[BUG] padding tokens are also masked in DataCollatorForLanguageModeling
## Environment info
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- `transformers` version: 4.3.2
- Platform: Linux
- Python version: 3.6
- PyTorch version (GPU?): 1.7.1 GPU
- Tensorflow version (GPU?): N/A
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: Sagemaker distributed data parallel

### Who can help 
@sgugger 

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## Information

Model I am using (Bert, XLNet ...): All models that use DataCollatorForLanguageModeling.

The bug is introduced in this [PR](https://github.com/huggingface/transformers/pull/8308). 

3 lines (241-243) are removed by mistake from this [line](https://github.com/huggingface/transformers/pull/8308/commits/74b3d7abce96c79bf8c35517857b4032b3d85a21#diff-046566f2b40a246c7d533457cd7f6f07830516da845b904086f36b3cfe0d5965L241).

Now padding tokens are also masked in MLM.

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)
* [ ] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

```
from transformers import DataCollatorForLanguageModeling
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('albert-base-v2')
data_collator = DataCollatorForLanguageModeling(
                tokenizer=tokenizer, mlm=True, mlm_probability=0.15
            )
tok = tokenizer('hello how are you!', add_special_tokens=True, truncation=True, max_length=256, padding='max_length')
data_collator([tok['input_ids']])
```

From the output you can easily see that the padding tokens are masked. Add back the three removed lines fix this bug.

## Expected behavior

padding token is not supposed to be mask-able in MLM.

I have similar issues.

"pad" token is not masked when I run bert-base-uncased model , but "pad" token can be masked when I run albert-base-v2

In examples/language-modeliing/run_mlm.py, I try to call tokenizer.get_special_tokens_mask.
```
print(tokenizer.get_special_tokens_mask([0, 100, 101, 102, 2, 3, 4], already_has_special_tokens=True))
```

Interestingly, "get_special_tokens_mask" function is called from "class PreTrainedTokenizerBase" when I run bert-base-uncased, but "get_special_tokens_mask" function is called from "class AlbertTokenizerFast"  whenn I run albert-base-v2.


In PretrainedToknizerBase class,
```
def get_special_tokens_mask(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
    ) -> List[int]:
   all_special_ids = self.all_special_ids  # cache the property
   special_tokens_mask = [1 if token in all_special_ids else 0 for token in token_ids_0]

   return special_tokens_mask
```


However in AlbertTokenizerFast class,
```
def get_special_tokens_mask(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
    ) -> List[int]:
    if already_has_special_tokens:
        if token_ids_1 is not None:
            raise ValueError(
                "You should not supply a second sequence if the provided sequence of "
                "ids is already formatted with special tokens for the model."
            )
        return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))

    if token_ids_1 is not None:
        return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
    return [1] + ([0] * len(token_ids_0)) + [1]
```

=> These two functions are different. Thus when I use bert, all_special_ids( it contains cls, sep, pad id) are ids which cannot be masked. But when i use albert, only cls, sep  ids cannot be masked. Thus pad token can be masked when i use albert.

I don't know why the functions are called from different class when I run bert-base-uncased or albert. 
Do you know why??

And is it correct that pad token will be masked in albert model?? 

[bert command]
```
%  python run_mlm.py --model_name_or_path bert-base-uncased --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train --do_eval --output_dir ./tmp/test-mlm --line_by_line

```

[albert command]
```
%  python run_mlm.py --model_name_or_path albert-base-v2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train --do_eval --output_dir ./tmp/test-mlm --line_by_line

```
 <|||||>Thanks for reporting! This is actually a bug in the `get_special_tokens_mask` method of most tokenizers. I will push a fix soon. In the meantime, you can workaround the problem by passing the `special_token_mask` the tokenizer returns to the data collator (which will actually be faster since it will avoid being recomputed):
```
tokenizer = AutoTokenizer.from_pretrained('albert-base-v2')
data_collator = DataCollatorForLanguageModeling(
                tokenizer=tokenizer, mlm=True, mlm_probability=0.15
            )
tok = tokenizer('hello how are you!',return_special_tokens_mask=True, truncation=True, max_length=256, padding='max_length')
data_collator([tok])
```