Support for OrderedConstraints, TemplateConstraints and LiteralConstraints in force_words_ids
### Feature request

As raised by @sijunhe in [this blog post](https://huggingface.co/blog/constrained-beam-search), the `force_words_ids` argument of the `model.generate()` method needs to be modified to support `OrderedConstraints` and `TemplateConstraints`.

In addition, there is a need for a `LiteralConstraints` subclass. This would enable generating exactly the same list of tokens given in the `force_words_ids` argument, which would in turn allow for the calculation of sentence perplexity across all language models in the library by making use of [the attribute implemented in this PR](https://github.com/huggingface/transformers/pull/14654). 

### Motivation

Currently, there is no standard way of calculating sentence perplexity and implementing it requires a lot of boilerplate code, which may not always work as intended. Third-party libraries such as [lm-scorer](https://github.com/simonepri/lm-scorer), which implemented this functionality, are no longer maintained and do not support all language models in the library.

### Your contribution

I would be interested in working on this PR as I'm the maintainer of a third-party library ( [hashformers](https://github.com/ruanchaves/hashformers) ) that performs sentence perplexity calculations with the Transformers library.
cc @gante<|||||>Hi @ruanchaves 👋 

I'm not sure whether I understand the issue you described above. Our generation methods return the sequence log probabilities, from which you can compute the sequence perplexity. What would be missing for your use case?

Regarding `force_words_ids`, I'm reluctant to add more features there -- it has low usage and a high maintenance cost. I might reconsider my position here if I see more demand for further functionality :)<|||||>Olá @gante !

> I'm not sure whether I understand the issue you described above. Our generation methods return the sequence log probabilities, from which you can compute the sequence perplexity. 

True, but I want the sequence log probabilities for a predefined sequence. I already have a sequence of tokens and I want the model to calculate its perplexity. I don't want the perplexity of a sequence generated through beamsearch or greedy search. 

When [lm_scorer](https://github.com/simonepri/lm-scorer) was conceived, there was no straightforward way to do this with `transformers`:

```python
# Return token probabilities (provide log=True to return log probabilities)
scorer.tokens_score("I like this package.")
# => (scores, ids, tokens)
# scores = [0.018321, 0.0066431, 0.080633, 0.00060745, 0.27772, 0.0036381]
# ids    = [40,       588,       428,      5301,       13,      50256]
# tokens = ["I",      "Ġlike",   "Ġthis",  "Ġpackage", ".",     "<|endoftext|>"]
```

Is this still the case? I hope you can point me in the right direction if new features were added since  [lm_scorer](https://github.com/simonepri/lm-scorer) was released.

> Regarding `force_words_ids`, I'm reluctant to add more features there -- it has low usage and a high maintenance cost. I might reconsider my position here if I see more demand for further functionality :)

I get it, but being able to calculate the perplexity of a predefined sequence sounds like an essential feature to me, regardless of where it is implemented.<|||||>Hey @ruanchaves 👋 

Yeah, we lack an easy interface to compute the logits of existing sentences, and that's something I really like to add ASAP! I'm planning to add it within the next month, but if you'd like to give me a hand you'd be more than welcome 🙌 

The planned interface is
```python
log_scores = model.compute_token_scores(tokens, normalize_logits)
```
where `tokens` is the tokenized input (so it can be used in different modalities) and `normalize_logits` is an optional boolean (defaulting to true) to control whether we want to renormalize the model logits<|||||>@gante , 

> Yeah, we lack an easy interface to compute the logits of existing sentences, and that's something I really like to add ASAP! I'm planning to add it within the next month, but if you'd like to give me a hand you'd be more than welcome 🙌

Good! This would close the issue for me, as it's the thing I'm actually looking for. I'll be watching your PRs and see if I can contribute somehow. 

Suggestion: consider adding the `compute_token_scores` method to masked language models as well. This has been implemented a few years ago at [awslabs/mlm-scoring](https://github.com/awslabs/mlm-scoring), but just like lm-scorer, it's no longer maintained. 