Add support for args to ProcessorMixin for backward compatibility

src/transformers/models/llava_onevision/processing_llava_onevision.py

@@ -57,11 +57,11 @@ class LlavaOnevisionProcessor(ProcessorMixin):
     r"""
     Constructs a LLaVa-Onevision processor which wraps a LLaVa-Onevision video processor, LLaVa-NeXT image processor and a LLaMa tokenizer into a single processor.
 
-    [`LlavaNextProcessor`] offers all the functionalities of [`LlavaOnevisionVideoProcessor`], [`LlavaNextImageProcessor`] and [`LlamaTokenizerFast`]. See the
+    [`LlavaNextProcessor`] offers all the functionalities of [`LlavaOnevisionVideoProcessor`], [`LlavaOnevisionImageProcessor`] and [`LlamaTokenizerFast`]. See the
     [`~LlavaOnevisionVideoProcessor.__call__`], [`~LlavaNextProcessor.__call__`] and [`~LlavaNextProcessor.decode`] for more information.
 
     Args:
-        image_processor ([`LlavaNextImageProcessor`], *optional*):
+        image_processor ([`LlavaOnevisionImageProcessor`], *optional*):
             The image processor is a required input.
         tokenizer ([`LlamaTokenizerFast`], *optional*):
             The tokenizer is a required input.
@@ -114,6 +114,7 @@ def __call__(
         self,
         images: ImageInput = None,
         text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        audio=None,
         videos: VideoInput = None,
         **kwargs: Unpack[LlavaOnevisionProcessorKwargs],
     ) -> BatchFeature:

src/transformers/processing_utils.py

@@ -38,7 +38,9 @@
 
 from .tokenization_utils_base import (
     PaddingStrategy,
+    PreTokenizedInput,
     PreTrainedTokenizerBase,
+    TextInput,
     TruncationStrategy,
 )
 from .utils import (
@@ -114,6 +116,9 @@ class TextKwargs(TypedDict, total=False):
             The side on which padding will be applied.
     """
 
+    text_pair: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]]
+    text_target: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]
+    text_pair_target: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]]
     add_special_tokens: Optional[bool]
     padding: Union[bool, str, PaddingStrategy]
     truncation: Union[bool, str, TruncationStrategy]
@@ -328,6 +333,7 @@ class ProcessorMixin(PushToHubMixin):
 
     attributes = ["feature_extractor", "tokenizer"]
     optional_attributes = ["chat_template"]
+    optional_call_args: List[str] = []
     # Names need to be attr_class for attr in attributes
     feature_extractor_class = None
     tokenizer_class = None
@@ -973,6 +979,64 @@ def validate_init_kwargs(processor_config, valid_kwargs):
             unused_kwargs = {k: processor_config[k] for k in unused_keys}
         return unused_kwargs
 
+    def prepare_and_validate_optional_call_args(self, *args):
+        """
+        Matches optional positional arguments to their corresponding names in `optional_call_args`
+        in the processor class in the order they are passed to the processor call.
+
+        Note that this should only be used in the `__call__` method of the processors with special
+        arguments. Special arguments are arguments that aren't `text`, `images`, `audio`, nor `videos`
+        but also aren't passed to the tokenizer, image processor, etc. Examples of such processors are:
+            - `CLIPSegProcessor`
+            - `LayoutLMv2Processor`
+            - `OwlViTProcessor`
+
+        Also note that passing by position to the processor call is now deprecated and will be disallowed
+        in future versions. We only have this for backward compatibility.
+
+        Example:
+            Suppose that the processor class has `optional_call_args = ["arg_name_1", "arg_name_2"]`.
+            And we define the call method as:
+            ```python
+            def __call__(
+                self,
+                text: str,
+                images: Optional[ImageInput] = None,
+                *arg,
+                audio=None,
+                videos=None,
+            )
+            ```
+
+            Then, if we call the processor as:
+            ```python
+            images = [...]
+            processor("What is common in these images?", images, arg_value_1, arg_value_2)
+            ```
+
+            Then, this method will return:
+            ```python
+            {
+                "arg_name_1": arg_value_1,
+                "arg_name_2": arg_value_2,
+            }
+            ```
+            which we could then pass as kwargs to `self._merge_kwargs`
+        """
+        if len(args):
+            warnings.warn(
+                "Passing positional arguments to the processor call is now deprecated and will be disallowed in v4.47. "
+                "Please pass all arguments as keyword arguments."
+            )
+        if len(args) > len(self.optional_call_args):
+            raise ValueError(
+                f"Expected *at most* {len(self.optional_call_args)} optional positional arguments in processor call"
+                f"which will be matched with {' '.join(self.optional_call_args)} in the order they are passed."
+                f"However, got {len(args)} positional arguments instead."
+                "Please pass all arguments as keyword arguments instead (e.g. `processor(arg_name_1=..., arg_name_2=...))`."
+            )
+        return {arg_name: arg_value for arg_value, arg_name in zip(args, self.optional_call_args)}
+
     def apply_chat_template(
         self,
         conversation: Union[List[Dict[str, str]]],

tests/models/altclip/test_processor_altclip.py

@@ -18,7 +18,7 @@
 import unittest
 
 from transformers import XLMRobertaTokenizer, XLMRobertaTokenizerFast
-from transformers.testing_utils import require_torch, require_vision
+from transformers.testing_utils import require_vision
 from transformers.utils import is_vision_available
 
 from ...test_processing_common import ProcessorTesterMixin
@@ -50,116 +50,3 @@ def get_rust_tokenizer(self, **kwargs):
 
     def get_image_processor(self, **kwargs):
         return CLIPImageProcessor.from_pretrained(self.model_id, **kwargs)
-
-    @require_torch
-    @require_vision
-    def test_unstructured_kwargs_batched(self):
-        if "image_processor" not in self.processor_class.attributes:
-            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
-        image_processor = self.get_component("image_processor")
-        tokenizer = self.get_component("tokenizer")
-
-        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
-        self.skip_processor_without_typed_kwargs(processor)
-
-        input_str = ["lower newer", "upper older longer string"]
-        image_input = self.prepare_image_inputs() * 2
-        inputs = processor(
-            text=input_str,
-            images=image_input,
-            return_tensors="pt",
-            crop_size={"height": 214, "width": 214},
-            padding="longest",
-            max_length=76,
-        )
-        self.assertEqual(inputs["pixel_values"].shape[2], 214)
-
-        self.assertEqual(len(inputs["input_ids"][0]), 7)
-
-    def test_structured_kwargs_nested(self):
-        if "image_processor" not in self.processor_class.attributes:
-            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
-        image_processor = self.get_component("image_processor")
-        tokenizer = self.get_component("tokenizer")
-
-        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
-        self.skip_processor_without_typed_kwargs(processor)
-
-        input_str = "lower newer"
-        image_input = self.prepare_image_inputs()
-
-        # Define the kwargs for each modality
-        all_kwargs = {
-            "common_kwargs": {"return_tensors": "pt"},
-            "images_kwargs": {"crop_size": {"height": 214, "width": 214}},
-            "text_kwargs": {"padding": "max_length", "max_length": 76},
-        }
-
-        inputs = processor(text=input_str, images=image_input, **all_kwargs)
-        self.skip_processor_without_typed_kwargs(processor)
-
-        self.assertEqual(inputs["pixel_values"].shape[2], 214)
-
-        self.assertEqual(len(inputs["input_ids"][0]), 76)
-
-    def test_structured_kwargs_nested_from_dict(self):
-        if "image_processor" not in self.processor_class.attributes:
-            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
-
-        image_processor = self.get_component("image_processor")
-        tokenizer = self.get_component("tokenizer")
-
-        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
-        self.skip_processor_without_typed_kwargs(processor)
-        input_str = "lower newer"
-        image_input = self.prepare_image_inputs()
-
-        # Define the kwargs for each modality
-        all_kwargs = {
-            "common_kwargs": {"return_tensors": "pt"},
-            "images_kwargs": {"crop_size": {"height": 214, "width": 214}},
-            "text_kwargs": {"padding": "max_length", "max_length": 76},
-        }
-
-        inputs = processor(text=input_str, images=image_input, **all_kwargs)
-        self.assertEqual(inputs["pixel_values"].shape[2], 214)
-
-        self.assertEqual(len(inputs["input_ids"][0]), 76)
-
-    def test_unstructured_kwargs(self):
-        if "image_processor" not in self.processor_class.attributes:
-            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
-        image_processor = self.get_component("image_processor")
-        tokenizer = self.get_component("tokenizer")
-
-        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
-        self.skip_processor_without_typed_kwargs(processor)
-
-        input_str = "lower newer"
-        image_input = self.prepare_image_inputs()
-        inputs = processor(
-            text=input_str,
-            images=image_input,
-            return_tensors="pt",
-            crop_size={"height": 214, "width": 214},
-            padding="max_length",
-            max_length=76,
-        )
-
-        self.assertEqual(inputs["pixel_values"].shape[2], 214)
-        self.assertEqual(len(inputs["input_ids"][0]), 76)
-
-    def test_image_processor_defaults_preserved_by_image_kwargs(self):
-        if "image_processor" not in self.processor_class.attributes:
-            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
-        image_processor = self.get_component("image_processor", crop_size=(234, 234))
-        tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length")
-
-        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
-        self.skip_processor_without_typed_kwargs(processor)
-
-        input_str = "lower newer"
-        image_input = self.prepare_image_inputs()
-
-        inputs = processor(text=input_str, images=image_input)
-        self.assertEqual(len(inputs["pixel_values"][0][0]), 234)

tests/models/chinese_clip/test_processor_chinese_clip.py

@@ -206,129 +206,3 @@ def test_model_input_names(self):
         inputs = processor(text=input_str, images=image_input)
 
         self.assertListEqual(list(inputs.keys()), processor.model_input_names)
-
-    def test_unstructured_kwargs_batched(self):
-        if "image_processor" not in self.processor_class.attributes:
-            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
-        image_processor = self.get_component("image_processor")
-        tokenizer = self.get_component("tokenizer")
-
-        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
-        self.skip_processor_without_typed_kwargs(processor)
-
-        input_str = ["lower newer", "upper older longer string"]
-        image_input = self.prepare_image_inputs() * 2
-        inputs = processor(
-            text=input_str,
-            images=image_input,
-            return_tensors="pt",
-            crop_size={"height": 214, "width": 214},
-            padding="longest",
-            max_length=76,
-        )
-        self.assertEqual(inputs["pixel_values"].shape[2], 214)
-
-        self.assertEqual(len(inputs["input_ids"][0]), 6)
-
-    def test_structured_kwargs_nested(self):
-        if "image_processor" not in self.processor_class.attributes:
-            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
-        image_processor = self.get_component("image_processor")
-        tokenizer = self.get_component("tokenizer")
-
-        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
-        self.skip_processor_without_typed_kwargs(processor)
-
-        input_str = "lower newer"
-        image_input = self.prepare_image_inputs()
-
-        # Define the kwargs for each modality
-        all_kwargs = {
-            "common_kwargs": {"return_tensors": "pt"},
-            "images_kwargs": {"crop_size": {"height": 214, "width": 214}},
-            "text_kwargs": {"padding": "max_length", "max_length": 76},
-        }
-
-        inputs = processor(text=input_str, images=image_input, **all_kwargs)
-        self.skip_processor_without_typed_kwargs(processor)
-
-        self.assertEqual(inputs["pixel_values"].shape[2], 214)
-
-        self.assertEqual(len(inputs["input_ids"][0]), 76)
-
-    def test_structured_kwargs_nested_from_dict(self):
-        if "image_processor" not in self.processor_class.attributes:
-            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
-
-        image_processor = self.get_component("image_processor")
-        tokenizer = self.get_component("tokenizer")
-
-        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
-        self.skip_processor_without_typed_kwargs(processor)
-        input_str = "lower newer"
-        image_input = self.prepare_image_inputs()
-
-        # Define the kwargs for each modality
-        all_kwargs = {
-            "common_kwargs": {"return_tensors": "pt"},
-            "images_kwargs": {"crop_size": {"height": 214, "width": 214}},
-            "text_kwargs": {"padding": "max_length", "max_length": 76},
-        }
-
-        inputs = processor(text=input_str, images=image_input, **all_kwargs)
-        self.assertEqual(inputs["pixel_values"].shape[2], 214)
-
-        self.assertEqual(len(inputs["input_ids"][0]), 76)
-
-    def test_unstructured_kwargs(self):
-        if "image_processor" not in self.processor_class.attributes:
-            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
-        image_processor = self.get_component("image_processor")
-        tokenizer = self.get_component("tokenizer")
-
-        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
-        self.skip_processor_without_typed_kwargs(processor)
-
-        input_str = "lower newer"
-        image_input = self.prepare_image_inputs()
-        inputs = processor(
-            text=input_str,
-            images=image_input,
-            return_tensors="pt",
-            crop_size={"height": 214, "width": 214},
-            padding="max_length",
-            max_length=76,
-        )
-
-        self.assertEqual(inputs["pixel_values"].shape[2], 214)
-        self.assertEqual(len(inputs["input_ids"][0]), 76)
-
-    def test_image_processor_defaults_preserved_by_image_kwargs(self):
-        if "image_processor" not in self.processor_class.attributes:
-            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
-        image_processor = self.get_component("image_processor", crop_size=(234, 234))
-        tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length")
-
-        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
-        self.skip_processor_without_typed_kwargs(processor)
-
-        input_str = "lower newer"
-        image_input = self.prepare_image_inputs()
-
-        inputs = processor(text=input_str, images=image_input)
-        self.assertEqual(len(inputs["pixel_values"][0][0]), 234)
-
-    def test_kwargs_overrides_default_image_processor_kwargs(self):
-        if "image_processor" not in self.processor_class.attributes:
-            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
-        image_processor = self.get_component("image_processor", crop_size=(234, 234))
-        tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length")
-
-        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
-        self.skip_processor_without_typed_kwargs(processor)
-
-        input_str = "lower newer"
-        image_input = self.prepare_image_inputs()
-
-        inputs = processor(text=input_str, images=image_input, crop_size=[224, 224])
-        self.assertEqual(len(inputs["pixel_values"][0][0]), 224)