Add ImageProcessorFast to Efficientnet processor 
Author: Yann-CV
Pull request: yes

# What does this PR do?

Following https://github.com/huggingface/transformers/issues/36978:
This pull request introduces a new fast image processor for EfficientNet models and integrates it into the existing codebase. The changes include updates to documentation, initialization files, and test cases to support the new `EfficientNetImageProcessorFast`.

### Integration of `EfficientNetImageProcessorFast`:

* [`docs/source/en/model_doc/efficientnet.md`](diffhunk://#diff-cee8ef04c44ba9bde1c112df04e193271b52309b393e78cfc6e4e3fdb6392821R46-R50): Added documentation for `EfficientNetImageProcessorFast`.
* [`src/transformers/__init__.py`](diffhunk://#diff-7723156f6b075b1bf1525f769d90a7cc0b5f233becdcbe28707aaa753960d897R1354): Included `EfficientNetImageProcessorFast` in the import structure and import statements. [[1]](diffhunk://#diff-7723156f6b075b1bf1525f769d90a7cc0b5f233becdcbe28707aaa753960d897R1354) [[2]](diffhunk://#diff-7723156f6b075b1bf1525f769d90a7cc0b5f233becdcbe28707aaa753960d897R6624)
* [`src/transformers/models/auto/image_processing_auto.py`](diffhunk://#diff-dc5c050927ed279b77bac41443778a1155c1cd7825aae50412d70b65bb96397fL58-R58): Updated the image processor mapping to include `EfficientNetImageProcessorFast`. [[1]](diffhunk://#diff-dc5c050927ed279b77bac41443778a1155c1cd7825aae50412d70b65bb96397fL58-R58) [[2]](diffhunk://#diff-dc5c050927ed279b77bac41443778a1155c1cd7825aae50412d70b65bb96397fL85-R85)
* [`src/transformers/models/efficientnet/__init__.py`](diffhunk://#diff-4ac3b97f46b61bd463102c9473fa6a202f9175f8f10ea56e1ac1a22e2ad8bb34R23): Added import for `EfficientNetImageProcessorFast`.

### Implementation of `EfficientNetImageProcessorFast`:

* [`src/transformers/models/efficientnet/image_processing_efficientnet_fast.py`](diffhunk://#diff-864fcb168f4bc5f8a11c57fb7d644e31b6af872dd57391121db4cf84190d7feeR1-R198): Added the implementation of the `EfficientNetImageProcessorFast` class, including methods for preprocessing, rescaling, and normalizing images.

### Testing and Dummy Objects:

* [`src/transformers/utils/dummy_torchvision_objects.py`](diffhunk://#diff-628ebb72d86e41f003144eae7255f5f0e9e60bbf5cb742b5b5447a0360cf1968R61-R67): Added a dummy class for `EfficientNetImageProcessorFast` to handle cases where `torchvision` is not available.
* [`tests/models/efficientnet/test_image_processing_efficientnet.py`](diffhunk://#diff-13b3debfbb1f79ee40a45ed62412b233fc6973d4ed3d8731eeab3782dd2db3adL22-R32): Updated test cases to include `EfficientNetImageProcessorFast` and ensure it is tested alongside the standard `EfficientNetImageProcessor`. [[1]](diffhunk://#diff-13b3debfbb1f79ee40a45ed62412b233fc6973d4ed3d8731eeab3782dd2db3adL22-R32) [[2]](diffhunk://#diff-13b3debfbb1f79ee40a45ed62412b233fc6973d4ed3d8731eeab3782dd2db3adR90) [[3]](diffhunk://#diff-13b3debfbb1f79ee40a45ed62412b233fc6973d4ed3d8731eeab3782dd2db3adL97-R123)



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## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

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Comments:
I am spotting an inconsistency between slow (in efficientnet) and fast resize (base class):

```
    def test_resize(self):
        torch.manual_seed(0)
        data =  torch.randint(0, 256, (1, 3, 480, 640), dtype=torch.uint8)
        for image_processor_class in self.image_processor_list:
            image_processor = image_processor_class(**self.image_processor_dict)
            if image_processor_class == EfficientNetImageProcessorFast:
                image = data
                print(f"image: {image.shape} - {image.mean(dtype=torch.float32)}")
                resized_image = image_processor.resize(
                    image,
                    size=SizeDict(height=18, width=18),
                    interpolation=F.InterpolationMode.NEAREST,
                    antialias=False,
                )
                print(f"resized_image: {resized_image.shape} - {resized_image.mean(dtype=torch.float32)}")
            else:
                image = data.squeeze().permute(1, 2, 0).numpy()
                print(f"image: {image.shape} - {image.mean()}")
                resized_image = image_processor.resize(
                    image,
                    size={"height": 18, "width": 18},
                    resample=PILImageResampling.NEAREST
                )
                print(f"resized_image: {resized_image.shape} - {resized_image.mean()}")
```

is returning

```
image: (480, 640, 3) - 127.5092306857639
resized_image: (18, 18, 3) - 126.91769547325103
image: torch.Size([1, 3, 480, 640]) - 127.50922393798828
resized_image: torch.Size([1, 3, 18, 18]) - 121.97119140625
```

You can obtain quite a gap in mean value for the same interpolation mode. `antialias` does not impact the result


All other method are outputing the same result (normalize and rescale) between slow and fast

using bilinear resizing is making the test pass (too much difference between PIL and torchvision with nearest)

@ydshieh @yonigozlan the test are failing on test non related to this pull request.
In addition of updating the  resize interpolation in tests, I took the freedom to fix the seed of numpy in the tests.