def _get_test_data_with_bpe_cont_marker(self, append_eos=True):
        """
        Args:
            append_eos: if True, each input sentence in the source tokens tensor
                will have an EOS appended to the end.

        Returns:
            vocabs: BPE vocab with continuation markers as suffixes to denote
                non-end of word tokens. This is the standard BPE format used in
                fairseq's preprocessing.
            x: input tensor containing numberized source tokens, with EOS at the
                end if append_eos is true
            src_lengths: and source lengths.
        """
        vocab = Dictionary()
        vocab.add_symbol("he@@")
        vocab.add_symbol("llo")
        vocab.add_symbol("how")
        vocab.add_symbol("are")
        vocab.add_symbol("y@@")
        vocab.add_symbol("ou")
        vocab.add_symbol("n@@")
        vocab.add_symbol("ew")
        vocab.add_symbol("or@@")
        vocab.add_symbol("k")

        src_tokens = [
            ["he@@", "llo", "n@@", "ew", "y@@", "or@@", "k"],
            ["how", "are", "y@@", "ou"],
        ]
        x, src_lengths = x, src_lengths = self._convert_src_tokens_to_tensor(
            vocab=vocab, src_tokens=src_tokens, append_eos=append_eos
        )
        return vocab, x, src_lengths

        Args:
            append_eos: if True, each input sentence in the source tokens tensor
                will have an EOS appended to the end.

        Returns:
            vocabs: BPE vocab with continuation markers as suffixes to denote
                non-end of word tokens. This is the standard BPE format used in
                fairseq's preprocessing.
            x: input tensor containing numberized source tokens, with EOS at the
                end if append_eos is true
            src_lengths: and source lengths.
        
infoxlm/fairseq/tests/test_noising.py