[core] Refactor condition embedders (and others) modules from pipelines

[sayakpaul]

What does this PR do?

We have a bunch of model-level components under src/diffusers/pipelines module. This PR factors them out of the pipelines module and places them under src/diffusers/models module strategically.

The main changes are in src/pipelines/pipeline_loading_utils.py which basically remaps to the components that were moved to src/diffusersmodels/. Open to other ways of handling it.

For reviewers

Main changes can be summarized as follows:

• Move an existing model-level module (e.g., audio_tokenizer_ace_step.py) to src/diffusers/models and deprecate gracefully.
• I have used my best judgment to determine which model gets to either of autoencoders and condition_embedders. When I wasn't sure, I simply placed them under others. No strong opinions here.
• scripts/smoke_test_relocated_pretrained.py is for transient testing until the PR is merged. Will delete it after. I have run the tests and models and pipelines load as expected.
• There is a tests/models/test_relocated_class_loading.py which will check if deprecation warnings are raised and will also help to remove those deprecation paths once the cycle is over.
• Similarly, there is a tests/pipelines/test_relocated_class_loading.py. It ensures that no deprecation warnings for the refactored modules are raised when loading the affected pipelines.

[HuggingFaceDocBuilderDev]

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[sayakpaul]

Open to suggestions on where to place these other than others.

[sayakpaul]

Going by the implementation of the module, it encodes into a tokenized representation (not the existing sense of tokenization done in language models). So, decided keep them under autoencoders.

[sayakpaul]

Open to moving them elsewhere. Since upsampling could be interpreted as decoding to a higher resolution, I thought of keeping them under autoencoders.

[sayakpaul]

This is so that we don't hit deprecation warnings.