T5 Tokenizer Adds Space after Each Added (Extra) Token
### System Info

- `transformers` version: 4.30.2
- Platform: Linux-5.4.0-146-generic-x86_64-with-glibc2.35
- Python version: 3.11.3
- Huggingface_hub version: 0.15.1
- Safetensors version: 0.3.1
- PyTorch version (GPU?): 2.0.1+cu117 (False)
- 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?: (NA)
- Using distributed or parallel set-up in script?: (NA)


### Who can help?

@Arthu

### 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

```ipython
In [1]: from transformers import AutoTokenizer

In [2]: tokenizer = AutoTokenizer.from_pretrained("./models/t5-base/")

In [3]: tokenizer.add_tokens(["asdfg"], special_tokens=False)
Out[3]: 1

In [4]: tokenizer.tokenize("asdfgwordtimeasdfgtime")
Out[4]: ['asdfg', '▁word', 'time', 'asdfg', '▁time']
```

### Expected behavior

tokenizer return `['asdfg', 'word', 'time', 'asdfg', 'time']`
I think a fix is in

https://github.com/huggingface/transformers/pull/24622

<|||||>FYI: that PR is not merged yet into `main` branch<|||||>Let's wait until we merge to close! <|||||>@ArthurZucker  Hi, this issue still exists after updating transformers to the latest 4.31.0 with #24622<|||||>Hey! 
It is adressed for slow tokenizer, which are part of transformers! Fast tokenizers will need to wait a bit. It is also linked to the conversion script and meta space that need to be used similar to Llama
```python 
In [6]: tokenizer = AutoTokenizer.from_pretrained("t5-base", legacy = False, use_fast = False)
/fsx/arthur/miniconda3/envs/py10/lib/python3.10/site-packages/transformers/models/t5/tokenization_t5.py:199: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.
For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.
- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.
- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.
- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.
  warnings.warn(

In [7]: tokenizer.add_tokens(["asdfg"], special_tokens=False)
Out[7]: 1

In [8]: tokenizer.tokenize("asdfgwordtimeasdfgtime")
Out[8]: ['asdfg', 'word', 'time', 'asdfg', 'time']
```
the key is that you need to set `legacy=False` and `use_fast = False` because fast tokenizer is not fixed yet 😉  