Improve handling of special tokens in Dictionary

https://github.com/pytorch/fairseq/blob/eb68afca0208a040d4e91eceae86f5f22ca24b04/fairseq/data/dictionary.py#L178-L190

When loading dict.txt that already contains special tokens such as <s> or <pad> (which are added by default in sentencepiece), these tokens appear twice in the fairseq dictionary. They are added once in Dictionary.__init__() and a second time from the dict.txt file in Dictionary.add_from_file(). This causes weird behaviours e.g. when using the model in https://github.com/huggingface/transformers.

Ideally Dictionary would not add the special tokens manually when loading an external dict.txt that already contains them (such as in https://github.com/huggingface/transformers). But I am afraid that this can break backward compatibility for people who already trained models with this "duplicated special tokens bug".

For instance:

>> print([fairseq_model.task.dictionary[i] for i in range(15)])
['<s>', '<pad>', '</s>', '<unk>', '<unk>', '<s>', '</s>', ',', '▁the', ...]

In the fill_mask() method for roberta, this is what happens:

>> tokens = self.task.source_dictionary.encode_line(
       '<s> ' + text_spans_bpe,
       append_eos=True,
       add_if_not_exist=False,
   )
   print(tokens)
tensor([[    5,  1285, 32004,     2]])

With the first token 5 being the <s> that was added as a string and matched to the token from dict.txt and the last token 2 corresponding to dictionary.eos().