Error in the RobertaTokenizer?
Hello, 

I think I spotted an error in the `RobertaTokenizer` (`AutoTokenizer`).

I tested the `tokenizer` function by using the code below:

```python
import torch
from torch.nn import CrossEntropyLoss
from matplotlib import pyplot as plt
from transformers import RobertaTokenizer, RobertaForMultipleChoice, AdamW, get_constant_schedule
from transformers import AutoTokenizer
import numpy as np
import pandas as pd
import pickle
import dill
from matplotlib.pyplot import plot, savefig, xlim, figure, ylim, legend, boxplot, setp, axes, xlabel, ylabel, xticks
import gc 
import math
import time
from random import seed
from random import randint
import sys
import statistics
from numpy import nan
import scipy.stats as ss
from statistics import mode
from pylab import *
from numpy import arange

# import the pre-trained HuggingFace RobertaTokenizer
tokenizer = AutoTokenizer.from_pretrained('roberta-base')

# get the pre-trained HuggingFace RobertaForMultipleChoice and resize the token embeddings after adding the special token
best_model_roberta = RobertaForMultipleChoice.from_pretrained('roberta-base', output_hidden_states = True)

sequence_a = 'china is a very large country .'
sequence_b = 'indeed'

tokenizer(sequence_a, sequence_b, padding = True, return_token_type_ids=True, return_tensors="pt")
```
and below is the corresponding output:

```
{
'input_ids': tensor([[ 0,  611,  1243,  16,  10,  182,  739,  247,  479, 2,  2, 2028, 10247, 2]]),
 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
}
```

The output above is not what I was expecting to get from the `tokenizer` function. Since I am inputting `sequence_a` along with `sequence_b`, I was expecting my `token_type_ids` to be: `tensor([[0,0,0,0,0,0,0,0,0,0,0,1,1,1]])`, but the output does not include any 1's...everything is 0. Perhaps this is the error within the `RobertaTokenizer`?

Thank you,
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