uniformize the kwargs for the rest of the processors in the list

src/transformers/models/clipseg/processing_clipseg.py

@@ -16,10 +16,28 @@
 Image/Text processor class for CLIPSeg
 """
 
+import sys
 import warnings
+from typing import List, Optional, Union
 
-from ...processing_utils import ProcessorMixin
-from ...tokenization_utils_base import BatchEncoding
+from ...image_utils import ImageInput
+from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin
+from ...tokenization_utils_base import BatchEncoding, PreTokenizedInput, TextInput
+
+
+if sys.version_info >= (3, 11):
+    from typing import Unpack
+else:
+    from typing_extensions import Unpack
+
+
+class CLIPSegImagesKwargs(ImagesKwargs, total=False):
+    visual_prompt: Optional[ImageInput]
+
+
+class CLIPSegProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: CLIPSegImagesKwargs
+    _defaults = {}
 
 
 class CLIPSegProcessor(ProcessorMixin):
@@ -58,7 +76,14 @@ def __init__(self, image_processor=None, tokenizer=None, **kwargs):
 
         super().__init__(image_processor, tokenizer)
 
-    def __call__(self, text=None, images=None, visual_prompt=None, return_tensors=None, **kwargs):
+    def __call__(
+        self,
+        text: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
+        images: Optional[ImageInput] = None,
+        audio=None,
+        videos=None,
+        **kwargs: Unpack[CLIPSegProcessorKwargs],
+    ):
         """
         Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
         and `kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to encode
@@ -79,14 +104,6 @@ def __call__(self, text=None, images=None, visual_prompt=None, return_tensors=No
                 NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape
                 (C, H, W), where C is a number of channels, H and W are image height and width.
 
-            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.
-
         Returns:
             [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
 
@@ -96,20 +113,43 @@ def __call__(self, text=None, images=None, visual_prompt=None, return_tensors=No
               `None`).
             - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
         """
+
+        output_kwargs = self._merge_kwargs(
+            CLIPSegProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        if output_kwargs["text_kwargs"].get("visual_prompt") is not None and audio is not None:
+            raise ValueError(
+                "You cannot provide `visual_prompt` as a positional argument and as a keyword argument at the same time."
+                "Please provide it only as a keyword argument (i.e. `visual_prompt=...`)."
+            )
+        if "visual_prompt" not in output_kwargs["text_kwargs"]:
+            warnings.warn(
+                "No `visual_prompt` kwarg was detected. The use of `visual_prompt` as an argument without specifying it explicitely as `visual_prompt=` will be deprecated in future versions."
+            )
+            # For backwards compatibility, we reuse `audio` as `visual_prompt` in case
+            # downstream users passed it as a positional argument
+            if audio is not None:
+                output_kwargs["text_kwargs"]["visual_prompt"] = audio
+
+        visual_prompt = output_kwargs["text_kwargs"].pop("visual_prompt", None)
+
         if text is None and visual_prompt is None and images is None:
             raise ValueError("You have to specify either text, visual prompt or images.")
 
         if text is not None and visual_prompt is not None:
             raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt.")
 
         if text is not None:
-            encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)
+            encoding = self.tokenizer(text, **output_kwargs["text_kwargs"])
 
         if visual_prompt is not None:
-            prompt_features = self.image_processor(visual_prompt, return_tensors=return_tensors, **kwargs)
+            prompt_features = self.image_processor(visual_prompt, **output_kwargs["images_kwargs"])
 
         if images is not None:
-            image_features = self.image_processor(images, return_tensors=return_tensors, **kwargs)
+            image_features = self.image_processor(images, **output_kwargs["images_kwargs"])
 
         if visual_prompt is not None and images is not None:
             encoding = {
@@ -128,7 +168,9 @@ def __call__(self, text=None, images=None, visual_prompt=None, return_tensors=No
             }
             return encoding
         else:
-            return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
+            return BatchEncoding(
+                data=dict(**image_features), tensor_type=output_kwargs["common_kwargs"]["return_tensors"]
+            )
 
     def batch_decode(self, *args, **kwargs):
         """

src/transformers/models/instructblipvideo/processing_instructblipvideo.py

@@ -17,26 +17,48 @@
 """
 
 import os
+import sys
 from typing import List, Optional, Union
 
 from ...image_processing_utils import BatchFeature
 from ...image_utils import VideoInput
-from ...processing_utils import ProcessorMixin
+from ...processing_utils import ProcessingKwargs, ProcessorMixin
 from ...tokenization_utils_base import (
     AddedToken,
     BatchEncoding,
-    PaddingStrategy,
     PreTokenizedInput,
     TextInput,
-    TruncationStrategy,
 )
-from ...utils import TensorType, logging
+from ...utils import logging
 from ..auto import AutoTokenizer
 
 
+if sys.version_info >= (3, 11):
+    from typing import Unpack
+else:
+    from typing_extensions import Unpack
+
+
 logger = logging.get_logger(__name__)
 
 
+class InstructBlipVideoProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "add_special_tokens": True,
+            "padding": False,
+            "truncation": None,
+            "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,
+        },
+    }
+
+
 class InstructBlipVideoProcessor(ProcessorMixin):
     r"""
     Constructs an InstructBLIPVideo processor which wraps a InstructBLIP image processor and a LLaMa/T5 tokenizer into a single
@@ -71,30 +93,24 @@ def __init__(self, image_processor, tokenizer, qformer_tokenizer=None, num_query
 
     def __call__(
         self,
-        images: VideoInput = None,
-        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        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,
+        images: Optional[VideoInput] = None,
+        text: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
+        audio=None,
+        videos=None,
+        **kwargs: Unpack[InstructBlipVideoProcessorKwargs],
     ) -> BatchFeature:
         """
         This method uses [`InstructBlipVideoImageProcessor.__call__`] method to prepare image(s) or video(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.
         """
+        output_kwargs = self._merge_kwargs(
+            InstructBlipVideoProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
         encoding = BatchFeature()
 
         if text is not None:
@@ -105,21 +121,10 @@ def __call__(
 
             _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=None,  # required to concatenate below
-                **kwargs,
+                **{
+                    **output_kwargs["text_kwargs"],
+                    "return_tensors": None,  # required to concatenate below
+                },
             )
 
             # if we know how many query tokens, expand text inside processor. We need this hacky manipulation
@@ -145,31 +150,14 @@ def __call__(
                     )
 
             # cast to desired return tensors type after concatenating
-            text_encoding = BatchEncoding(text_encoding, tensor_type=return_tensors)
+            text_encoding = BatchEncoding(text_encoding, tensor_type=output_kwargs["common_kwargs"]["return_tensors"])
             encoding.update(text_encoding)
-            qformer_text_encoding = self.qformer_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,
-            )
+            qformer_text_encoding = self.qformer_tokenizer(text=text, **output_kwargs["text_kwargs"])
             encoding["qformer_input_ids"] = qformer_text_encoding.pop("input_ids")
             encoding["qformer_attention_mask"] = qformer_text_encoding.pop("attention_mask")
 
         if images is not None:
-            image_encoding = self.image_processor(images, return_tensors=return_tensors)
+            image_encoding = self.image_processor(images, **output_kwargs["images_kwargs"])
             encoding.update(image_encoding)
 
         return encoding

src/transformers/models/llava_next_video/processing_llava_next_video.py

@@ -16,13 +16,20 @@
 Processor class for LLaVa-NeXT-Video.
 """
 
+import sys
 from typing import TYPE_CHECKING, List, Optional, Union
 
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput, VideoInput, get_image_size, to_numpy_array
-from ...processing_utils import ProcessorMixin
-from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
-from ...utils import TensorType, logging
+from ...processing_utils import ProcessingKwargs, ProcessorMixin
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import logging
+
+
+if sys.version_info >= (3, 11):
+    from typing import Unpack
+else:
+    from typing_extensions import Unpack
 
 
 if TYPE_CHECKING:
@@ -31,6 +38,17 @@
 logger = logging.get_logger(__name__)
 
 
+class LlavaNextVideoProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+        "common_kwargs": {
+            "return_tensors": "pt",
+        },
+    }
+
+
 class LlavaNextVideoProcessor(ProcessorMixin):
     r"""
     Constructs a LLaVa-NeXT-Video processor which wraps a LLaVa-NeXT image processor, LLaVa-NeXT-Video video processor and
@@ -88,12 +106,10 @@ def __init__(
     def __call__(
         self,
         text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
-        images: ImageInput = None,
-        videos: VideoInput = None,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: int = None,
-        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
+        images: Optional[ImageInput] = None,
+        videos: Optional[VideoInput] = None,
+        audio=None,
+        **kwargs: Unpack[LlavaNextVideoProcessorKwargs],
     ) -> BatchFeature:
         """
         Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
@@ -115,26 +131,6 @@ def __call__(
             videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
                 The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
                 tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
-            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
-                Select a strategy to pad the returned sequences (according to the model's padding side and padding
-                index) among:
-                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
-                  sequence if provided).
-                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
-                  acceptable input length for the model if that argument is not provided.
-                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
-                  lengths).
-            max_length (`int`, *optional*):
-                Maximum length of the returned list and optionally padding length (see above).
-            truncation (`bool`, *optional*):
-                Activates truncation to cut input sequences longer than `max_length` to `max_length`.
-            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.
 
         Returns:
             [`BatchFeature`]: A [`BatchFeature`] with the following fields:
@@ -145,13 +141,19 @@ def __call__(
               `None`).
             - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
         """
+        output_kwargs = self._merge_kwargs(
+            LlavaNextVideoProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
         if images is not None:
-            image_inputs = self.image_processor(images, return_tensors=return_tensors)
+            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
         else:
             image_inputs = {}
 
         if videos is not None:
-            videos_inputs = self.video_processor(videos, return_tensors=return_tensors)
+            videos_inputs = self.video_processor(videos, **output_kwargs["videos_kwargs"])
         else:
             videos_inputs = {}
 
@@ -203,14 +205,7 @@ def __call__(
                     sample = sample.replace(self.video_token, self.video_token * num_video_tokens)
                     prompt_strings.append(sample)
 
-        text_inputs = self.tokenizer(
-            prompt_strings,
-            return_tensors=return_tensors,
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-        )
-        print(text_inputs.keys())
+        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
 
         return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs})