….py (#998)

A lot of these fixes were intended for
#966 however that's blocked
until there's a new `transformers` release.

These improvements are general to all models and will enable PRs
resolving #806 and
#965

# Structure of `OutlinesLogitsProcessor`

The goal is to create a base class which allows a logits processors to
be implemented once and used for any `outlines.models` inference
library.

To accomplish this we must normalize the input array. It must have a
consistent type (`torch.Tensor`) and consistent dimensionality (2). We
can normalize both of these simply, and without any copy operations.

`mlx.core.array`, `numpy.array`, and `torch.Tensor` all support [pythons
array standard
`__dlpack__`](https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.__dlpack__.html).
This standard allows for casting between array types without copying.

`torch.Tensor` is the only input type which cannot always be cast to any
other type because torch tensors may live in GPU memory. Therefore, we
cast all arrays to `torch.Tensor`, implement logits processors using
torch methods, and convert back to the original array type in
`OutlinesLogitsProcessor`. See docstring of
`OutlinesLogitsProcessor.__call__()` for more details.

# Detailed Changes
- Rename `BaseLogitsProcessor` to `OutlinesLogitsProcessor`
- Ensure `OutlinesLogitsProcessor.process_logits()` is always passed a
2D batch request with `torch.Tensor` logits and `List` input_ids. Also
clean up code to be more readable in `OutlinesLogitsProcessor__call__()`
- Ensure `FSMLogitsProcessor` allows unstable sequence ordering (beam
search in transformers and vLLM change the order of sequences)
- Update `tests/generate/test_generate.py` to cover more permutations of
  - regex / text 
  - batch / single
  - greedy / multinomial / beam search
  - `stream()` / `generate()`
- Ensure performance stability with difference array libraries through
`benchmark_processors.py`