Fixed code style issue

Signed-off-by: Cheng, Penghui <penghui.cheng@intel.com>

Author: PenghuiCheng

optimum/exporters/openvino/model_patcher.py

@@ -367,9 +367,9 @@ def _llama_gemma_update_causal_mask_legacy(self, attention_mask, input_tensor, c
                 offset = 0
             mask_shape = attention_mask.shape
             mask_slice = (attention_mask.eq(0.0)).to(dtype=dtype) * min_dtype
-            causal_mask[
-                : mask_shape[0], : mask_shape[1], offset : mask_shape[2] + offset, : mask_shape[3]
-            ] = mask_slice
+            causal_mask[: mask_shape[0], : mask_shape[1], offset : mask_shape[2] + offset, : mask_shape[3]] = (
+                mask_slice
+            )
 
     if (
         self.config._attn_implementation == "sdpa"
@@ -1640,9 +1640,9 @@ def _dbrx_update_causal_mask_legacy(
                 offset = 0
             mask_shape = attention_mask.shape
             mask_slice = (attention_mask.eq(0.0)).to(dtype=dtype) * min_dtype
-            causal_mask[
-                : mask_shape[0], : mask_shape[1], offset : mask_shape[2] + offset, : mask_shape[3]
-            ] = mask_slice
+            causal_mask[: mask_shape[0], : mask_shape[1], offset : mask_shape[2] + offset, : mask_shape[3]] = (
+                mask_slice
+            )
 
     if (
         self.config._attn_implementation == "sdpa"

optimum/intel/neural_compressor/quantization.py

@@ -25,10 +25,10 @@
 import torch
 from datasets import Dataset, load_dataset
 from neural_compressor.config import PostTrainingQuantConfig
-from neural_compressor.utils.export import torch_to_int8_onnx
 from neural_compressor.model.onnx_model import ONNXModel
 from neural_compressor.model.torch_model import IPEXModel, PyTorchModel
 from neural_compressor.quantization import fit
+from neural_compressor.utils.export import torch_to_int8_onnx
 from packaging.version import parse
 from torch.utils.data import DataLoader, RandomSampler
 from transformers import (

optimum/intel/openvino/modeling_decoder.py

@@ -571,9 +571,11 @@ def _expand_outputs_for_generation(self, indicies, logits: torch.Tensor, past_ke
                 ):
                     past_key_values = tuple(
                         tuple(
-                            past_state[indicies]
-                            if not self.config.model_type == "chatglm"
-                            else past_state[:, indicies, ...]
+                            (
+                                past_state[indicies]
+                                if not self.config.model_type == "chatglm"
+                                else past_state[:, indicies, ...]
+                            )
                             for past_state in layer_past
                         )
                         for layer_past in past_key_values