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chore: enable pyupgrade fixes (#1806)

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# What does this PR do?

The goal of this PR is code base modernization.

Schema reflection code needed a minor adjustment to handle UnionTypes
and collections.abc.AsyncIterator. (Both are preferred for latest Python
releases.)

Note to reviewers: almost all changes here are automatically generated
by pyupgrade. Some additional unused imports were cleaned up. The only
change worth of note can be found under `docs/openapi_generator` and
`llama_stack/strong_typing/schema.py` where reflection code was updated
to deal with "newer" types.

Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
This commit is contained in:
Ihar Hrachyshka 2025-05-01 17:23:50 -04:00 • committed by GitHub
parent ffe3d0b2cd
commit 9e6561a1ec
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319 changed files with 2843 additions and 3033 deletions
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21
llama_stack/models/llama/llama4/preprocess.py
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@ -13,7 +13,6 @@
import math
from collections import defaultdict
from typing import Optional, Set, Tuple
import torch
import torchvision.transforms as tv
@ -52,7 +51,7 @@ class ResizeNormalizeImageTransform:
return self.tv_transform(image)
class VariableSizeImageTransform(object):
class VariableSizeImageTransform:
"""
This class accepts images of any size and dynamically resize, pads and chunks it
based on the image aspect ratio and the number of image chunks we allow.
@ -100,7 +99,7 @@ class VariableSizeImageTransform(object):
self.resample = tv.InterpolationMode.BILINEAR
@staticmethod
def get_factors(n: int) -> Set[int]:
def get_factors(n: int) -> set[int]:
"""
Calculate all factors of a given number, i.e. a dividor that leaves
no remainder. For example, if n=12, it will return {1, 2, 3, 4, 6, 12}.
@ -170,9 +169,9 @@ class VariableSizeImageTransform(object):
@staticmethod
def get_max_res_without_distortion(
image_size: Tuple[int, int],
target_size: Tuple[int, int],
) -> Tuple[int, int]:
image_size: tuple[int, int],
target_size: tuple[int, int],
) -> tuple[int, int]:
"""
Determines the maximum resolution to which an image can be resized to without distorting its
aspect ratio, based on the target resolution.
@ -223,8 +222,8 @@ class VariableSizeImageTransform(object):
def resize_without_distortion(
self,
image: torch.Tensor,
target_size: Tuple[int, int],
max_upscaling_size: Optional[int],
target_size: tuple[int, int],
max_upscaling_size: int | None,
) -> torch.Tensor:
"""
Used to resize an image to target_resolution, without distortion.
@ -289,10 +288,10 @@ class VariableSizeImageTransform(object):
def get_best_fit(
self,
image_size: Tuple[int, int],
image_size: tuple[int, int],
possible_resolutions: torch.Tensor,
resize_to_max_canvas: bool = False,
) -> Tuple[int, int]:
) -> tuple[int, int]:
"""
Determines the best canvas possible from a list of possible resolutions to, without distortion,
resize an image to.
@ -392,7 +391,7 @@ class VariableSizeImageTransform(object):
max_num_chunks: int,
normalize_img: bool = True,
resize_to_max_canvas: bool = False,
) -> Tuple[torch.Tensor, Tuple[int, int]]:
) -> tuple[torch.Tensor, tuple[int, int]]:
"""
Args:
image (PIL.Image): Image to be resized.