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README.md

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+---
+language: zh
+tags:
+- summarization
+- chinese
+inference: False
+---
+
+# Randeng-Pegasus-238M-Summary-Chinese
+
+- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM/blob/main/fengshen/examples/summary/randeng_pegasus_523M_summary.sh)
+- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/zh/latest/docs/%E7%87%83%E7%81%AF%E7%B3%BB%E5%88%97/Randeng-Pegasus-238M-Summary-Chinese.html)
+
+## 简介 Brief Introduction
+
+善于处理摘要任务，在数个中文摘要数据集上微调后的，中文版的PAGASUS-base。
+
+Good at solving text summarization tasks, after fine-tuning on multiple Chinese text summarization datasets, Chinese PAGASUS-base.
+
+## 模型分类 Model Taxonomy
+
+|  需求 Demand  | 任务 Task       | 系列 Series      | 模型 Model    | 参数 Parameter | 额外 Extra |
+|  :----:  | :----:  | :----:  | :----:  | :----:  | :----:  |
+| 通用 General | 自然语言转换 NLT | 燃灯 Randeng | PEFASUS |      238M      |    文本摘要任务-中文 Summary-Chinese    |
+
+## 模型信息 Model Information
+
+参考论文：[PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/pdf/1912.08777.pdf)
+
+基于[Randeng-Pegasus-238M-Chinese](https://huggingface.co/IDEA-CCNL/Randeng-Pegasus-238M-Chinese)，我们在收集的7个中文领域的文本摘要数据集（约4M个样本）上微调了它，得到了summary版本。这7个数据集为：education, new2016zh, nlpcc, shence, sohu, thucnews和weibo。
+
+Based on [Randeng-Pegasus-238M-Chinese](https://huggingface.co/IDEA-CCNL/Randeng-Pegasus-238M-Chinese), we fine-tuned a text summarization version (summary) on 7 Chinese text summarization datasets, with totaling around 4M samples. The datasets include: education, new2016zh, nlpcc, shence, sohu, thucnews and weibo.
+
+### 下游效果 Performance
+
+| datasets | rouge-1 | rouge-2 | rouge-L |
+| ---- | ---- | ---- | ---- |
+| LCSTS | 43.46 | 29.59 | 39.76 |
+
+## 使用 Usage
+
+```python
+from transformers import PegasusForConditionalGeneration
+# Need to download tokenizers_pegasus.py and other Python script from Fengshenbang-LM github repo in advance,
+# or you can download tokenizers_pegasus.py and data_utils.py in https://huggingface.co/IDEA-CCNL/Randeng_Pegasus_523M/tree/main
+# Strongly recommend you git clone the Fengshenbang-LM repo:
+# 1. git clone https://github.com/IDEA-CCNL/Fengshenbang-LM
+# 2. cd Fengshenbang-LM/fengshen/examples/pegasus/
+# and then you will see the tokenizers_pegasus.py and data_utils.py which are needed by pegasus model
+
+from tokenizers_pegasus import PegasusTokenizer
+
+model = PegasusForConditionalGeneration.from_pretrained("IDEA-CCNL/Randeng-Pegasus-238M-Summary-Chinese")
+tokenizer = PegasusTokenizer.from_pretrained("IDEA-CCNL/Randeng-Pegasus-238M-Summary-Chinese")
+
+text = "在北京冬奥会自由式滑雪女子坡面障碍技巧决赛中，中国选手谷爱凌夺得银牌。祝贺谷爱凌！今天上午，自由式滑雪女子坡面障碍技巧决赛举行。决赛分三轮进行，取选手最佳成绩排名决出奖牌。第一跳，中国选手谷爱凌获得69.90分。在12位选手中排名第三。完成动作后，谷爱凌又扮了个鬼脸，甚是可爱。第二轮中，谷爱凌在道具区第三个障碍处失误，落地时摔倒。获得16.98分。网友：摔倒了也没关系，继续加油！在第二跳失误摔倒的情况下，谷爱凌顶住压力，第三跳稳稳发挥，流畅落地！获得86.23分！此轮比赛，共12位选手参赛，谷爱凌第10位出场。网友：看比赛时我比谷爱凌紧张，加油！"
+inputs = tokenizer(text, max_length=1024, return_tensors="pt")
+
+# Generate Summary
+summary_ids = model.generate(inputs["input_ids"])
+tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+
+# model Output: 滑雪女子坡面障碍技巧决赛谷爱凌获银牌
+```
+
+## 引用 Citation
+
+如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)：
+
+If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970):
+
+```text
+@article{fengshenbang,
+  author    = {Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen and Ruyi Gan and Jiaxing Zhang},
+  title     = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
+  journal   = {CoRR},
+  volume    = {abs/2209.02970},
+  year      = {2022}
+}
+```
+
+也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
+
+You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
+
+```text
+@misc{Fengshenbang-LM,
+  title={Fengshenbang-LM},
+  author={IDEA-CCNL},
+  year={2021},
+  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
+}
+```

config.json

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+{
+  "activation_dropout": 0.1,
+  "activation_function": "relu",
+  "add_bias_logits": false,
+  "add_final_layer_norm": true,
+  "architectures": [
+    "PegasusForConditionalGeneration"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 2,
+  "classif_dropout": 0.0,
+  "classifier_dropout": 0.0,
+  "d_model": 768,
+  "decoder_attention_heads": 12,
+  "decoder_ffn_dim": 3072,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 12,
+  "decoder_start_token_id": 0,
+  "dropout": 0.1,
+  "encoder_attention_heads": 12,
+  "encoder_ffn_dim": 3072,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 12,
+  "eos_token_id": 1,
+  "extra_pos_embeddings": 1,
+  "force_bos_token_to_be_generated": false,
+  "forced_eos_token_id": 1,
+  "gradient_checkpointing": false,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "length_penalty": 0.8,
+  "max_length": 256,
+  "max_position_embeddings": 1024,
+  "model_type": "pegasus",
+  "normalize_before": true,
+  "normalize_embedding": false,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "scale_embedding": true,
+  "static_position_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.10.2",
+  "use_cache": true,
+  "vocab_size": 50000
+}

data_utils.py

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+# -*- coding: utf-8 -*-
+
+import re
+import six
+import unicodedata
+import torch
+import rouge
+import numpy as np
+import random
+# from fengshen.examples.pegasus.pegasus_utils import text_segmentate
+import sys
+
+sys.path.append('../../../')
+
+rouge = rouge.Rouge()
+
+
+is_py2 = six.PY2
+
+if not is_py2:
+    basestring = str
+
+
+def _is_chinese_char(cp):
+    """Checks whether CP is the codepoint of a CJK character."""
+    # This defines a "chinese character" as anything in the CJK Unicode block:
+    #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+    #
+    # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+    # despite its name. The modern Korean Hangul alphabet is a different block,
+    # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+    # space-separated words, so they are not treated specially and handled
+    # like the all of the other languages.
+    if ((cp >= 0x4E00 and cp <= 0x9FFF) or (cp >= 0x3400 and cp <= 0x4DBF)
+            or (cp >= 0x20000 and cp <= 0x2A6DF)
+            or (cp >= 0x2A700 and cp <= 0x2B73F)
+            or (cp >= 0x2B740 and cp <= 0x2B81F)
+            or (cp >= 0x2B820 and cp <= 0x2CEAF)
+            or (cp >= 0xF900 and cp <= 0xFAFF)
+            or (cp >= 0x2F800 and cp <= 0x2FA1F)):
+        return True
+
+    return False
+
+
+def _is_whitespace(char):
+    """Checks whether `char` is a whitespace character."""
+    # \t, \n, and \r are technically control characters but we treat them
+    # as whitespace since they are generally considered as such.
+    if char == " " or char == "\t" or char == "\n" or char == "\r":
+        return True
+    cat = unicodedata.category(char)
+    if cat == "Zs":
+        return True
+    return False
+
+
+def _is_control(char):
+    """Checks whether `char` is a control character."""
+    # These are technically control characters but we count them as whitespace
+    # characters.
+    if char == "\t" or char == "\n" or char == "\r":
+        return False
+    cat = unicodedata.category(char)
+    if cat.startswith("C"):
+        return True
+    return False
+
+
+def _is_punctuation(char):
+    """Checks whether `char` is a punctuation character."""
+    cp = ord(char)
+    # We treat all non-letter/number ASCII as punctuation.
+    # Characters such as "^", "$", and "`" are not in the Unicode
+    # Punctuation class but we treat them as punctuation anyways, for
+    # consistency.
+    if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (
+            cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
+        return True
+    cat = unicodedata.category(char)
+    if cat.startswith("P"):
+        return True
+    return False
+
+
+def is_string(s):
+    """判断是否是字符串
+    """
+    return isinstance(s, basestring)
+
+
+def is_stopwords(word, stopwords):
+    if word in stopwords:
+        return True
+    else:
+        return False
+
+
+def text_segmentate(text):
+    en_seg_pattern = '((?:\\!|\\?|\\.|\\n)+(?:\\s)+)'
+    ch_seg_pattern = '((?:？|！|。|\\n)+)'
+    try:
+        text = re.sub(en_seg_pattern, r'\1[SEP]', text)
+        # print("sub text: ", text)
+    except Exception as e:
+        print("input: ", text)
+        raise e
+    text = re.sub(ch_seg_pattern, r'\1[SEP]', text)
+    # print("sub ch text: ", text)
+    text_list = text.split("[SEP]")
+    text_list = list(filter(lambda x: len(x) != 0, text_list))
+    return text_list
+
+
+def load_stopwords(stopwords_path):
+    stopwords_dict = {}
+    with open(stopwords_path, "r") as rf:
+        for line in rf:
+            line = line.strip()
+            if line not in stopwords_dict:
+                stopwords_dict[line] = 0
+            else:
+                pass
+    return stopwords_dict
+
+
+def text_process(text, max_length):
+    """分割文本
+    """
+    texts = text_segmentate(text)
+
+    result, length = [], 0
+    for text in texts:
+        if length + len(text) > max_length * 1.3 and len(result) >= 3:
+            yield result
+            result, length = [], 0
+        result.append(text)
+        length += len(text)
+    if result and len(result) >= 3:
+        yield result
+
+
+def text_process_split_long_content(text, max_length):
+    """分割长文本
+    """
+    texts = text_segmentate(text)
+
+    result, sentence_num = "", 0
+    for text in texts:
+        if len(text) > 500:
+            if len(result) > 300 and sentence_num >= 3:
+                yield result
+                result, sentence_num = "", 0
+            else:
+                result, sentence_num = "", 0
+                continue
+        else:
+            if len(result) + len(text) > max_length * 1.1 and sentence_num >= 3:
+                yield result
+                result, sentence_num = "", 0
+            result += text
+            sentence_num += 1
+
+    if result and sentence_num >= 3:
+        yield result
+
+
+def gather_join(texts, idxs):
+    """取出对应的text，然后拼接起来
+    """
+    return ''.join([texts[i] for i in idxs])
+
+
+def gather_join_f1(texts_token, idsx):
+    join_texts = []
+    for id in idsx:
+        join_texts.extend(texts_token[id])
+    return join_texts
+
+
+def compute_rouge(source, target):
+    """计算rouge-1、rouge-2、rouge-l
+    """
+    source, target = ' '.join(source), ' '.join(target)
+    try:
+        scores = rouge.get_scores(hyps=source, refs=target)
+        return {
+            'rouge-1': scores[0]['rouge-1']['f'],
+            'rouge-2': scores[0]['rouge-2']['f'],
+            'rouge-l': scores[0]['rouge-l']['f'],
+        }
+    except ValueError:
+        return {
+            'rouge-1': 0.0,
+            'rouge-2': 0.0,
+            'rouge-l': 0.0,
+        }
+
+
+def remove_stopwords(texts, stopwords_dict):
+    for i, text in enumerate(texts):
+        texts[i] = list(filter(lambda x: x not in stopwords_dict, text))
+    return texts
+
+
+def pseudo_summary_f1(texts,
+                      stopwords,
+                      tokenizer,
+                      max_length,
+                      rouge_strategy="rouge-l"):
+    """构建伪标签摘要数据集
+    """
+    summary_rate = 0.25
+    max_length = max_length - 1
+    texts_tokens = []
+    sentece_idxs_vec = []
+    for text in texts:
+        if len(texts) == 0:
+            continue
+        try:
+            ids = tokenizer.encode(text.strip())[:-1]
+        except ValueError:
+            print("error, input : ", text)
+            raise ValueError
+        sentece_idxs_vec.append(ids)
+        tokens = [tokenizer._convert_id_to_token(token) for token in ids]
+        texts_tokens.append(tokens)
+
+    texts_tokens_rm = remove_stopwords(texts_tokens, stopwords)
+    source_idxs, target_idxs = list(range(len(texts))), []
+
+    assert len(texts_tokens) == len(texts)
+    # truncate_index = 0
+    while True:
+        sims = []
+        for i in source_idxs:
+            new_source_idxs = [j for j in source_idxs if j != i]
+            new_target_idxs = sorted(target_idxs + [i])
+            new_source = gather_join_f1(texts_tokens_rm, new_source_idxs)
+            new_target = gather_join_f1(texts_tokens_rm, new_target_idxs)
+            sim = compute_rouge(new_source, new_target)[rouge_strategy]
+            sims.append(sim)
+        new_idx = source_idxs[np.argmax(sims)]
+        del sims
+        source_idxs.remove(new_idx)
+        target_idxs = sorted(target_idxs + [new_idx])
+        source = gather_join(texts, source_idxs)
+        target = gather_join(texts, target_idxs)
+        try:
+            if (len(source_idxs) == 1
+                    or 1.0 * len(target) / len(source) > summary_rate):
+                break
+        except ZeroDivisionError as e:
+            print(e.meesage)
+            print(texts)
+            print("source: ", source)
+            print("target: ", target)
+
+    if len(source) < len(target):
+        source, target = target, source
+        source_idxs, target_idxs = target_idxs, source_idxs
+
+    return sentece_idxs_vec, source, target, source_idxs, target_idxs
+
+
+def get_input_mask(sentence_id_vec, indexs):
+    target_idxs = []
+    input_idxs = []
+    kMaskSentenceTokenId = 2
+    kEosTokenId = 1
+    mask_sentence_options_cumulative_prob = [0.9, 0.9, 1, 1]
+    for index in indexs:
+        target_idxs.extend(sentence_id_vec[index])
+        choice = random.uniform(0, 1)
+        if choice < mask_sentence_options_cumulative_prob[0]:
+            # print("mask index: ", index)
+            sentence_id_vec[index] = [kMaskSentenceTokenId]
+        elif choice < mask_sentence_options_cumulative_prob[1]:
+            # print("replace index: ", index)
+            replace_id = random.randint(0, len(sentence_id_vec))
+            sentence_id_vec[index] = sentence_id_vec[replace_id]
+        elif choice < mask_sentence_options_cumulative_prob[2]:
+            pass
+        else:
+            sentence_id_vec[index] = []
+
+    target_idxs.append(kEosTokenId)
+    # print(sentence_id_vec)
+    for index, sentence_id in enumerate(sentence_id_vec):
+        # print(index, sentence_id)
+        if len(sentence_id) == 0:
+            continue
+        input_idxs.extend(sentence_id_vec[index])
+
+    input_idxs.append(kEosTokenId)
+    return input_idxs, target_idxs
+
+
+def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int,
+                       decoder_start_token_id: int):
+    """
+    Shift input ids one token to the right.
+    """
+    shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+    shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
+    shifted_input_ids[:, 0] = decoder_start_token_id
+
+    if pad_token_id is None:
+        raise ValueError("self.model.config.pad_token_id has to be defined.")
+    # replace possible -100 values in labels by `pad_token_id`
+    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+    return shifted_input_ids
+
+
+def padding_to_maxlength(ids, max_length, pad_id):
+    cur_len = len(ids)
+    len_diff = max_length - cur_len
+    return ids + [pad_id] * len_diff, [1] * cur_len + [0] * len_diff

special_tokens_map.json

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+{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}

tokenizers_pegasus.py

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+
+from fengshen.examples.pegasus.data_utils import (
+    _is_control,
+    _is_punctuation,
+    _is_whitespace,
+    _is_chinese_char)
+from transformers import PreTrainedTokenizer
+from transformers import logging
+from typing import List, Optional, Tuple, Union
+import collections
+import os
+import unicodedata
+import re
+import jieba
+import sys
+
+sys.path.append("../../../../")
+
+jieba.dt.tmp_dir = os.path.expanduser(
+    "/cognitive_comp/dongxiaoqun/software/jieba/tmp/")
+# jieba.enable_parallel(8)
+jieba.initialize()
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
+
+
+def load_vocab(vocab_file):
+    """Loads a vocabulary file into a dictionary."""
+    vocab = collections.OrderedDict()
+    with open(vocab_file, "r", encoding="utf-8") as reader:
+        tokens = reader.readlines()
+    for index, token in enumerate(tokens):
+        token = token.rstrip("\n")
+        vocab[token] = index
+    return vocab
+
+
+def whitespace_tokenize(text):
+    """Runs basic whitespace cleaning and splitting on a piece of text."""
+    text = text.strip()
+    if not text:
+        return []
+    tokens = text.split()
+    return tokens
+
+
+class PegasusTokenizer(PreTrainedTokenizer):
+    # copy from BertTokenizer
+    r"""
+    Construct a Pegasus tokenizer. Based on WordPiece.
+    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+    this superclass for more information regarding those methods.
+    Args:
+        vocab_file (`str`):
+            File containing the vocabulary.
+        do_lower_case (`bool`, *optional*, defaults to `True`):
+            Whether or not to lowercase the input when tokenizing.
+        do_basic_tokenize (`bool`, *optional*, defaults to `True`):
+            Whether or not to do basic tokenization before WordPiece.
+        never_split (`Iterable`, *optional*):
+            Collection of tokens which will never be split during tokenization. Only has an effect when
+            `do_basic_tokenize=True`
+        unk_token (`str`, *optional*, defaults to `"[UNK]"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        sep_token (`str`, *optional*, defaults to `"[SEP]"`):
+            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+            sequence classification or for a text and a question for question answering. It is also used as the last
+            token of a sequence built with special tokens.
+        pad_token (`str`, *optional*, defaults to `"[PAD]"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        cls_token (`str`, *optional*, defaults to `"[CLS]"`):
+            The classifier token which is used when doing sequence classification (classification of the whole sequence
+            instead of per-token classification). It is the first token of the sequence when built with special tokens.
+        mask_token (`str`, *optional*, defaults to `"[MASK]"`):
+            The token used for masking values. This is the token used when training this model with masked language
+            modeling. This is the token which the model will try to predict.
+        tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+            Whether or not to tokenize Chinese characters.
+            This should likely be deactivated for Japanese (see this
+            [issue](https://github.com/huggingface/transformers/issues/328)).
+        strip_accents (`bool`, *optional*):
+            Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+            value for `lowercase` (as in the original BERT).
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    #     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    #     pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
+    #     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+
+    def __init__(self,
+                 vocab_file,
+                 do_lower_case=True,
+                 do_basic_tokenize=True,
+                 never_split=None,
+                 pad_token="<pad>",
+                 eos_token="</s>",
+                 unk_token="<unk>",
+                 mask_token="<mask_2>",
+                 mask_token_sent="<mask_1>",
+                 additional_special_tokens=None,
+                 sep_token="[SEP]",
+                 cls_token="[CLS]",
+                 tokenize_chinese_chars=True,
+                 strip_accents=None,
+                 offset=100,
+                 pre_tokenizer=lambda x: jieba.cut(x, HMM=False),
+                 **kwargs):
+        self.offset = offset
+
+        if additional_special_tokens is not None:
+            if not isinstance(additional_special_tokens, list):
+                raise TypeError(
+                    f"additional_special_tokens should be of type {type(list)}, \
+                     but is {type(additional_special_tokens)}"
+                )
+
+            additional_special_tokens_extended = (
+                ([mask_token_sent] + additional_special_tokens)
+                if mask_token_sent not in additional_special_tokens
+                and mask_token_sent is not None else additional_special_tokens)
+
+            # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
+            additional_special_tokens_extended += [
+                f"<unk_{i}>" for i in range(
+                    len(additional_special_tokens_extended), self.offset - 1)
+            ]
+
+            if len(set(additional_special_tokens_extended)) != len(
+                    additional_special_tokens_extended):
+                raise ValueError(
+                    f"Please make sure that the provided additional_special_tokens \
+                        do not contain an incorrectly shifted list of <unk_x> tokens. \
+                        Found {additional_special_tokens_extended}."
+                )
+            additional_special_tokens = additional_special_tokens_extended
+        else:
+            additional_special_tokens = [
+                mask_token_sent
+            ] if mask_token_sent is not None else []
+            # additional_special_tokens += [f"<unk_{i}>" for i in range(3, self.offset)]
+
+        # print("additional_special_tokens: ", additional_special_tokens)
+
+        if not os.path.isfile(vocab_file):
+            raise ValueError(
+                f"Can't find a vocabulary file at path '{vocab_file}'. \
+                To load the vocabulary from a Google pretrained "
+                "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+            )
+
+        super().__init__(
+            do_lower_case=do_lower_case,
+            do_basic_tokenize=do_basic_tokenize,
+            never_split=never_split,
+            unk_token=unk_token,
+            sep_token=sep_token,
+            pad_token=pad_token,
+            cls_token=cls_token,
+            mask_token=mask_token,
+            eos_token=eos_token,
+            tokenize_chinese_chars=tokenize_chinese_chars,
+            additional_special_tokens=additional_special_tokens,
+            strip_accents=strip_accents,
+            **kwargs,
+        )
+
+        self.pre_tokenizer = pre_tokenizer
+        self.mask_token_sent = mask_token_sent
+        self.vocab = load_vocab(vocab_file)
+
+        self.vocab[self.eos_token] = self.vocab.pop("[unused1]")
+        # self.vocab[self.eos_token] = self.vocab.pop("[unused2]")
+        self.vocab[self.pad_token] = self.vocab.pop("[PAD]")
+        self.vocab[self.unk_token] = self.vocab.pop("[UNK]")
+
+        if self.mask_token_sent is not None:
+            self.vocab[self.mask_token] = self.vocab.pop("[unused3]")
+            self.vocab[self.mask_token_sent] = self.vocab.pop("[unused2]")
+
+        self.ids_to_tokens = collections.OrderedDict([
+            (ids, tok) for tok, ids in self.vocab.items()
+        ])
+        self.do_basic_tokenize = do_basic_tokenize
+        if do_basic_tokenize:
+            self.basic_tokenizer = BasicTokenizer(
+                do_lower_case=do_lower_case,
+                never_split=never_split,
+                tokenize_chinese_chars=tokenize_chinese_chars,
+                strip_accents=strip_accents,
+            )
+        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab,
+                                                      unk_token=self.unk_token)
+
+    @property
+    def do_lower_case(self):
+        return self.basic_tokenizer.do_lower_case
+
+    @property
+    def vocab_size(self):
+        return len(self.vocab)
+
+    def get_vocab(self):
+        return dict(self.vocab, **self.added_tokens_encoder)
+
+    def _tokenize(self, text):
+        split_tokens = []
+        # print("pegasus_tokenizer: ", text)
+        for text in self.pre_tokenizer(text):
+            if text in self.vocab:
+                split_tokens.append(text)
+            else:
+                if self.do_basic_tokenize:
+                    for token in self.basic_tokenizer.tokenize(
+                            text, never_split=self.all_special_tokens):
+
+                        # If the token is part of the never_split set
+                        if token in self.basic_tokenizer.never_split:
+                            split_tokens.append(token)
+                        else:
+                            split_tokens += self.wordpiece_tokenizer.tokenize(
+                                token)
+                else:
+                    split_tokens = self.wordpiece_tokenizer.tokenize(text)
+        return split_tokens
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.vocab.get(token, self.vocab.get(self.unk_token))
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.ids_to_tokens.get(index, self.unk_token)
+
+    @staticmethod
+    def _cjk_punctuation():
+        return u'\uff02\uff03\uff04\uff05\uff06\uff07\uff08\uff09\uff0a\uff0b\uff0c\uff0d\uff0f\uff1a\uff1b\uff1c\uff1d\
+            \uff1e\uff20\uff3b\uff3c\uff3d\uff3e\uff3f\uff40\uff5b\uff5c\uff5d\uff5e\uff5f\uff60\uff62\
+            \uff63\uff64\u3000\u3001\u3003\u3008\u3009\u300a\u300b\u300c\u300d\u300e\u300f\u3010\u3011\u3014\
+            \u3015\u3016\u3017\u3018\u3019\u301a\u301b\u301c\u301d\u301e\u301f\u3030\u303e\u303f\u2013\u2014\
+            \u2018\u2019\u201b\u201c\u201d\u201e\u201f\u2026\u2027\ufe4f\ufe51\ufe54\u00b7\uff01\uff1f\uff61\u3002'
+
+    def convert_ids_to_tokens(
+            self,
+            ids: Union[int, List[int]],
+            skip_special_tokens: bool = False) -> Union[str, List[str]]:
+        """
+        Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
+        added tokens.
+        Args:
+            ids (`int` or `List[int]`):
+                The token id (or token ids) to convert to tokens.
+            skip_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not to remove special tokens in the decoding.
+        Returns:
+            `str` or `List[str]`: The decoded token(s).
+        """
+        if isinstance(ids, int):
+            if ids in self.added_tokens_decoder:
+                return self.added_tokens_decoder[ids]
+            else:
+                return self._convert_id_to_token(ids)
+        tokens = []
+        for index in ids:
+            index = int(index)
+            if skip_special_tokens and index in self.all_special_ids and index != 2:
+                continue
+            if index in self.added_tokens_decoder:
+                tokens.append(self.added_tokens_decoder[index])
+            else:
+                tokens.append(self._convert_id_to_token(index))
+        return tokens
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        # for token in
+        # tokens = tokens or self.ids_to_tokens(ids)
+        # tokens = [token for token in tokens if not self._is_special(token)]
+
+        text = ''
+        for i, token in enumerate(tokens):
+            if token[:2] == '##':
+                text += token[2:]
+            elif len(token) == 1 and _is_chinese_char(ord(token)):
+                text += token
+            elif len(token) == 1 and _is_punctuation(token):
+                text += token
+                text += ' '
+            elif i > 0 and _is_chinese_char(ord(text[-1])):
+                text += token
+            elif tokens == "</s>":
+                continue
+            else:
+                text += ' '
+                text += token
+
+        text = re.sub(' +', ' ', text)
+        text = re.sub('\' (re|m|s|t|ve|d|ll) ', '\'\\1 ', text)
+        punctuation = re.sub(' +', '', self._cjk_punctuation()).strip() + '+-/={(<['
+        punctuation_regex = '|'.join([re.escape(p) for p in punctuation])
+        punctuation_regex = '(%s) ' % punctuation_regex
+        text = re.sub(punctuation_regex, '\\1', text)
+        text = re.sub(r'(\d\.) (\d)', '\\1\\2', text)
+
+        return text.strip()
+        # out_string = " ".join(tokens).replace(" ##", "").strip()
+
+    def build_inputs_with_special_tokens(
+            self,
+            token_ids_0: List[int],
+            token_ids_1: Optional[List[int]] = None) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequences for sequence classification tasks by concatenating
+        and adding special tokens. A PEGASUS sequence has the following format, where `X` represents the sequence:
+        - single sequence: `X </s>`
+        - pair of sequences: `A B </s>` (not intended use)
+        BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
+        separator.
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+        Returns:
+            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+        """
+        if token_ids_1 is None:
+            return token_ids_0 + [self.eos_token_id]
+        return token_ids_0 + token_ids_1 + [self.eos_token_id]
+
+    def _special_token_mask(self, seq):
+        all_special_ids = set(
+            self.all_special_ids)  # call it once instead of inside list comp
+        # all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special
+
+        return [1 if x in all_special_ids else 0 for x in seq]
+
+    def get_special_tokens_mask(
+            self,
+            token_ids_0: List[int],
+            token_ids_1: Optional[List[int]] = None,
+            already_has_special_tokens: bool = False) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+
+        if already_has_special_tokens:
+            return self._special_token_mask(token_ids_0)
+        elif token_ids_1 is None:
+            return self._special_token_mask(token_ids_0) + [self.eos_token_id]
+        else:
+            return self._special_token_mask(token_ids_0 +
+                                            token_ids_1) + [self.eos_token_id]
+
+    def num_special_tokens_to_add(self, pair=False):
+        """Just EOS"""
+        return 1
+
+    def save_vocabulary(self,
+                        save_directory: str,
+                        filename_prefix: Optional[str] = None) -> Tuple[str]:
+        index = 0
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(
+                save_directory,
+                (filename_prefix + "-" if filename_prefix else "") +
+                VOCAB_FILES_NAMES["vocab_file"])
+        else:
+            vocab_file = (filename_prefix +
+                          "-" if filename_prefix else "") + save_directory
+        with open(vocab_file, "w", encoding="utf-8") as writer:
+            for token, token_index in sorted(self.vocab.items(),
+                                             key=lambda kv: kv[1]):
+                if index != token_index:
+                    logger.warning(
+                        f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
+                        " Please check that the vocabulary is not corrupted!")
+                    index = token_index
+                writer.write(token + "\n")
+                index += 1
+        return (vocab_file, )
+
+
+class BasicTokenizer(object):
+    """
+    Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
+    Args:
+        do_lower_case (`bool`, *optional*, defaults to `True`):
+            Whether or not to lowercase the input when tokenizing.
+        never_split (`Iterable`, *optional*):
+            Collection of tokens which will never be split during tokenization. Only has an effect when
+            `do_basic_tokenize=True`
+        tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+            Whether or not to tokenize Chinese characters.
+            This should likely be deactivated for Japanese (see this
+            [issue](https://github.com/huggingface/transformers/issues/328)).
+        strip_accents: (`bool`, *optional*):
+            Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+            value for `lowercase` (as in the original BERT).
+    """
+
+    def __init__(self,
+                 do_lower_case=True,
+                 never_split=None,
+                 tokenize_chinese_chars=True,
+                 strip_accents=None):
+        if never_split is None:
+            never_split = []
+        self.do_lower_case = do_lower_case
+        self.never_split = set(never_split)
+        self.tokenize_chinese_chars = tokenize_chinese_chars
+        self.strip_accents = strip_accents
+
+    def tokenize(self, text, never_split=None):
+        """
+        Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see
+        WordPieceTokenizer.
+        Args:
+            never_split (`List[str]`, *optional*)
+                Kept for backward compatibility purposes. Now implemented directly at the base class level (see
+                [`PreTrainedTokenizer.tokenize`]) List of token not to split.
+        """
+        # union() returns a new set by concatenating the two sets.
+        never_split = self.never_split.union(
+            set(never_split)) if never_split else self.never_split
+        text = self._clean_text(text)
+
+        # This was added on November 1st, 2018 for the multilingual and Chinese
+        # models. This is also applied to the English models now, but it doesn't
+        # matter since the English models were not trained on any Chinese data
+        # and generally don't have any Chinese data in them (there are Chinese
+        # characters in the vocabulary because Wikipedia does have some Chinese
+        # words in the English Wikipedia.).
+        if self.tokenize_chinese_chars:
+            text = self._tokenize_chinese_chars(text)
+        orig_tokens = whitespace_tokenize(text)
+        split_tokens = []
+        for token in orig_tokens:
+            if token not in never_split:
+                if self.do_lower_case:
+                    token = token.lower()
+                    if self.strip_accents is not False:
+                        token = self._run_strip_accents(token)
+                elif self.strip_accents:
+                    token = self._run_strip_accents(token)
+            split_tokens.extend(self._run_split_on_punc(token, never_split))
+
+        output_tokens = whitespace_tokenize(" ".join(split_tokens))
+        return output_tokens
+
+    def _run_strip_accents(self, text):
+        """Strips accents from a piece of text."""
+        text = unicodedata.normalize("NFD", text)
+        output = []
+        for char in text:
+            cat = unicodedata.category(char)
+            if cat == "Mn":
+                continue
+            output.append(char)
+        return "".join(output)
+
+    def _run_split_on_punc(self, text, never_split=None):
+        """Splits punctuation on a piece of text."""
+        if never_split is not None and text in never_split:
+            return [text]
+        chars = list(text)
+        i = 0
+        start_new_word = True
+        output = []
+        while i < len(chars):
+            char = chars[i]
+            if _is_punctuation(char):
+                output.append([char])
+                start_new_word = True
+            else:
+                if start_new_word:
+                    output.append([])
+                start_new_word = False
+                output[-1].append(char)
+            i += 1
+
+        return ["".join(x) for x in output]
+
+    def _tokenize_chinese_chars(self, text):
+        """Adds whitespace around any CJK character."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if self._is_chinese_char(cp):
+                output.append(" ")
+                output.append(char)
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+    def _is_chinese_char(self, cp):
+        """Checks whether CP is the codepoint of a CJK character."""
+        # This defines a "chinese character" as anything in the CJK Unicode block:
+        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+        #
+        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+        # despite its name. The modern Korean Hangul alphabet is a different block,
+        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+        # space-separated words, so they are not treated specially and handled
+        # like the all of the other languages.
+        if ((cp >= 0x4E00 and cp <= 0x9FFF)
+            or (cp >= 0x3400 and cp <= 0x4DBF)  #
+            or (cp >= 0x20000 and cp <= 0x2A6DF)  #
+            or (cp >= 0x2A700 and cp <= 0x2B73F)  #
+            or (cp >= 0x2B740 and cp <= 0x2B81F)  #
+            or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
+            or (cp >= 0xF900 and cp <= 0xFAFF)
+                or (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
+            return True
+
+        return False
+
+    def _clean_text(self, text):
+        """Performs invalid character removal and whitespace cleanup on text."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if cp == 0 or cp == 0xFFFD or _is_control(char):
+                continue
+            if _is_whitespace(char):
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+
+class WordpieceTokenizer(object):
+    """Runs WordPiece tokenization."""
+
+    def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
+        self.vocab = vocab
+        self.unk_token = unk_token
+        self.max_input_chars_per_word = max_input_chars_per_word
+
+    def tokenize(self, text):
+        """
+        Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
+        tokenization using the given vocabulary.
+        For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
+        Args:
+            text: A single token or whitespace separated tokens. This should have
+                already been passed through *BasicTokenizer*.
+        Returns:
+            A list of wordpiece tokens.
+        """
+
+        output_tokens = []
+        for token in whitespace_tokenize(text):
+            chars = list(token)
+            if len(chars) > self.max_input_chars_per_word:
+                output_tokens.append(self.unk_token)
+                continue
+
+            is_bad = False
+            start = 0
+            sub_tokens = []
+            while start < len(chars):
+                end = len(chars)
+                cur_substr = None
+                while start < end:
+                    substr = "".join(chars[start:end])
+                    if start > 0:
+                        substr = "##" + substr
+                    if substr in self.vocab:
+                        cur_substr = substr
+                        break
+                    end -= 1
+                if cur_substr is None:
+                    is_bad = True
+                    break
+                sub_tokens.append(cur_substr)
+                start = end
+
+            if is_bad:
+                output_tokens.append(self.unk_token)
+            else:
+                output_tokens.extend(sub_tokens)
+        return output_tokens