Return overflowing tokens if max_length is not given
## 🚀 Feature

The tokenizer can return overflowing tokens in the `encode_plus` method if 1. `return_overflowing_tokens=True`; and 2. if `max_length` is given.  I imagine that it could also be useful to allow to return the overflowing tokens when a max_length is not given but when the input is longer than the model's max_seq_len. As an example: if the input is 600 tokens long, and the model supports up to 512, then the tokenizer will cut down the input to 512 anyway, so the superfluous 88 tokens can then be returned. 

Example showing that the overflowing tokens are not returned, even though the input is trimmed. Would expect the trimmed tokens to be returned in the `overflowing_tokens` field.

```python
from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
text = 'I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas. I like bananas.'

encoded_inputs = tokenizer.encode_plus(text,
                                       return_tensors='pt',
                                       pad_to_max_length=True,
                                       return_overflowing_tokens=True,
                                       return_special_tokens_mask=True)
print(encoded_inputs.keys())
print(encoded_inputs['input_ids'].size())
# dict_keys(['special_tokens_mask', 'input_ids', 'token_type_ids', 'attention_mask'])
# torch.Size([1, 512])
```
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