Mistral-3: dtype mismatch between image preprocessor and model
Author: egallouis
Pull request: no
Labels: name: bug

### System Info

transformers\=\=5.0.0
pytorch\=\=2.10.0
python\=\=3.12.3
Platform: WSL

### Who can help?

@zucchini-nlp @Rocketknight1 

### Information

- [ ] The official example scripts
- [x] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)

### Reproduction

### Description

When using mistralai/Ministral-3-3B-Reasoning-2512 for image-text-to-text,
The dtype of the processed images is float32 and the dtype of the model weights bfloat16. Because of that, the Inference pipeline crashes as soon as the processed images are given to the model.
The problem has to do with the use of the rescale function of the image preprocessing:
https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/image_transforms.py#L89-L95

Here, the standard dtype is defined as float32. the Pixtral Processor apparently does not use this argument and the model does not transform the dtype of the pixels values beforehand.

### Minimal Reproducible Example
```python
import torch  
from transformers import pipeline  
  
if __name__ == '__main__':  
    messages = [  
        {  
            "role": "user",  
            "content": [  
                {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},  
                {"type": "text", "text": "What are these?"},  
            ],  
        },  
    ]  
    pipe = pipeline(  
        "image-text-to-text",  
        model="mistralai/Ministral-3-3B-Reasoning-2512",  # same for the instruction version  
        device="cpu",  # also "cpu", "cuda", and device_map="auto" have the same problem  
        dtype=torch.bfloat16,  # does not change, if left out
    )  
  
    result = pipe(messages)  
    print(result)
```

### Traceback
```
Traceback (most recent call last):  
  File "/home/user/project/issue.py", line 24, in <module>  
    result = pipe(messages)  
             ^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/transformers/pipelines/image_text_to_text.py", line 293, in __call__  
    return super().__call__(Chat(images), **kwargs)  
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/transformers/pipelines/base.py", line 1274, in __call__  
    return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)  
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/transformers/pipelines/base.py", line 1281, in run_single  
    model_outputs = self.forward(model_inputs, **forward_params)  
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/transformers/pipelines/base.py", line 1173, in forward  
    model_outputs = self._forward(model_inputs, **forward_params)  
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/transformers/pipelines/image_text_to_text.py", line 373, in _forward  
    generated_sequence = self.model.generate(**model_inputs, **generate_kwargs)  
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context  
    return func(*args, **kwargs)  
           ^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/transformers/generation/utils.py", line 2669, in generate  
    result = decoding_method(  
             ^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/transformers/generation/utils.py", line 2864, in _sample  
    outputs = self._prefill(input_ids, generation_config, model_kwargs)  
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/transformers/generation/utils.py", line 3853, in _prefill  
    return self(**model_inputs, return_dict=True)  
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl  
    return self._call_impl(*args, **kwargs)  
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl  
    return forward_call(*args, **kwargs)  
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/transformers/utils/generic.py", line 1002, in wrapper  
    outputs = func(self, *args, **kwargs)  
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/transformers/models/mistral3/modeling_mistral3.py", line 446, in forward  
    outputs = self.model(  
              ^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl  
    return self._call_impl(*args, **kwargs)  
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl  
    return forward_call(*args, **kwargs)  
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/transformers/utils/generic.py", line 1002, in wrapper  
    outputs = func(self, *args, **kwargs)  
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/transformers/models/mistral3/modeling_mistral3.py", line 313, in forward  
    image_features = self.get_image_features(  
                     ^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/transformers/utils/generic.py", line 1002, in wrapper  
    outputs = func(self, *args, **kwargs)  
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/transformers/models/mistral3/modeling_mistral3.py", line 232, in get_image_features  
    image_outputs = self.vision_tower(  
                    ^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl  
    return self._call_impl(*args, **kwargs)  
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl  
    return forward_call(*args, **kwargs)  
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/transformers/utils/generic.py", line 835, in wrapper  
    output = func(self, *args, **kwargs)  
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/transformers/models/pixtral/modeling_pixtral.py", line 496, in forward  
    patch_embeds = self.patch_conv(pixel_values)  
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl  
    return self._call_impl(*args, **kwargs)  
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl  
    return forward_call(*args, **kwargs)  
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 553, in forward  
    return self._conv_forward(input, self.weight, self.bias)  
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  
  File "/home/user/project/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 548, in _conv_forward  
    return F.conv2d(  
           ^^^^^^^^^
RuntimeError: Input type (torch.FloatTensor) and weight type (CPUBFloat16Type) should be the same or input should be a MKLDNN tensor and weight is a dense tensor
```

### Monkey-Patched working version
```python
import torch  
from transformers import pipeline  
  
if __name__ == '__main__':  
    messages = [  
        {  
            "role": "user",  
            "content": [  
                {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},  
                {"type": "text", "text": "What are these?"},  
            ],  
        },  
    ]  
    pipe = pipeline(  
        "image-text-to-text",  
        model="mistralai/Ministral-3-3B-Reasoning-2512",  # same for the instruction version  
        device="cpu",  # also "cpu", "cuda", and device_map="auto" have the same problem  
        dtype=torch.bfloat16,    
    )  
      
    # Patch the vision tower's forward method to convert inputs to bfloat16  
    original_forward = pipe.model.model.vision_tower.forward  
  
  
    def patched_forward(pixel_values, *args, **kwargs):  
        # Convert pixel_values to bfloat16 to match the model weights  
        pixel_values = pixel_values.to(torch.bfloat16)  
        return original_forward(pixel_values, *args, **kwargs)  
  
  
    pipe.model.model.vision_tower.forward = patched_forward  
      
      
    result = pipe(messages)  
    print(result)
```


### Expected behavior

Printed output dictionary with generated text tokens.

Comments:
The pipeline isn't casting the inputs to `dtype` when we take `chat_template` route. Will fix it, thanks for pointing out