fix lint

projects/LayoutLMv3/datasets/transforms/layoutlmv3_transforms.py

@@ -2,17 +2,17 @@
 from typing import Dict, List, Optional, Union
 
 from mmcv.transforms.base import BaseTransform
-
-from mmocr.registry import TRANSFORMS
-from projects.LayoutLMv3.utils.bio_label_utils import \
-    find_other_label_name_of_biolabel
 from transformers import LayoutLMv3ImageProcessor, LayoutXLMTokenizerFast
 from transformers.file_utils import PaddingStrategy
 from transformers.image_processing_utils import BatchFeature
 from transformers.image_utils import ChannelDimension
 from transformers.tokenization_utils_base import (BatchEncoding,
                                                   TruncationStrategy)
 
+from mmocr.registry import TRANSFORMS
+from projects.LayoutLMv3.utils.bio_label_utils import \
+    find_other_label_name_of_biolabel
+
 
 @TRANSFORMS.register_module()
 class LoadProcessorFromPretrainedModel(BaseTransform):

projects/LayoutLMv3/models/hf_layoutlmv3_wrapper.py

@@ -3,12 +3,12 @@
 
 import torch
 from mmengine.model import BaseModel
+from transformers import LayoutLMv3ForTokenClassification
+from transformers.modeling_outputs import TokenClassifierOutput
 
 from mmocr.registry import MODELS
 from projects.LayoutLMv3.utils.typing_utils import (OptSERSampleList,
                                                     SERSampleList)
-from transformers import LayoutLMv3ForTokenClassification
-from transformers.modeling_outputs import TokenClassifierOutput
 
 ForwardResults = Union[Dict[str, torch.Tensor], SERSampleList,
                        Tuple[torch.Tensor], torch.Tensor]

projects/LayoutLMv3/models/loss_processor.py

@@ -1,6 +1,7 @@
-from mmocr.registry import MODELS
 from transformers.trainer_pt_utils import LabelSmoother
 
+from mmocr.registry import MODELS
+
 
 @MODELS.register_module()
 class ComputeLossAfterLabelSmooth(LabelSmoother):