This PR updates the dummy-creation process that had been in place up to now. Instead of defining dummies in the lib, they're now dynamically created when the required dependencies for an object aren't found.

See below for an extract from the documentation explaining the current updated approach.

While we strive for minimal dependencies, some models have specific dependencies requirements that cannot be
worked around. We don't want for all users of transformers to have to install those dependencies to use other models,
we therefore mark those as soft dependencies rather than hard dependencies.

The transformers toolkit is not made to error-out on import of a model that has a specific dependency; instead, an
object for which you are lacking a dependency will error-out when calling any method on it. As an example, if
torchvision isn't installed, the fast image processors will not be available.

This object is still importable:

>>> from transformers import DetrImageProcessorFast
>>> print(DetrImageProcessorFast)
<class 'DetrImageProcessorFast'>

However, no method can be called on that object:

>>> DetrImageProcessorFast.from_pretrained()
ImportError: 
DetrImageProcessorFast requires the Torchvision library but it was not found in your environment. Checkout the instructions on the
installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.
Please note that you may need to restart your runtime after installation.

Let's see how to specify specific object dependencies.

Specifying Object Dependencies

Filename-based

All objects under a given filename have an automatic dependency to the tool linked to the filename

TensorFlow: All files starting with modeling_tf_ have an automatic TensorFlow dependency.

Flax: All files starting with modeling_flax_ have an automatic Flax dependency

PyTorch: All files starting with modeling_ and not valid with the above (TensorFlow and Flax) have an automatic
PyTorch dependency

Tokenizers: All files starting with tokenization_ and ending with _fast have an automatic tokenizers dependency

Vision: All files starting with image_processing_ have an automatic dependency to the vision dependency group;
at the time of writing, this only contains the pillow dependency.

Vision + Torch + Torchvision: All files starting with image_processing_ and ending with _fast have an automatic
dependency to vision, torch, and torchvision.

All of these automatic dependencies are added on top of the explicit dependencies that are detailed below.

Explicit Object Dependencies

We add a method called requires that is used to explicitly specify the dependencies of a given object. As an
example, the Trainer class has two hard dependencies: torch and accelerate. Here is how we specify these
required dependencies:

from .utils.import_utils import requires

@requires(backends=("torch", "accelerate"))
class Trainer:
    ...

Backends that can be added here are all the backends that are available in the import_utils.py module.