Different behaviour of AutoTokenizer and T5Tokenizer
### System Info

- `transformers` version: 4.22.2
- Platform: Linux-5.4.209-129.367.amzn2int.x86_64-x86_64-with-glibc2.26
- Python version: 3.9.13
- Huggingface_hub version: 0.10.0
- PyTorch version (GPU?): 1.12.1+cu102 (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?: NO
- Using distributed or parallel set-up in script?: NO

### Who can help?

_No response_

### Information

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

### Tasks

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

### Reproduction

The `T5Tokenizer` prepends a whitespace before the eos token when a new eos token is provided, while AutoTokenizer maintains the usual behaviour.

```python
from transformers import AutoTokenizer, T5Tokenizer

text = ["My name is Pietro", "I love pizza"]

tok = T5Tokenizer.from_pretrained("t5-small", bos_token="[bos]", eos_token="[eos]", sep_token="[sep]")
auto_tok = AutoTokenizer.from_pretrained("t5-small", bos_token="[bos]", eos_token="[eos]", sep_token="[sep]")
print(tok.batch_decode(tok(text)["input_ids"]))
print(auto_tok.batch_decode(tok(text)["input_ids"]))

#> ['My name is Pietro [eos]', 'I love pizza [eos]']
#> ['My name is Pietro[eos]', 'I love pizza[eos]']


tok = T5Tokenizer.from_pretrained("t5-small")
auto_tok = AutoTokenizer.from_pretrained("t5-small")
print(tok.batch_decode(tok(text)["input_ids"]))
print(auto_tok.batch_decode(tok(text)["input_ids"]))

#> ['My name is Pietro</s>', 'I love pizza</s>']
#> ['My name is Pietro</s>', 'I love pizza</s>']
```

### Expected behavior

The two tokenizer classes should be equivalent
When you're using `T5Tokenizer`, you're loading the slow version. When using `AutoTokenizer`, you're loading the fast version by default. 

Unfortunately, due to a difference in implementation, the fast and slow tokenizer can have some differences.

Pinging @SaulLu and @ArthurZucker for knowledge<|||||>Yes, noticed the same thing with GPT2, especially with OOV that are automatically converted to blank with the fast version! <|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.