handle unique consecutive issue with generating semantic token ids

- [x] accommodate variable lengthed audio, bring in eos token

- [x] make sure unique consecutive works with coarse transformer

- [x] pretty printing all discriminator losses to log

- [x] handle when generating semantic tokens, that last logits may not be necessarily the last in the sequence given unique consecutive processing

- [ ] complete full training code for soundstream, taking care of discriminator training

- [ ] figure out how to do the normalization across each dimension mentioned in the paper, but ignore it for v1 of the framework

- [ ] abstract out conditioning + classifier free guidance into external module or potentially a package

- [ ] add option to use flash attention

- [ ] simplify training even more within AudioLM class

- [ ] handle when generating semantic tokens, that last logits may not be necessarily the last in the sequence given unique consecutive processing

## Citations

        # start length and get running id output

        batch = ids.shape[0]

        start_length = ids.shape[-1]

        output = ids.clone()

        batch_range = rearrange(torch.arange(batch, device = device), 'b -> b 1')

        last_logit_indices = (ids != self.pad_id).sum(dim = -1).long()

        # sample from transformer

        for ind in range(start_length, max_length):

                **kwargs

            )

            last_logits = logits[:, -1]

            last_logits = logits[batch_range, last_logit_indices]

            last_logits = rearrange(last_logits, 'b 1 c -> b c')

            filtered_logits = top_k(last_logits, thres = filter_thres)

            sampled = gumbel_sample(filtered_logits, temperature = temperature, dim = -1)

            if all_rows_have_eos_id(output, self.eos_id):

                break

        output = mask_out_after_eos_id(output, self.pad_id)

            last_logit_indices += 1

        output = mask_out_after_eos_id(output, self.pad_id, include_eos = False)

        return output

    def forward_with_cond_scale(

setup(

  name = 'audiolm-pytorch',

  packages = find_packages(exclude=[]),

  version = '0.0.30',

  version = '0.0.31',

  license='MIT',

  description = 'AudioLM - Language Modeling Approach to Audio Generation from Google Research - Pytorch',

  author = 'Phil Wang',