    def _build_text_seq(self, text_feature, text_clip_indexs=None):
        """
        `text_feature`: all available clips.
        `text_clip_indexes`: clip sequence to build.
        """
        if text_clip_indexs is None:
            text_clip_indexs = [0]

        full_caps = []
        if isinstance(text_feature, dict):
            for clip_idx in text_clip_indexs:
                full_caps.extend(text_feature["cap"][clip_idx])
        else:
            full_caps = text_feature
        max_text_len = self.max_len - self.max_video_len - 3
        full_caps = full_caps[:max_text_len]
        full_caps = (
            [self.cls_token_id, self.sep_token_id] + full_caps + [self.sep_token_id]
        )
        text_pad_len = self.max_len - len(full_caps) - self.max_video_len
        padded_full_caps = full_caps + [self.pad_token_id] * text_pad_len
        caps = torch.LongTensor(padded_full_caps)
        cmasks = torch.zeros((len(padded_full_caps),), dtype=torch.bool)
        cmasks[: len(full_caps)] = 1

        return caps, cmasks
`text_feature`: all available clips.
`text_clip_indexes`: clip sequence to build.
examples/MMPT/mmpt/processors/processor.py