`PreTrainedTokenizerFast.build_inputs_with_special_tokens` doesn't add the special tokens
# 🐛 Bug

## Information

Model I am using (Bert, XLNet ...): BertTokenizer (but seems to apply to most)

Language I am using the model on (English, Chinese ...): English

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] 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

```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
tokenizer.build_inputs_with_special_tokens(["abc", "def"])
```

The output is `['abc', 'def']`.

## Expected behavior

The output should include the `[CLS]` and `[SEP]` tokens.

The problem is neither `PreTrainedTokenizerFast` nor its subclasses override `build_inputs_with_special_tokens`.

## Environment info
<!-- You can run the command `python transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version: 2.5.0
- Platform: Linux
- Python version: 3.7.6
- PyTorch version (GPU?): 1.4.0, w/o GPU
- Tensorflow version (GPU?): -
- Using GPU in script?: No.
- Using distributed or parallel set-up in script?: No.
Hi @bryant1410, 

Thanks for reporting the issue, as a workaround for now, can you try the following:

```python
tokenizer.tokenize("abc")
tokenizer.tokenizer("def")
```

It should do the same, let me know.
In the meantime I'll have a closer look at the function `tokenizer.build_inputs_with_special_tokens`<|||||>Should be fixed in https://github.com/huggingface/transformers/pull/2912