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from __future__ import annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
import torch
array = torch.Tensor
from typing import Union, Sequence, Literal
from torch.fft import * # noqa: F403
import torch.fft
# Several torch fft functions do not map axes to dim
def fftn(
x: array,
/,
*,
s: Sequence[int] = None,
axes: Sequence[int] = None,
norm: Literal["backward", "ortho", "forward"] = "backward",
**kwargs,
) -> array:
return torch.fft.fftn(x, s=s, dim=axes, norm=norm, **kwargs)
def ifftn(
x: array,
/,
*,
s: Sequence[int] = None,
axes: Sequence[int] = None,
norm: Literal["backward", "ortho", "forward"] = "backward",
**kwargs,
) -> array:
return torch.fft.ifftn(x, s=s, dim=axes, norm=norm, **kwargs)
def rfftn(
x: array,
/,
*,
s: Sequence[int] = None,
axes: Sequence[int] = None,
norm: Literal["backward", "ortho", "forward"] = "backward",
**kwargs,
) -> array:
return torch.fft.rfftn(x, s=s, dim=axes, norm=norm, **kwargs)
def irfftn(
x: array,
/,
*,
s: Sequence[int] = None,
axes: Sequence[int] = None,
norm: Literal["backward", "ortho", "forward"] = "backward",
**kwargs,
) -> array:
return torch.fft.irfftn(x, s=s, dim=axes, norm=norm, **kwargs)
def fftshift(
x: array,
/,
*,
axes: Union[int, Sequence[int]] = None,
**kwargs,
) -> array:
return torch.fft.fftshift(x, dim=axes, **kwargs)
def ifftshift(
x: array,
/,
*,
axes: Union[int, Sequence[int]] = None,
**kwargs,
) -> array:
return torch.fft.ifftshift(x, dim=axes, **kwargs)
__all__ = torch.fft.__all__ + [
"fftn",
"ifftn",
"rfftn",
"irfftn",
"fftshift",
"ifftshift",
]
_all_ignore = ['torch']
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