Uniformize model processors (#31368)

* add initial design for uniform processors + align model

* add uniform processors for altclip + chinese_clip

* add uniform processors for blip + blip2

* fix mutable default 👀

* add configuration test

* handle structured kwargs w defaults + add test

* protect torch-specific test

* fix style

* fix

* rebase

* update processor to generic kwargs + test

* fix style

* add sensible kwargs merge

* update test

* fix assertEqual

* move kwargs merging to processing common

* rework kwargs for type hinting

* just get Unpack from extensions

* run-slow[align]

* handle kwargs passed as nested dict

* add from_pretrained test for nested kwargs handling

* [run-slow]align

* update documentation + imports

* update audio inputs

* protect audio types, silly

* try removing imports

* make things simpler

* simplerer

* move out kwargs test to common mixin

* [run-slow]align

* skip tests for old processors

* [run-slow]align, clip

* !$#@!! protect imports, darn it

* [run-slow]align, clip

* [run-slow]align, clip

* update common processor testing

* add altclip

* add chinese_clip

* add pad_size

* [run-slow]align, clip, chinese_clip, altclip

* remove duplicated tests

* fix

* add blip, blip2, bridgetower

Added tests for bridgetower which override common. Also modified common
tests to force center cropping if existing

* fix

* update doc

* improve documentation for default values

* add model_max_length testing

This parameter depends on tokenizers received.

* Raise if kwargs are specified in two places

* fix

* removed copied from

* match defaults

* force padding

* fix tokenizer test

* clean defaults

* move tests to common

* add missing import

* fix

* adapt bridgetower tests to shortest edge

* uniformize donut processor + tests

* add wav2vec2

* extend common testing to audio processors

* add testing + bert version

* propagate common kwargs to different modalities

* BC order of arguments

* check py version

* revert kwargs merging

* add draft overlap test

* update

* fix blip2 and wav2vec due to updates

* fix copies

* ensure overlapping kwargs do not disappear

* replace .pop by .get to handle duplicated kwargs

* fix copies

* fix missing import

* add clearly wav2vec2_bert to uniformized models

* fix copies

* increase number of features

* fix style

* [run-slow] blip, blip2, bridgetower, donut, wav2vec2, wav2vec2_bert

* [run-slow] blip, blip_2, bridgetower, donut, wav2vec2, wav2vec2_bert

* fix concatenation

* [run-slow] blip, blip_2, bridgetower, donut, wav2vec2, wav2vec2_bert

* Update tests/test_processing_common.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* 🧹

* address comments

* clean up + tests

* [run-slow] instructblip, blip, blip_2, bridgetower, donut, wav2vec2, wav2vec2_bert

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

src/transformers/models/blip/processing_blip.py

@@ -19,9 +19,25 @@ Processor class for Blip.
 from typing import List, Optional, Union
 
 from ...image_utils import ImageInput
-from ...processing_utils import ProcessorMixin
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
-from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from ...tokenization_utils_base import BatchEncoding, PreTokenizedInput, TextInput
-from ...utils import TensorType
+
+
+class BlipProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "add_special_tokens": True,
+            "padding": False,
+            "stride": 0,
+            "return_overflowing_tokens": False,
+            "return_special_tokens_mask": False,
+            "return_offsets_mapping": False,
+            "return_token_type_ids": False,
+            "return_length": False,
+            "verbose": True,
+        },
+        "images_kwargs": {},
+    }
 
 
 class BlipProcessor(ProcessorMixin):
@@ -51,85 +67,54 @@ class BlipProcessor(ProcessorMixin):
     def __call__(
         self,
         images: ImageInput = None,
-        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        text: Optional[Union[str, List[str], TextInput, PreTokenizedInput]] = None,
-        add_special_tokens: bool = True,
+        audio=None,
-        padding: Union[bool, str, PaddingStrategy] = False,
+        videos=None,
-        truncation: Union[bool, str, TruncationStrategy] = None,
+        **kwargs: Unpack[BlipProcessorKwargs],
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_token_type_ids: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        **kwargs,
     ) -> BatchEncoding:
         """
         This method uses [`BlipImageProcessor.__call__`] method to prepare image(s) for the model, and
         [`BertTokenizerFast.__call__`] to prepare text for the model.
 
         Please refer to the docstring of the above two methods for more information.
+        Args:
+            images (`ImageInput`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. Both channels-first and channels-last formats are supported.
+            text (`TextInput`, `PreTokenizedInput`, `List[TextInput]`, `List[PreTokenizedInput]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Acceptable values are:
+                    - `'tf'`: Return TensorFlow `tf.constant` objects.
+                    - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                    - `'np'`: Return NumPy `np.ndarray` objects.
+                    - `'jax'`: Return JAX `jnp.ndarray` objects.
         """
         if images is None and text is None:
             raise ValueError("You have to specify either images or text.")
 
-        # Get only text
-        if images is None:
-            self.current_processor = self.tokenizer
-            text_encoding = self.tokenizer(
-                text=text,
-                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_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_token_type_ids=return_token_type_ids,
-                return_length=return_length,
-                verbose=verbose,
-                return_tensors=return_tensors,
-                **kwargs,
-            )
-            return text_encoding
-
-        # add pixel_values
-        encoding_image_processor = self.image_processor(images, return_tensors=return_tensors)
-
-        if text is not None:
-            text_encoding = self.tokenizer(
-                text=text,
-                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_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_token_type_ids=return_token_type_ids,
-                return_length=return_length,
-                verbose=verbose,
-                return_tensors=return_tensors,
-                **kwargs,
-            )
-        else:
         text_encoding = None
 
+        # add pixel_values encoding. If we also have text_encoding, update image encoding and return it.
+        # else, return the text encoding.
+        output_kwargs = self._merge_kwargs(
+            BlipProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+        if text is not None:
+            text_encoding = self.tokenizer(text, **output_kwargs["text_kwargs"])
+        if images is not None:
+            encoding_image_processor = self.image_processor(images, **output_kwargs["images_kwargs"])
+
             if text_encoding is not None:
                 encoding_image_processor.update(text_encoding)
-
             return encoding_image_processor
 
+        return text_encoding
+
     def batch_decode(self, *args, **kwargs):
         """
         This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please

src/transformers/models/blip_2/processing_blip_2.py

@@ -18,22 +18,38 @@ Processor class for BLIP-2.
 
 from typing import List, Optional, Union
 
+from ...image_processing_utils import BatchFeature
 from ...image_utils import ImageInput
-from ...processing_utils import ProcessorMixin
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import (
     AddedToken,
     BatchEncoding,
-    PaddingStrategy,
     PreTokenizedInput,
     TextInput,
-    TruncationStrategy,
 )
-from ...utils import TensorType, logging
+from ...utils import logging
 
 
 logger = logging.get_logger(__name__)
 
 
+class Blip2ProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "add_special_tokens": True,
+            "padding": False,
+            "stride": 0,
+            "return_overflowing_tokens": False,
+            "return_special_tokens_mask": False,
+            "return_offsets_mapping": False,
+            "return_token_type_ids": False,
+            "return_length": False,
+            "verbose": True,
+        },
+        "images_kwargs": {},
+    }
+
+
 class Blip2Processor(ProcessorMixin):
     r"""
     Constructs a BLIP-2 processor which wraps a BLIP image processor and an OPT/T5 tokenizer into a single processor.
@@ -67,58 +83,44 @@ class Blip2Processor(ProcessorMixin):
     def __call__(
         self,
         images: ImageInput = None,
-        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        text: Optional[Union[str, List[str], TextInput, PreTokenizedInput]] = None,
-        add_special_tokens: bool = True,
+        audio=None,
-        padding: Union[bool, str, PaddingStrategy] = False,
+        videos=None,
-        truncation: Union[bool, str, TruncationStrategy] = None,
+        **kwargs: Unpack[Blip2ProcessorKwargs],
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_token_type_ids: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        **kwargs,
     ) -> BatchEncoding:
         """
         This method uses [`BlipImageProcessor.__call__`] method to prepare image(s) for the model, and
         [`BertTokenizerFast.__call__`] to prepare text for the model.
 
         Please refer to the docstring of the above two methods for more information.
+        Args:
+            images (`ImageInput`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. Both channels-first and channels-last formats are supported.
+            text (`TextInput`, `PreTokenizedInput`, `List[TextInput]`, `List[PreTokenizedInput]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Acceptable values are:
+                    - `'tf'`: Return TensorFlow `tf.constant` objects.
+                    - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                    - `'np'`: Return NumPy `np.ndarray` objects.
+                    - `'jax'`: Return JAX `jnp.ndarray` objects.
         """
         if images is None and text is None:
             raise ValueError("You have to specify either images or text.")
-
+        output_kwargs = self._merge_kwargs(
-        # Get only text
+            Blip2ProcessorKwargs,
-        if images is None:
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            self.current_processor = self.tokenizer
-            text_encoding = self.tokenizer(
-                text=text,
-                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_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_token_type_ids=return_token_type_ids,
-                return_length=return_length,
-                verbose=verbose,
-                return_tensors=return_tensors,
             **kwargs,
         )
-            return text_encoding
+        # BC for explicit return_tensors
-
+        if "return_tensors" in output_kwargs["common_kwargs"]:
-        # add pixel_values
+            return_tensors = output_kwargs["common_kwargs"].pop("return_tensors", None)
-        encoding_image_processor = self.image_processor(images, return_tensors=return_tensors)
+        else:
-
+            return_tensors = None
+        encoding = BatchFeature(tensor_type=return_tensors)
         if text is not None:
             if isinstance(text, str):
                 text = [text]
@@ -126,24 +128,10 @@ class Blip2Processor(ProcessorMixin):
                 raise ValueError("Invalid input text. Please provide a string, or a list of strings")
 
             text_encoding = {}
-            _text_encoding = self.tokenizer(
+
-                text=text,
+            return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-                add_special_tokens=add_special_tokens,
+            _text_encoding = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
-                padding=padding,
+            output_kwargs["text_kwargs"]["return_tensors"] = return_tensors
-                truncation=truncation,
-                max_length=max_length,
-                stride=stride,
-                pad_to_multiple_of=pad_to_multiple_of,
-                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_token_type_ids=return_token_type_ids,
-                return_length=return_length,
-                verbose=verbose,
-                return_tensors=None,  # hardcode "None" here for prepending image tokens
-                **kwargs,
-            )
 
             # if we know how many query tokens, expand text inside processor. We need this hacky manipulation
             # because BLIP expects image tokens to be at the beginning even before BOS token
@@ -164,14 +152,14 @@ class Blip2Processor(ProcessorMixin):
                 )
 
             # cast to desired return tensors type
-            text_encoding = BatchEncoding(text_encoding, tensor_type=return_tensors)
+            encoding.update(BatchEncoding(text_encoding, tensor_type=return_tensors))
-        else:
+        # add pixel_values encoding. If we also have text_encoding, update image encoding and return it.
-            text_encoding = None
+        # else, return the text encoding.
 
-        if text_encoding is not None:
+        if images is not None:
-            encoding_image_processor.update(text_encoding)
+            image_encoding = self.image_processor(images, **output_kwargs["images_kwargs"])
-
+            encoding.update(image_encoding)
-        return encoding_image_processor
+        return encoding
 
     # Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer
     def batch_decode(self, *args, **kwargs):

src/transformers/models/bridgetower/processing_bridgetower.py

@@ -16,11 +16,29 @@
 Processor class for BridgeTower.
 """
 
-from typing import List, Optional, Union
+from typing import List, Union
 
-from ...processing_utils import ProcessorMixin
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
-from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from ...tokenization_utils_base import BatchEncoding, PreTokenizedInput, TextInput
-from ...utils import TensorType
+
+
+class BridgeTowerProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "add_special_tokens": True,
+            "padding": False,
+            "stride": 0,
+            "return_overflowing_tokens": False,
+            "return_special_tokens_mask": False,
+            "return_offsets_mapping": False,
+            "return_length": False,
+            "verbose": True,
+        },
+        "images_kwargs": {
+            "do_normalize": True,
+            "do_center_crop": True,
+        },
+    }
 
 
 class BridgeTowerProcessor(ProcessorMixin):
@@ -50,21 +68,9 @@ class BridgeTowerProcessor(ProcessorMixin):
         self,
         images,
         text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
-        add_special_tokens: bool = True,
+        audio=None,
-        padding: Union[bool, str, PaddingStrategy] = False,
+        videos=None,
-        truncation: Union[bool, str, TruncationStrategy] = None,
+        **kwargs: Unpack[BridgeTowerProcessorKwargs],
-        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,
-        **kwargs,
     ) -> BatchEncoding:
         """
         This method uses [`BridgeTowerImageProcessor.__call__`] method to prepare image(s) for the model, and
@@ -72,28 +78,14 @@ class BridgeTowerProcessor(ProcessorMixin):
 
         Please refer to the docstring of the above two methods for more information.
         """
-        encoding = self.tokenizer(
+        output_kwargs = self._merge_kwargs(
-            text=text,
+            BridgeTowerProcessorKwargs,
-            add_special_tokens=add_special_tokens,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            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,
             **kwargs,
         )
+        encoding = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
         # add pixel_values + pixel_mask
-        encoding_image_processor = self.image_processor(
+        encoding_image_processor = self.image_processor(images, **output_kwargs["images_kwargs"])
-            images, return_tensors=return_tensors, do_normalize=True, do_center_crop=True, **kwargs
-        )
         encoding.update(encoding_image_processor)
 
         return encoding

src/transformers/models/donut/processing_donut.py

@@ -19,8 +19,15 @@ Processor class for Donut.
 import re
 import warnings
 from contextlib import contextmanager
+from typing import List, Optional, Union
 
-from ...processing_utils import ProcessorMixin
+from ...image_utils import ImageInput
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+
+
+class DonutProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {}
 
 
 class DonutProcessor(ProcessorMixin):
@@ -63,7 +70,14 @@ class DonutProcessor(ProcessorMixin):
         self.current_processor = self.image_processor
         self._in_target_context_manager = False
 
-    def __call__(self, *args, **kwargs):
+    def __call__(
+        self,
+        images: ImageInput = None,
+        text: Optional[Union[str, List[str], TextInput, PreTokenizedInput]] = None,
+        audio=None,
+        videos=None,
+        **kwargs: Unpack[DonutProcessorKwargs],
+    ):
         """
         When used in normal mode, this method forwards all its arguments to AutoImageProcessor's
         [`~AutoImageProcessor.__call__`] and returns its output. If used in the context
@@ -72,28 +86,29 @@ class DonutProcessor(ProcessorMixin):
         """
         # For backward compatibility
         if self._in_target_context_manager:
-            return self.current_processor(*args, **kwargs)
+            return self.current_processor(images, text, **kwargs)
-
-        images = kwargs.pop("images", None)
-        text = kwargs.pop("text", None)
-        if len(args) > 0:
-            images = args[0]
-            args = args[1:]
 
         if images is None and text is None:
             raise ValueError("You need to specify either an `images` or `text` input to process.")
 
+        output_kwargs = self._merge_kwargs(
+            DonutProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
         if images is not None:
-            inputs = self.image_processor(images, *args, **kwargs)
+            inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
         if text is not None:
-            encodings = self.tokenizer(text, **kwargs)
+            encodings = self.tokenizer(text, **output_kwargs["text_kwargs"])
 
         if text is None:
             return inputs
         elif images is None:
             return encodings
         else:
-            inputs["labels"] = encodings["input_ids"]
+            inputs["labels"] = encodings["input_ids"]  # for BC
+            inputs["input_ids"] = encodings["input_ids"]
             return inputs
 
     def batch_decode(self, *args, **kwargs):

src/transformers/models/idefics3/processing_idefics3.py

@@ -17,12 +17,11 @@ Processor class for Idefics3.
 """
 
 import re
-import sys
 from typing import TYPE_CHECKING, Dict, List, Optional, Union
 
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput, is_valid_image, load_image
-from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin
+from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import AddedToken, BatchEncoding, TextInput
 from ...utils import logging
 
@@ -30,11 +29,6 @@ from ...utils import logging
 if TYPE_CHECKING:
     from ...tokenization_utils_base import PreTokenizedInput
 
-if sys.version_info >= (3, 11):
-    from typing import Unpack
-else:
-    from typing_extensions import Unpack
-
 logger = logging.get_logger(__name__)
 
 

src/transformers/models/mllama/processing_mllama.py

@@ -19,19 +19,9 @@ from typing import List, Optional, Union
 
 import numpy as np
 
-
-try:
-    from typing import Unpack
-except ImportError:
-    from typing_extensions import Unpack
-
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput
-from ...processing_utils import (
+from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack
-    ImagesKwargs,
-    ProcessingKwargs,
-    ProcessorMixin,
-)
 from ...tokenization_utils_base import (
     BatchEncoding,
     PreTokenizedInput,

src/transformers/models/omdet_turbo/processing_omdet_turbo.py

@@ -16,13 +16,12 @@
 Processor class for OmDet-Turbo.
 """
 
-import sys
 from typing import List, Optional, Tuple, Union
 
 from ...feature_extraction_utils import BatchFeature
 from ...image_transforms import center_to_corners_format
 from ...image_utils import ImageInput
-from ...processing_utils import ProcessingKwargs, ProcessorMixin, TextKwargs
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import (
     TensorType,
@@ -31,12 +30,6 @@ from ...utils import (
 )
 
 
-if sys.version_info >= (3, 11):
-    from typing import Unpack
-else:
-    from typing_extensions import Unpack
-
-
 class OmDetTurboTextKwargs(TextKwargs, total=False):
     task: Optional[Union[str, List[str], TextInput, PreTokenizedInput]]
 

src/transformers/models/wav2vec2/processing_wav2vec2.py

@@ -18,12 +18,18 @@ Speech processor class for Wav2Vec2
 
 import warnings
 from contextlib import contextmanager
+from typing import List, Optional, Union
 
-from ...processing_utils import ProcessorMixin
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from ...tokenization_utils_base import AudioInput, PreTokenizedInput, TextInput
 from .feature_extraction_wav2vec2 import Wav2Vec2FeatureExtractor
 from .tokenization_wav2vec2 import Wav2Vec2CTCTokenizer
 
 
+class Wav2Vec2ProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {}
+
+
 class Wav2Vec2Processor(ProcessorMixin):
     r"""
     Constructs a Wav2Vec2 processor which wraps a Wav2Vec2 feature extractor and a Wav2Vec2 CTC tokenizer into a single
@@ -66,35 +72,46 @@ class Wav2Vec2Processor(ProcessorMixin):
 
             return cls(feature_extractor=feature_extractor, tokenizer=tokenizer)
 
-    def __call__(self, *args, **kwargs):
+    def __call__(
+        self,
+        audio: AudioInput = None,
+        text: Optional[Union[str, List[str], TextInput, PreTokenizedInput]] = None,
+        images=None,
+        videos=None,
+        **kwargs: Unpack[Wav2Vec2ProcessorKwargs],
+    ):
         """
         When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's
         [`~Wav2Vec2FeatureExtractor.__call__`] and returns its output. If used in the context
         [`~Wav2Vec2Processor.as_target_processor`] this method forwards all its arguments to PreTrainedTokenizer's
         [`~PreTrainedTokenizer.__call__`]. Please refer to the docstring of the above two methods for more information.
         """
-        # For backward compatibility
-        if self._in_target_context_manager:
-            return self.current_processor(*args, **kwargs)
 
         if "raw_speech" in kwargs:
             warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.")
             audio = kwargs.pop("raw_speech")
-        else:
-            audio = kwargs.pop("audio", None)
-        sampling_rate = kwargs.pop("sampling_rate", None)
-        text = kwargs.pop("text", None)
-        if len(args) > 0:
-            audio = args[0]
-            args = args[1:]
 
         if audio is None and text is None:
             raise ValueError("You need to specify either an `audio` or `text` input to process.")
 
+        output_kwargs = self._merge_kwargs(
+            Wav2Vec2ProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+        # For backward compatibility
+        if self._in_target_context_manager:
+            return self.current_processor(
+                audio,
+                **output_kwargs["audio_kwargs"],
+                **output_kwargs["text_kwargs"],
+                **output_kwargs["common_kwargs"],
+            )
+
         if audio is not None:
-            inputs = self.feature_extractor(audio, *args, sampling_rate=sampling_rate, **kwargs)
+            inputs = self.feature_extractor(audio, **output_kwargs["audio_kwargs"])
         if text is not None:
-            encodings = self.tokenizer(text, **kwargs)
+            encodings = self.tokenizer(text, **output_kwargs["text_kwargs"])
 
         if text is None:
             return inputs

src/transformers/models/wav2vec2_bert/processing_wav2vec2_bert.py

@@ -17,12 +17,18 @@ Speech processor class for Wav2Vec2-BERT
 """
 
 import warnings
+from typing import List, Optional, Union
 
-from ...processing_utils import ProcessorMixin
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from ...tokenization_utils_base import AudioInput, PreTokenizedInput, TextInput
 from ..seamless_m4t.feature_extraction_seamless_m4t import SeamlessM4TFeatureExtractor
 from ..wav2vec2.tokenization_wav2vec2 import Wav2Vec2CTCTokenizer
 
 
+class Wav2Vec2BertProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {}
+
+
 class Wav2Vec2BertProcessor(ProcessorMixin):
     r"""
     Constructs a Wav2Vec2-BERT processor which wraps a Wav2Vec2-BERT feature extractor and a Wav2Vec2 CTC tokenizer into a single
@@ -63,7 +69,14 @@ class Wav2Vec2BertProcessor(ProcessorMixin):
 
             return cls(feature_extractor=feature_extractor, tokenizer=tokenizer)
 
-    def __call__(self, audio=None, text=None, **kwargs):
+    def __call__(
+        self,
+        audio: AudioInput = None,
+        text: Optional[Union[str, List[str], TextInput, PreTokenizedInput]] = None,
+        images=None,
+        videos=None,
+        **kwargs: Unpack[Wav2Vec2BertProcessorKwargs],
+    ):
         """
         Main method to prepare for the model one or several sequences(s) and audio(s). This method forwards the `audio`
         and `kwargs` arguments to SeamlessM4TFeatureExtractor's [`~SeamlessM4TFeatureExtractor.__call__`] if `audio` is not
@@ -71,17 +84,15 @@ class Wav2Vec2BertProcessor(ProcessorMixin):
         PreTrainedTokenizer's [`~PreTrainedTokenizer.__call__`] if `text` is not `None`. Please refer to the doctsring of the above two methods for more information.
 
         Args:
-            text (`str`, `List[str]`, `List[List[str]]`):
-                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
-                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
-                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
             audio (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
                 The audio or batch of audios to be prepared. Each audio can be NumPy array or PyTorch tensor. In case
                 of a NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels,
                 and T the sample length of the audio.
-            kwargs (*optional*):
+
-                Remaining dictionary of keyword arguments that will be passed to the feature extractor and/or the
+            text (`str`, `List[str]`, `List[List[str]]`):
-                tokenizer.
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
         Returns:
             [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
             - **input_features** -- Audio input features to be fed to a model. Returned when `audio` is not `None`.
@@ -91,15 +102,18 @@ class Wav2Vec2BertProcessor(ProcessorMixin):
             - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None` and `audio` is `None`.
         """
 
-        sampling_rate = kwargs.pop("sampling_rate", None)
-
         if audio is None and text is None:
             raise ValueError("You need to specify either an `audio` or `text` input to process.")
+        output_kwargs = self._merge_kwargs(
+            Wav2Vec2BertProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
 
         if audio is not None:
-            inputs = self.feature_extractor(audio, sampling_rate=sampling_rate, **kwargs)
+            inputs = self.feature_extractor(audio, **output_kwargs["audio_kwargs"])
         if text is not None:
-            encodings = self.tokenizer(text, **kwargs)
+            encodings = self.tokenizer(text, **output_kwargs["text_kwargs"])
 
         if text is None:
             return inputs

src/transformers/processing_utils.py

@@ -820,6 +820,8 @@ class ProcessorMixin(PushToHubMixin):
             "common_kwargs": {},
         }
 
+        used_keys = set()
+
         # get defaults from set model processor kwargs if they exist
         for modality in default_kwargs:
             default_kwargs[modality] = ModelProcessorKwargs._defaults.get(modality, {}).copy()
@@ -846,18 +848,29 @@ class ProcessorMixin(PushToHubMixin):
                             f"in a dictionary for {modality} and as a **kwarg."
                         )
                 elif modality_key in kwargs:
-                    kwarg_value = kwargs.pop(modality_key, "__empty__")
+                    # we get a modality_key instead of popping it because modality-specific processors
+                    # can have overlapping kwargs
+                    kwarg_value = kwargs.get(modality_key, "__empty__")
                 else:
                     kwarg_value = "__empty__"
                 if kwarg_value != "__empty__":
                     output_kwargs[modality][modality_key] = kwarg_value
-        # if something remains in kwargs, it belongs to common after flattening
+                    used_keys.add(modality_key)
-        if set(kwargs) & set(default_kwargs):
+
-            # here kwargs is dictionary-based since it shares keys with default set
+        # Determine if kwargs is a flat dictionary or contains nested dictionaries
-            [output_kwargs["common_kwargs"].update(subdict) for _, subdict in kwargs.items()]
+        if any(key in default_kwargs for key in kwargs):
+            # kwargs is dictionary-based, and some keys match modality names
+            for modality, subdict in kwargs.items():
+                if modality in default_kwargs:
+                    for subkey, subvalue in subdict.items():
+                        if subkey not in used_keys:
+                            output_kwargs[modality][subkey] = subvalue
+                            used_keys.add(subkey)
         else:
-            # here it's a flat dict
+            # kwargs is a flat dictionary
-            output_kwargs["common_kwargs"].update(kwargs)
+            for key in kwargs:
+                if key not in used_keys:
+                    output_kwargs["common_kwargs"][key] = kwargs[key]
 
         # all modality-specific kwargs are updated with common kwargs
         for modality in output_kwargs:

tests/models/altclip/test_processor_altclip.py

@@ -17,17 +17,12 @@
 import tempfile
 import unittest
 
-from transformers import XLMRobertaTokenizer, XLMRobertaTokenizerFast
+from transformers import AltCLIPProcessor, CLIPImageProcessor, XLMRobertaTokenizer, XLMRobertaTokenizerFast
 from transformers.testing_utils import require_vision
-from transformers.utils import is_vision_available
 
 from ...test_processing_common import ProcessorTesterMixin
 
 
-if is_vision_available():
-    from transformers import AltCLIPProcessor, CLIPImageProcessor
-
-
 @require_vision
 class AltClipProcessorTest(ProcessorTesterMixin, unittest.TestCase):
     processor_class = AltCLIPProcessor

tests/models/blip/test_processor_blip.py

@@ -17,7 +17,7 @@ import unittest
 
 import pytest
 
-from transformers.testing_utils import require_vision
+from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_vision_available
 
 from ...test_processing_common import ProcessorTesterMixin
@@ -139,3 +139,29 @@ class BlipProcessorTest(ProcessorTesterMixin, unittest.TestCase):
 
         # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
         self.assertListEqual(list(inputs.keys()), ["pixel_values", "input_ids", "attention_mask"])
+
+    @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},
+            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]), 24)

tests/models/blip_2/test_processor_blip_2.py

@@ -17,7 +17,7 @@ import unittest
 
 import pytest
 
-from transformers.testing_utils import require_vision
+from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_vision_available
 
 from ...test_processing_common import ProcessorTesterMixin
@@ -94,7 +94,7 @@ class Blip2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
         encoded_tok = tokenizer(input_str, return_token_type_ids=False)
 
         for key in encoded_tok.keys():
-            self.assertListEqual(encoded_tok[key], encoded_processor[key])
+            self.assertListEqual(encoded_tok[key], encoded_processor[key][0])
 
     def test_processor(self):
         image_processor = self.get_image_processor()
@@ -107,7 +107,7 @@ class Blip2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
 
         inputs = processor(text=input_str, images=image_input)
 
-        self.assertListEqual(list(inputs.keys()), ["pixel_values", "input_ids", "attention_mask"])
+        self.assertCountEqual(list(inputs.keys()), ["input_ids", "pixel_values", "attention_mask"])
 
         # test if it raises when no input is passed
         with pytest.raises(ValueError):
@@ -138,4 +138,31 @@ class Blip2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
         inputs = processor(text=input_str, images=image_input)
 
         # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
-        self.assertListEqual(list(inputs.keys()), ["pixel_values", "input_ids", "attention_mask"])
+        self.assertCountEqual(list(inputs.keys()), ["input_ids", "pixel_values", "attention_mask"])
+
+    @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")
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[TEST_PAD]"
+        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},
+            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]), 11)

tests/models/bridgetower/test_processing_bridgetower.py

@@ -0,0 +1,218 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import shutil
+import tempfile
+import unittest
+
+import numpy as np
+
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import (
+        AutoProcessor,
+        BridgeTowerImageProcessor,
+        BridgeTowerProcessor,
+        RobertaTokenizerFast,
+    )
+
+
+@require_vision
+class Blip2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = BridgeTowerProcessor
+
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+
+        image_processor = BridgeTowerImageProcessor()
+        tokenizer = RobertaTokenizerFast.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-itc")
+
+        processor = BridgeTowerProcessor(image_processor, tokenizer)
+
+        processor.save_pretrained(self.tmpdirname)
+
+    def get_tokenizer(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
+
+    def get_image_processor(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
+
+    def tearDown(self):
+        shutil.rmtree(self.tmpdirname)
+
+    def prepare_image_inputs(self):
+        """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
+        or a list of PyTorch tensors if one specifies torchify=True.
+        """
+
+        image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
+
+        image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
+
+        return image_inputs
+
+    # Some kwargs tests are overriden from common tests to handle shortest_edge
+    # and size_divisor behaviour
+
+    @require_torch
+    @require_vision
+    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={"shortest_edge": 234, "longest_edge": 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)
+
+    @require_torch
+    @require_vision
+    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": {"shortest_edge": 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)
+
+    @require_torch
+    @require_vision
+    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={"shortest_edge": 234})
+        tokenizer = self.get_component("tokenizer", max_length=117)
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[TEST_PAD]"
+        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={"shortest_edge": 224})
+        self.assertEqual(len(inputs["pixel_values"][0][0]), 224)
+
+    @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")
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[TEST_PAD]"
+        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={"shortest_edge": 214},
+            padding="longest",
+            max_length=76,
+        )
+        self.assertEqual(inputs["pixel_values"].shape[2], 214)
+
+        self.assertEqual(len(inputs["input_ids"][0]), 6)
+
+    @require_torch
+    @require_vision
+    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")
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[TEST_PAD]"
+        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={"shortest_edge": 214},
+            padding="max_length",
+            max_length=76,
+        )
+
+        self.assertEqual(inputs["pixel_values"].shape[2], 214)
+        self.assertEqual(len(inputs["input_ids"][0]), 76)
+
+    @require_torch
+    @require_vision
+    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")
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[TEST_PAD]"
+        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": {"shortest_edge": 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)

tests/models/donut/test_processing_donut.py

@@ -14,16 +14,32 @@
 # limitations under the License.
 
 
+import tempfile
 import unittest
 
-from transformers import DonutProcessor
+from transformers import DonutImageProcessor, DonutProcessor, XLMRobertaTokenizerFast
+from transformers.testing_utils import (
+    require_torch,
+    require_vision,
+)
+
+from ...test_processing_common import ProcessorTesterMixin
 
 
-class DonutProcessorTest(unittest.TestCase):
+class DonutProcessorTest(ProcessorTesterMixin, unittest.TestCase):
     from_pretrained_id = "naver-clova-ix/donut-base"
+    processor_class = DonutProcessor
 
     def setUp(self):
         self.processor = DonutProcessor.from_pretrained(self.from_pretrained_id)
+        self.tmpdirname = tempfile.mkdtemp()
+
+        image_processor = DonutImageProcessor()
+        tokenizer = XLMRobertaTokenizerFast.from_pretrained(self.from_pretrained_id)
+
+        processor = DonutProcessor(image_processor, tokenizer)
+
+        processor.save_pretrained(self.tmpdirname)
 
     def test_token2json(self):
         expected_json = {
@@ -49,3 +65,30 @@ class DonutProcessorTest(unittest.TestCase):
         actual_json = self.processor.token2json(sequence)
 
         self.assertDictEqual(actual_json, expected_json)
+
+    @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")
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[TEST_PAD]"
+        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},
+            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)

tests/models/wav2vec2/test_processor_wav2vec2.py

@@ -18,14 +18,19 @@ import shutil
 import tempfile
 import unittest
 
+import numpy as np
+
 from transformers.models.wav2vec2 import Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor
 from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES
 from transformers.utils import FEATURE_EXTRACTOR_NAME
 
+from ...test_processing_common import ProcessorTesterMixin
 from .test_feature_extraction_wav2vec2 import floats_list
 
 
-class Wav2Vec2ProcessorTest(unittest.TestCase):
+class Wav2Vec2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Wav2Vec2Processor
+
     def setUp(self):
         vocab = "<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" ")
         vocab_tokens = dict(zip(vocab, range(len(vocab))))
@@ -53,6 +58,9 @@ class Wav2Vec2ProcessorTest(unittest.TestCase):
         with open(self.feature_extraction_file, "w", encoding="utf-8") as fp:
             fp.write(json.dumps(feature_extractor_map) + "\n")
 
+        tokenizer = self.get_tokenizer()
+        tokenizer.save_pretrained(self.tmpdirname)
+
     def get_tokenizer(self, **kwargs_init):
         kwargs = self.add_kwargs_tokens_map.copy()
         kwargs.update(kwargs_init)
@@ -117,7 +125,6 @@ class Wav2Vec2ProcessorTest(unittest.TestCase):
         processor = Wav2Vec2Processor(tokenizer=tokenizer, feature_extractor=feature_extractor)
 
         input_str = "This is a test string"
-
         encoded_processor = processor(text=input_str)
 
         encoded_tok = tokenizer(input_str)
@@ -125,6 +132,22 @@ class Wav2Vec2ProcessorTest(unittest.TestCase):
         for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key], encoded_processor[key])
 
+    def test_padding_argument_not_ignored(self):
+        # padding, or any other overlap arg between audio extractor and tokenizer
+        # should be passed to both text and audio and not ignored
+
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = Wav2Vec2Processor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+        batch_duration_in_seconds = [1, 3, 2, 6]
+        input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
+
+        # padding = True should not raise an error and will if the audio processor popped its value to None
+        _ = processor(
+            input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
+        )
+
     def test_tokenizer_decode(self):
         feature_extractor = self.get_feature_extractor()
         tokenizer = self.get_tokenizer()

tests/models/wav2vec2_bert/test_processor_wav2vec2_bert.py

@@ -18,17 +18,21 @@ import shutil
 import tempfile
 import unittest
 
+import numpy as np
+
 from transformers.models.seamless_m4t import SeamlessM4TFeatureExtractor
 from transformers.models.wav2vec2 import Wav2Vec2CTCTokenizer
 from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES
 from transformers.models.wav2vec2_bert import Wav2Vec2BertProcessor
 from transformers.utils import FEATURE_EXTRACTOR_NAME
 
+from ...test_processing_common import ProcessorTesterMixin
 from ..wav2vec2.test_feature_extraction_wav2vec2 import floats_list
 
 
-# Copied from tests.models.wav2vec2.test_processor_wav2vec2.Wav2Vec2ProcessorTest with Wav2Vec2FeatureExtractor->SeamlessM4TFeatureExtractor, Wav2Vec2Processor->Wav2Vec2BertProcessor
+class Wav2Vec2BertProcessorTest(ProcessorTesterMixin, unittest.TestCase):
-class Wav2Vec2BertProcessorTest(unittest.TestCase):
+    processor_class = Wav2Vec2BertProcessor
+
     def setUp(self):
         vocab = "<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" ")
         vocab_tokens = dict(zip(vocab, range(len(vocab))))
@@ -40,7 +44,7 @@ class Wav2Vec2BertProcessorTest(unittest.TestCase):
             "eos_token": "</s>",
         }
         feature_extractor_map = {
-            "feature_size": 1,
+            "feature_size": 80,
             "padding_value": 0.0,
             "sampling_rate": 16000,
             "return_attention_mask": False,
@@ -56,6 +60,9 @@ class Wav2Vec2BertProcessorTest(unittest.TestCase):
         with open(self.feature_extraction_file, "w", encoding="utf-8") as fp:
             fp.write(json.dumps(feature_extractor_map) + "\n")
 
+        tokenizer = self.get_tokenizer()
+        tokenizer.save_pretrained(self.tmpdirname)
+
     def get_tokenizer(self, **kwargs_init):
         kwargs = self.add_kwargs_tokens_map.copy()
         kwargs.update(kwargs_init)
@@ -122,7 +129,6 @@ class Wav2Vec2BertProcessorTest(unittest.TestCase):
         processor = Wav2Vec2BertProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
 
         input_str = "This is a test string"
-
         encoded_processor = processor(text=input_str)
 
         encoded_tok = tokenizer(input_str)
@@ -130,6 +136,22 @@ class Wav2Vec2BertProcessorTest(unittest.TestCase):
         for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key], encoded_processor[key])
 
+    def test_padding_argument_not_ignored(self):
+        # padding, or any other overlap arg between audio extractor and tokenizer
+        # should be passed to both text and audio and not ignored
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = Wav2Vec2BertProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+        batch_duration_in_seconds = [1, 3, 2, 6]
+        input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
+
+        # padding = True should not raise an error and will if the audio processor popped its value to None
+        # processor(input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt")
+        _ = processor(
+            input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
+        )
+
     def test_tokenizer_decode(self):
         feature_extractor = self.get_feature_extractor()
         tokenizer = self.get_tokenizer()

tests/test_processing_common.py

@@ -16,6 +16,7 @@
 
 import inspect
 import json
+import random
 import tempfile
 from typing import Optional
 
@@ -31,11 +32,7 @@ from transformers.testing_utils import (
 from transformers.utils import is_vision_available
 
 
-try:
+global_rng = random.Random()
-    from typing import Unpack
-except ImportError:
-    from typing_extensions import Unpack
-
 
 if is_vision_available():
     from PIL import Image
@@ -48,6 +45,21 @@ def prepare_image_inputs():
     return image_inputs
 
 
+# Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list
+def floats_list(shape, scale=1.0, rng=None, name=None):
+    """Creates a random float32 tensor"""
+    if rng is None:
+        rng = global_rng
+
+    values = []
+    for batch_idx in range(shape[0]):
+        values.append([])
+        for _ in range(shape[1]):
+            values[-1].append(rng.random() * scale)
+
+    return values
+
+
 @require_torch
 @require_vision
 class ProcessorTesterMixin:
@@ -333,6 +345,135 @@ class ProcessorTesterMixin:
         self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0)
         self.assertEqual(inputs[self.text_input_name].shape[-1], 76)
 
+    #  text + audio kwargs testing
+    @require_torch
+    def test_tokenizer_defaults_preserved_by_kwargs_audio(self):
+        if "feature_extractor" not in self.processor_class.attributes:
+            self.skipTest(f"feature_extractor attribute not present in {self.processor_class}")
+        feature_extractor = self.get_component("feature_extractor")
+        if hasattr(self, "get_tokenizer"):
+            tokenizer = self.get_tokenizer(max_length=117, padding="max_length")
+        elif hasattr(self, "get_component"):
+            tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length")
+        else:
+            self.assertTrue(False, "Processor doesn't have get_tokenizer or get_component defined")
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[TEST_PAD]"
+        processor = self.processor_class(tokenizer=tokenizer, feature_extractor=feature_extractor)
+        self.skip_processor_without_typed_kwargs(processor)
+        input_str = "lower newer"
+        raw_speech = floats_list((3, 1000))
+        inputs = processor(text=input_str, audio=raw_speech, return_tensors="pt")
+        if "input_ids" in inputs:
+            self.assertEqual(len(inputs["input_ids"][0]), 117)
+        elif "labels" in inputs:
+            self.assertEqual(len(inputs["labels"][0]), 117)
+
+    @require_torch
+    def test_kwargs_overrides_default_tokenizer_kwargs_audio(self):
+        if "feature_extractor" not in self.processor_class.attributes:
+            self.skipTest(f"feature_extractor attribute not present in {self.processor_class}")
+        feature_extractor = self.get_component("feature_extractor")
+        if hasattr(self, "get_tokenizer"):
+            tokenizer = self.get_tokenizer(max_length=117)
+        elif hasattr(self, "get_component"):
+            tokenizer = self.get_component("tokenizer", max_length=117)
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[TEST_PAD]"
+        processor = self.processor_class(tokenizer=tokenizer, feature_extractor=feature_extractor)
+        self.skip_processor_without_typed_kwargs(processor)
+        input_str = "lower newer"
+        raw_speech = floats_list((3, 1000))
+        inputs = processor(text=input_str, audio=raw_speech, return_tensors="pt", max_length=112, padding="max_length")
+        if "input_ids" in inputs:
+            self.assertEqual(len(inputs["input_ids"][0]), 112)
+        elif "labels" in inputs:
+            self.assertEqual(len(inputs["labels"][0]), 112)
+
+    @require_torch
+    def test_unstructured_kwargs_audio(self):
+        if "feature_extractor" not in self.processor_class.attributes:
+            self.skipTest(f"feature_extractor attribute not present in {self.processor_class}")
+        feature_extractor = self.get_component("feature_extractor")
+        if hasattr(self, "get_tokenizer"):
+            tokenizer = self.get_tokenizer(max_length=117)
+        elif hasattr(self, "get_component"):
+            tokenizer = self.get_component("tokenizer", max_length=117)
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[TEST_PAD]"
+        processor = self.processor_class(tokenizer=tokenizer, feature_extractor=feature_extractor)
+        self.skip_processor_without_typed_kwargs(processor)
+
+        input_str = "lower newer"
+        raw_speech = floats_list((3, 1000))
+        inputs = processor(
+            text=input_str,
+            audio=raw_speech,
+            return_tensors="pt",
+            padding="max_length",
+            max_length=76,
+        )
+
+        if "input_ids" in inputs:
+            self.assertEqual(len(inputs["input_ids"][0]), 76)
+        elif "labels" in inputs:
+            self.assertEqual(len(inputs["labels"][0]), 76)
+
+    @require_torch
+    def test_doubly_passed_kwargs_audio(self):
+        if "feature_extractor" not in self.processor_class.attributes:
+            self.skipTest(f"feature_extractor attribute not present in {self.processor_class}")
+        feature_extractor = self.get_component("feature_extractor")
+        if hasattr(self, "get_tokenizer"):
+            tokenizer = self.get_tokenizer()
+        elif hasattr(self, "get_component"):
+            tokenizer = self.get_component("tokenizer")
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[TEST_PAD]"
+        processor = self.processor_class(tokenizer=tokenizer, feature_extractor=feature_extractor)
+        self.skip_processor_without_typed_kwargs(processor)
+
+        input_str = ["lower newer"]
+        raw_speech = floats_list((3, 1000))
+        with self.assertRaises(ValueError):
+            _ = processor(
+                text=input_str,
+                audio=raw_speech,
+                audio_kwargs={"padding": "max_length"},
+                padding="max_length",
+            )
+
+    @require_torch
+    @require_vision
+    def test_structured_kwargs_audio_nested(self):
+        if "feature_extractor" not in self.processor_class.attributes:
+            self.skipTest(f"feature_extractor attribute not present in {self.processor_class}")
+        feature_extractor = self.get_component("feature_extractor")
+        if hasattr(self, "get_tokenizer"):
+            tokenizer = self.get_tokenizer()
+        elif hasattr(self, "get_component"):
+            tokenizer = self.get_component("tokenizer")
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[TEST_PAD]"
+        processor = self.processor_class(tokenizer=tokenizer, feature_extractor=feature_extractor)
+        self.skip_processor_without_typed_kwargs(processor)
+
+        input_str = ["lower newer"]
+        raw_speech = floats_list((3, 1000))
+
+        # Define the kwargs for each modality
+        all_kwargs = {
+            "common_kwargs": {"return_tensors": "pt"},
+            "text_kwargs": {"padding": "max_length", "max_length": 76},
+            "audio_kwargs": {"padding": "max_length", "max_length": 66},
+        }
+
+        inputs = processor(text=input_str, audio=raw_speech, **all_kwargs)
+        if "input_ids" in inputs:
+            self.assertEqual(len(inputs["input_ids"][0]), 76)
+        elif "labels" in inputs:
+            self.assertEqual(len(inputs["labels"][0]), 76)
+
     # TODO: the same test, but for audio + text processors that have strong overlap in kwargs
     # TODO (molbap) use the same structure of attribute kwargs for other tests to avoid duplication
     def test_overlapping_text_kwargs_handling(self):