DPRReaderTokenizer does not generate the attention_mask properly
Hello,

It seems like the DPRReaderTokenizer does not generate the `attention_mask` properly.

Steps to reproduce on the master branch 

```bash
(venv) sergey_mkrtchyan test (master) $ python
Python 3.8.6 (v3.8.6:db455296be, Sep 23 2020, 13:31:39)
[Clang 6.0 (clang-600.0.57)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from transformers import DPRReaderTokenizer, DPRReader
>>> tokenizer = DPRReaderTokenizer.from_pretrained('facebook/dpr-reader-single-nq-base')
>>> model = DPRReader.from_pretrained('facebook/dpr-reader-single-nq-base')
>>> encoded_inputs = tokenizer(questions="What is love ?",
...                            titles="Haddaway",
...                            texts="What Is Love is a song recorded by the artist Haddaway",
...                            padding=True,
...                            return_tensors='pt')
>>> encoded_inputs
{'input_ids': tensor([[ 101, 2054, 2003, 2293, 1029,  102, 2018, 2850, 4576,  102, 2054, 2003,
         2293, 2003, 1037, 2299, 2680, 2011, 1996, 3063, 2018, 2850, 4576]]), 'attention_mask': tensor([True])}
```
Notice the `attention_mask` above is incorrect. It should have the same shape as the `input_ids` tensor. 

## Environment info
- `transformers` version: 4.2.0dev0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.6
- PyTorch version (GPU?): 1.7.1 (False)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help

Git blame says @lhoestq and @LysandreJik might be able to help :)

I believe the issue is in this part of the code

https://github.com/huggingface/transformers/blob/5f6721032af46cf491fe69c010805f8786bf63a1/src/transformers/models/dpr/tokenization_dpr.py#L254

(same thing for the fast tokenizer)

I fixed it locally by replacing the above line with

```Python
attention_mask = []
    for input_ids in encoded_inputs["input_ids"]:
        attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
```

I am happy to submit a PR if that looks reasonable to you.


Indeed, it doesn't! We would gladly welcome a PR!<|||||>Closed by #9663 :)