UnboundLocalError: local variable 'seq_length' referenced before assignment
### System Info

Hi, I am using the `ConvBertForTokenClassification` model in models.convbert and encountered the bug when passing only `input_embeds` to `forward()`.
The traceback says that at line 833 in modeling_convbert.py
```
if token_type_ids is None:
    if hasattr(self.embeddings, "token_type_ids"):
        buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
```
The seq_length is unassigned.

I noticed just above this piece of code in
```
elif input_ids is not None:
    input_shape = input_ids.size()
    batch_size, seq_length = input_shape
elif inputs_embeds is not None:
    input_shape = inputs_embeds.size()[:-1]
```
seq_length is not assigned if the program enters `elif inputs_embeds is not None`.

Not sure if it is the `batch_size, seq_length = input_shape` missing for `inputs_embeds` or I am not using the model correctly?

### Who can help?

text models: @ArthurZucker and @younesbelkada

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

passing only `inputs_embeds` and `attention_mask` to `ConvBertForTokenClassification` model.

### Expected behavior

There should be no error.
I think it might be a bug, request you to post a sample script of your usage to reproduce the error.<|||||>> I think it might be a bug, request you to post a sample script of your usage to reproduce the error.

```
import torch
from transformers import ConvBertConfig, ConvBertForTokenClassification
embeddings = torch.tensor([1])
mask = torch.tensor([1])
convbert_model_config = ConvBertConfig()
convbert_model = ConvBertForTokenClassification(convbert_model_config)
outputs = convbert_model(inputs_embeds=embeddings, attention_mask=mask)
```<|||||>Thanks for providing a reproducing script and thanks @raghavanone for jumping on this. Your fix looks  good! 