Adds OWLViT to models exportable with ONNX
Output for tests on my local machine:

```bash
(transformers) ➜  transformers git:(owlvit_onnx) ✗ RUN_SLOW=1 pytest tests/onnx/test_onnx_v2.py -v -k "owlvit" --full-trace
================================================================== test session starts ===================================================================
platform darwin -- Python 3.8.12, pytest-7.1.2, pluggy-1.0.0 -- /Users/dhruv/Documents/code/transformers/.venv/bin/python
cachedir: .pytest_cache
rootdir: /Users/dhruv/Documents/code/transformers, configfile: setup.cfg
collected 410 items / 408 deselected / 2 selected                                                                                                        

tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_101_owlvit_default PASSED                                                    [ 50%]
tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_on_cuda_101_owlvit_default PASSED                                            [100%]

==================================================================== warnings summary ====================================================================
tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_101_owlvit_default
  /Users/dhruv/Documents/code/transformers/src/transformers/image_utils.py:223: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
    def resize(self, image, size, resample=PIL.Image.BILINEAR, default_to_square=True, max_size=None):

tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_101_owlvit_default
  /Users/dhruv/Documents/code/transformers/src/transformers/models/owlvit/feature_extraction_owlvit.py:80: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
    resample=Image.BICUBIC,

tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_101_owlvit_default
tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_on_cuda_101_owlvit_default
  /Users/dhruv/Documents/code/transformers/src/transformers/models/owlvit/modeling_owlvit.py:272: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
    if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):

tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_101_owlvit_default
tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_on_cuda_101_owlvit_default
  /Users/dhruv/Documents/code/transformers/src/transformers/models/owlvit/modeling_owlvit.py:312: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
    if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):

tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_101_owlvit_default
tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_on_cuda_101_owlvit_default
  /Users/dhruv/Documents/code/transformers/src/transformers/models/owlvit/modeling_owlvit.py:709: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
    mask.fill_(torch.tensor(float("-inf")))

tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_101_owlvit_default
tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_on_cuda_101_owlvit_default
  /Users/dhruv/Documents/code/transformers/src/transformers/models/owlvit/modeling_owlvit.py:280: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
    if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):

tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_101_owlvit_default
tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_on_cuda_101_owlvit_default
  /Users/dhruv/Documents/code/transformers/src/transformers/models/owlvit/modeling_owlvit.py:289: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
    if attention_mask.size() != (bsz, 1, tgt_len, src_len):

tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_101_owlvit_default
tests/onnx/test_onnx_v2.py::OnnxExportTestCaseV2::test_pytorch_export_on_cuda_101_owlvit_default
  /Users/dhruv/Documents/code/transformers/.venv/lib/python3.8/site-packages/torch/onnx/symbolic_opset9.py:4592: UserWarning: Exporting aten::index operator of advanced indexing in opset 14 is achieved by combination of multiple ONNX operators, including Reshape, Transpose, Concat, and Gather. If indices include negative values, the exported graph will produce incorrect results.
    warnings.warn(

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
==================================================== 2 passed, 408 deselected, 14 warnings in 44.45s =====================================================
```

Note: Haven't tested this on GPU yet, don't have a GPU machine with me currently.

Also, this is for the `default` task of OWLViT. The `object-detection` task isn't supported by AutoModel yet, because of which if I add that to onnx it's failing currently. Should I make the change for AutoModel as well? 

cc: @ChainYo 
_The documentation is not available anymore as the PR was closed or merged._<|||||>@unography That's strange that it does not work for object detection. It should actually work, DETR and YOLOS are exportable to ONNX for instance (see [here](https://github.com/huggingface/transformers/blob/1ccd2515ed6d7da4ec46fe94aedbd8a86a2cde8e/src/transformers/onnx/features.py#L262)). What is the error you get when trying to export the model for object detection?<|||||>@regisss I think it just needs to be defined in the config for AutoModel, for Object detection [here](https://github.com/huggingface/transformers/blob/1ccd2515ed6d7da4ec46fe94aedbd8a86a2cde8e/src/transformers/models/auto/modeling_auto.py#L443)


This is the stacktrace - 

```bash
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

cls = <class 'transformers.models.auto.modeling_auto.AutoModelForObjectDetection'>
config = OwlViTConfig {
  "_commit_hash": "7cc55348dae46396474cd94bf00a542167a10f8d",
  "_name_or_path": "google/owlvit-base-pa...nsformers_version": "4.22.0.dev0",
    "typical_p": 1.0,
    "use_bfloat16": false
  },
  "vision_config_dict": null
}

kwargs = {}, trust_remote_code = False

    @classmethod
    def from_config(cls, config, **kwargs):
        trust_remote_code = kwargs.pop("trust_remote_code", False)
        if hasattr(config, "auto_map") and cls.__name__ in config.auto_map:
            if not trust_remote_code:
                raise ValueError(
                    "Loading this model requires you to execute the modeling file in that repo "
                    "on your local machine. Make sure you have read the code there to avoid malicious use, then set "
                    "the option `trust_remote_code=True` to remove this error."
                )
            if kwargs.get("revision", None) is None:
                logger.warning(
                    "Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure "
                    "no malicious code has been contributed in a newer revision."
                )
            class_ref = config.auto_map[cls.__name__]
            module_file, class_name = class_ref.split(".")
            model_class = get_class_from_dynamic_module(config.name_or_path, module_file + ".py", class_name, **kwargs)
            return model_class._from_config(config, **kwargs)
        elif type(config) in cls._model_mapping.keys():
            model_class = _get_model_class(config, cls._model_mapping)
            return model_class._from_config(config, **kwargs)
    
>       raise ValueError(
            f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
            f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping.keys())}."
        )
E       ValueError: Unrecognized configuration class <class 'transformers.models.owlvit.configuration_owlvit.OwlViTConfig'> for this kind of AutoModel: AutoModelForObjectDetection.
E       Model type should be one of DetrConfig, YolosConfig.

src/transformers/models/auto/auto_factory.py:412: ValueError
```<|||||>Hi @unography and @regisss! OWL-ViT is not a part of the object detection pipeline because it requires both image and search queries as input. 

We are planning to add a zero-shot-object-detection pipeline for OWL-ViT (see this [issue](https://github.com/huggingface/transformers/issues/18445)).

cc @sgugger @NielsRogge <|||||>Thanks for the information @alaradirik :)

@unography Let's keep only the default pipeline as you did then. I had to change one `.T` for `.t()` in `modeling_owlvit.py` to make the test pass, as in the PR of CLIP :laughing: Could you please change this?<|||||>Pinging @sgugger for final approval<|||||>@regisss ya sorry i missed the `.T` issue, i was testing on the nightly pytorch. should be fixed now<|||||>Hey @lewtun, would you like to have a look at this and merge if it looks good to you?<|||||>@lewtun Can you take a quick look at this PR and merge it when you approve? :slightly_smiling_face: 