Unable to load model by ignoring size mismatch; TypeError: __init__() got an unexpected keyword argument 'ignore_mismatched_sizes'
Author: swapnil3597
Pull request: no

I want to save the pre-trained model at a local path and later again load it using `from_pretrained` method. I'm doing this as I want to use hugging face on server with no internet.

I used following script to save the model:
```python3
from transformers import BertTokenizer, BertForSequenceClassification

pretrained_path = "pretrained_models/bert_base_uncased_pretrained/"

model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
model.save_pretrained(pretrained_path)
```


So I tried 2 approaches to load model from local path, but both aren't working.

### Approach 1:
Code Snippet 1:
```
model = BertForSequenceClassification.from_pretrained( 
                pretrained_path,
                num_labels = 27)
```
Error 1:
```bash
Traceback (most recent call last):
  File "<stdin>", line 5, in <module>
  File "/path/lib/python3.6/site-packages/transformers/models/auto/auto_factory.py", line 395, in from_pretrained
    return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
  File "/path/lib/python3.6/site-packages/transformers/modeling_utils.py", line 1220, in from_pretrained
    model, state_dict, pretrained_model_name_or_path, _fast_init=_fast_init
  File "/path/lib/python3.6/site-packages/transformers/modeling_utils.py", line 1360, in _load_state_dict_into_model
    raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
RuntimeError: Error(s) in loading state_dict for BertForSequenceClassification:
	size mismatch for classifier.weight: copying a param with shape torch.Size([2, 768]) from checkpoint, the shape in current model is torch.Size([27, 768]).
	size mismatch for classifier.bias: copying a param with shape torch.Size([2]) from checkpoint, the shape in current model is torch.Size([27]).
```


----
### Approach 2:
Code Snippet 2:
```python3
model = BertForSequenceClassification.from_pretrained( 
                pretrained_path,
                num_labels = 27, 
                ignore_mismatched_sizes=True)
```

Error 2:
```bash
Traceback (most recent call last):
  File "<stdin>", line 4, in <module>
  File "/path/python3.6/site-packages/transformers/modeling_utils.py", line 1179, in from_pretrained
    model = cls(config, *model_args, **model_kwargs)
TypeError: __init__() got an unexpected keyword argument 'ignore_mismatched_sizes'
```

Kindly specify the way to load model with size mismatch, or any other way to save and load model from local machine every time with different number of classes.


Comments:
HI, what version of transformers are you using? As `ignore_mismatched_sizes` option was newly added at v4.9.0, you should probably upgrade to v4.9.0+ in order to use it.

I tested the following snippet on Colab, it worked as expected. The transformers version I used is v4.9.0:
```
from transformers import BertTokenizer, BertForSequenceClassification

pretrained_path = "./test_path"
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
model.save_pretrained(pretrained_path)
model = BertForSequenceClassification.from_pretrained( 
                pretrained_path,
                num_labels = 27, 
                ignore_mismatched_sizes=True)
```

Output:

```
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at ./test_path and are newly initialized because the shapes did not match:
- classifier.weight: found shape torch.Size([2, 768]) in the checkpoint and torch.Size([27, 768]) in the model instantiated
- classifier.bias: found shape torch.Size([2]) in the checkpoint and torch.Size([27]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```

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