def _get_test_data_with_word_vocab(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: word vocab
            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("hello")
        vocab.add_symbol("how")
        vocab.add_symbol("are")
        vocab.add_symbol("you")
        vocab.add_symbol("new")
        vocab.add_symbol("york")
        src_tokens = [
            ["hello", "new", "york", "you"],
            ["how", "are", "you", "new", "york"],
        ]
        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: word vocab
            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