T5Tokenizer loses most special tokens after I add a new special token.
## Environment info
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- `transformers` version: 4.15.0
- Platform: CentOS
- Python version: 3.8.8
- PyTorch version (GPU?): 1.7.0+cu110
- Tensorflow version (GPU?): No
- Using GPU in script?:  Yes
- Using distributed or parallel set-up in script?: No

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## Information

I am using T5 and find a bug of T5Tokenizer.@patrickvonplaten

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [x] my own task or dataset: (give details below)

## To reproduce
Just run my code:
```python
from transformers import T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("t5-base")
print(tokenizer.all_special_ids)
tokenizer.add_special_tokens({"additional_special_tokens": ["[EVENT]"]})
print(tokenizer.all_special_ids)
```
## Expected behavior
I think the output should be:
[1, 2, 0, 32099, 32098, 32097, 32096, 32095, 32094, 32093, 32092, 32091, 32090, 32089, 32088, 32087, 32086, 32085, 32084, 32083, 32082, 32081, 32080, 32079, 32078, 32077, 32076, 32075, 32074, 32073, 32072, 32071, 32070, 32069, 32068, 32067, 32066, 32065, 32064, 32063, 32062, 32061, 32060, 32059, 32058, 32057, 32056, 32055, 32054, 32053, 32052, 32051, 32050, 32049, 32048, 32047, 32046, 32045, 32044, 32043, 32042, 32041, 32040, 32039, 32038, 32037, 32036, 32035, 32034, 32033, 32032, 32031, 32030, 32029, 32028, 32027, 32026, 32025, 32024, 32023, 32022, 32021, 32020, 32019, 32018, 32017, 32016, 32015, 32014, 32013, 32012, 32011, 32010, 32009, 32008, 32007, 32006, 32005, 32004, 32003, 32002, 32001, 32000]
[1, 2, 0, 32099, 32098, 32097, 32096, 32095, 32094, 32093, 32092, 32091, 32090, 32089, 32088, 32087, 32086, 32085, 32084, 32083, 32082, 32081, 32080, 32079, 32078, 32077, 32076, 32075, 32074, 32073, 32072, 32071, 32070, 32069, 32068, 32067, 32066, 32065, 32064, 32063, 32062, 32061, 32060, 32059, 32058, 32057, 32056, 32055, 32054, 32053, 32052, 32051, 32050, 32049, 32048, 32047, 32046, 32045, 32044, 32043, 32042, 32041, 32040, 32039, 32038, 32037, 32036, 32035, 32034, 32033, 32032, 32031, 32030, 32029, 32028, 32027, 32026, 32025, 32024, 32023, 32022, 32021, 32020, 32019, 32018, 32017, 32016, 32015, 32014, 32013, 32012, 32011, 32010, 32009, 32008, 32007, 32006, 32005, 32004, 32003, 32002, 32001, 32000, 32100]
However, the current output is:
[1, 2, 0, 32099, 32098, 32097, 32096, 32095, 32094, 32093, 32092, 32091, 32090, 32089, 32088, 32087, 32086, 32085, 32084, 32083, 32082, 32081, 32080, 32079, 32078, 32077, 32076, 32075, 32074, 32073, 32072, 32071, 32070, 32069, 32068, 32067, 32066, 32065, 32064, 32063, 32062, 32061, 32060, 32059, 32058, 32057, 32056, 32055, 32054, 32053, 32052, 32051, 32050, 32049, 32048, 32047, 32046, 32045, 32044, 32043, 32042, 32041, 32040, 32039, 32038, 32037, 32036, 32035, 32034, 32033, 32032, 32031, 32030, 32029, 32028, 32027, 32026, 32025, 32024, 32023, 32022, 32021, 32020, 32019, 32018, 32017, 32016, 32015, 32014, 32013, 32012, 32011, 32010, 32009, 32008, 32007, 32006, 32005, 32004, 32003, 32002, 32001, 32000]
[1, 2, 0, 32100]

So, it seems that a lot of special tokens get lost after I add a new special token.
Hey @zhaowei-wang98, 

The reason why your code sample behaves the way it does is because you completely overwrite the list of `"additional_special_tokens"`. If you just want to add a single token, you should rather use the `add_tokens(...)` function.
Think the following code-snippet should make things clearer:

```python
from transformers import T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("t5-base")
print("HEY")
print({k: v for k, v in tokenizer.get_vocab().items() if int(v) > 32000})
tokenizer.add_tokens(["[EVENT]"])
print("HEY")
print({k: v for k, v in tokenizer.get_vocab().items() if int(v) > 32000})
```<|||||>Thanks a lot. Sorry for my misunderstanding.<|||||>No worries!