PreTrainedTokenizerFast to BertTokenizer
Hi! I have a question about tokenizers.  
I made a unigram sentencepiece tokenizer using `tokenizers`.  
  
I use this tokenizer like following:  
  
```python
import json
from transformers import PreTrainedTokenizerFast
from tokenizers import SentencePieceUnigramTokenizer

tokens = list()
test_input = 'This is test input.'

# unigram.json is the file that saved by tokenizers library
# tokenizer.train()
# tokenizer.save_model()
with open('unigram.json', encoding='utf-8-sig') as f:
    json_file = json.load(f)
    vocab = json_file['vocab']
    for idx, v in enumerate(vocab):
        vocab[idx] = tuple(v)
        
tokenizer = SentencePieceUnigramTokenizer(vocab)
tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)

print(tokenizer.tokenize(test_input))
```
  
- Output
```
['▁T', 'h', 'is', '▁', 'is', '▁', 't', 'e', 'st', '▁', 'in', 'p', 'u', 't', '.']
```
  
I think the tokenizer's working. But I want the following format. (bert format)  
```
['[CLS]', '▁T', 'h', 'is', '▁', 'is', '▁', 't', 'e', 'st', '▁', 'in', 'p', 'u', 't', '.', '[SEP]']
```
  
I want to convert it to be easy to use, what can I do?  
Thank you.
I tried like this:  

```
from transformers import BertTokenizer

# Load my sentencepiece tokenizer
tokenizer = AutoTokenizer.from_pretrained('my_tokenizer_model')
tokenizer.build_inputs_with_special_tokens = BertTokenizer.build_inputs_with_special_tokens

print(tokenizer.tokenize('테스트 문장입니다.', add_special_tokens=True))
# ['▁테', '스트', '▁문', '장', '입니다']
```
  
I expected that if I change the `build_inputs_with_special_tokens` method, tokenizer would add [CLS] and [SEP].<|||||>You should be able to do so by adding a `post_processor` to your tokenizer: https://huggingface.co/docs/tokenizers/python/latest/quicktour.html#post-processing<|||||>@LysandreJik Thank you. But, this is not work for me.  
```
from tokenizers.processors import TemplateProcessing

tokenizer.post_processor = TemplateProcessing(
    single="[CLS] $A [SEP]",
    pair="[CLS] $A [SEP] $B:1 [SEP]:1",
    special_tokens=[
        ("[CLS]", tokenizer.vocab["[CLS]"]),
        ("[SEP]", tokenizer.vocab["[SEP]"]),
    ],
)
tokenizer.tokenize('Hi?')
# ['▁', 'H', 'i', '?']
```<|||||>The tokenizer is instance of `PreTrainedTokenizerFast `<|||||>Can you try doing it on the `SentencePieceUnigramTokenizer` instance (before it's converted to a `transformers` tokenizer?)<|||||>It works! Thank you! @LysandreJik 