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Enable RUF013 to enforce optional typing (#37266)

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* Enable RUF013 for Optional typing

Signed-off-by: cyy <cyyever@outlook.com>

* Add Optional to types

* Format code

Signed-off-by: cyy <cyyever@outlook.com>

---------

Signed-off-by: cyy <cyyever@outlook.com>
This commit is contained in:
Yuanyuan Chen
2025-05-08 18:39:56 +08:00
committed by GitHub
parent f6664ee713
commit 06c16de3d3
11 changed files with 41 additions and 32 deletions
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4
pyproject.toml
View File

@@ -20,7 +20,9 @@ line-length = 119
[tool.ruff.lint]
# Never enforce `E501` (line length violations).
ignore = ["C901", "E501", "E741", "F402", "F823" ]
select = ["C", "E", "F", "I", "W"]
# RUF013: Checks for the use of implicit Optional
# in type annotations when the default parameter value is None.
select = ["C", "E", "F", "I", "W", "RUF013"]
# Ignore import violations in all `__init__.py` files.
[tool.ruff.lint.per-file-ignores]

3
src/transformers/models/d_fine/convert_d_fine_original_pytorch_checkpoint_to_hf.py
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@@ -17,6 +17,7 @@ import argparse
import json
import re
from pathlib import Path
from typing import Optional
import requests
import torch
@@ -319,7 +320,7 @@ ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
}
def convert_old_keys_to_new_keys(state_dict_keys: dict = None):
def convert_old_keys_to_new_keys(state_dict_keys: Optional[dict] = None):
# Use the mapping to rename keys
for original_key, converted_key in ORIGINAL_TO_CONVERTED_KEY_MAPPING.items():
for key in list(state_dict_keys.keys()):

4
src/transformers/models/d_fine/modeling_d_fine.py
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@@ -1972,8 +1972,8 @@ class DFineConvNormLayer(nn.Module):
kernel_size: int,
stride: int,
groups: int = 1,
padding: int = None,
activation: str = None,
padding: Optional[int] = None,
activation: Optional[str] = None,
):
super().__init__()
self.conv = nn.Conv2d(

4
src/transformers/models/d_fine/modular_d_fine.py
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@@ -1054,8 +1054,8 @@ class DFineConvNormLayer(RTDetrConvNormLayer):
kernel_size: int,
stride: int,
groups: int = 1,
padding: int = None,
activation: str = None,
padding: Optional[int] = None,
activation: Optional[str] = None,
):
super().__init__(config, in_channels, out_channels, kernel_size, stride, padding=None, activation=activation)
self.conv = nn.Conv2d(

2
src/transformers/models/flava/image_processing_flava_fast.py
View File

@@ -66,7 +66,7 @@ class FlavaMaskingGenerator:
mask_group_max_patches: Optional[int] = None,
mask_group_min_patches: int = 16,
mask_group_min_aspect_ratio: Optional[float] = 0.3,
mask_group_max_aspect_ratio: float = None,
mask_group_max_aspect_ratio: Optional[float] = None,
):
if not isinstance(input_size, tuple):
input_size = (input_size,) * 2

9
src/transformers/models/internvl/convert_internvl_weights_to_hf.py
View File

@@ -15,6 +15,7 @@ import argparse
import gc
import os
import re
from typing import Optional
import torch
from einops import rearrange
@@ -116,7 +117,7 @@ chat_template = (
CONTEXT_LENGTH = 8192
def convert_old_keys_to_new_keys(state_dict_keys: dict = None, path: str = None):
def convert_old_keys_to_new_keys(state_dict_keys: Optional[dict] = None, path: Optional[str] = None):
"""
This function should be applied only once, on the concatenated keys to efficiently rename using
the key mappings.
@@ -303,7 +304,9 @@ def write_model(
del model
def write_tokenizer(save_dir: str, push_to_hub: bool = False, path: str = None, hub_dir: str = None):
def write_tokenizer(
save_dir: str, push_to_hub: bool = False, path: Optional[str] = None, hub_dir: Optional[str] = None
):
if LM_TYPE_CORRESPONDENCE[path] == "qwen2":
tokenizer = AutoTokenizer.from_pretrained(
"Qwen/Qwen2.5-VL-7B-Instruct",
@@ -355,7 +358,7 @@ def write_tokenizer(save_dir: str, push_to_hub: bool = False, path: str = None,
tokenizer.push_to_hub(hub_dir, use_temp_dir=True)
def write_image_processor(save_dir: str, push_to_hub: bool = False, hub_dir: str = None):
def write_image_processor(save_dir: str, push_to_hub: bool = False, hub_dir: Optional[str] = None):
image_processor = GotOcr2ImageProcessorFast(
do_resize=True,
size={"height": 448, "width": 448},

2
src/transformers/models/internvl/processing_internvl.py
View File

@@ -269,7 +269,7 @@ class InternVLProcessor(ProcessorMixin):
return BatchFeature(data={**text_inputs, **image_videos_inputs}, tensor_type=return_tensors)
def sample_indices_fn(
self, metadata: VideoMetadata, num_frames: int = None, initial_shift: Union[bool, float, int] = True
self, metadata: VideoMetadata, num_frames: Optional[int] = None, initial_shift: Union[bool, float, int] = True
):
"""
The function to generate indices of frames to sample from a video.

5
src/transformers/models/janus/convert_janus_weights_to_hf.py
View File

@@ -25,6 +25,7 @@ import gc
import json
import os
import re
from typing import Optional
import torch
from accelerate import init_empty_weights
@@ -167,7 +168,9 @@ def convert_state_dict_to_hf(state_dict):
return converted_state_dict
def ensure_model_downloaded(repo_id: str = None, revision: str = None, local_dir: str = None) -> str:
def ensure_model_downloaded(
repo_id: Optional[str] = None, revision: Optional[str] = None, local_dir: Optional[str] = None
) -> str:
"""
Ensures model files are downloaded locally, downloads them if not.
Returns path to local files.

18
src/transformers/models/janus/image_processing_janus.py
View File

@@ -98,7 +98,7 @@ class JanusImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
min_size: int = 14,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
@@ -106,7 +106,7 @@ class JanusImageProcessor(BaseImageProcessor):
do_normalize: bool = True,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_convert_rgb: bool = None,
do_convert_rgb: Optional[bool] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
@@ -411,13 +411,13 @@ class JanusImageProcessor(BaseImageProcessor):
def postprocess(
self,
images: ImageInput,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,
image_mean: List[float] = None,
image_std: List[float] = None,
input_data_format: str = None,
return_tensors: str = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[List[float]] = None,
image_std: Optional[List[float]] = None,
input_data_format: Optional[str] = None,
return_tensors: Optional[str] = None,
):
"""Applies post-processing to the decoded image tokens by reversing transformations applied during preprocessing."""
do_rescale = do_rescale if do_rescale is not None else self.do_rescale

18
src/transformers/models/janus/modular_janus.py
View File

@@ -1508,7 +1508,7 @@ class JanusImageProcessor(BlipImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
min_size: int = 14,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
@@ -1516,7 +1516,7 @@ class JanusImageProcessor(BlipImageProcessor):
do_normalize: bool = True,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_convert_rgb: bool = None,
do_convert_rgb: Optional[bool] = None,
**kwargs,
):
super().__init__(**kwargs)
@@ -1673,13 +1673,13 @@ class JanusImageProcessor(BlipImageProcessor):
def postprocess(
self,
images: ImageInput,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,
image_mean: List[float] = None,
image_std: List[float] = None,
input_data_format: str = None,
return_tensors: str = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[List[float]] = None,
image_std: Optional[List[float]] = None,
input_data_format: Optional[str] = None,
return_tensors: Optional[str] = None,
):
"""Applies post-processing to the decoded image tokens by reversing transformations applied during preprocessing."""
do_rescale = do_rescale if do_rescale is not None else self.do_rescale

4
src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2_fast.py
View File

@@ -14,7 +14,7 @@
# limitations under the License.
"""Fast Image processor class for MobileNetV2."""
from typing import List, Tuple
from typing import List, Optional, Tuple
from ...image_processing_utils_fast import BASE_IMAGE_PROCESSOR_FAST_DOCSTRING, BaseImageProcessorFast
from ...image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, PILImageResampling
@@ -42,7 +42,7 @@ class MobileNetV2ImageProcessorFast(BaseImageProcessorFast):
do_normalize = True
do_convert_rgb = None
def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple] = None):
def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[List[Tuple]] = None):
"""
Converts the output of [`MobileNetV2ForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.