from collections.abc import Callable, Iterable
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from typing import Any, Final, NamedTuple, ParamSpec, TypeVar
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from numpy._typing import _SupportsArrayFunc
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_T = TypeVar("_T")
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_Tss = ParamSpec("_Tss")
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_FuncT = TypeVar("_FuncT", bound=Callable[..., object])
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###
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ARRAY_FUNCTIONS: set[Callable[..., Any]] = ...
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array_function_like_doc: Final[str] = ...
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class ArgSpec(NamedTuple):
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args: list[str]
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varargs: str | None
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keywords: str | None
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defaults: tuple[Any, ...]
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def get_array_function_like_doc(public_api: Callable[..., Any], docstring_template: str = "") -> str: ...
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def finalize_array_function_like(public_api: _FuncT) -> _FuncT: ...
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#
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def verify_matching_signatures(
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implementation: Callable[_Tss, object],
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dispatcher: Callable[_Tss, Iterable[_SupportsArrayFunc]],
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) -> None: ...
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# NOTE: This actually returns a `_ArrayFunctionDispatcher` callable wrapper object, with
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# the original wrapped callable stored in the `._implementation` attribute. It checks
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# for any `__array_function__` of the values of specific arguments that the dispatcher
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# specifies. Since the dispatcher only returns an iterable of passed array-like args,
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# this overridable behaviour is impossible to annotate.
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def array_function_dispatch(
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dispatcher: Callable[_Tss, Iterable[_SupportsArrayFunc]] | None = None,
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module: str | None = None,
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verify: bool = True,
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docs_from_dispatcher: bool = False,
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) -> Callable[[_FuncT], _FuncT]: ...
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#
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def array_function_from_dispatcher(
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implementation: Callable[_Tss, _T],
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module: str | None = None,
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verify: bool = True,
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docs_from_dispatcher: bool = True,
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) -> Callable[[Callable[_Tss, Iterable[_SupportsArrayFunc]]], Callable[_Tss, _T]]: ...
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