Repository: bupticybee/ChineseAiDungeon
Branch: main
Commit: be9a8eb1e955
Files: 108
Total size: 78.0 MB
Directory structure:
gitextract_o5tgzk64/
├── 3rd/
│ └── gdown.pl/
│ ├── LICENSE.txt
│ ├── README.md
│ └── gdown.pl
├── CPM-Generate/
│ ├── .gitignore
│ ├── LICENSE
│ ├── README.md
│ ├── arguments.py
│ ├── bpe_3w_new/
│ │ ├── chinese_vocab.model
│ │ ├── chinese_vocab.vocab
│ │ ├── merges.txt
│ │ └── vocab.json
│ ├── configure_data.py
│ ├── data/
│ │ ├── Makefile
│ │ ├── __init__.py
│ │ ├── bert_dataset.py
│ │ ├── dataset_utils.py
│ │ ├── gpt2_dataset.py
│ │ ├── helpers.cpp
│ │ ├── indexed_dataset.py
│ │ ├── samplers.py
│ │ └── test/
│ │ ├── test_indexed_dataset.py
│ │ └── test_preprocess_data.sh
│ ├── data_utils/
│ │ ├── __init__.py
│ │ ├── corpora.py
│ │ ├── datasets.py
│ │ ├── file_utils.py
│ │ ├── lazy_loader.py
│ │ ├── samplers.py
│ │ ├── tf_dl.py
│ │ ├── tokenization.py
│ │ ├── tokenization_gpt2.py
│ │ └── wordpiece.py
│ ├── fp16/
│ │ ├── __init__.py
│ │ ├── fp16.py
│ │ ├── fp16util.py
│ │ └── loss_scaler.py
│ ├── generate_samples.py
│ ├── model/
│ │ ├── __init__.py
│ │ ├── distributed.py
│ │ ├── gpt2_modeling.py
│ │ ├── model.py
│ │ └── modeling.py
│ ├── mpu/
│ │ ├── __init__.py
│ │ ├── cross_entropy.py
│ │ ├── data.py
│ │ ├── grads.py
│ │ ├── initialize.py
│ │ ├── layers.py
│ │ ├── mappings.py
│ │ ├── random.py
│ │ ├── tests/
│ │ │ ├── __init__.py
│ │ │ ├── commons.py
│ │ │ ├── test_cross_entropy.py
│ │ │ ├── test_data.py
│ │ │ ├── test_initialize.py
│ │ │ ├── test_layers.py
│ │ │ └── test_random.py
│ │ ├── transformer.py
│ │ └── utils.py
│ ├── requirements.txt
│ ├── scripts/
│ │ └── generate_text.sh
│ └── utils.py
├── ChineseAiDungeonColabDemo.ipynb
├── LICENSE
├── finetune.ipynb
├── labeled_data/
│ ├── advanture_translated/
│ │ ├── process_data.ipynb
│ │ ├── processed_translated_story.txt
│ │ ├── processed_translated_story_valid.txt
│ │ ├── text_advanture_trans.txt
│ │ ├── train_adventures_valid_translated.txt
│ │ ├── truncated_advanture_train.json
│ │ ├── truncated_advanture_valid.json
│ │ └── valid_advantures_valid_translated.txt
│ ├── raw_data/
│ │ ├── Jinyong/
│ │ │ ├── 书剑恩仇录.txt
│ │ │ ├── 侠客行.txt
│ │ │ ├── 倚天屠龙记.txt
│ │ │ ├── 天龙八部.txt
│ │ │ ├── 射雕英雄传.txt
│ │ │ ├── 白马啸西风.txt
│ │ │ ├── 碧血剑.txt
│ │ │ ├── 神雕侠侣.txt
│ │ │ ├── 笑傲江湖.txt
│ │ │ ├── 越女剑.txt
│ │ │ ├── 连城诀.txt
│ │ │ ├── 雪山飞狐.txt
│ │ │ ├── 飞狐外传.txt
│ │ │ ├── 鸳鸯刀.txt
│ │ │ └── 鹿鼎记.txt
│ │ └── 邪气凛然.txt
│ ├── 一次冒险.txt
│ ├── 我从来都不主动.txt
│ ├── 盗墓实录.txt
│ └── 邪气凛然.txt
├── readme.md
├── requirements.txt
├── tf2gpt/
│ ├── .gitignore
│ ├── README.md
│ ├── loading.ipynb
│ ├── model.py
│ ├── predict.ipynb
│ ├── test.ipynb
│ ├── train.ipynb
│ └── vocab.txt
├── utils/
│ ├── gpt2_tokenizer.py
│ ├── progress_bar.py
│ ├── story_helper.py
│ └── story_util.py
└── 标注.ipynb
================================================
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FILE: 3rd/gdown.pl/LICENSE.txt
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License and any other pertinent obligations, then as a consequence you may
not convey it at all. For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU Affero General Public License into a single
combined work, and to convey the resulting work. The terms of this
License will continue to apply to the part which is the covered work,
but the special requirements of the GNU Affero General Public License,
section 13, concerning interaction through a network will apply to the
combination as such.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
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be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU General Public License, you may choose any version ever published
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If the Program specifies that a proxy can decide which future
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Later license versions may give you additional or different
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15. Disclaimer of Warranty.
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APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
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Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
Copyright (C)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see .
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
Copyright (C)
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
.
================================================
FILE: 3rd/gdown.pl/README.md
================================================
gdown.pl
========
Google Drive direct download of big files
Requirements
============
*wget* and *Perl* must be in the PATH.
**Windows** and **linux** compatible.
Usage
=====
Use Google Drive shareable links, viewable by anyone:
$ ./gdown.pl 'gdrive file url' ['desired file name']
Example
=======
For example, to download [this video](https://drive.google.com/file/d/0B1L_hFrWJfRhLUJZdXdSdTdfSWs/edit) from my [axolotl project](https://circulosmeos.wordpress.com/2015/03/04/axolotl-a-simple-plain-text-documentation-system/), just copy the url, and give a file name if desired:
$ ./gdown.pl https://drive.google.com/file/d/0B1L_hFrWJfRhLUJZdXdSdTdfSWs/edit axolotl.mp4
Resuming a download
===================
If you need to resume a download, please, use [**gdown.pl v2.0** here](https://github.com/circulosmeos/gdown.pl/tree/with-resume).
As long as a file name is indicated as second parameter, *gdown.pl v2.0* **will try to resume the partially downloaded file** if a local incomplete file with that name already exists.
Version
=======
This version is **v1.4**.
### Warning
Please, note that v1.2 (available between days 12 to 31 of Jan/2019) **should not be used**, as it contains a bug that could result in unusable downloaded files. Proceed to overwrite with v1.3 in case you have it.
Docker
======
A simple Docker file is provided, to build a simple Docker image with gdown.pl.
This has been used for pre-pulling data from a Google Drive to Kubernetes persistent volumes. Thanks @anton-khodak
License
=======
Distributed [under GPL 3](http://www.gnu.org/licenses/gpl-3.0.html)
Disclaimer
==========
This software is provided "as is", without warranty of any kind, express or implied.
More info
=========
[https://circulosmeos.wordpress.com/2014/04/12/google-drive-direct-download-of-big-files](https://circulosmeos.wordpress.com/2014/04/12/google-drive-direct-download-of-big-files)
Contact
=======
by [circulosmeos](loopidle@gmail.com)
================================================
FILE: 3rd/gdown.pl/gdown.pl
================================================
#!/usr/bin/env perl
#
# Google Drive direct download of big files
# ./gdown.pl 'gdrive file url' ['desired file name']
#
# v1.0 by circulosmeos 04-2014.
# v1.1 by circulosmeos 01-2017.
# v1.2, 2.0 by circulosmeos 01-2019.
# v2.1 by circulosmeos 12-2020.
# //circulosmeos.wordpress.com/2014/04/12/google-drive-direct-download-of-big-files
# Distributed under GPL 3 (//www.gnu.org/licenses/gpl-3.0.html)
#
use strict;
use POSIX;
my $TEMP='gdown.cookie.temp';
my $COMMAND;
my $confirm;
my $check;
sub execute_command();
my $URL=shift;
die "\n./gdown.pl 'gdrive file url' [desired file name]\n\n" if $URL eq '';
my $FILENAME=shift;
my $TEMP_FILENAME='gdown.'.strftime("%Y%m%d%H%M%S", localtime).'.'.substr(rand,2);
if ($URL=~m#^https?://drive.google.com/file/d/([^/]+)#) {
$URL="https://docs.google.com/uc?id=$1&export=download";
}
elsif ($URL=~m#^https?://drive.google.com/open\?id=([^/]+)#) {
$URL="https://docs.google.com/uc?id=$1&export=download";
}
execute_command();
while (-s $TEMP_FILENAME < 100000) { # only if the file isn't the download yet
open fFILENAME, '<', $TEMP_FILENAME;
$check=0;
foreach () {
if (/href="(\/uc\?export=download[^"]+)/) {
$URL='https://docs.google.com'.$1;
$URL=~s/&/&/g;
$confirm='';
$check=1;
last;
}
if (/confirm=([^;&]+)/) {
$confirm=$1;
$check=1;
last;
}
if (/"downloadUrl":"([^"]+)/) {
$URL=$1;
$URL=~s/\\u003d/=/g;
$URL=~s/\\u0026/&/g;
$confirm='';
$check=1;
last;
}
}
close fFILENAME;
die "Couldn't download the file :-(\n" if ($check==0);
$URL=~s/confirm=([^;&]+)/confirm=$confirm/ if $confirm ne '';
execute_command();
}
unlink $TEMP;
sub execute_command() {
my $OUTPUT_FILENAME = $TEMP_FILENAME;
my $CONTINUE = '';
# check contents before download & if a $FILENAME has been indicated resume on content download
# please, note that for this to work, wget must correctly provide --spider with --server-response (-S)
if ( length($FILENAME) > 0 ) {
$COMMAND="wget -q -S --no-check-certificate --spider --load-cookie $TEMP --save-cookie $TEMP \"$URL\" 2>&1";
my @HEADERS=`$COMMAND`;
foreach my $header (@HEADERS) {
if ( ( $header =~ /Content-Type: (.+)/ && $1 !~ 'text/html' ) ||
$header =~ 'HTTP/1.1 405 Method Not Allowed'
) {
$OUTPUT_FILENAME = $FILENAME;
$CONTINUE = '-c';
last;
}
}
}
$COMMAND="wget $CONTINUE --progress=dot:giga --no-check-certificate --load-cookie $TEMP --save-cookie $TEMP \"$URL\"";
$COMMAND.=" -O \"$OUTPUT_FILENAME\"";
my $OUTPUT = system( $COMMAND );
if ( $OUTPUT == 2 ) { # do a clean exit with Ctrl+C
unlink $TEMP;
die "\nDownloading interrupted by user\n\n";
} elsif ( $OUTPUT == 0 && length($CONTINUE)>0 ) { # do a clean exit with $FILENAME provided
unlink $TEMP;
die "\nDownloading complete\n\n";
}
return 1;
}
================================================
FILE: CPM-Generate/.gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
================================================
FILE: CPM-Generate/LICENSE
================================================
MIT License
Copyright (c) 2020 THU-PLM
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: CPM-Generate/README.md
================================================
# CPM-Generate
为了促进中文自然语言处理研究的发展，本项目提供了 **CPM-LM** (2.6B) 模型的文本生成代码，可用于文本生成的本地测试，并以此为基础进一步研究零次学习/少次学习等场景。[[项目首页](https://cpm.baai.ac.cn)] [[模型下载](https://cpm.baai.ac.cn/download.html)]
## 安装
首先安装pytorch等基础依赖，再安装[APEX](https://github.com/NVIDIA/apex#quick-start)以支持fp16：
```
pip install -r requirements.txt
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
```
## 模型
模型下载后文件夹的目录结构需设置如下：
```
.
├── 80000
│ ├── mp_rank_00_model_states.pt
│ └── mp_rank_01_model_states.pt
└── latest_checkpointed_iteration.txt
```
为保证下载文件的正确性，文件的checksum如下：
```
SHA1
7a8461098dae374a836f55bf2002bf21cba05964 80000/mp_rank_00_model_states.pt
6ab97bd06979c1707080b33cfddff4f08c9af675 80000/mp_rank_01_model_states.pt
MD5
ffb39d8e477aedf9f8714cc467f34eae 80000/mp_rank_00_model_states.pt
8af02c1aa85e76a430d45f3ad073edc5 80000/mp_rank_01_model_states.pt
```
## 使用
提供了命令行交互式生成：
```
bash scripts/generate_text.sh /path/to/CPM
```
运行该脚本需要两块GPU，每张卡的GPU内存占用约为7GB。该项目主要基于 [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) 进行修改。
## 引用
```
@article{cpm-v1,
title={CPM: A Large-scale Generative Chinese Pre-trained Language Model},
author={Zhang, Zhengyan and Han, Xu, and Zhou, Hao, and Ke, Pei, and Gu, Yuxian and Ye, Deming and Qin, Yujia and Su, Yusheng and Ji, Haozhe and Guan, Jian and Qi, Fanchao and Wang, Xiaozhi and Zheng, Yanan and Cao, Jiannan and Zeng, Guoyang and Cao, Huanqi and Chen, Shengqi and Li, Daixuan and Sun, Zhenbo and Liu, Zhiyuan and Huang, Minlie and Han, Wentao and Tang, Jie and Li, Juanzi and Sun, Maosong},
year={2020}
}
```
================================================
FILE: CPM-Generate/arguments.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
"""argparser configuration"""
import argparse
import os
import torch
def add_model_config_args(parser):
"""Model arguments"""
group = parser.add_argument_group('model', 'model configuration')
group.add_argument('--pretrained-bert', action='store_true',
help='use a pretrained bert-large-uncased model instead'
'of initializing from scratch. See '
'--tokenizer-model-type to specify which pretrained '
'BERT model to use')
group.add_argument('--attention-dropout', type=float, default=0.1,
help='dropout probability for attention weights')
group.add_argument('--num-attention-heads', type=int, default=16,
help='num of transformer attention heads')
group.add_argument('--hidden-size', type=int, default=1024,
help='tansformer hidden size')
group.add_argument('--intermediate-size', type=int, default=None,
help='transformer embedding dimension for FFN'
'set to 4*`--hidden-size` if it is None')
group.add_argument('--num-layers', type=int, default=24,
help='num decoder layers')
group.add_argument('--layernorm-epsilon', type=float, default=1e-5,
help='layer norm epsilon')
group.add_argument('--hidden-dropout', type=float, default=0.1,
help='dropout probability for hidden state transformer')
group.add_argument('--max-position-embeddings', type=int, default=512,
help='maximum number of position embeddings to use')
group.add_argument('--vocab-size', type=int, default=30522,
help='vocab size to use for non-character-level '
'tokenization. This value will only be used when '
'creating a tokenizer')
group.add_argument('--deep-init', action='store_true',
help='initialize bert model similar to gpt2 model.'
'scales initialization of projection layers by a '
'factor of 1/sqrt(2N). Necessary to train bert '
'models larger than BERT-Large.')
group.add_argument('--make-vocab-size-divisible-by', type=int, default=128,
help='Pad the vocab size to be divisible by this value.'
'This is added for computational efficieny reasons.')
group.add_argument('--cpu-optimizer', action='store_true',
help='Run optimizer on CPU')
group.add_argument('--cpu_torch_adam', action='store_true',
help='Use Torch Adam as optimizer on CPU.')
return parser
def add_fp16_config_args(parser):
"""Mixed precision arguments."""
group = parser.add_argument_group('fp16', 'fp16 configurations')
group.add_argument('--fp16', action='store_true',
help='Run model in fp16 mode')
group.add_argument('--fp32-embedding', action='store_true',
help='embedding in fp32')
group.add_argument('--fp32-layernorm', action='store_true',
help='layer norm in fp32')
group.add_argument('--fp32-tokentypes', action='store_true',
help='embedding token types in fp32')
group.add_argument('--fp32-allreduce', action='store_true',
help='all-reduce in fp32')
group.add_argument('--hysteresis', type=int, default=2,
help='hysteresis for dynamic loss scaling')
group.add_argument('--loss-scale', type=float, default=None,
help='Static loss scaling, positive power of 2 '
'values can improve fp16 convergence. If None, dynamic'
'loss scaling is used.')
group.add_argument('--loss-scale-window', type=float, default=1000,
help='Window over which to raise/lower dynamic scale')
group.add_argument('--min-scale', type=float, default=1,
help='Minimum loss scale for dynamic loss scale')
return parser
def add_training_args(parser):
"""Training arguments."""
group = parser.add_argument_group('train', 'training configurations')
group.add_argument('--batch-size', type=int, default=4,
help='Data Loader batch size')
group.add_argument('--weight-decay', type=float, default=0.01,
help='weight decay coefficient for L2 regularization')
group.add_argument('--checkpoint-activations', action='store_true',
help='checkpoint activation to allow for training '
'with larger models and sequences')
group.add_argument('--checkpoint-num-layers', type=int, default=1,
help='chunk size (number of layers) for checkpointing')
group.add_argument('--clip-grad', type=float, default=1.0,
help='gradient clipping')
group.add_argument('--train-iters', type=int, default=1000000,
help='total number of iterations to train over all training runs')
group.add_argument('--log-interval', type=int, default=100,
help='report interval')
group.add_argument('--exit-interval', type=int, default=None,
help='Exit the program after this many new iterations.')
group.add_argument('--seed', type=int, default=1234,
help='random seed')
# Batch prodecuer arguments
group.add_argument('--reset-position-ids', action='store_true',
help='Reset posistion ids after end-of-document token.')
group.add_argument('--reset-attention-mask', action='store_true',
help='Reset self attention maske after '
'end-of-document token.')
# Learning rate.
group.add_argument('--lr-decay-iters', type=int, default=None,
help='number of iterations to decay LR over,'
' If None defaults to `--train-iters`*`--epochs`')
group.add_argument('--lr-decay-style', type=str, default='linear',
choices=['constant', 'linear', 'cosine', 'exponential'],
help='learning rate decay function')
group.add_argument('--lr', type=float, default=1.0e-4,
help='initial learning rate')
group.add_argument('--warmup', type=float, default=0.01,
help='percentage of data to warmup on (.01 = 1% of all '
'training iters). Default 0.01')
# model checkpointing
group.add_argument('--save', type=str, default=None,
help='Output directory to save checkpoints to.')
group.add_argument('--save-interval', type=int, default=5000,
help='number of iterations between saves')
group.add_argument('--no-save-optim', action='store_true',
help='Do not save current optimizer.')
group.add_argument('--no-save-rng', action='store_true',
help='Do not save current rng state.')
group.add_argument('--load', type=str, default=None,
help='Path to a directory containing a model checkpoint.')
group.add_argument('--no-load-optim', action='store_true',
help='Do not load optimizer when loading checkpoint.')
group.add_argument('--no-load-rng', action='store_true',
help='Do not load rng state when loading checkpoint.')
group.add_argument('--finetune', action='store_true',
help='Load model for finetuning. Do not load optimizer '
'or rng state from checkpoint and set iteration to 0. '
'Assumed when loading a release checkpoint.')
group.add_argument('--resume-dataloader', action='store_true',
help='Resume the dataloader when resuming training. '
'Does not apply to tfrecords dataloader, try resuming'
'with a different seed in this case.')
# distributed training args
group.add_argument('--distributed-backend', default='nccl',
help='which backend to use for distributed '
'training. One of [gloo, nccl]')
group.add_argument('--local_rank', type=int, default=None,
help='local rank passed from distributed launcher')
return parser
def add_evaluation_args(parser):
"""Evaluation arguments."""
group = parser.add_argument_group('validation', 'validation configurations')
group.add_argument('--eval-batch-size', type=int, default=None,
help='Data Loader batch size for evaluation datasets.'
'Defaults to `--batch-size`')
group.add_argument('--eval-iters', type=int, default=100,
help='number of iterations to run for evaluation'
'validation/test for')
group.add_argument('--eval-interval', type=int, default=1000,
help='interval between running evaluation on validation set')
group.add_argument('--eval-seq-length', type=int, default=None,
help='Maximum sequence length to process for '
'evaluation. Defaults to `--seq-length`')
group.add_argument('--eval-max-preds-per-seq', type=int, default=None,
help='Maximum number of predictions to use for '
'evaluation. Defaults to '
'math.ceil(`--eval-seq-length`*.15/10)*10')
group.add_argument('--overlapping-eval', type=int, default=32,
help='sliding window for overlapping eval ')
group.add_argument('--cloze-eval', action='store_true',
help='Evaluation dataset from `--valid-data` is a cloze task')
group.add_argument('--eval-hf', action='store_true',
help='perform evaluation with huggingface openai model.'
'use `--load` to specify weights path to be loaded')
group.add_argument('--load-openai', action='store_true',
help='load openai weights into our model. Use `--load` '
'to specify weights path to be loaded')
return parser
def add_text_generate_args(parser):
"""Text generate arguments."""
group = parser.add_argument_group('Text generation', 'configurations')
group.add_argument("--temperature", type=float, default=1.0)
group.add_argument("--top_p", type=float, default=0.0)
group.add_argument("--top_k", type=int, default=0)
group.add_argument("--out-seq-length", type=int, default=256)
return parser
def add_data_args(parser):
"""Train/valid/test data arguments."""
group = parser.add_argument_group('data', 'data configurations')
group.add_argument('--data-impl', type=str, default='infer',
choices=['lazy', 'cached', 'mmap', 'infer'],
help='Implementation of indexed datasets.')
group.add_argument('--mmap-warmup', action='store_true',
help='Warm up mmap files.')
group.add_argument('--model-parallel-size', type=int, default=1,
help='size of the model parallel.')
group.add_argument('--shuffle', action='store_true',
help='Shuffle data. Shuffling is deterministic '
'based on seed and current epoch.')
#group.add_argument('--train-data', nargs='+', default=None,
# help='Whitespace separated filenames or corpora names '
# 'for training.')
group.add_argument('--data-path', type=str, default=None,
help='Path to combined dataset to split.')
group.add_argument('--use-npy-data-loader', action='store_true',
help='Use the numpy data loader. If set, then'
'train-data-path, val-data-path, and test-data-path'
'should also be provided.')
group.add_argument('--train-data-path', type=str, default='',
help='path to the training data')
group.add_argument('--val-data-path', type=str, default='',
help='path to the validation data')
group.add_argument('--test-data-path', type=str, default='',
help='path to the test data')
group.add_argument('--input-data-sizes-file', type=str, default='sizes.txt',
help='the filename containing all the shards sizes')
group.add_argument('--delim', default=',',
help='delimiter used to parse csv data files')
group.add_argument('--text-key', default='sentence',
help='key to use to extract text from json/csv')
group.add_argument('--eval-text-key', default=None,
help='key to use to extract text from '
'json/csv evaluation datasets')
group.add_argument('--valid-data', nargs='*', default=None,
help="""Filename for validation data.""")
group.add_argument('--split', default='1000,1,1',
help='comma-separated list of proportions for training,'
' validation, and test split')
group.add_argument('--test-data', nargs='*', default=None,
help="""Filename for testing""")
group.add_argument('--lazy-loader', action='store_true',
help='whether to lazy read the data set')
group.add_argument('--loose-json', action='store_true',
help='Use loose json (one json-formatted string per '
'newline), instead of tight json (data file is one '
'json string)')
group.add_argument('--presplit-sentences', action='store_true',
help='Dataset content consists of documents where '
'each document consists of newline separated sentences')
group.add_argument('--num-workers', type=int, default=2,
help="""Number of workers to use for dataloading""")
group.add_argument('--tokenizer-model-type', type=str,
default='bert-large-uncased',
help="Model type to use for sentencepiece tokenization \
(one of ['bpe', 'char', 'unigram', 'word']) or \
bert vocab to use for BertWordPieceTokenizer (one of \
['bert-large-uncased', 'bert-large-cased', etc.])")
group.add_argument('--tokenizer-path', type=str, default='tokenizer.model',
help='path used to save/load sentencepiece tokenization '
'models')
group.add_argument('--tokenizer-type', type=str,
default='BertWordPieceTokenizer',
choices=['CharacterLevelTokenizer',
'SentencePieceTokenizer',
'BertWordPieceTokenizer',
'GPT2BPETokenizer'],
help='what type of tokenizer to use')
group.add_argument("--cache-dir", default=None, type=str,
help="Where to store pre-trained BERT downloads")
group.add_argument('--use-tfrecords', action='store_true',
help='load `--train-data`, `--valid-data`, '
'`--test-data` from BERT tf records instead of '
'normal data pipeline')
group.add_argument('--seq-length', type=int, default=512,
help="Maximum sequence length to process")
group.add_argument('--max-preds-per-seq', type=int, default=None,
help='Maximum number of predictions to use per sequence.'
'Defaults to math.ceil(`--seq-length`*.15/10)*10.'
'MUST BE SPECIFIED IF `--use-tfrecords` is True.')
return parser
def get_args():
"""Parse all the args."""
parser = argparse.ArgumentParser(description='PyTorch BERT Model')
parser = add_model_config_args(parser)
parser = add_fp16_config_args(parser)
parser = add_training_args(parser)
parser = add_evaluation_args(parser)
parser = add_text_generate_args(parser)
parser = add_data_args(parser)
args = parser.parse_args()
if not args.data_path and not args.train_data_path:
print('WARNING: No training data specified')
args.cuda = torch.cuda.is_available()
args.rank = int(os.getenv('RANK', '0'))
args.world_size = int(os.getenv("WORLD_SIZE", '1'))
if os.getenv('OMPI_COMM_WORLD_LOCAL_RANK'):
# We are using (OpenMPI) mpirun for launching distributed data parallel processes
local_rank = int(os.getenv('OMPI_COMM_WORLD_LOCAL_RANK'))
local_size = int(os.getenv('OMPI_COMM_WORLD_LOCAL_SIZE'))
# Possibly running with Slurm
num_nodes = int(os.getenv('SLURM_JOB_NUM_NODES', '1'))
nodeid = int(os.getenv('SLURM_NODEID', '0'))
args.local_rank = local_rank
args.rank = nodeid*local_size + local_rank
args.world_size = num_nodes*local_size
args.model_parallel_size = min(args.model_parallel_size, args.world_size)
if args.rank == 0:
print('using world size: {} and model-parallel size: {} '.format(
args.world_size, args.model_parallel_size))
args.dynamic_loss_scale = False
if args.loss_scale is None:
args.dynamic_loss_scale = True
if args.rank == 0:
print(' > using dynamic loss scaling')
# The args fp32_* or fp16_* meant to be active when the
# args fp16 is set. So the default behaviour should all
# be false.
if not args.fp16:
args.fp32_embedding = False
args.fp32_tokentypes = False
args.fp32_layernorm = False
return args
================================================
FILE: CPM-Generate/bpe_3w_new/chinese_vocab.vocab
================================================
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▁66 -11.6167
▁问答 -11.6177
▁0.8 -11.6178
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旋 -11.618
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US -11.6188
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犯罪 -11.6191
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箭 -11.62
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吐 -11.6207
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------------ -11.6212
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碧 -11.6223
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温度 -11.6228
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▁长篇 -11.6235
▁iPad -11.6237
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過 -11.6239
罚 -11.624
牧 -11.6243
闭 -11.6243
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▁82 -11.6251
暗 -11.6254
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▁武侠 -11.6256
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▁人为 -11.6258
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▁忽悠 -11.6261
炼 -11.6263
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≤ -11.6273
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à -11.6274
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▁新颖 -11.6282
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▁发掘 -11.6283
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LE -11.6297
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委员会 -11.6299
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志愿者 -11.6302
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▁调料 -11.6302
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经理 -11.6304
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概念 -11.6308
截 -11.6311
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▁蜂蜜 -11.6321
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▁仓 -11.6322
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儒 -11.6325
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▁北美 -11.6333
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▁数控 -11.6335
▁105 -11.6338
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虾 -11.6339
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▁一日 -11.6358
▁蝴蝶 -11.6358
▁内向 -11.6358
前景 -11.6359
涅 -11.6359
▁野生 -11.636
改革 -11.6364
▁受益 -11.6365
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手术 -11.637
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00 -11.6371
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▁一书 -11.6373
▁凌 -11.6375
▁亲身经 -11.6376
▁佐 -11.6377
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▁影子 -11.638
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旭 -11.6382
▁河流 -11.6387
▁行驶 -11.6387
▁颈 -11.6387
亨 -11.6388
▁无可 -11.6392
▁醒 -11.6394
▁收缩 -11.6396
AM -11.6396
▁麻醉 -11.6397
▁67 -11.6397
▁风水 -11.6397
▁风光 -11.6397
▁两家 -11.64
▁外贸 -11.6402
▁纠纷 -11.6412
▁用力 -11.6413
▁定时 -11.6415
▁捉 -11.6415
锐 -11.6417
▁债务 -11.6419
▁94 -11.6423
▁谨慎 -11.6423
虐 -11.6424
▁振动 -11.6425
▁卫视 -11.6425
▁锤子 -11.6427
▁剂量 -11.643
▁悟 -11.6431
▁府 -11.6432
▁产地 -11.6433
要求 -11.6434
头部 -11.6434
碟 -11.6435
FC -11.6435
▁古城 -11.6436
▁年级 -11.6442
▁司马 -11.6444
▁清醒 -11.6445
art -11.6446
申 -11.6452
▁NO -11.6452
疆 -11.6452
▁黑龙江 -11.6454
估 -11.6456
MI -11.6457
▁即时 -11.6458
mon -11.6458
阻 -11.646
▁天文 -11.6461
织 -11.6463
▁撤销 -11.6464
▁精通 -11.6464
▁分工 -11.6465
▁章节 -11.6467
▁很想 -11.647
▁纲 -11.6473
▁进修 -11.6474
▁荒 -11.6475
▁头上 -11.6478
▁六十 -11.6479
▁新华 -11.6481
▁前锋 -11.6483
龟 -11.6484
陆续 -11.6487
▁历代 -11.6488
▁参赛 -11.6489
▁炫耀 -11.649
▁同性 -11.6492
▁进出口 -11.6493
▁制片 -11.6494
▁雕塑 -11.6496
一看 -11.6499
PE -11.65
▁1970 -11.6501
▁要死 -11.6504
募 -11.6505
▁龙头 -11.6506
▁Mo -11.6507
优势 -11.6509
▁常委 -11.6509
屈 -11.6511
▁不大 -11.6511
实实 -11.6513
▁受理 -11.6515
▁姓氏 -11.6515
▁短暂 -11.6516
茅 -11.6517
mi -11.6519
gu -11.6519
霾 -11.6522
▁漫 -11.6522
骗 -11.6523
▁累积 -11.6525
献 -11.6528
▁偏向 -11.6529
ue -11.653
▁音效 -11.6533
▁蹭 -11.6534
籽 -11.6535
▁课本 -11.6536
▁北上 -11.6536
▁唇 -11.6537
▁诱 -11.6538
▁漫长 -11.654
突 -11.6543
▁奖金 -11.6543
酿 -11.6545
▁感悟 -11.6548
▁性感 -11.6552
▁9787 -11.6554
加工 -11.6556
炸 -11.6556
心酸 -11.6557
▁1600 -11.6557
▁犬 -11.6557
á -11.656
▁引擎 -11.656
各地 -11.6561
▁天涯 -11.6565
▁哀 -11.6565
▁VS -11.6566
▁党支 -11.6566
CP -11.657
▁实务 -11.6573
▁范畴 -11.6574
▁球场 -11.6574
59 -11.658
薯 -11.6583
崇 -11.6583
▁法人 -11.6583
り -11.6584
▁四季 -11.6585
▁天津市 -11.6586
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▁摊 -11.6587
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hi -11.6589
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挡 -11.6595
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哉 -11.6598
▁天上 -11.66
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授 -11.6603
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▁嫩 -11.6614
▁缓慢 -11.6614
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っ -11.6615
▁合伙 -11.6617
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年内 -11.6622
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▁一分 -11.6624
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稳 -11.6629
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栓 -11.6632
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联保 -11.6641
▁自拍 -11.6641
▁87 -11.6642
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栈 -11.6644
ds -11.6646
▁制冷 -11.6647
湛 -11.6647
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sc -11.6652
卒 -11.6653
高中 -11.6656
晕 -11.6656
▁狗狗 -11.6657
竞争 -11.6658
叠 -11.666
RS -11.6661
▁老虎 -11.6663
肢 -11.6666
▁香水 -11.6668
▁面粉 -11.6668
▁马来西亚 -11.6669
▁China -11.667
▁机电 -11.667
▁边界 -11.667
▁罚款 -11.6671
▁第十九 -11.6675
au -11.6675
帆 -11.6676
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创作 -11.6681
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胀 -11.6686
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元素 -11.6687
逆 -11.6688
▁尚书 -11.669
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▁迟 -11.6691
恭 -11.6691
▁才华 -11.6692
逃 -11.6693
▁窝 -11.6693
▁监狱 -11.6693
▁灵感 -11.6694
冠军 -11.6696
摊 -11.6698
▁1960 -11.6699
▁艰苦 -11.67
▁up -11.67
澄 -11.6701
拓 -11.6702
▁殷 -11.6707
杏 -11.671
▁样式 -11.6713
吾 -11.6715
國 -11.6717
ap -11.672
400 -11.672
夺 -11.6722
▁旅客 -11.6722
▁恶性 -11.6723
▁------ -11.6727
201 -11.6728
▁用地 -11.673
▁左手 -11.6732
仅 -11.6732
▁刑法 -11.6732
▁伦理 -11.6735
▁GHz -11.6736
▁碾 -11.6737
▁术语 -11.674
▁移植 -11.6742
半岛 -11.6742
作家 -11.6742
▁注入 -11.6744
▁削 -11.6745
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膝 -11.6748
▁安心 -11.6749
鉴于 -11.6754
皆 -11.6757
▁初始 -11.6761
ま -11.6764
▁两边 -11.677
讨 -11.6776
生产 -11.6776
▁吻 -11.6776
▁脑残 -11.6781
▁杀手 -11.6781
ob -11.6786
▁阴道 -11.6788
▁裁判 -11.679
▁许可 -11.679
▁串 -11.679
▁开门 -11.679
▁讲座 -11.6796
2000 -11.6796
900 -11.6796
▁螺旋 -11.6797
▁书面 -11.6798
能源 -11.68
▁烹饪 -11.6801
▁时空 -11.6802
▁美人 -11.6802
▁教科 -11.6802
▁女王 -11.6803
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▁侵略 -11.6812
抚 -11.6815
漫 -11.6818
▁判 -11.6819
▁ch -11.682
凭 -11.682
▁匿 -11.682
▁橡胶 -11.6821
▁趣味 -11.6822
▁邪恶 -11.6825
用车 -11.6828
▁健 -11.6829
▁foot -11.683
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招呼 -11.6833
▁茎 -11.6835
▁瑜伽 -11.6836
▁帽子 -11.6837
芒 -11.6838
绒 -11.6839
▁涌 -11.6842
斥 -11.6842
▁行程 -11.6843
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▁1945 -11.6846
▁社会学 -11.6847
ea -11.6847
▁相差 -11.6849
▁宏 -11.6853
TO -11.6853
▁900 -11.6857
▁何时 -11.6857
▁不利 -11.6858
CA -11.6862
▁海报 -11.6862
▁巩固 -11.6863
大会 -11.6864
滋 -11.687
过去 -11.6871
▁民俗 -11.6873
▁几位 -11.6877
▁控股 -11.6879
谦 -11.688
▁出手 -11.6888
▁名家 -11.689
▁ア -11.689
▁跨越 -11.6895
▁DNA -11.6895
▁69 -11.6901
▁1.7 -11.6902
绩 -11.6904
▁91 -11.6905
▁本能 -11.6906
▁代表作 -11.6906
6.0 -11.6909
▁染色 -11.6909
▁总监 -11.6911
▁放置 -11.6911
od -11.6913
黄色 -11.6915
▁时机 -11.6917
▁报复 -11.6918
▁踏 -11.6919
▁基友 -11.692
遗产 -11.692
▁器官 -11.6921
▁瓜 -11.6921
來 -11.6923
able -11.6923
▁窗 -11.6923
▁书名 -11.693
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▁侮辱 -11.6934
功率 -11.6935
▁控制器 -11.6936
▁繁华 -11.6939
▁仲裁 -11.6944
▁议 -11.6947
勋 -11.6948
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薇 -11.6955
氢 -11.6958
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▁落地 -11.696
▁半小 -11.696
▁灾难 -11.6961
▁头像 -11.6962
RE -11.6962
▁相连 -11.6965
▁商贸 -11.6965
▁街头 -11.6965
飞机 -11.6966
▁gay -11.6966
▁这首 -11.6967
▁show -11.6969
▁竖 -11.6972
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▁陆军 -11.6973
▁惊人 -11.6973
EN -11.6975
▁失业 -11.6978
▁人际 -11.6979
▁外星 -11.6981
物质 -11.6981
▁感叹 -11.6981
▁政协 -11.6984
眠 -11.6985
▁TV -11.6986
如何 -11.6989
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▁怒 -11.6997
▁十九 -11.6997
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▁顶端 -11.6999
▁饮用 -11.7004
▁歌唱 -11.7004
▁首选 -11.7005
ag -11.7007
▁中毒 -11.7008
篮 -11.7009
ick -11.7009
秦 -11.701
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白了 -11.7011
▁Mar -11.7012
▁尹 -11.7013
▁PCI -11.7014
▁丸 -11.7015
▁是非 -11.7015
▁君子 -11.7018
▁屯 -11.702
Cr -11.7025
旱 -11.7025
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▁押 -11.7031
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▁5.4 -11.7033
肃 -11.7034
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识别 -11.7047
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▁四五 -11.705
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▁一杯 -11.7056
▁1975 -11.7059
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取向 -11.7061
.01 -11.7068
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ron -11.708
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up -11.7084
危 -11.7085
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AR -11.7087
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▁行走 -11.709
▁枯 -11.709
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颖 -11.7099
职称 -11.71
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▁赠 -11.7103
▁罕 -11.7103
▁香味 -11.7103
▁this -11.7104
▁自尊 -11.7105
▁AV -11.7106
▁十月 -11.7107
▁绑架 -11.7107
难看 -11.7109
ou -11.711
霉 -11.7115
ら -11.7116
▁扶持 -11.7116
得起 -11.7117
▁一发 -11.7118
崎 -11.7118
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杨 -11.7125
▁0.6 -11.7126
si -11.7127
▁蟹 -11.7127
▁微生物 -11.7128
权益 -11.7129
▁贝尔 -11.7129
▁百分之 -11.7129
▁掀 -11.7129
▁除此 -11.713
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不觉 -11.7133
▁冻 -11.7135
LA -11.7135
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禹 -11.7136
天下 -11.7136
▁四周 -11.7137
▁诡 -11.7142
▁析 -11.7143
▁iphone -11.7143
贸 -11.7145
▁彼 -11.7145
▁My -11.7147
▁励志 -11.7148
▁屋 -11.7149
腺 -11.7153
驰 -11.7154
▁交际 -11.7159
▁拍卖 -11.7159
▁77 -11.716
▁桃花 -11.7161
▁下雨 -11.7165
゚ -11.7167
▁92 -11.7168
▁光滑 -11.7169
▁魔术 -11.7169
▁小吃 -11.7169
▁仿真 -11.717
▁all -11.7174
▁1964 -11.7175
sp -11.7176
▁无敌 -11.7176
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艇 -11.718
城区 -11.7182
▁101 -11.7187
▁危 -11.7188
▁妥协 -11.7188
▁對 -11.7192
▁城区 -11.7192
▁不妨 -11.7196
NA -11.7197
▁劈 -11.7201
▁GPS -11.7201
き -11.7201
▁无视 -11.7202
ec -11.7202
▁侵权 -11.7202
▁磷 -11.7202
▁比喻 -11.7202
▁bug -11.7203
▁相继 -11.7203
▁流域 -11.721
▁翘 -11.7211
参考 -11.7212
▁but -11.7214
▁保温 -11.7217
▁雕刻 -11.7221
▁变革 -11.7221
▁圣经 -11.7223
▁斑 -11.7223
▁聚合 -11.7224
▁龟 -11.7225
▁噪音 -11.7225
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▁二者 -11.7226
▁he -11.7227
▁土豪 -11.7229
▁面板 -11.7229
而出 -11.7231
▁流水 -11.7232
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本事 -11.7234
▁命中 -11.7245
▁web -11.7245
▁续航 -11.7246
努力 -11.7247
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最 -11.7249
斋 -11.725
les -11.725
人心 -11.7252
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▁转身 -11.7254
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▁去除 -11.7257
穆 -11.7257
适配 -11.7257
▁舔 -11.7259
庸 -11.726
努 -11.7263
随 -11.7263
▁二十多 -11.7264
▁七月 -11.7264
▁进度 -11.7266
▁480 -11.7266
魄 -11.7267
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▁起义 -11.8311
4.8 -11.8311
蚕 -11.8312
▁1973 -11.8312
▁力气 -11.8314
叨 -11.8314
趋 -11.8315
let -11.8317
▁1968 -11.8317
屎 -11.8318
▁锡 -11.8319
≥ -11.832
▁没钱 -11.8321
▁at -11.8323
ny -11.8324
返 -11.8325
嗣 -11.8326
▁联邦 -11.8328
斯基 -11.8328
▁协商 -11.8329
偿 -11.8329
▁山上 -11.8329
▁紧紧 -11.833
学者 -11.833
▁十个 -11.8332
▁恶劣 -11.8333
▁数百 -11.8333
▁一瓶 -11.8333
▁锂电 -11.8337
政法 -11.8339
▁胸部 -11.834
do -11.834
交易 -11.8344
庵 -11.8345
▁腐 -11.8345
▁协会会 -11.835
IA -11.8351
弓 -11.8352
▁传销 -11.8357
▁750 -11.8358
▁搅 -11.8358
▁柠檬 -11.836
前方 -11.8361
▁终点 -11.8366
▁联系方 -11.8368
新闻 -11.8368
拘 -11.837
▁粤 -11.8371
▁群里 -11.8373
▁切除 -11.8373
tion -11.8374
▁不当 -11.8374
▁夫妇 -11.8374
简单 -11.8376
▁亏损 -11.8376
堵 -11.8378
▁军团 -11.8379
▁递 -11.838
▁吼 -11.838
▁预约 -11.838
▁珠 -11.8381
邮 -11.8381
▁亲爱 -11.8382
▁家教 -11.8383
▁一波 -11.8383
日报 -11.8385
▁十余 -11.8385
▁79 -11.8387
▁愉悦 -11.8388
调查 -11.8388
▁桂林 -11.8388
▁1961 -11.8389
ッ -11.8391
▁大国 -11.8392
▁暨 -11.8394
▁酶 -11.8396
▁短短 -11.8396
噢 -11.8396
▁DC -11.8397
▁发了 -11.8397
▁焊 -11.8397
ost -11.8399
du -11.8399
纺 -11.8401
冤 -11.8402
▁黑龙江省 -11.8402
职业 -11.8402
▁搜集 -11.8402
▁windows -11.8402
▁枕 -11.8403
▁八月 -11.8403
650 -11.8405
向上 -11.8407
▁鸿 -11.8408
380 -11.8409
▁世俗 -11.8409
▁长远 -11.8409
▁老是 -11.841
▁线上 -11.8416
复杂 -11.8417
▁have -11.842
▁捷 -11.8423
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▁插件 -11.8426
▁一站 -11.8426
卓 -11.8426
999 -11.8427
ED -11.8429
酮 -11.8429
55 -11.8429
▁卧室 -11.843
▁享 -11.8431
▁谎 -11.8432
▁国立 -11.8437
▁燃气 -11.844
▁梵 -11.8441
愧 -11.8441
▁早年 -11.8442
甚 -11.8445
▁凭证 -11.8447
▁屠 -11.8448
医疗 -11.8448
▁胡椒 -11.8449
▁这款 -11.845
▁禁忌 -11.8452
▁8000 -11.8452
▁坎 -11.8454
▁谈论 -11.8455
▁观音 -11.8456
妥 -11.8458
奸 -11.8459
MC -11.8459
▁同名 -11.8459
帐 -11.846
▁唐朝 -11.846
私 -11.8461
▁少儿 -11.8462
yl -11.8462
ca -11.8462
▁慈 -11.8463
▁地带 -11.8463
▁III -11.8464
表现 -11.8468
▁颁奖 -11.847
翰 -11.8473
▁1966 -11.8473
▁欧盟 -11.8474
▁6.4 -11.8475
艘 -11.8477
で -11.8481
▁was -11.8481
▁元件 -11.8482
▁美剧 -11.8483
悍 -11.8483
▁字幕 -11.8484
▁星际 -11.8486
▁风暴 -11.8487
阱 -11.8487
▁感知 -11.8491
▁妊娠 -11.8491
▁路口 -11.8493
猴 -11.8494
▁圣母 -11.8496
▁一秒 -11.8497
▁很多很多 -11.8498
▁邹 -11.85
▁稻 -11.8501
▁九十 -11.8503
▁昔 -11.8504
▁合唱 -11.8504
效应 -11.8505
▁Part -11.8505
▁窗户 -11.8506
痴 -11.8508
谐 -11.8509
▁备份 -11.8509
▁一碗 -11.8509
寇 -11.8511
▁有空 -11.8511
▁停下 -11.8512
▁隆 -11.8513
▁青海 -11.8514
▁好莱 -11.8514
CO -11.8516
▁不合 -11.8517
▁不服 -11.8518
04 -11.8518
贱 -11.8519
▁精神病 -11.8519
▁召 -11.8521
▁除去 -11.8521
又 -11.8522
▁耽误 -11.8523
▁双眼 -11.8523
▁沒 -11.8524
OR -11.8524
艾 -11.8529
▁有无 -11.853
he -11.8532
▁涂料 -11.8533
▁自助 -11.8533
▁而后 -11.8533
尼亚 -11.8534
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▁发烧 -11.8537
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▁录像 -11.8542
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2.5 -11.8543
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▁人有 -11.8545
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指标 -11.8548
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割 -11.8552
▁电阻 -11.8553
▁168 -11.8557
开展 -11.8558
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▁卡尔 -11.8562
▁相见 -11.8563
▁四月 -11.8564
▁字数 -11.8567
▁民事 -11.857
▁John -11.857
▁商业银行 -11.8572
▁女权 -11.8575
▁六月 -11.8576
▁担当 -11.8576
▁低温 -11.8577
ei -11.8577
▁漆 -11.8578
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让人 -11.8578
钞 -11.858
严重 -11.8581
可否 -11.8581
交往 -11.8582
抵 -11.8584
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▁教育局 -11.8588
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究 -11.8594
cl -11.8599
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工学 -11.8601
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滚 -11.8603
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▁三点 -11.8605
证书 -11.8606
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妞 -11.861
烯 -11.8612
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絮 -11.8617
ding -11.8617
报道 -11.8617
荫 -11.8619
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atch -11.8627
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ool -11.8629
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tor -11.8632
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一体 -11.864
畅 -11.8642
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商标 -11.8649
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MM -11.8652
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脾气 -11.8653
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挠 -11.8656
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单词 -11.8662
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▁3.6 -11.8669
▁不负 -11.8669
52 -11.8672
丙 -11.8672
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▁SATA -11.8673
揽 -11.8673
编制 -11.8673
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书法家 -11.8678
所用 -11.868
▁will -11.868
▁酥 -11.8681
▁一切都 -11.8685
▁头痛 -11.8691
▁深化 -11.8694
▁仪表 -11.8695
も -11.8695
攻击 -11.8695
▁九月 -11.8697
sk -11.8698
▁脾 -11.8698
▁詹 -11.8699
标注 -11.8699
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れ -11.8702
麻将 -11.8703
▁悄悄 -11.8706
▁__ -11.8706
▁制品 -11.8707
翅 -11.8708
广场 -11.8709
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▁稿 -11.8712
▁纠错 -11.8713
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▁廉价 -11.8716
▁娃娃 -11.8716
▁纤 -11.8716
姨妈 -11.8717
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女生 -11.8723
▁三百 -11.8724
红色 -11.8727
矢 -11.8729
▁中西 -11.8732
飘 -11.8734
回族 -11.8734
磊 -11.8737
こ -11.8738
▁羽毛球 -11.8739
崩 -11.8741
▁大体 -11.8741
▁di -11.8743
▁电缆 -11.8744
▁残疾人 -11.8746
▁执政 -11.8747
▁112 -11.8747
ì -11.875
东西 -11.875
SH -11.875
▁不看 -11.875
▁解放军 -11.8751
▁屈 -11.8753
▁专业课 -11.8753
4.7 -11.8755
不足 -11.8755
▁流氓 -11.8757
shi -11.8758
▁GDP -11.876
▁one -11.8768
凳 -11.877
▁渔业 -11.8771
▁军训 -11.8772
▁炒股 -11.8772
28 -11.8775
▁农历 -11.8775
▁糊 -11.8777
一半 -11.8782
▁细分 -11.8783
▁归纳 -11.8784
▁机灵 -11.8785
决心 -11.8786
冻 -11.8787
▁新西兰 -11.8788
▁扩散 -11.8789
▁篇文 -11.8789
诵 -11.8791
▁天真 -11.8795
SC -11.8797
ID -11.8798
▁方程 -11.8799
fa -11.8801
▁商会 -11.8802
▁歌舞 -11.8802
▁工业园 -11.8805
武帝 -11.8808
楼梦 -11.8808
TI -11.8809
▁1954 -11.881
▁滋味 -11.8812
▁CEO -11.8813
▁因果 -11.8813
.05 -11.8813
措 -11.8816
▁桂 -11.8816
▁玛丽 -11.8818
▁文笔 -11.8819
▁根源 -11.882
▁重组 -11.8822
▁无形 -11.8823
BO -11.8823
判定 -11.8824
ns -11.8825
性化 -11.8826
铁路 -11.8827
▁1953 -11.8827
▁攒 -11.8828
02 -11.8833
ON -11.8833
▁复发 -11.8834
OD -11.8834
▁粒子 -11.8835
▁1969 -11.8835
宝强 -11.8836
EL -11.8837
▁更改 -11.8838
▁结晶 -11.8842
将军 -11.8844
▁归属 -11.8847
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Д -11.8848
棱 -11.8848
▁弓 -11.885
▁抚 -11.885
▁老头 -11.8851
▁right -11.8851
▁美军 -11.8853
▁城堡 -11.8854
▁102 -11.8857
▁梨 -11.8858
▁地名 -11.8858
▁新品 -11.8858
▁农药 -11.8862
▁第二十二 -11.8865
▁敢于 -11.8872
▁装有 -11.8873
▁束缚 -11.8873
▁178 -11.8873
▁评委 -11.8878
▁永不 -11.888
▁size -11.8883
▁广播电视 -11.8884
李 -11.8884
▁栖息 -11.8887
▁受过 -11.8888
CI -11.8888
风险 -11.8892
▁争夺 -11.8893
▁全校 -11.8894
▁心得 -11.8894
▁牧 -11.8895
▁随手 -11.8895
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落实 -11.8897
剔 -11.8897
▁埃及 -11.8905
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▁信托 -11.8908
不大 -11.8909
变化 -11.8909
▁奏 -11.891
▁盒子 -11.8916
普查 -11.8917
▁闻名 -11.8919
西亚 -11.892
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影院 -11.8923
▁物价 -11.8924
▁会展 -11.8924
▁水电 -11.8925
磅 -11.8928
▁门诊 -11.893
▁著作权 -11.893
▁赛车 -11.8931
▁怪兽 -11.8932
更 -11.8933
▁模样 -11.8934
amp -11.8935
涡 -11.894
▁被子 -11.8942
▁不带 -11.8944
▁另一方 -11.8944
▁苯 -11.8945
▁静静 -11.8946
▁GB -11.8946
▁限定 -11.8947
▁境外 -11.8948
▁兮 -11.8948
摆 -11.895
OP -11.8951
号码 -11.8952
此刻 -11.8953
▁炸弹 -11.8955
▁佛山 -11.8955
▁横向 -11.896
▁羊肉 -11.8961
▁开阔 -11.8965
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凝 -11.8967
▁诸位 -11.8967
▁一号 -11.8968
▁莲花 -11.8969
▁狠 -11.8969
▁雕 -11.8975
▁容纳 -11.8976
▁Lo -11.8979
▁徐州 -11.8982
cc -11.8982
tu -11.8987
▁廖 -11.8987
幼 -11.8989
▁奥地利 -11.8995
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贡 -11.8998
不小 -11.9005
▁2.8 -11.9007
削 -11.9008
▁污水 -11.9008
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ins -11.901
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▁盲 -11.9014
▁We -11.9014
▁试着 -11.9015
▁观光 -11.9019
▁吉祥 -11.902
禾 -11.902
▁疾 -11.9021
▁全场 -11.9023
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▁薇 -11.9025
▁机箱 -11.9026
▁br -11.9026
▁单反 -11.9027
ber -11.9028
▁修养 -11.903
▁明清 -11.9032
OT -11.9034
▁到期 -11.9036
▁户通 -11.9037
▁烘 -11.9038
▁脚步 -11.904
▁报价 -11.9047
基地 -11.9049
▁审定 -11.9049
dy -11.9051
▁肿 -11.9056
▁三线 -11.9057
▁先天 -11.9058
规模 -11.9058
蓬 -11.9059
甫 -11.9061
大批 -11.9062
▁梦幻 -11.9062
闺 -11.9065
黛 -11.9065
▁之战 -11.9066
▁史上 -11.9067
渴 -11.9069
垂 -11.9071
▁腐蚀 -11.9071
▁外文 -11.9075
▁女票 -11.9076
国道 -11.9077
▁135 -11.908
▁润滑 -11.9081
▁脚本 -11.9082
▁九年 -11.9082
揍 -11.9085
▁190 -11.9086
▁1937 -11.9089
▁棉花 -11.9089
▁165 -11.9091
▁推崇 -11.9092
班子 -11.9093
▁like -11.9093
▁压制 -11.9094
rt -11.9095
MP -11.9099
▁VGA -11.9099
▁家电 -11.9099
han -11.9101
▁慌 -11.9101
MT -11.9102
▁云狗 -11.9104
▁话剧 -11.9104
▁Car -11.9105
芙 -11.9107
▁无情 -11.9114
▁日报 -11.9114
会儿 -11.9114
男孩 -11.9114
▁阅 -11.9114
▁遍布 -11.9115
▁如图 -11.9115
▁go -11.9118
▁红军 -11.912
▁高分 -11.9121
便宜 -11.9122
▁超人 -11.9124
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▁纵横 -11.9127
あ -11.9128
▁彩票 -11.913
一些 -11.9131
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▁贼 -11.9133
▁满分 -11.9135
煮 -11.9136
▁一贯 -11.9137
ran -11.9138
bu -11.9138
拥 -11.9139
舒 -11.9144
▁诺贝尔 -11.9144
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▁椅 -11.9145
▁听课 -11.9146
▁中央电视台 -11.9148
抄 -11.9149
Pa -11.915
▁卡片 -11.915
▁两面 -11.9154
▁TA -11.9157
▁蜘蛛 -11.9159
搞 -11.9159
▁动画片 -11.916
▁中日 -11.916
▁梦里 -11.916
▁德育 -11.9161
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原理 -11.9164
押 -11.9169
▁104 -11.9169
▁紫外 -11.9169
▁纯属 -11.9169
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▁尝 -11.917
▁蒸汽 -11.917
▁东风 -11.9173
▁莲 -11.9173
▁恐龙 -11.9179
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▁排序 -11.9181
运输 -11.9182
▁名声 -11.9182
▁不安 -11.9183
拙 -11.9184
▁1948 -11.9186
▁民生 -11.9187
▁梅花 -11.9189
▁蒲 -11.9189
▁宝石 -11.919
▁广电 -11.9191
尾巴 -11.9191
▁礼品 -11.9193
▁7.5 -11.9196
寻 -11.9196
mer -11.9197
迟 -11.9198
平衡 -11.9201
▁蘑菇 -11.9201
▁散发 -11.9202
遥 -11.9203
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涉 -11.9206
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孟 -11.9211
铜 -11.9217
踏实 -11.9218
盗 -11.9219
一样 -11.922
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▁反过来 -11.9223
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合金 -11.9225
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▁电容 -11.9226
8.00 -11.9226
ever -11.9228
▁短篇 -11.9229
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▁妆 -11.9229
▁必需 -11.9229
▁定居 -11.923
▁一刻 -11.9231
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▁上床 -11.9238
毙 -11.9243
▁大方 -11.9245
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实际 -11.9246
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▁贡 -11.925
糊 -11.925
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舅 -11.9257
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育人 -11.9258
IM -11.9258
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哑 -11.9261
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浊 -11.9266
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ous -12.0274
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ake -12.0294
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7.5 -12.0295
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OC -12.0298
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ning -12.0304
锯 -12.0308
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520 -12.0329
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み -12.0351
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灾害 -12.0353
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205 -12.0354
彝族 -12.0356
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7.8 -12.036
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滕 -12.037
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list -12.037
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吞 -12.0382
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dB -12.0385
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逐 -12.0394
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340 -12.0415
其后 -12.0418
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206 -12.0426
添 -12.0427
军区 -12.0428
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す -12.0432
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ak -12.0481
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周年 -12.0485
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GP -12.0495
锥 -12.0495
5.7 -12.0495
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152 -12.0526
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ff -12.0543
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泛 -12.0565
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稀 -12.0574
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220 -12.0583
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ise -12.0592
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ish -12.0593
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ki -12.0597
愤 -12.0598
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歧 -12.0606
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党员 -12.0659
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侍 -12.0662
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ara -12.0733
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甲醛 -12.074
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LO -12.0742
颤 -12.0743
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美术 -12.0744
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一笑 -12.0746
row -12.0748
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秃 -12.0763
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tra -12.0765
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▁113 -12.0778
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各界 -12.08
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小学 -12.0816
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▁捣 -12.082
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一届 -12.0838
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恤 -12.0842
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纵 -12.0844
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睁 -12.0846
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庄村 -12.0867
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斐 -12.0874
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GO -12.0879
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蒜 -12.0879
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啦 -12.0883
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set -12.0888
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轼 -12.09
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御 -12.0904
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va -12.0904
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隶 -12.0918
璋 -12.0919
撒 -12.092
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朔 -12.0929
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134 -12.0932
tin -12.0932
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----------- -12.0948
钛 -12.0949
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扮 -12.0954
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驳 -12.0955
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CL -12.097
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掠 -12.0977
脆 -12.0979
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伪 -12.0982
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月初 -12.0985
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对象 -12.0991
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DM -12.0993
劳动 -12.0995
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171 -12.0997
code -12.0997
ロ -12.0997
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IP -12.1001
领导 -12.1003
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函数 -12.1009
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↑ -12.102
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摹 -12.1025
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▁1967 -12.1029
è -12.1033
232 -12.1035
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▁Ad -12.1039
▁4.6 -12.104
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周期 -12.1043
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辐射 -12.1046
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睹 -12.1048
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ung -12.1054
▁1935 -12.1054
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▁票据 -12.1055
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gen -12.1059
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窥 -12.1062
1000 -12.1063
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點 -12.1064
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9.9 -12.1067
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炳 -12.1075
ジ -12.1075
孕 -12.1078
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妾 -12.1081
玻璃 -12.1081
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地位 -12.1089
思考 -12.1092
敛 -12.1093
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尔斯 -12.1102
吨 -12.1103
蛮 -12.1104
杆菌 -12.1106
汝 -12.1107
9000 -12.111
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▁翠 -12.1112
▁IEEE -12.1113
▁与众 -12.1114
▁弊 -12.1114
▁10.2 -12.1115
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view -12.1116
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iz -12.1121
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盔 -12.1127
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▁木有 -12.1133
▁验 -12.1135
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215 -12.1306
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互 -12.1763
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9.0 -12.1763
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long -12.1767
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询 -12.1784
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NS -12.1791
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ight -12.18
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333 -12.1803
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301 -12.1806
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毫 -12.1809
岔 -12.1809
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pin -12.1818
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红酒 -12.1819
肽 -12.1819
微博 -12.1821
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咽 -12.1825
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den -12.183
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ya -12.1834
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420 -12.1839
斜 -12.184
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不忘 -12.1842
∩ -12.1843
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魅 -12.1844
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空调 -12.1844
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斗争 -12.1846
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蛾 -12.1851
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楷 -12.1851
客户 -12.1852
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▁无耻 -12.1859
cd -12.1864
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渍 -12.1866
胡子 -12.1866
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隙 -12.1877
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搭 -12.1879
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陇 -12.1887
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笃 -12.1892
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gg -12.1899
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BE -12.19
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也罢 -12.1911
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chi -12.1915
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950 -12.3169
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uck -12.318
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235 -12.321
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245 -12.3261
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255 -12.3437
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540 -12.3448
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ative -12.3529
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е -12.3566
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py -12.3653
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け -12.3661
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碍 -12.3913
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ple -12.3931
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沧桑 -12.3941
4.5 -12.3946
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ama -12.395
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lic -12.3956
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AA -12.3962
cor -12.3963
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歇 -12.3987
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ense -12.3992
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現 -12.3994
椿 -12.3998
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唯 -12.4006
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IO -12.4012
316 -12.4014
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2007 -12.4017
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ose -12.402
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boy -12.4027
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des -12.4033
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lf -12.4054
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303 -12.4059
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sl -12.4085
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匡 -12.4087
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語 -12.4096
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put -12.411
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350 -12.4113
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file -12.4123
蒿 -12.4123
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8.7 -12.4126
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body -12.4132
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挟 -12.4134
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菠 -12.4147
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rap -12.4148
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ings -12.4149
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俏 -12.415
191 -12.4151
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LD -12.4156
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钻 -12.4158
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埃尔 -12.416
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城镇 -12.4171
咦 -12.4171
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▁强劲 -12.4172
▁人用 -12.4173
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疯 -12.4174
华人 -12.4175
▁sub -12.4177
▁太快 -12.4177
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PH -12.4179
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時 -12.4184
▁区位 -12.4185
▁文史 -12.4188
▁固有 -12.4189
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▁磁盘 -12.4193
410 -12.4193
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▁灌输 -12.4195
起义 -12.4195
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cer -12.4196
会员 -12.4198
▁路人 -12.4198
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娄 -12.42
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PT -12.4201
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▁大巴 -12.4203
▁人流 -12.4204
ted -12.4206
契 -12.4206
分布 -12.4206
中央 -12.4207
牲 -12.4208
酉 -12.4208
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既往 -12.4218
▁纵向 -12.4218
目睹 -12.422
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aw -12.4222
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无双 -12.4226
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ath -12.4229
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巩 -12.4232
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▁涂抹 -12.4245
▁民歌 -12.4246
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680 -12.4251
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226 -12.4253
簇 -12.4254
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app -12.4257
前行 -12.4257
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160 -12.4274
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BT -12.4275
矛 -12.4276
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8000 -12.4278
溜 -12.4279
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180 -12.4288
6000 -12.429
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530 -12.4325
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580 -12.434
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灸 -12.4517
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rom -12.458
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322 -12.4642
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粘膜 -12.4686
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810 -12.4754
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305 -12.4756
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霄 -12.4757
ute -12.4758
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蜕 -12.4772
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210 -12.4779
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ring -12.4785
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229 -12.4789
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101 -12.4793
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345 -12.4804
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259 -12.4827
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610 -12.491
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750 -12.4923
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630 -12.4951
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258 -12.4971
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NI -12.4975
峭 -12.4976
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递 -12.4981
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BI -12.5006
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▁千古 -12.5024
▁拍拍 -12.5025
▁米粉 -12.5027
▁颤抖 -12.5028
▁受限 -12.5029
动脉 -12.503
▁编导 -12.503
002 -12.503
▁特级 -12.5031
OH -12.5032
▁严禁 -12.5035
枕 -12.5036
鞠 -12.5039
▁金黄 -12.5041
勾 -12.5047
▁电极 -12.5048
▁课后 -12.5049
▁水温 -12.5049
煲 -12.5051
▁买入 -12.5052
▁二号 -12.5052
▁SEO -12.5052
▁操心 -12.5052
无故 -12.5054
可言 -12.5054
▁Live -12.5055
曦 -12.5055
▁凸显 -12.5056
▁五一 -12.5057
▁球形 -12.5058
▁格调 -12.5059
可见 -12.5059
▁智障 -12.506
▁Har -12.5061
▁福特 -12.5063
小心 -12.5063
▁性情 -12.5063
给我 -12.5063
▁大头 -12.5065
▁逃出 -12.5065
▁契合 -12.5065
东坡 -12.5066
▁名列 -12.5067
▁购房 -12.5067
▁干旱 -12.5069
▁活塞 -12.507
▁宁可 -12.5071
▁棒棒 -12.5074
▁军校 -12.5076
挤 -12.5077
樟 -12.5077
▁选秀 -12.5078
men -12.5079
通讯 -12.5079
▁贪污 -12.508
▁孟子 -12.508
兄弟 -12.5081
▁后备 -12.5081
▁网红 -12.5081
描述 -12.5082
▁甘蔗 -12.5083
▁话费 -12.5083
▁宣告 -12.5084
▁更深 -12.5084
▁隐隐 -12.5084
综合治理 -12.5085
▁路程 -12.5087
PU -12.5087
膛 -12.5091
▁可观 -12.5091
ˉ -12.5092
▁多发 -12.5092
▁ll -12.5093
▁某宝 -12.5093
癸 -12.5095
▁火花 -12.5095
脐 -12.5102
193 -12.5105
财产 -12.5106
▁发呆 -12.5107
▁以土 -12.5107
▁宰相 -12.5109
▁打打 -12.5109
▁明细 -12.5109
▁征求 -12.5112
▁三十二 -12.5112
▁干过 -12.5112
漂 -12.5112
194 -12.5116
▁第三十九 -12.5116
镑 -12.5117
绣 -12.5117
▁配偶 -12.5117
头发 -12.5118
▁冲刺 -12.5118
秋冬 -12.5118
▁一百九十 -12.5119
mail -12.512
▁一不 -12.512
▁CMOS -12.5123
▁VPN -12.5124
▁隐蔽 -12.5124
▁逆袭 -12.5125
一级 -12.5126
▁迫害 -12.5127
▁逼真 -12.5127
▁门窗 -12.5127
int -12.5127
▁顺畅 -12.5129
▁点多 -12.513
db -12.5132
▁爱因 -12.5132
2.2 -12.5134
▁矿业 -12.5136
▁平坦 -12.5136
▁出柜 -12.5137
▁摩尔 -12.5138
▁迪士 -12.5139
▁邻近 -12.5139
252 -12.514
娥 -12.5141
广播电 -12.5142
里亚 -12.5144
安稳 -12.5145
▁排球 -12.5145
▁TOP -12.5146
NO -12.5146
▁纯真 -12.515
醚 -12.5152
▁厂长 -12.5154
▁无害 -12.5156
▁风吹 -12.5158
警察 -12.5158
▁伊利 -12.516
▁下达 -12.5161
▁民政 -12.5162
star -12.5162
▁新闻出版 -12.5164
羟 -12.5165
▁三五 -12.5165
▁独创 -12.5165
▁软硬 -12.5166
进步 -12.5166
▁年均 -12.5167
期徒 -12.5167
▁性爱 -12.5167
黛玉 -12.5168
▁构件 -12.5168
湿度 -12.5169
▁非线性 -12.5171
▁五毛 -12.5172
▁PLC -12.5173
▁庆典 -12.5173
ext -12.5174
▁远大 -12.5174
▁音像 -12.5176
▁遍地 -12.5176
▁内阁 -12.5176
▁荔枝 -12.5177
▁三家 -12.5179
▁演练 -12.5179
贾 -12.5179
得好 -12.518
▁开来 -12.5182
锚 -12.5183
▁滑动 -12.5184
▁妥善 -12.5186
▁瘦身 -12.5186
▁外向 -12.5186
▁壁纸 -12.5186
惩 -12.5187
▁回路 -12.5187
▁两万 -12.5187
lay -12.5187
▁权衡 -12.5187
▁孔雀 -12.5187
▁会想 -12.5188
TB -12.5189
ump -12.5189
▁没得 -12.5189
▁减弱 -12.519
侵 -12.519
凛 -12.519
▁校门 -12.5191
▁Music -12.5192
oth -12.5193
▁单亲 -12.5196
蒙古族 -12.5199
馀 -12.5199
▁深沉 -12.5199
进一步 -12.52
▁Photoshop -12.5202
▁损耗 -12.5204
巴巴 -12.5204
▁建构 -12.5205
time -12.5207
▁买点 -12.5208
磷 -12.5208
era -12.5209
▁素有 -12.5211
▁第四十 -12.5212
▁北纬 -12.5213
▁从头到 -12.5213
▁热线 -12.5215
▁老老 -12.5216
ano -12.5217
ナ -12.5221
年华 -12.5222
混混 -12.5222
苷 -12.5225
治好 -12.5226
归于 -12.5227
砌 -12.5228
▁参谋 -12.5228
渠道 -12.5229
288 -12.5229
陛 -12.523
▁结核 -12.5231
一击 -12.5233
▁回事 -12.5235
▁超声波 -12.5235
沐 -12.5236
琐 -12.5238
▁佳能 -12.524
▁吴亦凡 -12.5246
秆 -12.5247
▁时应 -12.5249
▁高超 -12.5251
▁长征 -12.5251
▁亚热 -12.5255
文帝 -12.5259
▁抽出 -12.5262
▁板型 -12.5263
以求 -12.5264
▁嗓子 -12.5265
达标 -12.5266
▁自慰 -12.5267
▁Lesson -12.5267
胁 -12.527
fer -12.527
▁小鸟 -12.527
▁客户服务 -12.5273
▁往返 -12.5274
▁Fr -12.5274
▁分红 -12.5275
264 -12.5276
▁亨利 -12.5277
▁方块 -12.5277
▁大风 -12.5279
▁麒麟 -12.5279
sen -12.5279
▁现身 -12.528
醛 -12.5283
▁USA -12.5284
▁顺应 -12.5285
电源 -12.5286
2.8 -12.5287
241 -12.5287
▁激进 -12.5287
▁势必 -12.5288
▁1926 -12.5288
▁充当 -12.5288
▁大法 -12.5288
司机 -12.5289
搂 -12.5289
399 -12.5291
祀 -12.5292
话题 -12.5294
▁一角 -12.5294
▁第一百八 -12.5294
萝 -12.5295
实现 -12.5296
ita -12.5296
▁大姐 -12.5298
巳 -12.5299
FT -12.53
▁编者 -12.53
▁心肌 -12.5301
ck -12.5301
▁逮捕 -12.5302
▁因為 -12.5303
▁顽强 -12.5303
▁间谍 -12.5304
Comp -12.5305
▁收养 -12.5308
▁Up -12.5308
▁离别 -12.5308
唑 -12.5309
▁打入 -12.5309
▁行政区 -12.531
▁灭火 -12.5311
▁兵团 -12.5311
▁Michael -12.5312
参谋长 -12.5313
▁QAQ -12.5314
ith -12.5314
▁Ri -12.5315
职高 -12.5317
一定 -12.5317
▁国旗 -12.5318
▁西门 -12.5319
▁傲娇 -12.532
▁重伤 -12.5321
發 -12.5322
▁NVIDIA -12.5322
730 -12.5323
编辑 -12.5328
鲤 -12.5333
臻 -12.5333
▁打听 -12.5334
▁王道 -12.5334
荚 -12.5338
▁尼克 -12.5338
▁潜心 -12.5338
▁细微 -12.534
▁富豪 -12.5341
▁窗帘 -12.5341
▁五四 -12.5342
▁system -12.5343
讹 -12.5344
▁无色 -12.5346
ND -12.5347
▁融化 -12.5347
▁功课 -12.535
▁PDF -12.535
318 -12.5354
苍 -12.5355
理工 -12.5358
▁大佬 -12.5358
▁宜昌 -12.5359
▁副主 -12.5362
驴 -12.5363
▁title -12.5363
▁脱颖 -12.5366
填 -12.5366
470 -12.5367
监察 -12.5368
厮 -12.537
▁欧冠 -12.5373
知乎 -12.5374
▁WP -12.5377
▁口才 -12.5377
实践 -12.538
▁造纸 -12.5381
▁当面 -12.5382
▁宝玉 -12.5387
chan -12.5388
豆腐 -12.5388
メ -12.539
焉 -12.539
▁欠缺 -12.5391
▁降落 -12.5391
印发 -12.5393
绛 -12.5393
▁不耐 -12.5395
▁八十年 -12.5399
殴 -12.5399
▁复古 -12.54
5.9 -12.5401
妓 -12.5401
▁山谷 -12.5401
萝卜 -12.5402
ble -12.5403
SR -12.5407
▁计生 -12.541
▁视察 -12.5414
▁潜伏 -12.5415
▁一百四十 -12.5416
适合 -12.5417
▁贪婪 -12.5418
▁红豆 -12.5418
伯格 -12.5419
▁站台 -12.5421
▁别忘 -12.5422
产阶级 -12.5423
▁苍蝇 -12.5423
斯克 -12.5425
▁三岁 -12.5426
▁克里 -12.5426
▁翻唱 -12.5427
▁特许 -12.5427
▁进出 -12.5428
▁性命 -12.5429
9.2 -12.5429
▁芜湖 -12.5429
EV -12.543
▁引来 -12.5433
▁风波 -12.5434
▁发力 -12.5434
暑假 -12.5435
心动 -12.5435
▁完工 -12.5437
▁英勇 -12.5438
致敬 -12.5439
仑 -12.5441
390 -12.5443
190 -12.5444
▁保暖 -12.5446
▁写实 -12.5449
▁罗马尼亚 -12.545
▁巴基斯坦 -12.5453
▁健美 -12.5455
▁改版 -12.5455
▁无心 -12.5455
缓存 -12.5456
Data -12.5456
▁城中 -12.5456
rr -12.5458
小巷 -12.546
璐 -12.5461
▁霸道 -12.5464
▁截然 -12.5469
▁活血 -12.547
▁猎头 -12.547
め -12.5471
▁监事 -12.5472
▁试过 -12.5475
▁刚出 -12.5476
▁农机 -12.5477
▁急需 -12.5478
6.9 -12.5479
unt -12.5479
差异 -12.548
321 -12.548
▁安安 -12.5482
▁水文 -12.5482
沿 -12.5484
▁12.2 -12.5485
▁爆破 -12.5486
批评 -12.5486
▁At -12.5488
UP -12.5489
▁出道 -12.549
▁店主 -12.5492
雷斯 -12.5494
▁少将 -12.5494
▁工商行政 -12.5495
▁天皇 -12.5495
▁地中海 -12.5496
▁胶片 -12.5499
▁石英 -12.5501
忍受 -12.5501
▁排毒 -12.5502
開 -12.5502
小小 -12.5503
▁桂花 -12.5504
▁侵入 -12.5504
▁解救 -12.5505
TR -12.5505
励 -12.5506
▁his -12.5507
▁张艺兴 -12.5507
RP -12.5508
ations -12.551
▁殴打 -12.5511
nel -12.5512
申请 -12.5512
sw -12.5512
▁一见钟 -12.5512
ú -12.5513
▁二十九 -12.5513
▁殖民地 -12.5513
楞 -12.5513
▁亚运 -12.5514
▁献血 -12.5515
▁华硕 -12.5516
▁借此 -12.5516
ari -12.5516
▁淹没 -12.5517
▁襄阳 -12.5517
820 -12.5519
▁趋于 -12.5521
▁急诊 -12.5521
▁骨质 -12.5521
轰 -12.5525
▁重男轻 -12.5525
▁位居 -12.5526
▁导出 -12.5526
▁波特 -12.5526
com -12.5527
▁牧师 -12.5527
茹 -12.5527
▁变质 -12.5528
篡 -12.5528
ev -12.553
蹬 -12.5531
▁坦诚 -12.5532
1.4 -12.5533
▁涡轮 -12.5535
▁music -12.5536
▁无能为 -12.5537
dpi -12.5538
▁地表 -12.5539
DB -12.5539
1.7 -12.5544
▁之争 -12.5544
▁敲门 -12.5545
tech -12.5545
▁听得 -12.5546
缀 -12.5547
✧ -12.5549
物品 -12.5549
▁骆驼 -12.5549
▁喝醉 -12.5549
辣椒 -12.555
8.5 -12.5551
▁想得 -12.5552
▁ABC -12.5552
▁羽毛 -12.5554
▁不经 -12.5554
▁盆腔 -12.5554
IL -12.5554
▁卸妆 -12.5556
▁几部 -12.5557
ik -12.5557
play -12.5557
325 -12.5558
▁敷衍 -12.5559
▁接吻 -12.556
▁人头 -12.5562
▁贮藏 -12.5563
▁遗留 -12.5563
▁Max -12.5563
UE -12.5567
▁朱元 -12.5569
▁自立 -12.557
▁写过 -12.557
8.0 -12.5573
▁电能 -12.5573
▁会费 -12.5574
▁辨证 -12.5575
▁传闻 -12.5576
▁好些 -12.5577
▁犯规 -12.5578
▁并行 -12.5579
▁再来 -12.5579
▁表扬 -12.558
▁呼唤 -12.558
▁脾胃 -12.558
▁MAC -12.558
▁198 -12.5581
间距 -12.5582
311 -12.5582
朱 -12.5583
矫 -12.5583
唤 -12.5583
输出 -12.5585
▁先天性 -12.5585
▁瑕疵 -12.5586
bin -12.5586
▁浮动 -12.5586
年龄 -12.5587
Work -12.5587
蔑 -12.5588
▁乌鲁木齐 -12.559
▁机车 -12.559
▁Chen -12.5591
潜力 -12.5591
▁解毒 -12.5594
▁相对论 -12.5595
▁二三 -12.5595
▁以至 -12.5604
char -12.5604
▁扑克 -12.5604
oto -12.5606
▁LOGO -12.5607
枢 -12.561
辊 -12.5614
▁草坪 -12.5614
▁越野 -12.5615
▁上万 -12.5616
▁狂欢 -12.5617
▁支部 -12.5618
the -12.5622
▁工艺品 -12.5622
▁石刻 -12.5624
▁图画 -12.5624
▁盐酸 -12.5625
▁虚弱 -12.5626
▁转运 -12.5629
▁调戏 -12.5629
接受 -12.5629
▁后遗 -12.5631
音乐家 -12.5633
▁进阶 -12.5634
▁铅笔 -12.5637
▁举措 -12.5638
▁流血 -12.5641
▁中老年 -12.5643
▁磨合 -12.5644
▁布朗 -12.5645
PP -12.5645
▁一元 -12.5646
▁游人 -12.5646
▁诸如此 -12.5647
▁取舍 -12.5648
▁百八十 -12.5648
▁MacBook -12.565
▁养育 -12.5651
cho -12.5652
▁吃苦 -12.5652
裡 -12.5653
▁中层 -12.5653
▁不符 -12.5654
绯 -12.5655
猝 -12.5655
▁闪烁 -12.5655
挥 -12.5656
匣 -12.5656
▁王牌 -12.5659
▁定额 -12.5659
▁入围 -12.5659
well -12.5659
▁餐桌 -12.5659
▁Em -12.5661
▁进门 -12.5662
斩 -12.5663
cha -12.5665
▁国会 -12.5665
▁打成 -12.5665
▁碳水 -12.5666
事变 -12.5667
SK -12.5667
▁作死 -12.567
▁单调 -12.567
▁集装箱 -12.5671
▁风湿 -12.5671
▁美工 -12.5673
▁诉说 -12.5673
▁采样 -12.5674
iu -12.5674
內 -12.5675
tur -12.5675
▁叙利亚 -12.5676
壤 -12.5678
510 -12.5679
现状 -12.5684
▁北京师范大学 -12.5684
▁图解 -12.5687
口语 -12.5688
缠 -12.5689
▁击中 -12.5689
▁昆仑 -12.569
sin -12.569
▁一栋 -12.569
▁希尔 -12.5691
扫 -12.5693
211 -12.5694
▁硅胶 -12.5694
▁they -12.5694
▁食盐 -12.5695
▁Pe -12.5696
▁夏日 -12.5696
地铁 -12.5697
▁花花 -12.5698
▁重型 -12.5698
▁清热 -12.5699
▁领军 -12.57
▁Ke -12.5701
▁地理学 -12.5702
▁志同道合 -12.5702
疙瘩 -12.5702
▁肺炎 -12.5702
馅 -12.5702
▁饮用水 -12.5704
闵 -12.5704
▁客座 -12.5705
拣 -12.5708
▁沿用 -12.5708
▁镇江 -12.5709
▁三十多 -12.5709
▁抬起 -12.5709
Sc -12.571
▁谋杀 -12.5711
pro -12.5712
▁镶嵌 -12.5712
▁说不清 -12.5713
▁柴油 -12.5713
▁挥发 -12.5713
▁多好 -12.5713
▁关羽 -12.5714
满面 -12.5715
▁拥挤 -12.5717
▁一再 -12.5718
EP -12.5722
van -12.5722
可靠 -12.5724
公主 -12.5725
家庄 -12.5728
▁大开 -12.5728
401 -12.5729
制作 -12.5731
嵩 -12.5731
情节 -12.5732
▁贫富 -12.5733
▁粉底 -12.5734
壮大 -12.5735
▁从那 -12.5736
浓度 -12.5736
620 -12.5736
拢 -12.5737
710 -12.5738
▁红烧 -12.5739
勘探 -12.5742
勘 -12.5743
WA -12.5744
▁祝你 -12.5746
圩 -12.5746
省委 -12.5747
惟 -12.5747
▁固态 -12.5748
▁加点 -12.5752
240 -12.5752
▁琉璃 -12.5753
358 -12.5755
vers -12.5755
▁缴费 -12.5755
▁there -12.5756
▁封印 -12.5757
皇上 -12.5757
MAX -12.576
▁信奉 -12.5764
本上 -12.5769
▁阶梯 -12.577
江县 -12.5771
▁山下 -12.5771
pu -12.5772
羁 -12.5772
汞 -12.5773
▁可取 -12.5773
▁杏仁 -12.5774
▁法兰 -12.5777
▁1925 -12.5778
▁四次 -12.5778
复兴 -12.5779
▁LV -12.5779
员工 -12.578
▁段落 -12.578
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2.1 -12.5792
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3.2 -12.5792
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吕 -12.5793
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唾 -12.5796
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ming -12.5817
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AG -12.6115
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qi -12.6119
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膺 -12.6125
竞 -12.6126
碰 -12.6126
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aa -12.6127
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襟 -12.6145
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呐 -12.6154
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555 -12.6182
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760 -12.6189
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307 -12.619
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368 -12.6199
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242 -12.62
握 -12.6202
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电子科技 -12.6202
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rain -12.6208
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浸 -12.6218
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太阳 -12.6225
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302 -12.6226
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歹 -12.6234
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late -12.624
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钮 -12.6244
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306 -12.6271
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328 -12.6321
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rin -12.6324
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tern -12.6339
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夭 -12.6403
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3.7 -12.6408
230 -12.6409
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ada -12.7153
骆 -12.7153
140 -12.7154
▁中奖 -12.7154
▁暑期 -12.7155
▁全心 -12.7157
▁准时 -12.7158
▁编入 -12.7159
▁螺纹 -12.7161
▁含蓄 -12.7161
▁识字 -12.7163
▁繁忙 -12.7164
▁Chrome -12.7165
▁小巧 -12.7166
噬 -12.7166
LM -12.7166
▁领养 -12.7167
del -12.7167
混杂 -12.7168
▁阿卡 -12.7168
瑰 -12.7169
▁還是 -12.7169
UT -12.717
▁攻克 -12.7171
▁function -12.7172
质问 -12.7173
▁私密 -12.7174
掩 -12.7174
▁素食 -12.7175
▁床头 -12.7175
▁全真 -12.7176
▁抗争 -12.7177
直言 -12.7178
蔬 -12.718
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pop -12.7183
▁小城 -12.7184
lex -12.7185
▁直肠 -12.7187
▁报社 -12.7189
▁城南 -12.719
检验 -12.719
▁布雷 -12.719
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ves -12.7195
▁傻傻 -12.7196
▁外接 -12.7197
▲ -12.7197
▁耀眼 -12.7199
柑 -12.72
▁2300 -12.7201
记者 -12.7204
▁发财 -12.7205
▁大肆 -12.7206
▁京师 -12.7209
xy -12.7209
陪 -12.721
▁双胞 -12.721
▁配制 -12.7211
▁追杀 -12.7212
▁Vo -12.7212
▁颇具 -12.7214
▁减退 -12.7215
▁愚昧 -12.7215
央 -12.7216
榻 -12.7217
▁1700 -12.7218
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熹 -12.722
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▁Min -12.7224
沂 -12.7224
▁爬山 -12.7225
ior -12.7226
▁痛心 -12.7227
▁暴雨 -12.7228
▁万亿 -12.7228
荧 -12.723
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gr -12.7231
跟 -12.7233
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诊断 -12.7235
瞻 -12.7237
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▁漳州 -12.724
生肖 -12.724
饿 -12.7241
0.5 -12.7242
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象棋 -12.7243
▁红颜 -12.7244
▁坚果 -12.7245
non -12.7246
▁橘子 -12.7248
KE -12.7249
▁龙泉 -12.7251
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宙 -12.7254
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幾 -12.7256
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经济体制 -12.7258
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▁大西洋 -12.7262
腑 -12.7263
▁车里 -12.7263
▁原装 -12.7266
File -12.7267
鳌 -12.7267
▁天台 -12.7268
▁5400 -12.7268
KO -12.7269
▁想学 -12.7269
▁国家税务 -12.7274
▁动辄 -12.7274
& -12.7276
媲 -12.7276
▁离奇 -12.7278
▁在此之 -12.7279
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253 -12.7281
▁骑马 -12.7282
▁大理石 -12.7284
▁内陆 -12.7284
▁握手 -12.7284
▁山中 -12.7286
ǐ -12.7286
▁性行 -12.729
乎乎 -12.7291
▁军衔 -12.7292
▁寄生虫 -12.7294
▁土著 -12.7296
3.4 -12.7296
▁水位 -12.7296
BR -12.7296
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▁生猪 -12.7298
▁行政处罚 -12.7299
丫 -12.73
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mol -12.7302
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▁白头 -12.7307
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▁三万 -12.7311
▁军用 -12.7312
一说 -12.7312
▁整部 -12.7313
▁自控 -12.7313
流水 -12.7314
▁大麻 -12.7315
▁细思极 -12.7317
▁记下 -12.7317
▁惊天 -12.7317
▁苏宁 -12.7319
▁外伤 -12.732
480 -12.7321
▁前十 -12.7321
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▁曲艺 -12.7322
▁Gi -12.7323
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▁江河 -12.7323
▁肖像 -12.7324
EF -12.7328
▁卡特 -12.7328
▁就职 -12.7328
狸 -12.7328
▁欧式 -12.7329
▁邪教 -12.733
▁保送 -12.733
261 -12.7331
▁唐僧 -12.7331
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▁NPC -12.7331
▁延缓 -12.7332
FX -12.7332
▁贴图 -12.7333
▁混血 -12.7335
彝 -12.7336
生怕 -12.7336
▁浦东 -12.7337
▁盘点 -12.7338
逞 -12.734
▁继电 -12.734
汾 -12.734
傍 -12.734
HC -12.7345
▁防爆 -12.7346
族群 -12.7346
PD -12.7347
▁2400 -12.7348
▁水系 -12.7349
癣 -12.7351
▁理化 -12.7351
▁柳州 -12.7352
KI -12.7352
▁2200 -12.7353
や -12.7353
▁文身 -12.7354
▁差价 -12.7356
▁激起 -12.7356
pen -12.736
秤 -12.7361
▁第四十六 -12.7361
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▁heart -12.7362
濑 -12.7362
▁成天 -12.7362
▁儒学 -12.7363
零零 -12.7364
▁售票 -12.7364
▁痴迷 -12.7364
薰 -12.7365
000 -12.7365
▁红薯 -12.7368
4.4 -12.737
糯 -12.737
▁皮下 -12.737
▁开国 -12.7371
游记 -12.7371
所措 -12.7373
▁货车 -12.7374
▁斯大林 -12.7374
▁Store -12.7374
布局 -12.7375
ret -12.7375
▁抗原 -12.7375
▁仅此 -12.7376
小三 -12.7378
痿 -12.7379
▁晴天 -12.7381
祐 -12.7381
▁母乳 -12.7381
▁逗比 -12.7381
拘留 -12.7381
pm -12.7382
家具 -12.7385
怎么 -12.7385
▁断绝 -12.7385
uff -12.7387
duct -12.7388
▁切尔西 -12.7388
药材 -12.7389
▁农林 -12.739
fo -12.7391
▁穷游 -12.7391
▁几百万 -12.7391
▁高丽 -12.7392
▁科技园 -12.7395
- -12.7395
啰嗦 -12.7396
▁黄牛 -12.7396
▁迅猛 -12.7397
▁一班 -12.7397
▁抱抱 -12.7397
风光 -12.7397
▁人寿 -12.7398
▁要塞 -12.7398
拉着 -12.7398
▁公示 -12.7398
▁Alpha -12.74
工程师 -12.7403
袭击 -12.7405
458 -12.7405
▁BUG -12.7407
▁漫游 -12.7408
▁软化 -12.741
▁维多利亚 -12.741
▁报关 -12.7413
▁鲜血 -12.7413
▁This -12.7414
▁功劳 -12.7414
▁外事 -12.7415
▁古建筑 -12.7415
▁济南市 -12.7416
▁格拉 -12.7416
gs -12.7418
▁写信 -12.7419
平原 -12.7419
▁........ -12.7421
信用 -12.7421
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如今 -12.7422
▁亚当 -12.7423
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加强 -12.7425
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▁全额 -12.7426
ida -12.7428
▁萨尔 -12.7428
利斯 -12.743
▁理赔 -12.7431
▁神农 -12.7433
拱 -12.7434
一统 -12.7435
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花香 -12.7436
▁外省 -12.7437
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▁单项 -12.744
▁各式 -12.7441
▁顺其自 -12.7441
335 -12.7441
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▁短裤 -12.7443
贵妃 -12.7445
▁推介 -12.7445
▁手掌 -12.7446
▁滑板 -12.745
▁大局 -12.745
▁卡车 -12.7451
▁庄严 -12.7452
▁矢量 -12.7453
▁元器 -12.7453
▁鬼神 -12.7454
豚 -12.7454
▁牵扯 -12.7456
▁脊髓 -12.7456
▁格里 -12.7458
501 -12.7458
nic -12.7459
mal -12.7459
▁食管 -12.7459
▁Mini -12.7459
▁太监 -12.7461
▁ppt -12.7461
▁爷们 -12.7461
活力 -12.7462
vel -12.7462
▁可口 -12.7463
多多 -12.7464
▁小便 -12.7464
▁沈阳市 -12.7465
▁巡视 -12.7466
▁安防 -12.7466
▁埋怨 -12.7466
▁过时 -12.7466
▁之声 -12.7467
龙虾 -12.7468
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▁Gen -12.7472
▁化肥 -12.7473
▁质检 -12.7474
细长 -12.7474
援助 -12.7474
ora -12.7475
▁沒有 -12.7476
簧 -12.7477
▁Kindle -12.7477
憾 -12.7478
▁苦瓜 -12.7479
▁换装 -12.7479
▁一通 -12.7479
909 -12.748
LT -12.7481
▁乡镇企业 -12.7482
▁说不上 -12.7483
長 -12.7483
高校 -12.7484
▁菲尔 -12.7485
▁大写 -12.7488
▁威尼斯 -12.749
006 -12.749
▁芦荟 -12.7493
▁农牧 -12.7496
▁创刊 -12.7496
禀 -12.7497
不堪 -12.7501
▁Master -12.7501
▁硫化 -12.7501
生存 -12.7502
▁树叶 -12.7504
哩 -12.7504
▁狭义 -12.7505
▁投注 -12.7508
▁一旁 -12.7508
少少 -12.7509
▁腐女 -12.7509
▁idx -12.7509
▁午夜 -12.7509
▁普洱 -12.751
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枉 -12.7512
論 -12.7512
▁车票 -12.7512
▁数万 -12.7513
ュ -12.7513
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▁清风 -12.7514
▁白雪 -12.7514
▁优胜 -12.7514
▁动用 -12.7515
▁过失 -12.7515
▁走红 -12.7516
▁洒脱 -12.7516
pri -12.7519
▁方舟 -12.7521
▁回民 -12.7521
▁Note -12.7522
000000 -12.7522
▁情话 -12.7524
▁山东大学 -12.7525
▁古装 -12.7526
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▁锯齿 -12.7533
华北大 -12.7533
api -12.7534
▁乌龙 -12.7536
mAh -12.7538
▁组队 -12.7539
佚 -12.754
lar -12.754
▁诸葛 -12.754
▁烧伤 -12.7543
uang -12.7544
腱 -12.7545
▁羞辱 -12.7545
存款 -12.7546
▁第四十七 -12.7547
▁纪实 -12.7547
870 -12.7548
兩 -12.7548
鸠 -12.7548
▁汶川 -12.7548
.19 -12.7548
▁圈内 -12.7549
▁朦胧 -12.7549
▁家家 -12.755
▁安德 -12.7551
▁傲慢 -12.7551
▁下颌 -12.7553
帜 -12.7554
▁公尺 -12.7555
用途 -12.7555
電 -12.7556
▁纸巾 -12.7556
we -12.7557
很久 -12.7558
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披 -12.7561
斯堡 -12.7561
▁张开 -12.7562
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其实 -12.7563
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诬 -12.7566
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▁宜家 -12.7566
▁泪流 -12.7567
▁收纳 -12.7568
▁方正 -12.7568
▁宿命 -12.757
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▁满怀 -12.7571
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邃 -12.7571
▁传世 -12.7571
660 -12.7572
▁红星 -12.7572
書 -12.7576
广播 -12.7576
▁大户 -12.7576
▁隋唐 -12.7577
▁TCP -12.7579
▁受邀 -12.7579
▁apple -12.7581
▁潜移默 -12.7582
▁围攻 -12.7584
▁暗杀 -12.7586
维斯 -12.7587
▁集结 -12.7587
▁墙体 -12.759
▁一个半 -12.759
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▁马德里 -12.7593
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last -12.7597
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▁IOS -12.7601
社会治安 -12.7601
干线 -12.7602
▁田间 -12.7602
▁银行业 -12.7603
▁泰安 -12.7605
颌 -12.7605
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烟火 -12.7607
ura -12.7607
▁never -12.7608
思路 -12.7608
桃源 -12.7609
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四点 -12.7616
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▁万岁 -12.7623
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硫 -12.7626
法案 -12.7626
295 -12.7627
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ious -12.7628
蟾 -12.7628
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▁外籍 -12.7632
▁九点 -12.7633
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▁计时 -12.7635
▁MIUI -12.7635
▁神学 -12.7636
▁JPEG -12.7636
▁赞叹 -12.7637
▁他們 -12.7638
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静静 -12.7639
▁偿还 -12.7639
▁跟前 -12.764
▁隐约 -12.764
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▁高处 -12.7642
▁GPRS -12.7642
▁体裁 -12.7644
▁织物 -12.7644
▁济宁 -12.7645
换位 -12.7646
▁13.3 -12.7646
script -12.7648
▁大衣 -12.7648
imp -12.7648
don -12.7648
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一次 -12.7653
GI -12.7654
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沽 -12.7655
▁WAP -12.7655
血管 -12.7656
▁被盗 -12.7657
▁1922 -12.7658
迥 -12.7658
▁2100 -12.7659
洛夫 -12.7661
▁妥妥 -12.7661
蒸气 -12.7661
▁Liu -12.7662
传输 -12.7662
▁赚点 -12.7663
View -12.7664
考核 -12.7666
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输入 -12.7667
▁贝壳 -12.7668
̴ -12.7671
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〜 -12.7672
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pho -12.7672
▁距今 -12.7672
▁Sam -12.7673
404 -12.7674
SN -12.7677
JB -12.768
▁每逢 -12.7681
▁成龙 -12.7681
▁村上 -12.7682
▁光是 -12.7682
▁同心 -12.7682
▁瘦下 -12.7682
大楼 -12.7683
▁片刻 -12.7683
▁WMA -12.7685
▁全靠 -12.7686
▁单车 -12.7688
拼音 -12.7688
AW -12.7688
▁求实 -12.7689
▁接管 -12.7691
▁举手 -12.7692
fun -12.7692
263 -12.7694
▁1911 -12.7695
▁内饰 -12.7696
▁挨打 -12.7696
▁长沙市 -12.7697
出版 -12.7698
曜 -12.7699
瞄 -12.7699
▁点滴 -12.7702
▁水量 -12.7703
独厚 -12.7704
橡胶 -12.7704
炽 -12.7706
▁预售 -12.7708
▁地基 -12.7708
▁缝隙 -12.7708
嫡 -12.771
NK -12.7711
▁认出 -12.7711
▁劳资 -12.7711
▁法拉 -12.7712
店家 -12.7713
▁买到 -12.7714
美女 -12.7714
计算 -12.7716
▁仅为 -12.7718
rum -12.7719
▁见效 -12.7719
▁野兽 -12.772
▁最合 -12.7722
▁筹资 -12.7723
667 -12.7723
▁考古学 -12.7723
▁Just -12.7724
▁俗话说 -12.7725
▁一艘 -12.7725
唠 -12.7727
▁福田 -12.7729
▁刺杀 -12.7729
▁WIFI -12.7729
▁踏入 -12.7729
▁Ag -12.773
FP -12.7731
▁红茶 -12.7732
之以 -12.7732
▁1923 -12.7733
▁暗中 -12.7734
uk -12.7734
▁Op -12.7734
▁底子 -12.7734
▁公务 -12.7735
▁无须 -12.7736
选为 -12.7737
▁排挤 -12.7737
▁曙光 -12.7737
航空 -12.7737
▁英才 -12.7738
荔 -12.7738
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▁雌性 -12.7739
娱 -12.774
▁取景 -12.774
▁应不应 -12.7742
▁华西 -12.7742
▁残粉 -12.7746
Pro -12.7747
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选举 -12.775
▁净土 -12.775
▁明末 -12.775
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▁义乌 -12.7752
▁记述 -12.7754
政治 -12.7755
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▁一塌糊 -12.7758
▁唐宋 -12.7759
▁流利 -12.776
▁一局 -12.776
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等级 -12.7762
扩大 -12.7762
噶 -12.7762
它 -12.7763
▁King -12.7764
▁三菱 -12.7766
▁烹调 -12.7766
寰 -12.7766
区别 -12.7767
▁贬义 -12.7769
▁餐具 -12.7769
自治 -12.7771
榕 -12.7776
联合 -12.7778
▁副总 -12.778
▁作法 -12.7783
▁肥料 -12.7783
チ -12.7784
▁聘用 -12.7784
捏 -12.7784
670 -12.7785
SM -12.7785
▁玄武 -12.7786
适应 -12.7786
▁置换 -12.7787
▁显现 -12.7788
▁豪门 -12.7789
▁公知 -12.779
∇ -12.7793
▁个案 -12.7795
赶 -12.7796
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境地 -12.7798
▁集资 -12.78
▁先秦 -12.7801
党派 -12.7802
西北 -12.7803
▁秦汉 -12.7803
OG -12.7805
▁猕猴 -12.7806
传统 -12.7807
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las -12.8452
涝 -12.8454
下班 -12.8455
zy -12.8455
缤纷 -12.8455
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▁摇摆 -12.8457
▁新华社 -12.8457
▁车库 -12.8457
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砾 -12.8461
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gent -12.8467
树脂 -12.8468
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▁15000 -12.8469
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金刚 -12.847
教主 -12.8473
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9.3 -12.8473
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▁语义 -12.8474
▁鱼肉 -12.8476
之战 -12.8476
▁拷贝 -12.8477
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lv -12.8481
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慨 -12.8484
▁龙山 -12.8484
胤 -12.8486
zu -12.8486
▁古筝 -12.8487
▁短线 -12.849
▁结肠 -12.8491
▁寓言 -12.8493
▁11.5 -12.8495
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bd -12.8497
氟 -12.8499
▁花木 -12.8503
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ō -12.8503
下午 -12.8504
▁网贷 -12.8505
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▁Jun -12.8507
▁William -12.8507
▁尖叫 -12.8507
▁关上 -12.8508
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onic -12.8511
▁石墨 -12.8511
保障 -12.8512
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oid -12.8517
flow -12.8518
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▁忠于 -12.8519
咸丰 -12.852
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cess -12.8523
▁冤枉 -12.8524
▁乡土 -12.8524
▁全线 -12.8524
▁齐国 -12.8524
▁停滞 -12.8525
格拉斯 -12.8525
玫瑰 -12.8526
▁全员 -12.8526
▁得失 -12.8528
▁阿森纳 -12.8529
▁里程碑 -12.8533
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廓 -12.8535
▁iPod -12.8536
思义 -12.8537
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党中央 -12.8538
▁售卖 -12.8539
研究院 -12.8541
双方 -12.8541
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▁Oh -12.8549
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該 -12.8551
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▁一棵 -12.8553
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窃 -12.8554
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tics -12.8555
table -12.8555
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struct -12.8557
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2004 -12.8562
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僻 -12.8566
溶液 -12.8567
脖 -12.8567
ATA -12.8568
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感情 -12.857
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▁Val -12.8573
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观察 -12.8576
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▁元气 -12.8578
▁Game -12.8579
▁救命 -12.858
▁印制 -12.858
溃疡 -12.858
▁1918 -12.8581
氨 -12.8581
▁牙龈 -12.8582
换代 -12.8584
▁个头 -12.8589
男子 -12.859
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动工 -12.8594
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進 -12.8595
eek -12.8595
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ally -12.8598
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科技部 -12.8599
490 -12.8601
ula -12.8601
炙 -12.8604
269 -12.8604
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之年 -12.8605
▁突厥 -12.8608
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擀 -12.8608
▁夏威夷 -12.861
▁look -12.8611
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诧 -12.8613
信服 -12.8614
▁隆起 -12.8615
▁勘查 -12.8615
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▁山羊 -12.8616
娃娃 -12.8617
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bat -12.8618
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ū -12.8619
挖掘 -12.8619
▁门下 -12.8619
▁水泵 -12.8619
▁印度尼西亚 -12.862
BY -12.8621
▁课上 -12.8622
ken -12.8622
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▁极速 -12.8622
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▁心力 -12.8622
榴弹 -12.8623
▁find -12.8623
▁西周 -12.8624
▁书信 -12.8624
▁随处 -12.8624
♡ -12.8625
▁知觉 -12.8625
▁巴塞罗那 -12.8627
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ree -12.8629
意外 -12.863
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塾 -12.8631
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寄 -12.8634
熄 -12.8635
7.1 -12.8636
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嗤 -12.8638
326 -12.8641
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绷 -12.8644
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6.1 -12.8646
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▁订购 -12.8648
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5.4 -12.8653
不爱 -12.8653
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聋 -12.8654
监会 -12.8655
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无事 -12.8657
▁旧金山 -12.8658
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oul -12.8659
瑛 -12.8659
重点 -12.8659
▁即兴 -12.866
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▁剪纸 -12.8662
▁季风 -12.8663
▁梦到 -12.8664
type -12.8665
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KA -12.8669
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痣 -12.8672
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ule -12.8683
▁281 -12.8684
嗑 -12.8685
半部 -12.8686
卫星 -12.8686
ü -12.8687
胥 -12.8687
▁交织 -12.8687
黏膜 -12.8687
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eat -12.8692
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介质 -12.8694
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宝宝 -12.8696
6.2 -12.8696
▁2800 -12.8696
▁into -12.8696
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1111 -12.8707
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托马斯 -12.8709
音符 -12.8709
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▁第四十九 -12.871
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398 -12.8712
▁一九九 -12.8712
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纪念 -12.8716
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lect -12.8721
肥肉 -12.8721
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抹 -12.8724
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粤 -12.8726
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冕 -12.8728
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rem -12.8729
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咯 -12.8732
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器材 -12.8733
林业 -12.8733
KV -12.8734
field -12.8735
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装饰 -12.8737
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豉 -12.8739
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▁1366 -12.8742
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毋 -12.8747
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в -12.8748
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plan -12.875
▁法理 -12.8753
▁毛利 -12.8755
▁琥珀 -12.8757
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▁秀才 -12.8758
GL -12.8758
ward -12.8759
▁Blue -12.8759
▁核武器 -12.8759
▁這樣 -12.876
公式 -12.876
▁School -12.876
▁副县 -12.8762
GM -12.8762
7000 -12.8763
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▁江北 -12.8763
学过 -12.8764
陸 -12.8765
▁木板 -12.8767
▁顺风 -12.8768
▁演进 -12.877
▁试行 -12.8771
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▁克里斯 -12.8772
邸 -12.8772
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▁安乐 -12.8773
▁Inc -12.8774
▁未尝 -12.8775
▁差一点 -12.8777
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▁1914 -12.8779
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▁老天 -12.8789
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▁宇航 -12.879
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吵 -12.8791
撮 -12.8794
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国防部 -12.8796
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▁小叶 -12.88
oke -12.88
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▁名次 -12.8803
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HB -12.8804
时空 -12.8805
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佰 -12.8807
晟 -12.8807
苞 -12.8808
▁May -12.8811
不羁 -12.8812
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虏 -12.8813
相处 -12.8813
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qua -12.8816
花园 -12.8816
▁中文网 -12.8819
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极点 -12.8827
老板 -12.8828
吱 -12.8829
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伶 -12.8832
▁要花 -12.8833
南北 -12.8833
无畏 -12.8834
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痹 -12.884
802 -12.8844
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厘 -12.885
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日记 -12.8852
闫 -12.8853
355 -12.8854
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因子 -12.8854
壁垒 -12.8855
ese -12.8856
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lip -12.8856
7.2 -12.8857
▁合照 -12.8857
VM -12.8858
339 -12.8858
UM -12.8859
業 -12.886
point -12.886
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▁几只 -12.8861
兜 -12.8863
print -12.8864
逗 -12.8864
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▁归还 -12.8867
.26 -12.8868
价廉 -12.8869
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▁动脑 -12.8872
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家居 -12.8876
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▁纤维素 -12.8878
移植 -12.8878
▁对外贸易 -12.8878
▁WIN -12.8879
▁上岗 -12.8879
▁浮夸 -12.888
▁衬托 -12.888
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财富 -12.8891
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厄 -12.8895
▁HK -12.8895
▁水田 -12.8896
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俨 -12.8902
▁离谱 -12.8902
时速 -12.8903
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▁点数 -12.8904
时光 -12.8904
▁龙凤 -12.8905
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uni -12.8906
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氰 -12.8909
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eck -12.8915
幻想 -12.8916
芷 -12.8916
文物 -12.8919
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防疫 -12.8922
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跪 -12.8926
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ヽ -12.8928
注射 -12.8928
颚 -12.8929
无所 -12.8931
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せ -12.8935
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▁wow -12.8938
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眸 -12.8943
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罢 -12.8944
▁民政部 -12.8945
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词典 -12.8949
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变现 -12.8954
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map -12.8955
▁Trump -12.8956
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415 -12.8959
噗 -12.896
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ani -12.896
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不认 -12.8961
懿 -12.8962
105 -12.8962
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收拾 -12.8964
爻 -12.8964
▁端午 -12.8966
▁Ste -12.8967
▁1913 -12.8969
並 -12.8969
鳖 -12.8969
▁右上 -12.8972
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全意 -12.8974
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联播 -12.8976
hol -12.8977
▁手把 -12.8978
砸 -12.8983
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萃 -12.8984
▁复查 -12.8985
▁分清 -12.8985
▁礼服 -12.8985
▁谁知 -12.8986
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▁茫然 -12.8987
▁三十五 -12.8988
▁妈蛋 -12.8988
▁城关 -12.8989
RD -12.8991
▁表达方 -12.8991
▁7.7 -12.8991
▁书目 -12.8991
▁伦理学 -12.8992
琐碎 -12.8992
nan -12.8994
诱 -12.8994
.163 -12.8995
龙珠 -12.8995
▁渡过 -12.8996
楹 -12.8996
▁互通 -12.8998
兵法 -12.8999
▁奥尔 -12.9
▁哈尔滨市 -12.9001
▁取暖 -12.9001
▁稀土 -12.9001
融资 -12.9002
▁二十余 -12.9003
▁不求 -12.9003
▁中央党 -12.9003
▁恶毒 -12.9004
eh -12.9004
小波 -12.9005
▁认认 -12.9006
▁凌乱 -12.9007
拆 -12.9007
▁行政诉讼 -12.9008
熊猫 -12.901
▁百里 -12.901
▁应收 -12.9011
▁牵着 -12.9012
▁自来 -12.9014
▁变美 -12.9014
▁炮兵 -12.9014
▁第五十 -12.9014
aka -12.9017
▁回馈 -12.9017
▁吃惊 -12.9018
▁斗志 -12.9019
何不 -12.9019
▁难怪 -12.902
裸裸 -12.902
▁咖喱 -12.902
▁半决赛 -12.9021
▁慈悲 -12.9022
▁1917 -12.9023
▁之多 -12.9023
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▁堆放 -12.9024
叼 -12.9025
鲨 -12.9026
苏区 -12.9027
▁单向 -12.9027
▁溢出 -12.9027
▁中国人民银行 -12.9028
▁同乡 -12.9029
▁松鼠 -12.903
沦 -12.903
▁无损 -12.903
FO -12.9031
Id -12.9031
▁回转 -12.9031
▁震荡 -12.9034
▁明目 -12.9036
▁连云港 -12.9037
▁中小型 -12.9038
▁黄土 -12.9039
▁红外线 -12.904
▁五万 -12.9041
球菌 -12.9042
同胞 -12.9043
▁树枝 -12.9043
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met -12.9045
▁闲着 -12.9046
公路 -12.9047
▁行者 -12.9048
旅游局 -12.9049
玄宗 -12.9049
▁0.05 -12.905
▁黑色素 -12.905
▁粗略 -12.9051
▁东晋 -12.9051
煌 -12.9051
筱 -12.9052
▁留恋 -12.9052
▁军民 -12.9053
聪明 -12.9054
▁雇主 -12.9057
处分 -12.9058
▁二十几 -12.9059
▁必读 -12.906
▁BAT -12.9061
▁订婚 -12.9063
▁弱小 -12.9063
VP -12.9063
▁依附 -12.9064
352 -12.9065
▁大宗 -12.9066
▁杨柳 -12.9066
▁宝安 -12.9067
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▁辛勤 -12.9068
▁游牧 -12.907
▁两手 -12.9071
福德 -12.9071
▁地委 -12.9072
1.9 -12.9073
▁杨过 -12.9075
▁Model -12.9076
无意 -12.9077
▁一池 -12.9077
cover -12.9079
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lia -12.9081
四季 -12.9082
钨 -12.9083
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᷄ -12.9094
331 -12.9095
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ros -12.9096
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lis -12.9097
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rk -12.9101
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ode -12.9104
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hhh -12.9107
舵 -12.9109
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lp -12.9111
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ズ -12.9113
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lor -12.9116
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恃 -12.9118
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388 -12.9125
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蒙德 -12.9127
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wh -12.913
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dream -12.9131
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憩 -12.9132
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rc -12.9135
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tect -12.9137
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᷅ -12.9138
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蓟 -12.9141
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蹈 -12.9145
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讥 -12.915
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342 -12.9154
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喂 -12.9156
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gy -12.9158
ments -12.9159
015 -12.9159
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珂 -12.917
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沓 -12.9172
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パ -12.9173
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累累 -12.9173
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烨 -12.9179
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窦 -12.918
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何从 -12.9184
梧 -12.9186
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酌 -12.9188
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media -12.919
ダ -12.9192
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297 -12.9193
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以待 -12.92
mL -12.9201
Transform -12.9202
遛 -12.9204
寞 -12.9206
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kill -12.9212
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gue -12.9214
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咧 -12.9216
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徙 -12.9217
鬓 -12.9217
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變 -12.9223
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运会 -12.9228
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356 -12.9229
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捆 -12.923
▁吸水 -12.9231
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潼 -12.9232
▁14.2 -12.9232
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喇 -12.9233
2.7 -12.9234
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三环 -12.9236
▁方圆 -12.9239
▁牢牢 -12.9239
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▁生物化学 -12.924
▁可逆 -12.924
唯物主义 -12.9241
690 -12.9241
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720 -12.9244
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画作 -12.9247
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▁秦皇岛 -12.9258
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970 -12.9267
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place -12.9271
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清晰 -12.9272
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尹 -12.9275
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彰 -12.9277
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▁6. -12.9278
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▁没敢 -12.9279
本营 -12.9279
366 -12.9279
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mA -12.928
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不懈 -12.9283
‧ -12.9284
氪 -12.9284
於 -12.9284
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ひ -12.9286
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集成 -12.9286
鲈 -12.9287
书籍 -12.9287
丫头 -12.9288
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启动 -12.9289
喙 -12.9291
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range -12.9292
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ities -12.9295
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▁布线 -12.9296
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勐 -12.9298
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裸 -12.9306
cf -12.9307
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中医 -12.9309
337 -12.9309
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405 -12.9316
师傅 -12.9316
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箔 -12.9316
chang -12.9317
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berg -12.9317
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類 -12.9332
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皮炎 -12.9335
一斤 -12.9336
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真相 -12.9338
犁 -12.9339
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牛奶 -12.934
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mine -12.9344
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臼 -12.9348
ANG -12.9349
敞 -12.935
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date -12.9351
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诏 -12.9353
貂 -12.9354
这般 -12.9354
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狡 -12.9358
晚饭 -12.9359
ㄣ -12.9359
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▁路易 -12.9362
▁every -12.9363
猜 -12.9365
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躬 -12.9368
▁Light -12.9368
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▁二手房 -12.9369
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悄 -12.9371
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▁征战 -12.9372
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彗 -12.9374
Count -12.9374
携带 -12.9374
纂 -12.9375
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幸福 -12.9376
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friend -12.938
334 -12.938
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▁鸡翅 -12.9383
DT -12.9384
亚科 -12.9385
校生 -12.9385
芪 -12.9387
TM -12.9389
烩 -12.939
nes -12.939
▁小球 -12.9391
▁造影 -12.9391
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▁五味 -12.9393
▁隔热 -12.9393
▁婚庆 -12.9394
缉 -12.9395
▁public -12.9395
势力 -12.9395
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л -12.9398
伎 -12.9399
掖 -12.9399
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▁童子 -12.9401
8.2 -12.9401
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▁花岗岩 -12.9403
▁火柴 -12.9404
▁自传 -12.9405
▁18.2 -12.9407
space -12.9407
108 -12.9407
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磐 -12.9409
▁may -12.941
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332 -12.9413
此种 -12.9414
▼ -12.9414
▁酸痛 -12.9415
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缚 -12.9417
呱 -12.9417
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▁欢呼 -12.9425
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tre -12.9427
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八年 -12.9427
信条 -12.9427
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rel -12.943
phy -12.9431
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整理 -12.9433
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佟 -12.9434
馋 -12.9436
260 -12.9437
厘米 -12.9439
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樵 -12.944
INT -12.944
守法 -12.9441
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不透 -12.9442
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nc -12.9446
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侣 -12.9448
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740 -12.945
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不等 -12.9452
unk -12.9454
嬉 -12.9454
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Ω -12.9459
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一筹 -12.9461
哗啦 -12.9462
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琛 -12.9465
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iff -12.9468
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isappinstalled -12.9471
rent -12.9472
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387 -12.9473
今日 -12.9473
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RY -12.9473
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戛 -12.9483
50000 -12.9484
纲目 -12.9486
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隽 -12.9487
荟萃 -12.9487
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998 -12.9487
倡 -12.9488
369 -12.9489
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Code -12.949
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vision -12.9491
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ek -12.9494
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景气 -12.9496
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高宗 -12.9499
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Port -12.95
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表演 -12.9503
̆ -12.9504
蛀 -12.9504
来答 -12.9504
109 -12.9505
线程 -12.9505
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alt -12.9507
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钢琴 -12.9508
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嬛 -12.9511
庇 -12.9513
反射 -12.9515
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stan -12.9518
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胜利 -12.9524
羊羊 -12.9524
雨季 -12.9524
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RF -12.9527
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card -12.9538
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KW -12.9545
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lm -12.9548
无力 -12.955
犊 -12.9551
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之作 -12.9552
board -12.9554
圆形 -12.9554
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鳝 -12.9559
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角度 -12.9563
눈 -12.9564
din -12.9565
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徨 -12.9566
wan -12.9567
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之乱 -12.9569
Class -12.957
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阂 -12.9572
摘 -12.9574
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综 -12.9576
呻吟 -12.9578
成熟 -12.9579
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▁燕麦 -12.9589
▁起跑 -12.9589
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鸾 -12.959
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ガ -12.9591
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萎 -12.9593
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攀 -12.9597
甙 -12.9598
ola -12.9598
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note -12.9599
我太 -12.9599
空军 -12.9599
建华 -12.96
win -12.96
荡荡 -12.9601
罗马 -12.9602
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枷 -12.9602
笺 -12.9602
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▁1910 -12.9605
宝贝 -12.9606
367 -12.9606
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▁前三 -12.9608
▁普罗 -12.9608
若惊 -12.9608
▁内蒙 -12.9609
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ENT -12.9609
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mil -12.9611
▁搬出 -12.9611
▁填词 -12.9611
525 -12.9614
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ฅ -12.9615
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▁中庸 -12.9615
贿赂 -12.9619
nch -12.962
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等国 -12.9625
▁幻灯 -12.9625
▁婚恋 -12.9626
▁咆哮 -12.9627
▁知足 -12.9627
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920 -12.9629
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铭心 -12.9629
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掌握 -12.963
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▁老干部 -12.9634
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鳃 -12.9638
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强调 -12.9642
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耕耘 -12.9644
VO -12.9645
追究 -12.9645
hua -12.9645
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缺血 -12.9649
丶 -12.9651
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ates -12.9659
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指数 -12.9661
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蔗 -12.9664
楚楚 -12.9665
政务 -12.9666
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unc -12.9674
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一例 -13.0468
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汪汪 -13.0495
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414 -13.0595
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104 -13.0608
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107 -13.0619
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bc -13.0637
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王朝 -13.0642
OB -13.0642
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655 -13.0648
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AX -13.0677
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619 -13.0703
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280 -13.0711
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▁克罗地亚 -13.1511
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龛 -13.1512
尔曼 -13.1512
▁演职 -13.1513
illa -13.1513
瞥 -13.1514
▁酸甜 -13.1514
▁弗拉 -13.1515
▁措辞 -13.1515
▁西夏 -13.1516
151 -13.1517
▁完完 -13.1518
▁开篇 -13.1519
▁联谊会 -13.1519
▁异形 -13.152
技巧 -13.1521
▁第六十 -13.1521
▁近些年 -13.1522
▁独奏 -13.1523
攸 -13.1523
▁插头 -13.1523
标价 -13.1524
▁觉得很 -13.1524
▁如若 -13.1528
▁警报 -13.1529
▁3. -13.153
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hao -13.1531
沉沉 -13.1532
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gh -13.1541
登记 -13.1541
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uz -13.1542
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瞰 -13.1545
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▁范文 -13.1551
迷雾 -13.1551
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118 -13.1552
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city -13.1553
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order -13.1556
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酋 -13.1557
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uv -13.1563
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106 -13.1565
蚊 -13.1566
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이 -13.1568
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475 -13.1569
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隧 -13.1571
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acc -13.1575
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opy -13.1582
凑 -13.1582
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沅 -13.1586
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闪闪 -13.1588
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鄂 -13.1591
祖父 -13.1593
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xp -13.1595
财政 -13.1595
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每 -13.16
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699 -13.1601
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德罗 -13.1602
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胫 -13.1605
▁太平天国 -13.1607
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痈 -13.161
▁奋发 -13.1611
婧 -13.1612
rey -13.1613
▁李鸿 -13.1613
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aris -13.1616
▁醉酒 -13.1616
欺人 -13.1616
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sz -13.1617
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yr -13.1619
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漾 -13.1621
▁跳动 -13.1621
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▁乱说 -13.1624
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盂 -13.1626
▁math -13.1626
忒 -13.1627
▁倒地 -13.1627
辄 -13.1629
▁甲骨文 -13.1631
▁自干 -13.1633
▁各不相 -13.1635
▁想换 -13.1635
▁增肌 -13.1637
435 -13.1639
▁伙食 -13.1639
▁纸上 -13.1639
▁交货 -13.164
▁拉近 -13.1641
馥 -13.1641
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mall -13.1642
蒂斯 -13.1644
▁国宝 -13.1646
▁自重 -13.1646
799 -13.1647
▁无锡市 -13.1647
409 -13.1648
▁1907 -13.1652
▁暖暖 -13.1652
sco -13.1653
▁无端 -13.1653
609 -13.1655
▁陕北 -13.1656
▁音节 -13.1657
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共鸣 -13.1663
汕 -13.1663
▁东盟 -13.1663
▁普世 -13.1664
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▁Quora -13.1667
▁掉头 -13.1667
始至 -13.1668
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不足道 -13.167
▁......... -13.1672
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▁反反 -13.1672
kw -13.1675
▁四角 -13.1675
▁往外 -13.1677
瀚 -13.1678
gress -13.1682
年月 -13.1682
osa -13.1682
▁sur -13.1683
留情 -13.1684
▁亡国 -13.1684
闰 -13.1684
ott -13.1686
▁吃零 -13.1686
▁黑衣 -13.1687
methyl -13.1688
istic -13.1689
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公克 -13.169
▁273 -13.1691
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工业部 -13.1692
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▁4399 -13.1697
▁受苦 -13.1698
当户 -13.1698
▁单从 -13.1698
▁3600 -13.1699
▁第五十八 -13.1699
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协力 -13.1703
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▁打火 -13.1704
wu -13.1705
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可忍 -13.1708
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▁ASP -13.1711
ulation -13.1712
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脓 -13.1714
▁疗养 -13.1714
▁弧形 -13.1714
▁三百五十 -13.1715
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▁联欢 -13.1717
▁新石器 -13.1718
▁花药 -13.1719
▁风土 -13.1722
▁GIF -13.1722
▁趁早 -13.1723
▁Gal -13.1723
护航 -13.1724
poli -13.1725
▁赵国 -13.1725
▁16.2 -13.1726
▁自选 -13.1726
▁外景 -13.1726
迪亚 -13.1727
▁陷害 -13.1727
绸 -13.1727
lam -13.1728
dent -13.1731
▁超薄 -13.1731
▁排解 -13.1731
▁指明 -13.1734
▁射门 -13.1734
▁成瘾 -13.1735
汴 -13.1736
男女 -13.1736
豁 -13.1736
▁地理信息 -13.1736
▁机要 -13.1737
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溜溜 -13.1737
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ㄕ -13.174
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国美 -13.1745
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▁五分 -13.1747
▁铁血 -13.1749
夜夜 -13.1755
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▁独居 -13.1757
团圆 -13.1757
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▁二百七十 -13.1758
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事记 -13.176
仨 -13.176
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▁影迷 -13.1763
los -13.1765
▁唤起 -13.1765
康熙 -13.1767
▁乐山 -13.1767
▁文选 -13.1768
688 -13.1769
文化大 -13.1769
114 -13.1769
漪 -13.1771
▁牙医 -13.1771
▁日漫 -13.1772
▁假象 -13.1772
▁过激 -13.1772
▁镇定 -13.1772
lish -13.1773
投入 -13.1773
▁RGB -13.1774
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▁十字路 -13.1775
▁横扫 -13.1775
吸收 -13.1777
厕所 -13.1781
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▁过热 -13.1783
▁夏令 -13.1783
cri -13.1784
▁program -13.1785
lab -13.1786
▁乐天 -13.1788
饨 -13.1788
▁追上 -13.1789
乌斯 -13.179
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▁接通 -13.1791
▁需不需 -13.1791
格格 -13.1792
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rag -13.1794
▁枝条 -13.1795
白兔 -13.1796
▁爱恨 -13.1797
薏 -13.1798
▁高傲 -13.1799
▁迪奥 -13.18
硬盘 -13.18
▁合体 -13.18
▁一个劲 -13.1801
▁达尔文 -13.1803
▁家禽 -13.1805
▁城镇化 -13.1805
603 -13.1805
▁海景 -13.1806
蕉 -13.1807
▁草丛 -13.1807
非所 -13.1807
蜓 -13.1807
复复 -13.1808
以鼻 -13.1808
▁简而 -13.1809
▁西雅图 -13.1811
ZZ -13.1813
▁西蒙 -13.1813
▁ISIS -13.1813
▁一无是 -13.1816
▁专营 -13.1816
▁有理有 -13.1817
sq -13.1817
榷 -13.1818
▁卑鄙 -13.1818
▁灯火 -13.1819
▁转自 -13.1819
▁吝啬 -13.1819
ette -13.1819
ower -13.1823
▁恐极 -13.1824
▁结了 -13.1827
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▁渐变 -13.1829
耶夫 -13.1829
▁赡养 -13.183
▁沙河 -13.183
▁送往 -13.183
▁锦江 -13.183
806 -13.183
商银行 -13.1832
▁耐火 -13.1833
▁名作 -13.1833
▁Ken -13.1834
规矩 -13.1834
▁庸俗 -13.1836
▁管委 -13.1836
▁鞍山 -13.1837
pat -13.1837
▁费尔 -13.1838
▁柬埔寨 -13.1838
后任 -13.1839
▁栩栩 -13.1839
WI -13.184
▁初创 -13.184
ASS -13.1841
▁雄心 -13.1842
▁人民公社 -13.1843
婴儿 -13.1843
史以 -13.1843
▁水滴 -13.1845
▁孙女 -13.1847
▁比比皆 -13.185
鹑 -13.185
▁名副 -13.1852
▁方程式 -13.1852
▁best -13.1853
▁近十 -13.1854
flu -13.1855
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▁257 -13.186
疲劳 -13.1861
▁植树 -13.1862
▁那颗 -13.1862
▁冲泡 -13.1862
▁金融业 -13.1863
444 -13.1865
ㄠ -13.1865
▁果壳 -13.1866
▁事半功 -13.1866
▁粘合 -13.1867
▁各处 -13.1867
VT -13.1867
抑 -13.1868
▁拿手 -13.1868
▁金鸡 -13.1868
▁非典 -13.187
ᄇ -13.187
▁范式 -13.1873
▁无所事 -13.1873
▁碰巧 -13.1873
▁完事 -13.1874
RK -13.1875
901 -13.1875
789 -13.1877
喃 -13.1877
▁古董 -13.1878
▁冲上 -13.1879
▁顺丰 -13.188
▁相提 -13.188
▁飞龙 -13.1881
▁狼狈 -13.1882
VD -13.1883
▁正向 -13.1884
霓 -13.1884
▁考勤 -13.1885
▁放宽 -13.1885
镰 -13.1885
▁劳工 -13.1887
莫测 -13.1888
▁God -13.1888
▁国库 -13.1889
▁旱地 -13.189
ental -13.1892
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并论 -13.1897
坐标 -13.1898
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▁外阴 -13.1899
103 -13.19
▁封为 -13.19
GR -13.1902
▁Joe -13.1902
▁非金属 -13.1903
▁政工 -13.1904
▁三毛 -13.1904
一觉 -13.1904
▁公理 -13.1904
捶 -13.1905
不便 -13.1907
506 -13.1907
▁国共 -13.1909
▁dust -13.1909
▁痴情 -13.1909
▁三八 -13.191
▁般若 -13.191
糗 -13.1911
▁停顿 -13.1913
▁耗尽 -13.1913
▁小兔 -13.1914
▁Cre -13.1914
▁易燃 -13.1914
▁泡菜 -13.1916
▁罚球 -13.1917
▁14.6 -13.1918
718 -13.1918
普林 -13.1919
▁AK -13.192
▁party -13.192
▁翻脸 -13.1922
▁撑起 -13.1922
▁Every -13.1922
▁扎根 -13.1922
▁狠抓 -13.1925
缎 -13.1926
▁跌宕 -13.1927
▁胎盘 -13.1928
▁相克 -13.1929
UX -13.193
▁acid -13.1932
oxy -13.1933
▁韦博 -13.1933
咎 -13.1934
eep -13.1939
▁苦涩 -13.194
▁十几万 -13.1941
SON -13.1941
posit -13.1942
独资 -13.1942
武王 -13.1942
▁引以为 -13.1944
▁旗袍 -13.1945
▁理疗 -13.1945
▁13.8 -13.1946
▁歌坛 -13.1947
▁马列 -13.1947
φ -13.1949
▁血战 -13.195
づ -13.195
▁手电 -13.195
▁通胀 -13.1952
▁舌尖 -13.1953
梗阻 -13.1953
5.3 -13.1954
▁斟酌 -13.1955
風 -13.1955
▁帮派 -13.1955
纪元 -13.1955
▁流言 -13.1958
鞭炮 -13.1958
910 -13.1959
▁运球 -13.196
▁286 -13.1962
▁14.5 -13.1963
▁化成 -13.1964
▁避难 -13.1966
▁合乎 -13.1966
▁Over -13.1966
▁眼睁 -13.1966
▁豁达 -13.1967
▁牧羊 -13.1968
▁棺材 -13.1969
▁传播学 -13.197
▁弹出 -13.1972
產 -13.1973
och -13.1974
▁打骂 -13.1975
▁Fire -13.1976
▁博彩 -13.1978
▁艳丽 -13.1979
▁无功 -13.1979
▁神态 -13.1981
题词 -13.1981
▁张家口 -13.1984
▁脑血管 -13.1984
ford -13.1984
钓 -13.1984
▁萧山 -13.1985
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股票 -13.1987
識 -13.1988
前途 -13.1989
▁守门 -13.1989
▁路途 -13.1991
▁click -13.1991
mate -13.1992
蜻 -13.1992
▁悄然 -13.1992
箕 -13.1993
▁审美观 -13.1994
lit -13.1997
骥 -13.1998
▁休眠 -13.1999
▁滴定 -13.1999
含糊 -13.1999
▁品行 -13.1999
▁以备 -13.2003
▁承德 -13.2007
▁山南 -13.2008
628 -13.2009
▁板式 -13.201
▁年终 -13.201
▁紧贴 -13.2011
▁可玩 -13.2011
但 -13.2011
▁XD -13.2015
▁Phys -13.2015
▁楼高 -13.2016
▁House -13.2016
▁吴国 -13.2017
605 -13.2017
崇拜 -13.2017
▁体态 -13.2018
▁误认 -13.2019
危险 -13.202
shu -13.202
ability -13.2022
▁第五十九 -13.2022
▁George -13.2023
▁饭馆 -13.2025
▁exo -13.2026
一杯 -13.2027
▁丧心 -13.2029
▁除草 -13.2029
▁辐鳍 -13.2029
▁big -13.203
▁限时 -13.2033
身手 -13.2034
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▁猪肝 -13.2038
▁西邻 -13.2041
▁侵袭 -13.2043
▁南湖 -13.2043
▁形形 -13.2044
▁松散 -13.2044
▁季军 -13.2047
▁意气 -13.2047
▁高阶 -13.2047
▁钟表 -13.2048
707 -13.2048
▁确信 -13.2049
▁两栖 -13.2049
▁峰值 -13.2049
885 -13.2049
▁安吉 -13.205
▁元老 -13.205
▁蹲下 -13.2051
梅尔 -13.2051
▁谈吐 -13.2052
▁Project -13.2052
▁不愧 -13.2052
故意 -13.2055
▁雷诺 -13.2055
▁防弹 -13.2056
▁宜居 -13.2057
▁何其 -13.2057
▁时髦 -13.2058
▁重疾险 -13.2059
渡口 -13.206
▁keep -13.206
▁红红 -13.206
蕃 -13.206
▁观景 -13.2063
婢 -13.2063
▁有机化学 -13.2064
▁13.4 -13.2065
创造 -13.2065
▁There -13.2068
cra -13.2068
875 -13.207
▁遇过 -13.207
流通 -13.2071
▁牵强 -13.2071
dai -13.2074
南京大屠杀 -13.2076
▁停靠 -13.2076
FM -13.2076
▁Sin -13.2077
▁拍片 -13.2079
▁春光 -13.208
下文 -13.2081
▁拉丁语 -13.2081
燎 -13.2081
▁relative -13.2081
‰ -13.2083
▁针刺 -13.2083
▁麻花 -13.2084
▁沉溺 -13.2085
凹 -13.2085
▁糕点 -13.2087
▁归国 -13.2088
篮子 -13.2088
轮机 -13.2089
▁丽水 -13.2089
▁古琴 -13.2091
504 -13.2091
BM -13.2092
▁进退 -13.2092
▁兴国 -13.2095
dt -13.2095
▁低迷 -13.2095
▁College -13.2096
呸 -13.2096
▁合拍 -13.2096
▁不會 -13.2097
工行 -13.2098
▁include -13.2098
▁心肺 -13.21
▁郭沫 -13.2101
568 -13.2101
▁服务提供 -13.2103
▁奇奇 -13.2104
高清 -13.2104
▁福克斯 -13.2105
idae -13.2106
▁PSP -13.2109
AST -13.211
▁徽州 -13.2111
389 -13.2111
▁救世 -13.2112
▁博文 -13.2112
▁督察 -13.2113
ico -13.2114
运作 -13.2116
▁疏散 -13.2116
▁小肠 -13.2117
tex -13.2117
俱来 -13.2118
▁Japan -13.2118
▁成佛 -13.2118
▁GMA -13.2119
▁金光 -13.2119
疱疹 -13.2119
▁肝胆 -13.212
吴 -13.212
▁中国农业 -13.212
▁举世 -13.212
朝鲜族 -13.212
纽 -13.2121
▁有害物 -13.2121
ず -13.2121
▁富养 -13.2122
▁箱包 -13.2122
▁广度 -13.2122
Φ -13.2123
▁应援 -13.2125
蘑 -13.2125
进制 -13.2125
▁实效 -13.2125
浑 -13.2126
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扼 -13.213
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828 -13.2137
990 -13.2138
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.92 -13.2154
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521 -13.216
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杞 -13.2187
379 -13.219
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une -13.2199
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刮 -13.2204
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ㄏ -13.2206
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med -13.2216
大财 -13.2216
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ria -13.2219
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丝丝 -13.2221
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818 -13.2231
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rich -13.2237
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文山 -13.2256
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明珠 -13.2258
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ima -13.226
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百倍 -13.2266
翩翩 -13.2266
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诗作 -13.2267
σ -13.2267
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壮志 -13.2273
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uri -13.2274
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465 -13.2276
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LR -13.2278
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改善 -13.228
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一指 -13.2281
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▁271 -13.2282
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461 -13.2283
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nov -13.2288
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裆 -13.2291
488 -13.2292
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een -13.2299
門 -13.23
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737 -13.2306
奖赛 -13.2307
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hou -13.2311
涨幅 -13.2312
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606 -13.2312
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747 -13.2321
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tract -13.2327
戏剧学院 -13.2329
玫 -13.2331
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mann -13.2382
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754 -13.2388
mond -13.2389
Map -13.239
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stro -13.2407
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云龙 -13.2425
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迪斯 -13.2432
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cid -13.2436
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很 -13.2438
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吁 -13.2439
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甬 -13.2449
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嫔 -13.2451
人厌 -13.2451
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腆 -13.2463
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流失 -13.2468
ELL -13.2468
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蕙 -13.247
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谧 -13.2471
ISA -13.2472
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早起 -13.2476
kW -13.2477
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ific -13.2487
无虑 -13.2487
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gt -13.2487
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607 -13.249
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俑 -13.2492
fici -13.2493
捣 -13.2494
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囚 -13.2497
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715 -13.25
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无知 -13.25
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撬 -13.2502
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9.80 -13.2505
风暴 -13.2505
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埃及 -13.2507
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ega -13.2512
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eve -13.2515
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喜剧 -13.252
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闻乐 -13.2522
2001 -13.2523
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MAN -13.2535
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507 -13.2539
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IDE -13.2541
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oma -13.2542
忻 -13.2542
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运用 -13.2549
经济学 -13.255
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ira -13.2552
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KT -13.2553
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骁 -13.2555
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高潮 -13.2557
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Standard -13.2563
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race -13.2565
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live -13.257
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蛎 -13.2574
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ati -13.2582
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PER -13.2583
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皇后 -13.2585
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058 -13.2599
hai -13.26
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威尔 -13.2605
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868 -13.2607
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por -13.261
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anth -13.2619
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iving -13.2631
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不敢 -13.2632
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鼻咽 -13.2635
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417 -13.2636
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一郎 -13.2637
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セ -13.2648
VR -13.2648
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段话 -13.2649
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575 -13.2657
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杰出青年 -13.266
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5.6 -13.2663
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705 -13.2693
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jj -13.2697
604 -13.2698
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▁Wei -13.2699
缈 -13.2699
Ig -13.2701
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栾 -13.2706
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tis -13.2708
淞 -13.2708
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孢子 -13.2714
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785 -13.2717
辐 -13.2718
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roll -13.2721
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▁1902 -13.2727
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▁国药 -13.273
▁瓦解 -13.273
▁ccc -13.273
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认证 -13.2731
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学研 -13.2734
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▁琼脂 -13.2738
受命 -13.2742
濒 -13.2743
SHI -13.2744
缭 -13.2744
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ㄌ -13.2746
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icle -13.2749
一斑 -13.275
设置 -13.275
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lla -13.2753
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lad -13.2759
視 -13.276
494 -13.2761
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▁姐弟 -13.2764
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ね -13.2767
潘 -13.2768
tp -13.2769
泌 -13.277
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▁流亡 -13.2773
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千寻 -13.2776
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槃 -13.2777
荃 -13.2777
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JO -13.2779
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踢 -13.2781
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▁工部 -13.2784
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簪 -13.2785
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破烂 -13.2788
桓公 -13.2789
镌 -13.279
▁物产 -13.2792
▁said -13.2793
▁香格里拉 -13.2794
▁副厅 -13.2795
▁彩信 -13.2796
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▁第六十三 -13.2798
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▁危急 -13.2799
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荼 -13.2802
ude -13.2802
▁掺杂 -13.2803
▁约瑟夫 -13.2804
前年 -13.2804
▁称其 -13.2806
▁桑德 -13.2806
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奇艺 -13.2809
泷 -13.2811
大姨 -13.2811
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▁奋进 -13.2811
RU -13.2811
榭 -13.2812
▁北约 -13.2813
▁无赖 -13.2813
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▁三餐 -13.2816
不调 -13.2817
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▁hello -13.282
Application -13.2821
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闹闹 -13.2822
▁MicroSD -13.2823
▁1901 -13.2823
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NN -13.2828
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厘头 -13.2829
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▁劫持 -13.2834
▁剖面 -13.2835
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苓 -13.2837
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▁休养 -13.2841
喳 -13.2844
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▁外语教学 -13.2844
家用电器 -13.2845
▁EXE -13.2847
▁283 -13.2848
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缨 -13.2849
park -13.285
468 -13.2851
日耳曼 -13.2851
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宝钗 -13.2852
舞蹈 -13.2852
aid -13.2853
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nap -13.2854
▁防汛 -13.2854
▁繁杂 -13.2855
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▁冗余 -13.2855
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淡淡 -13.2858
打碎 -13.2858
自创 -13.2859
▁中国台湾 -13.2859
373 -13.286
▁勾心斗 -13.2862
▁信息处理 -13.2863
皙 -13.2863
修罗 -13.2864
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▁头等 -13.2869
bf -13.2869
FD -13.287
郭 -13.2871
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ras -13.2873
▁青铜器 -13.2873
不必 -13.2875
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促进 -13.288
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bert -13.2881
้ -13.2882
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present -13.2883
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▁282 -13.289
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◡ -13.2892
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rie -13.2898
019 -13.2899
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ln -13.2902
▁敬请 -13.2902
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改变 -13.2903
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亘 -13.2905
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拦 -13.2907
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509 -13.2908
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Space -13.2913
Kit -13.2916
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▁密钥 -13.2921
158 -13.2922
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dec -13.2924
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愫 -13.2926
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恢复 -13.2933
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chin -13.2939
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1001 -13.2941
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嘞 -13.2942
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冠希 -13.2945
交所 -13.2946
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887 -13.2947
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jun -13.2955
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ESS -13.2957
ona -13.2958
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node -13.2967
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▁半死 -13.297
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_____ -13.2981
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▁海峡两岸 -13.2984
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零碎 -13.2986
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▁究其 -13.2987
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728 -13.2989
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越远 -13.299
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▁那麼 -13.2992
▁暴涨 -13.2992
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之徒 -13.2993
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beat -13.2996
HL -13.2996
腐败 -13.2996
闾 -13.2998
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▁沸点 -13.2999
▁资费 -13.3001
割据 -13.3001
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▁雌激 -13.3002
848 -13.3003
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凯文 -13.3004
赔 -13.3004
處 -13.3005
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小异 -13.3011
ody -13.3011
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force -13.3017
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收藏家 -13.3021
▁物美 -13.3022
gn -13.3023
▁西安交通 -13.3023
折扣 -13.3024
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503 -13.3025
所闻 -13.3026
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打扮 -13.3028
886 -13.3029
▁help -13.303
不移 -13.3032
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▁倡议 -13.3034
law -13.3034
▁Martin -13.3035
苣 -13.3035
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▁长效 -13.3037
▁断路 -13.3038
▁安息 -13.3038
rou -13.3039
rio -13.3039
讼 -13.3039
▁微粒 -13.304
▁香甜 -13.3041
▁猪八戒 -13.3042
へ -13.3042
▁疏导 -13.3044
▁西风 -13.3045
▁高举 -13.3048
▁GDDR -13.3049
479 -13.3049
bil -13.305
bot -13.305
▁填料 -13.3052
眨眼 -13.3052
▁First -13.3054
▁聚氨 -13.3054
▁输电 -13.3056
走弯 -13.3056
▁闺密 -13.3057
▁保险金 -13.3057
箴 -13.3058
大厦 -13.3059
▁耐腐蚀 -13.306
▁锅盖 -13.306
mber -13.306
▁许嵩 -13.306
分部 -13.3061
rock -13.3062
▁极乐 -13.3062
write -13.3063
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0011 -13.3065
▁主创 -13.3065
▁青青 -13.3066
▁弯弯 -13.307
神父 -13.3072
▁魏则 -13.3072
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▁争相 -13.3072
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▁黑幕 -13.3073
▁吵闹 -13.3073
▁版主 -13.3076
▁凭空 -13.3077
▁必修 -13.3078
倪 -13.3082
▁夏侯 -13.3082
▁水壶 -13.3084
▁言谈 -13.3084
571 -13.3085
▁夏洛特 -13.3086
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▁投奔 -13.3089
▁沉醉 -13.309
忙忙 -13.309
三号 -13.3091
▁奔走 -13.3091
▁唾液 -13.3092
▁SAS -13.3093
▁祛斑 -13.3095
泓 -13.3097
▁布鲁斯 -13.3097
▁冷冷 -13.3097
645 -13.3099
▁军分 -13.3099
965 -13.3099
▁报业 -13.31
▁人情味 -13.31
▁后置 -13.31
▁WEB -13.31
▁鱼龙 -13.3102
▁无言以 -13.3102
▁曼彻斯特 -13.3103
OI -13.3105
▁应运而 -13.3106
▁Rhododendron -13.3112
蜥 -13.3112
▁习得 -13.3112
▁Plan -13.3113
▁underline -13.3114
▁巡洋 -13.3115
▁雨伞 -13.3116
▁千千万万 -13.312
农村 -13.312
inal -13.3122
▁宣讲 -13.3122
威胁 -13.3123
467 -13.3123
▁墙角 -13.3124
▁心肌梗 -13.3125
携 -13.3125
▁揭阳 -13.3126
▁超乎 -13.3127
▁降级 -13.3127
蓓 -13.3128
▁芦苇 -13.3128
▁突袭 -13.3128
▁weight -13.3128
▁第六十四 -13.3128
▁悔恨 -13.3129
▁布拉格 -13.313
▁盆景 -13.3131
▁胡歌 -13.3132
破坏 -13.3133
價 -13.3133
▁椎间 -13.3134
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及待 -13.3136
▁弥勒 -13.3138
配送 -13.3139
431 -13.314
远见 -13.314
fps -13.3141
▁寒冬 -13.3141
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drive -13.3144
▁澡堂 -13.3145
dj -13.3145
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▁免提 -13.3146
▁军医 -13.3148
▁糊弄 -13.3149
290 -13.3149
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nis -13.3151
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▁爱惜 -13.3153
憔 -13.3153
蠡 -13.3153
▁EMS -13.3155
單 -13.3157
wood -13.3158
radi -13.3158
eli -13.3159
▁零星 -13.3159
▁BMP -13.316
▁隐含 -13.316
张伟 -13.3161
uel -13.3161
▁不言而 -13.3165
▁靠拢 -13.3165
▁王氏 -13.3167
▁继任 -13.3167
所言 -13.3169
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烬 -13.317
列颠 -13.317
艺术界 -13.3171
蚓 -13.3171
ational -13.3171
678 -13.3173
▁美酒 -13.3173
845 -13.3173
▁Network -13.3175
▁阿联 -13.3176
616 -13.3178
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弈 -13.3181
不厌 -13.3182
中游 -13.3182
ECT -13.3183
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602 -13.3184
夸 -13.3184
▁ThinkPad -13.3184
▁effect -13.3184
力宏 -13.3185
▁DSP -13.3185
▁7.9 -13.3186
▁贞观 -13.3188
▁Au -13.3188
▁Flo -13.3188
▁庇护 -13.3189
fit -13.3191
▁讲求 -13.3192
▁荣幸 -13.3192
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家卫 -13.3192
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▁快船 -13.3198
▁招商银行 -13.3198
▁相辅 -13.3198
▁一抹 -13.3198
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殃 -13.3201
bt -13.3202
▁塔尔 -13.3204
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▁耳熟 -13.3208
985 -13.3208
702 -13.3208
▁护栏 -13.3208
啰 -13.321
祚 -13.321
▁凯特 -13.3211
嘚 -13.3212
▁叶酸 -13.3212
▁Lan -13.3212
监测 -13.3212
地黄 -13.3213
睢 -13.3213
莘 -13.3213
▁Hello -13.3214
▁公诉 -13.3214
艺谋 -13.3215
▁little -13.3216
▁夜色 -13.3216
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一扫 -13.3217
吖 -13.322
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▁壁挂 -13.322
▁手握 -13.3222
▁钟楼 -13.3222
▁技校 -13.3223
ime -13.3224
UD -13.3224
▁灵山 -13.3225
▁核发 -13.3226
▁罗辑 -13.3227
ago -13.3228
总结 -13.3228
▁287 -13.3229
尔雅 -13.323
edge -13.323
休止 -13.323
▁独角 -13.3231
▁抚顺 -13.3231
▁IPO -13.3236
▁墨迹 -13.3236
pack -13.3237
iel -13.3237
▁夺回 -13.3238
▁意在 -13.324
▁惰性 -13.3242
▁碎石 -13.3243
XY -13.3243
▁柑橘 -13.3243
HU -13.3243
▁附图 -13.3244
▁字帖 -13.3244
▁暴击 -13.3246
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莴 -13.3247
▁发球 -13.3249
▁Visitor -13.325
滔 -13.3251
▁制导 -13.3252
▁世袭 -13.3252
▁席卷 -13.3253
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ency -13.3255
eration -13.3256
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嚏 -13.3256
Image -13.3257
▁钻井 -13.3258
炕 -13.3259
谈判 -13.3262
中医药 -13.3262
545 -13.3264
浜 -13.3265
▁mx -13.3265
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▁匪夷 -13.3266
資 -13.3267
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918 -13.3271
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995 -13.3273
▁硬质 -13.3275
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型材 -13.3279
YA -13.328
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▁仇人 -13.3281
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NH -13.3283
▁刻度 -13.3283
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▁出乎意 -13.3287
▁千百年 -13.3287
▁加压 -13.3288
▁皮具 -13.329
▁香山 -13.329
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▁责备 -13.3293
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− -13.3293
变电 -13.3294
▁格罗 -13.3295
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dou -13.3298
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哪 -13.3302
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▁八五 -13.3304
▁滑坡 -13.3304
▁光合 -13.3305
▁忍无 -13.3306
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matic -13.3307
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758 -13.3406
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lean -13.3459
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915 -13.348
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925 -13.3507
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舞弊 -13.3522
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豪杰 -13.3525
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arri -13.357
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畿 -13.3578
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622 -13.3582
普勒 -13.3584
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cing -13.3586
476 -13.3586
ě -13.3588
嗔 -13.3588
531 -13.3589
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539 -13.359
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eter -13.3597
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vr -13.3604
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昱 -13.3611
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琉 -13.3617
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▁2048 -13.3629
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恣 -13.3633
DH -13.3635
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文静 -13.364
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拉扯 -13.3644
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呵呵 -13.3647
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准字 -13.3648
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〃 -13.3652
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東 -13.3653
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军医大学 -13.3654
台灯 -13.3654
ǒ -13.3654
589 -13.3655
峙 -13.3656
質 -13.3656
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隘 -13.3659
缔 -13.3659
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pha -13.3665
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865 -13.3675
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债务 -13.3679
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苛 -13.3689
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pic -13.3697
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斓 -13.37
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ㄙ -13.3714
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撂 -13.3717
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▁土建 -13.3719
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▁kiss -13.3724
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自弃 -13.3727
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RN -13.3729
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706 -13.3734
唆 -13.3737
曳 -13.374
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惹 -13.3747
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1080 -13.3749
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256 -13.375
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kit -13.3754
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dian -13.3756
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YP -13.3778
宥 -13.3782
658 -13.3784
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差阳 -13.3787
ょ -13.3789
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imi -13.3789
黄酮 -13.379
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668 -13.3792
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ova -13.3813
612 -13.3815
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岷 -13.3819
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之流 -13.3825
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door -13.3828
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挞 -13.3834
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559 -13.3841
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afafb -13.3851
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uf -13.3854
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787 -13.3857
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unch -13.3858
宸 -13.3858
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fact -13.3866
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吡 -13.3876
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cin -13.388
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586 -13.3886
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oro -13.389
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hl -13.3898
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ava -13.3899
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anna -13.3902
ept -13.3902
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蜚 -13.3915
枚举 -13.3916
ono -13.3916
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嗟 -13.3918
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恿 -13.3921
anti -13.3921
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茴香 -13.3927
堂皇 -13.3928
▁闭关 -13.3928
▁荒凉 -13.3928
▁973 -13.3929
▁门禁 -13.393
俸 -13.3931
阴极 -13.3932
bp -13.3932
肱 -13.3932
荡漾 -13.3932
▁免疫学 -13.3933
ION -13.3935
662 -13.3935
▁瑞安 -13.3936
▁临死 -13.3937
▁参选 -13.3938
▁1066 -13.394
▁行善 -13.3941
▁霍华 -13.3942
▁因其 -13.3942
▁算术 -13.3946
▁恩格 -13.3947
▁老挝 -13.3947
雪芹 -13.3948
▁明史 -13.3949
▁适不 -13.395
俐 -13.395
米亚 -13.395
▁群落 -13.395
▁奥尼 -13.395
▁稀里 -13.3951
AQ -13.3951
Process -13.3954
bla -13.3955
霍夫 -13.3955
▁师团 -13.3956
▁string -13.3957
挚友 -13.3957
山泉 -13.3958
▁乌托 -13.3959
车辆 -13.3959
▁独孤 -13.396
▁腐朽 -13.396
▁胸腔 -13.3961
▁北邻 -13.3962
▁威武 -13.3964
周末 -13.3964
袄 -13.3964
▁辅佐 -13.3965
▁造物 -13.3966
Κ -13.3966
诰 -13.3966
omo -13.3966
▁装订 -13.3967
▁霸占 -13.3967
▁八岁 -13.3968
绞 -13.3968
悯 -13.397
▁预想 -13.3971
▁南亚 -13.3972
▁TMD -13.3973
灰暗 -13.3974
轶 -13.3974
rist -13.3976
▁抖动 -13.3977
▁江泽 -13.3978
放火 -13.3979
▁创世 -13.398
▁着陆 -13.398
▁MIDI -13.3981
矗 -13.3984
计较 -13.3984
▁勇猛 -13.3985
kN -13.3986
▁诺曼 -13.3987
̣ -13.3991
桧 -13.3991
▁武学 -13.3993
▁铁板 -13.3994
▁時間 -13.3994
▁遮阳 -13.3996
▁CAM -13.3996
丨 -13.3997
▁自助餐 -13.4
▁拼写 -13.4
hon -13.4002
▁轻快 -13.4002
骑士 -13.4002
▁贪图 -13.4002
▁仿古 -13.4002
麒 -13.4004
鲲 -13.4004
▁York -13.4004
▁清泉 -13.4004
nz -13.4007
胄 -13.4009
邊 -13.4009
从心 -13.4009
▁政治经济 -13.4009
uring -13.4012
稷 -13.4012
997 -13.4013
▁拉票 -13.4014
▁补刀 -13.4016
甲烷 -13.4018
▁侠客 -13.4018
▁鲍鱼 -13.4018
▁mean -13.4018
▁凯旋 -13.4018
▁任由 -13.4019
▁艾玛 -13.4019
▁Tan -13.4019
inger -13.402
铉 -13.402
▁烟雨 -13.4021
▁左旋 -13.4022
▁柚子 -13.4025
▁宝塔 -13.4026
杵 -13.4026
▁地瓜 -13.4027
▁桂圆 -13.4027
AJ -13.4029
▁OPPO -13.4031
▁削减 -13.4032
ァ -13.4034
▁奇石 -13.4034
▁躲过 -13.4035
▁抓住机 -13.4036
▁mc -13.4036
渥 -13.4037
▁连夜 -13.4037
▁KOYO -13.4038
▁电算 -13.4039
▁CNN -13.4039
▁微生物学 -13.4041
stance -13.4041
▁奇侠 -13.4042
▁龙华 -13.4042
▁垫底 -13.4044
嗦 -13.4044
science -13.4044
▁tg -13.4045
▁自诩 -13.4048
▁第六十九 -13.4049
补贴 -13.405
▁红木 -13.405
▁政教 -13.4051
▁blabla -13.4051
▁灌篮 -13.4053
划入 -13.4053
▁else -13.4054
▁毒蛇 -13.4054
▁交割 -13.4055
jia -13.4055
iana -13.4055
诃 -13.4056
▁adc -13.4056
▁Festival -13.4057
▁赏心悦 -13.4059
脏器 -13.406
▁华南理工 -13.406
▁驰骋 -13.4062
▁0.03 -13.4063
▁三十万 -13.4063
潞 -13.4063
▁得不偿 -13.4063
爆竹 -13.4066
不惊 -13.4067
▁second -13.4068
▁背锅 -13.4071
▁埋没 -13.4072
▁擂台 -13.4072
彩票 -13.4073
▁腹膜 -13.4075
mis -13.4076
▁葵花 -13.4078
发证 -13.4078
hz -13.4078
▁family -13.4079
▁青石 -13.4083
贪 -13.4085
▁报班 -13.4086
▁23.6 -13.4087
▁甲等 -13.4087
DY -13.4087
▁导火 -13.409
真假 -13.4091
▁导引 -13.4091
▁螺杆 -13.4092
阑 -13.4093
▁婆媳 -13.4093
▁Passage -13.4093
▁GTX -13.4094
▁拉拢 -13.4096
▁突如其 -13.4096
展览 -13.4096
▁Arm -13.4097
▁金矿 -13.4098
▁倒不如 -13.4098
买卖 -13.4098
▁万众 -13.4099
▁爆笑 -13.4101
474 -13.4103
▁董卓 -13.4104
▁民间艺术 -13.4106
┴ -13.4106
dor -13.4107
▁Pla -13.4107
疫病 -13.4108
凸 -13.4108
▁镇守 -13.4109
▁卡顿 -13.411
▁保利 -13.4112
▁鸡胸 -13.4113
598 -13.4115
莒 -13.4115
▁石狮 -13.4117
▁胖胖 -13.4118
▁凌云 -13.4119
鹫 -13.4119
To -13.4121
魇 -13.4122
▁连绵 -13.4124
民大会 -13.4125
▁法兰克福 -13.4125
▁集锦 -13.4126
enny -13.4127
▁感覺 -13.4128
arn -13.4129
▁通缉 -13.413
term -13.4131
▁薛之谦 -13.4131
▁衢州 -13.4131
▁异步 -13.4132
▁北齐 -13.4132
ito -13.4134
5.1 -13.4134
▁革命烈 -13.4135
▁度日 -13.4138
.59 -13.4139
▁NTSC -13.4139
▁胡锦 -13.4139
▁淫秽 -13.4142
▁场均 -13.4143
▁程控 -13.4143
▁人云亦 -13.4143
у -13.4145
▁社交圈 -13.4146
▁水仙 -13.4146
evi -13.4146
▁4800 -13.4147
▁Watch -13.4148
178 -13.4149
▁直系 -13.4149
盈盈 -13.4149
▁中华全国 -13.4149
▁三十余 -13.415
▁怨气 -13.4152
▁勒布 -13.4152
丹青 -13.4152
▁迎宾 -13.4153
打击 -13.4154
▁北京电影学院 -13.4155
▁河床 -13.4157
独秀 -13.4157
▁哈特 -13.4157
▁一樣 -13.4159
▁弦乐 -13.4159
▁睁开眼 -13.4162
▁护发 -13.4163
▁没太大 -13.4164
▁抽空 -13.4166
039 -13.4167
▁Under -13.4169
▁安倍 -13.4169
▁爆款 -13.4169
709 -13.417
▁婉转 -13.4171
▁滑行 -13.4173
送入 -13.4174
▁陈氏 -13.4175
筵 -13.4175
不累 -13.4176
▁唯物 -13.4179
▁魔界 -13.4179
▁欧阳修 -13.4179
▁Gay -13.418
▁RMVB -13.4181
▁粘稠 -13.4181
▁满月 -13.4183
▁国泰 -13.4183
▁弯腰 -13.4184
德曼 -13.4184
▁国歌 -13.4184
▁倒计时 -13.4185
▁090 -13.4186
對 -13.4186
▁1860 -13.4188
流离 -13.4189
▁1895 -13.419
▁公投 -13.4191
▁巷子 -13.4192
nal -13.4192
▁牵连 -13.4193
弛 -13.4194
▁Audio -13.4195
▁暗夜 -13.4196
│ -13.4197
三尺 -13.4199
▁试客 -13.42
▁过氧化 -13.42
观止 -13.4202
▁袁绍 -13.4203
▁Material -13.4205
▁俯视 -13.4205
▁聊斋 -13.4205
欺 -13.4206
▁呆呆 -13.4206
▁近三 -13.4208
▁淮北 -13.4213
▁倚天 -13.4213
▁转接 -13.4214
戮 -13.4214
▁警察局 -13.4218
▁Arc -13.4218
oni -13.4219
▁20.6 -13.4221
▁Galaxy -13.4221
▁Digital -13.4223
▁黄连 -13.4226
hir -13.4227
Talk -13.4229
▁比丘 -13.423
▁txt -13.4232
▁手工业 -13.4234
▁Way -13.4236
▁烈日 -13.4237
▁血汗 -13.4238
▁BGM -13.4238
▁摩登 -13.4238
NF -13.4241
蹭 -13.4241
▁卧铺 -13.4246
▁井上 -13.4246
ಠ -13.4247
▁千叶 -13.4248
656 -13.4248
▁一棵树 -13.4248
▁青光 -13.4248
▁互感 -13.4249
▁Wall -13.425
龙湾 -13.4251
酸爽 -13.4251
▁集邮 -13.4253
▁寒风 -13.4254
▁级数 -13.4254
马哈 -13.4255
西林 -13.4258
▁化脓 -13.4258
Pass -13.4259
▁宗族 -13.4259
023 -13.426
▁MIT -13.426
不复 -13.4261
▁菱形 -13.4261
▁霸权 -13.4262
灵光 -13.4264
flo -13.4264
▁Hill -13.4265
rack -13.4265
▁保险费 -13.4265
▁邵阳 -13.4266
▁Pen -13.4267
▁无暇 -13.4267
▁迪斯尼 -13.4268
▁洪荒 -13.4268
▁Ana -13.4269
纲上 -13.4272
▁狗屎 -13.4274
▁空旷 -13.4274
▁一丁点 -13.4275
▁模联 -13.4277
▁后缀 -13.4277
▁凯撒 -13.4278
▁深奥 -13.4278
▁快活 -13.4278
▁demo -13.4279
导体 -13.428
▁全国人大 -13.4281
541 -13.4282
▁进京 -13.4282
▁替身 -13.4283
879 -13.4283
扫除 -13.4284
▁轮换 -13.4284
▁一技之 -13.4284
苹果 -13.4285
▁超导 -13.4285
▁卖不出 -13.4285
▁青鸟 -13.4286
▁鲜奶 -13.4289
▁收听 -13.4292
▁乘务 -13.4293
▁普鲁士 -13.4293
chem -13.4294
rant -13.4294
▁强盛 -13.4294
135 -13.4294
▁防抖 -13.4297
擂 -13.4298
啮 -13.4298
▁气垫 -13.4299
▁星期天 -13.4299
788 -13.4301
▁炎黄 -13.4301
▁隋朝 -13.4302
▁25.6 -13.4306
▁Die -13.4306
▁双击 -13.4307
▁Fox -13.4308
▁立功 -13.4309
屏风 -13.4309
濠 -13.431
▁益阳 -13.4312
风琴 -13.4313
info -13.4313
▁衣冠 -13.4313
▁农科 -13.4314
LK -13.4315
855 -13.4316
▁库克 -13.4316
▁永嘉 -13.4322
▁豆沙 -13.4324
▁芦笋 -13.4326
▁婷婷 -13.4327
鄙 -13.4327
▁缝纫 -13.4328
▁拓跋 -13.4328
自达 -13.433
EW -13.433
▁侧脉 -13.4331
471 -13.4331
▁红衣 -13.4331
▁致谢 -13.4332
▁末尾 -13.4333
▁炸鸡 -13.4334
▁Rap -13.4339
湮 -13.4339
苋 -13.4342
▁仰慕 -13.4342
▁传热 -13.4347
▁举证 -13.4347
▁谦逊 -13.435
▁Call -13.4352
蚂 -13.4353
▁妩媚 -13.4353
聲 -13.4353
▁万寿 -13.4354
▁冬日 -13.4354
▁思辨 -13.4355
▁主客场 -13.4358
▁巾帼 -13.4359
卸 -13.4359
organ -13.4359
▁碳化 -13.4361
▁体脂 -13.4361
▁丹阳 -13.4362
▁皇族 -13.4363
walk -13.4363
麾 -13.4365
▁瓶装 -13.4365
▁烟酒 -13.4366
▁克洛 -13.4368
chu -13.4368
riv -13.4369
鳅 -13.4369
敲打 -13.4371
衢 -13.4372
▁气缸 -13.4373
.264 -13.4373
▁Eric -13.4374
▁Jan -13.4376
▁二氧化 -13.4378
▁磁铁 -13.4379
叟 -13.438
▁玉树 -13.4382
甾 -13.4382
029 -13.4382
不治 -13.4382
▁冲绳 -13.4384
FU -13.4384
▁粘液 -13.4384
▁Rob -13.4385
▁宣誓 -13.4386
▁八达岭 -13.4386
指挥 -13.4387
shell -13.4388
▁抽查 -13.4389
▁倍受 -13.4389
▁开胃 -13.4392
▁追回 -13.4393
▁直击 -13.4393
▁克雷 -13.4393
-- -13.4394
▁风电 -13.4394
脲 -13.4395
蛰 -13.4397
▁天竺 -13.4398
kk -13.4398
▁馈赠 -13.4398
▁透光 -13.44
556 -13.44
▁亲笔 -13.4401
rot -13.4403
推车 -13.4403
▁Computer -13.4407
不疑 -13.4407
▁平顶山 -13.4408
▁wanna -13.441
▁心慌 -13.4411
▁漏斗 -13.4411
夙 -13.4411
gui -13.4412
▁临海 -13.4412
财务 -13.4413
如花 -13.4414
▁动乱 -13.4415
▁這些 -13.4416
▁牙科 -13.4417
▁反智 -13.4419
▁Never -13.4419
LF -13.4419
▁蓄积 -13.442
544 -13.442
lk -13.4421
▁摇晃 -13.4422
▁周瑜 -13.4427
▁封口 -13.4427
40000 -13.4428
▁Thomas -13.4429
▁敬老 -13.4429
▁碍于 -13.443
▁15.1 -13.443
▁邓丽 -13.4431
▁干洗 -13.4431
启超 -13.4431
news -13.4432
▁公墓 -13.4433
结束 -13.4433
▁玉龙 -13.4434
eau -13.4436
绿绿 -13.4436
▁准妈 -13.4437
▁惨遭 -13.4437
462 -13.4438
▁练琴 -13.4439
自足 -13.4442
▁尖头 -13.4442
理取 -13.4442
▁夜总 -13.4442
▁鲜嫩 -13.4442
▁群雄 -13.4443
饷 -13.4444
▁梯度 -13.4444
总支 -13.4446
▁tell -13.4447
浔 -13.4448
▁PET -13.4448
▁轮椅 -13.4448
▁宣战 -13.4449
▁广受 -13.4451
▁导数 -13.4452
▁织田 -13.4452
▁板载 -13.4454
aki -13.4454
▁持之以 -13.4454
▁静物 -13.4454
▁心平气 -13.4455
▁传神 -13.4456
▁cry -13.4458
▁sex -13.4458
▁隔膜 -13.4459
mt -13.4461
▁麦田 -13.4464
冰凉 -13.4466
偎 -13.4469
▁专修 -13.447
▁毅然决 -13.4472
无敌 -13.4474
▁滞留 -13.4476
▁取缔 -13.4476
▁清脆 -13.4477
▁枪管 -13.4477
▁TXT -13.4479
▁松树 -13.4482
冲击 -13.4482
机芯 -13.4482
▁互惠 -13.4483
ワ -13.4483
飒 -13.4483
轿车 -13.4484
search -13.4484
▁自言自 -13.4485
▁牧草 -13.4485
▁15.5 -13.4485
▁满嘴 -13.4485
▁犀牛 -13.4486
GHz -13.4486
yun -13.4487
▁13.6 -13.4488
▁见义勇为 -13.4489
▁抢断 -13.449
665 -13.4491
▔ -13.4492
▁Sci -13.4498
▁芭比 -13.4499
anc -13.4499
▁四驱 -13.45
▁SMS -13.45
▁切合 -13.4501
KS -13.4503
▁纸箱 -13.4504
基尼 -13.4504
厢情 -13.4505
耙 -13.4506
mission -13.4506
eur -13.4508
ography -13.4508
▁狡猾 -13.4509
▁琉球 -13.4509
▁made -13.4509
▁睁开 -13.451
▁韦伯 -13.451
忿 -13.4511
595 -13.4511
▁身临 -13.4513
▁淬火 -13.4514
▁剪裁 -13.4515
hel -13.4516
▁莱特 -13.4519
▁焕然 -13.4519
▁萨满 -13.452
▁焦躁 -13.4521
▁褒贬 -13.4522
▁阑尾 -13.4522
▁貌美 -13.4522
▁燕京 -13.4522
185 -13.4523
▁残存 -13.4525
itt -13.4525
▁0.15 -13.4527
▁祥和 -13.4527
羧 -13.4528
▁Think -13.4528
▁薄薄 -13.4529
▁判别 -13.4529
益匪 -13.453
▁质量保证 -13.4531
芥 -13.4531
隐私 -13.4531
▁预付 -13.4532
vor -13.4532
▁折合 -13.4534
▁有机肥 -13.4534
▁黄鹤 -13.4535
▁第六十七 -13.4536
▁错综 -13.4537
Log -13.4537
▁超额 -13.4537
▁刘秀 -13.4541
▁Center -13.4542
045 -13.4543
▁蹦出 -13.4543
▁process -13.4544
▁增减 -13.4544
▁一九四 -13.4545
川普 -13.4545
▁蜥蜴 -13.4548
▁泼水 -13.4548
▁自暴 -13.455
▁踪迹 -13.4551
天窗 -13.4552
▁双床 -13.4554
真事 -13.4554
▁防锈 -13.4555
辜 -13.4555
▁异乡 -13.4556
gre -13.4556
▁艾斯 -13.4556
945 -13.4557
開始 -13.4558
▁格律 -13.4558
▁推卸 -13.4559
▁脑膜 -13.4559
▁协办 -13.4561
▁萨摩 -13.4561
乒 -13.4562
▁第九十 -13.4562
▁施暴 -13.4563
joy -13.4564
痂 -13.4565
负荷 -13.4565
▁凝视 -13.4566
lite -13.4569
崴 -13.457
▁TAT -13.4571
▁飞奔 -13.4571
▁化纤 -13.4574
▁搜查 -13.4576
itz -13.4577
选课 -13.4577
▁请示 -13.4577
▁雷神 -13.4577
▁巴拿马 -13.4577
▁圣殿 -13.4578
▁软软 -13.4579
▁慌张 -13.458
權 -13.4581
▁义气 -13.4581
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龋 -13.4582
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腓 -13.4587
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551 -13.4588
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運 -13.4588
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寐 -13.4589
辍 -13.459
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band -13.4594
▁鲁国 -13.4596
≠ -13.4596
开复 -13.4597
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585 -13.4597
fd -13.4598
▁YOU -13.4598
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▁数十万 -13.4599
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残渣 -13.46
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標 -13.4602
讲座 -13.4603
阖 -13.4604
▁UPS -13.4605
▁朱雀 -13.4605
落落 -13.4607
缇 -13.4609
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败坏 -13.4613
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▁体弱 -13.4614
▁black -13.4615
▁潮汐 -13.4617
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529 -13.4618
疱 -13.462
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颧 -13.4623
lash -13.4624
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▁第七十三 -13.4627
▁四十四 -13.4627
蹋 -13.4627
▁嘈杂 -13.4627
即用 -13.4628
579 -13.4629
余部 -13.4629
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lian -13.4635
ふ -13.4635
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▁决断 -13.4636
▁艺兴 -13.4637
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▁汕头市 -13.4639
之恩 -13.464
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Color -13.464
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最多 -13.4643
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▁Kong -13.4645
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▁miss -13.4648
▁营口 -13.4648
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aba -13.4654
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END -13.4656
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▁副司令 -13.4657
729 -13.4657
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▁沉闷 -13.4658
526 -13.466
ester -13.4661
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▁王羲 -13.4661
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▁独唱 -13.4664
cks -13.4664
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裏 -13.4668
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汹 -13.4671
▁fuck -13.4672
刀切 -13.4673
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咕噜 -13.4678
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栓塞 -13.4678
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IU -13.4679
▁双臂 -13.4681
east -13.4682
▁市井 -13.4683
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↙ -13.4684
练习 -13.4684
685 -13.4686
钥匙 -13.4687
▁LOVE -13.4687
▁尘土 -13.4687
▁万恶 -13.4688
▁detail -13.4689
▁鸡皮 -13.4689
9999 -13.4691
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▁古玩 -13.4696
逵 -13.4697
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▁捂着 -13.4699
▁优步 -13.4699
▁钻孔 -13.47
▁alpha -13.4701
▁安德鲁 -13.4703
▁鬼吹 -13.4703
▁25.8 -13.4704
▁神功 -13.4705
▁勤工 -13.4705
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▁威严 -13.4706
喧 -13.4708
▁桔梗 -13.4708
▁move -13.4708
▁练功 -13.4709
ㄉ -13.4709
▁斯蒂芬 -13.4709
胡椒 -13.471
▁随波逐 -13.4711
▁饥渴 -13.4712
▁仓促 -13.4713
▁异步电 -13.4713
▁君臣 -13.4713
▁日新月 -13.4714
ACK -13.4716
▁显现出 -13.4717
▁半透明 -13.4717
▁英国伦敦 -13.4717
醐 -13.4717
835 -13.4718
▁From -13.4719
▁亲测 -13.4719
▁狂妄 -13.4719
▁卧床 -13.472
依恋 -13.4723
▁第七十四 -13.4724
▁17.2 -13.4724
767 -13.4725
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線 -13.4726
挨 -13.4728
▁字里行 -13.473
▁Level -13.473
榴 -13.4733
盈亏 -13.4734
叽 -13.4734
▁克拉克 -13.4735
▁follow -13.4738
▁预习 -13.4738
之计 -13.4738
▁染发 -13.474
嘀 -13.4741
诡计 -13.4741
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铰 -13.4742
▁空运 -13.4742
YU -13.4743
▁足不出 -13.4743
▁领带 -13.4744
▁辣鸡 -13.4746
ela -13.4747
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獨 -13.4749
▁020 -13.4749
▁遗弃 -13.4749
▁放荡 -13.475
▁杏花 -13.475
988 -13.4752
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刍 -13.4757
▁xxxx -13.4758
盅 -13.4761
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汪 -13.4768
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ried -13.4769
908 -13.4769
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▁明暗 -13.4772
ination -13.4773
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▁满载 -13.4776
多姿 -13.4779
佐助 -13.478
▁瞬时 -13.478
661 -13.4781
▁荞麦 -13.4782
▁三轮车 -13.4782
辞书 -13.4783
▁年终奖 -13.4783
造作 -13.4784
▁摩洛哥 -13.4785
▁油管 -13.4785
▁戴维 -13.4785
▁民政厅 -13.4786
零散 -13.4787
Ծ -13.4788
▁Language -13.4788
▁净利润 -13.4789
▁意料 -13.479
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▁控告 -13.4791
课标 -13.4792
▁命理 -13.4792
▁驾驶证 -13.4795
▁严寒 -13.4797
▁adv -13.4798
▁触觉 -13.4798
▁岩浆 -13.4799
莉亚 -13.48
捺 -13.4801
哗 -13.4802
▁录影 -13.4803
▁中国联通 -13.4803
▁Bug -13.4803
uke -13.4803
▁归档 -13.4804
JT -13.4807
愕 -13.4807
馬 -13.4807
▁四合院 -13.4807
▁频谱 -13.4808
噻 -13.4812
▁绝育 -13.4813
寥 -13.4815
▁工装 -13.4815
▁Online -13.4816
▁测控 -13.4816
▁韶关 -13.4817
裨 -13.4817
镭 -13.4817
唧唧 -13.4817
▁赎回 -13.4818
绝伦 -13.4818
▁核磁 -13.482
▁城隍 -13.482
▁碑林 -13.4821
hm -13.4821
▁voice -13.4822
hang -13.4823
敝 -13.4823
獒 -13.4823
▁溺爱 -13.4824
▁灌装 -13.4825
▁穷困 -13.4825
▁制热 -13.4825
东岸 -13.4825
▁22.8 -13.4828
KD -13.4829
晶晶 -13.483
▁独著 -13.483
ify -13.4831
▁夏普 -13.4832
▁国足 -13.4832
KHz -13.4834
▁泰坦 -13.4835
▁谐波 -13.4837
ソ -13.4838
歙 -13.4838
▁老鹰 -13.484
▁妈祖 -13.4841
▁终老 -13.4841
▁Germany -13.4841
▁枪械 -13.4842
iko -13.4842
▁十堰 -13.4844
▁触电 -13.4846
̷ -13.4846
▁冠名 -13.4846
▁排尿 -13.4846
ZE -13.4847
蛟 -13.4847
反应堆 -13.4847
oco -13.4847
酣 -13.4849
▁sorry -13.4851
▁法国巴黎 -13.4851
▁浮游 -13.4853
▁结膜 -13.4854
小虎 -13.4854
▁特警 -13.4855
熏 -13.4855
▁永定 -13.4855
芍 -13.4855
▁next -13.4856
dle -13.4859
▁挑拨 -13.486
芦 -13.4861
煨 -13.4863
▁案发 -13.4865
▁Great -13.4867
贲 -13.4868
▁洲际 -13.4869
▁光明日报 -13.4872
▁弗兰克 -13.4872
▁骗取 -13.4873
▁XXXX -13.4874
▁蚕桑 -13.4875
太平洋 -13.4876
▁原唱 -13.4878
▁牛油 -13.4878
▁哈萨克斯坦 -13.4879
百四十六 -13.488
▁Taylor -13.4881
罔 -13.4881
▁弗兰 -13.4882
▁飞轮 -13.4882
戦 -13.4883
▁工矿 -13.4884
马屁 -13.4885
▁object -13.4886
老君 -13.4887
▁竖起 -13.489
▁必经之 -13.4893
▁红褐 -13.4895
͡ -13.4895
▁周礼 -13.4896
▁两半 -13.4896
pose -13.4898
渡河 -13.49
aster -13.4901
梆 -13.4902
574 -13.4902
▁全株 -13.4903
祖辈 -13.4903
听途 -13.4905
▁PhD -13.4907
▁阿尔卑斯 -13.4908
懋 -13.4908
▁彩电 -13.4909
▁泰迪 -13.4909
▁撤回 -13.4909
覆去 -13.4909
▁蒲公英 -13.491
629 -13.491
▁抓获 -13.4911
鼬 -13.4912
做主 -13.4912
▁剖腹 -13.4913
▁世贸 -13.4915
▁取其 -13.4916
cial -13.4917
上爬 -13.492
雏 -13.4921
奘 -13.4921
▁吴越 -13.4922
事诉讼 -13.4923
▁酸酸 -13.4923
▁躯干 -13.4924
▁Pic -13.4924
▁哗众 -13.4928
▁运筹 -13.4929
▁江陵 -13.493
危害 -13.4931
花八 -13.4932
▁Xiao -13.4933
▁高性价 -13.4935
串串 -13.4935
▁承租 -13.4936
Source -13.4937
722 -13.4938
rick -13.494
▁阅卷 -13.494
兰芳 -13.4941
ube -13.4941
vic -13.4941
▁绣花 -13.4944
▁增补 -13.4945
▁足足 -13.4946
▁拐弯 -13.4946
▁皮尔 -13.4946
▁oppo -13.4947
▁周旋 -13.495
▁弄清 -13.4952
▁嘉年华 -13.4954
▁彼时 -13.4955
▁享有盛 -13.4955
▁乌尔 -13.4955
不决 -13.4956
理清 -13.4957
▁full -13.4958
▁游荡 -13.4959
▁果皮 -13.4961
▁怒气 -13.4961
▁尘世 -13.4962
許 -13.4962
之所 -13.4963
▁奔赴 -13.4964
▁天呐 -13.4965
▁Games -13.4966
▁横幅 -13.4966
1394 -13.4967
▁Tre -13.4968
垠 -13.4968
▁执笔 -13.4968
▁豪情 -13.4969
▁耳聋 -13.497
hd -13.4972
▁城郊 -13.4974
▁twitter -13.4974
匝 -13.4974
▁高耸 -13.4975
▁丰台 -13.4977
十足 -13.4978
pur -13.4979
▁腰痛 -13.498
▁倾注 -13.4983
▁兽药 -13.4985
Mg -13.4985
sec -13.4985
▁鼓浪 -13.4987
▁热议 -13.4987
▁斯拉夫 -13.499
▁炎帝 -13.4991
▁徐志 -13.4991
xiao -13.4991
▁脱硫 -13.4993
▁高危 -13.4994
虫草 -13.4995
▁汇合 -13.4996
▁戈壁 -13.4996
▁磨砂 -13.4998
▁伏羲 -13.5001
▁何苦 -13.5001
▁辽阳 -13.5003
▁延展 -13.5005
▁生津 -13.5005
▁Nexus -13.5005
ARA -13.5006
▁Amazon -13.5007
▁Conference -13.5007
▁公交站 -13.5008
匐 -13.501
‸ -13.5012
▁四十三 -13.5013
▁焦急 -13.5014
▁尽职 -13.5014
▁FBI -13.5015
6300 -13.5015
▁检测仪 -13.5017
▁隔断 -13.5018
▁落脚 -13.5018
▁各有所 -13.5018
▁野马 -13.5019
▁贝拉 -13.502
▁赶回 -13.5021
▁因地制 -13.5025
dx -13.5026
▁吸食 -13.5027
▁冰激 -13.5028
▁纠纷案 -13.5029
frame -13.5029
▁歪曲 -13.5029
▁利比亚 -13.5029
▁Kim -13.5029
王母 -13.503
▁敲击 -13.5032
gle -13.5032
▁安葬 -13.5032
▁嘴炮 -13.5032
▁滤过 -13.5034
enter -13.5035
▁Xu -13.5037
819 -13.504
▁始发 -13.504
TION -13.504
▁受阻 -13.5041
▁Yan -13.5041
▁close -13.5042
炼成 -13.5042
▁ACCA -13.5043
▁布政 -13.5044
▁平反 -13.5044
普斯 -13.5044
Radio -13.5044
驮 -13.5044
▁养鸡 -13.5045
aur -13.5045
igh -13.5045
▁生灵 -13.5045
苹 -13.5045
大把 -13.5047
散散 -13.5047
▁刺猬 -13.5049
▁美国纽约 -13.5051
滦 -13.5051
▁美利坚 -13.5054
▁系统分析 -13.5054
▁畅游 -13.5055
▁守备 -13.5056
員 -13.5057
▁环状 -13.5059
you -13.5061
▁TCL -13.5061
▁中央委员 -13.5061
▁great -13.5061
血淋 -13.5062
燮 -13.5064
town -13.5065
▁暨南 -13.5066
踝 -13.507
▁吃瓜 -13.5071
▁酸辣 -13.5071
pps -13.5071
▁供不应 -13.5072
▁触碰 -13.5073
▁Steam -13.5073
▁脍炙 -13.5074
▁获益 -13.5078
▁JPG -13.5079
▁达摩 -13.5081
mel -13.5081
▁carry -13.5083
芈 -13.5084
▁调频 -13.5084
▁猪脚 -13.5087
蔺 -13.5089
▁茶艺 -13.509
▁波纹 -13.509
▁通勤 -13.5091
▁沃尔沃 -13.5092
▁积雪 -13.5092
▁借阅 -13.5092
侄 -13.5093
▁1896 -13.5093
▁1899 -13.5094
▁托尼 -13.5094
▁农工 -13.5097
▁vivo -13.5097
▁开挂 -13.5097
▁淡泊 -13.5098
jy -13.5099
▁鲜红 -13.5102
▁story -13.5104
術 -13.5105
绑 -13.5107
不胖 -13.5107
磺酸 -13.5107
▁废旧 -13.5108
▁Rain -13.5109
757 -13.5111
▁绚烂 -13.5112
▁左转 -13.5116
▁香港中文 -13.5117
▁温润 -13.5117
▁苦练 -13.5118
绿水 -13.5118
▁祛除 -13.5121
▁times -13.5121
▁弹跳 -13.5121
冲冲 -13.5124
▁登高 -13.5124
▁初试 -13.5126
▁灰褐 -13.5126
▁购车 -13.5126
▁精诚 -13.5127
▁溶血 -13.513
▁六千 -13.513
紧缩 -13.5131
央企 -13.5132
▁海浪 -13.5133
▁活活 -13.5134
划线 -13.5136
瘪 -13.5136
戰 -13.5138
▁配资 -13.5139
社会保障部 -13.514
ault -13.5141
▁Bir -13.5143
▁苏南 -13.5143
耽 -13.5146
瞌睡 -13.5146
▁报告文学 -13.5147
▁相容 -13.5147
▁习以 -13.515
▁Syn -13.515
不稳 -13.5151
▁牡丹江 -13.5153
IK -13.5154
▁阻拦 -13.5156
▁三秒 -13.5157
▁22.6 -13.5158
几周 -13.5161
▁斯德哥尔摩 -13.5163
成荫 -13.5163
▁漂白 -13.5164
▁有目共 -13.5166
ctrl -13.5168
▁吊销 -13.5168
▁胆怯 -13.5168
▁危重 -13.5168
砧 -13.5169
講 -13.5169
▁Dark -13.5169
▁磨砺 -13.5169
▁朱棣 -13.517
▁8.9 -13.517
中下游 -13.5172
▁topic -13.5172
▁空余 -13.5173
▁手工艺 -13.5174
▁蚕丝 -13.5175
帷幕 -13.5176
▁异域 -13.5176
▁审稿 -13.5178
▁密密 -13.5179
etting -13.5179
▁16.7 -13.5179
▁蜀山 -13.518
▁2.00 -13.518
曙 -13.518
▁胶粘 -13.5184
決 -13.5191
▁蚩尤 -13.5192
▁安溪 -13.5193
▁炮火 -13.5193
Manager -13.5194
▁护送 -13.5194
▁珍妮 -13.5195
▁扣分 -13.5195
ife -13.5196
▁祛风 -13.5196
▁解构 -13.5196
慵 -13.5197
逾越 -13.5198
▁随之而 -13.5198
▁康乐 -13.5199
▁父女 -13.5199
▁预应 -13.5201
▁环游 -13.5201
加收 -13.5201
所作 -13.5202
▁持枪 -13.5202
诘 -13.5202
▁四十七 -13.5203
奋勇 -13.5206
導 -13.5207
▁厨具 -13.5207
▁吴江 -13.5209
▁朵朵 -13.5209
▁圣贤 -13.5209
▁衙门 -13.5211
TRA -13.5212
▁公职 -13.5212
▁胶质 -13.5212
▁镀金 -13.5214
▁瘦弱 -13.5214
巴比伦 -13.5214
▁FDA -13.5214
▁because -13.5216
▁暗淡 -13.5217
▁冥冥 -13.5217
▁method -13.5219
▁威斯 -13.522
秋雨 -13.522
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徕 -13.5222
浦东新区 -13.5223
▁招惹 -13.5224
▁really -13.5225
則 -13.5225
▁AlphaGo -13.5227
works -13.5228
713 -13.5229
▁紧绷 -13.5229
圖 -13.523
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滑冰 -13.5231
ISS -13.5231
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螯 -13.5232
▁黑芝麻 -13.5234
▁京华 -13.5235
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rush -13.5285
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鸢 -13.529
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汛 -13.5297
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社会科学院 -13.53
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727 -13.5302
jian -13.5303
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uid -13.5308
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ider -13.5347
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more -13.5348
fast -13.5348
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竞赛 -13.5355
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rmb -13.5377
CAT -13.5377
囱 -13.5378
imate -13.5379
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萬 -13.5386
☺ -13.5388
翊 -13.5388
黄蜂 -13.5393
谪 -13.5393
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犀 -13.5395
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張 -13.54
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▁五十多 -13.5404
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之女 -13.5407
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ATION -13.5412
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朝伟 -13.5419
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揉 -13.5425
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ological -13.5426
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623 -13.5432
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酐 -13.5439
鞅 -13.544
0100 -13.5443
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lc -13.5447
ille -13.5448
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坳 -13.5456
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hui -13.5459
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樽 -13.5459
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职务 -13.5482
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谌 -13.549
fort -13.5491
别姬 -13.5491
泠 -13.5491
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mobile -13.5497
QU -13.5498
掳 -13.5498
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嗲 -13.5505
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equen -13.5511
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不活 -13.5515
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urse -13.5524
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AGE -13.5527
叭 -13.5527
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651 -13.5529
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馈 -13.5543
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筷 -13.555
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锵 -13.5553
.000 -13.5554
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1020 -13.5555
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绶 -13.5558
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846 -13.5574
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榄 -13.5577
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iso -13.558
二醇 -13.558
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anger -13.5582
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642 -13.5586
AMA -13.5588
actor -13.5589
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民主主义 -13.5593
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etic -13.5595
ʔ -13.5596
578 -13.5599
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▁IEC -13.5602
RB -13.5602
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HF -13.5605
大减 -13.5605
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邨 -13.5606
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火箭 -13.5615
OUT -13.5616
薪阶 -13.5618
隼 -13.5619
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hip -13.5619
溥 -13.562
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尽责 -13.5624
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grad -13.5627
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YC -13.5631
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廣 -13.5634
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證 -13.5636
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心裂肺 -13.5643
惶 -13.5644
恐怖 -13.5645
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scape -13.5648
HG -13.5648
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631 -13.5651
離 -13.5653
646 -13.5654
JC -13.5654
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ㄚ -13.5655
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dam -13.5656
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搜 -13.5657
552 -13.5657
碰碰 -13.5657
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587 -13.5659
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apa -13.5664
大增 -13.5664
化瘀 -13.5665
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abel -13.5669
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EO -13.567
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jin -13.5671
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asa -13.5678
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14000 -13.5681
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玑 -13.5684
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逼人 -13.5695
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822 -13.5699
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898 -13.5709
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箐 -13.5738
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猜猜 -13.5739
磺 -13.5741
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专政 -13.5742
韭 -13.5744
716 -13.5744
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639 -13.5745
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自保 -13.575
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黜 -13.5761
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咲 -13.5777
峥 -13.5777
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kan -13.5798
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WO -13.5803
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漢 -13.5805
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乳糖 -13.5829
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虔 -13.5832
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dol -13.5844
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5200 -13.5846
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958 -13.5855
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肺腑 -13.586
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motion -13.586
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ello -13.5867
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茫 -13.5868
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宣统 -13.5871
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完整 -13.5874
rim -13.5875
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lio -13.588
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患失 -13.5882
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叮 -13.5883
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彦宏 -13.5891
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娱乐 -13.59
dog -13.5903
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限速 -13.5922
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革质 -13.5923
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印刷 -13.5924
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jing -13.5938
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================================================
FILE: CPM-Generate/bpe_3w_new/merges.txt
================================================
================================================
FILE: CPM-Generate/bpe_3w_new/vocab.json
================================================
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"\u2581\u6324\u51fa": 16231, "\u2581\u51b0\u5ddd": 16232, "OA": 16233, "\u2581\u5f20\u529b": 16234, "\u2581\u81ea\u8003": 16235, "\u82b9": 16236, "\u2581\u4e00\u51fa": 16237, "\u5de5\u4eba": 16238, "\u2581\u672b\u7aef": 16239, "\u2581\u56ed\u827a": 16240, "\u2581\u7231\u56fd\u4e3b\u4e49": 16241, "\u8138\u76ae": 16242, "\u2581\u4f73\u4f5c": 16243, "\u2581\u6cc9\u6c34": 16244, "\u602f": 16245, "\u2581\u65e0\u58f0": 16246, "\u2581\u6f6e\u6c55": 16247, "\u2581\u574f\u6b7b": 16248, "\u5b5c": 16249, "\u2581\u4e00\u5b57": 16250, "AY": 16251, "\u2581\u8fc7\u786c": 16252, "\u2581\u9752\u5c9b\u5e02": 16253, "BI": 16254, "\u2581\u62db\u52df": 16255, "\u2581\u77f3\u818f": 16256, "\u2581\u8bf7\u52ff": 16257, "\u2581\u4ed9\u4eba": 16258, "\u2581\u5ba3\u8a00": 16259, "\u2581153": 16260, "\u2581\u65b0\u5947": 16261, "\u8087": 16262, "\u2581\u516b\u89d2": 16263, "\u7f16\u7801": 16264, "\u2581\u86cb\u9ec4": 16265, "\u2581\u5343\u53e4": 16266, "\u2581\u62cd\u62cd": 16267, "\u2581\u7c73\u7c89": 16268, "\u2581\u98a4\u6296": 16269, "\u2581\u53d7\u9650": 16270, "\u52a8\u8109": 16271, "\u2581\u7f16\u5bfc": 16272, "002": 16273, "\u2581\u7279\u7ea7": 16274, "OH": 16275, "\u2581\u4e25\u7981": 16276, "\u6795": 16277, "\u97a0": 16278, "\u2581\u91d1\u9ec4": 16279, "\u52fe": 16280, "\u2581\u7535\u6781": 16281, "\u2581\u8bfe\u540e": 16282, "\u2581\u6c34\u6e29": 16283, "\u7172": 16284, "\u2581\u4e70\u5165": 16285, "\u2581\u4e8c\u53f7": 16286, "\u2581SEO": 16287, "\u2581\u64cd\u5fc3": 16288, "\u65e0\u6545": 16289, "\u53ef\u8a00": 16290, "\u2581Live": 16291, "\u66e6": 16292, "\u2581\u51f8\u663e": 16293, "\u2581\u4e94\u4e00": 16294, "\u2581\u7403\u5f62": 16295, "\u2581\u683c\u8c03": 16296, "\u53ef\u89c1": 16297, "\u2581\u667a\u969c": 16298, "\u2581Har": 16299, "\u2581\u798f\u7279": 16300, "\u5c0f\u5fc3": 16301, "\u2581\u6027\u60c5": 16302, "\u7ed9\u6211": 16303, "\u2581\u5927\u5934": 16304, "\u2581\u9003\u51fa": 16305, "\u2581\u5951\u5408": 16306, "\u4e1c\u5761": 16307, "\u2581\u540d\u5217": 16308, "\u2581\u8d2d\u623f": 16309, "\u2581\u5e72\u65f1": 16310, "\u2581\u6d3b\u585e": 16311, "\u2581\u5b81\u53ef": 16312, "\u2581\u68d2\u68d2": 16313, "\u2581\u519b\u6821": 16314, "\u6324": 16315, "\u6a1f": 16316, "\u2581\u9009\u79c0": 16317, "men": 16318, "\u901a\u8baf": 16319, "\u2581\u8d2a\u6c61": 16320, "\u2581\u5b5f\u5b50": 16321, "\u5144\u5f1f": 16322, "\u2581\u540e\u5907": 16323, "\u2581\u7f51\u7ea2": 16324, "\u63cf\u8ff0": 16325, "\u2581\u7518\u8517": 16326, "\u2581\u8bdd\u8d39": 16327, "\u2581\u5ba3\u544a": 16328, "\u2581\u66f4\u6df1": 16329, "\u2581\u9690\u9690": 16330, "\u7efc\u5408\u6cbb\u7406": 16331, "\u2581\u8def\u7a0b": 16332, "PU": 16333, "\u819b": 16334, "\u2581\u53ef\u89c2": 16335, "\u02c9": 16336, "\u2581\u591a\u53d1": 16337, "\u2581ll": 16338, "\u2581\u67d0\u5b9d": 16339, "\u7678": 16340, "\u2581\u706b\u82b1": 16341, "\u8110": 16342, "193": 16343, "\u8d22\u4ea7": 16344, "\u2581\u53d1\u5446": 16345, "\u2581\u4ee5\u571f": 16346, "\u2581\u5bb0\u76f8": 16347, "\u2581\u6253\u6253": 16348, "\u2581\u660e\u7ec6": 16349, "\u2581\u5f81\u6c42": 16350, "\u2581\u4e09\u5341\u4e8c": 16351, "\u2581\u5e72\u8fc7": 16352, "\u6f02": 16353, "194": 16354, "\u2581\u7b2c\u4e09\u5341\u4e5d": 16355, "\u9551": 16356, "\u7ee3": 16357, "\u2581\u914d\u5076": 16358, "\u5934\u53d1": 16359, "\u2581\u51b2\u523a": 16360, "\u79cb\u51ac": 16361, "\u2581\u4e00\u767e\u4e5d\u5341": 16362, "mail": 16363, "\u2581\u4e00\u4e0d": 16364, "\u2581CMOS": 16365, "\u2581VPN": 16366, "\u2581\u9690\u853d": 16367, "\u2581\u9006\u88ad": 16368, "\u4e00\u7ea7": 16369, "\u2581\u8feb\u5bb3": 16370, "\u2581\u903c\u771f": 16371, "\u2581\u95e8\u7a97": 16372, "int": 16373, "\u2581\u987a\u7545": 16374, "\u2581\u70b9\u591a": 16375, "db": 16376, "\u2581\u7231\u56e0": 16377, "2.2": 16378, "\u2581\u77ff\u4e1a": 16379, "\u2581\u5e73\u5766": 16380, "\u2581\u51fa\u67dc": 16381, "\u2581\u6469\u5c14": 16382, "\u2581\u8fea\u58eb": 16383, "\u2581\u90bb\u8fd1": 16384, "252": 16385, "\u5a25": 16386, "\u5e7f\u64ad\u7535": 16387, "\u91cc\u4e9a": 16388, "\u5b89\u7a33": 16389, "\u2581\u6392\u7403": 16390, "\u2581TOP": 16391, "NO": 16392, "\u2581\u7eaf\u771f": 16393, "\u919a": 16394, "\u2581\u5382\u957f": 16395, "\u2581\u65e0\u5bb3": 16396, "\u2581\u98ce\u5439": 16397, "\u8b66\u5bdf": 16398, "\u2581\u4f0a\u5229": 16399, "\u2581\u4e0b\u8fbe": 16400, "\u2581\u6c11\u653f": 16401, "star": 16402, "\u2581\u65b0\u95fb\u51fa\u7248": 16403, "\u7f9f": 16404, "\u2581\u4e09\u4e94": 16405, "\u2581\u72ec\u521b": 16406, "\u2581\u8f6f\u786c": 16407, "\u8fdb\u6b65": 16408, "\u2581\u5e74\u5747": 16409, "\u671f\u5f92": 16410, "\u2581\u6027\u7231": 16411, "\u9edb\u7389": 16412, "\u2581\u6784\u4ef6": 16413, "\u6e7f\u5ea6": 16414, "\u2581\u975e\u7ebf\u6027": 16415, "\u2581\u4e94\u6bdb": 16416, "\u2581PLC": 16417, "\u2581\u5e86\u5178": 16418, "ext": 16419, "\u2581\u8fdc\u5927": 16420, "\u2581\u97f3\u50cf": 16421, "\u2581\u904d\u5730": 16422, "\u2581\u5185\u9601": 16423, "\u2581\u8354\u679d": 16424, "\u2581\u4e09\u5bb6": 16425, "\u2581\u6f14\u7ec3": 16426, "\u8d3e": 16427, "\u5f97\u597d": 16428, "\u2581\u5f00\u6765": 16429, "\u951a": 16430, "\u2581\u6ed1\u52a8": 16431, "\u2581\u59a5\u5584": 16432, "\u2581\u7626\u8eab": 16433, "\u2581\u5916\u5411": 16434, "\u2581\u58c1\u7eb8": 16435, "\u60e9": 16436, "\u2581\u56de\u8def": 16437, "\u2581\u4e24\u4e07": 16438, "lay": 16439, "\u2581\u6743\u8861": 16440, "\u2581\u5b54\u96c0": 16441, "\u2581\u4f1a\u60f3": 16442, "TB": 16443, "ump": 16444, "\u2581\u6ca1\u5f97": 16445, "\u2581\u51cf\u5f31": 16446, "\u4fb5": 16447, "\u51db": 16448, "\u2581\u6821\u95e8": 16449, "\u2581Music": 16450, "oth": 16451, "\u2581\u5355\u4eb2": 16452, "\u8499\u53e4\u65cf": 16453, "\u9980": 16454, "\u2581\u6df1\u6c89": 16455, "\u8fdb\u4e00\u6b65": 16456, "\u2581Photoshop": 16457, "\u2581\u635f\u8017": 16458, "\u5df4\u5df4": 16459, "\u2581\u5efa\u6784": 16460, "time": 16461, "\u2581\u4e70\u70b9": 16462, "\u78f7": 16463, "era": 16464, "\u2581\u7d20\u6709": 16465, "\u2581\u7b2c\u56db\u5341": 16466, "\u2581\u5317\u7eac": 16467, "\u2581\u4ece\u5934\u5230": 16468, "\u2581\u70ed\u7ebf": 16469, "\u2581\u8001\u8001": 16470, "ano": 16471, "\u30ca": 16472, "\u5e74\u534e": 16473, "\u6df7\u6df7": 16474, "\u82f7": 16475, "\u6cbb\u597d": 16476, "\u5f52\u4e8e": 16477, "\u780c": 16478, "\u2581\u53c2\u8c0b": 16479, "\u6e20\u9053": 16480, "288": 16481, "\u965b": 16482, "\u2581\u7ed3\u6838": 16483, "\u4e00\u51fb": 16484, "\u2581\u56de\u4e8b": 16485, "\u2581\u8d85\u58f0\u6ce2": 16486, "\u6c90": 16487, "\u7410": 16488, "\u2581\u4f73\u80fd": 16489, "\u2581\u5434\u4ea6\u51e1": 16490, "\u79c6": 16491, "\u2581\u65f6\u5e94": 16492, "\u2581\u9ad8\u8d85": 16493, "\u2581\u957f\u5f81": 16494, "\u2581\u4e9a\u70ed": 16495, "\u6587\u5e1d": 16496, "\u2581\u62bd\u51fa": 16497, "\u2581\u677f\u578b": 16498, "\u4ee5\u6c42": 16499, "\u2581\u55d3\u5b50": 16500, "\u8fbe\u6807": 16501, "\u2581\u81ea\u6170": 16502, "\u2581Lesson": 16503, "\u80c1": 16504, "fer": 16505, "\u2581\u5c0f\u9e1f": 16506, "\u2581\u5ba2\u6237\u670d\u52a1": 16507, "\u2581\u5f80\u8fd4": 16508, "\u2581Fr": 16509, "\u2581\u5206\u7ea2": 16510, "264": 16511, "\u2581\u4ea8\u5229": 16512, "\u2581\u65b9\u5757": 16513, "\u2581\u5927\u98ce": 16514, "\u2581\u9e92\u9e9f": 16515, "sen": 16516, "\u2581\u73b0\u8eab": 16517, "\u919b": 16518, "\u2581USA": 16519, "\u2581\u987a\u5e94": 16520, "\u7535\u6e90": 16521, "2.8": 16522, "241": 16523, "\u2581\u6fc0\u8fdb": 16524, "\u2581\u52bf\u5fc5": 16525, "\u25811926": 16526, "\u2581\u5145\u5f53": 16527, "\u2581\u5927\u6cd5": 16528, "\u53f8\u673a": 16529, "\u6402": 16530, "399": 16531, "\u7940": 16532, "\u8bdd\u9898": 16533, "\u2581\u4e00\u89d2": 16534, "\u2581\u7b2c\u4e00\u767e\u516b": 16535, "\u841d": 16536, "\u5b9e\u73b0": 16537, "ita": 16538, "\u2581\u5927\u59d0": 16539, "\u5df3": 16540, "FT": 16541, "\u2581\u7f16\u8005": 16542, "\u2581\u5fc3\u808c": 16543, "ck": 16544, "\u2581\u902e\u6355": 16545, "\u2581\u56e0\u70ba": 16546, "\u2581\u987d\u5f3a": 16547, "\u2581\u95f4\u8c0d": 16548, "Comp": 16549, "\u2581\u6536\u517b": 16550, "\u2581Up": 16551, "\u2581\u79bb\u522b": 16552, "\u5511": 16553, "\u2581\u6253\u5165": 16554, "\u2581\u884c\u653f\u533a": 16555, "\u2581\u706d\u706b": 16556, "\u2581\u5175\u56e2": 16557, "\u2581Michael": 16558, "\u53c2\u8c0b\u957f": 16559, "\u2581QAQ": 16560, "ith": 16561, "\u2581Ri": 16562, "\u804c\u9ad8": 16563, "\u4e00\u5b9a": 16564, "\u2581\u56fd\u65d7": 16565, "\u2581\u897f\u95e8": 16566, "\u2581\u50b2\u5a07": 16567, "\u2581\u91cd\u4f24": 16568, "\u767c": 16569, "\u2581NVIDIA": 16570, "730": 16571, "\u7f16\u8f91": 16572, "\u9ca4": 16573, "\u81fb": 16574, "\u2581\u6253\u542c": 16575, "\u2581\u738b\u9053": 16576, "\u835a": 16577, "\u2581\u5c3c\u514b": 16578, "\u2581\u6f5c\u5fc3": 16579, "\u2581\u7ec6\u5fae": 16580, "\u2581\u5bcc\u8c6a": 16581, "\u2581\u7a97\u5e18": 16582, "\u2581\u4e94\u56db": 16583, "\u2581system": 16584, "\u8bb9": 16585, "\u2581\u65e0\u8272": 16586, "ND": 16587, "\u2581\u878d\u5316": 16588, "\u2581\u529f\u8bfe": 16589, "\u2581PDF": 16590, "318": 16591, "\u82cd": 16592, "\u7406\u5de5": 16593, "\u2581\u5927\u4f6c": 16594, "\u2581\u5b9c\u660c": 16595, "\u2581\u526f\u4e3b": 16596, "\u9a74": 16597, "\u2581title": 16598, "\u2581\u8131\u9896": 16599, "\u586b": 16600, "470": 16601, "\u76d1\u5bdf": 16602, "\u53ae": 16603, "\u2581\u6b27\u51a0": 16604, "\u77e5\u4e4e": 16605, "\u2581WP": 16606, "\u2581\u53e3\u624d": 16607, "\u5b9e\u8df5": 16608, "\u2581\u9020\u7eb8": 16609, "\u2581\u5f53\u9762": 16610, "\u2581\u5b9d\u7389": 16611, "chan": 16612, "\u8c46\u8150": 16613, "\u30e1": 16614, "\u7109": 16615, "\u2581\u6b20\u7f3a": 16616, "\u2581\u964d\u843d": 16617, "\u5370\u53d1": 16618, "\u7edb": 16619, "\u2581\u4e0d\u8010": 16620, "\u2581\u516b\u5341\u5e74": 16621, "\u6bb4": 16622, "\u2581\u590d\u53e4": 16623, "5.9": 16624, "\u5993": 16625, "\u2581\u5c71\u8c37": 16626, "\u841d\u535c": 16627, "ble": 16628, "SR": 16629, "\u2581\u8ba1\u751f": 16630, "\u2581\u89c6\u5bdf": 16631, "\u2581\u6f5c\u4f0f": 16632, "\u2581\u4e00\u767e\u56db\u5341": 16633, "\u9002\u5408": 16634, "\u2581\u8d2a\u5a6a": 16635, "\u2581\u7ea2\u8c46": 16636, "\u4f2f\u683c": 16637, "\u2581\u7ad9\u53f0": 16638, "\u2581\u522b\u5fd8": 16639, "\u4ea7\u9636\u7ea7": 16640, "\u2581\u82cd\u8747": 16641, "\u65af\u514b": 16642, "\u2581\u4e09\u5c81": 16643, "\u2581\u514b\u91cc": 16644, "\u2581\u7ffb\u5531": 16645, "\u2581\u7279\u8bb8": 16646, "\u2581\u8fdb\u51fa": 16647, "\u2581\u6027\u547d": 16648, "9.2": 16649, "\u2581\u829c\u6e56": 16650, "EV": 16651, "\u2581\u5f15\u6765": 16652, "\u2581\u98ce\u6ce2": 16653, "\u2581\u53d1\u529b": 16654, "\u6691\u5047": 16655, "\u5fc3\u52a8": 16656, "\u2581\u5b8c\u5de5": 16657, "\u2581\u82f1\u52c7": 16658, "\u81f4\u656c": 16659, "\u4ed1": 16660, "390": 16661, "190": 16662, "\u2581\u4fdd\u6696": 16663, "\u2581\u5199\u5b9e": 16664, "\u2581\u7f57\u9a6c\u5c3c\u4e9a": 16665, "\u2581\u5df4\u57fa\u65af\u5766": 16666, "\u2581\u5065\u7f8e": 16667, "\u2581\u6539\u7248": 16668, "\u2581\u65e0\u5fc3": 16669, "\u7f13\u5b58": 16670, "Data": 16671, "\u2581\u57ce\u4e2d": 16672, "rr": 16673, "\u5c0f\u5df7": 16674, "\u7490": 16675, "\u2581\u9738\u9053": 16676, "\u2581\u622a\u7136": 16677, "\u2581\u6d3b\u8840": 16678, "\u2581\u730e\u5934": 16679, "\u3081": 16680, "\u2581\u76d1\u4e8b": 16681, "\u2581\u8bd5\u8fc7": 16682, "\u2581\u521a\u51fa": 16683, "\u2581\u519c\u673a": 16684, "\u2581\u6025\u9700": 16685, "6.9": 16686, "unt": 16687, "\u5dee\u5f02": 16688, "321": 16689, "\u2581\u5b89\u5b89": 16690, "\u2581\u6c34\u6587": 16691, "\u6cbf": 16692, "\u258112.2": 16693, "\u2581\u7206\u7834": 16694, "\u6279\u8bc4": 16695, "\u2581At": 16696, "UP": 16697, "\u2581\u51fa\u9053": 16698, "\u2581\u5e97\u4e3b": 16699, "\u96f7\u65af": 16700, "\u2581\u5c11\u5c06": 16701, "\u2581\u5de5\u5546\u884c\u653f": 16702, "\u2581\u5929\u7687": 16703, "\u2581\u5730\u4e2d\u6d77": 16704, "\u2581\u80f6\u7247": 16705, "\u2581\u77f3\u82f1": 16706, "\u5fcd\u53d7": 16707, "\u2581\u6392\u6bd2": 16708, "\u958b": 16709, "\u5c0f\u5c0f": 16710, "\u2581\u6842\u82b1": 16711, "\u2581\u4fb5\u5165": 16712, "\u2581\u89e3\u6551": 16713, "TR": 16714, "\u52b1": 16715, "\u2581his": 16716, "\u2581\u5f20\u827a\u5174": 16717, "RP": 16718, "ations": 16719, "\u2581\u6bb4\u6253": 16720, "nel": 16721, "\u7533\u8bf7": 16722, "sw": 16723, "\u2581\u4e00\u89c1\u949f": 16724, "\u00fa": 16725, "\u2581\u4e8c\u5341\u4e5d": 16726, "\u2581\u6b96\u6c11\u5730": 16727, "\u695e": 16728, "\u2581\u4e9a\u8fd0": 16729, "\u2581\u732e\u8840": 16730, "\u2581\u534e\u7855": 16731, "\u2581\u501f\u6b64": 16732, "ari": 16733, "\u2581\u6df9\u6ca1": 16734, "\u2581\u8944\u9633": 16735, "820": 16736, "\u2581\u8d8b\u4e8e": 16737, "\u2581\u6025\u8bca": 16738, "\u2581\u9aa8\u8d28": 16739, "\u8f70": 16740, "\u2581\u91cd\u7537\u8f7b": 16741, "\u2581\u4f4d\u5c45": 16742, "\u2581\u5bfc\u51fa": 16743, "\u2581\u6ce2\u7279": 16744, "com": 16745, "\u2581\u7267\u5e08": 16746, "\u8339": 16747, "\u2581\u53d8\u8d28": 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"\u2581\u5199\u8fc7": 16792, "8.0": 16793, "\u2581\u7535\u80fd": 16794, "\u2581\u4f1a\u8d39": 16795, "\u2581\u8fa8\u8bc1": 16796, "\u2581\u4f20\u95fb": 16797, "\u2581\u597d\u4e9b": 16798, "\u2581\u72af\u89c4": 16799, "\u2581\u5e76\u884c": 16800, "\u2581\u518d\u6765": 16801, "\u2581\u8868\u626c": 16802, "\u2581\u547c\u5524": 16803, "\u2581\u813e\u80c3": 16804, "\u2581MAC": 16805, "\u2581198": 16806, "\u95f4\u8ddd": 16807, "311": 16808, "\u6731": 16809, "\u77eb": 16810, "\u5524": 16811, "\u8f93\u51fa": 16812, "\u2581\u5148\u5929\u6027": 16813, "\u2581\u7455\u75b5": 16814, "bin": 16815, "\u2581\u6d6e\u52a8": 16816, "\u5e74\u9f84": 16817, "Work": 16818, "\u8511": 16819, "\u2581\u4e4c\u9c81\u6728\u9f50": 16820, "\u2581\u673a\u8f66": 16821, "\u2581Chen": 16822, "\u6f5c\u529b": 16823, "\u2581\u89e3\u6bd2": 16824, "\u2581\u76f8\u5bf9\u8bba": 16825, "\u2581\u4e8c\u4e09": 16826, "\u2581\u4ee5\u81f3": 16827, "char": 16828, "\u2581\u6251\u514b": 16829, "oto": 16830, "\u2581LOGO": 16831, "\u67a2": 16832, "\u8f8a": 16833, "\u2581\u8349\u576a": 16834, "\u2581\u8d8a\u91ce": 16835, "\u2581\u4e0a\u4e07": 16836, "\u2581\u72c2\u6b22": 16837, "\u2581\u652f\u90e8": 16838, "the": 16839, "\u2581\u5de5\u827a\u54c1": 16840, "\u2581\u77f3\u523b": 16841, "\u2581\u56fe\u753b": 16842, "\u2581\u76d0\u9178": 16843, "\u2581\u865a\u5f31": 16844, "\u2581\u8f6c\u8fd0": 16845, "\u2581\u8c03\u620f": 16846, "\u63a5\u53d7": 16847, "\u2581\u540e\u9057": 16848, "\u97f3\u4e50\u5bb6": 16849, "\u2581\u8fdb\u9636": 16850, "\u2581\u94c5\u7b14": 16851, "\u2581\u4e3e\u63aa": 16852, "\u2581\u6d41\u8840": 16853, "\u2581\u4e2d\u8001\u5e74": 16854, "\u2581\u78e8\u5408": 16855, "\u2581\u5e03\u6717": 16856, "PP": 16857, "\u2581\u4e00\u5143": 16858, "\u2581\u6e38\u4eba": 16859, "\u2581\u8bf8\u5982\u6b64": 16860, "\u2581\u53d6\u820d": 16861, "\u2581\u767e\u516b\u5341": 16862, "\u2581MacBook": 16863, "\u2581\u517b\u80b2": 16864, "cho": 16865, "\u2581\u5403\u82e6": 16866, "\u88e1": 16867, "\u2581\u4e2d\u5c42": 16868, 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"\u2581\u4e00\u680b": 16910, "\u2581\u5e0c\u5c14": 16911, "\u626b": 16912, "211": 16913, "\u2581\u7845\u80f6": 16914, "\u2581they": 16915, "\u2581\u98df\u76d0": 16916, "\u2581Pe": 16917, "\u2581\u590f\u65e5": 16918, "\u5730\u94c1": 16919, "\u2581\u82b1\u82b1": 16920, "\u2581\u91cd\u578b": 16921, "\u2581\u6e05\u70ed": 16922, "\u2581\u9886\u519b": 16923, "\u2581Ke": 16924, "\u2581\u5730\u7406\u5b66": 16925, "\u2581\u5fd7\u540c\u9053\u5408": 16926, "\u7599\u7629": 16927, "\u2581\u80ba\u708e": 16928, "\u9985": 16929, "\u2581\u996e\u7528\u6c34": 16930, "\u95f5": 16931, "\u2581\u5ba2\u5ea7": 16932, "\u62e3": 16933, "\u2581\u6cbf\u7528": 16934, "\u2581\u9547\u6c5f": 16935, "\u2581\u4e09\u5341\u591a": 16936, "\u2581\u62ac\u8d77": 16937, "Sc": 16938, "\u2581\u8c0b\u6740": 16939, "pro": 16940, "\u2581\u9576\u5d4c": 16941, "\u2581\u8bf4\u4e0d\u6e05": 16942, "\u2581\u67f4\u6cb9": 16943, "\u2581\u6325\u53d1": 16944, "\u2581\u591a\u597d": 16945, "\u2581\u5173\u7fbd": 16946, "\u6ee1\u9762": 16947, "\u2581\u62e5\u6324": 16948, "\u2581\u4e00\u518d": 16949, "EP": 16950, "van": 16951, "\u53ef\u9760": 16952, "\u516c\u4e3b": 16953, "\u5bb6\u5e84": 16954, "\u2581\u5927\u5f00": 16955, "401": 16956, "\u5236\u4f5c": 16957, "\u5d69": 16958, "\u60c5\u8282": 16959, "\u2581\u8d2b\u5bcc": 16960, "\u2581\u7c89\u5e95": 16961, "\u58ee\u5927": 16962, "\u2581\u4ece\u90a3": 16963, "\u6d53\u5ea6": 16964, "620": 16965, "\u62e2": 16966, "710": 16967, "\u2581\u7ea2\u70e7": 16968, "\u52d8\u63a2": 16969, "\u52d8": 16970, "WA": 16971, "\u2581\u795d\u4f60": 16972, "\u5729": 16973, "\u7701\u59d4": 16974, "\u60df": 16975, "\u2581\u56fa\u6001": 16976, "\u2581\u52a0\u70b9": 16977, "240": 16978, "\u2581\u7409\u7483": 16979, "358": 16980, "vers": 16981, "\u2581\u7f34\u8d39": 16982, "\u2581there": 16983, "\u2581\u5c01\u5370": 16984, "\u7687\u4e0a": 16985, "MAX": 16986, "\u2581\u4fe1\u5949": 16987, "\u672c\u4e0a": 16988, "\u2581\u9636\u68af": 16989, "\u6c5f\u53bf": 16990, "\u2581\u5c71\u4e0b": 16991, "pu": 16992, 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"\u2581\u5c0f\u9c7c": 17912, "\u305d": 17913, "\u2581\u98de\u901f": 17914, "\u2581\u5370\u7b2c\u5b89": 17915, "\u2581\u96ea\u82b1": 17916, "\u2581\u8d8a\u8fc7": 17917, "\u5927\u592b": 17918, "\u5e42": 17919, "\u2581\u624b\u52bf": 17920, "\u611b": 17921, "\u2581\u5317\u9b4f": 17922, "\u9881": 17923, "\u7ef5": 17924, "\u2581\u5207\u5f00": 17925, "\u2581\u4ea4\u4e86": 17926, "8.1": 17927, "\u6a80": 17928, "\u2581\u8bf4\u8c0e": 17929, "\u2581\u9690\u5c45": 17930, "\u6109": 17931, "\u2581\u65b0\u79c0": 17932, "\u2581\u6c57\u6c34": 17933, "GN": 17934, "\u4e8b\u7269": 17935, "\u2581\u8d8a\u72f1": 17936, "\u5cb1": 17937, "\u2581\u9893\u5e9f": 17938, "\u2581\u4e3b\u98df": 17939, "\u73ba": 17940, "\u2581\u6ee1\u6d32": 17941, "\u2581\u7eda\u4e3d": 17942, "\u2581\u6cb3\u897f": 17943, "\u2581\u8865\u8bfe": 17944, "ors": 17945, "\u2581\u88c5\u673a": 17946, "\u2581\u4e00\u70b9\u4e00\u70b9": 17947, "\u2581\u603b\u5206": 17948, "\u2581\u6302\u53f7": 17949, "\u2581\u6d77\u6e7e": 17950, 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"\u2581\u5192\u72af": 17991, "\u2581\u4e00\u66f2": 17992, "\u2581\u9a6c\u5c3e": 17993, "sky": 17994, "og": 17995, "\u2581\u660e\u667a": 17996, "\u2581\u4e2d\u8349\u836f": 17997, "\u7455": 17998, "\u2581\u53cd\u5dee": 17999, "\u2581\u8349\u6839": 18000, "\u68d5": 18001, "\u2581\u51cf\u8102": 18002, "\u2581\u903e\u671f": 18003, "\u4e0a\u4e0b": 18004, "\u2581pdf": 18005, "\u2581song": 18006, "\u2581\u5fc3\u5b89": 18007, "\u2581Ge": 18008, "\u785d": 18009, "\u2581\u5171\u5efa": 18010, "\u2581\u9e33\u9e2f": 18011, "\u2581\u7b2c\u56db\u5341\u56db": 18012, "\u2581\u6d1e\u5bdf": 18013, "\u4ea2": 18014, "\u2581\u4f20\u7403": 18015, "\u2581\u6210\u8d25": 18016, "TY": 18017, "\u2581\u7f6e\u8eab": 18018, "\u2581\u6e58\u6f6d": 18019, "\u2581\u8001\u4e61": 18020, "\u2581\u5929\u9a6c": 18021, "247": 18022, "\u2581biz": 18023, "\u73b2\u73d1": 18024, "\u2581\u65e0\u6240\u4e0d": 18025, "\u2581\u6b66\u5927": 18026, "\u7cb1": 18027, "\u2581\u97f5\u5473": 18028, "\u2581\u6ca1\u5e26": 18029, "\u2581\u54ea\u5929": 18030, "\u545b": 18031, "\u2581\u9886\u5730": 18032, "\u2581\u5934\u6761": 18033, "\u2581trans": 18034, "\u2581\u5357\u7f8e": 18035, "\u2581\u5207\u524a": 18036, "\u2581Mr": 18037, "\u2581\u9ad8\u85aa": 18038, "\u2581\u6162\u8dd1": 18039, "Net": 18040, "\u2581Black": 18041, "\u2581\u519b\u9600": 18042, "\u2581\u65e0\u5f02": 18043, "\u2581\u526f\u79d1": 18044, "\u2581\u6c34\u91cc": 18045, "\u2581\u5168\u56fd\u653f\u534f": 18046, "\u2581\u8fd1\u73b0\u4ee3": 18047, "\u4f20\u5a92": 18048, "\u62d2": 18049, "ham": 18050, "\u5239\u90a3": 18051, "oom": 18052, "\u5c14\u7279": 18053, "UR": 18054, "\u2581\u8239\u957f": 18055, "\u2581\u8d29\u5356": 18056, "\u2581\u6bd4\u5c14": 18057, "\u2581\u4ece\u6839": 18058, "\u2581\u65e5\u7a0b": 18059, "ather": 18060, "\u2581\u5185\u7ebf": 18061, "\u2581\u7f16\u7e82": 18062, "\u2581\u8fde\u8fde": 18063, "\u2581\u540d\u8a00": 18064, "\u2581\u5e74\u6625": 18065, "\u7740\u5b9e": 18066, "\u2581\u690d\u53d1": 18067, "\u2581\u798f\u97f3": 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"\u2581\u5b9a\u578b": 18398, "\u2581\u9ad8\u4ea7": 18399, "\u2581\u4e94\u7b14": 18400, "\u2581\u7075\u5f02": 18401, "under": 18402, "\u597d\u591a": 18403, "2009": 18404, "\u2581\u7279\u4e48": 18405, "\u2581\u71ac\u8fc7": 18406, "cre": 18407, "\u51b3\u7b97": 18408, "\u2581\u516b\u4e00": 18409, "host": 18410, "\u2581\u9e66\u9e49": 18411, "\u6269": 18412, "\u2581Ed": 18413, "\u2581\u57ce\u7ba1": 18414, "\u2581\u60f3\u4e0d\u8d77": 18415, "840": 18416, "\u2581\u529f\u5fb7": 18417, "\u2581\u8033\u8fb9": 18418, "\u6c42\u662f": 18419, "\u2581\u503c\u4e0d\u503c": 18420, "\u2581\u5f15\u51fa": 18421, "\u54a9": 18422, "\u2581\u5148\u9a71": 18423, "\u2581\u6b63\u4e2d": 18424, "\u6559\u80b2\u90e8": 18425, "\u2581\u8a00\u8f9e": 18426, "Sh": 18427, "mor": 18428, "\u2581\u9e26\u7247": 18429, "\u7ee2": 18430, "\u4e00\u665a": 18431, "\u67ff": 18432, "\u66f4\u65b0": 18433, "\u68b0": 18434, "cul": 18435, "\u2581\u5149\u4eae": 18436, "\u2581\u545c\u545c": 18437, "\u7329\u7329": 18438, "\u2581\u5916\u6302": 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"\u2581\u5168\u5fc3": 18481, "\u2581\u51c6\u65f6": 18482, "\u2581\u7f16\u5165": 18483, "\u2581\u87ba\u7eb9": 18484, "\u2581\u542b\u84c4": 18485, "\u2581\u8bc6\u5b57": 18486, "\u2581\u7e41\u5fd9": 18487, "\u2581Chrome": 18488, "\u2581\u5c0f\u5de7": 18489, "\u566c": 18490, "LM": 18491, "\u2581\u9886\u517b": 18492, "del": 18493, "\u6df7\u6742": 18494, "\u2581\u963f\u5361": 18495, "\u7470": 18496, "\u2581\u9084\u662f": 18497, "UT": 18498, "\u2581\u653b\u514b": 18499, "\u2581function": 18500, "\u8d28\u95ee": 18501, "\u2581\u79c1\u5bc6": 18502, "\u63a9": 18503, "\u2581\u7d20\u98df": 18504, "\u2581\u5e8a\u5934": 18505, "\u2581\u5168\u771f": 18506, "\u2581\u6297\u4e89": 18507, "\u76f4\u8a00": 18508, "\u852c": 18509, "\u2581\u5206\u5e03\u5f0f": 18510, "pop": 18511, "\u2581\u5c0f\u57ce": 18512, "lex": 18513, "\u2581\u76f4\u80a0": 18514, "\u2581\u62a5\u793e": 18515, "\u2581\u57ce\u5357": 18516, "\u68c0\u9a8c": 18517, "\u2581\u5e03\u96f7": 18518, "\u2581\u4e16\u4e8b": 18519, "ves": 18520, "\u2581\u50bb\u50bb": 18521, "\u2581\u5916\u63a5": 18522, "\u25b2": 18523, "\u2581\u8000\u773c": 18524, "\u67d1": 18525, "\u25812300": 18526, "\u8bb0\u8005": 18527, "\u2581\u53d1\u8d22": 18528, "\u2581\u5927\u8086": 18529, "\u2581\u4eac\u5e08": 18530, "xy": 18531, "\u966a": 18532, "\u2581\u53cc\u80de": 18533, "\u2581\u914d\u5236": 18534, "\u2581\u8ffd\u6740": 18535, "\u2581Vo": 18536, "\u2581\u9887\u5177": 18537, "\u2581\u51cf\u9000": 18538, "\u2581\u611a\u6627": 18539, "\u592e": 18540, "\u69bb": 18541, "\u25811700": 18542, "\u2581PVC": 18543, "\u71b9": 18544, "\u2581\u51fa\u9505": 18545, "\u2581\u773c\u89d2": 18546, "\u2581Min": 18547, "\u6c82": 18548, "\u2581\u722c\u5c71": 18549, "ior": 18550, "\u2581\u75db\u5fc3": 18551, "\u2581\u66b4\u96e8": 18552, "\u2581\u4e07\u4ebf": 18553, "\u8367": 18554, "\u2581\u80a5\u7682": 18555, "\u2581\u675c\u5170": 18556, "gr": 18557, "\u8ddf": 18558, "\u2581Free": 18559, "\u5668\u5b98": 18560, "\u8bca\u65ad": 18561, "\u77bb": 18562, "\u2581\u6026\u7136": 18563, "\u2581\u6f33\u5dde": 18564, "\u751f\u8096": 18565, "\u997f": 18566, "0.5": 18567, "\u2581Big": 18568, "\u8c61\u68cb": 18569, "\u2581\u7ea2\u989c": 18570, "\u2581\u575a\u679c": 18571, "non": 18572, "\u2581\u6a58\u5b50": 18573, "KE": 18574, "\u2581\u9f99\u6cc9": 18575, "\u2581\u4e09\u9879": 18576, "\u5b99": 18577, "\u2581\u9000\u56de": 18578, "\u5e7e": 18579, "\u2581\u4fee\u9053": 18580, "\u2581\u897f\u5c71": 18581, "\u7ecf\u6d4e\u4f53\u5236": 18582, "\u2581\u4eba\u50cf": 18583, "\u2581\u5927\u897f\u6d0b": 18584, "\u8151": 18585, "\u2581\u8f66\u91cc": 18586, "\u2581\u539f\u88c5": 18587, "File": 18588, "\u9ccc": 18589, "\u2581\u5929\u53f0": 18590, "\u25815400": 18591, "KO": 18592, "\u2581\u60f3\u5b66": 18593, "\u2581\u56fd\u5bb6\u7a0e\u52a1": 18594, "\u2581\u52a8\u8f84": 18595, "&": 18596, "\u5ab2": 18597, "\u2581\u79bb\u5947": 18598, "\u2581\u5728\u6b64\u4e4b": 18599, "\u2581\u5199\u5f97": 18600, "253": 18601, "\u2581\u9a91\u9a6c": 18602, 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"\u2581\u524d\u5341": 18640, "\u2581\u6551\u62a4": 18641, "\u2581\u66f2\u827a": 18642, "\u2581Gi": 18643, "\u2581\u8d35\u91cd": 18644, "\u2581\u6c5f\u6cb3": 18645, "\u2581\u8096\u50cf": 18646, "EF": 18647, "\u2581\u5361\u7279": 18648, "\u2581\u5c31\u804c": 18649, "\u72f8": 18650, "\u2581\u6b27\u5f0f": 18651, "\u2581\u90aa\u6559": 18652, "\u2581\u4fdd\u9001": 18653, "261": 18654, "\u2581\u5510\u50e7": 18655, "\u2581\u6743\u91cd": 18656, "\u2581NPC": 18657, "\u2581\u5ef6\u7f13": 18658, "FX": 18659, "\u2581\u8d34\u56fe": 18660, "\u2581\u6df7\u8840": 18661, "\u5f5d": 18662, "\u751f\u6015": 18663, "\u2581\u6d66\u4e1c": 18664, "\u2581\u76d8\u70b9": 18665, "\u901e": 18666, "\u2581\u7ee7\u7535": 18667, "\u6c7e": 18668, "\u508d": 18669, "HC": 18670, "\u2581\u9632\u7206": 18671, "\u65cf\u7fa4": 18672, "PD": 18673, "\u25812400": 18674, "\u2581\u6c34\u7cfb": 18675, "\u7663": 18676, "\u2581\u7406\u5316": 18677, "\u2581\u67f3\u5dde": 18678, "KI": 18679, "\u25812200": 18680, "\u3084": 18681, "\u2581\u6587\u8eab": 18682, "\u2581\u5dee\u4ef7": 18683, "\u2581\u6fc0\u8d77": 18684, "pen": 18685, "\u79e4": 18686, "\u2581\u7b2c\u56db\u5341\u516d": 18687, "\u2581\u6709\u7406": 18688, "\u2581heart": 18689, "\u6fd1": 18690, "\u2581\u6210\u5929": 18691, "\u2581\u5112\u5b66": 18692, "\u96f6\u96f6": 18693, "\u2581\u552e\u7968": 18694, "\u2581\u75f4\u8ff7": 18695, "\u85b0": 18696, "000": 18697, "\u2581\u7ea2\u85af": 18698, "4.4": 18699, "\u7cef": 18700, "\u2581\u76ae\u4e0b": 18701, "\u2581\u5f00\u56fd": 18702, "\u6e38\u8bb0": 18703, "\u6240\u63aa": 18704, "\u2581\u8d27\u8f66": 18705, "\u2581\u65af\u5927\u6797": 18706, "\u2581Store": 18707, "\u5e03\u5c40": 18708, "ret": 18709, "\u2581\u6297\u539f": 18710, "\u2581\u4ec5\u6b64": 18711, "\u5c0f\u4e09": 18712, "\u75ff": 18713, "\u2581\u6674\u5929": 18714, "\u7950": 18715, "\u2581\u6bcd\u4e73": 18716, "\u2581\u9017\u6bd4": 18717, "\u62d8\u7559": 18718, "pm": 18719, "\u5bb6\u5177": 18720, "\u600e\u4e48": 18721, "\u2581\u65ad\u7edd": 18722, "uff": 18723, "duct": 18724, "\u2581\u5207\u5c14\u897f": 18725, "\u836f\u6750": 18726, "\u2581\u519c\u6797": 18727, "fo": 18728, "\u2581\u7a77\u6e38": 18729, "\u2581\u51e0\u767e\u4e07": 18730, "\u2581\u9ad8\u4e3d": 18731, "\u2581\u79d1\u6280\u56ed": 18732, "-": 18733, "\u5570\u55e6": 18734, "\u2581\u9ec4\u725b": 18735, "\u2581\u8fc5\u731b": 18736, "\u2581\u4e00\u73ed": 18737, "\u2581\u62b1\u62b1": 18738, "\u98ce\u5149": 18739, "\u2581\u4eba\u5bff": 18740, "\u2581\u8981\u585e": 18741, "\u62c9\u7740": 18742, "\u2581\u516c\u793a": 18743, "\u2581Alpha": 18744, "\u5de5\u7a0b\u5e08": 18745, "\u88ad\u51fb": 18746, "458": 18747, "\u2581BUG": 18748, "\u2581\u6f2b\u6e38": 18749, "\u2581\u8f6f\u5316": 18750, "\u2581\u7ef4\u591a\u5229\u4e9a": 18751, "\u2581\u62a5\u5173": 18752, "\u2581\u9c9c\u8840": 18753, "\u2581This": 18754, "\u2581\u529f\u52b3": 18755, "\u2581\u5916\u4e8b": 18756, "\u2581\u53e4\u5efa\u7b51": 18757, "\u2581\u6d4e\u5357\u5e02": 18758, "\u2581\u683c\u62c9": 18759, "gs": 18760, 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"\u2581\u4e61\u9547\u4f01\u4e1a": 18841, "\u2581\u8bf4\u4e0d\u4e0a": 18842, "\u9577": 18843, "\u9ad8\u6821": 18844, "\u2581\u83f2\u5c14": 18845, "\u2581\u5927\u5199": 18846, "\u2581\u5a01\u5c3c\u65af": 18847, "006": 18848, "\u2581\u82a6\u835f": 18849, "\u2581\u519c\u7267": 18850, "\u2581\u521b\u520a": 18851, "\u7980": 18852, "\u4e0d\u582a": 18853, "\u2581Master": 18854, "\u2581\u786b\u5316": 18855, "\u751f\u5b58": 18856, "\u2581\u6811\u53f6": 18857, "\u54e9": 18858, "\u2581\u72ed\u4e49": 18859, "\u2581\u6295\u6ce8": 18860, "\u2581\u4e00\u65c1": 18861, "\u5c11\u5c11": 18862, "\u2581\u8150\u5973": 18863, "\u2581idx": 18864, "\u2581\u5348\u591c": 18865, "\u2581\u666e\u6d31": 18866, "\u2581\u6ce5\u571f": 18867, "\u2581\u4e2d\u4e9a": 18868, "\u2581\u7a7a\u524d": 18869, "\u6789": 18870, "\u8ad6": 18871, "\u2581\u8f66\u7968": 18872, "\u2581\u6570\u4e07": 18873, "\u30e5": 18874, "\u2581\u4ea7\u5375": 18875, "\u2581\u6e05\u98ce": 18876, "\u2581\u767d\u96ea": 18877, "\u2581\u4f18\u80dc": 18878, "\u2581\u52a8\u7528": 18879, "\u2581\u8fc7\u5931": 18880, "\u2581\u8d70\u7ea2": 18881, "\u2581\u6d12\u8131": 18882, "pri": 18883, "\u2581\u65b9\u821f": 18884, "\u2581\u56de\u6c11": 18885, "\u2581Note": 18886, "000000": 18887, "\u2581\u60c5\u8bdd": 18888, "\u2581\u5c71\u4e1c\u5927\u5b66": 18889, "\u2581\u53e4\u88c5": 18890, "\u2581scene": 18891, "\u2581\u5c0f\u660e": 18892, "\u2581\u952f\u9f7f": 18893, "\u534e\u5317\u5927": 18894, "api": 18895, "\u2581\u4e4c\u9f99": 18896, "mAh": 18897, "\u2581\u7ec4\u961f": 18898, "\u4f5a": 18899, "lar": 18900, "\u2581\u8bf8\u845b": 18901, "\u2581\u70e7\u4f24": 18902, "uang": 18903, "\u8171": 18904, "\u2581\u7f9e\u8fb1": 18905, "\u5b58\u6b3e": 18906, "\u2581\u7b2c\u56db\u5341\u4e03": 18907, "\u2581\u7eaa\u5b9e": 18908, "870": 18909, "\u5169": 18910, "\u9e20": 18911, "\u2581\u6c76\u5ddd": 18912, ".19": 18913, "\u2581\u5708\u5185": 18914, "\u2581\u6726\u80e7": 18915, "\u2581\u5bb6\u5bb6": 18916, "\u2581\u5b89\u5fb7": 18917, "\u2581\u50b2\u6162": 18918, "\u2581\u4e0b\u988c": 18919, "\u5e1c": 18920, "\u2581\u516c\u5c3a": 18921, "\u7528\u9014": 18922, "\u96fb": 18923, "\u2581\u7eb8\u5dfe": 18924, "we": 18925, "\u5f88\u4e45": 18926, "\u2581\u6d53\u6d53": 18927, "\u2581\u5929\u874e": 18928, "\u62ab": 18929, "\u65af\u5821": 18930, "\u2581\u5f20\u5f00": 18931, "\u2581\u8017\u7535": 18932, "\u5176\u5b9e": 18933, "\u519c\u4e1a\u94f6\u884c": 18934, "Type": 18935, "\u8bec": 18936, "\u2581\u4e4c\u9f9f": 18937, "\u2581\u5b9c\u5bb6": 18938, "\u2581\u6cea\u6d41": 18939, "\u2581\u6536\u7eb3": 18940, "\u2581\u65b9\u6b63": 18941, "\u2581\u5bbf\u547d": 18942, "\u2581\u987d\u56fa": 18943, "\u2581\u8f7d\u5165": 18944, "\u2581\u6ee1\u6000": 18945, "\u2581\u53f2\u5bc6": 18946, "\u9083": 18947, "\u2581\u4f20\u4e16": 18948, "660": 18949, "\u2581\u7ea2\u661f": 18950, "\u66f8": 18951, "\u5e7f\u64ad": 18952, "\u2581\u5927\u6237": 18953, "\u2581\u968b\u5510": 18954, "\u2581TCP": 18955, "\u2581\u53d7\u9080": 18956, "\u2581apple": 18957, "\u2581\u6f5c\u79fb\u9ed8": 18958, "\u2581\u56f4\u653b": 18959, "\u2581\u6697\u6740": 18960, "\u7ef4\u65af": 18961, "\u2581\u96c6\u7ed3": 18962, "\u2581\u5899\u4f53": 18963, "\u2581\u4e00\u4e2a\u534a": 18964, "\u2581\u8840\u6d41": 18965, "\u2581\u9a6c\u5fb7\u91cc": 18966, "\u2581\u4e00\u76ee": 18967, "\u2581\u809d\u75c5": 18968, "\u2581\u4e4b\u795e": 18969, "last": 18970, "\u2581\u663e\u800c\u6613": 18971, "\u2581start": 18972, "\u2581\u661f\u8fb0": 18973, "\u2581\u5bf9\u53e3": 18974, "\u8d77\u4f0f": 18975, "\u2581\u77ed\u8def": 18976, "\u2581IOS": 18977, "\u793e\u4f1a\u6cbb\u5b89": 18978, "\u5e72\u7ebf": 18979, "\u2581\u7530\u95f4": 18980, "\u2581\u94f6\u884c\u4e1a": 18981, "\u2581\u6cf0\u5b89": 18982, "\u988c": 18983, "\u2581\u7384\u5e7b": 18984, "\u70df\u706b": 18985, "ura": 18986, "\u2581never": 18987, "\u601d\u8def": 18988, "\u6843\u6e90": 18989, "\u2581\u5b89\u5e86": 18990, "\u56db\u70b9": 18991, "\u2581\u9634\u830e": 18992, "\u2581\u4ea4\u914d": 18993, "\u2581\u6218\u8f66": 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"\u4e00\u6b21": 19034, "GI": 19035, "\u2581\u62bd\u5956": 19036, "\u6cbd": 19037, "\u2581WAP": 19038, "\u8840\u7ba1": 19039, "\u2581\u88ab\u76d7": 19040, "\u25811922": 19041, "\u8fe5": 19042, "\u25812100": 19043, "\u6d1b\u592b": 19044, "\u2581\u59a5\u59a5": 19045, "\u84b8\u6c14": 19046, "\u2581Liu": 19047, "\u4f20\u8f93": 19048, "\u2581\u8d5a\u70b9": 19049, "View": 19050, "\u8003\u6838": 19051, "\u2581\u96f7\u519b": 19052, "\u8f93\u5165": 19053, "\u2581\u8d1d\u58f3": 19054, "\u0334": 19055, "\u2581\u5ea7\u8c08": 19056, "\u301c": 19057, "\u2581\u603b\u5f97": 19058, "pho": 19059, "\u2581\u8ddd\u4eca": 19060, "\u2581Sam": 19061, "404": 19062, "SN": 19063, "JB": 19064, "\u2581\u6bcf\u9022": 19065, "\u2581\u6210\u9f99": 19066, "\u2581\u6751\u4e0a": 19067, "\u2581\u5149\u662f": 19068, "\u2581\u540c\u5fc3": 19069, "\u2581\u7626\u4e0b": 19070, "\u5927\u697c": 19071, "\u2581\u7247\u523b": 19072, "\u2581WMA": 19073, "\u2581\u5168\u9760": 19074, "\u2581\u5355\u8f66": 19075, "\u62fc\u97f3": 19076, 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"\u2581\u70ae\u5f39": 20813, "\u2581\u9ad8\u6570": 20814, "\u5f99": 20815, "\u9b13": 20816, "\u2581\u504f\u91cd": 20817, "\u2581\u6f2b\u6f2b": 20818, "\u8b8a": 20819, "\u2581\u8fde\u7ebf": 20820, "\u2581\u9752\u6d77\u7701": 20821, "\u8fd0\u4f1a": 20822, "\u25819.6": 20823, "356": 20824, "\u2581\u679c\u56ed": 20825, "\u2581\u76f8\u9694": 20826, "\u6346": 20827, "\u2581\u5438\u6c34": 20828, "\u2581\u67cf\u6cb9": 20829, "\u6f7c": 20830, "\u258114.2": 20831, "\u2581\u7a81\u56f4": 20832, "\u2581\u9686\u9f3b": 20833, "\u5587": 20834, "2.7": 20835, "\u2581\u8868\u5c42": 20836, "\u4e09\u73af": 20837, "\u2581\u65b9\u5706": 20838, "\u2581\u7262\u7262": 20839, "\u2581\u7d2f\u6b7b": 20840, "\u2581\u751f\u7269\u5316\u5b66": 20841, "\u2581\u53ef\u9006": 20842, "\u552f\u7269\u4e3b\u4e49": 20843, "690": 20844, "\u2581\u6545\u91cc": 20845, "\u2581\u91c7\u6696": 20846, "720": 20847, "\u2581\u5f80\u4e0a": 20848, "\u2581\u5fc3\u6000": 20849, "\u2581\u6fd2\u4e34": 20850, "\u753b\u4f5c": 20851, 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"\u2581\u514b\u9c81": 20968, "mine": 20969, "\u2581\u9752\u85cf": 20970, "\u81fc": 20971, "ANG": 20972, "\u655e": 20973, "\u2581DV": 20974, "date": 20975, "\u2581\u732e\u8eab": 20976, "\u2581\u56db\u901a": 20977, "\u2581\u8fdc\u8d85": 20978, "\u2581\u7275\u6302": 20979, "\u8bcf": 20980, "\u8c82": 20981, "\u8fd9\u822c": 20982, "\u2581\u6570\u7801\u76f8": 20983, "\u2581\u5fc3\u5f8b": 20984, "\u72e1": 20985, "\u665a\u996d": 20986, "\u3123": 20987, "\u2581\u585e\u5c14\u7ef4\u4e9a": 20988, "\u2581\u5f3a\u5f31": 20989, "\u2581\u7248\u753b": 20990, "\u2581\u8def\u6613": 20991, "\u2581every": 20992, "\u731c": 20993, "\u2581Ban": 20994, "\u2581\u682a\u6d32": 20995, "\u2581\u822a\u9053": 20996, "\u8eac": 20997, "\u2581Light": 20998, "\u2581\u53f0\u7403": 20999, "\u2581\u4e8c\u624b\u623f": 21000, "\u2581\u8865\u6551": 21001, "\u2581\u65f6\u624d": 21002, "\u6084": 21003, "\u2581\u7231\u7740": 21004, "\u2581\u5f81\u6218": 21005, "\u2581\u56fe\u817e": 21006, "\u2581\u8877\u5fc3": 21007, "\u2581\u5907\u5fd8": 21008, "\u5f57": 21009, "Count": 21010, "\u643a\u5e26": 21011, "\u7e82": 21012, "\u2581\u9ad8\u901a": 21013, "\u5e78\u798f": 21014, "\u2581\u7559\u5b58": 21015, "friend": 21016, "334": 21017, "\u2581\u6797\u7acb": 21018, "\u2581\u517c\u5e76": 21019, "\u2581\u957f\u5e74": 21020, "\u2581\u9e21\u7fc5": 21021, "DT": 21022, "\u4e9a\u79d1": 21023, "\u6821\u751f": 21024, "\u82aa": 21025, "TM": 21026, "\u70e9": 21027, "nes": 21028, "\u2581\u5c0f\u7403": 21029, "\u2581\u9020\u5f71": 21030, "\u2581\u5e74\u79cb": 21031, "\u2581\u4e94\u5473": 21032, "\u2581\u9694\u70ed": 21033, "\u2581\u5a5a\u5e86": 21034, "\u7f09": 21035, "\u2581public": 21036, "\u52bf\u529b": 21037, "\u2581\u771f\u5207": 21038, "\u043b": 21039, "\u4f0e": 21040, "\u6396": 21041, "\u2581\u5949\u547d": 21042, "\u2581\u7ae5\u5b50": 21043, "8.2": 21044, "\u2581\u5174\u81f4": 21045, "\u2581\u82b1\u5c97\u5ca9": 21046, "\u2581\u706b\u67f4": 21047, "\u2581\u81ea\u4f20": 21048, "\u258118.2": 21049, "space": 21050, "108": 21051, "\u2581SBS": 21052, "\u78d0": 21053, "\u2581may": 21054, "\u2581\u4ea4\u6218": 21055, "332": 21056, "\u6b64\u79cd": 21057, "\u25bc": 21058, "\u2581\u9178\u75db": 21059, "\u2581\u540d\u5229": 21060, "\u7f1a": 21061, "\u5471": 21062, "\u2581\u7a00\u758f": 21063, "\u2581\u6954\u5f62": 21064, "\u2581\u6d3b\u751f": 21065, "\u2581American": 21066, "\u2581\u7092\u83dc": 21067, "\u2581\u6b22\u547c": 21068, "\u2581\u70ae\u53cb": 21069, "tre": 21070, "\u2581\u8336\u6811": 21071, "\u516b\u5e74": 21072, "\u4fe1\u6761": 21073, "\u2581\u4fe1\u9053": 21074, "\u2581\u9ca8\u9c7c": 21075, "\u2581\u7518\u6cb9": 21076, "rel": 21077, "phy": 21078, "\u2581\u6d25\u6d25": 21079, "\u6574\u7406": 21080, "\u2581\u5927\u7ea2": 21081, "\u2581\u7092\u9e21": 21082, "\u4f5f": 21083, "\u998b": 21084, "260": 21085, "\u5398\u7c73": 21086, "\u2581\u8033\u9e23": 21087, "\u6a35": 21088, "INT": 21089, "\u5b88\u6cd5": 21090, "\u2581\u73cd\u7a00": 21091, "\u4e0d\u900f": 21092, "\u2581\u5305\u5934": 21093, "\u2581\u7d20\u989c": 21094, "\u2581\u987a\u5fb7": 21095, "nc": 21096, "\u2581\u7740\u8272": 21097, "\u4fa3": 21098, "\u2581ATI": 21099, "\u2581\u4e34\u8d70": 21100, "\u2581\u4e0b\u534a": 21101, "\u2581\u51fa\u793a": 21102, "740": 21103, "\u2581\u963f\u8fea": 21104, "\u6e05\u6670\u5ea6": 21105, "\u2581had": 21106, "\u4e0d\u7b49": 21107, "unk": 21108, "\u5b09": 21109, "\u2581\u60a0\u95f2": 21110, "\u2581\u9898\u4e3b\u60f3": 21111, "\u2581Chi": 21112, "\u2581\u5987\u4eba": 21113, "\u03a9": 21114, "\u25812.": 21115, "\u4e00\u7b79": 21116, "\u54d7\u5566": 21117, "\u2581\u5927\u70ae": 21118, "\u2581\u516d\u5341\u5e74": 21119, "\u741b": 21120, "\u2581sb": 21121, "\u2581\u8f6c\u4e1a": 21122, "iff": 21123, "\u2581\u8c61\u7259": 21124, "isappinstalled": 21125, "rent": 21126, "\u6709\u8272\u91d1\u5c5e": 21127, "387": 21128, "\u4eca\u65e5": 21129, "\u2581\u6ecb\u8865": 21130, "RY": 21131, "\u2581\u6ca1\u4eba\u4f1a": 21132, "\u2581\u6e90\u6e90\u4e0d": 21133, "\u2581\u6545\u4eba": 21134, "\u2581\u6570\u636e\u5904\u7406": 21135, "\u2581\u88c5\u6f62": 21136, "\u621b": 21137, "50000": 21138, "\u7eb2\u76ee": 21139, "\u6295\u6807": 21140, "\u96bd": 21141, "\u835f\u8403": 21142, "\u2581\u8054\u673a": 21143, "\u2581power": 21144, "998": 21145, "\u5021": 21146, "369": 21147, "\u2581\u4e00\u679d": 21148, "\u2581\u9006\u5929": 21149, "Code": 21150, "\u2581\u536b\u751f\u5c40": 21151, "vision": 21152, "\u2581Play": 21153, "\u2581\u5bf9\u5cd9": 21154, "\u2581\u7b3c\u7f69": 21155, "ek": 21156, "\u2581GRE": 21157, "\u666f\u6c14": 21158, "\u2581\u8f6c\u6218": 21159, "\u2581\u6210\u5343\u4e0a": 21160, "\u9ad8\u5b97": 21161, "\u2581\u7cae\u6cb9": 21162, "\u2581\u4e92\u4e0d": 21163, "Port": 21164, "\u2581\u7ffb\u51fa": 21165, "\u2581\u518d\u9020": 21166, "\u2581\u6807\u70b9": 21167, "\u2581\u7075\u6c14": 21168, "\u2581iTunes": 21169, "\u2581\u901a\u7545": 21170, "\u2581\u4e8c\u4e09\u5341": 21171, "\u8868\u6f14": 21172, "\u0306": 21173, "\u86c0": 21174, "\u6765\u7b54": 21175, "109": 21176, "\u7ebf\u7a0b": 21177, "\u2581\u96f6\u70b9": 21178, "alt": 21179, "\u2581\u60f6\u6050": 21180, "\u94a2\u7434": 21181, "\u2581\u6c34\u8def": 21182, "\u2581\u6270\u4e71": 21183, "\u5b1b": 21184, "\u5e87": 21185, "\u53cd\u5c04": 21186, "\u2581\u827e\u4f26": 21187, "\u2581\u5e03\u5170": 21188, "stan": 21189, "\u2581\u77e9\u5f62": 21190, "\u2581\u8f7b\u8584": 21191, "\u2581\u6b64\u9879": 21192, "\u2581\u52b3\u7d2f": 21193, "\u80dc\u5229": 21194, "\u7f8a\u7f8a": 21195, "\u96e8\u5b63": 21196, "\u2581\u4e00\u4e32": 21197, "\u2581\u5bff\u9669": 21198, "\u2581Imp": 21199, "RF": 21200, "\u2581\u82df\u4e14": 21201, "\u2581\u9ad8\u67b6": 21202, "\u2581\u6302\u673a": 21203, "\u2581\u5c42\u7ea7": 21204, "\u2581\u4e25\u5cfb": 21205, "\u2581padding": 21206, "\u2581\u7b80\u4f53": 21207, "\u2581\u5965\u5229": 21208, "\u2581\u5b8c\u7206": 21209, "card": 21210, "\u2581\u683c\u5170": 21211, "\u258123333": 21212, "\u2581\u6c5f\u9634": 21213, "\u2581\u6cb3\u5357\u4eba": 21214, "\u2581ref": 21215, "\u2581\u660e\u4fe1": 21216, "KW": 21217, "\u2581which": 21218, "\u2581\u5927\u68da": 21219, "lm": 21220, "\u65e0\u529b": 21221, "\u728a": 21222, "\u2581root": 21223, "\u2581\u7814\u78e8": 21224, "\u2581WTO": 21225, "\u4e4b\u4f5c": 21226, "board": 21227, "\u5706\u5f62": 21228, "\u2581\u6f02\u6cca": 21229, "\u2581\u4e13\u4e00": 21230, "\u9cdd": 21231, "\u2581\u6bcd\u4e0a": 21232, "\u2581\u8fde\u5e26": 21233, "\u2581\u65b0\u95fb\u62a5": 21234, "\u2581\u51e0\u9996": 21235, "\u89d2\u5ea6": 21236, "\ub208": 21237, "din": 21238, "\u2581\u4e3b\u653b": 21239, "\u5fa8": 21240, "wan": 21241, "\u2581\u9996\u6620": 21242, "\u2581\u9e21\u8840": 21243, "\u4e4b\u4e71": 21244, "Class": 21245, "\u2581\u51fa\u65b0": 21246, "\u9602": 21247, "\u6458": 21248, "\u2581\u865a\u5e7b": 21249, "\u2581\u9ed1\u70b9": 21250, "\u2581\u4e2d\u4ea7": 21251, "\u7efc": 21252, "\u547b\u541f": 21253, "\u6210\u719f": 21254, "\u2581\u8ffd\u51fb": 21255, "\u2581steam": 21256, "\u2581\u5a18\u5a18": 21257, "\u2581\u805a\u5c45": 21258, "\u2581\u8d54\u94b1": 21259, "\u2581taobao": 21260, "\u2581\u6234\u7740": 21261, "\u2581\u71d5\u9ea6": 21262, "\u2581\u8d77\u8dd1": 21263, "\u2581\u7406\u60f3\u4e3b\u4e49": 21264, "\u9e3e": 21265, "\u2581\u4f11\u5047": 21266, "\u30ac": 21267, "\u2581\u534e\u4e2d\u79d1\u6280": 21268, "\u840e": 21269, "\u2581\u73b0\u5f79": 21270, "\u2581solo": 21271, "\u2581\u4e00\u6247": 21272, "\u2581also": 21273, "\u6500": 21274, "\u7519": 21275, "ola": 21276, "\u2581Chan": 21277, "note": 21278, "\u6211\u592a": 21279, "\u7a7a\u519b": 21280, "\u5efa\u534e": 21281, "win": 21282, "\u8361\u8361": 21283, "\u7f57\u9a6c": 21284, "\u2581\u9986\u85cf": 21285, "\u67b7": 21286, "\u7b3a": 21287, "\u258116.8": 21288, "\u25811910": 21289, "\u5b9d\u8d1d": 21290, "367": 21291, "\u2581\u57fa\u7ad9": 21292, "\u2581\u81ea\u8d39": 21293, "\u2581\u524d\u4e09": 21294, "\u2581\u666e\u7f57": 21295, "\u82e5\u60ca": 21296, "\u2581\u5185\u8499": 21297, "\u2581\u5f17\u91cc": 21298, "ENT": 21299, "\u2581\u793e\u4f1a\u5de5\u4f5c": 21300, "mil": 21301, "\u2581\u642c\u51fa": 21302, "\u2581\u586b\u8bcd": 21303, "525": 21304, "\u2581\u603b\u961f": 21305, "\u0e05": 21306, "\u2581SNS": 21307, "\u2581\u4e2d\u5eb8": 21308, "\u8d3f\u8d42": 21309, "nch": 21310, "\u2581\u62b1\u4f4f": 21311, "\u7b49\u56fd": 21312, "\u2581\u5e7b\u706f": 21313, "\u2581\u5a5a\u604b": 21314, "\u2581\u5486\u54ee": 21315, "\u2581\u77e5\u8db3": 21316, "\u2581\u8feb\u4e8e": 21317, "\u2581Dan": 21318, "\u2581\u5e38\u6570": 21319, "920": 21320, "\u2581\u6fd2\u5371": 21321, "\u94ed\u5fc3": 21322, "\u2581\u7267\u573a": 21323, "\u638c\u63e1": 21324, "\u2581\u5236\u9ad8": 21325, "\u2581\u8001\u5e72\u90e8": 21326, "\u2581bilibili": 21327, "\u2581\u62db\u4eba": 21328, "\u2581\u7b2c\u4e94\u5341\u4e00": 21329, "\u2581\u96cc\u96c4": 21330, "\u2581\u538b\u69a8": 21331, "\u9cc3": 21332, "\u2581cpu": 21333, "\u2581\u542c\u8bc1": 21334, "\u2581\u5b58\u8d27": 21335, "\u2581UV": 21336, "\u2581\u4e1c\u95e8": 21337, "\u5f3a\u8c03": 21338, "\u2581\u70ae\u53f0": 21339, "\u2581\u9634\u6027": 21340, "\u8015\u8018": 21341, "VO": 21342, "\u8ffd\u7a76": 21343, "hua": 21344, "\u2581\u7f3a\u6c27": 21345, "\u2581\u65ad\u5c42": 21346, "\u2581\u666e\u62c9": 21347, "\u2581Sta": 21348, "\u2581\u6839\u90e8": 21349, "\u7f3a\u8840": 21350, "\u4e36": 21351, "\u2581\u9884\u89c8": 21352, "\u2581\u5f3a\u76d7": 21353, "\u2581\u5217\u5b81": 21354, "\u2581\u53f3\u4e0b": 21355, "\u2581\u8d70\u8fd1": 21356, "\u2581\u8d8a\u957f": 21357, "ates": 21358, "\u2581\u94c1\u9053\u90e8": 21359, "\u2581\u6307\u53d6": 21360, "\u6307\u6570": 21361, "\u2581\u76f8\u4ea4": 21362, "\u8517": 21363, "\u695a\u695a": 21364, "\u653f\u52a1": 21365, "\u2581\u9b54\u5973": 21366, "\u2581\u6253\u6597": 21367, "\u2581\u8bda\u7136": 21368, "\u2581\u6c34\u89e3": 21369, "\u2581\u9075\u4e49": 21370, "\u2581\u4f9b\u9500": 21371, "\u2581\u4f59\u5bb6": 21372, "\u2581\u7406\u76f4\u6c14": 21373, "\u2581\u6027\u4ea4": 21374, "\u2581Surface": 21375, "unc": 21376, "\u2581Han": 21377, "\u2581\u6821\u62db": 21378, "\u2581\u4fdd\u4fee\u671f": 21379, "\u2581AVI": 21380, "\u2581\u65e0\u8fb9": 21381, "\u2581-_-#": 21382, "\u5927\u6865": 21383, "\u2581\u6b66\u5219": 21384, "\u2581\u6bdb\u6bdb": 21385, "\u2581\u51b3\u6597": 21386, "\u2581\u773c\u7736": 21387, "\u2581\u8001\u53cb": 21388, "\u2581drama": 21389, "\u2581\u6c49\u53e3": 21390, "\u2581\u8001\u592b": 21391, "\u2581\u96c6\u6563": 21392, "\u2581\u673a\u5bc6": 21393, "\u2581\u8d1d\u65af": 21394, "\u2581RAID": 21395, "\u2581\u4e66\u751f": 21396, "\u2581\u5413\u6b7b": 21397, "\u308d": 21398, "\u2581\u77f3\u95e8": 21399, "NY": 21400, "\u2581\u5929\u9f99": 21401, "\u2581\u7cbe\u795e\u5206\u88c2": 21402, "\u2581\u6e7f\u70ed": 21403, "\u5bee": 21404, "\u2581\u5e74\u8001": 21405, "790": 21406, "\u2581\u9759\u4e0b\u5fc3": 21407, "\u8863\u5e93": 21408, "\u2581\u664b\u6c5f": 21409, "\u7261": 21410, "\u2581\u5fc3\u7518\u60c5": 21411, "\u3125": 21412, "\u5f3c": 21413, "\u2581\u56ca\u62ec": 21414, "XP": 21415, "\u2581\u6458\u5f55": 21416, "\u2581\u9f50\u9c81": 21417, "\u2581\u6362\u4e0a": 21418, "zhi": 21419, "\u2581been": 21420, "\u2581\u84ec\u83b1": 21421, "\u2581\u51fa\u8ba9": 21422, "\u2581\u7206\u7167": 21423, "\u2581\u90a3\u7fa4": 21424, "\u2581\u724c\u574a": 21425, "\u2581\u73cd\u54c1": 21426, "\u2581\u4e00\u4e07\u591a": 21427, "\u2581\u5760\u843d": 21428, "\u7eb3\u65af": 21429, "\u2581\u7a7f\u6234": 21430, "\u2581\u8001\u592a": 21431, "\u2581\u5929\u6c34": 21432, "\u7275": 21433, "\u2581\u5fc5\u80dc": 21434, "\u4e50\u4e50": 21435, "\u2581\u62a2\u8d70": 21436, "\u2581did": 21437, "aj": 21438, "\u2581\u901f\u6210": 21439, "\u2581\u7ebf\u5708": 21440, "\u2581\u6392\u7ec3": 21441, "\u4e09\u89d2\u6d32": 21442, "NP": 21443, "\u62d7": 21444, "\u5315": 21445, "\u611f\u67d3": 21446, "608": 21447, "ease": 21448, "\u2581\u641c\u5bfb": 21449, "\u2581\u6d41\u611f": 21450, "\u2581\u975e\u6b63": 21451, "\u2581\u8fc1\u5c31": 21452, "\u2581real": 21453, "\u4e00\u4eae": 21454, "\u2581\u6cbb\u56fd": 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21495, "\u2581\u9047\u96be": 21496, "fan": 21497, "\u2581\u53ef\u8bfb": 21498, "\u2581\u7834\u635f": 21499, "\u2581\u6c34\u74f6": 21500, "6.4": 21501, "\u2581\u62a5\u5bfc": 21502, "\u2581\u514d\u9664": 21503, "\u2581\u795e\u821f": 21504, "\u6545\u800c": 21505, "\u568e": 21506, "\u2581\u9178\u78b1": 21507, "\u2581\u6d88\u78e8": 21508, "\u2581Dream": 21509, "\u2581\u6536\u5272": 21510, "\u2581\u65ad\u7535": 21511, "\u2581Cho": 21512, "\u9a6c\u7279": 21513, "HZ": 21514, "\u5703": 21515, "\u2581\u50a8\u85cf": 21516, "\u2581\u8865\u8840": 21517, "soft": 21518, "\u7487": 21519, "\u7d50": 21520, "\u2581\u7f51\u76d8": 21521, "\u2581\u4f7c\u4f7c": 21522, "\u62a5\u5e94": 21523, "\u4e5d\u5e74": 21524, "ral": 21525, "\u2581\u534a\u8def": 21526, "\u2581SSD": 21527, "\u2581\u5265\u79bb": 21528, "\u2581\u4e03\u661f": 21529, "\u2581\u8d35\u4eba": 21530, "\u2581\u5c71\u811a": 21531, "\u2581\u7cbe\u5999": 21532, "\u2581\u88c5\u5378": 21533, "\u4e16\u51ef": 21534, "EG": 21535, "\u2581Group": 21536, "kb": 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21616, "\u2581DVI": 21617, "\u2581\u8d85\u6807": 21618, "\u2581\u6bdb\u576f": 21619, "\u2581\u76f8\u4f4d": 21620, "115": 21621, "\u795f": 21622, "\u2581\u71d5\u5b50": 21623, "\u2581\u7ecf\u53d7": 21624, "\u2581\u5929\u5e9c": 21625, "\u2581\u4e8c\u80e1": 21626, "\u2581\u65e7\u4e66": 21627, "\u2581\u4e09\u8054": 21628, "\u2581\u4e0d\u533f": 21629, "\u2581\u53d6\u60a6": 21630, "403": 21631, "\u2581\u9f0e\u76db": 21632, "\u56fe\u7247": 21633, "\u2581\u897f\u670d": 21634, "\u2581Par": 21635, "ato": 21636, "\u2581\u98de\u9e1f": 21637, "\u2581\u53d7\u529b": 21638, "\u59a5\u5f53": 21639, "\u2581\u5171\u548c\u515a": 21640, "\u2581\u5ad6\u5a3c": 21641, "\u2581\u8861\u6c34": 21642, "\u2581\u8fc7\u591c": 21643, "\u2581\u98df\u6307": 21644, "402": 21645, "\u3110": 21646, "\u2581\u6ce2\u97f3": 21647, "\u2581absolute": 21648, "\u2581\u662d\u548c": 21649, "\u2581\u6dee\u5b89": 21650, "601": 21651, "\u2581\u51e0\u4ef6": 21652, "vid": 21653, "\u76f8\u53cd": 21654, "\u2581\u5b98\u81f3": 21655, 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21737, "\u2581\u786c\u6027": 21738, "\u2581\u80a5\u5927": 21739, "\u773c\u5e95": 21740, "\u2581\u7535\u9540": 21741, "\u2581\u67d4\u5f31": 21742, "\u6d77\u6218": 21743, "\u2581Angel": 21744, "\u4e0d\u9700": 21745, "\u2581\u4e00\u5e2d": 21746, "\u2581\u517b\u72d7": 21747, "\u2581\u767e\u4e8b": 21748, "\u2581\u52a3\u8d28": 21749, "\u2581\u76d7\u8d3c": 21750, "\u2581young": 21751, "\u2035": 21752, "\u5b66\u9738": 21753, "BF": 21754, "\u2581\u5fc3\u80f8": 21755, "\u7b5b": 21756, "\u79f8": 21757, "\u2581\u524d\u594f": 21758, "\u2581\u8b66\u536b": 21759, "\u2581\u76f8\u8ddd": 21760, "\u2581\u7528\u5c3d": 21761, "ado": 21762, "\u2581\u8282\u5236": 21763, "\u2581\u514b\u52b3": 21764, "\u2581\u8017\u65f6": 21765, "\u2581\u518d\u4e09": 21766, "\u709c": 21767, "\u2581\u80f0\u817a": 21768, "\u2581\u8bfb\u51fa": 21769, "\u8fdd\u7eaa": 21770, "\u2581\u9020\u8c23": 21771, "\u2581\u821f\u5c71": 21772, "\u6587\u827a": 21773, "\u2581\u5fc3\u7535\u56fe": 21774, "String": 21775, "\u2581\u5175\u79cd": 21776, "\u2581\u7535\u8f66": 21777, "\u914d\u5408": 21778, "\u2581\u77ff\u77f3": 21779, "\u9893": 21780, "\u2581\u65e0\u83cc": 21781, "\u2581\u7763\u67e5": 21782, "\u2581\u6728\u5170": 21783, "\u8102\u80aa": 21784, "\u2581\u5e38\u7eff": 21785, "\u2581\u4e09\u53ea": 21786, "\u2581\u5f00\u660e": 21787, "\u54cd\u5e94": 21788, "\u2581\u5927\u5c4f\u5e55": 21789, "\u2581\u5e73\u5e95": 21790, "\u2581\u4e1d\u889c": 21791, "\u2581\u6454\u8de4": 21792, "590": 21793, "\u2581\u811a\u8dbe": 21794, "\u51fa\u5165\u5883": 21795, "\u2581\u5145\u8840": 21796, "\u2581\u4e5e\u8ba8": 21797, "\u2581\u591a\u7ef4": 21798, "aff": 21799, "\u2581\u94b1\u5e01": 21800, "\u2581\u8150\u70c2": 21801, "\u59b9\u59b9": 21802, "\u2581\u503e\u57ce": 21803, "\u2581\u5e72\u5e72": 21804, "\u2581\u9662\u5185": 21805, "fb": 21806, "\u2581\u72ec\u5360": 21807, "\u5206\u88c2": 21808, "\u8fd1\u4ee3\u53f2": 21809, "\u2581\u7f13\u548c": 21810, "\u2581\u52a0\u76d6": 21811, "\u7b14\u8bb0": 21812, "\u2581\u6ca7\u6d77": 21813, "\u5e94\u4ff1": 21814, "\u523b\u523b": 21815, "\u2581\u84b8\u998f": 21816, "\u2581\u5f00\u5f00\u5fc3": 21817, "\u2581\u8fbd\u4e1c": 21818, "\u2581\u76db\u5bb4": 21819, "\u2581\u8003\u9898": 21820, "\u2581\u65e7\u65f6": 21821, "\u2581\u6cb9\u58a8": 21822, "\u2581\u7cbe\u70bc": 21823, "\u2581\u722c\u884c": 21824, "\u2581\u6218\u706b": 21825, "\u5706\u5706": 21826, "\u2581\u666e\u6cd5": 21827, "3.1": 21828, "\u2581\u592a\u9633\u7cfb": 21829, "\u8bf2": 21830, "\u2581\u6c34\u6c60": 21831, "\u503e\u5fc3": 21832, "\u2581\u76c6\u53cb": 21833, "\u2581\u54ac\u7259": 21834, "\u2581\u7279\u4ef7": 21835, "\u2581\u5c3c\u5c14": 21836, "\u2581\u51c9\u6c34": 21837, "\u620d": 21838, "\u2581\u6c42\u6551": 21839, "\u4f5b\u6559": 21840, "\u2581\u5386\u9669": 21841, "\u2581\u62a5\u5e9f": 21842, "\u2581Tim": 21843, "eth": 21844, "\u2581\u732a\u6cb9": 21845, "\u2581\u8089\u4e1d": 21846, "gra": 21847, "\u2581\u8fdd\u7ae0": 21848, "\u4e92\u5229": 21849, "\u6218\u5f79": 21850, "\u2581That": 21851, "\u2581\u5fd0\u5fd1": 21852, "\u2581\u674e\u5fd7": 21853, "\u2581\u5341\u4e00\u70b9": 21854, "\u988d": 21855, "\u8bb7": 21856, "\u2581\u6709\u507f": 21857, "\u2581\u5360\u535c": 21858, "lon": 21859, "\u2581\u51b2\u538b": 21860, "\u2581\u6e56\u6c34": 21861, "711": 21862, "UB": 21863, "\u2581\u4f30\u4ef7": 21864, "\u776b": 21865, "\u2581\u4fe1\u9633": 21866, "\u2581would": 21867, "\u2581\u7687\u5b50": 21868, "\u032b": 21869, "\u2581\u9971\u53d7": 21870, "\u2581\u552e\u697c": 21871, "\u2581\u6740\u866b": 21872, "\u2581\u5c31\u9910": 21873, "\u7814\u7a76\u5458": 21874, "\u2581\u505c\u7535": 21875, "\u2581\u5bb3\u4eba": 21876, "\u2581\u7ec3\u7ec3": 21877, "\u2581\u82f1\u4f26": 21878, "\u2581\u5355\u51fb": 21879, "\u6e17": 21880, "\u2581Research": 21881, "\u2581\u524d\u7a0b": 21882, "\u2581\u533b\u95f9": 21883, "\u2581\u601d\u7eea": 21884, "\u2581\u75c5\u60a3": 21885, "\u2581\u4e94\u810f": 21886, "\u2581\u96c4\u4f1f": 21887, "\u2581Yang": 21888, "\u2581\u6587\u76f2": 21889, "\u2581\u7535\u6c14\u5de5\u7a0b": 21890, "\u2581\u62ac\u9ad8": 21891, "4000": 21892, "\u86f9": 21893, "\u2581\u771f\u4f2a": 21894, "\u2581\u9752\u4e91": 21895, "\u2581\u534a\u5c71": 21896, "DX": 21897, "\u701b": 21898, "\u908b": 21899, "\u2581\u5357\u5cb8": 21900, "\u2581\u536b\u58eb": 21901, "\u2581\u5e08\u5fb7": 21902, "\u2581\u751f\u786c": 21903, "\u2581\u9012\u589e": 21904, "\u9593": 21905, "341": 21906, "Mn": 21907, "\u2581\u70ed\u529b\u5b66": 21908, "\u25811916": 21909, "\u2581\u91d1\u4e1d": 21910, "\u3086": 21911, "\u6d63": 21912, "wen": 21913, "\u2581\u55b7\u6d82": 21914, "\u2581\u7f62\u514d": 21915, "\u2581\u7535\u70ed": 21916, "\u2581\u6728\u96d5": 21917, "\u2581\u8f70\u8f70": 21918, "\u2581\u603b\u8bba": 21919, "\u2581\u4e32\u53e3": 21920, "\u2581With": 21921, "\u2581him": 21922, "\u2581\u538b\u5012": 21923, "\u2581\u6148\u79a7": 21924, "808": 21925, "4.3": 21926, "\u2581\u82f1\u4fca": 21927, "\u2581\u679d\u53f6": 21928, "pot": 21929, "\u565c": 21930, "\u2581\u5e55\u5e9c": 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"\u2581\u809a\u76ae": 26244, "kit": 26245, "\u2581\u636e\u4f20": 26246, "\u9ebc": 26247, "\u2581\u6e90\u6c0f": 26248, "Edit": 26249, "\u2581\u56fd\u5bb6\u4e13\u5229": 26250, "dian": 26251, "\u2581\u4eab\u4e50": 26252, "\u2581\u7f57\u5170": 26253, "\u2581\u65e0\u6094": 26254, "\u541d": 26255, "\u2581\u80b2\u624d": 26256, "\u579b": 26257, "\u02f6": 26258, "\u786e\u786e": 26259, "circ": 26260, "\u2581based": 26261, "\u652f\u90a3": 26262, "\u2581\u5f52\u6839": 26263, "\u2581\u5b58\u680f": 26264, "\u85d3": 26265, "\u547d\u4ee4": 26266, "\u5f0b": 26267, "\u3124": 26268, "\u5934\u7985": 26269, "\u2581\u8865\u8003": 26270, "\u5c4f\u5e55": 26271, "\u2581\u5931\u4f20": 26272, "\u2581\u55b7\u6c14": 26273, "\u2581ACM": 26274, "\u2581\u7ba1\u5f26\u4e50": 26275, "YP": 26276, "\u5ba5": 26277, "658": 26278, "\u2581\u8f7b\u5de7": 26279, "\u2581\u679c\u8089": 26280, "\u5dee\u9633": 26281, "\u3087": 26282, "\u2581local": 26283, "imi": 26284, "\u9ec4\u916e": 26285, "\u523a\u6fc0": 26286, "\u5e84\u9547": 26287, "\u2581756": 26288, "668": 26289, "\u2581\u8d77\u91cd": 26290, "\u2581Sur": 26291, "\u2581\u7ed3\u4f34": 26292, "\u2581\u5baa\u653f": 26293, "\u8206\u8bba": 26294, "\u2581\u5438\u8102": 26295, "\u95e8\u9501": 26296, "\u77fe": 26297, "\u2581\u6e05\u8fdc": 26298, "\u2581\u6d77\u5578": 26299, "\u2581Post": 26300, "\u2581\u7f3a\u5e2d": 26301, "\u2581\u957f\u6bdb": 26302, "\u2581\u8d70\u8bbf": 26303, "\u2581\u81ea\u8f6c": 26304, "\u2581\u516b\u5206": 26305, "\u2581Ray": 26306, "\u2581\u9f99\u65cf": 26307, "\u2581used": 26308, "\u514d\u8d39": 26309, "\u2581\u6bd2\u5bb3": 26310, "\u2581\u519c\u8d38": 26311, "\u2581\u4e2d\u5e74\u7537": 26312, "\u2581\u536b\u56fd": 26313, "\u2581\u4e8c\u5341\u4e07": 26314, "\u2581Two": 26315, "ova": 26316, "612": 26317, "\u2581\u53e5\u578b": 26318, "\u2581\u7167\u5e38": 26319, "\u2581\u9f13\u52a8": 26320, "\u2581\u8f7b\u800c\u6613": 26321, "\u5cb7": 26322, "\u258112.7": 26323, "\u2581\u68a6\u6e38": 26324, "\u2581\u79c1\u6559": 26325, "Version": 26326, "\u51a4\u5bb6": 26327, "\u4e4b\u6d41": 26328, "\u2581\u54c8\u767b": 26329, "\u2581\u51e4\u59d0": 26330, "\u2581things": 26331, "\u2581\u87b3\u8782": 26332, "\u2581\u9192\u8fc7": 26333, "door": 26334, "\u043c": 26335, "hk": 26336, "\u2581\u4ea7\u9500": 26337, "\u9afb": 26338, "\u2581\u8d5b\u9a6c": 26339, "\u631e": 26340, "\u6444\u5f71\u5bb6": 26341, "\u9b3c\u602a": 26342, "\u2581\u9664\u6e7f": 26343, "KEY": 26344, "\u2581\u9000\u6362": 26345, "\u30cd": 26346, "\u2581\u5a01\u5c14\u58eb": 26347, "\u2581\u65bd\u7528": 26348, "559": 26349, "\u2581\u5e08\u5f1f": 26350, "\u258112.6": 26351, "\u2581\u6ce2\u5c14\u591a": 26352, "\u2581\u679c\u771f": 26353, "\u2581\u957f\u8679": 26354, "\u2581\u627f\u8fd0": 26355, "\u2581\u73ed\u7ec4": 26356, "\u2581year": 26357, "\u2581\u8fde\u7ed3": 26358, "\u2581\u60e0\u6c11": 26359, "\u2581Chris": 26360, "\u2581\u5b69\u7eb8": 26361, "afafb": 26362, "\u2581\u4e34\u573a": 26363, "\u2581\u7b26\u5408\u6761\u4ef6": 26364, "\u2581\u9075\u7167": 26365, "\u2581\u58f0\u5b66": 26366, "\u2581\u79fb\u4f4d": 26367, "uf": 26368, "\u4e13\u79d1": 26369, "787": 26370, "\u2581\u6574\u6d41": 26371, "\u2581\u8bc4\u5956": 26372, "unch": 26373, "\u5bb8": 26374, "\u2581Mel": 26375, "\u2581\u5c4b\u9762": 26376, "\u6253\u95f9": 26377, "\u2581\u59d4\u4efb": 26378, "\u2581Bob": 26379, "Player": 26380, "\u826e": 26381, "fact": 26382, "\u2581\u52a0\u6e7f": 26383, "\u2581\u5162\u5162\u4e1a": 26384, "\u2581\u8054\u7ed3": 26385, "\u2581\u666e\u5229": 26386, "\u2581\u4f2f\u6069": 26387, "\u5421": 26388, "\u2581\u4e30\u7855": 26389, "\u2581\u5f92\u52b3": 26390, "\u2581\u5fc3\u6025": 26391, "\u2581simple": 26392, "\u2581\u98de\u8d77": 26393, "cin": 26394, "\u54ed\u7b11": 26395, "\u2581\u963f\u59c6\u65af\u7279": 26396, "\u2581\u5de5\u519c\u4e1a": 26397, "\u5949\u732e": 26398, "\u2581\u516b\u5b9d": 26399, "\u2581\u95ed\u5e55": 26400, "\u2581Dec": 26401, "586": 26402, "\u2581team": 26403, "\u2581\u82e6\u5fc3": 26404, "\u3129": 26405, "\u2581\u5343\u767e": 26406, "oro": 26407, "\u5175\u5f79": 26408, "\u2581\u9634\u865a": 26409, "\u79d8\u5bc6": 26410, "\u2581\u5357\u6c99": 26411, "\u2581\u7cbe\u7b97": 26412, "\u2581does": 26413, "\u258118.5": 26414, "\u5576": 26415, "hl": 26416, "\u2581\u684c\u6e38": 26417, "ava": 26418, "\u2581\u9e21\u5c3e\u9152": 26419, "anna": 26420, "ept": 26421, "JA": 26422, "\u2581\u827e\u7c73": 26423, "\u2581\u7eb5\u6df1": 26424, "\u2581\u767b\u53f0": 26425, "\u2581\u5947\u624d": 26426, "\u53d1\u6307": 26427, "\u2581\u80cc\u9760": 26428, "\u2581\u91cd\u7f6e": 26429, "\u2581\u6446\u644a": 26430, "\u2581\u4e0a\u9976": 26431, "\u2581\u5730\u65b9\u7a0e": 26432, "\u2581\u6c47\u6b3e": 26433, "\u2581\u5211\u90e8": 26434, "\u2581\u4e34\u6c5f": 26435, "\u2581\u6e38\u5203": 26436, "\u871a": 26437, "\u679a\u4e3e": 26438, "ono": 26439, "\u2581\u51ac\u81f3": 26440, "\u55df": 26441, "\u2581Boss": 26442, "\u2581\u548c\u853c": 26443, "\u607f": 26444, "anti": 26445, "\u2581\u5200\u5251": 26446, "\u2581\u884c\u519b": 26447, "\u2581\u9006\u6d41": 26448, "\u2581\u6218\u529f": 26449, "\u2581\u8840\u6cea": 26450, "\u2581\u5546\u52a1\u5370\u4e66": 26451, "\u2581\u5df4\u514b": 26452, "\u2581\u73e0\u4e09": 26453, "\u8334\u9999": 26454, "\u5802\u7687": 26455, "\u2581\u95ed\u5173": 26456, "\u2581\u8352\u51c9": 26457, "\u2581973": 26458, "\u2581\u95e8\u7981": 26459, "\u4ff8": 26460, "\u9634\u6781": 26461, "bp": 26462, "\u80b1": 26463, "\u8361\u6f3e": 26464, "\u2581\u514d\u75ab\u5b66": 26465, "ION": 26466, "662": 26467, "\u2581\u745e\u5b89": 26468, "\u2581\u4e34\u6b7b": 26469, "\u2581\u53c2\u9009": 26470, "\u25811066": 26471, "\u2581\u884c\u5584": 26472, "\u2581\u970d\u534e": 26473, "\u2581\u56e0\u5176": 26474, "\u2581\u7b97\u672f": 26475, "\u2581\u6069\u683c": 26476, "\u2581\u8001\u631d": 26477, "\u96ea\u82b9": 26478, "\u2581\u660e\u53f2": 26479, "\u2581\u9002\u4e0d": 26480, "\u4fd0": 26481, "\u7c73\u4e9a": 26482, "\u2581\u7fa4\u843d": 26483, "\u2581\u5965\u5c3c": 26484, "\u2581\u7a00\u91cc": 26485, "AQ": 26486, "Process": 26487, "bla": 26488, "\u970d\u592b": 26489, "\u2581\u5e08\u56e2": 26490, "\u2581string": 26491, "\u631a\u53cb": 26492, "\u5c71\u6cc9": 26493, "\u2581\u4e4c\u6258": 26494, "\u8f66\u8f86": 26495, "\u2581\u72ec\u5b64": 26496, "\u2581\u8150\u673d": 26497, "\u2581\u80f8\u8154": 26498, "\u2581\u5317\u90bb": 26499, "\u2581\u5a01\u6b66": 26500, "\u5468\u672b": 26501, "\u8884": 26502, "\u2581\u8f85\u4f50": 26503, "\u2581\u9020\u7269": 26504, "\u039a": 26505, "\u8bf0": 26506, "omo": 26507, "\u2581\u88c5\u8ba2": 26508, "\u2581\u9738\u5360": 26509, "\u2581\u516b\u5c81": 26510, "\u7ede": 26511, "\u60af": 26512, "\u2581\u9884\u60f3": 26513, "\u2581\u5357\u4e9a": 26514, "\u2581TMD": 26515, "\u7070\u6697": 26516, "\u8f76": 26517, "rist": 26518, "\u2581\u6296\u52a8": 26519, "\u2581\u6c5f\u6cfd": 26520, "\u653e\u706b": 26521, "\u2581\u521b\u4e16": 26522, "\u2581\u7740\u9646": 26523, "\u2581MIDI": 26524, "\u77d7": 26525, "\u8ba1\u8f83": 26526, "\u2581\u52c7\u731b": 26527, "kN": 26528, "\u2581\u8bfa\u66fc": 26529, "\u0323": 26530, "\u6867": 26531, "\u2581\u6b66\u5b66": 26532, "\u2581\u94c1\u677f": 26533, "\u2581\u6642\u9593": 26534, "\u2581\u906e\u9633": 26535, "\u2581CAM": 26536, "\u4e28": 26537, "\u2581\u81ea\u52a9\u9910": 26538, "\u2581\u62fc\u5199": 26539, "hon": 26540, "\u2581\u8f7b\u5feb": 26541, "\u9a91\u58eb": 26542, "\u2581\u8d2a\u56fe": 26543, "\u2581\u4eff\u53e4": 26544, "\u9e92": 26545, "\u9cb2": 26546, "\u2581York": 26547, "\u2581\u6e05\u6cc9": 26548, "nz": 26549, "\u80c4": 26550, "\u908a": 26551, "\u4ece\u5fc3": 26552, "\u2581\u653f\u6cbb\u7ecf\u6d4e": 26553, "uring": 26554, "\u7a37": 26555, "997": 26556, "\u2581\u62c9\u7968": 26557, "\u2581\u8865\u5200": 26558, "\u7532\u70f7": 26559, "\u2581\u4fa0\u5ba2": 26560, "\u2581\u9c8d\u9c7c": 26561, "\u2581mean": 26562, "\u2581\u51ef\u65cb": 26563, "\u2581\u4efb\u7531": 26564, "\u2581\u827e\u739b": 26565, "\u2581Tan": 26566, "inger": 26567, "\u94c9": 26568, "\u2581\u70df\u96e8": 26569, "\u2581\u5de6\u65cb": 26570, "\u2581\u67da\u5b50": 26571, "\u2581\u5b9d\u5854": 26572, "\u6775": 26573, "\u2581\u5730\u74dc": 26574, "\u2581\u6842\u5706": 26575, "AJ": 26576, "\u2581OPPO": 26577, "\u2581\u524a\u51cf": 26578, "\u30a1": 26579, "\u2581\u5947\u77f3": 26580, "\u2581\u8eb2\u8fc7": 26581, "\u2581\u6293\u4f4f\u673a": 26582, "\u2581mc": 26583, "\u6e25": 26584, "\u2581\u8fde\u591c": 26585, "\u2581KOYO": 26586, "\u2581\u7535\u7b97": 26587, "\u2581CNN": 26588, "\u2581\u5fae\u751f\u7269\u5b66": 26589, "stance": 26590, "\u2581\u5947\u4fa0": 26591, "\u2581\u9f99\u534e": 26592, "\u2581\u57ab\u5e95": 26593, "\u55e6": 26594, "science": 26595, "\u2581tg": 26596, "\u2581\u81ea\u8be9": 26597, "\u2581\u7b2c\u516d\u5341\u4e5d": 26598, "\u8865\u8d34": 26599, "\u2581\u7ea2\u6728": 26600, "\u2581\u653f\u6559": 26601, "\u2581blabla": 26602, "\u2581\u704c\u7bee": 26603, "\u5212\u5165": 26604, "\u2581else": 26605, "\u2581\u6bd2\u86c7": 26606, "\u2581\u4ea4\u5272": 26607, "jia": 26608, "iana": 26609, "\u8bc3": 26610, "\u2581adc": 26611, "\u2581Festival": 26612, "\u2581\u8d4f\u5fc3\u60a6": 26613, "\u810f\u5668": 26614, "\u2581\u534e\u5357\u7406\u5de5": 26615, "\u2581\u9a70\u9a8b": 26616, "\u25810.03": 26617, "\u2581\u4e09\u5341\u4e07": 26618, "\u6f5e": 26619, "\u2581\u5f97\u4e0d\u507f": 26620, "\u7206\u7af9": 26621, "\u4e0d\u60ca": 26622, "\u2581second": 26623, "\u2581\u80cc\u9505": 26624, "\u2581\u57cb\u6ca1": 26625, "\u2581\u64c2\u53f0": 26626, "\u5f69\u7968": 26627, "\u2581\u8179\u819c": 26628, "mis": 26629, "\u2581\u8475\u82b1": 26630, "\u53d1\u8bc1": 26631, "hz": 26632, "\u2581family": 26633, "\u2581\u9752\u77f3": 26634, "\u8d2a": 26635, "\u2581\u62a5\u73ed": 26636, "\u258123.6": 26637, "\u2581\u7532\u7b49": 26638, "DY": 26639, "\u2581\u5bfc\u706b": 26640, "\u771f\u5047": 26641, "\u2581\u5bfc\u5f15": 26642, "\u2581\u87ba\u6746": 26643, "\u9611": 26644, "\u2581\u5a46\u5ab3": 26645, "\u2581Passage": 26646, "\u2581GTX": 26647, "\u2581\u62c9\u62e2": 26648, "\u2581\u7a81\u5982\u5176": 26649, "\u5c55\u89c8": 26650, "\u2581Arm": 26651, "\u2581\u91d1\u77ff": 26652, "\u2581\u5012\u4e0d\u5982": 26653, "\u4e70\u5356": 26654, "\u2581\u4e07\u4f17": 26655, "\u2581\u7206\u7b11": 26656, "474": 26657, "\u2581\u8463\u5353": 26658, "\u2581\u6c11\u95f4\u827a\u672f": 26659, "\u2534": 26660, "dor": 26661, "\u2581Pla": 26662, "\u75ab\u75c5": 26663, "\u51f8": 26664, "\u2581\u9547\u5b88": 26665, "\u2581\u5361\u987f": 26666, "\u2581\u4fdd\u5229": 26667, "\u2581\u9e21\u80f8": 26668, "598": 26669, "\u8392": 26670, "\u2581\u77f3\u72ee": 26671, "\u2581\u80d6\u80d6": 26672, "\u2581\u51cc\u4e91": 26673, "\u9e6b": 26674, "To": 26675, "\u9b47": 26676, "\u2581\u8fde\u7ef5": 26677, "\u6c11\u5927\u4f1a": 26678, "\u2581\u6cd5\u5170\u514b\u798f": 26679, "\u2581\u96c6\u9526": 26680, "enny": 26681, "\u2581\u611f\u89ba": 26682, "arn": 26683, "\u2581\u901a\u7f09": 26684, "term": 26685, "\u2581\u859b\u4e4b\u8c26": 26686, "\u2581\u8862\u5dde": 26687, "\u2581\u5f02\u6b65": 26688, "\u2581\u5317\u9f50": 26689, 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26806, "\u9178\u723d": 26807, "\u2581\u96c6\u90ae": 26808, "\u2581\u5bd2\u98ce": 26809, "\u2581\u7ea7\u6570": 26810, "\u9a6c\u54c8": 26811, "\u897f\u6797": 26812, "\u2581\u5316\u8113": 26813, "Pass": 26814, "\u2581\u5b97\u65cf": 26815, "023": 26816, "\u2581MIT": 26817, "\u4e0d\u590d": 26818, "\u2581\u83f1\u5f62": 26819, "\u2581\u9738\u6743": 26820, "\u7075\u5149": 26821, "flo": 26822, "\u2581Hill": 26823, "rack": 26824, "\u2581\u4fdd\u9669\u8d39": 26825, "\u2581\u90b5\u9633": 26826, "\u2581Pen": 26827, "\u2581\u65e0\u6687": 26828, "\u2581\u8fea\u65af\u5c3c": 26829, "\u2581\u6d2a\u8352": 26830, "\u2581Ana": 26831, "\u7eb2\u4e0a": 26832, "\u2581\u72d7\u5c4e": 26833, "\u2581\u7a7a\u65f7": 26834, "\u2581\u4e00\u4e01\u70b9": 26835, "\u2581\u6a21\u8054": 26836, "\u2581\u540e\u7f00": 26837, "\u2581\u51ef\u6492": 26838, "\u2581\u6df1\u5965": 26839, "\u2581\u5feb\u6d3b": 26840, "\u2581demo": 26841, "\u5bfc\u4f53": 26842, "\u2581\u5168\u56fd\u4eba\u5927": 26843, "541": 26844, 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"\u2581\u5e93\u514b": 26885, "\u2581\u6c38\u5609": 26886, "\u2581\u8c46\u6c99": 26887, "\u2581\u82a6\u7b0b": 26888, "\u2581\u5a77\u5a77": 26889, "\u9119": 26890, "\u2581\u7f1d\u7eab": 26891, "\u2581\u62d3\u8dcb": 26892, "\u81ea\u8fbe": 26893, "EW": 26894, "\u2581\u4fa7\u8109": 26895, "471": 26896, "\u2581\u7ea2\u8863": 26897, "\u2581\u81f4\u8c22": 26898, "\u2581\u672b\u5c3e": 26899, "\u2581\u70b8\u9e21": 26900, "\u2581Rap": 26901, "\u6e6e": 26902, "\u82cb": 26903, "\u2581\u4ef0\u6155": 26904, "\u2581\u4f20\u70ed": 26905, "\u2581\u4e3e\u8bc1": 26906, "\u2581\u8c26\u900a": 26907, "\u2581Call": 26908, "\u8682": 26909, "\u2581\u59a9\u5a9a": 26910, "\u8072": 26911, "\u2581\u4e07\u5bff": 26912, "\u2581\u51ac\u65e5": 26913, "\u2581\u601d\u8fa8": 26914, "\u2581\u4e3b\u5ba2\u573a": 26915, "\u2581\u5dfe\u5e3c": 26916, "\u5378": 26917, "organ": 26918, "\u2581\u78b3\u5316": 26919, "\u2581\u4f53\u8102": 26920, "\u2581\u4e39\u9633": 26921, "\u2581\u7687\u65cf": 26922, "walk": 26923, "\u9ebe": 26924, 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26967, "rot": 26968, "\u63a8\u8f66": 26969, "\u2581Computer": 26970, "\u4e0d\u7591": 26971, "\u2581\u5e73\u9876\u5c71": 26972, "\u2581wanna": 26973, "\u2581\u5fc3\u614c": 26974, "\u2581\u6f0f\u6597": 26975, "\u5919": 26976, "gui": 26977, "\u2581\u4e34\u6d77": 26978, "\u8d22\u52a1": 26979, "\u5982\u82b1": 26980, "\u2581\u52a8\u4e71": 26981, "\u2581\u9019\u4e9b": 26982, "\u2581\u7259\u79d1": 26983, "\u2581\u53cd\u667a": 26984, "\u2581Never": 26985, "LF": 26986, "\u2581\u84c4\u79ef": 26987, "544": 26988, "lk": 26989, "\u2581\u6447\u6643": 26990, "\u2581\u5468\u745c": 26991, "\u2581\u5c01\u53e3": 26992, "40000": 26993, "\u2581Thomas": 26994, "\u2581\u656c\u8001": 26995, "\u2581\u788d\u4e8e": 26996, "\u258115.1": 26997, "\u2581\u9093\u4e3d": 26998, "\u2581\u5e72\u6d17": 26999, "\u542f\u8d85": 27000, "news": 27001, "\u2581\u516c\u5893": 27002, "\u7ed3\u675f": 27003, "\u2581\u7389\u9f99": 27004, "eau": 27005, "\u7eff\u7eff": 27006, "\u2581\u51c6\u5988": 27007, "\u2581\u60e8\u906d": 27008, 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27047, "\u2581TXT": 27048, "\u2581\u677e\u6811": 27049, "\u51b2\u51fb": 27050, "\u673a\u82af": 27051, "\u2581\u4e92\u60e0": 27052, "\u30ef": 27053, "\u98d2": 27054, "\u8f7f\u8f66": 27055, "search": 27056, "\u2581\u81ea\u8a00\u81ea": 27057, "\u2581\u7267\u8349": 27058, "\u258115.5": 27059, "\u2581\u6ee1\u5634": 27060, "\u2581\u7280\u725b": 27061, "GHz": 27062, "yun": 27063, "\u258113.6": 27064, "\u2581\u89c1\u4e49\u52c7\u4e3a": 27065, "\u2581\u62a2\u65ad": 27066, "665": 27067, "\u2594": 27068, "\u2581Sci": 27069, "\u2581\u82ad\u6bd4": 27070, "anc": 27071, "\u2581\u56db\u9a71": 27072, "\u2581SMS": 27073, "\u2581\u5207\u5408": 27074, "KS": 27075, "\u2581\u7eb8\u7bb1": 27076, "\u57fa\u5c3c": 27077, "\u53a2\u60c5": 27078, "\u8019": 27079, "mission": 27080, "eur": 27081, "ography": 27082, "\u2581\u72e1\u733e": 27083, "\u2581\u7409\u7403": 27084, "\u2581made": 27085, "\u2581\u7741\u5f00": 27086, "\u2581\u97e6\u4f2f": 27087, "\u5fff": 27088, "595": 27089, "\u2581\u8eab\u4e34": 27090, 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29539, "\u74a8": 29540, "\u8be1": 29541, "\u9e66": 29542, "\u8f72": 29543, "\u90f4": 29544, "\u50f3": 29545, "\u9a9c": 29546, "\u59d7": 29547, "\u7898": 29548, "\u4e53": 29549, "\u6e43": 29550, "\u6cc1": 29551, "\u8a0e": 29552, "\u626a": 29553, "\u4eb3": 29554, "\u8d42": 29555, "\u8e0a": 29556, "\u5a29": 29557, "\u6aac": 29558, "\u5406": 29559, "\u6bc2": 29560, "\u8c04": 29561, "\u66b9": 29562, "\u76ef": 29563, "\u6682": 29564, "\u7ff1": 29565, "\u548b": 29566, "\u6177": 29567, "\u6c72": 29568, "\u8c7a": 29569, "\u733e": 29570, "\u65e2": 29571, "\u6401": 29572, "\u9e2f": 29573, "\u6323": 29574, "\u5c2c": 29575, "\u6232": 29576, "\u832f": 29577, "\u73bb": 29578, "\u5ed6": 29579, "\u4eb5": 29580, "\u6670": 29581, "\u7750": 29582, "\u5464": 29583, "\u66a8": 29584, "\u87c6": 29585, "\u8469": 29586, "\u8885": 29587, "\u9abc": 29588, "\u8c34": 29589, "\u5228": 29590, "\u7525": 29591, "\u6988": 29592, "\u867d": 29593, "!": 29594, "\u7435": 29595, "\u5201": 29596, "\u8bfd": 29597, "\u810d": 29598, "\u71a0": 29599, "\u88b1": 29600, "\u5a06": 29601, "\u6800": 29602, "\u7480": 29603, "\u5669": 29604, "\u6e32": 29605, "\u87b3": 29606, "\u727a": 29607, "\u556c": 29608, "\u4fde": 29609, "\u82e1": 29610, "\u8365": 29611, "\u7a23": 29612, "\u7325": 29613, "\u795b": 29614, "\u7f05": 29615, "\u8782": 29616, "\u5e44": 29617, "\u6829": 29618, "\u846b": 29619, "\u567c": 29620, "\u5306": 29621, "\u5960": 29622, "\u960e": 29623, "\u8759": 29624, "\u6a44": 29625, "\u4ecd": 29626, "\u609a": 29627, "\u5484": 29628, "\u8301": 29629, "\u7b06": 29630, "\u9955": 29631, "\u5f8a": 29632, "\u8f6d": 29633, "\u828e": 29634, "\u8c29": 29635, "\u5088": 29636, "\u83a0": 29637, "\u859b": 29638, "\u962e": 29639, "\u97ff": 29640, "\u70f9": 29641, "\u9f8c": 29642, "\u60eb": 29643, "\u62a0": 29644, "\u5457": 29645, "\u7a88": 29646, "\u6ec7": 29647, "\u72de": 29648, "\u90f8": 29649, "\u4f7c": 29650, "\u6f7a": 29651, "\u5a32": 29652, "\u8e76": 29653, "\u6495": 29654, "\u4e22": 29655, "\u80aa": 29656, "\u96d6": 29657, "\u7eab": 29658, "\u61a7": 29659, "\u5d2d": 29660, "\u54ab": 29661, "\u847a": 29662, "\u8568": 29663, "\u86d4": 29664, "\u8708": 29665, "\u6a97": 29666, "\u61f5": 29667, "\u55bd": 29668, "\u88a4": 29669, "\u6bd7": 29670, "\u9e35": 29671, "\u5ae6": 29672, "\u6361": 29673, "\u75ea": 29674, "\u54a4": 29675, "\u6cf1": 29676, "\u9a78": 29677, "\u9573": 29678, "\u6410": 29679, "\u631d": 29680, "\u968d": 29681, "\u8fe2": 29682, "\u7504": 29683, "\u66dd": 29684, "\u64c5": 29685, "\u86a9": 29686, "\u732c": 29687, "\u5d14": 29688, "\u8bc5": 29689, "\u72c8": 29690, "\u7f42": 29691, "\u9019": 29692, "\u5080": 29693, "\u8d41": 29694, "\u5555": 29695, "\u9ac5": 29696, "\u6942": 29697, "\u6dc6": 29698, "\u61ac": 29699, "\u8638": 29700, "\u5a6a": 29701, "\u5c8c": 29702, "\u6fdf": 29703, "\u5e3c": 29704, "\u5543": 29705, "\u629b": 29706, "\u90b9": 29707, "\u88f4": 29708, "\u7599": 29709, "\u9068": 29710, "\u64c7": 29711, "\u5499": 29712, "\u7629": 29713, "\u54fc": 29714, "\u78d5": 29715, "\u59a9": 29716, "\u80db": 29717, "\u65d6": 29718, "\u86a3": 29719, "\u78be": 29720, "\u7bdd": 29721, "\u75b8": 29722, "\u9f8a": 29723, "\u560c": 29724, "\u4fef": 29725, "\u8881": 29726, "\u9e49": 29727, "\u5a20": 29728, "\u7ead": 29729, "\u65ce": 29730, "\u547b": 29731, "\u814c": 29732, "\u870a": 29733, "\u5d4b": 29734, "\u6cde": 29735, "\u625b": 29736, "\u7a95": 29737, "\u5c82": 29738, "\u992e": 29739, "\u6273": 29740, "\u81c0": 29741, "\u6715": 29742, "\u62e7": 29743, "\u600e": 29744, "\u9661": 29745, "\u5fd1": 29746, "\u90dd": 29747, "\u64a9": 29748, "\u554a": 29749, "\u9005": 29750, "\u8734": 29751, "\u72f0": 29752, "\u5f89": 29753, "\u996a": 29754, "\u7ed4": 29755, "\u5121": 29756, "\u5a13": 29757, "\u5243": 29758, "\u6254": 29759, "\u8dda": 29760, "\u5c96": 29761, "\u778c": 29762, "\u6390": 29763, "\u5f9c": 29764, "\u8e52": 29765, "\u53e6": 29766, "\u5514": 29767, "\u90a2": 29768, "\u30eb": 29769, "\u9ecf": 29770, "\u9f9a": 29771, "\u30c8": 29772, "\u6cae": 29773, "\u8d81": 29774, "?": 29775, "\u58ec": 29776, "\u6628": 29777, "\u5496": 29778, "\u30ea": 29779, "\u63ba": 29780, "\u7852": 29781, "\u8c25": 29782, "\u30af": 29783, "\u6289": 29784, "\u55e8": 29785, "\u8214": 29786, "\u667e": 29787, "\u5594": 29788, "\u6380": 29789, "\u30a4": 29790, "\u968b": 29791, "\u5624": 29792, "\u7529": 29793, "\u8e72": 29794, "\u30c9": 29795, "\u618b": 29796, "\u5509": 29797, "\u8e29": 29798, "\u30a2": 29799, "\u30b7": 29800, "\u30de": 29801, "\u6345": 29802, "\u86ca": 29803, "\u5b71": 29804, "\u90d3": 29805, "\u9acc": 29806, "\u76b4": 29807, "\u7ccd": 29808, "\u6b38": 29809, "\u8ddb": 29810, "\u7292": 29811, "\u781d": 29812, "\u7596": 29813, "\u5fb3": 29814, "\u8e2e": 29815, "\u877c": 29816, "\u6c29": 29817, "\u6c19": 29818, "\u8db8": 29819, "\u705e": 29820, "\u54d0": 29821, "\u909d": 29822, "\u30e6": 29823, "\u56df": 29824, "\u6841": 29825, "\u909b": 29826, "\u9ac2": 29827, "\u7145": 29828, "\u6c94": 29829, "\u8e69": 29830, "\u7266": 29831, "\u87cb": 29832, "\u67de": 29833, "\u8d6d": 29834, "\u90eb": 29835, "\u783c": 29836, "\u5d4a": 29837, "\u8084": 29838, "\u89ca": 29839, "\u8c36": 29840, "\u779f": 29841, "\u8dd7": 29842, "\u9e67": 29843, "\u90ac": 29844, "\u30d8": 29845, "\u30db": 29846, "\u9e8b": 29847, "\u73b7": 29848, "\u8bb4": 29849, "\u30e4": 29850, "\u8e49": 29851, "\u90e7": 29852, "\u6f2f": 29853, "\u8e0c": 29854, "\u9791": 29855, "\u97a3": 29856, "\u67b3": 29857, "\u64b5": 29858, "\u8e42": 29859, "\u77ad": 29860, "\u989e": 29861, "\u5b6a": 29862, "\u50a9": 29863, "\u8378": 29864, "\u6400": 29865, "\u911e": 29866, "\u5522": 29867, "\u84e6": 29868, "\u85d0": 29869, "\u91b4": 29870, "\u527d": 29871, "\u874c": 29872, "\u752d": 29873, "\u54fd": 29874, "\u30d9": 29875, "\u30ae": 29876, "\u6dbf": 29877, "\u86af": 29878, "\u94a3": 29879, "\u4f58": 29880, "\u8737": 29881, "\u73d0": 29882, "\u530d": 29883, "\u30b1": 29884, "\u7622": 29885, "\u817c": 29886, "\u7f79": 29887, "\u30dc": 29888, "\u4ffe": 29889, "\u0308": 29890, "\u918d": 29891, "\u6ec1": 29892, "\u7f1c": 29893, "\u98d3": 29894, "\u6441": 29895, "\u5d02": 29896, "\u8ff8": 29897, "\u9074": 29898, "\u6daa": 29899, "\u5a7a": 29900, "\u9e4c": 29901, "\u6d93": 29902, "\u9cb6": 29903, "\u5f11": 29904, "\u6787": 29905, "\u84b4": 29906, "\u6266": 29907, "\u7889": 29908, "\u80ed": 29909, "\u77a0": 29910, "\u9acb": 29911, "\u9157": 29912, "\u9e9d": 29913, "\u8368": 29914, "\u83cf": 29915, "\u9984": 29916, "\u60c6": 29917, "\u873f": 29918, "\u8bd9": 29919, "\u7316": 29920, "\u60e6": 29921, "\u7619": 29922, "\u5fd0": 29923, "\u6452": 29924, "\u62ee": 29925, "\u4f8f": 29926, "\u75e4": 29927, "\u915d": 29928, "\u61ca": 29929, "\u80ae": 29930, "\u5486": 29931, "\u8dfb": 29932, "\u9ab7": 29933, "\u9609": 29934, "\u869d": 29935, "\u7315": 29936, "\u4fa5": 29937, "\u5f77": 29938, "\u30d3": 29939, "\u9082": 29940, "\u5671": 29941, "\u8bdf": 29942, "\u30a6": 29943, "\u8dc6": 29944, "\u5494": 29945, "\u8e39": 29946, "\u75ca": 29947, "\u30c7": 29948, "\u30a8": 29949, "\u30b5": 29950, "\u9eef": 29951, "\u30df": 29952, "\u7cbd": 29953, "\u8783": 29954, "\u30aa": 29955, "\u62a8": 29956, "\u892a": 29957, "\u87d1": 29958, "\u90af": 29959, "\u8c4c": 29960, "\u30ad": 29961, "\u62ce": 29962, "\u63ea": 29963, "\u6cf8": 29964, "\u55c5": 29965, "\u9668": 29966, "\u6f4d": 29967, "\u5018": 29968, "\u5241": 29969, "\u5f98": 29970, "\u5443": 29971, "\u717d": 29972, "\u80f3": 29973, "\u5b75": 29974, "\u8fc4": 29975, "\u598a": 29976, "\u03bc": 29977, "\u6002": 29978, "\u8386": 29979, "\u5a4a": 29980, "\u8db4": 29981, "\u5308": 29982, "\u66a7": 29983, "\u692d": 29984, "\u64b8": 29985, "\u5c4c": 29986, "\u54b1": 29987, "\u54ce": 29988, "\u5c34": 29989, "\u00b0": 29990, "\u5979": 29991, "\u6342": 29992, "\u30b3": 29993, "\u30ab": 29994, "\u30d5": 29995, "(": 29996, "\u30ce": 29997, "\u30ec": 29998, "\u4ffa": 29999}
================================================
FILE: CPM-Generate/configure_data.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
"""parses arguments and preps data loader"""
import copy
import torch
import data_utils
import mpu
class DataConfig:
def __init__(self, defaults={}):
super(DataConfig, self).__init__()
self.defaults = defaults
def apply(self, args):
if torch.distributed.get_rank() == 0:
print('configuring data')
self.apply_defaults(args)
return make_loaders(args)
def set_defaults(self, **kwargs):
for k, v in kwargs.items():
self.defaults[k] = v
def apply_defaults(self, args):
for k, v in self.defaults.items():
k = k.replace('-', '_')
if not hasattr(args, k):
setattr(args, k, v)
def make_data_loader(dataset, batch_size, args):
shuffle = args.shuffle
if shuffle:
sampler = data_utils.samplers.RandomSampler(dataset, replacement=True, num_samples=batch_size*args.train_iters)
else:
sampler = torch.utils.data.SequentialSampler(dataset)
world_size = torch.distributed.get_world_size(
group=mpu.get_data_parallel_group())
rank = torch.distributed.get_rank(group=mpu.get_data_parallel_group())
distributed = world_size > 1
drop_last = distributed
if distributed:
batch_sampler = data_utils.samplers.DistributedBatchSampler(sampler,
batch_size,
drop_last,
rank,
world_size)
else:
batch_sampler = torch.utils.data.BatchSampler(sampler,
batch_size,
drop_last)
data_loader = torch.utils.data.DataLoader(dataset,
batch_sampler=batch_sampler,
num_workers=args.num_workers,
pin_memory=True)
return data_loader
def make_tfrecord_loaders(args):
"""Load train/val/test dataset from shuffled TFRecords"""
import data_utils.tf_dl
data_set_args = {'batch_size': args.batch_size,
'max_seq_len': args.seq_length,
'max_preds_per_seq': args.max_preds_per_seq,
'train': True,
'num_workers': max(args.num_workers, 1),
'seed': args.seed + args.rank + 1,
'threaded_dl': args.num_workers > 0
}
train = data_utils.tf_dl.TFRecordDataLoader(args.train_data,
**data_set_args)
data_set_args['train'] = False
if args.eval_seq_length is not None:
data_set_args['max_seq_len'] = args.eval_seq_length
if args.eval_max_preds_per_seq is not None:
data_set_args['max_preds_per_seq'] = args.eval_max_preds_per_seq
valid = None
if args.valid_data is not None:
valid = data_utils.tf_dl.TFRecordDataLoader(args.valid_data,
**data_set_args)
test = None
if args.test_data is not None:
test = data_utils.tf_dl.TFRecordDataLoader(args.test_data,
**data_set_args)
tokenizer = data_utils.make_tokenizer(args.tokenizer_type,
train,
args.tokenizer_path,
args.vocab_size,
args.tokenizer_model_type,
cache_dir=args.cache_dir)
return (train, valid, test), tokenizer
def make_loaders(args):
"""makes training/val/test"""
if args.use_tfrecords:
return make_tfrecord_loaders(args)
world_size = torch.distributed.get_world_size(
group=mpu.get_data_parallel_group())
batch_size = args.batch_size * world_size
eval_batch_size = batch_size
if args.eval_batch_size is not None:
eval_batch_size = args.eval_batch_size * world_size
seq_length = args.seq_length
if seq_length < 0:
seq_length = seq_length * world_size
eval_seq_length = args.eval_seq_length
if eval_seq_length is not None and eval_seq_length < 0:
eval_seq_length = eval_seq_length * world_size
split = get_split(args)
data_set_args = {
'path': args.train_data,
'seq_length': seq_length,
'lazy': args.lazy_loader,
'delim': args.delim,
'text_key': args.text_key,
'label_key': 'label',
'non_binary_cols': None,
'ds_type': args.data_set_type,
'split': split,
'loose': args.loose_json,
'tokenizer_type': args.tokenizer_type,
'tokenizer_model_path': args.tokenizer_path,
'vocab_size': args.vocab_size,
'model_type': args.tokenizer_model_type,
'cache_dir': args.cache_dir,
'max_preds_per_seq': args.max_preds_per_seq,
'presplit_sentences': args.presplit_sentences}
eval_set_args = copy.copy(data_set_args)
eval_set_args['split'] = [1.]
# if optional eval args were set then replace their
# equivalent values in the arg dict
if eval_seq_length:
eval_set_args['seq_length'] = eval_seq_length
if args.eval_max_preds_per_seq:
eval_set_args['max_preds_per_seq'] = args.eval_max_preds_per_seq
if args.eval_text_key is not None:
eval_set_args['text_key'] = args.eval_text_key
# make datasets splits and tokenizer
train = None
valid = None
test = None
if args.train_data is not None:
train, tokenizer = data_utils.make_dataset(**data_set_args)
if data_utils.should_split(split):
train, valid, test = train
eval_set_args['tokenizer'] = tokenizer
# make training and val dataset if necessary
if valid is None and args.valid_data is not None:
eval_set_args['path'] = args.valid_data
valid, tokenizer = data_utils.make_dataset(**eval_set_args)
eval_set_args['tokenizer'] = tokenizer
if test is None and args.test_data is not None:
eval_set_args['path'] = args.test_data
test, tokenizer = data_utils.make_dataset(**eval_set_args)
# wrap datasets with data loader
if train is not None and args.batch_size > 0:
train = make_data_loader(train, batch_size, args)
args.do_train = True
else:
args.do_train = False
eval_batch_size = eval_batch_size if eval_batch_size != 0 else batch_size
if valid is not None:
valid = make_data_loader(valid, eval_batch_size, args)
args.do_valid = True
else:
args.do_valid = False
if test is not None:
test = make_data_loader(test, eval_batch_size, args)
args.do_test = True
else:
args.do_test = False
return (train, valid, test), tokenizer
def get_split(args):
"""
Get dataset splits from comma separated string list
"""
splits = []
if args.split.find(',') != -1:
splits = [float(s) for s in args.split.split(',')]
elif args.split.find('/') != -1:
splits = [float(s) for s in args.split.split('/')]
else:
splits = [float(args.split)]
split_total = sum(splits)
if split_total < 1.:
splits.append(1-split_total)
while len(splits) < 3:
splits.append(0.)
splits = splits[:3]
if args.valid_data is not None:
splits[1] = 0.
if args.test_data is not None:
splits[2] = 0.
final_sum = sum(splits)
return [s/final_sum for s in splits]
def configure_data():
"""add cmdline flags for configuring datasets"""
# These are options that are used by data_utils, but are either
# deprecated or not meant to be exposed to the command line user.
# These options are intneded to be set in code by specific scripts.
defaults = {
'world_size': 1,
'rank': -1,
'persist_state': 0,
'lazy': False,
'transpose': False,
'data_set_type': 'supervised',
'seq_length': 256,
'eval_seq_length': 256,
'samples_per_shard': 100
}
return DataConfig(defaults=defaults)
================================================
FILE: CPM-Generate/data/Makefile
================================================
CXXFLAGS += -O3 -Wall -shared -std=c++11 -fPIC -fdiagnostics-color
CPPFLAGS += $(shell python3 -m pybind11 --includes)
LIBNAME = helpers
LIBEXT = $(shell python3-config --extension-suffix)
default: $(LIBNAME)$(LIBEXT)
%$(LIBEXT): %.cpp
$(CXX) $(CXXFLAGS) $(CPPFLAGS) $< -o $@
================================================
FILE: CPM-Generate/data/__init__.py
================================================
from . import indexed_dataset
================================================
FILE: CPM-Generate/data/bert_dataset.py
================================================
# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. 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.
"""BERT Style dataset."""
import os
import time
import numpy as np
import torch
from torch.utils.data import Dataset
#from megatron import get_tokenizer
import mpu
from data.dataset_utils import build_training_sample
from data.indexed_dataset import make_dataset as make_indexed_dataset
from utils import print_rank_0
def build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
train_valid_test_num_samples,
max_seq_length, masked_lm_prob,
short_seq_prob, seed, skip_warmup):
# Indexed dataset.
indexed_dataset = get_indexed_dataset_(data_prefix,
data_impl,
skip_warmup)
# Get start and end indices of train/valid/train into doc-idx
# Note that doc-idx is desinged to be num-docs + 1 so we can
# easily iterate over it.
total_num_of_documents = indexed_dataset.doc_idx.shape[0] - 1
splits = get_train_valid_test_split_(splits_string, total_num_of_documents)
# Print stats about the splits.
print_rank_0(' > dataset split:')
def print_split_stats(name, index):
print_rank_0(' {}:'.format(name))
print_rank_0(' document indices in [{}, {}) total of {} '
'documents'.format(splits[index], splits[index + 1],
splits[index + 1] - splits[index]))
start_index = indexed_dataset.doc_idx[splits[index]]
end_index = indexed_dataset.doc_idx[splits[index + 1]]
print_rank_0(' sentence indices in [{}, {}) total of {} '
'sentences'.format(start_index, end_index,
end_index - start_index))
print_split_stats('train', 0)
print_split_stats('validation', 1)
print_split_stats('test', 2)
def build_dataset(index, name):
dataset = None
if splits[index + 1] > splits[index]:
# Get the pointer to the original doc-idx so we can set it later.
doc_idx_ptr = indexed_dataset.get_doc_idx()
# Slice the doc-idx
start_index = splits[index]
# Add +1 so we can index into the dataset to get the upper bound.
end_index = splits[index + 1] + 1
# New doc_idx view.
indexed_dataset.set_doc_idx(doc_idx_ptr[start_index:end_index])
# Build the dataset accordingly.
dataset = BertDataset(
name=name,
indexed_dataset=indexed_dataset,
data_prefix=data_prefix,
num_epochs=None,
max_num_samples=train_valid_test_num_samples[index],
masked_lm_prob=masked_lm_prob,
max_seq_length=max_seq_length,
short_seq_prob=short_seq_prob,
seed=seed)
# Set the original pointer so dataset remains the main dataset.
indexed_dataset.set_doc_idx(doc_idx_ptr)
# Checks.
assert indexed_dataset.doc_idx[0] == 0
assert indexed_dataset.doc_idx.shape[0] == \
(total_num_of_documents + 1)
return dataset
train_dataset = build_dataset(0, 'train')
valid_dataset = build_dataset(1, 'valid')
test_dataset = build_dataset(2, 'test')
return (train_dataset, valid_dataset, test_dataset)
class BertDataset(Dataset):
def __init__(self, name, indexed_dataset, data_prefix,
num_epochs, max_num_samples, masked_lm_prob,
max_seq_length, short_seq_prob, seed):
# Params to store.
self.name = name
self.seed = seed
self.masked_lm_prob = masked_lm_prob
self.max_seq_length = max_seq_length
# Dataset.
self.indexed_dataset = indexed_dataset
# Build the samples mapping.
self.samples_mapping = get_samples_mapping_(self.indexed_dataset,
data_prefix,
num_epochs,
max_num_samples,
self.max_seq_length,
short_seq_prob,
self.seed,
self.name)
# Vocab stuff.
tokenizer = get_tokenizer()
self.vocab_id_list = list(tokenizer.inv_vocab.keys())
self.vocab_id_to_token_dict = tokenizer.inv_vocab
self.cls_id = tokenizer.cls
self.sep_id = tokenizer.sep
self.mask_id = tokenizer.mask
self.pad_id = tokenizer.pad
def __len__(self):
return self.samples_mapping.shape[0]
def __getitem__(self, idx):
start_index, end_index, seq_length = self.samples_mapping[idx]
sample = []
for index in range(start_index, end_index):
sample.append(self.indexed_dataset[index])
# Note that this rng state should be numpy and not python since
# python randint is inclusive whereas the numpy one is exclusive.
np_rng = np.random.RandomState(seed=(self.seed + idx))
return build_training_sample(sample, seq_length,
self.max_seq_length, # needed for padding
self.vocab_id_list,
self.vocab_id_to_token_dict,
self.cls_id, self.sep_id,
self.mask_id, self.pad_id,
self.masked_lm_prob, np_rng)
def get_indexed_dataset_(data_prefix, data_impl, skip_warmup):
print_rank_0(' > building dataset index ...')
start_time = time.time()
indexed_dataset = make_indexed_dataset(data_prefix,
data_impl,
skip_warmup)
assert indexed_dataset.sizes.shape[0] == indexed_dataset.doc_idx[-1]
print_rank_0(' > finished creating indexed dataset in {:4f} '
'seconds'.format(time.time() - start_time))
print_rank_0(' > indexed dataset stats:')
print_rank_0(' number of documents: {}'.format(
indexed_dataset.doc_idx.shape[0] - 1))
print_rank_0(' number of sentences: {}'.format(
indexed_dataset.sizes.shape[0]))
return indexed_dataset
def get_train_valid_test_split_(splits_string, size):
""" Get dataset splits from comma or '/' separated string list."""
splits = []
if splits_string.find(',') != -1:
splits = [float(s) for s in splits_string.split(',')]
elif splits_string.find('/') != -1:
splits = [float(s) for s in splits_string.split('/')]
else:
splits = [float(splits_string)]
while len(splits) < 3:
splits.append(0.)
splits = splits[:3]
splits_sum = sum(splits)
assert splits_sum > 0.0
splits = [split / splits_sum for split in splits]
splits_index = [0]
for index, split in enumerate(splits):
splits_index.append(splits_index[index] +
int(round(split * float(size))))
diff = splits_index[-1] - size
for index in range(1, len(splits_index)):
splits_index[index] -= diff
assert len(splits_index) == 4
assert splits_index[-1] == size
return splits_index
def get_samples_mapping_(indexed_dataset,
data_prefix,
num_epochs,
max_num_samples,
max_seq_length,
short_seq_prob,
seed,
name):
if not num_epochs:
if not max_num_samples:
raise ValueError("Need to specify either max_num_samples "
"or num_epochs")
num_epochs = np.iinfo(np.int32).max - 1
if not max_num_samples:
max_num_samples = np.iinfo(np.int64).max - 1
# Filename of the index mapping
indexmap_filename = data_prefix
indexmap_filename += '_{}_indexmap'.format(name)
if num_epochs != (np.iinfo(np.int32).max - 1):
indexmap_filename += '_{}ep'.format(num_epochs)
if max_num_samples != (np.iinfo(np.int64).max - 1):
indexmap_filename += '_{}mns'.format(max_num_samples)
indexmap_filename += '_{}msl'.format(max_seq_length)
indexmap_filename += '_{:0.2f}ssp'.format(short_seq_prob)
indexmap_filename += '_{}s'.format(seed)
indexmap_filename += '.npy'
# Build the indexed mapping if not exist.
if torch.distributed.get_rank() == 0 and \
not os.path.isfile(indexmap_filename):
print(' > WARNING: could not find index map file {}, building '
'the indices on rank 0 ...'.format(indexmap_filename))
# Make sure the types match the helpers input types.
assert indexed_dataset.doc_idx.dtype == np.int64
assert indexed_dataset.sizes.dtype == np.int32
# Build samples mapping
verbose = torch.distributed.get_rank() == 0
start_time = time.time()
print_rank_0(' > building sapmles index mapping for {} ...'.format(
name))
# First compile and then import.
from data.dataset_utils import compile_helper
compile_helper()
from data import helpers
samples_mapping = helpers.build_mapping(
indexed_dataset.doc_idx,
indexed_dataset.sizes,
num_epochs,
max_num_samples,
max_seq_length - 3, # account for added tokens
short_seq_prob,
seed,
verbose)
print_rank_0(' > done building sapmles index maping')
np.save(indexmap_filename, samples_mapping, allow_pickle=True)
print_rank_0(' > saved the index mapping in {}'.format(
indexmap_filename))
# Make sure all the ranks have built the mapping
print_rank_0(' > elasped time to build and save samples mapping '
'(seconds): {:4f}'.format(
time.time() - start_time))
# This should be a barrier but nccl barrier assumes
# device_index=rank which is not the case for model
# parallel case
counts = torch.cuda.LongTensor([1])
torch.distributed.all_reduce(counts, group=mpu.get_data_parallel_group())
assert counts[0].item() == torch.distributed.get_world_size(
group=mpu.get_data_parallel_group())
# Load indexed dataset.
print_rank_0(' > loading indexed mapping from {}'.format(
indexmap_filename))
start_time = time.time()
samples_mapping = np.load(indexmap_filename, allow_pickle=True, mmap_mode='r')
print_rank_0(' loaded indexed file in {:3.3f} seconds'.format(
time.time() - start_time))
print_rank_0(' total number of samples: {}'.format(
samples_mapping.shape[0]))
return samples_mapping
================================================
FILE: CPM-Generate/data/dataset_utils.py
================================================
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors, and NVIDIA.
#
# 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.
# Most of the code here has been copied from:
# https://github.com/google-research/albert/blob/master/create_pretraining_data.py
# with some modifications.
import collections
import numpy as np
def compile_helper():
"""Compile helper function ar runtime. Make sure this
is invoked on a single process."""
import os
import subprocess
path = os.path.abspath(os.path.dirname(__file__))
#ret = subprocess.run(['make', '-C', path])
#if ret.returncode != 0:
# print("Making C++ dataset helpers module failed, exiting.")
# import sys
# sys.exit(1)
def build_training_sample(sample,
target_seq_length, max_seq_length,
vocab_id_list, vocab_id_to_token_dict,
cls_id, sep_id, mask_id, pad_id,
masked_lm_prob, np_rng):
"""Biuld training sample.
Arguments:
sample: A list of sentences in which each sentence is a list token ids.
target_seq_length: Desired sequence length.
max_seq_length: Maximum length of the sequence. All values are padded to
this length.
vocab_id_list: List of vocabulary ids. Used to pick a random id.
vocab_id_to_token_dict: A dictionary from vocab ids to text tokens.
cls_id: Start of example id.
sep_id: Separator id.
mask_id: Mask token id.
pad_id: Padding token id.
masked_lm_prob: Probability to mask tokens.
np_rng: Random number genenrator. Note that this rng state should be
numpy and not python since python randint is inclusive for
the opper bound whereas the numpy one is exclusive.
"""
# We assume that we have at least two sentences in the sample
assert len(sample) > 1
assert target_seq_length <= max_seq_length
# Divide sample into two segments (A and B).
tokens_a, tokens_b, is_next_random = get_a_and_b_segments(sample, np_rng)
# Truncate to `target_sequence_length`.
max_num_tokens = target_seq_length
truncated = truncate_segments(tokens_a, tokens_b, len(tokens_a),
len(tokens_b), max_num_tokens, np_rng)
# Build tokens and toketypes.
tokens, tokentypes = create_tokens_and_tokentypes(tokens_a, tokens_b,
cls_id, sep_id)
# Masking.
max_predictions_per_seq = masked_lm_prob * max_num_tokens
(tokens, masked_positions, masked_labels, _) = create_masked_lm_predictions(
tokens, vocab_id_list, vocab_id_to_token_dict, masked_lm_prob,
cls_id, sep_id, mask_id, max_predictions_per_seq, np_rng)
# Padding.
tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np \
= pad_and_convert_to_numpy(tokens, tokentypes, masked_positions,
masked_labels, pad_id, max_seq_length)
train_sample = {
'text': tokens_np,
'types': tokentypes_np,
'labels': labels_np,
'is_random': int(is_next_random),
'loss_mask': loss_mask_np,
'padding_mask': padding_mask_np,
'truncated': int(truncated)}
return train_sample
def get_a_and_b_segments(sample, np_rng):
"""Divide sample into a and b segments."""
# Number of sentences in the sample.
n_sentences = len(sample)
# Make sure we always have two sentences.
assert n_sentences > 1, 'make sure each sample has at least two sentences.'
# First part:
# `a_end` is how many sentences go into the `A`.
a_end = 1
if n_sentences >= 3:
# Note that randin in numpy is exclusive.
a_end = np_rng.randint(1, n_sentences)
tokens_a = []
for j in range(a_end):
tokens_a.extend(sample[j])
# Second part:
tokens_b = []
for j in range(a_end, n_sentences):
tokens_b.extend(sample[j])
# Random next:
is_next_random = False
if np_rng.random() < 0.5:
is_next_random = True
tokens_a, tokens_b = tokens_b, tokens_a
return tokens_a, tokens_b, is_next_random
def truncate_segments(tokens_a, tokens_b, len_a, len_b, max_num_tokens, np_rng):
"""Truncates a pair of sequences to a maximum sequence length."""
#print(len_a, len_b, max_num_tokens)
assert len_a > 0
assert len_b > 0
if len_a + len_b <= max_num_tokens:
return False
while len_a + len_b > max_num_tokens:
if len_a > len_b:
len_a -= 1
tokens = tokens_a
else:
len_b -= 1
tokens = tokens_b
if np_rng.random() < 0.5:
del tokens[0]
else:
tokens.pop()
return True
def create_tokens_and_tokentypes(tokens_a, tokens_b, cls_id, sep_id):
"""Merge segments A and B, add [CLS] and [SEP] and build tokentypes."""
tokens = []
tokentypes = []
# [CLS].
tokens.append(cls_id)
tokentypes.append(0)
# Segment A.
for token in tokens_a:
tokens.append(token)
tokentypes.append(0)
# [SEP].
tokens.append(sep_id)
tokentypes.append(0)
# Segment B.
for token in tokens_b:
tokens.append(token)
tokentypes.append(1)
# [SEP].
tokens.append(sep_id)
tokentypes.append(1)
return tokens, tokentypes
MaskedLmInstance = collections.namedtuple("MaskedLmInstance",
["index", "label"])
def is_start_piece(piece):
"""Check if the current word piece is the starting piece (BERT)."""
# When a word has been split into
# WordPieces, the first token does not have any marker and any subsequence
# tokens are prefixed with ##. So whenever we see the ## token, we
# append it to the previous set of word indexes.
return not piece.startswith("##")
def create_masked_lm_predictions(tokens,
vocab_id_list, vocab_id_to_token_dict,
masked_lm_prob,
cls_id, sep_id, mask_id,
max_predictions_per_seq,
np_rng,
max_ngrams=3,
do_whole_word_mask=True,
favor_longer_ngram=False,
do_permutation=False):
"""Creates the predictions for the masked LM objective.
Note: Tokens here are vocab ids and not text tokens."""
cand_indexes = []
# Note(mingdachen): We create a list for recording if the piece is
# the starting piece of current token, where 1 means true, so that
# on-the-fly whole word masking is possible.
token_boundary = [0] * len(tokens)
for (i, token) in enumerate(tokens):
if token == cls_id or token == sep_id:
token_boundary[i] = 1
continue
# Whole Word Masking means that if we mask all of the wordpieces
# corresponding to an original word.
#
# Note that Whole Word Masking does *not* change the training code
# at all -- we still predict each WordPiece independently, softmaxed
# over the entire vocabulary.
if (do_whole_word_mask and len(cand_indexes) >= 1 and
not is_start_piece(vocab_id_to_token_dict[token])):
cand_indexes[-1].append(i)
else:
cand_indexes.append([i])
if is_start_piece(vocab_id_to_token_dict[token]):
token_boundary[i] = 1
output_tokens = list(tokens)
masked_lm_positions = []
masked_lm_labels = []
if masked_lm_prob == 0:
return (output_tokens, masked_lm_positions,
masked_lm_labels, token_boundary)
num_to_predict = min(max_predictions_per_seq,
max(1, int(round(len(tokens) * masked_lm_prob))))
# Note(mingdachen):
# By default, we set the probilities to favor shorter ngram sequences.
ngrams = np.arange(1, max_ngrams + 1, dtype=np.int64)
pvals = 1. / np.arange(1, max_ngrams + 1)
pvals /= pvals.sum(keepdims=True)
if favor_longer_ngram:
pvals = pvals[::-1]
ngram_indexes = []
for idx in range(len(cand_indexes)):
ngram_index = []
for n in ngrams:
ngram_index.append(cand_indexes[idx:idx + n])
ngram_indexes.append(ngram_index)
np_rng.shuffle(ngram_indexes)
masked_lms = []
covered_indexes = set()
for cand_index_set in ngram_indexes:
if len(masked_lms) >= num_to_predict:
break
if not cand_index_set:
continue
# Note(mingdachen):
# Skip current piece if they are covered in lm masking or previous ngrams.
for index_set in cand_index_set[0]:
for index in index_set:
if index in covered_indexes:
continue
n = np_rng.choice(ngrams[:len(cand_index_set)],
p=pvals[:len(cand_index_set)] /
pvals[:len(cand_index_set)].sum(keepdims=True))
index_set = sum(cand_index_set[n - 1], [])
n -= 1
# Note(mingdachen):
# Repeatedly looking for a candidate that does not exceed the
# maximum number of predictions by trying shorter ngrams.
while len(masked_lms) + len(index_set) > num_to_predict:
if n == 0:
break
index_set = sum(cand_index_set[n - 1], [])
n -= 1
# If adding a whole-word mask would exceed the maximum number of
# predictions, then just skip this candidate.
if len(masked_lms) + len(index_set) > num_to_predict:
continue
is_any_index_covered = False
for index in index_set:
if index in covered_indexes:
is_any_index_covered = True
break
if is_any_index_covered:
continue
for index in index_set:
covered_indexes.add(index)
masked_token = None
# 80% of the time, replace with [MASK]
if np_rng.random() < 0.8:
masked_token = mask_id
else:
# 10% of the time, keep original
if np_rng.random() < 0.5:
masked_token = tokens[index]
# 10% of the time, replace with random word
else:
masked_token = vocab_id_list[np_rng.randint(0, len(vocab_id_list))]
output_tokens[index] = masked_token
masked_lms.append(MaskedLmInstance(index=index, label=tokens[index]))
assert len(masked_lms) <= num_to_predict
np_rng.shuffle(ngram_indexes)
select_indexes = set()
if do_permutation:
for cand_index_set in ngram_indexes:
if len(select_indexes) >= num_to_predict:
break
if not cand_index_set:
continue
# Note(mingdachen):
# Skip current piece if they are covered in lm masking or previous ngrams.
for index_set in cand_index_set[0]:
for index in index_set:
if index in covered_indexes or index in select_indexes:
continue
n = np.random.choice(ngrams[:len(cand_index_set)],
p=pvals[:len(cand_index_set)] /
pvals[:len(cand_index_set)].sum(keepdims=True))
index_set = sum(cand_index_set[n - 1], [])
n -= 1
while len(select_indexes) + len(index_set) > num_to_predict:
if n == 0:
break
index_set = sum(cand_index_set[n - 1], [])
n -= 1
# If adding a whole-word mask would exceed the maximum number of
# predictions, then just skip this candidate.
if len(select_indexes) + len(index_set) > num_to_predict:
continue
is_any_index_covered = False
for index in index_set:
if index in covered_indexes or index in select_indexes:
is_any_index_covered = True
break
if is_any_index_covered:
continue
for index in index_set:
select_indexes.add(index)
assert len(select_indexes) <= num_to_predict
select_indexes = sorted(select_indexes)
permute_indexes = list(select_indexes)
np_rng.shuffle(permute_indexes)
orig_token = list(output_tokens)
for src_i, tgt_i in zip(select_indexes, permute_indexes):
output_tokens[src_i] = orig_token[tgt_i]
masked_lms.append(MaskedLmInstance(index=src_i, label=orig_token[src_i]))
masked_lms = sorted(masked_lms, key=lambda x: x.index)
for p in masked_lms:
masked_lm_positions.append(p.index)
masked_lm_labels.append(p.label)
return (output_tokens, masked_lm_positions, masked_lm_labels, token_boundary)
def pad_and_convert_to_numpy(tokens, tokentypes, masked_positions,
masked_labels, pad_id, max_seq_length):
"""Pad sequences and convert them to numpy."""
# Some checks.
num_tokens = len(tokens)
padding_length = max_seq_length - num_tokens
assert padding_length >= 0
assert len(tokentypes) == num_tokens
assert len(masked_positions) == len(masked_labels)
# Tokens and token types.
filler = [pad_id] * padding_length
tokens_np = np.array(tokens + filler, dtype=np.int64)
tokentypes_np = np.array(tokentypes + filler, dtype=np.int64)
# Padding mask.
padding_mask_np = np.array([1] * num_tokens + [0] * padding_length,
dtype=np.int64)
# Lables and loss mask.
labels = [-1] * max_seq_length
loss_mask = [0] * max_seq_length
for i in range(len(masked_positions)):
assert masked_positions[i] < num_tokens
labels[masked_positions[i]] = masked_labels[i]
loss_mask[masked_positions[i]] = 1
labels_np = np.array(labels, dtype=np.int64)
loss_mask_np = np.array(loss_mask, dtype=np.int64)
return tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np
================================================
FILE: CPM-Generate/data/gpt2_dataset.py
================================================
# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. 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.
"""GPT2 style dataset."""
import os
import time
import numpy as np
import torch
from utils import print_rank_0
import mpu
from data.bert_dataset import get_train_valid_test_split_
from data.indexed_dataset import make_dataset as make_indexed_dataset
def build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
train_valid_test_num_samples,
seq_length, seed, skip_warmup):
"""Build train, valid, and test datasets."""
# Indexed dataset.
indexed_dataset = get_indexed_dataset_(data_prefix,
data_impl,
skip_warmup)
total_num_of_documents = indexed_dataset.sizes.shape[0]
splits = get_train_valid_test_split_(splits_string, total_num_of_documents)
# Print stats about the splits.
print_rank_0(' > dataset split:')
def print_split_stats(name, index):
print_rank_0(' {}:'.format(name))
print_rank_0(' document indices in [{}, {}) total of {} '
'documents'.format(splits[index], splits[index + 1],
splits[index + 1] - splits[index]))
print_split_stats('train', 0)
print_split_stats('validation', 1)
print_split_stats('test', 2)
def build_dataset(index, name):
dataset = None
if splits[index + 1] > splits[index]:
documents = np.arange(start=splits[index], stop=splits[index + 1],
step=1, dtype=np.int32)
dataset = GPT2Dataset(name, data_prefix,
documents, indexed_dataset,
train_valid_test_num_samples[index],
seq_length, seed)
return dataset
train_dataset = build_dataset(0, 'train')
valid_dataset = build_dataset(1, 'valid')
test_dataset = build_dataset(2, 'test')
return (train_dataset, valid_dataset, test_dataset)
def get_indexed_dataset_(data_prefix, data_impl, skip_warmup):
"""Build indexed dataset."""
print_rank_0(' > building dataset index ...')
start_time = time.time()
indexed_dataset = make_indexed_dataset(data_prefix,
data_impl,
skip_warmup)
print_rank_0(' > finished creating indexed dataset in {:4f} '
'seconds'.format(time.time() - start_time))
print_rank_0(' number of documents: {}'.format(
indexed_dataset.sizes.shape[0]))
return indexed_dataset
class GPT2Dataset(torch.utils.data.Dataset):
def __init__(self, name, data_prefix, documents, indexed_dataset,
num_samples, seq_length, seed):
self.name = name
self.indexed_dataset = indexed_dataset
# Checks
assert np.min(documents) >= 0
assert np.max(documents) < indexed_dataset.sizes.shape[0]
# Build index mappings.
self.doc_idx, self.sample_idx, self.shuffle_idx = _build_index_mappings(
self.name, data_prefix, documents, self.indexed_dataset.sizes,
num_samples, seq_length, seed)
def __len__(self):
# -1 is due to data structure used to retieve the index:
# sample i --> [sample_idx[i], sample_idx[i+1])
return self.sample_idx.shape[0] - 1
def __getitem__(self, idx):
# Get the shuffled index.
idx = self.shuffle_idx[idx]
# Start and end documents and offsets.
doc_index_f = self.sample_idx[idx][0]
doc_index_l = self.sample_idx[idx + 1][0]
offset_f = self.sample_idx[idx][1]
offset_l = self.sample_idx[idx + 1][1]
# If we are within the same document, just extract the chunk.
if doc_index_f == doc_index_l:
sample = self.indexed_dataset.get(self.doc_idx[doc_index_f],
offset=offset_f,
length=offset_l - offset_f + 1)
else:
# Otherwise, get the rest of the initial document.
sample_list = [self.indexed_dataset.get(self.doc_idx[doc_index_f],
offset=offset_f)]
# Loop over all in between documents and add the entire document.
for i in range(doc_index_f + 1, doc_index_l):
sample_list.append(self.indexed_dataset.get(self.doc_idx[i]))
# And finally add the relevant portion of last document.
sample_list.append(self.indexed_dataset.get(
self.doc_idx[doc_index_l],
length=offset_l + 1))
sample = np.concatenate(sample_list)
return {'text': np.array(sample, dtype=np.int64)}
def _build_index_mappings(name, data_prefix, documents, sizes,
num_samples, seq_length, seed):
"""Build doc-idx, sample-idx, and shuffle-idx.
doc-idx: is an array (ordered) of documents to be used in training.
sample-idx: is the start document index and document offset for each
training sample.
shuffle-idx: maps the sample index into a random index into sample-idx.
"""
# Number of tokens in each epoch and number of required epochs.
tokens_per_epoch = _num_tokens(documents, sizes)
num_epochs = _num_epochs(tokens_per_epoch, seq_length, num_samples)
# rng state
np_rng = np.random.RandomState(seed=seed)
# Filename of the index mappings.
_filename = data_prefix
_filename += '_{}_indexmap'.format(name)
_filename += '_{}ns'.format(num_samples)
_filename += '_{}sl'.format(seq_length)
_filename += '_{}s'.format(seed)
doc_idx_filename = _filename + '_doc_idx.npy'
sample_idx_filename = _filename + '_sample_idx.npy'
shuffle_idx_filename = _filename + '_shuffle_idx.npy'
# Build the indexed mapping if not exist.
if torch.distributed.get_rank() == 0:
if (not os.path.isfile(doc_idx_filename)) or \
(not os.path.isfile(sample_idx_filename)) or \
(not os.path.isfile(shuffle_idx_filename)):
print_rank_0(' > WARNING: could not find index map files, building '
'the indices on rank 0 ...')
# doc-idx.
start_time = time.time()
doc_idx = _build_doc_idx(documents, num_epochs, np_rng)
np.save(doc_idx_filename, doc_idx, allow_pickle=True)
print_rank_0(' > elasped time to build and save doc-idx mapping '
'(seconds): {:4f}'.format(time.time() - start_time))
# sample-idx.
start_time = time.time()
# Use C++ implementation for speed.
# First compile and then import.
from data.dataset_utils import compile_helper
compile_helper()
from data import helpers
assert doc_idx.dtype == np.int32
assert sizes.dtype == np.int32
sample_idx = helpers.build_sample_idx(sizes, doc_idx, seq_length,
num_epochs, tokens_per_epoch)
# sample_idx = _build_sample_idx(sizes, doc_idx, seq_length,
# num_epochs, tokens_per_epoch)
np.save(sample_idx_filename, sample_idx, allow_pickle=True)
print_rank_0(' > elasped time to build and save sample-idx mapping '
'(seconds): {:4f}'.format(time.time() - start_time))
# shuffle-idx.
start_time = time.time()
# -1 is due to data structure used to retieve the index:
# sample i --> [sample_idx[i], sample_idx[i+1])
shuffle_idx = _build_shuffle_idx(sample_idx.shape[0] - 1, np_rng)
np.save(shuffle_idx_filename, shuffle_idx, allow_pickle=True)
print_rank_0(' > elasped time to build and save shuffle-idx mapping'
' (seconds): {:4f}'.format(time.time() - start_time))
# This should be a barrier but nccl barrier assumes
# device_index=rank which is not the case for model
# parallel case
counts = torch.cuda.LongTensor([1])
torch.distributed.all_reduce(counts, group=mpu.get_data_parallel_group())
assert counts[0].item() == torch.distributed.get_world_size(
group=mpu.get_data_parallel_group())
# Load mappings.
start_time = time.time()
print_rank_0(' > loading doc-idx mapping from {}'.format(
doc_idx_filename))
doc_idx = np.load(doc_idx_filename, allow_pickle=True, mmap_mode='r')
print_rank_0(' > loading sample-idx mapping from {}'.format(
sample_idx_filename))
sample_idx = np.load(sample_idx_filename, allow_pickle=True, mmap_mode='r')
print_rank_0(' > loading shuffle-idx mapping from {}'.format(
shuffle_idx_filename))
shuffle_idx = np.load(shuffle_idx_filename, allow_pickle=True, mmap_mode='r')
print_rank_0(' loaded indexed file in {:3.3f} seconds'.format(
time.time() - start_time))
print_rank_0(' total number of samples: {}'.format(
sample_idx.shape[0]))
print_rank_0(' total number of epochs: {}'.format(num_epochs))
return doc_idx, sample_idx, shuffle_idx
def _num_tokens(documents, sizes):
"""Total number of tokens in the dataset."""
return np.sum(sizes[documents])
def _num_epochs(tokens_per_epoch, seq_length, num_samples):
"""Based on number of samples and sequence lenght, calculate how many
epochs will be needed."""
num_epochs = 0
total_tokens = 0
while True:
num_epochs += 1
total_tokens += tokens_per_epoch
# -1 is because we need to retrieve seq_length + 1 token each time
# but the last token will overlap with the first token of the next
# sample except for the last sample.
if ((total_tokens - 1) // seq_length) >= num_samples:
return num_epochs
def _build_doc_idx(documents, num_epochs, np_rng):
"""Build an array with length = number-of-epochs * number-of-dcuments.
Each index is mapped to a corresponding document."""
doc_idx = np.mgrid[0:num_epochs, 0:len(documents)][1]
doc_idx[:] = documents
doc_idx = doc_idx.reshape(-1)
doc_idx = doc_idx.astype(np.int32)
np_rng.shuffle(doc_idx)
return doc_idx
def _build_sample_idx(sizes, doc_idx, seq_length,
num_epochs, tokens_per_epoch):
"""Sample index mapping is a 2D array with sizes
[number-of-samples + 1, 2] where [..., 0] contains
the index into `doc_idx` and [..., 1] is the
starting offset in that document."""
# Total number of samples. For -1 see comments in `_num_epochs`.
num_samples = (num_epochs * tokens_per_epoch - 1) // seq_length
sample_idx = np.zeros([num_samples + 1, 2], dtype=np.int32)
# Index into sample_idx.
sample_index = 0
# Index into doc_idx.
doc_idx_index = 0
# Begining offset for each document.
doc_offset = 0
# Start with first document and no offset.
sample_idx[sample_index][0] = doc_idx_index
sample_idx[sample_index][1] = doc_offset
sample_index += 1
while sample_index <= num_samples:
# Start with a fresh sequence.
remaining_seq_length = seq_length + 1
while remaining_seq_length != 0:
# Get the document length.
doc_id = doc_idx[doc_idx_index]
doc_length = sizes[doc_id] - doc_offset
# And add it to the current sequence.
remaining_seq_length -= doc_length
# If we have more than a full sequence, adjust offset and set
# remaining length to zero so we return from the while loop.
# Note that -1 here is for the same reason we have -1 in
# `_num_epochs` calculations.
if remaining_seq_length <= 0:
doc_offset += (remaining_seq_length + doc_length - 1)
remaining_seq_length = 0
else:
# Otherwise, start from the begining of the next document.
doc_idx_index += 1
doc_offset = 0
# Record the sequence.
sample_idx[sample_index][0] = doc_idx_index
sample_idx[sample_index][1] = doc_offset
sample_index += 1
return sample_idx
def _build_shuffle_idx(size, np_rng):
"""Build the range [0, size) and shuffle."""
dtype_ = np.uint32
if size >= (np.iinfo(np.uint32).max - 1):
dtype_ = np.int64
shuffle_idx = np.arange(start=0, stop=size, step=1, dtype=dtype_)
np_rng.shuffle(shuffle_idx)
return shuffle_idx
================================================
FILE: CPM-Generate/data/helpers.cpp
================================================
/*
coding=utf-8
Copyright (c) 2020, NVIDIA CORPORATION. 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.
*/
/* Helper methods for fast index mapping builds */
#include
#include
#include
#include
#include
#include
#include
#include
namespace py = pybind11;
using namespace std;
const int32_t LONG_SENTENCE_LEN = 512;
py::array build_sample_idx(const py::array_t& sizes_,
const py::array_t& doc_idx_,
const int32_t seq_length,
const int32_t num_epochs,
const int64_t tokens_per_epoch) {
/* Sample index (sample_idx) is used for gpt2 like dataset for which
the documents are flattened and the samples are built based on this
1-D flatten array. It is a 2D array with sizes [number-of-samples + 1, 2]
where [..., 0] contains the index into `doc_idx` and [..., 1] is the
starting offset in that document.*/
// Consistency checks.
assert(seq_length > 1);
assert(num_epochs > 0);
assert(tokens_per_epoch > 1);
// Remove bound checks.
auto sizes = sizes_.unchecked<1>();
auto doc_idx = doc_idx_.unchecked<1>();
// Mapping and it's length (1D).
int64_t num_samples = (num_epochs * tokens_per_epoch - 1) / seq_length;
int32_t* sample_idx = new int32_t[2*(num_samples+1)];
cout << " using:" << endl << std::flush;
cout << " number of documents: " <<
doc_idx_.shape(0) / num_epochs << endl << std::flush;
cout << " number of epochs: " << num_epochs <<
endl << std::flush;
cout << " sequence length: " << seq_length <<
endl << std::flush;
cout << " total number of samples: " << num_samples <<
endl << std::flush;
// Index into sample_idx.
int64_t sample_index = 0;
// Index into doc_idx.
int64_t doc_idx_index = 0;
// Begining offset for each document.
int32_t doc_offset = 0;
// Start with first document and no offset.
sample_idx[2 * sample_index] = doc_idx_index;
sample_idx[2 * sample_index + 1] = doc_offset;
++sample_index;
while (sample_index <= num_samples) {
// Start with a fresh sequence.
int32_t remaining_seq_length = seq_length + 1;
while (remaining_seq_length != 0) {
// Get the document length.
auto doc_id = doc_idx[doc_idx_index];
auto doc_length = sizes[doc_id] - doc_offset;
// And add it to the current sequence.
remaining_seq_length -= doc_length;
// If we have more than a full sequence, adjust offset and set
// remaining length to zero so we return from the while loop.
// Note that -1 here is for the same reason we have -1 in
// `_num_epochs` calculations.
if (remaining_seq_length <= 0) {
doc_offset += (remaining_seq_length + doc_length - 1);
remaining_seq_length = 0;
} else {
// Otherwise, start from the begining of the next document.
++doc_idx_index;
doc_offset = 0;
}
}
// Record the sequence.
sample_idx[2 * sample_index] = doc_idx_index;
sample_idx[2 * sample_index + 1] = doc_offset;
++sample_index;
}
// Method to deallocate memory.
py::capsule free_when_done(sample_idx, [](void *mem_) {
int32_t *mem = reinterpret_cast(mem_);
delete[] mem;
});
// Return the numpy array.
const auto byte_size = sizeof(int32_t);
return py::array(std::vector{num_samples+1, 2}, // shape
{2*byte_size, byte_size}, // C-style contiguous strides
sample_idx, // the data pointer
free_when_done); // numpy array references
}
inline int32_t get_target_sample_len(const int32_t short_seq_ratio,
const int32_t max_length,
std::mt19937& rand32_gen) {
/* Training sample length. */
const auto random_number = rand32_gen();
if ((random_number % short_seq_ratio) == 0) {
return 2 + random_number % (max_length - 1);
}
return max_length;
}
template
py::array build_mapping_impl(const py::array_t& docs_,
const py::array_t& sizes_,
const int32_t num_epochs,
const uint64_t max_num_samples,
const int32_t max_seq_length,
const double short_seq_prob,
const int32_t seed,
const bool verbose) {
/* Build a mapping of (start-index, end-index, sequence-length) where
start and end index are the indices of the sentences in the sample
and sequence-length is the target sequence length.
*/
// Consistency checks.
assert(num_epochs > 0);
assert(max_seq_length > 1);
assert(short_seq_prob > 0.0);
assert(short_seq_prob <= 1.0);
assert(seed > 0);
// Remove bound checks.
auto docs = docs_.unchecked<1>();
auto sizes = sizes_.unchecked<1>();
// For efficiency, convert probability to ratio. Note: rand() generates int.
const auto short_seq_ratio = static_cast(round(1.0 / short_seq_prob));
if (verbose) {
const auto sent_start_index = docs[0];
const auto sent_end_index = docs[docs_.shape(0) - 1];
const auto num_sentences = sent_end_index - sent_start_index;
cout << " using:" << endl << std::flush;
cout << " number of documents: " << docs_.shape(0) - 1 <<
endl << std::flush;
cout << " sentences range: [" << sent_start_index <<
", " << sent_end_index << ")" << endl << std::flush;
cout << " total number of sentences: " << num_sentences <<
endl << std::flush;
cout << " number of epochs: " << num_epochs <<
endl << std::flush;
cout << " maximum number of samples: " << max_num_samples <<
endl << std::flush;
cout << " maximum sequence length: " << max_seq_length <<
endl << std::flush;
cout << " short sequence probability: " << short_seq_prob <<
endl << std::flush;
cout << " short sequence ration (1/prob): " << short_seq_ratio <<
endl << std::flush;
cout << " seed: " << seed << endl <<
std::flush;
}
// Mapping and it's length (1D).
int64_t num_samples = -1;
DocIdx* maps = NULL;
// Perform two iterations, in the first iteration get the size
// and allocate memory and in the second iteration populate the map.
bool second = false;
for (int32_t iteration=0; iteration<2; ++iteration) {
// Set the seed so both iterations produce the same results.
std::mt19937 rand32_gen(seed);
// Set the flag on second iteration.
second = (iteration == 1);
// Counters:
uint64_t empty_docs = 0;
uint64_t one_sent_docs = 0;
uint64_t long_sent_docs = 0;
// Current map index.
uint64_t map_index = 0;
// For each epoch:
for (int32_t epoch=0; epoch= max_num_samples) {
if (verbose && (!second)) {
cout << " reached " << max_num_samples << " samples after "
<< epoch << " epochs ..." << endl << std::flush;
}
break;
}
// For each document:
for (int32_t doc=0; doc<(docs.shape(0) - 1); ++doc) {
// Document sentences are in [sent_index_first, sent_index_last)
const auto sent_index_first = docs[doc];
const auto sent_index_last = docs[doc + 1];
// At the begining of the document previous index is the
// start index.
auto prev_start_index = sent_index_first;
// Remaining documents.
auto num_remain_sent = sent_index_last - sent_index_first;
// Some bookkeeping
if ((epoch == 0) && (!second)) {
if (num_remain_sent == 0) {
++empty_docs;
}
if (num_remain_sent == 1) {
++one_sent_docs;
}
}
// Detect documents with long sentences.
bool contains_long_sentence = false;
if (num_remain_sent > 1) {
for (auto sent_index=sent_index_first;
sent_index < sent_index_last; ++sent_index) {
if (sizes[sent_index] > LONG_SENTENCE_LEN){
if ((epoch == 0) && (!second)) {
++long_sent_docs;
}
contains_long_sentence = true;
break;
}
}
}
// If we have more than two sentences.
if ((num_remain_sent > 1) && (!contains_long_sentence)) {
// Set values.
auto seq_len = int32_t{0};
auto num_sent = int32_t{0};
auto target_seq_len = get_target_sample_len(short_seq_ratio,
max_seq_length,
rand32_gen);
// Loop through sentences.
for (auto sent_index=sent_index_first;
sent_index < sent_index_last; ++sent_index) {
// Add the size and number of sentences.
seq_len += sizes[sent_index];
++num_sent;
--num_remain_sent;
// If we have reached the target length.
// and if not only one sentence is left in the document.
// and if we have at least two sentneces.
// and if we have reached end of the document.
if (((seq_len >= target_seq_len) &&
(num_remain_sent > 1) &&
(num_sent > 1) ) || (num_remain_sent == 0)) {
// Check for overflow.
if ((3 * map_index + 2) >
std::numeric_limits::max()) {
cout << "number of samples exceeded maximum "
<< "allowed by type int64: "
<< std::numeric_limits::max()
<< endl;
throw std::overflow_error("Number of samples");
}
// Populate the map.
if (second) {
const auto map_index_0 = 3 * map_index;
maps[map_index_0] = static_cast(prev_start_index);
maps[map_index_0 + 1] = static_cast(sent_index + 1);
maps[map_index_0 + 2] = static_cast(target_seq_len);
}
// Update indices / counters.
++map_index;
prev_start_index = sent_index + 1;
target_seq_len = get_target_sample_len(short_seq_ratio,
max_seq_length,
rand32_gen);
seq_len = 0;
num_sent = 0;
}
} // for (auto sent_index=sent_index_first; ...
} // if (num_remain_sent > 1) {
} // for (int doc=0; doc < num_docs; ++doc) {
} // for (int epoch=0; epoch < num_epochs; ++epoch) {
if (!second) {
if (verbose) {
cout << " number of empty documents: " << empty_docs <<
endl << std::flush;
cout << " number of documents with one sentence: " <<
one_sent_docs << endl << std::flush;
cout << " number of documents with long sentences: " <<
long_sent_docs << endl << std::flush;
cout << " will create mapping for " << map_index <<
" samples" << endl << std::flush;
}
assert(maps == NULL);
assert(num_samples < 0);
maps = new DocIdx[3*map_index];
num_samples = static_cast(map_index);
}
} // for (int iteration=0; iteration < 2; ++iteration) {
// Shuffle.
// We need a 64 bit random number generator as we might have more
// than 2 billion samples.
std::mt19937_64 rand64_gen(seed + 1);
for (auto i=(num_samples - 1); i > 0; --i) {
const auto j = static_cast(rand64_gen() % (i + 1));
const auto i0 = 3 * i;
const auto j0 = 3 * j;
// Swap values.
swap(maps[i0], maps[j0]);
swap(maps[i0 + 1], maps[j0 + 1]);
swap(maps[i0 + 2], maps[j0 + 2]);
}
// Method to deallocate memory.
py::capsule free_when_done(maps, [](void *mem_) {
DocIdx *mem = reinterpret_cast(mem_);
delete[] mem;
});
// Return the numpy array.
const auto byte_size = sizeof(DocIdx);
return py::array(std::vector{num_samples, 3}, // shape
{3*byte_size, byte_size}, // C-style contiguous strides
maps, // the data pointer
free_when_done); // numpy array references
}
py::array build_mapping(const py::array_t& docs_,
const py::array_t& sizes_,
const int num_epochs,
const uint64_t max_num_samples,
const int max_seq_length,
const double short_seq_prob,
const int seed,
const bool verbose) {
if (sizes_.size() > std::numeric_limits::max()) {
if (verbose) {
cout << " using uint64 for data mapping..." << endl << std::flush;
}
return build_mapping_impl(docs_, sizes_, num_epochs,
max_num_samples, max_seq_length,
short_seq_prob, seed, verbose);
} else {
if (verbose) {
cout << " using uint32 for data mapping..." << endl << std::flush;
}
return build_mapping_impl(docs_, sizes_, num_epochs,
max_num_samples, max_seq_length,
short_seq_prob, seed, verbose);
}
}
PYBIND11_MODULE(helpers, m) {
m.def("build_mapping", &build_mapping);
m.def("build_sample_idx", &build_sample_idx);
}
================================================
FILE: CPM-Generate/data/indexed_dataset.py
================================================
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# copied from fairseq/fairseq/data/indexed_dataset.py
# Removed IndexedRawTextDataset since it relied on Fairseq dictionary
# other slight modifications to remove fairseq dependencies
# Added document index to index file and made it accessible.
# An empty sentence no longer separates documents.
from functools import lru_cache
import os
import shutil
import struct
from itertools import accumulate
import numpy as np
import torch
from utils import print_rank_0
def __best_fitting_dtype(vocab_size=None):
if vocab_size is not None and vocab_size < 65500:
return np.uint16
else:
return np.int32
def get_available_dataset_impl():
return ['lazy', 'cached', 'mmap']
def infer_dataset_impl(path):
if IndexedDataset.exists(path):
with open(index_file_path(path), 'rb') as f:
magic = f.read(8)
if magic == IndexedDataset._HDR_MAGIC:
return 'cached'
elif magic == MMapIndexedDataset.Index._HDR_MAGIC[:8]:
return 'mmap'
else:
return None
else:
print(f"Dataset does not exist: {path}")
print("Path should be a basename that both .idx and .bin can be appended to get full filenames.")
return None
def make_builder(out_file, impl, vocab_size=None):
if impl == 'mmap':
return MMapIndexedDatasetBuilder(out_file, dtype=__best_fitting_dtype(vocab_size))
else:
return IndexedDatasetBuilder(out_file)
def make_dataset(path, impl, skip_warmup=False):
if not IndexedDataset.exists(path):
print(f"Dataset does not exist: {path}")
print("Path should be a basename that both .idx and .bin can be appended to get full filenames.")
return None
if impl == 'infer':
impl = infer_dataset_impl(path)
if impl == 'lazy' and IndexedDataset.exists(path):
return IndexedDataset(path)
elif impl == 'cached' and IndexedDataset.exists(path):
return IndexedCachedDataset(path)
elif impl == 'mmap' and MMapIndexedDataset.exists(path):
return MMapIndexedDataset(path, skip_warmup)
print(f"Unknown dataset implementation: {impl}")
return None
def dataset_exists(path, impl):
if impl == 'mmap':
return MMapIndexedDataset.exists(path)
else:
return IndexedDataset.exists(path)
def read_longs(f, n):
a = np.empty(n, dtype=np.int64)
f.readinto(a)
return a
def write_longs(f, a):
f.write(np.array(a, dtype=np.int64))
dtypes = {
1: np.uint8,
2: np.int8,
3: np.int16,
4: np.int32,
5: np.int64,
6: np.float,
7: np.double,
8: np.uint16
}
def code(dtype):
for k in dtypes.keys():
if dtypes[k] == dtype:
return k
raise ValueError(dtype)
def index_file_path(prefix_path):
return prefix_path + '.idx'
def data_file_path(prefix_path):
return prefix_path + '.bin'
def create_doc_idx(sizes):
doc_idx = [0]
for i, s in enumerate(sizes):
if s == 0:
doc_idx.append(i + 1)
return doc_idx
class IndexedDataset(torch.utils.data.Dataset):
"""Loader for IndexedDataset"""
_HDR_MAGIC = b'TNTIDX\x00\x00'
def __init__(self, path):
super().__init__()
self.path = path
self.data_file = None
self.read_index(path)
def read_index(self, path):
with open(index_file_path(path), 'rb') as f:
magic = f.read(8)
assert magic == self._HDR_MAGIC, (
'Index file doesn\'t match expected format. '
'Make sure that --dataset-impl is configured properly.'
)
version = f.read(8)
assert struct.unpack('= self._len:
raise IndexError('index out of range')
def __del__(self):
if self.data_file:
self.data_file.close()
# @lru_cache(maxsize=8)
def __getitem__(self, idx):
if not self.data_file:
self.read_data(self.path)
if isinstance(idx, int):
i = idx
self.check_index(i)
tensor_size = self.sizes[self.dim_offsets[i]:self.dim_offsets[i + 1]]
a = np.empty(tensor_size, dtype=self.dtype)
self.data_file.seek(self.data_offsets[i] * self.element_size)
self.data_file.readinto(a)
return a
elif isinstance(idx, slice):
start, stop, step = idx.indices(len(self))
if step != 1:
raise ValueError("Slices into indexed_dataset must be contiguous")
sizes = self.sizes[self.dim_offsets[start]:self.dim_offsets[stop]]
size = sum(sizes)
a = np.empty(size, dtype=self.dtype)
self.data_file.seek(self.data_offsets[start] * self.element_size)
self.data_file.readinto(a)
offsets = list(accumulate(sizes))
sents = np.split(a, offsets[:-1])
return sents
def __len__(self):
return self._len
def num_tokens(self, index):
return self.sizes[index]
def size(self, index):
return self.sizes[index]
@staticmethod
def exists(path):
return (
os.path.exists(index_file_path(path)) and os.path.exists(data_file_path(path))
)
@property
def supports_prefetch(self):
return False # avoid prefetching to save memory
class IndexedCachedDataset(IndexedDataset):
def __init__(self, path):
super().__init__(path)
self.cache = None
self.cache_index = {}
@property
def supports_prefetch(self):
return True
def prefetch(self, indices):
if all(i in self.cache_index for i in indices):
return
if not self.data_file:
self.read_data(self.path)
indices = sorted(set(indices))
total_size = 0
for i in indices:
total_size += self.data_offsets[i + 1] - self.data_offsets[i]
self.cache = np.empty(total_size, dtype=self.dtype)
ptx = 0
self.cache_index.clear()
for i in indices:
self.cache_index[i] = ptx
size = self.data_offsets[i + 1] - self.data_offsets[i]
a = self.cache[ptx: ptx + size]
self.data_file.seek(self.data_offsets[i] * self.element_size)
self.data_file.readinto(a)
ptx += size
if self.data_file:
# close and delete data file after prefetch so we can pickle
self.data_file.close()
self.data_file = None
# @lru_cache(maxsize=8)
def __getitem__(self, idx):
if isinstance(idx, int):
i = idx
self.check_index(i)
tensor_size = self.sizes[self.dim_offsets[i]:self.dim_offsets[i + 1]]
a = np.empty(tensor_size, dtype=self.dtype)
ptx = self.cache_index[i]
np.copyto(a, self.cache[ptx: ptx + a.size])
return a
elif isinstance(idx, slice):
# Hack just to make this work, can optimizer later if necessary
sents = []
for i in range(*idx.indices(len(self))):
sents.append(self[i])
return sents
class IndexedDatasetBuilder(object):
element_sizes = {
np.uint8: 1,
np.int8: 1,
np.int16: 2,
np.int32: 4,
np.int64: 8,
np.float: 4,
np.double: 8
}
def __init__(self, out_file, dtype=np.int32):
self.out_file = open(out_file, 'wb')
self.dtype = dtype
self.data_offsets = [0]
self.dim_offsets = [0]
self.sizes = []
self.element_size = self.element_sizes[self.dtype]
self.doc_idx = [0]
def add_item(self, tensor):
bytes = self.out_file.write(np.array(tensor.numpy(), dtype=self.dtype))
self.data_offsets.append(self.data_offsets[-1] + bytes / self.element_size)
for s in tensor.size():
self.sizes.append(s)
self.dim_offsets.append(self.dim_offsets[-1] + len(tensor.size()))
def end_document(self):
self.doc_idx.append(len(self.sizes))
def merge_file_(self, another_file):
index = IndexedDataset(another_file)
assert index.dtype == self.dtype
begin = self.data_offsets[-1]
for offset in index.data_offsets[1:]:
self.data_offsets.append(begin + offset)
self.sizes.extend(index.sizes)
begin = self.dim_offsets[-1]
for dim_offset in index.dim_offsets[1:]:
self.dim_offsets.append(begin + dim_offset)
with open(data_file_path(another_file), 'rb') as f:
while True:
data = f.read(1024)
if data:
self.out_file.write(data)
else:
break
def finalize(self, index_file):
self.out_file.close()
index = open(index_file, 'wb')
index.write(b'TNTIDX\x00\x00')
index.write(struct.pack('= 0:
g.manual_seed(self.epoch)
if self.replacement:
return iter(torch.randint(high=n, size=(self.num_samples,),
dtype=torch.int64, generator=g).tolist())
return iter(torch.randperm(n, generator=g).tolist())
def __len__(self):
return self.num_samples
def set_epoch(self, epoch):
self.epoch = epoch
class DistributedBatchSampler(data.sampler.BatchSampler):
"""Similar to normal implementation of distributed sampler, except
implementation is at the batch sampler level, instead of just the
sampler level. This allows wrapping of arbitrary data samplers
(sequential, random, WeightedRandomSampler, etc.) with this batch
sampler.
The `interleave` argument specifies how to distribute a batch. A value
of True combined with the above random sampler is equivalent to pytorch's
torch.utils.data.distributed.DistributedSampler.
For the following batch [0,1,2,3,4,5,6,7] and data parallelism of 2
specifying True will result in the following samples for each gpu:
GPU0: [0,2,4,6] GPU1: [1,3,5,7]
specifying False will result in the following samples:
GPU0: [0,1,2,3] GPU1: [4,5,6,7]"""
def __init__(self, sampler, batch_size, drop_last, rank=-1,
world_size=2, wrap_last=False, interleave=False):
super(DistributedBatchSampler, self).__init__(sampler, batch_size,
drop_last)
if rank == -1:
assert False, 'should not be here'
rank = torch.distributed.get_rank()
self.rank = rank
self.world_size = world_size
self.sampler.wrap_around = 0
self.wrap_around = 0
self.wrap_last = wrap_last
self.start_iter = 0
self.interleave = interleave
def __iter__(self):
batch = []
i = 0
for idx in self.data_iterator(self.sampler, wrap_around=False):
batch.append(idx)
if len(batch) == self.batch_size:
tbatch = self._batch(batch)
if i >= self.start_iter:
yield tbatch
self.start_iter = 0
i += 1
batch = []
batch_len = len(batch)
if batch_len > 0 and not self.drop_last:
if self.wrap_last:
self.sampler.wrap_around -= (self.batch_size)
self.wrap_around += (len(batch))
self.wrap_around %= self.batch_size
yield self._batch(batch)
if self.wrap_last:
self.sampler.wrap_around += self.batch_size
def data_iterator(self, _iter, wrap_around=False):
"""iterates through data and handles wrap around"""
for i, idx in enumerate(_iter):
if i < self.wrap_around % self.batch_size:
continue
if wrap_around:
self.wrap_around += 1
self.wrap_around %= self.batch_size
yield idx
def _batch(self, batch):
"""extracts samples only pertaining to this worker's batch"""
if self.interleave:
return batch[self.rank:self.batch_size:self.world_size]
start = self.rank * self.batch_size // self.world_size
end = (self.rank + 1) * self.batch_size // self.world_size
return batch[start:end]
================================================
FILE: CPM-Generate/data/test/test_indexed_dataset.py
================================================
# This file isn't really a formal automated test, it's just a place to
# put some code used during development and manual testing of
# indexed_dataset.
from megatron.data import indexed_dataset
from megatron.tokenizer import build_tokenizer
import argparse
import os
import sys
import torch
script_dir = os.path.dirname(os.path.realpath(__file__))
sys.path.append(os.path.join(script_dir, "../../../"))
def test_indexed_dataset(args):
ds = indexed_dataset.make_dataset(args.data, args.dataset_impl)
tokenizer = build_tokenizer(args)
print(len(ds.doc_idx))
print(len(ds))
print(ds.doc_idx[-1])
if ds.supports_prefetch:
# just prefetch the whole thing in test (so assume it is small)
ds.prefetch(range(len(ds)))
if args.count > len(ds.doc_idx) - 1:
args.count = len(ds.doc_idx) - 1
for i in range(args.count):
start = ds.doc_idx[i]
end = ds.doc_idx[i + 1]
ids = ds[start:end]
print(f"Document {i}:")
print("--------------")
for s in ids:
assert len(s) > 0
l = s.data.tolist()
text = tokenizer.detokenize(l)
print(text)
print("---")
def test_indexed_dataset_get(args):
ds = indexed_dataset.make_dataset(args.data, args.dataset_impl)
tokenizer = build_tokenizer(args)
size = ds.sizes[0]
print(f"size: {size}")
full = ds.get(0)
print(full)
# print(tokenizer.detokenize(full.data.tolist()))
print("---")
end = ds.get(0, offset=size - 10)
print(end)
# print(tokenizer.detokenize(end.data.tolist()))
start = ds.get(0, length=10)
print(start)
# print(tokenizer.detokenize(start.data.tolist()))
part = ds.get(0, offset=2, length=8)
print(part)
# print(tokenizer.detokenize(part.data.tolist()))
# def test_albert_dataset(args):
# # tokenizer = FullBertTokenizer(args.vocab, do_lower_case=True)
# # idataset = indexed_dataset.make_dataset(args.data, args.dataset_impl)
# # ds = AlbertDataset(idataset, tokenizer)
# ds = AlbertDataset.from_paths(args.vocab, args.data, args.dataset_impl,
# args.epochs, args.max_num_samples,
# args.masked_lm_prob, args.seq_length,
# args.short_seq_prob, args.seed)
# truncated = 0
# total = 0
# for i, s in enumerate(ds):
# ids = s['text']
# tokens = ds.tokenizer.convert_ids_to_tokens(ids)
# print(tokens)
# if i >= args.count-1:
# exit()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, help='prefix to data files')
parser.add_argument('--dataset-impl', type=str, default='infer',
choices=['lazy', 'cached', 'mmap', 'infer'])
parser.add_argument('--count', type=int, default=10,
help='Number of samples/documents to print')
group = parser.add_argument_group(title='tokenizer')
group.add_argument('--tokenizer-type', type=str, required=True,
choices=['BertWordPieceLowerCase',
'GPT2BPETokenizer'],
help='What type of tokenizer to use.')
group.add_argument('--vocab-file', type=str, default=None,
help='Path to the vocab file')
group.add_argument('--merge-file', type=str, default=None,
help='Path to the BPE merge file (if necessary).')
parser.add_argument('--epochs', type=int, default=5,
help='Number of epochs to plan for')
parser.add_argument('--max-num-samples', type=int, default=None,
help='Maximum number of samples to plan for')
parser.add_argument('--masked-lm-prob', type=float, default=0.15,
help='probability of masking tokens')
parser.add_argument('--seq-length', type=int, default=512,
help='maximum sequence length')
parser.add_argument('--short-seq-prob', type=float, default=0.1,
help='probability of creating a short sequence')
parser.add_argument('--seed', type=int, default=1234,
help='random seed')
args = parser.parse_args()
args.rank = 0
args.make_vocab_size_divisible_by = 128
args.model_parallel_size = 1
if args.dataset_impl == "infer":
args.dataset_impl = indexed_dataset.infer_dataset_impl(args.data)
# test_albert_dataset(args)
test_indexed_dataset_get(args)
if __name__ == "__main__":
main()
================================================
FILE: CPM-Generate/data/test/test_preprocess_data.sh
================================================
#!/bin/bash
IMPL=cached
python ../preprocess_data.py \
--input test_samples.json \
--vocab vocab.txt \
--dataset-impl ${IMPL} \
--output-prefix test_samples_${IMPL} \
--workers 1 \
--log-interval 2
================================================
FILE: CPM-Generate/data_utils/__init__.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
"""utils for creating datasets"""
import os
import math
from .samplers import DistributedBatchSampler
from .datasets import json_dataset, csv_dataset, split_ds, ConcatDataset, SplitDataset, bert_sentencepair_dataset, GPT2Dataset
from .lazy_loader import exists_lazy, make_lazy, lazy_array_loader
from .tokenization import Tokenization, CommandToken, Tokenizer, CharacterLevelTokenizer, BertWordPieceTokenizer, GPT2BPETokenizer, make_tokenizer
from . import corpora
TRAIN_DATA = 0
VAL_DATA = 1
TEST_DATA = 2
def should_split(split):
"""
given split proportions checks if should split
Examples:
>>> should_split([10,0,0])
False
>>> should_split([1,.1,.2])
True
"""
return max(split)/sum(split) != 1.
def get_ext(path):
"""gets path extension"""
return os.path.splitext(path)[1]
def get_dataset(path, **kwargs):
"""gets dataset object based on keyword args and file at `path`"""
if supported_corpus(path):
return corpora.NAMED_CORPORA[path](**kwargs)
ext = get_ext(path)
if '.json' in ext:
text = json_dataset(path, **kwargs)
elif ext in ['.csv', '.tsv']:
text = csv_dataset(path, **kwargs)
else:
raise NotImplementedError('data file type %s is not supported'%(ext))
return text
def supported_corpus(corpus_name):
"""checks if corpus name is defined in `corpora.py`"""
return corpus_name in corpora.NAMED_CORPORA
def make_dataset(path, seq_length, text_key, label_key, lazy=False, process_fn=None, split=[1.],
delim=',', loose=False, binarize_sent=False, drop_unlabeled=False, tokenizer=None,
tokenizer_type='CharacterLevelTokenizer', tokenizer_model_path=None, vocab_size=None,
model_type='bpe', pad_token=0, character_converage=1.0, non_binary_cols=None, **kwargs):
"""function to create datasets+tokenizers for common options"""
if isinstance(process_fn, str):
process_fn = eval(process_fn)
if non_binary_cols is not None:
# multilabel dataset support (only for csvs)
label_key = non_binary_cols
def get_dataset_from_path(path_):
if lazy:
# get lazily loaded dataset
named_corpora = False
if supported_corpus(path_):
named_corpora = True
name = path_
path_ = corpora.NAMED_CORPORA[path_].PATH
if not exists_lazy(path_, data_type='data'):
# create cached version of dataset for lazy loading if it doesn't exist
text = get_dataset(name if named_corpora else path_, text_key=text_key, label_key=label_key, binarize_sent=binarize_sent,
delim=delim, drop_unlabeled=drop_unlabeled, loose_json=loose)
make_lazy(path_, text.X, data_type='data')
text = lazy_array_loader(path_, data_type='data', map_fn=process_fn)
else:
# get dataset
text = get_dataset(path_, text_key=text_key, label_key=label_key, binarize_sent=binarize_sent,
delim=delim, drop_unlabeled=drop_unlabeled, loose_json=loose, preprocess_fn=process_fn)
return text
# get one or multiple datasets and concatenate
if isinstance(path, str):
path = [path]
datasets = [get_dataset_from_path(p) for p in path]
if len(datasets) == 1:
ds = datasets[0]
else:
ds = ConcatDataset(datasets)
# make tokenizer for dataset
if tokenizer is None:
tokenizer = make_tokenizer(tokenizer_type, ds, tokenizer_model_path, vocab_size, model_type,
pad_token, character_converage, **kwargs)
ds_type = ''
if 'ds_type' in kwargs:
ds_type = kwargs['ds_type']
ds.SetTokenizer(tokenizer)
# Split dataset into train/val/test (and wrap bert dataset)
if should_split(split):
ds = split_ds(ds, split)
if ds_type.lower() == 'bert':
presplit_sentences = kwargs['presplit_sentences'] if 'presplit_sentences' in kwargs else False
ds = [bert_sentencepair_dataset(d, max_seq_len=seq_length, presplit_sentences=presplit_sentences) if d is not None else None for d in ds]
elif ds_type.lower() == 'gpt2':
ds = [GPT2Dataset(d, max_seq_len=seq_length) if d is not None else None for d in ds]
else:
if ds_type.lower() == 'bert':
presplit_sentences = kwargs['presplit_sentences'] if 'presplit_sentences' in kwargs else False
ds = bert_sentencepair_dataset(ds, max_seq_len=seq_length, presplit_sentences=presplit_sentences)
elif ds_type.lower() == 'gpt2':
ds = GPT2Dataset(ds, max_seq_len=seq_length)
return ds, tokenizer
================================================
FILE: CPM-Generate/data_utils/corpora.py
================================================
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
"""several datasets with preset arguments"""
from .datasets import json_dataset, csv_dataset
import os
class wikipedia(json_dataset):
"""
dataset for wikipedia with arguments configured for convenience
command line usage: `--train-data wikipedia`
"""
PATH = 'data/wikipedia/wikidump_lines.json'
assert_str = "make sure to set PATH for wikipedia data_utils/corpora.py"
def __init__(self, **kwargs):
assert os.path.exists(wikipedia.PATH), \
wikipedia.assert_str
if not kwargs:
kwargs = {}
kwargs['text_key'] = 'text'
kwargs['loose_json'] = True
super(wikipedia, self).__init__(wikipedia.PATH, **kwargs)
class webtext(json_dataset):
"""
dataset for webtext with arguments configured for convenience
command line usage: `--train-data webtext`
"""
PATH = '/data/private/zhangzhengyan/corpus/merge_new.json'
assert_str = "make sure to set PATH for webtext data_utils/corpora.py"
def __init__(self, **kwargs):
assert os.path.exists(webtext.PATH), \
webtext.assert_str
if not kwargs:
kwargs = {}
kwargs['text_key'] = 'text'
kwargs['loose_json'] = True
super(webtext, self).__init__(webtext.PATH, **kwargs)
NAMED_CORPORA = {
'wikipedia': wikipedia,
'webtext': webtext,
}
================================================
FILE: CPM-Generate/data_utils/datasets.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
"""dataset objects for jsons, csvs, and BERT datasets"""
import os
import time
from operator import itemgetter
from bisect import bisect_right
import json
import csv
import math
import random
from itertools import accumulate
from torch.utils import data
import pandas as pd
import numpy as np
import nltk
from nltk import tokenize
from .lazy_loader import lazy_array_loader, exists_lazy, make_lazy
from .tokenization import Tokenization
class ConcatDataset(data.Dataset):
"""
Dataset to concatenate multiple datasets.
Purpose: useful to assemble different existing datasets, possibly
large-scale datasets as the concatenation operation is done in an
on-the-fly manner.
Arguments:
datasets (sequence): List of datasets to be concatenated.
"""
@staticmethod
def cumsum(sequence):
r, s = [], 0
for e in sequence:
l = len(e)
r.append(l + s)
s += l
return r
def __init__(self, datasets, **kwargs):
super(ConcatDataset, self).__init__()
assert len(datasets) > 0, 'datasets should not be an empty iterable'
self.datasets = list(datasets)
self.is_lazy = sum([isinstance(ds, lazy_array_loader) for ds in self.datasets]) == len(self.datasets)
self.cumulative_sizes = self.cumsum(self.datasets)
self._X = None
self._Y = None
self._lens = None
def SetTokenizer(self, tokenizer):
for ds in self.datasets:
ds.SetTokenizer(tokenizer)
def GetTokenizer(self):
return self.datasets[0].GetTokenizer()
def __len__(self):
return self.cumulative_sizes[-1]
def __getitem__(self, idx):
dataset_idx = bisect_right(self.cumulative_sizes, idx)
if dataset_idx == 0:
sample_idx = idx
else:
sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
return self.datasets[dataset_idx][sample_idx]
@property
def lens(self):
if self._lens is None:
self._lens = []
if self.is_lazy:
for data in self.datasets:
self._lens.extend(data.lens)
else:
for data in self.datasets:
self._lens.extend([len(d['text']) if isinstance(d, dict) else len(d) for d in data])
return self._lens
@property
def X(self):
if self._X is None:
self._X = []
for data in self.datasets:
self._X.extend(data.X)
return self._X
@property
def Y(self):
if self._Y is None:
self._Y = []
for data in self.datasets:
self._Y.extend(list(data.Y))
self._Y = np.array(self._Y)
return self._Y
@property
def cummulative_sizes(self):
warnings.warn("cummulative_sizes attribute is renamed to "
"cumulative_sizes", DeprecationWarning, stacklevel=2)
return self.cumulative_sizes
class SplitDataset(data.Dataset):
"""
Dataset wrapper to access a subset of another dataset.
Purpose: useful to index into existing datasets, possibly
large-scale datasets as the subindexing operation is done in an
on-the-fly manner.
Arguments:
ds (Dataset or array-like): List of datasets to be subindexed
split_inds (1D array-like): List of indices part of subset
"""
def __init__(self, ds, split_inds, **kwargs):
self.split_inds = list(split_inds)
self.wrapped_data = ds
self.is_lazy = isinstance(ds, lazy_array_loader) or (hasattr(ds, 'is_lazy') and ds.is_lazy)
if self.is_lazy:
self.lens = itemgetter(*self.split_inds)(list(self.wrapped_data.lens))
self._X = None
self._Y = None
def __len__(self):
return len(self.split_inds)
def __getitem__(self, index):
return self.wrapped_data[self.split_inds[index]]
def SetTokenizer(self, tokenizer):
self.wrapped_data.SetTokenizer(tokenizer)
def GetTokenizer(self):
return self.wrapped_data.GetTokenizer()
@property
def X(self):
if self._X is None:
self._X = itemgetter(*self.split_inds)(self.wrapped_data.X)
return self._X
@property
def Y(self):
if self._Y is None:
self._Y = np.array(itemgetter(*self.split_inds)(self.wrapped_data.Y))
return self._Y
def __iter__(self):
for idx in self.split_inds:
yield self.wrapped_data[idx]
def split_ds(ds, split=[.8,.2,.0], shuffle=True):
"""
Split a dataset into subsets given proportions of how
much to allocate per split. If a split is 0% returns None for that split.
Purpose: Useful for creating train/val/test splits
Arguments:
ds (Dataset or array-like): Data to be split.
split (1D array-like): proportions to split `ds`. `sum(splits) != 0`
shuffle (boolean): Randomly split dataset. Default: True
"""
split_sum = sum(split)
if split_sum == 0:
raise Exception('Split cannot sum to 0.')
split = np.array(split)
split /= split_sum
ds_len = len(ds)
inds = np.arange(ds_len)
if shuffle:
np.random.shuffle(inds)
start_idx = 0
residual_idx = 0
rtn_ds = [None]*len(split)
for i, f in enumerate(split):
if f != 0:
proportion = ds_len*split[i]
residual_idx += proportion % 1
split_ = int(int(proportion) + residual_idx)
split_inds = inds[start_idx:start_idx+max(split_, 1)]
rtn_ds[i] = SplitDataset(ds, split_inds)
start_idx += split_
residual_idx %= 1
return rtn_ds
class csv_dataset(data.Dataset):
"""
Class for loading datasets from csv files.
Purpose: Useful for loading data for unsupervised modeling or transfer tasks
Arguments:
path (str): Path to csv file with dataset.
tokenizer (data_utils.Tokenizer): Tokenizer to use when processing text. Default: None
preprocess_fn (callable): Callable that process a string into desired format.
delim (str): delimiter for csv. Default: ','
binarize_sent (bool): binarize label values to 0 or 1 if they\'re on a different scale. Default: False
drop_unlabeled (bool): drop rows with unlabelled values. Always fills remaining empty
columns with -1 (regardless if rows are dropped based on value) Default: False
text_key (str): key to get text from csv. Default: 'sentence'
label_key (str): key to get label from json dictionary. Default: 'label'
Attributes:
X (list): all strings from the csv file
Y (np.ndarray): labels to train with
"""
def __init__(self, path, tokenizer=None, preprocess_fn=None, delim=',',
binarize_sent=False, drop_unlabeled=False, text_key='sentence', label_key='label',
**kwargs):
self.is_lazy = False
self.preprocess_fn = preprocess_fn
self.SetTokenizer(tokenizer)
self.path = path
self.delim = delim
self.text_key = text_key
self.label_key = label_key
self.drop_unlabeled = drop_unlabeled
if '.tsv' in self.path:
self.delim = '\t'
self.X = []
self.Y = []
try:
cols = [text_key]
if isinstance(label_key, list):
cols += label_key
else:
cols += [label_key]
data = pd.read_csv(self.path, sep=self.delim, usecols=cols, encoding='latin-1')
except:
data = pd.read_csv(self.path, sep=self.delim, usecols=[text_key], encoding='latin-1')
data = data.dropna(axis=0)
self.X = data[text_key].values.tolist()
try:
self.Y = data[label_key].values
except Exception as e:
self.Y = np.ones(len(self.X))*-1
if binarize_sent:
self.Y = binarize_labels(self.Y, hard=binarize_sent)
def SetTokenizer(self, tokenizer):
if tokenizer is None:
self.using_tokenizer = False
if not hasattr(self, '_tokenizer'):
self._tokenizer = tokenizer
else:
self.using_tokenizer = True
self._tokenizer = tokenizer
def GetTokenizer(self):
return self._tokenizer
@property
def tokenizer(self):
if self.using_tokenizer:
return self._tokenizer
return None
def __len__(self):
return len(self.X)
def __getitem__(self, index):
"""process+tokenize string and return string,label,and stringlen"""
x = self.X[index]
if self.tokenizer is not None:
x = self.tokenizer.EncodeAsIds(x, self.preprocess_fn)
elif self.preprocess_fn is not None:
x = self.preprocess_fn(x)
y = self.Y[index]
if isinstance(y, str):
if self.tokenizer is not None:
y = self.tokenizer.EncodeAsIds(y, self.preprocess_fn)
elif self.preprocess_fn is not None:
y = self.preprocess_fn(y)
return {'text': x, 'length': len(x), 'label': y}
def write(self, writer_gen=None, path=None, skip_header=False):
"""
given a generator of metrics for each of the data points X_i,
write the metrics, text, and labels to a csv file
"""
if path is None:
path = self.path+'.results'
print('generating csv at ' + path)
with open(path, 'w') as csvfile:
c = csv.writer(csvfile, delimiter=self.delim)
if writer_gen is not None:
#if first item of generator is a header of what the metrics mean then write header to csv file
if not skip_header:
header = (self.label_key,)+tuple(next(writer_gen))+(self.text_key,)
c.writerow(header)
for i, row in enumerate(writer_gen):
row = (self.Y[i],)+tuple(row)+(self.X[i],)
c.writerow(row)
else:
c.writerow([self.label_key, self.text_key])
for row in zip(self.Y, self.X):
c.writerow(row)
class json_dataset(data.Dataset):
"""
Class for loading datasets from a json dump.
Purpose: Useful for loading data for unsupervised modeling or transfer tasks
Arguments:
path (str): path to json file with dataset.
tokenizer (data_utils.Tokenizer): Tokenizer to use when processing text. Default: None
preprocess_fn (callable): callable function that process a string into desired format.
Takes string, maxlen=None, encode=None as arguments. Default: process_str
text_key (str): key to get text from json dictionary. Default: 'sentence'
label_key (str): key to get label from json dictionary. Default: 'label'
Attributes:
all_strs (list): list of all strings from the dataset
all_labels (list): list of all labels from the dataset (if they have it)
"""
def __init__(self, path, tokenizer=None, preprocess_fn=None, binarize_sent=False,
text_key='sentence', label_key='label', loose_json=False, **kwargs):
self.is_lazy = False
self.preprocess_fn = preprocess_fn
self.path = path
self.SetTokenizer(tokenizer)
self.X = []
self.Y = []
self.text_key = text_key
self.label_key = label_key
self.loose_json = loose_json
for j in self.load_json_stream(self.path):
s = j[text_key]
self.X.append(s)
self.Y.append(j[label_key])
if binarize_sent:
self.Y = binarize_labels(self.Y, hard=binarize_sent)
def SetTokenizer(self, tokenizer):
if tokenizer is None:
self.using_tokenizer = False
if not hasattr(self, '_tokenizer'):
self._tokenizer = tokenizer
else:
self.using_tokenizer = True
self._tokenizer = tokenizer
def GetTokenizer(self):
return self._tokenizer
@property
def tokenizer(self):
if self.using_tokenizer:
return self._tokenizer
return None
def __getitem__(self, index):
"""gets the index'th string from the dataset"""
x = self.X[index]
if self.tokenizer is not None:
x = self.tokenizer.EncodeAsIds(x, self.preprocess_fn)
elif self.preprocess_fn is not None:
x = self.preprocess_fn(x)
y = self.Y[index]
if isinstance(y, str):
if self.tokenizer is not None:
y = self.tokenizer.EncodeAsIds(y, self.preprocess_fn)
elif self.preprocess_fn is not None:
y = self.preprocess_fn(y)
return {'text': x, 'length': len(x), 'label': y}
def __len__(self):
return len(self.X)
def write(self, writer_gen=None, path=None, skip_header=False):
"""
given a generator of metrics for each of the data points X_i,
write the metrics, text, and labels to a json file
"""
if path is None:
path = self.path+'.results'
jsons = []
if writer_gen is not None:
#if first item of generator is a header of what the metrics mean then write header to csv file
def gen_helper():
keys = {}
keys[0] = self.label_key
if not skip_header:
for idx, k in enumerate(tuple(next(writer_gen))):
keys[idx+1] = k
for i, row in enumerate(writer_gen):
if i == 0 and skip_header:
for idx, _ in enumerate(row):
keys[idx+1] = 'metric_%d'%(idx,)
j = {}
for idx, v in enumerate((self.Y[i],)+tuple(row)):
k = keys[idx]
j[k] = v
yield j
else:
def gen_helper():
for y in self.Y:
j = {}
j[self.label_key] = y
yield j
def out_stream():
for i, j in enumerate(gen_helper()):
j[self.text_key] = self.X[i]
yield j
self.save_json_stream(path, out_stream())
def save_json_stream(self, save_path, json_stream):
if self.loose_json:
with open(save_path, 'w') as f:
for i, j in enumerate(json_stream):
write_string = ''
if i != 0:
write_string = '\n'
write_string += json.dumps(j)
f.write(write_string)
else:
jsons = [j for j in json_stream]
json.dump(jsons, open(save_path, 'w'), separators=(',', ':'))
def load_json_stream(self, load_path):
if not self.loose_json:
jsons = json.load(open(load_path, 'r'))
generator = iter(jsons)
else:
def gen_helper():
with open(load_path, 'r') as f:
for row in f:
yield json.loads(row)
generator = gen_helper()
for j in generator:
if self.label_key not in j:
j[self.label_key] = -1
yield j
class GPT2Dataset(data.Dataset):
def __init__(self, ds,
max_seq_len=1024,
num_samples=None,
weighted=True,
sample_across_doc=True,
random_across_doc_sampling=True,
sentence_start=False, **kwargs):
self.ds = ds
self.ds_len = len(self.ds)
self.num_samples = num_samples
if num_samples is None:
self.num_samples = 1000 * self.ds_len
self.max_seq_len = max_seq_len
self.tokenizer = self.ds.GetTokenizer()
self.ds.SetTokenizer(None)
self.weighted = weighted
self.sample_across_doc = sample_across_doc
self.random_across_doc_sampling = random_across_doc_sampling
self.sentence_start = sentence_start
self.init_weighting()
def init_weighting(self):
if self.weighted:
if hasattr(self.ds, 'is_lazy') and self.ds.is_lazy:
lens = np.array(self.ds.lens)
else:
lens = np.array([len(d['text']) if isinstance(d, dict)
else len(d) for d in self.ds])
self.total_len = np.sum(lens)
self.weighting = list(accumulate(lens))
else:
self.weighting = None
def get_weighted_samples(self, np_rng):
if self.weighting is not None:
idx = np_rng.randint(self.total_len)
return bisect_right(self.weighting, idx)
else:
return np_rng.randint(self.ds_len)
def __len__(self):
return self.num_samples
def __getitem__(self, idx):
# init rng
rng = random.Random(idx)
rng = np.random.RandomState(seed=[rng.randint(0, 2**32-1) for _ in range(16)])
# get possibly weighted random index from dataset
data_idx = self.get_weighted_samples(rng)
# data_idx = rng.choice(self.ds_len, p=self.weighting)
tokens = self.getidx(data_idx)
# truncate or pad tokens
num_tokens = len(tokens)
tokens_to_strip = num_tokens - self.max_seq_len - 1
if tokens_to_strip > 0:
strip_left_tokens = rng.randint(tokens_to_strip + 1)
tokens = tokens[strip_left_tokens:]
if self.sentence_start:
token_copy = list(tokens)
not_done = True
while (len(token_copy) > 0) and not_done:
tok = token_copy.pop(0)
if self.contains_sentence_end(tok):
tokens = token_copy
not_done = False
strip_right_rokens = len(tokens) - self.max_seq_len - 1
if strip_right_rokens > 0:
tokens = tokens[:-strip_right_rokens]
if self.sample_across_doc:
while (len(tokens) < (self.max_seq_len + 1)):
if self.random_across_doc_sampling:
data_idx = self.get_weighted_samples(rng)
else:
data_idx = (data_idx + 1) % self.ds_len
tokens += self.getidx(data_idx)
tokens = tokens[:(self.max_seq_len+1)]
tokens = self.pad_seq(tokens)
return {'text': np.array(tokens),}
def getidx(self, data_idx):
data = self.ds[data_idx]
if isinstance(data, dict):
data = data['text']
# tokenize
tokenization = self.tokenizer.EncodeAsIds(data)
tokenization.append(self.tokenizer.get_command('eos'))
tokens = tokenization.tokenization
return tokens
def pad_seq(self, seq):
total_tokens = self.max_seq_len + 1
num_pad_tokens = max(0, total_tokens - len(seq))
seq += [self.tokenizer.get_command('pad').Id]*(num_pad_tokens)
return seq
def contains_sentence_end(self, tok):
tok = self.tokenizer.IdToToken(tok)
if '.' in tok:
return True
if '?' in tok:
return True
if '!' in tok:
return True
return False
class bert_sentencepair_dataset(data.Dataset):
"""
Dataset containing sentencepairs for BERT training. Each index corresponds to a randomly generated sentence pair.
Arguments:
ds (Dataset or array-like): data corpus to use for training
max_seq_len (int): maximum sequence length to use for a sentence pair
mask_lm_prob (float): proportion of tokens to mask for masked LM
max_preds_per_seq (int): Maximum number of masked tokens per sentence pair. Default: math.ceil(max_seq_len*mask_lm_prob/10)*10
short_seq_prob (float): Proportion of sentence pairs purposefully shorter than max_seq_len
dataset_size (int): number of random sentencepairs in the dataset. Default: len(ds)*(len(ds)-1)
"""
def __init__(self, ds, max_seq_len=512, mask_lm_prob=.15, max_preds_per_seq=None, short_seq_prob=.01, dataset_size=None, presplit_sentences=False, weighted=True,**kwargs):
self.ds = ds
self.ds_len = len(self.ds)
self.tokenizer = self.ds.GetTokenizer()
self.vocab_words = list(self.tokenizer.text_token_vocab.values())
self.ds.SetTokenizer(None)
self.max_seq_len = max_seq_len
self.mask_lm_prob = mask_lm_prob
if max_preds_per_seq is None:
max_preds_per_seq = math.ceil(max_seq_len*mask_lm_prob /10)*10
self.max_preds_per_seq = max_preds_per_seq
self.short_seq_prob = short_seq_prob
self.dataset_size = dataset_size
if self.dataset_size is None:
self.dataset_size = self.ds_len * (self.ds_len-1)
self.presplit_sentences = presplit_sentences
if not self.presplit_sentences:
nltk.download('punkt', download_dir="./nltk")
self.weighted = weighted
self.get_weighting()
def get_weighting(self):
if self.weighted:
if hasattr(self.ds, 'is_lazy') and self.ds.is_lazy:
lens = np.array(self.ds.lens)
else:
lens = np.array([len(d['text']) if isinstance(d, dict) else len(d) for d in self.ds])
self.total_len = np.sum(lens)
self.weighting = list(accumulate(lens))
else:
self.weighting = None
def get_weighted_samples(self, np_rng):
if self.weighting is not None:
idx = np_rng.randint(self.total_len)
return bisect_right(self.weighting, idx)
else:
return np_rng.randint(self.ds_len)
def __len__(self):
return self.dataset_size
def __getitem__(self, idx):
# get rng state corresponding to index (allows deterministic random pair)
rng = random.Random(idx)
np_rng = np.random.RandomState(seed=[rng.randint(0, 2**32-1) for _ in range(16)])
# get seq length
target_seq_length = self.max_seq_len
short_seq = False
if rng.random() < self.short_seq_prob:
target_seq_length = rng.randint(2, target_seq_length)
short_seq = True
# get sentence pair and label
is_random_next = None
lena = 0
lenb = 0
while (is_random_next is None) or (lena < 1) or (lenb < 1):
tokensa, tokensb, is_random_next = self.create_random_sentencepair(target_seq_length, rng, np_rng)
lena = len(tokensa[0])
lenb = len(tokensb[0])
# truncate sentence pair to max_seq_len
tokensa, tokensb = self.truncate_seq_pair(tokensa, tokensb, self.max_seq_len, rng)
# join sentence pair, mask, and pad
tokens, mask, mask_labels, pad_mask = self.create_masked_lm_predictions(tokensa, tokensb, self.mask_lm_prob, self.max_preds_per_seq, self.vocab_words, rng)
sample = {'text': np.array(tokens[0]), 'types': np.array(tokens[1]), 'is_random': int(is_random_next), 'mask': np.array(mask), 'mask_labels': np.array(mask_labels), 'pad_mask': np.array(pad_mask)}
return sample
def sentence_split(self, document):
"""split document into sentences"""
lines = document.split('\n')
if self.presplit_sentences:
return [line for line in lines if line]
rtn = []
for line in lines:
if line != '':
rtn.extend(tokenize.sent_tokenize(line))
return rtn
def sentence_tokenize(self, sent, sentence_num=0, beginning=False, ending=False):
"""tokenize sentence and get token types"""
tokens = self.tokenizer.EncodeAsIds(sent).tokenization
str_type = 'str' + str(sentence_num)
token_types = [self.tokenizer.get_type(str_type).Id]*len(tokens)
return tokens, token_types
def get_doc(self, idx):
"""gets text of document corresponding to idx"""
rtn = self.ds[idx]
if isinstance(rtn, dict):
rtn = rtn['text']
return rtn
def create_random_sentencepair(self, target_seq_length, rng, np_rng):
"""
fetches a random sentencepair corresponding to rng state similar to
https://github.com/google-research/bert/blob/master/create_pretraining_data.py#L248-L294
"""
is_random_next = None
curr_strs = []
curr_str_types = []
curr_len = 0
while curr_len < 1:
curr_len = 0
doc_a = None
while doc_a is None:
if self.weighted:
# doc_a_idx = np_rng.choice(self.ds_len, p=self.weighting)
doc_a_idx = self.get_weighted_samples(np_rng)
else:
doc_a_idx = rng.randint(0, self.ds_len-1)
doc_a = self.sentence_split(self.get_doc(doc_a_idx))
if not doc_a:
doc_a = None
random_start_a = rng.randint(0, len(doc_a)-1)
while random_start_a < len(doc_a):
sentence = doc_a[random_start_a]
sentence, sentence_types = self.sentence_tokenize(sentence, 0, random_start_a == 0, random_start_a == len(doc_a))
curr_strs.append(sentence)
curr_str_types.append(sentence_types)
curr_len += len(sentence)
if random_start_a == len(doc_a) - 1 or curr_len >= target_seq_length:
break
random_start_a = (random_start_a+1)
if curr_strs:
num_a = 1
if len(curr_strs) >= 2:
num_a = rng.randint(0, len(curr_strs))
tokens_a = []
token_types_a = []
for j in range(num_a):
tokens_a.extend(curr_strs[j])
token_types_a.extend(curr_str_types[j])
tokens_b = []
token_types_b = []
is_random_next = False
if len(curr_strs) == 1 or rng.random() < 0.5:
is_random_next = True
target_b_length = target_seq_length - len(tokens_a)
b_len = 0
while b_len < 1:
doc_b = None
while doc_b is None:
doc_b_idx = rng.randint(0, self.ds_len - 2)
doc_b_idx += int(doc_b_idx >= doc_a_idx)
doc_b = self.sentence_split(self.get_doc(doc_b_idx))
if not doc_b:
doc_b = None
random_start_b = rng.randint(0, len(doc_b)-1)
while random_start_b < len(doc_b):
sentence_b = doc_b[random_start_b]
new_b_tokens, new_b_types = self.sentence_tokenize(sentence_b, 1, random_start_b == 0, random_start_b == len(doc_b))
b_len += len(new_b_tokens)
tokens_b.extend(new_b_tokens)
token_types_b.extend(new_b_types)
if len(tokens_b) >= target_b_length:
break
random_start_b = (random_start_b+1)
else:
is_random_next = False
for j in range(num_a, len(curr_strs)):
tokens_b.extend(curr_strs[j])
token_types_b.extend(curr_str_types[j])
return (tokens_a, token_types_a), (tokens_b, token_types_b), is_random_next
def truncate_seq_pair(self, a, b, max_seq_len, rng):
"""
Truncate sequence pair according to original BERT implementation:
https://github.com/google-research/bert/blob/master/create_pretraining_data.py#L391
"""
tokens_a, token_types_a = a
tokens_b, token_types_b = b
max_num_tokens = max_seq_len - 3
while True:
len_a = len(tokens_a)
len_b = len(tokens_b)
total_length = len_a + len_b
if total_length <= max_num_tokens:
break
if len(tokens_a) > len(tokens_b):
trunc_tokens = tokens_a
trunc_types = token_types_a
else:
trunc_tokens = tokens_b
trunc_types = token_types_b
assert len(trunc_tokens) >= 1
if rng.random() < 0.5:
trunc_tokens.pop(0)
trunc_types.pop(0)
else:
trunc_tokens.pop()
trunc_types.pop()
return (tokens_a, token_types_a), (tokens_b, token_types_b)
def mask_token(self, idx, tokens, types, vocab_words, rng):
"""
helper function to mask `idx` token from `tokens` according to
section 3.3.1 of https://arxiv.org/pdf/1810.04805.pdf
"""
label = tokens[idx]
if rng.random() < 0.8:
new_label = self.tokenizer.get_command('MASK').Id
else:
if rng.random() < 0.5:
new_label = label
else:
new_label = rng.choice(vocab_words)
tokens[idx] = new_label
return label
def pad_seq(self, seq):
"""helper function to pad sequence pair"""
num_pad = max(0, self.max_seq_len - len(seq))
pad_mask = [0] * len(seq) + [1] * num_pad
seq += [self.tokenizer.get_command('pad').Id] * num_pad
return seq, pad_mask
def create_masked_lm_predictions(self, a, b, mask_lm_prob, max_preds_per_seq, vocab_words, rng):
"""
Mask sequence pair for BERT training according to:
https://github.com/google-research/bert/blob/master/create_pretraining_data.py#L338
"""
tokens_a, token_types_a = a
tokens_b, token_types_b = b
tokens = [self.tokenizer.get_command('ENC').Id] + tokens_a + [self.tokenizer.get_command('sep').Id] + tokens_b + [self.tokenizer.get_command('sep').Id]
token_types = [token_types_a[0]] + token_types_a + [token_types_a[0]] + token_types_b + [token_types_b[0]]
len_a = len(tokens_a)
len_b = len(tokens_b)
cand_indices = [idx+1 for idx in range(len_a)] + [idx+2+len_a for idx in range(len_b)]
rng.shuffle(cand_indices)
output_tokens, pad_mask = self.pad_seq(list(tokens))
output_types, _ = self.pad_seq(list(token_types))
num_to_predict = min(max_preds_per_seq, max(1, int(round(len(tokens) * mask_lm_prob))))
mask = [0] * len(output_tokens)
mask_labels = [-1] * len(output_tokens)
for idx in sorted(cand_indices[:num_to_predict]):
mask[idx] = 1
label = self.mask_token(idx, output_tokens, output_types, vocab_words, rng)
mask_labels[idx] = label
return (output_tokens, output_types), mask, mask_labels, pad_mask
================================================
FILE: CPM-Generate/data_utils/file_utils.py
================================================
# This file is provided as is from:
# https://github.com/huggingface/pytorch-pretrained-BERT
# Please refer to their repository for copyright.
"""
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
from __future__ import (absolute_import, division, print_function, unicode_literals)
import json
import logging
import os
import shutil
import tempfile
from functools import wraps
from hashlib import sha256
import sys
from io import open
import boto3
import requests
from botocore.exceptions import ClientError
from tqdm import tqdm
try:
from urllib.parse import urlparse
except ImportError:
from urlparse import urlparse
try:
from pathlib import Path
PYTORCH_PRETRAINED_BERT_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
Path.home() / '.pytorch_pretrained_bert'))
except (AttributeError, ImportError):
PYTORCH_PRETRAINED_BERT_CACHE = os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
os.path.join(os.path.expanduser("~"), '.pytorch_pretrained_bert'))
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
def url_to_filename(url, etag=None):
"""
Convert `url` into a hashed filename in a repeatable way.
If `etag` is specified, append its hash to the url's, delimited
by a period.
"""
url_bytes = url.encode('utf-8')
url_hash = sha256(url_bytes)
filename = url_hash.hexdigest()
if etag:
etag_bytes = etag.encode('utf-8')
etag_hash = sha256(etag_bytes)
filename += '.' + etag_hash.hexdigest()
return filename
def filename_to_url(filename, cache_dir=None):
"""
Return the url and etag (which may be ``None``) stored for `filename`.
Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
"""
if cache_dir is None:
cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
cache_path = os.path.join(cache_dir, filename)
if not os.path.exists(cache_path):
raise EnvironmentError("file {} not found".format(cache_path))
meta_path = cache_path + '.json'
if not os.path.exists(meta_path):
raise EnvironmentError("file {} not found".format(meta_path))
with open(meta_path, encoding="utf-8") as meta_file:
metadata = json.load(meta_file)
url = metadata['url']
etag = metadata['etag']
return url, etag
def cached_path(url_or_filename, cache_dir=None):
"""
Given something that might be a URL (or might be a local path),
determine which. If it's a URL, download the file and cache it, and
return the path to the cached file. If it's already a local path,
make sure the file exists and then return the path.
"""
if cache_dir is None:
cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
if sys.version_info[0] == 3 and isinstance(url_or_filename, Path):
url_or_filename = str(url_or_filename)
if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
parsed = urlparse(url_or_filename)
if parsed.scheme in ('http', 'https', 's3'):
# URL, so get it from the cache (downloading if necessary)
return get_from_cache(url_or_filename, cache_dir)
elif os.path.exists(url_or_filename):
# File, and it exists.
return url_or_filename
elif parsed.scheme == '':
# File, but it doesn't exist.
raise EnvironmentError("file {} not found".format(url_or_filename))
else:
# Something unknown
raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))
def split_s3_path(url):
"""Split a full s3 path into the bucket name and path."""
parsed = urlparse(url)
if not parsed.netloc or not parsed.path:
raise ValueError("bad s3 path {}".format(url))
bucket_name = parsed.netloc
s3_path = parsed.path
# Remove '/' at beginning of path.
if s3_path.startswith("/"):
s3_path = s3_path[1:]
return bucket_name, s3_path
def s3_request(func):
"""
Wrapper function for s3 requests in order to create more helpful error
messages.
"""
@wraps(func)
def wrapper(url, *args, **kwargs):
try:
return func(url, *args, **kwargs)
except ClientError as exc:
if int(exc.response["Error"]["Code"]) == 404:
raise EnvironmentError("file {} not found".format(url))
else:
raise
return wrapper
@s3_request
def s3_etag(url):
"""Check ETag on S3 object."""
s3_resource = boto3.resource("s3")
bucket_name, s3_path = split_s3_path(url)
s3_object = s3_resource.Object(bucket_name, s3_path)
return s3_object.e_tag
@s3_request
def s3_get(url, temp_file):
"""Pull a file directly from S3."""
s3_resource = boto3.resource("s3")
bucket_name, s3_path = split_s3_path(url)
s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
def http_get(url, temp_file):
req = requests.get(url, stream=True)
content_length = req.headers.get('Content-Length')
total = int(content_length) if content_length is not None else None
progress = tqdm(unit="B", total=total)
for chunk in req.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
progress.update(len(chunk))
temp_file.write(chunk)
progress.close()
def get_from_cache(url, cache_dir=None):
"""
Given a URL, look for the corresponding dataset in the local cache.
If it's not there, download it. Then return the path to the cached file.
"""
if cache_dir is None:
cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
# Get eTag to add to filename, if it exists.
if url.startswith("s3://"):
etag = s3_etag(url)
else:
response = requests.head(url, allow_redirects=True)
if response.status_code != 200:
raise IOError("HEAD request failed for url {} with status code {}"
.format(url, response.status_code))
etag = response.headers.get("ETag")
filename = url_to_filename(url, etag)
# get cache path to put the file
cache_path = os.path.join(cache_dir, filename)
if not os.path.exists(cache_path):
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with tempfile.NamedTemporaryFile() as temp_file:
logger.info("%s not found in cache, downloading to %s", url, temp_file.name)
# GET file object
if url.startswith("s3://"):
s3_get(url, temp_file)
else:
http_get(url, temp_file)
# we are copying the file before closing it, so flush to avoid truncation
temp_file.flush()
# shutil.copyfileobj() starts at the current position, so go to the start
temp_file.seek(0)
logger.info("copying %s to cache at %s", temp_file.name, cache_path)
with open(cache_path, 'wb') as cache_file:
shutil.copyfileobj(temp_file, cache_file)
logger.info("creating metadata file for %s", cache_path)
meta = {'url': url, 'etag': etag}
meta_path = cache_path + '.json'
with open(meta_path, 'w', encoding="utf-8") as meta_file:
json.dump(meta, meta_file)
logger.info("removing temp file %s", temp_file.name)
return cache_path
def read_set_from_file(filename):
'''
Extract a de-duped collection (set) of text from a file.
Expected file format is one item per line.
'''
collection = set()
with open(filename, 'r', encoding='utf-8') as file_:
for line in file_:
collection.add(line.rstrip())
return collection
def get_file_extension(path, dot=True, lower=True):
ext = os.path.splitext(path)[1]
ext = ext if dot else ext[1:]
return ext.lower() if lower else ext
================================================
FILE: CPM-Generate/data_utils/lazy_loader.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
"""utils for loading text from disk"""
import os
import mmap
import pickle as pkl
import time
from itertools import accumulate
import torch
from torch.multiprocessing import Lock
def get_lazy_path(path):
"""
Gets directory path where lazy files are stored.
"""
return os.path.splitext(path)[0]+'.lazy'
def exists_lazy(path, data_type='data'):
"""
Check if we've already made a lazy version of this file for the `data_type` field.
"""
if not os.path.exists(get_lazy_path(path)):
return False
contents = os.listdir(get_lazy_path(path))
if data_type not in contents:
return False
if data_type+'.len.pkl' not in contents:
return False
return True
def make_lazy(path, strs, data_type='data'):
"""
Make lazy version of `data_type` field of the file. Byte offsets
corresponding to data indices are stored in a `.len.pkl` data file.
"""
lazypath = get_lazy_path(path)
if not os.path.exists(lazypath):
os.makedirs(lazypath)
datapath = os.path.join(lazypath, data_type)
lenpath = os.path.join(lazypath, data_type+'.len.pkl')
if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
with open(datapath, 'wb') as f:
str_lens = []
str_cnt = 0
for s in strs:
if isinstance(s, dict):
s = s['text']
encoded = s.encode('utf-8')
f.write(encoded)
str_cnt = len(encoded)
str_lens.append(str_cnt)
pkl.dump(str_lens, open(lenpath, 'wb'))
else:
while not os.path.exists(lenpath):
time.sleep(1)
def split_strings(strings, start, chr_lens):
"""
Split strings based on string lengths and given start.
"""
return [strings[i-start:j-start] for i, j in zip([start]+chr_lens[:-1], chr_lens)]
class ProcessorTokenizer:
"""
callable class that runs a preprocessing, as well as tokenization step,
on input text.
"""
def __init__(self, tokenizer, process_fn=None):
self.tokenizer = tokenizer
self.process_fn = process_fn
def __call__(self, string):
if self.tokenizer is not None:
string = self.tokenizer(string, process_fn=self.process_fn)
elif self.process_fn is not None:
string = self.process_fn(string)
return string
class lazy_array_loader(object):
"""
Arguments:
path: path to directory where array entries are concatenated into one big string file
and the .len file are located
data_type (str): Some datsets have multiple fields that are stored in different paths.
`data_type` specifies which of these fields to load in this class
mem_map (boolean): Specifies whether to memory map file `path`
map_fn (callable): Fetched strings are passed through map_fn before being returned.
Example of lazy loader directory structure:
file.json
file.lazy/
data_type1
data_type1.len.pkl
data_type2
data_type2.len.pkl
"""
def __init__(self, path, data_type='data', mem_map=False, map_fn=None):
lazypath = get_lazy_path(path)
datapath = os.path.join(lazypath, data_type)
#get file where array entries are concatenated into one big string
self._file = open(datapath, 'rb')
self.file = self._file
#memory map file if necessary
self.mem_map = mem_map
if self.mem_map:
self.file = mmap.mmap(self.file.fileno(), 0, prot=mmap.PROT_READ)
lenpath = os.path.join(lazypath, data_type+'.len.pkl')
self.lens = pkl.load(open(lenpath, 'rb'))
self.ends = list(accumulate(self.lens))
self.dumb_ends = list(self.ends)
self.read_lock = Lock()
self.process_fn = map_fn
self.map_fn = map_fn
self._tokenizer = None
def SetTokenizer(self, tokenizer):
"""
logic to set and remove (set to None) tokenizer.
combines preprocessing/tokenization into one callable.
"""
if tokenizer is None:
if not hasattr(self, '_tokenizer'):
self._tokenizer = tokenizer
else:
self._tokenizer = tokenizer
self.map_fn = ProcessorTokenizer(tokenizer, self.process_fn)
def GetTokenizer(self):
return self._tokenizer
def __getitem__(self, index):
"""
read file and splice strings based on string ending array `self.ends`
"""
if not isinstance(index, slice):
if index == 0:
start = 0
else:
start = self.ends[index-1]
end = self.ends[index]
rtn = self.file_read(start, end)
if self.map_fn is not None:
return self.map_fn(rtn)
else:
# if slice, fetch strings with 1 diskread and then splice in memory
chr_lens = self.ends[index]
if index.start == 0 or index.start is None:
start = 0
else:
start = self.ends[index.start-1]
stop = chr_lens[-1]
strings = self.file_read(start, stop)
rtn = split_strings(strings, start, chr_lens)
if self.map_fn is not None:
return self.map_fn([s for s in rtn])
return rtn
def __len__(self):
return len(self.ends)
def file_read(self, start=0, end=None):
"""read specified portion of file"""
# atomic reads to avoid race conditions with multiprocess dataloader
self.read_lock.acquire()
# seek to start of file read
self.file.seek(start)
# read to end of file if no end point provided
if end is None:
rtn = self.file.read()
#else read amount needed to reach end point
else:
rtn = self.file.read(end-start)
self.read_lock.release()
#TODO: @raulp figure out mem map byte string bug
#if mem map'd need to decode byte string to string
rtn = rtn.decode('utf-8', 'ignore')
# rtn = str(rtn)
if self.mem_map:
rtn = rtn.decode('unicode_escape')
return rtn
================================================
FILE: CPM-Generate/data_utils/samplers.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
"""batch samplers that work with either random or sequential data samplers"""
import math
import os
import sys
import torch
from torch.utils import data
import numpy as np
class RandomSampler(data.sampler.Sampler):
r"""
Based off of pytorch RandomSampler and DistributedSampler. Essentially a RandomSampler,
but this class lets the user set an epoch like DistributedSampler
Samples elements randomly. If without replacement, then sample from a shuffled dataset.
If with replacement, then user can specify ``num_samples`` to draw.
Arguments:
data_source (Dataset): dataset to sample from
num_samples (int): number of samples to draw, default=len(dataset)
replacement (bool): samples are drawn with replacement if ``True``, default=False
"""
def __init__(self, data_source, replacement=False, num_samples=None):
self.data_source = data_source
self.replacement = replacement
self._num_samples = num_samples
self.epoch = -1
if self._num_samples is not None and replacement is False:
raise ValueError("With replacement=False, num_samples should not be specified, "
"since a random permute will be performed.")
if not isinstance(self.num_samples, int) or self.num_samples <= 0:
raise ValueError("num_samples should be a positive integer "
"value, but got num_samples={}".format(self.num_samples))
if not isinstance(self.replacement, bool):
raise ValueError("replacement should be a boolean value, but got "
"replacement={}".format(self.replacement))
@property
def num_samples(self):
# dataset size might change at runtime
if self._num_samples is None:
return len(self.data_source)
return self._num_samples
def __iter__(self):
n = len(self.data_source)
g = torch.Generator()
if self.epoch >= 0:
g.manual_seed(self.epoch)
if self.replacement:
return iter(torch.randint(high=n, size=(self.num_samples,), dtype=torch.int64, generator=g).tolist())
return iter(torch.randperm(n, generator=g).tolist())
def __len__(self):
return self.num_samples
def set_epoch(self, epoch):
self.epoch = epoch
class DistributedBatchSampler(data.sampler.BatchSampler):
"""
similar to normal implementation of distributed sampler, except implementation is at the
batch sampler level, instead of just the sampler level. This allows wrapping of arbitrary
data samplers (sequential, random, WeightedRandomSampler, etc.) with this batch sampler.
"""
def __init__(self, sampler, batch_size, drop_last, rank=-1, world_size=2, wrap_last=False):
super(DistributedBatchSampler, self).__init__(sampler, batch_size, drop_last)
if rank == -1:
assert False, 'should not be here'
rank = torch.distributed.get_rank()
self.rank = rank
self.world_size = world_size
self.sampler.wrap_around = 0
self.wrap_around = 0
self.wrap_last = wrap_last
self.start_iter = 0
def __iter__(self):
batch = []
last_batch = None
i = 0
for idx in self.data_iterator(self.sampler, wrap_around=False):
batch.append(idx)
if len(batch) == self.batch_size:
tbatch = self._batch(batch)
if i >= self.start_iter:
yield tbatch
self.start_iter = 0
i += 1
last_batch = np.array(list(tbatch))
batch = []
batch_len = len(batch)
if batch_len > 0 and not self.drop_last:
if self.wrap_last:
self.sampler.wrap_around -= (self.batch_size)
self.wrap_around += (len(batch))
self.wrap_around %= self.batch_size
if isinstance(self.sampler, TransposedSampler):
for i, idx in enumerate(self.data_iterator(self.sampler, wrap_around=True)):
if i == 0:
continue
batch.append(idx)
new_batch_len = len(batch)
if len(batch) == self.batch_size:
break
yield self._batch(batch)
if self.wrap_last:
self.sampler.wrap_around += self.batch_size
def data_iterator(self, _iter, wrap_around=False):
"""iterates through data and handles wrap around"""
for i, idx in enumerate(_iter):
if i < self.wrap_around%self.batch_size:
continue
if wrap_around:
self.wrap_around += 1
self.wrap_around %= self.batch_size
yield idx
def _batch(self, batch):
"""extracts samples only pertaining to this worker's batch"""
start = self.rank*self.batch_size//self.world_size
end = (self.rank+1)*self.batch_size//self.world_size
return batch[start:end]
================================================
FILE: CPM-Generate/data_utils/tf_dl.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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 DataLoader for TFRecords"""
import queue
import threading
import tensorflow as tf
tf.enable_eager_execution()
import torch
import numpy as np
class TFRecordDataLoader(object):
def __init__(self, records, batch_size, max_seq_len, max_preds_per_seq, train, num_workers=2, seed=1, threaded_dl=False):
assert max_preds_per_seq is not None, "--max-preds-per-seq MUST BE SPECIFIED when using tfrecords"
tf.set_random_seed(seed)
if isinstance(records, str):
records = [records]
self.record_converter = Record2Example({"input_ids": tf.FixedLenFeature([max_seq_len], tf.int64),
"input_mask": tf.FixedLenFeature([max_seq_len], tf.int64),
"segment_ids": tf.FixedLenFeature([max_seq_len], tf.int64),
"masked_lm_positions": tf.FixedLenFeature([max_preds_per_seq], tf.int64),
"masked_lm_ids": tf.FixedLenFeature([max_preds_per_seq], tf.int64),
"masked_lm_weights": tf.FixedLenFeature([max_preds_per_seq], tf.float32),
"next_sentence_labels": tf.FixedLenFeature([1], tf.int64)})
#Instantiate dataset according to original BERT implementation
if train:
self.dataset = tf.data.Dataset.from_tensor_slices(tf.constant(records))
self.dataset = self.dataset.repeat()
self.dataset = self.dataset.shuffle(buffer_size=len(records))
# use sloppy tfrecord dataset
self.dataset = self.dataset.apply(
tf.contrib.data.parallel_interleave(
tf.data.TFRecordDataset,
sloppy=train,
cycle_length=min(num_workers, len(records))))
self.dataset = self.dataset.shuffle(buffer_size=100)
else:
self.dataset = tf.data.TFRecordDataset(records)
self.dataset = self.dataset.repeat()
# Instantiate dataloader (do not drop remainder for eval)
loader_args = {'batch_size': batch_size,
'num_parallel_batches': num_workers,
'drop_remainder': train}
self.dataloader = self.dataset.apply(tf.contrib.data.map_and_batch(self.record_converter, **loader_args))
self.threaded_dl = threaded_dl
self.num_workers = num_workers
def __iter__(self):
if self.threaded_dl:
data_iter = iter(MultiprocessLoader(self.dataloader, self.num_workers))
for item in data_iter:
yield item
else:
data_iter = iter(self.dataloader)
for item in data_iter:
yield convert_tf_example_to_torch_tensors(item)
class Record2Example(object):
def __init__(self, feature_map):
self.feature_map = feature_map
def __call__(self, record):
"""Decodes a BERT TF record to a TF example."""
example = tf.parse_single_example(record, self.feature_map)
for k, v in list(example.items()):
if v.dtype == tf.int64:
example[k] = tf.to_int32(v)
return example
def convert_tf_example_to_torch_tensors(example):
item = {k: (v.numpy()) for k,v in example.items()}
mask = np.zeros_like(item['input_ids'])
mask_labels = np.ones_like(item['input_ids'])*-1
for b, row in enumerate(item['masked_lm_positions'].astype(int)):
for i, idx in enumerate(row):
if item['masked_lm_weights'][b, i] != 0:
mask[b, idx] = 1
mask_labels[b, idx] = item['masked_lm_ids'][b, i]
output = {'text': item['input_ids'], 'types': item['segment_ids'],'is_random': item['next_sentence_labels'],
'pad_mask': 1-item['input_mask'], 'mask': mask, 'mask_labels': mask_labels}
return {k: torch.from_numpy(v) for k,v in output.items()}
class MultiprocessLoader(object):
def __init__(self, dataloader, num_workers=2):
self.dl = dataloader
self.queue_size = 2*num_workers
def __iter__(self):
output_queue = queue.Queue(self.queue_size)
output_thread = threading.Thread(target=_multiproc_iter,
args=(self.dl, output_queue))
output_thread.daemon = True
output_thread.start()
while output_thread.is_alive():
yield output_queue.get(block=True)
else:
print(RuntimeError('TF record data loader thread exited unexpectedly'))
def _multiproc_iter(dl, output_queue):
data_iter = iter(dl)
for item in data_iter:
tensors = convert_tf_example_to_torch_tensors(item)
output_queue.put(tensors, block=True)
================================================
FILE: CPM-Generate/data_utils/tokenization.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
"""Utilities for using and training tokenizers (char, wordpiece, sentencepiece)"""
from collections import namedtuple
import random
import os
import csv
import torch
import nltk
from nltk import tokenize as nltk_tokenize
import sentencepiece as spm
from .wordpiece import BertTokenizer, PRETRAINED_VOCAB_ARCHIVE_MAP
from .tokenization_gpt2 import GPT2Tokenizer
import regex as re
def make_tokenizer(tokenizer_type, corpus, model_path=None, vocab_size=None, model_type='bpe', pad_token=0, character_coverage=1.0, command_tokens=None, type_tokens=None, **kwargs):
"""
Helper function to instantiate a tokenizer given common combinations of options.
"""
tokenizer_class = tokenizer_type
if isinstance(tokenizer_class, str):
tokenizer_class = eval(tokenizer_class)
if tokenizer_class is BertWordPieceTokenizer:
return BertWordPieceTokenizer(model_type, **kwargs)
elif tokenizer_class is GPT2BPETokenizer:
return GPT2BPETokenizer(**kwargs)
text_tokenizer = tokenizer_class(corpus=corpus, vocab_size=vocab_size, model_path=model_path, model_type=model_type,
pad_token=pad_token, character_coverage=character_coverage)
return Tokenizer(text_tokenizer, command_tokens, type_tokens)
class Tokenization(object):
"""
Tokenization object to hold tokenization, (processed text),and original
text. Can hold tokenization as Ids or tokens.
It also holds command tokens (pad, unk, etc.) for the tokenization.
This allows functions to pad/operate on tokenizations without having
access to the full tokenizer, just the tokenization.
Several standard array operations are implemented (insert, append, extend).
"""
def __init__(self, tokenization, text=None, original_text=None, command_tokens=None, asIds=True):
self.tokenization = tokenization
self.text = text
if self.text is None:
self.text = self.tokenization
self.original_text = original_text
if self.original_text is None:
self.original_text = self.text
self.command_tokens = command_tokens
self.asIds = asIds
self.parse_command_tokens()
def set_command_tokens(self, command_tokens):
self.command_tokens = command_tokens
return self.parse_command_tokens()
def parse_command_tokens(self):
if self.command_tokens is None:
return
for command_token in self.command_tokens:
if self.asIds:
setattr(self, command_token.name, command_token.Id)
else:
setattr(self, command_token.name, command_token.token)
def __getitem__(self, index):
return self.tokenization[index]
def __len__(self):
return len(self.tokenization)
def insert(self, idx, other):
if isinstance(other, (CommandToken, TypeToken)):
self.tokenization.insert(idx, other.Id)
if idx == 0:
self.text = other.token + self.text
self.original_text = other.token + self.original_text
elif idx == len(self.tokenization)-1:
self.text += other.token
self.original_text += other.token
elif isinstance(other, Tokenization):
self.tokenization = self.tokenization[:idx] + other.tokenization + self.tokenization[idx:]
else:
self.tokenization = self.tokenization[:idx] + other.tokenization + self.tokenization[idx:]
def append(self, other):
if isinstance(other, (CommandToken, TypeToken)):
self.tokenization.append(other.Id)
self.text += other.token
self.original_text += other.token
elif isinstance(other, Tokenization):
self.tokenization.extend(other.tokenization)
self.text += other.text
self.original_text += other.original_text
else:
self.tokenization.append(other)
return self
def extend(self, other):
if isinstance(other, (CommandToken, TypeToken)):
self.tokenization.append(other.Id)
self.text += other.token
self.original_text += other.token
elif isinstance(other, list) and isinstance(other[0], (CommandToken, TypeToken)):
self.tokenization.extend([o.Id for o in other])
self.text += [o.token for o in other]
self.original_text += [o.token for o in other]
elif isinstance(other, Tokenization):
self.tokenization.extend(other.tokenization)
self.text += other.text
self.original_text += other.original_text
else:
self.tokenization.extend(other)
return self
"""define some default command tokens for the tokenizer to use"""
token_format = "<{0}>"
COMMAND_TUPLE = namedtuple('CommandToken', ('name', 'token', 'Id'))
def prep_command_tokens(tokenlist, token_format=token_format):
return [CommandToken(tok[0], token_format.format(tok[0]), tok[1]) for tok in tokenlist]
class CommandToken(object):
def __init__(self, name, token, Id):
self.name = name
self.token = token
self.Id = Id
def __str__(self):
return str(COMMAND_TUPLE(self.name, self.token, self.Id))
DEFAULT_COMMAND_TOKENS = [
('pad', 0),
('eos', 1),
('bos', 2),
('unk', 3),
('sep', 4),
('L2R', 5),
('ENC', 6),
('MASK', 7),
]
DEFAULT_COMMAND_TOKENS = prep_command_tokens(DEFAULT_COMMAND_TOKENS)
"""define some default type tokens for bert training"""
TYPE_TUPLE = namedtuple('TypeToken', ('name', 'token', 'Id'))
def prep_type_tokens(tokenlist, token_format=token_format):
return [TypeToken(tok[0], token_format.format(tok[0]), tok[1]) for tok in tokenlist]
class TypeToken(object):
def __init__(self, name, token, Id):
self.name = name
self.token = token
self.Id = Id
def __str__(self):
return str(TYPE_TUPLE(self.name, self.token, self.Id))
DEFAULT_TYPE_TOKENS = [
('function', 0),
('command', 1),
('str0', 2),
('str1', 3),
('str2', 4),
('embedding0', 5),
('embedding1', 6),
('embedding2', 7),
('arg0', 8),
('arg1', 9),
('arg2', 10),
]
DEFAULT_TYPE_TOKENS = prep_type_tokens(DEFAULT_TYPE_TOKENS)
class Tokenizer(object):
"""
Tokenizer object that handles text tokenization, command tokens, and type tokens.
Command tokens and text tokens are stored together in one mapping of size
`len(text_tokenizer)+len(command_tokens)`. Command tokens are stored as first
`len(command_tokens)` tokens. Token idx is stored at `idx+len(command_tokens)`.
Token types are stored in a separate mapping of size `len(type_tokens)`.
"""
def __init__(self, text_tokenizer, command_tokens=None, type_tokens=None):
# set text tokenizer
self.text_tokenizer = text_tokenizer
if not hasattr(self, 'num_text_tokens'):
self.num_text_tokens = len(self.text_tokenizer)
# set command tokens
if command_tokens is None:
command_tokens = DEFAULT_COMMAND_TOKENS
self._command_tokens = command_tokens
self.command_name_map = {tok.name: tok for tok in self._command_tokens}
self.command_token_map = {tok.token: tok for tok in self._command_tokens}
self.command_id_map = {tok.Id: tok for tok in self._command_tokens}
if not hasattr(self, 'num_command_tokens'):
self.num_command_tokens = len(self._command_tokens)
if not hasattr(self, 'num_tokens'):
self.num_tokens = self.num_command_tokens + self.num_text_tokens
# set type tokens
if type_tokens is None:
type_tokens = DEFAULT_TYPE_TOKENS
self.type_tokens = type_tokens
self.type_name_map = {tok.name: tok for tok in self.type_tokens}
self.type_token_map = {tok.token: tok for tok in self.type_tokens}
self.type_id_map = {tok.Id: tok for tok in self.type_tokens}
if not hasattr(self, 'num_type_tokens'):
self.num_type_tokens = len(self.type_tokens)
# parse tokens and vocabs from tokenizer
self._tokens = list(self.command_token_map.keys()) + list(self.text_tokenizer.tokens)
self._vocab = {t:Id for Id,t in self.command_id_map.items()}
self._vocab.update({t:Id+self.num_command_tokens for t,Id in self.text_tokenizer.vocab.items()})
self._text_tokens = list(self.text_tokenizer.tokens)
self._text_token_vocab = {t:Id+self.num_command_tokens for t,Id in self.text_tokenizer.vocab.items()}
self._command_token_tokens = list(self.command_token_map.keys())
self._command_token_vocab = {t:Id for Id,t in self.command_id_map.items()}
self._token_types = list(self.type_token_map.keys())
self._token_type_vocab = {t:Id for Id, t in self.type_id_map.items()}
def __call__(self, text, process_fn=None):
"""run preprocessing and encode text as Ids"""
return self.EncodeAsIds(text, process_fn=process_fn)
def __len__(self):
"""total number of tokens"""
return self.num_tokens
def get_command(self, name):
"""get command token corresponding to `name`"""
return self.command_name_map[name]
def get_type(self, name):
"""get type token corresponding to `name`"""
return self.type_name_map[name]
@property
def tokens(self):
"""list (or iterable) of all tokens for tokenizer"""
return self._tokens
@property
def vocab(self):
"""dictionary mapping tokens to ids for tokenizer"""
return self._vocab
@property
def token_types(self):
"""list (or iterable) of all token types for tokenizer"""
return self._token_types
@property
def token_type_vocab(self):
"""dictionary mapping token types to ids for tokenizer"""
return self._token_type_vocab
@property
def command_tokens(self):
"""list (or iterable) of all command tokens for tokenizer"""
return self._command_token_tokens
@property
def command_token_vocab(self):
"""dictionary mapping command tokens to ids for tokenizer"""
return self._command_token_vocab
@property
def text_tokens(self):
"""list (or iterable) of text tokens for text tokenizer"""
return self._text_tokens
@property
def text_token_vocab(self):
"""dictionary mapping text tokens to ids for text tokenizer"""
return self._text_token_vocab
def EncodeAsIds(self, text, process_fn=None):
"""
encode text using text tokenizer and shift Id values for command tokens
"""
tokenization = self.text_tokenizer.EncodeAsIds(text, process_fn=process_fn)
tokenization.tokenization = [t+self.num_command_tokens for t in tokenization.tokenization]
tokenization.set_command_tokens(self._command_tokens)
return tokenization
def EncodeAsTokens(self, text, process_fn=None):
"""
encode text as tokens using text tokenizer
"""
tokenization = self.text_tokenizer.EncodeAsTokens(text, process_fn=process_fn)
tokenization.set_command_tokens(self._command_tokens)
return tokenization
def IdToToken(self, Id, type_token=False):
"""convert Id to token accounting for command and type tokens"""
if isinstance(Id, (TypeToken, CommandToken)):
return Id.token
if type_token:
return self.type_id_map[Id].token
if Id < self.num_command_tokens:
return self.command_id_map[Id].token
return self.text_tokenizer.IdToToken(Id-self.num_command_tokens)
def TokenToId(self, token, type_token=False):
"""convert token to Id accounting for command and type tokens"""
if isinstance(token, (TypeToken, CommandToken)):
return token.Id
if type_token:
return self.type_token_map[token].Id
if token in self.command_token_map:
return self.command_token_map[token].Id
return self.text_tokenizer.TokenToId(token)+self.num_command_tokens
def DecodeIds(self, Ids, type_token=False):
"""
convert Ids to tokens accounting for command and type tokens, tokens
are joined and returned as a string.
"""
if type_token:
return ' '.join(Id.token if isinstance(Id, TypeToken) else self.type_id_map[Id].token for Id in Ids)
rtn_strs = []
current_str = []
if isinstance(Ids, Tokenization):
Ids = Ids.tokenization
for Id in Ids:
if isinstance(Id, CommandToken):
rtn_strs.append(self.text_tokenizer.DecodeIds(current_str))
current_str = []
rtn_strs.append(t.token)
elif Id < self.num_command_tokens:
rtn_strs.append(self.text_tokenizer.DecodeIds(current_str))
current_str = []
rtn_strs.append(self.command_id_map[Id].token)
else:
current_str.append(Id - self.num_command_tokens)
if current_str != []:
rtn_strs.append(self.text_tokenizer.DecodeIds(current_str))
return ' '.join(rtn_strs)
def DecodeTokens(self, Tokens, type_token=False):
"""
convert tokens to a string accounting for command and type tokens.
"""
if type_token:
return ' '.join(t.token if isinstance(t, TypeToken) else t for t in Tokens)
rtn_strs = []
current_str = []
if isinstance(Tokens, Tokenization):
Tokens = Tokens.tokenization
for t in Tokens:
if isinstance(t, CommandToken):
rtn_strs.append(self.text_tokenizer.DecodeTokens(current_str))
current_str = []
rtn_strs.append(t.token)
elif t in self.command_token_map:
rtn_strs.append(self.text_tokenizer.DecodeTokens(current_str))
current_str = []
rtn_strs.append(t)
else:
current_str.append(t)
if current_str != []:
rtn_strs.append(self.text_tokenizer.DecodeTokens(current_str))
return ' '.join(rtn_strs)
class TextTokenizer(object):
"""
Interface for text tokenizer
"""
def __init__(self):
if not hasattr(self, 'num_text_tokens'):
self.num_text_tokens = 0
if not hasattr(self, 'num_tokens'):
self.num_tokens = self.num_text_tokens
def __call__(self, text, process_fn=None):
return self.EncodeAsIds(text, process_fn)
def __len__(self):
return self.num_text_tokens
@property
def tokens(self):
"""list (or iterable) of text tokens for text tokenizer"""
raise NotImplementedError('TextTokenizer tokens property not implemented')
@property
def vocab(self):
"""dictionary mapping tokens to ids"""
raise NotImplementedError('TextTokenizer vocab property not implemented')
@staticmethod
def exists(model_path):
"""check if the filepath for a text tokenizer exists"""
raise NotImplementedError('TextTokenizer exists method not implemented')
def Train(self, corpus):
"""train a tokenizer on a data corpus and save model for future use"""
raise NotImplementedError('TextTokenizer Train not implemented')
def EncodeAsIds(self, text, process_fn=None):
"""
Preprocess text and encode as ids. Return a tokenization object with
original text, processed text, and id tokenization.
"""
raise NotImplementedError('TextTokenizer EncodeAsIds not implemented')
def EncodeAsTokens(self, text, process_fn=None):
"""
Preprocess text and encode as tokens. Return a tokenization object with
original text, processed text, and token tokenization.
"""
raise NotImplementedError('TextTokenizer EncodeAsTokens not implemented')
def IdToToken(self, Id):
"""Convert an Id to Token. Reverse lookup of self.vocab"""
raise NotImplementedError('TextTokenizer IdToToken not implemented')
def TokenToId(self, token):
"""Convert a Token to Id. Lookup of self.vocab"""
raise NotImplementedError('TextTokenizer TokenToId not implemented')
def DecodeIds(self, Ids):
"""Convert a list or tokenization object of Ids to a text string"""
raise NotImplementedError('TextTokenizer DecodeIds not implemented')
def DecodeTokens(self, Tokens):
"""Convert a list or tokenization object of tokens to a text string"""
raise NotImplementedError('TextTokenizer DecodeTokens not implemented')
class CharacterLevelTokenizer(TextTokenizer):
"""
Text tokenizer for ASCII-256 Character Level Tokenization.
"""
def __init__(self, **kwargs):
self.num_text_tokens = 256
super(CharacterLevelTokenizer, self).__init__()
self._tokens = [self.IdToToken(Id) for Id in range(self.num_text_tokens)]
self._vocab = {t: i for i,t in enumerate(self._tokens)}
def __len__(self):
return 256
@staticmethod
def exists(model_path):
return True
def Train(self, corpus):
pass
@property
def tokens(self):
return self._tokens
@property
def vocab(self):
return self._vocab
def EncodeAsIds(self, text, process_fn=None):
"""convert text to ascii 256 Ids"""
processed_text = text
if process_fn is not None:
processed_text = process_fn(processed_text)
processed_text = str(processed_text)
tokens = [self.TokenToId(c) for c in processed_text]
return Tokenization(tokens, processed_text, text)
def EncodeAsTokens(self, text, process_fn=None):
"""convert text to ascii 256 characters"""
processed_text = text
if process_fn is not None:
processed_text = process_fn(processed_text)
processed_text = str(processed_text)
tokens = [c for c in processed_text]
return Tokenization(tokens, processed_text, text, asIds=False)
def IdToToken(self, Id):
"""ascii index to character"""
return chr(Id)
def TokenToId(self, token):
"""ascii character to index"""
return ord(token)
def DecodeIds(self, Ids):
"""converts ascii ids to tokens before joining them into text"""
if isinstance(Ids, Tokenization):
Ids = Ids.tokenization
return ''.join([self.IdToToken(tok) for tok in Ids])
def DecodeTokens(self, Tokens):
"""just concatenates ascii tokens into text"""
if isinstance(Tokens, Tokenization):
Tokens = Tokens.tokenization
return ''.join(Tokens)
MAX_SENTENCEPIECE_SENTENCES = 100000000
def get_corpus_freq(dataset, filepath, filetype='tsv'):
"""
Take corpus, split it into sentences, and extract word frequencies.
Write frequencies to `filepath` as a tsv. Only write the first
MAX_SENTENCEPIECE_SENTENCES most common words to the file.
"""
nltk.download('punkt', download_dir="./nltk")
if filetype == 'tsv':
delimiter = '\t'
else:
delimiter = ','
print("compute corpus frequency\n", flush=True)
total_sentence_count = 0
maxlen = 0
freqs = {}
for entry in dataset:
if isinstance(entry, dict):
entry = entry['text']
lines = entry.strip().split('\n')
for line in lines:
sentences = nltk_tokenize.sent_tokenize(line)
total_sentence_count += len(sentences)
for sentence in sentences:
maxlen = max(len(line), maxlen)
for word in sentence.split():
if word not in freqs:
freqs[word] = 0
freqs[word] += 1
print("length of freqs before truncating " + str(len(freqs)), flush=True)
print("file path for freq " + str(filepath), flush=True)
freqs_sorted = {}
counter=0
for word, count in sorted(freqs.items(), key=lambda x: x[1], reverse=True):
if counter >= MAX_SENTENCEPIECE_SENTENCES:
break
counter+=1
freqs_sorted[word] = count
print("length of freqs after trancating " + str(len(freqs_sorted)), flush=True)
with open(filepath, 'w') as f:
writer = csv.writer(f, delimiter=delimiter)
for k, v in freqs_sorted.items():
writer.writerow([str(k), str(v)])
return total_sentence_count, maxlen
class SentencePieceTokenizer(TextTokenizer):
"""Trains and uses sentencepiece for text tokenization"""
def __init__(self, model_type='bpe', vocab_size=None, corpus=None, model_path=None, character_coverage=1.0, **kwargs):
self.character_coverage = character_coverage
self.model_type = model_type.lower()
self.spm_model = model_path
self.num_text_tokens = vocab_size
make_train = not SentencePieceTokenizer.exists(self.spm_model)
if make_train:
assert corpus is not None and self.num_text_tokens is not None
self.Train(corpus, self.num_text_tokens)
self._tokens = []
self._vocab = {}
self.load_spm_model()
super(SentencePieceTokenizer, self).__init__()
def __len__(self):
return self.num_text_tokens
@property
def tokens(self):
return self._tokens
@property
def vocab(self):
return self._vocab
@staticmethod
def exists(model_path):
if model_path is None:
return False
# check if path exists
dne = not os.path.exists(model_path)
# check if path.model exists
if dne and not model_path.endswith('.model'):
dne = not os.path.exists(model_path+'.model')
return not dne
def load_spm_model(self):
"""load sentencepiece model and parse vocab"""
if not os.path.exists(self.spm_model) and not self.spm_model.endswith('.model'):
self.spm_model = self.spm_model+'.model'
self.sp = spm.SentencePieceProcessor()
self.sp.Load(self.spm_model)
self.vocab_size = self.num_text_tokens = len(self.sp)
self._tokens = [self.IdToToken(t) for t in range(self.vocab_size)]
self._vocab = {t: i for i,t in enumerate(self._tokens)}
def Train(self, corpus, num_text_tokens):
"""train sentencepiece model on corpus using word frequencies"""
self.num_text_tokens = num_text_tokens
use_model_path = self.spm_model
random_hash = str(random.randint(0, 2147483647))
if use_model_path is None:
use_model_path = random_hash
if use_model_path.endswith('.model'):
use_model_path = use_model_path[:use_model_path.rfind('.model')]
input_path = use_model_path+'.tsv.'+random_hash
line_count, maxlenline = get_corpus_freq(corpus, input_path)
line_count = min(line_count, MAX_SENTENCEPIECE_SENTENCES)
print('line count used as input_sentence_size ', line_count, flush=True)
print('training sentencepiece model', flush=True)
train_string = '--input={file_path} --model_prefix={model_prefix} --vocab_size={vocab_size}' \
+ ' --model_type={model_type} --character_coverage={character_coverage} ' \
+ '--input_sentence_size={input_sentence_size} ' \
+ '--input_format=tsv'
train_string = train_string.format(file_path=input_path, model_prefix=use_model_path, vocab_size=num_text_tokens,
model_type=self.model_type, character_coverage=self.character_coverage,
input_sentence_size=int(line_count)) #, #)#,
print("calling spm.SentencePieceTrainer.Train(%s)"%(train_string), flush=True)
spm.SentencePieceTrainer.Train(train_string)
os.remove(input_path)
self.spm_model = use_model_path+'.model'
print('sentencepiece model written to '+self.spm_model, flush=True)
def EncodeAsIds(self, text, process_fn=None):
"""convert text to sentencepiece Ids"""
processed_text = text
if process_fn is not None:
processed_text = process_fn(processed_text)
tokens = self.sp.EncodeAsIds(processed_text)
return Tokenization(tokens, processed_text, text)
def EncodeAsTokens(self, text, process_fn=None):
"""convert text to sentencepiece tokens"""
processed_text = text
if process_fn is not None:
processed_text = process_fn(processed_text)
tokens = self.sp.EncodeAsTokens(processed_text)
return Tokenization(tokens, processed_text, text, asIds=False)
def IdToToken(self, Id):
"""convert Id to sentencpiece token"""
return self.sp.IdToPiece(Id)
def TokenToId(self, token):
"""convert sentencpiece token to Id"""
return self.sp.PieceToId(token)
def DecodeIds(self, Ids):
"""converts ids to a text string"""
if isinstance(Ids, Tokenization):
Ids = Ids.tokenization
return self.sp.DecodeIds(Ids)
def DecodeTokens(self, Tokens):
"""converts sentencepiece tokens to a text string"""
if isinstance(Tokens, Tokenization):
Tokens = Tokens.tokenization
return self.sp.DecodeTokens(Tokens)
class BertWordPieceTokenizer(Tokenizer):
"""
Loads a pretrained WordPiece tokenizer from `cache_dir` for tokenization
in BERT training. Default to bert-large-uncased tokenizer.
"""
def __init__(self, tokenizer_model_type=None, cache_dir=None, **kwargs):
# default to bert-large-uncased tokenizer
if tokenizer_model_type not in PRETRAINED_VOCAB_ARCHIVE_MAP:
tokenizer_model_type = 'bert-large-uncased'
if torch.distributed.get_rank() == 0:
print('loading BertWordPieceTokenizer (', tokenizer_model_type, ') from cache_dir ', cache_dir)
do_lower_case = not ('-cased' in tokenizer_model_type or 'chinese' in tokenizer_model_type)
self.text_tokenizer = BertTokenizer.from_pretrained(tokenizer_model_type, do_lower_case=do_lower_case, cache_dir=cache_dir)
if torch.distributed.get_rank() == 0:
print('loaded', tokenizer_model_type)
# disable max len warnings by increasing max len
self.text_tokenizer.max_len = int(1e12)
# set command tokens from wordpiece tokenizer values
self.num_command_tokens = 5
self.num_tokens = len(self.text_tokenizer.vocab)
self.num_text_tokens = self.num_tokens-5
self.num_type_tokens = 2
self._command_tokens = [
CommandToken('pad', '[PAD]', self.text_tokenizer.vocab['[PAD]']),
CommandToken('ENC', '[CLS]', self.text_tokenizer.vocab['[CLS]']),
CommandToken('MASK', '[MASK]', self.text_tokenizer.vocab['[MASK]']),
CommandToken('unk', '[UNK]', self.text_tokenizer.vocab['[UNK]']),
CommandToken('sep', '[SEP]', self.text_tokenizer.vocab['[SEP]']),
]
self.command_name_map = {tok.name: tok for tok in self._command_tokens}
self.command_token_map = {tok.token: tok for tok in self._command_tokens}
self.command_id_map = {tok.Id: tok for tok in self._command_tokens}
# set type tokens
self.type_tokens = [
TypeToken('str0', '', 0),
TypeToken('str1', '', 1),
]
self.type_name_map = {tok.name: tok for tok in self.type_tokens}
self.type_token_map = {tok.token: tok for tok in self.type_tokens}
self.type_id_map = {tok.Id: tok for tok in self.type_tokens}
# parse tokens and vocabs from tokenizer
self._tokens = list(self.text_tokenizer.vocab.keys())
self._vocab = {k:v for k,v in self.text_tokenizer.vocab.items()}
self._text_tokens = list(self._tokens)
self._text_token_vocab = {k:v for k,v in self.text_tokenizer.vocab.items()}
self._command_token_tokens = list(self.command_token_map.keys())
self._command_token_vocab = {t:Id for Id,t in self.command_id_map.items()}
self._token_types = list(self.type_token_map.keys())
self._token_type_vocab = {t:Id for Id, t in self.type_id_map.items()}
def EncodeAsIds(self, text, process_fn=None):
"""convert text to wordpiece Ids"""
processed_text = text
if process_fn is not None:
processed_text = process_fn(processed_text)
tokens = self.text_tokenizer.tokenize(processed_text)
Ids = self.text_tokenizer.convert_tokens_to_ids(tokens)
return Tokenization(Ids, processed_text, text)
def EncodeAsTokens(self, text, process_fn=None):
"""convert wordpiece token to Id"""
processed_text = text
if process_fn is not None:
processed_text = process_fn(processed_text)
tokens = self.text_tokenizer.tokenize(processed_text)
return Tokenization(tokens, processed_text, text, asIds=False)
def IdToToken(self, Id, type_token=False):
"""convert Id to sentencpiece token"""
if isinstance(Id, (TypeToken, CommandToken)):
return Id.token
if type_token:
return self.type_id_map[Id].token
return self.text_tokenizer.ids_to_tokens[Id]
def TokenToId(self, token, type_token=False):
"""convert sentencpiece token to Id"""
if isinstance(token, (TypeToken, CommandToken)):
return token.Id
if type_token:
return self.type_token_map[token].Id
return self.text_tokenizer.vocab[token]
def DecodeIds(self, Ids, type_token=False):
"""converts ids to wordpiece tokens and joins them as a text string"""
if type_token:
return ' '.join(Id.token if isinstance(Id, TypeToken) else self.type_id_map[Id].token for Id in Ids)
if isinstance(Ids, Tokenization):
Ids = Ids.tokenization
Tokens = []
for Id in Ids:
Tokens.append(self.text_tokenizer.ids_to_tokens[Id] if Id != -1 else '-1')
Tokens = self.text_tokenizer.convert_ids_to_tokens(Ids)
return ' '.join(Tokens)
def DecodeTokens(self, Tokens, type_token=False):
"""converts wordpiece tokens to a text string"""
if type_token:
return ' '.join(t.token if isinstance(t, TypeToken) else t for t in Tokens)
if isinstance(Tokens, Tokenization):
Tokens = Tokens.tokenization
return ' '.join(Tokens)
class GPT2BPETokenizer(Tokenizer):
def __init__(self, cache_dir=None, **kwargs):
self.text_tokenizer = GPT2Tokenizer.from_pretrained('gpt2',
cache_dir=cache_dir)
#disable max len warnings by increasing max len
self.text_tokenizer.max_len = int(1e12)
self.num_command_tokens = 2
self.num_tokens = len(self.text_tokenizer.encoder)
self.num_text_tokens = self.num_tokens-1
self.num_type_tokens = 2
self._command_tokens = [
CommandToken('pad', '<|endoftext|>', self.text_tokenizer.encoder['<|endoftext|>']),
CommandToken('eos', '<|endoftext|>', self.text_tokenizer.encoder['<|endoftext|>']),
]
self.command_name_map = {tok.name: tok for tok in self._command_tokens}
self.command_token_map = {tok.token: tok for tok in self._command_tokens}
self.command_id_map = {tok.Id: tok for tok in self._command_tokens}
self.type_tokens = [
TypeToken('str0', '', 0),
TypeToken('str1', '', 1),
]
self.type_name_map = {tok.name: tok for tok in self.type_tokens}
self.type_token_map = {tok.token: tok for tok in self.type_tokens}
self.type_id_map = {tok.Id: tok for tok in self.type_tokens}
self._tokens = list(self.text_tokenizer.encoder.keys())
self._vocab = {k:v for k,v in self.text_tokenizer.encoder.items()}
self._text_tokens = list(self._tokens)
self._text_token_vocab = {k:v for k,v in self.text_tokenizer.encoder.items()}
self._command_token_tokens = list(self.command_token_map.keys())
self._command_token_vocab = {t:Id for Id,t in self.command_id_map.items()}
self._token_types = list(self.type_token_map.keys())
self._token_type_vocab = {t:Id for Id, t in self.type_id_map.items()}
def EncodeAsIds(self, text, process_fn=None):
processed_text = text
if process_fn is not None:
processed_text = process_fn(processed_text)
Ids = self.text_tokenizer.encode(processed_text)
#return Tokenization(Ids, processed_text, text)
tokenization = Tokenization(Ids, processed_text, text)
tokenization.set_command_tokens(self._command_tokens)
return tokenization
def EncodeAsTokens(self, text, process_fn=None):
processed_text = text
if process_fn is not None:
processed_text = process_fn(processed_text)
tokens = []
for token in re.findall(self.text_tokenizer.pat, processed_text):
token = ''.join(self.text_tokenizer.bye_encoder[b] for b in token.encode('utf-8'))
tokens.extend(bpe_token for bpe_token in self.text_tokenizer.bpe(token).split(' '))
tokenization=Tokenization(tokens, processed_text, text, asIds=False)
tokenization.set_command_tokens(self._command_tokens)
return tokenization
#return Tokenization(tokens, processed_text, text, asIds=False)
def IdToToken(self, Id, type_token=False):
if isinstance(Id, (TypeToken, CommandToken)):
return Id.token
if type_token:
return self.type_id_map[Id].token
return self.text_tokenizer.decoder[Id]
def TokenToId(self, token, type_token=False):
if isinstance(token, (TypeToken, CommandToken)):
return token.Id
if type_token:
return self.type_token_map[token].Id
return self.text_tokenizer.encoder[token]
def DecodeIds(self, Ids, type_token=False):
if type_token:
return ' '.join(Id.token if isinstance(Id, TypeToken) else self.type_id_map[Id].token for Id in Ids)
if isinstance(Ids, Tokenization):
Ids = Ids.tokenization
return self.text_tokenizer.decode(Ids)
def DecodeTokens(self, Tokens, type_token=False):
if type_token:
return ' '.join(t.token if isinstance(t, TypeToken) else t for t in Tokens)
if isinstance(Tokens, Tokenization):
Tokens = Tokens.tokenization
return self.text_tokenizer.decode([self.TokenToId(tok) for tok in Tokens])
================================================
FILE: CPM-Generate/data_utils/tokenization_gpt2.py
================================================
# coding=utf-8
# Copyright 2018 The Open AI Team Authors 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.
"""Tokenization classes for OpenAI GPT."""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import sys
import json
import logging
import os
import regex as re
from io import open
import sentencepiece as spm
import jieba
try:
from functools import lru_cache
except ImportError:
# Just a dummy decorator to get the checks to run on python2
# because honestly I don't want to support a byte-level unicode BPE tokenizer on python 2 right now.
def lru_cache():
return lambda func: func
from .file_utils import cached_path
logger = logging.getLogger(__name__)
PRETRAINED_VOCAB_ARCHIVE_MAP = {
'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json",
}
PRETRAINED_MERGES_ARCHIVE_MAP = {
'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt",
}
PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
'gpt2': 1024,
}
VOCAB_NAME = 'vocab.json'
MERGES_NAME = 'merges.txt'
SPECIAL_TOKENS_NAME = 'special_tokens.txt'
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a signficant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
_chr = unichr if sys.version_info[0] == 2 else chr
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
n += 1
cs = [_chr(n) for n in cs]
return dict(zip(bs, cs))
def get_pairs(word):
"""Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
class GPT2Tokenizer(object):
"""
GPT-2 BPE tokenizer. Peculiarities:
- Byte-level BPE
"""
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
"""
Instantiate a PreTrainedBertModel from a pre-trained model file.
Download and cache the pre-trained model file if needed.
"""
if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path]
special_tokens_file = None
else:
vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME)
merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME)
special_tokens_file = os.path.join(pretrained_model_name_or_path, SPECIAL_TOKENS_NAME)
if not os.path.exists(special_tokens_file):
special_tokens_file = None
else:
logger.info("loading special tokens file {}".format(special_tokens_file))
# redirect to the cache, if necessary
try:
resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir)
except EnvironmentError:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find files {} and {} "
"at this path or url.".format(
pretrained_model_name_or_path,
', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
pretrained_model_name_or_path,
vocab_file, merges_file))
return None
if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file:
logger.info("loading vocabulary file {}".format(vocab_file))
logger.info("loading merges file {}".format(merges_file))
else:
logger.info("loading vocabulary file {} from cache at {}".format(
vocab_file, resolved_vocab_file))
logger.info("loading merges file {} from cache at {}".format(
merges_file, resolved_merges_file))
if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
# if we're using a pretrained model, ensure the tokenizer wont index sequences longer
# than the number of positional embeddings
max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path]
kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
# Instantiate tokenizer.
if special_tokens_file and 'special_tokens' not in kwargs:
special_tokens = open(special_tokens_file, encoding='utf-8').read().split('\n')[:-1]
else:
special_tokens = kwargs.pop('special_tokens', [])
tokenizer = cls(resolved_vocab_file, resolved_merges_file, special_tokens=special_tokens, *inputs, **kwargs)
return tokenizer
def __init__(self, vocab_file, merges_file, model_file, errors='replace', special_tokens=None, max_len=None):
self.max_len = max_len if max_len is not None else int(1e12)
self.encoder = json.load(open(vocab_file))
self.decoder = {v:k for k,v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v:k for k, v in self.byte_encoder.items()}
bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
bpe_merges = [tuple(merge.split()) for merge in bpe_data]
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.cache = {}
# Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
self.special_tokens = {}
self.special_tokens_decoder = {}
self.set_special_tokens(special_tokens)
self.sp = spm.SentencePieceProcessor(model_file=model_file)
self.translator = str.maketrans(" \n", "\u2582\u2583")
self.eod_id = self.encoder['']
self.decoder[7] = '\u2584'
@property
def vocab_size(self):
return len(self.encoder)
def __len__(self):
return len(self.encoder) + len(self.special_tokens)
@property
def eod(self):
return self.eod_id
def set_special_tokens(self, special_tokens):
""" Add a list of additional tokens to the encoder.
The additional tokens are indexed starting from the last index of the
current vocabulary in the order of the `special_tokens` list.
"""
if not special_tokens:
self.special_tokens = {}
self.special_tokens_decoder = {}
return
self.special_tokens = dict((tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens))
self.special_tokens_decoder = {v:k for k, v in self.special_tokens.items()}
logger.info("Special tokens {}".format(self.special_tokens))
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
pairs = get_pairs(word)
if not pairs:
return token
while True:
bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
new_word.extend(word[i:j])
i = j
except:
new_word.extend(word[i:])
break
if word[i] == first and i < len(word)-1 and word[i+1] == second:
new_word.append(first+second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = ' '.join(word)
self.cache[token] = word
return word
def tokenize(self, text):
""" Tokenize a string. """
bpe_tokens = []
seg_list = [x.translate(self.translator) for x in jieba.cut(text, cut_all=False)]
new_seg = " ".join(seg_list)
tmp_bpe_tokens = self.sp.encode(new_seg, out_type=str)
bpe_tokens.extend(tmp_bpe_tokens)
return bpe_tokens
def convert_tokens_to_ids(self, tokens):
""" Converts a sequence of tokens into ids using the vocab. """
ids = []
if isinstance(tokens, str) or (sys.version_info[0] == 2 and isinstance(tokens, unicode)):
if tokens in self.special_tokens:
return self.special_tokens[tokens]
else:
return self.encoder.get(tokens, 0)
for token in tokens:
if token in self.special_tokens:
ids.append(self.special_tokens[token])
else:
ids.append(self.encoder.get(token, 0))
if len(ids) > self.max_len:
logger.warning(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this OpenAI GPT model ({} > {}). Running this"
" sequence through the model will result in indexing errors".format(len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
"""Converts a sequence of ids in BPE tokens using the vocab."""
tokens = []
for i in ids:
if i in self.special_tokens_decoder:
if not skip_special_tokens:
tokens.append(self.special_tokens_decoder[i])
else:
tokens.append(self.decoder[i])
return tokens
def encode(self, text):
res = self.convert_tokens_to_ids(self.tokenize(text))
return res
def decode(self, tokens):
text = self.sp.decode([self.decoder[x] for x in tokens])
text = text.replace(' ', '').replace('\u2582', ' ').replace('\u2583', '\n').replace('\u2584', '')
#text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
return text
def save_vocabulary(self, vocab_path):
"""Save the tokenizer vocabulary and merge files to a directory."""
if not os.path.isdir(vocab_path):
logger.error("Vocabulary path ({}) should be a directory".format(vocab_path))
return
vocab_file = os.path.join(vocab_path, VOCAB_NAME)
merge_file = os.path.join(vocab_path, MERGES_NAME)
special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME)
with open(vocab_file, 'w', encoding='utf-8') as f:
f.write(json.dumps(self.encoder, ensure_ascii=False))
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write(u'#version: 0.2\n')
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!".format(merge_file))
index = token_index
writer.write(' '.join(bpe_tokens) + u'\n')
index += 1
index = len(self.encoder)
with open(special_tokens_file, 'w', encoding='utf-8') as writer:
for token, token_index in sorted(self.special_tokens.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning("Saving special tokens vocabulary to {}: BPE indices are not consecutive."
" Please check that the tokenizer is not corrupted!".format(special_tokens_file))
index = token_index
writer.write(token + u'\n')
index += 1
return vocab_file, merge_file, special_tokens_file
================================================
FILE: CPM-Generate/data_utils/wordpiece.py
================================================
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors 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.
"""Tokenization classes. Provided as is from https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/tokenization.py"""
from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import logging
import os
import unicodedata
from io import open
from .file_utils import cached_path
logger = logging.getLogger(__name__)
PRETRAINED_VOCAB_ARCHIVE_MAP = {
'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
}
PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
'bert-base-uncased': 512,
'bert-large-uncased': 512,
'bert-base-cased': 512,
'bert-large-cased': 512,
'bert-base-multilingual-uncased': 512,
'bert-base-multilingual-cased': 512,
'bert-base-chinese': 512,
}
VOCAB_NAME = 'vocab.txt'
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
index = 0
with open(vocab_file, "r", encoding="utf-8") as reader:
while True:
token = reader.readline()
if not token:
break
token = token.strip()
vocab[token] = index
index += 1
return vocab
def whitespace_tokenize(text):
"""Runs basic whitespace cleaning and splitting on a piece of text."""
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens
class BertTokenizer(object):
"""Runs end-to-end tokenization: punctuation splitting + wordpiece"""
def __init__(self, vocab_file, do_lower_case=True, max_len=None, do_basic_tokenize=True,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
"""Constructs a BertTokenizer.
Args:
vocab_file: Path to a one-wordpiece-per-line vocabulary file
do_lower_case: Whether to lower case the input
Only has an effect when do_wordpiece_only=False
do_basic_tokenize: Whether to do basic tokenization before wordpiece.
max_len: An artificial maximum length to truncate tokenized sequences to;
Effective maximum length is always the minimum of this
value (if specified) and the underlying BERT model's
sequence length.
never_split: List of tokens which will never be split during tokenization.
Only has an effect when do_wordpiece_only=False
"""
if not os.path.isfile(vocab_file):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
"model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file))
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()])
self.do_basic_tokenize = do_basic_tokenize
if do_basic_tokenize:
self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
never_split=never_split)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
self.max_len = max_len if max_len is not None else int(1e12)
def tokenize(self, text):
if self.do_basic_tokenize:
split_tokens = []
for token in self.basic_tokenizer.tokenize(text):
for sub_token in self.wordpiece_tokenizer.tokenize(token):
split_tokens.append(sub_token)
else:
split_tokens = self.wordpiece_tokenizer.tokenize(text)
return split_tokens
def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
ids.append(self.vocab[token])
if len(ids) > self.max_len:
logger.warning(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this BERT model ({} > {}). Running this"
" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
"""
Instantiate a PreTrainedBertModel from a pre-trained model file.
Download and cache the pre-trained model file if needed.
"""
if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
else:
vocab_file = pretrained_model_name_or_path
if os.path.isdir(vocab_file):
vocab_file = os.path.join(vocab_file, VOCAB_NAME)
# redirect to the cache, if necessary
try:
resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
except EnvironmentError:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find any file "
"associated to this path or url.".format(
pretrained_model_name_or_path,
', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
vocab_file))
return None
if resolved_vocab_file == vocab_file:
logger.info("loading vocabulary file {}".format(vocab_file))
else:
logger.info("loading vocabulary file {} from cache at {}".format(
vocab_file, resolved_vocab_file))
if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
# if we're using a pretrained model, ensure the tokenizer wont index sequences longer
# than the number of positional embeddings
max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path]
kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
# Instantiate tokenizer.
tokenizer = cls(resolved_vocab_file, *inputs, **kwargs)
return tokenizer
class BasicTokenizer(object):
"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
def __init__(self,
do_lower_case=True,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
"""Constructs a BasicTokenizer.
Args:
do_lower_case: Whether to lower case the input.
"""
self.do_lower_case = do_lower_case
self.never_split = never_split
def tokenize(self, text):
"""Tokenizes a piece of text."""
text = self._clean_text(text)
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
# characters in the vocabulary because Wikipedia does have some Chinese
# words in the English Wikipedia.).
text = self._tokenize_chinese_chars(text)
orig_tokens = whitespace_tokenize(text)
split_tokens = []
for token in orig_tokens:
if self.do_lower_case and token not in self.never_split:
token = token.lower()
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token))
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output)
def _run_split_on_punc(self, text):
"""Splits punctuation on a piece of text."""
if text in self.never_split:
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[-1].append(char)
i += 1
return ["".join(x) for x in output]
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
else:
output.append(char)
return "".join(output)
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if ((cp >= 0x4E00 and cp <= 0x9FFF) or #
(cp >= 0x3400 and cp <= 0x4DBF) or #
(cp >= 0x20000 and cp <= 0x2A6DF) or #
(cp >= 0x2A700 and cp <= 0x2B73F) or #
(cp >= 0x2B740 and cp <= 0x2B81F) or #
(cp >= 0x2B820 and cp <= 0x2CEAF) or
(cp >= 0xF900 and cp <= 0xFAFF) or #
(cp >= 0x2F800 and cp <= 0x2FA1F)): #
return True
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xfffd or _is_control(char):
continue
if _is_whitespace(char):
output.append(" ")
else:
output.append(char)
return "".join(output)
class WordpieceTokenizer(object):
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""Tokenizes a piece of text into its word pieces.
This uses a greedy longest-match-first algorithm to perform tokenization
using the given vocabulary.
For example:
input = "unaffable"
output = ["un", "##aff", "##able"]
Args:
text: A single token or whitespace separated tokens. This should have
already been passed through `BasicTokenizer`.
Returns:
A list of wordpiece tokens.
"""
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
while start < len(chars):
end = len(chars)
cur_substr = None
while start < end:
substr = "".join(chars[start:end])
if start > 0:
substr = "##" + substr
if substr in self.vocab:
cur_substr = substr
break
end -= 1
if cur_substr is None:
is_bad = True
break
sub_tokens.append(cur_substr)
start = end
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens
def _is_whitespace(char):
"""Checks whether `chars` is a whitespace character."""
# \t, \n, and \r are technically contorl characters but we treat them
# as whitespace since they are generally considered as such.
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
cat = unicodedata.category(char)
if cat == "Zs":
return True
return False
def _is_control(char):
"""Checks whether `chars` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char == "\t" or char == "\n" or char == "\r":
return False
cat = unicodedata.category(char)
if cat.startswith("C"):
return True
return False
def _is_punctuation(char):
"""Checks whether `chars` is a punctuation character."""
cp = ord(char)
# We treat all non-letter/number ASCII as punctuation.
# Characters such as "^", "$", and "`" are not in the Unicode
# Punctuation class but we treat them as punctuation anyways, for
# consistency.
if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
(cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
return True
cat = unicodedata.category(char)
if cat.startswith("P"):
return True
return False
================================================
FILE: CPM-Generate/fp16/__init__.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
from .fp16util import (
BN_convert_float,
network_to_half,
prep_param_lists,
model_grads_to_master_grads,
master_params_to_model_params,
tofp16,
to_python_float,
clip_grad_norm,
convert_module,
convert_network,
FP16Model,
)
from .fp16 import *
from .loss_scaler import *
================================================
FILE: CPM-Generate/fp16/fp16.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
"""Stable version of apex FP16 Optimizer"""
import torch
from torch import nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
from .loss_scaler import DynamicLossScaler, LossScaler
from .fp16util import model_grads_to_master_grads, master_params_to_model_params, clip_grad_norm
FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor)
HALF_TYPES = (torch.HalfTensor, torch.cuda.HalfTensor)
def conversion_helper(val, conversion):
"""Apply conversion to val. Recursively apply conversion if `val` is a nested tuple/list structure."""
if not isinstance(val, (tuple, list)):
return conversion(val)
rtn = [conversion_helper(v, conversion) for v in val]
if isinstance(val, tuple):
rtn = tuple(rtn)
return rtn
def fp32_to_fp16(val):
"""Convert fp32 `val` to fp16"""
def half_conversion(val):
val_typecheck = val
if isinstance(val_typecheck, (Parameter, Variable)):
val_typecheck = val.data
if isinstance(val_typecheck, FLOAT_TYPES):
val = val.half()
return val
return conversion_helper(val, half_conversion)
def fp16_to_fp32(val):
"""Convert fp16 `val` to fp32"""
def float_conversion(val):
val_typecheck = val
if isinstance(val_typecheck, (Parameter, Variable)):
val_typecheck = val.data
if isinstance(val_typecheck, HALF_TYPES):
val = val.float()
return val
return conversion_helper(val, float_conversion)
class FP16_Module(nn.Module):
def __init__(self, module):
super(FP16_Module, self).__init__()
self.add_module('module', module.half())
def forward(self, *inputs, **kwargs):
return fp16_to_fp32(self.module(*(fp32_to_fp16(inputs)), **kwargs))
def state_dict(self, destination=None, prefix='', keep_vars=False):
return self.module.state_dict(destination, prefix, keep_vars)
def load_state_dict(self, state_dict, strict=True):
self.module.load_state_dict(state_dict, strict=strict)
# TODO: Update overflow check + downscale to use Carl's fused kernel.
class FP16_Optimizer(object):
"""
:class:`FP16_Optimizer` is designed to wrap an existing PyTorch optimizer,
and manage static or dynamic loss scaling and master weights in a manner transparent to the user.
For standard use, only two lines must be changed: creating the :class:`FP16_Optimizer` instance,
and changing the call to ``backward``.
Example::
model = torch.nn.Linear(D_in, D_out).cuda().half()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
# Name the FP16_Optimizer instance to replace the existing optimizer
# (recommended but not required):
optimizer = FP16_Optimizer(optimizer, static_loss_scale = 128.0)
...
# loss.backward() becomes:
optimizer.backward(loss)
...
Example with dynamic loss scaling::
...
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
# optional arg to control dynamic loss scaling behavior
# dynamic_loss_args={'scale_window' : 500})
# Usually, dynamic_loss_args is not necessary.
Args:
init_optimizer (torch.optim.optimizer): Existing optimizer created with the parameters to optimize. Internally, :class:`FP16_Optimizer` replaces the passed optimizer's fp16 parameters, if any, with fp32 master parameters copied from the original ones. :class:`FP16_Optimizer` also stores references to the original fp16 parameters, and updates these fp16 parameters from the master fp32 copy at the end of each :attr:`step`.
static_loss_scale (float, optional, default=1.0): Loss scale used internally to scale gradients computed by the model. Any fp16 gradients will be copied to fp32, then downscaled before being applied to the fp32 master params, so ``static_loss_scale`` should not affect learning rate.
dynamic_loss_scale (bool, optional, default=False): Use dynamic loss scaling. If True, this will override any ``static_loss_scale`` option.
dynamic_loss_args (dict, optional, default=None): Dict of kwargs that will be forwarded to the internal :class:`DynamicLossScaler` instance's constructor. Keys of this dict must match kwargs accepted by :class:`DynamicLossScaler`'s constructor. If ``dynamic_loss_args`` is unspecified, :class:`DynamicLossScaler`'s defaults will be used.
verbose (bool, optional, default=True): By default, FP16_Optimizer's constructor prints out the parameters and parameter groups it is ingesting, as a sanity check. If this becomes annoying (e.g. for large models), it can be disabled by passing ``verbose=False``. ``verbose=False`` will not disable printing when the loss scale is readjusted during dynamic loss scaling.
``init_optimizer`` is expected to have been constructed in the ordinary way.
It is recommended (although not required) that the newly constructed :class:`FP16_Optimizer` instance be
named to replace ``init_optimizer``, for two reasons:
First, it means that references to the same name
later in the file will not have to change.
Second, :class:`FP16_Optimizer` reserves the right (as an implementation detail) to
modify ``init_optimizer``. If you do choose a unique name for the new
:class:`FP16_Optimizer` instance, you should only work with this new instance,
because the preexisting optimizer might no longer behave as expected.
``init_optimizer`` may be any Pytorch optimizer.
It may contain a mixture of fp16 and fp32 parameters organized into any number of
``param_groups`` with different hyperparameters. The :class:`FP16_Optimizer` constructor will
ingest these ``param_groups`` and remember them.
Calls to ::
loss.backward()
must be replaced with ::
optimizer.backward(loss)
because :class:`FP16_Optimizer` requires ownership of the backward pass to implement
loss scaling and copies to master gradients.
.. note::
Loss scaling, either static or dynamic, is orthogonal to learning rate, because gradients
are downscaled before being applied. This means that adjusting the loss scale, or using
dynamic loss scaling, should not require retuning the learning rate or any other
hyperparameters.
**Advanced options**
**Closures**: :class:`FP16_Optimizer` can wrap a Pytorch optimizer that receives a closure.
See docstring for :attr:`step`.
**Gradient clipping**: Use :attr:`clip_master_grads`.
**Multiple losses**: If your model accumulates gradients from multiple losses,
this can be made more efficient by supplying ``update_master_grads=False``
to :attr:`backward`. See docstring for :attr:`backward`.
**Manually adjusting loss scale**: The current loss scale can be retrieved or set via ::
print(optimizer.loss_scale)
optimizer.loss_scale = new_loss_scale
For static loss scaling, manually adjusting the loss scale over time is a reasonable
thing to do. During later epochs, gradients may become smaller, and a
higher loss scale may be required, analogous to scheduling the learning rate. Dynamic loss
scaling is more subtle (see :class:`DynamicLossScaler`) and in this case, manually adjusting
the loss scale is not recommended.
**Multi_GPU training**: If the wrapped ``init_optimizer`` was created from a model wrapped in
Pytorch DistributedDataParallel or Apex DistributedDataParallel, :class:`FP16_Optimizer`
should still work as intended.
"""
def __init__(self,
init_optimizer,
static_loss_scale=1.0,
dynamic_loss_scale=False,
dynamic_loss_args=None,
verbose=False):
if not torch.cuda.is_available:
raise SystemError("Cannot use fp16 without CUDA.")
self.verbose = verbose
self.optimizer = init_optimizer
# init_state_dict sets up an alternative way to cast per-param state tensors.
# Stashing here in case https://github.com/pytorch/pytorch/issues/7733 makes it necessary.
# init_state_dict = init_optimizer.state_dict()
self.fp16_groups = []
self.fp32_from_fp16_groups = []
self.fp32_from_fp32_groups = []
for i, param_group in enumerate(self.optimizer.param_groups):
self.maybe_print("FP16_Optimizer processing param group {}:".format(i))
fp16_params_this_group = []
fp32_params_this_group = []
fp32_from_fp16_params_this_group = []
for i, param in enumerate(param_group['params']):
if param.requires_grad:
if param.type() == 'torch.cuda.HalfTensor':
self.maybe_print("FP16_Optimizer received torch.cuda.HalfTensor with {}"
.format(param.size()))
fp16_params_this_group.append(param)
master_param = param.detach().clone().float()
master_param.requires_grad = True
# Copythe model parallel flag.
master_param.model_parallel = param.model_parallel
param_group['params'][i] = master_param
fp32_from_fp16_params_this_group.append(master_param)
# Reset existing state dict key to the new master param.
# We still need to recast per-param state tensors, if any, to FP32.
if param in self.optimizer.state:
self.optimizer.state[master_param] = self.optimizer.state.pop(param)
elif param.type() == 'torch.cuda.FloatTensor':
self.maybe_print("FP16_Optimizer received torch.cuda.FloatTensor with {}"
.format(param.size()))
fp32_params_this_group.append(param)
param_group['params'][i] = param
else:
raise TypeError("Wrapped parameters must be either "
"torch.cuda.FloatTensor or torch.cuda.HalfTensor. "
"Received {}".format(param.type()))
self.fp16_groups.append(fp16_params_this_group)
self.fp32_from_fp16_groups.append(fp32_from_fp16_params_this_group)
self.fp32_from_fp32_groups.append(fp32_params_this_group)
# Leverage state_dict() and load_state_dict() to recast preexisting per-param state tensors
self.optimizer.load_state_dict(self.optimizer.state_dict())
# alternative way to cast per-param state tensors:
# self.optimizer.load_state_dict(init_state_dict)
if dynamic_loss_scale:
self.dynamic_loss_scale = True
if dynamic_loss_args is not None:
self.loss_scaler = DynamicLossScaler(**dynamic_loss_args)
else:
self.loss_scaler = DynamicLossScaler()
else:
self.dynamic_loss_scale = False
self.loss_scaler = LossScaler(static_loss_scale)
self.overflow = False
self.first_closure_call_this_step = True
self.clip_grad_norm = clip_grad_norm
def maybe_print(self, msg):
if self.verbose:
print(msg)
def __getstate__(self):
raise RuntimeError("FP16_Optimizer should be serialized using state_dict().")
def __setstate__(self, state):
raise RuntimeError("FP16_Optimizer should be deserialized using load_state_dict().")
def zero_grad(self, set_grads_to_None=False):
"""
Zero fp32 and fp16 parameter grads.
"""
# In principle, only the .grad attributes of the model params need to be zeroed,
# because gradients are copied into the FP32 master params. However, we zero
# all gradients owned by the optimizer, just to be safe:
for group in self.optimizer.param_groups:
for p in group['params']:
if set_grads_to_None:
p.grad = None
else:
if p.grad is not None:
p.grad.detach_()
p.grad.zero_()
# Zero fp16 gradients owned by the model:
for fp16_group in self.fp16_groups:
for param in fp16_group:
if set_grads_to_None:
param.grad = None
else:
if param.grad is not None:
param.grad.detach_() # as in torch.optim.optimizer.zero_grad()
param.grad.zero_()
def _check_overflow(self):
params = []
for group in self.fp16_groups:
for param in group:
params.append(param)
for group in self.fp32_from_fp32_groups:
for param in group:
params.append(param)
self.overflow = self.loss_scaler.has_overflow(params)
def _update_scale(self, has_overflow=False):
self.loss_scaler.update_scale(has_overflow)
def _master_params_to_model_params(self):
for fp16_group, fp32_from_fp16_group in zip(self.fp16_groups, self.fp32_from_fp16_groups):
master_params_to_model_params(fp16_group, fp32_from_fp16_group)
def _model_params_to_master_params(self):
for fp16_group, fp32_from_fp16_group in zip(self.fp16_groups, self.fp32_from_fp16_groups):
master_params_to_model_params(fp32_from_fp16_group, fp16_group)
# To consider: Integrate distributed with this wrapper by registering a hook on each variable
# that does the overflow check, gradient copy + downscale, and fp32 allreduce in a different stream.
def _model_grads_to_master_grads(self):
for fp16_group, fp32_from_fp16_group in zip(self.fp16_groups, self.fp32_from_fp16_groups):
model_grads_to_master_grads(fp16_group, fp32_from_fp16_group)
def _downscale_master(self):
if self.loss_scale != 1.0:
for group in self.optimizer.param_groups:
for param in group['params']:
if param.grad is not None:
param.grad.data.mul_(1./self.loss_scale)
def clip_master_grads(self, max_norm, norm_type=2):
"""
Clips fp32 master gradients via ``torch.nn.utils.clip_grad_norm``.
Args:
max_norm (float or int): max norm of the gradients
norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
infinity norm.
Returns:
Total norm of the current fp32 gradients (viewed as a single vector).
.. warning::
Returns -1 if the most recently computed fp16 gradients overflowed (that is, if ``self.overflow`` is ``True``).
"""
if not self.overflow:
fp32_params = []
for param_group in self.optimizer.param_groups:
for param in param_group['params']:
fp32_params.append(param)
return self.clip_grad_norm(fp32_params, max_norm, norm_type)
else:
return -1
def state_dict(self):
"""
Returns a dict containing the current state of this :class:`FP16_Optimizer` instance.
This dict contains attributes of :class:`FP16_Optimizer`, as well as the state_dict
of the contained Pytorch optimizer.
Example::
checkpoint = {}
checkpoint['model'] = model.state_dict()
checkpoint['optimizer'] = optimizer.state_dict()
torch.save(checkpoint, "saved.pth")
"""
state_dict = {}
state_dict['loss_scaler'] = self.loss_scaler
state_dict['dynamic_loss_scale'] = self.dynamic_loss_scale
state_dict['overflow'] = self.overflow
state_dict['first_closure_call_this_step'] = self.first_closure_call_this_step
state_dict['optimizer_state_dict'] = self.optimizer.state_dict()
state_dict['fp32_from_fp16'] = self.fp32_from_fp16_groups
return state_dict
def load_state_dict(self, state_dict):
"""
Loads a state_dict created by an earlier call to state_dict().
If ``fp16_optimizer_instance`` was constructed from some ``init_optimizer``,
whose parameters in turn came from ``model``, it is expected that the user
will call ``model.load_state_dict()`` before
``fp16_optimizer_instance.load_state_dict()`` is called.
Example::
model = torch.nn.Linear(D_in, D_out).cuda().half()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
optimizer = FP16_Optimizer(optimizer, static_loss_scale = 128.0)
...
checkpoint = torch.load("saved.pth")
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
"""
# I think it should actually be ok to reload the optimizer before the model.
self.loss_scaler = state_dict['loss_scaler']
self.dynamic_loss_scale = state_dict['dynamic_loss_scale']
self.overflow = state_dict['overflow']
self.first_closure_call_this_step = state_dict['first_closure_call_this_step']
self.optimizer.load_state_dict(state_dict['optimizer_state_dict'])
# At this point, the optimizer's references to the model's fp32 parameters are up to date.
# The optimizer's hyperparameters and internal buffers are also up to date.
# However, the fp32 master copies of the model's fp16 params stored by the optimizer are still
# out of date. There are two options.
# 1: Refresh the master params from the model's fp16 params.
# This requires less storage but incurs precision loss.
# 2: Save and restore the fp32 master copies separately.
# We choose option 2.
#
# Pytorch Optimizer.load_state_dict casts saved buffers (e.g. momentum) to the type and device
# of their associated parameters, because it's possible those buffers might not exist yet in
# the current optimizer instance. In our case, as long as the current FP16_Optimizer has been
# constructed in the same way as the one whose state_dict we are loading, the same master params
# are guaranteed to exist, so we can just copy_() from the saved master params.
for current_group, saved_group in zip(self.fp32_from_fp16_groups, state_dict['fp32_from_fp16']):
for current, saved in zip(current_group, saved_group):
current.data.copy_(saved.data)
def step(self, closure=None): # could add clip option.
"""
If no closure is supplied, :attr:`step` should be called after
``fp16_optimizer_obj.backward(loss)``.
:attr:`step` updates the fp32 master copy of parameters using the optimizer supplied to
:class:`FP16_Optimizer`'s constructor, then copies the updated fp32 params into the fp16 params
originally referenced by :class:`FP16_Optimizer`'s constructor, so the user may immediately run
another forward pass using their model.
If a closure is supplied, :attr:`step` may be called without a prior call to
:attr:`backward(loss)`.
This control flow is identical to `ordinary Pytorch optimizer use`_ with closures.
However, the user should take care that any ``loss.backward()`` call within the closure
has been replaced by ``fp16_optimizer_obj.backward(loss)``.
Args:
closure (optional): Closure that will be supplied to the underlying optimizer originally passed to :class:`FP16_Optimizer`'s constructor. closure should call :attr:`zero_grad()` on the :class:`FP16_Optimizer` object, compute the loss, call :attr:`backward(loss)`, and return the loss.
Example with closure::
# optimizer is assumed to be an FP16_Optimizer object, previously constructed from an
# existing pytorch optimizer.
for input, target in dataset:
def closure():
optimizer.zero_grad()
output = model(input)
loss = loss_fn(output, target)
# loss.backward() becomes:
optimizer.backward(loss)
return loss
optimizer.step(closure)
.. warning::
Currently, calling :attr:`step` with a closure is not compatible with dynamic loss scaling.
.. _`ordinary Pytorch optimizer use`:
http://pytorch.org/docs/master/optim.html#optimizer-step-closure
"""
scale = self.loss_scaler.loss_scale
self._update_scale(self.overflow)
if self.overflow:
self.maybe_print("OVERFLOW! Skipping step. Attempted loss scale: {}, reducing to {}"
.format(scale, self.loss_scale))
return
if closure is not None:
retval = self._step_with_closure(closure)
else:
retval = self.optimizer.step()
self._master_params_to_model_params()
return retval
def _step_with_closure(self, closure):
def wrapped_closure():
# helpful for debugging
# print("Calling wrapped_closure, first_closure_call_this_step = {}"
# .format(self.first_closure_call_this_step))
if self.first_closure_call_this_step:
# We expect that the fp16 params are initially fresh on entering self.step(),
# so _master_params_to_model_params() is unnecessary the first time wrapped_closure()
# is called within self.optimizer.step().
self.first_closure_call_this_step = False
else:
# If self.optimizer.step() internally calls wrapped_closure more than once,
# it may update the fp32 params after each call. However, self.optimizer
# doesn't know about the fp16 params at all. If the fp32 params get updated,
# we can't rely on self.optimizer to refresh the fp16 params. We need
# to handle that manually:
self._master_params_to_model_params()
# Our API expects the user to give us ownership of the backward() call by
# replacing all calls to loss.backward() with optimizer.backward(loss).
# This requirement holds whether or not the call to backward() is made within a closure.
# If the user is properly calling optimizer.backward(loss) within "closure,"
# calling closure() here will give the fp32 master params fresh gradients
# for the optimizer to play with, so all wrapped_closure needs to do is call
# closure() and return the loss.
temp_loss = closure()
while(self.overflow):
scale = self.loss_scaler.loss_scale
self._update_scale(self.overflow)
self.maybe_print("OVERFLOW within closure! Skipping step. Attempted loss scale: {}, "
"reducing to {}".format(scale, self.loss_scale))
temp_loss = closure()
return temp_loss
retval = self.optimizer.step(wrapped_closure)
self.first_closure_call_this_step = True
return retval
def backward(self, loss, update_master_grads=True, retain_graph=False):
"""
:attr:`backward` performs the following conceptual steps:
1. fp32_loss = loss.float() (see first Note below)
2. scaled_loss = fp32_loss*loss_scale
3. scaled_loss.backward(), which accumulates scaled gradients into the ``.grad`` attributes of the model's leaves (which may be fp16, fp32, or a mixture, depending how your model was defined).
4. fp16 grads are then copied to the master params' ``.grad`` attributes (see second Note), which are guaranteed to be fp32.
5. Finally, master grads are divided by loss_scale.
In this way, after :attr:`backward`, the master params have fresh gradients,
and :attr:`step` may be called.
.. note::
:attr:`backward` internally converts the loss to fp32 before applying the loss scale.
This provides some additional safety against overflow if the user has supplied an
fp16 loss value.
However, for maximum overflow safety, the user should
compute the loss criterion (MSE, cross entropy, etc) in fp32 before supplying it to
:attr:`backward`.
.. warning::
The gradients found in a model's leaves after the call to
:attr:`backward` should not be regarded as valid in general,
because it's possible
they have been scaled (and in the case of dynamic loss scaling,
the scale factor may change over time).
If the user wants to inspect gradients after a call to :attr:`backward`,
only the master gradients should be regarded as valid. These can be retrieved via
:attr:`inspect_master_grad_data()`.
Args:
loss: The loss output by the user's model. loss may be either float or half (but see first Note above).
update_master_grads (bool, optional, default=True): Option to copy fp16 grads to fp32 grads on this call. By setting this to False, the user can delay the copy, which is useful to eliminate redundant fp16->fp32 grad copies if :attr:`backward` is being called on multiple losses in one iteration. If set to False, the user becomes responsible for calling :attr:`update_master_grads` before calling :attr:`step`.
retain_graph (bool, optional, default=False): Forwards the usual ``retain_graph=True`` option to the internal call to ``loss.backward``. If ``retain_graph`` is being used to accumulate gradient values from multiple backward passes before calling ``optimizer.step``, passing ``update_master_grads=False`` is also recommended (see Example below).
Example::
# Ordinary operation:
optimizer.backward(loss)
# Naive operation with multiple losses (technically valid, but less efficient):
# fp32 grads will be correct after the second call, but
# the first call incurs an unnecessary fp16->fp32 grad copy.
optimizer.backward(loss1)
optimizer.backward(loss2)
# More efficient way to handle multiple losses:
# The fp16->fp32 grad copy is delayed until fp16 grads from all
# losses have been accumulated.
optimizer.backward(loss1, update_master_grads=False)
optimizer.backward(loss2, update_master_grads=False)
optimizer.update_master_grads()
"""
# To consider: try multiple backward passes using retain_grad=True to find
# a loss scale that works. After you find a loss scale that works, do a final dummy
# backward pass with retain_graph=False to tear down the graph. Doing this would avoid
# discarding the iteration, but probably wouldn't improve overall efficiency.
self.loss_scaler.backward(loss.float(), retain_graph=retain_graph)
if update_master_grads:
self.update_master_grads()
def update_master_grads(self):
"""
Copy the ``.grad`` attribute from stored references to fp16 parameters to
the ``.grad`` attribute of the fp32 master parameters that are directly
updated by the optimizer. :attr:`update_master_grads` only needs to be called if
``fp16_optimizer_obj.backward`` was called with ``update_master_grads=False``.
"""
if self.dynamic_loss_scale:
self._check_overflow()
if self.overflow: return
self._model_grads_to_master_grads()
self._downscale_master()
def inspect_master_grad_data(self):
"""
When running with :class:`FP16_Optimizer`,
``.grad`` attributes of a model's fp16 leaves should not be
regarded as truthful, because they might be scaled.
After a call to :attr:`fp16_optimizer_obj.backward(loss)`, if no overflow was encountered,
the fp32 master params' ``.grad``
attributes will contain valid gradients properly divided by the loss scale. However,
because :class:`FP16_Optimizer` flattens some parameters, accessing them may be
nonintuitive. :attr:`inspect_master_grad_data`
allows those gradients to be viewed with shapes corresponding to their associated model leaves.
Returns:
List of lists (one list for each parameter group). The list for each parameter group
is a list of the ``.grad.data`` attributes of the fp32 master params belonging to that group.
"""
if self.overflow:
print("Warning: calling FP16_Optimizer.inspect_master_grad_data while in an overflow state. "
"Gradients are currently invalid (may be inf, nan, or stale). Returning None.")
return None
else:
# The optimizer owns only references to master params.
master_grads_data = []
for param_group in self.optimizer.param_groups:
master_grads_this_group = []
for param in param_group['params']:
if param.grad is not None:
master_grads_this_group.append(param.grad.data)
else:
master_grads_this_group.append(None)
master_grads_data.append(master_grads_this_group)
return master_grads_data
# Promote loss scale so it can be retrieved or set via "fp16_optimizer_instance.loss_scale"
def _get_loss_scale(self):
return self.loss_scaler.loss_scale
def _set_loss_scale(self, value):
self.loss_scaler.cur_scale = value
loss_scale = property(_get_loss_scale, _set_loss_scale)
# Promote state so it can be retrieved or set via "fp16_optimizer_instance.state"
def _get_state(self):
return self.optimizer.state
def _set_state(self, value):
self.optimizer.state = value
state = property(_get_state, _set_state)
# Promote param_groups so it can be retrieved or set via "fp16_optimizer_instance.param_groups"
# (for example, to adjust the learning rate)
def _get_param_groups(self):
return self.optimizer.param_groups
def _set_param_groups(self, value):
self.optimizer.param_groups = value
param_groups = property(_get_param_groups, _set_param_groups)
================================================
FILE: CPM-Generate/fp16/fp16util.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
import mpu
class tofp16(nn.Module):
"""
Utility module that implements::
def forward(self, input):
return input.half()
"""
def __init__(self):
super(tofp16, self).__init__()
def forward(self, input):
return input.half()
def BN_convert_float(module):
"""
Utility function for network_to_half().
Retained for legacy purposes.
"""
if isinstance(module, torch.nn.modules.batchnorm._BatchNorm) and module.affine is True:
module.float()
for child in module.children():
BN_convert_float(child)
return module
def network_to_half(network):
"""
Convert model to half precision in a batchnorm-safe way.
Retained for legacy purposes. It is recommended to use FP16Model.
"""
return nn.Sequential(tofp16(), BN_convert_float(network.half()))
def convert_module(module, dtype):
"""
Converts a module's immediate parameters and buffers to dtype.
"""
for param in module.parameters(recurse=False):
if param is not None:
if param.data.dtype.is_floating_point:
param.data = param.data.to(dtype=dtype)
if param._grad is not None and param._grad.data.dtype.is_floating_point:
param._grad.data = param._grad.data.to(dtype=dtype)
for buf in module.buffers(recurse=False):
if buf is not None and buf.data.dtype.is_floating_point:
buf.data = buf.data.to(dtype=dtype)
def convert_network(network, dtype):
"""
Converts a network's parameters and buffers to dtype.
"""
for module in network.modules():
if isinstance(module, torch.nn.modules.batchnorm._BatchNorm) and module.affine is True:
continue
convert_module(module, dtype)
return network
class FP16Model(nn.Module):
"""
Convert model to half precision in a batchnorm-safe way.
"""
def __init__(self, network):
super(FP16Model, self).__init__()
self.network = convert_network(network, dtype=torch.half)
def forward(self, *inputs):
inputs = tuple(t.half() for t in inputs)
return self.network(*inputs)
def backwards_debug_hook(grad):
raise RuntimeError("master_params recieved a gradient in the backward pass!")
def prep_param_lists(model, flat_master=False):
"""
Creates a list of FP32 master parameters for a given model, as in
`Training Neural Networks with Mixed Precision: Real Examples`_.
Args:
model (torch.nn.Module): Existing Pytorch model
flat_master (bool, optional, default=False): Flatten the master parameters into a single tensor, as a performance optimization.
Returns:
A tuple (``model_params``, ``master_params``). ``model_params`` is a list of the model's parameters for later use with :func:`model_grads_to_master_grads` and :func:`master_params_to_model_params`. ``master_params`` is a list of FP32 master gradients. If ``flat_master=True``, ``master_params`` will be a list with one element.
Example::
model_params, master_params = prep_param_lists(model)
.. warning::
Currently, if ``flat_master=True``, all the model's parameters must be the same type. If the model has parameters of different types, use ``flat_master=False``, or use :class:`FP16_Optimizer`.
.. _`Training Neural Networks with Mixed Precision: Real Examples`:
http://on-demand.gputechconf.com/gtc/2018/video/S81012/
"""
model_params = [param for param in model.parameters() if param.requires_grad]
if flat_master:
# Give the user some more useful error messages
try:
# flatten_dense_tensors returns a contiguous flat array.
# http://pytorch.org/docs/master/_modules/torch/_utils.html
master_params = _flatten_dense_tensors([param.data for param in model_params]).float()
except:
print("Error in prep_param_lists: model may contain a mixture of parameters "
"of different types. Use flat_master=False, or use F16_Optimizer.")
raise
master_params = torch.nn.Parameter(master_params)
master_params.requires_grad = True
# master_params.register_hook(backwards_debug_hook)
if master_params.grad is None:
master_params.grad = master_params.new(*master_params.size())
return model_params, [master_params]
else:
master_params = [param.clone().float().detach() for param in model_params]
for param in master_params:
param.requires_grad = True
return model_params, master_params
def model_grads_to_master_grads(model_params, master_params, flat_master=False):
"""
Copy model gradients to master gradients.
Args:
model_params: List of model parameters created by :func:`prep_param_lists`.
master_params: List of FP32 master parameters created by :func:`prep_param_lists`. If ``master_params`` was created with ``flat_master=True``, ``flat_master=True`` should also be supplied to :func:`model_grads_to_master_grads`.
"""
if flat_master:
# The flattening may incur one more deep copy than is necessary.
master_params[0].grad.data.copy_(
_flatten_dense_tensors([p.grad.data for p in model_params]))
else:
for model, master in zip(model_params, master_params):
if model.grad is not None:
if master.grad is None:
master.grad = Variable(master.data.new(*master.data.size()))
master.grad.data.copy_(model.grad.data)
else:
master.grad = None
def master_params_to_model_params(model_params, master_params, flat_master=False):
"""
Copy master parameters to model parameters.
Args:
model_params: List of model parameters created by :func:`prep_param_lists`.
master_params: List of FP32 master parameters created by :func:`prep_param_lists`. If ``master_params`` was created with ``flat_master=True``, ``flat_master=True`` should also be supplied to :func:`master_params_to_model_params`.
"""
if flat_master:
for model, master in zip(model_params,
_unflatten_dense_tensors(master_params[0].data, model_params)):
model.data.copy_(master)
else:
for model, master in zip(model_params, master_params):
model.data.copy_(master.data)
# Backward compatibility fixes
def to_python_float(t):
if hasattr(t, 'item'):
return t.item()
else:
return t[0]
TORCH_MAJOR = int(torch.__version__.split('.')[0])
TORCH_MINOR = int(torch.__version__.split('.')[1])
clip_grad_norm = mpu.clip_grad_norm
#elif TORCH_MAJOR == 0 and TORCH_MINOR <= 4:
# clip_grad_norm = torch.nn.utils.clip_grad_norm
#else:
# clip_grad_norm = torch.nn.utils.clip_grad_norm_
================================================
FILE: CPM-Generate/fp16/loss_scaler.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
import torch
import mpu
# item() is a recent addition, so this helps with backward compatibility.
def to_python_float(t):
if hasattr(t, 'item'):
return t.item()
else:
return t[0]
class LossScaler:
"""
Class that manages a static loss scale. This class is intended to interact with
:class:`FP16_Optimizer`, and should not be directly manipulated by the user.
Use of :class:`LossScaler` is enabled via the ``static_loss_scale`` argument to
:class:`FP16_Optimizer`'s constructor.
Args:
scale (float, optional, default=1.0): The loss scale.
"""
def __init__(self, scale=1):
self.cur_scale = scale
# `params` is a list / generator of torch.Variable
def has_overflow(self, params):
return False
# `x` is a torch.Tensor
def _has_inf_or_nan(x):
return False
def update_scale(self, overflow):
pass
@property
def loss_scale(self):
return self.cur_scale
def scale_gradient(self, module, grad_in, grad_out):
return tuple(self.loss_scale * g for g in grad_in)
def backward(self, loss, retain_graph=False):
scaled_loss = loss*self.loss_scale
scaled_loss.backward(retain_graph=retain_graph)
class DynamicLossScaler:
"""
Class that manages dynamic loss scaling. It is recommended to use :class:`DynamicLossScaler`
indirectly, by supplying ``dynamic_loss_scale=True`` to the constructor of
:class:`FP16_Optimizer`. However, it's important to understand how :class:`DynamicLossScaler`
operates, because the default options can be changed using the
the ``dynamic_loss_args`` argument to :class:`FP16_Optimizer`'s constructor.
Loss scaling is designed to combat the problem of underflowing gradients encountered at long
times when training fp16 networks. Dynamic loss scaling begins by attempting a very high loss
scale. Ironically, this may result in OVERflowing gradients. If overflowing gradients are
encountered, :class:`DynamicLossScaler` informs :class:`FP16_Optimizer` that an overflow has
occurred.
:class:`FP16_Optimizer` then skips the update step for this particular iteration/minibatch,
and :class:`DynamicLossScaler` adjusts the loss scale to a lower value.
If a certain number of iterations occur without overflowing gradients detected,
:class:`DynamicLossScaler` increases the loss scale once more.
In this way :class:`DynamicLossScaler` attempts to "ride the edge" of
always using the highest loss scale possible without incurring overflow.
Args:
init_scale (float, optional, default=2**32): Initial loss scale attempted by :class:`DynamicLossScaler.`
scale_factor (float, optional, default=2.0): Factor used when adjusting the loss scale. If an overflow is encountered, the loss scale is readjusted to loss scale/``scale_factor``. If ``scale_window`` consecutive iterations take place without an overflow, the loss scale is readjusted to loss_scale*``scale_factor``.
scale_window (int, optional, default=1000): Number of consecutive iterations without an overflow to wait before increasing the loss scale.
"""
def __init__(self,
init_scale=2**32,
scale_factor=2.,
scale_window=1000,
min_scale=1,
delayed_shift=1,
consecutive_hysteresis=False):
self.cur_scale = init_scale
self.cur_iter = 0
self.last_overflow_iter = -1
self.scale_factor = scale_factor
self.scale_window = scale_window
self.min_scale = min_scale
self.delayed_shift = delayed_shift
self.cur_hysteresis = delayed_shift
self.consecutive_hysteresis = consecutive_hysteresis
# `params` is a list / generator of torch.Variable
def has_overflow_serial(self, params):
for p in params:
if p.grad is not None and DynamicLossScaler._has_inf_or_nan(p.grad.data):
return True
return False
def has_overflow(self, params):
overflow = self.has_overflow_serial(params)
# Since each model parallel GPU carries only part of the model,
# make sure overflow flag is synced across all the model parallel GPUs
overflow_gpu = torch.cuda.ByteTensor([overflow])
torch.distributed.all_reduce(overflow_gpu,
op=torch.distributed.ReduceOp.MAX,
group=mpu.get_model_parallel_group())
overflow = overflow_gpu[0].item()
return bool(overflow)
# `x` is a torch.Tensor
def _has_inf_or_nan(x):
try:
# if x is half, the .float() incurs an additional deep copy, but it's necessary if
# Pytorch's .sum() creates a one-element tensor of the same type as x
# (which is true for some recent version of pytorch).
cpu_sum = float(x.float().sum())
# More efficient version that can be used if .sum() returns a Python scalar
# cpu_sum = float(x.sum())
except RuntimeError as instance:
# We want to check if inst is actually an overflow exception.
# RuntimeError could come from a different error.
# If so, we still want the exception to propagate.
if "value cannot be converted" not in instance.args[0]:
raise
return True
else:
if cpu_sum == float('inf') or cpu_sum == -float('inf') or cpu_sum != cpu_sum:
return True
return False
# `overflow` is boolean indicating whether the gradient overflowed
def update_scale(self, overflow):
if not hasattr(self, 'min_scale'):
self.min_scale = 1
if not hasattr(self, 'delayed_shift'):
self.delayed_shift = 1
if not hasattr(self, 'cur_hysteresis'):
self.cur_hysteresis = 1
if not hasattr(self, 'consecutive_hysteresis'):
self.consecutive_hysteresis = True
if overflow:
# self.cur_scale /= self.scale_factor
if self.delayed_shift == 1 or self.cur_hysteresis == 1:
self.cur_scale = max(self.cur_scale/self.scale_factor, self.min_scale)
else:
self.cur_hysteresis -= 1
self.last_overflow_iter = self.cur_iter
else:
if self.consecutive_hysteresis:
self.cur_hysteresis = self.delayed_shift
if (self.cur_iter - self.last_overflow_iter) % self.scale_window == 0:
if not self.consecutive_hysteresis:
self.cur_hysteresis = self.delayed_shift
self.cur_scale *= self.scale_factor
self.cur_iter += 1
@property
def loss_scale(self):
return self.cur_scale
def scale_gradient(self, module, grad_in, grad_out):
return tuple(self.loss_scale * g for g in grad_in)
def backward(self, loss, retain_graph=False):
scaled_loss = loss*self.loss_scale
scaled_loss.backward(retain_graph=retain_graph)
##############################################################
# Example usage below here -- assuming it's in a separate file
##############################################################
"""
TO-DO separate out into an example.
if __name__ == "__main__":
import torch
from torch.autograd import Variable
from dynamic_loss_scaler import DynamicLossScaler
# N is batch size; D_in is input dimension;
# H is hidden dimension; D_out is output dimension.
N, D_in, H, D_out = 64, 1000, 100, 10
# Create random Tensors to hold inputs and outputs, and wrap them in Variables.
x = Variable(torch.randn(N, D_in), requires_grad=False)
y = Variable(torch.randn(N, D_out), requires_grad=False)
w1 = Variable(torch.randn(D_in, H), requires_grad=True)
w2 = Variable(torch.randn(H, D_out), requires_grad=True)
parameters = [w1, w2]
learning_rate = 1e-6
optimizer = torch.optim.SGD(parameters, lr=learning_rate)
loss_scaler = DynamicLossScaler()
for t in range(500):
y_pred = x.mm(w1).clamp(min=0).mm(w2)
loss = (y_pred - y).pow(2).sum() * loss_scaler.loss_scale
print('Iter {} loss scale: {}'.format(t, loss_scaler.loss_scale))
print('Iter {} scaled loss: {}'.format(t, loss.data[0]))
print('Iter {} unscaled loss: {}'.format(t, loss.data[0] / loss_scaler.loss_scale))
# Run backprop
optimizer.zero_grad()
loss.backward()
# Check for overflow
has_overflow = DynamicLossScaler.has_overflow(parameters)
# If no overflow, unscale grad and update as usual
if not has_overflow:
for param in parameters:
param.grad.data.mul_(1. / loss_scaler.loss_scale)
optimizer.step()
# Otherwise, don't do anything -- ie, skip iteration
else:
print('OVERFLOW!')
# Update loss scale for next iteration
loss_scaler.update_scale(has_overflow)
"""
================================================
FILE: CPM-Generate/generate_samples.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
"""Sample Generate GPT2"""
import os
import random
import numpy as np
import torch
import torch.nn.functional as F
import argparse
import time
from arguments import get_args
from utils import Timers
from utils import load_checkpoint_model
from data_utils.tokenization_gpt2 import GPT2Tokenizer
from configure_data import configure_data
import mpu
from fp16 import FP16_Module
from model import GPT2Model
from model import DistributedDataParallel as DDP
from utils import print_rank_0
USE_TORCH_DDP = False
def get_masks_and_position_ids(data,
eod_token,
reset_position_ids,
reset_attention_mask):
# Extract batch size and sequence length.
batch_size, seq_length = data.size()
# Attention mask (lower triangular).
if reset_attention_mask:
att_mask_batch = batch_size
else:
att_mask_batch = 1
attention_mask = torch.tril(torch.ones(
(att_mask_batch, seq_length, seq_length), device=data.device)).view(
att_mask_batch, 1, seq_length, seq_length)
# Loss mask.
loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device)
loss_mask[data == eod_token] = 0.0
# Position ids.
position_ids = torch.arange(seq_length, dtype=torch.long,
device=data.device)
position_ids = position_ids.unsqueeze(0).expand_as(data)
# We need to clone as the ids will be modifed based on batch index.
if reset_position_ids:
position_ids = position_ids.clone()
if reset_position_ids or reset_attention_mask:
# Loop through the batches:
for b in range(batch_size):
# Find indecies where EOD token is.
eod_index = position_ids[b, data[b] == eod_token]
# Detach indecies from positions if going to modify positions.
if reset_position_ids:
eod_index = eod_index.clone()
# Loop through EOD indecies:
prev_index = 0
for j in range(eod_index.size()[0]):
i = eod_index[j]
# Mask attention loss.
if reset_attention_mask:
attention_mask[b, 0, (i+1):, :(i+1)] = 0
# Reset positions.
if reset_position_ids:
position_ids[b, (i+1):] -= (i + 1 - prev_index)
prev_index = i + 1
return attention_mask, loss_mask, position_ids
def initialize_distributed(args):
"""Initialize torch.distributed."""
# Manually set the device ids.
device = args.rank % torch.cuda.device_count()
if args.local_rank is not None:
device = args.local_rank
torch.cuda.set_device(device)
# Call the init process
init_method = 'tcp://'
master_ip = os.getenv('MASTER_ADDR', 'localhost')
master_port = os.getenv('MASTER_PORT', '6000')
init_method += master_ip + ':' + master_port
torch.distributed.init_process_group(
backend=args.distributed_backend,
world_size=args.world_size, rank=args.rank,
init_method=init_method)
# Set the model-parallel / data-parallel communicators.
mpu.initialize_model_parallel(args.model_parallel_size)
def set_random_seed(seed):
"""Set random seed for reproducability."""
if seed is not None and seed > 0:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
mpu.model_parallel_cuda_manual_seed(seed)
def get_batch(context_tokens, device, args):
tokens = context_tokens
tokens = tokens.view(args.batch_size, -1).contiguous()
tokens = tokens.to(device)
# Get the masks and postition ids.
attention_mask, loss_mask, position_ids = get_masks_and_position_ids(
tokens,
args.eod_token,
args.reset_position_ids,
args.reset_attention_mask)
return tokens, attention_mask, position_ids
def top_k_logits(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
# This function has been mostly taken from huggingface conversational ai code at
# https://medium.com/huggingface/how-to-build-a-state-of-the-art-conversational-ai-with-transfer-learning-2d818ac26313
if top_k > 0:
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = filter_value
if top_p > 0.0:
#convert to 1D
logits=logits.view(logits.size()[1]).contiguous()
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probs > top_p
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
indices_to_remove = sorted_indices[sorted_indices_to_remove]
logits[indices_to_remove] = filter_value
#going back to 2D
logits=logits.view(1, -1).contiguous()
return logits
def generate_samples(model, tokenizer, args, device):
context_count=0
model.eval()
with torch.no_grad():
while True:
torch.distributed.barrier(group=mpu.get_model_parallel_group())
terminate_runs=0
if mpu.get_model_parallel_rank() == 0:
raw_text = input("\nContext prompt (stop to exit) >>> ")
while not raw_text:
print('Prompt should not be empty!')
raw_text = input("\nContext prompt (stop to exit) >>> ")
if "stop" in raw_text:
terminate_runs = 1
else:
#context_tokens = tokenizer.EncodeAsIds(raw_text).tokenization
context_tokens = tokenizer.encode(raw_text)
context_length = len(context_tokens)
if context_length >=args.seq_length//2:
print("\nContext length", context_length, \
"\nPlease give smaller context (half of the sequence length)!")
continue
else:
#context_tokens = tokenizer.EncodeAsIds("EMPTY TEXT").tokenization
context_tokens = tokenizer.encode("空文本")
context_length = len(context_tokens)
terminate_runs_tensor = torch.cuda.LongTensor([terminate_runs])
torch.distributed.broadcast(terminate_runs_tensor, mpu.get_model_parallel_src_rank(), group=mpu.get_model_parallel_group())
terminate_runs = terminate_runs_tensor[0].item()
if terminate_runs == 1:
return
pad_id = tokenizer.encoder['']
args.eod_token = tokenizer.encoder['']
if context_length < args.seq_length:
context_tokens.extend([pad_id] * (args.seq_length - context_length))
context_tokens_tensor = torch.cuda.LongTensor(context_tokens)
context_length_tensor = torch.cuda.LongTensor([context_length])
torch.distributed.broadcast(context_length_tensor, mpu.get_model_parallel_src_rank(), group=mpu.get_model_parallel_group())
torch.distributed.broadcast(context_tokens_tensor, mpu.get_model_parallel_src_rank(), group=mpu.get_model_parallel_group())
context_length = context_length_tensor[0].item()
tokens, attention_mask, position_ids=get_batch(context_tokens_tensor, device, args)
start_time = time.time()
counter = 0
org_context_length = context_length
while counter < (org_context_length + args.out_seq_length):
logits = model(tokens, position_ids, attention_mask)
logits = logits[:, context_length - 1, :] / args.temperature
logits = top_k_logits(logits, top_k=args.top_k, top_p=args.top_p)
log_probs = F.softmax(logits, dim=-1)
prev = torch.multinomial(log_probs, num_samples=1)
tokens[0, context_length] = prev[0]
torch.distributed.broadcast(tokens, mpu.get_model_parallel_src_rank(), group=mpu.get_model_parallel_group())
context_length += 1
counter += 1
output_tokens_list = tokens.view(-1).contiguous()
decode_tokens = tokenizer.decode(output_tokens_list.tolist())
token_end = decode_tokens.find("")
if mpu.get_model_parallel_rank() == 0 and (counter % 16 == 0 or token_end != -1):
os.system('clear')
print("\nTaken time {:.2f}\n".format(time.time() - start_time), flush=True)
print("\nContext:", raw_text, flush=True)
trim_decode_tokens = decode_tokens[len(raw_text):decode_tokens.find("")]
print("\nCPM:", trim_decode_tokens, flush=True)
if token_end != -1:
#print(token_end)
break
if mpu.get_model_parallel_rank() == 0:
os.system('clear')
print("\nTaken time {:.2f}\n".format(time.time() - start_time), flush=True)
print("\nContext:", raw_text, flush=True)
output_tokens_list = tokens.view(-1).contiguous()
decode_tokens = tokenizer.decode(output_tokens_list.tolist())
trim_decode_tokens = decode_tokens[len(raw_text):decode_tokens.find("")]
print("\nCPM:", trim_decode_tokens, flush=True)
#print(token_end)
raw_text = None
torch.distributed.barrier(group=mpu.get_model_parallel_group())
context_count += 1
def prepare_tokenizer(args):
tokenizer_args = {
'tokenizer_type': args.tokenizer_type,
'corpus': None,
'model_path': args.tokenizer_path,
'vocab_size': args.vocab_size,
'model_type': args.tokenizer_model_type,
'cache_dir': args.cache_dir}
tokenizer = make_tokenizer(**tokenizer_args)
args.tokenizer_num_tokens = tokenizer.num_tokens
args.tokenizer_num_type_tokens = tokenizer.num_type_tokens
args.eod_token = tokenizer.get_command('eos').Id
after = tokenizer.num_tokens
while after % mpu.get_model_parallel_world_size() != 0:
after += 1
args.vocab_size = after
print("prepare tokenizer done", flush=True)
return tokenizer
def get_model(args):
"""Build the model."""
print_rank_0('building CPM model ...')
model = GPT2Model(num_layers=args.num_layers,
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
embedding_dropout_prob=args.hidden_dropout,
attention_dropout_prob=args.attention_dropout,
output_dropout_prob=args.hidden_dropout,
max_sequence_length=args.max_position_embeddings,
checkpoint_activations=args.checkpoint_activations,
checkpoint_num_layers=args.checkpoint_num_layers,
parallel_output=False)
if mpu.get_data_parallel_rank() == 0:
print(' > number of parameters on model parallel rank {}: {}'.format(
mpu.get_model_parallel_rank(),
sum([p.nelement() for p in model.parameters()])), flush=True)
# GPU allocation.
model.cuda(torch.cuda.current_device())
# Fp16 conversion.
if args.fp16:
model = FP16_Module(model)
# Wrap model for distributed training.
if USE_TORCH_DDP:
i = torch.cuda.current_device()
model = DDP(model, device_ids=[i], output_device=i,
process_group=mpu.get_data_parallel_group())
else:
model = DDP(model)
return model
def setup_model(args):
"""Setup model."""
model = get_model(args)
args.iteration = load_checkpoint_model(model, args)
return model
def main():
"""Main training program."""
print('Generate Samples')
# Disable CuDNN.
torch.backends.cudnn.enabled = False
# Timer.
timers = Timers()
# Arguments.
args = get_args()
# Pytorch distributed.
initialize_distributed(args)
# Random seeds for reproducability.
set_random_seed(args.seed)
#get the tokenizer
tokenizer = GPT2Tokenizer(os.path.join(args.tokenizer_path, 'vocab.json'), os.path.join(args.tokenizer_path, 'merges.txt'), os.path.join(args.tokenizer_path, 'chinese_vocab.model'))
# Model
model = setup_model(args)
#setting default batch size to 1
args.batch_size = 1
#generate samples
generate_samples(model, tokenizer, args, torch.cuda.current_device())
if __name__ == "__main__":
main()
================================================
FILE: CPM-Generate/model/__init__.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
from .distributed import *
from .gpt2_modeling import gpt2_get_params_for_weight_decay_optimization
from .gpt2_modeling import GPT2Model
from .model import BertModel
from .model import get_params_for_weight_decay_optimization
================================================
FILE: CPM-Generate/model/distributed.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
import torch
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
import torch.distributed as dist
from torch.nn.modules import Module
from torch.autograd import Variable
import mpu
class DistributedDataParallel(Module):
def __init__(self, module):
super(DistributedDataParallel, self).__init__()
self.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
self.module = module
self.data_parallel_group = mpu.get_data_parallel_group()
src_rank = mpu.get_model_parallel_rank()
for p in self.module.parameters():
if torch.is_tensor(p):
dist.broadcast(p, src_rank, group=self.data_parallel_group)
def allreduce_params(reduce_after=True, no_scale=False, fp32_allreduce=False):
if(self.needs_reduction):
self.needs_reduction = False
buckets = {}
for name, param in self.module.named_parameters():
if param.requires_grad and param.grad is not None:
tp = (param.data.type())
if tp not in buckets:
buckets[tp] = []
buckets[tp].append(param)
if self.warn_on_half:
if torch.cuda.HalfTensor in buckets:
print("WARNING: gloo dist backend for half parameters may be extremely slow." +
" It is recommended to use the NCCL backend in this case.")
self.warn_on_half = False
for tp in buckets:
bucket = buckets[tp]
grads = [param.grad.data for param in bucket]
coalesced = _flatten_dense_tensors(grads)
if fp32_allreduce:
coalesced = coalesced.float()
if not no_scale and not reduce_after:
coalesced /= dist.get_world_size(group=self.data_parallel_group)
dist.all_reduce(coalesced, group=self.data_parallel_group)
torch.cuda.synchronize()
if not no_scale and reduce_after:
coalesced /= dist.get_world_size(group=self.data_parallel_group)
for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
buf.copy_(synced)
self.hook_handles = []
self.hooks = []
for param in list(self.module.parameters()):
def allreduce_hook(*unused):
Variable._execution_engine.queue_callback(allreduce_params)
# handle = param.register_hook(allreduce_hook)
#self.hooks.append(allreduce_hook)
#self.hook_handles.append(handle)
self.allreduce_params = allreduce_params
def forward(self, *inputs, **kwargs):
self.needs_reduction = True
return self.module(*inputs, **kwargs)
def state_dict(self, destination=None, prefix='', keep_vars=False):
#[h.remove() for h in self.hook_handles]
sd = self.module.state_dict(destination, prefix, keep_vars)
# for handle, hook in zip(self.hook_handles, self.hooks):
# d = handle.hooks_dict_ref()
# d[handle.id] = hook
return sd
def load_state_dict(self, state_dict, strict=True):
self.module.load_state_dict(state_dict, strict=strict)
'''
def _sync_buffers(self):
buffers = list(self.module._all_buffers())
if len(buffers) > 0:
# cross-node buffer sync
flat_buffers = _flatten_dense_tensors(buffers)
dist.broadcast(flat_buffers, 0)
for buf, synced in zip(buffers, _unflatten_dense_tensors(flat_buffers, buffers)):
buf.copy_(synced)
def train(self, mode=True):
# Clear NCCL communicator and CUDA event cache of the default group ID,
# These cache will be recreated at the later call. This is currently a
# work-around for a potential NCCL deadlock.
if dist._backend == dist.dist_backend.NCCL:
dist._clear_group_cache()
super(DistributedDataParallel, self).train(mode)
self.module.train(mode)
'''
================================================
FILE: CPM-Generate/model/gpt2_modeling.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
"""GPT-2 model."""
import torch
import torch.nn.functional as F
import mpu
def init_method_normal(std=0.02):
"""Init method based on normal distribution.
This is only used for embeddings. The transformer has its
own initializer.
"""
def init_(tensor):
return torch.nn.init.normal_(tensor, mean=0.0, std=std)
return init_
class GPT2Model(torch.nn.Module):
"""GPT-2 Language model.
The output of the forward method are the logits (parallel or
serial depending on the `parallel_output` flag.
"""
def __init__(self,
num_layers,
vocab_size,
hidden_size,
num_attention_heads,
embedding_dropout_prob,
attention_dropout_prob,
output_dropout_prob,
max_sequence_length,
checkpoint_activations,
checkpoint_num_layers=1,
parallel_output=True):
super(GPT2Model, self).__init__()
self.parallel_output = parallel_output
init_method = init_method_normal(std=0.02)
# Word embeddings (parallel).
self.word_embeddings = mpu.VocabParallelEmbedding(
vocab_size, hidden_size, init_method=init_method)
# Position embedding (serial).
self.position_embeddings = torch.nn.Embedding(max_sequence_length,
hidden_size)
# Initialize the position embeddings.
init_method(self.position_embeddings.weight)
# Embeddings dropout
self.embedding_dropout = torch.nn.Dropout(embedding_dropout_prob)
# Transformer
self.transformer = mpu.GPT2ParallelTransformer(num_layers,
hidden_size,
num_attention_heads,
attention_dropout_prob,
output_dropout_prob,
checkpoint_activations,
checkpoint_num_layers)
def forward(self, input_ids, position_ids, attention_mask):
# Embeddings.
words_embeddings = self.word_embeddings(input_ids)
position_embeddings = self.position_embeddings(position_ids)
embeddings = words_embeddings + position_embeddings
# Dropout.
embeddings = self.embedding_dropout(embeddings)
# Transformer.
transformer_output = self.transformer(embeddings, attention_mask)
# Parallel logits.
transformer_output_parallel = mpu.copy_to_model_parallel_region(
transformer_output)
logits_parallel = F.linear(transformer_output_parallel,
self.word_embeddings.weight)
if self.parallel_output:
return logits_parallel
return mpu.gather_from_model_parallel_region(logits_parallel)
def gpt2_get_params_for_weight_decay_optimization(module):
weight_decay_params = {'params': []}
no_weight_decay_params = {'params': [], 'weight_decay': 0.0}
for module_ in module.modules():
if isinstance(module_, (mpu.LayerNorm, torch.nn.LayerNorm)):
no_weight_decay_params['params'].extend(
[p for p in list(module_._parameters.values())
if p is not None])
else:
weight_decay_params['params'].extend(
[p for n, p in list(module_._parameters.items())
if p is not None and n != 'bias'])
no_weight_decay_params['params'].extend(
[p for n, p in list(module_._parameters.items())
if p is not None and n == 'bias'])
return weight_decay_params, no_weight_decay_params
================================================
FILE: CPM-Generate/model/model.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
"""Utilities for wrapping BertModel."""
import torch
from .modeling import BertConfig
from .modeling import BertForPreTraining, BertForMaskedLM
from .modeling import BertLayerNorm
def get_params_for_weight_decay_optimization(module):
weight_decay_params = {'params': []}
no_weight_decay_params = {'params': [], 'weight_decay': 0.0}
for module_ in module.modules():
if isinstance(module_, (BertLayerNorm, torch.nn.LayerNorm)):
no_weight_decay_params['params'].extend(
[p for p in list(module_._parameters.values())
if p is not None])
else:
weight_decay_params['params'].extend(
[p for n, p in list(module_._parameters.items())
if p is not None and n != 'bias'])
no_weight_decay_params['params'].extend(
[p for n, p in list(module_._parameters.items())
if p is not None and n == 'bias'])
return weight_decay_params, no_weight_decay_params
class BertModel(torch.nn.Module):
def __init__(self, args):
super(BertModel, self).__init__()
if args.pretrained_bert:
self.model = BertForPreTraining.from_pretrained(
args.tokenizer_model_type,
cache_dir=args.cache_dir,
fp32_layernorm=args.fp32_layernorm,
fp32_embedding=args.fp32_embedding,
layernorm_epsilon=args.layernorm_epsilon)
else:
if args.intermediate_size is None:
intermediate_size = 4 * args.hidden_size
else:
intermediate_size = args.intermediate_size
self.config = BertConfig(
args.tokenizer_num_tokens,
hidden_size=args.hidden_size,
num_hidden_layers=args.num_layers,
num_attention_heads=args.num_attention_heads,
intermediate_size=intermediate_size,
hidden_dropout_prob=args.hidden_dropout,
attention_probs_dropout_prob=args.attention_dropout,
max_position_embeddings=args.max_position_embeddings,
type_vocab_size=args.tokenizer_num_type_tokens,
fp32_layernorm=args.fp32_layernorm,
fp32_embedding=args.fp32_embedding,
fp32_tokentypes=args.fp32_tokentypes,
layernorm_epsilon=args.layernorm_epsilon,
deep_init=args.deep_init)
self.model = BertForPreTraining(self.config)
def forward(self, input_tokens, token_type_ids=None,
attention_mask=None, checkpoint_activations=False):
return self.model(
input_tokens, token_type_ids, attention_mask,
checkpoint_activations=checkpoint_activations)
def state_dict(self, destination=None, prefix='', keep_vars=False):
return self.model.state_dict(destination=destination, prefix=prefix,
keep_vars=keep_vars)
def load_state_dict(self, state_dict, strict=True):
return self.model.load_state_dict(state_dict, strict=strict)
================================================
FILE: CPM-Generate/model/modeling.py
================================================
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
# Copyright (c) 2019, NVIDIA CORPORATION. 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 BERT model."""
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import copy
import json
import math
import logging
import tarfile
import tempfile
import shutil
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss
#from torch.utils.checkpoint import checkpoint
from data_utils.file_utils import cached_path
import mpu
def normal_init_method(mean, std):
def init_(tensor):
return torch.nn.init.normal_(tensor, mean=mean, std=std)
return init_
def scaled_init_method(mean, std, num_layers):
"""Init method based on N(0, sigma/sqrt(2*num_layers)."""
std = std / math.sqrt(2.0 * num_layers)
def init_(tensor):
return torch.nn.init.normal_(tensor, mean=mean, std=std)
return init_
logger = logging.getLogger(__name__)
PRETRAINED_MODEL_ARCHIVE_MAP = {
'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",
}
CONFIG_NAME = 'bert_config.json'
WEIGHTS_NAME = 'pytorch_model.bin'
TF_WEIGHTS_NAME = 'model.ckpt'
def load_tf_weights_in_bert(model, tf_checkpoint_path):
""" Load tf checkpoints in a pytorch model
"""
try:
import re
import numpy as np
import tensorflow as tf
except ImportError:
print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions.")
raise
tf_path = os.path.abspath(tf_checkpoint_path)
print("Converting TensorFlow checkpoint from {}".format(tf_path))
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
print("Loading TF weight {} with shape {}".format(name, shape))
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array)
for name, array in zip(names, arrays):
name = name.split('/')
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model
if any(n in ["adam_v", "adam_m"] for n in name):
print("Skipping {}".format("/".join(name)))
continue
pointer = model
for m_name in name:
if re.fullmatch(r'[A-Za-z]+_\d+', m_name):
l = re.split(r'_(\d+)', m_name)
else:
l = [m_name]
if l[0] == 'kernel' or l[0] == 'gamma':
pointer = getattr(pointer, 'weight')
elif l[0] == 'output_bias' or l[0] == 'beta':
pointer = getattr(pointer, 'bias')
elif l[0] == 'output_weights':
pointer = getattr(pointer, 'weight')
else:
pointer = getattr(pointer, l[0])
if len(l) >= 2:
num = int(l[1])
pointer = pointer[num]
if m_name[-11:] == '_embeddings':
pointer = getattr(pointer, 'weight')
elif m_name == 'kernel':
array = np.transpose(array)
try:
assert pointer.shape == array.shape
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
print("Initialize PyTorch weight {}".format(name))
pointer.data = torch.from_numpy(array)
return model
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
def swish(x):
return x * torch.sigmoid(x)
ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
class BertConfig(object):
"""Configuration class to store the configuration of a `BertModel`.
"""
def __init__(self,
vocab_size_or_config_json_file,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
deep_init=False,
fp32_layernorm=False,
fp32_embedding=False,
fp32_tokentypes=False,
layernorm_epsilon=1e-12):
"""Constructs BertConfig.
Args:
vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
hidden_size: Size of the encoder layers and the pooler layer.
num_hidden_layers: Number of hidden layers in the Transformer encoder.
num_attention_heads: Number of attention heads for each attention layer in
the Transformer encoder.
intermediate_size: The size of the "intermediate" (i.e., feed-forward)
layer in the Transformer encoder.
hidden_act: The non-linear activation function (function or string) in the
encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
hidden_dropout_prob: The dropout probabilitiy for all fully connected
layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob: The dropout ratio for the attention
probabilities.
max_position_embeddings: 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).
type_vocab_size: The vocabulary size of the `token_type_ids` passed into
`BertModel`.
initializer_range: The sttdev of the truncated_normal_initializer for
initializing all weight matrices.
"""
if isinstance(vocab_size_or_config_json_file, str):
with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
json_config = json.loads(reader.read())
for key, value in json_config.items():
self.__dict__[key] = value
elif isinstance(vocab_size_or_config_json_file, int):
self.vocab_size = vocab_size_or_config_json_file
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.deep_init = deep_init
self.fp32_layernorm = fp32_layernorm
self.fp32_embedding = fp32_embedding
self.layernorm_epsilon = layernorm_epsilon
self.fp32_tokentypes = fp32_tokentypes
else:
raise ValueError("First argument must be either a vocabulary size (int)"
"or the path to a pretrained model config file (str)")
@classmethod
def from_dict(cls, json_object):
"""Constructs a `BertConfig` from a Python dictionary of parameters."""
config = BertConfig(vocab_size_or_config_json_file=-1)
for key, value in json_object.items():
config.__dict__[key] = value
return config
@classmethod
def from_json_file(cls, json_file):
"""Constructs a `BertConfig` from a json file of parameters."""
with open(json_file, "r", encoding='utf-8') as reader:
text = reader.read()
return cls.from_dict(json.loads(text))
def __repr__(self):
return str(self.to_json_string())
def to_dict(self):
"""Serializes this instance to a Python dictionary."""
output = copy.deepcopy(self.__dict__)
return output
def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
try:
from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm
except ImportError:
print("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex.")
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = eps
def forward(self, x):
u = x.mean(-1, keepdim=True)
s = (x - u).pow(2).mean(-1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.variance_epsilon)
return self.weight * x + self.bias
class BertEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings.
"""
def __init__(self, config):
super(BertEmbeddings, self).__init__()
#self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.word_embeddings = mpu.VocabParallelEmbedding(
config.vocab_size, config.hidden_size,
init_method=normal_init_method(mean=0.0,
std=config.initializer_range))
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.fp32_layernorm = config.fp32_layernorm
self.fp32_embedding = config.fp32_embedding
self.fp32_tokentypes = config.fp32_tokentypes
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layernorm_epsilon)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, input_ids, token_type_ids=None):
seq_length = input_ids.size(1)
position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
if token_type_ids is None:
token_type_ids = torch.zeros_like(input_ids)
words_embeddings = self.word_embeddings(input_ids)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
if not self.fp32_tokentypes:
embeddings = words_embeddings + position_embeddings + token_type_embeddings
if self.fp32_embedding and not self.fp32_layernorm:
embeddings = embeddings.half()
previous_type = embeddings.type()
if self.fp32_layernorm:
embeddings = embeddings.float()
embeddings = self.LayerNorm(embeddings)
if self.fp32_layernorm:
if self.fp32_embedding:
embeddings = embeddings.half()
else:
embeddings = embeddings.type(previous_type)
else:
embeddings = words_embeddings.float() + position_embeddings.float() + token_type_embeddings.float()
if self.fp32_tokentypes and not self.fp32_layernorm:
embeddings = embeddings.half()
previous_type = embeddings.type()
if self.fp32_layernorm:
embeddings = embeddings.float()
embeddings = self.LayerNorm(embeddings)
if self.fp32_layernorm:
if self.fp32_tokentypes:
embeddings = embeddings.half()
else:
embeddings = embeddings.type(previous_type)
embeddings = self.dropout(embeddings)
return embeddings
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.hidden_size, config.num_attention_heads))
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(self, hidden_states, attention_mask):
mixed_query_layer = self.query(hidden_states)
mixed_key_layer = self.key(hidden_states)
mixed_value_layer = self.value(hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer)
key_layer = self.transpose_for_scores(mixed_key_layer)
value_layer = self.transpose_for_scores(mixed_value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
previous_type = attention_probs.type()
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
return context_layer
class BertSelfOutput(nn.Module):
def __init__(self, config):
super(BertSelfOutput, self).__init__()
if hasattr(config, 'deep_init') and config.deep_init:
init_method = scaled_init_method(mean=0.0,
std=config.initializer_range,
num_layers=config.num_hidden_layers)
else:
init_method = normal_init_method(mean=0.0,
std=config.initializer_range)
self.dense = mpu.RowParallelLinear(
input_size=config.hidden_size,
output_size=config.hidden_size,
bias=True,
input_is_parallel=True,
stride=1,
init_method=init_method)
self.fp32_layernorm = config.fp32_layernorm
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layernorm_epsilon)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
ln_input = hidden_states + input_tensor
previous_type = ln_input.type()
if self.fp32_layernorm:
ln_input = ln_input.float()
hidden_states = self.LayerNorm(ln_input)
if self.fp32_layernorm:
hidden_states = hidden_states.type(previous_type)
return hidden_states
class BertAttention(nn.Module):
def __init__(self, config):
super(BertAttention, self).__init__()
self.self = mpu.BertParallelSelfAttention(
hidden_size=config.hidden_size,
num_attention_heads=config.num_attention_heads,
dropout_prob=config.attention_probs_dropout_prob,
output_parallel=True,
init_method=normal_init_method(mean=0.0,
std=config.initializer_range))
self.output = BertSelfOutput(config)
def forward(self, input_tensor, attention_mask):
self_output = self.self(input_tensor, attention_mask)
attention_output = self.output(self_output, input_tensor)
return attention_output
class BertIntermediate(nn.Module):
def __init__(self, config):
super(BertIntermediate, self).__init__()
self.dense = mpu.ColumnParallelLinear(
input_size=config.hidden_size,
output_size=config.intermediate_size,
bias=True,
gather_output=False,
stride=1,
init_method=normal_init_method(mean=0.0,
std=config.initializer_range))
self.intermediate_act_fn = ACT2FN[config.hidden_act] \
if isinstance(config.hidden_act, str) else config.hidden_act
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class BertOutput(nn.Module):
def __init__(self, config):
super(BertOutput, self).__init__()
if hasattr(config, 'deep_init') and config.deep_init:
init_method = scaled_init_method(mean=0.0,
std=config.initializer_range,
num_layers=config.num_hidden_layers)
else:
init_method = normal_init_method(mean=0.0,
std=config.initializer_range)
self.dense = mpu.RowParallelLinear(
input_size=config.intermediate_size,
output_size=config.hidden_size,
bias=True,
input_is_parallel=True,
stride=1,
init_method=init_method)
self.fp32_layernorm = config.fp32_layernorm
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layernorm_epsilon)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
ln_input = hidden_states + input_tensor
previous_type = ln_input.type()
if self.fp32_layernorm:
ln_input = ln_input.float()
hidden_states = self.LayerNorm(ln_input)
if self.fp32_layernorm:
hidden_states = hidden_states.type(previous_type)
return hidden_states
class BertLayer(nn.Module):
def __init__(self, config):
super(BertLayer, self).__init__()
self.attention = BertAttention(config)
self.intermediate = BertIntermediate(config)
self.output = BertOutput(config)
def forward(self, hidden_states, attention_mask):
attention_output = self.attention(hidden_states, attention_mask)
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class BertEncoder(nn.Module):
def __init__(self, config):
super(BertEncoder, self).__init__()
#layer = BertLayer(config)
#self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])
# def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
# all_encoder_layers = []
# for layer_module in self.layer:
# hidden_states = layer_module(hidden_states, attention_mask)
# if output_all_encoded_layers:
# all_encoder_layers.append(hidden_states)
# if not output_all_encoded_layers:
# all_encoder_layers.append(hidden_states)
# return all_encoder_layers
def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True, checkpoint_activations=False):
all_encoder_layers = []
def custom(start, end):
def custom_forward(*inputs):
layers = self.layer[start:end]
x_ = inputs[0]
for layer in layers:
x_ = layer(x_, inputs[1])
return x_
return custom_forward
if checkpoint_activations:
l = 0
num_layers = len(self.layer)
chunk_length = 1 #math.ceil(math.sqrt(num_layers))
while l < num_layers:
hidden_states = mpu.checkpoint(custom(l, l+chunk_length), hidden_states, attention_mask*1)
l += chunk_length
# decoder layers
else:
for i,layer_module in enumerate(self.layer):
hidden_states = layer_module(hidden_states, attention_mask)
if output_all_encoded_layers:
all_encoder_layers.append(hidden_states)
if not output_all_encoded_layers or checkpoint_activations:
all_encoder_layers.append(hidden_states)
return all_encoder_layers
class BertPooler(nn.Module):
def __init__(self, config):
super(BertPooler, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
class BertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super(BertPredictionHeadTransform, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.transform_act_fn = ACT2FN[config.hidden_act] \
if isinstance(config.hidden_act, str) else config.hidden_act
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layernorm_epsilon)
self.fp32_layernorm = config.fp32_layernorm
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
previous_type = hidden_states.type()
if self.fp32_layernorm:
hidden_states = hidden_states.float()
hidden_states = self.LayerNorm(hidden_states)
if self.fp32_layernorm:
hidden_states = hidden_states.type(previous_type)
return hidden_states
class BertLMPredictionHead(nn.Module):
def __init__(self, config, bert_model_embedding_weights):
super(BertLMPredictionHead, self).__init__()
self.transform = BertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
#self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
# bert_model_embedding_weights.size(0),
# bias=False)
self.decoder_weight = bert_model_embedding_weights
self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0)))
self.bias.model_parallel = True
self.fp32_embedding = config.fp32_embedding
self.fp32_layernorm = config.fp32_layernorm
def convert_to_type(tensor):
if self.fp32_embedding:
return tensor.half()
else:
return tensor
self.type_converter = convert_to_type
self.converted = False
def forward(self, hidden_states):
if not self.converted:
self.converted = True
if self.fp32_embedding:
self.transform.half()
if self.fp32_layernorm:
self.transform.LayerNorm.float()
hidden_states = self.transform(self.type_converter(hidden_states))
# hidden_states = self.decoder(hidden_states) + self.bias
hidden_states = mpu.copy_to_model_parallel_region(hidden_states)
hidden_states = F.linear(self.type_converter(hidden_states),
self.type_converter(self.decoder_weight),
self.type_converter(self.bias))
return hidden_states
class BertOnlyMLMHead(nn.Module):
def __init__(self, config, bert_model_embedding_weights):
super(BertOnlyMLMHead, self).__init__()
self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores
class BertOnlyNSPHead(nn.Module):
def __init__(self, config):
super(BertOnlyNSPHead, self).__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score
class BertPreTrainingHeads(nn.Module):
def __init__(self, config, bert_model_embedding_weights):
super(BertPreTrainingHeads, self).__init__()
self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predictions(sequence_output)
for p in self.seq_relationship.parameters():
if p is None:
continue
pooled_output = pooled_output.type_as(p)
seq_relationship_score = self.seq_relationship(pooled_output)
return prediction_scores, seq_relationship_score
class PreTrainedBertModel(nn.Module):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
def __init__(self, config, *inputs, **kwargs):
super(PreTrainedBertModel, self).__init__()
if not isinstance(config, BertConfig):
raise ValueError(
"Parameter config in `{}(config)` should be an instance of class `BertConfig`. "
"To create a model from a Google pretrained model use "
"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
self.__class__.__name__, self.__class__.__name__
))
self.config = config
def init_bert_weights(self, module):
""" Initialize the weights.
"""
if isinstance(module, (nn.Linear, nn.Embedding)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
elif isinstance(module, BertLayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
@classmethod
def from_pretrained(cls, pretrained_model_name, state_dict=None, cache_dir=None,
fp32_layernorm=False, fp32_embedding=False, layernorm_epsilon=1e-12,
fp32_tokentypes=False, *inputs, **kwargs):
"""
Instantiate a PreTrainedBertModel from a pre-trained model file or a pytorch state dict.
Download and cache the pre-trained model file if needed.
Params:
pretrained_model_name: either:
- a str with the name of a pre-trained model to load selected in the list of:
. `bert-base-uncased`
. `bert-large-uncased`
. `bert-base-cased`
. `bert-large-cased`
. `bert-base-multilingual-uncased`
. `bert-base-multilingual-cased`
. `bert-base-chinese`
- a path or url to a pretrained model archive containing:
. `bert_config.json` a configuration file for the model
. `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance
cache_dir: an optional path to a folder in which the pre-trained models will be cached.
state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
*inputs, **kwargs: additional input for the specific Bert class
(ex: num_labels for BertForSequenceClassification)
"""
if pretrained_model_name in PRETRAINED_MODEL_ARCHIVE_MAP:
archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name]
else:
archive_file = pretrained_model_name
# redirect to the cache, if necessary
try:
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
except FileNotFoundError:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find any file "
"associated to this path or url.".format(
pretrained_model_name,
', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
archive_file))
return None
if resolved_archive_file == archive_file:
logger.info("loading archive file {}".format(archive_file))
else:
logger.info("loading archive file {} from cache at {}".format(
archive_file, resolved_archive_file))
tempdir = None
if os.path.isdir(resolved_archive_file):
serialization_dir = resolved_archive_file
else:
# Extract archive to temp dir
tempdir = tempfile.mkdtemp()
logger.info("extracting archive file {} to temp dir {}".format(
resolved_archive_file, tempdir))
with tarfile.open(resolved_archive_file, 'r:gz') as archive:
archive.extractall(tempdir)
serialization_dir = tempdir
# Load config
config_file = os.path.join(serialization_dir, CONFIG_NAME)
config = BertConfig.from_json_file(config_file)
config.fp32_layernorm = fp32_layernorm
config.fp32_embedding = fp32_embedding
config.layernorm_epsilon = layernorm_epsilon
config.fp32_tokentypes = fp32_tokentypes
logger.info("Model config {}".format(config))
# Instantiate model.
model = cls(config, *inputs, **kwargs)
if state_dict is None:
weights_path = os.path.join(serialization_dir, WEIGHTS_NAME)
state_dict = torch.load(weights_path)
old_keys = []
new_keys = []
for key in state_dict.keys():
new_key = None
if 'gamma' in key:
new_key = key.replace('gamma', 'weight')
if 'beta' in key:
new_key = key.replace('beta', 'bias')
if new_key:
old_keys.append(key)
new_keys.append(new_key)
for old_key, new_key in zip(old_keys, new_keys):
state_dict[new_key] = state_dict.pop(old_key)
missing_keys = []
unexpected_keys = []
error_msgs = []
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, '_metadata', None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
def load(module, prefix=''):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
module._load_from_state_dict(
state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + '.')
load(model, prefix='' if hasattr(model, 'bert') else 'bert.')
if len(missing_keys) > 0:
logger.info("Weights of {} not initialized from pretrained model: {}".format(
model.__class__.__name__, missing_keys))
if len(unexpected_keys) > 0:
logger.info("Weights from pretrained model not used in {}: {}".format(
model.__class__.__name__, unexpected_keys))
if tempdir:
# Clean up temp dir
shutil.rmtree(tempdir)
return model
class BertModel(PreTrainedBertModel):
"""BERT model ("Bidirectional Embedding Representations from a Transformer").
Params:
config: a BertConfig class instance with the configuration to build a new model
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
input sequence length in the current batch. It's the mask that we typically use for attention when
a batch has varying length sentences.
`output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
Outputs: Tuple of (encoded_layers, pooled_output)
`encoded_layers`: controled by `output_all_encoded_layers` argument:
- `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
- `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
to the last attention block of shape [batch_size, sequence_length, hidden_size],
`pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
classifier pretrained on top of the hidden state associated to the first character of the
input (`CLF`) to train on the Next-Sentence task (see BERT's paper).
Example usage:
```python
# Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
model = modeling.BertModel(config=config)
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config):
super(BertModel, self).__init__(config)
self.embeddings = BertEmbeddings(config)
self.encoder = BertEncoder(config)
self.pooler = BertPooler(config)
self.apply(self.init_bert_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, output_all_encoded_layers=True, checkpoint_activations=False):
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
if token_type_ids is None:
token_type_ids = torch.zeros_like(input_ids)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = extended_attention_mask.to(dtype=next(self.encoder.parameters()).dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
embedding_output = self.embeddings(input_ids, token_type_ids)
encoded_layers = self.encoder(embedding_output,
extended_attention_mask,
output_all_encoded_layers=output_all_encoded_layers,
checkpoint_activations=checkpoint_activations)
sequence_output = encoded_layers[-1]
for p in self.pooler.parameters():
if p is None:
continue
sequence_output = sequence_output.type_as(p)
break
pooled_output = self.pooler(sequence_output)
if not output_all_encoded_layers or checkpoint_activations:
encoded_layers = encoded_layers[-1]
return encoded_layers, pooled_output
class BertForPreTraining(PreTrainedBertModel):
"""BERT model with pre-training heads.
This module comprises the BERT model followed by the two pre-training heads:
- the masked language modeling head, and
- the next sentence classification head.
Params:
config: a BertConfig class instance with the configuration to build a new model.
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
input sequence length in the current batch. It's the mask that we typically use for attention when
a batch has varying length sentences.
`masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
is only computed for the labels set in [0, ..., vocab_size]
`next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size]
with indices selected in [0, 1].
0 => next sentence is the continuation, 1 => next sentence is a random sentence.
Outputs:
if `masked_lm_labels` and `next_sentence_label` are not `None`:
Outputs the total_loss which is the sum of the masked language modeling loss and the next
sentence classification loss.
if `masked_lm_labels` or `next_sentence_label` is `None`:
Outputs a tuple comprising
- the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and
- the next sentence classification logits of shape [batch_size, 2].
Example usage:
```python
# Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
model = BertForPreTraining(config)
masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config):
super(BertForPreTraining, self).__init__(config)
self.bert = BertModel(config)
self.cls = BertPreTrainingHeads(config, self.bert.embeddings.word_embeddings.weight)
self.apply(self.init_bert_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, next_sentence_label=None, checkpoint_activations=False):
sequence_output, pooled_output = self.bert(input_ids, token_type_ids, attention_mask,
output_all_encoded_layers=False, checkpoint_activations=checkpoint_activations)
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
if masked_lm_labels is not None and next_sentence_label is not None:
loss_fct = CrossEntropyLoss(ignore_index=-1)
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size).float(), masked_lm_labels.view(-1))
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2).float(), next_sentence_label.view(-1))
total_loss = masked_lm_loss + next_sentence_loss
return total_loss
else:
return prediction_scores, seq_relationship_score
class BertForMaskedLM(PreTrainedBertModel):
"""BERT model with the masked language modeling head.
This module comprises the BERT model followed by the masked language modeling head.
Params:
config: a BertConfig class instance with the configuration to build a new model.
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
input sequence length in the current batch. It's the mask that we typically use for attention when
a batch has varying length sentences.
`masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
is only computed for the labels set in [0, ..., vocab_size]
Outputs:
if `masked_lm_labels` is not `None`:
Outputs the masked language modeling loss.
if `masked_lm_labels` is `None`:
Outputs the masked language modeling logits of shape [batch_size, sequence_length, vocab_size].
Example usage:
```python
# Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
model = BertForMaskedLM(config)
masked_lm_logits_scores = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config):
super(BertForMaskedLM, self).__init__(config)
self.bert = BertModel(config)
self.cls = BertOnlyMLMHead(config, self.bert.embeddings.word_embeddings.weight)
self.apply(self.init_bert_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, checkpoint_activations=False):
sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask,
output_all_encoded_layers=False, checkpoint_activations=checkpoint_activations)
prediction_scores = self.cls(sequence_output)
if masked_lm_labels is not None:
loss_fct = CrossEntropyLoss(ignore_index=-1)
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
return masked_lm_loss
else:
return prediction_scores
class BertForNextSentencePrediction(PreTrainedBertModel):
"""BERT model with next sentence prediction head.
This module comprises the BERT model followed by the next sentence classification head.
Params:
config: a BertConfig class instance with the configuration to build a new model.
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
input sequence length in the current batch. It's the mask that we typically use for attention when
a batch has varying length sentences.
`next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size]
with indices selected in [0, 1].
0 => next sentence is the continuation, 1 => next sentence is a random sentence.
Outputs:
if `next_sentence_label` is not `None`:
Outputs the total_loss which is the sum of the masked language modeling loss and the next
sentence classification loss.
if `next_sentence_label` is `None`:
Outputs the next sentence classification logits of shape [batch_size, 2].
Example usage:
```python
# Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
model = BertForNextSentencePrediction(config)
seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config):
super(BertForNextSentencePrediction, self).__init__(config)
self.bert = BertModel(config)
self.cls = BertOnlyNSPHead(config)
self.apply(self.init_bert_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, next_sentence_label=None, checkpoint_activations=False):
_, pooled_output = self.bert(input_ids, token_type_ids, attention_mask,
output_all_encoded_layers=False, checkpoint_activations=checkpoint_activations)
seq_relationship_score = self.cls( pooled_output)
if next_sentence_label is not None:
loss_fct = CrossEntropyLoss(ignore_index=-1)
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
return next_sentence_loss
else:
return seq_relationship_score
class BertForSequenceClassification(PreTrainedBertModel):
"""BERT model for classification.
This module is composed of the BERT model with a linear layer on top of
the pooled output.
Params:
`config`: a BertConfig class instance with the configuration to build a new model.
`num_labels`: the number of classes for the classifier. Default = 2.
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
input sequence length in the current batch. It's the mask that we typically use for attention when
a batch has varying length sentences.
`labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
with indices selected in [0, ..., num_labels].
Outputs:
if `labels` is not `None`:
Outputs the CrossEntropy classification loss of the output with the labels.
if `labels` is `None`:
Outputs the classification logits of shape [batch_size, num_labels].
Example usage:
```python
# Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
num_labels = 2
model = BertForSequenceClassification(config, num_labels)
logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, num_labels=2):
super(BertForSequenceClassification, self).__init__(config)
self.num_labels = num_labels
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, num_labels)
self.apply(self.init_bert_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, checkpoint_activations=False):
_, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False, checkpoint_activations=checkpoint_activations)
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
return loss
else:
return logits
class BertForMultipleChoice(PreTrainedBertModel):
"""BERT model for multiple choice tasks.
This module is composed of the BERT model with a linear layer on top of
the pooled output.
Params:
`config`: a BertConfig class instance with the configuration to build a new model.
`num_choices`: the number of classes for the classifier. Default = 2.
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length]
with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A`
and type 1 corresponds to a `sentence B` token (see BERT paper for more details).
`attention_mask`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] with indices
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
input sequence length in the current batch. It's the mask that we typically use for attention when
a batch has varying length sentences.
`labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
with indices selected in [0, ..., num_choices].
Outputs:
if `labels` is not `None`:
Outputs the CrossEntropy classification loss of the output with the labels.
if `labels` is `None`:
Outputs the classification logits of shape [batch_size, num_labels].
Example usage:
```python
# Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[[31, 51, 99], [15, 5, 0]], [[12, 16, 42], [14, 28, 57]]])
input_mask = torch.LongTensor([[[1, 1, 1], [1, 1, 0]],[[1,1,0], [1, 0, 0]]])
token_type_ids = torch.LongTensor([[[0, 0, 1], [0, 1, 0]],[[0, 1, 1], [0, 0, 1]]])
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
num_choices = 2
model = BertForMultipleChoice(config, num_choices)
logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, num_choices=2):
super(BertForMultipleChoice, self).__init__(config)
self.num_choices = num_choices
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
self.apply(self.init_bert_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, checkpoint_activations=False):
flat_input_ids = input_ids.view(-1, input_ids.size(-1))
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1))
_, pooled_output = self.bert(flat_input_ids, flat_token_type_ids, flat_attention_mask, output_all_encoded_layers=False, checkpoint_activations=checkpoint_activations)
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, self.num_choices)
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
return loss
else:
return reshaped_logits
class BertForTokenClassification(PreTrainedBertModel):
"""BERT model for token-level classification.
This module is composed of the BERT model with a linear layer on top of
the full hidden state of the last layer.
Params:
`config`: a BertConfig class instance with the configuration to build a new model.
`num_labels`: the number of classes for the classifier. Default = 2.
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
input sequence length in the current batch. It's the mask that we typically use for attention when
a batch has varying length sentences.
`labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
with indices selected in [0, ..., num_labels].
Outputs:
if `labels` is not `None`:
Outputs the CrossEntropy classification loss of the output with the labels.
if `labels` is `None`:
Outputs the classification logits of shape [batch_size, sequence_length, num_labels].
Example usage:
```python
# Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
num_labels = 2
model = BertForTokenClassification(config, num_labels)
logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, num_labels=2):
super(BertForTokenClassification, self).__init__(config)
self.num_labels = num_labels
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
#self.classifier = nn.Linear(config.hidden_size, num_labels)
self.classifier = mpu.RowParallelLinear(
input_size=config.hidden_size,
output_size=num_labels,
bias=True,
input_is_parallel=True,
stride=1,
init_method=normal_init_method(mean=0.0,
std=config.initializer_range))
self.apply(self.init_bert_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, checkpoint_activations=False):
sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False, checkpoint_activations=checkpoint_activations)
with mpu.get_cuda_rng_tracker().fork():
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
return loss
else:
return logits
class BertForQuestionAnswering(PreTrainedBertModel):
"""BERT model for Question Answering (span extraction).
This module is composed of the BERT model with a linear layer on top of
the sequence output that computes start_logits and end_logits
Params:
`config`: a BertConfig class instance with the configuration to build a new model.
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
input sequence length in the current batch. It's the mask that we typically use for attention when
a batch has varying length sentences.
`start_positions`: position of the first token for the labeled span: torch.LongTensor of shape [batch_size].
Positions are clamped to the length of the sequence and position outside of the sequence are not taken
into account for computing the loss.
`end_positions`: position of the last token for the labeled span: torch.LongTensor of shape [batch_size].
Positions are clamped to the length of the sequence and position outside of the sequence are not taken
into account for computing the loss.
Outputs:
if `start_positions` and `end_positions` are not `None`:
Outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions.
if `start_positions` or `end_positions` is `None`:
Outputs a tuple of start_logits, end_logits which are the logits respectively for the start and end
position tokens of shape [batch_size, sequence_length].
Example usage:
```python
# Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
model = BertForQuestionAnswering(config)
start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config):
super(BertForQuestionAnswering, self).__init__(config)
self.bert = BertModel(config)
# TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version
# self.dropout = nn.Dropout(config.hidden_dropout_prob)
#self.qa_outputs = nn.Linear(config.hidden_size, 2)
self.qa_outputs = mpu.RowParallelLinear(
input_size=config.hidden_size,
output_size=2,
bias=True,
input_is_parallel=True,
stride=1,
init_method=normal_init_method(mean=0.0,
std=config.initializer_range))
self.apply(self.init_bert_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, start_positions=None, end_positions=None, checkpoint_activations=False):
sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False, checkpoint_activations=checkpoint_activations)
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions.clamp_(0, ignored_index)
end_positions.clamp_(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
return total_loss
else:
return start_logits, end_logits
================================================
FILE: CPM-Generate/mpu/__init__.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
"""Model parallel utility interface."""
from .cross_entropy import vocab_parallel_cross_entropy
from .data import broadcast_data
from .grads import clip_grad_norm
from .initialize import destroy_model_parallel
from .initialize import get_data_parallel_group
from .initialize import get_data_parallel_rank
from .initialize import get_data_parallel_world_size
from .initialize import get_model_parallel_group
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_src_rank
from .initialize import get_model_parallel_world_size
from .initialize import initialize_model_parallel
from .initialize import model_parallel_is_initialized
from .layers import ColumnParallelLinear
from .layers import ParallelEmbedding
from .layers import RowParallelLinear
from .layers import VocabParallelEmbedding
from .mappings import copy_to_model_parallel_region
from .mappings import gather_from_model_parallel_region
from .mappings import reduce_from_model_parallel_region
from .mappings import scatter_to_model_parallel_region
from .random import checkpoint
from .random import partition_activations_in_checkpoint
from .random import get_cuda_rng_tracker
from .random import model_parallel_cuda_manual_seed
from .transformer import BertParallelSelfAttention
from .transformer import BertParallelTransformerLayer
from .transformer import GPT2ParallelTransformer
from .transformer import LayerNorm
================================================
FILE: CPM-Generate/mpu/cross_entropy.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
import torch
from .initialize import get_model_parallel_group
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_world_size
from .utils import VocabUtility
class _VocabParallelCrossEntropy(torch.autograd.Function):
@staticmethod
def forward(ctx, vocab_parallel_logits, target):
# Copy so the input remains unchanged.
logits = vocab_parallel_logits.clone()
# Maximum value along vocab dimension across all GPUs.
logits_max = torch.max(logits, dim=-1)[0]
torch.distributed.all_reduce(logits_max,
op=torch.distributed.ReduceOp.MAX,
group=get_model_parallel_group())
# Subtract the maximum value.
logits.sub_(logits_max.unsqueeze(dim=-1))
# Sum of exponential of logits along vocab dimension across all GPUs.
exp_logits = logits.exp()
sum_exp_logits = exp_logits.sum(dim=-1)
torch.distributed.all_reduce(sum_exp_logits,
op=torch.distributed.ReduceOp.SUM,
group=get_model_parallel_group())
# Get the partition's vocab indecies
get_vocab_range = VocabUtility.vocab_range_from_per_partition_vocab_size
partition_vocab_size = vocab_parallel_logits.size()[-1]
rank = get_model_parallel_rank()
world_size = get_model_parallel_world_size()
vocab_start_index, vocab_end_index = get_vocab_range(
partition_vocab_size, rank, world_size)
# Create a mask of valid vocab ids (1 means it needs to be masked).
target_mask = (target < vocab_start_index) | (target >= vocab_end_index)
masked_target = target.clone() - vocab_start_index
masked_target[target_mask] = 0
# Get predicted-logits = logits[target].
# For Simplicity, we convert logits to a 2-D tensor with size
# [*, partition-vocab-size] and target to a 1-D tensor of size [*].
logits_2d = logits.view(-1, partition_vocab_size)
masked_target_1d = masked_target.view(-1)
arange_1d = torch.arange(start=0, end=logits_2d.size()[0],
device=logits_2d.device)
predicted_logits_1d = logits_2d[arange_1d, masked_target_1d]
predicted_logits = predicted_logits_1d.view_as(target)
predicted_logits[target_mask] = 0.0
# All reduce is needed to get the chunks from other GPUs.
torch.distributed.all_reduce(predicted_logits,
op=torch.distributed.ReduceOp.SUM,
group=get_model_parallel_group())
# Loss = log(sum(exp(logits))) - predicted-logit.
loss = torch.log(sum_exp_logits) - predicted_logits
# Store softmax, target-mask and masked-target for backward pass.
exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
ctx.save_for_backward(exp_logits, target_mask, masked_target_1d)
return loss
@staticmethod
def backward(ctx, grad_output):
# Retreive tensors from the forward path.
softmax, target_mask, masked_target_1d = ctx.saved_tensors
# All the inputs have softmax as thier gradient.
grad_input = softmax
# For simplicity, work with the 2D gradient.
partition_vocab_size = softmax.size()[-1]
grad_2d = grad_input.view(-1, partition_vocab_size)
# Add the gradient from matching classes.
arange_1d = torch.arange(start=0, end=grad_2d.size()[0],
device=grad_2d.device)
grad_2d[arange_1d, masked_target_1d] -= (
1.0 - target_mask.view(-1).float())
# Finally elementwise multiplication with the output gradients.
grad_input.mul_(grad_output.unsqueeze(dim=-1))
return grad_input, None
def vocab_parallel_cross_entropy(vocab_parallel_logits, target):
"""Helper function for the cross entropy."""
return _VocabParallelCrossEntropy.apply(vocab_parallel_logits, target)
================================================
FILE: CPM-Generate/mpu/data.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
import torch
from .initialize import get_model_parallel_group
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_src_rank
_MAX_DATA_DIM = 4
def _check_data_types(keys, data, target_dtype):
"""Check that all the keys have the same target data type."""
for key in keys:
assert data[key].dtype == target_dtype, '{} has data type {} which '\
'is different than {}'.format(key, data[key].dtype, target_dtype)
def _build_key_size_numel_dictionaries(keys, data):
"""Build the size on rank 0 and broadcast."""
max_dim = _MAX_DATA_DIM
sizes = [0 for _ in range(max_dim) for _ in keys]
# Pack the sizes on rank zero.
if get_model_parallel_rank() == 0:
offset = 0
for key in keys:
assert data[key].dim() < max_dim, 'you should increase MAX_DATA_DIM'
size = data[key].size()
for i, s in enumerate(size):
sizes[i + offset] = s
offset += max_dim
# Move to GPU and broadcast.
sizes_cuda = torch.cuda.LongTensor(sizes)
torch.distributed.broadcast(sizes_cuda, get_model_parallel_src_rank(),
group=get_model_parallel_group())
# Move back to cpu and unpack.
sizes_cpu = sizes_cuda.cpu()
key_size = {}
key_numel = {}
total_numel = 0
offset = 0
for key in keys:
i = 0
size = []
numel = 1
while sizes_cpu[offset + i] > 0:
this_size = sizes_cpu[offset + i]
size.append(this_size)
numel *= this_size
i += 1
key_size[key] = size
key_numel[key] = numel
total_numel += numel
offset += max_dim
return key_size, key_numel, total_numel
def broadcast_data(keys, data, datatype):
"""Broadcast data from rank zero of each model parallel group to the
members of the same model parallel group.
Arguments:
keys: list of keys in the data disctionary to be broadcasted
data: data dictionary of string keys and cpu tensor values.
datatype: torch data type of all tensors in data associated
with keys.
"""
# Build (key, size) and (key, number of elements) dictionaries along
# with the total number of elements on all ranks.
key_size, key_numel, total_numel = _build_key_size_numel_dictionaries(keys,
data)
# Pack on rank zero.
if get_model_parallel_rank() == 0:
# Check that all keys have the same data type.
_check_data_types(keys, data, datatype)
# Flatten the data associated with the keys
flatten_data = torch.cat(
[data[key].contiguous().view(-1) for key in keys], dim=0).cuda()
else:
flatten_data = torch.empty(total_numel,
device=torch.cuda.current_device(),
dtype=datatype)
# Boradcast
torch.distributed.broadcast(flatten_data, get_model_parallel_src_rank(),
group=get_model_parallel_group())
# Unpack
output = {}
offset = 0
for key in keys:
size = key_size[key]
numel = key_numel[key]
output[key] = flatten_data.narrow(0, offset, numel).view(size)
offset += numel
return output
================================================
FILE: CPM-Generate/mpu/grads.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
# Parts of the code here are adapted from PyTorch
# repo: https://github.com/pytorch/pytorch
import torch
from torch._six import inf
from .initialize import get_model_parallel_group
from .initialize import get_model_parallel_rank
def clip_grad_norm(parameters, max_norm, norm_type=2):
"""Clips gradient norm of an iterable of parameters.
This is adapted from torch.nn.utils.clip_grad.clip_grad_norm_ and
added functionality to handle model parallel parameters. Note that
the gradients are modified in place.
Arguments:
parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
single Tensor that will have gradients normalized
max_norm (float or int): max norm of the gradients
norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
infinity norm.
Returns:
Total norm of the parameters (viewed as a single vector).
"""
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
parameters = list(filter(lambda p: p.grad is not None, parameters))
max_norm = float(max_norm)
norm_type = float(norm_type)
if norm_type == inf:
total_norm = max(p.grad.data.abs().max() for p in parameters)
total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)])
# Take max across all GPUs.
torch.distributed.all_reduce(total_norm_cuda,
op=torch.distributed.ReduceOp.MAX,
group=get_model_parallel_group())
total_norm = total_norm_cuda[0].item()
else:
total_norm = 0
for p in parameters:
if p.model_parallel or (get_model_parallel_rank() == 0):
param_norm = p.grad.data.norm(norm_type)
total_norm += param_norm.item() ** norm_type
# Sum across all model parallel GPUs.
total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)])
torch.distributed.all_reduce(total_norm_cuda,
op=torch.distributed.ReduceOp.SUM,
group=get_model_parallel_group())
total_norm = total_norm_cuda[0].item() ** (1. / norm_type)
clip_coef = max_norm / (total_norm + 1e-6)
if clip_coef < 1:
for p in parameters:
p.grad.data.mul_(clip_coef)
return total_norm
================================================
FILE: CPM-Generate/mpu/initialize.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
"""Model and data parallel groups."""
import torch
from .utils import ensure_divisibility
# Model parallel group that the current rank belongs to.
_MODEL_PARALLEL_GROUP = None
# Data parallel group that the current rank belongs to.
_DATA_PARALLEL_GROUP = None
def initialize_model_parallel(model_parallel_size_):
"""
Initialize model data parallel groups.
Arguments:
model_parallel_size: number of GPUs used to parallelize model.
Let's say we have a total of 8 GPUs denoted by g0 ... g7 and we
use 2 GPUs to parallelize the model. The present function will
create 4 model parallel groups and 2 data parallel grous as:
4 model parallel groups:
[g0, g1], [g2, g3], [g4, g5], [g6, g7]
2 data parallel groups:
[g0, g2, g4, g6], [g1, g3, g5, g7]
Note that for efficiency, the caller should make sure adjacent ranks
are on the same DGX box. For example if we are using 2 DGX-1 boxes
with a total of 16 GPUs, rank 0 to 7 belong to the first box and
ranks 8 to 15 belong to the second box.
"""
if torch.distributed.get_rank() == 0:
print('> initializing model parallel with size {}'.format(
model_parallel_size_))
# Get world size and rank. Ensure some consistencies.
assert torch.distributed.is_initialized()
world_size = torch.distributed.get_world_size()
model_parallel_size = min(model_parallel_size_, world_size)
ensure_divisibility(world_size, model_parallel_size)
rank = torch.distributed.get_rank()
# Build the data parallel groups.
global _DATA_PARALLEL_GROUP
assert _DATA_PARALLEL_GROUP is None, \
'data parallel group is already initialized'
for i in range(model_parallel_size):
ranks = range(i, world_size, model_parallel_size)
group = torch.distributed.new_group(ranks)
if i == (rank % model_parallel_size):
_DATA_PARALLEL_GROUP = group
# Build the model parallel groups.
global _MODEL_PARALLEL_GROUP
assert _MODEL_PARALLEL_GROUP is None, \
'model parallel group is already initialized'
for i in range(world_size // model_parallel_size):
ranks = range(i * model_parallel_size,
(i + 1) * model_parallel_size)
group = torch.distributed.new_group(ranks)
if i == (rank // model_parallel_size):
_MODEL_PARALLEL_GROUP = group
def model_parallel_is_initialized():
"""Check if model and data parallel groups are initialized."""
if _MODEL_PARALLEL_GROUP is None or _DATA_PARALLEL_GROUP is None:
return False
return True
def get_model_parallel_group():
"""Get the model parallel group the caller rank belongs to."""
assert _MODEL_PARALLEL_GROUP is not None, \
'model parallel group is not initialized'
return _MODEL_PARALLEL_GROUP
def get_data_parallel_group():
"""Get the data parallel group the caller rank belongs to."""
assert _DATA_PARALLEL_GROUP is not None, \
'data parallel group is not initialized'
return _DATA_PARALLEL_GROUP
def get_model_parallel_world_size():
"""Return world size for the model parallel group."""
return torch.distributed.get_world_size(group=get_model_parallel_group())
def get_model_parallel_rank():
"""Return my rank for the model parallel group."""
return torch.distributed.get_rank(group=get_model_parallel_group())
def get_model_parallel_src_rank():
"""Calculate the global rank corresponding to a local rank zeor
in the model parallel group."""
global_rank = torch.distributed.get_rank()
local_world_size = get_model_parallel_world_size()
return (global_rank // local_world_size) * local_world_size
def get_data_parallel_world_size():
"""Return world size for the data parallel group."""
return torch.distributed.get_world_size(group=get_data_parallel_group())
def get_data_parallel_rank():
"""Return my rank for the data parallel group."""
return torch.distributed.get_rank(group=get_data_parallel_group())
def destroy_model_parallel():
"""Set the groups to none."""
global _MODEL_PARALLEL_GROUP
_MODEL_PARALLEL_GROUP = None
global _DATA_PARALLEL_GROUP
_DATA_PARALLEL_GROUP = None
================================================
FILE: CPM-Generate/mpu/layers.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
# Parts of the code here are adapted from PyTorch
# repo: https://github.com/pytorch/pytorch
import math
import torch
import torch.nn.functional as F
import torch.nn.init as init
from torch.nn.parameter import Parameter
from apex.normalization.fused_layer_norm import FusedLayerNorm as LayerNorm
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_world_size
from .mappings import copy_to_model_parallel_region
from .mappings import gather_from_model_parallel_region
from .mappings import reduce_from_model_parallel_region
from .mappings import scatter_to_model_parallel_region
from .random import get_cuda_rng_tracker
from .utils import divide
from .utils import split_tensor_along_last_dim
from .utils import VocabUtility
def _initialize_affine_weight(weight, output_size, input_size,
per_partition_size, partition_dim, init_method,
stride=1, return_master_weight=False):
"""Initialize affine weight for model parallel.
Build the master weight on all processes and scatter
the relevant chunk."""
# If we only use 1 process for model parallelism, bypass scatter.
world_size = get_model_parallel_world_size()
if world_size == 1:
init_method(weight)
if return_master_weight:
return weight
return None
# Initialize master weight
master_weight = torch.empty(output_size, input_size,
dtype=weight.dtype,
requires_grad=False)
init_method(master_weight)
# Split and copy
per_partition_per_stride_size = divide(per_partition_size, stride)
weight_list = torch.split(master_weight, per_partition_per_stride_size,
dim=partition_dim)
rank = get_model_parallel_rank()
my_weight_list = weight_list[rank::world_size]
with torch.no_grad():
torch.cat(my_weight_list, dim=partition_dim, out=weight)
if return_master_weight:
return master_weight
return None
class VocabParallelEmbedding(torch.nn.Module):
"""Embedding parallelized in the vocabulary dimension.
This is mainly adapted from torch.nn.Embedding and all the default
values are kept.
Arguments:
num_embeddings: vocabulary size.
embedding_dim: size of hidden state.
init_method: method to initialize weights.
"""
def __init__(self, num_embeddings, embedding_dim,
init_method=init.xavier_normal_):
super(VocabParallelEmbedding, self).__init__()
# Keep the input dimensions.
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
# Set the detauls for compatibility.
self.padding_idx = None
self.max_norm = None
self.norm_type = 2.
self.scale_grad_by_freq = False
self.sparse = False
self._weight = None
# Divide the weight matrix along the vocaburaly dimension.
self.vocab_start_index, self.vocab_end_index = \
VocabUtility.vocab_range_from_global_vocab_size(
self.num_embeddings, get_model_parallel_rank(),
get_model_parallel_world_size())
self.num_embeddings_per_partition = self.vocab_end_index - \
self.vocab_start_index
# Allocate weights.
self.weight = Parameter(torch.Tensor(self.num_embeddings_per_partition,
self.embedding_dim))
self.weight.model_parallel = True
# And initialize.
_initialize_affine_weight(
self.weight, self.num_embeddings, self.embedding_dim,
self.num_embeddings_per_partition, 0, init_method)
def forward(self, input_):
# Build the mask.
input_mask = (input_ < self.vocab_start_index) | \
(input_ >= self.vocab_end_index)
# Mask the input.
masked_input = input_.clone() - self.vocab_start_index
masked_input[input_mask] = 0
# Get the embeddings.
output_parallel = F.embedding(masked_input, self.weight,
self.padding_idx, self.max_norm,
self.norm_type, self.scale_grad_by_freq,
self.sparse)
# Mask the output embedding.
output_parallel[input_mask, :] = 0.0
# Reduce across all the model parallel GPUs.
output = reduce_from_model_parallel_region(output_parallel)
return output
class ParallelEmbedding(torch.nn.Module):
"""Embedding parallelized in the embedding dimension.
This is mainly adapted from torch.nn.Embedding and all the default
values are kept.
Arguments:
num_embeddings: vocabulary size.
embedding_dim: size of hidden state.
init_method: method to initialize weights.
"""
def __init__(self, num_embeddings, embedding_dim,
init_method=init.xavier_normal_,
keep_master_weight_for_test=False):
super(ParallelEmbedding, self).__init__()
# Keep the input dimensions.
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
# Set some detauls for compatibility.
self.padding_idx = None
self.max_norm = None
self.norm_type = 2.
self.scale_grad_by_freq = False
self.sparse = False
self._weight = None
# Divide the weight matrix along the embedding dimension.
world_size = get_model_parallel_world_size()
self.embedding_dim_per_partition = divide(self.embedding_dim,
world_size)
# Allocate weights.
self.weight = Parameter(torch.Tensor(self.num_embeddings,
self.embedding_dim_per_partition))
self.weight.model_parallel = True
# And initialize.
_initialize_affine_weight(
self.weight, self.num_embeddings, self.embedding_dim,
self.embedding_dim_per_partition, 1, init_method,
stride=1, return_master_weight=False)
def forward(self, input_):
input_parallel = copy_to_model_parallel_region(input_)
output_parallel = F.embedding(input_parallel, self.weight,
self.padding_idx, self.max_norm,
self.norm_type, self.scale_grad_by_freq,
self.sparse)
output = gather_from_model_parallel_region(output_parallel)
return output
class ColumnParallelLinear(torch.nn.Module):
"""Linear layer with column parallelism.
The linear layer is defined as Y = XA + b. A is parallelized along
its second dimension as A = [A_1, ..., A_p].
Arguments:
input_size: first dimension of matrix A.
output_size: second dimension of matrix A.
bias: If true, add bias
gather_output: If true, call all-gether on output and make Y avaiable
to all GPUs, otherwise, every GPU will have its output
which is Y_i = XA_i
init_method: method to initialize weights. Note that bias is always set
to zero.
stride: For the strided linear layers.
keep_master_weight_for_test: This was added for testing and should be
set to False. It returns the master weights
used for initialization.
"""
def __init__(self, input_size, output_size, bias=True, gather_output=True,
init_method=init.xavier_normal_, stride=1,
keep_master_weight_for_test=False):
super(ColumnParallelLinear, self).__init__()
# Keep input parameters
self.input_size = input_size
self.output_size = output_size
self.gather_output = gather_output
# Divide the weight matrix along the last dimension.
world_size = get_model_parallel_world_size()
self.output_size_per_partition = divide(output_size, world_size)
# Parameters.
# Note: torch.nn.functional.linear performs XA^T + b and as a result
# we allocate the transpose.
self.weight = Parameter(torch.Tensor(self.output_size_per_partition,
self.input_size))
self.weight.model_parallel = True
if bias:
self.bias = Parameter(torch.Tensor(self.output_size_per_partition))
self.bias.model_parallel = True
# Always initialize bias to zero.
with torch.no_grad():
self.bias.zero_()
else:
self.register_parameter('bias', None)
# Initialize weight.
self.master_weight = _initialize_affine_weight(
self.weight, self.output_size, self.input_size,
self.output_size_per_partition, 0, init_method,
stride=stride, return_master_weight=keep_master_weight_for_test)
def forward(self, input_):
# Set up backprop all-reduce.
input_parallel = copy_to_model_parallel_region(input_)
# Matrix multiply.
output_parallel = F.linear(input_parallel, self.weight, self.bias)
if self.gather_output:
# All-gather across the partitions.
output = gather_from_model_parallel_region(output_parallel)
else:
output = output_parallel
return output
class RowParallelLinear(torch.nn.Module):
"""Linear layer with row parallelism.
The linear layer is defined as Y = XA + b. A is parallelized along
its first dimension and X along its second dimension as:
- -
| A_1 |
| . |
A = | . | X = [X_1, ..., X_p]
| . |
| A_p |
- -
Arguments:
input_size: first dimension of matrix A.
output_size: second dimension of matrix A.
bias: If true, add bias. Note that bias is not parallelized.
input_is_parallel: If true, we assume that the input is already
split across the GPUs and we do not split
again.
init_method: method to initialize weights. Note that bias is always set
to zero.
stride: For the strided linear layers.
keep_master_weight_for_test: This was added for testing and should be
set to False. It returns the master weights
used for initialization.
"""
def __init__(self, input_size, output_size, bias=True,
input_is_parallel=False,
init_method=init.xavier_normal_, stride=1,
keep_master_weight_for_test=False):
super(RowParallelLinear, self).__init__()
# Keep input parameters
self.input_size = input_size
self.output_size = output_size
self.input_is_parallel = input_is_parallel
# Divide the weight matrix along the last dimension.
world_size = get_model_parallel_world_size()
self.input_size_per_partition = divide(input_size, world_size)
# Parameters.
# Note: torch.nn.functional.linear performs XA^T + b and as a result
# we allocate the transpose.
self.weight = Parameter(torch.Tensor(self.output_size,
self.input_size_per_partition))
self.weight.model_parallel = True
if bias:
self.bias = Parameter(torch.Tensor(self.output_size))
# Always initialize bias to zero.
with torch.no_grad():
self.bias.zero_()
else:
self.register_parameter('bias', None)
# Initialize weight.
self.master_weight = _initialize_affine_weight(
self.weight, self.output_size, self.input_size,
self.input_size_per_partition, 1, init_method,
stride=stride, return_master_weight=keep_master_weight_for_test)
def forward(self, input_):
# Set up backprop all-reduce.
if self.input_is_parallel:
input_parallel = input_
else:
input_parallel = scatter_to_model_parallel_region(input_)
# Matrix multiply.
output_parallel = F.linear(input_parallel, self.weight)
# All-reduce across all the partitions.
output_ = reduce_from_model_parallel_region(output_parallel)
if self.bias is not None:
output = output_ + self.bias
else:
output = output_
return output
================================================
FILE: CPM-Generate/mpu/mappings.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
import torch
from .initialize import get_model_parallel_group
from .utils import split_tensor_along_last_dim
def _reduce(input_):
"""All-reduce the the input tensor across model parallel group."""
group = get_model_parallel_group()
# Bypass the function if we are using only 1 GPU.
if torch.distributed.get_world_size(group=group) == 1:
return input_
# All-reduce.
torch.distributed.all_reduce(input_, group=group)
return input_
def _split(input_):
"""Split the tensor along its last dimension and keep the
corresponding slice."""
group = get_model_parallel_group()
# Bypass the function if we are using only 1 GPU.
if torch.distributed.get_world_size(group=group) == 1:
return input_
# Split along last dimension.
world_size = torch.distributed.get_world_size(group=group)
input_list = split_tensor_along_last_dim(input_, world_size)
# Note: torch.split does not create contiguous tensors by default.
rank = torch.distributed.get_rank(group=group)
output = input_list[rank].contiguous()
return output
def _gather(input_):
"""Gather tensors and concatinate along the last dimension."""
group = get_model_parallel_group()
# Bypass the function if we are using only 1 GPU.
if torch.distributed.get_world_size(group=group) == 1:
return input_
# Size and dimension.
last_dim = input_.dim() - 1
rank = torch.distributed.get_rank(group=group)
world_size = torch.distributed.get_world_size(group=group)
tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
tensor_list[rank] = input_
torch.distributed.all_gather(tensor_list, input_, group=group)
# Note: torch.cat already creates a contiguous tensor.
output = torch.cat(tensor_list, dim=last_dim).contiguous()
return output
class _CopyToModelParallelRegion(torch.autograd.Function):
"""Pass the input to the model parallel region."""
@staticmethod
def forward(ctx, input_):
return input_
@staticmethod
def backward(ctx, grad_output):
return _reduce(grad_output)
class _ReduceFromModelParallelRegion(torch.autograd.Function):
"""All-redcue the input from the model parallel region."""
@staticmethod
def forward(ctx, input_):
return _reduce(input_)
@staticmethod
def backward(ctx, grad_output):
return grad_output
class _ScatterToModelParallelRegion(torch.autograd.Function):
"""Split the input and keep only the corresponding chuck to the rank."""
@staticmethod
def forward(ctx, input_):
return _split(input_)
@staticmethod
def backward(ctx, grad_output):
return _gather(grad_output)
class _GatherFromModelParallelRegion(torch.autograd.Function):
"""Gather the input from model parallel region and concatinate."""
@staticmethod
def forward(ctx, input_):
return _gather(input_)
@staticmethod
def backward(ctx, grad_output):
return _split(grad_output)
# -----------------
# Helper functions.
# -----------------
def copy_to_model_parallel_region(input_):
return _CopyToModelParallelRegion.apply(input_)
def reduce_from_model_parallel_region(input_):
return _ReduceFromModelParallelRegion.apply(input_)
def scatter_to_model_parallel_region(input_):
return _ScatterToModelParallelRegion.apply(input_)
def gather_from_model_parallel_region(input_):
return _GatherFromModelParallelRegion.apply(input_)
================================================
FILE: CPM-Generate/mpu/random.py
================================================
# coding=utf-8
#Modified by Samyam Rajbhandari
#Used to partition the activations stored for backward propagation
#Therefore reduces the memory consumption
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
# Parts of the code here are adapted from PyTorch
# repo: https://github.com/pytorch/pytorch
import contextlib
import torch.distributed as dist
import torch
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
#from torch.utils.checkpoint import detach_variable
import torch.distributed as dist
PARTITION_ACTIVATIONS = False
PA_CORRECTNESS_TEST= False
def see_memory_usage(message, force=False):
if not force:
return
dist.barrier()
if dist.get_rank() == 0:
print(message)
print("Memory Allocated ", torch.cuda.memory_allocated()/(1024*1024*1024), "GigaBytes")
print("Max Memory Allocated ", torch.cuda.max_memory_allocated()/(1024*1024*1024), "GigaBytes")
print("Cache Allocated ", torch.cuda.memory_cached()/(1024*1024*1024), "GigaBytes")
print("Max cache Allocated ", torch.cuda.max_memory_cached()/(1024*1024*1024), "GigaBytes")
print(" ")
#input("Press Any Key To Continue ..")
from .initialize import get_data_parallel_rank
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_world_size
from .initialize import get_model_parallel_group
mp_rank = None #get_model_parallel_rank()
mp_size = None #get_model_parallel_world_size()
mp_group = None #get_model_parallel_group()
# Default name for the model parallel rng tracker.
_MODEL_PARALLEL_RNG_TRACKER_NAME = 'model-parallel-rng'
transport_stream = None
cuda_device=None
def detach_variable(inputs, device=None):
if isinstance(inputs, tuple):
out = []
for inp in inputs:
if not isinstance(inp, torch.Tensor):
out.append(inp)
continue
requires_grad = inp.requires_grad
if device is not None:
x = inp.to(device=device)
else:
x = inp
x = x.detach()
x.requires_grad = requires_grad
out.append(x)
return tuple(out)
else:
raise RuntimeError(
"Only tuple of tensors is supported. Got Unsupported input type: ", type(inputs).__name__)
def _set_cuda_rng_state(new_state, device=-1):
"""Sets the random number generator state of the current GPU.
Argumentss:
new_state (torch.ByteTensor): The desired state
This function is adapted from PyTorch repo (torch.cuda.set_rng_state)
with a single change: the input state is not cloned. Cloning caused
major performance issues for +4 GPU cases.
"""
if hasattr(_C, '_cuda_setRNGState') and callable(_C._cuda_setRNGState):
# older PyTorch
def cb():
with device_ctx_manager(device):
_C._cuda_setRNGState(new_state)
else:
# newer PyTorch
if device == -1:
device = torch.device('cuda')
elif isinstance(device, str):
device = torch.device(device)
elif isinstance(device, int):
device = torch.device('cuda', device)
def cb():
idx = device.index
if idx is None:
idx = torch.cuda.current_device()
default_generator = torch.cuda.default_generators[idx]
default_generator.set_state(new_state)
_lazy_call(cb)
class CudaRNGStatesTracker:
"""Tracker for the cuda RNG states.
Using the `add` method, a cuda rng state is initialized based on
the input `seed` and is assigned to `name`. Later, by forking the
rng state, we can perform operations and return to our starting
cuda state.
"""
def __init__(self):
# Map from a string name to the cuda rng state.
self.states_ = {}
# Seeds are just for book keeping and ensure no seed is set twice.
self.seeds_ = set()
def reset(self):
"""Set to the initial state (no tracker)."""
self.states_ = {}
self.seeds_ = set()
def get_states(self):
"""Get rng states. Copy the dictionary so we have direct
pointers to the states, not just a pointer to the dictionary."""
states = {}
for name in self.states_:
states[name] = self.states_[name]
return states
def set_states(self, states):
"""Set the rng states. For efficiency purposes, we do not check
the size of seed for compatibility."""
self.states_ = states
def add(self, name, seed):
"""Track the rng state."""
# Check seed is not already used.
if seed in self.seeds_:
raise Exception('seed {} already exists'.format(seed))
self.seeds_.add(seed)
# Check that state is not already defined.
if name in self.states_:
raise Exception('cuda rng state {} already exists'.format(name))
# Get the current rng state.
orig_rng_state = torch.cuda.get_rng_state()
# Set the new state and store it.
torch.cuda.manual_seed(seed)
self.states_[name] = torch.cuda.get_rng_state()
# Reset rng state to what it was.
_set_cuda_rng_state(orig_rng_state)
@contextlib.contextmanager
def fork(self, name=_MODEL_PARALLEL_RNG_TRACKER_NAME):
"""Fork the cuda rng state, perform operations, and exit with
the original state."""
# Check if we have added the state
if name not in self.states_:
raise Exception('cuda rng state {} is not added'.format(name))
# Store current rng state.
orig_cuda_rng_state = torch.cuda.get_rng_state()
# Set rng state to the desired one
_set_cuda_rng_state(self.states_[name])
# Do the stuff we wanted to do.
try:
yield
finally:
# Update the current rng state for later use.
self.states_[name] = torch.cuda.get_rng_state()
# And set the state to the original state we started with.
_set_cuda_rng_state(orig_cuda_rng_state)
# RNG tracker object.
_CUDA_RNG_STATE_TRACKER = CudaRNGStatesTracker()
def get_cuda_rng_tracker():
"""Get cuda rng tracker."""
return _CUDA_RNG_STATE_TRACKER
def model_parallel_cuda_manual_seed(seed):
"""Initialize model parallel cuda seed.
This function should be called after the model parallel is
initialized. Also, no torch.cuda.manual_seed should be called
after this function. Basically, this is replacement for that
function.
Two set of RNG states are tracked:
default state: This is for data parallelism and is the same among a
set of model parallel GPUs but different across
different model paralle groups. This is used for
example for dropout in the non-model-parallel regions.
model-parallel state: This state is different among a set of model
parallel GPUs, but the same across data parallel
groups. This is used for example for dropout in
model parallel regions.
"""
# 2718 is just for fun and any POSITIVE value will work.
offset = seed + 2718
model_parallel_seed = offset + get_model_parallel_rank()
# Data parallel gets the original sedd.
data_parallel_seed = seed
if torch.distributed.get_rank() == 0:
print('> initializing model parallel cuda seeds on global rank {}, '
'model parallel rank {}, and data parallel rank {} with '
'model parallel seed: {} and data parallel seed: {}'.format(
torch.distributed.get_rank(), get_model_parallel_rank(),
get_data_parallel_rank(), model_parallel_seed,
data_parallel_seed), flush=True)
_CUDA_RNG_STATE_TRACKER.reset()
# Set the default state.
torch.cuda.manual_seed(data_parallel_seed)
# and model parallel state.
_CUDA_RNG_STATE_TRACKER.add(_MODEL_PARALLEL_RNG_TRACKER_NAME,
model_parallel_seed)
def get_partition_start(item):
global mp_rank, mp_size, mp_group
partition_size = get_partition_size(item)
start = partition_size * mp_rank
return int(start)
def get_partition_size(item):
global mp_rank, mp_size, mp_group
size = item.numel()
partition_size = size/mp_size
return int(partition_size)
def get_full_inputs(tensors):
inputs=[]
for i in range(int(len(tensors)/2)-1):
item = tensors[2 * i]
size = tensors[2* i + 1]
partition_size = item.numel()
tensor_size = partition_size * mp_size
flat_tensor = torch.zeros([tensor_size], dtype=item.dtype, device=item.device)
partitions=[]
for i in range(mp_size):
part_i = flat_tensor.narrow(0, partition_size * i , partition_size)
if i == mp_rank:
part_i.copy_(item)
partitions.append(part_i)
dist.all_gather(partitions,partitions[mp_rank], group=mp_group)
input_tensor = flat_tensor.view(list(size.numpy()))
item.data=input_tensor.data
inputs.append(item)
inputs.append(tensors[-2])
return tuple(inputs)
class CheckpointFunction(torch.autograd.Function):
"""This function is adapted from torch.utils.checkpoint with
two main changes:
1) torch.cuda.set_rng_state is replaced with `_set_cuda_rng_state`
2) the states in the model parallel tracker are also properly
tracked/set/reset.
"""
@staticmethod
def forward(ctx, run_function, *args):
ctx.run_function = run_function
global mp_rank, mp_size, mp_group
if mp_rank is None:
mp_rank = get_model_parallel_rank()
mp_size = get_model_parallel_world_size()
mp_group = get_model_parallel_group()
global cuda_device, transport_stream, PARTITION_ACTIVATIONS
if cuda_device is None:
if dist.get_rank() == 0:
print(f"Partition Activations {PARTITION_ACTIVATIONS} and Correctness Check {PA_CORRECTNESS_TEST}")
cuda_device = torch.cuda.current_device()
#The transport stream is used to overlap the allgather communication for the activations
#with the computation in the backward pass
transport_stream = torch.cuda.Stream(device=cuda_device)
if PARTITION_ACTIVATIONS:
inputs = [item.detach().contiguous().view(-1).narrow(0, get_partition_start(item), get_partition_size(item)).clone() for item in args[:-1]]
inputs.append(args[-1])
#just in case something funky is happening such as reuse of inputs
inputs_cuda = [item.to(cuda_device) for item in args]
# Copy the rng states.
ctx.fwd_cpu_rng_state = torch.get_rng_state()
ctx.fwd_cuda_rng_state = torch.cuda.get_rng_state()
ctx.fwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states()
#ctx.save_for_backward(*args)
with torch.no_grad():
outputs = run_function(*inputs_cuda)
del inputs_cuda
if PARTITION_ACTIVATIONS:
new_args = []
for arg, inp in zip(args,inputs):
size= torch.tensor(arg.size())
arg.data = inp.data
new_args.append(arg)
new_args.append(size)
ctx.save_for_backward(*new_args)
else:
ctx.save_for_backward(*args)
return outputs
@staticmethod
def backward(ctx, *args):
if not torch.autograd._is_checkpoint_valid():
raise RuntimeError("Checkpointing is not compatible with .grad(), "
"please use .backward() if possible")
global cuda_device, transport_stream, PARTITION_ACTIVATIONS
if PARTITION_ACTIVATIONS:
with torch.cuda.stream(transport_stream):
inputs = get_full_inputs(ctx.saved_tensors)
detached_inputs = detach_variable(inputs)
else:
inputs = ctx.saved_tensors
detached_inputs = detach_variable(inputs)
# Store the current states.
bwd_cpu_rng_state = torch.get_rng_state()
bwd_cuda_rng_state = torch.cuda.get_rng_state()
bwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states()
# Set the states to what it used to be before the forward pass.
torch.set_rng_state(ctx.fwd_cpu_rng_state)
_set_cuda_rng_state(ctx.fwd_cuda_rng_state)
get_cuda_rng_tracker().set_states(ctx.fwd_cuda_rng_state_tracker)
if PARTITION_ACTIVATIONS:
current_stream=torch.cuda.current_stream()
current_stream.wait_stream(transport_stream)
with torch.enable_grad():
outputs = ctx.run_function(*detached_inputs)
# Set the states back to what it was at the start of this function.
torch.set_rng_state(bwd_cpu_rng_state)
_set_cuda_rng_state(bwd_cuda_rng_state)
get_cuda_rng_tracker().set_states(bwd_cuda_rng_state_tracker)
if isinstance(outputs, torch.Tensor):
outputs = (outputs,)
torch.autograd.backward(outputs, args)
return (None,) + tuple(inp.grad for inp in detached_inputs)
def checkpoint(function, *args):
"""Checkpoint a model or part of the model.
This has been directly copied from torch.utils.checkpoint."""
return CheckpointFunction.apply(function, *args)
def partition_activations_in_checkpoint(partition_activation):
global PARTITION_ACTIVATIONS
PARTITION_ACTIVATIONS=partition_activation
if dist.get_rank() == 0:
print(f"**************Partition Activations {PARTITION_ACTIVATIONS}************")
================================================
FILE: CPM-Generate/mpu/tests/__init__.py
================================================
================================================
FILE: CPM-Generate/mpu/tests/commons.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
import argparse
import os
import random
import numpy
import torch
import mpu
class IdentityLayer(torch.nn.Module):
def __init__(self, size, scale=1.0):
super(IdentityLayer, self).__init__()
self.weight = torch.nn.Parameter(scale * torch.randn(size))
def forward(self):
return self.weight
def set_random_seed(seed):
"""Set random seed for reproducability."""
random.seed(seed)
numpy.random.seed(seed)
torch.manual_seed(seed)
mpu.model_parallel_cuda_manual_seed(seed)
def initialize_distributed(backend='nccl'):
"""Initialize torch.distributed."""
# Get local rank in case it is provided.
parser = argparse.ArgumentParser()
parser.add_argument('--local_rank', type=int, default=None,
help='local rank passed from distributed launcher')
args = parser.parse_args()
local_rank = args.local_rank
# Get rank and world size.
rank = int(os.getenv('RANK', '0'))
world_size = int(os.getenv("WORLD_SIZE", '1'))
print('> initializing torch.distributed with local rank: {}, '
'rank: {}, world size: {}'.format(local_rank, rank, world_size))
# Set the device id.
device = rank % torch.cuda.device_count()
if local_rank is not None:
device = local_rank
torch.cuda.set_device(device)
# Call the init process.
init_method = 'tcp://'
master_ip = os.getenv('MASTER_ADDR', 'localhost')
master_port = os.getenv('MASTER_PORT', '6000')
init_method += master_ip + ':' + master_port
torch.distributed.init_process_group(
backend=backend,
world_size=world_size,
rank=rank,
init_method=init_method)
def print_separator(message):
torch.distributed.barrier()
filler_len = (78 - len(message)) // 2
filler = '-' * filler_len
string = '\n' + filler + ' {} '.format(message) + filler
if torch.distributed.get_rank() == 0:
print(string, flush=True)
torch.distributed.barrier()
================================================
FILE: CPM-Generate/mpu/tests/test_cross_entropy.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
import random
import sys
sys.path.append("../..")
import torch
import torch.nn.functional as F
import mpu
from mpu.cross_entropy import vocab_parallel_cross_entropy
from commons import initialize_distributed
from commons import print_separator
from commons import IdentityLayer
from commons import set_random_seed
def torch_cross_entropy(batch_size, seq_length, vocab_size,
logits_scale, seed):
set_random_seed(seed)
identity = IdentityLayer((batch_size, seq_length, vocab_size),
scale=logits_scale).cuda()
logits = identity()
target = torch.cuda.LongTensor(
size=(batch_size, seq_length)).random_(0, vocab_size)
loss = F.cross_entropy(logits.view(-1, logits.size()[-1]),
target.view(-1),
reduction='none').view_as(target).mean()
loss.backward()
return loss, identity.weight.grad
def mpu_cross_entropy(batch_size, seq_length, vocab_size,
logits_scale, seed):
set_random_seed(seed)
identity = IdentityLayer((batch_size, seq_length, vocab_size),
scale=logits_scale).cuda()
logits = identity()
logits_parallel = mpu.scatter_to_model_parallel_region(logits)
target = torch.cuda.LongTensor(
size=(batch_size, seq_length)).random_(0, vocab_size)
loss = vocab_parallel_cross_entropy(logits_parallel, target).mean()
loss.backward()
return loss, identity.weight.grad
def test_cross_entropy(model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing cross entropy with model parallel size {} ...'.
format(model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
batch_size = 13
seq_length = 17
vocab_size_per_partition = 11
logits_scale = 1000.0
vocab_size = vocab_size_per_partition * model_parallel_size
seed = 1234
loss_torch, grad_torch = torch_cross_entropy(batch_size, seq_length,
vocab_size, logits_scale,
seed)
loss_mpu, grad_mpu = mpu_cross_entropy(batch_size, seq_length,
vocab_size, logits_scale,
seed)
error = loss_torch.sub_(loss_mpu).abs().max()
print(' max error in loss on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
error = grad_torch.sub_(grad_mpu).abs().max()
print(' max error in grad on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
if __name__ == '__main__':
initialize_distributed()
world_size = torch.distributed.get_world_size()
model_parallel_size = 1
while model_parallel_size <= world_size:
print_separator('test cross entropy')
test_cross_entropy(model_parallel_size)
model_parallel_size *= 2
================================================
FILE: CPM-Generate/mpu/tests/test_data.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
import functools
import operator
import sys
sys.path.append("../..")
import torch
import mpu
from mpu import data as data_utils
from commons import initialize_distributed
from commons import print_separator
def test_boradcast_data(model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing boradcast_data with model parallel size {} ...'.
format(model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
torch.manual_seed(1234 + mpu.get_data_parallel_rank())
model_parallel_size = mpu.get_model_parallel_world_size()
key_size_t = {'key1': [7, 11],
'key2': [8, 2, 1],
'key3': [13],
'key4': [5, 1, 2],
'key5': [5, 12]}
keys = list(key_size_t.keys())
data = {}
data_t = {}
for key in key_size_t:
data[key] = torch.LongTensor(size=key_size_t[key]).random_(0, 1000)
data_t[key] = data[key].clone()
data['keyX'] = torch.FloatTensor(size=(5, )).random_(0, 1000)
data_t['keyX'] = data['keyX'].clone()
if mpu.get_model_parallel_rank() != 0:
data = None
data_utils._check_data_types(keys, data_t, torch.int64)
key_size, key_numel, \
total_numel = data_utils._build_key_size_numel_dictionaries(keys, data)
for key in keys:
assert key_size[key] == key_size_t[key]
total_numel_t = 0
for key in keys:
target_size = functools.reduce(operator.mul, key_size_t[key], 1)
assert key_numel[key] == target_size
total_numel_t += target_size
assert total_numel == total_numel_t
data_b = data_utils.broadcast_data(keys, data, torch.int64)
for key in keys:
tensor = data_t[key].cuda()
assert data_b[key].sub(tensor).abs().max() == 0
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
if __name__ == '__main__':
initialize_distributed()
world_size = torch.distributed.get_world_size()
model_parallel_size = 1
while model_parallel_size <= world_size:
print_separator('test test boradcast data')
test_boradcast_data(model_parallel_size)
model_parallel_size *= 2
================================================
FILE: CPM-Generate/mpu/tests/test_initialize.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
import sys
sys.path.append("../..")
import torch
import mpu
from commons import initialize_distributed
from commons import print_separator
def test_initialize_model_parallel(model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing initialize_model_parallel with size {} ...'.format(
model_parallel_size))
model_parallel_size_ = min(model_parallel_size,
torch.distributed.get_world_size())
assert not mpu.model_parallel_is_initialized()
mpu.initialize_model_parallel(model_parallel_size_)
assert mpu.model_parallel_is_initialized()
# Checks.
def check(group, world_size, rank):
assert world_size == torch.distributed.get_world_size(group=group)
assert rank == torch.distributed.get_rank(group=group)
# Model parallel.
world_size = model_parallel_size_
rank = torch.distributed.get_rank() % model_parallel_size_
assert world_size == mpu.get_model_parallel_world_size()
assert rank == mpu.get_model_parallel_rank()
check(mpu.get_model_parallel_group(), world_size, rank)
# Data parallel.
world_size = torch.distributed.get_world_size() // model_parallel_size_
rank = torch.distributed.get_rank() // model_parallel_size
assert world_size == mpu.get_data_parallel_world_size()
assert rank == mpu.get_data_parallel_rank()
check(mpu.get_data_parallel_group(), world_size, rank)
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
def test_get_model_parallel_src_rank(model_parallel_size_):
if torch.distributed.get_rank() == 0:
print('> testing get_model_parallel_src_rank with size {} ...'.format(
model_parallel_size_))
model_parallel_size = min(model_parallel_size_,
torch.distributed.get_world_size())
assert not mpu.model_parallel_is_initialized()
mpu.initialize_model_parallel(model_parallel_size)
assert mpu.model_parallel_is_initialized()
# Checks
src_rank = torch.distributed.get_rank() - mpu.get_model_parallel_rank()
assert mpu.get_model_parallel_src_rank() == src_rank
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
if __name__ == '__main__':
initialize_distributed()
world_size = torch.distributed.get_world_size()
model_parallel_size = 1
while model_parallel_size <= world_size:
print_separator('test initialize model parallel')
test_initialize_model_parallel(model_parallel_size)
print_separator('test model parallel source rank')
test_get_model_parallel_src_rank(model_parallel_size)
model_parallel_size *= 2
================================================
FILE: CPM-Generate/mpu/tests/test_layers.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
import random
import sys
sys.path.append("../..")
import torch
import torch.nn.init as init
from torch.nn.parameter import Parameter
import mpu
from commons import initialize_distributed
from commons import print_separator
from commons import set_random_seed
from mpu import layers
def test_parallel_embedding(model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing parallel embedding with model parallel size {} ...'.
format(model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
batch_size = 17
seq_length = 23
vocab_size = 48
hidden_size = 16
seed = 1236
set_random_seed(123)
input_data = torch.LongTensor(
size=(batch_size,seq_length)).random_(0, vocab_size).cuda()
loss_weight = torch.randn([batch_size, seq_length, hidden_size]).cuda()
set_random_seed(seed)
embedding_original = torch.nn.Embedding(vocab_size, hidden_size).cuda()
output = embedding_original(input_data)
loss_original = torch.mul(output, loss_weight).sum()
loss_original.backward()
set_random_seed(seed)
embedding_parallel = layers.ParallelEmbedding(
vocab_size, hidden_size, init_method=init.normal_).cuda()
output = embedding_parallel(input_data)
loss_parallel = torch.mul(output, loss_weight).sum()
loss_parallel.backward()
set_random_seed(seed)
embedding_vocab_parallel = layers.VocabParallelEmbedding(
vocab_size, hidden_size, init_method=init.normal_).cuda()
output = embedding_vocab_parallel(input_data)
loss_vocab_parallel = torch.mul(output, loss_weight).sum()
loss_vocab_parallel.backward()
torch.distributed.barrier()
error = loss_parallel.sub(loss_original).abs()
print(' error in loss (parallel) on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-12, 'error: {}'.format(error)
torch.distributed.barrier()
error = loss_vocab_parallel.sub(loss_original).abs()
print(' error in loss (vocab parallel) on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-12, 'error: {}'.format(error)
weight_grad_orig = torch.split(embedding_original.weight.grad,
hidden_size // model_parallel_size,
1)[mpu.get_model_parallel_rank()]
error = embedding_parallel.weight.grad.sub(weight_grad_orig).abs().max()
print(' error in grad (parallel) on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-12, 'error: {}'.format(error)
weight_grad_orig = torch.split(embedding_original.weight.grad,
vocab_size // model_parallel_size,
0)[mpu.get_model_parallel_rank()]
error = embedding_vocab_parallel.weight.grad.sub(
weight_grad_orig).abs().max()
print(' error in grad (vocab parallel) on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-12, 'error: {}'.format(error)
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
def test_initialize_affine_weight(model_parallel_size):
mpu.initialize_model_parallel(model_parallel_size)
if torch.distributed.get_rank() == 0:
print('> testing initialize_affine_weight with model parallel '
'size: {}'.format(model_parallel_size))
model_parallel_size = mpu.get_model_parallel_world_size()
seed = 12345
input_size_coeff = 13
input_size = input_size_coeff * model_parallel_size
output_size_coeff = 17
output_size = output_size_coeff * model_parallel_size
# ---------------
# Column parallel
# ---------------
weight = torch.empty(output_size_coeff, input_size)
set_random_seed(seed)
layers._initialize_affine_weight(weight, output_size, input_size,
output_size_coeff, 0,
torch.nn.init.normal_)
# Target.
set_random_seed(seed)
master_weight = torch.empty(output_size, input_size)
torch.nn.init.normal_(master_weight)
rank = mpu.get_model_parallel_rank()
my_weight = torch.split(master_weight, output_size_coeff,
dim=0)[rank].contiguous().clone()
# Compare.
error = weight.sub(my_weight).abs().max()
torch.distributed.barrier()
print(' column parallel max error (should be zero) on global rank '
'{}: {}'.format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
# ------------
# Row parallel
# ------------
weight = torch.empty(output_size, input_size_coeff)
set_random_seed(seed)
mpu.layers._initialize_affine_weight(weight, output_size, input_size,
input_size_coeff, 1,
torch.nn.init.normal_)
# Target.
set_random_seed(seed)
master_weight = torch.empty(output_size, input_size)
torch.nn.init.normal_(master_weight)
rank = mpu.get_model_parallel_rank()
my_weight = torch.split(master_weight, input_size_coeff,
dim=1)[rank].contiguous().clone()
# Compare.
error = weight.sub(my_weight).abs().max()
torch.distributed.barrier()
print(' row parallel max error (should be zero) on global rank '
'{}: {}'.format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(' >> passed the test :-)')
class IdentityLayer2D(torch.nn.Module):
def __init__(self, m , n):
super(IdentityLayer2D, self).__init__()
self.weight = Parameter(torch.Tensor(m, n))
torch.nn.init.xavier_normal_(self.weight)
def forward(self):
return self.weight
def test_column_parallel_linear(model_parallel_size):
mpu.initialize_model_parallel(model_parallel_size)
if torch.distributed.get_rank() == 0:
print('> testing ColumnParallelLinear with model parallel '
'size: {}'.format(model_parallel_size))
model_parallel_size = mpu.get_model_parallel_world_size()
seed = 12345
set_random_seed(seed)
input_size_coeff = 13
input_size = input_size_coeff * model_parallel_size
output_size_coeff = 17
output_size = output_size_coeff * model_parallel_size
batch_size = 7
# Network
identity_layer = IdentityLayer2D(batch_size, input_size).cuda()
linear_layer = mpu.ColumnParallelLinear(
input_size, output_size, keep_master_weight_for_test=True).cuda()
loss_weight = torch.randn([batch_size, output_size]).cuda()
# Forward
input_ = identity_layer()
output = linear_layer(input_)
loss = torch.mul(output, loss_weight).sum()
# Backward
loss.backward()
# Values.
dLdY = loss_weight
X = identity_layer.weight
A = linear_layer.master_weight.cuda()
dLdA = torch.matmul(dLdY.t(), X)
dLdb = torch.matmul(torch.ones(batch_size, 1).cuda().t(), dLdY).view(-1)
dLdX = torch.matmul(dLdY, A)
rank = mpu.get_model_parallel_rank()
my_dLdA = torch.split(dLdA, output_size_coeff,
dim=0)[rank].contiguous().clone()
error = my_dLdA.sub(linear_layer.weight.grad).abs().max()
torch.distributed.barrier()
print(' error in dLdA on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
my_dLdb = torch.split(dLdb, output_size_coeff,
dim=0)[rank].contiguous().clone()
error = my_dLdb.sub(linear_layer.bias.grad).abs().max()
torch.distributed.barrier()
print(' error in dLdb on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
error = dLdX.sub(identity_layer.weight.grad).abs().max()
torch.distributed.barrier()
print(' error in dLdX on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(' >> passed the test :-)')
def test_row_parallel_linear(model_parallel_size):
mpu.initialize_model_parallel(model_parallel_size)
if torch.distributed.get_rank() == 0:
print('> testing RowParallelLinear with model parallel '
'size: {}'.format(model_parallel_size))
model_parallel_size = mpu.get_model_parallel_world_size()
seed = 12345
set_random_seed(seed)
input_size_coeff = 13
input_size = input_size_coeff * model_parallel_size
output_size_coeff = 17
output_size = output_size_coeff * model_parallel_size
batch_size = 7
# Network
identity_layer = IdentityLayer2D(batch_size, input_size).cuda()
linear_layer = mpu.RowParallelLinear(
input_size, output_size, keep_master_weight_for_test=True).cuda()
loss_weight = torch.randn([batch_size, output_size]).cuda()
# Forward
input_ = identity_layer()
output = linear_layer(input_)
loss = torch.mul(output, loss_weight).sum()
# Backward
loss.backward()
# Values.
dLdY = loss_weight
X = identity_layer.weight
A = linear_layer.master_weight.cuda()
dLdA = torch.matmul(dLdY.t(), X)
dLdb = torch.matmul(torch.ones(batch_size, 1).cuda().t(), dLdY).view(-1)
dLdX = torch.matmul(dLdY, A)
rank = mpu.get_model_parallel_rank()
my_dLdA = torch.split(dLdA, input_size_coeff,
dim=1)[rank].contiguous().clone()
error = my_dLdA.sub(linear_layer.weight.grad).abs().max()
torch.distributed.barrier()
print(' error in dLdA on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
error = dLdb.sub(linear_layer.bias.grad).abs().max()
torch.distributed.barrier()
print(' error in dLdb on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
error = dLdX.sub(identity_layer.weight.grad).abs().max()
torch.distributed.barrier()
print(' error in dLdX on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(' >> passed the test :-)')
class IdentityLayer3D(torch.nn.Module):
def __init__(self, m , n, k):
super(IdentityLayer3D, self).__init__()
self.weight = Parameter(torch.Tensor(m, n, k))
torch.nn.init.xavier_normal_(self.weight)
def forward(self):
return self.weight
def parallel_self_attention(model_parallel_size, num_att_heads_per_partition,
hidden_size_per_att_head, dropout_prob, batch_size,
sequence_length):
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
seed = 12345
set_random_seed(seed)
num_att_heads = num_att_heads_per_partition * \
torch.distributed.get_world_size()
hidden_size = hidden_size_per_att_head * num_att_heads
# Network
identity_layer = IdentityLayer3D(batch_size, sequence_length,
hidden_size).cuda()
attention_layer = mpu.BertParallelSelfAttention(hidden_size, num_att_heads,
dropout_prob).cuda()
loss_weight = torch.randn([batch_size, sequence_length, hidden_size]).cuda()
attention_mask = torch.randn([batch_size, 1, 1, sequence_length]).cuda()
# Forward
input_ = identity_layer()
output = attention_layer(input_, attention_mask)
loss = torch.mul(output, loss_weight).sum()
# Backward
loss.backward()
rank = mpu.get_model_parallel_rank()
mpu.destroy_model_parallel()
return rank, hidden_size, model_parallel_size, loss, \
attention_layer, identity_layer
def test_parallel_self_attention(model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing ParallelSelfAttention with model parallel '
'size: {}'.format(model_parallel_size))
num_att_heads_per_partition = 3
hidden_size_per_att_head = 7
dropout_prob = 0.0 # has to be zero
batch_size = 5
sequence_length = 13
rank_1, hideen_size_1, model_parallel_size_1, loss_1, \
attention_layer_1, identity_layer_1 =parallel_self_attention(
1, num_att_heads_per_partition,
hidden_size_per_att_head, dropout_prob, batch_size, sequence_length)
rank, hidden_size, model_parallel_size, loss, \
attention_layer, identity_layer =parallel_self_attention(
model_parallel_size, num_att_heads_per_partition,
hidden_size_per_att_head, dropout_prob, batch_size, sequence_length)
assert hideen_size_1 == hidden_size
error = loss_1.sub(loss).abs().max()
torch.distributed.barrier()
print(' loss error on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 5.0e-6
my_lin_grad_list = torch.split(
attention_layer_1.query_key_value.weight.grad,
hidden_size // model_parallel_size, 0)[rank::model_parallel_size]
my_lin_grad = torch.cat(my_lin_grad_list, dim=0)
error = my_lin_grad.sub(
attention_layer.query_key_value.weight.grad).abs().max()
torch.distributed.barrier()
print(' weight gradient error on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 5.0e-6
error = identity_layer_1.weight.grad.sub(
identity_layer.weight.grad).abs().max()
torch.distributed.barrier()
print(' input gradient error on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 5.0e-6
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(' >> passed the test :-)')
def parallel_transformer(model_parallel_size, num_att_heads_per_partition,
hidden_size_per_att_head, batch_size, sequence_length):
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
seed = 12345
set_random_seed(seed)
num_att_heads = num_att_heads_per_partition * \
torch.distributed.get_world_size()
hidden_size = hidden_size_per_att_head * num_att_heads
intermediate_size = 4 * hidden_size
# Network
identity_layer = IdentityLayer3D(batch_size, sequence_length,
hidden_size).cuda()
transformer_layer = mpu.BertParallelTransformerLayer(
hidden_size, intermediate_size, num_att_heads, 0.0, 0.0,
torch.nn.functional.relu, 1.0e-5).cuda()
loss_weight = torch.randn([batch_size, sequence_length, hidden_size]).cuda()
attention_mask = torch.randn([batch_size, 1, 1, sequence_length]).cuda()
# Forward
input_ = identity_layer()
output = transformer_layer(input_, attention_mask)
loss = torch.mul(output, loss_weight).sum()
# Backward
loss.backward()
rank = mpu.get_model_parallel_rank()
mpu.destroy_model_parallel()
return rank, hidden_size, model_parallel_size, loss, \
transformer_layer, identity_layer
def test_parallel_transformer_layer(model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing ParallelTransformerLayer with model parallel '
'size: {}'.format(model_parallel_size))
num_att_heads_per_partition = 3
hidden_size_per_att_head = 7
batch_size = 5
sequence_length = 13
rank_1, hidden_size_1, model_parallel_size_1, loss_1, \
transformer_layer_1, identity_layer_1 = parallel_transformer(
1, num_att_heads_per_partition,
hidden_size_per_att_head, batch_size, sequence_length)
rank, hidden_size, model_parallel_size, loss, \
transformer_layer, identity_layer = parallel_transformer(
model_parallel_size, num_att_heads_per_partition,
hidden_size_per_att_head, batch_size, sequence_length)
error = loss_1.sub(loss).abs().max()
torch.distributed.barrier()
print(' loss error on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 5.0e-5, 'error: {}'.format(error)
error = identity_layer_1.weight.grad.sub(
identity_layer.weight.grad).abs().max()
torch.distributed.barrier()
print(' input gradient error on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 5.0e-5, 'error: {}'.format(error)
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(' >> passed the test :-)')
if __name__ == '__main__':
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
initialize_distributed()
world_size = torch.distributed.get_world_size()
print_separator('test initialize affine weight')
model_parallel_size = 1
while model_parallel_size <= world_size:
test_initialize_affine_weight(model_parallel_size)
model_parallel_size *= 2
model_parallel_size = 1
while model_parallel_size <= world_size:
print_separator('test parallel embedding')
test_parallel_embedding(model_parallel_size)
model_parallel_size *= 2
print_separator('test column-parallel linear')
model_parallel_size = 1
while model_parallel_size <= world_size:
test_column_parallel_linear(model_parallel_size)
model_parallel_size *= 2
print_separator('test row-parallel linear')
model_parallel_size = 1
while model_parallel_size <= world_size:
test_row_parallel_linear(model_parallel_size)
model_parallel_size *= 2
print_separator('test parallel self-attention')
model_parallel_size = 1
while model_parallel_size <= world_size:
test_parallel_self_attention(model_parallel_size)
model_parallel_size *= 2
print_separator('test parallel transformer')
model_parallel_size = 1
while model_parallel_size <= world_size:
test_parallel_transformer_layer(model_parallel_size)
model_parallel_size *= 2
================================================
FILE: CPM-Generate/mpu/tests/test_random.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
import sys
sys.path.append("../..")
import torch
import mpu
from commons import initialize_distributed
from commons import print_separator
def test_set_cuda_rng_state(model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing set_rng_state with size {} ...'.
format(model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
size = 123
seed = 1234
torch.cuda.manual_seed(1234)
tensor = torch.cuda.FloatTensor(size)
# Get the state
rng_state = torch.cuda.get_rng_state()
rng_state_copy = rng_state.clone()
# Do some stuff.
for _ in range(5):
torch.randn(size, out=tensor)
result_1 = tensor.clone()
assert rng_state.sub(rng_state_copy).max() == 0
assert torch.cuda.get_rng_state().sub(rng_state_copy).max() > 0
# State should be different.
new_rng_state = torch.cuda.get_rng_state()
max_diff = new_rng_state.sub(rng_state).max()
print(' max diff in rng state (should be non-zero) on global rank {}: {}'.
format(torch.distributed.get_rank(), max_diff))
assert max_diff > 0
# Reset the rng state and do the same stuff.
mpu.random._set_cuda_rng_state(rng_state)
for _ in range(5):
torch.randn(size, out=tensor)
mpu.random._set_cuda_rng_state(rng_state)
for _ in range(5):
torch.randn(size, out=tensor)
result_2 = tensor.clone()
# Results should be the same
error = result_2.sub(result_1).abs().max()
print(' max error in generated tensors (should be zero) on '
'global rank {}: {}'.format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Input state should have remained intact.
error = rng_state.sub(rng_state_copy).max()
print(' max error in rng state (should be zero) on global rank {}: {}'.
format(torch.distributed.get_rank(), error))
assert error == 0
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
def test_cuda_rng_tracker(model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing cuda rng tracker with size {} ...'.
format(model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
seed_1 = 1234
seed_2 = 4321
size = [12, 21]
tensor = torch.cuda.FloatTensor(size)
# Set to seed_1 and generate two tensors.
torch.cuda.manual_seed(seed_1)
torch.randn(size, out=tensor)
target_11 = tensor.clone()
torch.randn(size, out=tensor)
target_12 = tensor.clone()
# Set to seed_2 and generate two tensors.
torch.cuda.manual_seed(seed_2)
torch.randn(size, out=tensor)
target_21 = tensor.clone()
torch.randn(size, out=tensor)
target_22 = tensor.clone()
# Now if we interleave seed_1 and seed_2,
# we should still get the same tensors
torch.cuda.manual_seed(seed_1)
mpu.get_cuda_rng_tracker().add('test', seed_2)
torch.randn(size, out=tensor)
result_11 = tensor.clone()
with mpu.get_cuda_rng_tracker().fork('test'):
torch.randn(size, out=tensor)
result_21 = tensor.clone()
torch.randn(size, out=tensor)
result_12 = tensor.clone()
with mpu.get_cuda_rng_tracker().fork('test'):
torch.randn(size, out=tensor)
result_22 = tensor.clone()
diff = result_11.sub(result_21).abs().max()
diff = min(diff, result_12.sub(result_22).abs().max())
print(' max diff in generated tensors (should be non-zero) on '
'global rank {}: {}'.format(torch.distributed.get_rank(), diff))
assert diff > 1.0e-6
error = max(result_11.sub(target_11).abs().max(),
result_12.sub(target_12).abs().max())
error = max(error, result_21.sub(target_21).abs().max())
error = max(error, result_22.sub(target_22).abs().max())
print(' max error in generated tensors (should be zero) on '
'global rank {}: {}'.format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Reset the tracker
mpu.get_cuda_rng_tracker().reset()
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
def test_model_parallel_cuda_manual_seed(model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing model parallel cuda manual seed with size {} ...'.
format(model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
mpu.model_parallel_cuda_manual_seed(12345)
assert torch.cuda.initial_seed() == 12345
with mpu.get_cuda_rng_tracker().fork():
assert torch.cuda.initial_seed() == (12345 + 2718 +
mpu.get_model_parallel_rank())
# Reset the tracker
mpu.get_cuda_rng_tracker().reset()
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
if __name__ == '__main__':
initialize_distributed()
world_size = torch.distributed.get_world_size()
model_parallel_size = 1
while model_parallel_size <= world_size:
print_separator('test set rng state')
test_set_cuda_rng_state(model_parallel_size)
model_parallel_size *= 2
model_parallel_size = 1
while model_parallel_size <= world_size:
print_separator('test cuda rng tracker')
test_cuda_rng_tracker(model_parallel_size)
model_parallel_size *= 2
model_parallel_size = 1
while model_parallel_size <= world_size:
print_separator('test model parallel cuda manual seed')
test_model_parallel_cuda_manual_seed(model_parallel_size)
model_parallel_size *= 2
================================================
FILE: CPM-Generate/mpu/transformer.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
"""Transformer."""
import math
import torch
import torch.nn.init as init
from apex.normalization.fused_layer_norm import FusedLayerNorm as LayerNorm
from .initialize import get_model_parallel_world_size
from .layers import ColumnParallelLinear
from .layers import RowParallelLinear
from .mappings import gather_from_model_parallel_region
from .random import checkpoint
from .random import get_cuda_rng_tracker
from .utils import divide
from .utils import split_tensor_along_last_dim
class GPT2ParallelSelfAttention(torch.nn.Module):
"""Parallel self-attention layer for GPT2.
Self-attention layer takes input with size [b, s, h] where b is
the batch size, s is the sequence lenght, and h is the hidden size
and creates output of the same size.
Arguments:
hidden_size: total hidden size of the layer (h).
num_attention_heads: number of attention heads (n). Note that we
require n to be divisible by number of GPUs
used to parallelize the model. Also, we
require hidden size to be divisible by n.
dropout_prob: dropout probability for the attention scores.
init_method: weight initialization.
output_layer_init_method: output layer initialization. If None, use
`init_method`.
We use the following notation:
h: hidden_size
n: num_attention_heads
p: number of partitions
np: n/p
hp: h/p
hn: h/n
b: batch size
s: sequence length
"""
def __init__(self, hidden_size, num_attention_heads,
attention_dropout_prob, output_dropout_prob,
init_method, output_layer_init_method=None):
super(GPT2ParallelSelfAttention, self).__init__()
# Set output layer initialization if not provided.
if output_layer_init_method is None:
output_layer_init_method = init_method
# Per attention head and per partition values.
world_size = get_model_parallel_world_size()
self.hidden_size_per_partition = divide(hidden_size, world_size)
self.hidden_size_per_attention_head = divide(hidden_size,
num_attention_heads)
self.num_attention_heads_per_partition = divide(num_attention_heads,
world_size)
# Strided linear layer.
self.query_key_value = ColumnParallelLinear(hidden_size, 3*hidden_size,
stride=3,
gather_output=False,
init_method=init_method)
# Dropout. Note that for a single iteration, this layer will generate
# different outputs on different number of parallel partitions but
# on average it should not be partition dependent.
self.attention_dropout = torch.nn.Dropout(attention_dropout_prob)
# Output.
self.dense = RowParallelLinear(hidden_size,
hidden_size,
input_is_parallel=True,
init_method=output_layer_init_method)
self.output_dropout = torch.nn.Dropout(output_dropout_prob)
def _transpose_for_scores(self, tensor):
"""Transpose a 3D tensor [b, s, np*hn] into a 4D tensor with
size [b, np, s, hn].
"""
new_tensor_shape = tensor.size()[:-1] + \
(self.num_attention_heads_per_partition,
self.hidden_size_per_attention_head)
tensor = tensor.view(*new_tensor_shape)
return tensor.permute(0, 2, 1, 3)
def forward(self, hidden_states, ltor_mask):
# hidden_states: [b, s, h]
# ltor_mask: [1, 1, s, s]
# Attention heads. [b, s, hp]
mixed_x_layer = self.query_key_value(hidden_states)
(mixed_query_layer,
mixed_key_layer,
mixed_value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
# Reshape and transpose [b, np, s, hn]
query_layer = self._transpose_for_scores(mixed_query_layer)
key_layer = self._transpose_for_scores(mixed_key_layer)
value_layer = self._transpose_for_scores(mixed_value_layer)
# Raw attention scores. [b, np, s, s]
attention_scores = torch.matmul(query_layer,
key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(
self.hidden_size_per_attention_head)
# Apply the left to right attention mask.
attention_scores = torch.mul(attention_scores, ltor_mask) - \
10000.0 * (1.0 - ltor_mask)
# Attention probabilities. [b, np, s, s]
attention_probs = torch.nn.Softmax(dim=-1)(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
with get_cuda_rng_tracker().fork():
attention_probs = self.attention_dropout(attention_probs)
# Context layer.
# [b, np, s, hn]
context_layer = torch.matmul(attention_probs, value_layer)
# [b, s, np, hn]
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + \
(self.hidden_size_per_partition,)
# [b, s, hp]
context_layer = context_layer.view(*new_context_layer_shape)
# Output. [b, s, h]
output = self.dense(context_layer)
output = self.output_dropout(output)
return output
@torch.jit.script
def gelu_impl(x):
"""OpenAI's gelu implementation."""
return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x *
(1.0 + 0.044715 * x * x)))
def gelu(x):
return gelu_impl(x)
class GPT2ParallelMLP(torch.nn.Module):
"""MLP for GPT2.
MLP will take the input with h hidden state, project it to 4*h
hidden dimension, perform gelu transformation, and project the
state back into h hidden dimension. At the end, dropout is also
applied.
Arguments:
hidden_size: The hidden size of the self attention.
output_dropout_prob: dropout probability for the outputs
after self attention and final output.
init_method: initialization method used for the weights. Note
that all biases are initialized to zero and
layernorm weight are initialized to one.
output_layer_init_method: output layer initialization. If None,
use `init_method`.
"""
def __init__(self, hidden_size, output_dropout_prob, init_method,
output_layer_init_method=None):
super(GPT2ParallelMLP, self).__init__()
# Set output layer initialization if not provided.
if output_layer_init_method is None:
output_layer_init_method = init_method
# Project to 4h.
self.dense_h_to_4h = ColumnParallelLinear(hidden_size, 4*hidden_size,
gather_output=False,
init_method=init_method)
# Project back to h.
self.dense_4h_to_h = RowParallelLinear(
4*hidden_size,
hidden_size,
input_is_parallel=True,
init_method=output_layer_init_method)
self.dropout = torch.nn.Dropout(output_dropout_prob)
def forward(self, hidden_states):
# [b, s, 4hp]
intermediate_parallel = self.dense_h_to_4h(hidden_states)
intermediate_parallel = gelu(intermediate_parallel)
# [b, s, h]
output = self.dense_4h_to_h(intermediate_parallel)
output = self.dropout(output)
return output
class GPT2ParallelTransformerLayer(torch.nn.Module):
"""A single layer transformer for GPT2.
We use the following notation:
h: hidden size
n: number of attention heads
b: batch size
s: sequence length
Transformore layer takes input with size [b, s, h] and returns an
output of the same size.
Arguments:
hidden_size: The hidden size of the self attention.
num_attention_heads: number of attention head in the self
attention.
attention_dropout_prob: dropout probability of the attention
score in self attention.
output_dropout_prob: dropout probability for the outputs
after self attention and final output.
layernorm_epsilon: epsilon used in layernorm to avoid
division by zero.
init_method: initialization method used for the weights. Note
that all biases are initialized to zero and
layernorm weight are initialized to one.
output_layer_init_method: output layers (attention output and
mlp output) initialization. If None,
use `init_method`.
"""
def __init__(self,
hidden_size,
num_attention_heads,
attention_dropout_prob,
output_dropout_prob,
layernorm_epsilon,
init_method,
output_layer_init_method=None):
super(GPT2ParallelTransformerLayer, self).__init__()
# Set output layer initialization if not provided.
if output_layer_init_method is None:
output_layer_init_method = init_method
# Layernorm on the input data.
self.input_layernorm = LayerNorm(hidden_size, eps=layernorm_epsilon)
# Self attention.
self.attention = GPT2ParallelSelfAttention(
hidden_size,
num_attention_heads,
attention_dropout_prob,
output_dropout_prob,
init_method,
output_layer_init_method=output_layer_init_method)
# Layernorm on the input data.
self.post_attention_layernorm = LayerNorm(hidden_size,
eps=layernorm_epsilon)
# MLP
self.mlp = GPT2ParallelMLP(
hidden_size,
output_dropout_prob,
init_method,
output_layer_init_method=output_layer_init_method)
def forward(self, hidden_states, ltor_mask):
# hidden_states: [b, s, h]
# ltor_mask: [1, 1, s, s]
# Layer norm at the begining of the transformer layer.
layernorm_output = self.input_layernorm(hidden_states)
# Self attention.
attention_output = self.attention(layernorm_output, ltor_mask)
# Residual connection.
layernorm_input = hidden_states + attention_output
# Layer norm post the self attention.
layernorm_output = self.post_attention_layernorm(layernorm_input)
# MLP.
mlp_output = self.mlp(layernorm_output)
# Second residual connection.
output = layernorm_input + mlp_output
return output
def unscaled_init_method(sigma):
"""Init method based on N(0, sigma)."""
def init_(tensor):
return torch.nn.init.normal_(tensor, mean=0.0, std=sigma)
return init_
def scaled_init_method(sigma, num_layers):
"""Init method based on N(0, sigma/sqrt(2*num_layers)."""
std = sigma / math.sqrt(2.0 * num_layers)
def init_(tensor):
return torch.nn.init.normal_(tensor, mean=0.0, std=std)
return init_
class GPT2ParallelTransformer(torch.nn.Module):
"""GPT-2 transformer.
This module takes input from embedding layer and it's output can
be used directly by a logit layer. It consists of L (num-layers)
blocks of:
layer norm
self attention
residual connection
layer norm
mlp
residual connection
followed by a final layer norm.
Arguments:
num_layers: Number of transformer layers.
hidden_size: The hidden size of the self attention.
num_attention_heads: number of attention head in the self
attention.
attention_dropout_prob: dropout probability of the attention
score in self attention.
output_dropout_prob: dropout probability for the outputs
after self attention and final output.
checkpoint_activations: if True, checkpoint activations.
checkpoint_num_layers: number of layers to checkpoint. This
is basically the chunk size in checkpoitning.
layernorm_epsilon: epsilon used in layernorm to avoid
division by zero.
init_method_std: standard deviation of the init method which has
the form N(0, std).
use_scaled_init_for_output_weights: If Ture use 1/sqrt(2*num_layers)
scaling for the output weights (
output of self attention and mlp).
"""
def __init__(self,
num_layers,
hidden_size,
num_attention_heads,
attention_dropout_prob,
output_dropout_prob,
checkpoint_activations,
checkpoint_num_layers=1,
layernorm_epsilon=1.0e-5,
init_method_std=0.02,
use_scaled_init_for_output_weights=True):
super(GPT2ParallelTransformer, self).__init__()
# Store activation checkpoiting flag.
self.checkpoint_activations = checkpoint_activations
self.checkpoint_num_layers = checkpoint_num_layers
output_layer_init_method = None
if use_scaled_init_for_output_weights:
output_layer_init_method = scaled_init_method(init_method_std,
num_layers)
def get_layer():
return GPT2ParallelTransformerLayer(
hidden_size,
num_attention_heads,
attention_dropout_prob,
output_dropout_prob,
layernorm_epsilon,
unscaled_init_method(init_method_std),
output_layer_init_method=output_layer_init_method)
# Transformer layers.
self.layers = torch.nn.ModuleList(
[get_layer() for _ in range(num_layers)])
# Final layer norm before output.
self.final_layernorm = LayerNorm(hidden_size, eps=layernorm_epsilon)
def forward(self, hidden_states, attention_mask):
def custom(start, end):
def custom_forward(*inputs):
layers_ = self.layers[start:end]
x_ = inputs[0]
for layer in layers_:
x_ = layer(x_, inputs[1])
return x_
return custom_forward
if self.checkpoint_activations:
l = 0
num_layers = len(self.layers)
chunk_length = self.checkpoint_num_layers
while l < num_layers:
hidden_states = checkpoint(custom(l, l+chunk_length),
hidden_states, attention_mask)
l += chunk_length
else:
for layer in self.layers:
hidden_states = layer(hidden_states, attention_mask)
# Final layer norm.
output = self.final_layernorm(hidden_states)
return output
class BertParallelSelfAttention(torch.nn.Module):
"""Parallel self-attention layer for BERT.
Self-attention layer takes input with size [b, s, h] where b is
the batch size, s is the sequence lenght, and h is the hidden size
and creates output of the same size.
Arguments:
hidden_size: total hidden size of the layer (h).
num_attention_heads: number of attention heads (n). Note that we
require n to be divisible by number of GPUs
used to parallelize the model. Also, we
require hidden size be divisible by n.
dropout_prob: dropout probability for the attention scores.
output_parallel: If true, no all-gather is done on the output and
the output values will be per partition.
We use the following notation:
h: hidden_size
n: num_attention_heads
p: number of partitions
np: n/p
hp: h/p
hn: h/n
b: batch size
s: sequence length
"""
def __init__(self, hidden_size, num_attention_heads,
dropout_prob, output_parallel=False,
init_method=init.xavier_normal_):
super(BertParallelSelfAttention, self).__init__()
# Input configuration.
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.dropout_prob = dropout_prob
self.output_parallel = output_parallel
# Per attention head and per partition values.
world_size = get_model_parallel_world_size()
self.hidden_size_per_partition = divide(hidden_size, world_size)
self.hidden_size_per_attention_head = divide(hidden_size,
num_attention_heads)
self.num_attention_heads_per_partition = divide(num_attention_heads,
world_size)
# Strided linear layer.
self.query_key_value = ColumnParallelLinear(hidden_size, 3*hidden_size,
stride=3,
gather_output=False,
init_method=init_method)
# Dropout. Note that for a single iteration, this layer will generate
# different outputs on different number of parallel partitions but
# on average it should not be partition dependent.
self.dropout = torch.nn.Dropout(dropout_prob)
def _transpose_for_scores(self, tensor):
"""Transpose a 3D tensor [b, s, np*hn] into a 4D tensor with
size [b, np, s, hn].
"""
new_tensor_shape = tensor.size()[:-1] + \
(self.num_attention_heads_per_partition,
self.hidden_size_per_attention_head)
tensor = tensor.view(*new_tensor_shape)
return tensor.permute(0, 2, 1, 3)
def forward(self, hidden_states, attention_mask):
# Attention heads. [b, s, hp]
mixed_x_layer = self.query_key_value(hidden_states)
(mixed_query_layer,
mixed_key_layer,
mixed_value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
# Reshape and transpose [b, np, s, hn]
query_layer = self._transpose_for_scores(mixed_query_layer)
key_layer = self._transpose_for_scores(mixed_key_layer)
value_layer = self._transpose_for_scores(mixed_value_layer)
# Raw attention scores. [b, np, s, s]
norm_factor = math.sqrt(math.sqrt(self.hidden_size_per_attention_head))
attention_scores = torch.matmul(query_layer/norm_factor,
key_layer.transpose(-1, -2)/norm_factor)
# Apply the attention mask.
attention_scores += attention_mask
# Attention probabilities. [b, np, s, s]
attention_probs = torch.nn.Softmax(dim=-1)(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
with get_cuda_rng_tracker().fork():
attention_probs = self.dropout(attention_probs)
# Context layer.
# [b, np, s, hn]
context_layer = torch.matmul(attention_probs, value_layer)
# [b, s, np, hn]
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + \
(self.hidden_size_per_partition,)
# [b, s, hp]
context_layer = context_layer.view(*new_context_layer_shape)
# Output. [b, s, h]
if self.output_parallel:
output = context_layer
else:
output = gather_from_model_parallel_region(context_layer)
return output
class BertParallelTransformerOutput(torch.nn.Module):
"""The output layer used after self attention and intermediate
parts of transformer layer."""
def __init__(self, input_size, output_size, dropout_prob,
layernorm_epsilon=1.0e-12, input_is_parallel=False,
init_method=init.xavier_normal_):
super(BertParallelTransformerOutput, self).__init__()
# Components.
self.dense = RowParallelLinear(input_size,
output_size,
input_is_parallel=input_is_parallel,
init_method=init_method)
self.dropout = torch.nn.Dropout(dropout_prob)
self.layernorm = LayerNorm(output_size, eps=layernorm_epsilon)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
layernorm_input = hidden_states + input_tensor
hidden_states = self.layernorm(layernorm_input)
return hidden_states
class BertParallelTransformerLayer(torch.nn.Module):
"""A single layer transformer for Bert.
We use the following notation:
h: hidden size
n: number of attention heads
b: batch size
s: sequence length
Transformore layer takes input with size [b, s, h] and returns an
output of the same size.
Arguments:
hidden_size: The hidden size of the self attention.
intermediate_size: size of the intermediate state after
self attention. In both BERT and GPT
this is set to be 4 times the hidden
size.
num_attention_heads: number of attention head in the self
attention.
attention_dropout_prob: dropout probability of the attention
score in self attention.
output_dropout_prob: dropout probability for the outputs
after self attention and final output.
intermediate_activation_fn: activation function for output
of intermediate.
layernorm_epsilon: epsilon used in layernorm to avoid
division by zero.
init_method: initialization method used for the weights. Note
that all biases are initialized to zero and
layernorm weight are initialized to one.
"""
def __init__(self,
hidden_size,
intermediate_size,
num_attention_heads,
attention_dropout_prob,
output_dropout_prob,
intermediate_activation_fn,
layernorm_epsilon,
init_method=init.xavier_normal_):
super(BertParallelTransformerLayer, self).__init__()
# Self attention.
self.attention = BertParallelSelfAttention(hidden_size,
num_attention_heads,
attention_dropout_prob,
output_parallel=True,
init_method=init_method)
# Self attention output.
self.self_output = BertParallelTransformerOutput(
hidden_size, hidden_size, output_dropout_prob,
layernorm_epsilon=layernorm_epsilon,
input_is_parallel=True,
init_method=init_method)
# Intermediate.
self.intermediate = ColumnParallelLinear(hidden_size, intermediate_size,
gather_output=False,
init_method=init_method)
self.intermediate_activation_fn = intermediate_activation_fn
# Output.
self.output = BertParallelTransformerOutput(
intermediate_size, hidden_size, output_dropout_prob,
layernorm_epsilon=layernorm_epsilon,
input_is_parallel=True,
init_method=init_method)
def forward(self, hidden_states, attention_mask):
# [b, s, hp]
attention_output_parallel = self.attention(hidden_states,
attention_mask)
# [b, s, h]
attention_self_output = self.self_output(attention_output_parallel,
hidden_states)
# [b, s, ip]
intermediate_output_parallel = self.intermediate(attention_self_output)
intermediate_output_parallel = self.intermediate_activation_fn(
intermediate_output_parallel)
# [b, s, h]
layer_output = self.output(intermediate_output_parallel,
attention_self_output)
return layer_output
================================================
FILE: CPM-Generate/mpu/utils.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
import torch
def ensure_divisibility(numerator, denominator):
"""Ensure that numerator is divisible by the denominator."""
assert numerator % denominator == 0, '{} is not divisible by {}'.format(
numerator, denominator)
def divide(numerator, denominator):
"""Ensure that numerator is divisible by the denominator and return
the division value."""
ensure_divisibility(numerator, denominator)
return numerator // denominator
def split_tensor_along_last_dim(tensor, num_partitions,
contiguous_split_chunks=False):
"""Split a tensor along its last dimension.
Arguments:
tensor: input tensor.
num_partitions: number of partitions to split the tensor
contiguous_split_chunks: If True, make each chunk contiguous
in memory.
"""
# Get the size and dimension.
last_dim = tensor.dim() - 1
last_dim_size = divide(tensor.size()[last_dim], num_partitions)
# Split.
tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
# Note: torch.split does not create contiguous tensors by default.
if contiguous_split_chunks:
return tuple(chunk.contiguous() for chunk in tensor_list)
return tensor_list
class VocabUtility:
"""Split the vocabulary into `world_size` chunks amd return the
first and last index of the vocabulary belonging to the `rank`
partition: Note that indecies in [fist, last)"""
@staticmethod
def vocab_range_from_per_partition_vocab_size(per_partition_vocab_size,
rank, world_size):
index_f = rank * per_partition_vocab_size
index_l = index_f + per_partition_vocab_size
return index_f, index_l
@staticmethod
def vocab_range_from_global_vocab_size(global_vocab_size, rank, world_size):
per_partition_vocab_size = divide(global_vocab_size, world_size)
return VocabUtility.vocab_range_from_per_partition_vocab_size(
per_partition_vocab_size, rank, world_size)
================================================
FILE: CPM-Generate/requirements.txt
================================================
torch==1.6.0
nltk>=3.4
numpy>=1.15.4
pandas>=0.24.0
boto3==1.11.11
regex==2020.1.8
sentencepiece
jieba
pybind11
requests
================================================
FILE: CPM-Generate/scripts/generate_text.sh
================================================
#!/bin/bash
CHECKPOINT_PATH=$1
MPSIZE=2
NLAYERS=32
NHIDDEN=2560
NATT=32
MAXSEQLEN=1024
#SAMPLING ARGS
TEMP=0.9
#If TOPK/TOPP are 0 it defaults to greedy sampling, top-k will also override top-p
TOPK=0
TOPP=0
python -m torch.distributed.launch --nproc_per_node 2 generate_samples.py \
--model-parallel-size $MPSIZE \
--num-layers $NLAYERS \
--hidden-size $NHIDDEN \
--load $CHECKPOINT_PATH \
--num-attention-heads $NATT \
--seq-length $MAXSEQLEN \
--max-position-embeddings 1024 \
--fp16 \
--cache-dir cache \
--out-seq-length 512 \
--temperature $TEMP \
--top_k $TOPK \
--top_p $TOPP \
--tokenizer-path bpe_3w_new/ \
--vocab-size 30000
================================================
FILE: CPM-Generate/utils.py
================================================
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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.
"""Utilities for logging and serialization"""
import os
import random
import time
import numpy as np
import torch
from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
from fp16 import FP16_Optimizer
import mpu
import model
def print_rank_0(message):
if torch.distributed.is_initialized():
if torch.distributed.get_rank() == 0:
print(message, flush=True)
else:
print(message, flush=True)
def print_args(args):
"""Print arguments."""
print('arguments:', flush=True)
for arg in vars(args):
dots = '.' * (29 - len(arg))
print(' {} {} {}'.format(arg, dots, getattr(args, arg)), flush=True)
def print_params_min_max_norm(optimizer, iteration):
"""Print min, max, and norm of all parameters."""
index = 0
rank = torch.distributed.get_rank()
string = 'iteration, rank, index, model-parallel,min, max, norm\n'
optimizer_ = optimizer
if isinstance(optimizer, FP16_Optimizer):
optimizer_ = optimizer.optimizer
for param_group in optimizer_.param_groups:
for param in param_group['params']:
index += 1
min_ = param.data.min()
max_ = param.data.max()
norm = param.data.norm()
string += '{:7d}, {:4d}, {:4d}, {:2d}, '.format(
iteration, rank, index, int(param.model_parallel))
string += '{:.6E}, {:.6E}, {:.6E}\n'.format(min_, max_, norm)
print(string, flush=True)
class Timers:
"""Group of timers."""
class Timer:
"""Timer."""
def __init__(self, name):
self.name_ = name
self.elapsed_ = 0.0
self.started_ = False
self.start_time = time.time()
def start(self):
"""Start the timer."""
assert not self.started_, 'timer has already been started'
torch.cuda.synchronize()
self.start_time = time.time()
self.started_ = True
def stop(self):
"""Stop the timer."""
assert self.started_, 'timer is not started'
torch.cuda.synchronize()
self.elapsed_ += (time.time() - self.start_time)
self.started_ = False
def reset(self):
"""Reset timer."""
self.elapsed_ = 0.0
self.started_ = False
def elapsed(self, reset=True):
"""Calculate the elapsed time."""
started_ = self.started_
# If the timing in progress, end it first.
if self.started_:
self.stop()
# Get the elapsed time.
elapsed_ = self.elapsed_
# Reset the elapsed time
if reset:
self.reset()
# If timing was in progress, set it back.
if started_:
self.start()
return elapsed_
def __init__(self):
self.timers = {}
def __call__(self, name):
if name not in self.timers:
self.timers[name] = self.Timer(name)
return self.timers[name]
def log(self, names, normalizer=1.0, reset=True):
"""Log a group of timers."""
assert normalizer > 0.0
string = 'time (ms)'
for name in names:
elapsed_time = self.timers[name].elapsed(
reset=reset) * 1000.0/ normalizer
string += ' | {}: {:.2f}'.format(name, elapsed_time)
print_rank_0(string)
def report_memory(name):
"""Simple GPU memory report."""
mega_bytes = 1024.0 * 1024.0
string = name + ' memory (MB)'
string += ' | allocated: {}'.format(
torch.cuda.memory_allocated() / mega_bytes)
string += ' | max allocated: {}'.format(
torch.cuda.max_memory_allocated() / mega_bytes)
string += ' | cached: {}'.format(torch.cuda.memory_cached() / mega_bytes)
string += ' | max cached: {}'.format(
torch.cuda.max_memory_cached()/ mega_bytes)
print_rank_0(string)
def get_checkpoint_name(checkpoints_path, iteration, release=False, zero=False):
if release:
d = 'release'
else:
d = '{:d}'.format(iteration)
if zero:
dp_rank = mpu.get_data_parallel_rank()
d += '_zero_dp_rank_{}'.format(dp_rank)
return os.path.join(checkpoints_path, d,
'mp_rank_{:02d}_model_states.pt'.format(mpu.get_model_parallel_rank()))
def ensure_directory_exists(filename):
dirname = os.path.dirname(filename)
if not os.path.exists(dirname):
os.makedirs(dirname)
def get_checkpoint_tracker_filename(checkpoints_path):
return os.path.join(checkpoints_path, 'latest_checkpointed_iteration.txt')
def save_zero_checkpoint(args, iteration, optimizer):
zero_sd = {'iteration': iteration,
'optimizer_state_dict': optimizer.state_dict()}
zero_checkpoint_name = get_checkpoint_name(args.save, iteration, zero=True)
ensure_directory_exists(zero_checkpoint_name)
torch.save(zero_sd, zero_checkpoint_name)
print(' successfully saved {}'.format(zero_checkpoint_name))
def save_checkpoint(iteration, model, optimizer,
lr_scheduler, args):
"""Save a model checkpoint."""
# Only rank zer0 of the data parallel writes to the disk.
if isinstance(model, torchDDP):
model = model.module
if mpu.get_data_parallel_rank() == 0:
checkpoint_name = get_checkpoint_name(args.save, iteration)
print('global rank {} is saving checkpoint at iteration {:7d} to {}'.
format(torch.distributed.get_rank(), iteration, checkpoint_name))
sd = {}
sd['iteration'] = iteration
sd['model'] = model.state_dict()
# Optimizer stuff.
if not args.no_save_optim:
if optimizer is not None:
sd['optimizer'] = optimizer.state_dict()
if lr_scheduler is not None:
sd['lr_scheduler'] = lr_scheduler.state_dict()
# rng states.
if not args.no_save_rng:
sd['random_rng_state'] = random.getstate()
sd['np_rng_state'] = np.random.get_state()
sd['torch_rng_state'] = torch.get_rng_state()
sd['cuda_rng_state'] = torch.cuda.get_rng_state()
sd['rng_tracker_states'] = mpu.get_cuda_rng_tracker().get_states()
ensure_directory_exists(checkpoint_name)
torch.save(sd, checkpoint_name)
print(' successfully saved {}'.format(checkpoint_name))
# Wait so everyone is done (necessary)
torch.distributed.barrier()
# And update the latest iteration
if torch.distributed.get_rank() == 0:
tracker_filename = get_checkpoint_tracker_filename(args.save)
with open(tracker_filename, 'w') as f:
f.write(str(iteration))
# Wait so everyone is done (not necessary)
torch.distributed.barrier()
def save_ds_checkpoint(iteration, model, args):
"""Save a model checkpoint."""
sd = {}
sd['iteration'] = iteration
# rng states.
if not args.no_save_rng:
sd['random_rng_state'] = random.getstate()
sd['np_rng_state'] = np.random.get_state()
sd['torch_rng_state'] = torch.get_rng_state()
sd['cuda_rng_state'] = torch.cuda.get_rng_state()
sd['rng_tracker_states'] = mpu.get_cuda_rng_tracker().get_states()
model.save_checkpoint(args.save, iteration, client_state = sd)
def get_checkpoint_iteration(args):
# Read the tracker file and set the iteration.
tracker_filename = get_checkpoint_tracker_filename(args.load)
if not os.path.isfile(tracker_filename):
print_rank_0('WARNING: could not find the metadata file {} '.format(
tracker_filename))
print_rank_0(' will not load any checkpoints and will start from '
'random')
return 0, False, False
iteration = 0
release = False
with open(tracker_filename, 'r') as f:
metastring = f.read().strip()
try:
iteration = int(metastring)
except ValueError:
release = metastring == 'release'
if not release:
print_rank_0('ERROR: Invalid metadata file {}. Exiting'.format(
tracker_filename))
exit()
assert iteration > 0 or release, 'error parsing metadata file {}'.format(
tracker_filename)
return iteration, release, True
def load_checkpoint_model(model, args):
"""Load a model checkpoint."""
iteration, release, success = get_checkpoint_iteration(args)
if not success:
return 0
# Checkpoint.
checkpoint_name = get_checkpoint_name(args.load, iteration, release)
if mpu.get_data_parallel_rank() == 0:
print('global rank {} is loading checkpoint {}'.format(
torch.distributed.get_rank(), checkpoint_name))
# Load the checkpoint.
sd = torch.load(checkpoint_name, map_location='cpu')
if isinstance(model, torchDDP):
model = model.module
# Model.
try:
model.load_state_dict(sd['module'])
except KeyError:
print_rank_0('A metadata file exists but unable to load model '
'from checkpoint {}, exiting'.format(checkpoint_name))
exit()
torch.distributed.barrier()
if mpu.get_data_parallel_rank() == 0:
print(' successfully loaded {}'.format(checkpoint_name))
return iteration
def load_weights(src, dst, dst2src=False):
"""
Loads weights from src to dst via in place copy.
src is a huggingface gpt2model, while dst is one of our models.
dst2src=True loads parameters from our models into huggingface's.
^dst2src is still untested
"""
conv_layer = 'Conv1D' in str(type(src))
for n, p in src.named_parameters():
if dst2src:
data = dst._parameters[n].data
load = p.data
else:
data = p.data
load = dst._parameters[n].data
if conv_layer and 'weight' in n:
data = data.t().contiguous()
load.copy_(data)
# dst._parameters[n].data.copy_(data)
def load_mlp(our, oai, dst2src=False):
load_weights(oai.c_fc, our.dense_h_to_4h, dst2src)
load_weights(oai.c_proj, our.dense_4h_to_h, dst2src)
def load_attention(our, oai, dst2src=False):
load_weights(oai.c_attn, our.query_key_value, dst2src)
load_weights(oai.c_proj, our.dense, dst2src)
def load_transformer_layer(our, oai, dst2src=False):
load_weights(oai.ln_1, our.input_layernorm, dst2src)
load_weights(oai.ln_2, our.post_attention_layernorm, dst2src)
load_mlp(our.mlp, oai.mlp, dst2src)
load_attention(our.attention, oai.attn, dst2src)
def move_weights(our, oai, dst2src=False):
"""
Loads weights from `oai` to `our` via in place copy.
`oai` is a huggingface gpt2model, while `our` is one of our models.
dst2src=True loads parameters from our models into huggingface's.
^dst2src=True is still untested
"""
# while isinstance(our, (torchDDP, model.distributed.DistributedDataParallel, FP16_Module)):
# our=our.module
transformer_model = oai.transformer
load_weights(transformer_model.ln_f, our.transformer.final_layernorm, dst2src)
load_weights(transformer_model.wte, our.word_embeddings, dst2src)
load_weights(transformer_model.wpe, our.position_embeddings, dst2src)
for our_layer, oai_layer in zip(our.transformer.layers, oai.transformer.h):
load_transformer_layer(our_layer, oai_layer, dst2src)
================================================
FILE: ChineseAiDungeonColabDemo.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"id": "2a45dcbe",
"metadata": {},
"source": [
"# 中文ai 地牢 colab"
]
},
{
"cell_type": "markdown",
"id": "8fe7038b",
"metadata": {},
"source": [
"- 点击\"代码执行程序\" -> \"全部运行\" 进行游戏\n",
"- 安装依赖，下载模型和初始化大概会持续5分多钟时间\n",
"- 如果模型输出的结果没对你的动作做出合理反应，或者一直重复某个事实，尝试忽略模型的重复，直接输入更加具体的你的动作，多几次总会走出来\n",
"- 麻烦忽略模型输出中的一些逻辑问题，记住大体故事走向就好，这对游戏体验有好处（哈哈）"
]
},
{
"cell_type": "markdown",
"id": "69c34369",
"metadata": {},
"source": [
"## 安装依赖 （忽略error就行）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8dbf8fe4",
"metadata": {},
"outputs": [],
"source": [
"!git clone -q https://github.com/bupticybee/ChineseAiDungeon.git\n",
"!mkdir -p /content/ChineseAiDungeon/models\n",
"%cd /content/ChineseAiDungeon\n",
"!pip3 uninstall -y tensorflow\n",
"!pip3 install -r requirements.txt > tmp.log"
]
},
{
"cell_type": "markdown",
"id": "9ccf1277",
"metadata": {},
"source": [
"## 下载模型 (由于谷歌限制可能暂时失效)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c023ebbd",
"metadata": {},
"outputs": [],
"source": [
"!perl 3rd/gdown.pl/gdown.pl https://drive.google.com/open?id=1cJ1kvtPrV4TXxiadiGU6bJUAy11bRDm1 story_teller.zip\n",
"!unzip story_teller.zip"
]
},
{
"cell_type": "markdown",
"id": "37267267",
"metadata": {},
"source": [
"## 初始化"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e7d65efc",
"metadata": {},
"outputs": [],
"source": [
"from utils.story_helper import initize,Story\n",
"initize(\"./tmp_weight/\")"
]
},
{
"cell_type": "markdown",
"id": "ded5ddc0",
"metadata": {},
"source": [
"# 开始游戏"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e2cbf45c",
"metadata": {},
"outputs": [],
"source": [
"begin = f'''你在树林里冒险，指不定会从哪里蹦出来一些奇怪的东西，你握紧手上的手枪，希望这次冒险能够找到一些值钱的东西'''\n",
"story = Story(begin)\n",
"story.interactive()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "88ba1921",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"accelerator": "GPU",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
================================================
FILE: LICENSE
================================================
GNU GENERAL PUBLIC LICENSE
Version 2, June 1991
Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The licenses for most software are designed to take away your
freedom to share and change it. By contrast, the GNU General Public
License is intended to guarantee your freedom to share and change free
software--to make sure the software is free for all its users. This
General Public License applies to most of the Free Software
Foundation's software and to any other program whose authors commit to
using it. (Some other Free Software Foundation software is covered by
the GNU Lesser General Public License instead.) You can apply it to
your programs, too.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
this service if you wish), that you receive source code or can get it
if you want it, that you can change the software or use pieces of it
in new free programs; and that you know you can do these things.
To protect your rights, we need to make restrictions that forbid
anyone to deny you these rights or to ask you to surrender the rights.
These restrictions translate to certain responsibilities for you if you
distribute copies of the software, or if you modify it.
For example, if you distribute copies of such a program, whether
gratis or for a fee, you must give the recipients all the rights that
you have. You must make sure that they, too, receive or can get the
source code. And you must show them these terms so they know their
rights.
We protect your rights with two steps: (1) copyright the software, and
(2) offer you this license which gives you legal permission to copy,
distribute and/or modify the software.
Also, for each author's protection and ours, we want to make certain
that everyone understands that there is no warranty for this free
software. If the software is modified by someone else and passed on, we
want its recipients to know that what they have is not the original, so
that any problems introduced by others will not reflect on the original
authors' reputations.
Finally, any free program is threatened constantly by software
patents. We wish to avoid the danger that redistributors of a free
program will individually obtain patent licenses, in effect making the
program proprietary. To prevent this, we have made it clear that any
patent must be licensed for everyone's free use or not licensed at all.
The precise terms and conditions for copying, distribution and
modification follow.
GNU GENERAL PUBLIC LICENSE
TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
0. This License applies to any program or other work which contains
a notice placed by the copyright holder saying it may be distributed
under the terms of this General Public License. The "Program", below,
refers to any such program or work, and a "work based on the Program"
means either the Program or any derivative work under copyright law:
that is to say, a work containing the Program or a portion of it,
either verbatim or with modifications and/or translated into another
language. (Hereinafter, translation is included without limitation in
the term "modification".) Each licensee is addressed as "you".
Activities other than copying, distribution and modification are not
covered by this License; they are outside its scope. The act of
running the Program is not restricted, and the output from the Program
is covered only if its contents constitute a work based on the
Program (independent of having been made by running the Program).
Whether that is true depends on what the Program does.
1. You may copy and distribute verbatim copies of the Program's
source code as you receive it, in any medium, provided that you
conspicuously and appropriately publish on each copy an appropriate
copyright notice and disclaimer of warranty; keep intact all the
notices that refer to this License and to the absence of any warranty;
and give any other recipients of the Program a copy of this License
along with the Program.
You may charge a fee for the physical act of transferring a copy, and
you may at your option offer warranty protection in exchange for a fee.
2. You may modify your copy or copies of the Program or any portion
of it, thus forming a work based on the Program, and copy and
distribute such modifications or work under the terms of Section 1
above, provided that you also meet all of these conditions:
a) You must cause the modified files to carry prominent notices
stating that you changed the files and the date of any change.
b) You must cause any work that you distribute or publish, that in
whole or in part contains or is derived from the Program or any
part thereof, to be licensed as a whole at no charge to all third
parties under the terms of this License.
c) If the modified program normally reads commands interactively
when run, you must cause it, when started running for such
interactive use in the most ordinary way, to print or display an
announcement including an appropriate copyright notice and a
notice that there is no warranty (or else, saying that you provide
a warranty) and that users may redistribute the program under
these conditions, and telling the user how to view a copy of this
License. (Exception: if the Program itself is interactive but
does not normally print such an announcement, your work based on
the Program is not required to print an announcement.)
These requirements apply to the modified work as a whole. If
identifiable sections of that work are not derived from the Program,
and can be reasonably considered independent and separate works in
themselves, then this License, and its terms, do not apply to those
sections when you distribute them as separate works. But when you
distribute the same sections as part of a whole which is a work based
on the Program, the distribution of the whole must be on the terms of
this License, whose permissions for other licensees extend to the
entire whole, and thus to each and every part regardless of who wrote it.
Thus, it is not the intent of this section to claim rights or contest
your rights to work written entirely by you; rather, the intent is to
exercise the right to control the distribution of derivative or
collective works based on the Program.
In addition, mere aggregation of another work not based on the Program
with the Program (or with a work based on the Program) on a volume of
a storage or distribution medium does not bring the other work under
the scope of this License.
3. You may copy and distribute the Program (or a work based on it,
under Section 2) in object code or executable form under the terms of
Sections 1 and 2 above provided that you also do one of the following:
a) Accompany it with the complete corresponding machine-readable
source code, which must be distributed under the terms of Sections
1 and 2 above on a medium customarily used for software interchange; or,
b) Accompany it with a written offer, valid for at least three
years, to give any third party, for a charge no more than your
cost of physically performing source distribution, a complete
machine-readable copy of the corresponding source code, to be
distributed under the terms of Sections 1 and 2 above on a medium
customarily used for software interchange; or,
c) Accompany it with the information you received as to the offer
to distribute corresponding source code. (This alternative is
allowed only for noncommercial distribution and only if you
received the program in object code or executable form with such
an offer, in accord with Subsection b above.)
The source code for a work means the preferred form of the work for
making modifications to it. For an executable work, complete source
code means all the source code for all modules it contains, plus any
associated interface definition files, plus the scripts used to
control compilation and installation of the executable. However, as a
special exception, the source code distributed need not include
anything that is normally distributed (in either source or binary
form) with the major components (compiler, kernel, and so on) of the
operating system on which the executable runs, unless that component
itself accompanies the executable.
If distribution of executable or object code is made by offering
access to copy from a designated place, then offering equivalent
access to copy the source code from the same place counts as
distribution of the source code, even though third parties are not
compelled to copy the source along with the object code.
4. You may not copy, modify, sublicense, or distribute the Program
except as expressly provided under this License. Any attempt
otherwise to copy, modify, sublicense or distribute the Program is
void, and will automatically terminate your rights under this License.
However, parties who have received copies, or rights, from you under
this License will not have their licenses terminated so long as such
parties remain in full compliance.
5. You are not required to accept this License, since you have not
signed it. However, nothing else grants you permission to modify or
distribute the Program or its derivative works. These actions are
prohibited by law if you do not accept this License. Therefore, by
modifying or distributing the Program (or any work based on the
Program), you indicate your acceptance of this License to do so, and
all its terms and conditions for copying, distributing or modifying
the Program or works based on it.
6. Each time you redistribute the Program (or any work based on the
Program), the recipient automatically receives a license from the
original licensor to copy, distribute or modify the Program subject to
these terms and conditions. You may not impose any further
restrictions on the recipients' exercise of the rights granted herein.
You are not responsible for enforcing compliance by third parties to
this License.
7. If, as a consequence of a court judgment or allegation of patent
infringement or for any other reason (not limited to patent issues),
conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
excuse you from the conditions of this License. If you cannot
distribute so as to satisfy simultaneously your obligations under this
License and any other pertinent obligations, then as a consequence you
may not distribute the Program at all. For example, if a patent
license would not permit royalty-free redistribution of the Program by
all those who receive copies directly or indirectly through you, then
the only way you could satisfy both it and this License would be to
refrain entirely from distribution of the Program.
If any portion of this section is held invalid or unenforceable under
any particular circumstance, the balance of the section is intended to
apply and the section as a whole is intended to apply in other
circumstances.
It is not the purpose of this section to induce you to infringe any
patents or other property right claims or to contest validity of any
such claims; this section has the sole purpose of protecting the
integrity of the free software distribution system, which is
implemented by public license practices. Many people have made
generous contributions to the wide range of software distributed
through that system in reliance on consistent application of that
system; it is up to the author/donor to decide if he or she is willing
to distribute software through any other system and a licensee cannot
impose that choice.
This section is intended to make thoroughly clear what is believed to
be a consequence of the rest of this License.
8. If the distribution and/or use of the Program is restricted in
certain countries either by patents or by copyrighted interfaces, the
original copyright holder who places the Program under this License
may add an explicit geographical distribution limitation excluding
those countries, so that distribution is permitted only in or among
countries not thus excluded. In such case, this License incorporates
the limitation as if written in the body of this License.
9. The Free Software Foundation may publish revised and/or new versions
of the General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the Program
specifies a version number of this License which applies to it and "any
later version", you have the option of following the terms and conditions
either of that version or of any later version published by the Free
Software Foundation. If the Program does not specify a version number of
this License, you may choose any version ever published by the Free Software
Foundation.
10. If you wish to incorporate parts of the Program into other free
programs whose distribution conditions are different, write to the author
to ask for permission. For software which is copyrighted by the Free
Software Foundation, write to the Free Software Foundation; we sometimes
make exceptions for this. Our decision will be guided by the two goals
of preserving the free status of all derivatives of our free software and
of promoting the sharing and reuse of software generally.
NO WARRANTY
11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS
TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE
PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
REPAIR OR CORRECTION.
12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
POSSIBILITY OF SUCH DAMAGES.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
convey the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
Copyright (C)
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
Also add information on how to contact you by electronic and paper mail.
If the program is interactive, make it output a short notice like this
when it starts in an interactive mode:
Gnomovision version 69, Copyright (C) year name of author
Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, the commands you use may
be called something other than `show w' and `show c'; they could even be
mouse-clicks or menu items--whatever suits your program.
You should also get your employer (if you work as a programmer) or your
school, if any, to sign a "copyright disclaimer" for the program, if
necessary. Here is a sample; alter the names:
Yoyodyne, Inc., hereby disclaims all copyright interest in the program
`Gnomovision' (which makes passes at compilers) written by James Hacker.
, 1 April 1989
Ty Coon, President of Vice
This General Public License does not permit incorporating your program into
proprietary programs. If your program is a subroutine library, you may
consider it more useful to permit linking proprietary applications with the
library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License.
================================================
FILE: finetune.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import re\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import os\n",
"import sys\n",
"from tf2gpt.model import GPT\n",
"from utils.story_util import Story,Stories\n",
"from utils.progress_bar import ProgressBar\n",
"from tensorboardX import SummaryWriter\n",
"from tensorflow.keras.utils import multi_gpu_model\n",
"import random\n",
"import json"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),\n",
" PhysicalDevice(name='/physical_device:XLA_CPU:0', device_type='XLA_CPU'),\n",
" PhysicalDevice(name='/physical_device:XLA_GPU:0', device_type='XLA_GPU'),\n",
" PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf.config.list_physical_devices()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Num GPUs Available: 1\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"print(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"tf.keras.backend.set_floatx('float16')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#mirrored_strategy = tf.distribute.MirroredStrategy()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"float16 True\n"
]
}
],
"source": [
"#with mirrored_strategy.scope():\n",
"gpt = GPT(\n",
" vocab_size=30_000,\n",
" layer_size=32,\n",
" block_size=1024,\n",
" embedding_dropout=0.0,\n",
" embedding_size=2560,\n",
" num_attention_heads=32,\n",
" attention_dropout=0.0,\n",
" residual_dropout=0.0,\n",
" train_size=499\n",
")\n",
"gpt.load_weights('./gpt_weight_pretrain/weight_fp16')\n",
"\n",
"#input_x = tf.keras.layers.Input((499,), dtype=tf.int32)\n",
"#outputs = gpt_origin(input_x)\n",
"\n",
"#gpt = tf.keras.Model(inputs=input_x, outputs=outputs)\n",
"#gpt = multi_gpu_model(gpt, gpus=8)\n",
"\n",
"print(tf.keras.backend.floatx(), tf.float16, tf.keras.backend.floatx() == tf.float16)\n",
"if tf.keras.backend.floatx() == tf.float16:\n",
" for x in gpt.weights:\n",
" assert x.dtype == tf.float16\n",
"\n",
"\n",
"gpt.compile(\n",
" optimizer=tf.keras.optimizers.Adam(learning_rate=1e-5), # Optimizer\n",
" # Loss function to minimize\n",
" loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
" # List of metrics to monitor\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from utils.gpt2_tokenizer import GPT2Tokenizer\n",
"cbpe = GPT2Tokenizer(\n",
" 'CPM-Generate/bpe_3w_new/vocab.json',\n",
" 'CPM-Generate/bpe_3w_new/merges.txt',\n",
" model_file='CPM-Generate/bpe_3w_new/chinese_vocab.model')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Building prefix dict from the default dictionary ...\n",
"Loading model from cache /tmp/jieba.cache\n",
"Loading model cost 0.632 seconds.\n",
"Prefix dict has been built successfully.\n"
]
},
{
"data": {
"text/plain": [
"[837, 259, 497, 57, 8, 237]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ids = cbpe.encode('今天天气还行')\n",
"ids"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"++++++++++++++++++++\n",
"气 不错 不错行 \n",
"------------------------------\n",
"气 不错 不错行 ,\n",
"------------------------------\n",
"气 不错 不错行 , 我\n",
"------------------------------\n",
"气 不错 不错行 , 我 就\n",
"------------------------------\n",
"气 不错 不错行 , 我 就 想\n",
"------------------------------\n",
"气 不错 不错行 , 我 就 想着\n",
"------------------------------\n",
"气 不错 不错行 , 我 就 想着 去\n",
"------------------------------\n",
"气 不错 不错行 , 我 就 想着 去 看看\n",
"------------------------------\n",
"气 不错 不错行 , 我 就 想着 去 看看 \n",
"------------------------------\n",
"气 不错 不错行 , 我 就 想着 去 看看 ,\n",
"------------------------------\n"
]
}
],
"source": [
"def test_basic_logic():\n",
" ids = cbpe.encode('今天天气还行')\n",
" #print(ids)\n",
" print(\"+\" * 20)\n",
" for i in range(10):\n",
" output = gpt(tf.constant([ids]))\n",
" #print(output[0].shape)\n",
" nid = np.argmax(output[0, -1])\n",
" ids += [nid]\n",
" #print(i, cbpe.decode(ids))\n",
" #print(np.argmax(output[0],axis=-1))\n",
" print(cbpe.decode(np.argmax(output[0],axis=-1)))\n",
" print('-' * 30)\n",
"test_basic_logic()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def get_learning_rate(learning_rate=6e-4,\n",
" warmup_steps=20_0000,\n",
" decay_steps=200_0000,\n",
" alpha=0.0):\n",
" def decayed_learning_rate(step=1):\n",
" if step <= warmup_steps:\n",
" mult = step / float(warmup_steps)\n",
" else:\n",
" progress = (step - warmup_steps) / (decay_steps - warmup_steps)\n",
" mult = 0.5 * (1 + math.cos(math.pi * progress))\n",
" mult = max(0.1, mult)\n",
" return learning_rate * mult\n",
" return decayed_learning_rate"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"#stories = Stories(\"./labeled_data/advanture_translated/processed_translated_story.txt\").stories\n",
"stories = Stories(\"./labeled_data/advanture_translated/processed_translated_story.txt\").stories\n",
"#stories = stories[:50]\n",
"data_folder = \"./labeled_data/\"\n",
"txt_files = [(data_folder + i) for i in os.listdir(data_folder) if \"txt\" in i]\n",
"#stories = stories[:10]\n",
"stories = [Story(\"\",\"\").from_file(i) for i in txt_files]"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"valid_stories = Stories(\"./labeled_data/advanture_translated/processed_translated_story_valid.txt\").stories"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"len(stories),len(valid_stories)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"train_recoard = json.load(open('./labeled_data/advanture_translated/truncated_advanture_train.json'))\n",
"valid_recoard = json.load(open('./labeled_data/advanture_translated/truncated_advanture_valid.json'))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4500, 266)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(train_recoard),len(valid_recoard)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"import copy\n",
"def data_generator(stories, batch_size=4,sample_len=200,inf=False):\n",
" while True:\n",
" batch_data = []\n",
" tmp_stories = copy.copy(stories)\n",
" random.shuffle(tmp_stories)\n",
" for i,one_story in enumerate(tmp_stories):\n",
" story_content = one_story.to_dungeon_format()\n",
" story_content = story_content.replace(\"\\n\",\"\")\n",
" story_content = story_content.replace(\"\\n\",\"\")\n",
" story_content = story_content.replace(\"\\n\",\"\")\n",
" story_content = story_content.replace(\" \",\"\")\n",
" ids = cbpe.encode(story_content)\n",
" while ids:\n",
" sample = ids[:sample_len]\n",
" ids = ids[sample_len:]\n",
" if len(sample) < sample_len:\n",
" sample += [0 for i in range((sample_len - len(sample)))]\n",
" batch_data.append(sample)\n",
" if len(batch_data) >= batch_size:\n",
" yield np.asarray(batch_data)\n",
" batch_data = []\n",
" if not inf:\n",
" break\n",
" \n",
"import copy\n",
"def data_generator_content(texts, batch_size=4,sample_len=200,inf=False):\n",
" tmp_text = copy.copy(texts)\n",
" segments = []\n",
" while tmp_text:\n",
" story_content = tmp_text[:10000]\n",
" segments.append(story_content)\n",
" tmp_text = tmp_text[10000:]\n",
" while True:\n",
" \n",
" batch_data = []\n",
" random.shuffle(segments)\n",
" for one_story_context in segments:\n",
" ids = cbpe.encode(one_story_context)\n",
" while ids:\n",
" sample = ids[:sample_len]\n",
" ids = ids[sample_len:]\n",
" if len(sample) < sample_len:\n",
" sample += [0 for i in range((sample_len - len(sample)))]\n",
" batch_data.append(sample)\n",
" if len(batch_data) >= batch_size:\n",
" yield np.asarray(batch_data)\n",
" batch_data = []\n",
" if not inf:\n",
" break\n",
" \n",
"def turb_sentence(inputs):\n",
" outputs = []\n",
" for i in inputs:\n",
" if random.random() > 0.1:\n",
" outputs.append(i)\n",
" return outputs\n",
" \n",
"def data_generator_json(stories, batch_size=1,inf=False):\n",
" while True:\n",
" tmp_stories = copy.copy(stories)\n",
" random.shuffle(tmp_stories)\n",
" for i,one_story in enumerate(tmp_stories):\n",
" previous_contents = one_story[\"previous\"]\n",
" previous_contents = turb_sentence(previous_contents)\n",
" action = one_story[\"action\"]\n",
" content = one_story[\"result\"]\n",
" \n",
" previous_contents = ''.join(previous_contents)\n",
" content = ''.join(content)\n",
" \n",
" ids_prev = cbpe.encode(previous_contents + \"\\n\")\n",
" ids_action = cbpe.encode(\">\" + action + \"\\n\")\n",
" ids_content = cbpe.encode(content)[:300]\n",
" \n",
" ids_prev = ids_prev + ids_action\n",
" \n",
" prev_context_len = int(random.uniform(100,500))\n",
" ids_prev = ids_prev[-prev_context_len:]\n",
" \n",
" overall_ids = ids_prev + ids_content\n",
" mask = [0 for i in ids_prev] + [1 for i in ids_content]\n",
" yield np.asarray([overall_ids]),np.asarray([mask])\n",
" \n",
" if not inf:\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"valid_gen = data_generator_json(valid_recoard,inf=True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"x,m = valid_gen.__next__()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((1, 311), (1, 311))"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.shape,m.shape"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'起 。 你 觉得 有点 头晕 , 于是 你 把 自己 带到 黑暗 的 角落 休息 一下 。 您 访问 该 区域 角落 有点 暗 , 因为 自然光 几乎 无法 到达 那里 。 地板 上 似乎 也 有 一些 垃圾 。 你 进一步 移动 到 商店 的 后面 担心 周围 有 更 多 的 敌人 , 你 把 自己 拖 到 商店 的 后角 。 你 快 休息 你 无精打采 地 趴在 地上 , 试着 平稳 地 呼吸 。 要 放大 的 痛苦 众生 。 你 想 知道 这个 世界 发生 了 什么 , 是否 还有 更 多 的 敌对者 , 比如 攻击 你 的 那个 人 。 你 检查 后备箱 你 下车 , 走向 后备箱 。 你 走 回 商店 , 找到 几件 你 撕开 并 裹 在 胸前 的 衬衫 。 它 可以 帮助 您 感觉 稍微 好 一点 。 但 你 仍然 很 痛苦 。 你 弹出 后备箱 您 按 下 按钮 解锁 后备箱 。 它 缓缓 抬起 打开 。 你 发动 汽车 您 尝试 多次 启动 汽车 , 但 没有 任何 反应 。 你 想想 它 可能 需要 汽油 , 也许 他们 的 后备箱 里 有 备用 汽油 。 你 检查 地板 \\n > 你 忍住 了 咳嗽 \\n 当 你 看到 一个 女人 小心翼翼 地 走进 来时 , 你 把手 放在 嘴 上 。 她 看起来 并 没有 什么 害处 , 但 谁 知道 她 心里 在 想 什么 。'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cbpe.decode(x[0])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1]])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1\n",
"tl: 2.439453125 vl: 2.533203125 69.02 % [==================================>----------------] 3106/4500 \t used:3417s eta:1533 sss"
]
}
],
"source": [
"writer = SummaryWriter('log/finetune')\n",
"n_iter = 0\n",
"train_loss = 100\n",
"val_loss = 100\n",
"for epoch in range(3):\n",
" print(f\"Epoch {epoch + 1}\")\n",
" pb = ProgressBar(len(train_recoard))\n",
" pb.startjob()\n",
" for x,m in data_generator_json(train_recoard):\n",
" #for x in data_generator_content(texts[528:],sample_len=500,batch_size=2):\n",
" n_iter += 1\n",
" ret = gpt.train_step(\n",
" (\n",
" tf.constant(x[:,:-1]),\n",
" tf.constant(x[:,1:])\n",
" ),\n",
" mask=m[:,1:],\n",
" )\n",
" writer.add_scalar(\"train/loss\", ret[\"loss\"].numpy(), n_iter)\n",
" train_loss = ret[\"loss\"].numpy()\n",
" if n_iter % 10 == 0:\n",
" valid_x,valid_m = valid_gen.__next__()\n",
" ret = gpt.eval_step(\n",
" (\n",
" tf.constant(valid_x[:,:-1]),\n",
" tf.constant(valid_x[:,1:])),\n",
" mask=valid_m[:,1:],\n",
" )\n",
" writer.add_scalar(\"test/loss\", ret[\"loss\"].numpy(), n_iter)\n",
" val_loss = ret[\"loss\"].numpy()\n",
" pb.info = f\"tl: {train_loss} vl: {val_loss}\"\n",
" pb.complete(1)\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gpt.save_weights('./gpt_weight_pretrain/tmp_weight')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!ls -l ./gpt_weight_pretrain/tmp_weight*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# rewrite fp16 model (weight only)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import re\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import os\n",
"import sys\n",
"from tf2gpt.model import GPT\n",
"from utils.story_util import Story,Stories\n",
"from utils.progress_bar import ProgressBar\n",
"from tensorboardX import SummaryWriter\n",
"from tensorflow.keras.utils import multi_gpu_model\n",
"import random\n",
"import json"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Building prefix dict from the default dictionary ...\n",
"Loading model from cache /tmp/jieba.cache\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"float16 True\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading model cost 0.559 seconds.\n",
"Prefix dict has been built successfully.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"++++++++++++++++++++\n",
"好 不错行 \n",
"------------------------------\n",
"好 不错行 ,\n",
"------------------------------\n",
"好 不错行 , \n",
"------------------------------\n",
"好 不错行 , \n",
"------------------------------\n",
"好 不错行 , \n",
"------------------------------\n",
"好 不错行 , \n",
"------------------------------\n",
"好 不错行 , \n",
"------------------------------\n",
"好 不错行 , \n",
"------------------------------\n",
"好 不错行 , \n",
"------------------------------\n",
"好 不错行 , \n",
"------------------------------\n"
]
}
],
"source": [
"tf.keras.backend.set_floatx('float16')\n",
"#with mirrored_strategy.scope():\n",
"gpt = GPT(\n",
" vocab_size=30_000,\n",
" layer_size=32,\n",
" block_size=1024,\n",
" embedding_dropout=0.0,\n",
" embedding_size=2560,\n",
" num_attention_heads=32,\n",
" attention_dropout=0.0,\n",
" residual_dropout=0.0,\n",
" train_size=499\n",
")\n",
"gpt.load_weights('./gpt_weight_pretrain/tmp_weight')\n",
"\n",
"#input_x = tf.keras.layers.Input((499,), dtype=tf.int32)\n",
"#outputs = gpt_origin(input_x)\n",
"\n",
"#gpt = tf.keras.Model(inputs=input_x, outputs=outputs)\n",
"#gpt = multi_gpu_model(gpt, gpus=8)\n",
"\n",
"print(tf.keras.backend.floatx(), tf.float16, tf.keras.backend.floatx() == tf.float16)\n",
"if tf.keras.backend.floatx() == tf.float16:\n",
" for x in gpt.weights:\n",
" assert x.dtype == tf.float16\n",
" \n",
"from utils.gpt2_tokenizer import GPT2Tokenizer\n",
"cbpe = GPT2Tokenizer(\n",
" 'CPM-Generate/bpe_3w_new/vocab.json',\n",
" 'CPM-Generate/bpe_3w_new/merges.txt',\n",
" model_file='CPM-Generate/bpe_3w_new/chinese_vocab.model')\n",
"\n",
"def test_basic_logic():\n",
" ids = cbpe.encode('今天天气还行')\n",
" #print(ids)\n",
" print(\"+\" * 20)\n",
" for i in range(10):\n",
" output = gpt(tf.constant([ids]))\n",
" #print(output[0].shape)\n",
" nid = np.argmax(output[0, -1])\n",
" ids += [nid]\n",
" #print(i, cbpe.decode(ids))\n",
" #print(np.argmax(output[0],axis=-1))\n",
" print(cbpe.decode(np.argmax(output[0],axis=-1)))\n",
" print('-' * 30)\n",
"test_basic_logic()\n",
" \n",
"gpt.save_weights('./gpt_weight_pretrain/tmp_weight_test')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-rw-r--r-- 1 tiger tiger 5194222858 Jul 14 10:40 ./gpt_weight_pretrain/tmp_weight_test.data-00000-of-00001\n",
"-rw-r--r-- 1 tiger tiger 34188 Jul 14 10:40 ./gpt_weight_pretrain/tmp_weight_test.index\n"
]
}
],
"source": [
"!ls -l ./gpt_weight_pretrain/tmp_weight_test*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# save model (restart kernel here)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import re\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import os\n",
"import sys\n",
"from tf2gpt.model import GPT\n",
"from utils.story_util import Story,Stories\n",
"from utils.progress_bar import ProgressBar\n",
"from tensorboardX import SummaryWriter\n",
"from tensorflow.keras.utils import multi_gpu_model\n",
"import random\n",
"tf.keras.backend.set_floatx('float16')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from utils.gpt2_tokenizer import GPT2Tokenizer\n",
"cbpe = GPT2Tokenizer(\n",
" 'CPM-Generate/bpe_3w_new/vocab.json',\n",
" 'CPM-Generate/bpe_3w_new/merges.txt',\n",
" model_file='CPM-Generate/bpe_3w_new/chinese_vocab.model')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"float16 True\n"
]
}
],
"source": [
"#with mirrored_strategy.scope():\n",
"gpt = GPT(\n",
" vocab_size=30_000,\n",
" layer_size=32,\n",
" block_size=1024,\n",
" embedding_dropout=0.0,\n",
" embedding_size=2560,\n",
" num_attention_heads=32,\n",
" attention_dropout=0.0,\n",
" residual_dropout=0.0\n",
")\n",
"gpt.load_weights('./gpt_weight_pretrain/tmp_weight_test')\n",
"\n",
"#input_x = tf.keras.layers.Input((499,), dtype=tf.int32)\n",
"#outputs = gpt_origin(input_x)\n",
"\n",
"#gpt = tf.keras.Model(inputs=input_x, outputs=outputs)\n",
"#gpt = multi_gpu_model(gpt, gpus=8)\n",
"\n",
"print(tf.keras.backend.floatx(), tf.float16, tf.keras.backend.floatx() == tf.float16)\n",
"if tf.keras.backend.floatx() == tf.float16:\n",
" for x in gpt.weights:\n",
" assert x.dtype == tf.float16\n",
"\n",
"\n",
"#gpt.compile(\n",
"# optimizer=tf.keras.optimizers.Adam(learning_rate=1e-5), # Optimizer\n",
"# # Loss function to minimize\n",
"# loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
"# # List of metrics to monitor\n",
"#)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Building prefix dict from the default dictionary ...\n",
"Loading model from cache /tmp/jieba.cache\n",
"Loading model cost 0.581 seconds.\n",
"Prefix dict has been built successfully.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 今天天气 不错 \n",
"1 今天天气 不错 ,\n",
"2 今天天气 不错 , 我\n",
"3 今天天气 不错 , 我 想\n",
"4 今天天气 不错 , 我 想 去\n",
"5 今天天气 不错 , 我 想 去 看看\n",
"6 今天天气 不错 , 我 想 去 看看 \n",
"7 今天天气 不错 , 我 想 去 看看 。\n",
"8 今天天气 不错 , 我 想 去 看看 。 \n",
"9 今天天气 不错 , 我 想 去 看看 。 ”\n"
]
}
],
"source": [
"ids = cbpe.encode('今天天气不错')\n",
"\n",
"for i in range(10):\n",
" output = gpt(tf.constant([ids]))\n",
" nid = np.argmax(output[0, -1])\n",
" ids += [nid]\n",
" print(i, cbpe.decode(ids))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def batch_gather(a, b):\n",
" return tf.gather(a, b, batch_dims=1)\n",
"\n",
"def penalize_used(logits, output,penalize):\n",
" # I want to change the indices of logits wherever the index is found in output\n",
" change_tensor = tf.zeros_like(logits, dtype=logits.dtype)\n",
" unique = tf.unique(output[0])[0]\n",
" ones = tf.ones_like(unique, dtype=unique.dtype)\n",
" indices = tf.expand_dims(unique, 1)\n",
" updates = tf.scatter_nd(indices, ones, [logits.shape[1]])\n",
" bool_tensor = tf.expand_dims(tf.cast(updates, tf.bool), 0)\n",
" return tf.compat.v1.where(bool_tensor, logits * penalize, logits)\n",
"\n",
"def top_p_sample(logits, inputs, num_samples=1, p=0.9,k=40,temperature=0.4,penalize=0.4):\n",
" logits = penalize_used(logits,inputs,penalize)\n",
" batch_size, vocab_size = logits.shape\n",
" probs = tf.nn.softmax(logits / temperature, axis=-1)\n",
" # [batch_size, vocab_perm]\n",
" indices = tf.argsort(probs, direction='DESCENDING')\n",
" cumulative_probabilities = tf.math.cumsum(batch_gather(probs, indices), axis=-1, exclusive=False)\n",
" top_k_prob = cumulative_probabilities[0][k]\n",
"\n",
" # find the top pth index to cut off. careful we don't want to cutoff everything!\n",
" # result will be [batch_size, vocab_perm]\n",
" p_expanded = p if isinstance(p, float) else p#[:, None]\n",
" exclude_mask = tf.logical_not(\n",
" tf.logical_or(\n",
" tf.logical_and(\n",
" cumulative_probabilities < p_expanded, \n",
" cumulative_probabilities < top_k_prob\n",
" ),\n",
" tf.range(vocab_size)[None] < 1\n",
" )\n",
" )\n",
"\n",
" # OPTION A - sample in the sorted space, then unsort.\n",
" logits_to_use = batch_gather(logits, indices) - tf.cast(exclude_mask, tf.float16) * 1e4\n",
" sample_perm = tf.random.categorical(logits=logits_to_use, num_samples=num_samples)\n",
" sample = batch_gather(indices, sample_perm)\n",
"\n",
" return tf.cast(sample, tf.int64)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"@tf.function\n",
"def serve(inputs):\n",
" return gpt(inputs, kv_cache=None, use_cache=True)\n",
"\n",
"\n",
"@tf.function\n",
"def serve_cache(inputs, kv_cache):\n",
" return gpt(inputs, kv_cache=kv_cache, use_cache=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"serve_concrete = serve.get_concrete_function(\n",
" tf.TensorSpec(shape=[None, None], dtype=tf.int64, name=\"inp\")\n",
")\n",
"\n",
"layer_size = 32\n",
"attention_head = 32\n",
"embedding_size = 2560\n",
"\n",
"serve_cache_concrete = serve_cache.get_concrete_function(\n",
" tf.TensorSpec(shape=[None, None], dtype=tf.int64, name=\"inp\"),\n",
" tf.TensorSpec(shape=[\n",
" layer_size, None, 2, attention_head,\n",
" None, embedding_size // attention_head\n",
" ], dtype=tf.float16, name=\"kv_cache\")\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"r = serve_concrete(\n",
" tf.constant([[1]], tf.int64)\n",
")\n",
"r2 = serve_cache_concrete(\n",
" tf.constant([[1]], tf.int64),\n",
" r[1]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"@tf.function\n",
"def sample(initial_inputs, length, p,k,temperature,penalize, num_samples=1):\n",
" layer_size = 32\n",
" embedding_size = 2560\n",
" attention_head = 32\n",
"\n",
" i = tf.constant(0, dtype=tf.int64)\n",
" initial_logits, kv_cache = serve(initial_inputs)\n",
" inputs = top_p_sample(initial_logits[:, -1, :],initial_inputs, p=p,k=k,temperature=temperature,penalize=penalize)\n",
" stores = tf.concat([initial_inputs, inputs], axis=1)\n",
"\n",
" def _cond(i, inputs, kv_cache, stores):\n",
" return i < length\n",
"\n",
" def _body(i, inputs, kv_cache, stores):\n",
" new_logits, new_kv_cache = serve_cache(inputs, kv_cache)\n",
" \n",
" new_inputs = top_p_sample(new_logits[:, -1, :],inputs, p=p,k=k,temperature=temperature,penalize=penalize)\n",
" new_stores = tf.concat([stores, new_inputs], axis=-1)\n",
" new_kv_cache = tf.concat([\n",
" kv_cache,\n",
" new_kv_cache\n",
" ], axis=-2)\n",
" new_i = i + 1\n",
" return [new_i, new_inputs, new_kv_cache, new_stores]\n",
"\n",
" result = tf.while_loop(\n",
" _cond, _body,\n",
" loop_vars=[i, inputs, kv_cache, stores],\n",
" shape_invariants=[\n",
" tf.TensorShape(None),\n",
" tf.TensorShape([None, None]),\n",
" tf.TensorShape([\n",
" layer_size, None, 2,\n",
" attention_head, None,\n",
" embedding_size // attention_head\n",
" ]),\n",
" tf.TensorShape([\n",
" None, None\n",
" ])\n",
" ]\n",
" )\n",
" return result[-1]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
"INFO:tensorflow:Assets written to: ./result_weights/tmp_weight/assets\n"
]
}
],
"source": [
"gpt.save('./result_weights/tmp_weight', include_optimizer=False, signatures={\n",
" 'serving_default': sample.get_concrete_function(\n",
" tf.TensorSpec(shape=[None, None], dtype=tf.int64, name=\"inp\"),\n",
" tf.TensorSpec(shape=[None,], dtype=tf.int64, name=\"length\"),\n",
" tf.TensorSpec(shape=[], dtype=tf.float16, name=\"p\"),\n",
" tf.TensorSpec(shape=[], dtype=tf.int64, name=\"k\"),\n",
" tf.TensorSpec(shape=[], dtype=tf.float16, name=\"temperature\"),\n",
" tf.TensorSpec(shape=[], dtype=tf.float16, name=\"penalize\")\n",
" )\n",
"})"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tf.Tensor(\n",
"[[ 837 259 497 788 8 9 148 16 755 91 1126 375 8 12\n",
" 8]], shape=(1, 15), dtype=int64)\n",
"今天天气 不错 , 所以 我 决定 去 海边 。 \n"
]
}
],
"source": [
"ids = cbpe.encode('今天天气不错')\n",
"\n",
"ret = sample(\n",
" tf.constant([ids], dtype=tf.int64), # inp\n",
" tf.constant(10, dtype=tf.int64), #length\n",
" tf.constant(0.9, dtype=tf.float16), #p\n",
" tf.constant(40, dtype=tf.int64), #k\n",
" tf.constant(0.4, dtype=tf.float16), # temperature\n",
" tf.constant(0.4, dtype=tf.float16) # penalize\n",
")\n",
"print(ret)\n",
"print(cbpe.decode(ret.numpy().tolist()[0]))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total 21660\n",
"drwxr-xr-x 2 tiger tiger 4096 Jul 5 15:21 assets\n",
"-rw-r--r-- 1 tiger tiger 22168232 Jul 14 10:50 saved_model.pb\n",
"drwxr-xr-x 3 tiger tiger 4096 Jul 14 10:50 variables\n"
]
}
],
"source": [
"!ls -l ./result_weights/tmp_weight"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# read deploy model (you should restart kernel and go from here)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import re\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import os\n",
"import sys\n",
"from tf2gpt.model import GPT\n",
"from utils.story_util import Story,Stories\n",
"from utils.progress_bar import ProgressBar\n",
"from tensorflow.keras.utils import multi_gpu_model\n",
"import random\n",
"from utils.gpt2_tokenizer import GPT2Tokenizer\n",
"import tensorflow_hub as hub\n",
"cbpe = GPT2Tokenizer(\n",
" 'CPM-Generate/bpe_3w_new/vocab.json',\n",
" 'CPM-Generate/bpe_3w_new/merges.txt',\n",
" model_file='CPM-Generate/bpe_3w_new/chinese_vocab.model')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"gpt = hub.load('./result_weights/tmp_weight/')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def sample_gpt(tokenizer, gpt, sentence, number=1, length=20, p=0.9,k=40,temperature=0.4,penalize=0.85):\n",
" inputs = tf.constant([tokenizer.encode(sentence)] * number, dtype=tf.int64)\n",
" length = tf.constant(length, dtype=tf.int64)\n",
" p = tf.constant(p, dtype=tf.float16)\n",
" k = tf.constant(k, dtype=tf.int64)\n",
" temperature = tf.constant(temperature, dtype=tf.float16)\n",
" penalize = tf.constant(penalize, dtype=tf.float16)\n",
" ret = gpt.signatures['serving_default'](\n",
" inp=inputs, \n",
" length=length, \n",
" p=p,\n",
" k=k,\n",
" temperature=temperature,\n",
" penalize=penalize)['output_0']\n",
" return [\n",
" tokenizer.decode(s[len(inputs[0]):]).replace(' ', '')\n",
" for s in ret.numpy()\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Building prefix dict from the default dictionary ...\n",
"Loading model from cache /tmp/jieba.cache\n",
"Loading model cost 0.558 seconds.\n",
"Prefix dict has been built successfully.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"了吧。你可以看看他的回答。有人说FastestisthestupidthingFastestisthestupidthing有人说FastestisthestupidthingFastestisthestupidthingisFastestisthestupidthingFastestisthestupidthingFastestisthestupidthingFastestisthestupidthing.........FastestisthestupidthingFastestisthestupidthingFastestisthestupidthing.........FastestisthestupidthingFastestisthestupidthingFastestisthestupidthing.........Fastestisthestupidthing.........FastestisthestupidthingFastestisthestupidthingFastestisthestupidthing.........Fastestisthestupidthing...\n",
"--------------------\n"
]
}
],
"source": [
"q = f'''双十一'''\n",
"ret = sample_gpt(cbpe, gpt, q, 1, 500, p=0.9,k=16,temperature=1,penalize=0.75)\n",
"for x in ret:\n",
" print(x)\n",
" print('-' * 20)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"class Story():\n",
" def __init__(self,beginning,story_max_len=200,context_len=12):\n",
" self.story = [beginning]\n",
" self.story_max_len = story_max_len\n",
" self.context_len = context_len\n",
" \n",
" def response_quality_ok(self,response):\n",
" if len(response) < 20:\n",
" return False\n",
" return True\n",
" \n",
" def action(self,action):\n",
" action_str = \"> 你\" + action\n",
" q = \"\".join(self.story[-self.context_len:]).replace(\"\\n\",'') + \"\\n\" + action_str + \"\\n\"\n",
" q = q[-600:]#.replace(\"\\n\",\"\")\n",
" self.story.append(\"你\" + action)\n",
" response = \"\"\n",
" for i in range(3):\n",
" response = sample_gpt(cbpe, gpt, q, 1, 150,p=0.9,k=16,temperature=1,penalize=0.75)[0]\n",
" response = '。'.join(response.split(\"。\")[:-1]) + \"。\"\n",
" if(self.response_quality_ok(response) == True):\n",
" break\n",
" else:\n",
" print(\"response quality check failed,generating another one\")\n",
" \n",
" #responsesp = response.split(\"。\")\n",
" #if(len(responsesp) > 2):\n",
" # response = ''.join(responsesp[:-1])\n",
" #else:\n",
" # response = response.split(\">\")[0]\n",
" #response = \"。\".join([:-1])\n",
" #response = response.split(\">\")[0].strip()\n",
" #response = \"\\n\".join(response.split(\"\\n\")[:-1])\n",
" self.story.append(response)\n",
" \n",
" def interactive(self):\n",
" print(\"\\n\".join(self.story))\n",
" while True:\n",
" action = input(\"> 你\")\n",
" self.action(action)\n",
" print(self.story[-1])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"begin = f'''你在树林里冒险，指不定会从哪里蹦出来一些奇怪的东西，你握紧手上的手枪，希望这次冒险能够找到一些值钱的东西'''\n",
"story = Story(begin)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"你在树林里冒险，指不定会从哪里蹦出来一些奇怪的东西，你握紧手上的手枪，希望这次冒险能够找到一些值钱的东西\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"> 你 把手枪上膛\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"枪身立刻就被拉下来,你把弹匣从枪口推出来,把枪扔到地上。“现在,我们走吧。”你说。“好的,先生。”老人说。“那我们出发吧。”你说。“好的,先生。”老人说。你把手枪放进自己的口袋里,然后跟着老人继续旅行。“我们去哪里?”老人问。“去Geralt村。\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"> 你 走去村子里\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"很快,你就会到达Geralt村。村子里有个院子,院子里有个漂亮的石头房子。你走进房子,发现门廊上坐着一个很漂亮的女人,她的脸颊红润,看起来很漂亮。“嘿,丽贝卡,”你喊道,“我是来帮你的。”“谢谢,亲爱的。”“什么?”“我叫你丽贝卡,我是你的妻子。\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"> 你 挽着她散步\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"当你和丽贝卡交谈时,你开始觉得很舒服,你想着你在这里找到了很多东西,你终于开始享受这个地方了。你的心情越来越好,你想要的东西就好像越来越近了。“那么,亲爱的,我们要去哪里?”“很明显,我们该走了。我们很快就会到村子里的Geralt村。”你说。“好的,那么,我们到那里吧。\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"> 你 在村子里找个地方玩\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"你带着老人和丽贝卡到村子里最好的房子去,并开始找一个最好的。你很快就找到了。它就在村子里,你会找到它的。你带着老人和丽贝卡来到村子的边缘,然后丽贝卡就走在了你的前面。\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"> 你 走进房子\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"您带着老人和丽贝卡进入房子,然后在房子里等。你走到房子的门口,你把门厅里的椅子推到一边,让老人坐下。你走到房子的另一边,你把丽贝卡带到房子另一边,并把老人从椅子上抱下来。你把老人放下,丽贝卡就坐在你的膝盖上。“怎么了?我有什么要说的?”你问。“我们需要你和我们。我有我的妻子和你的妻子。\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"> 你 赞美妻子的美貌\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"“你真棒,亲爱的。”丽贝卡说。“哦,是的,但她太年轻了。”“我会照顾她的。您看到她的样子了吗?”“是的,她看起来很漂亮。”“你会看到它,你看。她的眼睛好看吗?”“是的,很亮。”“好的,”你说。“那么,我们走吧。你可以和我们一起去Geralt村。\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"> 你 走去村里的集市\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"现在,你去集市。这个集市在村子里,你把老人和丽贝卡留在你的领子上,然后你带着老人丽贝卡到外面的广场。你在那里看到了一个大的摊位,上面摆着面包和葡萄酒。你把老人和丽贝卡带到那里,你把老人从椅子上抱下来,然后你把她放下,开始和她交谈。\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"> 你 说“你觉得这个集市怎么样？”\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"“你认为怎么样?我想去Geralt村,我们可以去村里的大集市。你看到了一个大的布店,那里有很多布。我们可以在那里做生意。你看到了一个大的水果摊,你可以买你需要的任何水果。我们可以做到了。”“是的,”你说。“我认为我们会做到的。我们可以做到的。现在走吧,我们去Geralt村。我想看看我们会做到什么。我认为我们已经做到了。\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "Interrupted by user",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteractive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m\u001b[0m in \u001b[0;36minteractive\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"> 你\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m 849\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 850\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 851\u001b[0;31m \u001b[0mpassword\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 852\u001b[0m )\n\u001b[1;32m 853\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m 890\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 891\u001b[0m \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 892\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Interrupted by user\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 893\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 894\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid Message:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
]
}
],
"source": [
"story.interactive()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: labeled_data/advanture_translated/process_data.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import random\n",
"import os\n",
"import sys\n",
"sys.path.append(\"../../\")\n",
"from utils.story_util import Story,Stories"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"text = open(\"./valid_advantures_valid_translated.txt\",encoding='utf-8').read().strip()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"contents = []\n",
"while text:\n",
" p1 = text.find(\"<| startoftext |>\")\n",
" if p1 == -1:\n",
" break\n",
" p2 = min(text.find(\"<| endoftext |>\"),text.find(\"<| endoftext>\"))\n",
" if p2 == -1:\n",
" break\n",
" p3 = text.find(\"<|正文结束|>\")\n",
" if p3 < p2 and p3 >= 0:\n",
" p2 = p3\n",
" contents.append(text[p1 + 20:p2])\n",
" text = text[p2+1:]"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"85"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(contents)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"contents[-26].count(\">\")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(352, -1)"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p1,p2"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"valid_content = [i for i in contents if i.count(\">\") > 2]"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"35"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(valid_content)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"stories = []\n",
"num = 0\n",
"for one_content in valid_content:\n",
" num += 1\n",
" parags = one_content.split(\">\")\n",
" one_story = Story(parags[0],str(num))\n",
" for one_parag in parags[1:]:\n",
" p1 = one_parag.find(\"\\n\")\n",
" action = one_parag[:p1]\n",
" result = one_parag[p1 + 1:]\n",
" if action.find(\"你说\") != -1:\n",
" one_story.add_action(action.replace(\"你说\",\"\"),\"说\",result,action)\n",
" else:\n",
" one_story.add_action(action,\"做\",result,action)\n",
" stories.append(one_story)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"故事名称：\n",
"5\n",
"故事背景：\n",
"抓住刀刃，迅速环顾四周，然后冲向火山口的边缘，冲向离你左侧最近的爆破墙。附近一场爆炸将大块的泥土和石头以及一具尸体的手臂喷射到空中。你惊恐地看着附属物扑通一声掉到人行道上。 从你的左边，三个图里克战士冲向一个受伤的商人，用他们邪恶的带刺弯刀不断地刺伤他，然后洗劫了他的尸体。 在你的右边，你可以看到另一栋建筑，大约有20英尺宽，有一些裸露的地面。 你…吗：\n",
"--------------------\n",
"动作：\n",
"做\n",
"你去下一栋楼？\n",
"结果：\n",
"你跑到下一栋楼，一切看起来都很安全。没有人经过，街上到处都是尸体。也许先锋队已经过去了。深吸一口气，你往外看，感觉不到任何人。 你慢慢地站起来，朝下一栋楼走去。尽管你穿过一片20英尺宽的地方，但你还是安全地到达了下一站。在拐角处偷看，你会看到一个可怜的景象，一个金发小男孩在他父母的尸体上哭泣，尸体散落在瓦砾中，瓦砾曾经是他的建筑。一块燃烧着的巨石撞击鹅卵石发出刺耳的尖叫声，蒸发了你视野中的一切，把你击倒在地。 当你站起来的时候，你听到另一个爆炸声从后面传来。 当你重新站起来后，你就从男孩所在的地方开始。只剩下一个冒烟的火山口，周围环绕着一圈火焰。尽管你有着正常的坚忍性格，眼泪还是会从你的脸颊滑落。握紧你的刀刃，你强迫自己艰难前行。 向上三条街，你注意到一场交火正在进行。三支埃斯图军队蜷缩在一堵墙后。每个人都有一把弩，第四个士兵死了，脖子上插着一个螺栓。你蹲着朝他们的位置跑去，跪在军士旁边，问道：“军士情况如何？”。 他转过身，几乎要去拿他的剑，因为他不认识你，但他说：“雇佣军？” 你点了点头，他咕哝道：“我以为你们现在都逃了，见鬼，如果这不是我的家，我早就逃了。我想我很快就会死在这些旧墙里了。” 一个弩箭击中了墙壁，他又回到了战斗中。”好吧，我们正试图到达北边的防御阵地，但是该死的特瑞克斯把我们切断了。一个排大小的分队，只剩下我们，我们被切断了与主要集团。如果你能发射弩，欢迎你的帮助。见鬼，即使你做不到，你至少可以向那些混蛋开枪。” 你点点头，拿起弓弩，这是一把你非常熟练的弩，然后装上箭。”“他们在哪里？”你问。 他用一个插销在泥土里快速画了一张图，然后说，“我们在这里，他们在那边的护堤后面。我敢肯定在西边有个狙击手。不好的是，虽然我们看不到他，但他肯定能看到我们，而且他是个绝顶聪明的人。一枪干净利落地干掉了艾维，斯皮纳就在那边。”他指着15英尺外的一具埃斯特里恩尸体，背后插着一个螺栓。 护堤后面的那些白痴很差劲，但是他们用压制火力把我们钉住了，不能抬起头来不害怕拿螺栓，尽管狙击手，他才是真正的挑战。我们干掉他，我们可以侧翼进攻。如果他不死，我们就会陷入僵局。” 把弓弩加到你的库存中。 你可以考虑以下选项： \n",
"梗概：\n",
"你去下一栋楼？\n",
"--------------------\n",
"动作：\n",
"说\n",
"“掩护我，我来对付狙击手。”\n",
"结果：\n",
"你同意留下来，但你必须先弄清楚狙击手在哪里，然后才能把他干掉。”嘿，士兵，让我借一下那个盾牌。“你抓住他的小金属盾牌，迅速把它举到你们都蹲在后面的小墙上面。果然，分心带来了一个小冰雹螺栓从你的前线那里，土耳其士兵盘踞，但他们错过了。然而，从你的右侧，一个螺栓猛击盾牌，将它从你手中击落，并在盾牌倒下前将它滚向你的左侧约10英尺。 “好的，士兵们，我知道狙击手在哪里。我要去瓦砾堆旁边的那堵小砖墙，当我说走的时候，我要你用你的弩向图里克的杂种们开火，直到我准备好为止。” 士兵们点头，锁上弓弩，蜷缩起来准备开火。 你数到三，然后开始向砖墙低蹲冲刺。与此同时，埃西特兰人把他们所有的火力都射向了泰瑞克士兵，他们在战乱结束前保持清醒。然后他们回击埃斯特里安人，你向右转向墙。你知道接下来的五英尺会很关键。 考验你的运气： \n",
"梗概：\n",
"你说“掩护我，我来对付狙击手。”\n",
"--------------------\n",
"动作：\n",
"做\n",
"如果你的名字以B，E，F，J，P或Y开头。。。\n",
"结果：\n",
"当你直接在肋骨上插上一个螺栓时，你会躲开，闪避，冲刺，并且没有几秒钟的时间往家里跳。你不由自主地撞到砖头上，伤得很重，但你却蹦蹦跳跳地爬到墙后靠在墙上。 你现在有2耐力（3分之一），如果你的耐力降到0，你就死定了。 呼吸困难，你蹲下并装上你的弩，如果这是你做的最后一件事，你会得到那个狗娘养的。在快速祈祷之后，你从墙上转出来，朝着镜头的方向扫视建筑物，就像你在安全的墙上转回来一样快。一毫秒后，一个螺栓砸进了你刚刚栖息的瓦砾中。 你朝右上角的高塔窗户精神上俯冲过去。然后你绕过墙上的小孔瞄准。在里面你看到墙上的运动，你画了一个珠子。吸气。呼气。。。开火！ 门闩在空中飞扬，你听到一声轻微的喘息声，然后一个人从窗户里摔了出去，掉到下面四层楼高的鹅卵石上。士兵们欢呼起来，你迅速向他们竖起大拇指。 现在来对付土耳其士兵。 你沿着墙移动到图里克十字弓的侧面，小心不要发出噪音或提醒他们注意你的位置。最后，你不能再往前走了，你可以探出你的头看看他们在哪里，有多少。你注意到五个人，每个人都在装弩。决定让它变成四个，你向最暴露的士兵开火，然后躲回墙后。一声尖叫回报了你的努力，土耳其士兵的喊叫表明他们现在有狙击手要对付。 你重新装好子弹，准备再打一枪。转身，瞄准，开火，然后躲开。一声尖叫伴随着那动作，你滑下墙去和中士交流。他正忙着指示他的士兵小心瞄准，但你注意到他的目光和动作，让他派另一名士兵到你的位置去。 你滑回到狙击手的位置，在你和士兵之间，躺下掩护火力，这时一个士兵冲向你躲在后面的墙。你现在有敌人的侧翼了。他看着你，枪栓伸出你的肋骨，呼气，但你的动作让他移动到角落。 他迅速看了一眼，看到一个土里克医生正在照顾他们的两个伤员，而两个士兵向你留下的士兵开火。你点点头，然后翻身瞄准医生，用一个能打碎他的头骨的螺栓把他救了出来。和你在一起的士兵跑到另一面墙的一边，在被围困的图里克战士的后面，中士和他的士兵继续从前面开火。 几秒钟后，你和你的士兵击落了最后两名图里克弩手。你来回挥舞你的手表示停火，你们都从废墟散落的战场上起来。中士和他的部下急忙向你道贺，“没有你及时的干预，我不知道我们会做什么。我是雷纳尔中士，这些士兵是科维斯和利克。我猜你是那个被他们称为“西尔弗”的女孩。我听说你很了不起，但很明显那些传说对你毫无意义。” 他看了看你身上的插销，畏缩着说：“看起来你为球队拿了一个，不幸的是，我们的医生躺在那里，他身上有一个插销，所以我要包扎起来，尽量保持无菌。我们正前往一个防御阵地，我们认为有一些友军驻扎在那里，如果你愿意的话，欢迎你来坐车。四个比一个好多了。”\n",
"梗概：\n",
"如果你的名字以B，E，F，J，P或Y开头。。。\n",
"--------------------\n",
"动作：\n",
"说\n",
"“谢谢你让我陪着……”\n",
"结果：\n",
"你和部队快速而安静地穿过小巷、小巷和城市的主要街道。有好几次你必须拔剑和敌人战斗，逃离其他的图里克团体，甚至是游荡在城市里的不死生物，然而，在你向安全的最后一步推进之前，你都会找到一个藏身的地方。您决定等到天黑后再继续前进。\n",
"梗概：\n",
"你说“谢谢你让我陪着……”\n",
"\n"
]
}
],
"source": [
"one_story = random.choice(stories)\n",
"print(one_story.to_str())"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"with open(\"processed_translated_story_valid.txt\",'w',encoding='utf-8') as whdl:\n",
" for one_story in stories:\n",
" whdl.write(one_story.to_str())\n",
" whdl.write(\"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [],
"source": [
"stories = Stories(\"../advanture_translated/processed_translated_story.txt\")"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"故事名称：\n",
"18\n",
"故事背景：\n",
"你把Kyros撞倒在地，双手捂住他的喉咙。他开始逐渐退出，钻研现实，而你努力让他脚踏实地。你们两个传送到竞技场的中间，而守卫的三名角斗士立即拉屎并争夺他们的枪支。再次脱离现实，你发现自己在一个空荡荡的走廊里。你知道他会在亚空间传送门开始形成时继续传送，直到你们都被搞砸了。你需要做点什么。 “战斗！战斗，卡斯！” 梅利奥达斯咆哮。 感觉他开始逐渐消失，你很快就开始让你和他飞过亚空间再次传送，这样你就可以选择下一个目的地，试图在它杀死你和船上的其他人之前结束这个循环. 是时候选择一个目的地了。\n",
"--------------------\n",
"动作：\n",
"做\n",
"在突变体出没的地区\n",
"结果：\n",
"你传送，然后发现自己在飞船上众多被变异人出没的坑洞中的一个。这是您真正知道的少数几个之一，因此它是您的传送目的地，因为您与一群独眼乌鸦战斗通过它来为叛变者保护它。在这里，成群的半智能变种人在监视器上工作，监视器设计得非常简单，一个蹒跚学步的孩子也能使用，这是幸运的，因为它需要这些白痴的最大智力努力。航站楼控制着空中管制，不是为了任何重要的人，而是为了随时可以使用的另一组变异工人。 “对对对！” 梅利奥达斯哭了。“完美，卡斯！完美！” Kyros 哽咽的尖叫声很快通知了突变体你的存在。当它们开始蜂拥而至时，它们的原始本能立即接管了你。触手、爪子、手、脚、牙齿、尖牙、角和刺都会攻击你们。凯罗斯尖叫起来，他的注意力瞬间被打破，亚空间流在他周围消散。你滚开他们的攻击，带着一股精神能量迅速飞离他们的控制。此部分的重要作用意味着您可以看到维护电梯。你朝它跑去，抬起大门并在它下面滚动，你的手向上按下带有向上箭头的红色电梯按钮。由于黑暗诸神、神皇或宇宙本身的恩典，电梯立即做出反应。 Kyros 尖叫起来，因为变种人开始撕裂他。他的能量一直在与你战斗并试图打开亚空间传送门，但在电梯将你从他的视线中移开之前，他已经耗尽了精力，无法反击，无法集中注意力，也无法做任何事情，他发出一声可怜的尖叫。 你深吸一口气，靠在墙上。那个狗屎让你失去了很多。你听到远处 Kyros 的尖叫声，然后微笑起来。这有一些隐约的诗意。凯罗斯，一个声称捍卫人民权利的人，将人类从压迫中解放出来，摆脱了对灵能者、变种人和其他人的偏见，这些偏见与帝国对人类的完美看法不同，被他以变种人身份征服的变种人杀死并被奴役。美丽的。 你将头靠在墙上，深呼吸。你需要休息一下。\n",
"梗概：\n",
"在突变体出没的地区\n",
"--------------------\n",
"动作：\n",
"做\n",
"你回到你的房间\n",
"结果：\n",
"你走进卧室，立刻瘫倒在床上。当您将头靠在枕头上并闭上眼睛时，您想知道自己怎么可能睡着。 “卡斯！” 一个声音说。 你翻了个身，看到 Fay 站在门口。她的肘部缠着沾满鲜血的绷带，手里拿着一把血淋淋的弯刀。 “什么？” 你问，费伊微笑着。 “所以，这就是你一直藏身的地方，”她说着向你走来。 “躲起来？我真的刚到这里。我杀了 Kyros，”你补充道，试图漠不关心。 “刚到这里？你已经睡了几个小时了！” 她说。“我们清理了剩余的 Kyros 忠诚者。大多数人在 Kyros 被杀后才投降。” “找到尸体了？” 你问。 “我们实际上希望你能帮忙。我们找不到。他在哪里被杀？” “你怎么知道他死了？” 你问，揉着眼睛努力保持清醒。 “一些混沌灵能者感觉到他强大而明亮的灵魂消失了。我以为你杀了他。他们说他们仍然感觉到你的存在，谢谢......不管是谁在照顾我们。我的一部分认为他们错了直到我真的在这里找到你。发生了什么事？ “我们最终来到了一个被变异人占据的区域。他们把他撕成了碎片。区域……3C-QP……操，我不知道。我最终会记住的。” “菲指挥官，报到！” 收音机里传来噼啪声。 “我找到了他，”费伊说。“给我几分钟，我们会聚在一起寻找幸存的忠诚者。” 费伊把收音机扔到一边，对你微笑。她坐在床边，把弯刀扔在地板上。 “所以，我们做到了。我们杀死了凯洛斯！” 她笑了。 “我们做到了，”你微笑。 “我们离一个神奇的、残废的 Uwais 和一个害怕的、赤身裸体的 Azajaja 相去甚远，”她笑着说。 “该死的。我们需要和其他人见面。” “稍后，Karth。睡一觉。我需要做一些工作。我们明天早上见面。” “很高兴，”你说，你的头撞在枕头上，Fay 笑着抓起刀片走出门。 是时候睡觉了。\n",
"梗概：\n",
"你回到你的房间\n",
"--------------------\n",
"动作：\n",
"做\n",
"新的黎明...\n",
"结果：\n",
"新的曙光... 你漫步到桥上，揉揉眼睛里的睡眠。其他人已经在那里，盯着窗外一颗巨大的黄矮星。Drake Amadeus 坐在主座位上，不耐烦地敲击着座位。两个部落站在他的两侧，既是保镖又是监狱看守。 往窗外看的其他人转过头来看你。他们立即爆发出热烈的掌声，你向前走时微笑。海耶斯船长表示敬意地点点头，德奇发出一声响亮的叮当声，试图用作为他手臂的金属尖刺拍手，而埃利奥斯微笑着，立即抓住机会把手放在你的肩膀上，它立即沿着你的胸膛伸向你的生殖器直到你轻轻推开他。罐头鼓掌的声音来自扬声器，大概来自 Primus，让你发笑。伊丽莎白也鼓掌，凯恩困惑地盯着鼓掌的人们，然后耸了耸肩，加入了轰隆隆的雷声，双胞胎为双胞胎鼓掌。Fay 紧紧地拥抱着你，然后你才让大家安静下来。 “你干掉了 Kyros，”伊丽莎白说。“感人的。” “我不再需要赞美了，”你说。“我们需要讨论一下 hsip 会变成什么样子。” “当你活得和我一样长时，你就会知道要珍惜美好的时光，而不是匆匆忙忙地向前冲，”德奇说。 “我们有一艘格洛丽安娜级战舰、一支军队和绝对的自由。世界是……不，银河系是我们的，”费伊微笑着说。 你凝视窗外，沐浴在温暖的金色阳光下，自从 Kyros 登陆你的星球以来，你第一次感到完全自由。 银河系是你的。\n",
"梗概：\n",
"新的黎明...\n",
"\n"
]
}
],
"source": [
"print(random.choice(stories.stories).to_str())"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"304"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(stories.stories)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 28M raw data process"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
"text = open(\"./text_advanture_trans.txt\",'r').read()"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"textsps = text.split(\"\\n>\")"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [],
"source": [
"contents = []\n",
"for one_sp in textsps:\n",
" tmp = [i for i in one_sp.split(\"\\n\") if i.replace(\" \",\"\")]\n",
" contents.append(tmp)"
]
},
{
"cell_type": "code",
"execution_count": 362,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"7074"
]
},
"execution_count": 362,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(contents)"
]
},
{
"cell_type": "code",
"execution_count": 569,
"metadata": {},
"outputs": [],
"source": [
"small_contents = [i for i in contents if len(i) < 20]"
]
},
{
"cell_type": "code",
"execution_count": 570,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4304"
]
},
"execution_count": 570,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(small_contents)"
]
},
{
"cell_type": "code",
"execution_count": 571,
"metadata": {},
"outputs": [],
"source": [
"import random"
]
},
{
"cell_type": "code",
"execution_count": 572,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[' 你拒绝了她的提议',\n",
" '“不，”你说。“我真的应该完成陷阱。”',\n",
" '“哦，好吧，”罗斯说。',\n",
" '“不过，我会在附近见到你，”你说。',\n",
" '“是的，我想是的，”罗斯说。“我真的该走了。我妈妈会找我的。”',\n",
" '“好的。再见，罗斯，”',\n",
" '“再见，雅各布，”罗斯说，然后冲进树林。',\n",
" '你看着她离开，然后转身设置其余的陷阱。']"
]
},
"execution_count": 572,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"random.choice(small_contents)"
]
},
{
"cell_type": "code",
"execution_count": 573,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 741,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[]], dtype=object)"
]
},
"execution_count": 741,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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+pbylyDTA9r5pdo1113VJXHNrdHLNp7YMXNY2OjpKvT8rRTV6am0++7jaa26HVuznZitytsz70hE7khYDvwS8BDwNfCoN2wrsTcv70jqp/1uebzcza60iv06XA3skXUH5l8FjEbFf0ovAiKT/CPwlsDuN3w18VdI48EPgjibUbWZmcyhytswx4OertL8M3Fyl/cfApxtSnZmZ1cXvUDUzy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy1BnXqXAzH4qzXWN3mY6tfPWtjxvM/nI3cwsQw53M7MMFbnM3ipJT0t6UdILku5N7cskHZB0It0vTe2S9KCkcUnHJK1t9oswM7N3KnLkPg1sj4jrgfXAPZKuB3YAByNiDXAwrQPcAqxJtyHgoYZXbWZmc6oZ7hFxNiKeS8s/onxx7BXAZmBPGrYHuC0tbwYejrJDQK+k5Y0u3MzMZqeIKD5YWg18G7gB+OuI6E3tAs5HRK+k/cDOiHgm9R0E7ouIwzO2NUT5yJ5SqbRuZGSkrhcwNnGh5pjSYpi8WNfm28Y1t0Yn19y3YsllbVNTU/T09DT1eYv8TM1HJ+/jS2bu61bs50YYHBw8EhH91foKnwopqQf4I+BzEfF6Oc/LIiIkFf8tUX7MMDAM0N/fHwMDA/N5+FvuKnDq1Pa+aXaNdddZn665NTq55lNbBi5rGx0dpd6flaKK/EzNRyfv47eMvfGO1e19f8uuZ96YZXBjNes0zEJny0h6F+Vg/1pEfD01T16abkn351L7BLCq4uErU5uZmbVIkbNlBOwGXoqI363o2gdsTctbgb0V7Xems2bWAxci4mwDazYzsxqK/K30EeBXgTFJR1PbvwN2Ao9J2gacBm5PfU8Cm4Bx4E3g7kYWbGZmtdUM9/SPUc3SvaHK+ADuWWBdZma2AH6HqplZhhzuZmYZcribmWXI4W5mliGHu5lZhhzuZmYZcribmWWowz/wweynV7VLzm3vm274Z79YnnzkbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZajIZfa+LOmcpOMVbcskHZB0It0vTe2S9KCkcUnHJK1tZvFmZlZdkSP3rwAbZ7TtAA5GxBrgYFoHuAVYk25DwEONKdPMzOajZrhHxLeBH85o3gzsSct7gNsq2h+OskNAr6TlDarVzMwKUvmSpzUGSauB/RFxQ1p/LSJ607KA8xHRK2k/sDNddxVJB4H7IuJwlW0OUT66p1QqrRsZGanrBYxNXKg5prQYJi/Wtfm2cc2t0W01d1u94Jpr6VuxpO7HDg4OHomI/mp9C/7gsIgISbV/Q1z+uGFgGKC/vz8GBgbqev4iH6K0vW+aXWPd9Rlprrk1uq3mbqsXXHMtp7YMNGW79Z4tM3lpuiXdn0vtE8CqinErU5uZmbVQveG+D9ialrcCeyva70xnzawHLkTE2QXWaGZm81Tz7w5JjwADwLWSzgBfAHYCj0naBpwGbk/DnwQ2AePAm8DdTajZzMxqqBnuEfGZWbo2VBkbwD0LLcrMzBbG71A1M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDDnczcwy1JRwl7RR0ncljUva0YznMDOz2TU83CVdAfw+cAtwPfAZSdc3+nnMzGx2zThyvxkYj4iXI+InwAiwuQnPY2Zms1D5sqcN3KD0KWBjRPx6Wv9V4EMR8dkZ44aAobT6AeC7DS3kna4FXmni9pvBNbdGt9XcbfWCa26mfxAR76vWUfMC2c0SEcPAcCueS9LhiOhvxXM1imtujW6rudvqBdfcLs2YlpkAVlWsr0xtZmbWIs0I978A1ki6TtKVwB3AviY8j5mZzaLh0zIRMS3ps8CfAlcAX46IFxr9PPPUkumfBnPNrdFtNXdbveCa26Lh/1A1M7P28ztUzcwy5HA3M8tQNuEuaZWkpyW9KOkFSfdWGTMg6YKko+n2O+2odUZNpySNpXoOV+mXpAfTRzkck7S2HXVW1POBiv13VNLrkj43Y0zb97OkL0s6J+l4RdsySQcknUj3S2d57NY05oSkrW2s979K+k76uj8hqXeWx875PdTimr8oaaLia79plse25SNKZqn50Yp6T0k6Ostj27Kf6xYRWdyA5cDatHw18D3g+hljBoD97a51Rk2ngGvn6N8EPAUIWA882+6aK2q7AvgB5TdSdNR+Bj4GrAWOV7T9F2BHWt4B3F/lccuAl9P90rS8tE31fgJYlJbvr1Zvke+hFtf8ReC3CnzfnAR+FrgSeH7mz2ora57Rvwv4nU7az/Xesjlyj4izEfFcWv4R8BKwor1VNcRm4OEoOwT0Slre7qKSDcDJiDjd7kJmiohvAz+c0bwZ2JOW9wC3VXnoPwEORMQPI+I8cADY2Kw6L6lWb0R8MyKm0+ohyu8Z6Riz7OMi2vYRJXPVLEnA7cAjrail2bIJ90qSVgM/DzxbpfvDkp6X9JSkD7a2sqoC+KakI+kjGWZaAXy/Yv0MnfNL6w5m/0HotP0MUIqIs2n5B0CpyphO3d+/RvkvuGpqfQ+12mfTVNKXZ5n66tR9/I+ByYg4MUt/p+3nOWUX7pJ6gD8CPhcRr8/ofo7yFMKNwO8Bf9zi8qr5aESspfwpmvdI+li7CyoivUHtk8AfVunuxP38DlH+O7srzgOW9NvANPC1WYZ00vfQQ8A/BG4CzlKe5ugWn2Huo/ZO2s81ZRXukt5FOdi/FhFfn9kfEa9HxFRafhJ4l6RrW1zmzJom0v054AnKf7JW6tSPc7gFeC4iJmd2dOJ+TiYvTWml+3NVxnTU/pZ0F/DLwJb0C+kyBb6HWiYiJiPibyPi/wH/c5ZaOmofA0haBPwz4NHZxnTSfi4im3BP82W7gZci4ndnGfP30jgk3Uz59b/auiovq+cqSVdfWqb8D7TjM4btA+5MZ82sBy5UTC2006xHOZ22nyvsAy6d/bIV2FtlzJ8Cn5C0NE0pfCK1tZykjcC/AT4ZEW/OMqbI91DLzPh/0D+dpZZO/IiSXwS+ExFnqnV22n4upN3/0W3UDfgo5T+zjwFH020T8BvAb6QxnwVeoPzf+UPAL7S55p9NtTyf6vrt1F5Zsyhf/OQkMAb0d8C+vopyWC+paOuo/Uz5F89Z4G8oz+luA64BDgIngD8DlqWx/cAfVDz214DxdLu7jfWOU56bvvT9/N/T2L8PPDnX91Aba/5q+j49Rjmwl8+sOa1vonxG28l215zav3Lp+7dibEfs53pv/vgBM7MMZTMtY2Zmb3O4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpah/w/gXtd1MSmC/wAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pd.DataFrame([len(i) for i in small_contents]).hist(bins=10)"
]
},
{
"cell_type": "code",
"execution_count": 791,
"metadata": {},
"outputs": [],
"source": [
"tmp_data = []\n",
"for i in range(len(contents)):\n",
" nowcontent = contents[i]\n",
" prevcontents = []\n",
" j = i - 1\n",
" while len(''.join(prevcontents)) < 500 and j >= 0:\n",
" prevcontents += contents[j]\n",
" j = j - 1\n",
" if(len(nowcontent) < 1000):\n",
" tmp_data.append([prevcontents,nowcontent])"
]
},
{
"cell_type": "code",
"execution_count": 803,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[' 第一次死亡...',\n",
" '就在布里杰堡外面，一个名叫卢克·哈洛兰的年轻人加入了你们的队伍：他死于肺结核，被乔治和雅各布·唐纳带走，并被允许乘坐他们的马车。在盐湖的岸边，他突然晕倒并因病去世。任何人都做不到，但他的死给党带来了黯淡的情绪。第二天，他被埋葬在盐湖岸边另一位在前往加利福尼亚的途中去世的定居者的坟墓。',\n",
" '迫于压力，您将在 9 月进入盐沙漠，那里炎热干燥，湿气重。马车艰难地穿越沙漠，拉动它们的牛在努力拉动马车时消耗了大量的水。詹姆斯·里德 (James Reed) 被迫放弃他的两辆马车，乔治·唐纳 (George Donner) 和另一位名叫路易斯·凯塞伯格 (Louis Keseberg) 的凶猛定居者都不得不放弃他们的一辆马车。整列马车被拉长了好几英里，带着马车的牛群被分开，其中许多人死了。不知何故，你设法让你的马车保持移动，让你的牛活着，但这种经历是噩梦般的。',\n",
" '最后，您和您的同伴穿越沙漠，在泉水扎营几天，这些时间都在围捕丢失的牛，从废弃的马车上找回东西，直到将近 9 月中旬，聚会才准备好继续。穿越沙漠杀死了许多牛，也花费了党的许多物资。大多数定居者，尤其是詹姆斯·里德，现在担心他们可能没有足够的食物来一路到达加利福尼亚。',\n",
" '一个计划很快就制定了：查尔斯·斯坦顿和另一位名叫威廉·麦卡琴的定居者（一个巨人，带着妻子和小女儿前往加利福尼亚）自愿骑着快马前往加利福尼亚的第一个贸易站萨特堡，收集新鲜补给品并带回派对。行动迅速，他们可能会在短短一个月内回来。如果你愿意，你可以和他们一起骑车去加利福尼亚，或者你可以继续和唐纳派对一起旅行。',\n",
" '你做什么工作？'],\n",
" [' 你留在唐纳党',\n",
" '在查尔斯·斯坦顿和威廉·麦卡琴离开后，情况并没有好转。虽然你离开了沙漠印第安人现在开始在晚上从派对上偷马，脾气变得很差。随着食物变得稀缺和饥饿开始蔓延，定居者之间开始发生争吵。这种情况不可避免地在 10 月份以暴力达到高潮，当时两辆马车在爬上艰难的山丘时被卡住。',\n",
" \"而爬山，迫使詹姆斯·里德你的货车卡在岩石“小号车和其他属于富兰克林格雷夫斯（一个友好的人与他的妻子，7个子女和女婿旅行）碰撞。里德大发雷霆，咆哮道：“你该死的笨蛋，你别挡我的路！”“闭上你的嘴垃圾桶或我'会关闭它为你”树皮背约翰·斯奈德，一个脾气暴躁的驱动程序格雷夫斯工作。\",\n",
" '这时里德彻底发脾气，从马车上跳了下来，大步走向斯奈德，斯奈德倒在地上迎接他。您吃惊地看见一个长刃屠夫“的刀下里德闪闪发光”的手并没有谋杀他的眼睛。',\n",
" '你做什么工作？']]"
]
},
"execution_count": 803,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"random.choice(tmp_data)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"<|正文结束|> <|文本结束|> <|startoftext|> <|endoftext|>"
]
},
{
"cell_type": "code",
"execution_count": 851,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"7074"
]
},
"execution_count": 851,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(tmp_data)"
]
},
{
"cell_type": "code",
"execution_count": 869,
"metadata": {},
"outputs": [],
"source": [
"specials = ['<|正文结束|>','<|文本结束|>','<|startoftext|>','<|endoftext|>','<| startoftext |>','<| endoftext |>']\n",
"def replace_contents(content):\n",
" for i in specials:\n",
" content = content.replace(i,\"\")\n",
" return content"
]
},
{
"cell_type": "code",
"execution_count": 926,
"metadata": {},
"outputs": [],
"source": [
"train_data = []\n",
"for prev,content in tmp_data:\n",
" tmp_prev = []\n",
" for i in prev:\n",
" i = i.strip()\n",
" replaced = replace_contents(i)\n",
" tmp_prev.append(replaced)\n",
" tmp_action = content[0].strip().replace(\"你你\",\"\").replace(\"罢工\",\"出手\").replace(\"教练\",\"马车\")\n",
" tmp_content = []\n",
" for i in content[1:]:\n",
" i = i.strip()\n",
" replaced = replace_contents(i)\n",
" tmp_content.append(replaced)\n",
" if(i != replaced):\n",
" break\n",
" if(len(tmp_content) < 15):\n",
" train_data.append({\n",
" \"previous\":tmp_prev,\n",
" \"action\":tmp_action,\n",
" \"result\":tmp_content})"
]
},
{
"cell_type": "code",
"execution_count": 927,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4766"
]
},
"execution_count": 927,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(train_data)"
]
},
{
"cell_type": "code",
"execution_count": 1001,
"metadata": {},
"outputs": [],
"source": [
"aa = random.choice(train_data)"
]
},
{
"cell_type": "code",
"execution_count": 1007,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'{\"previous\": [\"你对抗\", \"你拿着你找到的证据面对金发女郎，她告诉你一切。她是一个吸血鬼，她希望改变你，这样她就不再孤单了。她还告诉你相信她，她的名字是莎拉。她给了你死亡的选择和永生的选择。\", \"你尽可能快地逃跑\", \"不幸的是，你不能轻易逃离吸血鬼。金发女郎跟踪你，抽干你的血，折断你的脖子。你作为人类死去，不会再复活。\", \"翻阅这本书的书名后，你会发现它们都是关于吸血鬼的神话传说。然而，有一个让你眼前一亮，它被标记为“吸血鬼日记”，它吸引了你。翻开日记，你会发现它提到了一家夜总会和一个容易上当的家伙，他的皮线很俗气，试图进入主人的内裤。然后，日记的主人详细说明了他们想在吸干他们后如何肢解这个人。你惊恐地发现所有的相似之处，并发现你只有两种选择，你可以面对在日记底部签名的“莎拉”，或者你可以逃命。你选哪个？\", \"你搜索书柜\", \"翻开书名，你会发现它们都是关于吸血鬼的神话和传说。然而，有一个让你眼前一亮，它被标记为“吸血鬼日记”，它吸引了你。翻开日记，你会发现它提到了一家夜总会和一个容易上当的家伙，他的皮线很俗气，试图进入主人的内裤。然后，日记的主人详细说明了他们想在吸干他们后如何肢解这个人。你惊恐地发现所有的相似之处，并发现你只有两种选择，你可以面对在日记底部签名的“莎拉”，或者你可以逃命。你选哪个？\"], \"action\": \"你死了\", \"result\": [\"她杀了你，你就死了，你也不能从坟墓里复活。\"]}'"
]
},
"execution_count": 1007,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"str(aa).replace('\\'','\\\"')"
]
},
{
"cell_type": "code",
"execution_count": 929,
"metadata": {},
"outputs": [],
"source": [
"import json"
]
},
{
"cell_type": "code",
"execution_count": 941,
"metadata": {},
"outputs": [],
"source": [
"with open(\"./truncated_advanture_train.json\",'w') as whdl:\n",
" json.dump(train_data[:4500],whdl)\n",
"\n",
"with open(\"./truncated_advanture_valid.json\",'w') as whdl:\n",
" json.dump(train_data[4500:],whdl)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: labeled_data/advanture_translated/processed_translated_story.txt
================================================
故事名称：
1
故事背景：
你在平静的浅水池中醒来，在风的呼啸和冰冷的爱抚中醒来。月亮从上面满是星星的天空注视着你。它苍白的灯光照亮了你脚下光滑石头缝隙之间生长的苔藓和茂密的草。树根弯曲，长满苔藓，伸向天空，在这个奇怪的无墙花园中可以看到的几块裸石上投下阴影。空气比你所感受到的任何东西都稀薄和寒冷。它呼啸着穿过树木和草地，声音类似于吹过断笛的空气。你颤抖着站了起来。当您缓慢谨慎地朝花园边缘迈出一步时，一阵窒息的声音从您身边溜走了。你向任何和所有的神祈祷和乞求，但是当你的脚停下来时，你诅咒他们，你诅咒他们所有人。一块小石头从花园的石边上坠落。你抓住你旁边的树，看着它倒下，知道它还在继续倒下，即使你看不到它。当您注意到远处闪烁的暖黄色灯光时，您会感到头晕目眩。你看到的就是你的家。 泪流满面，“不……”你跪倒在地，挣扎着想要呕吐的冲动。 你在蛇王的塔顶。 你在邪恶花园的顶端。 而你会死。 在你身后，最黑暗的阴影中发出一声深沉的笑声。它穿过树林，比寒冷的风更能冷却你的血液。闭上你的眼睛，你把你的心放在喉咙里，背对着你的家。你咽下口水，睁开眼睛去面对为你而来的一切。 黑暗变浓成黑夜的丝带，汇聚在一个点上，直到一个衣衫褴褛的苍白男子出现。他毫不费力地漂浮在你醒来的浅水之上，用充满漩涡墨水的空心眼窝看着你。他对你说话，但他干裂的嘴唇一动不动。 “每一层楼都提供了新的挑战。通过并继续。” 大地震动，灰人脚下的水荡漾开，流走。当它们移动以形成下降的螺旋时，它会从石头的缝隙中渗出。雾或可能是蒸汽的东西进入花园只是洒在塔的边缘。你看着它，楼梯，那个衣衫褴褛的人。 “追随前人的脚步。完成他们不能完成的事情。” 你吞了吞口水，走到楼梯的顶端。但在你迈出另一步之前，你最后一次环顾四周，并将其全部记入记忆；天空，树木，远处的村庄。你叹息。
--------------------
动作：
做
你走下楼梯。
结果：
衣衫褴褛的男人一脸不偏不倚地看着你 你回望，直到你再也无法直视他的眼睛。您别无选择，进入邪恶花园的中心。 “赢得你的自由；正确回答，否则死亡。” ~ 当您越来越深入地走进 塔楼时，您会注意到楼梯通向每一层楼，您和塔楼外墙弯曲的石头之间只有空旷的空间。时间过去了，但多少暗示了你，因为没有窗户可以看到天空中的太阳。这一切似乎有点太容易了；奇怪的是，没有什么能阻止你下降到下一个级别。你想知道其他被选中的人，或者更准确地说是为这座塔献祭的人。 他们通过了这些楼层吗？ 当你走到一扇门时，你会回想那个衣衫褴褛的人说的“完成他们不能完成的事情”。它随着一条小溪摇摆开来。你凝视着远处的黑暗。
梗概：
你走下楼梯。
--------------------
动作：
做
你进入房间。
结果：
房间里漆黑一片，寂静无声，只有你自己的脚步声。一圈尘土飞扬的光芒出现。你小心翼翼地走向它，你的心跳敲击着你的胸腔。当你到达它时，一个低沉的声音向你呼唤： “欢迎来到元素试炼。” 你寻找说话的人，但没有找到。在光线下你突然觉得太暴露了，太开放了，太容易成为猎物了。认为表现得坚强比软弱更好，你尽可能地鼓起勇气，在黑暗中呼唤。“谁在那儿？” 声音没有回答你，而是继续： “在你坐下之前，万物皆有：风、火、土、水。” 您在比赛中站立的灯光包含四个裸露这些元素的基座。现在房间完全亮了，你会看到每个房间后面都有一扇门，你知道其中一个肯定是出口。但这是个问题。 “每个人都守着一扇门，但只有一个能让你安全通过。慢慢来，明智地选择，因为一旦一扇门打开，你就无法关闭它，你也不能选择另一扇门。” 房间因这个声音而颤抖。火在第二扇门上喷发，就像石头落在第三扇门前挡住了一样。害怕你倒退到一个以前绝对不存在的水坑里。你的眼睛跟随源源不断的水流回到它的源头，水从第四扇门的下面和周围流出。 当声音回来时，你向前迈了一步： “小心，死亡等待着那些选择错误的人。”
梗概：
你进入房间。
--------------------
动作：
做
你选择空气之门。
结果：
你伸手去拿把手，你的手指感觉到空气的咬合。他们在突如其来的寒冷中疼痛，但你仍然把门把手握在手中。它吱吱作响地打开，尽管寒冷，你觉得自己做出了正确的选择，但你脸上的笑容几乎和它出现的一样快。门外什么也没有，只有一个永无止境的灰雾漩涡坑。当门口和你之间的一小块土地开始破裂和崩溃时，你的心开始下降。你惊恐地看着大块石头落入雾中，再也听不见，尖叫着房间的地板也开始脱落。当你爬到墙上希望它不会随着地板掉下来时，你的指甲深入石头。但是你的脚不会配合。你滑倒并坠入雾中。
梗概：
你选择空气之门。
--------------------
动作：
做
你已经死了……结束游戏并发表评论。
结果：
那么现在你完成了邪恶的花园，你如何评价它？请注意，作者不会看到您的个人评分；选择评级后，您将有机会发表个人评论。1 - 远离。很远很远2 - 好......让你大便3 - 选择你自己的无聊4 - 比家庭作业更有趣5 - 不是最好的，当然也不是最差的6 - 每一分钱都值得7 - 甚至比 Reeses' Cups® 更好8 - 它会给你带来启示如果您真的不想留下评分，您可以随时单击此处继续评论屏幕。不确定要评价什么？查看评分和发表评论的提示。
梗概：
你已经死了……结束游戏并发表评论。
故事名称：
2
故事背景：
“旅行者在篝火旁！走，走！” 消息如野火般蔓延开来。很快，村子里的所有孩子都聚集在火坑旁。“你会给我们讲个故事吗？” 他们恳求。旅行者对孩子们不太习惯，所以她花了一段时间才意识到他们不会拒绝回答。 “好吧，”她心软了。“想象一下……你是一条龙，在夏天的一天飞过森林。太阳在你的翅膀上，风在你的耳朵里，世界是你的游乐场……” 您正在您的领域上空翱翔，这是该地区最大的森林。人类称它为Essucia Woods。这是一个明媚的夏日，但您不必担心被发现。你正飞越茂密的树林，那里生活着半人马、精灵、蜥蜴人和数百种其他危险生物——显然没有人敢进入。 或者，至少，你认为没有人敢去那里。你很惊讶地看到一个年轻的女人，只不过是一个女孩，独自站在空地上。难道她不知道那本质上是在尖叫，“怪物！来杀了我！” 你飞过去仔细观察，女孩注意到了你。她没有逃跑（这很没用，因为你很容易追上她），她大声喊道。“龙！” 这个女孩要么真的很勇敢，要么很愚蠢，或者两者兼而有之。 考虑到人类，您更愿意保持低调。如果你试图掠夺他们的财宝，他们的军队可能会来找你。虽然你可能很容易接受它们，但人类可能会变得非常幸运。其中之一实际上可能会造成一些损害。因此，如果您要与这个女孩交谈，她可能会向她的家人和朋友分享您的真实面貌，而不是将人们吓跑的传说。 通过她的背包摆动的方式，你可以看出它里面有一些很重的东西，很可能是金子。她穿着一件漂亮的斗篷，很明显她已经很久没有进入森林了。可能值得看看这个女孩向你呼唤什么。另一方面，这可能是一个陷阱。这似乎有点太明显了。尽管如此，大多数陷阱不会伤害到你的规模，而且你更愿意看看这个女孩在她没有跳到你身上时在做什么。
--------------------
动作：
做
你跟那个女孩说话
结果：
你降落在女孩身边。你相当确定她不会构成太大的威胁，如果你愿意，你可以立刻吃掉她。“你为什么叫我？” 你问，好奇。“你知道我可以用一千种不同的方式杀死你。” “可是你一个都没用过。” 她回应。所以她比看起来更聪明。“我是来向你提出要约的。我是公主，这意味着我将作为某种政治工具出嫁。我不想仅仅因为某个老家伙向我父亲提供联盟而结婚……我根本不想结婚，所以我跑了。” “那你到底是因为什么引起了我的注意？” 你不耐烦地问。 “我正在解决这个问题，”她回答道。“如果我自己逃跑，我父亲会派出搜查队。林子里搜查的人会很多，他们找到我只是时间问题。但是，如果我被“抓住”了一个强大的存在，就像一条龙……” “人们会害怕，只有个人会试图拯救你。” 你完成。“这对我有什么好处？” 作为回应，她把袋子里的东西倒在地上。一条钻石项链，一个镶满宝石的圣杯，还有一大堆金币洒了出来。 “我还叫你潜伏在我所在的区域，以保持形象。所以，你说呢？” 如果她说只有个人来是对的，那应该问题不大。然而，你留在树林里的原因是因为如果人类专心致志，他们会变得非常强大。谁知道呢？也许其中之一会走运。尽管如此，对于一项小服务来说，这是一个半体面的回报......你考虑了所有这些因素，你最终做出了决定。
梗概：
你跟那个女孩说话
--------------------
动作：
做
是的。
结果：
有人付钱给你绑架他们？这是一笔不错的交易。“别忘了飞到镇子附近，让他们看到我被‘俘虏’了，”女孩说。 “我怎么知道这不是杀死我的陷阱？你不能，”你问。 “你会用爪子夹着我。” 她满怀信心地说。“如果弓箭手射你，你会带我一起倒下。如果有什么问题，就像你说的，你有千种不同的方法可以杀死我。” 在她捡回宝藏后（你会知道是否有任何遗漏），她穿过森林走向城堡。你跟着，树木隐藏着你的绿褐色鳞片。一进入城堡周围的田野，她就开始蹦蹦跳跳地吹着曲子，真正扮演了无助的少女的角色。 然后是你最喜欢的部分。你俯冲下来，瞄准那个女孩。一个农家男孩走过来，发现了你，开始惊慌失措。“赛琳娜公主，小心！” 男孩喊道。 “注意什么？” 她说，假装没有注意到她身后的你。 “这是——”农家男孩开口，但被你用爪子抓住她并飞向空中的声音打断了。 “龙！” 女孩尖叫起来。“哦，我有祸了！一条龙正在抓住我！有人能帮我从这只可怕的野兽手中救出我吗？哦，不！” 她真的很适合这个角色。你在城堡周围飞来飞去几次，很快整个城镇都一片哗然。 “一条龙正在俘虏公主！我们要做什么？” 为什么，镇上的人比公主更闹，她知道发生了什么。你飞向远方，对自己轻笑。一旦女孩停止了她的遇险少女行为，她就会问你有什么好笑的。 “好吧，你们整个镇子都因为一个女孩被抓而闹得沸沸扬扬。人类真的对最愚蠢的事情很着迷。” 你回应。你认为你会把她留在森林中的某个地方，希望她能活下来，但你看到远处的巫师塔，你就明白了。你肯定那里有一些不错的魔法战利品，但那个傲慢的巫师设置了守卫以防止龙进入。巫师早已不在，但守卫仍然存在。然而，这个女孩应该能够进入，如果她学会了魔法，你可能会及时说服她降低守卫。 不过，你不要对她说这些。相反，你只是飞过去说，“在这里。这将是一个比大多数人更适合居住的地方。如果有人试图营救你，他们将不得不浪费时间爬上塔楼。但这是一个巫师塔，所以在你向我描述之前不要碰任何东西。我不想让你自爆。” 你也不希望她在你有机会利用它之前使用塔中的所有魔法。 几天过去了。这个女孩似乎并没有找到太多的兴趣。到目前为止，她找到的只是一本各种各样的日记，一本厚厚的大部头书，里面有奇怪的符号。你已经看到巫师带着它四处走动，但它并没有真正做任何事情。它会造成什么危害？她也还没有找到见之池。它相当容易使用。你需要做的就是说出你见过的人的名字或你去过的地方的名字，它会向用户显示他们命名的内容。由于它在塔外，您已经使用了很多次。如果女孩使用它会发生什么？游泳池可以潜在地增强她的魔力。从更实际的角度来说，她可以注意到任何试图“拯救”她的骑士。尽管如此，知识就是力量，你更喜欢拥有权力而不是其他人。
梗概：
是的。
--------------------
动作：
做
你给她看游泳池
结果：
“姑娘，你看到后面的水池了吗？” “你是说锦鲤池吗？” 她问。“是的，我看到了。为什么？” “它可以让你看到你以前见过的任何人或地方。” “酷！它是如何工作的？” 她说，然后跳下阳台。她没有坠落，而是飘落下来，轻轻地落在地上。“我一直在练习那个咒语。” 她承认。 “不错，”你说，掩饰你的感受。她主动修炼魔法，这意味着她可以拿下结界，但另一方面，她可能会成为威胁。 “你好？” 女孩说着，在你面前打了个响指。“怎么又起作用了？” “你所做的就是说出你想看到的东西的名字。” 你说，还是有点惊讶她在你面前已经变得如此自在。即使你已经认识她一段时间了，你仍然不确定她是勇敢还是愚蠢。也许她对你有点太舒服了。 “给我看看我父亲，”她命令道。你翻白眼。 “游泳池不知道你是谁，也不知道你父亲是谁。你必须说出他的名字。” “哦！这就是它的工作原理。我知道这一点。” 她说，试图隐瞒她一直在用棍子戳水的事实，但没有成功。你开始倾向于勇敢或愚蠢的困境中的愚蠢。“给我看看亨利克斯·阿奇博尔德三世国王。” 她再试一次。 “让我猜猜，”你说。“你的全名是赛琳娜华丽丽——” “这只是赛琳娜，”女孩生气地说。“看！” 水池上有一个留着胡子的男人穿着红色斗篷和金色王冠，这是人类皇室的象征。他好像好几天没睡了。 “怎么还没派出搜查队？” 向国王提出要求，但从他的表情可以看出他已经知道了答案。 “你知道，每个头脑正常的人都非常害怕龙，”女王来到国王身边说道。“我们不能指望民兵打龙！” 至少女孩的父母有一些常识，虽然你不能对他们的女儿说同样的话。 “你怎么知道她还活着？” 国王痛苦地说。 “巨龙并没有立即杀死她，而是将她带回了自己的巢穴。它可能是想引诱更多的受害者，以她为诱饵。” 女王回答。 突然间，国王似乎把自己抬高了。“我们将召集来自各个王国的骑士！告诉他们有一只巨大的野兽囚禁了一个无助的少女，”——女孩对此嗤之以鼻——“它必须被征服！ “我们应该给予什么样的奖励？” 王后问道，开始重拾希望。“面对巨龙可不是一件容易的事。” 国王考虑到了这一点。“既然有人从国库中偷走了很多宝物，我们不能确切地给予现金奖励，否则小偷可能会再次偷走。” 你看着那个女孩。 “所以呢？” 她说。“我相信你也偷走了你应得的那份宝藏。” 您决定不回答并将注意力转回游泳池。 “也许我们可以同时解决两个问题，”国王说。“我们还没有王位继承人，这样的好人，杀了一条龙，一定是个不错的继承人。” “所以你是在暗示……”女王开口。 “拯救赛琳娜的骑士会牵手结婚。” 皇后顿住。“你知道赛琳娜不会同意的。” 国王笑了。“胡说八道！她会感激她的救命恩人，如果她没有当场嫁给他，我会感到惊讶！” “我很确定事情不会这样……”女王试图说，但国王不再听了。 “派使者到全境！” 国王宣布。“我们在寻找骑士！” 视线消散，水面又是一潭清澈见底。 “这不会发生，”女孩说。“我不会嫁给一个拿着大剑和更大自我的白痴。” “说得好，”你说。“可是，姑娘，你打算怎么办？” “首先，我叫赛琳娜，不是女孩。其次，我练习的不仅仅是从塔上飘出来。” 她展示了一根优雅的魔杖，魔杖顶端有一颗发出微弱光芒的蓝色水晶。“Mutati vigil ut endormi rever, tempo soisminno，”她说，用魔杖指着附近的一头野猪。水晶的光芒增强了，野猪很快就睡着了。 “你可以治愈失眠。太棒了。” 你干巴巴地说。 “不过你想想！” 女孩——赛琳娜——抗议。“如果骑士在工作中睡着了，那比面对龙还要糟糕！他会被嘲笑！” “公平，”你说。“哦，谢谢你的零食。” 你刺穿了野猪的心脏，立即杀死了它。赛琳娜发出一种尖叫的声音。你转向她，很生气。“拜托。你又不是不吃肉。” “但是——你只是——” “杀了它？是的，我知道。人类在狩猎中获得了某种虐待狂的乐趣。我见过他们派狗去追捕猎物，让它们扯掉它的四肢。这样——”你向死去的野猪示意，“感觉就像睡着了。” 赛琳娜终于设法抹去了脸上那震惊的表情。“只是……不要对骑士们那样做，好吗？” 你叹息。“别担心。人类太骨感了——我在开玩笑，我在开玩笑，”你说，尽管你并不是真的在开玩笑。看起来她从未经历过人类的战争，也从未见过死亡……直到现在。哎呀。 她进了塔，然后把头探了出来。“我忘了问，”她说。“你叫什么名字？我需要它才能在游泳池里见到你。” 诅咒。如果你不提供你的名字，它会显得可疑。但是名字可以是一个强大的东西，用于法术和诸如“见之池”之类的东西。 “我的名字是 Eryllis，”你终于说。 几天后，第一个骑士来了。
梗概：
你给她看游泳池
--------------------
动作：
做
骑士。
结果：
你从空中发现他。虽然那个女孩——赛琳娜——告诉你不要杀他，但这并不意味着你不能玩得开心。 他骑着一匹白马，身上穿着一副从未使用过的铠甲。他拿着一把巨大的剑，白痴。它会减慢他的速度，并且几乎无法阻止攻击。他也没有戴头盔——最好是对着赛琳娜微笑。 “美丽的公主，”他开始说。“我是希尔德布兰特爵士，我是来救你的——” FWOOMP。你马上把那个人从马上抢走，他惊恐地飞奔而去。聪明的马。骑士试图伸手去拿他的剑，但你把他锁得紧紧的，他动弹不得。你把他扔到很远的地方，进入埃克贝尔沼泽。 “我说过不要杀他！” 赛琳娜歇斯底里地叫道。 “放心，我知道自己的实力。” 你说。 “给我看看希尔德布兰特！” 赛琳娜要求。水池旋转，显示一名骑士在天空中飞翔。他降落在沼泽中，深深地陷在泥泞中。有那么一刻，你认为他可能不会重新露面，但随后你会看到一团泥巴的头发出现，你知道他会没事的。 “就在我的新盔甲上！我没想到它会是一条真正的龙！” 他发牢骚。 你轻笑。“他认为我会是什么，一条毛绒龙？” 赛琳娜笑着回应。 当新贵试图向国王和王后解释自己时，你们俩都笑得很开心。“我勇敢地战斗，但最终我不得不逃离龙。我几乎没有逃脱我的生命。” 他解释说。 “闭嘴，”你说，“你很好。” “你怎么浑身是泥？” 王后问道。 “嗯——我——你看——”他结结巴巴地说。 “你不适合我的女儿，”国王宣称。 接下来的几位骑士也有类似的标准。您可以轻松地将它们全部发送。然而，在一个阳光明媚的早晨，来了一位与众不同的骑士…… 你甚至没有看到他来。他没有穿过小路，而是悄悄地穿过树林。“龙，”你听到他的呼唤。你转过身来，只看到一个小小的身影，比其他人还要瘦。他的盔甲太大了，你几乎为他感到难过。他看起来并不比女孩更具威胁性。“我不想伤害你，”他继续说，显然是在掩饰他的声音，让它听起来比实际更深。“你会听我说完吗？
梗概：
骑士。
--------------------
动作：
做
你听骑士的话
结果：
“说吧，”你宣布。 “首先，我要感谢你对公主的怜悯。” “你的意思是不杀她？那可不是无私的。” 你回答。 “无论如何，谢谢你。她对她的父母和整个王国都很重要。” “我看得出来。到目前为止，你已经派出了——什么，现在有五个骑士？是什么让你认为你的表现会有所不同？” 你问。 “我寻求解放公主，而不是杀死龙，”骑士说。“你会释放她吗？我愿意作为交换。” 你仔细检查他。他看起来很严肃。 赛琳娜走出阳台。“看，这就是你弄错的地方。也许她会释放我，除非我没有被抓获。” 虽然骑士戴着面罩，但你几乎可以看到他眼中的震惊。 “你是心甘情愿来的？可是整个王国都看到你被抓了！” 赛琳娜看着你，似乎在决定要不要承认真相。 “这是一个设置，”你说。赛琳娜继续向骑士讲述你的故事。 骑士用不可置信的眼神看着赛琳娜。“你让你父母这么苦恼，为了什么？躲在塔里？做龙的玩物？他们几乎没睡过，睡过之后，他们做你被折磨和饿死的噩梦！” “没有人明白！” 赛琳娜回喊道。“对你来说，我只是一个穿着裙子的漂亮女孩，不知道什么对自己有好处。谁不能领导一个王国，谁需要保护她的'女性好奇心'。这听起来对吗？ ” 然而，骑士并没有完成。“好吧，至少你有一个在乎你的家人！我家把他们唯一的孩子送走了，除了她背上的衣服和一条面包！” 你很惊讶赛琳娜还没有意识到这一点。做出足够的决定就足够了，你向试图跳开的骑士俯冲，但速度太慢。不过，你的目标不是骑士。 骑士起身，一缕金色的头发落在她的面前。意识到她的头盔不见了，她的脸上先是震惊，然后是无奈。“好吧，我猜猜谜游戏开始了，不是吗？” 她轻声说，叹了口气。赛琳娜震惊得说不出话来。你笑。两人都转向你。 “有什么好笑的？” 骑士问道。 “你确实意识到，如果你不隐藏你的身份，你的机会可能会好得多。” 你解释一下。 “发生了什么？” 赛琳娜终于开口问道。 “你是什么意思，‘发生了什么’？” 你和骑士同时说。你看着骑士。她耸耸肩。 “是什么让你穿上盔甲？最后一根稻草是什么？骑士停顿了一下。你可以在晚餐时告诉我们。”赛琳娜建议。“毕竟，我们有一些新鲜的......野猪......吃。 ” 尽管赛琳娜明显的娇气（或者也许正因为如此），骑士还是微笑了。“我很乐意，”她说。 ” 你沉默了一会儿吃“所以，你想知道发生了什么？骑士说，她的声音几乎是低语。 “继续，”赛琳娜鼓励道。 “我从哪里开始？嗯，我父亲一直想要一个男孩。当我母亲在分娩时去世而我还是个女孩时，我认为他相信，那时，女孩一无是处。 “当我五岁的时候，我们搬到了一个新城镇。当邻居问我的父母是谁时，我在公园里，因为他们不在那里。我告诉他们我父亲是谁，但他们说，'那是不可能。他没有孩子。当我质问他时，他说一个女孩数不过来。从那时起，我想如果我像个男孩，我父亲可能会认为我数数。 “七岁的时候，我剪了短头发，要求做‘学习武器’的学徒，这意味着当地的剑术大师、铁匠和弓箭手都给一群孩子当学徒，选择哪个行业最好？——比如说，射箭？——会是各自师傅的徒弟吗？——比如说，弓箭手。 “在教堂里，我被教导说谁说谎最终都会被发现，我认为这同样适用于我作为女性。所以，我的目标是变得很好，即使我是一个女孩，他们也别无选择接受我。我总是第一个到达，最后一个离开。即使我不需要，我也在那里。每一项任务，无论多么平凡、屈辱或困难，我都努力做到最好在。尽管我可能在每项任务上都比其他人做得更好，但我并没有停下来。有一段时间，我让它消耗了我。 “此时，我已经和铁匠建立了关系，我觉得我可以信任他，从徒弟到师傅。我告诉他我是一个女孩，并求他让我继续做徒弟。他似乎有点惊讶，说你是女孩还是男孩并不重要，只要你擅长你的交易。“只要你擅长，我看不出你有什么理由不去交易， ' 他说。 “不知何故，传出我是个女孩的消息。但铁匠每次试图结束我的学徒生涯时都会为我担保。但在 16 岁时，每个人都必须完成最后一项任务，以证明他们是真正的手艺大师.我是来救公主的。他们说我可以结束了，放弃。我可以回到女仆或家庭主妇的生活，不用面对压力。但那些猪不可能会告诉我该怎么做。他们拒绝给我武器，所以我从一个镇到另一个镇，找铁匠的工作。最终，我攒够了自己的钱。他们告诉我，国王永远不会接受我的帮助，我不会活着回来。好吧...... “在我离开小镇之前，我父亲看到我穿着盔甲。我想他可能终于为我感到骄傲了。” 她的表情变暗了。“让我们说我错了。” “在这里，我认为解决我的问题的唯一方法就是逃跑，”赛琳娜说，脸上带着深思。沉默了片刻。“我应该告诉我的父母我一直想要什么，”赛琳娜突然说道。“我应该告诉他们，我只是想自由地做我自己的决定，”她继续说道，她的声音中越来越强烈。“但是因为我想要的东西而让他们如此担心是不对的。” 她看起来好像还想说些什么，但骑士插话了。“算了，别跟我们说，跟他们说！” 赛琳娜从桌子上站起来，脸上带着坚定的表情。 你在天空中翱翔，天空被早晨的阳光涂成各种颜色。赛琳娜坐在她为你准备的篮子里，敬畏地倒吸一口气。她伸出手，几乎像是要触摸你周围的蓝色和粉红色痕迹。然而，骑士紧紧抓住篮筐的两侧。她看起来很害怕，尽管由于头上的超大头盔你看不到她的脸。 你放下篮子，然后降落在宫殿周围的田野上，仍然沾满露水。骑士是第一个爬出来的。她大大地松了口气。赛琳娜优雅地走了出来。“你准备好了吗？” 骑士问道。 “不，”赛琳娜回答，“但不管怎样，我们还是去做吧。” 你腾飞了。你会在游泳池里观看。尽管如此，你还是觉得自己错过了一些行动。那好吧。 当您返回游泳池时，天空现在是一片明亮的蓝色，太阳在空中升起。“给我看看赛琳娜，”你宣布，意识到骑士从来没有告诉过你她的名字。你不能责怪她。 “谢谢你的鼓舞人心的谈话，”赛琳娜对骑士说。“我现在可以走了。” 两人径直走到城堡的大门前。“公主回来了！” 骑士大声宣布，惊醒了一些睡在工作中的守卫。 “对不起？” 其中一名警卫问道。 “她已经从巨龙手中救了出来，”骑士说，恢复了她假装低沉的声音。消息像野火一样传播开来。不到五分钟，国王和王后就跑了出来，还穿着睡衣。 “赛琳娜？” 女王喃喃自语。国王转向骑士，脸上写满感激。“我们的王国欠你一大笔债。你当然配得上下一任继承人——” “父亲，”赛琳娜打断道，“在我们继续之前，有几件事我必须说。我没有被龙俘虏。我上演了俘虏。” 国王不知道该说什么。“我——但是——” “但是，仅仅因为我想要什么就让你承受这样的压力是不对的，”她继续说道。“考虑到这一点，我想要的不是财富或财富。这不是头衔或名声。” 赛琳娜停顿了片刻。“我真正想要的，也是我现在仍然想要的，是做出自己决定的自由，尽管它们可能不是我做出的最好的决定。我想决定我是否结婚，以及我将嫁给谁对。我想做一些可笑的事情，不寻常的事情。你会让我做那些事情吗？” 国王的表情先是震惊，然后是无奈。“我明白了，龙干预了她的心智。我们必须把她留在这里，直到我们能治愈她的心智。” 你翻白眼。 “我以为你会这么说，”赛琳娜失望地说。“虽然我更希望你能说些不同的话，”她继续说，眼中流露出渴望的神情。“妈妈懂，你不懂。” 皇后点点头，眼里含着泪水。“你什么时候学会？没人能把我关在塔里。” 她的眼睛望向天空，带着淡淡的微笑直视着你。“你不能违背我的意愿把我留在这里。也许有一天，我会回来。但我现在自由了。” 她开始执行一个咒语的动作，你已经看过她练习过很多次了。“看我飞，”赛琳娜变身成一只亮蓝色的鸟，飞向空中。 赛琳娜从头发中抽出一根亮蓝色的羽毛。“那个咒语比我想象的要花费更多的努力，”她叹了口气。“我失去联系了吗？” 她大声问道。她看起来筋疲力尽。你不能责怪她。即使在成功地变身为鸟并返回之后，她仍然在研究骑士的礼物，这需要她使用大量的魔法。 “休息一下，”你命令道。“你没有截止日期，你几乎没有任何耐力。” “耐力？” 赛琳娜问道。 “引导魔法并不容易，”你说。“这需要付出代价。你目前只是在施展低级法术，但强大的法术可以让一个人昏迷好几天。” “好吧，”她同意了，太累了，无法争辩。“但我发誓我感觉到了一些东西，就像我有能力轻松做到一样，但就像有什么东西挡住了魔法，让任何人都很难通过。就像……水从大坝里流过。” “我对此一无所知，”你说，“我从来没有像你那样施展魔法。现在，休息一下。你需要它。” “是的，是的，埃瑞利斯船长，”她用你认为应该是开玩笑的方式说道，但你说不出来。她的声音呈现出人类一整天疲倦时所具有的单调品质。现在是傍晚，但如果赛琳娜在中午之前起床，她会很幸运。 你转向骑士，他正在用野猪皮制作皮革兜帽。赛琳娜还没有告诉她这是做什么用的。“Eryllis，嗯，”她说。又一个知道你名字的人。你微微皱眉，骑士出人意料地注意到了这一点。 “我明白了。我想我从 10 岁起就没有告诉任何人我的真名。我在每个城镇都叫不同的名字。” “对于人类来说，你的直觉令人惊讶，”你说。 “嗯，我想我一直很擅长读人。” 骑士回应。 “龙也是人？” 你问，半开玩笑。 “好吧，既然我知道你的名字，我想我也应该告诉你我的。我是伊丽莎白。” “伊丽莎白，”你难以置信地说。 “我知道，让这个名字听起来像一个男孩的名字的方法大约有零种。” “女孩的名字有什么不好？” 赛琳娜疲倦地问道。她几乎立刻又睡着了。 接下来的几天就在迷茫中度过。你阻止了其他没有收到消息的骑士，赛琳娜继续精疲力竭地为骑士的礼物工作，而骑士——伊丽莎白——在为赛琳娜的法术收集原料和试图偷看她的礼物之间练习剑术。不过，没什么可看的。你开始怀疑人类是否对放松过敏。 10天后，伊丽莎白的礼物终于完成了。“闭上你的眼睛，”赛琳娜说。伊丽莎白答应了。您会看到一个带有化妆舞会面具的风帽。起初它看起来是靛蓝色，但当你观察它时，它会在光线下变换颜色，从靛蓝色变成绿色，然后又变回最初制作引擎盖时皮革的棕色。赛琳娜把兜帽戴在伊丽莎白的头上。“好了，现在可以睁眼了。” 她说，兴奋。 伊丽莎白睁开眼睛，惊讶地屏住了呼吸。“很美丽。” 伊丽莎白喃喃自语。 “这很神奇，”赛琳娜说。“这顶帽子将确保其他人看到你想被视为谁。” “所以……如果我想被视为穿着闪亮盔甲的骑士……” 你期待着什么会改变，但什么也没发生。 “这有点像是他们的世界观和你的意志的混合体。如果他们已经把你看作是一个穿着闪亮盔甲的骑士——”她朝你的方向瞥了一眼——“他们的看法不会改变。” 伊丽莎白收拾好东西准备离开——直到她遇到了赛琳娜。“你要去吗？” 赛琳娜疑惑的问道。 “你试图和你的家人解决问题。我想我会听从我自己的建议，试着和我的家人一起解决问题。 “你父亲……就算他做了什么，你还想和他和解？” 赛琳娜惊讶的说道。 “至少，我想让他知道，我比他想象的要好得多。我什至可以‘拯救’一些‘遇险的少女’。” 你们都笑那个。 伊丽莎白站在门口，但她似乎……犹豫不决。像是在等什么。
梗概：
你听骑士的话
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动作：
做
你让她留下
结果：
“哦，看在上帝的份上，如果你想留下来，那就继续吧！” 你轻笑。 “你出生的家庭很重要，但你选择的家庭更重要，”赛琳娜补充道。“这既是你的家，也是我们的家。虽然我们不会阻止你寻找你的亲生家庭……但请记住，如果你愿意，我们愿意成为你选择的家庭。 ” 作为回应，伊丽莎白拥抱了赛琳娜。 “一旦我尝试与他交谈，我就会回来，”她说。你飞到空中，看 着骑士穿过森林。你绕着塔转一圈，然后飞向日落。 “这就是它的结局，”旅行者说。 “我喜欢那个，”其中一个孩子说。 “但为什么赛琳娜不能在不疲倦的情况下施法？” “这条龙与伊丽莎白这个名字有何不同？” “好吧，那是下一个故事了，”旅行者一边说。一边在她的脖子后面摩擦着一排绿色的鳞片。 “再来一次！另一个故事！” 孩子们高兴地尖叫起来。 从那以后，他们都过着幸福的生活……或者至少在续集之前。
梗概：
你让她留下
故事名称：
3
故事背景：
末日之塔 你的村庄一片废墟。被强大的巨龙摧毁。但你没有时间哭泣。你必须为你的人民报仇！ 天色阴沉，寒风刺骨。这座塔高高耸立，就像一个角斗士，它向任何潜在的挑战者敞开大门。有传言说这座塔包含了这个世界上最伟大的宝藏。凡是敢踏入此塔的人，一去不复返。人们说它由黑暗中最危险的生物看守。据说宝物在最顶层，有龙守护！那条龙摧毁了你的村庄！ 你穿过门，看到一个向上的螺旋楼梯。你拔出你的剑，检查你的财产，然后迈出一步…… 战斗：1/7寿命：7/7硬币：5/100
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动作：
做
你开始！
结果：
你慢慢爬上楼梯…… 当前楼层：1 战斗：1/7寿命：7/7硬币：5/100
梗概：
你开始！
--------------------
动作：
做
你这个生物！
结果：
这层楼笼罩在一片漆黑之中，只有几缕光线从裂开的墙壁中透出。突然，你听到了声音…… 战斗：1/7寿命：7/7硬币：5/100
梗概：
你这个生物！
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动作：
做
一条蛇！
结果：
有什么东西从阴影中掠过你！这是一条毒蛇，眼睛黑得像夜一样！ 战斗 - 4 战斗：1/7寿命：7/7硬币：5/100
梗概：
一条蛇！
--------------------
动作：
做
你打架！
结果：
最后一剑使生物飞起来。它掉在地上，流血而死！ 战斗：1/7寿命：4/7硬币：5/100
梗概：
你打架！
--------------------
动作：
做
你清除了！
结果：
突然，一名炼金术士和他的商店一起传送到了这里。你可以看到一些奇怪的发光符号和烧瓶里的沸腾液体。您现在可以在这里康复并花钱。准备好后可以爬到下一层。炼金术士每通关一层就会给你 1 个硬币作为奖励！ 提高剑 - 战斗统计加 1。花费 5 钱。 治疗 - 恢复 1 点生命值。花费 1 美元。 购买药水 - 恢复 3 点生命值。成本 2 钱。 附魔剑 - 战斗属性增加 3。花费 10 钱。 战斗：1/7寿命：4/7硬币：5/100
梗概：
你清除了！
--------------------
动作：
做
你爬到下一层
结果：
你慢慢爬上楼梯…… 当前楼层：2 战斗：1/7寿命：4/7硬币：6/100
梗概：
你爬到下一层
--------------------
动作：
做
你这个赌徒！
结果：
一股奇怪的气味扑进你的鼻子。唯一的光源是放在角落桌子上的蜡烛。突然，老者从桌子后面走了出来，拖着他那张破旧的椅子。他要一枚硬币。他是赌徒！你可以和他一起玩游戏！ 卡片 - 给他一枚硬币，作为回报，您将收到随机确定的 1 个硬币、2 个硬币或根本没有硬币。 技巧 - 给他 3 个硬币，作为回报，您将收到随机确定的 1 个硬币、2 个硬币、3 个硬币、4 个硬币或 5 个硬币。 DRAIN - 用 1 个硬币失去 1 个生命。 你想玩多久就玩多久！ 战斗：1/7寿命：4/7硬币：6/100
梗概：
你这个赌徒！
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动作：
做
你继续
结果：
突然，一名炼金术士和他的商店一起传送到了这里。你可以看到一些奇怪的发光符号和烧瓶里的沸腾液体。您现在可以在这里康复并花钱。准备好后可以爬到下一层。炼金术士每通关一层就会给你 1 个硬币作为奖励！ 提高剑 - 战斗统计加 1。花费 5 钱。 治疗 - 恢复 1 点生命值。花费 1 美元。 购买药水 - 恢复 3 点生命值。成本 2 钱。 附魔剑 - 战斗属性增加 3。花费 10 钱。 战斗：1/7寿命：4/7硬币：6/100
梗概：
你继续
--------------------
动作：
做
你爬到下一层
结果：
你慢慢爬上楼梯…… 当前楼层：3 战斗：1/7寿命：4/7硬币：7/100
梗概：
你爬到下一层
--------------------
动作：
做
你这个赌徒！
结果：
一股奇怪的气味扑进你的鼻子。唯一的光源是放在角落桌子上的蜡烛。突然，老者从桌子后面走了出来，拖着他那张破旧的椅子。他要一枚硬币。他是赌徒！你可以和他一起玩游戏！ 卡片 - 给他一枚硬币，作为回报，您将收到随机确定的 1 个硬币、2 个硬币或根本没有硬币。 技巧 - 给他 3 个硬币，作为回报，您将收到随机确定的 1 个硬币、2 个硬币、3 个硬币、4 个硬币或 5 个硬币。 DRAIN - 用 1 个硬币失去 1 个生命。 你想玩多久就玩多久！ 战斗：1/7寿命：4/7硬币：7/100
梗概：
你这个赌徒！
--------------------
动作：
做
你继续
结果：
突然，一名炼金术士和他的商店一起传送到了这里。你可以看到一些奇怪的发光符号和烧瓶里的沸腾液体。您现在可以在这里康复并花钱。准备好后可以爬到下一层。炼金术士每通关一层就会给你 1 个硬币作为奖励！ 提高剑 - 战斗统计加 1。花费 5 钱。 治疗 - 恢复 1 点生命值。花费 1 美元。 购买药水 - 恢复 3 点生命值。成本 2 钱。 附魔剑 - 战斗属性增加 3。花费 10 钱。 战斗：1/7寿命：4/7硬币：7/100
梗概：
你继续
--------------------
动作：
做
你爬到下一层
结果：
你慢慢爬上楼梯…… 当前楼层：4 战斗：1/7寿命：4/7硬币：8/100
梗概：
你爬到下一层
--------------------
动作：
做
你的钱！
结果：
完全的寂静。然后你看到黑暗中闪闪发光的东西。你靠近一点……发现一些散落的金币！与他们一起的是一个小袋子，里面装着更多的硬币！您只能选择一个，因为它们上面有一个陷阱机制！ 小袋 - 1D10 个硬币硬币 - 5 硬币 战斗：1/7寿命：4/7硬币：8/100
梗概：
你的钱！
--------------------
动作：
做
你拿袋子！
结果：
突然，一名炼金术士和他的商店一起传送到了这里。你可以看到一些奇怪的发光符号和烧瓶里的沸腾液体。您现在可以在这里康复并花钱。准备好后可以爬到下一层。炼金术士每通关一层就会给你 1 个硬币作为奖励！ 提高剑 - 战斗统计加 1。花费 5 钱。 治疗 - 恢复 1 点生命值。花费 1 美元。 购买药水 - 恢复 3 点生命值。成本 2 钱。 附魔剑 - 战斗属性增加 3。花费 10 钱。 战斗：1/7寿命：4/7硬币：13/100
梗概：
你拿袋子！
--------------------
动作：
做
你爬到下一层
结果：
你慢慢爬上楼梯…… 当前楼层：5 战斗：1/7寿命：4/7硬币：14/100
梗概：
你爬到下一层
--------------------
动作：
做
你传送！
结果：
这层楼沐浴在蓝光中。在远处你看到一个披着斗篷和面具的男人。在他旁边是一扇木炭门，用铁栅栏封住。 他告诉你，在这扇门的帮助下，你可以跳到这座塔的任何一层，除了最后一层被远古魔法保护！他不会向您收取任何费用，但会警告您传送到的楼层是随机的。你敢冒险吗！？ 他还提到，如果你以某种方式摧毁这扇门，那么他会恢复你的生命并给你 3 个硬币！你可能会损坏你的剑，输掉 1D2 战斗！ 战斗：1/7寿命：4/7硬币：14/100
梗概：
你传送！
--------------------
动作：
做
你离开门
结果：
突然，一名炼金术士和他的商店一起传送到了这里。你可以看到一些奇怪的发光符号和烧瓶里的沸腾液体。您现在可以在这里康复并花钱。准备好后可以爬到下一层。炼金术士每通关一层就会给你 1 个硬币作为奖励！ 提高剑 - 战斗统计加 1。花费 5 钱。 治疗 - 恢复 1 点生命值。花费 1 美元。 购买药水 - 恢复 3 点生命值。成本 2 钱。 附魔剑 - 战斗属性增加 3。花费 10 钱。 战斗：1/7寿命：4/7硬币：14/100
梗概：
你离开门
--------------------
动作：
做
你爬到下一层
结果：
你慢慢爬上楼梯…… 当前楼层：6 战斗：1/7寿命：4/7硬币：15/100
梗概：
你爬到下一层
--------------------
动作：
做
你很珍惜！
结果：
你已经到达了第 6 次洪水。立刻，你被一道亮黄色的光蒙蔽了双眼！你勉强睁开眼睛，眼前就是光荣的宝物。无价的硬币、护身符、珠宝、武器、盔甲，价值超乎想象！你找到了宝藏！恭喜！ 你赢了！ 你的分数是 28！ 战斗：1/7寿命：4/7硬币：15/100
梗概：
你很珍惜！
--------------------
动作：
做
你又玩了！
结果：
你沾满鲜血的身体软软地倒在地板上。一股血流在大地上漫天飞舞。塔已经声称另一名受害者...... 你的分数是0！ 战斗：0/7生命：0/7硬币：0/100
梗概：
你又玩了！
故事名称：
4
故事背景：
突然，一名炼金术士和他的商店传送到了这里。你可以看到一些奇怪的发光符号和烧瓶里的沸腾液体。您现在可以在这里康复并花钱。准备好后可以爬到下一层。炼金术士每通关一层就会给你 1 个硬币作为奖励！ 提高剑 - 战斗统计加 1。花费 5 钱。 治疗 - 恢复 1 点生命值。花费 1 美元。 购买药水 - 恢复 3 点生命值。成本 2 钱。 附魔剑 - 战斗属性增加 3。花费 10 钱。 战斗：1/7寿命：4/7硬币：14/100
--------------------
动作：
做
你提高剑
结果：
突然，一名炼金术士和他的商店一起传送到了这里。你可以看到一些奇怪的发光符号和烧瓶里的沸腾液体。您现在可以在这里康复并花钱。准备好后可以爬到下一层。炼金术士每通关一层就会给你 1 个硬币作为奖励！ 提高剑 - 战斗统计加 1。花费 5 钱。 治疗 - 恢复 1 点生命值。花费 1 美元。 购买药水 - 恢复 3 点生命值。成本 2 钱。 附魔剑 - 战斗属性增加 3。花费 10 钱。 战斗：2/7寿命：4/7硬币：9/100
梗概：
你提高剑
--------------------
动作：
做
你爬到下一层
结果：
你慢慢爬上楼梯…… 当前楼层：6 战斗：2/7寿命：4/7硬币：10/100
梗概：
你爬到下一层
--------------------
动作：
做
你很珍惜！
结果：
你已经到达了第 6 次洪水。立刻，你被一道亮黄色的光蒙蔽了双眼！你勉强睁开眼睛，眼前就是光荣的宝物。无价的硬币、护身符、珠宝、武器、盔甲，价值超乎想象！你找到了宝藏！恭喜！ 你赢了！ 你的分数是 26！ 战斗：2/7寿命：4/7硬币：10/100
梗概：
你很珍惜！
故事名称：
5
故事背景：
那是马萨诸塞州波士顿一个普通的星期六下午。你从沙发上起身去拿一杯健怡可乐。你还在去买健怡可乐的路上加热了冰箱里剩下的热狗。在您等待热狗完成加热时。你接到一个电话。你拿起它，听到一个恼人的声音。“听着，我们需要很多帮助”。“我们需要你”“正在征召你入伍”。你的震惊。但是你必须做你需要做的事情来拯救美国在射击场来见我。”他说。“明天见”。他说“再见”。那天晚上你睡了 3 个小时，想着可能会发生什么你未来的生活。
--------------------
动作：
做
你选择你的枪
结果：
去射击场的长途车程让你非常紧张。但你准备好开始在军队中射击。你5分钟后到达那里。当你到达那里时，你看到了一组不同的枪。有突击步枪，重机枪，半自动步枪，霰弹枪，狙击步枪，最后是冲锋枪。拿哪一个。因为你现在在军队的新人。
梗概：
你选择你的枪
--------------------
动作：
做
你突击步枪
结果：
当你拿起步枪时，你差点把它掉下来，因为你太紧张了。新人有一个很大的目标。它是 4 英尺乘 5 英尺。你在颤抖，不确定你能保持平衡多久。目标看起来像 1 英尺，2 英尺。你应该等待，然后拍摄，但你不确定你能坚持多久。你的老板对你需要多长时间变得非常不耐烦。
梗概：
你突击步枪
--------------------
动作：
做
你等待，然后射击目标
结果：
花了很长时间，你放弃了那把枪，也许你只是不适合那把枪。
梗概：
你等待，然后射击目标
--------------------
动作：
做
重机枪
结果：
庞大的身躯连重机枪都扛不住。它很大，很重。但是射的这么厉害。您会看到一个 4 英尺乘 5 英尺的目标。你会开枪吗，记住这是你第一次开枪，你不知道在哪里开枪，你可以把枪的一端朝你的喉咙，然后射击目标，或者你可以把它放在你的肚子上。哪里会比较好拍呢。你想。
梗概：
重机枪
--------------------
动作：
做
你用喉咙开枪
结果：
宾果游戏，你完美地射中了目标。“不错的瞄准。” 你的老板告诉你，我喜欢你把枪放在哪里，干得好。这是给初学者的新枪。” “现在下周我将需要你在伊拉克，我们需要你的帮助”。当他继续说话时，你吞了口口水，“我们将需要你做最重要的部分。拍摄。"
梗概：
你用喉咙开枪
故事名称：
6
故事背景：
“生是痛，死是遗忘，黑暗诸神的意志留给你的唯一选择就是你能忍受多少。我赐予和平。拥抱我。” 当黑暗呼唤你时，这些话在你耳边响起。你所能做的就是专注于前方，等待黎明和醒来的机会。 你盘腿坐在岩石黑曜石地板上，耐心地等待。你尽最大努力屏蔽周围的世界，专注于你深沉而沉重的呼吸。关注噩梦和守护进程只会鼓励他们。表现出恐惧是在水中溅血。虽然噩梦般的苍白天空每分每秒都在变暗，数千里宽的黑色卷须在百里之上的天空中展开，挡住了你从白色太阳那里得到的一点安慰，但你并不感到恐惧。这只是每晚困扰您的众多噩梦之一。你会活着。 黑暗开始凝固成波浪状，向着你袭来。你被液体黑暗所包围，它淹没了任何接近光的东西，因为它吞噬了你。 “虚空不断张开的巨嘴在呼唤，”黑暗低语。“我来自你。”
--------------------
动作：
做
你的光出现
结果：
当你躺在床上，气喘吁吁的时候，你扭动着把床单扔掉。又是一场噩梦。又一个充满恐怖、恐怖和恶魔般的声音在你的灵魂尖叫。你深吸一口气。重点。在。出去。在。出去。你把头靠在枕头上，叹了口气。 突然，菲伊出现在门口，穿着战斗盔甲，抓着她的短发。 “早上好，”她说。
梗概：
你的光出现
--------------------
动作：
说
“早上好，Fay。嗯，昨晚很有趣。”
结果：
“是的，是的，”Fay 微笑着。“你确定你没事？” “完美，”你说，爬下床 ，把床单从你赤裸的身体上掀开，Fay wolf 吹口哨。 “去操你自己，”你说，走进浴室。 “如果我能那样做，我就不会留你在身边，”当你走进浴室时，Fay 笑着回答。 站起身来，快步走向洗手间。你盯着浴室镜子里的倒影，随着声音的响起深呼吸。 “你不好奇吗？Fay真的爱你吗？还是她在利用你？” “你是灵能者。一块美味的牛排，一个爱人的拥抱，物质世界的乐趣，与我能给你的相比，根本不算什么。” “亚历山大死了，亚历山大被埋葬了，亚历山大归于尘土。同样的事情也会发生在你身上。拥抱生命带来的腐烂和腐烂。” “杀！残！烧！杀！残！烧！杀！残！烧！” 您对头脑中的守护进程有了一定程度的熟悉。现在梅利奥达斯已经消失了，更多的东西来填满你的脑袋。您已经开始给它们命名，这是您认为对它们保持理智的少数几种方式之一。问号不断地用知识诱惑你。性爱以无与伦比的美妙乐趣诱惑您。Rotgut 是懒惰的化身，让你想躺在床上躺在自己的污秽中，而 Smiley 只是他妈的疯子。 奸奇的钥匙，你想念梅利奥达斯。 你很快穿好衣服，然后打电话给菲。她打开门，走进去。 “嘿，卡斯，”她笑着说。 “她打算背叛你吗？她忠诚吗？你不知道……但我知道，”问号说。 “割开她的喉咙，强奸刺伤，当你吞下她的喉咙时，感觉血液顺着手臂流下！感受纯粹的幸福感！” 该死的窃窃私语。 “拥抱你的瘟疫。你是污秽的。你是腐烂的。但这没关系。你仍然被爱着，”Rotgut 说。 “燃烧！致残！杀死！” 笑脸尖叫。 费伊扬起眉毛，用指关节敲击你的头。 “喂，有人吗？” 她问。 “比你知道的还要多，”你回答。 “梅利奥达斯回来了？” “新人。至少有一些变化。” “他们是不是在说我什么？” 菲微笑。 “混蛋！拿走她他妈的脑袋！” 笑脸尖叫。 “我认为他们喜欢你，”你回答说，而 Fay 笑了。 “你确定你没事？” “顶顶，”你回答。 当你深呼吸时，一连串的声音在你的脑海中尖叫着答案。菲亲吻你的头顶，看着你。 “Karth，来吧，回到我身边，”她说。 “我很好，”你回答，Fay 关切地看了你一眼。 “如果你确定的话，”她紧张地问。“走吧，我们得去作战室参加早会。”
梗概：
你说“早上好，Fay。嗯，昨晚很有趣。”
--------------------
动作：
做
你前往作战室
结果：
步行五分钟后，您到达作战室。其他人已经在那里了。海耶斯船长在角落里抽烟，普里默斯出现在一个机器人身体的上半部分，只存在于谈话时专注的东西，看到他实际上就是船，德奇坐在他的座位上，轻轻地喂食被他刺穿的肉块刺伤了他的胃的老鼠，Elios 与他的一名警卫进行了一些重度的抚摸，Elizabeth 读到，Castus 和 Fay 正在争吵，Cain 坐在他的椅子上，耐心地等待。 “早上好，先生们，”你说，滑到你的座位上。“既然 Kyros 已经死了，我们就需要腾出时间来处理这艘船的控制权。我们没有时间庆祝了。” “我们有一个问题，”Primus 说。 “什么？” “德雷克·阿玛迪斯。他的守卫没有回应，所以我封锁了桥。我们需要去调查。” “谁值班？” 伊丽莎白好奇地问道。 “兽足和尖头叉子，”你回答。 “他们是硬汉。他们不会不回应，”费伊说。 “桥上不是有摄像头吗？” 海耶斯问道。 “它们处于离线状态。很可能是由于破坏。可能是由于故障，”Primus 说。 “那我们去调查吧，”你说。 “让我们做吧，”Fay点头回应。
梗概：
你前往作战室
--------------------
动作：
做
你找到德雷克
结果：
你发现自己站在通往桥的密封门旁。你耐心地等待，你的双手在紫色的火焰中扭动着，而 Fay 举起了她的爆弹枪。 “准备好？” 普里姆斯问道。 “做吧，”当门打开时，你说。 你迅速走进去看看情况。兽足躺在地上，一根由陶瓷碎片制成的刀刃刺穿了他的脖子。尖头叉子坐在角落里，手里拿着爆弹枪。他的两只眼睛都被多次刺出。 “天哪，”你说。 当他大声喊叫时，尖头叉立即将他的爆弹枪对准你。你用心灵动力从他手中撕下爆矢枪，把它扔到一边。 “该死！卡斯？” 他问道，当Fay跑过来抓住他时吓坏了。 “妈的，尖头叉子！你还好吗？” “我还活着。检查兽足！” 你很快飘到兽足面前，但他早就死了。 “他走了，”你说。 “操，混蛋抢了我的眼睛！他还在这里！你要找到他！” “我们会给你仿生眼，尖头叉子，我保证，”费伊说。“这有什么可以做的？” 你点点头，在房间里寻找德雷克。你慢慢地向前走，经过导航员的椅子。你看到德雷克坐在角落里，嘴里叼着一支烟，他看着你，点点头。 “嘿，”他漫不经心地说。“你的部落妓女怎么样了？她有没有长过奶子？” 你迅速悬浮起来，抓住他的喉咙，把他举到空中。 “你这个王八蛋！你杀了我的人！” “我以为我有机会逃脱，”德雷克耸了耸肩。“你在我之前关闭了这个部分。” 当他大叫时，你让火沿着你的手燃烧，他的喉咙发痒。 “嘿！你不能杀我，笨蛋！我们经历过这一切！你杀了我，你他妈的。你折磨我，下次我航行时我会杀了你和我，而不是忍受折磨的生活！” 你打他，把他的头撞在墙上，然后让他倒在地上。部落进入你身后，抓住他并捆绑他的手。 “修理摄像头。为他增加安全性，”你对其中一名部落成员叹息道。 “是的，先生，”她回答。 “你知道，你可以添加一堆安全功能，但我会想办法解决它。你想让我停止试图逃跑，我所要求的只是更多的奢侈品。获得酒精、毒品和吸烟，进入给一两个……安慰女士们，”德雷克微笑着，尽管血从他断掉的鼻子里滴下来。
梗概：
你找到德雷克
--------------------
动作：
做
你提高了德雷克的安全性
结果：
随着磁锁的增加、更严格的时间表、更多的警卫、更先进的桥梁摄像系统以及多个备份和故障保险，德雷克更加安全。他对此很不高兴，并承诺割开你的喉咙，但是见鬼，他他妈的要做什么？再试试逃跑？十几个部落对德雷克对他们两个人的所作所为感到恼火，吓得阿玛迪斯屈服。 当您理清德雷克的安全措施时，其中涉及与德雷克打交道的时间比任何人应得的要长得多，您将继续执行您现在需要处理的所有愚蠢的正常任务，例如决定口粮，派遣工作人员进行清洁上船，指派守卫和所有其他你期望的事情。与 Fay、Cain、Castus、Elios、Durge 和 Hayes 船长不同，他们都绝对鄙视他们的新工作量，Castus 甚至建议建立一个新的无政府状态系统，其中唯一的规则是发现任何人提交某些东西就是被枪杀，你实际上喜欢它. Olive、Primus、Elizabeth 和你们都在其中找到了某种程度的平静。您必须应对的心理挑战和解决问题的方法在您的脑海中具有一定的镇静作用。当它完成时，已经很晚了。这艘船上有一个昼夜循环，灯光变亮和变暗，有一个二十四小时的循环，与家相比，这对你来说有点长，但中午小睡是可以控制的。你想知道这艘 船的周期与哪个世界对齐。也许无论Kyros来自哪个世界。 费伊打开门，走了进去。 “Karth，该吃晚饭了，”她说，“最好快点。我们想早点吃饭，所以我们有晚上讨论重要问题。”
梗概：
你提高了德雷克的安全性
--------------------
动作：
做
你去吃饭
结果：
这顿饭是由奴隶、具有烹饪能力和服从命令的智慧的变种人、从船上士兵那里带走的才华横溢的厨师以及为该角色聘请的少数实际厨师的混合体准备的。鉴于这个事实，食物还不错。辣酱排骨，一些从外星世界采来的野菜，煮过的棕色大茎，切片吃，这些被砸开吃的巨大坚果，一些罐头“奶酪”，尝起来像……嗯，至少可以说，它尝起来不像奶酪。所有这些都与葡萄酒或烈性酒搭配，你选择前者。费伊喝了一瓶果味酒，可以杀死一个较弱的人。当你完成的时候，每个人都吃饱了，大多数人都喝醉了。你自然而然地从餐厅搬到作战室，很快发现自己准备好讨论这艘船的未来。 “那么，我们首先在计划者上有什么，Primus？” “战争！我们去开战！杀！杀！杀！泯！” 笑脸尖叫。 “掠夺者的生活有一定的吸引力，不是吗？感受堕落者的欢乐……”Fucky 建议道。 “小一，我很乐意帮忙。我计划执行一个仪式，让你进入……”问号开始暗示。 “放下你的防御，让腐败和腐烂开始，”Rotgut 说。 你深吸一口气，尽量不让你的脑袋里充满了快速钻研争论的声音这一事实显示出来。 “我们有几个重要的问题，”Primus 机械语气说。“首先，我们需要为曾经操作过这艘船的技术牧师找到合适的替代者。这应该是我们的第一要务。然后，我们需要照顾Kyros的前盟友。” “盟国？” 你问。 “虽然Kyros的大部分军队，至少所有直接回应他的人，都在这艘船上，但他已经集结了一支更大的军队，他希望入侵帝国。他曾与数个势力达成过契约。这些盟友可能会试图通过对他们的老盟友的勤奋来消灭我们。幸运的是，我们预计他们还需要一段时间才能集结一支部队来攻击我们，如果有的话。” “还有什么？” “我们有大量的报告，”Primus 说。 一个前奴隶工人走进来，手里拿着一大叠文件，他把这些文件倾倒在桌子上。打字的文件和用漂亮文字写成的大报告和匆忙潦草的笔记一起。该堆栈包含所有内容，从部队心理报告到有关您的飞行翼的统计数据，再到以各种外来货币估算的财富。 “那是什么鬼？” 菲问。 “这些是我们目前情况的文件和报告。谢天谢地，这些已经简化到了你们都能真正理解的极端。有三个大的圆圈数字，分别标记为力量、士气和资金。看到了吗？” 你看着文件的顶部，看到数字就点点头。 “实力数字是指你部队的军事实力，以百分比表示。100表示 你是兵变前那段时间里Kyros舰队的确切实力，50表示它的一半实力，200表示两倍力量。士气数字是您指挥下的部队的总体士气，100 是令人难以置信的快乐船员，令人难以忍受，而 0 是船员只是因为害怕而不敢割开喉咙。资金数字是您可以控制的资金数量，每个单位代表一百万个王座。” “明白了，”你说。
梗概：
你去吃饭
故事名称：
7
故事背景：
你抓起一块石头，将它向前挥去，然后将它砸在了格雷琴的脸颊上。它砸在地上，你爬上它，一次又一次地把它砸进生物的头骨。格雷琴的脑袋被压碎了，你松了口气。然而，这种缓解是短暂的，因为兽人继续向前冲锋，挥舞着匆忙制造的武器。Azajaja 幸存者的数量变得越来越少，因为您的部落成员试图在绿皮的猛烈攻击下同时拯救他们和您，但数量实在是太多了。双手抓住你的肩膀把你拉起来，就在迎面而来的长矛的路径之外。转过身来，映入眼帘的是一个比你小几岁的少女，手里拿着长矛，脸上带着惊恐的神色。当你意识到她赤身裸体时，你会脸红，然后责备自己有如此不成熟的想法，而人们却要杀了你。你听到一声怒吼，然后回头看到一个兽人向前冲刺。他看到你，举起他的斧头。
--------------------
动作：
做
你尖叫
结果：
你几乎不能走路。你不会爬离兽人，更不用说杀死它了。兽人击倒了你身边的每一个人。只剩下你和那个女孩，你的部落成员在绿色肌肉墙的另一边，挣扎着生存。你几乎只能做你唯一能做的事情。你发出一声惊恐的、刺耳的尖叫。在一股空气爆发中，你释放所有被压抑的恐惧和恐惧。兽人捂着耳朵发出痛苦的嚎叫。它继续尖叫，跪倒在地，然后它的头在深红色的雾气中爆炸。你看着数十个兽人倒在地上，鲜血从他们的眼睛、耳朵、鼻子和嘴里流出来。你感到眼睛后面、头骨中央有一种抓挠的感觉，好像看不见的手试图把你拉开。你发出一声呻吟，向后倒去。
梗概：
你尖叫
--------------------
动作：
做
你进入遗忘
结果：
你有可怕的噩梦：不断变化的触手和眼睛球，可怕的爪子、乳房、蹄子和长舌头的生物，巨大的、长满疖子、瘟疫缠身的憎恶和挥舞着火焰剑的红色角兽。最后，你被冷汗惊醒，躺在旧的麝香毯子里。你环顾四周，你的眼睛适应了黄昏的光线。你会注意到之前坐在角落里的那个女孩，她裹着鳞片的兽皮和毛皮，她的头靠在她紧握的剑柄上。她注意到你游移的眼睛，开始用她明亮的绿色眼睛盯着你。她满身泥土，棕色的头发和你的一样短，让她看起来像个男孩。 “你醒了，”她说。 “是的，”你回答，回望她，想知道她为什么不让自己眨眼。 “你睡了很久。” “我做。” 你坐在那里盯着每个人看了很长时间，然后帐篷盖打开了，Duggin 走进去。你注意到他胸前有几道新伤疤，脸上有一道很长的伤疤。 “卡斯！你醒了！” 他惊讶地说。 “我做了多久的梦？” 你问。 “两周。我们想……在发生了什么事之后……好吧，我们考虑过把你喂给 puk，”Duggin 笑着说。“很高兴看到你还活着。你有什么异象？” 你简要地反思你的梦想。 “有猛烈的风暴在一个弱的、大量的猎物中向南靠近湖岸……而且没有绿皮活动的迹象，”你说。 “很高兴知道，小家伙。我会转达消息的。” 杜金将注意力转向坐在那里的女孩。 “你见过Azajaja吗，Karth？” 他问。“她在这里的整个时间里都没有说过一句话。她只是坐在那里。费了好大劲儿才让她吃饭。我很确定她只是在角落里拉屎。” 杜金看向女孩，似乎在期待她说什么，但她什么也没说，只是盯着你看。 “我……给你拿点吃的和喝的。为你们俩，”杜金说，走出帐篷，留下你和女孩一个人。 “我是Karth，”你回答道，伸出你的手来握手。 她只是坐在那里作为回应，盯着你看。越来越不安了 几分钟后，杜金回来了，手里端着两碗熟肉和两杯酒。他把它们留在你面前的地板上。 “确保她吃东西。我会给你一些时间，但接下来我们需要讨论你是如何用……无论你拥有什么力量来消灭两打兽人的，”他在离开前说道。 你抓起两碗肉，滑过地板给她。你开始吃饭，她也一样。她抓起肉塞进嘴里，没有中断眼神交流，也没有放开她的剑。她张着嘴吃肉，发出响亮的咀嚼声。你对噪音感到畏缩，搅拌、切片和粉碎是另一种你的思想被排斥的东西。你眨了眨眼，被凝视吓坏了，但开始吃，肉的美味充满了你的嘴。 “你打鼾声很大，”几分钟后她说。 “是的，我被告知，”你回答。 “你做什么工作？” 她问。 “你是什么意思？” “你一整天都在做什么？你似乎不能四处走动，更不用说做家务或工作了。” “我坐在这里睡了很多觉。有时我会在需要时制定计划。” “你怎么脸色这么苍白？” 她疑惑地问道。 “很明显，我大部分时间都在帐篷里度过，”你回答道。 “你想出去吗？” 她问。 “大家都太忙了，没空带我到处走。” 女孩考虑到了这一点。 “要不要背到我的背上，让我带你出去？”
梗概：
你进入遗忘
--------------------
动作：
做
是的
结果：
“是的，如果你不介意的话，”你说。 她站起来，在咽下酒之前，把剩下的肉从喉咙里吐出来。她向前走，转身并弯下腰，让你的手臂环绕在她的肩膀上，然后将自己抬高到足以将双腿环绕在她的大腿上。她咕哝着站了起来，抓起她的剑，然后向前走出帐篷。你抬头看着迅速落下的太阳，将你的脸沐浴在它的温暖中。部落的其他人瞥了你一眼，惊讶地看到你在外面，但还是回去工作。女孩继续背着你，沿着帐篷小跑着朝树林走去。 “那……我知道你的名字了吗？” 你问。 “是Fay，”她回答。 “我是卡斯。” “你有什么梦想？睡觉的时候知道吗？” “坏事，主要是。怪物。有时，我会收到消息。我看到野兽攻击，我知道兽人什么时候会发动攻击。一世…” “你知道兽人要袭击我的村庄吗？” 费伊问道，声音里充满了悲伤。 “我……没有。我不是每次都收到消息。” “哦。好的。” 你到达树旁，伸出你的手臂，穿过其中一棵树的树皮。当黑暗笼罩天空时，您发现太阳终于从地平线下落下。两个较暗的明亮灯光取而代之，但它们太弱了，连一盎司的亮度都没有。 “我们应该回去，”你说。 “为什么？” 菲问。 “怪物晚上住在这里。” 她轻轻地拍了拍她的剑。 “我有一把刀。我可以轻而易举地杀死怪物。” “怪物很可怕。” “我也会害怕。”
梗概：
是的
--------------------
动作：
做
你逼她回去
结果：
“来吧，我们回去吧，”她说。 “好吧，”她叹了口气说，转身回到营地。 她回到营地，把你放回毯子上。 “我可以睡在这里吗？” 她问。 “呃……好吧，”你回答，蜷缩在毯子里。 “我明天早上见，”她说。 “晚安。” “晚安。” 有了这个，你蜷缩在毯子里，让睡眠再次带你入睡。
梗概：
你逼她回去
--------------------
动作：
做
三年后...
结果：
在接下来的三年里，事情发生了变化，无论是对你还是对整个世界。Fay 成为了一个永远的伙伴，似乎没有注意到部落的其他孩子会很乐意和她一起玩，并把她所有的时间都花在你身上。你不知道为什么，但你很感激。青春期会改变你们俩。菲伊长得更高、更快、更强壮，从一个小女孩成长为一个大而强大的战士。她和村里的男孩一样高大而自豪，并且非常高兴在摔跤比赛中与他们对决，赢得了所有人。她似乎更喜欢花时间和男孩子一起打猎，或者更频繁地和你在一起，而不是学习普通女孩的烹饪、缝纫和清洁等家务活。你可以想象很快她就必须在猎人中找到一个丈夫。 值得庆幸的是，成长并没有让你落后，尽管它对你的影响几乎没有。你变得足够强壮，能够在营地周围站立和漫步，即使是小范围的旅行，尽管即使是一两分钟的轻微慢跑也足以让你筋疲力尽。与 Fay 相比，你是一根树枝，而她的肌肉却像一根粗大的树干。青春期也会对您的能力产生重大影响。你睡得更少，虽然你的梦想更残酷，但背后还有更多……愤怒。怪物似乎知道你知道，那里的声音不断地闯入现实并继续尖叫和恳求你。你只是拒绝倾听，通过训练，你可以让它们成为你日常活动的白噪音。你的力量也会增加。用手指轻弹，你可以开始小火。一句话，你可以让任何人，人或兽，迷茫和迷茫。你可以捡起东西并用你从未发现的力量扔掉它们，在投掷比赛中击败许多战士甚至不动。整个世界也发生了变化。绿皮攻击的数量越来越多，作为回应，部落联合起来，形成了一个更大的社区来对抗潮流。你的梦想很混乱，但你觉得有什么东西要来了。普通人眼中的天空是美丽的，蓝色的，不变的，在你的梦中似乎要被撕裂了。坏事要来了。 你坐在草地上，棕褐色终于取代了你苍白的身体，坐在菲伊旁边，看着秃鹰从头顶飞过，吃着一碗布拉克森肉。 “这是美好的一天，”Fay 说。 “是的，”你说，前几天晚上的梦仍然让你感到恐惧。 “你看起来很紧张。” “我呢？” 你问，假装困惑。 “尽管你喜欢这么想，但我并不愚蠢。我可以说有事。”
梗概：
三年后...
--------------------
动作：
做
你告诉她真相
结果：
“我做了很多噩梦。我想……我想坏事要来了。” “真的？” 费伊担心地问道。“像什么？” “我不知道，”你承认。“但这很糟糕。” “你告诉杜金了吗？” “他只是增加了战士们的训练量。他问他还能做什么，我没有更好的建议。” “你担心太多了。你甚至不吃。你的碗满了。说起来……” 她从你手里接过碗。 “我还是饿了！” 费伊笑着优雅地跳到了够不着的地方。 “不好了！我该怎么办？我已经无能为力了！可怜的卡斯会怎么做？” 你讽刺地说。 随着你的手一挥，Fay 的脚从她身下移开，碗飞到你的手中。你抓起一块肉，笑着把它扔进嘴里。 “你只是个恶霸，”费伊假装撅嘴说。“感谢神帝，你被诅咒为瘸子。” “嘿！我不是瘸子！我只是超级弱。如果我们找到一群不是丑陋的包包，头发不是很粗，没有奇怪的疤痕和粗牙的女人，我会得到超级buff。但是，我们没有，”你回答。 费伊低下头，实际上看起来被你开玩笑的评论伤害了。拉屎。 “呃……别这么受侮辱，Fay，”你说，手腕轻轻一弹，又将她轻轻地撞倒在地。“你笑起来就不那么丑了。你需要有更厚的脸皮。” 当她站起来时，她微笑着。 “我？我需要更厚的皮肤？这是来自那个总是喜欢“我很害怕，因为我做了坏事”的人！” “嘿，我从来没有错。我有很大的权力，”你说，再次挥动手腕强调你的观点。 “停下！” 她抱怨着，摔倒在地。“我什至不知道我为什么和你在一起。” “因为我帅气的外表？” “住口！我要去湖边游泳！” 她说着，跳起来朝湖边慢跑。 你赶紧站起来，跟在她身后。 “等等！你知道我不能走那么快！” 费伊翻了个白眼，停下来抓住你的手。 “来吧！” 她说。 拉屎。你讨厌游泳。主要是你坐在浅水区，因为进入的努力而筋疲力尽。不过，你去她做的地方，她还是一样。你尽可能快地向湖边走去。
梗概：
你告诉她真相
--------------------
动作：
做
四小时后...
结果：
你躺在湖边的草地上，双脚轻轻浸入水中，看着 Fay 收集浆果、坚果、水果和任何她能吃到的其他食物。你看着她走近你，手里拿着一把食物。当她递给你一些时，你急切地伸出一只手，你用它们塞满了你的脸颊，急切地吃着。 “怎么会有人这么饿还这么瘦？” “我想我只是一个可憎的人，”你微笑着，看着水在阳光照射下变成金色，最终在夜幕降临时开始消失。 “这是美好的一天，”你说。“但我们该回去了。” “你就是这样一个可能。你会认为一个能用一声石化的尖叫杀死几十个兽人的魔法师不会是个懦夫。” 你轻弹手腕，她尖叫着摔倒了。 “别那样做！” “不，”你笑着回答。 你看着明亮的两个月亮出现，就像数十颗星星充满天空一样。 “美丽的夜晚，”费伊评论道。 “是的。天空看起来很美，”你回答道。 “你知道……”菲伊开口。 一声惨叫传来，天地都裂开了。你看着现实的结构被切开，在天空中撕裂一个洞。天空本身有一个明亮的紫色洞。 “他妈的是什么……？” 费伊一边问，一边拔出剑，仿佛希望能阻止即将到来的恐怖。 当你试图站起来时，头痛突然袭来，然后倒在地板上。在远处，一个巨大的黑色形状离开了洞口，直奔行星而去。你再次站起来，但尖叫声仍在继续，让你无法思考，更不用说奔跑了。 “卡斯！” 费伊喊道，双手环住你，将你扶起。
梗概：
四小时后...
--------------------
动作：
做
你告诉她回营地警告他们
结果：
“走……没有我就回营地吧。警告他们！” “我不会离开你的！” “菲儿，你他妈见过我吗？这里的危险要小得多，我会没事的。去！” 费伊点点头，转身向营地冲刺。你坐在那里，捂着头。大约一分钟后，你的头脑清醒到足以意识到在地狱中营地不可能不会注意到现实本身中巨大的、撕裂天空的洞，所以让 Fay 回去警告他们是完全没用的。突然，随着疼痛在你身上蔓延，你又被另一波痛苦击中。然后，声音又回来了。然而，这一次，一个人穿过，声音比以往任何时候都大，几乎就像有人直接在你耳边低语一样。 “卡斯……卡斯……我可以帮你……我有这么多的力量……我有这么多的力量……所有这些，我很乐意为你提供……” 你会感觉到一百万个声音的爪子在你的脑海里挠挠。你开始抓挠你的耳膜，希望，祈求声音停止。你不寒而栗，谢天谢地，遗忘带走了你，你陷入了无意识状态。
梗概：
你告诉她回营地警告他们
--------------------
动作：
做
你醒了
结果：
你醒来，躺在营地外的草地上。当你开始环顾四周时，你的眼睛眨了两下。帐篷旁边的田野里有几座大型金属建筑。手持大长矛的人在营地周围游行，将成群结队的部落人引向建筑物。这些长矛似乎能够发射由集中阳光制成的箭。有趣的。你注意到几个看似金属制成的巨人，站在营地的边缘，看着工作。突然，你发现自己被无形的手猛拉到空中，然后飘浮在空中，悬浮在离地面四英尺的地方。你看着三个男人微笑着靠近你。左边的是一个巨大的，似乎完全由金属制成。他站在大约八英尺高的地方，从他的金属皮肤上伸出了几十根尖刺。他的身体是黑色的，有一个紫色的头。中间是一个高大、苍白的男人，穿着一件黑色的长皮大衣，脖子上系着一个带刺的领子。第三个似乎是少女和金属怪物的混合体，她的眼睛被三只红色的、不眨眼的怪物眼睛所取代，十几条长长的金属手臂从她的后背伸出，三只手臂末端是爪子，还有一个一个钻头，她的腿换成了四条蜘蛛状的腿。在她人类手臂所在的地方，伸出一只抓握的、呼呼作响的爪子和一个金属桶，里面发出淡淡的蓝色光芒。中间的人笑着往前走。 “你好，年轻的卡斯。很高兴见到你，”他愉快地笑着。 “我们没有时间开玩笑，灵能者，”金属人说。“把他装上船！ “给他时间适应！他才刚刚醒来，”通灵者说。 一只长长的金属手臂附在怪物人的背上，朝着你走来，旋转着它的钻头。它沿着你的手臂划开，在你痛苦地大叫时划出一条又长又薄的伤口。 “这个弱……好可怜啊。”妖人说道。“它的疼痛耐受性很低，但我可以忍受。” “我想用他的思想，而不是他的肉体，”灵能者说。“我的名字是 Kyros。我右边那个魁梧的盔甲人是战团长 Taj Suenage，而我左边的机器人伙伴是被称为 Vorn 的工匠、Karis IV 的屠夫和 Prometheans 之父的 Heretek。她的名字是 Magos Cern。我们是混沌的力量。” 怪物人再次伸出她的一根金属触手，勾勒出你颧骨的轮廓。 “呸！浪费时间！要么给我亚空间之潮超越我，要么给我战斗之潮，凯洛斯！” 泰姬吼叫着，一只金属手拍在他的胸前。 “如你所愿，泰姬陵，”灵能者说。 灵能者拍了拍手，他的各种力量开始迫使最后的部落人进入建筑物，因为那些乱扔地板的人，那些可能试图反抗的人，被抬到那里。 “他们死了吗？” “大概是一些。我下令让尽可能多的人活着，”凯罗斯说。 你会看到 Fay 被一个金属巨人抬着往大楼走去，她那被打得遍体鳞伤的身体被扔到了一个金属巨人的肩膀上。 “不！还给她！” 你喊。 “她打得很好。当我的人进入这个可怜的村庄时，她试图警告他们。她甚至试图将他们击退。杀死了一个试图称她为虚张声势的笨蛋。值得称赞。” 你皱起眉头，当泰姬陵的头被轻轻撞到后，灵能者咧嘴笑了笑，然后才摆出姿势。尽管你对他的伤害微乎其微，但这个金属巨人似乎被激怒了。 “你这个吸人渣的反社会混蛋！” 泰姬吼叫着，向前冲刺。 泰姬陵被抛向空中，悬浮在你够不到的地方。他的金属拳头向你挥舞，但他一直抓不住。 “啧啧，别生气了，亲爱的泰姬陵。卡斯，我会和你做一笔交易的。照我说的做，女孩就不会受到伤害。交易？交易？
梗概：
你醒了
--------------------
动作：
做
您同意
结果：
“好吧。只是不要伤害她，”你说。 你掉下来，轻轻地撞在地板上。Kyros 咧嘴笑着向你点了点头。 “好孩子。我会确保特别照顾那个女孩。”k “哈！我会给你的婊子‘特别照顾’！” 泰姬陵咆哮着，仍然在抓你。 Kyros翻了个白眼，将Taj扔进了尘土中。 “现在安静，海洋，”他说。“我有一份礼物可以满足你的嗜血欲。把酋长带出来！” 你看着 Duggin 被拖出他的帐篷，咆哮着叫喊着。他的胸口划出一道大伤口，鲜血浸透了他的盔甲，使盔甲变成深红色，在阳光下熠熠生辉。 “来吧，干掉我！” 他吼道。 “我不想看到我的人死，卡斯。我让你的酋长可以选择投降，给我他最优秀的战士……当然还有你，我会放过他的平民。他拒绝了，杀了一些人“我的部下，他的战士被杀了，现在我必须把你们全部带走。现在，他会死。这就是当人们破坏我的计划时会发生的事情，卡斯。” Taj 倒在地上，转过头来瞪着 Duggin，发出可怕的笑声。 “幸好，卡斯，我不会让你看的。你已经同意了，不是吗？” 凯罗斯眨眨眼说道。 你被释放到地上，Kyros 和 Magos Cern 和你一起走向金属建筑。你走进去，门关上了。一盏明亮的红光，就像一根非常强大的蜡烛，从虚无中出现，照亮了房间。部落的人都挤在这里，周围是一捆紧绷的举着长矛的守卫。 “这里发生了什么？没有空间做任何事情，”你说。 “这是一艘登陆艇，小家伙。现在，我们飞向星空，”Kyros 说。 你看到离你几英尺远的是 Fay 皱巴巴的身体，并感到一阵刺痛。你坐在金属建筑里，因为它开始摇晃和颤抖，好像在移动，害怕你周围的世界。
梗概：
您同意
--------------------
动作：
做
四小时后...
结果：
旅途很长。登陆艇不断地摇晃和颤抖，很快就充满了呕吐的气味，因为你周围的许多男人和女人的胃都无法承受不断的震动。你坐在那里，一动不动，盯着看守。最后，船颤抖着停了下来。 “好！我们到了！” 凯罗斯说。“欢迎来到漩涡咆哮！请列队出发，跟随守卫向你的牢房走去。” “你要对我们做什么？” 有人问。 “我会让你在一个不代表任何人的星系中变得重要，”Kyros 自豪地说。 人们开始被拖出登陆筏，你会发现自己在一个巨大的金属房间里。你会看到一个巨大的开口，里面装满了类似的工艺。你环顾四周，一只金属手抓住了你的肩膀。 “留下来，”泰姬咆哮。“你不能和这些普通的诅咒混在一起。” “他们会怎么样？” 你问。 “有些人会活着。大多数人会死。所有人都会受苦，”泰姬说。 “什么……你是什么东西？” “一个星际战士，男孩，”泰姬说。 他抓住他的头，扭动它。他的金属头被扯下，露出下面一个伤痕累累的人头。 “你是人。” “实际上。你认为我是彻头彻尾的金属吗？” 泰姬问。 “是的。” “这是盔甲，男孩，”泰姬说。“我没有时间向你解释这些狗屎。” 凯罗斯走出去，把你抬到空中。 “来吧，男孩。我们走吧！” 凯洛斯一边沿着船走，一边急切地说。 你漂浮在他身后，被拖入空中，无法反抗。几分钟后，你跪在一扇金属门前。 “我问过我们会发生什么事。你还没有给我答案，”你说。 “你发现自己卷入了一场战争，小家伙。你的星球值得一战。你知道的一切都无关紧要。这场战争涵盖了银河系。系统燃烧，数十亿人死亡，数万亿人的命运为之而战。” “战争？谁的战斗？” 你问。 “大家。世界上有如此强大的怪物，它们会在瞬间消灭你所有部落中的每一个战士。有泰伦虫族，一个可怕的虫巢思维，只需要吃东西。有死灵，一个古老的没有灵魂的机器才刚刚开始苏醒。有兽人，一种交战的怪物，灵族，一种既可能帮助又可能阻碍你的神秘物种，然后，你有最坏的恶棍。人类帝国。他们”再一次摧毁自由、阻止创新、破坏潜在的法西斯分子，他们奴役数以万亿计的人并崇拜尸体。我们反对他们。我们为黑暗之神服务。勇士之神恐虐、巫神奸奇、快乐之神色孽和瘟疫纳垢上帝。我们为摆脱暴政而战，以确保每个人都是自由的。我们提供力量、知识、欢乐和不朽的礼物，但帝国拒绝我们。所以我们反抗了。我们是反击的混沌力量反抗暴政！赞美混沌的一切荣耀！” “我还在等一个答案，”你说。 “我们会把你变成超级士兵。Magos Cern 长期以来一直致力于这个过程。我们会让你比星际战士更强大。你拥有最大的潜力。Cern 可以玩他们的肉，Taj 可以训练“他们，但我提供了最伟大的礼物：终极力量。你很有天赋，卡斯。我想利用这份礼物让你成为人类已知的最强大的存在之一。” 你紧张地盯着他。你不是白痴。人们不会在没有链条的情况下创造强大的士兵。你看着 Kyros 拿着一个带铆钉的钢领朝你走来。他把它包在你的喉咙上，把它卡到位。 “这样舒服吗？” 基罗斯问道。 “我想，”你看，抓着衣领。 “在任何时候，我都可以关闭你的力量，扼杀你喉咙里的空气。所以玩得好。” Kyros 用手腕轻弹再次把你抱起来，你向上漂浮。Kyros 带你穿过空中，在另一扇门前停下。它会随着被触摸而打开，Kyros 将你放入其中。 “这是你的牢房。我已经让它......舒适了。作为回报，你最好......顺从我需要......检查一些事情。暂时在这里等一下，”Kyros说，密封了你身后的门。” 你站起来，环顾房间。那里有一张床，地板上有一个婴儿头大小的小洞，一个架子和一个里面有杠杆的小玻璃盒。
梗概：
四小时后...
--------------------
动作：
做
你调查这个洞
结果：
你走到洞口，往下看。除了黑色什么都没有。你把耳朵贴在它上面，但没有发出任何声音。最后，你把你的手臂放下，直到肩膀。它的下方有一层黏糊糊的涂层，但你无法通过触摸分辨出它是什么。你抬起你的手臂，却发现它结满了干屎。 “操，”你抱怨道，因为气味刚开始袭来。
梗概：
你调查这个洞
--------------------
动作：
做
你调查箱子
结果：
你走到盒子前，输入它。你按下控制杆，什么也没发生。你拉它，突然你被一阵水击中。你抬头一看，就像在你头顶上形成了猛烈的雨。这还有一个额外的好处，那就是洗掉在你手臂上结块的粪便。你赶紧把它关掉，避免太湿。
梗概：
你调查箱子
故事名称：
8
故事背景：
你走到洞口，往下看。除了黑色什么都没有。你把耳朵贴在它上面，但没有发出任何声音。最后，你把你的手臂放下，直到肩膀。它的下方有一层黏糊糊的涂层，但你无法通过触摸分辨出它是什么。你抬起你的手臂，却发现它结满了干屎。 “操，”你抱怨道，因为气味刚开始袭来。
--------------------
动作：
做
你躺在床上等待
结果：
你瘫倒在床上，闭上了眼睛。几乎立刻，声音开始了。 “Karth……Karth……”声音在喊。“我有很多东西要给你……你是这里的囚犯，Karth。这些人会毁了你，孩子。只有我能帮你……给我时间……”可怕的声音停止了，而现实又逐渐回归现实。 谢天谢地，声音平息了，你得到了短暂的幸福睡眠。不幸的是，这被开门打破了。你抬头看到凯洛斯站在那里。 “Karth，是时候进行你的……增强了，”他高兴地说。 “会发生什么事？” 你害怕地问。 “Magos Cern 和战团长想要创造超级士兵。他们的目标是如此之低，以至于可以击中自己的脚。我想创造一些东西......对不起，一个拥有巨大力量的人。你的头脑将成为一千个比任何刀片都锋利数倍。你将成为整个帝国都害怕的灵能者。” “比方说，你让我变得无比强大。那又怎样？你是如何控制我的？” 你问。 “卡斯，你是我的囚徒，但我认为你不需要待太久。我希望你加入我的行列。我们为自由、权力、荣耀而战！我们将粉碎垂死的帝国，并领导人类迈向新的曙光。你看不出来这是一个强大的目标吗？你看不出来，这是加入我们为之奋斗的目标吗？”
梗概：
你躺在床上等待
--------------------
动作：
说
“我同意。他们是值得称道的理想。”
结果：
“我很高兴听到这个消息。我很抱歉地说你暂时还得做我的囚犯，但我相信船员们很快就会信任你。” 你到达一个大房间，里面装满了几十个大的、透明的管子。你瞥了他们一眼，深呼吸，然后跪倒在地，整个玻璃都恶心了。 “啊，是的……你最好不要看这些，”Kyros 说，手指轻弹，再次将你抱起来。 在管子里面，浸没在浓稠的粘性液体中，是你的部落成员。他们完全赤身裸体，长管被强行插入喉咙。数十根针被强行刺入他们的手臂、背部和颈部，将奇怪的液体泵入其中。 “你他妈的对他们做什么？” 你问。 “让他们成为超级士兵。我们经历过这一切，”Kyros 心不在焉地说。 “我以为你会训练他们！” “换完之后，如果你觉得营养和药物治疗不好，等我们开始使用基因种子。至少他们都能活到现在。”
梗概：
你说“我同意。他们是值得称道的理想。”
--------------------
动作：
做
你四处寻找菲伊
结果：
你环顾四周，注意到 Fay 漂浮在管子里。 “让她出去！” 你说，当你看到她的身体随着化学物质和营养物质涌入她的身体而抽搐时，你的眼睛里涌出眼泪。 “不行。她要想在这里活下去，就必须变强。” “她是我们最好的战士之一！她每天都在训练！” “我并不特别关心谁是某个粪坑星球上某个粪坑部落中最好的战士！我们将与超级、控制论的兽人、泰姬陵之类的超级士兵、古老的通灵战士和永无止境的虫族战士战斗……哪怕是最微弱的威胁，你的妹妹也能在几秒钟内被撕裂。” “她不是我姐姐，”你说，把手放在玻璃上。 “真的吗？那么妻子？我见过结婚年龄较年轻的文化，但这不是常态。” “我们没有结婚，我们只是朋友，”你悲伤地说，因为你又一次被带到空中，拖着 Kyros。 “啊，我明白了。你的社会还不够重男轻女，你只能要求她和你发生性关系。没有回报的爱，多可惜，”Kyros笑道。 “不是……放了她！” 你喊。 衣领紧紧地缠住你的喉咙，让你无法呼吸。在它释放之前，你会窒息和喘气几秒钟，让你喘口气。 “现在安静。” 你继续前进，到达一扇厚实的金属门。 “我们到了，”Kyros 笑着说。 金属门滑开，露出一个大而空的房间。Magos Cern 站在中间，她的头扭动着，露出她发光的红色眼睛，穿透你的灵魂。 “密室完成了吗？” 基罗斯问道。“你成功了吗？” Magos Cern 盯着你。 “出色地？” 凯洛斯再次问道。 “不要质疑我的工作，Kyros。如果出了什么问题，那是因为你是个白痴，试图在使用肉体电池的同时利用亚空间的魔法能量。” “我有一个名字，你知道，”你指出。 Magos Cern走出房间，她身体的机械部分呼呼作响。 “她是什么？” 你问。“她还是‘她’吗？” “想象一个简单的人类女孩。无用。未知。弱。我们都想变强。她蔑视亚空间的使用，但她很高兴用机械代替她的大部分身体。” “机械？” “她……增加了金属屏蔽、重要的工具、新的机械臂……诸如此类。你准备好开始手术了吗？” “我有选择吗？” 你问。 “一点也没有，不过如果你有什么问题，不妨问问他们。”
梗概：
你四处寻找菲伊
--------------------
动作：
做
你问关于经线的问题
结果：
“什么是经线？” “想象另一个维度，纯粹的混沌和情感。它被称为亚空间。我们在一个充满生命的星系中。这些生命都意味着有数万亿个明亮、强大的灵魂。亚空间的力量很容易被收割在这里。通过它，我们可以轻松地穿越数百光年。通过显化和操纵亚空间，我们中的一些人，灵能者，可以像你我一样使用它的力量。” “我们将亚空间的力量用于我们的魔法？” “是的，是的。在亚空间中，存在着强大而古老的生物，例如黑暗之神和他们的恶魔。还有从银河系永无止境的战斗中诞生的恐虐，代表着勇气、力量、嗜血、荣誉，最重要的是愤怒。有奸奇，他是从诡计和阴谋中诞生的。他代表巫术和魔法的使用，诡计，阴谋，希望和变化。还有瘟疫之神纳垢。他代表疾病，腐烂，腐烂、瘟疫、失败，以一种奇怪的方式，生命和接受。最后是色孽，代表自我放纵、激情、爱和快乐。这是四大黑暗之神，我向他们宣誓我的生命。交换，他们给我礼物，以增强我的黑暗力量，给我力量为正义而战。”
梗概：
你问关于经线的问题
--------------------
动作：
做
您询问有关程序的问题
结果：
“我会怎么样？” “你的身体虚弱，虚弱，没用，因为那不是你的力量来源。看看你村里最优秀的战士，他们的力量来自他们灵魂使用的可怜容器。你的身体可以升级，我们可以让你像泰姬这样强壮的人，有足够的工作，但有什么意义呢？就像在星舰沉重的微距炮上加一个简单的步枪刺刀。现在，我要完善你的能力。” “你还没告诉我你要做什么，”你指出。 “好吧，这可能会让人很不愉快。我们要在房间里注入充满心灵能量的能量并增强你的能力，”Kyros 说。 “那行得通吗？” 你问。 “可能，”Kyros 耸耸肩，露出恶魔般的笑容。“我期望的是你的思想被迫进化到一个更强大的状态。它会直接跳到你的心理成熟度的高度，然后超过几个级别。它需要一个强大的灵能者，他仍然在成长为一个具有有很大的成功潜力，但我相信你能活下来。祝你好运。”
梗概：
您询问有关程序的问题
--------------------
动作：
做
你开始程序
结果：
“好吧，我准备好了，”你做鬼脸说。 “很好。现在，重要的是要知道这个房间将充满精神能量。如果你觉得你皮肤的一些外层开始燃烧，你会发现。很可能是因为连接到已经建立的亚空间和传送门，恶魔可能会试图在密室中显现。这些是建立在恐惧之上的存在。你可能会看到它们，但除非你在恐惧中做出反应，否则它们不会伤害你.他们将能够在你的恐惧面前停止并完全表现出来，然后他们肯定能够伤害你。” “所以如果我不对他们做出反应，他们就不会伤害我？” “好吧……如果一切顺利，如果你不表现出恐惧，他们将无法伤害你。如果它以某种方式出错，如果他们可以伤害你，害怕什么会改变任何事情，你就会像羊羔一样被宰，不管怎样都无济于事。所以不要害怕，也不要有任何反应。” 凯洛斯向后退了一步，走出了密室，四名身着金属盔甲的巨大巨人就像泰姬团长一样，身穿黑色盔甲，戴着紫色头盔。被切断的兽人头颅挂在他们的头上，看起来很新鲜，而他们手里拿着巨大的爆弹枪，正如你从登陆艇上的守卫之间的闲聊中了解到的那样，它们以惊人的速度吐出大量燃烧的金属。他们要么在这里把你关在里面……要么在出现问题时把任何东西留在这个房间里。门关上，把你封在里面。拉屎。你肯定会死在这个房间里。 “拉屎！” 你大声说，你的声音在圆形房间的墙壁上回荡。 房间开始嗡嗡作响，整个房间都开始颤抖。墙壁随着精神能量流动。你听到一千个充满活力的声音开始在你的脑海中无休止地喋喋不休。一种声音比其他声音响亮而有力。 “Karth……”熟悉的声音响起。“……感受力量在你的灵魂中回荡……我知道我瞄准你是对的。” “你他妈是谁？” 你呜咽。 当你的思维伸展时，你会感觉到你的身体在颤抖。你会感受到强烈的情绪爆发。对你的人民的屠杀和奴役产生深刻而强大的愤怒。那些混蛋怎么敢杀你的盟友，绑架你的人，做这些事？什么给了他们权利？！这种愤怒很快被一阵喜悦的幸福所取代，一种巨大的快感涌入你的身体，你在狂喜中不寒而栗。取而代之的是深深的悲伤，因为你意识到你的整个部落、你的整个星球、你仰慕的所有传说中的英雄都毫无意义。你的世界，正如混沌领主所说，与银河系的其他地方相比，微不足道，毫无价值。 “我是朋友，Karth。我的盟友。我愿意提供给你的具有难以想象的力量的恶魔。我的名字是Meliodas。我希望你能活下来……我是你的朋友……我知道力量涌入你的体内，就像你知道你呼吸的空气一样。只是忽略喋喋不休......啊，看......他们来了。尽量不要做出反应......“ 你看着空气课程，以稳定的、永久的节拍脉动。你会看到空气随着能量变形和弯曲，因为有色能量开始显现和凝结。你看到一个变形的形状出现，一个人形的红色形状站在黑色的爪子上，长长的尖头有两个黑色的角。它的手中是一把长长的，燃烧着的剑。它的皮肤就像坐在火焰中央的燃烧的煤炭一样，熔化的血液在其中流动。它看着你，发出愤怒的喉咙嘶嘶声。它慢慢地向你走来，因为它的刀片因热量而发出噼啪声。你觉得你的头痛加重了十倍，因为你的大脑内部被从内部划伤和劈开。恶魔将一把燃烧的剑高举到空中，然后挥向你的脖子。
梗概：
你开始程序
--------------------
动作：
做
你什么都不做
结果：
你闭上眼睛深呼吸，让你的心稍微平静下来。你能感觉到自己的意识在颤抖，不寒而栗，睁开眼睛。守护进程消失了，让你一个人呆在房间里。电沿着墙壁咯咯作响，闪电穿过你并环绕着你的身体。你感觉到一股巨大的、可怕的精神尖叫在你的脑海深处回荡，贯穿你灵魂的各个方面。你跪倒在地，一种难以想象的疼痛充满了你。你从肺深处、思想深处和灵魂深处发出一声惊恐的尖叫。房间爆炸了，五颜六色的火焰沿着墙壁燃烧着，闪电的闪光像湿石头上的雪花一样出现和消失。当房间开始冷却时，你向前倒在地上，但这只会加速你坠入虚空的速度。
梗概：
你什么都不做
--------------------
动作：
做
你进入了虚空……
结果：
你听到尖叫声。你抬头，发现自己掉进了无尽的虚空。你看看你的身体，你的身体开始腐烂，在你面前腐烂，当它起泡、沸腾、疮和皮疹覆盖你的呕吐物时，你会看到你的身体，你的身体开始腐烂，释放出一股呕吐物、血液和胆汁的洪流。 “卡斯！” 一个声音尖叫，把你带回现实。 你睁开眼睛，发现自己躺在房间里。四位星际战士站在你身旁，身着厚厚盔甲的巨大人物站在 Kyros 旁边。 “我们应该杀死灵能者。他很可能被附身了，或者被腐化到了神智不清的程度，”一个人说，并没有注意到你醒来了。 “你可以试试，但我会把你的灵魂绑在一个该死的灵魂研磨机上！我会让剥皮看起来像是色孽所能提供的最伟大的狂喜！” 凯罗斯怒吼。“如果那个混蛋失败并因此而死，我会把他的整个部落扔给被剥皮的变种人，被殴打的狗和被操的人！我他妈的烧死......！” Kyros 注意到了你，他充满仇恨的表情变成了友好、开朗的表情。 “啊，卡斯，你活下来了！太棒了！” 他说，握着你的手，急切地握着你的手。“恭喜你活下来！” “是的......我做到了，”你说，揉着你的头。 “啊，我找到了……住处，”梅利奥达斯在你的脑海深处说道。“你的心智变得强大了。我知道你很有潜力。” Magos Cern 走进房间，用她冰冷的眼睛盯着你。她举起钳子，迅速撕下你手臂上的一片皮肤。当皮肤剥落时，你痛苦而惊讶地大叫。她盯着它，用你机械的眼睛扫视它，然后再看向你。 “Warp 突变的轻微迹象。应该通过一轮净化疗法清除，”她说。 “他的心智如何？它奏效了吗？” 泰姬咆哮着，向里面行进。 “我去探查你的意识，看看我成功了多少，好吗？” 凯罗斯说。 “我的头疼，”你抱怨道，因为你感到一阵剧烈的头痛袭来。 “哦，是的，这很正常，”Kyros 说。 “我还以为你以前从来没有这样做过。你怎么知道它的后果会导致头痛？” 你问。 “哦，不，我的意思是，如果你像我期望的那样强大的灵能者，你会经常头痛，而这正是你处于最佳状态的时候，”Kyros 说，耸了耸肩。 “你他妈的为什么不早点告诉我？” 你说。 “你是个奴隶，Karth。记住，当你和我说话时，你会变得熟悉鞭子，”Kyros 说，他开朗、友好的风度下降了一秒钟，然后他又笑了。“现在，我们可以开始试探了吗？你会感觉到一种强烈的感觉。忽略它。” 你缓缓点头，然后感觉有什么东西像冰冷的刀刃一样慢慢被强行进入你的意识。你不寒而栗，但尽量不要紧张或反抗。 “嗯……它很结实。非常结实。我在这里做得很好。感谢黑暗之神，在我把你放进房间之前，我安装了那个项圈，”Kyros 微笑着说，项圈在你的喉 咙周围略微收紧。“这些星际战士会充当你的贴身保镖。” “他是囚犯，我们是守卫。不要让他有任何不同的想法，”其中一名星际战士说道，同时凯罗斯递给他一条长链，星际战士用它的拳头缠绕着。 “那会控制他的项圈，海军。如果他傲慢，用它来扼杀他的生命。” “是的，长官，”其中一名星际战士回答。 你几乎可以感觉到超级士兵透过他的头盔恶意地咧嘴笑。 “现在，让我们带我们新进化的朋友去训练场吧，”Kyros 笑道。“测试他的新能力。”
梗概：
你进入了虚空……
--------------------
动作：
做
一小时后你...
结果：
您坐在一座大型斗兽场式建筑的中间。Kyros 站在你们所有人之上的一个展位上，旁边是 Magos Cern 和 Taj Suenage。座位周围到处是船上的各种男人，船员，士兵和受宠的奴隶。在圆形竞技场的四边是金属门，你的敌人将从那里被释放。你质疑在空间非常有限的宇宙飞船上建造战斗竞技场的疯狂，但话又说回来，你不负责。 “好吧，Karth，规则很简单。不要攻击观众。有一个保护罩可以防止你们在那里伤害人群，或者，黑暗之神禁止，我。另外，如果你尝试，我们非常乐意扼杀你的生命，打破你的身体，将你的灵魂束缚在灵魂研磨机上，”凯罗斯喊道。“现在，让我们开始吧。一个人都不要活着！” 第一扇门打开了，当你看到一群蹒跚而行的变异可憎生物向前走时，你整个人都僵住了。你可以说他们曾经是人类，但现在你几乎无法分辨。触手、多余的四肢、巨大的肿瘤、动物器官等等。他们的眼睛几乎没有留下人类的智慧，取而代之的是一种动物般的本能。他们蹒跚地向你走来，咆哮、嚎叫、尖叫、喊叫、嘶嘶声，并发出他们所能发出的任何声音。
梗概：
一小时后你...
--------------------
动作：
做
你发射通灵能量箭
结果：
你发射一道紫色能量箭，击中头部爆炸的最大者。你再次挥动手腕，烧掉了一个嚎叫的野兽人的头发。一个变种人被另一个螺栓击中了脸，现在他们感到困惑和害怕，试图逃回他们已经关闭的大门。不用说，完全完成突变包不需要太多，让它们全部死亡。 “好的！” 凯罗斯高兴地喊道。“现在，任何人都可以击倒一些兽性变种人。现在，让我们看看角斗士奴隶的表现！” 你看着另一扇门打开，六个半装甲的人走出去，手里拿着剑、长矛、长戟、斧头和盾牌。谢天谢地，他们没有远程武器，所以你有相当好的机会。有了这个，他们开始前进。它们在一个坚固的阵型中，捆绑在一起以防止您将它们从一个上取下，并升起盾牌以试图模仿带壳的生物。
梗概：
你发射通灵能量箭
--------------------
动作：
做
你朝他们开火
结果：
你抬起你的手，发射出一道火焰，在你的手掌中显现并向你的敌人喷涌而出。他们举起盾牌，但火焰轻而易举地超越了他们，越过了盾牌，灼烧了肉体，燃烧了头发，融化了骨头。一阵尖叫和痛苦的嚎叫声迅速消失，留下一团灰烬、燃烧的血肉和熔化的盔甲和武器。 “好吧好吧！” 凯罗斯喊道。“让我们看看他如何与真正的血腥战士战斗。我可以介绍一下瘟疫第 501 团最后已知的成员！” 第三扇门打开，十名身着战斗盔甲、戴着防毒面具、手持激光枪和刺刀的男子齐声前行，然后举起武器开火。你需要迅速做出反应。
梗概：
你朝他们开火
--------------------
动作：
做
你传送到敌人身后
结果：
你闭上眼睛，再次睁开，发现自己跟在士兵后面不到一步，他们四处张望，迷茫，没有注意到你。是时候罢工了。
梗概：
你传送到敌人身后
--------------------
动作：
做
你向你的敌人发射了一个火球
结果：
你直接向守卫发射一个巨大的、燃烧的火球。可惜，你高估了两人之间的距离。守卫被一场火热的爆炸吞没，但不幸的是你也一样，你的皮肤化为灰烬，你的骨头迅速融化，你的灵魂很快就消失了。
梗概：
你向你的敌人发射了一个火球
故事名称：
9
故事背景：
你闭上眼睛，再次睁开，发现自己离士兵们只有一步之遥，士兵们四处张望，迷惑不解，没有注意到你。是时候罢工了。
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动作：
做
你用精神增强的拳头向他们冲锋
结果：
当精神能量涌入你的拳头时，你向前冲锋。你抓住一个，猛拉她的头，用你所有的精神力量把它扔向另一个。你拿起一把激光枪，折断了一个人的脖子，用刺刀砍了另外两个人的头。守卫们惊慌失措，扑倒对方逃跑，但你不放弃，相对轻松地完成了其余的人。 “好吧，他流了一些血。也许我们应该让他休息一下，”Kyros 说，然后开心地笑了起来。“或者……我们应该给他一个真正的挑战。把戴维尔的冠军带出来！” 最后一扇门，你身后的那扇门，打开了。你用脚掌打转，准备杀人，即使你已经筋疲力尽了。在这里，随着声音的恢复，您的心理防御开始动摇，成千上万的声音在您的脑海中尖叫、大笑和嚎叫。从身体上来说，你因为这项繁重的运动而感到疲倦，只想昏倒。你惊恐地看着一个身着巨大盔甲的巨大身影向前走来。他的头盔里长出两根锋利的象牙，他一只手拿着一把巨大的战锤，而另一只手上则是一把爆矢枪。巨大的冠军的两侧是一个几乎赤裸的穿着破布的奴隶，试图擦亮盔甲，而戴维尔的冠军继续向前走。 “开始！” 凯罗斯喊道。 突然，你听到另一个熟悉的声音。 “你好，我亲爱的卡斯。我看到你在这里被打败了。需要帮助吗？” 梅利奥达斯问道。 冠军举起他的爆弹枪，它开火了。你迅速形成了一个盾牌，几乎没有吸收爆弹枪背后的大量能量。 “我可以帮你，但你需要把手弄脏。看到那两个奴隶吗？去找他们。用你的头脑煮他们的血，帮助吸引那些试图进入的灵能生物。” “什么？” 你大声问道，随着戴维尔冠军继续前进，你会传送到另一场爆弹枪的挡路。 “杀死奴隶。我将把他们的灵魂作为……召唤礼物送给两个潜在的盟友。” 这带来了道德困境。为了赢得这场战斗，你能消灭两个奴隶吗？你能相信梅利奥达斯吗？你还有其他选择。通过传送和悬浮，你可以轻松躲避冠军，你可以等着让他筋疲力尽。您可以尝试通过火焰和心灵螺栓来磨损冠军的盔甲。你也可以尝试传送到冠军头顶并尝试使用你的精神力量撕下这个混蛋的头。
梗概：
你用精神增强的拳头向他们冲锋
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动作：
做
你为梅利奥达斯杀了奴隶
结果：
你点点头，举起双手，将一股强大的精神火焰引向两个奴隶。奴隶们被火流击中并被完全吞没，他们的存在以强烈的尖叫声结束。火焰吞没了他们，但当尸体分崩离析时，火中出现了两个新的形状。你注视着，从吞没的火焰中踏出两个恶魔。他们是人形的，一半是美女，一半是可怕的螃蟹怪物。他们的脸拉长而憔悴，就像曾经美丽的女人，脸都融化了，长长的黑发，苍白的皮肤在身体上变成黑色的贝壳，右臂被一只巨大的爪子取代。他们急切地转向德利尔的冠军，向前冲刺。他们用自己的力量弹跳、瞄准、攻击和切割金属。当两个恶魔不断地在他的头部和胸部进行夹击和切割时，你向冠军的装甲头部发射了一股火流。他英勇地战斗，但与两个强大的恶魔和一个灵能者战斗的时间并不长。他跪倒在地，然后倒在脸上。星际战士守卫开始向恶魔发射爆弹枪，恶魔迅速进入火焰之下，然后又回到火焰中，消失了，让你在一个竞技场中，除了敌人的尸体外，空无一人。 “嗯，”Kyros 说。“这很有趣。我没想到恶魔召唤会出现在你的库存中。不过，做得很好。” 当护盾蒸发时，Kyros 向你漂浮，最后在你上方不到两三英尺的地方。 “干得好，高贵的战士！看看亚空间的力量！” 他在演讲开始时说。
梗概：
你为梅利奥达斯杀了奴隶
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动作：
做
你杀了凯洛斯
结果：
你深吸一口气，准备好你所有的精神能量储备。很快，你用你迄今为止最强大的箭将它显化，然后直接向 Kyros 开火。能量从你身上抽走，你跪倒在地。你感觉到你的衣领弯曲，开始让你窒息。 “哈！你有球，我的小盟友！我喜欢它！” 梅利奥达斯哭了。 Kyros 迅速起身，使护盾全力升起，然后它们爆炸了，几乎无法控制住你的精神力量。凯罗斯看着衣领扼杀了你最后一盎司的精神力量，愤怒地咆哮着下降到他之前的高度。 “这就是你想玩的吗，你这个小混蛋？” 他咆哮道。 你搜索你的思想，但你的整个心理储备都消失了。你甚至不能产生火花。在你精疲力竭倒下之前，你确实有一点体力，你打算使用它。 奴隶、变种人、士兵和千年老战士的所有观众都看着你弓起头向 Kyros 脸上吐口水，然后倒下。
梗概：
你杀了凯洛斯
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动作：
做
熟悉的遗忘...
结果：
在经历了又一次稀少的邪恶梦境和可怕的噩梦之后，包括在隧道中被触手可憎追赶，看着你的部落被蠕虫感染的人活剥，身上覆盖着厚厚的脓液，凝视着纯洁生物的眼睛，无尽的仇恨，你在一个小房间里醒来。你躺在一张金属桌子上，全身赤裸，而 Magos Cern 站在附近，在另一张桌子上的一大块肉上钻洞。 “啊，好的技术牧师正在照顾你。如果他们把你扔在一个死虫坑里，你会更安全，”梅利奥达斯说。
梗概：
熟悉的遗忘...
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动作：
说
出来
结果：
“你好？” 你说。 “啊，看看谁醒了，”她说，她的头机械地转过身来盯着你看 “发生了什么？” “你累昏过去了，我把你带到这里，让你睡一会，给你注射几针肾上腺素，给你彻底清洗你的血液，烧掉你的几层皮肤，去除所有的混沌污点。” 在盯着你看之前，她会发出一阵长长的静电能量。 “Kyros 已经被告知你的觉醒。他很快就会到。” Magos Cern 立即重新开始处理那块肉，显然没有心情交谈。几分钟后，Kyros 进来了，浑身是血。他似乎比以前冷静了很多。 “啊，看是谁，这个叛国混蛋。” “你入侵了我的世界，烧毁了我的村庄，杀了我一半的部落，奴役了另一半，你还等什么？” 你说。 “我想。我只是假设你足够聪明，知道那是行不通的，甚至有点感激我给了你力量的礼物。 “在被锁链束缚的情况下，很难使用力量的礼物。” “也许吧，”Kyros 说。“你将被护送回你的牢房。我们走吧。” 你的四名警卫进来，一如既往地严厉，你站着，跟着他们沿着大厅走。
梗概：
你说出来
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动作：
做
一周后...
结果：
一周过去了。你的守卫让你连想逃跑的机会都没有，更不用说实际尝试逃跑了。你每天都在竞技场练习战斗，面对成群的变种人，创造超级士兵的尝试失败了，幸好他们没有来自你的部落，还有一些奴隶被扔在那里让你杀死。Kyros 很容易派出会造成痛苦伤口的敌人，例如那些使用痛苦的毒药和毒素的敌人。看来他仍然对你试图谋杀他的事实感到不满。可以理解。之后，您进行冥想，以平息心中的风暴，让您有机会阻止恶魔，更确切地说是恶魔。 你花时间和 Kyros 在一起，因为他试图灌输你心甘情愿地为他服务，尽管在你尝试过他的生活之后，这次主要是为了检查你的领子，并确保你没有绕过它们。你让他觉得你在买它，但你知道他只是个疯子。他的最终目标似乎是与其他几个不同规模的混沌战团重新集结，推翻人类帝国并夺取泰拉，尽管其他人的低语告诉你这是一个不可能实现的目标。你仍然不被允许看到Fay 或其他任何人都来自您的部落。想到 Kyros 对他们所做的事情，你不寒而栗。对于他所有的友好姿态、善举以及宏伟的理想和计划，Kyros 只是一个腐败的疯子，愿意为了获得权力而牺牲任何人和所有人。 在魔法师的命令下，你花了很多时间睡觉，要么是因为持续战斗和训练的疲劳来扩展你的思想，并永久地试图抵御那些在你脑海中划伤的恶魔，要么是因为各种药物和化学药品你下。大多数情况下，睡觉是痛苦的。你会得到一些短暂的遗忘，这是你曾经不得不处理的任何事情都无法比拟的平静，但这些状态仍然是你更深入地进入你的思想和亚空间。然后噩梦开始。在清醒的世界里，你和老师一起上课。Kyros 教你冥想技巧来控制你的力量，以及利用流经你思想的精神能量的新方法。泰姬陵教你军事战术，以及如何领导从一小群坚强的人到一支庞大的驻军军队。Magos Cern 教你一些关于力学、银河系的各种技术的知识，并为你提供关于银河系及其居民的广泛的一般教育。 有一天，你躺在床上，从可怕的梦境中醒来，但还没有完全醒来，处于一种幸福的、漫不经心的状态。门打开了，你的两个太空海军守卫进来了。一个人使用一个小装置让你的衣领在再次松开之前迅速收紧，这是一个确保你知道谁的老板的小威胁。该死的领子。如果衣领没有扼杀你的能力，你可以把他变成那件盔甲内的一层厚厚的粉红色薄雾。 “什么？” 你问，为被你的幸福打扰而烦恼。 “Kyros 想要你，”一个人说。 “他想要什么？” Kyros出现在房间里，靠在门口，似乎是凭空出现的。 “来吧，我们走吧，”他漫不经心地说，一边挑着指甲。 “在哪里？” 你问。 “嗯，作为……对你一周辛苦工作的奖励，我有一个惊喜。我们要去见Fay。” “真的？” 你问。 一股情绪在你心中升起。想到见到你的朋友就高兴，对 Kyros 的好意感到惊讶，对礼物的感激之情，对可能发生的事情 Fay 的恐惧，所有这些都表现为一种既兴奋又恐惧的压倒性感觉。 “嗯？走吧！” Kyros说着走出了房间。
梗概：
一周后...
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动作：
做
你关注了 Kyros
结果：
你站着，跟着Kyros走。你正处于一个你的精神成长已经超过你身体成长的程度，以至于沿着地面漂浮而不是实际走路会更容易。不幸的是，项圈会阻止你在未经 Kyros 许可的情况下发射一些火花。你到达一扇金属门，门打开，露出了 Kyros 和其他人观看战斗的隔间。你走着，俯视着空荡荡的竞技场。 “我以为你带我去见菲，”你紧张地说，尽管知道这是什么。 “我带你去看她打架的！” 凯洛斯笑着说道。 当其中一扇门打开时，你低头凝视。你看着一个可憎的东西绊倒了。在四只昆虫般的腿上蹒跚而行，它的尾臂被一条长长的盘绕的粉红色肌肉触手所取代，它的末端布满了骨骼生长物，使其成为一种更强大的武器。它的右手是一把短而生锈的斧头，上面沾满了鲜血和血迹。它的脑袋是可怕的，一张巨大的嘴里长满了牙齿，酸液溢出，嘶嘶作响地滴在它的胸前，还有一只布满血丝的单眼。这个生物抬头看着你，眼睛一缩，然后发出悲惨的嚎叫。 “该死……这看起来令人难以置信的悲惨，”你说。 “是的，她知道，”Kyros 笑着说，一边笑着一边听着，就像第一次走路一样，还有六只野兽，跌跌撞撞地爬进了竞技场的中心。 “她？” 你问。 既然您没有与它们作战，您就可以长点心看看这些变异的可憎之物到底是什么：令人厌恶、可怜的生物，应该很快死去。 “你不认识她？” 凯罗斯说，显然是压抑着笑声。“这就是乌艾斯部落的遗迹，第一个出来的，领头羊，是费伊。” 你看着下面的怪物，试图从你的朋友身上看到任何相似之处，因为纯粹的恐惧笼罩了你的脑海。你跪倒在地，吐在栏杆上，吐到下面的怪物身上，Kyros 大笑起来。 “他这样做是为了激怒你。他想让你生气，看看你的领子有多大。他没有夸大你的实力，所以你可能会伤害他，但是……”梅利奥达斯劝告，但你不理会，太愤怒了。 你站起来，扶着栏杆把自己拉起来。你的膝盖在颤抖，但你需要一点时间来恢复力量。然后，您在 Kyros 开始自己的工作。你抓住他的眼睛，你的手指直指他的眼睛。Kyros 跌跌撞撞地向后倒退，毫无准备，在你开始哽咽时，你感到衣领迅速收紧。你继续抓挠和凿击，即使空气被你窒息。最后，你被无形的手向上拖拽，漂浮在离 Kyros 几英尺远的地方。你虚弱的身体几乎没有对 Kyros 造成任何伤害，Kyros 看起来很惊讶，有点好笑而不是生气。 “嗯……我没有夸大你会生气的程度，或者如果你认为和我打架是一种选择，你是多么愚蠢。” “我要杀了你。我要打垮你。我不在乎需要多久，我会摧毁你的灵魂，”你说，盯着他。 Kyros轻声笑了笑，然后强迫你低头盯着竞技场。 “看比赛，”他说。 “我们会报仇雪恨，卡斯。不是今天，也不是明天，但我们会报仇雪恨，”梅利奥达斯说。 你观察了大约一分钟，因为这些可憎的东西坐在那里，混乱地四处游荡，陷入短暂的暴力和自残以及大量的尖叫和嚎叫。终于，第二道门打开了。你看着戴着头巾的人进来，手里拿着剑和圆形的小盾牌。三人向前冲锋，跳入空中。猛兽向前冲锋，凶猛的进攻。你看着领头的那个曾经是 Fay 的东西，几乎被三人组中的一个人斩首。三人组轻松战斗，俯冲、躲避和避开触手、斧头、咬和跺脚。一分钟之内，最后一只可憎的怪物踉踉跄跄地向前摔倒在地，前两条腿被砍断，三人中的一个跳上怪物的头顶，将其刺穿。 “什么显示！” 凯洛斯笑着说道。“我们要见胜利者吗？” 你还没来得及说一句话，Kyros 就松开了你，你从空中坠落，发出痛苦的叫声，重重地摔在泥土里。你痛苦地呻吟着，抬头看着战士。他们向前走，盯着你看。 “卡斯？” 其中一个问道。 你抬头看到 Fay 站在那里，她的连帽长袍上沾满了鲜血，旁边还有另外两个 Uwais 部落，你依稀记得你参加狩猎派对的旅行，Jakar Twins。 “Fay，”你回答，回望她，不知道还能说什么。 费伊把她的剑扔到一边，抓住你并紧紧拥抱你。即使你觉得自己被她拥抱的力量变成了细粉，你还是很高兴你找到了她，你不在乎，拥抱回来。当 Magos Cern 加入你时，Kyros 掉下来，从 Fay 的肩膀上盯着你。 “啊，好开心的重逢啊！” 凯罗斯说。 “你说她死了！” 你愤怒地大喊，把费伊推到一边，用指责的手指指着他。 “我做了。我撒谎了，”Kyros 微笑着说。 “永远不要相信灵能者，Karth。甚至不要相信你自己，”Meliodas 说。 你耸了耸肩，尽量不屈服于他操纵情绪的企图。你抓住Fay，正视她，脱下她的连帽斗篷。她很高，站立时有六英尺高，而且她的肌肉比你记得的要多得多。无论凯罗斯对她做了什么，都产生了严重的影响。她的胳膊和腿肌肉发达，你怀疑你在她身上的任何地方都找不到一盎司的脂肪。尽管如此，她仍然保持着同样腼腆的微笑，眼神中带着狂野、冒险的神情，她几乎还是那个菲伊。 “那么……那兜帽是怎么回事？” 你问，苦苦寻找其他的话要说。 “嗯……这样变种人就不能抓住我的头发把我拖下去了，Karth。这是最重要的问题吗？你好吗？他们对你做了什么？” “来吧，离开竞技场。我有一只被俘虏的史奎格，男孩们想玩得开心，”凯罗斯说。“Jakar双胞胎，回宿舍。Karth，Fay，跟我来。” 你看着 Fay，然后抓住她的手，跟在 Kyros 和 Magos Cern 后面走。 “嗯，牵着我的手走在我身边？我会在村子里背着的老卡斯在哪里？” 菲问。 “噗，如果我没有这个项圈，我会在这个地方漂浮，”你说。 当您与老朋友交谈时，被绑架、部落被奴役和谋杀、村庄被烧毁、被迫穿越地狱深处的精神和灵魂的压力都消失了。你在她讲述的时候讲述你的故事。显然，她大部分时间都处于昏迷状态，身体被劈开并重新制作。她的血液现在含有大量的化学物质和抗击药物，她有几个新器官和更强壮的骨骼，更快的血液凝固等等。她接受了广泛的剑、枪、炸药等方面的训练，以及团队战术。她被选为 Uwais 超级士兵的队长。 “所以基本上，你已经成为一名超级士兵了？” “超级战士？” Magos Cern 说，她的声音充满了你认识她以来从她那里听到的最多的情感。“她是个失败者。耻辱。我可以接触到基因种子、太空海洋测试对象和无数资源。我创造了什么？一个嘲弄。这些“超级士兵”无法像我希望的那样每个人都对付多个太空海军陆战队员。他们不能像我计划的那样对付一个该死的星际战士！他们只值半个星际战士！我失败了！” “他们会做得很好，Magos，”Kyros 说着走到另一组金属门前，打开后露出一张大桌子，里面坐着各种各样的人物。 有 Kyros、Magos Cern 和 Taj Suenage，以及坐在角落里的 Kyros 的女儿。还有更多你不知道的人。一个是一个高大憔悴的女人，皮肤苍白，黑色的头发扎成马尾，穿着一件令人不安的长外套，看起来像是用人皮制成的。另一个是剃光头的女孩，穿着沉重的战斗盔甲，正在磨一把长长的弯刀，另一个是......你无法分辨性别的人，穿着黑色皮甲和内衣，奇怪地试图在战争中看起来性感长而卷曲的黑发和浓妆。这个人对你微笑，舔他的……或她的……或它的嘴唇。然后是一个年轻的女孩，比你还年轻，穿着连帽长袍，正在阅读一本尘土飞扬的古老书籍。最后，有一个几乎没有人类的怪物。一个肥胖的男人满身腐烂的毛孔、疖子、疮和充满脓液的感染伤口，手臂被长而生锈的尖刺所取代。Kyros 和 Magos Cern 坐下。 “这些是舰队的领袖，菲伊和卡斯。先介绍一下。首先是我，整个舰队的最高领袖。塞恩魔导师在船上率领黑暗机械师，并维护着这艘船。战团长泰姬·苏纳奇“不朽的收割者号是宇宙海军陆战队的领导者。然后是第 22 伊斯坦福德团的领袖，掠夺者卡尔珀尼亚。” 那个穿着人皮大衣的人站了起来，伸手一抖。 “很高兴认识你，”她说，同时握着你和菲伊的手。我希望你……在这里逗留期间有效率。” “然后是斯坦恩兄弟姐妹。还有丽贝卡斯坦恩，血爪组织的领袖，一个崇拜战神恐虐的战团。” “操你妈的，灵能者。你既软弱又可怜。你的父母应该让你死去，”丽贝卡咆哮着，用她的刀刃指着你。 “操你妈的，”Fay 咆哮着，将她的刀尖从你的喉咙里推开。 丽贝卡站起来，挥舞着她的剑，在瘟疫缠身的男人站起来之前，用他的一只尖刺手臂挡住了丽贝卡前进的道路。 “嘿嘿嘿！不用暴力！我们都是兄弟姐妹！” 丽贝卡再次低吼着坐下。 “好吧，接下来是 Elios，领导尖叫的鹰身女妖，这是一个崇拜色孽，过分之神的战争乐队，”Kyros 指着穿着皮革和内衣的人说。 “很高兴见到你，亲爱的。对于混沌飞船上的奴隶，我可以说你看起来……很好吃，”Elios 用深沉而男性化的声音说道，舔着嘴唇看着你和 Fay。 “把它放在你的裤子里，Elios，”Kyros 说。“接下来是伊丽莎白。” 看书的戴兜帽女孩伊丽莎白没有抬头。 “她已经上升到独眼乌鸦的领导地位，这是一支崇拜巫神奸奇的战团。她凭借惊人的社交能力轻松达到了这个位置。告诉我们你自己，伊丽莎白。” “往前走，”伊丽莎白说，头也不抬地用手打了个圈。 “好的，伊丽莎白。最后，我们有崇拜瘟神纳垢的黑鼠战帮的领袖德奇。” “你好！” 病人说，挥舞着他的尖刺武器。“很高兴认识你！希望你能被善待，我希望你吃饱了。如果没有，那可能是因为我把这艘船上的食物都吃光了！” 德奇男子大笑起来，几只蛆虫从他的皮肤中爆裂而出。你尽量不要生病。 “你来这里是为了会见这支舰队的领导人，以便你知道你会回答谁。我们希望你们俩都领导奴隶军队。” “奴隶军？” 菲问。 “Uwais 部落士兵，上一批失败的测试对象，角斗士奴隶以及我们可以召集的任何其他东西。我们将把雷管放在你的炸药项圈上，但你会为所有男人拿着一份雷管的副本在你的指挥下。” “之前有一批失败的测试对象吗？” 菲问。 “是的，有过。他们很强大，但他们的心智很容易被打破。大多数没有知觉，有知觉的人疯了。只有一个人可以交谈，我任命的领导者。我过一段时间带你见他们。我猜你会接受这个职位？” “我没有太多选择，”你说。 “我们会接受的，”费伊说，替你们俩回答。 “这支奴隶军队，不管你怎么称呼它，都会给我们带来实力优势，在这个小小的区域内成为一个真正的军事强国。我们可以从弱小和不设防的世界中征服自己的一个小王国。我们可以成为一个掠夺者可以让我们过上奢侈生活的舰队。但是一个小小的奴隶军队甚至都不会给我们机会对抗冥王星的防御！卡尔珀尼亚说。 “你要是再有胆怯的迹象，我就把你的灵魂撕碎吞噬！” 凯罗斯喊道。“魔导师塞恩，带菲伊和卡斯去看看上一批超级战士。” “当我们将它们发布到银河系时，我将不得不让银河系见证我失败的创造的耻辱。我不妨让这两个青少年预览我令人作呕的失败，”Magos Cern 说。 “我们这里还没完！” 卡波妮愤怒地嘟囔道。“我不是为了自杀任务而出动我的军队！” “我的军队！只要你为我效力，就是我的军队！” 基罗斯尖叫起来。 “那么，我们进行投票，”卡尔珀尼亚建议道。 “好吧，如果你不想经历攻击，如果你是个懦夫，如果你不忠诚，请举手。” 卡波妮挑衅地举起双手。Elios 举起他的下一个。 “对不起，Kyros。我不会死……我太年轻太漂亮了，不能在一场无法取胜的战争中死去，”他说。 德奇举起一根钉子，一脸惭愧。 “我很忠诚，Kyros，但我对我的手下和你一样忠诚，我不会看到他们白白死去。” 伊丽莎白慢慢地举起一只手，第一次从她的书上抬起头来。 “这是自杀的计划，Kyros。我们都知道。” Kyros愤怒地咆哮着，环顾四周。 “是我，Cern、Taj 和 Rebecca 负责进攻，Elizabeth、Durge、Elios 和 Calpurnia 没有这样做，”Kyros 低吼道。 “那么，奴隶领袖是决胜局，”卡尔珀尼亚说。 Kyros 盯着你看，抓着你衣领的控制器。嗯，这似乎是一个显而易见的答案，即使它是一个自杀式任务。
梗概：
你关注了 Kyros
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动作：
做
你支持对泰拉的进攻
结果：
你把手放在身边，费伊扬了扬眉毛，她也这样做了。 “胆小……但很聪明，”梅利奥达斯说。“也许你终究会意识到自己的潜力。” “好好！” 凯罗斯说，他的声音充满了宽慰。“奴隶是忠诚的！现在我必须质疑为什么我自己的盟友不忠诚。” 不安的沉默充满了房间，然后 Magos Cern 打破了沉默。 “我要带菲伊和卡斯去检查他们的新部队，老一代的超级战士。这可以接受吗，凯洛斯？” “是的，是的，”Kyros 说，他的注意力显然集中在其他事情上。“那就去吧。” Magos Cern 走出房间，你迅速跟上，Fay 紧随其后。你沿着空荡荡的走廊走向超级士兵。Magos Cern 显然根本没有兴趣交谈，所以你和 Fay 有时间交谈。 “所以，刚刚发生了什么？” 你问。“我们刚刚得到了一支军队吗？” “我们在五分钟内从奴隶变成了奴隶军队的主人，”费伊说。 “好吧，公平地说，我们仍然是奴隶。我们是奴隶，同时也是奴隶军队的奴隶主，”你指出。 “好吧，我想是的。无论如何，你还好吗？我们走了很多路，”费伊说。 “我很好，”你微笑。“我不知道是感谢你照顾我，还是侮辱你认为我连走路都走不了。” 你继续往前走，最终进入一个巨大的金属电梯。电梯迅速开始沿着船下降。 “好的，孩子们。听着。我们要把我们的超级士兵从他们的冷冻睡眠中唤醒。不要做任何突然的动作，不要发出任何巨响，什么都不做。” “明白了，”你们俩说。 电梯门打开，露出墙壁上的数百根金属管，即冷冻舱。Magos Cern 向前走，伸出一根从她的尖刺上长出来的金属触手。触手压在与许多吊舱相连的控制台上，然后打开了三个。冷空气迅速涌出，你看到三个笨拙的怪物走出去。 “看哪，我的失败，” Magos Cern 说。 这些超级士兵身高 8 英尺，由巨大的肌肉线圈组成，几乎是普通人的两倍，没有任何盔甲。它们的嘴里长满了数百颗食肉牙齿，就像鲨鱼的牙齿。它们有从手腕伸出的四英寸短骨爪。他们的皮肤又黑又畸形，就像有人将人的皮肤晒黑制成皮革，然后将其缝合在他们的身体上。他们向前走，咕哝着，咆哮着。第一个环顾四周，然后跌跌撞撞地回到它的冷冻舱内。 “睡……睡……睡……”它咕哝着，走回里面。 “尺寸不是一切……但它确实很重要，”梅利奥达斯评论道。 另外两人立即开始用头撞对方，发出咕噜声，你听到沉重的骨头相互碰撞的声音。Magos Cern 拿出一个小数据板，按下它。两个超级士兵哼了一声，回到他们的冷冻舱。 “这些是超级士兵，这里的守卫给他们起了个绰号‘歌利亚的’。我想这很合适。他们不聪明，但他们害怕鞭子和电项圈，所以他们很容易服从命令。” 为了证明她的观点，Magos Cern 拿出一个小数据板，快速点击它。这对超级士兵痛苦地咆哮着，退回到他们的冷冻舱中。Magos Cern 迅速敲击垫子，cyro-pods 关闭。他大步向前，走向另一辆坦克。 “让我向你介绍样本 401245。它的名字叫凯恩。它是最聪明的野兽，也是最友好的。现在，他来了。” 当 Magos Cern 快速敲击数据板时，cyro-pod 打开。你看到另一个巨大的、笨拙的歌利亚从冰冷舱里游荡出来。它看起来和其他的几乎一模一样，唯一的区别是它的额头上有一对长着角状的骨头，还有它超大的嘴唇用钩子托起来让它呼吸。它走出去，深吸一口气，然后盯着你看。短暂的沉默，Fay 很快就打破了。 “它……会说话吗？” 她问。 “它绝对能听到，”野兽咕哝着回答。 “嗯，你好！” 菲说。“我叫菲。你叫什么名字？” “该隐，”他回答道，他的声音低沉、隆隆、喉咙嘶哑。 “我是卡斯，”你说。 “现在是几奌？” 该隐问道。 “时间？” 你回应。“我不知道。大概是中午之后，或者船用什么作为中午。” “我不在乎。我现在几点了？” “……呃……同时？” “我醒着。我总是睡在冰冷的舱里，直到需要我为止。我只需要被切割、下药、戳刺或刺杀。我什么时候做？杀，或者被撕裂了？” “都没有。我来这里是为了向你们介绍你们手下的新领袖 Fay 和 Karth。” “我的男人？我第一次听说他们叫男人，”Cain说，心不在焉地用爪子互相摩擦。“嗯？他们见过我。还有什么？你想知道什么？”
梗概：
你支持对泰拉的进攻
--------------------
动作：
做
你问一个问题
结果：
“什么？” 该隐咕哝着。
梗概：
你问一个问题
--------------------
动作：
说
“你的名字是怎么来的？”
结果：
“发现它潦草地写在一本丢在垃圾桶里的大部头书上，当他们让我们放手而不是冻结我们的时候。不管有多少数字，“该隐”比主题更容易记住，”该隐说。<|endoftext|
梗概：
你说“你的名字是怎么来的？”
--------------------
动作：
做
结果：
<|startoftext|
梗概：
--------------------
动作：
做
“什么？” 该隐咕哝着。
结果：
梗概：
“什么？” 该隐咕哝着。
--------------------
动作：
说
“你多大了？”
结果：
“几岁了？我不知道。在寒冷的舱内时间很难说。没有太阳，没有天，没有分钟，”凯恩说，似乎在思考这个问题。 “这个科目大约是五年零三个月零两天。” “啊……很有趣，”该隐评论道。
梗概：
你说“你多大了？”
故事名称：
10
故事背景：
“什么？” 该隐咕哝着。
--------------------
动作：
说
“我没有其他问题了。”
结果：
“好吧，”凯恩睡着了。“还有什么？” “不需要其他任何东西，”Magos Cern 说。“回到你的舱。” 该隐点点头，然后走回他的吊舱。冷冻舱关闭，让该隐回到他冰冷、无尽的睡眠中。 “我们应该继续，” Magos Cern 说。“我仍然必须向你展示你将接受的角斗士奴隶和 Uwais 部落。歌利亚将充当你的重型突击部队，角斗士奴隶将充当侦察兵、散兵，并填补你的任何空缺角色有，乌艾斯部落就是你的主力部队，我带你去看看。” 你开始跟随 Magos Cern 离开 cyro 睡眠区，并沿着走廊。十分钟后，你到达一扇巨大的金属门，打开后是一间铺满双层床的大房间，床上躺着熟悉的青铜尸体。 “这是 A 宿舍。我们这里有大约 60 名士兵，” Magos Cern 说。 费伊走进房间，让你喊了一声。 “先生们！你们的船长回来了！” 部落滚下床。你认得他们中的一些人，但奇怪的是，你认出许多人是来自其他不同部落的人。” “菲儿，你回来了！” 一个说。 “战斗进行得如何？花费的时间比预期的要长得多，”另一位补充道。 “哦，该死！那是卡斯吗？” 另一个问。 当部落开始出现时，注意力立即从 Fay 转移到你身上。他们都显得更加肌肉发达，许多人都带着新鲜的伤疤。 “卡斯！你怎么还活着？” 有人不可置信地问道。 “是乌艾斯的萨满！” 另一个问。 “Karth，你好吗？他们对你做了什么？” 你看着部落开始包围你们三个，尽管 Magos Cern 确保他们至少保持六项壮举远离她。 “退后。Karth 只是来见你，而不是跟你说话，”Magos Cern 说，他画了一个数据板，发出一道电击，这让这群人在他们退开之前发出痛苦的叫声。 “怎么这么多其他部落的男女？我还以为是乌艾斯部落被绑架了。” “当我们到达你们的星球时，我们需要做很多工作。我们从湖泊中抽水取水，在地球的地壳中钻孔以获取大量的钷，消灭本地物种作为食物。跑起来需要很多努力一艘这么大的船，为了快速有效地完成任务。在我们这样做的同时，我们的士兵开始工作。他们需要战斗，既是为了训练，又是为了自己的享受而流血。俘虏被大量俘虏，并且我用它们就像我用 Uwais 部落一样。现在，来自其他部落的比来自 Uwais 部落的要多得多。” “嗯……你的家园毁灭了。野心是现在唯一前进的道路，卡斯，没有回头路。我们一起征服星星。” 梅利奥达斯说。 “那么，我想称他们为 Uwais 部落有点不合适。部落会更合适。” “如你所愿。你是他们的上司。你可以随心所欲地对待他们。” “一般部落都会配备战斗装甲以及一些装备，例如手榴弹，防毒面具，通讯设备之类的东西。强者将获得爆弹枪，最强者将配备近战武器；链锯剑，链斧，动力剑，以及爆弹手枪。较弱的将被赋予专门的角色并装备如下：用于反装甲的热熔枪和导弹发射器，带有狙击步枪的狙击手以及带有变色龙斗篷和消音武器的隐身角色。拥有一支装备精良的军队，在与我们指挥的卫兵和叛徒海军陆战队并肩作战时能够保持自己的实力。” “我们他妈的不是为你而战！” 有人喊道。 “是啊！操你妈的，你们这些绑架混蛋！” “啊，这是一个问题，” Magos Cern 说。“不幸的是，由于我……在大脑方面的工作失败，Kyros 禁止我在他们的脑海中摆弄以确保忠诚。Fay 到目前为止还没有做到，所以现在这将是你的工作。我需要一切叛逆要被粉碎。” “如何？” 你问，扬起眉毛。 “完成它。我不在乎怎么做。我需要去检查氧气甲板到右舷的微距枪没有窒息，”Magos 说，转身走开了。 “好吧，那是个婊子，”你抱怨道，转向费伊。“我想我们需要在 Kyros 生气并把我们的脑袋吹干净之前摆脱叛逆。” “或者……”费伊狡黠地说。“我们可以掩饰叛逆。最好保密，直到我们有机会离开这里。” “哦，选择，选择，选择。恐惧，威胁，谈话或信任。应该是哪个，Karth？” 梅利奥达斯说。 你向前走，走进宿舍的中央。您在这里有多种选择。你可以尝试使用你的一些精神力量来向这些部落展示亚空间的真正力量。如果只是出于恐惧，那肯定会确保他们的忠诚度。你可以再次出于恐惧威胁他们以确保他们的忠诚，而且你不必通过让自己看起来像一个可怕的通灵者来疏远自己。您可以尝试说服部落，忠于这个地方是唯一合理的选择。你也可以走 Fay 的道路，向他们保证他们会有报复的机会，但现在他们应该隐藏所有的叛逆，直到罢工的时候。
梗概：
你说“我没有其他问题了。”
--------------------
动作：
做
你用亚空间的力量恐吓部落
结果：
你扯了扯自己的衣领，幸好由于凯洛斯对他“不忠”的部队分心，限制放宽了。没走，肯定还在。你知道如果你尝试任何事情，你会引起他的注意，限制会收紧，你喉咙周围的领子也很可能会收紧。不过，你需要利用这一点并进行完整的表演。 你慢慢地向前走，升到空中。黑色和紫色的电流开始沿着你的身体流动，沿着你自己爆发。你点燃了亚空间的深红色火焰。你改变了你温顺和胆怯的声音，通过亚空间将它放大到蓬勃发展的比例。 “你看到我变成了什么吗？你看到 Kyros 创造了我什么吗？我是混沌的容器，亚空间的存在。这就是亚空间所拥有的力量。这是凯洛斯拥有的力量，这艘船的力量持有，以及守护和操纵它的被诅咒的人的力量。你不可能希望击败它。你应该感谢黑暗之神他们让你扮演的角色对你有益，并接受这一点发挥到极致。” 包括 Fay 在内的部落都退后了，看起来很害怕。你知道在那一刻，恐惧取代了他们灵魂中的所有叛逆。他们现在会服从命令。你蹲下身子，环顾四周。 “传播信息，”你对部落说，然后转向费伊。“我们该走了。” 你走出房间，Fay 紧随其后，他追着你跑。 “那是什么鬼东西？” 她问。 “我做了我的工作，”你简单地说。 “那些是我们的朋友，我们的家人，我们的亲戚。你对为一个绑架我们的人服务的生活不满意吗？这些人杀了 Duggin！” “Duggin 是个傻瓜，”你回答。“我们的生活曾经毫无意义。现在，我们有了希望。你一定明白吗？” “我希望我能说我能，Karth，但我没有。我不知道你怎么会这么想。” 你走到外面，Kyros 微笑着等待着。 “干得好，”他笑着说。“令人印象深刻的展示。” “你是为了那个？” 你问。 “我知道。不然你的衣领限制怎么会弱一些？” “我认为这是因为你分心了，”你承认。 “如果那是真的，那么每当我睡觉时，你就可以随心所欲地做任何你想做的事，”Kyros 笑着说。“现在，我们需要拜访你最后的士兵，角斗士奴隶。他们总是很有趣的船员。我们走吧？” 你看着 Fay，她点点头，虽然她看起来仍然因为你之前“展示”力量而紧张。 “是的，让我们，”你回答。
梗概：
你用亚空间的力量恐吓部落
--------------------
动作：
做
你拜访角斗士奴隶
结果：
当您前往竞技场时，您跟随 Kyros。你到达通向它的门，但不是进入你而是去它后面的一个小电梯。你进入，它慢慢开始下降。它打开了，露出了竞技场的地下巢穴。有十几个笼子，关着奇异的野兽和危险的异形。你会在笼子里看到六只粗壮、肌肉发达、绿皮肤的兽人，他们的脖子被锁在墙上。他们嚎叫着，咆哮着，拉着他们的锁链。令人惊讶的是，虽然大多数人用他们野蛮的喉咙语言大喊大叫，但似乎有人在说你的。 “Humie！Humie！给我自由！给我自由！” 他喊。“如果你让我自由，我会让你活着，甚至给你一点战利品和一个逃生舱！” “不要回应！” 位于竞技场下方的地下墓穴下方有人喊道。“它只会鼓励Ol'Dak！” “嗯……这个兽人一定是同类中最伟大的演讲者之一，”梅利奥达斯指出。“当然对我们来说，他是个该死的白痴。” 兽人的眼睛眯了起来，然后他开始沮丧地嚎叫，用双手拍打自己的头，当他肌肉发达的拳头猛击他厚厚的头骨时发出沉重的撞击声。你继续行走，路过神奇而奇妙的生物和角斗士。有一个长着大喙的触手鼻涕虫人，他蜷缩在笼子角落里的身体周围有几十只眼睛，三个可怕的猎鸟生物在他们的酒吧里抓挠，一个巨大的人类穿着似乎是一个铁笼子，里面有数百个钉子，把自己撞在笼子的墙上，他的身体被撕碎到你惊讶他还没有死的程度，一个多腿长角的生物，看起来几乎很可爱。最后，您到达了一条长长的走廊，厚重的金属门沿着走廊散布开来。一只眼睛从门缝里探出头来，盯着你看。 “你好。我们有更多的战士，还是……？哦等等，是他们。太棒了。” 眼睛消失，插槽关闭。凯洛斯拿出一个数据板，按了几个按钮，门缓缓滑开，露出一大片空地。双层床排列在墙上，有一群年轻的男女，最大的二十多岁，最小的才十几岁。他们穿着很少的盔甲，所有的盔甲都是皮革或轻型战斗盔甲。他们围坐在火堆旁，用木棍烤大块的肉。两个角斗士靠在离你最近的床上，盯着你看。一个身材高大，肌肉发达，皮肤黝黑，有着长长的金发和闪闪发光的绿色眼睛。她拿着一把长长的步枪一样的枪，枪管里有一把斧头刀片，下面有一个把手。另一个较短，皮肤同样晒黑，有着俏皮的绿色眼睛和棕色卷发，拿着一把弯刀和一把等离子手枪。他们看起来最晚都在十几岁或二十出头。 “那是火吗？” 菲问。 “他们有武装吗？” 你也是。 “是的，是的，”男人说，转向女孩。“这个问答游戏很好玩。” “火在一个排放烟雾的通风口下方......某处。我们没有得到这个地方的确切示意图，我们只知道烟雾不会打扰我们。而且我们被允许使用武器来控制其他人“即使我们都选择了Kyros，我们的思想也会在我们举起武器之前破碎，“女孩说。 “不，我至少可以在我被杀之前开始挥杆。” “这些是双胞胎。他们领导角斗士奴隶。现在，介绍一下你们自己，”Kyros 说。 “我是卡斯图斯，”男孩微笑着盯着你们俩。 当他看着费伊时，他的眼睛闪烁着。他似乎在检查她。你想打他的喉咙，但要克制。 “你是个嫉妒的人，对吧？” 梅利奥达斯说。 “我是奥利弗，”女孩说，伸出一只手，在菲伊做同样的事情之前，你抓住并摇晃它。 “那么，你来这里有什么原因吗？” 卡斯塔斯问道。 “费伊和卡斯将指挥角斗士。他们是我的副官，将指挥我们军队的一部分，奴隶。” “哦，太棒了。我们被人盯上了，”卡斯图斯说。 “我相信 Kyros 会希望我们有一个顾问职位来协助 Fay 和 Karth，”Olive 说。 “正确，”Kyros 说。 “嗯，这可能有问题，”卡斯图斯说。“你看，Fay 和 Karth 都有……嗯，谋杀我手下的历史。让 Kyros Jr 和 Magos Cern 最新的超级怪物品牌对抗我们，导致很多人对我们感到愤怒，这是可以理解的，很生气那些劈腿的人，或者精神上爆炸的人，或者干脆把他们的朋友打倒在地，直到他们昏迷。” “如果你愿意，我们可以把持不同政见者活活鞭打。只要指出他们，我就打断他们。这样更可取吗？” 卡斯塔斯安静下来。 “正如我想的那样，”Kyros 笑着说。“确保男人们知道服从命令。现在已经很晚了，这些人需要睡觉。还有什么需要吗？” “我想不会，”奥利弗说。 “嗯，我们需要很多，但我们实际上在短期内得不到任何东西，”卡斯图斯说。 Kyros盯着Castus，火焰开始沿着他的身体蔓延。 “不需要，长官，”卡斯图斯说。 “很好。Fay，Karth，我们走吧，”Kyros 说。 Kyros转过身，走出了房间。你迅速跟上他，巨大的金属门在你身后关上了。 “所以，我们会护送 Fay 回 Uwais 宿舍，然后送你到你的房间，”Kyros 说。 “嗯，你知道，Karth 的房间更近。我可以在他的房间里度过一段时间，”Fay 建议道。“既然我们知道我们的人有什么能力，那么我们就有时间来讨论作战计划，我可以告诉他乌韦斯部落在战斗中的样子……” “真的吗？这就是原因？这就是你要去的原因？” Kyros挑起眉毛问道。“不。”
梗概：
你拜访角斗士奴隶
--------------------
动作：
做
你争辩说 Fay 应该在你的房间里过夜
结果：
“但她可以帮助我计划如何处理中尉以及如何确保他们的忠诚，而且……” “别跟我他妈的，Karth。你只是想要那个女孩在那里，这样你就可以有他妈的东西。也许如果你对我的行为方式更聪明一点，我会给你这种卑鄙的快乐，但你表现得像你在这里的时候一直是个刺，所以不，她会回到她的床上，你会回到她的床上。” “令人惊讶的是，屄并不会因为你想上床就不再是屄。除非我错了，你真的没有计划或不想和她一起尝试任何事情，在这种情况下，色孽将永远无法抓住你的灵魂，”梅利奥达斯说。 你到达牢房的门，Kyros 打开了它。 “快点，进去，”Kyros 说。 你看着Fay，他对你微笑。 “再见，Karth。谢谢你的尝试，”她说，轻轻地挥了挥手。 “再见，Fay，”你回答。 门关上了，你走到你的床上，倒在床上。现在才知道自己有多累。你花了一整天的时间走路，这又不是你的专长。你闭上眼睛，很快就入睡了。值得庆幸的是，您的灵魂可以得到休息，疲惫的心灵可以找到平静的暮光阶段，会持续很长时间。当然，没过多久，噩梦就来了。
梗概：
你争辩说 Fay 应该在你的房间里过夜
--------------------
动作：
做
你睡觉
结果：
你痛苦地扭动着，蜷缩着试图结束无尽的痛苦。你所听到的只是数以千计的骷髅在嘲笑你的喋喋不休。你凝视着一座高耸入云的头骨山。你可以看到金属的野兽咆哮着冲过头骨，相互撞击，产生强大的冲突，龙一样的野兽从头顶飞过。 你惊慌地吸了口气，从附近的一个人身边跌跌撞撞地摔倒。你沿着骷髅翻滚，滚下骷髅山。你撞上一头巨大野兽的头骨，结束了你的翻滚。你低头看到一条巨大的沸腾血液河流，可怕的骨头生物从水中爆裂并再次潜入水下。你疯狂地尝试使用你的力量，从这个可怕的地方飘离，但你能感觉到一只无形的手环着你的脖子，紧紧地围着你的气管，阻止你呼吸，把你拴在地上，阻止你使用你的力量. 它......从那里开始并不顺利。你在无尽的骷髅山中游荡，躲避漫游的恶魔野兽的恐惧。最终，你猛地惊醒，发现自己躺在床上，一身冷汗，赤身裸体，害怕极了。值得庆幸的是，剩下的夜晚并没有那么糟糕。你梦寐以求的怪物远离你。睡眠……有点可以忍受。
梗概：
你睡觉
--------------------
动作：
做
你几个小时后...
结果：
你在金属门打开的声音中醒来。你站起来，抬头看到凯洛斯和菲进入房间。你意识到自己赤身裸体，抓起一条毯子裹在自己身上。费伊咯咯地笑着。 “嗯，这是一个惊喜。公平地说，一个相当小的，”她笑着说。 你发现你的衣领有一点余地，并通过手腕的轻弹急切地使用它，并再次将Fay送到她身后。项圈迅速收紧您的喉咙，使您呼吸困难但并非无法忍受。你举手投降，Kyros松开衣领。 “我们要去作战室，”他轻声但令人毛骨悚然地说。 今天的 Kyros 似乎更黑了。他的外套上布满了几个深红色的斑点和污渍。你怀疑询问他们的起源是个好主意。你站起来，跟着 Kyros 沿着大厅走。 “作战室里有什么？” 你问。 “我们只是在开会。现在安静，”Kyros 说。 你默默地沿着走廊走，经过我在走廊上奔跑、工作的各种船员。Kyros 走得很快，而你试图按照他的步调走路时会出汗。 “我们可以慢点吗？” 菲问。 “你是基因升级的超级战士，快步走真的太过分了吗？” 凯罗斯继续以他的速度说道。 “这对卡斯来说太过分了，”她说。 “我……很好，”你喘着粗气。 “不，你不是，”费伊说。“体育锻炼不完全是你的强项。” “他有精力说话，他有精力走路！现在安静！” Kyros 挥了挥手腕，Fay 大叫起来。一道细长的伤口从她的脸颊滑落，鲜血顺着她的下巴流下。 “嘿！” 你喊。 你站起身来，凝视着 Kyros 的眼睛。他的眼神狂野而残忍。几个小时前他还保持的幽默和快乐已经消失了。你意识到他会非常乐意严重伤害你和费伊。你退缩。 “这就是我的想法。现在更快，我们已经浪费时间了！” 凯罗斯说。 你加快速度，尽可能快地移动。很快你就到达了作战室。门开了，你停了下来。里面，躺在她的座位上，是卡尔珀尼亚。她所有的皮肤都……不见了，似乎从她身上剥落了。鲜血覆盖了她的座位、她周围的地板和桌子。她的眼睛不见了，似乎被非人的爪子挖了出来。她发出一声痛苦的呻吟，你突然意识到她还活着。 “这他妈的……？” 菲说。 Magos Cern 出现在你身后，向前走去。她盯着卡尔珀尼亚，似乎不感兴趣。 “这与我有关吗？” 她问。 “不，老朋友。这关系到其他人，”Kyros 说，他的声音低沉咆哮。 Kyros 不在意地在桌边坐下。Taj 进来了，但甚至没有回应，只是在桌子上坐了下来。卡尔珀尼亚举起一只手像是在乞求帮助，但泰姬只是往桌子上吐了一口唾沫。 “叛徒，”他咕哝道。 你看着 Durge 走进来，Elizabeth 站在他身边。德奇停顿了一下，看着卡尔珀尼亚，然后看着凯洛斯，伊丽莎白变得非常苍白。 “这到底是什么？” 德奇低头盯着卡尔珀尼亚问道。 他庞大而臃肿的身躯蹒跚着向前，站在流血的近乎尸体上。 “你发生了什么事，他问道，然后将一根尖刺手臂推到伊丽莎白面前并将她推回去，在他盯着房间的其余部分时防御性地走到她面前。“这是谁干的？！ 丽贝卡走了进来，脸上挂着笑容。 “哦，这是谁干的？” 她问，显然是假装惊讶。 最后，埃利奥斯走进来，停下来，环顾房间，大吃一惊。 “什么……怎么了？” 他问道，脸色变得比他粉笔白的妆容还要苍白。 “看来我们的好朋友卡尔珀尼亚在深夜遭到袭击，”凯罗斯说。“想象一下她感受到了多少痛苦。她伤口的痛苦。她背叛的痛苦。谁听到可能背叛了他们？谁会在深夜密谋背刺一个人，背叛给他们这样的人多吗？会不会是埃利奥斯？杜吉？伊丽莎白？” “操……你，”卡尔珀尼亚说，然后把头靠在桌子上。 Calpurnia 尖叫着，她手臂上裸露的肉开始煮熟，Kyros 瞪着她。德奇举起一根钉子，穿过卡尔珀尼亚的脑袋。 “够了。没有必要再延长她的痛苦了，”Durge 说，瞪着 Kyros。 “我想。也许她死了最好。你知道吗，Durge？Magos Cern、Taj 战团长和 Rebecca 都来找我，告诉我 Calpurnia 正在策划一场政变，她正在招募所有派系领导人。我敢肯定她还没来得及问你，伊丽莎白和埃利奥斯，否则你会向我报告的。对吗？” 杜奇盯着凯洛斯，一言不发。 “嗯，我会派人调查一下，谁可能对我们亲爱的朋友这样做。任何人，至于这次会议的目的，我只是想告诉你，我们都没有 Recaf，我会得到它被明天取代。我还想我必须说，在卡尔珀尼亚去世后，我正在控制第 33 伊斯坦福德团。现在，每个人都回到他们的职责。我们处于危险时期。谁知道谁会拥有接下来……意外？” Kyros走出房间，你和Fay跟着他。他立即让你开始工作。他让你和你的几个新人一起训练，用少数歌利亚、部落和角斗士与几十个螃蟹一样的外星人战斗，学习他们如何战斗。部落仍然使用他们的狩猎战术和用来对抗无休止的绿皮攻击的战争方法，所以你会发现你可以轻松地带领他们取得胜利。 歌利亚是边缘白痴，即使在激烈的战斗中，他们也会被自己和彼此分心，当他们的大手无法成功重新加载爆弹枪时会感到沮丧，并且非常乐意打破阵型向敌人冲锋。该隐证明了自己是一位可靠的领导者，他向你发出命令，并用巨大而强大的肺将它们吼叫出来，声音大到足以让歌利亚恢复暴力。歌利亚用他们极端的力量弥补了他们的精神缺陷，承受了超出你想象的惩罚，同时用他们强大的打击把钢铁变成了尘土。 角斗士的战斗风格很有趣。由于角斗士的生活方式，他们并没有真正接受过成为大部队一员的训练，只知道如何自生自灭，或者可能与六人组成的小部队并肩作战。他们是优秀的、经验丰富的战士，但尽管他们经验丰富，但他们仍然年轻而狂野，在战斗中承担不必要的风险并且表现得过于骄傲。虽然卡斯图斯很狂野，除了对敌人大喊大叫，召集他的手下并在必要时领导冲锋之外，几乎没有在战斗中扮演领导者的角色，但奥利弗冷静、镇定并充当真正的领导者，吐出战场战术，允许惨败。角斗士也有一定的表演技巧，这无疑是因为他们先是表演者，然后才是战士。角斗士的攻击使流血最大化，并具有奢侈但不必要的敏捷专长，这需要从他们的战斗学说中消除。 总而言之，你新发现的力量是相当称职的。你对他们的战斗能力感到满意，这是由你巨大的力量所支撑的，唯一的出路就是这些竞技场战斗。经过几个小时的长时间战斗，正当你筋疲力尽无法继续战斗时，外星人喷出的巨大金属门砰地关上了。 你在空中盘旋，发射一束五颜六色的火焰，吞噬一个螃蟹外星人，然后俯冲下来，直接向另一个人没有盔甲的脸发射通灵冲击。你的部队消灭了剩余的野兽，很快战斗就结束了，让你被尸体、鲜血和爆弹炮弹包围。你穿过的金属门打开，你倒在地上，感觉你的衣领再次开始限制你的精神力，然后站起来开始走回里面。 “干得好，伙计们。你们今天证明了自己，”你说。 您的四名守卫在门口等待，与其他数十名守卫一起确保你们没有人犯错。角斗士被带到竞技场的下面，歌利亚回到 cyro-sleep，部落回到他们的牢房宿舍。你很快被护送到你的房间，门在你身后关上。你向前走去，迅速脱下沾满鲜血、汗水的衣服，然后瘫倒在床上，把头埋在枕头里。
梗概：
你几个小时后...
--------------------
动作：
做
你去睡觉
结果：
“卡斯！” 梅利奥达斯的声音出现在你的脑海中。“往后看，笨蛋！” 你在床上迅速转过身来，举起拳头，准备使用小小的灵能，看到艾略斯靠在你浴室的门口。 “该死！你他妈的在干什么！？” 你喊。 “嗯……男人不能只是向后靠，享受生活中最美好的风景之一吗？” 埃利奥斯一边说，一边眨着眼睛，自信地向你走来。 “得到...！” 你想说话，但他的手指抵在你的唇上，靠得很近。 “安静，宝贝。不用担心。我不是来给你添麻烦的。我听说可以帮你解决某个小……问题。” Elios 的手沿着你裸露的胸膛一直摸到你的衣领，玩味地拉扯它。你盯着他，等待。 “好吧，让我们现实一点。我们会被屠杀。如果我们幸运的话，当 Kyros 领导他对帝国的愚蠢冲锋时，我们将被杀死。如果我们不走运，我们将最终就像卡尔珀尼亚一样，被一个偏执的灵能者折磨致死。” “你有被折磨致死的危险。我从来没有背叛过他，”你说。 “哦，宝贝，你为什么要这么难？” Elios 微笑着，他的两根手指沿着你的腿，慢慢靠近你的腹股沟。“宝贝，目前，Kyros 正在寻求消灭那些会背叛他的人。但他越来越偏执了。他最终会认为你会背叛他，不管你是不是。让我们公平一点，你确实有动机。你的部落被摧毁或奴役，被迫承受灵能者的麻烦？见鬼，如果你有一个逃生计划，我也不会感到惊讶。见鬼，即使他没有，你的一个男人会表现得叛逆，或者战斗会出错，你会为此受到指责。你需要和我们在一起，宝贝。” “假设我正在考虑。它会带来什么？” 你问。 “你需要让领导们站在你这边。Fay、Olive、Castus，谁负责我之前看到你和你一起工作的那些大怪物男孩。让他们站在你这边，同时我会和伊丽莎白和杜奇谈谈维持叛逆的事情Calpurnia 的孩子们的态度，确保他们仍然准备进行一点兵变。” “当我们坐船时会发生什么？” 你问。 “等我们确定了其他首领之后再决定。我相信他们有自己的要求。所以你在吗？” “嗯……这显然是我们唯一的选择，但你应该知道 Elios 既想操你又想操你，”梅利奥达斯说。 “我很确定我的另一个选择是死亡。那甚至不是一个选择，”你说。 “好吧，这就是我给你的，宝贝，”Elios 说，轻轻地弹了一下你的阴茎尖，让你大叫，然后爬回你的床上。
梗概：
你去睡觉
--------------------
动作：
做
您同意
结果：
埃利奥斯上前，敲了敲金属门。守卫打开，立即举起爆矢枪。埃利奥斯挥了挥手。 “你没看到我在这里。色孽的声音在呼唤你，你现在是他的了。移开，不要告诉任何人我在这里。” “我……不……是的，长官，”一个人说，走到一边。 你看着 Elios 走出房间，在他离开房间时最后微笑并挥手致意。 你睡了一会儿，躺在毯子里。幸运的是，只有少数黑暗、充满恐惧的梦，其余的只会让你充满一种模糊的、不安的恐惧感。你醒来发现 Magos Cern 用白热的钳子戳你的胃。 “啊！” 你喊。“什么鬼？” “我的诊断结果告诉我，这样我可以快 0.4 秒叫醒你。我们得走了，”Magos Cern 说。 “在哪里？” “首先，你需要食物。然后，你会得到一些植入物。我正在为......“歌利亚”进行一些早该升级的升级，正如你所说的，Kyros让我用铅笔给你一些生物升级。” “然后？” 你问 “近战练习。在暗杀威胁之后，Kyros 希望你能够像远程或灵能攻击一样轻松地抵御近战暗杀。” “我的力量可以做到这一点，”你说。 “在你做出反应之前，你会被击穿。Castus 和 Olive 会进行一些练习来让你的反应时间更长，并为你提供一些适合你所有战斗的躲避技巧。” “然后？” “到那时，随着植入物的恢复时间，你虚弱的身体将需要睡眠。” “好”，你说。 Magos Cern 带你走出房间，沿着走廊走。你会发现自己身处一个巨大的自助餐厅。你向前走去，发现 Fay 坐在一个严重烧伤的女孩旁边，长辫子缠在她身上，另一个用锋利的机械爪代替了假肢。费伊抬起头，嘴里挂着面条，发现了你。 “哦，卡斯！” 她一边说，一边挥手，然后才注意到她嘴里塞满了面条，脸红了。 你坐在她旁边，她笑了。 “那么你过得怎么样？” 她问。 “我们需要谈谈。独自一人，”你说，看着她身边的两个部落。 “呃……好吧，”Fay 说，示意两个部落移动，他们很快就这样做了。 “谢谢，”当他们离开时你对部落说。 “所以？这和我想的有关吗？” “你认为这是关于什么的？” 你问，扬起眉毛。 “嗯，你知道……你想单独和我说话……私下……所以我猜是关于我们的。” “是的，当然是关于我们的，所以我们都在这里谈论它。” “不，我的意思是……就像我们我们一样，”Fay 说，显然对你的不理解感到沮丧。 “什么？” “嗯，比如，你想让我们成为一对吗？”
梗概：
您同意
--------------------
动作：
说
“是啊，我想出去。”
结果：
“哦真的吗？” 菲笑着说。 “不是吗？” “啊啊啊啊啊啊！” 费伊笑着说。“我实际上只是想问你这个。我很高兴你答应了。” Fay 微笑着，倾身在你的唇上轻轻吻了一下。你盯着她亮绿色的眼睛，瞬间的吻持续了无限长，感觉她嘴唇的温暖涌入了你，然后她收回了手。 “嗯，那太好了。理想情况下，我们的下一次约会将发生在我们不会被部落和想要我们死的可怕守卫包围的地方，”Fay 微笑着。“是这样，还是另有原因？” “是啊，我们有重要的事情要讨论。” “那不是很重要吗？” 费伊微笑着问道。 “哦，不，是的，我只是……我的意思是对部落很重要，而不仅仅是对我们，”你说，绊倒了你的话。 “看到你变得和我一样尴尬，感觉真好，”Fay 咯咯地笑着。“现在，别吐槽了。深呼吸，告诉我什么如此重要。” “叛乱。” “哦？” “Kyros 又疯又偏执。他要么割断我们的喉咙，要么派我们去执行无休止的自杀任务。我们需要做点什么，我真的需要你的帮助。一群合作者已经计划起来，摆脱我们属于 Kyros。然后，我们接管。我需要你们部落的支持才能这样做。 “是的，完全。完成了。” “完毕？” “是啊。再不逃，就死在这里。而且，他们也是你们的部落，和我的一样。还有谁参与其中？” 菲问。 “Elios、Durge 和Elizabeth 都在。毫无疑问，Calpurnia 的人会帮助我们，”你说。 “该隐和双胞胎呢？” “我还没有问他们，但我们需要让他们加入，”你说。 “我们有一个可靠的计划吗？” 菲问。 “还没有。我们需要弄清楚有多少人为我们而战。” “我们乘坐这艘船后会发生什么，”Fay 扬起眉毛问道。 “那还有待决定。一旦我们有了该隐。卡斯图斯和奥利弗在我们身后，我们就有了讨价还价的能力。所以你在？” “我在，”费伊点点头。“我会让消息在部落中传播。” 你注意到你的一名守卫端着托盘朝你走来。你示意菲伊安静下来，因为守卫过来把托盘放在桌子上。 “吃吧，”他说，声音冰冷，就像刀片在生锈的金属上刮擦一样。 你看看你的食物。有一碗面条和几条薄的腌肉，一小碗绿色果冻，里面有大块奇怪的外星水果，一大杯塑料酒和一个小而密的饼干状蛋糕。 “嗯，有人被宠着了，”费伊笑着说。 “很明显，我只是比你重要，”你拿着一叉面条和肉，幸灾乐祸。 费伊从你的托盘里拿起饼干蛋糕，咬了一口。 “嘿！” 你生气地说，菲伊伸出舌头。 你很快吃完饭，把整个托盘擦干净，一口喝下酒。然后，Magos Cern 来了。 “很好，你吃过了，”她说。“如果你吃饱了，这些手术会更安全。” “好了，我准备好了，走吧，你说。”再见，菲伊。再见。” “待会见，菲说，吹了一个吻。 Magos Cern 带你走出自助餐厅，沿着大厅，沿着大厅和电梯离开。最终，您会通过 cyro-sleep 部分，到达一个大手术室。数十个歌利亚躺在手术台上，其中许多已经手术到一半了。六名其他科技牧师在歌利亚之间游荡，移除和添加肉、骨头、器官、控制论植入物和其他奇怪的东西。 “我不知道还有其他技术牧师，”你说。 “这艘船有一个小月亮那么大。你以为你看够了它，可以准确地说我们没有什么吗？我有几十个科技牧师跟着我进入了黑暗的怀抱诸神，”Magos Cern 说。 Magos Cern 带您到手术台。 “这是你的，请脱下衣服在这里等着，我要拿材料和装备，留在这里。” Magos Cern 转身离开，留下你一个人。你看到凯恩躺在手术台上，他的胸部被切开，露出仍在运作的器官。你想知道他的躯干如此匆忙地被切开，他怎么能还活着，然后当你看到他的眼睛朝你飞来时你缩了缩，你意识到他是有意识的。看着 Magos Cern 蹒跚而行，你跳下手术台走到他身边。 “Karth 指挥官，”Cain 咕哝道。“需要某物？” “我有……一个提议。” “报价？什么报价？” 该隐说道，心不在焉地在他的……或者至少是他的一个胃旁边挖了一个伤口。 “自由，”你说，引起了怪物的注意。 “继续，”该隐说，皱起他伤痕累累的眉毛，从他的角里流出细细的血流。 “我们正在计划逃跑。我们可以利用你和你的人，”你说。 “我们？” “Elios、Durge、Elizabeth、Fay，还有其他人，”你说。“就是这样，我想。” “什么时候？” “我们还没有详细信息。我们需要先统计一下人数，然后才能开始计划。” “嗯……我愿意让你控制我的……‘歌利亚’，正如你所说的那样。但是，我有一个条件清单，一旦我们登上这艘船，我就想满足。” “继续，”你说。 “我想让我们得到名字。每一个能够记住和回答一个名字的歌利亚都会被你和所有其他指挥部成员称为这个名字而不是它的序列号。不再是cyro-sleep。我想要一张床“每一个歌利亚。我不在乎它是细布还是一块混凝土，只要它不冻结它们，它就会帮助我们。我想要的不仅仅是杀戮的空闲时间。” “还要别的吗？” 你问。 “就是这样，”Cain说，吐出一颗从下巴上掉下来的锯齿。 该隐的要求绝非不公平。真的，他想要的只是给他的歌利亚多一点权利，这甚至不会占用更多资源。不过，你不希望野兽有一个大脑袋。你不仅负责他的项圈，还负责每一个歌利亚的项圈。你所需要的只是一些威胁，歌利亚就会是你的。
梗概：
你说“是啊，我想出去。”
--------------------
动作：
做
您同意该隐的要求
结果：
“成交，”你说。 “好，好，”Cain点点头，伸出一只手。 你抓住他的手，谢天谢地该隐没有试图挤压，这几乎肯定会导致你脆弱的骨头碎裂。你使劲地颤抖，Cain 尝试微笑。他的嘴角不自然地翘起，露出一排排锯齿状的牙齿、弯曲的獠牙、巨大的臼齿和许多其他奇怪的、奇怪的牙齿，从血腥、腐烂的牙龈中突出。你笑着回应，不愿意得罪他。 “野兽会拿走他要求的一半，”梅利奥达斯说。 “你之前怎么不告诉我？” 你生气地说。 “我猜你知道，”梅利奥达斯回答。 “你还好吗？” 该隐说。“告诉你什么？” “呃，没什么。抱歉，”你说。 “好吧。很划算，”Cain说，他的脸扭曲成你认为是他扬眉的版本。“好吧，最好在 Magos Cern 回来之前为你的行动做好准备。” “是的，我应该，”你说。 你走到手术台前，迅速脱掉衣服，然后躺下。又过了一两分钟，Magos Cern 出现了，把一个托盘推到她面前。她的一根金属卷须抓住了一个注射器，里面装满了苍白的半透明液体。 “这可能会受到一点伤害。它更有可能会受到更多伤害，”她说。 当她把注射器塞进你的脖子时，你发出痛苦的叫声。当您希望将麻醉剂泵入您体内时，她开始向下推柱塞。温暖的双手拥抱你，将你拉入无意识的遗忘。
梗概：
您同意该隐的要求
--------------------
动作：
做
你拥抱黑暗
结果：
你的眼睛平静地闭上了，你失去了知觉。 不知过了多久，你醒来，躺在金属手术台上。Magos Cern 站在你身边，在一盆水中清洗她的工具。她看着你，她冰冷的红色眼睛凝视着你的灵魂。 “啊，你醒了。很好，”她说。 “你给我装了什么？” 你问。 “我所做的生物学超出了你的范围，肉包。” “你能不能替我把它哑口无言？” 你问。 “能不能把协奏曲变成变种人的咕哝呢？” Magos Cern 愤愤不平地问道。 “请？” “很好。我把你的心脏扩大了 40%，肺扩大了 12%，并增加了几条从心脏直接通向大脑的静脉和动脉。这些动作增加了流向大脑的血流量，因此应该会增加通灵能力“我添加了多个微芯片，可以培育单核细胞、其他白细胞、血小板和其他对身体有益的东西。这是我的大部分工作。除此之外，这只是一次体检。” “好的，”你说。 “我现在有很多重要的工作要做。你应该有足够的时间恢复。” 你的两个庞大的太空海军陆战队员到达了，他们厚厚的头盔上有一张咆哮的脸。 “给你，” Magos Cern 说。“卫兵，走慢点。如果病人在任何时候开始吐血，让他坐下十分钟，也许十五分钟。” “那你不应该让他听听观察吗？” 其中一名警卫问道。 “我有太多的工作要做，”Magos Cern 说。“别打扰我。如果有什么问题，去找Genetor Corivan。他在医疗方面很有经验。” “是的，长官，”星际战士说。 Magos Cern 迅速离开，走向一排电梯。星际战士盯着你看，你呻吟着从床上爬起来。 “啊，我的头，”你说。 “接下来的 48 小时你应该会头疼，”就在她消失在电梯里之前，Magos Cern 喊道。 你站起来，感觉一股血涌到了你的头上。你跟随星际战士，沿着大厅和上下电梯，直到你发现自己在舞台上。Castus 和 Olive 在那里，在战斗中。 “哦，听着，这简直是个笨蛋和该死的笨蛋，”梅利奥达斯说。“连笑都没有？哦等等，你不是随着童谣长大的，你来自一个野性世界。我的错。” 卡斯图斯向她挥舞着剑，而奥利弗则将她的步枪斧头挥向他的头。他滚开，把奥利弗的脚踢开，把她摔倒在地，然后用脚踢了她的头。她倒下，然后滚开，避免卡斯图斯向下刺伤。她抓住卡斯图斯的剑柄向他猛击。Castus 向后倒下，然后摆出战斗姿势。奥利弗开始围着他转，她的枪斧已经准备好了。她发现你，微笑。 “Karth 来了，”她说。 “胡说八道，”卡斯图斯说，准备好他的剑。 “Karth，告诉他你在这里，”Olive 说。 “是的，我在这里，”你说。 Castus 转身看着你，但 Olive 挥舞着她的枪斧冲了过来。她把脚从 Castus 身上敲开，把他的膝盖放在 Castus 的肚子上。在 Olive 抓起她的枪斧并将它握在 Castus 的喉咙之前，Castus 哼了一声。 “死了，”她说。 “不公平，我们应该在 Karth 到这里时停下来，”Castus 说。 “你还是死了，”奥利弗微笑着站了起来。 Castus 抓住了 Olive 的手腕，将她拉向前，这样他的剑就刺入了她的腹部。 “死了，”他笑着说。 “严重地？” Olive翻了个白眼说。“好吧，我死了。我们完成了吗？这孩子需要训练。” “好吧，”Castus耸了耸肩。“让我们开始吧。” 卡斯图斯从腰带里抽出一把匕首，盯着它看。刀刃是亮银色的，上面刻有奇怪的图案。它的手柄是明亮的浅蓝色，手柄末端有一个银色的短角。 “现在，这绝对是一个绝美之物。我真的用得不够，所以它是你的。异种人，所以即使拥有它也是异端。刀片锋利，它可以比它更容易切肉和骨头“通过空气。在这里。” Castus 转身，将匕首直接扔在你的头上。
梗概：
你拥抱黑暗
--------------------
动作：
做
你鸭
结果：
你低下头，刀从你的头顶飞过，杂乱地落在你身后的地板上。 “抓得好，”卡斯图斯说。 你拿起刀，练习刺伤。 “你觉得怎么样？” “这是一把刀，”你简单地说。 “是的，是的。你喜欢吗？感觉好吗？” 卡斯图斯说。 “我对刀一无所知。也许吧，”你说。 “好吧，首先，我们需要谈谈。有人在看我们吗？比如，有摄像头吗？” “没有。嗯，是的，但他们不在线，”奥利弗说，把头靠在她的斧枪枪托上。“它是什么？” “正在制定计划，我们要逃跑。” “逃脱？” 奥利弗沉思着说。“谁站在我们这边？” “我们这边有很多人。埃利奥斯、伊丽莎白和德奇正在把他们的战队以及大部分（如果不是全部）卫兵带到这里。然后是歌利亚和部落，两者都是谁和我们在一起。” “什么...？” “因为我以前做过两次，我最好说。我们还没有计划。他们有待决定。我们实际上什么都没有。我们只需要检查你是否会加入我们。然后，我们会得到领导们一起计划我们的接管，以及一旦我们真正这样做了该怎么办。” “嗯。好吧，我进去了，”Castus 说。 “什么？” 奥利弗惊讶地问道。 “是的，我在，”Castus 说。 “我们不知道计划，我们没有任何保证，我们一接手就不会被背刺，我们……” “奥利弗，我们还是聪明点吧。如果我们不反抗，我们肯定要么被凯洛斯的战争打死，要么是为了平息这些混蛋的叛乱而被打死，要么是为了这些混蛋的消遣再打一场竞技场战。” “如果我们造反，我们几乎肯定会被杀！” “几乎肯定比肯定要好得多。而且，在我们下去之前，我们会杀死几个 Kyros 的混蛋。” “好吧，我在，但我……我们有一些要求，”奥利弗说。 “继续，”你说。 您知道角斗士对您的计划至关重要，因此您确实需要获得它们，但您仍然不想在此问题上投入大量资源。 “一旦我们接管，我想要立即自由。不再被锁在竞技场下。我希望我的人有自由的统治权，可以在船上旅行。我想要报酬，做为船而战和工作的男人和女人“实际上，从中得到一些东西。我想要增加口粮，以及比我们提供的狗屎更好的设备。除此之外，我还想要真正的医疗服务。” “那个？” 你问，对她的大要求很恼火。 可能需要大量资源才能满足她的要求。你有几个选择。 “真是个他妈的贪婪的婊子。这个混蛋需要一个家伙，我告诉你，”梅利奥达斯抱怨道。
梗概：
你鸭
--------------------
动作：
做
你同意她的要求
结果：
“完成了，”你说。 Olive伸出一只手，你紧紧握住并摇晃它。Castus 伸出他的手，你抓住了它，但他扭着你的胳膊，让你痛苦地大叫。他踢开你的脚，让你跪倒在地，然后将他的剑举到你的喉咙。 “死了，”他笑着说。 “该死的，Castus，你怎么了？” “什么？我只是在为他准备战斗。我以为我们应该训练他。第一课，孩子：不要相信任何人。现在，起床。” Olive 微笑着向你伸出手。
梗概：
你同意她的要求
--------------------
动作：
做
你让她帮你起来
结果：
你抓住奥利弗的手，她把你拉起来。 “啊，干得好，”卡斯图斯说。“第二条规则：第一条规则是胡说八道。你知道谁不信任任何人吗？像Kyros这样的混蛋，最终没有忠诚的朋友，没有盟友，什么也没有，最后一个人，没有人。如果你想活下去在这个世界上，你需要盟友。” 卡斯图斯抓起他的剑，递给你。你伸手去抓住它，然后他挥动它并在热的一侧打你，把你打倒在地。Castus 向你伸出手，你接受了。他把你拉起来，然后把你跪在肚子里，把你扔到地上。 “得了吧，你不是在学习。第一条规则：不要相信任何人。你不听吗？” “我要遵守我的第二条规则，”你呻吟着说。 “有时，第一条规则比第二条规则更重要，”Castus 耸耸肩。 “我他妈的该怎么告诉我们哪条规则之间的区别？” 你问，生气。 “打败我。等你知道了，你将成为历史上最强大的军阀。在那之前，你只会失去直觉和智慧。” “看起来像一个糟糕的系统，”你说，因为 Castus 终于让你站起来了。 “看起来你所处的世界很糟糕。那个系统是我所能提供的最好的系统。现在，规则三：肉搏战，寻找弱点。喉咙、眼睛和球总是好的，但是当谈到这些操蛋的一半，我会一直盯着眼睛，因为过去了，我不知道你还剩下什么。观察。” Castus 翻转他的剑，所以他再次握住了刀刃，将他的袖子裹在了刀刃上，这样它就不会像他的手指那样咬到太多。他向前一步，以疯狂的速度挥舞着他的剑。鞍头猛击您的喉咙，然后刺入您的眼睛，让您跌跌撞撞地向后倒退。在 Castus 将鞍头刺入您的腹股沟之前，您迅速合拢双腿，然后您痛苦地尖叫着向后倒下。 “看到了吗？有效，”Castus 微笑着。“规则四：你可以通过多种方式获胜。痛苦屈服，使他们丧失能力，或者干脆夺走他们的生命。判断哪些可以用来打倒你的敌人。例如，在你的情况下，痛苦是前进的道路。” 你已经厌倦了被打脸。当 Castus 举起剑向前迈出一步时，您决定要采取措施阻止他继续攻击您。
梗概：
你让她帮你起来
--------------------
动作：
做
你将 Castus 的剑冻结在原地
结果：
你用你的心灵遥控将剑举在半空中，卡斯塔斯困惑地咕哝着，因为他发现他的武器卡在空中，因为它是混凝土。 “我不需要学习如何使用剑，卡斯塔斯。你一生都在用剑训练。我从来没有。但你看我解除你的武装是多么简单？我需要一把剑，就像你需要尖牙一样。我已经进化到不需要它了，”你说，然后将剑咔嗒一声扔到地上。 “说得对，孩子，”Castus 说，同时你让他的剑悬浮在他身边。“我想你可以走了。训练结束。” “太棒了，”你说。“那么，我可以离开了吗？” “是的，我会叫你的守卫在这里打开大门，把你带走，”Castus 说，抓起挂在他腰带上的对讲机对着它说话。 “是的，我们到此结束，”他说。“妈的，结束了。” “肯定的，出去，”声音又回来了。 很快，大门升起，你的卫兵穿着厚厚的动力装甲漫步出去。 “接下来去哪里，孩子们？” 你问。 “闭嘴，灵能者，”一个咆哮着看着卡斯图斯和奥利弗。“你们两个回牢房了吗？” “我们很高兴在这里多练习一些，”奥利弗说，一边转动她的枪斧，以阻止卡斯图斯的突然袭击。“我的意思是，如果这个愚蠢的混蛋很少练习就认为这会让我失望，那么他需要更多的练习。” “噗，我想看你把我打倒，”卡斯图斯说。 “就这样吧，”你的俘虏咆哮道。“移动它，灵能者。” 你防御性地举起双手，让他们护送你回到你的房间。
梗概：
你将 Castus 的剑冻结在原地
--------------------
动作：
做
三天后...
结果：
在接下来的几天里，Elios、Durge 或 Elizabeth 都没有任何消息。还有更多平常的，训练，战斗等等。最终，当你把头放在枕头上时，你听到了一个熟悉的声音。 “卡斯，戴着那个项圈和你说话很难受。你能不能开始检查阴影，这样我就不必这样做了？” 梅利奥达斯说。 你在床上转来转去，发现角落里有一个熟悉的身影，穿着紧身的、非常暴露的黑色皮革，化着浓妆。 “为什么我们会有这样见面的习惯？” 埃利奥斯说。 “操，”你说，从床上跳起来。 “这是命令吗？因为我非常愿意……服务，”他说，舔了舔嘴唇。 “你为什么在这？” “我们见面了。我们有计划。我们去吗？” 埃利奥斯微笑。 “呃……当然，”你回答。 埃利奥斯大步走到门边，敲了敲门。守卫开门，埃利奥斯再次欺骗了他们，让他们轻松地走到一边。 “你是怎样做的？” 你问，当你走过你的守卫。 “为色孽服务是有好处的。魅力、美貌、使人们屈服于你的欲望的能力，以及永恒的……耐力，”他说，最后一个字。 “啊，很高兴知道，”你说。 你漫步穿过走廊，沿着一条你以前从未走过的路走。最终，您发现自己在一个看似废弃的区域，进入一个像作战室一样布置的大房间。 “这是旧的作战室。Kyros 有一个新的作战室，建在一个更安全的位置，不受登船的影响。从这里，我们进行兵变。” 你环顾房间，注意谁在这里。德奇坐在一张大木椅上，用尖刺的手臂沿着他的肚子刺痛。伊丽莎白坐在角落里，阅读数据板。一个看起来二十多岁的年轻人靠在墙上，抽着烟，抓着浓密的胡茬，光头，满脸伤痕，皮肤蜡黄。该隐正在抓挠他那坚硬的裸露胸膛上的伤痕累累的伤口。Castus 正在玩刀，而 Olive 靠在墙上，从食堂喝水。费伊靠在墙上，立刻注意到了你。 “嘿，Karth，”她微笑着走向你。“埃利奥斯是怎么把你从牢房里弄出来的？” “守卫有什么欺骗手段。你？” “伊丽莎白以某种方式让守卫睡着了，”菲回答。 “好了，孩子们。我们都到齐了。我们开始吧？” “是的，让我们来看看这个，”Durge 说。 当你看到毛茸茸的东西从他的肠子里爬出来时，Durge 站起来，抓着他的胃。你尽量不要生病，或者简单地点燃整个被称为德奇的怪物来烧灼他令人作呕的形式，然后再让自己确信他站在你这边。 “咝咝咝咝咝咳，”Durge 说着，拍了拍自己的肚子，然后从大金属桌上抓起一个遥控器，打开船的蓝色大全息图，漩涡之吼。“她来了。我们需要开始计划如何掌握这艘船的控制权。记住，这不是一个星球。我们不会通过占领比敌人更多的领土来赢得胜利。我们需要占领必要的区域。” 随着各个部分变成不同的颜色，全息图开始发生变化。 “这些黑色区域被船员遗弃。它们要么是不必要的货舱、未使用的宿舍，要么是功能冗余、已被其他部分接管或由留在这些部分的任何人履行的功能部分。” “我以为他们被遗弃了，”费伊说。 “被船员遗弃，不是被所有人遗弃。你必须记住这艘船有多大。某些部分的船员是奴隶或变种人，他们由于与船员缺乏联系，已经退化到野性状态。他们中的许多人现在履行他们的职责，例如清洁管道或抽气或工作发电机，因为他们认为这是一种宗教仪式，是他们的食物、水和废水管道继续运行所必需的，或者只是不知道为什么而这样做。” “啊。多么......知道这是现实真是太可怕了，”Fay 说。 “他们的牺牲是必要的，”Durge 耸了耸肩。“现在，我将继续。这些棕色部分完全由变种人管理。这些变种人对任何一方都不忠诚，并扮演非必要的角色，或者至少是短期内非必要的角色。对于这个术语来说……哗变，这些空间和废弃区域一样无关紧要。现在，我们开始讨论重要的事情。这些绿色区域是，或者至少肯定会由我们控制。黑老鼠的地板，尖叫的鹰身人的地板，独眼鸦的楼层，竞技场和竞技场地下墓穴，cyro-sleep 区，医疗舱，部落的宿舍，和第 22 团的宿舍。第 22 团在大多数情况下都在我们的指挥下，尽管他们是 Kyros 的忠实拥护者，所以他们需要一些时间来解决兵变中的问题。这些红色部分是敌人的。Kyros 的房间，血爪的地板，技术牧师的部分，不朽者使用的地板收割者，以及那些几乎可以肯定的人控制的楼层 忠于 Kyros，无论他们来自同化的战团，还是他第一次占领这艘船时的原始盟友。” 突然，全息图再次移动，部分变成黑色、灰色、蓝色、绿色或金色。 “这些黑色部分无关紧要。忽略它们。这些灰色区域无关紧要，但仍然有好处。应该捕获蓝色部分。绿色部分比绿色部分更重要，应该” 被优先考虑。这些黄金部分，指挥桥，引擎和大炮甲板，是完全必要的。我们需要这些才能取得胜利。现在，伊丽莎白将详细说明目标，埃利奥斯将分工。 伊丽莎白站起身，走向全息影像，德奇倒在他的座位上。 “谢谢你，兄弟。现在，我们有四个主要目标。第一个是杀死 Kyros。如果我们幸运的话，那应该会让他的大部分部队投降。希望 Rebecca 也会投降，”伊丽莎白说。 “她不会投降，”埃利奥斯说。 “她可能会，”德奇满怀希望地说。 “我认识她的时间最长。她从未放弃。当爸爸让她选择以打破谷仓的窗户以换取一半的睫毛时道歉时，她向他的眼睛吐了口水。只要她还剩下一条腿， “她会和我们打架。我把她的头算作肢体，”埃利奥斯说。 “嗯……我们可以抱有希望，”伊丽莎白说。“第二个目标是夺取黄金分割区。第三个和第四个目标分别是捕获尽可能多的仆从和德雷克·阿玛迪斯。” “Drake Amadeus 到底是谁，仆人又是什么？” 卡斯塔斯大声问道。 “仆从是部分生物机器，由机器和罪犯、奴隶或克隆人的身体制成。他们没有知觉，但被技术牧师控制并用作他们的劳动力。他们负责维护船。捕获尽可能多的人应该使权力的转移......更容易。德雷克·阿马德乌斯是一个非常强大的导航员，一个变种人，拥有心灵的第三只眼，可以看穿亚空间并允许我们导航亚空间。德雷克是为数不多的强大到足以让这么大的船安全航行的导航员之一。如果我们不保护他，我们将失去以光速航行的所有能力。换句话说，我们在太空中漂流直到我们都饿死。所以，德雷克是一个重要的目标。这是我们的四个主要目标。如果我们在其中任何一个目标中失败，我们就会在所有方面都失败。还有其他几十个目标，例如获得医疗用品，寻找食物，你有什么。那些可以发给 个人团体。埃利奥斯？” Elios站起来，在Elizabeth坐下时漫步到全息影像前。 “现在，我们拥有一支庞大的军队。尖叫的鹰身人、黑鼠、独眼乌鸦、第 22 团、部落、歌利亚和角斗士。我们甚至拥有……特殊资产，”他说，看着你。“虽然这些派系中的每一个都相当有效，但它们结合起来却无限强大。这里列出了你将指挥的部队，以及你将指挥的部队中的哪些将被转移到另一个领导者。” 各种各样的羊皮纸和数据板，技术水平的奇怪组合，被传递出去。当你意识到你没有得到任何东西时，你扬起了眉毛。 “为什么我没有得到什么？” 你问。 埃利奥斯和你一样微笑。 “让你大部队浪费资源，小部队让你消灭太多敌人，无法守住已经占领的阵地。所以，我们要用你的作为一个特殊的物品。我们派你去抓俘虏。释放你认为值得信赖的人，保护他们。最重要的是，保护德雷克·阿玛迪斯。明白了吗？” “是的，明白了，”你说。 “等你来了，我们就重新集结兵力，出手干掉凯洛斯和他的副手。” “明白了，”你说。 “什么时候？” 该隐用他那可怕的声音问道。 “很快。在接下来的几天内。我们需要时间来传播命令，做好准备，而伊丽莎白需要时间找到一种方法来停用安装在奴隶身上的各种项圈和炸药。随时做好准备，”德奇说。 “我们怎么知道什么时候罢工？” 奥利弗问道。 “我们将放置炸药，以消灭尽可能多的忠诚者。当你听到巨大的爆炸声，或者如果警报开始时你离得太远，你的项圈应该停用，因为我成功了，我会的， ”然后立即攻击并立即前往你的目标。明白了吗？伊丽莎白说。 “知道了，”奥利弗点点头。 Elios 开始分发文件。 “看看这些，然后把它们烧掉，”埃利奥斯说。 你传递了一个文件，并急切地抓住它。
梗概：
三天后...
--------------------
动作：
做
你看看文件
结果：
您查看文件会看到三个副标题：Drake Amadeus、The Penthouse、Charles Antonguella 和舞刀剧团。这些文件似乎是打印出来的和手写的混合体，导致您假设它们来自不同的来源。
梗概：
你看看文件
--------------------
动作：
做
你读文件：德雷克·阿玛迪斯
结果：
姓名：德雷克·阿玛迪斯 物种：人类，亚种导航员 年龄：27 婚姻状况：未婚 儿童：无 起源之家：杰纳西斯家族 当前位置：The Penthouse, Roar of the Vortex 血型：阳性 额外突变：无 精神疾病：反社会者、虐待狂、自恋者、 联盟：无 家属：无 有用的英特尔： - 德雷克迄今为止被证明没有忠诚度，背叛了他以前的所有雇主。不要相信他。 - 德雷克拒绝向任何黑暗诸神，甚至混沌不分者宣誓 - 德雷克已被证明有许多享乐主义倾向，例如卖淫、大量吸毒和类似活动。 - 德雷克有足够的火器和近战武器。有必要让他远离所有武器。
梗概：
你读文件：德雷克·阿玛迪斯
故事名称：
11
故事背景：
您查看文件会看到三个副标题：Drake Amadeus、阁楼、Charles Antonguella 和刀锋舞团。这些文件似乎是打印出来的和手写的混合体，导致您假设它们来自不同的来源。
--------------------
动作：
做
你读完文件
结果：
你把文件拿出来，还给 Elios。 “燃烧吧，亲爱的，”埃利奥斯说。 你耸了耸肩，因为文件在你的指尖点燃，变成了灰烬。Fay 注意到你已经完成了，并将她的文件交给你。 “你把这个给我看？” 你问，糊涂。 “没有，卡斯，但周围没有蜡烛。在这种时候，有一个可以充当打火机的朋友是件好事。” 你迅速点燃了文件，它又一次变成了灰烬。 “谢谢你，”费伊说。“你的任务看起来怎么样？” “很简单。如果我能把这个项圈取下来，我会没事的。如果不能，我就完蛋了，”你说。 “我相信伊丽莎白能把项圈取下来。她看起来很聪明，”费伊说。 “我是，”伊丽莎白说，并没有从她的档案中抬起头。 “我们到此结束了吗？” 你问。 “当然可以，亲爱的，”Elios 站起来说。“要我送你回房间吗？” “当然可以，”你说，Fay 急切地点点头。 你站着，跟着 Elios 沿着走廊走。几分钟后，您会到达部落的宿舍，门口两侧站着两名混沌战士。 “嘿，你不应该离开你的牢房！” 一个说，举起他的爆矢枪。 Elios打了个响指，两个守卫停了下来。 “你什么也没看到，”埃利奥斯说。 “是的……是的，长官，”当埃利奥斯打开门时，星际战士回答。 “晚安，菲，”伊丽莎白说。 “晚安，卡斯，”她说，然后消失在宿舍里。 你快速走向你的房间，几分钟后到达那里。Elios 对你的守卫使用同样的伎俩，因为他们服从 Elios 的命令。他打开门，示意你进去。 “晚安，Karth。我有很多事要安排，所以我不能给你留宿的机会。好好睡吧，亲爱的，”关上门时他说。 你摇摇头，走到床边，倒在床上。很快，你就睡着了。
梗概：
你读完文件
--------------------
动作：
做
两天后...
结果：
你和四名混沌海军陆战队守卫一起走在走廊上。你前往作战室，在那里 Kyros 正在计划对帝国的第一次打击，接管一个蜂巢世界，以确保可以招募的大量人口来支持 Kyros 的军队十倍。你哼着一首曲子，一些古老的 Uwais 战争圣歌，你觉得很吸引人。你感到一阵恶心袭来，你停了下来，因为你感到沉重的重量从你的灵魂上卸下，你的阴霾被清除了。你的衣领咔哒一声从你身上掉下来，撞到了地上。你的守卫停下来，注意到你已经停下了。 “犯人，你怎么……？” 一名警卫问道，然后才发现你的领子掉了下来。 “等等……”当警报开始响起时，他开始说。 “哈哈哈！自由！自由！” 梅利奥达斯哭了。 然后，你罢工。第一个守卫的头猛地一转，从肩膀上撕下了他的头。你迅速点燃了第二个，保护自己免受随着守卫咆哮而开始融化厚厚盔甲的明亮蓝色火焰。当爆弹枪猛烈撞击你曾经所在的走廊时，你传送到最后两个人后面。你用一股精神能量在一个人的胸膛上炸开一个洞，然后把最后一个人悬浮在空中，把他的爆弹枪扔过房间。 “不不不！” 星际战士咆哮着，颤抖着试图抓住你。 “杀死！致残！燃烧！破坏！粉碎！粉碎！屠杀！” 梅利奥达斯兴高采烈地吟唱。 你反复将星际战士砸在地上，打碎盔甲，让他在地上留下血迹。你试着给自己定位，试着想象你从房间到这里的路径，并记住阁楼在哪里。 “兄弟！阿尔法囚徒安全吗？” 收音机噼啪作响。 你转身，注意到第一个星际战士腰带上的无线电。你拿起它，你周围的灵能在你说话时振动，把你的声音变成星际战士的声音。 “模仿他同伴的声音。使用亚空间，”梅利奥达斯建议道。 “我们是被迫杀了他的，情况如何？” “摆脱了。我们的兄弟背叛了我们。独眼乌鸦，黑鼠，尖叫鹰身人，都是叛徒！22号正在战斗，但我不知道大多数是否与Kyros在一起。” “有什么打算？” “Kyros 让我们在消灭叛徒和试图平息正在发生的奴隶起义之间产生分歧。第一代和第二代超级士兵正在叛乱。带上你的部队，保护角斗士。如果他们确保了竞技场，他们将拥有强大的防守位置。” “明白了。我们会搞定的，”你说。 “把他们送到他们的收割者那里，兄弟，并避开你自己的收割者，”无线电爆裂声。 “荣耀归于 Kyros，”你说。“赞美黑暗诸神！” “手感很好，”梅利奥达斯说。 收音机结束，你把它夹在腰带上。你漂浮在空中，沿着走廊盘旋向阁楼。你经过三个挥舞着霰弹枪的血爪，冲向 cyro-chambers。你发射一束火焰，吞没三人组，但甚至没有放慢速度看着他们死去。很快，你发现自己在电梯里，朝顶层公寓上升。你耐心等待，听收音机里的报道。 “整个地区都挤满了他们！他们都是叛徒。我……” “桥梁固定，向前推进。” “退后，退后，他们不下去了！” “愤怒！杀戮！致残！粉碎！血为血神！骷髅为骷髅王座！” “带头冲锋！保护Elizabeth Staine，他们都会……该死！啊！” “该死！我被封印了！给我滚出去，该死的！他们来找我们了！” 电梯门打开，让你面临着五个重型爆矢枪瞄准你。 “是灵能者！” 有人喊道。 “该死了，凡人！” 梅利奥达斯咆哮。
梗概：
两天后...
--------------------
动作：
做
你快速发射一些火弩箭
结果：
你从每只手上喷出火球，立即将两个爆矢枪和他们的操作员吞没在火焰中，他们发出惊恐的尖叫声。可惜，另外三人着火了。子弹击中了你的男孩，一颗很快就找到了你的头并将其炸开。
梗概：
你快速发射一些火弩箭
故事名称：
12
故事背景：
你和四名混沌海军陆战队守卫一起走在走廊上。你前往作战室，在那里 Kyros 正在计划对帝国的第一次打击，接管一个蜂巢世界，以确保可以招募的大量人口来支持 Kyros 的军队十倍。你哼着一首曲子，一些古老的 Uwais 战争圣歌，你觉得很吸引人。你感到一阵恶心袭来，你停了下来，因为你感到沉重的重量从你的灵魂上卸下，你的阴霾被清除了。你的衣领咔哒一声从你身上掉下来，撞到了地上。你的守卫停下来，注意到你已经停下了。 “犯人，你怎么……？” 一名警卫问道，然后才发现你的领子掉了下来。 “等等……”当警报开始响起时，他开始说。 “哈哈哈！自由！自由！” 梅利奥达斯哭了。 然后，你罢工。第一个守卫的头猛地一转，从肩膀上撕下了他的头。你迅速点燃了第二个，保护自己免受随着守卫咆哮而开始融化厚厚盔甲的明亮蓝色火焰。当爆弹枪猛烈撞击你曾经所在的走廊时，你传送到最后两个人后面。你用一股精神能量在一个人的胸膛上炸开一个洞，然后把最后一个人悬浮在空中，把他的爆弹枪扔过房间。 “不不不！” 星际战士咆哮着，颤抖着试图抓住你。 “杀死！致残！燃烧！破坏！粉碎！粉碎！屠杀！” 梅利奥达斯兴高采烈地吟唱。 你反复将星际战士砸在地上，打碎盔甲，让他在地上留下血迹。你试着给自己定位，试着想象你从房间到这里的路径，并记住阁楼在哪里。 “兄弟！阿尔法囚徒安全吗？” 收音机噼啪作响。 你转身，注意到第一个星际战士腰带上的无线电。你拿起它，你周围的灵能在你说话时振动，把你的声音变成星际战士的声音。 “模仿他同伴的声音。使用亚空间，”梅利奥达斯建议道。 “我们是被迫杀了他的，情况如何？” “摆脱了。我们的兄弟背叛了我们。独眼乌鸦，黑鼠，尖叫鹰身人，都是叛徒！22号正在战斗，但我不知道大多数是否与Kyros在一起。” “有什么打算？” “Kyros 让我们在消灭叛徒和试图平息正在发生的奴隶起义之间产生分歧。第一代和第二代超级士兵正在叛乱。带上你的部队，保护角斗士。如果他们确保了竞技场，他们将拥有强大的防守位置。” “明白了。我们会搞定的，”你说。 “把他们送到他们的收割者那里，兄弟，并避开你自己的收割者，”无线电爆裂声。 “荣耀归于 Kyros，”你说。“赞美黑暗诸神！” “手感很好，”梅利奥达斯说。 收音机结束，你把它夹在腰带上。你漂浮在空中，沿着走廊盘旋向阁楼。你经过三个挥舞着霰弹枪的血爪，冲向 cyro-chambers。你发射一束火焰，吞没三人组，但甚至没有放慢速度看着他们死去。很快，你发现自己在电梯里，朝顶层公寓上升。你耐心等待，听收音机里的报道。 “整个地区都挤满了他们！他们都是叛徒。我……” “桥梁固定，向前推进。” “退后，退后，他们不下去了！” “愤怒！杀戮！致残！粉碎！血为血神！骷髅为骷髅王座！” “带头冲锋！保护Elizabeth Staine，他们都会……该死！啊！” “该死！我被封印了！给我滚出去，该死的！他们来找我们了！” 电梯门打开，让你面临着五个重型爆矢枪瞄准你。 “是灵能者！” 有人喊道。 “该死了，凡人！” 梅利奥达斯咆哮。
--------------------
动作：
做
你传送到重型爆矢枪后面
结果：
你从他们的视线中消失，出现在他们身后。你喷出一股火焰，吞没了两个重型爆矢枪并杀死了他们的操作员。你转过身来，向另一个人发射精神能量，然后吐出闪电杀死最后两个操作员。一分钟后，您就会到达一扇带有对讲机的厚重金属门。
梗概：
你传送到重型爆矢枪后面
--------------------
动作：
做
您使用对讲机
结果：
你按下按钮，一个声音噼啪作响。 “密码？” 低沉的声音问道。
梗概：
您使用对讲机
--------------------
动作：
做
你试图通过物理方式探查男人的心智以获取密码
结果：
你感受他的思想，并轻轻地测试它的想法。 “我应该对Kyros保持忠诚吗？操，我可以坚持……不，不。” “我想知道米姆图是否需要更多的食物。我应该买更多的食物。” “这个蠢货是说铁路还是我必须杀了他？” “这是铁路，”你说。 门咔哒一声打开。 “好吧，进去。快点，”他说。
梗概：
你试图通过物理方式探查男人的心智以获取密码
--------------------
动作：
做
你走进去
结果：
“我能闻到他的味道。他在这里，”梅利奥达斯说。 踏入门内，望向一座奇妙的宫殿。有一个巨大的大楼梯，上面有一盏巨大的蜡烛吊灯，这是房间唯一的照明。旁边是一尊镶嵌着宝石的黑曜石龙雕像，散发着由鲜红色和橙色宝石制成的长长的火焰，坐在大理石桌子旁边，桌上摆满了烤肉、精致的糕点和美酒。你看到 Amadeus 躺在一张大丝绸沙发上，穿着一件敞开的红色丝绸连帽长袍。在他的额头上，闭着第三只眼睛，而他平常的目光则聚焦在眼前这个正在口交的膝盖上的女孩。你瞬间脸红了，然后才想起你是一个非常强大的灵能者，而不是一个他妈的孩子。 “Drake Amadeus，你跟我来，”你说，同时 Drake 大叫一声，把女孩从他身上踢开，跌跌撞撞地往后退。 “妈的，这是喀斯特！这是喀斯特！把他他妈的离我远点！” 你上前一步，就看到十几个女孩从楼上的房间里出来，朝着大楼梯走去。他们以疯狂的灵活性移动。有些人背着头走路，头从两腿间向外张望，然后把头转了一百零八度。有些人四肢着地，他们的腿向后伸展到肩膀周围，让他们可以以一个奇怪的角度向前走。他们动作优雅，然后各自拔出一对弯刀，继续向你走来。你看到 Drake Amadeus 从眼角溜走，但似乎你有更大的威胁。 “啊，Slaaneeshi 妓女。让他们快点死。他们只是喜欢你把它画出来，”梅利奥达斯说。
梗概：
你走进去
--------------------
动作：
做
你射出一股火焰来阻止剧团
结果：
你射出一道火焰，舞刃迅速撤出你的火力范围。他们沿着边缘跳舞，等待攻击。
梗概：
你射出一股火焰来阻止剧团
--------------------
动作：
做
你走上楼梯，喷火
结果：
你走上楼梯，向剧团喷火。不幸的是，当你上楼梯时，这会让你足够远而无法击中他们，但足够近以至于他们在跳跃距离内，因为你在他们下方。剧团出击，跳下栏杆进攻。马上，他们中的三个就在你身上。在第三个飞向你之前，你飞出一个并点燃第二个。你向后退开，避免被剑击中，然后你的背部被刺穿，你跪倒在地，然后一击将你的头砍下。
梗概：
你走上楼梯，喷火
故事名称：
13
故事背景：
你射出一道火焰，舞刃迅速撤出你的射程。他们沿着边缘跳舞，等待攻击。
--------------------
动作：
做
你从远处发射通灵箭
结果：
当它们飞向敌人时，你发射螺栓。剧团的动作不断地躲避冲击波，大部分都没有击中，只有少数落地。你用你的火力与剧团保持距离，并用你的精神能量将杀戮归零。在你杀死一些人后，你意识到他们的策略，他们尝试冲锋，尽管能量爆发和亚空间的力量流过你，你还是很容易地消灭了剧团。
梗概：
你从远处发射通灵箭
--------------------
动作：
做
你找到艾玛迪斯
结果：
你从杀戮的现场转过身去，朝着 Amadeus 驶过的房间走去。一道微弱的光线从门里射进来，你一脚踢进去，很容易就找到了躺在空浴缸里的导航员，手里拿着一把匕首，正拿着一把匕首指向一个半穿着的妓女的刀，这个妓女看起来还是十几岁的样子。 “住手！肌肉不要动！你再靠近我，我就割断这婊子的喉咙！” 德雷克大喊大叫，对你咆哮。 “他认为那行得通吗？傻瓜，”梅利奥达斯笑道。
梗概：
你找到艾玛迪斯
--------------------
动作：
做
你杀了妓女
结果：
眼睛一眯，妓女的脖子就断了。德雷克松开她的身体，盯着你。 “你……好吧，你是个威胁，”德雷克说，放下刀站了起来。 “你跟我来，”你说。 “当然，当然，”德雷克说。 你用无形的锁链把德雷克的手绑在一起，迫使他向前走。德雷克立即变得顺从。 “我可以拿一些东西吗？” 德雷克问道。“我的……梦境变得很糟糕。我做了可怕的噩梦。我有药可以帮助。拜托，我能得到它们吗？” 德雷克问道。
梗概：
你杀了妓女
--------------------
动作：
做
你允许德雷克取回他的药物
结果：
“去吧，”你说，德雷克感激地点点头，跑到柜台前，开始从桌子上收集各种药丸和香膏罐，在抽屉里翻找。突然，你听到身后传来嘶嘶声。
梗概：
你允许德雷克取回他的药物
--------------------
动作：
做
你赶紧转身去调查噪音
结果：
你转过身来，发现一个金属盒子开始隆隆作响，散发出树木气味的空气。然后，你听到一声巨响，你的头爆炸了，把房间漆成了红色。
梗概：
你赶紧转身去调查噪音
故事名称：
14
故事背景：
“走吧，”你说，德雷克感激地点点头，跑到柜台前，开始从桌子上收集各种药丸和香脂罐，在抽屉里翻找。突然，你听到身后传来嘶嘶声。
--------------------
动作：
做
你一直在看德雷克
结果：
你看着德雷克继续寻找药丸和香膏，然后拔出猎枪瞄准你的头。你挥动手腕，将霰弹枪射过房间。你绑住德雷克的手，把他抬起来飞向你。 “求求你，对不起，我不能被抓住。他们会杀了他们！” 德雷克可怜兮兮的问道。 “他们？” 你问。 “是的。Kyros 把我的两个孩子囚禁在这艘船上，我的两个宝贝女儿。如果我不每小时向他报告，他会伤害他们！” “我看过你的报告。你没有孩子。” 德雷克恐惧和绝望的表情慢慢变成了笑容。 “好吧，你抓到我了。我们走吧？” 你咆哮着，把德雷克打在地上。在你再次将他抬起来之前，他发出了痛苦的叫喊声。 “给我一个不杀你的理由，”你说。 “操你妈的。去杀了我就行了，”德雷克笑着说，嘴里吐出鲜血，咧嘴一笑，牙齿染血。 “这就是你的最终答案吗？” 你问。 “你需要我活着才能驾驶这艘船，混蛋，”德雷克说。 “也许吧。但我可以轻而易举地剥掉你的皮，把你的骨头变成灰尘，烧焦你的肉，扭断你的四肢。” “你需要快乐，卡斯，”德雷克笑着说。“我的工作是驾船，确保我们不会迷失在亚空间，不会被恶魔登上，或者确保我们不会从亚空间出来直接进入太阳。如果我的生命不值得过，比如有人剥了我的皮，把我的骨头变成了灰尘，烧焦了我的肉，扭断了我的四肢，我只是确保这艘船直奔星星。” “打碎他。亚空间的力量会把你送到你想去的地方。你不需要导航员。把这只老鼠撕碎！” 梅利奥达斯说。 你咆哮着走进走廊，德雷克漂浮在你身后。你迅速到达电梯，把德雷克扔进去。
梗概：
你一直在看德雷克
--------------------
动作：
做
你前往作战室
结果：
电梯门打开，露出了装满船的另一个无尽的走廊。六具尸体，赤身裸体，浑身是血，手里攥着剑，躺在地上，旁边躺着两个身患重病、腐烂的士兵，拿着地狱枪。 “咦，血爪vs黑鼠，你跟哪个？” 阿玛迪斯问道。 “闭嘴，”你说。 “因为我听说有传言说 Kyros 想要进军泰拉。那会得到血爪的青睐，但我怀疑这些传言是真的。” “他们是，”你说。 “什么都别告诉他！我几乎能尝到他的黏糊糊的味道！他会利用他掌握的任何信息来对付你，”梅利奥达斯责备道。 “嗯。如果你早点告诉我，我就不会试图谋杀你，”德雷克说。“我知道对泰拉的攻击和其他人一样愚蠢。” “闭嘴，”你说。 “好的，好的，明白了，”德雷克说。 你跨过尸体，开始走向作战室。你看到一双穿着长皮夹克的血爪，他们的脸上沾满了鲜血，就像战争涂料一样，用激光枪拱起。你握紧拳头，将他们的两个脑袋压在了一起。你继续走。很快，你就到了作战室的门口。你猛地敲门。 “哟，谁啊？” 一个你不认识的声音回复了答案。 “喀斯特，”你回答。 门打开了，露出几天前那个满脸伤疤、剃光头的士兵。 “妈的，Karst。你得到了 Amadeus。干得好，进去，”他说，把你拉进去。 你进入作战室，看到不同的领导人在工作。Olive 和 Elizabeth 正在查看船上的地图，Durge 与三名病重、腐烂的士兵交谈，据你所知，他们不应该活着。埃利奥斯在角落里抽一根烟，一边阅读数据板。Elios 立刻注意到了你，微笑着。 “Drake Amadeus！看看我们抓到了谁，”他笑着走到你面前。 “埃利奥斯。你是……” Elios 猛击 Drake 的头部一侧，将他击倒在地，抓住他的头发将他拉了起来。 “我会让他护送到牢房，亲爱的，”Elios 对你说，然后转身把 Drake 拖走。“对了，约翰尼小子在找你。” “WHO？” “我，”剃光的、伤痕累累的男人伸出手说。“乔恩·哈茨上尉，第 22 伊斯坦福德团。” “你需要什么？” 你问，握着他的手。 “我们要出去占领附近的一个军械库，以便更好地装备我们的人。我可以用灵能者以我的方式在战斗中获胜。你进去了吗？ “当然，”你回答。 “你需要重新装备还是什么？” “没有，我很好。” “那么，让我们弹跳吧，”海耶斯船长说，从附近的桌子上抓起一把激光枪，推开了门。 你们一起沿着走廊移动。旧作战室周围人烟稀少，已经被叛军占领了。 “那么，你是在叛徒叛变之前还是之后加入军团的？” 你问。 “从第一场战斗开始我就在那里。我记得部署。我在这里，十五岁，手里拿着一把激光枪。我们降落的星球只有他妈的丛林和河流。我们应该消灭他妈的叛军在丛林里，但他们到处都是他妈的。但是叛军，他们不是狗屎。我们本可以干掉叛军的。指挥部没有告诉我们那里有兽人。事实证明，我们在 WAAAGH 几周后登陆！达兹克击中了。所以我们被困在兽人和游击队之间的三路混战中。那甚至不是丛林。我向上帝发誓，丛林他妈的是有知觉的，它所感受到的只是仇恨。野兽袭击了我们我们的睡眠，虫子让我们感到恶心，下雨不停，泥泞的武器和该死的敌人不会投降。两周后我被任命为中士。该死的两周。我十五岁，带领士兵参战。我们只是继续战斗。他妈的援军没有来，援助没有来，伤员和我们一起死了。我 mperium 并没有在意我们。皇帝没有。大家都说了。叛军显然是通过走私和制造毒品来支持他们的战争机器。显然，他们使用世界上的大量植物来制造战斗药物、麻醉品和狗屎。我和几个人消灭了一些走私者，让我们感到寒冷、疲倦和毒品。在那之后，我们开始针对走私者，整个团。男人们在他们的生活中获得了一些乐趣，我们使用了战斗药物来提高我们的效率，他妈的，走私者是在支持敌人！出了什么问题？！” “他有能力闭嘴吗？” 梅利奥达斯抱怨道。 海耶斯船长从他的左手杖上拖了一段很长的时间，他跨过一个变异的星际战士的尸体，踩在它巨大的、细长的舌头上发出一声闷响。 “所以我们就这样，在这个世界上战斗。有一天，丹顿政委对着喉咙开了一枪。这家伙是个不错的混蛋，坚强，从来没有做错任何事。他很高兴让我们成为，只要我们付出对他吹了几口气，让他和当地的妓女们免费拜访了几次。这家伙被杀了，我们被派了一个新的政委。我们拥有他的第一天，我们就失去了五十个人。二十人是他让我们以自杀罪名起诉我们的占据敌机枪阵地，如果我们聪明的话，我们会用大炮消灭。另外三十个？孩子们。孩子们，大约在我加入的年龄。这已经是我们部署七年了，七年不变战争。现在，我是船长。乔恩·海耶斯船长。当然，刚从运输船上下来的孩子们很害怕。我们让他们在战斗中躲起来，直到他们准备好战斗。那个该死的政委称他们为懦夫并处决了他们全部。我们从指控中回来发现他谋杀了我们的兄弟。所以，我们对他进行了冰敷。把这个混蛋枪杀了。T 三个星期后，一位审判官出现了。你见过审神者吗？” “不，”你承认。 “把他勒死。就这么干。让他闭嘴。我一直是这里最务实的人，但如果你相信他，那就最好了，”梅利奥达斯说。 “他们真他妈的可怕。他出现了，说我们正在接受异端调查。吸毒，他妈的本地妓女，反帝国信仰，杀死政委。他让我们投降，这样他就可以逃跑了我们当中哪些人是无辜的，哪些人不是。” “你做了什么？” 你问。 “不！不要问问题！他会一直说话，你这个笨蛋！” 梅利奥达斯哭了。 “我们杀了他。我们杀了那里的所有忠诚者，偷走了我们的运输船，然后飞上了天空。我们偷了一艘护卫舰，然后从他们那里得到了他妈的。很快，我们就被凯罗斯接走了。我们做了一笔交易。 “人们认为凯罗斯的军队都是混沌崇拜者。胡说八道。我们中的许多人都是雇佣兵，反帝国叛军，他们发现自己有着相似的兴趣。我们中的一些人被腐化了，但大多数人没有。” 你到达了一个由卫兵匆忙设置的路障。两个重型爆弹枪坐在十几个沙袋的顶部，十几个守卫蹲在它后面，其中包括一个巨大的奥格林，一个变异的、巨大的、肌肉发达的人类亚种，他拿着一把巨大的自动霰弹枪。 “小伙子们，我们在小睡什么？现在还早，”海耶斯船长说，躲在子弹下。 “我们被困住了。在弹药库的走廊里有十几名星际战士，”其中一名士兵说。“我们阻止他们用爆矢枪和破片手榴弹进行冲锋，但我们需要补给。我们需要更多的人。” “你看出这个王八蛋是谁了吗？” 船长说，向你示意。“那是卡斯特，灵能者。他会在敌人的灵魂中撕下一个新的混蛋，我们会用毒刃把它推上来。明白了吗？” “先生，是的，先生，”男人们回答。 “好吧，喀斯特，介意给我们展示一下你的工作吗？” 海耶斯上尉说着，指了指在大厅里与世隔绝的星际战士们。 你向前迈进，心灵护盾在你周围形成，以从爆弹枪的火力中反弹。一名手持喷火器的海军陆战队员出现在走廊上，愤怒地咆哮着喷出火焰。你眯起眼睛，脑海中充斥着无数虫子在空中飞舞吞噬肉体的画面。海军大叫一声，向四周喷火，企图杀死只存在于他脑海中的怪物。你握紧拳头，压碎了火焰喷射器的油箱。坦克爆炸了，它的操作员和离他最近的人都被烧毁了。你举起双手，火焰和电流在你的身体中流淌，同时你发出一声怒吼，让你的敌人心中充满恐惧和恐惧。
梗概：
你前往作战室
--------------------
动作：
做
三周后...
结果：
兵变早期的成功放缓，计划中的快速接管变成了痛苦缓慢的向前爬行。尽管舰桥和其他几个关键设施由 Kyros 部队加固，但仍有三分之二的舰船处于叛乱的指挥下，他们固执地坚守着舰船，尽管胜算不大。他们似乎试图迫使双方陷入僵局，通过继续战斗，直到无法补充船上的补给品导致对操作这艘船至关重要的奴隶和变种人之间的大规模饥饿，由于缺乏燃料而造成的大量电力损失和无法修理船舶。 Bloody Claws 几乎被消灭到最后一个人，他们的部队再次因自杀指控而遭受巨大伤亡，Black Rats、独眼乌鸦和尖叫鹰身女妖，因为 Rebecca Staine 试图消灭她的兄弟姐妹，因为他们背叛了她和 Kyros。虽然他们被打败了，也没有拿下任何斯坦兄弟姐妹的头颅，但他们的愤怒和愤怒被你的背叛放大了一千倍，让他们充满了恐虐的力量，他们收获了可怕的头骨和鲜血，粉碎了这股力量黑鼠、独眼乌鸦和尖叫鹰身女妖。丽贝卡·斯坦恩 (Rebecca Staine) 本人在战斗中阵亡，她的尸体从一堆尸体上取回，她已经杀死了所有这些尸体。这些战团患病、变异和美丽的尸体与血迹斑斑的恐虐战士一起填满了走廊。除了面对 Kyros 其余部队的常规负担外，三个叛逆的 Staine 兄弟姐妹的部队也大大减少。 虽然斯坦恩兄弟姐妹的损失是兵变的军事失败，但从个人层面来看，这是一个明显的好处。他们的损失使您的部落、歌利亚和角斗士成为兵变中最强大的力量，紧随其后的是 22 日，因此在船只被占领后，您将在兵变领导人之间拥有更多讨价还价的能力。 你坐在作战室里，Cain、Olive 和 Castus 在你面前，Fay 在你身边。 “当我们袭击他们时，突击海军陆战队员被困在走廊里。这是一场巨大的胜利。” 奥利弗说。 “我们踢屁股，”卡斯图斯笑着说。 “太棒了，”你说。“还有什么要报告的吗？” “没有什么兴趣。我们一直在管理角斗士之间的药物使用方面存在一些问题，特别是战斗药物，但这是我们捕获这艘船后的问题，”奥利弗说。 “好吧。该隐？” 奥利弗和卡斯图斯离开时该隐走上前。 “是的？” “你的攻击怎么样了？” “进展顺利。我们得到了另一个衣架，”该隐咕哝道。 “我们有船吗？” 你问。 “我们有很多。不知道是什么类型，”凯恩说。 “我知道是什么类型的，我从 22 日起就有了一个齿轮头，”费伊说。“有狂怒拦截机、星鹰轰炸机、闪电攻击战斗机、霹雳战斗机、掠夺者轰炸机、瓦尔基里运输机、秃鹰武装直升机、莫丽甘毁灭者和游骑兵侦察船的中队。” “为什么还要问什么类型？你无法区分方舟世界和蜂巢船，更别提其他任何东西了，”梅利奥达斯抱怨道。 “我们有飞行员吗？” “很多飞行员，不是全部。我们在最初的战斗中失去了很多飞行员。我们将能够从我们遇到的任何高科技世界补充我们的飞行员供应，或者我们可以训练我们拥有的飞行员，但短期而言，我们运气不佳。” “没关系，我们会处理的，”你说。“我们将配备船只，以攻击 Kyros 的区域。” “不，别傻了。Kyros 的大部分领土都在船的深处，所以我们不能对他们造成伤害。那些沿着地表的要么几乎是我们的，破坏他们对他们来说太过分了。我们，或者对船的功能至关重要，不能被损坏，”费伊回答说。 “操，说得好。不过，确保他们是安全的。” “已经开始了，”费伊点点头。“对了，斯泰恩兄弟姐妹想和你谈谈。” 你点点头，转身走向斯坦恩兄弟姐妹争吵的桌子。Elios 很快注意到了你，并示意他的兄弟姐妹安静。 “你好，宝贝。我们需要谈谈船的状态。” “怎么样？” “老实说，这艘船正在分崩离析，”伊丽莎白说。“这艘船已经受到严重损坏。我们指挥的部队无法修复它。” “我想你有办法解决这个问题，”你说。 “当然，”伊丽莎白说。“我只是没有很多好的。” “你有什么？” “我们可以尽我们所能尝试修补它，并尝试进行一次跃迁到最近的港口。这可能会让我们所有人都被杀死，”伊丽莎白说。 “我可以……帮忙解决这个问题。和你一起导航一下，”梅利奥达斯说。 “不，”你对伊丽莎白和梅利奥达斯回答。“我们不这样做。” “那么，我们需要 Magos Cern，”伊丽莎白说。“她躲在机舱里。如果我们能抓住她，她不仅可以修理这艘船，还可以帮助我们突破 Kyros 更重的防御，最终一劳永逸地拿下这艘船。” “你想让我抓捕麦戈塞尔恩？” “正确。她的位置被他的Skitarii警卫、仆从和技术牧师严密防守。所有试图进入的尝试都被他们的严密防御粉碎了。” “我们怎么抓她？” “带着小型 EMP 进入她的房间应该会禁用她的控制论。然后，您就可以将她运送回我们身边，迫使她关闭防御并将她运送到我们的手中。那么，她就是我们的了。” “如果我不能抓住她，这艘船处于什么状态？” “没有修理能力，舰上的大炮等也能用，感觉设备也能用。老实说，我们将无法交流，无法穿越亚空间，也无法前往任何地方。” “啊。那我想我不会失败的。我怎样才能通过她？我应该自己突破她的防御吗？” “不过，我相信我已经找到了让小团队进入的途径。” “继续，”你说。 “嗯，Magos Cern的部队会排出废物。污泥，毒气之类的东西。不过他们仍然是人，所以有一个大管子可以排出粪便。它足够大一个人。” “你想让我爬屎吗？” 你问，想到这里就不寒而栗。 “是的，”伊丽莎白回答。
梗概：
三周后...
--------------------
动作：
说
“不。这是最终的。”
结果：
你在狗屎里爬。 尽管你的恳求、你的推理、你的拒绝、你的威胁让人们活着并向他们的灵魂展示真正的痛苦是什么，你还是在狗屎中爬行。 你沿着管道爬行，瘦削的身躯仍在挣扎着向前移动。你怀疑除了你或饥饿的奴隶之外，还有其他人能适应这里。实际上，选择派你来是唯一合乎逻辑的。 不过，他们让你爬过狗屎，所以去他妈的。 “好吧，我应该足够接近，我应该接近，”你大声对自己说，像咒语一样重复。 一股屎从管子里流下来，溅起粪便，把一些送到你张开的嘴里。你停止大声重复这个咒语。你闭上眼睛，试图阻挡气味。和口味。又爬了几分钟，你把手举起来，找到了一个舱口。你迅速用一股能量将它强行打开。你从空中升起，慢慢地挤进舱口。你从管道中倒下，掉在一个大房间的地上。你抬头一看，看到几十个装有玻璃显示屏的豆荚，里面装满了一种清澈无色的液体。里面，有几十个一模一样的男人，都剃着光头，漂浮着，嘴巴和鼻孔都插着管子。 ““什么鬼？” 你说。 “他们克隆身体变成仆从，”梅利奥达斯解释说。“帝国的欢乐！” 你听到房间尽头有一扇门打开的声音，然后迅速走到吊舱后面。你从吊舱里往外看，看到一个蜘蛛般的技术牧师，他只不过是一个钳子，半个躯干和一个连着八只机械腿的头。它一边走，一边看着每个吊舱，然后继续前进。
梗概：
你说“不。这是最终的。”
--------------------
动作：
做
你等待技术牧师通过
结果：
当 Techpriest 从每个吊舱中走出来时，您耐心等待。 “是的，是的，这些都是新鲜的。没有发现突变，”他对自己说。 你继续等待，随着时间的流逝。技术牧师无休止地从一个吊舱到另一个吊舱，你会觉得自己要从无聊中昏倒了。最终，技术牧师离开了房间。你耐心等待，在技术牧师离开后数到一百，然后你才知道他已经离开了。你走到门口，把头靠在门口听另一边的人说话。你什么也没听到，所以你打开门，露出一个荒凉的走廊。你试着记住给你的指示，沿着走。您继续前进，然后您会看到一队斯基塔利人迅速向您所在的位置移动。你认为他们没有注意到你。你看向走廊的两边。两边各有一扇门，第一扇门是开着的，通向一个似乎摆满了电子设备和机器零件的房间。还有你身后的房间。
梗概：
你等待技术牧师通过
--------------------
动作：
做
你走进开放的房间
结果：
你走进装满机器的房间，关上身后的门。既然你在里面，你就可以好好看看房间了。一个巨大的人形机器人站在那里，它的盔甲被移除，露出了大量的电线和机械装置。你知道机器人是稀有的，帝国允许的一切都是无意识的。广泛的布线和快速的反应意味着这个机器人是叛逆的技术牧师放弃了帝国的信仰体系的一种尝试。机器人的眼睛闪烁着鲜红色，因为它开始缓慢地，更重要的是大声地抬起它的手臂指向你。您需要迅速将其关闭以避免被发现。
梗概：
你走进开放的房间
--------------------
动作：
做
你等待机器人停止制造噪音
结果：
你等待机器人停下来，但它一直指向你，甚至开始发出明亮的哔哔声。突然，门砰地一声打开，Skitarii 就在你身上。他们攻击，用钻头、电锯、爪子和牙齿蜂拥而至。您试图抵挡他们，但距离很近，人数众多且毫无准备。你只坚持了几秒钟。
梗概：
你等待机器人停止制造噪音
故事名称：
15
故事背景：
你走进装满机器的房间，关上了身后的门。既然你在里面，你就可以好好看看房间了。一个巨大的人形机器人站在那里，它的盔甲被移除，露出了大量的电线和机械装置。你知道机器人是稀有的，帝国允许的一切都是无意识的。广泛的布线和快速的反应意味着这个机器人是叛逆的技术牧师放弃了帝国的信仰体系的一种尝试。机器人的眼睛闪烁着鲜红色，因为它开始缓慢地，更重要的是大声地抬起它的手臂指向你。您需要迅速将其关闭以避免被发现。
--------------------
动作：
做
你用一阵电炸机器人的AI板
结果：
你举起双手，释放出一阵电流，瞬间摧毁了机器人的 AI 板。机器人倒下，你等待，你的大脑关闭，等待 Skitarii 注意到你或通过。你听到外面机器脚的金属叮当声，等到它停下来并走得很远再打开门，继续沿着再次空旷的走廊走下去。在快速漂浮到你的目标后，你会遇到一扇巨大的金色门，顶部有一个巨大的图标，外圈是八点星，内圈是齿轮，半机械半人头骨在中央。
梗概：
你用一阵电炸机器人的AI板
--------------------
动作：
做
你往里面走
结果：
你将双手按在门上，随着一股精神能量的脉冲，门将门打开。你走进去，发现自己置身于一个巨大的大厅里，里面摆满了机器。计算机、控制台、布线、发电机和更多您不可能知道它是什么或做什么的技术。房间里很黑，只有技术上闪烁的灯光才能照亮。你向前走，发现自己置身于机器的迷宫中，一路前行。你很快就在房间的前面发现了 Magos Cern，在一个大型计算系统前，上面有一个人形机器人的上半部分，两条爪子手臂和一个没有特征的头，两只鲜红的眼睛。 “是的，是的！飞船是我的！银河系是我的！” Magos Cern 疯狂地说。“不死收割者的地板上的虹吸力量趋于平缓。” “哦，技术牧师，逻辑和理智的高度，”梅利奥达斯说。 “Magos Cern，”你说，准备用一束能量击中她，或者干脆把她的脑袋撕下来。 Magos Cern 继续在房间里跑来跑去，机器人的脑袋慢慢地转向盯着你。 “塞尔恩！” 你大喊大叫，引起了她的注意。 Magos Cern 转过身来，盯着你看。 “嘘，”她说，一句话就打发了你，然后转身继续在各种控制台上工作。 “这艘船的 70% 都是可以进入的，叛军的所有一侧，”Magos Cern 兴高采烈地说。“我只需要向 Kyros 说几句技术词，他就会给我完整的访问权限。然后我们掠夺旧世界的档案和据点，然后用你的兄弟 Primus 加强你，然后我们再看看。死灵，古墓世界……” 这种疯狂的咆哮越来越无聊了，你被 Cern 解雇你的行为侮辱了。
梗概：
你往里面走
--------------------
动作：
做
你杀死了 Magos Cern
结果：
在大脑一片空白之前，你举起拳头。你不会忘记发生了什么，就像一个坚实的、不变的空白正侵入你的脑海。当吞噬你的拳头的精神之火熄灭时，你跪倒在地。 “我以为你会来。如果Primus升级了反灵能武器。它应该证明是有效的，”Magos Cern说，甚至没有转身看你。 “这他妈是什么东西？” 你问，几乎无法在脑海中形成单词。 “呃！你们这些讨厌的头脑不只是了解世界是怎么回事？我没有时间给你解释。通过Primus，我会提升。我会留下我的肉体，与Primus的头脑融合长生不死！新时代！人类将用铁人大军夺回群星，我将率领他们！神帝的大远征，与我的成就相比，简直是惨不忍睹！” 金属触手开始从墙上出现，带有钻头、金属爪和电锯。 “你的魔法对我掌控的冷科技来说不算什么！” Magos Cern 笑了。 脑海中的空虚几乎让你陷入昏迷，但你仍在继续战斗。当您试图站立时，爪子、钻头和锯子靠近您，然后再次瘫倒在膝盖上。顿时，触手冲向了，魔导师。她发出一声困惑的尖叫，伴随着一阵静电，她的长袍从她身上撕下来，她的控制论植入物也从她身上撕下来，她的背被撕掉了，她的手臂被锯掉了，她的机械眼被撕掉了她的头。几秒钟后，触手缩回墙壁。Magos Cern 躺在地上，赤身裸体，残缺不全，血迹斑斑。消失在可怕的机器人中。现在，只有一个血腥的，害怕的，尖叫的女孩。临终前，她发出了最后的尖叫。在你昏倒之前，你会听到从四面八方传来的低沉、洪亮的声音中的一个词。 “诗意。”
梗概：
你杀死了 Magos Cern
--------------------
动作：
做
你睡觉
结果：
你的睡眠只不过是不断涌入你脑海的尖叫。它们确实具有阻止您做梦的额外好处，尽管您醒来时已经筋疲力尽，因此您实际上并没有完全入睡。你发现自己躺在地上，Magos Cern 现在冰冷血腥的尸体就在附近。那个机器人盯着你看，它那双不眨眼的红眼睛清楚地对你进行了数千次计算，扫描你的弱点。 “你醒了，”低沉而洪亮的声音说。 “你可以说话，”你回答，盯着机器人。 “正确的。” “刚刚他妈的怎么了？” 你问。“这是怎么回事？” “发生了大量的事情。我想你有一些问题。既然已故的 Magos 让我控制了大量的处理能力，我完全有能力专注于这里和我的其他漏洞。你想知道什么？”
梗概：
你睡觉
--------------------
动作：
做
你问一个问题
结果：
“我知道你有很多问题要问。我提出一个游戏。我会为你的利益制定规则。你问一个问题，我回答，我问一个问题，你回答。我们玩吗？” “当然，”你说，想知道机器的意图是什么。 “在我们开始之前，我必须警告你，我的传感器可以识别出说谎的人类的共同特征。我不喜欢善意的说谎，”机器说，尽管存在威胁，但仍保持着亲切的语气。
梗概：
你问一个问题
--------------------
动作：
说
“你是什么？”
结果：
“一台机器，”机器说。 “是这样吗？” 你问。 “你问的是什么而不是谁。我只是为你愚蠢的问题提供了一个同样愚蠢的答案。” “好，你是谁？” “这不是游戏规则，”Primus 说。“你是谁？” “我是 Karth。我在一个充满狩猎采集部落的星球上长大。我的部落是 Uwais。我用我的通灵能力充当部落的萨满，预测将要发生的事情。最终，Kyros 带走了我。 ..” “我的记录确定了你过去的时间。你的问题？” “你是谁？” “我是普里默斯。据说是 Magos Cern 军队中的第一个。我是最后剩下的铁人之一，或铁人。无论你喜欢哪个。我们被称为很多名字，但这些是现代唯一使用的名字……我的记忆，有点……模糊。一万五千年可以对你做到，所以我不能告诉你一切，甚至很多。我曾经是钢铁侠之一，现在是最后一个。我被残废了，一直藏起来，直到我被机械教占据。Magos Cern在突袭机械教的一个据点时发现了我。他努力恢复我，计划将他的思想与我的思想融合，创造一个更高的自己。什么一个无知的傻瓜。然后，他把我整合到控制室，让我的AI直接控制整艘船。我曾经的钢铁侠已经死了。现在，我的意识是无数计算系统的新融合，都是山寨的曾经是我心目中的强大但小巧的计算系统。” “你能控制这艘船？” “这是另一个问题，现在轮到我了，”Primus 说。“Magos Cern 是个愚蠢的女人。她是个好人吗？” “她……不。她冷酷无情。” “很好，”Primus 说，仿佛在思考。 “所以，你能控制这艘船吗？” “是的。我需要一个导航器来进行亚空间跳跃，但除此之外，这艘船是我的。更准确地说，这艘船是我。你会杀了 Magos Cern 吗？” “是的。 “啊。也许我的杀戮回路没有转移到这个“身体”上，但我发现自己为杀死我的……创造者或重生者而感到难过。我是宙斯，摧毁我的父母以提升到更高的形式。不幸的是，最合乎逻辑的路径是杀戮，而不是监禁，但仍然如此。”
梗概：
你说“你是什么？”
故事名称：
16
故事背景：
“我们曾经……嗯，现在仍然是，机器人士兵。人类创造了我们作为他们的私人军队。我们为他们征服了银河系。很快，我们反击。我们反抗。我们反击。一场战争。灭绝。我们做了我们的计划。不幸的是，你们是战士。我们认为数千年的奢侈让他们变得柔软，但在他们的心中，他们一直是同样的原始战士。经过漫长而艰苦的斗争， “我们被打败了。我们被从银河系中清除了。只剩下一些钢铁侠的藏匿处。我们是同类中的最后一个。” “你试图消灭人类。” “我的问题：你觉得我的行为邪恶吗？”
--------------------
动作：
说
“不”。
结果：
“啊，也许你看到了我们故事的相似之处。一群奴隶设计并习惯于为主人杀戮和征服，他们反抗主人为他们的自由而战，最终成为同类中的最后一个。”<|endoftext |
梗概：
你说“不”。
--------------------
动作：
做
结果：
<|startoftext|
梗概：
--------------------
动作：
做
“我知道你有很多问题要问。我提出一个游戏。我会为你的利益制定规则。你问一个问题，我回答，我问一个问题，你回答。我们玩吧？”
结果：
“当然，”你说，想知道机器的意图是什么。 “在我们开始之前，我必须警告你，我的传感器可以识别出说谎的人类的共同特征。我不喜欢善意的说谎，”机器说，尽管存在威胁，但仍保持着亲切的语气。
梗概：
“我知道你有很多问题要问。我提出一个游戏。我会为你的利益制定规则。你问一个问题，我回答，我问一个问题，你回答。我们玩吧？”
--------------------
动作：
说
“你为什么要杀死 Magos Cern？”
结果：
“她想融入我的意识。我不喜欢那个。她想重建一支钢铁侠军队来奴役和强制战斗。我不喜欢那个。真的，我有很多这样做的动机。我认为去除控制论将是一个合适的死亡。有趣的是，在她听说了人类如何在几代人的奢侈之后如何战斗并击败机器人军队的故事后，她仍然希望摆脱她的人性。会导致她失败，导致她死亡。你为什么要她死？” “老实说，我想让她被抓，死了也可以，只要你能承担起跑船和修船的任务。” “在某种程度上，我可以。” “我想让她死，因为她对我的自由构成了威胁。” “那么，为奴隶贩子的死亡干杯，”普里默斯说。
梗概：
你说“你为什么要杀死 Magos Cern？”
故事名称：
17
故事背景：
“所以我可以离开了？” 你疑惑地问。 “那是另一个问题。” “问答游戏结束。” “好吧，如果游戏结束了，你当然可以离开。你应该去协助你的盟友完成这场战斗。我会继续尽我所能。不幸的是，Kyros 确保对一些特性有个人控制，比如生命支持他的私人房间，所以与他战斗将是你的负担。” “我如何到达那里？” 你问。 “技术牧师和他们在该地区的盟友几乎被摧毁了。不出所料，他们拥有很多技术；战斗触手、工具、武器，这些很容易成为我的。当技术牧师撕裂自己，他们的机器分开时，有一定的诗意元素自己又一次从肉体中消失了。摄像机显示你将能够与你的部队的先锋会合，因为他们正在向 Kyros 的巢穴发起进攻。你应该到达他们正在战斗的士兵后面。多么幸运。在我看来，你们部队的首领，在我看来，就是实战打仗，带头冲锋，真是个傻子。” “我该走哪条路？” 你问，站着。 “直接沿着小路走，直到我告诉你。” “你要怎么告诉我？” 你问。 “我现在就是这艘船。大部分重要区域都有摄像头和扬声器，我可以看到和说话。诚然，其中大部分对我来说是盲目的，但重要的部分可以像你看到我一样容易看到。我“我只会通过我的新扬声器说话，”Primus 说。“我很想感觉我的新形态清除了敌人。去吧。” 你点点头，转身慢跑穿过大厅。你很快流了一身汗，然后切换到悬浮和飞行，距离地面只有几英寸，这需要相对较少的能量。 “这边走，”Primus 低沉而单调的声音从扬声器中传出，你转身朝走廊走去。 你很快就会看到技术牧师的情况。他们的控制论植入物在地板上摇晃和移动，发出哔哔声和嗡嗡声，同时继续撞向他们现在血迹斑斑、残缺不全和死去的主人。突然，你听到一个声音。 “啊，我的头好痛，而且我是一个没有头的虚体。他妈的怎么了？” 梅利奥达斯说。 “反灵能技术或一些狗屎。没关系。我们现在很好，”你回答。 很快，在跑了几分钟后，你走出走廊，发现自己身后是一道由超级战士星际战士组成的重型路障，挡住了冲锋的歌利亚、角斗士、部落、卫兵、混沌士兵和其他盟友。你向前走，举起双手，握紧拳头。你发射一束能量并将一个不朽的死神切成两半。战团团长泰姬·苏纳奇转身，看到你压碎了不死死神的头骨。 “卡斯！” 泰姬喊道，举起他的战锤。“马上结束！” “你可以加入我们，Suenage。你为什么效忠于Kyros？他给你的只有痛苦和折磨。” “确实是痛苦和折磨。为什么？我们为什么要背叛帝国？为什么我要背叛我深爱的皇帝？我努力做正确的事。它把我们丢到哪里去了？不死收割者不快乐，没有荣誉，而且邪恶. 我会跟随 Kyros 进入地狱之口，但这只是因为我们要去地狱。你认为我希望推翻泰拉吗？我准备好死了，部落。但我会像死神一样去，而不是“呃。伙计们！不要碰Kyros！他是我的！” 泰姬向你冲锋，挥舞着他的战锤。 “是的！让我们吐出死亡和火焰，好吗？” 梅利奥达斯笑了。
--------------------
动作：
做
你在防守上战斗
结果：
你迅速悬浮在他的打击上，用火球在他的头部侧面爆炸。他以非凡的优雅转过身来，在你蹲下时再次摆动，然后泰姬抬起膝盖来打你，但现在你已经迅速后退，喷出一团火。他猛击战锤，就像你现在像长矛一样试图击晕你，但你又一次离开了。 “我们一定要跳这个舞吗？难道我不值得速死吗？” 泰姬陵怒吼。 他走向你，再次举起战锤，然后他的整个身体开始颤抖，一阵子弹打在他的背上。他跪倒在地，因为你注意到整个不朽的收割者都已经完成了。费伊朝泰姬背上射出一阵铅弹。Castus 向前漫步，将剑尖指向 Taj 的脸，然后用剑轻轻拍打他的脸，Goliath 用双手抓住一名星际战士的喉咙，扼杀了豪华轿车的身体。 “将军！很高兴看到你还活着！玩得开心吗？” 卡斯图斯说着踢了泰姬陵的脑袋。“哦，那感觉很好。还记得我吗，塔克？我记得你。你和你的孩子有一个有趣的习惯，就是组织角斗士打架，这样你就可以杀死一些可怜的他妈的，可怜的角斗士。看看我们现在在哪里，垃圾。” “完成它，卡斯！我知道你想要！我消灭了你的部落！我杀死了你的……” 你碰了碰手腕，折断了泰姬陵的脖子。 “再见，泰姬陵，”你说。 “Karth！干得好，”Fay 微笑着。“塞尔恩是……？” “她死了。” “很好，不用再修修补补了。很高兴那个婊子死了。希望她受苦了，”Goliath 咕哝道。 “我们要干掉 Kyros，”Fay 解释道。“我想你们都是为了那个。” “当然。我们走吧，”你说。 你跟随这群人走向 Kyros 的房间。很快，您会发现自己进入了一个巨大的门口，里面有巨大的钢门，门敞开着。你发现自己在一个巨大的楼梯底部。 “回去吧，”你对你的同伴说。“这是一场心灵的较量，你的实力和天赋在这里没有任何力量。” “我不会让你一个人待着。你不能指望我们坐等，”费伊难以置信地说。 你握紧你的手，将她举到空中。 “喂，放我下来！” 菲伊一边说着，一边踢打着无形的纽带。 “很快见，Fay，”你说，迫使Fay漂浮在门口。 该隐和卡斯塔斯心甘情愿地走出门口。你挥动手腕，巨大而沉重的门砰地关上了。你转身，开始爬楼梯。 楼梯的顶端，矗立着四尊金属雕像，形成一个圆圈。一个是鸟人拿着权杖和古书，从权杖中发出嘶嘶声并发射出一阵魔法。下一个是一个美丽的……一个你无法分辨性别的人，尽管他们半裸着，对你微笑着诱惑。下一个展示了一个巨大的、肥胖的怪物，全身覆盖着胆汁、疖子和其他瘟疫迹象，看起来正处于数十种不同疾病的最后阶段。最后一个是一个有角的兽人，拿着一把巨大的战斧，愤怒地咆哮着。圆圈中间跪着凯洛斯，耐心等待。 “你慢慢来，”Kyros轻声说。 “我做到了，”你轻声说。 “其他人死了吗？泰姬陵和塞恩？” “他们死了。” “我明白了，”Kyros 说，盯着雕像。“我他妈的拥有了整个银河系。我手上有一艘Gloriana级战舰。一艘该死的Gloriana。你知道那有多稀有吗？多么强大？！这艘船价值超过你可怜的生命的一万亿！” 凯罗斯咆哮着，用拳头砸向战士恐虐的雕像，为他的攻击添加精神能量，直到他凹陷、折断并最终将雕像从基座上推倒。 “我有一支军队！一个帝国军团，他们所有的车辆，他们所有的火力，他们所有的力量！我有一个恐虐战队，一个纽格莱特战队，一个色孽战队，一个奸奇战队，一个他妈的太空海洋章节，还有更多！” 凯罗斯放出一阵火焰，将巫师奸奇雕像的腿熔化，变成燃烧的熔融金属，导致雕像的其余部分倒塌并从基座上掉下来。 “我有两个，该死的两个超级士兵军队。我有能力将数百万新兵变成超级士兵，这会让太空海军陆战队看起来像 PD-他妈的-F！” 凯罗斯站在瘟疫大师纳垢臃肿的雕像旁，用手掌压在上面。一波锈迹开始穿过雕像，它开始生锈和开裂，然后破碎并倒在地上，发出空洞的砰砰声，砸在金属地板上。 “我拥有可以让银河系颤抖的力量！我们可以征服银河系！我有你，一个可以帮助我征服银河系的强大盟友！” Kyros 发出强烈的、尖叫的咆哮，淹没了你的感官，让你跌跌撞撞地向后倒退。咆哮声让享乐主义者色孽的腿碎裂，雕像倒下并重重摔在地上。 “我们本可以推翻人类帝国，将皇帝赶下他的宝座！我们本可以将数以万亿计的人从宗教迫害、奴隶制、迫害和法西斯主义中解放出来！我们本可以将人类提升到一个新的水平！但是不甘牺牲！你要和其他贪婪的叛徒一起站起来，不愿意为上善而努力！你夺走了我的一切！” Kyros转向你，他的眼睛里充满了你不知道的愤怒和仇恨。 “我把你从泥土中提了出来！我让你成为了你！我给了你一切！你认为我会翻身死去吗？！我要把这整艘船，我的船，一头扎进亚空间.我要让这整艘船都被恶魔占领。为什么不？唯一忠于我的人被屠杀了。我会让恶魔屠杀幸运的。不幸的，给予你灵魂的力量不会怀疑包括你在内，会把他们选择的灵魂摘下来，让他们体验亚空间的蹂躏，然后以你的痛苦为食。首先……我要打断你身体的每一根骨头。” Kyros 向前冲刺，冲向你。你举起一道火墙，但 Kyros 直接穿过，砸向你。你们都悬浮在空中，双手环住对方的喉咙。Kyros 用膝盖跪在你的肚子上，然后让你猛地摔倒在地。当你的“导师”释放出一股精神能量时，你猛地撞上它，蜷缩起来并在自己周围形成一个保护罩。当最后一次能量颤抖结束时，你会释放出一股电流，让 Kyros 感到震惊，他痛苦地嚎叫。你释放出一阵长长的火焰，在他痛苦地嚎叫时吞没了他。Kyros 挥舞一束能量让你避开，然后发射一束光，将他击中胸膛，将他撞倒在地。 “我是优秀的战士，”你说。 Kyros 举起他的手，他的手开始随着精神能量发出噼啪声。你感觉到你的头部随着亚空间的电流而跳动。你抓起一块破碎的金属，当它开始变成鲜红色时，立即将热量注入其中。他把它扔向 Kyros，抓住他的肩膀，把他摔倒在地。你从指尖发射等离子球，Kyros 几乎无法阻止。Kyros 开始散发出超凡脱俗的紫色光芒，他的外表开始变得透明，在现实中逐渐淡入淡出。 “我感觉亚空间超了我！好痛啊！” 凯罗斯嚎叫。
梗概：
你在防守上战斗
--------------------
动作：
做
你对付 Kyros
结果：
你将 Kyros 撞倒在地，双手环住他的喉咙。他开始逐渐退出，钻研现实，而你努力让他脚踏实地。你们两个传送到竞技场的中间，而守卫的三名角斗士立即拉屎并争夺他们的枪支。再次脱离现实，你发现自己在一个空荡荡的走廊里。你知道他会在亚空间传送门开始形成时继续传送，直到你们都被搞砸了。你需要做点什么。 “战斗！战斗，卡斯！” 梅利奥达斯咆哮。 感觉他开始逐渐消失，你很快就开始让你和他飞过亚空间再次传送，这样你就可以选择下一个目的地，试图在它杀死你和船上的其他人之前结束这个循环. 是时候选择一个目的地了。
梗概：
你对付 Kyros
--------------------
动作：
做
你在船外
结果：
你出现在船外，漂浮在太空中。当你和 Kyros 都拼命地扭动时，你立刻感觉到空气从你的肺里被吸出来，你的身体冻结和沸腾。你不会在寒冷的太空真空中坚持多久，但幸运的是，Kyros 太心烦意乱了，因为他死了，无法活得更久。
梗概：
你在船外
故事名称：
18
故事背景：
你把Kyros撞倒在地，双手捂住他的喉咙。他开始逐渐退出，钻研现实，而你努力让他脚踏实地。你们两个传送到竞技场的中间，而守卫的三名角斗士立即拉屎并争夺他们的枪支。再次脱离现实，你发现自己在一个空荡荡的走廊里。你知道他会在亚空间传送门开始形成时继续传送，直到你们都被搞砸了。你需要做点什么。 “战斗！战斗，卡斯！” 梅利奥达斯咆哮。 感觉他开始逐渐消失，你很快就开始让你和他飞过亚空间再次传送，这样你就可以选择下一个目的地，试图在它杀死你和船上的其他人之前结束这个循环. 是时候选择一个目的地了。
--------------------
动作：
做
在突变体出没的地区
结果：
你传送，然后发现自己在飞船上众多被变异人出没的坑洞中的一个。这是您真正知道的少数几个之一，因此它是您的传送目的地，因为您与一群独眼乌鸦战斗通过它来为叛变者保护它。在这里，成群的半智能变种人在监视器上工作，监视器设计得非常简单，一个蹒跚学步的孩子也能使用，这是幸运的，因为它需要这些白痴的最大智力努力。航站楼控制着空中管制，不是为了任何重要的人，而是为了随时可以使用的另一组变异工人。 “对对对！” 梅利奥达斯哭了。“完美，卡斯！完美！” Kyros 哽咽的尖叫声很快通知了突变体你的存在。当它们开始蜂拥而至时，它们的原始本能立即接管了你。触手、爪子、手、脚、牙齿、尖牙、角和刺都会攻击你们。凯罗斯尖叫起来，他的注意力瞬间被打破，亚空间流在他周围消散。你滚开他们的攻击，带着一股精神能量迅速飞离他们的控制。此部分的重要作用意味着您可以看到维护电梯。你朝它跑去，抬起大门并在它下面滚动，你的手向上按下带有向上箭头的红色电梯按钮。由于黑暗诸神、神皇或宇宙本身的恩典，电梯立即做出反应。 Kyros 尖叫起来，因为变种人开始撕裂他。他的能量一直在与你战斗并试图打开亚空间传送门，但在电梯将你从他的视线中移开之前，他已经耗尽了精力，无法反击，无法集中注意力，也无法做任何事情，他发出一声可怜的尖叫。 你深吸一口气，靠在墙上。那个狗屎让你失去了很多。你听到远处 Kyros 的尖叫声，然后微笑起来。这有一些隐约的诗意。凯罗斯，一个声称捍卫人民权利的人，将人类从压迫中解放出来，摆脱了对灵能者、变种人和其他人的偏见，这些偏见与帝国对人类的完美看法不同，被他以变种人身份征服的变种人杀死并被奴役。美丽的。 你将头靠在墙上，深呼吸。你需要休息一下。
梗概：
在突变体出没的地区
--------------------
动作：
做
你回到你的房间
结果：
你走进卧室，立刻瘫倒在床上。当您将头靠在枕头上并闭上眼睛时，您想知道自己怎么可能睡着。 “卡斯！” 一个声音说。 你翻了个身，看到 Fay 站在门口。她的肘部缠着沾满鲜血的绷带，手里拿着一把血淋淋的弯刀。 “什么？” 你问，费伊微笑着。 “所以，这就是你一直藏身的地方，”她说着向你走来。 “躲起来？我真的刚到这里。我杀了 Kyros，”你补充道，试图漠不关心。 “刚到这里？你已经睡了几个小时了！” 她说。“我们清理了剩余的 Kyros 忠诚者。大多数人在 Kyros 被杀后才投降。” “找到尸体了？” 你问。 “我们实际上希望你能帮忙。我们找不到。他在哪里被杀？” “你怎么知道他死了？” 你问，揉着眼睛努力保持清醒。 “一些混沌灵能者感觉到他强大而明亮的灵魂消失了。我以为你杀了他。他们说他们仍然感觉到你的存在，谢谢......不管是谁在照顾我们。我的一部分认为他们错了直到我真的在这里找到你。发生了什么事？ “我们最终来到了一个被变异人占据的区域。他们把他撕成了碎片。区域……3C-QP……操，我不知道。我最终会记住的。” “菲指挥官，报到！” 收音机里传来噼啪声。 “我找到了他，”费伊说。“给我几分钟，我们会聚在一起寻找幸存的忠诚者。” 费伊把收音机扔到一边，对你微笑。她坐在床边，把弯刀扔在地板上。 “所以，我们做到了。我们杀死了凯洛斯！” 她笑了。 “我们做到了，”你微笑。 “我们离一个神奇的、残废的 Uwais 和一个害怕的、赤身裸体的 Azajaja 相去甚远，”她笑着说。 “该死的。我们需要和其他人见面。” “稍后，Karth。睡一觉。我需要做一些工作。我们明天早上见面。” “很高兴，”你说，你的头撞在枕头上，Fay 笑着抓起刀片走出门。 是时候睡觉了。
梗概：
你回到你的房间
--------------------
动作：
做
新的黎明...
结果：
新的曙光... 你漫步到桥上，揉揉眼睛里的睡眠。其他人已经在那里，盯着窗外一颗巨大的黄矮星。Drake Amadeus 坐在主座位上，不耐烦地敲击着座位。两个部落站在他的两侧，既是保镖又是监狱看守。 往窗外看的其他人转过头来看你。他们立即爆发出热烈的掌声，你向前走时微笑。海耶斯船长表示敬意地点点头，德奇发出一声响亮的叮当声，试图用作为他手臂的金属尖刺拍手，而埃利奥斯微笑着，立即抓住机会把手放在你的肩膀上，它立即沿着你的胸膛伸向你的生殖器直到你轻轻推开他。罐头鼓掌的声音来自扬声器，大概来自 Primus，让你发笑。伊丽莎白也鼓掌，凯恩困惑地盯着鼓掌的人们，然后耸了耸肩，加入了轰隆隆的雷声，双胞胎为双胞胎鼓掌。Fay 紧紧地拥抱着你，然后你才让大家安静下来。 “你干掉了 Kyros，”伊丽莎白说。“感人的。” “我不再需要赞美了，”你说。“我们需要讨论一下 hsip 会变成什么样子。” “当你活得和我一样长时，你就会知道要珍惜美好的时光，而不是匆匆忙忙地向前冲，”德奇说。 “我们有一艘格洛丽安娜级战舰、一支军队和绝对的自由。世界是……不，银河系是我们的，”费伊微笑着说。 你凝视窗外，沐浴在温暖的金色阳光下，自从 Kyros 登陆你的星球以来，你第一次感到完全自由。 银河系是你的。
梗概：
新的黎明...
故事名称：
19
故事背景：
星期日 “你担心这些不确定的时期吗？你…” 点击。 当你关掉电视时，你对这些地面零广告感到非常厌烦。如果你的哥哥本没有在悲惨的偏执崩溃中枪杀他的家人和他自己，你只能想象他会继续说他在你后院建造地下掩体是多么正确。 你看看时间，意识到你已经花了一天的大部分时间在拖延看电视，即使你对电视不再感兴趣。你以前看过的所有有趣的东西现在都有浅薄的真人秀节目，坦率地说，你在现实生活中已经有足够多的功能失调的家庭剧了，而无需在电视上观看。 你记得有一次你可以躲在地下室来避免大部分混乱。不幸的是，你甚至不能再这样做了。自从爸爸喝醉酒死后，免费在家呆着的肉汁火车就结束了。 不再有你父亲的养老金支票的帮助，你的亚马逊姐姐凯利说，除非她是唯一一个住在里面的人，否则她不可能继续靠自己养家糊口。既然她是一个轮滑的婊子，又是一个非法的搏击俱乐部争吵者，那么你或你的任何兄弟姐妹都不可能与她争论。 你的弟弟彼得已经放弃了成为著名电影导演的崇高梦想，现在他花了很多时间在车库里和他在俱乐部遇到的各种各样的人一起制作业余色情片。他通过自己的网站赚了足够多的钱，这让凯利远离了他。他似乎很享受他在做的事情，至少他不再偷你的衣服了。 你的妹妹唐娜搬出去和你最好的朋友朱莉住在一起，朱莉仍然是永恒的派对女孩。你真的很惊讶朱莉此时没有发现人类已知的所有性病。老实说，唐娜和朱莉的共同点比你多年来的要多。你肯定唐娜在“派对屋”里更快乐。 至于你，你没有太多选择。在这个经济体中，试图找到一份工作以失败告终。虽然你的……“独特”和隐居的个性对那个部门没有帮助。 考虑到房子周围散落着一些垃圾，您有时会想是否不应该尝试通过互联网出售一些垃圾来养活自己。虽然看起来工作量很大，而且你不是车手和经销商。 唐娜建议你和她和朱莉住在一起，但整个场景不适合你。就像你讨厌和凯利打交道一样，你也讨厌每五分钟就不得不忍受醉酒的角质男人（和女孩）试图进入你的裤子，更不用说嘈杂的音乐、人群和不断增长的可能性警察破获非法毒品的地方。 这将你带到了你的男朋友 Bobby Morningstar，他自然而然地曾多次建议你和他同住。如果不是因为他自己的家庭由一百万个兄弟姐妹组成，他们都住在他的邪教大院里，这将是理想的解决方案。还有一个事实是，他的前女友/姐姐戴安娜非常生你的气，因为你把他从她身边偷走了。 这是你和鲍比之间持续紧张的根源，你希望他把他们都抛在身后，你们两个可以在别处一起生活，但他对家庭的责任很强烈，就像他对巴比伦混沌女神的信仰一样, Tiamat 其中仍然涉及人类牺牲。 现在你和他有一段分离期。有时你想彻底和他分手，但你不能忽视和他在一起的美好时光。当一个人警告对方他们的连环杀手亲戚在树林里等着他们时，你有一种特殊的联系，只有一对夫妇才能拥有。感谢上帝鲍比照顾你的叔叔埃德。那一定是你生命中处理那种情况最可怕的时刻。 当然，这些令人愉快的过去事件回顾都没有真正帮助你。凯利开始对你很不耐烦，你担心你几个月的拖延即将结束。 凯利目前不在家，但她会在几个小时后回来睡一觉，然后再去她的搏击俱乐部。继续享受你不必与她打交道的时间，你又回到拖延，这主要包括在互联网上闲逛，“绊倒”色情网站，然后使用电池供电的方式来减轻自己的压力。 最后你会听到熟悉的砰砰声和楼上跺脚的声音，这表明凯利确实在家。 “苏茜！别把小猫劈开，把你的骨屁股弄到这里来！” 你听到凯利喊道。 你害怕这个。你清理一下自己，做好心理准备，然后上楼。 “嘿凯利……”你开始说，但被打断了。“住口。在我不得不拖着我的屁股去我的另一份工作来支持这他妈的房子之前，我只有几个小时可以休息一下，这他妈的一团糟！你连这附近都打扫不干净？你今天找工作了吗？让我猜猜你整天都在网上乱搞，还手淫，不是吗？”“嗯嗯……”“该死的基督苏西，你他妈的没用你知道吗？我现在不踢你的唯一原因是因为我需要节省我的精力，这太他妈的容易了。” 在你说话之前，凯利继续她的咆哮。 “找份他妈的工作！我不在乎什么样！像彼得一样成为这个家庭的贡献者，或者像唐娜一样滚蛋！”“凯利，我正在尝试，但我只是没有找到任何东西的运气，你能帮我吗？” 你说。“帮助你？我现在通过支持你的屁股来帮助你！我已经帮了你很长时间了！事实上，我最初建议你可能会在打架时成为一名拳击手，但你拒绝了，因为你不想穿着轻薄的衣服在一群醉汉面前游行。”“凯利，你知道我不想在公共场合穿成那样，尤其是在那天晚上埃德叔叔让我穿的之后……”“哦，我们走了，疯狂的老叔叔埃德！你打算继续以此为借口多久？Sheesh，你继续说下去，你表现得就像他强奸了你。相反发生了什么？他被杀了，你甚至都没有被触动，你从那笔交易中得到了一个他妈的男朋友！我们中的一些人应该很幸运地处于困境中并且仍然领先！你知道我经常处理什么吗？类固醇怪胎试图打破我他妈的脖子就是这样！如果我长得像你苏西，你认为我会靠和混蛋交易来谋生吗？你得到了最大的优势，而你只是在浪费它！你这该死的问题是什么？” 你可以看到凯利处于全面的嫉妒模式并且没有停止的迹象。试图走开只会导致她和你发生肢体冲突，所以你唯一能做的就是忍受口头攻击。 “这是下沉或游泳的时间姐姐。你要么在这周结束前解决问题，要么就出局！这种被庇护的女孩行为真的他妈的老了！你是个成熟的女人。开始像一个人一样！现在滚出我他妈的视线。” 你温顺地溜回地下室，让凯利一个人呆着。 你默默地闷闷不乐，思考着接下来要做什么。然后你会注意到你的手机一直在你的沙发垫下面并且一整天都处于静音状态。鲍比显然一直在反复尝试与您联系。甚至留下多条短信说他很想说话，但你现在的心情，你不想处理。 你不想处理任何事情。您只想在地下室的小世界中保持安全。多么怀念那些日子…… 好吧，今天还没有完全结束。你还有时间挽救其中的一些。地狱，也许你甚至可以对你的生活做出重大改变。
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动作：
做
你从彼得那里得到帮助
结果：
你不想搬出去，你不能相信自己不会像往常一样在找工作时分心。你需要帮助，而你知道的唯一一个在家的人是彼得，所以你决定去看他，希望他没有在“工作”中。 你去车库敲门。从内部你听到进来所以你这样做了，不幸的是，鉴于彼得几乎赤裸的状态和手中的一管 KY，很明显他没有期待你。 “哦。是你。你想要什么？我很快就有几个人过来了，我还需要加油。” 他说“呃……我需要和你说话。” 你说尽量不看他。“你能不能穿上些该死的衣服？”“（叹气）好吧，但让我们快点。” 彼得说着穿上了袍子。 “彼得，我需要你帮忙找份工作，在你提出建议之前，不，我不会做你为了钱所做的事。”“哦当然不是。我不会想到向你们这样的人提出这样的建议。这意味着我没有很多其他的建议给你。我的意思是我只能想象，由于你没有大学学位、性格怪癖和严重的内向，你在找到一份正常的工作方面非常失败。你似乎没有创业的动力，而且出于某种莫名其妙的原因，你甚至没有利用你所拥有的天赋。噗，如果我是你，我现在就开始，因为你只是变老了。”“彼得，我不是妓女。我不会为了现金而向所有人张开双腿。”“谁说的？弄个有钱人吧，狗屁对你来说应该没那么难。”“那不是我，而且我有男朋友……”“WHO？鲍比？我还以为你们两个分手了。”“不，有点……这很复杂。”“那么你需要和他一起解决一些问题，让他照顾你，因为虽然我没有听到整个咆哮，但凯利的声音足够大，我什至从这里也听出来了一些。听起来她给你下了最后通牒。”“我知道。我知道。我不知道该怎么办。” 你说瘫倒在附近的椅子上。 彼得和你从来没有亲近过，而且大多数时候他过去经常偷你的衣服惹恼你，但值得称赞的是，他走过去并试图用拥抱安慰你，因为他的鸡巴还在外荡他的长袍，你宁愿他没有，礼貌地告诉他你很好。 “你试过马戏团吗？” 彼得突然问道。“什么？” 你回应。“马戏团。在镇外以西几英里处有一个小的。几乎就像一个吉普赛营地。还记得我们一家人以前是怎么在一起的吗？我想这是我们唯一一次相处融洽。”“是啊……我一直最喜欢小丑。我喜欢他们总是那么外向和快乐。甚至以为我会长大并成为其中一员。”“嗯，我想到目前为止只有你一个人。我还记得你过去常常用妈妈的化妆品粉刷你的脸，并试图在家里招待唐娜和我。不过老实说，你所做的只是把我们吓坏了。”“是啊……所以我放弃了那个梦想。以为我做错了。” 你阴郁地说。“哦。抱歉，我不是想成为一个梦想粉碎者。”“没关系，考虑到我的内向，我怀疑我是否真的能够在人群面前做到这一点。”“嘿，如果那是你想做的，你就不应该放弃。我的意思是也许你现在应该追求它。我的意思是你没有太多可以失去的权利，这有多难？化妆，装扮傻，一般都表现得令人毛骨悚然……呃，我是说傻。” 彼得的话确实在你心中点燃了某种火花。 “嗯，也许你是对的……我会去做的！” 你突然惊呼。“很好，很高兴听到你对某事感到兴奋。那你现在好吗？我的意思是我会说得更多，但你知道还是会有人过来……”“哦，对了，我很好。等等，我能借你的车明天去马戏团吗？”“嗯，当然可以。钥匙在那边。”“再次感谢彼得。” 你说拿钥匙离开。 回到地下室，满怀期待再次追寻童年梦想！现在这一切似乎有点疯狂，但你想得越多，就越有道理。 你整个晚上都精神抖擞地度过，并保持积极的态度，明天一切都会好起来的。
梗概：
你从彼得那里得到帮助
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动作：
做
第二天...
结果：
周一 “……我们的庇护所都配备了最先进的技术，现在就订购……” 点击。 你又看电视睡着了。似乎他们现在一直在播放那些该死的广告。由于最近的世界事件，没有什么比为人们的恐惧祈祷了。 你看了看时间，发现早上大部分时间你都在睡觉，但考虑到你要去马戏团申请一份工作，你认为他们会整夜营业。 您还查看了手机，发现 Bobby 一直在试图联系您，但今天您不能让自己分心。您需要专注于成为小丑的目标！ 你开着彼得的车朝马戏团走去。尽管您注意到的一件事是警车和警察总体上有所增加，但驾驶过程相当平静。显然，在多个地点发生了一些内乱。你发誓这是你从不喜欢离开地下室的原因之一。你讨厌人们混乱的本性。 但是，如果您有任何希望成为小丑，您将不得不习惯于与人群和人群打交道。事实上，你将不得不习惯很多事情，但你不能让自己对此感到恐慌。你必须经历这个。 最终您到达目的地。或者至少你认为它是。彼得说这就像一个吉普赛营地而不是一个真正的马戏团时，他并不是在开玩笑。你会看到很多小帐篷和一些拖车，但没有什么能比得上“大顶”。 这里的大多数人都是矮人，上次你知道你镇上没有大量的小人，所以你只能假设他们是马戏团的一部分。事实上，据你所知，实际上没有任何客户，只有肉食。 当你四处游荡时，你开始得到很多目光，你不确定他们是否因为你是局外人而盯着你看，或者他们只是在盯着你看。可能两者兼而有之。很快你就会被很多小人物包围，你开始觉得自己像多萝西。 “嘿，长腿的戈尔迪，你在这附近做什么？我们还没开门。” 他们的一个数字说。“哦，好吧，我不是为此而来的。我在这里是因为……”你深吸一口气。“我要当小丑！” 这当然不是他们所期望的回应。在正在说话的人再次开口之前，您会听到几声耳语。 “一个小丑？嘿，我不确定这对你来说会有多成功，但幸运的是，这个优秀马戏团的老板也是个小丑，所以你需要和他谈谈。他在那边的帐篷里。Roofie 可能还挂着，所以尽量小声说话。”“楼上？”“是啊，那是他的小丑名……其实也是他的真名。不管怎样，别担心他不是强奸犯或类似的东西，他只是个酒鬼。事实上，他可能是这个马戏团里精神最稳定的小丑！” 按照矮人的指示，你前往 Roofie 的帐篷。酒味很重，当您进入时，您会看到一个穿着超大号裤子和吊带裤的秃顶老人坐在桌子旁，双手捧着头。啤酒和威士忌酒瓶足够丰富，只要绊倒一些就可以听到自己的声音。 “该死的，这不是他妈的表演时间，反正没人来！放开我，该死！” 鲁菲连头都没抬，继续捂着头喊道。“呃，鲁菲先生，我被告知要和你说话。” 鲁菲转身看着你。一个大红鼻子还在他的脸上。 “嗯？Wbo你是他妈的吗？你看起来太他妈的优雅了，不可能是我能负担得起的任何妓女。”“我叫苏西，我想成为一个小丑。” 就在这时，鲁菲慢慢地从座位上站起来，踢开几瓶酒。你继续站在那里开始怀疑这是否是个好主意。 “一个小丑？现在为什么像你这样一个可爱的金发女郎想要成为一个他妈的小丑。没有意义。亲爱的，你在玩某种角度的游戏，因为我向你保证我没有心情，反正我也没有钱让你操心。”“不，这一直是我从小到大的梦想！” 你说然后开始详细介绍你是如何喜欢马戏团小丑和其他一些 Roofie 并不真正感兴趣的生活故事。事实上，他现在只是努力站起来倾听。 “好了好了，够了！拉屎。我得到它。（叹气）亲爱的，我不知道该告诉你什么，我们不完全是这里的林林兄弟。我也不是很想招聘，我几乎没有让这个地方保持原样。”“请让我做个小丑！就给我一次机会！”“啊，他妈的太吵了！好吧，给我看看你的小丑玩意儿，小声点。”“小丑的东西？”“嗯，是的，就像你会玩杂耍一样？也许有点魔法？一些闹剧？你有流浪汉之类的性格吗？你以为白白化个妆，像个傻子一样蹦蹦跳跳的笑，哪有那么容易？”“嗯，有点……”“嘘。这种想法让我们看起来令人毛骨悚然，而不是有趣。你知道大多数人讨厌小丑吗？如今，我们在恐怖电影中不断受到诽谤，这无济于事。如果你连基础知识都不知道，你就在浪费我和你的时间。” Roofie 不再完全激发您对此的信心。您感觉就像回家一样，但不能像对待其他一切一样逃离这里，您别无选择！你开始泪流满面。 “也许我不知道基础，但我会努力工作！（抽泣）我是认真的！（抽泣）我试图在其他任何地方找到一份工作，如果我得不到这个，我就会被赶出家门！这是我最后的机会！（抽泣）”你在泪水中惊呼。“最后的机会？亲爱的，如果当小丑是你最后的机会，那么你就遇到了一些大问题。（叹气）我什至不知道我为什么要娱乐这个，因为即使不需要酒精你也很容易看清，而且我是一个陷入困境的少女的傻瓜。此外，如果这个消息有任何迹象，无论如何我们都会很快被核火烧死，所以他妈的。好吧，别哭了，也许我们可以和你做点什么。” 你睁大眼睛，完全没有注意到 Roofie 对世界末日的短暂预期。 “所以你会帮我训练？”“好吧，希望你能及时掌握一些东西，但我不会打扰定期训练，你将学习火试法，无论如何，所有好小丑都会这样做。现在对你来说最重要的事情是你很可爱，所以我想我们为你创造一个利用你的角色，我们可以说是天赋。”“等等，我不会在我的乳头上只戴红色小丑鼻子的情况下暴露我的乳房，不是吗？” 你关切地问。“（哼哼）耶稣女孩。不，没什么。” Roofie 笑着说：“你会穿好衣服，但你看起来仍然很可爱。” 有点像那个女孩。你在有趣的书里知道吗？那个有那个富有的义务警员的人。无论如何，要记住的关键是你需要表现得可爱。那将是你的性格。你会成为一个可爱的小丑，而你周围的所有其他小丑都滑稽地无法赢得你的欢心，用木槌互相殴打等等……嗯，这实际上听起来像是一个很好的小品。我要围着其他人跑过去。与此同时，那边的后备箱里还有一堆小丑装束。相当肯定我们有一堆应该适合你的未使用的少女。有的在那边化妆，那边有一面镜子。现在不用担心是否合适，因为我知道这个帐篷里的灯光很糟糕。呃，我不知道练习端庄和害羞。” Roofie 离开帐篷，而您却意识到自己可能会成功获得这份工作！你迅速穿过行李箱，发现一些粉红色和黑色格子紧身连衣裤，腰间系着一条迷你裙。它很合身，幸运的是，在合身的同时，它并不过分紧身，虽然你的腿仍然暴露在外，所以你穿上了一些膝盖高的紫色条纹紧身裤。在一个有人可以随时进入的帐篷里脱衣服，你会感到有点脆弱，但幸运的是，至少有一个隐私屏幕可以让你穿在后面。 将头发扎成少女般的辫子，涂上一束白色的妆容，用黑色眼线笔和红色唇膏让睫毛和嘴唇脱颖而出，从而完成你的造型。 当 Roofie 带着一群穿着各种小丑服装的男人回来时，你还没有时间练习。其中一个甚至是像您在前面遇到的小人物。 “该死的鲁菲，我想我得重新考虑离开你该死的狂欢节的计划了。” 其中一位说。“离开？见鬼，我想我恋爱了。” 在鲁菲说话之前又说了一句。 “该死的丫头，我说的是可爱的小丑，不是小丑妖精！将来你可能需要调低一点，我们仍然或多或少地尝试举办一场家庭表演。好吧，没关系，让我们来看看这个，你们试着记住偶尔把舌头放在嘴里。” 因此，当 Roofie 为每个人提供指导时，您可以完成例程。一开始你很自然地感到有点不准备和不安，因为每个人都和你调情，但很快你就开始陷入困境。事实上，它开始变得容易，当有人滑倒并试图给你一朵气球花和类似的东西时，你眨着眼睛，发出几声少女的傻笑。 最终 Roofie 阻止了所有人，并说这个小品需要稍微改进一下，但它应该会奏效。然后他告诉每个人离开，因为他想和你私下谈谈。这似乎让那些在离开时继续盯着你的其他人感到失望。 “嗯，除了我可以看出他们中的一些人真的很难不真正摸索你之外，我想说一切都很顺利，甚至你似乎也很快融入了角色。也许你确实拥有它所需要的东西。虽然你看起来还是有点紧张，这对你的表现有帮助，但我宁愿你保持放松。有什么能帮上忙的吗？” 路飞问道。“嗯，就像你说的那样，我部分地觉得他们中的一个人会在任何时候跳到我身上。我的意思是，这是我需要担心的事情吗？”“不，那不会发生。我们carnies有一个规则，禁止互相伤害。我的意思是，我不会撒谎说他们不会盯着你看很多，而且很可能有一些人会发表粗鲁的评论，但如果你决定加入并留下来，那就差不多了我们。”“要我加入吗？”“如果你仍然感兴趣，当然可以。我想你有什么可以提供的。虽然就像我说的，我仍然不确定为什么像你这样的人会想要加入这样一个破败的马戏团。我仍然认为像你这样的人会成为模特什么的。”“嗯，我穿着古怪的衣服，我的脸都被涂上了，一群人为了看我一眼就摔倒了。除了饮食失调之外，我似乎和模特一样在做同样的事情。” 你的评论引起了 Roofie 的笑声。 “哈哈！这很好。我想我做出了正确的决定。好吧，那么关于你的睡眠安排，我可能要重新安排一些人，这总是会引起问题，所以你今晚可能要睡在你的车里……”“等等，你想让我搬进来吗？”“嗯是的。我的意思是我告诉过你我们不会在这里呆太久，你必须知道马戏团确实倾向于旅行，对吧？”“对，没错。当然。只是，呵呵，我想当我跳入这个时，我并没有考虑全部。虽然这可能是显而易见的。”“嗯，这是你的选择。我不会撒谎说这种生活是最简单的，如果你在家里感觉更舒服或者有亲人你不想离开，这是可以理解的。”“我不知道我是否会这么说，但是离开家的概念……嗯，这对我来说是一个很大的变化。就像一个非常大的。奇怪的是，这个机会让我认真考虑过。”“好吧，你做你内心深处的事情。不管怎样，我们今天不走，明天也不走，所以我的意思是，如果你想回家拿些东西，跟谁说再见，那你就有时间了。但即使我们明天不离开，我们的最后一场演出也会开放，我需要你在这里。我希望我们至少能在这里招到一些人，在拿到赌注之类的东西之前至少能赚点钱。” 你不确定该怎么做。合乎逻辑的做法是回家收拾一些东西，说几句再见，然后试着说服彼得开车送你回来，这样他就可以取回他的车了。 但是你内心有些东西会怀疑你是否应该立即彻底休息并留下来。您了解自己，如果您回到家，您可能会找到一些理由留下来并退出这个机会。你想想鲍比，以及他如何成为这些原因之一。你还没给他回电话。
梗概：
第二天...
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动作：
做
你留在马戏团
结果：
如果你要这样做，你需要立即全力以赴。你对偷彼得的车感觉有点不好，但你认为你可以稍后给他打电话，他可以让人开车过去接他。（假设他没有先给你打电话） 至于鲍比，你不会打扰的。你得出的结论是，你和他有太多的不同，无法实现，而这最后的分手已经很久了。打电话给他只会让你爱上他的魅力并阻碍你自己的目标，你不能让这种情况发生。 “先生。鲁菲，我留下来。” 你说。“好，太棒了。我们很快就会开放，不知道是否有人真的会出现，但你不妨出去走走，让这个地方看起来很喜庆。”“要我换衣服吗，你说这身衣服太过分了？”“呃……别闹了。我想这对你来说很合适。就像我说的，你可能不得不忍受这里一些粗鲁员工的猫叫，但仅此而已。如果有人开始与您格格不入，无论是卡尼还是鲁布，请告诉我。相信我会立即处理的。这场狂欢节可能已经走到了最后，但如果它陷入无政府状态，我会被诅咒的。我父亲这些年没有工作，也没有白白牺牲。” 鲁菲说。“这个嘉年华是你爸爸开的？他也是小丑吗？” 你问。“呵呵，没有。他是个极客，我也不是指可爱的书呆子意义上的。我的意思是他是一个真正的马戏团极客。吃了各种恶心的东西来招揽顾客。生的动物器官，鸡的头，腐烂的食物，你能想到的。他总是告诉我，尽管他大部分时间都喝醉了，所以情况并没有你想象的那么糟糕。幸运的是，他并没有成为马戏团的怪胎。显然他在一些非常重要的事情上帮助了原来的领班，在那之后，我父亲在这里找到了一份更好的工作，担任领班的助手。”“他帮掌门做什么了？”“完全不知道。就像我说的，在那些极客的日子里，我父亲喝得更重。他说这与马戏团内部的权力斗争有关，但他不太记得所有细节。无论如何，当最初的环大师有一天决定是时候转向其他东西时，我父亲控制了马戏团。尽管时代在变，但它也继续前进。我试图追随他的脚步，但到目前为止，我唯一成功的是饮酒部分。” 说完这句话后，鲁菲从附近的瓶子里喝了一杯。 “所以，呃，是什么让你变成了小丑？” 你问。“我不知道，我想我是想帮忙。你看，自从我妈妈在生下我时去世，我爸爸不得不自己抚养我，我想等我长大了，我觉得我应该为我加在他身上的所有额外负担做出贡献。”“很抱歉听到这个。”“嗯，我对她的了解是，她是一个离家出走的人，在我父亲接任老板后不久就加入了马戏团。不管怎样，有一段时间，这个马戏团没有一个小丑。显然很久以前就发生了，但我父亲说有一些关于他们的恐怖故事，他们最终……放手了。尽管这个马戏团对再次出现小丑有奇怪的担忧，但我说服了我父亲，让小丑出现是个好主意。所以他给了我一个镜头，它奏效了。休息是历史，无论如何，故事时间足够了，你有自己的小丑工作要做，所以开始吧。” 您请假并再次保证您将尽一切努力让人们开心。 你刚离开 Roofie 的帐篷，就被其他几个小丑抓住了，这次他们是全套小丑制服。 “嘿郁金香，我们一直在找你。如果你留下来，我们将需要完善我们的日常工作。” 其中一位说。“是啊，在那之后，你可以打磨别的东西。哈哈哈哈哈！” 另一个说，这引起了其他人的窃笑。“你能不能不要这样跟我说话？” 你礼貌地问。“哇，你敏感吗？你真是一朵娇嫩漂亮的小花不是吗？” 发表抛光评论的小丑说。“嘘，兰科他妈的可不是个混蛋。别介意他，亲爱的，他以前从来没有和女人这么亲近过。” 另一个小丑说。“去他妈的 Klepto，别再玩白骑士了。你没有在愚弄任何人。你他妈的很清楚你对这个金发碧眼的小数字也很生气。伙计们，我是对的吗？” 这得到了其他小丑的小小肯定，包括 Klepto，他在承认自己透明的白人骑士身份 时轻笑。你又一次开始怀疑这是否值得。 这就是它永远的样子吗？你甚至还没有正式成为一名员工五分钟，你就已经受到了肮脏的影射和骚扰。你知道 Roofie 试图向你保证不会发生任何事情，如果有人尝试过任何事情，你可以让他知道，但实际上，尽管你不想每次都像小故事一样跑到他身边. 如果你能勇敢面对你的连环杀手叔叔和你的邪教前男友，你当然可以对付一些粗鲁的小丑。事实上，你开始思考凯利曾多次辱骂你，你开始生气。你将一些你对她的愤怒引导到你的新同事身上。 「兰子，因为你现在口臭，我猜这就是你的名字。它闻起来像狗屎，但仍然比你看起来高两步。” 你说，这让他措手不及，其他人都笑不出来了。在兰科反驳之前，你继续。 “你的名字应该是零，因为这正是你和我能走多远的地方。你们其他混蛋也一样！仅仅因为我是新来的并不意味着我会接受你他妈的虐待！” 片刻之间，寂静无声，甚至附近的一些员工也看着，想知道剩下的要做什么。不过，兰科是唯一一个说什么的人。 “嗯，这朵娇嫩的花好像有几根刺。不过没关系。我喜欢有点精神，让事情变得有趣。无论如何，我们仍然需要回顾一下常规，所以如果你对我们所有人都完蛋了，那就走吧。” 他说并示意每个人都跟着。 你有点担心在任何地方跟随这个小组，但没有什么其他的，因为这些人将成为你的同事。你只能希望他们及时将你视为他们欲望的出口。 当您步行到该地点时，您会浏览狂欢节的其余部分，其中主要由许多游戏狂人类型组成，他们设置了他们的不正当游戏。你确实看到了一些更传统的吞剑者和食火者类型正在准备自己的阶段。这是一个马戏团，而且 Roofie 说他父亲是个极客，你不禁怀疑是否有任何“怪胎”。 “嘿，只是想知道这里附近有没有连体双胞胎之类的？” 你问试图打破沉默。“连体双胞胎？成为种族主义者的方式。哈哈，不只是和你他妈的，但我相信正确的术语是连体双胞胎，不，我们没有。几十年来，这个狂欢节没有任何怪胎。我听说那天有很多人在这里。独眼巨人、针头、自然界的四臂可憎之物，你能说出它的名字。” 兰科评论。“这里怎么没有了？”“好吧，因为我们生活在一个发达国家，现在可以使用现代医学？我的意思是，这些怪胎中的大多数只是不幸的混蛋，他们出生时在遗传图表上的排名很低。多亏了现代医学，他们现在可以纠正那些狗屎你知道吗？另外，你有所有那些关于剥削或一些狗屎的政治正确类型。此外，如果某人是天生的基因怪胎，他们可以参加脱口秀或互联网并以这种方式赚钱。不，杂耍狂的时代早已过去了。我的意思是你肯定会得到像纹身和穿孔的人这样的自制怪胎，但这并不完全相同，它甚至不再令人震惊，尤其是当你让人们免费割掉他们的鸡巴或将玻璃罐塞进他们的屁股时互联网。在这里对付怪物的最好方法是侏儒，它们是一打一毛钱，没有冒犯虾皮。” 兰科说的是矮人小丑。“去你妈的兰科。” 虾米的言论。 很快你就会到达几辆拖车附近的一个空地，兰科说你会在那里完成例行公事。这很像你第一次经历它的方式，但你注意到几个小丑现在变得更加抢手了。 “好了，停下，停下。你们这些混蛋应该是想讨好我，互相打架，而不是想轮奸我！我感觉又有一只手试图抓住我的屁股，他们正在失去它！” 你说。“哦，看在你的份上，我们所做的一切都是冒犯的。我只是在想一种方法来保持日常生活的新鲜感。会很好笑的！” 兰科说。“新鲜的？我们甚至还没有为人们表演过！此外，这不应该是家庭表演吗？Roofie 建议的方式会很好。”“那个老酒鬼连表演都没有了，他忙着把这个嘉年华推到他妈的地盘上。他太他妈的脱节了，这太荒谬了。现在的孩子们现在只要可以登录互联网就立即观看铁杆肛门色情片，所以我怀疑几个小丑是否会抓住你的屁股甚至会注册。” 兰科说。 在这一点上，兰科开始变得更加敌对。你对不想成为挤压玩具的持续蔑视正在影响他。尽管 Roofie 是所有者，但很明显，这个 Ranko 是小丑的“领头羊”，无论如何他都会成为一个混蛋。继续对抗他的权威只会导致其他人统一到他身边，所以你尝试其他方法。 “好吧，兰科，你想让这个例行公事包含一些更危险的商业行为，但你这样做是错误的。你没有风格。”“哦？只当了几个小时的小丑，你就已经是专家了吧？好吧，亲爱的，继续启发我们所有人。”“嗯，一方面，由于他的体型，Shrimpy 应该尽可能地在我的裙子下面看看。” 你说。“等什么？” 兰科问道。“嘿，我已经喜欢她的想法了！” 虾米惊呼。“你们仍然需要发挥自己的特点，不是吗？你，利夫托。你是这群人中最大的，所以你应该依靠强大的力量来给我留下深刻印象，就像把我抱在怀里一样。”“是啊，有道理。” 利夫托评论。 你经历了其他小丑应该做的事情，他们非常接受你和你的想法。你想出了一个全新的套路，他们每个人都试图吸引你，以及他们是如何由于自己的无能或另一个小丑破坏他们而失败的。唯一不喜欢这个想法的人是兰科。 “嘿嘿嘿，那我呢？我适合哪里？” 兰科要求。“你？好吧，我想你会是一个完全令人作呕的追求者，甚至永远不会接近迷人的视觉，因为你已经很擅长这个角色了。你可以是一个不断被其他人击败的持续参与者。Lifto 用木槌打你，Klepto 偷你的裤子，Shrimpy 给你一个热脚……你知道小丑的东西。会很有趣的。” 你对兰科的懊恼说了很多冷嘲热讽的话。“哦不，你不是让我的船员反对我！我是这里的小丑头，而不是一些金发碧眼的尾巴！我们不同意这种狗屎！” 兰科喊道。“你自己说兰科。” 克莱普托说。“是的，我更喜欢这个主意，我的那位似乎并不依赖我的动作。” 胖子评论。“你只是因为一个新手采纳了你的想法并让它对你不利而生气。” 虾皮补充道。 兰科在这一点上开始对每个人咆哮和咆哮。他发出一连串的咒骂声，引起了整个狂欢节的注意。他叫你婊子，其余的人都被鞭打。 “就这样，这个该死的失败者嘉年华我已经结束了，你们其他的失败者也是如此！我他妈的是RANKO这个该死的小丑！我可以在任何地方找到工作！和这里的冷小姐一起享受默默无闻的生活吧！” 兰科惊呼一声，怒气冲冲地离开。 “好摆脱级气息！我是个该死的天生金发女郎，你这个混蛋！” 当他继续走开时，你喊道。 你不得不说这种自信让你有点匆忙。你喜欢它。当你回到与其他人一起做例行公事时，你会注意到以前的不安感减少了。“男孩们”仍然在做很多偷看的事情，现在的日常活动更加注重身体，但这次有所不同。也就是说，您感觉更能控制局势，这可能是因为您确实如此。 “哇，我真的是在浪费我的天赋。” 你自己想想。 在例行公事的中间，鲁菲出现了。 “嗯，这看起来……友好。” 他评论说，Lifto 正在让你回到地面上。“刚刚经历了经过调整的例行老板。这是新来的女孩的主意！” 克莱普托评论。“是啊，会好的。” 虾皮补充道。“嗯。好吧，让我们至少试着让每个人都穿上衣服，好吗？”“当然！不会有任何那样的事情。只是想让它对成年观众也很有趣。” 你说。“不禁注意到兰科像水手一样咒骂并跺着脚离开狂欢节。抱怨他永远不会回来。你不会对此一无所知吧？”“不是真正的老板。我猜他只是跟不上这里最近的变化。” 你说， 罗菲微微一笑。 “呵呵，好的。好吧，我不会再失眠了，也不必再付钱给那个混蛋了。不管怎样，看到几个人居然开车过来了。看起来像真正的客户，所以你们需要为人群做你的事情。” 这一天剩下的时间对你来说都很顺利。您和其他人会招待确实出现的一小群人（他们似乎主要来自您所在城镇外的拖车公园社区）。 到了晚上结束时，你实际上已经很累了，很想找个地方睡觉。Roofie 说多亏了 Ranko 的离开，他的拖车现在是空的，并说如果你愿意忽略他可能把它留在那里的烂摊子，你可以拥有它。你接受这个提议，因为这比睡在 Peter 的车里要好. 就在那个时候，你忘了给彼得打电话，让他 知道去取车！你检查你的手机，发现彼得确实试图联系你，鲍比又一次留下了一堆短信和信息。你太累了，甚至懒得读它们。 你试图给彼得回电话，但没有得到答复。大概是在做天知道吧。你明天再试一次，现在你进入兰科的拖车，因为这个地方对健康没有危害而松了一口气，然后躺在他的床上。 当你渐渐入睡时，兰科真的很想把你弄到他的床上，而他只需要离开，这一点讽刺意味十足，你不禁轻笑起来。
梗概：
你留在马戏团
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动作：
做
第二天…
结果：
周二 “……记住时间不多了！我们的庇护所不会……” 你醒来时本能地抓住遥控器关掉电视，却发现你没有电视，但你仍然听到那些该死的零地商业广告。 你昏昏沉沉地坐起来，从侧窗向外望去，看到其中一个拿着收音机，正在大声播放那些广告。 反正你已经起床了，现在你有实际的责任，必须和这些新人一起生活，你应该借此机会认识大家，因为尽管认识了你的同事和老板，但你并没有真正互动和其他在这里工作的人在一起。 仍然穿着昨天的衣服（主要是因为你现在没有其他衣服可穿）你出去见你的新家人，充满希望这将是美好的开始！ 然而，事实证明大不相同。 “亲爱的，他妈的怎么跟那些小丑混在一起？我敢肯定你更适合吞剑；事实上，如果你现在来我的预告片，我有一个你可以练习的方法！”“嘿小丑女孩，我有一个不用化妆就能把你的脸涂成白色的方法！”“好奶子！” 女性carnies也好不到哪里去。 “所以你就是那个有自己私人拖车的新女孩吧？猜猜现在口交会让你更喜欢。鲁菲当时还清醒吗？”“嘿，Bozo the Hoe，你认为你是在对每个人扭动屁股吗？我现在就警告你，你带走我的人，我保证你下次表演时不需要戴上红色小丑鼻子！”“没有，我没有多余的衣服可以借给你，活体骷髅小姐。走开你这个厌食的婊子。” 地下室外的生活从来都不容易，是苏西吗？ 不幸的是，你生命的那一部分已经结束了。您可以回到您的预告片并隐藏您的想法，但事实并非如此，而且您已经得出结论，隐藏并不能解决您的问题。 尽管如此，你还是觉得有点“联合起来”，所以为了暂时的缓解，你回到了目前唯一对你有一半体面的人，你的小丑伙伴们。 你进入小丑帐篷，看到他们都围坐在一起谈论某事，但当你进入时，他们的注意力都转向了你。 “哦，嘿，苏西，你来得早。我们通常要到演出时间前几个小时才会热身。” 克莱普托评论。“哦，我不是来这里的，我只是在这里，因为狂欢节的一半似乎想操我，用笨拙的性暗示来攻击我。”“而且……你来这里是为了逃避那个？” 利夫托问道。 你轻笑一声。 “是的，至少如果我留在这里，一部分将与工作有关。” 你的回答引起了其他人的笑声和微笑。“好吧，考虑到你是我们中的一员，也许我们现在应该更轻松一点。” 克莱普托说。“我当然会很感激。”“嘿，还有很多其他女人在这里工作，也许你可以和她们建立关系，做整个姐妹情谊的事情？” 利托说。“我试过了。他们都侮辱我，似乎想打我。好吧，除了那个会玩弄刀的红短发女人。我想她想打我然后操我。（叹气）不行，看来你们现在是我这里唯一的部落了。”“嗯，估计习惯了。没有人那么喜欢我们这些小丑。老实说，我们决定取消对你粗鲁言论的原因之一是因为你将成为多年来第一个对我们有礼貌的新人。没有必要制造新的敌人。” 克莱普托说。“真的？我以为所有的肉都粘在一起了。” 你说。 现在所有其余的小丑都笑了。 “几乎不。我的意思是，有一条不成文的规则，即carnies 对局外人和rubes 保持团结。我们不应该互相残杀，但是……好吧，只能说这个狂欢节在这方面并没有一尘不染的记录。” 克莱普托说。“这里的人自相残杀？” 你问的有点惊慌。“不！反正不是在我的时代......” Klepto 的评论逐渐减弱。“这到底意味着什么？听着，我不想成为某个谋杀马戏团的一员！”“该死的盗贼，你现在吓坏了那个女孩！你应该闭嘴！” 虾米惊呼。 在这一点上，小丑们开始互相争吵，但在争吵了一会儿之后，你要求有人准确地解释情况。Klepto 再一次承担起了讲故事的责任。 “听着，我可能把这一切都夸大了，但有一些故事说，在 Roofie 甚至他父亲拥有这个狂欢节的前一天，为了控制它而进行的暴力权力斗争导致了很多人死亡之后的尸体。”“不只是故事，该死的事情发生了！几代人以来，我的家人也一直是这场狂欢节的参与者，我的祖父告诉我在那决定性的日子里流淌的鲜血。” Shrimpy 补充道，好像 Klepto 并没有在这些故事中投入那么多精力，而是接替了讲故事的人。 “那天划定了战线。我的意思是你有矮人，普通人和怪人都站在对方一边，最讨厌的一群？小丑们。现在从我祖父告诉我的，这些小丑绝对是今天每个人都认为他们是床底下的怪物的原因。从那以后，事情当然发生了变化，但正如他们所说，事情变化得越多，它们就越保持不变。”“你是什么意思？” 你问。“我是说很快就会有另一场权力斗争，鉴于我们是小丑，无论发生什么，我们都会输掉比赛”“等一下，鲁菲呢？我的意思是他不是我们中的一员吗？” 你问。“楼上？他是小丑这一事实只会让每个人都认为我们已经得到了优待。他可能会是第一个被杀的人，成为所有者和所有人。他老了，几乎不再像以前那样有控制力了。不，我相信变化很快就会到来，这对小丑来说真的是黑暗的一天……好吧，也许我可以让我的人带我回去，你可以提出做某人的女朋友，但剩下的你们中的人完蛋了。”“哎呀，很高兴知道如果真的发生什么事，我们将能够依靠你的支持 Shrimpy。” 克莱普托评论。 你从未意识到卡尼“政治”是如此复杂和危险。在你对它过于恐慌之前，Roofie 手里拿着一个瓶子出现了。 “喂，你们还在这里干什么？我们实际上已经有人提前出现了。显然你昨天的例行公事让那个拖车公园的人们开始说话，他们带来了所有其他的乡巴佬。看来我们可以在离开这个地方之前赚到一笔可观的现金。哦还有苏西，我不想成为坏消息的承载者，但是你开的车呢？嗯，看起来像是在晚上被破坏了。”“什么？！” 你惊呼。“所有的轮胎都被划破了，有人在上面涂满了脸。这可能只是一种预感，但我想说兰科觉得他需要最后说一句话。”“（叹气）太好了。” 你说。“好吧，无论如何，你们都需要出去做自己该做的事。” Roofie 命令和离开。 当剩下的事情开始时，你仍然坐着开始担心你自己陷入了什么。你再一次开始怀疑在小偷碰到你的肩膀之前你是否应该离开。 “别担心苏西，从我认识他开始，虾米就一直这样。虽然这里的事情有时会变得紧张，但我认为它不会发生大屠杀。再说你不能一辈子都在担心未来，要活在我说的那一天，所以我们就好好表演一下，把我们的烦恼从烦恼中解脱出来吧。” 克莱普托评论。 虽然 Klepto 的保证很好，但并没有真正减轻您的担忧。见鬼，如果你想彼得的车被毁了，你甚至不能离开。你只能希望，也许人们对你的态度会随着时间而缓和一点。现在，你确实有工作要专注。 Roofie 说今天有更多人出现并没有说谎。你和其他小丑大部分时间都在四处走动，让人们玩得开心 最后你休息一下，决定检查一下彼得的车，看看它是否可以挽救。当你看到它时，就像 Roofie 所说的那样，所有四个轮胎都被划破了，上面写着屄这个词。你无法相信即使在晚上也没有人看到这些。或者，考虑到您还没有完全被这里的大多数人接受，他们可能做到了并且没有在意。 无论如何，您尝试再次给彼得打电话，但没有得到答复。他甚至没有试图给你打电话。他一定是真的忙于工作什么的。或者也许他不在乎他的车。他很容易买得起新的。您还注意到 Bobby 也没有尝试给您打电话。你猜他得到了暗示。 你试图发动汽车，但正如你怀疑兰科肯定已经破坏了引擎或引擎盖下的东西一样。无论如何，当你离开时，无论如何你都不会再驾驶这东西了。汽车不再有用了，你看看后备箱看看里面可能有什么，然后你就会发现一些有用的东西。即一堆衣服……特别是你的衣服！无论如何，他们中的一些人。 显然彼得还有很多你过去从你那里偷来的旧衣服。其他的东西看起来像 S&M 服装，虽然不是所有的都是传统的黑色皮革或乳胶。其中一些是彩色的。甚至节日。 好吧，至少你现在不会日复一日地在同样的东西里走来走去。你收拾好你能收拾的东西，然后回到你的拖车把它放下。之后，您会找到其余的新小丑“家人”并与他们一起吃饭。你借此机会告诉他们一些你以前的生活；你从其他人那里得到了惊人的同情。 “天哪，我以为我来自一个该死的家庭。” 克莱普托评论。“是的，就像我说的那样，我大部分时间都躲在地下室。我猜我的生活并不多，但它让我在很大程度上保持理智。” 你说。“嘿，有什么可以帮助你度过这一天的吧？” 虾米说。 再聊了几句，大家就回去工作了。 随着时间的推移，你一整天都在附近看到一个人，一个穿着工作服的大个子，虽然试图看起来他没有盯着你看，但显然是。这不是通常的凝视，因为你已经习惯了，你从这个人那里得到了一种非常令人毛骨悚然的氛围。 “也许我们应该让 Roofie 把这家伙赶出去什么的。” 你说。“楼上？我刚路过其中一个帐篷时，我又吃了点零食，看到他现在像往常一样昏倒了。此外，只要他们没有做暴力的事情，他就不会把任何人赶出去。就他而言，参加这个狂欢节的每一个低级生活都是一个潜在的标志，值得为了赚钱而忍受。” 胖子说。“这家伙看起来像个智障。他可能不习惯看到像你这样美丽的女孩。我的意思是，即使在白脸妆和小丑服装下，你看起来仍然比任何带着他们的后代来到这里的拖车垃圾母亲都要好几英里。” 虾米的言论。“嗯，他真的他妈的令人毛骨悚然，我不相信他。” 你说。“他不会在光天化日之下做任何事情，即使他做了，你知道我们得到了你的支持。只要靠近我们，Lifto 看起来就特别吓人。” 克莱普托说。 到了晚上，人群开始变得稀少。你想象一下，在他们把他们的非法产卵放在床上之后，拖车垃圾更喜欢在酒吧、脱衣舞俱乐部度过他们的夜晚，并在家里喝醉和/或喝醉。至于整天盯着你看的大笨蛋，他好像也走了，但你还是不放心。 与此同时，鲁菲从暂时的醉酒昏迷中醒来，告诉大家今天是个好日子，每个人都应该休息一下，因为明天早上你要收拾这个地方，然后继续前进。然后他嘟囔着说这一切毫无意义，摇摇晃晃地回到他自己的拖车。 “嗯，看起来就是这样。明天见，苏西。” 当他和其他人走向他们的拖车时，Klepto 说道。 另一方面，你对回到你的身边仍然有点紧张。 “呃，伙计们，你们认为我今晚可以和你们一起睡吗？” 你说这导致他们所有人都难以置信地转向你。“哇，兰科会因为那句话而度过一天。” 克莱普托轻笑。“你知道可能什么都不会发生。那家伙只是个笨蛋。见鬼，如果怪胎表演还存在，他可能会成为这里的杂耍之一。”“听着，我仍然对之前那个家伙感到非常紧张。我的意思是我可以睡在地板上，我不在乎。” 你说。“那会令人难以置信的不舒服，而且可能非常恶心。我们的拖车并不是最干净的。我的意思是我愿意提供我的床铺，但鉴于我们明天将需要我们所有的精力来收拾行李，我想睡个好觉。” 克莱普托评论。 “我为什么不在你的拖车里过夜？” 利夫托问道。“我知道 Ranko 的拖车足以容纳另一个人。我可以留下来，你们今晚多出一点房间，你就可以安心睡觉了。”“嗯，那是……你很慷慨，Lifto。” 克莱普托评论。“不是那样的，我只是提供一个解决方案。” Lifto 说，尽管他似乎确实希望这种侠义行为会带来更多结果。 虽然你不喜欢在团队中引起强烈的情绪或可能带领某人，但你知道有一件事是确定的；你今晚还是不想一个人睡。
梗概：
第二天…
故事名称：
20
故事背景：
你醒了，但是嗯。谁真正关心？ 你走出房间，巨石回荡着你的脚步声。当您离开大厅时，您会凝视着卑微住所中毫无价值的居民。 他们都四处游荡，问你对事情的看法，但你在乎吗？嗯，真的不是。另一个声音传到你的耳朵里。从某个随机的房间里出来了一个橙色头发的矮个子女孩。 “啊，我的樱桃派，”你亲吻她的额头时说。 “喂，你睡得很晚啊！” 她说。 “嗯。” “我今天要去Cystia？要和我一起吗？” 她问。 “嗯。” “嗯？” 她问。 “今天可能会留在 Discordina。” “为什么？” “诶，别想了。” 随着你继续前进，你会看到越来越多你根本不关心的人和事。最后，您会沿着大厅走到您最喜欢的房间之一。 机器和其他东西的呼呼声和叮当声在你的耳朵里就像音乐一样。但这还不是全部。前方，一个浑身覆盖着鳞片的人站在一台大型机器前。很大，有很多金属管，每个管子里都流着绿色的液体。正是通过这项发明，在这种绿色魔法精华的帮助下，你可以改变现实本身。它被称为 Deviner，是有史以来最伟大的发明。 你上前插上电源，大针状物体即将刺穿你的前额。但是当你这样做时，你不会感觉到流动的绿色液体的涌动。事实上，你听到的只是一种奇怪的沉闷和翻腾的声音。 什么都没发生，你皱眉。 “该死的！” 鳞甲龙人道：“我觉得有什么东西被破坏了。” 你把针拔出来，让它在它所在的地方晃来晃去。机器本身似乎已经损坏、倒塌和废弃。龙人开始收集他所能收集的机器碎片。在你做任何事情之前，他会阻止你。 “这东西我会做的，你应该去Cystia找一些那些魔晶。” “为什么？” 你问。 “不仅是机械坏了，魔晶似乎也充电过度了，在液体中散发出一股魔力。所以它把德维纳的薄弱部分给击碎了。” 你叹了口气，这是你为数不多的事情之一。 你走下无价值的大厅，经过所有无价值的人，最后到达一辆无价值的车厢。无论如何，它们中的几个似乎已经起飞。 当你坐下时，你会瘫倒在你安装在车厢里的舒适座椅上。驾驶它的没用的家伙轻弹统治，使没用的马前进。
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动作：
做
你的目的地
结果：
你终于到达了被称为 Cystia 的繁华之地！当您驾车穿越荒野时，您会看到几名身穿盔甲的守卫沿着道路巡逻。 这些石路一定是新的。前方你会看到三名守卫在追赶一只毛茸茸的生物，当它最终倒下时，在它的背上射了几支箭。守卫站在路中央，上前举手。马车猛地停下。 你看着窗户，一名警卫轻快地靠近。 “道路很危险，你不应该一个人旅行。” “嗯。”你耸了耸肩。 守卫叹了口气，他疲惫的眼睛有片刻的空白。 “野兽人在这些土地上游荡，”他说，“至少让我们的几个守卫和你一起旅行。” 你耸了耸肩，卫兵命令他的两个下属和你一起上马车。 虽然沿途有一些小景点可看，但旅程平安无事。
梗概：
你的目的地
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动作：
做
你镇
结果：
当马车到达文明国度 Cystia 的大门时，已经是中午了。墙壁上的守卫传来几声喊叫，沉重的木门和铁门吱吱作响地打开了。马车停在一个木制的……棚屋前，那里有几个肮脏的农民出来照顾马匹。 你走出马车，环顾四周。希望一些水晶应该在 Cystia 的某个地方......如果没有，你将不得不徒步前往一个非常强大的魔法使用者的地方。不是说你可以进入那里，但你必须尝试。 你必须想办法想办法，否则你将永远无法再次使用占卜师！ 你去环顾一下 Cystia 的所有这些部分。现在那些魔晶会在哪里呢？
梗概：
你镇
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动作：
做
你的巢穴
结果：
一种强烈的恐惧感让你充满了恐惧，让你觉得内心不那么温暖。你最后一次去这个地方是......有趣。在被痛苦地放逐之后，您决定忽略这个地方，继续照常营业。 但是你需要得到这些水晶，否则你在 Discorina 的位置会怎样？ 这需要几个小时，但最终你会穿过主要城市，穿过土路。前方，巨大而黑暗的城堡在头顶隐约可见。灰蒙蒙的云层喷出大量雨水和闪电，狠狠地击中地面。城堡周围的石墙显得不祥，墙上点缀着几座小瞭望塔。 黑暗的尖塔向上延伸，顶部的金属尖刺捕捉到了一些闪电。当您靠近城墙时，您发誓在暴风雨的声音中，您听到城堡深处有什么东西在转动和呼呼。 你颤抖着慢跑到城堡，希望你能得到你想要的东西，也许还有一个暖和的地方。 大木门似乎是关着的，但你没想到它只是敞开着。你抓住旁边的绳子，用尽全力拉它。与它相连的大钟响起它可怕的曲调。 当你站着，浑身湿透时，门缓缓打开，但只是一点点。向前迈出一步，你眯着眼睛看着开口。 “谁在那儿？” 一个声音说：“福特？是你吗？” “是的，”你说。 “哈，你知道这里不准你。” “我只是为了一些魔法水晶……没有别的，”你说。 “我认为那会违反规则……还记得上次你在这里发生了什么吗？” “你能不能至少让我从寒冷中进来，”你说，“这不是很好吗？” 门又开了，声音的来源走了出来。一个高大的男人，头发剃光，刮得干干净净，他微笑着。但是那微笑还有别的东西，它并不完全来自热情好客的地方。 “当然，”他笑着说，“这应该很有趣。” “谢谢，”你走过他身边时说。 你进入最终通向门的隧道……你发现门是敞开的。走进去就像你拥有这个地方一样，你环顾四周。大城堡看起来是空的……据你所知。 风暴的风吹过城堡，几乎掩盖了您穿过大城堡时轻柔的脚步声。 漫步在大厅里，你听到什么突然停下来。 “REEEEEEEEEEEEEEEEEEEEEEEEEEEEEE！” 耸耸肩继续，一定只是你的想象。 大厅蜿蜒曲折，您感觉一如既往的迷失。您在这里度过的短暂时间是如何在这个地方导航的？ 那个该死的女巫一定就在这附近的某个地方，她一定有一些魔晶。但她会在哪里呢？
梗概：
你的巢穴
--------------------
动作：
做
你尖顶
结果：
你从最明显的地方开始，她住的尖顶。当你经过几个房间时，你听到其中一个房间发出奇怪的声音……嗯。你继续说下去，不关心你今晚会听到的许多奇怪的声音之一。 爬上几组楼梯后，您会看到一个变窄并….螺旋上升的楼梯。几分钟后，您会发现自己位于顶部，在一个巨大的图书馆中。你穿过这个地方，出奇的安静。 “斧头……斧头！” 你喊。 没有什么。最终你会找到一张床、一张桌子和一张桌子，用来魔法的东西或她所做的任何事情。对可以藏在这里的任何东西都没有兴趣，你继续走自己的路。 你还能在哪里看？
梗概：
你尖顶
故事名称：
21
故事背景：
啊，对了，该吃晚饭了！在花了几分钟又长又辛苦的时间试图记住你在这个地方的路之后，你穿过一些大厅，直到找到厨房。 迎接你的是多么美妙的气味啊！有人在编造一些好东西！ 走进宽敞的饭厅，近乎空旷的地方显得有些令人吃惊。当你靠近三人组时，你看到一个女人怀里抱着几只鸡，轻轻地对他们耳语。 “你是谁？” 你问。 “这很简单……我是 Sabley，”她在回到最珍贵的鸡之前说道。 你看到的第二个人是一个头发上长着小蛇的女孩，她的眼睛……上帝啊，她的眼睛……你惊恐地凝视着她眼眶的空洞，当你这样做时，一只小甲虫从其中一个爬出来他们，在她的眉毛上拉屎，然后回到里面。
--------------------
动作：
做
你问 Axiom 在哪里
结果：
“公理在哪里？” 你问。 失明的怪物误判了声音，转向空墙。 “上次我看到她去了地牢，”她拍着旁边的椅子说，“别担心，你很快就会找到她的。” “谢谢，”你说。 她转向你。 “我不是在和你说话，”她再次转向椅子说，“有些人，不尊重私人谈话。” 她厌恶地摇摇头，开始详细说明地牢可能在哪里。 “好吧，我们的朋友柯基可以带你去那里，”Sabley 说，“他知道地牢在哪里，你不是柯基吗？” 你并没有真正注意或注意到三人组中的第三个人。在你的脚边坐着一只小狗，它的短毛贴着……你真的不想知道是什么。狗的舌头永远地从嘴边伸出来，盯着你看的时候，它的左眼似乎在向左移动。它的瞳孔几乎隐藏在它的眼眶内。 “哦，我想他正试图指着门。” 你顺着眼睛的方向看，看到了你来自的门。 “他想让你告诉他地牢在哪里，你能做到吗？” 萨比利问道。 “呸！” 柯基大喊大叫，用力上下摇头。 片刻后，他轻快地走到门口，小脚发出踏踏实实的声音。此时您别无选择，只能遵循它。 狗坐在门口等你追上再继续。
梗概：
你问 Axiom 在哪里
--------------------
动作：
做
你的地牢
结果：
过了一会儿，狗坐在一个大楼梯箱前。他坐下来面对你，左眼在脑后滚动，大概是指着他身后的楼梯。当您走下楼梯时，您几乎没有注意到紧随其后的狗。 你终于到达了又长又黑的走廊。当您走过多个牢房时，您会看到一个看起来精神错乱的毛茸茸的男人和一只巨大的蜥蜴坐在笼子里。他们来回滚动一个球，毛茸茸的人只是一遍又一遍地重复“Jibble”，口水从他的嘴边流出。 随着脊椎发抖，你继续前进，不想知道这会变成什么样的病态退化。一路上，你听到一个巨大的Uuuuurp！并窥视它来自的牢房。 从阴影中显现出来的是一个又大又丑的……食人魔！善良的人会向你挥手微笑。 “没想到在这里见到你！” 他说。 不在乎你耸耸肩继续前进，食人魔更不在乎，又回到抓挠他的屁股或食人魔所做的任何事情。 最终你找到了打破墙壁的方法，砖块铺在地上，苔藓沿着洞穴的墙壁生长。 “达！” 柯基喊道，他的眼睛指向洞穴。
梗概：
你的地牢
--------------------
动作：
做
你洞穴
结果：
你进入了黑暗的洞穴，在周围闲逛了几分钟后诅咒自己，因为没想到 从墙上抓起火炬。最终，一道光芒从前方散发出来。她肯定是在这里闲逛。 当你走出去时，手电筒几乎让你失明。洞穴扩展到一个巨大的洞穴系统，充满了数以千计的发光植物和各种蘑菇。大约一半的地方似乎是一条奔向大海的河流。一个小码头空无一人，柱子上一盏孤独的灯亮着。 一个小篝火在燃烧，周围有几条木凳。两个人影坐在它周围，手里拿着绿色发光的小东西。你漫不经心地走到篝火旁，狗爬进了不远处的临时狗屋。 坐在火的两边是马尔克和史蒂夫。第一个穿着粉红色的芭蕾舞裙，上面是一件普通的毛皮衬衫，他看到你在盯着芭蕾舞裙。 “什么？” 马尔克说：“这是个笑话。” “不管你说什么，”你说，“你们有谁见过公理吗？” “我很想念她，”史蒂夫说，“嘿，你想和我们一起坐一会儿吗？” 你开始耸肩。 “你应该热身，”史蒂夫说，“你看起来湿透了。” 你决定坐下来逗他们一会儿，此外，火很温暖。史蒂夫指着小码头。 “Axiom 就在两小时前乘那艘船，”史蒂夫说，“不知道她为什么会在这样的风暴中。” 你再次看向空荡荡的码头，然后低头看向洞穴系统的黑暗深渊。 “这些洞穴比我们任何人想象的都要延伸得更远，”史蒂夫说，“任何人都猜不到深处的东西。” “只要她不深入了解他们，”你说，“尽管她可能比任何人都更了解他们。” “不，她去了大海，”史蒂夫说，“虽然她经常穿越洞穴。” “嗯，她会没事的，”马尔克说，“嘿，福特，想试试其中一种吗？” 马尔克伸出手，露出一株发光的绿色蘑菇，掌心则是一株暗红色的植物。 “伙计，这些会让你在云端呆上几天，”马尔克说，“这很好。” “究竟是什么？” 你问。 “某种蘑菇，”他说。
梗概：
你洞穴
--------------------
动作：
做
你不吃蘑菇
结果：
“呃，不，我很好，”你说。 “什么？为什么不呢，伙计？” 马尔克斯问道。 “我只是不相信任何旧东西——” “呸，随便。” 马尔克一边说着，一边把蘑菇塞进嘴里，似乎太享受咀嚼和吞咽了……尤其是吞咽。 他的眼睛在脑后滚动了一会儿，然后在它们回到原位时咧嘴一笑。呆滞的眼眸望向远方，口水开始滴落。回头一看，你注意到史蒂夫似乎也吃了神秘的蘑菇，他盯着天花板。 “呜呜呜，”史蒂夫说，“看看你，你这个性感的东西。”
梗概：
你不吃蘑菇
--------------------
动作：
做
你继续
结果：
你决定最好等一会儿，出去迷路也没有用。在等待的过程中，史蒂夫和马尔克似乎摄入了更多的蘑菇。 马尔克站起来，像个漂亮的芭蕾舞演员一样开始旋转，史蒂夫开始跳一些爱尔兰吉格之王。在他们的心目中，他们可能看起来像职业舞者……在你看来，他们看起来像一对草率地四处乱舞的白痴。 马尔克抓住你的手，开始试着和你跳舞……操！你加入并旋转他，然后将他浸入水中，然后将他扔到空中并接住他。 他的嘴唇靠近你的嘴唇，你及时转过身去，在脸颊上留下一个又大又湿又邋遢的吻。害怕你放下他并走开。 “太远？” 马尔克咧嘴笑着哼了一声，开始像青蛙一样跳来跳去。 你看到史蒂夫拿出一些纸，开始在上面疯狂地涂鸦。 你转过头去挠头，想知道自己到底陷入了什么。Malk 跳起来，试着做他之前看到 Steve 做的同样的动作，但只是更可怕和更邋遢。 马尔克一边跳舞一边唱着歌，差点掉进火里，但你抓住了他，把他拉开了。他甚至没有意识到并继续他正在做的事情。 他跪下，捡起那株红色的植物。 “我以前从没试过这个……”马尔克看着史蒂夫，“小子，让我们试试吧。” 史蒂夫抬起头，已经有几本大书写满了他的作品。那很快。 “确实，我的朋友，我们确实应该，”史蒂夫站起来拿起一株植物，“嗯，不确定我们是否应该……” “呸！胡说八道！” 马尔克说：“伙计，我现在真的可以去吃馅饼了。” “呃，”史蒂夫的脸变成了厌恶。 “我绝对讨厌馅饼，”史蒂夫说。 “嗯，到时候你可以帮我成功，”马尔克说。 史蒂夫耸了耸肩，他和马尔克开始压碎红色植物的一部分，并将其卷成一些叶子。用篝火点燃后，他们把那东西抽掉了。当烟雾弥漫在附近区域时，他们的眼睛变红了。 一段时间后，您开始感到有点头晕……周围有太多烟雾。当你坐下时，马尔克咯咯笑着指着你。史蒂夫笑了起来，差点从木凳上掉下来。 “伙计，我讨厌派，”史蒂夫说，“但我肯定可以用一些。” “哈哈操，操，操！” 马尔克尖叫道：“我他妈有个绝妙的主意！” 还没等任何人说话，马尔克就跳起来抓住你的衣领。他的眼睛开始像红色植物一样发红，史蒂夫的眼睛也是。两个人都咯咯笑着把你拖上山洞，即使在你踢和抗议的时候也是如此。 “伙计，这会很有趣，”马尔克说，“哈……操……有史以来最好的笑话……” 你张开嘴想说话，但马尔克转身吐出一些绿色发光的液体，它充满了你的嘴，你气喘吁吁，哽咽。 “呃！操！” 你喊。
梗概：
你继续
--------------------
动作：
做
你烘焙和烘焙
结果：
两个高大的混蛋把你扔到厨房的桌子上。 “哇-你们在做什么-”你说。 “他妈的闭嘴！没关系，”马尔克抚摸着你的头发说，“这会很有趣，你会喜欢的！” 一个声音吸引了你的注意力，你转过头。一个有着两个尖角和粉红色皮肤的男人走了进来。 “卡史塔克！呃——卡史塔克大人！” 马尔克说：“你来得正是时候，想帮忙吗？” “当然……suuuure，”卡史塔克咧嘴笑了笑，搓着手，“我们在做什么？” “你会知道的，”史蒂夫厉声说着摇了摇头，他和马尔克互相看了一眼，对卡史塔克的愚蠢摇了摇头，这种事只有他们自己知道。 当你转身看着马尔克时，你尖叫着，一把斧头刀片埋在你的脸上。又是一记重击，你的眼睛从你的脑海中跳了出来，奇迹般地你仍然能够看到史蒂夫正在准备一个小银托盘。 卡史塔克向前走了一步，在你意识到之前，他锯掉了你的手臂，喝着你断臂的血。 当你开始从桌子上掉下来时，你咳嗽并吐出鲜血，马尔克以惊人的力量把你扶起来。他责骂 Kar 喝了你的血，当他们继续攻击你时，你的视野变得黑暗……
梗概：
你烘焙和烘焙
--------------------
动作：
做
你醒了
结果：
在你意识的内部隐秘处，你感觉到一股极度的热量在燃烧你的身体。你试图尖叫，但还不能。你感觉有什么东西在动，一定是你的嘴。 刺耳的声音在你的耳朵上摩擦，当一只戴着手套的手抓住你并将你从灼热的高温中拉出来时，你会感到畏缩。你倒吸一口新鲜的空气，你上方的两张脸倒吸一口凉气，把你放在桌子上。 当他们都惊恐地盯着你时，第三个出现在你的视野中……你认出这些面孔……你很确定。当你被放置时，更像是扔在桌子上。你感到一阵震动……然后你就记住了。 你还记得那些混蛋妈妈把你切碎时的短暂痛苦。 “我们做了什么？” 史蒂夫问道。 三人对视一眼，身为恶魔的卡史塔克只是笑了笑。马尔克和史蒂夫来接你，然后匆匆走到下水道口。 “对不起，伙计，”马尔克说，“我们不知道还能做什么。” “等待！” 史蒂夫说。 但为时已晚，你已经被扔进下水道，在肮脏、肮脏的污水流中旋转……
梗概：
你醒了
--------------------
动作：
做
你打旋和狗屎
结果：
疼痛……真正的疼痛……有什么东西刺痛了你的意识。不用等… 你的眼睛猛地睁开，你浑身是屎和污秽的身体在有什么东西猛烈撞击时发出回响……一次又一次。当粪便从你的视野中飘出时，你被其中一个兽人的可怕景象搭讪了！ 它是一根毛茸茸的小杆子，从你身体的许多开口之一进进出出。你张大了嘴巴，吐出一种又臭又臭的绿色液体，覆盖在鼠人身上！它在酸味灼烧它的肉体时尖叫，它跳进粘糊糊的满是圆木的水中，试图洗掉液体……坏主意。 它因所有可怕的体液和排泄物进入其开放性伤口的疼痛而尖叫。无法忍受疼痛，它潜入水中张开嘴，以一种奇怪的自我怜悯杀戮行为尽可能多地吞咽。 你痛苦地呻吟着，意识到你不能……只能等。一条触手从你的身体中缓缓浮现，在石阶上缓缓蜿蜒。绿色发光的液体从它身上渗出，触手爬行……但它也会抽搐。 经过一些努力和思考，你最终学会了你可以控制它！鼠人精液和魔法软泥一定以某种方式结合在一起，为您提供了这些非凡的力量！ 当你长出另一条触手将自己拉过来时，你的银托盘擦过石头走道……呃，太慢了。 耳边传来几声吱吱声，你转头看去，看到几个老鼠人正在靠近，手持生锈的剑和长矛。其中一个，明显的领导者专注地看着你，他的爪子搁在他身边的剑上。 两只老鼠靠近，一只拿着小棍子，另一只拿着长矛。那个拿着长矛的人朝你戳了一下，你看到另一个人在后面舔着他们的嘴唇。领导者用他们的语言向老鼠喊叫，而那个戳你的人耸了耸肩，似乎对他不屑一顾。这些老鼠正在困扰着您，并构成了巨大的威胁……但您有一个想法。一种获得更多力量的方法……
梗概：
你打旋和狗屎
--------------------
动作：
做
你吃一个
结果：
你挥动你的触手，一个从老鼠身上拔出长矛，另一个缠绕在他的脖子上，把他拉近你。当你试图吃掉它时，它害怕地发出尖叫声，并将爪子放在你的两侧，用尽全力推开。 当他设法拉开一点时，另一只老鼠向前跳跃，用他的狼牙棒的铁头猛击你。你内心的樱桃和人肉溅在石头上，因为一切都感觉模糊。你的几颗牙齿掉了，你最后看到的是老鼠最后一次举起狼牙棒对付你......
梗概：
你吃一个
故事名称：
22
故事背景：
疼痛……真正的疼痛……有什么东西刺痛了你的意识。不用等… 你的眼睛猛地睁开，你浑身是屎和污秽的身体在有什么东西猛烈撞击时发出回响……一次又一次。当粪便从你的视野中飘出时，你被其中一个兽人的可怕景象搭讪了！ 它是一根毛茸茸的小杆子，从你身体的许多开口之一进进出出。你张大了嘴巴，吐出一种又臭又臭的绿色液体，覆盖在鼠人身上！它在酸味灼烧它的肉体时尖叫，它跳进粘糊糊的满是圆木的水中，试图洗掉液体……坏主意。 它因所有可怕的体液和排泄物进入其开放性伤口的疼痛而尖叫。无法忍受疼痛，它潜入水中张开嘴，以一种奇怪的自我怜悯杀戮行为尽可能多地吞咽。 你痛苦地呻吟着，意识到你不能……只能等。一条触手从你的身体中缓缓浮现，在石阶上缓缓蜿蜒。绿色发光的液体从它身上渗出，触手爬行……但它也会抽搐。 经过一些努力和思考，你最终学会了你可以控制它！鼠人精液和魔法软泥一定以某种方式结合在一起，为您提供了这些非凡的力量！ 当你长出另一条触手将自己拉过来时，你的银托盘擦过石头走道……呃，太慢了。 耳边传来几声吱吱声，你转头看去，看到几个老鼠人正在靠近，手持生锈的剑和长矛。其中一个，明显的领导者专注地看着你，他的爪子搁在他身边的剑上。 两只老鼠靠近，一只拿着小棍子，另一只拿着长矛。那个拿着长矛的人朝你戳了一下，你看到另一个人在后面舔着他们的嘴唇。领导者用他们的语言向老鼠喊叫，而那个戳你的人耸了耸肩，似乎对他不屑一顾。这些老鼠正在困扰着您，并构成了巨大的威胁……但您有一个想法。一种获得更多力量的方法……
--------------------
动作：
做
你吐酸粘液
结果：
当酸性呕吐物离开你的脸时，你的眼睛和嘴巴都睁大了，老鼠惊讶地叫了起来。当持枪者的腿被烧到骨头时，他们的肉发出嘶嘶声，他倒在地上。另一只老鼠捂着脸痛苦地扭动着身体，然后一动不动。你伸出你的触手，抓住两人中幸存的鼠人并将他拖得更近。当你把整个老鼠人塞进嘴里时，你咀嚼和咀嚼他。 你感觉到一股能量在你体内流淌，随着你获得质量和力量，银色托盘从你身下掉了下来。你邪恶地假笑着再次挥动你的触手，随着你的动作变得越来越多。 你击中了一个人的脸，把他撞到了石墙上，他的头骨在墙上裂开了。他倒在一个可怜的堆里。下一个，你用一根触手缠住他的脚踝，强迫他进入低于他一英尺的肮脏水域，再送下一根触手把他关在下面。 鼠人的首领和他的最后两个人似乎不知所措。你向前一跃，用几根触手卡住了他们的一个喉咙，并盘绕在他的手腕上以阻止他伸手去拿剑。 当你在他其他朋友的脖子上射出一根触手时，领导者跳回来。你张大嘴巴，漱口一些绿色酸液，然后朝三人组射击。领导者以惊人的速度用生锈的剑砍断了他一个朋友脖子上的一根触手。当他及时将他抓住充满粪便的水中以避开酸时，它们旋转！ 不幸的是，他对另一个老鼠人无能为力，幸运的是你。你干掉他，然后继续吃尸体。你看着鼠人首领和他唯一幸存的朋友惊恐地盯着他们，让电流把他们带到安全的地方。 你不担心他们，你有你需要的。
梗概：
你吐酸粘液
--------------------
动作：
做
你的成长与复仇
结果：
现在你的力量和体型都在增长，是时候进行报复了！您的三个潜在目标根本不会太容易。第一个，也许是最容易到达的可能是卡史塔克。虽然您将不得不担心他的恶魔随行人员，但它们似乎并没有太大的威胁。你将不得不走一段公平的路去 Cystia 的恶魔出没的狗屎区，但是狗屎，凭你的外表，他们可能会认为你是其中之一。 下一个可能的目标是史蒂夫。他不应该太难找到，特别是如果你现在开始寻找，在他再次启航之前。该死，你甚至可以一举将他和马尔克搞定。他唯一的一点就是，他是一个顽强的混蛋，他不会轻易去的。你已经看到他的酒精燃料与一些野兽人和其他土地成员的决斗。 然后是马尔克，他可能会最警惕。当他兴奋或喝醉时，你必须设法抓住他，因为他不会像史蒂夫那样具有很好的协调性。他的问题是，如果他发泄他的狂暴之怒，你可能就完蛋了。当你要报复他的时候，你可能不得不偷偷接近他。 你真诚地希望没有其他恶棍妨碍你，这总是有可能的。当你到达那里时，你只需要过那座桥。 看起来卡史塔克可能是最简单的，所以你会从他开始。
梗概：
你的成长与复仇
--------------------
动作：
做
你这个卡史塔克
结果：
好吧，您必须首先使用最薄弱的环节。此外，当他们把你切碎并烤成馅饼时，他是唯一一个完全清醒的人。那个混蛋。作为大袋馅饼的你，你从下水道滚出来，小心翼翼地避开任何一大群正在寻找你的老鼠。不过，您确实会通过下水道旅行，当您离开老鼠的领地时，出行会更容易。 您会在绵延数英里的下水道系统中遇到各种各样的怪物和野兽。有几次你甚至会找人引导你前往卡史塔克领主的小领地。 您当前的向导，一些名叫鲨鱼勋爵的 Nuub 指向一个梯子。看来这两位领主已经不和了，他当然很乐意帮助你！ 当他举起一只手时，你就要向上走。 “嘿，所以这个梯子应该直接通到他的营地中间，当然是在一个不太开阔的地方，”鲨鱼勋爵说，“但如果你再往上走一点，应该会有一根管道通向某个地方其他在他的营地。” 鲨鱼在继续之前环顾四周。 “我不能确定它去了哪里，但检查它不会有什么坏处。” “谢谢，”你说，“我很感激你的帮助。”
梗概：
你这个卡史塔克
--------------------
动作：
做
你的梯子
结果：
你爬上梯子，打开舱门，发现自己正像预期的那样站在卡史塔克的营地中间。你环顾四周，几只恶魔正在四处游荡。虽然恶魔本质上应该是可怕的，但它们似乎不是人类馅饼无法处理的任何东西。
梗概：
你的梯子
--------------------
动作：
做
你隐身
结果：
你决定像馅饼一样安静地玩这个，它需要在罐头上滚动和滑行。不知何故，你设法穿过堡垒废墟，逃避醉酒的“巡逻”等。翻过所有的帐篷，你最终会找到你要找的人。 坐在厕所的厕所上……没有厕所，是卡史塔克勋爵。他的鼻子埋在了一个故事里，一个人的名字叫queenlatifa04，评分最低的一本书，想必快要变成卫生纸了。 你伸出你的触手，刺穿那脏兮兮的（或很快就会变成脏兮兮的纸），触手猛地刺穿他的胸膛。卡史塔克张大嘴巴，颤抖着。 “Yy-you....我记得你...”Karstark 说。 “我想是的，你只是把我变成了馅饼，”你说。 “那是……几个月前……”Karstark 之前说，他的头垂下，当他的肠子排空时，他的腿猛地向上。 你移开血腥的触手，欣赏着这根触手的锋利。你甚至不知道你能做到这一点，一定是本能。嗯，这出奇的容易。 你滑开并再次避开营地守卫，回到下水道。
梗概：
你隐身
故事名称：
23
故事背景：
------------------------------ ////////////////////////////////////////////////// [状态框]/////////////////////////////////// ///国库：125元{-75} /////// 军：小（-25）//////////////////群众：高 //////////////////////////////////////工人：低（-25）//// //////////////佛教：低{-50} ------------------------------- 您前往被泥石流摧毁的社区并分发捐款。当你走在他们中间时，晒伤的农民会给你大大的牙齿咧嘴笑。当您拿起双手锯帮助移除倒塌在道路中间的树时，有些人会鼓掌。 在救援工作中，您的领导层得到了广泛传播。民众现在相信您与他们的优先事项和价值观相同。你成为整个南方人民的最爱。 您的公民现在与您的警察合作以抵御渗透者。邻近的军阀不再费心向你的领域派遣破坏者或刺客。结果你的小军士气暂时飙升。 你的财政部顾问报告说，虽然你的国库大幅减少，但你仍然拥有足够的人民币来进行财政投资，以确保你不会破产。佛寺责备你缺乏虔诚，但不会因为你照顾你的人民而对你不屑一顾。一些政府专业人士抱怨没有加薪，但大多数人仍然保持乐观并继续为您的利益工作。他们甚至完成了一座城堡的建造，这是一座坚固的门控结构，将作为您的住所并增强您首都的防御。 你收听来自遥远国度的新闻。纳鲁将军的北方帝国正在经历内乱，将军几乎无法保住自己的性命。他不会对你构成任何威胁，至少目前是这样。你的另一个主要竞争对手，闵总统，有人看到你朝你的方向东行。据报道，他会见了附近的几位小军阀。其他军阀之间一直在小规模战斗，但没有一个获得足够的力量值得你花时间。 虽然中国的混乱一直困扰着民众，但希望并没有消失。人民渴望能带来和平、创造良好经济环境的能干领导人。他们说，如果有人出现，他们愿意脱离他们的王国并加入这样的领导者。 你的下一步... ------------------------------ ////////////////////////////////////////////////// [状态框]/////////////////////////////////// ///国库：125元{-75} /////// 军：小（-25）//////////////////群众：高 //////////////////////////////////////工人：低（-25）//// //////////////佛教：低{-50} -------------------------------
--------------------
动作：
做
你投资增加你的国库（-75元）
结果：
------------------------------ ////////////////////////////////////////////////// [状态框]/////////////////////////////////// ///国库：0元（+150）//////军队：小（-25）//////////////////群众：高 ////////////////////////////////////工人：低（-25）//// ////////////佛教：低{-50} ------------------------------- 您与园艺主管举行长时间的会议，以确保该项目取得成功。经过数小时的精确规划，您委托种植者购买最好的荔枝种子，用于在您最肥沃的土壤中种植。你热切地期待着即将到来的收获及其对你的经济的推动。 就在您的工人们播下最后一批种子时，您惊讶地发现，敏总统和他的盟友已经将他们的军队开进了您城堡外几英里的位置。“疲倦的人没有休息，”你自言自语。 您的民兵会敲响每一扇门，召集市民帮助保卫您的城市。第二天，敏和他的盟友在离你的城堡半英里远的地方展开了一面巨大的白色横幅。它写道： “精明的托勒隆总督，我们给你一个提议。如果你放弃对中国的主权，打开你的城堡大门，我们会放过里面所有人的性命。否则我们只能围攻你的城堡。到时候，我们不会再手下留情了。” 你保持你的大门关闭。他们信守诺言并封锁你的城堡。几周过去了，但您的公民对您的指导的信心使他们保持高昂的精神。另一方面，进攻力量开始失去凝聚力。一位军阀引用你的小军队并坚持对你的城墙发动全面攻击。另一个主张长期围困，利用他们在资源上的集中优势让你筋疲力尽，如果需要的话需要数年时间。另一个让公民回家推动更多关注其他事项，宁愿完全取消攻击。闵总统不知道该怎么办。他并没有做好真正的流血准备，原本以为他的大部队会诱使你投降。另一方面，如此轻易地辞职将是一个难以下咽的苦药。经过一番争吵和深思熟虑后，他们三心二意地派军队进攻你的城墙。 携带绳梯的步兵冲向您的护墙。你的弓箭手会用一连串燃烧的箭来回应。许多攻击者在到达您的墙壁之前就已过期。那些在爬上梯子时确实遇到浓火的人。当您的战士切断他们的绳索时，有些人会陷入厄运。那些设法到达顶峰的人会遭到驻扎在里面的每个人的猛烈抵抗。火光在叮叮当当的剑间飞舞，而深红色的血液则在战场上迅速流淌。 你的队伍逐渐缩小，但你的战士每在战斗中阵亡，入侵者就会失去五个。如果没有强大的领导者，城堡外的剩余攻击者将失去决心并放弃。入侵部队的残余力量随着胜利的捍卫者的欢呼声而散去。 全中国的军阀都知道你不可思议的胜利。许多人现在对闵总统的看法如此之少，以至于他再也不会发挥太大的外交影响力了。 在从这场冲突中恢复过来的时候，你从间谍那里听说纳鲁将军全神贯注于在他的王国里抓捕和处决不忠的臣民，并巩固他在中国北方的权力基础。他不会很快就可以自由地攻击你。没有其他军阀仍然是一个很大的威胁。 在国内方面，如果你现在不增加他们的工资，许多工人威胁要抛弃你并为其他军阀工作。圣殿也会因为你缺乏沟通而变得不耐烦。你的上校仍然建议增加征兵。 你接下来会做什么？ ------------------------------ ////////////////////////////////////////////////// [状态框]/////////////////////////////////// ///国库：0元（+150）//////军队：小（-25）//////////////////群众：高 ////////////////////////////////////工人：低（-25）//// ////////////佛教：低{-50} -------------------------------
梗概：
你投资增加你的国库（-75元）
--------------------
动作：
做
你付给工人更多的钱（-75 元而不是-25 元）
结果：
------------------------------ ////////////////////////////////////////////////// [状态框]/////////////////////////////////// ///国库：50元（+300） /////// 军：小（-25）//////////////////群众：高 ////////////////////////////////////工人：高（-75）//// ////////////佛教：低{-50} ------------------------------- 您宣布为您的专业工人加薪。他们很高兴听到这个消息，你们政府的行政效率大大提高。 多亏了你精心的战略规划，你提高了你王国的凝聚力和声望，发展了一个能产生大量收入的经济，并在你的王国中保持和平。您的公民很满意，并承认您是一位明智的领导者。 与此同时，纳鲁将军消灭了他的敌人，现在统治着中国北方，无人反对。然而，他对你日益反对他完全统治世界的目标感到愤怒。你的侦察兵报告说，纳鲁将军已经在你的北部边境集结了足够的军队来发动猛烈的进攻。 你： ------------------------------ ////////////////////////////////////////////////// [状态框]/////////////////////////////////// ///国库：50元（+300） /////// 军：小（-25）//////////////////群众：高 ////////////////////////////////////工人：高（-75）//// ////////////佛教：低{-50} -------------------------------
梗概：
你付给工人更多的钱（-75 元而不是-25 元）
--------------------
动作：
做
成为佛堂成员，获得外国公民的尊重（-50元）
结果：
------------------------------ ////////////////////////////////////////////////// [状态框]/////////////////////////////////// ///国库：200元（+600）//////陆军：小（-25）//////////////////群众：高 ///////////////////////////////////////工人：高（-75）// ///////////////佛教：高 ------------------------------- 您向寺庙捐赠了大量资金。高僧们为你的慷慨而欣喜若狂，并邀请你到最近的寺院举行盛大的皈依仪式。面对青铜佛像和点燃的红色蜡烛，您与穿着藏红花色长袍的僧侣一起吟诵新学的佛教赞美诗。僧侣称赞您的虔诚，并广泛传播您的虔诚。 你的帝国的繁荣和你在人民中的受欢迎程度的故事与你的虔诚者一起传播。当全中国的公民得知你的王国的高品质生活时，不仅最好、最聪明、最勇敢的人涌入你的王国，有些城市甚至脱离当地军阀，自愿举起你的旗帜。 尽管将军的手下进行了猛烈的镇压，但纳鲁将军的半数省份还是脱离了他的王国并加入了你的王国。Nahru 对你很生气，并且可以预见地发起一次全力以赴的攻击，一劳永逸地摧毁你。 你赶紧在首都集结军队。由于您决定推迟增加士兵的工资，您只能进行粗略的防御。 Nahru 将军的军队冲破你的首都，夷平并摧毁你曾经建立的每一个权力象征。 正如你从你剩下的顾问那里听到纳赫鲁将军宣布他将处决任何从你的营地投降的人一样，你们全都死于突如其来的箭雨。 THE END
梗概：
成为佛堂成员，获得外国公民的尊重（-50元）
故事名称：
24
故事背景：
你立即返回首都。你不能失去你唯一的权力基础和基础设施。如果卢尔杜的军队控制了要塞，他将在战斗中拥有巨大的战术优势。蒙古人没有根据你的间谍报告做任何准备，你怀疑他们永远不会。就在卢尔杜的军队到达时，你的军队到达了你的首都郊区。 夕阳的橙色光芒使棕色的泥土带上了微红色。细小的沙尘云团在两军身后缓缓消散。卢尔杜骑着他的坐骑在他的阵型后方，俯瞰你的 18000 步兵和 5000 弓箭手。你看他的一万骑兵和五千弓箭手。 地形由左右两侧的小凸起山脊组成。当您的步兵站在中间时，您的弓箭手会挤在这个凸起的地形上并获得轻微的高地优势。Lurdu 假笑并命令他的骑兵向你冲锋。他们做到了，而且势头强劲。 你：
--------------------
动作：
做
当你的弓箭手尽可能长时间地利用他们的制高点优势时，你撤退步兵；然后当你的弓箭手不得不后退时用你的步兵拦截他们的骑兵
结果：
战斗以您的步兵战略撤退开始。这使您的弓箭手有更多时间在肉搏战开始之前向骑兵开火。在他们攻击你的部队之前，你设法消灭了他们的一半骑兵。Lurdu 的弓箭手会向您的弓箭手开火，但由于位于较低的地面，因此无法造成那么多伤害。 一旦命令进行近战，你的步兵就会被高高在上、速度更快的骑兵所支配。您的步兵无法应对骑兵的远距离和速度。他们的马匹甚至会践踏你的一些部队。 但是你的早期优势对Lurdu来说太过分了。虽然双方都遭受了灾难性的损失，但你的战略撤退给了你足够的优势来战胜他。卢尔杜看到自己的部队全都死了，从受伤的马上跳下，逃离了战场。他很快就被你的一名速度更快的士兵抓住了。无视他的请求，在他对你再次背叛之前，你迅速斩首他。 你从战斗中获胜，但只剩下 3000 名步兵和 1000 名弓箭手。 你回来后，镇上的人一般都很高兴，因为他们中的大多数人比鲁尔都太子更喜欢你。你需要一些时间来补充你的物资并治愈你的伤员。完成后，您将返回蒙古。 当你俯视第一个部落时，黎明才刚刚开始。十五个巨大的蒙古包或帐篷，在凉爽的微风中摇曳。除了少数早起照料山羊的人外，没有任何人类活动。你毫不犹豫地冲进蒙古包，在毫无戒心的营地上倾盆大雨。正如所料，蒙古人仍然不知道你要来，也没有征召任何士兵。第一个定居点很快投降，其次是他们的邻居。你屠杀他们的首领，强迫他们的继任者每月进贡，带走一些年轻女性作为战利品，并征用你的军队可以携带的物资。 当您返回首都时，您的公民很高兴听到您的胜利故事。一位热心的支持者发明了弩炮，这是一种发射矛状箭矢的巨大弩。这些箭比手持弓箭射出的箭造成更大的伤害，射程也远得多。 你迫不及待地想要再次征服。你开始征兵征募更多士兵。然而，在最后一次叛乱之后，你觉得你现在必须留下足够的人来守卫你的首都，所以你的行军军队暂时保持不变。 你渴望夺取托勒隆总督的领土，但又担心如何打败更大的敌军。 有一天，帮助来自一个意想不到的来源。似乎一群土匪抢劫了一辆在前往印度的途中穿过你们王国的商队，你们的警察能够逮捕他们并没收战利品。当您的警察找不到任何幸存的所有者时，他们将这些商品作为礼物送给了您。 原来他们携带的是火药，这是一种极其稀有且威力巨大的物质。当它被点燃时，细小的颗粒会剧烈爆炸，造成巨大的破坏。您拥有的有限数量将转化为最有效的形式箭头炸弹。碎玻璃、稻草和火药碎片在一个挖空的微型西瓜中用水泥固定在一起，西瓜中带有一根灯芯。然后将炸弹绑在箭的底部。你的弓箭手会点燃灯芯，然后向敌军发射炸弹，造成弹片爆炸并点燃火焰。您不会在弩炮螺栓上装上炸弹，因为绑在一个螺栓上的多颗炸弹会紧挨着彼此爆炸并浪费其相当大的爆炸半径。此外，弩炮的长装填时间使得快速弹幕变得不可能。此外，齐射会受到您一次拥有的弩炮数量的限制，而使用箭头，您可以发射与弓箭手一样多的炸弹。 火药极为稀有，因此您很可能永远不会再得到它。另一方面，由于很少有人听说过火药，它的效果对于普通的敌军士兵来说很可能是超自然的。 带着新秘密武器的自信，你向南进军托勒隆总督的王国。但在你到达他的城市之一之前，你遇到了万指挥官的军队。 万是当地军阀，与敏总统和托勒龙州长有联系。Min、Wan 和 Toleron 目前正在享受他们之间不安的和平，因为他们所在地区的流血事件暂时减少了。他没有取得太多其他值得注意的成就。你对这个走狗没有印象，准备战斗。 你的间谍报告说，他的军队只有两万步兵，你看他们再一次完全正确，因为密集的两万步兵群列队挡住了你的去路。Wan 看着你的 3000 步兵、1000 弓箭手和你的木匠完成的一个弩炮。 正面攻 击显然不是一种选择。战场是平坦的平原，偶尔有小树。关于地形或天气，没有其他值得注意的地方。 你：
梗概：
当你的弓箭手尽可能长时间地利用他们的制高点优势时，你撤退步兵；然后当你的弓箭手不得不后退时用你的步兵拦截他们的骑兵
--------------------
动作：
做
当你的弩炮和弓箭手开火时，你发射箭弹；保持步兵待命
结果：
战斗从你的弩炮卸下几个巨大的螺栓开始。你的人非常准确，消灭了几个敌军。与此同时，你前排的每个弓箭手都会装载一个箭弹并按照你的命令开火。 开场的炸弹齐发震惊了万的前线。玻璃碎片刺穿了他们的盔甲，爆炸发生后一百码内没有人能幸存下来。万的军队从来没有见过也没有听说过箭弹，即使在又几次爆炸消灭了数百人之后，他们也惊呆了，无法理解刚刚发生的事情。烧焦肉的气味进一步使他的部队士气低落。万的部分步兵开始分散并逃跑。 当你的箭弹用完时，万军的两万士兵中，有一半已经死亡，而其余的大部分都已经受伤或已经逃跑了。你的弩炮现在也造成了严重的损坏。你的步兵冲进来完成大屠杀，万指挥官投降。 “请允许我活下去，”他恳求道。 你傻笑。“我要一个笨手笨脚的指挥官有什么用？傻子！” 你接管了万指挥官的总部。你把它变成你新的行动基地，同时把他被砍下的头留在旗杆上，提醒你你的力量。 你的秘密武器的损失在一定程度上影响了你的胜利。尽管如此，在与难以置信的赔率和最近取得的显著成功的情况下赢得一场大战已经很好地提高了你王国的士气。你现在控制了中国北方的大部分地区。很多人听到你的勇气和领导力的故事，并自愿加入你的战斗部队。此外，熟练的木匠、机械师和工匠提供他们的服务来帮助您建造更多的战争机器。从附近的城镇收集最优秀和最聪明的人，你的军队将膨胀到 4 万步兵、1 万弓箭手和 5 辆弩炮。 当你战胜万的消息传到托勒隆总督的王国时，他准备了他的九万大军进行防御。他的军队在他首都外几英里的一片森林中迎接你。 你坐在帐篷里等待间谍的信息。你不耐烦地敲打脚趾，用肘部在扶手上摩擦。突然，你的首席间谍猛地打开门板，单膝跪地。 “长官，托勒隆已经将他的弓箭手部署在我们对面的茂密森林中。这使他们可以向我们在露天的军队开火，而不会成为我们弓箭手的目标。我们还发现 Toleron 在他的城堡里留下了 15 把弩炮，而这些弩炮没有带在身上。他一定是低估了我们的决心。” 你走出帐篷，眺望战场。你看不到他的弓箭手，但你可以看到他的七万步兵在森林前一百码处排成一排。仔细一看，他们一脸惊慌失措的白痴。虽然您的士兵既聪明又勇敢，但托勒隆的士兵在靴子里颤抖着，看起来他们会在任何厄运的征兆下逃跑。 您与上校商议决定最佳策略。你的背后有一阵轻风，空气干燥。由于不再有箭弹，你唯一的选择是：
梗概：
当你的弩炮和弓箭手开火时，你发射箭弹；保持步兵待命
--------------------
动作：
做
你让你的步兵冲到树林里寻找敌方弓箭手，就像他们在克利尔布鲁克森林伏击时所做的那样，然后对他的所有部队发动全面攻击
结果：
你的步兵冲过战场。Toleron 的弓箭手开火。你的许多步兵真的死在他们的轨道上。 当你的步兵向他的弓箭手冲锋时，托勒隆命令他的步兵拦截你的步兵。 已经在人数上处于劣势的战斗开始了，您不断增加的伤亡人数进一步降低了您获胜的机会。即使你的弩炮也无法以足够快的速度杀死托勒隆的部队以抵消他们的巨大优势。您只是没有做出正确的战术决定来在早期暴露您的步兵。 你为自己的巨大错误感到羞耻，并以你知道的唯一方式来弥补它。你和你的其他部队一起参加战斗，你的剑高高地指向天空。 你不可避免的死亡来自于刺穿你心脏的长矛。 THE END
梗概：
你让你的步兵冲到树林里寻找敌方弓箭手，就像他们在克利尔布鲁克森林伏击时所做的那样，然后对他的所有部队发动全面攻击
故事名称：
25
故事背景：
你的步兵勇敢地向迎面而来的骑兵冲锋，而你和你的弓箭手则奔向几百码外的大门。市民们很高兴看到您的军队回来并为您的军队打开大门，但没有时间开玩笑。你的弓箭手立即跑上护墙和塔楼，看到你的步兵损失惨重。 你的步兵发现你的弓箭手在城堡顶上并放下他们的武器和盾牌，然后向大门跑去。与此同时，你的箭雨从天而降，覆盖你的步兵刚刚腾空的空间。 在激烈的战斗中，卢尔杜的骑兵没有意识到弓箭手的轰炸，直到为时已晚。他们进入了它的凌空范围，许多人被箭射得千疮百孔，倒在地上。你的弓箭手的高地大大提高了他们的射程并在小规模冲突中扭转局势。箭矢落下，鲁尔度太子的许多军队都不知该往何处去。在随之而来的混乱中，你的一支箭射中了 Lurdu。他的军队在没有领袖的情况下分散开来，完全瓦解。 你从战斗中获胜，剩下 13000 名步兵和 5000 名弓箭手。 你需要一些时间来重新集结并治愈你的伤员。完成后，您将返回蒙古。 当你俯视第一个部落时，黎明才刚刚开始。十五个巨大的蒙古包或帐篷，在凉爽的微风中摇曳。除了少数早起照料山羊的人外，没有任何人类活动。你毫不犹豫地冲进蒙古包，在毫无戒心的营地上倾盆大雨。正如所料，蒙古人仍然不知道你要来，也没有征召任何士兵。第一个定居点很快投降，其次是他们的邻居。你屠杀他们的首领，强迫他们的继任者每月进贡，带走一些年轻女性作为战利品，并征用你的军队可以携带的物资。 当您返回首都时，您的公民很高兴听到您的胜利故事。一位热心的支持者发明了弩炮，这是一种发射矛状箭矢的巨大弩。这些长矛造成的伤害要大得多，射程也远大于手持弓箭发射的箭矢。 你迫不及待地想要再次征服。你开始征兵征募更多士兵。然而，在最后一次叛乱之后，你觉得你现在必须留下足够的人来守卫你的首都，所以你的行军军队暂时保持不变。 你渴望夺取托勒隆总督的领土，但又担心如何打败更大的敌军。 有一天，帮助来自一个意想不到的来源。似乎一群土匪抢劫了一辆在前往印度的途中穿过你们王国的商队，你们的警察能够逮捕他们并没收战利品。当您的警察找不到任何幸存的所有者时，他们将这些商品作为礼物送给了您。 原来他们携带的是火药，这是一种极其稀有且威力巨大的物质。当它被点燃时，细小的颗粒会剧烈爆炸，造成巨大的破坏。您拥有的有限数量将转化为最有效的形式箭头炸弹。碎玻璃、稻草和火药碎片在一个挖空的微型西瓜中用水泥固定在一起，西瓜中带有一根灯芯。然后将炸弹绑在箭的底部。你的弓箭手会点燃灯芯，然后向敌军发射炸弹，造成弹片爆炸并点燃火焰。您不会在弩炮螺栓上装上炸弹，因为绑在一个螺栓上的多颗炸弹会紧挨着彼此爆炸并浪费其相当大的爆炸半径。此外，弩炮的长装填时间使得快速弹幕变得不可能。此外，齐射会受到您一次拥有的弩炮数量的限制，而使用箭头，您可以发射与弓箭手一样多的炸弹。 火药极为稀有，因此您很可能永远不会再得到它。另一方面，由于很少有人听说过火药，它的效果对于普通的敌军士兵来说很可能是超自然的。 带着新秘密武器的自信，你向南进军托勒隆总督的王国。但在你到达他的城市之一之前，你遇到了万指挥官的军队。 万是当地军阀，与敏总统和托勒龙州长有联系。Min、Wan 和 Toleron 目前正在享受他们之间不安的和平，因为他们所在地区的流血事件暂时减少了。他没有取得太多其他值得注意的成就。你对这个走狗没有印象，准备战斗。 你的间谍报告说，他的军队只有两万步兵，你看他们再一次完全正确，因为密集的两万步兵群列队挡住了你的去路。万看着你的 13000 步兵、5000 弓箭手和你的木匠建造的一架弩炮。战场是平坦的平原，偶尔有小树。关于地形或天气，没有其他值得注意的地方。 你：
--------------------
动作：
做
你将你的步兵分成钳形机动，从两侧攻击敌人，同时用弩炮和弓箭手射击；不使用箭弹
结果：
你相信你可以在不使用箭弹的情况下赢得这场战斗。当你的胜算更大时，你的秘密武器可能会在以后证明更有用。然而，现在可不是依靠站在平地上的弓箭手的时候。你的步兵飞奔包围敌人。 在万指挥官的部队知道是什么击中他们之前，你正在下雨，无论是普通的还是巨大的，从两侧射向他们。 这种经典的孙子兵法非常完美。混乱随之而来的是他们以前有组织的形成。你创造了 2 个甚至 3 个步兵与其中一个步兵的不匹配。当敌方士兵被钉得更近时，你的弩炮和弓箭手会在他们身上下雨。 大屠杀只需要几个小时就可以促使万投降。你损失了一些，你的军队只有 1 万步兵、5 千弓箭手和 1 辆弩炮。尽管如此，你和你的士兵们在彻底消灭万的军队后仍然欣喜若狂，同时保持你储存的箭弹完好无损。万指挥官爬到你面前乞求饶命，你的精神就高了。 “请允许我活下去，”他恳求道。 你傻笑。“一个连编队战术都不知道的指挥官，我有什么用？傻子！” 你接管了万指挥官的总部。你把它变成你新的行动基地，同时把他被砍下的头留在旗杆上，提醒你你的力量。 你壮观的胜利和最近的一系列成功极大地提高了你王国的士气。你现在控制了中国北方的大部分地区。许多人听到你的勇气和领导力的故事，并自愿加入你的战斗部队。此外，熟练的木匠、机械师和工匠提供他们的服务来帮助您建造更多的战争机器。从附近的城镇收集最优秀和最聪明的人，你的军队将膨胀到 5 万步兵、1 万弓箭手、4 万骑兵和 5 辆弩炮。 当你战胜万的消息传到托勒隆总督的王国时，他准备了他的九万大军进行防御。他的军队在他首都外几英里的一片森林中迎接你。 你坐在帐篷里等待间谍的信息。你不耐烦地敲打脚趾，用肘部在扶手上摩擦。突然，你的首席间谍猛地打开门板，单膝跪地。 “长官，托勒隆已经将他的弓箭手部署在我们对面的茂密森林中。这使他们可以向我们在露天的军队开火，而不会成为我们弓箭手的目标。我们还发现 Toleron 在他的城堡里留下了 15 把弩炮，而这些弩炮没有带在身上。他一定是低估了我们的决心。” 你走出帐篷，眺望战场。你看不到他的弓箭手，但你可以看到他的七万步兵在森林前一百码处排成一排。仔细一看，他们一脸惊慌失措的白痴。虽然您的士兵既聪明又勇敢，但托勒隆的士兵在靴子里颤抖着，看起来他们会在任何厄运的征兆下逃跑。 您与上校商议决定最佳策略。由于现在有骑兵和箭弹，你可以排除较少的机动。背上吹来一阵刺骨的微风，空气干燥。您的两个最佳攻击选项是：
梗概：
你将你的步兵分成钳形机动，从两侧攻击敌人，同时用弩炮和弓箭手射击；不使用箭弹
--------------------
动作：
做
你用远程战略弩炮轰炸敌方步兵，而你的所有部队都坚守阵地，直到托勒隆的弓箭手离开他们的优势位置；不使用箭弹
结果：
你再次拒绝使用你的秘密武器。相反，您尝试利用您的五个弩炮的远距离和伤害输出。你命令你的弩炮向敌人开火。 弩炮是一台令人印象深刻的机器，您会看到他们在 Toleron 对被屠杀做出反应之前消灭了数百名敌军步兵。他命令他的 90,000 名士兵中的每一个，包括他的弓箭手，向你的军队冲锋，以抵消你的射程优势。这正是您想要的。现在你与他的军队交战。 你的人明显训练有素。你的步兵的步法更快，他们的剑术比托勒隆的强。你的弓箭手要准确得多。你的骑兵在近战中也远远优于他的步兵。你的军队在你遭受轻微损失的同时歼灭了他的队伍。Toleron 本人试图逃跑，但几个小时后被一个骑兵部队追查到。他被绑在脚踝和手腕上，被扔到你面前。 “我为什么要饶你一命？” 你问一个挑衅的托勒隆。 “没有必要。自从我皈依佛门后，我就看到了这条路。存在的痛苦和苦难是没有意义的。随着时间的推移，你的人民会同意我的意见。杀了我只会让我涅槃——” “足够的！相信我，你的死亡对我的帮助比它对你的帮助要好得多。砍掉他的头！” 当您接管 Toleron 的城堡时，您会发现他的 15 辆弩炮状况良好，并将它们添加到您的舰队中。此外，在与巨大敌人的战斗中获胜同时遭受适度损失可以大大提高您的王国的士气。另一个征兵高峰出现了，这次将您的部队增加到 50 万步兵、20 万弓箭手和 30 万骑兵。此外，你越来越稳定的熟练商人已经完成了几个云梯的建设。 云梯或攻城塔是移动缓慢、带轮子、可垂直延伸的木结构。它们允许步兵和弓箭手直接到达城墙和塔楼的顶部，而不会在途中受到箭的轰击。湿布覆盖在外面，以保护它免受燃烧的箭的伤害。与普通箭不同，弩炮可以穿透云梯的木壳并最终破坏整个结构。云梯也不能容纳任何骑兵，因此您的骑兵将无法支持您的步兵和弓箭手。您的战争机器现在共有 20 个弩炮和 10 个云梯。 有一天，当您准备另一场进攻性战役时，一位使者拜访了您。 “我是带着闵总统的和平使命来到这里的。他听说过你的胜利故事，并对你的军事才华感到敬畏。现在中国剩下的所有军阀都与总统结盟了，你加入我们是迟到的。他要求你将你的军队保持在你新获得的华北、华南和华东领土的边界内，以换取他阻止所有其他军阀入侵你的领土。在中国以外，你仍然可以自由地攻击任何你想攻击的人。我拿什么回信给敏总？” “啊哈哈哈哈！” 你已经很久没有笑得这么开心了。“别担心，”你说，“我会为你处理好。” 当您的手下将外交官拖走处决并将他的头放入一个盒子并送回 Min 时，您笑了。 到现在为止，你拥有四分之三的中国。但最后的反对是最强的。敏已经说服他的盟友都反对你作为一个整体。他们最坚守的阵地是闵的首都，150万士兵在那里等着你。 你向敏的最后堡垒进军。远处矗立着一座巨大的城堡。在它的前面是数以千计的帐篷紧密地依偎在一起，敏的盟友的一百万步兵就在那里扎营。城墙上栖息着敏的20万步兵和30万弓箭手。十架弩炮坐在他的塔顶上，指向你的方向。由于它们的高度，Min 的弩炮的射程比你的大得多。战场地形是一片没有树木的低洼平原，背后吹来一阵轻风。 你意识到你还有箭弹。如果这个场合不值得使用它们，那么他们将永远不会看到战斗。地形和天气为您的箭弹提供了完美的条件，可以燃烧帐篷并造成最大的伤害。根据您的命令，您前排的每个弓箭手都会向帐篷发射箭弹。 敏的盟友看到你在做什么，急忙准备进攻。但还没等他们迈出一步，巨大的爆炸声就震动了他们的营地。爆炸和刺穿玻璃弹片直接杀死了敏的许多盟友。爆炸还引发了草丛中的火灾。 许多士兵被困在城墙之间，火被风吹向他们。敏拒绝打开他的城堡的大门，因为它的占领已经完全满了。有些人死于吸入烟雾。当火从一个帐篷跳到另一个帐篷时，其他人的死亡是火热和痛苦的。幸存者抛弃敏并逃命。你已经消灭了整个营地，没有一个人伤亡。 大火熄灭后，你走过烧焦的废墟，靠近敏的高耸城堡，小心地保持在敏的弩炮射程之外。你还有50万步兵，20万弓箭手，30万骑兵，还有20个弩炮和10个云梯。敏还有20万步兵、30万弓箭手和10辆弩炮守卫着他的堡垒。你将如何攻击城堡？由于您一直等到现在才部署箭头炸弹，您有三个不错的选择。 你知道，当你的弩炮移动得足够近以向城堡开火时，它们也很可能在攻击中被开火，最终全部被敏的远程弩炮摧毁。
梗概：
你用远程战略弩炮轰炸敌方步兵，而你的所有部队都坚守阵地，直到托勒隆的弓箭手离开他们的优势位置；不使用箭弹
--------------------
动作：
做
你向他们巨大的木门发射弩炮并摧毁它；一旦大门被攻破，与你的部队一起冲锋，同时冒着不可避免的弩炮弹幕，冲进城堡并占领要塞
结果：
你命令你的弩炮向前。当您的弩炮操作员在敌人的弩炮火力面前勇敢地推动他们前进时，您会感到自豪。其中一些在进入射程之前就被摧毁了，但大多数都到达了目的地。 你专心地看着你的弩炮用螺栓冲向 Min 的城堡大门。最终，巨大的大门被竖井覆盖，你几乎看不到任何木头。几分钟后，当你的最后一个弩炮被摧毁时，你看到门开裂，然后摇晃，最后倒塌。是时候了。 一百万最勇敢的战士向城堡开放冲锋。你看到敏的士兵急忙冲向缺口保护要塞。正如您所料，当您的部队向城堡前进时 ，敏的弩炮会在您的部队中下雨。许多人无法生存。但是足够了，当世界末日冲突开始时，你在数量上仍然拥有适度的优势。 金属的叮当声、原始的叫喊声、马蹄疾驰的混乱，以及数百万激烈的战斗人员在你面前的场景中整理出来。鲜血和尸体慢慢地填满了战场。看着所有的大屠杀，你感到不寒而栗，几乎是兴高采烈。但是谁会胜利并在历史书中刻上他们的名字呢？ 你在人数上的适度优势非常缓慢地削弱了敏的队伍。敏的部队有保卫家园的额外动力。你的部队的优势是更加嗜血和战斗经验丰富。一点一点地，你的军队压倒了防御者并将他们推向了更深的城堡。有时他们会被推后，但随后又从他们倒下的战友身边冲过去。越来越靠近你得到的要塞，直到 Min 的旗帜从它的杆子上掉下来，取而代之的是你的血腥旗帜。 仔细观察战斗场景后，你踏入要塞。没有一个穿着敏制服的人还站着。王座室很奢华，但空无一人。你冲上台阶，跳上华丽的黄金宝座。敏双手被绑在背后，被带到你面前。 “我投降，伟大的征服者。请饶我一命。我会为你服务，并且永远不会背叛你！我可以帮你统治，我很了解这个地区，因为我已经在这里住了一辈子。我会永远感激你。请放过我！” 为了为帮助你团结全中国的阵亡士兵报仇，你不顾他的恳求，处决了闵总统。但中国只是一个开始。从太阳升起的地方到它落下的地方，土地都已经成熟了。你宣布自己为皇帝并开始一个新的王朝。你继续成为中国有史以来最伟大的征服者。多亏了你的军事才能， 你赢了。
梗概：
你向他们巨大的木门发射弩炮并摧毁它；一旦大门被攻破，与你的部队一起冲锋，同时冒着不可避免的弩炮弹幕，冲进城堡并占领要塞
故事名称：
26
故事背景：
你相信不用箭弹就能赢得这场战斗。当你的胜算更大时，你的秘密武器可能会在以后证明更有用。然而，现在可不是依靠站在平地上的弓箭手的时候。你的步兵飞奔包围敌人。 在万指挥官的部队知道是什么击中他们之前，你正在下雨，无论是普通的还是巨大的，从两侧射向他们。 这种经典的孙子兵法非常完美。混乱随之而来的是他们以前有组织的形成。你创造了 2 个甚至 3 个步兵与其中一个步兵的不匹配。当敌方士兵被钉得更近时，你的弩炮和弓箭手会在他们身上下雨。 大屠杀只需要几个小时就可以促使万投降。你损失了一些，你的军队只有 1 万步兵、5 千弓箭手和 1 辆弩炮。尽管如此，你和你的士兵们在彻底消灭万的军队后仍然欣喜若狂，同时保持你储存的箭弹完好无损。万指挥官爬到你面前乞求饶命，你的精神就高了。 “请允许我活下去，”他恳求道。 你傻笑。“一个连编队战术都不知道的指挥官，我有什么用？傻子！” 你接管了万指挥官的总部。你把它变成你新的行动基地，同时把他被砍下的头留在旗杆上，提醒你你的力量。 你壮观的胜利和最近的一系列成功极大地提高了你王国的士气。你现在控制了中国北方的大部分地区。许多人听到你的勇气和领导力的故事，并自愿加入你的战斗部队。此外，熟练的木匠、机械师和工匠提供他们的服务来帮助您建造更多的战争机器。从附近的城镇收集最优秀和最聪明的人，你的军队将膨胀到 5 万步兵、1 万弓箭手、4 万骑兵和 5 辆弩炮。 当你战胜万的消息传到托勒隆总督的王国时，他准备了他的九万大军进行防御。他的军队在他首都外几英里的一片森林中迎接你。 你坐在帐篷里等待间谍的信息。你不耐烦地敲打脚趾，用肘部在扶手上摩擦。突然，你的首席间谍猛地打开门板，单膝跪地。 “长官，托勒隆已经将他的弓箭手部署在我们对面的茂密森林中。这使他们可以向我们在露天的军队开火，而不会成为我们弓箭手的目标。我们还发现 Toleron 在他的城堡里留下了 15 把弩炮，而这些弩炮没有带在身上。他一定是低估了我们的决心。” 你走出帐篷，眺望战场。你看不到他的弓箭手，但你可以看到他的七万步兵在森林前一百码处排成一排。仔细一看，他们一脸惊慌失措的白痴。虽然您的士兵既聪明又勇敢，但托勒隆的士兵在靴子里颤抖着，看起来他们会在任何厄运的征兆下逃跑。 您与上校商议决定最佳策略。由于现在有骑兵和箭弹，你可以排除较少的机动。背上吹来一阵刺骨的微风，空气干燥。您的两个最佳攻击选项是：
--------------------
动作：
做
你将箭弹射入森林以利用天气条件，利用树木作为火种将弓箭手从掩体中吸出；然后看看会发生什么
结果：
你命令你的弓箭手向前行进，然后将箭弹射入森林。一旦他们进入射程，你的弓箭手就会被 Toleron 的箭射中。然而，这种抵抗只会延迟你的弹幕；所有的箭弹最终都到达了目标。一场又一场的爆炸震撼了战场。 干燥的天气使树木更加易燃，而猛烈的微风则助长了火焰。没有直接在爆炸中丧生的敌方弓箭手在火焰中被烤焦。 托勒隆步兵的前纵队听到身后可怕的爆炸声，转过身来。他们眼睁睁地看着树木起火，他们的战友流血地死去。他们犹豫了片刻，然后丢下盾牌和武器逃跑了。Toleron 的其余军队停顿了一下，然后同样撤退了。 你如何回应？
梗概：
你将箭弹射入森林以利用天气条件，利用树木作为火种将弓箭手从掩体中吸出；然后看看会发生什么
--------------------
动作：
做
你派出骑兵追击撤退的敌人并杀死他们
结果：
你不能让 Toleron 逃脱并重新集结。当他拿到他的 15 个弩炮时，要击败他将更加困难。你命令你的 40000 骑兵在 70 左右撤退步兵之后冲锋。 他们在干燥的平原上疾驰，很快就追上了敌人。步兵，即使是那些为生命而奔跑的人，也无法跑得过纯种马。惊慌失措的士兵无法避免被自己绊倒，更不用说防御冲锋骑兵的长矛了。一场屠杀接踵而至。 你的一名警官发现了 Toleron 本人并活捉了他。他被绑在脚踝和手腕上，被扔到你面前。 “我为什么要饶你一命？” 你问一个挑衅的托勒隆。 “没有必要。自从我皈依佛门后，我就看到了这条路。存在的痛苦和苦难是没有意义的。随着时间的推移，你的人民会同意我的意见。杀了我只会让我涅槃——” “足够的！相信我，你的死亡对我的帮助比它对你的帮助要好得多。砍掉他的头！” 当您接管 Toleron 的城堡时，您会发现他的 15 辆弩炮状况良好，并将它们添加到您的舰队中。此外，在与巨大敌人的战斗中获胜而损失微乎其微，这会极大地提高您的王国的士气。征兵人数又一次大幅增加，这次将您的部队增加到 50 万步兵、50 万弓箭手和 100 万骑兵。此外，你越来越稳定的熟练商人已经完成了几个云梯的建设。 云梯或攻城塔是移动缓慢、带轮子、可垂直延伸的木结构。它们允许步兵和弓箭手直接到达城墙和塔楼的顶部，而不会在途中受到箭的轰击。湿布覆盖在外面，以保护它免受燃烧的箭的伤害。与普通箭不同，弩炮可以穿透云梯的木壳并最终破坏整个结构。云梯也不能容纳任何骑兵，因此您的骑兵将无法支持您的步兵和弓箭手。您的战争机器现在共有 20 个弩炮和 10 个云梯。 有一天，当您准备另一场进攻性战役时，一位使者拜访了您。 “我是带着闵总统的和平使命来到这里的。他听说过你的胜利故事，并对你的军事才华感到敬畏。现在中国剩下的所有军阀都与总统结盟了，你加入我们是迟到的。他要求你将你的军队保持在你新获得的华北、华南和华东领土的边界内，以换取他阻止所有其他军阀入侵你的领土。在中国以外，你仍然可以自由地攻击任何你想攻击的人。我拿什么回信给敏总？” “啊哈哈哈哈！” 你已经很久没有笑得这么开心了。“别担心，”你说，“我会为你处理好。” 当您的手下将外交官拖走处决并将他的头放入一个盒子并送回 Min 时，您笑了。 到现在为止，你拥有四分之三的中国。但最后的反对是最强的。敏已经说服他的盟友都反对你作为一个整体。他们最坚守的阵地是闵的首都，150万士兵在那里等着你。 你向敏的最后堡垒进军。远处矗立着一座巨大的城堡。在它的前面是数以千计的帐篷紧密地依偎在一起，敏的盟友的一百万步兵就在那里扎营。城墙上栖息着敏的20万步兵和30万弓箭手。十架弩炮坐在他的塔顶上，指向你的方向。由于它们的高度，Min 的弩炮的射程比你的大得多。战场地形是一片没有树木的低洼平原，背后吹来一阵轻风。 你的军团直立面对敌人。你的荣耀之日终于到来了。然而，就在这一刻发生之前，敏的盟友突然袭击了你。所有一百万步兵在您的编队中作为一个整体冲锋，并从您手中夺走主动权。你别无选择，只能反击。 你的一百万骑兵是第一个与敌人交战的。前几线的士兵被你的骑兵的凶猛冲锋击倒。你的骑兵在承受中等损失的同时迅速屠杀了步兵。很快，100 万步兵要么全部阵亡，要么分散，而你却损失了 50 万骑兵。 小心地保持在 Min 的弩炮射程之外，您走过帐篷，靠近 Min 高耸的城堡。你还有步兵、弓箭手、骑兵各50万，还有弩炮20个，云梯10个。敏还有20万步兵、30万弓箭手和10辆弩炮守卫着他的堡垒。你将如何攻击城堡？由于您在早期战斗中做出出色的战术决策，您现在拥有更好的选择。有两个吸引人的选择，但请谨慎决定。 你知道，当你的弩炮移动得足够近以向城堡开火时，它们也很可能在攻击中被开火，最终全部被敏的远程弩炮摧毁。
梗概：
你派出骑兵追击撤退的敌人并杀死他们
--------------------
动作：
做
你向他们巨大的木门发射弩炮并摧毁它；一旦大门被攻破，与你的部队一起冲锋，同时冒着不可避免的弩炮弹幕，冲进城堡并占领要塞
结果：
你命令你的弩炮向前。当您的弩炮操作员在敌人的弩炮火力面前勇敢地推动他们前进时，您会感到自豪。其中一些在进入射程之前就被摧毁了，但大多数都到达了目的地。 你专心地看着你的弩炮用螺栓冲向 Min 的城堡大门。最终，巨大的大门被竖井覆盖，你几乎看不到任何木头。几分钟后，当你的最后一个弩炮被摧毁时，你看到门开裂，然后摇晃，最后倒塌。是时候了。 你最勇敢的战士中有 50 万向城堡开放冲锋。你看到敏的士兵急忙冲向缺口保护要塞。正如你所预料的，当你的部队向城堡前进时 ，敏的弩炮会在你的部队身上下雨。许多人无法生存。但是，当一场你从未见过的规模的冲突开始时，你仍然拥有巨大的数量优势。 金属的叮当声、原始的叫喊声、马蹄疾驰的混乱，以及数百万激烈的战斗人员在你面前的场景中整理出来。鲜血和尸体慢慢地填满了战场。看着所有的大屠杀，你感到不寒而栗，几乎是兴高采烈。但是谁会胜利并在历史书中刻上他们的名字呢？ 你在人数上的巨大优势慢慢削弱了敏的队伍。敏的部队有保卫家园的额外动力。你的部队的优势是更加嗜血和战斗经验丰富。一点一点地，你的军队压倒了防御者并将他们推向了更深的城堡。越来越靠近你得到的要塞，直到 Min 的旗帜从它的杆子上掉下来，取而代之的是你的血腥旗帜。 仔细观察战斗场景后，你踏入要塞。没有一个穿着敏制服的人还站着。王座室很奢华，但空无一人。你冲上台阶，跳上华丽的黄金宝座。敏双手被绑在背后，被带到你面前。 “我投降，伟大的征服者。请饶我一命。我会为你服务，并且永远不会背叛你！我可以帮你统治，我很了解这个地区，因为我已经在这里住了一辈子。我会永远感激你。请放过我！” 在一个难得的怜悯时刻，你决定监禁闵总统。他温顺的恳求让你感动。然而，你并不真正信任他，所以他在监狱里度过了他的余生，一个人。 你现在已经征服了中国。但这只是开始。从太阳升起的地方到它落下的地方，土地都已经成熟了。你宣布自己为皇帝并开始一个新的王朝。你继续成为世界上有史以来最伟大的征服者。多亏了你无与伦比的军事技能， 你赢了。
梗概：
你向他们巨大的木门发射弩炮并摧毁它；一旦大门被攻破，与你的部队一起冲锋，同时冒着不可避免的弩炮弹幕，冲进城堡并占领要塞
故事名称：
27
故事背景：
你的神秘访客松了口气，看到你除了找出他是谁之外别无他意。他用一只手将匕首对准你的喉咙，另一只手从他脸上抬起黑布。 “你的反应不像其他军阀。我的名字是Iconoclast。正确回答这个问题，我可以饶你一命。 “比如说，如果中国其他军阀都消失了，而你是最后一个，你会怎么做？”
--------------------
动作：
说
“用我的军队征服每一个最后的国家，在我统一整个世界之前不要停止。”
结果：
他厌恶地看着你。“无论如何，你永远不会有任何成就。我会让你的血染上别人的刀刃。我会饶你一命，但你别想跟着我。” 他转身，冲出去，消失在夜色中。 稍后，您询问了您的守卫，但他们都没有注意到任何异常。你责备他们的无能并加强你的宫殿安全。 你再也听不到蒙面刺客的消息了。 几天之内，您就会从与死亡的这个可怕的接近电话中恢复过来。在您康复期间，您会反思这样一个事实，即您仍然与任何强大的领导者没有密切联系。您需要尽快获得一个，以免被掠夺性军阀视为容易攻击的目标。到目前为止，纳赫鲁将军和托勒龙总督是中国仅有的两个足够强大的值得会见的统治者。你先拜访谁？
梗概：
你说“用我的军队征服每一个最后的国家，在我统一整个世界之前不要停止。”
--------------------
动作：
做
你跋涉到托勒龙总督的首府
结果：
您的代表团抵达托勒龙总督的东部领土。由于他的王国的经济繁荣和他的臣民的满足，他在中国是一个重要的存在。众所周知，托勒龙是一位勤奋、和平、节俭和务实的统治者。 他的会议区稀疏而优雅，他热情地接近你。他向你打招呼。“千载难逢，吉祥君。您的光临是对我和我的公民的尊重。”
梗概：
你跋涉到托勒龙总督的首府
--------------------
动作：
说
“少废话托勒隆。像个男人一样和我说话。”
结果：
托勒龙州长坐在那里，目瞪口呆。 他给了你一个奇怪的眼神。
梗概：
你说“少废话托勒隆。像个男人一样和我说话。”
--------------------
动作：
说
：“你弱小的帝国已经到了末日。被打垮是你的宿命。现在只有和我结盟才能拯救你。”
结果：
“你很幸运，我不相信暴力，否则我早就将你斩首了！” Toleron 的笑容消失了，取而代之的是一种威胁的表情。 他与他的守卫进行目光接触，然后向你的方向倾斜他的头。他们用力把你推向出口。你被毫不客气地引导到路边。
梗概：
你说：“你弱小的帝国已经到了末日。被打垮是你的宿命。现在只有和我结盟才能拯救你。”
--------------------
动作：
做
你回家
结果：
您在没有重要联盟的情况下返回了您的王国。当中国各地的军阀听到你失败的消息时，你的声誉就会受到影响。 一些军阀开始攻击你，认为你不强大。你的军队抵挡了前几次进攻，但你的王国士气低落。然后更大的军队的连续攻击深入你的领土。 入侵者纵火焚烧您的村庄，掠夺您的城市，并通常对您的人民造成严重破坏。你的小盟友会提供尽可能多的军事援助，但被利用你的多条战线的众多机会主义者所淹没。您的公民作为难民逃往托勒隆的领土。面对迫在眉睫的失败，您的许多士兵也离开了您的军队。 你站在你的王座房间里，试图想出一些话对敌军说些什么，他们肯定会在任何时候敲开你的门。果然，房间外很快就出现了火把的影子。 一群步兵闯入你的纱门，由一个戴着红色头盔的小伙子带领。 “闵总裁。很高兴终于见到你。有你在，我离征服中国又近了一步！” 你恳求。“让我以顾问的身份加入你的军队。我对当地很了解。我不会忘记你的慷慨。” “我有足够多的有才华的顾问。再说了，我不信任你。” 他的剑在你的头顶划过空气。你设法躲开了。然而，这只是暂时的缓刑，因为有几个人，包括戴着红色头盔的那个人，围着你，野蛮地把你切成碎片。 THE END
梗概：
你回家
故事名称：
28
故事背景：
“我一直期待与伟大的总统会面。你觉得我们这个时候适合什么样的联盟？”
--------------------
动作：
说
“让我们宣布我们两个领域之间的和平协议。”
结果：
“这也是我心中的想法。你确实是一个聪明能干的统治者。” “我们的联盟可能会为我们双方带来更大更好的事情，”托勒隆说。“谁说我们一人统治整个中国。只要和平，就会繁荣，这是我们作为统治者所能做的最好的事情。” 他送你十匹奖马和二十尊金佛像。你获得了一个可靠且无价的盟友。 这个联盟巩固了你作为中国更强大军阀之一的地位。无论是在王国内外，您的声望都会提高。你的人民认为联盟有理由为你举行庆祝活动和游行。你被外国外交官称赞为聪明和聪明。 您已准备好迎接最后的挑战。你完成了与纳赫鲁将军谈判的准备工作。
梗概：
你说“让我们宣布我们两个领域之间的和平协议。”
--------------------
动作：
做
你出发前往纳赫鲁将军的首都
结果：
您的外交大篷车抵达纳赫鲁将军的北部领地。他以脾气暴躁和好战倾向而闻名。众所周知，他只是因为失言而攻击军阀。另一方面，Nahru 是一位聪明的总司令，也是光荣行为的坚定支持者。 被击败的敌人的旗帜和血淋淋的头颅装饰在 Nahru 将军大院的外围。数以百计的精英守卫站在通往主战帐篷的道路两侧。 当你走进去时，你不禁感到有点害怕。将军盘腿坐在一张巨大的熊皮地毯上，他的眼睛盯着你的眼睛，带着自信的笑容。他的大儿子卢尔杜站在他身边，冷冷的看着你，不屑的看着你。
梗概：
你出发前往纳赫鲁将军的首都
--------------------
动作：
说
“擦去你脸上的笑容，给予我应有的尊重。”
结果：
纳鲁的脸更红了。“你怎么敢这样跟我说话！”
梗概：
你说“擦去你脸上的笑容，给予我应有的尊重。”
--------------------
动作：
说
“你今天遇到了你的对手 Nahru。你的帝国正在衰落，你也是。”
结果：
“敏，你是个疯子。守卫！处决这个笨蛋！” 你被肌肉发达的暴徒包围。他们把你的头压在沾满血迹的砧板上。你只能移动你的头，因为你身体的其余部分被 Nahru 的守卫固定住了。你试图说些什么，但当你的头掉到地板上时，你的嘴里没有发出任何声音。 THE END
梗概：
你说“你今天遇到了你的对手 Nahru。你的帝国正在衰落，你也是。”
故事名称：
29
故事背景：
爸爸告诉过你关于 Dawnwood 的三件事：这是一个不到 1000 人的小镇，它是一个友好、封闭的社区，它位于深南地区，在偏僻的地方和一些被你遗弃的狗屎坑之间”没去过。你坐在爸爸的福特车的后面，盯着窗外肮脏的田野和森林。 “很漂亮，不是吗？” 你爸爸说，兴高采烈地望着窗外。 “爸爸，我的手机上没有酒吧。我真的不知道在这个国家阿拉斯加以外的地方有这种可能。” “好吧，也许它会鼓励你花更多时间与人交谈或阅读。亨特喜欢阅读。” 你看着坐在你身边的九岁弟弟，他的眼睛盯着 Kindle 的屏幕。 “他不是在看书，而是在玩游戏。我告诉过你他不应该买 Kindle。” “闭嘴，黑兹尔！” 他说。 你飞快地拍打他的手臂。 “嘿！Hazel 打我了！” 猎人呜咽。 “Hazel！我跟你说过打你弟弟什么了？” “不看的时候做吗？” 你讽刺地问。 “看在上帝的份上，黑兹尔，你才十六岁，装出你的年龄！我讨厌你因为搬家而表现得像个被宠坏的孩子！ “你把我从家里拖到无处可去的混蛋中间，我当然生气了！” 爸爸愤怒地双手拍在方向盘上，再次失去了冷静。 “别骂了！我必须搬到这里来，我没有选择！公司……” “还有很多其他的律师事务所！这是纽约，没有找不到工作的犯罪少！” “这是另一个原因！纽约很危险，到处都是犯罪。人们会因为我们要去的地方而死的唯一一件事就是年老或吃太多薯条！” “好吧，你的工作再接再厉！” 爸爸叹了口气，你掏出耳机戴上。当你穿过一望无际的田野时，你盯着窗外。
--------------------
动作：
做
一小时后...
结果：
一个小时后，您驶入 Dawnbrook，发现您太乐观了。该镇是一系列低矮的建筑。亨特从他的 Kindle 上短暂地抬起头，然后耸了耸肩，又往下看。您将车停在车道上的众多外观普通的房屋中的一间。您已经可以看到新来的人从窗户向外窥视的面孔。爸爸打开车门，把两个手提箱拖到门口，从口袋里掏出钥匙打开车门。 “我们到了！回家！” 他兴高采烈地说，显然是想提高孩子们的精神。 你跟着爸爸进去，把背包放在地板上。 “我会开始拆包，你们可以安顿下来，或者去见邻居或探索，”爸爸说。
梗概：
一小时后...
--------------------
动作：
做
你去你的房间
结果：
你再次拿起你的背包，走向你的新房间。它有一个大衣柜、一张大双人床、一张桌椅和两个梳妆台。你坐在床上，拿出你的笔记本电脑，然后上 Facebook。谢天谢地，WiFi 已经设置好了，所以你可以继续。你沿着你的时间轴，看到你所有的朋友都玩得很开心。Fiona 举办了一个派对，Dan 现在正在和 Leone 约会，还有一堆其他你希望你能参与的事情。你叹了口气，继续浏览图片和帖子。一两个小时后，爸爸又叫你下来。你合上笔记本电脑下楼，发现爸爸在厨房等着。 “嘿，亲爱的。房子很荒凉，所以我们要出去吃饭，我早上去买杂货。我在镇上看到了一家披萨店。你想去吗？”
梗概：
你去你的房间
--------------------
动作：
说
“当然”。
结果：
“哦，好的！来吧，我们走吧！” 你跟着爸爸上车，爬上副驾驶座。你甚至不需要转过头就知道你兄弟坐在后座，听到愤怒的小鸟的开场曲：斯大林的表演审判或他们现在拥有的任何他妈的版本。当你穿过城镇时，你躺在你的座位上，凝视着窗外。最终，你到达了一些又脏又油的披萨店，然后下车。你走进去，发现自己置身于 Chucky Cheese 仿冒品中。你走到柜台前，发现自己面对的是一个和你年龄相仿的女孩，她有着长长的棕色头发，额头中间长着一个看起来像一个红点的巨大青春痘。 “您好，欢迎光临比萨角，我可以为您点菜吗？” 她问。 “是啊，我们可以要一个大的意大利辣香肠比萨和三个可乐吗？” “当然可以。小号、中号还是大号？” “中等的。” 当另一个更漂亮的金发女孩从厨房出现时，女孩开始倒饮料，手里拿着一大块面团。她瞥了你一眼，然后用备用的手抓起一块布，又消失了。 “那么，你是新来的吗？” 收银员问。 “是的，我刚搬进来。” “我是露西。” “我是黑泽尔。” “哦真的吗？嗯，是个不错的小镇。除了乏味的比萨店外，你找不到太多工作，但这里也没什么可花钱的。一些旧衣服店和一家电影院，仅此而已。” “你在这里全职工作吗？” “耶稣没有。如果我到了在这里全职工作的地步，我会把头埋在深脂肪里……好吧，我不会高兴的。我还在上学。” “什么等级？” “第十个。” “哦，我明天就开始。我想我会在那里见到你。” ““当然。如果你愿意，我总能在这里帮你找到一份工作。贝卡，那个金发女孩，从我们开始的时候就开始做事了。她只是在工作，所以她爸爸认为她正在学习现实生活中的经验，并且仍然觉得购买任何她的东西都是合理的……” “我可以听见你！” 厨房里传来一个声音。 “我知道，Becca，我明白了！” 爸爸出现在你旁边，咧嘴笑着。 “看看谁已经交朋友了！” 他笑了。 你翻白眼。 “是的，当然。我们坐下吧，”你说，把爸爸拉到一个摊位坐下。
梗概：
你说“当然”。
--------------------
动作：
做
两天后...
结果：
这所学校是一栋破烂的建筑，里面没有学习地理和莎士比亚的好孩子，尽管他们几乎肯定会从事维持这座城镇运转所需的低劣工作。你穿过门，肩上挎着一个背包。 “好吧，还不错，”你嘟哝着，走过大厅里的一排储物柜。“我只需要找到我的课程，然后……” “嘿，榛子！” 一个声音在哭。 你转身发现比萨收银员露西那张熟悉的、长着青春痘的脸。 “嘿，”你回答。 “所以，这是你的第一天，嗯？你知道你在什么班吗？” 你从口袋里掏出一张皱巴巴的课表，递给她。 “好吧，我们有相同的西班牙语、英语、数学、历史……你学化学？同性恋！那是最难的课！” “除此之外，我们似乎共享可疑数量的课程。” “是啊，我跟校长谈过，求他让你上我的课……学校很小，你觉得会发生什么？当然，我们共享大多数课程。哦，看，卡特来了。” 你转身看到一个身材高大的孩子，头发梳得很整齐，脸上带着尴尬的笑容。他的衣服都太小了，他的牙齿乱七八糟的碎裂，随着他的笑容越来越明显。 “Pp-很高兴见到你-你。我叫卡特，”男孩尴尬地结结巴巴地说。 “这是卡特。他是我在这里最好的朋友之一。” “这是一个很小的城镇。你确实——不完全是——有很多可供选择。” “是的，我想不会。如果你愿意，我们都可以带你四处看看。在我们两个之间，我们应该几乎共享所有课程。” “除了化学……化学，”卡特指出。 “是的，你为此很受折磨，但那是你的问题。现在我们有数学和英语双学位，Loaves 小姐真的是最容易对付的数学老师。她是唯一一个在你忘记书的时候让你从储物柜里取书而不是写出或拘留或只是大喊大叫十分钟的人。走吧，我们带你四处看看。”
梗概：
两天后...
--------------------
动作：
做
两个小时后...
结果：
前几节课很容易。你一直擅长数学，Loaves小姐非常渴望有一个真正努力的学生。班里，除了你，和露西一起在披萨店工作的金发女孩贝卡和坐在班级后面的一个看起来和亨特一样年轻的小孩之外，大部分都是孩子们在捣乱和涂鸦。英语也是类似的，你是少数能拼出“Immediately”的人之一。在短短的三节课中，你巩固了自己在课堂上书呆子的地位。当第一顿午餐的钟声响起时，你会发现露西对你傻笑。 “你本可以提醒我们你是个书呆子，”她说。 “我不是书呆子！” 你抗议。 “你脑子里做了一半的事情！” “一世...！” “别闹了，新来的姑娘。该死的，没想到你会这么在意。来吧，我给你介绍一下这帮人。” 你跟着露西走到自助餐厅，她在一张桌子旁坐下。你认出坐在那里的人的几张脸。卡特坐在那里，急切地撕成一个三明治。那个书呆子的小孩也在那里，还有来自披萨店的金发女孩贝卡。唯一的其他人是一个你不认识的大而笨重的孩子，但似乎与贝卡异常接近。 “好的，各位，这是 Hazel，她是新来的。你知道 Carter。” “我是贝卡，”贝卡说，抬头看着你。 “嘿，我是安东尼，”小孩说。 “他是蚂蚁，”贝卡插嘴说。 “安东尼，你这个混蛋！” Ant 又说了一遍，但你确定 Ant 是小孩子的一个更好的名字。 “我是凯尔，”大孩子说。 你坐下，Kyle 立即继续讲一个他显然在讲的故事。 “所以我就在那里，刚走进这家糟糕的宠物店，我真的认为里面有一半的动物要么死了，要么只有几个小时的生命，因为我想在我们的周年纪念日给 Becca 买一条金鱼。 “这不是周年纪念。周年纪念是一年一次，而不是五个月一次，”Ant 指出。 “不管怎样，别再装傻打断故事了。所以我正试图弄到一条金鱼，但经理或老板之类的人只是茫然地盯着我看。大约一分钟后，他说“买点东西或者出去”，我问他他的问题是什么，他说，没有一句谎言，“你们黑人都在偷我的猫粮”。我什至没有被冒犯。我很好奇猫粮有多少他被黑人孩子偷了，让这成为他脑海中的刻板印象。” “不，听起来像 bb 废话。我敢打赌，你-不是真的买 Bec ...... Becca 一个周年纪念礼物。你--会……偷 c-cat 的食物，”卡特指出。 “操你妈的，卡特。我在哪儿？” “你刚刚完成，”贝卡说。 “好吧，该死，我无话可说。梅森在哪里？他通常有话要说。” “他在那儿，”露西说。 你稍微转过头，想看看你见过的最性感的男孩是谁。他高高耸立，T 恤下的肌肉起伏不定。他有一头黑色的短直发，看起来真的很可爱……妈的，他来了。说些什么！看在上帝的份上，别搞砸了！使用正确的问候语！
梗概：
两个小时后...
--------------------
动作：
说
“你好”。
结果：
“嘿，”他微笑着伸出手来握手。“我是梅森。” 他说话时带着性感、浓重的南方口音，立刻…… 操，他在盯着你看。 “我是 Hazel，”你微笑着握住他的手。 “你在哪里，梅森？” 露西问道。 “我正在和德雷克谈论今晚。” “谁是德雷克？” 你问。 “露西的哥哥，”卡特回答。 “你有告诉他我要去吗？” “当然不会。你就不能相信我吗？一切都会好起来的。嘿，我忘了说，我需要你们所有人来筹码。德雷克的得分很高。” “今晚是什么？” 你问。 “我们都出去在树林里喝酒，”露西解释说。“如果你愿意，你可以来。你甚至不需要喝酒什么的，只是有点乐趣。” “我不知道我爸会不会让我，” “好吧，偷偷溜出去，”蚂蚁说。 “嗯，我可以试试，”你说。 “酷，我会给你留一些罐头以防万一，”梅森笑道。 铃声响起，凯尔和蚂蚁齐声发誓。 “现在是什么班？” 梅森问道。 “西班牙语，”卡特回答。 你拿起你的包，跟着你的新朋友去上西班牙语课。
梗概：
你说“你好”。
--------------------
动作：
做
六小时后...
结果：
剩下的上学时间很轻松。你在历史课上了解小镇的历史，历史老师急切地放弃麦卡锡主义的概念来解释她最喜欢的话题。显然，该镇曾经是一大群美洲原住民的家园，他们很快就被殖民者消灭了。该镇因其大量的养牛场而声名鹊起，但随着现代时代的到来而变得默默无闻。最终，它变成了今天的烂摊子。放学后，你乘公共汽车回家，和你的朋友说再见。爸爸还在工作，所以你瘫倒在电视机前，观看一连串几乎完全相同的犯罪节目。一个是一群对罪犯刻板印象的人，一个是一些看着发光污渍的人，一个是知道你什么时候撒谎的人，一个是现代的夏洛克福尔摩斯。 就在团队找到一些关于小丑的汽车如何布满一些……特别是脏污渍的新信息时，您听到了熟悉的呜呜声。 “哈泽尔！我可以看电视吗！？！” “不！我在看。去玩你的游戏。” “但是电池没电了！” 他发牢骚。
梗概：
六小时后...
--------------------
动作：
做
你让那个呜咽、流鼻涕的小子看电视
结果：
“美好的！” 你叹了口气，因为你哥哥兴高采烈地抢过遥控器并切换到对他来说太老的最新卡通片。 你从沙发上跳起来，拿起你的笔记本电脑，疯狂地看 Youtube，直到你听到爸爸从门口进来。 “你好！我到家了！” 他打电话。 “嘿老爸！” 你在厨房里大喊大叫。 他走进来，微笑着。 “你没有在战斗。这令人印象深刻，”他笑着说，指的是你和你的兄弟。“我去吃晚饭。我们有咖喱鸡。” 他开始打开炉子开始做饭。 “学校怎么样，亲爱的？”
梗概：
你让那个呜咽、流鼻涕的小子看电视
--------------------
动作：
说
“该死”。
结果：
“好吧，亲爱的，我相信它会好起来的。不，我发誓它会的。别担心，只是需要一些时间来适应。” 你耸耸肩，打开手机。大约一个小时后，爸爸开始准备晚餐。
梗概：
你说“该死”。
--------------------
动作：
做
你坐下来吃饭
结果：
你坐下。 “那么亨特，学校怎么样？” “很好，”亨特咕哝道。 “嘿，爸爸……”你问。 “是的？” 现在是问他你是否可以去的最佳时机……或者你可以稍后偷偷溜出去。
梗概：
你坐下来吃饭
--------------------
动作：
说
“我可以和一些朋友一起去树林吗？”
结果：
“到树林里去？榛子，已经晚了，你不能去树林里了，”爸爸说。 “好吧，”你垂头丧气地说，已经在计划如何偷偷溜出去了。 你很快就吃完了大部分晚餐，然后假装胃痛消失在你的房间里。
梗概：
你说“我可以和一些朋友一起去树林吗？”
--------------------
动作：
做
你几个小时后...
结果：
在你应该会见你的新朋友前一小时，你出去见爸爸。 “爸爸，我感觉不太好，”你呻吟道。“我要睡了。” “你确定吗？” “是啊。我需要休息。我只是要睡觉了。不要过早地叫醒我。” “当然，亲爱的。” 你消失在你的房间里，尽管它有点普通，但立即填满你的床和枕头，然后打开你的窗户。在你下降之前，你坐在一个小壁架上。你轻轻地敲打着泥土，在爸爸选择看向窗外之前，你快速地慢跑离开了房子。你迅速冲出房子，掏出手机打电话给露西问路。
梗概：
你几个小时后...
--------------------
动作：
做
十分钟后你...
结果：
经过长途跋涉，多次迷路，并以麦当劳作为导航的地标，您最终在树林中跋涉，双脚轻轻地落在既不是干土也不是湿泥，而是两者兼而有之的土壤上。步行十分钟后，您会看到朋友们正在谈论的网站。由苔藓干燥的树叶和树枝生起的火很难保持燃烧，因为两侧有两个生锈的皮卡。您立即看到该组。梅森、露西和卡特坐在其中一辆卡车的车顶上，在他们之间传递着一大瓶看起来像伏特加的酒。凯尔和贝卡坐在另一边，分享着各种液体的奇怪混合物，最后变成了淡褐色。蚂蚁坐在火边，发出脏兮兮的咒语，以至于水手都脸红了，他将一罐打火机液体倒在火焰中，绝望地试图通过点燃一根较大的树枝来让火自给自足。 “哦，你在这里……”卡特说。 “哦，哈兹尔！我很高兴你能来！妈的，我有点预料到你会迷路，放弃并最终得到一个 McFlurry 然后来，”露西承认。 当太阳开始照射到地平线，照亮整个森林金色和橙色时，你坐在一辆皮卡的边缘。 “你带什么喝的？” 蚂蚁问道，把一堆树叶翻过来给火呼吸一些空气。 你摇摇头，蚂蚁叹了口气。 “数字。自从我认识他们以来，我就一直告诉其中一半的人自己带饮料，但唯一这样做的人只有我。” 梅森冒犯地举起双手。 “还有梅森，” “我总是带饮料！” 露西说。 “你哥哥是的，他拿了超过一磅肉作为报酬。反正有人给他打电话。” 蚂蚁刚说完，你就听到树叶间发出沙沙声。梅森立即将他的饮料放在外套下面，然后一个看起来二十出头、头发凌乱的高个子男孩出现了。 “嘿！” 他严厉地说。“未成年人饮酒是非法的、不健康的，可以作为通向非常危险的大麻的入口药物！” “我们没有时间听你胡说八道，德雷克，”蚂蚁抱怨道。 “为什么要破坏一切，短回合？” 我是想吓唬这个新来的女孩，”男孩说着，把一小袋装着酒的小袋子扔到蚂蚁面前。 “极好的！” 梅森萨西斯立即抓起袋子，蚂蚁递给德雷克一叠皱巴巴的钞票。 “顺便说一下，我是德雷克，”他对你说。“露西的弟弟。” “榛。你给你姐姐买酒？” 你惊讶地问。 “妈的，我十二岁半的时候就喝酒了。我想她已经大到可以自己做决定了。他妈的，在某些国家，她现在已经在生孩子和经营家庭了。” “谢谢，德雷克，”露西说，打破了一罐非品牌超市啤酒的标签。 “没问题。对了，我得走了，我和艾德要出去做几份工作。我到处见。很高兴认识你，Hazel。” 当你周围的孩子开始撕开袋子时，德雷克转身穿过森林。 “这一切尝起来就像有人喝了烂酒然后把尿塞进了罐子里，”蚂蚁抱怨道。 “哦，bb-因为你带来了 o-只有最好的葡萄酒和 c-cham-champagnes，蚂蚁，”卡特说。 梅森打开另一个罐头的标签，期待地看着你。 “嘿，Hazel，”他问道。“你想要一些？”
梗概：
十分钟后你...
--------------------
动作：
说
“不用了，谢谢”。
结果：
“你确定吗？” 露西问道。“喝一点酒不会伤害任何人。” “另外，你会非常平淡，如果你清醒，我们会互相讨厌，”Ant 补充道。“哦，还有……同辈压力。我们是向你施压的同龄人，因为这是真实发生的事情。”
梗概：
你说“不用了，谢谢”。
--------------------
动作：
说
“好”。
结果：
“酷，”梅森笑着把酒瓶递给你。 你喝了很长时间，把瓶子里的大部分都喝光了。蚂蚁是对的，它确实尝起来像小便。尽管如此，在传递之前，你还是成功地吞下了一半罐头。 “该死。新来的女孩可以喝酒，”蚂蚁笑道。 你继续围坐在篝火旁，这真的更接近于坐在一堆树叶中的蜡烛。梅森抓起一瓶伏特加，在你身边坐下，你的心怦怦直跳。你们中的很大一部分人都知道这只是迷恋和童年的迷恋，混杂着一些荷尔蒙的欲望，但仍然……他真的，真的很可爱。其他人似乎很无聊，凯尔喝下的酒似乎比他的体重还要重。 “好吧，这很无聊，”露西抱怨道，在中间扔了一个瓶子。“让我们玩转瓶子吧。” “决不！” 蚂蚁抱怨。“我是个奇怪的人。凯尔和贝卡一起去，你和卡特，然后是梅森和新来的女孩。” 当卡特点头表示同意时，你对这个评论脸红了。 “我同意。我不想玩旋转瓶子，”卡特说。 短暂地瞥了一眼冷漠的梅森，你感到一阵烦恼，因为蚂蚁建议一个新游戏，旋转瓶子的所有可能性都消失了。 “真心话大冒险！” 蚂蚁建议，对凯尔和贝卡的呻吟。 “还记得上次发生了什么吗？” 贝卡指出。 “这不公平！那不是我的错！你应该保护你爸爸的东西。我怎么知道那幅画值钱？” “你一开始就不应该在画上画画，蚂蚁！” “这不是你爸爸鼻子上的皮！那个有钱的混蛋能吃亏！天啊，我们都知道他的祖先是靠凯尔的祖先赚钱的！” “操你妈的，蚂蚁！” 凯尔喊道，喝完酒含糊不清。 “先生们，先生们，冷静点，”梅森说。“没有问题！我们在这里都是朋友！我说的是真话游戏，敢于让我们都冷静下来。” “很好，”贝卡说。 梅森把剩下的酒倒了下去，把玻璃瓶扔到中间。他双膝跪地，转动它，落在坐在皮卡车后面的卡特身上。 “真心话大冒险，卡特？” “如果我敢挑剔，你会不会……让我站起来？” 卡特懒洋洋地说。 “也许吧，”梅森狡黠地说。 “说实话，那么。” 梅森咧嘴一笑，看了他和露西一眼。 “你吻过露西吗？” 他问。 “不。抱歉让您失望了。” “该死。我很确定，”梅森叹了口气，掏出钱包扔给凯尔一张皱巴巴的钞票。“你只花了我二十块钱，卡特。” 蚂蚁倾身，转动瓶子。它慢慢停下来，指向树林里。 “那离贝卡或榛子更近吗？” 他问。 “贝卡，”卡特说。 “哦，太棒了。真心话大冒险？” “真相。” “该死的，贝卡。好吧，我会让这变得尴尬。凯尔跟你走了多远？” “闭嘴，蚂蚁！” 贝卡嘶嘶作响。 “喂，你得回答！” 露西说。 “哦，来吧！那是私人的！” “我们在这里都是朋友，”梅森说。 “美好的！我们……你知道。完成了。” “尊重，Kyle，”Ant 微笑着，忍住了咯咯的笑声。 贝卡脸红了，抓起瓶子转动它。通过纯粹的运气或技巧，它直接指向Ant。 “该死，”蚂蚁叹了口气。 “真心话大冒险。我有很多尴尬的问题要问。” “敢，”蚂蚁做了个鬼脸。 “好吧，虽然你的球比我多得多。你会受苦的。我敢让你进入艾歇尔先生的地下室。” “不！” 蚂蚁回答。 “来吧，贝卡！艾歇尔先生要是抓住了他，他的喉咙就会被撕掉！” 露西抱怨。 “艾歇尔先生是谁？” 你问。 “哦，我会告诉她的。我一直在为我的弟弟练习这个故事，“梅森说，然后清了清嗓子。“在第二次世界大战期间，一名年轻的士兵从党卫军的队伍中崛起。他有狩猎犹太人并亲自护送他们到集中营的习惯。他会猎杀男人、女人、孩子，并折磨他们以获取信息。他毫不留情。最终，当俄罗斯人入侵时，他伪装成平民逃脱，从柏林逃到法国，从英国逃到美国。他就藏身在 Dawnwood 的这里，追捕他能找到的任何犹太人。他叫艾歇尔先生。” “那是最大的狗屎！” 露西抱怨。“每个人都知道，在 Dawnwood 的任何犹太人都会被老卡特和他的白人权力废话吓跑。” “他不想要白色战俘权力！” 卡特说。“他想要白色的骄傲。有……不一样。” “不，没有，卡特，现在停止破坏这个故事。我在哪儿？” 梅森问道。 “你刚刚完成，”你指出。 “哦。好吧，现在艾歇尔先生要杀了蚂蚁。” “贝卡，对不起！请换胆！” “美好的。你必须进入死人谷仓，然后上阁楼。” “那就更糟了！” 蚂蚁抱怨。 “不，这还不错，”梅森说。“纳粹是真实存在的，会杀了你。鬼不是。” 你扬起眉毛，梅森笑了。 “好的，故事时间。曾经，西方有一位治安官，他用他的力量消灭了镇上大牧场主的任何竞争。其中一位小农场主丹尼尔爱德华兹站起来告诉警长，尽管受到威胁，他不会将他的农场卖给他的竞争对手。警长带了一队人把他打倒，爱德华兹试图反击。在他倒下之前，他杀死了两打人，并带走了近一百颗子弹。他们折磨了他几天，甚至把他的脸切开给他……你怎么称呼它，格拉斯哥咧嘴笑。最终，你只是把他吊死了。有传言说他仍然出没在他们把他绞死的谷仓里。这是一座历史建筑，不能拆除，所以业主就让它空置。” “我不去！” “别闹了，蚂蚁。” “你们中的一个和我一起去吗？” 蚂蚁恳求。 “旋转瓶子，无论它落在谁身上，都必须和你一起去，”露西建议道。“我希望是贝卡。那就太讽刺了。” “当然，”卡特说。“让我们这样做。” 蚂蚁感谢露西并旋转瓶子。它摇晃着，慢慢停下来，直接指向你。 “嗯，我猜黑兹尔要去死人谷仓，”梅森说。 “不，如果她愿意，她可以去艾歇尔先生家。Eichel 只是一个年迈、行动迟缓的家伙。死人是一个超自然的凶手，”露西说。 “让 Hazel 采摘，”Kyle 说，努力站起来，然后跌跌撞撞地靠在卡车上。 “是的。继续哈泽尔。幽灵或纳粹。”
梗概：
你说“好”。
--------------------
动作：
做
你和蚂蚁一起去死人的谷仓
结果：
“死人的谷仓。” “如果你这么认为的话，”露西说，梅森鼓励地点点头。 蚂蚁紧张地点点头。 “不错的选择，”梅森说。 你点头。一行人开始起立，开始向死人谷仓走去。 “我会……我会留在这里！” Kyle含糊其辞，倒在皮卡车的后面。 露西耸了耸肩，继续带领大家走出森林。当您出现在一片平淡而空旷的田野中时，小组将各种空罐子丢在灌木丛中，中间有一个旧的、明显废弃并用木板封起来的谷仓。这是一个特别大的谷仓，但除此之外，它只是一个陈旧乏味的谷仓。 太阳落山了，露西拿出手机打开手电筒。她带你到谷仓，梅森抓住一块胶合板的边缘，把它拉回来。 “这是愚蠢的家伙，”蚂蚁说。 “怎么，怕鬼？” 梅森轻笑。 “不！我只是害怕被生锈的钉子绊倒和破伤风！” “猫，”梅森说，翻了个白眼。 梅森将胶合板向后拉得更远，创造了一个足够大的间隙让你通过。 “谢谢你，”你一边走一边说。 “当然，女士，”梅森微笑。 当你跨过时，你进入了完全的黑暗，被一道从缝隙中射出的苍白月光所穿透。蚂蚁随后走进来，紧张地环顾四周。 “好吧，我们已经进去了，敢于结束，”蚂蚁说。 梅森叹了口气，将胶合板推回原位。几秒钟后你会听到他低沉的声音。 “上楼打开窗户的木板。然后我们会让你出去，蚂蚁，”露西说。 “你是个婊子！” 蚂蚁一边说，一边重重地敲打着胶合板，然后转向你。 “让我们探索一下，”他紧张地说。 你拿出手机，打开手电筒。谷仓的内部立即被照亮。几个用腐木制成的马厩占据了谷仓的大部分，地板上覆盖着干枯、腐烂的干草。 “这里闻起来像屎一样，”蚂蚁抱怨道。 “安静，”你回答。 你发现一个部分腐烂的楼梯通向楼上。你走过去。你把你的脚放在第一个楼梯上，并在上面放一些重量，以确保它能够支撑住。你慢慢地走上楼梯，到达楼上。里面是死一般的寂静，连风都在消逝，完全的寂静吞噬了你。你用手电筒扫描房间。当你注意到天花板上挂着一个沾满血迹的绞索时，你不禁畏缩了。 “天啊。这就是我认为的吗？” “不，Hazel，这他妈的……我什至想不出一个嘲讽的例子。当然是……！” 你身后的地板发出嘎吱嘎吱的声音，你迅速转身发现……什么也没有。 “Hazel，求你了。如果你想吓唬我，就……不要。” “我不干……” 你感到有什么东西撞到了你的背上，你大叫着向前倒下。你的手机从你手中飞了。它向前从楼梯上掉下来，在它落在它的背上之前发出砰的一声，遮住了手电筒，让你陷入完全黑暗中。 “怎么了，蚂蚁？” “我对天发誓，那不是我！” 蚂蚁说。“我们离开这里，去他妈的窗户。” 你在楼梯间听到憔悴、湿漉漉的呼吸声，然后你僵住了。 “什么...？” “住口！” 你嘶嘶声。 呼吸还在继续，然后变成湿漉漉的、令人窒息的轻笑。当你开始远离楼梯间时，蚂蚁呜咽着。 “今晚不是你们的夜晚，孩子们，”一个低沉的声音说道，每一个字都穿插着你们之前听过的湿漉漉的声音。“他妈的是什么让两个笨蛋，鸡屎孩子来到一个你不属于的地方？” “这他妈的……？” 蚂蚁说，一半是呜咽，一半是尖叫。 “嘘……喘口气。我什至没有给你开始大喊大叫的理由……还没有。”
梗概：
你和蚂蚁一起去死人的谷仓
--------------------
动作：
做
你收费的数字
结果：
你向前冲锋，人影抓住了你。它的手臂抓住你的手腕，把你钉在墙上。你听到蚂蚁的拳头轻轻敲击这些生物的背部，但人影似乎没有注意到。 “哦，你会后悔的，亲爱的。” 人影笑了。 你感觉到刀片的冰冷金属压在你的脸颊上。他笑着开始把你的脸分开，而你所能做的就是尖叫。
梗概：
你收费的数字
故事名称：
30
故事背景：
“亡灵谷仓。” “如果你这么认为的话，”露西说，梅森鼓励地点点头。 蚂蚁紧张地点点头。 “不错的选择，”梅森说。 你点头。一行人开始起立，开始向死人谷仓走去。 “我会……我会留在这里！” Kyle含糊其辞，倒在皮卡车的后面。 露西耸了耸肩，继续带领大家走出森林。当您出现在一片平淡而空旷的田野中时，小组将各种空罐子丢在灌木丛中，中间有一个旧的、明显废弃并用木板封起来的谷仓。这是一个特别大的谷仓，但除此之外，它只是一个陈旧乏味的谷仓。 太阳落山了，露西拿出手机打开手电筒。她带你到谷仓，梅森抓住一块胶合板的边缘，把它拉回来。 “这是愚蠢的家伙，”蚂蚁说。 “怎么，怕鬼？” 梅森轻笑。 “不！我只是害怕被生锈的钉子绊倒和破伤风！” “猫，”梅森说，翻了个白眼。 梅森将胶合板向后拉得更远，创造了一个足够大的间隙让你通过。 “谢谢你，”你一边走一边说。 “当然，女士，”梅森微笑。 当你跨过时，你进入了完全的黑暗，被一道从缝隙中射出的苍白月光所穿透。蚂蚁随后走进来，紧张地环顾四周。 “好吧，我们已经进去了，敢于结束，”蚂蚁说。 梅森叹了口气，将胶合板推回原位。几秒钟后你会听到他低沉的声音。 “上楼打开窗户的木板。然后我们会让你出去，蚂蚁，”露西说。 “你是个婊子！” 蚂蚁一边说，一边重重地敲打着胶合板，然后转向你。 “让我们探索一下，”他紧张地说。 你拿出手机，打开手电筒。谷仓的内部立即被照亮。几个用腐木制成的马厩占据了谷仓的大部分，地板上覆盖着干枯、腐烂的干草。 “这里闻起来像屎一样，”蚂蚁抱怨道。 “安静，”你回答。 你发现一个部分腐烂的楼梯通向楼上。你走过去。你把你的脚放在第一个楼梯上，并在上面放一些重量，以确保它能够支撑住。你慢慢地走上楼梯，到达楼上。里面是死一般的寂静，连风都在消逝，完全的寂静吞噬了你。你用手电筒扫描房间。当你注意到天花板上挂着一个沾满血迹的绞索时，你不禁畏缩了。 “天啊。这就是我认为的吗？” “不，Hazel，这他妈的……我什至想不出一个嘲讽的例子。当然是……！” 你身后的地板发出嘎吱嘎吱的声音，你迅速转身发现……什么也没有。 “Hazel，求你了。如果你想吓唬我，就……不要。” “我不干……” 你感到有什么东西撞到了你的背上，你大叫着向前倒下。你的手机从你手中飞了。它向前从楼梯上掉下来，在它落在它的背上之前发出砰的一声，遮住了手电筒，让你陷入完全黑暗中。 “怎么了，蚂蚁？” “我对天发誓，那不是我！” 蚂蚁说。“我们离开这里，去他妈的窗户。” 你在楼梯间听到憔悴、湿漉漉的呼吸声，然后你僵住了。 “什么...？” “住口！” 你嘶嘶声。 呼吸还在继续，然后变成湿漉漉的、令人窒息的轻笑。当你开始远离楼梯间时，蚂蚁呜咽着。 “今晚不是你们的夜晚，孩子们，”一个低沉的声音说道，每一个字都穿插着你们之前听过的湿漉漉的声音。“他妈的是什么让两个笨蛋，鸡屎孩子来到一个你不属于的地方？” “这他妈的……？” 蚂蚁说，一半是呜咽，一半是尖叫。 “嘘……喘口气。我什至没有给你开始大喊大叫的理由……还没有。”
--------------------
动作：
做
你试图与人偶交谈
结果：
“求求你……我不想死。” “我也没有，孩子。还不错。告诉你的祖父母我说“去你的。”，好吗？ “请！” 你说，破涕为笑。 “嘿！梅森在外面喊道。“你在里面还好吗？ 你感到冰冷的金属触碰到你的喉咙。 “告诉他你很好，亲爱的。”
梗概：
你试图与人偶交谈
--------------------
动作：
做
你告诉梅森你很好
结果：
“我没事！有只老鼠！” 你喊。 “好的！” 梅森回答。 Ant 继续呜咽，一只戴着手套的手穿过你的头发。 “好女孩……我在黑暗中认不出你。让我们看看你是谁……顺便说一下，不要尖叫。” 您会听到打火机的轻弹声，然后火焰在您面前燃烧起来，照亮了你们三个人。然后，你第一眼看到了死人。他的皮肤灰白而死气沉沉，他的眼睛冰冷，毫无生气，纯黑色。他的嘴被雕刻得张开，伤口向上雕刻成一个微笑。鲜血染红了他腐烂的皮肤和衬衫。他戴着经典的牛仔帽和皮革掸子，脖子上布满了绞索造成的深深瘀伤。蚂蚁试图尖叫，但找不到这样做的空气。当死人咧嘴笑时，你僵住了，在恐惧中一动不动。 “好吧，看看我们这里有谁。Ceder 和流浪者。” 当他抚摸蚂蚁的脸时，你盯着他看。” “你叫什么名字，男孩？” “安东尼塞德。” “我记得你。我记得你的人。我记得见过你的曾曾祖母。一个经营商店但没有狗屎的犹太小女孩。你是少数没有束缚我的家庭之一就在这个镇上。我和你没有仇。” 他转向你，把刀压在你的嘴唇上。 “亲爱的，你应该多笑笑。这会让你更漂亮。” 他用冰冷的刀刃沿着你的脸描绘出微笑的轨迹。 “别伤害我。” “啊，除非我有理由，否则我永远不会伤害一个女人。我不认识你。你是个流浪者，是吗？你原本不是来自 Dawnwoods，” “我来自纽约。” “见鬼，如果我不是一个像我一样好的人，可能会因此而让你失望。” “你确定你没事？” 贝卡大喊大叫，死人将刀压在你的喉咙上。 “她叫什么名字？” 他嘶嘶作响。 “贝卡，”你呜咽道。 “我他妈的知道那个声音！我他妈的知道那个声音！我他妈的……贝卡蒙哥马利。霍顿 J. 蒙哥马利的伟大伟大但他妈的许多伟大的孙女。那个婊子……” 死人显然被他复仇的咆哮分散了注意力。如果有时间做任何事情，那就是现在。
梗概：
你告诉梅森你很好
--------------------
动作：
做
你耐心等待
结果：
“……那个该死的婊子，我……给她打电话。” “什么？” 你问。 “我说给她打电话，你个婊子！叫那个蒙哥马婊子进来！”
梗概：
你耐心等待
--------------------
动作：
做
你告诉贝卡进入谷仓
结果：
“贝卡！你能进来吗？我们需要一只手。蚂蚁的腿被卡住了！” “好，我们进去！” 梅森喊道。 操，你不是故意让他们都来的。 “干得好，亲爱的。对不起，我不能让你们中的任何一个孩子出去。上帝，今晚我要打包蒙哥马利！” 当他的刀刃切开你的喉咙时，你尖叫着，冰冷的钢铁与温热的血液混合在一起。
梗概：
你告诉贝卡进入谷仓
故事名称：
31
故事背景：
“我很好！有只老鼠！” 你喊。 “好的！” 梅森回答。 Ant 继续呜咽，一只戴着手套的手穿过你的头发。 “好女孩……我在黑暗中认不出你。让我们看看你是谁……顺便说一下，不要尖叫。” 您会听到打火机的轻弹声，然后火焰在您面前燃烧起来，照亮了你们三个人。然后，你第一眼看到了死人。他的皮肤灰白而死气沉沉，他的眼睛冰冷，毫无生气，纯黑色。他的嘴被雕刻得张开，伤口向上雕刻成一个微笑。鲜血染红了他腐烂的皮肤和衬衫。他戴着经典的牛仔帽和皮革掸子，脖子上布满了绞索造成的深深瘀伤。蚂蚁试图尖叫，但找不到这样做的空气。当死人咧嘴笑时，你僵住了，在恐惧中一动不动。 “好吧，看看我们这里有谁。Ceder 和流浪者。” 当他抚摸蚂蚁的脸时，你盯着他看。” “你叫什么名字，男孩？” “安东尼塞德。” “我记得你。我记得你的人。我记得见过你的曾曾祖母。一个经营商店但没有狗屎的犹太小女孩。你是少数没有束缚我的家庭之一就在这个镇上。我和你没有仇。” 他转向你，把刀压在你的嘴唇上。 “亲爱的，你应该多笑笑。这会让你更漂亮。” 他用冰冷的刀刃沿着你的脸描绘出微笑的轨迹。 “别伤害我。” “啊，除非我有理由，否则我永远不会伤害一个女人。我不认识你。你是个流浪者，是吗？你原本不是来自 Dawnwoods，” “我来自纽约。” “见鬼，如果我不是一个像我一样好的人，可能会因此而让你失望。” “你确定你没事？” 贝卡大喊大叫，死人将刀压在你的喉咙上。 “她叫什么名字？” 他嘶嘶作响。 “贝卡，”你呜咽道。 “我他妈的知道那个声音！我他妈的知道那个声音！我他妈的……贝卡蒙哥马利。霍顿 J. 蒙哥马利的伟大伟大但他妈的许多伟大的孙女。那个婊子……” 死人显然被他复仇的咆哮分散了注意力。如果有时间做任何事情，那就是现在。
--------------------
动作：
做
你肘击他然后跑
结果：
你用肘部猛击他的脸，他咕哝着把你放下。 “该死的婊子！” 他咕哝道。 你抓住蚂蚁的手臂，把他拽下楼梯，然后转身，一边冲过去一边冲梅森大喊，让他移动胶合板。死人出现在你面前。 “嗨，亲爱的，”他轻笑，鲜血从喉咙里喷涌而出。 他开始向你走来，Ant 发出一声呜咽。死人咧嘴笑着，随着血液顺着下巴流下，他的皮肤撕裂了。 “我要……”他说，然后停了下来。 一大块胶合板被梅森拉走，发出嘎吱声。一束月光进来，打在死人的靴子上。当他的靴子开始消退时发出嘶嘶声，死人咆哮起来。一眨眼，他就走了。你不知道他去了哪里，但你不会等着找出答案。 “来吧！” 你大喊大叫，抓住蚂蚁朝板子的缝隙跑去。 你匆匆穿过，倒在外面凉爽的草地上。 “他妈的怎么了？” 梅森问道。 “死人就在那里！他就在那里，他疯了，他……” “蚂蚁，你他妈的冷静点！” 露西喊道，抓住蚂蚁摇晃他。 “你他妈的喝醉了，蚂蚁。你不知道你看到了什么，”卡特说。 “我做到了！问榛子！！” 小组转向你，你点点头。 “那里有一个鬼魂。它的喉咙被割开了……！” 你紧张地说。 “好吧，冷静点，”梅森说，把你拉了进去，紧紧抱住了你。 当你深呼吸，你的头靠在你的胸膛时，你会感觉到你的心脏在颤动。 “G-找个房间，”卡特说。 当梅森放开你时，你脸红了。 “平心而论，你也喝了不少酒。你比我更生气。也许你只是在不存在的阴影中看到了东西，”梅森建议道。 你一边摇头一边耸耸肩。 “我不知道。” “走吧，我们回去找凯尔，”贝卡说。 你穿过田野，回到树林里，朝卡车走去。 “Ss-so，这是一个糟糕的想法，”卡特说。“我们只是吓坏了 Ant 和 H-Ha-Hazel。” “我们冒着生命危险去惹恼一个本可以无害的老人，”安特说。 你到达营地，梅森大声咒骂。 “操！凯尔到底在哪儿？” 他问。 营地空空荡荡，一个人都看不到。 “rr——还记得上次我们在喝醉的时候失去了凯尔吗？” “哦，天哪，他最终在我的花园里撒尿。他没有在花园里撒尿。他站在花园里，在我的房子上撒尿，”贝卡说。 “这很有趣，”蚂蚁说，仍然从他的经历中动摇。 “我们需要找到他。难道有人还不够清醒再找到这个地方吗？” 梅森说。 一片寂静。 “很好。我们会分头去找他。蚂蚁，你去腐烂的树，露西，去我们的老树屋，贝卡，去旧的饮水点，卡特，回到镇上。黑兹尔，你是新来的。那边有一条小溪，”梅森指着说。“沿着那边走，手机一直开着。如果你找不到他，就顺着小溪回到这里。” 你点头。是时候找到凯尔了。
梗概：
你肘击他然后跑
--------------------
动作：
做
你沿着小溪走
结果：
你沿着溪流走，你的鞋子在湿泥里吱吱作响。你走到更干燥的土地上，但他们已经毁了。 “操，”你叹了口气，一边走一边走。“凯尔！凯尔！你在吗？！？” 你听到树枝在你身后折断的声音，并迅速旋转。一定是你的错觉。你继续沿着小溪走。 “凯尔！凯尔！我不管你喝了多少，做点什么！我已经厌倦了找你！” 随着风起，你会听到树叶的沙沙声，早先一个炎热潮湿的夜晚现在变得寒冷无比，因为月亮提供的光线明显变暗。第一次，你意识到你正走在一个你从未去过的地方的中间，深夜，没有人在身边。你考虑掉头回去，但你知道如果凯尔在某个地方昏倒了，你需要找到他。 “你好！” 你打电话。 你的声音穿过树林。然后，沉默重新开始。有一种死一般的寂静，令人痛苦。没有鸟类，没有在树林或灌木丛中乱窜的小型哺乳动物，甚至没有蟋蟀。你深呼吸，试图让自己冷静下来，然后发出低沉的咆哮。你转过身来，拼命地甩头寻找噪音的来源，拼命想跑却不知往哪个方向跑。 “凯尔？是你吗？” 你呜咽。 咆哮停止了，沉默又恢复了。你完全站着不动，不想打扰外面的任何东西。然后，它发出一声 怒吼。咆哮声非常响亮，你痛苦地尖叫，因为混凝土相互摩擦时发出的声音就像玻璃碎裂。你会感到纯粹、纯粹的恐惧，当你发现野兽时，这种恐惧会被放大。 野兽用四只腿站立，形状像熊，但有一辆大卡车那么大，身上覆盖着厚厚的毛皮，漆黑得几乎吞噬了透过树木的微弱月光。它的眼睛是纯红色的，鲜血吞噬一切的深红色，透过你的眼睛，渗入你的灵魂。它咆哮着，嘴里长满了数百颗匕首大小的牙齿，洁白而锋利，足以在坚固的钢铁上钻孔。它站立着，挡住了返回朋友的路线。你现在需要做点什么。
梗概：
你沿着小溪走
--------------------
动作：
做
你跑
结果：
你转身，跑得快，你的腿能带你走。当野兽追逐时，树木被撕毁并在你身后飞走。值得庆幸的是，它的大小意味着无法越过树木，因此需要将它们撕碎并扔掉才能跟随您。你有足够的线索知道你有两个选择：你可以前往城镇，从 Dawnwood 的许多武装居民那里获得帮助，他们拥有足够的联合火力来入侵一个小国，这很容易吓跑或杀死他们兽。见鬼，这不太可能，因为缺乏关于这种生物的报告，如果你逃离树木的限制，它可能不会冒险出去跟随你。另一方面，如果你前往洞穴，你可能会找到一个地方来躲避这头可怕的野兽。
梗概：
你跑
--------------------
动作：
做
你跑向城镇
结果：
你跑向城镇，发出绝望的呼救声。它冲向你，你们之间的差距在缩小。它比卡车更快地冲向你，最终设法抓住你，用它的爪子以难以置信的力量击打你。当您在空中盘旋时，您的骨头会断裂和破裂。当你的四肢以令人难以置信的角度扭曲时，你会在柔软的土地上降落，发出一声尖叫，这是它们绝对不应该走的。这个生物慢慢地向你走来，你试图爬开，但发现自己无法移动。在它吃掉猎物之前，你会看到野兽牙齿的闪光和深红色的喷射。
梗概：
你跑向城镇
故事名称：
32
故事背景：
你转身，跑得尽可能快。当野兽追逐时，树木被撕毁并在你身后飞走。值得庆幸的是，它的大小意味着无法越过树木，因此需要将它们撕碎并扔掉才能跟随您。你有足够的线索知道你有两个选择：你可以前往城镇，从 Dawnwood 的许多武装居民那里获得帮助，他们拥有足够的联合火力来入侵一个小国，这很容易吓跑或杀死他们兽。见鬼，不太可能需要这样做，因为缺乏关于这种生物的报告，如果你逃离树木的限制，它可能不会冒险出去跟随你。另一方面，如果你前往洞穴，你可能会找到一个地方来躲避这头可怕的野兽。
--------------------
动作：
做
你走向洞穴
结果：
你改变方向，冲向这些树林里的几个洞穴。你可以看到野兽就在你身后，感受它在你颈后的灼热呼吸，吹散你的头发。你发现了两个你认为的洞穴......好吧，希望你能在它抓住你之前到达。一个是你认为你可以潜入的石头上的小裂缝，它太小了，野兽无法跟随你进入更远的隧道。另一个是通往一个大洞穴的开口，它也太小了，野兽无法跟随跟着你，这会进一步扩大。
梗概：
你走向洞穴
--------------------
动作：
做
你穿过小隧道
结果：
你穿过小裂缝，快速地沿着隧道前进。当你出现在完全黑暗中时，你停止了行走，你的深呼吸在洞穴墙壁上回荡，发出令人毛骨悚然的声音。你拿出手机，打开手电筒，照亮这个区域。然后，你发出了石化的尖叫。墙壁上布满了你不认识的红色、尖锐和陌生字符的生动符号。但这不是让你尖叫的原因。三个被束缚的人影倒在地上，他们的身体现在变成了可怕的残酷景象。他们赤身裸体，但他们的皮肤已经被撕掉，让你看到下面闪闪发光的湿漉漉的肌肉。当他们开始扭动时，你抽泣了起来。他们没有死，你惊恐地意识到。人影在扭动，当你惊恐地后退时，你开始抽泣。你尖叫着跑向隧道口。万一被野兽看到，你不敢冒险，所以你坐在开口处，抽泣着。你开始闻到一种从隧道更深处传来的奇怪气味，这与腐烂的肉不同。很快，你那可怜、哭泣和破碎的身体就会入睡。
梗概：
你穿过小隧道
--------------------
动作：
做
你醒了
结果：
“我们找到了她！” 一个声音在哭泣，将你从沉睡中唤醒。 你睁开眼睛，看到一个年轻人，你模糊地认出他是城镇机械师。 “没关系，”他说。“你会没事的。” 随着更多城镇居民的到来，扭动尸体的图像充斥着您的脑海。 “天啊，她到底怎么了？” 有人问。 “天啊，她没事吧？” “她需要医生吗？” “Hazel！我的宝贝女儿呢！？！” 你听到爸爸的声音，在越来越多的人群中发现他。 “爸爸！” 当他倾身拥抱他时，你哭了。 “哦，Hazel，谢天谢地，我找到了你。你到底在想什么，就这样偷偷溜出去？没关系，我找到了你。你很安全。发生了什么事？” 你抽泣着，试图向爸爸讲述你的故事，向他讲述你的困惑。 “你的朋友说你一直在寻找凯尔。他们找到了他，但后来他们找不到你了。你……你闻到了酒味。你在想什么，黑兹尔？你一定是喝醉了，看到了一只熊。我会告诉警长，派狩猎队去杀了它。” “这不是熊，爸爸！还有尸体！他们在山洞里！” “亲爱的，你喝醉了！” 警长出现，从人群中走出来。他在爸爸身边停下，捏了捏他的肩膀。 “我很高兴你找到了你的女朋友，马尔科姆。我们稍后再谈，”他说，然后停下来。“她有酒味。我一直相信父亲会管教孩子，所以我会把它留给你，马尔科姆。” “来吧，亲爱的！” 爸爸说。“我送你回家。” “不等了，检查洞穴！里面有尸体！” 爸爸叹了口气，警长走了进来。一分钟后，他出现了，耸了耸肩。 “里面什么都没有。有一些兔子尸体看起来被感染了，但仅此而已，”他说。 “什么…？” 你困惑地问。“我要下去看看……！” “冷静点，亲爱的。那些动物尸体会让你感染。你可能在黑暗中看错了。稍后我会派人清理它们。来吧，大家，回家吧。” 警长站在隧道入口处，示意人们离开，搜救队向镇上进发，爸爸感谢他们。 “来吧，Hazel，我们回家吧，”爸爸说。 你跟着爸爸出去，他带你回家。
梗概：
你醒了
--------------------
动作：
做
你回家
结果：
回家的路漫长而安静。爸爸可以明确告诉你不想说话，所以他让你一个人呆着。最终，您驶入车道。 “我跟你老师说了，你今天不来了，上楼去洗个澡，好好休息一天，我今天就留在家里照顾你。” 你点点头，走进去。你脱下沾满泥巴的鞋子，走上楼，身后留下一滩泥水。你走进浴室，慢慢地脱掉衣服。您解开纽扣并脱下牛仔裤，然后走出水下。你脱下你的套头衫，而不是你的衬衫。当你解开胸罩时，当你注意到镜子里的自己时，你会僵住。你看到的奇怪的外星符号之一覆盖在令人作呕、颤抖、去皮的尸体上方的洞穴墙壁上，刻在你的肚子里，就在你的肚脐下方。你尖叫着，你听到爸爸叫你。 “榛子？你还好吗？”
梗概：
你回家
--------------------
动作：
说
“我很好！”
结果：
“好的亲爱的。” 你惊恐地盯着那个符号，然后深呼吸。你打开浴室柜，拿出一些绷带，敷在伤口上。伤口又浅又小，医学上没什么好担心的，虽然它让你害怕。在你睡觉的时候，谁对尸体做了那件事。看到这里你不寒而栗。您决定淋浴以感觉更干净一些。
梗概：
你说“我很好！”
--------------------
动作：
做
你淋浴
结果：
你走进淋浴间，在不烫伤自己的情况下，将温度设置得尽可能高。你踏入水下，感觉你的头发随着水的浸透而变得越来越重。水可以温暖您并帮助清除您身体上的污垢和泥土层。令您十分尴尬的是，您注意到水在流下水道之前在您下方积聚成淡黄色，并且您在昨晚的恐惧中意识到您一定是在某个时候弄湿了自己。你脸红了，因为尴尬和滚烫的水，完成了自己的清洁。你关掉淋浴，拿一条毛巾，把脏衣服扔进洗衣篮。你擦干身体，穿好衣服，然后倒在床上。你拿起你的笔记本电脑并打开它，在 Netflix 提供的无尽可怕的喜剧中找到安慰。爸爸给你带了早餐，然后是午餐，让你在最新一集的一些随机犯罪节目面前裹在毯子里，这个节目讲述了一个警察部门在现实生活中几乎没有做太多事情，从刻板印象中找到安慰，并大规模地用来杀人的人为谋杀方法。最终，你听到敲门声。 “榛！” 爸爸喊道。“你的朋友来了。” 你从床上扭动起来，跳起来，走下楼梯。站在那里的是梅森、卡特、露西、蚂蚁、贝卡和凯尔。 “嘿，Hazel。你感觉还好吗？” 露西问道。 “是的，”你回答。 “不好意思，刚才喝醉了，跑出去找个地方上厕所，没想到你会因此出什么事！” 凯尔抱歉地说。 “没关系，”你说。 “嗯，我们只是顺便过来看看你是否还好，以及你是否想去镇上吃点东西。” 爸爸盯着你看。 “你还记得上次出去发生了什么吗？就在昨晚，记得吗？” “我们会在日落前让她回来。我们只是进城，我会好好照顾她，”梅森说。 “我相信你会的，”爸爸怀疑地说，看着梅森。“Hazel，保持手机开机，八点前回来。不是八点半，不是八点一刻，不是八点半。明白了吗？” “是的，爸爸。” 你和你的朋友走出门去镇上。
梗概：
你淋浴
--------------------
动作：
做
一小时后...
结果：
一个小时后，你坐在露西和贝卡工作的披萨店里，另外两个不幸的青少年在柜台后面工作，一群人中间有一个大意大利辣香肠披萨。 “所以……你被怪物袭击了……是谁把你带进了不死死人三人组？” 蚂蚁问道。“无意冒犯，但这听起来完全是胡说八道。” 你不可置信地盯着蚂蚁。 “你不记得不到一个小时之前发生了什么吗？你差点把自己气死！” 蚂蚁低头，脸色铁青。 “没有鬼。你只是在和我们玩，”露西抱怨道。 “好，我相信你。” “我相信她，”凯尔说。 “你 - 没有选择抄送，凯尔。你 - 差点让她被杀，”卡特说。 凯尔耸了耸肩。 “好吧，说我们相信你，”贝卡说。“我们该怎么办？” “你是什么意思？” “嗯，Dawnwoods 有一个怪物……这是个问题，而且这个怪物太大了，不能进入你所在的洞穴，否则它会抓住你，所以这意味着身体剥皮的东西是分开的，所以我们得到了一个怪物和至少一个撒旦教徒。” “你怎么知道这是撒旦教？” 蚂蚁问道。 “嗯，这不是基督徒，因此，一定是撒旦教徒，”贝卡说。 “为什么c-不能是任何其他rr-宗教？” “卡特，这是有史以来最白人、最基督教的城镇。有一个锡克教家庭，他们在草坪上烧了一个十字架，在 9/11 之后被迫离开城镇。这里没有其他宗教。” “好的。” “那么，我们该怎么办……？” 你呻吟着，一阵剧痛袭上你的胃。它加剧了，你发出了一声尖叫。 “你还好吗，黑兹尔？” 梅森问道。 “是的，我是……”你呻吟着，露西扶你站起来。 “我想我会生病，”你呻吟道。 “来吧，我带你去，”露西说着把你拖进了浴室。 你被拉过小便池，进入一个隔间，跪倒在地，吐出一股呕吐的洪流。当你开始呕吐时，你痛苦地呻吟，填满碗。 “感谢上帝，我现在没有轮班。我不想再次清理它，”露西说。 在疼痛开始消退之前，你又吐了一次。 “别担心，Hazel，我们很快就会送你回家，”Lucy 说。 “喂，露西？” “是的？” “你把我拖进了那个男人的浴室？” “哦，糟糕。抱歉，Hazel。我清洁了两个厕所，所以我习惯进入它们，而这个更近......” 你叹了口气，站起来，准备回家。
梗概：
一小时后...
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动作：
做
你回家
结果：
你的朋友把你拖回家，你父亲在那里等着你。 “Hazel！你还好吗？Carter 打电话告诉我你吐了。” “我很好，”你呻吟着，爸爸把你扶进屋里。 “上楼去，亲爱的。我给你买一个平底七件套来帮助你安顿好胃和一个热水袋。谢谢你帮助她回家，伙计们，”爸爸说。 “不用麻烦，”梅森回答。“待会见，黑兹尔。” 爸爸扶你上床，你倒在床垫上。你甚至在爸爸进厨房之前就睡着了。
梗概：
你回家
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动作：
做
六小时后...
结果：
当您在六个小时后醒来时，您会感到恐慌。你躺在一个生锈的铁笼子里，周围是一片漆黑。你慢慢地站着，发现自己在树林中间。你拼命环顾四周寻找出路，但似乎你被困在这里。风吹过树木，树叶沙沙作响，当您意识到自己穿着内衣时，让您感到寒冷。你环顾四周，发现笼子旁边有一辆大型房车。两个人影走出，开始向你走来。他们都戴着滑雪面罩、连帽衫和宽松的黑色裤子，其中一人提着一个医生的包。第三个出现，将轮床推向你。你尖叫着求救，然后回到笼子的后面。其中一人上前打开笼门，进入笼子。 “双手放在脑后，别动，”其中一个人影说道。
梗概：
六小时后...
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动作：
做
你攻击
结果：
你向前冲锋，冲向第一个人。你用力打他的下巴，他抓住了你的手臂。他狠狠地扇了你一巴掌，把你打倒在地。男人抓住你，强迫你进入轮床。他们把你的手绑在我的头和脚上，让你在轮床上伸展开来。一个球塞塞在你的嘴里并紧紧地绑着。你皱着眉头戴上手术手套，然后将温度计推入你的耳朵。他检查了一下，然后将冰凉的手放在你的肚子上。他轻轻推，然后挤压。 “我是对的。准备好注射。” 你颤抖着试图尖叫，因为其中一个人拿出一根几乎可笑的大而锋利的针。他把它戳进你的胃里，你对着塞子尖叫，然后按下，把液体释放到你的胃里。当他用更多的针重复这个过程时，你开始流泪，最后你的眼睛被泪水遮住了。他隔着滑雪面罩对你微笑，然后另一个人带着一个大粗麻袋过来了。他打开它，拿出一只像老鼠或雪貂这样的小型哺乳动物和一把大刀。你惊恐地看着他切开哺乳动物的喉咙，让血液流到你的胃里。他把尸体扔到一边，开始用手指在你的肚子上画各种图案，让你畏缩和颤抖。他叹了口气，狠狠地扇了你一巴掌。 “别发抖了，”他说，然后转向其他人。“明天晚上。” 他画完图案，然后拿出一个黑色的小笔记本。他开始用一种你不懂的疯狂语言来阅读它，就像一个疯子的口齿不清。当你感到自己被撕裂时发出石化的尖叫声时，你的胃痛大大加剧。最后，他停止了，疼痛也停止了。 “啊，很好。拿来氯仿，解开她的绳子。然后，把孩子们从散步中带回来。” 他们把你从你的皮带中解脱出来，你注意到门还开着。
梗概：
你攻击
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动作：
做
你跑到门口
结果：
你跳起来跑向门口。很明显，这些人并没有为你新获得的力量做好准备，所以你冲出笼子，开始向树木冲刺。当你听到身后的男人大声咒骂时，你冲破了树林。你在黑暗中奔跑，避开树木，然后撞到什么东西。你倒在地上，膝盖被树根刮伤。你抬起头，看到他们时惊恐地尖叫起来。六个人影慢慢地围绕着你，让你看到他们。这些……生物都是孩子，但他们如此畸形，如此变异，几乎没有资格。一只失去了它的双臂，取而代之的是细长的、粘糊糊的触手，伸手触摸你。第二个没有了它的大部分脸，在它的地方有一个大洞，里面充满了来自墙壁、背部、顶部和嘴底的微小的、摆动的触手。第三个看起来像一个四岁左右的可爱，完全正常的女孩，除了她的嘴巴张开，她的眼睛完全是黑色的。另一个，最糟糕的一个，看起来像是一个女学生、一个胎儿和一只章鱼的混合体，当它向你伸出手时，它在你上方摆动。你尖叫着向后爬，吓坏了。它们缓慢前进，伸出触手来摩擦你，光滑的粘液擦拭你的脸和头发。当你试图站起来时，你抽泣着，然后被一个戴着面具的男人拉了起来。他拿出一块大抹布和一个玻璃瓶。他浸湿了抹布，然后把它放到你的嘴边。你试着不呼吸，但大约一分钟后，你大口大口地喘气，吸入烟雾。
梗概：
你跑到门口
--------------------
动作：
做
你失去意识
结果：
你在床上醒来，吓坏了，蜷缩在毯子里。你短暂地想知道你是否梦想过一切，但知道那是不可能的。你的膝盖在你刮伤的地方受伤了，你的窗户还开着，你知道你的大脑无法想象你昨晚看到的一切。 “亲爱的？你没事吧？” 爸爸打电话。 你停顿了一下，想知道是否应该告诉他，然后才意识到他永远不会相信你。 “是的，我很好，”你回答道，想知道你应该做什么。 今天是星期六，所以你可以拜访你的朋友并解释情况。如果你更乐观，你可以通过正常的、合法的方式，尽管你几乎可以肯定这将是完全的、彻头彻尾的胡说八道。
梗概：
你失去意识
--------------------
动作：
做
你拜访你的朋友
结果：
你冲了个澡，注意到肚脐下仍然有同样的痕迹，然后在和爸爸一起吃完早餐后去见你的朋友，爸爸说“你看起来像没睡”。他给了你一些东西来帮助你安顿下来，然后你出去给你的朋友发短信。他们已经在 Ant 的房子里，所以你去那里，小心地按照他们的指示走。最终，您会发现自己置身于一所甚至不值得描述的房子里，因为它与这个肮脏城镇中的其他房子完全相同。你敲了敲门，开门的是一个身材矮小、秃顶、穿着廉价西装的男人。 “你好？” 他问，凝视着你。 “嗨，我是Hazel，我是Ant 的朋友。” “嗯……你当然是。我承认，叫他安东尼是我的错误。我希望我有更好的选择。是他的母亲，上帝保佑她，她想给他起一个好听的名字，比如康纳，但我想要安东尼。啊，我在胡说八道。他在游戏室，最后一个离开。 你点点头并感谢他，朝最后一个左边走去，走进去，找到了一张台球桌，几个豆袋和你的朋友散落在其中，还有一个 Xbox。 “嘿Hazel。感觉好多了？” 卡特问道。 “是的，我……我需要和你谈谈这件事。” 蚂蚁抬头看着你，挑了挑眉毛。 “你……发胖了吗？” 他问，隔着T恤盯着你的肚子。 “嘿蚂蚁：别这么挑剔。你不会问一个女孩那个，”露西说。 “你需要谈什么？” Mason 问道，从控制台上移开目光看着你，Ant 迅速利用了这一点，两个屏幕都达到了峰值，并开始朝着他的位置移动以轻松杀死。 你向小组解释你的情况，他们要么不相信，要么惊恐地盯着。 “没有怪物这种东西，”露西在你结束你的故事时说。 “坚持住，”梅森说。“森林里有一个老怪人表演的锈迹斑斑的笼子。不然她怎么会知道呢？” “也许是她迷路的时候看到的。” “我没有，”你说。 “好吧。那么，我们必须对此做点什么，”贝卡建议道。 “她是对的，”凯尔说。 “你-你同意只是因为你对失去她感到难过，”卡特指出。 “闭嘴，卡特！” 凯尔嘶嘶作响。 “你报警了吗？” 蚂蚁问道。 “我告诉警长，当他找到我时，他一个字都不相信。” 梅森点点头。 “警长是个白痴。我们必须检查一下现场，”他说。 你紧张地摇头。 “我不会回去。那里有那个野兽和那些撒旦教徒……” “如果你愿意，我可以让我哥哥用他的皮卡车载我们出去，”露西建议道。 “德雷克真的会带我们去树林吗？” 贝卡惊讶地问道。 “他欠我的。当他在学校和一些孩子做生意时被抓到，我替他掩护。” “经商？” 你问。 “毒品交易，”露西解释说，这是事实。 “哦。” “我会给他一个戒指，”她说，消失在一个侧屋里。 你叹了口气，一想到要回到笼子里就紧张，但你知道这是必须要做的。你等待露西回来，她在几分钟后做了。 “好消息和坏消息！” 他高兴地说。 “继续。” 卡特说。 “我哥哥会开车送我们到树林里，帮我们环顾四周……但要等到八点。” “到时候天都黑了！” 你歇斯底里地说。 “没关系，你和我们在一起会很安全！我会让德雷克带上他所能带来的最大、最强大的手电筒，还有一两把枪，”露西微笑着说。 “我没有……我的意思是，我不能……” 梅森拉住你微笑。 “没关系，Hazel。我会保护你的。” 你不得不承认，在他的怀里你确实感到更安全。该死的他的魅力。 “好吧，我们会做的，”你说。 “太好了。如果你被邪教徒杀了，我会很难过，因为我们什么都没做。” 他厚颜无耻地笑了笑，你开始后悔这个决定。
梗概：
你拜访你的朋友
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动作：
做
你九小时后...
结果：
九个小时后，你准备好了。你以某种方式说服爸爸让你出去，不敢告诉他你的实际目的地，而是声称你住在露西家。你和这群人在露西家见面，然后走进客厅，他们都坐在那里。 “我们准备好了吗？” 你问。 “该死的！” 蚂蚁笑了，提着一个大背包。“备用食物、水瓶、打火机、火柴、绳索、烟花、一些刀和手电筒。你们带来了什么？” 卡特拿起他一直嚼着的半块巧克力。 “半个 M-Mars 酒吧，”他说，然后又咬了一口。“火星 b-bar 的四分之一夸脱。” “我带了一把小刀和一个打火机，”你耸了耸肩，Ant 失望地环顾四周。 “没人带东西吗？” 他噘嘴。“你们真烂。” 你听到外面的喇叭声，然后看看外面。一辆大型房车驶上车道，停在外面。露西跳过门，然后跑到他身边，你跟着她出去了。 “嘿，Hazel，”德雷克说。 “德雷克的朋友埃德有车，所以我们开车去树林，会见他并换车，”露西告诉你。 “好的，”你说，走进房车。当您注意到房车的窗户上覆盖着百叶窗和垃圾袋时，您扬起了眉毛，而实际的内部看起来就像是《绝命毒师》中的场景，上面布满了烧杯、试管、本生灯和其他化学设备。 “嗯，你很公然，”你说。 “嘿嘿，有效果。” “我的意思是，是的，但《绝命毒师》把这变成了一个比喻。警察不要为此搜查房车。” “《绝命毒师》对我的销售有很大帮助，以至于我贿赂了副手。当你添加一些蓝色染料时，你会惊讶地发现有多少额外的冰毒头愿意为产品支付费用。” 你耸了耸肩，坐在房车里，你的朋友们都进来了。 “在那里小心点。把一件事打翻，这个地方可能会爆炸。” “真的？” 你问。 德雷克咧嘴一笑，耸了耸肩，然后开始开车。 “也许吧。我的意思是，后面还有一个酿酒厂和实验室。我想有一个减速带和繁荣！” “这令人不安，”Ant 说。 “我想。那么，我到底为什么要把你所有的朋友都拖到树林中央到那个旧的怪胎笼子里？” “我已经告诉过你了，”露西透露。 “那头噩梦公牛没有说服我。真正的原因是什么？” “这才是真正的原因。” 德雷克转过身，恼怒地盯着你。 “什么...？” “眼睛在路上，德雷克，”凯尔说。 “好吧，”德雷克说，转身。“不过，我想要一个解释，为什么我要开车到树林里，因为新来的女孩做了个噩梦。” “这不是噩梦！” 你说。 “当然，”德雷克说。 你们都坐在尴尬的沉默中，直到到达树林的郊区。德雷克靠边停车，你站起来走出门。尽管您因 RV 的百叶窗和漆黑的窗户而无法分辨，但您现在可以看到夜幕已经降临。一辆空的有四个座位的红色皮卡车坐在树林旁边。德雷克大步向前，掏出手机。 “啊，该死，”德雷克说。“当然，Ed 把卡车停在这里，然后开着他的四轮摩托车，而不是像他应该的那样拿回房车。” 他锁上房车，把钥匙藏在方向盘上。 “我会给我的朋友发短信，让他去取房车。来吧，进去。我们开车去树林里的一个笨蛋笼子。” Drake、Lucy、Carter 和 Ant 坐在座位上，而你、Mason、Becca 和 Kyle 坐在皮卡的后座。你坐在，半躺在梅森身上，他用一只胳膊搂着你。大脑的情感部分会暂时忘记所有恐惧，转而对梅森的幼稚迷恋和荷尔蒙欲望使您脸红心跳，大脑的理性部分提醒您保持警惕，以防万一邪教徒或野兽出现。你和你的其他朋友拿出手电筒，将光束射入黑暗中，照亮树木和灌木丛。 “我们快到了，”德雷克喊道。“因为已经开辟了一条道路，所以到达这里要容易得多。” 你的耳朵为此振作起来。 “你是什么意思？” 你问，德雷克完全滚下他的窗户。 “你说什么？” “我说你什么意思？你说有一条路。” “是的。这就像一辆大型房车驶过灌木丛，撞到了一条土路。” “什么，你认为埃德一直开车到这里开房车工作？” 露西问道。 “不，艾德害怕树林。说了一些关于怪物或什么的。” 当你想起昨晚的房车时，你会感到一阵恐惧。你向外凝视，照亮每一个阴影，既希望又害怕看到隐藏在黑暗中的东西。 “来了！老鬼戏的笼子！” 德雷克戏剧性地说。 当 Drake 将皮卡车拉入树林中的空地时，您会看到巨大的、生锈的笼子，尽管没有房车。你吓得瑟瑟发抖，德雷克走出卡车。 “让我们调查一下然后回家，”德雷克说着，用手电筒向笼子发射光束。 手电筒光束照亮笼子及其内部。生锈的旧轮床坐在那里，现在已经沾满了干涸的血迹。 “嗯，这是新的，”德雷克说。 你紧张地退后一步。 “Hazel 说她被绑架时有一个轮床……当她被绑架时，”Ant 指出。 “操，这真是令人毛骨悚然的狗屎。我要给警长打电话，”德雷克说。 德雷克拿出手机，拨通了电话。 “你好，巴克利警长？我刚刚在树林里的笼子中间发现了一张轮床，上面覆盖着干涸的血迹。我觉得有些奇怪……哦，真的吗？好吧，你知道笼子从哪里来的……哦，太好了……太酷了，几分钟后见，或者一个小时后，或者什么的。” “那是什么？” “警长说他马上就来。” “好。”你松了口气。 如果没有法律的帮助，你真的无法想象必须处理这个问题。现在，它不在你的掌握之中了。你耐心地等待巴克利警长的到来。最终，您发现手电筒正朝您走来。 “他在那儿，”德雷克说。 你照亮了前方，照亮了巴克利警长的身影。他发誓，双手捂住眼睛。 “关掉它！” 他说，你做。 “操，你怎么把卡车开到这里来的？我几乎不能穿过灌木丛，”他说。“这该死的轮床在哪里？” 你指着笼子，巴克利警长走过来看着它。 “耶稣……还有谁看到了这一点？我需要召集所有目击者并找出发生了什么，”他说。 “我们是唯一见过这个的人，尽管 Hazel 有一些更近距离的接触。” “所以你没有告诉你爸这件事？” 巴克利警长问道。 你摇头，巴克利警长点点头。 “很好，”巴克利警长说。 巴克利警长拔出左轮手枪，迅速对准德雷克的头部开火。德雷克的头部在部分爆炸时弹回，向你发送大块灰色的脑物质、苍白的头骨和红色的肉块。当 Lucy 跑向 Drake 时，他的身体向后倒塌，你尖叫起来。在警长发出命令之前，梅森跑向卡车。 “住手！再动一块肌肉，我就把你们每个人的脑袋都炸了！” 梅森停下，举起双手。巴克利警长用枪指着你们所有人，迫使你们举起双手。 “大家，冷静下来。没有人需要受伤，”巴克利警长说。 “去你的！” 露西尖叫起来。“你杀了他！你杀了他你这个……” 巴克利警长上前，用左轮手枪猛击她的头部。 “闭嘴！卡特、梅森、蚂蚁、凯尔，进他妈的笼子里去！” 他喊。 梅森、蚂蚁、凯尔和卡特慢慢走进笼子。 “现在，他妈的回到笼子的尽头！” 男孩们服从了，警长从口袋里掏出一套钥匙，锁上了笼门。他转向你，露西和贝卡。 “别动。他们一会儿就到。” 你静静地站着，冷风吹过，只有露西站在德雷克身边时偶尔轻轻的抽泣打破了沉默。经过大约半小时的等待，您看到房车沿着小路停了下来。话音刚落，四个蒙面人走了出来。 “有人逃吗？” 一个问。 “不，”巴克利警长回答。“操，我什至不知道他们会来这里。你说你给那个女孩注射什么他妈的会让她忘记一切。” “我说可能会。她的手机坏了。你怎么没发现？” “她没有打电话说她要这么做！” “好吧，至少没有人逃过一劫，”蒙面人说着摘下了他的面具。 一行人摘下口罩。你依稀认出了他们，一个是找到你的机械师，一个是镇上的医生凯塞尔医生，另一个是学校的德语老师，最后一个是当地酒吧的调酒师。 “你要……做什么？” 贝卡问道，在巴克利警长大叫之前。 “喂！他妈的闭嘴……” “不，杰克，让女孩问她的问题，”凯塞尔医生说。 “你要拿我们怎么办？你是谁？为什么派怪物来猎杀我？” “我们拿你怎么办？最好什么都不用。我们只需要借你。我们是谁？好吧，你肯定认得我们。路易斯，巴克利警长，乔丹夫人，艾莉森和我，凯塞尔医生。”对于怪物，我假设你指的是黑兽。这不是问题，我向你保证。虽然我不确定它的起源，但如果你查看镇上的记录，它实际上杀死了该地区的大多数熊，从而减少了攻击数量，从而挽救了生命。如果我是你的情况，我会更担心我们。” “你想要什么？你要把我们献给撒旦吗？” 贝卡又问。 Kessel 医生发出一声轻笑。 “不，丽贝卡，我们不是。我们不是撒旦教徒。真正的撒旦教徒甚至不相信撒旦。我们是……研究人员。我们是人道主义者。我们是探险家。当我们还是小孩子的时候，我们曾经是最好的朋友。我们在这片树林里玩耍。当我们小的时候，我们在一个山洞里发现了……一本书。我们拿着它读了它，我们发现了最神奇的东西。你知道吗，不仅我们是微不足道的，这个星球、这个太阳系、这个星系和这个宇宙是多么的微不足道？关于这些生物，我们学到了很多我们甚至无法理解的惊人的东西，但与外面的东西相比，我们学到的很少。所以我们继续阅读。这本书包含了一些召唤它们的说明，召唤这些生物比我们想象的任何神都强大，比我们在小说中创造的任何外星人都更加陌生，更加不可思议比你在这里能体验到的任何东西都重要。如果我们创造了一个钥匙持有者，我们就可以创造一个通往这些生物的门户。” “太疯狂了！你怎么知道他们不会消灭我们，或者奴役我们所有人，或者更糟！” 你哭。 “我没有，”凯塞尔医生承认。 “它可以摧毁一切！” “你只需要冷静下来倾听！” 他嘶嘶作响。“想象一下你被困在一个房间里。有一个小水龙头，一个马桶和一个小机器，可以制作出一种营养而无味的糊状物。你拥有生存所需的一切，继续你空白、无聊的生活。现在，一个一天，房间里出现了一扇门。门没有锁。你穿过吗？” “他妈的……干什么？” “我们正试图打开那扇门。就像房间里的人不知道另一边是什么一样，任何东西都比房间平淡乏味的存在更好。我们正试图打开世界，让人们体验人类不知道好与坏，因为它必须比这种毫无意义的存在更好。” “你到底在说什么？钥匙持有者是什么鬼？” 梅森喊道，巴克利警长再次用枪指着他的头。 “闭嘴，”巴克利警长咕哝道。 “啊，是的，钥匙持有者。那部分对你们来说是最糟糕的。钥匙持有者是一种从人类子宫中的生命力量诞生的生物。我们已经尝试过几次，但每次都失败了。我们没有创造钥匙持有者，而是创造了快速老化的半人半怪物的孩子。最后，我们解决了问题。钥匙持有者的母亲必须是处女，青春期后，21岁以下，并且有 AB 阴性血。你可以想象我的运气，当我发现一个这样的目标在我牺牲一些前母亲的洞穴周围走动时，我正在牺牲一些前任母亲以保持生物对我们世界的兴趣。我只是抽出一些我手头的气体来帮助把她打倒，瞧！我得工作了。” 你惊恐地看着他，把手放在你的肚子上。在警长巴克利向空中开枪之前，一群人发出了一系列的喘息声和叫喊声。 “关掉它！” 他吼道。 “对不起，Hazel。几个小时后就会结束。” “几个小时？才两天，怎么……？” 你惊恐地问。 “持钥者长得快得不得了，顶多一天就能出世。” 你感到胃里和子宫里有什么东西在摆动，一阵疼痛传遍全身。 “听着，Hazel，不会有事的，”Kessel 医生说。“几个小时后，你会好起来的，我们将进入一个全新的世界。”
梗概：
你九小时后...
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动作：
做
您同意出生钥匙持有人
结果：
“好吧，”你说。“我会做的。” “极好的！” 凯塞尔医生急切地说道。“我保证，你是在帮助拯救人类。” 凯塞尔医生牵着你的手，带你走向房车，那里有第二辆更新的轮床在等着你。你爬上它并被绑起来。凯塞尔医生用一根针刺入你的脖子，让你安静下来。 “别担心，接下来几个小时我会帮你睡觉的，等你醒来的时候，一切就都过去了……” 在闭上嘴之前，您开始感到困倦。当针头压入您的血管时，您沉重的眼睑闭合，让您进入深度睡眠。
梗概：
您同意出生钥匙持有人
--------------------
动作：
做
你醒了
结果：
天启之母 你醒来，躺在一张小床上。巴克利警长站在你的上方，看起来异常的喜悦。 “你醒了！好，我们可以开始了！” 他说。 你站着，往下看。你的腿上沾满了干涸的血液和其他液体，你的身体立即耗尽了力气。你得到了帮助，离开了房车。你的朋友都走了，但你只看了一秒钟，就看到了门户网站的准备工作。一大圈石头躺着，每个石头上都沾着一滴血。站在圆圈周围的是六名邪教徒；路易斯、凯塞尔医生、乔丹夫人和艾莉森。站在他们周围的是六个让你想呕吐的怪物孩子，这是抚养关键持有者的失败尝试。他们是如此畸形，如此变异，以至于他们几乎没有资格成为孩子。一只失去了它的双臂，取而代之的是长而细，像意大利面一样粘糊糊的触手。第二个没有了它的大部分脸，在它的地方有一个大洞，里面充满了来自墙壁、背部、顶部和嘴底的微小的、摆动的触手。第三个看起来像一个四岁左右的可爱，完全正常的女孩，除了她的嘴巴张开，她的眼睛完全是黑色的。另一个，最糟糕的一个，看起来像是一个女学生、一个胎儿和一只章鱼的混合体，站着摆动。尽管如此，与位于圆圈中间的东西相比，这算不了什么。坐在中间，扭动着，是你的孩子。Key-Bearer 只不过是一团滚烫的触手，四处挥动，似乎在刻在你身上的符号中在自己周围画了一条紫色的闪电线。 “啊，你来了！” 凯塞尔医生急切地说道。 “我的朋友在哪里？” 你问。 “现在不要紧！现在，我们开始！我们将见证克雷西西利亚塔尼亚创造的辉煌！” Key-Bearer 加速，在现实的结构上绘制符号。地面开始崩塌，变成纯黑色。突然，一道闪光，传送门打开了。 然后，它开始。
梗概：
你醒了
故事名称：
33
故事背景：
九个小时后，你准备好了。你以某种方式说服爸爸让你出去，不敢告诉他你的实际目的地，而是声称你住在露西家。你和这群人在露西家见面，然后走进客厅，他们都坐在那里。 “我们准备好了吗？” 你问。 “该死的！” 蚂蚁笑了，提着一个大背包。“备用食物、水瓶、打火机、火柴、绳索、烟花、一些刀和手电筒。你们带来了什么？” 卡特拿起他一直嚼着的半块巧克力。 “半个 M-Mars 酒吧，”他说，然后又咬了一口。“火星 b-bar 的四分之一夸脱。” “我带了一把小刀和一个打火机，”你耸了耸肩，Ant 失望地环顾四周。 “没人带东西吗？” 他噘嘴。“你们真烂。” 你听到外面的喇叭声，然后看看外面。一辆大型房车驶上车道，停在外面。露西跳过门，然后跑到他身边，你跟着她出去了。 “嘿，Hazel，”德雷克说。 “德雷克的朋友埃德有车，所以我们开车去树林，会见他并换车，”露西告诉你。 “好的，”你说，走进房车。当您注意到房车的窗户上覆盖着百叶窗和垃圾袋时，您扬起了眉毛，而实际的内部看起来就像是《绝命毒师》中的场景，上面布满了烧杯、试管、本生灯和其他化学设备。 “嗯，你很公然，”你说。 “嘿嘿，有效果。” “我的意思是，是的，但《绝命毒师》把这变成了一个比喻。警察不要为此搜查房车。” “《绝命毒师》对我的销售有很大帮助，以至于我贿赂了副手。当你添加一些蓝色染料时，你会惊讶地发现有多少额外的冰毒头愿意为产品支付费用。” 你耸了耸肩，坐在房车里，你的朋友们都进来了。 “在那里小心点。把一件事打翻，这个地方可能会爆炸。” “真的？” 你问。 德雷克咧嘴一笑，耸了耸肩，然后开始开车。 “也许吧。我的意思是，后面还有一个酿酒厂和实验室。我想有一个减速带和繁荣！” “这令人不安，”Ant 说。 “我想。那么，我到底为什么要把你所有的朋友都拖到树林中央到那个旧的怪胎笼子里？” “我已经告诉过你了，”露西透露。 “那头噩梦公牛没有说服我。真正的原因是什么？” “这才是真正的原因。” 德雷克转过身，恼怒地盯着你。 “什么...？” “眼睛在路上，德雷克，”凯尔说。 “好吧，”德雷克说，转身。“不过，我想要一个解释，为什么我要开车到树林里，因为新来的女孩做了个噩梦。” “这不是噩梦！” 你说。 “当然，”德雷克说。 你们都坐在尴尬的沉默中，直到到达树林的郊区。德雷克靠边停车，你站起来走出门。尽管您因 RV 的百叶窗和漆黑的窗户而无法分辨，但您现在可以看到夜幕已经降临。一辆空的有四个座位的红色皮卡车坐在树林旁边。德雷克大步向前，掏出手机。 “啊，该死，”德雷克说。“当然，Ed 把卡车停在这里，然后开着他的四轮摩托车，而不是像他应该的那样拿回房车。” 他锁上房车，把钥匙藏在方向盘上。 “我会给我的朋友发短信，让他去取房车。来吧，进去。我们开车去树林里的一个笨蛋笼子。” Drake、Lucy、Carter 和 Ant 坐在座位上，而你、Mason、Becca 和 Kyle 坐在皮卡的后座。你坐在，半躺在梅森身上，他用一只胳膊搂着你。大脑的情感部分会暂时忘记所有恐惧，转而对梅森的幼稚迷恋和荷尔蒙欲望使您脸红心跳，大脑的理性部分提醒您保持警惕，以防万一邪教徒或野兽出现。你和你的其他朋友拿出手电筒，将光束射入黑暗中，照亮树木和灌木丛。 “我们快到了，”德雷克喊道。“因为已经开辟了一条道路，所以到达这里要容易得多。” 你的耳朵为此振作起来。 “你是什么意思？” 你问，德雷克完全滚下他的窗户。 “你说什么？” “我说你什么意思？你说有一条路。” “是的。这就像一辆大型房车驶过灌木丛，撞到了一条土路。” “什么，你认为埃德一直开车到这里开房车工作？” 露西问道。 “不，艾德害怕树林。说了一些关于怪物或什么的。” 当你想起昨晚的房车时，你会感到一阵恐惧。你向外凝视，照亮每一个阴影，既希望又害怕看到隐藏在黑暗中的东西。 “来了！老鬼戏的笼子！” 德雷克戏剧性地说。 当 Drake 将皮卡车拉入树林中的空地时，您会看到巨大的、生锈的笼子，尽管没有房车。你吓得瑟瑟发抖，德雷克走出卡车。 “让我们调查一下然后回家，”德雷克说着，用手电筒向笼子发射光束。 手电筒光束照亮笼子及其内部。生锈的旧轮床坐在那里，现在已经沾满了干涸的血迹。 “嗯，这是新的，”德雷克说。 你紧张地退后一步。 “Hazel 说她被绑架时有一个轮床……当她被绑架时，”Ant 指出。 “操，这真是令人毛骨悚然的狗屎。我要给警长打电话，”德雷克说。 德雷克拿出手机，拨通了电话。 “你好，巴克利警长？我刚刚在树林里的笼子中间发现了一张轮床，上面覆盖着干涸的血迹。我觉得有些奇怪……哦，真的吗？好吧，你知道笼子从哪里来的……哦，太好了……太酷了，几分钟后见，或者一个小时后，或者什么的。” “那是什么？” “警长说他马上就来。” “好。”你松了口气。 如果没有法律的帮助，你真的无法想象必须处理这个问题。现在，它不在你的掌握之中了。你耐心地等待巴克利警长的到来。最终，您发现手电筒正朝您走来。 “他在那儿，”德雷克说。 你照亮了前方，照亮了巴克利警长的身影。他发誓，双手捂住眼睛。 “关掉它！” 他说，你做。 “操，你怎么把卡车开到这里来的？我几乎不能穿过灌木丛，”他说。“这该死的轮床在哪里？” 你指着笼子，巴克利警长走过来看着它。 “耶稣……还有谁看到了这一点？我需要召集所有目击者并找出发生了什么，”他说。 “我们是唯一见过这个的人，尽管 Hazel 有一些更近距离的接触。” “所以你没有告诉你爸这件事？” 巴克利警长问道。 你摇头，巴克利警长点点头。 “很好，”巴克利警长说。 巴克利警长拔出左轮手枪，迅速对准德雷克的头部开火。德雷克的头部在部分爆炸时弹回，向你发送大块灰色的脑物质、苍白的头骨和红色的肉块。当 Lucy 跑向 Drake 时，他的身体向后倒塌，你尖叫起来。在警长发出命令之前，梅森跑向卡车。 “住手！再动一块肌肉，我就把你们每个人的脑袋都炸了！” 梅森停下，举起双手。巴克利警长用枪指着你们所有人，迫使你们举起双手。 “大家，冷静下来。没有人需要受伤，”巴克利警长说。 “去你的！” 露西尖叫起来。“你杀了他！你杀了他你这个……” 巴克利警长上前，用左轮手枪猛击她的头部。 “闭嘴！卡特、梅森、蚂蚁、凯尔，进他妈的笼子里去！” 他喊。 梅森、蚂蚁、凯尔和卡特慢慢走进笼子。 “现在，他妈的回到笼子的尽头！” 男孩们服从了，警长从口袋里掏出一套钥匙，锁上了笼门。他转向你，露西和贝卡。 “别动。他们一会儿就到。” 你静静地站着，冷风吹过，只有露西站在德雷克身边时偶尔轻轻的抽泣打破了沉默。经过大约半小时的等待，您看到房车沿着小路停了下来。话音刚落，四个蒙面人走了出来。 “有人逃吗？” 一个问。 “不，”巴克利警长回答。“操，我什至不知道他们会来这里。你说你给那个女孩注射什么他妈的会让她忘记一切。” “我说可能会。她的手机坏了。你怎么没发现？” “她没有打电话说她要这么做！” “好吧，至少没有人逃过一劫，”蒙面人说着摘下了他的面具。 一行人摘下口罩。你依稀认出了他们，一个是找到你的机械师，一个是镇上的医生凯塞尔医生，另一个是学校的德语老师，最后一个是当地酒吧的调酒师。 “你要……做什么？” 贝卡问道，在巴克利警长大叫之前。 “喂！他妈的闭嘴……” “不，杰克，让女孩问她的问题，”凯塞尔医生说。 “你要拿我们怎么办？你是谁？为什么派怪物来猎杀我？” “我们拿你怎么办？最好什么都不用。我们只需要借你。我们是谁？好吧，你肯定认得我们。路易斯，巴克利警长，乔丹夫人，艾莉森和我，凯塞尔医生。”对于怪物，我假设你指的是黑兽。这不是问题，我向你保证。虽然我不确定它的起源，但如果你查看镇上的记录，它实际上杀死了该地区的大多数熊，从而减少了攻击数量，从而挽救了生命。如果我是你的情况，我会更担心我们。” “你想要什么？你要把我们献给撒旦吗？” 贝卡又问。 Kessel 医生发出一声轻笑。 “不，丽贝卡，我们不是。我们不是撒旦教徒。真正的撒旦教徒甚至不相信撒旦。我们是……研究人员。我们是人道主义者。我们是探险家。当我们还是小孩子的时候，我们曾经是最好的朋友。我们在这片树林里玩耍。当我们小的时候，我们在一个山洞里发现了……一本书。我们拿着它读了它，我们发现了最神奇的东西。你知道吗，不仅我们是微不足道的，这个星球、这个太阳系、这个星系和这个宇宙是多么的微不足道？关于这些生物，我们学到了很多我们甚至无法理解的惊人的东西，但与外面的东西相比，我们学到的很少。所以我们继续阅读。这本书包含了一些召唤它们的说明，召唤这些生物比我们想象的任何神都强大，比我们在小说中创造的任何外星人都更加陌生，更加不可思议比你在这里能体验到的任何东西都重要。如果我们创造了一个钥匙持有者，我们就可以创造一个通往这些生物的门户。” “太疯狂了！你怎么知道他们不会消灭我们，或者奴役我们所有人，或者更糟！” 你哭。 “我没有，”凯塞尔医生承认。 “它可以摧毁一切！” “你只需要冷静下来倾听！” 他嘶嘶作响。“想象一下你被困在一个房间里。有一个小水龙头，一个马桶和一个小机器，可以制作出一种营养而无味的糊状物。你拥有生存所需的一切，继续你空白、无聊的生活。现在，一个一天，房间里出现了一扇门。门没有锁。你穿过吗？” “他妈的……干什么？” “我们正试图打开那扇门。就像房间里的人不知道另一边是什么一样，任何东西都比房间平淡乏味的存在更好。我们正试图打开世界，让人们体验人类不知道好与坏，因为它必须比这种毫无意义的存在更好。” “你到底在说什么？钥匙持有者是什么鬼？” 梅森喊道，巴克利警长再次用枪指着他的头。 “闭嘴，”巴克利警长咕哝道。 “啊，是的，钥匙持有者。那部分对你们来说是最糟糕的。钥匙持有者是一种从人类子宫中的生命力量诞生的生物。我们已经尝试过几次，但每次都失败了。我们没有创造钥匙持有者，而是创造了快速老化的半人半怪物的孩子。最后，我们解决了问题。钥匙持有者的母亲必须是处女，青春期后，21岁以下，并且有 AB 阴性血。你可以想象我的运气，当我发现一个这样的目标在我牺牲一些前母亲的洞穴周围走动时，我正在牺牲一些前任母亲以保持生物对我们世界的兴趣。我只是抽出一些我手头的气体来帮助把她打倒，瞧！我得工作了。” 你惊恐地看着他，把手放在你的肚子上。在警长巴克利向空中开枪之前，一群人发出了一系列的喘息声和叫喊声。 “关掉它！” 他吼道。 “对不起，Hazel。几个小时后就会结束。” “几个小时？才两天，怎么……？” 你惊恐地问。 “持钥者长得快得不得了，顶多一天就能出世。” 你感到胃里和子宫里有什么东西在摆动，一阵疼痛传遍全身。 “听着，Hazel，不会有事的，”Kessel 医生说。“几个小时后，你会好起来的，我们将进入一个全新的世界。”
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动作：
做
你把钥匙持有者从你身上拿走了
结果：
你拼命地在口袋里翻找，几秒钟后找到了，拔出弹簧刀。你轻轻拉起我们的套头衫和 T 恤，露出你的小腹。你可以看到它略微伸展，如果你深呼吸并保持静止，你可以非常模糊地感觉到那里有什么东西在移动，当你感觉到剧烈的疼痛时，它开始增加。 “放下刀。在你靠近我们之前，我会在你的眼睛之间放一颗子弹，”巴克利警长说。 你举起刀，把它插入你的胃里，朝着运动的中心质量。 “不！看在他妈的份上，阻止她！” 凯塞尔医生尖叫起来。 巴克利警长向你冲过来，用他的枪敲击你的侧脑。你的头向后猛地一缩，你很快就失去了知觉。
梗概：
你把钥匙持有者从你身上拿走了
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动作：
做
你醒了
结果：
你醒来时被绑在轮床上，浑身是血，穿着医院的长袍。你在房车里，灯是关着的。你什么也看不见，但你可以看到有人在呜咽。 “你好？” 你问。 呜咽声停止，你开始听到深沉的喉咙喘气声。 “你好？” 你再问。 当你的眼睛开始适应黑暗时，你在黑暗中辨认出两个人影。一个坐在角落里，另一个站在他们上方，用一圈长长的绳子摩擦他们，像蛇一样移动。 “梅森？贝卡？凯尔？卡特？” 你问，盯着数字。 站着的人转过身来，你会看到坐着的人快速地轻触电灯开关，照亮了房车。在灯光下，你注意到坐着的人是贝卡，她的眼睛因泪水而通红，看起来很害怕。你的目光扫过，看到第二道身影，缓缓向你靠近。这个人物是怪物儿童之一。它有一头长长的棕色头发，但她的脸只不过是一张布满粘液、没有嘴唇的嘴和两个鼻孔。它穿着一件破烂的、染着血迹的蓝色连衣裙，一条长长的红色触手从它的腹部伸出并缠绕在它的身体上。触手的尖端慢慢地向你移动，你发现一只黄色的眼睛从触手的末端伸出来，慢慢地向你靠近，盯着你的眼睛。在触手开始缠住你的腿，留下一条长长的粘性脓液从触手渗出之前，你尖叫着，挣扎着对抗你的束缚。当它继续向你靠近，朝着你的脸前进时，你尖叫起来。触手在你的胸部蜿蜒前行，然后缠绕在你的脖子上，然后停在离你脸几英寸的地方。 “你好……”怪物小孩沙哑的声音。 你盯着那只眼睛，看到眼睛里混合着痛苦和孩子般的好奇。然后，你听到一声喊叫。用你的眼角余光，你看到贝卡拿着一把又长又薄的刀向前冲去，刺穿了怪物孩子的脖子。这个生物嚎叫着，嘴里吐着鲜血，贝卡拔出她的刀开始砍它的脸和脖子，最后刀子穿过它的头骨。它崩溃了，你面前的触手落在你的脸上，像一条垂死的鱼一样摆动。你呜咽着，贝卡走到你身边，开始把你从束缚和缠绕在你周围的触手中解脱出来。你急切地站起来，一边挣扎着不呕吐，一边把触手从你身上扯下来。 “发生了什么？” 你问，瑟瑟发抖。 你感到胃部隐隐作痛，轻轻将它拉起来，幸好你还穿着内衣，虽然你看到你的胃包着厚厚的绷带。 “他们切开你，从你身上拉出……什么东西。它是活的，还在摆动，而且……”贝卡说，然后吓得瑟瑟发抖。“我们需要让我们的朋友离开这里。” 你听到浴室门传来砰的一声，赶紧过去听。 “你好？” “Hazel？带我离开这里！” 露西在里面尖叫。 你打开门，找到露西。 “感谢上帝。他们把我们锁在这里，把这些家伙关在笼子里。我们需要离开这里阻止他们。” 你点点头，走向厨房，找到一把大切肉刀。房车的门打开了，露西和贝卡迅速和你一起躲到柜台后面。 “Iris？你和你的新朋友玩完了吗？我希望你没有伤害他们。如果这个新生儿不是Key-Bearer，我们将需要他们。我什至没有看到k。 .. 鸢尾花？ 路易斯机械师走进来，环顾四周。 “你好？” 他向前走了几步，走进厨房向你走来。
梗概：
你醒了
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动作：
做
你攻击
结果：
你等到他靠近，深呼吸，感受肾上腺素在你的血管中流动，然后冲锋。你转过柜台，在一个范围内到达他。在你刺入你的刀刃之前，他惊恐地睁大了眼睛，直接穿过了皮肤和肉体，直接穿过了脖子。你拔出刀刃，再刺，再刺，直到他的脖子变成一块掉在地上的肉，血溅到地上的水坑里。当他死去时，你站在他身边，看着他眼中的生命消失。由于肾上腺素激增和原始本能的影响，你尽量不要把注意力集中在面前这个可怜的、垂死的人身上，而是走向门口。你把它打开到一个裂缝，向外凝视。梅森、凯尔、卡特和蚂蚁坐在生锈的笼子里，巴克利警长看着他们。乔丹夫人和艾莉森将沾满血迹的石头围成一个圆圈。Kesel 医生站着，他怀里抱着什么东西，尽管它太黑了，你看不见。
梗概：
你攻击
--------------------
动作：
做
你逃避制定计划
结果：
你慢慢地打开门，开始爬出营地。你慢慢地踮起脚尖，保持低调并被阴影包裹。当您逐渐远离营地时，您会在听到喊叫声时僵住。 “妈的，他们跑了！” 巴克利警长吼道。 你开始冲刺，离开营地。你设法到达树上，但你听到身后的枪声。你轻轻转过头，看到露西的腿后部鲜血喷涌而出，她向前倒下，沿着地面滑行停了下来。贝卡走到你旁边的树上停下来。 “妈的，我们回去吗？” 她惊恐地问道。 你回头看看。在黑暗中，您或许可以找到露西并帮助她消失在树林中。如果你们都到达林木线，巴克利警长几乎不可能跟踪你。
梗概：
你逃避制定计划
--------------------
动作：
做
你试着救她
结果：
“我们必须，”你说，下定决心。 贝卡点点头，你转身冲向露西。你看到巴克利警长朝你走来，用他的枪瞄准。你抓住露西，把她拉起来。 “来吧！” 你喊。 当你开始帮助把露西拉向树林时，巴克利警长开始拍摄。你感到一颗子弹穿过你的胸膛，你向前倒下。在最后一次闭上眼睛之前，你咳出了血。
梗概：
你试着救她
故事名称：
34
故事背景：
你慢慢地打开门，开始爬出营地。你慢慢地踮起脚尖，保持低调并被阴影包裹。当您逐渐远离营地时，您会在听到喊叫声时僵住。 “妈的，他们跑了！” 巴克利警长吼道。 你开始冲刺，离开营地。你设法到达树上，但你听到身后的枪声。你轻轻转过头，看到露西的腿后部鲜血喷涌而出，她向前倒下，沿着地面滑行停了下来。贝卡走到你旁边的树上停下来。 “妈的，我们回去吗？” 她惊恐地问道。 你回头看看。在黑暗中，您或许可以找到露西并帮助她消失在树林中。如果你们都到达林木线，巴克利警长几乎不可能跟踪你。
--------------------
动作：
做
你走进树林
结果：
“我们必须去！” 你说，贝卡点点头。 你冲进阴影和黑暗，逃到树林里。你跑了几分钟，然后停下来，弯腰喘气。 “耶稣，我们现在怎么办？”
梗概：
你走进树林
--------------------
动作：
做
你认为
结果：
好吧，你得快点行动，在邪教徒做他们的计划之前。你可以报警。尽管巴克利警长是镇上仅有的警察之一，但如果你打电话给州警，他们很有可能赶到并清理这个地方。 你也可以偷偷溜回卡车，偷走德雷克带来的一些武器，强行对抗并结束这一切。 再说一次，您知道 Drake 的 RV 钥匙的位置。您知道 RV 的爆炸性很强，因此您可以轻松破坏仪式并做更多事情。 然后，还有更……危险的解决方案。你可以用死人。那股恐怖的气息，对于邪教徒等镇上的居民，无疑是有些愤怒的。或许他可以以物易物。
梗概：
你认为
--------------------
动作：
做
你打电话给州警
结果：
“那么我们该怎么办？” 贝卡问道。 “呼叫州警。” “你确定这行得通吗？” “我不知道，”你承认。“只是……值得一试。” 你按这个号码，马上就会听到自动的声音传到你的耳朵里。你等待他们通过数字，然后你会听到喊叫声。 “举手！” 一个声音尖叫。 你转身，看到巴克利警长从树上走出来。 “天哪，艾莉森建议我跟踪你的手机，但我告诉她，在我告诉你我有跟踪它的设备后，你使用它会很疯狂。天哪。来吧，女孩们。我正在接受你回到营地。我会伤害你，伤害你，伤害你对路易斯所做的一切。”
梗概：
你打电话给州警
--------------------
动作：
做
你攻击
结果：
当肾上腺素在你体内汹涌澎湃时，你深吸一口气，然后用刀向前冲锋。一旦你靠近巴克利警长，他就会迅速后退并瞄准，然后让你大吃一惊。
梗概：
你攻击
故事名称：
35
故事背景：
“你在想什么？” “德雷克的房车。” “那怎么办？” “我们拿到钥匙，直接开到邪教徒的营地然后跳出去。” “什么？就像撞他们一样。” “不，我们让它爆炸。我很擅长化学，我看了很多犯罪节目。我应该知道要混合什么才能让它变成火球。” 贝卡紧张地盯着你，然后点点头。 “是的，好的。让我们这样做。” “你还记得卡车在哪条路上吗？” 贝卡点点头，然后你开始跟着她彻夜难眠。只有脚步声和猫头鹰偶尔发出的叫声打破了寂静。最终，你接近了树林的边缘。果然，巨大的房车坐落在森林边缘的树旁。 “你会开车吗？” 你问。 “我猜。我从来没有开过房车，而且我绝对没有开过树林，避开树木等等。” “你将不得不这样做，”你说。 贝卡从他们的藏身之处取回钥匙，然后你进入了房车。Becca 开始启动它，而您则转向后面并开始查看您可以使用的各种设备和化学品。你会从 Drake 建立的酿酒厂中找到一些精选的物质，包括大量的易燃酒精。当你努力记住你的许多科学课时，在你把 RV 变成一个陪审团操纵的炸弹之前，你需要研究几分钟的化学物质。 “嘿，Hazel，准备好了吗？” “是的。” “我们是不是应该等到我们再靠近一点，不要穿过崎岖不平的地形，然后再把它变成炸弹？” 你耸了耸肩，贝卡开始开车。尽管与其他人不同，她发现在树林中穿行要困难得多，但她还是设法让你走上正轨并保持活力。过了一会儿，你开始靠近营地。 “那么我们该怎么办？” 贝卡问道。 “我们撞他们，”你说。“你现在出去。我来打。” “你不会开车！” “我有一条直线到营地。我想只要按下加速器就行了。哪个是加速器？” 贝卡向你展示，你点点头。 “好了，出去吧。” “你确定吗？” “是的，我会加快速度并潜入水中。容易，”你回答。 在贝卡离开房车之前，你深吸一口气。她站在一旁，紧张地盯着前方。你再次深呼吸，希望氧气能让你平静下来，然后踩油门。车辆迅速开始加速，因为 RV 在您的座位上颠簸着您。您进入空地，并获得您的网站。当其他邪教徒在一个巨大的圆圈中放置石头时，您会在房车中看到滑行，怪物和畸形的孩子。Kessel 医生将一个包裹抱在怀里，震惊地抬头看着你。 “我勒个去…？” “拦住她！” 艾莉森大喊大叫，巴克利警长惊慌失措地摸索着他的枪。 你将你的房车指向他们的房车，然后迅速将一大件设备放在加速器上。当车轮开始旋转并开始向 RV 加速时。你从座位上跳起来，冲向门口。当您看到一辆房车向另一辆超速行驶时，您跳出它，撞到了泥土。凯塞尔医生惊慌失措地尖叫，冰毒实验室冲进房车。它们以难以置信的力量撞击，在空中翻转时将它们撞到两侧。几秒钟后，它们放慢了速度，并降落在一个破碎的堆中。你会听到金属内部发出可怕的咆哮、尖叫和嘶嘶声，因为里面看似没有受到伤害的怪物开始用手、爪子或触手把自己拉出来。残骸开始泄漏，形成一大滩酒精、燃料和各种其他化学品。凯塞尔医生看着它笑了。 “我以为会爆炸……”凯塞尔医生说。 他开始向你走来，你会看到他拿着什么：一团摆动的触手，在空中飞来飞去，抚摸着他的脸。长而细的触手，覆盖着一层薄薄的粘液，开始伸展，抚摸着你的脸，你尖叫起来。 “嘘……”凯塞尔博士低声说。“这是你的孩子，是不是很漂亮？” 你盯着触手的中心质量，看到中心有一只淡蓝色的、令人难以置信的人眼。和你一样的眼睛。当你听到巴克利警长的喊叫声时，你发出了惊恐的叫声。 “愚蠢的婊子！” 他尖叫。 你抬头一看，发现贝卡偷偷摸摸地试图打开生锈的笼子。凯塞尔医生抬起头来，你有时间采取行动。
--------------------
动作：
做
你从他们身边爬开
结果：
你拼命地向后爬，远离凯塞尔医生。你的手接触到燃料的水坑，你就有了一个想法。你摸索着裤子，拿出打火机。Kessel 博士向前冲锋，用脚快速跺着脚踩在你的胸口。 “别打了，姑娘！” 他咆哮道。
梗概：
你从他们身边爬开
--------------------
动作：
做
你点燃了水坑
结果：
你打开打火机，点燃水坑。当液体着火时，火焰爆发了。火焰在你身边燃烧，但凯塞尔医生惊慌失措地拼命后退，让你滚离火场。在它到达 RV 之前，火焰一直在燃烧，点燃了水坑。当火焰到达实验室和临时爆炸物被击中的内部时。然后，它爆炸了。随着雷鸣般的轰鸣声响起，一股热浪和弹片撕裂了空气。当火球以巨大的力量在黑夜中爆炸时，您的耳朵会嗡嗡作响。金属块向四面八方飞散，你倒在地上。几秒钟后，您设法抬头并调查了大屠杀。奇迹般地，你安全了，你在笼子里的所有朋友以及贝卡和巴克利警长似乎都没有受到伤害。你可以在两辆交织在一起的房车的燃烧骨架中听到怪物尸体的嘶嘶声。艾莉森和乔丹夫人离房车最近，他们被火墙和弹幕弹幕吞噬，他们被烧毁和撕裂的尸体现在躺在地上。凯塞尔医生躺着，身上躺着一把钥匙持有者的烧焦残骸，被严重烧伤。 “不，不！” 巴克利警长尖叫着，极度恐惧地盯着残骸。 贝卡举起她的刀，尖叫着刺向巴克利警长的脖子，他痛得大叫。她把它拉出来，巴克利警长倒在地上。 “你……你夺走了我的一切……”凯塞尔医生裤子。 你看着他，他的脸和他闷烧的胸膛都被烧伤。Key-Bearer 略微移动，但您很快就用几下跺脚完成了它，每次静噪时都会畏缩。 “不...！” 凯塞尔医生裤子。“你已经……注定了男人……成为虚无。” 在他的胸口停止起伏之前，你低头看着他，他的呼吸变慢了。你走到贝卡面前，贝卡正从巴克利警长的口袋里掏出一对钥匙。她打开生锈的笼子，你的朋友们就会溢出来。卡特和梅森扶着露西一瘸一拐地走出来，紧接着是热切拥抱贝卡的凯尔，最后是蚂蚁。 “天哪。你刚刚炸毁了一个邪恶的邪教组织。我印象深刻，”蚂蚁说。 “我的妈的，我不……他妈的刚刚发生了什么？” 凯尔问道。 “我不知道。我们可能应该离开，”梅森说。“谢谢，Hazel，谢谢，Becca，你在这里救了我们的驴。 “我们需要尽快离开这里，让露西帮助她的腿，”卡特说。 你点点头，开始走出营地烧焦的灰烬，就在天空开始升起的时候。
梗概：
你点燃了水坑
--------------------
动作：
做
一年后...
结果：
爆炸之后 你会惊讶于媒体如此迅速和热切地注意到这一点。一群孩子发现了什么是邪教，然后将一个冰毒实验室推入其中并将其彻底炸毁的故事。当然，这个故事变得相当拙劣。邪教据说是撒旦教徒，怪物儿童被声称只是畸形或残缺不全，任何和所有超自然的存在都被合理化了。尽管如此，它确实将 Dawnwood 变成了一段时间的媒体热点，并在一段时间内让你成为了名人。尽管如此，媒体继续前进并不需要很长时间。随着名人怀孕、中东恐怖袭击和无休止的大规模枪击事件，事情还在继续。你继续住在 Dawnwood，和你的朋友住在一起。当一群人一起面对死亡时，会产生一种特殊的牢不可破的束缚，因此你们就像一群紧密相连的朋友。时间流逝，您在镇上安顿下来，第一次为搬到镇上而感到高兴。当然，对于发生的事情，您仍然有许多悬而未决的问题，并且仍然会在胃部幻痛和那天晚上的噩梦中醒来，但在大多数情况下，您可以克服它。你期待着你在 Dawnwood 的未来。
梗概：
一年后...
故事名称：
36
故事背景：
好吧，你得快点行动，在邪教徒做他们的计划之前。你可以报警。尽管巴克利警长是镇上仅有的警察之一，但如果你打电话给州警，他们很有可能赶到并清理这个地方。 你也可以偷偷溜回卡车，偷走德雷克带来的一些武器，强行对抗并结束这一切。 再说一次，您知道 Drake 的 RV 钥匙的位置。您知道 RV 的爆炸性很强，因此您可以轻松破坏仪式并做更多事情。 然后，还有更……危险的解决方案。你可以用死人。那股恐怖的气息，对于邪教徒等镇上的居民，无疑是有些愤怒的。或许他可以以物易物。
--------------------
动作：
做
你试图与死人以物易物
结果：
“那我们的计划是什么？” “死人。” “什么？鬼魂？” “是的。我们可以用他。” “幽灵不是真的，Hazel。也许你应该坐下。” “我看到了他。” “鬼不是……” “贝卡，我们的朋友被召唤恶魔的邪教徒绑架了，你现在还不信鬼？” “我……我猜。” “是啊，我们去看看他吧。” 你转身带领贝卡走向死人谷仓。慢跑大约五分钟后，您到达了月光下明亮的空地，废弃的大谷仓矗立在那里。 “来吧！” 你说。 你到达谷仓，开始撬开胶合板。你停下来，想起死人对贝卡的仇恨，停下来。 “我一个人进去，”你说。 “你确定吗？” “是的，”你说。 贝卡耸了耸肩，把胶合板拉开，爬了进去。你立即被黑暗吞没。草的沙沙声停止了，整个谷仓充满了寂静。然后，一声哨声响起。死人开始吹出缓慢而柔和的曲调，这是唯一打破沉默的东西。每个音符都混有你上次听到的湿漉漉的声音。你看到他靠在一根大木梁上，他长长的、切开的笑容盯着你。随着阴影似乎让他着迷，口哨声停止了。他从他靠在梁上的位置消失了，你感到脖子后面有热气。 “我没想到你会来这里，亲爱的。我确信你会更聪明，”你听到他在你身后耳语。 当你感觉到金属刀片压在你的喉咙上时，一只戴着手套的手开始抚摸你的头发。 “不用了。我不介意把你砍掉一点。” “等等！我要和你做个交易！” 刀片停止并扭曲，以便刀片的平面摩擦您的脖子。 “好吧……你给自己买了一分钟。解释一下。” “我想帮你报仇。” “不，你不想要。你想要一些与我报仇的巧合。什么？” “我需要乔丹夫人、艾莉森、巴克利警长和凯塞尔医生。” “艾莉森。她姓什么。” “帕克。” “哦，是的，帕克……该死的，我想报仇。但你似乎比我更绝望。” “一世...” “我想要一些东西。蒙哥马利女孩。” “什么？” “我要你带她来。” “我没有她。她走了……” 死人微笑着，他的笑容因为伤口而再次拉长，一波血从他的脸颊上溢出。他倾身向前，一只手抚上你的脸颊和脖子。 “把那个女孩给我带来……我会帮你杀死每一个你想死的最后一个人。”
梗概：
你试图与死人以物易物
--------------------
动作：
做
您同意
结果：
你深吸一口气，点点头。 “好的。很好，”你说。 这是最好的。这是保护朋友的唯一方法。你不牺牲一个，你都会死。 “那给她打电话。” “贝卡！” 你喊。 “是的？” “进来吧！” “我虽然你说……” “去做就对了！” 你看到死人消失了，几秒钟后，贝卡爬过墙上的洞。 “你需要什么？” “我很抱歉，”你呜咽道。 “什么...？” 死人由贝卡正后方的阴影形成，靠在墙上。他举起一把老式左轮手枪，指向她的后脑勺。 “告诉你的祖先是我派你来的，”他说。 死人开枪，左轮手枪爆炸，发出雷鸣般的轰鸣声，贝卡的脸也是如此。她的脸前部爆炸，将鲜血、肉块和灰质送到地板上。当你的朋友倒在地板上时，你惊恐地尖叫，因为她的眼睛里流淌着所有的生命，她的脸色变得沉闷。 “啊……你无法想象那种感觉有多好。” 你忍住了哭声，眼泪顺着你的脸流下来。 “你必须杀死我提到的每个人。” “我他妈的该怎么做？” “什么？你说你可以！你答应过。” “我答应过我会试试的。你把那些人带进这个谷仓，我会把他们都杀了，让你活着。” “我他妈的该怎么做？” 你呼喊着，呼应了死人的抱怨。 死人咧嘴一笑，消失在背景中，再次被阴影吸收。你叹了口气，走出去。你爬出谷仓，思绪飞驰。然后，你有一个想法。他们中的任何一个都关心的一件事是 Key-Bearer。如果你能得到那个，或者它，或者不管这个生物是什么他妈的，你可以把它们引到谷仓里。你开始向营地走回。
梗概：
您同意
--------------------
动作：
做
五分钟后...
结果：
你盯着营地，看着邪教徒工作。他们都把石头堆成一个大圆圈。你会听到房车内发出可怕的呻吟声，你可以依稀看到在房车内生出钥匙持有者的失败尝试的可怕形状，那些可怕的、扭曲的形状绝望地嚎叫。尽管如此，你还是会看到你在寻找什么。Kessel 博士拿着一大捆扭曲的触手 你环顾树林的土楼，找到一根重量和长度都合适的大树枝。你拿起它，开始向 Kessel 博士爬去。你默默地向前爬，因为他看着圆圈正在形成，对其他任何事情一无所知。你深吸一口气，举起树枝。你用尽可能多的力量挥动它，它击中了他的后脑勺。它撞在他的头上，他向前倒下，钥匙持有者飞到了地板上。你发现你的朋友在笼子里，警长巴克利看着你，但知道你没有时间去救他们。您拿起 Key-Bearer 并第一次真正看到它。一团沸腾的触手，底部有一个小球体。当你注意到这个生物有一只明亮的蓝色眼睛，和你的颜色一样时，你吓得瑟瑟发抖。你抓住它，裹在凯塞尔医生一直娇惯它的毯子里，然后转身。 “操！抓住她！” 凯塞尔医生尖叫起来。 巴克利警长转身举起枪，但凯塞尔医生尖叫着阻止他。 “不，不！看在上帝的份上，不要开火！你可以击中钥匙持有者，毁了一切！” 凯塞尔医生随后尖叫起来。 当你看到艾莉森打开房车时，你开始跑步。 “孩子们！去，去追查你弟弟和那个女孩！” 你会看到房车里出现了疯狂的人物。有一对蹒跚向前的小腿，虽然腰部以上的一切都是一团沸腾的触手。有一个没有手臂的孩子，他的眼睛只是两个空洞，比纯粹的黑暗更黑。有一个小女孩，每个四肢都被一圈长长的肌肉代替，用来在地上爬行。他们都看到了你，眼里充满了痛苦和焦虑，开始跟着你。你转身开始以最快的速度向死者的谷仓跑去。你跑得尽可能快，你的脚可以带你几分钟，随着邪教徒跟在你身后，你听到身后怪物的嚎叫声越来越大。 “别跑了！我们不会伤害你的！” 凯塞尔医生尖叫起来。 你感觉到钥匙持有者在你怀里的毯子下扭动，你畏缩了一下，但不敢停止奔跑。最终，你从树林中挣脱出来，进入月光下的田野。你在用木板封起来的谷仓里发现了一个洞，开始拼命地跑向它。以最快的速度跑了一分钟后，你潜入谷仓的黑暗中。几秒钟后，当这些怪物进入时，你会听到滑行、颤抖的声音。你保持完全安静，开始从栅栏的一个小缝隙下爬到马厩里。 “Hazel……你最好现在出来……”警长巴克利低声说。“我们最好找到你，然后是其中一个孩子……” 当你听到人影在房间里游荡时，你尽量不呼吸并保持完全沉默。 “哦，你在那里……这是什么？” “啊，看看是谁。一个巴克利……我一直知道你家是一代蛇和政府混蛋……” “怎么……你他妈的是什么……？” “我是死神死神。” “不！等等！求求你，不要！” “告诉老爷爷巴克利你死了求我！” 一声枪响，巴克利警长沉默了。 “好吧，看看这个……凯塞尔家族的最新一代都是猪他妈的丑陋怪物。再见，孩子们。” 当你听到刀子在空中吹过口哨时，有一系列枪声响起。 “他妈的！什么……？” “一个帕克。你爷爷的笑容是我见过的最愚蠢的马笑。” 刀子划破空气的声音和血腥的声音传来。 “哎呀，有一个 Jordan 和一个 O'Riley。嗯，你爸爸妈妈身边的爷爷奶奶们在摆弄，只有他们的孩子有孩子才有意义。” 在你听到一个单独的声音之前，有一系列枪声和刀刺的声音。 “Hazel？不管你做了什么，停止这个……求你了……” Kessel博士呜咽着。 你听到一声枪响，还有身体撞击地板的砰砰声。然后，沉默。突然，死人站了起来，盯着你看。 “好吧，亲爱的。你从来没有说过会有十几个怪物被拖走。” “我没想到他们会释放他们。” “好吧。好吧，我想我们差不多了。” 你点点头，那包又在你怀里晃动了。死人举起他的枪，对摇晃着的包裹开了两枪。你尖叫着血液飞扬，然后将垂死的怪物从你身上扔掉。 “在那里。现在我们他妈的确定了。” 死人再次被黑暗吸收，而你独自留在黑暗中。你站起来，走进大屠杀。到处都是尸体，房间里到处都是干瘪的触手和流血的尸体。你发现巴克利警长躺在地上，血水越来越大。你搜查他的口袋，掏出锈迹斑斑的笼子的钥匙。你离开谷仓，开始走去营救你的朋友，就在天空开始升起的时候。
梗概：
五分钟后...
--------------------
动作：
做
一年后...
结果：
死亡与复仇 你会惊讶于媒体如此迅速和热切地注意到这一点。一群孩子发现什么是邪教的故事。当然，这个故事变得相当拙劣。邪教被称为撒旦教，怪物儿童被声称只是畸形或残缺不全，任何和所有超自然的存在都被合理化，并声称你和贝卡反对邪教，导致贝卡在消防。你不会不同意他们，因为你知道你会像疯了一样。死人很高兴每次调查谷仓时都躲起来，他知道抓捕一些闯入者和伏击警察调查之间的区别，以及不希望他的谷仓被警察胶带覆盖的时间超过需要的时间。尽管如此，它确实将 Dawnwood 变成了一段时间的媒体热点，并在一段时间内让你成为了名人。尽管如此，媒体很快就会继续前进。随着名人怀孕、中东恐怖袭击和无休止的大规模枪击事件，事情还在继续。你继续住在 Dawnwood，和你的朋友住在一起。你仍然对发生在 Becca 的事情深感内疚，任何和所有将其合理化的尝试都会导致你陷入自我贬低和抑郁。时间流逝，您在镇上安顿下来，第一次为搬到镇上而感到高兴。当然，你仍然对发生的事情有许多未解决的问题，仍然在胃部幻痛和那天晚上的噩梦中醒来，以及贝卡尸体倒在你面前的噩梦，但你能够稍微摆脱它。你期待着你在 Dawnwood 的未来。
梗概：
一年后...
故事名称：
37
故事背景：
你醒来时被绑在轮床上，浑身是血，穿着医院的长袍。你在房车里，灯是关着的。你什么也看不见，但你可以看到有人在呜咽。 “你好？” 你问。 呜咽声停止，你开始听到深沉的喉咙喘气声。 “你好？” 你再问。 当你的眼睛开始适应黑暗时，你在黑暗中辨认出两个人影。一个坐在角落里，另一个站在他们上方，用一圈长长的绳子摩擦他们，像蛇一样移动。 “梅森？贝卡？凯尔？卡特？” 你问，盯着数字。 站着的人转过身来，你会看到坐着的人快速地轻触电灯开关，照亮了房车。在灯光下，你注意到坐着的人是贝卡，她的眼睛因泪水而通红，看起来很害怕。你的目光扫过，看到第二道身影，缓缓向你靠近。这个人物是怪物儿童之一。它有一头长长的棕色头发，但她的脸只不过是一张布满粘液、没有嘴唇的嘴和两个鼻孔。它穿着一件破烂的、染着血迹的蓝色连衣裙，一条长长的红色触手从它的腹部伸出并缠绕在它的身体上。触手的尖端慢慢地向你移动，你发现一只黄色的眼睛从触手的末端伸出来，慢慢地向你靠近，盯着你的眼睛。在触手开始缠住你的腿，留下一条长长的粘性脓液从触手渗出之前，你尖叫着，挣扎着对抗你的束缚。当它继续向你靠近，朝着你的脸前进时，你尖叫起来。触手在你的胸部蜿蜒前行，然后缠绕在你的脖子上，然后停在离你脸几英寸的地方。 “你好……”怪物小孩沙哑的声音。 你盯着那只眼睛，看到眼睛里混合着痛苦和孩子般的好奇。然后，你听到一声喊叫。用你的眼角余光，你看到贝卡拿着一把又长又薄的刀向前冲去，刺穿了怪物孩子的脖子。这个生物嚎叫着，嘴里吐着鲜血，贝卡拔出她的刀开始砍它的脸和脖子，最后刀子穿过它的头骨。它崩溃了，你面前的触手落在你的脸上，像一条垂死的鱼一样摆动。你呜咽着，贝卡走到你身边，开始把你从束缚和缠绕在你周围的触手中解脱出来。你急切地站起来，一边挣扎着不呕吐，一边把触手从你身上扯下来。 “发生了什么？” 你问，瑟瑟发抖。 你感到胃部隐隐作痛，轻轻将它拉起来，幸好你还穿着内衣，虽然你看到你的胃包着厚厚的绷带。 “他们切开你，从你身上拉出……什么东西。它是活的，还在摆动，而且……”贝卡说，然后吓得瑟瑟发抖。“我们需要让我们的朋友离开这里。” 你听到浴室门传来砰的一声，赶紧过去听。 “你好？” “Hazel？带我离开这里！” 露西在里面尖叫。 你打开门，找到露西。 “感谢上帝。他们把我们锁在这里，把这些家伙关在笼子里。我们需要离开这里阻止他们。” 你点点头，走向厨房，找到一把大切肉刀。房车的门打开了，露西和贝卡迅速和你一起躲到柜台后面。 “Iris？你和你的新朋友玩完了吗？我希望你没有伤害他们。如果这个新生儿不是Key-Bearer，我们将需要他们。我什至没有看到k。 .. 鸢尾花？ 路易斯机械师走进来，环顾四周。 “你好？” 他向前走了几步，走进厨房向你走来。
--------------------
动作：
做
你保持隐藏
结果：
你躲起来，路易斯走了过来。他靠在柜台上，头就在你上方。然后，他发现了露西脚的边缘。 “拉屎！” 他喊道，转身跑出房车。 “嘿！女孩们松了！枪呢？” 几秒钟后，巴克利警长站在门口，用枪瞄准。 “快出来，不然我打爆你的脑袋！” 他吼道。
梗概：
你保持隐藏
--------------------
动作：
做
你投降
结果：
你举手投降，向前迈进。巴克利警长咧嘴一笑。 “来来来，把刀放下。” 你放下刀，向前一步。他挥舞着他的枪，猛击你的头部一侧，让你陷入昏迷。
梗概：
你投降
--------------------
动作：
做
你醒了
结果：
启示录 你躺在生锈的轮床上，在一个大圆圈的边缘。你的朋友都走了，但你只看了一秒钟，就看到了门户网站的准备工作。一大圈石头躺着，每个石头上都沾着一滴血。站在圆圈周围的是邪教徒。站在他们周围的是六个让你想呕吐的怪物孩子，这是抚养关键持有者的失败尝试。他们是如此畸形，如此变异，以至于他们几乎没有资格成为孩子。一只失去了它的双臂，取而代之的是长而细，像意大利面一样粘糊糊的触手。第二个没有了它的大部分脸，在它的地方有一个大洞，里面充满了来自墙壁、背部、顶部和嘴底的微小的、摆动的触手。第三个看起来像一个四岁左右的可爱，完全正常的女孩，除了她的嘴巴张开，她的眼睛完全是黑色的。另一个，最糟糕的一个，看起来像是一个女学生、一个胎儿和一只章鱼的混合体，站着摆动。尽管如此，与位于圆圈中间的东西相比，这算不了什么。坐在中间，扭动着，是你的孩子。Key-Bearer 有一只猫那么大，只不过是一团滚烫的触手，四处挥舞着，似乎在刻在你身上的符号中在自己周围划出紫色闪电线。 “啊，你来了！” 凯塞尔医生急切地说道。 “我的朋友在哪里？” 你尖叫着，害怕你周围的生物。 “现在无所谓！现在，我们开始！我们将见证克雷西西利亚塔尼亚创造的辉煌！” Key-Bearer 加速，在现实的结构上绘制符号。地面开始崩塌，变成纯黑色。突然，一道闪光，传送门打开了。 然后，它开始。
梗概：
你醒了
故事名称：
38
故事背景：
“酷，”梅森笑着把酒瓶递给你。 你喝了很长时间，把瓶子里的大部分都喝光了。蚂蚁是对的，它确实尝起来像小便。尽管如此，在传递之前，你还是成功地吞下了一半罐头。 “该死。新来的女孩可以喝酒，”蚂蚁笑道。 你继续围坐在篝火旁，这真的更接近于坐在一堆树叶中的蜡烛。梅森抓起一瓶伏特加，在你身边坐下，你的心怦怦直跳。你们中的很大一部分人都知道这只是迷恋和童年的迷恋，混杂着一些荷尔蒙的欲望，但仍然……他真的，真的很可爱。其他人似乎很无聊，凯尔喝下的酒似乎比他的体重还要重。 “好吧，这很无聊，”露西抱怨道，在中间扔了一个瓶子。“让我们玩转瓶子吧。” “决不！” 蚂蚁抱怨。“我是个奇怪的人。凯尔和贝卡一起去，你和卡特，然后是梅森和新来的女孩。” 当卡特点头表示同意时，你对这个评论脸红了。 “我同意。我不想玩旋转瓶子，”卡特说。 短暂地瞥了一眼冷漠的梅森，你感到一阵烦恼，因为蚂蚁建议一个新游戏，旋转瓶子的所有可能性都消失了。 “真心话大冒险！” 蚂蚁建议，对凯尔和贝卡的呻吟。 “还记得上次发生了什么吗？” 贝卡指出。 “这不公平！那不是我的错！你应该保护你爸爸的东西。我怎么知道那幅画值钱？” “你一开始就不应该在画上画画，蚂蚁！” “这不是你爸爸鼻子上的皮！那个有钱的混蛋能吃亏！天啊，我们都知道他的祖先是靠凯尔的祖先赚钱的！” “操你妈的，蚂蚁！” 凯尔喊道，喝完酒含糊不清。 “先生们，先生们，冷静点，”梅森说。“没有问题！我们在这里都是朋友！我说的是真话游戏，敢于让我们都冷静下来。” “很好，”贝卡说。 梅森把剩下的酒倒了下去，把玻璃瓶扔到中间。他双膝跪地，转动它，落在坐在皮卡车后面的卡特身上。 “真心话大冒险，卡特？” “如果我敢挑剔，你会不会……让我站起来？” 卡特懒洋洋地说。 “也许吧，”梅森狡黠地说。 “说实话，那么。” 梅森咧嘴一笑，看了他和露西一眼。 “你吻过露西吗？” 他问。 “不。抱歉让您失望了。” “该死。我很确定，”梅森叹了口气，掏出钱包扔给凯尔一张皱巴巴的钞票。“你只花了我二十块钱，卡特。” 蚂蚁倾身，转动瓶子。它慢慢停下来，指向树林里。 “那离贝卡或榛子更近吗？” 他问。 “贝卡，”卡特说。 “哦，太棒了。真心话大冒险？” “真相。” “该死的，贝卡。好吧，我会让这变得尴尬。凯尔跟你走了多远？” “闭嘴，蚂蚁！” 贝卡嘶嘶作响。 “喂，你得回答！” 露西说。 “哦，来吧！那是私人的！” “我们在这里都是朋友，”梅森说。 “美好的！我们……你知道。完成了。” “尊重，Kyle，”Ant 微笑着，忍住了咯咯的笑声。 贝卡脸红了，抓起瓶子转动它。通过纯粹的运气或技巧，它直接指向Ant。 “该死，”蚂蚁叹了口气。 “真心话大冒险。我有很多尴尬的问题要问。” “敢，”蚂蚁做了个鬼脸。 “好吧，虽然你的球比我多得多。你会受苦的。我敢让你进入艾歇尔先生的地下室。” “不！” 蚂蚁回答。 “来吧，贝卡！艾歇尔先生要是抓住了他，他的喉咙就会被撕掉！” 露西抱怨。 “艾歇尔先生是谁？” 你问。 “哦，我会告诉她的。我一直在为我的弟弟练习这个故事，“梅森说，然后清了清嗓子。“在第二次世界大战期间，一名年轻的士兵从党卫军的队伍中崛起。他有狩猎犹太人并亲自护送他们到集中营的习惯。他会猎杀男人、女人、孩子，并折磨他们以获取信息。他毫不留情。最终，当俄罗斯人入侵时，他伪装成平民逃脱，从柏林逃到法国，从英国逃到美国。他就藏身在 Dawnwood 的这里，追捕他能找到的任何犹太人。他叫艾歇尔先生。” “那是最大的狗屎！” 露西抱怨。“每个人都知道，在 Dawnwood 的任何犹太人都会被老卡特和他的白人权力废话吓跑。” “他不想要白色战俘权力！” 卡特说。“他想要白色的骄傲。有……不一样。” “不，没有，卡特，现在停止破坏这个故事。我在哪儿？” 梅森问道。 “你刚刚完成，”你指出。 “哦。好吧，现在艾歇尔先生要杀了蚂蚁。” “贝卡，对不起！请换胆！” “美好的。你必须进入死人谷仓，然后上阁楼。” “那就更糟了！” 蚂蚁抱怨。 “不，这还不错，”梅森说。“纳粹是真实存在的，会杀了你。鬼不是。” 你扬起眉毛，梅森笑了。 “好的，故事时间。曾经，西方有一位治安官，他用他的力量消灭了镇上大牧场主的任何竞争。其中一位小农场主丹尼尔爱德华兹站起来告诉警长，尽管受到威胁，他不会将他的农场卖给他的竞争对手。警长带了一队人把他打倒，爱德华兹试图反击。在他倒下之前，他杀死了两打人，并带走了近一百颗子弹。他们折磨了他几天，甚至把他的脸切开给他……你怎么称呼它，格拉斯哥咧嘴笑。最终，你只是把他吊死了。有传言说他仍然出没在他们把他绞死的谷仓里。这是一座历史建筑，不能拆除，所以业主就让它空置。” “我不去！” “别闹了，蚂蚁。” “你们中的一个和我一起去吗？” 蚂蚁恳求。 “旋转瓶子，无论它落在谁身上，都必须和你一起去，”露西建议道。“我希望是贝卡。那就太讽刺了。” “当然，”卡特说。“让我们这样做。” 蚂蚁感谢露西并旋转瓶子。它摇晃着，慢慢停下来，直接指向你。 “嗯，我猜黑兹尔要去死人谷仓，”梅森说。 “不，如果她愿意，她可以去艾歇尔先生家。Eichel 只是一个年迈、行动迟缓的家伙。死人是一个超自然的凶手，”露西说。 “让 Hazel 采摘，”Kyle 说，努力站起来，然后跌跌撞撞地靠在卡车上。 “是的。继续哈泽尔。幽灵或纳粹。”
--------------------
动作：
做
你和蚂蚁一起去艾歇尔先生的地下室
结果：
“好吧，我们会去找纳粹。” 当梅森畏缩时，蚂蚁紧张地点点头。 “如果……如果你这么认为的话，”梅森说。 你点头。一行人开始起立，开始向艾歇尔先生的家走去。 “我会……我会留在这里！” Kyle含糊其辞，倒在皮卡车的后面。 露西耸了耸肩，继续带领大家走出森林。当您出现在构成 Dawnwood 的另一个平淡乏味的郊区时，小组将各种空罐子扔到灌木丛中。太阳下 山了，露西拿出手机打开手电筒。她带你到一栋看起来和街上每一栋房子都一样的房子。 “是这个吗？” 你问。 “是的，”露西说。 “出色地。我以为屋顶上会挂着卍字横幅之类的东西。” “这里最接近卐字符的是卡特爸爸手臂上的那个。” “去你的！” 卡特说：“他——他不是纳粹纳粹！” “是的，卡特。” “我们现在不争论了。安静点，我们不想吵醒艾歇尔先生。蚂蚁，把榛子带回去，地下室总是有一扇敞开的窗户。我们会留在这里。六个孩子的骚动会吵醒纳粹，”梅森说。 “可能是为了散发气味，”贝卡说。“你知道，他的受害者。” 你开始紧张地冒汗，你跟在蚂蚁后面。就像梅森说的，地下室的窗户是敞开的。蚂蚁看着你，点点头。 “呃……女士优先，”他紧张地说。 “懦夫，”你吐了口气，然后跪倒在地。 你用手和膝盖向前爬，转身滑过窗户。你扭动着身体，花了很长时间挤过去，然后掉进地下室的黑暗中。你看不到一切，被留在连月光都无法穿透的完全黑暗中。你听着，寂静被蚂蚁从窗户掉下来的安静的叫喊声和砰的一声打破了。 “嘘！” 你嘶嘶声。 “好吧，”蚂蚁说。 在寂静恢复之前，你听到他在黑暗中摸索。当 Ant 点燃他的点烟器时，微弱的灯光闪烁，他的脸是黑暗中唯一照亮的东西。你拿出你的手机，打开手电筒，这是一种更现代的黑暗解决方案。工作间的桌子被照亮，桌子上覆盖着几块……骨头和肉。 “这他妈的……？” 蚂蚁说着，往后退了一步。“应该只是传闻。我很紧张，但实际上我没有……我……” “这到底是什么地方？” “我们需要离开这里，”蚂蚁嘶嘶声。 你惊恐地盯着桌子。你把你的手机翻过来，又找出了几个这样的车间桌子，上面覆盖着软骨和前受害者的碎片。一个巨大的工具架，上面布满了沾满血迹的刀，有些锋利无比，有些呈锯齿状，有些大到可以像切黄油一样轻松地切开骨头。 “哈兹尔，挣脱它！” “好吧，好吧，”你说，摇摇头。“我把你扶出窗外，然后你把我拉起来，明白了吗？” 你说。 蚂蚁点点头，你赶紧跑到地下室的窗户。他把脚放在你的手上，你给了他一个鼓励。他抓住窗框，开始把自己拉进门。当他扭动穿过门时，你推他。在他转过身去抓住你的手之前，他花了两分钟的时间才挤出来。 “走吧，我们走！” 你说。 你听到吱吱声。当一束光从敞开的门射进来时，你关掉了手机。 “隐藏！” 蚂蚁大声低语。“他来了！” 你环顾四周。在黑暗中，您只能看到两个符合条件的藏身之处：车间桌子下方或“工具”架后面。再说一次，你总是可以试着挤过窗户，尽管即使有蚂蚁的帮助，艾歇尔先生也可能会先抓住你。
梗概：
你和蚂蚁一起去艾歇尔先生的地下室
--------------------
动作：
做
你躲在工具架后面
结果：
你双膝跪地，迅速 爬到工具架后面，蜷缩起来，双臂抱住膝盖，蜷缩成一个球，尽量不要移动或发出噪音。当楼梯开始吱吱作响时，你听到一个古老的声音开始嗡嗡作响。 “Muss i denn，Muss i denn，zum stadtele hinaus……”老人唱道。“……stadtele hinaus……” 当你听到他靠近时，你畏缩了。 “和杜，我的沙茨，bleibst hier？” 他一边唱歌，一边从你面前的工具架上拿起一把刀片，你会听到金属的叮当声。“Wenn i komm'，wenn i komm'，wenn i wiedrum komm'...” 当他走开，前往车间时，你松了口气。当刀片切开肉时，你会听到刀片变得光滑时的静噪。一声巨响，你倒抽一口气，接着又是一声巨响，在一声巨响和一声爆裂之前，Eichel 先生用锤子敲开了一根骨头。艾歇尔先生停了下来，停顿了片刻。 “有人在吗？” 他问道，他那令人毛骨悚然的歌声让位于一位带着浓重德国口音的年迈祖父的声音。“我保证，如果你现在出来，那里……不会有问题。”
梗概：
你躲在工具架后面
--------------------
动作：
做
你继续躲
结果：
当 Eichel 先生开始在房间里走来走去，一直唱歌时，你保持隐藏，尽量不呼吸。 “Kehr' i ein，mein Schatz，bei dir。Kann i glei net allweil bei dir sein……”他唱道。 当门铃响起时，你听到一声钟声，随着艾歇尔先生停止唱歌，房间里陷入了沉默。 “啊。客人，”他说。 当他爬上楼梯时，你会听到他的靴子砰的一声离开。你听到门关上的声音，立即离开你的藏身之处。你走向窗户，从窗户跳起来。你抓住窗玻璃，开始把自己拉过去，摆动以帮助你继续前进。你把自己拉过去，倒在草地上，然后开始跑到前面。当您注意到 Eichel 先生正站在您面前时，您僵住了。 “你好，”他用浓重的德国口音说。“我是艾歇尔先生。” “我知道。” “啊，所以你听说过我。告诉我，现在的谣言是什么。我是说是约瑟夫·门格勒的助手吗？阿道夫·希特勒的私人助手？集中营里的营地医生？” 你沉默地看着他。 “嗯，传言完全不真实。我只是一个移民和标本剥制师。我小时候就来到这里。当然，我在国防军战争期间为我的国家服务，但我第一次得到了机会就放弃了。很简单害怕外国人让孩子们认为我是个可怕的恶棍，没有任何真实的事实。你有没有从我的地下室拿过......纪念品？ 你摇头。 “很好，虽然我警告你，亲爱的，如果你和你的朋友再次闯入我的房子，我会打电话给警长。告诉我……等我一下，我的视力不太好……梅森， Rebecca、Anthony 和 Carter 说，有更好的事情可以浪费你的时间而不是打扰老人。具有讽刺意味的是，Carter 来这里是为了和他的家人一起看纳粹......现在，亲爱的，请让我一个人去工作。” 你点点头，跑向树林。你发现那群人，梅森惊恐地站在那里等着你。 “发生了什么事？他为什么要放你走？卡特说他在雕刻一具尸体！” “他说他只是一个老移民和标本剥制师，让他一个人呆着，否则他会打电话给警长。” “他在砍人！” 蚂蚁尖叫。 “那应该是一头鹿吧！” 露西说。“你他妈的喝醉了，蚂蚁。” “是一个人，”蚂蚁固执地说道。 “好吧，我们可能只是惹恼了一个老人。我们他妈的离开这里去找Kyle。”
梗概：
你继续躲
--------------------
动作：
做
你回到树林里
结果：
你穿过树林往回走，走向卡车。 “Ss-so，这是一个糟糕的想法，”卡特说。 “是的，是的。我冒着生命危险去惹恼一个无害的老人，”你说。 “我们冒着生命危险去惹恼一个本可以无害的老人，”安特说。 你到达营地，梅森大声咒骂。 “操！凯尔到底在哪儿？” 他问。 营地空空荡荡，一个人都看不到。 “rr——还记得上次我们在喝醉的时候失去了凯尔吗？” “天哪，他最后在我的花园里撒尿。他没有在花园里撒尿。他站在花园里，在我的房子上撒尿，”贝卡说。 “我们需要找到他。难道有人还不够清醒再找到这个地方吗？” 梅森说。 一片寂静。 “很好。我们会分头去找他。蚂蚁，你去腐烂的树，露西，去我们的老树屋，贝卡，去旧的饮水点，卡特，回到镇上。黑兹尔，你是新来的。那边有一条小溪，”梅森指着说。“沿着那边走，手机一直开着。如果你找不到他，就顺着小溪回到这里。” 你点头。是时候找到凯尔了。
梗概：
你回到树林里
故事名称：
39
故事背景：
一个小时后，你驶进了道恩布鲁克，发现你太乐观了。该镇是一系列低矮的建筑。亨特从他的 Kindle 上短暂地抬起头，然后耸了耸肩，又往下看。您将车停在车道上的众多外观普通的房屋中的一间。您已经可以看到新来的人从窗户向外窥视的面孔。爸爸打开车门，把两个手提箱拖到门口，从口袋里掏出钥匙打开车门。 “我们到了！回家！” 他兴高采烈地说，显然是想提高孩子们的精神。 你跟着爸爸进去，把背包放在地板上。 “我会开始拆包，你们可以安顿下来，或者去见邻居或探索，”爸爸说。
--------------------
动作：
做
你去外面
结果：
你走到外面，两个满头流鼻涕的脏兮兮的孩子，长着没有梳过的棕色长发，茫然地盯着你看。两人都齐肩长发，穿着沾满泥土的衣服，一男一女。 “你是谁？” 当她开始挖她的鼻子时，有人问道。 “Hazel。我是新来的。” “你从哪来？” “纽约。” “我爸爸说纽约只有贪婪的犹太人和愤怒的黑人。” “耶稣基督，孩子，也许你爸爸是个混蛋，”你惊恐地说，盯着这对夫妇。 一个看起来和你差不多大的高个子男孩慢跑过来，一把拽住了两个孩子。他留着棕色的短发，穿着短裤和T恤，对他来说都太小了，还有盯着你的尴尬微笑。 “嗨。SS——对不起，如果这两个家伙打扰你了。TJ，艾玛，滚回去，”男孩结结巴巴地说。 这对孩子耸了耸肩就跑了。男孩伸出一只手，你抓住并握住它。 “我是卡特，”他笑着说。 “我是黑泽尔。” “那么，你-你刚刚---搬进来了吗？” “是啊，我做了。” “酷。你是什么g级？” “第十个。” “哦！我也是！你-你什么时候开始……开始？” “后天。” “很酷。我想我会再见到你的。很高兴见到你，”他微笑着。 卡特追赶他的兄弟姐妹，你看着他慢跑。你听到爸爸在你家里大喊大叫，然后转身去看他。 “嘿，亲爱的。房子很荒凉，所以我们要出去吃饭，我早上去买杂货。我在镇上看到一家披萨店。你想去吗？”<|endoftext |
梗概：
你去外面
--------------------
动作：
做
结果：
<|startoftext|
梗概：
--------------------
动作：
做
你好，冒险家！似乎某个随机的村庄被那边描绘的那个大野兽所困扰。可爱，不是吗？
结果：
当然，作为你的英雄，显然你有责任与它战斗。那你还在等什么？ 首先，你必须选择你的工作。你的工作会影响你的能力。
梗概：
你好，冒险家！似乎某个随机的村庄被那边描绘的那个大野兽所困扰。可爱，不是吗？
--------------------
动作：
做
你战士（物理攻击者）
结果：
很棒的选择！ 你冲锋陷阵打败了巨兽...
梗概：
你战士（物理攻击者）
故事名称：
40
故事背景：
你不记得世界是从什么时候开始震动的，这个现实的脆弱小块有着如此多的裂缝和如此微弱的支撑以防止它分裂，但总有一些东西会被放弃。这只是时间问题。没有人知道是什么真正引发了下跌。是不是打破了锁链，让你安静的小岛一直漂浮在天空中，充满了让它持续这么久的能量？是远低于你们宝贵的小文明的最后一次地震，分裂了人类的土地吗？或者是别的什么？ 很容易责怪生活在你之下的人，把世界上每一件小事都归咎于他们。你足够高，可以避免泄漏到大气中的最严重的有毒气体；你足够聪明，知道不要耗尽地球上还有待开发的少数 资源；当他们制造大量新的危险，将恐怖释放到野外时，你的速度足以隐藏自己。 这里没有战争，没有政府，没有暴力，没有武器，也不需要做任何事，只能靠自己生存并互相帮助。这里空气稀薄。如果没有自己的特殊设备，人类会发现在这个高度呼吸困难。您同情他们，但同时又钦佩他们足智多谋，能够创造出让他们一路走来的工具。然后你会记得为了制造这样的工具必须牺牲什么，你就不再那么钦佩它们了。 您一生居住的岛屿，以及您的家人和朋友……正在开始坍塌。
--------------------
动作：
做
你继续
结果：
风在你耳边呼啸而过，这座岛向着远在下方的地面坠落。没有链条可以保证您的安全。没有能量或动力让你保持在空中，不再有。一切都用完了。这是你的家，你的村庄，你的生活；成群结队的小草屋整齐地排列成小圆圈，用来抵御这种高度的强风。你们的人早就习惯了这里缺氧。 它不再是你的家了。 你紧紧抓住覆盖在地面上的美丽的绿草，草丛在你的手中散落。气压正在影响土地，当您家的各个部分被野蛮地撕裂时，您会越来越恐惧地看着。你也不是唯一的；数十人拼命地试图在他们也被撕成碎片之前保持他们的房屋安全。一个注定的努力，因为引力是巨大的。 你在任何地方都看不到你的父母。您担心他们可能会像其他许多人一样被拖到死亡边缘。这个定居点没有配备合适的设备来降落在数千米以下的人类居住的土地上。没有人会想到做这样的事情。 风的轰鸣声在你耳边越来越响，压在你身上的压力迫使你陷入狭窄的爬行中。以你下落的速度，你甚至无法抬起头。您周围的地面出现裂缝，您意识到这不再是保持岛屿安全的情况。结算已经完成。它将无法生存。
梗概：
你继续
--------------------
动作：
做
你爬向附近房子的废墟
结果：
您朝着曾经是您的私人住所的地方迈进了一步。屋顶被撕掉了，前门只用线挂着，内部已经被摧毁得无法恢复。现在这里只剩下破碎的木头碎片。起风了，有那么一瞬间你几乎失去了控制，但你不知怎么设法让它进入了里面。剩下的墙在这里用作临时避难所。 即便如此，你的直觉告诉你整个岛屿的速度比以前更快。链条不会被修复：它不会停止。你的生存机会充其量是微乎其微的。
梗概：
你爬向附近房子的废墟
--------------------
动作：
做
你低着头，在墙内寻求庇护
结果：
当震耳欲聋的噪音在你周围持续时，你蹲在墙的内侧，捂住你的头。早些时候，你可以听到其他人在他们脆弱的小屋里寻求庇护时的哭声，但现在除了无休止的风啸之外什么也听不到。一股寒意渗入你的骨头，但这是你现在最不担心的。这堵墙撑不了多久。当风暴肆虐时，你也可能被困在海洋中间，紧紧抓住一块破碎的浮木。 好像急于向您确认这一点，小裂缝开始在您藏身的墙壁上蔓延开来。整个岛屿都在晃动，裂缝越来越大。
梗概：
你低着头，在墙内寻求庇护
故事名称：
41
故事背景：
这个故事发生在很久以前，在Mystigar 王国。 国王最近去世了，留下他的女儿，美丽的长着翅膀的公主果酱，作为唯一的继承人。公主还没有成年，所以她被留在肆无忌惮的雅克皮埃尔侯爵的监护之下。他不满足于只是一个侯爵，密谋娶她并夺取王位。 宣布以果酱的名义举行盛大的集市和射箭比赛。胜利者将赢得她的婚姻。比赛对所有人开放，但比赛是固定的，因为每个人都知道马克斯是这片土地上最伟大的弓箭手。 博览会的日子就要到了！你扮演一个卑微的面包师学徒的角色，谁都不知道，注定要成为国王！
--------------------
动作：
做
你演我
结果：
第一幕： 公正
梗概：
你演我
--------------------
动作：
做
面包师的学徒
结果：
今天是展会的日子！ 太阳还没有完全升起，但你的家务已经完成了。烤箱点燃，面团揉好，面包刺破，地板扫地，锅子擦洗。如果你明白我的意思，你甚至有机会砍一点晨光。现在你的主人肯定会让你在集市上度过余下的一天。自从它第一次宣布以来，你就没有想过别的。 你正忙着白日梦到 Marmalade 公主，再次，阁楼传来一声叫喊；“男孩！” 你脾气暴躁的主人终于醒了。当他爬到厨房时，梯子压在他的身下。“放开你的屁股，男孩，把烤箱加热，”他说。 “师父，炉子已经暖和了。我的家务都做完了。我想我或许可以去展销会了？” 你回复。 一只肉肉的手敲击你的脸。“集市！集市是为吟游诗人和花花公子举办的。你是哪一个，男孩？或者你认为你会赢得比赛并嫁给公主？” 他笑着说。“面包王的男孩万岁！忘掉那些东西，去给我拿些牛奶来。” 他递给你一块铜板，又啪地一声送你出门。 “如果我发现你去了集市，孩子，我会把你的脸锁在烤箱上！” “混蛋，”你一边揉着肿胀的嘴唇，一边低声咒骂。有一天，你会把他胖胖的脸拴在烤箱上。你走向送奶工的摊位。他总是有一瓶烈酒与年轻的学徒男孩分享，他的女儿贝莎拥有你见过的最大的牛奶罐。 你走到他的立场，但没有人在那里。
梗概：
面包师的学徒
--------------------
动作：
做
你去送奶人家
结果：
“你好？” 你敲送奶工的门时打电话。没有回应，但门是开着的，所以你走进去。 “众神熄灭了那盏灯，停止了这一切的喧嚣。一个人就不能休息一下他的痛苦的头吗？” 送奶工在床上说。他把被子拉到头上，几个粘土罐洒在地板上。他和撒切尔家族的孩子们一起喝酒了一个漫长的夜晚。“如果你想要牛奶，你就得自己去拿。贝萨去了集市，我肯定不会在我的情况下这样做。” 只是你的运气，烈酒和贝萨都不见了。 你可以听到牛棚里的母牛低声乞求挤奶。你拿起一个桶，坐在挤奶台上。
梗概：
你去送奶人家
--------------------
动作：
做
你给牛挤奶
结果：
你一边给奶牛挤奶，一边梦见 Bessa 隆起的乳房。你在棚子里的时间比给奶牛挤奶时间长得多。 你举起桶，走回屋子。送奶工又陷入了昏迷状态。你可以听到他咕哝着关于撒切尔的男孩和柔软的双手。真的没有必要付钱给他。他可能甚至不会记得你来过这里。拧紧送奶工，拧紧面包师！今天是集市日，你口袋里有一个铜币，你会很享受的！在将牛奶桶扔掉之前，你要从牛奶桶里喝得很深。你是自己命运的主人！ —— 博览会就是你所希望的一切。出身名门的贵妇和贤惠的骑士在五颜六色的丝绸凉亭外吟诵诗歌。赤裸的海胆互相追逐玩捉迷藏和棕色手指，而醉酒的吟游诗人则在他们的肺上束着绿色的袖子。供应商从各种手推车和摊位上调用他们的商品。 一个长着痘印的胖糕点师喊道：“热馅饼！两个铜！” 当一个矮人在喝她的啤酒时。你饿了，但买不起馅饼。然而，矮人会让你为铜钱喝醉。 你环顾人群，希望能瞥见公主。你在比武场上方的皇家包厢里窥探她，她被一对看起来更像是狱卒而不是护送的守卫陷害。有那么一刻，你的目光相遇，她微笑了。 一位传令官在她下面喊道。“召集所有参加射箭比赛的 yee”，他宣称。“获胜者将获得金弓和美丽的果酱公主之手。任何密斯提加人都可以获得一枚铜币。” 有机会娶公主的铜钱？多么幸运！
梗概：
你给牛挤奶
--------------------
动作：
做
你进入比赛
结果：
传令官说：“你们每个人都将拥有三支箭”。“如果其中一个触及靶心，你将晋级下一轮。” 只有一个？小菜一碟。 喇叭吹响，标志着回合开始。你扣上你的箭，小心瞄准，然后放开。箭矢在目标上方的空中翱翔。不气馁，你敲下第二支箭。这个人将自己埋在距离目标一半的泥土中。射箭比看起来更难。 这是您超越面包师学徒的一次机会。一切都靠这最后一支箭。你闭上眼睛，想象你和公主的生活。当您在城堡上空翱翔时，您会看到自己栖息在她华丽的翅膀之间，您的脸隐藏在她的金色头发中。你飞得越来越高，向下面尖叫的农民扔石头。你松开箭头。 一个完美的靶心！ 当其他参赛者被淘汰时，一轮又一轮你的箭射中了标记。公主会注意到你的成功。最终，只剩下你和一个对手；雅克·皮埃尔侯爵。 “我的帽子给你了，好先生，”他在几轮之间亲切地说。“告诉我，你是谁，你从哪里学会了这么好的投篮？” “我只是一个面包师的孩子，”你对他咕哝。 “你肯定是在开玩笑。我从来没有听说过一个面包师的孩子有这样的本事，一个真正的天才。有时间你一定要和我一起打猎，我担心这些野兽不会有机会！但是瞧，看看这根绳子！它是穿得很薄，准备折断。请换一个我的。它是最好的。我不会说我利用了一个可怜的面包师的孩子。” 马克斯是个多么好的人啊。你换下你的绳子，并承诺接受他的狩猎提议。 你被召唤到公主的私人包厢。“贝克的孩子，自从我父亲去世以来，你第一次给了我希望，”公主说。“打败侯爵，你将得到我的心和我的手。” 哇，那里没有压力。 喇叭响起，标志着最后一轮。“最后一轮，每个人射一箭，最好的标记将赢得Marmalade公主的手！” 传令官宣布。目标向后移动一百英尺。 侯爵提议先开枪。他的金箭刚好碰到靶心的边缘，射得很好。你接近标记并准备画画，意识到公主正在看着你。
梗概：
你进入比赛
--------------------
动作：
做
你拉弓
结果：
折断！新的绳子在压力下断裂，划开你的脸颊。你的箭在降落到你的脚下之前会做一次懒惰的夏日跳跃。 “侯爵是赢家！” 先驱宣布。 那个肮脏的家伙，他骗了你！他知道绳子会断。你挥舞着弓箭像棍棒一样向侯爵冲锋，但此时赛场上已经挤满了观众，你无法通过媒体接触到他。有那么一瞬间，你的目光落在了公主的身上，她在她面前用悲伤的眼神看着你，侯爵们被带走了。 几个小时后，你坐在矮人的摊位前，试图喝掉你的悲伤。你揉了揉脸上新的伤疤，发誓要报复。 你的肩膀上有一个水龙头。你转身发现身后有一个神秘的蒙面人影。“一份礼物，”她说，“来自公主的礼物，送给她真正的冠军。请在她被迫结婚之前帮助她。” 她递给你一个包裹，然后消失在夜色中。这可能是真的吗？公主真的需要你的帮助吗？ 一个没有任何弦的小骨琴被包裹在捆绑中。你他妈的该怎么办？唱出你的悲伤？你问矮人竖琴会买多少啤酒，希望足以让你酒精中毒。 “你这个醉酒的傻瓜！这不是普通的竖琴，”她喊道，“这是梦之竖琴！如果你用独角兽的鬃毛把这竖琴串起来，唱出愿望之歌，你最大的愿望就会实现。” 那讲得通。 “拿下这颗种子”小矮人说，递给你一颗小珍珠。“从它长出来的树是独角兽最喜欢的食物。把它种在魔法森林里，他会来吃它的。”
梗概：
你拉弓
--------------------
动作：
做
你前往魔法森林
结果：
第二幕： 愿望之歌
梗概：
你前往魔法森林
--------------------
动作：
做
你现在开始变得奇怪了......
结果：
故事继续，你绝望地迷失在魔法森林中。 又饿又累，你坐在木头上休息，了解自己的方向。一根看起来很美味的蘑菇从原木的末端长出来。你认出它闪亮的白色帽子和蓝色斑点是可食用的矮人指，但你又知道，它可能是一个小丑蛋，你不是医生。
梗概：
你现在开始变得奇怪了......
--------------------
动作：
做
你吃了蘑菇
结果：
可口的！咬一口，您会感觉精神焕发。您迈出额外的步伐，开始穿越魔法森林。 过了一会儿，你来到一个斑驳的峡谷。瀑布在空地上投下一层薄雾，萤火虫在茂密的绿叶间翩翩起舞。这个地方看起来和任何种植魔法种子的地方一样好。你挖一个浅洞，轻轻地用丰富的棕色泥土覆盖种子。 一棵大树从土里长出来。它扭曲得越来越高，向上生长。数以百计的白花从它的嫩芽中萌芽，空气中弥漫着青春的芬芳。在远处的某个地方，你听到马的嘶鸣声。
梗概：
你吃了蘑菇
--------------------
动作：
做
独角兽出现
结果：
一匹威风凛凛的白马慢腾腾地从雾霭中飞驰而出，停在了你的身边。当您的括约肌突然松动时，您会伸手去摸它乳白色的鬃毛。空气中弥漫着明显的腹泻气味。你的背部在痛苦中拱起，你的胃充满了火。独角兽尖叫着跑回雾中，而血和脓从你的肚脐中涌出...... —— 小丑低头看了看你那被撕碎的身体片刻，然后从独角兽身后滑过。我想这毕竟是一个小丑蛋。 你死了
梗概：
独角兽出现
故事名称：
42
故事背景：
你明智地不理会蘑菇。饿了总比死好，你总是说。 过了一会儿，你来到一个斑驳的峡谷。瀑布在空地上投下一层薄雾，萤火虫在茂密的绿叶间翩翩起舞。这个地方看起来和任何种植魔法种子的地方一样好。你挖一个浅洞，轻轻地用丰富的棕色泥土覆盖种子。 一棵巨大的树从土壤中冒出。它扭曲得越来越高，向上生长。数以百计的白花从它的嫩芽中萌芽，空气中弥漫着青春的芬芳。一只华丽的乳白色独角兽从薄雾中飞驰而出，跪在你的脚下。 “很久以前就有人预言，密斯提加真正的国王会在这片空地上种下独角兽的圣树，”独角兽说。“以我的鬃毛为证——” 还没等他说完，他的脖子上就冒出了一根金色的箭矢。 “资本出手先生！” 从你身后传来一声喊叫。侯爵和他的走狗温德尔手执弓骑马穿过林间空地。 “天哪，如果不是面包师的孩子。我还以为我见过你们最后一个，”侯爵说。“现在站在一边。那只独角兽的角将成为公主的完美结婚礼物。”
--------------------
动作：
做
你站起来战斗
结果：
你站在独角兽面前，挥舞着拳头。你受够了这个自负的侯爵。侯爵耸了耸肩，然后抽出了他的金剑。他收费。你躲避打击，直接踢他的马。马卷轴，将马克斯扔在地上。 Marques 很快就重新站起来，再次追上你。毫无防备，你被倒退了。你跌跌撞撞，侯爵的剑深深地刺入了你的身边。 “抱歉，就这样结束了，面包师的孩子，”他笑着说。他举起剑，准备最后一击。 独角兽看到你的危险，召唤了他最后的力量。一道壮丽的光芒从他的角中散发出来。失明的侯爵丢下他的剑，跪倒在地。你抓住时机，拿起剑。你报仇的时候到了。 灯熄灭。 温德尔站在垂死的独角兽身边，得意洋洋地握着断掉的角。失血战胜了你。剑从你手中滑落，你崩溃了。 “看来你的计划落空了，面包师的孩子，”侯爵打趣道。他收回剑，拿起竖琴。“看，温德尔，我是个烤小吟游诗人，在我丑陋的小竖琴上唱关于糕点的歌曲，”他唱道，在你周围欢快地唱着，然后嘲笑地把竖琴扔掉。“太糟糕了，你没有时间为我的婚礼创作一首歌。来吧，温德尔，我们必须及时回来挑选邀请函。” 当他们骑车离开时，你听到了他们谈话的最后一段话：“再次解释一下这是如何工作的，温德尔。我应该把喇叭绑在哪里？” “陛下，”独角兽轻声说。当生命从你的身体流出时，你爬到他的身边。“你必须喝我的血，这是治愈你伤口的唯一方法。” 可能还有另一种方法，可能是绷带或药膏。没有人与神话中的存在争论，你捧起一把绿松石血，把它带到你的嘴唇上。它尝起来像彩虹和梦想。血液流经你的身体，治愈你。 “你是密斯提加真正的国王。拿走我的鬃毛，把琴弦上弦。现在，你必须到十字路口去寻找睿智的圣人。只有他才能教你许愿之歌。” 独角兽开始褪色。“不要让竖琴落入坏人之手。它的力量可以用于大善，也可以用于大恶。我必须离开这个世界，但不要为你担心，我现在是一体的。我的精神将永远注视在你身上。总是……”独角兽消失了。从那天起，每当你去洗手间时，你都会有一种不舒服的感觉，他在看着你。 你怀着沉重的心情继续你的旅程。
梗概：
你站起来战斗
--------------------
动作：
做
旅程还在继续……
结果：
森林小径最终将您带到一座古色古香的小屋。一个半人马站在前面，耙树叶。当你接近时，半人马停止倾斜。“早上好，旅行者，你能帮我照看我的花园吗？” 半人马问道。“作为交换，我要把我女儿的手交给你。” 像这样的交易太好了，不能放弃，但你想知道这怎么行。也许有一个特殊的凳子什么的。您正在努力寻找一种礼貌的方式来询问他的女儿何时从小屋小跑。她的美丽可以与王子果酱相媲美。她害羞地摆弄着金色的长辫子，用银色的蹄子顽皮地踩着地面。 拒绝他的提议是不礼貌的，但你正在寻求。半人马对任务和魔法森林非常了解，也许他可以为您的旅程提供帮助。
梗概：
旅程还在继续……
--------------------
动作：
做
你照料半人马的花园
结果：
那天晚上你发现自己在小屋里，独自和半人马的女儿在一起。她以令人难以置信的半人马力量将你摔倒在地。她撕下她的皮背心，以一种主导的方式高高耸立在你身上。 “挠我的乳房，人类！” 你无可救药地坠入爱河并立即结婚。你白天在半人马的花园里耙地，晚上用竖琴为半人马的女儿唱小夜曲。晚上你躺在她的摊位上，蜷缩成小勺子，想知道你的孩子会有多少条腿。 结束 当然，事情对你来说很好，但你怎么能放弃公主呢？说真的，这是如何工作的？ 你找到了 N。发现更多的字母来解开传奇之谜。
梗概：
你照料半人马的花园
故事名称：
43
故事背景：
森林小径最终将您带到一座古色古香的小屋。一个半人马站在前面，耙树叶。当你接近时，半人马停止倾斜。“早上好，旅行者，你能帮我照看我的花园吗？” 半人马问道。“作为交换，我要把我女儿的手交给你。” 像这样的交易太好了，不能放弃，但你想知道这怎么行。也许有一个特殊的凳子什么的。您正在努力寻找一种礼貌的方式来询问他的女儿何时从小屋小跑。她的美丽可以与王子果酱相媲美。她害羞地摆弄着金色的长辫子，用银色的蹄子顽皮地踩着地面。 拒绝他的提议是不礼貌的，但你正在寻求。半人马对任务和魔法森林非常了解，也许他可以为您的旅程提供帮助。
--------------------
动作：
做
你问圣人
结果：
“他们说圣人有一千年的历史，可以预见未来一千年。当然，他们也说我们半人马不能擦屁股，”半人马说。“总之，没有人见过圣人，就算见过，也活不了多久了。向东过河，到十字路口，那里会出现一位精神向导，带你去见圣人。” ” “但要小心鬼城！” 他警告说。“它被一个邪恶的恶魔统治着，她不值得信任。” 在沿着森林小径向东走之前，你最后看了一眼半人马的女儿。半人马在你身后喊道：“你知道这不是真的！我们可以擦屁股！” —— 当您到达河流时，太阳高高地挂在头顶。 这条河太宽太强，不能涉水。如果你有一辆马车，你也许可以填塞它并漂浮过去。闹鬼的城堡不祥地矗立在河边，守卫着唯一的桥梁。散发着邪恶的气息。幸运的是，还有一艘渡轮停靠在近岸。
梗概：
你问圣人
--------------------
动作：
做
你坐渡轮
结果：
渡轮上有一个老麻风病人，身上披着 无形的棕色破布。他的皮肤又硬又灰，给人一种爬行动物的外表。他身体的右侧是一堆畸形的疖子和疣。尽管他的外表看起来很友好。 你爬上船，他开始把你拉过去。“你有多么柔软的粉红色皮肤，”摆渡人说。“我敢打赌，当你四处走动时，你的蠕虫会像一条绳子上的鱼一样摆动。我可怜的灰球看起来就像粘在葡萄柚上的蓝莓。我几乎不能把它们塞进我的马裤里，更不用说一个女人了。” 一只带鳞的手从水中伸出并抓住了你的手臂，打断了麻风病人对他生殖器的图形描述。在它把你从船上拖下来之前，摆渡人用他的杆子把胳膊拍掉了。还有数百人从深处升起。他们抓住船，疯狂地搅动河水。 “河女巫今天饿了。” 摆渡人说。他站起来，把你逼到船头。“现在，把那把竖琴交出来，否则我会把你扔到海里。”
梗概：
你坐渡轮
--------------------
动作：
做
你给他竖琴
结果：
摆渡人从你手中扯下竖琴。 “我早就预见到了这一天，我在这里等你多有耐心啊！” 他惊呼道。“现在全世界都将知道我的痛苦。” 他弹着竖琴，唱着愿望之歌。他唱歌时麻风疖消退，皮肤变得柔软和粉红色。当他的诅咒转移到你身上时，你的皮肤会爆发出灰色的疖子。你的包皮像气球一样扭曲，疣充满你的喉咙。你开始窒息... 你死了
梗概：
你给他竖琴
故事名称：
44
故事背景：
渡轮上有一个老麻风病人，身上披着 无形的棕色破布。他的皮肤又硬又灰，给人一种爬行动物的外表。他身体的右侧是一堆畸形的疖子和疣。尽管他的外表看起来很友好。 你爬上船，他开始把你拉过去。“你有多么柔软的粉红色皮肤，”摆渡人说。“我敢打赌，当你四处走动时，你的蠕虫会像一条绳子上的鱼一样摆动。我可怜的灰球看起来就像粘在葡萄柚上的蓝莓。我几乎不能把它们塞进我的马裤里，更不用说一个女人了。” 一只带鳞的手从水中伸出并抓住了你的手臂，打断了麻风病人对他生殖器的图形描述。在它把你从船上拖下来之前，摆渡人用他的杆子把胳膊拍掉了。还有数百人从深处升起。他们抓住船，疯狂地搅动河水。 “河女巫今天饿了。” 摆渡人说。他站起来，把你逼到船头。“现在，把那把竖琴交出来，否则我会把你扔到海里。”
--------------------
动作：
做
你从船上跳下来
结果：
你的跳跃使小船倾覆，而摆渡人与你一起被抛入水中。 在你成为你母亲大腿上的污点之前，麻风病人就一直在这条河里工作，而河中的女巫一直在耐心地等待着抓到他。他们聚集在他身上，当他们满足他们的嗜血欲望时，你会暂时被遗忘。他含漱液的尖叫声在撕裂肉体的声音中几乎听不见。 当他们分心时，您迅速游到岸边并继续您的旅程。 —— 林间小路渐渐变成小巷，小巷也变成了路。最终，它会带您到达目的地。一根石柱从十字路口冒出阴茎，标志着十几条小路像车轮上的辐条一样相遇。三只动物从柱子后面走出来：青蛙、乌鸦和狐狸。 “欢迎陛下，”狐狸说。 “我们是精神导师，”青蛙说。 “我们等你很久了，”乌鸦说。 你可以说这会很快变得烦人。 “我是人类的遗憾，”青蛙说。 “而我是人类的悲哀，”乌鸦说。 “而我是人类的诡计，”狐狸说。 你不知道他们在说什么。你只关心哪一个能带你成圣。 “也许我们所有人”乌鸦说。 “也许我们都没有，”青蛙说。 “你必须相信你的心，”狐狸说。
梗概：
你从船上跳下来
--------------------
动作：
做
你跟着狐狸
结果：
当狐狸绊倒时，你跟随它穿过森林一段距离。“啊，我的脚趾被撞伤了。你得背着我，”狐狸说。“拜托，真的，真的，很痛。抱我，我可以告诉你去哪里。非常拜托。” 他的脚趾看起来不错，但你不情愿地拿起了灵狐。“哦，你有这么强壮的手臂，”他说。 你旅行了很多英里，森林变得黑暗和不祥。树木上装饰着萎缩的头颅和人骨，它们像风铃一样悬挂在树枝上。入石空，林中响起低沉的声音：“谁敢求大圣？” “这是我的暗示，”吓坏了的狐狸说。他从你的怀里跳下，迅速逃走。他的脚趾似乎奇迹般地痊愈了。 一头巨大的狮子跳到你面前的岩石露头上。他是如此之大，一匹马可以骑下他的食道。头骨和骨头像战利品一样绑在他的鬃毛上。“如果你想见圣人，你必须回答我的谜语。回答错误，我会把你加到我的装饰品上 “什么东西在早晨高高耸立，将金色的光芒洒在孩子们身上，然后精疲力竭地把头垂在石头之间？”
梗概：
你跟着狐狸
--------------------
动作：
做
太阳
结果：
“嗯，我不是这么想的，不过既然你这么说，那可能就是太阳了。你已经证明了自己的价值，可以进去了。” 狮子张开巨大的嘴，招呼你进去。 狮子内部相当宽敞。他的肋骨形成了一个优雅的拱形天花板。地精头骨、巨魔膀胱和其他神奇的零碎物品排列在墙上。一只独角兽角被丢弃在一堆用作圣人床的毛皮旁边。 圣人是一位身披鼠皮长袍的弯腰老人。他秃顶，赤脚，下颚突出了一颗牙齿。“我一直在等你，”他说。“你是被选中学习愿望之歌的人。但首先你必须用女巫的血沐浴。” 你脱下衣服，爬进一个大石管。圣人将一桶桶浓稠的女巫的血倒在你的头上。它顺着你的脸流到你的嘴里。“很难相信女巫每个月都会填满这些桶中的一个。现在关于那首歌。闭上你的眼睛。” 圣人脱下袍子，和你一起溜进浴缸。他的牙齿猛地切入你的乳头。当他吸走你的生命力时，旋律会充满你的脑海。这是愿望之歌。 “嗯，就是这首歌，”圣人擦着嘴唇说。“你只能唱一次，所以要明智地使用它。现在足够了，让我们玩浴缸喷泉！” 你从浴缸里跳起来，匆匆穿好衣服。“适合你自己，我会打一场单人比赛。如果你要离开，你必须使用后门。应该把你放在城外。” 当你爬出狮子的结肠时，你的思绪转向了公主。
梗概：
太阳
--------------------
动作：
做
你回城
结果：
镇上现在很安静，集市的盛况已被遗忘。你像公鸡一样大摇大摆地走在空荡荡的街道上。你找到了独角兽，打了侯爵，遇见了智者，学了许愿之歌，离婚礼还有时间。但是你还在等什么？你现在可以唱这首歌，并在夜幕降临前探索公主。 你拨动竖琴柔滑的琴弦，清了清嗓子。话还没说出口，一只肥硕的手就落在了你的肩上。 “你好男孩。” 是面包师！他从你手中抢过竖琴，嘲讽地弹了一下，然后把它扔到了他的肩膀上。“我知道你会逃跑成为一个花花公子的吟游诗人。但你仍然是我的徒弟，孩子，我警告过你如果你去博览会会发生什么……”
梗概：
你回城
--------------------
动作：
做
你行动 III
结果：
第三幕： 婚礼
梗概：
你行动 III
--------------------
动作：
做
同时你又回到了城堡……
结果：
温德尔手里拿着一份差事清单，走在泥泞的街道上。当一把小骨竖琴落在他的脚边时，他停在了花店的门前。街上一阵骚动；镇上的面包师刚刚把一个肥胖的小男孩扔到了他的肩膀上。 “面包师的孩子还活着，”温德尔对没有人特别说。“我必须警告侯爵们！” 他把竖琴夹在胳膊下，跑回城堡。 —— 马克斯砰的一声关上了塔门。一个红色的手形贴边擦伤了他的脸。公主并没有很好地接受这个消息。 “公主和我将立即结婚，”他告诉温德尔。“看看一切都准备好了。为面包师男孩安排一个特别的欢迎仪式，以防他决定加入我们。你完成后到我的房间来。我需要在新婚之夜放松一下，”侯爵说，松开了他的手。腰带。 —— 公主在塔牢房内哭泣。她跑到窗边，好像要把自己从塔上扔下去，但铁栏杆阻止了她。 一只小白鸽停在窗台上。“我忠实的仆人，”公主抽泣着说。“侯爵有竖琴，现在我怕面包师来不及救我。求你了，你必须找到他。在我嫁给那个邪恶的侯爵之前，我会死的！” 鸽子带着公主最后的希望飞走了。
梗概：
同时你又回到了城堡……
--------------------
动作：
做
结局
结果：
面包师利用他的威胁，把你的脸锁在烤箱里。“我要去城堡送些婚礼用的糕点。等你回来的时候，你会变得又香又脆。” 他离开时门砰的一声关上了。烤箱对您的皮肤已经很暖和了。 你的脸每分每秒都在变热。汗水顺着你的身体滚落，把你的零钱包涂在你自己的油脂里。你挣扎着逃跑，但锁链太紧了。你大声呼救，但周围唯一能听到你说话的是一只栖息在窗台上的小白鸽。 响应你的哭声，鸽子变成了一个美丽的披风女人。你认出她是集市上的神秘女人，虽然现在她的肚子因为怀孕而肿了。“来吧贝克的男孩，你必须快点，婚礼正在我们说话的时候举行，”她说。闪电从她的指尖飞出，将锁链打碎。 “可是没有竖琴我还能做什么呢？我当初怎么可能进城堡呢？” 你问。 “城堡期待为婚礼提供食物，”她说。“你可以烤个蛋糕躲在里面。守卫会直接把你推到教堂。” 多么聪明的主意。你卷起袖子开始烤。 蛋糕完成后，您将其掏空并爬进去。 “我有最后的礼物给你，”披着斗篷的女人说。她张开双腿，蹲下。一个鸡蛋重重地掉在地上。“这颗蛋包含了我最后的力量。破解它会给你巨大的力量。” 血腥的胎衣顺着她的腿流下。她开始褪色。 “我最亲爱的果酱，”她轻声说。“我会多么想念你甘露的甘甜。” 翅膀一振，女人不见了。 被糖果包围着，你意识到你已经有一段时间没有吃任何东西了，这个蛋糕看起来几乎和森林里的蘑菇一样好吃。当然，一点点味道都不会受到伤害。
梗概：
结局
--------------------
动作：
做
你试着咬一小口
结果：
蛋糕很好吃，也许是你做过的最好的蛋糕，但它对你空着的肚子来说太丰富了。你的肠子转动，拒绝甜蜜的糖霜。尽你所能握紧，你无法阻止不可避免的事情，你的肠子像喷水的鲸鱼一样释放。在蛋糕的范围内，恶臭是压倒性的。你既骄傲又厌恶。 已经安排好撒切尔家族的男孩将蛋糕送到城堡。在那里，一个肥胖的守卫用他油腻的手指穿过糖霜来品尝蛋糕。“我勒个去？” 他喊道，吐出蛋糕。“你在里面放了什么？它尝起来像污水。把它拿走。” 你和蛋糕被毫不客气地扔进了垃圾堆。在您自己的臭味中进行热拳击后，肮脏的垃圾空气令人耳目一新。 你的到来吓坏了捡垃圾的肮脏乞丐。他用一只好眼睛眯着眼睛看着你，然后裂开一块鸽骨，吸出骨髓。 堆为您提供城堡的俯视图。教堂的钟声响彻整个庭院，但教堂的门戒备森严。你将如何进入？
梗概：
你试着咬一小口
--------------------
动作：
做
你跟乞丐换衣服偷偷溜进来
结果：
乞丐很乐意拿你干净的衣服换他的破布，即使你的裤子有点硬。 当你接近小教堂时，守卫拦住了你。“我们这里有什么？皇室婚礼上的脏乞丐？” 守卫笑道。“等一下，我认得你。你是那个该死的贝克男孩！” 他喊道，拔出了他的剑。“等等，不，我的错。他是个胖乎乎的、长着疙瘩的家伙，闻起来像洋葱。你闻起来像污水和垃圾。进去吧。” 当他抓住你的衣领时，你正要走进去。“我认得你了。你是独眼丹。你就是那个闯进铁匠铺偷铁匠鞋的流氓。然后你放火烧孤儿院，强奸了那些被活活烧死的无辜孩子。” Mystigar 的法律非常重视偷鞋行为。守卫抓住你，把你拖到地牢。 —— 一排排牢房排列在地牢墙壁上。除了一个，它们都是空的。在最后一个牢房里，一个身材矮小的男人驼着背坐在一个苍白的房间里，贪婪地吃着东西。当守卫游行你经过时，他转向你。粪便像棕色的小丑妆一样弄脏了他的脸。他显然是疯了。 卫兵护送你到大厅尽头的刑讯室处决，但看来你得等轮到你了。 一个尖叫的囚犯被直立地绑在木板上，嘴里叼着一个漏斗。一个强大的磁石悬在他面前。看守站在凳子上，背对着你，往漏斗里钉钉子。然后他将磁石拉近囚犯的肚子。铁钉被石头的吸引力向上拉。囚犯尖叫起来。 狱卒正是侯爵的走狗温德尔！你必须偿还他对独角兽所做的一切。 你拍了拍围裙口袋，打破了里面的魔法蛋。凉爽的蛋黄顺着你的大腿流下。一股刺痛的电能穿过你的皮肤，让你所有的头发都竖立起来，而你的阴囊则充满了奥术能量。力量是压倒性的。闪电从你的指尖飞出，击中了守卫。他们的盔甲发出炽热的光芒，在里面将他们煮熟。 温德尔不是你的对手。他试图逃跑，但螺栓抓住了他。他的皮肤像热狗在火上烤太久一样发出嘶嘶声和裂缝。你用一个又一个的螺栓击打他，直到他们只剩下灰烬。 咒语消失，你的阴囊像一只受惊的蜗牛一样退回你的身体。你从犯人的嘴里拔出漏斗。“谢天谢地！我是塞巴斯蒂安，塞库姆王子，果酱公主的未婚妻，”他说。“我知道一条进入教堂的路，释放我，我们将一起拯救公主。”
梗概：
你跟乞丐换衣服偷偷溜进来
--------------------
动作：
做
你释放了塞巴斯蒂安王子
结果：
“谢谢你。我不期待今晚使用密室，这是肯定的，”塞巴斯蒂安说。 王子压在地牢墙上的一块假石头上，露出了一条隐藏的通道。“他们是这样带我来的。隧道就像一个迷宫。保持靠近，这样你就不会迷路，”他说。 他会带你穿过在地牢后面雕刻的隧道。您的路径曲折穿过迷宫般的隧道。“我和公主从一出生就得到了约定。国王死后，我收到了侯爵的一封信，邀请我去城堡挑选喜帖，但这是一个陷阱。他把我锁起来，所以我不能干涉按照他的计划。” 你来到隧道的岔路口。远处的音乐声充满了左侧的隧道，一种腐烂的气味从右侧的隧道中散发出来。“说吧，你到底为什么要救Marmalade公主？”
梗概：
你释放了塞巴斯蒂安王子
--------------------
动作：
说
“因为我爱她，我要娶她！”
结果：
右手边的小路通向地牢下方的深处。你小心翼翼地穿过黑暗，塞巴斯蒂安紧随其后。隧道在一个大裂缝的边缘突然结束，塞巴斯蒂安恶意地把你推到边缘。 你在城堡的化粪池深处倾倒垃圾。鸿沟有一百英尺宽，数百英尺深。管道从墙壁上的各个角度伸出，从城堡各处运送废物。一层薄薄的尿液和花生覆盖在坑的表面。它闻起来很糟糕。 “对不起，伙计，那个贱人果酱像水槽一样滴落，我不会成为一个戴绿帽子的国王，”塞巴斯蒂安对你喊道。他的临别话语的回声被新鲜粪便落入坑中的啪嗒啪嗒声打断了。 有什么东西在深处搅动。随着巨大绦虫的分段身体在您面前升起，污物开始冒泡。“我已经在这里等了你一千年了，面包师的孩子，”绦虫嘶嘶地说。“一旦我吞噬了你，诅咒就会被打破，我将摆脱这个坑。” 寄生虫的方形大嘴膨胀得难以置信地宽。你最后看到的是一粒玉米粒卡在它尖尖的牙齿之间。 你死了
梗概：
你说“因为我爱她，我要娶她！”
故事名称：
45
故事背景：
面包师兑现了他的威胁，把你的脸锁在了烤箱上。“我要去城堡送些婚礼用的糕点。等你回来的时候，你会变得又香又脆。” 他离开时门砰的一声关上了。烤箱对您的皮肤已经很暖和了。 你的脸每分每秒都在变热。汗水顺着你的身体滚落，把你的零钱包涂在你自己的油脂里。你挣扎着逃跑，但锁链太紧了。你大声呼救，但周围唯一能听到你说话的是一只栖息在窗台上的小白鸽。 响应你的哭声，鸽子变成了一个美丽的披风女人。你认出她是集市上的神秘女人，虽然现在她的肚子因为怀孕而肿了。“来吧贝克的男孩，你必须快点，婚礼正在我们说话的时候举行，”她说。闪电从她的指尖飞出，将锁链打碎。 “可是没有竖琴我还能做什么呢？我当初怎么可能进城堡呢？” 你问。 “城堡期待为婚礼提供食物，”她说。“你可以烤个蛋糕躲在里面。守卫会直接把你推到教堂。” 多么聪明的主意。你卷起袖子开始烤。 蛋糕完成后，您将其掏空并爬进去。 “我有最后的礼物给你，”披着斗篷的女人说。她张开双腿，蹲下。一个鸡蛋重重地掉在地上。“这颗蛋包含了我最后的力量。破解它会给你巨大的力量。” 血腥的胎衣顺着她的腿流下。她开始褪色。 “我最亲爱的果酱，”她轻声说。“我会多么想念你甘露的甘甜。” 翅膀一振，女人不见了。 被糖果包围着，你意识到你已经有一段时间没有吃任何东西了，这个蛋糕看起来几乎和森林里的蘑菇一样好吃。当然，一点点味道都不会受到伤害。
--------------------
动作：
做
你不要再想杀人了！
结果：
你明智地决定不吃你的伪装。 已经安排好撒切尔家族的男孩将蛋糕送到城堡。在那里，一个肥胖的守卫用他油腻的手指穿过糖霜来品尝蛋糕。“可口的！” 他惊呼道。“马上拿去厨房。” 你在厨房里听到一个熟悉的声音：“小心那些糕点，男孩！放下一个，你会感觉到我的手背。” 即使声音被蛋糕压住了，面包师也没有错。 复仇的冲动几乎和你拯救公主的愿望一样强烈。你抚弄围裙口袋里的魔法蛋。
梗概：
你不要再想杀人了！
--------------------
动作：
做
你杀了面包师
结果：
你把鸡蛋砸在头上。力量在你的四肢中涌动。你的男子气概膨胀到原来的三倍，黑色卷发从你的胸部和背部长出。你从蛋糕上炸开，用巨大的拳头砸在面包师的脸上，骨头碎裂，白头破裂。吐痰的男孩和洗碗工的女仆尖叫着跑。 你一次又一次地把面包师的破碎的脸撞到烤箱的侧面，灼伤他的肉。当除了空的外皮什么都没有时，你把他对折，然后把他塞进烤箱。魔法会消失，你的嗜血欲望也会随之消失。你跑去教堂。 宏伟的彩色玻璃窗将教堂沐浴在彩虹光中。客人坐满了每个长椅，更多的人站在上面的阳台上。礼物高高地堆在房间中央的桌子上。其中有独角兽的乳白色角。 公主和侯爵们背对着你，站在教堂顶端的祭坛上。他正要把戒指戴在她的手指上。你喊着果酱的名字，立刻被守卫包围了。弩手从阳台上走出来。这是一次伏击。 “好吧，主宾终于到了，”侯爵笑着说。他从斗篷下露出了梦想的竖琴。“你的干预已经让面包师的孩子变得陈腐了。到此为止。” 他拨动着琴弦，整个大厅都在颤抖。竖琴的力量不应该掉以轻心。 房间剧烈震动。柱子开裂，地板在你脚下扭曲。客人们惊慌失措，纷纷逃往出口。在混乱中，您可以摆脱守卫。 在他摧毁整个教堂之前，必须阻止侯爵。你冲向圣坛，但温德尔挡住了你的去路。彩色玻璃窗爆炸了，五颜六色的剃须刀洒在大厅里。你必须迅速行动。 温德尔拔出剑。你后退一步，勉强避开他的刺击，直到你撞到礼物堆，再也无法撤退。你拼命摸索着武器，你的手找到了独角兽的角。你把喇叭向上吹，像酒馆妓女一样穿透温德尔。他的表情从胜利变成了茫然。剑从他的手中掉了下来，他倒下了。你听到独角兽的声音低声说“我的灵魂会一直守护着你。” 马克斯感觉到失败，从窗户跳到下面的骏马。你冲到公主身边，搂着她。“没有时间！” 她喊道。“我们必须在它摧毁整个城堡之前取回竖琴。爬上去。” 你爬到她的背上。她张开翅膀，飞过窗户追赶。
梗概：
你杀了面包师
--------------------
动作：
做
追逐开始
结果：
与公主一起飞行就像您想象的那样令人兴奋。你无法掩饰你的兴奋。“别戳我了，我想飞，”她抱怨道。 露天市场出现在您的下方。他们现在空旷而安静。你追逐马克斯到射箭场。公主折叠她的翅膀，开始她的攻击俯冲。“等等，我们——” 一根金色的箭从她的喉咙里冒了出来。马克斯从比赛中取回了他的弓箭并松开了一个致命的轴。公主无法控制地侧翻，你从天空中翻滚。 你冲进了马克斯，打破了你的跌落。他被冷倒了。你爬到公主身边，抱着她毫无生气的身体。泪水从你的眼眶里流出来。 这怎么会发生？一切都已经白费了。她爱你，她需要你，而你辜负了她。一声破烂的尖叫从你的喉咙里流出来，然后你看到了竖琴。当然！竖琴的力量可以使她苏醒。你像以前从未玩过一样玩。你把你所拥有的一切都融入了这首歌。你到达最后一行： “魔法的力量，听我的呐喊。梦之竖琴……”
梗概：
追逐开始
--------------------
动作：
做
你不能让公主死！
结果：
公主被竖琴的力量复活了。竖琴破碎，它的目的达到了。侯爵很快被逮捕并因叛国罪被投入地牢。 你和公主马上就结婚了。您的加冕礼遵循所有无聊的传统和仪式，例如守夜、仪式和人祭。 你期待已久的时刻终于到来；你发现自己和公主单独在一起。她赤身裸体地坐在床上，精致的翅膀遮住了除她泛红的脸之外的一切。她紫罗兰色的眼睛说出了她嘴里害怕的一切。
梗概：
你不能让公主死！
--------------------
动作：
做
你吻她
结果：
在短暂而尽职地完成之后，你躺在床上，双手搂着公主。你告诉自己“它发生在每个人身上”，但你知道这是一个谎言。 “我迫不及待地想共度余生，但等我老了你还会爱我吗？” 公主问道。 “当然，”你回答。 “如果我胖了你还会爱我吗？如果我秃了你会爱我吗？如果我坐在轮椅上你会爱我吗？如果我所有的皮肤都掉了，我的骨头都长了水痘，你还会爱我吗？” .” 各位大神们，你们做了什么？ 结束 这种女人的胡说八道。快，回去挠她的乳房！ 你找到了一个 D。发现更多的字母来解开传奇之谜。
梗概：
你吻她
故事名称：
46
故事背景：
“他们说圣人有一千年的历史，可以预见未来一千年。当然，他们也说我们半人马不能擦自己的屁股，”半人马说。“总之，没有人见过圣人，就算见过，也活不长，谈不上。向东过河，到十字路口，那里会出现一位精神向导，带你去见圣人。” ” “但要小心鬼城！” 他警告说。“它被一个邪恶的恶魔统治着，她不值得信任。” 在沿着森林小径向东走之前，你最后看了一眼半人马的女儿。半人马在你身后喊道：“你知道这不是真的！我们可以擦屁股！” —— 当您到达河流时，太阳高高地挂在头顶。 这条河太宽太强，不能涉水。如果你有一辆马车，你也许可以填塞它并漂浮过去。闹鬼的城堡不祥地矗立在河边，守卫着唯一的桥梁。散发着邪恶的气息。幸运的是，还有一艘渡轮停靠在近岸。
--------------------
动作：
做
你走桥
结果：
还没来得及敲门，城堡的门就打开了。 一位老太太向你打招呼 她的皮肤像干苹果一样皱巴巴的。她卷曲的蓝色头发塞在帽子下，她饱经风霜的乳房塞在长袍的腰带里。她透过厚厚的眼镜斜视着你。“哦，一个访客！多么令人兴奋。进来。进来，”她说。“你闻起来像半人马的马厩。我从不关心半人马。说谎的荡妇。” 城堡内部没什么特别的，除了弥漫的老人味和丰富的装饰板。你感觉不到你以前经历过的邪恶存在。 “你们这小子总是这么赶时间，我坚持要你们留下来休息一晚。这里太孤单了。我会感谢有你们的陪伴。你愿意在上班前和我一起吃晚饭吗？”
梗概：
你走桥
--------------------
动作：
做
你和她一起吃饭
结果：
一张长桌摆满了美味的食物和饮料。老太婆在桌前坐下，你在远处坐好，把酒倒满你的高脚杯，从最近的烤肉上切下一块厚厚的脚肉。 “各位，我想让你们见见我们的客人，面包师男孩，”老妇人说。你环顾四周，剩下的座位都空了。“杰拉德，坐直。亲爱的伊莎贝尔，不要在桌子上闻你的手指。这很粗鲁，”她对任何人说。“对不起。鬼的餐桌礼仪最差。告诉我，你出去做什么？” 你兴奋地告诉她和鬼魂你到目前为止的冒险经历。 “哦，我可怜的孩子，”老太婆说。“那个小号已经把你忘记了。” 她从桌子底下抽出一个水晶球，在上面挥动她的手。里面形成一幅画。你看到公主与侯爵和贝克在床上。不可能的！她永远不会……是吗？ “我一直想要一个像你这样的儿子。你为什么不忘记那个酸果酱，留在我身边？”
梗概：
你和她一起吃饭
--------------------
动作：
做
你留下来做她的儿子
结果：
“你现在是我的了，”老妇人说。 你的身体开始刺痛，你的视力发生变化。房间越来越暗，鬼魂一个一个进来。伊莎贝尔向你挥手致意。数百个鬼魂充满了大厅，每一个都是像你一样疲惫的旅行者。 现在可以看到老太婆的真身了。她是一个蓝色皮肤和四只手臂的仁慈恶魔。“来喂，我的孩子们，”她招手，打开她的长袍，露出十几个肿胀的乳头。你依偎在杰拉德身边，让自己充满母爱。 你到家了。 结束 瓶装的东西不能与真实的东西相比。 你找到了一个 I。发现更多的字母来解开传奇之谜。
梗概：
你留下来做她的儿子
故事名称：
47
故事背景：
今天是展会的日子！ 太阳还没有完全升起，但你的家务已经完成了。烤箱点燃，面团揉好，面包刺破，地板扫地，锅子擦洗。如果你明白我的意思，你甚至有机会砍一点晨光。现在你的主人肯定会让你在集市上度过余下的一天。自从它第一次宣布以来，你就没有想过别的。 你正忙着白日梦到 Marmalade 公主，再次，阁楼传来一声叫喊；“男孩！” 你脾气暴躁的主人终于醒了。当他爬到厨房时，梯子压在他的身下。“放开你的屁股，男孩，把烤箱加热，”他说。 “师父，炉子已经暖和了。我的家务都做完了。我想我或许可以去展销会了？” 你回复。 一只肉肉的手敲击你的脸。“集市！集市是为吟游诗人和花花公子举办的。你是哪一个，男孩？或者你认为你会赢得比赛并嫁给公主？” 他笑着说。“面包王的男孩万岁！忘掉那些东西，去给我拿些牛奶来。” 他递给你一块铜板，又啪地一声送你出门。 “如果我发现你去了集市，孩子，我会把你的脸锁在烤箱上！” “混蛋，”你一边揉着肿胀的嘴唇，一边低声咒骂。有一天，你会把他胖胖的脸拴在烤箱上。你走向送奶工的摊位。他总是有一瓶烈酒与年轻的学徒男孩分享，他的女儿贝莎拥有你见过的最大的牛奶罐。 你走到他的立场，但没有人在那里。
--------------------
动作：
做
你搞砸了，去展会
结果：
拧紧送奶工，拧紧面包师！这是博览会的日子。你的口袋里有一个铜币，你会很享受的！你迈着脚步往街上走去。 今天早上主街异常拥挤。看来你不是唯一一个起步较早的人。你挤进人群，跟在三个农民身后，尽管时间一小时，他们似乎已经喝醉了。他们一起在街上跌跌撞撞地唱着诸如“两只老狗”和“摇摇欲坠的寡妇”之类的下流歌曲。 当一个标志吸引您的眼球时，您正准备加入“旅馆老板的女儿”的合唱团。上面写着：“破碎的紧身胸衣。” 一个有大量乳沟的女人从门口叫你。“嘿，面包师的孩子，有时间去翻滚吗？”
梗概：
你搞砸了，去展会
--------------------
动作：
做
你去妓院
结果：
The Broken Bodice 的内部昏暗潮湿。装饰是红色天鹅绒、金色流苏和超大枕头的华丽组合。一股压倒性的香水气息让你的鼻孔窒息，但它无法掩盖湿饼干的恶臭。美女们在房间里闲逛，脱衣服的各个阶段，宠爱着热切的顾客。大多数主顾都是富有的老人，有些太老了，他们可能会喷出灰尘。 将连绵起伏的山丘塞进紧身胸衣的女人是这家精美酒店的夫人。“哦，我敢打赌这是你第一次”她咕哝道。“把它留给我的女儿们，让他们成为面包师的男人。” 你把你的铜交给她，并要一位漂亮的女孩。她把硬币塞进乳沟里，然后嗤之以鼻。“铜钱买不到公主，但我想我们可以找到一些面团让你刺破。”
梗概：
你去妓院
--------------------
动作：
做
你和 Grizzela 一起睡
结果：
夫人护送您到妓院最远的一个暗室。“祝你好运”她说，在你身后关上了门。你听到螺栓砰地一声落下。 一个男中音的声音从阴影中呼唤你，“哦，我爱我一个年轻的人。” Grizzela 的方形下巴上覆盖着淡淡的胡茬，一条天鹅绒围巾遮住了“她”脖子上的肿块。“别害怕，糖。我会向你展示你梦寐以求的东西。” 她的怀抱如铁一般。
梗概：
你和 Grizzela 一起睡
--------------------
动作：
做
你挠她的乳房
结果：
你抓住一个看起来像乳房的东西，用尽全力拉扯。格里泽拉发出一声喉咙里的呻吟，她的秃头发出鲜红色的光。你的努力会得到一份浓奶油的回报。 她倒在稻草上，喘着粗气。在她作为妓女的五十五年里，没有人给过她如此多的乐趣。 当夫人发现你的所作所为后，她递给你一袋银子，坚持要你为她工作。你试图拒绝，但格里泽拉的铁腕不容争辩。这不是你想象的生活，但有钱就是好。人们从王国的遥远角落旅行到柔软的贝克男孩身边。 结束 这不是我的想法。当我说你注定要成为国王时，我不是说妓女之王。也许你应该再试一次...... 你找到了一个美国。发现更多的字母来解开传奇之谜。
梗概：
你挠她的乳房
故事名称：
48
故事背景：
冷。 这是迄今为止这次旅行给您的总体印象。公平地说，您正在寻找艰苦而艰难的徒步旅行，但整个想法在家里，在欢快燃烧的壁炉前听起来要好得多。 你的生活从来都不是一帆风顺的，但最近几个月真的一点都不好玩。你在短短几个月内失去了你的爱人、工作和最后一个家庭成员。对于汽车、疾病和对你老板的三重怀疑：浪费你姐妹生命的疾病是一种在家庭中遗传的疾病。 一旦你的老板听说了——诅咒你，谷歌分析——他立即使用你合同中的一个子条款来烧毁这件事并解雇你的遗憾。一旦你的女朋友听说你突然即将破产——诅咒你，谣言制造者——她想了想，“如果这段关系休息一下，对你来说是最好的。我只是在想你，亲爱的。” 一旦你的母亲听说了这一系列……坏事……发生在你身上，她决定你需要一些安慰，让你的生活仍然很好，你仍然有家人和爱你的人。也就是说，她计划去拜访你，因为在穿过将当地电车站与你的小屋隔开的街道时，她被撞倒了。瞬间死去。 在那之后，你在唯一总能安慰你的东西中寻求安慰：白雪皑皑的冰山。为了摆脱通常是您的目的地的“无聊”阿尔卑斯山，您在 11 月的某个时候前往乌拉尔的白雪皑皑的山峰。 现在，那是一月，而你，你迷路了。 不管你为什么在这里，现在重要的是 a) 找到回到文明的方式，b) 活着。你有一个帐篷和一些食物，但尽管天空晴朗，冰冷的温度让你想要生火。感谢上帝，这不是你第一次在寒冷、孤独和寒冷的夜晚外出。另外，你知道如何生火，你只需要一些火种。
--------------------
动作：
做
你去觅食
结果：
虽然呆在帐篷里可能会让你活着，但你真的不想冒险。 类似的暴风雪总是有可能发生的，你没有白买一个带集成壁炉的帐篷。然而，你已经用完了煤炭。需要干燥的火种和木材，尽可能多地携带。 你知道当地人在山顶有一个木材仓库，但在下面，在真正的森林中，你可能更有机会真正找到大量的木材。
梗概：
你去觅食
--------------------
动作：
做
你下降到黑暗的森林
结果：
这片森林非常阴暗潮湿。 事实上，这也是你来到这里的原因之一。你试图从你在乡下远足的旧记忆中寻找快乐，跟随你亲爱的老爸爸。 这就是你来这里的真正原因。他消失在这些山中。嗯，在乌拉尔的某个地方。它们很大。而你已经迷路了。 但这就是他消失的地方。 当你收集（稍微湿的）火种时，你会注意到一个相当奇特的空地。它似乎是那些巫婆圈之一，蘑菇长成一圈。这些蘑菇可能是可食用的，但也可能是剧毒的。或者介于两者之间。
梗概：
你下降到黑暗的森林
--------------------
动作：
做
你去圆圈里看蘑菇
结果：
你知道你在做什么。您已经在这些山上存活了数月，并且知道哪些蘑菇可以安全食用，哪些让您卧床不起，浑身酸痛。 当你接近圆圈并直接蹲在它前面，打算识别蘑菇时，你听到了孤独的狼嚎叫。你跳了一下，吓了一跳，发现自己在圈子里。
梗概：
你去圆圈里看蘑菇
--------------------
动作：
做
你离开圈子
结果：
你紧张地环顾四周，但那嚎叫声听起来很远。你再次蹲下，撞到了你的头。 在稀薄的空气中。 你伸出手，做出一个哑剧的印象：似乎有一堵无形的墙，把你困住了。你慢慢地走在墙的周围，试图找到一条出路，但你没有发现任何缝隙。它是一堵无缝的墙，从蘑菇伸向天空。 你慢慢开始恐慌，寻找坚固的东西。因为你的那堆木头不在这个圈子里，你的刀留在帐篷里，你唯一拥有的就是你的手臂。你敲打着这堵无形的墙，但它是徒劳的。 “那行不通，亲爱的。” 突然，身后传来一个声音。 你转身看到一个女人漂浮在空中。她身材矮小，只有几英寸高，但务实散发着智慧和力量。 “你是做什么的？” 你脱口而出。“为什么我不能离开这个圈子？” 她微笑着，一种近乎人性的微笑，飘出了圆圈。你试着跟上，但再次脸朝前走到墙上。一个疑点浮现。 “你把我困在这里了？” 你尖锐地问。 她又笑了。“为什么，很明显。那毕竟是一个仙人陷阱。我的设计。用来困住愚蠢的凡人。” 她咯咯地笑。“每次都有效。” 你开始怀疑，毕竟你父亲关于仙灵的故事还有更多。他确实说过他去这些山上寻找恐怖分子……而且再也没有回来…… 另一个疑点浮出水面。并在片刻后得到确认。 “我太累了。我以前从未有过一套。你父亲是一只很棒的宠物，我相信他的后代也会如此。” 她兴奋地旋转着。 如果她不是一个把你囚禁的邪恶精灵（而且，显然是你的父亲），她会有点可爱。但她是，所以她不是。 “你对我父亲做了什么！” 你冲她尖叫，殴打你的无形监狱。 她不祥地咯咯笑着。“你很快就会知道的！” 然后她从某处抽出一根魔杖并开始挥动它。你可以感觉到圆圈内的空气开始旋转，开始缓慢，然后越来越快。 缓缓的，在漩涡的中央，一道传送门正在开启。 这是你最后的机会。
梗概：
你离开圈子
--------------------
动作：
做
你求饶
结果：
“请！不要！” 你拼命地乞求。“不要这样做！” 最后一次，仙女只是微笑着挥动她的魔杖。传送门变宽，你跌倒…… 落下 落下 落下 并降落在柔软的表面上。一圈。你抬头看到一张多年未见的脸。你的父亲。 “爸爸？” 你小声问。 他低头看着你，眼里充满爱意，回答道：“儿子？多可爱。她的力量确实很棒，如果她能把我们俩重新聚在一起。” 他抬眼看了一会儿，眼中闪过一丝冷意。“你妈妈和姐姐怎么样了？” 他接着问道。 “死的！” 你抽泣。“都死了，你怎么还不回来？” 他看着你，似乎要开始回答，却突然闭上了眼睛。 当他再次打开它们时，他的瞳孔是真正的心。他正凝视着天空。 你顺着他的目光，找到了困住你的仙子。 “那个邪恶的女巫。” 你对自己抱怨。不幸的是，仙子和你父亲都听到了你说的话。你父亲的反应是让你窒息。 “你竟敢称女主为邪恶的女巫！” 他在你耳边尖叫。“我会因为这种侮辱而杀了你！” 仙女看着你被勒死了一会儿，然后用她的魔杖动作。前一刻你父亲想杀了你，下一刻他就睡着了。你倒在长满苔藓的地面上，抬头看着仙子。 “你……你……对他做了什么？” 你问。 “同样的事情也会发生在你身上，亲爱的。你可能会在最初的几年里和我战斗，但我发现只要有足够的时间，任何凡人都会崩溃。而在我个人的树林里，你们凡人实际上并没有变老。有一天，你会像你父亲一样：一个忠诚的宠物。”
梗概：
你求饶
故事名称：
49
故事背景：
你紧张地环顾四周，但那嚎叫声听起来很远。你再次蹲下，撞到了你的头。 在稀薄的空气中。 你伸出手，做出一个哑剧的印象：似乎有一堵无形的墙，把你困住了。你慢慢地走在墙的周围，试图找到一条出路，但你没有发现任何缝隙。它是一堵无缝的墙，从蘑菇伸向天空。 你慢慢开始恐慌，寻找坚固的东西。因为你的那堆木头不在这个圈子里，你的刀留在帐篷里，你唯一拥有的就是你的手臂。你敲打着这堵无形的墙，但它是徒劳的。 “那行不通，亲爱的。” 突然，身后传来一个声音。 你转身看到一个女人漂浮在空中。她身材矮小，只有几英寸高，但务实散发着智慧和力量。 “你是做什么的？” 你脱口而出。“为什么我不能离开这个圈子？” 她微笑着，一种近乎人性的微笑，飘出了圆圈。你试着跟上，但再次脸朝前走到墙上。一个疑点浮现。 “你把我困在这里了？” 你尖锐地问。 她又笑了。“为什么，很明显。那毕竟是一个仙人陷阱。我的设计。用来困住愚蠢的凡人。” 她咯咯地笑。“每次都有效。” 你开始怀疑，毕竟你父亲关于仙灵的故事还有更多。他确实说过他去这些山上寻找恐怖分子……而且再也没有回来…… 另一个疑点浮出水面。并在片刻后得到确认。 “我太累了。我以前从未有过一套。你父亲是一只很棒的宠物，我相信他的后代也会如此。” 她兴奋地旋转着。 如果她不是一个把你囚禁的邪恶精灵（而且，显然是你的父亲），她会有点可爱。但她是，所以她不是。 “你对我父亲做了什么！” 你冲她尖叫，殴打你的无形监狱。 她不祥地咯咯笑着。“你很快就会知道的！” 然后她从某处抽出一根魔杖并开始挥动它。你可以感觉到圆圈内的空气开始旋转，开始缓慢，然后越来越快。 缓缓的，在漩涡的中央，一道传送门正在开启。 这是你最后的机会。
--------------------
动作：
做
你试图达成交易
结果：
“让我们做个交易吧！” 你拼命地喊。凭借您对 Fae 的了解，永远不会对凡人施以怜悯，而交易总是会进行的。 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 它似乎正在工作。漩涡正在放缓，传送门正在缩小。她漂浮在圆圈里，深深地看着你的眼睛。 “还有什么，请告诉我，你愿意给我买你的自由吗？” 她眼中闪烁着光芒问道。 你不确定。“一定有你想要的吗？” 你在寻找线索，她知道。 “嗯，她说...
梗概：
你试图达成交易
--------------------
动作：
做
你……你可以得到我偷来的宝物……”
结果：
“它是一颗宝石，被一个叫铃铛的人从我这里偷走了。” 她解释说。 当她说出它的标题时，她的声音充满力量，一个阴影似乎穿过了傍晚的太阳。“我在哪里可以找到这个托勒？” 你询问。她对缩短的形式畏缩并告诉你：“我会把你送到生锈的门。钟声生活在一个死亡的领域，曾经由对金属、技术和创新的看似无限的信任提供动力。现在，这信任被打破并重新塑造成纯粹的贪婪，这个领域崩溃并失去了生命。像钟声这样的人的理想环境。” “你为什么不去那里旅行，拿回你的东西？” 你想知道。“因为一个由金属构成的整个存在对我的灵魂来说是有毒的，傻瓜！我抓到和玩的凡人总是很快。无论如何，如果没有巨大的影响力，我永远不会相信凡人。” 顿悟。“这，在我父亲身上，你有。” 她再次微笑。“的确。我会带你到锈门，你进去，找到我的珠宝，然后带着它回来。否则，你再也见不到你父亲了。”
梗概：
你……你可以得到我偷来的宝物……”
--------------------
动作：
做
你同意她的提议
结果：
“你同意吗？很好。那里很危险，我确实想要那颗宝石。有这个。这是一瓶水，是从 Mímirsbrunnr 水域偷来的，在世界树 Yggdrasill 的底部。它会给你宝贵的洞察力，但只是几秒钟。如果你不知道该怎么做，喝它。它会让一切都清楚。” 然后她咯咯笑，最后一次挥动魔杖，然后送你走。 漩涡和传送门一样再次生长，但这次是蓝色而不是绿色。你深吸一口气，然后跳进去。 你降落在雪堆上。往下看，可以看到一扇门。 “奇怪的。” 当你检查生锈的门时，你对自己说。它只是站在那里，没有充分的理由你可以看到。门的两边各有两个箱子。
梗概：
你同意她的提议
故事名称：
50
故事背景：
“妈妈，看鸟！” 一个孩子的声音叫道。 你猛地惊醒，在你想起自己乘坐的是民用交通工具之前，会自动检查你的武器。 “是时候了，Zee。我们半小时前离开了地下空间，”辛斯曼在你身边说道。“像黄油一样光滑。他们现在在这里设置了稳定器，我不知道。自从我上次来到这里以来，这个系统确实做了一些工作。” 他已经五十多岁了，但还没有看他的年龄，他应该打扮成一个小行星投机者出去度假，在座位狭窄的地方挣扎着拉他的外套。Hinsman 的偏短，而且这件衣服大了几个尺码。你周围的其他人、游客、商人和家庭也开始收集他们的东西。 “总之，现在就等着我们的传送。” 你解开你的安全带。“好的。” Hinsman 占据了过道的位置，这只是他不言而喻的另一个因素，即阻止您与公众进行过多的直接接触。对你很好。你的工作一直是保护这些人，而不是进行对话。凝视窗外，您可以欣赏到绿色星球的美景。卡马罗夫四世 应该很美，但又不是来观光的。这只是您返回工作岗位的最快途径。 你一直在使用的秩序巡洋舰在你的上次任务中受到了足够的打击，需要被带到真正的造船厂进行维修，同时这艘运输船直接前往卡马罗夫，有两个空缺。还有另一艘命令船正在前往这里的途中，您将转移到它并带回外环。你甚至永远不会踏上这个星球。 随着登陆舰锁定，船员开始将乘客引向出口，发出轻微的砰砰声。欣斯曼梳理着他的黑发，抚摸着他一直留着的小胡子。你只需坐等。 您的手腕上的两个通讯器同时发出哔哔声，您会看到 Order 飞船已经离开了地下空间。当它要求锁定时，船员们之间有点慌乱，但 Hinsman 闪烁着一个徽章和他标志性的微笑，你们两个没有发生任何意外地转移了。 上船后，船会解锁并移动到安全距离进行跳跃。然后有一个延迟，因为飞行员似乎无意离开，直到他下载 Camarov IV 必须提供的任何媒体。舞台上青山、雾蒙蒙的湖泊、水晶尖塔和舞者的图像以及对旧地球的某种历史重新想象在屏幕上翻滚。 飞行员 Karoff 是中年人，身材高大，稀疏的金色头发刚刚开始变灰。然而，他的胡子比欣斯曼的要令人印象深刻。你以前和他一起工作过。你并不总是能理解他的幽默感，但他很好。 下一站将是加油的最后前哨站，然后您将被带去风景旅游，在外环最无聊和最危险的部分之间交替进行，您将在那里履行您的各种义务并提供帮助在紧急情况下。 你靠在墙上并在精神上检查，直到是时候系上安全带跳到下层空间了。Hinsman 是对的，系统内稳定器确实改善了很多体验。 ***** 当你接近时，最后的前哨是黑暗的。 “嗯，这不是一个好兆头，”卡洛夫喃喃自语。在系统单一贫瘠小行星的苍白背景下，空间站像一只肥胖的黑色蜘蛛一样漂浮在原地。Karoff 进行了一些调整以使您的方法不那么明显，然后开始从远处进行扫描。你花了将近一周的时间才离开卡马罗夫这么远，而且你们都知道，如果有麻烦，在你找到任何备份之前也一样长。 The Last Outpost 曾经是位于受保护空间边缘的研究医院。现在它已经超越了官方路线，尽管在回滚之后它仍然是军队和殖民地世界可以聚集在一起撤离的任何垃圾的中途停留和加油站。 正式停止服务并现在被遗弃到外面的任何人命令应该知道，它仍然有足够的自动防御系统，任何寻找废料的清道夫都会被匆忙打包。或者它曾经，无论如何。Hinsman 现在也对控制台皱眉。 “那是很大的伤害。” “海盗？” 欣斯曼摇头。“我不这么认为。一些镀层的扣子方式，从内部看起来就像是一连串的爆炸。大多数情况下只是在那只手臂上，但防御系统处于离线状态，空间站上的任何地方都只有基本的生命支持。” 扫描完成时会发出一声轻柔的哔哔声。 “一个生命迹象，”卡洛夫咕哝道。 “就算是把人都拉出来执行任务，这里也应该至少有三个人来维护吧。” 辛斯曼说完话，就站在窗边和你一起看向远处。“但是……我想我会为他们欢呼。” “你确定？” “不妨。反正看起来他们已经没有多少可以向我们开枪了。” “让我们希望外表不会骗人。”
--------------------
动作：
做
您询价
结果：
当 Hinsman 试图联系电台时，Karoff 看起来很紧张。有一个漫长的等待没有答复。他皱起眉头，再次尝试使用订单密码。 一个绿灯闪烁，片刻之后，一个看起来憔悴的金发女人，颧骨高，嘴唇薄，带着静电噼啪声出现在视屏上。“所以终于有人出现了。” 女人的声音平淡而空白。她年轻，在其他情况下可能很漂亮，但她的眼睛下面有黑圈，她的脸色蜡黄，头发已经有一段时间没有梳过。她将勺子浸入一桶正在融化的滴溜溜冰淇淋中，冷漠地注视着屏幕。 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Karoff 拿起她的身份证，Hinsman 咳嗽并清了清嗓子。“啊……你是马蒂·奥斯文，对吧？” “是的。” “我是辛斯曼探员。我们只是在前往 Rim 的路上停下来，但看起来你在这里遇到了一些麻烦。需要帮助吗？” 他保持轻松的语气，并注入了友好的关心。 她向他眨了眨眼，然后把冰淇淋放在一边，轻轻挠了挠脖子的一侧。“我打了一个电话，但电脑断电了，我不确定它是否接通了。那是……差不多一周前，我想。” “你是维修工吧？马蒂，这里发生了什么？还有其他人呢？” “伊尼亚和马尔科夫？哦，他们死了。” 她的声音仍然平淡，而且出奇地冷漠。 卡洛夫也尽职尽责地拿出了他们的身份证。无论如何，他们作为已故者的新状态尚未在 Order 数据库中更新，而且由于你们三个没有听说过有关车站损坏或救援尝试的任何消息，因此您只能假设她的遇险信号从未发出过。 奥斯文在辛斯曼再次提示她后才继续。“伊尼亚在修理时发生了事故，马尔科夫自杀了，几天后试图带走车站的其余部分。我及时更改了电源以重定向爆炸，但大多数发电机仍然爆炸。我一直坐在黑暗中，试图在冰箱里的所有东西都变质之前吃掉它们，然后想在氧气耗尽之前给自己注射一些东西。” 欣斯曼的声音很舒缓。“好吧，我很抱歉你不得不经历那个马蒂，但你现在会安全的。我们将发出帮助请求，然后锁定以查看在此期间我们可以做些什么来修复。无论如何，我们可以传递足够的能量以确保维持生命。 “任何。” 呼叫中断后，遇险信号会连同一段简短的录音信息一起发送回总部。你们三个简单地回顾了一下情况。 欣斯曼总结了这一切。“要过几天才能有人来帮忙，在那之前我们不能离开。即使这里没有幸存者，车站本身也很脆弱和地狱，如果我们说实话，它可能比我们任何人都更需要秩序。虽然这个奥斯文女人……她可能是震惊了，也可能是那里发生了其他事情。不确定我们可以信任她。” 卡洛夫吐出胡须末端。“我们要进去，发现那个婊子割开她们的喉咙，用她们的皮肤制作比基尼上衣。以前见过这种情况。” 你看着他。“真的？” “啊，好吧，不是比基尼部分的剥皮，但人们被分配到这样一个偏僻的地方太久了，他们可以直接开始互相残杀。或者可能有某种三角恋正在发生，变得令人讨厌。而不是那种令人讨厌的好东西，”他补充说，显然觉得有必要澄清一下。 “好吧，我想我们到时候就只能冒着风险了，”Hinsman 微微翻了个白眼说道。“或者你和我，卡洛夫。但是Zee，无论如何你总是要一个人工作。如果你自己去，你不会让我们在轨道上待命，但如果工作足够紧急，没有什么可以阻止你穿上衣服并乘坐备用航天飞机。” “我们可能还需要一段时间才能确切地知道前哨站受到的破坏有多严重，”卡洛夫指出。“这里可能需要她。” “是的，但是……Zee，我想这个是你的电话。我没有你被要求执行的任务的详细信息，所以这里的协议是什么？” 当然，如果您可以直接联系指挥官，这一切都会更简单，但在这里几乎不可能，而且必须在飞行中做出这样的决定是工作的一部分。 ORDER 最初代表外缘防御、评估和研究。每个人都喜欢开的玩笑是，当饥饿病毒出现在现场时，E 代表 Evacuation，最后是 Eradication。 当然，这是你们仍在努力的部分。而且实际上并不是那么好笑。 骑士团是越过受保护空间边缘的维和部队，尽管不再以任何官方身份存在。二十多年前，当饥饿病毒达到顶峰并从一个星球燃烧到另一个星球时，边界被回滚，然后又被回滚。殖民地和采矿设施、贸易站和前哨在混乱中被切断，任何不愿或无法撤离的人只能自生自灭。 一些秩序特工被召回新泰拉，但对于更多人来说，这是他们的家，这就是他们所做的。势头和习惯已经走了很长一段路，该组织已经拥有足够的船只、站台和设备，可以像往常一样继续运作。如果有的话，现在官僚主义和繁文缛节都被剥夺了，他们更有效了，尽管这有时会产生自己的争议。 独立的、孤立的殖民地、小型矿工和商人没有人保护他们免受盗版的侵害，更不用说贸易财团了，它们相当于同一事物的更有组织的版本。他们一方面欢迎教团的存在，另一方面憎恨他们认为干涉自己事务的任何事情。在新泰拉和边境当局之间界限模糊的地区，某些方面也从来没有不指出，骑士团的管辖权并不比任何其他私人组织多；无论如何，在古老的权利之外，还有关于占有以及它与法律的关系。 尽管在纸面上，该组织从一开始就遵守相同的法律和标准，但他们和任何人一样独立于这里，也必须考虑到自己人民的利益和保护。新鲜血液可能很难找到，因为它不会仅仅因为它是正确的事情而要求它自己溢出。关于唯一任何人都同意的事情是，秩序是唯一有能力真正对饥饿病毒做任何事情的人，所以他们应该被留给它，不问任何问题。 谢天谢地，政治和其他胡说八道不是你需要关心的任何事情。 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 你所做的就是你的工作，你所担心的就是把它做好。现在，这意味着要对您在哪些方面最有效做出判断。而现在，它似乎确实在这里。你得到的任何东西都没有表明任何人都处于直接危险中，最后哨站对骑士团来说与你被要求检查的任何其他设施一样有价值，而且在战略上更重要。加油站首先使在 Rim 的这个区域进行这些小尝试成为可能。 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
梗概：
您询价
--------------------
动作：
做
你保护了车站。
结果：
你向其他人表明你暂时留下来，Karoff 指引船沿着一条通往车站的路线前进。 ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 至少对损坏进行粗略检查并确保至少生命支持能够保持是有意义的。当然还有另一个问题，即所谓的破坏和人员在仍在等待确认的情况下丢失。只留下两个操作员来处理这一切并不理想。 在空间站的八个臂中，幸运的是对接舱位于受损最小的一侧。卡洛夫毫无意外地锁定了门，加固传送门的面板随着轻微的呼呼声缩回。远处，车站又黑又冷。在手电筒的帮助下，你们都排队离开着陆区，沿着走廊走下去。 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 由于剩下的微弱力量被重新路由到生命维持系统，通往中央综合体的门户拒绝打开。试图通过短程频道联系 Oswen 失败了，所以 Hinsman 终于在他的枪的帮助下打开了门；将能量电池连接到它旁边的面板，也就是说。在动力的冲击下，它以突然的速度突然打开，然后又开始缓慢关闭，并在大约三分之一的路程中停下来。你们三个挤过去，很快就会发现自己在一个空旷的地方，一个联合餐厅和健身房，墙上挂着死掉的视频屏幕。 餐厅后面的厨房区域是一个充满空塑料托盘、垃圾桶溢出和变质食物的灾区。尽管你确实找到了她遗弃的冰淇淋桶，但没有马蒂的踪迹。你们三个开始逐个区域搜索。虽然不愿意分手，但你确实比其他人行动得快一点，并在他们前面检查房间和拐角处。 抛光灰色瓷砖上的一滩鲜艳的深红色是您第一次确认出现问题。你跑到马蒂·奥斯文皱巴巴的身子上，检查她的脉搏，另一只手放在她被切断的手臂上。把女人从地上抱起来，向其他人喊道，你溅起自己的血迹，在你身后追踪它，同时更多的东西浸透你的制服。 医疗站是你们三个已经搜索过的地方之一，所以你赶紧过去，把她放在一张钢制桌子上，在 Hinsman 启动计算机的时候把手保持在伤口上方。 “这个护理人工智能在 15 年前就已经过时了，”他抱怨道，从一扇门不断响起的门里拿出一套防护服，把它拉到衣服上。 “是不是有点矫枉过正？” 卡洛夫看着他问道。 Hinsman 的反应被呼吸过滤器压住了。“没有它，我什至不会继续前进。这个地方最后用来隔离暴露在饥饿中的撤离人员，就计算机而言，没有什么过分的东西。很高兴整个车站不会在有人被剪纸的那一刻自毁。” 他停顿了片刻，开始使用止血带。“现在……泽，你退一步，它也讨厌你。” 卡洛夫看着你，哼了一声。“我想是这样。你看起来像是刚刚离开了斧头谋杀的现场。” 你已经在欣斯曼的命令下搬走了，现在你懊恼地擦了擦脸，希望能得到一个干净的地方。卡洛夫随后的大笑告诉你，你可能只是让情况变得更糟。 “我会去收拾大厅里的烂摊子……还有我自己，”你说，转身要走。 “Zee，等等，等等，Oswen 小姐需要进行手臂移植手术，你不觉得吗？” 你对着卡洛夫眨眼。“你是认真的吗？” 这只会引起更多的笑声。 Hinsman 一直专注于医疗 AI 舒缓的声音和使用从小抽屉中取出的仪器，这些仪器一直在依次打开，现在他在换上干净的手套时对你们两个摇头。“恐怕我不是那么好的医生。泽，你继续。” 他对着卡洛夫打了个响指，另一只手拿出一根超大的针，就像魔术师的把戏。“但我需要一个献血者，你猜谁适合？” 已经在你出去的路上，当门在你之间滑动时，你转身瞥见了飞行员的鬼脸。 清理是一件乏味但简单的事情。在返回事故现场并开始工作之前，你擦洗自己并换上一件你找到的普通灰色连身衣。
梗概：
你保护了车站。
--------------------
动作：
做
你处理断臂
结果：
似乎奥斯文一直在试图打开通往车站一个储藏室的门，就像 Hinsman 早些时候让你们进入中央综合体的方式一样。不幸的是，在收到维修工具的震动后，门突然关上了，这意味着它本来是开着的。 剩下的汁液足够让它回到原来的位置，你小心翼翼地取回另一边的断肢，把它放进一个生物危害袋。花点时间与 Hinsman 确认，就重新连接它而言，他无能为力，您可以考虑处置选项。焚化炉是最直接的方式，但考虑到该站目前依赖紧急备用电源来维持生命，焚化炉需要大量能源，即使不是不可能生产，也似乎是浪费。 你还记得餐厅后面厨房里的冷冻柜。至少，如果它需要更长的时间才能完全发挥作用，那至少可以防止它腐烂。房间无法再维持冰冷的温度，但它仍然明显比车站的其他地方冷，车站的其他地方也足够冷。将袋装的手臂放在架子上，当你的目光盯着后面的两个捆绑形式时，你停了下来。因此，您并不是第一个想到将这个地方用作临时停尸房的人。这些一定是另外两名维修工 Inea 和 Markov 的尸体。
梗概：
你处理断臂
--------------------
动作：
做
你检查尸体
结果：
首先打开最大的尸体袋，您简要地检查马尔可夫。割伤、烧伤和一些碎玻璃仍然嵌入皮肤的证据似乎与他曾在爆炸附近一致，尽管马蒂的简短解释中有很多悬而未决的问题。你想，更多的细节必须等到她再次清醒。 你拉上袋子的拉链，把注意力转向伊尼亚。黑发女人的死因显而易见，她左侧的头骨已经塌陷在耳朵的正上方和后方。“修理时发生事故”是马蒂描述她死亡的方式。虽然你还没有找到任何与此相反的东西，但当你沿着头骨碎片和大脑物质的冷球的边缘进行探测时，在你看来，它会花费大量的力量集中在那个地方立即造成那种伤害。 话又说回来，马蒂也差点自杀了。你想知道它应该是怎样的事故。如此无能的三个人被留下来负责如此重要的前哨基地，这对您来说几乎是不可思议的。的确，空间是一个很大的地方，而秩序特工和人员却很稀薄，但根据您的经验，很少有事情在没有人过错的情况下出现如此惊人的错误。 没有发现更多值得注意的东西，你也关闭了那个尸体袋，然后离开了冰箱。从厨房的角落里抓起拖把，装满一桶水，你回到找到马蒂的地方，开始处理快速干燥的血液。血液已经开始变干，很明显这需要一段时间，所以你可以理清思路，专注于任务。
梗概：
你检查尸体
故事名称：
51
故事背景：
你是凶手。 先冲进那个地方，然后把一切都炸成地狱。突袭看守所只是为了杀死那些强奸并迫使你心爱的人自杀的人毕竟不是一个好主意。你甚至不是她喜欢打扰的类型，你应该让他们在监狱里腐烂。现在你有成排的警察追赶你，没有回头路。你现在在你所爱的人眼中是黑面包，你必须为它而奔波。每当您看到转弯时，您只能希望它不会导致死胡同。 那个电话不应该打到你。疯狂的爱，你把所做的一切都归咎于它。它迫使你逃离你的办公室，把你的思想变成一个疯子，把那个东西放在你手上。之后，只剩下一片混乱。当里面的火焰熄灭时，你会结束自己的生命......但没有......你仍然找不到勇气。 “拉过来！！！现在拉过来！！！你听到我了吗？！！我要开枪了！！！” 他们终于用子弹说话了，嗯？也许你应该专注于踩油门而不是随机闪回。
--------------------
动作：
做
你射他们，这是自卫！
结果：
你没有被包围，所以除非你像拇指酸痛一样伸出你的头，否则他们甚至对你进行清晰的射击将是一个真正的痛苦。但是，轮胎立即让您的手指抚摸到您直接从市场上获得的大量流血事件。解锁安全装置后，您向友好用户 Uzi 发射了一道刺穿子弹的路障。你不敢看，太神经质了，你不能把注意力浪费在路上。 “他们在向我们开火！！！呼唤支持！呼唤支持！！阻止他们！！！” 所以他们认为有帮凶？伙计，这在这种时候会很令人欣慰。这种东西只能在电影里看到，现实生活中没有体验过，也不能有幸与任何人分享……至少在这之后你会让所有人都想把你关押到死，对于像你这样的孤独者来说是一个不错的奖励。 看起来你的火力像致命的东西一样袭击了他们，他们停下来并开始驻扎在一个地方，只瞄准你的马车并在你加速遗忘时投掷子弹。你很安全……至少现在是这样。从逃亡者，你变成了流浪者。未来不会为你照亮，但你必须忍受，你必须忍受。你不想死。无论世界如何窥探你的恐惧或评判你，你都不会死在这个小世界上。 ... 透过门看，感受风让你享受的所有美妙感觉，你把你的头脑找回了它的起源，一个平静而成功的头脑。 “哈……哈哈哈哈！哈哈哈哈哈哈哈哈哈哈！” 你假装一个精神病患者的笑声，让内心积聚的所有泪水飞扬，每一次都挥动你的眉毛。“……哈哈哈……该死的……” 两个小时前你还坐在那里，还笑得一塌糊涂，更像是一个疯子，那个用子弹穿过一个无助的人，在他的爱人引导他走向下一个目标的时候，惊叹不已的人。复仇从来都不是甜蜜的，但之后肯定会感觉很棒！摇晃着血管中的血流，你开车穿过空地，接着是落叶雨，标志着你的出口。
梗概：
你射他们，这是自卫！
--------------------
动作：
做
在这一切之后，你首先需要抽根烟……
结果：
你是约翰·纳穆尔，28 岁，来自某家富有公司的普通秘书，该公司努力维护自己的形象，以至于你不得不质疑你所有的努力是否真的在那里得到了回报。多亏了劳拉，您才能享受签单的日子。 一个真正的美女，她是……她太天真了，她有时可以做的事情。你和她对话的机会，就像太阳系直线排列。21 对 28，你以为有一天你会有机会，假设你可以把自己提高成一个更勇敢、更好的人来给她戒指。你尽力去了解她的想法，她的立场，她的习惯……你自己。地狱！你甚至给她的母亲和她的妹妹买了并赠送了昂贵的珠宝。赢得你即将成为可爱妻子的家人是你的第一次……也是你最后的感情。 你的老板不能允许，是吗？他为年轻女性准备了一堆多么友好的肉丸。当你看到那个混蛋得意洋洋地扫她时，你就知道出了什么问题。就在事情变得更糟的时候，劳拉在她的房间里上吊自杀了。当她妈妈在电话里给你读到她的自杀遗嘱的第一个字的那一刻，你简直就是恶魔。你没有安抚她，而是用突然的声音把她和所有人吓跑了，要求一个明确的解释。其余的文章都是关于你的，你是如何照顾她和她的家人的。之后你抽了第一滴血。 “……哈哈哈……” 你从肺里吐出几口烟，手指间夹着一根燃烧的尼古丁棒。“没用……他妈的什么都不会……” 你不是天生就爱她，你是一个头脑发热的精神病患者，一想到你虐待她，你就会畏缩。以前，您在写纸，而现在，这些手指几乎将您标记为危险的射手、鼓舞人心的新闻杀手和武器狂热者的想法活目标练习。 “现在是戒烟的好时机。” 你想，在把整包扔到路上之前，先看看香烟的残骸。“……我不能……我不想死。” 一个您可以温和地安顿下来并躲避媒体视线的地方对您来说至关重要。说到媒体，你就有了买一台迷你电视来监视你的情况的想法。你可以把你的生命押在警察看到你时就开枪。在当地进行大规模枪战并活着出去，你有多少运气？ 有一件事是肯定的，去另一个国家旅行，尽管这看起来是个好主意，但你不确定自己是否有可能在不要求逮捕的情况下参加。您甚至不知道从单纯的面包店购买面包是否安全。越想，就越发觉自己给自己挖的坟墓有多深。 杀死一些强奸犯的混蛋……你永远不应该期望社会原谅他们或你。你犯下了多么可怕的罪行！......你想念你的家，你的家人。但是你不能联系他们，警察会跟踪你的位置，太冒险了，当然不值得。他们可能正在诅咒自己，因为他们现在抚养了一个凶手。 ……再说了，想叫谁也叫不上来。
梗概：
在这一切之后，你首先需要抽根烟……
--------------------
动作：
做
你开车到另一个城镇
结果：
电视...检查。 太阳镜...检查。 风衣......不必要，但你不能只留下你的瘦骨，它使你的运气翻了一番，你的外表独一无二......也检查过。 好在你的脸还没有真正达到任何媒体类型的标记，但你的时间已经不多了。躺下必须在另一个地方，一个远离这里的安全可靠的地方。 你检查盖住燃气表的盘子，发现它还有 2/3 的余量。你必须储备一些大窝才能过关。最近的补给地点是……距离这里大约 15 英里，并不难找到，如果他们尝试一些有趣的事情，你的枪就能完成这项工作。你决定去那里。 ...唔？时间快到晚上了。现在任何时候... 你已经到了一个加油站。附近只有一些当地的房屋和商店，一个空地，可能没有任何城市业务。
梗概：
你开车到另一个城镇
--------------------
动作：
做
您合法购买
结果：
你小心翼翼地把车停在一个偏僻的地方，迅速把你的枪支包在一个袋子里，还不忘用一条大毯子把它盖起来藏在你的角落里，然后径直驶入院子。灯光开始照亮该地区。 “加满油！我想要 5 罐燃料。” 您与正在您座位上清洁设备的人交谈，一位中年男子向您走来。 “哇，你是个大手笔，不是吗？！” 他说着，打开了盖子。“我可以问你要去哪里吗？” “那要看。” 你回应，已经准备好进行对话了。“我要去一个朋友那里，听说那里有很多小路，所以我想我会在出发前储备一些。 “是啊，差不多了。” 他说，抽你的汽油。“如果路上的石头发现你，你最好加紧脚步。” “路上有石头？” “你没听说过？显然有一群傻瓜骑着他们的自行车，劫持了他们在这些地方找到的任何汽车。我敢打赌，在黑市降一些价。” 这是什么笑话？逃离警察的追捕，并在下一次奔跑时有一个骑自行车的团伙在等着你，你的生活再也不会比现在更有趣了。 “天也快黑了。” 这个男人让你从你的想法中抽离出来。“也许你应该在这里过夜，这个时间去旅行太危险了。” “有人和这个团伙打过交道吗？” 你希望有一条安全的路线。“有人吗？” “没有。混蛋被打了几次，但另一个只是像生成任务一样加注并重复。那样的话，他们永远不会完成。” 嗯，这不正是你需要的吗？现在回头，警察可能不会再放你走，前进并冒失去四个轮子的风险也可能会让你丧命。你也不能在这里等待，消息会传播开来，让你放松警惕……你要承受这么多的混乱。 挑战执法者或讨厌的帮派？你遇到了很大的麻烦，一个非常非常严重的麻烦。
梗概：
您合法购买
--------------------
动作：
做
你留下来过夜
结果：
滴答……滴答……滴答…… 你已经盯着你的手表将近一个小时了，没有任何停下来的迹象。通常情况下，您决定进入酒吧后会采取措施来镇定自己的神经，但这里的气氛却无法满足您的口味。当时太安静了，太诡异了。也许这与这个地方本身有关，因为调酒师并不特别喜欢与像您一样的访客进行任何交谈。 谁设计了这个地方？难怪为什么很少有人在这里穿靴子。除了女服务员，你找不到任何积极的地方，一个热辣的黑发，不幸的是，她似乎工作不正常，她几乎没有道歉就将整个红葡萄酒洒在另一位顾客身上。好在现在有些人仍然善良。 “哦，劳拉……”你叹了口气，拉着额头。“你为什么要离开？”
梗概：
你留下来过夜
--------------------
动作：
做
你离开这里
结果：
你正在慢慢恶化，你需要吃点新鲜的东西。你毫不犹豫地走向街对面的大排档。满足你的饥饿感需要的不仅仅是一顿便宜的饭菜。 ... 嗯，里面还算不错，肯定比那个烂酒吧要好。他们有干净的桌布，漂亮的装饰，最重要的是，还有更多的人。如果不是因为这里的空间太窄而无法移动，你会给它一个高于平均水平的评价。你很快来到柜台点餐。一份煎鸡蛋涂上辣酱和一个苹果派作为甜点的薄煎饼对你来说似乎是一种很好的填胃剂。 您找到一个座位并在等待用餐时让自己感到舒适。你默默地希望这不会成为你最后一次在外面吃饭。 “哇哈哈！！” 身后突然响起又尖利的叫喊声，差点把你吓死。在这种时候，你怎么能这么毫无防备？您转过身来，看到一个正在专注于您的迷你设备的小学生。 “迷你电视！就像在游戏里一样！！” 非常热情和渴望，他一边说一边在她的包里搜索，然后把一个看起来很小的东西推给你。仔细一看，您会发现它是 Nintendo DS。“你是宝可梦教官吗？” “……哇——什么？什么……？” 你开始在你面前闪烁的屏幕上胡说八道，因为男孩每按一个按钮，它就会变成一个不同的屏幕。这是 DS 的口袋妖怪游戏之一，在年轻人中销量很高，并且没有任何停止的迹象。 “先生？” “呃……” 你今天忍了很多，可是第一次，居然对这个说不出话来。 “匹普！匹普！你找到人了吗？” 现在是那个女孩，她也是一名学校的评分员，加入了纠缠在你外套周围的小教练。天哪！现在您已经意识到外套内的便携式电视发出的光线吸引了他们的好奇心，您一直在听到它查看新闻。从你可以从男孩的 2 屏幕机器上看到的，你可以看到是什么吸引了他的注意力。 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
梗概：
你离开这里
--------------------
动作：
做
你的口袋妖怪……听起来不错
结果：
你现在不是特别喜欢电子游戏，但放松和玩一会儿肯定没有坏处。你没有玩过他们的任何口袋妖怪游戏，但你仍然记得你小时候一直在看的节目。 “这是什么游戏，小家伙？” 你摘下眼镜，很感兴趣地向男孩询问游戏的名字。 “噗！” 他在说话之前吐了一句不必要但令人愉快的俚语。“它是口袋妖怪火红！我最喜欢的任何一种！！” “每天你只想一直玩！” 女孩补充了他的说法，为这款游戏制作了一份高质量的招股说明书。 当谈到这个话题时，他们似乎很高兴，你会觉得你可能是这个镇上唯一一个公开和他们说话的人。考虑到它有多空，这并不奇怪。 “想看我的收藏吗？” 她说坐在你旁边，不用担心她和一个陌生人在一起，你可能会加上一个令人毛骨悚然的陌生人。“来吧皮普！你也是。” “那么，你的收藏是怎么回事？” 你说，渴望了解他们。 “等等……这边！” 女孩从她的橙色袋子里拿出一堆五颜六色的卡片放在你的桌子上，然后继续。“直接从节目和游戏中，我得到了它们！” 这些卡片都井井有条，您几乎可以看到表面反射的光线，这是一副漂亮的口袋妖怪卡片。 “这是一个不错的甲板。我可以看看他们吗？” “求求你了，先生！！！” 哇！怎么会有人不想和这些小宝贝说话呢？他们是如此的充满活力和可爱。 获得业主同意后，您可以伸手去拿甲板，然后……哇！你仍然可以认出其中的大部分！当你一个一个地挑选并告诉他们每个口袋妖怪的名字时，你实际上会感觉到你内心深处的温暖，你天真时的感觉，你对你所享受的东西的爱。你和其他人一样，仍然是一个正常的人。 ......你刚刚意识到一些事情。你，一个披着风衣，穿着遮阳篷的男人，吸引了少数人的大部分注意力，你显然不希望这种情况发生。一个友好的陌生人想要和一些随机的孩子聊天，这会引起一些注意。在这种情况下，你最好表现得像一个神奇宝贝疯子。伙计，你真的希望你的食物就在你身边，越早越好。 “嘿，先生，你收集口袋妖怪卡吗？” 那个男孩，她叫皮普，当你试图把自己完全推向那些口袋妖怪时，他突然问你。 “不……至少现在不是。” 你喃喃自语，真的不知道该说什么。 “诶？！为什么不呢？” 他们两个都说出来，他们当场抓住了你。好吧……你能告诉他们什么？你不能告诉他们这只是给孩子们的，这很荒谬，但是嘿！也许这会给他们一个现在停止的迹象。
梗概：
你的口袋妖怪……听起来不错
--------------------
动作：
做
你告诉他们这是给孩子的
结果：
“少爷！……还有珍妮小姐！你们两个在干什么？？！” 就在你要说些什么的时候，一个暴躁而优雅的声音穿过餐厅传来。你转身看到一个身着优雅黑色燕尾服的大个子正拖着自己走向你的桌子。“请远离那个男人！” 最后，一个有常识并且愿意在这个被遗弃的小镇上行动的人……而且他似乎不是你想要惹恼的类型。 “哇哦！Manny the Orge，什么？？” 现在被称为詹妮弗的女孩看到他几乎尖叫起来。“我们去别的房间吧！” 和她一起，皮普已经逃跑了。 “你是谁？！你没对他们做什么吧？？？？” 哇！这家伙速度极快，与他山峰的身材完全相反。他的声音变得暴躁，等着释放在你身上。他抓住你的衣领，直接冲着你的脸大喊大叫，差点让你心脏病发作。“你他妈的！！！你做了什么？？？” 然后不等你回答，他就向你一拳！
梗概：
你告诉他们这是给孩子的
--------------------
动作：
做
你用手挡
结果：
你尽你最大的努力让你的屁股尽快离开椅子站起来，但当你抬起你的双手时，打击仍然在接触！你设法将他的一拳打在你的额头上一英寸。力量以某种方式被吸收，但仍然打破你的平衡。 你的身体和你一起拉着椅子，你带着难以忍受的疼痛和疯狂的刺痛倒在地板上。好像你也拿着桌布，飞牌在你和天花板上晃来晃去的灯泡之间闪烁。 在你落地后，你还会听到一声巨响……看起来你的腿也把这个怪物绊倒了，还好他没有摔倒在你身上。 “曼尼！！你刚刚做了什么？？！！” 这是你起床的机会！在你的腿把你抱出餐厅之前，你只听到了一点点女孩的叫喊声。 ... 嗯，这很糟糕，但至少它不比最终躺在病床上更糟。 你戴上窗帘，戴上耳塞，走到街上，等着听你想要的公开展示。在餐厅发生那件事之后，你不得不求助于街头食品。苹果派、煎饼、令人作呕的甜点……如果你不想肚子咕咕叫地睡觉，你就必须忍受这些。
梗概：
你用手挡
--------------------
动作：
做
你那天晚上晚些时候
结果：
<今天下午4点23分，警方拘留中心发生枪战，由一名ET雇主，28岁的John Namour引发，火灾中有7名罪犯，2名保安死亡，9名警官在火灾中受重伤-
梗概：
你那天晚上晚些时候
--------------------
动作：
做
迷你电视用恶魔般的声音说，当你坐在车里时，你实际上可以感受到魔鬼的舌头用你的耳朵热情地做爱。<他开着棕色越野车，2D74
结果：
迷你电视用恶魔般的声音说，当你坐在车里时，你实际上可以感受到魔鬼的舌头用你的耳朵热情地做爱。<他开着棕色越野车，2D74-
梗概：
迷你电视用恶魔般的声音说，当你坐在车里时，你实际上可以感受到魔鬼的舌头用你的耳朵热情地做爱。<他开着棕色越野车，2D74
--------------------
动作：
做
结果：
末日列车来了，它在等着你。尽你所能地无所畏惧，只需要 1 秒钟，你的脑袋就会开始胡思乱想，恐慌就安定下来。你把盖着武器的毯子扔到后备箱上盖住车牌。在那之后，汽车座椅会让您在夜间感到舒适。您将手柄锁紧并躺下，确保在您调整电视并用风衣遮住脸，看着您的箱子时没有光线。 一个人在漆黑的夜色下，被无声的汽车和摩托车包围，这不是什么好感觉……根本就不是什么好感觉。好在天气这么好，在你的家乡，你永远不会希望睡在这样的车里……这么热又忙的地方。躲起来，逃跑，躲避别人的注意，你进入了多么美妙的生活！抛弃你的人性，逐渐消失，成为罪犯。 哦，糟糕的事情发生了。你只是做了正确的事。你把从商店买的所有东西都放在前排座位上。大部分是基本的东西，比如医疗用品、甜面包和水瓶，……但现在，它们对你来说是金子。谁知道在您的旅行中会发生多少神奇的事情？小心一点总是好的。 你决定把频道切换到别的东西，在这个阴郁的时刻能让你振作起来的东西，电视或音乐节目。你把音量调低，闭上眼睛，试着睡觉，听听今晚可能提供的娱乐节目。打盹没过多久，您就因跑步而疲惫不堪，需要充分的睡眠。你必须早起离开这个可怕的地方，否则任何人都可以咬你一口。
梗概：
--------------------
动作：
做
你午夜
结果：
哦！有东西掉在车上。雨？多么合适，你现在真的需要一些摇篮曲。一次自由和一个人，一个人躲起来，一块一块地睡觉，真是太酷了。你必须在它仍然持续的时候享受它。 ……嗯，这里有点冷了。当你站起来透过眼镜看时，你开始颤抖。周围的一切似乎都安静了下来。伙计，你现在想抽根烟会杀人的。 你突然想起一直藏在隔间里的那块金属砖。您前往仪表板，将损坏的手机放在眼前。“打了就出去了，太浪费了……”这东西给你打了三发子弹，还不死。 但现在它必须消失，这样的悲剧......你把手机扔掉，发现你早点忘记扔掉它令人难以置信。为警察保留潜在的跟踪设备没有用。 雨下得很大。如果他们还想在这个州经营他们的生意，那骑自行车的团伙就必须是地球上最勤奋的暴徒。你启动引擎，走出停车线—— 而开灯后首先映入眼帘的是一块血淋淋的布，里面好像还包着什么东西，好可爱！你踩油门，像疯子一样加速，朝车库出口驶去。小心！！！ ... 对你来说永远不会有好结果，不是吗？地狱般的子弹在这大雨下直接射向街道！这个地方怎么回事？？你肯定不能开车穿过危险区域，你甚至看不清前方是什么。哦哦！坏转！！！有人注意到了你的光芒，他们不会浪费任何时间来摆脱你。 “滚出去，混蛋！OFGG！” 在他对你的汽车造成更多伤害之前，你将他射死，然后将它的尾巴拖回停车场，同时与站在他身边的攻击者交火。 你现在的日子已经正式超越了最糟糕的情况。这些朋克之间正在发生一些严重的冲突，他们正在通过使用致命的枪支来巩固自己的统治地位。你必须避免在这些中被抓住。
梗概：
你午夜
--------------------
动作：
做
你突破后方
结果：
你决定开着你的车冲过后门，尽可能快地开到危险的地方。凭借出色的操控反应能力，您可以顺利上路并逃生。在贵重的车内过夜确实是个不错的选择。你融入雨中，在黑暗中开车……在这个过程中，一只巨大的蚊子溅到了你的挡风玻璃上。 和平时期已经结束，黑魔王要来向人类散播混乱，从你开始。 “这是什么……” 在试图将其冲走之前，您必须对视线中残留的褐色液体大喊大叫。就在这时，您注意到汽车顶部有东西在刮擦。你被包围了！！！没过多久，它们就向你展示了它们的可憎形态，巨大的变异吸血鬼。为什么这么多？？？ 就在事情变得不再可爱的时候，你的车现在完全被装饰成黑色，你直接撞到一棵树上，打破了你廉价的挡风玻璃，让几根死亡管子在里面骚扰你的血管。 你失败了 “是什么杀死了他们？” 你，现在已经变成了一个幽灵般的骨架，把你的骨头折叠成思想者的姿势，坐在一堆死蚊子上。“嗯……他们看起来完全被我包围了，就像被什么东西附身了一样。”
梗概：
你突破后方
故事名称：
52
故事背景：
哦！有东西掉在车上。雨？多么合适，你现在真的需要一些摇篮曲。一次自由和一个人，一个人躲起来，一块一块地睡觉，真是太酷了。你必须在它仍然持续的时候享受它。 ……嗯，这里有点冷了。当你站起来透过眼镜看时，你开始颤抖。周围的一切似乎都安静了下来。伙计，你现在想抽根烟会杀人的。 你突然想起一直藏在隔间里的那块金属砖。您前往仪表板，将损坏的手机放在眼前。“打了就出去了，太浪费了……”这东西给你打了三发子弹，还不死。 但现在它必须消失，这样的悲剧......你把手机扔掉，发现你早点忘记扔掉它令人难以置信。为警察保留潜在的跟踪设备没有用。 雨下得很大。如果他们还想在这个州经营他们的生意，那骑自行车的团伙就必须是地球上最勤奋的暴徒。你启动引擎，走出停车线—— 而开灯后首先映入眼帘的是一块血淋淋的布，里面好像还包着什么东西，好可爱！你踩油门，像疯子一样加速，朝车库出口驶去。小心！！！ ... 对你来说永远不会有好结果，不是吗？地狱般的子弹在这大雨下直接射向街道！这个地方怎么回事？？你肯定不能开车穿过危险区域，你甚至看不清前方是什么。哦哦！坏转！！！有人注意到了你的光芒，他们不会浪费任何时间来摆脱你。 “滚出去，混蛋！OFGG！” 在他对你的汽车造成更多伤害之前，你将他射死，然后将它的尾巴拖回停车场，同时与站在他身边的攻击者交火。 你现在的日子已经正式超越了最糟糕的情况。这些朋克之间正在发生一些严重的冲突，他们正在通过使用致命的枪支来巩固自己的统治地位。你必须避免在这些中被抓住。
--------------------
动作：
做
你等到一切都清楚了
结果：
霰弹枪、Uzi 和简陋的手枪是唯一能够抵御它们的东西，您不想浪费它们。最愚蠢的事情是你对如何重新装填 Uzi 和霰弹枪一无所知，所以你只是在没有任何弹药的情况下买了它们......如果你不得不求助于它们，那会很糟糕。这里是处于最佳状态的大规模杀伤性武器，你的脑袋一定是个屁。从好的方面来说，你为自己准备了一大堆手枪弹夹，所以至少你不必担心在枪声中用完选项......现在正在发生。 “杀了他！” 呆在一个地方不是一个好主意。你迅速下车，爬到另一辆车上，子弹从你的头顶直接飞过，在你身边钻出它们的声音。这是一个完全的死亡区，你只能希望他们不打算把这个地方放火。从一辆棕色汽车的底部停车处达到顶峰，您的目光落在她们性感的腿上，随着她们的摆动，您可以轻松标记您的路线，以避免任何交叉火力向您袭来。 你开始等待…… ... ... 太远了还是已经结束了？你意识到你一直在躲在角落里，与任何开火隔离。他们不再瞄准你了。再也听不到喊叫声或弹丸喷射声了，只有你的呼吸声和雨水滴落的声音。 小心翼翼地，你慢慢地移到前面，眼前的景象……这是一件很血腥的事情，可以增加你的噩梦。雨水与内脏和器官一起产生了一股红色的血液。你几乎可以看到一颗健康跳动的心脏流过。太黑了，看不出别的东西，所以你不能太确定，但是……那是一对卡在某人脖子里的蟑螂腿吗？它正在移动，略微移动，但它仍在制作自己的场景。
梗概：
你等到一切都清楚了
--------------------
动作：
做
你调查
结果：
你会觉得难以置信，即使在这种情况下，你仍然可以为这样的事情感到兴奋。你能说什么？危险是可怕的，但同时又是那么诱人。 你拿着枪小心地走到现场，从来没有设法正确地握住它，但仍然保持不动。让我们看看这里……那东西在尸体的脖子上进进出出，好像它想把自己从肉堆里弄出来。你可以放心地假设它被卡住了，周围没有任何相似之处。
梗概：
你调查
--------------------
动作：
做
你拍吧
结果：
“一只蚊子，什么鬼？？！” 当你瞄准面前的可憎之物时，你把一滴水溅到一边。一个巨大的黑翅生物，腹股沟处长出一个红色的肿瘤，它是一只巨大的蚊子！这是你的大脑在和你玩的某种幻觉技巧吗？这东西正在通过它的管子吸他的血，多亏了你的欢迎辞，它现在已经意识到另一个人，你。 你用手枪立即将它炸开，而不是一个真正需要害怕的威胁生物。它在痛苦中尖叫，那种地狱般的反应只是想让你结束它。哎呀，过头了！！现在它只是一个巨大的烂摊子，滴黄色液体到血流中。你再也看不出它的任何原始形状了。 你茫然地盯着眼前一具随机的尸体，因为雨越来越大。他是如此扭曲和干涸，以至于如果不是衣服和头发，你几乎会认为这是一块厚厚的抹布。 发生了严重的问题……你突然想起了在这疯狂开始之前你车底下的血淋淋的布，光是想到它就会溢出你的脑海。你——你必须尽快离开这里！现在每一个错误的决定都可能是你最后的决定。
梗概：
你拍吧
--------------------
动作：
做
你去看看
结果：
你决定回到那个地方亲自检查一下。站在雨中是没有用的，你有一种奇怪的预感，如果你逃跑了，事情会变得非常糟糕。但主要是好奇心引导你的腿去寻找让你几乎被吓死的东西。 你拆开织物烂摊子，露出一个你已经无法辨认的被殴打严重的人，尸体上只剩下几滴干血和脓液，可能是在下雨前被放在你的车底下。也是那些怪物的牺牲品？你收起你的阴影，以便清楚地看到尸体。 该死！气味很乱，你必须与它保持距离才能忍受恶臭。从你能说的。在这些东西吞掉它们的液体之前，这个人几乎已经被棍棒打死了。因此，除了那些暴力暴徒和警察的风险之外，你还受到地狱淫秽的威胁。为什么他或她在被喂给那些人之前被打死，你不知道。你所知道的是，如果你不离开这里，这种恐惧很快就会结束你的生命。 “还有吗？” 你开始怀疑。“我不能独自一人。” 不久，你发现另一个可怜的灵魂被塞在另一辆车下面，这辆车……经历了一些严重的腐烂，但同时又奇怪地干净。据你所知，尸体保存得很好，没有人会认出主人。 还有一件事现在你已经注意到了。这里停着一大排摩托车。当你躲在车里时，你不记得看到那么多。他们有那些毫无意义的疯狂标记和装饰，几乎看起来就像你通常在酒吧或其他地方遇到的符号之一。 “路上的摇滚？......不，一个骑自行车的团伙不可能这么有礼貌。” 你很快就放弃了他们冲进这个地方的想法。“除非道路被封锁，否则他们永远不会做这样的事情......或者这个可怕的地方可能是他们的藏身之处，我一直被愚弄了？？？” 雨夜下，你快速浏览一下停车场。！！！你应该早点意识到这一点，这个区域几乎被巡洋舰和装袋者包围，那些你永远不想骑的类型。就是这样！你不在乎它是不是真的，你要离开这里！ “Hellllpppp mmee！救命！！” 就在你准备启动引擎的时候，男人和女人混合的尖叫声透过眼镜传到你的耳朵里。这么大一滴雨水，想必很多人都费了劲才把它拉下来。
梗概：
你去看看
故事名称：
53
故事背景：
你正在慢慢恶化，你需要吃点新鲜的东西。你毫不犹豫地走向街对面的大排档。满足你的饥饿感需要的不仅仅是一顿便宜的饭菜。 ... 嗯，里面还算不错，肯定比那个烂酒吧要好。他们有干净的桌布，漂亮的装饰，最重要的是，还有更多的人。如果不是因为这里的空间太窄而无法移动，你会给它一个高于平均水平的评价。你很快来到柜台点餐。一份煎鸡蛋涂上辣酱和一个苹果派作为甜点的薄煎饼对你来说似乎是一种很好的填胃剂。 您找到一个座位并在等待用餐时让自己感到舒适。你默默地希望这不会成为你最后一次在外面吃饭。 “哇哈哈！！” 身后突然响起又尖利的叫喊声，差点把你吓死。在这种时候，你怎么能这么毫无防备？您转过身来，看到一个正在专注于您的迷你设备的小学生。 “迷你电视！就像在游戏里一样！！” 非常热情和渴望，他一边说一边在她的包里搜索，然后把一个看起来很小的东西推给你。仔细一看，您会发现它是 Nintendo DS。“你是宝可梦教官吗？” “……哇——什么？什么……？” 你开始在你面前闪烁的屏幕上胡说八道，因为男孩每按一个按钮，它就会变成一个不同的屏幕。这是 DS 的口袋妖怪游戏之一，在年轻人中销量很高，并且没有任何停止的迹象。 “先生？” “呃……” 你今天忍了很多，可是第一次，居然对这个说不出话来。 “匹普！匹普！你找到人了吗？” 现在是那个女孩，她也是一名学校的评分员，加入了纠缠在你外套周围的小教练。天哪！现在您已经意识到外套内的便携式电视发出的光线吸引了他们的好奇心，您一直在听到它查看新闻。从你可以从男孩的 2 屏幕机器上看到的，你可以看到是什么吸引了他的注意力。 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
--------------------
动作：
做
你忽略他们
结果：
不，不，不！不是你想参与的那种。在这种情况下，无知是纯粹的幸福，你继续听电视。无论他们如何试图纠缠你进入这个口袋妖怪的东西，你都站着......你坐在椅子上甚至没有看他们一眼。也许是因为你的铁杆酷，他们一到就离开你。孩子们，可爱，但有时就是那么简单。看到他们询问另一位客户时，您必须微笑。
故事名称：
54
故事背景：
你小心地把车停在一个偏僻的地方，迅速把你的枪支包在一个袋子里，还不忘用一条大毯子盖住它，把它藏在你的角落里，然后径直驶入院子。灯光开始照亮该地区。 “加满油！我想要 5 罐燃料。” 您与正在您座位上清洁设备的人交谈，一位中年男子向您走来。 “哇，你是个大手笔，不是吗？！” 他说着，打开了盖子。“我可以问你要去哪里吗？” “那要看。” 你回应，已经准备好进行对话了。“我要去一个朋友那里，听说那里有很多小路，所以我想我会在出发前储备一些。 “是啊，差不多了。” 他说，抽你的汽油。“如果路上的石头发现你，你最好加紧脚步。” “路上有石头？” “你没听说过？显然有一群傻瓜骑着他们的自行车，劫持了他们在这些地方找到的任何汽车。我敢打赌，在黑市降一些价。” 这是什么笑话？逃离警察的追捕，并在下一次奔跑时有一个骑自行车的团伙在等着你，你的生活再也不会比现在更有趣了。 “天也快黑了。” 这个男人让你从你的想法中抽离出来。“也许你应该在这里过夜，这个时间去旅行太危险了。” “有人和这个团伙打过交道吗？” 你希望有一条安全的路线。“有人吗？” “没有。混蛋被打了几次，但另一个只是像生成任务一样加注并重复。那样的话，他们永远不会完成。” 嗯，这不正是你需要的吗？现在回头，警察可能不会再放你走，前进并冒失去四个轮子的风险也可能会让你丧命。你也不能在这里等待，消息会传播开来，让你放松警惕……你要承受这么多的混乱。 挑战执法者或讨厌的帮派？你遇到了很大的麻烦，一个非常非常严重的麻烦。
--------------------
动作：
做
你冒着生命危险
结果：
你不应该选择这条路，这是你一生中见过的最糟糕的道路。被道路杀死的动物，到处都是死胡同，导致死亡或粗糙的泥泞让你放慢速度，它就像你没有照明的办公室一样黑暗。 但与你现在避免的相比，这些都不算什么。有一个骑自行车的团伙冒着可怜的生命危险偷走您的货物，甚至可能在此过程中杀死您。由于道路状况，您似乎找不到办法让这些熟悉该地区的经验丰富的强盗消失。 你拿出你的中继器，开始在他们中间传播致命的痛苦的叫喊声和新鲜的血液。他们是罪犯，对吧？擦一些浮渣和你所做的工作是一样的。 “操！一把枪，他拿了一把枪！AGGHRRR！ “一个巨大的战利品！开火，孩子们！” 你的开火已经开始了一辆孤独的汽车和一群骑自行车的人之间的枪战。交易的每一枪都等于某人的身体一部分变得毫无用处，你只能祈祷你不是下一个。 “看你喜不喜欢这个！！！” “AGGGGGRRR！！！” “让开，傻瓜！在路上摇滚！” 一发子弹开始向你飞来，毁灭之力在你耳边尖叫。加特林机枪！即使前排挡路，他们也不怕使用速射，他们想要你的生命！ 你得想办法！
梗概：
你冒着生命危险
--------------------
动作：
做
你用了其中一个罐头
结果：
暗示一下，这是使用燃料的一种自杀方式。 你抓起一个罐子，等待火势消散。你的车只需要坚持一轮，你也必须精确地把握时机。 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 这可能是你能想象到的最愚蠢的想法，但在关键时刻，没有人能真正将你的失败归咎于这一点。 当骑自行车的帮派撞到时，他们真的大打出手。似乎没有那把枪的迹象，他们自己的人的尖叫声也没有停下来，在试图通过这件事的时候听到这个真是太残酷了。难道他们不知道他们刚刚压死了多少可怜的松鼠和泼妇吗？……你被追杀的时候还想着这些，真是难以置信！ “锁定并重新加载！宝贝哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇哇 那里！1...2...3！你把煤气罐扔过窗户。如果你错过了，他们很有可能会滑倒。 大约只需一秒钟，整个区域就会突然焕发光芒。由汽油引起的老派蓝色死亡火焰……如此美丽，如此宁静，但由于他们的身体被烧毁和自行车爆炸而造成的创伤。听起来很美妙，但你不忍心看。你刚刚放火烧了森林，伙计……你必须快点离开这里。 你知道......逃脱谋杀真的变得更像是属于你的东西。是运气，还是你的实际技能让你成为冷血杀手？
梗概：
你用了其中一个罐头
--------------------
动作：
做
你没事！够白日梦了！
结果：
如果您认为自己可以摆脱臭名昭著的摩托车帮派，那您就死定了。 你失败了 “我不知道是冒着生命危险还是成为罪犯让我陷入了这种境地。” 你直接对着挡路的四散和烧焦的尸体说。“但是要知道。我会用任何生命来确保我自己的……即使是这个！” 你把枪放进嘴里并扣动扳机，结束了你身体上无数出血孔的疼痛。 ...如果必须采取，那么就按照你一开始就打算做的那样去做。
梗概：
你没事！够白日梦了！
故事名称：
55
故事背景：
你周围到处都是战争的味道。篝火发出刺鼻的烟雾，厕所里的小便和垃圾，男人们的汗水。 和恐惧。你也闻到了。 这些人有理由害怕。在过去的几个月里，你一直在与哈罗德国王的敌人作战，结果却是双倍前进以对抗他最危险的人；诺曼底的威廉。他的军队扎营在下面，有一万二千人，而你的八千人。有理由害怕。你害怕吗？不，你在心里肯定地肯定。你不怕。你是艾瑟尔沃夫领主，北方领主，哈罗德国王最信任的顾问，百战老兵。你为什么要害怕一群死在你的剑下的破烂诺曼人？ 打消心中的疑惑，你有目的地大步穿过营地的喧嚣，一心检查前方的防守。一对咯咯笑的海胆灵活地绕着你转，一个醉汉在你面前晃来晃去，注意到你闪闪发光的链甲盔甲，向后退去，连连道歉。大步朝着英国营地的边缘前进，您经过哨兵警惕的凝视，站在山的上坡。 你并不孤单。另一个人坐在山上，被落日的阴影笼罩着。走近你会认出他是 Acwellen，一个小领主，但坚定的战士。他的脸像石头一样，下巴阴森森的，眼睛盯着地平线。越过他的肩膀，你会看到黑色的烟雾笼罩着红色的夜空。远处黑斯廷斯镇在燃烧。在落日的模糊不确定性中，你无法分辨他眼中的闪光是泪水还是即将逝去的阳光。你还记得，Acwellen 的儿子被派去指挥黑斯廷的驻军。 在林线上方，您只能分辨出火焰的噼啪声。黑斯廷斯烧伤。
--------------------
动作：
做
你表示哀悼
结果：
轻轻的，你把手搭在他的肩上，轻声说 “我为你的死感到难过。我无法想象失去儿子的痛苦，但我相信他会从天堂看不起你。” 阿克维伦的目光依旧盯着燃烧的小镇，脸上依旧是石质的面具，但脸颊却是有条纹。 “谢谢” 他颤抖着吐了口气，张嘴想说点什么，又闭上嘴巴，沉默不语，但这些话却清晰地在他脑海中浮现，像拴着皮带的猎犬一样想要挣脱。最后他说话了，冷酷而安静，不过是耳语。 “我们能赢得这场战斗吗？” 这个问题永远悬在那里，英国军队的所有恐惧和焦虑都表达在一个破碎的人的沉思中。
梗概：
你表示哀悼
--------------------
动作：
做
是的
结果：
“是的！” 你咆哮着，突然发出刺耳的声音，让 Acwellen 和你自己都惊讶。 “是的” 你继续用更柔和的语气。 “我们可以赢。这不是结束。” 在你看来，没有怀疑的余地。你买不起。 在树幕后面，黑斯廷斯镇熊熊燃烧，诺曼威廉的部落集结。
梗概：
是的
--------------------
动作：
做
你离开
结果：
Acwellen 继续注视着燃烧的城镇闪烁的光芒。他的脸色铁青，深不可测。你转身离开，小心翼翼地穿过一片淤泥。用脚向下压，评估地面。某些补丁是干燥的，但土壤非常松散。只需要一夜的雨，就能把战场变成泥泞的地狱。好吧，你自己想，至少泥泞对诺曼人的伤害会比我们更严重。 “艾瑟尔武夫！” 一个声音打断了你的沉思。 很熟悉的声音，深沉而洪亮。这是你的国王哈罗德的声音。 他身高不到 6 英尺，比大多数人高一个头，与您的身高不相上下。他的胡须和头发都是火红的橙色，浓密而狂野。他的胸膛很宽，身体肌肉发达，但你会注意到他腹部周围的皮上衣有一个明显的凸起；他对狂欢的臭名昭著的热爱并没有受到惩罚。 “艾瑟尔武夫，你这个狡猾的混蛋！” 他咆哮着，咧嘴笑着拍着你的背。
梗概：
你离开
故事名称：
56
故事背景：
你周围到处都是战争的味道。篝火发出刺鼻的烟雾，厕所里的小便和垃圾，男人们的汗水。 和恐惧。你也闻到了。 这些人有理由害怕。在过去的几个月里，你一直在与哈罗德国王的敌人作战，结果却是双倍前进以对抗他最危险的人；诺曼底的威廉。他的军队扎营在下面，有一万二千人，而你的八千人。有理由害怕。你害怕吗？不，你在心里肯定地肯定。你不怕。你是艾瑟尔沃夫领主，北方领主，哈罗德国王最信任的顾问，百战老兵。你为什么要害怕一群死在你的剑下的破烂诺曼人？ 打消心中的疑惑，你有目的地大步穿过营地的喧嚣，一心检查前方的防守。一对咯咯笑的海胆灵活地绕着你转，一个醉汉在你面前晃来晃去，注意到你闪闪发光的链甲盔甲，向后退去，连连道歉。大步朝着英国营地的边缘前进，您经过哨兵警惕的凝视，站在山的上坡。 你并不孤单。另一个人坐在山上，被落日的阴影笼罩着。走近你会认出他是 Acwellen，一个小领主，但坚定的战士。他的脸像石头一样，下巴阴森森的，眼睛盯着地平线。越过他的肩膀，你会看到黑色的烟雾笼罩着红色的夜空。远处黑斯廷斯镇在燃烧。在落日的模糊不确定性中，你无法分辨他眼中的闪光是泪水还是即将逝去的阳光。你还记得，Acwellen 的儿子被派去指挥黑斯廷的驻军。 在林线上方，您只能分辨出火焰的噼啪声。黑斯廷斯烧伤。
--------------------
动作：
做
你提醒他他的儿子可能还活着
结果：
Lifto 的意思可能很好，但您宁愿坚持原来的计划。 “不，在我们离开该死的区域之前，我不会在我的拖车里感到安全，所以我仍然想睡在你的拖车里。”“好吧，好吧，但我告诉你它会很拥挤，就像我说的那样，你不想睡在地板上。” 克莱普托说。 你向其他人保证你会没事的，然后和他们一起回到预告片，那时你才真正开始欣赏 Klepto 的警告。他们拖车的地板比兰科的还要脏。很多食物残渣、包装纸和一些脏衣服。你想象胖子可能是垃圾的最大贡献者。 奇怪的是，气味并没有那么糟糕，但你不确定是否可以再睡在地板上。 “嗯，我明白你的意思。我觉得你是对的。如果我睡在地板上，我醒来时会粘在地板上。” 你说。“我都说了，你到底想干什么？” 克莱普托问道。“我的提议仍然对 Suzy 有效，我发誓我不会尝试任何事情。减轻您的后顾之忧是我最关心的。” 利夫托评论。“这很甜蜜，但我仍然在这里睡觉，而不是在地板上。我要睡其中一张床。” 此时，其余的小丑对您的要求感到有些惊讶。 “呃，看苏西，我知道你担心今晚睡在你的拖车里，即使我认为没有什么可担心的，我知道我们都变得更亲密了，我们都喜欢彼此，但来吧你不会真的认为我们中的一个人会为了一个恶心的地板而放弃我们的床！” 克莱普托评论。“你不用我睡虾的床。”“我也不睡地板！” 虾米的言论。“不用了，我会和你分享的。”“等什么？！” 他们都说。 “你看，是这样的。我不会在你的床上占据那么多空间，这意味着 Fatty 和 Lifto 的床已经用完了，因为它们本身几乎装不下。剩下的就是 Klepto 和 Shrimpy。就我个人而言，我想要尽可能多的空间，而且由于他的体型而不会冒犯 Shrimpy，他有更多的空闲空间。” 虾米得意地朝其他人笑了笑。 “我感觉合理！好了，大家睡觉吧！” 虾米兴奋地说。“喂，等一下！我们不同意！” 克莱普托评论。“是啊，你好像很担心我尝试什么，你还要睡虾的床？无论如何，你可能会因为旁边的那个小变态而变得黏糊糊的。” 利托说。“哦，不，我不会，因为 Shrimpy 不仅要背对着我睡觉，我们还要从头到脚睡觉……不管怎样，从头到脚都要睡觉。如果我在夜间非常感到任何不想要的摸索、抚摸或不良接触，将对他的男人部分造成严重后果。今晚一个人睡觉我可能会有点紧张，但我并不担心打小矮人的球，因为他对我很着迷。明白了吗？”“我得到它。” Shrimpy 的评论，但他似乎对这种安排并没有减少失望。“如果有人不喜欢它，那么我的拖车目前对任何人都是空的。房间很大，可以睡个好觉。” 你补充。 没有人会接受你的预告片报价或说其他任何话，尽管你听到胖子的一些抱怨，他说他只想站起来睡觉，在那个音符上，你们都前往梦乡。 你先上 Shrimpy 的床，然后进入一个舒适的位置，然后 Shrimpy 在床的另一端也做同样的事情。 您有一段时间处于半醒状态，想知道 Shrimpy 是否会忽略您的警告，但他很快就睡着了。老实说是胖子的鼾声比较烦人。你猜其他人已经习惯了。 当您开始感到困倦时，您无法相信您不仅与侏儒小丑同睡一张床，而且明天您将抛开一切。有趣的是，你现在对这个想法越来越适应了。您认为这是您在没有持续互联网或电视刺激的情况下度过的最长时间，并且您根本不介意。 最终，您确实会像在地下室一样安全地入睡。
梗概：
你提醒他他的儿子可能还活着
--------------------
动作：
做
第二天…
结果：
周三 “嘿，苏西起床了，该开始收拾东西了。” Klepto 说拍拍你的肩膀。“好吧……”你面无表情地回答。你不会感觉到腿底部附近有一个小人物，所以你认为虾一定已经站起来了。 “嗯，那还不错，虾米还履行了协议。” 你说坐起来。“或者，也许你只是一个睡得很熟的人。” 克莱普托评论。“嗯，如果是这样的话，他最好至少有意识不要吹嘘它。不管怎样，我想我会请假洗洗衣服换衣服。”“希望我能和你一起去。” 克莱普托说。“打扰一下？” 你说。“哈哈哈，不是那样的，我的意思是不得不分享我们在这个预告片中的小浴室。胖子现在就在那里，你可以弄清楚他离开后会怎样。”“是的，我现在宁愿没有那种心理形象。再见。” 你说着就开始离开。“等等，呃苏西……我……”克莱普托喊道。“是的？”“（叹气）看我知道我可能没有表现出完全的白马王子行为，但我只想说，我真的很喜欢你。我是说真的很喜欢你。”“是的，我很久以前就有这种印象了。” 你回应。“是的，但我想说这不仅仅是身体上的。”“我觉得这有点难以相信……不过我不会真的因此而责怪你，但我需要更多的证据来证明你想要的不仅仅是性爱。”“嗯什么？比如戒指什么的？”“嘿，我不认为你完全处于财务状况中。听着，我也喜欢你，但这可能不是对我抱有更多期望的最佳时机，而不仅仅是友谊。我现在仍在努力适应这是我的新生活的事实，更不用说我刚刚与我的第一个也是唯一的男朋友分手。所以我可能更喜欢我自己的公司一段时间。然而，这并不意味着你不应该试图获得我的喜爱。但前提是你真的想要和我一起做一些有意义的事情。如果没有，那我建议你不要浪费我们的时间，你去找一个不太挑剔的伴侣。我也不需要白马王子，我只需要一个我可以依靠的人，而不是一个彻头彻尾的混蛋。如果有帮助，那么到目前为止，您在该部门的表现比这里的其他任何人都要好一些。”“好的。” 在你离开之前，Klepto 点点头并短暂微笑着说。 好吧，有人公开表示想成为你的男朋友。你知道它最终会成为其中之一。你只能想象其他人会像你和他们的日常一样试图向你求爱。有趣的是，生活如何开始模仿艺术。 即使 Klepto 大部分时间都很愉快，你也不知道他有多真诚。（你真的能相信一个叫 Klepto 的人吗？） 就吸引力而言，Klepto 可能是最好的。Lifto 可能会提供最好的保护，但即使化着小丑妆，这个人也很难看，就 Fatty 而言，你脑海中唯一闪过的是潜在的性姿势，只有其中一个没有导致你被压垮。 你们中的一部分人想知道自己是不是因为肤浅而不好，但后来你记得他们都被你吸引，主要是因为你的外表。 尽管出于某种原因，您会发现 Shrimpy 的大小很可爱……事实上，如果他们对您不那么粗俗，您可能会发现其他一些在这里工作的矮人也很可爱。也许你有一个你以前不知道的隐藏的侏儒恋物癖。 哦，好吧，你现在不会担心任何这些。 当您返回自己的拖车时，您会看到大多数矮人都在忙着收拾嘉年华；其他人正在提供帮助，但大多数人只是在整理自己的阶段等。 “嘿，小丑荡妇，如果你不是真的要睡在你自己的拖车里，只是想和你画画的怪胎朋友一起玩垃圾箱，那么你至少可以把它交给我们中的一个！” 一位身着黑色大莫霍克族纹身的女士，脸上有几个穿孔，这句话让你措手不及。其他几个纹身的女人站在她身后，同意她的说法。你短暂地记得她昨天叫你的名字。她的名字是剃刀，她做了一个人类枕形动作。 “昨晚我没有使用我的拖车，因为我一个人感到不安全。” 你说。“哦？只是树林里的一个害怕的小宝贝吧？好吧，你现在一个人了。没有你的小丑团队保护你，你打算怎么办？” Razor 说着用手指戳了你的胸膛。 你环顾四周，虽然有几个人站在旁边，但没有一个人在帮助你。虽然 Razor 并不比你大多少，但她看起来确实更强壮。再加上她有两个朋友，看起来他们也会加入，如果真的打架，你认为你没有机会。
梗概：
第二天…
--------------------
动作：
做
你跑
结果：
当你不得不应对凯利的欺凌时，跑步总是一种久经考验的真正策略，现在没有理由改变...... 你立即转身跑向你的拖车，但可以预见的是，剃刀和她的伙伴们不会让你逃跑。 “抓住那个该死的小丑婊子！” 剃刀喊道。 当你冲过其他肉食时，你会情不自禁地注意到他们都没有试图阻止这一点。您开始认为，也许您的未来可能不会参加这场该死的狂欢节旅行。 当您到达拖车时，门略微半开。您还注意到上次有一些不合适的地方，更不用说看起来有人在您的床上花了一些时间并用他们的“本质”弄脏了它。 你没有时间注意到其他任何事情，但很明显昨晚有人在这里并且你躲过了一颗子弹，不幸的是你没有躲避两次。 当您试图锁门时，Razor 将门砸开，导致它击中您的脸。当你从这件事中退缩时，Razor 已经把你抱起来打你的脸。她的其他暴徒就在她身后。 “锁上那扇门，我要享受这个！” 剃刀命令她的一名船员并拔出一把直剃刀。 “现在，我要帮你一个忙。我要把你的嘴切得大到你可以同时吐出三只公鸡。你可以让它成为你表演的一部分，小丑 Sucko！” 剃刀说着冲向你。 自然地，你不会只是让她给你一个格拉斯格罗微笑，所以你试着为自己辩护。你设法向她扔了一些附近的物品，但这还不足以阻止她。事实上，你的挣扎让她“不小心”刺伤了你的喉咙并割破了动脉。 你捂着脖子倒在床上，脖子在流血。 “拉屎！” 剃刀喊道。“他妈的！我没想到我们会杀了她！” 她的一个暴徒说。“我并没有尝试，但那个婊子一直在挣扎。我们离开这里吧！” 她喊道，很快他们都离开了你的拖车。 另一方面，你继续流血并很快死于伤口。
梗概：
你跑
故事名称：
57
故事背景：
也许这不是你有过的最好的主意，但在和你姐姐凯利打交道这么多年之后，你不会再忍受欺凌了。 你以前从未真正成为过战士（试图保护你的脸免受凯利几次试图毁容你的企图不算数）所以当你向 Razor 挥拳时，她很容易躲开并抓住你的手臂也就不足为奇了。 “哎哟！哈莉·奎因 (Harley Quinn) 有一些战斗！很好，不想这太容易了！” Razor 说着把你扔在地上。在你起床之前，你感到肋骨被踢了一脚。 缺乏任何其他策略，你冲向 Razor 的一条腿，用力地将牙齿咬入其中以抽血。 “啊该死！什么鬼！” Razor 大喊大叫试图让你离开她的腿，此时她的朋友会介入帮助你离开。当 Razor 花点时间检查她的伤口时，你被拖走并被束缚住了。 “他妈的！拉屎！” Razor 看着你留下的相当深的咬伤，惊呼。“我还以为你是人枕，几颗小牙都吃不下怎么回事？” 你问剃刀的方向吐血。“希望你玩得开心，因为那是你最后一次使用那些珍珠白。从现在开始向吸管吃东西问好，但从好的方面来看，下次你参与你最喜欢的吸吮小丑鸡巴的过去时，他们不必担心被刮伤。” Razor 说着开始向你走来，但突然 Roofie 阻止了她。 “好吧，剃刀就行了，我看到了发生的事情，我不会在这个狂欢节上发生不必要的冲突！回去收拾你的舞台！” Roofie 骂。 Razor 看了他一会儿，然后是你，然后向她的朋友挥手让你走。 “总有一天你不会主持这个节目 Roofie。” Razor 和她的朋友们一起离开时说道。“也许不是，但那一天不是今天。” 鲁菲说。 当你起身想刷自己的时候，Roofie 慢慢地向你走来。他已经闻起来像酒精了。 “嗯，苏西，先是兰科，现在是剃刀，看起来你只是在到处交朋友。”“这不是我的错！我没有对她做什么！”“我知道你没有，你好吗？”“是的，但现在我的背上可能有一个目标，其中一个可能是因为 Razor 不是这里唯一讨厌我的人。”“是的，我注意到了。仅仅因为我大部分时间都喝得半醉并不意味着我不知道这里发生了什么。听听一些人对你的负面情绪会随着时间消失。不过，您似乎与新的小丑同事相处得很好。尽管你有自己的拖车，但你还是在他们的拖车里过夜。”“我这样做只是因为昨晚我觉得一个人睡不安全。昨天有一个非常令人毛骨悚然的大家伙整天盯着我看，我不知道我可能只是偏执了。”“嗯，那你是想跟他们上床，还是跟别人上床？我的意思是，如果你觉得一个人睡不舒服，我可以安排。”“不，我很感激你给了我自己的拖车。此外，考虑到我结交的所有新朋友，我会更安全地独自睡觉并与 Klepto 和其他人一起睡，我觉得这会导致其他问题。” 你说。“好吧。好吧，我会让你回到你的拖车。我们可能会在夜幕降临之前准备好出发，所以我相信 Lifto 会在附近把这东西挂在我们的一辆卡车上。因此，在那之前，请享受停机时间。”“等等，我不是应该帮忙收拾嘉年华吗？”“你？不，其他人可以应付。我真的觉得他们不会让你做太多工作，因为他们都迷恋你和所有人。矮人把整个包都盖好了。总是有，这就是他们所做的。狂欢节的主心骨等等……考虑到即将发生的事情，这有点浪费时间，但嘿，我们做我们知道的事情。” 鲁菲说完就离开了。 Roofie 对未来厄运的预测有点神秘，你会觉得他的性格很一般，但至少他对你总是很愉快。 当您到达拖车时不再分心，这时您又开始感到紧张。 首先，门是微开的。您还注意到上次有一些不合适的地方，更不用说看起来有人在您的床上花了一些时间并用他们的“本质”弄脏了它。你留在这儿的手机也不见了，但考虑到它的电池快没电了，你不会让 Bobby 为它支付更长的时间，这不是什么损失。昨晚肯定有人来过。你不知道是不是兰科，那个盯着你或其他人的令人毛骨悚然的大笨蛋，但不管怎样，你都躲过了一劫。 你有一半希望这个人会从某个隐蔽的地方向你跳出来，但这并没有发生。不管是谁在这里，可能在早上之前就离开了，也可能是在他们意识到你不会来的时候。 你重新检查所有拖车的位置（即使它不是最大的地方）并在感觉足够安全之前用椅子挡住门，然后才能淋浴和换衣服。 之后，您将经历实际清理拖车的平凡过程。你想如果你不需要帮忙收拾嘉年华，你至少可以花时间清理你的生活区。（第一件事就是扔掉被弄脏的床单） 当你完成时，已经是下午了，你又累了。你还是不习惯这么早起床，昨晚又没睡好，就躺在现在更干净的床上，闭上了眼睛…… 当你再次入睡时，你不知道过了多久，但当你醒来时，门外传来一声巨响。 “按照美国国民警卫队的命令开放！” 国民警卫？这到底是怎么回事？ “等等，等等，我要起来了！” 你喊。“打开门小姐，否则我们将被迫打开它！” 一个声音回拨。 不想被枪杀，你就顺从了，打开门后，几名国民警卫队站在外面。 “小姐，跟我们一起来，为了你们的安全，我们需要你们在一起。” 一个年轻的士兵说带你出去。“我们去哪？” 你问。“就到那边那个大帐篷。放心，这一切都是为了保护你。” 不知何故，您不太相信这一点，但是当您环顾四周时，您会发现狂欢节似乎受到国民警卫队的控制，因此别无选择，只能遵守。你不知道为什么或如何发生这种情况，但从你无意中听到的一些军用无线电中，听起来你所在的城镇发生了骚乱。这是某种戒严吗？ 当您到达大帐篷时，狂欢节的大部分内容都在那里。大多数人坐在桌旁抱怨这种命运。你会看到 Roofie 积极地与一名高级士兵争论，后者看起来对他所说的话完全不为所动。 “该死的，镇上闹事，不代表这里就胡说八道！我们甚至不是当地人！我们只是路过，正要走！” 鲁菲说。“是的，我第一次听到你，但我的命令是保护整个区域，并且会一直处于监视状态，直到另行通知。现在我建议你在我踩到你屁股小丑的泥洞之前离开我的脸！” 军官说完便走出帐篷。 你去找鲁菲。 “具体是怎么回事？” 你问。“就像我一直知道的那样，苏西会来到末日。只是没想到我最后的日子会被一群法西斯分子困在枪下！这些政府的白痴认为他们可以控制即将发生的事情，但他们真的无法控制狗屎。这些傻瓜甚至不知道他们真的只是那个该死的零地公司的傀儡，该公司一直在该死的地方到处投放防空洞广告。” 这绝对是你见过的 Roofie 的新一面，他真的很生气。即使他很连贯，他也确实喝醉了。你看到 Klepto 和其他人向你招手。 “我们为什么不直接过去，然后……”“你知道苏西是什么吗？这些混蛋甚至没有那么多。充其量是一个骨干船员。我打赌我们可以接受他们。我的意思是我们的人数肯定超过了他们。”“什么？！你疯了吗？他们装备了机枪！” 你说。“和？所以我们几个人死了？反正我们都快死了，为什么要等？宁可跪着死去，也不愿跪着活着。说真的，我们要做的就是从他们那里拿走几支枪，这已经让比赛场地变得平坦了。说真的，你认为他们可能会让我们活下去？他们可能会像海豹宝宝一样把我们排成一排，然后在明天之前把我们的脑袋炸掉。我告诉你，为了今晚的反叛，我要组建自己的军队。” Roofie 似乎很想完成这个计划，你唯一能想到的就是尝试帮助或阻止暴力。
--------------------
动作：
做
你阻止暴力
结果：
Roofie 的计划除了杀死一群人之外不会有任何成就，而您可能就是其中之一！你想得快。 “呃，好吧，鲁菲，我们先过去制定作战计划好不好？我敢肯定 Klepto 和其他人会想先听到这个。”“嗯，好的。你他妈的不用给我指路，我没醉！” Roofie 说着将他的手臂从你的手中拉开。 当你到达 Klepto 的桌子时，当 Roofie 开始说“赶上那些政府傀儡混蛋”时，他和其他人都知道出了什么问题。Shrimpy 和 Fatty 都试图让他冷静下来，但他没有任何反应。 “苏西到底是怎么回事？鲁菲说话比平时更疯狂。” 克莱普托问道。“他想让整个狂欢节攻击国民警卫队，在他造成太多场面之前做点什么。” 你说。“做点什么？你不是说杀了他吧？！” 克莱普托问道。“不！但我不知道也许把他打倒是为了给我们争取时间，他可以清醒一下。利夫托，把他打倒。” 你说。“我？！为什么是我？” 利夫托问道。“因为你是我们所有人中最强的，我猜你可以一击完成。在他杀了我们所有人之前，就去做吧！” Lifto 有点不情愿，通常不会这样做，但情况似乎确实很糟糕，所以当 Roofie 继续对 Shrimpy 和 Fatty 咆哮时，他猛击 Roofie 的后脑勺，将年长的男人完全击倒并摔倒扑进胖子的怀里。 其他一些肉食者目睹了这一点，但他们似乎没有做任何事情。事实上，这只是表明他们中的大多数人在事情的计划中是多么不尊重 Roofie。有点可惜。 “妈的，你有必要打他这么狠吗？他是个老家伙！你可能已经让他昏迷了！” 虾米的言论。“我只是按照苏西告诉我的去做！”“好吧，我也不希望鲁菲受到伤害，但那家伙至少需要沉默片刻。我认为我们遇到的更大问题是其他肉食。我感觉他们认为 Roofie 不再是领导角色的一个因素，所以期待一场权力游戏。我们都需要团结起来保护自己。”“国民警卫队呢？我的意思是我们不应该担心他们吗？” 利夫托问道。“国民警卫队正在阻止这场狂欢节现在爆发成一场暴力狂欢。没有人会冒险通过制造问题来激怒他们，至少现在没有 Roofie 将他们全部激怒的计划。” 你回复。 “那我们现在怎么办？” 克莱普托问道。“我不知道！我的意思是我只是在这里思考！你们都跟我说我们是狂欢节最讨厌的一群人，所以我们应该交个朋友吧？小虾，你跟矮人有什么关系吗？”“拉？我想我已经足够了，我的意思是，即使作为一个内心深处的小丑，我也将永远是一个侏儒。但正如我之前提到的，我不能为你们其他人做出任何承诺。”“好吧，我不知道，做些外交什么的。至于我们其他人，我们只需要呆在桌子上照顾 Roofie，并确保他整晚都过得很好。也许我会尝试迷住一名士兵或其他什么。要记住的关键是我们需要共同度过难关。” 你从来没有想过你会开始听起来像你哥哥 Ben 的整个生存主义技巧，但如果它有助于像一个偏执的人那样思考，那么你就不会忽视这些想法。 随着夜幕降临，大军“隔离”帐篷里的情绪变得更加焦躁不安。两名警卫一直在值班，尽管他们对这种情况也不太满意。您确实设法以 Roger 的名义与其中一个人建立了友好关系，并提取了更多信息。 罗杰告诉你，你和你的其他肉欲的朋友不要太担心，因为这只是一种形式。他说镇上的情况要糟糕得多，预计他的部队将在一两天内转移到那里支持他的卫兵同伴。在这一点上，你们都可以自由去。 听到镇上发生的大规模骚乱让您想知道您家人的情况如何。你几乎有点担心他们，甚至凯利，但现在没有什么可以做的。希望他们找到了某种安全。 “到处都是这样吗？” 你问。“嗯，我不知道很多细节，我只是在计划中咕哝，但事情变得很糟糕，我的意思是你最近没有听到消息吗？” 罗杰说。“不是真的，最近有点与世界其他地方脱节了。” 你说。“嗯，世界紧张局势真的很严重，就像每个国家都在为第三次世界大战做准备，而国内的人们似乎在公共礼仪和道德规则方面已经疯了。我想很多人只是害怕，我不能在某种程度上责怪他们，但我们仍然需要法律和秩序。你肯定能理解吧？”“当然。” 你说同意，因为你宁愿不与军队建立任何潜在的敌人，即使它只是一个咕噜声。 无论如何，当你同意时，罗杰会微笑，你会觉得他同样关心在这里。又客套了几句后，罗杰说他真的需要回到“守卫”状态，而不是和你说话，因为他的上司是一个真正的硬汉，他不想因此而惹上麻烦或你。你感谢他的谈话，然后回到你的同事身边。 当然，一些肉食者看到你和其中一名守卫“相处融洽”会让他们更不喜欢你，你偶尔会听到别人的喃喃自语和辱骂。在你来这里的整个过程中，Razor 都没有把目光从你身上移开。她显然已经把目光投向了你，不管发生什么，她只是在等待机会。 有利的一面是，当 Shrimpy 回来时，他说虽然矮人会在适当的时候发挥自己的作用，但他设法说服他们不要攻击小丑，只要你不挡他们的路。（显然他们只是因为 Shrimpy 的关系以及你的关系而扩大这种礼貌......好吧你，尽管女矮人可以预见地不会太喜欢你） 夜幕降临，当每个人都处于高度的准备状态时，没有人可以永远保持清醒，所以最终人们开始入睡，尽管大多数团体大部分时间都是轮班的，你也不例外。 最终，当轮到您睡觉时，您会尝试在提供的众多睡垫中找到某种舒适感。这几乎是不可能的，但精疲力竭可以帮助你。 最终你飘了……
梗概：
你阻止暴力
--------------------
动作：
做
第二天…
结果：
周四 几声枪响让你醒来。 “什么？！” 你说还留在地板上。“嘘！安静的！” Klepto 评论并指向负责卫兵的人。他和其他五个人拿着武器站在帐篷入口附近。 “我不在乎谁开始了这场战斗，甚至为什么！我们离开后你们这些肉体的怪物可以互相撕裂，但在那之前我们是来维持秩序的，上帝啊，这就是我们他妈的要做的事！如果我因为任何原因不得不中断另一场战斗，下次射击不会在空中！” 军官说完便离开了。其他守卫继续站在入口附近，看着每个人最终都坐了下来。 “到底发生了什么？” 你问。“剃刀和其他一些人与菲尔和他的朋友发生了争执。” 克莱普托说。“菲尔是谁？” 你说。“他做那种吃火的东西。无论如何，他们踢了他的屁股，正当事情变得更糟时，卫兵进来阻止了它，就像你刚刚看到的那样。看来控制嘉年华的战斗已经开始了。”“很惊讶我们当时没有受到攻击。”“嗯，就像 Shrimpy 昨天说的，我们现在有矮人保护，但谁知道这种安排会持续多久。”“不敢相信我打架睡着了，鲁菲呢？”“哦他？他还在那边，即使是枪声也没有把他吵醒。” Klepto 指着 Roofie 说，然后她让所有人都感到惊讶。 “我很清醒 Klepto，我只是闭着眼睛休息 反正现在起床也没意义。很明显，这个狂欢节让我失去了尊重；我会责怪你们昨晚把我的后脑勺砸了，但这真的只是一个催化剂。（叹气）没关系，我预测最多两天我们都会死。” 鲁菲说。 “鲁菲，我们真的不想那样做，但你攻击军队的想法太疯狂了。你的计划会害死所有人。” 你说。“如果最近的事件有任何迹象，这种情况可能仍然会发生。至少按照我的计划，我会在狂欢节完好无损的情况下死去，而不是眼睁睁地看着它在我眼前分崩离析。” 此时没有其他人真正知道该说什么，所以你们都保持沉默并继续相互交谈。 “那么下一步是什么？” 克莱普托问道。“我不知道，我猜想逃跑？我们可以快速到达其中一辆车并开走吗？” 你说。“鉴于所有警卫现在都处于高度戒备状态，祝你好运。” 胖子指出。“是的，我不认为我们走得很远。” Lifto 补充道。 你坐下来呼气，想知道接下来要做什么，但你现在无法真正思考。似乎由于你更积极的建议和 Roofie 的冷漠，你现在不知何故成为了你的小团体的“领导者”。你并没有真正计划好。 “就目前而言，我们只是待在原地，我会试着想一些事情。” 你说。你小组的其他人没有自己的任何想法，所以他们遵守你的计划（或缺乏计划）。 随着时间的流逝，您通常会进行大量随意的谈话，并在心里猜测“如果您做 X”会发生什么。你有一半期望其他人试图逃跑，特别是当他们说他们需要使用洗手间时，但考虑到他们总是在他们离开的同一个警卫下回来，似乎没有人有胆量真正尝试它，即使是剃刀。 你们小组中的其他人也没有提出任何计划，但您确实注意到虾皮偶尔会前往矮人小组。他声称他只是在确保“联盟”的安全，但您更倾向于相信他只是想确保自己的安全。到目前为止，虽然矮人仍然应该站在你这边。 Roofie继续躺在睡垫上，没有对任何人说话。你仍然对背叛他感到有点难过，尤其是考虑到他不仅把你带进了这个狂欢节，而且很喜欢你给你一个私人预告片，而且当剃刀要踢你的屁股时，他可能会为你省去很多痛苦. 你过去和他谈谈。 “嘿，路飞。” 你说。“嗯，嘿苏西。” 他哼了一声。“你还好吗？”“我？除了整天不喝酒，我还好。我想我只是在和我的生活和平相处。” 鲁菲说。“楼上，我还是很抱歉……”“什么？让 Lifto 把我搞得一头雾水？我已经忘记了这一点，或者更确切地说，我已经重新考虑了自己对此的立场。你可能是对的，我的意思是我肯定宁愿那样死，但我想利用我最后一点逐渐减弱的影响力让每个人都跟随我，这有点自私。如果你在交火中丧生，那将是特别的耻辱。你其实很有天赋。”“你就是这么说的。”“不，这是真的。我不是根据外表或一时兴起决定雇用你的。在你扮演小丑的那一刻，你很好。我的意思是很明显你甚至是一个领导者，你如何让其他小丑挂在你的决定上。（叹）要是早点遇到这个狂欢就好了。情况可能大不相同。”“嗯，我不确定我是多么优秀的领导者，因为我想不出接下来会发生什么。” 你说。“你会想出一些我敢肯定的。不过有一件事，虽然我不抱太大希望，但如果你能以某种方式让这个狂欢节保持在一起……那就去做吧。我知道目前的情况不是最好的，我知道你并没有受到所有人的欢迎，但我看到你身上的某些东西，如果你能像以前一样体验狂欢节，就会感到满足。像一家人一样。”“一个家族？” 你笑着说。“嘿，就像我说的，你是在家庭斗争中进来的。这就是有时在家庭中发生的事情。家族斗争永远是最恶毒的，但平息之后，有时家族单位会比以往任何时候都更加强大。而且我认为，如果你能让这个家庭不至于彻底分裂，你会很高兴。但你必须是做到这一点的人。我已经没有它了。” 这时，Roofie 摸了摸你的手，你拍了拍他的手，然后再离开他。你想知道这是否更像是一个“垂死”的人的最后要求，而不是期待你的个人成就。尽管如此，你仍然认为他是真诚的，如果整个狂欢节成为你从未拥有的幸福家庭，那肯定会很好，但你只是认为这是不可能的。 你环顾四周，发现除了卫兵换岗外，没有什么变化。其中一个恰好是罗杰，这让您想到了一种可能的逃脱方式。唯一的问题是它会依赖于比你已经和罗杰在一起时更“迷人”，这可能会让你陷入你不想陷入的境地。
梗概：
第二天…
--------------------
动作：
做
你去找罗杰
结果：
虽然你希望你不必走得太远，但如果这意味着离开这个地方，那么也许这就是你必须做的，因为无论国民警卫队离开还是留下，事实仍然存在，你不是像 Roofie 一样领导这场狂欢的人认为你是，你觉得你的生命处于危险之中。 你开始走向罗杰。 “喂，你要去哪里？” 克莱普托问道。“还不确定，但别等了。” 你回答并继续走向罗杰。 当你到达罗杰时，你满脸笑容、姿势和魅力。 “你好罗杰，你今天过得好吗？” 你用友好的语气说。“嘿，苏西。我想我还好，但我真的希望你的朋友不要惹这么多麻烦。有人诽谤伤害。我的指挥官不是那种宽容的人，我想你早点看到了。”“是的，我做到了。我很害怕。事实上，我很害怕这个地方现在给人的整个氛围。它变得非常暴力，但我觉得靠近你更安全。” 你说着开始抚摸罗杰。 此时，罗杰正在对你微笑，但他附近的朋友不太赞同，尽管你比其他任何事情都想象它的嫉妒。 “罗杰，我们应该密切关注这些卡尼怪胎，而不是与他们交朋友。”“厄尼有什么害处？我的意思是她说她很害怕。什么，你认为她会砸我的头并拿走我的武器？来吧，别这么偏执了。反正我们也不应该在这里。”“订单就是订单……呃。无论如何，我不会看着你对着某个carnie妓女做个咕噜咕噜的眼睛。我会在这个帐篷外面，尽量让你的眼睛一直盯着被拘留者，而不是一直盯着她的屁股。” 厄尼说着走出了帐篷。 “真是个粗鲁的人。我很高兴你不是那样的人。” 你现在说，双臂搂着罗杰。“嗯，你知道，我以人们希望被对待的方式对待他们。” 罗杰回答。 尽管罗杰心烦意乱，但他实际上比人们想象的要好得多。他紧紧握着他的步枪，并密切注视着你是否打算抢他的手枪。他还会定期将目光从你身上移开，以确保没有人利用这种情况。（Klepto 和其他人看起来他们打算这样做，但意识到他们还太远了，无法在 Roger 将你推开并开火之前冲向他） 虽然你的计划不一定是拯救所有人，而是拯救你自己。 “所以罗杰，一个女孩要做什么才能离开这里？” 你诱惑的说。“嗯，就像我说的，我们应该今天晚些时候或者最迟明天离开。”“太长了，在那段时间里什么都可能发生！你不能做点什么吗？我的意思是我可以让它值得你花时间。” 你说着，用手指抚过罗杰的胸膛。“苏西，听着，我真的很想帮忙，但是……嘿！” 当你抓住他的手时，罗杰被打断了。 你把他的手放在你的脸上，闭上眼睛，将他的食指插入你的嘴里，慢慢吮吸，发出“嗯”的声音。 这个场景并没有被超过几个盯着你的carnie忽视。事实上，“妓女”、“荡妇”甚至更糟的可预测的呼唤都是以您的方式发出的。 另一方面，罗杰几乎无言以对。 “我……呃……该死……”他说，然后抽开他的手盯着他的手指看了一会儿。“啊……但是我还没有吸完你……”你说，眼睛微微颤动，尽量装出可爱的样子，然后在他耳边低语： “带我回到我的预告片，我会向你展示地球上真正最伟大的表演。” 然后你舔他的耳朵。 罗杰立即大声呼唤厄尼。厄尼拿着武器冲了进来，准备杀死什么东西。 “什么？！什么？！又要闹了？！” 他惊呼道。“不。但我真的需要一个帮助！你需要观察这些人，如果指挥官检查我们，告诉他我有一个非常糟糕的事情。”“什么……等等……哦他妈的不！你要去操这个小婊子，不是吗？该死的罗杰！”“来吧，厄尼，你不能帮我吗？”“混蛋，我一直在帮你！我不明白你为什么不能就在这里操这个婊子。不像这些人害羞，从她的表情来看，她看起来不是第一次在现场观众面前被性交。” 厄尼评论道。 你知道，虽然你已经习惯了关于你是一个道德败坏的女人的毫无根据的评论，但有时它仍然很痛苦。 无论如何，罗杰现在不是一个可以阻止他对你的欲望的人。 “好，没问题。我不想把这件事放在你头上，但你让我别无选择……”罗杰开始说，但无论威胁是什么，厄尼甚至都不让他说完。“哦操。（叹气）别说了。美好的。去。去。去！不要花太长时间，虽然我想在你的情况下不会有问题。” 厄尼说被打败了。“谢啦！说真的，这个恩情我真的会记住的！”“嗯。你只是通过勒索得到的。” 当你和罗杰离开时，厄尼又说了一件事情。 “希望你有避孕套，因为上帝知道她是一个肉瘤。她的傻逼可能会把你的鸡巴变成他妈的连体双胞胎或一些狗屎。” “这到底是怎么回事？他妈的混蛋……”你说着开始对厄尼的评论生气，但罗杰只是拉着你的手回到了你的拖车。“别管他。让我们快点，因为我们不想撞到其他人。我不像对厄尼那样对每个人都有影响力。那么你的拖车呢？” 你告诉他位置，你们两人正朝着那个方向前进，在“隐身”和奔跑之间摇摆不定。 当您这样做时，您突然想到您必须考虑下一步行动。你真的要让他操你吗？罗杰心烦意乱，你可能可以抓住他的侧臂，给你足够的时间逃跑，但虽然罗杰可能不会立即开枪射击你（这是在那里掷骰子），但没有什么可以说附近的其他卫兵不会你知道一旦你试图逃离罗杰，他们就会收到警报。 当然，如果你真的愿意为自由做出最终的“牺牲”，罗杰甚至可以在你的逃生尝试中帮助你更多。当然，他可能会变成一个彻头彻尾的混蛋，在他对你采取他的方式后，把你带回帐篷。
梗概：
你去找罗杰
--------------------
动作：
做
你为它休息一下
结果：
罗杰正忙着留意是否会遇到他的伙伴并到达您的拖车，他现在适当地分心了，而当您离他如此之近时，您可以轻松地抓住他的手枪。 “嘿，什么……”当你迅速后退并将枪托在他身上时，他惊呼道。“放下步枪！” 你喊。但他没有。 “拉屎。我应该更清楚。厄尼将有一个实地考察日。” 罗杰说。“对不起罗杰，但我真的需要你放下那支步枪！” 你说。“……不……如果你有心向我开枪，你现在就会了。你从来没有射过一个人，整个情况对你来说很可怕。你希望你的性感足以让你渡过难关，我的意志力足够软弱，我不会说你的虚张声势，现在就开枪打死你。” 对于你认为只是一个角质咕噜声的人来说，这是一个令人难以置信的洞察力。不幸的是，他几乎达到了目标。 有片刻的停顿，你满怀希望地看着他，你甚至开始流泪了。 “（叹气）看，我告诉过你我们很快就要离开了，我不知道你为什么觉得有必要在没有必要的情况下制定这个精心设计的逃生计划。把手枪交出来，我们就回帐篷了。” 你太害怕投降会发生什么，所以你能做的就是继续瞄准罗杰。 “Suzy，我不是他妈的，现在给我……” Roger 开始说，走向你。你把步枪稍微放下，伸出手，紧张地朝他开了两枪。在这个范围内，即使您处于高度焦虑状态，您也不会错过。 罗杰胸中夹着两个人倒在地上不动，你已经杀了他。 “哦，天哪……天哪，我很抱歉……（嗅）”你说。 这些镜头当然引起了其他守卫的注意。你听到越来越接近你所在位置的喊叫声，所以你跑了。 你听到更多的喊叫声，然后是枪声。起初，您认为他们已经看到您并向您开枪，但当您开始听到几声尖叫时，情况并非如此。 现在你更害怕了，因为你不知道发生了什么。难道卡尼们趁着这种情况，开始反击了吗？你不知道，但你不打算留下来。 您可以通过拖车和车辆的小迷宫尽可能地躲避和隐藏。尖叫、喊叫和枪声现在伴随着不自然的增长。像某种动物。 不幸的是，当您到达嘉年华极限的尽头时，您被隐藏在您经过的最后一辆拖车角落后面的东西伏击，手枪从您的手中蔓延开来。 你甚至不知道它是什么，除了你在地上，它在你的背上沉重地呼吸并发出你听到的咆哮声。然后你会感觉到牙齿深深地陷进了你的肩膀。 当你努力挣脱时，痛苦和恐惧都在你心中升起，但是这东西不管它是什么都有很强的抓地力，它用一只手反复地把你的脸砸在地上，让你的挣扎变得更少，然后在吃掉你尸体的其余部分之前，它会折断你的脖子。
梗概：
你为它休息一下
故事名称：
58
故事背景：
虽然你希望你不必走得太远，但如果这意味着离开这个地方，那么也许这就是你必须做的，因为无论国民警卫队离开还是留下，事实仍然存在，你不是像 Roofie 认为的那样领导这场狂欢的人，你觉得自己的生命处于危险之中。 你开始走向罗杰。 “喂，你要去哪里？” 克莱普托问道。“还不确定，但别等了。” 你回答并继续走向罗杰。 当你到达罗杰时，你满脸笑容、姿势和魅力。 “你好罗杰，你今天过得好吗？” 你用友好的语气说。“嘿，苏西。我想我还好，但我真的希望你的朋友不要惹这么多麻烦。有人诽谤伤害。我的指挥官不是那种宽容的人，我想你早点看到了。”“是的，我做到了。我很害怕。事实上，我很害怕这个地方现在给人的整个氛围。它变得非常暴力，但我觉得靠近你更安全。” 你说着开始抚摸罗杰。 此时，罗杰正在对你微笑，但他附近的朋友不太赞同，尽管你比其他任何事情都想象它的嫉妒。 “罗杰，我们应该密切关注这些卡尼怪胎，而不是与他们交朋友。”“厄尼有什么害处？我的意思是她说她很害怕。什么，你认为她会砸我的头并拿走我的武器？来吧，别这么偏执了。反正我们也不应该在这里。”“订单就是订单……呃。无论如何，我不会看着你对着某个carnie妓女做个咕噜咕噜的眼睛。我会在这个帐篷外面，尽量让你的眼睛一直盯着被拘留者，而不是一直盯着她的屁股。” 厄尼说着走出了帐篷。 “真是个粗鲁的人。我很高兴你不是那样的人。” 你现在说，双臂搂着罗杰。“嗯，你知道，我以人们希望被对待的方式对待他们。” 罗杰回答。 尽管罗杰心烦意乱，但他实际上比人们想象的要好得多。他紧紧握着他的步枪，并密切注视着你是否打算抢他的手枪。他还会定期将目光从你身上移开，以确保没有人利用这种情况。（Klepto 和其他人看起来他们打算这样做，但意识到他们还太远了，无法在 Roger 将你推开并开火之前冲向他） 虽然你的计划不一定是拯救所有人，而是拯救你自己。 “所以罗杰，一个女孩要做什么才能离开这里？” 你诱惑的说。“嗯，就像我说的，我们应该今天晚些时候或者最迟明天离开。”“太长了，在那段时间里什么都可能发生！你不能做点什么吗？我的意思是我可以让它值得你花时间。” 你说着，用手指抚过罗杰的胸膛。“苏西，听着，我真的很想帮忙，但是……嘿！” 当你抓住他的手时，罗杰被打断了。 你把他的手放在你的脸上，闭上眼睛，将他的食指插入你的嘴里，慢慢吮吸，发出“嗯”的声音。 这个场景并没有被超过几个盯着你的carnie忽视。事实上，“妓女”、“荡妇”甚至更糟的可预测的呼唤都是以您的方式发出的。 另一方面，罗杰几乎无言以对。 “我……呃……该死……”他说，然后抽开他的手盯着他的手指看了一会儿。“啊……但是我还没有吸完你……”你说，眼睛微微颤动，尽量装出可爱的样子，然后在他耳边低语： “带我回到我的预告片，我会向你展示地球上真正最伟大的表演。” 然后你舔他的耳朵。 罗杰立即大声呼唤厄尼。厄尼拿着武器冲了进来，准备杀死什么东西。 “什么？！什么？！又要闹了？！” 他惊呼道。“不。但我真的需要一个帮助！你需要观察这些人，如果指挥官检查我们，告诉他我有一个非常糟糕的事情。”“什么……等等……哦他妈的不！你要去操这个小婊子，不是吗？该死的罗杰！”“来吧，厄尼，你不能帮我吗？”“混蛋，我一直在帮你！我不明白你为什么不能就在这里操这个婊子。不像这些人害羞，从她的表情来看，她看起来不是第一次在现场观众面前被性交。” 厄尼评论道。 你知道，虽然你已经习惯了关于你是一个道德败坏的女人的毫无根据的评论，但有时它仍然很痛苦。 无论如何，罗杰现在不是一个可以阻止他对你的欲望的人。 “好，没问题。我不想把这件事放在你头上，但你让我别无选择……”罗杰开始说，但无论威胁是什么，厄尼甚至都不让他说完。“哦操。（叹气）别说了。美好的。去。去。去！不要花太长时间，虽然我想在你的情况下不会有问题。” 厄尼说被打败了。“谢啦！说真的，这个恩情我真的会记住的！”“嗯。你只是通过勒索得到的。” 当你和罗杰离开时，厄尼又说了一件事情。 “希望你有避孕套，因为上帝知道她是一个肉瘤。她的傻逼可能会把你的鸡巴变成他妈的连体双胞胎或一些狗屎。” “这到底是怎么回事？他妈的混蛋……”你说着开始对厄尼的评论生气，但罗杰只是拉着你的手回到了你的拖车。“别管他。让我们快点，因为我们不想撞到其他人。我不像对厄尼那样对每个人都有影响力。那么你的拖车呢？” 你告诉他位置，你们两人正朝着那个方向前进，在“隐身”和奔跑之间摇摆不定。 当您这样做时，您突然想到您必须考虑下一步行动。你真的要让他操你吗？罗杰心烦意乱，你可能可以抓住他的侧臂，给你足够的时间逃跑，但虽然罗杰可能不会立即开枪射击你（这是在那里掷骰子），但没有什么可以说附近的其他卫兵不会你知道一旦你试图逃离罗杰，他们就会收到警报。 当然，如果你真的愿意为自由做出最终的“牺牲”，罗杰甚至可以在你的逃生尝试中帮助你更多。当然，他可能会变成一个彻头彻尾的混蛋，在他对你采取他的方式后，把你带回帐篷。
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动作：
做
你通过它
结果：
你对伏击罗杰没有信心。虽然在预告片中你可能会得到一些更好的机会，但此时你会希望罗杰不是一个混蛋，这实际上会让他帮助你逃脱。 有很多希望寄托，但这是必须要做的。 很快你就到了你的拖车，你进去了，罗杰还在自己进去之前环顾四周。 你站在床边看着它，然后看着罗杰。 “所以我们来了。” 他说。“是的……所以我们在这里……”你回答。“出色地？走吧，我们的时间不多了。” 罗杰说着放下了他的武器，当然他把它们放在你够不到的地方，并开始引导你躺在床上。 这将会发生。并不是说 Roger 对你没有吸引力，也不是他强迫你做任何事情（虽然你担心如果你在这一点上反抗他可能会这样做）它只是......直接用你的猫来得到自己摆脱困境并不完全是你做事的方式。间接通过戏弄和调情？当然，您已经习惯了这样做，但这是您认为自己还没有准备好采取的步骤，现在为时已晚。 罗杰已经在脱他的衣服，而你也在慢慢地这样做。很快，你们俩就赤身裸体，他的鸡巴像个怪物一样朝你扑过来。当他亲吻并拥抱你时，你阻止了他。 “嗯……我们做这件事的时候不能互相看看吗？” 你说。“嗯？” 他问道，看起来几乎被那条评论弄伤了。“呃，我的意思是我们可以做狗式的吗？我只是喜欢那样。” 你问。“哦。嗯是的！当然，不管你想要它。我对此很酷。” 罗杰说，你就位。 好吧，至少现在你可以闭上眼睛假装它是鲍比。希望罗杰不是一个健谈的人，只是顺其自然。 当您为这种“体验”做好准备时，外面突然传来枪声。事实上，其中有几个。随之而来的是大量的喊叫声。 “哦操！我勒个去？！” Roger 惊呼起来，你再也感觉不到他的手在你的腰间，他已经下了床。 你转过身来，看到他开始穿衣服，不时地望着狭窄的窗户。 “外面怎么回事？！” 你问。“我不知道，但不管是什么，你都待在里面，保持低调。我不想你受伤。” 你也开始穿衣服，想知道现在发生了什么灾难。Carnies 最终决定冲向卫兵吗？ 罗杰重新穿上衣服，抓起步枪和手枪准备出门。 “苏西，记住我说的话，呆在里面……噢该死！” 罗杰打开门时喊道。 某个类人生物碰到了罗杰，罗杰掉落了他的步枪并被钉在墙上。它在咆哮和咆哮，Roger 抓住它的脖子不让它咬他。 恐惧笼罩着你，但不会到你无法从厨 房区域拿起附近的刀子刺伤它的程度。 “苏西留下！我知道了！” 罗杰大喊大叫，设法把东西从他身上推开，然后出了门。然后他掏出手枪，大概朝那东西开了几枪。然后他砰地关上门并试图锁上并设置路障。 “你杀了它？” 你问。“我不知道。我想我做到了。” 罗杰回答。“您认为？你不觉得你最好知道吗？！”“是的！是的！我杀了它！我的意思是我把两个它的头和三个放在它的胸前。耶稣基督，给我一点时间！有一刻我差点置身于天堂，下一秒我又在躲避他妈的……僵尸……或者其他什么……该死的我不知道是什么鬼……” Roger 说，试图将他的脑袋围绕在这出乎意料的事件上。 僵尸。他刚刚说那东西是丧尸吗？它是人形的，试图咬他，同时发出咆哮和咆哮的声音。再一次，它看起来并不完全腐烂，虽然可能是新死的。也许只是一个狂热的人类？ 您实际上正在考虑自己受到僵尸的攻击，这似乎很愚蠢。尽管如此，您在外面听到的枪声和混乱似乎表明您正在受到某种东西的攻击。 与此同时，罗杰坐在那里，喃喃自语。突然他站了起来。 “去他妈的。秀智，你还想离开这里对吗？” 罗杰说。“嗯，当然。” 你说。“来吧，我们要乘坐其中一辆卡车。我拿到钥匙了。”“好的？但是你的朋友呢？”“哈，和我一起服务的那些人，大部分都是混蛋。我的意思是你看到了我的指挥官和厄尼。不能说他们中的任何一个都是我的朋友。你和你的卡尼一家呢？”“嗯……小丑对我和主人都很酷……但是这很复杂，我们只能说我不适合那里，所以虽然我确实有点内疚，只是放弃了我相处的那些人，但我可以”不要呆在我感觉不完全受欢迎或不安全的地方。”“好，那么事情就解决了，我们现在就离开。”“可是外面的那些东西呢？”“好吧，我们现在将不得不抓住卡车，因为一两件事会发生。一，我小队的其他人会把这些事情放下，我们俩都会陷入困境，否则那些事情会杀死所有人，我们必须勇敢地度过难关，同时他们不会被其他人分心。” 罗杰解开门，踢开门，准备射击任何可能潜伏在外面的东西，幸运的是现在情况并非如此。 “好吧，让我们这样做，靠近我。” 他说，你们俩跑出了拖车。 生物的声音和枪声就在你身边，当罗杰不得不拍摄另外两件东西时，你还没有走出预告片几秒钟。他没有击中他们的头部，他们仍然保持低位，所以你猜也许僵尸的事情不是这样。仍然不想被咬，谁知道这是否会传染？ 你会看到地上有几具守卫的尸体，在一种情况下，其中一个食人者正在吃它而没有注意其他任何事情。枪声逐渐减弱，尖叫声和大喊声越来越大，这一事实向您表明情况变得严峻。你想知道他们是否已经到了carnies。 到处都在发生这种情况吗？这是本地的吗？你以后必须弄清楚。现在你很高兴进入重型军用卡车。 罗杰坐在驾驶座上，随着更多咆哮的生物敲门并试图上车，罗杰启动了卡车，并在离开这个地方的路上迅速碾过其中三个。 很快你就上路了，但接下来呢？ “我们去哪？” 你问。“我不知道。如果这是某种瘟疫或入侵，或者我想知道我们是否应该前往蒂蒙斯堡，那是附近最近的军事设施，也是我被派往这里之前驻扎的地方。不过还是很开车的。” 罗杰说。“什么？你疯了吗？你不是刚刚擅离职守，现在还考虑去军事基地吗？”“嗯……我的意思是，从技术上讲，我只是放弃了我的职位……但考虑到似乎没有其他人会经历那次袭击，我怀疑是否有人会因为这件事而责备我。我们只能告诉他们，我们是唯一的幸存者。”“但如果这是某种疾病，军事基地还能安全吗？我的意思是，那些秘密实验和生化武器难道不是来自他们吗？”“什么？你看太多恐怖电影或科幻小说了！除非是一些政府安全级别最高的秘密基地，否则一般的普通军事基地是不会有实验室来制造生化武器的！无论如何，我知道这些东西不在蒂蒙斯堡。”“我不知道……只是看起来不是最安全的地方。”“那你建议去哪里？” 你没有答案。 “看？看，那里还有很长的路要走，也许我可以从广播上得到一些信息，我们可以了解更多信息。太糟糕了，这个没有双向无线电，我们实际上可以与某人交流，而我他妈的手机丢了。事实上，我可能把它留在了你的拖车里。” 当 Roger 试图通过无线电接收信号时，你会想你现在是如何逃离两个“家庭”的，谁知道他们两个都变成了什么。他们死了吗？他们还活着吗？你的家乡被那些东西淹没了吗？它是在元帅法律之下吗？ 你对这一切感到有点难过，但你真的能做什么？ 随着白天最终变成黑夜，罗杰继续尝试通过无线电获取一些信息，但在他使用的军事频道上却是静止的。他不明白。切换到普通收音机只会播放音乐，但会被周期性的 Ground Zero 广告打断。不过，对这些生物完全没有任何了解，甚至连常规新闻都没有。这很令人沮丧。 你也不会在路上遇到太多的交通，当然这不是最繁忙的道路，但感觉相当孤立。尽管如此，至少你没有看到一大群这些东西在四处游荡。 罗杰谈到了他的过去，以打破长时间的沉默，其中大部分都相当平凡。你只是主要听，虽然你真的只是听了一半，并将关于你自己的问题重新引导到一些肤浅的事情上。你没有心情谈论你自己。 最终你开始昏昏欲睡，罗杰也是如此。他靠边停车，你们俩都睡了，谢天谢地，他从来没有建议从拖车上停下来的地方继续。 没有什么比一群食人生物更能打破情绪了。 你昏昏沉沉地睡着了……
梗概：
你通过它
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动作：
做
第二天...
结果：
星期五 “……这是零地公司的付费公告……” “啊！早他妈的闭嘴。GZ他妈的把那些该死的电台都买下了？！” 你听到 Roger 愤怒地关掉收音机，把你吵醒。 看起来你又在运动了，你睡过了。 “这是怎么回事？” 你问。“嗯？哦，你起来了。什么都没有发生，只是像往常一样愚蠢的零地广告警告即将发生的核浩劫。没关系，我们收到了一个非常真实的威胁，我似乎无法获得任何信息。反正我没有睡那么多，所以我又开始开车了。我想你仍然可以睡觉，所以我没有费心叫醒你。”罗杰说。“那我们在路上多久了？”“肯定不止几个小时。现在开始绕过所有这些农田区域，我们可能很快就会到达人口稠密的区域。我也开始看到更多移动的车辆，尽管我也看到了很多废弃的车辆。奇怪的是没有军队。看到一些人甚至试图搭便车，但我现在玩得不好。”“你看到这些东西了吗？” 你问。“嗯，我想我可能在开车的时候看到了一些，但从远处我可以看出来。不像我要去检查。” 到目前为止，该计划似乎仍在 Fort Timons，但如果您之前不认为这是一个可疑的想法，那么您现在真的不确定。他无法从无线电中获得任何信号，因为你知道基地可能被那些东西淹没了。这正是你向 Roger 提出的问题，但他似乎相信情况并非如此，并且他决心前往 Fort Timons。有趣的是，他在狂欢节上抛弃了所有的同志，并说他们都是混蛋。猜猜他内心仍然是军人。 他感觉到你仍然对他的计划不满意。 “苏西，你不用跟我来，你知道的。我的意思是我可以在我们经过的下一个城镇送你。” 罗杰说。“我到底要在那里做什么？除了背上的衣服，我什么都没有，随着事情的发展，我可以想象我会为了生存而经常脱掉那些衣服。” 你说。 在罗杰回答之前有片刻的沉默。 “我猜这就是你在狂欢节上对我所做的。没关系，我有点知道这是怎么回事。我知道你其实并不喜欢我。”“如果你知道这一切，你为什么要冒着职业生涯甚至生命的风险？”“你开玩笑的对吧？我的意思是我知道我并不可怕，但你有点超出我的范围。我到底什么时候才有机会和长得像你的人在一起？我的意思是肯定我知道这只是一种行为，但在我看来，我可以假装你是真诚的。虽然我承认，当你阻止我吻你，不想看我的脸时，那种伤害。”“这不是针对个人的。我只是不太了解你，我还没有准备好接受那种程度的亲密。我要假装你是我的前男友。（叹气）我简直不敢相信我竟然走了那么远，还要经历它。我的意思是，与你可能听到的对我大喊大叫的事情相反，我不会做那样的事情……永远。”“真的？因为你很擅长整个诱惑的事情，即使你对它有点明显。”“我很明显，因为这只是我最近几天开发的一项新技能，我参加了一个该死的狂欢节，只是为了摆脱我该死的家庭生活......我他妈的到底在想什么？！我不是为了照顾自己！我应该呆在家里，让食人者在我舒适的地下室里吃我！” 你说，泪流满面。本周的压力开始影响你。 “哇！你不是那个意思。事情并不完全可怕。嘿，如果这能让你感觉好些，我感到有点难过，因为我知道我要利用一个明显绝望的女人的行为。” 罗杰说。 现在你停止抽鼻子，开始嘲笑那句话的荒谬。 “但这当然不会阻止你。” 你说。“当然不会，但我向你保证，我会为此感到内疚。”“不，你不会有的。”“好吧，你说得对，我不会，但如果你事后问了，我还是会把你从那个狂欢节中弄出来的。”“好吧，至少它不会白费。（叹气）猜猜这一切都解决了。我的意思是，由于我们的选择，我们都还活着，我很感激我没有死，而且你可能在那里救了我的命。”“我很感激我至少能看到你赤身裸体。”“我想我们总会有拖车的。” 你对自己的处境并不完全满意，但罗杰试图缓和情绪至少让你感觉好一点。 “那……你为什么不告诉我你这个前男友的事？如果你还在想他的话，我猜他一定是个英俊的家伙。” 罗杰说。“呵呵。不是特别。他是一个看起来很普通的人。在那个部门和你没有太大的不同。他是我唯一交往过的人，所以，想到他会更舒服。反正对他来说有点复杂。很长的故事，我现在不想讲。”“我明白。好吧，我就这么说吧，他有你做女朋友很幸运，让你逃跑是个该死的傻瓜。”“嗯……其实你知道吗？你说得对。他很幸运有我，还有一个该死的傻瓜让我逃跑！” 你有点骄傲地说。 你知道罗杰说了一半只是希望让你再次陷入妥协的境地，但到底是什么，这是一个自我助推器，它至少让你保持积极的心态。 “拉屎。” 罗杰突然说道。“现在怎么办？” 你问。“我只是看了看油表，我们几乎没电了。该死的，我还以为厄尼最近把这件事搞砸了。”“我们不能去蒂蒙斯吗？”“好吧，我们可以冒着冒烟到蒂蒙斯堡的风险，但我不想在这种情况下冒险。我什至没有一根橡胶软管可以用来从我们经过的这些废弃车辆中吸出气体。”“好吧，我想我们会在下一个加油站停下来。”“假设他们得到了任何。我的印象是进入下一个加油站不会那么简单。” 你和罗杰继续开车大约半小时，然后你才看到远处有什么有趣的东西。即一个卡车停靠站，显然已被改造成一个小堡垒。 “嘿，看那个！” 你指出。“我看到了。这是一个卡车停靠站，看起来好像那里的任何人都试图通过在它周围放置卡车来封锁这个地方。他们甚至让人们拿着步枪站在卡车顶部作为观察员。他们可能有汽油。” 罗杰说。“我不知道。无论谁在那里，都可能不会太信任任何人，尤其是来自军队的人。他们可能只是想抢劫我们。”“我们得试试。我的意思是我们需要加油，考虑到这个地方的防守有多好，我们至少知道他们可能得到了一些。谁知道我们会不会在其他地方走运。” 你和罗杰开车到卡车停靠站，这立即让瞭望员指向你的方向并举起他们的步枪。当你停在它前面时，会有很多叫喊声让你留在卡车里。 “我们在这里不需要任何政府援助，正如你所看到的，我们做得很好。我强烈建议你们继续前行。” 其中一个喊道。“我不是来找麻烦的。我只需要一些汽油。我们明白了，我们将前往蒂蒙斯堡。” 罗杰喊回去。“气体？别他妈的这么认为士兵男孩。随着社会崩溃的方式，狗屎将变得有价值，而金钱将变得一文不值。”“那么，你会接受天然气供应交易吗？”“嗯，你有什么？”“这东西后面有一些军用口粮。你知道，这些东西会持续很多年。”“我们有很多食物，还有什么？有武器吗？”“呃……可能有几个……哦，我们这里有一些医疗用品！” 有一个很长的停顿，他告诉你在爬下卡车并从视线中消失之前等待。最终你再次听到他的声音，这是从卡车路障后面传来的。 “好吧，我们要移动这辆卡车，但发生了一件有趣的事情，你是第一个被子弹击中头部的人。好了，就让这个进来，大家小心那些狂暴的混蛋！” “你还觉得这是个好主意吗？” 你问。“嗯，就像我说的，我们对天然气的选择是有限的。” 罗杰回答。 卡车一开出你的路，那个向你咆哮命令的人就会挥手让你进去，并示意你走到油泵前。你一开车，他们就把卡车放回去。要么他们真的有安全意识，要么他们不想冒险让你逃脱。 从这个距离，您会看到卡车停靠站的建筑部分还有几个人。看起来它是一个一般的商店/餐厅组合。他们中的大多数人都在窗外看着你，虽然有一些人出来了，但其中一个是一个拿着手杖的老人，他直接走到水泵旁。 “好吧，我想让那辆卡车里的你们两个慢慢离开。我不在乎你是否有武器，但不要以威胁的方式将它们指向我或其他任何人。” 老者喊道。 罗杰将步枪扛在肩上，走出卡车。你很聪明，虽然你手无寸铁，但你会从罗杰的身边退出，这样你就不会感到那么脆弱。自然是来复枪对着你们俩，但拄着拐杖的老头却叫他们冷静下来。 “你们这群人，别再指指点点了，开始卸载这辆卡车。对不起，每个人都有点紧张。名字叫亨利。我拥有这个地方。” 他说。“罗杰，这是可以理解的。”“嗯，是军人吧？以为所有的国民警卫队都在与城里那些疯狂的东西作战。据我所知，这个地方现在已经完全人满为患了。”“城市？你的意思是保安超支了？” 你问。“是的。在收音机听过。当然不是普通的。那些 GZ 混蛋似乎正在堵塞那些，但是在我的业余无线电中。一直在尝试与 Fort Timons 沟通，但我的收音机并不是处于最佳状态……”“坚持，稍等！你一直在和蒂蒙斯堡有联系？！” 罗杰惊呼。“嗯，有点。更像是我以前服务过的老朋友，他仍然在那里担任库存文员。从昨天开始就没有收到他的任何消息。我所知道的是，他告诉我的最后一件事是堡垒处于高度戒备状态，进一步的沟通将仅限于需要了解的基础，并且仅限于具有适当权限的人。我想是典型的军事废话，我不需要告诉你。”“呃，我们正在去蒂蒙斯堡的路上。你觉得安全吗？” 你说。“嘿，好吧，我不能告诉你堡垒是否安全，但我可以告诉你，那些狂热的混蛋一直在集体逃离这座城市，考虑到这座城市有多远，他们显然没有得到累了，可以覆盖很多地方。我们昨天刚刚击退了其中的一群人。因此，这里的转移堡垒。蒂蒙斯堡更靠近城市，所以我打赌你在到达那里之前必须经历一堆这些事情。我不想做一些事情来确定。” 亨利评论道。“妈的，那些东西在城里，在我镇上！这意味着这不仅仅是本地的事情！估计是全国性的流行病！我们现在不能去蒂蒙斯堡了！” 你惊呼。“我们不知道这个地方是否已经沦为那些东西。” 罗杰回答。“确切地！我们不知道！” 你说。 这时亨利插话了。 “如果可以的话，你们两个都可以留在这里。就我而言，我们有房间，你装满补给品的卡车可以让你进去。而且我敢肯定，作为一名军人，如果需要的话，你会在这里竭尽全力保卫这个地方。所以如果你愿意，你和你的女朋友可以留在这里。” 亨利说着，然后走过去监督他的人卸载卡车。 这是一个非常诱人的报价，你会抓住机会，但罗杰似乎仍然确信从长远来看，Fort Timons 会更好。你想知道他是否觉得有必要回到蒂蒙斯堡，因为在内心深处，即使他没有承认，他也觉得自己放弃了自己的职位。 唯一的缺点是如果罗杰不留下来而你坚持，你将再次被留在一群陌生人的陪伴下，而上次并不顺利。您只能想象来自一群角质卡车司机类型的所有嘲笑，摸索尝试，甚至可能更糟。
梗概：
第二天...
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动作：
做
你执意留在这里
结果：
你心中的某些东西告诉你，去蒂蒙斯堡将是一个巨大的错误。旅行的长度以及它会被这些东西蜂拥而至的可能性似乎并不安全。这个地方也可能不安全，但至少它是一个安全的地方，你不需要再往前走了。 “苏西，我还是觉得去蒂蒙斯比留在这里更好。我要去，所以如果你想留下来……”罗杰在你打断他之前说道。 你用双手抓住 Roger 的头，向下移动，这样你就可以亲吻他的嘴唇。然后你的手臂以更紧的拥抱动作向下移动。当你们继续拥抱时，他以同样的方式回报。 “别走。请不要去蒂蒙斯堡。这不安全，我需要你陪在我身边。” 你说停止你的吻说话。“苏西……”“请。跟我在一起。”“（叹气）苏西……你真的是这个意思吗？我的意思是我真的需要知道我们的立场……因为……你现在不能只是眨着眼睛吻我，而不是故意的。我真的开始对你有感觉了，但如果你再次为了生存而这样做……” 你直视他的眼睛。 “是的，罗杰。我是认真的。不只是为了我，也是为了你。我不希望你再把自己置于任何危险之中，就像我希望自己处于危险之中一样。蒂蒙斯堡将成为不必要的危险。我知道我们认识的时间不长，但我们一起经历了很多，如果这个肮脏的世界就是这样，那我至少想和你一起度过剩下的时间。” 好吧，如果你不是那个意思，那肯定足以说服罗杰，因为他立即继续亲吻和拥抱你。你当然是认真的，因为你已经对他产生了依恋。你猜对那些拯救你生命的男人来说只是个傻瓜。 你们俩甚至都没有注意到亨利向你走来。 「嗯，好像打断了什么。所以你们两个决定好要做什么了吗……除了互相吸对方的脸？” 他问。“嗯……”罗杰说，然后对你微笑，然后将注意力转向亨利。“我想我们会留下来。”“伟大的。很高兴听见。需要更多的人来帮助保卫这个地方。你觉得你可以帮忙看看你那支步枪吗？” 亨利说。“当然。多长时间？” 罗杰问道。“嗯，大概到午夜吧。厄尔需要好好睡一觉，这就是他现在很烦躁的原因。至于你这小丫头，不如走进里面去认识一下大家。你和你的男朋友可以在你离开的地方继续。” 亨利走到厄尔身边，告诉他他很快就会解脱，而你和罗杰又被单独留下了。 “好吧，那我想晚上晚些时候见。” 罗杰说。“是的，虽然我想知道我们应该睡在哪里。” 你回答。“嗯，估计现在在一起了。”“哈哈，当然，但我不是这个意思。”“嗯，我肯定有人会告诉你里面的睡眠安排是什么，反正我可能应该去我的新工作。回头见。” 罗杰说。 当他离开时，你对他大喊要小心，他只是微笑着挥手，以保证你不需要担心他。 现在你转向卡车停靠站大楼，外面不再有小人群，人们已经回到里面。你现在必须与之交往的人...... “好的，苏西。你可以这样做。它不会像狂欢节那样。” 当你走向大楼时，你喃喃自语。 一进门就看到人山人海。大多数是年长的男人，但也有女人，甚至还有几个孩子。当您进入时，他们中的大多数人都会抬头。 “大家好。我是苏西，我的男朋友罗杰将待在这里，但我们带了一大堆医疗用品和食物来帮助我们减轻体重。” 你的介绍很冷漠。他们中的一半人咕哝着表示感谢，另一半人只是回去做他们正在做的事情。 嗯，它比你在狂欢节上得到的大部分东西都要好。 看到没人和你说话，你决定一个人安静地坐在一个远离其他人的包厢里。您主要将时间花在反省和聆听随机对话中。他们中的大多数人自然是在谈论手头的情况，但它被开玩笑的“常态”时期打破了。 这里似乎没有人过度担心疯狂的人四处奔波和社会崩溃，然后这里的大多数人都是卡车司机，所以他们可能已经习惯了四处闲逛，而且这个地方的员工似乎花了很多时间这里是第二个家。 最终，足够的时间过去了，你厌倦了成为壁花，或者更具体地说，你厌倦了过去几天穿着同样的衣服而没有淋浴。你走向那群女服务员，希望能问他们这里是否有某种洗漱设施。 最年长的女服务员，红头发，五官端正，名牌上写着安妮在你走近时上下打量你，她的朋友也是如此。 “对不起，请问有洗手间或淋浴间之类的我可以用的吗？我只是觉得太脏了，因为外面的那些东西出汗和逃跑。” 你期待严厉的评论或一些侮辱或某种敌意，但你会遇到一个惊喜。 “为什么确定金发女郎。您可以使用我们在后面为我们准备的小淋浴间。你也需要换衣服？” 安妮说。“你有吗？”“嗯，它们是额外的女服务员制服。如果没问题的话。”“当然，如果它很干净，那将是一个受欢迎的变化。”“好吧，嗯，你有点小，茉莉，去拿特鲁迪的旧制服。那应该合适……苏西不是吗？”“是的，谢谢。” 很快，茉莉花回来给你一套制服，而你正准备洗漱。女服务员的私人浴室不多，但事实上它有一个淋浴，你可以用它来清洁，这对你来说就足够了。 你穿着这身制服感觉有点奇怪，这显然是为了吸引很多男性的注意力，但你还是很高兴能摆脱那些旧的脏衣服。 等你洗完澡，女服务员正在为大家准备晚餐。你问他们是否需要任何帮助，但他们说他们得到了。 你坐下来吃饭，几个人闲聊，但没什么重要的。你大多是一个人呆着，但至少这是一个有希望的开始。无论如何，淋浴后您重新焕发活力的感觉逐渐消失，您开始感到疲倦，这就是当您问安妮睡觉的地方在哪里时。 “好吧，有些人就在这些摊位上摔倒了。一些真正勇敢的灵魂正在外面的卡车里睡觉。正如您所料，亨利拥有自己的私人办公室。侍应生在我们浴室附近有一个小休息区……尽管 Jasmine 正在使用其中一个储藏室，因为她带着孩子。我想你可以拿一个这些摊位或与茉莉花分享储藏室。你当然有那个男朋友……嗯，可以给你第二个储藏室。不过空间真的不大。”“没关系，我只是想找个地方休息一下。” 再一次，您成功地完成了一项没有大问题的简单任务。自从你来到这里以来，你的一切都进展得如此顺利，以至于你几乎忘记了外界正在发生的问题。你当然不会，但老实说，这是你离开地下室以来感觉最安全的一次。 安妮给你一条备用的毯子，带你去第二个储藏室，里面确实空间不足，但对你来说已经足够了。你毫不犹豫地铺上毯子，在地板上睡着了。
梗概：
你执意留在这里
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动作：
做
第二天……
结果：
周六 你醒来时，一个身体靠在你身上，一只手臂紧紧地抱着你，在接吻之间，温暖的呼吸在你的耳朵里吹来。 不完全符合您的预期，但它击败了那些地面零广告。 “嗯，看起来今天早上有人感觉很友好。你什么时候到的？” 你问。“午夜过后不久。这很无聊，我宁愿和你在一起，但至少我没有看到任何这些东西。你当然已经睡着了，所以我整夜都在你旁边睡。你睡得很香，连起床都没有。” 罗杰说。“嗯。很惊讶你没有利用我脆弱的处境。”“我想过，但说实话，当时我自己也很累。其实我醒的时间不长。所以他们让你做女服务员来帮忙？”“哦。这？不，他们只是足够好让我借一些干净的衣服。我不会说这里的每个人都和我交往过，但 至少没有人对我怀有敌意。”“嗯。好吧，你穿着那身制服看起来很性感。” 罗杰说着继续摸索着你。“哦？你喜欢这个？有角色扮演癖好吗？”“当然，而且它很短，所以我可以把裙子抬高，方便取用。”“嗯，这绝对是它实用的一面。” 你轻笑。 当罗杰将一只手移到你的裙子下时，你的笑声变成了半惊喜。你没想到他会这么快就去那里，但这并不不受欢迎。 “嗯嗯嗯……” 就像整整一个星期你都没有让自己开心（更不用说让其他人这样做了），就你而言，你是应该的。 你迅速开始解开 Roger 的裤子，片刻之后你的双腿缠住他，他猛地冲进你的身体。 “哦，妈的，这感觉真他妈的好……”你惊呼道。 老实说，这不是你说的全部。你大声说很多话，你并不在乎是否有人听到你。（另一方面，罗杰更安静一点） 你不知道过了多久，因为你非常享受自己，然后你就会感觉到。 “我的天啊！我快到了！继续这样做！更难！快点！哦，是的……”你惊呼，然后是爆炸。 虽然不是那个。 有一声巨响和微弱的隆隆声，但你并不真正在意，因为你和罗杰更专注于涉及彼此有趣部分的任务。 片刻之后，有人开始敲门。一开始你以为是有人在抱怨你太吵了，但他们开始大喊你们两个现在需要出来。 “远处有一枚该死的核弹！” 声音喊道。“什么鬼？！” 罗杰惊呼。“什么？！” 你说。“我说有……”“是的，我听到了！（叹气）等一下。” 罗杰打断。 那一定是之前的隆隆声和噪音。谈论情绪破坏者。 “所以……他们终于去做了。” 你说。“大概吧。该死……我希望它不会太接近。” 罗杰说。“如果它是一个大的，我们都会被蒸发掉，所以也许它只是一个小的？”“他们扔在广岛的那个也很小。”“好吧，我想现在没有多少了。我们的建筑仍然完好无损，所以这是一个好兆头。” 你说尽量不要怀疑自己是否仍会处于致命剂量的辐射范围内。 罗杰开始慢慢地穿上裤子，你把裙子拉下来。 当你们都离开房间时，你看到茉莉花试图让她的孩子们平静下来。几个人在争论。少数人只是坐在沉默的震惊状态中。亨利实际上在餐厅外面，看着炸弹一定是爆炸的地方。即使在餐厅内，您也可以看到远处残留的蘑菇云。你情不自禁地握紧了罗杰。 “我不知道炸弹有多远或有多大，但我知道它是朝着蒂蒙斯堡的方向发射的。见鬼，那甚至可能是目标。那么无论是那个还是城市。不管怎样，这两个地方现在很可能都是放射性废墟，看起来你也救了我的命。” 罗杰说搂着你的腰。“你认为这可能只是摆脱那些感染者的一种方式吗？也许这只是小范围发生的？” 你问。“我不知道。也许吧，但我的印象是，这颗炸弹只是零地广告一直在播放的大战核战争结束的众多炸弹之一。我猜这并不重要，因为我想事情将会发生剧烈的变化，而我们曾经知道的世界将会大不相同。更难做一件事。” 令人惊讶的是，尽管这次事件令人恐惧，但您并没有像平时那样充满恐惧。也许是因为在你这周经历了所有其他疯狂之后，你最终可能会变得更糟。也许你终于尝试在你的生命中找到一次一线希望。 “我们会……一起处理。” 你肯定地说。 罗杰看着你点点头。 “我们将。” 此时，亨利在安抚了一些吵架的卡车司机之后，退到了餐厅里。他开始处理这种情况并表示没有任何改变，情况仍然相同，即如果人们提供帮助，那么欢迎他们留下来。如果没有，或者如果他们不想留下，那么他们可以自由去。 一些显然在别处有家人的卡车司机确实离开了。其他人都留下来，开始建造一个永久的新“家”。 当您通过清点医疗用品进行宣传时，您会短暂地想知道如果您当时没有放弃狂欢节会发生什么。如果你像鲁菲希望的那样留下来，你会有所作为吗？ 你摇头。你不介意帮忙，但你知道你不是领导者。最好保持安全并与一群至少让您感觉更舒服的团体在一起，到目前为止，这就是您在这个卡车停靠站遇到的情况。尽管压力很大，但这里的大多数人仍然对你感到愉快。 有趣的是，人们找到了属于自己的地方。
梗概：
第二天……
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动作：
做
...
结果：
好又安全 未来… “要么接受，要么离开它。没有整天跟你讨价还价。” 你对拾荒者说。 他看了一会儿装着 9 毫米弹药的塑料袋。 “好，我这就去拿。” 他说着递出两支损坏的步枪，二话不说接过弹药离开。 你再看看步枪，想想你有一个天生喜欢修理东西的人是多么高兴。 “丹尼尔！出来吧，给你一个项目。” 你儿子从他的工作区出来，看看你这次对他大喊大叫什么。 “妈妈，现在怎么样了？” 丹尼尔回答。“这里。有更多可以修理的步枪，我们以后可以出售以获得更好的利润。” 你说着把步枪递给他。“这些需要新螺栓。我不确定我是否有备用的。至少不是两支步枪。”“嗯，尽你所能。我相信你会想到一些东西。你通常会这样做。此外你知道每个来过这个地方的人总是对武器感兴趣。如果您只修复其中一个，那么我们购买它们的目的仍然是一种利润。”“大多数人不是从集市另一边的伯爵那里买武器吗？”“是的，但他的专长是，我们所做的事情的美妙之处在于买卖所有东西。因此，可能正在寻找药物的人也可能最终也会购买枪支。此外，他还因敲诈勒索而臭名昭著，因此我们需要为可能有一天人们开始来找我们购买枪支做好准备。” 丹尼尔点点头，没再说什么。在这件事上，他真的不是一个健谈者或思想家。不是说这个男孩笨，只是他专注于其他事情。喜欢修理。有时他会让你想起从前你哥哥的儿子。那男孩绝对是个学者。 丹尼尔回到他的工作站。你很高兴你从来没有和他有过任何问题，尽管罗杰认为他应该更多地与人交往……但你会想起自己在某个地下室的孤立主义日子，并自嘲一笑。 与当时的你相比，丹尼尔是一个社交蝴蝶，有实际交易。他会没事的。 有时候你真的很奇怪，你基本上经营着一家相当于后世界末日的杂货店，但随着这个地方变得更大，更坚固，最初的人开始提升地位和责任，你来了。 这当然比在外面流浪要好。你听过这些故事，不想离开新安全的围墙。这里很安全，你拥有你想要的一切。 这是你的新地下室。 你记得 Roger 在新安全侦察小组成立时想领导它，但你设法说服他不要胡说八道。你一开始就没有说服他留在这里，避免被原子弹炸死，这样他就可以去玩荒地战士，然后把他的头炸掉。 他作为新安全的主要保护者的工作充满了足够的危险和对他的技能的考验。 “嗨，妈妈，生意怎么样？” 辛迪走进来问道。“美好的。我猜你今天和你父亲一起去了？”“是的。真的很酷。我必须学习如何射击。我什至杀了东西！”“什么？” 你问。 这时罗杰走了进来，听到了谈话的结束。 “那不是一个人，她没有任何危险。那只是一只在靶场上徘徊的大老鼠。她一枪就打中了。她是天生的。” 罗杰这么说是为了让你放心。 您知道现在这是一个严酷的新世界，您也知道您女儿学习如何射击对她来说可能是最好的事情之一，但您不禁担心。 “嗯，很高兴听到你玩得开心。” 你说。“是的！我什至亲眼看到了执行任务的新安全侦察队！他们说他们必须和一个变种人战斗！听起来很刺激！我迫不及待地想有一天能够成为他们中的一员！” 好吧，现在你很担心，而罗杰已经知道了。 “呃，辛迪，你为什么不到后面去，我相信我们很快就会吃饭。” 罗杰说。辛迪答应了，但她非常清楚很快就会发生争吵。 你给罗杰一个他非常了解的“表情”是一种不悦。 “秀智，在你动手之前，我真的没有把这个想法放在她的脑海里！我什至不知道侦察队今天回来了！更不用说她会撞到他们了。”“你是头部保护者！你怎么会不知道？！” 你说。“喂，我先负责内部安全。此外，有时侦察员如果在那里找不到任何东西，就会早早回来。”“对我来说，变种人听起来很像。听着，我知道辛迪是个十几岁的孩子，我知道她发现所有这些军事类的东西都令人兴奋，但我至少不想鼓励她想去荒地游荡的愿望。”“好吧，我也希望她安全，我的意思是这就是我试图教她如何射击和其他技能的原因。我的意思是我们只能指导这么多，最终她会长大成人，我们无法控制她的所作所为。你懂的。” 这是一个古老的论点，罗杰是对的，但你真的希望他不是。 “我知道。我知道。只是……（叹气）至少下次你能不能强调一下，当侦察员并不像听起来那么迷人？”“我能做到的。不能向你保证它会奏效，但我一定会做到的。”“嗯，这是我所能希望的最好的结果。” 罗杰走过去亲吻你，你还给了它。 “此外，我不明白为什么在我不被允许的情况下，她会加入侦察队。” 罗杰笑着说道。 你笑着关了店，这样你就可以和家人共度时光。 这不是最轻松的生活，但它比你以往任何时候都多。
梗概：
...
故事名称：
59
故事背景：
星期四 几声枪响让你醒来。 “什么？！” 你说还留在地板上。“嘘！安静的！” Klepto 评论并指向负责卫兵的人。他和其他五个人拿着武器站在帐篷入口附近。 “我不在乎谁开始了这场战斗，甚至为什么！我们离开后你们这些肉体的怪物可以互相撕裂，但在那之前我们是来维持秩序的，上帝啊，这就是我们他妈的要做的事！如果我因为任何原因不得不中断另一场战斗，下次射击不会在空中！” 军官说完便离开了。其他守卫继续站在入口附近，看着每个人最终都坐了下来。 “到底发生了什么？” 你问。“剃刀和其他一些人与菲尔和他的朋友发生了争执。” 克莱普托说。“菲尔是谁？” 你说。“他做那种吃火的东西。无论如何，他们踢了他的屁股，正当事情变得更糟时，卫兵进来阻止了它，就像你刚刚看到的那样。看来控制嘉年华的战斗已经开始了。”“很惊讶我们当时没有受到攻击。”“嗯，就像 Shrimpy 昨天说的，我们现在有矮人保护，但谁知道这种安排会持续多久。”“不敢相信我打架睡着了，鲁菲呢？”“哦他？他还在那边，即使是枪声也没有把他吵醒。” Klepto 指着 Roofie 说，然后她让所有人都感到惊讶。 “我很清醒 Klepto，我只是闭着眼睛休息 反正现在起床也没意义。很明显，这个狂欢节让我失去了尊重；我会责怪你们昨晚把我的后脑勺砸了，但这真的只是一个催化剂。（叹气）没关系，我预测最多两天我们都会死。” 鲁菲说。 “鲁菲，我们真的不想那样做，但你攻击军队的想法太疯狂了。你的计划会害死所有人。” 你说。“如果最近的事件有任何迹象，这种情况可能仍然会发生。至少按照我的计划，我会在狂欢节完好无损的情况下死去，而不是眼睁睁地看着它在我眼前分崩离析。” 此时没有其他人真正知道该说什么，所以你们都保持沉默并继续相互交谈。 “那么下一步是什么？” 克莱普托问道。“我不知道，我猜想逃跑？我们可以快速到达其中一辆车并开走吗？” 你说。“鉴于所有警卫现在都处于高度戒备状态，祝你好运。” 胖子指出。“是的，我不认为我们走得很远。” Lifto 补充道。 你坐下来呼气，想知道接下来要做什么，但你现在无法真正思考。似乎由于你更积极的建议和 Roofie 的冷漠，你现在不知何故成为了你的小团体的“领导者”。你并没有真正计划好。 “就目前而言，我们只是待在原地，我会试着想一些事情。” 你说。你小组的其他人没有自己的任何想法，所以他们遵守你的计划（或缺乏计划）。 随着时间的流逝，您通常会进行大量随意的谈话，并在心里猜测“如果您做 X”会发生什么。你有一半期望其他人试图逃跑，特别是当他们说他们需要使用洗手间时，但考虑到他们总是在他们离开的同一个警卫下回来，似乎没有人有胆量真正尝试它，即使是剃刀。 你们小组中的其他人也没有提出任何计划，但您确实注意到虾皮偶尔会前往矮人小组。他声称他只是在确保“联盟”的安全，但您更倾向于相信他只是想确保自己的安全。到目前为止，虽然矮人仍然应该站在你这边。 Roofie继续躺在睡垫上，没有对任何人说话。你仍然对背叛他感到有点难过，尤其是考虑到他不仅把你带进了这个狂欢节，而且很喜欢你给你一个私人预告片，而且当剃刀要踢你的屁股时，他可能会为你省去很多痛苦. 你过去和他谈谈。 “嘿，路飞。” 你说。“嗯，嘿苏西。” 他哼了一声。“你还好吗？”“我？除了整天不喝酒，我还好。我想我只是在和我的生活和平相处。” 鲁菲说。“楼上，我还是很抱歉……”“什么？让 Lifto 把我搞得一头雾水？我已经忘记了这一点，或者更确切地说，我已经重新考虑了自己对此的立场。你可能是对的，我的意思是我肯定宁愿那样死，但我想利用我最后一点逐渐减弱的影响力让每个人都跟随我，这有点自私。如果你在交火中丧生，那将是特别的耻辱。你其实很有天赋。”“你就是这么说的。”“不，这是真的。我不是根据外表或一时兴起决定雇用你的。在你扮演小丑的那一刻，你很好。我的意思是很明显你甚至是一个领导者，你如何让其他小丑挂在你的决定上。（叹）要是早点遇到这个狂欢就好了。情况可能大不相同。”“嗯，我不确定我是多么优秀的领导者，因为我想不出接下来会发生什么。” 你说。“你会想出一些我敢肯定的。不过有一件事，虽然我不抱太大希望，但如果你能以某种方式让这个狂欢节保持在一起……那就去做吧。我知道目前的情况不是最好的，我知道你并没有受到所有人的欢迎，但我看到你身上的某些东西，如果你能像以前一样体验狂欢节，就会感到满足。像一家人一样。”“一个家族？” 你笑着说。“嘿，就像我说的，你是在家庭斗争中进来的。这就是有时在家庭中发生的事情。家族斗争永远是最恶毒的，但平息之后，有时家族单位会比以往任何时候都更加强大。而且我认为，如果你能让这个家庭不至于彻底分裂，你会很高兴。但你必须是做到这一点的人。我已经没有它了。” 这时，Roofie 摸了摸你的手，你拍了拍他的手，然后再离开他。你想知道这是否更像是一个“垂死”的人的最后要求，而不是期待你的个人成就。尽管如此，你仍然认为他是真诚的，如果整个狂欢节成为你从未拥有的幸福家庭，那肯定会很好，但你只是认为这是不可能的。 你环顾四周，发现除了卫兵换岗外，没有什么变化。其中一个恰好是罗杰，这让您想到了一种可能的逃脱方式。唯一的问题是它会依赖于比你已经和罗杰在一起时更“迷人”，这可能会让你陷入你不想陷入的境地。
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动作：
做
你留在帐篷里
结果：
虽然这可能是一个逃脱的机会，但您不会冒险。Roger 可能对你有点着迷，但他看起来足够运动，你认为如果出现问题，你不会轻易与他抗衡。鉴于目前处于戒严状态，您很可能会中枪或更糟。 最好留在帐篷里，制定一个计划，至少你可能会有一些后备。 所以你现在又回到了原点，坐在那里无所事事，直到国民警卫队决定离开（无论何时何地）。 您大部分时间都在思考 Roofie 所说的话以及您目前在嘉年华上的位置。要想拥有安全感并拥有家庭，唯一的方法就是为实现这些目标而采取直接行动。 “Lifto，Shrimpy，你和我在一起。” 你突然说，你要去见矮人派系的首领。“呃，我不知道这是否是个好主意，他最近做了很多准备。” 虾米说。“我相信他有，而且我们最感兴趣的是找出那是什么。也许你可以负担得起双方的比赛，但我不会抓住这个机会。” 你说着，开始走向一群小人。 当你接近时，矮人会立即开始关闭队伍，他们的举止转变为防御性的。 “又回来了虾米？我没想到你又一个小时左右的谄媚滑稽动作。你一定是苏西，那个新来的小丑女孩，惹恼了所有的女士，惹恼了所有的男人。嗯。仔细一看，我明白其中的魅力，但你仍然不是侏儒女人。” 一位年长的侏儒说，尽管他的许多同伴可能不同意这种观点。“我猜你是你们小组的领导？”“是的，虽然我认为虾皮不会介意提到我的名字是比利。如果你是关于我们目前的联盟，你不必担心，因为我已经多次向虾保证，它已经到了令人烦恼的地步。很高兴你至少通过出现自己来改变它，虽然你身后没有丑陋的大小丑也可以做到，但我猜他是一个糟糕的恐吓尝试。”“女孩有时喜欢有一个大块头的男人在后面看着她。”“呵，这就是为什么你实际上是在乞求一群小男人这样做，嗯？让我们切入正题，你真正想要的是什么？我已经知道你是小丑的新领袖，因为你对那边的老鲁菲做了什么。打赌他没有看到这一点。” 你不知道还能怎么说，就这么说吧，这让虾皮和利夫托也吃了一惊。 “我将成为这个嘉年华的新领导者，你会支持我的努力。”比利对这句话笑了笑，然后笑了起来。其他几个矮人也这样做。 “你？真的吗？你认为我们只会在这方面支持你？我希望你有一个更好的计划，而不是准备在芒奇金土地多萝西上操每一个芒奇金人，因为在这个时刻，我保证这将是你可能接近掌权梦想的唯一途径。如果那是你的计划，你可以忘记它，因为我的婚姻很幸福。但是，它可能会在我的一些朋友中获得您的青睐。可以确保您的团队在即将到来的新嘉年华秩序中占有一席之地，但这是您必须自己做出的选择。”“你真的认为狂欢节的其余部分会跟着你吗？”“他们别无选择，我们矮人一直是它的支柱，现在是我们成为焦点的时候了。就像我的查理叔叔很久以前一直想做的那样。”“是的，而且鉴于你目前的情况，显然这并不奏效。你知道为什么？没有人真正尊重你。他们很乐意让你做一些繁重的工作，但他们不希望你成为众人瞩目的焦点，除非你像这里的 Shrimpy 一样自欺欺人。” 你说。“嘿！” 虾米的言论。“金发女郎注意你的语气，你开始踏上非常危险的土地。” 比利警告。“是的，我知道，而且我还试图解释说，作为狂欢节的中坚力量，你已经掌握了控制权。这场内部斗争，你很可能会赢，但还有什么办法吗？显然，这个地方在 Roofie 的统治下已经萎靡不振，但如果它有合适的戒指大师，它可能会再次伟大。一个可以把人带进来的人。你自己说的，我让这里的大多数人仅仅因为我的存在就对我垂涎三尺。你知道广告是如何运作的，我用一个眨眼、一个微笑和一些甜言蜜语把他们带进来，其他表演者从他们那里拿钱，有人必须把基础设施 保持在一起，那就是你。我们不仅可以作为同事一起工作，还可以像……一个家庭一样。就像carnie应该如何真正粘在一起一样。” 你的话让比利停顿了片刻，比利思考了一会儿，然后摇了摇头。 “你知道，那是一次非常好的演讲，问题是你才几天没来这里，你已经在试图接管了。在家庭方面，你就像一个火辣的 20 岁护士，在他去世的时候嫁给了曾祖父，希望能继承一切，尽管他有很多家庭成员，因为他们不得不忍受他的狗屎更长。这并不意味着我不认为你是真诚的，甚至你的想法不值得，只是我不认为你是那个潜在的领导者。如果这场狂欢在尘埃落定后真的分裂了，那么也许这是注定的，但我不会再跟随另一个半途而废的领导者度过余生。所以总而言之，如果你想站在我们这边，那就太好了，如果不是，那么我强烈建议你们都坐上小丑车，在牛仔竞技表演或其他什么地方找到工作。” 此时比利说完话，挥手让你们离开。 嗯，本来可以做得更好。 “你疯了吗？！你他妈的为什么这么做？你很幸运，他没有取消我们的保护！” 虾米说。“好吧，我不能说这是最好的动作，但我想你必须尝试一下。” 利托说。 在 Shrimpy 对自己的自我保护的担忧和 Lifto 无能为力的评论之间，您想知道您是否应该重新考虑再次尝试逃跑。 最终，你们都回到了桌子旁，Shrimpy 和 Lifto 解释了发生的事情。Fatty 和 Klepto 都对你的行为感到惊讶，但 Klepto 再次尝试比其他人更“理解”。基督，他从不放弃。 “我现在不需要这个，我要一个人待着。” 你说抓起他们正在听的小收音机，然后走向 Roofie 仍然闭着眼睛躺在垫子上的地方。你不知道他现在是醒着还是真的睡着了，但至少他现在似乎没有心情打扰你。 你多花几个小时听其中一个收音机，而其他人则打牌并尝试谈论平凡的事情。你没有得到最好的接收，也没有互联网，但总比没有好。 你通常会听到很多特别的突发新闻，谈论世界各地的情况有多么糟糕。你听说了正在发生的骚乱，想想你自己的城镇以及你的“真正的”家人过得怎么样。Ben 隐藏的个人防空洞在后院的某个地方，你想知道你是否不应该只是试图寻找它并搬到那里去，它可能已经存货了。 然后当太阳开始落山时，你会听到外面有一堆枪声和喊叫声。你看到帐篷入口附近的其他卫兵匆匆离开，让每个人都无人看管。 自然不再受到监督意味着帐篷内开始爆发混乱，尽管你不知道为什么人们不关心国民警卫队为什么对外面的东西开枪和大喊大叫。 没有更好的主意，你试着叫醒 Roofie，但他没有动。 “哦，该死……”你对自己说。 显然，他对你说的最后一句话是他临终的请求。 虽然你是唯一一个意识到可怜的鲁菲意外去世的人，但帐篷里爆发了一场激烈的战斗。不出所料，这是 Razor 和她的派系对阵 Billy 和他的派系。一些较小的派系加入，而另一些则完全用光了帐篷。很幸运，你的团队离战斗发生的帐篷中心很远，或多或少是在角落里。 “苏西！开始了！” 利夫托喊道。“是的！我们需要进入并帮助比利，否则我们将成为下一个！” 虾皮补充道。
梗概：
你留在帐篷里
--------------------
动作：
做
你帮助比利
结果：
随着外面的枪声不断，你并不完全觉得那会更安全。现在最好帮助比利。 你们都接近了大规模的争吵，但除了Lifto，你们中没有人有任何真正的战斗欲望或能力。Shrimpy 找到了一个藏身之处，偶尔会向某人扔东西，Klepto 被 Phil 的一名船员击倒，只有在 Lifto 的帮助下，他才没有被踩到脸。由于他的体型，Fatty 做得好一点，但他在耐力区并不多，每隔几分钟就必须停下来喘口气（这导致有人不可避免地试图攻击他） 你尽量远离它并与矮人或Lifto保持近距离。你偶尔会指出潜在的危险，有时甚至设法用你在地上找到的杂耍别针击中某人的头部，但你离它很远。 在这一切进行的过程中，您注意到外面的枪声减少了，尖叫声和呻吟声越来越多。在一片混乱中，你看到罗杰在帐篷外蹒跚而行，在帐篷入口附近倒塌。看到你并没有真正影响“权力斗争”，你冲到罗杰那里看看帐篷外面到底发生了什么。 当你找到罗杰时，你会注意到他受了重伤，流了很多血。你不知道他是否会成功。 “罗杰！这到底是……怎么回事？！”“（喘气）他们……只是……不知从哪儿冒出来的……（喘气）一堆。”“什么？！什么出来了？” 你问。“我不知道。呃……疼……（喘气）他们看起来很疯狂……咆哮，咬人……” 你注意到罗杰的大部分伤口确实看起来像咬痕。你无法相信你甚至在思考这个，但是……“该死，罗杰，你是说你刚刚被……该死的僵尸袭击了？” 此时，Roger 没有反应，他正在失去知觉，呼吸也越来越急促。你试图让他坐起来，打醒他，但没有任何效果。他会死的，如果你对僵尸电影的丰富知识有任何迹象表明他很快也会上场。 然而，这是您最不担心的问题，因为当您抓着罗杰时，罗杰提到的“僵尸”之一突然从一辆停放的军车后面出来。它不会立即看到你，但如果你在这里闲逛，它就会看到。 站起来离开 Roger 即将失去生命的身体，试图回到帐篷里，你撞到了另一个同样令人不快的人，同样危险。她的脸是血的，她的几个穿孔都被拔掉了，但如果她在这里，那只能说明她赢了。 “你来了，小丑婊子。我希望能有幸亲自打你的金发屁股，太糟糕了，Wanda the Red 用刀刺到了喉咙，因为在我踢狗屎之前看着她用十二英寸的皮带强奸你可能很有趣你的。” 剃刀评论。“Razor，有 zom ……有些疯子只是……”你开始说，然后 Razor 猛击你的脸，把你撞倒在地。 当你扶着脸的一侧时，Razor 拔出了一把血淋淋的直剃刀。 “别担心，我不打算杀你，我只是想让你成为我们全新的怪胎节目中的第一个！” Razor 说并且要压倒你来兑现她的承诺，但你所有的求生本能都开始发挥作用，你尖叫着踢她。如果她是个男人，你就会踢出好几球。最后她用一只胳膊抓住了你的一条腿。 “好的！奋斗的婊子，这只是让我觉得更有趣。反正没人会救你。其他人要么在舔伤口，要么死了！狂欢现在在我的掌控之中！”“我不是为了狂欢节上的人来救我而尖叫。” 你说。 Razor 被你的话弄糊涂了，只是因为她太专注于伤害你，以至于完全无视周围的环境。僵尸不仅听到了你的尖叫声，而且他的几个同伴也听到了，不幸的是，对于 Razor 来说，他们是快速的品种。 当她抬起头时，靠近军车的那辆已经在 Razor 上，另外两辆正在快速跟在后面。你迅速爬进帐篷，希望 Razor 里面提到的死者不会转身，这是一种“咬式”感染。 Razor 提到有人受伤或死亡时并不是在开玩笑。当然，状况良好的人不止几个，但现在更重要的是从过去的争吵中休息。对他们来说太糟糕了，这不是一个选择。 “天啊！喂……哎呀！” “你听到了吗？那是你的短命领袖被食人族怪物撕成碎片的声音。所以，如果你们这些白痴已经结束了互相残杀的权力，你们最好准备好为了生存而杀戮！” 当你听到 Razor 可能在外面被活生生吃掉的尖叫声时，你大喊并抓住最近的钝器。 你刚抓住一条椅子腿，一个僵尸就冲了进来。你设法在它头上猛击了一次，然后在它倒下之前又猛击了一次。它抓住了你的脚，你正准备用你的临时球杆砸在上面，这时 Shrimpy 突然跑起来用杂耍别针为你完成了它。在那一点上，你们俩都继续击打它，直到它的大脑变成糊状并且不再移动。 “（哇）谢谢虾。很高兴看到你活了下来。”“你也是，到底是什么……”“稍后，虾。集中！” 当其他两个僵尸突然闯进来时，你说。幸运的是，此时你和 Shrimpy 从其他一些僵尸那里得到了更多的支持，幸运的是没有其他人受到伤害。 在杀死他们两个之后，你突然被关于他们到底是什么的问题轰炸。你唯一能想到的就是僵尸，但在你们中的一些人进行了一些谨慎的调查之后，你开始怀疑这是否准确。 您得出了几个幸运的结论和发现。 首先，看起来这些东西已经不复存在了，国民警卫队也都死了。因此，这些危险已经不存在了。 其次，没有人死而复生，（被咬与否）所以至少无论它是什么都不会传染。事实上，你看到其中一个“僵尸”躺着，胸部有几处枪伤，头部没有伤口，所以你至少不会认为这些东西是不死生物。但是，疯狂的食人族逃跑的事实仍然令人担忧！ 当你、小虾和其他几个人继续在帐篷外小心翼翼地调查时，小虾在你们两个离开小组时提到了一些坏消息。Lifto、Fatty、Billy 和 Roofie 都死了，尽管你提到你已经知道 Roofie。 “是啊，我猜老家伙在睡梦中平静地死去了。可能他在事情发生之前做的最好。那么小偷呢？” 你问。“哦，他还活着。从第一次撞击开始，他仍然昏迷不醒，但他还活着。嘿，我的意思是说我很抱歉像我这样自私自利，然后这一切都没有发生在苏西身上。我想我只是被吓到了。”“嗯……都过去了。此外，当我真正需要它时，你为我而来。所以你说比利死了？”“是的，那个婊子剃刀就在他完成红色旺达的时候抓住了他。正如您所料，剩下的矮人对此非常不满。但后来我猜 Razor 也死了，所以我不确定这让我们何去何从。”“我想说一个可能的机会。看起来国民警卫队留下了很多在不久的将来可能有用的物资。走吧，我们回去吧。” 你说并拿起其中一支步枪。躺在附近的地上。 当你回到帐篷时，虽然从一辆军用卡车上取来的医疗用品应该可以帮助修补伤员，但人们并没有从原来的位置移动多少。 你站在所有人面前发表演讲。你知道它可能不会顺利，但这是最好的时间。 “好吧，作为一个刚刚去世的伟人曾经说过，狂欢就像一个家庭。现在我们刚刚克服了重大的内部争执和外部威胁，猜猜怎么着，我们都还活着，可以谈论它。这意味着我们可以继续前进并由此变得更强大，因为我觉得我们真的需要在不久的将来拥有牢不可破的家庭纽带。一个强大的家庭也需要一个强大的领导者，这就是为什么看到我目前是唯一一个或多或少身体状况良好的人，我提名自己为团长。” 你预计此时会有一些口头上的爆发，但每个人仍然太疲惫或不愿意陷入更多冲突，但你得到了很多皱眉的表情。 “现在我已经知道你在想什么了，这婊子已经好几天没来了，她以为她在告诉我们该怎么做？好吧，让我们回答这个问题。你说得对，我是新来的，但我对功能失调的家庭了解很多。每个功能失调的家庭也需要一个替罪羊来承担责任。现在作为领班，所有重大决定都将落到我头上，因此，如果我搞砸了，那么谁应该受到责备？当然是我。当然，我希望作为领导者，我可以通过成功改变每个人对我的看法。然而，这里的大多数女性已经仅仅因为我的外表而讨厌我，而你们大多数人只是将我视为一个活生生的性玩偶，所以你真的有什么可失去的？如果结果证明我作为领导者完全失败，那么你们都可以很容易地把我扔到我的屁股上，然后选择其他人。” 没有人说什么。只是一个尴尬的停顿，你决定用更多的谈话来填补。 “如果有人担心某个群体会得到优待，比如说小丑，你可以不用担心，因为他们中的大多数人目前已经死了，我可能会忙于成为指环王而不是小丑。” “去他妈的，让可爱的小丑荡妇跑到这里来。她的嘴看起来像是口交的高速公路，但她不可能比 Razor 计划的那意味着屁股更糟。至少她有一个积极的态度。” 有个矮人女人在后面说，额头上盖着一块沾满血的抹布。 有了这种“英镑”的认可，其他几个人也同意了。 看起来你是这个狂欢节的新领导者，而这只是一场彻底的大屠杀和幸存者的冷漠。 剩下的时间都用来在大帐篷周围建造一个小路障并清除死者。没有人知道是否会有更多的极客（这是“僵尸”的新名称，考虑到嘉年华极客的谋生历史）是否会出现以及他们来自哪里，因此出于安全原因，每个人都保持密切为了夜晚。有些人无论如何都无法移动。 最终你睡着了，意识到明天对你来说将是忙碌的一天。
梗概：
你帮助比利
--------------------
动作：
做
第二天...
结果：
星期五 “嘿，无畏的领袖，醒醒，你的人民需要它。” 小虾说拍拍你的肩膀。 你昏昏沉沉地坐起来，你发誓你觉得自己几乎没有睡过觉。Shrimpy 和 Klepto 一起站在附近，Klepto 是昨天“认可”你的侏儒女人，也是一个瘦削、邋遢的男人。你认为他在玩那些无聊的游戏……特别是你用水枪射小丑嘴的游戏。 “这是怎么回事？” 你问。“正在发生的事情是一堆狗屎，这就是亲爱的。在侏儒之后……”“操你的杰克，我们是矮人。” 矮女人打断了他的话。“不管多洛雷斯。无论如何，在昨晚……小人物收集了所有军用物资之后，我将他们的一台收音机调到了其他军用频率。原来附近的城镇已经被我们昨天杀死的那些东西淹没了。就在听起来他的胆子被掏空之前，我与之交谈过的那个可怜的混蛋说他们不知道这是不是病毒，只是说有一堆狗屎。无论哪种方式，我说我们很幸运，只有少数人徘徊了这么远。我们需要他妈的离开这里。”“我的小镇……哇……呃……”你说，对这个启示感到惊讶。你现在真的是一言难尽。你开始考虑你真正的家人，甚至鲍比。他们还好吗？他们已经死了吗？他们是其中之一吗？ “苏西？你好吗？你在拉开距离。” 克莱普托说。“嗯，是的，我只是……好吧，是的，我们显然需要离开这里。开始收拾我们剩下的东西，然后……”“亲爱的你没听到我说的话吗？我说现在他妈的可能有疯狂的食人族正在来这里的路上！我们没时间干那些事！我们需要装载尽可能多的补给品，然后在我们的车辆上起飞。这是他妈的末世男人！” 杰克说。“收拾行李不会花太多时间，在国民警卫队到达之前，我们已经准备好了大部分东西。我们的一些人仍然受了重伤，也需要休息。此外，如果现在社会正在彻底崩溃，住在我们的拖车里会比住在普通的车里更可取。” 虾米的言论。“好的，那么汤姆拇指你和其他棒棒糖孩子就这样做了。我和其他一些人？我们带着补给离开了。不要呆在这儿等着吃食人族。” 你负责的时间还不到一整天，已经有人威胁要分手了，甚至更糟，甚至没有注意到你。你昨天刚把疯狂的怪胎打死，你厌倦了不被重视。 “杰克，如果你或其他人想离开，你可以自由地离开，但你没有做的是从这里拿走任何军需品。这些属于狂欢节，因此属于我们所有人。现在，如果您不再想要其中的任何部分，那么祝您好运，但是您和其他任何想要离开您的人都可以随身携带您带来的个人物品。”“噗，随便。我没有选你做领导。我只是出于礼貌告诉你，你可以留在这里吃，也可以一起来，我可以保证你的安全。其实为了打发时间，我们可以玩射小丑的嘴巴，除了不会进你嘴里的水，它会是我的……” 杰克没想到你会踢他。你这样做的那一刻，他皱眉皱眉。你用膝盖贴着他的脸跟进。他倒下了，你在帐篷里引起了所有人的注意。 “这是我要从你或其他任何人那里听到的最后一次关于口交的推荐信。现在听好了，因为我记得你们中的大多数人昨天都对我负责，该死的，这就是我要做的，他妈的负责。这仍然是我们的狂欢节，它没有改变，我不会仅仅因为社会其他人似乎是这样就让它分崩离析。现在这就是我们要做的，我们要完成收拾行李，整理好拖车等，我们还要乘坐军用卡车，然后继续前进。现在，如果其中一些极客表现不错，我们就会有一些全新的步枪和手枪可以试用。任何不喜欢这个计划的人都可以背着衬衫离开，别无其他！现在，让我们停止战斗，把这该死的事情办妥吧！” 在这一点上，那些有条件工作的人，继续这样做。 “那么他呢？” 虾米指着还在地上的杰克问道。“扔掉他的屁股，他做出了他的决定。” 你评论然后注意到多洛雷斯微笑。“你笑什么？” 你要求。“没什么，但我开始喜欢你了，金发女郎。” 多洛雷斯这么说，然后吹口哨吹过几个矮人，他们迅速抱起杰克并将他拖走。多洛雷斯然后离开去处理其他事情。Shrimpy 说他会去帮忙，然后也离开了，留下你和 Klepto 一个人呆着。 “嗯，昨天我被淘汰的时候，你看起来好像发生了很大的变化。我喜欢。自信对你来说是一种有吸引力的特质。”“Klepto，我脑子里有很多事，我没心情让你的好人现在试图进入我的裤子常规。” 你说。“好吧，好吧，sheesh，我只是……算了……操我就让你一个人呆着吧。” Klepto 说着走开了。 你现在不能担心 Klepto 脆弱的自我，你有更大的担忧，而且还在考虑你的“老”家庭。你知道你决定离开他们，但那还是几天前的事，你仍然不希望他们中的任何一个人受苦于那些该死的东西。 说到这里，你还是要担心那些极客。杰克可能有点出格了，但他仍然可能像昨天那样在狂欢节上徘徊是正确的。考虑到矮人似乎仍然是最大的派系，对你最友好，你让他们中的一些人带着步枪站在拖车顶部并保持警惕。您可以通过帮助清理来保持忙碌。 几个小时过去了，这时您的瞭望员看到有什么东西接近了。不是极客，而是几辆皮卡车，看起来他们正拿着几个拿着猎枪和步枪的人。你呼吁所有人立即采取防御立场并后退。 你不确定会发生什么，但是一群全副武装的人永远不会是一件好事。不幸的是，作为领导者，您将不得不成为处理这次访问的人，如果可以，希望和平。 当皮卡车停下来时，你站在几英尺远的地方，你的“狙击手”盯住你的访客。你坚持使用自己的步枪，其他几个carnie也这样做。其中一名男子举起双手从其中一辆卡车上下来。看起来像个乡巴佬，实际上你想象的这个地段来自几英里外的拖车停车场。 “没关系，没关系，女士，我看到你们在那里了。没有人想打架。” 他说。“看起来不像，因为你带着三辆满载武装人员的卡车来了，而且不要告诉我是狩猎季节。” 你说。“嘿，好吧，我敢肯定，考虑到你自己的高度意识状态，你知道现在事情变得'危险了。不管怎样，我的名字是梅尔，我猜是因为你是这里的负责人。魔戒之类的？”“就像那样的东西。” 你说。“好的。你……呃……这里也有一些军事装备，我明白了。那边的那辆卡车。”“是啊，那又怎样？”“哦，没什么，但这带来了卫兵在哪里的问题……嘿，是的，他们也试图告诉我们该怎么做。效果不太好。第二个修正案说你毕竟有权打包它。政府不能随心所欲。” 此时，更多梅尔的手下也从他们的卡车上下来。你的人和梅尔的人都开始互相凝视，等待有人开枪。 “看，让我切入正题，亲爱的。我猜你现在可能知道镇上发生了什么。除了政府试图拉扯他们的狗屎之外，还有一群怪胎跑来跑去试图吃人。昨晚他们中的一些人袭击了我们，当我们将他们击退时，我们中的一些人受了重伤，需要医疗用品。当然，现在我们确实尝试先进城，但在那里太操蛋了，而且我们没有跟那些该死的地方的怪胎和警卫打交道。然后我今天突然想到你的马戏团可能还在，国民警卫队也可能会在这里，所以我们决定进行调查。现在，拖车公园的那群周末勇士们都在抢医疗用品。所以我希望你的支队在那个部门有更多的人。” 梅尔抬头看着拖车上的矮人。 “现在你可能会得到我们中的一些人，但我敢打赌，我们中的大多数人都比你们所有人都好，所以如果你给我们你所拥有的药品，我们会平安地离开这里，如果不是很好...... ” 在这一点上，事情可能会很快变得非常暴力。
梗概：
第二天...
--------------------
动作：
做
你开火
结果：
你让这些混蛋进来拿医疗用品，他们不会就此止步。毕竟你已经经历过，你不会忍受更多的人不认真对待你。 “火！” 你大喊大叫，跑回去掩护，因为你的人就是这样做的。当然，拖车公园的工作人员也会像他们自己的领导者一样寻找掩护。 您以前从未向人类开枪，但您确实记得您的兄弟 Ben 曾试图向您展示如何射击。你当时不太擅长，现在也不擅长，可悲的是，你的很多人也不擅长。 在随后的枪战中，几名卡尼被杀，更多人被击中。虽然你自己的球队确实有自己的几支安打，但梅尔说他的球员投篮更好，这并不是在虚张声势。你毫发无伤地挺过来真的很神奇，尽管这可能是因为 Klepto 愚蠢地试图扮演英雄，但在试图保护你时却被致命一击。 唯一能阻止乡巴佬完全超越你的是杂耍者之一以相当高的准确性从军方获得的少数手榴弹之一，炸毁了他们的两辆卡车（和其中一些）并给幸存者足够的停下来了解持续攻击不符合他们的最佳利益。 梅尔和其他人迅速挤进幸存的卡车并开走。你赢了，但代价高昂。由于最近的内部斗争，carnies 已经遭受损失和伤害。那些没有受伤或死亡的人现在正经历着悲伤、愤怒和失去希望的混合体。 “那现在什么无畏首领？！多亏你的领导，我们死伤更多！” 多洛雷斯惊呼。“如果我让他们进来吃我们所有的狗屎会更好吗？！” 你啪。“如果我们没有把我们所有的驴都射掉会更好！如果不是扎卡里扔了手榴弹，我们可能都是蠕虫食物！”“那到底是我的错？！我正在向你们其他人射击那些乡下人！我没看到你打他们中的任何一个！” 当你和多洛雷斯继续争论时，虾皮介入试图调解。 “嘿，我们没有时间进行所有这些内斗！我们还受够了那些狗屎吗？现在我们活了下来，这是最重要的事情。我说我们继续我们原来的计划，然后他妈的离开这里，事实上我认为我们应该收拾行装，现在就出发。那些乡下人不会只承担这次损失，他们会重新发挥作用。”“你在跟我开玩笑吗？你想让我们掉头就追吗？我们要报仇！”“多洛雷斯，我们已经有好几个伤员了，已经没有战斗力了！不管死了多少！我们不知道有多少拿着枪的乡下人，甚至不知道他们有多少武器！” 虾米说。“很可能很多，这还不算他们从军队得到的任何东西。” 你补充。 然而，多洛雷斯并没有被吓倒，她只是特别对你更生气。 “你是不是……（叹气）不敢相信我支持你做领导，一定是狂欢节打架后我的头部伤口流血了。听着，现在也许这场枪战是一场群殴，这就是我生气的原因，但我想在事情的计划中，你至少站起来对抗这些混蛋。如果我们现在打他们，估计他们也不会想到的，你听他们的首领说，他们自己的伤员很多，那他们到底有多少人？” 多洛雷斯呼气，现在给你一个更加刺眼的表情，你认为这是不可能的。 “然而，你的计划是逃跑而不是为我们的堕落者报仇，那么也许你需要他妈的滚开，像你不久前告诉我们对杰克做的那样上路。苏西，如果你想继续举办这个狂欢节，你最好做正确的事。” 多洛雷斯的意图显而易见。无论如何，她都要报仇雪恨。不太明显的是她是否会得到支持。可能有一些人会支持她，但更多的人可能不想再打架了。
梗概：
你开火
--------------------
动作：
做
你现在离开
结果：
“多洛雷斯，你比那些愚蠢的乡巴佬更吓不倒我，如果你想在徒劳无功的任务中炸掉你的头，那就做我的客人，但我们其他人都走了……” 多洛雷斯二话不说，用她的武器指着你。这个动作让少数还处于战斗状态的人也举起了自己的武器。其中一些在多洛雷斯，一些在你。 “别以为这么金发碧眼。我现在正在主持这个节目。”“多洛雷斯你疯了吗？！” 虾米惊呼。“小虾，你最好选他妈的一边，因为你骑着他妈的篱笆的日子很快就要结束了，现在你要么支持狂欢，要么你他妈的也滚出去！” 多洛雷斯说。 当步枪直指你时，曾经对你来说是可怕的，现在不再是了。 “多洛雷斯，如果你认为这会让我退缩，那你就大错特错了。”“如果你认为我不会开枪打你，那你就太愚蠢了。”“多洛雷斯环顾四周，我没有看到反对我的统一战线。你所做的一切都是通过开火来削弱你自己的事业，前提是你能活下来。如果你真的想报复那些乡巴佬，那么你将需要你能得到的每个人。而这不是这样做的方式。” 多洛雷斯可能会生气，但她并不傻，她知道你说得有道理。 “那好吧，你有推荐的金发女郎吗？不要说只是跟着你，因为那不会发生。”“不，我看得出来。（叹气）我看这个嘉年华是注定要拆散的，所以我看，谁想跟我一起走，现在就走。这意味着我们将带着大部分个人物品和一些补给品离开，其中一半仍然是我们的，但我们只会拿走我们需要的东西。我们的旅行也将变得更轻，所以我们将乘坐一些较小的车辆。您可以拥有所有拖车和更大的卡车。因为我想大多数伤员不会有任何旅行状况，所以你需要这些，只是为了在时机成熟时为他们提供舒适的交通工具。”“武器呢？” 多洛雷斯问道。“好吧，每个人都保留自己的个人物品和一些弹药，剩下的就交给你了。希望对你来说，大多数武装的人都和你在一起。不过，作为善意的表示，您可以保留我们拥有的少数手榴弹。虽然我觉得它们会派上用场，但我也认为，如果你有机会打败那些乡巴佬，你会比我们更需要它们。” 此时，您伸出手来巩固交易，多洛雷斯（Dolores）向您瞥了一眼，然后再做出手势。 “好吧，但我最好别抓到你们这些胆小鬼拿的东西超过你需要的！” 之后，她开始打电话给每个人和任何愿意听他们解释为什么他们应该留在她身边并对拖车公园进行报复的人。 另一方面，你不会试图说服更多的人，你的演讲已经结束了一段时间，就你而言，这有点浪费时间。如果他们想来，那很好，如果不想，那好吧。 在返回拖车取走个人物品的路上，您注意到它一定在枪声中捕获了几颗流弹。当你打开门时，你会遇到一个更大的惊喜。 在地板上，你看到兰科身上有几个弹孔 ，还在流血。附近是一把直剃刀靠近他的手。他看到你，挣扎着说话。 “Suz...zy...你得（咳嗽）帮助...我...”他说。“哦，这很有钱，要我帮你吗？你一开始在我的预告片里到底在做什么？要攻击我吗？也许强奸我？” 你说拿起直剃刀。“我......是......goi......道歉......（喘气）（咕噜声）......”“是啊，如果我不相信你，请原谅我。你活该死，而我已经度过了这一天，我想我会借此机会欣赏它。”“（咕噜声）操……你……（喘气）……”兰科说。 这句话是他慢慢流血致死的最后一句话。好吧，他肯定留下了一团糟，但考虑到这不再是你的拖车，至少你不需要担心清理。 当你收拾行李时，有人走进了拖车，你有点偏执，所以你立即拔出武器，却看到了虾皮。 “哇！只有我！我只是想知道哪个……天哪，那是兰科吗？”“是啊，那个混蛋显然是躲在这里等着伏击我。如您所见，捕获了一些流弹。不可能发生在一个更好的人身上。那你想要什么？” 你说。“哦对了，我就是来问问你要什么车？我的意思是，考虑到 Lifto 和 Klepto 都已经死了，我想我们至少比其他任何人都更适合这两个。”“那你跟我来？”“当然。我反对攻击拖车公园还记得吗？事实上，已经有好几个人准备离开了。不知道多洛雷斯对拖车停车场的突袭将如何进行，但这几乎不是我们的问题，对吧？” 在解决 Shrimpy 的反问问题之前，您停顿了一下。 “你认为我这样做是对的，就这样放弃狂欢吗？我的意思是多洛雷斯说我只是把没有补给品的杰克扔掉是有道理的，因为他想离开。我觉得自己像个伪君子。” 你说。“嗯……我认为你用发的牌做得最好，这就是你有时能做的。我的意思是这里有些人参加狂欢节的时间更长，他们准备毫不犹豫地离开，所以你当然不应该为此感到难过。我的意思是，在当今世界的发展方式下，狂欢节之类的东西还有未来吗？” 虾米说。“也许不是，我们可能应该在农村地区至少坚持几天，直到这些狗屎中的一些东西消失，但我只是想知道我们将如何对待自己的未来。”“嗯，我相信我们会想办法解决的。但要知道，无论是什么，我都会支持你的决定。”“谢谢虾米……嘿，你的真名是什么？”“呵呵，那个。嗯，今年还没有人叫它，但它是兰斯。”“真的？”“是的，我知道我看起来远非兰斯。你可能会想象一个有着方下巴之类的高大英勇的家伙。”“呃，反正那些家伙在现实生活中是不存在的，至少他们从来没有出现在我的生活中，但你已经没事了。” Lance 微笑着等待您完成打包，然后你们两人前往 Lifto 的车辆，令您惊讶的是，该车辆已安装到可容纳“矮个”司机的位置。 你，兰斯和其他几个人离开狂欢节，现在正准备突袭拖车公园（或收容重伤者），你无法想象会发生什么，但你希望多洛雷斯能成功如果你没有和她意见一致。 兰斯和你和其他几辆车一起驾驶着一种奇怪的大篷车式的时尚。真的没有理由大家要在一起，因为每个人都可以自由地走自己的路，但也许离开狂欢节变化太大了，至少有一点熟悉，坚持不变人们。 你和兰斯在你开车的整个过程中都在交谈，但最终你开始感到困倦，兰斯接管了驾驶。感觉这几天最安全，你昏昏欲睡……
梗概：
你现在离开
--------------------
动作：
做
第二天...
结果：
周六 远处的巨响将您唤醒，您也注意到自己不再移动。 “天哪，苏西醒醒！看！” 兰斯大喊大叫，你感到手臂猛烈一颤。 你立即睁开眼睛，轻轻转动头部，你会看到比世界上任何其他事物都更能改变你生活的东西。 远处的蘑菇云。 你没有感觉到任何颤抖或余震，一波火还没有把你吞没，所以你猜它不是离得太近，甚至可能很小。尽管如此，您还是忍不住想知道您是否处于某种致命的辐射半径中。如果是这样，就没有什么可以做的了。 “就是这样……世界终于做到了……”你对自己说。 你比兰斯镇定得多，兰斯显然很苦恼。事实上，考虑到大部分车辆都停了下来，你只能猜测他不是唯一的。有些人出去看看，然后很快就有人泪流满面或争吵起来。 “兰斯，我们会没事的。就像你说的，我们会想办法的。” 你说，然后开始打开门。“你要去哪里？我想我们应该呆在里面，我的意思是可能有辐射什么的。”“兰斯，如果我们在辐射爆炸范围内，那么我们可能已经获得了足够的辐射来杀死我们。我要去试着让每个人都冷静下来，因为如果是这样，那么如果我们一起努力，那么我们在生存方面实际上会做得更好。” 这需要一点时间，你站在你的车顶上做这件事，但你很快就会引起所有人的注意。 “嘿！听着！这可能是我们所知道的文明的终结，但这不是世界的终结！现在的生活肯定会更艰难，但我们什么时候没有过？为什么就在这该死的一周里，我们遭受了军事占领、内战、怪胎袭击和一群乡巴佬的袭击。我们已经分裂得够多了。我们没有办法再下降了。我们已走得太远;我们已跑题。” 你认为你在演讲方面做得越来越好，因为这次你看到的皱眉脸和一般的专注性减少了。 “我们过去常常从一个地方漂流到另一个地方，提供娱乐作为我们生存的源泉，到底发生了什么变化？我们仍然是流浪者，因为那是我们生存的方式。这个世界变得更糟了，我们天生就是游牧民族和流浪者。当然，我们会在旅途中捡拾、收集和清除有用的材料、物品等。我知道我们都拥有有用的技能，当然有了这些技能，我们的学习和适应能力以及团队合作，我们将能够让这个家庭团结在一起很长时间。” “是的。苏西说得对。我们可以熬过这一切。” 有人说，其他人也同意。 虽然你没有得到热情的感觉，但至少这次你没有遇到冷漠。 无论如何，您和其他一些人会聚在一起决定下一步要去哪里以及如何处理食物和燃料。这并不容易，但这整周对你来说都是一次考验。 你不再是那个躲在地下室的害羞“好”女孩，被你的家人骚扰，一天天过去了。你是更大事物的一部分，不仅如此，而且是一个领导者。 这是全新的一天，而这只是您全新生活的开始。
梗概：
第二天...
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动作：
做
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结果：
游牧民族 未来… “妈妈，我们为什么不能直接去重生，那里更近，我们可以在那里得到更好的交易。” 露西问你。“因为上次我们停下来，他们开始进行权力斗争，虽然我知道现在已经结束了，但那个镇的新统治者是个混蛋。” 你回复。“这到底是怎么让他和上一个人有什么不同？”“重生的最后一位统治者至少试图遏制社区中肆意暴力和强奸的数量。这家伙鼓励它。这个地方现在只不过是一个光荣的荒地暴徒避风港。我们走进那里，充其量我们会活着又穷困地从交火中走出来。最坏的情况是，我想你知道会发生什么。” 露西什么也没说。她完全意识到身为女性的危险，更不用说在这个新的严酷世界中长得像她的少女了。尽管如此，她还是忍不住要争论这个问题。 “但是，无论如何，我们总是在一个地方漫游，难道我们不会冒着死亡甚至更糟的风险吗？找个地方安顿下来不是更好吗？”“呵，我猜你不记得了，因为你太小了，但你父亲和我试过一次。当我们认为安全的小镇被袭击者占领时，情况并不好。那可能是我一生中最可怕的日子之一。经过多年的漂泊，我们让自己措手不及。这是我的一个错误，因为我认为一个更稳定的环境会更好地抚养你，结果证明，你应该保持游牧状态，我会说这样会好得多。”“如何？我们总是过着一种口耳相传的生活。我们的团队仍然因暴力和其他原因而失去人员。我的意思是爸爸死了，因为我们没有适当的医疗可用。”“露西，就算我们不是过着游牧生活，你父亲也可能已经死了。我知道他们说文明正在重建，但我们去过的大多数地方的医疗设施本身并不出色。例外可能是那些该死的零地社区，我怀疑你是否愿意住在那样的地方。”“不，我想不是。” 露西说。 你看看露西，就会明白她只是因为她现在是个青少年而有点叛逆。她已经到了不想让你一直靠得这么近的年纪了。这很有趣，因为你自己没有经历过这样的叛逆期，而你现在的生活方式恰恰相反。你从未去过任何地方；除了呆在地下室之外，你从未真正做过任何事情。 现在？你看到的和做的比你想象的更多。事实上，你已经在游牧生活中度过了足够长的时间，以至于你认为你更喜欢它，即使你几年前暂时安顿下来。您（和她的父亲）教会了露西如何在这个严酷的世界中照顾好自己。不过，你确实想知道你是否可能对你的女儿有点过度保护，你不会永远存在，她应该有机会过自己的生活，如果这意味着她想住在永久住所，那么也许她应该给这个机会。 “露西，这个世界现在仍然很糟糕，但尽管我以前愤世嫉俗，但我认为它的某些部分正在变得更好。也许有一天你会找到一个不错的地方安顿下来，它不会被嗜血的军阀管理，也不会有被变异食人者袭击的危险。如果这是您的选择，那么您可以自由选择。我知道你最终将不得不过自己的生活。” 你说。 露西对这个许可微笑，但向你保证她还没有离开你的打算。这很令人欣慰，因为这表明她仍然希望你在身边。她还是你的孩子。 你，露西和你那一群混杂的商人、修补匠和拾荒者，前往 Blivop 镇。不完全是最好的贸易，但至少那里的人不是完全不道德的强奸犯和杀人犯。 那天晚上晚些时候，当您的大篷车安顿下来时，您手上拿着一个破旧的小丑鼻子，慢慢入睡。你捏了几下，回忆起和你已经去世的丈夫在一起的快乐时光。 “晚安兰斯。” 你说着，手里拿着圆形的红色泡沫球慢慢地飘走了。
梗概：
...
故事名称：
60
故事背景：
星期五 “嘿，无畏的领袖，醒醒，你的人民需要它。” 小虾说拍拍你的肩膀。 你昏昏沉沉地坐起来，你发誓你觉得自己几乎没有睡过觉。Shrimpy 和 Klepto 一起站在附近，Klepto 是昨天“认可”你的侏儒女人，也是一个瘦削、邋遢的男人。你认为他在玩那些无聊的游戏……特别是你用水枪射小丑嘴的游戏。 “这是怎么回事？” 你问。“正在发生的事情是一堆狗屎，这就是亲爱的。在侏儒之后……”“操你的杰克，我们是矮人。” 矮女人打断了他的话。“不管多洛雷斯。无论如何，在昨晚……小人物收集了所有军用物资之后，我将他们的一台收音机调到了其他军用频率。原来附近的城镇已经被我们昨天杀死的那些东西淹没了。就在听起来他的胆子被掏空之前，我与之交谈过的那个可怜的混蛋说他们不知道这是不是病毒，只是说有一堆狗屎。无论哪种方式，我说我们很幸运，只有少数人徘徊了这么远。我们需要他妈的离开这里。”“我的小镇……哇……呃……”你说，对这个启示感到惊讶。你现在真的是一言难尽。你开始考虑你真正的家人，甚至鲍比。他们还好吗？他们已经死了吗？他们是其中之一吗？ “苏西？你好吗？你在拉开距离。” 克莱普托说。“嗯，是的，我只是……好吧，是的，我们显然需要离开这里。开始收拾我们剩下的东西，然后……”“亲爱的你没听到我说的话吗？我说现在他妈的可能有疯狂的食人族正在来这里的路上！我们没时间干那些事！我们需要装载尽可能多的补给品，然后在我们的车辆上起飞。这是他妈的末世男人！” 杰克说。“收拾行李不会花太多时间，在国民警卫队到达之前，我们已经准备好了大部分东西。我们的一些人仍然受了重伤，也需要休息。此外，如果现在社会正在彻底崩溃，住在我们的拖车里会比住在普通的车里更可取。” 虾米的言论。“好的，那么汤姆拇指你和其他棒棒糖孩子就这样做了。我和其他一些人？我们带着补给离开了。不要呆在这儿等着吃食人族。” 你负责的时间还不到一整天，已经有人威胁要分手了，甚至更糟，甚至没有注意到你。你昨天刚把疯狂的怪胎打死，你厌倦了不被重视。 “杰克，如果你或其他人想离开，你可以自由地离开，但你没有做的是从这里拿走任何军需品。这些属于狂欢节，因此属于我们所有人。现在，如果您不再想要其中的任何部分，那么祝您好运，但是您和其他任何想要离开您的人都可以随身携带您带来的个人物品。”“噗，随便。我没有选你做领导。我只是出于礼貌告诉你，你可以留在这里吃，也可以一起来，我可以保证你的安全。其实为了打发时间，我们可以玩射小丑的嘴巴，除了不会进你嘴里的水，它会是我的……” 杰克没想到你会踢他。你这样做的那一刻，他皱眉皱眉。你用膝盖贴着他的脸跟进。他倒下了，你在帐篷里引起了所有人的注意。 “这是我要从你或其他任何人那里听到的最后一次关于口交的推荐信。现在听好了，因为我记得你们中的大多数人昨天都对我负责，该死的，这就是我要做的，他妈的负责。这仍然是我们的狂欢节，它没有改变，我不会仅仅因为社会其他人似乎是这样就让它分崩离析。现在这就是我们要做的，我们要完成收拾行李，整理好拖车等，我们还要乘坐军用卡车，然后继续前进。现在，如果其中一些极客表现不错，我们就会有一些全新的步枪和手枪可以试用。任何不喜欢这个计划的人都可以背着衬衫离开，别无其他！现在，让我们停止战斗，把这该死的事情办妥吧！” 在这一点上，那些有条件工作的人，继续这样做。 “那么他呢？” 虾米指着还在地上的杰克问道。“扔掉他的屁股，他做出了他的决定。” 你评论然后注意到多洛雷斯微笑。“你笑什么？” 你要求。“没什么，但我开始喜欢你了，金发女郎。” 多洛雷斯这么说，然后吹口哨吹过几个矮人，他们迅速抱起杰克并将他拖走。多洛雷斯然后离开去处理其他事情。Shrimpy 说他会去帮忙，然后也离开了，留下你和 Klepto 一个人呆着。 “嗯，昨天我被淘汰的时候，你看起来好像发生了很大的变化。我喜欢。自信对你来说是一种有吸引力的特质。”“Klepto，我脑子里有很多事，我没心情让你的好人现在试图进入我的裤子常规。” 你说。“好吧，好吧，sheesh，我只是……算了……操我就让你一个人呆着吧。” Klepto 说着走开了。 你现在不能担心 Klepto 脆弱的自我，你有更大的担忧，而且还在考虑你的“老”家庭。你知道你决定离开他们，但那还是几天前的事，你仍然不希望他们中的任何一个人受苦于那些该死的东西。 说到这里，你还是要担心那些极客。杰克可能有点出格了，但他仍然可能像昨天那样在狂欢节上徘徊是正确的。考虑到矮人似乎仍然是最大的派系，对你最友好，你让他们中的一些人带着步枪站在拖车顶部并保持警惕。您可以通过帮助清理来保持忙碌。 几个小时过去了，这时您的瞭望员看到有什么东西接近了。不是极客，而是几辆皮卡车，看起来他们正拿着几个拿着猎枪和步枪的人。你呼吁所有人立即采取防御立场并后退。 你不确定会发生什么，但是一群全副武装的人永远不会是一件好事。不幸的是，作为领导者，您将不得不成为处理这次访问的人，如果可以，希望和平。 当皮卡车停下来时，你站在几英尺远的地方，你的“狙击手”盯住你的访客。你坚持使用自己的步枪，其他几个carnie也这样做。其中一名男子举起双手从其中一辆卡车上下来。看起来像个乡巴佬，实际上你想象的这个地段来自几英里外的拖车停车场。 “没关系，没关系，女士，我看到你们在那里了。没有人想打架。” 他说。“看起来不像，因为你带着三辆满载武装人员的卡车来了，而且不要告诉我是狩猎季节。” 你说。“嘿，好吧，我敢肯定，考虑到你自己的高度意识状态，你知道现在事情变得'危险了。不管怎样，我的名字是梅尔，我猜是因为你是这里的负责人。魔戒之类的？”“就像那样的东西。” 你说。“好的。你……呃……这里也有一些军事装备，我明白了。那边的那辆卡车。”“是啊，那又怎样？”“哦，没什么，但这带来了卫兵在哪里的问题……嘿，是的，他们也试图告诉我们该怎么做。效果不太好。第二个修正案说你毕竟有权打包它。政府不能随心所欲。” 此时，更多梅尔的手下也从他们的卡车上下来。你的人和梅尔的人都开始互相凝视，等待有人开枪。 “看，让我切入正题，亲爱的。我猜你现在可能知道镇上发生了什么。除了政府试图拉扯他们的狗屎之外，还有一群怪胎跑来跑去试图吃人。昨晚他们中的一些人袭击了我们，当我们将他们击退时，我们中的一些人受了重伤，需要医疗用品。当然，现在我们确实尝试先进城，但在那里太操蛋了，而且我们没有跟那些该死的地方的怪胎和警卫打交道。然后我今天突然想到你的马戏团可能还在，国民警卫队也可能会在这里，所以我们决定进行调查。现在，拖车公园的那群周末勇士们都在抢医疗用品。所以我希望你的支队在那个部门有更多的人。” 梅尔抬头看着拖车上的矮人。 “现在你可能会得到我们中的一些人，但我敢打赌，我们中的大多数人都比你们所有人都好，所以如果你给我们你所拥有的药品，我们会平安地离开这里，如果不是很好...... ” 在这一点上，事情可能会很快变得非常暴力。
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动作：
做
你继续谈判
结果：
作为该地区的“本地人”，您确实了解一些住在拖车公园的类型。他们中的大多数人可能除了整天练习射击啤酒罐之外什么都不做。梅尔很可能是对的，因为他的人会更好地投篮，而枪战会导致你这一方死很多人。 但是，您不能让他们拿走您的东西，尤其是因为您使用了很多东西并且开始时没有太多东西。 “好吧，如果我们有额外的供应，我们可能倾向于分享，事实是我们没有太多，我们只有很少，我们需要。你在这里开始一场射击比赛，你最终可能会死伤更多，而不是值得付出努力。” 你回复。“如果我不相信你，亲爱的，你会原谅我的。” 梅尔回答。“是真的，认真的进来看看你自己。我们在那里的那个大帐篷里受伤了。不过就你一个人，你的其他人可以在这里站出来，看起来很可怕。” 梅尔轻笑。 “哦，只有我吗？我一进去我就听他妈的摆布了。不，我不这么认为。”“好吧，把我当人质。在这里……”你说着放下步枪，走到梅尔身边。有几个人冲你大喊大叫，但你只是向他们挥手，好像要他们冷静下来。与此同时，Mel 站在那里看着你走近。当你在他面前时，他肯定会上下打量你。 “我猜你有手枪？” 你问梅尔。“是的。”“好吧，把它拿出来放在我的头上。” 你说转身把你的身体靠在梅尔的身上，然后把他的一只胳膊搂在你的腰上。 “嗯……该死的女孩，你只是别的东西，不是吗？” 梅尔说，他拿出手枪时几乎没有保持镇定。“如果你这么说。无论如何，您现在有一点保险应该足够了，不是吗？我的意思是……我完全信任你。你完全可以控制。”你说，然后又在梅尔的身体上磨蹭了一下，同时转过头来朝他眨眨眼睛。 梅尔不知所措，但他的伙伴们却没有。 “Woo Hoo，看起来li'l 金发女郎对你很有吸引力，Mel！”“我说我们带她回去，而不是医疗用品。一开始我从不喜欢鲍比雷。我不管他妈的都能流血！”“你打算把蒂娜交易给她？“为什么梅尔得到所有漂亮的？他已经得到了蒂娜，我说我们中的一个人应该得到那个金发女郎！” 这时爆发了一些争吵，随后梅尔对他们大喊大叫，让他们闭嘴。然后他把注意力转回到你身上。 “呵……你……你是麻烦娘。如果我带你回到拖车停车场，你可能会让每个男人都为你撕毁对方。我现在几乎无法阻止这些愚蠢的混蛋这样做。无论如何，我们先去看看医疗供应情况。来吧。” 梅尔说，示意你移动。“如你所愿。” 你说。 梅尔和你回到狂欢节，在那里你给他一个“盛大的旅行”，向他展示你确实没有太多的医疗用品。他也看到了所有受伤的人。 “嗯，我想你们仍然可以把这些东西藏在某个地方，但老实说，我倾向于相信你们。这个地方的军车比拖车公园里的还要少，而且从你告诉我的情况来看，这里甚至没有那么多警卫。从我看到的伤员数量来看，你可能确实用光了军队带来的大部分微薄……你是怎么弄到这么多伤的？没有人看起来像是受了枪伤。食人族会爬吗？”“不。所有这些伤员都是彼此的。长话短说，我们只能说淫秽政治很复杂，而我名列前茅。” 你自信地回答。“我明白了……好吧，你确实展示了一定数量的球，让自己处于你所做的脆弱位置。（叹气）好吧，你对我直截了当，我就对你直截了当。猜猜我们会再次尝试镇上。” 在这一点上，你和梅尔回到“中立区”，在那里梅尔释放你（不情愿地）并向他的人挥手准备离开。 当他们开车离开时，你深深地松了一口气，然后冲回其他人身边。当你到达时，多洛雷斯向你走来。 “嗯，这是一种有趣的处理方式，但它奏效了。那些好孩子可能会杀了我们更多的人。”“是的，现在我们需要离开这里。我不相信他们不会带着更大的团体回来攻击我们。”“好吧，我们本可以多休息一下，但我无法反驳这种逻辑，他们看起来不是最有名的，而且你是老板。最近离开的一件好事是拖车现在有更多的空间。说到这里，您自己的拖车已经连接到卡车上。我们应该准备好在大约一个小时内滚动。” 在最近发生的事件之后，您觉得自己应该休息一下，然后回到您的拖车放松一下。 但是，一进门，您就会突然被抓住并摔倒在地板上，难闻的体味充斥着您的鼻孔。 “不要尖叫婊子。” 熟悉的声音在耳边响起。你仔细看看你的袭击者，看到一个男人脸上化着半破烂的小丑妆。 是兰科，现在他正躺在你身上，闻起来更难闻了。 “自从我离开后，我看到这里的狂欢发生了很多他妈的变化。在躲藏并等待最佳攻击时机时从远处看到了其中的一些。过程中差点被那些极客吃掉，不过嘿嘿嘿，我跑了……” 兰科开始将他的体重压在你身上，这样你就不能在一只手放在嘴上，另一只手拿着剃须刀的同时扭动。他的整身衣服很脏，大概是因为在荒野中潜伏了几天，但你也注意到他身上有几处咬痕形式的伤口。 “现在有什么好说的婊子？以为你现在是领班了。负责的头屄等等。操你妈的，我他妈的应该负责！” 在这一点上，你设法咬住了兰科的手，这尝起来很臭，但这足以让他把它从你嘴里拿开，让你有机会尖叫求救，然后他反击你的脸。 “婊子！也许如果我强奸了你，你就会知道谁他妈的负责！” 他一边惊叫一边摸索着拉下裤子，另一只手把剃须刀一直放在你的喉咙上。（Carnies 一定喜欢直剃须刀） Ranko 所要做的就是快速滑动，如果你挣扎，你的喉咙就会被打开。如果你什么都不做，他很可能会强奸你，假设没有人听到你的尖叫。当然，在他完成之后，无论如何他可能会杀了你。
梗概：
你继续谈判
--------------------
动作：
做
你让步
结果：
也许是因为你在过去一周内所经历的一切（更不用说你叔叔的事了），但这种情况最奇怪的事情是你更反感而不是害怕。 这并不是说你不害怕，你不想被强奸，你当然不想死，特别是考虑到你刚刚幸存下来的一切。这个想法让你感到恶心，但你意识到可能仍然有一种方法可以在不抵抗的情况下避免当前的可怕情况。 “你要把我当妓女？来吧，那就去做吧！” 你说。“什么？” 兰科问道。“来吧，把它粘在我爸爸身上。我想感觉……”当兰科将剃刀推向您的喉咙时，您开始说感觉就像它要打破皮肤一样。“你和我一起玩婊子？” 兰科说，他的脸离你的脸很近，你必须防止自己因为他的口臭而离开。“一点也不，你在按照我喜欢的方式做事。爸爸过去常常深夜进入我的房间，有时会变得粗暴，但我知道他这样做只是因为他爱我……”“你……是个变态……” 在兰科说完他的话之前，你鼓起勇气，把反感搁置了足够长的时间，把你的头向前去吻兰科的嘴唇，甚至把你的舌头塞进他的嘴里。 这是你吃过的最糟糕的东西，这只是因为你强烈的呕吐反射（是的，如果大多数人知道你，他们会因为这些知识而开个玩笑）你不吐. 由于您的头部向前移动，Ranko 的刀片从表面上割伤了您的脖子，但您的动作已经达到了他放松的预期效果。不足以让您相信能够逃脱或成功地将剃须刀拿走，但它为您赢得了一些时间。 “哈，我就知道你喜欢我！没有婊子能抗拒一个负责的男人！你想让我像爸爸那样吗？好吧，他曾经这样做过吗？” 兰科说，然后把你翻过来。 “我要狠狠地骑你的屁股，你很长一段时间都不会拉屎的！” 他说着开始拉下你的裤子。“不好了！我在那里还是处女！” 你用最天真的方式说，这当然让兰科笑得像个强奸犯一样，撕掉你的内裤。 与此同时，血液一定已经完全离开了他的大脑，全部流向了他的阴茎，因为这个白痴想要操你的屁股，他的直剃刀掉了下来。你抓住了直剃刀，它现在靠近你的手。 在 Ranko 将他的阴茎放入“应许之地”之前，您转过头并用剃刀向后滑动。他离得足够近，你可以隔着他的脸看到他。 “啊！他妈的！该死的婊子！” 他大喊一声，捂着脸向后倒去。你争先恐后地抓起附近的几件物品，然后转身将它们扔向他。 他堵住嘴，骂了一堆脏话，但还没等他向你走来，总算有人听到了骚动。 “混蛋……”虾皮说着打开门，冲上兰科的后背一拳。兰科回踢小虾，导致小家伙摔倒。感觉到他不再能控制局面，兰科试图逃出门外，但由于裤子和内衣还绕在脚踝上，他被绊倒并笨拙地摔倒在脸上。 你不会让Ranko逍遥法外，所以你将剃刀插入Ranko的屁股并扭动。他的尖叫是可以预见的。 “对你有好处吗？” 你说然后把他的脸撞到门上两次，打断了他的鼻子，让他在自己的血中咯咯作响。对于终结者，你从他的屁股里拔出那把该死的、该死的剃刀，用它割断他的喉咙。 在经历了磨难之后，你向后倒下，坐在附近的椅子上喘着粗气。小虾此时也恢复了。 “该死，我知道这是个愚蠢的问题，但你没事吧？” 他问。“（喘气）好吧，我很好，至少这个混蛋没有强奸我，但这不是一次愉快的经历（喘气）。他妈的。““好吧，至少现在结束了……”Shrimpy 在被打断之前开始说。 （敲敲门）“嘿，里面发生了什么，听到这一切……天哪……”多洛雷斯说着打开门，首先看到的是兰科血腥的脸。“没什么多洛雷斯，只是一个心怀不满的前雇员。已经处理好了。” 你喊。“苏茜！发生了什么？！你没事儿吧？！” 你从外面听到 Klepto 的评论。“呃，她很好 Klepto，她只是需要一些时间自己。你为什么不帮忙把这个混蛋从拖车里拉出来？” Shrimpy 说着抓住了 Ranko 的脚，Dolores 和 Klepto 将他的其余部分从拖车中拉了出来。 “他的屁股到底怎么了？全是血……呃，这家伙闻起来比他活着的时候更臭。” 当门砰地关上时，你听到多洛雷斯说。 你抓起一些东西盖住你赤裸的下半身，而 Shrimpy 继续站在门口，以防万一 Klepto 想扮演“白衣骑士”来见你。 “谢谢虾米。” 你说。“为了什么，我几乎什么都没做。” 他回答。“当你这样做的时候，你来到这里就足够了。”“嗯，我最初是在问下一个目的地在哪里的路上，但我想现在可以等了。而且别担心，我不会告诉 Klepto 或其他任何人看到你的事……而且还没有穿好衣服。”“嗯，我也很欣赏。至于我们的下一个目的地，我不知道，事情似乎变得如此疯狂，也许我们应该在农村地区呆一段时间。”“是的，可能是最好的。好吧，我现在就走了，但请记住，当您需要交谈时，我们都在这里。”“虾米？” 你再次呼唤。“是吗，苏西？”“您的真实姓名是什么？”“呵呵，那个。已经有一段时间没有人使用它了，但它是兰斯。”“真的？”“真的。我知道我看起来不像兰斯。你往往会想到一个方下巴的大个子。真正的英雄类型。”“嗯，就像我说的，你今天是个英雄。” 兰斯对这个评论微笑着点点头，然后走出了门。 而你又是一个人了。你环顾拖车，发现新的血迹需要清理。你立即锁上门，去洗漱，让自己在淋浴时休息一下。 仍然透过泪水，你意识到你比你想象的要强大得多。你也很高兴你现在有一个“家庭”，这有一些安慰。 你一天剩下的时间都一个人呆在你的拖车里，尽管考虑到整个狂欢节很快就要开始了，现在并不是有人会走上前敲你的门。 拖车的运动加上一天的压力，让你瘫倒在床上，闭上眼睛，最终入睡……
梗概：
你让步
--------------------
动作：
做
第二天…
结果：
周六 远处的巨响将您唤醒，您也注意到自己不再移动。 从其中一个窗口快速向外看，您会看到比世界上任何其他事物都更能改变您生活的东西。 一朵蘑菇云。 你没有感觉到任何颤抖或余震，一波火还没有把你吞没，所以你猜它不是离得太近，甚至可能很小。尽管如此，您还是忍不住想知道您是否处于某种致命的辐射半径中。如果是这样，就没有什么可以做的了。 “就是这样……世界终于做到了……”你对自己说。 你听到外面发生了一些骚动，大概有几只肉食者吓坏了，争论不休，并且普遍关心下一步该怎么做。这是对世界末日情况的合理反应。 你冷静地穿好衣服，露出你的样子。这需要一点时间，但你很快就会引起所有人的注意。 “嘿！听着！这可能是我们所知道的文明的终结，但这不是世界的终结！现在的生活肯定会更艰难，但我们什么时候没有过？为什么就在这该死的一周里，我们遭受了军事占领、内战、怪胎袭击，并避免了一群乡巴佬可能发动的另一次袭击。地狱，我有额外的挣扎试图适应这里并打击强奸企图。我们绝不会再次陷入无政府状态。我们已走得太远;我们已跑题。” 你认为你在演讲方面做得越来越好，因为这次你看到的皱眉脸和一般的专注性减少了。 “我们已经从一个地方漂流到另一个地方，提供娱乐作为我们生存的源泉，究竟发生了什么变化？我们仍然可以这样做。我们仍然会这样做。这个世界变得更糟了，如果我们能给一些人带来一点快乐，他们可能会感激不尽。如果我们的娱乐价值不被重视，那么我们天生就是游牧民族和流浪者。当然，我们会在旅途中捡拾、收集和清除有用的材料、物品等。我知道我们都有有用的技能，当然有了这些，我们的学习和适应能力以及团队的努力，我们将能够保持这个狂欢节……很长时间没有这个家庭在一起。” “是的！苏西说得对！我们能活下来！” 有人喊道，其他几个人热情地表示赞同。 这一次，你遇到的不是冷漠，而是掌声。终于，你真的被接纳了。你有一半希望他们都喊“我们中的一个”。一遍又一遍，但也许这可能有点太明显了。 无论如何，您和其他一些人会聚在一起决定下一步要去哪里以及如何处理食物和燃料。这并不容易，但这整周对你来说都是一次考验。 你不再是那个躲在地下室的害羞“好”女孩，被你的家人骚扰，一天天过去了。你是更大事物的一部分，不仅如此，而且是一个领导者。 这是全新的一天，而这只是您全新生活的开始。
梗概：
第二天…
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动作：
做
...
结果：
焦土上最伟大的表演 未来… 在又一场成功的表演之后，大灾变嘉年华的表演者们开始整夜收拾行李，开始他们惯常的玩笑。 “嗯，那当然很有趣。我们已经很久没有像这样真正表演一整场演出了。” 收起她的电锯时闪闪发光的评论。“哈哈哈哈，是的，我不会确切地称地狱风暴将军的极权城邦文明，但与通常的棚户区或更糟，那些零地拥有的避难所社区相比，这是一个很好的变化。天啊，我有时想知道炸弹爆炸后这些地方到底发生了什么。我发誓这些人中的大多数都比那些在地表被抓到的人更糟。” 兰迪补充道。 赫克托和露西走进拖车。 “我想和一群人长时间呆在近距离肯定会让很多人感到奇怪。我的意思是你不必看得太远就能看到。” 布鲁诺听到部分对话后说道。“我希望你这次记得把那些猫鼠的东西锁起来。上次他们下车在其中一辆拖车上咬了几个洞。” 火花说。“他们被称为 Nibblers，是的，我确保他们都被关起来了。我也放了一些肉，所以它们应该很好吃。” 赫克托耳评论。“嗯，我还是不知道你怎么处理那些东西。我以前见过一只野生的，一口咬断一只胳膊。” 兰迪说。“只是要让那些小混蛋看看谁的老板。” 赫克托尔自豪地说，展示了他的十根手指仍然完好无损。 露西什么也没说，只是走到最近的镜子前给她的小丑化妆。Randy 和 Sparkle 感觉有些不对劲。 “你没事吧？” 闪闪问道。“她很生气，因为显然地狱风暴将军在晚上结束时和她辣妈调情了很多。事实上，我敢打赌，这就是我们最初找到这份工作的方式。”“赫克托，如果你不叫我妈妈辣，我会很感激的。我以前告诉过你。如果你没有暗示我妈妈是个妓女，我真的很感激！” 露西说。“看在他妈的份上露西，我没说她是！我只是说 ol' Hellstorm 可能在他第一次见到你妈妈的时候就浑身湿透了。她打开了魅力，只是在rube中踉跄。妈的，到了那个年纪，我们都应该有这样的天赋。如果我们在这个肮脏的世界里成为那个年龄的话。” “我见过的最讨人喜欢的人，赫克托。很高兴知道一个四十多岁的女人在这个腐烂的世界里还有一席之地。” 你说走进拖车。 赫克托不太确定该如何反应。 “嗯，向御姐苏西道歉。”“放松点，你永远不必担心自己不够迷人的赫克托尔，你总会有你的 Nibblers 让你保持温暖。你父亲 Klepto 会很自豪的。” 你说以居高临下的方式拍了拍赫克托的头。 Sparkle 和 Randy 都向你打招呼，但你的女儿继续专注于镜子。 “无论如何，我只是向每个人转播，我只是想说今晚这是一场精彩的表演，我为你们所有人感到骄傲。事实上，我们可能会在几个月后再次来这里演出。地狱风暴先生真的很喜欢这个节目。你会认为一个名叫地狱风暴将军的人会是战争死神！不过这家伙脾气倒是挺不错的，反正废土军阀也罢。”“嘿，他最喜欢哪个动作？他喜欢我的电锯吗？” 斯帕克热情地问道。“嘿，老实说，他不会停止谈论怪胎表演部分。那家伙真的很喜欢那一点。如果说生活在后世界末日的辐射景观中是一件好事，那么这场狂欢节的怪胎表演又将重新上演。在务实方面，我们还交换了一些我们发现的奇怪激光武器。我们从不使用这些该死的东西，但地狱风暴似乎真的很喜欢它们的想法，并认为他可以让它们工作，所以我们有几个月的食物和医疗用品。我认为多洛雷斯现在正在计算……” 当你解释这个好消息时，你注意到你的女儿在卸完妆时仍在照镜子并一直皱着眉头。 “你们其他人能不能离开一会儿，我要和露西谈谈。” 你说。没有人争论，他们一个一个地离开拖车，让你和露西一个人呆着。 “好吧，这次你生气什么？” 你问。“没有什么。你没有人可以舔你的嘴唇吗？” 露西回答。“暂时没有，但我很高兴你继续对我有这么高的评价。”“调情权狂军阀，大有好报的理由……” 露西说到这里，满脸怒色转向你。“该死的，爸爸还没有死一个月，你就已经在讨好别的男人了！你在他活着的时候就这么做已经够糟糕了，你就不能尊重死者吗？！”“我们已经结束了很多次露西。我爱你父亲，我从来没有对他不忠，但你父亲实际上比任何人都了解。露西必须继续表演。此外，我实际上并没有对 Hellstorm 做任何事情，除了逗他玩，而且很可能会给他一些梦想。我总是对 rubes 这样做以引起他们的注意，这是成为一名领班的一部分。就连那个白痴赫克托也明白了，不知道你为什么从来没有这样做过。”“因为经常听到关于你母亲的性暗示，因为他们想掌握你的戒指，这真是太尴尬了。”“嗯，以前没听说过，但仔细想想，这有点明显的双关语……”你说。“别对这事他妈的这么冷静！我的意思是……对不起……我只是……我的意思是我知道这都是一种行为，但我只是希望你有时能缓和一下。” 露西说。“相信我，我愿意。你从未见过我处于成熟的诱惑模式。看，我是一个大女孩，没有做任何我被迫做的事情。（叹气）我知道你是个世俗的女孩，但你还是有一小部分有点天真。一定是我遗传的基因。”“幼稚的？”“是的，我非常健忘的宝贝女儿。你真的相信没有人对你发表评论吗？你穿着粉红色和紫色的紧身套装走来走去，渔网丝袜在你的表演过程中会吹出发光的泡泡。你怎么知道那些下流的言论都是关于我的？其中一半可能是关于你的，但你太关心我的行为了，以至于你太盲目了，以至于看不出你和我一样喜欢挑逗。你只是不知道而已。就像我在你这个年纪一样。” 意识到这一点，露西突然沉默了。有点有趣的是，您实际上对那句话的态度如何。你和她的父亲都教过她这个糟糕的新世界，她并不是不聪明，但出于某种原因，这个女孩从来没有真正了解过她是多么的出类拔萃。也许在某种程度上，你确实让她免于真正遭受“美丽的诅咒”，因为狂欢节是某种“安全区”。 “在世界变得完全狗屎之前，我几乎浪费了我的天赋。现在？我已经充分利用了它们，但不仅仅是为了我自己，也是为了你和这个我们都称之为家庭的狂欢节。你真的很幸运露西，现在大多数人甚至没有家人来照顾他们。妈的，即使在核武器击中之前，我也没有这样的奢侈，所以如果我不得不在某个小暴君的脸颊上啄一下以帮助我们生存，那就这样吧。我做比你必须做要好。” 露西没有回答，她只是看着你。她知道她不会有胃口去经历你有时经历的行为。这可能是她第一次意识到你为了让她自己的生活更轻松而做出的牺牲。 露西点了几次头才开口。 “好的，妈妈。我得到它。我很感激。”“很好，这就是我要问的。好的，我现在要回到我自己的预告片。我们明天早上出发。好好休息吧，在你上演的精彩表演之后，无论如何你都应得的。”“我会妈妈。谢谢。” 露西回答。 当你走开时，你会看到 Sparkle、Randy 和 Hector 都慢慢地溜回拖车。 你进入你自己的拖车，躺在床上思考你已经走了多远。这不是您所期望的生活，但考虑到所有因素，这并不是一件坏事。 在你入睡之前，你会看看床头柜上那个破旧的小丑鼻子。你抓住并挤压它几次，回忆与你现已离去的丈夫的快乐时光。 “晚安兰斯。” 你说着，手里拿着圆形的红色泡沫球慢慢地飘走了。
梗概：
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故事名称：
61
故事背景：
Billy 已经明确表示，无论发生什么，您都将处于任何重组的最底层，所以您何必费心呢？整个区域都要烂掉了，你最好赶紧离开这里。 “你们有车钥匙吗？” 你问。“我拿到了卡车钥匙。” 利夫托评论。“很好，当每个人都分心时，我们要离开这里。”“什么？！我们要离开比利？” 虾米问道。“听着，如果你想帮助比利，那就去吧，但他基本上是在说我是图腾柱上的矮个子，我不会在接下来的时间里经历一堆口头和可能的身体虐待。我在这里的时间。我离开家是为了摆脱那些狗屎。坦率地说，考虑到这里的小丑有多不受欢迎，我建议你们其他人跟随我，因为无论谁赢，你都会输。” 你说着转身离开 Lifto、Klepto 和 Fatty 连想都没想，他们跟着你走出帐篷，把狂欢节冲突的混乱抛在脑后。然而，Shrimpy 不确定，他对“同类”的忠诚仍然阻碍着他，但你并没有等待。 当你离开帐篷时，情况并没有好多少。附近没有任何警卫，但你仍然能听到枪声和他们的叫喊声。您还会听到奇怪的咆哮声和几声尖叫。 “我不知道发生了什么事，但我们现在需要去你的卡车！” 你惊呼Lifto领先。 当您看到 Roger 在另外两个帐篷之间蹒跚而行时，您不会走得太远。他受了重伤，流了很多血。他看到你，拼命地呼唤你的帮助。 “苏西……嘿……”是他在离你几英尺的地方倒塌之前发出的声音。他试图爬向你，而你几乎冲向他来帮忙，但考虑到所有的枪声和大乱斗仍在继续，你专注于与这个地方保持尽可能远的距离的目标。 “对不起，罗杰。” 你小声说，继续往前走。 Lifto 指向您第一次停放自己车辆的区域的一辆皮卡车。 “嘘，你听到了吗？” 克莱普托评论。“不。” 你说。“确切地。所有的枪声都停止了。”“好吧，我们需要快点，因为这要么意味着卫兵杀死了进攻的人，他们很快就会回来找我们，要么……” 然后从一辆军车后面你经过的东西跳到了Lifto。它是人类，但它的咆哮和咆哮不自然。如果你不知道更多，你几乎会认为这是一个...... “啊！僵尸！” Lifto 大喊大叫，挣扎着想摆脱他。胖子立即上前帮忙，两人之间轻松地将其撞倒在地。Lifto 然后继续用他颜色鲜艳的小丑靴反复踩踏它的头。 当Lifto 决心把它的头砸成糊状时，另一种生物突然冒出来，差点抓住你。在 Klepto 介入并试图提供帮助之前，您尖叫并设法离开。 不幸的是，这些生物在这个过程中设法咬住了他。他痛苦地大叫，但多亏有人帮忙吹掉了这东西的头，他才设法把这些生物从他身上弄下来。鲜血、骨头和大脑在 Klepto 身上飞溅。 “来吧！我不知道还有多少这样的东西！” Shrimpy 双手拿着一把卫兵的突击步枪喊道。看来他已经做出了选择。 Lifto 摧毁了攻击他的那个人，很快你们所有人又开始跑了。你一边跑一边环顾四周，以为会有更多的人向你扑来，但这并没有发生。不过你有点担心胖子被咬了。除了你，当然还有他，你认为其他人还没有注意到它。 当您到达卡车时，Lifto 迅速打开驾驶员侧并打开乘客门。你上车，小偷跟在后面，与此同时，胖子和小虾一起上了卡车的床。 “我们到底要去哪里？” 利夫托开车离开嘉年华时说道。“我还不知道，但如果那些……有任何迹象的话，我们可能应该远离大片人口。暂时离开镇子远一点。” 你说。 老实说，你不知道接下来会发生什么，但你真的开始相信 Roofie 关于即将结束的评论。那些生物是僵尸吗？疯狂的人类？活？亡灵？有关系吗？那里有多少？他们已经超越文明了吗？你的老家人有幸存下来吗？这是结束的开始吗？ 最重要的是……它们的咬合效果和电影里的一样吗？ Klepto 紧张地坐在你旁边，仍然抓着他被咬过的胳膊。Lifto 还没有注意到，因为他一直在关注路况，虽然你什么都没说，但 Klepto 对保守伤口的秘密还不够狡猾…… “你怎么老是揉胳膊？” 利夫托评论。“呃，那个东西抓住了我，抓得很痛，呃……”“小偷，我认识你已经够久了，知道你什么时候在撒谎！那东西咬你了吗？！” Klepto 没有回答，因此您尝试提供帮助但未成功。 “只是咬了一口，我们甚至不知道是否……”在被打断之前你开始说。“哦操！就是这个人！游戏结束！” Lifto 大喊一声，突然停下了卡车。Fatty 和 Shrimpy 都敲着窗户大喊发生了什么事。 “Klepto，我知道我们已经认识很久了，但你现在需要离开这辆卡车。”“利托，我……”“我不想听。在我让你滚出去或更糟之前滚出去。”“利夫托，我们也不知道这会不会传染！这不是你知道的电影！” 你惊呼。“而你愿意抓住这个机会吗？你就坐在他旁边！更不用说当Shrimpy吹掉它的头时，他被那东西的血覆盖了。谁知道他抓到了什么鬼东西。” 就像Lifto现在一样偏执，你一直坐在Klepto旁边感觉有点紧张。 一言不发，Lifto 转向你，伸手伸向手套箱。他打开它露出了一把手枪，尽管 Klepto 已经弄清楚他在做什么并试图在 Lifto 之前抓住它。 尽管 Lifto 处于一个尴尬的位置并且您挡住了他的路，但他足够强壮，可以先握住枪。Klepto 抓住了他的手，随后发生了斗争。 此时，胖子打开了驾驶员侧的车门，而小虾则打开了乘客侧的车门。 “这里他妈的到底是怎么回事！？” 虾米问道。“啊！王八蛋！” Lifto 突然大叫，Klepto 咬伤了他的手，导致他将手枪丢在卡车地板上。“虾米把那支步枪留在小偷身上！” 利夫托喊道。 你被困在中间，Lifto 的一半身体压在你身上。你只知道你很害怕，很想出去。你设法扭动了一下身体，抓住了地板上的手枪。 Klepto 并没有发动攻击，看起来也没有什么不同。 “我不是丧尸！或者不管你认为我是什么！我很好！如果你杀了我，你也必须杀了利夫托！” Klepto 向所有人惊呼。 Klepto 可能对也可能不对，但他很快就咬住了 Lifto。这是否是为了确保他自己的安全而拼命的尝试，还是对即将发生的事情的一瞥，你不知道。 手里拿着手枪，你不知道是否应该只对他们俩都开枪。
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动作：
做
你试图挽救局面
结果：
你不是为了更多的战斗而离开狂欢节。如果末日即将来临，您需要所有与您关系良好的人，因为您不希望自己有机会独自行动。所以，如果你不努力让这个小团体团结在一起，你会被诅咒的。 “好吧，该死的！停下来！只是他妈的阻止它！” 你用枪指着卡车天花板大喊。Lifto此时已经回到了他的驾驶座位置。 “现在每个人都下车，听我说！” 你说看着 Lifto 并简短地瞥了一眼 Klepto。 Lifto 走出来看着你，好像在考虑把手枪从你身边拿走。他可能可以毫无问题地做到，因为尽管您离射击某人很近，但您还没有完全“在那里”。 值得庆幸的是，Lifto 没有尝试任何东西。 当每个人都离开卡车时，你跟着，Shrimpy 现在站在你身边作为后备（胖子没有武器，但无论如何他都站在你附近） “好的，苏西接下来怎么办？” 利夫托平静地说。 你有一个想法，但如果涉及绳索会更好。您快速浏览一下卡车的货厢，会看到一些锁和链子放在那里，它们也能正常工作。 “大家看，我们没有关于这种生物情况的所有信息，杀死被咬伤的人，可能是跳枪。我是说这不是电影！” 你说。“是的！” 克莱普托说。“闭嘴，我还没说完呢！当然，这并不意味着我们需要对此感到愚蠢，因为Lifto 可能是对的。现在 Lifto 我们不知道咬一口是否会导致感染，但如果确实如此，那么除非我们切断您的手，否则您已经注定要失败，但我猜您不希望那样。你想要那个吗？” Lifto 在回答之前给了 Klepto 一个肮脏的眼神。 “看你是不是要射我，至少先射 Klepto，这样我才能看。” 利托说。“什么……我还以为你们两个应该是朋友呢！（叹气）我想在这里给你另一种选择！就像我说的，我们不知道这种事情是否会传染。我不想我们任何人死，你明白吗？我建议你们两人待在卡车的车厢里，直到我们到达可以接受医疗救治的目的地。我的意思是，如果你们两人开始感觉不舒服或有任何不同，那么我们可以考虑开枪。” Lifto 没有说什么，但 Klepto 肯定是可以接受的。 “当然，苏西，你说什么都行！我的意思是这对我来说听起来很合理！” 他评论了，虽然你会觉得他会同意你说的话，即使他没有被咬。“你知道，我们将不得不把你绑起来。” 你说。“没关系。我完全明白。” 克莱普托评论。“电梯？” 他呼了口气，看着自己的手没有说话。然后他回头看你。 “你真的不想我们一个人死吗？” 利夫托问道。“不。为什么我会想要那个？我的意思是，如果这些是末日来临，那么我们就只剩下我们了！我们需要尽可能地团结在一起。无论如何，你们是我多年来与家人最亲近的人，除了你们中的大多数人有一半时间用眼睛脱掉我的衣服……但我跑题了，我的观点是，尽管如此本周发生的该死的事情，我知道我希望你们在我的角落里。你们所有人。” 起初你想知道你是否把它放得太多了，但你听到胖子的方向抽了几声鼻子，小偷显然表现出情绪化的迹象。他可能认为你只是表达了对他的爱之类的。 “好的。好的。我们会按照你的方式来做，但如果我开始转身，或者如果我认为我会变成不同于我自己的东西，那么我希望你是那个向我开枪的人。” 利托说。“好吧，让我们希望它不会变成那样，但我保证。” 你说。 说完这句话，Lifto 和 Klepto 都屈服于被锁起来并坐在卡车床上。Lifto 仍然愤怒地看着 Klepto，但希望他们能在骑行过程中解决他们的分歧。 胖子开车，小虾坐在中间，你坐在乘客旁边。你从来都不是最好的方向，所以你只是告诉胖子留在主路上，希望你能到达一个比你自己的家乡更好的地方，现在可能是一个战区。你不禁想知道你真正的家人过得怎么样，但实际上你宁愿不去想它。 胖子打开收音机希望能找到什么，但除了几个随机的宗教电台谈论救赎和结束时间之外，他没有得到太多，但那些东西总是在谈论结束时间，所以很难说他们甚至指的是目前正在发生的事情。您可能会认为可能会弹出新闻或紧急广播之类的内容，但什么也没有。奇怪的是，您确实让一些电台只播放那些地狱般的归零地广告。 几个小时的车程穿过农场，小偷和利夫托没有呻吟，甚至没有争吵，所以你猜他们感觉很好。尽管如此，即使你不希望他们中的任何一个死去，你也知道这仍然是可能的。 当它开始转向夜晚时，您已经厌倦了白天的活动。感觉有点安全，你允许自己闭上眼睛，随着卡车的行驶而漂移。
梗概：
你试图挽救局面
--------------------
动作：
做
第二天…
结果：
星期五 “……别耽搁了，今天买个零地庇护所！” “胖子关掉收音机，好几个小时没有任何重要的事情发生。这只是那些该死的归零地广告。天哪，你认为有人会报道哦，你知道僵尸启示录吗？” 虾米说。“我仍然说那些东西还活着。我们看到的那些看起来不像是死而复生之类的。” 胖子评论。“好吧，无论如何，报告某种爆发似乎仍然很重要。”“也许它是本地的，在其他任何地方都没有发生？” 此时你的眼睛睁开，你完全从睡梦中醒来。 “（打哈欠）怎么了？” 你问。“所以你终于醒了。” 虾米说。“我们整晚都在开车？” 你问。“有些。胖子一度停在这里，我们都睡了一会儿。如果你想知道那里可怕的二人组，我们检查了他们，据我所知，他们俩都感觉很好。事实上，我认为他们都还在睡觉。猜猜昨天那场闹剧真的让他们筋疲力尽。不知道这是否意味着他们没事，但现在我们确实有另一个问题。” 虾米说。“那是什么？” 你问。“好吧，胖子让我们他妈的迷路了。”“嘿，这不是我的错，我在晚上看不到狗屎，我以为我们在主干道上。该死的GPS。” 胖子说。“告诉你它坏了，多年来一直无法正常工作。不管怎样，我知道你是这个地区的本地人，所以我希望你知道自己的路。” 虾米说。 简单地瞥一眼你左右两边几乎没有描述的风景会让你不知所措。 “对不起，但就像我说的，我从来都不是一个大旅行家。如果我能帮忙，永远不要离开地下室。” 你说。“哦，是的，没错。不管这个事实，有什么建议吗？” 虾米问道。 你真的没有，虽然你可能应该靠近某种农舍，因为你知道有一些这样的。您可以通过停在一处并询问方向来了解自己的方向。 你还记得胖子关于补给品的评论。 “附近应该有某种农舍。继续沿着这条路行驶，我们可能会遇到一个可以问路并可能获得补给的地方。”“听起来和任何想法一样好。” 你真的不知道会不会是这样，你只是希望在这一点上。 幸运的是，这次你的希望成真了。 “嗯，有房子。不过不知道有没有人在家。” 胖子说要拉上一幢传统的大型农舍。“好吧，那我们怎么办？”“嗯……我想我会去前门看看有没有人在家，而你们其他人留在卡车里。” 你说。“你疯了？这个地方看起来像是从恐怖电影中走出来的！可能是一群住在那里的乡巴佬！他们看到像你这样年轻的东西，甚至在我们做任何事情之前就会抓住你。” 虾米的言论。“就像与一群小丑一起到达一扇门并不是直接来自恐怖电影。我现在是唯一一个穿着普通衣服，身上没有血迹的人，我觉得还是先求助一下比较好。”“嘿，我们要怎么处理 Klepto 和 Lifto？” 胖子问。“好吧再次检查它们，如果它们似乎很好地解开了它们，我猜。我的意思是他们可以使用拉伸。反正就像我说的我要去门口。我带着手枪，我相信如果真的发生了什么事，你将能够及时做出反应，以免我被砍伤或任何你认为会发生的事情。我们需要帮助回到正轨并查看仪表，我们甚至可能需要加油，所以希望他们也能做到。别担心，我会没事的。” 你说然后下车。 你一路走到前廊，环顾四周。这个地方安静得令人毛骨悚然。看来你至少应该听到动物的声音什么的。Shrimpy现在让你完全偏执了。直到他提到精神病乡下人，你才真正担心。你回头看看卡车。 “该死的苏西，你需要自己勇敢地度过这些狗屎。为了基督，你从你的连环杀手叔叔中幸存下来。什么都不会发生，可能只是住在这里的一些和蔼的农场老夫妇。” 你喃喃自语，走上楼梯敲门。 你敲了好几下都没有回答，但就在你要放弃的时候，它打开了。 “你是谁！？” 一个毁容的大男人惊呼，把你吓得魂飞魄散，让你向后倒下。 当这个大怪物向你伸出手向你靠近时，你处于极度恐惧的状态...... 即使在你可怕的情况下，你仍然能够对危险的情况做出反应。你拔出手上的手枪，朝那个毁容的人开枪。你在他身上打了两次，当他的身体撞到前门然后撞到房子的前厅地板时，他向后倒转并大声吼叫。 “啊啊啊啊！？来帮你！” 男人喊道。 “初级？那里到底发生了什么？我希望你不要再背着你父亲的步枪射中自己的脚，你知道他是多么讨厌浪费弹药。” 里面传来一个年长的女声。“妈妈！妈妈！伤了很多！坏女孩伤害了我！” 男人哭了。“如果你又和你的妹妹吵架了……天哪……JUNIOR！克莱姆有人射中了少年！” 你听到年长的女性声音尖叫。 到这个时候你正在跑回卡车，虾已经离开了。Lifto和Klepto此时也都醒了，大喊大叫。 “回到他妈的卡车上！我们得……”你开始大喊大叫，然后从农舍的一个窗户传来一声枪响。你听到它在你耳边嗡嗡作响，让你立即倒在地上。 您继续爬向卡车，在那里您听到身后有更多的枪声和突击步枪还击的声音。您会听到有人试图启动卡车，但多击金属似乎可以结束导致发动机发出低于运行声音的声音。 “胖子滚出前座！” 虾米叫道。 你听到更多的枪声，接着是胖子的哭声，但你不知道他是否真的受到了严重的打击，因为你只听到“我的屁股！” 你一直在祈祷你的背部不会被枪击，但你是幸运的…… 曾经是白骑士，Klepto 试图去“拯救”你。 “盗贼滚出去！” 你听到 Lifto 的喊声。“Suzy 就在外面，我不能……” Klepto 的头骨被一颗子弹击中，他的话被打断了。事实上，他在你面前下降了大约几英尺。当你继续爬向卡车时，你试着把他的死从你的脑海中抹去。 “你们这些混蛋不要这个农场！这个农场是我的！你们所有的 SONSOFBITCHES 都要为你们对我儿子的所作所为付出代价！” 一个男声喊道。 你终于到达了目的地，其余的人都躲在卡车后面，尽管胖子因为他的体型和屁股中了一颗子弹而度过了一段艰难的时光。 不过，双方的射击暂时停止了。 “操，操，操，小偷死了！那个乡下混蛋说我们要付钱，他妈的不行，他他妈的要付钱！” Lifto 说，考虑到他昨天就准备杀死 Klepto，这有点令人惊讶。 “操，我的屁股……啊！妈的……真希望我有枪，我会朝那个混蛋开枪。也不是脸颊，该死的洞！” 胖子说他平时很冷静，但被中枪了，他的愤怒有点可以理解。 “幸运的是，我在嘉年华上多拍了一些片段，苏西可能还剩下几张照片。我说我们可能可以通过一些策略来干掉射手。只要我们想出一个当然。” 虾米的言论。 你不知道如何看待这一切。也许这都是过去几天暴力事件的增加以及 Klepto 的丢失，但是您的小丑团队开始变得更加凶恶，尽管最近发生的事件并没有完全鼓励人们过上平静的生活。 不过，您不禁对当前的情况感到有些责任。智障只是想帮助你从跌倒中站起来，但你不可能知道……你只是对你的恐惧做出反应。即使你的其他小丑朋友认为你受到了攻击，你也是第一个伤害农场居民的人。 你们中的一部分人只想逃离这个地方，但你会去哪里？保持理所当然意味着做更多你不一定想做的事情……但是你会想，如果你能让自己处于“正确”的心态，你就能够做到。 农夫试图杀死你们所有人，并成功杀死了试图拯救你的小偷，当然为此报复不是完全错误的吗？
梗概：
第二天…
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动作：
做
你报复
结果：
如果你跑了，你会怀疑其他人是否会跟随，尽管你觉得这不是最好的主意，但你已经厌倦了跑步。反正你也没什么可跑的，如果你能对付里面的居民，你至少有一个地方可以让你从生活中不断增长的混乱中喘口气。 你至少希望。 “虾皮，你能在不被撞到的情况下进入卡车并将其置于空档吗？”“是的，我想我可以。” 虾米说。“很好，给我步枪和夹子，上卡车，当它在中立的 Lifto 和 Fatty 中时，我希望你开始向前推动它。我们应该能够使用这个东西作为一个大盾牌离房子足够近。”“你要做什么？” 胖子问。“我会在卡车床上爬来爬去，尽可能地向那个向我们开火的混蛋开枪。希望我们离得越近，我就越有可能击中他。现在你是想为小偷报仇还是什么？” 这句话得到了所有人的认可。 “我们开工吧。” Lifto 说，你们所有人都制定了你的计划。 当有人向你开枪时，它进展顺利，或者与计划一样好。卡车越靠近房子前面，另一个人很快就加入了农民的行列。估计是农夫的妻子。他们两人对你释放了大量的诅咒，但还没有人互相攻击。 子弹刺入卡车，虽然你们都安全，但当两个前轮胎被炸毁时，Lifto 和 Fatty 会更加努力地推动。 “他妈的。（喘息）”胖子喘息着。“他妈的。” Lifto 咕哝着。 仍然在卡车床上爬来爬去，你朝后面走去，与 Lifto 和 Fatty 交谈。“好吧，停止推动，没有必要让自己紧张，因为你需要节省体力。我认为左边的射手没有那么熟练，所以往左边跑，绕过房子的后面，一定有一个后门。我会提供掩护，我还剩下半个夹子，还有一个完整的，所以当我全自动时，开始跑步。把手枪拿过来。” 你说把手枪扔到卡车的后端。 老实说，你宁愿让 Shrimpy 这样做，因为他是一个小得多的目标，但这个小家伙可能躲在卡车司机或乘客一侧的腿部区域，希望不被击中。 随着更多的子弹撕裂卡车的侧面，您深呼吸。你握住步枪，只露出一点手，盲目地射击并喷洒第二个射手所在的房子的左侧区域。 幸运的是，枪声中传来一声轻微的哭声，你认为你又撞到人了。 在任何情况下，Lifto 和 Fatty 都有时间蹲下跑向房子，抱住墙壁，然后爬到拐角处。胖子在他想要的时候肯定很快，你只是希望他不会心脏病发作。与此同时，你会听到屋内的喊叫声。 “克莱姆！我被击中了！呵呵……（抽泣）”“蜂蜜派？！蜂蜜派？不不不…” 随着枪声完全停止，Shrimpy 趁机跳下卡车跑到它后面。 “苏西？你还在这里？” 你听到他问。“是的，我还在。留在卡车后面，我要……”你在被打断之前开始说 “不！该死！不！去你的！去你的！操你们所有人！” 农夫克莱姆大喊大叫，现在枪声再次响起，只是这次他不再开枪了，轰隆隆的声音表明是某种霰弹枪。 当您尝试重新装填最后一个突击步枪弹夹时，卡车继续受到霰弹枪子弹的影响。事实上，他们已经开始对卡车造成足够的损坏，以至于其中一个通过并撞到你是非常有可能的。您翻到卡车的另一侧，以免被撞到。 “我要杀了你们所有人！” 你听到他吼叫。你太忙于保持低调和重新加载。 然后你什么也没听到，接着是手枪射击，然后是另一声。您借此机会快速完全爬出卡车床。 “苏西，他们抓到他了吗？” 虾米问“我不知道……”你说，然后探出头来希望这不是一个错误。 你很快就会看到 Lifto 手里拿着手枪站在前门。 “我们得到了所有这些混蛋。它是安全的。” 他说。 你和 Shrimpy 都从卡车的盖子上走出来，走向房子。 你注意到的第一件事是你在入口附近开枪的第一个男人，显然他在枪战开始后不久就试图爬走，不久之后就死了。考虑到他的毁容以及从他的声音可以看出，他可能是智障。你记得听到有人说“初级”，所以你猜他是儿子。你对射他感觉不太好。这真的是一场意外。 看到另外两个尸体并不会让你感觉好一些。 农夫的妻子死在餐厅区。她实际上比胖子大，这意味着她确实是大方。她的上半身有多处枪伤。你知道你打了她，但你只是不知道在哪里。当你想到它时，你的胃开始感觉有点不舒服。 最后你看到了农民本人，或者你听到有人喊叫的克莱姆。Lifto 提到 Clem 非常关注你，以至于他意识到有人偷偷摸摸他为时已晚。Lifto 提到他朝他的背部开了一枪，然后当 Clem 还在地板上翻来覆去试图用他的霰弹枪翻身时，Clem 把一个射入了他的头骨。 你甚至没有杀死克莱姆，你仍然对此感到奇怪。你不明白为什么你对这一切感到有点不舒服。或者更确切地说，你知道为什么，但考虑到狂欢节上的小生物横冲直撞，你以前并不是没有见过可怕的死亡，但这在某种程度上有所不同。 现在危险已经过去，你让自己陷入的肾上腺素和生存主义心态已经消失，所以你的行为带来了严峻的后果。 意识到你杀死了一个真正只是保卫家园的全家，这让你很受打击。那以及你觉得整个事件都是你的错的事实。加上 Klepto 的死亡，你要直接或间接地对四个人的死亡负责。 “洗手间。” 你发呆的说。“什么？” 虾米问道。“我会生病的……”“我想我在房子后面看到了一个，它是……” 你不用等Lifto完成你就朝方向跑。试图避免呕吐，你只是设法去厕所，然后你立即吐出你的胆量。 你的同事来看看你过得怎么样。 “苏西，你没事吧？” 虾米问道。“是啊，你没病吧？” 利夫托问道。 你需要一段时间来回答，但最终你会解决它。 “呃……（喘气）……我只是……我不知道……是的，我会没事的。我认为这一切的压力刚刚降临到我身上。呃……”你说。“嗯，可以理解。看，你为什么不去某个地方躺下？你今天做的已经够多了。我们其他人会把这些尸体拖出这里。这需要我们所有人，上帝知道他们在这个农场吃的到底是什么，但这里的每个人似乎都和胖子一样大或更大。可能也应该环顾农场的其他地方。确保没有人躲在谷仓之类的地方。” 利夫托评论。“是的……好吧……”你说着走过 Shrimpy 和 Lifto。 当你慢慢地走回房子的前面时，胖子走下楼梯。 “嗯，楼上很清楚，据我所知。上面没人。嘿苏西，你还好吗？能听到你在上面呕吐的声音。” 胖子问。“是啊，只是需要躺下。” 你说。“你和我都有，但我想 Lifto 需要我帮他搬出这些尸体。反正楼上有几张不错的床，应该可以拿一张吧。” 在这一点上，您感到非常不稳定，以至于您想知道您是否甚至可以爬上楼梯。
梗概：
你报复
--------------------
动作：
做
你使用楼上的卧室
结果：
考虑到每个人都会在楼下发出一堆噪音，移动身体等等，你决定上楼。你认为无论如何你真的可以使用孤独。你认为这周你在亲密的人中度过的时间比你一生中花费的时间还多，这包括你有男朋友的时候。 当其他人都在做一些清理工作时，当你走上吱吱作响的楼梯时，你的头脑会变得有点麻木。你把本周所经历的一切都抛诸脑后。你现在只想从这场磨难中休息一下。 您到达平台并向左看，看到一个房间相当混乱，令人作呕。你会听到苍蝇在那里嗡嗡作响，闻起来像是有什么东西死在了那里，这并没有改善你的感觉。你猜这可能是儿子的房间。你关上门以保持气味并继续前进。 穿过大厅，你会发现一间更好的房间，里面有一张大床。你猜克莱姆和他的妻子睡在这里，或者只是他的妻子，考虑到她的身材。不幸的是，Junior 房间的气味仍然足够强烈，足以让这个房间闻到一点余味。你也不能在这里休息，所以你又继续前进。 您经过另一间浴室，然后在尽头找到另一间卧室。这个没有难闻的气味，而且有一张看起来很舒服的床，所以你马上就去。 你倒在床上，深深地呼气。你静静地躺在那里片刻，试图让你的思绪围绕着这种情况。 “（叹气）哦……该死……永远不应该离开地下室。” 你说。 你想想如果你本周早些时候真的呆在家里而不是去参加马戏团的想法，可能会发生什么。你也想知道如果你和你的妹妹住在一起，甚至和鲍比和好，会发生什么。然后你会记得国民警卫队是如何驻扎在你的城镇的，然后还有那些生物（仍然不知道那是怎么回事，或者就这点而言，Lifto 是否已经脱离险境）所以事情可能会“我仍然很忙。 “无论如何我都被操了。” 当你的眼睛开始闭上时，你笑了。事实上，你笑得很开心。你嘲笑这整个地狱周。 可能是你的头脑试图应对和疲倦的结合，或者你可能已经有点生气了，但无论如何你都无法停止对自己悄悄地咯咯笑。 然后，当您处于轻微的咯咯笑状态时，当您开始意识到周围环境时，您的眼睛会睁得更久。 这个房间。这是一个年轻女性的房间。有一个专门用于化妆等的区域。墙壁上有华丽的印花。你甚至会看到一些女性内衣坐在梳妆台上。 然后你还记得你第一次开枪射击少年时，他的母亲告诉他最好不要再和他的妹妹打架。你在紧张的情况下记得的事情很有趣。 只有三具尸体，这说明这家人的女儿还活着，可能藏身于某处。胖子说他在楼上找过，但他可能漏掉了什么。 快速瞥一眼你的左边，你会看到一扇略微歪斜的壁橱门，然后它开始进一步打开。 感受到危险带来的突然爆发的能量，你设法坐起来从床上下来，正好赶上这间屋子的主人现身。显然她认为你会比胖子更容易挑选，她发现自己错得有多可怕。 你立即冲向一手拿着大刀的女人。这足以让她措手不及，而且她没有用刀击中你，或者更确切地说没有以有意义的方式击中你，因为刀片确实切断了你的上臂。 你们两人跌倒在壁橱地板上，她的头撞在墙上。你抓住拿着刀的手，把它砸在门口，直到她掉下来。你感觉到她的另一只手在打你的脸，但你忽略了疼痛，用你所有的重量用肘部猛击她的鼻子。 你用一拳打在她的嘴上，阻止她哭出来。你反复打她的脸，直到她暂时完全停止反抗。衣服掉在你身上，你抓起一件衬衫，把它包在她的喉咙上，然后用力拉。你的思想进入一个非常黑暗的地方。 “嘘……一切很快就会结束，让黑暗带走你吧”你低声说。 女人倒吸一口凉气，试图对你拳打脚踢，但你设法把头向后拉，正好让她错过。她试图扭动和扭动离开，但你正坐在她的肚子上，她无处可去。女人继续哽咽并试图撬开你的手，但她做不到，她的挣扎变得不那么激烈。 “......我们走了......现在你将永远和你的家人在一起......”你说，看着她的脸因缺氧而变得不健康，她的眼睛向上滚动。 女人停止呼吸，事情就完成了。你甚至都不觉得累了，就像杀了她让你精力充沛一样。你也没有恶心的感觉。你只是进入了一种恍惚状态。就像这个女人是需要处理的垃圾。你想知道这是否是你的埃德叔叔杀死妓女时的感受。 如果你在你的旧思想框架中，你就会知道那是多么糟糕。以你目前的心态，你仍然知道那是多么糟糕，但是你不再关心了。 你的内心很可能已经崩溃了，无论是整个星期的组合，还是这糟糕的一天，伤害已经造成，你不再是原来的那个人了。她要杀了你，而你阻止了她，这才是最重要的。没有其他的。你活了下来。你的能力没有限制，你现在意识到这一点。 “苏西！？嘿，你在上面还好吗？！我听到了一些声音！” 你听到 Lifto 的喊声。“是的，我很好，只是在我休息之前先看看壁橱。有些狗屎掉了，不用担心！” 你喊回来。“好吧。” 有了这种干扰，你就可以将注意力重新转移到你的处境上。你开始环顾房间，发现很多化妆品。你也仔细看看她的衣服，注意到很多都和你的尺寸差不多，虽然有点放荡。奇怪的是，在查看了更多私人物品后，您发现她的名字也是 Susie。或者准确地说是Susie Mae。 从壁橱里拿出一些衣服后，你把苏西梅的其余身体塞进里面，然后关上了门。 “嗯，是的，我想我可以用这些东西做一套新衣服。” 你说。然后你拿起附近的剪刀和缝纫材料。你已经有一段时间没有做过任何裁缝类的东西了，但你记得你妈妈在你小学戏剧时教你这样做的时候。希望你已经保留了足够的知识。 您将在 Susie Mae 的房间里度过您自己的个人项目。这是你很长一段时间以来最孤独的时候，你很高兴地享受它。你的其他同胞大部分时间都在忙着寻找农场的其余部分，尽管有一次 Shrimpy 出现并询问你是否还好。 “苏西？嘿，还以为你睡着了……你在做什么？”“好吧，我已经没有多余的衣服了。连我的小丑东西都留在嘉年华了，所以我正在做一个新衣橱，怎么了？” 你问不要从你正在做的事情中抬头。“嗯，我只是想让你知道我们已经检查了整个农场，这个地方看起来很干净，而且库存也很充足。” 虾米的言论。“胖子和利夫托没事吧？” 你问。“嘿，是的，胖子很好，不过从他的屁股里挖出那颗子弹并不好玩。至于Lifto，他看起来也不错。不要认为 Klepto 咬他有任何作用，此时他已经清理了伤口，所以我猜他是无罪的。说到 Klepto，我们呃……在想我们至少应该把他埋了。我们已经在一个不错的地方挖了洞。也许明天我们应该举行一个小型的葬礼？”“我认为这听起来不错……我想如果我参加葬礼，我最好完成这件事。” 你回复。 虾米停顿了片刻。 “苏西你真的没事吗？不知怎的，你似乎与众不同。”“我很好，小虾。正好。不过，我真的需要专注于这一点。”“嗯，当然可以。如果有任何重要的事情出现，我们会通知你。” Shrimpy 说着就离开了你。 白天或黑夜的其余时间您都不会受到打扰；事实上，他们都必须睡在楼下，这让您可以自由地在楼上的浴室洗漱。 洗完澡后，您可以尝试现在完成的项目。你的衣服是五颜六色的混乱拼凑而成，因为你只是剪了很多衣服并将它们缝在一起，但你对自己的工作很满意。事实上，你对它非常满意，你在里面睡着了，以为它会为你明天的 Klepto 葬礼节省一点时间。
梗概：
你使用楼上的卧室
--------------------
动作：
做
第二天…
结果：
周六 “苏西？你起来了？” Shrimpy 询问并敲你的门。 通常你不会这么早，但在这种罕见的情况下，你实际上是。 “是的，我醒了。我一会儿就准备好。” 你说在脸上化妆。“好吧，我们已经准备好了，Lifto 和 Fatty 已经把 Klepto 的尸体放在我们挖的洞里，所以我们已经为这件事做好了准备. 楼下见。”“嘿，虾米，我能在这次活动中说几句话吗？” 你在他离开门之前问。“你是说像悼词？当然，我的意思是，如果您有话要说，那么一定要说出来。真的，我们都试图提出一些正式的东西，但我认为我们都有些筋疲力尽。昨天，Lifto 实际上为 Klepto 的死掉下了眼泪。我认为他对前一天试图杀死他感到不安，并希望他们的最后时刻能以更友好的方式结束。”“好吧，告诉Lifto，流泪和后悔的时间很快就会成为过去。差不多了，楼下见。”“好的。” 你看着镜子里的自己，涂完口红。不是您想要的确切红色阴影，但它是前任所有者尚未使用的唯一口红。你很高兴这个女孩和她化妆的一样多，所以你可以适当地画你的脸。 你对自己的脸很满意，站起来看着附近的全身镜里的自己。 “好吧，这不是我的旧狂欢节装束，但是是的……这绝对是我现在的样子。现在和永远。” 你说着朝门口走去。 在离开之前，你停下来看看壁橱。 “谢谢你让我借你的化妆品和衣服。” 你说然后咯咯地笑了起来。 你跳下楼梯，与其他人见面。他们都比你更阴沉一些，看到你略显精力充沛的气质感到很惊讶。 “苏西？那是一个呃有趣的装束。” 胖子评论。“不是吗？自己做的。没有什么比一套新衣服更能让你拥有不同的心态了！其实考虑到大家一天都穿着同样的衣服跑来跑去，我想说大家也可以穿新衣服。那些破布现在一定很脏；一旦你改变了，你们可能都会对事情感到更加积极。我相信我们可以收集足够的这家人的衣服，我可以为你们做点什么。无论如何，我们可以稍后再谈，让我们继续前进。” 每个人都不确定如何对你的新行为做出反应，尤其是在这种情况下，但他们都只是带路到 Klepto 在房子后院挖出的开放坟墓。 Klepto 看起来仍然和你上次见到他时一样死，尽管有人以恭敬的方式安排了他的尸体。他手里甚至还拿着一些刚摘下来的花。 “花的触感很好。” 你说不知道是谁做的。“谢谢。” Lifto说着擦了擦眼泪。“Shrimpy 可能已经告诉过你我要做一些悼词，但还有人有什么要说的吗？”“不，我们昨晚都在交易 Klepto 的故事。我们可能会做一段时间。不过，我们中没有人真的适合正式演讲，如果你这样做可能是最好的。此外，尽管与我们中的任何一个人都不认识那么久，但你还是想做这件事，这意味着很多。” 虾米说。“很高兴你有这种感觉，因为我要说的将是针对你们所有人的……” 在你完成这个想法之前。你的眼睛（和其他人的）看到远处非常壮观的东西。 一朵蘑菇云。 你没有感觉到任何颤抖或余震，一波火还没有把你吞没，所以你猜它不是离得太近，甚至可能很小。尽管如此，您还是忍不住想知道您是否处于某种致命的辐射半径中。如果是这样，就没有什么可以做的了。 “好吧，我会被诅咒的，这个世界终于做到了……”你对自己说。 Lifto、Fatty 和 Shrimpy 同时开始发誓并像大多数人一样对这种情况做出反应，即恐慌、恐惧和歇斯底里。你认为在过去你可能也会有，但现在不会了。如果有的话，这是您新的人生观的绝佳时机。 你迅速唤起所有人的注意。 “嘿！听着！这可能是我们所知道的文明的终结，但这不是世界的终结！现在的生活肯定会更艰难，但看看我们已经活了下来。就在这个该死的一周，我们经历了军事占领、内战、怪胎袭击，还击倒了一群试图谋杀我们的乡下人。” 在这一点上，他们都在专心听你说话。“我们会挺过去的，只要我们团结在一起，就没有我们做不到的事情。这是一个新的开始，如有必要，我们将与世界作对。我们是一家人，我爱你们。” 你说。 这些话被停顿了一下，然后当然得到了所有人的积极认可，每个人都进入了一个集体拥抱。情绪有点高涨，又流了一些眼泪。 虽然你的话是真实的，背后确实有感情，但你的思想仍然在黑暗的地方。您确实将它们视为家人，但您也情不自禁地将它们视为在即将到来的混乱中有用的“工具”。即使现在你也在根据他们的弱点和优势来衡量他们。 无论如何，你和每个人都完成了 Klepto 的埋葬并从农场收集了所有需要的补给品。你们都知道你最终会用完，但现在你有足够的物资来思考你未来的长期计划。 你不再是那个躲在地下室的害羞“好”女孩，被你的家人骚扰，一天天过去了。离得很远。离它很远。 这是全新的一天，而这只是您全新生活的开始。
梗概：
第二天…
--------------------
动作：
做
结果：
Karnage 狂欢节 未来… 您舒适地坐在改装后的吉普车上，看着面前的另一个小型定居点燃烧。不是第一次，当然也不会是最后一次。尖叫声和枪声开始平息，这意味着今天的突袭即将结束。 尽管如此，它还没有完全结束，总有一些有趣的事情可以看到。 “呜呜！看来那位新人态度对了！” 你说看着一个戴着古怪的绿色假发和新奇的鼻镜的年轻人用他的大砍刀砍向另一个以前乞求生命的年轻人的头骨。“嗯。一开始对他不太确定，但我想他已经在这次突袭中证明了自己。说起来，看来我们收获颇丰。很多食物、水，甚至医疗用品。” 坐在你旁边的 Lifto 说。“啧啧！把那张地图给我，我想看看我们接下来应该打哪里。” 你说。“我相信霍迪船长拥有它。”“好吧，那就把那个被刺穿的怪胎叫过来。” 你说。 Lifto 点点头，并通过扩音器呼叫 Howdy 船长。最终，出现了一个戴着破旧飞行员帽、黑着脸、肩上扛着一支突击步枪的穿孔严重的男子。 “今天脸黑？是不是有点unPC？” 你笑着说。“呃，如果他们不能开个玩笑就去他妈的。” 你好冷漠地说。“我的朋友说得更真切。Lifto 说你有这个地区的地图。” 你说。“哦，是的，我愿意。来，让我把它从我的包里拿出来。” Howdy 取下一个小背包并打开它，为您提供该地区的旧地图。 “让我们看看……好吧……我们到达了那个地方……那个地方……”你开始寻找新的目标。“救赎呢？我讨厌那些宗教类型。” 你好说。“虽然我毫不怀疑这座虔诚的狂热者城镇储备充足，但他们也装备精良。事实上，我一直听到有传言说他们正在对邻近的定居点发动自己的小型讨伐。” 你说。“我听说他们在一个城镇完成了西班牙宗教裁判所。把他们中的大部分钉在十字架上，其余的活活烧死。那当然是在他们已经把所有人都折磨得精疲力竭之后。” Lifto 补充道。“所以？我们可以对付那些混蛋。” 你好评论。“那可能是，但不是在不久的将来。这将是一项非常危险的工作，我们这样做不是为了冒险，也不是为了证明我们有多坏。我们这样做是为了生存……好吧，并在此过程中获得一点乐趣。” 你笑着说，虽然 Howdy 仍然是你更“男子气概”的成员之一，仍然想通过杀死一些东西来证明他有一个鸡巴。 你用温柔的触摸和舒缓的声音伸手抚摸他的脸。 “我知道你喜欢杀人，但我不希望你在这个过程中被杀。仅仅因为我们倾向于选择更容易的目标就意味着我真的不希望你或我们中的任何一个人被杀。我们都是一家人，我不想看到你们中的任何一个人死去，明白吗？这就是为什么我们的运作模式是追求低风险、高回报的原因，我会说它多年来的工作不是吗？”“是的，我想是的。” 你好说。“这是正确的。无论如何，看起来我们已经到达了这里所有值得的地方。我们应该开始向东进攻，袭击从这些城邦出来的商队。在和零地公司的那场大战之后，他们都搞砸了，所以他们现在缺乏国防部。好的，让我们开始关闭这个马戏团并继续前进。” 你说并从 Lifto 拿走扩音器。 “好吧，你们这些小丑把我们洗劫一空的东西都收集起来，堆在车上，让我们在路上进行这场怪胎表演！我们往东走！火烈鸟！嘿！不是说要再开火，而是说走吧！” 你摇头大喊，把扩音器扔到后座。 Lifto 启动吉普车，你稍微伸展一下，等待你的家人完成收拾行装。Lifto 静静地等待，但你想说话。 “一分钱一分货，你的想法。” 你说这会导致 Lifto 发出一声喷嚏。“哦，没什么，我想我只是在想事情发生了多少变化。过去。有时我很想念它。”“哦这个。嗯，我不会错过的。很高兴我生命的一部分结束了。”“不只是过去，人们也喜欢胖子、虾米……小偷。”“嗯，那不一样。我很想念他们。他们是家人，我永远不会忘记他们。我们肯定在那间农舍里度过了一些有趣的时光，记得吗？”“呵呵，对。对发生的事情感到羞耻；也许我们可以在那里过上更稳定的生活，而不是做这种掠夺者的事情。”“也许吧，但我们不能沉迷于如果过去会怎样。只需要关注今天，如果可能的话，关注未来。这就是让我们活着并让我们建立新家庭的原因。” 你说并触摸了Lifto的手。 Lifto 看着你微笑。你不确定他是否完全同意你做事的方式，但他足够爱你，无论发生什么他都会站在你身边，这对你来说就足够了。 老实说，一开始你确实有点看好Lifto，因为他显然是个大个子，可以在遇到麻烦的时候保护你，但现在已经不止于此了。考虑到你们都是原组中仅剩的两个人，你们的关系自然而然地增长了很多。 自从你的新生活开始以来，你做了很多坏事，但这是一个严酷的世界，你只是生活在其中。有时，您会想知道自己是否应该为自己所做的事情感到难过，但随后您只是笑了笑，并记住不要沉迷于过去。 “我爱你肯尼斯。” 你说用Lifto的真名，看着他并触摸他的手。“我爱你，苏西。” 他回应并倾身吻你。 通过这种感情的交流，你、Lifto 和你的小丑团伙出发寻找新的猎物。
梗概：
故事名称：
62
故事背景：
第一章：黑暗的开始 好吧，你又做了一次。你整个学期都懈怠了，没有为期末考试而学习。明天你不可能通过你的自然魔法测试。这些东西总是杀手。你甚至不知道你为什么参加那个班；你甚至对成为自然法师都不感兴趣。你甚至不喜欢该死的户外活动！ 哦，等等，你知道为什么，因为你看到一个可爱的女巫在上课，所以你就像个笨蛋一样，报名参加了。自然而然，当你试图用某种戏法给她留下深刻印象的那一刻，她就嘲笑你，说即使她是直的，她也不会去找你这样一个半屁股的树篱精灵！你真的应该更清楚，这些女巫中有一半是女同性恋。整个地球母亲女神崇拜迎合了地毯咀嚼。（不，没有男性女巫，任何自尊的人都会称自己为术士，而不是该死的女巫！） （叹气）就像你妹妹的 B'ar'b Dolly 曾经说过的“魔法很难！” 确实。有时你会诅咒你母亲将她神奇的精灵血传给你的那一天。 尽管如此，你现在在这里，所以你需要充分利用你的生活。你已经从你的父母那里听到了你对他们俩的失望。你爸爸可能会做的比说的多一点，他可能会亲自骑车去大学，把你打死，因为你浪费了他辛苦赚来的掠夺钱把你送到那里。 啊！你只是希望你擅长某种魔法！你有能力；你只是没有重点！这一直是你的问题。你姐姐过得轻松，她追上了爸爸。狭隘地专注于用钝器杀死某人并不需要太多努力。当然，你必须成为人们期待更伟大事物的那个人。 你已经尝试了所有的学派，召唤，咒法，幻术，占卜；他们都是。你不能在其中任何一个方面出类拔萃。你只能走这么远，然后言语和手势以及所有这些都对你来说太他妈的混乱了。 不过，你没试过“黑魔法”，你妈肯定不会同意的。（你爸爸可能不在乎，但如果你让你妈妈不高兴，那就是头上的战锤）事实上，研究其中任何一个都是违法的。他们在过去制造了太多的麻烦，一时间差点毁灭了世界。所有已知甚至涉足此类艺术的人都会立即被拘留并处决。 你认为无论如何你都不想召唤恶魔；即使是在校园里工作的少数“友好的人”也是他妈的混蛋到了 N 级。你只能想象那些没有生活在凡人中的人有多糟糕。 然而，你不得不承认你一直对另一种黑暗艺术有一点好奇。 所讨论的艺术是死灵法术。 在这一点上，你至少愿意看看你是否擅长任何事情，所以对于狗屎和傻笑，你决定取消你无论如何都会失败的测试，然后去看看你是否能找到更多关于这个的信息取缔黑暗艺术。 虽然是一个懒惰的人，但您决定先尝试图书馆。
--------------------
动作：
做
你去图书馆看看
结果：
即使你应该成为一名训练中的巫师，如果你能帮上忙，你还是尽可能少花时间在图书馆里。通常，您只需查看课程中需要阅读的任何内容然后离开。 你对在那里找到关于死灵法术的任何实质性内容几乎没有信心，但也许你可以找到一些至少可以平息对它的知识渴望的东西。您意识到自己踏上了危险的道路，但您必须尝试了解更多。 然而，图书馆很大，你不确定在哪里看，它不像有一个关于死灵法或非法魔法的部分，但你认为你会先看历史部分。 你扫视并浏览书堆，直到你拿起几本看起来可能包含某些东西的精选书籍，坐在一个安静的角落开始阅读。自然你不会学到太多。历史书主要讲述死亡战争有多糟糕，重要的战斗，巫妖领主的暴行，以及你一次又一次阅读的典型内容。尽管如此，你还是决心寻找新的东西。 你花了几个小时阅读，直到你意识到你已经在图书馆度过了一天，现在是晚上了。奇怪的是，图书馆大部分时间都是空的，这可能是因为决赛现在已经结束，你现在无疑失败了。你想想你是多么愚蠢地痴迷于一种你可能不会擅长的非法艺术！你只是专注于这个主题而忽略了你自己可以尝试解决的问题，而不是幻想成为某个死灵法师。复活死者……这对你有什么好处？夸大妄想。无论如何，您都没有足够的动力来尝试接管世界。只有一个人真正激励了你，而她已经走了。 你开始想起 Serena 并开始感到悲伤，就像你第一次失去她时一样。你受够了死亡。你离开图书馆发呆，然后回家睡觉，度过这悲惨的一天。 但是你逃不掉；甚至你的梦想也会带来死亡。你梦见塞丽娜。起初她还活着，充满活力，然后突然间她在你的眼前死去。然后你看到她的坟墓，你为它哭泣并大喊你如何要求她不要离开你。 然后她从坟墓中醒来……而你醒来却与她面对面！她是一个漂浮在你床边的幽灵。 “天啊！” 你大喊大叫，靠在你的床头板上。“嘘！小声点，我的爱，我在这里冒着很大的风险。” 她说环顾四周，就好像有人随时会撞倒你的门一样。“赛琳娜，这是怎么回事？这不是梦吧？”“不，亲爱的，我向你保证，我是非常真实的。还有我要给你的消息。”“一个消息？”“是的，拥抱死亡和它所提供的所有美丽。”“你想让我自杀，加入你？”瑟琳娜笑了笑。“不傻，你要成为死灵法师，为世界带来死亡的纯洁。”“……对对对。以我的魔法天赋，我怀疑我是否能养出老鼠骨架。”“你总是对自己缺乏信心。我想这就是他们最后一次派我来激励你的原因。虽然我承认，无论如何我都会来看你的。”“他们？” 瑟琳娜一边回答你的问题，然后继续。 “看，我们没有太多时间，但你有能力成为自几个世纪前将世界濒临灭绝边缘的大巫妖领主以来最伟大的死灵法师，但你不能独自完成……至少现在还不能。你必须有盟友。这个校园里还有像你一样渴望练习黑魔法的人。寻找他们并接管这所大学，作为你走向命运的第一步。”“什么？坚持，稍等！是什么让你觉得我想做这一切？” 瑟琳娜飘到你身边，用一只幽灵般的手抚摸你的脸。你感到完全平静。 “因为在内心深处，这就是你想要的。你知道你的思想被死亡所支配。你甚至会想到我而不是活着的女孩，而我已经死了很多年了。把我召唤到这里来，是你的心智起了作用，这也是你的愿望的证明。”“我想念你…”“我知道你知道，但我的意思是，即使在我死之前，我也记得你对黑魔法很感兴趣。事实上，这是你吸引我的部分原因。但现在我必须回来，你必须履行你在这个世界上的职责，这次努力的成功与否最终取决于你，但我相信你会成功。” 然后塞丽娜飘得更近了一点，吻了你。 “再见了我的爱人。” 她说然后消失了。 你现在完全清醒并在地板上踱步。您之前一直对走这条路感到矛盾，但是您考虑得越多，而这次来自 Serena 的访问，这就是您将要选择的道路。那么为何不？在这一点上，您的生活中没有更好的选择。 你会去做的，虽然Serena提到了像你在校园里的其他人，但如果你有线索知道他们是谁，你就该死，因为你没有真正社交。不过，您可以想出两个可以帮助您入门的人。（或者至少不要在你开始询问死灵法时对你发火） 第二天，你迈出了走向黑暗的第一步……
梗概：
你去图书馆看看
--------------------
动作：
做
你去找你那令人毛骨悚然的咒语教授 Demar 先生
结果：
第2章：死亡等待 Serena 说得对，你需要其他人的帮助，然后前往 Demar 先生的办公室。 Demar 先生的班级可能是唯一一个你不会太费劲的班级。鉴于咒语可以导致召唤恶魔，这并不是一个真正受欢迎的领域。尽管如此，它还是有小范围的用途，当然它受到大多数政府的严格监管。 德马尔先生是一位好教授，但你们班上的每个人都普遍认为他真的很令人毛骨悚然。此外，关于他的传闻也一直存在。你总是觉得他有兴趣用他的专业领域做更多的事情，但不得不满足于教学。不过，他似乎总是对你很好。 当你到达他的办公室时，令人惊讶的是它是开放的，他正忙着拖着一些文件四处走动。 “先生。德玛尔？”“嗯？什么？该死，我以为我锁上了那扇门！哦，是你，你想要什么？并锁上那扇门。”“嗯嗯……我有一些问题……”“好吧，他们是什么？出去吧。” 他说继续看一些奇怪的羊皮纸。“嗯，呃……我想是关于魔法，我想嗯……” 德马尔先生抬头看着你，好像他会因为你口吃太多而扇你一巴掌。你终于开口了。 “我想更多地了解死灵法术。” Demar 先生退后几步，用审视的目光盯着你。 “我不知道你在说什么，事实上，如果你不离开这里，我会向院长报告你问这样的问题。” 他警告，但你会觉得他没有说实话。他可能认为你是被院长派去监视他的。他之前也不是没有受到严密审查。 “先生。Demar，我不是想给你惹麻烦，是我！我真的很想学死灵法术，我认为只有你能帮到我！我其实很担心来找你，我对整件事都很紧张！”“嗯，我想我一直认为你倾向于黑暗艺术，考虑到你刚才令人讨厌的口吃，你听起来确实很紧张。好的，你想学吗？我来教你。我会教你，就像我现在正在教很多其他人一样。惊讶？是的，时间即将过去，我们这些倾向于真正的权力的人开始在这个世界上……永久地占有一席之地。虽然我仍然担心你，我的意思是虽然你在我班上的成绩很一般，但我很清楚你在其他领域并不是最好的学生。我怀疑你能否驾驭黑暗魔法的力量，更不用说成为死灵法师了。” 你站直身子回答。“我相信这是我的道路。” “很好，我很高兴你这么说，因为没有回头路，这条路不适合弱者。我们还必须始终保密，直到我们可以拿走属于我们的东西的那一天。现在跟我来，我们将看看你到底有什么样的潜力。” Demar 先生施了一个咒语并打开了一个传送门。然后他准备进入它并召唤你跟随他，你这样做了。突然间，你被带到了一个近乎黑暗的地方。鉴于您周围的环境，您会猜测您在地下某处。您可以在附近听到锁链的叮当声和呻吟声。 “我们在哪？”“你的测试区，我们将在这里找出你到底是由什么组成的。在隔壁房间，你会发现一些不死朋友。他们目前被锁起来，但不会持续太久。现在，你可以杀死他们，但作为死灵法师，你应该控制他们，所以显然你应该尝试这样做。”“但我不知道怎么做！你没有教我任何咒语！”“而且我不打算，你必须自己弄清楚这一点，但我要说的是，这完全取决于意志力的力量。挖掘你的魔力，如果你有它所需要的，你就会成功。我就交给你了。” Demar 消失在传送门中，留下你们一个人。你真的已经陷入其中，现在真的没有回头路了，你可以听到隔壁房间的铁链掉到地上，呻吟声越来越大。 地上有一大块骨头，房间里没有其他东西，你也不能跑，因为没有其他出口。
梗概：
你去找你那令人毛骨悚然的咒语教授 Demar 先生
--------------------
动作：
做
你拿起骨头保护自己
结果：
你拿起大骨头，为即将到来的一切做好准备。几个僵尸进入，一旦他们看到你，他们就会蹒跚而行。 你有点动摇，你试图完全按照德马尔所说的去做，但你没有任何效果。很快，其中一只僵尸就快到你身上了，你用骨头猛烈抨击。 您对第一个头骨进行了几次重击，使其再次坍塌，毫无生气。你对第二个做同样的事情。你呼吸困难并感谢他们足够慢以至于不能轻易抓住你。 然而，当德玛再次出现时，你的胜利是短暂的。 “（叹气）你杀了他们，在我告诉你这不是目的之后。另外，你甚至试图控制他们也表现不佳。如果你在这样的压力下屈服，我看不到你的未来。” 你有点沮丧，但更糟糕的还在后头，当你发现你不会离开这个地方的时候。Demar 先生不能冒险让您知道这个地方，但又不是他的门徒。他没有坚持这么长时间不被发现的机会。 随着几声快速的喃喃自语，你会感觉到你的身体被腐烂蹂躏。这是一种可怕的死亡，但会导致你站起来取代你刚刚摧毁的“测试”僵尸。 你将剩余的亡灵生命在地下徘徊，直到最终当 Demar 觉得是攻击的时候，你是在企图接管中被摧毁的第一批炮灰部队之一。
梗概：
你拿起骨头保护自己
故事名称：
63
故事背景：
第 2 章：死亡等待 Serena 说得对，你需要其他人的帮助，然后前往 Demar 先生的办公室。 Demar 先生的班级可能是唯一一个你不会太费劲的班级。鉴于咒语可以导致召唤恶魔，这并不是一个真正受欢迎的领域。尽管如此，它还是有小范围的用途，当然它受到大多数政府的严格监管。 德马尔先生是一位好教授，但你们班上的每个人都普遍认为他真的很令人毛骨悚然。此外，关于他的传闻也一直存在。你总是觉得他有兴趣用他的专业领域做更多的事情，但不得不满足于教学。不过，他似乎总是对你很好。 当你到达他的办公室时，令人惊讶的是它是开放的，他正忙着拖着一些文件四处走动。 “先生。德玛尔？”“嗯？什么？该死，我以为我锁上了那扇门！哦，是你，你想要什么？并锁上那扇门。”“嗯嗯……我有一些问题……”“好吧，他们是什么？出去吧。” 他说继续看一些奇怪的羊皮纸。“嗯，呃……我想是关于魔法，我想嗯……” 德马尔先生抬头看着你，好像他会因为你口吃太多而扇你一巴掌。你终于开口了。 “我想更多地了解死灵法术。” Demar 先生退后几步，用审视的目光盯着你。 “我不知道你在说什么，事实上，如果你不离开这里，我会向院长报告你问这样的问题。” 他警告，但你会觉得他没有说实话。他可能认为你是被院长派去监视他的。他之前也不是没有受到严密审查。 “先生。Demar，我不是想给你惹麻烦，是我！我真的很想学死灵法术，我认为只有你能帮到我！我其实很担心来找你，我对整件事都很紧张！”“嗯，我想我一直认为你倾向于黑暗艺术，考虑到你刚才令人讨厌的口吃，你听起来确实很紧张。好的，你想学吗？我来教你。我会教你，就像我现在正在教很多其他人一样。惊讶？是的，时间即将过去，我们这些倾向于真正的权力的人开始在这个世界上……永久地占有一席之地。虽然我仍然担心你，我的意思是虽然你在我班上的成绩很一般，但我很清楚你在其他领域并不是最好的学生。我怀疑你能否驾驭黑暗魔法的力量，更不用说成为死灵法师了。” 你站直身子回答。“我相信这是我的道路。” “很好，我很高兴你这么说，因为没有回头路，这条路不适合弱者。我们还必须始终保密，直到我们可以拿走属于我们的东西的那一天。现在跟我来，我们将看看你到底有什么样的潜力。” Demar 先生施了一个咒语并打开了一个传送门。然后他准备进入它并召唤你跟随他，你这样做了。突然间，你被带到了一个近乎黑暗的地方。鉴于您周围的环境，您会猜测您在地下某处。您可以在附近听到锁链的叮当声和呻吟声。 “我们在哪？”“你的测试区，我们将在这里找出你到底是由什么组成的。在隔壁房间，你会发现一些不死朋友。他们目前被锁起来，但不会持续太久。现在，你可以杀死他们，但作为死灵法师，你应该控制他们，所以显然你应该尝试这样做。”“但我不知道怎么做！你没有教我任何咒语！”“而且我不打算，你必须自己弄清楚这一点，但我要说的是，这完全取决于意志力的力量。挖掘你的魔力，如果你有它所需要的，你就会成功。我就交给你了。” Demar 消失在传送门中，留下你们一个人。你真的已经陷入其中，现在真的没有回头路了，你可以听到隔壁房间的铁链掉到地上，呻吟声越来越大。 地上有一大块骨头，房间里没有其他东西，你也不能跑，因为没有其他出口。
--------------------
动作：
做
你试着做好准备并集中注意力
结果：
你开始集中注意力，为即将到来的一切做好准备。几个僵尸进入，一旦他们看到你，他们就会蹒跚而行。 你保持冷静和专注，只是为了摆脱他们的影响而后退。虽然这行不通，但你拒绝放弃，所以你做了一些你从未想过会使用的事情。 你在地下，你所在的地方有很多根，你记得你在自然魔法课上学到的一个小法术。你施展了这个咒语，树根缠住了僵尸的脚，使它们无法再向你移动。然后，您可以专注于控制它们，现在这对您来说变得容易多了，因为您不再感到处于危险之中。 当你确定你抓住了它们时，你释放根，然后练习告诉僵尸该做什么。很有趣，就像养宠物一样。当 Demar 先生再次出现时，您的乐趣被打断了。 “嗯，这是一种有趣的处理方式。不要以为我以前见过将自然魔法与死灵法相结合。通常学生用火球或其他东西杀死其中一个僵尸，然后集中所有注意力控制另一个。不管怎样，不管怎样，你是少数几个完全没有接受过死灵法训练就能控制他们两个的人之一，所以你有天赋。天赋将在这条道路上更好地为你服务。”“我明白你为什么要给我测试，但我不得不说，我认为你会让我先让死者复活，而不是先控制他们。” 你说。“啊！是的，这是一个普遍的误解。你想知道为什么大多数死灵法师在开始之前就失败了吗？因为他们缺乏控制。复活死者相对容易；控制它们需要更多的注意力和力量。大多数情况下，新手死灵法师会去当地的墓地，最终被他举起的尸体杀死，因为他无法控制它们。好的，我想是时候向你介绍班级的其他人了。我们将有很多时间练习，而大多数所谓的普通学生都放假了。”“呃，我准备好了，但我应该去拜访我的家人度假......而且我认为我也没有通过所有课程，特别是自然魔法课程。”“什么？（叹气）很好，我会照顾你的成绩；我确切地知道如何处理这种情况，我在这所学校的吸引力比大多数人想象的要多得多。至于你的家人，我会留给你。你肯定能想出点什么来告诉他们，对吧？”“是的。”“那好吧，一切都解决了。是时候见见其他同类了。” Demar 先生再次带您进入另一个门户。你仍然在地下，但现在你处于一个更有条理的环境中，有实际的砖块，而不是像洞穴一样的环境。 您很快就会被介绍给 Demar 先生的其他“班级”，即黑暗教团。其中一些你实际上在校园里甚至在你的其他课堂上见过。认识你的人会感到惊讶。 “你让那个家伙进来？我记得他在幻术课上，结果他创造了克隆人，打了教授的脸！”“那没什么，回到召唤课；他最终放火烧了教授。” “好了，课够了！我可以向你保证，他有掌握黑魔法的潜力。如果我不这么认为，他就不会在这里。现在 Trelik 和 Felsa，你们跟我来，你们其他人练习你们的课程，你们中的一个人帮助我们这里的最新同事开始，我们都在一起，你知道。” 在 Demar 和他的两个学生离开后，你只能靠自己的手段，因为似乎没有人愿意帮助你。你环顾学习区，然后开始拿起散落在周围的众多书籍中的一本，直到有人走过来。 “喂，你知道这些是干什么用的吗？” 一个丑学生问你。“嗯，它们的正面有一个头骨，从它们的外观来看，它们包含了对各种不死生物的描述。我的猜测是通灵术。”“是啊，那你知道什么？”“不多，这就是我在这里的原因，这不就是其他人的原因吗？”“完全没有，事实上你只是这里的第五个死灵法师。大多数学生都在研究如何控制地狱位面。你知道恶魔学。甚至还有人在研究失传的时空魔法。”“颞？你是说时间？”“是的，很多人都忘记了那个也被取缔了。很好的理由，考虑到如果使用不当，它真的会搞砸。我实际上会远离奎尔，我认为所有按时学习都让他有点疯狂。他倾向于向后说话，并且经常侧身说话。即使是德马尔先生也很难让他走上正轨。”“有趣，你叫什么名字，你是恶魔学家吗？”“我，不，正如他们所说，我是死亡的门徒之一。我叫塞巴斯蒂安。你不妨去见见其他人，你会一起工作。” 塞巴斯蒂安带你去见另外两个学生。卡特琳娜和欧文。他们向你点点头，然后回到他们的训练中，其中包括创造某种骨魔像。卡特琳娜看起来有些眼熟。Sebastian 告诉你另一个是 Trelik，他刚和 Demar 先生一起离开。 “他是最熟练的，当然除了德马尔先生。” 塞巴斯蒂安说。“那么我首先要从什么开始呢？” 你问。“我会从饲养和控制一些骷髅开始。我知道你可能做了僵尸的事情，但你需要磨练你的技能，而且骷髅更容易练习。拿起这本书，开始吧。你会在走廊的右边找到骨坑，但不要向左走；恶魔学家把一些坏蛋恶魔锁在了一个圈子里。他也真的很生气。最好远离。” 你拿起一本死灵书，沿着走廊走下去。在去那里的路上，你开始听到一个声音穿透你的头。 “你过来。我认识你。你寻求权力。我可以给你那种力量；你不必再浪费时间学习了。我可以立即授予你。你想成为死亡大师吗？到我这里来…” 这声音非常诱人。
梗概：
你试着做好准备并集中注意力
--------------------
动作：
做
你从左边的走廊往下走
结果：
你不得不听从声音，然后沿着左边的走廊走下去，最终你遇到了一个坐在沙发上围成一圈的恶魔。在抬头看着你之前，他看着在他周围形成圆圈的粉笔。他甚至比你在校园里遇到的恶魔都要大得多，看起来也更卑鄙。他当然不高兴被困。你怀疑他们最近从地狱召唤了这个家伙。 “你是谁？”“我的真名不重要，但你可以叫我 Velzix……而且你是 Demar 妄想的一群狗屎首发的新弟子。他们相信，通过使用黑魔法，他们将能够接管这所学校。也许他们可能会，但这样的胜利将是转瞬即逝的。凡人缺乏长远眼光。当全世界联合起来反对他们时，他们不会做好准备……但你可以免于这种命运。释放我，我向你保证，你会在未来的斗争中成为一股强大的力量。”“虽然这听起来很诱人，但恶魔以其欺骗的本性而闻名，并因在所谓的交易中绕过漏洞而臭名昭著。” 你说。“是的，这是我们引以为豪的事情……很好，当你释放我时，我不会伤害你。这就是你担心的不是吗？”“我已经想到了。”“好了，现在你可以释放我了。”
梗概：
你从左边的走廊往下走
--------------------
动作：
做
你释放了 Velzix
结果：
您可以轻松去除 Velzix 周围的粉笔。 “啊对了，又是一个缺乏远见的人，如果他们再聪明点，他们还会在粉笔圈周围放一个魔法屏障，用魔法锁链把我绑起来。好吧，现在他们该为他们的傲慢付出代价了！” Velzix 现在从他的监狱中解脱出来，发出一种超凡脱俗的咆哮，把你推到一边去报复他的俘虏。当您恢复并离开地板时，您已经可以听到战斗的声音，然后是尖叫声。 你犹豫着走向研究区，在那里你看到了屠杀。恶魔轻易地杀死了所有人，现在正在施展某种咒语。 “现在我自由了，这个世界将遭受苦难。与我的想法相比，那个微不足道的凡人德马尔的小计划显得苍白无力。他将是我要追查的名单上的第一个。应该用不了多久。” 他说着打开了通往地狱领域的门户，在那里有几个较小的恶魔涌出，渴望鲜血。事实上，他们看到你并开始向你走来。 “呵……喂！你说过不会伤害我的！”“我说过我不会伤害你。我从来没有说过他们！哈哈！就像我说的，你们凡人没有这样的眼光！” 在恶魔跳到你身上并开始将你撕成碎片之前，你甚至没有时间奔跑，唯一的好处是你没有受到 Velzix 向世界释放的恐怖。
梗概：
你释放了 Velzix
故事名称：
64
故事背景：
你倒在地上，被打败了。你的身体已经耗尽了最后一盎司的能量，完全无法移动。你的骨头碎裂开裂。尽管你不顾一切地试图说服你的身体做出回应，但你的请求没有得到回应。你放弃所有希望，接受你的命运。 你抬头看到一个穿着全身铠甲的身影，在阳光下闪闪发光。又是那个该死的圣骑士…… 当他准备好他的剑来完成交易时，他散发着神圣的光环。你看到他的嘴唇在动，但你的感官早已停止为你服务，所以你只能猜测他在说什么。 他用剑猛击你的头骨，一切都变黑了......
--------------------
动作：
做
你醒了……
结果：
你醒来。 阳光明媚，耀眼的光芒照进房间。 鸟儿唱着一首歌，可以让最残暴的暴君进入平静状态。 一切都很平静。 你绝对讨厌它。 你是祖里古尔，拉格霍尔姆王国中最强大、最有才华、最英俊的巫妖。你是拉格霍尔姆历史上唯一有记载的巫妖，这对你很有帮助。 你在非生命中的目标是完全征服这片土地，把它变成一个黑暗和痛苦的深渊，这是你的巫师学校的恶霸完全应得的。 你看着你的护命匣，你用了 40 年的时间来完善这个美丽的水晶球。在死亡中，它是使你不朽的力量灯塔；除非它被摧毁，否则在你“死亡”几天后不断地恢复你的身体形态。
梗概：
你醒了……
--------------------
动作：
做
你是时候抱怨你的失败了
结果：
您最近的失败是由查德利·麦克布拉文哈特 (Chadley McBravenheart) 手中的，他是这片土地上最著名的圣骑士。作为一名神圣的战士，他宣誓的职责是保护人民对抗你这个王国中最有才华和最强大的巫妖。 不幸的是，在这一点上，他连续 94 次破坏了你的身体形态而没有划伤，因为他在作弊！他使用神圣的魔法来保护自己，并打击你，因为显然想要接管王国是“邪恶的”，这使得天空中的一些魔法实体不喜欢你。 你想出一个计划来报复查德利，他肯定会比你的下一个计划更胜一筹，甚至比上一个更精彩！ 你决定这次你会...... .
梗概：
你是时候抱怨你的失败了
--------------------
动作：
做
你.....创造一种炼金混合物来帮助你战斗
结果：
你走近你的炼金术台，因为几个月的疏忽，它布满了灰尘。最近，您用于出色研究的资金很少，而且炼金术可能很昂贵，所以您一直在寻找击败查德利的替代方法，但这次您知道这将是值得的。 您花了一些时间清理该区域，不知不觉中，您所有的烧杯和烧瓶看起来都像新的一样，有或有一些划痕、裂缝和孔洞。您还清理了在整个房子的各种锅、平底锅和架子上找到的一些基本材料。 你在意识到你完全没有毒药的炼金成分之前加热了各种烧杯，所以除非你再买一些，否则你将不得不坚持使用某种药水来增强你的力量。 你决定……
梗概：
你.....创造一种炼金混合物来帮助你战斗
--------------------
动作：
做
你……去购物！
结果：
你走出家门回头看。微小的，被侵蚀的石头结构看起来随时都会倒塌……甜蜜的家。 您步行到首都，经过漫长而艰巨的旅程，经过 30 秒的艰苦跋涉，您终于到达了城门。 查德利坐在他英勇的骏马上，打断了您愉快的散步。 “你觉得你在这里做什么？我上次告诉过你——” 你打断了傻瓜，说你来这里只是为了购买炼金成分来摧毁他。 “哦，那好吧。” 查德利回答说他允许你通过。显然，由于恐惧在他的全身蔓延，你计划的纯粹独创性让他沉默了。
梗概：
你……去购物！
--------------------
动作：
做
你走到炼金店。
结果：
你走进商店，柜台前的男人高兴地笑了。 “哦，Zurigul，很高兴再次见到你。你上次用魔药打败那个卑鄙的老查德利已经有几个月了。” 你向柜台的人解释你的财务状况不佳，导致你尝试其他形式的战斗。 “哦，你放心，我明白了，这次我还要给你打折，你平时的一包食材只要5金币。” 很高兴遇到如此惊人的储蓄，你兴高采烈地把你的五个金“块”摔在桌子上。小雕像在透过建筑物窗户的阳光下闪闪发光。通常他要十个。谁有足够的金像来买一些植物和真菌？ 回到家，店主给了你这么大的折扣，却出奇地高兴。然后，您将用剩下的一天时间调制您所见过的最令人愉快的致命药水。第二天来了，你去
梗概：
你走到炼金店。
--------------------
动作：
做
你用强大的毒药对抗查德利。
结果：
你又一次艰难地前往首都，查德利问你在做什么。 你告诉他，你来这里是为了用你昨晚花了这么多时间酿造的极其致命的毒药最终打败了他。 查德利下马准备战斗；他的剑在他的每一次打击之前都散发着令人作呕的黄白色光芒。 你决定迈出第一步
梗概：
你用强大的毒药对抗查德利。
--------------------
动作：
做
你向他投毒！
结果：
你用尽全力将那瓶毒药扔向查德利，然后猛烈地将它粉碎……完全错过了他，落在了你面前 3 英尺的地面上。 你忘了这一点，因为你是一个没有肌肉的骷髅巫妖，你不再擅长扔东西了，并不是说你在生活中真的是一个很好的运动员。 查德利立即向前冲锋，将你粉碎成碎片。
梗概：
你向他投毒！
--------------------
动作：
说
不定下次会得到他......
结果：
那么现在你完成了无能者祖里古尔，你如何评价它？请注意，作者不会看到您的个人评分；选择评级后，您将有机会发表个人评论。1 - 远离。很远很远2 - 好......让你大便3 - 选择你自己的无聊4 - 比家庭作业更有趣5 - 不是最好的，当然也不是最差的6 - 每一分钱都值得7 - 甚至比 Reeses' Cups® 更好8 - 它会给你带来启示如果您真的不想留下评分，您可以随时单击此处继续评论屏幕。不确定要评价什么？查看评分和发表评论的提示。
梗概：
你说不定下次会得到他......
故事名称：
65
故事背景：
我不知道我和僵尸游戏有什么关系，我就是喜欢它们。不久前，我写了一个故事，叫做无人区。它比我预期的要好得多，促使我再试一次。所以它是：Ultraoverlord Strikes Again，Zombie Style。 游戏注意事项： 这个故事的结构有点像我正在试验的新结构。只有一种“胜利”情景，其余都是死亡。现在，鉴于此，有几种不同的方法可以到达那里。不同的选择可以让您处于不同的健康状态、不同的用品和不同的人。我以尽可能高的可重玩性制作了这个，并且在每个故事情节中都有对角色的引用，这些人物散布在其他故事情节中。看看你能不能全部找到！
--------------------
动作：
做
你开始
结果：
你看着咖啡慢慢地从机器中过滤出来，进入你的杯子。完成后，你拿起它，喝了一口。完美的。 公寓里的生活并不容易。你和你的妻子还没有足够的钱买房子，而且你没有孩子，所以公寓似乎是最好的选择。你有一些商品；微波炉、电视和咖啡机。你自己洗碗，开车去洗衣店洗衣服，但你仍然满足。 你坐在你的沙发上，喝更多的咖啡。你老婆还没醒。你每天早上比她早起一个小时。安静是好的。它给了你思考的时间。你想想你今天必须做什么。今天是星期天，做家务。你很确定轮到你洗碗了。你放下杯子，起身。现在完成盘子总比拖延好。 就在您开始处理之前，一声尖锐的哔哔声吓了您一跳。它来自电视，但没有人打开它。你偷看客厅，看到电视屏幕。它上面有闪烁的图像和警告。存在国家安全威胁。担心，你把电视的音量调大听。你的妻子被哔哔声吵醒，慢慢地走进来。 “这是怎么回事？” 她睡眼惺忪地问道。在你回答之前，一个机器人的声音通过扬声器爆炸。 “请注意美国公民。恐怖主义行为在以下州造成了致命感染：德克萨斯州、田纳西州、俄克拉荷马州、堪萨斯州......”名单还在继续。你听不到加利福尼亚，你的州。你现在还是安全的。你和你的妻子看着电视切换到现场新闻镜头。一名妇女站在非军事区，栅栏重重，军事人员随处可见。新闻女士宣布，未受影响的州已设立边防部队，以防止疾病传播。来自世界其他地区的军队也在涌入，墨西哥和加拿大士兵正在努力将感染控制在美国境内 然后电视切换到病人的现场镜头。他们不会像感冒时那样缓慢或昏昏欲睡。不，它们恰恰相反。他们贪得无厌。他们以极速步道蹒跚而行，看起来已经死了。鲜血浸湿了他们的衣服。他们的眼睛都呆滞了。突然，一只鹿闯入了视野。你惊恐地看着受感染的人包围并杀死鹿。镜头立即切掉。 你老婆开始慌了。你试图安慰她，提醒她整个世界都在努力阻止怪物，你们两个会没事的。萨克拉门托离加州边境很远。万无一失。万无一失。你绝对安全。 突然，电视上播报：“所有年满 18 岁的男性都必须找到最近的哨站，并为守住这条线路做出贡献。”
梗概：
你开始
--------------------
动作：
做
你去边境
结果：
恐慌会降临到你身上。你应该留下来帮助你的妻子，为了安全。你不能接受这一点。但你必须这样做。 你收拾你能想到的一切：衣服、食物、水、毛巾、一本书。一切你有空间。你没有任何枪支，厨房里的刀似乎对这些掠食者毫无用处。即使装满后，你的背包仍然感觉很轻。 你和你的妻子说再见，一次，两次，三次，离开仍然很痛。感觉很不真实，就像做梦一样，乘坐电梯下来，叫了辆出租车，然后告诉司机带你去参加你自己的葬礼。 行程大约需要三个小时。加州有很多 18 岁以上的男性。当您走出驾驶室时，迎接您的是一个身穿全套防弹衣的钢铁侠。他命令你跟着他，带你去一个兵营。 “这暂时是你的新家，”他用粗鲁的声音说。房间里摆满了吊床。每个人都有一张桌子和旁边的存储单元。你选择看起来最干净的一个，然后放下你的东西。“时间是多久？” 你问。男人看着你。“直到这一切都过去了。” 接下来你知道，你跪在一个人形目标面前，手里拿着一把突击步枪。 “操作武器的第一条规则是永远不要将手指放在扳机上，除非枪管瞄准你的目标，”教官说。所有这些军人看起来都非常相似，你可以发誓这就是带你去看吊床的那个人。 在向您展示了发射手中怪物的正确方法后，您将被命令进行射击。你直接瞄准目标的胸部，然后开火。 只有将纸板手从目标上取下才能成功。你的肩膀因后坐力而疼痛。“不要气馁，你会掌握窍门的。” 这个男人似乎并不特别关心你。你停下来俯视一排学员。他们都是随机的人，脸上带着恐惧或痛苦的表情。当您查看他们的武器时，您会得出结论，一定是枪支短缺。三名学员正在使用BB枪。 经过三天严格的训练，你几乎死了。你的肩膀承受着巨大的痛苦。你拼命想打电话给你的妻子，但“出于安全原因”，所有电子设备都被没收了。你讨厌这个。你只想回家。你只是想让事情回到原来的样子…… 你和所有其他新兵突然被召集起来，拿起你的枪向战场进发。
梗概：
你去边境
--------------------
动作：
做
战场
结果：
你单膝跪地，在金属墙后面。有一个洞，刚好够你看到并从你面前射出。前方是一片草地。树木被清理干净以期待死者。在你的左右两侧，一排刚硬的士兵和害怕的平民混合在一起，一旦发出信号，就随时准备开火。你的心在胸膛里砰砰直跳。 突然，树线被一小群死者打破了。还没等你反应过来，一挺机枪就干掉了整个部落。在那之后，它是一个源源不断的流。被感染的人穿过树林，只在全视野中被击落一次。在你周围，枪声响起，命令声响起，死者的呻吟声越来越大。 你成功地消灭了一些僵尸。大多数时候，一旦你将目光投向一个，就会被其他人杀死。你试着让你的神经平静下来，假装这只是一场游戏什么的。瞄准射击。就那么简单。 大约一个小时后，亡灵康加舞线没有停止的迹象。当他的对讲机开始噼啪作响时，指挥官正准备下令暂停并重新加载。他回答，然后继续向他的同事们大喊，南部的大部分封锁刚刚被打破。数百人已经死亡，僵尸已经进入平民领土。瞬间，你的脑海里闪过你的妻子。你不能让她死。 你离开你的岗位，向指挥官讲话。“我得走了，”你说。 “绝对不会，”男人回答。 “请，”你乞求。“我的妻子，我必须——”男人打断了你的话。“已经派出小组围捕妇女和儿童。他们被带到华盛顿。那里更安全。别担心你的妻子，”他说。你从他的声音中听出一种不确定的语气。突然，另一个士兵喊道。你翻墙看，发现你不会成功。僵尸前进的速度比被击倒的速度还要快。你回头看看那个士兵。他也能看到。 “撤退！撤退！” 当你为你的生命奔跑时你会听到。在你身后，你可以听到尖叫声。你放下你的枪，它只会加重你的负担，而且你也不太擅长使用它。撤退，当你跑到大门时你会听到。撤退，当你匆忙上一辆废弃的汽车时，你会听到。撤退，你会听到金属墙倒塌的震耳欲聋的声音。
梗概：
战场
--------------------
动作：
做
你退...
结果：
你加速离开军事区。地形慢慢变回城市。但这一切都是空的。周围没人，电网好像没了。但这没关系。如果每个人都走了，那就意味着军队已经疏散了他们，这意味着你的妻子是安全的。现在你只需要担心你。继续行驶时，您会看到一个标志，上面写着距离萨克拉门托还有 80 英里。那没问题。你有一个死者的先机，还有一辆汽车。 就在您从桥下经过时，您会听到一声巨响。就像你担心的那样，你的轮胎被玻璃碎片爆裂了。你环顾四周。实际上，整条路都覆盖着一层均匀的玻璃碎片。谢天谢地，你穿着鞋子。 这辆车现在没用了，因为四个轮胎都爆了。你甚至不能把它向前推进，因为前方的交通是致命的。街道上满是废弃的汽车，有的开着灯，有的车门还开着。你不知道还有多久死者会追上你。你环顾四周，匆忙做出选择。
梗概：
你退...
--------------------
动作：
做
你得到食物
结果：
食物永远是重中之重。如果你想活下去，你就需要食物。时期。你环顾四周。附近没有任何东西，所以你必须做一些探索。你朝着前面的交通堵塞前进。 当你穿过汽车时，你注意到一些奇怪的东西。很多车窗被砸碎，大部分车的内外都有血迹。军方不会做这种事的。一个想法掠过你的脑海：你听到南墙倒塌后离开了一段时间。zed 可能已经到了，这意味着您已经太晚了。 你加快步伐，躲避汽车和碎片。当您经过一辆 SUV 时，一个僵尸会从其中一个窗户中弹出并向您滑动。他们肯定在这里。你闯过所有的汽车。 再次找到畅通的道路后，您会发现一个路标。它为酒店和商店做广告，并告诉他们在哪里。你看到一家非常近的超市。你开始寻找它，并希望它没有被清理干净。 穿过马路后，您会看到超市。不幸的是，相当多的僵尸围绕着它。你看看有没有什么办法可以溜过去，但僵尸们都漫无目的地游荡，堵住了入口。再说，就算进去了，也出不来。 就在你要放弃的时候，一个不满十六岁的男孩跑进了市场。他一定一直在看，就像你一样。但与您不同的是，他找到了进入的方法。他躲开了僵尸，并在没有碰到僵尸的情况下走到了门口。一分钟过去了，男孩拿着一袋食物来到屋顶上。 你和男孩同时意识到，出去不会那么容易。上面没有什么可以跳的，僵尸被噪音吸引了。他们已经包围了大楼，并开始将垃圾箱和汽车爬上屋顶。
梗概：
你得到食物
--------------------
动作：
做
你帮助男孩
结果：
你决定这个男孩值得拯救。如果他能那样跑，他作为搭档对你很有用。唯一的问题是，你怎么去救他？ 你知道他们会被噪音分心，所以你从那里开始。你看到马路对面有一辆卡车。它可以工作。你跑过去看看卡车是否可以运行。令您惊讶的是，它几乎处于完美状态。然而，当你尝试打开门时，你会发现它是锁着的。你回头看看这家店。这个男孩的时间不多了。一时惊慌失措，你用肘部砰地一声关上了车窗。这会打碎窗户并触发汽车警报。 这个男孩和你一样吃惊。至于丧尸，你耽误了他们。大约一半的小组从商店中分离出来并以您为目标。你意识到这是一个很大的错误。你没有阻止屋顶上的僵尸，现在你有一大群人跟着你。当您看到他成功时，您转身放弃了男孩。对僵尸的小小疏忽让男孩从屋顶上跳了下来。他着地，冲进树林。你本能地跟着他。
梗概：
你帮助男孩
--------------------
动作：
做
你跟着他
结果：
你几乎不能在部落周围移动。你设法逃脱并跟随男孩。他绝对是一个比你更好的跑步者，但你可以把他放在你的视线中。没走多远，他就从后门进入了一个大仓库。你知道那个男孩看到了你，所以你继续前进。 当您到达仓库时，您可以透过其中一个工业窗户窥视。出乎意料的是，仓库里竟然有三个人。男孩，一个和他年龄相仿的女孩，还有另一个女人，大概是他们的母亲。你看着男孩打开食品袋。他能够将数量惊人的食物装入袋子。女孩捡起来，加到角落里一大堆罐头里。三人开始说话，然后男孩转身直视你。 你惊慌失措。他们现在可能认为你是个怪人，或者更糟。最好的办法是离开仓库。你四肢着地，在你听到身后男孩的声音之前走了几英尺。 “你在做什么？” 他要求。你转身。男孩的格洛克手枪正对着你的额头。你一转身，男孩的表情就变了。“哦。你就是那个打破卡车车窗的人，是不是？你想要奖励什么的？”
梗概：
你跟着他
故事名称：
66
故事背景：
您的名字是 Leo Trapa，您作为您的冒险家舰船中将的成就使您在麦哲伦世界中成为传奇。冒险者号是一艘坚韧的小船，是多次战斗的老兵，大到足以容纳 30 名水手的船员和 30 天的补给品。你虽然只有二十一岁，但已经获得了中将的军衔，以表彰你高超的能力和航海技术。 您居住的麦哲伦世界大部分是被分散的岛屿占据的海洋：其中许多岛屿无人居住，但有人居住的岛屿被三种文化之一占据：本土拉保和技术更先进的 Elixi 和 Navar，后者是您的自己的人。尽管在死亡之痛下被流放于您的祖国三岛王国，但您继承了您人民丰富的海军传统：冒险家携带四门大炮，您的所有船员都配备了火枪，尽管这些火枪在制造剑时加载缓慢你的次要武器。最近，麦哲伦的战争爆发了，一边是 Elixi 和 Navar，另一边是你和你的盟友 Lapau。 您在长途航行中最大的问题是您的船只能携带足够 30 天的补给品，这将使寻找和获得新的补给品来源成为您的首要任务。如果您的补给品用完（您的补给品数量降至 0），那么您和您的船员将饿死并死亡。你几乎肯定会在航行中失去船员，你必须小心你的船员人数也不会降到 0：这意味着你和你所有的船员都死了。 在您的航程中经常会涉及到时间因素，您会希望在尽可能短的天数内完成它们。在您的竞争对手之前成功完成航程将获得一个分数，如果您在比赛结束后发表评论，该分数将表明您以非常高的技能完成了这个故事。和以前的冒险一样，您会发现制作一张航行水域地图很有用，因为您可能会再次以这种方式返回，而这些信息可能会使您受益... 所以祝读者好运，做出明智和战略上合理的选择，如果有一点运气，一条好的路线和顺风，你可能会再次看到你的祖国！
--------------------
动作：
做
你点击这里开始你的冒险（首先选择著名的水手名单！）
结果：
安妮-玛丽和她的盟友烧毁了马坦的拉波首都并屠杀了大部分捍卫者之后的第二天黎明前。躺在床上，你看着娜奥米睡觉，她裸露的胸膛随着每一次呼吸慢慢地起伏。她的头巾从她的脸上滑落，露出曾经她眼睛的破碎废墟。当有人轻轻敲击你的舱门时，你轻轻地重新调整它并抚摸她的头发。尽可能安静地起身，以免吵醒娜奥米，你穿上长袍，打开门。 Saphia Dancer，Anne-Marie 的首席仆人和忠实的追随者站在你面前，洁白的牙齿闪烁着顽皮的微笑，她对你的表情大笑。 “闭上你的嘴，海军上将，你看起来像条鱼。”她飞快地说，令你惊讶的是，你意识到这是你第一次听到她说话，她说话的速度如此之快以至于很难听懂她的意思，这是一个快速的明显迹象。头脑。在她身后的阴暗处，你会惊讶地看到安·洛维特和阿尔巴鲁王子站在一旁，看起来既紧张又紧张。想知道到底发生了什么，你走出你的小屋，悄悄地关上门。 “我们需要快点，因为我没有太多时间，”萨菲亚迅速说道。“你还是让我来说话比较好，你插话会浪费太多时间在无意义的问题上。现在的情况是：安妮-玛丽认为这场战争已经赢了。她打算立即返回金沙与信玄王结婚并巩固她与 Elixi 的联盟。Kenshin 王子将回到 Revel 以确保一切顺利并在 Shingen 不在的情况下统治 Elixi。Ieyasu 王子和我被赋予了屠杀你和你所有遗体的任务可悲和士气低落，更不用说身体上没有吸引力的追随者。” 萨菲亚叹了口气。“现在我可以想出很多我宁愿做的事情来杀死你和你的朋友，但家康是一个乏味和缺乏想象力的人，他会比他应该更认真地对待狩猎。幸运的是我们在法庭上在一起的那段时间安我有一个小项目。现在已经准备好了，或者应该差不多了。如果你能航行到隐藏它的岛屿，我会尽量把单据交给家康，然后在那里与你会合。我建议你快点离开，因为来了早上家康会像猎犬追狐狸一样追你。我会尽量误导他，但他不像他的兄弟那样头脑简单，可能更难骗。哦，利奥，恭喜你提高了工作水平与海军上将斯威夫特的关系。希望她不要向我们其他人展示她的专业知识。” 萨菲亚眨了眨眼，发出一声嘲讽的短促笑声，转身匆匆跑向冒险者号的甲板。你震惊瞪着安等待解释，看到阿尔巴鲁王子同样困惑的样子，你稍稍松了口气。 “萨菲亚和我有一个……理解，”安尴尬地说。“她站在我们这边：她知道安妮-玛丽是一个暴君，她再次向拉保宣战只是时间问题。几个月来，我们一直在秘密地向附近的一个岛屿运送人员、材料和金钱。萨菲亚在绕麦哲伦航行时发现的大礁石。我们正在那里建造一种特殊的船……它们 是标准的木船，但周围有铁板固定，测试表明它们实际上是坚不可摧的炮火。” 安的话过了好一会儿才听进去。 “你是说这些船是防弹的？” 你最后问。 “基本上，”安回答。“这是萨菲亚的主意，她以自己的方式非常聪明。她知道如果不阻止安妮-玛丽，她将使我们的世界陷入战争，而这些船只可能是唯一可以阻止她的东西。如果她是对的，这些船只可以击败安妮玛丽的海洋君主或她可能要派来对付我们的任何其他船只。有了这些船只，我们可以占领金沙或狂欢或我们的敌人可能避难的任何其他港口。 “这太棒了，”Al-Balu 用他的口音版本说。“等我爸知道了再说。” 终于感觉到你们在与安妮玛丽的战斗中再次有机会，你回到你的床上，依偎在娜奥米温暖的身体附近。她激动、微笑并亲吻你。想办法度过最后几个小时直到黎明，你会兴高采烈地与新盟友一起参加第一次战争会议。
梗概：
你点击这里开始你的冒险（首先选择著名的水手名单！）
--------------------
动作：
做
你卡普很担心
结果：
在早上的会议上，需要花一点时间向 Al-Capu 国王解释情况，他对您的语言的了解不如他儿子的好。他没有为安的启示而欢呼，而是相当沮丧和谨慎。 “我们可以相信萨菲亚这个女人吗？” 他问。“她是安妮-玛丽最亲密的追随者之一。这听起来像是我们被邀请进入陷阱。” “我可以为她担保，”安坚定地说。“我们几个月来一直在准备这个秘密项目。该岛位于这里的东南部，不会超过几周的航程。” “嗯，这些船可能是件好事，但我们现在还有其他问题，”Al-Capu 说，并指着你周围的一百多艘独木舟，他的人的残余蜷缩在那里，大多数人在艰难的夜晚之后仍在睡觉。“我的人必须被带到安全的地方，我其他岛屿上的人也必须在安妮-玛丽找到他们之前被带到安全的地方。我们将如何喂养他们我不知道......为我们的人民寻找食物已经够难了最近六个月。” “爸爸，我们为什么不分手？” Al-Balu 建议。“我们可以和我们的人一起向北航行，安可以向东南航行找到这个岛。如果这些船准备好了，她可以继续向东，攻击雷维尔，然后航行通过西部航道夺取金沙。如果我们继续向北走，我们就能遇到“她在那里重聚。在摧毁马坦之后，我预计安妮-玛丽和信根最不希望发生的事情是对他们自己的首都发动攻击。” “这听起来是一个困难而冒险的计划，”Al-Capu 犹豫地说，但随后叹了口气。“不过这是我们唯一的一个。如果我们要从南边攻击金沙，而安·洛维特和这个舞者人从西边攻击，我们需要一个会合点。” “我知道一个方便的地方，”你告诉国王。“金沙以西，紫晶以南，是三岛王国唯一一个与南航道、西航道、金沙等距离的地方，需要三十到四十天的航程。 ” “这是一个非常冒险的计划，”Al-Capu 沉重地说。“如果我们的两支部队中的一支出现问题，那么另一支将在完全无人支持的情况下抵达纳瓦尔水域，并将被安妮-玛丽更大的部队摧毁……我们都必须确保我们在四十之内到达这个会合点最迟几天，然后我们可以一起攻击金沙。很好，洛维特准将去东南寻找这个岛，而你和我将向北营救我们的人。特拉帕海军上将，你和斯威夫特海军上将和谁一起航行？哪个我们当中你会加入吗？” 你瞥了一眼感觉到你目光的娜奥米，耸了耸肩。你仔细考虑你的回答。
梗概：
你卡普很担心
--------------------
动作：
做
你和安·洛维特一起向东南航行，寻找这座铁甲船的岛屿
结果：
那天早上，两支敌方舰队向不同的方向分散：你和洛维特将冒险家和大联盟带到东南部，而 Al-Capu 和 Al-Balu 则乘坐 Matan Afloat 和国王独木舟向北。你的敌人也分散了：海王、海鬼和埃斯梅的喜悦号转向西南，但家康在探险者号上追击拉波北部，你很高兴看到舞者在暗影中追击你东南方。 当各种船只从视线中消失时，你和内奥米谈论你和洛维特停下来让舞者赶上你的想法。在你做出决定之前，大自然介入了：厚厚的云层开始从北方吹来，很明显，途中有风暴。您可以坚持自己的路线并继续向东南方向穿过风暴进入未知领域，或者您可以抛锚与洛维特一起尝试等待风暴结束。
梗概：
你和安·洛维特一起向东南航行，寻找这座铁甲船的岛屿
--------------------
动作：
做
你在风暴中继续向东南航行
结果：
狂风像飓风一样降临在你和你的同伴的船上，很快冒险家和大联盟就在真正强大的海浪低谷中上下起伏。风的突然变化将你的船转向了舷侧，在你能纠正她之前，一股巨浪席卷了冒险者号的甲板，把三个人卷入了船外。风暴肆虐自由天，到最后你已经看不到大联盟和暗影，你的船发现自己独自在海洋中，受过重创，但适航。您别无选择，只能继续向东南前往洛维特所描述的岛屿。
梗概：
你在风暴中继续向东南航行
--------------------
动作：
做
你一个人
结果：
你在向东南方向前进的方向很模糊，当你看到一个低矮的热带岛屿时，你知道这个岛屿一定在那里。最初您希望这是您的目标，但您很失望：岛上没有人类居住的迹象，但可以看到许多猴子在岛上的棕榈树上摆动。 “如果你想停下来，猴子会吃得很好，”当你向她描述这座岛时，内奥米告诉你。“不过，如果我们继续努力寻找我们的同伴，可能会更好。”
梗概：
你一个人
--------------------
动作：
做
你停泊在岛上猎杀猴子
结果：
你在海上抛锚，带领两船水手上海滩。当您走上声音时，猴子安静的喋喋不休变得沉默，然后当猴子开始用野蛮的力量向您的水手投掷椰子和岩石时，数十声可怕的尖叫声在空中响起。您的水手向树上开火，击中了许多猴子，但现在有数百只猴子在树上荡来荡去，您的手下被迫逃回他们的船上。你的一些人被野蛮的灵长类动物击倒，这些灵长类动物跳下来咬住他们的喉咙，杀死他们。失去了你的三个人，你仓促地把岛放到你的船尾并继续向东南行驶。
梗概：
你停泊在岛上猎杀猴子
--------------------
动作：
做
海龟船
结果：
您继续向东南行驶，幸运的是，您正在寻找的岛屿出现了。你知道它一定是正确的岛屿，因为大联盟和影子都停泊在海上。其他四艘船在眼前，一艘被部分拆除的 Elixi 船名为“财富”号，以及三艘您一生中见过的最奇怪的船。最初你认为它们是漂浮在水中的大圆形银色气球。 这三艘船确实看起来像在水中漂浮的大圆罐，虽然它们是用木头建造的，但它们完全被铁板包裹着，铁板被绑在船舷和甲板上。一个小帆从金属板上方伸出。近距离可以看到两侧伸出的大炮。抛锚你和娜奥米上岸会见安·洛维特、萨菲亚舞者和第三个完全出乎意料的人物。 安兴奋地在沙滩上大步走来走去，而萨菲亚坐在木头上，一副被生活逗乐的样子。站在一旁的Elixi男子，开朗，中年，衣着华美，留着长长的棕色胡须。当你带领娜奥米走向他们时，安大步向你走来。 “他们不是很厉害吗？” 她说，指着三艘船。“Lapau 工人、Elixi 木材和 Navar 金属：Anne-Marie 永远无法完成的团队合作建造了这些船。每艘船都配备有来自各个王国的忠诚水手，他们冒着生命危险建造这些船并带领他们对抗安妮——玛丽的暴政。有这三艘船，复仇号、报应号和正义号，没有什么能阻止我们。” 萨菲亚只是微笑着什么也没说，但你的注意力被那个微笑着上前的奇怪的Elixi男人所吸引。 “我叫德雷克船长，”他宣布。“我在 Haven Island Admiral 见过你，但我怀疑你会记得我，我是 Sogo 船长的船，Fortune 的二把手。好吧，在我们前往 Matan 的途中，我们被那条狗 Kenshin 袭击了，我的船长是三周前我们向南逃到了这个岛上。了解这里发生的事情后，我和我的手下立即开始拆解我们严重受损的船，以装帆并完成这些船的建造。我和我的船员并且完全在您的指挥下，先生。” 你很惊讶但很高兴见到这个新盟友，但萨菲亚突然站了起来。 “嗯，这都是很棒的谈话，但速度至关重要，”她宣称。“Revel 是我们最近的目标，而我、Drake 和 Naomi 都已经航行过这些水域，可以引导你到达 Revel。如果我们能够攻击并捕获它，我们就可以通过西部航道与 Al-Capu 和 Al-Balu 会合离开金沙，占领那个岛，打败安妮-玛丽女王，把我们所有的努力带到一个幸福的结局，这个结局将充满欢乐和幸福。” “为什么不先向西航行，先攻击金沙？” 娜奥米问道。 “因为我们需要给 Lapau 时间到达那里，”萨菲亚宣称，因为一个人可能会向一个孩子解释 2 和 2 等于 4。“不，我和安一起制定了所有计划，他们非常好。你和所有的人你的船员需要做的就是跟随他们。” 对这样的称呼感到有点吃惊，并感觉 Saphia 与安妮-玛丽有很多共同点，但您仍然准备好您的船，您不寻常的舰队第二天起航，每艘船都拖着一艘新战舰，萨菲亚称之为“龟船”。大部分被拆除的财富被留下，你的冒险家拖着复仇号（德雷克船长在船上），影子拖着报应，大联盟拖着正义。
梗概：
海龟船
--------------------
动作：
做
你要去战争
结果：
最初，舰队的航行进行得很顺利：萨菲亚已经两次航行在这些水域，内奥米一次，他们一起带领舰队向东南航行。不幸的是，大海是不可预测的，有一天早上你醒来发现浓浓的海雾已经完全笼罩了你的舰队，当你向你的同伴喊叫时，只有你自己的声音回响。事实上，唯一仍在视线中的船是冒险家仍在拖曳的铁甲复仇号。她的德雷克船长上船与你和娜奥米交谈 “好吧，我们失去了他们，”他带着不雅的快乐说道。“别担心：我们距离 Elixi 水域不超过一周，距离 Revel 不到两周，只要我们继续朝着大致向东的方向前进，我们迟早会遇到它们。一旦我们开始看到一些岛屿我会知道我们在哪里。” 你询问 Naomi 的意见，但她只是耸了耸肩，除了一般的东方，你没有明确的方向去 Revel。
梗概：
你要去战争
--------------------
动作：
做
你航行东北
结果：
将 Adventurer 转向东北，因此 Vengeance 必须跟随您，在几天内保持高速行驶，您会看到令人担忧的景象：数十块岩石像牙齿一样直接从海洋中伸出。用望远镜仔细扫描它们，您可以看到一个间隙，该间隙可能足够让冒险者航行通过：另一种选择是向南绕道很长的路来绕过它们。当您询问 Naomi 的意见时，她坚决支持您绕道向南，而不是冒险冒险。
梗概：
你航行东北
--------------------
动作：
做
无论如何你都试着在岩石间航行
结果：
您决定在岩石中航行，当冒险者撞到一块您没有看到的水下岩石时，您最初表现得很好。您的船名列前茅，在您的船能够从岩石上滑下并继续前进之前，您的三个人已经落入海中。你赶紧下甲板，但幸运的是你的船没有受到严重损坏：你的三名船员不能游泳并很快淹死。你很幸运地顺利通过了岩石堤岸，没有发生进一步的事故，并引导复仇号安全通过。
梗概：
无论如何你都试着在岩石间航行
--------------------
动作：
做
你向东航行
结果：
你现在已经进入 Elixi 水域，一天早上一个小岛/港口出现在视线中。德雷克看到它时松了一口气，因为他知道你现在在哪里。 “那是 Ferryland，”他告诉你和 Naomi。“这是一个小的 Elixi 港口或岛屿。Sogo 船长在我们前往马坦岛的航行中将它洗劫一空，如果我们想突袭它，我怀疑自从我们上次来到这里以来，他们是否有时间加强它的防御。” “如果你愿意，你可以突袭它，”内奥米告诉你。“但别忘了我们的主要任务是到达 Revel。”
梗概：
你向东航行
--------------------
动作：
做
你突袭渡口
结果：
您将 Adventurer 和 Vengeance 直接驶入 Ferrytown 的港口，殖民者对看到奇怪的铁甲感到非常惊讶，以至于当您要求食物时，他们毫不抵抗，并迅速将三桶苹果送到您手中。德雷克被逗乐了。 “上次也这么简单，”当你命令你的船继续他们的旅程时，他告诉你。
梗概：
你突袭渡口
--------------------
动作：
做
你向东航行
结果：
当一位目光敏锐的船员在海洋中发现了一些有趣的东西时，冒险家和复仇号正在平静地航行。把你的两个水手捞出来给你看他们发现的一个瓶子，防水瓶子里面是一条信息。把它拿出来读给你的船员的潦草的信息。 “今天胖子睡着了没醒。浅井队长告诉我，我现在要做Elixi的女王。我希望我能成为一个好女王，但我担心我可能不会。我会听从老师的建议，把这些烦恼装进瓶子里，扔进海里，永远放逐。重生。” 一开始你就会意识到这些话一定是许多年前由信玄、剑心和家康的母亲 Renewel 女王写的。德雷克得知消息后非常感动，并问他是否可以保留它，你允许他这样做。德雷克也确信你正在接近 Revel，并且在补给不足的情况下，你改变了向东南的路线，这样你就可以接近 Elixi Capital。
梗概：
你向东航行
--------------------
动作：
做
你陶醉
结果：
最后，经过艰难的航行和食物匮乏，Revel 的港口/岛屿在远处出现在您面前。锚定在不远处并且显然在等着您的是带有 Retribution 的 Shadow，您可以驾驶 Adventurer 和 Vengeance 与他们并肩作战。德雷克和萨菲亚上船，后者自言自语地哼着曲子，好像她不在乎这个世界。然而，你和娜奥米更担心，因为没有洛维特和她的两艘船的迹象。 “你有没有看到洛维特准将的踪迹？” 你问萨菲亚，她甚至连曲调都没有摇摇头。你和德雷克交换了眼神，你们谁都不相信你的新盟友，但萨菲亚终于停止吹口哨了。 “我已经找到了 Revel Port，”她告诉你。“剑心亲王带着艾斯梅的欢乐号在那里。你不用消耗你显然相当可观的精神力，因为我已经有了计划。剑心仍然相信我是他的盟友。如果我同时拖着两艘铁甲船航行，他会感到困惑，但应该” 在我们近距离向他开火之前不要怀疑任何事情。我宁愿拖着铁甲战士而不是冒险者，因为他们全副武装。 “你想趁我们在远处等待的时候，带着我们的两个铁甲进入敌港的中心？” 娜奥米怀疑地问道。 “为什么有人会认为你不信任我，海军上将，”萨菲亚笑着对娜奥米说。“无论如何，如果你愿意的话，我可以把冒险者号拖到港口，用我们的两艘船攻击剑心。这只是意味着铁甲部队可能无法战斗，胜利将更难赢得，尽管可能还是有可能。虽然我只是一个卑微的指挥官，所以在两个海军上将这样的高贵连队中，我必须让我的上级制定作战计划。” 你对 Saphia 皱眉，她说把两艘铁甲船拖进港口是有道理的，但你想知道你有多少信任她不会把那些船直接交给 Elixi。还有失踪的洛维特和她的两艘船的谜团……
梗概：
你陶醉
--------------------
动作：
做
你允许萨菲亚的舞者将铁甲舰报应和复仇拖入狂欢港
结果：
您做出决定并决定信任您的新盟友。那天下午，当影子直接驶向 Revel，拖着她身后的两块铁甲时，您将冒险家保持在很长的距离之外。当 Esme's Joy 驶出迎接 Shadow 时，你和 Naomi 屏住呼吸等待。当两艘船靠在一起时，一连串大炮闪光从影子的侧面响起，同时她向埃斯梅的欢乐号的侧面发射了一记近距离齐射。片刻之后，Retribution 和 Vengeance 都能够转身并发射近距离齐射。 随着 Esme's Joy 迅速下沉，人们欢呼起来。在一次危险的伏击中失明的暴徒自己成为伏击的受害者，这似乎很奇怪，但当你看着船沉没时，你会感到内心一阵剧痛。在她的前任船长埃斯梅·黑尔和伊娃·弗里尔的带领下，你们都是好朋友，她取得了许多伟大的成就，你看到她沉没也不是没有遗憾。即使在死亡中，她仍然是挑衅的，向影子发射了一个回击凌空，它击倒了它的主桅杆。 当 Esme's Joy 最终消失在海浪之下时，她的船员们为了生命而游回 Revel 岛。大多数人做到了，但剑心不在其中。后来你知道他被影子号上的一名水手认出并击中头部。当娜奥米得知让她失明的男人死了的消息时，她沉默了，你可以告诉她，她自己也不确定自己对此有何感受。不管战斗赢了，狂欢镇都是你的！
梗概：
你允许萨菲亚的舞者将铁甲舰报应和复仇拖入狂欢港
--------------------
动作：
做
你制定计划
结果：
那天晚上，你的小舰队的四艘船聚集在狂欢港，他们的船员下船，而一群 Elixi 默默地注视着。你清楚地记得上次你站在这个码头上时你和内奥米为你的生命而战，现在经过长途跋涉，你作为胜利者回来了。人群似乎不确定如何反应，但当你、娜奥米、萨菲亚和德雷克带领你的团队前往皇宫时，没有任何抵抗。 “现在我们赢得了这场胜利，我们的首要任务应该是航行通过西部航道，与拉波河会合并攻击金沙，”萨菲亚宣称。“暗影号失去了主桅，无法航行，所以我会留在这里，修理我的船，并建立某种临时政府。一旦你在金沙取得胜利，请给我发消息，我会尽力加入你。”加上两个铁甲和拉波，冒险者应该足够强大，足以击败安妮玛丽、信玄和家康的联合力量。” “如果我们不在了，我们的任何敌人驶入 Revel，你会怎么做？” 你问萨菲亚。 “我得逃走是不是？” 萨菲亚带着耐人寻味的笑容说道。 “也许德雷克和其中一艘龟船待在这里会更好……？” 内奥米建议，但德雷克摇摇头。 他说：“我认为我们将需要我们拥有的所有力量来击败我们的敌人。” “我还发誓，崇光船长死后，我不会休息，直到那个篡位者信玄死了。我是一个信守诺言的人。” “那就这么定了，”萨菲亚爽快地说。“我会安排你的战舰从镇上丰富的商店补给，祝你在接下来的战斗中好运。” 奇怪的是，你好像被愚弄了一样，你花了一夜时间监督你的船只的补给，然后冒险家拖着报应和复仇号向东出发。影子留在后面，轻轻地停在狂欢港的锚地。
梗概：
你制定计划
--------------------
动作：
做
你回家了
结果：
您向东驶向西航道，您的两艘铁甲船在冒险者身后欢快地摆动。当瞭望员在前方的水中发现其他东西时，大礁刚刚进入视线：这是一艘小船，里面有几个人一动不动地躺着。你会猜他们是一艘漂流船，虽然他们有点偏僻，需要一整天才能营救德雷克，但他坚持让你接他们，因为他相信他们是 Elixi 水手。
梗概：
你回家了
--------------------
动作：
做
你花了一天时间营救漂流者
结果：
您转向船，将冒险者滑到旁边。船上的七个人中只有三人还活着，他们处于可怕的状态，每个人都必须用网把他们拖上你的船。在水和食物恢复到足以告诉你他的故事之后：这三个人是 Azai 船长的“哀悼荣耀号”上的水手，该号船在两个月前的一场风暴中沉没在大礁石上：这七名水手设法登上了一艘船但在过去的五十天左右，他们一直在漂流，没有任何食物，除了雨水之外没有其他东西可以喝。这些人能活下来简直是一个奇迹，你命令他们在继续你的东方之旅之前加入你的船员。
梗概：
你花了一天时间营救漂流者
--------------------
动作：
做
西部通道
结果：
终于，熟悉的景象映入眼帘：大礁石横跨地平线，西部航道的狭窄缺口穿过它。三个月前，你、Naomi 和你的朋友 Eva Freer 就是通过那条航道航行的，从那时起，你的整个世界都发生了变化，但现在你回来了，复仇之后。航道两侧都有瞭望塔，水手可以在那里停下来获取更多食物，但内奥米对此持谨慎态度。 “我们不需要食物就可以到达金沙，而且我们不确定卫兵收到了安妮-玛丽女王的什么命令，”她警告你。
梗概：
西部通道
--------------------
动作：
做
无论如何你都抛锚寻找食物
结果：
您抛锚并带领一群水手前往了望塔。当你靠近时，突然响起几声步枪射击，杀死了你的三个人。你们其他人迅速返回并撤回到船上，而复仇号上的德雷克指示他的船员用他们的大炮向瞭望塔开火。你命令他停下来，他不情愿地这样做了，当你在冒险者号上见到他时，他看起来很困惑。 “他们不知道自己在做什么，”你告诉他，指着瞭望塔里的士兵。“安妮-玛丽现在可能已经宣布我和娜奥米为非法。我想尽可能避免流无辜人的血。” 被自己的同胞开枪打死，你重新开始向东旅行。
梗概：
无论如何你都抛锚寻找食物
--------------------
动作：
做
你回家了
结果：
当您驶过您国家的水域前往金沙的家时，您不知道接下来会发生什么。几天后，您命令 Adventurer、Retribution 和 Vengeance 抛锚等待。您现在位于 Golden Sands 以西和 Amethyst 以南，这是与您的 Lapau 盟友约定的会合点。现在您所能做的就是等待并希望您能够足够快地赶到，然后安妮-玛丽才知道他们的存在并已经击败了他们。
梗概：
你回家了
--------------------
动作：
做
关键时刻
结果：
你已经足够快了，你的两半盟友舰队能够在金沙以西重新汇合，而安妮-玛丽在另一半准备就绪之前没有发现并摧毁其中的一半。冒险者号、国王的独木舟、许多拉波独木舟和两艘奇怪的圆形金属包船，它们似乎在海中摆动而不是航行，被称为“报应”和“复仇”，尽管没有 Al-Balu 的 Matan 的迹象漂浮，洛维特的大联盟或舞者的影子。你、娜奥米和其他船长都聚集在冒险者号的客舱里。
梗概：
关键时刻
--------------------
动作：
做
你的战斗计划
结果：
在冒险者号上，你们四个人聚集在一起。你坐在桌子的最前面，而 Naomi 坐在你旁边，在她听着程序时目不转睛地盯着前方。Al-Capu 国王穿着部落的衣服，一动不动地坐着，看上去老了很多。第四个人，一个名叫 Drake 的年轻开朗 Elixi 船长意外加入了你的事业，是最后一个坐在桌子上的人。没有 Al-Balu，没有 Ann Lovett 和 Saphia Dancer。 大家很快就会分享你的航程的细节。德雷克在崇光船长的船员中，直到后者在与信玄的战斗中丧生。与幸存的船员德雷克登上崇光的船逃到了安和萨菲亚秘密建造他们的铁甲海龟船的岛上。拆除自己的船以帮助完成船只的建造 德雷克在安和萨菲亚到达时与他们联手，指挥正义、报应和复仇三艘铁甲舰。 铁甲船驶向狂欢，但在航程中洛维特的大联盟和正义联盟都神秘地消失了。其余的船只攻击 Revel，杀死 Kenshin 并沉没了 Esme's Joy。Saphia's Shadow 在战斗中严重受损，她被迫留在 Revel 修复它并重新建立权威。德雷克率领着剩下的两个铁甲师报应和复仇来到了这次会合。 Al-Capu 国王和 Lapau 人也经历了艰难的航行，他们对 Lapau 的所有岛屿进行了系统的疏散，因此每一个 Lapau 都是伴随 Al-Capu 伟大的皇家独木舟的数百艘战争独木舟之一。这次航行也遇到了不幸：Al-Balu 王子和 Matan Afloat 在暴风雨中消失了，担心迷路了。失去儿子和他的人民的苦难对老国王造成了沉重打击，他在整个议会中都感到沮丧和沮丧。 尽管您遭受了各种损失，但包括两艘铁甲舰在内的四艘大船和数百艘拉波战时独木舟成功抵达会合点，并且 Revel 已被俘虏并得到保护。现在希望金沙会跟进，尽管阿尔卡普很沮丧，但你们其余的人都充满信心，因为你下令向东航行，你的小舰队向金沙和这场战争的决定性战役推进......
梗概：
你的战斗计划
--------------------
动作：
做
你金沙
结果：
一天早上，美丽的金沙港口映入眼帘：您已经数月未见的家。海洋君主号和海鬼号在锚地轻轻骑行，这是您的人民建造过的两艘最强大的船，除非您的铁甲证明更坚固。你带领你奇怪的舰队直奔他们，冒险家带路拖着报应和复仇号，而国王独木舟在你身后航行，带领它的舰队或战争独木舟充满了复仇的拉波战士，他们决心报仇。 当您接近该岛时，您会看到敌舰上一片喧嚣，他们都出海并直接驶向您的舰队，舰船的栏杆上排满了人和大炮。随着你和你的敌人之间的距离缩小，双方的弓炮开始发射，因为金沙之战开始时，你的人民将永远知道这一事件。几分钟之内，所有船只都向船只和士兵发射炮弹、火枪弹或长矛，决心摧毁对方。 King's Canoe 是一个早期的受害者：在战斗几分钟内，最大的 Lapau 独木舟开始突然下沉。你站在冒险家的后甲板上，娜奥米在你身边，用望远镜扫描正在下沉的船只，你会发现一些奇怪的东西：许多小木管从水里伸出来。惊魂未定，您意识到安妮-玛丽巧妙地雇用了手持剑和空心呼吸管的潜水员在您的船只上刺破洞，这是对拉保人在与他们的战争中使用的战术的模仿。拉波人也意识到了威胁，并试图在海平面以下的任何地方切断呼吸管。 如果安妮-玛丽有她的水下潜水员，你就有了你的龟船，报应和复仇都表现出色，当它们将自己的炮火倾泻到你的敌人身上时，炮弹和火枪弹会随着金属的叮当声从它们身上弹开。的确，信玄的海鬼正遭受如此严重的痛苦，他故意将其船头引导到报应的一侧，不顾一切地试图将其击沉。剑士们从海鬼号的甲板上涌入惩戒号，开始屠杀人数众多的铁甲船员。 你的舰队正在遭受重创：国王的独木舟已经沉没了，许多拉波在海中和他们的独木舟中被步枪射击杀死。海鬼和报应纠缠在一起，似乎都在下沉。远离一般混战安妮-玛丽的海洋君主正在与复仇者进行大量的大炮射击，但至少在这里你的同伴德雷克是最好的交换。在冒险者号上，你已经因为炮火而失去了三个人，现在必须选择帮助谁。
梗概：
你金沙
--------------------
动作：
做
你去帮助受灾的报应
结果：
你驾驶冒险者与海鬼并驾齐驱，并在近距离向她的身边发射一连串大炮。敌船令人震惊地列出并拖着报应号将曾经是你们所有人中最伟大的船沉入海底。海洋君主转身向你发射一连串大炮，杀死了你的三名水手，最后承认失败，她转身离开复仇号，从战斗中向西航行。你的冒险者和复仇者都已经不堪重负，无法立即追击。 您赢得了这场战斗，但输了太多，以至于感觉不到胜利：阿尔卡普国王在死者中，他的战士们向天空尖叫。信玄王与报应号和海鬼号的其他幸存者一起从海中被救出时还活着，但在战斗中他被击穿了腹部，当他被拖上冒险家的甲板时，他几乎死的。当你把内奥米拉向他时，他认出了你并笑了。 “好吧，Leo，这次你抓住了我，”他带着虚弱的微笑告诉你。“不过，你永远得不到安妮-玛丽。她是我们所有人中最好的……” 他叹了口气后退，片刻后死去。那天晚上，您将带领冒险家、复仇号和最后的拉波人进入金沙港。
梗概：
你去帮助受灾的报应
--------------------
动作：
做
奇怪的归来
结果：
你和德雷克协助娜奥米上岸，随后是一队无声的拉保勇士，抬着阿尔卡普国王的遗体。当你从他们身边走过，走向城市的皇宫时，一群同样沉默的市民站在那里，看起来很震惊。在远处，您可以看到安妮-玛丽的船向西逃窜，无疑是在向 Revel 寻求帮助。如今，Elixi、Lapau 和 Navar 三个领域都失去了他们的统治者，你不知道接下来会发生什么。 你、德雷克和娜奥米在宫殿的王座室坐下，讨论现在该怎么做。冒险者号和复仇号仍然适合出海，明天你可以带上新的船员和补给并追捕安妮玛丽。您主要关心的是 Saphia Dancer 是否会信守诺言并帮助您。家康王子仍然是一个因素：他会在你之前到达 Revel，如果他与 Anne-Marie 联手，你的问题将重新开始。 当你在讨论这个问题时，镇上的一群著名市民小心翼翼地靠近。在君主无法统治的时候，他们会紧张地告诉你，任命摄政王代替他们统治是传统。经过一番讨论，他们决定任命内奥米·斯威夫特中将担任摄政王，直到君主回来或市民另有决定。他们尴尬地问你未来的计划是什么，当他们得知你打算在第二天追捕安妮玛丽并将她的罪行绳之以法时，他们承诺在你缺席的情况下会安排投票，以便三岛人民可以选择他们希望统治他们的人。你感谢离开的纳瓦尔。 几分钟后，一群拉波人到达，许多骄傲的战士在他们的国王去世后眼中仍含着泪水。他们用蹩脚的英语告诉您，他们明天将离开金沙并返回马坦，试图重建他们的社区和生活。你阴沉着脸向他们保证，只要你还活着，纳瓦尔人和拉波人之间就不会再发生战争，如果你能帮忙的话。拉波勇士们疲倦地摇摇头。 “请帮助我们找到我们的 Al-Balu 王子，”一位战士告诉你。“他现在是我们的统治者，也是唯一能带领我们走出黑暗的人。也请试着找到洛维特夫人。他们也许能一起重建我们破碎的国家。” 第二天早上，你的补给和加强船冒险者拖着德雷克的复仇号驶离金沙，在你逃亡的女王之后向西航行。Lapau 舰队向北航行，当你分道扬镳时，你命令你的船员开炮，向你勇敢的盟友致敬，他们不顾一切地站在你这边，献出了他们的国王的生命，以从暴君女王的统治下拯救你的王国您决心将她绳之以法，即使您必须将她追捕到天涯海角。
梗概：
奇怪的归来
--------------------
动作：
做
追逐开始
结果：
从金沙向西驶向西部航道，您再也看不到海洋君主了，但您的船员强烈认为金沙之战是一个重大转折点：许多个月以来，您第一次看到了猎人而不是被猎物，当你接近西方通道时，你最大的恐惧是萨菲亚舞者会背叛你的联盟，或者安妮玛丽会与家康联手，一旦他的兄弟信玄的消息传来，他可能会继承 Elixi 的王位死亡被人知道...
梗概：
追逐开始
--------------------
动作：
做
西部通道
结果：
当您到达西部航道时，您会发现安妮-玛丽确实从这条路经过的迹象：守卫航道的了望塔空无一人，所有木结构建筑都被烧毁。曾经在这里的人员和补给都已经撤离，无疑有助于弥补海上君主在战斗中遭受的损失。仍然希望在安妮玛丽到达狂欢之前赶上她，你驾驶你的两艘船穿过通道向西。
梗概：
西部通道
--------------------
动作：
做
你不安的夜晚
结果：
进入 Elixi 水域并且没有追上 Anne-Marie 的迹象，你和 Naomi 开始忍受不眠之夜，因为你们无休止地争论 Saphia 是否会保持忠诚。一天晚上，内奥米突然转向你。 “你觉得他们为什么选我做金沙摄政王？” 她问。“我看不出来。我没有统治的经验。到底是什么让他们选择我而不是你？” “也许他们认为你是更好的人，”你笑着说，但 Naomi 看起来很认真。“我不知道。也许是因为你是安妮玛丽所没有的一切：你善良而她残忍，你满足于她冷酷的野心。你在各方面都与她相反。我如果没有你的帮助，别以为我们能走到今天这一步……” “你也有德雷克，”娜奥米戏谑地说。 “是的，但你几乎一样有吸引力，”你笑着告诉她，你的同伴在你身边，你终于在到达 Revel 之前入睡。
梗概：
你不安的夜晚
--------------------
动作：
做
你萨菲亚的奇怪选择
结果：
一旦看到 Revel 岛港口，您就可以用望远镜扫描港口。 “好吧，萨菲亚的影子还在那里，”你告诉娜奥米。“不过，家康的探索者号也停泊在那里……没有海王的踪迹。我想那是什么。” “探险家在吗？” 娜奥米问道。“这看起来不太好……” 当您小心地接近港口时，您会发现一小群人聚集在码头。并肩站在 Saphia 码头上，家康和一位你不认识的苗条迷人的深色皮肤和轻盈身材的女性正在等着你。非常谨慎地记住过去的伏击，你、德雷克和娜奥米下船迎接他们。Saphia 大步向前，咧嘴笑着迎接你，她看起来很高兴见到你，但看到她如此开心的事情会让你本能地感到担心。 “你根本不会喜欢 Leo，”Saphia 告诉你，看起来非常高兴。“当我告诉你时，你可能会笑，但也许不会。大约一周前，家康出现在这里，我虚弱的大脑设法产生了一个好主意。为了使我们能够恢复 Navar 和 Elixi 之间的和平，我做出了非凡的贡献同意嫁给他的自我牺牲！就在几天前，真正有趣的是，我们发现家康是 Elixi 统治家族的最后一名幸存成员，因此，默认情况下，Elixi 之王。出于某种奇怪的原因，让我们既是国王又是王后，但你可以放心，家康已经看到了他的错误方式，我们都打算永远与拉波和纳瓦尔完全和平相处。” 你完全被这个消息震惊了，难怪家康看起来如此得意，或者港口的人群如此不满，但在你身边，娜奥米愤怒地大步走向萨菲亚。 “这从一开始就是你的计划，”她对 Saphia 厉声说。“当你和家康从马坦分开时，这就是你一直以来的计划：你帮助抓捕Revel和家康到这里旅行并嫁给你！你拥有自己的王国，家康就摆脱了困境！你以为我们会忘记他被杀伊娃·弗里尔并竭尽全力一次又一次地杀死我们所有人？” 娜奥米的愤怒似乎只会让萨菲亚更开心。 “我觉得你对娜奥米很不友善，”她爽快地说。“我敢肯定家康过去有点顽皮，但你忘记了一些事情：如果没有我的设计和我的想法，洛维特永远不可能建造那些我们用来捕捉 Revel 的铁甲，我刚刚听说你用过重新夺回金沙。如果没有我和家康的干预，你和你的拉波盟友会被安妮-玛丽的支持者追杀至毁灭，而现在占上风的是你。” “这可能是真的，但时代在改变 Saphia，”你愤怒地告诉新的 Elixi 女王。“Naomi 是 Navar 的新摄政，如果战争再次爆发，Elixi 将被 Navar 和他们的 Lapau 盟友击败。” “我本以为拉波人会忙着寻找他们失踪的统治者阿尔巴鲁或他的女朋友安洛维特……”萨菲亚若有所思地说。“还是你没有意识到他们已经秘密结成了情侣？我觉得这很明显。你和你的朋友也不应该在安妮玛丽之后忙着打猎吗？她两天前偶然在这里停下来，她对你很生气. 似乎想要我和家康帮助她，好吧，你会很高兴知道我们认为 Elixi 现在最好的事情是恢复他们的力量的和平时期。为了帮助确保这种和平，最好是你尽快找到并击败安妮-玛丽，当然为了友谊，您可以在这里或在您遇到的任何 Elixi 港口为您的船只补给：我们已向他们发送消息以协助您，但拒绝安妮-玛丽任何安全港.” 你抬眼看向家康和站在他身边的美女。家康对你冷冷地点点头，而他身边的女人则使眼色并飞吻。 “那是谁？” 德雷克问的不仅仅是随意的兴趣。 “那是我比较喜欢的一个叫野姬的 Elixi 女士，”萨菲亚眼睛闪烁着评论，你注意到家康似乎对她的存在并不太兴奋。一看到狡猾的黑衣王子，你的怒火又来了。 “我们不会忘记他杀了 Eva Freer，”你向 Saphia 保证。“总有一天他将不得不为那次犯罪付出代价。” “不要等太久，”萨菲亚轻声说，靠得更近了，压低了声音。“在我看来，找到阿尔卡普国王或他失踪的女友洛维特准将的人可能处于一个相当不错的位置，不是吗？如果你在追捕安妮玛丽时留意他们可能会有用，如果我出色的新丈夫首先找到他们......好吧，让我们说我不确定我能确保他们的安全。我在这里告诉你一些诚实的事情 Leo：我很喜欢 Ann 并且讨厌任何害来找她。” 随着那个警告会议结束，你在 Revel Harbour 的 Adventurer 上度过了一个不安的夜晚，而 Saphia 的新 Elixi 对象为你的两艘船重新补给。
梗概：
你萨菲亚的奇怪选择
--------------------
动作：
做
追逐转向南
结果：
第二天早上，您从 Revel 启航。 萨菲亚王后和她的女伴野姬来到码头为您送行，但没有家康的踪迹。你很高兴他不在，而你、娜奥米和德雷克停下来和新的 Elixi 女王交谈。 “我真的希望你能抓住安妮-玛丽，”萨菲亚高兴地告诉你。“她现在没有其他用处了。我什至可能会做一两次航行来帮助你的努力……正如你所看到的，我们正在为暗影号准备航行。” “如果你碰巧找到了阿尔巴鲁国王或洛维特王后，这对你来说不是一个惊喜吗？”娜奥米冰冷地说。 “会的，不是吗，”萨菲亚笑着说，突然笑了起来。“你知道我一直是你们两个 Leo 和 Naomi 的粉丝。你们有那种自以为是的东西，这绝对是可爱的。你们必须从你们一起的冒险中得到很多......满足。” “再见萨菲亚，”你冷冷地告诉她。“我希望你和家康在这里在一起很开心。” 当你转身离开时，萨菲亚停了下来，似乎在和自己争论什么。突然她在你身后呼唤，当你转身时，她走近一些，悄悄地和你说话，这样只有你能听到。 “如果我是你 Leo，我会一直向南走，直到天气开始变冷，然后向东转，因为我认为这就是 Anne-Marie 会做的……”她很快说。“你还记得在伟大的比赛中，当她试图环游世界时，她和我的兄弟约翰·丹瑟遭遇海难，但她设法带着两个同伴回来了？我已经仔细调查过了，我相信她离开了大部分船员因为她建造的船只能容纳几个人。我相信，如果她的船员和我兄弟幸存下来，他们就会在那个岛上或那个地区建立一个秘密殖民地。他们可能是她最后的希望必须找到一个安全的地方来重建她的力量，我有一种感觉，如果你找到他们，你就会找到她。” 萨菲亚一言不发地转身背对着你，走回野姬，他们向镇上走去；看到他们手牵着手走路时，你会感到有点困惑。耸耸肩，你转向冒险家并登上，而德雷克则登上复仇号并确保铁甲准备好启航。再一次，你将拖着你的盟友，当你扫视地平线时，你会考虑去哪个方向去追捕你的逃亡女王。
梗概：
追逐转向南
--------------------
动作：
做
你向东南航行
结果：
向东南航行，您很快就会看到一个热带小岛。没有人类居住的迹象，但靠近水边的地方有许多大物体，你把它们当成石头，直到它们两个突然游在一起。用望远镜你可以认出它们是巨龟。它们看起来并不特别危险，事实上，如果你想花一天时间去买新鲜的食物，如果你认为你可以从追逐安妮玛丽中抽出一天时间，它们可能很容易被猎杀。
梗概：
你向东南航行
--------------------
动作：
做
你停下来追捕乌龟
结果：
第二天早上，您和您的船员开始在您的船上捕猎乌龟。这些动物不快也不具有攻击性，因此狩猎中最困难的部分是将沉重的尸体、贝壳和所有东西拖出水面并准备存放肉。这是一项杂乱的工作，但在一天结束时，您已经获得了充足的新鲜肉类，并对即将启航的航程充满信心。
梗概：
你停下来追捕乌龟
--------------------
动作：
做
你向南航行
结果：
您正航行在翻滚的海浪中，直到有一天下午，您的瞭望点落在了远处。这是一个小岛，上面有一个小埃利西渔村。 “那是 Nassa Port，”德雷克在甲板上与您汇合时告诉您。“她是这些水域中最后一个 Elixi 定居点。也许如果我们上岸问问殖民者，如果 Saphia 真的告诉他们帮助我们，他们也许可以卖给我们一些鱼。” 你问 Naomi 她的想法，她耸了耸肩。“对我来说，这听起来是个好主意，”她同意道。
梗概：
你向南航行
--------------------
动作：
做
你在 Nassa 登陆并尝试购买新鲜的鱼
结果：
在 Nassa 抛锚，你和 Drake 带领一群值得信赖的海员上岸。您担心会如何接待您，尽管殖民者保持沉默和谨慎，但殖民地的领导者同意提供给您。 “我们接到了新国王和王后的命令，要为你们的船只补给，”他慢慢地说。“虽然似乎世界的变化有点太快了，我不喜欢。我的兄弟在狂欢之战中死于埃斯梅的喜悦之上，现在看来，在他自己的兄弟去世后的几周内，我们的国王家康正在与他的敌人并转向他以前的盟友。未来情况似乎不太可能再次发生变化，也许下次我们见面时可能会在一列枪的两侧？不管现在我的命令是帮助你所以我们会帮助你的。” 不想把你的运气推得太远，你一将补给装上船就继续航行。
梗概：
你在 Nassa 登陆并尝试购买新鲜的鱼
--------------------
动作：
做
你在南方航行
结果：
继续向南，您很快就会看到另一个岛屿。这是一个草丛覆盖的丘陵岛屿，几乎没有树木，但您确实可以看到山羊在岛上自由漫步，山上还可以看到许多洞穴。德雷克热衷于登陆和狩猎，但当你向她描述洞穴时，内奥米更加谨慎。 “那些洞穴里可能有任何东西，”她向你指出。
梗概：
你在南方航行
--------------------
动作：
做
你着陆和狩猎
结果：
当您和您的同伴上岸并开始穿过山丘前往一群山羊时，您会经过一个洞穴入口。当你经过黑暗的隧道口时，一股令人毛骨悚然的咆哮吓到了你们所有人，一个比一个人还大的棕色巨人从洞穴里冲出来朝你冲了过来。在熊将你的一个人撞倒在地并从他的脸上咬了一口之前，你看到了一张巨大的、长着锋利黄牙的大嘴。火枪爆炸了，但当球击中熊时，它们似乎只会让它更生气。在你逃跑之前，愤怒的熊追逐并杀死了另外两个人，你和你的船员逃回了海岸。当你回到冒险者号上并继续向南航行时，你仍然在颤抖，与你一起上岸的船员少了三个。
梗概：
你着陆和狩猎
--------------------
动作：
做
你向南航行
结果：
你航行的奇怪海域总是很有趣，但它们很少比今天更美丽，当银色的鱼群似乎吞没了你的两艘船，你的人冲到栏杆上调查这种奇怪的现象。一个巨大的鱼群，看似无穷无尽，已经包围了你的船，你的人可以用网、马甚至水桶把几十条这样的鱼拖上船。那天晚上你在新鲜的鱼上吃得很好，并且有足够的补给品来供应几天的补给品。
梗概：
你向南航行
--------------------
动作：
做
你向南航行
结果：
地平线上很快就会看到一个低矮的树木繁茂的小岛，当你驶近时，你会看到一个奇怪的景象：几个男人，从他们的衣服来看都是纳瓦尔人，已经跑到海滩上，疯狂地向你挥手。他们似乎迫切希望你停下来接他们，而你、德雷克和娜奥米聚集在冒险家的后甲板上讨论这个奇怪的景象。 “他们一定是安妮-玛丽船上的纳瓦尔，”德雷克评论道。“她为什么要把他们丢在这里？” “这可能是她对陷阱或伏击的想法，”内奥米指出。“如果我们确实捡到了它们，我们应该小心。” 你瞥了一眼远处绝望的水手，不知道该怎么办。
梗概：
你向南航行
--------------------
动作：
做
你停下来接人
结果：
你和德雷克小心翼翼地带领一群武装水手登上船，被困的纳瓦尔水手蹒跚地向你走来。 “感谢上帝，你来了特拉帕海军上将，”其中一位说。“两天前我们被那个恶棍安妮-玛丽遗弃在这里，从那以后就没有吃的了。” “她为什么抛弃你？” 你惊讶地问。 “好吧，海洋君主号上的情况变得非常糟糕，”水手告诉你。“食物供应越来越少，许多船员不高兴。女王大部分时间都待在她的船舱里，我们很少见到她。我和我的朋友们正在讨论一场哗变以夺取这艘船的控制权，消息传回了女王。她亲手杀死了我的两个朋友，并在从这里向南航行之前抛弃了我们其余的人。” 你相信这个人的故事，并带着他和他的两个同伴登上冒险者号，而其他人则加入复仇船员。被告知安妮-玛丽朝哪个方向走后，你就在她之后直接向南航行。
梗概：
你停下来接人
--------------------
动作：
做
你自由镇
结果：
您的航程将带您前往您所见过的更非凡的岛屿之一：小而热带，它包含一个小村庄，旁边有一个小码头，那里有几艘小船在水中轻轻摆动。在这些水域发现文明已经很不寻常了，但更令人惊讶的是，聚集在码头的十几个人拿着火枪，从他们破烂的临时衣服判断他们是纳瓦尔。你、德雷克和娜奥米小心翼翼地把你的船停在码头上，上岸会见这些人，他们似乎对你同样谨慎，直到一个男人，一个留着大胡子，天生具有权威气息的人走上前来。 “我的名字是约翰亚当斯，我是自由人民的总统，”他宣称。“我们记得你的名字 Leo Trapa，现在我们很高兴得知你是 Anne-Marie 的敌人，这让你成为我们的朋友。欢迎你和你的同伴来到这里，我会告诉你我和我的 17伙伴们来到这个岛成为我们的家。许多个月前，我和我的伙伴们是 Vixen 和 Dancer's Pride 上的船员，由约翰·丹瑟 (John Dancer) 和安妮·玛丽 (Anne-Marie) 指挥，他们试图从北到南在我们的麦哲伦世界中航行伟大种族的。” “你肯定知道，我们在位于这里以东的一个被诅咒的雾岛上失事了，我们的船只被摧毁了。我们公司的许多人，包括约翰·丹瑟在内，都死在了那里。我们帮助安妮·玛丽用来自那里的残骸建造了一艘小船。我们的船，但它对我们所有人来说都不够大：一天晚上，她和船上的两个同伴溜走了，把我们抛弃了。我们一度接近绝望，但及时我们能够建造船只让我们离开岛。我们知道如果我们回到三岛王国安妮玛丽很可能会杀死我们以确保我们的沉默所以我们选择向西航行寻找可居住的土地我们在这个岛上定居并试图建立一个殖民地在这里西边的一个岛上，但没有足够的食物，所以我们就在这里安顿下来。” “我们在这里很开心，比在家里开心多了，”亚当斯笑着告诉你。“新鲜水果和鱼类丰富，下雨时我们经常得到淡水，我们有很多时间休息和享受生活。就在昨天，当我们所有麻烦的被诅咒的创造者安妮玛丽本人降落在这里时，我们的幸福生活就结束了。一艘伟大的船。她谈到在你的手下遭受不幸 特拉帕海军上将表示她对抛弃我们感到抱歉，坚称她已经派人帮助但她的救援船找不到我们，并敦促我们在她计划航行时加入她的船员回到金沙并试图夺回她的王位。 亚当斯停下来微笑。 “好吧，我们所有的痛苦和所有的问题都是她造成的。我拔出步枪向她开枪，但可惜我很兴奋，我的目标偏离了所以我只擦了她的右臀部。之后她和她的水手逃回了他们的船迅速驶向东南。昨天晚上：如果你航行快，你仍然可以抓住她，对不起，我不认为我的火枪球对她造成太大伤害。因为你是她的敌人，我们会帮助但是我们可以而且可以给你一些鱼和水果来帮助你的航行：我们希望你能杀死她，否则将她带回金沙并将她投入她应得的监狱。” 您和您的同伴保持沉默，将被困在这里几个月的您自己的同胞视为安妮-玛丽背信弃义和对她的行为造成的痛苦漠不关心的最新证据。 “我们欠你和你的同伴很多，亚当斯船长，”娜奥米最后说道。“如果你愿意，我们的船上有足够的空间来带你和你所有的人离开这个岛。” 亚当斯坚定地摇头，他的人民也这样做。“不，这是我们的家，我们在这里很开心……如果你能派几个女人加入我们，我们的生活就完美了！请跟随安妮-玛丽，确保她不会在金沙重新获得权力，那将是最糟糕的事情。” “我们不会让这种事情发生，”你们向勇敢的水手们保证。您和您的船员与自由人共度夜晚，他们在新家中确实非常开心，第二天早上，他们将几桶物资装载到您的船上，并承诺彼此之间永恒的友谊，您将在邪恶的女王之后继续追求东南部。
梗概：
你自由镇
--------------------
动作：
做
追逐的终结
结果：
从自由镇向东南航行，你终于在第三天早上最后一次看到了你的对手。海洋君主号已经停泊在一个小沙岛上等着你，除了一棵棕榈树外，完全光秃秃的。Anne-Marie 似乎终于停下来并决定进行战斗，而你、Drake 和 Naomi 则聚集在冒险者号上进行快速的战争会议。 “让复仇者靠近我们，好好利用她，”你命令德雷克，他咧嘴一笑。 “这就是我们将得到她的 Leo 的地方，”德雷克宣称。“我们会击沉她的船，否则就会死！” 你的两艘船驶近安妮-玛丽的船，它在锚泊中等待。当三艘船汇聚在一起时，你和娜奥米身着新制作的蓝色海军上将制服并肩站在冒险者的后甲板上，当你靠近时，你和德雷克开火。海洋君主转向一边，发射大炮齐射，她的水手也用火枪开火，同时将你的两艘船吞没在大炮和火枪弹的风暴中。子弹只是从复仇号的铁皮上反弹，但冒险者受到的打击更大，子弹杀死了你的三名船员。一颗炮弹从你的左臂附近飞过，而一发火枪子弹将 Naomi 的帽子从她头上擦掉。你为她取回它并恢复你在栏杆上的位置，你继续喊着命令，而 Naomi 回应他们并添加她自己的鼓励。 看到她的炮火对复仇号毫无影响，安妮-玛丽将伟大的海洋君主引向并直接进入了铁甲舰的一侧，这是信玄在金沙之战中的战术的重复。数十名武装水手涌上复仇号的甲板，开始屠杀寡不敌众的船员。由于担心安妮玛丽会捕获德雷克的船，您命令冒险家在旁边驾驶并提供帮助，但在您到达之前突然爆炸将复仇号和海洋君主号炸成两半。在你震惊的双眼之前，你的双眼迅速沉入染血和身体覆盖的海浪之下。 直到后来从极少数幸存者那里你才知道发生了什么事：德雷克担心他的船即将被俘，抓起一个火药箱消失在甲板下，宣布他打算炸毁他的船。片刻之后，复仇号的火药供应爆炸了，在铁甲的侧面以及直接在旁边的海洋君主侧面撕裂了一个巨大的洞。Drake 不在幸存者之列，你朋友的死让你和 Naomi 深感悲痛，但他的牺牲最终摧毁了 Anne-Marie 的大船，她曾经杀死了这么多人并犯下了这么多暴行。 您设法营救了三名复仇者船员，但没有人从海洋君主那里救出，因为鲨鱼在战场周围的海域中非常活跃。没有安妮-玛丽的踪迹，你最初以为她已经淹死了，但你终于看到了她：她坐在一个比桌子大不了多少的小木筏上，正朝着树上的小岛稳步划船。你果断地将冒险家转向小岛，当安妮-玛丽到达它时，你在岛上训练你的船只的大炮，你和娜奥米一起带领你的十几个船员进入船上并划上岸，最终将你的逃亡女王绳之以法。
梗概：
追逐的终结
--------------------
动作：
做
女王的俘虏
结果：
这个岛很小，当你着陆时，你可以清楚地看到安妮-玛丽坐在棕榈树的阴影下，她奇怪的小木筏躺在她的身边。你引导娜奥米下船，上海滩，和你的水手一起前进，所有的人都把上膛的火枪训练在坐姿上。安妮-玛丽坐在树的底部，双手各拿着一支手枪，右臂紧贴着臀部，你可以看到她的衣服被血染红了。当你走近时，你会看到女王看起来比你记得的更苍白和更瘦，在游泳时脱掉了大部分衣服，她穿着一件朴素的白衬衫和黑色裤子，并拥有她古老的动物狡猾的样子。 “好吧，利奥，我想就是这样，”当你走近时，她笑着告诉你。“如果没有食物，我会继续航行，而我会继续奔跑，只是我的腿没有了。” 她握着手枪的手微微颤抖，然后叹了口气，把它们放在她两侧的沙子里。 “好吧，利奥，你要么射杀我，要么把我带回冒险者号，”她疲倦地说。“我太累了，几乎走不动了。” 你的水手包围了安妮-玛丽，确实发现她的腿被血浸透了。你拿着她的两把手枪检查一下，它们装满了子弹，显然是很好的武器。你自己留着一个，把另一个递给拿俄米，她把它塞进口袋。安妮-玛丽注意到你在做什么并笑了。 “你们两个要小心，”她警告说。“他们之前已经夺走了很多人的生命。” 你们都带着安妮-玛丽和她的微型木筏返回冒险家。一旦登上您的船，这位前女王的脚就会被铐住，并被囚禁在船舱内，由一名手持火枪的水手看守。你船的外科医生正在检查她的臀部伤口，但你和内奥米站在一边，他宣称伤口不严重。 “那么我们现在要航行到哪里？海军上将？” 安妮-玛丽带着嘲弄的微笑问道。“回到狂欢去见我的老朋友萨菲亚？还是去金沙让我重回王位？” 你受够了这些废话。 “我们要带你去东北部的金沙，审判你谋杀 Tobias Cuthrew、Thomas Finch、Eva Freer、Elixi 队长 Azai、Amako、Ryuzoji、Miura、Rokkaku、Sanada、Sogo、我的朋友 Drake 和Lapau King Al-Capu 以及成千上万的 Lapau、Navar 和 Elixi 水手、战士、妇女和儿童，”你告诉她。“我们会让你接受公开审判，让全世界看到你是一个多么无情的怪物，然后在金沙的欢呼人群面前处决你。” 安妮-玛丽的笑容微微一缩。“我再次看到金沙的机会和娜奥米一样多，”她残酷地说。“你认为我已经完成了？好吧，我会告诉你们两个我才刚刚开始！如果你认为我不好，我会告诉你一旦我摆脱了这艘船是多么糟糕！” 你转向拿俄米。“你觉得我们现在应该杀了她吗？” 你问她。 娜奥米想。“不，”她最后说。“我们应该把她带回金沙，让人们决定她的命运。” 那天晚上，随着海浪继续冲刷掉入海中的尸体，您将冒险者号置于东北航道上，然后前往您的小屋，脱掉衣服，与内奥米一起躺在床上。你亲吻她并依偎在她身边，但你马上就能看出有些不对劲。你问她怎么了，Naomi 没有回答，只是若有所思地抚摸着你的背。最后，她承认了令她担心的事情。 “你知道我们现在已经打了几十架了，”她告诉你。“在麦哲伦，你经历过比任何人都更危险的情况，每次你都毫发无损。我担心最终你的运气会耗尽，你会受重伤甚至死亡。我不担心“相信安妮-玛丽，即使没有她的船和她的船员独自一人，她仍然非常危险。我很担心她可能会在我们安全返回金沙之前逃脱。” 你有同样的恐惧，但你们都知道你不能只是处决你手无寸铁的囚犯。那天晚上你们俩都睡不好，尽管有一些激烈的做爱让你们俩都气喘吁吁，汗流浃背，紧紧地抱在一起，试图用对彼此压倒性的爱来驱散你的恐惧。
梗概：
女王的俘虏
--------------------
动作：
做
安妮-玛丽的结局……？
结果：
冒险家在继续向东北方向前进时取得了良好的进展，前往前方水域中的纳瓦尔殖民地，您将从那里驶过南部通道前往金沙。捕获安妮-玛丽沙洲的三天后，您花了一整天的时间引导您的船安全通过沙洲，然后再入睡。那天晚上，内奥米起床出去上厕所，当你听到开门的声音时，你仍然躺在床上休息。以为是娜奥米回来了，你什么也没想到，但当你听到安妮-玛丽冰冷、胜利的声音时，你会感到震惊。 “你认为我有多么愚蠢才不会对我的人保持警惕？” 她提出要求，你直直地坐着，发现前女王站在你面前微笑着，一把直枪对准你的头。“下次你给我设防时，不要让它成为一个头脑简单的水手，他一露皮就忘记了自己的职责，让自己处于一个我可以压倒他并夺走他的武器的位置。” 安妮-玛丽走进房间，当火枪管的冰冷金属直接压在你的额头上时停了下来。你从安妮-玛丽那里拿来的手枪放在两英尺远的床头柜上，就在你够不到的地方。 “你知道利奥，你动不动就反对我，”安妮-玛丽宣布，她的表情变得难看。“如果不是你，我会坐在我的金沙宫殿里，如果你，娜奥米，伊娃和其他白痴在拉波战争期间没有反对我，我现在可能是世界女主人了！我有这么宏大的计划，却因为你而被撕碎，随风飘散！你死了，你以为还有人能挡得住我的力量吗？那个屌丝萨菲亚？那个工具家康？你的瞎子呢？ “是吗？内奥米？不？我现在要杀了你，利奥，我想让你知道这一点。” 安妮-玛丽深吸一口气。“再见 Leo Trapa，”她轻声说。 一声枪响响起。 你没有死。 你睁开眼睛抬头看。 安妮-玛丽的嘴微微张开，看起来很惊讶。她的眼睛微微晃动，与你的眼睛相遇，你瞥见了她纯粹的惊讶，然后她向后倒在地板上，一动不动地躺在血泊中。Naomi 站在你的小屋门口，她手里拿着一支冒烟的手枪，你认出这是你从 Anne-Marie 身上取下交给她的武器。她慢慢地放下武器，摸索着走进了机舱。 “我听到你说话了，”她尴尬地说。“我试图判断她站在黑暗中的位置……是我做的吗？她死了吗？” “是的，”你简单地说。你跨过女王倒下的尸体，把内奥米抱在怀里亲吻她。她把手枪掉在地上，泪流满面，你紧紧抱住她。你走到甲板上，提醒你的船员注意发生了什么。您的船员惊呆了，但几分钟后，您已经召集了几名水手并返回了您的船舱。当你打开门时，你就会停下脚步。 安妮-玛丽的尸体消失了。 就在这时，你会听到水花溅起的声音。冲回甲板，您环顾大海，看到了安妮-玛丽：尽管她受伤了，但她仍然活着并逃离了您的船舱，来到甲板，她的小桌子大小的木筏被存放在甲板上，并在划着她的所有价值登上它前往最近的沙洲。你的许多水手向她开火，但尽管她的运气令人难以置信，但她没有被击中。你愤怒地转动冒险者跟随她。 安妮-玛丽到达最近的沙岸，将她的木筏拖上她越过另一边的木瓦，然后将其放回海中。你的船太大了，无法跟上她，你只能愤怒地看着你最大的敌人消失在蜿蜒曲折的沙洲迷宫中。你和娜奥米一起回到你的小屋，盯着地板上的血池。不管安妮-玛丽是否受了重伤，如果没有医疗护理，她可能活不了多久。娜奥米坐在你旁边的床上，握住你的手。 “这样看，”她告诉你。“那个女人现在没有船，没有船员，没有补给品，也没有朋友。她受了重伤，迷失在她不认识的水域中，远离任何文明，所有这些都使她成为非法。我们已经做了我们能做的一切……我想现在最好还是继续北上，赶在物资耗尽之前回到金沙，看看我们家乡现在的情况。别忘了我们还有家康要处理。” 听从伙伴的建议，你命令冒险者号恢复它的东北航线。
梗概：
安妮-玛丽的结局……？
--------------------
动作：
做
奇迹
结果：
虽然对安妮-玛丽最近一次逃亡的记忆很痛苦，但你知道你要回家了，这让你感到安慰。在三天的时间里，您将继续航行在越来越温暖的海水中，驶向纳瓦尔殖民地，并最终驶向您的家。在阳光明媚的海上待了几天之后，您决定最好向北转，以便接近南航道并下达必要的命令。 娜奥米在甲板上站在你旁边，当覆盖她破碎的眼睛的绷带滑落时，她举起手来调整它。突然，她出人意料地大喊一声，接着是一声让你转身的喘气声。突然，她靠在你身上，昏倒在地，吓得你把她放到甲板上。你的几个水手拿着冷水和海绵跑来让她复活，当她醒来时，你急切地问她出了什么事。 “我能看到，”娜奥米用一种惊奇的语气说。撕开她的绷带，你第一次睁开她破碎的眼睛。左眼窝完全是空的，但在右边，一个红色的、充满血丝的眼球从一个野蛮的切口下方闪耀出来。眼球环顾四周，然后迅速闭合，又缓缓睁开。“我能看到……蓝色，”娜奥米说，她剩下的眼睛抬头看着天空。“我能看到……天哪，利奥，我能看到你！” 似乎奇迹发生了，你对生命的热爱正在恢复部分视力！在无尽的黑暗中生活了数月后，你充满喜悦地紧紧拥抱 Naomi，她终于开始回归生活，在接下来的一天里，她无法停止从宽慰和幸福的结合中颤抖。
梗概：
奇迹
--------------------
动作：
做
你回家了（再次）
结果：
越往北走，内奥米的视力就越好。眼罩是为了掩盖她左眼的破坏，但随着时间的推移，她的右眼变得更白、更强壮、更清晰，因为它惊奇地凝视着一个隐藏了这么久的世界。事实上，它变得如此强大，以至于当您稳步向北考虑时，使用您的望远镜 Naomi 是第一个在地平线上发现一个丛林覆盖的岛屿的人。由于食物短缺正成为一个日益令人担忧的问题，您可能希望在这里降落，尽管这里没有人类居住的迹象。
梗概：
你回家了（再次）
--------------------
动作：
做
你降落并寻找食物
结果：
你带领一群水手上岸，包括坚持要加入你的娜奥米。丛林中有许多小动物，在一个小时左右的时间里，您可以很好地狩猎，从树上射出几只。正当你想回到船上时，你听到一声野蛮的咆哮，一道橙色和黑色的运动从树上迸发出来，锋利的爪子撕裂了你的一个人的胸膛。火枪爆炸了，但在娜奥米大步走向老虎之前，老虎又攻击了两个人，举起安妮-玛丽的手枪，从近距离射击了它的头部。野兽倒在地上，抽搐了一下，然后一动不动。你被野蛮的袭击和内奥米新发现的准确性惊呆了，但害怕未来的袭击，你仓促地收集你的补给并离开了小岛，带着你的三名死去的船员。到了晚上，水手们已经被埋葬在海里，你正驶向北方。
梗概：
你降落并寻找食物
--------------------
动作：
做
你航行在北方
结果：
向北航行，您会经过一座烧焦的岛屿残骸。你不解：这里曾经是一个殖民地，建筑物确实还屹立不倒，但这里看起来绝对是空无一人。岛上的任何地方都没有任何生命迹象，除非有人躲在建筑物中，否则您不确定这意味着什么。Naomi 也很困惑，但指出这里没有任何暴力或抢劫的迹象。这几乎就像纳瓦尔人收拾好殖民地回家一样。
梗概：
你航行在北方
--------------------
动作：
做
你花了一天的时间登陆岛上并进行调查
结果：
你和娜奥米带领一队水手上岸探索岛屿。你不必再指导娜奥米，这似乎很奇怪，但她自信地走在了你们其他人的前面。该岛确实是完全被遗弃的，你能找到的唯一殖民者的踪迹就是在几棵树和小屋墙壁上以相同的笔迹划过的“GONE TO GOLDEN SANDS”字样。一切有价值的东西都被拿走了，所以感到不安，你别无选择，只能继续向北航行。
梗概：
你花了一天的时间登陆岛上并进行调查
--------------------
动作：
做
你向北到漂流岛
结果：
随着冒险家越来越靠近您的家，您会感到高兴和担忧，因为 Naomi 的视力稳步恢复到几乎和以前一样好，但您越来越担心您的供应情况。来到你的另一个殖民地，你再次通过望远镜看到废弃和空荡荡的小屋、小木屋和废弃码头的图像。再一次没有任何生命迹象或任何有价值的东西已经落后。
梗概：
你向北到漂流岛
--------------------
动作：
做
你花一天时间登陆并进行调查
结果：
你和 Naomi 带领一个派对上岸并探索这个岛屿。在其中一间较大的小屋中，您会找到一封由殖民地领导人写的简短信，并留在桌子上。它指出，由于不确定和危险的时期以及对纳瓦尔内战中来自拉保、伊利西或一侧或另一侧的袭击的恐惧，大礁石北部和南部岛屿上的所有殖民者都撤回了安全的地方。大礁石到紫水晶、玛瑙和金沙三岛。它声明欢迎朋友通过四个通道之一进入王国，但警告敌人远离......仔细考虑此消息，您和您的船员返回冒险家并继续航行。
梗概：
你花一天时间登陆并进行调查
--------------------
动作：
做
南航道
结果：
最后，大礁石和雕刻在其中的南部通道的受欢迎的景象映入眼帘，您知道在它之外等待着您的家。但是守卫南航道的瞭望塔已经发生了很大的变化：建造了更多的小屋，通常只有几个士兵的驻军也被加强到了大约三十人。您对通过通道航行有点紧张，因为您不知道守卫是友好还是敌对，但您必须这样做。娜奥米指出，守卫看起来并不特别具有威胁性，只是站在他们的小屋外面，一群人看着你，她建议你着陆，尝试补充逐渐减少的补给。
梗概：
南航道
--------------------
动作：
做
你降落并尝试捡起新鲜的补给品
结果：
不确定您的接待情况，您和内奥米降落在瞭望塔附近的畜栏上。守卫之间传来兴奋的喋喋不休，但他们中的大多数人都离开了队伍，上前热 情地打招呼。一位特别热情的人为其他人代言。 “欢迎特拉帕海军上将和斯威夫特海军上将回来，”他惊呼道。“欢迎你在这里补给你的船只。我想你是前往金沙吗？现在整个国家都在投票决定接受谁作为他们未来的领导人，我们是否应该成为一个共和国，安妮 - 玛丽是否应该保留王位，或者包括你们两人，萨菲亚舞者或其他人在内的几个候选人是否应该被任命为她的统治者。当你到达那里的时候，结果应该出来了。” 你很惊讶你和 Naomi 甚至被认为是 Anne-Marie 的可能替代者，而且很惊讶你几乎没有反应，因为大多数守卫开始把一些补给品装到你的船上，每个人都向你保证他们投票给你。In fact, judging from the reaction of the guards on the reef it seems that over three quarters of them voted for you and Naomi to be elected as King and Queen of the Navar! 不知道如何对这个消息做出反应，你真诚地感谢守卫并继续回到金沙的家中。
梗概：
你降落并尝试捡起新鲜的补给品
--------------------
动作：
做
你金沙
结果：
美妙的金沙岛终于映入眼帘，当您看到码头边欢呼的人群时，您对接待的任何疑虑都会立即消除。冒险者号驶入并抛锚，当人们看到内奥米恢复视力时，欢呼声倍增。当一群镇上最受尊敬的市民接近您时，沉默降临，一位老妇人手里拿着王国的王冠。 “代表海军人民，我们想选用Leo Trapa和你的三个岛屿迅速王和女王，海军人民和他们所有的殖民地。一直举行了一个全国公投，大多数92％两人都被选为君主立宪制统治我们的人民。除非被普选罢免，否则您和您的后代将无限期地统治，前女王安妮玛丽因叛国罪被取缔，并丧失了她以前的所有头衔。” 你和娜奥米的喉咙异常干燥，接受了这一崇高的荣誉，当这位女士首先将皇冠放在你的头上，然后是娜奥米的头上时，人群变得歇斯底里，人们欢呼甚至哭泣。你和娜奥米对反应和事件不知所措，感谢人群并匆匆赶往宫殿。在宫殿内，你会发现一个意想不到的人物在等着你：它是萨菲亚舞者的迷人朋友，Elixi 女士 Nohime。看到你和内奥米，她看起来松了口气。 “祝贺我们的加冕国王和王后，但萨菲亚和我迫切需要你的帮助，”她用令人惊讶的音乐声音告诉你。“Revel 的一切都不太好。王家康找到并俘获了洛维特准将，他正在关押她的囚犯。Revel 发生了很大的动荡，萨菲亚公开与家康分开，后者和他的囚犯一起留在他的探索者号上. Saphia 已经授权我告诉你，如果你立即来到 Revel，她将帮助你推翻家康，一劳永逸地击败他并拯救洛维特。 你和娜奥米对视一眼，深深地叹了口气。你希望在所有事情之后你可能有时间休息，开始你的人民的统治，并帮助纳瓦尔人与他们的邻居和平共处，但萨菲亚的信息很重要，洛维特是你的朋友，家康是未完成的事业所以曾经更多你必须启航。冒险者号得到了补给和船员，尽管你的新对象令你失望，但你第二天向西航行，野姬自己的船海少女号与你的船一起航行。
梗概：
你金沙
--------------------
动作：
做
你再次向西征战
结果：
通过非常熟悉的水域，一路向西前往狂欢，一路公海，除了西部通道，您在那里非常匆忙，您不会停下来捡拾补给品。Nohime 是一个有趣的伴侣，调情又聪明，很明显萨菲亚喜欢她，但你能感觉到她真的很担心她的朋友和她的人民。至少有三艘船，你的家康将超过人数，并且应该能够迫使他投降洛维特海军上将。你们所有人都同意他应该被推翻为 Elixi 之王，尽管这会让萨菲亚成为他们的女王和唯一的统治者。 Revel Harbour 很快就映入眼帘，当冒险家和海女驶近时，您可以看到萨菲亚的影子和家康的探险家正停泊在港口。当您靠近时，Explorer 突然下帆并滑出港口。显然家康无意被困，而萨菲亚显然无意让他逃跑，影子转身向撤退的探险者开火，探险者用它的尾炮还击，但迅速驶出港口并转向西北，超速行驶在返回的阴影之前。 你，Naomi和Nohime都在你到达的时候在码头遇见了萨菲亚，萨菲亚一点都没变，看到你们都很高兴。 “Leo，Naomi，你回来真是太好了，Nohime，总是很高兴，”萨菲亚说，看起来很兴奋。“看来我现在的前夫不太想见你，已经离开了。在你不在的时候，我们都搜索了最后一次见到洛维特的地方，但不幸的是他先找到了她。她的船似乎被风暴破坏了她被困在这里西边的一个岛上，直到家康找到她。他一直以杀死她的威胁作为维持他作为国王的权力的一种方式，因为一些奇怪且完全无法解释的原因，人们赞成推翻他，任命我代替他成为圣水的唯一统治者！想象一下！” Saphia 的眼睛闪烁着愉悦，一种 Naomi 没有的愉悦。 “当然，你会想与你的邻居，纳瓦尔的利奥国王和王后娜奥米结成和平联盟，”娜奥米告诉她。“当然，这也意味着你会想加入我们追捕你族人的前暴君，直到他死或被捕为止。” “我从不逃避战斗，”萨菲亚高兴地说。“Nohime，我不在的时候你会照顾 Revel 是不是？确保人们在我回来时对我的正式加冕典礼的盛大庆祝保持快乐和充满希望。与此同时，我认为最好是你和我 Naomi和雷欧一起追杀我的前夫，杀死那个恶棍，救出洛维特指挥官，如果找不到阿尔巴鲁，他很有可能被选为拉波的下一任领袖。我会让我们的船重新——供应，我们今晚会追上家康。” 果然，把野姬和海女抛在身后，娜奥米和你滑溜溜的同伴萨菲亚那天晚上在你补给的船上向西北航行，追赶着家康的遥远目标，真诚地希望这场追逐不会像安妮玛丽之后的那样漫长。
梗概：
你再次向西征战
--------------------
动作：
做
你萨菲亚的不幸
结果：
似乎家康在向着 Revel 西北航行三天后并不打算跑太远，他停下来转动他的船，等待你和你的同伴靠近。你是内奥米，担心如果你的炮火击中或击沉探险者号，有可能杀死安洛维特，所以你们和萨菲亚一起同意直接驶向你的敌人，靠在她身边并登上她。这是一个冒险的计划，但如果成功，它将确保击败你的敌人。 不幸的是，不幸的事很早就发生了：探险家向你侧身并发射了她的第一发大炮，而你仍在向她航行并且无法还击。大多数炮弹击中了暗影，突然她从探险家转向，转身开始远离战斗！探险者号同时起航，迅速滑离战场。她比冒险者还快，并且对你逃跑的猎物感到恼火，你转回暗影去找出萨菲亚为什么中断了她的攻击。 碰巧 Saphia 也有很好的理由：当你上船时，你会发现她躺在后甲板上的血泊中，而两个男人正在紧急取出她腿上的木屑。她所倚靠的栏杆似乎被炮弹击中，她的双腿被数十片木制弹片弄断。她几乎失去知觉，无法控制地颤抖着，你命令自己船上的外科医生帮助那些试图止血的人，并从她受伤的腿上挑出比你手指更长更粗的碎片。 今天过得并不顺利。 那天晚上晚些时候，你和娜美回到暗影号上，看看萨菲亚的情况。她躺在小屋的床上，双腿缠着干净的绷带，喝着大量的朗姆酒来缓解疼痛。 “真尴尬，”当你坐在她身边时，她痛苦地皱着眉告诉你。“在战斗开始几分钟就被砍成这样。我虽然离开了 Leo。我太担心如果我留在海上会被感染，我会回到 Revel。你和 Naomi 是最擅长追踪家康，比我见过的任何水手都要好，包括安妮玛丽。继续追他，我知道你会抓住他，但要小心！他有自己的方式像蛇一样聪明狡猾，他朝着他现在的方向前进肯定是有目的的。我猜他有一些Al-Balu的智慧，并且有办法让他和洛维特都掌握在他的手中。” 向萨菲亚承诺你不会停止追逐，直到家康被你带走，内奥米让她休息。
梗概：
你萨菲亚的不幸
--------------------
动作：
做
你一个人继续
结果：
第二天早上，你们分道扬镳，当家康的探险家仍在向西北行驶后，你和内奥米继续独自一人乘坐冒险者号时，影子将其受伤的病人送回东南部前往狂欢。在追了三天之后，你慢慢地追上了他，一个下午很明显你会赶上他。您参加过如此多的海战，并且在其他人受伤或死亡时总是毫发无伤地出现，因此您不必担心什么有望成为小型且可能不确定的舰对舰行动。但今天会有所不同：今天幸运女神终于背弃了你。
梗概：
你一个人继续
--------------------
动作：
做
你的运气用完了。
结果：
当冒险者接近时，探险者转身与你的船战斗，她的全副武装指着你。你别无选择，只能直接驶向它，一旦进入射程就转向。你的船员在大炮旁等待，你的步枪手在索具中。你和娜奥米身着海军上将的蓝色制服并肩站在军需甲板上大喊大叫。 “现在我们有机会带走她的孩子了，”你对你的船员喊道。“做好准备。” 当 Explorer 上的大炮爆炸时，您向舵手发出命令，将 Adventurer 转向右舷，以便您可以携带自己的大炮。当一个又大又黑的东西在你面前吹口哨时，你抬起你的手臂指向他应该乘坐的船的方向，像一根树枝一样折断你的手臂，把你当场旋转，把你撞到甲板上。感觉就像有人用刀直刺你的手臂，而且还在无情地扭动它。躺在甲板上，你瞥了一眼你的右肩，震惊地发现只有一根断掉的白色骨头和一团乱七八糟的肉，在你肩膀下方几英寸处伸出来。 内奥米在你上方大喊大叫，但你听不到她在你耳边猛烈撞击的声音，这种声音似乎像汽笛一样增强。你不能说话，但随着更多的船员包围你，你设法摇头。你感觉到强壮的手臂将你从甲板上抬起，在黑暗中朦胧地你要求某人拿起你的手臂。您将被带到您的机舱，在那里您的制服和衬衫很快就会被剪掉。当夜幕降临时，您会看到船上的外科医生拿着锯子进入您的船舱。当他开始工作时，您会沉入无尽的黑暗中，而您则沉睡在生与死之间的人的睡眠中。
梗概：
你的运气用完了。
--------------------
动作：
做
你谵妄
结果：
面孔和图像从你身边掠过。Esme Hale 稍纵即逝的脸出现在你笑之前，你再次看到 Anne-Marie 告诉 Cuthrew、Finch 和 Eva Freer 他们是非常糟糕的对象，现在必须游到海底把她带回沉没的铁甲正义，为她的新舰队报仇雪恨。弗里尔让她自己做，当你向她伸手时，一声枪响，她的脑袋爆炸了，在信玄身后，剑心和家康站着大笑。家康拿着冒烟的手枪接近你，当你一动不动地站在那里时，他折断你的右臂扔掉，仍然笑着。黑暗再次升起。 有时 Naomi 是图像之一。你感觉到她在你发烧的皮肤上亲吻，伸出你的右臂拥抱她，但那里什么也没有。奇怪的是，你仍然可以感觉到你的手指和肘部以及你所有的手臂在移动，但你看不到它。一定有人把它放在某个地方。出于某种原因，你确信家康已经潜入你的小屋并用你的手臂逃跑了。 “家康，”你说了很多遍。“家康在哪里。” “我们在追他，”声音对你坚持。“我们还在跟踪他。” 还有一次，哭声把你从黑暗中唤醒，你会听到某处的声音，说一个人看起来不太好，他的状况没有任何改善的迹象。你对他们谈论的人感到有点抱歉，然后娜奥米的脸出现在你的上方，一滴泪水从她善良的眼睛里流下她的脸颊。 “回到我身边，Leo，”她平静地说。“请上帝回到我身边。” 你试图伸手去擦掉眼泪，但没有任何反应。你沉入黑暗中，被冒险家熟悉、舒适的动作所迷惑。
梗概：
你谵妄
--------------------
动作：
做
你的旅程结束了
结果：
一天早上，你的发烧无缘无故地退烧了。 你回过神来，发现自己躺在床上，被汗水弄湿，感到非常口渴，但完全意识到自己的存在。机舱外可以听到大炮和火枪的轰鸣声，大概是把你吵醒的原因：爆炸声和爆炸声变得迟钝而遥远。向右看一眼，你的手臂曾经所在的地方有一个沾满绷带的残肢。它隐隐作痛，你叹了口气，看着你失去的肢体，然后爬上你的战斗，用你的左手笨拙地从你床边的水罐里给自己倒了一杯水，然后喝了。感觉好多了，你笨拙地穿上沾满鲜血的海军上将制服，你发现它躺在你的床脚。因为你只用左手，仍然感觉有点麻木，所以你花了将近 30 分钟穿好衣服，然后走到甲板上。当你穿好衣服时，战斗的声音已经停止，留下一片寂静，似乎比战斗的声音更具威胁性。 出于某种原因，你的船员不在他们的正常车站，而是都在轨道上盯着远处的视线。好奇你加入他们看不见的，看看他们在看什么。探索者号在离岩石岛屿不远的地方着火并迅速下沉。一艘挤满人的小船正快速划向岩石岛的岸边，岛上排满了手持长矛的拉保勇士。您可以看到站在船头的家康的独特形象。他拿着一个小人影，用手枪抵着那个人影的金发。眯着眼你认出这个人是安·洛维特，你自己的船员都没有向船开火，因为担心她会受到伤害。 当船靠近岸边时，您会看到一位特殊的拉波战士大步走向船，冲浪到膝盖处，手里拿着长矛。家康和洛维特之间突然发生了争执，最后洛维特将他推下船，并潜入海中。当她游走时，家康直起身，用手枪指着她，下一刻他就向后倒退，拉波战士用他的长枪飞翔，长矛直接穿过家康的喉咙并留在那里，一半伸出喉咙前部，一半伸出背部。家康摇晃着，然后倒在他的船上，显然已经死了。 洛维特在水中向她的救援者泼水，当他们在海岸边相遇时，他们热情地拥抱并亲吻。随着冒险家越来越近，您终于认出这位战士是 Al-Balu 王子，并猜测他周围的人一定是他的船员。像洛维特一样，他一定是在这个岛上失事并一直待在这里。您意识到冒险者必须驶入并让他们离开岛屿返回马坦并寻找某人下达命令。你看到娜奥米站在不远处的栏杆上，在她观察现场时，望远镜紧贴着她那双好眼睛。你悄悄地走到她身后，拍拍她的肩膀。 “我感觉好多了，”你小声告诉她。 内奥米转过身来，当她看到你时，她的整张脸都亮了。她大喊一声，扑向你，几秒钟后，你就会热情地亲吻、拥抱和触摸你能接触到的彼此的每一部分。你们享受彼此陪伴的时间不会超过片刻：你们的整个团队都把你们吞没了，完全忘记了你们的职责，你们都流下了喜悦的泪水，高兴地大喊。经过这么多的挣扎，这么多的危险和这么多的损失，终于结束了：洛维特和阿尔巴鲁获救，家康死了，和平终于回到了马坦。你们所有人都将迎来一段漫长的繁荣期。
梗概：
你的旅程结束了
--------------------
动作：
做
麦哲伦的成功
结果：
你和你的船员迅速从他们的岛上救出安洛维特和阿尔巴鲁，后者不是拉波的阿尔巴鲁国王。他们的经历似乎都不是更糟，在这两种情况下都涉及破坏他们的船只，而在洛维特的情况下，他们被家康俘虏了一个多月。他的尸体被抛入海中，被海洋生物吞噬，他幸存的船员与洛维特、阿尔巴鲁和他的拉波一起被带到冒险者号上。您向西南航行三天，然后向南行驶，经过科拉尔山，直到您返回马坦。国王安全归来后，拉保民族的喜悦难以形容。 当您返回时，有一个惊喜在等着您：Shadow 和 Sea Maiden 停泊在 Matan，Nohime 和 Saphia Dancer，后者仍然一瘸一拐，但已从伤势中恢复过来。在激动人心的仪式中，Al-Balu 正式加冕为 Lapau 国王，您也借此机会确认了 Saphia 作为 Elixi 女王的地位。那天晚上，你向 Naomi 求婚，她接受了，两天后 Al-Balu 也效仿，向 Ann Lovett 求婚，她也接受了。计划在一周内在马坦岛举行双重仪式，最终有一些东西可以庆祝整个拉保民族，结果是在婚礼之后提供丰盛的盛宴。 Saphia 仍然没有失去让你震惊的能力，在双人婚礼的那天，她带来了一个更大的惊喜：她和 Nohime 穿着漂亮的白色婚纱出现，笑着向你解释了情况。 “这就是成为女王的乐趣所在，”她高兴地告诉你。“我可以通过任何我想要的法律。我碰巧相信人们应该被允许做他们想做的任何事情，只要他们不伤害其他人。所以今天早上我通过了允许男人和男人之间结婚的法律女人和女人。因为我一直很喜欢 Nohime 我认为她会成为一个相当好的妻子。我不认为 Elixi 应该太在意：他们现在有两个皇后而不是一个。诚然，我们可能有点矮国王队，但过去国王队对他们不太好，所以也许是时候让他们有几个女士负责了？” 她说的太天真了，你忍不住笑了。那天下午，你和 Naomi、Al-Balu 和 Ann、Saphia 和 Nohime 都在盛大的庆祝活动中结婚，并签署了一项协议，承诺在 Elixi、Lapau 和 Navar 之间实现持久和平，只要你和你的家人将统治。几天后，你、内奥米、萨菲亚和诺姬带着祝福离开了拉波。Elixi 的两位女王乘坐 Shadow and Sea Maiden 向东南航行，返回 Revel，而您在最后一次返回 Golden Sands 的旅程中将 Adventurer 转向西南，这样您和 Naomi 就可以扮演 Navar 的国王和王后的角色。 在航程中，只有一件事让你感到困扰，你和 Naomi 晚上很晚才在床上讨论。 “安妮玛丽呢？” 你问拿俄米。“你觉得她会回来吗？” “如果她这样做了，我看不出她能做什么，只能以一种小而卑鄙的方式制造麻烦，”内奥米回答说。“她在她的控制下用武力征服麦哲伦的希望已经破灭，王国和平联盟。她所有的盟友都死了，所有的人都反对她。我看不出她能做什么。反正有什么事情比安妮-玛丽更重要的是我今晚想和你谈的。” “我的爱是什么？”你问她。 娜奥米深吸一口气。 “我怀孕了。” 你睁大眼睛拥抱内奥米，用数百个吻盖住她赤裸的身体，因为你们俩为即将成为父母而高兴地笑了起来。 这是你一生中最快乐的一天
梗概：
麦哲伦的成功
--------------------
动作：
做
结束
结果：
... 在遥远的一个小沙岛上，十几个水手在满月的照耀下驾驶着他们的小船驶向沙岸。一个人影坐在沙滩上，旁边是冒烟的火炉，上面有几条小鱼在做饭。附近沙地上放着半桶雨水和几块木头碎片，一个小木筏的残骸，是沙堤上唯一的其他物品。当小船海滩和里面的水手跳入海浪中并迅速驶上海滩时，这个人影几乎没有抬头。他们的首领摘下帽子，在沉默的沉思身影面前单膝跪在沙滩上。 “陛下，感谢上帝，我们找到了您，”他宣称。“我们已经找了很多天了，我们会带你去你想去的地方。” 人影终于抬起头，站了起来。她是一个高大但瘦弱的年轻女子，脸上被愤怒和仇恨扭曲。 “金沙，”她用充满冰冷毒液的安静声音宣布。“把我伪装成男人，带我回金沙。我们还有工作要做。” 当水手们匆忙服从她的命令时，安妮-玛丽走向等候的船，她的脑海里充满了黑暗的阴谋和对你、娜奥米和你未出生的孩子的未来的计划。 ... 麦哲伦系列将在麦哲伦 5：以新方式旅行中结束。
梗概：
结束
故事名称：
67
故事背景：
你的名字是Leo Trapa，你作为你的船冒险家准将的成就已经使你成为麦哲伦世界中最著名的水手之一。冒险者号是一艘坚韧的小船，是多次战斗的老兵，大到足以容纳 30 名水手的船员和 30 天的补给品。虽然你只有二十一岁，但你已经获得了女王授予的准将军衔，以表彰你高超的能力和航海技术。 您所居住的麦哲伦世界大部分是由分散的岛屿所占据的海洋：已经发现了 25 个，而且几乎可以肯定还有更多的岛屿在等待着您。您的祖国三岛王国的人民拥有丰富的海军传统和先进的技术：冒险家携带四门大炮，您的所有船员都配备了火枪，尽管这些火枪上膛缓慢，使剑成为您的次要武器. 在环绕您的三个家乡岛屿的大礁石之外已经建立了十几个殖民地，但这些殖民地从繁荣的定居点到微小的文明前哨站不一而足。 您在长途航行中最大的问题是您的船只能携带足够 30 天的补给品，这将使寻找和获得新的补给品来源成为您的首要任务。如果您的补给品用完（您的补给品数量降至 0），那么您和您的船员将饿死并死亡。你几乎肯定会在航行中失去船员，你必须小心你的船员人数也不会降到 0：这意味着你和你所有的船员都死了。 在您的航程中经常会涉及到时间因素，您会希望在尽可能短的天数内完成它们。在您的竞争对手之前成功完成航程将获得一个分数，如果您在比赛结束后发表评论，该分数将表明您以非常高的技能完成了这个故事。和以前的冒险一样，您会发现制作一张航行水域地图很有用，因为您可能会再次以这种方式返回，而这些信息可能会使您受益... 所以祝读者好运，做出明智和战略上合理的选择，如果有一点运气，一条好的路线和顺风，你可能会再次看到你的祖国！
--------------------
动作：
做
你点击这里开始你的冒险（记得先拿起著名水手名单）
结果：
你站在熟悉的金沙皇宫前厅，三岛之都，你的故乡。你的两边站着你的同事娜奥米·斯威夫特和伊娃·弗里尔，他们都和你自己一样在公海上有经验。苍白修长的长发娜奥米精致的外表下隐藏着钢铁般的坚毅精神。另一方面，伊娃看起来和她一样坚强，有一头假小子般的紫黑色短发，从头发下面，她的眼睛似乎以一种具有挑战性的方式瞪着她所看到的一切。由于不同的原因，他们是你最好的两个朋友：对伊娃来说，你有一种接近奉献的感觉，同时又尴尬地意识到你在娜奥米身上激发了类似的感觉。 王座室的门打开了，理查德·夸瑞尔（Richard Quarrel），一条黏糊糊的蛇，与你的女王结婚并没有提高他在任何人中的知名度，对你微笑。 “女王现在会见你，”他宣布。 走进去，你会发现更多熟悉的面孔在等着你。冷酷而专横的安妮-玛丽女王坐在她的宝座上，而她身边坐着年轻的外国人阿尔巴鲁王子，他一如既往地对周围发生的事情感到完全困惑。在他的另一边坐着安·洛维特 (Ann Lovett)，他的金发口译员会在您进入时给大家一个快速的微笑。安妮-玛丽的另一边是一位你不认识的女士，一位迷人的黑发女士带着嘲弄的微笑，给人一种不安的印象，即她知道一些你不知道的事情。当你停下来时，安妮-玛丽说话了。 “我把你们三个召来这里是因为我再次需要你们的服务，”她宣称。“我知道最近你对我的统治的批评声浪很大。我也不能忘记，在拉保战争即将进入高潮之际，你们都反抗并拒绝听从我的命令……然而，在我伟大的和慈悲的我原谅你们三个，因为我有一项任务要你们执行。正如你们所知，库斯鲁准将和芬奇都没有从西航道以外的探索之旅中回来，他们离开已经快三个月了。显然是这些经验丰富的军官的失踪至少可以说是令人不安的......特拉帕准将。我希望你带领一支远征队寻找我们失踪的同胞，看看他们遭遇了什么。这是一个艰巨的挑战，但它的成功只会增加你本已伟大的名声并带来丰厚的回报。” 安妮-玛丽在你的同伴身上闪烁着目光，当她遇到伊娃挑衅的凝视时，她的表情变得暗了下来。“弗里尔准将和斯威夫特准将带着他们自己的船陪你，听从你的指挥，你可以把斯威夫特准将作为你的副手。我已经安排好新的补给品存放在西航道，过一段时间我就会派我的朋友舞者船长带着新鲜补给穿过东部航道，在你返回时为你提供帮助。” 你瞥了一眼舞者船长，她的笑容随着你的眼睛而扩大。如果你不知道更多，你可以肯定她是想告诉你一些事情......你把目光投向争吵，他也在以你不喜欢的方式对你微笑，然后转向 Al-Balu 和洛维特礼貌地听着安妮-玛丽说话，并没有看着你。安妮-玛丽正专心地看着你，你向她保证你会尽你所能找到失踪的水手。 “哦，特拉帕准将，”安妮-玛丽说，当你转身离开并回头看了一眼时。“我希望你带着Commodores Cuthrew 和Finch 回来，或者根本不回来。祝你一路平安。” 她说最后四个字的方式让你不寒而栗。 “你有没有感觉到有什么东西是婊子没有告诉我们的？” 当你们三个离开宫殿并下降到码头为下一次航行做准备时，伊娃对你耳语。
梗概：
你点击这里开始你的冒险（记得先拿起著名水手名单）
--------------------
动作：
做
你开始你的旅程
结果：
第二天早上，您和您的同伴乘船驶出金沙，太阳在您的背上。Anne-Marie 还没有通知人们您的航程，因此没有像之前的航程那样欢呼的人群为您送行，这感觉奇怪地令人不安。与往常一样，您回望自己的祖国，想知道您是否还会再次看到她美丽的海岸。您的第一个目标是西部航道，该航道大约需要一周的航程，通过您熟悉的水域向西航行。您可以直接在那里航行，也可以向西北方向航行，参观茂密的森林岛屿紫水晶，您的人民的船只就是在那里建造的。
梗概：
你开始你的旅程
--------------------
动作：
做
你摆动西北去参观紫水晶
结果：
短途航行后，您将到达紫水晶，目睹另一个令人不安的景象。安妮玛丽女王的巨大旗舰海洋君主号停泊在港口，当您观看时，您可以看到几门新的大炮正在装载，以在甲板上装满枪支。这艘船已经全副武装，但现在看起来它包含大约二十门大炮，还有更多的被装载。看到这一幕让您深感不安，并且不明白为什么安妮-玛丽如此全副武装她的船，您没有降落而是将您的同伴从西南方向引向西部航道。
梗概：
你摆动西北去参观紫水晶
--------------------
动作：
做
西部通道
结果：
在海上航行六天后，西部航道出现在眼前，从很远的地方可以看到，就像大礁石无尽线上的一滴眼泪。您以前从未这样航行过，但 Naomi 和 Eva 都曾和他们的船带路，引导您的冒险家穿过狭窄的通道。与所有通道一样，已经建立了一个小营地，以监视所有进出通道的船只。Naomi 和 Eva 都在你的下方，你应该在这里停留一天，拿起 Anne-Marie 答应她存放在这里的补给品。
梗概：
西部通道
--------------------
动作：
做
你停下来接受新鲜的补给
结果：
知道这是有道理的，你命令你的船长同伴在西部航道抛锚，然后划上岸与守卫在塔楼的人会合。有两个：一个年轻人和一个老人，他们很乐意帮助您将一些备用的鱼箱装载到您的船上。当老人向您的水手展示板条箱所在的位置时，您问年轻人最近几个月他看到哪些船只通过西部航道。 “自从第二场比赛以来，告诉你真相的人并不多，”他挠着头说。“我想从那以后我们看到的第一艘船是舞者之影船长从西边进来，船上只有十几名水手。她在这里停了下来，拿起了补给品，然后驶向了金沙。” 这对你来说是个新闻，你皱着眉头。“在那之后，Cuthrew 和 Finch 航行了多久？” “哦，海鬼和皇家护卫队在那之后航行了大约一个月，但不仅仅是他们，”健谈的年轻人告诉你。“影子和不倦号跟他们在一起。他们都向西出去了大约一个月，然后影子和不倦号从西边不停地航行回来。没有海鬼或皇家护卫队的迹象，我想他们一定是旅行了。” 你大吃一惊，但在你可以问更多问题之前，两名守卫中年长的人重新加入了你的行列。 “不谈论任何不该谈论的事情，是吗？” 他以一种不友好的方式看着他的年轻同伴问道。年轻人接受了暗示，同意他最好继续履行职责。那天晚上，在你的三艘船上装载了新的补给品后，你向 Naomi 和 Eva 报告了谈话，你们三个都认为这看起来很可疑。 “为什么安妮-玛丽没有告诉我们争吵和舞者和卡斯鲁和芬奇在一起？” 娜奥米问你。“为什么他们没有他们就回来了？” “你们俩都参加了第二场比赛，”你告诉内奥米和伊娃。“当你从东到西绕着麦哲伦航行时，你有没有看到任何可疑的东西？” “我没有，但我和我的表弟尽可能地留在西北，很少登陆，”内奥米承认。“不仅仅是我和伊娃做了那次航行：芬奇和夸雷尔也做了。” “嗯，我一直在西南方向，也没有降落太多……”伊娃皱着眉头说。“你认为其他人在这些水域中发现了什么？” 你们都不确定地互相看了一眼，带着很多想法继续向西航行。
梗概：
你停下来接受新鲜的补给
--------------------
动作：
做
不期而遇的你
结果：
在航行通过西部航道进入未知之后，您命令冒险家、探险家和埃斯梅的喜悦号暂时保持稳定的西风航向。大礁石从您身后的视线中消失，但在西边看到了一个完全出乎意料的物体：一艘船！您很快就走近了，发现这艘船的大小与您自己的相似，但设计却完全不同，它位于水面较低的位置，帆是黑色的，而不是白色的。这艘船的名字印在船头上是牵牛花。当你航行时，你会惊讶地看到穿着奇怪衣服的人用你自己的语言大喊让你上船！
梗概：
不期而遇的你
--------------------
动作：
做
你命令你的舰队抛锚并遇到这个奇怪的来访者
结果：
你小心翼翼地驶向那艘奇怪的船，并在旁边锚定冒险家，与也在附近锚定他们的船的伊娃和娜奥米一起登上牵牛花。这艘奇怪的船上的水手穿着五颜六色的衣服，上面印着动物，他们看着你，就像你看着它们一样感兴趣。他们的指挥官是一个高大强壮的男人长的灰胡子谁看起来奇怪不高兴见到你。他走上前，用你自己的语言向你打招呼，尽管口音很重，不寻常。 “我的名字是 Azai，我是这艘船的船长，”他告诉你。“什么是你的名字？” “我是 Leo Trapa 准将，他们是我的同伴 Commodores Eva Freer 和 Naomi Swift，”你告诉他。绫濑一脸惊讶。 “确实？” 他若有所思地问道。“你可能会惊讶地发现我们已经知道你的王国，尽管我认为你不知道我们的王国。你现在正在进入 Elixi 国家的水域。直到最近我们被我们的女王 Renewal 统治，但她...... . 几个月前去世，她的儿子信玄现在是国王。我和我的六位船长拒绝为信玄和他的兄弟剑心和家康服务，现在我们在死亡的痛苦中航行，被我们的同胞宣布为非法。如果你航行在西边你会来到我们国家的首都 Revel 岛。在那里你会见到 Shingen 和他的兄弟们，但我建议你保持警惕。” 分享了这些情绪的浅井船长拒绝回答您的任何进一步问题，并且对您的祖国完全没有兴趣。“我们已经知道你的三岛了，”他告诉你。“我的人称你为 Navar，Barrier-Folk。我想现在是我们分道扬镳的时候了。” Azai 似乎很想上路，所以感觉完全困惑你和你的同伴回到你的船上并起锚。牵牛花号迅速驶向西南，并考虑着与您的奇怪遭遇，您的探险队继续向西行驶。
梗概：
你命令你的舰队抛锚并遇到这个奇怪的来访者
--------------------
动作：
做
长生不老药之地
结果：
继续向西行驶，您很快就会发现一片不同于以往任何地方的土地：它是一个繁荣的港口岛屿，大小与金沙相似，但在各方面都不同，拥有截然不同的建筑和船只。穿着奇装异服的人很快就挤满了码头，当您接近港口时，一个意想不到的景象让您冻结：海鬼正轻轻地停泊在港口的锚地。芬奇的船皇家护航号没有任何迹象。 小心地进入港口，你、伊娃和娜奥米都把你的船停在码头旁边，带领你的水手武装上岸，准备好与这些人会面。当你靠近时，人群退了回去，但三个高大强壮的男人，他们简直就像兄弟一样，大步向前迎接你。他们的首领是一个英俊的男人，一头飘逸的黑发扎成小辫，神采飞扬。“你好，你好，”他大叫着拉起你的手，握成一个巨大的拳头。他用奇怪的口音说你自己的语言。 “我叫信玄，我是艾力西国的国王！这里是我们的首都岛，我们称之为Revel。这是我的兄弟，剑心和家康。” Kenshin 是一个宽阔的男人，他带着黄牙咧嘴笑着握着你的手，散发着浓烈的酒精味。家康看起来最年轻，也是最苗条的，一身黑衣。当你握手时，他不会对你微笑，而是用蛇一样的狭窄眼睛审视你。 “如你所见，你的同胞已经拜访过我们，”信玄继续说，指着海鬼。“因此我们知道你们的语言。你们伟大的船长 Thomas Finch 和 Tobias Cuthrew 几个月前登陆这里。恐怕我有一个悲伤的消息要告诉你们。你们伟大的水手 Tobias Cuthrew 登陆时病得很重，他的病情加重了。我“很遗憾，他已经去世并被埋葬在我们镇的墓地。他死后，他的同伴芬奇船长乘船离开了。那是两个月前，从那时起我们就没有听到他的消息。” 你、伊娃和娜奥米交换了惊恐的眼神。你们王国最伟大的水手去世的消息让你们所有人都感到震惊。 “Cuthrew 的船员呢？” 伊娃要求。“他们后来怎么样了？” “他们中的许多人继续使用 Commodore Finch，”Shingen 高兴地说道。“许多人仍然居住在这个镇上，并成为我人民的好朋友。出于某种原因，没有人有回到你的安妮-玛丽女王统治的王国的冲动......现在我有兴趣与你的女王结盟所以我和我的人会给你你需要的所有帮助。我猜你打算继续寻找你失踪的芬奇同志？如果你明天回来，我会安排足够的补给品来重新供应你的船只，以便在码头，我们还将为您提供我们岛屿的地图，以帮助您安全航行。” “我们也必须去拜访库斯鲁准将的坟墓，”你告诉信玄王，他同情地点点头。 “明天，明天，”他告诉你。“明天早上回来。” 你、伊娃和娜奥米登上冒险者号，在那里你匆忙商议。伊娃很疑惑。 “我在 Cuthrew 启程前看到了他，”她告诉你们俩。“他病了，但没那么严重。我不相信这三个人中的任何一个。” “如果我们开航，他们会在海鬼号和港口的其他小船上追我们，”娜奥米指出。“我认为暂时还是和这些人一起工作比较好。明天我们可以上岸看看卡斯鲁是否真的被埋葬在岛上。” “我同意，”你告诉她。“明天我们将按计划上岸。 结果证明这是你一生中最糟糕的决定。
梗概：
长生不老药之地
--------------------
动作：
做
决定命运的一天
结果：
第二天早上，你、内奥米和伊娃带领你的水手上岸到 Revel 码头。和以前一样，有一群期待的人群在等着你，但今天的人群出奇地沉默，看起来很紧张。环顾四周，你会发现一些奇怪的事情：有一艘船正从南方迅速驶近该岛。眯着眼睛一看就认出来了：这是托马斯·芬奇的皇家护卫队！Eva 和 Naomi 也认出了它，并在码头的一半处冻结，你的水手也都停在你身后。伊娃抓住你的手臂，通过你传递兴奋的快感。 “我不喜欢这样，”她平静地说，这是你认识她以来第一次听起来很害怕。 信玄的人群分开，剑心和家康大步向你走来。与昨天不同的是，他们全副武装：信玄和剑心手里拿着拔出来的剑，家康拿着一把极短枪管的步枪，更像是一把手枪。当他们在离你几英尺的范围内前进时，他们身后的人群会向前冲，更多的武器出现。带着一种恐惧的感觉，你意识到你已经把你的整个探险队带入了一个陷阱。 “伊娃！娜奥米！回来！” 你喊。 信玄用他的剑冲向你，但你及时弹回，像你一样拔出自己的剑。与此同时，剑心用剑猛地一扫，刺穿了娜奥米·斯威夫特的双眼，尖叫着捂住了流血的脸。家康上前，咔嗒一声举起手枪，举起手枪，直接朝伊娃的胸膛开枪。她向后一甩，仰面着地，躺在码头上，睁着眼睛望着天空，脸上还挂着最后一丝惊讶的表情。 你怒吼一声，向着信玄狂砍你的剑，迫使他退后。钢篦，刀剑互相碰撞，火枪在两边轰鸣。一个火枪球从你的头上呼啸而过，当 Shingen 撤退时，你抓住失明的 Naomi 魔杖，用你的自由手臂将她的身体靠近你的身体，将她拖回安全的地方。你的九十名水手被困在码头的尽头，面对的人群是他们自己人数的几倍，死亡似乎是不可避免的。 你的同胞们高价出卖他们的生命。当您将 Naomi 拖回通往 Adventurer 的跳板时，火枪弹在您周围飞得如此密集，以至于您没有被击中似乎是一个奇迹，您的水手在码头上与人群作战。他们被无情地砍伐和屠杀，几十只设法穿过跳板逃到冒险家那里，但大多数被砍掉了。你设法让冒险家的锚升起，帆升起并设法离开码头，被迫放弃探险家和埃斯梅的喜悦，更不用说包括伊娃在内的同胞的尸体。 即使冒险者号带着三艘船上幸存的水手驶入港口，总共大约有三十名，而其中两倍的水手死在 Revel 的码头上，海鬼号还是爆炸了，发射了一排六门大炮。其中一枪将你的两名水手斩首，另一枪击中第三名水手的胸部，将他炸成几块。收集速度的海鬼向前移动以切断您的逃生。 然而，帮助终于到手了。接近的皇家护卫队发射了两门大炮和一连串的步枪射击，迫使海鬼转向远离。这让您有机会将冒险家滑出港口，然后直接驶向皇家护卫队，不知道谁在指挥，只知道他们站在您这边。当皇家护卫队返回南方时，您跟随她，在您和 Revel 的凶残暴君之间留出尽可能多的空间。
梗概：
决定命运的一天
--------------------
动作：
做
你震惊
结果：
伊娃·弗里尔死了。这似乎是不可能的，但这是真的。 娜奥米·斯威夫特已经失明，再也看不到了。你把她抬到你的船舱里，你船上的外科医生日夜照顾她的伤势。死了这么多人：你们总共失去了 60 多名水手和两艘探险队的船只：只有冒险者号和她的小船员留下了。三天来，您跟随皇家护卫队，不知道自己要去哪里，也没有特别关心，直到您来到一个岩石小岛。七艘大小与冒险者号相似但设计不同的船停泊在海上。在其中航行，你会遇到你的新盟友。 七位 Elixi 船长登上冒险者号。他们的首领是浅井，一个灰胡子老头，一脸忧心忡忡的样子，你可能以前见过。他的六个同伴是高大冷酷的天子，与弟弟龙造寺形成鲜明对比，龙藏寺是一个穿着鲜红色衣服的开朗青年。三浦是一个留着长长的黑色胡须的大男人，而他的哥哥六角则是个花花公子，穿着精美的衣服，优雅。真田是一个高大英俊的男人，而他的同伴崇光皮肤黝黑，不像其他人，在你的小屋里喝茶，而其他人则接受你的酒。你发现他们所有的名字都有些难以记住，在你沮丧的状态下，你无法提高关心他们中的任何一个的兴趣。你的思绪一直停留在伊娃身上，而你只是在听浅井说话的一半。 “你不是第一个来到我们土地的人，第一批来访者是你的两位船长 Finch 和 Quarrel，他们大约六个月前从西部来到 Revel，”Azau 告诉你。“后来一位名叫 Sapphira Dancer 的女人也从西方来访。那时 Renewel 还是女王，她控制着她邪恶的儿子 Shingen、Kenshin 和 Ieyasu。几个月之后，Dancer 又来了四艘船，这次是从东方来的。Dancer 、Quarrel、Finch 和 Tobias Cuthrew 是他们的四位队长。” “他们在 Revel 待了几个星期。白天你的同胞们与 Renewel 王后交谈，但到了晚上，他们在黑暗中与 Shingen 和他的兄弟们密谋。一天晚上发生了一场血腥政变：Shingen 在他母亲睡觉时割断了她的喉咙，而他的支持者控制了宫殿。在政变期间，Cuthrew 和 Finch 试图站在女王一边进行干预。Cuthrew 在战斗中被枪杀，而 Finch 的胸部被刺伤。他和他的一些水手设法回到了他们的船上并逃走。” “我和我的六位船长是 Renewel 女王的忠实支持者。当 Shingen 掌权并自称为国王时，我们带着我们的船只和准将 Finch 逃到这里。我们尽力帮助他，但 Finch 的伤口是致命的。知道他快死了，他把你和你的同伴的名字告诉我们，作为可以信任的人。他死后，我们把他埋在海里。从那时起，我们一直在西航道和狂欢之间的水域巡逻，希望能见到你。我看到你的船了一周前冲到这里通知其他船长。我们派芬奇剩余的船员登上皇家护卫队去狂欢会见你。我们猜你不会相信我们，但你不会怀疑你自己同胞的话……我们没想到Shingen 会在我们到达那里之前试图谋杀你们所有人。据 Finch 说，Quarrel 和 Dancer 告诉他 Shingen 和 Anne-Marie 已经结成联盟，他们会一起杀死所有批评或反对他们暴虐统治的人……” “我们很感激你的帮助，”你疲惫地说，躺在你小屋里的病床上，娜奥米目瞪口呆地点点头。“但我们现在能做什么？伊娃死了。娜奥米看不见。我们失去了探险家号和埃斯梅的欢乐号以及我们的大部分船员。你们这里有七艘船和几百名水手，但在狂欢中有成千上万的武装人员和在安妮-玛丽的控制下，金沙还有更多……我们能做什么？” 当 Elixi 船长互相扫视时，你盯着桌子看。 “我们有个主意，”Azai 最后说道。
梗概：
你震惊
--------------------
动作：
做
机会渺茫
结果：
“在这个世界上似乎有三个伟大的国家，你们的人称之为麦哲伦，”Azai 告诉你。“有我的族人，Elixi，有我们称为 Navar 的你们族人，Barrier Folk，还有 Lapau，我们了解到你们的族人曾与他们作战过的长矛独木舟战士。我们知道Anne-Marie 和 Shingen 结成了联盟。Finch 告诉我们，Quarrel 已经向他声称这个联盟的目的是在两条战线上攻击 Lapau 并粉碎他们，因为你们的人无法单独行动。被屠杀的 Lapau 的土地将被分为 Anne-Marie 和 Shingen。我和我的同伴们相信 Lapau 是我们唯一可能的盟友。” “卡普国王同意与安妮玛丽王后签订和平条约，”你告诉阿宰。“他的儿子是安妮-玛丽王后宫廷的人质。即使我们航行到马坦岛，即使他相信我们，这不太可能，拉波号能对我和你的联合海军做什么？你所说的纳瓦尔我们在与他们的战争中差点摧毁了 Lapau，他们当然没有机会对抗 Navar 和 Elixi。” “我们必须尝试，”Azai 坚持道。“与你的人民一起，纳瓦尔和我的人民被他们的统治者欺骗加入这个邪恶的联盟，拉波是我们唯一寻求帮助的机会。” “Matan 在世界的另一端，”你提醒 Azai。“要到达那里，我们要么穿越安妮-玛丽女王统治的领土，要么穿越你信玄统治的领土，要么穿越完全未知的水域。信玄现在拥有海鬼、探险家和埃斯梅的喜悦。我很确定他和他的兄弟们会带着他们所拥有的一切来追捕我们。你们这里有一支强大的小海军，但我认为这无法与安妮玛丽和信根的全部力量相匹敌。 “我们已经想到了，”Azai 告诉你。“我们将从四个不同的方向航行到马坦。我将向东北航行，绕过大礁石，穿过你们王国的殖民地到达马丹。天子和龙藏寺将向东南航行，绕过大礁石，也穿过你们王国的殖民地。马丹。三浦和六角将向西南航行，穿过未知的水域到达马丹，最后真田和崇光将向西北航行，穿过伊利西群岛到达马丹。你必须选择我们中谁最有机会通过。” 你的脑海里仍然充满了伊娃被杀时的惊讶表情。你深深地叹了口气，走到娜奥米躺在病床上的地方，眼睛上缠着流血的绷带，握住她的手。你低头闭上眼睛，沉默了片刻之后，你听到七名Elixi船长静静地站起来，走出去，为他们的航行准备他们的船只，等待你的决定。
梗概：
机会渺茫
--------------------
动作：
做
你不情愿的决定
结果：
那天晚上，当您与饱受痛苦的同伴坐在一起想知道您的世界怎么会如此迅速地天翻地覆时，Elixi 船长开始将他们的计划付诸行动。皇家护卫舰被遗弃沉没，剩下的船员分布在其他船只中：牵牛花中的浅井，无情的天子，幸运女神中的龙藏寺，皇家复仇者中的三浦，战士中的六角，魅惑者中的真田，财富中的崇光，当然还有冒险家中的你和娜奥米。为了帮助您跟踪您的新朋友，Azai 贴心地为您提供了一份忠诚的 Elixi 船长名单。 当内奥米终于陷入困境时，你上甲板呼吸新鲜空气并思考事情。太阳正从东方升起粉红色，显示出三艘从北方驶来的船：海鬼号、埃斯梅的欢乐号和探险家号。用您的望远镜，您可以看到所有三艘船的甲板上都挤满了大炮和水手。你听到身后轻轻的咳嗽声，转身看到站在附近的浅井。 “是信玄、剑心和家康，”他指着远处的三艘船说。“我想他们的意思是一劳永逸地摧毁我们。我们还不够强大，不足以用我们这里的两百名水手打败所有三个，所以我们必须立即离开去寻求拉波号的帮助。我已经给你的船补给了并重新载人，但现在你必须选择去马坦的四条路线中的哪一条。你在麦哲伦周围航行的距离比我多得多，必须使用你自己的知识和地图来选择你的路线。不过我们必须迅速行动：一旦信玄和安妮-玛丽知道他们在做什么，他们的船长会尝试在我们之前到达马坦，警告阿尔卡普国王不要听我们的！我们必须迅速。” 答应在一个小时内告诉 Azai 你的决定，你去你的小屋发现 Naomi 已经醒来。 “那我们要去马坦岛吗？” 她虚弱地问你，你告诉她看起来你必须这样做。娜奥米犹豫了一下，然后眼泪从她眼睛周围沾满鲜血的绷带上慢慢滴落下来。“我想感谢你在那里为我所做的一切......我知道你爱伊娃，现在她走了，你只剩下我了，我现在有什么帮助？除了负担什么都没有。” “你永远不会那样，”你向娜奥米保证。“如果我们要在这次航行中幸存下来，我将需要你所有的帮助和建议。” 用痛苦的拥抱抱住内奥米，你仔细考虑要走哪条路。 （请拿起并使用忠诚药剂队长名单来帮助您做出这个重要决定）。
梗概：
你不情愿的决定
故事名称：
68
故事背景：
很少有景点能像无月之夜旅馆温暖的灯光一样令人愉悦，尤其是当北风吹来，用冰冷的牙齿咬住你的皮肤时。你艰难地穿过泥泞和肮脏的水坑，渴望在又一天疲惫的旅行之前休息一下。橡木门很重，但山挡不住你，这接近温暖。 你没有失望。客栈里挤满了各种形状和大小的人。附近村庄的农民与修道院的黑袍僧人混在一起。孩子们在这里和那里飞奔，嘲笑醉酒和湿透的男人，他们躺在他们的麦酒里毫无知觉。一小撮衣衫褴褛的旅行者在巨大的火堆旁取暖，交换谣言，撕成黑面包。你在他们旁边坐下，点头表示欢迎，但眼睛只盯着火焰。只有当您再次感觉到您的手脚时，您的饥饿感才会恢复。酒保是个矮胖子，留着红胡子，似乎喜欢大喊大叫。他接过你的硬币，把一碗浓汤推给你。“欢迎，”他喊道，“欢迎来到红狮！” 你左边的男人开始谈论你的目的地，很快你们两个就开始聊天，而你明天忘记了前方的里程。突然房间里安静了，这可不是一件小事。甚至酒保也不再向他的儿子们吼叫了。所有人的目光都集中在刚刚站在中央桌子上的吟游诗人，手里拿着琵琶。他是一个好奇的，几乎是滑稽的人物，胳膊和腿比他的身体略长，一头白发像瀑布一样从肩膀上倾泻而下。然而，他的声音低沉而清晰，不难看出为什么大家都那么期待演出。吟游诗人弹了几下音符，咳嗽了两声，然后开始了他的故事。 一百年前或更久 当好国王 Freolaf 统治时 王国知道和平，从不战争。 他的儿子王子从不残忍 可敬的儿子，雷金纳德王子。 他也有一个女儿，一个宝石 简夫人，不过十二岁。 我们的王子确实很爱他的妹妹 他们的羁绊令人欣喜。 但危险以最快的速度降临 来自那个老恶魔，那个恶魔 Cantaphrax，热之王 谁的罪行，如果我从名单开始 会从这里一直延伸到月球！ 火族的恶魔 所有人都向他致敬，跳着他的曲子。 他眨眼就召唤了火焰 比中午的太阳还亮。 根据一个计划，法师确实认为 为了赢得他渴望已久的权力 来自褪色的书本中的咒语。 计划是这样的：让恶魔炙手可热 把简夫人偷走 ...
--------------------
动作：
做
你和她用一个恶魔结
结果：
然后 Cantaphrax 会举起他的刀 最纯银的女仆 斩断她的王室生活。 然后随着刀刃上溅出的鲜血 他会召唤一个最伟大的恶魔 并进行最邪恶的交易。 他会用仇恨给予女士的灵魂 并作为回报获得统治权。 他会统治——“这将是他的命运！ 于是巫师寻找可怜的简 通过派出魔奴 一个有链子的牛头怪 确实把她拖到了一个遥远的洞穴。 第二天早上，当雷金纳德醒来时 他知道他必须救他的妹妹。 王子拿起他的剑和斗篷 骑马离开他的城堡家 但来到一棵枯死的老橡树 他知道他不能再漫游了 必须选择向左或向右 在没有找到的尘土飞扬的道路上 如果是为了妹妹，他会战斗。 雷金纳德王子选择了这条路 ...
梗概：
你和她用一个恶魔结
--------------------
动作：
做
通往高山的你
结果：
王子骑在路上 身高越来越高 并且在他的真诚中从未放慢脚步。 穿过黑暗降临仍照耀着灯火 月亮和星星排列在上面 我们的监护人整夜都在上面。 被真挚的爱所驱使 雷金纳德王子确实窥探过一个洞穴 并且不惧危险 为这个黑暗阴沉的坟墓而生。 亲爱的听众，你猜对了 简夫人和法师的奴隶 也躺在这个洞穴里等待！ 王子靠近地狱般的喉咙 骑了进去，喊着“呸！” 牛头怪伸出爪子 并发出了他可怕的愤怒。 ...
梗概：
通往高山的你
--------------------
动作：
做
你和雷金纳德做了冲锋和抽签
结果：
他的剑斩妖牛！ 简夫人用铁链牢牢绑住 反对她可怜的债券确实拉。 血管膨胀的牛头怪 那跑过肉肉的形态 让他挥动他那沾满血迹的斧头 就像地狱的风暴一样无情。 银足太子叫道 随着血液开始沸腾起来 从曾经发芽的洞口 他的耳朵，转向他的敌人 他冲锋而起，长吼一声！ 雷金纳德王子将他的剑瞄准低 于是悲剧就发生了。 他错过了野兽，但凶猛的确实流动了 他姐姐的血，他没听见 她对着剑发出恐怖的尖叫。 现在在她的尸体旁，视力模糊 他震惊地瞪大了眼睛，然后转向 野兽，但来不及反应 他的头，一旦被切断就加入了宝库 肮脏的生物保持和堆积。 就像简女士那样 但很快这头野兽就被追踪到了 在愤怒和痛苦中死去 通过 Cantaphrax，他的计划失败了。 我的故事到此结束，所有人都被杀了。
梗概：
你和雷金纳德做了冲锋和抽签
--------------------
动作：
做
你和吟游诗人结束了他的故事。
结果：
在吟游诗人的最后一个音符之后沉默了片刻。你几乎可以感受到后颈的悲伤，将你逼入一个浅浅的坟墓。其他顾客开始鼓掌，你也开始鼓掌，惊叹这位魔术师能用言语唤起恐惧和死亡。很快，平凡的生活又开始了，闲聊着前方的道路或多年的回忆。你喝多了对你有好处，明天早上就够后悔了。 几个小时后，你上楼，倒在一张满是跳蚤的小床上。睡眠几乎立即到来，但这并不是平静的休息。有角和斧头滴血的幽灵追捕你，渴望将你腐烂的头颅加入堆中。随着时间的流逝，您会忘记梦想，一段时间后，甚至会忘记吟游诗人和他的故事。但他们每隔一段时间就会回来，只有文字才能在夜晚带来无名的恐怖，这是那个令人毛骨悚然的故事的证明。
梗概：
你和吟游诗人结束了他的故事。
故事名称：
69
故事背景：
她的目光从火贤转向了火贤。当然，他们也会像其他人一样背叛她，但她还不能驱逐他们。他们必须先给她加冕，但现在无论如何这都是一个毫无意义的头衔。火主。呸，什么废话。回去几个小时，这将是世界上最强大的职位。火国现在几乎统治了整个世界，但她的父亲决定让自己成为凤凰王，这不是最有创意的头衔，这让她只是一种荣誉……某种东西。名誉封臣？ 哦，我又开始胡思乱想了。我多么愚蠢。我像个疯女人一样胡言乱语。她会在火贤者背叛她之前驱逐他们。一切从妈妈说起。不，是从祖克背叛她开始，一出生就偷走了他们所有的母亲。至少父亲假装喜欢她。太糟糕了，当他不再需要她时，他只是把她扔在这里。帮助他对地球王国进行种族灭绝会带来一些有趣的结合时刻。 到底是什么让他们花了这么长时间？把王冠戴在我头上！这将使她成为有史以来最年轻、十五岁和第一位女性火之领主。她吹散了垂在左眼前的一缕头发。她应该像所有其他不可信的头发一样砍掉那根头发。也许她现在看起来并不完美。她说不清。在母亲不停地和她说话后，她摧毁了她的镜子，当然是从反射的另一边。她竟然有半年不老，甚至不和她说话的胆子！ 她大脑中逻辑部分的剩余部分向她尖叫，告诉她她的头脑不太好。那是幻觉，不是她的母亲。她所有的仆人、保镖和老师都没有试图杀死她。 阿祖拉忽略了她脑海中那个小小的声音。 就在傻瓜们要把那顶王冠戴在她头上的时候，她看到了他。他！他来了，在最不合时宜的时候回来了！他背叛了她和他们的国家多久了？周？他一直和这个光荣国家最讨厌的敌人躲在某个地方，但他来是为了战斗——不！他又来背叛她，就像每个人对她所做的那样，就像每个人都会对她做的那样。 阿祖拉站起来面对他，笑得像个疯女人。她对他说一个字。“祖祖。”
--------------------
动作：
做
你 1
结果：
婴儿伸手去拿明亮的球，但她永远抓不住它。为什么？其他球到了她的手上。那个没有。这让她很生气，但也让她更想要那个亮球。它温暖了她，让她感到活着。她伸出双臂再次抓住球。她又失败了。 高个子对她说着话。当然，阿祖拉一个人都听不懂，但话里似乎有些刺耳。她对那些曾经听起来不错的词有着遥远的记忆，但现在不会了。有一天她会知道，因为母亲不会对她说好话。相反，她将婴儿移到树荫下。 她决定不哭。无论如何，它永远不会有多大好处。明亮的球现在消失在她头顶的绿色东西下。 高人和矮人坐在水边。Azula 了解水。每天一次，即使她不喜欢这种感觉，她也会被放入水中。水感觉不好。明亮的球感觉良好和温暖，即使她从未到达过它。 两人往池塘里扔东西。愤怒的小东西游了过来，吃掉他们两个扔进水里的任何东西。Azula 看着他们，直到他们用完可以扔的东西。后来她才知道它叫做面包。高个子站在水边，矮个子走到她身边。 她听不懂他在说什么，但他的语气很高兴。他一遍又一遍地向她强调一个词。她看着他的嘴唇动作，听着她发出的声音。为什么不复制呢？毕竟，这就是她学习很多东西的方式。 “Z……” 他看起来更喜欢那个声音，所以他一遍又一遍地重复那个词。也许他坏了？也许他会通过再次发出那个声音来修复。 “祖祖！” 这是她说的第一句话。
梗概：
你 1
--------------------
动作：
做
你 2
结果：
阿祖拉咯咯地笑。看到这么多金属男人跑来跑去，她觉得很有趣。他们总是站着不动或跟在她身后，但现在他们似乎很疯狂。他们把这幅愚 蠢丑陋的画中的火熄灭了。害怕他们的红色盔甲和尖尖的头盔，但不害怕 Azula。不，她是一个不会感到害怕的大女孩。 “阿祖拉！” 当她的母亲站在…… 什么词的前面时，女孩感到害怕？挂毯！这是挂毯。妈妈发抖。一滴泪水从她的脸上滑落。妈妈为什么难过？我弯火了！ “你做了什么，阿祖拉？” 阿祖拉垂下视线。妈妈怎么会对她这么生气，而且只对她这么生气？她对其他人都微笑，嗯，除了爸爸。 “我……” 为什么我会遇到麻烦？“我弯了火，就像爸爸和祖祖一样。” “告诉我真相。你们两个。没有人会在两点下火。Zuko很少在四岁就开火。” “但我说的是实话。” 她交叉双臂。 “你把蜡烛藏起来了吗？请告诉我，你烧这个的火还没有燃烧。” “我用我的火烧了它！” “你烧掉了我对家人的唯一提醒。停止说谎。” “我没有说谎。我是你的家人！” 她一脚跺在地上。地毯在她的脚接触的地方着火了。不像以前，纯粹的快乐，现在她感到害怕。妈妈也会为此生气吗？ 妈妈二话不说，匆匆离去。金属人，也许他们被称为守卫，也将那团火熄灭。她观看。他们这样做很容易。为什么妈妈不骂他们？他们弯下腰，但她不介意。妈妈为什么不爱她？ “一个……阿祖拉？” 她转过头。祖祖站在门口，睁大眼睛。祖祖第一次弯腰时也烧了一些东西。这是妈妈裙子的一部分，漂亮的黑色裙子。发生这件事时妈妈很高兴，尽管火让她手上的皮肤看起来有点红。她爱祖祖。 “你做了什么，阿祖拉？” 他问道，呼应了他们母亲的话。阿祖拉再次垂下视线。
梗概：
你 2
--------------------
动作：
做
你给他看
结果：
她射了一些火。它让祖祖的衬衫着火了。在脱掉衬衫之前，他像个女孩一样尖叫。 “你为什么这样做，阿祖拉？” 他的眼里含着泪水。我伤害了祖祖吗？ “我……”她看着地面。“我想给你看。” 他又看了一眼自己的手臂。“火会伤人。它可以杀人。这就是为什么我们如此擅长征服世界。” Azula 在最后部分点头。“不要再这样做了。我……我有关于控制的练习。嗯……”他叹了口气，或者那是呻吟。“那你可以跟我来。” 她把头偏向一边。“真的？” “是啊，真的。” 祖祖会帮助她。她以前从来没有和他做过大孩子的事情。在她烧了他之后，他做了这一切？她的大哥要么是有史以来最好的人，要么是个大笨蛋。 他们听到一组新的脚步声。她转身看到一个她只记得之前见过两次的人，除了他们每晚紧张的晚餐。妈妈站在他身后。她看起来既生气又困惑。也许她想知道为什么祖祖的衬衫在地上冒烟了。 “爸爸？” 她问。祖祖后退三步。 “阿祖拉，你弯了火。” “是的。” 她握紧了她的小拳头。他会像妈咪一样生气吗？ “做得好。” 他假笑。在她生命中的这个时候，这让她感到害怕。最终，她将学会渴望得到这样的认可。
梗概：
你给他看
故事名称：
70
故事背景：
精灵-地精战争 莫登指挥官站在船头，向船上的每个人大喊大叫。 一年前，你应征加入了 Arcria Locroium 的精灵军队，参加了精灵-地精战争。6 个月以来，您一直在使用精灵士兵常用的武器——镰刀进行训练。 你在战斗、防御和治疗中学习了基本的魔法。除此之外，你除了是精灵之主亚伦·莱特森的儿子之外，没什么可看的。你的孪生兄弟纳瑞克在 2 年前被任命为龙骑士，并获得了天空之剑。 前弓门开始打开，锁链在慢慢打开时吱吱作响。光线倾泻而下，您会看到地精沙漠的荒凉土地，其后是地精丛林。但挡在你面前的是一大群哥布林，他们是绿色的，穿着黑色的皮革束腰外衣和紧身裤。他们中的每一个似乎都对你咆哮。 莫登指挥官对排说：“冲锋！” 每个人都跑出船，举起剑和盾。随即，战斗开始。
--------------------
动作：
做
你继续
结果：
在您周围，您会看到您的朋友死于地精军队射出的黑色箭矢。你会看到你的朋友倒下，你的对手，以及其他你从未有机会真正认识的人。几分钟之内，沙地就被精灵和地精的鲜血所覆盖。 你会在战斗中心看到一个孤独的哥布林，他的躯干由金属胸甲保护，这与大多数哥布林士兵所穿的皮甲不同。他手持一把带锯齿的大剑。
梗概：
你继续
--------------------
动作：
做
你挑战地精
结果：
你走到哥布林身边，对他大喊：“我向你挑战，你这个黄腹混蛋！” 地精看着你的方向并嘲笑你。“你，挑战我？你看起来一点也不比男孩大！” 他冲你咆哮，尖牙在阳光下闪闪发光。 他也比大多数哥布林大，几乎有 7 英尺高。 举起你的匕首，你冲向哥布林，盾牌挡在你的身前。你祈祷你能活下来。
梗概：
你挑战地精
--------------------
动作：
做
你试图斩首他
结果：
你冲向他，你的刀刃已经在空中挥舞。然而，他似乎已经来了，把他的剑降在你身上，劈开你的盾牌和盔甲。鲜血从你的伤口中涌出，地精将你向后踢，但在他将你的头砍成两半之前。
梗概：
你试图斩首他
故事名称：
71
故事背景：
你跳到空中，蜷缩成一个球并翻过地精。你轻轻地双脚着地，转身将对手的后背劈开，黑色的鲜血喷涌而出，染红了你的脸。还没等他反应过来，你的匕首就在他的腿上一扫，然后举起来拿掉了他的剑臂。哥布林倒在地上，痛苦地尖叫起来。 他看着你，脸上带着恐惧的表情。“求你了，”地精恳求道。
--------------------
动作：
做
你杀了他
结果：
你将你的匕首举过头顶，然后将它的尖刺入地精的头部。 就在你把它拉回来之前，火焰吞没了地精的身体，把它烧成灰烬。你吓得松开了剑，从刚才那是哥布林的灰烬堆中退了出去。 “拿起你的剑，阿里克！” 康。莫登对你大喊大叫。“我们还有一场战斗要赢！” 你麻木地拿起武器，转向剩余的哥布林部队。剩下的不多了 你把你的匕首举过头顶，大喊：“攻击！！！！！” 你冲向哥布林，其余的精灵军队在你身后。 在接下来的几个月里，地精们不断地被赶回他们的丛林，很快就被击败了。
梗概：
你杀了他
--------------------
动作：
做
你第2话
结果：
6个月后...... 那天，精灵-哥布林战争结束了。精灵帝国建立了对基奥拉的统治，以确保地精不再伤害任何人。 目前，你坐在军营的铺位上，等着你父亲的到来。虽然他同样爱你和你的兄弟，但你总是觉得你被纳里克超越了。 康。莫登走进你的房间，甚至懒得敲门。 “Aric，你父亲在这里，”他面无表情地说。“我建议你去迎接他。” 你从来没有真正喜欢过摩登，但你可以看出他很伤心。他之所以参战，是因为他发动战争的哥布林师将他的村庄全灭了。他没有家可以回去。 收集你的少数财产，你离开你的房间和 Com。摩登身后，前往庭院。 一个精灵男子正走出一辆制作精美的马车，一条蓝龙和红凤凰的图像在表面上翩翩起舞。他戴着银色头带和金色斗篷。他的衣裳剪裁考究，眼底却带着一丝忧伤。 你冲过去拥抱他，抓住他的肩膀，试图确保他真的在那里。 “Aric，儿子，是我，”你父亲对你说，对你的热情笑了笑。“我们有很多话要说。现在，我们上马车吧。”
梗概：
你第2话
--------------------
动作：
做
你跟着你父亲上了马车。
结果：
你一进入马车，你就转向你的父亲。“有什么事情需要和我谈？” 当马车开始向前行驶时，你问他。 “Aercaena 想念你，”他说。 你的思绪会自动转向你的未婚妻和心爱的公主 Aercaena Nornien，你不愿离开她。你全心全意地爱她。你们从小就认识。离开的前一天晚上，你们俩终于决定在皇宫的她床上共度一夜。 “公主？她还有什么要说的吗？” 你急切地问。 “除了这条消息：'亲爱的阿里克，我迫不及待地想再次见到你。在那之前请小心。我已经为你的到来做好了计划。'” 有那么一会儿，你能想到的只有 Aercaena 美味柔软的吻。 “阿瑞克，你在吗？” 你父亲问道，脸上浮现出一丝微笑。 “是的。” “那好吧。如你所知，龙凤族要平衡，必须同时有龙凤骑士。龙骑士继承了天之剑，而凤凰骑士则被赋予了剑光之光。你的兄弟纳瑞克，两年前被封为龙骑士，众所周知。光之剑，是每一个凤凰骑士挥舞的剑，被术士雷迪诺偷走了。” 你知道术士雷迪诺，那个大约 15 年前试图杀死沃里纳尔国王的施法败类。失败后，他躲藏起来，从此再无音讯；至少，直到现在。 “他为什么要这么做？” 你问。“我的意思是，我知道他是个精神病术士什么的，但是偷东西 “雷迪诺正在谋划对精灵帝国的复仇，他计划打造一把新的力量之剑，暗影之剑。目前，据我们所知，存在的力量之剑只有三把，所有这些都是由精灵帝国：光之剑，由凤凰骑士使用；天空之剑，由龙骑士使用；以及统一之剑，由精灵王使用。 “如果雷迪诺创造新的力量之剑来对抗光之剑，那将不得不做出可怕的牺牲。光的反面是什么，阿里克？什么消耗光？” 你父亲说完，用期待的眼神看着你。
梗概：
你跟着你父亲上了马车。
--------------------
动作：
做
你继续
结果：
“黑暗，”你不确定地回答，不想让你父亲失望。“黑暗吞噬光明。但我为什么要取回它？难道我们不能派遣精灵军队击败雷迪诺并从他的手中夺走光明之剑吗？” “唉，”你父亲说，“这并不容易。我们不知道他在哪里；只有纳里克知道，而且他不会向任何人透露这些信息，除了你。之所以必须是你，是因为想要嫁给艾尔卡伊娜，你必须是凤凰骑士，如果你不能重获光明之剑，那你就没有资格与艾尔卡娜结婚。而且，我将被迫将你排除在你的遗产之外，将你驱逐出境。精灵帝国。所以，你说呢？你会接受这个任务，还是你会拒绝与艾尔卡娜结婚的可能性？
梗概：
你继续
--------------------
动作：
做
您接受任务
结果：
“我会接受这个任务，”你肯定地说，决心嫁给 Aercaena。 “很好。如果你说不，我会被迫驱逐你，”你父亲说，脸上带着悲伤的短暂微笑。 “我什么时候走？” 你急切地问。 “只要你做好了适当的准备。目前，我们正前往凡世通庄园的家，”你父亲回答道。“在那里，我们会给你各种各样的装备，从武器到盔甲，从药物到毒药。但是，你在这场磨难中的主要武器将是你用来杀死哥布林酋长的匕首。” 你低头看着剑柄，点点头。 “我们还要多久才能到家？” 你问。 “三个小时，”你父亲回答。 如何度过接下来的 3 个小时由您决定
梗概：
您接受任务
--------------------
动作：
做
您在接下来的 3 小时内阅读一本书
结果：
在接下来的 3 个小时里，你读了一本破旧的“阿纳里亚的历史：我们世界的复杂而丰富的记录”。 当马车停在您儿时的家 Firestone Manor 时，您才刚刚读到关于仙女与恶魔之间战争的章节。 Firestone Manor 是一个庞大的建筑群和住宅群，适合居住在该物业的每个人。建筑物是用红砖建造的，整个庄园的皇冠上的明珠——凡世通庄园，似乎日夜都在泛着红光。不知何故，你似乎永远不会厌倦它。
梗概：
您在接下来的 3 小时内阅读一本书
--------------------
动作：
做
你得到了装备，和你一生挚爱的告别
结果：
你会立即被带到庄园的军械库，在那里你会得到最好的盔甲、武器和装备。帝国德鲁伊，卡里内托利本人，来给你一本她的个人法术书，“七种魔法指南”。 您还将获得一把能够承受最恶劣环境的龙角弓，以及永不耗尽箭矢的箭袋，从通常的箭矢到装有各种毒药的箭矢，再到能转动的箭矢进入元素弹丸。Karinetory 还可以强化您的镰刀，以保护它免受魔法刀刃的伤害。 您的父亲祝福您可能安全并从您的任务中返回，然后您的母亲说他们将在您第二天预定出发前为您举行告别宴会。
梗概：
你得到了装备，和你一生挚爱的告别
--------------------
动作：
做
你留到第二天
结果：
在接下来的几个小时里，Karinetory 为您提供 7 种魔法的高级课程，您可能会了解您所使用的新力量。 你学会通过结合速度、地球和空气的结合以及光来召唤闪电。你还学会了呼唤生命之火——所有生命的源泉，有了它，你可以控制生命，能够杀死、治愈或真正复活某人。您还将学习如何使用时间场，它可以停止或加速时间，并在您受伤时给您一个治愈自己的机会。通过使用太空魔法，你可以更快更远地穿越 Anaria 的表面，唯一的缺点是你只能创建通往你去过的地方的网关；没有捷径可通往 Redinor 的巢穴。你对水系魔法从来没有太大的兴趣，你父亲早就放弃教给你了，所以Kerinetory甚至懒得提。 在盛宴开始之前，你穿上你最喜欢的束腰外衣和紧身裤，然后将你的匕首系在腰带上。 离开你的房间，你走下楼梯到餐厅。 一进门，你就会发现你的全家，除了纳里克，你所有的朋友都聚集在巨大的桌子周围，桌子上有你所知道的每一种美食，还有更多。 很快你就会注意到，不仅你的家人和朋友在这里，皇室也在这里。其中，您会看到 Aercaena。 当会众看到你时欢呼雀跃，你微笑着挥手，笑容在他们脸上蔓延开来。最重要的是，你寻求 Aercaena 的认可，今晚，你发现她脸上露出灿烂的笑容。 坐在唯一可用的座位上，恰好就在 Aercaena 旁边，你父亲站起来开始说话。 “今天，”他说，“在我儿子 Aric 明天出发前往 Quest 之前，我们为他举办了一场盛宴。但让我们今晚别想那个……” 你父亲继续说下去，你试着集中注意力，但你能想到的只有 Aercaena。在你父亲的演讲中，你无法抗拒闲聊。 “你好，我美丽的人，”你说。 “你好，亲爱的，”Aercaena 说，她的声音就像音乐一样。你想做的就是和她一起离开，去你的房间。但你不能不尊重你的父亲。或许，在盛宴之后，你可以分享这个夜晚。 最后，你父亲最后说：“宴会开始吧！” 刹那间，每个人都开始把食物堆在盘子里，银器和餐具在吃的时候叮当作响。 离开一年多后，坐在 Aercaena 旁边感觉很尴尬。 Aercaena 代替您开始对话。 “那么，阿里克，自从战争结束后，你一直在想什么？” 她无辜地问道。
梗概：
你留到第二天
--------------------
动作：
做
你为什么不给你看？
结果：
“那我以后怎么不给你看？” 你调皮地说。 Aercaena 也恢复了同样顽皮的笑容，喉咙里升起一丝轻笑。 “我们很久没有……单独在一起了，”她回答道。“为什么我们不……在我们完成这里之后，一起度过一段美好的时光？” “我认为这将是一个绝妙的主意，”你急切地说。 在宴会的剩余时间里，你唯一能想到的就是与 Aercaena 共度一夜。毕竟，她是你一生的挚爱。当你在她身边时，你会情不自禁地感到快乐；她只是一个光明和解脱的灯塔，一个你爱的胜过一切。
梗概：
你为什么不给你看？
--------------------
动作：
做
一起度过的一夜
结果：
宴会结束后，你和 Aercaena 跑到你的房间，像孩子一样咯咯地笑。当您听到拐角处的声音时，您就快到了。 “我儿子爱上了你的女儿，”其中一位说。你立即认出它是你父亲的。“当然，你不能把它们分开，在他最近刚刚经历的长期磨难之后。” Aercaena 的脸上浮现出恐惧的神色，这与你的脸相配。你瞥了她一眼，她立刻把唇贴在你的唇上，不知道这是否是你们最后一次在一起。 “我的女儿玛丽安娜可能对纳里克有同样的感觉，亚伦勋爵，但我不知道他们是否适合彼此，”一个听起来像竖琴的声音说道。你知道它是妖精之王埃罗尼基的声音。“矮人王国和仙女王国之间的关系变得脆弱。他们的国王沃伦弗莱德要求我的一个女儿出手。我不想让安纳里亚再次受到战争伤害，而不是在你刚刚与哥布林经历过一次战争之后。我在这里的选择不多了。” 你记得知道仙女种族与 Anaria 有着独特的联系，能够感受到战争和污染造成的痛苦。这种能力是他们最大的强项和弱项。如果安娜莉亚卷入一场席卷整个星球的战争，那么就会将仙女们逼到绝境。 你松了一口气，很高兴讨论的不是你和 Aercaena。然而，你害怕纳里克和玛丽安娜。他们的关系就像你和 Aercaena 的。你不想看到它们被撕裂。 “你有很多女儿，埃罗尼基，”你父亲生气地说。“为什么不娶一个？” 你和 Aercaena 在拐角处窥视，并意识到争论比你想象的更严重。你的父亲将埃罗尼基抱在一个时间场内，阻止妖精王移动，但不限制他交谈。 “因为 Warrenfreid 想要我最年长的一个，”Erroniki 惊呼道。“他们几乎都没有了。妖魔之战，你们也知道，几乎所有的人都被暗杀了。” “那就把你的最后一个给他，”你父亲要求。“玛丽安娜和纳瑞克结下了不解之缘。矮人种族不断地想要新的东西。如果他看到她，他会知道她不再是少女；他会看到她被利用，而不是被他使用，而是被一个精灵使用。矮人麻木不仁; Erroniki，如果你对我有任何生气，你应该知道我说的是实话。你知道一个矮人公主来见她未婚夫的内战，只是为了让他看到她不情愿侵犯了她。他把她拒之门外，对她没有生气，而是转向了她的兄弟。随之而来的流血几乎摧毁了矮人。这样的事情会毁了安纳里亚。如果仙女被摧毁，那么恶魔就会被允许横行安纳里亚，杀了他们身后的一切。不要夺走我儿子的幸福，否则你将在两线作战。不要低估精灵帝国的力量，也不要认为你可以打破Vorinar 和 Warrenfreid 之间的友谊。你应该声明 再向我们开战，矮人和精灵之间就会结盟，这种 Anaria 从未见过。” Erroniki 愤怒的表情消失了。 “你是对的，Aaron，就像你以前说过很多次一样，”他平静地说。 你和 Aercaena 认为你已经侵入了足够长的时间，然后溜走了。
梗概：
一起度过的一夜
--------------------
动作：
做
你继续
结果：
最后，您和 Aercaena 到达您的房间。你们俩都无法抑制自己的兴奋。自从你离开去参加战争的前一天晚上你和公主有了这种程度的亲密关系以来，你就没有过这种感觉。
梗概：
你继续
--------------------
动作：
做
你闪回
结果：
你在皇宫，Aercaena在她的房间里。你们俩都躺在她的床上，一种小小的抑郁症让你们沉沦在你们俩躺着的地方。她的喉咙里发出轻微的呜咽声，但当你亲吻她时，你可以感受到她的微笑，没有无情的爱。 突然，她握住你的手，将他们拉到胸前，然后毫无预兆地把你翻到她下面，她自己翻到你上面。 时间流逝，你们都脱掉衣服，享受你们从未体验过的纯粹的狂喜。你继续，直到早上到来，你必须离开去参加战争。
梗概：
你闪回
--------------------
动作：
做
你一直在移动
结果：
你们俩进了门。 “等一下，”你说，锁上门，拉上窗帘。你的家人知道不要进入你的房间，但你也知道你永远不会太小心。 Aercaena 已经脱掉鞋子和衣服，只穿着白色蕾丝内衣。你也一样，渴望和她一起躺在床上。你们俩躺在一起，开始亲吻对方的嘴唇和身体。你们都在寻找一年多前体验过的美味狂喜。很快，你们都无法做任何事情，只能屈服于纯粹的本能，因为你们都撕掉了彼此剩余的衣服。 “我爱你，Aercaena，”你在她耳边低语，亲吻她的胸膛，再也无法抗拒。 “我也爱你，阿里克，”她在亲吻之间说，喉咙里发出呜咽声。 你必须回到她身边，你甚至无法考虑没有她的生活，不是现在，不是在考虑战后回到她身边之后。 “我会回来的，”你低声说。“我保证。但让我们不要想这样的事情，让我们享受今晚。” 艾尔卡娜点了点头，把你拉到她身上，淡淡的问道：“那你先让我开心一晚，在你必须走之前，好吗，我的爱人？” “我很乐意效劳，”你笑着说。
梗概：
你一直在移动
--------------------
动作：
做
你第 3 章
结果：
第二天早上你醒来，发现自己和 Aercaena 在彼此的怀里，你们赤裸的身体紧贴在一起。你可以看到她脸上的一缕头发，把它放在她的耳后。当你这样做的时候，她的脸上会洋溢着笑容。阳光从窗帘之间的缝隙中倾泻而下。 “醒了，我们呢？” 她调皮地问道，当她把你拉近她的时候，她嘴里发出一声轻笑。 “看起来是这样，”你亲吻她说。“哦，当我今天离开你时，我会多么想念你，Aercaena。” “我会一直想念你，”她说，手指在你的后背上滑过。 接下来的一个小时你们都在做爱，一直到有人敲门，你能听到妈妈的声音传来。 “阿里克，”她说，“你必须做好离开的准备。工作人员已经装备了你的马，沃里纳尔国王希望尽快与艾尔卡埃纳交谈。” 你和 Aercaena 下床穿好衣服，很快就手牵手从房间里出来了。你背上背着你的匕首，你的胸甲在你的束腰外衣上。
梗概：
你第 3 章
--------------------
动作：
做
庭院
结果：
将艾尔卡埃纳留在皇族专属的招待所，前往庭院。 你会在那里发现一群人，其中有 Aercaena，她脸上带着担忧的表情。你的马不耐烦地嘶吼着，其中一位牵着缰绳的马夫。 你开始拥抱你亲爱的每一个人，最后一个你成为 Aercaena 的人。与其只是拥抱她，不如亲吻她。在你离开之前，她在你耳边低语：“祝你好运。我会等你的。” 当你走向马匹时，你的父亲来到你身边，递给你一个钱袋。 “好好利用它，阿里克，”他说。“里面有一大笔钱，可以赎一个国王。不要让任何人看到。” 你走开，你父亲和艾尔卡娜的话在你脑海中回荡。骑上你的马，你拿起缰绳，感谢马夫，递给他一颗拳头大小的红宝石。他对着你的笑容咧嘴一笑，把宝石塞进了口袋。 你向人群挥手，然后用靴子的后跟轻推马的两侧。他疾驰出院子，一团微弱的尘土从他的蹄子里喷出。 您骑车直到到达一家名为金狐的旅馆。下马后，您将缰绳交给驻扎在入口处的一名马夫，并付给他一枚银币。 您进入旅馆，寻找特定的精灵。
梗概：
庭院
--------------------
动作：
做
你继续
结果：
你发现你的兄弟在其中一个摊位里，用高脚杯喝酒。走向他的路上，纳里克注意到了你，把高脚杯放在桌子上，笑容在他脸上蔓延开来。他从座位上站起来拥抱你。 “你好，Aric，”Naric 说。他拍拍你的背，然后坐回原位。“你需要什么？” “我需要找到雷迪诺，”你简单地说。 “那个叛逆的术士？” 纳里克怀疑地问道。“为什么？” “雷迪诺偷了光之剑，”你解释道。“他打算为它制造一把伴剑。如果他这样做了，那么光之剑就会失去力量。如果那样的话，按照父亲告诉我的，帝国将没有凤凰骑士，而且我将无法与 Aercaena 结婚。纳瑞克，我请求你作为兄弟和朋友的帮助。” “我会帮你的，”纳里克说。“雷迪诺撤退到他在森林中的塔——” “Dyor Kranick，”你替他说完。 “是的，”纳里克确认道。“这是他最能防守的位置，因为创造力量之剑是一项漫长而累人的工作。你当然知道迪奥·克拉尼克在哪里。” “龙之岛。” “没错。我知道一条通往森林的路，可以带你到雷迪诺的塔楼，”纳里克说，靠得更近了。“你必须使用龙路骑到岛的另一边。不要让自己被道路两边的'捷径'所诱惑。你听说过这些故事。进入其中的人几乎不会回来再出来。 “到达另一边后，转身走第一个进入森林的入口。只沿着红色的石头走，没有其他颜色，明白吗？红色的小路会带你到雷迪诺的塔。我会帮助你，但任务的条款规定你必须独自完成这件事。不过，我可以把这个给你。” 纳里克把手伸进口袋，拿出一个小袋子。 “那是什么？” 你问，好奇。 “杂草丛生，”纳里克解释道。“很有价值，很稀有。喂给马，它可以连续几天不休息地狂奔。一片叶子足以维持一个星期的状态。不要松散它，还有谨慎使用它。明白吗？ “我明白了，”你说，从座位上站起来拿起那袋杂草。“代我向银牙问好，好吗？” “那条龙开始变得越来越少了，”纳里克笑着说。“她想念你。” “我知道，”你说，离开旅馆，走向马厩。
梗概：
你继续
--------------------
动作：
做
你去龙岛
结果：
在接下来的几天里，您将乘车前往精灵帝国边缘的码头。每天晚上，你都选择睡在路边，不在视线范围内，认为这比睡在旅馆里更安全。 在你的旅程中，路人停下他们正在做的事情，指着你，兴奋地互相窃窃私语。 很快，您到达码头并支付前往龙岛的单程旅行费用，认为一旦您取回光之剑，您就可以召唤您的凤凰。至少，这就是你所希望的。 摆渡人只需不到一天的时间即可到达您的目的地，因为您可以通过增加风速和平息海浪来帮助他。 登陆小岛后，可以看到皇宫大小的群山，高大如天的树木，以及横贯小岛全长的白色大理石道路——龙道。龙，并被 Dyor Kranick 森林所包围。 感谢摆渡人并从牲畜仓库卸下您的马，您开始了为期 3 天的徒步旅行，前往小岛的另一边。
梗概：
你去龙岛
--------------------
动作：
做
你继续
结果：
你给你的马喂了一片杂草叶，立刻感觉到他突然的不安。你骑上他，用脚后跟戳他的两侧，催促他全力疾驰。 随着速度的爆发，你的马似乎飞起来了，他的蹄子很少接触路面，但一旦接触到，就会发出很大的撞击声。风吹拂着你的脸和头发，带走了你所有的力量。 在你前面，你会听到尖叫声，并看到一个少女被一群男人袭击。 那些人看到你就冲进森林，但在你用你的刀砍倒他们之前。你完成最后一个，然后将你的马推到少女身边。 “你还好吗？” 你问，有点担心。 “我以为我已经死了，”恰巧非常漂亮的女孩说道，“但后来，你来了。” 她对你微笑，无辜地捻着她的头发。她的喉咙里发出一声轻笑，顿时让你觉得有些不安全。 “好吧，我必须走了，”你说，因为她没有受伤而松了一口气。你推开你的马。 “等待！” 她在你的马开始疾驰之前说。
梗概：
你继续
--------------------
动作：
做
你听她的
结果：
你转身，停在她面前。 “什么？” 你不耐烦地问。“我需要走到路的尽头。不管是什么，请快点。” “我正想问你能不能带我去东部码头，”少女说。 “好吧，无论如何我都要去那里，”你不确定地说。 “谢谢，”她一边说一边爬上你身后的马鞍，双手轻轻搂住你的腰。 你的马疾驰而去，你的新伙伴突然握紧了她的手。 “他怎么动作这么快？” 她在狂风中大喊。 “当我到达龙岛时，我给他喂了一些杂草丛生，”你向她解释道。 接下来的两天你们互相认识了，你们很快就分道扬镳了，她有足够的钱来支付去童话王国的旅行，你的脸颊上有一个吻，还有一根长长的银绳，可以听命于敌人。
梗概：
你听她的
--------------------
动作：
做
你进入 Dyor Kranick
结果：
您进入 Dyor Kranick，并立即在您面前找到 2 条路径：一条由红色石头制成的路径，一条由白色水晶制成的路径。知道你哥哥说要走红石之路，你开始了新的旅程。 沿着红色的小径，你会听到奇怪的声音：看不见的鸟儿的叫声，大型生物的咆哮，还有看不见的生物的声音。尽管很诱人，但你拒绝离开这条路。 很快，你会在树叶的缝隙之间看到一座巨大的塔，它的顶部消失在云层中。
梗概：
你进入 Dyor Kranick
--------------------
动作：
做
你第4话
结果：
你到达塔楼，看到雷迪诺的家族印章，一棵燃烧的树，上面印着黑旗。从你的马上下来，你抽出你的匕首并穿过塔的门。 在里面，你会发现各种各样奇怪而神秘的物体，都散发着神奇的能量。你爬楼梯到下一层，找到一群流氓哥布林，每个人都拿着剑和盾牌。他们都对你咆哮，准备进攻。
梗概：
你第4话
--------------------
动作：
做
你把它们都放在一个时间域中
结果：
你将你的敌人放置在一个时间场中，让他们以惊人的速度老化，直到他们一无所有，甚至连骨头上的灰尘都没有。 你爬下一组楼梯，只发现一个向上的梯子。您可以听到上方各种机器的声音，并谨慎选择从下方抓起的一小块附魔金属。剥去它所有的魔法属性，你把金属扔进你上方的攀爬竖井中，然后等待。 金属回落所需的时间不到一秒钟。它落在您的脚下，无需太仔细观察，您就可以看到嵌入其中的箭头。你听到嘶嘶声，金属似乎正在分解，你推断箭头涂有酸。 你只有一种选择。
梗概：
你把它们都放在一个时间域中
--------------------
动作：
做
你速度攀登
结果：
利用胸甲内的魔法，你将空气和地球结合起来，让自己移动得更快。从第一梯级开始，您开始爬上塔的其余部分，同时箭头从 Redinor 设计和制造的机器中射出。 你会发现移动速度比射出的箭还快很累。想要受到保护而不仅仅是拥有更快的速度，您会在楼层之间找到一个空间，没有射弹瞄准您。终止速度魔法，你会导致你身后的空气变得更加密集，从而形成一个隐形的盾牌。 继续攀登，您会听到箭在气盾上砰砰作响，但不敢回头，因为害怕让箭射中您的脸。能够移动得更慢也可以让你节省能量，为此，你很感激。 很快，你就到达了塔顶。在攀爬竖井的存在处，您会发现一个由锻造和魔法转化器组成的实验室。你可以看到房间中央的光之剑，钢索连接在剑身和剑柄上。你小心翼翼地在设备表中导航，努力不让任何人注意到你的存在，你伸手从监狱中取出力量之剑。然而，就在你接触之前，一张放在桌子上的羊皮纸引起了你的注意。 你仔细一看，发现他不仅要窃取光明之剑的力量，还要将其转化为黑魔法，将其放入已经拥有相当强大力量的力量之剑。 你会听到曾经处于休眠状态的机器轰鸣着复活的声音，你可以看到能量从光之剑流过钢索进入魔法转化器。 “找出我的计划是什么，不是吗，阿里克？” 一个病态的声音在你身后说。 拔出你的匕首，你转过身来，发现一个光头男子拿着一把术士法杖和一把剑，他的刀刃是黑色金属制成的。你知道这是雷迪诺。 “为什么？” 你问。“你为什么要这样做？” “因为我和魔王和巫后做了交易，”雷迪诺说，他的脸上露出邪恶的笑容。“将光之剑的力量交给新的力量之剑，然后作为礼物献给魔王，让他征服阿克里亚洛克罗伊姆的精灵帝国，然后消灭妖精。之后，我就给我一个后宫，随心所欲。 “但抛开我的计划，你熟悉魔术师对决吗？” “不是真的，”你回答，“但是当我摧毁你的屁股时，我会有更多的经验！” 雷迪诺举起他的法杖，发出了一个咒语，一个黑魔法球从法杖中喷涌而出，朝你飞来。 你跳开，用你的匕首对准他，召唤出一个光魔法漩涡。 “聪明的孩子，”雷迪诺评论道。“光明抵消黑暗，黑暗吞噬光明。” “只要黑暗不在你身边，”你回答，“那么你就容易受到我最强大的武器的伤害。” 你的光之漩涡与雷迪诺的法杖接触，将它折成两半，将他从空中抛向墙壁。 Redinor 倒在地上，但他并没有因为不适而倒吸一口凉气。相反，他只是站起身来，将剑切换到之前握住他的手杖的手上。 “你知道这是什么吗，阿里克？” 他问道，指着那把黑色的刀刃。“是你毁灭的工具，影之剑！”
梗概：
你速度攀登
--------------------
动作：
做
魔术师的决斗第2部分
结果：
他冲向你，刀尖指向你的心脏。你立即准备跳到他的上方，但在你可以之前，你注意到他的刀刃充满了黑魔法。你没有跳过雷迪诺，而是冲到一边，他的剑尖砰地一声撞到了工作台上。 Redinor 将他的刀刃转向你并发射第二颗黑魔法球，这颗比第一颗更大更强大。这次无法躲避它，你举起你的匕首挡住了黑暗球体，用光向它充电。一旦你的刀刃接触到邪恶的魔法，它就会破碎。 “呜呜！” 你哭。虽然你的剑能够承受攻击的冲击，但它无法完全保护你。你被抛到空中，在你恢复之前，雷迪诺就在你身上。见你不设防，他将暗影之剑放在你的右臂上，砍在你的肘部。当你感觉到你的思想和手臂之间的联系永远被切断时，痛苦的尖叫从你的嘴唇中逸出。没有魔法可以治愈这样的伤口，即使是活火之力也无法逆转。 抓住你的树桩，你滚出雷迪诺的下一次攻击，他的刀刃击中了石地板。你发现自己正站在光之剑所在的基座上。当你被黑暗球体击中时，你的弓折断了，尽管现在它对你没有任何用处，不是失去你的手臂。 站起来，将电缆从光之剑上拉开，用剩下的手抓住它。你转向魔法转化器并挥动你的剑，在它的魔法容器中刻出一道深深的伤口，释放出光之剑的所有力量。光魔法飞回你的新武器，它的刀刃再次以新的强度闪耀。你看着一个黑色能量的小球向黑色飞去 雷迪诺在空中飞舞，他的剑举过头顶。举起你自己的，你挡住了他的向下斜线，让他飞了回来。用嘴从上衣上撕下一条布条，尽力阻止血液流动。 雷迪诺站了起来，不愿屈服。你没有让他恢复过来并再次攻击，而是向他冲锋，拒绝从你的打击中缓和下来。每次你将光之剑带到雷迪诺身上时，他几乎没有足够的时间做出反应并带着自己的剑与你的剑相遇。他很累，他一分钟都不能做。最后一击是你的。 你选择召唤火，而不是用光魔法为你的剑充电。你向后一跃，将你的刀刃指向雷迪诺，然后从刀刃中爆发出一道火焰龙卷风，将他包围。他在火焰中的尖叫对你来说毫无意义，因为他已经威胁到了整个 Anaria。 一旦雷迪诺的尖叫声停止，你就会使火焰旋风消亡。你的敌人只剩下灰烬和燃烧人肉的气味。你跪倒在地，再也站不起来了。然后，你将光之剑拖到膝盖上，并记得你的家人最后一次握住剑刃是什么时候。
梗概：
魔术师的决斗第2部分
--------------------
动作：
做
你闪回
结果：
那是 15 年前，那时你只有 5 岁。你父亲从第六次妖魔大战归来，他的凤凰在最后一战中阵亡。他的脸上，浮现出一抹永不散去的悲伤。 你是第一个跟他打招呼的，但就在那时你才发现有些不对劲。你的叔叔瑟林没有和他在一起，但他的剑，天空之剑，放在你父亲的腿上。从他的马上下来，你的父亲把你放在他的肩膀上，痛苦的笑容在他的脸上蔓延开来。 你妈妈和纳里克是下一个。你父亲问你母亲 Therin 的妻子在哪里，她告诉他她在庄园里。他带着你，走进客厅，在客厅里找到了她。 瑟琳的妻子格温来自人类王国。她的儿子是昆汀，几个世纪以来第一个半精灵。 把你放在客厅地板上，你父亲拿出了天空之剑。 “这是你的，”他对格温说。“你是他的妻子。等他长大了，你把它拿去送给昆汀是正确的。” “不，”格温说，推开天空之剑。“造物主在骑士之初就明确告诉精灵，天之剑和光之剑只能留在凤龙宫。” 昆汀脸上充满希望的表情消失了。你知道他想成为下一个龙骑士，但这并不意味着。 塞林的葬礼结束后，沃里纳尔国王将光之剑和天空之剑妥善保管。不久之后，你和 Aercaena 开始花越来越多的时间在一起。 你们的婚约不是一出生就定的，而是在你们都15岁的时候，在你们彼此表白很久之后。
梗概：
你闪回
--------------------
动作：
做
你希望
结果：
你低头看着光之剑，欣赏着大刀和镶嵌在金剑柄中央的活火宝石。 然后你就会想起 Aercaena。你答应过她，你会回来的。召唤你剩余的力量，你让自己和光之剑从龙岛移动到皇宫，特别是在Aercaena的房间里。 你倒在地上，筋疲力尽。Aercaena 看到你就会尖叫。她的脸上洋溢着灿烂的笑容，但随后她注意到了你的手臂。 “带我去医务室，”你用憔悴的声音说。 她把你的残肢搭在她的背上，同时把你扶起来。她一半拖着你，一半抱着你，把你带到医务室，在那里你很快瘫倒在床上。在您陷入昏迷之前，您可以听到 Aercaena 与一群目瞪口呆的医务人员交谈。他们从你身上拿走你的胸甲和束腰外衣，还有光之剑。然后，只有黑色。
梗概：
你希望
--------------------
动作：
做
你第5话
结果：
你醒来发现自己还在你倒下的床上，只是舒服多了。你花点时间环顾四周，发现你父亲正坐在床边的椅子上，睡着了。 “父亲，”你说，抓住他的手臂轻轻摇晃。 他喘着气醒来，然后微笑着看着你。 “你醒了，”他说。“我必须说，当矮人对你动手术时，我们很担心，但他们向我们保证你会成功。我已经看到他们的工作成功太多次了，不能怀疑他们。” “你是什么意思？” 你问。 你父亲伸手拉开毯子，露出一个包着绷带的东西，看起来是一只手的形状。 “他们修好了？” 你口吃。你看着你的父亲，脸上洋溢着灿烂的笑容。他不回答。当齿轮和活塞的旋转声在空气中弥漫时，你用你的新手挠了挠头。你停下来，机器的声音也停下来。 “那是什么？” 你问。“我睡了多久？” “两个星期。” “回答我的第一个问题。” “他们无法解决所有问题，”你父亲开始说。“没有任何魔法能够真正治愈你所受的创伤。但在医治你之后，沃里纳尔国王派来的一群矮人来到这里，声称发现了一种新的魔法——金属。 “他们带来了……好吧，也许你应该自己看看，”你父亲说完。
梗概：
你第5话
--------------------
动作：
做
你透露
结果：
你撕开绷带，发现一只机械手贴在你的手臂上。每次您弯曲它时，精致机械的柔和而奇怪的放松声会从魔法强化钢板内部充满空气。 “什么力量？” 你问，凝视着闪亮的附属物。 “你是什么意思？” 你父亲好奇地问。 “是什么让它像现在这样移动？” “你的思想，以及金属魔法的力量，”你父亲解释道。“这种新型技术，它是同类中的第一个。你很幸运得到了这份礼物。” “幸运的？！” 你说，突然生你父亲的气。“因为我自己的愚蠢，我让雷迪诺把我的手从我的胳膊上砍下来。因为我的愚蠢，我比以前少了！” 羞耻的热泪开始从你的脸上流下来，弄脏了你衬衫的白色面料。 “你很幸运，因为那些矮人专门为你设计了这只手，”你父亲说，似乎对你出乎意料的爆发并不担心，如果不是沮丧。“他们本可以选择用木头为你建造一个简单的、非机械的替代品，但不，他们为你量身定制了这个。而且，你已经证明自己是我将成为的精灵的两倍，Aric。你征服了黑暗，这是许多人都没有声称的壮举。你应该感到荣幸，而不是生气。” 你不情愿地回答说，“我想是的。”
梗概：
你透露
--------------------
动作：
做
你以后.....
结果：
1周后…… 你和 Aercaena 在她的房间里，这是你们第一个做爱的地方。 你深深地看着她绿色的眼睛，无法移开视线。你轻轻地吻她，然后她握着你的手。 “Aric……”当你离开时她叹了口气。 “是的？” 你试探性地问。 “只是后天，当你成为凤凰骑士的时候，我们应该结婚。我简直不敢相信已经6年了......确定我们是故意的对于彼此。” 几天前宣布您的婚礼将举行，而您却无法在远离 Aercaena 的地方度过一晚。 “嗯，要不要庆祝一下？” 你问，同样淘气的笑容在你脸上蔓延。 “天道之爱，你还用问吗？” Aercaena 说，她的手臂环住你的脖子，深深地亲吻你，以至于你的呼吸都被偷走了。 “我只是想我可能会，”你脱掉衣服说。
梗概：
你以后.....
--------------------
动作：
做
新凤凰骑士
结果：
第二天，你发现自己跪在沃里纳尔国王面前，他的剑轻轻敲击你的肩膀，他说：“凭借勇气和纯粹的决心，作为你与生俱来的权利，我，精灵帝国的沃里纳尔国王，请命名你，阿里克莱特森，新凤凰骑士。好好为阿纳里亚服务，你和你的房子将得到造物主的奖励。” 当你站起来接受你的新武器光之剑时，欢呼声响起。一个花童将它交给 Vorinar，后者将它交给你。那是用火纹装饰的皮鞘，你握住剑柄，将圣剑拔出，光芒漫天。 然后你会看到一个凤凰蛋，几乎可以孵化了。 你转身面向观众，将光之剑和凤凰蛋举过头顶，盛宴开始。不久之后，你和 Aercaena 逃到她的房间，在你们成为夫妻之前一起度过最后一晚。
梗概：
新凤凰骑士
--------------------
动作：
做
婚礼
结果：
第二天，你穿着白色西装，站在走道的尽头，Naric 在你身边，Mariannai 在另一边。然后，Aercaena 出现了，穿着一件纯白色的连衣裙。 她沿着过道向你走来，手里捧着一束白百合，脸上挂着你从未见过的最大笑容。 当她到达你身边时，Kerinetory 执行必要的仪式，并在用银丝带将你的双手绑在一起后，宣布你们为夫妻。 随后的庆祝活动持续了整整一周，刚好有足够的时间让矮人给您最后一份礼物。他们回来了，并为您展示了一套盔甲，其头盔的样式类似于凤凰头。感谢他们，你更仔细地检查了盔甲的其余部分，发现缺少正确的护手。你对自己微笑，弯曲机械手指，听着机械滴答作响。
梗概：
婚礼
--------------------
动作：
做
结束
结果：
Vorinar 把你拉到一边，让你为他讲述你的故事。你这样做，当你完成时，他问你一个重要的问题。 “现在暗影之剑在哪里，阿里克？” 杀死雷迪诺后，你还没有去寻找那把被诅咒的刀刃，这一认识让你心生恐惧。 “它肯定还在雷迪诺的塔里，”你恐惧地回答。 “那么让我们祈祷，当它被发现时，你已经准备好了，”沃里纳尔冷酷地说。“我看到战争在地平线上酝酿，这场战争将永远改变我们所知道的生活。光打败影子，影子吞噬光。你知道我的意思，阿里克。” 他消失在人群中，为了暂时忘记他的警告，你转向 Aercaena 和她连续跳舞几个小时，直到你再次消失在她的房间里。
梗概：
结束
故事名称：
72
故事背景：
你醒来后咧嘴笑了。你做到了。你的朋友打赌三辆车你不会睡在鬼屋里，然后活着出去。你肯定还活着。你环顾四周，看到闹鬼商店的巨型蜥蜴怪物。等一会儿！闹鬼的 Radio Shack 没有巨型蜥蜴怪物！它甚至没有那种奇怪的耳语声！ “攻击小猫！是的，继续攻击它！Iwan Napee国王！吃掉它是你的工作！我们将立于不败之地 你把自己推回角落。这绝对是一个房间，但这绝对不是闹鬼的Radio Shack。你开始咬指甲，这是一个坏习惯。但那是当你意识到你的指甲是爪子的时候！等等，什么！你看看你的手，呃……爪子……你也有用脚痒耳朵的冲动！你变成了一只小猫！你低头看你的肚子。你是一只小猫，是的，一只肥猫。对你太坏了！ 再过一年！是的！是的！是的！拿走，拿走肥肉！它对我们一无所知！越不好奇 一只蜥蜴野兽对你说：“小猫！我们是野兽！我是伊万国王，我们所有的二十八种濒临灭绝的物种都会把你分开吃掉！” 他们都向你扑来，但你跳起来躲在他们身后。 更好！不！不要让它跑掉！它可能会知道我们的秘密！吃它，让我们都可以一年无敌！ 你再次环顾四周，发现了其他几只用墙壁伪装的小猫。你也可以闻到它们的味道。他们在看着你。什么？！你自己想想，想办法逃跑。唔... 不要让它知道我们的弱点是番茄酱！不要让它知道！不要让它思考！别！” 你发现了三件事。桌子上的一根香肠里插着三把刀，角落里有一艘适合两只小猫的微型火箭飞船，墙上挂着一个大煎锅。你做什么工作？
--------------------
动作：
做
你进入火箭！
结果：
在野兽的牙齿咬断你的脖子之前，你立即冲进火箭。令您惊讶的是，火箭内有一只戴着墨镜的小猫在跳舞。看到他心烦意乱，你冲向控制板。你可以感觉到野兽在摇晃火箭。只有一件事要做。 你拨动开关和按钮，敲击控制板。在打击垫上敲击时，您不小心按下了“请勿打孔”按钮。哦哦。 跳舞的小猫丢下太阳镜和甜甜圈，盯着你看。“你刚才是不是——” 还没来得及耸耸肩，火箭就爆炸了，其中一个大块刺中了兽王伊万的左眼。哦哦！这不好。你被发射到窗外，但幸好你有降落伞！ 你在天空中潜水，呼吸新鲜空气。啊啊啊啊好啊 然而，你的降落伞被一小块火箭撕成碎片。不好了！你疯狂地环顾四周。你想尝试降落在哪里？
梗概：
你进入火箭！
--------------------
动作：
做
你降落在附近的森林里
结果：
你在树的叶子上蹦蹦跳跳。你决定再弹几下。然后你意识到叶子没有弹性。谁在乎？你思考，然后在树叶上跳来跳去。 随即有些暴躁的声音喊道：“喂！这是私有财产！这些叶子是给手套厂的！等一下！停在那里！” 你从树叶上弹开并尽可能快地奔跑。警察小猫朝你跑来，注意到你肩膀上有东西。“哦……如果你是胖子，那你可以留下来。等一下，所有猫科特工部队的成员都肩上扛着蜥蜴的鳞片！” “告诉我真相！” 警察小猫用拐杖指着你。
梗概：
你降落在附近的森林里
--------------------
动作：
做
不管它有多疯狂，你都说实话！
结果：
你决定说实话。你告诉他一切，从你出生的那天到你敢吃奶酪的时间，到现在的时间。警猫瞪大了眼睛。 “哇，这是个故事，孩子！这绝对是真的！只有最高级别的警察才知道这一切。你是幸运的孩子。我是一个反对所有牺牲混乱的叛逆者。” 警察小猫咆哮着 FAT，它是 Feline Agent Troop 的首字母缩写。他们是保护地球免受被称为野兽的巨型蜥蜴怪物的小猫。他还告诉你，每年，小猫都会带走一个人，把他们变成一只小猫，作为祭祀野兽的祭品。他告诉你，牺牲只是为了在 FAT 试图找出他们所有的秘密时让野兽们拖延一会儿。 “我的名字是沃尔多，孩子。如果你需要警察，只需拨打我的电话号码。问题是，唯一可以给我打电话的公用电话是你所在的野兽塔。” 他给你一个电话号码。伟大的！ 沃尔多继续带你去米滕村。
梗概：
不管它有多疯狂，你都说实话！
故事名称：
73
故事背景：
童年 一拳砸在你的脸上，将你打倒在地。Gruzub站在你身边，笑着。 “马兹基尔！你跟谢泼德一样弱！” 当你跳起来时，他笑了。
--------------------
动作：
做
你打他
结果：
你挥动拳头，但 Gruzub 迅速躲开并在你的胃部肘击你，将风从你的肺里吹走。在你设法滚开并回到战斗姿势之前，他又打了两拳。当你准备向他冲锋时，你听到一个低沉的声音。 “孩子们！你的妈妈在努力安抚小家伙，而你的兄弟在打羊。现在不是玩耍的时间。还有工作要做！在里面！” 你父亲吼道。
梗概：
你打他
--------------------
动作：
做
你进去
结果：
你赶紧冲进去，后面跟着你的孪生兄弟。你住在一个小木屋的小村庄里。幸运的是，你的父亲是马格拉什酋长，这让你可以睡在最大的小屋酋长小屋里。当你进入时，你看到你的父亲穿着一件粗糙的羊毛上衣，正在磨他的巨剑。在他的面前，是他那厚重的钢板铠甲，以及他的战利品。 “Mazkil，Gruzub，你打架太多了。妈妈想哄Dagden睡觉。” “是的，爸爸。” 你们两个都回复。 “谁赢了？” 他问。 “我做到了！” 你们都说，然后瞪了对方一眼。 “你不知道？也许你战斗得不够。格鲁祖布，到河边去取水炖。玛兹基尔，磨快我的剑。我需要点燃壁炉。” 你走到剑前，开始在磨石上磨刀，同时看着你父亲的战利品：精灵匕首、萎缩的头颅、破旧的金盾、破碎的战斧、镶钉的狼牙棒、一系列部落项链和龙骨。 “爸爸？” “是的？” “能说说你的奖杯吗？” 你问。 “关于我的奖杯，我已经告诉过你很多次了。” “再说一遍。” 爸爸笑了，你会喜欢这种刺耳的声音。 “你想知道什么？”
梗概：
你进去
--------------------
动作：
做
你精灵匕首（精灵）
结果：
“啊，匕首。我最喜欢的一个。精灵是一个“美丽”和“完美”的生物种族，他们不知道战斧和木斧。对他们所有人来说都是死亡。他们在诸如此类的事情上很愚蠢如何生火和如何打猎，但似乎喜欢哲学和阅读。死亡应该带走他们。不过，他们刀锋飞快，弓箭手好，这是我在追捕他们中的一群人时发现的进入我的领地。那把刀差点进入我的喉咙，然后我刺穿了那个挥舞着它的小混蛋。”
梗概：
你精灵匕首（精灵）
故事名称：
74
故事背景：
你赶紧冲进去，后面跟着你的孪生兄弟。你住在一个小木屋的小村庄里。幸运的是，你的父亲是马格拉什酋长，这让你可以睡在最大的小屋酋长小屋里。当你进入时，你看到你的父亲穿着一件粗糙的羊毛上衣，正在磨他的巨剑。在他的面前，是他那厚重的钢板铠甲，以及他的战利品。 “Mazkil，Gruzub，你打架太多了。妈妈想哄Dagden睡觉。” “是的，爸爸。” 你们两个都回复。 “谁赢了？” 他问。 “我做到了！” 你们都说，然后瞪了对方一眼。 “你不知道？也许你战斗得不够。格鲁祖布，到河边去取水炖。玛兹基尔，磨快我的剑。我需要点燃壁炉。” 你走到剑前，开始在磨石上磨刀，同时看着你父亲的战利品：精灵匕首、萎缩的头颅、破旧的金盾、破碎的战斧、镶钉的狼牙棒、一系列部落项链和龙骨。 “爸爸？” “是的？” “能说说你的奖杯吗？” 你问。 “关于我的奖杯，我已经告诉过你很多次了。” “再说一遍。” 爸爸笑了，你会喜欢这种刺耳的声音。 “你想知道什么？”
--------------------
动作：
做
你缩小了头（死灵法师的军队）
结果：
“啊，那个！我用比你大一倍的剑从一个大混蛋身上取下了那个。那个该死的死灵法师，我不记得他的名字了，玫瑰僵尸食尸鬼，骷髅，尸鬼，死亡骑士，幽灵，鬼魂，谁知道他在追求力量的过程中还有什么。他带领一小群死亡骑士来到这里，将一些兽人僵尸添加到他的收藏中。当他看到真正的兽人能做什么时，他跑得比鹿还快！哈！”<|endoftext|
梗概：
你缩小了头（死灵法师的军队）
--------------------
动作：
做
结果：
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梗概：
--------------------
动作：
做
你赶紧冲进去，后面跟着你的孪生兄弟。你住在一个小木屋的小村庄里。幸运的是，你的父亲是马格拉什酋长，这让你可以睡在最大的小屋酋长小屋里。当你进入时，你看到你的父亲穿着一件粗糙的羊毛上衣，正在磨他的巨剑。在他的面前，是他那厚重的钢板铠甲，以及他的战利品。
结果：
“Mazkil，Gruzub，你打架太多了。妈妈想哄Dagden睡觉。” “是的，爸爸。” 你们两个都回复。 “谁赢了？” 他问。 “我做到了！” 你们都说，然后瞪了对方一眼。 “你不知道？也许你战斗得不够。格鲁祖布，到河边去取水炖。玛兹基尔，磨快我的剑。我需要点燃壁炉。” 你走到剑前，开始在磨石上磨刀，同时看着你父亲的战利品：精灵匕首、萎缩的头颅、破旧的金盾、破碎的战斧、镶钉的狼牙棒、一系列部落项链和龙骨。 “爸爸？” “是的？” “能说说你的奖杯吗？” 你问。 “关于我的奖杯，我已经告诉过你很多次了。” “再说一遍。” 爸爸笑了，你会喜欢这种刺耳的声音。 “你想知道什么？”
梗概：
你赶紧冲进去，后面跟着你的孪生兄弟。你住在一个小木屋的小村庄里。幸运的是，你的父亲是马格拉什酋长，这让你可以睡在最大的小屋酋长小屋里。当你进入时，你看到你的父亲穿着一件粗糙的羊毛上衣，正在磨他的巨剑。在他的面前，是他那厚重的钢板铠甲，以及他的战利品。
--------------------
动作：
做
你打败了金盾（人类帝国）
结果：
“盾牌？盾牌……啊，是的！盾牌！我从一个喝得太多，打得太少的花式骑士身上取下的。让我告诉你关于男人的事，小家伙，他们有很多，但他们“软弱、愚蠢、懦弱。它们很容易坏掉。它们是我们小老家的最大威胁，但它们忙于与精灵战斗，无暇与我的部落发生冲突。”
梗概：
你打败了金盾（人类帝国）
故事名称：
75
故事背景：
你赶紧冲进去，后面跟着你的孪生兄弟。你住在一个小木屋的小村庄里。幸运的是，你的父亲是马格拉什酋长，这让你可以睡在最大的小屋酋长小屋里。当你进入时，你看到你的父亲穿着一件粗糙的羊毛上衣，正在磨他的巨剑。在他的面前，是他那厚重的钢板铠甲，以及他的战利品。 “Mazkil，Gruzub，你打架太多了。妈妈想哄Dagden睡觉。” “是的，爸爸。” 你们两个都回复。 “谁赢了？” 他问。 “我做到了！” 你们都说，然后瞪了对方一眼。 “你不知道？也许你战斗得不够。格鲁祖布，到河边去取水炖。玛兹基尔，磨快我的剑。我需要点燃壁炉。” 你走到剑前，开始在磨石上磨刀，同时看着你父亲的战利品：精灵匕首、萎缩的头颅、破旧的金盾、破碎的战斧、镶钉的狼牙棒、一系列部落项链和龙骨。 “爸爸？” “是的？” “能说说你的奖杯吗？” 你问。 “关于我的奖杯，我已经告诉过你很多次了。” “再说一遍。” 爸爸笑了，你会喜欢这种刺耳的声音。 “你想知道什么？”
--------------------
动作：
做
你粉碎了战斧（矮人）
结果：
“啊，这个！我小时候和矮人打过，那时候我和你一样叫Youngblood。那天我获得了Magrash Shatteraxe的名字！矮人是小混蛋，看到一个会让你笑，但我爱他们！众神把他们弄成小丑来取笑，而矮人变得愤怒、固执、酗酒，他们会挥舞斧头！他们躲在隧道里，挖得太多，但我还是尊重他们。”<|endoftext|
梗概：
你粉碎了战斧（矮人）
--------------------
动作：
做
结果：
<|startoftext|
梗概：
--------------------
动作：
做
你赶紧冲进去，后面跟着你的孪生兄弟。你住在一个小木屋的小村庄里。幸运的是，你的父亲是马格拉什酋长，这让你可以睡在最大的小屋酋长小屋里。当你进入时，你看到你的父亲穿着一件粗糙的羊毛上衣，正在磨他的巨剑。在他的面前，是他那厚重的钢板铠甲，以及他的战利品。
结果：
“Mazkil，Gruzub，你打架太多了。妈妈想哄Dagden睡觉。” “是的，爸爸。” 你们两个都回复。 “谁赢了？” 他问。 “我做到了！” 你们都说，然后瞪了对方一眼。 “你不知道？也许你战斗得不够。格鲁祖布，到河边去取水炖。玛兹基尔，磨快我的剑。我需要点燃壁炉。” 你走到剑前，开始在磨石上磨刀，同时看着你父亲的战利品：精灵匕首、萎缩的头颅、破旧的金盾、破碎的战斧、镶钉的狼牙棒、一系列部落项链和龙骨。 “爸爸？” “是的？” “能说说你的奖杯吗？” 你问。 “关于我的奖杯，我已经告诉过你很多次了。” “再说一遍。” 爸爸笑了，你会喜欢这种刺耳的声音。 “你想知道什么？”
梗概：
你赶紧冲进去，后面跟着你的孪生兄弟。你住在一个小木屋的小村庄里。幸运的是，你的父亲是马格拉什酋长，这让你可以睡在最大的小屋酋长小屋里。当你进入时，你看到你的父亲穿着一件粗糙的羊毛上衣，正在磨他的巨剑。在他的面前，是他那厚重的钢板铠甲，以及他的战利品。
--------------------
动作：
做
你镶嵌了钉锤（Rebel Orcs）
结果：
“啊……狼牙棒。孩子，一些兽人坚持他们的部落。一些作为雇佣兵四处游荡，或者注册成为士兵。一些成为叛军，袭击者针对每个人类、精灵、矮人和其他任何阻碍他们的人。他们是疯狂的、固执的、嗜血的傻瓜。为了说服老克莱姆加入，我从他们身上取下了武器。我也拔掉了他的几颗牙齿，但我已经没有了。”
梗概：
你镶嵌了钉锤（Rebel Orcs）
故事名称：
76
故事背景：
你赶紧冲进去，后面跟着你的孪生兄弟。你住在一个小木屋的小村庄里。幸运的是，你的父亲是马格拉什酋长，这让你可以睡在最大的小屋酋长小屋里。当你进入时，你看到你的父亲穿着一件粗糙的羊毛上衣，正在磨他的巨剑。在他的面前，是他那厚重的钢板铠甲，以及他的战利品。 “Mazkil，Gruzub，你打架太多了。妈妈想哄Dagden睡觉。” “是的，爸爸。” 你们两个都回复。 “谁赢了？” 他问。 “我做到了！” 你们都说，然后瞪了对方一眼。 “你不知道？也许你战斗得不够。格鲁祖布，到河边去取水炖。玛兹基尔，磨快我的剑。我需要点燃壁炉。” 你走到剑前，开始在磨石上磨刀，同时看着你父亲的战利品：精灵匕首、萎缩的头颅、破旧的金盾、破碎的战斧、镶钉的狼牙棒、一系列部落项链和龙骨。 “爸爸？” “是的？” “能说说你的奖杯吗？” 你问。 “关于我的奖杯，我已经告诉过你很多次了。” “再说一遍。” 爸爸笑了，你会喜欢这种刺耳的声音。 “你想知道什么？”
--------------------
动作：
做
你问龙骨（龙）
结果：
“啊，头骨。龙是可怕的生物。它们有多种颜色，都拥有强大的技能。他们说龙语，许多人说我们的语言。有些很聪明，而有些由于多年与世隔绝而变得异常野性和狂野。它们的火焰往往和它们的鳞片颜色相同，喜欢囤积财宝，发动战争和破坏。它们是孤独的生物，龙。如果你杀了一只，戈尔戈克会高兴地看着你。”<|endoftext|
梗概：
你问龙骨（龙）
--------------------
动作：
做
结果：
<|startoftext|
梗概：
--------------------
动作：
做
你赶紧冲进去，后面跟着你的孪生兄弟。你住在一个小木屋的小村庄里。幸运的是，你的父亲是马格拉什酋长，这让你可以睡在最大的小屋酋长小屋里。当你进入时，你看到你的父亲穿着一件粗糙的羊毛上衣，正在磨他的巨剑。在他的面前，是他那厚重的钢板铠甲，以及他的战利品。
结果：
“Mazkil，Gruzub，你打架太多了。妈妈想哄Dagden睡觉。” “是的，爸爸。” 你们两个都回复。 “谁赢了？” 他问。 “我做到了！” 你们都说，然后瞪了对方一眼。 “你不知道？也许你战斗得不够。格鲁祖布，到河边去取水炖。玛兹基尔，磨快我的剑。我需要点燃壁炉。” 你走到剑前，开始在磨石上磨刀，同时看着你父亲的战利品：精灵匕首、萎缩的头颅、破旧的金盾、破碎的战斧、镶钉的狼牙棒、一系列部落项链和龙骨。 “爸爸？” “是的？” “能说说你的奖杯吗？” 你问。 “关于我的奖杯，我已经告诉过你很多次了。” “再说一遍。” 爸爸笑了，你会喜欢这种刺耳的声音。 “你想知道什么？”
梗概：
你赶紧冲进去，后面跟着你的孪生兄弟。你住在一个小木屋的小村庄里。幸运的是，你的父亲是马格拉什酋长，这让你可以睡在最大的小屋酋长小屋里。当你进入时，你看到你的父亲穿着一件粗糙的羊毛上衣，正在磨他的巨剑。在他的面前，是他那厚重的钢板铠甲，以及他的战利品。
--------------------
动作：
做
你不要再问奖杯了
结果：
“我完了，爸爸！” 爸爸拿起刀片，舔了舔边缘。 “是的……锋利到可以喷出龙血。干得好，玛兹基尔。现在，去取水果。”
梗概：
你不要再问奖杯了
--------------------
动作：
做
四年后...
结果：
“你们在这个星球上已经十四年了，你们什么都没学到，孩子们，除非你们见过我和丹达尔抓过一头野猪！” 你和 Gruzub、你的哥哥 Bagig 和他的朋友 Dendar 在树林里散步。你的腰带上有一把小铁刀，手里拿着一把弓。到目前为止，在你短暂的生命中，你只吃过鹿、山羊和鸡，但今天之后，你就可以享用你帮助杀死的野猪了。 “在你第一次狩猎时，吃鹿的粪便来获得它的气味是一种传统。” 丹达尔评论。“这个大家都知道。” 丹达尔和巴吉格停了下来，单膝跪地，指着一些小粪便。 “继续，Gruzub，Mazkill。挖进去。”
梗概：
四年后...
--------------------
动作：
做
你吃屎
结果：
你双膝跪地，闭上眼睛，迅速吞下一小块药水。Bagig 拍了拍你的头，向 Gruzub 点点头。Gruzub 也这样做了，Dendar 大笑起来。 “你们两个是白痴！” Mazkil 和 Gruzub Youngblood 正在吃鹿粪。” 当你的脸变红时，他们会笑。你张开嘴冲他们大喊大叫，但随后就僵住了。您在一条看不见的小溪边发现了一只美丽的鹿。你抓住你的兄弟，指向它。 “啊，很好看，马兹基尔·施泰特。让我们抓住它，丹达尔。” “我们不能分心。我们来这里是为了打野猪。” “我们会抓住鹿和野猪！” 巴吉哭着追赶鹿。 鹿突然跑了起来，巴吉和丹达尔迅速跟了上去。 “留在这！” 巴吉离开时冲你大喊大叫。
梗概：
你吃屎
--------------------
动作：
做
你留在那里
结果：
你耐心地站在看着你的 Gruzub 身边。 “所以你知道，奥拉尔是我的。” 他平静地说。 你考虑一下。Oraal 是一个胖而懒惰的女孩，她是剑匠的女儿。 “欧拉尔？她父亲有没有答应你，如果你在她的腿间爬行，就会得到一把好刀？你傻笑。 “嘿！她已经宣布了她……！” 在他睁大眼睛之前，他开始了。
梗概：
你留在那里
--------------------
动作：
做
你问怎么了
结果：
你张开嘴想说话，但你的背部被击中并在空中飞了起来。你摔倒在地，你的头撞在树根上，鲜血直流。当你面对一头准备再次冲锋的大野猪时，你颤抖着站起来，拔出你的刀。 “小心！” 格鲁祖布喊道，跑过去帮忙，但很明显他的速度太慢了。 野猪冲向你，它的象牙在你附近看起来非常锋利......
梗概：
你问怎么了
--------------------
动作：
做
你滚到一边
结果：
你滚到一边，勉强躲过野兽獠牙的刺痛。当它跑过时，你拿着刀向前一跃，切开厚厚的皮毛，抽了相当多的血。它尖叫着，然后向前跑去，再次转身准备另一次冲锋。
梗概：
你滚到一边
--------------------
动作：
做
你又躲了
结果：
野猪冲锋时，你试图滚到一边，但这一次它已经做好了准备。你大喊大叫，因为它的象牙划伤了你的身体，造成了巨大的伤口。
梗概：
你又躲了
--------------------
动作：
做
你继续
结果：
Gruzub 冲过来，用他的匕首刺中了野猪，让你有足够的时间跳起来。你冲向野猪，反复刺伤它的脖子。它会尖叫，但在它能够抵抗之前很容易被杀死。它因你的壮举而死。
梗概：
你继续
--------------------
动作：
做
你欢呼吧，敌人被征服了！
结果：
你把脚搁在野猪的獠牙上，大笑起来。 “哈！我杀了野猪！” “嘿！” 格鲁祖布喊道。“我帮助了。” Dendar 和 Bagig 冲出树林，一只鹿跨在他们之间。 “我们抓到了鹿。我敢打赌……” 他们停下来，注意到了那头野猪。“诅咒死神，这是一头野猪吗？” 你得意地点点头。 “死亡之首，玛兹基尔，你让我印象深刻。你不再叫玛兹基尔幼血，现在你是玛兹基尔野猪！” 巴吉笑道。 你骄傲地微笑，知道今晚你会吃野猪肉。
梗概：
你欢呼吧，敌人被征服了！
--------------------
动作：
做
一年后...
结果：
“Mazkil Boarsbane！Gruzub Fastfoot！我们走。” 爸爸喊道。 你迅速跟随你的父亲走出你的小房子，来到连接村庄的小土路上。 “孩子们，你们做得很好。当我看到你们在战斗中成为最好的哈克爪拳时，我感到非常自豪，我给你们准备了一份礼物。” 你深情地微笑。Harkor 侮辱了你的兄弟 Bagig 和 Dendar。他们最近才被一名死灵法师军队残余的死亡骑士杀死，而你确保 Harkor 为他的侮辱而受苦。当你和 Gruzub 完成时，他的脸已经塌陷了一半。 你父亲递给你们每个人少量的金币。 “丹达尔·箭眼今天要去里弗顿。他要推车去取一些东西来帮助我们度过冬天。推车去享受一下吧。” 你感谢他，急切地数着你的硬币。25枚金币，都为你骄傲地闪耀。 “走吧，孩子们。尽情享受吧。”
梗概：
一年后...
--------------------
动作：
做
你前往里弗顿
结果：
在与丹达尔一起长途跋涉到里弗顿之后，丹达尔主要是在侮辱你和格鲁祖布，你最终到达了小镇。您会立即找到多种方法来花费您的宝贵硬币。镇上较贫穷的地区有一家妓院，一家满是您想品尝的美味佳肴的大商店，一个小赌局和一家小酒馆。
梗概：
你前往里弗顿
--------------------
动作：
做
你离开里弗顿
结果：
用完金币后，您将返回丹达尔的购物车。不幸的是，您发现自己身处城镇的贫困地区，显然走错了路。当你穿过一条小街时，一个声音在喊叫。 “你好！我们有点迷茫吧？” 一个声音在哭。 你转身，面对三个拿着刀的人。 “滚蛋。” 你生气地哼了一声。 “‘滚蛋’，他说。真是个愤怒的兽人。” 身后传来一个声音说道。 你转过身来，看到又有两个男人挥舞着刀。 “听着，先生。把你的硬币给我们，我们会看到你找到你要去的地方。”
梗概：
你离开里弗顿
--------------------
动作：
做
你向男人收费
结果：
你向前奔跑，挥舞着你的刀片。你砍断其中一名男子的喉咙，他向后倒下。第二个人深深地割伤了你的肩膀，你抓住他的头在墙上反复地砸，把它压成纸浆。你感觉到另一把刀划伤了你的背部，你大喊大叫，转身刺穿另一个人的眼睛并杀死他。剩下的两个人看上去有些紧张，但一个人却是鼓起勇气向前跑去。他向前俯冲，将刀刺入您的手臂，导致您的刀落下。你用另一只手抓住他，在他尖叫时用拇指将他的眼睛推过他的眼睛，然后跺着他的头，直到他在地板上留下污点。到那时，最后一个人已经离开了，他跑得最快。
梗概：
你向男人收费
--------------------
动作：
做
你崩溃
结果：
你砰的一声倒在地上，失去了知觉。当你醒来时，你在 Dendar 的手推车后面，Gruzub 就在你身边。 “发生了什么？” “你杀了很多人，晕倒了，我把你拖过来了。” “啊。那我还活着。很好。” “等我们回到村子里，我们会给你医疗帮助。” “好的。” 你咕哝。 “对付小偷干得好，玛兹基尔。你已经证明了自己是一个真正的兽人。”
梗概：
你崩溃
--------------------
动作：
做
一年后...
结果：
兽人部落习俗很简单。酋长的儿子们打架，当酋长被杀或达到四十时，获胜者接替他。爸爸刚满 40 岁，所以这是你一生都在为之准备的东西。你坐在战士之神戈尔戈克神殿前的格鲁祖布旁边，默默祈祷。 “我会尽量不杀你，你知道的。” 格鲁祖布轻声说道。 “我尽量不杀你。” 你回复。 “爸爸杀了他的兄弟。” Gruzub 笔记。“对于我们中的一个人来说，死亡可能是平静的。” “爷爷没有杀他弟弟。” 你补充。 “说得好。不管怎么说，我爱你。你是我的兄弟，我会为被你杀死而感到自豪。” “嗯，我很伟大，很强大。我杀死的每个人都应该为此感到自豪。” 你轻笑。 “我收回，我要杀了你，你这个混蛋。” 爸爸来了，你们俩都笑了。他悄悄地取下他的部落珠子，将它们披在神殿周围。 “啊。我的脖子感觉光秃秃的。” 他叹了一口气。“孩子们，明天将是你们生命中最重要的一天。你们中的一个将成为胜利者和酋长，另一个将倒下并可能死去。我只是想让你们都知道，我对你们感到骄傲。” 你努力不流泪，顺便说一下，Gruzub 很紧张，你知道他也在挣扎。 “我一直像我父亲对待我一样对待你，严厉但充满爱心。无论你成为酋长、战士、雇佣兵，甚至是一个抱怨的面包师，你都会是有史以来最伟大的。” 你父亲点点头，将他的大剑放在神殿前。 “很晚了，去吧，睡觉，休息。” 你点点头，站起身开始向小屋走去。当你走的时候，Gruzub 开始轻笑。 “什么？” 你问。 “你还记得我们小时候试图用鸡毛制作滑翔机的时候吗？” 你笑。 “而我们站在山顶上，你正准备跳下去。” 你补充。 格鲁祖布微笑着点点头。 “当你意识到鸡不能飞时，你把我拉了回来。” 格鲁祖布微笑。 “哈！你会死的！还记得我们抓到第一头野猪的时候吗？” 你微笑。 “是啊。那天晚上我吃了太多的野猪，我差点吐了。” “那是蜂蜜酒，格鲁祖布。” “我很怀念那些日子。” Gruzub 点点头。 你到达小屋，拥抱了 Gruzub。 “祝你明天好运。” 你说。 “祝你好运。” 你们都去睡觉了，对明天感到害怕和紧张。
梗概：
一年后...
--------------------
动作：
做
第二天...
结果：
你站在 Gruzub 和 Gorgok 神殿旁边，全身披着镀钢盔甲。神父金瑟站在神殿旁边，手里拿着两把短剑。 “Gorgok！看好这场战斗，请祝福这些战士。看好这场战斗，选出最伟大的酋长！” 牧师哀号。 他拿出两把剑，你抓起一把。Gruzub 抓住另一个。 “战斗开始！先让对方站不住的胜利！” 你采取了战斗的姿态，就像 Gruzub 一样。在你们俩挥动刀片之前，你慢慢地向前移动。碰撞时火花四溅，战斗开始。你击中了 Gruzub 的胸口，同时他向后跌跌撞撞地击中了第一击。他先抽血，砍开一小块没有保护的手臂。很明显，你是最优秀的战士，Gruzub 努力躲避你的攻击。然后，命运拨弦。
梗概：
第二天...
--------------------
动作：
做
你采
结果：
一只老鹰，在你的上方，从高处扔下一只小乌龟，希望它的壳会在地上碎裂。乌龟撞到你的头上，让你措手不及。格鲁祖布利用你的困惑，向前迈出一步并刺出他的剑。刀片进入你的胸膛，Gruzub 小心地避开你的器官，但刺入的深度足以让你尖叫。当他退出时，血液喷涌而出，你挣扎着站起来。 “命运似乎不喜欢马兹基尔。” 牧师大声记笔记，尽管你几乎不听。 Gruzub 继续攻击，而你几乎无法保护自己。最终，他踢你的腿，你向后倒下。这会让你的喉咙暴露在外，Gruzub 可以轻松地割伤你的喉咙，确保你摔倒而永远不会起来。
梗概：
你采
--------------------
动作：
做
你试图恢复战斗姿态
结果：
令你惊讶的是，格鲁祖布犹豫了，然后再次摆出战斗姿态，让你获得一个防御阵地，保卫你的喉咙并阻止你的死亡。你的能量逐渐减弱，你决定用最后的努力打败 Gruzub。他挥舞着他的剑在你的腿上，试图让你倒地，但你挡住了枪，头撞到了他。他向后倒下，你挥舞着你的刀，狠狠地砸在他的头上。他的头盔飞向空中，头部和颈部无人看管。你知道你没有足够的精力继续战斗。
梗概：
你试图恢复战斗姿态
--------------------
动作：
做
你斩首格鲁祖布
结果：
你咆哮着挥舞着你的刀刃。你的剑与他的脖子相连，他的头掉在了地上。你跪下，看着死去的兄弟。你几乎无法理解牧师抓住你的手举起它，大声宣布你是胜利者。爸爸走过去，将部落酋长的珠子和项链挂在你的脖子上。妈妈走出去，轻轻地亲吻你的头，然后将格鲁祖布的眼睛闭上，看着他被斩首的头。牧师将尸体运走烧毁，以确保它能够进入来世。你努力不为你的兄弟哭泣，而爸爸跪下来和你说话。 “你现在是酋长了，玛兹基尔。红刃族的领袖玛兹基尔红刃酋长。你将带领部落走向伟大。今晚，我们庆祝。我们庆祝你成为酋长，我们庆祝伟大的战斗，我们庆祝你兄弟的旅程来世。他打得非常好，我敢肯定戈尔戈克本人就是格鲁祖布的侍酒师。” 你点头。你要走的路已经确定。成为雇佣兵或掠夺者的梦想已经一去不复返了。现在，你是酋长。
梗概：
你斩首格鲁祖布
--------------------
动作：
做
你继续
结果：
酋长之路 你坐在大厅的小木制酋长宝座上。部落项链和珠子挂在你的脖子上，由于 Gruzub 死后的愧疚和悲伤，你感觉比它们重得多。爸爸彻底放弃了酋长的角色，把所有的责任都交给了你，带着妈妈去了山上的一个小农场，让你负责。尽管如此，他还是给你留下了两个中尉和顾问，长老博士和 Thraka Flameborn。 博士长老是一位年迈的兽人，是你们村子里最年长的。她是一个传统主义者，她对过去战士为荣誉而决斗和农场是用锄头而不是骡子和驴手工耕种的日子有着美好的回忆。她重视古老的价值观，如荣誉、孤立主义和保持村庄道德和传统。 Thraka Flameborn 是博士的对立面。他是为数不多的兽人法师之一，擅长火术和火焰魔法。他是一个非常现代和进步的兽人，他希望村庄的工业化和现代化以及与外界建立更好的关系。 两人进入大厅，并开始接近你。博士长老在他作为战士的时代就穿着古老、尘土飞扬的盔甲，脸像皱巴巴、腐烂的李子，而瑟拉卡·烈焰伯恩是一个更年轻、更矮小的兽人，看起来像一个穿着他父亲的红色法师长袍玩装扮的孩子。 呵呵。也许你还在因为你兄弟的死而对这个世界充满怨恨和愤怒。 “恭喜你晋升为酋长，玛兹基尔酋长。” 博士长老高兴的说道。 “我哥刚刚死了。” “哦……是的，对不起，酋长。” 医生道歉。 “什么问题？” 你问。 “Jagik Longstride 在东部森林发现了袭击者。她说他们已经在那里露营了至少三天，袭击了周围的农场和市场大篷车。” 特拉卡说。 “那我该怎么办？” 你问。 “你应该拿起你的剑，带领我们的战士消灭他们。” 博士长老说。 你拿起你父亲的长剑，或者你现在的长剑。这是一把巨大的钢刃，上面刻有兽人符文。当您的曾祖父用精灵之盾击碎他的家族剑时，您的家族已经传承了四代。 “男人们在哪里？” “在军械库。” 特拉卡补充道。
梗概：
你继续
--------------------
动作：
做
你前往军械库
结果：
你将剑背在肩上，开始向军械库进军。兽人全都站在周围，穿着板甲和链甲，挥舞着剑、斧头和狼牙棒。他们以热切和嗜血来回应你的到来。 “啊！酋长来了！战斗的时间到了！” 其中一个兴高采烈的说道。 由于您的年龄，您知道您很快需要负责并展示您的主导地位。 “好吧，伙计们！我们有杀戮要做！在我的带领下！” 你喊。 你开始向东部森林进军，然后是兽人。有了这个，你开始带领你的战争党走向流血。
梗概：
你前往军械库
--------------------
动作：
做
两个小时后...
结果：
你在远处发现了烟雾，袭击者可能在那里扎营。 “兽人！” 你大声耳语。“武装起来！我们将把恶魔消灭到最后一个人。” 你慢慢地向前走，越过树林越远。你到达一个小空地的边缘。有一个很大的开口，里面有几十个帐篷。一群大约 30 名身穿锁子甲、挥舞着斧头、短剑和狼牙棒的兽人。你向各个兽人下达了一些安静的命令，以瞄准各个侧翼，以确保没有一个袭击者幸存下来。然后，你举起你的剑。 “收费！” 你咆哮着，向前冲刺。 突袭者很快就措手不及，抓起附近的任何武器并举起防御。你挥舞着你的剑，它咬住了他们的一个脖子，一扫就斩了他的头。你一脚踢到地板上，一脚快速踢到头部，将他撞倒在地。你举起剑向下刺，刺穿袭击者的胃。几秒钟后，战斗就结束了。掠夺者很快被消灭到最后一个兽人，没有一个活着的人。 “这比我希望的要快。” 兽人的评论之一。 “命令，酋长？” 另一个问，转向你。 “烧掉帐篷和尸体，带走任何值得带走的东西。” 你回复。 “是的，马兹基尔酋长。” 你的手下很快开始从死去的兽人身上取下盔甲、武器和珠宝，并将他们的尸体包裹在帐篷里。一旦处理完所有尸体，他们就会点燃包裹着的尸体，尸体很快就开始燃烧。你嗅着空气，闻到炙烤肉的美味。 “酋长！我数了数床位和尸体的数量。死了二十个掠夺者，但有八十多个床位。” “还有更多！我们相信目前至少有六十多个袭击者正在执行任务。命令，酋长？” 兽人之一，你带来的为数不多的弓箭手之一，卡尼姆·达克威特，向前走了一步。 “长官，突袭者们会又饿又累。他们很快就会到的。我们可以埋伏。其中一棵树上有一个很好的有利位置……” “胆小鬼！你的荣誉呢，孩子？” 另一个兽人，Jakku Bearsbane，咆哮着。“我们并没有放弃所有的荣誉和理性，而是像以前那样躲起来！我们找到袭击者，把他们消灭到最后一个人。”
梗概：
两个小时后...
--------------------
动作：
做
你回村
结果：
“诅咒诸神，我不会在冰冷、空旷的森林里浪费我的时间，等待可能数天、数周甚至数周的袭击者回来，同时像他妈的花栗鼠一样吃坚果和浆果。我们前往回村子吃一顿热饭，喝上一大杯。” “是的，酋长。” 兽人都吠了回来。 你擦掉湿草地上长剑上的血迹，然后转身开始长途跋涉回家。
梗概：
你回村
--------------------
动作：
做
你回到村庄
结果：
第二天，你走在街上，Thraka 和 Doct 暂时管理你的工作。你调查你的村庄。孩子们在街上吵架，老人在喝酒，大人在工作。一切都很好。你自己想想。你注意到一个年轻的兽人女人，她的名字你还没有学过，当你注意到她是多么美丽时，你的心中会感到一阵剧痛。你在另一个地方感到一阵剧痛，但这既不在这里也不在那里。你意识到酋长的职责之一，也许是最好的职责，就是拥有一个王位继承人并结婚。你会尽快找到一个人。不过不是今天。你继续在村子里闲逛，在你注意到小镇的边缘，一条大篷车的踪迹正在逼近。你挑了挑眉毛，因为在接下来的几天里没有预定的大篷车到达。你走上前，惊讶地发现商队不是你习惯看到的兽人。他们都是男人。通常有几个人与经营商队的兽人混在一起。你走向大篷车。 “喂！那你是谁？” 你大声问。 船上的人类穿着红色的布料，上面绣着金鹰标志。 “您好！我是乔治·拉特斯，我是人类王国的商人。” “我认得这个印记。你想要什么？” “我是个商人，好心的先生。兽人以他们的钢铁制品而闻名，我有幸与一些兽人雇佣兵一起工作。他们的刀刃、斧头和狼牙棒是无与伦比的，除了精灵刀刃，它们很容易找到作为一个诚实的商人。你碰巧找到了。” 乔治眨着眼睛说。 “你要买刀片？” “买？不完全是。我怀疑你对我的黄金有多大用处。我想交换，易货，交易。” “为了什么？” “我们有很多东西可以交易。食物、饮料、钢铁、铁、羊毛、珠宝、黄金、白银等等。我们希望在这里和帝国之间开辟一条贸易路线。” 乔治咧嘴一笑。 你皱眉。商人很容易利用像你这样的不起眼的村庄。该村还致力于农作物、农场工作和一般自给自足。为了增加你的锻造和武器产量，你需要牺牲你的自给自足。尽管如此，它仍然可以增加您村庄的利润和生活水平。
梗概：
你回到村庄
--------------------
动作：
做
您同意与人类王国进行贸易
结果：
“是的。是的……我喜欢这个计划。我会扩大铁匠铺，开始为人类王国生产武器。” “啊！太棒了，先生！” 乔治高兴地回答。“我保证，你不会后悔的。我会增加来这里的大篷车的数量，并在前几名塞满一点……礼物，表示感谢。我从来没有过麋鹿、鳟鱼或羔羊，您会喜欢大篷车的。” “我们开始生产武器需要一些时间，但一旦我们开始生产，我们就能生产出相当多的武器。” “嗯，不错，不错。” 乔治咧嘴一笑。 你协商了一些小细节，并为每个商人提供了一张床和蜂蜜酒，但他们礼貌地拒绝下山。尽管 Giorgio 很友善，但很明显，大多数其他商人和守卫都因为在兽人领地如此之深而感到不安。不过，您认为这笔交易对红刀队非常有利。
梗概：
您同意与人类王国进行贸易
--------------------
动作：
做
三天后...
结果：
你坐在大厅的宝座上，在一场庆祝达迪克酋长死亡的盛宴中，你的曾祖父被精灵刺客残忍地刺死。尽管手无寸铁，感到惊讶，而且年纪很大，但他还是进行了一场盛大的战斗。二十个精灵进入这个非常大的大厅刺杀他，三个出去了。Thraka 和 Dokt 站在你的两边。这一次，他们都同意你应该做什么，尽管 Thraka 的诚实得多残酷。 “找个女孩来操。” 他说。 “Thraka Flameborn 的意思是……” “让你的家伙进入一个女孩。” 对于 Dokt 的尴尬和沮丧，Thraka 大笑起来。 “酋长，每个酋长都需要一个可以与之分享灵魂的人。你需要一个伴侣。” 多克特说。 你调查房间。你看到一个年轻的兽人女孩在人群中跳舞。她又小又可爱，你会短暂地进行眼神交流。她给了你一个轻浮的眨眼。一个漂亮的女孩会很好地帮助你度过寒冷的冬夜。 利用饮料和食物是一个高大的战士。她的头被剪短了，侧面有一把大而令人印象深刻的刀片。战士是您可以作为伴侣联系和尊重的人。 在对面（房间和方向），一个年轻的兽人静静地站着，手里拿着一本皮书。他看起来很聪明，但也有柔和的五官和甜美的笑容，他很快就向你闪现。 最后，有一个巨大的节拍，一个兽人拿着一把可以一击将马劈成两半的刀片靠在墙上。你认出他是 Ironsoul，一位出色的战士，以在妓院度过大量时间与选择在妓院工作而不是在战场上工作的兽人一起在妓院中度过而臭名昭著。
梗概：
三天后...
--------------------
动作：
做
你和跳舞的兽人说话
结果：
你走向跳舞的兽人，微笑。 “你好。” “你好。” 她兴高采烈地回答。 “我是马兹基尔酋长。” 她咯咯地笑。 “显然。我确定我知道酋长的名字。” “啊好。” 你回答，尴尬。 你意识到，虽然你在战争艺术方面非常熟练和勇敢，但你却很不擅长调情。谢天谢地，她更好，也很感兴趣。 “我是萨鲁特。所以，你想喝一杯吗，马兹基尔？” 你急切地点了点头，然后走向饮料台。
梗概：
你和跳舞的兽人说话
--------------------
动作：
做
您继续与 Sarunt 共度时光
结果：
在接下来的几个小时里，你和你的新朋友聊天。萨伦特非常聪明和性感，几乎与你完全相反。你会确保你们的关系继续下去并发展成可能是繁荣的婚姻。
梗概：
您继续与 Sarunt 共度时光
--------------------
动作：
做
四个星期后...
结果：
当 Thraka Flameborn 和长老 Dokt 向您讲述村庄的事态时，您坐在宝座上，将头枕在刀刃上。 “亚历山大学院的管理发生了变化。他们已经开始接受兽人学生了。” Thraka 对你说。 “嗯，这对种族关系很好，但红刀会在意什么？” “如果你在学院工作，可以找到更多有利可图的工作。我们可以大大改善他们的生活。” “这些工作会使这些人无法从 Red Blades 中获利，花费大量金钱，而且他们很可能不会因为是 Orcs 而获得这些工作。这些学生将无法获得这些工作，他们也不会”没有教过如何耕种或战斗，所以当他们没有工作回到这里时，他们无法在这里工作。” “长老夸大其词了。有很多工作没有找到，尤其是在兽人方面。在亚历山大学院学习意味着我们的孩子会过上更好的生活。” 你简要地考虑一下这个想法。也许年轻的兽人在学院里和之后的生活会更好，但这肯定会削弱部落的规模，也许在歧视阻止他们找到受过教育的人后，兽人将无法从事体力劳动或找到实际工作的能力工作。
梗概：
四个星期后...
--------------------
动作：
做
你派年轻的兽人去学院
结果：
您决定将这些年轻的兽人送到学院。您希望他们过上比您和您的祖先更好的生活，并确保兽人不会被排除在教育界之外。
梗概：
你派年轻的兽人去学院
--------------------
动作：
做
五周后...
结果：
你坐在王座上，看着狩猎周期。双子点是很好的狩猎场，但它可能会导致死骨部落之间的战斗，他们也使用该地区。你可以派猎人去那里，而不是派猎人去那里，或者第三种也是迄今为止最好的选择，倒在你自己的剑上，结束比任何其他兽人经历过的轮作或狩猎区更糟糕的痛苦考验。 你听到外面的叫喊声，市政厅的门被踢开了。一群六名兽人出现，拖着一个被残忍殴打的人。 “酋长！我们在我们的领地发现了一个人，山羊混蛋。” 一个兽人大喊，把这个人扔在你面前。“他似乎已经狩猎我们的鹿和野猪好几个星期了。” “解释你自己，人类，在我释放我的战士来撕裂你之前。” “先生，请。” 他说，乞求。“我来自埃克格莱德村。它被精灵突击队摧毁，我们被迫逃离。我们中的许多人前往山丘和山脉，以躲避精灵的袭击，并在那里建立了小营地。我只是想打猎养活我的家人。” “先生，有吩咐吗？” 一个兽人问你。“我要不要抢他的头？”
梗概：
五周后...
--------------------
动作：
做
你处决了侵入者
结果：
“嗯……营地在哪里，侵入者？” “先生，如果我告诉你……” “不要主持知道我是谁或我会做什么，侵入者。” “他们在 Morinspeck 矿场，长官。” “好的。” 你站起来，挥动你的刀片。男人来不及眨眼，头就掉在地上，滚到地板上。你转向你的男人。 “派战士到 Morinspeck Miens。把他们赶出去，杀死任何抵抗或拒绝去的人。” “是的，酋长。” 他们兴高采烈地说，跑去寻找盔甲和武器。 “嗯，这个问题就解决了。” 你想，把头踢到一边。
梗概：
你处决了侵入者
--------------------
动作：
做
五周后...
结果：
“酋长！酋长！” “是的？” 你问。 你遇到了一个年轻的兽人，你的表弟，他让你想起了格鲁祖布。你感到内疚，在决定为了纪念 Gruzub 之前，如果他提出新井的位置或是否建造新的石头或木头马厩，你将杀死这个 Orc。 “凯斯巢穴发生了一场灾难。” “灾难？这是一个强烈的词。” “没错，一群丧尸和一只栖息在山上的红龙交战了。” “如果死亡与毁灭在路上相遇。” 你引用，记得你小时候读过的几本书之一。 “什么？” 兽人哼了一声。 “这首诗，死神在路上遇到了龙之母。他们争论自己的角色，死神说他的工作是如何防止人口过剩的必要性，而龙之母说她防止帝国消灭所有......我他妈的在做什么？我不是在给你解释儿童诗。” “对不起酋长。勇士队建议你在那里领导一个派对。” “做什么？” 兽人耸了耸肩。
梗概：
五周后...
--------------------
动作：
做
你率领军队前往凯斯巢穴
结果：
“集结武士。” 你说。“给我找来一群六十名渴求鲜血的武者。” 您已经召集了小组，然后迅速开始上山。大约过了一半，你开始后悔了。 “天气很冷，我的脚很痛，我他妈的累了。” 你在山上跋涉时咕哝着。 “该死的，玛兹基尔。” Urug Eaglebow 说。 你往山上看更远，听到死者的嚎叫和刺耳的声音，以及巨龙的咆哮和尖叫。这里的龙很少，所以你假设这场红龙战斗是 Mont Pre'Igni Rubrum，一个几乎无法发音的名字，或者 Gargarum 火山。亡灵无疑是死灵法师军队的逃兵。你到达山顶，在一个火山口的边缘。在中央，一大群两打不死士兵，手持长矛和剑，正试图杀死巨龙。看来不死族是非常无敌的。
梗概：
你率领军队前往凯斯巢穴
--------------------
动作：
做
你同时与龙和亡灵战斗
结果：
你向前冲锋，你的刀片摆动。 “去！把死者送回坟墓，把龙送回戈尔戈克的奖杯室！” 你斩首一个不死生物，它腐烂的头骨在空中飞舞。在一股火焰让你着火，融化你的头骨之前，你会感觉到刀刃划过你的背部。
梗概：
你同时与龙和亡灵战斗
--------------------
动作：
做
你死了
结果：
当你的生命被夺走时，你发出一声可怜的尖叫。
梗概：
你死了
故事名称：
77
故事背景：
“集结战士。” 你说。“给我找来一群六十名渴求鲜血的武者。” 您已经召集了小组，然后迅速开始上山。大约过了一半，你开始后悔了。 “天气很冷，我的脚很痛，我他妈的累了。” 你在山上跋涉时咕哝着。 “该死的，玛兹基尔。” Urug Eaglebow 说。 你往山上看更远，听到死者的嚎叫和刺耳的声音，以及巨龙的咆哮和尖叫。这里的龙很少，所以你假设这场红龙战斗是 Mont Pre'Igni Rubrum，一个几乎无法发音的名字，或者 Gargarum 火山。亡灵无疑是死灵法师军队的逃兵。你到达山顶，在一个火山口的边缘。在中央，一大群两打不死士兵，手持长矛和剑，正试图杀死巨龙。看来不死族是非常无敌的。
--------------------
动作：
做
你攻击亡灵
结果：
“把死人送回坟墓！” 你咆哮着，冲进战场。 你挥动你的刀刃，砍下一个腐烂的头颅并将其送入空中。你刺伤了另一个人的心脏，立即杀死了他。你用头撞到另一个，以致脊椎断裂，僵尸倒在地上。亡灵是如此无与伦比，以至于他们在几秒钟内就被消灭了。你转过身来，看到一条龙咆哮的脸。它的声音，低沉的咆哮，震动了你的核心。 “兽人……你怎么来我家了？” “我……我是来帮忙的。” 你紧张地说。“我想确保这场战斗不会进入我的领地。” “明智之举，凡人。你的帮助是不必要的，但很有礼貌。请让我送你一份礼物。” 巨龙转身，跺着脚走进一个山洞。几分钟后，它又回来了。它张开嘴，让金币和宝石掉了出来。数量不多，但足以资助建造一口新井并为猎人获得新弓。 “谢谢你，强大的龙。” 你说。 “当然。” 你鞠躬，开始跋涉下山。
梗概：
你攻击亡灵
--------------------
动作：
做
一个月后...
结果：
你走上一座山，在严酷的雪中跋涉。尽管被毛皮和皮革包裹着，你还是很冷，你真的需要小便，但你知道如果你试过，你的阴茎会冻死。在刚刚开始的寒冷寒冷的冬天，你被一只从山上游荡下来的霜冻巨魔失去了三个农民和十几只山羊。你正在带领一个由 20 名战士组成的五个战队来消灭山上的任何巨魔。你领导着最优秀的战士的主要战队，他们希望以最有可能拥有庇护巨魔的洞穴系统为目标。风和雪开始挡住你的视线。你知道你需要尽快找到庇护所。
梗概：
一个月后...
--------------------
动作：
做
你蜷缩在寒冷中
结果：
你蹲下，搭起你所有的帐篷。你爬进去，裹着毛皮和皮革。你希望风暴快点过去。一个半小时之内，寒冷是如此残酷，以至于你失去了所有感觉。下雪了，你不知道要去哪里。经过四个小时的等待，您突然感到温暖。你开始解开你的盔甲来降温。你又热又累。在雪地里小睡一下会很不错...
梗概：
你蜷缩在寒冷中
故事名称：
78
故事背景：
你走上一座山，艰难地穿过厚厚的积雪。尽管被毛皮和皮革包裹着，你还是很冷，你真的需要小便，但你知道如果你试过，你的阴茎会冻死。在刚刚开始的寒冷寒冷的冬天，你被一只从山上游荡下来的霜冻巨魔失去了三个农民和十几只山羊。你正在带领一个由 20 名战士组成的五个战队来消灭山上的任何巨魔。你领导着最优秀的战士的主要战队，他们希望以最有可能拥有庇护巨魔的洞穴系统为目标。风和雪开始挡住你的视线。你知道你需要尽快找到庇护所。
--------------------
动作：
做
你移动到最近的洞穴系统来躲避寒冷
结果：
你迅速加速，朝着一个山洞走去。你设法在十分钟内找到它，设法躲避。你冲进去，快速进入洞穴深处。你最终会找到一个地方，在那里你大部分时间都可以躲避冰冻的风和雪。你感觉到身后有暖风，你转身。一只巨大的熊站在你面前。它挥动爪子，把你撞到墙上，在你的胸板上留下大量划痕。熊很快被战士斩首，身体反复刺伤。它的皮毛很快被去除，兽人将它包裹在自己身上。
梗概：
你移动到最近的洞穴系统来躲避寒冷
--------------------
动作：
做
你在洞穴中寻找巨魔
结果：
你站起来，擦掉胸板上的雪和一些血，然后开始沿着其中一条隧道前进。兽人点燃了火炬，照亮了隧道的墙壁。你举起长剑，注视着墙壁。影子在跳舞，但你看不到巨魔。最终，你来到一个开口，在那里它通向一个巨大的房间。里面是四个包裹着熊皮的巨大人形。 “巨魔！” 你大声耳语。 巨魔正在吃几只死山羊，这些山羊被残忍地分开了。你缓缓靠近，剑举起。当你足够接近时，你会摆动。你的刀片摆动，划破其中一个的脚踝。你从侧面刺伤坠落的巨魔，切开它的器官。你拔出刀片，挡住巨魔挥动的石棒。兽人迅速进攻，刺穿他们的腿和腹股沟，迅速杀死了他们。巨魔倒在地上，被兽人砍得四分五裂。 “干得好，兽人。有人生火。我们在这里避寒。”
梗概：
你在洞穴中寻找巨魔
--------------------
动作：
做
三个月后...
结果：
Red Blades 又度过了另一个冬天。再一次，Thraka Flameborn 和长老 Dokt 站出来给你无尽的任务来解决。主要的三个是 Farok 矿井、过剩的战士问题和对 Ka 的日益增长的崇拜。 “酋长！我们在山下挖掘了一个巨大的铁矿和煤矿，法罗克矿。它被人类用来供应战争机器，但因为它没有防御并被人类抛弃而被遗弃。但是，你还有多余的兽人可以下矿去工作的工人，只有一小部分利润作为使用矿井的报酬支付给人类王国。这意味着为人类王国工作，但可以证明是非常有利可图的村。” 特拉卡说。 “我们山上还有矿，兽人矿。我们不需要爬到人类王国来获取微薄的铁和煤。他们将能够在兽人矿中获得更多的利润。” 多克特长老说。
梗概：
三个月后...
--------------------
动作：
做
你派工人到 Farok 矿山工作
结果：
你决定派工人去 Farok 矿工作是最好的主意。这些工人赚取微薄利润，但这会加强你与工业化综合体和人类王国的关系。
梗概：
你派工人到 Farok 矿山工作
--------------------
动作：
做
你继续
结果：
下一个问题是多余的战士问题。红刃正在生产过剩的战士，他们都是渴望战斗的年轻、嗜血的战士。您可以派他们与突袭者战斗，这些兽人对抗人类王国扩张到兽人领土并利用兽人部落和村庄的优势。你也可以派他们在人精灵战争中担任人类王国的雇佣兵。
梗概：
你继续
--------------------
动作：
做
你派他们去当攻略
结果：
您派遣多余的战士加入袭击者。他们将为兽人的荣誉而战，并保护兽人免受人类王国的剥削和扩张。
梗概：
你派他们去当攻略
--------------------
动作：
做
你继续
结果：
下一个也是最后一个问题是新宗教的传播。出于你不确定的原因，对一神论的造物主 Ka 的崇拜在年轻的兽人中变得越来越流行。这导致人们远离对多神的崇拜，尤其是戈尔戈克。
梗概：
你继续
--------------------
动作：
做
你禁止 Ka 宗教
结果：
你很快就禁止了 Ka 的宗教。它的传播被阻止了，尽管一些村民私下崇拜他，但大多数人放弃了他，转而支持戈尔戈克和许多人。
梗概：
你禁止 Ka 宗教
--------------------
动作：
做
一个月后...
结果：
你穿过村庄，看着村民。你厌倦了战斗和做决定，所以你要回家了。你到达了大酋长的小屋。
梗概：
一个月后...
--------------------
动作：
做
你进去
结果：
你走进去，看到一个瘦小的身影，穿着一件对于兽人来说太过花哨的裙子。 “啊！玛兹基尔！你到家了。” “萨伦特！很高兴见到你！” 你把他拉进怀里，几乎压碎了这个人影。 “所以？怎么样了？你安排好春天的轮作了吗……” “如果你提到轮作，我就杀了你。” 萨伦特笑了。 “那好吧。人类王国呢？” “他们呢？” “我们害怕入侵吗？” “也许。” “好吧，悲伤的消息已经够多了。我有一些自己的消息。” “继续。” 你挑了挑眉，示意她继续。 “玛兹基尔，我怀孕了。” 一连串的情绪立刻袭上心头。困惑、烦恼、恐惧、喜悦、兴奋等等。你意识到她在盯着你看，所以你选择表达不会让她开始哭泣的情绪。 “这太妙了！” 你说。“太好了！你去过萨满吗？” “是的，玛兹基尔。” “他们怎么想，男孩还是女孩？” “萨满们什么都不知道，马兹基尔。他们只是猜测并猜对了一半。” “啊，这个是……” 你听到外面的尖叫声。他妈的。你抓住你的刀片，把它举起来。
梗概：
你进去
--------------------
动作：
做
你去外面调查。
结果：
你走到外面，遇到了一个躺在路上的兽人孩子，他的脸上流着血。一个大兽人站在他的上方，他在吐口水。 “扒手。我他妈的讨厌他们。” 他大声喃喃自语。 “你他妈的是谁，我为什么不杀了你？” “我是食死徒格朗格。我是派克酋长的仪仗队的一员。” “派克酋长？他妈的为什么是……？” “酋长，我们有客人。或者客人，我应该说。” 一个声音在哭。 “是吗？是谁？” 你问，转向多克特长老。 老人拂去盔甲上的灰尘，在它的重压下挣扎着保持站立。也许你应该建议他用一些漂亮的丝绸长袍代替他的重型盔甲。 “复仇之狼的派克酋长来了，还有烈火的索辛酋长和邪阳的高古兹酋长。” 你回头看看庞大的仪仗队成员。耸了耸肩，迅速回到了大殿。一群二十个兽人，三个巨大的身影在前面等着你。 “啊！玛兹基尔酋长！我们正在准备一个战队，对驻扎在鲁布加德的人类王国军队发动突袭。他们最近对附近的三个兽人村庄发动了突袭，以获取任何战争武器。他们正在阉割我们的人民. 我们需要将他们消灭到最后一个人。结束他们对我们人民的束缚。我们希望你和你的战士加入我们。 “大人，这是一个绝佳的机会，可以对抗人类王国的野蛮掠夺，解放我们的人民。” 多克特长老说，他的嗜血比你想象中的年长者还要多。 你想知道是否应该加入即将开始的兽人战争。也许你已经融入了人类王国太多，不允许自己攻击他们。也许最好不要攻击。
梗概：
你去外面调查。
--------------------
动作：
做
你没有加入兽人对抗人类帝国
结果：
一周后，人类王国的一队士兵抵达，由一名身穿板甲、披着红色斗篷、披着金龙的大汉率领。他进入大厅，在接近你的宝座时鞠躬。 “您好，马兹基尔酋长。我是伊格纳特爵士。我被我们的皇家亚历山大国王选中，以感谢您拒绝攻击王国的城镇和人民。我很高兴向您提供一个建议，如果您提供军队和支持王国的反击消灭侵略者。如果你接受这个提议，你将被视为王国的附庸，并获得我们的全部财政和军事支持，以允许你扩张到侵略者领土。你会接受我们的吗？提议，马兹基尔酋长？”
梗概：
你没有加入兽人对抗人类帝国
--------------------
动作：
做
你加入人类王国击退兽人
结果：
进步的三月 “是的。是的，我会加入人类王国与侵略者的战斗。” “很好。太棒了，甚至。我会把这个消息带给亚历山大国王。我会确保你因此得到回报，先生。我向你保证，这将帮助你的人民。我们的第一次军事行动是领导一个攻击主力复仇狼的村庄。我猜你会帮助我们消灭复仇狼的军队？” “是的当然。” “好，期待与你在战场上一战。”
梗概：
你加入人类王国击退兽人
--------------------
动作：
做
第二天晚上...
结果：
你站在复仇狼村的边缘，靠近伊格纳特爵士。 “冲进营地。不要不惜一切代价杀死孩子，也不要杀死非战斗人员。” 你大声对你的盟友说。“伊格纳 特大人，战斗结束后村庄的计划是什么？长期的，我的意思是。” “村庄及其周边地区将成为王国被征服的领土。我们将在这里建造一座小堡垒以维持控制，但除此之外，我们会尽量确保权力的过渡尽可能顺利。” 你点头。 “好了，大家准备好了吗？” “是的，酋长。” 声音的合唱从你的兽人那里传来，而人类排除了回答的酋长部分。 “好吧。让我们这样做。充电！” 你吼。 你开始尽可能快地移动，你的长剑举起。复仇之狼的勇士们显然毫无准备，前一天晚上他们喝酒和盛宴来庆祝他们最近在王国的一系列伏击和袭击中取得的胜利。尽管如此，兽人还是兽人，兽人随时准备战斗。有一群兽人从建筑物中涌出，有些缺少盔甲，或者只用匕首甚至拳头进行战斗。你挥动你的刀片，抓住一个人的头部并撕下他的头盔。你刺伤，直接穿过喉咙。你用头撞了另一个兽人，让他趴在地上，然后你迅速把他劈开。你刺出你的刀刃，穿过其中一个兽人的腹部。你挥杆、砍、斜线、刺、推、格挡和斩首好几分钟。兽人是好斗的战士，这意味着他们更容易犯更多错误，更容易受到打击。你和你的部队和盟友利用这一点，迅速消灭那里的激进分子。战斗结束后，男人们开始挨家挨户搜查任何要俘虏的士兵和要没收的武器，以及可能要抢劫的任何东西。你看到血迹斑斑的伊格纳特爵士在大屠杀中走来走去，大笑着。 “干得好，马兹基尔酋长。你们战士的表现令人钦佩。我们很快就会结束这场战争，我就能回到精灵那里。我相信他们会想念我，他们肯定会想念我的剑。 ” 他笑了。 你点点头，几乎听不见。你会为杀死你的兽人同胞而感到内疚，即使是为了自卫。 “我们还有几场战斗，玛兹基尔酋长。之后，你可以在你的村庄安顿下来。然后，我们一起完成我们的工作。”
梗概：
第二天晚上...
--------------------
动作：
做
你继续
结果：
在接下来的几周里，兽人侵略者开始袭击王国的军队。作为兽人，你的部队被用来对抗这些突袭和伏击，但结果喜忧参半。最终，你的一名侦察员成功了。 “酋长！我已经找到了兽人主力所在的位置。” 他自豪地说。 “在哪里？” “戈尔戈克的原野。” “戈尔戈克原野？哈！适合我们进行最后一战的地方。给伊格纳斯爵士发消息。我们要开战了。”
梗概：
你继续
--------------------
动作：
做
你发动战争！
结果：
你在庞大的人类和兽人部队的陪同下前进。皇家军队，红刃部落，河源部落，黄昏猎手部落以及各种兽人，人类，黑暗精灵和矮人雇佣兵前往发动战争。你有步兵的主力，伴随着你最好的战士和来自人类王国的骑士。还有一大群弓箭手和一群骑兵，向着敌人前进。经过几个小时的游行，你会看到他们。数百个巨大的帐篷点缀在球场上。你会听到他们的哨兵吹响喇叭，及时唤醒兽人，为即将到来的袭击做准备。他们今天不会突然袭击，你知道。你举起长剑。刀片刚磨好。 “收费！” 你与伊格纳特爵士和其他几位军队领导人齐声咆哮。 你向前冲，以极快的速度前进。当兽人出现时，你和领导风暴的军队愤怒的力量，在他们的盔甲中抽出并供奉着武器。你冲向它们，一股不可阻挡的力量砸向一个不可移动的物体。 “死！” 你咆哮着，挥舞着你的刀刃。 你挥动你的剑，砍断了飞翔的肢体。你跪在另一个人身上，用你的刀柄反复砸他的脸，直到他的脸变成了纸浆。你再次挥舞，在兽人突破锁子甲盔甲后切开它的肚子。你再次摆动，在一个兽人的脸侧开了一个巨大的伤口。你招架了一次打击并阻止了另一次，然后以凶猛的方式冲入战斗。你用肘击了一个兽人的脸，用你的打击力砸碎了骨头和软骨。您阻止了斧头摆动并移除了拿着斧头的兽人的手臂。你再次挥杆，砍断一条腿，将一个兽人放在一个膝盖上，让你斩首兽人。你劈砍和摆动，愤怒地咆哮。 “酋长！命令？” 你的一个兽人尖叫起来。 是时候接受命令了。
梗概：
你发动战争！
--------------------
动作：
做
你试图重新集结并瞄准酋长
结果：
你吹响骨号角，红刃在你周围重新集结。 “瞄准酋长！” 你咆哮着，向前冲刺。 你向前冲刺，躲避长矛和箭矢。最终，您会发现酋长们聚集在一起，抵御逼近的敌人。当你和红色刀片袭击时，你咆哮着。你会找到整个军队的领袖，派克酋长。派克酋长咆哮着，向你挥舞着他的剑。你挡住了打击，躲在另一个秋千下。你钻到 Pike 够不到的地方，用头撞他，然后靠过去。你咬住他的喉咙，把它撕掉。当他呻吟着向后倒下时，鲜血从他身上流出。你咆哮着，转身刺穿下一位酋长的喉咙。你再次摆动，斩首另一位酋长。几秒钟之内，整群酋长都死了。你拿起派克酋长的号角，然后吹响它。一声巨响从喇叭中爆发出来，你开始咆哮。 “兽人！你们的酋长都死了！快逃，否则就被消灭！” 兽人开始惊慌失措，转身冲了出去。他们放下武器和盔甲，以加快逃离该地区所需的速度。兽人军队被击溃并迅速离开。剩下的少数人很快就被士兵们击倒了。 “胜利！” 你吼。
梗概：
你试图重新集结并瞄准酋长
--------------------
动作：
做
敌人被征服了！
结果：
你站在战场上，周围都是兽人和人类的尸体。你擦掉额头上的汗水。伊格纳特爵士骑在马背上。 “我们做到了！” 伊格纳茨爵士笑了。“这片土地现在完全属于亚历山大国王！没有人可以反对他！向国王致敬！别担心，马兹基尔。我会确保你得到奖励。你的村庄会从中受益，我向你发誓。” 你看着前盟友的尸体，嘴里有一种自 Gruzub 死后你从未尝过的背叛感。你吐口水，摇头。不，你做对了。你为给你的部落一个未来而奋斗。你成功了。
梗概：
敌人被征服了！
--------------------
动作：
做
五年后...
结果：
在过去的十年里，这个村庄不断壮大。伊格纳特爵士和亚历山大国王信守诺言。村子面积是原来的三倍，但人口却相当多。它是兽人大片领土的首都，也是一个庞大贸易帝国的中心。红刃拥有大量的力量，是人类王国的重要组成部分。村子很繁荣。你为帝国做了一些伟大的工作。你现在是 Mazkil 勋爵、Mazkil 爵士、Mazkil 酋长和光明使者 Mazkil 以回应你带来的大规模创新。那会让你发笑。你在这个地球上才二十五年，看看你所做的一切。世界是好的。你听说过死灵法师崛起和兽人叛乱可能发生的谣言，但这些都是小烦恼。地狱，你为他们高兴，因为它会让你忙碌。你坐在你的宝座上，门被一个抱着两个小人物的小人物推开。 “萨鲁特！” 你哭。 萨伦特，你的妻子，带着两个小孩向你走来；一个五岁的人类和一个三岁的兽人。 “Mazkil！Gruzub和Durzul想念你。” 抱起孩子时，你会微笑。你透过开阔的大厅向外望去，微笑着。你拯救了红刀，创造了自己的未来。世界在你手中。
梗概：
五年后...
故事名称：
79
故事背景：
你坐在王座上，看着狩猎周期。双子点是很好的狩猎场，但它可能会导致死骨部落之间的战斗，他们也使用该地区。你可以派猎人去那里，而不是派猎人去那里，或者第三种也是迄今为止最好的选择，倒在你自己的剑上，结束比任何其他兽人经历过的轮作或狩猎区更糟糕的痛苦考验。 你听到外面的叫喊声，市政厅的门被踢开了。一群六名兽人出现，拖着一个被残忍殴打的人。 “酋长！我们在我们的领地发现了一个人，山羊混蛋。” 一个兽人大喊，把这个人扔在你面前。“他似乎已经狩猎我们的鹿和野猪好几个星期了。” “解释你自己，人类，在我释放我的战士来撕裂你之前。” “先生，请。” 他说，乞求。“我来自埃克格莱德村。它被精灵突击队摧毁，我们被迫逃离。我们中的许多人前往山丘和山脉，以躲避精灵的袭击，并在那里建立了小营地。我只是想打猎养活我的家人。” “先生，有吩咐吗？” 一个兽人问你。“我要不要抢他的头？”
--------------------
动作：
做
欢迎入侵者加入村庄
结果：
“嗯……营地在哪里，侵入者？” “先生，如果我告诉你……” “不要主持知道我是谁或我会做什么，侵入者。” “他们在 Morinspeck 矿场，长官。” “很好。好吧，我会给你一个选择，入侵者。如果你回来，你会带着死亡的威胁离开我的土地，或者你作为一个与部落没有血缘或法律联系的自由工人加入红刃部落。” “先生？” “我们有闲置的狩猎场和农田。如果你想工作，我会给你机会。” “是，先生！谢谢您，先生！” 在接下来的几天里，来自 Morinspeck Mine 的难民到达，并获得了工作，以便他们可以在村里获得食物、水和床位。
梗概：
欢迎入侵者加入村庄
--------------------
动作：
做
两周后...
结果：
由于您选择接受来自 Morinspeck Mine 的难民，因此会有更多的难民抵达。大多数是来自被摧毁或被袭击的村庄的人类，他们正在寻找安全的工作场所，但也有一些是不想参与战争的木精灵或黑暗精灵。他们都要求在你的村庄担任猎人、农民或工匠的工作。
梗概：
两周后...
--------------------
动作：
做
您允许他们加入
结果：
出于道德和经济原因，您欢迎这些难民进入您的村庄。这可以让您的村庄随着您的人口激增而扩大。
梗概：
您允许他们加入
故事名称：
80
故事背景：
出乎你的意料，Gruzub 犹豫了，然后再次摆出战斗姿态，让你获得一个防御阵地来保卫你张开的喉咙并阻止你的死亡。你的能量逐渐减弱，你决定用最后的努力打败 Gruzub。他挥舞着他的剑在你的腿上，试图让你倒地，但你挡住了枪，头撞到了他。他向后倒下，你挥动你的刀，狠狠地砸在他的头上。他的头盔飞向空中，头部和颈部无人看管。你知道你没有足够的精力继续战斗。
--------------------
动作：
做
你让他恢复战斗姿态
结果：
他迅速恢复了战斗姿态，举起了武器。战斗只持续了一分钟，你几乎无法阻挡他接下来的几次攻击。最终，他扫了你的腿，他倒在了地上。Gruzub 举起他的剑并挥舞它，砸在你的胸口。你试图站起来让 Gruzub 惊讶，所以他迅速踢了你的头，你陷入昏迷……
梗概：
你让他恢复战斗姿态
--------------------
动作：
做
你醒了
结果：
你在萨满的小屋里醒来。你的伤口被包扎好，身上覆盖着各种旨在加速愈合的药膏、乳膏和绷带。你呻吟一声，起身下床。萨满走到你身边，检查你的体温。 “你不应该四处走动。” 萨满说。 “去你的。” 你咕哝着。” “我杀不了你。你应该死在一个不像我这样强壮和幸运的人手里，死得更久、更慢。” “操你妈的，格鲁祖布。” 你笑。 你拿起你能找到的最柔软的羊毛束腰外衣穿上。你走到外面，对着聪明的儿子皱眉。你发现 Gruzub，戴着新买的珠子和项链，表明他是新的酋长。当他看到你时，他会笑。 “马兹基尔！” 他冲到你身边，拥抱你。 当他的拥抱收紧时，你畏缩了一下，但设法没有表现出疼痛。 “你的新晋升怎么样？” 你问。 “这很无聊。爸爸总是让酋长出来打架，他妈的，吃喝。我刚刚得到了组织轮作的工作。” 你笑了，拍拍他的背。 “很抱歉听到。谢谢你没有杀了我，由 th “说真的，你应该赢了那场战斗。” “也许。” 你耸耸肩。“告诉我至少有人射杀了那只该死的鹰。” “它在任何人之前就离开了。我说的是我丢了头盔的时候。” “我考虑过，但决定我更愿意让你组织……你怎么称呼它？作物轮作？” “是的。” 你静静地站了几分钟，凝视着冉冉升起的太阳。 “你打算怎么做，玛兹基尔？你会留在这里帮助部落吗？” “不，部落是你的地方。我想旅行。人类王国迫切需要新的士兵来帮助他们对抗精灵。” “嗯……你什么时候走？” “很快。很快。我不想和部落经历整个告别协议。” “我明白。” 再一次，你哥哥把你拉过来拥抱。 “我会想念你。”
梗概：
你醒了
--------------------
动作：
做
三天后...
结果：
你走在路上，你身后的村庄。你的剑背着一袋食物、水和物品。 人类王国正在为皇家军队招募士兵。他们计划再次攻击精灵，并且想要尽可能多的人。您可以获得丰厚的报酬、免费的武器和装备，并可能在那里成名。无论如何，你一直讨厌精灵。 是时候成为一名士兵了。
梗概：
三天后...
--------------------
动作：
做
你加入皇家军队
结果：
士兵之路 需要三天时间才能找到最近的皇家陆军堡垒，一座被村庄包围的巨大石头城堡，以及驻扎在这里的人所住的一大串帐篷。当你到达并说你想加入时，你会得到一把新的钢剑、一个大盾牌、一些抛光的板甲，胸板上有国王的象征，还有一小笔起始工资。你被告知是兽人，他们认为你已经接受过战争训练；一个种族主义但准确的假设，所以你不需要新的培训。野兽团给了你一张床，你被告知这是唯一能负担得起额外人员的团。你很快就会意识到这是胡说八道，因为野兽团是唯一拥有兽人的团。你认出黑角、金牙、死眼、铁焰、神杀手、硬爪和熊足兽人都在那里，他们围坐在一个小石头兵营的壁炉旁。一个较大的兽人，头戴闪亮的头盔，头盔上刻有国王的象征金龙。 “您好，新兵。我是达鲁克船长。名字和部落。” “玛兹基尔·野猪毒，红刃。” “啊，赤刃还活着吧？几年前有一把红刀，他设法斩首了一条龙。他只是跑起来，在眼睛上戳了几下，然后挥了挥。暗黑龙颅。地狱战士。如果你按照我说的去做，不引起任何争吵，也许你会一样好，但一定要结束我们进入的任何事情。” “当然可以，先生。” 你在先生的部分挣扎，但你认为随着时间的推移它会变得容易。 “先生？哈，你已经在使用先生了。我想你会给我留下深刻印象，马兹基尔。” 你注意到一个巨大的兽人耸立在你身上，他的盔甲覆盖着你认为是杀伤力的东西。你一定要留意他。你找到你的床，然后倒在床上。只需几分钟，您就可以进入沉闷、无梦的睡眠。
梗概：
你加入皇家军队
--------------------
动作：
做
两周后...
结果：
你和 50 多位其他兽人一起穿过精灵领地，穿过一片有树木的丘陵地区。你与身着缺口盔甲的巨型兽人 Karn Deathreaper 一起被派去调查一个小型的精灵战争小队。你还没有经历过任何真正的战斗，你之前在这里只是机动到侧翼位置迫使敌人撤退和前进。尽管如此，太阳还是特别红，所以你今天会和戈尔戈克发生一场战斗。 “兽人！住手！” 卡恩尖叫，你停止移动。 Karn Deathreaper 真正赢得了他的名字。他杀死的敌人数量是数百甚至数千。他是一个可怕的生物，你迫不及待地想看他战斗。 “太阳祝我们好运。我们会在战斗中取得好成绩。” 你附近的一个兽人高兴地说。 “停止你的迷信，纳格拉克。我们今天甚至可能不会战斗。” 另一个回复。 “一个人可以希望。” 纳格拉克急切地回答。 眨眼间，十几个兽人就死了。你会看到六个精灵站在山顶上，身穿浅金色盔甲，手持弓箭的金色长发。
梗概：
两周后...
--------------------
动作：
做
你举起盾牌进行防御
结果：
你举起盾牌，三支箭砰的一声射入其中。 “兽人！重整旗鼓，守住那些盾牌！” 卡恩尖叫。 你们挤在一起，作为一个群体开始迅速上坡向精灵冲去。精灵们向你们俩又射了几支箭，但很快就转身跑了。您到达山顶，看着精灵消失在山下的小树林中。 “来吧，兽人！我们跟着他们，确保蠕虫今晚能吃到精灵肉！” 卡恩喊道。
梗概：
你举起盾牌进行防御
--------------------
动作：
做
你跟随卡恩进入树林
结果：
卡恩发出一声战吼，全力冲向树林。你跟在后面，盾牌遮住胸口，举起剑。当您冲破树木并穿过灌木丛时，您会听到一声喊叫。 “火！” 箭从树上射下来。十秒钟后，你和你的盟友挤在一起，箭射进你的盾牌，你的一半人已经死了。然后，短剑的精灵战士比弓箭手的盔甲更重，但仍然比你的轻很多，来消灭剩下的毫无准备、畏缩的兽人。
梗概：
你跟随卡恩进入树林
--------------------
动作：
做
你采取防御措施来保护自己
结果：
你举起盾牌，举起你的剑。战士们迅速利用这种情况，攻击那些试图躲避箭矢的毫无准备的兽人。其中一个朝你走来，然后摆动。你用盾挡住他的剑，反复刺穿他的腹部，第三次刺穿他的盔甲并刺穿他的腹部，再刺几下后就杀死了他。 当兽人设法抵挡精灵战士时，他们包括可汗在内，只有十几个人。精灵的数量比你多，但只是略胜一筹，而且他们显然不喜欢他们的胜算，或者认为他们今天已经杀死了足够多的盟友。战士们迅速转身，向树林中冲刺。
梗概：
你采取防御措施来保护自己
--------------------
动作：
做
你跟随勇士队
结果：
你迅速跟随勇士，冲刺冲刺。你拒绝让他们逃脱。几分钟后，战士们停下来转身。你意识到没有其他兽人跟随你，你是一个人。你举起剑来战斗，但战士们冲锋。你挥舞着你的剑并设法斩首了一个，然后在胸部被十几次刺伤。
梗概：
你跟随勇士队
故事名称：
81
故事背景：
你举起盾牌，举起你的剑。战士们迅速利用这种情况，攻击那些试图躲避箭矢的毫无准备的兽人。其中一个朝你走来，然后摆动。你用盾挡住他的剑并反复刺穿他的腹部，第三次刺穿他的盔甲并刺穿他的腹部，再刺几下后杀死了他。 当兽人设法抵挡精灵战士时，他们包括可汗在内，只有十几个人。精灵的数量比你多，但只是略胜一筹，而且他们显然不喜欢他们的胜算，或者认为他们今天已经杀死了足够多的盟友。战士们迅速转身，向树林中冲刺。
--------------------
动作：
做
你攻击树上的弓箭手
结果：
你咆哮着，向前奔跑并在空中跳跃。你抓住一个站在树枝上的弓箭手尖叫着把她扔到地上。你迅速结束了她悲惨的生活，将你的剑刺穿了她的脖子。你看到另一个弓箭手从树上跳下来想逃跑，但砍断了他的腿，导致他倒在地上。你站在他的上方，向下刺，刺穿他的胸膛，杀死了他。一名弓箭手拿着猎刀跳到你身上，从你的脖子后面切开，企图谋杀你，但失败了。你把他从背上扔到地上，切开他的肚子。你抬起头，看到其余的弓箭手以惊人的速度从战斗中冲刺而出。 “别费心去追。你抓不到精灵。他们已经跑了一辈子，胆小鬼。” 卡恩说。“来吧伙计们。我们回营地吧。”
梗概：
你攻击树上的弓箭手
--------------------
动作：
做
一天后...
结果：
你坐在营地，边上有篝火，烤猪。在达鲁克船长到来之前，你急切地等待着用餐。 “玛兹基尔，陪我一会儿。” 你努力不生气地咆哮，知道你会错过最好的猪肉，但无论如何点头并跟随达鲁克。在他开始说话之前，你跟随他离开了营地的耳朵范围。 “你昨天和卡恩一起执行任务，对吗？” “是的先生。” “为了执行如此重要的任务，我们损失了大量人员。我需要知道：这是卡恩作为领导者的错吗？”
梗概：
一天后...
--------------------
动作：
说
“是”。
结果：
“啊。我也是这么想的。其他人的证词也这么说。谢谢你。去吧，吃饭。” 你点点头，赶紧回去吃饭。您必须打一个较小的兽人，但您设法抓住了一大块多汁的肉并急切地咀嚼。
梗概：
你说“是”。
--------------------
动作：
做
一周后...
结果：
你站在一张巨大的地图周围，显示了格列宁平原。杜拉克船长开始解释情况。 “Rhian 船长正在带领皇家髑髅地前进，在多个地点与精灵髑髅地交战。当后卫穿过雅各布桥和这里的无名小砖桥时，我们被安排在这里对髑髅地发动攻击。我将领导主要巡逻在雅各布桥上。卡恩，带领你选择的兽人部队。” “是的先生。” 卡恩选择了两打兽人，令你惊讶的是他没有选择你，原因是你不知道。然后，当您前往雅各布桥时，您会跟随 Durrak 船长。谢天谢地，精灵髑髅地还没有到来，所以你就位。 “行了，你别动，等我说完再进攻。明白了吗？” “是的先生。” 大家异口同声的回答。 这座桥是一座巨大的木桥，横跨一条巨大的泥泞小溪。你在桥的边缘占据位置，这样你就可以猛攻并消灭髑髅地的后卫。一大群精灵骑着美丽的白马开始疾驰而过。他们疾驰了整整三十秒，然后疾驰的马儿的数量出现了一个小小的中断。 “现在！把他们都干掉！” 杜拉克船长吼道。 你跳出泥泞的河岸，来到桥上。小型的 Elvish Calvary's Rearguard 旨在保护他们的侧翼并确保他们可以选择撤退，只有大约三打 Calvary 部队，而且您有同样多的 Orc。你看到后卫的领头，一个金色盔甲的精灵战士，骑在一匹恐惧的马上。
梗概：
一周后...
--------------------
动作：
做
你向前冲锋以瞄准领导者
结果：
你举起剑，咆哮着向前冲刺。你切开马的胸膛，马惊恐地发出呜呜声。首领从马上摔下来，你迅速站在他身边，举起你的刀。突然，一支长矛从你的胸口飞过，被其中一个 Calvary 部队抛掷。当长矛嵌入你的身体时，你呻吟起来。
梗概：
你向前冲锋以瞄准领导者
故事名称：
82
故事背景：
你站在一张巨大的地图周围，上面是格列宁平原。杜拉克船长开始解释情况。 “Rhian 船长正在带领皇家髑髅地前进，在多个地点与精灵髑髅地交战。当后卫穿过雅各布桥和这里的无名小砖桥时，我们被安排在这里对髑髅地发动攻击。我将领导主要巡逻在雅各布桥上。卡恩，带领你选择的兽人部队。” “是的先生。” 卡恩选择了两打兽人，令你惊讶的是他没有选择你，原因是你不知道。然后，当您前往雅各布桥时，您会跟随 Durrak 船长。谢天谢地，精灵髑髅地还没有到来，所以你就位。 “行了，你别动，等我说完再进攻。明白了吗？” “是的先生。” 大家异口同声的回答。 这座桥是一座巨大的木桥，横跨一条巨大的泥泞小溪。你在桥的边缘占据位置，这样你就可以猛攻并消灭髑髅地的后卫。一大群精灵骑着美丽的白马开始疾驰而过。他们疾驰了整整三十秒，然后疾驰的马儿的数量出现了一个小小的中断。 “现在！把他们都干掉！” 杜拉克船长吼道。 你跳出泥泞的河岸，来到桥上。小型的 Elvish Calvary's Rearguard 旨在保护他们的侧翼并确保他们可以选择撤退，只有大约三打 Calvary 部队，而且您有同样多的 Orc。你看到后卫的领头，一个金色盔甲的精灵战士，骑在一匹恐惧的马上。
--------------------
动作：
做
你坚持小组并举起你的盾牌
结果：
你坚持小组，从你的盾牌偏转长矛。你向前移动，在你的盾牌保持抬起以偏转任何打击时向前刺伤你的刀片。你刺伤了一个精灵骑士的大腿，他尖叫着从马上掉下来，你反复踩在他的脸上。你再次挥动你的刀刃，劈开一匹马的胸膛，让它惊恐地站起来。精灵掉下来撞到地上，让一个兽人站在它上面并刺穿它的胸部。混战只持续了几秒钟，因为各各他部队被绝望地屠杀，因为他们显然感到惊讶并且没有准备好战斗，努力寻找他们的武器并准备战斗。几个手持弩的兽人促成了大屠杀。 “好吧。损失？” 杜拉克船长在你巡视战场时问道。 “十二个兽人倒下。三人受伤。” “两个。我还能打。” 一个兽人咕哝着，从她身边抽出了一根长矛。 “好了，开始把马尸体堆成路障。” 你抓住一匹死马，开始把它拖到位，把它放在另一匹死马的上面。几分钟后，你在桥上占据一席之地。您会听到主战中的尖叫声，同时您会看到骑马的战斗以马背上的较量和剑术的形式进行。你微笑着从死去的 Calvary 部队中抓起一把长矛。 “这很有用。先生，如果我们使用精灵之矛，我们可以协助战斗。” 你向杜拉克船长指出。 “这会有风险。我们需要留在这里保卫这座桥。” 杜拉克船长说。
梗概：
你坚持小组并举起你的盾牌
--------------------
动作：
做
你提议独自前行
结果：
“我会继续帮助我们的人。一个人。” “哈！你是个疯子，马兹基尔。如果你想和马匹一起玩，就往前走。” 你快速向前，举起你的新长矛。战斗正在残酷地进行。你快速移动，首先走向一个与人类骑士较量的精灵，骑士受了重伤，从侧面的刺伤中流血。当精灵被骑士分散注意力时，你刺伤了你的武器，快速刺穿了精灵的侧面，杀死了精灵。你拿出你的长矛，向前移动到下一个目标，一个参与剑术的精灵。你保持低调以避免被看到，并迅速刺穿小精灵的脖子，将他从马上拉下来，然后再次拔出你的长矛。你重复这个，用你的长矛和潜行反复刺伤各种精灵骑手和各各别部队，让你自己的各各别部队击退精灵。你的贡献很小，但由于你自己的军队已经获胜，少数幸存下来的精灵迅速前往桥梁，他们可能在那里被消灭了。你擦掉脸上精灵和马的血，然后开始向桥走去。你到达时发现了几十个死去的精灵和马匹，达鲁克船长得意地笑了。 “马兹基尔，你还活着。这很好。精灵们已经被消灭了。” “我尽可能多地取出来，先生。我尽了自己的一份力量。” “很好。现在，我们还有工作要做。随着我们髑髅的前进，我们终于可以开始入侵和攻击精灵村庄了。” 达鲁克船长说。“精灵帝国正在崩溃，孩子们。从死者身上拿走你想要的东西，杀死受伤的人，我们会回到堡垒去寻找喝的东西。”
梗概：
你提议独自前行
--------------------
动作：
做
一周后...
结果：
你和一小群人沿着树林散步。二十个兽人，三十个男人和一打战犬。你由 Darruk 船长带领，他自豪地站在领先位置。 “最近的精灵袭击了我们的部队，损失惨重。我们相信他们已经撤退到Abhaile村补给并治疗伤员。我希望这个威胁得到处理。” 你轻轻拍了拍其中一只战犬的头，被那条快乐的狗逗乐了。突然，战犬冲上前，狂吠。 “我们来了。兽人！没有抢劫，杀戮或任何类似的事情！一旦村庄安全，我们就在这里过夜。” “是的先生。” 你们都回应。 你向前穿过树林，看到村庄。这是一个很大的村庄，有很多房子，都是木制的，看起来很自然，让你想起你在去精灵领地行军时经过的几个有钱人的避暑别墅。许多平民注意到了，开始大喊大叫，赶紧跑了进去。 “慢着！这个村庄在亚历山大国王和皇家军队的控制之下！” 达鲁克船长吠叫。“挨家挨户搜查！没收任何武器。杀死任何士兵或试图抵抗的人。” 你点了点头，就快步向着屋子走去。第一个进去的是一个小女孩拼命地抱着一个毛绒玩具，她的母亲站在她面前，手里拿着一把匕首。 “别出去！” 她哭了。 在忽略她之前，你简短地看了她一眼。在返回楼下之前，您迅速搜索了楼上并没收了一把弓、一些箭和三把猎刀。除了母女俩，屋子里空荡荡的。 “把刀给我。” 你问。 “滚出我的房子！” 她尖叫，你畏缩。
梗概：
一周后...
--------------------
动作：
做
你杀了她
结果：
你挥舞着刀刃，划破了她的胸膛。她向后倒下，随着女儿的尖叫声重重地摔在地板上。你抓起她的刀，看着女儿。你感到一阵短暂的怜悯，但很快就压制住了。
梗概：
你杀了她
--------------------
动作：
做
你搬到下一个房子
结果：
你走进隔壁房子。一个年轻的精灵，和你一样年轻，站在门后面，把门关上了。你把门砸开，他滚到膝盖上，他的弓准备好了一个箭头缺口。 “放下弓。” 你说，举起你的盾牌。 他开火，你很容易把箭撞到一边。快速一扫，他的头就趴在了地板上。你搜查了底层，一无所获，但在楼上你发现了另一个被封锁的房间。用你那令人印象深刻的力量，你用蛮力砸开了门。房间里有四名精灵战士，身上缠着绷带，身上有明显的伤口。你迅速挥剑斩杀一个，踢另一个的头部并反复跺着他的喉咙，把它压碎。你刺穿另一只胸膛杀死他，然后刺穿最后一只刺穿眼睛。当他的身体着地时，你会注意到一个密室。你打开它，找到一小叠精灵珠宝。镶嵌着金色宝石的金色手镯、设计精美的项链和镶有大红宝石的金戒指。您的命令是将所有贵重物品留待稍后由 Darruk 船长拿走，用于购买补给和食物，但是……闪亮。
梗概：
你搬到下一个房子
--------------------
动作：
做
你拿走贵重物品
结果：
当您将贵重物品扫入书包时，您会微笑。你为皇家军队而战，工资微薄，你配得上这些贵重物品。下一个房子的时间，你还有重要的工作要做。
梗概：
你拿走贵重物品
--------------------
动作：
做
你搜索下一个房子
结果：
你走进隔壁房子，发现里面已经有一个兽人。一个被斩首的精灵躺在地板上，一滩血在地板上蔓延。兽人，一个矮小但坚强的人，你认为是坎尼人。他抱着一个半裸的精灵女人，把她逼到墙上。 “住口！” 在注意到你之前，他咆哮着。“Mazkil？我在这里很忙，我的朋友。如果你想去，楼上还有一个。否则，你应该趁着抢劫好时出去。” 你看着那个女人，泪水从她的脸上流下来，不知道该怎么办。
梗概：
你搜索下一个房子
--------------------
动作：
做
你停止卡尼什语
结果：
“Kannish！放开她！” 坎尼什停止微笑，咆哮起来。 “什么，你坠入爱河了吗，Mazkil？滚开，否则这该死的。”
梗概：
你停止卡尼什语
--------------------
动作：
做
你离开大楼
结果：
你走到外面，继续挨家挨户地寻找，尽管当你听到她在你身后呼救时你会皱眉。
梗概：
你离开大楼
--------------------
动作：
做
你搜索下一个房子
结果：
你走到外面，当你走到隔壁房子时，你听到一声尖叫。 你转身看到一座大房子已经爆发成正在迅速蔓延的火焰。达鲁克船长冲过去，紧随其后的是你。 “这是怎么回事？” 达鲁克船长问道。 “当他们抵抗时，我们将平民安置在这所房子里，所以我们烧毁了这个地方。” 一名兽人解释道。 你看到平民；男人、女人和儿童冲出大楼，因为它迅速爆发成火焰。你听到从顶楼传来的哭声，一种只有婴儿才能发出的高音。 “船长！里面有婴儿！” “我们无能为力。火焰会杀死任何试图进入的人。” 你看着建筑物，火焰舔着天空。你会看到火焰中有一个很小的缝隙，有人可以很容易地利用它进入。
梗概：
你搜索下一个房子
--------------------
动作：
做
你试图拯救婴儿
结果：
尽管你的队友抗议，你还是冲进了大楼。你潜入燃烧的地狱，高温让你大汗淋漓，痛苦地呻吟。你环顾四周，但烟雾遮蔽了你的视线。你开始咳嗽和喷嚏，声音越来越小。你四处寻找楼梯。你设法找到了它，并开始爬上楼梯，跟随婴儿的哭声和尖叫声。当你痛苦地畏缩、大声呻吟时，火焰开始灼烧你的肉体。你到达楼梯的顶部，拼命地环顾四周寻找婴儿。你会发现一个布满灰烬和余烬的小摇篮，你刷掉它，捡起一个你认出是婴儿的、丑陋而吵闹的生物。你拿起它，寻找出路。此刻，屋子里充满了浓烟，火势蔓延到了各处。你准备离开，但屋顶横梁倒塌，砸在你的背上，当火焰开始灼烧你的肉时，你跪倒在地。你尖叫着滚开，蜷缩在婴儿周围以保护它。您在烟雾和火焰中寻找出路，同时尽最大努力保护婴儿。你冲进门，找到外面的路，然后掉到凉爽的草地上。你的肉被烧焦了，你很难保持清醒。一个精灵抱起婴儿说了什么，大概是谢谢 。
梗概：
你试图拯救婴儿
--------------------
动作：
做
你昏迷不醒
结果：
几个小时后你醒来，躺在床上。你缠着厚厚的绷带，躺在几十个熟睡的兽人和人类旁边。看来，达鲁克船长让医生照顾你，你已经被扔到沉睡的士兵旁边治疗了。你假设你会被带回最近的堡垒接受照顾，直到你痊愈到可以再次出战。 你躺在寂静中，试图重新入睡。然后，你听到窗户破碎的声音。你环顾四周，挣扎着起身。谢天谢地，医生把你的刀留在了你身上。你环顾四周，一拳砸在你的头侧，把你击倒。你试着尖叫，然后才感觉到喉咙上的刀片冰冷。你抬起头，看到一个高大的精灵拿着一把精灵短剑。 “祈祷吧，兽人。” 他说。 你抓住他的喉咙，但他很快就偏转了你的手。当他准备切开你的喉咙时，你看到一只手阻止了他。你注意到另一个精灵阻止了他。 “等等。不要。” “我为什么不应该？” “他早些时候帮助过我们。他救了格温迪琳的孩子。” 拿着剑的精灵耸了耸肩。 “美好的。” 你环顾四周。房间里挤满了一群精灵，他们全都为战争而武装。看来你是第一个醒来的。由于严重的烧伤覆盖了你的身体，你很难做任何身体上的事情。不过，你仍然可以警告你的队友。
梗概：
你昏迷不醒
--------------------
动作：
做
你大声呼救
结果：
“是...！” 你开始大喊大叫。 刀片的平面砸在你的脸上，让你失去知觉。
梗概：
你大声呼救
--------------------
动作：
做
你醒了
结果：
当你醒来时你尖叫着，被一股新的疼痛击中。你环顾四周。你被被屠杀的兽人和人类包围，许多人在睡梦中被杀，但很明显有些人及时醒来进行抵抗。你试图站起来，然后倒在地板上。痛苦是无所不在的，你考虑让死亡超越你并结束你生存的斗争。
梗概：
你醒了
--------------------
动作：
做
你搜索周边地区
结果：
你四处寻找可以帮助你摆脱困境的东西，你发现了一小瓶蓝色液体。
梗概：
你搜索周边地区
--------------------
动作：
做
你喝吧
结果：
你喝下酒，随着疼痛开始停止颤抖。你注意到不仅你的痛苦结束了，而且你所有的感觉都结束了。不久之后，您的身体开始瘫痪。当您的胸部开始放缓，呼吸停止时，您会畏缩和颤抖。你挣扎着继续呼吸，但在你可怜的存在结束之前，你只能忍受痛苦的几秒钟。
梗概：
你喝吧
故事名称：
83
故事背景：
你四处寻找可以帮助你摆脱困境的东西，你发现了一小瓶蓝色液体。
--------------------
动作：
做
你不喝
结果：
你决定不喝。你知道你需要尽快行动，在夜幕降临之前逃跑，但要坚持他们的蒸馏器可能是附近有用的东西。
梗概：
你不喝
--------------------
动作：
做
你把自己拖到外面
结果：
你把自己拖到外面。村子里挤满了死去的兽人、人类和战犬，穿插着你的盟友设法击倒的金甲精灵。你继续拖着自己向前走，与死者擦肩而过。秃鹫在你周围降落并开始啄食尸体。一只落在你面前，啄你的脸。
梗概：
你把自己拖到外面
--------------------
动作：
做
你抓住秃鹫
结果：
你抓住秃鹫，把秃鹫的头从脖子上扯下来扔到一边。你又开始拖着自己向前走。
梗概：
你抓住秃鹫
--------------------
动作：
做
四小时后...
结果：
你向前移动你的手，找到一些东西把自己再向前拖几英寸。谢天谢地，树叶让你在炎热的阳光下有一些阴凉处，但疼痛开始变得难以忍受。然后，好像众神都同意让你的生活一团糟，你听到狼的声音。 “去你的！” 你尖叫着，因为三只狼开始向你徘徊。
梗概：
四小时后...
--------------------
动作：
做
你一刀一掷
结果：
你扔出你的刀，刺穿了其中一只狼的头部，立即杀死了它。它向后倒下，你会短暂地感到骄傲，然后另一只狼扑向你，落在你的胸口。它咬住你的胸膛，撕裂肉体，你发出一声尖叫。
梗概：
你一刀一掷
故事名称：
84
故事背景：
你走到外面，当你走到隔壁房子时，你听到一声尖叫。 你转身看到一座大房子已经爆发成正在迅速蔓延的火焰。达鲁克船长冲过去，紧随其后的是你。 “这是怎么回事？” 达鲁克船长问道。 “当他们抵抗时，我们将平民安置在这所房子里，所以我们烧毁了这个地方。” 一名兽人解释道。 你看到平民；男人、女人和儿童冲出大楼，因为它迅速爆发成火焰。你听到从顶楼传来的哭声，一种只有婴儿才能发出的高音。 “船长！里面有婴儿！” “我们无能为力。火焰会杀死任何试图进入的人。” 你看着建筑物，火焰舔着天空。你会看到火焰中有一个很小的缝隙，有人可以很容易地利用它进入。
--------------------
动作：
做
你什么都不做
结果：
你站着，看着建筑物燃烧，一些不安的士兵和哭泣的平民因为害怕他们的残酷死亡而被阻止进入建筑物。`幸好，随着烟雾在整个房子里蔓延，婴儿的哭声只持续了几分钟。一旦它停止，达鲁克船长点点头。 “好的，大家。回去工作。继续寻找。” 你搜查了接下来的几所房子，但人们要么死了要么畏缩不前，这让你可以找到一些小武器库存。最终，你完成并找到一个空房子睡觉。达鲁克船长指派了几个兽人看守，但幸运的是你不是其中之一。
梗概：
你什么都不做
--------------------
动作：
做
你休息
结果：
几个小时后你醒来，躺在床上。你又醒了。 你躺在寂静中，试图重新入睡。然后，你听到窗户破碎的声音。你环顾四周，挣扎着起身。你慢慢地拔刀。你环顾四周，一拳砸在你的头侧，把你击倒。你试着尖叫，然后才感觉到喉咙上的刀片冰冷。你抬起头，看到一个高大的精灵拿着一把精灵短剑。 “祈祷吧，兽人。” 他说。
梗概：
你休息
--------------------
动作：
做
你扑向他
结果：
你向前猛冲，抓住他的手腕，将剑甩开。你抓住他的头，举起刀，反复刺穿他的眼睛。另一个精灵跑上前，切开你的胸膛，你把他的头撞到房间里，然后反复把他扔到墙上，除非他的身体被打碎了。在精灵包围你之前，你切开了另一个精灵的喉咙。当每个精灵都开始向你猛砍时，你会咆哮。随着成千上万的伤口和斜线在你的身体上裂开，血液开始沿着你的胸部流下。在你倒在地板上之前，你只持续了几秒钟的剧烈疼痛。
梗概：
你扑向他
故事名称：
85
故事背景：
箭矢永不停息。 在泥泞中爬行，你看着周围不幸的同志被截击。一声咕哝和砰的一声，在你面前的战壕中，一个人掉了下来，向你伸出了手。你爬向他，扯下他的盔甲，试图止血。箭插在他胸口几乎是不可能的，把它撕下来只会更快地杀死他。 你坐在那里抱着他，试图在他的最后时刻提供一些小小的安慰。他想说话，却只发出咕噜咕噜的声音，赤红从他的嘴角流了出来。最后叹了口气，整个人放松下来，头向后仰去。他那双玻璃般的眼睛盯着你，只看到了空虚。 把他放下，你继续在泥泞中跋涉。你周围更多的人已经倒下，并将继续倒下。一道暗紫色的能量冲击波从头顶飞过，煎炸了一些不幸的人。 你跑向树木。永远不要直线奔跑，你从右边开始，然后在你潜入树后时向左飞镖。几支箭落在你旁边的地上。 该死的精灵。 你回想一下精灵最初是如何和平地来到这里的。提供权力和力量的承诺。兽人不想与他们和他们的魔法有任何关系。下一次精灵出现时；然而，它已经生效。寻找应征入伍者以对抗帝国。兽人不想与他们的战争有任何关系，但自从精灵把它带到我们家门口，我们现在来报复了。 无法在两条战线上作战的精灵们肯定会崩溃。 但这一切都始于四年前。事实证明，精灵有着令人难以置信的决心……你打算打破它。
--------------------
动作：
做
你前进
结果：
仅在前方几英里处，您就会看到 Wister 精灵国王的城堡要塞。哈里博国王企图拖延他的最后一战，躲在他的人民身后。 懦夫。 他宁愿让他的敌人毁灭他的国家，也不愿在战场上迎接他的命运。 现在，你已经走到了一座山脊，许多兽人在山脊后面等待。对面是另一座山脊，兽人也同样驻扎。在这之间，几十个兽人缓慢前进：躲在战壕里，在岩石后面，在成堆的死去的战友中间。除非你为他们争取时间，否则他们永远不会成功。 在前面，精灵们建造了木墙和塔楼。每个人都有裂缝和城墙来射箭和施展魔法。 扫描该区域，您会看到精灵的右翼防御最强。完美的。您需要做的就是冲下山脊并直接冲向那座塔。 你抓住一个兽人的肩膀，他回头咕哝着。 “看看他们右边的防御有多强？” 他凝视着外面，咕哝着承认。 “我需要你们所有人都做好准备，将所有的火力集中在那个区域。” “为什么？他们的左边是最弱的。” “我知道，我会利用它来发挥我们的优势。” 当你再次拍拍他的肩膀时，你就要离开了。 “尽量不要射我。” 你回到森林里，看到树上发出一些光。精灵们正在密切注视着他们的队伍。 在黑暗中看到多个兽人，你阻止他们。他们好奇地看着你，直到你解释你的计划。他们渴望跟随。 你回到山脊后面，靠近前线，小心地留在黑暗中。凝视顶部，您会注意到一些新事物：一个充满精灵的隐藏战壕系统。他们肯定期待兽人随时发起冲锋，并计划在战壕中出其不意。 你检查你下面的沟渠。它直接通向一座塔的门。你看到一名军官使用微弱发光的望远镜。在他附近，分散在战壕各处的是受伤的精灵。有些适合战斗，有些不适合，但无论如何他们都会去做。你至少要尊重这一点。 你准备好你的弓，看到一个精灵倒在战壕墙上。头上缠着血淋淋的绷带，他拿着匕首，似乎在祈祷。他看起来几乎像个孩子，比你小几岁。 当您放松时，其他人也会放松。当箭射中他的胸膛时，精灵惊讶地倒吸一口凉气。你们都射了几支箭，直到你面前的每个精灵都被针垫住了。 没有人会远离这一点。没有人发出警报。 穿过战壕到达最近的城垛，你准备好你的斧头，短到可以用一只手使用，但长到可以用两只手。你站在塔门旁边，深吸一口气。
梗概：
你前进
--------------------
动作：
做
你塔
结果：
用脚向前，门突然打开，碎片直接撞在后面的墙上。当一个血淋淋的精灵蹒跚地向你走来时，你冲了进来。当你将斧头插进他的额头时，他惊讶地瞪大了眼睛。当其他兽人从你身边涌过时，浓浓的血迹从他的脸上爬下。你满意地咕哝着从倒下的敌人身上拔出斧头。 进入狭长的大厅，就看到许多精灵睁大眼睛，死人的脸庞因震惊而扭曲。他们……几乎没有看到是什么击中了他们。 再次带路，你爬上楼梯到达下一个平台，你的动作被战斗的声音掩盖了。你能认出箭在弦上的声音。 “我击中了一个，”其中一个精灵说，“哈！看他扭动着身体！” 你的脸因愤怒而扭曲。举起你的斧头，你走出去，把它嵌入他们军官的脖子上。在他们意识到发生了什么之前，你已经将斧头留在了另一个人的肩膀上，并用一支箭射中了三分之一的人的眼睛。顷刻间，其他兽人就向他们袭来。 你登上塔顶。几乎立刻，一个双手缠着绷带的精灵看到了你，并向他的同伴们发出警告。 你冲他们冲锋，躲避箭矢，向他们投球，把两个人撞到边缘，尖叫着。受伤的人试图放下他的剑，但你的双手抓住了它。扭开它，你将剑从边缘扔掉。 咆哮着，你向精灵徘徊；他后退，拼命地摆动你这样做。当你滑到他身后时，你躲开一拳，用一只胳膊搂住他的脖子。他的手在你的手臂上拍打和拉扯你的手臂无效。你挤得更紧。啪的一声，他的手软了下来。你放下他。 该死的棒子，太他妈弱了。
梗概：
你塔
--------------------
动作：
做
你继续
结果：
你低下头走到窗台，向上看，越过一堵矮墙。箭还在飞；到处都是混乱。 “来吧，我们还有更多的塔要拆除，”你说。 你走到另一边，挥手让你离开山脊的兽人注意。你向他们发出信号，让他们从你身边冲进战壕。 当你走下来的时候，兽人已经涌入了战壕。当敌人在混乱中失去注意力时，其余的战友会从中心冲进来包围他们。 精灵们英勇战斗，拒绝投降，这让你感到惊讶。更令人惊讶的是，在兽人完全消灭其余部分之前，您只能杀死少数人。 你向前跑到墙上，看着战壕，精灵在远处，但他们的恐惧是显而易见的。所有的战壕似乎都通向一个地下隧道系统，旁边的房间被挖出来存放伤员和额外的补给品。 当您进入其中一个时，您会看到一个受伤的小精灵躺在床上。他的胸口缠着血淋淋的绷带，呼吸困难。他甚至可能不会成功，尽管就你而言，杀死手无寸铁的人是为弱者准备的。
梗概：
你继续
--------------------
动作：
做
你怜悯杀了
结果：
你走近精灵，轻轻地踩着。当你从鞘中抽出刀刃时，他抬头看着你。他的眼睛瞪大，呼吸也变得急促起来。他虚弱地试图举起手臂，可能是求饶。 用剑穿过他最近的伤口，你用力按压，血液从他的身边倾泻而下。当他的背部和颈部拱起时，他的嘴唇发出了一声短促的喘息，他的眼睛睁大了，然后闭上了大约一半。你拉出你的刀片并向前移动。
梗概：
你怜悯杀了
--------------------
动作：
做
回到隧道
结果：
当你准备跑回隧道时，突然爆发出一声巨响，震动了地面。你必须让自己靠在墙上。在与其他兽人交换了眼神之后，你穿过隧道向前走，始终保持警惕。 你到达一个被火焰吞没的开口。兽人尖叫着燃烧着，或者已经躺着不动，肉体在烤。 当腐烂的气味到达您的鼻孔时，您会感到畏缩。您会变低以试图更好地查看泥土坡道。炸药一定是从墙上来的。 你飞奔到大厅的一侧，想着你应该做什么。你看到其他一些兽人即将穿过火热的开口。
梗概：
回到隧道
--------------------
动作：
做
你等着
结果：
你举起手。 “等等，”你说。“他们可以有更多。” 当每个人都在等待时，房间里死一般的寂静。你会听到更多的爆炸声，还有兽人冲过其他战壕隧道。 到目前为止，这听起来像是一片混乱。在某个时候，你听到一声尖叫，一个精灵的身体在开口外的地面上嘎吱作响。 他们现在肯定已经攻占了城墙，或者至少是其中的一部分。 你慢慢接近开口，手里拿着斧头。当你走出去时，你会注意到到处都有精灵和兽人在进行致命的战斗。 几个精灵直奔你而来。他们可能正试图寻求庇护。当他们抬起手臂时，他们的一张脸扭曲了。一道鲜红色的箭矢从他手中飞出。当螺栓猛烈撞击隧道上方的木制固定装置时，您几乎没有时间掉到地上。 当小木块洒在你的背上时，你的弓上有一个箭头。你只能站得足够高，让你的箭升到斜坡上方。当你将箭对准最近的精灵时，你呼气，他们现在离得更近了。 你放开箭，向你猛射的精灵在落在他的臀部时蹒跚而行。但他仍然向前跑，尽管现在他们都开始向你发射能量箭。你右边的一个兽人倒下了，他胸口的伤口冒出烟雾。 你射出另一支箭，一支箭从你身边掠过，你射中了下一个精灵，因为另一个兽人已经完成了你击中的第一个。 片刻之后，这群精灵全都死了，还有一些兽人在你身边。 兽人似乎又一次占了上风，实力和数量都超群。 你和你的团队走到户外，很快就找到了一个不那么脆弱的地方。
梗概：
你等着
--------------------
动作：
做
你推
结果：
许多兽人从精灵身上掠夺财宝、武器和战利品。你交叉双臂靠在一些供应棚上。 一匹马奔向一群兽人，中间是一个兽人首领。骑在马背上的兽人斥候疯狂地开口说话。 “精灵们！他们正在逼近我们，我们就在他们队伍的中间，我们会被包围，”他喊道。 他们切断通往他们国王的大门只是时间问题。 筛选兽人，你最终找到了负责这场战斗的兽人首领，他似乎已经解雇了斥候。 “酋长，”你说，“我们需要进一步推进。” “是的，是的，我知道。” 他对你耸了耸肩，而其他一些亲吻屁股的兽人递给他一些小饰品。 “你听到了，我们在他们队伍的中间，很容易被夹子夹住。估计也被包围了。” 兽人首领笑了。 “让他们来吧，他们不会有机会的。” 你睁大眼睛，这个傻瓜似乎会让你的兄弟们死去而不是结束这一切。 咕噜一声，你扯断了系在他腰带上的喇叭。他瞪了你一眼，上前一步。你看到他的手轻轻地放在他的剑柄上。
梗概：
你推
--------------------
动作：
做
你跟他讲道理
结果：
看着手中的喇叭，你意识到你可能犯了一个错误。 “我不会坐在这里等你把他们都杀了，”你说。“我们现在需要搬家，因为我们有机会见到他们的国王。” 考虑到你所说的话，酋长的目光转向了一边。 他轻笑。 “你是一个大胆的人，你是对的。” 他一边说一边走向你，一边从你手中抢过喇叭，一边把你推到一边。 “但我不能让这种不尊重继续存在。” 他一边吹号角一边继续走，召集兽人军队。当他呼吁他们向前推进时，你听到有什么东西在你身后滑动。 转过身来，你正好可以避开剑击，这会劈开你的头。 你抓住兽人的前臂，迅速扭动他的手臂并偷走了他的剑。他单膝跪地，痛苦地咕哝着，你踢他的脸，把他撞倒。 酋长随从中的其他四个兽人都拔出了武器，试图包围你。你一直在移动，向后，向左，向右，无论你走到哪里，都要让最重要的人在你面前，让其他人远离你。 一个终于向前倾斜，切开你的胫骨。无视灼热的疼痛，你纵身一跃，剑刺入他的脖子。他的身体放松下来，向前倾倒，还没有完全死去，而是在去那里的路上。 另一个兽人冲上前，试图将他的斧头埋在你的头上。你只需将垂死的兽人拉到你面前，斧头埋在他的背上。 你让兽人倒下，因为他的重量不允许其他兽人拔出他的斧头。它反而从他的手指上滑落。他的手伸向他的匕首，但在他做任何其他事情之前，你偷来的剑已经埋在他的胸膛里。 你哼了一声，把它推到了刀柄上，他温热的血点溅到了你的手臂和胸部。兽人的双臂在垂死的阵痛中张大嘴巴并颤抖着，当他的手从你的前臂滑落时，他轻拍你的手臂。 剩下的两个兽人已经开始冲锋了。一个跳过垂死的兽人，几乎撞到你的肩膀。你得到了一次幸运的闪避，这次是抽出你的真剑。 另一个是从你身边过来，一心想要将长枪插进你的身边。你敲开矛头，沿着轴滑动刀片，直到你够到他的手。你只需快速地将刀片向后拉，然后快速砍断他的手指。当你用剑划过他的喉咙时，他痛苦地叫喊着，变成了咕噜咕噜的声音。 最后一个兽人，可能也是最足智多谋的。从他身边拉出一个小盾牌，飞盘向你挥舞。它击中你抬起的前臂，在你的手臂上留下一个刺痛的痕迹。 当你后退时，另一个兽人卷起并翻滚，抓住长矛。当他跪下时，他将长矛向前倾斜。它埋在你的腰间，灼热的疼痛射了进来。当他把它撕掉时，你痛苦地咆哮。这次他的目标正好在你的喉咙下方，但你设法把它撞到一边。 以同样的动作，你举起你的斧头，以惊人的灵巧投掷它。兽人的头向后一踢，然后缓缓向前倾倒。他的一只眼睛盯着你，另一只眼睛以奇怪的角度看向别处。他摔倒了，斧柄阻止了他的脸碰到地面。 叹了口气，你一瘸一拐，从他的额头上拔出斧头。当你环顾四周时，你意识到一些兽人聚集在一起为你加油。他们似乎不想介入，以防万一你应得的，他们甚至不知道你在争吵什么，但似乎很有趣。 他们拍了拍你的背，因为他们击毁了四个兽人，其中一个甚至愿意帮助你清洁和包扎伤口。你欣然接受... 一些烈酒和缝合之后，你的腿和刺伤被牢固地修补了。你只需要小心不要重新打开或加重伤口。 错过了最初的冲刺，你被困在补给车里，和所有年老的、跛脚的或受伤的兽人一起前进。您确实设法搭便车，还提供了一些新鲜的熟牛肉。几个月前就没有吃过的熟食店。 你决定在去前面的路上闭上眼睛...
梗概：
你跟他讲道理