Training a tokenizer - add argument for preprocessing the input
### Feature request

I am training my huggingface tokenizer on my own corpora, and I want to save it with a preprocessing step. That is, if I pass some text to it, I want it to apply the preprocessing and then tokenize the text, instead of explicitly preprocessing it before that. A good example is BERTweet: https://github.com/VinAIResearch/BERTweet and their `tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base", normalization=True)` (here normalization=True indicates that the input will be preprocessed according to some function). I want the same to apply when I train a tokenizer with a custom preprocessing function. My code is:

    from pathlib import Path
    from tokenizers import ByteLevelBPETokenizer
    
    def preprocess(text):
        return text
    
    paths = [str(x) for x in Path('data').glob('*.txt')]
    
    tokenizer = ByteLevelBPETokenizer()
    
    tokenizer.train(files=paths, vocab_size=50_000, min_frequency=2,
                    special_tokens=['<s>', '<pad>', '</s>', '<unk>', '<mask>'])
    
    tokenizer.save_model('CustomBertTokenizer')


Now, when I load the tokenizer: 

    from transformers import RobertaTokenizerFast
    sentence = 'Hey'
    tokenizer = RobertaTokenizerFast.from_pretrained('CustomBertTokenizer')
    tokenizer(sentence)

I want `sentence` to be preprocessed with the `preprocess` function, and then tokenized. So I want to pass like an argument : preprocessing=True, or something like that. How can I do it?

How can I achieve this?


### Motivation

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### Your contribution

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I have also posted my question in  the huggingface forum: https://discuss.huggingface.co/t/save-tokenizer-with-argument/17389<|||||>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.

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