[WIP] Refactor Attention Modules

[DN6]

What does this PR do?

Draft PR to address some issues with the attention modules.

1. Moves a number of transformer related blocks/objects in the attention module that would have a better home under models/transformers

2. Moves the definition of the Attention module into attention.py, rather than have it live in attention_prcessors.py.

3. We have a very large number of processors, but with #11368 we should no longer need a good chunk of them and they can be deprecated. I think with these changes we would end up with ~3 processors per model (Attn, IPAdapter, PAG)

4. Make it so that we can bump up our minimum supported Torch version to >=2.0 and use the F.sdpa API for all processors.

5. There was some discussion around naming of the processors: https://huggingface.slack.com/archives/C065E480NN9/p1737130514639479 We landed on calling the processors something like AttnProcessorsSDPA, but with #11368 we no longer need to use a dedicated processor per backend, so I think it's okay to just have the class be named AttnProcessor.

6. Move processor definitions into the model files, so we don't end up with very large files containing all processors.

7. Introduce AttentionModuleMixin that contains all common methods related to attention operations. New attention modules would inherit from this class.

8. Introduce an AttentionMixin for models so that methods like set_processor are not duplicated across models. Although we can probably just add the methods of this Mixin to ModelMixin

9. Using Flux as an example here to show how we can define a single Processor to support both fused/unfused qkv projections.

Fixes # (issue)

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