[ERROR] Tokenizer and TokenizerFast ??? 
# 🐛 Bug

## Information

Model I am using (Bert, XLNet ...): BERT

Language I am using the model on (English, Chinese ...): 'bert-base-multilingual-cased'

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:

1. `from transformers import *`
2. `tokenizer = BertTokenizerFast.from_pretrained('bert-base-multilingual-cased')`
3. `tokenizer.decode(tokenizer.encode('mở bài lạc trôi'))` --> wrong 


but:

1. `from transformers import *`
2. `tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')`
3. `tokenizer.decode(tokenizer.encode('mở bài lạc trôi'))` --> true

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## Expected behavior
the decode sentence after encoding and decoding using TokenizerFast should be true
<!-- A clear and concise description of what you would expect to happen. -->

## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version:
- Platform: Pytorch and TF
- Python version: 3.6
- PyTorch version (GPU?): GPU
- Tensorflow version (GPU?): 2.2
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No

This is related to https://github.com/huggingface/transformers/issues/2917. In the slow tokenizers, when `do_lower_case=False` we don't strip accents, while we do it when `do_lower_case=True`. In the fast tokenizers, this is controlled by the `strip_accents` option, which is `True` here.

@thomwolf How do you think we should fix this?<|||||>Yes let's do it @n1t0  and stick to the official bert tokenizer behavior in the fast tokenizers as well.