Uniformize kwargs for Udop processor and update docs (#33628)

* Add optional kwargs and uniformize udop

* cleanup Unpack

* nit Udop

src/transformers/models/udop/modeling_udop.py

@@ -1790,7 +1790,7 @@ class UdopForConditionalGeneration(UdopPreTrainedModel):
         >>> # one can use the various task prefixes (prompts) used during pre-training
         >>> # e.g. the task prefix for DocVQA is "Question answering. "
         >>> question = "Question answering. What is the date on the form?"
-        >>> encoding = processor(image, question, words, boxes=boxes, return_tensors="pt")
+        >>> encoding = processor(image, question, text_pair=words, boxes=boxes, return_tensors="pt")
 
         >>> # autoregressive generation
         >>> predicted_ids = model.generate(**encoding)

src/transformers/models/udop/processing_udop.py

@@ -18,10 +18,38 @@ Processor class for UDOP.
 
 from typing import List, Optional, Union
 
+from transformers import logging
+
+from ...image_processing_utils import BatchFeature
 from ...image_utils import ImageInput
-from ...processing_utils import ProcessorMixin
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack
-from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
-from ...utils import TensorType
+
+
+logger = logging.get_logger(__name__)
+
+
+class UdopTextKwargs(TextKwargs, total=False):
+    word_labels: Optional[Union[List[int], List[List[int]]]]
+    boxes: Union[List[List[int]], List[List[List[int]]]]
+
+
+class UdopProcessorKwargs(ProcessingKwargs, total=False):
+    text_kwargs: UdopTextKwargs
+    _defaults = {
+        "text_kwargs": {
+            "add_special_tokens": True,
+            "padding": False,
+            "truncation": False,
+            "stride": 0,
+            "return_overflowing_tokens": False,
+            "return_special_tokens_mask": False,
+            "return_offsets_mapping": False,
+            "return_length": False,
+            "verbose": True,
+        },
+        "images_kwargs": {},
+    }
 
 
 class UdopProcessor(ProcessorMixin):
@@ -49,6 +77,8 @@ class UdopProcessor(ProcessorMixin):
     attributes = ["image_processor", "tokenizer"]
     image_processor_class = "LayoutLMv3ImageProcessor"
     tokenizer_class = ("UdopTokenizer", "UdopTokenizerFast")
+    # For backward compatibility. See transformers.processing_utils.ProcessorMixin.prepare_and_validate_optional_call_args for more details.
+    optional_call_args = ["text_pair"]
 
     def __init__(self, image_processor, tokenizer):
         super().__init__(image_processor, tokenizer)
@@ -57,28 +87,16 @@ class UdopProcessor(ProcessorMixin):
         self,
         images: Optional[ImageInput] = None,
         text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
-        text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
+        # The following is to capture `text_pair` argument that may be passed as a positional argument.
-        boxes: Union[List[List[int]], List[List[List[int]]]] = None,
+        # See transformers.processing_utils.ProcessorMixin.prepare_and_validate_optional_call_args for more details,
-        word_labels: Optional[Union[List[int], List[List[int]]]] = None,
+        # or this conversation for more context: https://github.com/huggingface/transformers/pull/32544#discussion_r1720208116
-        text_target: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        # This behavior is only needed for backward compatibility and will be removed in future versions.
-        text_pair_target: Optional[
+        #
-            Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]
+        *args,
-        ] = None,
+        audio=None,
-        add_special_tokens: bool = True,
+        videos=None,
-        padding: Union[bool, str, PaddingStrategy] = False,
+        **kwargs: Unpack[UdopProcessorKwargs],
-        truncation: Union[bool, str, TruncationStrategy] = False,
+    ) -> BatchFeature:
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-    ) -> BatchEncoding:
         """
         This method first forwards the `images` argument to [`~UdopImageProcessor.__call__`]. In case
         [`UdopImageProcessor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
@@ -93,6 +111,20 @@ class UdopProcessor(ProcessorMixin):
         Please refer to the docstring of the above two methods for more information.
         """
         # verify input
+        output_kwargs = self._merge_kwargs(
+            UdopProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+            **self.prepare_and_validate_optional_call_args(*args),
+        )
+
+        boxes = output_kwargs["text_kwargs"].pop("boxes", None)
+        word_labels = output_kwargs["text_kwargs"].pop("word_labels", None)
+        text_pair = output_kwargs["text_kwargs"].pop("text_pair", None)
+        return_overflowing_tokens = output_kwargs["text_kwargs"].get("return_overflowing_tokens", False)
+        return_offsets_mapping = output_kwargs["text_kwargs"].get("return_offsets_mapping", False)
+        text_target = output_kwargs["text_kwargs"].get("text_target", None)
+
         if self.image_processor.apply_ocr and (boxes is not None):
             raise ValueError(
                 "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."
@@ -103,69 +135,47 @@ class UdopProcessor(ProcessorMixin):
                 "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."
             )
 
-        if return_overflowing_tokens is True and return_offsets_mapping is False:
+        if return_overflowing_tokens and not return_offsets_mapping:
             raise ValueError("You cannot return overflowing tokens without returning the offsets mapping.")
 
         if text_target is not None:
             # use the processor to prepare the targets of UDOP
             return self.tokenizer(
-                text_target=text_target,
+                **output_kwargs["text_kwargs"],
-                text_pair_target=text_pair_target,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                stride=stride,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_token_type_ids=return_token_type_ids,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_length=return_length,
-                verbose=verbose,
-                return_tensors=return_tensors,
             )
 
         else:
             # use the processor to prepare the inputs of UDOP
             # first, apply the image processor
-            features = self.image_processor(images=images, return_tensors=return_tensors)
+            features = self.image_processor(images=images, **output_kwargs["images_kwargs"])
+            features_words = features.pop("words", None)
+            features_boxes = features.pop("boxes", None)
+
+            output_kwargs["text_kwargs"].pop("text_target", None)
+            output_kwargs["text_kwargs"].pop("text_pair_target", None)
+            output_kwargs["text_kwargs"]["text_pair"] = text_pair
+            output_kwargs["text_kwargs"]["boxes"] = boxes if boxes is not None else features_boxes
+            output_kwargs["text_kwargs"]["word_labels"] = word_labels
 
             # second, apply the tokenizer
             if text is not None and self.image_processor.apply_ocr and text_pair is None:
                 if isinstance(text, str):
                     text = [text]  # add batch dimension (as the image processor always adds a batch dimension)
-                text_pair = features["words"]
+                output_kwargs["text_kwargs"]["text_pair"] = features_words
 
             encoded_inputs = self.tokenizer(
-                text=text if text is not None else features["words"],
+                text=text if text is not None else features_words,
-                text_pair=text_pair if text_pair is not None else None,
+                **output_kwargs["text_kwargs"],
-                boxes=boxes if boxes is not None else features["boxes"],
-                word_labels=word_labels,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                stride=stride,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_token_type_ids=return_token_type_ids,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_length=return_length,
-                verbose=verbose,
-                return_tensors=return_tensors,
             )
 
             # add pixel values
-            pixel_values = features.pop("pixel_values")
             if return_overflowing_tokens is True:
-                pixel_values = self.get_overflowing_images(pixel_values, encoded_inputs["overflow_to_sample_mapping"])
+                features["pixel_values"] = self.get_overflowing_images(
-            encoded_inputs["pixel_values"] = pixel_values
+                    features["pixel_values"], encoded_inputs["overflow_to_sample_mapping"]
+                )
+            features.update(encoded_inputs)
 
-            return encoded_inputs
+            return features
 
     # Copied from transformers.models.layoutlmv3.processing_layoutlmv3.LayoutLMv3Processor.get_overflowing_images
     def get_overflowing_images(self, images, overflow_to_sample_mapping):
@@ -198,7 +208,20 @@ class UdopProcessor(ProcessorMixin):
         """
         return self.tokenizer.decode(*args, **kwargs)
 
+    def post_process_image_text_to_text(self, generated_outputs):
+        """
+        Post-process the output of the model to decode the text.
+
+        Args:
+            generated_outputs (`torch.Tensor` or `np.ndarray`):
+                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+                or `(sequence_length,)`.
+
+        Returns:
+            `List[str]`: The decoded text.
+        """
+        return self.tokenizer.batch_decode(generated_outputs, skip_special_tokens=True)
+
     @property
-    # Copied from transformers.models.layoutlmv3.processing_layoutlmv3.LayoutLMv3Processor.model_input_names
     def model_input_names(self):
-        return ["input_ids", "bbox", "attention_mask", "pixel_values"]
+        return ["pixel_values", "input_ids", "bbox", "attention_mask"]

tests/models/udop/test_processor_udop.py

@@ -12,8 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import json
-import os
 import shutil
 import tempfile
 import unittest
@@ -34,7 +32,7 @@ from transformers.testing_utils import (
     require_torch,
     slow,
 )
-from transformers.utils import FEATURE_EXTRACTOR_NAME, cached_property, is_pytesseract_available, is_torch_available
+from transformers.utils import cached_property, is_pytesseract_available, is_torch_available
 
 from ...test_processing_common import ProcessorTesterMixin
 
@@ -55,20 +53,19 @@ if is_pytesseract_available():
 class UdopProcessorTest(ProcessorTesterMixin, unittest.TestCase):
     tokenizer_class = UdopTokenizer
     rust_tokenizer_class = UdopTokenizerFast
-    maxDiff = None
     processor_class = UdopProcessor
+    maxDiff = None
 
     def setUp(self):
-        image_processor_map = {
-            "do_resize": True,
-            "size": 224,
-            "apply_ocr": True,
-        }
-
         self.tmpdirname = tempfile.mkdtemp()
-        self.feature_extraction_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME)
+        image_processor = LayoutLMv3ImageProcessor(
-        with open(self.feature_extraction_file, "w", encoding="utf-8") as fp:
+            do_resize=True,
-            fp.write(json.dumps(image_processor_map) + "\n")
+            size=224,
+            apply_ocr=True,
+        )
+        tokenizer = UdopTokenizer.from_pretrained("microsoft/udop-large")
+        processor = UdopProcessor(image_processor=image_processor, tokenizer=tokenizer)
+        processor.save_pretrained(self.tmpdirname)
 
         self.tokenizer_pretrained_name = "microsoft/udop-large"
 
@@ -80,15 +77,15 @@ class UdopProcessorTest(ProcessorTesterMixin, unittest.TestCase):
     def get_tokenizer(self, **kwargs) -> PreTrainedTokenizer:
         return self.tokenizer_class.from_pretrained(self.tokenizer_pretrained_name, **kwargs)
 
+    def get_image_processor(self, **kwargs):
+        return LayoutLMv3ImageProcessor.from_pretrained(self.tmpdirname, **kwargs)
+
     def get_rust_tokenizer(self, **kwargs) -> PreTrainedTokenizerFast:
         return self.rust_tokenizer_class.from_pretrained(self.tokenizer_pretrained_name, **kwargs)
 
     def get_tokenizers(self, **kwargs) -> List[PreTrainedTokenizerBase]:
         return [self.get_tokenizer(**kwargs), self.get_rust_tokenizer(**kwargs)]
 
-    def get_image_processor(self, **kwargs):
-        return LayoutLMv3ImageProcessor.from_pretrained(self.tmpdirname, **kwargs)
-
     def tearDown(self):
         shutil.rmtree(self.tmpdirname)
 
@@ -153,7 +150,7 @@ class UdopProcessorTest(ProcessorTesterMixin, unittest.TestCase):
         input_str = "lower newer"
         image_input = self.prepare_image_inputs()
 
-        inputs = processor(text=input_str, images=image_input)
+        inputs = processor(images=image_input, text=input_str)
 
         self.assertListEqual(list(inputs.keys()), processor.model_input_names)
 
@@ -472,7 +469,7 @@ class UdopProcessorIntegrationTests(unittest.TestCase):
             question = "What's his name?"
             words = ["hello", "world"]
             boxes = [[1, 2, 3, 4], [5, 6, 7, 8]]
-            input_processor = processor(images[0], question, words, boxes, return_tensors="pt")
+            input_processor = processor(images[0], question, text_pair=words, boxes=boxes, return_tensors="pt")
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]
@@ -488,7 +485,9 @@ class UdopProcessorIntegrationTests(unittest.TestCase):
             questions = ["How old is he?", "what's the time"]
             words = [["hello", "world"], ["my", "name", "is", "niels"]]
             boxes = [[[1, 2, 3, 4], [5, 6, 7, 8]], [[3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3]]]
-            input_processor = processor(images, questions, words, boxes, padding=True, return_tensors="pt")
+            input_processor = processor(
+                images, questions, text_pair=words, boxes=boxes, padding=True, return_tensors="pt"
+            )
 
             # verify keys
             expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]