Chat template: update for processor (#35953)

* update

* we need batched nested input to always process correctly

* update a bit

* fix copies

src/transformers/models/aria/image_processing_aria.py

@@ -110,6 +110,8 @@ class AriaImageProcessor(BaseImageProcessor):
             The resampling filter to use if resizing the image.
     """
 
+    model_input_names = ["pixel_values", "pixel_mask", "num_crops"]
+
     def __init__(
         self,
         image_mean: List[float] = None,

src/transformers/models/aria/modular_aria.py

@@ -499,6 +499,8 @@ class AriaImageProcessor(BaseImageProcessor):
             The resampling filter to use if resizing the image.
     """
 
+    model_input_names = ["pixel_values", "pixel_mask", "num_crops"]
+
     def __init__(
         self,
         image_mean: List[float] = None,
@@ -997,6 +999,10 @@ class AriaProcessor(ProcessorMixin):
     def model_input_names(self):
         tokenizer_input_names = self.tokenizer.model_input_names
         image_processor_input_names = self.image_processor.model_input_names
+
+        # Remove `num_crops`, it is popped and used only when processing. Make a copy of list when remocing
+        # otherwise `self.image_processor.model_input_names` is also modified
+        image_processor_input_names = [name for name in image_processor_input_names if name != "num_crops"]
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
 
 

src/transformers/models/aria/processing_aria.py

@@ -158,6 +158,10 @@ class AriaProcessor(ProcessorMixin):
     def model_input_names(self):
         tokenizer_input_names = self.tokenizer.model_input_names
         image_processor_input_names = self.image_processor.model_input_names
+
+        # Remove `num_crops`, it is popped and used only when processing. Make a copy of list when remocing
+        # otherwise `self.image_processor.model_input_names` is also modified
+        image_processor_input_names = [name for name in image_processor_input_names if name != "num_crops"]
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))