When using bert-base-chinese model, except for the first one, other uppercase English letters that are the same in succession will be ignored. And the input_id of different uppercase English characters is the same
Author: longweiwei
Pull request: no

## Environment info
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- `transformers` version: **4.12.3**
- Platform: **macos**
- Python version: 1.7.0
- PyTorch version (GPU?):No
- Tensorflow version (GPU?):-
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
- 

### Who can help
@LysandreJik
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- Benchmarks: @patrickvonplaten
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- Text generation: @patrickvonplaten @narsil
- Tokenizers: @LysandreJik
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- Pipelines: @Narsil
- Speech: @patrickvonplaten, @anton-l
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## Information

Model I am using (Bert, XLNet ...):

The problem arises when using:
* [x] the official example scripts: (give details below)
* [ ] 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

Steps to reproduce the behavior:
 here is code:
1.tokenizer = BertTokenizer.from_pretrained("bert-base-chinese")
2.encode = tokenizer(["A", "B", "AAA"])

result:
1. The input_id  of 'A' and 'B' are both 100.
2. After AAA is tokenized, only A is left.


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## Expected behavior
1. Don't omit any English letters after tokenized. 
2. The input_id corresponding to different English letters should be different
<!-- A clear and concise description of what you would expect to happen. -->


Comments:
hi!
@vanpelt @pvl @arfon @xeb  
can someone hele me？ 
thank you all very much.

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