Loading mT5 checkpoint will load from UMT5 class
### System Info

- `transformers` version: 4.31.0.dev0
- Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- Huggingface_hub version: 0.14.1
- Safetensors version: 0.3.1
- PyTorch version (GPU?): 2.0.0+cu117 (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?

@ArthurZucker 

### Information

- [X] The official example scripts
- [ ] 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

```Python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained('google/mt5-small')
print(type(model))
#transformers.models.umt5.modeling_umt5.UMT5ForConditionalGeneration
```

### Expected behavior

@ArthurZucker Thank you for the recent integration of umT5. However, from the latest branch of transformers, loading normal mT5 will load from UMT5 class. Of course this does not happen with 4.30.2.
cc @ArthurZucker <|||||>Hey! Indeed one of our CI test is failing because of that. Looking into it now! <|||||>Yep, the issue is that in the `CONFIG_MAPPING_NAMES` `umt5` maps to mt5 (since they have the same configuration file). This is messing with the overall mapping. A custom coming has to be create, or find a way to properly update! 😉 <|||||>Hmm. The values in `CONFIG_MAPPING(_NAMES)` is used as keys when creating `MODEL_MAPPING`. We should remove the entries of `umt5` in `CONFIG_MAPPING_NAMES` and other mappings.

Those models should be loaded in a non-auto way. 

<|||||>We can't just remove every mapping, some of our checks and doc require them. Let's just add a config for UMT5.