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
## 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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Models:

- ALBERT, BERT, XLM, DeBERTa, DeBERTa-v2, ELECTRA, MobileBert, SqueezeBert: @LysandreJik
- T5, BART, Marian, Pegasus, EncoderDecoder: @patrickvonplaten
- Blenderbot, MBART: @patil-suraj
- Longformer, Reformer, TransfoXL, XLNet, FNet, BigBird: @patrickvonplaten
- FSMT: @stas00
- Funnel: @sgugger
- GPT-2, GPT: @patrickvonplaten, @LysandreJik
- RAG, DPR: @patrickvonplaten, @lhoestq
- TensorFlow: @Rocketknight1
- JAX/Flax: @patil-suraj
- TAPAS, LayoutLM, LayoutLMv2, LUKE, ViT, BEiT, DEiT, DETR, CANINE: @NielsRogge
- GPT-Neo, GPT-J, CLIP: @patil-suraj
- Wav2Vec2, HuBERT, SpeechEncoderDecoder, UniSpeech, UniSpeechSAT, SEW, SEW-D, Speech2Text: @patrickvonplaten, @anton-l

If the model isn't in the list, ping @LysandreJik who will redirect you to the correct contributor.

Library:

- Benchmarks: @patrickvonplaten
- Deepspeed: @stas00
- Ray/raytune: @richardliaw, @amogkam
- Text generation: @patrickvonplaten @narsil
- Tokenizers: @LysandreJik
- Trainer: @sgugger
- Pipelines: @Narsil
- Speech: @patrickvonplaten, @anton-l
- Vision: @NielsRogge, @sgugger

Documentation: @sgugger

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HF projects:

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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. -->

hi!
@vanpelt @pvl @arfon @xeb  
can someone hele me？ 
thank you all very much.<|||||>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/master/CONTRIBUTING.md) are likely to be ignored.