DPR AutoModel loading incorrect architecture for DPRContextEncoders
## Environment info
- `transformers` version: 4.10.2
- Platform: Darwin-20.6.0-x86_64-i386-64bit
- Python version: 3.7.7
- PyTorch version (GPU?): 1.9.0 (False)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help

Model type `dpr`: @LysandreJik @patrickvonplaten @lhoestq 

## Information

Model I am using:
 * https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base
 * https://huggingface.co/facebook/dpr-question_encoder-single-nq-base

## To reproduce

Loading a DPR context encoder `DPRContextEncoder` using `AutoModel.from_pretrained` is actually loading `DPRQuestionEncoder` instead, and later fails.

Steps to reproduce the behavior:

`AutoModel.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base')`

```
File "venv/lib/python3.7/site-packages/transformers/modeling_utils.py", line 579, in _init_weights
    raise NotImplementedError(f"Make sure `_init_weigths` is implemented for {self.__class__}")
NotImplementedError: Make sure `_init_weigths` is implemented for <class 'transformers.models.dpr.modeling_dpr.DPRQuestionEncoder'>
```

Note in the above that it's trying to use the `DPRQuestionEncoder` even though the config for this context encoder is correct and points to `architecture=DPRContextEncoder`.

Using explicitly the `DPRContextEncoder.from_pretrained` works just fine, so it looks like this is somewhere in `AutoModel`.

`DPRContextEncoder.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base')`

## Expected behavior

Using `AutoModel.from_pretrained` should pick the correct architecture for a `DPRContextEncoder`.

Unfortunately, `AutoModel` and its variants currently only support 1-to-1 model mapping according to the model name e.g. `DPR`. So in this case, the one model that maps to AutoModel is `DPRQuestionEncoder`<|||||>Ok, that kind of makes sense 🙃 Is there an easy way to change that or `DPR` models so it also looks at the architecture in the config?<|||||>To the best of my knowledge, this would be a major change of auto factory because the mapping file defines all `Auto-` models all together, not for each specific model. Only modifying `DPR`-related models might break the consistency of them.<|||||>@joshdevins - could you check whether the PR linked above solves the issue? <|||||>@patrickvonplaten Sorry, I realise now that there are two problems. Your PR fixes the problem that they didn't implement `_init_weights`, so that error is now gone. The `AutoModel` problem is still that `AutoModel.load_pretrained` is selecting `DPRQueryEncoder` even when the model architecture (as specified also in the `config.json`) is actually `DPRContextEncoder`.

```python
import torch
import transformers

model_id = "facebook/dpr-ctx_encoder-single-nq-base"
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
input_ids = tokenizer("This is an example sentence.", return_tensors="pt")["input_ids"]

auto_model = transformers.AutoModel.from_pretrained(model_id)
context_model = transformers.DPRContextEncoder.from_pretrained(model_id)

auto_output = auto_model(input_ids)
context_output = context_model(input_ids)
```

```python
> type(auto_model)
transformers.models.dpr.modeling_dpr.DPRQuestionEncoder

> type(context_model)
transformers.models.dpr.modeling_dpr.DPRContextEncoder

> torch.all(torch.eq(auto_output["pooler_output"], context_output["pooler_output"]))
tensor(False)
```<|||||>Note that my workaround is basically this 🤷 

```python
config = AutoConfig.from_pretrained(model_id)
getattr(transformers, config.architectures[0]).from_pretrained(model_id)
```<|||||>@joshdevins - ah yeah I think we can't really do anything against the second problem the way it is implemented now...maybe it might makes sense to implement a `AutoModel.from_pretrained(...)` that relies on `config.architectures` in the future...<|||||>I guess that makes sense. I wonder if this is the only model that has this scenario? It seems the way `sentence-transformers` does things also makes sense. They have a second config containing all the pooling and normalization layers after the transformer.<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.