Fast and normal tokenizer generate different output when handling consecutive spaces.
### System Info

Transformer version: 4.29.2

### Who can help?

_No response_

### Information

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

### Tasks

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

### Reproduction

Code to reproduce:
```python
import transformers

fast = transformers.AutoTokenizer.from_pretrained('openlm-research/open_llama_7b', use_fast=True, cache_dir='hf_cache')
normal = transformers.AutoTokenizer.from_pretrained('openlm-research/open_llama_7b', use_fast=False, cache_dir='hf_cache')

print(fast.__class__, fast('a    b'))
print(normal.__class__, normal('a    b'))
```

output:
```text
<class 'transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast'> {'input_ids': [1, 260, 31822, 31822, 31822, 284], 'token_type_ids': [0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1]}
<class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'> {'input_ids': [1, 260, 284], 'attention_mask': [1, 1, 1]}
```

### Expected behavior

Same output
cc @ArthurZucker <|||||>This seems to be pretty close to #24918. 
I don't know how they converted the fast tokenizer but it seems wrong. I suggest you open an issue at https://hf.co/openlm-research/open_llama_7b/discussions, as if you use the `huggyllama/llama-7b` model, you will not have this issue: 

```python 
import transformers

>>> fast = transformers.AutoTokenizer.from_pretrained('openlm-research/open_llama_7b', use_fast=True, cache_dir='hf_cache')
>>> normal = transformers.AutoTokenizer.from_pretrained('openlm-research/open_llama_7b', use_fast=False, cache_dir='hf_cache')

>>> print(fast.__class__, fast('a    b'))
<class 'transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast'> {'input_ids': [1, 263, 1678, 289], 'attention_mask': [1, 1, 1, 1]}
>>> print(normal.__class__, normal('a    b'))
<class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'> {'input_ids': [1, 263, 1678, 289], 'attention_mask': [1, 1, 1, 1]}
```