Update processing_phi3_v.py

processing_phi3_v.py

@@ -328,13 +328,13 @@ class Phi3VProcessor(ProcessorMixin):
         self.img_tokens = [f"<|image_{i + 1}|>" for i in range(1000000)]
 
     def __call__(
-            self,
-            text: Union[TextInput, List[TextInput]],
-            images: ImageInput = None,
-            padding: Union[bool, str, PaddingStrategy] = False,
-            truncation: Union[bool, str, TruncationStrategy] = None,
-            max_length=None,
-            return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
+        self,
+        text: Union[TextInput, List[TextInput]],
+        images: ImageInput = None,
+        padding: Union[bool, str, PaddingStrategy] = False,
+        truncation: Union[bool, str, TruncationStrategy] = None,
+        max_length=None,
+        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
     ) -> BatchFeature:
         """
         Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
@@ -415,11 +415,9 @@ class Phi3VProcessor(ProcessorMixin):
     def get_special_image_token_id(self):
         return self.tokenizer.convert_tokens_to_ids(self.special_image_token)
 
-    def _convert_images_texts_to_inputs(self, images, texts, padding=False, truncation=None, max_length=None,
-                                        return_tensors=None):
+    def _convert_images_texts_to_inputs(self, images, texts, padding=False, truncation=None, max_length=None, return_tensors=None):
         if not len(images):
-            model_inputs = self.tokenizer(texts, return_tensors=return_tensors, padding=padding, truncation=truncation,
-                                          max_length=max_length)
+            model_inputs = self.tokenizer(texts, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length)
             return BatchFeature(data={**model_inputs})
 
         pattern = r"<\|image_\d+\|>"