

TITLE
Unexpected/wrong handling of added special tokens in special_tokens_mask (GPT1, BERT, possibly others)

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## 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: 3.1.0
- Platform: Linux-5.4.0-52-generic-x86_64-with-glibc2.29
- Python version: 3.8.5
- PyTorch version (GPU?): 1.6.0+cpu (False)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: **No**
- Using distributed or parallel set-up in script?: **No**

### Who can help
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 albert, bert, GPT2, XLM: @LysandreJik 
 tokenizers: @mfuntowicz
 Trainer: @sgugger
 Speed and Memory Benchmarks: @patrickvonplaten
 Model Cards: @julien-c
 Translation: @sshleifer
 Summarization: @sshleifer
 TextGeneration: @TevenLeScao 
 examples/distillation: @VictorSanh
 nlp datasets: [different repo](https://github.com/huggingface/nlp)
 rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
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 Bart: @sshleifer
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Most appropriate seems @mfuntowicz (tokenization), blame says @thomwolf.
## Information

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

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)

I am adding special tokens (`BOS`, `SEP` and `EOS`) to GPT1 tokenizer in order to format and fine-tune a GPT model a bit differently. I am also making use of the convenient `return_special_tokens_mask` argument in `encode_plus()`, though it does not seem to mark the added custom special tokens as special in the returned mask.

The same is also true when adding custom special tokens to BERT tokenizer. I did not check beyond these two.
The problem for GPT seems to be that `get_special_tokens_mask()` in `tokenization_utils.py` does not seem to take into account any special tokens.
```python
def get_special_tokens_mask(
    self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False
) -> List[int]:
    return [0] * ((len(token_ids_1) if token_ids_1 else 0) + len(token_ids_0))
```

For BERT, it only seems to take into account `[CLS]` and `[SEP]`.



## To reproduce

```python
from transformers import OpenAIGPTTokenizer

tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-gpt")
tokenizer.add_special_tokens({
    "bos_token": "<bos>",
    "sep_token": "<sep>",
    "eos_token": "<eos>"
})

# Does not work this way either
# tokenizer.add_special_tokens({
#     "additional_special_tokens": ["<bos>", "<sep>", "<eos>"]
# })

encoded = tokenizer.encode_plus("<bos> State your name, rank and intention <sep> The Doctor, doctor, fun. <eos>",
                                return_special_tokens_mask=True)
print(encoded["input_ids"])
print(encoded["special_tokens_mask"])  # This returns all zeros
```

## Expected behavior
I would expect that the additional special tokens also get marked as special, i.e. that the `special_tokens_mask` in above snippet returns `[1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1]`
