def mask_tokens(
            self, inputs: torch.Tensor, special_tokens_mask: Optional[torch.Tensor] = None
        ) -> Tuple[torch.Tensor, torch.Tensor]:
            """
            Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
            """
            inputs = inputs.clone()
            labels = inputs.clone()
            # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
            probability_matrix = torch.full(labels.shape, self.mlm_probability)
            if special_tokens_mask is None:
                special_tokens_mask = [
                    self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
                ]
                special_tokens_mask = torch.tensor(special_tokens_mask, dtype=torch.bool)
            else:
                special_tokens_mask = special_tokens_mask.bool()

            probability_matrix.masked_fill_(special_tokens_mask, value=0.0)
            masked_indices = torch.bernoulli(probability_matrix).bool()
            labels[~masked_indices] = -100  # We only compute loss on masked tokens

            # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
            indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices
            inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)

            # 10% of the time, we replace masked input tokens with random word
            indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced
            random_words = torch.randint(len(self.tokenizer), labels.shape, dtype=torch.long)
            inputs[indices_random] = random_words[indices_random]

            # The rest of the time (10% of the time) we keep the masked input tokens unchanged
            return inputs, labels

            Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
            
train.py