Processor accepts any kwargs

src/transformers/models/blip/processing_blip.py

@@ -39,10 +39,11 @@ class BlipProcessor(ProcessorMixin):
     """
 
     attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = []
     image_processor_class = "BlipImageProcessor"
     tokenizer_class = ("BertTokenizer", "BertTokenizerFast")
 
-    def __init__(self, image_processor, tokenizer):
+    def __init__(self, image_processor, tokenizer, **kwargs):
         tokenizer.return_token_type_ids = False
         super().__init__(image_processor, tokenizer)
         self.current_processor = self.image_processor

src/transformers/models/blip_2/processing_blip_2.py

@@ -39,11 +39,12 @@ class Blip2Processor(ProcessorMixin):
     """
 
     attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = []
     image_processor_class = "BlipImageProcessor"
     tokenizer_class = "AutoTokenizer"
 
     # Copied from transformers.models.blip.processing_blip.BlipProcessor.__init__
-    def __init__(self, image_processor, tokenizer):
+    def __init__(self, image_processor, tokenizer, **kwargs):
         tokenizer.return_token_type_ids = False
         super().__init__(image_processor, tokenizer)
         self.current_processor = self.image_processor

src/transformers/models/fuyu/processing_fuyu.py

@@ -322,10 +322,11 @@ class FuyuProcessor(ProcessorMixin):
     """
 
     attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = []
     image_processor_class = "FuyuImageProcessor"
     tokenizer_class = "AutoTokenizer"
 
-    def __init__(self, image_processor, tokenizer):
+    def __init__(self, image_processor, tokenizer, **kwargs):
         super().__init__(image_processor=image_processor, tokenizer=tokenizer)
         self.image_processor = image_processor
         self.tokenizer = tokenizer

src/transformers/models/idefics/processing_idefics.py

@@ -173,6 +173,7 @@ class IdeficsProcessor(ProcessorMixin):
     """
 
     attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = ["image_size", "add_end_of_utterance_token"]
     image_processor_class = "IdeficsImageProcessor"
     tokenizer_class = "LlamaTokenizerFast"
 

src/transformers/models/idefics2/processing_idefics2.py

@@ -61,6 +61,7 @@ class Idefics2Processor(ProcessorMixin):
     """
 
     attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = ["image_seq_len", "chat_template"]
     image_processor_class = "Idefics2ImageProcessor"
     tokenizer_class = "AutoTokenizer"
 

src/transformers/models/instructblip/processing_instructblip.py

@@ -40,15 +40,16 @@ class InstructBlipProcessor(ProcessorMixin):
             An instance of [`BlipImageProcessor`]. The image processor is a required input.
         tokenizer (`AutoTokenizer`):
             An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input.
-        qformer_tokenizer (`AutoTokenizer`):
+        qformer_tokenizer (`AutoTokenizer`, *optional*):
             An instance of ['PreTrainedTokenizer`]. The Q-Former tokenizer is a required input.
     """
 
     attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = []
     image_processor_class = "BlipImageProcessor"
     tokenizer_class = "AutoTokenizer"
 
-    def __init__(self, image_processor, tokenizer, qformer_tokenizer):
+    def __init__(self, image_processor, tokenizer, qformer_tokenizer=None, **kwargs):
         super().__init__(image_processor, tokenizer)
 
         # add QFormer tokenizer
@@ -167,7 +168,11 @@ def save_pretrained(self, save_directory, **kwargs):
     # overwrite to load the Q-Former tokenizer from a separate folder
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
+        processor = super().from_pretrained(pretrained_model_name_or_path, **kwargs)
+
+        # if return_unused_kwargs a tuple is returned where the second element is 'unused_kwargs'
+        if isinstance(processor, tuple):
+            processor = processor[0]
         qformer_tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="qformer_tokenizer")
-        args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, **kwargs)
-        args.append(qformer_tokenizer)
-        return cls(*args)
+        processor.qformer_tokenizer = qformer_tokenizer
+        return processor

src/transformers/models/instructblipvideo/processing_instructblipvideo.py

@@ -40,15 +40,16 @@ class InstructBlipVideoProcessor(ProcessorMixin):
             An instance of [`InstructBlipVideoImageProcessor`]. The image processor is a required input.
         tokenizer (`AutoTokenizer`):
             An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input.
-        qformer_tokenizer (`AutoTokenizer`):
+        qformer_tokenizer (`AutoTokenizer`, *optional*):
             An instance of ['PreTrainedTokenizer`]. The Q-Former tokenizer is a required input.
     """
 
     attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = []
     image_processor_class = "InstructBlipVideoImageProcessor"
     tokenizer_class = "AutoTokenizer"
 
-    def __init__(self, image_processor, tokenizer, qformer_tokenizer):
+    def __init__(self, image_processor, tokenizer, qformer_tokenizer=None, **kwargs):
         super().__init__(image_processor, tokenizer)
 
         # add QFormer tokenizer
@@ -164,7 +165,11 @@ def save_pretrained(self, save_directory, **kwargs):
     # overwrite to load the Q-Former tokenizer from a separate folder
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
+        processor = super().from_pretrained(pretrained_model_name_or_path, **kwargs)
+
+        # if return_unused_kwargs a tuple is returned where the second element is 'unused_kwargs'
+        if isinstance(processor, tuple):
+            processor = processor[0]
         qformer_tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="qformer_tokenizer")
-        args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, **kwargs)
-        args.append(qformer_tokenizer)
-        return cls(*args)
+        processor.qformer_tokenizer = qformer_tokenizer
+        return processor

src/transformers/models/kosmos2/processing_kosmos2.py

@@ -54,10 +54,11 @@ class Kosmos2Processor(ProcessorMixin):
     """
 
     attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = ["num_patch_index_tokens"]
     image_processor_class = "CLIPImageProcessor"
     tokenizer_class = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
 
-    def __init__(self, image_processor, tokenizer, num_patch_index_tokens=1024):
+    def __init__(self, image_processor, tokenizer, num_patch_index_tokens=1024, *kwargs):
         tokenizer.return_token_type_ids = False
 
         self.eod_token = "</doc>"

src/transformers/models/llava/processing_llava.py

@@ -42,10 +42,11 @@ class LlavaProcessor(ProcessorMixin):
     """
 
     attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = ["chat_template"]
     image_processor_class = "AutoImageProcessor"
     tokenizer_class = "AutoTokenizer"
 
-    def __init__(self, image_processor=None, tokenizer=None, chat_template=None):
+    def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs):
         super().__init__(image_processor, tokenizer, chat_template=chat_template)
 
     def __call__(

src/transformers/models/llava_next/processing_llava_next.py

@@ -42,10 +42,11 @@ class LlavaNextProcessor(ProcessorMixin):
     """
 
     attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = ["chat_template"]
     image_processor_class = "AutoImageProcessor"
     tokenizer_class = "AutoTokenizer"
 
-    def __init__(self, image_processor=None, tokenizer=None, chat_template=None):
+    def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs):
         super().__init__(image_processor, tokenizer, chat_template=chat_template)
 
     def __call__(

src/transformers/models/llava_next_video/processing_llava_next_video.py

@@ -53,11 +53,12 @@ class LlavaNextVideoProcessor(ProcessorMixin):
     # video and image processor share same args, but have different processing logic
     # only image processor config is saved in the hub
     attributes = ["video_processor", "image_processor", "tokenizer"]
+    valid_kwargs = ["chat_template"]
     image_processor_class = "LlavaNextImageProcessor"
     video_processor_class = "LlavaNextVideoImageProcessor"
     tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
 
-    def __init__(self, video_processor=None, image_processor=None, tokenizer=None, chat_template=None):
+    def __init__(self, video_processor=None, image_processor=None, tokenizer=None, chat_template=None, **kwargs):
         super().__init__(video_processor, image_processor, tokenizer, chat_template=chat_template)
 
     def __call__(

src/transformers/models/paligemma/processing_paligemma.py

@@ -85,16 +85,21 @@ class PaliGemmaProcessor(ProcessorMixin):
             The image processor is a required input.
         tokenizer ([`LlamaTokenizerFast`], *optional*):
             The tokenizer is a required input.
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
     """
 
     attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = ["chat_template"]
     image_processor_class = "SiglipImageProcessor"
     tokenizer_class = ("GemmaTokenizer", "GemmaTokenizerFast")
 
     def __init__(
         self,
         image_processor=None,
         tokenizer=None,
+        chat_template=None,
+        **kwargs,
     ):
         if image_processor is None:
             raise ValueError("You need to specify an `image_processor`.")
@@ -113,7 +118,7 @@ def __init__(
         tokenizer.add_bos_token = False
         tokenizer.add_eos_token = False
 
-        super().__init__(image_processor, tokenizer)
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
 
     def __call__(
         self,

src/transformers/models/video_llava/processing_video_llava.py

@@ -37,14 +37,17 @@ class VideoLlavaProcessor(ProcessorMixin):
             The image processor is a required input.
         tokenizer ([`LlamaTokenizerFast`], *optional*):
             The tokenizer is a required input.
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
     """
 
     attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = ["chat_template"]
     image_processor_class = "VideoLlavaImageProcessor"
     tokenizer_class = "AutoTokenizer"
 
-    def __init__(self, image_processor=None, tokenizer=None):
-        super().__init__(image_processor, tokenizer)
+    def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs):
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
 
     def __call__(
         self,

src/transformers/processing_utils.py

@@ -320,6 +320,7 @@ class ProcessorMixin(PushToHubMixin):
     feature_extractor_class = None
     tokenizer_class = None
     _auto_class = None
+    valid_kwargs: List[str] = []
 
     # args have to match the attributes class attribute
     def __init__(self, *args, **kwargs):
@@ -648,21 +649,23 @@ def from_args_and_dict(cls, args, processor_dict: Dict[str, Any], **kwargs):
         processor_dict = processor_dict.copy()
         return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
 
-        # Unlike image processors or feature extractors whose `__init__` accept `kwargs`, processor don't have `kwargs`.
-        # We have to pop up some unused (but specific) arguments to make it work.
+        # We have to pop up some unused (but specific) kwargs and then validate that it doesn't contain unused kwargs
+        # If we don't pop, some specific kwargs will raise a warning
         if "processor_class" in processor_dict:
             del processor_dict["processor_class"]
 
         if "auto_map" in processor_dict:
             del processor_dict["auto_map"]
 
+        unused_kwargs = cls.validate_init_kwargs(processor_config=processor_dict, valid_kwargs=cls.valid_kwargs)
         processor = cls(*args, **processor_dict)
 
         # Update processor with kwargs if needed
         for key in set(kwargs.keys()):
             if hasattr(processor, key):
                 setattr(processor, key, kwargs.pop(key))
 
+        kwargs.update(unused_kwargs)
         logger.info(f"Processor {processor}")
         if return_unused_kwargs:
             return processor, kwargs
@@ -887,6 +890,19 @@ def model_input_names(self):
         first_attribute = getattr(self, self.attributes[0])
         return getattr(first_attribute, "model_input_names", None)
 
+    @staticmethod
+    def validate_init_kwargs(processor_config, valid_kwargs):
+        kwargs_from_config = processor_config.keys()
+        unused_kwargs = {}
+        unused_keys = set(kwargs_from_config) - set(valid_kwargs)
+        if unused_keys:
+            unused_key_str = ", ".join(unused_keys)
+            logger.warning(
+                f"Some kwargs in processor config are unused and will not have any effect: {unused_key_str}. "
+            )
+            unused_kwargs = {k: processor_config[k] for k in unused_keys}
+        return unused_kwargs
+
     def apply_chat_template(
         self,
         conversation: Union[List[Dict[str, str]]],