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Use pyupgrade --py39-plus to improve code (#36843)

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This commit is contained in:
cyyever
2025-03-20 22:39:44 +08:00
committed by GitHub
parent 3e8f0fbf44
commit ce091b1bda
33 changed files with 285 additions and 308 deletions
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46
src/transformers/image_processing_utils_fast.py
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@@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,8 +12,9 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Iterable
from functools import lru_cache, partial
from typing import Any, Dict, Iterable, List, Optional, Tuple, TypedDict, Union
from typing import Any, Optional, TypedDict, Union
import numpy as np
@@ -77,8 +77,8 @@ def validate_fast_preprocess_arguments(
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
image_mean: Optional[Union[float, list[float]]] = None,
image_std: Optional[Union[float, list[float]]] = None,
do_pad: Optional[bool] = None,
size_divisibility: Optional[int] = None,
do_center_crop: Optional[bool] = None,
@@ -128,14 +128,14 @@ def safe_squeeze(tensor: "torch.Tensor", axis: Optional[int] = None) -> "torch.T
return tensor
def max_across_indices(values: Iterable[Any]) -> List[Any]:
def max_across_indices(values: Iterable[Any]) -> list[Any]:
"""
Return the maximum value across all indices of an iterable of values.
"""
return [max(values_i) for values_i in zip(*values)]
def get_max_height_width(images: List["torch.Tensor"]) -> Tuple[int]:
def get_max_height_width(images: list["torch.Tensor"]) -> tuple[int]:
"""
Get the maximum height and width across all images in a batch.
"""
@@ -147,7 +147,7 @@ def get_max_height_width(images: List["torch.Tensor"]) -> Tuple[int]:
def divide_to_patches(
image: Union[np.array, "torch.Tensor"], patch_size: int
) -> List[Union[np.array, "torch.Tensor"]]:
) -> list[Union[np.array, "torch.Tensor"]]:
"""
Divides an image into patches of a specified size.
@@ -171,16 +171,16 @@ def divide_to_patches(
class DefaultFastImageProcessorKwargs(TypedDict, total=False):
do_resize: Optional[bool]
size: Optional[Dict[str, int]]
size: Optional[dict[str, int]]
default_to_square: Optional[bool]
resample: Optional[Union["PILImageResampling", "F.InterpolationMode"]]
do_center_crop: Optional[bool]
crop_size: Optional[Dict[str, int]]
crop_size: Optional[dict[str, int]]
do_rescale: Optional[bool]
rescale_factor: Optional[Union[int, float]]
do_normalize: Optional[bool]
image_mean: Optional[Union[float, List[float]]]
image_std: Optional[Union[float, List[float]]]
image_mean: Optional[Union[float, list[float]]]
image_std: Optional[Union[float, list[float]]]
do_convert_rgb: Optional[bool]
return_tensors: Optional[Union[str, TensorType]]
data_format: Optional[ChannelDimension]
@@ -427,8 +427,8 @@ class BaseImageProcessorFast(BaseImageProcessor):
def _fuse_mean_std_and_rescale_factor(
self,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
image_mean: Optional[Union[float, list[float]]] = None,
image_std: Optional[Union[float, list[float]]] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
device: Optional["torch.device"] = None,
@@ -446,8 +446,8 @@ class BaseImageProcessorFast(BaseImageProcessor):
do_rescale: bool,
rescale_factor: float,
do_normalize: bool,
image_mean: Union[float, List[float]],
image_std: Union[float, List[float]],
image_mean: Union[float, list[float]],
image_std: Union[float, list[float]],
) -> "torch.Tensor":
"""
Rescale and normalize images.
@@ -471,7 +471,7 @@ class BaseImageProcessorFast(BaseImageProcessor):
def center_crop(
self,
image: "torch.Tensor",
size: Dict[str, int],
size: dict[str, int],
**kwargs,
) -> "torch.Tensor":
"""
@@ -576,7 +576,7 @@ class BaseImageProcessorFast(BaseImageProcessor):
do_convert_rgb: bool = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
device: Optional["torch.device"] = None,
) -> List["torch.Tensor"]:
) -> list["torch.Tensor"]:
"""
Prepare the input images for processing.
"""
@@ -599,8 +599,8 @@ class BaseImageProcessorFast(BaseImageProcessor):
size: Optional[SizeDict] = None,
crop_size: Optional[SizeDict] = None,
default_to_square: Optional[bool] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
image_mean: Optional[Union[float, list[float]]] = None,
image_std: Optional[Union[float, list[float]]] = None,
data_format: Optional[ChannelDimension] = None,
**kwargs,
) -> dict:
@@ -701,7 +701,7 @@ class BaseImageProcessorFast(BaseImageProcessor):
def _preprocess(
self,
images: List["torch.Tensor"],
images: list["torch.Tensor"],
do_resize: bool,
size: SizeDict,
interpolation: Optional["F.InterpolationMode"],
@@ -710,8 +710,8 @@ class BaseImageProcessorFast(BaseImageProcessor):
do_rescale: bool,
rescale_factor: float,
do_normalize: bool,
image_mean: Optional[Union[float, List[float]]],
image_std: Optional[Union[float, List[float]]],
image_mean: Optional[Union[float, list[float]]],
image_std: Optional[Union[float, list[float]]],
return_tensors: Optional[Union[str, TensorType]],
**kwargs,
) -> BatchFeature:
@@ -749,7 +749,7 @@ class BaseImageProcessorFast(BaseImageProcessor):
class SemanticSegmentationMixin:
def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple] = None):
def post_process_semantic_segmentation(self, outputs, target_sizes: list[tuple] = None):
"""
Converts the output of [`MobileNetV2ForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.