def load_dataset(self, split, epoch=0, combine=False, **kwargs):
        """Load a given dataset split.

        Args:
            split (str): name of the split (e.g., train, valid, test)
        """
        paths = self.args.data.split(':')
        assert len(paths) > 0
        data_path = paths[epoch % len(paths)]

        languages = [
            name for name in os.listdir(data_path)
            if os.path.isdir(os.path.join(data_path, name))
        ]
        print("| Training on {0} languages: {1}".format(len(languages), languages))
        print("| Language to id mapping: ", {
                lang: id for id, lang in enumerate(languages)
            }
        )

        mask_whole_words = self._get_whole_word_mask()
        lang_datasets = []
        for lang_id, language in enumerate(languages):
            split_path = os.path.join(data_path, language, split)

            dataset = data_utils.load_indexed_dataset(
                split_path,
                self.source_dictionary,
                self.args.dataset_impl,
                combine=combine,
            )
            if dataset is None:
                raise FileNotFoundError('Dataset not found: {} ({})'.format(split, split_path))

            # create continuous blocks of tokens
            dataset = TokenBlockDataset(
                dataset,
                dataset.sizes,
                self.args.tokens_per_sample - 1,  # one less for <s>
                pad=self.source_dictionary.pad(),
                eos=self.source_dictionary.eos(),
                break_mode=self.args.sample_break_mode,
            )
            print('| loaded {} blocks from: {}'.format(len(dataset), split_path))

            # prepend beginning-of-sentence token (<s>, equiv. to [CLS] in BERT)
            dataset = PrependTokenDataset(dataset, self.source_dictionary.bos())

            src_dataset, tgt_dataset = MaskTokensDataset.apply_mask(
                dataset,
                self.source_dictionary,
                pad_idx=self.source_dictionary.pad(),
                mask_idx=self.mask_idx,
                seed=self.args.seed,
                mask_prob=self.args.mask_prob,
                leave_unmasked_prob=self.args.leave_unmasked_prob,
                random_token_prob=self.args.random_token_prob,
                freq_weighted_replacement=self.args.freq_weighted_replacement,
                mask_whole_words=mask_whole_words,
            )

            lang_dataset = NestedDictionaryDataset(
                {
                    'net_input': {
                        'src_tokens': PadDataset(
                            src_dataset,
                            pad_idx=self.source_dictionary.pad(),
                            left_pad=False,
                        ),
                        'src_lengths': NumelDataset(src_dataset, reduce=False),
                    },
                    'target': PadDataset(
                        tgt_dataset,
                        pad_idx=self.source_dictionary.pad(),
                        left_pad=False,
                    ),
                    'nsentences': NumSamplesDataset(),
                    'ntokens': NumelDataset(src_dataset, reduce=True),
                    'lang_id': RawLabelDataset([lang_id] * src_dataset.sizes.shape[0]),
                },
                sizes=[src_dataset.sizes],
            )
            lang_datasets.append(lang_dataset)

        if split == self.args.train_subset:
            # For train subset, additionally up or down sample languages.
            dataset_lengths = np.array(
                [len(d) for d in lang_datasets],
                dtype=float,
            )
            sample_probs = self._get_sample_prob(dataset_lengths)
            print("| Sample probability by language: ", {
                    lang: "{0:.4f}".format(sample_probs[id])
                    for id, lang in enumerate(languages)
                }
            )
            size_ratio = (sample_probs * dataset_lengths.sum()) / dataset_lengths
            print("| Up/Down Sampling ratio by language: ", {
                    lang: "{0:.2f}".format(size_ratio[id])
                    for id, lang in enumerate(languages)
                }
            )

            resampled_lang_datasets = [
                ResamplingDataset(
                    lang_datasets[i],
                    size_ratio=size_ratio[i],
                    seed=self.args.seed,
                    epoch=epoch,
                    replace=size_ratio[i] >= 1.0,
                )
                for i, d in enumerate(lang_datasets)
            ]
            dataset = ConcatDataset(resampled_lang_datasets)
        else:
            dataset = ConcatDataset(lang_datasets)
            lang_splits = [split]
            for lang_id, lang_dataset in enumerate(lang_datasets):
                split_name = split + '_' + languages[lang_id]
                lang_splits.append(split_name)
                self.datasets[split_name] = lang_dataset

            # [TODO]: This is hacky for now to print validation ppl for each
            # language individually. Maybe need task API changes to allow it
            # in more generic ways.
            if split in self.args.valid_subset:
                self.args.valid_subset = self.args.valid_subset.replace(
                    split, ','.join(lang_splits)
                )

        with data_utils.numpy_seed(self.args.seed + epoch):
            shuffle = np.random.permutation(len(dataset))

        self.datasets[split] = SortDataset(
            dataset,
            sort_order=[
                shuffle,
                dataset.sizes,
            ],
        )
Load a given dataset split.

        Args:
            split (str): name of the split (e.g., train, valid, test)
        
infoxlm/fairseq/fairseq/tasks/multilingual_masked_lm.py