def prepare_pet_batch()

in scripts/adapet/ADAPET/src/data/RecordReader.py [0:0]

• 44 lines of code
• 7 McCabe index (conditional complexity)

    def prepare_pet_batch(self, batch, mode="PET1"):
        '''
        Prepare for train
        :param batch:
        :return:
        '''
        list_passage = batch["input"]["passage"]
        list_question = batch["input"]["question"]
        list_true_entity = batch["input"]["true_entity"]
        list_false_entities = batch["input"]["false_entities"]
        list_lbl = batch["output"]["lbl"]

        bs = len(list_passage)

        assert(bs == 1)

        list_input_ids = []
        list_mask_idx = np.ones((bs, self.config.max_num_lbl, self.get_num_lbl_tok())) * self.config.max_text_length - 1
        list_lbl_choices = []

        for b_idx, (p, q, te, fe, lbl) in enumerate(zip(list_passage, list_question, list_true_entity, list_false_entities, list_lbl)):
            mask_txt_split_tuple = []

            true_num_lbl_tok = self.get_lbl_num_lbl_tok(te)
            max_num_lbl_tok = true_num_lbl_tok
            for idx, wrong_enty in enumerate(fe):
                num_lbl_tok = self.get_lbl_num_lbl_tok(wrong_enty)
                if num_lbl_tok > max_num_lbl_tok:
                    max_num_lbl_tok = num_lbl_tok

            txt_trim = -1
            pattern = self.pet_patterns[self._pet_names.index(mode)]

            for idx, txt_split in enumerate(pattern):
                mask_txt_split_inp = txt_split.replace("[PASSAGE]", p).replace("[QUESTION]", q + " [SEP]").replace("[MASK] ", "[MASK] " * max_num_lbl_tok).replace("@highlight", "-")
                mask_txt_split_tuple.append(mask_txt_split_inp)

                # Trim the paragraph
                if "[PASSAGE]" in txt_split:
                    txt_trim = idx

            input_ids, mask_idx = tokenize_pet_txt(self.tokenizer, self.config, mask_txt_split_tuple[0],
                                                   mask_txt_split_tuple[1], mask_txt_split_tuple[2],
                                                   mask_txt_split_tuple[0], mask_txt_split_tuple[1],
                                                   mask_txt_split_tuple[2], txt_trim)

            list_mask_idx[b_idx, 0, :true_num_lbl_tok] = range(mask_idx, mask_idx + true_num_lbl_tok)


            for idx, wrong_enty in enumerate(fe):
                num_lbl_tok = self.get_lbl_num_lbl_tok(wrong_enty)
                list_mask_idx[b_idx, (idx+1), :num_lbl_tok] = range(mask_idx, mask_idx + num_lbl_tok)


            list_input_ids.append(input_ids)
            candidates = [te]
            candidates.extend(fe)
            list_lbl_choices.append(candidates)

        return torch.tensor(list_input_ids).to(device), torch.tensor(list_mask_idx).to(device), list_lbl_choices