apply style

Author: l-bat

optimum/exporters/openvino/model_configs.py

@@ -149,9 +149,9 @@ def init_model_configs():
         "Gemma3ForConditionalGeneration",
     )
 
-    TasksManager._TRANSFORMERS_TASKS_TO_MODEL_LOADERS[
-        "image-text-to-text"
-    ] = TasksManager._TRANSFORMERS_TASKS_TO_MODEL_LOADERS["text-generation"]
+    TasksManager._TRANSFORMERS_TASKS_TO_MODEL_LOADERS["image-text-to-text"] = (
+        TasksManager._TRANSFORMERS_TASKS_TO_MODEL_LOADERS["text-generation"]
+    )
 
     TasksManager._TRANSFORMERS_TASKS_TO_MODEL_LOADERS["video-text-to-text"] = "AutoModelForVision2Seq"
 

optimum/exporters/openvino/model_patcher.py

@@ -430,9 +430,9 @@ 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"
@@ -2073,9 +2073,9 @@ 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/openvino/modeling_base.py

@@ -111,9 +111,9 @@ def __init__(
         for idx, key in enumerate(model.inputs):
             names = tuple(key.get_names())
             input_names[next((name for name in names if "/" not in name), names[0])] = idx
-            input_dtypes[
-                next((name for name in names if "/" not in name), names[0])
-            ] = key.get_element_type().get_type_name()
+            input_dtypes[next((name for name in names if "/" not in name), names[0])] = (
+                key.get_element_type().get_type_name()
+            )
         self.input_names = input_names
         self.input_dtypes = input_dtypes
 
@@ -122,9 +122,9 @@ def __init__(
         for idx, key in enumerate(model.outputs):
             names = tuple(key.get_names())
             output_names[next((name for name in names if "/" not in name), names[0])] = idx
-            output_dtypes[
-                next((name for name in names if "/" not in name), names[0])
-            ] = key.get_element_type().get_type_name()
+            output_dtypes[next((name for name in names if "/" not in name), names[0])] = (
+                key.get_element_type().get_type_name()
+            )
 
         self.output_names = output_names
         self.output_dtypes = output_dtypes

optimum/intel/openvino/modeling_diffusion.py

@@ -881,9 +881,11 @@ def reshape(
             self.text_encoder.model = self._reshape_text_encoder(
                 self.text_encoder.model,
                 batch_size,
-                getattr(self.tokenizer, "model_max_length", -1)
-                if "Gemma" not in self.tokenizer.__class__.__name__
-                else -1,
+                (
+                    getattr(self.tokenizer, "model_max_length", -1)
+                    if "Gemma" not in self.tokenizer.__class__.__name__
+                    else -1
+                ),
             )
 
         if self.text_encoder_2 is not None: