def _compute_offsets(self, token_ids, time_precision=0.02):
        """
        Compute offsets for a given tokenized input

        Args:
            token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
                List of tokenized input ids. Can be obtained using the `__call__` method.
            time_precision (`float`, `optional`, defaults to 0.02):
                The time ratio to convert from token to time.
        """
        offsets = []
        token_ids = np.array(token_ids)
        if token_ids.shape[0] > 1 and len(token_ids.shape) > 1:
            raise ValueError("Can only process a single input at a time")
        timestamp_begin = self.all_special_ids[-1] + 1
        timestamp_tokens = token_ids >= timestamp_begin

        consecutive = np.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0] + 1
        if consecutive.shape[0] == 0 and timestamp_tokens.sum() <= 1:
            # either there are no timestamps or there are no consecutive ones
            return []
        elif np.where(timestamp_tokens)[0][-1] + 1 not in consecutive:
            # we add the final timestamp if it is not already in the list
            consecutive = np.append(consecutive, np.where(timestamp_tokens)[0][-1] + 1)

        last_slice = np.where(timestamp_tokens)[0][0]
        for current_slice in consecutive:
            sliced_tokens = token_ids[last_slice:current_slice]
            if len(sliced_tokens) > 1:
                start_timestamp_position = sliced_tokens[0].item() - timestamp_begin
                end_timestamp_position = sliced_tokens[-1].item() - timestamp_begin
                # strip timestamp tokens from the text output
                sliced_tokens = self._preprocess_token_ids(sliced_tokens)
                text = self._decode(sliced_tokens)
                text = self._filter_timestamp_ids(text)
                offsets.append(
                    {
                        "text": text,
                        "timestamp": (
                            start_timestamp_position * time_precision,
                            end_timestamp_position * time_precision,
                        ),
                    }
                )
            last_slice = current_slice

        return offsets

        Compute offsets for a given tokenized input

        Args:
            token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
                List of tokenized input ids. Can be obtained using the `__call__` method.
            time_precision (`float`, `optional`, defaults to 0.02):
                The time ratio to convert from token to time.
        
dpkd/transformers/src/transformers/models/whisper/tokenization_whisper.py