from collections.abc import Callable, Iterable

from typing import Any, Final, NamedTuple

from numpy._utils import set_module as set_module

type _FuncLike = type | Callable[..., object]

type _Dispatcher[**_Tss] = Callable[_Tss, Iterable[object]]

###

ARRAY_FUNCTIONS: set[Callable[..., Any]] = ...

array_function_like_doc: Final[str] = ...

class ArgSpec(NamedTuple):

args: list[str]

varargs: str | None

keywords: str | None

defaults: tuple[Any, ...]

def get_array_function_like_doc(public_api: Callable[..., object], docstring_template: str = "") -> str: ...

def finalize_array_function_like[FuncLikeT: _FuncLike](public_api: FuncLikeT) -> FuncLikeT: ...

#

def verify_matching_signatures[**Tss](implementation: Callable[Tss, object], dispatcher: _Dispatcher[Tss]) -> None: ...

# NOTE: This actually returns a `_ArrayFunctionDispatcher` callable wrapper object, with

# the original wrapped callable stored in the `._implementation` attribute. It checks

# for any `__array_function__` of the values of specific arguments that the dispatcher

# specifies. Since the dispatcher only returns an iterable of passed array-like args,

# this overridable behaviour is impossible to annotate.

def array_function_dispatch[**Tss, FuncLikeT: _FuncLike](

dispatcher: _Dispatcher[Tss] | None = None,

module: str | None = None,

verify: bool = True,

docs_from_dispatcher: bool = False,

) -> Callable[[FuncLikeT], FuncLikeT]: ...

#

def array_function_from_dispatcher[**Tss, T](

implementation: Callable[Tss, T],

module: str | None = None,

verify: bool = True,

docs_from_dispatcher: bool = True,

) -> Callable[[_Dispatcher[Tss]], Callable[Tss, T]]: ...