AlkantarClanX12
Your IP : 3.137.180.62
import abc
import collections
import collections.abc
import functools
import operator
import sys
import types as _types
import typing
# Please keep __all__ alphabetized within each category.
__all__ = [
# Super-special typing primitives.
'ClassVar',
'Concatenate',
'Final',
'LiteralString',
'ParamSpec',
'ParamSpecArgs',
'ParamSpecKwargs',
'Self',
'Type',
'TypeVarTuple',
'Unpack',
# ABCs (from collections.abc).
'Awaitable',
'AsyncIterator',
'AsyncIterable',
'Coroutine',
'AsyncGenerator',
'AsyncContextManager',
'ChainMap',
# Concrete collection types.
'ContextManager',
'Counter',
'Deque',
'DefaultDict',
'NamedTuple',
'OrderedDict',
'TypedDict',
# Structural checks, a.k.a. protocols.
'SupportsIndex',
# One-off things.
'Annotated',
'assert_never',
'assert_type',
'clear_overloads',
'dataclass_transform',
'get_overloads',
'final',
'get_args',
'get_origin',
'get_type_hints',
'IntVar',
'is_typeddict',
'Literal',
'NewType',
'overload',
'Protocol',
'reveal_type',
'runtime',
'runtime_checkable',
'Text',
'TypeAlias',
'TypeGuard',
'TYPE_CHECKING',
'Never',
'NoReturn',
'Required',
'NotRequired',
]
# 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.
_marker = object()
def _check_generic(cls, parameters, elen=_marker):
"""Check correct count for parameters of a generic cls (internal helper).
This gives a nice error message in case of count mismatch.
"""
if not elen:
raise TypeError(f"{cls} is not a generic class")
if elen is _marker:
if not hasattr(cls, "__parameters__") or not cls.__parameters__:
raise TypeError(f"{cls} is not a generic class")
elen = len(cls.__parameters__)
alen = len(parameters)
if alen != elen:
if hasattr(cls, "__parameters__"):
parameters = [p for p in cls.__parameters__ if not _is_unpack(p)]
num_tv_tuples = sum(isinstance(p, TypeVarTuple) for p in parameters)
if (num_tv_tuples > 0) and (alen >= elen - num_tv_tuples):
return
raise TypeError(f"Too {'many' if alen > elen else 'few'} parameters for {cls};"
f" actual {alen}, expected {elen}")
if sys.version_info >= (3, 10):
def _should_collect_from_parameters(t):
return isinstance(
t, (typing._GenericAlias, _types.GenericAlias, _types.UnionType)
)
elif sys.version_info >= (3, 9):
def _should_collect_from_parameters(t):
return isinstance(t, (typing._GenericAlias, _types.GenericAlias))
else:
def _should_collect_from_parameters(t):
return isinstance(t, typing._GenericAlias) and not t._special
def _collect_type_vars(types, typevar_types=None):
"""Collect all type variable contained in types in order of
first appearance (lexicographic order). For example::
_collect_type_vars((T, List[S, T])) == (T, S)
"""
if typevar_types is None:
typevar_types = typing.TypeVar
tvars = []
for t in types:
if (
isinstance(t, typevar_types) and
t not in tvars and
not _is_unpack(t)
):
tvars.append(t)
if _should_collect_from_parameters(t):
tvars.extend([t for t in t.__parameters__ if t not in tvars])
return tuple(tvars)
NoReturn = typing.NoReturn
# Some unconstrained type variables. These are used by the container types.
# (These are not for export.)
T = typing.TypeVar('T') # Any type.
KT = typing.TypeVar('KT') # Key type.
VT = typing.TypeVar('VT') # Value type.
T_co = typing.TypeVar('T_co', covariant=True) # Any type covariant containers.
T_contra = typing.TypeVar('T_contra', contravariant=True) # Ditto contravariant.
ClassVar = typing.ClassVar
# On older versions of typing there is an internal class named "Final".
# 3.8+
if hasattr(typing, 'Final') and sys.version_info[:2] >= (3, 7):
Final = typing.Final
# 3.7
else:
class _FinalForm(typing._SpecialForm, _root=True):
def __repr__(self):
return 'typing_extensions.' + self._name
def __getitem__(self, parameters):
item = typing._type_check(parameters,
f'{self._name} accepts only a single type.')
return typing._GenericAlias(self, (item,))
Final = _FinalForm('Final',
doc="""A special typing construct to indicate that a name
cannot be re-assigned or overridden in a subclass.
For example:
MAX_SIZE: Final = 9000
MAX_SIZE += 1 # Error reported by type checker
class Connection:
TIMEOUT: Final[int] = 10
class FastConnector(Connection):
TIMEOUT = 1 # Error reported by type checker
There is no runtime checking of these properties.""")
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)
# 3.8+:
if hasattr(typing, 'Literal'):
Literal = typing.Literal
# 3.7:
else:
class _LiteralForm(typing._SpecialForm, _root=True):
def __repr__(self):
return 'typing_extensions.' + self._name
def __getitem__(self, parameters):
return typing._GenericAlias(self, parameters)
Literal = _LiteralForm('Literal',
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 # noqa
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
# 3.7.2+
if hasattr(typing, 'OrderedDict'):
OrderedDict = typing.OrderedDict
# 3.7.0-3.7.2
else:
OrderedDict = typing._alias(collections.OrderedDict, (KT, VT))
Counter = typing.Counter
ChainMap = typing.ChainMap
AsyncGenerator = typing.AsyncGenerator
NewType = typing.NewType
Text = typing.Text
TYPE_CHECKING = typing.TYPE_CHECKING
_PROTO_WHITELIST = ['Callable', 'Awaitable',
'Iterable', 'Iterator', 'AsyncIterable', 'AsyncIterator',
'Hashable', 'Sized', 'Container', 'Collection', 'Reversible',
'ContextManager', 'AsyncContextManager']
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 list(base.__dict__.keys()) + list(annotations.keys()):
if (not attr.startswith('_abc_') and attr not in (
'__abstractmethods__', '__annotations__', '__weakref__',
'_is_protocol', '_is_runtime_protocol', '__dict__',
'__args__', '__slots__',
'__next_in_mro__', '__parameters__', '__origin__',
'__orig_bases__', '__extra__', '__tree_hash__',
'__doc__', '__subclasshook__', '__init__', '__new__',
'__module__', '_MutableMapping__marker', '_gorg')):
attrs.add(attr)
return attrs
def _is_callable_members_only(cls):
return all(callable(getattr(cls, attr, None)) for attr in _get_protocol_attrs(cls))
def _maybe_adjust_parameters(cls):
"""Helper function used in Protocol.__init_subclass__ and _TypedDictMeta.__new__.
The contents of this function are very similar
to logic found in typing.Generic.__init_subclass__
on the CPython main branch.
"""
tvars = []
if '__orig_bases__' in cls.__dict__:
tvars = typing._collect_type_vars(cls.__orig_bases__)
# Look for Generic[T1, ..., Tn] or Protocol[T1, ..., Tn].
# If found, tvars must be a subset of it.
# If not found, tvars is it.
# Also check for and reject plain Generic,
# and reject multiple Generic[...] and/or Protocol[...].
gvars = None
for base in cls.__orig_bases__:
if (isinstance(base, typing._GenericAlias) and
base.__origin__ in (typing.Generic, Protocol)):
# for error messages
the_base = base.__origin__.__name__
if gvars is not None:
raise TypeError(
"Cannot inherit from Generic[...]"
" and/or Protocol[...] multiple types.")
gvars = base.__parameters__
if gvars is None:
gvars = tvars
else:
tvarset = set(tvars)
gvarset = set(gvars)
if not tvarset <= gvarset:
s_vars = ', '.join(str(t) for t in tvars if t not in gvarset)
s_args = ', '.join(str(g) for g in gvars)
raise TypeError(f"Some type variables ({s_vars}) are"
f" not listed in {the_base}[{s_args}]")
tvars = gvars
cls.__parameters__ = tuple(tvars)
# 3.8+
if hasattr(typing, 'Protocol'):
Protocol = typing.Protocol
# 3.7
else:
def _no_init(self, *args, **kwargs):
if type(self)._is_protocol:
raise TypeError('Protocols cannot be instantiated')
class _ProtocolMeta(abc.ABCMeta):
# This metaclass is a bit unfortunate and exists only because of the lack
# of __instancehook__.
def __instancecheck__(cls, instance):
# We need this method for situations where attributes are
# assigned in __init__.
if ((not getattr(cls, '_is_protocol', False) or
_is_callable_members_only(cls)) and
issubclass(instance.__class__, cls)):
return True
if cls._is_protocol:
if all(hasattr(instance, attr) and
(not callable(getattr(cls, attr, None)) or
getattr(instance, attr) is not None)
for attr in _get_protocol_attrs(cls)):
return True
return super().__instancecheck__(instance)
class Protocol(metaclass=_ProtocolMeta):
# There is quite a lot of overlapping code with typing.Generic.
# Unfortunately it is hard to avoid this while these live in two different
# modules. The duplicated code will be removed when Protocol is moved to typing.
"""Base class for protocol classes. Protocol classes are defined as::
class Proto(Protocol):
def meth(self) -> int:
...
Such classes are primarily used with static type checkers that recognize
structural subtyping (static duck-typing), for example::
class C:
def meth(self) -> int:
return 0
def func(x: Proto) -> int:
return x.meth()
func(C()) # Passes static type check
See PEP 544 for details. Protocol classes decorated with
@typing_extensions.runtime act as simple-minded runtime protocol that checks
only the presence of given attributes, ignoring their type signatures.
Protocol classes can be generic, they are defined as::
class GenProto(Protocol[T]):
def meth(self) -> T:
...
"""
__slots__ = ()
_is_protocol = True
def __new__(cls, *args, **kwds):
if cls is Protocol:
raise TypeError("Type Protocol cannot be instantiated; "
"it can only be used as a base class")
return super().__new__(cls)
@typing._tp_cache
def __class_getitem__(cls, params):
if not isinstance(params, tuple):
params = (params,)
if not params and cls is not typing.Tuple:
raise TypeError(
f"Parameter list to {cls.__qualname__}[...] cannot be empty")
msg = "Parameters to generic types must be types."
params = tuple(typing._type_check(p, msg) for p in params) # noqa
if cls is Protocol:
# Generic can only be subscripted with unique type variables.
if not all(isinstance(p, typing.TypeVar) for p in params):
i = 0
while isinstance(params[i], typing.TypeVar):
i += 1
raise TypeError(
"Parameters to Protocol[...] must all be type variables."
f" Parameter {i + 1} is {params[i]}")
if len(set(params)) != len(params):
raise TypeError(
"Parameters to Protocol[...] must all be unique")
else:
# Subscripting a regular Generic subclass.
_check_generic(cls, params, len(cls.__parameters__))
return typing._GenericAlias(cls, params)
def __init_subclass__(cls, *args, **kwargs):
if '__orig_bases__' in cls.__dict__:
error = typing.Generic in cls.__orig_bases__
else:
error = typing.Generic in cls.__bases__
if error:
raise TypeError("Cannot inherit from plain Generic")
_maybe_adjust_parameters(cls)
# Determine if this is a protocol or a concrete subclass.
if not cls.__dict__.get('_is_protocol', None):
cls._is_protocol = any(b is Protocol for b in cls.__bases__)
# Set (or override) the protocol subclass hook.
def _proto_hook(other):
if not cls.__dict__.get('_is_protocol', None):
return NotImplemented
if not getattr(cls, '_is_runtime_protocol', False):
if sys._getframe(2).f_globals['__name__'] in ['abc', 'functools']:
return NotImplemented
raise TypeError("Instance and class checks can only be used with"
" @runtime protocols")
if not _is_callable_members_only(cls):
if sys._getframe(2).f_globals['__name__'] in ['abc', 'functools']:
return NotImplemented
raise TypeError("Protocols with non-method members"
" don't support issubclass()")
if not isinstance(other, type):
# Same error as for issubclass(1, int)
raise TypeError('issubclass() arg 1 must be a class')
for attr in _get_protocol_attrs(cls):
for base in other.__mro__:
if attr in base.__dict__:
if base.__dict__[attr] is None:
return NotImplemented
break
annotations = getattr(base, '__annotations__', {})
if (isinstance(annotations, typing.Mapping) and
attr in annotations and
isinstance(other, _ProtocolMeta) and
other._is_protocol):
break
else:
return NotImplemented
return True
if '__subclasshook__' not in cls.__dict__:
cls.__subclasshook__ = _proto_hook
# We have nothing more to do for non-protocols.
if not cls._is_protocol:
return
# Check consistency of bases.
for base in cls.__bases__:
if not (base in (object, typing.Generic) or
base.__module__ == 'collections.abc' and
base.__name__ in _PROTO_WHITELIST or
isinstance(base, _ProtocolMeta) and base._is_protocol):
raise TypeError('Protocols can only inherit from other'
f' protocols, got {repr(base)}')
cls.__init__ = _no_init
# 3.8+
if hasattr(typing, 'runtime_checkable'):
runtime_checkable = typing.runtime_checkable
# 3.7
else:
def runtime_checkable(cls):
"""Mark a protocol class as a runtime protocol, so that it
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 the
one-offs in collections.abc such as Hashable.
"""
if not isinstance(cls, _ProtocolMeta) or not cls._is_protocol:
raise TypeError('@runtime_checkable can be only applied to protocol classes,'
f' got {cls!r}')
cls._is_runtime_protocol = True
return cls
# Exists for backwards compatibility.
runtime = runtime_checkable
# 3.8+
if hasattr(typing, 'SupportsIndex'):
SupportsIndex = typing.SupportsIndex
# 3.7
else:
@runtime_checkable
class SupportsIndex(Protocol):
__slots__ = ()
@abc.abstractmethod
def __index__(self) -> int:
pass
if hasattr(typing, "Required"):
# 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.
TypedDict = typing.TypedDict
_TypedDictMeta = typing._TypedDictMeta
is_typeddict = typing.is_typeddict
else:
def _check_fails(cls, other):
try:
if sys._getframe(1).f_globals['__name__'] not in ['abc',
'functools',
'typing']:
# Typed dicts are only for static structural subtyping.
raise TypeError('TypedDict does not support instance and class checks')
except (AttributeError, ValueError):
pass
return False
def _dict_new(*args, **kwargs):
if not args:
raise TypeError('TypedDict.__new__(): not enough arguments')
_, args = args[0], args[1:] # allow the "cls" keyword be passed
return dict(*args, **kwargs)
_dict_new.__text_signature__ = '($cls, _typename, _fields=None, /, **kwargs)'
def _typeddict_new(*args, total=True, **kwargs):
if not args:
raise TypeError('TypedDict.__new__(): not enough arguments')
_, args = args[0], args[1:] # allow the "cls" keyword be passed
if args:
typename, args = args[0], args[1:] # allow the "_typename" keyword be passed
elif '_typename' in kwargs:
typename = kwargs.pop('_typename')
import warnings
warnings.warn("Passing '_typename' as keyword argument is deprecated",
DeprecationWarning, stacklevel=2)
else:
raise TypeError("TypedDict.__new__() missing 1 required positional "
"argument: '_typename'")
if args:
try:
fields, = args # allow the "_fields" keyword be passed
except ValueError:
raise TypeError('TypedDict.__new__() takes from 2 to 3 '
f'positional arguments but {len(args) + 2} '
'were given')
elif '_fields' in kwargs and len(kwargs) == 1:
fields = kwargs.pop('_fields')
import warnings
warnings.warn("Passing '_fields' as keyword argument is deprecated",
DeprecationWarning, stacklevel=2)
else:
fields = None
if fields is None:
fields = kwargs
elif kwargs:
raise TypeError("TypedDict takes either a dict or keyword arguments,"
" but not both")
ns = {'__annotations__': dict(fields)}
try:
# Setting correct module is necessary to make typed dict classes pickleable.
ns['__module__'] = sys._getframe(1).f_globals.get('__name__', '__main__')
except (AttributeError, ValueError):
pass
return _TypedDictMeta(typename, (), ns, total=total)
_typeddict_new.__text_signature__ = ('($cls, _typename, _fields=None,'
' /, *, total=True, **kwargs)')
class _TypedDictMeta(type):
def __init__(cls, name, bases, ns, total=True):
super().__init__(name, bases, ns)
def __new__(cls, name, bases, ns, total=True):
# Create new typed dict class object.
# This method is called directly when TypedDict is subclassed,
# or via _typeddict_new when TypedDict is instantiated. This way
# TypedDict supports all three syntaxes described in its docstring.
# Subclasses and instances of TypedDict return actual dictionaries
# via _dict_new.
ns['__new__'] = _typeddict_new if name == 'TypedDict' else _dict_new
# Don't insert typing.Generic into __bases__ here,
# or Generic.__init_subclass__ will raise TypeError
# in the super().__new__() call.
# Instead, monkey-patch __bases__ onto the class after it's been created.
tp_dict = super().__new__(cls, name, (dict,), ns)
if any(issubclass(base, typing.Generic) for base in bases):
tp_dict.__bases__ = (typing.Generic, dict)
_maybe_adjust_parameters(tp_dict)
annotations = {}
own_annotations = ns.get('__annotations__', {})
msg = "TypedDict('Name', {f0: t0, f1: t1, ...}); each t must be a type"
own_annotations = {
n: typing._type_check(tp, msg) for n, tp in own_annotations.items()
}
required_keys = set()
optional_keys = set()
for base in bases:
annotations.update(base.__dict__.get('__annotations__', {}))
required_keys.update(base.__dict__.get('__required_keys__', ()))
optional_keys.update(base.__dict__.get('__optional_keys__', ()))
annotations.update(own_annotations)
for annotation_key, annotation_type in own_annotations.items():
annotation_origin = get_origin(annotation_type)
if annotation_origin is Annotated:
annotation_args = get_args(annotation_type)
if annotation_args:
annotation_type = annotation_args[0]
annotation_origin = get_origin(annotation_type)
if annotation_origin is Required:
required_keys.add(annotation_key)
elif annotation_origin is NotRequired:
optional_keys.add(annotation_key)
elif total:
required_keys.add(annotation_key)
else:
optional_keys.add(annotation_key)
tp_dict.__annotations__ = annotations
tp_dict.__required_keys__ = frozenset(required_keys)
tp_dict.__optional_keys__ = frozenset(optional_keys)
if not hasattr(tp_dict, '__total__'):
tp_dict.__total__ = total
return tp_dict
__instancecheck__ = __subclasscheck__ = _check_fails
TypedDict = _TypedDictMeta('TypedDict', (dict,), {})
TypedDict.__module__ = __name__
TypedDict.__doc__ = \
"""A simple typed name space. At runtime it is equivalent to a plain dict.
TypedDict creates a dictionary type that expects all of its
instances to have a certain set of keys, with each key
associated with a value of a consistent type. This expectation
is not checked at runtime but is only enforced by type checkers.
Usage::
class Point2D(TypedDict):
x: int
y: int
label: str
a: Point2D = {'x': 1, 'y': 2, 'label': 'good'} # OK
b: Point2D = {'z': 3, 'label': 'bad'} # Fails type check
assert Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')
The type info can be accessed via the Point2D.__annotations__ dict, and
the Point2D.__required_keys__ and Point2D.__optional_keys__ frozensets.
TypedDict supports two additional equivalent forms::
Point2D = TypedDict('Point2D', x=int, y=int, label=str)
Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})
The class syntax is only supported in Python 3.6+, while two other
syntax forms work for Python 2.7 and 3.2+
"""
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
"""
return isinstance(tp, tuple(_TYPEDDICT_TYPES))
if hasattr(typing, "assert_type"):
assert_type = typing.assert_type
else:
def assert_type(__val, __typ):
"""Assert (to the type checker) that the value is of the given type.
When the type checker encounters a call to assert_type(), it
emits an error if the value is not of the specified type::
def greet(name: str) -> None:
assert_type(name, str) # ok
assert_type(name, int) # type checker error
At runtime this returns the first argument unchanged and otherwise
does nothing.
"""
return __val
if hasattr(typing, "Required"):
get_type_hints = typing.get_type_hints
else:
import functools
import types
# replaces _strip_annotations()
def _strip_extras(t):
"""Strips Annotated, Required and NotRequired from a given type."""
if isinstance(t, _AnnotatedAlias):
return _strip_extras(t.__origin__)
if hasattr(t, "__origin__") and t.__origin__ in (Required, NotRequired):
return _strip_extras(t.__args__[0])
if isinstance(t, typing._GenericAlias):
stripped_args = tuple(_strip_extras(a) for a in t.__args__)
if stripped_args == t.__args__:
return t
return t.copy_with(stripped_args)
if hasattr(types, "GenericAlias") and isinstance(t, types.GenericAlias):
stripped_args = tuple(_strip_extras(a) for a in t.__args__)
if stripped_args == t.__args__:
return t
return types.GenericAlias(t.__origin__, stripped_args)
if hasattr(types, "UnionType") and isinstance(t, types.UnionType):
stripped_args = tuple(_strip_extras(a) for a in t.__args__)
if stripped_args == t.__args__:
return t
return functools.reduce(operator.or_, stripped_args)
return t
def get_type_hints(obj, globalns=None, localns=None, include_extras=False):
"""Return type hints for an object.
This is often the same as obj.__annotations__, but it handles
forward references encoded as string literals, adds Optional[t] if a
default value equal to None is set and recursively replaces all
'Annotated[T, ...]', 'Required[T]' or 'NotRequired[T]' with 'T'
(unless 'include_extras=True').
The argument may be a module, class, method, or function. The annotations
are returned as a dictionary. For classes, annotations include also
inherited members.
TypeError is raised if the argument is not of a type that can contain
annotations, and an empty dictionary is returned if no annotations are
present.
BEWARE -- the behavior of globalns and localns is counterintuitive
(unless you are familiar with how eval() and exec() work). The
search order is locals first, then globals.
- If no dict arguments are passed, an attempt is made to use the
globals from obj (or the respective module's globals for classes),
and these are also used as the locals. If the object does not appear
to have globals, an empty dictionary is used.
- If one dict argument is passed, it is used for both globals and
locals.
- If two dict arguments are passed, they specify globals and
locals, respectively.
"""
if hasattr(typing, "Annotated"):
hint = typing.get_type_hints(
obj, globalns=globalns, localns=localns, include_extras=True
)
else:
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
# 3.7-3.8
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
# 3.7-3.9
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):
class _TypeAliasForm(typing._SpecialForm, _root=True):
def __repr__(self):
return 'typing_extensions.' + self._name
@_TypeAliasForm
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")
# 3.7-3.8
else:
class _TypeAliasForm(typing._SpecialForm, _root=True):
def __repr__(self):
return 'typing_extensions.' + self._name
TypeAlias = _TypeAliasForm('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.""")
# Python 3.10+ has PEP 612
if hasattr(typing, 'ParamSpecArgs'):
ParamSpecArgs = typing.ParamSpecArgs
ParamSpecKwargs = typing.ParamSpecKwargs
# 3.7-3.9
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'):
ParamSpec = typing.ParamSpec
# 3.7-3.9
else:
# Inherits from list as a workaround for Callable checks in Python < 3.9.2.
class ParamSpec(list):
"""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):
super().__init__([self])
self.__name__ = name
self.__covariant__ = bool(covariant)
self.__contravariant__ = bool(contravariant)
if bound:
self.__bound__ = typing._type_check(bound, 'Bound must be a type.')
else:
self.__bound__ = None
# for pickling:
try:
def_mod = sys._getframe(1).f_globals.get('__name__', '__main__')
except (AttributeError, ValueError):
def_mod = None
if def_mod != 'typing_extensions':
self.__module__ = def_mod
def __repr__(self):
if 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
# 3.7-3.9
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))
)
# 3.7-3.9
@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
# 3.9
elif sys.version_info[:2] >= (3, 9):
@_TypeAliasForm
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)
# 3.7-8
else:
class _ConcatenateForm(typing._SpecialForm, _root=True):
def __repr__(self):
return 'typing_extensions.' + self._name
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):
class _TypeGuardForm(typing._SpecialForm, _root=True):
def __repr__(self):
return 'typing_extensions.' + self._name
@_TypeGuardForm
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,))
# 3.7-3.8
else:
class _TypeGuardForm(typing._SpecialForm, _root=True):
def __repr__(self):
return 'typing_extensions.' + self._name
def __getitem__(self, parameters):
item = typing._type_check(parameters,
f'{self._name} accepts only a single type')
return typing._GenericAlias(self, (item,))
TypeGuard = _TypeGuardForm(
'TypeGuard',
doc="""Special typing form used to annotate the return type of a user-defined
type guard function. ``TypeGuard`` only accepts a single type argument.
At runtime, functions marked this way should return a boolean.
``TypeGuard`` aims to benefit *type narrowing* -- a technique used by static
type checkers to determine a more precise type of an expression within a
program's code flow. Usually type narrowing is done by analyzing
conditional code flow and applying the narrowing to a block of code. The
conditional expression here is sometimes referred to as a "type guard".
Sometimes it would be convenient to use a user-defined boolean function
as a type guard. Such a function should use ``TypeGuard[...]`` as its
return type to alert static type checkers to this intention.
Using ``-> TypeGuard`` tells the static type checker that for a given
function:
1. The return value is a boolean.
2. If the return value is ``True``, the type of its argument
is the type inside ``TypeGuard``.
For example::
def is_str(val: Union[str, float]):
# "isinstance" type guard
if isinstance(val, str):
# Type of ``val`` is narrowed to ``str``
...
else:
# Else, type of ``val`` is narrowed to ``float``.
...
Strict type narrowing is not enforced -- ``TypeB`` need not be a narrower
form of ``TypeA`` (it can even be a wider form) and this may lead to
type-unsafe results. The main reason is to allow for things like
narrowing ``List[object]`` to ``List[str]`` even though the latter is not
a subtype of the former, since ``List`` is invariant. The responsibility of
writing type-safe type guards is left to the user.
``TypeGuard`` also works with type variables. For more information, see
PEP 647 (User-Defined Type Guards).
""")
# Vendored from cpython typing._SpecialFrom
class _SpecialForm(typing._Final, _root=True):
__slots__ = ('_name', '__doc__', '_getitem')
def __init__(self, getitem):
self._getitem = getitem
self._name = getitem.__name__
self.__doc__ = getitem.__doc__
def __getattr__(self, item):
if item in {'__name__', '__qualname__'}:
return self._name
raise AttributeError(item)
def __mro_entries__(self, bases):
raise TypeError(f"Cannot subclass {self!r}")
def __repr__(self):
return f'typing_extensions.{self._name}'
def __reduce__(self):
return self._name
def __call__(self, *args, **kwds):
raise TypeError(f"Cannot instantiate {self!r}")
def __or__(self, other):
return typing.Union[self, other]
def __ror__(self, other):
return typing.Union[other, self]
def __instancecheck__(self, obj):
raise TypeError(f"{self} cannot be used with isinstance()")
def __subclasscheck__(self, cls):
raise TypeError(f"{self} cannot be used with issubclass()")
@typing._tp_cache
def __getitem__(self, parameters):
return self._getitem(self, parameters)
if hasattr(typing, "LiteralString"):
LiteralString = typing.LiteralString
else:
@_SpecialForm
def LiteralString(self, params):
"""Represents an arbitrary literal string.
Example::
from pip._vendor.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"):
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"):
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 pip._vendor.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'):
Required = typing.Required
NotRequired = typing.NotRequired
elif sys.version_info[:2] >= (3, 9):
class _ExtensionsSpecialForm(typing._SpecialForm, _root=True):
def __repr__(self):
return 'typing_extensions.' + self._name
@_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:
class _RequiredForm(typing._SpecialForm, _root=True):
def __repr__(self):
return 'typing_extensions.' + self._name
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, "Unpack"): # 3.11+
Unpack = typing.Unpack
elif sys.version_info[:2] >= (3, 9):
class _UnpackSpecialForm(typing._SpecialForm, _root=True):
def __repr__(self):
return 'typing_extensions.' + self._name
class _UnpackAlias(typing._GenericAlias, _root=True):
__class__ = typing.TypeVar
@_UnpackSpecialForm
def Unpack(self, parameters):
"""A special typing construct to unpack a variadic type. For example:
Shape = TypeVarTuple('Shape')
Batch = NewType('Batch', int)
def add_batch_axis(
x: Array[Unpack[Shape]]
) -> Array[Batch, Unpack[Shape]]: ...
"""
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:
class _UnpackAlias(typing._GenericAlias, _root=True):
__class__ = typing.TypeVar
class _UnpackForm(typing._SpecialForm, _root=True):
def __repr__(self):
return 'typing_extensions.' + self._name
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="""A special typing construct to unpack a variadic type. For example:
Shape = TypeVarTuple('Shape')
Batch = NewType('Batch', int)
def add_batch_axis(
x: Array[Unpack[Shape]]
) -> Array[Batch, Unpack[Shape]]: ...
""")
def _is_unpack(obj):
return isinstance(obj, _UnpackAlias)
if hasattr(typing, "TypeVarTuple"): # 3.11+
TypeVarTuple = typing.TypeVarTuple
else:
class TypeVarTuple:
"""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):
self.__name__ = name
# for pickling:
try:
def_mod = sys._getframe(1).f_globals.get('__name__', '__main__')
except (AttributeError, ValueError):
def_mod = None
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"):
reveal_type = typing.reveal_type
else:
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"):
assert_never = typing.assert_never
else:
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 hasattr(typing, 'dataclass_transform'):
dataclass_transform = typing.dataclass_transform
else:
def dataclass_transform(
*,
eq_default: bool = True,
order_default: bool = False,
kw_only_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 pip._vendor.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.
- ``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,
"field_specifiers": field_specifiers,
"kwargs": kwargs,
}
return cls_or_fn
return decorator
# We have to do some monkey patching to deal with the dual nature of
# Unpack/TypeVarTuple:
# - We want Unpack to be a kind of TypeVar so it gets accepted in
# Generic[Unpack[Ts]]
# - We want it to *not* be treated as a TypeVar for the purposes of
# counting generic parameters, so that when we subscript a generic,
# the runtime doesn't try to substitute the Unpack with the subscripted type.
if not hasattr(typing, "TypeVarTuple"):
typing._collect_type_vars = _collect_type_vars
typing._check_generic = _check_generic
# Backport typing.NamedTuple as it exists in Python 3.11.
# 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.
if sys.version_info >= (3, 11):
NamedTuple = typing.NamedTuple
else:
def _caller():
try:
return sys._getframe(2).f_globals.get('__name__', '__main__')
except (AttributeError, ValueError): # For platforms without _getframe()
return None
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:
class_getitem = typing.Generic.__class_getitem__.__func__
nm_tpl.__class_getitem__ = classmethod(class_getitem)
# update from user namespace without overriding special namedtuple attributes
for key in ns:
if key in _prohibited_namedtuple_fields:
raise AttributeError("Cannot overwrite NamedTuple attribute " + key)
elif key not in _special_namedtuple_fields and key not in nm_tpl._fields:
setattr(nm_tpl, key, ns[key])
if typing.Generic in bases:
nm_tpl.__init_subclass__()
return nm_tpl
def NamedTuple(__typename, __fields=None, **kwargs):
if __fields is None:
__fields = kwargs.items()
elif kwargs:
raise TypeError("Either list of fields or keywords"
" can be provided to NamedTuple, not both")
return _make_nmtuple(__typename, __fields, module=_caller())
NamedTuple.__doc__ = typing.NamedTuple.__doc__
_NamedTuple = type.__new__(_NamedTupleMeta, 'NamedTuple', (), {})
# On 3.8+, alter the signature so that it matches typing.NamedTuple.
# The signature of typing.NamedTuple on >=3.8 is invalid syntax in Python 3.7,
# so just leave the signature as it is on 3.7.
if sys.version_info >= (3, 8):
NamedTuple.__text_signature__ = '(typename, fields=None, /, **kwargs)'
def _namedtuple_mro_entries(bases):
assert NamedTuple in bases
return (_NamedTuple,)
NamedTuple.__mro_entries__ = _namedtuple_mro_entries