NLP-预训练模型-2020：Pegasus（天马）模型【专为生成式摘要定制的“预训练模型”】【预训练数据集：C4、XSum、CNN/DM】【只需1000个样本就可微调出效果超出T5、Bart的模型】_pegasus.py

随着MASS和T5的问世，seq2seq结构的生成式摘要模型也逐渐成熟起来，在更大更丰富的语料上进行训练的摘要模型，表现一度超过了抽取式模型，成为CNNDM等语料上的SOTA。

Pegasus模型提出的baseline是standard Transformer，模型结构如下图，使用正弦余弦绝对位置编码；Pegasus模型训练了两个不同参数大小的模型，PEGASUS-base和PEGASUS-large，前者使用Transformer-base，后者使用Transformer-large；Pegasus模型还提出了没有pre-training的PEGASUS-base，即Transformer-base，作为对比：

Pegasus模型提出，如果pre-training task在形式上与finetuning task类似，则有利于提升finetuning task的表现；为此，Pegasus模型提出一个专用于abstractive summarization task的pre-training task，即gap sentences generation（GSG），相关细节将在3.2.2中介绍。

Pegasus模型提出，如果pre-training corpus与finetuning corpus的type相似，则有利于提升finetuning task的表现；为此，Pegasus模型提出两个pre-training corpus，C4和HugeNews；前者是[T5][Raffle et al.]提出的，大小750GB，且绝大部分文章不是news-type的；后者是论文收集的news-type articles corpus，大小3.8TB，其中包含CNNDM、NYT等corpus；

同样地，finetuning corpus共有12个，其中6个corpus是news-type的。

Pegasus模型首先分别在两个pre-trained corpus上进行pre-training，然后分别在不同的finetuning corpus上进行finetuning，对比corpus type对下游任务的影响；

实验显示，在C4上预训练的模型在non-news-type的finetuning corpus（wikihow/reddit）上的表现更好，在HugeNews上预训练的模型在news-type的finetuning corpus（XSum/ CNNDM）上的表现更好，说明pre-training corpus type对下游任务的影响很大，in-domain training可以提高下游任务的表现：

训练时，目标函数是MLE loss，使用Adafactor optimizer，square root learning rate decay，在beam search时使用length penalty；

预测时，Pegasus模型没有使用任何防止重复生成的机制，但Pegasus模型发现生成的摘要中重复生成的比例非常小；这似乎说明，模型重复生成是因为encoder和decoder没有像pre-trained model一样经过充分的预训练，没有在全词汇表上建立良好的语言模型，因而只能围绕少数几个学习较好的点重复生成内容；

Pegasus模型对比BPE和SentencePiece Unigram在不同词汇表规模下的影响，如下图：

在news-type corpus上，两种tokenizers的效果差不多，但是在non-news-type corpus上，SentencePiece Unigram的表现要好得多；

Pegasus模型发现，在CNNDM、BIGPATENT等corpus中，测试集文档的长度经常会超过训练集文档的最长长度，但是PEGASUS可以在最长1024个tokens的长度的测试集上泛化得很好；Pegasus模型认为，该现象证明了正弦余弦位置向量在长输入上具有较好的泛化能力，使得模型可以处理超出训练长度的输入文档；

Pegasus模型在finetuning corpus上的表现如下图，可以发现：（1）在C4上训练的PEGASUS在non-news corpus上达到了SOTA，在HugeNews上训练的PEGASUS在news corpus上达到了SOTA（2）从Transformer-base到PEGASUS-large的提升，在规模越小的数据集上越大，说明pre-training对小数据集具有重要的作用；

Pegasus模型测试模型在low-source corpus上的zero-shot预测的效果，发现往往只需要几百至几千的样本上finetuning，就可以达到Transformer-base在全数据集上训练达到的结果：

一、导语

近些年 Transformers 在海量语料上进行自监督预训练再到下游各种NLP任务(当然也包括文本摘要)上微调的方案已取得巨大成功。但是，尚未有针抽象文本摘要(abstractive text summarization)定制预训练目标。此外，目前抽象文本摘要任务也缺乏跨领域的系统评价。

为此，本文提出了一种新的自监督预训练目标：GSG(Gap Sentences Generation)，以适配 Transformer-based 的 encoder-decoder 模型在海量文本语料上预训练。在 PEGASUS 中， 将输入文档中的“重要句子”删除或者遮蔽，再利用剩余的句子在输出中生成这些被删除或遮蔽的句子。从输入和输出看，该目标与文本摘要类似。

本文以 12 个文本摘要数据集(包括新闻、科学、故事、使用说明、电子邮件、专利和立法议案)对最好的 PEGASUS 模型进行全面测试。实验结果是：PEGASUS 刷新 12 个数据集的 ROUGE 得分记录。另外，PEGASUS 模型在处理低资源摘要数据集也显示出惊人的性能，在 6 个数据集上仅以 1000 个样本就超过了之前的最先进结果。最后，本文还对 PEGASUS 模型生成的摘要结果进行人工评测，结果表明本文的模型在多个数据集上达到与人工摘要相媲美的性能。

二、前言

抽象文本摘要是一项极具挑战的自然语言处理任务，因为这要求理解长篇文章、压缩资讯以及生成语言。目前主流的解决方案是用 seq2seq，让神经网路学习把输入序列映射到输出序列。这些 seq2seq 模型最初是使用 RNN，但因为基于 Transformer encoder-decoder 的各种模型在处理长序列中的依赖关系表现更好，所以逐渐更受青睐。

各种 Transformer 模型与自监督预训练技术(如 BERT、GPT-2、 RoBERTa、XLNet、ALBERT、T5、ELECTRA)相结合，已被证明是学习生成通用语言的强大框架。之前的工作中，预训练使用的自监督目标对下游应用有一定程度的不可知性，即不考虑下游任务，如此有利于模型通用性的学习。本文认为如果预训练的自监督目标更接近最终的任务，那么最终的下游任务能取得更好的结果。

实验证明，将输入文档中部分句子遮蔽掉，用剩余的句子生成被遮蔽掉句子的这种预训练目标很适用于文本摘要任务。这种预训练目标确实适合于抽象摘要，因为它非常类似于下游任务，从而促进模型对整个文档的理解和类似摘要的生成。需要指出的是，选择重要句子比随机选择或者选择前几句的结果性能都要好。

在 C4 语料上预训练出的最好 PEGASUS 模型，参数只有 568M，但在 12 个评测数据集上评测能够比肩此前最优结果，甚至超越它们刷新纪录。另外，本文为进一步提升最先进结果，引入了一个新收集的文本语料库，该语料库由新闻类文章组成包括 XSum 和 CNN/DailyMail 摘要数据集，统称为 HugeNews。此外，将本文的模型应用了低资源文本摘要任务上时，实验结果表明本文的模型能够非常快速适用于少量监督对的微调，并仅以 1000 个样本即在 6 个数据集中斩获桂冠。最后，还将文本模型的结果与人工摘要结果做对比，结果表明本文的模型可以达到与人工摘要相媲美的效果。

总结下本文的贡献：

• 提出了一个新的自监督的预训练目标(GSG)用于抽象摘要任务，并研究相应的句子选择策略。
• 用多个领域的摘要任务数据集对 GSG 进行广泛评测，并仔细地选择最佳的模型设置，训练一个参数量仅为 568M 的 PEGASUS 模型。该模型在全部的 12 个下游数据集上能够超过或与当前最先进水平持平。
• 对于低资源任务数据集，通过微调 PEGASUS 模型，可以在广泛的领域实现良好的抽象摘要效果。在多个任务上，仅需 1000 个样本就超过了以前的最先进的结果。
• 对模型结果进行人工评估，结果表明在 XSum, CNN/DailyMail 和 Reddit TIFU 上的摘要效果与人工摘要比肩。

三、PEGASUS模型最大输入长度、词表大小

Pegasus模型的单词和/或句子的最大输入长度是多少？这实际上取决于你的训练前准备。您可以创建一个pegagsus模型，该模型支持100个令牌或10000个令牌的长度。例如：

• 模型pegasus-cnn_dailymail支持1024个Token输入；
• 模型pegasus-xsum支持512个Token输入；

1、pegasus-cnn_dailymail模型

# https://github.com/huggingface/transformers/blob/master/src/transformers/models/pegasus/modeling_pegasus.py
from transformers import PegasusTokenizer, PegasusForConditionalGeneration

tokenizer = PegasusTokenizer.from_pretrained(r'D:\Pretrained_Model\pegasus-cnn_dailymail')
model = PegasusForConditionalGeneration.from_pretrained(r'D:\Pretrained_Model\pegasus-cnn_dailymail')

max_input_len = tokenizer.max_len_single_sentence
print("pegasus-cnn_dailymail模型---->最大输入长度为：", max_input_len)

vocab_size = len(tokenizer)
print("pegasus-cnn_dailymail模型---->词表大小为：", vocab_size)

text = "This is a test sentence Embedding."
tokenized_text = tokenizer.tokenize(text)
print("tokenized_text = ", tokenized_text)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

打印结果：

pegasus-cnn_dailymail模型---->最大输入长度为： 1023
pegasus-cnn_dailymail模型---->词表大小为： 96103
tokenized_text =  ['▁This', '▁is', '▁a', '▁test', '▁sentence', '▁Embed', 'ding', '.']
1
2
3

2、pegasus-xsum模型

# https://github.com/huggingface/transformers/blob/master/src/transformers/models/pegasus/modeling_pegasus.py
from transformers import PegasusTokenizer, PegasusForConditionalGeneration

tokenizer = PegasusTokenizer.from_pretrained(r'D:\Pretrained_Model\pegasus-xsum')
model = PegasusForConditionalGeneration.from_pretrained(r'D:\Pretrained_Model\pegasus-xsum')

max_input_len = tokenizer.max_len_single_sentence
print("pegasus-xsum 模型---->最大输入长度为：", max_input_len)

vocab_size = len(tokenizer)
print("pegasus-xsum 模型---->词表大小为：", vocab_size)

text = "This is a test sentence Embedding."
tokenized_text = tokenizer.tokenize(text)
print("tokenized_text = ", tokenized_text)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

打印结果：

pegasus-xsum 模型---->最大输入长度为： 511
pegasus-xsum 模型---->词表大小为： 96103
tokenized_text =  ['▁This', '▁is', '▁a', '▁test', '▁sentence', '▁Embed', 'ding', '.']
1
2
3

3、pegasus-large模型

# https://github.com/huggingface/transformers/blob/master/src/transformers/models/pegasus/modeling_pegasus.py
from transformers import PegasusTokenizer, PegasusForConditionalGeneration

tokenizer = PegasusTokenizer.from_pretrained(r'D:\Pretrained_Model\pegasus-large')
model = PegasusForConditionalGeneration.from_pretrained(r'D:\Pretrained_Model\pegasus-large')

max_input_len = tokenizer.max_len_single_sentence
print("pegasus-large 模型---->最大输入长度为：", max_input_len)

vocab_size = len(tokenizer)
print("pegasus-large 模型---->词表大小为：", vocab_size)

text = "This is a test sentence Embedding."
tokenized_text = tokenizer.tokenize(text)
print("tokenized_text = ", tokenized_text)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

打印结果：

pegasus-large 模型---->最大输入长度为： 1023
pegasus-large 模型---->词表大小为： 96103
tokenized_text =  ['▁This', '▁is', '▁a', '▁test', '▁sentence', '▁Embed', 'ding', '.']
1
2
3

四、PEGASUS模型结构

本文假设预训练自监督的目标越接近最终的任务则结果性能越好。在 PEGASUS 预训练中，将文件里的几个完整句子删除，而模型的目标就是要恢复这些句子，换句话说，用来预训练的输入是有缺失部分句子的文档，而输出则是缺失句子的串连。

这是一项难以置信的艰巨任务，甚至对人人类来说也是不可能的，我们并不期望模型能完美地解决它。然而，这样一个具有挑战性的任务促使模型学习到关于语言的知识和这个世界的一般事实，以及如何从整个文档中提取信息，以便生成类似于微调摘要任务的输出。

这种自监督的优点是，可以创建与文档一样多的示例，而不需要任何人工注释，而这通常是纯监督系统的阿喀琉斯之踵。

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained(r'D:\Pretrained_model\pegasus-cnn_dailymail')
model = AutoModelForSeq2SeqLM.from_pretrained(r'D:\Pretrained_model\pegasus-cnn_dailymail')

print(model)
1
2
3
4
5
6

from transformers import PegasusTokenizer, PegasusForConditionalGeneration

tokenizer = PegasusTokenizer.from_pretrained(r'D:\Pretrained_Model\pegasus-cnn_dailymail')
model = PegasusForConditionalGeneration.from_pretrained(r'D:\Pretrained_Model\pegasus-cnn_dailymail')

print(model)
1
2
3
4
5
6

PegasusForConditionalGeneration(
  (model): PegasusModel(
    (shared): Embedding(96103, 1024, padding_idx=0)
    (encoder): PegasusEncoder(
      (embed_tokens): Embedding(96103, 1024, padding_idx=0)
      (embed_positions): PegasusSinusoidalPositionalEmbedding(1024, 1024)
      (layers): ModuleList(
        (0): PegasusEncoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (1): PegasusEncoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (2): PegasusEncoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (3): PegasusEncoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (4): PegasusEncoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (5): PegasusEncoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (6): PegasusEncoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (7): PegasusEncoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (8): PegasusEncoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (9): PegasusEncoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (10): PegasusEncoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (11): PegasusEncoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (12): PegasusEncoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (13): PegasusEncoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (14): PegasusEncoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (15): PegasusEncoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
      )
      (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
    )
    (decoder): PegasusDecoder(
      (embed_tokens): Embedding(96103, 1024, padding_idx=0)
      (embed_positions): PegasusSinusoidalPositionalEmbedding(1024, 1024)
      (layers): ModuleList(
        (0): PegasusDecoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (1): PegasusDecoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (2): PegasusDecoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (3): PegasusDecoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (4): PegasusDecoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (5): PegasusDecoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (6): PegasusDecoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (7): PegasusDecoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (8): PegasusDecoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (9): PegasusDecoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (10): PegasusDecoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (11): PegasusDecoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (12): PegasusDecoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (13): PegasusDecoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (14): PegasusDecoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
        (15): PegasusDecoderLayer(
          (self_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): PegasusAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1024, out_features=4096, bias=True)
          (fc2): Linear(in_features=4096, out_features=1024, bias=True)
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
      )
      (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
    )
  )
  (lm_head): Linear(in_features=1024, out_features=96103, bias=False)
)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516

五、PEGASUS模型源码

1、configuration_pegasus.py

# coding=utf-8
# Copyright 2021, Google and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PEGASUS model configuration"""

from transformers import PretrainedConfig

PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}


class PegasusConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`PegasusModel`]. It is used to instantiate an
    PEGASUS model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of the PEGASUS
    [google/pegasus-large](https://huggingface.co/google/pegasus-large) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 50265):
            Vocabulary size of the PEGASUS model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`PegasusModel`] or [`TFPegasusModel`].
        d_model (`int`, *optional*, defaults to 1024):
            Dimensionality of the layers and the pooler layer.
        encoder_layers (`int`, *optional*, defaults to 12):
            Number of encoder layers.
        decoder_layers (`int`, *optional*, defaults to 12):
            Number of decoder layers.
        encoder_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        decoder_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer decoder.
        decoder_ffn_dim (`int`, *optional*, defaults to 4096):
            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
        encoder_ffn_dim (`int`, *optional*, defaults to 4096):
            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
        activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        activation_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for activations inside the fully connected layer.
        classifier_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for classifier.
        max_position_embeddings (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        init_std (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        encoder_layerdrop: (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        decoder_layerdrop: (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        scale_embedding (`bool`, *optional*, defaults to `False`):
            Scale embeddings by diving by sqrt(d_model).
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models)
        forced_eos_token_id (`int`, *optional*, defaults to 1):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.

    Example:

    ```python
    >>> from transformers import PegasusModel, PegasusConfig

    >>> # Initializing a PEGASUS google/pegasus-large style configuration
    >>> configuration = PegasusConfig()

    >>> # Initializing a model from the google/pegasus-large style configuration
    >>> model = PegasusModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "pegasus"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

    def __init__(
        self,
        vocab_size=50265,
        max_position_embeddings=1024,
        encoder_layers=12,
        encoder_ffn_dim=4096,
        encoder_attention_heads=16,
        decoder_layers=12,
        decoder_ffn_dim=4096,
        decoder_attention_heads=16,
        encoder_layerdrop=0.0,
        decoder_layerdrop=0.0,
        use_cache=True,
        is_encoder_decoder=True,
        activation_function="gelu",
        d_model=1024,
        dropout=0.1,
        attention_dropout=0.0,
        activation_dropout=0.0,
        init_std=0.02,
        decoder_start_token_id=0,
        classifier_dropout=0.0,
        scale_embedding=False,
        pad_token_id=0,
        eos_token_id=1,
        forced_eos_token_id=1,
        **kwargs
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.d_model = d_model
        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_layers = encoder_layers
        self.encoder_attention_heads = encoder_attention_heads
        self.decoder_ffn_dim = decoder_ffn_dim
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.activation_function = activation_function
        self.init_std = init_std
        self.encoder_layerdrop = encoder_layerdrop
        self.decoder_layerdrop = decoder_layerdrop
        self.classifier_dropout = classifier_dropout
        self.use_cache = use_cache
        self.num_hidden_layers = encoder_layers
        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=pad_token_id,
            eos_token_id=eos_token_id,
            is_encoder_decoder=is_encoder_decoder,
            decoder_start_token_id=decoder_start_token_id,
            forced_eos_token_id=forced_eos_token_id,
            **kwargs,
        )

    @property
    def num_attention_heads(self) -> int:
        return self.encoder_attention_heads

    @property
    def hidden_size(self) -> int:
        return self.d_model
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165

2、tokenization_pegasus.py

# coding=utf-8
# Copyright 2020 Google and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from shutil import copyfile
from typing import Dict, List, Optional, Tuple

import sentencepiece as spm

from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.utils import logging


SPIECE_UNDERLINE = "▁"

VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "google/pegasus-xsum": 512,
}


logger = logging.get_logger(__name__)


class PegasusTokenizer(PreTrainedTokenizer):
    r"""
    Construct a PEGASUS tokenizer. Based on `SentencePiece <https://github.com/google/sentencepiece>`__.

    This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods.
    Users should refer to this superclass for more information regarding those methods.

    Args:
        vocab_file (:obj:`str`):
            `SentencePiece <https://github.com/google/sentencepiece>`__ file (generally has a `.spm` extension) that
            contains the vocabulary necessary to instantiate a tokenizer.
        pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
            The token used for padding, for example when batching sequences of different lengths.
        eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
            The end of sequence token.

            .. note::

                When building a sequence using special tokens, this is not the token that is used for the end of
                sequence. The token used is the :obj:`sep_token`.
        unk_token (:obj:`str`, `optional`, defaults to :obj:`"<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.
        mask_token (:obj:`str`, `optional`, defaults to :obj:`"<mask_2>"`):
            The token used for masking single token values. This is the token used when training this model with masked
            language modeling (MLM). This is the token that the PEGASUS encoder will try to predict during pretraining.
            It corresponds to `[MASK2]` in `PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive
            Summarization <https://arxiv.org/pdf/1912.08777.pdf>`__.
        mask_token_sent (:obj:`str`, `optional`, defaults to :obj:`"<mask_1>"`):
            The token used for masking whole target sentences. This is the token used when training this model with gap
            sentences generation (GSG). This is the sentence that the PEGASUS decoder will try to predict during
            pretraining. It corresponds to `[MASK1]` in `PEGASUS: Pre-training with Extracted Gap-sentences for
            Abstractive Summarization <https://arxiv.org/pdf/1912.08777.pdf>`__.
        additional_special_tokens (:obj:`List[str]`, `optional`):
            Additional special tokens used by the tokenizer. If no additional_special_tokens are provided <mask_2> and
            <unk_2, ..., unk_102> are used as additional special tokens corresponding to the `original PEGASUS
            tokenizer
            <https://github.com/google-research/pegasus/blob/939830367bcf411193d2b5eca2f2f90f3f9260ca/pegasus/ops/pretrain_parsing_ops.cc#L66>`__
            that uses the tokens 2 - 104 only for pretraining
    """
    vocab_files_names = VOCAB_FILES_NAMES

    offset = 103  # entries 2 - 104 are only used for pretraining
    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        vocab_file,
        pad_token="<pad>",
        eos_token="</s>",
        unk_token="<unk>",
        mask_token="<mask_2>",
        mask_token_sent="<mask_1>",
        additional_special_tokens=None,
        **kwargs
    ):
        if additional_special_tokens is not None:
            assert isinstance(
                additional_special_tokens, list
            ), 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
                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]
            additional_special_tokens += [f"<unk_{i}>" for i in range(2, self.offset)]

        super().__init__(
            eos_token=eos_token,
            unk_token=unk_token,
            mask_token=mask_token,
            pad_token=pad_token,
            mask_token_sent=mask_token_sent,
            additional_special_tokens=additional_special_tokens,
            **kwargs,
        )
        self.vocab_file = vocab_file
        self.sp_model = spm.SentencePieceProcessor()
        self.sp_model.Load(vocab_file)
        self.mask_token_sent = mask_token_sent

        # add special tokens to encoder dict
        self.encoder: Dict[int, str] = {
            0: self.pad_token,
            1: self.eos_token,
            2: self.mask_token_sent,
            3: self.mask_token,
        }
        # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
        # mask_token_sent is already added to list -> so start at 1
        self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1, self.offset - 1)})
        self.decoder: Dict[str, int] = {v: k for k, v in self.encoder.items()}

    @property
    def vocab_size(self) -> int:
        return len(self.sp_model) + self.offset

    def get_vocab(self) -> Dict[str, int]:
        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
        vocab.update(self.added_tokens_encoder)
        return vocab

    def __getstate__(self):
        state = self.__dict__.copy()
        state["sp_model"] = None
        return state

    def __setstate__(self, d):
        self.__dict__ = d
        self.sp_model = spm.SentencePieceProcessor()
        self.sp_model.Load(self.vocab_file)

    def _tokenize(self, text, sample=False):
        """Take as input a string and return a list of strings (tokens) for words/sub-words"""
        if not sample:
            pieces = self.sp_model.EncodeAsPieces(text)
        else:
            pieces = self.sp_model.SampleEncodeAsPieces(text, 64, 0.1)
        return pieces

    def _convert_token_to_id(self, token: str) -> int:
        """ Converts a token (str) to an id using the vocab. """
        if token in self.decoder:
            return self.decoder[token]
        elif token in self.added_tokens_decoder:
            return self.added_tokens_decoder[token]
        sp_id = self.sp_model.piece_to_id(token)
        return sp_id + self.offset

    def _convert_id_to_token(self, index: int) -> str:
        """Converts an index (integer) to a token (str) using the vocab."""
        if index in self.encoder:
            return self.encoder[index]
        elif index in self.added_tokens_encoder:
            return self.added_tokens_encoder[index]
        else:
            token = self.sp_model.IdToPiece(index - self.offset)
        return token

    def convert_tokens_to_string(self, tokens):
        """ Converts a sequence of tokens (string) in a single string. """
        out_string = self.sp_model.decode_pieces(tokens)
        return out_string

    def num_special_tokens_to_add(self, pair=False):
        """Just EOS"""
        return 1

    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

        assert all_special_ids == set(
            range(len(self.additional_special_tokens) + 3)
        ), f"There should be 3 special tokens: mask_token, pad_token, and eos_token + {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}"

        return [1 if x in all_special_ids else 0 for x in seq]

    def get_special_tokens_mask(
        self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False
    ) -> List[int]:
        """Get list where entries are [1] if a token is [eos] or [pad] else 0."""
        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) + [1]
        else:
            return self._special_token_mask(token_ids_0 + token_ids_1) + [1]

    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=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 (:obj:`List[int]`):
                List of IDs to which the special tokens will be added.
            token_ids_1 (:obj:`List[int]`, `optional`):
                Optional second list of IDs for sequence pairs.

        Returns:
            :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
        """
        if token_ids_1 is None:
            return token_ids_0 + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_0 + token_ids_1 + [self.eos_token_id]

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        if not os.path.isdir(save_directory):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
            return
        out_vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )

        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
            copyfile(self.vocab_file, out_vocab_file)

        return (out_vocab_file,)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262

3、modeling_pegasus.py

# coding=utf-8
# Copyright 2021, Google and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch PEGASUS model. """

import copy
import math
import random
from typing import Optional, Tuple

import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss

from transformers.activations import ACT2FN
from transformers.file_utils import (
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    replace_return_docstrings,
)
from transformers.modeling_outputs import (
    BaseModelOutput,
    BaseModelOutputWithPastAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
    Seq2SeqLMOutput,
    Seq2SeqModelOutput,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging
from .configuration_pegasus import PegasusConfig


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "PegasusConfig"
_TOKENIZER_FOR_DOC = "PegasusTokenizer"


PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "google/pegasus-large",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
]


# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
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

    assert pad_token_id is not None, "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


# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):
    """
    Make causal mask used for bi-directional self-attention.
    """
    bsz, tgt_len = input_ids_shape
    mask = torch.full((tgt_len, tgt_len), float("-inf"))
    mask_cond = torch.arange(mask.size(-1))
    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
    mask = mask.to(dtype)

    if past_key_values_length > 0:
        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1)
    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)


# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
    """
    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
    """
    bsz, src_len = mask.size()
    tgt_len = tgt_len if tgt_len is not None else src_len

    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

    inverted_mask = 1.0 - expanded_mask

    return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min)


# Copied from transformers.models.marian.modeling_marian.MarianSinusoidalPositionalEmbedding with Marian->Pegasus
class PegasusSinusoidalPositionalEmbedding(nn.Embedding):
    """This module produces sinusoidal positional embeddings of any length."""

    def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
        super().__init__(num_positions, embedding_dim)
        self.weight = self._init_weight(self.weight)

    @staticmethod
    def _init_weight(out: nn.Parameter):
        """
        Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
        the 2nd half of the vector. [dim // 2:]
        """
        n_pos, dim = out.shape
        position_enc = np.array(
            [[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)]
        )
        out.requires_grad = False  # set early to avoid an error in pytorch-1.8+
        sentinel = dim // 2 if dim % 2 == 0 else (dim // 2) + 1
        out[:, 0:sentinel] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
        out[:, sentinel:] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
        out.detach_()
        return out

    @torch.no_grad()
    def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
        """`input_ids_shape` is expected to be [bsz x seqlen]."""
        bsz, seq_len = input_ids_shape[:2]
        positions = torch.arange(
            past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
        )
        return super().forward(positions)


# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->Pegasus
class PegasusAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        dropout: float = 0.0,
        is_decoder: bool = False,
        bias: bool = True,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        assert (
            self.head_dim * num_heads == self.embed_dim
        ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {num_heads})."
        self.scaling = self.head_dim ** -0.5
        self.is_decoder = is_decoder

        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)

    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def forward(
        self,
        hidden_states: torch.Tensor,
        key_value_states: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        """Input shape: Batch x Time x Channel"""

        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None
        bsz, tgt_len, embed_dim = hidden_states.size()

        # get query proj
        query_states = self.q_proj(hidden_states) * self.scaling
        # get key, value proj
        if is_cross_attention and past_key_value is not None:
            # reuse k,v, cross_attentions
            key_states = past_key_value[0]
            value_states = past_key_value[1]
        elif is_cross_attention:
            # cross_attentions
            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
        elif past_key_value is not None:
            # reuse k, v, self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)
        else:
            # self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)

        if self.is_decoder:
            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_states, value_states)

        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
        key_states = key_states.view(*proj_shape)
        value_states = value_states.view(*proj_shape)

        src_len = key_states.size(1)
        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))

        assert attn_weights.size() == (
            bsz * self.num_heads,
            tgt_len,
            src_len,
        ), f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"

        if attention_mask is not None:
            assert attention_mask.size() == (
                bsz,
                1,
                tgt_len,
                src_len,
            ), f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

        attn_weights = F.softmax(attn_weights, dim=-1)

        if layer_head_mask is not None:
            assert layer_head_mask.size() == (
                self.num_heads,
            ), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

        if output_attentions:
            # this operation is a bit akward, but it's required to
            # make sure that attn_weights keeps its gradient.
            # In order to do so, attn_weights have to reshaped
            # twice and have to be reused in the following
            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
        else:
            attn_weights_reshaped = None

        attn_probs = F.dropout(attn_weights, p=self.dropout, training=self.training)

        attn_output = torch.bmm(attn_probs, value_states)

        assert attn_output.size() == (
            bsz * self.num_heads,
            tgt_len,
            self.head_dim,
        ), f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"

        attn_output = (
            attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
            .transpose(1, 2)
            .reshape(bsz, tgt_len, embed_dim)
        )

        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights_reshaped, past_key_value


# Copied from transformers.models.mbart.modeling_mbart.MBartEncoderLayer with MBart->Pegasus
class PegasusEncoderLayer(nn.Module):
    def __init__(self, config: PegasusConfig):
        super().__init__()
        self.embed_dim = config.d_model
        self.self_attn = PegasusAttention(
            embed_dim=self.embed_dim,
            num_heads=config.encoder_attention_heads,
            dropout=config.attention_dropout,
        )
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout
        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        layer_head_mask: torch.Tensor,
        output_attentions: bool = False,
    ):
        """
        Args:
            hidden_states (:obj:`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
            attention_mask (:obj:`torch.FloatTensor`): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            layer_head_mask (:obj:`torch.FloatTensor`): mask for attention heads in a given layer of size
                `(config.encoder_attention_heads,)`.
            output_attentions (:obj:`bool`, `optional`):
                Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
                returned tensors for more detail.
        """
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states, attn_weights, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            layer_head_mask=layer_head_mask,
            output_attentions=output_attentions,
        )
        hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = F.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states

        if hidden_states.dtype == torch.float16 and (
            torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
        ):
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs


# Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer with MBart->Pegasus
class PegasusDecoderLayer(nn.Module):
    def __init__(self, config: PegasusConfig):
        super().__init__()
        self.embed_dim = config.d_model

        self.self_attn = PegasusAttention(
            embed_dim=self.embed_dim,
            num_heads=config.decoder_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
        )
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout

        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.encoder_attn = PegasusAttention(
            self.embed_dim,
            config.decoder_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
        )
        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        encoder_layer_head_mask: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = True,
    ):
        """
        Args:
            hidden_states (:obj:`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
            attention_mask (:obj:`torch.FloatTensor`): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            encoder_hidden_states (:obj:`torch.FloatTensor`): cross attention input to the layer of shape `(seq_len, batch, embed_dim)`
            encoder_attention_mask (:obj:`torch.FloatTensor`): encoder attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            layer_head_mask (:obj:`torch.FloatTensor`): mask for attention heads in a given layer of size
                `(config.encoder_attention_heads,)`.
            encoder_layer_head_mask (:obj:`torch.FloatTensor`): mask for encoder attention heads in a given layer of
                size `(config.encoder_attention_heads,)`.
            past_key_value (:obj:`Tuple(torch.FloatTensor)`): cached past key and value projection states
            output_attentions (:obj:`bool`, `optional`):
                Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
                returned tensors for more detail.
        """
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

        # Self Attention
        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        # add present self-attn cache to positions 1,2 of present_key_value tuple
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            past_key_value=self_attn_past_key_value,
            attention_mask=attention_mask,
            layer_head_mask=layer_head_mask,
            output_attentions=output_attentions,
        )
        hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states

        # Cross-Attention Block
        cross_attn_present_key_value = None
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = hidden_states
            hidden_states = self.encoder_attn_layer_norm(hidden_states)

            # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
            hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
                hidden_states=hidden_states,
                key_value_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                layer_head_mask=layer_head_mask,
                past_key_value=cross_attn_past_key_value,
                output_attentions=output_attentions,
            )
            hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
            hidden_states = residual + hidden_states

            # add cross-attn to positions 3,4 of present_key_value tuple
            present_key_value = present_key_value + cross_attn_present_key_value

        # Fully Connected
        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = F.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights, cross_attn_weights)

        if use_cache:
            outputs += (present_key_value,)

        return outputs


class PegasusPreTrainedModel(PreTrainedModel):
    config_class = PegasusConfig
    base_model_prefix = "model"

    def _init_weights(self, module):
        std = self.config.init_std
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, PegasusSinusoidalPositionalEmbedding):
            pass
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

    @property
    def dummy_inputs(self):
        pad_token = self.config.pad_token_id
        input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
        dummy_inputs = {
            "attention_mask": input_ids.ne(pad_token),
            "input_ids": input_ids,
            "decoder_input_ids": input_ids,
        }
        return dummy_inputs


PEGASUS_START_DOCSTRING = r"""
    This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
    methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
    pruning heads etc.)

    This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
    subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
    general usage and behavior.

    Parameters:
        config (:class:`~transformers.PegasusConfig`):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""

PEGASUS_GENERATION_EXAMPLE = r"""
    Summarization example::

        >>> from transformers import PegasusTokenizer, PegasusForConditionalGeneration

        >>> model = PegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum')
        >>> tokenizer = PegasusTokenizer.from_pretrained('google/pegasus-xsum')

        >>> ARTICLE_TO_SUMMARIZE = (
        ... "PG&E stated it scheduled the blackouts in response to forecasts for high winds "
        ... "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were "
        ... "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."
        ... )
        >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')

        >>> # Generate Summary
        >>> summary_ids = model.generate(inputs['input_ids'])
        >>> print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
"""

PEGASUS_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

            Indices can be obtained using :class:`~transformers.PegasusTokenizer`. See
            :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
            details.

            `What are input IDs? <../glossary.html#input-ids>`__
        attention_mask (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            `What are attention masks? <../glossary.html#attention-mask>`__
        decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
            Indices of decoder input sequence tokens in the vocabulary.

            Indices can be obtained using :class:`~transformers.PegasusTokenizer`. See
            :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
            details.

            `What are input IDs? <../glossary.html#input-ids>`__

            Pegasus uses the :obj:`pad_token_id` as the starting token for :obj:`decoder_input_ids` generation. If
            :obj:`past_key_values` is used, optionally only the last :obj:`decoder_input_ids` have to be input (see
            :obj:`past_key_values`).
        decoder_attention_mask (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
            Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
            also be used by default.

            If you want to change padding behavior, you should read :func:`modeling_pegasus._prepare_decoder_inputs`
            and modify to your needs. See diagram 1 in `the paper <https://arxiv.org/abs/1910.13461>`__ for more
            information on the default strategy.
        head_mask (:obj:`torch.Tensor` of shape :obj:`(num_layers, num_heads)`, `optional`):
            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in ``[0, 1]``:

            - 1 indicates the head is **not masked**,
            - 0 indicates the heas is **masked**.

        decoder_head_mask (:obj:`torch.Tensor` of shape :obj:`(num_layers, num_heads)`, `optional`):
            Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in ``[0, 1]``:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        encoder_outputs (:obj:`tuple(tuple(torch.FloatTensor)`, `optional`):
            Tuple consists of (:obj:`last_hidden_state`, `optional`: :obj:`hidden_states`, `optional`:
            :obj:`attentions`) :obj:`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)`,
            `optional`) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
            cross-attention of the decoder.
        past_key_values (:obj:`Tuple[Tuple[torch.Tensor]]` of length :obj:`config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.

            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
            instead of all :obj:`decoder_input_ids`` of shape :obj:`(batch_size, sequence_length)`.
        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
            vectors than the model's internal embedding lookup matrix.
        decoder_inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, target_sequence_length, hidden_size)`, `optional`):
            Optionally, instead of passing :obj:`decoder_input_ids` you can choose to directly pass an embedded
            representation. If :obj:`past_key_values` is used, optionally only the last :obj:`decoder_inputs_embeds`
            have to be input (see :obj:`past_key_values`). This is useful if you want more control over how to convert
            :obj:`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.

            If :obj:`decoder_input_ids` and :obj:`decoder_inputs_embeds` are both unset, :obj:`decoder_inputs_embeds`
            takes the value of :obj:`inputs_embeds`.
        use_cache (:obj:`bool`, `optional`):
            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
            decoding (see :obj:`past_key_values`).
        output_attentions (:obj:`bool`, `optional`):
            Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
            tensors for more detail.
        output_hidden_states (:obj:`bool`, `optional`):
            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
            more detail.
        return_dict (:obj:`bool`, `optional`):
            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
"""


class PegasusEncoder(PegasusPreTrainedModel):
    """
    Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
    :class:`PegasusEncoderLayer`.

    Args:
        config: PegasusConfig
        embed_tokens (torch.nn.Embedding): output embedding
    """

    def __init__(self, config: PegasusConfig, embed_tokens: Optional[nn.Embedding] = None):
        super().__init__(config)

        self.dropout = config.dropout
        self.layerdrop = config.encoder_layerdrop

        embed_dim = config.d_model
        self.padding_idx = config.pad_token_id
        self.max_source_positions = config.max_position_embeddings
        self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0

        if embed_tokens is not None:
            self.embed_tokens = embed_tokens
        else:
            self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)

        self.embed_positions = PegasusSinusoidalPositionalEmbedding(
            config.max_position_embeddings,
            embed_dim,
            self.padding_idx,
        )
        self.layers = nn.ModuleList([PegasusEncoderLayer(config) for _ in range(config.encoder_layers)])
        self.layer_norm = nn.LayerNorm(config.d_model)

        self.init_weights()

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Args:
            input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                provide it.

                Indices can be obtained using :class:`~transformers.PegasusTokenizer`. See
                :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__`
                for details.

                `What are input IDs? <../glossary.html#input-ids>`__
            attention_mask (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.

                `What are attention masks? <../glossary.html#attention-mask>`__
            head_mask (:obj:`torch.Tensor` of shape :obj:`(num_layers, num_heads)`, `optional`):
                Mask to nullify selected heads of the attention modules. Mask values selected in ``[0, 1]``:

                - 1 indicates the head is **not masked**,
                - 0 indicates the heas is **masked**.

            inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
                Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded
                representation. This is useful if you want more control over how to convert :obj:`input_ids` indices
                into associated vectors than the model's internal embedding lookup matrix.
            output_attentions (:obj:`bool`, `optional`):
                Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
                returned tensors for more detail.
            output_hidden_states (:obj:`bool`, `optional`):
                Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors
                for more detail.
            return_dict (:obj:`bool`, `optional`):
                Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale

        embed_pos = self.embed_positions(input_shape)

        hidden_states = inputs_embeds + embed_pos

        hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)

        # expand attention_mask
        if attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)

        encoder_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        # check if head_mask has a correct number of layers specified if desired
        if head_mask is not None:
            assert head_mask.size()[0] == (
                len(self.layers)
            ), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
        for idx, encoder_layer in enumerate(self.layers):
            if output_hidden_states:
                encoder_states = encoder_states + (hidden_states,)
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            dropout_probability = random.uniform(0, 1)
            if self.training and (dropout_probability < self.layerdrop):  # skip the layer
                layer_outputs = (None, None)
            else:
                if getattr(self.config, "gradient_checkpointing", False) and self.training:

                    def create_custom_forward(module):
                        def custom_forward(*inputs):
                            return module(*inputs, output_attentions)

                        return custom_forward

                    layer_outputs = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(encoder_layer),
                        hidden_states,
                        attention_mask,
                        (head_mask[idx] if head_mask is not None else None),
                    )
                else:
                    layer_outputs = encoder_layer(
                        hidden_states,
                        attention_mask,
                        layer_head_mask=(head_mask[idx] if head_mask is not None else None),
                        output_attentions=output_attentions,
                    )

                hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        hidden_states = self.layer_norm(hidden_states)

        if output_hidden_states:
            encoder_states = encoder_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
        )


class PegasusDecoder(PegasusPreTrainedModel):
    """
    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a :class:`PegasusDecoderLayer`

    Args:
        config: PegasusConfig
        embed_tokens (torch.nn.Embedding): output embedding
    """

    def __init__(self, config: PegasusConfig, embed_tokens: Optional[nn.Embedding] = None):
        super().__init__(config)
        self.dropout = config.dropout
        self.layerdrop = config.decoder_layerdrop
        self.padding_idx = config.pad_token_id
        self.max_target_positions = config.max_position_embeddings
        self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0

        if embed_tokens is not None:
            self.embed_tokens = embed_tokens
        else:
            self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)

        self.embed_positions = PegasusSinusoidalPositionalEmbedding(
            config.max_position_embeddings,
            config.d_model,
            self.padding_idx,
        )
        self.layers = nn.ModuleList([PegasusDecoderLayer(config) for _ in range(config.decoder_layers)])
        self.layer_norm = nn.LayerNorm(config.d_model)

        self.init_weights()

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
        # create causal mask
        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
        combined_attention_mask = None
        if input_shape[-1] > 1:
            combined_attention_mask = _make_causal_mask(
                input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length
            ).to(self.device)

        if attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
            combined_attention_mask = (
                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
            )

        return combined_attention_mask

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        head_mask=None,
        encoder_head_mask=None,
        past_key_values=None,
        inputs_embeds=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Args:
            input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                provide it.

                Indices can be obtained using :class:`~transformers.PegasusTokenizer`. See
                :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__`
                for details.

                `What are input IDs? <../glossary.html#input-ids>`__
            attention_mask (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.

                `What are attention masks? <../glossary.html#attention-mask>`__
            encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, encoder_sequence_length, hidden_size)`, `optional`):
                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
                of the decoder.
            encoder_attention_mask (:obj:`torch.LongTensor` of shape :obj:`(batch_size, encoder_sequence_length)`, `optional`):
                Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
                selected in ``[0, 1]``:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.

                `What are attention masks? <../glossary.html#attention-mask>`__
            head_mask (:obj:`torch.Tensor` of shape :obj:`(num_layers, num_heads)`, `optional`):
                Mask to nullify selected heads of the attention modules. Mask values selected in ``[0, 1]``:

                - 1 indicates the head is **not masked**,
                - 0 indicates the heas is **masked**.

            encoder_head_mask (:obj:`torch.Tensor` of shape :obj:`(num_layers, num_heads)`, `optional`):
                Mask to nullify selected heads of the attention modules in encoder to avoid performing cross-attention
                on hidden heads. Mask values selected in ``[0, 1]``:

                - 1 indicates the head is **not masked**,
                - 0 indicates the heas is **masked**.

            past_key_values (:obj:`Tuple[Tuple[torch.Tensor]]` of length :obj:`config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
                Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up
                decoding.

                If :obj:`past_key_values` are used, the user can optionally input only the last
                :obj:`decoder_input_ids` (those that don't have their past key value states given to this model) of
                shape :obj:`(batch_size, 1)` instead of all :obj:`decoder_input_ids`` of shape :obj:`(batch_size,
                sequence_length)`.
            inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
                Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded
                representation. This is useful if you want more control over how to convert :obj:`input_ids` indices
                into associated vectors than the model's internal embedding lookup matrix.
            output_attentions (:obj:`bool`, `optional`):
                Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
                returned tensors for more detail.
            output_hidden_states (:obj:`bool`, `optional`):
                Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors
                for more detail.
            return_dict (:obj:`bool`, `optional`):
                Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

        # past_key_values_length
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale

        attention_mask = self._prepare_decoder_attention_mask(
            attention_mask, input_shape, inputs_embeds, past_key_values_length
        )

        # expand encoder attention mask
        if encoder_hidden_states is not None and encoder_attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])

        # embed positions
        positions = self.embed_positions(input_shape, past_key_values_length)

        hidden_states = inputs_embeds + positions

        hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
        next_decoder_cache = () if use_cache else None

        # check if head_mask has a correct number of layers specified if desired
        if head_mask is not None:
            assert head_mask.size()[0] == (
                len(self.layers)
            ), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
        for idx, decoder_layer in enumerate(self.layers):
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            if output_hidden_states:
                all_hidden_states += (hidden_states,)
            dropout_probability = random.uniform(0, 1)
            if self.training and (dropout_probability < self.layerdrop):
                continue

            past_key_value = past_key_values[idx] if past_key_values is not None else None

            if getattr(self.config, "gradient_checkpointing", False) and self.training:

                if use_cache:
                    logger.warn(
                        "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
                        "`use_cache=False`..."
                    )
                    use_cache = False

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(*inputs, output_attentions, use_cache)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    attention_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    head_mask[idx] if head_mask is not None else None,
                    encoder_head_mask[idx] if encoder_head_mask is not None else None,
                    None,
                )
            else:

                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=encoder_attention_mask,
                    layer_head_mask=(head_mask[idx] if head_mask is not None else None),
                    encoder_layer_head_mask=(encoder_head_mask[idx] if encoder_head_mask is not None else None),
                    past_key_value=past_key_value,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                )
            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

                if encoder_hidden_states is not None:
                    all_cross_attentions += (layer_outputs[2],)

        hidden_states = self.layer_norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if not return_dict:
            return tuple(
                v
                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
                if v is not None
            )
        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            cross_attentions=all_cross_attentions,
        )


@add_start_docstrings(
    "The bare PEGASUS Model outputting raw hidden-states without any specific head on top.",
    PEGASUS_START_DOCSTRING,
)
class PegasusModel(PegasusPreTrainedModel):
    def __init__(self, config: PegasusConfig):
        super().__init__(config)

        padding_idx, vocab_size = config.pad_token_id, config.vocab_size
        self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)

        self.encoder = PegasusEncoder(config, self.shared)
        self.decoder = PegasusDecoder(config, self.shared)

        self.init_weights()

    def get_input_embeddings(self):
        return self.shared

    def set_input_embeddings(self, value):
        self.shared = value
        self.encoder.embed_tokens = self.shared
        self.decoder.embed_tokens = self.shared

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    @add_start_docstrings_to_model_forward(PEGASUS_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        decoder_input_ids=None,
        decoder_attention_mask=None,
        head_mask=None,
        decoder_head_mask=None,
        encoder_outputs=None,
        past_key_values=None,
        inputs_embeds=None,
        decoder_inputs_embeds=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Returns:

        Example::

            >>> from transformers import PegasusTokenizer, PegasusModel

            >>> tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-large")
            >>> model = PegasusModel.from_pretrained("google/pegasus-large")

            >>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt").input_ids  # Batch size 1
            >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
            >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)

            >>> last_hidden_states = outputs.last_hidden_state
        """

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                head_mask=head_mask,
                inputs_embeds=inputs_embeds,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )
        # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
            encoder_outputs = BaseModelOutput(
                last_hidden_state=encoder_outputs[0],
                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
            )

        # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=encoder_outputs[0],
            encoder_attention_mask=attention_mask,
            head_mask=decoder_head_mask,
            encoder_head_mask=head_mask,
            past_key_values=past_key_values,
            inputs_embeds=decoder_inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if not return_dict:
            return decoder_outputs + encoder_outputs

        return Seq2SeqModelOutput(
            last_hidden_state=decoder_outputs.last_hidden_state,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )


@add_start_docstrings(
    "The PEGASUS Model with a language modeling head. Can be used for summarization.", PEGASUS_START_DOCSTRING
)
class PegasusForConditionalGeneration(PegasusPreTrainedModel):
    base_model_prefix = "model"
    _keys_to_ignore_on_load_missing = [
        r"final_logits_bias",
        r"encoder\.version",
        r"decoder\.version",
        r"lm_head\.weight",
        r"embed_positions\.weight",
    ]

    def __init__(self, config: PegasusConfig):
        super().__init__(config)
        self.model = PegasusModel(config)
        self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
        self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)

        self.init_weights()

    def get_encoder(self):
        return self.model.get_encoder()

    def get_decoder(self):
        return self.model.get_decoder()

    def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
        new_embeddings = super().resize_token_embeddings(new_num_tokens)
        self._resize_final_logits_bias(new_num_tokens)
        return new_embeddings

    def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
        old_num_tokens = self.final_logits_bias.shape[-1]
        if new_num_tokens <= old_num_tokens:
            new_bias = self.final_logits_bias[:, :new_num_tokens]
        else:
            extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
            new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
        self.register_buffer("final_logits_bias", new_bias)

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    @add_start_docstrings_to_model_forward(PEGASUS_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
    @add_end_docstrings(PEGASUS_GENERATION_EXAMPLE)
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        decoder_input_ids=None,
        decoder_attention_mask=None,
        head_mask=None,
        decoder_head_mask=None,
        encoder_outputs=None,
        past_key_values=None,
        inputs_embeds=None,
        decoder_inputs_embeds=None,
        labels=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for computing the masked language modeling loss. Indices should either be in ``[0, ...,
            config.vocab_size]`` or -100 (see ``input_ids`` docstring). Tokens with indices set to ``-100`` are ignored
            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``.

        Returns:

        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if labels is not None:
            if decoder_input_ids is None:
                decoder_input_ids = shift_tokens_right(
                    labels, self.config.pad_token_id, self.config.decoder_start_token_id
                )

        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            decoder_input_ids=decoder_input_ids,
            encoder_outputs=encoder_outputs,
            decoder_attention_mask=decoder_attention_mask,
            head_mask=head_mask,
            decoder_head_mask=decoder_head_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            decoder_inputs_embeds=decoder_inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias

        masked_lm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))

        if not return_dict:
            output = (lm_logits,) + outputs[1:]
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output

        return Seq2SeqLMOutput(
            loss=masked_lm_loss,
            logits=lm_logits,
            past_key_values=outputs.past_key_values,
            decoder_hidden_states=outputs.decoder_hidden_states,
            decoder_attentions=outputs.decoder_attentions,
            cross_attentions=outputs.cross_attentions,
            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
            encoder_hidden_states=outputs.encoder_hidden_states,
            encoder_attentions=outputs.encoder_attentions,
        )

    def prepare_inputs_for_generation(
        self,
        decoder_input_ids,
        past=None,
        attention_mask=None,
        head_mask=None,
        use_cache=None,
        encoder_outputs=None,
        **kwargs
    ):
        # cut decoder_input_ids if past is used
        if past is not None:
            decoder_input_ids = decoder_input_ids[:, -1:]

        return {
            "input_ids": None,  # encoder_outputs is defined. input_ids not needed
            "encoder_outputs": encoder_outputs,
            "past_key_values": past,
            "decoder_input_ids": decoder_input_ids,
            "attention_mask": attention_mask,
            "head_mask": head_mask,
            "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
        }

    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
        return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)

    @staticmethod
    def _reorder_cache(past, beam_idx):
        reordered_past = ()
        for layer_past in past:
            # cached cross_attention states don't have to be reordered -> they are always the same
            reordered_past += (
                tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
            )
        return reordered_past


# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->Pegasus
class PegasusDecoderWrapper(PegasusPreTrainedModel):
    """
    This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
    used in combination with the :class:`~transformers.EncoderDecoderModel` framework.
    """

    def __init__(self, config):
        super().__init__(config)
        self.decoder = PegasusDecoder(config)

    def forward(self, *args, **kwargs):
        return self.decoder(*args, **kwargs)


# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->Pegasus
class PegasusForCausalLM(PegasusPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        config = copy.deepcopy(config)
        config.is_decoder = True
        config.is_encoder_decoder = False
        self.model = PegasusDecoderWrapper(config)

        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.init_weights()

    def get_input_embeddings(self):
        return self.model.decoder.embed_tokens

    def set_input_embeddings(self, value):
        self.model.decoder.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model.decoder = decoder

    def get_decoder(self):
        return self.model.decoder

    @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        head_mask=None,
        encoder_head_mask=None,
        past_key_values=None,
        inputs_embeds=None,
        labels=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Args:
            input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                provide it.

                Indices can be obtained using :class:`~transformers.PegasusTokenizer`. See
                :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__`
                for details.

                `What are input IDs? <../glossary.html#input-ids>`__
            attention_mask (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.

                `What are attention masks? <../glossary.html#attention-mask>`__
            encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
                if the model is configured as a decoder.
            encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
                in the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
            head_mask (:obj:`torch.Tensor` of shape :obj:`(num_layers, num_heads)`, `optional`):
                Mask to nullify selected heads of the attention modules. Mask values selected in ``[0, 1]``:

                - 1 indicates the head is **not masked**,
                - 0 indicates the heas is **masked**.

            encoder_head_mask (:obj:`torch.Tensor` of shape :obj:`(num_layers, num_heads)`, `optional`):
                Mask to nullify selected heads of the attention modules in encoder to avoid performing cross-attention
                on hidden heads. Mask values selected in ``[0, 1]``:

                - 1 indicates the head is **not masked**,
                - 0 indicates the heas is **masked**.

            past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
                Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up
                decoding.

                If :obj:`past_key_values` are used, the user can optionally input only the last ``decoder_input_ids``
                (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
                instead of all ``decoder_input_ids`` of shape :obj:`(batch_size, sequence_length)`.
            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                Labels for computing the masked language modeling loss. Indices should either be in ``[0, ...,
                config.vocab_size]`` or -100 (see ``input_ids`` docstring). Tokens with indices set to ``-100`` are
                ignored (masked), the loss is only computed for the tokens with labels in ``[0, ...,
                config.vocab_size]``.
            use_cache (:obj:`bool`, `optional`):
                If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
                decoding (see :obj:`past_key_values`).

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.
            output_attentions (:obj:`bool`, `optional`):
                Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
                returned tensors for more detail.
            output_hidden_states (:obj:`bool`, `optional`):
                Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors
                for more detail.
            return_dict (:obj:`bool`, `optional`):
                Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.

        Returns:

        Example::

            >>> from transformers import PegasusTokenizer, PegasusForCausalLM

            >>> tokenizer = PegasusTokenizer.from_pretrained('facebook/bart-large')
            >>> model = PegasusForCausalLM.from_pretrained('facebook/bart-large', add_cross_attention=False)
            >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
            >>> outputs = model(**inputs)

            >>> last_hidden_states = outputs.last_hidden_state
        """

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model.decoder(
            input_ids=input_ids,
            attention_mask=attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            head_mask=head_mask,
            encoder_head_mask=encoder_head_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        logits = self.lm_head(outputs[0])

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithCrossAttentions(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )

    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, use_cache=None, **kwargs):
        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
        if attention_mask is None:
            attention_mask = input_ids.new_ones(input_ids.shape)

        if past:
            input_ids = input_ids[:, -1:]
        # first step, decoder_cached_states are empty
        return {
            "input_ids": input_ids,  # encoder_outputs is defined. input_ids not needed
            "attention_mask": attention_mask,
            "past_key_values": past,
            "use_cache": use_cache,
        }

    @staticmethod
    def _reorder_cache(past, beam_idx):
        reordered_past = ()
        for layer_past in past:
            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
        return reordered_past

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548

五、直接使用Pegasus预训练模型

1、pegasus-cnn_dailymail预训练模型

1.1 方式01：from transformers import PegasusTokenizer, PegasusForConditionalGeneration

from transformers import PegasusTokenizer, PegasusForConditionalGeneration

tokenizer = PegasusTokenizer.from_pretrained(r'D:\Pretrained_model\pegasus-cnn_dailymail')
model = model = PegasusForConditionalGeneration.from_pretrained(r'D:\Pretrained_model\pegasus-cnn_dailymail')

text = """
         (CNN)For the second time during his papacy, Pope Francis has announced a new group of bishops and archbishops set to become cardinals -- and they come from all over the world.
        Pope Francis said Sunday that he would hold a meeting of cardinals on February 14 "during which I will name 15 new Cardinals who, coming from 13 countries from every continent, manifest the indissoluble links between the Church of Rome and the particular Churches present in the world," according to Vatican Radio.
        New cardinals are always important because they set the tone in the church and also elect the next pope, CNN Senior Vatican Analyst John L. Allen said. They are sometimes referred to as the princes of the Catholic Church.
        The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar.
        "This is a pope who very much wants to reach out to people on the margins, and you clearly see that in this set," Allen said. "You're talking about cardinals from typically overlooked places, like Cape Verde, the Pacific island of Tonga, Panama, Thailand, Uruguay."
        But for the second time since Francis' election, no Americans made the list.
        "Francis' pattern is very clear: He wants to go to the geographical peripheries rather than places that are already top-heavy with cardinals," Allen said.
        Christopher Bellitto, a professor of church history at Kean University in New Jersey, noted that Francis announced his new slate of cardinals on the Catholic Feast of the Epiphany, which commemorates the visit of the Magi to Jesus' birthplace in Bethlehem.
        "On feast of three wise men from far away, the Pope's choices for cardinal say that every local church deserves a place at the big table."
        In other words, Francis wants a more decentralized church and wants to hear reform ideas from small communities that sit far from Catholicism's power centers, Bellitto said.
        That doesn't mean Francis is the first pontiff to appoint cardinals from the developing world, though. Beginning in the 1920s, an increasing number of Latin American churchmen were named cardinals, and in the 1960s, St. John XXIII, whom Francis canonized last year, appointed the first cardinals from Japan, the Philippines and Africa.
        In addition to the 15 new cardinals Francis named on Sunday, five retired archbishops and bishops will also be honored as cardinals.
        Last year, Pope Francis appointed 19 new cardinals, including bishops from Haiti and Burkina Faso.
        CNN's Daniel Burke and Christabelle Fombu contributed to this report.
"""
# CNN/DM答案：
# @highlight
# The 15 new cardinals will be installed on February 14
# @highlight
# They come from countries such as Myanmar and Tonga
# @highlight
# No Americans made the list this time or the previous time in Francis' papacy

inputs = tokenizer(text, max_length=1024, truncation=True, return_tensors='pt')

print('inputs = ', inputs)

summary_ids = model.generate(inputs['input_ids'])

print('\nsummary_ids = ', summary_ids)

print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39

打印结果：

inputs =  {'input_ids': tensor([[  143, 40155,   158,   581,   109,   453,   166,   333,   169, 95987,
           108, 11481,  7756,   148,  1487,   114,   177,   456,   113, 35712,
           111, 66941,   116,   323,   112,   460, 30726,   116,  1315,   111,
           157,   331,   135,   149,   204,   109,   278,   107, 11481,  7756,
           243,  1342,   120,   178,   192,  1137,   114,   988,   113, 30726,
           116,   124,  1538,  1265,   198, 35871,   162,   125,   138,   442,
           738,   177, 18345,   170,   108,   792,   135,  1428,  1105,   135,
           290, 10156,   108, 14451,   109,   115,  8597, 32478,  1784,   317,
           109,  1887,   113,  6807,   111,   109,   970, 24353,   799,   115,
           109,   278,   745,   992,   112, 20525,  4474,   107,   351, 30726,
           116,   127,   329,   356,   262,   157,   323,   109,  4104,   115,
           109,  1588,   111,   163, 14094,   109,   352, 32577,   108, 11869,
          4244, 20525, 18672,  1084,  1054,   107,  6611,   243,   107,   322,
           127,  1254,  3795,   112,   130,   109, 54407,   113,   109,  4569,
          1887,   107,   139,   177, 30726,   116,   331,   135,  1105,   253,
           130, 16958,   108,   351,  3571,   111, 14838,   107,   198,   287,
           117,   114, 32577,   170,   221,   249,  1728,   112,  1111,   165,
           112,   200,   124,   109, 11691,   108,   111,   119,  2312,   236,
           120,   115,   136,   323,   745,  6611,   243,   107,   198,   417,
           131,   216,  1767,   160, 30726,   116,   135,  2222, 10912,  1262,
           108,   172,  5365, 23288,   108,   109,  3755,  2273,   113, 43439,
           108, 14668,   108,  6398,   108, 32671,   496,   343,   118,   109,
           453,   166,   381,  7756,   131,  2974,   108,   220,  3361,   266,
           109,   467,   107,   198, 59883,   131,  2293,   117,   221,   786,
           151,   285,  1728,   112,   275,   112,   109, 12483, 26941, 30713,
          3317,   880,   197,  1262,   120,   127,   506,   349,   121, 22564,
           122, 30726,   116,   745,  6611,   243,   107,  8751,  5706,  1418,
           497,   108,   114,  4609,   113,  1588,   689,   134, 69328,   502,
           115,   351,  3477,   108,  3151,   120,  7756,  1487,   169,   177,
         11598,   113, 30726,   116,   124,   109,  4569, 26717,   113,   109,
         60574,   108,   162, 56784,   109,   558,   113,   109, 33806,   112,
          1694,   131, 25910,   115, 26163,   107,   198,  1189, 11733,   113,
           339,  5509,  1024,   135,   571,   429,   108,   109, 11481,   131,
           116,  2257,   118, 30726,   416,   120,   290,   391,  1588,  8068,
           114,   295,   134,   109,   461,   826,   496,   222,   176,   989,
           108,  7756,  1728,   114,   154, 24500,  1588,   111,  1728,   112,
          1232,  6243,   675,   135,   360,  1724,   120,  2051,   571,   135,
         52403,   131,   116,   484,  3853,   108,  5706,  1418,   497,   243,
           107,   485,   591,   131,   144,  1021,  7756,   117,   109,   211,
           110, 39619, 18827,   112, 17717, 30726,   116,   135,   109,  1690,
           278,   108,   577,   107, 16591,   115,   109,  8821,   116,   108,
           142,  2186,   344,   113,  5249,   655,  1588,  3635,   195,  1729,
         30726,   116,   108,   111,   115,   109,  6939,   116,   108,   873,
           107,  1084, 61939, 12964,   108,  2901,  7756, 24828,  3792,   289,
           232,   108,  4486,   109,   211, 30726,   116,   135,  2466,   108,
           109,  6802,   111,  1922,   107,   222,   663,   112,   109,   738,
           177, 30726,   116,  7756,  1729,   124,  1342,   108,   668,  5774,
         66941,   116,   111, 35712,   138,   163,   129,  7051,   130, 30726,
           116,   107,  2882,   232,   108, 11481,  7756,  4486,  1925,   177,
         30726,   116,   108,   330, 35712,   135, 17256,   111, 58499, 55600,
           107, 11869,   131,   116,  4767, 18834,   111,  2333, 65534, 15391,
         28929,  5674,   112,   136,   731,   107,     1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}

summary_ids =  tensor([[    0,   139,   177, 30726,   116,   331,   135,  1105,   253,   130,
         16958,   108,   351,  3571,   111, 14838,   110,   107,   106,  1667,
          3361,   266,   109,   467,   118,   109,   453,   166,   381,  7756,
           131,  2974,   110,   107,     1]])

["The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar .<n>No Americans made the list for the second time since Francis' election ."]
["The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar .<n>No Americans made the list for the second time since Francis' election ."]

Process finished with exit code 0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83

1.2 方式02：from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained(r'D:\Pretrained_model\pegasus-cnn_dailymail')
model = AutoModelForSeq2SeqLM.from_pretrained(r'D:\Pretrained_model\pegasus-cnn_dailymail')

text = """
         (CNN)For the second time during his papacy, Pope Francis has announced a new group of bishops and archbishops set to become cardinals -- and they come from all over the world.
        Pope Francis said Sunday that he would hold a meeting of cardinals on February 14 "during which I will name 15 new Cardinals who, coming from 13 countries from every continent, manifest the indissoluble links between the Church of Rome and the particular Churches present in the world," according to Vatican Radio.
        New cardinals are always important because they set the tone in the church and also elect the next pope, CNN Senior Vatican Analyst John L. Allen said. They are sometimes referred to as the princes of the Catholic Church.
        The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar.
        "This is a pope who very much wants to reach out to people on the margins, and you clearly see that in this set," Allen said. "You're talking about cardinals from typically overlooked places, like Cape Verde, the Pacific island of Tonga, Panama, Thailand, Uruguay."
        But for the second time since Francis' election, no Americans made the list.
        "Francis' pattern is very clear: He wants to go to the geographical peripheries rather than places that are already top-heavy with cardinals," Allen said.
        Christopher Bellitto, a professor of church history at Kean University in New Jersey, noted that Francis announced his new slate of cardinals on the Catholic Feast of the Epiphany, which commemorates the visit of the Magi to Jesus' birthplace in Bethlehem.
        "On feast of three wise men from far away, the Pope's choices for cardinal say that every local church deserves a place at the big table."
        In other words, Francis wants a more decentralized church and wants to hear reform ideas from small communities that sit far from Catholicism's power centers, Bellitto said.
        That doesn't mean Francis is the first pontiff to appoint cardinals from the developing world, though. Beginning in the 1920s, an increasing number of Latin American churchmen were named cardinals, and in the 1960s, St. John XXIII, whom Francis canonized last year, appointed the first cardinals from Japan, the Philippines and Africa.
        In addition to the 15 new cardinals Francis named on Sunday, five retired archbishops and bishops will also be honored as cardinals.
        Last year, Pope Francis appointed 19 new cardinals, including bishops from Haiti and Burkina Faso.
        CNN's Daniel Burke and Christabelle Fombu contributed to this report.
"""
# CNN/DM答案：
# @highlight
# The 15 new cardinals will be installed on February 14
# @highlight
# They come from countries such as Myanmar and Tonga
# @highlight
# No Americans made the list this time or the previous time in Francis' papacy

inputs = tokenizer.encode(text)
inputs = torch.tensor([inputs])

print('inputs = ', inputs)

summary_ids = model.generate(inputs)

print('\nsummary_ids = ', summary_ids)

print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41

打印结果：

inputs =  tensor([[  143, 40155,   158,   581,   109,   453,   166,   333,   169, 95987,
           108, 11481,  7756,   148,  1487,   114,   177,   456,   113, 35712,
           111, 66941,   116,   323,   112,   460, 30726,   116,  1315,   111,
           157,   331,   135,   149,   204,   109,   278,   107, 11481,  7756,
           243,  1342,   120,   178,   192,  1137,   114,   988,   113, 30726,
           116,   124,  1538,  1265,   198, 35871,   162,   125,   138,   442,
           738,   177, 18345,   170,   108,   792,   135,  1428,  1105,   135,
           290, 10156,   108, 14451,   109,   115,  8597, 32478,  1784,   317,
           109,  1887,   113,  6807,   111,   109,   970, 24353,   799,   115,
           109,   278,   745,   992,   112, 20525,  4474,   107,   351, 30726,
           116,   127,   329,   356,   262,   157,   323,   109,  4104,   115,
           109,  1588,   111,   163, 14094,   109,   352, 32577,   108, 11869,
          4244, 20525, 18672,  1084,  1054,   107,  6611,   243,   107,   322,
           127,  1254,  3795,   112,   130,   109, 54407,   113,   109,  4569,
          1887,   107,   139,   177, 30726,   116,   331,   135,  1105,   253,
           130, 16958,   108,   351,  3571,   111, 14838,   107,   198,   287,
           117,   114, 32577,   170,   221,   249,  1728,   112,  1111,   165,
           112,   200,   124,   109, 11691,   108,   111,   119,  2312,   236,
           120,   115,   136,   323,   745,  6611,   243,   107,   198,   417,
           131,   216,  1767,   160, 30726,   116,   135,  2222, 10912,  1262,
           108,   172,  5365, 23288,   108,   109,  3755,  2273,   113, 43439,
           108, 14668,   108,  6398,   108, 32671,   496,   343,   118,   109,
           453,   166,   381,  7756,   131,  2974,   108,   220,  3361,   266,
           109,   467,   107,   198, 59883,   131,  2293,   117,   221,   786,
           151,   285,  1728,   112,   275,   112,   109, 12483, 26941, 30713,
          3317,   880,   197,  1262,   120,   127,   506,   349,   121, 22564,
           122, 30726,   116,   745,  6611,   243,   107,  8751,  5706,  1418,
           497,   108,   114,  4609,   113,  1588,   689,   134, 69328,   502,
           115,   351,  3477,   108,  3151,   120,  7756,  1487,   169,   177,
         11598,   113, 30726,   116,   124,   109,  4569, 26717,   113,   109,
         60574,   108,   162, 56784,   109,   558,   113,   109, 33806,   112,
          1694,   131, 25910,   115, 26163,   107,   198,  1189, 11733,   113,
           339,  5509,  1024,   135,   571,   429,   108,   109, 11481,   131,
           116,  2257,   118, 30726,   416,   120,   290,   391,  1588,  8068,
           114,   295,   134,   109,   461,   826,   496,   222,   176,   989,
           108,  7756,  1728,   114,   154, 24500,  1588,   111,  1728,   112,
          1232,  6243,   675,   135,   360,  1724,   120,  2051,   571,   135,
         52403,   131,   116,   484,  3853,   108,  5706,  1418,   497,   243,
           107,   485,   591,   131,   144,  1021,  7756,   117,   109,   211,
           110, 39619, 18827,   112, 17717, 30726,   116,   135,   109,  1690,
           278,   108,   577,   107, 16591,   115,   109,  8821,   116,   108,
           142,  2186,   344,   113,  5249,   655,  1588,  3635,   195,  1729,
         30726,   116,   108,   111,   115,   109,  6939,   116,   108,   873,
           107,  1084, 61939, 12964,   108,  2901,  7756, 24828,  3792,   289,
           232,   108,  4486,   109,   211, 30726,   116,   135,  2466,   108,
           109,  6802,   111,  1922,   107,   222,   663,   112,   109,   738,
           177, 30726,   116,  7756,  1729,   124,  1342,   108,   668,  5774,
         66941,   116,   111, 35712,   138,   163,   129,  7051,   130, 30726,
           116,   107,  2882,   232,   108, 11481,  7756,  4486,  1925,   177,
         30726,   116,   108,   330, 35712,   135, 17256,   111, 58499, 55600,
           107, 11869,   131,   116,  4767, 18834,   111,  2333, 65534, 15391,
         28929,  5674,   112,   136,   731,   107,     1]])

summary_ids =  tensor([[    0,   139,   177, 30726,   116,   331,   135,  1105,   253,   130,
         16958,   108,   351,  3571,   111, 14838,   110,   107,   106,  1667,
          3361,   266,   109,   467,   118,   109,   453,   166,   381,  7756,
           131,  2974,   110,   107,     1]])

["The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar .<n>No Americans made the list for the second time since Francis' election ."]
["The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar .<n>No Americans made the list for the second time since Francis' election ."]

Process finished with exit code 0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62

1.3 方式03:使用自定义模型（根据源码修改）

from pegasus_source_whx.tokenization_pegasus import PegasusTokenizer
from pegasus_source_whx.modeling_pegasus import PegasusForConditionalGeneration

tokenizer = PegasusTokenizer.from_pretrained(r'D:\Pretrained_model\pegasus-cnn_dailymail')
model = model = PegasusForConditionalGeneration.from_pretrained(r'D:\Pretrained_model\pegasus-cnn_dailymail')

text = """
         (CNN)For the second time during his papacy, Pope Francis has announced a new group of bishops and archbishops set to become cardinals -- and they come from all over the world.
        Pope Francis said Sunday that he would hold a meeting of cardinals on February 14 "during which I will name 15 new Cardinals who, coming from 13 countries from every continent, manifest the indissoluble links between the Church of Rome and the particular Churches present in the world," according to Vatican Radio.
        New cardinals are always important because they set the tone in the church and also elect the next pope, CNN Senior Vatican Analyst John L. Allen said. They are sometimes referred to as the princes of the Catholic Church.
        The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar.
        "This is a pope who very much wants to reach out to people on the margins, and you clearly see that in this set," Allen said. "You're talking about cardinals from typically overlooked places, like Cape Verde, the Pacific island of Tonga, Panama, Thailand, Uruguay."
        But for the second time since Francis' election, no Americans made the list.
        "Francis' pattern is very clear: He wants to go to the geographical peripheries rather than places that are already top-heavy with cardinals," Allen said.
        Christopher Bellitto, a professor of church history at Kean University in New Jersey, noted that Francis announced his new slate of cardinals on the Catholic Feast of the Epiphany, which commemorates the visit of the Magi to Jesus' birthplace in Bethlehem.
        "On feast of three wise men from far away, the Pope's choices for cardinal say that every local church deserves a place at the big table."
        In other words, Francis wants a more decentralized church and wants to hear reform ideas from small communities that sit far from Catholicism's power centers, Bellitto said.
        That doesn't mean Francis is the first pontiff to appoint cardinals from the developing world, though. Beginning in the 1920s, an increasing number of Latin American churchmen were named cardinals, and in the 1960s, St. John XXIII, whom Francis canonized last year, appointed the first cardinals from Japan, the Philippines and Africa.
        In addition to the 15 new cardinals Francis named on Sunday, five retired archbishops and bishops will also be honored as cardinals.
        Last year, Pope Francis appointed 19 new cardinals, including bishops from Haiti and Burkina Faso.
        CNN's Daniel Burke and Christabelle Fombu contributed to this report.
"""
# CNN/DM答案：
# @highlight
# The 15 new cardinals will be installed on February 14
# @highlight
# They come from countries such as Myanmar and Tonga
# @highlight
# No Americans made the list this time or the previous time in Francis' papacy

inputs = tokenizer(text, max_length=1024, truncation=True, return_tensors='pt')

print('inputs = ', inputs)

summary_ids = model.generate(inputs['input_ids'])

print('\nsummary_ids = ', summary_ids)

print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40

打印结果：

inputs =  {'input_ids': tensor([[  143, 40155,   158,   581,   109,   453,   166,   333,   169, 95987,
           108, 11481,  7756,   148,  1487,   114,   177,   456,   113, 35712,
           111, 66941,   116,   323,   112,   460, 30726,   116,  1315,   111,
           157,   331,   135,   149,   204,   109,   278,   107, 11481,  7756,
           243,  1342,   120,   178,   192,  1137,   114,   988,   113, 30726,
           116,   124,  1538,  1265,   198, 35871,   162,   125,   138,   442,
           738,   177, 18345,   170,   108,   792,   135,  1428,  1105,   135,
           290, 10156,   108, 14451,   109,   115,  8597, 32478,  1784,   317,
           109,  1887,   113,  6807,   111,   109,   970, 24353,   799,   115,
           109,   278,   745,   992,   112, 20525,  4474,   107,   351, 30726,
           116,   127,   329,   356,   262,   157,   323,   109,  4104,   115,
           109,  1588,   111,   163, 14094,   109,   352, 32577,   108, 11869,
          4244, 20525, 18672,  1084,  1054,   107,  6611,   243,   107,   322,
           127,  1254,  3795,   112,   130,   109, 54407,   113,   109,  4569,
          1887,   107,   139,   177, 30726,   116,   331,   135,  1105,   253,
           130, 16958,   108,   351,  3571,   111, 14838,   107,   198,   287,
           117,   114, 32577,   170,   221,   249,  1728,   112,  1111,   165,
           112,   200,   124,   109, 11691,   108,   111,   119,  2312,   236,
           120,   115,   136,   323,   745,  6611,   243,   107,   198,   417,
           131,   216,  1767,   160, 30726,   116,   135,  2222, 10912,  1262,
           108,   172,  5365, 23288,   108,   109,  3755,  2273,   113, 43439,
           108, 14668,   108,  6398,   108, 32671,   496,   343,   118,   109,
           453,   166,   381,  7756,   131,  2974,   108,   220,  3361,   266,
           109,   467,   107,   198, 59883,   131,  2293,   117,   221,   786,
           151,   285,  1728,   112,   275,   112,   109, 12483, 26941, 30713,
          3317,   880,   197,  1262,   120,   127,   506,   349,   121, 22564,
           122, 30726,   116,   745,  6611,   243,   107,  8751,  5706,  1418,
           497,   108,   114,  4609,   113,  1588,   689,   134, 69328,   502,
           115,   351,  3477,   108,  3151,   120,  7756,  1487,   169,   177,
         11598,   113, 30726,   116,   124,   109,  4569, 26717,   113,   109,
         60574,   108,   162, 56784,   109,   558,   113,   109, 33806,   112,
          1694,   131, 25910,   115, 26163,   107,   198,  1189, 11733,   113,
           339,  5509,  1024,   135,   571,   429,   108,   109, 11481,   131,
           116,  2257,   118, 30726,   416,   120,   290,   391,  1588,  8068,
           114,   295,   134,   109,   461,   826,   496,   222,   176,   989,
           108,  7756,  1728,   114,   154, 24500,  1588,   111,  1728,   112,
          1232,  6243,   675,   135,   360,  1724,   120,  2051,   571,   135,
         52403,   131,   116,   484,  3853,   108,  5706,  1418,   497,   243,
           107,   485,   591,   131,   144,  1021,  7756,   117,   109,   211,
           110, 39619, 18827,   112, 17717, 30726,   116,   135,   109,  1690,
           278,   108,   577,   107, 16591,   115,   109,  8821,   116,   108,
           142,  2186,   344,   113,  5249,   655,  1588,  3635,   195,  1729,
         30726,   116,   108,   111,   115,   109,  6939,   116,   108,   873,
           107,  1084, 61939, 12964,   108,  2901,  7756, 24828,  3792,   289,
           232,   108,  4486,   109,   211, 30726,   116,   135,  2466,   108,
           109,  6802,   111,  1922,   107,   222,   663,   112,   109,   738,
           177, 30726,   116,  7756,  1729,   124,  1342,   108,   668,  5774,
         66941,   116,   111, 35712,   138,   163,   129,  7051,   130, 30726,
           116,   107,  2882,   232,   108, 11481,  7756,  4486,  1925,   177,
         30726,   116,   108,   330, 35712,   135, 17256,   111, 58499, 55600,
           107, 11869,   131,   116,  4767, 18834,   111,  2333, 65534, 15391,
         28929,  5674,   112,   136,   731,   107,     1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}

summary_ids =  tensor([[    0,   139,   177, 30726,   116,   331,   135,  1105,   253,   130,
         16958,   108,   351,  3571,   111, 14838,   110,   107,   106,  1667,
          3361,   266,   109,   467,   118,   109,   453,   166,   381,  7756,
           131,  2974,   110,   107,     1]])

["The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar .<n>No Americans made the list for the second time since Francis' election ."]
["The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar .<n>No Americans made the list for the second time since Francis' election ."]

Process finished with exit code 0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83

2、pegasus-large预训练模型

# https://github.com/huggingface/transformers/blob/master/src/transformers/models/pegasus/modeling_pegasus.py
from transformers import PegasusTokenizer, PegasusForConditionalGeneration


tokenizer = PegasusTokenizer.from_pretrained(r'D:\Pretrained_Model\pegasus-large')
model = PegasusForConditionalGeneration.from_pretrained(r'D:\Pretrained_Model\pegasus-large')

max_input_len = tokenizer.max_len_single_sentence
print("pegasus-large 模型---->最大输入长度为：", max_input_len)

vocab_size = len(tokenizer)
print("pegasus-large 模型---->词表大小为：", vocab_size)

text = """
         (CNN)For the second time during his papacy, Pope Francis has announced a new group of bishops and archbishops set to become cardinals -- and they come from all over the world.
        Pope Francis said Sunday that he would hold a meeting of cardinals on February 14 "during which I will name 15 new Cardinals who, coming from 13 countries from every continent, manifest the indissoluble links between the Church of Rome and the particular Churches present in the world," according to Vatican Radio.
        New cardinals are always important because they set the tone in the church and also elect the next pope, CNN Senior Vatican Analyst John L. Allen said. They are sometimes referred to as the princes of the Catholic Church.
        The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar.
        "This is a pope who very much wants to reach out to people on the margins, and you clearly see that in this set," Allen said. "You're talking about cardinals from typically overlooked places, like Cape Verde, the Pacific island of Tonga, Panama, Thailand, Uruguay."
        But for the second time since Francis' election, no Americans made the list.
        "Francis' pattern is very clear: He wants to go to the geographical peripheries rather than places that are already top-heavy with cardinals," Allen said.
        Christopher Bellitto, a professor of church history at Kean University in New Jersey, noted that Francis announced his new slate of cardinals on the Catholic Feast of the Epiphany, which commemorates the visit of the Magi to Jesus' birthplace in Bethlehem.
        "On feast of three wise men from far away, the Pope's choices for cardinal say that every local church deserves a place at the big table."
        In other words, Francis wants a more decentralized church and wants to hear reform ideas from small communities that sit far from Catholicism's power centers, Bellitto said.
        That doesn't mean Francis is the first pontiff to appoint cardinals from the developing world, though. Beginning in the 1920s, an increasing number of Latin American churchmen were named cardinals, and in the 1960s, St. John XXIII, whom Francis canonized last year, appointed the first cardinals from Japan, the Philippines and Africa.
        In addition to the 15 new cardinals Francis named on Sunday, five retired archbishops and bishops will also be honored as cardinals.
        Last year, Pope Francis appointed 19 new cardinals, including bishops from Haiti and Burkina Faso.
        CNN's Daniel Burke and Christabelle Fombu contributed to this report.
"""
# CNN/DM答案：
# @highlight
# The 15 new cardinals will be installed on February 14
# @highlight
# They come from countries such as Myanmar and Tonga
# @highlight
# No Americans made the list this time or the previous time in Francis' papacy

inputs = tokenizer(text, max_length=1024, truncation=True, return_tensors='pt')

print('inputs = ', inputs)

summary_ids = model.generate(inputs['input_ids'])

print('\nsummary_ids = ', summary_ids)

print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47

打印结果：

pegasus-large 模型---->最大输入长度为： 1023
pegasus-large 模型---->词表大小为： 96103
inputs =  {'input_ids': tensor([[  143, 40155,   158,   581,   109,   453,   166,   333,   169, 95987,
           108, 11481,  7756,   148,  1487,   114,   177,   456,   113, 35712,
           111, 66941,   116,   323,   112,   460, 30726,   116,  1315,   111,
           157,   331,   135,   149,   204,   109,   278,   107, 11481,  7756,
           243,  1342,   120,   178,   192,  1137,   114,   988,   113, 30726,
           116,   124,  1538,  1265,   198, 35871,   162,   125,   138,   442,
           738,   177, 18345,   170,   108,   792,   135,  1428,  1105,   135,
           290, 10156,   108, 14451,   109,   115,  8597, 32478,  1784,   317,
           109,  1887,   113,  6807,   111,   109,   970, 24353,   799,   115,
           109,   278,   745,   992,   112, 20525,  4474,   107,   351, 30726,
           116,   127,   329,   356,   262,   157,   323,   109,  4104,   115,
           109,  1588,   111,   163, 14094,   109,   352, 32577,   108, 11869,
          4244, 20525, 18672,  1084,  1054,   107,  6611,   243,   107,   322,
           127,  1254,  3795,   112,   130,   109, 54407,   113,   109,  4569,
          1887,   107,   139,   177, 30726,   116,   331,   135,  1105,   253,
           130, 16958,   108,   351,  3571,   111, 14838,   107,   198,   287,
           117,   114, 32577,   170,   221,   249,  1728,   112,  1111,   165,
           112,   200,   124,   109, 11691,   108,   111,   119,  2312,   236,
           120,   115,   136,   323,   745,  6611,   243,   107,   198,   417,
           131,   216,  1767,   160, 30726,   116,   135,  2222, 10912,  1262,
           108,   172,  5365, 23288,   108,   109,  3755,  2273,   113, 43439,
           108, 14668,   108,  6398,   108, 32671,   496,   343,   118,   109,
           453,   166,   381,  7756,   131,  2974,   108,   220,  3361,   266,
           109,   467,   107,   198, 59883,   131,  2293,   117,   221,   786,
           151,   285,  1728,   112,   275,   112,   109, 12483, 26941, 30713,
          3317,   880,   197,  1262,   120,   127,   506,   349,   121, 22564,
           122, 30726,   116,   745,  6611,   243,   107,  8751,  5706,  1418,
           497,   108,   114,  4609,   113,  1588,   689,   134, 69328,   502,
           115,   351,  3477,   108,  3151,   120,  7756,  1487,   169,   177,
         11598,   113, 30726,   116,   124,   109,  4569, 26717,   113,   109,
         60574,   108,   162, 56784,   109,   558,   113,   109, 33806,   112,
          1694,   131, 25910,   115, 26163,   107,   198,  1189, 11733,   113,
           339,  5509,  1024,   135,   571,   429,   108,   109, 11481,   131,
           116,  2257,   118, 30726,   416,   120,   290,   391,  1588,  8068,
           114,   295,   134,   109,   461,   826,   496,   222,   176,   989,
           108,  7756,  1728,   114,   154, 24500,  1588,   111,  1728,   112,
          1232,  6243,   675,   135,   360,  1724,   120,  2051,   571,   135,
         52403,   131,   116,   484,  3853,   108,  5706,  1418,   497,   243,
           107,   485,   591,   131,   144,  1021,  7756,   117,   109,   211,
           110, 39619, 18827,   112, 17717, 30726,   116,   135,   109,  1690,
           278,   108,   577,   107, 16591,   115,   109,  8821,   116,   108,
           142,  2186,   344,   113,  5249,   655,  1588,  3635,   195,  1729,
         30726,   116,   108,   111,   115,   109,  6939,   116,   108,   873,
           107,  1084, 61939, 12964,   108,  2901,  7756, 24828,  3792,   289,
           232,   108,  4486,   109,   211, 30726,   116,   135,  2466,   108,
           109,  6802,   111,  1922,   107,   222,   663,   112,   109,   738,
           177, 30726,   116,  7756,  1729,   124,  1342,   108,   668,  5774,
         66941,   116,   111, 35712,   138,   163,   129,  7051,   130, 30726,
           116,   107,  2882,   232,   108, 11481,  7756,  4486,  1925,   177,
         30726,   116,   108,   330, 35712,   135, 17256,   111, 58499, 55600,
           107, 11869,   131,   116,  4767, 18834,   111,  2333, 65534, 15391,
         28929,  5674,   112,   136,   731,   107,     1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}

summary_ids =  tensor([[    0,   143, 40155,   158,   581,   109,   453,   166,   333,   169,
         95987,   108, 11481,  7756,   148,  1487,   114,   177,   456,   113,
         35712,   111, 66941,   116,   323,   112,   460, 30726,   116,  1315,
           111,   157,   331,   135,   149,   204,   109,   278,   107, 11481,
          7756,   243,  1342,   120,   178,   192,  1137,   114,   988,   113,
         30726,   116,   124,  1538,  1265,   198, 35871,   162,   125,   138,
           442,   738,   177, 18345,   170,   108,   792,   135,  1428,  1105,
           135,   290, 10156,   108, 14451,   109,   115,  8597, 32478,  1784,
           317,   109,  1887,   113,  6807,   111,   109,   970, 24353,   799,
           115,   109,   278,   745,   992,   112, 20525,  4474,   107,     1]])

['(CNN)For the second time during his papacy, Pope Francis has announced a new group of bishops and archbishops set to become cardinals -- and they come from all over the world. Pope Francis said Sunday that he would hold a meeting of cardinals on February 14 "during which I will name 15 new Cardinals who, coming from 13 countries from every continent, manifest the indissoluble links between the Church of Rome and the particular Churches present in the world," according to Vatican Radio.']
['(CNN)For the second time during his papacy, Pope Francis has announced a new group of bishops and archbishops set to become cardinals -- and they come from all over the world. Pope Francis said Sunday that he would hold a meeting of cardinals on February 14 "during which I will name 15 new Cardinals who, coming from 13 countries from every continent, manifest the indissoluble links between the Church of Rome and the particular Churches present in the world," according to Vatican Radio.']

Process finished with exit code 0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91

设置预测最大长度

# https://github.com/huggingface/transformers/blob/master/src/transformers/models/pegasus/modeling_pegasus.py
from transformers import PegasusTokenizer, PegasusForConditionalGeneration


tokenizer = PegasusTokenizer.from_pretrained(r'D:\Pretrained_Model\pegasus-large')
model = PegasusForConditionalGeneration.from_pretrained(r'D:\Pretrained_Model\pegasus-large')

max_input_len = tokenizer.max_len_single_sentence
print("pegasus-large 模型---->最大输入长度为：", max_input_len)

vocab_size = len(tokenizer)
print("pegasus-large 模型---->词表大小为：", vocab_size)

text = """
         (CNN)For the second time during his papacy, Pope Francis has announced a new group of bishops and archbishops set to become cardinals -- and they come from all over the world.
        Pope Francis said Sunday that he would hold a meeting of cardinals on February 14 "during which I will name 15 new Cardinals who, coming from 13 countries from every continent, manifest the indissoluble links between the Church of Rome and the particular Churches present in the world," according to Vatican Radio.
        New cardinals are always important because they set the tone in the church and also elect the next pope, CNN Senior Vatican Analyst John L. Allen said. They are sometimes referred to as the princes of the Catholic Church.
        The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar.
        "This is a pope who very much wants to reach out to people on the margins, and you clearly see that in this set," Allen said. "You're talking about cardinals from typically overlooked places, like Cape Verde, the Pacific island of Tonga, Panama, Thailand, Uruguay."
        But for the second time since Francis' election, no Americans made the list.
        "Francis' pattern is very clear: He wants to go to the geographical peripheries rather than places that are already top-heavy with cardinals," Allen said.
        Christopher Bellitto, a professor of church history at Kean University in New Jersey, noted that Francis announced his new slate of cardinals on the Catholic Feast of the Epiphany, which commemorates the visit of the Magi to Jesus' birthplace in Bethlehem.
        "On feast of three wise men from far away, the Pope's choices for cardinal say that every local church deserves a place at the big table."
        In other words, Francis wants a more decentralized church and wants to hear reform ideas from small communities that sit far from Catholicism's power centers, Bellitto said.
        That doesn't mean Francis is the first pontiff to appoint cardinals from the developing world, though. Beginning in the 1920s, an increasing number of Latin American churchmen were named cardinals, and in the 1960s, St. John XXIII, whom Francis canonized last year, appointed the first cardinals from Japan, the Philippines and Africa.
        In addition to the 15 new cardinals Francis named on Sunday, five retired archbishops and bishops will also be honored as cardinals.
        Last year, Pope Francis appointed 19 new cardinals, including bishops from Haiti and Burkina Faso.
        CNN's Daniel Burke and Christabelle Fombu contributed to this report.
"""
# CNN/DM答案：
# @highlight
# The 15 new cardinals will be installed on February 14
# @highlight
# They come from countries such as Myanmar and Tonga
# @highlight
# No Americans made the list this time or the previous time in Francis' papacy

inputs = tokenizer(text, max_length=1024, truncation=True, return_tensors='pt')

print('inputs = ', inputs)

summary_ids = model.generate(inputs['input_ids'], max_length = 20)

print('\nsummary_ids = ', summary_ids)

print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47

打印结果：

pegasus-large 模型---->最大输入长度为： 1023
pegasus-large 模型---->词表大小为： 96103
inputs =  {'input_ids': tensor([[  143, 40155,   158,   581,   109,   453,   166,   333,   169, 95987,
           108, 11481,  7756,   148,  1487,   114,   177,   456,   113, 35712,
           111, 66941,   116,   323,   112,   460, 30726,   116,  1315,   111,
           157,   331,   135,   149,   204,   109,   278,   107, 11481,  7756,
           243,  1342,   120,   178,   192,  1137,   114,   988,   113, 30726,
           116,   124,  1538,  1265,   198, 35871,   162,   125,   138,   442,
           738,   177, 18345,   170,   108,   792,   135,  1428,  1105,   135,
           290, 10156,   108, 14451,   109,   115,  8597, 32478,  1784,   317,
           109,  1887,   113,  6807,   111,   109,   970, 24353,   799,   115,
           109,   278,   745,   992,   112, 20525,  4474,   107,   351, 30726,
           116,   127,   329,   356,   262,   157,   323,   109,  4104,   115,
           109,  1588,   111,   163, 14094,   109,   352, 32577,   108, 11869,
          4244, 20525, 18672,  1084,  1054,   107,  6611,   243,   107,   322,
           127,  1254,  3795,   112,   130,   109, 54407,   113,   109,  4569,
          1887,   107,   139,   177, 30726,   116,   331,   135,  1105,   253,
           130, 16958,   108,   351,  3571,   111, 14838,   107,   198,   287,
           117,   114, 32577,   170,   221,   249,  1728,   112,  1111,   165,
           112,   200,   124,   109, 11691,   108,   111,   119,  2312,   236,
           120,   115,   136,   323,   745,  6611,   243,   107,   198,   417,
           131,   216,  1767,   160, 30726,   116,   135,  2222, 10912,  1262,
           108,   172,  5365, 23288,   108,   109,  3755,  2273,   113, 43439,
           108, 14668,   108,  6398,   108, 32671,   496,   343,   118,   109,
           453,   166,   381,  7756,   131,  2974,   108,   220,  3361,   266,
           109,   467,   107,   198, 59883,   131,  2293,   117,   221,   786,
           151,   285,  1728,   112,   275,   112,   109, 12483, 26941, 30713,
          3317,   880,   197,  1262,   120,   127,   506,   349,   121, 22564,
           122, 30726,   116,   745,  6611,   243,   107,  8751,  5706,  1418,
           497,   108,   114,  4609,   113,  1588,   689,   134, 69328,   502,
           115,   351,  3477,   108,  3151,   120,  7756,  1487,   169,   177,
         11598,   113, 30726,   116,   124,   109,  4569, 26717,   113,   109,
         60574,   108,   162, 56784,   109,   558,   113,   109, 33806,   112,
          1694,   131, 25910,   115, 26163,   107,   198,  1189, 11733,   113,
           339,  5509,  1024,   135,   571,   429,   108,   109, 11481,   131,
           116,  2257,   118, 30726,   416,   120,   290,   391,  1588,  8068,
           114,   295,   134,   109,   461,   826,   496,   222,   176,   989,
           108,  7756,  1728,   114,   154, 24500,  1588,   111,  1728,   112,
          1232,  6243,   675,   135,   360,  1724,   120,  2051,   571,   135,
         52403,   131,   116,   484,  3853,   108,  5706,  1418,   497,   243,
           107,   485,   591,   131,   144,  1021,  7756,   117,   109,   211,
           110, 39619, 18827,   112, 17717, 30726,   116,   135,   109,  1690,
           278,   108,   577,   107, 16591,   115,   109,  8821,   116,   108,
           142,  2186,   344,   113,  5249,   655,  1588,  3635,   195,  1729,
         30726,   116,   108,   111,   115,   109,  6939,   116,   108,   873,
           107,  1084, 61939, 12964,   108,  2901,  7756, 24828,  3792,   289,
           232,   108,  4486,   109,   211, 30726,   116,   135,  2466,   108,
           109,  6802,   111,  1922,   107,   222,   663,   112,   109,   738,
           177, 30726,   116,  7756,  1729,   124,  1342,   108,   668,  5774,
         66941,   116,   111, 35712,   138,   163,   129,  7051,   130, 30726,
           116,   107,  2882,   232,   108, 11481,  7756,  4486,  1925,   177,
         30726,   116,   108,   330, 35712,   135, 17256,   111, 58499, 55600,
           107, 11869,   131,   116,  4767, 18834,   111,  2333, 65534, 15391,
         28929,  5674,   112,   136,   731,   107,     1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}

summary_ids =  tensor([[    0,   143, 40155,   158,   581,   109,   453,   166,   333,   169,
         95987,   108, 11481,  7756,   148,  1487,   114,   177,   456,   113,
         35712,   111, 66941,   116,   323,   112,   460, 30726,   116,  1315,
           111,   157,   331,   135,   149,   204,   109,   278,   107, 11481,
          7756,   243,  1342,   120,   178,   192,  1137,   114,   988,     1]])

['(CNN)For the second time during his papacy, Pope Francis has announced a new group of bishops and archbishops set to become cardinals -- and they come from all over the world. Pope Francis said Sunday that he would hold a meeting']
['(CNN)For the second time during his papacy, Pope Francis has announced a new group of bishops and archbishops set to become cardinals -- and they come from all over the world. Pope Francis said Sunday that he would hold a meeting']

Process finished with exit code 0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86

五、微调Pegasus预训练模型（pegasus-cnn_dailymail）

# https://github.com/huggingface/notebooks/blob/master/examples/summarization.ipynb
import nltk
import numpy as np
from datasets import load_dataset, load_metric
from transformers import AutoTokenizer
from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer

model_checkpoint = r"D:\Pretrained_Model\pegasus-cnn_dailymail"
raw_datasets = load_dataset("xsum")
metric = load_metric("rouge")

print('raw_datasets = ', raw_datasets)
print("raw_datasets['train'][0] = ", raw_datasets['train'][0])

tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

prefix = "summarize: "


def preprocess_function(examples):
    inputs = [prefix + doc for doc in examples["document"]]
    model_inputs = tokenizer(inputs, max_length=1024, truncation=True)

    # Setup the tokenizer for targets
    with tokenizer.as_target_tokenizer():
        labels = tokenizer(examples["summary"], max_length=128, truncation=True)

    model_inputs["labels"] = labels["input_ids"]
    return model_inputs


def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
    # Replace -100 in the labels as we can't decode them.
    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

    # Rouge expects a newline after each sentence
    decoded_preds = ["\n".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds]
    decoded_labels = ["\n".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels]

    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
    # Extract a few results
    result = {key: value.mid.fmeasure * 100 for key, value in result.items()}

    # Add mean generated length
    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]
    result["gen_len"] = np.mean(prediction_lens)

    return {k: round(v, 4) for k, v in result.items()}


tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)

# ----------------------------------- Fine-tuning the model -----------------------------------
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
batch_size = 1
model_name = model_checkpoint.split("/")[-1]
args = Seq2SeqTrainingArguments(
    "finetuned-xsum",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    weight_decay=0.01,
    save_total_limit=3,
    num_train_epochs=1,
    predict_with_generate=True,
    fp16=True,
    push_to_hub=False,
)

data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)

trainer = Seq2SeqTrainer(
    model,
    args,
    train_dataset=tokenized_datasets["test"],
    eval_dataset=tokenized_datasets["validation"],
    data_collator=data_collator,
    tokenizer=tokenizer,
    compute_metrics=compute_metrics
)

trainer.train()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86

---

---

---

参考资料：
2015-2019年摘要模型（Summarization Model）发展综述（二）
谷歌飞马PEGASUS - 生成式自动摘要预训练模型
ICML 2020 | PEGASUS(天马)：地表最强文本摘要生成模型
T5 PEGASUS：开源一个中文生成式预训练模型
华人博士一作：自动生成摘要超越BERT！帝国理工&谷歌提出新模型Pegasus
谷歌开源“穷人版”摘要生成NLP模型：训练成本低，只要1000个样本就能打败人类
ICML 2020 | Google提出最强生成式摘要预训练模型——天马