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The introduction of a decoder introduces 2 changes:
- We need to be able to specify a separate mask in the cross
attention to mask the positions corresponding to padding tokens in the
encoder state.
- The self-attention in the decoder needs to be causal on top of not
attending to padding tokens.

the definition of `get_masks` would blow with the proper combination of
arguments. It was just a matter of moving a definition outside of a
control structure.

We currenctly instantiate encoders and decoders for the seq2seq by
passing the `is_decoder` keyword argument to the `from_pretrained`
classmethod. On the other hand, the model class looks for the value
of the `is_decoder` attribute in its config.

In order for the value to propagate from the kwarg to the configuration
we simply need to define `is_decoder` as an attribute to the base
`PretrainedConfig`, with a default at `False`.

the data provided by Li Dong et al. were already tokenized, which means
that they are not compatible with  all the models in the library. We
thus process the raw data directly and tokenize them using the models'
tokenizers.