avoid errors when the size of `input_ids` passed to `PrefixConstrainedLogitsProcessor` is zero
Author: HiDolen
Pull request: yes

# What does this PR do?

`PrefixConstrainedLogitsProcessor.__call__()` does not account for the case where the size of the input `input_ids` is zero.

It can lead to an error when executing `input_ids.view(-1, self._num_beams, input_ids.shape[-1])`:

```text
RuntimeError: cannot reshape tensor of 0 elements into shape [-1, 1, 0] because the unspecified dimension size -1 can be any value and is ambiguous
```

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Fixes noamgat/lm-format-enforcer#132


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Comments:
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Hi @HiDolen 👋  Thank you for opening the PR!

Technically, we might want to apply `prefix_allowed_tokens_fn()` if `input_ids` is empty. Instead of providing an early `return` in that case, can we change how we iterate over the batch and the beam items so as to not hit this exception?

e.g.
```py
for row_id in range(input_ids.shape[0]):
    batch_id = row_id // self._num_beams
    beam_id = row_id % self._num_beams
    sent = input_ids[row_id, ...]
    (...)
```

> Hi @HiDolen 👋 Thank you for opening the PR!
> 
> Technically, we might want to apply `prefix_allowed_tokens_fn()` if `input_ids` is empty. Instead of providing an early `return` in that case, can we change how we iterate over the batch and the beam items so as to not hit this exception?
> 
> e.g.
> 
> ```python
> for row_id in range(input_ids.shape[0]):
>     batch_id = row_id // self._num_beams
>     beam_id = row_id % self._num_beams
>     sent = input_ids[row_id, ...]
>     (...)
> ```

How adout this? Sorry for the late reply.

```diff
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
    mask = torch.full_like(scores, -math.inf)
-   if input_ids.shape[-1] == 0:
-       return scores + mask
+   batch_size = input_ids.shape[0] // self._num_beams

-   for batch_id, beam_sent in enumerate(input_ids.view(-1, self._num_beams, input_ids.shape[-1])):
-       for beam_id, sent in enumerate(beam_sent):
+   for batch_id in range(batch_size):
+       for beam_id in range(self._num_beams):
+           sent = input_ids[batch_id * self._num_beams + beam_id]
            prefix_allowed_tokens = self._prefix_allowed_tokens_fn(batch_id, sent)
            if len(prefix_allowed_tokens) == 0:
                raise ValueError(
                    f"`prefix_allowed_tokens_fn` returned an empty list for batch ID {batch_id}."
                    f"This means that the constraint is unsatisfiable. Please check your implementation"
                    f"of `prefix_allowed_tokens_fn` "
                )
            mask[batch_id * self._num_beams + beam_id, prefix_allowed_tokens] = 0

    scores_processed = scores + mask
    return scores_processed
```