ponies in collections.abc such as Iterable.
For example::
@runtime_checkable
class Closable(Protocol):
def close(self): ...
assert isinstance(open('/some/file'), Closable)
Warning: this will check only the presence of the required methods,
not their type signatures!
"""
if not issubclass(cls, typing.Generic) or not getattr(cls, '_is_protocol', False):
raise TypeError('@runtime_checkable can be only applied to protocol classes,'
' got %r' % cls)
cls._is_runtime_protocol = True
# Only execute the following block if it's a typing_extensions.Protocol class.
# typing.Protocol classes don't need it.
if isinstance(cls, _ProtocolMeta):
# PEP 544 prohibits using issubclass()
# with protocols that have non-method members.
# See gh-113320 for why we compute this attribute here,
# rather than in `_ProtocolMeta.__init__`
cls.__non_callable_proto_members__ = set()
for attr in cls.__protocol_attrs__:
try:
is_callable = callable(getattr(cls, attr, None))
except Exception as e:
raise TypeError(
f"Failed to determine whether protocol member {attr!r} "
"is a method member"
) from e
else:
if not is_callable:
cls.__non_callable_proto_members__.add(attr)
return cls
# The "runtime" alias exists for backwards compatibility.
runtime = 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
# Update this to something like >=3.13.0b1 if and when
# PEP 728 is implemented in CPython
_PEP_728_IMPLEMENTED = False
if _PEP_728_IMPLEMENTED:
# 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.
# Also on 3.13, PEP 705 adds the ReadOnly[] qualifier.
# PEP 728 (still pending) makes more changes.
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
def _get_typeddict_qualifiers(annotation_type):
while True:
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]
else:
break
elif annotation_origin is Required:
yield Required
annotation_type, = get_args(annotation_type)
elif annotation_origin is NotRequired:
yield NotRequired
annotation_type, = get_args(annotation_type)
elif annotation_origin is ReadOnly:
yield ReadOnly
annotation_type, = get_args(annotation_type)
else:
break
class _TypedDictMeta(type):
def __new__(cls, name, bases, ns, *, total=True, closed=False):
"""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()
readonly_keys = set()
mutable_keys = set()
extra_items_type = None
for base in bases:
base_dict = base.__dict__
annotations.update(base_dict.get('__annotations__', {}))
required_keys.update(base_dict.get('__required_keys__', ()))
optional_keys.update(base_dict.get('__optional_keys__', ()))
readonly_keys.update(base_dict.get('__readonly_keys__', ()))
mutable_keys.update(base_dict.get('__mutable_keys__', ()))
base_extra_items_type = base_dict.get('__extra_items__', None)
if base_extra_items_type is not None:
extra_items_type = base_extra_items_type
if closed and extra_items_type is None:
extra_items_type = Never
if closed and "__extra_items__" in own_annotations:
annotation_type = own_annotations.pop("__extra_items__")
qualifiers = set(_get_typeddict_qualifiers(annotation_type))
if Required in qualifiers:
raise TypeError(
"Special key __extra_items__ does not support "
"Required"
)
if NotRequired in qualifiers:
raise TypeError(
"Special key __extra_items__ does not support "
"NotRequired"
)
extra_items_type = annotation_type
annotations.update(own_annotations)
for annotation_key, annotation_type in own_annotations.items():
qualifiers = set(_get_typeddict_qualifiers(annotation_type))
if Required in qualifiers:
required_keys.add(annotation_key)
elif NotRequired in qualifiers:
optional_keys.add(annotation_key)
elif total:
required_keys.add(annotation_key)
else:
optional_keys.add(annotation_key)
if ReadOnly in qualifiers:
mutable_keys.discard(annotation_key)
readonly_keys.add(annotation_key)
else:
mutable_keys.add(annotation_key)
readonly_keys.discard(annotation_key)
tp_dict.__annotations__ = annotations
tp_dict.__required_keys__ = frozenset(required_keys)
tp_dict.__optional_keys__ = frozenset(optional_keys)
tp_dict.__readonly_keys__ = frozenset(readonly_keys)
tp_dict.__mutable_keys__ = frozenset(mutable_keys)
if not hasattr(tp_dict, '__total__'):
tp_dict.__total__ = total
tp_dict.__closed__ = closed
tp_dict.__extra_items__ = extra_items_type
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, closed=False, **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)
if closed is not False and closed is not True:
kwargs["closed"] = closed
closed = False
fields = kwargs
elif kwargs:
raise TypeError("TypedDict takes either a dict or keyword arguments,"
" but not both")
if kwargs:
if sys.version_info >= (3, 13):
raise TypeError("TypedDict takes no keyword arguments")
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, closed=closed)
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, "ReadOnly"): # 3.13+
get_type_hints = typing.get_type_hints
else: # <=3.13
# 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, ReadOnly):
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).
""")
# 3.13+
if hasattr(typing, 'TypeIs'):
TypeIs = typing.TypeIs
# 3.9
elif sys.version_info[:2] >= (3, 9):
@_ExtensionsSpecialForm
def TypeIs(self, parameters):
"""Special typing form used to annotate the return type of a user-defined
type narrower function. ``TypeIs`` only accepts a single type argument.
At runtime, functions marked this way should return a boolean.
``TypeIs`` 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 ``TypeIs[...]`` as its
return type to alert static type checkers to this intention.
Using ``-> TypeIs`` 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 intersection of the type inside ``TypeGuard`` and the argument's
previously known type.
For example::
def is_awaitable(val: object) -> TypeIs[Awaitable[Any]]:
return hasattr(val, '__await__')
def f(val: Union[int, Awaitable[int]]) -> int:
if is_awaitable(val):
assert_type(val, Awaitable[int])
else:
assert_type(val, int)
``TypeIs`` also works with type variables. For more information, see
PEP 742 (Narrowing types with TypeIs).
"""
item = typing._type_check(parameters, f'{self} accepts only a single type.')
return typing._GenericAlias(self, (item,))
# 3.8
else:
class _TypeIsForm(_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,))
TypeIs = _TypeIsForm(
'TypeIs',
doc="""Special typing form used to annotate the return type of a user-defined
type narrower function. ``TypeIs`` only accepts a single type argument.
At runtime, functions marked this way should return a boolean.
``TypeIs`` 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 ``TypeIs[...]`` as its
return type to alert static type checkers to this intention.
Using ``-> TypeIs`` 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 intersection of the type inside ``TypeGuard`` and the argument's
previously known type.
For example::
def is_awaitable(val: object) -> TypeIs[Awaitable[Any]]:
return hasattr(val, '__await__')
def f(val: Union[int, Awaitable[int]]) -> int:
if is_awaitable(val):
assert_type(val, Awaitable[int])
else:
assert_type(val, int)
``TypeIs`` also works with type variables. For more information, see
PEP 742 (Narrowing types with TypeIs).
""")
# 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,
)
""")
if hasattr(typing, 'ReadOnly'):
ReadOnly = typing.ReadOnly
elif sys.version_info[:2] >= (3, 9): # 3.9-3.12
@_ExtensionsSpecialForm
def ReadOnly(self, parameters):
"""A special typing construct to mark an item of a TypedDict as read-only.
For example:
class Movie(TypedDict):
title: ReadOnly[str]
year: int
def mutate_movie(m: Movie) -> None:
m["year"] = 1992 # allowed
m["title"] = "The Matrix" # typechecker error
There is no runtime checking for this property.
"""
item = typing._type_check(parameters, f'{self._name} accepts only a single type.')
return typing._GenericAlias(self, (item,))
else: # 3.8
class _ReadOnlyForm(_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,))
ReadOnly = _ReadOnlyForm(
'ReadOnly',
doc="""A special typing construct to mark a key of a TypedDict as read-only.
For example:
class Movie(TypedDict):
title: ReadOnly[str]
year: int
def mutate_movie(m: Movie) -> None:
m["year"] = 1992 # allowed
m["title"] = "The Matrix" # typechecker error
There is no runtime checking for this propery.
""")
_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(warnings, "deprecated"):
deprecated = warnings.deprecated
else:
_T = typing.TypeVar("_T")
class deprecated:
"""Indicate that a class, function or overload is deprecated.
When this decorator is applied to an object, the type checker
will generate a diagnostic on usage of the deprecated object.
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: ...
The warning specified by *category* will be emitted at runtime
on use of deprecated objects. For functions, that happens on calls;
for classes, on instantiation and on creation of subclasses.
If the *category* is ``None``, no warning is emitted at runtime.
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.
Static type checker behavior is not affected by the *category*
and *stacklevel* arguments.
The deprecation message passed to the decorator is saved in the
``__deprecated__`` attribute on the decorated object.
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 __init__(
self,
message: str,
/,
*,
category: typing.Optional[typing.Type[Warning]] = DeprecationWarning,
stacklevel: int = 1,
) -> None:
if not isinstance(message, str):
raise TypeError(
"Expected an object of type str for 'message', not "
f"{type(message).__name__!r}"
)
self.message = message
self.category = category
self.stacklevel = stacklevel
def __call__(self, arg: _T, /) -> _T:
# Make sure the inner functions created below don't
# retain a reference to self.
msg = self.message
category = self.category
stacklevel = self.stacklevel
if category is None:
arg.__deprecated__ = msg
return arg
elif isinstance(arg, type):
import functools
from types import MethodType
original_new = arg.__new__
@functools.wraps(original_new)
def __new__(cls, *args, **kwargs):
if cls is arg:
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 cls.__init__ is object.__init__ and (args or kwargs):
raise TypeError(f"{cls.__name__}() takes no arguments")
else:
return original_new(cls)
arg.__new__ = staticmethod(__new__)
original_init_subclass = arg.__init_subclass__
# We need slightly different behavior if __init_subclass__
# is a bound method (likely if it was implemented in Python)
if isinstance(original_init_subclass, MethodType):
original_init_subclass = original_init_subclass.__func__
@functools.wraps(original_init_subclass)
def __init_subclass__(*args, **kwargs):
warnings.warn(msg, category=category, stacklevel=stacklevel + 1)
return original_init_subclass(*args, **kwargs)
arg.__init_subclass__ = classmethod(__init_subclass__)
# Or otherwise, which likely means it's a builtin such as
# object's implementation of __init_subclass__.
else:
@functools.wraps(original_init_subclass)
def __init_subclass__(*args, **kwargs):
warnings.warn(msg, category=category, stacklevel=stacklevel + 1)
return original_init_subclass(*args, **kwargs)
arg.__init_subclass__ = __init_subclass__
arg.__deprecated__ = __new__.__deprecated__ = msg
__init_subclass__.__deprecated__ = msg
return arg
elif callable(arg):
import functools
@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}"
)
# 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"):
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:
expect_val = 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
# deal with TypeVarLike defaults
# required TypeVarLikes cannot appear after a defaulted one.
if alen < elen:
# since we validate TypeVarLike default in _collect_type_vars
# or _collect_parameters we can safely check parameters[alen]
if getattr(parameters[alen], '__default__', None) is not None:
return
num_default_tv = sum(getattr(p, '__default__', None)
is not None for p in parameters)
elen -= num_default_tv
expect_val = f"at least {elen}"
things = "arguments" if sys.version_info >= (3, 10) else "parameters"
raise TypeError(f"Too {'many' if alen > elen else 'few'} {things}"
f" for {cls}; actual {alen}, expected {expect_val}")
else:
# Python 3.11+
def _check_generic(cls, parameters, elen):
"""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")
alen = len(parameters)
if alen != elen:
expect_val = elen
if hasattr(cls, "__parameters__"):
parameters = [p for p in cls.__parameters__ if not _is_unpack(p)]
# deal with TypeVarLike defaults
# required TypeVarLikes cannot appear after a defaulted one.
if alen < elen:
# since we validate TypeVarLike default in _collect_type_vars
# or _collect_parameters we can safely check parameters[alen]
if getattr(parameters[alen], '__default__', None) is not None:
return
num_default_tv = sum(getattr(p, '__default__', None)
is not None for p in parameters)
elen -= num_default_tv
expect_val = f"at least {elen}"
raise TypeError(f"Too {'many' if alen > elen else 'few'} arguments"
f" for {cls}; actual {alen}, expected {expect_val}")
typing._check_generic = _check_generic
# Python 3.11+ _collect_type_vars was renamed to _collect_parameters
if hasattr(typing, '_collect_type_vars'):
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 = []
# required TypeVarLike cannot appear after TypeVarLike with default
default_encountered = False
for t in types:
if (
isinstance(t, typevar_types) and
t not in tvars and
not _is_unpack(t)
):
if getattr(t, '__default__', None) is not None:
default_encountered = True
elif default_encountered:
raise TypeError(f'Type parameter {t!r} without a default'
' follows type parameter with a default')
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)
typing._collect_type_vars = _collect_type_vars
else:
def _collect_parameters(args):
"""Collect all type variables and parameter specifications in args
in order of first appearance (lexicographic order).
For example::
assert _collect_parameters((T, Callable[P, T])) == (T, P)
"""
parameters = []
# required TypeVarLike cannot appear after TypeVarLike with default
default_encountered = False
for t in args:
if isinstance(t, type):
# We don't want __parameters__ descriptor of a bare Python class.
pass
elif isinstance(t, tuple):
# `t` might be a tuple, when `ParamSpec` is substituted with
# `[T, int]`, or `[int, *Ts]`, etc.
for x in t:
for collected in _collect_parameters([x]):
if collected not in parameters:
parameters.append(collected)
elif hasattr(t, '__typing_subst__'):
if t not in parameters:
if getattr(t, '__default__', None) is not None:
default_encountered = True
elif default_encountered:
raise TypeError(f'Type parameter {t!r} without a default'
' follows type parameter with a default')
parameters.append(t)
else:
for x in getattr(t, '__parameters__', ()):
if x not in parameters:
parameters.append(x)
return tuple(parameters)
typing._collect_parameters = _collect_parameters
# 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, val in ns.items():
if key in _prohibited_namedtuple_fields:
raise AttributeError("Cannot overwrite NamedTuple attribute " + key)
elif key not in _special_namedtuple_fields:
if key not in nm_tpl._fields:
setattr(nm_tpl, key, ns[key])
try:
set_name = type(val).__set_name__
except AttributeError:
pass
else:
try:
set_name(val, nm_tpl, key)
except BaseException as e:
msg = (
f"Error calling __set_name__ on {type(val).__name__!r} "
f"instance {key!r} in {typename!r}"
)
# BaseException.add_note() existed on py311,
# but the __set_name__ machinery didn't start
# using add_note() until py312.
# Making sure exceptions are raised in the same way
# as in "normal" classes seems most important here.
if sys.version_info >= (3, 12):
e.add_note(msg)
raise
else:
raise RuntimeError(msg) from e
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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