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[BE][PYFMT] migrate PYFMT for torch/[p-z]*/ to ruff format (#144552)

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Pull Request resolved: https://github.com/pytorch/pytorch/pull/144552
Approved by: https://github.com/ezyang
This commit is contained in:
Xuehai Pan
2025-08-06 20:57:29 +00:00
committed by PyTorch MergeBot
parent fd606a3a91
commit 5cedc5a0ff
65 changed files with 446 additions and 522 deletions
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33
torch/utils/_python_dispatch.py
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@ -302,14 +302,12 @@ class BaseTorchDispatchMode(TorchDispatchMode):
# Subtypes which have __tensor_flatten__ and __tensor_unflatten__.
class TensorWithFlatten(Protocol):
def __tensor_flatten__(self) -> tuple[Sequence[str], object]:
...
def __tensor_flatten__(self) -> tuple[Sequence[str], object]: ...
@staticmethod
def __tensor_unflatten__(
inner_tensors: int, flatten_spec: int, outer_size: int, outer_stride: int
) -> torch.Tensor:
...
) -> torch.Tensor: ...
# It would be really nice to be able to say that the return of
# is_traceable_wrapper_subclass() is Intersection[torch.Tensor,
@ -318,26 +316,20 @@ class TensorWithFlatten(Protocol):
shape: torch._C.Size
@overload
def stride(self, dim: None = None) -> tuple[int, ...]:
...
def stride(self, dim: None = None) -> tuple[int, ...]: ...
@overload
def stride(self, dim: int) -> int:
...
def stride(self, dim: int) -> int: ...
@overload
def size(self, dim: None = None) -> tuple[int, ...]:
...
def size(self, dim: None = None) -> tuple[int, ...]: ...
@overload
def size(self, dim: int) -> int:
...
def size(self, dim: int) -> int: ...
def storage_offset(self) -> int:
...
def storage_offset(self) -> int: ...
def dim(self) -> int:
...
def dim(self) -> int: ...
@overload
def to(
@ -347,8 +339,7 @@ class TensorWithFlatten(Protocol):
copy: bool = False,
*,
memory_format: Optional[torch.memory_format] = None,
) -> torch.Tensor:
...
) -> torch.Tensor: ...
@overload
def to(
@ -359,8 +350,7 @@ class TensorWithFlatten(Protocol):
copy: bool = False,
*,
memory_format: Optional[torch.memory_format] = None,
) -> torch.Tensor:
...
) -> torch.Tensor: ...
@overload
def to(
@ -370,8 +360,7 @@ class TensorWithFlatten(Protocol):
copy: bool = False,
*,
memory_format: Optional[torch.memory_format] = None,
) -> torch.Tensor:
...
) -> torch.Tensor: ...
def is_traceable_wrapper_subclass(t: object) -> TypeIs[TensorWithFlatten]: