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from typing import (
Literal as L,
Any,
overload,
TypeVar,
Protocol,
)
from numpy import generic
from numpy._typing import (
ArrayLike,
NDArray,
_ArrayLikeInt,
_ArrayLike,
)
_SCT = TypeVar("_SCT", bound=generic)
class _ModeFunc(Protocol):
def __call__(
self,
vector: NDArray[Any],
iaxis_pad_width: tuple[int, int],
iaxis: int,
kwargs: dict[str, Any],
/,
) -> None: ...
_ModeKind = L[
"constant",
"edge",
"linear_ramp",
"maximum",
"mean",
"median",
"minimum",
"reflect",
"symmetric",
"wrap",
"empty",
]
__all__: list[str]
# TODO: In practice each keyword argument is exclusive to one or more
# specific modes. Consider adding more overloads to express this in the future.
# Expand `**kwargs` into explicit keyword-only arguments
@overload
def pad(
array: _ArrayLike[_SCT],
pad_width: _ArrayLikeInt,
mode: _ModeKind = ...,
*,
stat_length: None | _ArrayLikeInt = ...,
constant_values: ArrayLike = ...,
end_values: ArrayLike = ...,
reflect_type: L["odd", "even"] = ...,
) -> NDArray[_SCT]: ...
@overload
def pad(
array: ArrayLike,
pad_width: _ArrayLikeInt,
mode: _ModeKind = ...,
*,
stat_length: None | _ArrayLikeInt = ...,
constant_values: ArrayLike = ...,
end_values: ArrayLike = ...,
reflect_type: L["odd", "even"] = ...,
) -> NDArray[Any]: ...
@overload
def pad(
array: _ArrayLike[_SCT],
pad_width: _ArrayLikeInt,
mode: _ModeFunc,
**kwargs: Any,
) -> NDArray[_SCT]: ...
@overload
def pad(
array: ArrayLike,
pad_width: _ArrayLikeInt,
mode: _ModeFunc,
**kwargs: Any,
) -> NDArray[Any]: ...
|
