Uniformize kwargs for Pixtral processor (#33521)

* add uniformized pixtral and kwargs

* update doc

* fix _validate_images_text_input_order

* nit

docs/source/en/model_doc/pixtral.md

@@ -24,7 +24,7 @@ The Pixtral model was released by the Mistral AI team on [Vllm](https://github.c
 Tips:
 
 - Pixtral is a multimodal model, the main contribution is the 2d ROPE on the images, and support for arbitrary image size (the images are not padded together nor are they resized)
-- This model follows the `Llava` familiy, meaning image embeddings are placed instead of the `[IMG]` token placeholders. 
+- This model follows the `Llava` familiy, meaning image embeddings are placed instead of the `[IMG]` token placeholders.
 - The format for one or mulitple prompts is the following:
 ```
 "<s>[INST][IMG]\nWhat are the things I should be cautious about when I visit this place?[/INST]"
@@ -35,7 +35,7 @@ This model was contributed by [amyeroberts](https://huggingface.co/amyeroberts)
 
 Here is an example of how to run it:
 
-```python 
+```python
 from transformers import LlavaForConditionalGeneration, AutoProcessor
 from PIL import Image
 
@@ -51,7 +51,7 @@ IMG_URLS = [
 ]
 PROMPT = "<s>[INST]Describe the images.\n[IMG][IMG][IMG][IMG][/INST]"
 
-inputs = processor(text=PROMPT, images=IMG_URLS, return_tensors="pt").to("cuda")
+inputs = processor(images=IMG_URLS, text=PROMPT, return_tensors="pt").to("cuda")
 generate_ids = model.generate(**inputs, max_new_tokens=500)
 ouptut = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
 

src/transformers/models/pixtral/__init__.py

@@ -43,7 +43,8 @@
 
 
 if TYPE_CHECKING:
-    from .configuration_pixtral import PixtralProcessor, PixtralVisionConfig
+    from .configuration_pixtral import PixtralVisionConfig
+    from .processing_pixtral import PixtralProcessor
 
     try:
         if not is_torch_available():

src/transformers/models/pixtral/processing_pixtral.py

@@ -16,18 +16,36 @@
 Processor class for Pixtral.
 """
 
-from typing import List, Optional, Union
+import sys
+from typing import List, Union
 
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput, is_valid_image, load_image
-from ...processing_utils import ProcessorMixin
-from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
-from ...utils import TensorType, is_torch_device, is_torch_dtype, is_torch_tensor, logging, requires_backends
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, _validate_images_text_input_order
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import is_torch_device, is_torch_dtype, is_torch_tensor, logging, requires_backends
 
 
+if sys.version_info >= (3, 11):
+    from typing import Unpack
+else:
+    from typing_extensions import Unpack
+
 logger = logging.get_logger(__name__)
 
 
+class PixtralProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+        "images_kwargs": {},
+        "common_kwargs": {
+            "return_tensors": "pt",
+        },
+    }
+
+
 # Copied from transformers.models.idefics2.processing_idefics2.is_url
 def is_url(val) -> bool:
     return isinstance(val, str) and val.startswith("http")
@@ -143,12 +161,11 @@ def __init__(
 
     def __call__(
         self,
-        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
         images: ImageInput = None,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length=None,
-        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        audio=None,
+        videos=None,
+        **kwargs: Unpack[PixtralProcessorKwargs],
     ) -> BatchMixFeature:
         """
         Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
@@ -158,26 +175,13 @@ def __call__(
         of the above two methods for more information.
 
         Args:
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                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 (`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).
-            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
-                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.
-            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:
 
@@ -195,6 +199,15 @@ def __call__(
             `None`).
             - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
         """
+        # check if images and text inputs are reversed for BC
+        images, text = _validate_images_text_input_order(images, text)
+
+        output_kwargs = self._merge_kwargs(
+            PixtralProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
         if images is not None:
             if is_image_or_image_url(images):
                 images = [[images]]
@@ -209,7 +222,7 @@ def __call__(
                     "Invalid input images. Please provide a single image or a list of images or a list of list of images."
                 )
             images = [[load_image(im) for im in sample] for sample in images]
-            image_inputs = self.image_processor(images, patch_size=self.patch_size, return_tensors=return_tensors)
+            image_inputs = self.image_processor(images, patch_size=self.patch_size, **output_kwargs["images_kwargs"])
         else:
             image_inputs = {}
 
@@ -246,16 +259,9 @@ def __call__(
                 while "<placeholder>" in sample:
                     replace_str = replace_strings.pop(0)
                     sample = sample.replace("<placeholder>", replace_str, 1)
-
                 prompt_strings.append(sample)
 
-        text_inputs = self.tokenizer(
-            prompt_strings,
-            return_tensors=return_tensors,
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-        )
+        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
         return BatchMixFeature(data={**text_inputs, **image_inputs})
 
     # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama

src/transformers/processing_utils.py

@@ -27,7 +27,7 @@
 import numpy as np
 
 from .dynamic_module_utils import custom_object_save
-from .image_utils import ChannelDimension, is_vision_available, valid_images
+from .image_utils import ChannelDimension, is_valid_image, is_vision_available
 
 
 if is_vision_available():
@@ -1003,6 +1003,20 @@ def _validate_images_text_input_order(images, text):
     in the processor's `__call__` method before calling this method.
     """
 
+    def is_url(val) -> bool:
+        return isinstance(val, str) and val.startswith("http")
+
+    def _is_valid_images_input_for_processor(imgs):
+        # If we have an list of images, make sure every image is valid
+        if isinstance(imgs, (list, tuple)):
+            for img in imgs:
+                if not _is_valid_images_input_for_processor(img):
+                    return False
+        # If not a list or tuple, we have been given a single image or batched tensor of images
+        elif not (is_valid_image(imgs) or is_url(imgs)):
+            return False
+        return True
+
     def _is_valid_text_input_for_processor(t):
         if isinstance(t, str):
             # Strings are fine
@@ -1019,11 +1033,11 @@ def _is_valid_text_input_for_processor(t):
     def _is_valid(input, validator):
         return validator(input) or input is None
 
-    images_is_valid = _is_valid(images, valid_images)
-    images_is_text = _is_valid_text_input_for_processor(images) if not images_is_valid else False
+    images_is_valid = _is_valid(images, _is_valid_images_input_for_processor)
+    images_is_text = _is_valid_text_input_for_processor(images)
 
     text_is_valid = _is_valid(text, _is_valid_text_input_for_processor)
-    text_is_images = valid_images(text) if not text_is_valid else False
+    text_is_images = _is_valid_images_input_for_processor(text)
     # Handle cases where both inputs are valid
     if images_is_valid and text_is_valid:
         return images, text