the `tokenize_chinese_chars` argument is not always taken into account with the fast version of the bert tokenizer
## Environment info
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- `transformers` version: 4.16.0.dev0
- Platform: Linux-5.11.0-46-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyTorch version (GPU?): 1.10.1+cu102 (False)
- Tensorflow version (GPU?): 2.7.0 (False)
- Flax version (CPU?/GPU?/TPU?): 0.3.6 (cpu)
- Jax version: 0.2.26
- JaxLib version: 0.1.75
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: no

### Who can help
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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)
* [ ] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

```python
from transformers import BertTokenizer, BertTokenizerFast

list_of_commun_chinese_char = ["的", "人", "有"]
text = "".join(list_of_commun_chinese_char)
print(text)
# 的人有

model_name = "bert-base-uncased"

tokenizer_slow = BertTokenizer.from_pretrained(model_name, tokenize_chinese_chars=False)
tokenizer_slow.tokenize(text)
# ['的', '##人', '##有']

tokenizer_slow = BertTokenizer.from_pretrained(model_name, tokenize_chinese_chars=True)
tokenizer_slow.tokenize(text)
# ['的', '人', '有']

tokenizer_fast = BertTokenizerFast.from_pretrained(model_name, tokenize_chinese_chars=False)
tokenizer_fast.tokenize(text)
# ['的', '人', '有']


tokenizer_fast = BertTokenizerFast.from_pretrained(model_name, tokenize_chinese_chars=True)
tokenizer_fast.tokenize(text)
# ['的', '人', '有']
```

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## Expected behavior
If the user indicates `tokenize_chinese_chars=False` when he initializes a fast bert tokenizer, we expect that this characteristic is reflected on the tokenizer. In other words, in the previous example, we expect that:

```python
tokenizer_fast = BertTokenizerFast.from_pretrained(model_name, tokenize_chinese_chars=False)
tokenizer_fast.tokenize(text)
# ['的', '##人', '##有']
```