[WIP] Hard error when ignoring tensors.
Author: Narsil
Pull request: yes

# What does this PR do?

 Better selection/error when saving a checkpoint.

- Find all names we should normally drop (those are in the transformers
  config)
- Find all disjoint tensors (for those we can safely trigger a copy to
  get rid of the sharing before saving)
- Clone those disjoint tensors getting rid of the issue
- Find all identical names (those should be declared in the config
  but we try to find them all anyway.)
- For all identical names:
  - If they are in the config, just ignore them everything is fine
  - If they are not, warn about them.
- For all remainder tensors which are shared yet neither identical NOR
  disjoint. raise a hard error.

* Adding a failing test on `main` that passes here.

* We don't need to keep the subfolder logic in this test.


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Fixes # (issue)


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Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_27484). All of your documentation changes will be reflected on that endpoint.

Removing the `.clone()` [here](https://github.com/huggingface/optimum/blob/15a162824d0c5d8aa7a3d14ab6e9bb07e5732fb6/optimum/bettertransformer/models/base.py#L154), I can confirm that

```python
from transformers import AutoModel
from optimum.bettertransformer import BetterTransformer
import torch

model = AutoModel.from_pretrained("bert-base-uncased")

model_bt = BetterTransformer.transform(model)

model_reverse = BetterTransformer.reverse(model_bt)

model_reverse.save_pretrained("bt-reverse-saved")

model = AutoModel.from_pretrained("bert-base-uncased")
model_reverse = AutoModel.from_pretrained("bt-reverse-saved")

assert torch.equal(model.encoder.layer[0].attention.self.query.weight, model_reverse.encoder.layer[0].attention.self.query.weight)
assert torch.equal(model.encoder.layer[0].attention.self.key.weight, model_reverse.encoder.layer[0].attention.self.key.weight)
assert torch.equal(model.encoder.layer[0].attention.self.value.weight, model_reverse.encoder.layer[0].attention.self.value.weight)
```

works fine with this fix (while was failing on main).

Forgot to merge after resolving conflicts with main. Merging now