FlaxDataCollatorForT5MLM :ValueError: all input arrays must have the same shape 
### System Info

- transformers version: 4.27.1
- Platform: Linux-5.18.10-76051810-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.11.1
- PyTorch version (GPU?): 2.0.0.dev20230202+cu116 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: No

### Who can help?

@patil-suraj @patrickvonplaten

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

I am following the script to reproduce the above https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/run_t5_mlm_flax.py#L336-L346

If I give the `mean_noise_span_length ` > 1, for any value of noise_density, i get the ouput
```
 prompt = "The cute dog walks in the green park"
    encoded = tokenizer(prompt, truncation=False, padding=False, return_tensors="pt").input_ids
    batch_size =1
    input_length = encoded.shape[1]
    denoiser = FlaxDataCollatorForT5MLM(tokenizer,.35,3)
    mask_indices = np.asarray([denoiser.random_spans_noise_mask(input_length) for i in range(batch_size)])
    labels_mask = ~mask_indices
    input_ids_sentinel = denoiser.create_sentinel_ids(mask_indices.astype(np.int8))
    labels_sentinel = denoiser.create_sentinel_ids(labels_mask.astype(np.int8))
    input_ids = denoiser.filter_input_ids(encoded, input_ids_sentinel)
    labels  =  denoiser.filter_input_ids(encoded, labels_sentinel)
```
If I give the `mean_noise_span_length `  == 1, for many value of noise_density, i get the error

```
Traceback (most recent call last):
  File "/home/alex/coding/tranformer_learn/t5_denoising.py", line 133, in <module>
    mask_indices = np.asarray([denoiser.random_spans_noise_mask(input_length) for i in range(batch_size)])
  File "/home/alex/coding/tranformer_learn/t5_denoising.py", line 133, in <listcomp>
    mask_indices = np.asarray([denoiser.random_spans_noise_mask(input_length) for i in range(batch_size)])
  File "/home/alex/coding/tranformer_learn/t5_denoising.py", line 94, in random_spans_noise_mask
    np.stack([nonnoise_span_lengths, noise_span_lengths], axis=1), [num_noise_spans * 2]
  File "<__array_function__ internals>", line 200, in stack
  File "/home/alex/.local/lib/python3.10/site-packages/numpy/core/shape_base.py", line 464, in stack
    raise ValueError('all input arrays must have the same shape')
ValueError: all input arrays must have the same shape
```

Basically, the two arrays are different  lengths in numpy stack 
```
  interleaved_span_lengths = np.reshape(
            np.stack([nonnoise_span_lengths, noise_span_lengths], axis=1), [num_noise_spans * 2]
        )
```
From what I could make out this happens when `num_noise_spans` == `num_noise_tokens` when `mean_noise_span_length == 1`

```
num_noise_spans = int(np.round(num_noise_tokens / self.mean_noise_span_length))
```
Code that can be run https://gist.github.com/alexcpn/b9bb2b0f01833d1bb862502faf99bab8

### Expected behavior

There should not be exception
cc @sanchit-gandhi @ArthurZucker maybe<|||||>Hey @alexcpn - great job at digging into the issue and thanks for the gist! It does indeed look like the case that we're hitting this error based on how we compute the `num_noise_spans`:
https://github.com/huggingface/transformers/blob/aec10d162f59d809ead3990ef78c51918b622f38/examples/flax/language-modeling/run_t5_mlm_flax.py#L274

Would you like to open a PR to fix this so that it's robust for `mean_noise_span_length == 1`?

The code is largely ported from the original T5 pre-processing, which can be found here: https://github.com/google-research/text-to-text-transfer-transformer/blob/main/t5/data/preprocessors.py<|||||>HI @sanchit-gandhi ; I have tried to demo the problem and the possible correction; Please find the pull request here https://github.com/huggingface/transformers/pull/22938