From 23e051135e1b74239361c97c95bf0b69dce134f4 Mon Sep 17 00:00:00 2001
From: weili-nlp <weili.ucas.ict@gmail.com>
Date: Fri, 20 May 2022 18:01:06 +0800
Subject: [PATCH 1/4] open release code for UNIMO-2

---
 NLP/UNIMO-2/.idea/UNIMO2-Open.iml             |    12 +
 NLP/UNIMO-2/.idea/misc.xml                    |     4 +
 NLP/UNIMO-2/.idea/modules.xml                 |     8 +
 NLP/UNIMO-2/.idea/vcs.xml                     |     6 +
 NLP/UNIMO-2/.idea/workspace.xml               |    90 +
 NLP/UNIMO-2/CHANGELOG.md                      |    24 +
 NLP/UNIMO-2/README.md                         |   216 +
 NLP/UNIMO-2/ci.yml                            |    18 +
 NLP/UNIMO-2/data/download_MNLI-AX.sh          |    14 +
 NLP/UNIMO-2/env_local/env.sh                  |    25 +
 NLP/UNIMO-2/env_local/utils.sh                |   105 +
 NLP/UNIMO-2/images/cross-modal.graffle        |   Bin 0 -> 700658 bytes
 NLP/UNIMO-2/images/cross-modal.png            |   Bin 0 -> 450406 bytes
 NLP/UNIMO-2/images/paper.png                  |   Bin 0 -> 211698 bytes
 NLP/UNIMO-2/images/textual.png                |   Bin 0 -> 156160 bytes
 NLP/UNIMO-2/images/visual.png                 |   Bin 0 -> 99776 bytes
 .../model_files/config/grounded_base_en.json  |    31 +
 .../model_files/config/grounded_large_en.json |    31 +
 .../model_files/config/roberta_base_en.json   |    14 +
 .../model_files/config/roberta_large_en.json  |    14 +
 .../model_files/config/vit_base_en.json       |    26 +
 .../model_files/config/vit_large_en.json      |    26 +
 .../dict/roberta_base_en.encoder.json         |     1 +
 .../dict/roberta_base_en.vocab.bpe            | 50001 +++++++++++++++
 .../dict/roberta_base_en.vocab.txt            | 50265 ++++++++++++++++
 NLP/UNIMO-2/requirements.txt                  |    60 +
 .../MNLI-AX/model_conf                        |    58 +
 .../classification-grounded/MNLI-AX/run.sh    |   132 +
 .../classification-grounded/method_conf       |     5 +
 .../img2txt-grounded/coco-oscar/model_conf    |    80 +
 .../script/img2txt-grounded/coco-oscar/run.sh |   124 +
 .../script/img2txt-grounded/method_conf       |     5 +
 .../Flickr30k-fleet/model_conf                |    62 +
 .../retrieval-grounded/Flickr30k-fleet/run.sh |   127 +
 .../script/retrieval-grounded/method_conf     |     5 +
 NLP/UNIMO-2/setup.cfg                         |    80 +
 NLP/UNIMO-2/setup.py                          |    18 +
 NLP/UNIMO-2/src/__init__.py                   |     0
 NLP/UNIMO-2/src/__main__.py                   |    21 +
 NLP/UNIMO-2/src/args/__init__.py              |    12 +
 .../src/args/classification_grounded_args.py  |   130 +
 NLP/UNIMO-2/src/args/img2txt_oscar_args.py    |   146 +
 .../src/args/retrieval_grounded_args.py       |   134 +
 NLP/UNIMO-2/src/eval/__init__.py              |    12 +
 NLP/UNIMO-2/src/eval/gen_eval.py              |   131 +
 NLP/UNIMO-2/src/finetune/__init__.py          |     0
 .../src/finetune/classifier_grounded.py       |   201 +
 NLP/UNIMO-2/src/finetune/glue_eval.py         |   222 +
 NLP/UNIMO-2/src/finetune/img2txt_oscar.py     |  1690 +
 NLP/UNIMO-2/src/finetune/img_eval.py          |   218 +
 .../src/finetune/retrieval_grounded_fleet.py  |   256 +
 NLP/UNIMO-2/src/finetune/trigram_blocking.py  |   175 +
 NLP/UNIMO-2/src/launch.py                     |   137 +
 NLP/UNIMO-2/src/model/__init__.py             |     0
 NLP/UNIMO-2/src/model/roberta_tokenization.py |   633 +
 NLP/UNIMO-2/src/model/transformer_encoder.py  |   400 +
 NLP/UNIMO-2/src/model/unimo_grounded.py       |  1494 +
 .../src/model/unimo_grounded_baseline.py      |   948 +
 .../src/model/vision_transformer_encoder.py   |   400 +
 NLP/UNIMO-2/src/reader/__init__.py            |     0
 .../__pycache__/__init__.cpython-37.pyc       |   Bin 0 -> 174 bytes
 .../reader/classification_grounded_reader.py  |   252 +
 .../src/reader/img2txt_oscar_reader.py        |   404 +
 .../src/reader/retrieval_grounded_reader.py   |   399 +
 .../src/reader/unimo_grounded_batching.py     |   552 +
 NLP/UNIMO-2/src/run_classifier_grounded.py    |   515 +
 NLP/UNIMO-2/src/run_img2txt_oscar.py          |   337 +
 .../src/run_retrieval_grounded_fleet.py       |   326 +
 NLP/UNIMO-2/src/utils/__init__.py             |     0
 NLP/UNIMO-2/src/utils/args.py                 |    57 +
 NLP/UNIMO-2/src/utils/bleu.py                 |   107 +
 NLP/UNIMO-2/src/utils/cmrc2018_eval.py        |   153 +
 NLP/UNIMO-2/src/utils/extract_eval_res.py     |    64 +
 NLP/UNIMO-2/src/utils/fp16.py                 |   204 +
 NLP/UNIMO-2/src/utils/image_utils.py          |   240 +
 NLP/UNIMO-2/src/utils/init.py                 |   141 +
 NLP/UNIMO-2/src/utils/new_stat_res.py         |    99 +
 NLP/UNIMO-2/src/utils/optimization.py         |   136 +
 NLP/UNIMO-2/src/utils/optimization_fleet.py   |   106 +
 .../src/utils/optimization_fleet_new.py       |    92 +
 NLP/UNIMO-2/src/utils/pos_emb_interpolate.py  |    50 +
 NLP/UNIMO-2/src/utils/stat_res.py             |    72 +
 NLP/UNIMO-2/src/utils/utils.py                |    47 +
 83 files changed, 113400 insertions(+)
 create mode 100644 NLP/UNIMO-2/.idea/UNIMO2-Open.iml
 create mode 100644 NLP/UNIMO-2/.idea/misc.xml
 create mode 100644 NLP/UNIMO-2/.idea/modules.xml
 create mode 100644 NLP/UNIMO-2/.idea/vcs.xml
 create mode 100644 NLP/UNIMO-2/.idea/workspace.xml
 create mode 100644 NLP/UNIMO-2/CHANGELOG.md
 create mode 100644 NLP/UNIMO-2/README.md
 create mode 100644 NLP/UNIMO-2/ci.yml
 create mode 100644 NLP/UNIMO-2/data/download_MNLI-AX.sh
 create mode 100644 NLP/UNIMO-2/env_local/env.sh
 create mode 100644 NLP/UNIMO-2/env_local/utils.sh
 create mode 100644 NLP/UNIMO-2/images/cross-modal.graffle
 create mode 100644 NLP/UNIMO-2/images/cross-modal.png
 create mode 100644 NLP/UNIMO-2/images/paper.png
 create mode 100644 NLP/UNIMO-2/images/textual.png
 create mode 100644 NLP/UNIMO-2/images/visual.png
 create mode 100644 NLP/UNIMO-2/model_files/config/grounded_base_en.json
 create mode 100644 NLP/UNIMO-2/model_files/config/grounded_large_en.json
 create mode 100644 NLP/UNIMO-2/model_files/config/roberta_base_en.json
 create mode 100644 NLP/UNIMO-2/model_files/config/roberta_large_en.json
 create mode 100644 NLP/UNIMO-2/model_files/config/vit_base_en.json
 create mode 100644 NLP/UNIMO-2/model_files/config/vit_large_en.json
 create mode 100644 NLP/UNIMO-2/model_files/dict/roberta_base_en.encoder.json
 create mode 100644 NLP/UNIMO-2/model_files/dict/roberta_base_en.vocab.bpe
 create mode 100644 NLP/UNIMO-2/model_files/dict/roberta_base_en.vocab.txt
 create mode 100644 NLP/UNIMO-2/requirements.txt
 create mode 100644 NLP/UNIMO-2/script/classification-grounded/MNLI-AX/model_conf
 create mode 100644 NLP/UNIMO-2/script/classification-grounded/MNLI-AX/run.sh
 create mode 100644 NLP/UNIMO-2/script/classification-grounded/method_conf
 create mode 100644 NLP/UNIMO-2/script/img2txt-grounded/coco-oscar/model_conf
 create mode 100644 NLP/UNIMO-2/script/img2txt-grounded/coco-oscar/run.sh
 create mode 100644 NLP/UNIMO-2/script/img2txt-grounded/method_conf
 create mode 100644 NLP/UNIMO-2/script/retrieval-grounded/Flickr30k-fleet/model_conf
 create mode 100644 NLP/UNIMO-2/script/retrieval-grounded/Flickr30k-fleet/run.sh
 create mode 100644 NLP/UNIMO-2/script/retrieval-grounded/method_conf
 create mode 100644 NLP/UNIMO-2/setup.cfg
 create mode 100755 NLP/UNIMO-2/setup.py
 create mode 100644 NLP/UNIMO-2/src/__init__.py
 create mode 100644 NLP/UNIMO-2/src/__main__.py
 create mode 100644 NLP/UNIMO-2/src/args/__init__.py
 create mode 100644 NLP/UNIMO-2/src/args/classification_grounded_args.py
 create mode 100644 NLP/UNIMO-2/src/args/img2txt_oscar_args.py
 create mode 100644 NLP/UNIMO-2/src/args/retrieval_grounded_args.py
 create mode 100644 NLP/UNIMO-2/src/eval/__init__.py
 create mode 100644 NLP/UNIMO-2/src/eval/gen_eval.py
 create mode 100644 NLP/UNIMO-2/src/finetune/__init__.py
 create mode 100644 NLP/UNIMO-2/src/finetune/classifier_grounded.py
 create mode 100644 NLP/UNIMO-2/src/finetune/glue_eval.py
 create mode 100644 NLP/UNIMO-2/src/finetune/img2txt_oscar.py
 create mode 100644 NLP/UNIMO-2/src/finetune/img_eval.py
 create mode 100644 NLP/UNIMO-2/src/finetune/retrieval_grounded_fleet.py
 create mode 100644 NLP/UNIMO-2/src/finetune/trigram_blocking.py
 create mode 100644 NLP/UNIMO-2/src/launch.py
 create mode 100644 NLP/UNIMO-2/src/model/__init__.py
 create mode 100644 NLP/UNIMO-2/src/model/roberta_tokenization.py
 create mode 100644 NLP/UNIMO-2/src/model/transformer_encoder.py
 create mode 100644 NLP/UNIMO-2/src/model/unimo_grounded.py
 create mode 100644 NLP/UNIMO-2/src/model/unimo_grounded_baseline.py
 create mode 100644 NLP/UNIMO-2/src/model/vision_transformer_encoder.py
 create mode 100644 NLP/UNIMO-2/src/reader/__init__.py
 create mode 100644 NLP/UNIMO-2/src/reader/__pycache__/__init__.cpython-37.pyc
 create mode 100644 NLP/UNIMO-2/src/reader/classification_grounded_reader.py
 create mode 100644 NLP/UNIMO-2/src/reader/img2txt_oscar_reader.py
 create mode 100644 NLP/UNIMO-2/src/reader/retrieval_grounded_reader.py
 create mode 100644 NLP/UNIMO-2/src/reader/unimo_grounded_batching.py
 create mode 100644 NLP/UNIMO-2/src/run_classifier_grounded.py
 create mode 100644 NLP/UNIMO-2/src/run_img2txt_oscar.py
 create mode 100644 NLP/UNIMO-2/src/run_retrieval_grounded_fleet.py
 create mode 100644 NLP/UNIMO-2/src/utils/__init__.py
 create mode 100644 NLP/UNIMO-2/src/utils/args.py
 create mode 100644 NLP/UNIMO-2/src/utils/bleu.py
 create mode 100644 NLP/UNIMO-2/src/utils/cmrc2018_eval.py
 create mode 100644 NLP/UNIMO-2/src/utils/extract_eval_res.py
 create mode 100644 NLP/UNIMO-2/src/utils/fp16.py
 create mode 100644 NLP/UNIMO-2/src/utils/image_utils.py
 create mode 100644 NLP/UNIMO-2/src/utils/init.py
 create mode 100644 NLP/UNIMO-2/src/utils/new_stat_res.py
 create mode 100644 NLP/UNIMO-2/src/utils/optimization.py
 create mode 100644 NLP/UNIMO-2/src/utils/optimization_fleet.py
 create mode 100644 NLP/UNIMO-2/src/utils/optimization_fleet_new.py
 create mode 100644 NLP/UNIMO-2/src/utils/pos_emb_interpolate.py
 create mode 100644 NLP/UNIMO-2/src/utils/stat_res.py
 create mode 100644 NLP/UNIMO-2/src/utils/utils.py

diff --git a/NLP/UNIMO-2/.idea/UNIMO2-Open.iml b/NLP/UNIMO-2/.idea/UNIMO2-Open.iml
new file mode 100644
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new file mode 100644
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new file mode 100644
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diff --git a/NLP/UNIMO-2/CHANGELOG.md b/NLP/UNIMO-2/CHANGELOG.md
new file mode 100644
index 000000000..9045fa643
--- /dev/null
+++ b/NLP/UNIMO-2/CHANGELOG.md
@@ -0,0 +1,24 @@
+Changelog
+===
+以下记录了项目中所有值得关注的变更内容，其格式基于[Keep a Changelog]。
+
+本项目版本遵守[Semantic Versioning]和[PEP-440]。
+
+[Unreleased]
+---
+### Added
+- 这里记录新添加的内容
+### Changed
+- 这里记录变更的内容
+
+0.1.0 - 2022-05-05
+---
+### Added
+- 创建项目
+
+
+[Unreleased]: http://icode.baidu.com/repos/baidu/personal-code/UNIMO2-Open/merge/0.1.0...master
+
+[Keep a Changelog]: https://keepachangelog.com/zh-CN/1.0.0/
+[Semantic Versioning]: https://semver.org/lang/zh-CN/
+[PEP-440]: https://www.python.org/dev/peps/pep-0440/
diff --git a/NLP/UNIMO-2/README.md b/NLP/UNIMO-2/README.md
new file mode 100644
index 000000000..18ec6f583
--- /dev/null
+++ b/NLP/UNIMO-2/README.md
@@ -0,0 +1,216 @@
+UNIMO
+====
+Code for the findings of ACL2022 long paper [UNIMO-2: End-to-End Unified Vision-Language Grounded Learning](https://arxiv.org/pdf/2203.09067.pdf)
+
+
+Abstract
+---
+
+Vision-Language Pre-training (VLP) has achieved impressive performance on various cross-modal downstream tasks. 
+However, most existing methods can only learn from aligned image-caption data and rely heavily on expensive regional 
+features, which greatly limits their scalability and performance. In this paper, we propose an end-to-end unified-modal 
+pre-training framework, namely UNIMO-2, for joint learning on both aligned image-caption data and unaligned image-only 
+and text-only corpus. We build a unified Transformer model to jointly learn visual representations, textual 
+representations and semantic alignment between images and texts. In particular, we propose to conduct grounded learning 
+on both images and texts via a sharing grounded space, which helps bridge unaligned images and texts, and align the 
+visual and textual semantic spaces on different types of corpora. The experiments show that our grounded learning 
+method can improve textual and visual semantic alignment for improving performance on various cross-modal tasks. 
+Moreover, benefiting from effective joint modeling of different types of corpora, our model also achieves impressive 
+performance on single-modal visual and textual tasks. Our code and models are public at the UNIMO project page 
+\url{https://unimo-ptm.github.io}.
+
+![UNIMO-2](images/paper.png#pic_center)
+
+
+
+Dependencies
+---
+python3.7.4\
+cuda-10.1\
+cudnn_v7.6\
+nccl2.4.2\
+java1.8
+paddlepaddle-gpu==2.1.2\
+pyrouge==0.1.3
+
+
+Pre-trained Models
+---
+Similar to UNIMO, UNIMO-2 adopts large-scale text corpus, image collections and image-text aligned datasets as the pre-training data. 
+We provide pre-trained UNIMO-2 models:
+
+```
+cd /path/to/model_files
+wget --no-check-certificate -q https://unimo-2.bj.bcebos.com/model/UNIMO-2.tar.gz
+tar -zxf UNIMO-2.tar.gz
+```
+
+
+Experiments
+---
+
+Our fine-tuning experiments are carried on V100 GPU. Here are the results from the UNIMO-2 model:
+
+
+1 Cross-Modal  Tasks
+---
+
+
+### (1) Image-Text Retrieval
+
+#### Download Flickr30k dataset:
+
+```
+cd /path/to/data
+wget --no-check-certificate -q https://unimo-2.bj.bcebos.com/data/Flickr30k.tar.gz
+tar -zxf Flickr30k.tar.gz
+```
+
+#### Run the following common to train and evaluate on the Flickr30k dataset:
+
+```
+bash ./script/retrieval-grounded/Flickr30k-fleet/run.sh
+```
+
+#### Evaluation Results:
+
+Results of Image Retrieval task on Flickr30k dataset
+
+|   Model   | R@1 |  R@5  |  R@10  |
+| ----------- | ------- | ------- | ------- |
+|   UNIMO-2 (zero-shot)  |  72.70 | 91.18 | 94.60  |
+|   UNIMO-2 (finetuned)  |  80.14 | 95.58 | 97.75  |
+
+Results of Text Retrieval task on Flickr30k dataset
+
+|   Model   |  R@1  |  R@5  |  R@10  |
+| ----------- | ------- | ------- | ------- |
+|   UNIMO-2 (zero-shot)  |  88.46 | 96.84 | 98.92  |
+|   UNIMO-2 (finetuned) | 92.01 | 99.31 | 99.51 |
+
+
+
+### (2) Image Caption Generation
+
+#### Download COCO Caption dataset:
+
+```
+cd /path/to/data
+wget --no-check-certificate -q https://unimo-2.bj.bcebos.com/data/coco.tar.gz
+tar -zxf coco.tar.gz
+```
+
+#### Download evaluation script:
+
+```
+mkdir src/eval/tasks
+cd src/eval/tasks
+wget --no-check-certificate -q https://unimo.bj.bcebos.com/eval_script/coco.tar.gz
+tar -zxf coco.tar.gz
+```
+
+#### Run the following common to train and evaluate on the COCO Caption dataset:
+
+```
+bash ./script/img2txt-grounded/coco-oscar/run.sh
+```
+
+
+#### Evaluation Results:
+
+|   Model   | BLUE4 | CIDEr |
+| ----------- | ------- | ------- |
+|   UNIMO-2 |  39.7  |  131.2  |
+
+
+
+### (3) Visual Entailment
+####todo
+
+
+
+### (4) Visual Question Answering (VQA)
+####todo
+
+
+
+
+
+2 Visual Tasks
+---
+
+### (1) Image Classification
+####todo
+
+### (2) Zero-shot Image Classification
+####todo
+
+
+
+3 Textual Tasks
+---
+
+### (1) Natural Language Inference
+
+#### Download MNLI-AX dataset:
+```
+cd /path/to/data
+wget --no-check-certificate -q https://unimo-2.bj.bcebos.com/data/MNLI-AX.tar.gz
+tar -zxf MNLI-AX.tar.gz
+```
+
+#### Run the following common to train and evaluate on the MNLI-AX dataset:
+
+```
+bash ./script/classification/MNLI-AX/run.sh
+```
+
+
+#### Evaluation Results:
+
+|   Model   | Acc-(m/mm) |
+| ----------- | ------- |
+|   UNIMO-2  |  87.5/87.5 |
+
+
+
+
+### (2) Sentiment Classification
+####todo
+
+
+
+
+
+### (3) Similarity Tasks
+####todo
+
+
+
+
+
+### (4) Linguistic Acceptability Judgments
+####todo
+
+
+
+
+
+Citation
+---
+If you find our paper and code useful, please cite the following paper:
+```
+@article{li2022unimo,
+  title={UNIMO-2: End-to-End Unified Vision-Language Grounded Learning},
+  author={Li, Wei and Gao, Can and Niu, Guocheng and Xiao, Xinyan and Liu, Hao and Liu, Jiachen and Wu, Hua and Wang, Haifeng},
+  journal={arXiv preprint arXiv:2203.09067},
+  year={2022}
+}
+```
+
+Contact information
+---
+
+For help or issues using UNIMO-2, please submit a GitHub issue.
+
+For personal communication related to UNIMO, please contact Wei Li (liwei85@baidu.com), Can Gao (gaocan01@baidu.com), Guocheng Niu (niuguocheng@baidu.com).
diff --git a/NLP/UNIMO-2/ci.yml b/NLP/UNIMO-2/ci.yml
new file mode 100644
index 000000000..c83a1c7e4
--- /dev/null
+++ b/NLP/UNIMO-2/ci.yml
@@ -0,0 +1,18 @@
+Global:
+    tool : build_submitter
+
+Default:
+    profile : [publish]
+
+Profiles:
+    - profile:
+      name : dev
+      env: DECK_CENTOS6U3_K3
+      command : python setup.py bdist_wheel
+      release : true
+
+    - profile:
+      name :  publish
+      env: DECK_CENTOS6U3_K3
+      command : python setup.py bdist_wheel
+      release : true
diff --git a/NLP/UNIMO-2/data/download_MNLI-AX.sh b/NLP/UNIMO-2/data/download_MNLI-AX.sh
new file mode 100644
index 000000000..7c2be9397
--- /dev/null
+++ b/NLP/UNIMO-2/data/download_MNLI-AX.sh
@@ -0,0 +1,14 @@
+data_name=MNLI-AX
+data_tar=${data_name}.tar.gz
+bos_url=https://3m-gup.bj.bcebos.com/data/nlu_data/MNLI-AX.tar.gz
+
+rm -rf $data_name
+wget --no-check-certificate -q $bos_url
+if [[ $? -ne 0 ]]; then
+    echo "url link: $bos_url"
+    echo "download data failed"
+    exit 1
+fi
+tar zxf $data_tar
+rm -f $data_tar
+exit 0
diff --git a/NLP/UNIMO-2/env_local/env.sh b/NLP/UNIMO-2/env_local/env.sh
new file mode 100644
index 000000000..5f87f5c72
--- /dev/null
+++ b/NLP/UNIMO-2/env_local/env.sh
@@ -0,0 +1,25 @@
+#!/usr/bin/env bash
+set -x
+#在LD_LIBRARY_PATH中添加cuda库的路径
+export LD_LIBRARY_PATH=/home/work/cuda-10.1/lib64${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}
+export LD_LIBRARY_PATH=/home/work/cuda-10.1/extras/CUPTI/lib64:$LD_LIBRARY_PATH
+#在LD_LIBRARY_PATH中添加cudnn库的路径
+export LD_LIBRARY_PATH=/home/work/cudnn/cudnn_v7.6/cuda/lib64:$LD_LIBRARY_PATH
+#需要先下载NCCL，然后在LD_LIBRARY_PATH中添加NCCL库的路径
+export LD_LIBRARY_PATH=/home/work/nccl2.4.2_cuda10.1/lib:$LD_LIBRARY_PATH
+#如果FLAGS_sync_nccl_allreduce为1，则会在allreduce_op_handle中调用cudaStreamSynchronize（nccl_stream），这种模式在某些情况下可以获得更好的性能
+export FLAGS_sync_nccl_allreduce=1
+#表示分配的显存块占GPU总可用显存大小的比例，范围[0,1]
+export FLAGS_fraction_of_gpu_memory_to_use=1
+#表示是否使用垃圾回收策略来优化网络的内存使用，<0表示禁用，>=0表示启用
+export FLAGS_eager_delete_tensor_gb=1.0
+#是否使用快速垃圾回收策略
+export FLAGS_fast_eager_deletion_mode=1
+#垃圾回收策略释放变量的内存大小百分比，范围为[0.0, 1.0]
+export FLAGS_memory_fraction_of_eager_deletion=1
+
+export iplist=`hostname -i`
+#http_proxy
+unset http_proxy
+unset https_proxy
+set +x
diff --git a/NLP/UNIMO-2/env_local/utils.sh b/NLP/UNIMO-2/env_local/utils.sh
new file mode 100644
index 000000000..e91bb204a
--- /dev/null
+++ b/NLP/UNIMO-2/env_local/utils.sh
@@ -0,0 +1,105 @@
+#!/bin/bash
+
+#set -u
+
+function check_iplist() {
+
+    if [ ${iplist:-} ]; then
+        #paddle envs
+        export PADDLE_PSERVER_PORT=9184
+        export PADDLE_TRAINER_IPS=${iplist} 
+        #export PADDLE_CURRENT_IP=`/sbin/ip a | grep inet | grep global | awk '{print $2}' | sed 's/\/[0-9][0-9].*$//g'`
+        export PADDLE_CURRENT_IP=`hostname -i`
+        
+        iparray=(${iplist//,/ })
+        for i in "${!iparray[@]}"; do
+        if [ ${iparray[$i]} == ${PADDLE_CURRENT_IP} ]; then
+            export PADDLE_TRAINER_ID=$i
+        fi
+        done
+        
+        export TRAINING_ROLE=TRAINER
+        #export PADDLE_PSERVERS=127.0.0.1
+        export PADDLE_INIT_TRAINER_COUNT=${#iparray[@]}
+        export PADDLE_PORT=${PADDLE_PSERVER_PORT}
+        export PADDLE_TRAINERS=${PADDLE_TRAINER_IPS}
+        export POD_IP=${PADDLE_CURRENT_IP}
+        export PADDLE_TRAINERS_NUM=${PADDLE_INIT_TRAINER_COUNT}
+            #is model_config
+        export PADDLE_IS_LOCAL=0
+        echo "****************************************************"
+  
+        #paddle debug envs
+        export GLOG_v=0
+        export GLOG_logtostderr=1
+        
+        #nccl debug envs
+        export NCCL_DEBUG=INFO
+        #export NCCL_IB_DISABLE=1
+        #export NCCL_IB_GDR_LEVEL=4
+        export NCCL_IB_GID_INDEX=3
+        #export NCCL_SOCKET_IFNAME=eth2
+    fi
+}
+
+function ReadINIfile() {
+    Section=$1
+    Key=$2
+    Configfile=$3
+    ReadINI=`awk -F '=' '/\['$Section'\]/{a=1}a==1&&$1~/'$Key'/{print $2;exit}' $Configfile`
+    echo "$ReadINI"
+}
+
+function get_gpu_id() {
+    gpu_node=$1
+    selected_gpus=""
+    gpu_array=("0" "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15")
+    for(( i=0;i<${gpu_node};i++ )) do
+        if [[ ${selected_gpus} == "" ]]; then
+                selected_gpus=${gpu_array[i]}
+        else
+                selected_gpus=${selected_gpus}","${gpu_array[i]}
+        fi
+    done;
+    echo "${selected_gpus}"
+}
+
+function get_init_step() {
+    init_model=$1
+    cur_step=`basename $init_model`
+    arr_cur_step=(${cur_step//_/ })
+    if [[ ${arr_cur_step[0]} == "step" ]] && [[ ${#arr_cur_step[@]} == 2 ]]; then
+        init_step=${arr_cur_step[1]}
+    else
+        init_step=0
+    fi
+    echo $init_step
+}
+
+function is_port_used() {
+    port=$1
+
+    ret=`lsof -i:$port`
+    ret=$(echo $ret | grep "LISTEN")
+    if [[ $ret != "" ]];
+    then
+        echo "1"
+    else
+        echo "0"
+    fi
+}
+
+function is_ports_used() {
+    port=$1
+    nums=$2
+
+    ret="0"
+    for((i=0;i<${nums};i++)) do
+        tmp=`is_port_used $[$port+$i]`
+        if [[ ${tmp} == "1" ]]; then
+            ret="1"
+            break;
+        fi
+    done;
+    echo $ret
+}
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diff --git a/NLP/UNIMO-2/model_files/config/grounded_base_en.json b/NLP/UNIMO-2/model_files/config/grounded_base_en.json
new file mode 100644
index 000000000..3c5671fa2
--- /dev/null
+++ b/NLP/UNIMO-2/model_files/config/grounded_base_en.json
@@ -0,0 +1,31 @@
+{
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "max_position_embeddings": 514,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "text_attention_probs_dropout_prob": 0.1,
+  "text_hidden_act": "gelu",
+  "text_hidden_dropout_prob": 0.1,
+  "text_hidden_size": 768,
+  "text_max_position_embeddings": 514,
+  "text_num_attention_heads": 12,
+  "text_num_hidden_layers": 12,
+  "text_vocab_size": 50265,
+  "vit_attention_probs_dropout_prob": 0.0,
+  "vit_hidden_act": "gelu",
+  "vit_hidden_dropout_prob": 0.0,
+  "vit_hidden_size": 768,
+  "vit_num_attention_heads": 12,
+  "vit_num_hidden_layers": 12,
+  "vit_layer_norm_eps": 1e-12,
+  "initializer_range": 0.02,
+  "type_vocab_size": 0,
+  "use_neg_lm_loss": false,
+  "fusion_method": "mul",
+  "num_labels": 2,
+  "resolution": 16,
+  "image_size": 224
+}
\ No newline at end of file
diff --git a/NLP/UNIMO-2/model_files/config/grounded_large_en.json b/NLP/UNIMO-2/model_files/config/grounded_large_en.json
new file mode 100644
index 000000000..d3831a95b
--- /dev/null
+++ b/NLP/UNIMO-2/model_files/config/grounded_large_en.json
@@ -0,0 +1,31 @@
+{
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "max_position_embeddings": 514,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "text_attention_probs_dropout_prob": 0.1,
+  "text_hidden_act": "gelu",
+  "text_hidden_dropout_prob": 0.1,
+  "text_hidden_size": 1024,
+  "text_max_position_embeddings": 514,
+  "text_num_attention_heads": 16,
+  "text_num_hidden_layers": 24,
+  "text_vocab_size": 50265,
+  "vit_attention_probs_dropout_prob": 0.0,
+  "vit_hidden_act": "gelu",
+  "vit_hidden_dropout_prob": 0.0,
+  "vit_hidden_size": 1024,
+  "vit_num_attention_heads": 16,
+  "vit_num_hidden_layers": 24,
+  "vit_layer_norm_eps": 1e-12,
+  "initializer_range": 0.02,
+  "type_vocab_size": 0,
+  "use_neg_lm_loss": false,
+  "fusion_method": "mul",
+  "num_labels": 2,
+  "resolution": 16,
+  "image_size": 224
+}
diff --git a/NLP/UNIMO-2/model_files/config/roberta_base_en.json b/NLP/UNIMO-2/model_files/config/roberta_base_en.json
new file mode 100644
index 000000000..6d5673f45
--- /dev/null
+++ b/NLP/UNIMO-2/model_files/config/roberta_base_en.json
@@ -0,0 +1,14 @@
+{
+  "attention_probs_dropout_prob": 0.1, 
+  "hidden_act": "gelu", 
+  "hidden_dropout_prob": 0.1, 
+  "hidden_size": 768,   
+  "initializer_range": 0.02, 
+  "max_position_embeddings": 514,
+  "num_attention_heads": 12, 
+  "num_hidden_layers": 12, 
+  "type_vocab_size": 0,
+  "sent_type_vocab_size": 0, 
+  "task_type_vocab_size": 0, 
+  "vocab_size": 50265
+}
diff --git a/NLP/UNIMO-2/model_files/config/roberta_large_en.json b/NLP/UNIMO-2/model_files/config/roberta_large_en.json
new file mode 100644
index 000000000..9a95c3557
--- /dev/null
+++ b/NLP/UNIMO-2/model_files/config/roberta_large_en.json
@@ -0,0 +1,14 @@
+{
+  "attention_probs_dropout_prob": 0.1, 
+  "hidden_act": "gelu", 
+  "hidden_dropout_prob": 0.1, 
+  "hidden_size": 1024,
+  "initializer_range": 0.02, 
+  "max_position_embeddings": 514,
+  "num_attention_heads": 16, 
+  "num_hidden_layers": 24, 
+  "type_vocab_size": 0,
+  "sent_type_vocab_size": 0, 
+  "task_type_vocab_size": 0, 
+  "vocab_size": 50265
+}
diff --git a/NLP/UNIMO-2/model_files/config/vit_base_en.json b/NLP/UNIMO-2/model_files/config/vit_base_en.json
new file mode 100644
index 000000000..ea0b08b8d
--- /dev/null
+++ b/NLP/UNIMO-2/model_files/config/vit_base_en.json
@@ -0,0 +1,26 @@
+{
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 514,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "vit_attention_probs_dropout_prob": 0.0,
+  "vit_hidden_act": "gelu",
+  "vit_hidden_dropout_prob": 0.0,
+  "vit_hidden_size": 768,
+  "vit_num_attention_heads": 12,
+  "vit_num_hidden_layers": 12,
+  "type_vocab_size": 0,
+  "vocab_size": 50265,
+  "use_neg_lm_loss": false,
+  "fusion_method": "mul",
+  "txt_mask_ratio": 0.15,
+  "vl_mask_ratio": 0.3,
+  "num_labels": 2,
+  "resolution": 16,
+  "image_size": 224,
+  "num_image_codebook": 16384
+}
\ No newline at end of file
diff --git a/NLP/UNIMO-2/model_files/config/vit_large_en.json b/NLP/UNIMO-2/model_files/config/vit_large_en.json
new file mode 100644
index 000000000..97f426ba7
--- /dev/null
+++ b/NLP/UNIMO-2/model_files/config/vit_large_en.json
@@ -0,0 +1,26 @@
+{
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 514,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "vit_attention_probs_dropout_prob": 0.0,
+  "vit_hidden_act": "gelu",
+  "vit_hidden_dropout_prob": 0.0,
+  "vit_hidden_size": 768,
+  "vit_num_attention_heads": 12,
+  "vit_num_hidden_layers": 12,
+  "type_vocab_size": 0,
+  "vocab_size": 50265,
+  "use_neg_lm_loss": false,
+  "fusion_method": "mul",
+  "txt_mask_ratio": 0.15,
+  "vl_mask_ratio": 0.3,
+  "num_labels": 2,
+  "resolution": 16,
+  "image_size": 224,
+  "num_image_codebook": 1024
+}
diff --git a/NLP/UNIMO-2/model_files/dict/roberta_base_en.encoder.json b/NLP/UNIMO-2/model_files/dict/roberta_base_en.encoder.json
new file mode 100644
index 000000000..1f1d9aaca
--- /dev/null
+++ b/NLP/UNIMO-2/model_files/dict/roberta_base_en.encoder.json
@@ -0,0 +1 @@
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"sk": 8135, "\u0120solutions": 8136, "\u0120atmosphere": 8137, "\u0120blame": 8138, "umes": 8139, "\u0120Nob": 8140, "CA": 8141, "umps": 8142, "rical": 8143, "\u0120Putin": 8144, "\u0120Dest": 8145, "oric": 8146, "\u0120PA": 8147, "\u0120respectively": 8148, "wan": 8149, "\u0120fifth": 8150, "\u00e2\u0126\u00a2": 8151, "\u0120Cry": 8152, "\u0120governor": 8153, "resident": 8154, "\u0120purchased": 8155, "\u0120hack": 8156, "\u0120intense": 8157, "obs": 8158, "\u0120origin": 8159, "\u0120define": 8160, "\u0120careful": 8161, "***": 8162, "\u0120shoulder": 8163, "Click": 8164, "\u0120tied": 8165, "\u0120destruction": 8166, "oured": 8167, "\u0120nobody": 8168, "\u0120ho": 8169, "\u0120Exper": 8170, "\u0120tip": 8171, "\";": 8172, "\u0120technique": 8173, "\u0120jur": 8174, "\u0120Pok": 8175, "bow": 8176, "\u0120legend": 8177, "\u0120accord": 8178, "\u0120busy": 8179, "\u0120Intel": 8180, "\u0120hang": 8181, "aki": 8182, ".]": 8183, "\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136": 8184, "\u0120surgery": 8185, "\u0120reprodu": 8186, "\u0120uniform": 8187, "\u0120scenes": 8188, "code": 8189, "\u012062": 8190, "lisher": 8191, "\u0120Have": 8192, "phia": 8193, "\u0120crypt": 8194, "\u0120recon": 8195, "\u0120scream": 8196, "\u0120adopted": 8197, "\u0120scores": 8198, "Ne": 8199, "\u0120Italian": 8200, "including": 8201, "BO": 8202, "\u0120indicated": 8203, "\u0120entertain": 8204, "Gu": 8205, "Text": 8206, "iel": 8207, "\u0120twenty": 8208, "\u0120engage": 8209, "offs": 8210, "\u0120Pacific": 8211, "\u0120smile": 8212, "\u0120personnel": 8213, "\u0120toler": 8214, "\u0120doors": 8215, "\u0120tone": 8216, "\u0120machines": 8217, "\u0120entering": 8218, "tenance": 8219, "CO": 8220, "\u0120Jersey": 8221, "\u0120forest": 8222, "\u0120horse": 8223, "\u0120complaint": 8224, "\u0120Spring": 8225, "yo": 8226, "\u0120Plus": 8227, "eding": 8228, "\u0120Return": 8229, "quarters": 8230, "ials": 8231, "cow": 8232, "\u0120academic": 8233, "\u0120fruit": 8234, "\u01201996": 8235, "ogether": 8236, "\u0120wine": 8237, "\u0120pursu": 8238, "\u0120Steven": 8239, "\u0120licens": 8240, "Who": 8241, "\u0120clothes": 8242, "rection": 8243, "\u0120squad": 8244, "\u0120stable": 8245, "\u0120raw": 8246, "zens": 8247, "Star": 8248, "uties": 8249, "ancer": 8250, "\u0120keys": 8251, "\u0120Mu": 8252, "\u0120complicated": 8253, "iger": 8254, "\u0120Text": 8255, "\u0120absor": 8256, "\u012068": 8257, "\u0120funny": 8258, "\u0120relief": 8259, "\u0120Lew": 8260, "\u0120Cook": 8261, "\u0120chart": 8262, "\u0120drawing": 8263, "GE": 8264, "\u0120module": 8265, "\u0120Bull": 8266, "ILL": 8267, "\u0120salt": 8268, "00000000": 8269, "ille": 8270, "\u0120resource": 8271, "away": 8272, "adelphia": 8273, "\u0120Bru": 8274, "\u012067": 8275, "\u0120somebody": 8276, "\u0120participate": 8277, "\u0120rose": 8278, "wered": 8279, "\u0120muscle": 8280, "\u0120consent": 8281, "\u0120continuing": 8282, "\u0120Guardian": 8283, "\u0120Order": 8284, "regon": 8285, "\u0120rear": 8286, "\u0120provision": 8287, "\u0120liked": 8288, "rient": 8289, "\u0120bra": 8290, "Trans": 8291, "\u0120meetings": 8292, "\u0120tox": 8293, "\u0120convent": 8294, "\u0120auto": 8295, "\u0120recording": 8296, "\u0120Soft": 8297, "001": 8298, "\u0120Roll": 8299, "\u0120programming": 8300, "\u0120pic": 8301, "\u0120proved": 8302, "\u0120stab": 8303, "\u0120Ast": 8304, "\u0120caption": 8305, "ulating": 8306, "\u0120Attack": 8307, "\u0120newly": 8308, "\u01201997": 8309, "fr": 8310, "\u0120discipl": 8311, "\u0120Greek": 8312, "\u0120edition": 8313, "\u0120Does": 8314, "\u0120Box": 8315, "ifle": 8316, "acket": 8317, "\u0120passes": 8318, "\u0120guest": 8319, "\u0120acceler": 8320, "itals": 8321, "UD": 8322, "\u0120authent": 8323, "\u0120Rest": 8324, "oval": 8325, "ta": 8326, "uine": 8327, "\u0120armor": 8328, "\u0120Town": 8329, "\u0120compat": 8330, "\u0120inches": 8331, "Despite": 8332, "\u0120assign": 8333, "herent": 8334, "\u0120prepare": 8335, "\u0120Meg": 8336, "ockey": 8337, "\u0120depends": 8338, "\u0120tracks": 8339, "watch": 8340, "\u0120lists": 8341, "\u0120Northern": 8342, "\u0120alter": 8343, "rec": 8344, "\u0120Eastern": 8345, "\u0120condem": 8346, "\u0120everywhere": 8347, "?'": 8348, "\u0120affili": 8349, "\u0120fought": 8350, "\":{\"": 8351, "\u0120mac": 8352, "itarian": 8353, "\u0120scope": 8354, "\u0120AL": 8355, "aws": 8356, "arms": 8357, "\u0120que": 8358, "\u0120enjoyed": 8359, "nesota": 8360, "\u0120aggressive": 8361, "\u0120Story": 8362, "\u0120IV": 8363, "\u0120recipe": 8364, "\u0120rarely": 8365, "\u0120Medical": 8366, "value": 8367, "angel": 8368, "aying": 8369, "omething": 8370, "\u0120subsection": 8371, "\u0120southern": 8372, "\u0120frequency": 8373, "rete": 8374, "rolled": 8375, "ults": 8376, "\u0120Nic": 8377, "\u0120behalf": 8378, "\u0120sequence": 8379, "abet": 8380, "\u0120controversial": 8381, "\u0120comprom": 8382, "\u0120worker": 8383, "\u0120mainly": 8384, "\u0120algorith": 8385, "\u0120Major": 8386, "orce": 8387, "gender": 8388, "\u0120organized": 8389, "\u0120fake": 8390, "\u0120concluded": 8391, "\u0120ED": 8392, "\u0120Exec": 8393, "rage": 8394, "\u0120chances": 8395, "berry": 8396, "\u0120Trad": 8397, "\u0120configuration": 8398, "\u0120withdraw": 8399, "\u0120fro": 8400, "udes": 8401, "\u0120Brother": 8402, "\u0120Brian": 8403, "\u0120tries": 8404, "\u0120samples": 8405, "\u0120bid": 8406, "\u0120Golden": 8407, "\u0120photograph": 8408, "ifest": 8409, "\u0120DO": 8410, "\u0120Parliament": 8411, "****************": 8412, "Rem": 8413, "\u0120contest": 8414, "\u0120signing": 8415, "px": 8416, "\u0120Zeal": 8417, "\u00e2\u0136\u0122\u00e2\u0136\u0122": 8418, "Ear": 8419, "\u0120exit": 8420, "Before": 8421, "\u0120Corpor": 8422, "null": 8423, "month": 8424, "\u0120racial": 8425, "otted": 8426, "\u0120Veg": 8427, "\u0120Reuters": 8428, "\u0120sword": 8429, "pson": 8430, "\u0120Romney": 8431, "aed": 8432, "\u0120trib": 8433, "\u0120inner": 8434, "\u0120protocol": 8435, "\u0120Bi": 8436, "\u0120Miami": 8437, "everal": 8438, "press": 8439, "\u0120shipping": 8440, "\u0120Amendment": 8441, "\u0120Howard": 8442, "connect": 8443, "\u0120Disc": 8444, "\u0120Jac": 8445, "iamond": 8446, "\u0120Therefore": 8447, "ses": 8448, "\u0120Princess": 8449, "\u0120USB": 8450, "\u0120Anth": 8451, "\u0120surveillance": 8452, "\u0120apolog": 8453, "\u012061": 8454, "owa": 8455, "\u0120fulf": 8456, "js": 8457, "\u0120luck": 8458, "usted": 8459, "\u0120\u00c2\u00a7": 8460, "ni": 8461, "\u0120anticip": 8462, "eman": 8463, "\u0120winner": 8464, "\u0120silver": 8465, "lla": 8466, "icity": 8467, "\u0120unusual": 8468, "\u0120crack": 8469, "\u0120ties": 8470, "ez": 8471, "\u0120practical": 8472, "\u0120province": 8473, "\u0120Place": 8474, "\u0120priority": 8475, "ICE": 8476, "\u0120describes": 8477, "\u0120branch": 8478, "Form": 8479, "aska": 8480, "missions": 8481, "bi": 8482, "\u0120porn": 8483, "\u0120Turk": 8484, "\u0120enthus": 8485, "\u0120fighters": 8486, "\u012008": 8487, "\u0120Detroit": 8488, "\u0120foundation": 8489, "avid": 8490, "Are": 8491, "\u0120judgment": 8492, "cling": 8493, "\u0120solve": 8494, "\u0120Design": 8495, "Where": 8496, "hesis": 8497, "\u0120Tro": 8498, "after": 8499, "\u0120neutral": 8500, "\u0120Palestinian": 8501, "\u0120Hollywood": 8502, "\u0120advis": 8503, "\u0120Non": 8504, "yes": 8505, "olis": 8506, "\u0120reputation": 8507, "\u0120smell": 8508, "\u0120bread": 8509, "\u0120Bul": 8510, "\u0120Beach": 8511, "\u0120claiming": 8512, "\u0120genetic": 8513, "\u0120technologies": 8514, "\u0120upgrade": 8515, "rows": 8516, "\u0120developer": 8517, "\u0120Josh": 8518, "\u0120Disney": 8519, "erved": 8520, "ipal": 8521, "\u0120unex": 8522, "\u0120barely": 8523, "then": 8524, "\u0120Pub": 8525, "\u0120illness": 8526, "etary": 8527, "\u0120Bal": 8528, "\u0120patch": 8529, "\u0120butt": 8530, "\u0120stupid": 8531, "\u0120Dog": 8532, "\u0120Dallas": 8533, "front": 8534, "iece": 8535, "\u0120protests": 8536, "\u0120chat": 8537, "oenix": 8538, "\u0120wing": 8539, "\u0120parliament": 8540, "\u012077": 8541, "osexual": 8542, "\u0120render": 8543, "ptions": 8544, "\u0120Coast": 8545, "osa": 8546, "\u0120Greg": 8547, "hop": 8548, "\u0120Management": 8549, "\u0120bitcoin": 8550, "\u0120recover": 8551, "\u0120incorpor": 8552, "orne": 8553, "\u0120Using": 8554, "\u0120preced": 8555, "\u0120threatened": 8556, "\u0120spiritual": 8557, "\u0120Event": 8558, "\u0120Fred": 8559, "\u0120advertising": 8560, "\u0120improvements": 8561, "\u0120Custom": 8562, "\u0120errors": 8563, "\u0120sensitive": 8564, "\u0120Navy": 8565, "\u0120cream": 8566, "Look": 8567, "\u0120exclusive": 8568, "\u0120comprehens": 8569, "\u0120deleg": 8570, "\u0120conce": 8571, "\u0120remem": 8572, "\u0120structures": 8573, "\u0120stored": 8574, "ND": 8575, "\u01201000": 8576, "UP": 8577, "\u0120Budd": 8578, "AF": 8579, "woman": 8580, "\u0120Academy": 8581, "\u00f0\u0141": 8582, "sea": 8583, "\u0120temporary": 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"limited": 10698, "Size": 10699, "\u0120meters": 10700, "\u0120rocket": 10701, "essee": 10702, "\u0120certificate": 10703, "\u0120Iranian": 10704, "ASS": 10705, "\u0120grid": 10706, "Dec": 10707, "\u0120rolling": 10708, "commun": 10709, "\u0120Sweden": 10710, "bury": 10711, "\u0120tissue": 10712, "\u0120racism": 10713, "\u0120Local": 10714, "\u0120mystery": 10715, "\u0120examine": 10716, "\u0120stem": 10717, "\u0120sits": 10718, "\u0120hoped": 10719, "oting": 10720, "\u0120dialogue": 10721, "\u0120persu": 10722, "Watch": 10723, "lay": 10724, "MAN": 10725, "\u0120chronic": 10726, "\u0120Portland": 10727, "market": 10728, "\u0120SEC": 10729, "\u0120parallel": 10730, "\u0120scandal": 10731, "\u0120carries": 10732, "\u0120phenomenon": 10733, "human": 10734, "acker": 10735, "\u0120Ox": 10736, "\u0120retirement": 10737, "tainment": 10738, "ovie": 10739, "\u0120Gear": 10740, "\u0120duties": 10741, "\u0120dose": 10742, "\u0120scroll": 10743, "MB": 10744, "inf": 10745, "\u0120sauce": 10746, 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"\u0120interpretation": 10794, "\u0120dependent": 10795, "\u0120coins": 10796, "\u0120Bol": 10797, "Video": 10798, "\u0120Justin": 10799, "\u0120fatal": 10800, "\u0120cooking": 10801, "\u0120confusion": 10802, "ipher": 10803, "\u0120custody": 10804, "\u0120Morgan": 10805, "omach": 10806, "\u0120Governor": 10807, "\u0120restaurants": 10808, "eling": 10809, "\u0120acknowledged": 10810, "\u0120ther": 10811, "\u0120genes": 10812, "ching": 10813, "Hey": 10814, "\u0120tactics": 10815, "\u0120Mexican": 10816, "\u0120vend": 10817, "\u0120hes": 10818, "quer": 10819, "\u0120noting": 10820, "\u0120Cameron": 10821, "\u0120targeting": 10822, "rock": 10823, "\u0120credits": 10824, "\u0120emotions": 10825, "\u0120representatives": 10826, "news": 10827, "\u0120legislative": 10828, "\u0120removing": 10829, "\u0120tweeted": 10830, "\u0120Carter": 10831, "\u0120Fixed": 10832, "\u0120forcing": 10833, "\u0120speaker": 10834, "\u0120males": 10835, "\u0120Vietnam": 10836, "lined": 10837, "\u0120concepts": 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"ighth": 10887, "\u0120Ruby": 10888, "\u0120Sure": 10889, "olving": 10890, "\u0120jun": 10891, "OST": 10892, "\u0120imposed": 10893, "\u0120astron": 10894, "\u0120correl": 10895, "\u0120NS": 10896, "\u0120Kit": 10897, "\u0120Future": 10898, "burn": 10899, "\u0120immune": 10900, "ocus": 10901, "\u0120courses": 10902, "\u0120String": 10903, "\u0120lean": 10904, "\u0120ghost": 10905, "\u0120outcomes": 10906, "\u0120expense": 10907, "\u0120everyday": 10908, "\u0120acceptable": 10909, "Ah": 10910, "\u0120equipped": 10911, "\u0120orange": 10912, "FR": 10913, "\u0120Dutch": 10914, "Though": 10915, "\u0120Rank": 10916, "QU": 10917, "\u0120Roberts": 10918, "what": 10919, "rend": 10920, "\u0120disappear": 10921, "\u0120spawn": 10922, "\u0120Lam": 10923, "ois": 10924, "\u0120deserve": 10925, "\u0120minimal": 10926, "\u0120nervous": 10927, "\u0120Would": 10928, "\u0120rook": 10929, "\u0120Vancouver": 10930, "\u0120resign": 10931, "shire": 10932, "\u0120Works": 10933, "\u0120Build": 10934, "\u0120affordable": 10935, "\u0120Gary": 10936, "\u0120Arena": 10937, "\u0120hanging": 10938, "\u0120implications": 10939, "\u0120Song": 10940, "\u0120maintaining": 10941, "\u0120guards": 10942, "CON": 10943, "\u0120derived": 10944, "\u0120executed": 10945, "\u0120theories": 10946, "\u0120quoted": 10947, "\u0120Andre": 10948, "oga": 10949, "seless": 10950, "info": 10951, "\u0120Belg": 10952, "\u0120tears": 10953, "\u0120Surv": 10954, "\u0120birthday": 10955, "igious": 10956, "immer": 10957, "\u0120spectrum": 10958, "\u0120architecture": 10959, "\u0120recruit": 10960, "arma": 10961, "Table": 10962, "\u0120monsters": 10963, "\u0120Gov": 10964, "\u0120destination": 10965, "\u0120attractive": 10966, "\u0120foss": 10967, "\u0120Moreover": 10968, "\u0120presents": 10969, "THE": 10970, "\u0120reply": 10971, "pton": 10972, "\u0120cum": 10973, "\u0120delight": 10974, "\u0120affects": 10975, "\u0120donations": 10976, "\u0120Toy": 10977, "\u0120Him": 10978, "MENT": 10979, "\u0120overcome": 10980, "itched": 10981, "\u0120Fantasy": 10982, "\u0120Hat": 10983, "\u0120Beast": 10984, "bott": 10985, "\u0120investigations": 10986, "Run": 10987, "\u0120hunting": 10988, "di": 10989, "fund": 10990, "\u0120sessions": 10991, "estyle": 10992, "\u0120portray": 10993, "oids": 10994, "Yeah": 10995, "\u0120communicate": 10996, "\u0120comedy": 10997, "\u0120Yang": 10998, "\u0120belt": 10999, "\u0120Marine": 11000, "\u0120predicted": 11001, "Play": 11002, "\u0120importantly": 11003, "\u0120remarkable": 11004, "\u0120eliminate": 11005, "David": 11006, "\u0120bind": 11007, "VID": 11008, "\u0120advocates": 11009, "\u0120Gaza": 11010, "imp": 11011, "DB": 11012, "\u0120Na": 11013, "\u0120Similar": 11014, "IES": 11015, "\u0120charity": 11016, "vas": 11017, "math": 11018, "\u0120\u00e2\u0138": 11019, "oker": 11020, "ndum": 11021, "\u0120caps": 11022, "\u0120Hal": 11023, "2000": 11024, "ean": 11025, "\u0120fleet": 11026, "\u0120recre": 11027, "Right": 11028, "\u0120sleeping": 11029, "ijing": 11030, 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11124, "flow": 11125, "\u0120dismissed": 11126, "ibraries": 11127, "cap": 11128, "ele": 11129, "\u0120McG": 11130, "\u0120Harvard": 11131, "\u0120Conservative": 11132, "\u0120CBS": 11133, "png": 11134, "\u0120roots": 11135, "\u0120Having": 11136, "umbled": 11137, "\u0120Fun": 11138, "\\/": 11139, "\u0120Search": 11140, "plex": 11141, "\u0120discussing": 11142, "\u0120continu": 11143, "\u0120Tai": 11144, "\u0120Wik": 11145, "Free": 11146, "fit": 11147, "\u0120refuse": 11148, "\u0120managing": 11149, "\u0120synd": 11150, "ipedia": 11151, "walk": 11152, "\u0120professionals": 11153, "\u0120guidance": 11154, "\u0120universities": 11155, "\u0120assemb": 11156, "untu": 11157, "Finally": 11158, "ASE": 11159, "\u0120Auto": 11160, "\u0120Had": 11161, "\u0120anniversary": 11162, "LD": 11163, "\u0120Dur": 11164, "\u0120Ultimate": 11165, "ihad": 11166, "product": 11167, "\u0120transit": 11168, "\u0120restore": 11169, "\u0120explaining": 11170, "\u0120asset": 11171, "\u0120transferred": 11172, 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"\u0120politically": 11889, "awks": 11890, "\u0120Bloom": 11891, "\u0120worship": 11892, "eger": 11893, "uter": 11894, "\u0120FO": 11895, "\u0120mathemat": 11896, "\u0120sentenced": 11897, "\u0120disk": 11898, "\u0120Marg": 11899, "\u0120/*": 11900, "PI": 11901, "\u0120optional": 11902, "\u0120babies": 11903, "\u0120seeds": 11904, "\u0120Scottish": 11905, "\u0120thy": 11906, "]]": 11907, "\u0120Hitler": 11908, "PH": 11909, "ngth": 11910, "\u0120recovered": 11911, "inge": 11912, "\u0120powder": 11913, "\u0120lips": 11914, "\u0120designer": 11915, "\u0120disorders": 11916, "\u0120courage": 11917, "\u0120chaos": 11918, "\"},{\"": 11919, "\u0120carrier": 11920, "bably": 11921, "High": 11922, "\u0120RT": 11923, "esity": 11924, "len": 11925, "\u0120routes": 11926, "uating": 11927, "Fil": 11928, "NOT": 11929, "wall": 11930, "sburgh": 11931, "\u0120engaging": 11932, "\u0120JavaScript": 11933, "orer": 11934, "lihood": 11935, "\u0120unions": 11936, "\u0120Federation": 11937, "\u0120Tesla": 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"icating": 12364, "\u0120LED": 12365, "\u0120females": 12366, "Station": 12367, "eller": 12368, "\u0120Oakland": 12369, "\u0120mechanical": 12370, "iology": 12371, "\u0120Var": 12372, "\u0120robust": 12373, "ettings": 12374, "otta": 12375, "\u0120theoret": 12376, "\u0120retain": 12377, "kward": 12378, "\u0120da": 12379, "\u0120deployed": 12380, "del": 12381, "\u0120Andy": 12382, "\u0120subscribe": 12383, "web": 12384, "\u0120na": 12385, "\u0120Michel": 12386, "\u0120partially": 12387, "\u0120Comey": 12388, "\u0120crown": 12389, "\u0120Maj": 12390, "\u0120Blu": 12391, "rator": 12392, "Day": 12393, "INT": 12394, "\u0120documented": 12395, "\u0120GDP": 12396, "gi": 12397, "chell": 12398, "\u0120brutal": 12399, "\u0120Bab": 12400, "stration": 12401, "\u0120theft": 12402, "\u0120tube": 12403, "@@": 12404, "\u0120query": 12405, "\u0120Lincoln": 12406, "\u0120publishing": 12407, "\u0120wore": 12408, "orical": 12409, "\u0120ric": 12410, "\u0120notable": 12411, "\u0120subsequently": 12412, "nex": 12413, "\u0120observe": 12414, "\u0120Boe": 12415, "\u0120codes": 12416, "main": 12417, "WH": 12418, "\u0120SL": 12419, "\u0120residential": 12420, "avan": 12421, "\u0120mas": 12422, "arest": 12423, "adeon": 12424, "OUT": 12425, "\u0120sophistic": 12426, "ante": 12427, "\u0120cens": 12428, "\u0120**": 12429, "\u0120mortality": 12430, "\u0120yours": 12431, "\u0120occasions": 12432, "\u0120recalled": 12433, "\u0120Driver": 12434, "\u0120vocal": 12435, "\u0120bathroom": 12436, "\u0120shops": 12437, "\u0120collaboration": 12438, "\u0120Obamacare": 12439, "\u0120Cell": 12440, "Char": 12441, "Super": 12442, "Cre": 12443, "\u0120tends": 12444, "\u0120torn": 12445, "\u0120economics": 12446, "avery": 12447, "\u0120Raid": 12448, "\u0120Sem": 12449, "\u0120shoulders": 12450, "\u0120expecting": 12451, "\u0120examination": 12452, "ename": 12453, "\u0120UI": 12454, "iability": 12455, "olas": 12456, "\u0120Amb": 12457, "\u0120Dra": 12458, "\u0120midfield": 12459, "\u0120IC": 12460, 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12746, "\u0120misc": 12747, "\u0120freely": 12748, "\u0120muscles": 12749, "\u0120lineup": 12750, "\u0120impacts": 12751, "ousing": 12752, "omi": 12753, "acular": 12754, "\u0120controlling": 12755, "agine": 12756, "cery": 12757, "hell": 12758, "\u0120ranking": 12759, "\u0120Nich": 12760, "\u0120Ave": 12761, "128": 12762, "\u0120highway": 12763, "\u0120incons": 12764, "\u0120binding": 12765, "\u0120struggles": 12766, "\u0120Pittsburgh": 12767, "\u0120gray": 12768, "rin": 12769, "\u0120comics": 12770, "\u0120Sport": 12771, "\u0120relatives": 12772, "\u0120fright": 12773, "\u0120probe": 12774, "\u0120Portug": 12775, "\u0120voc": 12776, "\u0120tu": 12777, "\u0120Corps": 12778, "\u0120possibilities": 12779, "\u0120qualify": 12780, "wcsstore": 12781, "\u0120libraries": 12782, "\u0120migrants": 12783, "\u0120entries": 12784, "\u0120consecutive": 12785, "vals": 12786, "\u0120Chairman": 12787, "\u0120hill": 12788, "IME": 12789, "\u0120Gard": 12790, "\u0120inequality": 12791, "fox": 12792, "\u0120Save": 12793, "\u0120cort": 12794, "claimed": 12795, "\u0120traits": 12796, "\u0120pour": 12797, "\u0120missiles": 12798, "\u0120essence": 12799, "\u0120sends": 12800, "\u0120alliance": 12801, "\u0120wishes": 12802, "\u0120Christopher": 12803, "Big": 12804, "NY": 12805, "\u0120Jacob": 12806, "san": 12807, "urred": 12808, "\u0120SO": 12809, "lly": 12810, "\u0120advocate": 12811, "\u0120Bond": 12812, "\u0120\"/": 12813, "Using": 12814, "\u0120districts": 12815, "\u0120Gate": 12816, "\u0120Bir": 12817, "ridge": 12818, "\u0120Naz": 12819, "\u0120Rs": 12820, "boards": 12821, "\u0120Ga": 12822, "\u0120Reagan": 12823, "\u0120influenced": 12824, "1000": 12825, "apy": 12826, "\u0120challenged": 12827, "\u0120barg": 12828, "\u0120faculty": 12829, "\u0120Fif": 12830, "\u0120acquire": 12831, "Ac": 12832, "\u0120insect": 12833, "\u0120instruments": 12834, "\u0120leaf": 12835, "thodox": 12836, "Message": 12837, "\u0120tale": 12838, "\u0120thereby": 12839, "\u0120trap": 12840, "\u0120strongest": 12841, "\u0120Military": 12842, "isible": 12843, "\u01201984": 12844, "etheless": 12845, "\u0120flexible": 12846, "\u0120kills": 12847, "\u0120finishing": 12848, "\u0120Size": 12849, "\u0120reduces": 12850, "\u0120epid": 12851, "\u0120orientation": 12852, "full": 12853, "\u0120trace": 12854, "\u0120laser": 12855, "\u0120oppose": 12856, "\u0120editing": 12857, "\u0120momentum": 12858, "\u00e4\u00ba": 12859, "show": 12860, "VI": 12861, "\u0120Lad": 12862, "\u01201985": 12863, "\u0120murdered": 12864, "900": 12865, "uther": 12866, "\u0120probability": 12867, "\u0120Poll": 12868, "\u0120reluct": 12869, "\u0120Chem": 12870, "\u0120Montreal": 12871, "\u0120adequate": 12872, "\u0120Poland": 12873, "\u0120Sheriff": 12874, "umph": 12875, "\u0120ok": 12876, "\u0120000": 12877, "\u0120\"[": 12878, "\u0120operators": 12879, "\u0120Fer": 12880, "\u0120modes": 12881, "\u0120Eve": 12882, "\u0120discipline": 12883, "NET": 12884, "Hand": 12885, "\u0120oral": 12886, "\u0120WE": 12887, "email": 12888, "JP": 12889, "\u0120Palestinians": 12890, "\u0120hence": 12891, "\u0120Less": 12892, "\u0120overl": 12893, "dig": 12894, "\u0120intimid": 12895, "\u0120Coal": 12896, "\u0120ranging": 12897, "tha": 12898, "\u0120distant": 12899, "\u0120fib": 12900, "\u0120Index": 12901, "\u0120Wonder": 12902, "\u0120Pel": 12903, "hattan": 12904, "\u0120Hug": 12905, "\u00c3\u0139": 12906, "rait": 12907, "\u0120wrapped": 12908, "\u0120RPG": 12909, "\u0120chemicals": 12910, "\u0120Money": 12911, "\u0120frozen": 12912, "\u0120indirect": 12913, "\u0120Against": 12914, "End": 12915, "\u0120uncomfortable": 12916, "\u0120Gallery": 12917, "\u0120Posted": 12918, "\u00d8\u00a7": 12919, "onduct": 12920, "\u0120consequence": 12921, "\u0120bitter": 12922, "\u01201987": 12923, "pop": 12924, "\u0120countless": 12925, "\u0120Alaska": 12926, "ffff": 12927, "\u0120departure": 12928, "\u0120refund": 12929, "\u0120Ian": 12930, "iated": 12931, "\u0120seeks": 12932, "\u0120mechanics": 12933, "\u0120jurisdiction": 12934, "lynn": 12935, "\u0120alike": 12936, "\u0120Hunt": 12937, "athon": 12938, "\u0120resolved": 12939, "\u0120cache": 12940, "\u0120distinction": 12941, "direct": 12942, "\u0120encount": 12943, "oub": 12944, "beat": 12945, "\u0120Country": 12946, "search": 12947, "\u0120continuous": 12948, "\u0120modest": 12949, "\u0120Rail": 12950, "thood": 12951, "130": 12952, "BUG": 12953, "\u0120criminals": 12954, "\u0120indication": 12955, "\u0120encountered": 12956, "last": 12957, "\u0120Wy": 12958, "\u0120ideology": 12959, "\u0120PDF": 12960, "security": 12961, "])": 12962, "\u0120Jimmy": 12963, "\u0120EN": 12964, "\u0120hiring": 12965, "Tem": 12966, "\u0120pig": 12967, "aunt": 12968, "\u0120Crystal": 12969, "\u0120penalties": 12970, "\u0120capability": 12971, "\u0120py": 12972, "\u0120productive": 12973, "\u0120balanced": 12974, "\u0120GeForce": 12975, "click": 12976, "olitan": 12977, "ods": 12978, "\u0120afterwards": 12979, "\u0120playoffs": 12980, "\u0120Gill": 12981, "User": 12982, "\u0120backs": 12983, "pub": 12984, "tag": 12985, "\u0120absurd": 12986, "piring": 12987, "\u0120citing": 12988, "\u0120trillion": 12989, "\u0120obligation": 12990, "\u0120maxim": 12991, "ahoo": 12992, "cf": 12993, "umi": 12994, "\u0120Alpha": 12995, "\u0120Nelson": 12996, "\u0120pursuant": 12997, "initely": 12998, "\u0120fract": 12999, "entry": 13000, "bery": 13001, "\u0120Thor": 13002, "Added": 13003, "\u0120DJ": 13004, "\u0120Gene": 13005, "\u0120awkward": 13006, "Stud": 13007, "\u0120wallet": 13008, "\u0120Divine": 13009, "arios": 13010, "\u0120releasing": 13011, "\u0120edited": 13012, "\u0120accomplished": 13013, "Best": 13014, "\u0120edges": 13015, "\u0120planes": 13016, "\u0120feeding": 13017, "\"},\"": 13018, "\u0120disclosure": 13019, "\u0120grain": 13020, "airy": 13021, "oons": 13022, "ernand": 13023, "VR": 13024, "\u0120reasonably": 13025, "\u0120drum": 13026, "\u0120partial": 13027, "\u0120graphic": 13028, "\u0120unprecedented": 13029, "\u0120advised": 13030, "Micro": 13031, "\u0120Assad": 13032, "points": 13033, "scar": 13034, "\u0120Zone": 13035, "ttes": 13036, "\u0120700": 13037, "vo": 13038, "\u0120Hamp": 13039, "\u0120fixes": 13040, "\u0120caution": 13041, "\u0120strings": 13042, "\u0120panels": 13043, "\u0120leak": 13044, "\u0120pricing": 13045, "rowth": 13046, "\u0120Error": 13047, "\u0120Saints": 13048, "fix": 13049, "\u0120observations": 13050, "\u0120Abs": 13051, "\u0120suggestion": 13052, "\u0120Ukrainian": 13053, "\u0120barrier": 13054, "\u0120painted": 13055, "Bet": 13056, "imir": 13057, "\u0120Spect": 13058, "pot": 13059, "orneys": 13060, "\u0120compound": 13061, "\u0120bears": 13062, "\u0120Rush": 13063, "\u0120luxury": 13064, "Sum": 13065, "\u0120orbit": 13066, "\u0120Marc": 13067, "\u0120exempt": 13068, "\u0120Trail": 13069, "\u0120MO": 13070, "\u0120Hans": 13071, "\u0120Weapon": 13072, "ocused": 13073, "uminum": 13074, "\u0120Jerry": 13075, "\u0120bust": 13076, "\u0120AG": 13077, "\u0120Wiki": 13078, "\u0120endless": 13079, "\u0120Vlad": 13080, "\u0120Bah": 13081, "\u0120Radeon": 13082, "keys": 13083, "\u0120Survey": 13084, "\u0120Viol": 13085, "define": 13086, "lean": 13087, "\u0120commod": 13088, "\u0120revenues": 13089, "\u00c5\u012f": 13090, "\u0120furniture": 13091, "\u0120casting": 13092, "\u0120diplomatic": 13093, "\u0120Players": 13094, "\u0120Killed": 13095, "\u0120modify": 13096, "\u0120innovative": 13097, "\u0120Abu": 13098, "nor": 13099, "\u0120bonds": 13100, "\u0120coaching": 13101, "Mer": 13102, "\u0120modules": 13103, "\u0120Patriots": 13104, "\u0120enhanced": 13105, "\u0120proceedings": 13106, "\u0120teammates": 13107, "\u0120128": 13108, "ardo": 13109, "\u0120compromise": 13110, "\u0120Much": 13111, "\u0120flew": 13112, "\u0120Edge": 13113, "\u0120unnecessary": 13114, "\u0120doctrine": 13115, "report": 13116, "\u0120Orlando": 13117, "\u0120Profile": 13118, "\u0120playoff": 13119, "friendly": 13120, "\u0120complain": 13121, "\u0120MC": 13122, "\u0120Opt": 13123, "\u0120GB": 13124, "\u0120beaten": 13125, "\u0120golf": 13126, "\u0120placement": 13127, "Bit": 13128, "\u0120newsletter": 13129, "\u01202019": 13130, "visor": 13131, "rawl": 13132, "\u0120iPad": 13133, "\u0120acted": 13134, "\u0120juice": 13135, "\u0120decks": 13136, "PN": 13137, "success": 13138, "\u0120Half": 13139, "\u0120deleted": 13140, "\u0120secrets": 13141, "\u0120asylum": 13142, "Mart": 13143, "\u0120Activ": 13144, "\u0120Guy": 13145, "\u0120Ts": 13146, "\u0120dys": 13147, "\u0120assuming": 13148, "\u0120mana": 13149, "\u0120subur": 13150, "\u0120125": 13151, "Media": 13152, "ARY": 13153, "ride": 13154, "cp": 13155, "\u0120difficulties": 13156, "\u0120collecting": 13157, "\u0120bankrupt": 13158, "non": 13159, "\u0120composed": 13160, "\u0120volt": 13161, "\u0120militants": 13162, "\u0120>>>": 13163, "\u0120Mormon": 13164, "tor": 13165, "\u0120particles": 13166, "\u0120Bart": 13167, "ryption": 13168, "\u0120admin": 13169, "\u0120squee": 13170, "VIDIA": 13171, "\u0120creator": 13172, "iameter": 13173, "icular": 13174, "NBC": 13175, "\u0120grabbed": 13176, "\u0120nodd": 13177, "\u0120rated": 13178, "\u0120rotation": 13179, "\u0120grasp": 13180, "\u0120excessive": 13181, "\u0120EC": 13182, "\u0120Whit": 13183, "\u0120inventory": 13184, "aults": 13185, "\u0120FB": 13186, "\u0120ecosystem": 13187, "\u0120billions": 13188, "\u0120venture": 13189, "named": 13190, "\u0120defender": 13191, "oute": 13192, "Instead": 13193, "irable": 13194, "War": 13195, "\u0120assumption": 13196, "\u0120bite": 13197, "\u0120earthqu": 13198, "tail": 13199, "space": 13200, "\u0120gifts": 13201, "boys": 13202, "\u0120inevitable": 13203, "\u0120structural": 13204, "\u0120beneficial": 13205, "\u0120compelling": 13206, "hole": 13207, "ervation": 13208, "\u0120coat": 13209, "oj": 13210, "incarn": 13211, "\u0120Years": 13212, "\u0120determining": 13213, "\u0120rhetoric": 13214, "\u0120boundaries": 13215, "\u0120whites": 13216, "Ant": 13217, "addy": 13218, ")-": 13219, "raham": 13220, "etermin": 13221, "\u0120harvest": 13222, "\u0120Conc": 13223, "\u0120laptop": 13224, "\u0120Match": 13225, "\u0120enjoying": 13226, "cca": 13227, "ollar": 13228, "\u0120trips": 13229, "\u0120addiction": 13230, "\u0120Sak": 13231, "\u0120powered": 13232, "\u0120cous": 13233, "\u0120Russians": 13234, "iere": 13235, "\u0120retrie": 13236, "quality": 13237, "\u0120differ": 13238, "\u0120kingdom": 13239, "\u0120Laur": 13240, "\u0120Capitol": 13241, "\u0120conclusions": 13242, "\u0120Altern": 13243, "\u0120Nav": 13244, "\u0120transparent": 13245, "BER": 13246, "Group": 13247, "\u0120Complete": 13248, "\u0120infer": 13249, "\u0120intrig": 13250, "\u0120insane": 13251, "RO": 13252, "ophob": 13253, "isen": 13254, "qual": 13255, "Michael": 13256, "\u0120museum": 13257, "\u0120Pope": 13258, "\u0120reset": 13259, "rative": 13260, "five": 13261, "\u0120aggreg": 13262, "ittees": 13263, "ository": 13264, "\u0120carb": 13265, "\u0120Record": 13266, "\u0120decides": 13267, "\u0120Fix": 13268, "\u0120exceptions": 13269, "\u0120Commissioner": 13270, "uns": 13271, "\u0120Environmental": 13272, "\u0120legendary": 13273, "istence": 13274, "\u0120tunnel": 13275, "km": 13276, "\u0120insult": 13277, "\u0120troll": 13278, "\u0120shake": 13279, "\u0120detention": 13280, "ques": 13281, "\u0120Chrome": 13282, "\u0120Files": 13283, "\u0120subt": 13284, "\u0120prospects": 13285, "\u0120prol": 13286, "render": 13287, "proof": 13288, "\u0120performances": 13289, "Str": 13290, "\u0120href": 13291, "ername": 13292, "\u0120achievement": 13293, "\u0120fut": 13294, "Full": 13295, "\u0120Leban": 13296, "google": 13297, "\u00e3\u0125\u012a": 13298, "ampa": 13299, "Maybe": 13300, "\u0120projected": 13301, "\u0120Emb": 13302, "\u0120colleg": 13303, "\u0120awards": 13304, "\u0120\u00e2\u0136": 13305, "Gold": 13306, "\u0120Blake": 13307, "\u0120Raj": 13308, "ifting": 13309, "\u0120pending": 13310, "\u0120instinct": 13311, "\u0120developments": 13312, "Connect": 13313, "\u0120Mand": 13314, "\u0120WITH": 13315, "\u0120Philippines": 13316, "profile": 13317, "\u0120altogether": 13318, "\u0120Bund": 13319, "\u0120TD": 13320, "oooo": 13321, "amped": 13322, "iph": 13323, "\u0120steam": 13324, "\u0120oldest": 13325, "\u0120detection": 13326, "ulpt": 13327, "\u0120\u00e7": 13328, "\u0120Wayne": 13329, "2006": 13330, "fa": 13331, "\u0120circles": 13332, "\u0120Fu": 13333, "\u0120donors": 13334, "appropriate": 13335, "\u0120Dakota": 13336, "jamin": 13337, "\u0120motivated": 13338, "\u0120purchases": 13339, "\u0120Louisiana": 13340, "\u0120Spl": 13341, "\u0120globe": 13342, "\u0120105": 13343, "zip": 13344, "call": 13345, "\u0120departments": 13346, "\u0120sustainable": 13347, "105": 13348, "\u0120OP": 13349, "ifiers": 13350, "\u0120prevented": 13351, "\u0120incomp": 13352, "\u0120Commander": 13353, "\u0120dominated": 13354, "\u0120\u00c2\u00bb": 13355, "\u0120invested": 13356, "\u0120complexity": 13357, "\u0120incl": 13358, "\u0120ensuring": 13359, "\u0120realm": 13360, "ync": 13361, "\u0120Independent": 13362, "rained": 13363, "\u0120Jen": 13364, "\u0120Flight": 13365, "\u0120athe": 13366, "\u0120speculation": 13367, "\u0120TE": 13368, "ocate": 13369, "tic": 13370, "\u0120plaint": 13371, "herry": 13372, "\u0120toy": 13373, "\u0120111": 13374, "\u0120plates": 13375, "status": 13376, "\u0120Isa": 13377, "\u0120devoted": 13378, "Cop": 13379, "\u0120ES": 13380, "255": 13381, "urrency": 13382, "Main": 13383, "\u0120slaves": 13384, "\u0120pepper": 13385, "\u0120quotes": 13386, "\u0120ceiling": 13387, "\u0120Fish": 13388, "\u0120transformation": 13389, "\u0120fraction": 13390, "\u0120advantages": 13391, "\u0120toile": 13392, "\u0120stunning": 13393, "\u0120moist": 13394, "breaking": 13395, "si": 13396, "\u0120Location": 13397, "\u0120Medium": 13398, "\u0120texts": 13399, "\u0120ugly": 13400, "\u0120bio": 13401, ".\u00e2\u0122\u0136": 13402, "\u0120Based": 13403, "\u0120trains": 13404, "\u0120Wing": 13405, "\u0120Ancient": 13406, "\u0120Records": 13407, "\u0120Hope": 13408, "Special": 13409, "adesh": 13410, "obi": 13411, "[/": 13412, "\u0120temporarily": 13413, "Ver": 13414, "hu": 13415, "oser": 13416, "\u0120overnight": 13417, "\u0120mamm": 13418, "\u0120Treasury": 13419, "\u0120Venezuel": 13420, "\u0120Mega": 13421, "\u0120tar": 13422, "\u0120expects": 13423, "black": 13424, "orph": 13425, "\\\\\\\\": 13426, "\u0120acceptance": 13427, "\u0120radar": 13428, "sis": 13429, "\u0120junior": 13430, "\u0120frames": 13431, "\u0120observation": 13432, "acies": 13433, "Power": 13434, "\u0120Advanced": 13435, "Mag": 13436, "ologically": 13437, "\u0120Mechan": 13438, "\u0120sentences": 13439, "\u0120analysts": 13440, "aughters": 13441, "forcement": 13442, "\u0120vague": 13443, "\u0120clause": 13444, "\u0120directors": 13445, "\u0120evaluate": 13446, "\u0120cabinet": 13447, "Matt": 13448, "\u0120Classic": 13449, "Ang": 13450, "\u0120cler": 13451, "\u0120Buck": 13452, "\u0120researcher": 13453, "\u0120160": 13454, "\u0120poorly": 13455, "\u0120experiencing": 13456, "\u0120Ped": 13457, "\u0120Manhattan": 13458, "\u0120freed": 13459, "\u0120themes": 13460, "advant": 13461, "\u0120nin": 13462, "\u0120praise": 13463, "104": 13464, "\u0120Libya": 13465, "best": 13466, "\u0120trusted": 13467, "\u0120cease": 13468, "\u0120dign": 13469, "Direct": 13470, "\u0120bombing": 13471, "\u0120migration": 13472, "\u0120Sciences": 13473, "\u0120municipal": 13474, "\u0120Average": 13475, "\u0120glory": 13476, "\u0120revealing": 13477, "\u0120arena": 13478, "\u0120uncertainty": 13479, "\u0120battlefield": 13480, "iao": 13481, "God": 13482, "\u0120cinem": 13483, "rape": 13484, "elle": 13485, "apons": 13486, "\u0120listing": 13487, "\u0120waited": 13488, "\u0120spotted": 13489, "keley": 13490, "\u0120Audio": 13491, "eor": 13492, "arding": 13493, "idding": 13494, "igma": 13495, "\u0120Neg": 13496, "\u0120lone": 13497, "\u0120----": 13498, "exe": 13499, "deg": 13500, "\u0120transf": 13501, "\u0120wash": 13502, "\u0120slavery": 13503, "\u0120exploring": 13504, "\u0120WW": 13505, "atson": 13506, "\u0120encl": 13507, "lies": 13508, "\u0120Creek": 13509, "\u0120wooden": 13510, "Manager": 13511, "\u0120Brand": 13512, "ummy": 13513, "\u0120Arthur": 13514, "\u0120bureaucr": 13515, "\u0120blend": 13516, "arians": 13517, "Further": 13518, "\u0120supposedly": 13519, "\u0120winds": 13520, "\u01201979": 13521, "\u0120gravity": 13522, "\u0120analyses": 13523, "\u0120Travel": 13524, "\u0120Veter": 13525, "\u0120dumb": 13526, "\u0120alternate": 13527, "gal": 13528, "\u0120consumed": 13529, "\u0120effectiveness": 13530, ".''": 13531, "\u0120paths": 13532, "onda": 13533, "LA": 13534, "\u0120Strong": 13535, "\u0120enables": 13536, "\u0120escaped": 13537, "\u0120\"\"": 13538, "\u0120112": 13539, "\u01201983": 13540, "\u0120smiled": 13541, "\u0120tendency": 13542, "Fire": 13543, "\u0120pars": 13544, "\u0120Roc": 13545, "\u0120lake": 13546, "\u0120fitness": 13547, "\u0120Ath": 13548, "\u0120Horn": 13549, "\u0120hier": 13550, "\u0120impose": 13551, "mother": 13552, "\u0120pension": 13553, "icut": 13554, "borne": 13555, "iciary": 13556, "._": 13557, "\u0120SU": 13558, "\u0120polar": 13559, "isy": 13560, "engu": 13561, "itialized": 13562, "ATA": 13563, "write": 13564, "\u0120exercises": 13565, "\u0120Diamond": 13566, "otypes": 13567, "\u0120harmful": 13568, "onz": 13569, "\u0120printing": 13570, "story": 13571, "\u0120expertise": 13572, "\u0120Ger": 13573, "\u0120tragedy": 13574, "\u0120Fly": 13575, "\u0120divid": 13576, "ampire": 13577, "stock": 13578, "Mem": 13579, "\u0120reign": 13580, "\u0120unve": 13581, "\u0120amend": 13582, "\u0120Prophet": 13583, "\u0120mutual": 13584, "\u0120Fac": 13585, "\u0120replacing": 13586, "Har": 13587, "\u0120Circuit": 13588, "\u0120throat": 13589, "\u0120Shot": 13590, "\u0120batteries": 13591, "\u0120toll": 13592, "\u0120addressing": 13593, "\u0120Medicaid": 13594, "\u0120pupp": 13595, "\u0120Nar": 13596, "olk": 13597, "\u0120equity": 13598, "MR": 13599, "\u0120Hispan": 13600, "\u0120Large": 13601, "mid": 13602, "Dev": 13603, "\u0120exped": 13604, "\u0120demo": 13605, "\u0120Marshall": 13606, "ergus": 13607, "\u0120fiber": 13608, "\u0120divorce": 13609, "\u0120Create": 13610, "\u0120slower": 13611, "\u0120Parker": 13612, "\u0120Student": 13613, "\u0120Training": 13614, "Return": 13615, "\u0120Tru": 13616, "\u0120cub": 13617, "\u0120Reached": 13618, "\u0120panic": 13619, "\u0120quarters": 13620, "\u0120rect": 13621, "\u0120treating": 13622, "\u0120rats": 13623, "\u0120Christianity": 13624, "oler": 13625, "\u0120sacred": 13626, "\u0120declare": 13627, "ulative": 13628, "eting": 13629, "\u0120delivering": 13630, "estone": 13631, "\u0120tel": 13632, "\u0120Larry": 13633, "\u0120meta": 13634, "accept": 13635, "artz": 13636, "\u0120Roger": 13637, "handed": 13638, "\u0120header": 13639, "\u0120trapped": 13640, "\u0120Century": 13641, "\u0120knocked": 13642, "\u0120Oxford": 13643, "\u0120survivors": 13644, "bot": 13645, "\u0120demonstration": 13646, "\u0120dirt": 13647, "\u0120assists": 13648, "OME": 13649, "\u0120Draft": 13650, "ortunate": 13651, "folio": 13652, "pered": 13653, "usters": 13654, "gt": 13655, "\u0120Lock": 13656, "\u0120judicial": 13657, "verted": 13658, "\u0120secured": 13659, "outing": 13660, "\u0120Books": 13661, "\u0120hosting": 13662, "\u0120lifted": 13663, "length": 13664, "\u0120jer": 13665, "\u0120wheels": 13666, "\u0120Range": 13667, "umbnails": 13668, "\u0120diagnosis": 13669, "tech": 13670, "\u0120Stewart": 13671, "\u0120Pract": 13672, "\u0120nationwide": 13673, "\u0120dear": 13674, "\u0120obligations": 13675, "\u0120grows": 13676, "\u0120mandatory": 13677, "\u0120suspicious": 13678, "!'": 13679, "Apr": 13680, "Great": 13681, "\u0120mortgage": 13682, "\u0120prosecutor": 13683, "\u0120editorial": 13684, "\u0120Kr": 13685, "\u0120processed": 13686, "ungle": 13687, "\u0120flexibility": 13688, "Earlier": 13689, "\u0120Cart": 13690, "\u0120Sug": 13691, "\u0120focuses": 13692, "\u0120startup": 13693, "\u0120breach": 13694, "\u0120Tob": 13695, "cycle": 13696, "\u00e3\u0122\u012e": 13697, "rose": 13698, "\u0120bizarre": 13699, "\u00e3\u0122\u012f": 13700, "\u0120vegetables": 13701, "$$": 13702, "\u0120retreat": 13703, "oshi": 13704, "\u0120Shop": 13705, "\u0120Ground": 13706, "\u0120Stop": 13707, "\u0120Hawaii": 13708, "\u0120Ay": 13709, "Perhaps": 13710, "\u0120Beaut": 13711, "uffer": 13712, "enna": 13713, "\u0120productivity": 13714, "Fixed": 13715, "control": 13716, "\u0120absent": 13717, "\u0120Campaign": 13718, "Green": 13719, "\u0120identifying": 13720, "\u0120regret": 13721, "\u0120promoted": 13722, "\u0120Seven": 13723, "\u0120eru": 13724, "neath": 13725, "aughed": 13726, "\u0120Pin": 13727, "\u0120Living": 13728, "Cost": 13729, "omatic": 13730, "mega": 13731, "\u0120Nig": 13732, "ocy": 13733, "\u0120inbox": 13734, "\u0120empire": 13735, "\u0120horizont": 13736, "\u0120branches": 13737, "\u0120metaph": 13738, "Active": 13739, "edi": 13740, "\u0120Film": 13741, "\u0120Something": 13742, "\u0120mods": 13743, "incial": 13744, "\u0120Original": 13745, "Gen": 13746, "\u0120spirits": 13747, "\u0120earning": 13748, "Hist": 13749, "\u0120riders": 13750, "\u0120sacrific": 13751, "MT": 13752, "\u0120VA": 13753, "\u0120Salt": 13754, "\u0120occupation": 13755, "\u0120Mi": 13756, "\u0120disg": 13757, "lict": 13758, "\u0120nit": 13759, "\u0120nodes": 13760, "eem": 13761, "\u0120Pier": 13762, "\u0120hatred": 13763, "psy": 13764, "\u00e3\u0125\u012b": 13765, "\u0120theater": 13766, "\u0120sophisticated": 13767, "\u0120defended": 13768, "\u0120besides": 13769, "\u0120thoroughly": 13770, "\u0120Medicare": 13771, "\u0120blamed": 13772, "arently": 13773, "\u0120crying": 13774, "FOR": 13775, "priv": 13776, "\u0120singing": 13777, "\u0120Il": 13778, "\u0120cute": 13779, "oided": 13780, "olitical": 13781, "\u0120Neuro": 13782, "\u00e5\u00a4": 13783, "\u0120donation": 13784, "\u0120Eagles": 13785, "\u0120Give": 13786, "Tom": 13787, "\u0120substantially": 13788, "\u0120License": 13789, "\u0120Ja": 13790, "\u0120grey": 13791, "\u0120Animal": 13792, "\u0120ER": 13793, "\u0120Und": 13794, "\u0120keen": 13795, "\u0120conclude": 13796, "\u0120Mississippi": 13797, "Engine": 13798, "\u0120Studios": 13799, "Press": 13800, "overs": 13801, "llers": 13802, "\u0120350": 13803, "\u0120Rangers": 13804, "\u0120rou": 13805, "erto": 13806, "Ep": 13807, "issa": 13808, "ivan": 13809, "\u0120seal": 13810, "\u0120Regist": 13811, "display": 13812, "\u0120weaken": 13813, "uum": 13814, "\u0120Commons": 13815, "\u0120Say": 13816, "\u0120cultures": 13817, "\u0120laughed": 13818, "\u0120slip": 13819, "\u0120treatments": 13820, "izable": 13821, "mart": 13822, "\u0120Rice": 13823, "\u0120beast": 13824, "\u0120obesity": 13825, "\u0120Laure": 13826, "iga": 13827, "Which": 13828, "holder": 13829, "\u0120elderly": 13830, "\u0120pays": 13831, "\u0120complained": 13832, "\u0120crop": 13833, "\u0120proc": 13834, "\u0120explosive": 13835, "\u0120Fan": 13836, "\u0120Arsenal": 13837, "Author": 13838, "eful": 13839, "\u0120meals": 13840, "\u0120(-": 13841, "idays": 13842, "\u0120imagination": 13843, "\u0120annually": 13844, "\u0120ms": 13845, "asures": 13846, "Head": 13847, "ikh": 13848, "matic": 13849, "\u0120boyfriend": 13850, "\u0120Computer": 13851, "\u0120bump": 13852, "\u0120surge": 13853, "\u0120Craig": 13854, "\u0120Kirk": 13855, "Del": 13856, "mediate": 13857, "\u0120scenarios": 13858, "\u0120Mut": 13859, "\u0120Stream": 13860, "\u0120competitors": 13861, "\u00d9\u0126": 13862, "\u0120Stanford": 13863, "\u0120Resources": 13864, "azed": 13865, "bage": 13866, "\u0120organis": 13867, "\u0120Release": 13868, "\u0120separately": 13869, "\u0120habits": 13870, "\u0120measurements": 13871, "\u0120Close": 13872, "\u0120accompany": 13873, "\u0120gly": 13874, "\u0120tang": 13875, "\u0120Rou": 13876, "\u0120plugin": 13877, "\u0120convey": 13878, "\u0120Challenge": 13879, "oots": 13880, "jan": 13881, "\u0120curs": 13882, "\u0120Relations": 13883, "keeper": 13884, "\u0120approaching": 13885, "ping": 13886, "Speaking": 13887, "\u0120arrangement": 13888, "\u0120VI": 13889, "arettes": 13890, "\u0120affecting": 13891, "\u0120permits": 13892, "because": 13893, "\u0120useless": 13894, "\u0120Hus": 13895, "!!!!": 13896, "\u0120destroying": 13897, "Unfortunately": 13898, "\u0120fascinating": 13899, "Sem": 13900, "\u0120electoral": 13901, "\u0120transparency": 13902, "\u0120Chaos": 13903, "\u0120volunteer": 13904, "\u0120statistical": 13905, "\u0120activated": 13906, "rox": 13907, "Web": 13908, "HE": 13909, "\u0120Hampshire": 13910, "isive": 13911, "Map": 13912, "\u0120trash": 13913, "\u0120Lawrence": 13914, "stick": 13915, "Cr": 13916, "\u0120rings": 13917, "EXT": 13918, "\u0120operational": 13919, "opes": 13920, "Does": 13921, "\u0120Evans": 13922, "\u0120witnessed": 13923, "Port": 13924, "\u0120launching": 13925, "econom": 13926, "wear": 13927, "\u0120Particip": 13928, "umm": 13929, "cules": 13930, "\u0120RAM": 13931, "\u0120Tun": 13932, "\u0120assured": 13933, "\u0120binary": 13934, "\u0120betray": 13935, "\u0120exploration": 13936, "\u0120Fel": 13937, "\u0120admission": 13938, "itated": 13939, "Sy": 13940, "\u0120avoided": 13941, "\u0120Simulator": 13942, "\u0120celebrated": 13943, "\u0120Electric": 13944, "\u00a5\u0140": 13945, "\u0120cluster": 13946, "itzerland": 13947, "health": 13948, "Line": 13949, "\u0120Nash": 13950, "aton": 13951, "\u0120spare": 13952, "\u0120enterprise": 13953, "\u0120DIS": 13954, "cludes": 13955, "\u0120flights": 13956, "\u0120regards": 13957, "\u0120\u00c3\u0139": 13958, "half": 13959, "\u0120trucks": 13960, "\u0120contacts": 13961, "\u0120uncons": 13962, "\u0120Climate": 13963, "\u0120immense": 13964, "NEW": 13965, "occ": 13966, "ective": 13967, "\u0120embod": 13968, "\u0120patrol": 13969, "\u0120beside": 13970, "\u0120viable": 13971, "\u0120creep": 13972, "\u0120triggered": 13973, "verning": 13974, "\u0120comparable": 13975, "ql": 13976, "\u0120gaining": 13977, "asses": 13978, "\u0120();": 13979, "\u0120Grey": 13980, "\u0120MLS": 13981, "sized": 13982, "\u0120prosper": 13983, "\"?": 13984, "\u0120polling": 13985, "\u0120shar": 13986, "\u0120RC": 13987, "\u0120firearm": 13988, "orient": 13989, "\u0120fence": 13990, "\u0120variations": 13991, "giving": 13992, "\u0120Pi": 13993, "ospel": 13994, "\u0120pledge": 13995, "\u0120cure": 13996, "\u0120spy": 13997, "\u0120violated": 13998, "\u0120rushed": 13999, "\u0120stroke": 14000, "\u0120Blog": 14001, "sels": 14002, "\u0120Ec": 14003, ",''": 14004, "\u0120pale": 14005, "\u0120Collins": 14006, "terror": 14007, "\u0120Canadians": 14008, "\u0120tune": 14009, "\u0120laboratory": 14010, "\u0120nons": 14011, "tarian": 14012, "\u0120disability": 14013, "\u0120Gam": 14014, "\u0120singer": 14015, "alg": 14016, "\u0120Senior": 14017, "\u0120traded": 14018, "\u0120Warrior": 14019, "\u0120infring": 14020, "\u0120Franklin": 14021, "\u0120strain": 14022, "\u0120Swedish": 14023, "\u0120seventh": 14024, "\u0120Benn": 14025, "\u0120Tell": 14026, "\u0120syndrome": 14027, "\u0120wondered": 14028, "iden": 14029, "++++": 14030, "igo": 14031, "\u0120purple": 14032, "\u0120journalism": 14033, "\u0120rebel": 14034, "\u0120fu": 14035, "blog": 14036, "\u0120invite": 14037, "rencies": 14038, "\u0120Contact": 14039, "Israel": 14040, "\u0120Content": 14041, "\u0120cheer": 14042, "\u0120bedroom": 14043, "\u0120Engineering": 14044, "\u0120Queens": 14045, "\u0120dwell": 14046, "\u0120PlayStation": 14047, "\u0120Dim": 14048, "\u0120Colon": 14049, "lr": 14050, "\u0120operates": 14051, "\u0120motivation": 14052, "USA": 14053, "astered": 14054, "Core": 14055, "\u0120Truth": 14056, "olo": 14057, "OSE": 14058, "\u0120Memory": 14059, "\u0120predec": 14060, "\u0120anarch": 14061, "\u01201920": 14062, "\u0120Yam": 14063, "\u00c3\u00a8": 14064, "bid": 14065, "\u0120grateful": 14066, "\u0120excitement": 14067, "\u0120treasure": 14068, "\u0120longest": 14069, "ctive": 14070, "\u0120deserves": 14071, "\u0120reserves": 14072, "\u0120cops": 14073, "\u0120Ottawa": 14074, "\u0120Egyptian": 14075, "anked": 14076, "\u0120artif": 14077, "\u0120hypothesis": 14078, ":/": 14079, "\u0120purchasing": 14080, "\u0120lovely": 14081, "HP": 14082, "\u0120divide": 14083, "\u0120strictly": 14084, "\u0120questioning": 14085, "\u0120taxpayers": 14086, "\u0120Joy": 14087, "\u0120rolls": 14088, "\u0120Heavy": 14089, "\u0120ports": 14090, "\u0120magnetic": 14091, "\u0120inflamm": 14092, "\u0120brush": 14093, "tics": 14094, "\u00e2\u012a\u0134": 14095, "\u0120bottles": 14096, "ppy": 14097, "\u0120padd": 14098, "\u00e3\u0124\u00af": 14099, "million": 14100, "\u0120devastating": 14101, "\u0120compiled": 14102, "\u0120medication": 14103, "\u0120twelve": 14104, "\u0120Perry": 14105, "Space": 14106, "imb": 14107, "your": 14108, "\u0120leaked": 14109, "\u0120Tar": 14110, "\u0120unity": 14111, "\u0120infected": 14112, "\u0120traveled": 14113, "IDE": 14114, "\u0120McDonald": 14115, "txt": 14116, "\u0120Princ": 14117, "\u0120interven": 14118, "\u0120Taiwan": 14119, "\u0120Pow": 14120, "\u0120bearing": 14121, "\u0120Thread": 14122, "\u0120zones": 14123, "izards": 14124, "unks": 14125, "Chapter": 14126, "llor": 14127, "\u0120\u00c2\u00b7": 14128, "\u0120wounds": 14129, "\u0120discretion": 14130, "\u0120succeeded": 14131, "iking": 14132, "\u0120iconic": 14133, "Call": 14134, "\u0120screening": 14135, "\u0120Mis": 14136, "icts": 14137, "\u0120ministers": 14138, "\u0120separation": 14139, "Player": 14140, "\u0120bip": 14141, "\u0120beloved": 14142, "\u0120counting": 14143, "\u0120Eye": 14144, "around": 14145, "inging": 14146, "\u0120tablet": 14147, "\u0120offence": 14148, "inance": 14149, "have": 14150, "\u0120Info": 14151, "\u0120Ninja": 14152, "\u0120protective": 14153, "\u0120Cass": 14154, "Mac": 14155, "\u0120Quality": 14156, "North": 14157, "\u0120ic": 14158, "\u0120Cuba": 14159, "\u0120Chronicle": 14160, "\u0120Property": 14161, "\u0120fastest": 14162, "otos": 14163, "\u0120Germ": 14164, "OWN": 14165, "\u0120boom": 14166, "\u0120Stanley": 14167, "erguson": 14168, "\u0120clever": 14169, "\u0120enters": 14170, "mode": 14171, "terior": 14172, "\u0120Sens": 14173, "\u0120linear": 14174, "ARK": 14175, "\u0120comparing": 14176, "\u0120purely": 14177, "\u0120safer": 14178, "\u0120Potter": 14179, "\u0120cups": 14180, "RT": 14181, "\u0120gluc": 14182, "\u0120attributed": 14183, "\u0120dupl": 14184, "\u0120Pap": 14185, "\u0120precious": 14186, "\u0120pa": 14187, "ictionary": 14188, "\u0120Tig": 14189, "\u0120Too": 14190, "olutions": 14191, "stan": 14192, "\u0120robots": 14193, "\u0120lobb": 14194, "\u0120statute": 14195, "\u0120prevention": 14196, "western": 14197, "160": 14198, "\u0120Active": 14199, "\u0120Maria": 14200, "hal": 14201, "None": 14202, "ellar": 14203, "\u0120KB": 14204, "\u0120Partners": 14205, "\u0120Single": 14206, "\u0120Following": 14207, "ango": 14208, "acious": 14209, "\u0120thou": 14210, "\u0120kg": 14211, "\u0120influential": 14212, "\u0120Friends": 14213, "Sur": 14214, "ainted": 14215, "\u0120forums": 14216, "\u0120starter": 14217, "\u0120citizenship": 14218, "\u0120Election": 14219, "onge": 14220, "otation": 14221, "osph": 14222, ";;;;": 14223, "utical": 14224, "pur": 14225, "eren": 14226, "\u0120accusations": 14227, "bitious": 14228, "abbit": 14229, "\u0120Ord": 14230, "Posted": 14231, "irk": 14232, "\u0120sensitivity": 14233, "iche": 14234, "\u0120Amy": 14235, "\u0120Fab": 14236, "\u0120summit": 14237, "\u0120pedest": 14238, "\u0120rubber": 14239, "\u0120agricultural": 14240, "\u0120cancel": 14241, "AE": 14242, "\u0120inaug": 14243, "\u0120contam": 14244, "\u0120firmly": 14245, "iw": 14246, "stage": 14247, "\u0120Kan": 14248, "\u0120tier": 14249, "\u0120invention": 14250, "\u0120translated": 14251, "\u0120Rules": 14252, "Box": 14253, "Twitter": 14254, "IDS": 14255, "\u0120pizza": 14256, "\u0120debug": 14257, "\u0120Drop": 14258, "vs": 14259, "\u0120horses": 14260, "big": 14261, "\u0120boring": 14262, "\u0120hood": 14263, "\u0120McCain": 14264, "atched": 14265, "\u0120Bros": 14266, "\u0120skip": 14267, "\u0120essay": 14268, "stat": 14269, "\u0120Legends": 14270, "\u0120ammunition": 14271, "auc": 14272, "\u0120shooter": 14273, "\u0120unh": 14274, "\u0120supplied": 14275, "\u0120generic": 14276, "\u0120SK": 14277, "iban": 14278, "yrics": 14279, "\u0120255": 14280, "\u0120climbing": 14281, "Former": 14282, "\u0120flip": 14283, "\u0120jumping": 14284, "\u0120frustration": 14285, "\u0120Terry": 14286, "\u0120neighborhoods": 14287, "\u0120median": 14288, "bean": 14289, "\u0120brains": 14290, "Following": 14291, "\u0120shaped": 14292, "\u0120draws": 14293, "\u0120altered": 14294, "Jack": 14295, "\u0120recipes": 14296, "\u0120skilled": 14297, "wealth": 14298, "achi": 14299, "election": 14300, "\u0120behaviors": 14301, "deals": 14302, "\u0120Until": 14303, "Fe": 14304, "\u0120declaration": 14305, "marks": 14306, "\u0120Between": 14307, "celona": 14308, "\u0120reson": 14309, "\u0120bubble": 14310, "Among": 14311, "\u0120imperial": 14312, "GS": 14313, "\u0120feminist": 14314, "2005": 14315, "\u0120Kyle": 14316, "\u0120accounting": 14317, "\u0120Tele": 14318, "\u0120Tyr": 14319, "\u0120connecting": 14320, "\u0120rehab": 14321, "\u0120Pred": 14322, "sim": 14323, "\u0120meantime": 14324, "\u0120physician": 14325, "MW": 14326, "\u0120Campbell": 14327, "\u0120Brandon": 14328, "\u0120contributing": 14329, "\u0120Rule": 14330, "\u0120Weight": 14331, "\u0120Nap": 14332, "\u0120interactive": 14333, "\u0120vag": 14334, "\u0120helmet": 14335, "\u0120Comb": 14336, "four": 14337, "\u0120shipped": 14338, "\u0120completing": 14339, "\u0120PD": 14340, "PDATE": 14341, "\u0120spreading": 14342, "\u0120scary": 14343, "erving": 14344, "\u0120Gas": 14345, "\u0120frank": 14346, "school": 14347, "\u0120romantic": 14348, "\u0120stabil": 14349, "Rob": 14350, "\u0120accurately": 14351, "\u0120acute": 14352, "\u0120Hann": 14353, "\u0120symbols": 14354, "\u0120civilization": 14355, "\u0120AW": 14356, "\u0120lightning": 14357, "\u0120considers": 14358, "\u0120venue": 14359, "\u0120\u00d7": 14360, "\u0120oven": 14361, "\u0120SF": 14362, "his": 14363, "\u0120nu": 14364, "\u0120Learn": 14365, "\u0120peoples": 14366, "\u0120std": 14367, "\u0120slee": 14368, "\u0120slic": 14369, "\u0120Statistics": 14370, "\u0120corners": 14371, "\u0120Baker": 14372, "\u0120:)": 14373, "mentation": 14374, "olver": 14375, "\u0120laughing": 14376, "\u0120Todd": 14377, "onde": 14378, "\u0120Hills": 14379, "\u0120nuts": 14380, "\u0120Woman": 14381, "plane": 14382, "\u0120liver": 14383, "\u0120Inside": 14384, "Sorry": 14385, "\u0120agrees": 14386, "\u0120fundament": 14387, "\u0120Fisher": 14388, "\u0120auction": 14389, "\u0120threads": 14390, "glas": 14391, "\u0120Basic": 14392, "\u0120Nat": 14393, "\u0120lacking": 14394, "\u0120celebration": 14395, "ju": 14396, "\u0120silly": 14397, "Euro": 14398, "\u0120tatt": 14399, "ighty": 14400, "controlled": 14401, "Test": 14402, "\u0120Singh": 14403, "\u0120rage": 14404, "\u0120rhyth": 14405, "offic": 14406, "\u0120Phantom": 14407, "\u0120headlines": 14408, "\u0120responding": 14409, "\u0120Morning": 14410, "\u0120vitamin": 14411, "\u0120boots": 14412, "\u0120Site": 14413, "alin": 14414, "pi": 14415, "\u0120viral": 14416, "\u0120UC": 14417, "DER": 14418, "\u0120Sex": 14419, "\u0120stocks": 14420, "current": 14421, "\u0120churches": 14422, "\u0120Rare": 14423, "\u0120Murphy": 14424, "\u0120denial": 14425, "\u0120Gaming": 14426, "\u0120toug": 14427, "\u0120nick": 14428, "\u0120makers": 14429, "\u0120Ronald": 14430, "\u0120generous": 14431, "\u0120Doc": 14432, "\u0120Morris": 14433, "\u0120transformed": 14434, "\u0120Normal": 14435, "\u0120104": 14436, "\u0120Kickstarter": 14437, "\u0120Upon": 14438, "Online": 14439, "\u0120IRS": 14440, "\u0120wrap": 14441, "\u0120loving": 14442, "\u0120arrives": 14443, "\u0120Due": 14444, "\u0120heter": 14445, "\u0120Made": 14446, "\u0120rental": 14447, "\u0120belongs": 14448, "\u0120attorneys": 14449, "\u0120crops": 14450, "\u0120matched": 14451, "ulum": 14452, "oline": 14453, "109": 14454, "\u0120dispar": 14455, "\u0120buyers": 14456, "\u0120Cambridge": 14457, "\u0120ethics": 14458, "roups": 14459, "\u0120justified": 14460, "\u0120marginal": 14461, "\u0120respected": 14462, "winning": 14463, "\u0120nodded": 14464, "\u0120Serge": 14465, "\u0120Former": 14466, "Craft": 14467, "################": 14468, "\u0120Warner": 14469, "\u0120dash": 14470, "ete": 14471, "\u0120entert": 14472, "\u0120Escape": 14473, "outheast": 14474, "\u0120knees": 14475, "\u0120Bomb": 14476, "\u0120rug": 14477, "Pass": 14478, "\u0120attitudes": 14479, "government": 14480, "\u0120Prior": 14481, "\u0120qualities": 14482, "\u0120notification": 14483, "\u0120Phone": 14484, "lie": 14485, "\u0120anticipated": 14486, "\u0120Combat": 14487, "\u0120Barry": 14488, "\u01201982": 14489, "Users": 14490, "oner": 14491, "\u0120computing": 14492, "\u0120Connecticut": 14493, "\u0120lesser": 14494, "\u0120peers": 14495, "\u0120Cu": 14496, "\u0120technically": 14497, "\u0120submission": 14498, "\u0120Universal": 14499, "\u0120manually": 14500, "ourge": 14501, "\u0120respondents": 14502, "\u0120BTC": 14503, "\u0120Host": 14504, "\u0120fare": 14505, "\u0120Bird": 14506, "\u0120receipt": 14507, "also": 14508, "\u0120jack": 14509, "\u0120agriculture": 14510, "\u0120skull": 14511, "\u0120!=": 14512, "\u0120passive": 14513, "\u0120CI": 14514, "\u0120societies": 14515, "\u0120reminded": 14516, "\u0120interference": 14517, "Buy": 14518, "\u0120\u00e2\u013e": 14519, "gon": 14520, "\u0120scrutiny": 14521, "\u0120Witch": 14522, "\u0120conducting": 14523, "\u0120\u00e3\u0125": 14524, "\u0120exchanges": 14525, "\u0120Mitchell": 14526, "\u0120inhabit": 14527, "\u0120twist": 14528, "BD": 14529, "\u0120wherever": 14530, "groupon": 14531, "\u0120jokes": 14532, "\u0120Benjamin": 14533, "\u0120Random": 14534, "frame": 14535, "\u0120Lions": 14536, "\u0120highlighted": 14537, "\u0120Arkansas": 14538, "Ent": 14539, "\u0120pile": 14540, "\u0120prelim": 14541, "gs": 14542, "minded": 14543, "\u0120felony": 14544, "\u0120GA": 14545, "\u0120Luck": 14546, "\u0120practically": 14547, "\u0120Bos": 14548, "\u0120actress": 14549, "Dam": 14550, "\u0120Bou": 14551, "\u0120visa": 14552, "\u0120embedded": 14553, "\u0120hybrid": 14554, "\u0120earliest": 14555, "\u0120sooner": 14556, "social": 14557, "\u0120HA": 14558, "\u0120steep": 14559, "\u0120disadvant": 14560, "\u0120exploit": 14561, "\u0120Egg": 14562, "\u0120Ultra": 14563, "\u0120necessity": 14564, "Local": 14565, "iege": 14566, "\u0120dated": 14567, "\u0120masses": 14568, "\u0120subscription": 14569, "pless": 14570, "\u0120anonym": 14571, "\u0120presumably": 14572, "Blue": 14573, "Their": 14574, "asketball": 14575, "\u0120Philip": 14576, "\u0120comed": 14577, "loaded": 14578, "rane": 14579, "\u0120reflection": 14580, "China": 14581, "\u0120extends": 14582, "\u0120forming": 14583, "\u0120unders": 14584, "2001": 14585, "\u0120grat": 14586, "\u0120concentrations": 14587, "\u0120insulin": 14588, "\u0120secular": 14589, "\u0120whilst": 14590, "\u0120winners": 14591, "Advertisements": 14592, "\u0120deliberately": 14593, "\u0120Working": 14594, "\u0120sink": 14595, "etics": 14596, "dale": 14597, "\u0120mandate": 14598, "\u0120gram": 14599, "\u0120vacation": 14600, "\u0120warnings": 14601, "ripp": 14602, "\u0120THAT": 14603, "\u0120commentary": 14604, "\u0120intu": 14605, "\u0120aest": 14606, "\u0120reasoning": 14607, "\u0120breakdown": 14608, "\u0120Zombie": 14609, "\u0120-->": 14610, "\u0120Political": 14611, "cott": 14612, "\u0120thrust": 14613, "\u0120technological": 14614, "\u0120deciding": 14615, "\u0120trafficking": 14616, "Long": 14617, "Welcome": 14618, "prising": 14619, "\u0120Communications": 14620, "\u0120endors": 14621, "\u0120swift": 14622, "\u0120metabol": 14623, "coins": 14624, "resa": 14625, "\u0120HTTP": 14626, "\u0120enroll": 14627, "\u0120Happy": 14628, "usr": 14629, "intage": 14630, "\u0120[\"": 14631, "uably": 14632, "\u0120Material": 14633, "\u0120repeal": 14634, "Sept": 14635, "kh": 14636, "\u0120Modi": 14637, "\u0120underneath": 14638, "\u0120IL": 14639, "shore": 14640, "\u0120diagnosed": 14641, "aceutical": 14642, "\u0120shower": 14643, "aux": 14644, "\u0120Switch": 14645, "\u0120Strength": 14646, "\u0120jihad": 14647, "national": 14648, "\u0120trauma": 14649, "ussy": 14650, "oni": 14651, "\u0120consolid": 14652, "\u0120calories": 14653, "\u0120Flynn": 14654, "agged": 14655, "168": 14656, "\u0120Pink": 14657, "\u0120fulfill": 14658, "\u0120chains": 14659, "\u0120notably": 14660, "\u0120AV": 14661, "Life": 14662, "\u0120Chuck": 14663, "mus": 14664, "\u0120Urban": 14665, "\u0120Hend": 14666, "\u0120deposit": 14667, "\u0120Sad": 14668, "\u0120affair": 14669, "ORK": 14670, "ieval": 14671, "\u0120FDA": 14672, "\u0120trop": 14673, "\u0120Overall": 14674, "\u0120virtue": 14675, "\u0120satisfaction": 14676, "aund": 14677, "\u0120lun": 14678, "\u0120Switzerland": 14679, "\u0120Operation": 14680, "process": 14681, "\u0120shook": 14682, "\u0120counties": 14683, "leased": 14684, "\u0120Charlotte": 14685, "112": 14686, "\u0120transcript": 14687, "\u0120redd": 14688, "push": 14689, "\u0120Hey": 14690, "\u0120Analysis": 14691, "[\"": 14692, "\u0120alternatives": 14693, "ardless": 14694, "\u0120eleph": 14695, "\u0120prejud": 14696, "\u0120Leaf": 14697, "Having": 14698, "\u0120Hub": 14699, "\u0120expressions": 14700, "\u0120Volume": 14701, "\u0120shocking": 14702, "\u0120Reds": 14703, "\u0120readily": 14704, "\u0120planets": 14705, "adata": 14706, "\u0120collapsed": 14707, "\u0120Madrid": 14708, "\u0120irrit": 14709, "ipper": 14710, "\u0120Enc": 14711, "\u0120Wire": 14712, "\u0120buzz": 14713, "\u0120GP": 14714, "asha": 14715, "\u0120accidentally": 14716, "uru": 14717, "\u0120frustrated": 14718, "\u0120SA": 14719, "\u0120hungry": 14720, "\u0120Huff": 14721, "\u0120labels": 14722, "anto": 14723, "\u0120EP": 14724, "\u0120barriers": 14725, ")|": 14726, "\u0120Berkeley": 14727, "\u0120Jets": 14728, "\u0120pairs": 14729, "\u0120Lan": 14730, "James": 14731, "\u0120Bear": 14732, "\u0120humor": 14733, "\u0120Liberty": 14734, "\u0120magnitude": 14735, "\u0120aging": 14736, "\u0120Mason": 14737, "\u0120friendship": 14738, "umbling": 14739, "\u0120emerge": 14740, "\u0120newspapers": 14741, "\u0120ambitious": 14742, "\u0120Richards": 14743, "aternal": 14744, "\u01201981": 14745, "\u0120cookies": 14746, "\u0120sculpt": 14747, "\u0120pursuit": 14748, "Location": 14749, "\u0120scripts": 14750, "pc": 14751, "\u0120arrangements": 14752, "\u0120diameter": 14753, "\u0120loses": 14754, "amation": 14755, "\u0120liqu": 14756, "\u0120Jake": 14757, "arette": 14758, "\u0120understands": 14759, "\u0120Zen": 14760, "vm": 14761, "\u0120approve": 14762, "\u0120wip": 14763, "\u0120ultra": 14764, "\u0120intend": 14765, "\u0120DI": 14766, "ascular": 14767, "\u0120stays": 14768, "\u0120Kor": 14769, "\u0120Kl": 14770, "\u0120investing": 14771, "La": 14772, "\u0120believing": 14773, "bad": 14774, "mouth": 14775, "\u0120taxpayer": 14776, "\u00e3\u0125\u0125": 14777, "\u0120Quebec": 14778, "\u0120lap": 14779, "\u0120Swiss": 14780, "drop": 14781, "\u0120drain": 14782, "iri": 14783, "etc": 14784, "ften": 14785, "\u0120Nex": 14786, "\u0120straw": 14787, "\u0120screaming": 14788, "\u0120counted": 14789, "\u0120damaging": 14790, "\u0120ambassador": 14791, "century": 14792, "\u0120prox": 14793, "\u0120arrests": 14794, "uv": 14795, "ilateral": 14796, "\u0120Charg": 14797, "\u0120prescribed": 14798, "\u0120independently": 14799, "\u0120fierce": 14800, "\u0120Baby": 14801, "\u0120brave": 14802, "\u0120suits": 14803, "=>": 14804, "\u0120baseline": 14805, "\u0120Rate": 14806, "\u0120islands": 14807, "\u0120((": 14808, "green": 14809, "ixels": 14810, "\u0120namely": 14811, "\u0120Village": 14812, "than": 14813, "amy": 14814, "Version": 14815, "gmail": 14816, "entials": 14817, "\u0120Sud": 14818, "\u0120Melbourne": 14819, "\u0120arriving": 14820, "\u0120quantum": 14821, "eff": 14822, "ropolitan": 14823, "Tri": 14824, "\u0120funeral": 14825, "\u0120IR": 14826, "\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124": 14827, "\u0120Cob": 14828, "itably": 14829, "\u0120turb": 14830, "\u0120combo": 14831, "Review": 14832, "\u0120deployment": 14833, "uity": 14834, "\u0120Bott": 14835, "\u0120invisible": 14836, "\u0120rendering": 14837, "\u0120unlocked": 14838, "\u0120aqu": 14839, "\u0120Vladimir": 14840, "\u0120pad": 14841, "\u0120Brain": 14842, "\u0120Legacy": 14843, "dragon": 14844, "\u0120Kurdish": 14845, "\u0120sounded": 14846, "\u0120detained": 14847, "\u0120DM": 14848, "gary": 14849, "\u0120daughters": 14850, "\u0120disturbing": 14851, "uka": 14852, "\u0120Parad": 14853, "\u0120tast": 14854, "\u0120unfortunate": 14855, "\u0120ul": 14856, "emin": 14857, "\u0120attendance": 14858, "trl": 14859, "\u0120parks": 14860, "\u0120Memorial": 14861, "\u0120Alice": 14862, "othy": 14863, "guard": 14864, "\u0120Dise": 14865, "\u0120Shan": 14866, "\u0120Forum": 14867, "Rich": 14868, "\u0120shifted": 14869, "uez": 14870, "\u0120lighter": 14871, "\u0120Magn": 14872, "\u0120cod": 14873, "Sch": 14874, "hammad": 14875, "Pub": 14876, "350": 14877, "\u0120Pokemon": 14878, "\u0120prototype": 14879, "\u0120unre": 14880, "Base": 14881, "\u0120Students": 14882, "\u0120Reply": 14883, "\u0120Communist": 14884, "\u0120gau": 14885, "\u0120Tyler": 14886, "IZ": 14887, "\u0120participated": 14888, "\u0120suprem": 14889, "\u0120Details": 14890, "\u0120vessels": 14891, "rod": 14892, "\u0120tribe": 14893, "keep": 14894, "\u0120assumptions": 14895, "\u0120pound": 14896, "\u0120crude": 14897, "\u0120Available": 14898, "\u0120swimming": 14899, "\u0120inclusion": 14900, "\u0120advances": 14901, "culation": 14902, "\u0120conservation": 14903, "\u0120overd": 14904, "\u0120Buffalo": 14905, "Article": 14906, "edge": 14907, "\u0120awa": 14908, "\u0120Madison": 14909, "\u0120sidew": 14910, "\u0120catast": 14911, "\u0120Krist": 14912, "ucle": 14913, "\u0120Highway": 14914, "\u0120Terror": 14915, "\u0120activation": 14916, "\u0120unconscious": 14917, "\u0120Satan": 14918, "\u0120Susan": 14919, "illery": 14920, "\u0120arranged": 14921, "iop": 14922, "\u0120rumors": 14923, "urring": 14924, "think": 14925, "\u0120Keith": 14926, "\u0120Kind": 14927, "\u0120avoiding": 14928, "byn": 14929, "nut": 14930, "\u0120Speaker": 14931, "rus": 14932, "names": 14933, "\u0120guilt": 14934, "\u0120Olympics": 14935, "\u0120sail": 14936, "\u0120Mes": 14937, "levant": 14938, "\u0120Columbus": 14939, "aft": 14940, "City": 14941, "South": 14942, "\u0120Harvey": 14943, "\u0120Pun": 14944, "Several": 14945, "\u0120mentally": 14946, "\u0120impress": 14947, "mount": 14948, "\u0120Ubuntu": 14949, "\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136": 14950, "\u0120Superman": 14951, "\u0120MPs": 14952, "\u0120intentions": 14953, "\u0120Racing": 14954, "\u0120likelihood": 14955, "\u0120240": 14956, "Total": 14957, "\u0120toys": 14958, "\u0120Watson": 14959, "\u0120urge": 14960, "Lear": 14961, "\u0120Paper": 14962, "\u0120occurring": 14963, "\u0120Beng": 14964, "\u0120Cert": 14965, "\u0120stones": 14966, "Tim": 14967, "\u0120Twin": 14968, "zb": 14969, "\u0120Dynam": 14970, "\u0120politician": 14971, "kens": 14972, "\u0120Enterprise": 14973, "UTERS": 14974, "\u0120abol": 14975, "\u0120refresh": 14976, "\u0120arbitrary": 14977, "pection": 14978, "\u0120troubles": 14979, "\u0120});": 14980, "tv": 14981, "\u0120pilots": 14982, "\u0120distribute": 14983, "\u0120audit": 14984, "\u0120pause": 14985, "original": 14986, "\u0120rivals": 14987, "\u00c2\u00a3": 14988, "Fig": 14989, "TL": 14990, "abil": 14991, "rying": 14992, "Lin": 14993, "ioned": 14994, "lon": 14995, "\u0120fancy": 14996, "\u0120crashed": 14997, "\u0120tract": 14998, "\u0120shed": 14999, "\u0120consume": 15000, "Based": 15001, "download": 15002, "init": 15003, "\u0120voltage": 15004, "Introdu": 15005, "\u0120condemned": 15006, "\u0120Finance": 15007, "respect": 15008, "\u0120excluded": 15009, "\u0120establishing": 15010, "heric": 15011, "\u0120heritage": 15012, "\u0120spectacular": 15013, "\u0120unst": 15014, "\u0120Snowden": 15015, "\u0120Lane": 15016, "San": 15017, "\u0120protections": 15018, "struction": 15019, "incinn": 15020, "\u0120macro": 15021, "Custom": 15022, "iosity": 15023, "\u0120esp": 15024, "\u0120functioning": 15025, "\u0120mush": 15026, "\u0120puzzle": 15027, "\u0120ethical": 15028, "Mal": 15029, "\u0120governing": 15030, "\u0120Ferguson": 15031, "\u0120restored": 15032, "\u0120stressed": 15033, "\u0120Counter": 15034, "\u0120Kas": 15035, "clip": 15036, "ANS": 15037, "\u0120seiz": 15038, "UK": 15039, "byss": 15040, "oldown": 15041, "api": 15042, "\u0120permanently": 15043, "ounters": 15044, "West": 15045, "Through": 15046, "Light": 15047, "atoes": 15048, "\u0120neat": 15049, "\u0120cord": 15050, "urer": 15051, "\u0120severely": 15052, "\u0120Aven": 15053, "\u0120interrog": 15054, "\u0120triple": 15055, "Given": 15056, "Number": 15057, "\u0120arise": 15058, "\u0120sher": 15059, "plant": 15060, "\u0120flower": 15061, "\u0120Cou": 15062, "\u0120ate": 15063, "\u0120newer": 15064, "bul": 15065, "\u0120meanwhile": 15066, "\u0120Lair": 15067, "\u0120adjustment": 15068, "\u0120Copyright": 15069, "\u0120divers": 15070, "iological": 15071, "\u0120gamers": 15072, "oat": 15073, "\u0120historically": 15074, "\u0120analog": 15075, "\u0120longtime": 15076, "\u0120prescription": 15077, "\u0120Mist": 15078, "\u0120Hyper": 15079, "\u0120Maine": 15080, "\u0120Deity": 15081, "\u0120multipl": 15082, "\u0120Reincarn": 15083, "\u0120Hyd": 15084, "\u0120Pic": 15085, "Sil": 15086, "rants": 15087, "\u0120Cris": 15088, ".;": 15089, "({": 15090, "ependence": 15091, "\u0120recy": 15092, "ateur": 15093, "\u0120quad": 15094, "\u0120glob": 15095, "\u0120conced": 15096, "team": 15097, "\u0120capitalist": 15098, "\u0120Lot": 15099, "\u0120royal": 15100, "\u0120Cyber": 15101, "\u0120blacks": 15102, "metic": 15103, "riv": 15104, "\u0120Danny": 15105, "\u0120spo": 15106, "\u0120RO": 15107, "\u0120animated": 15108, "rypted": 15109, "\u0120Deputy": 15110, "\u0120rendered": 15111, "FE": 15112, "\u0120streak": 15113, "\u0120clouds": 15114, "\u0120Doug": 15115, "~~~~~~~~": 15116, "\u0120discour": 15117, "\u0120Veh": 15118, "\u0120psychology": 15119, "\u0120Journey": 15120, "\u0120crystal": 15121, "\u0120Frost": 15122, "\u0120suspicion": 15123, "\u0120relate": 15124, "orus": 15125, "\u0120Crypt": 15126, "\u0120NVIDIA": 15127, "comed": 15128, "uting": 15129, "incinnati": 15130, "\u0120vulnerability": 15131, "ostic": 15132, "\u0120isolation": 15133, "\u0120cooling": 15134, "\u0120Coalition": 15135, "\u0120119": 15136, "Four": 15137, "\u0120Deal": 15138, "\u0120\u00e2\u012b": 15139, "semble": 15140, "rament": 15141, "\u0120Barcelona": 15142, "\u0120102": 15143, "\u0120cocaine": 15144, "ocalypse": 15145, "Feb": 15146, "ogenic": 15147, "\u0120mutation": 15148, "\u0120cryptoc": 15149, "\u0120Kel": 15150, "\u0120Git": 15151, "ais": 15152, "\u0120sisters": 15153, "ANK": 15154, "\u0120activate": 15155, "Ter": 15156, "\u0120dread": 15157, "ylon": 15158, "\u0120propri": 15159, "Aust": 15160, "\u0120Default": 15161, "\u0120outdoor": 15162, "\u0120sheer": 15163, "ceive": 15164, "\u0120gently": 15165, "\u00d0\u00be": 15166, "Program": 15167, "\u0120\u00e2\u0128\u0134": 15168, "\u0120vegan": 15169, "\u0120Crus": 15170, "\u0120responsibilities": 15171, "\u0120HR": 15172, "OLD": 15173, "\u0120prevents": 15174, "\u0120stiff": 15175, "\u0120Were": 15176, "\u0120athletic": 15177, "\u0120Score": 15178, "\u0120):": 15179, "\u0120columns": 15180, "\u0120Loc": 15181, "available": 15182, "\u0120Fram": 15183, "\u0120Sessions": 15184, "\u0120companion": 15185, "\u0120packs": 15186, "140": 15187, "\u0120Knights": 15188, "\u0120fart": 15189, "\u0120streams": 15190, "\u0120shore": 15191, "\u0120appeals": 15192, "\u0120Performance": 15193, "haul": 15194, "\u0120Stra": 15195, "\u0120Nag": 15196, "103": 15197, "\u0120Transportation": 15198, "BB": 15199, "Ev": 15200, "zan": 15201, "Public": 15202, "\u0120twin": 15203, "ulsion": 15204, "Mult": 15205, "\u0120electro": 15206, "\u0120statue": 15207, "ationally": 15208, "\u0120Nort": 15209, "\u0120inspection": 15210, "/*": 15211, "igue": 15212, "\u0120compassion": 15213, "\u0120Tales": 15214, "\u0120Stein": 15215, "\u0120Screen": 15216, "\u0120Bug": 15217, "\u0120Lion": 15218, "girl": 15219, "\u0120withdrawal": 15220, "\u0120objectives": 15221, "\u0120bloody": 15222, "\u0120preliminary": 15223, "\u0120jacket": 15224, "\u0120dimensions": 15225, "\u0120Cool": 15226, "\u0120Occup": 15227, "\u0120wreck": 15228, "\u0120doubled": 15229, "anking": 15230, "\u01201975": 15231, "\u0120glasses": 15232, "\u0120Wang": 15233, "prov": 15234, "Path": 15235, "connected": 15236, "\u0120Multi": 15237, "\u0120Norway": 15238, "agonist": 15239, "\u0120feared": 15240, "\u0120touching": 15241, "\u0120arguably": 15242, "\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af": 15243, "\u0120NCAA": 15244, "chem": 15245, "\u0120spat": 15246, "\u0120WWE": 15247, "\u0120Cel": 15248, "igger": 15249, "\u0120attacker": 15250, "\u0120Join": 15251, "object": 15252, "etta": 15253, "\u0120eliminated": 15254, "det": 15255, "\u0120destruct": 15256, "\u0120Lucas": 15257, "ctuary": 15258, "180": 15259, "\u0120Brady": 15260, "\u0120Blues": 15261, "Bay": 15262, "aukee": 15263, "\u0120timeline": 15264, "\u0120delegates": 15265, "written": 15266, "ufficient": 15267, "\u0120shapes": 15268, "Copyright": 15269, "ouble": 15270, "service": 15271, "\u0120pione": 15272, "\u0120colleges": 15273, "\u0120rows": 15274, "\u0120spite": 15275, "\u0120assessed": 15276, "360": 15277, "\u0120lease": 15278, "\u0120confidential": 15279, "cker": 15280, "\u0120Manning": 15281, "\u0120Voice": 15282, "\u0120sealed": 15283, "\u0120calculate": 15284, "NO": 15285, "\u0120Assistant": 15286, "\u0120teenager": 15287, "ulent": 15288, "atherine": 15289, "\u0120mock": 15290, "\u0120diamond": 15291, "\u0120fest": 15292, "\u0120switched": 15293, "\u0120resume": 15294, "\u0120Puerto": 15295, "\u0120lanes": 15296, "iration": 15297, "\u0120Similarly": 15298, "\u0120rod": 15299, "\u0120Sel": 15300, "\u0120Palace": 15301, "\u0120Limited": 15302, "eous": 15303, "\u0120variant": 15304, "\u0120ward": 15305, "\u0120))": 15306, "Show": 15307, "OOK": 15308, "Alex": 15309, "\u0120Nep": 15310, "bris": 15311, "\u0120Wikipedia": 15312, "\u0120exceptional": 15313, "\u0120manages": 15314, "\u0120Draw": 15315, "Again": 15316, "\u0120copper": 15317, "utt": 15318, "\u0120exports": 15319, "\u0120portfolio": 15320, "\u0120elevated": 15321, "Rated": 15322, "\u0120Otherwise": 15323, "\u0120Tact": 15324, "\u0120Shel": 15325, "\u0120TX": 15326, "\"\u00e2\u0122\u0136": 15327, "\u0120resur": 15328, "\u0120Wa": 15329, "venant": 15330, "\u0120monetary": 15331, "people": 15332, "Email": 15333, "\u0120fifty": 15334, "\u0120Sweet": 15335, "\u0120Malaysia": 15336, "\u0120confusing": 15337, "\u0120Rio": 15338, "uda": 15339, "utenant": 15340, "\");": 15341, "\u0120praised": 15342, "\u0120volumes": 15343, "turn": 15344, "\u0120mature": 15345, "\u0120nonprofit": 15346, "\u0120passionate": 15347, "\u0120Private": 15348, "\u0120103": 15349, "\u0120descend": 15350, "\u00e7\u00a5\u0140": 15351, "uffy": 15352, "headed": 15353, "Whether": 15354, "rien": 15355, "zech": 15356, "beit": 15357, "\u0120chrom": 15358, "\u0120McM": 15359, "\u0120dancing": 15360, "\u0120eleg": 15361, "\u0120Noticed": 15362, "115": 15363, "\u0120advocacy": 15364, "ENTS": 15365, "ambling": 15366, "\u0120Minor": 15367, "\u0120Finn": 15368, "\u0120priorities": 15369, "\u0120thereof": 15370, "\u0120Stage": 15371, "\u0120Rogers": 15372, "\u0120substitute": 15373, "\u0120Jar": 15374, "\u0120Jefferson": 15375, "\u0120lightly": 15376, "102": 15377, "\u0120Lisa": 15378, "uits": 15379, "ysical": 15380, "\u0120shifts": 15381, "\u0120drones": 15382, "\u0120workplace": 15383, "\u0120resid": 15384, "ensed": 15385, "ahn": 15386, "\u0120preferences": 15387, "server": 15388, "\u0120debates": 15389, "doc": 15390, "\u0120Gods": 15391, "\u0120helicopter": 15392, "\u0120honour": 15393, "\u0120considerably": 15394, "eded": 15395, "\u0120Female": 15396, "\u0120Anne": 15397, "\u0120reun": 15398, "\u0120Face": 15399, "\u0120Hallow": 15400, "\u0120Budget": 15401, "\u0120condemn": 15402, "\u0120tender": 15403, "Prof": 15404, "ocratic": 15405, "\u0120Turner": 15406, "\u0120Agric": 15407, "\u01201976": 15408, "\u0120apt": 15409, "disc": 15410, "\u0120Fighter": 15411, "\u0120Aur": 15412, "\u0120garbage": 15413, "input": 15414, "\u0120Karl": 15415, "\u0120Oliver": 15416, "\u0120Language": 15417, "kn": 15418, "Non": 15419, "\u0120Clar": 15420, "\u0120traditions": 15421, "\u0120advertisement": 15422, "\u0120Sor": 15423, "\u0120archive": 15424, "\u0120villages": 15425, "750": 15426, "\u0120implementing": 15427, "waukee": 15428, "\u0120dietary": 15429, "\u0120switching": 15430, "Republic": 15431, "\u0120velocity": 15432, "\u0120cit": 15433, "\u0120Awards": 15434, "\u0120financing": 15435, "\u0120lasted": 15436, ")]": 15437, "\u0120reminder": 15438, "Person": 15439, "\u0120precision": 15440, "\u0120designers": 15441, "\u0120Fried": 15442, "\u0120Border": 15443, "\u0120tragic": 15444, "\u0120wield": 15445, "\u0120initiatives": 15446, "\u0120Tank": 15447, "wer": 15448, "\u0120joins": 15449, "Ro": 15450, "inery": 15451, "\u0120arrow": 15452, "\u0120generating": 15453, "founder": 15454, "\u0120searches": 15455, "\u0120randomly": 15456, "Access": 15457, "\u0120batch": 15458, "\u0120posed": 15459, "lat": 15460, "\u0120pursuing": 15461, "asa": 15462, "\u0120testified": 15463, "forming": 15464, "\u0120Shar": 15465, "wiki": 15466, "\u0120Either": 15467, "Sometimes": 15468, "\u0120senators": 15469, "\u0120Johnny": 15470, "\u0120Taliban": 15471, "\u0120GPS": 15472, "\":\"/": 15473, "\u00e3\u0123\u00ae\u00e5": 15474, "\u0120analyzed": 15475, "\u0120Rubio": 15476, "\u0120Movement": 15477, "opard": 15478, "iii": 15479, "Stand": 15480, "fight": 15481, "\u0120ignoring": 15482, "iang": 15483, "\u0120GN": 15484, "soever": 15485, "\u0120STAT": 15486, "\u0120refusing": 15487, "\u0120sweat": 15488, "\u0120bay": 15489, "PORT": 15490, "irmed": 15491, "aky": 15492, "\u0120dispro": 15493, "\u0120labeled": 15494, "\u0120108": 15495, "Hello": 15496, "\u0120pleasant": 15497, "aba": 15498, "\u0120triumph": 15499, "\u0120aboard": 15500, "\u0120incom": 15501, "\u0120Crow": 15502, "lett": 15503, "\u0120folk": 15504, "\u0120chase": 15505, "``": 15506, "\u0120Brus": 15507, "\u0120teens": 15508, "cue": 15509, "\u0120terrain": 15510, "hyd": 15511, "ilight": 15512, "ORY": 15513, "Support": 15514, "ews": 15515, "lli": 15516, "raints": 15517, "\u0120Cand": 15518, "\u0120abused": 15519, "achment": 15520, "larg": 15521, "Bas": 15522, "\u0120Cancer": 15523, "\u01201978": 15524, "\u0120supporter": 15525, "access": 15526, "\u0120Termin": 15527, "\u0120Tampa": 15528, "\u0120ANY": 15529, "\u0120newest": 15530, "\u0120Criminal": 15531, "edu": 15532, "\u01201930": 15533, "\u0120admits": 15534, "\u0120ende": 15535, "\u0120failures": 15536, "urate": 15537, "fulness": 15538, "cycl": 15539, "\u0120Subject": 15540, "\u0120infinite": 15541, "three": 15542, "WA": 15543, "pit": 15544, "\u0120Install": 15545, "Rad": 15546, "iliation": 15547, "GM": 15548, "\u0120continent": 15549, "\u0120accommodate": 15550, "\u0120Clay": 15551, "\u0120pup": 15552, "\u0120Function": 15553, "\u0120hammer": 15554, "\u0120Alberta": 15555, "\u0120revised": 15556, "\u0120minorities": 15557, "\u0120measurement": 15558, "Connell": 15559, "\u0120disable": 15560, "\u0120Mix": 15561, "Incre": 15562, "\u0120fork": 15563, "\u0120Rosen": 15564, "\u0120implies": 15565, "umblr": 15566, "ANG": 15567, "\u0120proteins": 15568, "\u0120aggression": 15569, "\u0120facilitate": 15570, "SN": 15571, "\u0120illegally": 15572, "uer": 15573, "\u0120academ": 15574, "\u0120puzz": 15575, "\u0120Shift": 15576, "pay": 15577, "ollo": 15578, "\u0120audiences": 15579, "Build": 15580, "\u0120noble": 15581, "\u0120syntax": 15582, "\u00e2\u013a\u0127": 15583, "\u0120beam": 15584, "\u0120Bed": 15585, "\u0120Ald": 15586, "\u0120origins": 15587, "video": 15588, "\u01201977": 15589, "\u0120Assault": 15590, "\u0120garage": 15591, "Team": 15592, "\u0120verdict": 15593, "\u0120dwar": 15594, "\u0120Virtual": 15595, "event": 15596, "Keep": 15597, "\u0120sentiment": 15598, "\u0120wildlife": 15599, "shirt": 15600, "\u0120burg": 15601, "\u0120recommendation": 15602, "represent": 15603, "\u0120gallery": 15604, "owners": 15605, "\u0120scholar": 15606, "\u0120convenience": 15607, "\u0120Swift": 15608, "\u0120convinc": 15609, "Cap": 15610, "\u0120warfare": 15611, "\u0120Visual": 15612, "\u0120constitute": 15613, "\u0120abort": 15614, "\u0120Weather": 15615, "\u0120Looking": 15616, "\u0120Hem": 15617, "\u0120martial": 15618, "\u0120incoming": 15619, "etition": 15620, "\u0120tolerance": 15621, "\u0120Created": 15622, "\u0120flows": 15623, "\u0120Elder": 15624, "\u0120souls": 15625, "\u0120foul": 15626, "\u0120Pain": 15627, "\u0120CAN": 15628, "\u0120220": 15629, "bc": 15630, "hend": 15631, "\u0120genius": 15632, "Real": 15633, "\u0120Wr": 15634, "ometer": 15635, "pad": 15636, "\u0120limiting": 15637, "\u0120Si": 15638, "\u0120Lore": 15639, "\u0120Adventures": 15640, "\u0120varied": 15641, "Disc": 15642, "fin": 15643, "\u0120Personal": 15644, "Chris": 15645, "\u0120invented": 15646, "\u0120dive": 15647, "\u0120Rise": 15648, "\u0120oz": 15649, "\u0120Comics": 15650, "\u0120expose": 15651, "\u0120Reb": 15652, "letters": 15653, "site": 15654, "imated": 15655, "\u0120hacking": 15656, "\u0120educated": 15657, "\u0120Nobody": 15658, "\u0120depri": 15659, "\u0120incentive": 15660, "\u00e3\u0124\u00b7": 15661, "\u0120oversight": 15662, "\u0120tribes": 15663, "\u0120Belgium": 15664, "\u0120licensing": 15665, "ourt": 15666, "Product": 15667, "ahl": 15668, "\u0120Gem": 15669, "\u0120specialist": 15670, "\u0120cra": 15671, "anners": 15672, "\u0120Corbyn": 15673, "\u01201973": 15674, "READ": 15675, "\u0120summar": 15676, "\u0120overlook": 15677, "\u0120Application": 15678, "\u0120inappropriate": 15679, "\u0120downloaded": 15680, "Que": 15681, "\u0120Bears": 15682, "\u0120thumb": 15683, "\u0120Character": 15684, "\u0120Reincarnated": 15685, "\u0120Sid": 15686, "\u0120demonstrates": 15687, "sky": 15688, "\u0120Bloomberg": 15689, "\u0120Array": 15690, "\u0120Results": 15691, "\u0120Fourth": 15692, "\u0120EDT": 15693, "\u0120Oscar": 15694, "cend": 15695, "\u0120106": 15696, "\u0120NULL": 15697, "\u0120HERE": 15698, "match": 15699, "\u0120Brun": 15700, "\u0120glucose": 15701, "ieg": 15702, "egu": 15703, "\u0120certified": 15704, "\u0120relie": 15705, "\u0120humanitarian": 15706, "\u0120prayers": 15707, "King": 15708, "\u0120nan": 15709, "hou": 15710, "108": 15711, "ulu": 15712, "\u0120renewable": 15713, "\u0120distinguish": 15714, "\u0120dense": 15715, "\u0120Vent": 15716, "\u0120Package": 15717, "\u0120Boss": 15718, "\u0120editors": 15719, "\u0120migr": 15720, "Tra": 15721, "\u0120Peters": 15722, "\u0120Arctic": 15723, "2004": 15724, "\u0120Cape": 15725, "\u0120locally": 15726, "\u0120lasting": 15727, "\u0120handy": 15728, ".).": 15729, "Pan": 15730, "\u0120RES": 15731, "Index": 15732, "\u0120tensions": 15733, "\u0120formerly": 15734, "\u0120ideological": 15735, "\u0120sensors": 15736, "\u0120dealers": 15737, "\u0120defines": 15738, "Sk": 15739, "\u0120proceeds": 15740, "\u0120proxy": 15741, "azines": 15742, "\u0120Bash": 15743, "\u0120Pad": 15744, "\u0120Craft": 15745, "ealous": 15746, "\u0120sheets": 15747, "ometry": 15748, "June": 15749, "clock": 15750, "TT": 15751, "\u0120Theatre": 15752, "\u0120Buzz": 15753, "\u0120chapters": 15754, "\u0120millenn": 15755, "\u0120dough": 15756, "\u0120Congressional": 15757, "\u0120imagined": 15758, "avior": 15759, "\u0120clinic": 15760, "\u01201945": 15761, "\u0120holder": 15762, "root": 15763, "olester": 15764, "\u0120restart": 15765, "BN": 15766, "\u0120Hamas": 15767, "\u0120Job": 15768, "\u0120orb": 15769, "\u0120ram": 15770, "\u0120disclose": 15771, "\u0120translate": 15772, "\u0120immigrant": 15773, "\u0120annoying": 15774, "\u0120treaty": 15775, "anium": 15776, "\u0120Tea": 15777, "\u0120Legion": 15778, "\u0120crowds": 15779, "\u0120Bec": 15780, "\u0120Aer": 15781, "ohyd": 15782, "Bro": 15783, "Looking": 15784, "\u0120lbs": 15785, "\u0120aggress": 15786, "\u0120seam": 15787, "\u0120intercept": 15788, "\u0120MI": 15789, "mercial": 15790, "activ": 15791, "\u0120Cit": 15792, "\u0120dimension": 15793, "\u0120consistency": 15794, "\u0120rushing": 15795, "\u0120Douglas": 15796, "\u0120trim": 15797, "Install": 15798, "icker": 15799, "\u0120shy": 15800, "106": 15801, "\u0120mentions": 15802, "pelled": 15803, "\u0120Tak": 15804, "cost": 15805, "\u0120classroom": 15806, "\u0120fortune": 15807, "driven": 15808, "\u0120unle": 15809, "\u0120Wheel": 15810, "\u0120investor": 15811, "\u0120Masters": 15812, "kit": 15813, "\u0120associations": 15814, "\u0120Evolution": 15815, "oping": 15816, "uscript": 15817, "\u0120provincial": 15818, "\u0120Walter": 15819, "avi": 15820, "SO": 15821, "\u0120unlimited": 15822, "English": 15823, "\u0120Cards": 15824, "\u0120Ebola": 15825, "nered": 15826, "\u0120revenge": 15827, "\u0120outright": 15828, "umper": 15829, "\u0120fitting": 15830, "\u0120Solid": 15831, "\u0120formally": 15832, "\u0120problematic": 15833, "\u0120hazard": 15834, "\u0120encryption": 15835, "\u0120straightforward": 15836, "\u0120AK": 15837, "\u0120pse": 15838, "\u0120Orb": 15839, "\u0120Chamber": 15840, "\u0120Mak": 15841, "Contents": 15842, "\u0120loyalty": 15843, "\u0120lyrics": 15844, "\u0120Sym": 15845, "\u0120welcomed": 15846, "\u0120cooked": 15847, "\u0120monop": 15848, "\u0120nurse": 15849, "\u0120misleading": 15850, "\u0120eternal": 15851, "\u0120shifting": 15852, "\u0120+=": 15853, "Vis": 15854, "\u0120institutional": 15855, "illary": 15856, "\u0120pant": 15857, "VERT": 15858, "\u0120ACC": 15859, "\u0120Enh": 15860, "\u0120incon": 15861, "\u0120REUTERS": 15862, "\u0120donated": 15863, "\u00e2\u0122\u00a6\u00e2\u0122\u00a6\u00e2\u0122\u00a6\u00e2\u0122\u00a6": 15864, "Intern": 15865, "\u0120exhibit": 15866, "\u0120tire": 15867, "\u0120Ric": 15868, "\u0120Champion": 15869, "\u0120Muhammad": 15870, "NING": 15871, "\u0120Soccer": 15872, "\u0120mobility": 15873, "\u0120varying": 15874, "\u0120Movie": 15875, "\u0120lord": 15876, "oak": 15877, "Field": 15878, "\u0120vector": 15879, "usions": 15880, "\u0120scrap": 15881, "\u0120enabling": 15882, "make": 15883, "Tor": 15884, ".*": 15885, "||": 15886, "\u0120Website": 15887, "\u0120NPC": 15888, "\u0120socialist": 15889, "\u0120Billy": 15890, "\u0120Additional": 15891, "\u0120cargo": 15892, "\u0120farms": 15893, "\u0120Soon": 15894, "\u0120Prize": 15895, "\u0120midnight": 15896, "\u0120900": 15897, "seen": 15898, "\u0120Spot": 15899, "\u0120sheep": 15900, "\u0120sponsored": 15901, "\u0120Hi": 15902, "\u0120Jump": 15903, "\u01201967": 15904, "Microsoft": 15905, "\u0120Agent": 15906, "\u0120charts": 15907, "dir": 15908, "\u0120adjacent": 15909, "\u0120tricks": 15910, "\u0120manga": 15911, "\u0120exagger": 15912, "/>": 15913, "football": 15914, "\u0120FCC": 15915, "GC": 15916, "\u0120Tier": 15917, "andra": 15918, "OUND": 15919, "%),": 15920, "\u0120fruits": 15921, "VC": 15922, "\u0120AA": 15923, "Rober": 15924, "\u0120midst": 15925, "\u00e2\u0139": 15926, "anka": 15927, "\u0120legislature": 15928, "\u0120Neil": 15929, "\u0120tourists": 15930, "\"\"": 15931, "\u0120Warning": 15932, "\u0120Nevertheless": 15933, "\u0120Official": 15934, "\u0120Whatever": 15935, "\u0120mold": 15936, "\u0120drafted": 15937, "\u0120substances": 15938, "\u0120breed": 15939, "\u0120tags": 15940, "\u0120Task": 15941, "\u0120verb": 15942, "\u0120manufactured": 15943, "comments": 15944, "\u0120Polish": 15945, "Prov": 15946, "\u0120determines": 15947, "Obama": 15948, "kers": 15949, "\u0120utterly": 15950, "\u0120sect": 15951, "sche": 15952, "\u0120Gates": 15953, "\u0120Chap": 15954, "\u0120aluminum": 15955, "\u0120zombie": 15956, "\u0120Touch": 15957, "\u0120UP": 15958, "\u0120satisfy": 15959, "\u0120predomin": 15960, "ascript": 15961, "\u0120elaborate": 15962, "\u01201968": 15963, "\u0120measuring": 15964, "\u0120Vari": 15965, "anyahu": 15966, "\u0120sir": 15967, "ulates": 15968, "idges": 15969, "ickets": 15970, "\u0120Spencer": 15971, "TM": 15972, "oubted": 15973, "\u0120prey": 15974, "\u0120installing": 15975, "\u0120Cab": 15976, "reed": 15977, "reated": 15978, "Supp": 15979, "\u0120wrist": 15980, "\u0120Kerry": 15981, "107": 15982, "\u0120Kle": 15983, "\u0120Rachel": 15984, "\u0120cotton": 15985, "\u0120ARE": 15986, "\u0120Ele": 15987, "Control": 15988, "\u0120loads": 15989, "\u0120Dod": 15990, "anas": 15991, "bone": 15992, "\u0120classical": 15993, "\u0120Regional": 15994, "\u0120Integ": 15995, "VM": 15996, "\u0120desires": 15997, "\u0120autism": 15998, "supported": 15999, "\u0120Message": 16000, "\u0120compact": 16001, "writer": 16002, "\u0120109": 16003, "\u0120Hurricane": 16004, "cision": 16005, "\u0120cycles": 16006, "\u0120drill": 16007, "\u0120colleague": 16008, "\u0120maker": 16009, "German": 16010, "\u0120mistaken": 16011, "Sun": 16012, "\u0120Gay": 16013, "\u0120whatsoever": 16014, "\u0120sells": 16015, "\u0120Airl": 16016, "liv": 16017, "\u0120Option": 16018, "\u0120solved": 16019, "\u0120sectors": 16020, "\u0120horizontal": 16021, "\u0120equation": 16022, "\u0120Skill": 16023, "\u0120Bio": 16024, "gement": 16025, "\u0120Snap": 16026, "\u0120Legal": 16027, "\u0120trademark": 16028, "\u0120makeup": 16029, "\u0120assembled": 16030, "\u0120saves": 16031, "\u0120Halloween": 16032, "\u0120Vermont": 16033, "\u0120FROM": 16034, "\u0120farming": 16035, "\u0120Podcast": 16036, "acceptable": 16037, "\u0120Higher": 16038, "\u0120asleep": 16039, "ullivan": 16040, "\u0120referen": 16041, "\u0120Lev": 16042, "\u0120bullets": 16043, "oko": 16044, "HC": 16045, "\u0120stairs": 16046, "\u0120maintains": 16047, "\u0120Lower": 16048, "\u0120Vi": 16049, "\u0120marine": 16050, "\u0120acres": 16051, "\u0120coordinator": 16052, "\u0120Joh": 16053, "\u0120counterparts": 16054, "\u0120Brothers": 16055, "\u0120indict": 16056, "bra": 16057, "\u0120chunk": 16058, "\u0120cents": 16059, "Home": 16060, "\u0120Month": 16061, "\u0120accordingly": 16062, "ifles": 16063, "\u0120Germans": 16064, "\u0120Syn": 16065, "Hub": 16066, "\u0120eyeb": 16067, "\u00e2\u0136\u0122\u00e2\u0136\u0122\u00e2\u0136\u0122\u00e2\u0136\u0122": 16068, "\u0120ranges": 16069, "\u0120Holland": 16070, "\u0120Robot": 16071, "fc": 16072, "Mike": 16073, "\u0120plasma": 16074, "\u0120swap": 16075, "\u0120athlete": 16076, "\u0120Rams": 16077, ",'\"": 16078, "\u0120infections": 16079, "\u0120corrid": 16080, "\u0120vib": 16081, "\u0120patches": 16082, "\u0120traditionally": 16083, "\u0120revelation": 16084, "\u0120sweep": 16085, "\u0120glance": 16086, "\u0120inex": 16087, "2003": 16088, "\u0120Raw": 16089, "working": 16090, "osures": 16091, "\u0120Dat": 16092, "\u0120Lynch": 16093, "\u0120leverage": 16094, "\u0120Reid": 16095, "\u0120correlation": 16096, "iances": 16097, "avascript": 16098, "\u0120repository": 16099, "retty": 16100, "\u01201972": 16101, "240": 16102, "\u0120oun": 16103, "pol": 16104, "\u0120Reed": 16105, "\u0120tactical": 16106, "isite": 16107, "Apple": 16108, "\u0120Quinn": 16109, "\u0120raped": 16110, "illo": 16111, "Europe": 16112, "\u0120algorithms": 16113, "\u0120Rodrig": 16114, "iu": 16115, "\u0120illum": 16116, "\u0120fame": 16117, "\u0120introducing": 16118, "\u0120delays": 16119, "\u0120Raiders": 16120, "\u0120whistle": 16121, "\u0120novels": 16122, "\u0120Really": 16123, "\u0120deriv": 16124, "\u0120publications": 16125, "\u0120Neither": 16126, "\u0120Commerce": 16127, "\u0120aston": 16128, "language": 16129, "Notes": 16130, "\u0120Roth": 16131, "\u0120Fear": 16132, "\u0120mate": 16133, "\u0120parade": 16134, "\u0120QB": 16135, "\u0120maneu": 16136, "\u0120Cincinnati": 16137, "mitting": 16138, "\u0120waist": 16139, "\u0120Rew": 16140, "\u0120discont": 16141, "\u00d0\u00b0": 16142, "\u0120staring": 16143, "\u0120alias": 16144, "\u0120securities": 16145, "\u0120toilet": 16146, "\u0120Jedi": 16147, "\u0120unlaw": 16148, "vised": 16149, "////////": 16150, "](": 16151, "\u0120Weiss": 16152, "\u0120prest": 16153, "\u0120Compan": 16154, "\u0120memo": 16155, "\u0120Grace": 16156, "July": 16157, "\u0120Elite": 16158, "center": 16159, "\u0120Stay": 16160, "\u0120galaxy": 16161, "\u0120tooth": 16162, "\u0120Settings": 16163, "\u0120subjected": 16164, "\u00e3\u0124\u00a6": 16165, "\u0120lineback": 16166, "\u0120retailers": 16167, "\u0120Want": 16168, "\u0120dangers": 16169, "Air": 16170, "\u0120voluntary": 16171, "eway": 16172, "\u0120interpreted": 16173, "otine": 16174, "\u00c3\u00a7": 16175, "\u0120pel": 16176, "Service": 16177, "\u0120Eventually": 16178, "\u0120careers": 16179, "\u0120threaten": 16180, "\u0120memor": 16181, "\u0120Bradley": 16182, "ancies": 16183, "sn": 16184, "\u0120Unknown": 16185, "National": 16186, "\u0120shadows": 16187, "ailand": 16188, "\u0120Dash": 16189, "Everyone": 16190, "izzard": 16191, "March": 16192, "=(": 16193, "\u0120pulls": 16194, "\u0120stranger": 16195, "\u0120backwards": 16196, "\u0120Bernard": 16197, "imensional": 16198, "\u0120chron": 16199, "\u0120theoretical": 16200, "ktop": 16201, "\u0120ware": 16202, "\u0120Investig": 16203, "\u0120Initi": 16204, "\u0120Operations": 16205, "oven": 16206, "ocide": 16207, "*/": 16208, "\u0120flames": 16209, "\u0120Cash": 16210, "shit": 16211, "\u0120cab": 16212, "\u0120Analy": 16213, "\u0120Seah": 16214, "\u0120defining": 16215, "\u0120ordering": 16216, "\u0120immun": 16217, "\u0120persistent": 16218, "ACH": 16219, "Russian": 16220, "mans": 16221, "\u0120hind": 16222, "\u0120photography": 16223, "\u00c2\u00a9": 16224, "\u0120hug": 16225, "\u0120107": 16226, "\u0120Hence": 16227, "iots": 16228, "udeau": 16229, "\u0120subsidies": 16230, "\u0120routinely": 16231, "\u0120Device": 16232, "itic": 16233, "\u0120disgust": 16234, "lander": 16235, "\u01201940": 16236, "\u0120assignment": 16237, "\u0120Besides": 16238, "wick": 16239, "\u0120Dust": 16240, "usc": 16241, "structed": 16242, "111": 16243, "develop": 16244, "\u0120fond": 16245, "\u0120intersection": 16246, "\u0120dignity": 16247, "\u0120commissioner": 16248, "Without": 16249, "reach": 16250, "\u0120cartoon": 16251, "\u0120scales": 16252, "\u00e3\u0125\u0143": 16253, "FIG": 16254, "\u0120surveys": 16255, "\u0120Indonesia": 16256, "\u0120artwork": 16257, "\u0120unch": 16258, "\u0120cycling": 16259, "unct": 16260, "auer": 16261, "orate": 16262, "\u0120Obviously": 16263, "\u0120characterized": 16264, "feld": 16265, "\u0120affirm": 16266, "\u0120innings": 16267, "\u0120\u00e9": 16268, "\u0120aliens": 16269, "\u0120cloth": 16270, "etooth": 16271, "\u0120Certain": 16272, "\u00c2\u00a7": 16273, "\u0120digest": 16274, "know": 16275, "\u0120XL": 16276, "\u0120predictions": 16277, "\u0120din": 16278, "WAR": 16279, "\u0120aftermath": 16280, "Example": 16281, "\u0120Success": 16282, "\u0120Thr": 16283, "IGN": 16284, "\u0120miner": 16285, "Bus": 16286, "\u0120clarity": 16287, "heimer": 16288, "\u0120OUT": 16289, "\u0120Send": 16290, "\u0120Circle": 16291, "\u0120Diet": 16292, "\u0120pronounced": 16293, "\u0120creators": 16294, "\u0120earthquake": 16295, "attery": 16296, "geons": 16297, "\u0120od": 16298, "\u0120laying": 16299, "orp": 16300, "Ult": 16301, "project": 16302, "\u0120undermin": 16303, "\u0120sequel": 16304, "Sam": 16305, "\u0120Darkness": 16306, "\u0120reception": 16307, "bull": 16308, "YS": 16309, "\u0120Vir": 16310, "\u0120sequences": 16311, "\u0120Coin": 16312, "\u0120outfit": 16313, "\u0120Wait": 16314, "119": 16315, "\u0120delivers": 16316, "......": 16317, "\u0120blown": 16318, "\u0120Esc": 16319, "\u0120Math": 16320, "perm": 16321, "\u0120Ul": 16322, "\u0120glim": 16323, "\u0120facial": 16324, "\u0120greenhouse": 16325, "\u0120tokens": 16326, "/-": 16327, "\u0120Annual": 16328, "\u0120ONE": 16329, "\u0120teenage": 16330, "\u0120Physical": 16331, "\u0120Lang": 16332, "\u0120Celt": 16333, "\u0120sued": 16334, "ividually": 16335, "\u0120patience": 16336, "chair": 16337, "regular": 16338, "\u0120aug": 16339, "inv": 16340, "except": 16341, "\u0120Lil": 16342, "\u0120nest": 16343, "fd": 16344, "sum": 16345, "\u0120Chase": 16346, "Russia": 16347, "\u0120Jennifer": 16348, "\u0120offseason": 16349, "Overall": 16350, "Fore": 16351, "\u0120riot": 16352, "Aud": 16353, "former": 16354, "\u0120defenders": 16355, "\u0120CT": 16356, "iotic": 16357, "ribly": 16358, "\u0120automated": 16359, "\u0120penis": 16360, "\u0120insist": 16361, "\u0120diagram": 16362, "\u0120SQL": 16363, "\u0120Garc": 16364, "\u0120witch": 16365, "client": 16366, "ierra": 16367, "ambers": 16368, "\u0120recount": 16369, "far": 16370, "Very": 16371, "osterone": 16372, "\u0120appreciated": 16373, "\u0120Perfect": 16374, "Section": 16375, "\u0120doses": 16376, "ocaust": 16377, "\u0120costly": 16378, "\u0120grams": 16379, "\u0120Shi": 16380, "\u0120wrestling": 16381, "\u01201971": 16382, "\u0120trophy": 16383, "\u0120nerve": 16384, "\u0120Kaz": 16385, "\u0120Experience": 16386, "\u0120pledged": 16387, "\u0120playback": 16388, "\u0120creativity": 16389, "bye": 16390, "\u0120attackers": 16391, "\u0120holders": 16392, "\u0120Coach": 16393, "\u0120PhD": 16394, "\u0120transfers": 16395, "\u0120colored": 16396, "\u0120Hindu": 16397, "\u0120drown": 16398, "\u0120listened": 16399, "\u0120WA": 16400, "iasm": 16401, "PO": 16402, "\u0120appealing": 16403, "\u0120disclosed": 16404, "\u0120Chicken": 16405, "agging": 16406, "\u0120pleaded": 16407, "\u0120navigation": 16408, "\u0120Returns": 16409, "\u0120[[": 16410, "ROR": 16411, "EA": 16412, "\u0120photographer": 16413, "\u0120Rider": 16414, "ippers": 16415, "\u0120slice": 16416, "\u0120erect": 16417, "\u0120hed": 16418, "issance": 16419, "\u0120Vikings": 16420, "urious": 16421, "\u0120appet": 16422, "oubtedly": 16423, "Child": 16424, "\u0120authentic": 16425, "oos": 16426, "\u0120Making": 16427, "\u0120announcing": 16428, "\u0120bod": 16429, "\u0120meter": 16430, "\u0120Nine": 16431, "\u0120Rogue": 16432, "\u0120workforce": 16433, "\u0120renewed": 16434, "\u0120organisations": 16435, "acs": 16436, "PLE": 16437, "Short": 16438, "\u0120compounds": 16439, "\u0120Visit": 16440, "\u0120envelop": 16441, "earth": 16442, "\u0120supportive": 16443, "ggle": 16444, "\u0120Brussels": 16445, "\u0120Guild": 16446, "Create": 16447, "REL": 16448, "\u0120averaged": 16449, "\u01201969": 16450, "riages": 16451, "\u0120lengthy": 16452, "\u0120forgot": 16453, "Okay": 16454, "\u0120Erd": 16455, "\u0120dealer": 16456, "\u0120recession": 16457, "DD": 16458, "\u0120desperately": 16459, "\u0120hunger": 16460, "\u0120sticks": 16461, "\u0120mph": 16462, "\u0120Faith": 16463, "\u0120intentionally": 16464, "\u0120demol": 16465, "ueller": 16466, "\u0120Sale": 16467, "\u0120debris": 16468, "spring": 16469, "\u0120leap": 16470, ">>>>": 16471, "\u0120containers": 16472, "selling": 16473, "ranean": 16474, "attering": 16475, "\u0120commented": 16476, "\u0120CM": 16477, "onut": 16478, "\u0120woods": 16479, "especially": 16480, "\u0120organize": 16481, "ivic": 16482, "\u0120Woods": 16483, "anga": 16484, "squ": 16485, "\u0120maj": 16486, "amon": 16487, "\u0120axis": 16488, "\u01201974": 16489, "\u0120Denmark": 16490, "\u0120warrior": 16491, "\u0120Pand": 16492, "\u0120outlined": 16493, "\u0120BO": 16494, "insula": 16495, "zilla": 16496, "ebook": 16497, "\u0120dare": 16498, "\u0120searched": 16499, "\u0120navigate": 16500, "Sn": 16501, "writing": 16502, "\u0120united": 16503, "Japan": 16504, "\u0120Hebrew": 16505, "\u0120flame": 16506, "\u0120relies": 16507, "\u0120catching": 16508, "\u0120Sho": 16509, "\u0120imprisonment": 16510, "\u0120pockets": 16511, "\u0120closure": 16512, "\u0120Fam": 16513, "tim": 16514, "adequ": 16515, "Activity": 16516, "\u0120recruiting": 16517, "\u0120WATCH": 16518, "\u0120Argentina": 16519, "dest": 16520, "\u0120apologize": 16521, "oro": 16522, "\u0120lacks": 16523, "\u0120tuned": 16524, "\u0120Griffin": 16525, "\u0120infamous": 16526, "\u0120celebrity": 16527, "sson": 16528, "\u0120----------------------------------------------------------------": 16529, "\u0120Isis": 16530, "\u0120Display": 16531, "\u0120credibility": 16532, "\u0120economies": 16533, "\u0120headline": 16534, "\u0120Cowboys": 16535, "\u0120indef": 16536, "\u0120lately": 16537, "\u0120incentives": 16538, "button": 16539, "\u0120Mob": 16540, "Aut": 16541, "\u0120resigned": 16542, "\u0120Om": 16543, "camp": 16544, "\u0120profiles": 16545, "\u0120schemes": 16546, "olphins": 16547, "ayed": 16548, "Clinton": 16549, "enh": 16550, "\u0120Yahoo": 16551, "\u0120abst": 16552, "\u0120ank": 16553, "suits": 16554, "\u0120wished": 16555, "\u0120Marco": 16556, "udden": 16557, "\u0120sphere": 16558, "\u0120Bishop": 16559, "\u0120incorporated": 16560, "\u0120Plant": 16561, "114": 16562, "\u0120hated": 16563, "pic": 16564, "\u0120donate": 16565, "\u0120lined": 16566, "\u0120beans": 16567, "\u0120stealing": 16568, "\u0120costume": 16569, "\u0120sheriff": 16570, "\u0120forty": 16571, "\u0120intact": 16572, "\u0120adapted": 16573, "\u0120travelling": 16574, "bart": 16575, "\u0120nicely": 16576, "\u0120dried": 16577, "\u0120scal": 16578, "osity": 16579, "NOTE": 16580, "\u0120Bh": 16581, "\u0120Broncos": 16582, "\u0120Ign": 16583, "\u0120intimate": 16584, "\u0120chemistry": 16585, "\u0120optimal": 16586, "Deb": 16587, "\u0120Generation": 16588, "\u0120],": 16589, "ichi": 16590, "\u0120Wii": 16591, "\u0120YOUR": 16592, "ventions": 16593, "Write": 16594, "\u0120popul": 16595, "unning": 16596, "\u0120Wor": 16597, "Vol": 16598, "\u0120queen": 16599, "heads": 16600, "KK": 16601, "\u0120analyze": 16602, "opic": 16603, "earchers": 16604, "\u0120dot": 16605, "legraph": 16606, "astically": 16607, "\u0120upgrades": 16608, "\u0120cares": 16609, "\u0120extending": 16610, "\u0120freeze": 16611, "\u0120inability": 16612, "\u0120organs": 16613, "\u0120pretend": 16614, "\u0120outlet": 16615, "113": 16616, "olan": 16617, "\u0120Mall": 16618, "uling": 16619, "talk": 16620, "\u0120expressing": 16621, "\u0120Always": 16622, "\u0120Begin": 16623, "files": 16624, "\u0120licenses": 16625, "%%": 16626, "\u0120Mitt": 16627, "\u0120filters": 16628, "\u0120Milwaukee": 16629, "GN": 16630, "\u0120unfold": 16631, "Mo": 16632, "\u0120nutrition": 16633, "ppo": 16634, "Bo": 16635, "\u0120founding": 16636, "\u0120undermine": 16637, "\u0120easiest": 16638, "\u0120Czech": 16639, "\u0120Mack": 16640, "\u0120sexuality": 16641, "\u0120Nixon": 16642, "Win": 16643, "\u0120Arn": 16644, "\u0120Kin": 16645, "\u00e3\u0124\u00a3": 16646, "icer": 16647, "\u0120fortun": 16648, "\u0120surfaces": 16649, "aghd": 16650, "\u0120carriers": 16651, "\u0120PART": 16652, "\u0120Tib": 16653, "\u0120interval": 16654, "\u0120frustrating": 16655, "\u0120Ship": 16656, "\u0120Armed": 16657, "ffe": 16658, "\u0120boats": 16659, "\u0120Abraham": 16660, "inis": 16661, "\u0120suited": 16662, "thread": 16663, "iov": 16664, "abul": 16665, "\u0120Venezuela": 16666, "\u0120tom": 16667, "super": 16668, "\u0120castle": 16669, "although": 16670, "ioxide": 16671, "eches": 16672, "\u0120evolutionary": 16673, "\u0120negotiate": 16674, "\u0120confronted": 16675, "Remember": 16676, "\u0120170": 16677, "Such": 16678, "\u0120911": 16679, "mult": 16680, "\u0120Abyss": 16681, "urry": 16682, "kees": 16683, "spec": 16684, "\u0120Barbara": 16685, "\u0120belonging": 16686, "\u0120villain": 16687, "istani": 16688, "\u0120accountable": 16689, "\u0120portions": 16690, "\u0120Decl": 16691, "Ur": 16692, "\u0120Kate": 16693, "gre": 16694, "\u0120magazines": 16695, "UCK": 16696, "\u0120regulate": 16697, "omon": 16698, "\u0120Almost": 16699, "\u0120overview": 16700, "\u0120scram": 16701, "\u0120loot": 16702, "\u0120Fitz": 16703, "\u0120characteristic": 16704, "\u0120Snake": 16705, "say": 16706, "\u0120Rico": 16707, "\u0120trait": 16708, "\u0120Joined": 16709, "aucus": 16710, "\u0120adaptation": 16711, "\u0120Airlines": 16712, "\u0120archae": 16713, "\u0120Ide": 16714, "\u0120bikes": 16715, "\u0120literary": 16716, "\u0120influences": 16717, "\u0120Used": 16718, "Creat": 16719, "\u0120plea": 16720, "\u0120Defence": 16721, "\u0120Assass": 16722, "\u0120pond": 16723, "ULT": 16724, ")\"": 16725, "\u0120evaluated": 16726, "\u0120obtaining": 16727, "\u0120demographic": 16728, "\u0120vigil": 16729, "aley": 16730, "\u0120spouse": 16731, "\u0120Seahawks": 16732, "respons": 16733, "\u0120Belt": 16734, "umatic": 16735, "\u0120rises": 16736, "runner": 16737, "\u0120Michelle": 16738, "\u0120potent": 16739, "race": 16740, "\u0120PAC": 16741, "Find": 16742, "olesterol": 16743, "ISS": 16744, "\u0120Introduced": 16745, "resses": 16746, "ignment": 16747, "Os": 16748, "\u0120Tu": 16749, "\u0120Dex": 16750, "icides": 16751, "\u0120sparked": 16752, "\u0120Laura": 16753, "\u0120Bryant": 16754, "\u0120smiling": 16755, "\u0120Nexus": 16756, "\u0120defendants": 16757, "\u0120Catal": 16758, "\u0120dishes": 16759, "shaped": 16760, "\u0120prolong": 16761, "mt": 16762, "($": 16763, "\u00e3\u0122\u0124": 16764, "\u0120calculations": 16765, "\u0120Same": 16766, "\u0120piv": 16767, "HH": 16768, "\u0120cancelled": 16769, "\u0120grin": 16770, "\u0120territories": 16771, "istically": 16772, "Come": 16773, "\u0120Parent": 16774, "Project": 16775, "\u0120neglig": 16776, "\u0120Privacy": 16777, "\u0120ammo": 16778, "LECT": 16779, "olutely": 16780, "\u0120Epic": 16781, "\u0120misunder": 16782, "wal": 16783, "April": 16784, "mos": 16785, "pathy": 16786, "\u0120Carson": 16787, "\u0120albums": 16788, "\u0120Easy": 16789, "\u0120pistol": 16790, "<<": 16791, "\u0120\\(": 16792, "target": 16793, "help": 16794, "\u0120interpre": 16795, "conscious": 16796, "\u0120Housing": 16797, "\u0120Joint": 16798, "127": 16799, "\u0120beers": 16800, "science": 16801, "\u0120Firefox": 16802, "effective": 16803, "\u0120Cabin": 16804, "\u0120Okay": 16805, "\u0120Applic": 16806, "\u0120spacecraft": 16807, "\u0120SR": 16808, "vet": 16809, "\u0120Strange": 16810, "SB": 16811, "\u0120corps": 16812, "iberal": 16813, "efficient": 16814, "\u0120prevalence": 16815, "\u0120economists": 16816, "118": 16817, "Thread": 16818, "ordable": 16819, "ODE": 16820, "\u0120Cant": 16821, "=-=-": 16822, "ifiable": 16823, "\u0120Around": 16824, "\u0120pole": 16825, "\u0120willingness": 16826, "CLA": 16827, "\u0120Kid": 16828, "\u0120complement": 16829, "\u0120scattered": 16830, "\u0120inmates": 16831, "\u0120bleeding": 16832, "every": 16833, "\u0120queue": 16834, "\u0120Train": 16835, "\u0120hij": 16836, "\u0120melee": 16837, "pleted": 16838, "\u0120digit": 16839, "\u0120gem": 16840, "official": 16841, "\u0120lifting": 16842, "\u00d0\u00b5": 16843, "Requ": 16844, "itutes": 16845, "\u0120packaging": 16846, "\u0120Workers": 16847, "hran": 16848, "\u0120Lebanon": 16849, "olesc": 16850, "\u0120punished": 16851, "\u0120Juan": 16852, "\u0120jam": 16853, "\u0120Document": 16854, "\u0120mapping": 16855, "icates": 16856, "\u0120inevitably": 16857, "\u0120vanilla": 16858, "\u0120Ton": 16859, "\u0120watches": 16860, "\u0120leagues": 16861, "\u0120initiated": 16862, "degree": 16863, "portion": 16864, "\u0120recalls": 16865, "\u0120ruin": 16866, "\u0120melt": 16867, "IAN": 16868, "\u0120hem": 16869, "Exp": 16870, "\u0120baking": 16871, "\u0120Colomb": 16872, "atible": 16873, "\u0120radius": 16874, "plug": 16875, "\u0120IF": 16876, "etically": 16877, "\u0120fict": 16878, "HER": 16879, "\u0120Tap": 16880, "atinum": 16881, "\u0120ink": 16882, "\u0120coh": 16883, "\u0120Wizard": 16884, "both": 16885, "tex": 16886, "\u0120spends": 16887, "\u0120Currently": 16888, "\u0120Pit": 16889, "\u0120neurons": 16890, "ignt": 16891, "\u0120rall": 16892, "\u0120buses": 16893, "building": 16894, "\u0120adjustments": 16895, "\u0120cried": 16896, "iblical": 16897, "atted": 16898, "\u0120Zion": 16899, "\u0120Matter": 16900, "\u0120meditation": 16901, "\u0120Dennis": 16902, "\u0120ours": 16903, "\u0120Tab": 16904, "\u0120rankings": 16905, "ortal": 16906, "\u0120advers": 16907, "\u0120surrender": 16908, "\u0120Gob": 16909, "cium": 16910, "omas": 16911, "imeter": 16912, "\u0120multiplayer": 16913, "\u0120heroin": 16914, "\u0120optimistic": 16915, "\u0120indicator": 16916, "\u0120Brig": 16917, "\u0120grocery": 16918, "\u0120applicant": 16919, "\u0120Rocket": 16920, "vid": 16921, "Exception": 16922, "pent": 16923, "\u0120organizing": 16924, "\u0120encounters": 16925, "\u0120TOD": 16926, "\u0120jewel": 16927, "Save": 16928, "\u0120Christie": 16929, "\u0120heating": 16930, "\u0120lazy": 16931, "\u0120CP": 16932, "\u0120cousin": 16933, "Config": 16934, "\u0120regener": 16935, "\u0120nearest": 16936, "\u0120achieving": 16937, "ENS": 16938, "throw": 16939, "\u0120Richmond": 16940, "antle": 16941, "2002": 16942, "\u0120anten": 16943, "bird": 16944, "133": 16945, "\u0120narc": 16946, "raint": 16947, "unny": 16948, "\u0120Hispanic": 16949, "ournaments": 16950, "\u0120prophe": 16951, "\u0120Thailand": 16952, "\u0120Ti": 16953, "\u0120injection": 16954, "\u0120inherit": 16955, "ravis": 16956, "\u0120medi": 16957, "\u0120whoever": 16958, "\u0120DEBUG": 16959, "GP": 16960, "\u0120Hud": 16961, "Card": 16962, "prom": 16963, "\u0120por": 16964, "\u0120overhead": 16965, "Law": 16966, "\u0120violate": 16967, "\u0120heated": 16968, "\u0120descriptions": 16969, "\u0120achievements": 16970, "\u0120Beer": 16971, "\u0120Quant": 16972, "Was": 16973, "\u0120eighth": 16974, "\u0120Iv": 16975, "\u0120specialized": 16976, "UPDATE": 16977, "\u0120Delta": 16978, "Pop": 16979, "Jul": 16980, "\u0120Ask": 16981, "ophy": 16982, "\u0120newsletters": 16983, "\u0120Tool": 16984, "\u0120gard": 16985, "\u0120Confeder": 16986, "\u0120GMT": 16987, "\u0120Abbott": 16988, "\u0120immunity": 16989, "\u0120VM": 16990, "Islam": 16991, "\u0120implicit": 16992, "wd": 16993, "\u01201944": 16994, "ravity": 16995, "ometric": 16996, "\u0120surviving": 16997, "urai": 16998, "\u0120Prison": 16999, "\u0120rust": 17000, "\u0120Sketch": 17001, "\u0120bees": 17002, "\u0120Theory": 17003, "\u0120merit": 17004, "Tex": 17005, "chat": 17006, "\u0120mim": 17007, "\u0120paste": 17008, "\u0120Koch": 17009, "\u0120ignorance": 17010, "\u0120Shoot": 17011, "\u0120basement": 17012, "United": 17013, "\u0120Advis": 17014, "height": 17015, "\u0120foster": 17016, "\u0120detain": 17017, "information": 17018, "\u0120neural": 17019, "';": 17020, "\u0120proves": 17021, "allery": 17022, "\u0120invitation": 17023, "umbers": 17024, "\u0120cattle": 17025, "\u0120bicycle": 17026, "zi": 17027, "\u0120consultant": 17028, "\u0120apology": 17029, "\u0120Tiger": 17030, "\u0120123": 17031, "999": 17032, "\u0120individually": 17033, "rt": 17034, "igion": 17035, "\u0120Brazilian": 17036, "\u0120disturb": 17037, "\u0120entrepreneurs": 17038, "\u0120forests": 17039, "cerpt": 17040, "plates": 17041, "pher": 17042, "clipse": 17043, "\u0120twitter": 17044, "\u0120acids": 17045, "ographical": 17046, "hum": 17047, "\u0120Bald": 17048, "ifully": 17049, "\u0120compiler": 17050, "\u0120DA": 17051, "\u0120donor": 17052, "asi": 17053, "\u0120tribal": 17054, "lash": 17055, "\u0120Config": 17056, "\u0120applicants": 17057, "\u0120salaries": 17058, "135": 17059, "Putin": 17060, "\u0120Focus": 17061, "irs": 17062, "\u0120misconduct": 17063, "\u0120Haz": 17064, "\u0120eaten": 17065, "Mobile": 17066, "Muslim": 17067, "\u0120Marcus": 17068, "viol": 17069, "\u0120favorable": 17070, "\u0120stub": 17071, "adin": 17072, "\u0120Hob": 17073, "\u0120faithful": 17074, "\u0120electronics": 17075, "\u0120vacuum": 17076, "wait": 17077, "backed": 17078, "economic": 17079, "dist": 17080, "\u0120tenure": 17081, "\u0120sincere": 17082, "\u0120Together": 17083, "\u0120Wave": 17084, "\u0120progression": 17085, "\u0120denying": 17086, "\u0120distress": 17087, "braska": 17088, "third": 17089, "\u0120mixing": 17090, "\u0120colonial": 17091, "\u0120privately": 17092, "\u0120unrest": 17093, "aternity": 17094, "\u0120premises": 17095, "anti": 17096, "gregation": 17097, "\u0120licence": 17098, "\u0120Hind": 17099, "\u0120Samuel": 17100, "\u0120convincing": 17101, "\u0120Ace": 17102, "\u0120Rust": 17103, "\u0120Netanyahu": 17104, "\u0120handles": 17105, "\u0120Patch": 17106, "oriented": 17107, "aho": 17108, "\u0120Gonz": 17109, "\u0120hackers": 17110, "claimer": 17111, "\u0120customs": 17112, "\u0120Gran": 17113, "fighters": 17114, "\u0120luc": 17115, "\u0120manuscript": 17116, "arenthood": 17117, "\u0120devil": 17118, "\u0120warriors": 17119, "\u0120offenders": 17120, "William": 17121, "\u0120holidays": 17122, "\u0120nightmare": 17123, "\u0120lever": 17124, "ifferent": 17125, "Stat": 17126, "\u0120exhibition": 17127, "puted": 17128, "\u0120Pure": 17129, "\u0120alpha": 17130, "\u0120enthusiasm": 17131, "\u0120Representatives": 17132, "EAR": 17133, "\u0120Typ": 17134, "\u0120wheat": 17135, "\u0120Alf": 17136, "\u0120correction": 17137, "\u0120evangel": 17138, "ATT": 17139, "Miss": 17140, "\u0120soup": 17141, "\u0120implied": 17142, "param": 17143, "\u0120sexy": 17144, "\u0120Lux": 17145, "\u0120republic": 17146, "patch": 17147, "ablish": 17148, "\u0120icons": 17149, "\u0120fathers": 17150, "\u0120GET": 17151, "\u0120Carib": 17152, "\u0120regulated": 17153, "\u0120Cohen": 17154, "\u0120Bobby": 17155, "\u0120ner": 17156, "\u0120bent": 17157, "ventory": 17158, "\u0120Along": 17159, "\u0120EST": 17160, "\u0120Wallace": 17161, "\u0120murders": 17162, "rise": 17163, "kell": 17164, "\u0120Commonwealth": 17165, "\u0120nasty": 17166, "eta": 17167, "\u0120MIT": 17168, "\u0120administered": 17169, "\u0120genuinely": 17170, "Editor": 17171, "nick": 17172, "\u0120hydro": 17173, "********************************": 17174, "\u0120Ble": 17175, "\u0120fines": 17176, "\u0120gorge": 17177, "ausible": 17178, "rh": 17179, "\u0120apple": 17180, "mentioned": 17181, "\u0120rope": 17182, "otyp": 17183, "HR": 17184, "\u0120disappointing": 17185, "\u0120cage": 17186, "nik": 17187, "\u0120doubts": 17188, "\u0120FREE": 17189, "prints": 17190, "\u0120MUST": 17191, "\u0120vendors": 17192, "\u0120Inqu": 17193, "\u0120liberals": 17194, "\u0120contractor": 17195, "\u0120upside": 17196, "children": 17197, "\u0120tricky": 17198, "\u0120regulators": 17199, "charged": 17200, "liter": 17201, "\u0120***": 17202, "\u0120rebell": 17203, "lang": 17204, "\u0120locals": 17205, "\u0120physicians": 17206, "\u0120hey": 17207, "arse": 17208, "tm": 17209, "\u0120Lex": 17210, "\u0120behavioral": 17211, "successful": 17212, "FX": 17213, "\u0120brick": 17214, "ovic": 17215, "\u0120conform": 17216, "\u0120reviewing": 17217, "\u0120insights": 17218, "\u0120biology": 17219, "\u0120Remove": 17220, "\u0120Extra": 17221, "\u0120committing": 17222, "induced": 17223, "ignty": 17224, "igm": 17225, "\u0120atomic": 17226, "Common": 17227, "\u0120EM": 17228, "\u0120Pere": 17229, "\u0120Items": 17230, "eh": 17231, "\u0120preserved": 17232, "\u0120Hood": 17233, "\u0120prisoner": 17234, "\u0120bankruptcy": 17235, "\u0120gren": 17236, "ushes": 17237, "\u0120exploitation": 17238, "\u0120signatures": 17239, "\u0120finan": 17240, "],\"": 17241, "\u0120MR": 17242, "\u0120meg": 17243, "remlin": 17244, "\u0120musicians": 17245, "\u0120selecting": 17246, "\u0120examining": 17247, "INK": 17248, "lated": 17249, "Hi": 17250, "\u0120artic": 17251, "\u0120pets": 17252, "\u0120impair": 17253, "\u0120MAN": 17254, "\u0120tablets": 17255, "include": 17256, "Range": 17257, "\u0120caut": 17258, "\u0120logs": 17259, "\u0120mounting": 17260, "\u0120unaware": 17261, "\u0120dynamics": 17262, "\u0120Palestine": 17263, "\u0120Quarter": 17264, "\u0120Purple": 17265, "\u0120ma": 17266, "\u0120Import": 17267, "\u0120collections": 17268, "ciation": 17269, "\u0120successor": 17270, "\u0120clone": 17271, "\u0120aiming": 17272, "\u0120possessed": 17273, "\u0120sticking": 17274, "\u0120shaking": 17275, "\u0120locate": 17276, "\u0120Hockey": 17277, "Turn": 17278, "170": 17279, "\u0120fifteen": 17280, "\u0120Harrison": 17281, "\u0120continuously": 17282, "\u0120TC": 17283, "\u0120Valent": 17284, "\u0120Rescue": 17285, "\u0120bypass": 17286, "amount": 17287, "\u0120mast": 17288, "\u0120protects": 17289, "\u0120artistic": 17290, "\u0120sometime": 17291, "\u0120shoe": 17292, "\u0120shouted": 17293, "ificant": 17294, "etitive": 17295, "\u0120Register": 17296, "\u0120Jin": 17297, "\u0120concentrated": 17298, "lington": 17299, "onies": 17300, "\u0120generator": 17301, "yrim": 17302, "\u0120Armen": 17303, "\u0120clearing": 17304, "ido": 17305, "\u0120TW": 17306, "alph": 17307, "\u0120ladies": 17308, "Hard": 17309, "\u0120dialog": 17310, "\u0120inputs": 17311, "\u00e6\u013e": 17312, "\u0120poses": 17313, "\u0120slots": 17314, "\u0120Premium": 17315, "\u0120leaks": 17316, "\u0120bosses": 17317, "\u0120113": 17318, "course": 17319, "Acc": 17320, "\u0120Newton": 17321, "\u0120Austria": 17322, "\u0120Mage": 17323, "\u0120teaches": 17324, "abad": 17325, "\u0120wears": 17326, "\u0120cyl": 17327, "\u0120curse": 17328, "\u0120Sales": 17329, "\u0120Wings": 17330, "\u0120psy": 17331, "\u0120gaps": 17332, "\u0120Iceland": 17333, "\u0120Pinterest": 17334, "\u0120landlord": 17335, "\u0120definitions": 17336, "\u0120Ker": 17337, "\u0120sufficiently": 17338, "\u0120Pence": 17339, "\u0120Architect": 17340, "\u0120surpass": 17341, "\u0120114": 17342, "\u0120superhero": 17343, "\u0120Disease": 17344, "\u0120priests": 17345, "\u0120Culture": 17346, "\u0120definitive": 17347, "\u0120secretly": 17348, "\u0120Dance": 17349, "install": 17350, "chief": 17351, "\u0120Jessica": 17352, "Would": 17353, "Updated": 17354, "\u0120locker": 17355, "\u0120Kay": 17356, "\u0120memorial": 17357, "\u00e8\u00a6": 17358, "fat": 17359, "\u0120disgu": 17360, "\u0120flavors": 17361, "\u0120Baseball": 17362, "\u0120Resistance": 17363, "\u0120kicks": 17364, "\u0120env": 17365, "\u0120teenagers": 17366, "Dark": 17367, "\u0120CAR": 17368, "\u0120halt": 17369, "\u0120LG": 17370, "\u0120Gabriel": 17371, "\u0120fever": 17372, "\u0120satur": 17373, "\u0120mall": 17374, "\u0120affiliate": 17375, "\u0120Sleep": 17376, "\u0120Specific": 17377, "\u0120Vel": 17378, "\u0120jar": 17379, "\u0120Sacred": 17380, "\u0120Edwards": 17381, "\u0120ACL": 17382, "\u0120retained": 17383, "\u0120Giant": 17384, "\u0120limitation": 17385, "inces": 17386, "\u0120refusal": 17387, "\u0120Tale": 17388, "\u0120Butler": 17389, "\u0120accidents": 17390, "\u0120CSS": 17391, "\u0120imported": 17392, "\u0120Copy": 17393, "\u00ce\u00b1": 17394, "ERT": 17395, "zel": 17396, "\u0120divisions": 17397, "hots": 17398, "\u0120Alb": 17399, "\u0120DS": 17400, "Loader": 17401, "Washington": 17402, "atisf": 17403, "\u0120Creative": 17404, "\\.": 17405, "\u0120Autom": 17406, "redict": 17407, "\u0120receptor": 17408, "\u0120Carlos": 17409, "Method": 17410, "oka": 17411, "\u0120malicious": 17412, "\u0120stepping": 17413, ",[": 17414, "\u0120Dad": 17415, "\u0120attraction": 17416, "\u0120Effects": 17417, "\u0120Pirate": 17418, "\u0120Cer": 17419, "\u0120Industry": 17420, "\u0120Rud": 17421, "\u0120charter": 17422, "\u0120dining": 17423, "\u0120insists": 17424, "\u0120configure": 17425, "\u0120(#": 17426, "\u0120Simple": 17427, "\u0120Scroll": 17428, "UTC": 17429, "175": 17430, "\u0120Kon": 17431, "\u0120marketplace": 17432, "\u0120\u00e3\u0124": 17433, "\u0120refres": 17434, "\u0120gates": 17435, "erred": 17436, "\u0120Pod": 17437, "\u0120behave": 17438, "Frank": 17439, "node": 17440, "\u0120endorsed": 17441, "hett": 17442, "asive": 17443, "\u0120Homeland": 17444, "\u0120rides": 17445, "\u0120Leave": 17446, "erness": 17447, "\u0120flooding": 17448, "AFP": 17449, "\u0120risen": 17450, "\u0120continually": 17451, "\u0120unanim": 17452, "\u0120Contract": 17453, "\u0120Pas": 17454, "\u0120guided": 17455, "\u0120Chile": 17456, "bd": 17457, "\u0120succ": 17458, "ptic": 17459, "\u0120committees": 17460, "\u0120Luther": 17461, "\u0120Anyone": 17462, "\u0120sab": 17463, "124": 17464, "\u0120pixel": 17465, "\u0120Bak": 17466, "\u0120Tag": 17467, "\u0120Bennett": 17468, "Enter": 17469, "small": 17470, "\u0120Presidential": 17471, "\u0120pul": 17472, "\u0120contrace": 17473, "archive": 17474, "\u0120coastal": 17475, "\u0120Kids": 17476, "192": 17477, "\u00e2\u0122\u00b2": 17478, "icky": 17479, "INGTON": 17480, "\u0120wolf": 17481, "\u0120Stalin": 17482, "Tur": 17483, "idget": 17484, "amas": 17485, "\u0120Unless": 17486, "\u0120sponsor": 17487, "\u0120morph": 17488, "\u0120Choose": 17489, "\u0120runner": 17490, "\u0120unbel": 17491, "\u0120mud": 17492, "\u0120Mana": 17493, "\u0120dubbed": 17494, "\u0120godd": 17495, "urers": 17496, "window": 17497, "\u0120relied": 17498, "\u0120celebrating": 17499, "osc": 17500, "\u0120135": 17501, "\u0120lobbying": 17502, "\u0120incomplete": 17503, "\u0120restriction": 17504, "\u0120incap": 17505, "itus": 17506, "\u0120expectation": 17507, "\u0120Apollo": 17508, "\u0120intens": 17509, "\u0120sync": 17510, "GH": 17511, "\u0120manipulation": 17512, "BY": 17513, "\u0120spear": 17514, "\u0120breasts": 17515, "\u0120volcan": 17516, "ilia": 17517, "Material": 17518, "\u0120formats": 17519, "\u0120Bast": 17520, "\u0120parliamentary": 17521, "\u0120snake": 17522, "\u0120servants": 17523, "\u0120Trudeau": 17524, "\u0120Grim": 17525, "\u0120Arabic": 17526, "\u0120SCP": 17527, "\u0120Boys": 17528, "station": 17529, "\u0120prospective": 17530, "orde": 17531, "initialized": 17532, "\u0120bored": 17533, "ABLE": 17534, "\u0120accessed": 17535, "\u0120taxi": 17536, "\u0120Shell": 17537, "aiden": 17538, "ursed": 17539, "inates": 17540, "\u0120Insurance": 17541, "\u0120Pete": 17542, "September": 17543, "650": 17544, "\u0120adventures": 17545, "\u0120Cover": 17546, "\u0120tribute": 17547, "\u0120sketch": 17548, "\u0120empower": 17549, "\u0120\u00d8": 17550, "\u0120Glenn": 17551, "\u0120Daw": 17552, "=\\\"": 17553, "\u0120Politics": 17554, "\u0120guides": 17555, "\u0120dioxide": 17556, "\u0120Gore": 17557, "\u0120Bright": 17558, "\u0120Sierra": 17559, "\u0120valued": 17560, "cond": 17561, "\u0120pointer": 17562, "Select": 17563, "\u0120risky": 17564, "\u0120absorb": 17565, "images": 17566, "\u0120refuses": 17567, "\u0120bonuses": 17568, "___": 17569, "\u0120hilar": 17570, "\u0120Features": 17571, "220": 17572, "\u0120Collector": 17573, "Foot": 17574, "\u01201964": 17575, "culus": 17576, "\u0120dawn": 17577, "\u0120workout": 17578, "\u0120LO": 17579, "\u0120philosophical": 17580, "\u0120Sandy": 17581, "\u0120Youth": 17582, "\u0120liable": 17583, "Af": 17584, "blue": 17585, "\u0120overturn": 17586, "lessness": 17587, "\u0120Tribune": 17588, "\u0120Ing": 17589, "\u0120factories": 17590, "\u0120catches": 17591, "\u0120prone": 17592, "\u0120matrix": 17593, "\u0120login": 17594, "\u0120inacc": 17595, "\u0120exert": 17596, "sys": 17597, "\u0120needle": 17598, "\u0120Qur": 17599, "\u0120notified": 17600, "oulder": 17601, "tx": 17602, "\u0120reminds": 17603, "\u0120publishers": 17604, "\u0120nort": 17605, "\u0120git": 17606, "\u0120flies": 17607, "\u0120Emily": 17608, "\u0120flowing": 17609, "\u0120Alien": 17610, "\u0120Strateg": 17611, "\u0120hardest": 17612, "\u0120modification": 17613, "API": 17614, "\u0120MY": 17615, "\u0120crashes": 17616, "stairs": 17617, "number": 17618, "\u0120urging": 17619, "channel": 17620, "\u0120Falcon": 17621, "\u0120inhabitants": 17622, "\u0120terrifying": 17623, "\u0120utilize": 17624, "\u0120banner": 17625, "\u0120cigarettes": 17626, "\u0120senses": 17627, "\u0120Holmes": 17628, "\u0120practition": 17629, "\u0120Phillips": 17630, "otto": 17631, "\u0120compile": 17632, "Model": 17633, "\u0120Ko": 17634, "\u0120[]": 17635, "Americans": 17636, "\u0120Terms": 17637, "\u0120medications": 17638, "\u0120Ana": 17639, "\u0120fundamentally": 17640, "\u0120Notice": 17641, "\u0120weaker": 17642, "\u01200000": 17643, "\u0120garlic": 17644, "\u0120outbreak": 17645, "\u0120economist": 17646, "\u0120Birth": 17647, "\u0120obstacles": 17648, "arcer": 17649, "\u0120Orthodox": 17650, "\u0120placebo": 17651, "\u0120Crew": 17652, "aspberry": 17653, "\u0120Angels": 17654, "\u0120discharge": 17655, "\u0120destructive": 17656, "117": 17657, "\u0120Rising": 17658, "\u0120dairy": 17659, "late": 17660, "\u0120collision": 17661, "\u0120Tigers": 17662, "eanor": 17663, "ocumented": 17664, "\u0120Invalid": 17665, "\u0120dont": 17666, "\u0120Liter": 17667, "\u0120Va": 17668, "\u0120hydrogen": 17669, "\u0120variants": 17670, "\u0120Browns": 17671, "\u01201965": 17672, "\u0120indigenous": 17673, "\u0120trades": 17674, "\u0120remainder": 17675, "\u0120swept": 17676, "\u0120Impact": 17677, "\u0120redist": 17678, "\u0120unint": 17679, "graduate": 17680, "\u00e3\u0125\u0137": 17681, "\u0120WILL": 17682, "\u00e3\u0123\u00ae\u00e7": 17683, "\u0120Critical": 17684, "\u0120fisher": 17685, "\u0120vicious": 17686, "\u0120reversed": 17687, "Year": 17688, "\u0120Sox": 17689, "\u0120shootings": 17690, "\u0120filming": 17691, "\u0120touchdowns": 17692, "aires": 17693, "mel": 17694, "\u0120grandfather": 17695, "\u0120affection": 17696, "ingle": 17697, "\u0120overly": 17698, "Additional": 17699, "\u0120supreme": 17700, "\u0120Grad": 17701, "\u0120sporting": 17702, "\u0120mercy": 17703, "\u0120Brooks": 17704, "ounty": 17705, "\u0120performs": 17706, "\u0120tightly": 17707, "\u0120demons": 17708, "\u0120killings": 17709, "\u0120faction": 17710, "\u0120Nova": 17711, "auts": 17712, "\u0120undoubtedly": 17713, "arin": 17714, "\u0120underway": 17715, "rak": 17716, "\u0120liv": 17717, "\u0120Region": 17718, "\u0120briefing": 17719, "sers": 17720, "cloud": 17721, "\u0120Mik": 17722, "usp": 17723, "\u0120prediction": 17724, "azor": 17725, "\u0120portable": 17726, "\u0120Gand": 17727, "\u0120presenting": 17728, "\u01201080": 17729, "\u00c2\u00bb": 17730, "ushi": 17731, "\u0120Spark": 17732, "thereum": 17733, "\u0120justification": 17734, "\u0120Ny": 17735, "\u0120contractors": 17736, "mingham": 17737, "\u0120Style": 17738, "\u00e5\u0127": 17739, "\u0120Chronicles": 17740, "\u0120Picture": 17741, "\u0120proving": 17742, "\u0120wives": 17743, "sett": 17744, "\u0120molecules": 17745, "\u0120Fairy": 17746, "\u0120consisting": 17747, "\u0120pier": 17748, "alone": 17749, "inition": 17750, "\u0120nucle": 17751, "json": 17752, "\u0120gotta": 17753, "\u0120mobil": 17754, "\u0120verbal": 17755, "arium": 17756, "\u0120monument": 17757, "ucked": 17758, "\u0120256": 17759, "Tech": 17760, "minecraft": 17761, "\u0120Track": 17762, "\u0120tile": 17763, "\u0120compatibility": 17764, "asis": 17765, "\u0120sadd": 17766, "\u0120instructed": 17767, "\u0120Mueller": 17768, "\u0120lethal": 17769, "\u0120hormone": 17770, "\u0120orche": 17771, "else": 17772, "\u0120skelet": 17773, "\u0120entertaining": 17774, "\u0120minimize": 17775, "again": 17776, "\u0120undergo": 17777, "\u0120constraints": 17778, "\u0120cigarette": 17779, "\u0120Islamist": 17780, "\u0120travels": 17781, "\u0120Panthers": 17782, "lings": 17783, "Care": 17784, "\u0120lawsuits": 17785, "uras": 17786, "\u0120cryst": 17787, "\u0120lowered": 17788, "\u0120aerial": 17789, "\u0120combinations": 17790, "\u0120haun": 17791, "\u0120cha": 17792, "\u0120vine": 17793, "\u0120quantities": 17794, "\u0120linking": 17795, "bank": 17796, "\u0120soy": 17797, "Bill": 17798, "\u0120Angela": 17799, "\u0120recipient": 17800, "\u0120Protest": 17801, "\u0120socket": 17802, "\u0120solidarity": 17803, "\u0120\u00e2\u0128": 17804, "mill": 17805, "\u0120varies": 17806, "\u0120Pakistani": 17807, "Dragon": 17808, "\u0120une": 17809, "\u0120horizon": 17810, "\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142": 17811, "\u0120provinces": 17812, "\u0120frankly": 17813, "\u0120enacted": 17814, "notes": 17815, "['": 17816, "\u0120192": 17817, "ocracy": 17818, "\u0120endorsement": 17819, "\u0120overtime": 17820, "True": 17821, "Lab": 17822, "licted": 17823, "\u0120DNC": 17824, "\u0120beats": 17825, "\u0120Jamie": 17826, "152": 17827, "\u0120INT": 17828, "Contact": 17829, "\u0120accounted": 17830, "hash": 17831, "\u0120Packers": 17832, "pires": 17833, "\u0120lesbian": 17834, "\u0120amendments": 17835, "\u0120hopeful": 17836, "\u0120Finland": 17837, "\u0120spotlight": 17838, "\u0120configured": 17839, "\u0120troubled": 17840, "\u0120gaze": 17841, "\u0120Calgary": 17842, "\u0120reliability": 17843, "\u0120insurg": 17844, "swer": 17845, "buy": 17846, "\u0120Skin": 17847, "\u0120pixels": 17848, "\u0120handgun": 17849, "\u0120paras": 17850, "\u0120categor": 17851, "\u0120EL": 17852, "\u0120Rex": 17853, "Indeed": 17854, "\u0120kinda": 17855, "\u0120conjunction": 17856, "\u0120Bryan": 17857, "\u0120Manufact": 17858, "yang": 17859, "Plus": 17860, "SQL": 17861, "ishment": 17862, "\u0120dominate": 17863, "\u0120nail": 17864, "\u0120oath": 17865, "\u0120erupt": 17866, "\u0120Fine": 17867, "itbart": 17868, "\u0120Chip": 17869, "\u0120Abd": 17870, "\u0120Nam": 17871, "\u0120buyer": 17872, "\u0120dissent": 17873, "Leaks": 17874, "Contin": 17875, "\u0120rider": 17876, "\u0120Someone": 17877, "\u0120illusion": 17878, "cin": 17879, "\u0120Boeing": 17880, "\u0120inadequ": 17881, "ovation": 17882, "iants": 17883, "\u0120rebuild": 17884, "450": 17885, "\u0120Destiny": 17886, "SW": 17887, "\u0120Till": 17888, "Hit": 17889, "iaz": 17890, "\u0120Bangl": 17891, "achers": 17892, "\u0120Reform": 17893, "\u0120segments": 17894, "\u0120systematic": 17895, "dc": 17896, "\u0120Conservatives": 17897, "\u0120portal": 17898, "hor": 17899, "\u0120Dragonbound": 17900, "\u0120dragged": 17901, "omo": 17902, "\u0120thee": 17903, "advert": 17904, "\u0120Reports": 17905, "\u0120Et": 17906, "\u0120barrels": 17907, "August": 17908, "\u0120comparisons": 17909, "\u0120hex": 17910, "\u0120anthrop": 17911, "\"[": 17912, "borough": 17913, "abi": 17914, "\u0120pictured": 17915, "playing": 17916, "\u0120Address": 17917, "\u0120Mirror": 17918, "Smith": 17919, "\u0120tires": 17920, "\u0120NPR": 17921, "AAAA": 17922, "\u0120classification": 17923, "\u0120Than": 17924, "\u0120Harm": 17925, "\u0120RA": 17926, "\u0120rejection": 17927, "mination": 17928, "\u0120ranged": 17929, "\u0120Falls": 17930, "DI": 17931, "Host": 17932, "\u00e3\u0124\u00b4": 17933, "\u0120Example": 17934, "listed": 17935, "thirds": 17936, "\u0120safegu": 17937, "brand": 17938, "\u0120probable": 17939, "Canada": 17940, "ITION": 17941, "\u0120Qaeda": 17942, "\u0120chick": 17943, "\u0120imports": 17944, "hit": 17945, "loc": 17946, "WW": 17947, "\u0120blew": 17948, "\u0120anytime": 17949, "\u0120wholes": 17950, "iked": 17951, "\u0120calculation": 17952, "create": 17953, "\u0120Ori": 17954, "\u0120upgraded": 17955, "\u0120appar": 17956, "utory": 17957, "\u0120Mol": 17958, "Brit": 17959, "\u0120Jong": 17960, "INAL": 17961, "\u0120Starting": 17962, "\u0120dice": 17963, "urtle": 17964, "\u0120relying": 17965, "closure": 17966, "\u0120profitable": 17967, "\u0120slaughter": 17968, "\u0120Manual": 17969, "caster": 17970, "\u0120\"$": 17971, "\u0120feather": 17972, "\u0120Simply": 17973, "ieves": 17974, "\u0120deterior": 17975, "\u0120PCI": 17976, "\u0120stamp": 17977, "\u0120flaws": 17978, "\u0120shade": 17979, "hammer": 17980, "\u0120passport": 17981, "\u0120conting": 17982, "amel": 17983, "\u0120observers": 17984, "\u0120neglect": 17985, "\u0120RB": 17986, "\u0120Brotherhood": 17987, "\u0120skeptical": 17988, "family": 17989, "usk": 17990, "\u0120emotionally": 17991, "\u00e2\u013b": 17992, "\u0120Beta": 17993, "asonable": 17994, "idity": 17995, "\u0120Mul": 17996, "\u0120kicking": 17997, "\u0120Carm": 17998, "ollah": 17999, "VERTIS": 18000, "\u0120Athen": 18001, "\u0120ladder": 18002, "\u0120Bullet": 18003, "\u00e5\u00a3": 18004, "0001": 18005, "\u0120Wildlife": 18006, "\u0120Mask": 18007, "\u0120Nan": 18008, "Rev": 18009, "\u0120unacceptable": 18010, "legal": 18011, "\u0120crowded": 18012, "agi": 18013, "\u0120Cox": 18014, "je": 18015, "\u0120morality": 18016, "\u0120fuels": 18017, "\u0120cables": 18018, "\u0120mankind": 18019, "\u0120Caribbean": 18020, "\u0120anchor": 18021, "\u0120byte": 18022, "\u0120Often": 18023, "\u0120Oz": 18024, "\u0120crafted": 18025, "\u0120historian": 18026, "\u0120Wu": 18027, "\u0120towers": 18028, "\u0120Citizens": 18029, "\u0120helm": 18030, "\u0120credentials": 18031, "\u0120singular": 18032, "\u0120Jesse": 18033, "\u0120tackles": 18034, "\u0120contempt": 18035, "\u0120afore": 18036, "\u0120Shadows": 18037, "\u0120nil": 18038, "\u0120urgent": 18039, "apple": 18040, "blood": 18041, "\u0120von": 18042, "\u0120offline": 18043, "\u0120breathe": 18044, "\u0120jumps": 18045, "\u0120irrelevant": 18046, "oxic": 18047, "omal": 18048, "important": 18049, "Jim": 18050, "\u0120gloves": 18051, "arming": 18052, "depth": 18053, "\u0120talents": 18054, "ookie": 18055, "\u0120SB": 18056, "\u0120palm": 18057, "uffs": 18058, "esta": 18059, "IGH": 18060, "\u0120canon": 18061, "\u0120Verizon": 18062, "\u0120Ple": 18063, "\u0120coupled": 18064, "velt": 18065, "\u0120fundraising": 18066, "\u0120Getting": 18067, "\u0120DLC": 18068, "\u0120mathematical": 18069, "\u0120HS": 18070, "\u0120Cardinals": 18071, "telling": 18072, "\u0120sponsors": 18073, "\u0120\u00cf": 18074, "\u0120Bulls": 18075, "option": 18076, "\u0120propose": 18077, "\u0120memorable": 18078, "\u0120embraced": 18079, "\u0120declining": 18080, "Health": 18081, "eda": 18082, "\u0120};": 18083, "\u0120spam": 18084, "mile": 18085, "\u0120pitcher": 18086, "\u0120Eight": 18087, "\u0120caring": 18088, "utic": 18089, "role": 18090, "\u0120airline": 18091, "ernandez": 18092, "\u0120Athlet": 18093, "\u0120certification": 18094, "uxe": 18095, "riger": 18096, "\u0120empir": 18097, "\u0120sensation": 18098, "\u0120dism": 18099, "\u0120bolt": 18100, "\u0120evolve": 18101, "House": 18102, "\u0120consultation": 18103, "\u0120Duty": 18104, "\u0120touches": 18105, "\u0120Nathan": 18106, "\u0120faint": 18107, "had": 18108, "\"(": 18109, "\u0120Consumer": 18110, "\u0120Extreme": 18111, "\u0120127": 18112, "\u0120Herm": 18113, "\u0120Sacrament": 18114, "izoph": 18115, "\u0120anxious": 18116, "ulously": 18117, "\u0120socially": 18118, "\u0120UTC": 18119, "\u0120solving": 18120, "\u0120Letter": 18121, "History": 18122, "educ": 18123, "Price": 18124, "));": 18125, "\u0120reload": 18126, "amic": 18127, "\u0120pork": 18128, "\u0120discourse": 18129, "\u0120tournaments": 18130, "airo": 18131, "\u0120Kur": 18132, "\u0120Costa": 18133, "\u0120violating": 18134, "\u0120interfere": 18135, "\u0120recreational": 18136, "uffle": 18137, "\u0120speeches": 18138, "\u0120needing": 18139, "\u0120remembers": 18140, "\u0120credited": 18141, "nia": 18142, "focused": 18143, "amera": 18144, "\u0120bru": 18145, "umbs": 18146, "\u0120Cuban": 18147, "\u0120preceding": 18148, "\u0120nonsense": 18149, "acial": 18150, "\u0120smartphones": 18151, "\u0120Stories": 18152, "Sports": 18153, "\u0120Emergency": 18154, "ouncing": 18155, "efined": 18156, "\u0120ber": 18157, "\u0120consulting": 18158, "\u0120masters": 18159, "heastern": 18160, ".\"[": 18161, "\u0120Running": 18162, "\u0120suscept": 18163, "\u0120Feng": 18164, "America": 18165, "prises": 18166, "stitial": 18167, "\u0120Weekly": 18168, "\u0120Greater": 18169, "modules": 18170, "ifter": 18171, "Graphics": 18172, "uler": 18173, "\u0120wholly": 18174, "\u0120suppress": 18175, "\u0120concealed": 18176, "\u0120happily": 18177, "\u0120accepts": 18178, "\u0120Enjoy": 18179, "\u0120rivers": 18180, "\u0120Except": 18181, "225": 18182, "\u0120NHS": 18183, "\u0120McConnell": 18184, "\u0120pussy": 18185, "ferred": 18186, "utable": 18187, "\u0120attain": 18188, "\u0120>=": 18189, "\u0120deposits": 18190, "rophic": 18191, "\u0120notorious": 18192, "\u0120Shaw": 18193, "ilitation": 18194, "\u0120epidemic": 18195, "allic": 18196, "\u0120smallest": 18197, "ovich": 18198, "\u0120accessories": 18199, "perties": 18200, "\u0120surplus": 18201, "\u0120Mech": 18202, "\u0120ambig": 18203, "\u0120Immigration": 18204, "\u0120chim": 18205, "eval": 18206, "\u0120practicing": 18207, "\u0120Mystery": 18208, "\u0120domains": 18209, "\u0120Silicon": 18210, "apps": 18211, "\u0120kilometers": 18212, "ea": 18213, "\u0120Smash": 18214, "\u0120warranty": 18215, "\u0120nost": 18216, "sil": 18217, "rev": 18218, "Jon": 18219, "\u0120Dublin": 18220, "\u0120tastes": 18221, "\u0120bout": 18222, "great": 18223, "error": 18224, "\u0120switches": 18225, "\u0120Bapt": 18226, "DO": 18227, "oki": 18228, "\u0120sourced": 18229, "produ": 18230, "\u0120attachment": 18231, "\u0120Issue": 18232, "\u0120Question": 18233, "Join": 18234, "\u0120fitted": 18235, "\u0120unlawful": 18236, "^^": 18237, "erek": 18238, "\u0120authentication": 18239, "\u0120stole": 18240, "\u0120accountability": 18241, "label": 18242, "Search": 18243, "\u0120albeit": 18244, "atican": 18245, "funded": 18246, "\u0120Adding": 18247, "\u0120IQ": 18248, "\u0120submar": 18249, "lit": 18250, "aque": 18251, "\u0120Learning": 18252, "\u0120integer": 18253, "Master": 18254, "\u0120Chrom": 18255, "\u0120premier": 18256, "Op": 18257, "\u0120Liu": 18258, "\u0120blessed": 18259, "\u0120Globe": 18260, "\u0120Response": 18261, "\u0120legitim": 18262, "\u0120Merkel": 18263, "\u0120disposal": 18264, "\u00c2\u00b4": 18265, "\u0120gauge": 18266, "peat": 18267, "\u0120induced": 18268, "\u0120questionable": 18269, "arthy": 18270, "\u0120Vit": 18271, "\u0120Feed": 18272, "Until": 18273, "Ut": 18274, "worthy": 18275, "RY": 18276, "\u0120Herald": 18277, "\u0120Hammer": 18278, "\u0120medal": 18279, "\u0120Rivers": 18280, "\u0120Hack": 18281, "\u0120clarify": 18282, "\u0120tracked": 18283, "\u0120autonomous": 18284, "\u0120tenant": 18285, "\u0120Qatar": 18286, "erie": 18287, "\u0120grim": 18288, "\u0120Monitor": 18289, "\u0120resistant": 18290, "\u0120Spec": 18291, "\u0120Wells": 18292, "NAS": 18293, "148": 18294, "\u0120miners": 18295, "iotics": 18296, "\u0120misses": 18297, "116": 18298, "gian": 18299, "git": 18300, "\u0120Eyes": 18301, "pres": 18302, "\u0120graduated": 18303, "\u0120angel": 18304, "\u0120synchron": 18305, "\u0120efficiently": 18306, "\u0120transmitted": 18307, "Harry": 18308, "\u0120globally": 18309, "ENCE": 18310, "\u0120Montana": 18311, "raged": 18312, "\u0120Prevention": 18313, "\u0120piss": 18314, "\u0120Ll": 18315, "\u0120shelf": 18316, "\u0120BJP": 18317, "\u0120Testament": 18318, "\u0120Late": 18319, "iker": 18320, "\u0120Happ": 18321, "\u0120Julian": 18322, "hall": 18323, "\u0120spont": 18324, "\u0120shutdown": 18325, "\u0120inconsistent": 18326, "\u0120subscribers": 18327, "\u0120skeleton": 18328, "\u0120Nebraska": 18329, "\u0120inspire": 18330, "\u0120Void": 18331, "Feed": 18332, "\u0120angles": 18333, "\u0120Springs": 18334, "\u0120benchmark": 18335, "\u0120vaccines": 18336, "izophren": 18337, "sexual": 18338, "uffed": 18339, "\u0120shine": 18340, "\u0120Kath": 18341, "\u0120gesture": 18342, "inea": 18343, "\u0120rip": 18344, "\u0120oppression": 18345, "\u0120conscience": 18346, "bt": 18347, "\u0120Lum": 18348, "\u0120incidence": 18349, "\u0120Fa": 18350, "wr": 18351, "\u0120mineral": 18352, "\u0120Spurs": 18353, "alky": 18354, "\u0120thunder": 18355, "\u0120opio": 18356, "Being": 18357, "\u0120Palm": 18358, "\u0120wasted": 18359, "\u0120lb": 18360, "iaries": 18361, "\u0120Initiative": 18362, "\u0120curric": 18363, "\u0120marker": 18364, "\u0120McL": 18365, "\u0120extensions": 18366, "\u0120Pv": 18367, "\u0120Arms": 18368, "\u0120offerings": 18369, "\u0120defenses": 18370, "\u0120vendor": 18371, "\u0120contradict": 18372, "\u0120Colin": 18373, "\u0120reddit": 18374, "\u0120peripher": 18375, "122": 18376, "\u0120sins": 18377, "Edit": 18378, "ICT": 18379, "Soft": 18380, "\u0120Shah": 18381, "\u0120administrator": 18382, "\u0120Trip": 18383, "\u0120pornography": 18384, "\u0120tuition": 18385, "inence": 18386, "\u0120Progress": 18387, "\u0120catalog": 18388, "\u0120suite": 18389, "\u0120hike": 18390, "\u0120reproductive": 18391, "engine": 18392, "\u0120drought": 18393, "\u0120Noah": 18394, "\u0120230": 18395, "\u0120dude": 18396, "\u0120relaxed": 18397, "\u0120partition": 18398, "\u0120participant": 18399, "\u0120telesc": 18400, "\u0120feas": 18401, "\u0120FF": 18402, "owner": 18403, "\u0120sweeping": 18404, "\u0120lenses": 18405, "\u0120matchup": 18406, "\u0120Repl": 18407, "ournals": 18408, "\u0120credible": 18409, "\u0120grandmother": 18410, "\u0120thermal": 18411, "\u0120subscribing": 18412, "\u0120identities": 18413, "colm": 18414, "UCT": 18415, "\u0120reluctant": 18416, "users": 18417, "\u0120Cort": 18418, "\u0120assisted": 18419, "OSS": 18420, "ATIONS": 18421, "ISH": 18422, "\u0120pharmaceutical": 18423, "icable": 18424, "adian": 18425, "\u0120Sonic": 18426, "\u0120Fury": 18427, "\u0120Mong": 18428, "AH": 18429, "\u0120Psychology": 18430, "\u0120phosph": 18431, "\u0120treats": 18432, "\u0143\u0136": 18433, "\u0120steadily": 18434, "\u0120Hello": 18435, "\u0120relates": 18436, "\u0120clue": 18437, "Expl": 18438, "auth": 18439, "\u0120revision": 18440, "\u0120eld": 18441, "osion": 18442, "\u0120bron": 18443, "144": 18444, "rikes": 18445, "\u0120mines": 18446, "\u0120blanket": 18447, "\u0120Fail": 18448, "eled": 18449, "\u0120Imagine": 18450, "\u0120Planned": 18451, "aic": 18452, "Request": 18453, "Mad": 18454, "\u0120Horse": 18455, "\u0120Eagle": 18456, "\u0120capac": 18457, "157": 18458, "\u0120ling": 18459, "\u0120Nice": 18460, "\u0120Parenthood": 18461, "minster": 18462, "ogs": 18463, "ensitive": 18464, "Nothing": 18465, "\u0120carn": 18466, "Fin": 18467, "\u0120PE": 18468, "\u0120rifles": 18469, "\u0120LP": 18470, "Sand": 18471, "\u0120guiActive": 18472, "\u0120tourist": 18473, "CNN": 18474, "\u0120unveiled": 18475, "\u0120predecessor": 18476, "}{": 18477, "uber": 18478, "\u0120offshore": 18479, "\u0120optical": 18480, "\u0120Rot": 18481, "\u0120Pearl": 18482, "eton": 18483, "\u0120stared": 18484, "\u0120farther": 18485, "atility": 18486, "contin": 18487, "\u0120Gy": 18488, "\u0120Foster": 18489, "\u0120Coc": 18490, "rients": 18491, "\u0120designing": 18492, "\u0120Economy": 18493, "ONG": 18494, "Women": 18495, "\u0120Nancy": 18496, "erver": 18497, "\u0120mascul": 18498, "\u0120casualties": 18499, "\u0120225": 18500, "\u0120Sullivan": 18501, "\u0120Choice": 18502, "\u0120aster": 18503, "ws": 18504, "\u0120hotels": 18505, "\u0120considerations": 18506, "\u0120couch": 18507, "\u0120Strip": 18508, "\u0120Gn": 18509, "\u0120manipulate": 18510, "lied": 18511, "\u0120synthetic": 18512, "\u0120assaulted": 18513, "\u0120offenses": 18514, "\u0120Drake": 18515, "\u0120impe": 18516, "October": 18517, "\u0120Heritage": 18518, "hl": 18519, "\u0120Blair": 18520, "Unlike": 18521, "\u0120grief": 18522, "\u0120450": 18523, "\u0120opted": 18524, "\u0120resignation": 18525, "ilo": 18526, "\u0120verse": 18527, "\u0120Tomb": 18528, "\u0120upt": 18529, "\u0120aired": 18530, "\u0120Hook": 18531, "\u0120MLB": 18532, "\u0120assumes": 18533, "outed": 18534, "\u0120Vers": 18535, "\u0120inferior": 18536, "\u0120bundle": 18537, "\u0120DNS": 18538, "ographer": 18539, "\u0120multip": 18540, "\u0120Souls": 18541, "\u0120illustrated": 18542, "\u0120tactic": 18543, "\u0120dressing": 18544, "\u0120duo": 18545, "Conf": 18546, "\u0120relent": 18547, "\u0120cant": 18548, "\u0120scarce": 18549, "\u0120candy": 18550, "\u0120CF": 18551, "\u0120affiliated": 18552, "\u0120sprint": 18553, "ylan": 18554, "\u0120Garcia": 18555, "\u0120junk": 18556, "Print": 18557, "exec": 18558, "Crit": 18559, "\u0120portrait": 18560, "iries": 18561, "\u0120OFF": 18562, "\u0120disputes": 18563, "WR": 18564, "Love": 18565, "\u00e3\u0123\u0126": 18566, "\u0120Reyn": 18567, "\u0120hipp": 18568, "opath": 18569, "\u0120floors": 18570, "\u0120Feel": 18571, "\u0120worries": 18572, "\u0120settlements": 18573, "\u0120Pos": 18574, "\u0120mosque": 18575, "\u0120finals": 18576, "\u0120crushed": 18577, "\u0120Probably": 18578, "\u0120Bot": 18579, "\u0120Mans": 18580, "\u0120Period": 18581, "\u0120sovereignty": 18582, "\u0120seller": 18583, "\u0120apost": 18584, "\u0120amateur": 18585, "\u0120dorm": 18586, "\u0120consuming": 18587, "\u0120armour": 18588, "\u0120Roose": 18589, "\u0120intensive": 18590, "\u0120eliminating": 18591, "\u0120Sunni": 18592, "\u0120Aleppo": 18593, "jin": 18594, "\u0120advise": 18595, "pal": 18596, "\u0120Halo": 18597, "\u0120descent": 18598, "\u0120simpler": 18599, "\u0120booth": 18600, "STR": 18601, "Later": 18602, "\u0120Cave": 18603, "===": 18604, "\u0120mol": 18605, "\u0120fist": 18606, "\u0120shotgun": 18607, "supp": 18608, "\u0120robbery": 18609, "Effect": 18610, "\u0120obscure": 18611, "\u0120Professional": 18612, "\u0120embassy": 18613, "\u0120militant": 18614, "\u0120incarcer": 18615, "\u0120generates": 18616, "\u0120launches": 18617, "\u0120administrators": 18618, "\u0120shaft": 18619, "\u0120circular": 18620, "\u0120freshman": 18621, "\u0120Wes": 18622, "\u0120Joel": 18623, "\u0120Drew": 18624, "\u0120Duncan": 18625, "\u0120Apparently": 18626, "sight": 18627, "\u0120Internal": 18628, "\u0120Individual": 18629, "\u0120FE": 18630, "\u0120bore": 18631, "\u0120Mt": 18632, "\u0120broadly": 18633, "\u0120Options": 18634, "ountain": 18635, "ipes": 18636, "\u0120Videos": 18637, "204": 18638, "\u0120hills": 18639, "\u0120simulation": 18640, "\u0120disappointment": 18641, "itan": 18642, "\u0120Laboratory": 18643, "\u0120upward": 18644, "\u0120boundary": 18645, "\u0120darker": 18646, "hart": 18647, "\u0120dominance": 18648, "Cong": 18649, "\u0120Oracle": 18650, "\u0120Lords": 18651, "\u0120scholarship": 18652, "\u0120Vincent": 18653, "ede": 18654, "\u0120Rah": 18655, "\u0120encourages": 18656, "rov": 18657, "\u0120quo": 18658, "\u0120premise": 18659, "\u0120Crisis": 18660, "\u0120Holocaust": 18661, "\u0120rhythm": 18662, "\u0120metric": 18663, "club": 18664, "\u0120transported": 18665, "\u0120nod": 18666, "\u0120Pist": 18667, "\u0120ancestors": 18668, "\u0120Freder": 18669, "thumbnails": 18670, "\u0120CE": 18671, "OND": 18672, "Phil": 18673, "venge": 18674, "\u0120Products": 18675, "castle": 18676, "\u0120qualifying": 18677, "\u0120Karen": 18678, "VERTISEMENT": 18679, "\u0120mighty": 18680, "\u0120explanations": 18681, "\u0120fixing": 18682, "Di": 18683, "\u0120declaring": 18684, "\u0120anonymity": 18685, "\u0120juven": 18686, "\u0120Nord": 18687, "\u0120Doom": 18688, "\u0120Actually": 18689, "Ok": 18690, "phis": 18691, "\u0120Desert": 18692, "\u0120116": 18693, "IK": 18694, "\u0120FM": 18695, "\u0120incomes": 18696, "VEL": 18697, "okers": 18698, "\u0120pecul": 18699, "\u0120lightweight": 18700, "gue": 18701, "\u0120accent": 18702, "\u0120increment": 18703, "\u0120Chan": 18704, "\u0120complaining": 18705, "\u0120Baghd": 18706, "\u0120midfielder": 18707, "\u0120overhaul": 18708, "Process": 18709, "\u0120Hollow": 18710, "\u0120Titans": 18711, "Small": 18712, "manuel": 18713, "\u0120Unity": 18714, "\u0120Events": 18715, "Sty": 18716, "\u0120disproportion": 18717, "nesty": 18718, "enes": 18719, "\u0120Cod": 18720, "\u0120demonstrations": 18721, "\u0120Crimson": 18722, "\u0120OH": 18723, "\u0120enrolled": 18724, "\u0120cel": 18725, "\u0120Brett": 18726, "\u0120aide": 18727, "\u0120heels": 18728, "\u0120broadband": 18729, "\u0120marking": 18730, "\u0120wizard": 18731, "\u0120NJ": 18732, "\u0120Chiefs": 18733, "\u0120ingredient": 18734, "\u0120dug": 18735, "\u0120Shut": 18736, "urchase": 18737, "endor": 18738, "\u0120farmer": 18739, "\u0120Goldman": 18740, "129": 18741, "155": 18742, "Order": 18743, "\u0120lion": 18744, "iably": 18745, "\u0120stain": 18746, "array": 18747, "ilitary": 18748, "\u0120FAQ": 18749, "\u0120exploded": 18750, "\u0120McCarthy": 18751, "\u0120Tweet": 18752, "\u0120Greens": 18753, "eking": 18754, "ln": 18755, "ensen": 18756, "\u0120motorcycle": 18757, "\u0120particle": 18758, "\u0120cholesterol": 18759, "Bron": 18760, "\u0120stair": 18761, "\u0120oxid": 18762, "\u0120desirable": 18763, "ibles": 18764, "\u0120theor": 18765, "forcing": 18766, "\u0120promotional": 18767, "ovo": 18768, "boot": 18769, "\u0120Bonus": 18770, "rawling": 18771, "\u0120shortage": 18772, "\u0120Psy": 18773, "\u0120recruited": 18774, "\u0120infants": 18775, "\u0120testosterone": 18776, "\u0120deduct": 18777, "\u0120distinctive": 18778, "\u0120firmware": 18779, "built": 18780, "145": 18781, "\u0120explored": 18782, "\u0120factions": 18783, "\u0120vide": 18784, "\u0120tattoo": 18785, "\u0120financially": 18786, "\u0120fatigue": 18787, "\u0120proceeding": 18788, "constitutional": 18789, "\u0120miser": 18790, "\u0120chairs": 18791, "gging": 18792, "ipple": 18793, "\u0120dent": 18794, "\u0120disreg": 18795, "\u00e7\u0136": 18796, "stant": 18797, "llo": 18798, "bps": 18799, "akening": 18800, "\u0120abnormal": 18801, "\u0120ERA": 18802, "\u00e5\u00a3\u00ab": 18803, "\u0120HBO": 18804, "\u0120MAR": 18805, "\u0120concess": 18806, "\u0120servant": 18807, "\u0120aspir": 18808, "lav": 18809, "\u0120Panel": 18810, "amo": 18811, "\u0120precip": 18812, "\u0120recordings": 18813, "\u0120proceeded": 18814, "\u0120colony": 18815, "\u0120Tang": 18816, "ablo": 18817, "\u0120stripped": 18818, "Left": 18819, "too": 18820, "\u0120potatoes": 18821, "\u0120finest": 18822, "%).": 18823, "\u0120crap": 18824, "\u0120Zach": 18825, "abases": 18826, "\u0120Goth": 18827, "\u0120billionaire": 18828, "wolf": 18829, "\u0120sanction": 18830, "SK": 18831, "\u0120logged": 18832, "Po": 18833, "eyed": 18834, "unal": 18835, "\u0120cricket": 18836, "\u0120armies": 18837, "\u0120uncovered": 18838, "Cloud": 18839, "\u00c3\u00b3n": 18840, "\u0120rebounds": 18841, "\u0120mes": 18842, "Oper": 18843, "Pac": 18844, "\u0120nationally": 18845, "\u0120inserted": 18846, "pict": 18847, "\u0120governance": 18848, "\u00d0\u00b8": 18849, "\u0120privileges": 18850, "GET": 18851, "\u0120favorites": 18852, "imity": 18853, "\u0120lover": 18854, "them": 18855, "empl": 18856, "\u0120gorgeous": 18857, "Ann": 18858, "\u0120slipped": 18859, "\u0120veto": 18860, "Bob": 18861, "\u0120slim": 18862, "ucc": 18863, "\u0120Fame": 18864, "uddenly": 18865, "\u0120denies": 18866, "\u0120Maur": 18867, "\u0120distances": 18868, "\u0120wanna": 18869, "tar": 18870, "\u0120SER": 18871, "\u0120\u00e2\u012a": 18872, "\u0120lemon": 18873, "athetic": 18874, "\u0120literal": 18875, "\u0120distinguished": 18876, "\u0120answering": 18877, "GI": 18878, "\u0120religions": 18879, "\u0120Philos": 18880, "\u0120Lay": 18881, "\u0120compos": 18882, "irements": 18883, "\u0120Kos": 18884, "inez": 18885, "rolling": 18886, "\u0120youngest": 18887, "andise": 18888, "\u0120Born": 18889, "\u0120altar": 18890, "amina": 18891, "\u0120Boot": 18892, "voc": 18893, "\u0120digging": 18894, "\u0120pressures": 18895, "\u0120len": 18896, "264": 18897, "\u0120assassination": 18898, "\u0120Birmingham": 18899, "\u0120Myth": 18900, "\u0120sovereign": 18901, "\u0120Artist": 18902, "\u0120Photograph": 18903, "\u0120depicted": 18904, "\u0120dispens": 18905, "orthy": 18906, "\u0120ambul": 18907, "integ": 18908, "\u0120Cele": 18909, "\u0120Tibet": 18910, "\u0120hierarchy": 18911, "\u0120cu": 18912, "\u0120preseason": 18913, "\u0120Peterson": 18914, "\u0120colours": 18915, "\u0120worrying": 18916, "\u0120backers": 18917, "\u0120Palmer": 18918, "\u0120\u00ce\u00bc": 18919, "\u0120contributor": 18920, "\u0120hearings": 18921, "\u0120urine": 18922, "\u0120\u00d9": 18923, "ourgeois": 18924, "Similar": 18925, "\u0120Zimmer": 18926, "something": 18927, "\u0120USC": 18928, "\u0120strengths": 18929, "\u0120FI": 18930, "\u0120logging": 18931, "Asked": 18932, "\u0120Thai": 18933, "inqu": 18934, "\u0120Walt": 18935, "\u0120crews": 18936, "itism": 18937, "301": 18938, "\u0120sharply": 18939, "umed": 18940, "\u0120redirect": 18941, "rators": 18942, "Inf": 18943, "\u0120Weapons": 18944, "\u0120teasp": 18945, "1999": 18946, "Live": 18947, "\u0120Especially": 18948, "\u0120Ster": 18949, "\u0120Veterans": 18950, "\u0120intro": 18951, "otherapy": 18952, "\u0120malware": 18953, "\u0120breeding": 18954, "\u0120molecular": 18955, "\u0120Route": 18956, "\u0120Comment": 18957, "ochem": 18958, "\u0120ain": 18959, "Season": 18960, "\u0120linebacker": 18961, "\u00c4\u00ab": 18962, "\u0120Economics": 18963, "esar": 18964, "\u0120Lives": 18965, "\u0120Emma": 18966, "\u0120kin": 18967, "\u0120Territ": 18968, "\u0120planted": 18969, "oton": 18970, "\u0120Butter": 18971, "\u0120Spons": 18972, "PER": 18973, "\u0120dungeon": 18974, "\u0120symbolic": 18975, "\u0120filmed": 18976, "\u0120diets": 18977, "\u0120concludes": 18978, "\u0120certainty": 18979, "\u0120Format": 18980, "\u0120strangers": 18981, "format": 18982, "\u0120Phase": 18983, "\u0120copied": 18984, "\u0120metres": 18985, "lda": 18986, "\u0120Users": 18987, "\u0120deliberate": 18988, "\u0120washed": 18989, "\u0120Lance": 18990, "imation": 18991, "\u0120improper": 18992, "\u0120Genesis": 18993, "ickr": 18994, "\u0120Kush": 18995, "\u0120realise": 18996, "\u0120embarrassing": 18997, "alking": 18998, "bucks": 18999, "\u0120verified": 19000, "\u0120outline": 19001, "years": 19002, "\u0120Income": 19003, "202": 19004, "\u0120zombies": 19005, "Final": 19006, "\u0120Millenn": 19007, "\u0120modifications": 19008, "\u0120Vision": 19009, "\u0120Moses": 19010, "verb": 19011, "iterranean": 19012, "\u0120Jet": 19013, "\u0120naval": 19014, "\u0120Agg": 19015, "\u0120url": 19016, "\u0120victories": 19017, "\u0120nonetheless": 19018, "\u0120injust": 19019, "\u0120Fact": 19020, "\u00e7\u013c": 19021, "\u0120insufficient": 19022, "review": 19023, "facebook": 19024, "\u0120negotiating": 19025, "\u0120guarantees": 19026, "imen": 19027, "utenberg": 19028, "\u0120gambling": 19029, "\u0120congr": 19030, "Loading": 19031, "\u0120nevertheless": 19032, "\u0120presidents": 19033, "\u0120Industrial": 19034, "\u0120118": 19035, "\u0120poured": 19036, "\u0120Tory": 19037, "\u0120175": 19038, "\u0120:=": 19039, "Scott": 19040, "angered": 19041, "Tok": 19042, "\u0120organizers": 19043, "Mat": 19044, "\u0120Growth": 19045, "\u0120adul": 19046, "\u0120ensures": 19047, "\u0120117": 19048, "\u00e9\u00be\u012f\u00e5": 19049, "\u0120massacre": 19050, "\u0120grades": 19051, "before": 19052, "ADVERTISEMENT": 19053, "\u0120Slow": 19054, "\u0120MMA": 19055, "\u00e2\u0122\u0136\"": 19056, "\u0120Vatican": 19057, "Qaeda": 19058, "\u0120owe": 19059, "6666": 19060, "\u0120Sorry": 19061, "\u0120Grass": 19062, "\u0120backgrounds": 19063, "\u0120exhausted": 19064, "\u0120clan": 19065, "\u0120compromised": 19066, "\u0120Elf": 19067, "\u0120Isaac": 19068, "enson": 19069, "Invest": 19070, "IFA": 19071, "\u0120interrupted": 19072, "\u00e3\u0125\u012b\u00e3\u0125\u00a9": 19073, "\u0120twisted": 19074, "\u0120Dragons": 19075, "Mode": 19076, "\u0120Kremlin": 19077, "\u0120fertil": 19078, "heres": 19079, "phan": 19080, "\u0120Node": 19081, "fed": 19082, "\u0120Orc": 19083, "\u0120unwilling": 19084, "Cent": 19085, "\u0120priorit": 19086, "\u0120graduates": 19087, "\u0120subjective": 19088, "\u0120issuing": 19089, "\u0120Lt": 19090, "\u0120viewer": 19091, "\u0120woke": 19092, "Thus": 19093, "brook": 19094, "\u0120depressed": 19095, "\u0120bracket": 19096, "\u0120Gor": 19097, "\u0120Fighting": 19098, "\u0120striker": 19099, "Report": 19100, "\u0120Portugal": 19101, "\u0120neo": 19102, "wed": 19103, "199": 19104, "\u0120fleeing": 19105, "shadow": 19106, "identified": 19107, "USE": 19108, "Steam": 19109, "\u0120stretched": 19110, "\u0120revelations": 19111, "arted": 19112, "\u0120Dw": 19113, "\u0120alignment": 19114, "eston": 19115, "\u0120Jared": 19116, "Sep": 19117, "\u0120blogs": 19118, "update": 19119, "gom": 19120, "risk": 19121, "\u0120clash": 19122, "\u0120Hour": 19123, "\u0120runtime": 19124, "\u0120unwanted": 19125, "\u0120scam": 19126, "\u0120rack": 19127, "\u0120enlight": 19128, "onest": 19129, "\u0120Ferr": 19130, "\u0120convictions": 19131, "\u0120piano": 19132, "\u0120circulation": 19133, "\u0120Welcome": 19134, "\u0120backlash": 19135, "\u0120Wade": 19136, "\u0120receivers": 19137, "otive": 19138, "Jeff": 19139, "\u0120networking": 19140, "\u0120Prep": 19141, "\u0120Explorer": 19142, "\u0120lecture": 19143, "\u0120uploaded": 19144, "\u0120Meat": 19145, "BLE": 19146, "\u0120Nazis": 19147, "\u0120Synd": 19148, "stud": 19149, "roots": 19150, "rians": 19151, "\u0120portrayed": 19152, "\u0120??": 19153, "\u0120Buddha": 19154, "sun": 19155, "Robert": 19156, "\u0120Complex": 19157, "\u0120oversee": 19158, "\u0120stealth": 19159, "Title": 19160, "\u0120Jobs": 19161, "\u0120Kum": 19162, "\u0120appreciation": 19163, "\u0120MOD": 19164, "\u0120basics": 19165, "\u0120clips": 19166, "\u0120nursing": 19167, "\u0120proposition": 19168, "\u0120realised": 19169, "\u0120NYC": 19170, "\u0120allocated": 19171, "rium": 19172, "aran": 19173, "\u0120Production": 19174, "\u0120Vote": 19175, "\u0120smugg": 19176, "\u0120hunter": 19177, "azer": 19178, "\u0120Changes": 19179, "\u0120fluct": 19180, "yon": 19181, "Array": 19182, "\u0120kits": 19183, "Water": 19184, "\u0120uncommon": 19185, "\u0120resting": 19186, "ells": 19187, "would": 19188, "\u0120pursued": 19189, "\u0120assertion": 19190, "ometown": 19191, "\u0120Mosul": 19192, "\u0120Platform": 19193, "iolet": 19194, "\u0120shareholders": 19195, "\u0120trails": 19196, "Pay": 19197, "\u0120Enforcement": 19198, "types": 19199, "\u0120Anonymous": 19200, "\u0120satisfying": 19201, "ilogy": 19202, "\u0120('": 19203, "wave": 19204, "city": 19205, "Steve": 19206, "\u0120confrontation": 19207, "\u0120Eld": 19208, "Capt": 19209, "ahan": 19210, "htm": 19211, "\u0120Ctrl": 19212, "ONS": 19213, "230": 19214, "ifa": 19215, "holding": 19216, "\u0120delicate": 19217, "\u0120jaw": 19218, "\u0120Going": 19219, "orum": 19220, "Sal": 19221, "\u0120dull": 19222, "\u0120Beth": 19223, "\u0120prisons": 19224, "\u0120ego": 19225, "\u0120Elsa": 19226, "avorite": 19227, "\u0120Gang": 19228, "\u0120Nuclear": 19229, "\u0120spider": 19230, "atsu": 19231, "\u0120sampling": 19232, "\u0120absorbed": 19233, "\u0120Pharm": 19234, "ieth": 19235, "\u0120bucket": 19236, "\u0120Recomm": 19237, "OF": 19238, "\u0120Factory": 19239, "ANCE": 19240, "\u0120bacter": 19241, "Has": 19242, "\u0120Observ": 19243, "121": 19244, "\u0120premiere": 19245, "Develop": 19246, "\u0120currencies": 19247, "Cast": 19248, "\u0120accompanying": 19249, "\u0120Nashville": 19250, "\u0120fatty": 19251, "\u0120Brend": 19252, "\u0120locks": 19253, "\u0120centered": 19254, "\u0120UT": 19255, "aughs": 19256, "orie": 19257, "\u0120Affordable": 19258, "vance": 19259, "DL": 19260, "emet": 19261, "\u0120throne": 19262, "\u0120Bluetooth": 19263, "\u0120naming": 19264, "ifts": 19265, "ADE": 19266, "\u0120corrected": 19267, "\u0120promptly": 19268, "\u0120STR": 19269, "\u0120genome": 19270, "\u0120cope": 19271, "\u0120valley": 19272, "\u0120rounded": 19273, "\u0120Kend": 19274, "alion": 19275, "pers": 19276, "\u0120tourism": 19277, "\u0120stark": 19278, "vl": 19279, "\u0120blowing": 19280, "\u0120Schedule": 19281, "std": 19282, "\u0120unhappy": 19283, "\u0120litigation": 19284, "cedes": 19285, "\u0120android": 19286, "\u0120integral": 19287, "erers": 19288, "uded": 19289, "tax": 19290, "\u0120reiter": 19291, "\u0120Motors": 19292, "ociated": 19293, "\u0120wonders": 19294, "\u0120Apost": 19295, "ucking": 19296, "\u0120Roosevelt": 19297, "fram": 19298, "\u0120yields": 19299, "\u0120constitutes": 19300, "awk": 19301, "Interest": 19302, "\u0120interim": 19303, "\u0120breakthrough": 19304, "\u0120Cher": 19305, "\u0120prosec": 19306, "\u0120Dj": 19307, "\u0120MT": 19308, "Resp": 19309, "\u0120PT": 19310, "\u0120sperm": 19311, "edit": 19312, "BT": 19313, "Linux": 19314, "country": 19315, "league": 19316, "\u0120dick": 19317, "\u0120oct": 19318, "\u0120inserting": 19319, "\u0120scra": 19320, "\u0120Brewing": 19321, "\u01201966": 19322, "\u0120runners": 19323, "\u0120plun": 19324, "idy": 19325, "\u0120Dian": 19326, "\u0120dysfunction": 19327, "\u0120exclusion": 19328, "\u0120disgr": 19329, "\u0120incorporate": 19330, "\u0120reconc": 19331, "\u0120nominated": 19332, "\u0120Archer": 19333, "draw": 19334, "achelor": 19335, "\u0120writings": 19336, "\u0120shallow": 19337, "\u0120hast": 19338, "\u0120BMW": 19339, "\u0120RS": 19340, "\u0120thigh": 19341, "\u01201963": 19342, "\u0120lamb": 19343, "\u0120favored": 19344, "agle": 19345, "\u0120cooler": 19346, "\u0120Hours": 19347, "\u0120GU": 19348, "\u0120Origin": 19349, "\u0120glimpse": 19350, "--------------------": 19351, "Lim": 19352, "\u0120cheek": 19353, "\u0120jealous": 19354, "-'": 19355, "\u0120harness": 19356, "\u0120Poison": 19357, "\u0120disabilities": 19358, "neapolis": 19359, "\u0120outlook": 19360, "\u0120notify": 19361, "\u0120Indianapolis": 19362, "\u0120abrupt": 19363, "nsic": 19364, "\u0120encrypted": 19365, "\u0120forfe": 19366, "reath": 19367, "\u0120rabb": 19368, "\u0120foundations": 19369, "\u0120compliment": 19370, "\u0120Interview": 19371, "\u0120Swe": 19372, "\u0120adolesc": 19373, "\u0120monitors": 19374, "\u0120Sacramento": 19375, "\u0120timely": 19376, "\u0120contempl": 19377, "\u0120positioned": 19378, "\u0120posters": 19379, "phies": 19380, "iovascular": 19381, "void": 19382, "\u0120Fifth": 19383, "\u0120investigative": 19384, "OUN": 19385, "\u0120integrate": 19386, "\u0120INC": 19387, "isha": 19388, "iblings": 19389, "\u0120Request": 19390, "\u0120Rodriguez": 19391, "\u0120slides": 19392, "\u0120DX": 19393, "\u0120feminism": 19394, "\u0120datas": 19395, "\u0120bend": 19396, "irus": 19397, "\u0120Nigeria": 19398, "Fox": 19399, "Change": 19400, "\u0120airplane": 19401, "\u0120Laden": 19402, "\u0120publicity": 19403, "ixty": 19404, "\u0120commitments": 19405, "\u0120aggregate": 19406, "\u0120displaying": 19407, "\u0120Arrow": 19408, "\u0120122": 19409, "\u0120respects": 19410, "android": 19411, "six": 19412, "\u0120Sha": 19413, "\u0120restoration": 19414, ")\\": 19415, "WS": 19416, "oys": 19417, "\u0120illustrate": 19418, "without": 19419, "126": 19420, "\u0120\u00e2\u0136\u0124": 19421, "\u0120pickup": 19422, "nels": 19423, "\u0120....": 19424, "food": 19425, "\u0120Fen": 19426, ")?": 19427, "\u0120phenomena": 19428, "\u0120companions": 19429, "\u0120Write": 19430, "\u0120spill": 19431, "\u0120bridges": 19432, "\u0120Updated": 19433, "\u0120Fo": 19434, "\u0120insects": 19435, "ASHINGTON": 19436, "\u0120scare": 19437, "iltr": 19438, "\u0120Zhang": 19439, "\u0120severity": 19440, "\u0120indul": 19441, "149": 19442, "\u0120Coffee": 19443, "\u0120norms": 19444, "\u0120pulse": 19445, "\u0120FT": 19446, "\u0120horrific": 19447, "\u0120Destroy": 19448, "\u0120JSON": 19449, "\u0120olive": 19450, "\u0120discusses": 19451, "Rest": 19452, "Elect": 19453, "\u0120Winn": 19454, "\u0120Surviv": 19455, "\u0120Hait": 19456, "Sure": 19457, "oped": 19458, "\u0120rooted": 19459, "\u0120Ske": 19460, "\u0120Bronze": 19461, "\u0120lol": 19462, "Default": 19463, "\u0120commodity": 19464, "redited": 19465, "\u0120libertarian": 19466, "\u0120forbidden": 19467, "\u0120gran": 19468, "\u00e0\u00a8": 19469, "\u0120lag": 19470, "enz": 19471, "drive": 19472, "\u0120mathematics": 19473, "\u0120wires": 19474, "\u0120critically": 19475, "\u0120carbohyd": 19476, "\u0120Chancellor": 19477, "\u0120Eddie": 19478, "\u0120banning": 19479, "\u0120Fri": 19480, "\u0120complications": 19481, "etric": 19482, "\u0120Bangladesh": 19483, "\u0120bandwidth": 19484, "Stop": 19485, "\u0120Originally": 19486, "\u0120halfway": 19487, "ynasty": 19488, "shine": 19489, "\u0120tales": 19490, "rities": 19491, "avier": 19492, "\u0120spinning": 19493, "\u0120WHO": 19494, "\u0120neighbourhood": 19495, "bach": 19496, "\u0120commerce": 19497, "\u0120Sle": 19498, "BU": 19499, "\u0120entrepreneur": 19500, "\u0120peculiar": 19501, "\u0120Comments": 19502, "fre": 19503, "320": 19504, "ICS": 19505, "\u0120imagery": 19506, "\u0120Canon": 19507, "\u0120Electronic": 19508, "short": 19509, "((": 19510, "Dig": 19511, "\u0120commem": 19512, "uced": 19513, "\u0120inclined": 19514, "\u0120Summon": 19515, "\u0120cliff": 19516, "\u0120Mediterranean": 19517, "\u0120poetry": 19518, "\u0120prosperity": 19519, "\u0120Rece": 19520, "\u0120pills": 19521, "member": 19522, "\u0120finale": 19523, "unc": 19524, "\u0120Gig": 19525, "\u00e4\u00bd": 19526, "\u0120lod": 19527, "\u0120backward": 19528, "-+": 19529, "\u0120Forward": 19530, "\u0120thri": 19531, "sure": 19532, "\u0120soap": 19533, "\u0120FX": 19534, "RES": 19535, "\u0120Sexual": 19536, "oulos": 19537, "\u0120foolish": 19538, "\u0120righteous": 19539, "\u0120coff": 19540, "terrorism": 19541, "ustain": 19542, "oter": 19543, "\u0120abuses": 19544, "next": 19545, "\u0120abusive": 19546, "\u0120thereafter": 19547, "\u0120prohibition": 19548, "\u0120SUP": 19549, "\u0120dip": 19550, "\u0120ripped": 19551, "\u0120inherited": 19552, "\u0120bats": 19553, "stru": 19554, "GT": 19555, "\u0120flawed": 19556, "phabet": 19557, "\u0120fog": 19558, "doors": 19559, "\u0120imaging": 19560, "\u0120digits": 19561, "\u0120Hungary": 19562, "\u0120arrog": 19563, "\u0120teachings": 19564, "\u0120protocols": 19565, "\u0120Banks": 19566, "\u00e0\u00b8": 19567, "pound": 19568, "\u0120Curt": 19569, ".\")": 19570, "./": 19571, "\u0120exemption": 19572, "endix": 19573, "\u0120Mull": 19574, "\u0120improves": 19575, "\u0120Gamer": 19576, "dimensional": 19577, "Icon": 19578, "\u0120Margaret": 19579, "Status": 19580, "dates": 19581, "\u0120intends": 19582, "\u0120depict": 19583, "\u0120parked": 19584, "Joe": 19585, "\u0120Marines": 19586, "chnology": 19587, "!).": 19588, "\u0120judged": 19589, "\u0120weights": 19590, "Ray": 19591, "\u0120apartments": 19592, "hester": 19593, "\u0120reinforce": 19594, "\u0120offender": 19595, "occup": 19596, "\u0120sore": 19597, "ept": 19598, "\u0120PHP": 19599, "\u0120Brow": 19600, "\u0120authorization": 19601, "\u0120Risk": 19602, "\u0120Delaware": 19603, "\u0120QU": 19604, "\u0120notifications": 19605, "\u0120sunlight": 19606, "\u0120exclude": 19607, "dat": 19608, "\u0120mesh": 19609, "\u0120Sudan": 19610, "\u0120belonged": 19611, "\u0120subway": 19612, "\u0120noon": 19613, "\u0120Interior": 19614, "olics": 19615, "\u0120Lakers": 19616, "\u0120coding": 19617, "Disclaimer": 19618, "Calif": 19619, "Old": 19620, "\u0120disl": 19621, "?????": 19622, "\u0120confirms": 19623, "\u0120recruitment": 19624, "\u0120homicide": 19625, "Consider": 19626, "\u0120Jeffrey": 19627, "fty": 19628, "};": 19629, "\u0120objection": 19630, "doing": 19631, "\u0120Leo": 19632, "Want": 19633, "\u0120glow": 19634, "\u0120Clarke": 19635, "\u0120Norman": 19636, "\u0120verification": 19637, "\u0120packet": 19638, "\u0120Formula": 19639, "\u0120plag": 19640, "esville": 19641, "\u0120shouting": 19642, "\u0120ov": 19643, "\u0120REC": 19644, "\u0120Bub": 19645, "\u0120ninth": 19646, "\u0120energ": 19647, "\u0120validity": 19648, "\u0120ups": 19649, "jack": 19650, "\u0120neighboring": 19651, "\u0120Nec": 19652, "eworks": 19653, "\u0120Hab": 19654, "arez": 19655, "\u0120spine": 19656, "\u0120eventual": 19657, "\u0120Leaders": 19658, "\u0120Carn": 19659, "\u0120probation": 19660, "\u0120romance": 19661, "msg": 19662, "\u0120Mechanical": 19663, "ERY": 19664, "Rock": 19665, "\u0120partisan": 19666, "Node": 19667, "assets": 19668, "minent": 19669, "\u0120foreigners": 19670, "\u0120testify": 19671, "\u0120Usually": 19672, "lords": 19673, "\u0120Gren": 19674, "\u0120Powell": 19675, "BIL": 19676, "\u0120sr": 19677, "\u0120addict": 19678, "\u0120shells": 19679, "\u0120sigh": 19680, "\u0120Yale": 19681, "ternity": 19682, "\u0120750": 19683, "EU": 19684, "\u0120Rifle": 19685, "\u0120patron": 19686, "ema": 19687, "\u0120Bannon": 19688, "anity": 19689, "\u0120tropical": 19690, "\u0120VII": 19691, "cross": 19692, "Everything": 19693, "\u0120ISO": 19694, "\u0120humble": 19695, "assing": 19696, "\u0120FIG": 19697, "\u0120updating": 19698, "yson": 19699, "\u0120calcium": 19700, "\u0120competent": 19701, "\u0120steering": 19702, "Prot": 19703, "\u0120SY": 19704, "\u0120Finals": 19705, "\u0120Rug": 19706, "159": 19707, "137": 19708, "\u0120Golf": 19709, "\u0120126": 19710, "\u0120accommodation": 19711, "\u0120Hughes": 19712, "\u0120aesthetic": 19713, "artisan": 19714, "\u0120Twilight": 19715, "\u0120prince": 19716, "\u0120Agriculture": 19717, "\u0120Disco": 19718, "\u0120precedent": 19719, "\u0120typing": 19720, "authorized": 19721, "Option": 19722, "\u0120Aub": 19723, "lishes": 19724, "acht": 19725, "mag": 19726, "Peter": 19727, "\u0120UFO": 19728, "monton": 19729, "\u0120Lith": 19730, "\u0120arom": 19731, "\u0120securing": 19732, "\u0120confined": 19733, "private": 19734, "\u0120swords": 19735, "\u0120markers": 19736, "\u0120metabolic": 19737, "select": 19738, "\u0120Curse": 19739, "\u0120Ot": 19740, "gressive": 19741, "\u0120incumb": 19742, "\u0120Saga": 19743, "\u0120priced": 19744, "\u0120clearance": 19745, "Content": 19746, "\u0120drilling": 19747, "\u0120notices": 19748, "\u0120bourgeois": 19749, "\u0120vest": 19750, "\u0120cookie": 19751, "\u0120Guardians": 19752, "rys": 19753, "inyl": 19754, "\u0120124": 19755, "\u0120plausible": 19756, "ongh": 19757, "\u0120Odin": 19758, "\u0120conception": 19759, "\u0120Yuk": 19760, "\u0120Baghdad": 19761, "\u0120Flag": 19762, "Austral": 19763, "\u0120IBM": 19764, "\u0120internationally": 19765, "\u0120WikiLeaks": 19766, "IED": 19767, "\u0120cyn": 19768, "\u0120chooses": 19769, "\u0120Pill": 19770, "\u0120combining": 19771, "\u0120radi": 19772, "\u0120Mohammed": 19773, "defense": 19774, "atching": 19775, "Subject": 19776, "iciency": 19777, "Frame": 19778, "\u0120{\"": 19779, "\u0120chess": 19780, "\u0120timer": 19781, "190": 19782, "\u0120tin": 19783, "\u0120ordinance": 19784, "emetery": 19785, "\u0120accusing": 19786, "\u0120noticeable": 19787, "\u0120centres": 19788, "\u0120lid": 19789, "\u0120Mills": 19790, "imgur": 19791, "\u0120zoom": 19792, "ergic": 19793, "\u0120compression": 19794, "prim": 19795, "find": 19796, "\u0120surg": 19797, "\u0120pand": 19798, "\u0120Kee": 19799, "\u0120Chad": 19800, "cellence": 19801, "oyle": 19802, "\u0120socialism": 19803, "\u0120Travis": 19804, "\u0120MHz": 19805, "\u0120guild": 19806, "ALLY": 19807, "\u0120Subscribe": 19808, "\u0120Related": 19809, "\u0120occurrence": 19810, "itching": 19811, "\u0120fictional": 19812, "\u0120crush": 19813, "\u0120EA": 19814, "cod": 19815, "mix": 19816, "\u0120Triple": 19817, "\u0120retrieve": 19818, "\u0120stimulus": 19819, "\u0120psychiat": 19820, "\u0120Door": 19821, "\u0120homosexuality": 19822, "\u0120elementary": 19823, "\u0120cellular": 19824, "idian": 19825, "\u0120Laun": 19826, "\u0120intriguing": 19827, "\u0120foam": 19828, "\u0120Bass": 19829, "idi": 19830, "itsu": 19831, "\u0120assure": 19832, "\u0120congrat": 19833, "\u0120businessman": 19834, "\u0120Boost": 19835, "close": 19836, "\u0120lied": 19837, "\u0120sciences": 19838, "\u0120Omega": 19839, "\u0120Graphics": 19840, "\u0120<=": 19841, "spoken": 19842, "\u0120connectivity": 19843, "Saturday": 19844, "\u0120Avengers": 19845, "\u0120toggle": 19846, "\u0120ankle": 19847, "\u0120nationalist": 19848, "model": 19849, "\u0120Pool": 19850, "ophobia": 19851, "Var": 19852, "\u0120Mons": 19853, "atories": 19854, "\u0120aggressively": 19855, "Clear": 19856, "Forge": 19857, "acters": 19858, "\u0120hedge": 19859, "\u0120pipes": 19860, "\u0120blunt": 19861, "\u0120sq": 19862, "\u0120remotely": 19863, "Wed": 19864, "asers": 19865, "\u0120refriger": 19866, "\u0120tiles": 19867, "\u0120rescued": 19868, "\u0120comprised": 19869, "insky": 19870, "\u0120manif": 19871, "avanaugh": 19872, "\u0120prolifer": 19873, "\u0120aligned": 19874, "xml": 19875, "\u0120triv": 19876, "\u0120coordination": 19877, "\u0120PER": 19878, "\u0120Quote": 19879, "134": 19880, "bf": 19881, "\u0120Saw": 19882, "\u0120termination": 19883, "\u0120190": 19884, "\u0120additions": 19885, "\u0120trio": 19886, "\u0120projections": 19887, "\u0120positively": 19888, "\u0120inclusive": 19889, "\u0120membr": 19890, "1990": 19891, "older": 19892, "\u0120practiced": 19893, "inkle": 19894, "Arch": 19895, "\u0120starters": 19896, "arius": 19897, "\u0120intermediate": 19898, "\u0120Benef": 19899, "\u0120Killer": 19900, "\u0120interventions": 19901, "\u0120Kil": 19902, "\u0120Flying": 19903, "Inv": 19904, "\u0120premature": 19905, "\u0120psychiatric": 19906, "\u0120indie": 19907, "\u0120collar": 19908, "\u0120Rainbow": 19909, "afi": 19910, "\u0120disruption": 19911, "\u0120FOX": 19912, "casting": 19913, "\u0120misdem": 19914, "cro": 19915, "\u0120wipe": 19916, "ardon": 19917, "\u0120bast": 19918, "\u0120Tommy": 19919, "\u0120Representative": 19920, "\u0120belly": 19921, "\u0120PO": 19922, "\u0120Breitbart": 19923, "132": 19924, "\u0120messaging": 19925, "Should": 19926, "References": 19927, "\u0120GRE": 19928, "istical": 19929, "LP": 19930, "\u0120Cav": 19931, "\u0120Crazy": 19932, "\u0120intuitive": 19933, "keeping": 19934, "\u0120Moss": 19935, "\u0120discontin": 19936, "\u0120Module": 19937, "\u0120unrelated": 19938, "\u0120Practice": 19939, "\u0120Transport": 19940, "\u0120statistically": 19941, "orns": 19942, "\u0120sized": 19943, "pu": 19944, "\u0120caf": 19945, "\u0120Worlds": 19946, "\u0120Rodgers": 19947, "\u0120Lun": 19948, "\u0120Comic": 19949, "living": 19950, "\u0120cared": 19951, "\u0120climbed": 19952, "){": 19953, "\u0120consisted": 19954, "\u0120medieval": 19955, "folk": 19956, "\u0120hacked": 19957, "\u0120dire": 19958, "\u0120Hermione": 19959, "\u0120tended": 19960, "ceans": 19961, "Daniel": 19962, "went": 19963, "\u0120legislators": 19964, "\u0120redes": 19965, "games": 19966, "\u0120gn": 19967, "amiliar": 19968, "\u0120++": 19969, "ggy": 19970, "threat": 19971, "\u0120magnet": 19972, "\u0120perceive": 19973, "\u0120zip": 19974, "\u0120indictment": 19975, "\u0120critique": 19976, "gard": 19977, "\u0120Safe": 19978, "\u0120Cream": 19979, "\u0120advent": 19980, "oba": 19981, "\u0120vowed": 19982, "ousands": 19983, "\u0120ski": 19984, "\u0120abortions": 19985, "uart": 19986, "\u0120stunned": 19987, "\u0120advancing": 19988, "\u0120lacked": 19989, "\u0120\\\"": 19990, "\u0120schizophren": 19991, "\u0120elegant": 19992, "\u0120conferences": 19993, "\u0120canceled": 19994, "\u0120Hudson": 19995, "\u0120Hopefully": 19996, "\u0120trump": 19997, "\u0120frequencies": 19998, "\u0120meteor": 19999, "\u0120Junior": 20000, "\u0120Fleet": 20001, "\u0120Malcolm": 20002, "\u0120Tools": 20003, "\u0120........": 20004, "\u0120hobby": 20005, "\u0120Europeans": 20006, "\u01201500": 20007, "\u0120Into": 20008, "\u0120sway": 20009, "\u0120Appro": 20010, "\u0120Compl": 20011, "Community": 20012, "\u0120tide": 20013, "\u0120Summit": 20014, "\u00e4\u00bb": 20015, "\u0120intervals": 20016, "\u0120Ether": 20017, "\u0120habitat": 20018, "\u0120Stevens": 20019, "lishing": 20020, "\u0120Domain": 20021, "\u0120triggers": 20022, "\u0120chasing": 20023, "\u0120charm": 20024, "\u0120Flower": 20025, "itored": 20026, "\u0120blessing": 20027, "\u0120textures": 20028, "Five": 20029, "\u0120liquor": 20030, "RP": 20031, "FIN": 20032, "\u01201962": 20033, "CAR": 20034, "Unknown": 20035, "\u0120resil": 20036, "\u0120Lily": 20037, "\u0120abundance": 20038, "\u0120predictable": 20039, "rar": 20040, "\u0120bullshit": 20041, "leen": 20042, "chet": 20043, "Mor": 20044, "Much": 20045, "\u00e4\u00b9": 20046, "\u0120emphasized": 20047, "\u0120crust": 20048, "\u0120primitive": 20049, "\u0120enjoyable": 20050, "\u0120Pictures": 20051, "\u0120teammate": 20052, "pler": 20053, "\u0120Tol": 20054, "\u0120Kane": 20055, "\u0120summoned": 20056, "thy": 20057, "rama": 20058, "\u0120Honda": 20059, "\u0120realizing": 20060, "\u0120quicker": 20061, "\u0120concentrate": 20062, "clear": 20063, "\u0120210": 20064, "\u0120Erdogan": 20065, "aris": 20066, "\u0120responds": 20067, "\u0120BI": 20068, "\u0120eligibility": 20069, "\u0120pushes": 20070, "\u0120Idaho": 20071, "\u0120aggrav": 20072, "\u0120ruins": 20073, "urations": 20074, "\u0120bans": 20075, "\u0120anat": 20076, "share": 20077, "\u0120grind": 20078, "hin": 20079, "umen": 20080, "\u0120utilities": 20081, "\u0120Yankees": 20082, "\u0120databases": 20083, "\u0120DD": 20084, "\u0120displaced": 20085, "\u0120dependencies": 20086, "\u0120stimulation": 20087, "hun": 20088, "houses": 20089, "\u0120Pretty": 20090, "\u0120Ravens": 20091, "\u0120TODAY": 20092, "\u0120associates": 20093, "\u0120therape": 20094, "cled": 20095, "\u0120deer": 20096, "\u0120repairs": 20097, "rentice": 20098, "\u0120receptors": 20099, "\u0120remed": 20100, "\u0120Ce": 20101, "\u0120marriages": 20102, "\u0120ballots": 20103, "\u0120Soldier": 20104, "\u0120hilarious": 20105, "opl": 20106, "138": 20107, "\u0120inherently": 20108, "\u0120ignorant": 20109, "\u0120bounce": 20110, "\u0120Easter": 20111, "RELATED": 20112, "\u0120Currency": 20113, "EV": 20114, "\u00e3\u0125\u0140": 20115, "\u0120Lead": 20116, "\u0120deceased": 20117, "Brien": 20118, "\u0120Musk": 20119, "JS": 20120, "\u0120merge": 20121, "hearted": 20122, "creat": 20123, "mitt": 20124, "mund": 20125, "\u0120\u00e2\u0122\u012d": 20126, "\u0120Bag": 20127, "\u0120projection": 20128, "\u0120java": 20129, "\u0120Standards": 20130, "\u0120Leonard": 20131, "\u0120coconut": 20132, "\u0120Population": 20133, "\u0120traject": 20134, "\u0120imply": 20135, "\u0120curiosity": 20136, "\u0120DB": 20137, "\u0120Fresh": 20138, "\u0120Por": 20139, "\u0120heavier": 20140, "neys": 20141, "gomery": 20142, "\u0120deserved": 20143, "\u0120phrases": 20144, "\u0120GC": 20145, "\u0120yeast": 20146, "desc": 20147, "Death": 20148, "\u0120reboot": 20149, "\u0120metadata": 20150, "ICAL": 20151, "\u0120repay": 20152, "\u0120Independence": 20153, "\u0120suburban": 20154, "icals": 20155, "\u0120atop": 20156, "\u0120allocation": 20157, "generation": 20158, "\u0120Gram": 20159, "\u0120moisture": 20160, "\u0120pine": 20161, "\u0120Liberals": 20162, "\u0120aides": 20163, "\u0120underest": 20164, "\u0120Berry": 20165, "\u0120ceremon": 20166, "370": 20167, "astrous": 20168, "\u0120Pirates": 20169, "\u0120tense": 20170, "\u0120Industries": 20171, "\u0120Appeals": 20172, "\u0120Near": 20173, "\u0120\u00e8\u00a3\u0131\u00e7": 20174, "\u0120lovers": 20175, "\u0120CAP": 20176, "\u0120Craw": 20177, "\u0120giants": 20178, "\u0120efficacy": 20179, "Element": 20180, "\u0120Behavior": 20181, "\u0120Toyota": 20182, "\u0120intest": 20183, "Priv": 20184, "AI": 20185, "\u0120maneuver": 20186, "\u0120perfection": 20187, "\u0120bang": 20188, "paper": 20189, "rill": 20190, "George": 20191, "border": 20192, "inters": 20193, "\u0120Seth": 20194, "\u0120clues": 20195, "\u0120Levi": 20196, "\u0120Revenue": 20197, "147": 20198, "\u0120vapor": 20199, "\u0120fortunate": 20200, "\u0120threatens": 20201, "\u0120vet": 20202, "\u0120dependency": 20203, "ersed": 20204, "article": 20205, "\u0120Blizzard": 20206, "\u0120chlor": 20207, "\u0120minus": 20208, "\u0120Bills": 20209, "\u0120cryptocurrency": 20210, "\u0120metabolism": 20211, "tering": 20212, "\u0120pestic": 20213, "steps": 20214, "\u0120Treasure": 20215, "racted": 20216, "\u0120Constant": 20217, "\u0120temp": 20218, "139": 20219, "\u0120Detective": 20220, "urally": 20221, "\u0120recovering": 20222, "\u0120cortex": 20223, "\u0120144": 20224, "closed": 20225, "\u0120prejudice": 20226, "aunted": 20227, "\u0120storms": 20228, "\u0120NOW": 20229, "\u0120machinery": 20230, "Address": 20231, "\u0120compelled": 20232, "270": 20233, "\u0120despair": 20234, "bane": 20235, "\u0120vegetable": 20236, "\u0120beds": 20237, "Learn": 20238, "\u0120colorful": 20239, "\u0120spike": 20240, "\u0120margins": 20241, "\u0120sympathy": 20242, "\u0120workshop": 20243, "\u0120CBC": 20244, "Sat": 20245, "\u0120burns": 20246, "\u0120Gender": 20247, "\u0120129": 20248, "\u0120Cable": 20249, "\u0120debts": 20250, "\u0120Theresa": 20251, "\u0120reflecting": 20252, "\u0120airst": 20253, "\u0120rim": 20254, "ramid": 20255, "\u0120weaknesses": 20256, "Writ": 20257, "oggle": 20258, "ti": 20259, "\u0120Charge": 20260, "\u0120weighed": 20261, "\u0120(.": 20262, "\u0120laughter": 20263, "\u0120router": 20264, "\u0120Democracy": 20265, "Dear": 20266, "\u0120hasht": 20267, "\u0120dy": 20268, "\u0120hints": 20269, "running": 20270, "\u0120finishes": 20271, "arus": 20272, "Mass": 20273, "result": 20274, "ascus": 20275, "\u0120vintage": 20276, "\u0120conqu": 20277, "\u0120wildly": 20278, "acist": 20279, "\u0120lingu": 20280, "\u0120protagonist": 20281, "strom": 20282, "teenth": 20283, "\u0120Solo": 20284, "mac": 20285, "filled": 20286, "\u0120renown": 20287, "itives": 20288, "\u0120motive": 20289, "\u0120Antar": 20290, "\u0120Mann": 20291, "\u0120Adjust": 20292, "\u0120rockets": 20293, "\u0120troubling": 20294, "ei": 20295, "\u0120organisms": 20296, "assis": 20297, "Christian": 20298, "\u0120145": 20299, "\u0120Hass": 20300, "\u0120swall": 20301, "\u0120wax": 20302, "\u0120Survival": 20303, "VS": 20304, "\u0120Murd": 20305, "vd": 20306, "standard": 20307, "\u0120dragons": 20308, "\u0120acceleration": 20309, "rational": 20310, "final": 20311, "\u0120paired": 20312, "\u0120Ethereum": 20313, "\u0120interfaces": 20314, "\u0120resent": 20315, "\u0120artifacts": 20316, "\u00c5\u00ab": 20317, "arel": 20318, "\u0120competitor": 20319, "\u0120Nicholas": 20320, "\u0120Surface": 20321, "cpp": 20322, "\u0120Tot": 20323, "\u0120economically": 20324, "\u0120organised": 20325, "\u0120enforced": 20326, "inho": 20327, "\u0120varieties": 20328, "\u0120abdom": 20329, "\u0120Bailey": 20330, "idav": 20331, "\u0120Salv": 20332, "paid": 20333, "\u0120altitude": 20334, "essert": 20335, "\u0120Gutenberg": 20336, "area": 20337, "opoulos": 20338, "\u0120professors": 20339, "iggs": 20340, "\u0120Fate": 20341, "hey": 20342, "\u01203000": 20343, "Dist": 20344, "\u0120twins": 20345, "cill": 20346, "\u0120Maps": 20347, "\u0120traps": 20348, "\u0120weed": 20349, "\u0120Kiss": 20350, "\u0120yoga": 20351, "\u0120recipients": 20352, "\u0120Westminster": 20353, "\u0120pools": 20354, "\u0120Walmart": 20355, "188": 20356, "\u0120Schools": 20357, "attack": 20358, "\u0120ARM": 20359, "paragraph": 20360, "Warning": 20361, "jl": 20362, "\u0120selfish": 20363, "anchez": 20364, "\u0120Heights": 20365, "Fre": 20366, "\u0120Soph": 20367, "\u0120--------------------------------": 20368, "tml": 20369, "333": 20370, "\u0120raids": 20371, "\u0120satellites": 20372, "KEY": 20373, "\u0120lasts": 20374, "\u00d1\u0124": 20375, "Ins": 20376, "\u0120Dame": 20377, "\u0120unpredict": 20378, "///": 20379, "ghai": 20380, "\u0120artillery": 20381, "\u0120cruise": 20382, "\u0120gel": 20383, "\u0120Cabinet": 20384, "\u0120blows": 20385, "\u0120Esp": 20386, "\u0120proximity": 20387, "othe": 20388, "\u0120Skills": 20389, "\u0120Upper": 20390, "obo": 20391, "\u0120NDP": 20392, "\u0120enjoys": 20393, "\u0120repeating": 20394, "\u0120Construction": 20395, "\u0120Questions": 20396, "Hillary": 20397, "\u0120uint": 20398, "\u0120processors": 20399, "\u0120Gibson": 20400, "\u0120Multiple": 20401, "qa": 20402, "\u0120Bom": 20403, "\u0120Miles": 20404, "ventional": 20405, "\u0120hurts": 20406, "skin": 20407, "\u0120AIDS": 20408, "\u0120advisers": 20409, "\u0120Root": 20410, "\u0120methodology": 20411, "\u0120Dale": 20412, "\u0120deton": 20413, "\u0120Knowledge": 20414, "sequently": 20415, "\u0120121": 20416, "\u0120connects": 20417, "Cy": 20418, "\u0120Danger": 20419, "\u0120contributors": 20420, "\u0120Bent": 20421, "\u0120brass": 20422, "\u0120Guns": 20423, "into": 20424, "\u0120Fortune": 20425, "\u0120broker": 20426, "balance": 20427, "\u0120lengths": 20428, "\u0120vic": 20429, "\u0120averaging": 20430, "\u0120appropriately": 20431, "\u0120Camera": 20432, "\u0120sandwich": 20433, "\u0120CDC": 20434, "\u0120coordinate": 20435, "\u0120navig": 20436, "\u0120goodness": 20437, "laim": 20438, "\u0120brake": 20439, "\u0120extremist": 20440, "\u0120Wake": 20441, "\u0120Mend": 20442, "\u0120Tiny": 20443, "\u0120COL": 20444, "\u0120RF": 20445, "\u0120Dual": 20446, "\u0120Wine": 20447, "Case": 20448, "\u0120refined": 20449, "\u0120lamp": 20450, "Lead": 20451, "\u0120bapt": 20452, "\u0120Carb": 20453, "\u0120Sadd": 20454, "\u0120Minneapolis": 20455, "PDF": 20456, "Early": 20457, "\u0120Hidden": 20458, "Its": 20459, "\u0120TIME": 20460, "\u0120pap": 20461, "\u0120commissioned": 20462, "\u0120Few": 20463, "\u0120Colts": 20464, "\u0120Bren": 20465, "\u0120bothered": 20466, "\u0120likewise": 20467, "Exper": 20468, "\u0120Schw": 20469, "cry": 20470, "nn": 20471, "\u0120Mitch": 20472, "imon": 20473, "MG": 20474, "bm": 20475, "UMP": 20476, "rays": 20477, "\u0120registry": 20478, "\u0120270": 20479, "achine": 20480, "rella": 20481, "anting": 20482, "00000": 20483, "\u0120ruined": 20484, "spot": 20485, "\u0120ta": 20486, "\u0120maximize": 20487, "\u0120inconven": 20488, "Dead": 20489, "Human": 20490, "Enabled": 20491, "\u0120Marie": 20492, "\u0120chill": 20493, "\u0120Paradise": 20494, "\u0120starring": 20495, "\u0120Latino": 20496, "\u0120Protocol": 20497, "\u0120EVER": 20498, "\u0120suppliers": 20499, "message": 20500, "\u0120Brock": 20501, "\u0120serum": 20502, "\u00e2\u0138\u012a\u00e2\u0138\u012a\u00e2\u0138\u012a\u00e2\u0138\u012a": 20503, "\u0120encomp": 20504, "\u0120ambition": 20505, "uese": 20506, "\u0120arrows": 20507, "Andrew": 20508, "\u0120antenna": 20509, "\u01201961": 20510, "\u0120Bark": 20511, "\u0120bool": 20512, "\u00e3\u0124\u00aa": 20513, "\u0120Storage": 20514, "\u0120railway": 20515, "\u0120tougher": 20516, "\u0120Cad": 20517, "\u0120washing": 20518, "Py": 20519, "']": 20520, "embed": 20521, "\u0120Memphis": 20522, "ackle": 20523, "\u0120famously": 20524, "\u0120Fortunately": 20525, "ovies": 20526, "\u0120mindset": 20527, "\u0120sneak": 20528, "\u0120Dh": 20529, "RAW": 20530, "\u0120Simpson": 20531, "\u0120livest": 20532, "\u0120landmark": 20533, "\u0120cement": 20534, "Low": 20535, "\u0120thrilled": 20536, "\u0120Course": 20537, "inel": 20538, "\u0120chuck": 20539, "idate": 20540, "global": 20541, "\u0120whit": 20542, "\u0120\u00ef\u00bf\u00bd": 20543, "adays": 20544, "ski": 20545, "\u0120SV": 20546, "\u0120viruses": 20547, "306": 20548, "\u0120Respons": 20549, "\u0120theaters": 20550, "\u0120Branch": 20551, "\u0120Geneva": 20552, "\u0120MK": 20553, "\u0120unbeliev": 20554, "\u0120communist": 20555, "Original": 20556, "\u0120Received": 20557, "\u0120Transfer": 20558, "\u0120Arg": 20559, "Input": 20560, "\u0120Strategy": 20561, "\u0120palace": 20562, "thening": 20563, "Dri": 20564, "\u0120sentencing": 20565, "umbnail": 20566, "\u0120pins": 20567, "recy": 20568, "\u0120siblings": 20569, "Getting": 20570, "\u0120BU": 20571, "\u0120Northwest": 20572, "\u0120prolonged": 20573, "\u0120Sakura": 20574, "Comb": 20575, "\u0120Bour": 20576, "\u0120inadequate": 20577, "\u0120Kash": 20578, "\u0120username": 20579, "\u0120Improve": 20580, "\u0120battling": 20581, "\u0120MAC": 20582, "\u0120curriculum": 20583, "\u0120soda": 20584, "\u0120Cannon": 20585, "\u0120sensible": 20586, "spons": 20587, "December": 20588, "\u0120wicked": 20589, "\u0120Pengu": 20590, "\u0120dictators": 20591, "\u0120Hearts": 20592, "ogyn": 20593, "\u0120similarities": 20594, "\u0120Stats": 20595, "\u0120hollow": 20596, "itations": 20597, "\":[": 20598, "\u0120hover": 20599, "\u0120Listen": 20600, "sch": 20601, "Sund": 20602, "\u0120cad": 20603, "\u0120Parks": 20604, "\u0120lur": 20605, "\u0120hype": 20606, "\u0120Lem": 20607, "NAME": 20608, "isure": 20609, "Friday": 20610, "\u0120shoots": 20611, "\u0120closes": 20612, "\u0120db": 20613, "\u0120Ridge": 20614, "\u0120Different": 20615, "\u0120replies": 20616, "\u0120Broadway": 20617, "opers": 20618, "\u0120intoler": 20619, "\u0120Zeus": 20620, "akespe": 20621, "\u0120proprietary": 20622, "\u0120requesting": 20623, "\u0120controllers": 20624, "\u0120MIN": 20625, "imedia": 20626, "becca": 20627, "\u0120expans": 20628, "\u0120oils": 20629, "Bot": 20630, "\u0120Chand": 20631, "\u0120printer": 20632, "\u0120topped": 20633, "\u0120POL": 20634, "\u0120Earlier": 20635, "Social": 20636, "avin": 20637, "\u0120decreases": 20638, "\u0120Seb": 20639, "\u0120specifications": 20640, "\u0120Blast": 20641, "\u0120Kurt": 20642, "\u0120freel": 20643, "Brown": 20644, "\u0120dilig": 20645, "roe": 20646, "\u0120Problem": 20647, "\u0120Quad": 20648, "\u0120decentral": 20649, "\u0120Vector": 20650, "anut": 20651, "\u0120plugins": 20652, "\u0120Gregory": 20653, "\u0120fucked": 20654, "elines": 20655, "\u0120Ambassador": 20656, "take": 20657, "\u0120cleans": 20658, "ongyang": 20659, "Anonymous": 20660, "stro": 20661, "\"}": 20662, "aline": 20663, "\u0120Odd": 20664, "\u0120Eug": 20665, "216": 20666, "\u0120boil": 20667, "\u0120Powers": 20668, "\u0120nurses": 20669, "Obviously": 20670, "\u0120Technical": 20671, "\u0120exceeded": 20672, "ORS": 20673, "\u0120extremists": 20674, "\u0120traces": 20675, "expl": 20676, "\u0120comr": 20677, "\u0120Sach": 20678, ")/": 20679, "\u0120masks": 20680, "\u0120sci": 20681, "Bon": 20682, "\u0120regression": 20683, "wegian": 20684, "\u0120advisor": 20685, "itures": 20686, "\u0120Vo": 20687, "example": 20688, "\u0120Instruct": 20689, "\u0120siege": 20690, "\u0120reductions": 20691, "ptr": 20692, "\u0120statutory": 20693, "\u0120removes": 20694, "\u0120puck": 20695, "redits": 20696, "\u0120bee": 20697, "\u0120salad": 20698, "\u0120promotions": 20699, "\u0120Joshua": 20700, "withstanding": 20701, "ETH": 20702, "\u0120Cha": 20703, "imus": 20704, "\u0120expenditure": 20705, "aunting": 20706, "\u0120delighted": 20707, "\u0120155": 20708, "beh": 20709, "\u0120carpet": 20710, "\u0120Spart": 20711, "\u0120jungle": 20712, "lists": 20713, "\u0120bullying": 20714, "\u0120Nobel": 20715, "\u0120Glen": 20716, "\u0120referenced": 20717, "\u0120introduces": 20718, "sein": 20719, "\u0120chopped": 20720, "glass": 20721, "\u0120Wrest": 20722, "\u0120neutrality": 20723, "\u0120\u00e2\u013b": 20724, "\u0120investigator": 20725, "\u0120shelves": 20726, "\u0120unconstitutional": 20727, "\u0120reproduction": 20728, "\u0120merchant": 20729, "mia": 20730, "\u0120metrics": 20731, "\u0120explosives": 20732, "\u0120Sonia": 20733, "\u0120bodily": 20734, "\u0120thickness": 20735, "\u0120predominantly": 20736, "\u0120Ability": 20737, "\u0120monitored": 20738, "ICH": 20739, "\u0120].": 20740, "\u0120Martinez": 20741, "\u0120visibility": 20742, "\u0120queries": 20743, "\u0120genocide": 20744, "\u0120Warfare": 20745, "Query": 20746, "\u0120studios": 20747, "\u0120embry": 20748, "\u0120corridor": 20749, "\u0120cleaned": 20750, "complete": 20751, "\u0120MH": 20752, "\u0120enrollment": 20753, "INGS": 20754, "\u0120impacted": 20755, "\u0120disastrous": 20756, "\u0120Yun": 20757, "\u0120Claire": 20758, "\u0120Basically": 20759, "yt": 20760, "usterity": 20761, "\u0120indirectly": 20762, "wik": 20763, "\u0120dod": 20764, "\u0120Carr": 20765, "\u0120amp": 20766, "\u0120prohibit": 20767, "\u0120Initial": 20768, "\u0120Rd": 20769, "iji": 20770, "\u0120educate": 20771, "corn": 20772, "iott": 20773, "\u0120Beauty": 20774, "\u0120detective": 20775, "\u0120Conn": 20776, "since": 20777, "\u0120stagger": 20778, "\u0120obese": 20779, "\u0120bree": 20780, "ologic": 20781, "isse": 20782, "walker": 20783, "\u0120blades": 20784, "\u0120lawful": 20785, "func": 20786, "\u0120Behind": 20787, "\u0120appetite": 20788, "\u0120(*": 20789, "\u0120tennis": 20790, "\u0120offspring": 20791, "\u0120jets": 20792, "\u0120structured": 20793, "\u0120aforementioned": 20794, "Nov": 20795, "\u0120scaling": 20796, "fill": 20797, "\u0120stew": 20798, "\u0120curb": 20799, "\u0120Stephan": 20800, "edIn": 20801, "SF": 20802, "obic": 20803, "\u00e9\u0143\u0136": 20804, "oug": 20805, "\u0120MM": 20806, "\u0120genetically": 20807, "opez": 20808, "136": 20809, "\u0120umb": 20810, "ancers": 20811, "\u0120cohort": 20812, "\u0120merchandise": 20813, "\u0120imposing": 20814, "\u0120Legislature": 20815, "\u0120Archive": 20816, "ivia": 20817, "\u0120Naval": 20818, "\u0120offences": 20819, "\u0120miracle": 20820, "\u0120snapped": 20821, "\u0120foes": 20822, "\u0120extensively": 20823, "\u0120Raf": 20824, "\u0120cater": 20825, "edience": 20826, "Kit": 20827, "\u0120Bin": 20828, "\u0120recommends": 20829, "\u0120Cities": 20830, "\u0120rigid": 20831, "\u0120READ": 20832, "\u0120Noble": 20833, "\u0120Tian": 20834, "\u0120certificates": 20835, "antis": 20836, "oiler": 20837, "\u0120Buddhist": 20838, "did": 20839, "\u0120surveyed": 20840, "\u0120downward": 20841, "\u0120prints": 20842, "\u0120Motion": 20843, "ronics": 20844, "\u0120Sans": 20845, "ossibly": 20846, "uctions": 20847, "\u0120colonies": 20848, "\u0120Danish": 20849, "unit": 20850, "\u0120spoil": 20851, "\u0120advisory": 20852, "berries": 20853, "Plan": 20854, "\u0120specification": 20855, "ophers": 20856, "\u0120Resource": 20857, "\u0120shirts": 20858, "prisingly": 20859, "communications": 20860, "\u0120trivial": 20861, "\u0120mentioning": 20862, "isexual": 20863, "\u0120supplements": 20864, "\u0120supervision": 20865, "BP": 20866, "vor": 20867, "\u0120wit": 20868, "\u0120cooldown": 20869, "\u0120plaintiff": 20870, "\u0120Reviews": 20871, "\u0120Sri": 20872, "\u0120Mint": 20873, "\u0120Sugar": 20874, "\u0120afterward": 20875, "\u0120Priest": 20876, "\u0120Investment": 20877, "ogene": 20878, "\u0120Taking": 20879, "\u0120stretching": 20880, "\u0120inflammation": 20881, "\u0120Tehran": 20882, "\u0120lining": 20883, "\u0120freezing": 20884, "\u0120Entity": 20885, "\u0120inspiring": 20886, "special": 20887, "price": 20888, "\u0120sue": 20889, "\u0120Porter": 20890, "ounge": 20891, "ETA": 20892, "\u0120Derek": 20893, "\u0120Luis": 20894, "uo": 20895, "ymph": 20896, "\u0120exterior": 20897, "ihil": 20898, "\u0120Ashley": 20899, "inator": 20900, "\u0120nutrients": 20901, "\u0120Thrones": 20902, "\u0120finances": 20903, "\u0120Inspect": 20904, "\u0120specially": 20905, "\u0120Required": 20906, "\u0120PTS": 20907, "\u0120Violence": 20908, "ointed": 20909, "shots": 20910, "\u0120excerpt": 20911, "coon": 20912, "INS": 20913, "\u0120Gri": 20914, "\u0120recognised": 20915, "Week": 20916, "Young": 20917, "\u0120vom": 20918, "isle": 20919, "\u0120Curry": 20920, "\u0120Buddh": 20921, "\u0120notebook": 20922, "\u0120durable": 20923, "/?": 20924, "\u0120Gad": 20925, "\u0120Pupp": 20926, "\u0120forgive": 20927, "park": 20928, "\u0120personalities": 20929, "analysis": 20930, "clamation": 20931, "\u0120elevator": 20932, "\u0120warehouse": 20933, "\u0120Role": 20934, "unn": 20935, "\u0120illustration": 20936, "\u0120Scan": 20937, "\u0120atmospheric": 20938, "Import": 20939, "ANC": 20940, "ricted": 20941, "fu": 20942, "010": 20943, "\u0120arche": 20944, "\u0120rewarded": 20945, "akespeare": 20946, "\u0120internally": 20947, "\u0120RBI": 20948, "alker": 20949, "\u0120elephant": 20950, "owitz": 20951, "\u0120Pizza": 20952, "\u0120bipartisan": 20953, "\u00c3\u00a9s": 20954, "\u0120slowed": 20955, "\u0120Stark": 20956, "\u0120override": 20957, "OUS": 20958, "\u0120320": 20959, "undreds": 20960, "\u0120Deck": 20961, "\u0120Census": 20962, "bee": 20963, "146": 20964, "otor": 20965, "\u0120ip": 20966, "\u0120ub": 20967, "ocations": 20968, "\u0120Button": 20969, "rice": 20970, "\u0120cripp": 20971, "fff": 20972, "\u0120originated": 20973, "\u0120overwhelmed": 20974, "appa": 20975, "\u0120foremost": 20976, "\u00e2\u0122\u0133": 20977, "\u0120LEG": 20978, "release": 20979, "eatured": 20980, "atches": 20981, "\u0120reps": 20982, "\u0120lending": 20983, "\u0120Reference": 20984, "\u0120Client": 20985, "165": 20986, "venth": 20987, "Complete": 20988, "\u0120Patrol": 20989, "\u0120sworn": 20990, "cam": 20991, "\u0120shuttle": 20992, "\u0120Ralph": 20993, "\u0120hometown": 20994, "-,": 20995, "onal": 20996, "\u0120BP": 20997, "\u00e5\u0131": 20998, "\u0120persuade": 20999, "\u0120Alexand": 21000, "\u0120combines": 21001, "\u0120vivid": 21002, "\u0120Lag": 21003, "\u0120encoding": 21004, "\u0120salvation": 21005, "wen": 21006, "\u0120Recovery": 21007, "iya": 21008, "University": 21009, "\u0120Biden": 21010, "\u0120budgets": 21011, "\u0120Texans": 21012, "fits": 21013, "\u0120honored": 21014, "\u0120python": 21015, "TD": 21016, "###": 21017, "clone": 21018, "\u0120blink": 21019, "\u0120Liquid": 21020, "\u0120unemployed": 21021, "\u0120clashes": 21022, "\u0120Counsel": 21023, "\u0120directing": 21024, "\u0120punct": 21025, "\u0120Falcons": 21026, "\u0120shark": 21027, "\u0120Damascus": 21028, "\u0120jeans": 21029, "\u0120embark": 21030, "\u0120seize": 21031, "\u0120upwards": 21032, "280": 21033, "\u0120Ez": 21034, "\u0120Anything": 21035, "\u0120exotic": 21036, "lower": 21037, "\u0120Creator": 21038, "\u0120Um": 21039, "\u0120suburbs": 21040, "berger": 21041, "\u0120Wend": 21042, "\u0120mint": 21043, "\u0120XX": 21044, "\u0120Dro": 21045, "\u0120suffers": 21046, "\u0120herb": 21047, "tree": 21048, "\u0120fragile": 21049, "\u0120flooded": 21050, "\u0120Alcohol": 21051, "olean": 21052, "nyder": 21053, "\u0120KO": 21054, "Fram": 21055, "\u0120136": 21056, "\u0120owed": 21057, "\u0120Melee": 21058, "\u0120Hash": 21059, "\u0120whisk": 21060, "\u0120sudo": 21061, "rr": 21062, "Quick": 21063, "appro": 21064, "\u0120ii": 21065, "\u0120Examples": 21066, "hee": 21067, "\u0120promotes": 21068, "perature": 21069, "kar": 21070, "\u0120Honor": 21071, "\u0120sodium": 21072, "\u0120Lif": 21073, "rosso": 21074, "intendent": 21075, "\u0120correspondent": 21076, "Found": 21077, "secret": 21078, "\u0120identifies": 21079, "agne": 21080, "\u0120lou": 21081, "\u0120PP": 21082, "\u0120coincidence": 21083, "move": 21084, "\u0120militia": 21085, "\u0120infiltr": 21086, "\u0120Primary": 21087, "\u0120pitching": 21088, "\u0120Ib": 21089, "\u0120GOOD": 21090, "\u00e3\u0124\u00b8": 21091, "\u0120Wizards": 21092, "iral": 21093, "\u0120Venus": 21094, "RR": 21095, "\u0120\u00e2\u0122\u0137": 21096, "\u0120Casey": 21097, "\u0120sadly": 21098, "\u0120admire": 21099, "\u0120embarrassed": 21100, "cb": 21101, "Mel": 21102, "\u0120tubes": 21103, "\u0120beautifully": 21104, "\u0120Queensland": 21105, "Below": 21106, "rez": 21107, "quet": 21108, "pleasant": 21109, "\u0120\u00c2\u00ab": 21110, "Camp": 21111, "\u0120decisive": 21112, "1998": 21113, "\u0120Lamb": 21114, "utton": 21115, "hn": 21116, "\u0120Jagu": 21117, "aunder": 21118, "\u0120Cord": 21119, "\u0120clerk": 21120, "\u0120caffe": 21121, "\u0120wiped": 21122, "\u0120reim": 21123, "\u0120Mountains": 21124, "\u0120imprisoned": 21125, "\u0120develops": 21126, "\u0120Pra": 21127, "\u0120modeling": 21128, "Anyone": 21129, "ancel": 21130, "\u0120Sit": 21131, "\u0120shields": 21132, "\u0120lawn": 21133, "\u0120cardiovascular": 21134, "\u0120demonstrating": 21135, "\u0120parse": 21136, "\u0120Israelis": 21137, "\u0120euros": 21138, "143": 21139, "\u0120glorious": 21140, "inski": 21141, "ecd": 21142, "\u0120conditioning": 21143, "\u0120helpless": 21144, "\u0120microsc": 21145, "\u0120Harbor": 21146, "\u0120stakes": 21147, "\u0120260": 21148, "\u0120unequ": 21149, "\u0120Floyd": 21150, "\u0120damp": 21151, "\u0120apparatus": 21152, "\u0120Laws": 21153, "\u0120counters": 21154, "\u0120induce": 21155, "atable": 21156, "\u0120Ahmed": 21157, "\u0120slam": 21158, "November": 21159, "\u0120persist": 21160, "\u0120imminent": 21161, "\u00c3\u00a1n": 21162, "\u0120shred": 21163, "\u0120phases": 21164, "\u0120Edmonton": 21165, "\u0120Armstrong": 21166, "\u0120Meet": 21167, "\u0120Kitty": 21168, "\u00d1\u0122": 21169, "circ": 21170, "\u0120Adult": 21171, "\u0120arose": 21172, "\u0120Xen": 21173, "Dan": 21174, "gow": 21175, "\u0120superf": 21176, "\u0120Admir": 21177, "\u0120endure": 21178, "\u0120keyword": 21179, "yrus": 21180, "\u0120yarn": 21181, "\u0120pathway": 21182, "\u0120Hopkins": 21183, "midt": 21184, "\u0120censorship": 21185, "dependent": 21186, "\u0120instructor": 21187, "Sources": 21188, "\u0120toe": 21189, "\u0120balloon": 21190, "Nob": 21191, "\u0120swear": 21192, "\u0120Castro": 21193, "\u0120gloss": 21194, "\u0120Kavanaugh": 21195, "\u0120remarkably": 21196, "Photos": 21197, "\u0120Nom": 21198, "\u0120Southeast": 21199, "yers": 21200, "\u0120validation": 21201, "\u0120cannon": 21202, "\u0120Victory": 21203, "\u0120Pierre": 21204, "\u0120cautious": 21205, "Audio": 21206, "\u0120fetch": 21207, "\u0120Gift": 21208, "\u0120Hyp": 21209, "\u0120remedy": 21210, "ZE": 21211, "\u0120scent": 21212, "\u0120beard": 21213, "\u0120Rut": 21214, "-\"": 21215, "\u0120patents": 21216, "Hy": 21217, "\u0120unjust": 21218, "\u0120potato": 21219, "\u0120forthcoming": 21220, "\u0120chef": 21221, "\u0120Rift": 21222, "affe": 21223, "\u0120ROM": 21224, "\u0120Launch": 21225, "\u0120pads": 21226, "\u0120Neo": 21227, "\u0120onset": 21228, "\u0120squeeze": 21229, "safe": 21230, "\u0120prefix": 21231, "\u0120TM": 21232, "\u0120Nearly": 21233, "\u0120Clinical": 21234, "\u0120Mental": 21235, "otiation": 21236, "\u0120Unic": 21237, "antry": 21238, "\u0120Cir": 21239, "\u0120epit": 21240, "\u00c3\u00a6": 21241, "\u0120extracted": 21242, "versely": 21243, "riad": 21244, "\u0120strains": 21245, "\u0120tops": 21246, "\u0120poem": 21247, "\u0120Randy": 21248, "\u0120Maple": 21249, "THER": 21250, "upiter": 21251, "\u0120SSD": 21252, "\u013c\u00e9": 21253, "\u0120uncon": 21254, "pering": 21255, "\u0120slept": 21256, "iners": 21257, "\u0120underwater": 21258, "\u0120Evidence": 21259, "gone": 21260, "205": 21261, "\u0120historians": 21262, "\u0120synthesis": 21263, "\u0120frog": 21264, "basketball": 21265, "\u0120vibrant": 21266, "\u0120subord": 21267, "\u0120365": 21268, "\u0120Dial": 21269, "\u0120cooperate": 21270, "HAHA": 21271, "\u0120greeted": 21272, "158": 21273, "\u0120jazz": 21274, "\u0120intox": 21275, "\u0120Walking": 21276, "\u0120supervisor": 21277, "\u0120Fusion": 21278, "\u0120Mercedes": 21279, "send": 21280, "Ham": 21281, "sd": 21282, "nl": 21283, "\u0120tours": 21284, "\u0120FIFA": 21285, "\u0120culp": 21286, "gd": 21287, "304": 21288, "\u0120pleas": 21289, "\u0120illustrates": 21290, "\u0120Colombia": 21291, "\u0120highlighting": 21292, "\u0120Summary": 21293, "\u0120exposing": 21294, "\u0120Dru": 21295, "\u0120irony": 21296, "ritional": 21297, "\u0120Carroll": 21298, "\u0120Ellis": 21299, "Pict": 21300, "\u0120Rapt": 21301, "\u0120adapter": 21302, "\u0120unm": 21303, "\u0120corpse": 21304, "\u0120celebrities": 21305, "Den": 21306, "atum": 21307, "\u0120Apocalypse": 21308, "\u0120Wag": 21309, "lining": 21310, "\u0120hormones": 21311, "Rub": 21312, "\u0120Xi": 21313, "\u0120Vaults": 21314, "208": 21315, "alkyrie": 21316, "inosaur": 21317, "\u0120feeds": 21318, "vity": 21319, "\u0120defeating": 21320, "Wait": 21321, "\u0120emphasize": 21322, "\u0120Steelers": 21323, "yrinth": 21324, "leys": 21325, "\u0120Whenever": 21326, "Currently": 21327, "\u0120Clock": 21328, "\u0120collectively": 21329, "anyon": 21330, "\u0120JP": 21331, "\u0120mentality": 21332, "\u0120downloads": 21333, "\u0120surroundings": 21334, "\u0120Barnes": 21335, "\u0120flagship": 21336, "\u0120indicators": 21337, "\u0120grapp": 21338, "January": 21339, "\u0120Elemental": 21340, "\u0120Athena": 21341, "ibal": 21342, "\u0120sights": 21343, "\u0120capita": 21344, "\u0120Treaty": 21345, "\u0120voiced": 21346, "\u0120Gaz": 21347, "lette": 21348, "\u0120ya": 21349, "\u0120expired": 21350, "Legend": 21351, "Hot": 21352, "nature": 21353, "\u0120unstable": 21354, "\u0120280": 21355, "\u00c3\u00ba": 21356, "Comment": 21357, "ALE": 21358, "\u0120quests": 21359, "\u0120handler": 21360, "nis": 21361, "\u0120versatile": 21362, "\u0120conceal": 21363, "engeance": 21364, "\u0120Interactive": 21365, "\u0120obsessed": 21366, "\u0120Dogs": 21367, "\u0120cracked": 21368, "Sound": 21369, "sv": 21370, "\u0120Dylan": 21371, "roads": 21372, "fx": 21373, "\u0120Catholics": 21374, "\u0120Hag": 21375, "\u0120slammed": 21376, "\u0120glowing": 21377, "sale": 21378, "\u0120tissues": 21379, "\u0120Chi": 21380, "nee": 21381, "\u0120cher": 21382, "sic": 21383, "urrection": 21384, "\u0120bacon": 21385, "ulatory": 21386, ").\"": 21387, "\u0120irregular": 21388, "FORM": 21389, "assed": 21390, "\u0120intentional": 21391, "\u0120compensate": 21392, "\u0120Speaking": 21393, "\u0120Sets": 21394, "153": 21395, "\u0120conventions": 21396, "bands": 21397, "emade": 21398, "\u0120ecc": 21399, "\u0120Winston": 21400, "\u0120Assassin": 21401, "\u0120Belgian": 21402, "\u0120dependence": 21403, "\u0120niche": 21404, "\u0120bark": 21405, "\u0120Jazz": 21406, "\u0120disadvantage": 21407, "\u0120gasoline": 21408, "\u0120165": 21409, "\u00e7\u013c\u0126": 21410, "essa": 21411, "module": 21412, "angular": 21413, "OY": 21414, "\u0120Treatment": 21415, "itas": 21416, "olation": 21417, "\u0120Arnold": 21418, "\u0120feud": 21419, "\u0120Nest": 21420, "\u0120theatre": 21421, "ewater": 21422, "\u0120minors": 21423, "olicy": 21424, "\u0120Haven": 21425, "division": 21426, "\u0120trunk": 21427, "Far": 21428, "\u0120Pull": 21429, "\u0120capturing": 21430, "\u01201800": 21431, "\u0120Teen": 21432, "\u0120exempl": 21433, "\u0120clinics": 21434, "\u0120Burg": 21435, "\u0120substit": 21436, "\u0120payload": 21437, "\u0120Lav": 21438, "\u0120Troy": 21439, "\u0120Witness": 21440, "\u0120fragments": 21441, "\u0120passwords": 21442, "\u0120gospel": 21443, "\u0120Gin": 21444, "\u0120tenants": 21445, "olith": 21446, "Six": 21447, "Previous": 21448, "\u0120Ages": 21449, "\u0120Darwin": 21450, "\u0120blat": 21451, "\u0120empathy": 21452, "smith": 21453, "bag": 21454, "\u0120Echo": 21455, "\u0120Camb": 21456, "\u0120Madd": 21457, "\u0120Boo": 21458, "\u0120rede": 21459, "\u0120Burning": 21460, "\u0120smoothly": 21461, "\u0120Adrian": 21462, "\u0120Vampire": 21463, "\u0120Monsters": 21464, "steam": 21465, "Style": 21466, "Ma": 21467, "rea": 21468, "\u0120Dwar": 21469, "alyst": 21470, "ursor": 21471, "\u0120elimination": 21472, "\u0120crypto": 21473, "cht": 21474, "\u0120Eternal": 21475, "\u00e2\u0122\u00a6]": 21476, "\u0120Sorce": 21477, "Ill": 21478, "NER": 21479, "\u0120uh": 21480, "Conclusion": 21481, "wage": 21482, "\u0120respir": 21483, "\u0120reminis": 21484, "hetical": 21485, "\u0120gy": 21486, "\u0120utilized": 21487, "icidal": 21488, "\u01201900": 21489, "\u0120hunters": 21490, "\u0120Swan": 21491, "\u0120React": 21492, "\u0120visitor": 21493, "\u0120Thanksgiving": 21494, "308": 21495, "Posts": 21496, "\u0120hips": 21497, "1997": 21498, "omers": 21499, "\u0120knocking": 21500, "\u0120Vehicle": 21501, "\u0120til": 21502, "\u0120138": 21503, "\u0120mi": 21504, "\u0120Investigation": 21505, "\u0120Kenya": 21506, "\u0120casino": 21507, "\u0120motives": 21508, "\u0120regain": 21509, "rex": 21510, "\u0120weekends": 21511, "\u0120stabbed": 21512, "boro": 21513, "\u0120exploited": 21514, "\u0120HAVE": 21515, "\u0120Television": 21516, "cock": 21517, "\u0120preparations": 21518, "\u0120endeav": 21519, "\u0120Remote": 21520, "\u0120Maker": 21521, "\u0120Produ": 21522, "\u0120Evan": 21523, "\u0120informational": 21524, "\u0120Louisville": 21525, "154": 21526, "\u0120Dreams": 21527, "\u0120plots": 21528, "\u0120Runner": 21529, "\u0120hurting": 21530, "\u0120academy": 21531, "\u0120Montgomery": 21532, "nm": 21533, "\u0120Lanc": 21534, "\u0120Alz": 21535, "210": 21536, "elong": 21537, "\u0120retailer": 21538, "\u0120arising": 21539, "\u0120rebellion": 21540, "\u0120blonde": 21541, "played": 21542, "\u0120instrumental": 21543, "Cross": 21544, "\u0120retention": 21545, "\u0120therapeutic": 21546, "\u0120seas": 21547, "\u0120infantry": 21548, "\u0120Clint": 21549, "\u0120prompting": 21550, "\u0120bitch": 21551, "\u0120stems": 21552, "\u0120Kra": 21553, "\u0120thesis": 21554, "\u0120Bog": 21555, "rued": 21556, "\u0120kings": 21557, "\u0120clay": 21558, "ificent": 21559, "\u0120YES": 21560, "\u0120Thing": 21561, "\u0120Cubs": 21562, "veyard": 21563, "elsh": 21564, "inarily": 21565, "\u0120Ey": 21566, "\u0120Rolling": 21567, "\u0120evolving": 21568, "India": 21569, "\u0120recognizes": 21570, "\u0120graduation": 21571, "isers": 21572, "\u0120fertility": 21573, "\u0120Milan": 21574, "Command": 21575, "\u0120boxing": 21576, "\u01201943": 21577, "\u0120gluten": 21578, "\u0120Emir": 21579, "\u0120idol": 21580, "\u0120conceived": 21581, "\u0120Creation": 21582, "Merit": 21583, "uddy": 21584, "ussions": 21585, "\u0120Lieutenant": 21586, "ietal": 21587, "\u0120unchanged": 21588, "\u0120Scale": 21589, "\u0120Crimea": 21590, "balls": 21591, "atorial": 21592, "\u0120depths": 21593, "\u0120empirical": 21594, "\u0120transm": 21595, "\u0120unsafe": 21596, "missible": 21597, "comfort": 21598, "156": 21599, "\u0120mechanic": 21600, "002": 21601, "lins": 21602, "\u0120smoked": 21603, "Pos": 21604, "\u0120slowing": 21605, "\u0120lav": 21606, "Texas": 21607, "\u0120cheating": 21608, "\u0120Metropolitan": 21609, "ethyl": 21610, "\u0120discovering": 21611, "asse": 21612, "\u0120pencil": 21613, "\u0120Pyongyang": 21614, "\u0120closet": 21615, "\u0120Sheet": 21616, "\u0120Entry": 21617, "oustic": 21618, "\u0120myst": 21619, "erate": 21620, "ariat": 21621, "\u0120minerals": 21622, "\u0120musician": 21623, "\u0120Pul": 21624, "\u0120Maz": 21625, "249": 21626, "\u0120permissions": 21627, "\u0120iv": 21628, "enary": 21629, "ickers": 21630, "\u0120Bing": 21631, "hea": 21632, "enable": 21633, "\u0120griev": 21634, "\u0120asserted": 21635, "\u0120Colonel": 21636, "\u0120affidav": 21637, "wo": 21638, "\u0120seated": 21639, "\u0120Ride": 21640, "\u0120paintings": 21641, "\u0120Pix": 21642, "\u0120137": 21643, "ishi": 21644, "umbai": 21645, "gotten": 21646, "\u0120Earl": 21647, "\u0120inning": 21648, "\u0120census": 21649, "\u0120travelled": 21650, "\u0120Consult": 21651, "185": 21652, "bind": 21653, "\u0120simplicity": 21654, "\u0120overlooked": 21655, "\u0120Helpful": 21656, "\u0120monkey": 21657, "\u0120overwhelmingly": 21658, "Blood": 21659, "\u0120Flint": 21660, "\u0120Jama": 21661, "\u0120Present": 21662, "\u0120Rage": 21663, "\u0120TA": 21664, "ptive": 21665, "\u0120turnout": 21666, "wald": 21667, "\u0120Dolphins": 21668, "\u0120VPN": 21669, "\u0120onion": 21670, "\u0120crafting": 21671, "mma": 21672, "\u0120Mercury": 21673, "\u0120arrange": 21674, "\u0120alerts": 21675, "\u0120OT": 21676, "zbollah": 21677, "\u0120gases": 21678, "\u0120Richardson": 21679, "sal": 21680, "lar": 21681, "\u0120frost": 21682, "\u0120lowering": 21683, "\u0120acclaim": 21684, "\u0120startups": 21685, "\u0120Gain": 21686, "essment": 21687, "\u0120guardian": 21688, "\u00e4\u00ba\u00ba": 21689, "\u0120Pie": 21690, "\u0120Links": 21691, "\u0120merits": 21692, "\u0120awake": 21693, "\u0120parental": 21694, "\u0120exceeds": 21695, "\u0120idle": 21696, "\u0120Pilot": 21697, "\u0120eBay": 21698, "\u0120Accept": 21699, "ipeg": 21700, "Cam": 21701, "\u0120Kot": 21702, "\u0120traders": 21703, "olitics": 21704, "unker": 21705, "\u0120Pale": 21706, "osi": 21707, "anmar": 21708, "\u01201947": 21709, "\u0120Fell": 21710, "estial": 21711, "itating": 21712, "GF": 21713, "\u0120Sr": 21714, "ifted": 21715, "\u0120connector": 21716, "\u0120Bone": 21717, "illes": 21718, "260": 21719, "hma": 21720, "\u0120overlap": 21721, "\u0120GitHub": 21722, "\u0120cleaner": 21723, "\u0120Baptist": 21724, "\u0120WAS": 21725, "\u0120lungs": 21726, "\u00d1\u0123": 21727, "\u0120BUT": 21728, "\u0120cite": 21729, "\u0120pitched": 21730, "reatment": 21731, "\u0120trophies": 21732, "\u0120Nu": 21733, "386": 21734, "\u0120Pride": 21735, "\u0120attendees": 21736, "[]": 21737, "179": 21738, "\u0120spatial": 21739, "\u0120prizes": 21740, "\u0120Religion": 21741, "\u0120showcase": 21742, "\u0120Category": 21743, "vidia": 21744, "Target": 21745, "Property": 21746, "?,": 21747, "\u0120fusion": 21748, "pie": 21749, "\u0120UCLA": 21750, "\u0120soundtrack": 21751, "\u0120princess": 21752, "\u0120Caval": 21753, "should": 21754, "\u0120limbs": 21755, "Background": 21756, "\u0120lonely": 21757, "\u0120cores": 21758, "\u0120Tail": 21759, "sheet": 21760, "\u0120132": 21761, "Ra": 21762, "\u00e3\u0124\u00ab": 21763, "\u0120Bolt": 21764, "\u0120booked": 21765, "\u0120administer": 21766, "\u0120equals": 21767, "wy": 21768, "\u0120observing": 21769, "\u0120Baron": 21770, "\u0120Adobe": 21771, "\u0120virgin": 21772, "\u0120Socialist": 21773, "Move": 21774, "ghazi": 21775, "\u0120Linda": 21776, "212": 21777, "\u0120brewing": 21778, "\u0120merchants": 21779, "burse": 21780, "\u0120divor": 21781, "\u0120metals": 21782, "\u0120Ner": 21783, "\u0120sums": 21784, "\u0120Enemy": 21785, "\u0120envision": 21786, "\u0120granting": 21787, "\u0120Honey": 21788, "\u0120Skyrim": 21789, "\u0120socio": 21790, "graded": 21791, "\u0120selective": 21792, "WASHINGTON": 21793, "\u01201948": 21794, "\u0120Sirius": 21795, "\u0120Gross": 21796, "activity": 21797, "\u0120Ivan": 21798, "\u0120furious": 21799, "BSD": 21800, "\u0120Previous": 21801, "\u0120responsive": 21802, "\u0120charitable": 21803, "\u0120leaning": 21804, "\u0120Pew": 21805, "\u0120violates": 21806, "\\\\\\\\\\\\\\\\": 21807, "\u0120Coming": 21808, "wire": 21809, "\u0120poet": 21810, "\u0120resolutions": 21811, "command": 21812, "\u0120Portuguese": 21813, "\u0120nickname": 21814, "\u0120deaf": 21815, "February": 21816, "\u0120recognise": 21817, "\u0120entirety": 21818, "\u0120seasonal": 21819, "placed": 21820, "\u0120Telegraph": 21821, "\u0120microphone": 21822, "ouring": 21823, "\u0120grains": 21824, "\u0120governed": 21825, "\u0120postp": 21826, "\u0120Waters": 21827, "inement": 21828, "\u0120undocumented": 21829, "\u0120Comcast": 21830, "\u0120fox": 21831, "\u0120assaults": 21832, "reon": 21833, "many": 21834, "\u0120Jenkins": 21835, "\u0120Anyway": 21836, "\u0120assessments": 21837, "\u0120downs": 21838, "\u0120Mouse": 21839, "\u0120superb": 21840, "kt": 21841, "\u0120Dow": 21842, "\u0120taxation": 21843, "401": 21844, "\u0120smiles": 21845, "\u0120undertaken": 21846, "\u0120exh": 21847, "\u0120enthusiastic": 21848, "\u0120twent": 21849, "\u0120governmental": 21850, "\u0120autonomy": 21851, "\u0120Technologies": 21852, "\u0120Chain": 21853, "\u0120prevalent": 21854, "fb": 21855, "\u0120nicotine": 21856, "ogram": 21857, "job": 21858, "\u0120awaiting": 21859, "\u0120Menu": 21860, "\u0120deputies": 21861, "kov": 21862, "ishops": 21863, "Button": 21864, "\u0120Shanghai": 21865, "\u0120diesel": 21866, "\u0120Duck": 21867, "Ryan": 21868, "\u0120PCs": 21869, "NF": 21870, "jury": 21871, "ente": 21872, "\u0120inaccurate": 21873, "eddy": 21874, "Whatever": 21875, "\u0120showc": 21876, "\u0120Nad": 21877, "odus": 21878, "etr": 21879, "\u0120plaintiffs": 21880, "\u0120WOR": 21881, "\u0120Assange": 21882, "\u0120privat": 21883, "\u0120premiums": 21884, "\u0120tam": 21885, "URL": 21886, "\u0120elites": 21887, "\u0120Ranger": 21888, "ottenham": 21889, "\u0120Hoff": 21890, "\u0120Athens": 21891, "\u0120definite": 21892, "\u0120sighed": 21893, "\u0120evenly": 21894, "211": 21895, "\u0120Amber": 21896, "akia": 21897, "\u0120mailing": 21898, "\u0120crashing": 21899, "\u0120Confederate": 21900, "rugged": 21901, "Wal": 21902, "\u0120Depths": 21903, "\u0120juvenile": 21904, "\u0120reactor": 21905, "Introduction": 21906, "\u0120Deluxe": 21907, "1995": 21908, "\u0120Sanchez": 21909, "\u0120Mead": 21910, "ivable": 21911, ":-": 21912, "\u0120Planning": 21913, "\u0120Trap": 21914, "quin": 21915, "\u0120Protect": 21916, "vered": 21917, "Information": 21918, "\u0120kidney": 21919, "innamon": 21920, "las": 21921, "\u0120policing": 21922, "\u0120tolerate": 21923, "\u0120Qi": 21924, "\u0120biased": 21925, "Fort": 21926, "\u0120Ki": 21927, "save": 21928, "\u0120privileged": 21929, "\u0120beasts": 21930, "\u0120Glas": 21931, "\u0120Cinem": 21932, "\u0120comeback": 21933, "Sunday": 21934, "\u0120extinction": 21935, "hops": 21936, "\u0120transmit": 21937, "\u0120doubles": 21938, "\u0120Flat": 21939, "167": 21940, "\u0120disputed": 21941, "\u0120injustice": 21942, "foo": 21943, "Vict": 21944, "roleum": 21945, "\u0120Julie": 21946, "Context": 21947, "\u0120Rarity": 21948, "issue": 21949, "Component": 21950, "\u0120counseling": 21951, "anne": 21952, "dark": 21953, "\u0120objections": 21954, "uilt": 21955, "\u0120gast": 21956, "\u0120plac": 21957, "\u0120unused": 21958, "\u00e3\u0125\u0129": 21959, "\u0120Trial": 21960, "\u0120Jas": 21961, "hedral": 21962, "obb": 21963, "\u0120temporal": 21964, "\u0120PRO": 21965, "\u0120NW": 21966, "\u0120Anniversary": 21967, "Large": 21968, "\u0120therm": 21969, "\u0120david": 21970, "\u0120systemic": 21971, "\u0120Shir": 21972, "mut": 21973, "\u0120Nept": 21974, "address": 21975, "\u0120scanning": 21976, "\u0120understandable": 21977, "\u0120canvas": 21978, "Cat": 21979, "\u0120Zoo": 21980, "\u0120angels": 21981, "LO": 21982, "\u0120Statement": 21983, "\u0120Sig": 21984, "ovable": 21985, "\u0120Away": 21986, "sharing": 21987, "ocrats": 21988, "stated": 21989, "\u0120weighing": 21990, "Nor": 21991, "wild": 21992, "Bey": 21993, "\u0120astonishing": 21994, "\u0120Reynolds": 21995, "\u0120opener": 21996, "\u0120trainer": 21997, "\u0120surgical": 21998, "pn": 21999, "\u0120adjusting": 22000, "wheel": 22001, "\u0120frown": 22002, "ervative": 22003, "\u0120suspend": 22004, "Within": 22005, "tein": 22006, "\u0120obstacle": 22007, "\u0120liberties": 22008, "ymes": 22009, "\u0120uranium": 22010, "ansom": 22011, "anol": 22012, "uba": 22013, "\u0120Loss": 22014, "\u0120arous": 22015, "\u0120Henderson": 22016, "Wow": 22017, "spl": 22018, "cur": 22019, "\u0120\u00c2\u0143": 22020, "\u0120theirs": 22021, "Damage": 22022, "\u0120downloading": 22023, "\u0120discern": 22024, "\u0120Sto": 22025, "\u0120Fla": 22026, "\u0120hath": 22027, "\u0120Aj": 22028, "\u0120unpleasant": 22029, "European": 22030, "expensive": 22031, "\u0120screenshot": 22032, "\u0120UV": 22033, "\u0120allied": 22034, "\u0120Persian": 22035, "\u0120monopoly": 22036, "\u0120atom": 22037, "\u0120Redskins": 22038, "\"><": 22039, "\u0120cancell": 22040, "\u0120cinema": 22041, "131": 22042, "fair": 22043, "\u0120Alfred": 22044, "\u0120duck": 22045, "args": 22046, "223": 22047, "\u0120ISI": 22048, "\u0120signaling": 22049, "inar": 22050, "\u0120laughs": 22051, "\u0120forwards": 22052, "\u0120reckless": 22053, "\u0120listeners": 22054, "ativity": 22055, "\u0120vastly": 22056, "nant": 22057, "Less": 22058, "\u0120Hunting": 22059, "\u0120Scientific": 22060, "ITED": 22061, "\u0120knight": 22062, "\u0120HTC": 22063, "usa": 22064, "tmp": 22065, "\u0120rude": 22066, "\u0120Legendary": 22067, "\u0120arises": 22068, "Bad": 22069, "\u0120Claim": 22070, "peg": 22071, "\u0120realities": 22072, "Think": 22073, "\u0120\u00c2\u00b0": 22074, "\u0120rode": 22075, "\u0120strive": 22076, "\u0120anecd": 22077, "\u0120shorts": 22078, "\u0120hypothes": 22079, "\u0120coordinated": 22080, "\u0120Gandhi": 22081, "\u0120FPS": 22082, "RED": 22083, "\u0120susceptible": 22084, "\u0120shrink": 22085, "\u0120Chart": 22086, "Help": 22087, "\u0120ion": 22088, "deep": 22089, "ribes": 22090, "\u0120Kai": 22091, "\u0120Customer": 22092, "Summary": 22093, "\u0120cough": 22094, "wife": 22095, "\u0120lend": 22096, "\u0120positioning": 22097, "\u0120lottery": 22098, "\u0120Canyon": 22099, "\u0120fade": 22100, "\u0120bronze": 22101, "\u0120Kenny": 22102, "\u0120boasts": 22103, "\u0120Enhanced": 22104, "record": 22105, "\u0120emergence": 22106, "\u0120akin": 22107, "\u0120Bert": 22108, "itous": 22109, "\u00e2\u0138\u0133": 22110, "\u0120stip": 22111, "\u0120exchanged": 22112, "omore": 22113, "alsh": 22114, "\u0120reservoir": 22115, "\u0120standpoint": 22116, "WM": 22117, "\u0120initiate": 22118, "\u0120decay": 22119, "\u0120brewery": 22120, "\u0120terribly": 22121, "\u0120mortal": 22122, "levard": 22123, "\u0120revis": 22124, "NI": 22125, "elo": 22126, "\u0120confess": 22127, "\u0120MSNBC": 22128, "\u0120submissions": 22129, "Controller": 22130, "\u0120202": 22131, "\u0120Ruth": 22132, "});": 22133, "\u0120Azure": 22134, "\u0120.\"": 22135, "206": 22136, "\u0120Marketing": 22137, "\u0120laund": 22138, "iencies": 22139, "\u0120renowned": 22140, "\u0120Trou": 22141, "\u0120NGO": 22142, "blems": 22143, "\u0120terrified": 22144, "\u0120warns": 22145, "\u0120pert": 22146, "\u0120unsure": 22147, "480": 22148, "alez": 22149, "ultz": 22150, "\u0120Outside": 22151, "\u0120styl": 22152, "\u0120Underground": 22153, "\u0120panc": 22154, "\u0120dictionary": 22155, "\u0120foe": 22156, "riminal": 22157, "\u0120Norwegian": 22158, "\u0120jailed": 22159, "\u0120maternal": 22160, "\u00c3\u00a9e": 22161, "\u0120Lucy": 22162, "cop": 22163, "Cho": 22164, "\u0120unsigned": 22165, "\u0120Zelda": 22166, "\u0120Insider": 22167, "\u0120Continued": 22168, "\u0120133": 22169, "\u0120Naruto": 22170, "\u0120Majority": 22171, "169": 22172, "\u0120Wo": 22173, "\u00e3\u0124\u0135": 22174, "\u0120pastor": 22175, "\u0120informal": 22176, "\u00d0\u00bd": 22177, "anthrop": 22178, "join": 22179, "\u00e3\u0123\u0139": 22180, "itational": 22181, "NP": 22182, "\u0120Writing": 22183, "fn": 22184, "\u0120Bever": 22185, "195": 22186, "\u0120yelling": 22187, "\u0120drastically": 22188, "\u0120eject": 22189, "\u0120neut": 22190, "\u0120thrive": 22191, "\u0120Frequ": 22192, "oux": 22193, "\u0120possesses": 22194, "\u0120Senators": 22195, "\u0120DES": 22196, "\u0120Shakespeare": 22197, "\u0120Franco": 22198, "\u0120LB": 22199, "uchi": 22200, "\u0120incarn": 22201, "\u0120founders": 22202, "Function": 22203, "\u0120brightness": 22204, "\u0120BT": 22205, "\u0120whale": 22206, "\u0120Theater": 22207, "mass": 22208, "\u0120Doll": 22209, "Something": 22210, "\u0120echoed": 22211, "\u0120Hex": 22212, "crit": 22213, "afia": 22214, "\u0120goddess": 22215, "\u0120eleven": 22216, "\u0120Preview": 22217, "\u0120Aurora": 22218, "\u0120401": 22219, "ulsive": 22220, "\u0120Logan": 22221, "inburgh": 22222, "\u0120Centers": 22223, "\u0120ONLY": 22224, "\u0120Aid": 22225, "\u0120paradox": 22226, "\u0120hurd": 22227, "\u0120LC": 22228, "Due": 22229, "court": 22230, "\u0120offended": 22231, "\u0120evaluating": 22232, "\u0120Matthews": 22233, "\u0120tomb": 22234, "\u0120payroll": 22235, "\u0120extraction": 22236, "\u0120Hands": 22237, "ifi": 22238, "\u0120supernatural": 22239, "\u0120COMM": 22240, "]=": 22241, "dogs": 22242, "\u0120512": 22243, "\u0120Meeting": 22244, "Richard": 22245, "\u0120Maximum": 22246, "\u0120ideals": 22247, "Things": 22248, "mand": 22249, "\u0120Regardless": 22250, "\u0120humili": 22251, "buffer": 22252, "Little": 22253, "\u0120Dani": 22254, "\u0120Nak": 22255, "\u0120liberation": 22256, "\u0120Abe": 22257, "\u0120OL": 22258, "\u0120stuffed": 22259, "aca": 22260, "inda": 22261, "raphic": 22262, "\u0120mosqu": 22263, "\u0120campaigning": 22264, "\u0120occupy": 22265, "Squ": 22266, "rina": 22267, "\u0120Wel": 22268, "\u0120VS": 22269, "\u0120physic": 22270, "\u0120puls": 22271, "rint": 22272, "oaded": 22273, "ETF": 22274, "\u0120Archives": 22275, "\u0120venues": 22276, "hner": 22277, "\u0120Turbo": 22278, "\u0120lust": 22279, "\u0120appealed": 22280, "quez": 22281, "ilib": 22282, "\u0120Timothy": 22283, "\u0120omn": 22284, "dro": 22285, "\u0120obsession": 22286, "\u0120Savage": 22287, "1996": 22288, "Global": 22289, "Jes": 22290, "214": 22291, "\u0120sliding": 22292, "\u0120disappro": 22293, "\u0120Magical": 22294, "\u0120voluntarily": 22295, "gb": 22296, "aney": 22297, "\u0120prophet": 22298, "\u0120Rein": 22299, "\u0120Julia": 22300, "\u0120Worth": 22301, "aurus": 22302, "\u0120bounds": 22303, "ieu": 22304, ")))": 22305, "\u0120crore": 22306, "\u0120Citizen": 22307, "Sky": 22308, "\u0120columnist": 22309, "\u0120seekers": 22310, "ondo": 22311, "ISA": 22312, "\u0120Length": 22313, "\u0120nostalg": 22314, "\u0120newcom": 22315, "\u0120detrim": 22316, "entric": 22317, "375": 22318, "\u0120GE": 22319, "\u0120autop": 22320, "\u0120academics": 22321, "AppData": 22322, "\u0120Shen": 22323, "\u0120idiot": 22324, "\u0120Transit": 22325, "\u0120teaspoon": 22326, "Wil": 22327, "KO": 22328, "\u0120Comedy": 22329, ">,": 22330, "\u0120populated": 22331, "WD": 22332, "\u0120pigs": 22333, "\u0120Oculus": 22334, "\u0120sympathetic": 22335, "\u0120marathon": 22336, "198": 22337, "\u0120seizure": 22338, "sided": 22339, "\u0120dop": 22340, "irtual": 22341, "Land": 22342, "\u0120Floor": 22343, "osaurs": 22344, "...]": 22345, "\u0120los": 22346, "\u0120subsidiary": 22347, "EY": 22348, "\u0120Parts": 22349, "\u0120Stef": 22350, "\u0120Judiciary": 22351, "\u0120134": 22352, "\u0120mirrors": 22353, "\u0120ket": 22354, "times": 22355, "\u0120neurolog": 22356, "\u0120cav": 22357, "\u0120Guest": 22358, "\u0120tumor": 22359, "scill": 22360, "\u0120Lloyd": 22361, "Est": 22362, "\u0120clearer": 22363, "\u0120stereotypes": 22364, "\u0120dur": 22365, "nothing": 22366, "Reddit": 22367, "\u0120negotiated": 22368, "------------------------": 22369, "235": 22370, "\u0120flown": 22371, "\u0120Seoul": 22372, "\u0120Resident": 22373, "\u0120SCH": 22374, "\u0120disappearance": 22375, "\u0120Vince": 22376, "grown": 22377, "\u0120grabs": 22378, "ril": 22379, "\u0120Infinite": 22380, "\u0120Twenty": 22381, "\u0120pedestrian": 22382, "\u0120jersey": 22383, "\u0120Fur": 22384, "\u0120Infinity": 22385, "\u0120Elliott": 22386, "\u0120mentor": 22387, "\u0120morally": 22388, "\u0120obey": 22389, "secure": 22390, "iffe": 22391, "\u0120antibiotics": 22392, "angled": 22393, "\u0120Freeman": 22394, "\u0120Introduction": 22395, "Jun": 22396, "\u0120marsh": 22397, "icans": 22398, "\u0120EVENTS": 22399, "ochond": 22400, "Wall": 22401, "iculty": 22402, "\u0120misdemeanor": 22403, "\u0120ly": 22404, "Thomas": 22405, "\u0120Resolution": 22406, "\u0120animations": 22407, "\u0120Dry": 22408, "\u0120intercourse": 22409, "\u0120Newcastle": 22410, "\u0120Hog": 22411, "\u0120Equipment": 22412, "177": 22413, "\u0120territorial": 22414, "\u0120archives": 22415, "203": 22416, "Filter": 22417, "\u0120Munich": 22418, "\u0120commanded": 22419, "\u0120Wand": 22420, "\u0120pitches": 22421, "\u0120Croat": 22422, "\u0120ratios": 22423, "\u0120Mits": 22424, "\u0120accumulated": 22425, "\u0120Specifically": 22426, "\u0120gentleman": 22427, "acerb": 22428, "\u0120penn": 22429, "\u0120aka": 22430, "\u0120Fuk": 22431, "\u0120intervene": 22432, "\u0120Refuge": 22433, "\u0120Alzheimer": 22434, "\u0120succession": 22435, "ohan": 22436, "does": 22437, "Lord": 22438, "\u0120separat": 22439, "\u0120correspondence": 22440, "\u0120shiny": 22441, "Prior": 22442, "\u0120sulf": 22443, "\u0120miserable": 22444, "\u0120dedication": 22445, "().": 22446, "\u0120specialists": 22447, "\u0120defects": 22448, "\u0120Cult": 22449, "\u0120Xia": 22450, "\u0120jeopard": 22451, "\u0120Ore": 22452, "Ability": 22453, "\u0120lear": 22454, "\u0120ambitions": 22455, "\u0120BMI": 22456, "\u0120Arabs": 22457, "\u01201942": 22458, "\u0120preservation": 22459, "ificate": 22460, "\u0120ashamed": 22461, "loss": 22462, "\u0120Restaur": 22463, "\u0120resemble": 22464, "\u0120enrich": 22465, "\u0120KN": 22466, "\u0120Clan": 22467, "float": 22468, "\u0120playable": 22469, "ITT": 22470, "\u0120harmony": 22471, "arrison": 22472, "\u0120Weinstein": 22473, "were": 22474, "\u0120poisoning": 22475, "\u0120Comput": 22476, "\u0120WordPress": 22477, "major": 22478, "\u0120Valve": 22479, "Fan": 22480, "\u0120Throw": 22481, "\u0120Romans": 22482, "\u0120Depression": 22483, "ados": 22484, "\u0120tortured": 22485, "\u0120balancing": 22486, "bottom": 22487, "\u0120acquiring": 22488, "\u0120Monte": 22489, "ardi": 22490, "\u0120aura": 22491, "\u0120##": 22492, "\u0120Standing": 22493, "\u0120Atlas": 22494, "CF": 22495, "\u0120intrins": 22496, "\u0120Benghazi": 22497, "\u0120camping": 22498, "\u0120tapped": 22499, "blade": 22500, "strous": 22501, "\u0120Rabb": 22502, "\u0120Written": 22503, "tip": 22504, "\u0120Neigh": 22505, "sterdam": 22506, "\u0120Allow": 22507, "\u0120Healing": 22508, "\u0120Rhod": 22509, "num": 22510, "\u0120caffeine": 22511, "\u0120Percent": 22512, "\u0120boo": 22513, "\u0120apples": 22514, "305": 22515, "\u0120welcoming": 22516, "\u0120applaud": 22517, "\u0120austerity": 22518, "\u00c2\u00b1": 22519, "\u0120Reality": 22520, "efe": 22521, "\u00e5\u00ae": 22522, "\u0120sucks": 22523, "\u0120tabs": 22524, "\u0120PayPal": 22525, "\u0120backpack": 22526, "\u0120gifted": 22527, "abulary": 22528, "\u0120Scout": 22529, "irteen": 22530, "\u0120chin": 22531, "\u0120omitted": 22532, "\u0120negatively": 22533, "\u0120accessing": 22534, "\u0120Earn": 22535, "\u0120ambulance": 22536, "\u0120headphones": 22537, "\u0120205": 22538, "\u0120Refresh": 22539, "president": 22540, "\u0120Kitchen": 22541, "\u0120Entered": 22542, "\u0120Snyder": 22543, "005": 22544, "omical": 22545, "\u0120borrowed": 22546, "\u0120Nem": 22547, "\u0120aviation": 22548, "\u0120stall": 22549, "rimination": 22550, "\u0120uniforms": 22551, "itime": 22552, "\u0120Simmons": 22553, "energy": 22554, "ablished": 22555, "yy": 22556, "qualified": 22557, "\u0120rallies": 22558, "\u0120Stuart": 22559, "flight": 22560, "\u0120gangs": 22561, "rag": 22562, "\u0120vault": 22563, "lux": 22564, "\u0120Compar": 22565, "\u0120designation": 22566, "209": 22567, "\u0120Jos": 22568, "dollar": 22569, "zero": 22570, "\u0120wells": 22571, "303": 22572, "\u0120constituents": 22573, "\u0120heck": 22574, "\u0120cows": 22575, "\u0120commanders": 22576, "\u0120differential": 22577, "\u0120Catherine": 22578, "299": 22579, "\u0120valve": 22580, "\u0120brace": 22581, "\u0120perspectives": 22582, "cert": 22583, "fact": 22584, "icularly": 22585, "\u0120McN": 22586, "planes": 22587, "\u0120intric": 22588, "\u0120peas": 22589, "ovan": 22590, "\u0120tossed": 22591, "retch": 22592, "\u0120Lopez": 22593, "\u0120unfamiliar": 22594, "death": 22595, "\u0120Apart": 22596, "\u0120Chang": 22597, "\u0120relieved": 22598, "rophe": 22599, "\u0120airports": 22600, "\u0120freak": 22601, "util": 22602, "Mill": 22603, "\u0120Chin": 22604, "\u0120Owen": 22605, "male": 22606, "\u0120Broken": 22607, "\u0120Winds": 22608, "rob": 22609, "rising": 22610, "\u0120firefighters": 22611, "\u0120authoritarian": 22612, "\u0120148": 22613, "Bitcoin": 22614, "external": 22615, "\u0120browsers": 22616, "ichever": 22617, "orian": 22618, "\u0120unb": 22619, "\u0120poke": 22620, "\u0120Zot": 22621, "Mid": 22622, "\u0120Popular": 22623, "\u0120covert": 22624, "\u0120contributes": 22625, "\u0120650": 22626, "\u0120contention": 22627, "Gate": 22628, "\u0120consoles": 22629, "\u0120chromos": 22630, "\u0120IX": 22631, "\u0120visually": 22632, "\u0120Eisen": 22633, "\u0120jewelry": 22634, "\u0120delegation": 22635, "\u0120accelerate": 22636, "\u0120Riley": 22637, "\u0120slope": 22638, "\u0120indoor": 22639, "itially": 22640, "\u0120hugely": 22641, "\u0120tunnels": 22642, "\u0120fined": 22643, "\u0120directive": 22644, "\u0120forehead": 22645, "ustomed": 22646, "\u0120skate": 22647, "Music": 22648, "gas": 22649, "\u0120recognizing": 22650, "ambo": 22651, "\u0120overweight": 22652, "\u0120Grade": 22653, "\u00d9\u012c": 22654, "\u0120sounding": 22655, "\u0120locking": 22656, "\u0120REM": 22657, "Store": 22658, "\u0120excav": 22659, "\u0120Likewise": 22660, "\u0120Lights": 22661, "\u0120elbow": 22662, "\u0120Supply": 22663, "wic": 22664, "\u0120handsome": 22665, "1994": 22666, "Coll": 22667, "\u0120adequately": 22668, "\u0120Associate": 22669, "\u0120strips": 22670, "\u0120crackdown": 22671, "\u0120marvel": 22672, "\u0120Kun": 22673, "\u0120passages": 22674, "@@@@": 22675, "\u0120Tall": 22676, "\u0120thoughtful": 22677, "namese": 22678, "\u0120prostitution": 22679, "business": 22680, "\u0120ballistic": 22681, "personal": 22682, "cig": 22683, "izational": 22684, "Round": 22685, "\u0120\u00c2\u0142\u0120\u00c2\u0142\u0120\u00c2\u0142\u0120\u00c2\u0142": 22686, "\u0120Coleman": 22687, "\u0120admitting": 22688, "\u0120Plug": 22689, "\u0120bitcoins": 22690, "\u0120Suz": 22691, "\u0120fairness": 22692, "\u0120supplier": 22693, "\u0120catastrophic": 22694, "\u0120Helen": 22695, "oqu": 22696, "Marc": 22697, "\u0120Articles": 22698, "gie": 22699, "\u0120endangered": 22700, "\u0120destiny": 22701, "\u0120Volt": 22702, "olia": 22703, "axis": 22704, "\u0120cheat": 22705, "\u0120unified": 22706, "ICO": 22707, "quote": 22708, "302": 22709, "\u0120Sed": 22710, "\u0120suppression": 22711, "\u0120analyzing": 22712, "\u0120squat": 22713, "\u0120figuring": 22714, "\u0120coordinates": 22715, "\u0120chunks": 22716, "\u01201946": 22717, "\u0120subp": 22718, "\u0120wiki": 22719, "\u0120Forbes": 22720, "\u0120Jupiter": 22721, "\u0120Erik": 22722, "imer": 22723, "\u0120Commercial": 22724, "\\)": 22725, "\u0120legitimacy": 22726, "\u0120dental": 22727, "\u0120Mean": 22728, "\u0120deficits": 22729, "550": 22730, "Originally": 22731, "\u0120Horror": 22732, "\u0120contamination": 22733, "llah": 22734, "\u0120confisc": 22735, "\u0120Clare": 22736, "TB": 22737, "\u0120Failed": 22738, "aned": 22739, "\u0120ruler": 22740, "\u0120Controller": 22741, "\u0120feminists": 22742, "Fix": 22743, "gay": 22744, "207": 22745, "\u0120rabbit": 22746, "Third": 22747, "owntown": 22748, "\u0120glue": 22749, "\u0120volatile": 22750, "\u0120shining": 22751, "\u0120foll": 22752, "\u0120impaired": 22753, "\u0120supers": 22754, "\u00e6\u012a": 22755, "\u0120clutch": 22756, "\u013c\u00e9\u0128\u0134": 22757, "\u0120prolet": 22758, "\u0120(!": 22759, "\u0120yelled": 22760, "\u0120Kiev": 22761, "\u0120Ern": 22762, "\u0120Shock": 22763, "KB": 22764, "\u0120situated": 22765, "query": 22766, "\u0120Nas": 22767, "\u0120annex": 22768, "character": 22769, "\u0120Holiday": 22770, "\u0120automation": 22771, "\u0120Jill": 22772, "\u0120Remastered": 22773, "\u0120linem": 22774, "\u0120wilderness": 22775, "\u0120Horizon": 22776, "\u0120Guinea": 22777, "AZ": 22778, "\u0120mainland": 22779, "\u0120secrecy": 22780, "LEASE": 22781, "\u0120punk": 22782, "\u0120Province": 22783, "(),": 22784, "Speed": 22785, "\u0120handing": 22786, "\u0120Sebast": 22787, "Sir": 22788, "rase": 22789, "\u0120journals": 22790, "\u0120congest": 22791, "\u0120Tut": 22792, "irrel": 22793, "\u0120schizophrenia": 22794, "\u0120misogyn": 22795, "healthy": 22796, "Iron": 22797, "\u0120reacted": 22798, "-$": 22799, "252": 22800, "\u0120plural": 22801, "\u0120plum": 22802, "\u0120bargain": 22803, "\u0120grounded": 22804, "finder": 22805, "\u0120disse": 22806, "\u0120Laz": 22807, "OOD": 22808, "\u0120atroc": 22809, "Factory": 22810, "\u0120minions": 22811, "\u0120ori": 22812, "\u0120Brave": 22813, "\u0120PRE": 22814, "\u0120Myanmar": 22815, "\u0120Hod": 22816, "\u0120expedition": 22817, "\u0120explode": 22818, "\u0120Coord": 22819, "\u0120extr": 22820, "\u0120Brief": 22821, "\u0120ADHD": 22822, "\u0120hardcore": 22823, "feeding": 22824, "\u0120dile": 22825, "\u0120Fruit": 22826, "\u0120vaccination": 22827, "\u0120Mao": 22828, "osphere": 22829, "\u0120contests": 22830, "-|": 22831, "\u0120fren": 22832, "isphere": 22833, "Rom": 22834, "\u0120Sharp": 22835, "\u0120Trend": 22836, "\u0120disconnect": 22837, "\u00e2\u0122\u00a2\u00e2\u0122\u00a2": 22838, "\u0120persecution": 22839, "Earth": 22840, "\u0120healthier": 22841, "384": 22842, "\u0120cob": 22843, "\u0120Trinity": 22844, "OWS": 22845, "ANN": 22846, "\u0120specialty": 22847, "\u0120gru": 22848, "\u0120cooperative": 22849, "why": 22850, "Starting": 22851, "\u0120Issues": 22852, "stre": 22853, "ensor": 22854, "\u0120185": 22855, "Adv": 22856, "!?": 22857, "\u0120Revel": 22858, "emia": 22859, "\u0120Hulk": 22860, "\u0120celebrations": 22861, "\u0120Sou": 22862, "raud": 22863, "\u0120Klein": 22864, "\u0120unreal": 22865, "context": 22866, "\u0120partnerships": 22867, "\u0120adopting": 22868, "tical": 22869, "\u0120splash": 22870, "\u0120Hezbollah": 22871, "category": 22872, "cyclop": 22873, "xton": 22874, "\u0120Dot": 22875, "urdy": 22876, "tz": 22877, "\u0120envelope": 22878, "\u0120NL": 22879, "\u00e2\u0137": 22880, "\u0120wherein": 22881, "Spec": 22882, "184": 22883, "\u0120telev": 22884, "aliation": 22885, "\u0120myths": 22886, "\u00e5\u00b0": 22887, "\u0120rigorous": 22888, "\u0120communicating": 22889, "\u0120observer": 22890, "\u0120rehe": 22891, "\u0120Wash": 22892, "\u0120apologized": 22893, "\u0120Tin": 22894, "\u0120expenditures": 22895, "workers": 22896, "document": 22897, "\u0120hesitate": 22898, "\u0120Lenin": 22899, "\u0120unpredictable": 22900, "\u0120renewal": 22901, "cler": 22902, "okia": 22903, "\u0120CONT": 22904, "\u0120postseason": 22905, "Tokens": 22906, "\u0120exacerb": 22907, "\u0120betting": 22908, "\u0120147": 22909, "\u0120elevation": 22910, "Wood": 22911, "\u0120Solomon": 22912, "194": 22913, "004": 22914, "output": 22915, "\u0120redund": 22916, "\u0120Mumbai": 22917, "\u0120pH": 22918, "\u0120reproduce": 22919, "\u0120Duration": 22920, "MAX": 22921, "\u0120bog": 22922, "CBS": 22923, "\u0120Balance": 22924, "\u0120Sgt": 22925, "\u0120Recent": 22926, "\u0120cd": 22927, "\u0120popped": 22928, "\u0120incompet": 22929, "prop": 22930, "ayan": 22931, "guy": 22932, "Pacific": 22933, "\u0120tyr": 22934, "\u0120{{": 22935, "\u0120Mystic": 22936, "\u0120Dana": 22937, "\u0120masturb": 22938, "\u0120geometry": 22939, "\u00c3\u00a2": 22940, "\u0120Correct": 22941, "\u0120trajectory": 22942, "\u0120distracted": 22943, "\u0120foo": 22944, "\u0120Welsh": 22945, "Luc": 22946, "mith": 22947, "\u0120rugby": 22948, "\u0120respiratory": 22949, "\u0120triangle": 22950, "\u0120215": 22951, "\u0120undergraduate": 22952, "\u0120Superior": 22953, "changing": 22954, "_-": 22955, "\u0120rightly": 22956, "\u0120referee": 22957, "\u0120lucrative": 22958, "\u0120unauthorized": 22959, "\u0120resembles": 22960, "\u0120GNU": 22961, "\u0120Derby": 22962, "\u0120pathways": 22963, "\u0120Led": 22964, "\u0120endurance": 22965, "\u0120stint": 22966, "\u0120collector": 22967, "Fast": 22968, "\u0120dots": 22969, "\u0120nationals": 22970, "\u0120Securities": 22971, "\u0120whip": 22972, "Param": 22973, "\u0120learns": 22974, "Magic": 22975, "\u0120detailing": 22976, "moon": 22977, "\u0120broadcasting": 22978, "\u0120baked": 22979, "265": 22980, "holm": 22981, "\u0120Sah": 22982, "\u0120Hussein": 22983, "\u0120Courtesy": 22984, "174": 22985, "\u0120146": 22986, "\u0120geographic": 22987, "peace": 22988, "\u0120judging": 22989, "\u0120Stern": 22990, "Bur": 22991, "\u0120storyline": 22992, "Gun": 22993, "\u0120Stick": 22994, "245": 22995, "307": 22996, "\u00e3\u0124\u00b4\u00e3\u0125\u00b3": 22997, "\u0120Administrator": 22998, "\u0120burnt": 22999, "\u0120pave": 23000, "choes": 23001, "Exec": 23002, "\u0120campuses": 23003, "Result": 23004, "\u0120mutations": 23005, "\u0120Charter": 23006, "\u0120captures": 23007, "\u0120compares": 23008, "\u0120badge": 23009, "Scient": 23010, "\u0120erad": 23011, "iery": 23012, "oi": 23013, "ettes": 23014, "\u0120Estate": 23015, "\u0120strap": 23016, "\u0120proudly": 23017, "\u0120fried": 23018, "\u0120withdrawn": 23019, "\u0120Voy": 23020, "phony": 23021, "Items": 23022, "\u0120Pierce": 23023, "bard": 23024, "\u0120annotation": 23025, "anton": 23026, "illon": 23027, "Impro": 23028, "...)": 23029, "\u0120happier": 23030, "------": 23031, "adjust": 23032, "\u0120staffers": 23033, "\u0120activism": 23034, "\u0120perf": 23035, "\u0120alright": 23036, "Need": 23037, "\u0120commence": 23038, "\u0120opioid": 23039, "\u0120Amanda": 23040, "Es": 23041, "\u0120Pars": 23042, "\u0120Kaw": 23043, "Works": 23044, "248": 23045, "\u0120indo": 23046, "tc": 23047, "endant": 23048, "\u0120Moto": 23049, "\u0120legalization": 23050, "OTE": 23051, "\u0120tasked": 23052, "\u0120tsp": 23053, "\u0120ACTIONS": 23054, "166": 23055, "\u0120refreshing": 23056, "\u0120NR": 23057, "\u0120Perez": 23058, "\u0120infringement": 23059, "SY": 23060, "Listen": 23061, "inning": 23062, "ku": 23063, "\u0120rotate": 23064, "program": 23065, "arah": 23066, "Design": 23067, "\u0120(\u00c2\u00a3": 23068, "\u0120storing": 23069, "\u0120warrants": 23070, "\u0120judgement": 23071, "\u0120Brist": 23072, "usually": 23073, "photo": 23074, "\u0120Ran": 23075, "\u0120Pine": 23076, "\u0120outrageous": 23077, "\u0120Valentine": 23078, "luence": 23079, "\u0120Everybody": 23080, "Altern": 23081, "\u0120relevance": 23082, "\u0120terminated": 23083, "\u0120dessert": 23084, "\u0120fulfilled": 23085, "\u0120prosecuted": 23086, "\u0120Words": 23087, "\u0120migrant": 23088, "\u0120cultivation": 23089, "\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124": 23090, "idelity": 23091, "\u0120Vern": 23092, "\u0120Login": 23093, "\u0120metaphor": 23094, "\u0120Tip": 23095, "\u0120recruits": 23096, "\u0120Pig": 23097, "ribing": 23098, "\u0120enthusiasts": 23099, "exper": 23100, "\u0120frightening": 23101, "\u0120Hair": 23102, "anson": 23103, "strate": 23104, "\u0120hi": 23105, "Height": 23106, "\u0120owning": 23107, "none": 23108, "\u0120dislike": 23109, "\u0120knives": 23110, "pherd": 23111, "\u0120loudly": 23112, "\u0120APIs": 23113, "Display": 23114, "\u0120Lac": 23115, "\u0120USS": 23116, "abl": 23117, "verages": 23118, "Jew": 23119, "\u0120172": 23120, "\u0120Historical": 23121, "atoon": 23122, "\u0120Physics": 23123, "intern": 23124, "\u0120warmth": 23125, "\u0120topp": 23126, "DM": 23127, "\u0120gunman": 23128, "\u0120emperor": 23129, "odi": 23130, "\u00e3\u0125\u00a3": 23131, "inatory": 23132, "\u0120Rib": 23133, "\u0120131": 23134, "\u0120Saturn": 23135, "\u0120Shining": 23136, "\u0120waking": 23137, "Quotes": 23138, "\u0120comedian": 23139, "enberg": 23140, "\u00c2\u00bd": 23141, "\u0120believers": 23142, "\u0120paperwork": 23143, "custom": 23144, "\u0120lev": 23145, "\u0120lament": 23146, "\u0120pouring": 23147, "222": 23148, "political": 23149, "\u0120Supplement": 23150, "maid": 23151, "\u0120cruelty": 23152, "\u0120tread": 23153, "ysics": 23154, "Aw": 23155, "rites": 23156, "\u0120modifier": 23157, "\u0120Position": 23158, "Adam": 23159, "lb": 23160, "ubs": 23161, "\u0120imperfect": 23162, "\u0120clusters": 23163, "\u0120Engineer": 23164, "\u0120Cherry": 23165, "\u0120inauguration": 23166, "\u0120Sau": 23167, "\u0120embodiment": 23168, "\u0120Uncle": 23169, "\u0120overr": 23170, "\u0120explosions": 23171, "cule": 23172, "\u0120Princeton": 23173, "\u0120Andrea": 23174, "\u0120incorrectly": 23175, "\u0120earnest": 23176, "\u0120pilgr": 23177, "\u0120Sprint": 23178, "\u0120sleeve": 23179, "\u0120hears": 23180, "\u0120Amazing": 23181, "\u0120browsing": 23182, "agin": 23183, "\u0120homeland": 23184, "\u0120haw": 23185, "\u0120diving": 23186, "istered": 23187, "178": 23188, "\u0120bargaining": 23189, "\u0120Arcade": 23190, "\u0120delegate": 23191, "terson": 23192, "................................................................": 23193, "\u0120Jacksonville": 23194, "275": 23195, "\u0120stagn": 23196, "\u0120adam": 23197, "\u0120Sherman": 23198, "CB": 23199, "\u0120suburb": 23200, "\u0120Foods": 23201, "\u0120converting": 23202, "\u0120Arist": 23203, "\u0120chambers": 23204, "love": 23205, "\u0120amino": 23206, "\u0120Gan": 23207, "\u0120madness": 23208, "mc": 23209, "\u0120USE": 23210, "defined": 23211, "\u0120ultr": 23212, "indust": 23213, "\u0120wolves": 23214, "lance": 23215, "Additionally": 23216, "\u0120cracks": 23217, "asia": 23218, "\u0120Reason": 23219, "\u0120Pump": 23220, "\u0120accidental": 23221, "\u0120Laser": 23222, "\u0120Rid": 23223, "\u0120initialized": 23224, "elli": 23225, "\u0120unnamed": 23226, "\u0120noun": 23227, "\u0120Passed": 23228, "\u0120hostage": 23229, "\u0120Ethiop": 23230, "shirts": 23231, "\u0120unrel": 23232, "\u0120Embassy": 23233, "\u01201941": 23234, "\u0120atoms": 23235, "\u0120purported": 23236, "164": 23237, "\u0120Fi": 23238, "\u0120gallons": 23239, "\u0120Monica": 23240, "\u0120pg": 23241, "enment": 23242, "\u0120sorted": 23243, "\u0120Gospel": 23244, "\u0120heights": 23245, "\u0120traced": 23246, "\u0120undergoing": 23247, "Shell": 23248, "\u0120sacks": 23249, "\u0120proportions": 23250, "\u0120halluc": 23251, "Font": 23252, "acet": 23253, "\u0120warmer": 23254, "\u0120INTER": 23255, "\u0120grabbing": 23256, "Plug": 23257, "\u0120realization": 23258, "\u0120Burke": 23259, "\u0120enchant": 23260, "ATER": 23261, "\u0120Seed": 23262, "\u0120abundant": 23263, "FM": 23264, "\u0120civic": 23265, "Vs": 23266, "isi": 23267, "\u0120vow": 23268, "\u0120reper": 23269, "\u0120Partnership": 23270, "\u0120penetration": 23271, "\u0120axe": 23272, "\u0120shattered": 23273, "\u0120Zombies": 23274, "\u0120vinyl": 23275, "\u0120Alert": 23276, "eon": 23277, "\u0120obliged": 23278, "\u0120Illust": 23279, "\u0120Plaza": 23280, "\u0120Frontier": 23281, "\u0120davidjl": 23282, "\u0120Serial": 23283, "\u0120Hav": 23284, "\u0120Nutrition": 23285, "Bi": 23286, "\u0120\u00e2\u0138\u012a": 23287, "\u0120Jays": 23288, "linux": 23289, "\u0120hurry": 23290, "\u0120voy": 23291, "\u0120hopeless": 23292, "\u0120Stealth": 23293, "\u0120\u00e3\u0123": 23294, "essors": 23295, "ttle": 23296, "borg": 23297, "\u0120Safari": 23298, "fell": 23299, "\u0120wary": 23300, "due": 23301, "\u0120Above": 23302, "Ha": 23303, "ELL": 23304, "\u0120notor": 23305, "\u0120Won": 23306, "Too": 23307, "\u0120occupations": 23308, "\u0120possessions": 23309, "\u0120inviting": 23310, "\u0120predators": 23311, "\u0120accelerated": 23312, "\u0120157": 23313, "uterte": 23314, "\u0120Cube": 23315, "east": 23316, "account": 23317, "Give": 23318, "\u0120transplant": 23319, "redients": 23320, "idable": 23321, "\u0120screenshots": 23322, "\u0120Gund": 23323, "\u0120FS": 23324, "\u0120travelers": 23325, "\u0120sensory": 23326, "\u0120Fiat": 23327, "\u0120Rockets": 23328, "\u0130\u012d": 23329, "_{": 23330, "Friend": 23331, "\u0120charming": 23332, "ALS": 23333, "\u0120enjoyment": 23334, "mph": 23335, "\u01205000": 23336, "\u0120REG": 23337, "\u00d9\u0128": 23338, "bia": 23339, "\u0120compilation": 23340, "rost": 23341, "\u0120VP": 23342, "\u0120Schne": 23343, "2019": 23344, "\u0120copying": 23345, "MORE": 23346, "\u0120Flore": 23347, "falls": 23348, "215": 23349, "total": 23350, "\u0120disciples": 23351, "double": 23352, "\u0120exceeding": 23353, "\u0120smashed": 23354, "\u0120conceptual": 23355, "\u0120Romania": 23356, "\u0120Brent": 23357, "\u0120ICE": 23358, "\u0120Tou": 23359, "\u0120grap": 23360, "\u0120nails": 23361, "189": 23362, "\u00e3\u0125\u013a": 23363, "\u0120procure": 23364, "eur": 23365, "\u0120confirming": 23366, "\u0120Cec": 23367, "awi": 23368, "\u0120Eden": 23369, "\u0120ng": 23370, "\u0120engineered": 23371, "atics": 23372, "\u0120hooked": 23373, "\u0120disgusting": 23374, "\u0120Murder": 23375, "\u00e3\u0124\u00bf": 23376, "Library": 23377, "\u0120168": 23378, "Almost": 23379, "hematic": 23380, "Menu": 23381, "\u0120Notre": 23382, "\u0120Jur": 23383, "\u0120kidnapped": 23384, "\u0120hacker": 23385, "\u0120Jade": 23386, "\u0120creepy": 23387, "\u0120drawings": 23388, "\u0120Sponsor": 23389, "\u0120cyclists": 23390, "\u0120Goblin": 23391, "\u0120optimized": 23392, "\u0120staged": 23393, "\u0120McD": 23394, "between": 23395, "Age": 23396, "eno": 23397, "Sex": 23398, "\u0120Wide": 23399, "nings": 23400, "avis": 23401, "\u0120incapable": 23402, "\u0120Kob": 23403, "\u0120rewarding": 23404, "\u0120Lone": 23405, "olescent": 23406, "\u0120contracted": 23407, "\u0120sticky": 23408, "Jose": 23409, "Ball": 23410, "fest": 23411, "\u0120Input": 23412, "\u0120Recently": 23413, "\u0120tomat": 23414, "square": 23415, "Application": 23416, "\u0120nitrogen": 23417, "\u0120duplicate": 23418, "\u0120Recon": 23419, "\u0120Dear": 23420, "London": 23421, "\u0120intra": 23422, "\u0120dock": 23423, "\u0120outreach": 23424, "\u0120Million": 23425, "\u0120mammals": 23426, "ampton": 23427, "VAL": 23428, "\u0120snaps": 23429, "\u0120dos": 23430, "\u0120Whole": 23431, "\u0120Ready": 23432, "Try": 23433, "\u0120Winnipeg": 23434, "earance": 23435, "\u0120incurred": 23436, "renched": 23437, "\u0120NSW": 23438, "ilot": 23439, "raine": 23440, "\u0120cube": 23441, "got": 23442, "\u0120runway": 23443, "etermined": 23444, "\u0120Hawks": 23445, "\u0120survivor": 23446, "\u0120Wish": 23447, "\u0120Din": 23448, "\u0120DEF": 23449, "\u0120Vault": 23450, "187": 23451, "\u0120mushrooms": 23452, "\u0120crisp": 23453, "bey": 23454, "\u0120Discovery": 23455, "\u0120developmental": 23456, "\u0120paradigm": 23457, "\u0120chaotic": 23458, "\u0120Tsu": 23459, "\u0120333": 23460, "bons": 23461, "\u0120bacterial": 23462, "\u0120commits": 23463, "\u0120cosmic": 23464, "\u0120mega": 23465, "ocative": 23466, "\u0120Paint": 23467, "ophobic": 23468, "\u0120vain": 23469, "\u0120carved": 23470, "\u0120Thief": 23471, "\u0120Gul": 23472, "owship": 23473, "\u0120cites": 23474, "\u0120Edinburgh": 23475, "\u0120diminished": 23476, "\u0120acknowledges": 23477, "\u0120Kills": 23478, "\u0120microw": 23479, "\u0120Hera": 23480, "\u0120seniors": 23481, "\u0120whereby": 23482, "Hop": 23483, "atron": 23484, "\u0120unavailable": 23485, "\u0120Nate": 23486, "\u0120480": 23487, "\u0120slated": 23488, "\u0120Rebecca": 23489, "\u0120Battery": 23490, "\u0120grammar": 23491, "\u0120headset": 23492, "\u0120cursor": 23493, "\u0120excluding": 23494, "anye": 23495, "aundering": 23496, "ebin": 23497, "\u0120feasible": 23498, "\u0120Publishing": 23499, "\u0120Labs": 23500, "\u0120Cliff": 23501, "\u0120Ferrari": 23502, "\u0120pac": 23503, "visible": 23504, "marked": 23505, "pell": 23506, "\u0120polite": 23507, "\u0120staggering": 23508, "\u0120Galactic": 23509, "\u0120superst": 23510, "\u0120paran": 23511, "\u0120Officers": 23512, "\u00e3\u0122\u0123": 23513, "\u0120specifics": 23514, "ulus": 23515, "239": 23516, "\u0120Paste": 23517, "AMP": 23518, "\u0120Panama": 23519, "\u0120Delete": 23520, "anguard": 23521, "restrial": 23522, "\u0120heroic": 23523, "\u0120Dy": 23524, "\u00d8\u00a7\u00d9\u0126": 23525, "\u0120incumbent": 23526, "\u0120crunch": 23527, "tro": 23528, "\u0120scoop": 23529, "\u0120blogger": 23530, "\u0120sellers": 23531, "uren": 23532, "\u0120medicines": 23533, "\u0120Caps": 23534, "\u0120Animation": 23535, "oxy": 23536, "\u0120outward": 23537, "\u0120inquiries": 23538, "229": 23539, "\u0120psychologist": 23540, "\u0120Sask": 23541, "evil": 23542, "\u0120contaminated": 23543, "\u00e3\u0124\u00a8": 23544, "herence": 23545, "\u0120branded": 23546, "\u0120Abdul": 23547, "zh": 23548, "\u0120paragraphs": 23549, "\u0120mins": 23550, "\u0120correlated": 23551, "erb": 23552, "\u0120impart": 23553, "\u0120milestone": 23554, "\u0120Solutions": 23555, "otle": 23556, "\u0120undercover": 23557, "\u0120marched": 23558, "\u0120Chargers": 23559, "fax": 23560, "\u0120Secrets": 23561, "\u0120ruth": 23562, "weather": 23563, "\u0120feminine": 23564, "\u0120sham": 23565, "\u0120prestigious": 23566, "iggins": 23567, "\u0120sung": 23568, "history": 23569, "ettle": 23570, "ggie": 23571, "\u0120outdated": 23572, "oland": 23573, "\u0120perceptions": 23574, "\u0120Session": 23575, "\u0120Dodgers": 23576, "uj": 23577, "\u0120END": 23578, "Doc": 23579, "\u0120deficiency": 23580, "Grand": 23581, "\u0120Joker": 23582, "\u0120retrospect": 23583, "\u0120diagnostic": 23584, "\u0120harmless": 23585, "\u0120rogue": 23586, "\u0120Aval": 23587, "Equ": 23588, "\u0120transc": 23589, "\u0120Robertson": 23590, "\u0120Depending": 23591, "\u0120Burns": 23592, "ivo": 23593, "\u0120hostility": 23594, "Features": 23595, "\u0135\u013a": 23596, "\u0120discomfort": 23597, "\u0120LCD": 23598, "specified": 23599, "\u0120Expect": 23600, "340": 23601, "\u0120imperative": 23602, "\u0120Regular": 23603, "Chinese": 23604, "\u0120statewide": 23605, "\u0120symm": 23606, "\u0120loops": 23607, "\u0120autumn": 23608, "Nick": 23609, "\u0120shaping": 23610, "\u0120quot": 23611, "\u0120cherry": 23612, "\u0120Crossref": 23613, "\u00e8\u00a6\u013c\u00e9\u0128\u0134": 23614, "Standard": 23615, "heed": 23616, "\u0120Dell": 23617, "\u0120Vietnamese": 23618, "\u0120ost": 23619, "\u0120Valkyrie": 23620, "OA": 23621, "Assad": 23622, "\u0120rebound": 23623, "\u0120Traffic": 23624, "places": 23625, "\u00e6\u013a": 23626, "\u0120Buc": 23627, "172": 23628, "\u0120shelters": 23629, "\u0120insisting": 23630, "\u0120Certainly": 23631, "\u0120Kenneth": 23632, "\u0120TCP": 23633, "\u0120penal": 23634, "\u0120Replay": 23635, "heard": 23636, "\u0120dialect": 23637, "iza": 23638, "\u0120FY": 23639, "itcher": 23640, "\u0120DL": 23641, "\u0120spiral": 23642, "\u0120quarterbacks": 23643, "\u0120hull": 23644, "\u0120google": 23645, "\u0120todd": 23646, "\u0120Sterling": 23647, "\u0120Plate": 23648, "\u0120spying": 23649, "mbol": 23650, "\u0120Realm": 23651, "\u0120Proced": 23652, "\u0120Crash": 23653, "\u0120terminate": 23654, "\u0120protesting": 23655, "Center": 23656, "guided": 23657, "\u0120uncover": 23658, "\u0120boycott": 23659, "\u0120realizes": 23660, "sound": 23661, "\u0120pretending": 23662, "\u0120Vas": 23663, "1980": 23664, "\u0120framed": 23665, "\u0120139": 23666, "\u0120descended": 23667, "\u0120rehabilitation": 23668, "\u0120borrowing": 23669, "\u0120Buch": 23670, "\u0120blur": 23671, "Ron": 23672, "\u0120Frozen": 23673, "enza": 23674, "Chief": 23675, "\u0120Poor": 23676, "\u0120translates": 23677, "MIN": 23678, "\u0120212": 23679, "JECT": 23680, "\u0120erupted": 23681, "\u0120successes": 23682, "SEC": 23683, "\u0120plague": 23684, "\u0120gems": 23685, "doms": 23686, "\u0120stretches": 23687, "\u0120Spy": 23688, "\u0120storytelling": 23689, "Credit": 23690, "\u0120Push": 23691, "\u0120traction": 23692, "\u0120ineffective": 23693, "\u0120Luna": 23694, "\u0120tapes": 23695, "\u0120analytics": 23696, "ercise": 23697, "\u0120programmes": 23698, "\u0120Carbon": 23699, "\u0120behold": 23700, "heavy": 23701, "\u0120Conservation": 23702, "\u0120FIR": 23703, "\u0120sack": 23704, "termin": 23705, "ricks": 23706, "\u0120housed": 23707, "\u0120unusually": 23708, "Ice": 23709, "\u0120executing": 23710, "\u0120Moroc": 23711, "eday": 23712, "\u0120editions": 23713, "\u0120smarter": 23714, "\u0120BA": 23715, "\u0120outlaw": 23716, "\u0120vanished": 23717, "iba": 23718, "ALSE": 23719, "\u0120Silva": 23720, "238": 23721, "Could": 23722, "\u0120philosopher": 23723, "\u0120evacuated": 23724, "Secret": 23725, "142": 23726, "\u0120visas": 23727, "\u00e3\u0124\u00ac": 23728, "\u0120Malt": 23729, "\u0120Clearly": 23730, "\u0120Niger": 23731, "\u0120Cairo": 23732, "\u0120Fist": 23733, "380": 23734, "\u0120XML": 23735, "auto": 23736, "itant": 23737, "\u0120reinforced": 23738, "Record": 23739, "\u0120Survivor": 23740, "GHz": 23741, "\u0120screws": 23742, "parents": 23743, "\u0120oceans": 23744, "mares": 23745, "\u0120brakes": 23746, "vasive": 23747, "\u0120hello": 23748, "\u0120SIM": 23749, "rimp": 23750, "\u0120ore": 23751, "\u0120Armour": 23752, "247": 23753, "\u0120terrific": 23754, "\u0120tones": 23755, "141": 23756, "\u0120Minutes": 23757, "Episode": 23758, "\u0120curves": 23759, "\u0120inflammatory": 23760, "\u0120batting": 23761, "\u0120Beautiful": 23762, "Lay": 23763, "\u0120unpop": 23764, "vable": 23765, "\u0120riots": 23766, "\u0120Tactics": 23767, "baugh": 23768, "\u0120Cock": 23769, "\u0120orgasm": 23770, "\u0120Sas": 23771, "\u0120constructor": 23772, "etz": 23773, "Gov": 23774, "\u0120antagon": 23775, "\u0120theat": 23776, "\u0120deeds": 23777, "hao": 23778, "cuts": 23779, "\u0120McCl": 23780, "\u0120um": 23781, "\u0120Scientists": 23782, "\u0120grassroots": 23783, "yssey": 23784, "\"]=>": 23785, "\u0120surfaced": 23786, "\u0120shades": 23787, "\u0120neighbours": 23788, "\u0120advertis": 23789, "oya": 23790, "\u0120merged": 23791, "Upon": 23792, "\u0120gad": 23793, "\u0120anticipate": 23794, "Anyway": 23795, "\u0120slogan": 23796, "\u0120disrespect": 23797, "Iran": 23798, "\u0120TB": 23799, "acted": 23800, "\u0120subpoen": 23801, "mediately": 23802, "OOOO": 23803, "\u0120waiver": 23804, "\u0120vulnerabilities": 23805, "ottesville": 23806, "\u0120Huffington": 23807, "Josh": 23808, "\u0120DH": 23809, "Monday": 23810, "\u0120Ellen": 23811, "Know": 23812, "xon": 23813, "items": 23814, "228": 23815, "\u0120fills": 23816, "\u0120Nike": 23817, "\u0120cumulative": 23818, "andals": 23819, "Ir": 23820, "\u0120\u00ec": 23821, "\u0120friction": 23822, "igator": 23823, "\u0120scans": 23824, "\u0120Vienna": 23825, "ldom": 23826, "\u0120performers": 23827, "Prim": 23828, "\u0120bidding": 23829, "Mur": 23830, "\u0120leaned": 23831, "\u0120Prix": 23832, "alks": 23833, "\u0120[\u00e2\u0122\u00a6]": 23834, "\u0120Twitch": 23835, "\u0120Developer": 23836, "\u0120Gir": 23837, "\u0120callback": 23838, "Abstract": 23839, "\u0120accustomed": 23840, "\u0120freedoms": 23841, "\u0120PG": 23842, "uracy": 23843, "\u0120lump": 23844, "isman": 23845, ",,,,": 23846, "1992": 23847, "\u0120RED": 23848, "\u0120worm": 23849, "Match": 23850, "\u0120Platinum": 23851, "IJ": 23852, "\u0120Owner": 23853, "Trivia": 23854, "compl": 23855, "\u0120newborn": 23856, "\u0120fantas": 23857, "Own": 23858, "\u01201959": 23859, "\u0120sympath": 23860, "\u0120ubiqu": 23861, "\u0120outputs": 23862, "\u0120allev": 23863, "\u0120prag": 23864, "Kevin": 23865, "\u0120favors": 23866, "\u0120burial": 23867, "\u0120nurt": 23868, "solete": 23869, "cache": 23870, "\u0120156": 23871, "\u0120unlocks": 23872, "techn": 23873, "Making": 23874, "\u0120conquer": 23875, "adic": 23876, "\u00e6\u0138": 23877, "\u0120elf": 23878, "\u0120electorate": 23879, "\u0120Kurds": 23880, "\u0120Stack": 23881, "\u0120Samurai": 23882, "\u0120\u00e2\u013a\u0127": 23883, "\u0120{}": 23884, "\u0120Said": 23885, "\u0120Fallout": 23886, "\u0120kindness": 23887, "\u0120Customs": 23888, "\u0120Boulevard": 23889, "\u0120helicopters": 23890, "otics": 23891, "\u0120Veget": 23892, "comment": 23893, "\u0120criticised": 23894, "\u0120polished": 23895, "\u0120Remix": 23896, "\u0120Cultural": 23897, "\u0120recons": 23898, "\u0120doi": 23899, "atem": 23900, "Screen": 23901, "\u0120barred": 23902, "Comments": 23903, "\u0120Generally": 23904, "\u0120slap": 23905, "720": 23906, "Vari": 23907, "pine": 23908, "\u0120empt": 23909, "\u0120hats": 23910, "\u0120Playing": 23911, "lab": 23912, "average": 23913, "forms": 23914, "\u0120Cotton": 23915, "\u0120cans": 23916, "\u0120DON": 23917, "\u0120Somalia": 23918, "Crypt": 23919, "\u0120Increases": 23920, "Ever": 23921, "modern": 23922, "\u0120surgeon": 23923, "3000": 23924, "\u0120randomized": 23925, "================================================================": 23926, "Bern": 23927, "impl": 23928, "\u0120COR": 23929, "\u0120proclaim": 23930, "thouse": 23931, "\u0120toes": 23932, "\u0120ample": 23933, "\u0120preserving": 23934, "\u0120disbel": 23935, "grand": 23936, "Besides": 23937, "\u0120silk": 23938, "\u0120Pattern": 23939, "hm": 23940, "\u0120enterprises": 23941, "\u0120affidavit": 23942, "\u0120Advisory": 23943, "\u0120advertised": 23944, "\u0120Religious": 23945, "sections": 23946, "psych": 23947, "\u0120Fields": 23948, "aways": 23949, "\u0120hashtag": 23950, "\u0120Nightmare": 23951, "\u0120vampire": 23952, "\u0120forensic": 23953, "rossover": 23954, "nar": 23955, "\u0120navy": 23956, "\u0120vacant": 23957, "\u0120Duel": 23958, "\u0120hallway": 23959, "\u0120facebook": 23960, "identally": 23961, "\u0120NRA": 23962, "\u0120matt": 23963, "\u0120hurricane": 23964, "\u0120Kirby": 23965, "\u0120Puzzle": 23966, "\u0120skirt": 23967, "oust": 23968, "dullah": 23969, "\u0120analogy": 23970, "inion": 23971, "\u0120tomatoes": 23972, "\u0120NV": 23973, "\u0120Peak": 23974, "\u0120Meyer": 23975, "\u0120appointments": 23976, "\u0120masc": 23977, "\u0120alley": 23978, "rehend": 23979, "\u0120charities": 23980, "\u0120undo": 23981, "\u0120destinations": 23982, "\u0120Testing": 23983, "\"></": 23984, "\u0120destined": 23985, "\u0120implements": 23986, "\u0120Harold": 23987, "RECT": 23988, "\u0120optimization": 23989, "\u0120kilometres": 23990, "\u0120cmd": 23991, "\u0120impairment": 23992, "\u0120unsuccessful": 23993, "\u0120swiftly": 23994, "\u0120Glasgow": 23995, "arten": 23996, "\u0120Shares": 23997, "\u0120Answer": 23998, "\u0120Album": 23999, "\u0120nutritional": 24000, "\u00e3\u0125\u0138": 24001, "\u0120Fut": 24002, "\u0120bloc": 24003, "\u0120NFC": 24004, "\u0120wholesale": 24005, "\u0120CW": 24006, "\u0120neglected": 24007, "\u0120launcher": 24008, "\u0120announcements": 24009, "OULD": 24010, "comb": 24011, "\u0120rotating": 24012, "\u0120rests": 24013, "\u0120Ticket": 24014, "chedel": 24015, "Lou": 24016, "\u0120Vic": 24017, "\u0120\"'": 24018, "\u0120templates": 24019, "\u0120replaces": 24020, "Arc": 24021, "::::": 24022, "\u0120Gilbert": 24023, "\u0120illnesses": 24024, "\u0120schedules": 24025, "\u0120heterosexual": 24026, "LINE": 24027, "\u0120herein": 24028, "\u0120coerc": 24029, "\u0120decreasing": 24030, "\u0120deportation": 24031, "sudo": 24032, "\u0120Indigenous": 24033, "\u0120weighs": 24034, "Along": 24035, "');": 24036, "\u0120Bengals": 24037, "707": 24038, "\u0120joints": 24039, "verts": 24040, "\u0120149": 24041, "naire": 24042, "\u0120simplest": 24043, "\u0120lore": 24044, "1080": 24045, "fiction": 24046, "\u0120Database": 24047, "\u0120reservation": 24048, "\u0120sou": 24049, "\u0120sanctuary": 24050, "audio": 24051, "aple": 24052, "\u0120vegetarian": 24053, "\u0120anticipation": 24054, "micro": 24055, "\u0120enduring": 24056, "\u0120departed": 24057, "\u0120sidewalk": 24058, "\u0120prohibits": 24059, "\u0120Font": 24060, "\u0120compute": 24061, "\u0120Sect": 24062, "\u0120158": 24063, "Battle": 24064, "\u0120bomber": 24065, "\u0120distraction": 24066, "\u0120endured": 24067, "\u0120practitioners": 24068, "\u0120disturbed": 24069, "\u0120drank": 24070, "ordered": 24071, "\u0120surprises": 24072, "seat": 24073, "Security": 24074, "\u0120Wisdom": 24075, "ogo": 24076, "\u0120subparagraph": 24077, "\u0120Peninsula": 24078, "\u0120Origins": 24079, "iren": 24080, "\u0120Pav": 24081, "iggle": 24082, "\u0120gratitude": 24083, "\u0120Gravity": 24084, "overty": 24085, "iman": 24086, "ctr": 24087, "\u0120Caesar": 24088, "could": 24089, "gem": 24090, "\u0120skies": 24091, "\u0120champ": 24092, "\u0120agreeing": 24093, "Family": 24094, "Div": 24095, "176": 24096, "\u0120messy": 24097, "umption": 24098, "Federal": 24099, "erno": 24100, "\u0120Chat": 24101, "Beyond": 24102, "\u0120devote": 24103, "\u0120Walsh": 24104, "\u0120dumped": 24105, "\u0120accumulation": 24106, "stad": 24107, "hibition": 24108, "\u0120smokers": 24109, "\u0120inspector": 24110, "French": 24111, "issan": 24112, "\u0120Vita": 24113, "\u0120researching": 24114, "RAM": 24115, "\u0120Celtics": 24116, "\u0120cloak": 24117, "\u0120Terra": 24118, "Mary": 24119, "sold": 24120, "\u0120DOM": 24121, "mods": 24122, "Intel": 24123, "\u0120multitude": 24124, "\u0120Improved": 24125, "\u0120reliance": 24126, "\u0120artifact": 24127, "\u0120alarming": 24128, "Prom": 24129, "hon": 24130, "TION": 24131, "medium": 24132, "\u0120reflex": 24133, "\u0120Excel": 24134, "\u0120weakened": 24135, "163": 24136, "224": 24137, "\u0120costumes": 24138, "\u0120uniquely": 24139, "\u0120sorrow": 24140, "\u0120mansion": 24141, "wp": 24142, "\u0120salv": 24143, "\u0120Grove": 24144, "bsp": 24145, "\u0120Sniper": 24146, "\u0120Shipping": 24147, "\u0120POW": 24148, "\u0120undis": 24149, "\u0120branding": 24150, "Girl": 24151, "\u0120Ahmad": 24152, "\u0120Lakes": 24153, "\u0120Corey": 24154, "\u0120inheritance": 24155, "enery": 24156, "\u0120packing": 24157, "\u0120Prest": 24158, "Dest": 24159, "FW": 24160, "\u0120regulator": 24161, "locked": 24162, "\u0120contested": 24163, "\u0120Melissa": 24164, "\u0120Duc": 24165, "\u0120unpopular": 24166, "\u0120stacked": 24167, "\u01201917": 24168, "\u0120yearly": 24169, "\u0120stare": 24170, "\u0120assessing": 24171, "\u00c3\u00b8": 24172, "\u0120beverages": 24173, "\u0120competitions": 24174, "\u0120strengthening": 24175, "along": 24176, "\u0120Lud": 24177, "\u0120melted": 24178, "stanbul": 24179, "\u0120bounty": 24180, "ENC": 24181, "\u0120Lands": 24182, "\u0120declares": 24183, "\u0120customize": 24184, "\u0120composite": 24185, "\u00e3\u0125\u00ac": 24186, "CM": 24187, "ographics": 24188, "\u0120Temp": 24189, "\u0120contender": 24190, "\u0120insign": 24191, "\u0120LAN": 24192, "\u0120disasters": 24193, "inspired": 24194, "\u0120judgments": 24195, "ustainable": 24196, "ursion": 24197, "\u0120variance": 24198, "\u0120Ultimately": 24199, "\u0120--------": 24200, "uador": 24201, "\u0120RX": 24202, "\u0120melting": 24203, "\u0120Extended": 24204, "\u0120Twe": 24205, "Major": 24206, "\u0120Bil": 24207, "\u0120syrup": 24208, "quick": 24209, "\u0120Holder": 24210, "\u0120innocence": 24211, "ULE": 24212, "\u0120Might": 24213, "9999": 24214, "\u0120fal": 24215, "\u0120continuity": 24216, "\u01201953": 24217, "\u0120BS": 24218, "still": 24219, "Lat": 24220, "\u0120Abuse": 24221, "\u0120unsupported": 24222, "xxxxxxxx": 24223, "\u0120institute": 24224, "\u0120fragment": 24225, "\u0120Pep": 24226, "Western": 24227, "\u0120Cause": 24228, "\u0120Frag": 24229, "\u0120Ars": 24230, "\u00e0\u00a5": 24231, "astics": 24232, "\u0120bishop": 24233, "\u0120crosses": 24234, "\u0120154": 24235, "\u0120Upgrade": 24236, "\u0120mitigate": 24237, "\u0120Raymond": 24238, "Mods": 24239, "\u0120tomato": 24240, "\u0120stumbled": 24241, "\u0120differs": 24242, "Initial": 24243, "\u0120Raspberry": 24244, "\u0120ignores": 24245, "\u0120tant": 24246, "\u00c3\u0142": 24247, "\u0120relay": 24248, "\u0120bisexual": 24249, "\u0120confession": 24250, "\u0120dement": 24251, "inas": 24252, "\u0120Heather": 24253, "platform": 24254, "driving": 24255, "bourg": 24256, "\u0120Mush": 24257, "\u0120hyster": 24258, "Details": 24259, "\u0120drift": 24260, "\u0120Wald": 24261, "\u0120Luckily": 24262, "orf": 24263, "\u0120expire": 24264, "\u0120Punch": 24265, "zyme": 24266, "gold": 24267, "\u0120unpaid": 24268, "\u0120Trent": 24269, "\u0120unarmed": 24270, "\u0120illicit": 24271, "\u0120Tottenham": 24272, "\u0120smash": 24273, "International": 24274, "inker": 24275, "\u0120sting": 24276, "\u0120Saddam": 24277, "\u0120ART": 24278, "\u0120truths": 24279, "birth": 24280, "\u0120sober": 24281, "\u0120Nit": 24282, "\u0120ib": 24283, "\u0120usable": 24284, "\u0120stacks": 24285, "\u0120Sylv": 24286, "\u0120northeast": 24287, "\u0120domination": 24288, "\u0120Mour": 24289, "ENSE": 24290, "\u0120Measure": 24291, "\u0120programmer": 24292, "\u0120<-": 24293, "182": 24294, "\u0120Condition": 24295, "\u0120backyard": 24296, "irling": 24297, "\u0120Jeb": 24298, "\u0120Creed": 24299, "\u0120Hang": 24300, "\u0120COMP": 24301, "FER": 24302, "\u0120Ish": 24303, "\u0120detectives": 24304, "---------------": 24305, "\u0120Messenger": 24306, "\u0120looph": 24307, "\u0120gateway": 24308, "151": 24309, "\u0120Materials": 24310, "\u0120DT": 24311, "\u0120doomed": 24312, "odo": 24313, "\u0120slices": 24314, "\u0120emailed": 24315, "\u0120Perl": 24316, "\u0120renov": 24317, "UTH": 24318, "odynam": 24319, "\u0120Southwest": 24320, "getic": 24321, "\u0120TPP": 24322, "\u0120optimism": 24323, "\u0120Tow": 24324, "ulators": 24325, "protected": 24326, "yles": 24327, "\u00c2\u00ab": 24328, "\u0120exile": 24329, "env": 24330, "Prop": 24331, "\u0120Zimmerman": 24332, "\u00d9\u0130": 24333, "Ca": 24334, "omaly": 24335, "\u00e3\u0125\u0128": 24336, "\u0120railroad": 24337, "Lee": 24338, "232": 24339, "\u0120replicate": 24340, "\u0120comfortably": 24341, "actly": 24342, "\u0120rav": 24343, "\u0120telescope": 24344, "\u0120honesty": 24345, "\u0120Pepper": 24346, "\u0120Bring": 24347, "\u0120richest": 24348, "\u0120outdoors": 24349, "\u0120halls": 24350, "\u0120contend": 24351, "ISE": 24352, "\u0120submitting": 24353, "\u0120naive": 24354, "arations": 24355, "\u0120143": 24356, "\u0120poised": 24357, "responsible": 24358, "\u0120socks": 24359, "\u0120Skull": 24360, "Question": 24361, "\u0120discoveries": 24362, "Joined": 24363, "\u0120Enemies": 24364, "\u0120Wireless": 24365, "\u0120Revenge": 24366, "\u0120puzzles": 24367, "\u0120ceased": 24368, "290": 24369, "criptions": 24370, "\u0120Console": 24371, "\u0120boiling": 24372, "\u0120discrep": 24373, "\u0120deduction": 24374, "\u0120arsenal": 24375, "XXXX": 24376, "\u0120Amsterdam": 24377, "roximately": 24378, "\u0120Shane": 24379, "\u0120posing": 24380, "\u0120ACLU": 24381, "\u0120Companies": 24382, "\u0120theology": 24383, "\u0120Ug": 24384, "quarter": 24385, "\u0120Hank": 24386, "Coin": 24387, "\u0120Lv": 24388, "\u0120allegation": 24389, "\u0120Avoid": 24390, "\u0120indefinitely": 24391, "\u0120commodities": 24392, "\u0120brig": 24393, "\u0120Manit": 24394, "\u0120tenth": 24395, "method": 24396, "\u0120Knicks": 24397, "\u0120\u00e2\u0122\u0130": 24398, "\u0120invoked": 24399, "Dial": 24400, "ARA": 24401, "\u0120caucus": 24402, "227": 24403, "\u0120Jab": 24404, "\u0120ounces": 24405, "bay": 24406, "\u0120buddy": 24407, "fan": 24408, "234": 24409, "\u0120Hil": 24410, "adh": 24411, "\u0120TY": 24412, "\u0120IND": 24413, "\u01201939": 24414, "\u0120iteration": 24415, "\u0120Gonzalez": 24416, "\u0120Vert": 24417, "\u0120IO": 24418, "emb": 24419, "rera": 24420, "ench": 24421, "\u0120Requirements": 24422, "\u0120Wins": 24423, "\u0120livestock": 24424, "hours": 24425, "\"\u00e2\u0122\u00a6": 24426, "bral": 24427, "Marg": 24428, "\u0120Done": 24429, "\u0120wasting": 24430, "inged": 24431, "groups": 24432, "\u0120wishing": 24433, "\u0120Tumblr": 24434, "\u0120tapping": 24435, "\u0120nationalism": 24436, "\u0120Byr": 24437, "\u0120squares": 24438, "\u0120Actions": 24439, "\u00e3\u0125\u00a5": 24440, "Inside": 24441, "debug": 24442, "\u0120append": 24443, "\u0120stubborn": 24444, "\u0120Cind": 24445, "Tell": 24446, "\u0120tearing": 24447, "\u0120Rey": 24448, "orc": 24449, "\u0120Dayton": 24450, "\u0120NH": 24451, "\u0120Madness": 24452, "Charl": 24453, "\u0120Morrison": 24454, "filter": 24455, "\u0120accuse": 24456, "\u0120./": 24457, "\u0120torrent": 24458, "\u0120declines": 24459, "gallery": 24460, "Mine": 24461, "\u0120negotiation": 24462, "\u0120Bashar": 24463, "opia": 24464, "1993": 24465, "emort": 24466, "\u0120Novel": 24467, "\u0120Fang": 24468, "ersive": 24469, "\u0120Instant": 24470, "\u0120roller": 24471, "Around": 24472, "\u0120Elections": 24473, "Games": 24474, "\u0120inexpensive": 24475, "\u0120wors": 24476, "\u0120vul": 24477, "\u0120Hole": 24478, "\u0120unbelievable": 24479, "\u0120nause": 24480, "\u0120entr": 24481, "boat": 24482, "\u0120STE": 24483, "\u0120bush": 24484, "\u0120Hassan": 24485, "\u0120wo": 24486, "\u0120paused": 24487, "\u0120Mig": 24488, "lived": 24489, "\u0120scout": 24490, "\u0120lith": 24491, "Published": 24492, "duino": 24493, "cool": 24494, "\u0120circulating": 24495, "idas": 24496, "\u0120Pam": 24497, "violent": 24498, "\u0120Crawford": 24499, "uddle": 24500, "\u0120Letters": 24501, "Guard": 24502, "morph": 24503, "\u0120wandering": 24504, "\u0120sophomore": 24505, "\u0120queer": 24506, "\u0120Blind": 24507, "rue": 24508, "\u0120Marriage": 24509, "Dom": 24510, "\u0120padding": 24511, "\u0120folders": 24512, "\u0120meaningless": 24513, "\u0120candidacy": 24514, "afort": 24515, "\u0120whistlebl": 24516, "\u0120Identified": 24517, "\u0120cigar": 24518, "\u0120hid": 24519, "\u0120Dubai": 24520, "\u0120posture": 24521, "\u0120hiking": 24522, "\u0120Terminal": 24523, "Legendary": 24524, "\u0120TP": 24525, "\u0120ATK": 24526, "\u0120Starbucks": 24527, "\u0120Riot": 24528, "1991": 24529, "\u0120Bottom": 24530, "effic": 24531, "\u0120Eugene": 24532, "\u0120Wyoming": 24533, "\u0120Rocky": 24534, "\u0120salmon": 24535, "\u0120metro": 24536, "\u0120bilateral": 24537, "\u0120celebrates": 24538, "Length": 24539, "billion": 24540, "Bat": 24541, "\u0120releg": 24542, "\u0120pseudo": 24543, "DT": 24544, "\u0120Rhode": 24545, "Parent": 24546, "pletion": 24547, "\u0120attribut": 24548, "\u0120tuning": 24549, "\u0120NOTE": 24550, "\u0120Rebel": 24551, "icus": 24552, "Fund": 24553, "\u0120cocktail": 24554, "\u0120501": 24555, "\u0120spoon": 24556, "\u0120brutality": 24557, "\u0120unite": 24558, "\u0120microbi": 24559, "\u0120Reich": 24560, "positive": 24561, "\u0120amazed": 24562, "\u0120NT": 24563, "Desc": 24564, "ECTION": 24565, "\u0120falsely": 24566, "\u0120Highlander": 24567, "\u0120Crist": 24568, "\u0120Victorian": 24569, "\u0120distributions": 24570, "their": 24571, "\u0120Einstein": 24572, "\u0120pod": 24573, "\u0120epidem": 24574, "\u0120heap": 24575, "\u0120Ranch": 24576, "\u0120anthem": 24577, "\u0120reapp": 24578, "\u0120Auburn": 24579, "\u0120concurrent": 24580, "\u0120Throughout": 24581, "\u0120POST": 24582, "\u00e2\u013a": 24583, "\u0120homemade": 24584, "kick": 24585, "Beg": 24586, "\u0120chassis": 24587, "counter": 24588, "\u0120merger": 24589, "\u0120laps": 24590, "217": 24591, "union": 24592, "\u0120Trigger": 24593, "\u0120debated": 24594, "\u0120silently": 24595, "\u0120restraint": 24596, "Bal": 24597, "0000000": 24598, "\u0120formidable": 24599, "\u0120Filip": 24600, "\u0120sacrifices": 24601, "Food": 24602, "\u0120dwarf": 24603, "\u0120Sequ": 24604, "inian": 24605, "Moreover": 24606, "\u0120tangible": 24607, "opsis": 24608, "\u0120Minecraft": 24609, "\u0120Registration": 24610, "oan": 24611, "\u0120representations": 24612, "\u0120thirst": 24613, "\u0120corp": 24614, "irement": 24615, "Made": 24616, "loe": 24617, ">\"": 24618, "cats": 24619, "*.": 24620, "\u0120gestures": 24621, "general": 24622, "League": 24623, "\u0120packets": 24624, "\u0120Inspector": 24625, "\u0120Berg": 24626, "\u0120fraudulent": 24627, "\u0120criticize": 24628, "Fun": 24629, "\u0120blaming": 24630, "ndra": 24631, "\u0120slash": 24632, "\u0120Eston": 24633, "\u0120proposing": 24634, "\u0120whales": 24635, "\u0120therapist": 24636, "\u0120subset": 24637, "\u0120leisure": 24638, "ELD": 24639, "\u0120CVE": 24640, "\u0120Activity": 24641, "\u0120culmin": 24642, "shop": 24643, "\u0120DAY": 24644, "ischer": 24645, "\u0120Admiral": 24646, "\u0120Attacks": 24647, "\u01201958": 24648, "\u0120memoir": 24649, "\u0120folded": 24650, "\u0120sexist": 24651, "\u0120153": 24652, "\u0120LI": 24653, "\u0120readings": 24654, "\u0120embarrassment": 24655, "\u0120Employment": 24656, "wart": 24657, "chin": 24658, "\u0120continuation": 24659, "lia": 24660, "Recently": 24661, "\u0120duel": 24662, "\u0120evacuation": 24663, "\u0120Kashmir": 24664, "\u0120disposition": 24665, "\u0120Rig": 24666, "\u0120bolts": 24667, "\u0120insurers": 24668, "467": 24669, "Mex": 24670, "\u0120retaliation": 24671, "\u0120misery": 24672, "\u0120unreasonable": 24673, "raining": 24674, "Imm": 24675, "\u0120PU": 24676, "emer": 24677, "\u0120genital": 24678, "\u00e3\u0124\u00b3": 24679, "\u0120Candy": 24680, "\u0120onions": 24681, "\u0120Patt": 24682, "liner": 24683, "\u0120conceded": 24684, "\u0120fa": 24685, "\u0120forc": 24686, "\u0120Hernandez": 24687, "\u0120Geoff": 24688, "debian": 24689, "\u0120Teams": 24690, "\u0120cries": 24691, "\u0120homeowners": 24692, "237": 24693, "ABC": 24694, "\u0120stitch": 24695, "\u0120statistic": 24696, "\u0120headers": 24697, "\u0120Biology": 24698, "\u0120motors": 24699, "\u0120GEN": 24700, "\u0120Lip": 24701, "\u0120hates": 24702, "\u0120heel": 24703, "Self": 24704, "ipl": 24705, "EDIT": 24706, "orting": 24707, "\u0120annot": 24708, "\u0120Speech": 24709, "oldemort": 24710, "\u0120Javascript": 24711, "\u0120LeBron": 24712, "\u0120footprint": 24713, "\u0120fn": 24714, "\u0120seizures": 24715, "nas": 24716, "hide": 24717, "\u01201954": 24718, "\u0120Bee": 24719, "\u0120Declaration": 24720, "\u0120Katie": 24721, "\u0120reservations": 24722, "NR": 24723, "female": 24724, "\u0120saturated": 24725, "\u0120biblical": 24726, "\u0120trolls": 24727, "Device": 24728, "photos": 24729, "\u0120drums": 24730, "\u00e3\u0125\u012b\u00e3\u0125\u00a9\u00e3\u0124\u00b4\u00e3\u0125\u00b3": 24731, "Night": 24732, "fighter": 24733, "\u0120Hak": 24734, "riber": 24735, "\u0120cush": 24736, "\u0120disciplinary": 24737, "baum": 24738, "\u0120GH": 24739, "\u0120Schmidt": 24740, "ilibrium": 24741, "\u0120sixty": 24742, "\u0120Kushner": 24743, "rots": 24744, "\u0120pund": 24745, "\u0120Rac": 24746, "\u0120springs": 24747, "\u0120conve": 24748, "Business": 24749, "Fall": 24750, "\u0120qualifications": 24751, "\u0120verses": 24752, "\u0120narciss": 24753, "\u0120Koh": 24754, "\u0120Wow": 24755, "\u0120Charlottesville": 24756, "edo": 24757, "\u0120interrogation": 24758, "\u0120Wool": 24759, "365": 24760, "Brian": 24761, "\u0120\u00e2\u013e\u0135": 24762, "\u0120alleges": 24763, "onds": 24764, "idation": 24765, "\u0120Jackie": 24766, "yu": 24767, "\u0120lakes": 24768, "\u0120worthwhile": 24769, "\u0120crystals": 24770, "\u0120Juda": 24771, "\u0120comprehend": 24772, "\u0120flush": 24773, "\u0120absorption": 24774, "\u0120OC": 24775, "\u0120frightened": 24776, "\u0120Chocolate": 24777, "Martin": 24778, "\u0120buys": 24779, "\u0120bucks": 24780, "\u0120appell": 24781, "\u0120Championships": 24782, "\u0120listener": 24783, "\u0120Defensive": 24784, "\u0120cz": 24785, "uds": 24786, "\u0120Mate": 24787, "\u0120replay": 24788, "\u0120decorated": 24789, "\u0120sunk": 24790, "\u0120VIP": 24791, "\u0120Ank": 24792, "\u0120195": 24793, "aaaa": 24794, "Nobody": 24795, "\u0120Milk": 24796, "\u0120Gur": 24797, "\u0120Mk": 24798, "\u0120Sara": 24799, "\u0120seating": 24800, "\u0120Wid": 24801, "Track": 24802, "\u0120employs": 24803, "\u0120gigantic": 24804, "APP": 24805, "\u00e3\u0124\u00a7": 24806, "inventory": 24807, "\u0120towel": 24808, "atche": 24809, "lasting": 24810, "\u0120TL": 24811, "\u0120latency": 24812, "\u0120kne": 24813, "Ber": 24814, "meaning": 24815, "\u0120upheld": 24816, "\u0120playground": 24817, "\u0120mant": 24818, "Side": 24819, "\u0120stereo": 24820, "\u0120northwest": 24821, "\u0120exceptionally": 24822, "\u0120rays": 24823, "\u0120recurring": 24824, "Drive": 24825, "\u0120upright": 24826, "\u0120abduct": 24827, "\u0120Marathon": 24828, "\u0120goodbye": 24829, "\u0120alphabet": 24830, "hp": 24831, "\u0120courtroom": 24832, "rington": 24833, "othing": 24834, "Tag": 24835, "\u0120diplomats": 24836, "\u0120barbar": 24837, "\u0120Aqua": 24838, "183": 24839, "3333": 24840, "\u0120maturity": 24841, "\u0120instability": 24842, "\u0120Apache": 24843, "\u0120===": 24844, "\u0120fasting": 24845, "\u0120Grid": 24846, "ModLoader": 24847, "\u0120152": 24848, "Abs": 24849, "\u0120Operating": 24850, "etti": 24851, "\u0120acquaint": 24852, "Donnell": 24853, "\u0120Kem": 24854, "\u0120Forge": 24855, "\u0120armored": 24856, "Mil": 24857, "\u0120philosophers": 24858, "invest": 24859, "Players": 24860, "\u00e2\u012a": 24861, "\u0120myriad": 24862, "\u0120comrades": 24863, "Rot": 24864, "\u0120remembering": 24865, "\u0120corresponds": 24866, "\u0120programmers": 24867, "\u0120Lynn": 24868, "\u0120olig": 24869, "\u0120coherent": 24870, "ynchron": 24871, "\u0120Chemical": 24872, "\u0120jugg": 24873, "pair": 24874, "posts": 24875, "Eye": 24876, "\u0120Inner": 24877, "\u0120semester": 24878, "ottest": 24879, "\u0120Emirates": 24880, "ricanes": 24881, "orously": 24882, "mits": 24883, "\u0120Wis": 24884, "\u0120dodge": 24885, "location": 24886, "\u0120faded": 24887, "Amazon": 24888, "\u0120Proceed": 24889, "\u0120INFO": 24890, "journal": 24891, "\u0120Truck": 24892, "Ten": 24893, "\u0120217": 24894, "\u0120statutes": 24895, "mobile": 24896, "\u0120Types": 24897, "Recomm": 24898, "buster": 24899, "pex": 24900, "\u0120legends": 24901, "\u0120headache": 24902, "faced": 24903, "\u0120WiFi": 24904, "ifty": 24905, "\u0120HER": 24906, "\u0120circuits": 24907, "ERROR": 24908, "226": 24909, "olin": 24910, "\u0120cylinder": 24911, "ospace": 24912, "ikers": 24913, "Prem": 24914, "Quant": 24915, "\u0120conflicting": 24916, "\u0120slightest": 24917, "\u0120forged": 24918, "ionage": 24919, "Stephen": 24920, "\u0120Kub": 24921, "\u0120Opportun": 24922, "\u0120Heal": 24923, "\u0120blo": 24924, "\u0120rulers": 24925, "\u0120huh": 24926, "\u0120submarine": 24927, "fy": 24928, "asser": 24929, "\u0120allowance": 24930, "\u0120Kasich": 24931, "\u0120Tas": 24932, "\u0120Australians": 24933, "ForgeModLoader": 24934, "\u0120\u00e2\u0128\u0133": 24935, "\u0120Matrix": 24936, "amins": 24937, "\u01201200": 24938, "\u0120Acqu": 24939, "236": 24940, "Document": 24941, "\u0120Breaking": 24942, "193": 24943, "\u0120Subst": 24944, "\u0120Roller": 24945, "\u0120Properties": 24946, "\u0120NI": 24947, "tier": 24948, "\u0120crushing": 24949, "\u0120advocating": 24950, "Furthermore": 24951, "keepers": 24952, "\u0120sexism": 24953, "xd": 24954, "\u0120caller": 24955, "\u0120Sense": 24956, "chieve": 24957, "\u0120TF": 24958, "\u0120fueled": 24959, "\u0120reminiscent": 24960, "\u0120obsess": 24961, "urst": 24962, "\u0120uphold": 24963, "\u0120Fans": 24964, "hetics": 24965, "\u0120\u00e2\u0139": 24966, "\u0120Bath": 24967, "\u0120beverage": 24968, "\u0120oscill": 24969, "254": 24970, "\u0120poles": 24971, "\u0120gradual": 24972, "\u0120exting": 24973, "\u0120Suff": 24974, "\u0120Suddenly": 24975, "\u0120liking": 24976, "\u01201949": 24977, "unciation": 24978, "amination": 24979, "\u0120Omar": 24980, "\u0120LV": 24981, "\u0120Consequently": 24982, "\u0120synthes": 24983, "\u0120GIF": 24984, "\u0120pains": 24985, "\u0120interacting": 24986, "uously": 24987, "incre": 24988, "\u0120rumor": 24989, "\u0120Scientology": 24990, "197": 24991, "\u0120Zig": 24992, "\u0120spelling": 24993, "\u0120ASS": 24994, "\u0120extingu": 24995, "mson": 24996, "\u0120gh": 24997, "\u0120remarked": 24998, "\u0120Strategic": 24999, "\u0120MON": 25000, "\u00e5\u00a5": 25001, "gae": 25002, "\u0120WHAT": 25003, "Eric": 25004, "\u0120Campus": 25005, "\u0120methane": 25006, "\u0120imagin": 25007, "JUST": 25008, "\u0120Alm": 25009, "XT": 25010, "iq": 25011, "\u0120RSS": 25012, "\u0120wrongdoing": 25013, "atta": 25014, "\u0120bigot": 25015, "\u0120demonstrators": 25016, "\u0120Calvin": 25017, "\u0120Villa": 25018, "\u0120membrane": 25019, "\u0120Awesome": 25020, "\u0120benefic": 25021, "268": 25022, "\u0120magnificent": 25023, "\u0120Lots": 25024, "Greg": 25025, "\u0120Boris": 25026, "\u0120detainees": 25027, "\u0120Herman": 25028, "\u0120whispered": 25029, "\u0120awe": 25030, "Professor": 25031, "funding": 25032, "\u0120physiological": 25033, "\u0120Destruction": 25034, "\u0120limb": 25035, "\u0120manipulated": 25036, "\u0120bubbles": 25037, "\u0120pseud": 25038, "\u0120hydra": 25039, "\u0120Bristol": 25040, "\u0120stellar": 25041, "\u0120Expansion": 25042, "\u0120Kell": 25043, "\u0120Interestingly": 25044, "\u0120mans": 25045, "\u0120dragging": 25046, "\u0120ecological": 25047, "\u0120Fit": 25048, "\u0120gent": 25049, "\u0120benefited": 25050, "\u0120Haiti": 25051, "\u0120polyg": 25052, "\u00e3\u0125\u0130": 25053, "\u01202030": 25054, "\u0120prow": 25055, "\u0120reconstruction": 25056, "\u0120wast": 25057, "\u0120psychic": 25058, "\u0120Greeks": 25059, "Handler": 25060, "162": 25061, "\u0120Pulse": 25062, "\u0120solicit": 25063, "\u0120sys": 25064, "\u0120influx": 25065, "\u0120Gentle": 25066, "percent": 25067, "\u0120proliferation": 25068, "\u0120taxable": 25069, "\u0120disregard": 25070, "\u0120escaping": 25071, "\u0120ginger": 25072, "\u0120withstand": 25073, "\u0120devastated": 25074, "\u0120Dew": 25075, "series": 25076, "\u0120injected": 25077, "elaide": 25078, "\u0120turnover": 25079, "heat": 25080, "\u013b\u0124": 25081, "Happy": 25082, "\u0120Silent": 25083, "\u00e3\u0124\u0143": 25084, "ivism": 25085, "\u0120irrational": 25086, "AMA": 25087, "\u0120reef": 25088, "rub": 25089, "\u0120162": 25090, "\u0120bankers": 25091, "\u0120Ethics": 25092, "vv": 25093, "\u0120criticisms": 25094, "Kn": 25095, "186": 25096, "Movie": 25097, "\u0120Tories": 25098, "\u0120nood": 25099, "\u0120distortion": 25100, "False": 25101, "odore": 25102, "\u0120tasty": 25103, "Research": 25104, "\u0120UID": 25105, "-)": 25106, "\u0120divorced": 25107, "\u0120MU": 25108, "\u0120Hayes": 25109, "\u0120Isn": 25110, "iani": 25111, "\u0120HQ": 25112, "\u0120\"#": 25113, "ignant": 25114, "\u0120traumatic": 25115, "\u0120Ling": 25116, "Hun": 25117, "\u0120sabot": 25118, "online": 25119, "random": 25120, "\u0120renamed": 25121, "rared": 25122, "KA": 25123, "dead": 25124, "\u00c3\u00a9t": 25125, "\u0120Assistance": 25126, "\u0120seaf": 25127, "++++++++": 25128, "\u0120seldom": 25129, "\u0120Webb": 25130, "\u0120boolean": 25131, "ulet": 25132, "\u0120refrain": 25133, "\u0120DIY": 25134, "rule": 25135, "\u0120shutting": 25136, "\u0120utilizing": 25137, "loading": 25138, "\u0120Param": 25139, "coal": 25140, "ooter": 25141, "\u0120attracting": 25142, "\u0120Dol": 25143, "\u0120hers": 25144, "agnetic": 25145, "\u0120Reach": 25146, "imo": 25147, "\u0120discarded": 25148, "\u0120Pip": 25149, "015": 25150, "\u00c3\u00bcr": 25151, "\u0120mug": 25152, "Imagine": 25153, "COL": 25154, "\u0120cursed": 25155, "\u0120Shows": 25156, "\u0120Curtis": 25157, "\u0120Sachs": 25158, "speaking": 25159, "\u0120Vista": 25160, "\u0120Framework": 25161, "ongo": 25162, "\u0120subreddit": 25163, "\u0120crus": 25164, "\u0120Oval": 25165, "Row": 25166, "growing": 25167, "\u0120installment": 25168, "\u0120glac": 25169, "\u0120Advance": 25170, "ECK": 25171, "\u0120LGBTQ": 25172, "LEY": 25173, "\u0120acet": 25174, "\u0120successive": 25175, "\u0120Nicole": 25176, "\u01201957": 25177, "Quote": 25178, "\u0120circumstance": 25179, "ackets": 25180, "\u0120142": 25181, "ortium": 25182, "\u0120guessed": 25183, "\u0120Frame": 25184, "\u0120perpetrators": 25185, "\u0120Aviation": 25186, "\u0120Bench": 25187, "\u0120handc": 25188, "Ap": 25189, "\u01201956": 25190, "259": 25191, "rand": 25192, "NetMessage": 25193, "din": 25194, "urtles": 25195, "hig": 25196, "\u0120VIII": 25197, "ffiti": 25198, "\u0120Swords": 25199, "bial": 25200, "\u0120kidnapping": 25201, "device": 25202, "\u0120barn": 25203, "\u0120Eli": 25204, "aucas": 25205, "Send": 25206, "Constructed": 25207, "\u0120\u00c2\u00bd": 25208, "\u0120needles": 25209, "\u0120advertisements": 25210, "\u0120vou": 25211, "\u0120exhibited": 25212, "\u0120Fortress": 25213, "Ask": 25214, "Berry": 25215, "TYPE": 25216, "\u0120cancers": 25217, "umping": 25218, "\u0120Territory": 25219, "\u0120prud": 25220, "\u0120nas": 25221, "\u0120atheist": 25222, "\u0120balances": 25223, "\u00e3\u0123\u0141": 25224, "\u0120Shawn": 25225, "&&": 25226, "\u0120landsc": 25227, "\u0120RGB": 25228, "\u0120petty": 25229, "\u0120excellence": 25230, "\u0120translations": 25231, "\u0120parcel": 25232, "\u0120Chev": 25233, "East": 25234, "\u0120Output": 25235, "imi": 25236, "\u0120ambient": 25237, "\u0120Threat": 25238, "\u0120villains": 25239, "\u0120550": 25240, "ICA": 25241, "\u0120taller": 25242, "\u0120leaking": 25243, "cup": 25244, "\u0120polish": 25245, "\u0120infectious": 25246, "\u0120KC": 25247, "\u0120@@": 25248, "background": 25249, "\u0120bureaucracy": 25250, "\u0120Sai": 25251, "unless": 25252, "itious": 25253, "\u0120Skype": 25254, "Atl": 25255, "IDENT": 25256, "008": 25257, "\u0120hypocr": 25258, "\u0120pitchers": 25259, "\u0120guessing": 25260, "\u0120FINAL": 25261, "Between": 25262, "\u0120villagers": 25263, "\u0120252": 25264, "fashion": 25265, "\u0120Tunis": 25266, "Beh": 25267, "\u0120Exc": 25268, "\u0120MID": 25269, "288": 25270, "\u0120Haskell": 25271, "196": 25272, "\u0120NOR": 25273, "\u0120specs": 25274, "\u0120invari": 25275, "\u0120glut": 25276, "\u0120Cars": 25277, "\u0120impulse": 25278, "\u0120honors": 25279, "gel": 25280, "\u0120jurisdictions": 25281, "\u0120Bundle": 25282, "ulas": 25283, "California": 25284, "\u0120Increase": 25285, "\u0120pear": 25286, "\u0120singles": 25287, "\u0120cues": 25288, "\u0120underwent": 25289, "\u0120WS": 25290, "\u0120exaggerated": 25291, "\u0120dubious": 25292, "\u0120flashing": 25293, "LOG": 25294, ")].": 25295, "Journal": 25296, "tg": 25297, "Van": 25298, "\u0120Istanbul": 25299, "\u0120Insp": 25300, "\u0120Franken": 25301, "Draw": 25302, "\u0120sadness": 25303, "\u0120ironic": 25304, "\u0120Fry": 25305, "xc": 25306, "\u0120164": 25307, "isch": 25308, "Way": 25309, "\u0120Protestant": 25310, "horn": 25311, "\u0120unaff": 25312, "\u0120Viv": 25313, "illas": 25314, "\u0120Productions": 25315, "\u0120Hogan": 25316, "\u0120perimeter": 25317, "\u0120Sisters": 25318, "\u0120spontaneous": 25319, "\u0120downside": 25320, "\u0120descendants": 25321, "\u0120orn": 25322, "worm": 25323, "Japanese": 25324, "\u01201955": 25325, "\u0120151": 25326, "\u0120Doing": 25327, "elsen": 25328, "umbles": 25329, "\u0120radically": 25330, "\u0120Drum": 25331, "\u0120Bach": 25332, "\u0120liabilities": 25333, "\u0120OB": 25334, "\u0120Elementary": 25335, "\u0120meme": 25336, "ynes": 25337, "\u0120fingerprint": 25338, "\u0120Grab": 25339, "\u0120undertake": 25340, "Members": 25341, "\u0120Reader": 25342, "\u0120Sims": 25343, "god": 25344, "\u0120hypothetical": 25345, "scient": 25346, "\u0120AJ": 25347, "\u0120charism": 25348, "\u0120admissions": 25349, "\u0120Missile": 25350, "trade": 25351, "\u0120exercising": 25352, "\u0120Background": 25353, "Written": 25354, "\u0120vocals": 25355, "whether": 25356, "\u0120vi": 25357, "\u0120Winner": 25358, "\u0120litter": 25359, "\u0120Shooting": 25360, "STEM": 25361, "\u00e3\u0124\u00a1": 25362, "\u0120AFL": 25363, "\u0120variability": 25364, "\u0120eats": 25365, "\u0120DPS": 25366, "brow": 25367, "\u0120elephants": 25368, "\u0120strat": 25369, "\u0120\u00c5": 25370, "\u0120settlers": 25371, "Matthew": 25372, "\u0120inadvert": 25373, "HI": 25374, "\u0120IMF": 25375, "\u0120Goal": 25376, "\u0120nerves": 25377, "Johnson": 25378, "eye": 25379, "ablishment": 25380, "Thursday": 25381, "BILITY": 25382, "Had": 25383, "amoto": 25384, "hetamine": 25385, "eps": 25386, "\u0120mitochond": 25387, "\u0120compressed": 25388, "\u0120Trevor": 25389, "\u0120Animals": 25390, "Tool": 25391, "Lock": 25392, "\u0120tweak": 25393, "\u0120pinch": 25394, "\u0120cancellation": 25395, "Pot": 25396, "\u0120focal": 25397, "\u0120Astron": 25398, "173": 25399, "\u0120ASC": 25400, "\u0120OTHER": 25401, "umni": 25402, "\u0120demise": 25403, "dl": 25404, "\u00d9\u0127": 25405, "Semitism": 25406, "\u0120cracking": 25407, "\u0120collaborative": 25408, "\u0120explores": 25409, "sql": 25410, "\u0120herbs": 25411, "\u0120configurations": 25412, "mis": 25413, "\u0120Result": 25414, "acey": 25415, "\u0120Smoke": 25416, "\u0120sanct": 25417, "elia": 25418, "\u0120degener": 25419, "\u0120deepest": 25420, "\u0120screamed": 25421, "\u0120nap": 25422, "Software": 25423, "\u0120STAR": 25424, "EF": 25425, "\u0120Xin": 25426, "sponsored": 25427, "manship": 25428, "233": 25429, "\u0120primaries": 25430, "\u0120filtering": 25431, "\u0120assemble": 25432, "mil": 25433, "\u0120Myers": 25434, "bows": 25435, "\u0120punched": 25436, "Mic": 25437, "\u0120innovations": 25438, "\u0120func": 25439, "ando": 25440, "\u0120fracking": 25441, "\u0120Vul": 25442, "\u00d0\u00be\u00d0": 25443, "oshop": 25444, "\u0120Immun": 25445, "\u0120settling": 25446, "\u0120adolescents": 25447, "\u0120rebuilding": 25448, "\u0120transforming": 25449, "\u0120parole": 25450, "\u0120harbor": 25451, "\u0120booking": 25452, "otional": 25453, "ongevity": 25454, "\u0120Yo": 25455, "bug": 25456, "\u0120emerges": 25457, "\u0120Methods": 25458, "\u0120Chu": 25459, "Pres": 25460, "\u0120Dungeons": 25461, "\u0120trailing": 25462, "\u0120Rum": 25463, "\u0120Hugh": 25464, "\u00e5\u00a4\u00a9": 25465, "\u0120Era": 25466, "\u0120Battles": 25467, "Results": 25468, "\u0120Trading": 25469, "\u0120versa": 25470, "css": 25471, "axies": 25472, "heet": 25473, "\u0120greed": 25474, "1989": 25475, "\u0120gardens": 25476, "\u0120contingent": 25477, "Park": 25478, "\u0120Leafs": 25479, "hook": 25480, "robe": 25481, "\u0120diplomacy": 25482, "\u0120Fuel": 25483, "\u0120Invasion": 25484, "\u0120upgrading": 25485, "Male": 25486, "\u0120elic": 25487, "\u0120relentless": 25488, "\u0120Covenant": 25489, "apesh": 25490, "\u0120Trop": 25491, "Ty": 25492, "production": 25493, "arty": 25494, "\u0120punches": 25495, "ako": 25496, "cyclopedia": 25497, "\u0120Rabbit": 25498, "\u0120HDMI": 25499, "\u0120141": 25500, "\u0120foil": 25501, "ItemImage": 25502, "\u0120FG": 25503, "\u0120implementations": 25504, "\u0120Pom": 25505, "ixtures": 25506, "\u0120await": 25507, "\u0120330": 25508, "amus": 25509, "\u0120umbrella": 25510, "\u0120foresee": 25511, "separ": 25512, "\u0120circumcision": 25513, "\u0120peripheral": 25514, "Say": 25515, "\u0120Expert": 25516, "Inc": 25517, "\u0120withdrew": 25518, "\u0120Anders": 25519, "fried": 25520, "\u0120radioactive": 25521, "\u0120Opening": 25522, "\u0120boarding": 25523, "\u0120ND": 25524, "\u0120overthrow": 25525, "Activ": 25526, "WP": 25527, "\u0120Acts": 25528, "\u00d7\u013b": 25529, "\u0120motions": 25530, "vic": 25531, "\u0120Mighty": 25532, "\u0120Defender": 25533, "aer": 25534, "\u0120thankful": 25535, "\u0120Killing": 25536, "\u0120Bris": 25537, "moil": 25538, "\u0120predicting": 25539, "266": 25540, "choice": 25541, "\u0120killers": 25542, "\u0120incub": 25543, "\u0120Chest": 25544, "athering": 25545, "\u0120proclaimed": 25546, "flower": 25547, "ossom": 25548, "umbledore": 25549, "\u0120Cycling": 25550, "\u0120Occupy": 25551, "AGES": 25552, "Pen": 25553, "\u0120Yug": 25554, "\u0120packaged": 25555, "\u0120heightened": 25556, "cot": 25557, "stack": 25558, "Cond": 25559, "\u0120stamps": 25560, "mage": 25561, "\u0120persuaded": 25562, "\u0120ensl": 25563, "\u0120Cardinal": 25564, "\u0120solitary": 25565, "\u0120possessing": 25566, "\u0120Cork": 25567, "\u0120evid": 25568, "\u0120Tay": 25569, "\u0120blues": 25570, "\u0120extremism": 25571, "\u0120lunar": 25572, "\u0120clown": 25573, "Techn": 25574, "\u0120festivals": 25575, "\u0120PvP": 25576, "\u0120Lar": 25577, "\u0120consequently": 25578, "present": 25579, "\u0120someday": 25580, "\u00e7\u0130\u012d": 25581, "\u0120Meteor": 25582, "\u0120touring": 25583, "culture": 25584, "\u0120beaches": 25585, "Ship": 25586, "cause": 25587, "\u0120Flood": 25588, "\u00e3\u0125\u00af": 25589, "\u0120purity": 25590, "those": 25591, "\u0120emission": 25592, "bolt": 25593, "\u0120chord": 25594, "\u0120Scripture": 25595, "Lu": 25596, "\u0120${": 25597, "created": 25598, "Others": 25599, "258": 25600, "\u0120elemental": 25601, "\u0120annoyed": 25602, "\u0120AE": 25603, "dan": 25604, "\u0120Sag": 25605, "Researchers": 25606, "\u0120fairy": 25607, "\u00e2\u0122\u0135\u00e2\u0122\u0135": 25608, "============": 25609, "Smart": 25610, "GGGG": 25611, "\u0120skeletons": 25612, "\u0120pupils": 25613, "linked": 25614, "\u0120urgency": 25615, "enabled": 25616, "\u0120Fuck": 25617, "\u0120councill": 25618, "rab": 25619, "UAL": 25620, "TI": 25621, "\u0120lifes": 25622, "\u0120confessed": 25623, "Bug": 25624, "\u0120harmon": 25625, "\u0120CONFIG": 25626, "\u0120Neutral": 25627, "Double": 25628, "\u0120staple": 25629, "\u0120SHA": 25630, "British": 25631, "\u0120SNP": 25632, "ATOR": 25633, "oco": 25634, "\u0120swinging": 25635, "gex": 25636, "oleon": 25637, "plain": 25638, "\u0120Missing": 25639, "\u0120Trophy": 25640, "vari": 25641, "ranch": 25642, "\u0120301": 25643, "440": 25644, "0000000000000000": 25645, "\u0120restoring": 25646, "\u0120haul": 25647, "ucing": 25648, "nerg": 25649, "\u0120futures": 25650, "\u0120strategist": 25651, "question": 25652, "\u0120lateral": 25653, "\u0120Bard": 25654, "\u0120sor": 25655, "\u0120Rhodes": 25656, "\u0120Downtown": 25657, "?????-": 25658, "\u0120Lit": 25659, "\u0120Bened": 25660, "\u0120coil": 25661, "street": 25662, "\u0120Portal": 25663, "FILE": 25664, "\u0120Gru": 25665, "*,": 25666, "231": 25667, "neum": 25668, "\u0120sucked": 25669, "\u0120rapper": 25670, "\u0120tendencies": 25671, "\u0120Lauren": 25672, "cellaneous": 25673, "267": 25674, "\u0120browse": 25675, "\u0120overc": 25676, "header": 25677, "oise": 25678, "\u0120beet": 25679, "\u0120Gle": 25680, "Stay": 25681, "\u0120mum": 25682, "\u0120typed": 25683, "\u0120discounts": 25684, "Talk": 25685, "\u0120Og": 25686, "existing": 25687, "\u0120Sell": 25688, "uph": 25689, "CI": 25690, "\u0120Austrian": 25691, "\u0120Warm": 25692, "\u0120dismissal": 25693, "\u0120averages": 25694, "camera": 25695, "\u0120allegiance": 25696, "LAN": 25697, "=\"#": 25698, "\u0120commentators": 25699, "\u0120Setting": 25700, "\u0120Midwest": 25701, "\u0120pharmac": 25702, "\u0120EXP": 25703, "\u0120stainless": 25704, "Chicago": 25705, "\u0120tan": 25706, "244": 25707, "\u0120countryside": 25708, "\u0120Vac": 25709, "295": 25710, "\u0120pinned": 25711, "\u0120crises": 25712, "\u0120standardized": 25713, "Task": 25714, "\u0120Jail": 25715, "\u0120Docker": 25716, "colored": 25717, "forth": 25718, "\"},": 25719, "\u0120patrons": 25720, "\u0120spice": 25721, "\u0120mourn": 25722, "\u0120Mood": 25723, "\u0120laundry": 25724, "\u0120equip": 25725, "\u0120Mole": 25726, "yll": 25727, "\u0120THC": 25728, "nation": 25729, "\u0120Sherlock": 25730, "\u0120issu": 25731, "\u0120Kre": 25732, "\u0120Americas": 25733, "\u0120AAA": 25734, "\u0120systematically": 25735, "\u0120contra": 25736, "\u0120Sally": 25737, "\u0120rationale": 25738, "\u0120carriage": 25739, "\u0120peaks": 25740, "\u0120contradiction": 25741, "ensation": 25742, "\u0120Failure": 25743, "\u0120props": 25744, "\u0120namespace": 25745, "\u0120cove": 25746, "fields": 25747, "\u00e3\u0124\u012d": 25748, "\u0120wool": 25749, "\u0120Catch": 25750, "\u0120presumed": 25751, "\u0120Diana": 25752, "ragon": 25753, "igi": 25754, "\u0120hamm": 25755, "\u0120stunt": 25756, "\u0120GUI": 25757, "\u0120Observatory": 25758, "\u0120Shore": 25759, "\u0120smells": 25760, "annah": 25761, "\u0120cockpit": 25762, "\u0120Duterte": 25763, "850": 25764, "\u0120oppressed": 25765, "breaker": 25766, "\u0120Contribut": 25767, "\u0120Peru": 25768, "\u0120Monsanto": 25769, "\u0120Attempt": 25770, "\u0120commanding": 25771, "\u0120fridge": 25772, "\u0120Rin": 25773, "\u0120Chess": 25774, "uality": 25775, "\u0120ol": 25776, "Republican": 25777, "\u0120Glory": 25778, "\u0120WIN": 25779, ".......": 25780, "agent": 25781, "reading": 25782, "\u0120inh": 25783, "Jones": 25784, "\u0120clicks": 25785, "alan": 25786, "\u0120[];": 25787, "\u0120Majesty": 25788, "\u0120Ced": 25789, "opus": 25790, "atel": 25791, "\u00c3\u00aa": 25792, "ARC": 25793, "\u0120Ecuador": 25794, "\u00e3\u0125\u0142": 25795, "\u0120Kuro": 25796, "\u0120rituals": 25797, "\u0120captive": 25798, "\u0120ounce": 25799, "\u0120disagreement": 25800, "\u0120slog": 25801, "fuel": 25802, "Pet": 25803, "Mail": 25804, "\u0120exercised": 25805, "\u0120solic": 25806, "\u0120rainfall": 25807, "\u0120devotion": 25808, "\u0120Assessment": 25809, "\u0120robotic": 25810, "options": 25811, "\u0120RP": 25812, "\u0120Families": 25813, "\u0120Flames": 25814, "\u0120assignments": 25815, "007": 25816, "akedown": 25817, "\u0120vocabulary": 25818, "Reilly": 25819, "\u0120caval": 25820, "gars": 25821, "\u0120suppressed": 25822, "\u0120SET": 25823, "\u0120Johns": 25824, "\u0120warp": 25825, "broken": 25826, "\u0120statues": 25827, "\u0120advocated": 25828, "\u0120275": 25829, "\u0120peril": 25830, "omorph": 25831, "\u0120Femin": 25832, "perfect": 25833, "\u0120hatch": 25834, "Lib": 25835, "512": 25836, "\u0120lifelong": 25837, "313": 25838, "\u0120cheeks": 25839, "\u0120numbered": 25840, "\u0120Mug": 25841, "Body": 25842, "ravel": 25843, "Weight": 25844, "\u0120Jak": 25845, "\u0120Heath": 25846, "\u0120kissing": 25847, "\u0120JUST": 25848, "\u0120waving": 25849, "upload": 25850, "\u0120insider": 25851, "\u0120Progressive": 25852, "\u0120Filter": 25853, "tta": 25854, "\u0120Beam": 25855, "\u0120violently": 25856, "ipation": 25857, "\u0120skepticism": 25858, "\u01201918": 25859, "\u0120Annie": 25860, "\u0120SI": 25861, "\u0120genetics": 25862, "\u0120onboard": 25863, "atl": 25864, "\u0120Friedman": 25865, "\u0120Bri": 25866, "ceptive": 25867, "\u0120pirate": 25868, "\u0120Reporter": 25869, "278": 25870, "\u0120mythology": 25871, "\u0120eclipse": 25872, "\u0120skins": 25873, "\u0120glyph": 25874, "ingham": 25875, "Files": 25876, "Cour": 25877, "women": 25878, "\u0120regimes": 25879, "\u0120photographed": 25880, "Kat": 25881, "\u0120MAX": 25882, "Officials": 25883, "\u0120unexpectedly": 25884, "\u0120impressions": 25885, "Front": 25886, ";;;;;;;;": 25887, "\u0120supremacy": 25888, "\u0120sang": 25889, "\u0120aggravated": 25890, "\u0120abruptly": 25891, "\u0120Sector": 25892, "\u0120excuses": 25893, "\u0120costing": 25894, "idepress": 25895, "Stack": 25896, "\u0120RNA": 25897, "obil": 25898, "\u0120ghosts": 25899, "ldon": 25900, "atibility": 25901, "Topics": 25902, "\u0120reimburse": 25903, "\u0120HM": 25904, "\u0120Deg": 25905, "\u0120thief": 25906, "yet": 25907, "ogenesis": 25908, "leaning": 25909, "\u0120Kol": 25910, "\u0120Basketball": 25911, "\u0120fi": 25912, "\u0120Seeing": 25913, "\u0120recycling": 25914, "\u0120[-": 25915, "Congress": 25916, "\u0120lectures": 25917, "Psy": 25918, "\u0120nep": 25919, "\u0120maid": 25920, "\u0120oriented": 25921, "AX": 25922, "\u0120respectful": 25923, "rene": 25924, "flush": 25925, "\u0120Unloaded": 25926, "request": 25927, "grid": 25928, "\u0120Alternatively": 25929, "\u0120Hugo": 25930, "\u0120decree": 25931, "\u0120Buddhism": 25932, "andum": 25933, "Android": 25934, "\u0120Congo": 25935, "\u0120Joyce": 25936, "\u0120acknowledging": 25937, "hesive": 25938, "\u0120Tomorrow": 25939, "\u0120Hiro": 25940, "thren": 25941, "\u0120Maced": 25942, "\u0120hoax": 25943, "\u0120Increased": 25944, "\u0120Pradesh": 25945, "Wild": 25946, "______": 25947, "161": 25948, "\u0120aunt": 25949, "\u0120distributing": 25950, "\u0120Tucker": 25951, "\u0120SSL": 25952, "\u0120Wolves": 25953, "Building": 25954, "oult": 25955, "\u0120Luo": 25956, "\u0120Yas": 25957, "\u0120Spir": 25958, "\u0120Shape": 25959, "\u0120Cambod": 25960, "\u0120IPv": 25961, "\u0120ml": 25962, "\u0120extrad": 25963, "390": 25964, "\u0120Penny": 25965, "dream": 25966, "\u0120stationed": 25967, "optional": 25968, "eworthy": 25969, ".</": 25970, "\u0120undertaking": 25971, "\u0120chickens": 25972, "\u0120stimuli": 25973, "\u0120Else": 25974, "igators": 25975, "\u0120Beginning": 25976, "ctory": 25977, "\u0120prepares": 25978, "\u0120delta": 25979, "\u0120vicinity": 25980, "tool": 25981, "\u0120workshops": 25982, "MHz": 25983, "\u0120accusation": 25984, "\u0120histories": 25985, "ropolis": 25986, "\u0120Churchill": 25987, "\u0120neon": 25988, "\u0120baff": 25989, "dies": 25990, "maybe": 25991, "\u0120\u00e8\u00a3\u0131\u00e8\u00a6\u013c\u00e9\u0128\u0134": 25992, "\u0120symptom": 25993, "ECH": 25994, "\u0120Manuel": 25995, "\u0120banana": 25996, "\u0120HB": 25997, "\u0120****": 25998, "\u0120Koreans": 25999, "coll": 26000, "FB": 26001, "\u0120praying": 26002, "\u0120Cannot": 26003, "\u0120Mile": 26004, "\u0120embracing": 26005, "\u0120Silk": 26006, "393": 26007, "oters": 26008, "FD": 26009, "\u0120daylight": 26010, "alias": 26011, "\u0120Brigade": 26012, "\u0120Hannah": 26013, "\u0120clergy": 26014, "\u0120southeast": 26015, "\u0120alcoholic": 26016, "\u0120proposes": 26017, "livion": 26018, "\u0120calculating": 26019, "\u0120stimulate": 26020, "\u0120splitting": 26021, "eight": 26022, "\u0120Indy": 26023, "plays": 26024, "\u0120Pik": 26025, "\u0120domest": 26026, "\u0120forgiveness": 26027, "\u0120Rings": 26028, "patient": 26029, "kinson": 26030, "Mont": 26031, "igible": 26032, ";\"": 26033, "\u0120periodically": 26034, "ammad": 26035, "\u0120Britt": 26036, "pard": 26037, "\u0120arbitration": 26038, "\u0120Schneider": 26039, "\u0120Corporate": 26040, "\u0120Maya": 26041, "\u0120snakes": 26042, "aum": 26043, "\u0120blasted": 26044, "\u0120mysteries": 26045, "\u0120revive": 26046, "ocamp": 26047, "\u0120Dodge": 26048, "\u0120Opera": 26049, "279": 26050, "\u0120orphan": 26051, "\u0120specifies": 26052, "\u0120Mets": 26053, "Duration": 26054, "Hen": 26055, "\u0120fireworks": 26056, "\u0120prosecute": 26057, "\u0120Tillerson": 26058, "dp": 26059, "usage": 26060, "liness": 26061, "\u0120Debian": 26062, "\u0120224": 26063, "rises": 26064, "\u0120Infect": 26065, "atra": 26066, "\u0120RR": 26067, "\u0120Lor": 26068, "diff": 26069, "\u0120Charleston": 26070, "\u0120acoustic": 26071, "\u0120amuse": 26072, "330": 26073, "\u0120cer": 26074, "\u0120Tac": 26075, "\u0120[+": 26076, "\u0120cardiac": 26077, "\u0120Restaurant": 26078, "ergy": 26079, "\u0120fuzz": 26080, "\u0120bites": 26081, "\u0120hazardous": 26082, "\u0120brighter": 26083, "rans": 26084, "\u0120Stephanie": 26085, "extra": 26086, "RET": 26087, "\u0120Christine": 26088, "\u0120Sue": 26089, "statement": 26090, "\u0120bolster": 26091, "\u0120antit": 26092, "Radio": 26093, "BIT": 26094, "\u00e3\u0124\u00b0": 26095, "\u0120visions": 26096, "\u0120Concept": 26097, "\u0120inline": 26098, "\u0120Philosophy": 26099, "isans": 26100, "\u0120Irving": 26101, "\u00c3\u00a3": 26102, "taking": 26103, "\u0120inconsist": 26104, "\u0120Kumar": 26105, "\u0120lig": 26106, "\u0120Schumer": 26107, "\u0120Regulations": 26108, "\u0120Hz": 26109, "thro": 26110, "\u0120Voldemort": 26111, "\u0120MED": 26112, "\u0120Frederick": 26113, "Pad": 26114, "221": 26115, "\u0120alleging": 26116, "\u0120Communication": 26117, "\u0120167": 26118, "\u0120forecasts": 26119, "\u0120spiders": 26120, "Organ": 26121, "\u0120Participants": 26122, "\u0120Ops": 26123, "design": 26124, "Close": 26125, "\u0120facto": 26126, "\u0120bombers": 26127, "resistant": 26128, "ategories": 26129, "School": 26130, "\u0120homework": 26131, "\u0120corro": 26132, "Tuesday": 26133, "\u0120Brendan": 26134, "\u0120MX": 26135, "\u0120TS": 26136, "\u0120Stri": 26137, "\u0120stakeholders": 26138, "\u0120Millennium": 26139, "\u0120transferring": 26140, "Jud": 26141, "\u0120tac": 26142, "\u01201600": 26143, "\u0120SDK": 26144, "rb": 26145, "\u0120interpretations": 26146, "\u0120SG": 26147, "\u0120upstairs": 26148, "\u0120Harvest": 26149, "\u0120vagina": 26150, "\u0120ingest": 26151, "xf": 26152, "\u0120Orion": 26153, "\u0120Joey": 26154, "\u0120sandwic": 26155, "\u0120immortal": 26156, "\u0120flipped": 26157, "ortex": 26158, "threatening": 26159, "\u0120sniper": 26160, "\u0120converts": 26161, "\u0120installations": 26162, "\u0120Bulgar": 26163, "orsche": 26164, "mails": 26165, "\u0120lure": 26166, "\u0120narrowly": 26167, "\u0120grenade": 26168, "\u0120Ging": 26169, "\u0120underwear": 26170, "--------------": 26171, "\u0120chased": 26172, "\u0120VAL": 26173, "\u0120parenting": 26174, "\u0120Hamb": 26175, "\u0120Blaz": 26176, "\u0120anarchist": 26177, "\u0120Median": 26178, "\u0120Programs": 26179, "\u00ce\u00bd": 26180, "\u0120obj": 26181, "\u0120Nokia": 26182, "orman": 26183, "anqu": 26184, "atism": 26185, "opa": 26186, "\u0120fulfilling": 26187, "\u0120puppy": 26188, "\u0120entit": 26189, "\u0120Sebastian": 26190, "\u0120shooters": 26191, "\u0120richer": 26192, "\u00e8\u00a1": 26193, "\u0120tempted": 26194, "\u0120ATT": 26195, "\u0120CV": 26196, "\u0120tore": 26197, "Resource": 26198, "\u0120Devils": 26199, "408": 26200, "inational": 26201, "\u0120assurance": 26202, "\u0120Darren": 26203, "\u0120whichever": 26204, "posure": 26205, "\u0120fury": 26206, "Stock": 26207, "\u0120universally": 26208, "response": 26209, "\u0120oak": 26210, "\u0120workload": 26211, "\u0120Corner": 26212, "eele": 26213, "\"...": 26214, "\u0120deprived": 26215, "kowski": 26216, "\u0120casts": 26217, "\u0120affiliation": 26218, "\u0120Ach": 26219, "\u0120Asked": 26220, "athe": 26221, "\u0120lact": 26222, "\u0120Thu": 26223, "rm": 26224, "\u0120airlines": 26225, "\u0120notions": 26226, "Format": 26227, "\u0120FAA": 26228, "\u00e3\u0125\u012c": 26229, "driver": 26230, "\u0120transcend": 26231, "Settings": 26232, "\u0120Prosecut": 26233, "\u0120spinal": 26234, "\u0120defaults": 26235, "FK": 26236, "\u0120prefers": 26237, "rendered": 26238, "thus": 26239, "film": 26240, "\u0120tiger": 26241, "\u0120Spicer": 26242, "recogn": 26243, "\u0120Rugby": 26244, "Network": 26245, "\u0120pity": 26246, "\u0120compartment": 26247, "casters": 26248, "\u0120Monroe": 26249, "\u0120720": 26250, "\u0120corrections": 26251, "\u0120dopamine": 26252, "\u0120AZ": 26253, "Cut": 26254, "\u0120roomm": 26255, "\u0120speculate": 26256, "Hash": 26257, "\u0120restrictive": 26258, "1111": 26259, "redible": 26260, "onel": 26261, "\u0120rampant": 26262, "reported": 26263, "\u0120Suite": 26264, "\u0120Minimum": 26265, "alys": 26266, "azard": 26267, "loop": 26268, "\u0120lent": 26269, "sha": 26270, "\u0120vandal": 26271, "menu": 26272, "\u0120Boehner": 26273, "\u0120narratives": 26274, "\u0120authenticity": 26275, "269": 26276, "anic": 26277, "duty": 26278, "285": 26279, "\u0120thanked": 26280, "\u0120betrayed": 26281, "lift": 26282, "\u0120southwest": 26283, "\u0120Dexter": 26284, "\u0120Bod": 26285, "\u0120keywords": 26286, "Average": 26287, "DIS": 26288, "\u0120ethnicity": 26289, "!),": 26290, "\u0120Nationals": 26291, "\u00e1\u00b9": 26292, "\u0120Tah": 26293, "ioxid": 26294, "\u0120widget": 26295, "\u0120pasta": 26296, "\u0120billing": 26297, "\u0120trilogy": 26298, "\u0120Lines": 26299, "\u0120sniff": 26300, "\u0120nephew": 26301, "Late": 26302, "\u0120princip": 26303, "\u0120Loop": 26304, "\u0120Marxist": 26305, "\u0120dissolved": 26306, "\u0120contexts": 26307, "\u0120Amount": 26308, "\u0120Spike": 26309, "\u0120totals": 26310, "\u0120organizer": 26311, "\u0120uprising": 26312, "ships": 26313, "YY": 26314, "\u0120Northeast": 26315, "money": 26316, "gradation": 26317, "\u0120goalkeeper": 26318, "\u0120Hear": 26319, "\u0120steak": 26320, "\u0120BuzzFeed": 26321, "\u0120solemn": 26322, "\u0120Scand": 26323, "\u0120popping": 26324, "\u0120adhere": 26325, "\u0120Alleg": 26326, "byte": 26327, "\u0120Wolver": 26328, "\u0120unin": 26329, "\u0120recol": 26330, "itud": 26331, "\u0120mimic": 26332, "ibus": 26333, "\u0120predicts": 26334, "\u0120Keeper": 26335, "iating": 26336, "\u0120deception": 26337, "\u0120learnt": 26338, "\u0120diary": 26339, "\u0120conditional": 26340, "\u0120relic": 26341, "\u0120invoke": 26342, "ienced": 26343, "\u00e5\u012a": 26344, "\u0120Pont": 26345, "\u0120cellphone": 26346, "\u0120speeding": 26347, "\u0120tackling": 26348, "\u0120nude": 26349, "opened": 26350, "\u0120Manafort": 26351, "\u01201952": 26352, "\u0120majors": 26353, "\u0120Silence": 26354, "\u0120logistics": 26355, "\u0120weighted": 26356, "\u0120Psychiat": 26357, "\":[\"": 26358, "\u0120sickness": 26359, "\u0120dividends": 26360, "zon": 26361, "Release": 26362, "\u0120Keys": 26363, "\u0120Ich": 26364, "\u0120enz": 26365, "\u0120Fernand": 26366, "\u0120\u00ce\u00b1": 26367, "\u0120meanings": 26368, "\u0120penny": 26369, "\u0120stern": 26370, "\u0120lar": 26371, "\u0120Published": 26372, "\u0120backdrop": 26373, "Kim": 26374, "\u0120Synt": 26375, "\u0120debuted": 26376, "wm": 26377, "\u0120Isle": 26378, "\u0120regulating": 26379, "otti": 26380, "\u0120Scholars": 26381, "icester": 26382, "\u0120Chef": 26383, "\u0120pops": 26384, "\u0120Launcher": 26385, "\u0120Various": 26386, "\u0120commenting": 26387, "oslav": 26388, "enzie": 26389, "\u0120rivalry": 26390, "\u00e2\u0124\u00ac": 26391, "Really": 26392, "\u0120orc": 26393, "\u0120bean": 26394, "\u0120Judy": 26395, "Notice": 26396, "\u0120Bike": 26397, "?]": 26398, "\u0120rented": 26399, "sten": 26400, "\u0120forefront": 26401, "\u0120Baldwin": 26402, "\u0120yielded": 26403, "tails": 26404, "Prime": 26405, "\u0120Sources": 26406, "icator": 26407, "Sean": 26408, "\u0120marching": 26409, "Output": 26410, "\u0120Jungle": 26411, "\u0120reside": 26412, "zzle": 26413, "\u0120Andrews": 26414, "\u0120torque": 26415, "Basic": 26416, "Actually": 26417, "strap": 26418, "penter": 26419, "\u0120exams": 26420, "\u0120Ya": 26421, "\u0120159": 26422, "\u0120Decision": 26423, "\u0120ransom": 26424, "eteenth": 26425, "ensing": 26426, "213": 26427, "\u0120sunset": 26428, "404": 26429, "\u0120Rapid": 26430, "\u0120Hein": 26431, "\u0120Aboriginal": 26432, "\u0120organism": 26433, "\u0120Sever": 26434, "\u0120cla": 26435, "aji": 26436, "Simple": 26437, "\u0120Flavor": 26438, "\u0120Eval": 26439, "prus": 26440, "\u0120chorus": 26441, "DAY": 26442, "\u0120denounced": 26443, "\u0120biography": 26444, "\u0120Turnbull": 26445, "Recent": 26446, "Normal": 26447, "lections": 26448, "Word": 26449, "\u0120ferry": 26450, "\u0120Wagner": 26451, "hom": 26452, "Unit": 26453, "\u0120supermarket": 26454, "\u0120Sith": 26455, "\u0120nominees": 26456, "\u0120dictatorship": 26457, "iddler": 26458, "\u0120announces": 26459, "\u0120Them": 26460, "\u0120Neptune": 26461, "\u0120deity": 26462, "\u0120Yi": 26463, "\u0120monarch": 26464, "ARR": 26465, "\u0120invaded": 26466, "\u0120Hok": 26467, "untary": 26468, "Certain": 26469, "ega": 26470, "\u0120kidding": 26471, "\u0120Regulation": 26472, "\u0120tray": 26473, "\u0120photographers": 26474, "\u0120Arcane": 26475, "\u0120discharged": 26476, "\u0120evangelical": 26477, "\u0120interchange": 26478, "\u0120filmmaker": 26479, "\u0120Endless": 26480, "\u0120290": 26481, "\u0120Salvador": 26482, "ASY": 26483, "\u0120Signal": 26484, "\u0120wrath": 26485, "\u00e2\u013e": 26486, "lot": 26487, "'/": 26488, "\u0120projectile": 26489, "\u0120employing": 26490, "\u0120Interface": 26491, "191": 26492, "atellite": 26493, "\u0120Rath": 26494, "package": 26495, "\u0120indications": 26496, "Jason": 26497, "\u0120args": 26498, "\u0120GHz": 26499, "\u0120tilt": 26500, "nants": 26501, "won": 26502, "\u00e3\u0124\u00b5": 26503, "redd": 26504, "rescent": 26505, "\u0120Calendar": 26506, "\u0120modular": 26507, "\u0120assisting": 26508, "\u0120redeem": 26509, "\u0120Bean": 26510, "\u0120worsh": 26511, "\u0120decentralized": 26512, ")...": 26513, "377": 26514, "\u0120arrays": 26515, "\u0120accomplishments": 26516, "\u00ce\u00bf": 26517, "dot": 26518, "\u0120mutually": 26519, "\u0120obstruct": 26520, "\u0120misrepresent": 26521, "orest": 26522, "ionic": 26523, "ruce": 26524, "%;": 26525, "\u0120knowingly": 26526, "porting": 26527, "inently": 26528, "Ari": 26529, "\u0120Schultz": 26530, "Da": 26531, "\u0120Cere": 26532, "\u0120obsolete": 26533, "\u0127\u012d": 26534, "give": 26535, "\u0120bait": 26536, "\u0120enlarg": 26537, "Neill": 26538, "\u01201933": 26539, "\u0120reconsider": 26540, "\u0120Sergeant": 26541, "\u0120Diane": 26542, "\u0120Cogn": 26543, "\u0120Icon": 26544, "Position": 26545, "\u0120fost": 26546, "\u0120stirring": 26547, "seven": 26548, "\u0120SpaceX": 26549, "uggets": 26550, "\u0120medd": 26551, "Gal": 26552, "\u0120Sister": 26553, "Boy": 26554, "\u0120triggering": 26555, "Taking": 26556, "\u0120screams": 26557, "\u0120causal": 26558, "\u0120awaken": 26559, "Arm": 26560, "297": 26561, "\u0120dispatched": 26562, "\u0120FALSE": 26563, "\u0120organizational": 26564, "\u0120Tong": 26565, "\u0120dilemma": 26566, "demon": 26567, "Spl": 26568, "\u0120hooks": 26569, "uding": 26570, "\u0120validate": 26571, "\u0120potion": 26572, "\u0120claw": 26573, "\u0120burgl": 26574, "\u0120quir": 26575, "ACA": 26576, "\u0120Brennan": 26577, "\u0120durability": 26578, "\u0120bombings": 26579, "\u0120Window": 26580, "\u0120culprit": 26581, "325": 26582, "Therefore": 26583, "umbered": 26584, "performance": 26585, "warts": 26586, "\u0120enforcing": 26587, "\u0120Blow": 26588, "\u0120reprint": 26589, "ifax": 26590, "alpha": 26591, "\u0120sinister": 26592, "\u0120burger": 26593, "fighting": 26594, "Score": 26595, "\u0120Stones": 26596, "iem": 26597, "405": 26598, "chemy": 26599, "\u0120vinegar": 26600, "nom": 26601, "\u0120prevailing": 26602, "\u0120Latest": 26603, "\u00c2\u00b6": 26604, "\u0120ba": 26605, "\u0120Writer": 26606, "\u0120177": 26607, "\u0120Conway": 26608, "\u0120collects": 26609, "\u0120quantitative": 26610, "\u0120horrors": 26611, "ogens": 26612, "\u0120Slov": 26613, "\u0120lays": 26614, "haw": 26615, "\u0120Slash": 26616, "\u0120nightclub": 26617, "\u0120Davies": 26618, "\u0120bride": 26619, "\u0120Scarlet": 26620, "ymm": 26621, "\u0120Applications": 26622, "velength": 26623, "\u0120revival": 26624, "\u0120softly": 26625, "\u0120zoo": 26626, "itaire": 26627, "Cur": 26628, "\u0120electrom": 26629, "\u0120planting": 26630, "OTO": 26631, "\u0120Elements": 26632, "\u0120swallow": 26633, "porter": 26634, "\u0120laptops": 26635, "\u0120peanut": 26636, "\u0120lobbyists": 26637, "\u00ce\u00b2": 26638, "Panel": 26639, "\u0120Joan": 26640, "imil": 26641, "tnc": 26642, "\u0120resisted": 26643, "\u0120outwe": 26644, "\u0120retaining": 26645, "atri": 26646, "\u0120poorer": 26647, "\u0120Syrians": 26648, "\u0120Hammond": 26649, "\u0120weld": 26650, "uder": 26651, "topic": 26652, "\u0120TT": 26653, "ricia": 26654, "\u0120thieves": 26655, "Lic": 26656, "\u0120Gust": 26657, "\u0120Ways": 26658, "areth": 26659, "243": 26660, "\u0120broadcaster": 26661, "shield": 26662, "assium": 26663, "uble": 26664, "\u0120airstrikes": 26665, "onso": 26666, "\u0120pedal": 26667, "\u0120collectors": 26668, "\u0120Vander": 26669, "\u0120Mesa": 26670, "\u0120dictator": 26671, "\u0120dir": 26672, "enton": 26673, "cart": 26674, "score": 26675, "adder": 26676, "Cry": 26677, "\u0120ssh": 26678, "gger": 26679, "\u0120drunken": 26680, "\u0120GS": 26681, "\u0120Seat": 26682, "\u0120cornerback": 26683, "\u0120skipped": 26684, "\u0120Researchers": 26685, "\u0120Audi": 26686, "Reference": 26687, "\u0120haunted": 26688, "\u00c3\u00ab": 26689, "\u0120Clinic": 26690, "cz": 26691, "\u0120ps": 26692, "\u0120Paladin": 26693, "\u0120Recipe": 26694, "\u0120stigma": 26695, "oppy": 26696, "\u0120monkeys": 26697, "\u0120Hawk": 26698, "Sad": 26699, "\"/>": 26700, "\u0120Workshop": 26701, "\u0120Retail": 26702, "\u0120Avatar": 26703, "625": 26704, "Na": 26705, "\u0120VC": 26706, "\u0120Secure": 26707, "MY": 26708, "1988": 26709, "ossip": 26710, "\u0120prostate": 26711, "\u0120unden": 26712, "\u0120gamer": 26713, "\u0120Contents": 26714, "\u0120Warhammer": 26715, "\u0120Sentinel": 26716, "310": 26717, "\u0120segregation": 26718, "\u0120Flex": 26719, "\u0120MAY": 26720, "\u0120drills": 26721, "\u0120Drugs": 26722, "Islamic": 26723, "\u0120spur": 26724, "\u0120cafe": 26725, "\u0120imaginary": 26726, "\u0120guiding": 26727, "\u0120swings": 26728, "\u0120Theme": 26729, "oby": 26730, "\u0120nud": 26731, "\u0120begging": 26732, "\u0120strongh": 26733, "\u0120rejecting": 26734, "\u0120pedestrians": 26735, "\u0120Prospect": 26736, "Rare": 26737, "sle": 26738, "\u0120concessions": 26739, "\u0120Constitutional": 26740, "\u0120beams": 26741, "\u0120fibers": 26742, "poon": 26743, "\u0120instincts": 26744, "property": 26745, "\u0120BIG": 26746, "Sanders": 26747, "imates": 26748, "\u0120coating": 26749, "\u0120corpses": 26750, "\u0120TRUE": 26751, "checked": 26752, "\u0120166": 26753, "Ash": 26754, "\u0120JS": 26755, "\u0120Fiction": 26756, "\u0120communal": 26757, "\u0120energetic": 26758, "oooooooo": 26759, "\u0120nowadays": 26760, "ILD": 26761, "ibo": 26762, "\u0120SUV": 26763, "Ren": 26764, "\u0120dwelling": 26765, "Silver": 26766, "\u0120tally": 26767, "\u0120Moving": 26768, "\u0120coward": 26769, "\u0120generals": 26770, "\u0120horns": 26771, "\u0120circulated": 26772, "\u0120robbed": 26773, "\u0120Unlimited": 26774, "\u0120harassed": 26775, "\u0120inhibit": 26776, "\u0120composer": 26777, "\u0120Spotify": 26778, "\u0120spreads": 26779, "364": 26780, "\u0120suicidal": 26781, "\u0120noises": 26782, "\u0120Stur": 26783, "\u0120saga": 26784, "\u0120Kag": 26785, "iso": 26786, "\u0120theoretically": 26787, "Money": 26788, "\u0120similarity": 26789, "\u0120sliced": 26790, "utils": 26791, "inges": 26792, "\"-": 26793, "\u0120anth": 26794, "\u0120imped": 26795, "Module": 26796, "Throughout": 26797, "\u0120menus": 26798, "committee": 26799, "andi": 26800, "obj": 26801, "inav": 26802, "fired": 26803, "\u0120Abdullah": 26804, "\u0120undead": 26805, "\u0120fonts": 26806, "Hold": 26807, "ENG": 26808, "\u0120sustainability": 26809, "\u0120flick": 26810, "\u0120razor": 26811, "\u0120Fest": 26812, "\u0120Characters": 26813, "\u0120wording": 26814, "\u0120populist": 26815, "\u0120criticizing": 26816, "\u0120muse": 26817, "vine": 26818, "\u0120cardboard": 26819, "\u0120kindly": 26820, "\u0120fringe": 26821, "\u0120Theft": 26822, "icultural": 26823, "\u0120governors": 26824, "\u0120\u00ef\u00bf\u00bd\u00ef\u00bf\u00bd\u00ef\u00bf\u00bd\u00ef\u00bf\u00bd": 26825, "\u0120163": 26826, "\u0120timeout": 26827, "\u0120Auth": 26828, "Children": 26829, "AU": 26830, "\u0120redemption": 26831, "\u0120Alger": 26832, "\u01201914": 26833, "\u0120waved": 26834, "\u0120astronauts": 26835, "ograms": 26836, "\u0120swamp": 26837, "\u0120Finnish": 26838, "\u0120candle": 26839, "\u0120tonnes": 26840, "utm": 26841, "\u0120ray": 26842, "\u0120spun": 26843, "\u0120fearful": 26844, "articles": 26845, "\u0120caus": 26846, "orically": 26847, "\u0120Requires": 26848, "\u0120Gol": 26849, "\u0120pope": 26850, "\u0120inaugural": 26851, "\u0120gle": 26852, "ADA": 26853, "\u0120ISIL": 26854, "\u0120Offensive": 26855, "\u0120watchdog": 26856, "\u0120balcon": 26857, "entity": 26858, "\u0120Hoo": 26859, "\u0120gallon": 26860, "ACC": 26861, "\u0120doubling": 26862, "\u0120implication": 26863, "\u0120Sight": 26864, "\u0120doctr": 26865, "-------": 26866, "\u0120\\\\": 26867, "\u0120malt": 26868, "Roll": 26869, "\u0120\u00e2\u012b\u00a5": 26870, "\u0120recap": 26871, "adding": 26872, "uces": 26873, "\u0120Bend": 26874, "figure": 26875, "\u0120turkey": 26876, "\u0120societal": 26877, "\u0120Tickets": 26878, "\u0120commercially": 26879, "\u0120spicy": 26880, "\u0120216": 26881, "\u0120Ramp": 26882, "\u0120superiority": 26883, "\u00c3\u00af": 26884, "\u0120Tracker": 26885, "Carl": 26886, "\u0120Coy": 26887, "\u0120Patriot": 26888, "\u0120consulted": 26889, "\u0120listings": 26890, "\u0120slew": 26891, "reenshot": 26892, "\u0120Gone": 26893, "\u0120[...]": 26894, "309": 26895, "\u0120hottest": 26896, "\u00d8\u00b1": 26897, "\u0120rocky": 26898, "\u0120Diaz": 26899, "\u0120massage": 26900, "\u0120paraly": 26901, "\u0120pony": 26902, "Az": 26903, "\u0120cartridge": 26904, "\u0120NZ": 26905, "\u0120snack": 26906, "\u0120Lamar": 26907, "plement": 26908, "\u0120Leslie": 26909, "\u0120mater": 26910, "\u0120snipp": 26911, "246": 26912, "\u0120jointly": 26913, "\u0120Brisbane": 26914, "\u0120iPod": 26915, "\u0120pumping": 26916, "\u0120goat": 26917, "\u0120Sharon": 26918, "ealing": 26919, "\u0120coron": 26920, "\u0120anomal": 26921, "rahim": 26922, "\u0120Connection": 26923, "\u0120sculpture": 26924, "\u0120scheduling": 26925, "\u0120Daddy": 26926, "athing": 26927, "\u0120eyebrows": 26928, "\u0120curved": 26929, "\u0120sentiments": 26930, "\u0120drafting": 26931, "Drop": 26932, "([": 26933, "\u0120nominal": 26934, "\u0120Leadership": 26935, "\u0120Grow": 26936, "\u0120176": 26937, "\u0120constructive": 26938, "ivation": 26939, "\u0120corrupted": 26940, "gerald": 26941, "\u0120Cros": 26942, "\u0120Chester": 26943, "\u0120Lap": 26944, "\u00e3\u0123\u00aa": 26945, "OTH": 26946, "DATA": 26947, "\u0120almond": 26948, "probably": 26949, "Imp": 26950, "\u0120feast": 26951, "\u0120Warcraft": 26952, "Flor": 26953, "\u0120checkpoint": 26954, "\u0120transcription": 26955, "\u0120204": 26956, "\u0120tweaks": 26957, "\u0120relieve": 26958, "Science": 26959, "\u0120performer": 26960, "Zone": 26961, "\u0120turmoil": 26962, "igated": 26963, "hibit": 26964, "\u0120Cafe": 26965, "themed": 26966, "\u0120fluor": 26967, "bench": 26968, "\u0120decom": 26969, "\u0120Unt": 26970, "\u0120Barrett": 26971, "\u0120Facts": 26972, "\u0120tasting": 26973, "\u0120PTSD": 26974, "\u0120Seal": 26975, "\u0120Judaism": 26976, "\u0120Dynamic": 26977, "\u0120Cors": 26978, "Ve": 26979, "\u0120Ming": 26980, "\u0120Transform": 26981, "von": 26982, "\u0120Defenders": 26983, "\u0120Tactical": 26984, "\u0120Von": 26985, "\u0120Univers": 26986, "\u0120distorted": 26987, "\u0120Breath": 26988, "?'\"": 26989, "\u0120agon": 26990, "\u0120Deadly": 26991, "\u0120lan": 26992, "\u0120Cycle": 26993, "orned": 26994, "\u0120reliably": 26995, "\u0120glor": 26996, "\u0120Monkey": 26997, "\u00e3\u0125\u00a1": 26998, "\u0120adren": 26999, "\u0120microwave": 27000, "\u0120Alban": 27001, "ircraft": 27002, "digit": 27003, "smart": 27004, "\u0120Dread": 27005, "\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af\u00c2\u00af": 27006, "{{": 27007, "\u0120Rochester": 27008, "\u0120simplified": 27009, "\u0120inflicted": 27010, "\u0120takeover": 27011, "\u0120yourselves": 27012, "aditional": 27013, "\u0120muscular": 27014, "KS": 27015, "\u0120ingen": 27016, "Tax": 27017, "\u0120Feature": 27018, "277": 27019, "\u0120cruc": 27020, "\u0120crate": 27021, "\u0120unidentified": 27022, "\u0120acclaimed": 27023, "\u0120Manga": 27024, "\u0120Frances": 27025, "\u0120Nepal": 27026, "\u0120Gerald": 27027, "\u0120Kuwait": 27028, "\u0120slain": 27029, "\u0120Heb": 27030, "\u0120Goku": 27031, "\u00e3\u0123\u00ae\u00e6": 27032, "286": 27033, "Mrs": 27034, "\u0120Cody": 27035, "\u0120Sanctuary": 27036, "016": 27037, "\u0120dismant": 27038, "\u0120dataset": 27039, "\u0120Hond": 27040, "buck": 27041, "\u0120Patterson": 27042, "\u0120palette": 27043, "\u0120GD": 27044, "icol": 27045, "\u0120Lodge": 27046, "\u0120planetary": 27047, "akin": 27048, "\u0120Registered": 27049, "abwe": 27050, "\u0120Petersburg": 27051, "\u0120hailed": 27052, "\u0120Piece": 27053, "Sche": 27054, "\u0120DOJ": 27055, "\u0120enumer": 27056, "181": 27057, "\u0120Observer": 27058, "\u0120Bold": 27059, "founded": 27060, "commerce": 27061, "\u0120exploits": 27062, "\u0120Finding": 27063, "URN": 27064, "\u0120Sne": 27065, "\u0120Acid": 27066, "ayette": 27067, "\u0120Values": 27068, "\u0120drastic": 27069, "\u0120architectural": 27070, "\u0120\".": 27071, "\u00d7\u0137": 27072, "umped": 27073, "\u0120wrapping": 27074, "\u0120widow": 27075, "\u0120Slayer": 27076, "lace": 27077, "once": 27078, "Germany": 27079, "avoid": 27080, "\u0120temples": 27081, "PAR": 27082, "\u00c3\u00b4": 27083, "\u0120Lucifer": 27084, "\u0120Flickr": 27085, "lov": 27086, "forces": 27087, "\u0120scouting": 27088, "\u0120louder": 27089, "tesy": 27090, "\u0120beforehand": 27091, "\u00c4\u0135": 27092, "\u0120Neon": 27093, "\u0120Wol": 27094, "\u0120Typically": 27095, "\u0120Politico": 27096, "-+-+": 27097, "\u0120builder": 27098, "\u0120derive": 27099, "Kill": 27100, "\u0120poker": 27101, "\u0120ambiguous": 27102, "\u0120lifts": 27103, "\u0120cyt": 27104, "\u0120ribs": 27105, "oodle": 27106, "\u0120Sounds": 27107, "hair": 27108, "\u0120Syndrome": 27109, "tf": 27110, "\u0120proportional": 27111, "uid": 27112, "\u0120pertaining": 27113, "\u0120Kindle": 27114, "\u0120Negro": 27115, "\u0120reiterated": 27116, "\u0120Tonight": 27117, "oths": 27118, "\u0120Cornell": 27119, "\u0120owing": 27120, "\u0120208": 27121, "elfare": 27122, "ocating": 27123, "\u0120Birds": 27124, "Subscribe": 27125, "\u0120essays": 27126, "\u0120burdens": 27127, "\u0120illustrations": 27128, "arious": 27129, "ERAL": 27130, "\u0120Calcul": 27131, "\u0120xen": 27132, "\u0120LinkedIn": 27133, "\u0120Jung": 27134, "\u0120redesign": 27135, "Connor": 27136, "296": 27137, "\u0120reversal": 27138, "\u0120Adelaide": 27139, "\u0120LL": 27140, "\u0120sinking": 27141, "\u0120gum": 27142, "USH": 27143, "capt": 27144, "\u0120Grimm": 27145, "\u0120footsteps": 27146, "\u0120CBD": 27147, "ispers": 27148, "\u0120prose": 27149, "Wednesday": 27150, "\u0120Movies": 27151, "edin": 27152, "\u0120overturned": 27153, "\u0120contentious": 27154, "USB": 27155, "~~~~~~~~~~~~~~~~": 27156, "\u0120Copper": 27157, "\u0120pointless": 27158, "NV": 27159, "values": 27160, "olphin": 27161, "dain": 27162, "\u0120deposited": 27163, "\u0120GW": 27164, "\u0120preceded": 27165, "\u0120Cla": 27166, "\u0120Golem": 27167, "\u0120Nim": 27168, "\u0120\u00ce\u00b2": 27169, "\u0120Engineers": 27170, "middle": 27171, "\u0120flatt": 27172, "operative": 27173, "\u0120councils": 27174, "imbabwe": 27175, "elin": 27176, "\u0120stressful": 27177, "\u0120LD": 27178, "\u0120resh": 27179, "lake": 27180, "\u0120wheelchair": 27181, "\u0120Alternative": 27182, "\u0120optimize": 27183, "operation": 27184, "\u0120peek": 27185, "\u0120oneself": 27186, "igil": 27187, "\u0120transitions": 27188, "opathy": 27189, "blank": 27190, "\u0120169": 27191, "171": 27192, "________________________________________________________________": 27193, "\u0120laundering": 27194, "Enc": 27195, "\u0120DEC": 27196, "\u0120workouts": 27197, "\u0120spikes": 27198, "\u0120dinosaurs": 27199, "\u0120discriminatory": 27200, "Pool": 27201, "Rather": 27202, "385": 27203, "RNA": 27204, "testers": 27205, "eto": 27206, "\u0120Identity": 27207, "\u0120vein": 27208, "\u0120Burton": 27209, "\u0120arcade": 27210, "420": 27211, "Ultimately": 27212, "\u0120Sadly": 27213, "\u00c3\u00b0": 27214, "pill": 27215, "\u0120cubic": 27216, "\u0120Spectrum": 27217, "these": 27218, "states": 27219, "\u0120unofficial": 27220, "hawks": 27221, "\u0120EVERY": 27222, "\u0120rainbow": 27223, "\u0120incarceration": 27224, "anding": 27225, "\u0120syll": 27226, "\u0120Everton": 27227, "\u0120179": 27228, "\u0120Serbia": 27229, "\u0120189": 27230, "meter": 27231, "\u0120Mickey": 27232, "\u0120antiqu": 27233, "\u0120factual": 27234, "neck": 27235, "\u0120Nare": 27236, "norm": 27237, "must": 27238, "\u0120highways": 27239, "\u0120glam": 27240, "\u0120dividing": 27241, "\u0120Squadron": 27242, "\u0120Martha": 27243, "\u0120births": 27244, "Cover": 27245, "////////////////": 27246, "\u0120Wong": 27247, "Phot": 27248, "\u0120ALS": 27249, "rio": 27250, "\u0120Nonetheless": 27251, "\u0120Lemon": 27252, "\u0120206": 27253, "\u0120EE": 27254, "\u0120derivative": 27255, "\u0120WWII": 27256, "vote": 27257, "\u0120therein": 27258, "\u0120separating": 27259, "446": 27260, "sync": 27261, "\u0120Streets": 27262, "\u0120ratt": 27263, "\u0120municipality": 27264, "\u0120Shortly": 27265, "\u0120monk": 27266, "),\"": 27267, "\u0120scrub": 27268, "\u0120operatives": 27269, "Neither": 27270, "Place": 27271, "\u0120Limit": 27272, "Female": 27273, "\u0120Actor": 27274, "Character": 27275, "\u0120constituted": 27276, "357": 27277, "\u0120protested": 27278, "\u0120Straw": 27279, "\u0120Height": 27280, "ilda": 27281, "\u0120Typh": 27282, "\u0120floods": 27283, "\u0120cosmetic": 27284, "WAY": 27285, "perture": 27286, "upon": 27287, "tons": 27288, "essing": 27289, "\u0120Pocket": 27290, "\u0120rooft": 27291, "\u0120Caucas": 27292, "\u0120antidepress": 27293, "\u0120incompatible": 27294, "ECD": 27295, "\u0120opera": 27296, "\u0120Contest": 27297, "\u0120generators": 27298, "lime": 27299, "Defense": 27300, "1987": 27301, "forum": 27302, "\u0120savage": 27303, "\u0120Hungarian": 27304, "nz": 27305, "\u0120metallic": 27306, "\u0120expelled": 27307, "\u0120residency": 27308, "\u0120dresses": 27309, "666": 27310, "\u0120Clement": 27311, "fires": 27312, "Category": 27313, "\u0120geek": 27314, "alis": 27315, "\u0120cemetery": 27316, "educated": 27317, "\u0120crawl": 27318, "\u0120Unable": 27319, "\u0120Tyson": 27320, "akis": 27321, "\u0120pardon": 27322, "\u0120Wra": 27323, "\u0120strengthened": 27324, "\u0120Fors": 27325, "335": 27326, "\u0120HC": 27327, "\u0120Mond": 27328, "\u0120visuals": 27329, "\u0120Beatles": 27330, "ettlement": 27331, "\u0120\u00ef": 27332, "gro": 27333, "\u0120bash": 27334, "\u0120poorest": 27335, "\u0120excel": 27336, "\u0120aspirations": 27337, "\u0120Municip": 27338, "ensible": 27339, "\u0120ceremonies": 27340, "\u0120intimidation": 27341, "\u0120CONTR": 27342, "beck": 27343, "\u0120Kap": 27344, "asu": 27345, "\u0120trademarks": 27346, "\u0120Sew": 27347, "\u0120Competition": 27348, "network": 27349, "\u0120Arri": 27350, "\u0120Tet": 27351, "Roaming": 27352, "WC": 27353, "Dat": 27354, "\u0120sob": 27355, "\u0120pairing": 27356, "\u0120overdose": 27357, "SAY": 27358, "aber": 27359, "\u0120revolt": 27360, "\u0120Fah": 27361, "acting": 27362, "eq": 27363, "estation": 27364, "Fight": 27365, "\u0120Marks": 27366, "273": 27367, "\u0120178": 27368, "Raw": 27369, "\u00e3\u0123\u012d": 27370, "349": 27371, "blocks": 27372, "\u0120verge": 27373, "estine": 27374, "\u0120Podesta": 27375, "\u0120invasive": 27376, "\u0120profoundly": 27377, "\u0120Ao": 27378, "each": 27379, "\u0120lest": 27380, "interpret": 27381, "\u0120shrinking": 27382, "\u0120errone": 27383, "\u0120chees": 27384, "lys": 27385, "\u0120Ivy": 27386, "\u0120Directory": 27387, "\u0120hinted": 27388, "VICE": 27389, "\u0120contacting": 27390, "\u0120Gent": 27391, "hei": 27392, "\u0120labeling": 27393, "\u0120mercury": 27394, "\u0120Lite": 27395, "\u0120expires": 27396, "\u0120destabil": 27397, "ritis": 27398, "cu": 27399, "\u0120feathers": 27400, "\u0120steer": 27401, "\u0120programmed": 27402, "\u0120Vader": 27403, "Going": 27404, "\u0120Elim": 27405, "\u0120yo": 27406, "\u0120Miche": 27407, "\u0120203": 27408, "\u0120sleeves": 27409, "\u0120bully": 27410, "\u0120Humans": 27411, "368": 27412, "\u0120compress": 27413, "\u0120Banner": 27414, "ARS": 27415, "\u0120awhile": 27416, "\u0120calib": 27417, "\u0120sponsorship": 27418, "\u0120Difficulty": 27419, "\u0120Papers": 27420, "\u0120identifier": 27421, "}.": 27422, "\u0120yog": 27423, "\u0120Shia": 27424, "\u0120cleanup": 27425, "\u0120vibe": 27426, "introdu": 27427, "imming": 27428, "Australia": 27429, "\u0120outlines": 27430, "\u0120Youtube": 27431, "train": 27432, "\u0120Makes": 27433, "\u0120deported": 27434, "\u0120centr": 27435, "\u0120Dug": 27436, "\u0120Boulder": 27437, "\u0120Buffy": 27438, "\u0120injunction": 27439, "\u0120Harley": 27440, "\u0120Groups": 27441, "\u0120Dumbledore": 27442, "\u0120Clara": 27443, "\u0120\"-": 27444, "\u0120sacrificed": 27445, "eph": 27446, "Shadow": 27447, "ibling": 27448, "\u0120freelance": 27449, "\u0120evidently": 27450, "phal": 27451, "\u0120retains": 27452, "Mir": 27453, "\u0120finite": 27454, "dar": 27455, "\u0120Cous": 27456, "\u0120repaired": 27457, "\u0120periodic": 27458, "\u0120championships": 27459, "\u0120asteroid": 27460, "blind": 27461, "\u0120expressly": 27462, "\u0120Astros": 27463, "\u0120scaled": 27464, "\u0120geographical": 27465, "\u0120Rapids": 27466, "Enjoy": 27467, "\u0120elastic": 27468, "\u0120Mohamed": 27469, "Market": 27470, "begin": 27471, "\u0120discovers": 27472, "\u0120telecommunications": 27473, "\u0120scanner": 27474, "\u0120enlarge": 27475, "\u0120sharks": 27476, "\u0120psychedel": 27477, "\u0120Rouge": 27478, "\u0120snapshot": 27479, "isine": 27480, "XP": 27481, "\u0120pesticides": 27482, "\u0120LSD": 27483, "\u0120Distribution": 27484, "really": 27485, "\u0120degradation": 27486, "\u0120disguise": 27487, "\u0120biom": 27488, "\u0120EXT": 27489, "\u0120equations": 27490, "\u0120hazards": 27491, "\u0120Compared": 27492, ")*": 27493, "\u0120virtues": 27494, "\u0120elders": 27495, "\u0120enhancing": 27496, "\u0120Across": 27497, "eros": 27498, "angling": 27499, "\u0120combust": 27500, "ucci": 27501, "\u0120concussion": 27502, "\u0120contraception": 27503, "\u0120Kang": 27504, "\u0120expresses": 27505, "\u0120aux": 27506, "\u0120Pione": 27507, "\u0120exhibits": 27508, "Debug": 27509, "OTAL": 27510, "\u0120Already": 27511, "\u0120Wheeler": 27512, "\u0120expands": 27513, "?:": 27514, "\u0120reconciliation": 27515, "\u0120pirates": 27516, "\u0120purse": 27517, "\u0120discourage": 27518, "\u0120spectacle": 27519, "Rank": 27520, "\u0120wraps": 27521, "\u0120Thought": 27522, "\u0120impending": 27523, "Opp": 27524, "\u0120Anglo": 27525, "\u0120EUR": 27526, "\u0120screwed": 27527, "retched": 27528, "\u0120encouragement": 27529, "models": 27530, "\u0120confuse": 27531, "mmm": 27532, "\u0120Vitamin": 27533, "\u00e2\u0138\u0133\u00e2\u0138\u0133": 27534, "Cru": 27535, "\u0120knights": 27536, "\u0120discard": 27537, "\u0120bishops": 27538, "\u0120Wear": 27539, "\u0120Garrett": 27540, "kan": 27541, "\u00e3\u0125\u0141": 27542, "\u0120masculine": 27543, "capital": 27544, "\u0120Aus": 27545, "\u0120fatally": 27546, "thanks": 27547, "\u0120AU": 27548, "\u0120Gut": 27549, "1200": 27550, "\u012000000000": 27551, "\u0120surrog": 27552, "\u0120BIOS": 27553, "raits": 27554, "\u0120Watts": 27555, "\u0120resurrection": 27556, "\u0120Electoral": 27557, "\u0120Tips": 27558, "4000": 27559, "\u0120nutrient": 27560, "\u0120depicting": 27561, "\u0120sprink": 27562, "\u0120muff": 27563, "\u0120LIM": 27564, "\u0120Sample": 27565, "psc": 27566, "ibi": 27567, "generated": 27568, "\u0120specimens": 27569, "\u0120dissatisf": 27570, "\u0120tailored": 27571, "\u0120holdings": 27572, "\u0120Monthly": 27573, "\u0120Eat": 27574, "poons": 27575, "\u0120nec": 27576, "\u0120Cage": 27577, "\u0120Lotus": 27578, "\u0120Lantern": 27579, "\u0120frontier": 27580, "\u0120pensions": 27581, "\u0120joked": 27582, "\u0120Hardy": 27583, "=-=-=-=-": 27584, "rade": 27585, "UID": 27586, "\u0120rails": 27587, "\u0120emit": 27588, "\u0120slate": 27589, "\u0120smug": 27590, "\u0120spit": 27591, "\u0120Calls": 27592, "\u0120Jacobs": 27593, "feat": 27594, "\u0120UE": 27595, "\u0120restruct": 27596, "\u0120regeneration": 27597, "\u0120energies": 27598, "\u0120Connor": 27599, "OHN": 27600, "\u0120Cheese": 27601, "\u0120ger": 27602, "\u0120resurrect": 27603, "management": 27604, "NW": 27605, "\u0120presently": 27606, "\u0120Bruins": 27607, "Member": 27608, "\u0120Mang": 27609, "idan": 27610, "\u0120boosting": 27611, "wyn": 27612, "+.": 27613, "requisite": 27614, "\u0120NYPD": 27615, "\u0120Megan": 27616, "\u0120Conditions": 27617, "\u0120pics": 27618, "nesium": 27619, "\u0120Rash": 27620, "\u0120174": 27621, "\u0120Ducks": 27622, "\u0120embro": 27623, "zu": 27624, "onian": 27625, "religious": 27626, "\u0120craz": 27627, "\u0120ACA": 27628, "\u0120Zucker": 27629, "EMA": 27630, "\u0120Pros": 27631, "Weapon": 27632, "\u0120Knox": 27633, "\u0120Arduino": 27634, "\u0120stove": 27635, "\u0120heavens": 27636, "\u0120Purchase": 27637, "\u0120herd": 27638, "\u0120fundraiser": 27639, "Digital": 27640, "5000": 27641, "\u0120proponents": 27642, "/\u00e2\u0122\u012d": 27643, "\u0120jelly": 27644, "\u0120Visa": 27645, "\u0120monks": 27646, "\u0120advancement": 27647, "\u0120Wer": 27648, "\u0120187": 27649, "eus": 27650, "ertility": 27651, "\u0120fetal": 27652, "\u01201936": 27653, "Lo": 27654, "\u0120outfits": 27655, "\u0120staircase": 27656, "bomb": 27657, "\u0120customized": 27658, "clair": 27659, "Tree": 27660, "\u0120mapped": 27661, "\u0120Considering": 27662, "\u0120Torres": 27663, "\u0120methyl": 27664, "\u0120approximate": 27665, "\u0120doom": 27666, "\u0120Hansen": 27667, "\u0120crossover": 27668, "\u0120standalone": 27669, "\u00e4\u00bc": 27670, "\u0120invites": 27671, "\u0120graveyard": 27672, "\u0120hp": 27673, "DonaldTrump": 27674, "\u0120escort": 27675, "Gar": 27676, "\u0120predecessors": 27677, "\u0120hay": 27678, "\u0120enzyme": 27679, "\u0120Straight": 27680, "visors": 27681, "Ing": 27682, "aneously": 27683, "\u0120Applied": 27684, "\u0120fec": 27685, "\u0120Durant": 27686, "\u0120outspoken": 27687, "orb": 27688, "\u0120zeal": 27689, "\u0120disgrace": 27690, "').": 27691, "\u0120Cheng": 27692, "289": 27693, "\u0120Rena": 27694, "\u0120Suicide": 27695, "294": 27696, "\u0120outraged": 27697, "\u0120Newman": 27698, "\u0120Nvidia": 27699, "\u0120Aber": 27700, "\u0120Bers": 27701, "\u0120recreation": 27702, "Window": 27703, "\u0120DP": 27704, "xe": 27705, "\u0120pedoph": 27706, "\u0120fallout": 27707, "amboo": 27708, "\u0120presentations": 27709, "\u0120Apps": 27710, "\u0120html": 27711, "345": 27712, "\u0120XXX": 27713, "\u0120rubbing": 27714, "\u0120Leather": 27715, "\u0120humidity": 27716, "seys": 27717, "established": 27718, "\u0120Units": 27719, "646": 27720, "\u0120respectable": 27721, "Auto": 27722, "\u0120thriving": 27723, "\u0120Innovation": 27724, "angs": 27725, "Extra": 27726, "regulation": 27727, "298": 27728, "pick": 27729, "Examples": 27730, "\u0120CJ": 27731, "Attack": 27732, "\u0120dracon": 27733, "LT": 27734, "\u0120sticker": 27735, "rers": 27736, "\u0120sunny": 27737, "Iss": 27738, "regulated": 27739, "dim": 27740, "\u0120Abstract": 27741, "\u0120husbands": 27742, "Office": 27743, "omination": 27744, "itars": 27745, "ANGE": 27746, "ascal": 27747, "\u0120Kris": 27748, "\u0120Infantry": 27749, "\u0120malf": 27750, "\u0120Athe": 27751, "\u0120Rally": 27752, "balanced": 27753, "........................": 27754, "OUP": 27755, "\u0120molecule": 27756, "metics": 27757, "\u0120Split": 27758, "\u0120Instructions": 27759, "\u0120Nights": 27760, "cards": 27761, "\u0120tug": 27762, "\u0120cone": 27763, "\u00e5\u0143": 27764, "\u0120tx": 27765, "\u0120Discussion": 27766, "\u0120catastrophe": 27767, "ppe": 27768, "gio": 27769, "\u0120communism": 27770, "\u0120halted": 27771, "\u0120Guant": 27772, "clean": 27773, "\u0120Sched": 27774, "\u0120Kanye": 27775, "\u0120wander": 27776, "\u0120Seriously": 27777, "\u0120188": 27778, "ennial": 27779, "follow": 27780, "productive": 27781, "\u0120Flow": 27782, "\u0120Sail": 27783, "\u0120craw": 27784, "\u0120simulations": 27785, "oru": 27786, "angles": 27787, "\u0120Nolan": 27788, "\u0120menstru": 27789, "470": 27790, "\u0120207": 27791, "aja": 27792, "\u0120casually": 27793, "boarding": 27794, "\u0120222": 27795, "ovy": 27796, "\u0120Numbers": 27797, "umat": 27798, "OE": 27799, "287": 27800, "\u0120Clemson": 27801, "\u0120certs": 27802, "\u0120slid": 27803, "\u0120Tribe": 27804, "\u0120toast": 27805, "\u0120fortunes": 27806, "\u0120fals": 27807, "\u0120Committees": 27808, "\u0120gp": 27809, "\u0120fiery": 27810, "\u0120Nets": 27811, "\u0120Anime": 27812, "Package": 27813, "\u0120Compare": 27814, "laughter": 27815, "infect": 27816, "\u0120atrocities": 27817, "\u0120justices": 27818, "\u0120insults": 27819, "\u0120Vernon": 27820, "\u0120shaken": 27821, "\u0120persona": 27822, "estamp": 27823, "367": 27824, "brain": 27825, "\u0120experimenting": 27826, "Ken": 27827, "\u0120Electronics": 27828, "\u0120161": 27829, "domain": 27830, "\u0120graphical": 27831, "bishop": 27832, "\u0120whopping": 27833, "\u0120Evangel": 27834, "\u0120advertisers": 27835, "\u0120Spear": 27836, "\u0120bids": 27837, "\u0120destroys": 27838, "utz": 27839, "\u0120undersc": 27840, "\u0120ADD": 27841, "\u0120ants": 27842, "\u0120Cum": 27843, "ipples": 27844, "\u0120Fill": 27845, "\u0120glanced": 27846, "\u0120indicted": 27847, "\u0120Eff": 27848, "\u0120miscon": 27849, "\u0120Desktop": 27850, "\u0120abide": 27851, "\u00e3\u0125\u0122": 27852, "\u0120Io": 27853, "\u0120Coul": 27854, "\u0120capsule": 27855, "\u0120Chrys": 27856, "MON": 27857, "\u0120undes": 27858, "\u0120IRA": 27859, "\u0120citation": 27860, "\u0120dictate": 27861, "\u0120Networks": 27862, "\u0120Conflict": 27863, "\u0120Stuff": 27864, "xa": 27865, "isec": 27866, "\u0120Chemistry": 27867, "\u0120quarterly": 27868, "Williams": 27869, "anan": 27870, "Opt": 27871, "\u0120Alexandria": 27872, "outheastern": 27873, "\u0120Springfield": 27874, "\u0120Blacks": 27875, "\u0120geography": 27876, "242": 27877, "\u0120utmost": 27878, "\u0120Exxon": 27879, "abouts": 27880, "EVA": 27881, "\u0120Enable": 27882, "\u0120Barr": 27883, "\u0120disagreed": 27884, "\u0120Cyprus": 27885, "\u0120dementia": 27886, "\u0120labs": 27887, "\u0120ubiquitous": 27888, "\u0120LOVE": 27889, "\u0120consolidated": 27890, "sr": 27891, "\u0120creamy": 27892, "\u0120Timber": 27893, "Regardless": 27894, "\u0120Certificate": 27895, "\u0120\"...": 27896, "ogenous": 27897, "Captain": 27898, "\u0120insulting": 27899, "\u0120Soros": 27900, "\u0120Instr": 27901, "\u0120Bulgaria": 27902, "better": 27903, "\u0120sucking": 27904, "\u0120Davidson": 27905, "atz": 27906, "\u0120collateral": 27907, "gif": 27908, "\u0120plagued": 27909, "\u0120Cancel": 27910, "\u0120Gardner": 27911, "RB": 27912, "\u0120sixteen": 27913, "Remove": 27914, "uristic": 27915, "cook": 27916, "Rod": 27917, "\u0120comprising": 27918, "fle": 27919, ")\u00e2\u0122\u0136": 27920, "\u0120Viking": 27921, "growth": 27922, "agonal": 27923, "\u0120srf": 27924, "afety": 27925, "mot": 27926, "Nearly": 27927, "stown": 27928, "\u0120Factor": 27929, "\u0120automobile": 27930, "\u0120procedural": 27931, "mask": 27932, "ampires": 27933, "\u0120disappears": 27934, "jab": 27935, "315": 27936, "\u01201951": 27937, "needed": 27938, "\u0120daring": 27939, "leader": 27940, "\u0120podium": 27941, "\u0120unhealthy": 27942, "\u0120mund": 27943, "\u0120pyramid": 27944, "ocre": 27945, "\u0120kissed": 27946, "\u0120dreamed": 27947, "\u0120Fantastic": 27948, "\u0120Gly": 27949, "\u00e5\u012c": 27950, "\u0120greatness": 27951, "\u0120spices": 27952, "\u0120metropolitan": 27953, "\u0120compuls": 27954, "iets": 27955, "1016": 27956, "\u0120Sham": 27957, "\u0120Pyr": 27958, "flies": 27959, "\u0120Midnight": 27960, "\u0120swallowed": 27961, "\u0120genres": 27962, "\u0120Lucky": 27963, "\u0120Rewards": 27964, "\u0120dispatch": 27965, "\u0120IPA": 27966, "\u0120Apply": 27967, "\u0120aven": 27968, "alities": 27969, "312": 27970, "things": 27971, "\u0120().": 27972, "\u0120mates": 27973, "\u0120Sz": 27974, "\u0120COP": 27975, "olate": 27976, "OFF": 27977, "\u0120recharge": 27978, "caps": 27979, "\u0120Yorker": 27980, "icone": 27981, "\u0120galaxies": 27982, "ileaks": 27983, "Dave": 27984, "\u0120Puzz": 27985, "\u0120Celtic": 27986, "\u0120AFC": 27987, "276": 27988, "\u0120Sons": 27989, "\u0120affirmative": 27990, "Hor": 27991, "\u0120tutorials": 27992, "\u0120CITY": 27993, "\u0120Rosa": 27994, "\u0120Extension": 27995, "Series": 27996, "\u0120fats": 27997, "\u0120rab": 27998, "lis": 27999, "\u0120unic": 28000, "\u0120eve": 28001, "\u0120Spin": 28002, "\u0120adulthood": 28003, "typ": 28004, "\u0120sectarian": 28005, "\u0120checkout": 28006, "\u0120Cycl": 28007, "Single": 28008, "\u0120martyr": 28009, "\u0120chilling": 28010, "888": 28011, "oufl": 28012, "\u0120];": 28013, "\u0120congestion": 28014, "mk": 28015, "\u0120Whereas": 28016, "\u01201938": 28017, "urrencies": 28018, "erion": 28019, "\u0120boast": 28020, "\u0120Patients": 28021, "\u0120chap": 28022, "\u0120BD": 28023, "realDonaldTrump": 28024, "\u0120examines": 28025, "hov": 28026, "\u0120startling": 28027, "\u0120Babylon": 28028, "wid": 28029, "omew": 28030, "brance": 28031, "\u0120Odyssey": 28032, "wig": 28033, "\u0120torch": 28034, "\u0120Vox": 28035, "\u0120Moz": 28036, "\u0120Troll": 28037, "\u0120Ans": 28038, "Similarly": 28039, "\u0120Ful": 28040, "006": 28041, "Unless": 28042, "\u0120Alone": 28043, "stead": 28044, "\u0120Publisher": 28045, "rights": 28046, "tu": 28047, "\u0120Doesn": 28048, "\u0120professionally": 28049, "\u0120clo": 28050, "icz": 28051, "\u0120steals": 28052, "\u0120\u00e1": 28053, "1986": 28054, "\u0120sturdy": 28055, "\u0120Johann": 28056, "\u0120medals": 28057, "\u0120filings": 28058, "\u0120Fraser": 28059, "done": 28060, "\u0120multinational": 28061, "\u0120feder": 28062, "\u0120worthless": 28063, "\u0120pest": 28064, "Yesterday": 28065, "ankind": 28066, "\u0120gays": 28067, "\u0120borne": 28068, "\u0120POS": 28069, "Picture": 28070, "\u0120percentages": 28071, "251": 28072, "rame": 28073, "\u0120potions": 28074, "AMD": 28075, "\u0120Lebanese": 28076, "\u0120rang": 28077, "\u0120LSU": 28078, "ongs": 28079, "\u0120peninsula": 28080, "\u0120Clause": 28081, "ALK": 28082, "oha": 28083, "\u0120MacBook": 28084, "\u0120unanimous": 28085, "\u0120lenders": 28086, "\u0120hangs": 28087, "\u0120franchises": 28088, "orers": 28089, "\u0120Updates": 28090, "\u0120isolate": 28091, "andro": 28092, "Soon": 28093, "\u0120disruptive": 28094, "\u0120Surve": 28095, "\u0120stitches": 28096, "\u0120Scorp": 28097, "\u0120Dominion": 28098, "\u0120supplying": 28099, "Arg": 28100, "\u0120turret": 28101, "\u0120Luk": 28102, "\u0120brackets": 28103, "*)": 28104, "\u0120Revolutionary": 28105, "\u0120Honest": 28106, "\u0120noticing": 28107, "\u0120Shannon": 28108, "\u0120afforded": 28109, "\u0120tha": 28110, "\u0120Janet": 28111, "!--": 28112, "\u0120Narendra": 28113, "\u0120Plot": 28114, "Hol": 28115, "sever": 28116, "eenth": 28117, "\u0120obstruction": 28118, "\u01201024": 28119, "staff": 28120, "jas": 28121, "orget": 28122, "scenes": 28123, "laughs": 28124, "\u0120Fargo": 28125, "crime": 28126, "\u0120orchestr": 28127, "\u0120delet": 28128, "iliary": 28129, "rieved": 28130, "\u0120militar": 28131, "\u0120Greene": 28132, "\u00e2\u0139\u0131": 28133, "\u00e3\u0123\u00a6": 28134, "\u0120Guards": 28135, "\u0120unleashed": 28136, "\u0120Weber": 28137, "\u0120adjustable": 28138, "\u0120caliber": 28139, "\u0120motivations": 28140, "\u0120\u00c3\u0142": 28141, "mAh": 28142, "\u0120Lanka": 28143, "handle": 28144, "\u0120pent": 28145, "\u0120Rav": 28146, "\u0120Angular": 28147, "\u0120Kau": 28148, "umbing": 28149, "\u0120philanthrop": 28150, "\u0120dehyd": 28151, "\u0120toxicity": 28152, "eer": 28153, "\u0120YORK": 28154, "witz": 28155, "\u00e5\u00bc": 28156, "\u0120IE": 28157, "community": 28158, "\u0120AH": 28159, "\u0120retali": 28160, "\u0120massively": 28161, "\u0120Daniels": 28162, "\u0120DEL": 28163, "\u0120carcin": 28164, "Url": 28165, "\u0120routing": 28166, "\u0120NPCs": 28167, "\u0120RAF": 28168, "ryce": 28169, "\u0120waived": 28170, "\u0120Guatem": 28171, "Everybody": 28172, "\u0120covenant": 28173, "\u0120173": 28174, "\u0120relaxing": 28175, "\u0120quart": 28176, "almost": 28177, "\u0120guarded": 28178, "\u0120Soldiers": 28179, "\u0120PLAY": 28180, "\u0120outgoing": 28181, "LAND": 28182, "\u0120rewrite": 28183, "\u0120MOV": 28184, "\u0120Imper": 28185, "\u0120Solution": 28186, "\u0120phenomenal": 28187, "\u0120longevity": 28188, "\u0120impat": 28189, "\u0120Nissan": 28190, "irie": 28191, "\u0120odor": 28192, "\u0120Zar": 28193, "oks": 28194, "\u0120militias": 28195, "\u0120SPEC": 28196, "\u0120tolerated": 28197, "arser": 28198, "\u0120Bradford": 28199, "+,": 28200, "\u0120surreal": 28201, "sf": 28202, "Canadian": 28203, "\u0120resemblance": 28204, "\u0120carbohydrate": 28205, "VIEW": 28206, "\u0120accessory": 28207, "meal": 28208, "largest": 28209, "iegel": 28210, "Someone": 28211, "\u0120toughest": 28212, "oso": 28213, "\u0120funnel": 28214, "\u0120condemnation": 28215, "luent": 28216, "\u0120wired": 28217, "\u0120Sunset": 28218, "Jesus": 28219, "\u0120PST": 28220, "\u0120Pages": 28221, "\u0120Tycoon": 28222, "\u0120PF": 28223, "\u0120selections": 28224, "\u0120\u00e0\u00a4": 28225, "partisan": 28226, "\u0120highs": 28227, "\u0120Rune": 28228, "\u0120crafts": 28229, "lead": 28230, "\u0120Parents": 28231, "\u0120reclaim": 28232, "eker": 28233, "\u0120Allied": 28234, "aeper": 28235, "\u0120looming": 28236, "\u0120beneficiaries": 28237, "\u0120Hull": 28238, "Students": 28239, "Jewish": 28240, "dj": 28241, "\u0120pact": 28242, "template": 28243, "\u0120Officials": 28244, "\u0120Baylor": 28245, "\u0120hemp": 28246, "\u0120youths": 28247, "\u0120Levels": 28248, "\u0120Xiao": 28249, "\u0120Ches": 28250, "\u0120endeavor": 28251, "\u0120Removed": 28252, "\u0120hippocamp": 28253, "Hell": 28254, "\u00e3\u0124\u012c": 28255, "805": 28256, "\u0120dinosaur": 28257, "\u0120Wrath": 28258, "\u0120Indonesian": 28259, "\u0120calculator": 28260, "\u0120Dictionary": 28261, "\u0120420": 28262, "\u0120MAG": 28263, "(_": 28264, "!,": 28265, "tarians": 28266, "\u0120restricting": 28267, "racuse": 28268, "\u0120weekday": 28269, "OUNT": 28270, "\u0120shrugged": 28271, "leground": 28272, "\u0120bald": 28273, "\u0120Doctors": 28274, "\u0120touted": 28275, "\u0120Maxwell": 28276, "\u0120214": 28277, "\u0120diplomat": 28278, "\u0120repression": 28279, "\u0120constituency": 28280, "vice": 28281, "ranked": 28282, "\u0120Napoleon": 28283, "gang": 28284, "\u0120Forever": 28285, "tun": 28286, "\u0120bulb": 28287, "\u0120PDT": 28288, "\u0120Cisco": 28289, "VEN": 28290, "\u0120resumed": 28291, "Steven": 28292, "\u0120Manitoba": 28293, "\u0120fabulous": 28294, "\u0120Agents": 28295, "1984": 28296, "\u0120amusing": 28297, "\u0120Mysteries": 28298, "\u0120orthodox": 28299, "floor": 28300, "\u0120questionnaire": 28301, "\u0120penetrate": 28302, "\u0120filmmakers": 28303, "\u0120Unc": 28304, "\u0120stamped": 28305, "\u0120thirteen": 28306, "\u0120outfield": 28307, "\u0120forwarded": 28308, "\u0120appra": 28309, "\u0120aided": 28310, "try": 28311, "\u0120unfocused": 28312, "\u0120Liz": 28313, "\u0120Wendy": 28314, "\u0120Scene": 28315, "Charg": 28316, "\u0120rejects": 28317, "\u0120leftist": 28318, "\u0120Providence": 28319, "\u0120Brid": 28320, "regn": 28321, "\u0120prophecy": 28322, "\u0120LIVE": 28323, "499": 28324, "\u0120forge": 28325, "\u0120FML": 28326, "\u0120intrinsic": 28327, "\u0120Frog": 28328, "\u0120wont": 28329, "\u0120Holt": 28330, "\u0120famed": 28331, "CLUS": 28332, "aepernick": 28333, "\u0120Hate": 28334, "\u0120Cay": 28335, "\u0120registering": 28336, "ortality": 28337, "ropy": 28338, "ocalyptic": 28339, "aan": 28340, "nav": 28341, "\u0120fascist": 28342, "IFIED": 28343, "\u0120implicated": 28344, "\u0120Resort": 28345, "\u0120Chandler": 28346, "\u0120Brick": 28347, "Pin": 28348, "ysc": 28349, "Usage": 28350, "\u0120Helm": 28351, "usra": 28352, "\u00e2\u013a\u0127\u00e2\u013a\u0127": 28353, "\u0120Abbas": 28354, "\u0120unanimously": 28355, "\u0120keeper": 28356, "\u0120addicted": 28357, "???": 28358, "\u0120helmets": 28359, "\u0120antioxid": 28360, "apsed": 28361, "808": 28362, "giene": 28363, "\u0120waits": 28364, "\u0120minion": 28365, "raved": 28366, "\u0120Porsche": 28367, "\u0120dreaming": 28368, "\u0120171": 28369, "\u0120Cain": 28370, "\u0120unfor": 28371, "asso": 28372, "\u0120Configuration": 28373, "kun": 28374, "hardt": 28375, "\u0120nested": 28376, "\u0120LDS": 28377, "LES": 28378, "\u0120tying": 28379, "enos": 28380, "\u0120cue": 28381, "\u0120Marqu": 28382, "skirts": 28383, "\u0120clicked": 28384, "\u0120expiration": 28385, "\u0120Accordingly": 28386, "\u0120WC": 28387, "\u0120blessings": 28388, "\u0120addictive": 28389, "\u0120Narr": 28390, "yx": 28391, "\u0120Jaguars": 28392, "\u0120rents": 28393, "\u0120Siber": 28394, "\u0120tipped": 28395, "ousse": 28396, "\u0120Fitzgerald": 28397, "\u0120hierarch": 28398, "outine": 28399, "\u0120wavelength": 28400, ">.": 28401, "chid": 28402, "\u0120Processing": 28403, "/+": 28404, "ranking": 28405, "Easy": 28406, "\u0120Construct": 28407, "\u0120tet": 28408, "insured": 28409, "HUD": 28410, "\u0120quoting": 28411, "\u0120communicated": 28412, "inx": 28413, "\u0120inmate": 28414, "\u0120erected": 28415, "\u0120Absolutely": 28416, "\u0120Surely": 28417, "\u0120unim": 28418, "\u0120Throne": 28419, "heid": 28420, "\u0120claws": 28421, "\u0120superstar": 28422, "\u0120Lenn": 28423, "\u0120Whis": 28424, "Uk": 28425, "abol": 28426, "\u0120sket": 28427, "\u0120Niet": 28428, "\u0120perks": 28429, "\u0120affinity": 28430, "\u0120openings": 28431, "phasis": 28432, "\u0120discriminate": 28433, "Tip": 28434, "vc": 28435, "\u0120grinding": 28436, "\u0120Jenny": 28437, "\u0120asthma": 28438, "holes": 28439, "\u0120Homer": 28440, "\u0120registers": 28441, "\u0120Glad": 28442, "\u0120creations": 28443, "\u0120lithium": 28444, "\u0120applause": 28445, "until": 28446, "Justice": 28447, "\u0120Turks": 28448, "\u0120scandals": 28449, "\u0120bake": 28450, "tank": 28451, "Mech": 28452, "\u0120Means": 28453, "\u0120Maid": 28454, "Republicans": 28455, "isal": 28456, "windows": 28457, "\u0120Santos": 28458, "\u0120vegetation": 28459, "338": 28460, "tri": 28461, "\u0120flux": 28462, "insert": 28463, "\u0120clarified": 28464, "\u0120mortg": 28465, "\u0120Chim": 28466, "\u0120Tort": 28467, "\u0120disclaim": 28468, "metal": 28469, "\u0120Aside": 28470, "\u0120induction": 28471, "\u0120infl": 28472, "\u0120atheists": 28473, "amph": 28474, "\u0120ether": 28475, "\u0120Vital": 28476, "\u0120Built": 28477, "Mind": 28478, "\u0120weaponry": 28479, "SET": 28480, "\u0120186": 28481, "admin": 28482, "gam": 28483, "contract": 28484, "afa": 28485, "\u0120derivatives": 28486, "\u0120snacks": 28487, "\u0120churn": 28488, "Econom": 28489, "\u0120capped": 28490, "\u0120Understanding": 28491, "\u0120Hers": 28492, "\u0120Iz": 28493, "\u0120duct": 28494, "IENT": 28495, "aughty": 28496, "\u0120\u00e2\u013e\u0136": 28497, "\u0120NP": 28498, "\u0120sailing": 28499, "Initialized": 28500, "\u0120ted": 28501, "\u0120reactors": 28502, "\u0120Lomb": 28503, "\u0120choke": 28504, "\u0120Worm": 28505, "\u0120admiration": 28506, "\u0120swung": 28507, "ensibly": 28508, "\u0120rash": 28509, "\u0120Goals": 28510, "\u0120Important": 28511, "Shot": 28512, "\u0120Ras": 28513, "\u0120trainers": 28514, "\u0120Bun": 28515, "Working": 28516, "\u0120harmed": 28517, "\u0120Pandora": 28518, "\u0120LTE": 28519, "\u0120mushroom": 28520, "\u0120CHAR": 28521, "\u0120Fee": 28522, "\u0120Moy": 28523, "Born": 28524, "oliberal": 28525, "\u0120Martial": 28526, "\u0120gentlemen": 28527, "\u0120lingering": 28528, "Official": 28529, "\u0120graffiti": 28530, "\u0120Names": 28531, "Der": 28532, "\u0120quint": 28533, "istrate": 28534, "azeera": 28535, "\u0120NOTICE": 28536, "\u0120Florence": 28537, "\u0120payable": 28538, "\u0120depicts": 28539, "\u0120Species": 28540, "Heart": 28541, "\u00e2\u0136\u0122\u00e2\u0136\u0122\u00e2\u0136\u0122\u00e2\u0136\u0122\u00e2\u0136\u0122\u00e2\u0136\u0122\u00e2\u0136\u0122\u00e2\u0136\u0122": 28542, "\u0120enclosed": 28543, "Increases": 28544, "Daily": 28545, "\u0120Lis": 28546, "\u0120enactment": 28547, "\u0120Bacon": 28548, "\u0120Steele": 28549, "demand": 28550, "\u0120183": 28551, "\u0120mouths": 28552, "\u0120stranded": 28553, "\u0120enhancement": 28554, "011": 28555, "\u0120Whats": 28556, "\u0120healed": 28557, "eny": 28558, "\u0120Rab": 28559, "\u0120340": 28560, "\u0120Labyrinth": 28561, "roach": 28562, "\u0120Yosh": 28563, "\u0120Clippers": 28564, "\u0120concerts": 28565, "Internet": 28566, "355": 28567, "\u0120stickers": 28568, "\u0120termed": 28569, "\u0120Axe": 28570, "\u0120grandparents": 28571, "France": 28572, "\u0120Clim": 28573, "\u0120Uh": 28574, "ulic": 28575, "\u0120thrill": 28576, "centric": 28577, "\u0120Overview": 28578, "\u0120Conduct": 28579, "\u0120substantive": 28580, "\u0120182": 28581, "mur": 28582, "\u0120stray": 28583, "\u0120Coff": 28584, "\u0120repetitive": 28585, "\u0120Forgotten": 28586, "\u0120qualification": 28587, "ewitness": 28588, "\u0120Zimbabwe": 28589, "\u0120simulated": 28590, "\u0120JD": 28591, "253": 28592, "\u0120Ware": 28593, "\u0120unsc": 28594, "Times": 28595, "\u0120summons": 28596, "\u0120disconnected": 28597, "\u0120184": 28598, "cius": 28599, "\u0120Gujar": 28600, "odka": 28601, "\u0120erase": 28602, "\u0120Tobacco": 28603, "elected": 28604, "\u0120uncont": 28605, "\u0120Shepard": 28606, "\u0120Lamp": 28607, "\u0120alerted": 28608, "\u0120operative": 28609, "arna": 28610, "uint": 28611, "\u0120negligence": 28612, "acements": 28613, "\u0120supra": 28614, "\u0120prevail": 28615, "\u0120Shark": 28616, "\u0120belts": 28617, "\u00e3\u0123\u00ab": 28618, "\u0120tighter": 28619, "Engineers": 28620, "\u0120inactive": 28621, "\u0120exponent": 28622, "\u0120Willie": 28623, "aples": 28624, "\u0120heir": 28625, "\u0120Hits": 28626, "iann": 28627, "\u0120Says": 28628, "\u0120currents": 28629, "\u0120Bengal": 28630, "\u0120arist": 28631, "Buffer": 28632, "\u0120breeze": 28633, "\u0120Wesley": 28634, "Cola": 28635, "\u0120pronoun": 28636, "\u0120deed": 28637, "\u0120Kling": 28638, "\u0120oft": 28639, "\u0120inflict": 28640, "\u0120punishing": 28641, "\u0120nm": 28642, "iku": 28643, "ODUCT": 28644, "014": 28645, "\u0120subsidy": 28646, "\u0120DEA": 28647, "\u0120Herbert": 28648, "\u0120Jal": 28649, "Bank": 28650, "\u0120deferred": 28651, "\u0120shipment": 28652, "Bott": 28653, "\u0120alle": 28654, "bearing": 28655, "HTML": 28656, "Offline": 28657, "\u0120213": 28658, "\u0120scrolling": 28659, "\u0120scanned": 28660, "\u0120Libyan": 28661, "\u0120TOP": 28662, "chrom": 28663, "dt": 28664, "column": 28665, "PsyNetMessage": 28666, "Zero": 28667, "\u0120torso": 28668, "050": 28669, "\u00e2\u0137\u0132": 28670, "\u0120imperson": 28671, "\u0120Schwartz": 28672, "udic": 28673, "\u0120pissed": 28674, "\u0120Sapp": 28675, "257": 28676, "\u0120ISPs": 28677, "ogl": 28678, "\u0120supervised": 28679, "\u0120adolescent": 28680, "\u0120attained": 28681, "\u0120Delivery": 28682, "\u0120Bunny": 28683, "\u01201937": 28684, "\u0120miniature": 28685, "\u0120os": 28686, "\u0120370": 28687, "608": 28688, "\u0120Mourinho": 28689, "\u0120innate": 28690, "\u0120tempo": 28691, "\u0120NM": 28692, "\u0120Fallen": 28693, "009": 28694, "\u0120provocative": 28695, "Streamer": 28696, "\u0120Benedict": 28697, "\u0120Bolshe": 28698, "\u0120turtle": 28699, "\u0120PCB": 28700, "\u0120Equal": 28701, "Director": 28702, "\u0120Rend": 28703, "\u0120fluids": 28704, "Authorities": 28705, "\u0120cousins": 28706, "requency": 28707, "\u0120Neighbor": 28708, "sets": 28709, "shared": 28710, "Charles": 28711, "password": 28712, "\u0120gears": 28713, "\u0120211": 28714, "\u0120Hardware": 28715, "rika": 28716, "\u0120upstream": 28717, "Hom": 28718, "\u0120disproportionately": 28719, "ivities": 28720, "\u0120undefined": 28721, "\u0120electrons": 28722, "\u0120commemor": 28723, "Eventually": 28724, "\u0120><": 28725, "\u0120irresponsible": 28726, "218": 28727, "\u0120Released": 28728, "\u0120OVER": 28729, "\u0120IGN": 28730, "\u0120Bread": 28731, "stellar": 28732, "\u0120Sage": 28733, "tted": 28734, "damage": 28735, "edition": 28736, "\u0120Prec": 28737, "\u0120lime": 28738, "\u0120confinement": 28739, "\u0120calorie": 28740, "weapon": 28741, "\u0120differing": 28742, "\u0120Sina": 28743, "mys": 28744, "amd": 28745, "\u0120intricate": 28746, "kk": 28747, "\u0120PAT": 28748, "\u00c3\u00a3o": 28749, "stones": 28750, "links": 28751, "\u0120ranch": 28752, "Semitic": 28753, "\u0120differentiate": 28754, "\u0120Singer": 28755, "occupied": 28756, "\u0120fortress": 28757, "cmd": 28758, "\u0120interception": 28759, "\u0120Ankara": 28760, "\u0120rept": 28761, "\u0120Solitaire": 28762, "\u0120remake": 28763, "pred": 28764, "\u0120dared": 28765, "autions": 28766, "\u0120BACK": 28767, "Running": 28768, "\u0120debugging": 28769, "\u0120graphs": 28770, "399": 28771, "\u0120Nigel": 28772, "\u0120bun": 28773, "\u0120pillow": 28774, "\u0120progressed": 28775, "fashioned": 28776, "\u0120obedience": 28777, "ERN": 28778, "\u0120rehears": 28779, "Cell": 28780, "tl": 28781, "Sher": 28782, "\u0120herald": 28783, "\u0120Payment": 28784, "\u0120Cory": 28785, "\u0120Dept": 28786, "\u0120repent": 28787, "\u0120Weak": 28788, "uckland": 28789, "\u0120pleasing": 28790, "\u0120shortages": 28791, "\u0120jurors": 28792, "\u0120Kab": 28793, "qqa": 28794, "Anti": 28795, "\u0120wow": 28796, "\u0120RCMP": 28797, "\u0120tsun": 28798, "\u0120Sic": 28799, "\u0120comprises": 28800, "\u0120spies": 28801, "\u0120precinct": 28802, "nu": 28803, "\u0120urges": 28804, "\u0120timed": 28805, "\u0120stripes": 28806, "\u0120Boots": 28807, "\u0120yen": 28808, "Advanced": 28809, "\u0120discrete": 28810, "\u0120Archangel": 28811, "employment": 28812, "Diff": 28813, "\u0120monuments": 28814, "\u0120209": 28815, "worker": 28816, "\u0120196": 28817, "\u0120Ig": 28818, "utterstock": 28819, "TPS": 28820, "Jac": 28821, "\u0120homelessness": 28822, "\u0120commentator": 28823, "\u0120racially": 28824, "fing": 28825, "seed": 28826, "Ele": 28827, "ellation": 28828, "\u0120ethanol": 28829, "\u0120parish": 28830, "\u0120Dong": 28831, "\u0120Awakening": 28832, "\u0120deviation": 28833, "\u0120Bearing": 28834, "\u0120Tsuk": 28835, "\u0120recess": 28836, "\u0120lymph": 28837, "\u0120Cannabis": 28838, "\u00e5\u013e": 28839, "\u0120NEWS": 28840, "\u0120dra": 28841, "\u0120Stefan": 28842, "\u0120Wrong": 28843, "\u0120SAM": 28844, "\u0120loosely": 28845, "\u0120interpreter": 28846, "\u0120Plain": 28847, "Government": 28848, "\u0120bigotry": 28849, "\u0120grenades": 28850, "avez": 28851, "pictured": 28852, "\u0120mandated": 28853, "\u0120Monk": 28854, "\u0120Pedro": 28855, "\u0120lava": 28856, "274": 28857, "\u0120cynical": 28858, "\u0120Scrolls": 28859, "locks": 28860, "Mp": 28861, "\u0120congregation": 28862, "ornings": 28863, "phil": 28864, "\u0120Ibid": 28865, "\u0120ferv": 28866, "\u0120disappearing": 28867, "\u0120arrogant": 28868, "syn": 28869, "\u0120Maver": 28870, "\u0120Suit": 28871, "241": 28872, "\u0120abbre": 28873, "ackers": 28874, "Pa": 28875, "\u0120Yel": 28876, "Whenever": 28877, "\u0120235": 28878, "\u0120Vine": 28879, "\u0120Anat": 28880, "\u0120extinct": 28881, "LET": 28882, "\u0120executable": 28883, "VERS": 28884, "oxide": 28885, "DNA": 28886, "\u0120Prel": 28887, "\u0120resentment": 28888, "\u0120comprise": 28889, "\u0120Aviv": 28890, "\u0120interceptions": 28891, "\u0120prolific": 28892, "INA": 28893, "\u0120Erin": 28894, "thought": 28895, "219": 28896, "\u0120Psychiatry": 28897, "unky": 28898, "chemist": 28899, "Ho": 28900, "\u0120McCoy": 28901, "\u0120bricks": 28902, "Los": 28903, "rily": 28904, "\u0120USSR": 28905, "\u0120rud": 28906, "\u0120laud": 28907, "\u0120Wise": 28908, "\u0120Emerald": 28909, "\u0120revived": 28910, "\u0120damned": 28911, "\u0120Repair": 28912, "idem": 28913, "ctica": 28914, "\u0120patriarch": 28915, "\u0120Nurs": 28916, "meg": 28917, "\u0120cheapest": 28918, "reements": 28919, "empty": 28920, "\u0120Celebr": 28921, "\u0120deprivation": 28922, "chanted": 28923, "\u0120Thumbnails": 28924, "Energy": 28925, "\u0120Ethan": 28926, "\u0120Qing": 28927, "\u0120opposes": 28928, "WIND": 28929, "vik": 28930, "\u0120Mau": 28931, "\u0120SUB": 28932, "667": 28933, "GRE": 28934, "\u0120Volunte": 28935, "nton": 28936, "Cook": 28937, "\u00e5\u0132": 28938, "esque": 28939, "\u0120plummet": 28940, "\u0120suing": 28941, "\u0120pronounce": 28942, "\u0120resisting": 28943, "\u0120Fishing": 28944, "\u0120Trials": 28945, "\u0120yell": 28946, "\u0120310": 28947, "\u0120induct": 28948, "\u0120personalized": 28949, "often": 28950, "Reb": 28951, "EMBER": 28952, "\u0120viewpoint": 28953, "\u0120existential": 28954, "())": 28955, "remove": 28956, "MENTS": 28957, "lasses": 28958, "\u0120evapor": 28959, "\u0120aisle": 28960, "meta": 28961, "\u0120reflective": 28962, "\u0120entitlement": 28963, "\u0120devised": 28964, "music": 28965, "ascade": 28966, "\u0120winding": 28967, "offset": 28968, "\u0120accessibility": 28969, "kered": 28970, "Better": 28971, "\u0120Johnston": 28972, "thinking": 28973, "Snow": 28974, "\u0120Croatia": 28975, "\u0120Atomic": 28976, "271": 28977, "348": 28978, "\u0120textbook": 28979, "\u0120Sixth": 28980, "\u0120\u00d8\u00a7\u00d9\u0126": 28981, "\u0120slider": 28982, "\u0120Burger": 28983, "bol": 28984, "Sync": 28985, "\u0120grandchildren": 28986, "\u0120cerv": 28987, "+)": 28988, "\u0120eternity": 28989, "\u0120tweeting": 28990, "\u0120speculative": 28991, "\u0120pivotal": 28992, "\u0120WP": 28993, "\u0120TER": 28994, "ynamic": 28995, "\u0120upl": 28996, "\u0120Cats": 28997, "perhaps": 28998, "\u0120classmates": 28999, "\u0120blatant": 29000, "'-": 29001, "\u0120lakh": 29002, "antine": 29003, "\u0120Borg": 29004, "iom": 29005, "/(": 29006, "\u0120Athletic": 29007, "\u0120sar": 29008, "OTA": 29009, "\u0120Hoffman": 29010, "Nevertheless": 29011, "\u0120adorable": 29012, "\u0120spawned": 29013, "Associated": 29014, "\u0120Domestic": 29015, "\u0120implant": 29016, "\u0120Luxem": 29017, "\u0120Kens": 29018, "\u0120pumps": 29019, "\u0120SAT": 29020, "Attributes": 29021, "509": 29022, "avour": 29023, "\u0120centralized": 29024, "\u0120TN": 29025, "\u0120freshly": 29026, "\u0120Achieve": 29027, "\u0120outsiders": 29028, "herty": 29029, "\u0120Ree": 29030, "\u0120Towers": 29031, "\u0120Dart": 29032, "akable": 29033, "\u0120mp": 29034, "\u0120Heavenly": 29035, "\u0120ripe": 29036, "\u0120Caroline": 29037, "ryan": 29038, "\u0120classics": 29039, "\u0120retiring": 29040, "\u0120228": 29041, "\u0120ah": 29042, "\u0120dealings": 29043, "\u0120punching": 29044, "\u0120Chapman": 29045, "Options": 29046, "maxwell": 29047, "volume": 29048, "\u0120stal": 29049, "\u0120exported": 29050, "\u0120Quite": 29051, "\u0120numerical": 29052, "Burn": 29053, "Fact": 29054, "\u0120Keystone": 29055, "\u0120trending": 29056, "\u0120altering": 29057, "\u0120Africans": 29058, "478": 29059, "\u0120MN": 29060, "\u0120Knock": 29061, "\u0120temptation": 29062, "\u0120prestige": 29063, "Overview": 29064, "\u0120Traditional": 29065, "\u0120Bahrain": 29066, "Private": 29067, "\u0120HOU": 29068, "\u0120barr": 29069, "\u0120Tat": 29070, "Cube": 29071, "USD": 29072, "\u0120Grande": 29073, "\u0120Gat": 29074, "\u0120Flo": 29075, "\u0120resides": 29076, "\u0120indec": 29077, "volent": 29078, "\u0120perpetual": 29079, "ubes": 29080, "\u0120worldview": 29081, "\u0120Quantum": 29082, "\u0120filtered": 29083, "\u0120ensu": 29084, "orgetown": 29085, "ERSON": 29086, "\u0120Mild": 29087, "379": 29088, "OTT": 29089, "\u00c3\u00a5": 29090, "\u0120vitamins": 29091, "\u0120ribbon": 29092, "\u0120sincerely": 29093, "\u0120Hin": 29094, "\u0120eighteen": 29095, "\u0120contradictory": 29096, "\u0120glaring": 29097, "\u0120expectancy": 29098, "\u0120conspir": 29099, "\u0120monstrous": 29100, "\u0120380": 29101, "reci": 29102, "\u0120handic": 29103, "\u0120pumped": 29104, "\u0120indicative": 29105, "\u0120rapp": 29106, "\u0120avail": 29107, "\u0120LEGO": 29108, "\u0120Marijuana": 29109, "1985": 29110, "erton": 29111, "\u0120twentieth": 29112, "################################": 29113, "\u0120Swamp": 29114, "\u0120valuation": 29115, "\u0120affiliates": 29116, "adjusted": 29117, "\u0120Facility": 29118, "262": 29119, "\u0120enzymes": 29120, "itudinal": 29121, "\u0120imprint": 29122, "Site": 29123, "\u0120installer": 29124, "\u0120TRA": 29125, "mology": 29126, "linear": 29127, "\u0120Collective": 29128, "igating": 29129, "\u0120Token": 29130, "\u0120speculated": 29131, "KN": 29132, "\u0120Cly": 29133, "ority": 29134, "\u0120defer": 29135, "\u0120inspectors": 29136, "approved": 29137, "RM": 29138, "\u0120Suns": 29139, "\u0120informing": 29140, "\u0120Syracuse": 29141, "ibli": 29142, "765": 29143, "\u0120glove": 29144, "\u0120authorize": 29145, "\u00e2\u0122\u00a6\u00e2\u0122\u00a6\u00e2\u0122\u00a6\u00e2\u0122\u00a6\u00e2\u0122\u00a6\u00e2\u0122\u00a6\u00e2\u0122\u00a6\u00e2\u0122\u00a6": 29146, "\u0120Cruise": 29147, "\u0120contracting": 29148, "shell": 29149, "IFE": 29150, "\u0120Jewel": 29151, "pract": 29152, "\u0120Photoshop": 29153, "\u0120Knowing": 29154, "harm": 29155, "\u0120attractions": 29156, "adan": 29157, "etus": 29158, "018": 29159, "wagen": 29160, "Alt": 29161, "\u0120multiply": 29162, "\u0120equilibrium": 29163, ":{": 29164, "\u0120Fighters": 29165, "\u0120Edgar": 29166, "\u0120fourteen": 29167, "Govern": 29168, "\u0120misuse": 29169, "\u0120abusing": 29170, "\u0120ancestry": 29171, "ramer": 29172, "644": 29173, "\u0120worms": 29174, "\u0120thicker": 29175, "\u0120Combine": 29176, "\u0120peasants": 29177, "\u0120vind": 29178, "\u0120conquest": 29179, "\u0120mocked": 29180, "\u0120cinnamon": 29181, "\u0120Cald": 29182, "\u0120Gallup": 29183, "\u0120avoidance": 29184, "\u0120incarnation": 29185, "\u0120Strat": 29186, "\u0120tasted": 29187, "enta": 29188, "\u0120Neal": 29189, "pared": 29190, "\u0120terminology": 29191, "jection": 29192, "Scientists": 29193, "\u0120INS": 29194, "\u0120Dee": 29195, "\u0120directories": 29196, "Road": 29197, "\u0120Shap": 29198, "bright": 29199, "\u0120Directors": 29200, "\u0120Column": 29201, "\u0120bob": 29202, "\u0120preferably": 29203, "\u0120glitch": 29204, "furt": 29205, "\u0120eg": 29206, "idis": 29207, "CBC": 29208, "\u0120surrendered": 29209, "\u0120testament": 29210, "336": 29211, "uggest": 29212, "\u0120Nil": 29213, "another": 29214, "\u0120pathetic": 29215, "\u0120Donna": 29216, "\u0120218": 29217, "\u0120Avery": 29218, "\u0120whiskey": 29219, "\u0120fixture": 29220, "\u0120Conquest": 29221, "\u0120bets": 29222, "Occ": 29223, "\u0120Leicester": 29224, "].\"": 29225, "\u0120));": 29226, "\u0120flashes": 29227, "456": 29228, "\u0120masked": 29229, "gebra": 29230, "\u0120computed": 29231, "chel": 29232, "auder": 29233, "\u0120defeats": 29234, "\u0120Liberation": 29235, "\u0120Osama": 29236, "\u0120Vive": 29237, "Changes": 29238, "Channel": 29239, "\u0120tariffs": 29240, "\u0120mage": 29241, "\u0120Sax": 29242, "\u0120inadvertently": 29243, "\u0120CRE": 29244, "\u0120Reaper": 29245, "inky": 29246, "grading": 29247, "\u0120stereotyp": 29248, "\u0120curl": 29249, "\u0120FANT": 29250, "\u0120frameworks": 29251, "Mom": 29252, "\u0120Anch": 29253, "\u0120flavour": 29254, "carbon": 29255, "\u0120permitting": 29256, "letcher": 29257, "\u0120Mozilla": 29258, "\u0120Parking": 29259, "\u0120Champ": 29260, "Scroll": 29261, "\u0120murderer": 29262, "\u0120rested": 29263, "\u0120owes": 29264, "\u0120Poss": 29265, "ADD": 29266, "IFF": 29267, "resolution": 29268, "\u0120Mining": 29269, "\u0120comparative": 29270, "Dim": 29271, "\u0120neighbouring": 29272, "\u0120AST": 29273, "\u0120Toxic": 29274, "\u0120biases": 29275, "\u0120gunfire": 29276, "urous": 29277, "\u0120Moment": 29278, "1983": 29279, "\u0120pervasive": 29280, "ttp": 29281, "\u0120Normally": 29282, "rir": 29283, "Sarah": 29284, "\u0120Albany": 29285, "\u0120unsett": 29286, "\u0120SMS": 29287, "ipers": 29288, "layer": 29289, "\u0120Whites": 29290, "uple": 29291, "\u0120turbo": 29292, "\u0120Leeds": 29293, "\u0120thats": 29294, "\u0120Miner": 29295, "MER": 29296, "\u0120Reign": 29297, "\u0120perme": 29298, "\u0120Blitz": 29299, "\u01201934": 29300, "\u0120intimidating": 29301, "tube": 29302, "\u0120eccentric": 29303, "abolic": 29304, "boxes": 29305, "\u0120Associates": 29306, "votes": 29307, "\u0120simulate": 29308, "umbo": 29309, "astery": 29310, "\u0120shipments": 29311, "FFFF": 29312, "anth": 29313, "\u0120seasoned": 29314, "\u0120experimentation": 29315, "\u00e2\u0138\u0142": 29316, "laws": 29317, "Meet": 29318, "iddles": 29319, "antics": 29320, "Rating": 29321, "ISIS": 29322, "hift": 29323, "\u0120fronts": 29324, "buf": 29325, "017": 29326, "\u0120unatt": 29327, "\u0120Dil": 29328, "leases": 29329, "\u0120Gardens": 29330, "777": 29331, "touch": 29332, "vell": 29333, "458": 29334, "\u0120=====": 29335, "saving": 29336, "\u0120erosion": 29337, "\u0120Quin": 29338, "\u0120earns": 29339, "\u0120accomplishment": 29340, "\u0120Wei": 29341, "\u0120<[": 29342, "_____": 29343, "\u0120irrig": 29344, "\u0120Teddy": 29345, "\u0120conquered": 29346, "\u0120Armored": 29347, "\u0120asserts": 29348, "\u0120manipulating": 29349, "r\u00c3\u00a9": 29350, "\u0120transcripts": 29351, "Gallery": 29352, "\u0120plotting": 29353, "Neil": 29354, "\u0120betrayal": 29355, "loader": 29356, "\u0120Sul": 29357, "\u0120displacement": 29358, "\u0120royalty": 29359, "\u0120WI": 29360, "heit": 29361, "\u0120Devices": 29362, "allel": 29363, "\u0120municipalities": 29364, "\u0120canal": 29365, "Stars": 29366, "\u0120UAE": 29367, "\u0120\"\u00e2\u0122\u00a6": 29368, "\u0120CU": 29369, "above": 29370, "\u0120resonance": 29371, "\u0120guiActiveUn": 29372, "added": 29373, "\u0120Braves": 29374, "\u0120Ibn": 29375, "\u0120hereby": 29376, "\u0120BRE": 29377, "\u0120shareholder": 29378, "\u0120Hir": 29379, "\u0120Ji": 29380, "\u0120strangely": 29381, "\u0120admired": 29382, "\u0120plight": 29383, "\u0120bachelor": 29384, "\u0120Pole": 29385, "ciplinary": 29386, "Tony": 29387, "\u0120Armenian": 29388, "\u0120unman": 29389, "\u0120Zionist": 29390, "Stage": 29391, "iscover": 29392, "\u0120automotive": 29393, "\u0120sidelines": 29394, "\u0120slick": 29395, "\u0120Renaissance": 29396, "\u0120FUN": 29397, "Images": 29398, "\u0120Haj": 29399, "\u0120ping": 29400, "\u0120shortcut": 29401, "\u0120Blvd": 29402, "\u0120Looks": 29403, "\u0120bursts": 29404, "\u0120clamp": 29405, "\u0120mish": 29406, "\u0120sorting": 29407, "\u0120patriot": 29408, "\u0120correctness": 29409, "\u0120Scandinav": 29410, "\u0120Cavaliers": 29411, "python": 29412, "azar": 29413, "\u0120375": 29414, "\u0120Jaune": 29415, "409": 29416, "\u0120detrimental": 29417, "\u0120stabbing": 29418, "\u0120poisoned": 29419, "\u0120fountain": 29420, "ocent": 29421, "orst": 29422, "\u0120Mari": 29423, "\u0120rains": 29424, "\u0120Overs": 29425, "\u0120Institution": 29426, "udget": 29427, "AMY": 29428, "tale": 29429, "\u0120KR": 29430, "\u0120Prices": 29431, "\u0120headaches": 29432, "\u0120landsl": 29433, "\u0120Aura": 29434, "Bonus": 29435, "\u0120Zhao": 29436, "\u0120Hip": 29437, "\u0120hops": 29438, "\u0120Kurdistan": 29439, "\u0120exploiting": 29440, "ryn": 29441, "\u0120hypocrisy": 29442, "opening": 29443, "\u0120gunshot": 29444, "\u0120wed": 29445, "interstitial": 29446, "Interstitial": 29447, "\u0120amen": 29448, "Breaking": 29449, "\u0120marketed": 29450, "Wire": 29451, "\u0120Crowd": 29452, "Continue": 29453, "\u0120Known": 29454, "\u0120Effective": 29455, "orean": 29456, "izons": 29457, "Joseph": 29458, "\u0120escalation": 29459, "username": 29460, "\u0120curtain": 29461, "ATES": 29462, "\u0120PAR": 29463, "\u0120Miy": 29464, "\u0120counterfe": 29465, "lene": 29466, "\u0120contenders": 29467, "daily": 29468, "\u0120Asc": 29469, "\u0120Phillip": 29470, "mostly": 29471, "\u0120filename": 29472, "hene": 29473, "\u0120resembling": 29474, "\u0120staging": 29475, "\u0120Chloe": 29476, "\u0120wiring": 29477, "Hon": 29478, "\u0120Renew": 29479, "ottage": 29480, "\u0120Hybrid": 29481, "much": 29482, "\u0120strokes": 29483, "\u0120policymakers": 29484, "APTER": 29485, "\u0120Arkham": 29486, "plot": 29487, "\u0120assistants": 29488, "\u0120deport": 29489, "\u0120Sega": 29490, "\u0120influenza": 29491, "\u0120Cursed": 29492, "\u0120Kobe": 29493, "\u0120skinny": 29494, "Provider": 29495, "\u0120Rip": 29496, "\u0120incremental": 29497, "products": 29498, "BF": 29499, "\u0120dome": 29500, "\u0120Credits": 29501, "\u0120losers": 29502, "ints": 29503, "\u0120Betty": 29504, "\u0120Talent": 29505, "\u0120DAM": 29506, "Lv": 29507, "Ess": 29508, "\u0120dens": 29509, "temp": 29510, "Judge": 29511, "odic": 29512, "\u0120'(": 29513, "URES": 29514, "etsk": 29515, "VO": 29516, "\u0120retrieved": 29517, "\u0120architects": 29518, "\u00d9\u0129": 29519, "\u0120ethic": 29520, "\u0120Secondary": 29521, "stocks": 29522, "adia": 29523, "\u0120325": 29524, "\u0120Opinion": 29525, "\u0120simultaneous": 29526, "\u0120dizz": 29527, "ulp": 29528, "\u0120smuggling": 29529, "ippery": 29530, "Random": 29531, "facing": 29532, "\u0120Das": 29533, "\u0120stockp": 29534, "\u0120disclosures": 29535, "pointer": 29536, "\u0120coral": 29537, "\u0120Selection": 29538, "\u0120Pike": 29539, "ivalent": 29540, "\u0120ruthless": 29541, "\u0120Rim": 29542, "\u0120ensuing": 29543, "\u0120Experiment": 29544, "\u0120congressman": 29545, "\u0120believer": 29546, "\u0120unspecified": 29547, "\u0120Mord": 29548, "\u0120knowledgeable": 29549, "\u0120VERY": 29550, "TX": 29551, "\u0120straps": 29552, "\u0120turf": 29553, "apeshifter": 29554, "\u0120marital": 29555, "\u0120flock": 29556, "\u00e3\u0123\u0128": 29557, "263": 29558, "AMES": 29559, "\u0120Opposition": 29560, "\u0120treasures": 29561, "\u0120GOD": 29562, "\u0120modeled": 29563, "\u0120WORLD": 29564, "\u0120([": 29565, "\u0120Usage": 29566, "HF": 29567, "\u0120$(": 29568, "ussed": 29569, "\u0120pioneer": 29570, "Eight": 29571, "parse": 29572, "bread": 29573, "ritz": 29574, "\u0120Miranda": 29575, "\u0120Kant": 29576, "++)": 29577, "oren": 29578, "\u0120provoked": 29579, "\u0120breeds": 29580, "\u0120Includes": 29581, "\u0120Pastebin": 29582, "\u0120Flip": 29583, "Java": 29584, "\u0120brink": 29585, "\u0120rumored": 29586, "\u0120unseen": 29587, "\u0120garnered": 29588, "\u0120Defin": 29589, "alted": 29590, "\u0120tattoos": 29591, "\u0120hesitation": 29592, "isitions": 29593, "\u0120Weaver": 29594, "\u0120Reporting": 29595, "\u0120therapies": 29596, "\u0120consultants": 29597, "\u0120residual": 29598, "\u0120Mali": 29599, "\u0120Roma": 29600, "iago": 29601, "\u0120Residents": 29602, "ubi": 29603, "\u0120remedies": 29604, "\u0120adaptive": 29605, "\u0120Alive": 29606, "\u0120Barcl": 29607, "\u0120wallets": 29608, "crypt": 29609, "etermination": 29610, "\u0120Pelosi": 29611, "\u0120slipping": 29612, "otonin": 29613, "\u0120alliances": 29614, "patrick": 29615, "iris": 29616, "\u0120orth": 29617, "\u0120Perkins": 29618, "\u0120DeV": 29619, "\u0120Gets": 29620, "\u0120drying": 29621, "gee": 29622, "forest": 29623, "\u0120Forget": 29624, "orem": 29625, "339": 29626, "\u0120vaguely": 29627, "\u0120Dion": 29628, "\u0120Porn": 29629, "\u0120HOW": 29630, "\u0120pneum": 29631, "\u0120rubble": 29632, "\u0120Taste": 29633, "encia": 29634, "\u0120Gel": 29635, "\u0120dst": 29636, "\u0120245": 29637, "\u0120Morocco": 29638, "inflamm": 29639, "\u0120Twins": 29640, "\u0120bots": 29641, "daughter": 29642, "\u0120Balk": 29643, "\u0120brethren": 29644, "\u0120logos": 29645, "\u0120gobl": 29646, "fps": 29647, "\u0120subdivision": 29648, "\u0120pawn": 29649, "\u0120squeezed": 29650, "\u0120morale": 29651, "\u0120DW": 29652, "'\"": 29653, "\u0120knot": 29654, "ooky": 29655, "\u0120divisive": 29656, "\u0120boosted": 29657, "chy": 29658, "\u00e3\u0125\u0132": 29659, "ifact": 29660, "\u0120newcomers": 29661, "\u0120Wrestling": 29662, "\u0120scouts": 29663, "wolves": 29664, "Rat": 29665, "\u0120nineteenth": 29666, "\u0120Osborne": 29667, "Stats": 29668, "\u0120empowered": 29669, "\u0120psychopath": 29670, "\u0120OEM": 29671, "uggage": 29672, "\u0120PK": 29673, "\u0120Mohammad": 29674, "Pak": 29675, "\u0120anarchists": 29676, "\u0120Extract": 29677, "esthes": 29678, "\u0120Stockholm": 29679, "loo": 29680, "\u0120Graph": 29681, "\u0120deploying": 29682, "\u0120Stranger": 29683, "\u0120Mold": 29684, "\u0120staffer": 29685, "\u0120discounted": 29686, "uckle": 29687, "please": 29688, "\u0120Landing": 29689, "\u00c3\u0143a": 29690, "\u0120193": 29691, "\u0120ante": 29692, "\u0120repetition": 29693, "\u0120+/-": 29694, "\u0120parody": 29695, "\u0120lively": 29696, "AAA": 29697, "\u0120Horus": 29698, "\u0120pits": 29699, "inders": 29700, "LOC": 29701, "\u0120Venice": 29702, "406": 29703, "\u0120Discover": 29704, "\u00e2\u0128": 29705, "ellectual": 29706, "\u0120pens": 29707, "\u0120eyel": 29708, "iguous": 29709, "Impl": 29710, "\u0120joking": 29711, "\u0120inval": 29712, "\u0120Belfast": 29713, "\u0120creditors": 29714, "\u0120Skywalker": 29715, "ovsky": 29716, "\u0120ceasefire": 29717, "\u0120seals": 29718, "isoft": 29719, ")).": 29720, "\u0120Felix": 29721, "ITS": 29722, "\u0120tresp": 29723, "\u0120Blockchain": 29724, "eware": 29725, "\u0120Schwar": 29726, "enne": 29727, "mounted": 29728, "\u0120Beacon": 29729, "lesh": 29730, "\u0120immensely": 29731, "\u0120cheering": 29732, "Employ": 29733, "scene": 29734, "ishly": 29735, "atchewan": 29736, "\u0120Nicolas": 29737, "\u0120drained": 29738, "\u0120Exit": 29739, "\u0120Azerb": 29740, "jun": 29741, "\u0120floated": 29742, "uania": 29743, "Deep": 29744, "\u0120superv": 29745, "\u0120mystical": 29746, "\u0120Dollar": 29747, "\u0120Apostle": 29748, "\u0120REL": 29749, "\u0120Provided": 29750, "\u0120Bucks": 29751, "\u00e3\u0125\u00b4": 29752, "cutting": 29753, "\u0120enhancements": 29754, "\u0120Penguins": 29755, "\u0120Isaiah": 29756, "\u0120jerk": 29757, "\u0120Wyn": 29758, "\u0120stalled": 29759, "\u0120cryptocurrencies": 29760, "\u0120Roland": 29761, "single": 29762, "\u0120lumin": 29763, "\u0120Fellow": 29764, "\u0120Capacity": 29765, "\u0120Kazakh": 29766, "WN": 29767, "\u0120financed": 29768, "389": 29769, "\u0120tid": 29770, "\u0120collusion": 29771, "\u0120Myr": 29772, "\u00ee\u0122": 29773, "Senator": 29774, "\u0120pediatric": 29775, "\u0120neatly": 29776, "\u0120sandwiches": 29777, "\u0120Architecture": 29778, "\u0120tucked": 29779, "\u0120balcony": 29780, "\u0120earthquakes": 29781, "quire": 29782, "Future": 29783, "\u0120hefty": 29784, "\u00e9\u0139": 29785, "\u0120specializes": 29786, "\u0120stresses": 29787, "\u0120sender": 29788, "\u0120misunderstanding": 29789, "\u0120epile": 29790, "\u0120provoke": 29791, "\u0120Colors": 29792, "\u0120dismay": 29793, "uko": 29794, "[_": 29795, "586": 29796, "neutral": 29797, "\u0120donating": 29798, "\u0120Randall": 29799, "Multi": 29800, "\u0120conveniently": 29801, "\u0120Sung": 29802, "\u0120Coca": 29803, "\u0120tents": 29804, "\u0120Acceler": 29805, "\u0120partnered": 29806, "272": 29807, "irming": 29808, "\u0120BAS": 29809, "sometimes": 29810, "\u0120objected": 29811, "ubric": 29812, "posed": 29813, "LCS": 29814, "grass": 29815, "\u0120attributable": 29816, "VIS": 29817, "Israeli": 29818, "\u0120repeats": 29819, "\u0120RM": 29820, "vag": 29821, "uta": 29822, "inous": 29823, "\u0120inert": 29824, "\u0120Miguel": 29825, "\u00e6\u0143": 29826, "\u0120Hawaiian": 29827, "Board": 29828, "\u0120artific": 29829, "\u0120Azerbai": 29830, "asio": 29831, "\u0120Rent": 29832, "AIN": 29833, "\u0120appliances": 29834, "\u0120nationality": 29835, "\u0120asshole": 29836, "\u0120Neb": 29837, "\u0120notch": 29838, "hani": 29839, "\u0120Bride": 29840, "Availability": 29841, "\u0120intercepted": 29842, "\u0120continental": 29843, "\u0120swelling": 29844, "\u0120Perspect": 29845, "bies": 29846, ".<": 29847, "ithmetic": 29848, "\u0120Lara": 29849, "\u0120tempting": 29850, "addr": 29851, "\u0120overseeing": 29852, "clad": 29853, "\u0120DV": 29854, "\u0120Gingrich": 29855, "\u0120mun": 29856, "\u0120Appropri": 29857, "\u0120alterations": 29858, "\u0120Patreon": 29859, "\u0120havoc": 29860, "\u0120disciplines": 29861, "\u0120notoriously": 29862, "akuya": 29863, "ieri": 29864, "?).": 29865, "\u0120Went": 29866, "\u0120silicon": 29867, "\u0120tremb": 29868, "Container": 29869, "Known": 29870, "\u0120mortar": 29871, "este": 29872, "icka": 29873, "Arthur": 29874, "\u0120Previously": 29875, "\u0120Marty": 29876, "\u0120sparse": 29877, "gins": 29878, "\u0120inward": 29879, "\u0120Participant": 29880, "Copy": 29881, "\u0120Misc": 29882, "\u0120antibiotic": 29883, "\u0120Retro": 29884, "\u0120elusive": 29885, "\u0120assail": 29886, "\u0120Battalion": 29887, "\u0120Bought": 29888, "\u0120diminish": 29889, "\u0120Europa": 29890, "session": 29891, "\u0120Dangerous": 29892, "iesel": 29893, "\u0120disbelief": 29894, "\u0120blasts": 29895, "extreme": 29896, "\u0120Boyd": 29897, "\u0120Projects": 29898, "\u0120Guys": 29899, "\u0120undergone": 29900, "\u0120grill": 29901, "\u0120Dwight": 29902, "\u0120197": 29903, "USER": 29904, "\u0120filesystem": 29905, "\u0120clocks": 29906, "Taylor": 29907, "\u0120wrapper": 29908, "\u0120folding": 29909, "ousand": 29910, "\u0120Philippine": 29911, "ATIONAL": 29912, "\u0120Perth": 29913, "\u0120ashes": 29914, "\u0120accumulate": 29915, "\u0120Gateway": 29916, "Shop": 29917, "orkshire": 29918, "Han": 29919, "\u0120Barrel": 29920, "\u0120Leh": 29921, "\u0120XV": 29922, "\u0120whim": 29923, "\u0120repo": 29924, "\u0120CG": 29925, "\u0120Mam": 29926, "\u0120incorporating": 29927, "\u0120bailout": 29928, "\u0120linguistic": 29929, "\u0120disinteg": 29930, "CLE": 29931, "\u0120cinematic": 29932, "\u0120Fiber": 29933, "Syn": 29934, "ilion": 29935, "\u0120Compos": 29936, "chens": 29937, "\u0120neoc": 29938, "\u0120boiled": 29939, "FINE": 29940, "ono": 29941, "uncle": 29942, "iken": 29943, "\u0120BM": 29944, "\u00ce\u00b9": 29945, "\u0120receipts": 29946, "\u0120disposed": 29947, "\u0120Thirty": 29948, "\u0120Rough": 29949, "\u0120ABS": 29950, "\u0120notwithstanding": 29951, "ollen": 29952, "#$": 29953, "\u0120unreliable": 29954, "\u0120bloom": 29955, "\u0120mediocre": 29956, "\u0120tram": 29957, "\u0120Tasman": 29958, "\u0120shakes": 29959, "\u0120manifesto": 29960, "\u0120MW": 29961, "\u0120satisfactory": 29962, "\u0120shores": 29963, "\u0120computation": 29964, "\u0120assertions": 29965, "ormons": 29966, "arag": 29967, "abit": 29968, "Democrats": 29969, "\u0120Loot": 29970, "\u0120Volks": 29971, "haired": 29972, "\u0120gravitational": 29973, "Sing": 29974, "\u0120Miz": 29975, "\u0120throttle": 29976, "\u0120tyranny": 29977, "\u0120Views": 29978, "\u0120robber": 29979, "\u0120Minority": 29980, "\u0120shrine": 29981, "scope": 29982, "purpose": 29983, "\u0120nucleus": 29984, "ourcing": 29985, "\u0120USDA": 29986, "\u0120DHS": 29987, "wra": 29988, "\u0120Bowie": 29989, "Scale": 29990, "\u0120BEL": 29991, "xi": 29992, "Iter": 29993, "\u0120(),": 29994, "wright": 29995, "\u0120sailors": 29996, "oused": 29997, "NASA": 29998, "\u0120Proof": 29999, "\u0120Mineral": 30000, "token": 30001, "\u0120FD": 30002, "Rew": 30003, "\u0120ell": 30004, "630": 30005, "\u0120chancellor": 30006, "\u0120Gos": 30007, "\u0120amounted": 30008, "\u0120Recre": 30009, "omez": 30010, "\u0120Optim": 30011, "\u0120Olive": 30012, "\u0120tracker": 30013, "owler": 30014, "\u0120Unique": 30015, "Root": 30016, "\u0120maritime": 30017, "\u0120Quran": 30018, "\u0120Adapt": 30019, "\u0120ecosystems": 30020, "\u0120Repeat": 30021, "\u0120Soy": 30022, "\u0120IMP": 30023, "\u0120graduating": 30024, "andem": 30025, "Pur": 30026, "\u0120Reset": 30027, "\u0120Trick": 30028, "\u0120Philly": 30029, "\u0120Tue": 30030, "\u0120Malaysian": 30031, "\u0120climax": 30032, "\u0120bury": 30033, "\u0120conspic": 30034, "\u0120Southampton": 30035, "\u0120Flowers": 30036, "\u0120escorted": 30037, "\u0120Educational": 30038, "\u0120IRC": 30039, "\u0120brutally": 30040, "eating": 30041, "\u0120pillar": 30042, "\u0120Sang": 30043, "\u0120Jude": 30044, "arling": 30045, "\u0120Amnesty": 30046, "\u0120reminding": 30047, "\u0120Administrative": 30048, "hesda": 30049, "\u0120flashed": 30050, "\u0120PBS": 30051, "perate": 30052, "feature": 30053, "\u0120swipe": 30054, "\u0120graves": 30055, "oultry": 30056, "261": 30057, "breaks": 30058, "\u0120Guer": 30059, "\u0120shrimp": 30060, "\u0120Voting": 30061, "quist": 30062, "\u0120analytical": 30063, "\u0120tablespoons": 30064, "\u0120SOU": 30065, "\u0120researched": 30066, "\u0120disrupted": 30067, "\u0120jour": 30068, "\u0120replica": 30069, "\u0120cartoons": 30070, "bians": 30071, "})": 30072, "copy": 30073, "Got": 30074, "ouched": 30075, "PUT": 30076, "\u0120swarm": 30077, "notations": 30078, "said": 30079, "\u0120rebuilt": 30080, "\u0120collaborate": 30081, "\u0120raging": 30082, "\u0120nar": 30083, "\u0120demographics": 30084, "\u0120DDR": 30085, "\u0120distrust": 30086, "ossier": 30087, "\u0120Kro": 30088, "\u0120pumpkin": 30089, "\u0120regrets": 30090, "\u0120fatalities": 30091, "\u0120Lens": 30092, "\u0120Ole": 30093, "pd": 30094, "\u0120puppet": 30095, "\u0120Outlook": 30096, "\u0120Stam": 30097, "Ol": 30098, "Fair": 30099, "UU": 30100, "\u0120rewritten": 30101, "\u00c4\u00b1": 30102, "\u0120fascinated": 30103, "\u0120vectors": 30104, "\u0120tribunal": 30105, "uay": 30106, "\u0120Mats": 30107, "\u0120Coins": 30108, "[[": 30109, "\u0120181": 30110, "\u0120renders": 30111, "\u0120Kaepernick": 30112, "\u0120espionage": 30113, "\u0120summ": 30114, "\u0120ditch": 30115, "Account": 30116, "\u0120spreadsheet": 30117, "\u0120mutant": 30118, "past": 30119, "407": 30120, "\u0120dye": 30121, "\u0120initiation": 30122, "\u01204000": 30123, "\u0120punishable": 30124, "\u0120thinner": 30125, "\u0120Khal": 30126, "\u0120intermedi": 30127, "Dun": 30128, "\u0120Gotham": 30129, "\u0120eagerly": 30130, "\u0120vaginal": 30131, "powers": 30132, "VW": 30133, "\u0120WATCHED": 30134, "\u0120predator": 30135, "amsung": 30136, "\u0120disparity": 30137, "\u0120[*": 30138, "\u0120amph": 30139, "\u0120outskirts": 30140, "\u0120Spirits": 30141, "\u0120skeletal": 30142, "\u00d0\u00bb": 30143, "\u0120Rear": 30144, "\u0120issuance": 30145, "\u0120Logic": 30146, "released": 30147, "ZZ": 30148, "\u0120Bound": 30149, "Entry": 30150, "\u0120exits": 30151, "isol": 30152, "\u0120Founder": 30153, "\u0120wre": 30154, "\u0120Greenland": 30155, "\u0120MMO": 30156, "taker": 30157, "INC": 30158, "\u00e3\u0123\u00be": 30159, "\u0120hourly": 30160, "henko": 30161, "\u0120fantasies": 30162, "\u0120disob": 30163, "\u0120demolition": 30164, "\u00e3\u0125\u012d": 30165, "\u0120enlisted": 30166, "ratulations": 30167, "\u0120misguided": 30168, "\u0120ensured": 30169, "\u0120discouraged": 30170, "mort": 30171, "\u0120flank": 30172, "\u0120cess": 30173, "\u0120reacts": 30174, "\u0120Sere": 30175, "sensitive": 30176, "\u0120Serpent": 30177, "assad": 30178, "\u0120247": 30179, "\u0120calmly": 30180, "busters": 30181, "\u0120bleed": 30182, "\u0120Stro": 30183, "\u0120amusement": 30184, "\u0120Antarctica": 30185, "\u0120scept": 30186, "\u0120Gaw": 30187, "aq": 30188, "asonic": 30189, "\u0120sprawling": 30190, "native": 30191, "aturated": 30192, "\u0120Battlefield": 30193, "IVERS": 30194, "EB": 30195, "\u0120Gems": 30196, "\u0120Northwestern": 30197, "\u0120Films": 30198, "\u0120Automatic": 30199, "\u0120apprehend": 30200, "\u00e3\u0123\u00a8": 30201, "\u0120guiName": 30202, "\u0120backend": 30203, "\u0120evidenced": 30204, "geant": 30205, "012": 30206, "\u0120Siege": 30207, "\u0120externalTo": 30208, "\u0120unfocusedRange": 30209, "\u0120guiActiveUnfocused": 30210, "\u0120guiIcon": 30211, "\u0120externalToEVA": 30212, "\u0120externalToEVAOnly": 30213, "Fri": 30214, "chard": 30215, "enaries": 30216, "\u0120chiefs": 30217, "\u0120cf": 30218, "\u0120HUD": 30219, "\u0120corrobor": 30220, "\u0120dB": 30221, "\u0120Taken": 30222, "\u0120Patricia": 30223, "rail": 30224, "\u0120Charm": 30225, "\u0120Libertarian": 30226, "rieve": 30227, "Personal": 30228, "\u0120OUR": 30229, "geries": 30230, "\u0120dumping": 30231, "\u0120neurological": 30232, "itimate": 30233, "\u0120Clintons": 30234, "rafted": 30235, "\u0120Molly": 30236, "\u0120terminals": 30237, "register": 30238, "\u0120flare": 30239, "\u0120encoded": 30240, "\u0120autopsy": 30241, "pel": 30242, "machine": 30243, "\u0120exemptions": 30244, "\u0120Royals": 30245, "distance": 30246, "\u0120drafts": 30247, "\u0120lame": 30248, "\u0120Cunning": 30249, "\u0120spouses": 30250, "\u0120Markets": 30251, "\u0120Carrier": 30252, "\u0120implying": 30253, "\u0120Yak": 30254, "sid": 30255, "\u0120loser": 30256, "\u0120vigilant": 30257, "\u0120impeachment": 30258, "\u0120augmented": 30259, "\u0120Employees": 30260, "\u0120unintended": 30261, "ternally": 30262, "\u0120Watt": 30263, "\u0120recognizable": 30264, "essim": 30265, "\u00e6\u013f": 30266, "\u0120coated": 30267, "rha": 30268, "\u0120lieutenant": 30269, "\u0120Legislation": 30270, "published": 30271, "444": 30272, "013": 30273, "\u0120ideally": 30274, "\u0120Password": 30275, "\u0120simplify": 30276, "\u0120Meta": 30277, "\u0120MRI": 30278, "\u0120pleading": 30279, "organized": 30280, "handler": 30281, "\u0120unravel": 30282, "correct": 30283, "\u0120icy": 30284, "\u0120paranoid": 30285, "\u0120passer": 30286, "\u0120inspections": 30287, "ofer": 30288, "\u0120Healthcare": 30289, "283": 30290, "\u0120Brut": 30291, "iola": 30292, "forge": 30293, "\u0120Medieval": 30294, "MSN": 30295, "ievers": 30296, "\u0120Programming": 30297, "\u00e5\u012b": 30298, "\u0120223": 30299, "mu": 30300, "\u0120CLE": 30301, "uga": 30302, "\u0120shoppers": 30303, "\u0120informative": 30304, "\u0120Plans": 30305, "\u0120supplementation": 30306, "\u0120Tests": 30307, "tyard": 30308, "ocytes": 30309, "\u0120Vega": 30310, "\u0120Gujarat": 30311, "ermanent": 30312, "Except": 30313, "\u0120LOT": 30314, "alla": 30315, "\u0120Cumm": 30316, "\u0120Osw": 30317, "\u0120venom": 30318, "\u0120Debt": 30319, "\u0120DOWN": 30320, "\u0120reunion": 30321, "\u0120muc": 30322, "\u0120Relief": 30323, "\u0120geop": 30324, "\u0120\u00f0\u0141\u013a": 30325, "alogue": 30326, "Anth": 30327, "echo": 30328, "\u0120corros": 30329, "\u0120replication": 30330, "\u0120Blazing": 30331, "\u0120Daughter": 30332, "\u0120inflic": 30333, "\u0120Lindsey": 30334, "\u00d9\u012a": 30335, "284": 30336, "Exit": 30337, "\u0120gloom": 30338, "TAIN": 30339, "\u0120undermining": 30340, "\u0120advising": 30341, "hidden": 30342, "\u0120overflow": 30343, "\u0120gor": 30344, "urdue": 30345, "\u0120echoes": 30346, "enhagen": 30347, "\u0120impuls": 30348, "drug": 30349, "cash": 30350, "\u0120async": 30351, "\u0120mirac": 30352, "atts": 30353, "punk": 30354, "\u0120pivot": 30355, "\u0120Legislative": 30356, "\u0120bloggers": 30357, "\u0120Claw": 30358, "sburg": 30359, "dyl": 30360, "\u0120Recommend": 30361, "\u0120verte": 30362, "\u0120prohibiting": 30363, "\u0120Panther": 30364, "Jonathan": 30365, "\u0120omin": 30366, "\u0120hateful": 30367, "281": 30368, "\u0120Orche": 30369, "\u0120Murdoch": 30370, "downs": 30371, "\u0120asymm": 30372, "GER": 30373, "Always": 30374, "\u0120informs": 30375, "\u0120WM": 30376, "\u0120Pony": 30377, "\u0120Appendix": 30378, "\u0120Arlington": 30379, "Jam": 30380, "\u0120medicinal": 30381, "\u0120Slam": 30382, "ITIES": 30383, "\u0120reaff": 30384, "\u0120Ri": 30385, "FG": 30386, "Spring": 30387, "bool": 30388, "\u0120thighs": 30389, "\u0120markings": 30390, "\u0120Raqqa": 30391, "\u0120Lak": 30392, "poll": 30393, "tsky": 30394, "\u0120Morty": 30395, "\u0120Definition": 30396, "\u0120debunk": 30397, "endered": 30398, "\u0120Leone": 30399, "avers": 30400, "\u0120mortgages": 30401, "Apparently": 30402, "Nic": 30403, "haus": 30404, "\u0120Thousands": 30405, "auld": 30406, "\u0120mash": 30407, "shoot": 30408, "\u0120diarr": 30409, "\u0120consciously": 30410, "Hero": 30411, "eas": 30412, "\u0120Naturally": 30413, "\u0120Destroyer": 30414, "\u0120dashboard": 30415, "services": 30416, "Rog": 30417, "\u0120millennials": 30418, "\u0120invade": 30419, "-(": 30420, "\u0120commissions": 30421, "\u0120Auckland": 30422, "\u0120broadcasts": 30423, "\u0120frontal": 30424, "\u0120crank": 30425, "\u0120Historic": 30426, "\u0120rumours": 30427, "CTV": 30428, "\u0120steril": 30429, "\u0120booster": 30430, "rocket": 30431, "\u00e3\u0124\u00bc": 30432, "utsche": 30433, "\u0120PI": 30434, "\u0120233": 30435, "\u0120Producer": 30436, "\u0120Analytics": 30437, "\u0120invaluable": 30438, "\u0120unintention": 30439, "\u0120CY": 30440, "\u0120scrutin": 30441, "\u0120gigg": 30442, "\u0120engulf": 30443, "\u0120proletariat": 30444, "\u0120hacks": 30445, "\u0120Hew": 30446, "arak": 30447, "\u0120Slime": 30448, "ielding": 30449, "agher": 30450, "\u0120Elliot": 30451, "\u0120telecom": 30452, "\u0120219": 30453, "ultan": 30454, "\u0120Arbor": 30455, "\u0120Scouts": 30456, "Ban": 30457, "\u0120lifespan": 30458, "\u0120blasp": 30459, "388": 30460, "\u0120judiciary": 30461, "\u0120Continental": 30462, "asking": 30463, "McC": 30464, "LED": 30465, "\u0120baggage": 30466, "\u0120Sorcerer": 30467, "\u0120remnants": 30468, "\u0120Griffith": 30469, "etsu": 30470, "\u0120Subaru": 30471, "\u0120Personality": 30472, "designed": 30473, "ushima": 30474, "agnar": 30475, "\u0120recoil": 30476, "\u0120passions": 30477, "\\\":": 30478, "\u0120tee": 30479, "\u0120abolition": 30480, "\u0120Creating": 30481, "jac": 30482, "\u0120194": 30483, "019": 30484, "\u0120pillars": 30485, "riched": 30486, "/\"": 30487, "tk": 30488, "\u0120livelihood": 30489, "\u0120roasted": 30490, "ahon": 30491, "\u0120Hutch": 30492, "assert": 30493, "\u0120dividend": 30494, "\u0120knit": 30495, "\u0120daunting": 30496, "\u0120disturbance": 30497, "\u0120shale": 30498, "\u0120cultivated": 30499, "\u0120refrigerator": 30500, "LB": 30501, "\u0120NET": 30502, "\u0120commercials": 30503, "\u0120thinkers": 30504, "455": 30505, "\u0120chop": 30506, "Broad": 30507, "\u0120suspicions": 30508, "\u0120tagged": 30509, "lifting": 30510, "\u0120stylish": 30511, "\u0120Shields": 30512, "Shortly": 30513, "\u0120tails": 30514, "Auth": 30515, "STE": 30516, "\u0120GAME": 30517, "\u0120seism": 30518, "\u0120Kis": 30519, "ologne": 30520, "\u0120cowork": 30521, "\u0120forcibly": 30522, "\u0120thyroid": 30523, "\u0120PB": 30524, "ANE": 30525, "married": 30526, "horse": 30527, "\u0120polymer": 30528, "\u0120Chal": 30529, "odor": 30530, "DEBUG": 30531, "\u0120Context": 30532, "\u0120bliss": 30533, "\u0120pinpoint": 30534, "\u0120Mathemat": 30535, "legram": 30536, "\u0120Weekend": 30537, "\u0120labelled": 30538, "\u0120bart": 30539, "itles": 30540, "\u0120estrogen": 30541, "\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136": 30542, "\"'": 30543, "\u0120visibly": 30544, "\u0120outsider": 30545, "aida": 30546, "Area": 30547, "\u0120dissemin": 30548, "\u0120dishonest": 30549, "\u0120Closed": 30550, "\u0120Bulletin": 30551, "\u0120Ramsey": 30552, "sword": 30553, "\u0120XI": 30554, "ourced": 30555, "Same": 30556, "346": 30557, "\u0120Repe": 30558, "\u0120Kou": 30559, "cake": 30560, "emis": 30561, "Cache": 30562, "\u0120Meaning": 30563, "\u0120Enlight": 30564, "onomy": 30565, "\u0120manifestation": 30566, "sworth": 30567, "Jay": 30568, "\u0120chore": 30569, "\u00c3\u00b6r": 30570, "Dream": 30571, "\u0120sanctioned": 30572, "\u0120culturally": 30573, "\u0120Ara": 30574, "Nav": 30575, "\u0120theological": 30576, "\u0120strut": 30577, "\u0120VO": 30578, "\u0120Handbook": 30579, "\u0120constructing": 30580, "\u0120\u00c2\u00b6": 30581, "\u0120Benefits": 30582, "\u0120Psychological": 30583, "sac": 30584, "\u00e5\u00b8": 30585, "policy": 30586, "\u0120Matters": 30587, "\u0120Reported": 30588, "\u0120Byte": 30589, "\u0120vitro": 30590, "\u0120Maiden": 30591, "\u0120lam": 30592, "\u0120Jennings": 30593, "\u0120garment": 30594, "\u0120Rutgers": 30595, "\u0120Stafford": 30596, "\u0120Wellington": 30597, "\u0120intermitt": 30598, "\u0120npm": 30599, "\u0120ordeal": 30600, "\u0120plugged": 30601, "ooming": 30602, "inished": 30603, "framework": 30604, "\u0120timber": 30605, "\u0120cass": 30606, "\u0120850": 30607, "iless": 30608, "\u0120Redux": 30609, "768": 30610, "Stre": 30611, "\u0120surpassed": 30612, "whel": 30613, "\u0120parallels": 30614, "\u0120veil": 30615, "\u0120GI": 30616, "\u0120REST": 30617, "\u0120readiness": 30618, "sort": 30619, "\u0120modifying": 30620, "\u0120Slate": 30621, "ruff": 30622, "\u0120marble": 30623, "\u0120infrared": 30624, "\u0120auditor": 30625, "\u0120FANTASY": 30626, "\u0120Poverty": 30627, "\u0120SPD": 30628, "\u0120\"(": 30629, "Ky": 30630, "RAY": 30631, "\u0120executions": 30632, "\u0120Beverly": 30633, "\u0120Marxism": 30634, "\u0120Burst": 30635, "\u0120Kali": 30636, "estones": 30637, "Clearly": 30638, "Ell": 30639, "\u00e3\u0123\u00a7": 30640, "\u0120Proceedings": 30641, "Token": 30642, "IFIC": 30643, "\u00c3\u00b1a": 30644, "Central": 30645, "\u0120Haley": 30646, "\u0120Drama": 30647, "\u0120formations": 30648, "ORN": 30649, "Books": 30650, "\u0120dominating": 30651, "\u0120Flyers": 30652, "\u0120Companion": 30653, "\u0120disciplined": 30654, "\u0120Yugoslav": 30655, "\u0120Spells": 30656, "\u0120vengeance": 30657, "\u0120landlords": 30658, "Len": 30659, "\u0120Ogre": 30660, "anoia": 30661, "\u0120piercing": 30662, "\u0120congreg": 30663, "\u0120scorer": 30664, "obia": 30665, "\u0120nickel": 30666, "\u0120Learns": 30667, "\u0120rejo": 30668, "\u0120masterpiece": 30669, "Flash": 30670, "\u0120inhabited": 30671, "\u0120OpenGL": 30672, "\u0120Dud": 30673, "\u0120ICO": 30674, "\u0120arter": 30675, "\u0120plur": 30676, "\u0120mastery": 30677, "\u0120longstanding": 30678, "sted": 30679, "\u0120wines": 30680, "\u0120televised": 30681, "\u0120Shrine": 30682, "\u0120Bayern": 30683, "\u0120\u00e2\u0135\u013a": 30684, "\u0120enclosure": 30685, "john": 30686, "\u0120prophets": 30687, "\u0120Resurrection": 30688, "\u0120Orders": 30689, "\u0120uneven": 30690, "rals": 30691, "\u0120dwind": 30692, "\u0120Lah": 30693, "\u0120Sloven": 30694, "378": 30695, "\u0120insistence": 30696, "affle": 30697, "\u0120Clone": 30698, "\u0120hardship": 30699, "\u0120Congressman": 30700, "\u0120plead": 30701, "\u0120reviewers": 30702, "\u0120cured": 30703, "\u01201935": 30704, "asley": 30705, "fake": 30706, "\u0120Thinking": 30707, "ydia": 30708, "PART": 30709, "\u0120Dota": 30710, "oit": 30711, "\u0120whipped": 30712, "\u0120bouncing": 30713, "\u0120Hispanics": 30714, "comings": 30715, "\u0120cannabin": 30716, "\u0120Chambers": 30717, "\u0120Zack": 30718, "Optional": 30719, "\u0120coats": 30720, "\u0120prowess": 30721, "\u0120Norton": 30722, "\u0120plainly": 30723, "\u0120freight": 30724, "\u0120inhibition": 30725, "\u0120clam": 30726, "\u0120303": 30727, "kef": 30728, "aleigh": 30729, "Luke": 30730, "\u0120psycho": 30731, "atorium": 30732, "MED": 30733, "\u0120treaties": 30734, "\u0120indisc": 30735, "\u0120dc": 30736, "OPS": 30737, "\u0120resilient": 30738, "\u0120Interstate": 30739, "\u0120slack": 30740, "\u0120mundane": 30741, "\u0120establishes": 30742, "359": 30743, "\u0120strained": 30744, "\u0120nond": 30745, "Sus": 30746, "\u0120caste": 30747, "arate": 30748, "ieving": 30749, "\u0120unfairly": 30750, "\u0120parser": 30751, "onial": 30752, "ursive": 30753, "Via": 30754, "\u0120Otto": 30755, "\u0120Authorities": 30756, "stroke": 30757, "KR": 30758, "\u0120Mercy": 30759, "\u0120furnished": 30760, "\u0120outset": 30761, "\u0120metic": 30762, "1982": 30763, "olithic": 30764, "\u0120Tent": 30765, "ogical": 30766, "\u0120Aircraft": 30767, "\u0120hides": 30768, "\u0120Became": 30769, "\u0120educators": 30770, "reaching": 30771, "\u0120volatility": 30772, "\u0120toddler": 30773, "\u0120NASCAR": 30774, "\u0120Twelve": 30775, "\u0120Highlights": 30776, "\u0120grape": 30777, "\u0120splits": 30778, "\u0120peasant": 30779, "\u0120reneg": 30780, "\u0120MSI": 30781, "Temp": 30782, "stars": 30783, "\u0120trek": 30784, "\u0120Hyde": 30785, "binding": 30786, "\u0120realism": 30787, "\u0120oxide": 30788, "\u0120Hos": 30789, "\u0120mounts": 30790, "\u0120biting": 30791, "\u0120collapsing": 30792, "\u0120postal": 30793, "\u0120museums": 30794, "\u0120detached": 30795, "\u0120respecting": 30796, "\u0120monopol": 30797, "\u0120workflow": 30798, "\u0120Cake": 30799, "Template": 30800, "\u0120Organisation": 30801, "\u0120persistence": 30802, "369": 30803, "Coming": 30804, "Brad": 30805, "\u0120redundant": 30806, "\u0120GTA": 30807, "\u0120bending": 30808, "\u0120revoked": 30809, "\u0120offending": 30810, "\u0120framing": 30811, "\u0120printf": 30812, "Commun": 30813, "members": 30814, "Outside": 30815, "\u0120construed": 30816, "\u0120coded": 30817, "FORE": 30818, "\u0120chast": 30819, "Chat": 30820, "Indian": 30821, "\u0120Yard": 30822, "?!\"": 30823, "\u0120Ports": 30824, "\u0120Xavier": 30825, "\u0120RET": 30826, "'.\"": 30827, "\u0120Boat": 30828, "ivated": 30829, "icht": 30830, "umerable": 30831, "Ds": 30832, "\u0120Dunn": 30833, "\u0120coffin": 30834, "\u0120securely": 30835, "\u0120Raptors": 30836, "\u0120Bes": 30837, "Installation": 30838, "\u0120inception": 30839, "\u0120Healthy": 30840, "endants": 30841, "\u0120psychologists": 30842, "\u0120Sheikh": 30843, "cultural": 30844, "\u0120BlackBerry": 30845, "shift": 30846, "Fred": 30847, "oche": 30848, "\u0120cakes": 30849, "\u0120SEO": 30850, "\u0120Gian": 30851, "\u0120Asians": 30852, "ogging": 30853, "element": 30854, "\u0120pundits": 30855, "\u0120Vaugh": 30856, "\u0120Gavin": 30857, "\u0120hitter": 30858, "\u0120drowned": 30859, "\u0120chalk": 30860, "\u0120Zika": 30861, "\u0120measles": 30862, "802": 30863, "\u00e2\u0122\u00a6..": 30864, "\u0120AWS": 30865, "]\"": 30866, "\u0120distort": 30867, "\u0120Mast": 30868, "\u0120antibodies": 30869, "\u0120Mash": 30870, "Memory": 30871, "\u0120Uganda": 30872, "\u0120Prob": 30873, "\u0120vomiting": 30874, "\u0120Turns": 30875, "\u0120occupying": 30876, "\u0120evasion": 30877, "\u0120Therapy": 30878, "\u0120promo": 30879, "\u0120electr": 30880, "\u0120blueprint": 30881, "\u0120Dre": 30882, "priced": 30883, "\u0120Depot": 30884, "\u0120alleviate": 30885, "\u0120Somali": 30886, "marg": 30887, "nine": 30888, "\u0120nostalgia": 30889, "\u0120Shepherd": 30890, "\u0120cavalry": 30891, "\u0120torped": 30892, "\u0120Bloody": 30893, "xb": 30894, "\u0120sank": 30895, "\u0120goalt": 30896, "reportprint": 30897, "embedreportprint": 30898, "cloneembedreportprint": 30899, "\u0120Initially": 30900, "\u0120Fischer": 30901, "\u0120noteworthy": 30902, "cern": 30903, "\u0120inefficient": 30904, "rawdownload": 30905, "rawdownloadcloneembedreportprint": 30906, "cation": 30907, "\u0120Dynasty": 30908, "lag": 30909, "DES": 30910, "\u0120distinctly": 30911, "\u0120Estonia": 30912, "\u0120openness": 30913, "\u0120gossip": 30914, "ruck": 30915, "Width": 30916, "\u0120Ibrahim": 30917, "\u0120petroleum": 30918, "\u0120avatar": 30919, "\u0120Hed": 30920, "atha": 30921, "\u0120Hogwarts": 30922, "\u0120caves": 30923, "678": 30924, "\u0120safeguard": 30925, "\u0120Mog": 30926, "isson": 30927, "\u0120Durham": 30928, "slaught": 30929, "\u0120Graduate": 30930, "\u0120subconscious": 30931, "\u0120Excellent": 30932, "\u0120Dum": 30933, "-----": 30934, "\u0120piles": 30935, "\u0120WORK": 30936, "\u0120Garn": 30937, "\u0120Fol": 30938, "\u0120ATM": 30939, "\u0120avoids": 30940, "\u0120Tul": 30941, "\u0120bleak": 30942, "ELY": 30943, "ivist": 30944, "lightly": 30945, "Pers": 30946, "\u0120Dob": 30947, "\u0120LS": 30948, "\u0120insanity": 30949, "\u00ce\u00b5": 30950, "atalie": 30951, "Enlarge": 30952, "\u0120twists": 30953, "\u0120faulty": 30954, "\u0120piracy": 30955, "\u0120impover": 30956, "\u0120rugged": 30957, "\u0120Fashion": 30958, "\u0120sands": 30959, "'?": 30960, "swick": 30961, "\u0120natives": 30962, "\u0120hen": 30963, "\u0120Noise": 30964, "\u00e3\u0125\u0139": 30965, "\u0120greens": 30966, "\u0120freezer": 30967, "\u0120dynasty": 30968, "\u0120Fathers": 30969, "\u0120Newark": 30970, "\u0120archaeological": 30971, "\u0120ot": 30972, "obar": 30973, "\u0120blockade": 30974, "\u0120allerg": 30975, "LV": 30976, "\u0120debit": 30977, "\u0120RFC": 30978, "\u0120Milton": 30979, "\u0120Pressure": 30980, "\u0120willingly": 30981, "\u0120disproportionate": 30982, "\u0120oppressive": 30983, "\u0120diamonds": 30984, "\u0120belongings": 30985, "1970": 30986, "\u0120bells": 30987, "\u0120imperialism": 30988, "\u0120227": 30989, "\u0120exploding": 30990, "\u0120Eclipse": 30991, "\u01201919": 30992, "\u0120rant": 30993, "\u0120nominations": 30994, "347": 30995, "\u0120peacefully": 30996, "rica": 30997, "\u0120FUCK": 30998, "\u0120vibration": 30999, "malink": 31000, "\u0120ropes": 31001, "\u0120Ivanka": 31002, "\u0120Brewery": 31003, "\u0120Booker": 31004, "\u0120Owens": 31005, "goers": 31006, "Services": 31007, "\u0120Snape": 31008, "\u0120191": 31009, "395": 31010, "\u0120299": 31011, "justice": 31012, "\u0120bri": 31013, "\u0120discs": 31014, "\u0120prominently": 31015, "\u0120vulgar": 31016, "\u0120skipping": 31017, "lves": 31018, "\u0120tsunami": 31019, "374": 31020, "\u0120Urug": 31021, "\u0120Eid": 31022, "recated": 31023, "phen": 31024, "\u0120faults": 31025, "\u0120Started": 31026, "950": 31027, "\u0120pi": 31028, "\u0120detector": 31029, "\u0120bastard": 31030, "\u0120validated": 31031, "SpaceEngineers": 31032, "OURCE": 31033, "\u0120(~": 31034, "\u0120unsur": 31035, "\u0120affirmed": 31036, "\u0120fascism": 31037, "\u0120resolving": 31038, "\u0120Chavez": 31039, "\u0120Cyn": 31040, "\u0120detract": 31041, "Lost": 31042, "\u0120rigged": 31043, "\u0120homage": 31044, "\u0120Bruno": 31045, "555": 31046, "eca": 31047, "\u0120presses": 31048, "\u0120humour": 31049, "\u0120spacing": 31050, "\u0120'/": 31051, "olkien": 31052, "Coun": 31053, "OPER": 31054, "Tre": 31055, "Son": 31056, "\u0120Cambodia": 31057, "ierre": 31058, "mong": 31059, "ozy": 31060, "\u0120liquidity": 31061, "\u0120Soviets": 31062, "\u0120Fernando": 31063, "\u0120229": 31064, "\u0120slug": 31065, "\u0120Catalan": 31066, "electric": 31067, "\u0120scenery": 31068, "\u0120Hearth": 31069, "\u0120constrained": 31070, "\u0120goalie": 31071, "\u0120Guidelines": 31072, "\u0120Ammo": 31073, "\u0120Pearson": 31074, "\u0120taxed": 31075, "\u0120fetus": 31076, "Response": 31077, "\u0120Alexis": 31078, "thia": 31079, "Guy": 31080, "\u0120reconstruct": 31081, "\u0120extremes": 31082, "\u0120concluding": 31083, "\u0120Peg": 31084, "ooks": 31085, "\u0120deductions": 31086, "Rose": 31087, "\u0120groundbreaking": 31088, "\u0120Targ": 31089, "\u00e3\u0125\u0123": 31090, "\u0120Reve": 31091, "resource": 31092, "\u0120moons": 31093, "\u0120electromagnetic": 31094, "\u0120amidst": 31095, "\u0120Viktor": 31096, "NESS": 31097, "BACK": 31098, "\u0120commute": 31099, "\u0120Anaheim": 31100, "\u0120fluctuations": 31101, "640": 31102, "\u0120noodles": 31103, "\u0120Copenhagen": 31104, "\u0120Tide": 31105, "\u0120Grizz": 31106, "\u0120SEE": 31107, "\u0120pipelines": 31108, "\u0120scars": 31109, "endo": 31110, "agus": 31111, "\u0120ETF": 31112, "/#": 31113, "\u0120Become": 31114, "448": 31115, "\u0120visc": 31116, "\u0120Recommended": 31117, "\u0120jumper": 31118, "\u0120cognition": 31119, "\u0120assassin": 31120, "\u0120witnessing": 31121, "\u0120Setup": 31122, "\u0120lac": 31123, "vim": 31124, "ISM": 31125, "pages": 31126, "SSL": 31127, "358": 31128, "\u0120adject": 31129, "industrial": 31130, "lore": 31131, "chery": 31132, "\u0120glitter": 31133, "\u0120calf": 31134, "Florida": 31135, "\u0120spoilers": 31136, "\u0120succeeds": 31137, "\u0120chanting": 31138, "\u0120slogans": 31139, "\u0120Tracy": 31140, "Visit": 31141, "rology": 31142, "\u0120mornings": 31143, "\u0120lineage": 31144, "\u0120sip": 31145, "\u0120intensely": 31146, "\u0120flourish": 31147, "\u0120Sleeping": 31148, "\u0120Fem": 31149, "orpor": 31150, "\u0120Klan": 31151, "\u0120Darth": 31152, "hack": 31153, "\u0120Nielsen": 31154, "\u0120tumors": 31155, "\u0120procurement": 31156, "\u0120Yorkshire": 31157, "\u0120raided": 31158, "KY": 31159, "Anna": 31160, "\u0120//[": 31161, "\u0120Disorder": 31162, "\u0120Mustang": 31163, "\u0120Wen": 31164, "\u0120Trying": 31165, "sq": 31166, "\u0120deliveries": 31167, "\u0120shutter": 31168, "\u0120cerebral": 31169, "\u0120bipolar": 31170, "\u0120CN": 31171, "lass": 31172, "jet": 31173, "\u0120debating": 31174, ">:": 31175, "\u0120eagle": 31176, "grades": 31177, "\u0120Dixon": 31178, "UGC": 31179, "MAS": 31180, "\u0120Draco": 31181, "\u0120Machines": 31182, "affer": 31183, "\u0120eman": 31184, "\u00c2\u00b2": 31185, "pron": 31186, "\u0120Gym": 31187, "\u0120comparatively": 31188, "\u0120Tribunal": 31189, "PRO": 31190, "\u0120lex": 31191, "\u0120fertile": 31192, "\u0120depressing": 31193, "\u0120superficial": 31194, "essential": 31195, "\u0120Hunters": 31196, "gp": 31197, "\u0120prominence": 31198, "Liber": 31199, "\u0120Ancest": 31200, "otechnology": 31201, "\u0120mocking": 31202, "\u0120Traff": 31203, "\u0138\u013c": 31204, "Medium": 31205, "Iraq": 31206, "\u0120psychiatrist": 31207, "Quantity": 31208, "\u0120Lect": 31209, "\u0120noisy": 31210, "520": 31211, "GY": 31212, "\u0120slapped": 31213, "\u0120MTV": 31214, "\u0120para": 31215, "pull": 31216, "Multiple": 31217, "asher": 31218, "\u0120nour": 31219, "\u0120Seg": 31220, "Spell": 31221, "vous": 31222, "ordial": 31223, "Senior": 31224, "\u0120Goldberg": 31225, "\u0120Plasma": 31226, "need": 31227, "\u0120messenger": 31228, "eret": 31229, "\u0120teamed": 31230, "\u0120literacy": 31231, "\u0120Leah": 31232, "\u0120Doyle": 31233, "\u0120emitted": 31234, "UX": 31235, "\u0120evade": 31236, "\u0120maze": 31237, "\u0120wrongly": 31238, "\u0120Lars": 31239, "\u0120stereotype": 31240, "\u0120pledges": 31241, "\u0120aroma": 31242, "\u0120MET": 31243, "\u0120acre": 31244, "\u0120OD": 31245, "\u0120ff": 31246, "\u0120breweries": 31247, "\u0120Hilton": 31248, "undle": 31249, "\u0120Kak": 31250, "\u0120Thankfully": 31251, "\u0120Canucks": 31252, "inctions": 31253, "\u0120Appears": 31254, "\u0120coer": 31255, "\u0120undermined": 31256, "rovers": 31257, "Andre": 31258, "\u0120blaze": 31259, "umers": 31260, "\u0120famine": 31261, "amphetamine": 31262, "ulkan": 31263, "Amount": 31264, "\u0120desperation": 31265, "wikipedia": 31266, "development": 31267, "\u0120Corinth": 31268, "ussia": 31269, "Jackson": 31270, "LI": 31271, "Native": 31272, "Rs": 31273, "Ohio": 31274, "\u0120Kathleen": 31275, "Fortunately": 31276, "\u0120attendant": 31277, "\u0120Preferred": 31278, "\u0120Didn": 31279, "\u0120Vs": 31280, "Mis": 31281, "\u0120respondent": 31282, "\u0120boun": 31283, "stable": 31284, "\u0120paved": 31285, "\u0120unexpl": 31286, "\u0120Cheney": 31287, "LM": 31288, "\u0120Cull": 31289, "blown": 31290, "\u0120confronting": 31291, "ocese": 31292, "serving": 31293, "Wi": 31294, "\u0120Lithuania": 31295, "anni": 31296, "\u0120stalk": 31297, "hd": 31298, "\u0120vener": 31299, "APH": 31300, "ynchronous": 31301, "URR": 31302, "umably": 31303, "historic": 31304, "Half": 31305, "Hay": 31306, "\u0120resilience": 31307, "spection": 31308, "\u0120abandoning": 31309, "Obs": 31310, "\u0120Debbie": 31311, "\u0120gradient": 31312, "\u0120Plaint": 31313, "\u0120Canal": 31314, "ARCH": 31315, "\u0120expansive": 31316, "\u0120fung": 31317, "\u0120bounced": 31318, "Und": 31319, "\u0120precautions": 31320, "\u0120clarification": 31321, "\u0120dagger": 31322, "\u0120grips": 31323, "\u0120\u00c2\u00b5": 31324, "\u0120Rivera": 31325, "\u0120Undead": 31326, "isites": 31327, "\u0120FIRST": 31328, "\u00c3\u00b1o": 31329, "audi": 31330, "\u0120hostages": 31331, "\u0120compliant": 31332, "\u0120alumni": 31333, "Seven": 31334, "\u0120cybersecurity": 31335, "either": 31336, "Collect": 31337, "\u0120invariably": 31338, "\u0120Soci": 31339, "\u0120lawmaker": 31340, "\u0120ale": 31341, "\u0120Personally": 31342, "Nazi": 31343, "\u0120customization": 31344, "\u0120Proc": 31345, "\u0120Saskatchewan": 31346, "eaturing": 31347, "\u0120spared": 31348, "\u0120discontinued": 31349, "\u0120computational": 31350, "\u0120Motorola": 31351, "\u0120supremacist": 31352, "governmental": 31353, "\u0120paradise": 31354, "\u0120Downing": 31355, "\u0120Nikon": 31356, "\u0120catalyst": 31357, "berra": 31358, "Toronto": 31359, "875": 31360, "beta": 31361, "\u0120Macron": 31362, "\u0120unrealistic": 31363, "vector": 31364, "\u0120Vehicles": 31365, "itiveness": 31366, "\u0120RV": 31367, "\u0120Colbert": 31368, "sin": 31369, "oji": 31370, "entin": 31371, "\u0120Krish": 31372, "hello": 31373, "ffield": 31374, "oky": 31375, "\u0120Tate": 31376, "\u0120maple": 31377, "\u0120aids": 31378, "chemical": 31379, "334": 31380, "nuts": 31381, "\u0120Warp": 31382, "\u0120xx": 31383, "\u0120Robb": 31384, "umerous": 31385, "_-_": 31386, "ftime": 31387, "\u0120VW": 31388, "\u0120winger": 31389, "\u0120Dome": 31390, "tools": 31391, "\u0120PV": 31392, "\u0120Georgetown": 31393, "\u0120geared": 31394, "\u0120jihadists": 31395, "\u0120cp": 31396, "\u0120steroids": 31397, "Mother": 31398, "clerosis": 31399, "\u0120DRM": 31400, "nesia": 31401, "\u0120linger": 31402, "\u0120immersive": 31403, "\u0120COUN": 31404, "\u0120outweigh": 31405, "ensual": 31406, "Band": 31407, "\u0120transforms": 31408, "matched": 31409, "psons": 31410, "\u0120Judicial": 31411, "factor": 31412, "\u0120referral": 31413, "\u0120oddly": 31414, "\u0120Wenger": 31415, "Bring": 31416, "\u0120Bows": 31417, "602": 31418, "ICLE": 31419, "\u0120lions": 31420, "\u0120Academic": 31421, "\u0120Thorn": 31422, "\u0120Raider": 31423, "kefeller": 31424, "Storage": 31425, "Lower": 31426, "\u0120Ort": 31427, "\u0120Equality": 31428, "ALT": 31429, "\u0120SOC": 31430, "Types": 31431, "\u0120lyn": 31432, "\u0120Asset": 31433, "coat": 31434, "TPP": 31435, "CVE": 31436, "\u0120Pioneer": 31437, "application": 31438, "Modern": 31439, "\u0120HK": 31440, "Environment": 31441, "Alright": 31442, "Rain": 31443, "IPP": 31444, "\u0120Shiite": 31445, "\u0120mound": 31446, "\u0120Abilities": 31447, "condition": 31448, "Staff": 31449, "\u0120competence": 31450, "\u0120Moor": 31451, "\u0120Diablo": 31452, "\u0120withheld": 31453, "\u0120ostensibly": 31454, "\u0120Brom": 31455, "\u0120msg": 31456, "\u0120denomin": 31457, "\u0120References": 31458, "\u0120FP": 31459, "\u0120plunged": 31460, "\u0120pamph": 31461, "moving": 31462, "central": 31463, "\u0120downright": 31464, "\u0120fading": 31465, "Tal": 31466, "Typ": 31467, "\u0120Thy": 31468, "ukes": 31469, "ithe": 31470, "\u0120ove": 31471, "\u0120battled": 31472, "\u0120seafood": 31473, "\u0120figur": 31474, "\u0120RD": 31475, "crop": 31476, "\u0120squads": 31477, "{\\": 31478, "\u00e0\u00b9": 31479, "\u0120Eh": 31480, "\u0120interviewing": 31481, "\u0120Qin": 31482, "\u0120aspiring": 31483, "PLIC": 31484, "\u0120clauses": 31485, "\u0120Gast": 31486, "\u0120Nir": 31487, "\u0120luggage": 31488, "\u0120hose": 31489, "\u0120systemd": 31490, "\u0120descending": 31491, "\u0120Revised": 31492, "\u0120Rails": 31493, "align": 31494, "709": 31495, "337": 31496, "\u0120fug": 31497, "charging": 31498, "tags": 31499, "\u0120uter": 31500, "kish": 31501, "WARNING": 31502, "490": 31503, "profits": 31504, "\u0120voyage": 31505, "\u0120ace": 31506, "\u0120Vanguard": 31507, "\u0120Tanks": 31508, "\u0120Muk": 31509, "\u0120226": 31510, "Safe": 31511, "Armor": 31512, "\u0120volcanic": 31513, "\u0120womb": 31514, "\u0120MIL": 31515, "\u0120beginner": 31516, "\u0120Recogn": 31517, "\u0120AAP": 31518, "PLAY": 31519, ")!": 31520, "\u0120detecting": 31521, "cn": 31522, "\u0120breaches": 31523, "Basically": 31524, "\u0120Pag": 31525, "\u0120Municipal": 31526, "\u0120Indie": 31527, "\u0120Laf": 31528, "\u0120Disable": 31529, "\u0120Olson": 31530, "\u0120restrained": 31531, "\u0120rulings": 31532, "\u0120humane": 31533, "events": 31534, "\u0120Cinema": 31535, "displayText": 31536, "\u0120Hatch": 31537, "actionDate": 31538, "onnaissance": 31539, "\u0120assaulting": 31540, "\u0120Lug": 31541, "CHAT": 31542, "\u0120vigorous": 31543, "\u0120Perse": 31544, "\u0120intolerance": 31545, "\u0120Snapchat": 31546, "\u0120Sharks": 31547, "\u0120dummy": 31548, "\u0120Diagn": 31549, "\u0120Guitar": 31550, "imeters": 31551, "403": 31552, "REG": 31553, "Ax": 31554, "\u0120separates": 31555, "\u0120Mahm": 31556, "\u0120tv": 31557, "jah": 31558, "OOL": 31559, "Circ": 31560, "\u0120Windsor": 31561, "ussian": 31562, "\u0120intuition": 31563, "\u0120disdain": 31564, "\u0120Donovan": 31565, "\u0120221": 31566, "Emb": 31567, "\u0120condemning": 31568, "\u0120generosity": 31569, "zzy": 31570, "\u0120panties": 31571, "\u0120Prevent": 31572, "ActionCode": 31573, "ANA": 31574, "342": 31575, "externalActionCode": 31576, "\u0120specifying": 31577, "\u0120crystall": 31578, "Jere": 31579, "\u0120rupt": 31580, "\u0120Apprentice": 31581, "\u0120profiling": 31582, "\u00d0\u00ba": 31583, "Strike": 31584, "\u0120sideline": 31585, "\u0120obligated": 31586, "\u0120occult": 31587, "\u0120bureaucratic": 31588, "antically": 31589, "rupted": 31590, "negative": 31591, "\u0120Ethiopia": 31592, "\u0120Civic": 31593, "\u0120insiders": 31594, "eligible": 31595, "\u0120TVs": 31596, "\u0120BAR": 31597, "\u0120TI": 31598, "iologist": 31599, "\u0120AIR": 31600, "\u0120substituted": 31601, "Arab": 31602, "\u0120Saul": 31603, "\u0120Yog": 31604, "prem": 31605, "\u0120builders": 31606, "\u0120stationary": 31607, "\u0120doubtful": 31608, "\u0120vigorously": 31609, "\u0120thrilling": 31610, "Physical": 31611, "\u0120Carey": 31612, "\u0120Hydra": 31613, "geoning": 31614, "\u0120Sly": 31615, "yton": 31616, "\u0120borrowers": 31617, "\u0120Parkinson": 31618, "\u0120\u00eb": 31619, "\u0120Jamaica": 31620, "\u0120satir": 31621, "\u0120insurgents": 31622, "\u0120Firm": 31623, "\u0120isot": 31624, "\u0120Karn": 31625, "ourning": 31626, "akens": 31627, "docs": 31628, "little": 31629, "\u0120Monaco": 31630, "CLASS": 31631, "Turkey": 31632, "Ly": 31633, "\u0120Conan": 31634, "assic": 31635, "\u0120starred": 31636, "\u0120Pacers": 31637, "eties": 31638, "\u0120tipping": 31639, "Moon": 31640, "\u0120Rw": 31641, "same": 31642, "\u0120cavity": 31643, "\u0120goof": 31644, "\u0120Zo": 31645, "Shock": 31646, "ummer": 31647, "\u0120emphasizes": 31648, "\u0120regrett": 31649, "\u0120novelty": 31650, "\u0120envy": 31651, "\u0120Passive": 31652, "rw": 31653, "505": 31654, "\u0120indifferent": 31655, "\u0120Rica": 31656, "\u0120Himself": 31657, "\u0120Freddie": 31658, "\u0120adip": 31659, "\u00e4\u00b8\u0122": 31660, "\u0120breakout": 31661, "\u0120hurried": 31662, "\u0120Huang": 31663, "\u0120Disk": 31664, "\u0120roaming": 31665, "?????-?????-": 31666, "UV": 31667, "\u0120Ricky": 31668, "\u0120Sigma": 31669, "\u0120marginalized": 31670, "\u0120edits": 31671, "\u0120304": 31672, "memory": 31673, "\u0120specimen": 31674, "293": 31675, "\u00e3\u0123\u00af": 31676, "\u0120vertically": 31677, "\u0120audition": 31678, "\u0120Heck": 31679, "\u0120caster": 31680, "\u0120Holdings": 31681, "adal": 31682, "\u0120Cron": 31683, "\u0120Liam": 31684, "\u0120deflect": 31685, "Pick": 31686, "\u0120Debug": 31687, "REF": 31688, "\u0120versatility": 31689, "othes": 31690, "classified": 31691, "\u0120Mahar": 31692, "\u0120Hort": 31693, "Counter": 31694, "stasy": 31695, "noticed": 31696, "331": 31697, "\u0120Shim": 31698, "fuck": 31699, "\u0120Bie": 31700, "\u0120airing": 31701, "\u0120Protein": 31702, "\u0120Holding": 31703, "\u0120spectators": 31704, "iliated": 31705, "\u0120Thatcher": 31706, "nosis": 31707, "\u00e3\u0125\u00bc\u00e3\u0125\u00b3": 31708, "Tele": 31709, "Boston": 31710, "\u0120Templ": 31711, "stay": 31712, "\u0120declarations": 31713, "479": 31714, "Volume": 31715, "\u0120Designer": 31716, "\u0120Overwatch": 31717, "idae": 31718, "\u0120onwards": 31719, "\u0120nets": 31720, "\u0120Manila": 31721, "particularly": 31722, "\u0120politic": 31723, "oother": 31724, "\u0120portraits": 31725, "\u0120pavement": 31726, "cffff": 31727, "\u0120saints": 31728, "\u0120beginners": 31729, "ESPN": 31730, "\u0120shortcomings": 31731, "\u00e2\u0137\u0132\u00e2\u0137\u0132": 31732, "\u0120comet": 31733, "\u0120Organic": 31734, "quel": 31735, "\u0120hospitalized": 31736, "Break": 31737, "\u0120peel": 31738, "dylib": 31739, "aspx": 31740, "urances": 31741, "\u0120TIM": 31742, "Pg": 31743, "\u0120readable": 31744, "\u0120Malik": 31745, "\u0120muzzle": 31746, "\u0120benchmarks": 31747, "dal": 31748, "\u0120Vacc": 31749, "\u0120Hicks": 31750, "609": 31751, "\u0120Biblical": 31752, "heng": 31753, "\u0120overload": 31754, "\u0120Civilization": 31755, "\u0120immoral": 31756, "\u0120fries": 31757, "\u00e3\u0124\u0134": 31758, "\u0120reproduced": 31759, "\u0120formulation": 31760, "jug": 31761, "irez": 31762, "gear": 31763, "\u0120coached": 31764, "MpServer": 31765, "\u0120SJ": 31766, "\u0120Kw": 31767, "Init": 31768, "deal": 31769, "\u0120Oro": 31770, "\u0120Loki": 31771, "\u0120Songs": 31772, "\u0120232": 31773, "\u0120Louise": 31774, "asionally": 31775, "\u0120uncond": 31776, "ollywood": 31777, "\u0120progressives": 31778, "\u0120Enough": 31779, "\u0120Doe": 31780, "\u0120wreckage": 31781, "\u0120brushed": 31782, "\u0120BaseType": 31783, "\u0120zoning": 31784, "ishable": 31785, "hetically": 31786, "\u0120Caucus": 31787, "\u0120Hue": 31788, "\u0120karma": 31789, "\u0120Sporting": 31790, "\u0120trader": 31791, "\u0120seeming": 31792, "\u0120Capture": 31793, "430": 31794, "bish": 31795, "\u0120tunes": 31796, "\u0120indoors": 31797, "\u0120Sphere": 31798, "\u0120Dancing": 31799, "TERN": 31800, "\u0120nob": 31801, "\u0120GST": 31802, "maps": 31803, "\u0120peppers": 31804, "Fit": 31805, "\u0120oversees": 31806, "\u0120Rabbi": 31807, "\u0120Ruler": 31808, "vertising": 31809, "office": 31810, "xxx": 31811, "\u0120raft": 31812, "Changed": 31813, "\u0120textbooks": 31814, "Links": 31815, "\u0120Omn": 31816, "\u00e3\u0122\u0133": 31817, "\u0120inconvenience": 31818, "\u0120Donetsk": 31819, "=~": 31820, "\u0120implicitly": 31821, "\u0120boosts": 31822, "\u0120Bones": 31823, "\u0120Boom": 31824, "Courtesy": 31825, "\u0120sensational": 31826, "ANY": 31827, "\u0120greedy": 31828, "eden": 31829, "\u0120inexper": 31830, "\u0120Ler": 31831, "\u0120Vale": 31832, "\u0120tighten": 31833, "\u0120EAR": 31834, "\u0120Num": 31835, "\u0120ancestor": 31836, "Sent": 31837, "\u0120Horde": 31838, "urgical": 31839, "allah": 31840, "\u0120sap": 31841, "amba": 31842, "\u0120Spread": 31843, "twitch": 31844, "\u0120grandson": 31845, "\u0120fracture": 31846, "\u0120moderator": 31847, "\u0120Seventh": 31848, "\u0120Reverse": 31849, "\u0120estimation": 31850, "Choose": 31851, "\u0120parach": 31852, "\u0120barric": 31853, "\u00e3\u0122\u0132": 31854, "\u0120compass": 31855, "\u0120allergic": 31856, "\u00e2\u0122\u0137": 31857, "OTHER": 31858, "errilla": 31859, "\u0120wagon": 31860, "\u0120zinc": 31861, "\u0120rubbed": 31862, "\u0120Fuller": 31863, "\u0120Luxembourg": 31864, "\u0120Hoover": 31865, "\u0120liar": 31866, "\u0120Evening": 31867, "\u0120Cobb": 31868, "esteem": 31869, "\u0120selector": 31870, "\u0120Brawl": 31871, "isance": 31872, "\u0120Ek": 31873, "\u0120troop": 31874, "\u0120guts": 31875, "\u0120Appeal": 31876, "\u0120Tibetan": 31877, "\u0120routines": 31878, "\u0120Ment": 31879, "\u0120summarized": 31880, "steamapps": 31881, "\u0120tranqu": 31882, "\u01201929": 31883, "oran": 31884, "\u0120Authent": 31885, "\u0120gmaxwell": 31886, "\u0120apprehens": 31887, "\u0120poems": 31888, "\u0120sausage": 31889, "\u0120Webster": 31890, "urus": 31891, "\u0120themed": 31892, "\u0120lounge": 31893, "\u0120charger": 31894, "Spoiler": 31895, "\u0120spilled": 31896, "hog": 31897, "\u0120Sunder": 31898, "\u0120Ain": 31899, "\u0120Angry": 31900, "\u0120disqual": 31901, "\u0120Frequency": 31902, "\u0120Ethernet": 31903, "\u0120helper": 31904, "Percent": 31905, "\u0120horrifying": 31906, "\u0120ail": 31907, "\u0120Allan": 31908, "EEE": 31909, "\u0120Crossing": 31910, "449": 31911, "\u0120holog": 31912, "\u0120Puzzles": 31913, "\u0120Goes": 31914, "erenn": 31915, "604": 31916, "\u00e3\u0123\u0131": 31917, "\u0120Rafael": 31918, "\u0120atten": 31919, "\u0120Emanuel": 31920, "\u0120upro": 31921, "\u0120Susp": 31922, "Psych": 31923, "\u0120Trainer": 31924, "\u0120NES": 31925, "\u0120Hunts": 31926, "becue": 31927, "\u0120counselor": 31928, "Rule": 31929, "\u0120toxins": 31930, "\u0120banners": 31931, "rifice": 31932, "\u0120greeting": 31933, "\u0120frenzy": 31934, "\u0120allocate": 31935, "\u0120*)": 31936, "expr": 31937, "503": 31938, "\u0120Chick": 31939, "\u0120Torn": 31940, "\u0120consolidation": 31941, "\u0120Fletcher": 31942, "switch": 31943, "frac": 31944, "clips": 31945, "\u0120McKin": 31946, "\u0120Lunar": 31947, "Month": 31948, "ITCH": 31949, "\u0120scholarly": 31950, "raped": 31951, "398": 31952, "\u01201910": 31953, "\u0120egreg": 31954, "\u0120insecure": 31955, "\u0120victorious": 31956, "cffffcc": 31957, "\u0120singled": 31958, "\u0120elves": 31959, "\u0120Wond": 31960, "burst": 31961, "\u0120camoufl": 31962, "\u0120BLACK": 31963, "\u0120conditioned": 31964, "\u00e7\u012b": 31965, "answered": 31966, "\u0120compulsory": 31967, "ascist": 31968, "\u0120podcasts": 31969, "\u0120Frankfurt": 31970, "bnb": 31971, "\u0120neoliberal": 31972, "\u0120Keyboard": 31973, "\u0120Belle": 31974, "warm": 31975, "\u0120trusts": 31976, "\u0120insured": 31977, "\u0120Bucc": 31978, "usable": 31979, "607": 31980, "\u0120Plains": 31981, "\u01201890": 31982, "\u0120sabotage": 31983, "\u0120lodged": 31984, "felt": 31985, "\u0120ga": 31986, "\u0120Narc": 31987, "\u0120Salem": 31988, "\u0120seventy": 31989, "\u0120Blank": 31990, "pocket": 31991, "\u0120whisper": 31992, "\u0120mating": 31993, "omics": 31994, "\u0120Salman": 31995, "\u0120Kad": 31996, "\u0120angered": 31997, "\u0120collisions": 31998, "\u0120extraordinarily": 31999, "\u0120coercion": 32000, "Ghost": 32001, "birds": 32002, "\u00e8\u0122": 32003, "kok": 32004, "\u0120permissible": 32005, "avorable": 32006, "\u0120pointers": 32007, "\u0120dissip": 32008, "aci": 32009, "\u0120theatrical": 32010, "\u0120Cosmic": 32011, "\u0120forgetting": 32012, "\u0120finalized": 32013, "\u00e5\u00a4\u00a7": 32014, "yout": 32015, "library": 32016, "\u0120booming": 32017, "\u0120Believe": 32018, "\u0120Teacher": 32019, "\u0120Liv": 32020, "\u0120GOODMAN": 32021, "\u0120Dominican": 32022, "ORED": 32023, "\u0120Parties": 32024, "\u0120precipitation": 32025, "\u0120Slot": 32026, "Roy": 32027, "\u0120Combined": 32028, "\u0120integrating": 32029, "\u0120chrome": 32030, "\u0120intestinal": 32031, "\u0120Rebell": 32032, "\u0120matchups": 32033, "\u0120blockbuster": 32034, "\u0120Loren": 32035, "\u0120Levy": 32036, "\u0120preaching": 32037, "\u0120Sending": 32038, "\u0120Purpose": 32039, "rax": 32040, "fif": 32041, "\u0120authoritative": 32042, "\u0120PET": 32043, "astical": 32044, "\u0120dishon": 32045, "\u0120chatting": 32046, "\u0120\"$:/": 32047, "Connection": 32048, "\u0120recreate": 32049, "\u0120delinqu": 32050, "\u0120broth": 32051, "\u0120Dirty": 32052, "\u0120Admin": 32053, "zman": 32054, "\u0120scholarships": 32055, "\u0120253": 32056, "contact": 32057, "alsa": 32058, "767": 32059, "creen": 32060, "abbage": 32061, "\u01201915": 32062, "\u0120blended": 32063, "\u0120alarmed": 32064, "Language": 32065, "356": 32066, "\u0120blends": 32067, "\u0120Changed": 32068, "Wolf": 32069, "\u0120hepat": 32070, "Creating": 32071, "\u0120persecut": 32072, "\u0120sweetness": 32073, "arte": 32074, "\u0120forfeiture": 32075, "\u0120Roberto": 32076, "impro": 32077, "NFL": 32078, "\u0120Magnet": 32079, "Detailed": 32080, "\u0120insignificant": 32081, "\u0120POLIT": 32082, "\u0120BBQ": 32083, "\u0120CPS": 32084, "\u0120seaw": 32085, "aminer": 32086, "mL": 32087, "endif": 32088, "finals": 32089, "\u0120265": 32090, "uish": 32091, "\u0120})": 32092, "\u0120Problems": 32093, "\u0120emblem": 32094, "\u0120seriousness": 32095, "\u0120parsing": 32096, "\u0120substitution": 32097, "\u0120pressured": 32098, "\u0120recycled": 32099, "aleb": 32100, "Ruby": 32101, "\u0120proficiency": 32102, "Driver": 32103, "\u0120Wester": 32104, ":'": 32105, "AFTA": 32106, "\u0120mantle": 32107, "\u0120Clayton": 32108, "flag": 32109, "\u0120practitioner": 32110, "covered": 32111, "\u0120Struct": 32112, "addafi": 32113, "425": 32114, "\u0120Township": 32115, "\u0120Hydro": 32116, "Louis": 32117, "343": 32118, "\u0120condo": 32119, "\u0120Tao": 32120, "\u0120utilization": 32121, "\u0120nausea": 32122, "\u0120Dems": 32123, "ridges": 32124, "pause": 32125, "\u0120formulas": 32126, "\u0120challenger": 32127, "376": 32128, "\u0120defective": 32129, "\u0120Railway": 32130, "\u0120PubMed": 32131, "\u0120yogurt": 32132, "lbs": 32133, "\u0120Norfolk": 32134, "OPE": 32135, "\u0120Moody": 32136, "\u0120distributor": 32137, "\u0120scrolls": 32138, "\u0120extracts": 32139, "Stan": 32140, "\u0120viability": 32141, "\u0120exposes": 32142, "\u0120starvation": 32143, "\u0120Steps": 32144, "\u0120Dodd": 32145, "few": 32146, "STD": 32147, "332": 32148, "\u0120closures": 32149, "\u0120complementary": 32150, "\u0120Sasha": 32151, "umpy": 32152, "\u0120monet": 32153, "\u0120articulate": 32154, "\u0120Doct": 32155, "killer": 32156, "\u0120scrim": 32157, "\u0120264": 32158, "\u0120prostitutes": 32159, "\u0120severed": 32160, "\u0120attachments": 32161, "\u0120cooled": 32162, "Lev": 32163, "\u0120Falk": 32164, "fail": 32165, "\u0120policeman": 32166, "\u0120Dag": 32167, "\u0120prayed": 32168, "\u0120Kernel": 32169, "\u0120clut": 32170, "\u0120cath": 32171, "\u0120anomaly": 32172, "Storm": 32173, "emaker": 32174, "\u0120Breakfast": 32175, "uli": 32176, "oire": 32177, "JJ": 32178, "hz": 32179, "Operation": 32180, "\u0120Sick": 32181, "354": 32182, "\u0120Guatemala": 32183, "Rate": 32184, "\u0120exposures": 32185, "faces": 32186, "\u0120Archae": 32187, "raf": 32188, "\u0120Mia": 32189, "\u01202025": 32190, "\u0120opaque": 32191, "\u0120disguised": 32192, "\u0120Headquarters": 32193, "Sah": 32194, "\u0120pots": 32195, "978": 32196, "\u0120Malf": 32197, "\u0120frowned": 32198, "\u0120poisonous": 32199, "\u0120Convers": 32200, "eeks": 32201, "\u0120crab": 32202, ".\"\"": 32203, "\u0120treason": 32204, "\u0120ranc": 32205, "\u0120escalating": 32206, "\u0120warr": 32207, "\u0120mobs": 32208, "\u0120lamps": 32209, "\u0120Sunshine": 32210, "\u0120Brunswick": 32211, "Phones": 32212, "\u0120spelled": 32213, "\u0120Skip": 32214, "\u01202050": 32215, "\u01201911": 32216, "\u0120Pluto": 32217, "\u0120Amend": 32218, "\u0120meats": 32219, "387": 32220, "\u0120stomp": 32221, "\u0120Zhou": 32222, "\u0120Leviathan": 32223, "\u0120Hazard": 32224, "adv": 32225, "\u0120Orwell": 32226, "\u0120aloud": 32227, "\u0120bumper": 32228, "\u0120Anarch": 32229, "ubuntu": 32230, "\u0120Serious": 32231, "fitting": 32232, "\u0120Optional": 32233, "\u0120Cecil": 32234, "REAM": 32235, "\u0120serotonin": 32236, "\u0120cultivate": 32237, "agogue": 32238, "}\\": 32239, "\u0120mosques": 32240, "\u0120Sunny": 32241, "\u0120reactive": 32242, "revolution": 32243, "\u0120Lup": 32244, "\u0120Fedora": 32245, "\u0120defenseman": 32246, "\u0120VID": 32247, "istine": 32248, "\u0120drowning": 32249, "\u0120Broadcasting": 32250, "\u0120thriller": 32251, "\u0120Scy": 32252, "\u0120accelerating": 32253, "\u0120directs": 32254, "odied": 32255, "bike": 32256, "duration": 32257, "\u0120painfully": 32258, "Redd": 32259, "\u0120productions": 32260, "\u0120gag": 32261, "\u0120whist": 32262, "\u0120sock": 32263, "\u0120infinitely": 32264, "\u0120Concern": 32265, "\u0120Citadel": 32266, "\u0120lieu": 32267, "\u0120candles": 32268, "ogeneous": 32269, "arger": 32270, "\u0120heavenly": 32271, "inflammatory": 32272, "Performance": 32273, "Cs": 32274, "ructose": 32275, "azaki": 32276, "\u0120pessim": 32277, "\u0120inference": 32278, "\u0120powd": 32279, "\u0120Zoe": 32280, "\u0120paints": 32281, "\u0120dazz": 32282, "pta": 32283, "-----------": 32284, "\u0120inspir": 32285, "\u0120Experimental": 32286, "\u0120Knife": 32287, "regor": 32288, "bors": 32289, "\u0120showers": 32290, "romeda": 32291, "\u0120saint": 32292, "\u0120benign": 32293, "\u0120Jiang": 32294, "\u0120envisioned": 32295, "\u0120shroud": 32296, "IFT": 32297, "HO": 32298, "\u0120shuff": 32299, "\u0120ICC": 32300, "\u0120segreg": 32301, "\u0120revisit": 32302, "ighthouse": 32303, "Li": 32304, "\u0120substrate": 32305, "\u0120Seas": 32306, "\u0120Reward": 32307, "\u0120Hep": 32308, "\u0120Brass": 32309, "sbm": 32310, "\u0120eliminates": 32311, "\u0120stamina": 32312, "\u0120VAT": 32313, "\u0120Loan": 32314, "\u0120constraint": 32315, "\u0120appropriated": 32316, "\u0120pes": 32317, "\u0120ALE": 32318, "ranging": 32319, "\u0120404": 32320, "392": 32321, "\u0120intellectuals": 32322, "achu": 32323, "\u0120restructuring": 32324, "\u0120Levin": 32325, "\u0120runes": 32326, "\u0120delightful": 32327, "\u0120carbohydrates": 32328, "\u0120Models": 32329, "\u0120Expo": 32330, "\u0120transporting": 32331, "alloc": 32332, "\u0120ringing": 32333, "Samsung": 32334, "\u0120scarcely": 32335, "\u0120URLs": 32336, "\u0120MAS": 32337, "\u0120prototypes": 32338, "\u0120narrator": 32339, "\u0120CPUs": 32340, "cdn": 32341, "\u0120Barton": 32342, "\u0120decidedly": 32343, "\u0120Shu": 32344, "ixir": 32345, "ocious": 32346, "\u0120Myst": 32347, "Nintendo": 32348, "\u0120reuse": 32349, "\u0120forgiven": 32350, "Few": 32351, "inical": 32352, "nat": 32353, "\u0120seamless": 32354, "\u0120Eva": 32355, "\u0120EVE": 32356, "\u0120JO": 32357, "landers": 32358, "\u0120softer": 32359, "negie": 32360, "\u0120transient": 32361, "\u0120orbital": 32362, "\u0120fulfil": 32363, "\u0120Kom": 32364, "Hopefully": 32365, "\u0120dynamically": 32366, "\u0120Hunger": 32367, "\u00e5\u013d": 32368, "\u0120Armenia": 32369, "elman": 32370, "berto": 32371, "\u0120pige": 32372, "\u0120IDs": 32373, "limit": 32374, "\u0120veins": 32375, "\u0120soaring": 32376, "packs": 32377, "Golden": 32378, "\u0120Crab": 32379, "istor": 32380, "\u0120RPM": 32381, "\u0120$$": 32382, "gression": 32383, "\u0120jihadist": 32384, "\u0120gamble": 32385, "\u0120careg": 32386, "\u0120inflated": 32387, "Face": 32388, "\u0120Firearms": 32389, "\u0120Emmanuel": 32390, "\u00e2\u013f": 32391, "\u0120shocks": 32392, "grab": 32393, "\u0120splend": 32394, "\u0120HPV": 32395, "abortion": 32396, "Above": 32397, "Entity": 32398, "players": 32399, "\u0120commenced": 32400, "ulence": 32401, "\u0120fulfillment": 32402, "\u0120embodiments": 32403, "\u0120Welfare": 32404, "\u0120hail": 32405, "\u0120<@": 32406, "tten": 32407, "\u0120catcher": 32408, "\u0120Jazeera": 32409, "\u0120volcano": 32410, "\u0120stabilize": 32411, "\u0120Handler": 32412, "\u0120intensified": 32413, "\u0120Abrams": 32414, "\u0120humiliation": 32415, "paced": 32416, "605": 32417, "\u0120CentOS": 32418, "Specific": 32419, "\u0120heed": 32420, "\u0120CAM": 32421, "\u0120Galile": 32422, "Die": 32423, "\u0120abolished": 32424, "\u0120Thomson": 32425, "\u0120Teachers": 32426, "\u0120Wass": 32427, "jong": 32428, "\u0120ISBN": 32429, "\u0120Allies": 32430, "shake": 32431, "\u00e5\u00b7": 32432, "vict": 32433, "Howard": 32434, "\u0120deem": 32435, "\u0120exceedingly": 32436, "\u0120Smartstocks": 32437, "ibe": 32438, "\u0120doorway": 32439, "\u0120competed": 32440, "igmat": 32441, "\u0120nationalists": 32442, "\u0120groom": 32443, "\u0120Keen": 32444, "\u0120disposable": 32445, "decl": 32446, "\u0120Tolkien": 32447, "\u0120Scheme": 32448, "\u0120biod": 32449, "\u0120avid": 32450, "\u0120Elon": 32451, "agar": 32452, "\u0120TSA": 32453, "Roman": 32454, "\u0120artificially": 32455, "\u0120advisors": 32456, "XL": 32457, "\u0120Inferno": 32458, "366": 32459, "\u0120tedious": 32460, "\u0120Photography": 32461, "\u0120Carrie": 32462, "\u0120trope": 32463, "\u0120Sandra": 32464, "\u0120decimal": 32465, "Queen": 32466, "\u0120Gundam": 32467, "\u0120OM": 32468, "otech": 32469, "NBA": 32470, "\u01201932": 32471, "\u0120entrenched": 32472, "\u0120Marion": 32473, "\u0120fraternity": 32474, "Labour": 32475, "Henry": 32476, "\u0120latitude": 32477, "Either": 32478, "\u0120enhances": 32479, "\u0120Potential": 32480, "\u0120shines": 32481, "idad": 32482, "\u0120breadth": 32483, "\u0120capacities": 32484, "\u0120\u00f0\u0141\u013b\u0124": 32485, "\u0120Bronx": 32486, "\u0120sexes": 32487, "\u0120differentiation": 32488, "\u0120heavyweight": 32489, "\u0120Taj": 32490, "dra": 32491, "\u0120migrate": 32492, "\u0120exhaustion": 32493, "\u0120RUN": 32494, "elsius": 32495, "\u0120Cuomo": 32496, "\u0120guitars": 32497, "\u0120clones": 32498, "\u0120Somew": 32499, "\u0120Pry": 32500, "-------------": 32501, "\u0120warranted": 32502, "cycles": 32503, "\u0120salvage": 32504, "\u0120disks": 32505, "RANT": 32506, "\u0120NGOs": 32507, "\u0120Martian": 32508, "\":[{\"": 32509, "\u0120addicts": 32510, "ojure": 32511, "illet": 32512, "\u0120amazingly": 32513, "artments": 32514, "pixel": 32515, "\u0120GPUs": 32516, "Layout": 32517, "\u00e8\u00a3": 32518, "\u0120Tamil": 32519, "\u0120Basil": 32520, "\u0120impartial": 32521, "\u0120Structure": 32522, "fork": 32523, "bryce": 32524, "\u0120ridge": 32525, "\u0120Hamburg": 32526, "rious": 32527, "\u0120blitz": 32528, "cigarettes": 32529, "\u0120canned": 32530, "402": 32531, "\u0120ironically": 32532, "\u0120compassionate": 32533, "\u0120Hawkins": 32534, ".#": 32535, "\u0120Cathedral": 32536, "\u0120rallied": 32537, "internal": 32538, "\u0120quota": 32539, "stakes": 32540, "TEXT": 32541, "mom": 32542, "\u0120completes": 32543, "\u0120238": 32544, "\u0120shrug": 32545, "\u00e3\u0125\u0133": 32546, "\u0120Ninth": 32547, "\u0120revise": 32548, "\u0120Provider": 32549, "\u0120treacher": 32550, "\u0120quasi": 32551, "\u0120PRES": 32552, "\u0120deposition": 32553, "\u0120confidentiality": 32554, "issors": 32555, "\u0120imbalance": 32556, "\u0120spanning": 32557, "\u0120angular": 32558, "\u0120Cul": 32559, "communication": 32560, "\u0120Nora": 32561, "\u0120Genius": 32562, "opter": 32563, "\u0120sacked": 32564, "Spot": 32565, "\u0120finely": 32566, "\u0120CHR": 32567, "282": 32568, "waves": 32569, "Palest": 32570, "\u0120Rohing": 32571, "NL": 32572, "\u00e8\u00bf": 32573, "\u0120shitty": 32574, "\u0120Scalia": 32575, "475": 32576, "Progress": 32577, "\u0120referencing": 32578, "\u0120classrooms": 32579, "abee": 32580, "\u0120sod": 32581, "hesion": 32582, "708": 32583, "\u0120Zuckerberg": 32584, "\u0120Finish": 32585, "\u0120Scotia": 32586, "\u0120Savior": 32587, "\u0120Installation": 32588, "antha": 32589, "(-": 32590, "\u0120302": 32591, "\u0120Punk": 32592, "\u0120crater": 32593, "youtu": 32594, "\u0120roast": 32595, "\u0120influencing": 32596, "\u0120dup": 32597, "\u0120JR": 32598, "\u0120Grav": 32599, "\u0120stature": 32600, "\u0120bathrooms": 32601, "Aside": 32602, "Wiki": 32603, "mean": 32604, "\u0120Zak": 32605, "\u0120Ones": 32606, "\u0120Nath": 32607, "\u0120hypert": 32608, "\u0120commencement": 32609, "Civil": 32610, "\u0120moderately": 32611, "\u0120distributors": 32612, "\u0120breastfeeding": 32613, "\u0120980": 32614, "\u0120Sik": 32615, "\u0120Cig": 32616, "\u0120AMER": 32617, "RIP": 32618, "\u0120Career": 32619, "usting": 32620, "\u0120messed": 32621, "\u0120eh": 32622, "\u0120Jensen": 32623, "/$": 32624, "\u0120blackmail": 32625, "\u0120conversions": 32626, "\u0120scientifically": 32627, "\u0120mantra": 32628, "paying": 32629, "\u0120ivory": 32630, "\u0120Courts": 32631, "OUGH": 32632, "auntlet": 32633, "Serial": 32634, "Brow": 32635, "\u0120Hundreds": 32636, "323": 32637, "\u0120pee": 32638, "\u0120linux": 32639, "\u0120submer": 32640, "\u0120Principal": 32641, "485": 32642, "\u0120DSL": 32643, "\u0120Cousins": 32644, "\u0120doctrines": 32645, "\u0120Athletics": 32646, "\u0120315": 32647, "\u0120Karma": 32648, "\u0120attent": 32649, "urger": 32650, "\u0120prescribe": 32651, "\u0120encaps": 32652, "\u0120Came": 32653, "\u0120secretive": 32654, "\u0120Crimes": 32655, "dn": 32656, "Clean": 32657, "\u0120Egyptians": 32658, "\u0120Carpenter": 32659, "\u0120ll": 32660, "Hum": 32661, "\u0120Milo": 32662, "\u0120capitalists": 32663, "\u0120briefed": 32664, "Twe": 32665, "\u0120Basin": 32666, "elvet": 32667, "Mos": 32668, "\u0120plunge": 32669, "\u0120Kaiser": 32670, "\u0120Fuj": 32671, "illin": 32672, "\u0120safeguards": 32673, "\u0120oste": 32674, "\u0120Opportunity": 32675, "\u0120Mafia": 32676, "\u0120Calling": 32677, "apa": 32678, "urban": 32679, "brush": 32680, "illard": 32681, "c\u00c3\u00a9": 32682, "intelligence": 32683, "\u0120Lob": 32684, "\u0120Druid": 32685, "\u0120smoother": 32686, "\u0120footing": 32687, "\u0120motorists": 32688, "arcity": 32689, "\u0120masculinity": 32690, "\u0120mism": 32691, "\u0120abdominal": 32692, "\u0120Tavern": 32693, "\u0120Roh": 32694, "\u0120escapes": 32695, "signed": 32696, "Anthony": 32697, "\u0120sacrificing": 32698, "\u0120intimacy": 32699, "\u0120anterior": 32700, "\u0120Kod": 32701, "\u0120motif": 32702, "\u0120graz": 32703, "\u0120visualization": 32704, "\u0120guitarist": 32705, "\u0120Trotsky": 32706, "magic": 32707, "Dar": 32708, "\u0120Mori": 32709, "\u0120wards": 32710, "\u0120toilets": 32711, "lest": 32712, "\u0120teleport": 32713, "\u0120Sundays": 32714, "\u0120Plat": 32715, "ETS": 32716, "\u0120eSports": 32717, "Patrick": 32718, "\u0120Katherine": 32719, "enko": 32720, "\u0120hassle": 32721, "\u0120Mick": 32722, "ggles": 32723, "\u0120hob": 32724, "aintain": 32725, "\u0120airborne": 32726, "\u0120spans": 32727, "\u0120chili": 32728, "\u0120aperture": 32729, "\u0120volunteered": 32730, "\u0120Incident": 32731, "\u0120Fres": 32732, 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"\u0120Biological": 32827, "\u0120Aware": 32828, "owell": 32829, "\u00cf\u0126": 32830, "\u0120Beau": 32831, "shaw": 32832, "Hack": 32833, "\u0120Julius": 32834, "USS": 32835, "olson": 32836, "auna": 32837, "cru": 32838, "\u0120Maurice": 32839, "\u0120Ik": 32840, "\u0120sequencing": 32841, "\u0120radicals": 32842, "\u0120(?,": 32843, "virtual": 32844, "\u0120anyways": 32845, "\u0120reperc": 32846, "\u0120handlers": 32847, "\u0120hesitant": 32848, "\u00e9\u0125": 32849, "\u0120MF": 32850, "plementation": 32851, "associated": 32852, "\u0120campaigned": 32853, "\u0120Yue": 32854, "utations": 32855, "\u0120Yoga": 32856, "\u0120simmer": 32857, "\u0120rods": 32858, "\u0120melody": 32859, "\u0120convoy": 32860, "videos": 32861, "\u0120screened": 32862, "Neg": 32863, "ochemical": 32864, "\u0120())": 32865, "\u0120ultras": 32866, "\u0120antip": 32867, "\u0120Islanders": 32868, "704": 32869, "\u0120fetish": 32870, "\u0120ridiculously": 32871, "\u0120Kart": 32872, "\u0120mitochondrial": 32873, 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32920, "\u0120410": 32921, "\u0120outing": 32922, "\u0120Memories": 32923, "appointed": 32924, "\u0120countered": 32925, "uld": 32926, "posing": 32927, "\u0120firewall": 32928, "\u0120Wast": 32929, "\u0120Wet": 32930, "worked": 32931, "seller": 32932, "\u0120repealed": 32933, "ereo": 32934, "assuming": 32935, "BLIC": 32936, "mite": 32937, "\u0120CEOs": 32938, "\u0120Chapel": 32939, "elligent": 32940, "________________________": 32941, "Dog": 32942, "\u0120wart": 32943, "\u0120subscriber": 32944, "sports": 32945, "\u0120begged": 32946, "\u0120MV": 32947, "\u0120semif": 32948, "ethical": 32949, "\u0120preach": 32950, "\u0120revital": 32951, "\u0120punitive": 32952, "\u0120shortcuts": 32953, "\u0120instituted": 32954, "\u0120Warsaw": 32955, "\u0120abdomen": 32956, "\u0120KING": 32957, "\u0120superintendent": 32958, "\u0120fry": 32959, "\u0120Geo": 32960, "TOR": 32961, "\u0120contradictions": 32962, "aptic": 32963, "\u0120landscapes": 32964, "bugs": 32965, "\u0120clust": 32966, "\u0120volley": 32967, "cribed": 32968, "\u0120tandem": 32969, "\u0120robes": 32970, "WHAT": 32971, "\u0120promoter": 32972, "\u0120eloqu": 32973, "reviewed": 32974, "\u0120DK": 32975, "\u0120Plato": 32976, "\u0120fps": 32977, "Tank": 32978, "\u0120Derrick": 32979, "\u0120prioritize": 32980, "asper": 32981, "\u0120Honduras": 32982, "\u0120Completed": 32983, "nec": 32984, "\u0120mog": 32985, "nir": 32986, "\u0120Mayo": 32987, "DEF": 32988, "stall": 32989, "inness": 32990, "\u0120Volkswagen": 32991, "\u0120precaution": 32992, "\u0120Mell": 32993, "iak": 32994, "istries": 32995, "\u0120248": 32996, "\u0120overlapping": 32997, "Senate": 32998, "\u0120Enhance": 32999, "resy": 33000, "racial": 33001, "ORTS": 33002, "\u0120Mormons": 33003, "Strong": 33004, "\u0120Coch": 33005, "Mexico": 33006, "\u0120Maduro": 33007, "\u0120jars": 33008, "\u0120cane": 33009, "Wik": 33010, "olla": 33011, "ifference": 33012, "\u0120physicist": 33013, "\u0120Maggie": 33014, "\u0120285": 33015, "\u0120depiction": 33016, "\u0120McLaren": 33017, "Ju": 33018, "\u0120slows": 33019, "\u0120commissioners": 33020, "\u0120Willow": 33021, "\u0120Explos": 33022, "hovah": 33023, "\u0120technician": 33024, "\u0120homicides": 33025, "\u0120Flav": 33026, "\u0120Truman": 33027, "\u012010000": 33028, "uctor": 33029, "\u0120shader": 33030, "Newsletter": 33031, "457": 33032, "\u0120rever": 33033, "\u0120hardened": 33034, "\u0120whereabouts": 33035, "\u0120redevelop": 33036, "\u0120carbs": 33037, "\u0120travers": 33038, "\u0120squirrel": 33039, "\u0120follower": 33040, "\u0120sings": 33041, "508": 33042, "\u0120rabbits": 33043, "emonium": 33044, "\u0120documenting": 33045, "\u0120misunderstood": 33046, ")'": 33047, "Rick": 33048, "ggies": 33049, "\u0120premie": 33050, "\u0120skating": 33051, "\u0120passports": 33052, "\u0120fists": 33053, "ageddon": 33054, "Haw": 33055, "ACP": 33056, "080": 33057, "\u0120Thoughts": 33058, "\u0120Carlson": 33059, "\u0120priesthood": 33060, "hua": 33061, "\u0120dungeons": 33062, "\u0120Loans": 33063, "\u0120antis": 33064, "\u0120familiarity": 33065, "\u0120Sabb": 33066, "opal": 33067, "\u0120Ink": 33068, "strike": 33069, "\u0120cram": 33070, "\u0120legalized": 33071, "\u0120cuisine": 33072, "\u0120fibre": 33073, "Travel": 33074, "\u0120Monument": 33075, "ODY": 33076, "ethy": 33077, "\u0120interstate": 33078, "\u0120PUR": 33079, "emporary": 33080, "\u0120Arabian": 33081, "developed": 33082, "\u0120saddle": 33083, "\u0120github": 33084, "\u0120Offer": 33085, "\u0120ISP": 33086, "rolet": 33087, "\u0120SUPER": 33088, "\u0120Denis": 33089, "\u0120multiplier": 33090, "\u0120stirred": 33091, "Interestingly": 33092, "\u0120customary": 33093, "\u0120billed": 33094, "hex": 33095, "\u0120multiplied": 33096, "\u0120flipping": 33097, "\u0120Crosby": 33098, "\u0120fundamentals": 33099, "iae": 33100, "\u0120Played": 33101, "\u0120Atom": 33102, "amazon": 33103, "\u0120Flam": 33104, "eez": 33105, "activated": 33106, "\u0120tablespoon": 33107, "\u0120liberalism": 33108, "\u0120Palin": 33109, "\u0120Patel": 33110, "Num": 33111, "\u0120TAM": 33112, "\u0120surn": 33113, "\u0120Reloaded": 33114, "\u0120coined": 33115, "\"],": 33116, "\u0120Clash": 33117, "\u0120Agu": 33118, "\u0120pragmatic": 33119, "\u0120Activate": 33120, "\u0120802": 33121, "\u0120trailers": 33122, "\u0120silhou": 33123, "\u0120probes": 33124, "\u0120circus": 33125, "\u0120Bain": 33126, "\u0120Lindsay": 33127, "\u0120Abbey": 33128, "Delivery": 33129, "\u0120concession": 33130, "\u0120gastro": 33131, "\u0120Sprite": 33132, "\u00c4\u0141": 33133, "andel": 33134, "\u0120gimm": 33135, "\u0120autobi": 33136, "\u0120Turtle": 33137, "\u0120wonderfully": 33138, "\u0120Haram": 33139, "\u0120Worldwide": 33140, "\u0120Handle": 33141, "\u0120theorists": 33142, "\u0120sleek": 33143, "\u0120Zhu": 33144, "ographically": 33145, "EGA": 33146, "\u0120Owners": 33147, "aths": 33148, "\u0120Antarctic": 33149, "natal": 33150, "=\"\"": 33151, "flags": 33152, "````": 33153, "\u0120sul": 33154, "Kh": 33155, "\u0120potassium": 33156, "\u0120lineman": 33157, "\u0120cereal": 33158, "\u0120Seasons": 33159, "\u01202022": 33160, "\u0120mathematic": 33161, "\u0120astronomers": 33162, "professional": 33163, "\u0120fares": 33164, "cknowled": 33165, "\u0120chi": 33166, "\u0120youngsters": 33167, "\u0120mistakenly": 33168, "\u0120hemisphere": 33169, "\u0120Divinity": 33170, "rone": 33171, "\u0120\",": 33172, "rings": 33173, "\u0120attracts": 33174, "vana": 33175, "\u00e5\u00b9": 33176, "CAP": 33177, "\u0120playlist": 33178, "\u0120porch": 33179, "\u00e3\u0123\u00a3": 33180, "\u0120incorporates": 33181, "\u0120soak": 33182, "\u0120asserting": 33183, "\u0120Terrorism": 33184, "\u0120Pablo": 33185, "Ja": 33186, "cester": 33187, "\u0120fearing": 33188, "\u0120Prayer": 33189, "\u0120escalated": 33190, "GW": 33191, "\u0120robe": 33192, "\u0120Brighton": 33193, "acists": 33194, "\u0120Symphony": 33195, "\u0120Dwarf": 33196, "\u0120Parade": 33197, "\u0120Lego": 33198, "\u0120inexpl": 33199, "\u0120lords": 33200, "leaf": 33201, "RAG": 33202, "liber": 33203, "\u0120cigars": 33204, "\u0120Jehovah": 33205, "606": 33206, "WINDOWS": 33207, "\u0120Liberia": 33208, "ebus": 33209, "Heavy": 33210, "\u0120lubric": 33211, "\u0120RW": 33212, "anguages": 33213, "\u0120narrowed": 33214, "computer": 33215, "\u0120Ember": 33216, "\u0120murdering": 33217, "\u0120downstream": 33218, "\u0120Tuls": 33219, "\u0120Tables": 33220, "Topic": 33221, "\u0120Accuracy": 33222, "=/": 33223, "lost": 33224, "\u0120Rei": 33225, "\u0120progresses": 33226, "bear": 33227, "\u0120establishments": 33228, "Justin": 33229, "\u0120Peach": 33230, "\u0120Gomez": 33231, "\u00e5\u00bf": 33232, "\u0120Triangle": 33233, "Ident": 33234, "\u0120Hive": 33235, "Resources": 33236, "\u0120mixes": 33237, "\u0120Assuming": 33238, "Mu": 33239, "\u0120hypoc": 33240, "\u0120sane": 33241, "\u0120Wan": 33242, "idious": 33243, "Success": 33244, "\u0120io": 33245, "Angel": 33246, "\u0120dangerously": 33247, "\u0120Creature": 33248, "WORK": 33249, ":[": 33250, "\u0120Katrina": 33251, "Listener": 33252, "Miller": 33253, "\u0120Idlib": 33254, "hang": 33255, "\u0120circumvent": 33256, "href": 33257, "\u0120celestial": 33258, "\u0120Weeks": 33259, "\u0120Pug": 33260, "\u0120Dalton": 33261, "\u0120subpoena": 33262, "uku": 33263, "\u0120persisted": 33264, "pei": 33265, "olding": 33266, "\u0120Documents": 33267, "\u0120Hast": 33268, "\u0120CENT": 33269, "\u0120primer": 33270, "\u0120synonymous": 33271, "\u0120nib": 33272, "ombs": 33273, "\u0120notation": 33274, "\u0120Dish": 33275, "\u0120Atmosp": 33276, "\u0120forbid": 33277, "\u0120ANG": 33278, "pattern": 33279, "los": 33280, "\u0120projectiles": 33281, "brown": 33282, ".\",": 33283, "\u0120Venom": 33284, "\u0120fiercely": 33285, "ublished": 33286, "\u0120Uran": 33287, "\u0120Nicarag": 33288, "410": 33289, "\u0120CAL": 33290, "OTOS": 33291, "\u0120Miracle": 33292, "\u0120Enchant": 33293, "\u0120guarding": 33294, "append": 33295, "Attach": 33296, "\u0120leveled": 33297, "\u0120condoms": 33298, "ihilation": 33299, "649": 33300, "\u0120nightmares": 33301, "\u0120THEY": 33302, "\u0120START": 33303, "\u0120Kinn": 33304, "\u0120roommate": 33305, "\u0120hygiene": 33306, "opping": 33307, "Job": 33308, "\u0120lvl": 33309, "\u0120VER": 33310, "\u0120Keeping": 33311, "abetic": 33312, "\u0120formatting": 33313, "erala": 33314, "\u0120revisions": 33315, "\u0120resurg": 33316, "Tel": 33317, "\u0120Goodman": 33318, "353": 33319, "pod": 33320, "\u0120indisp": 33321, "\u0120Translation": 33322, "\u0120gown": 33323, "\u0120Mund": 33324, "\u0120cis": 33325, "\u0120bystand": 33326, "collect": 33327, "\u0120Punjab": 33328, "actively": 33329, "\u0120Gamb": 33330, "tell": 33331, "\u0120importing": 33332, "gencies": 33333, "\u0120locom": 33334, "\u0120Brill": 33335, "Holy": 33336, "\u0120Berger": 33337, "\u0120showdown": 33338, "\u0120responders": 33339, "ILY": 33340, "\u0120takedown": 33341, "leted": 33342, "\u0120mattered": 33343, "\u0120predictive": 33344, "\u0120overlay": 33345, "GPU": 33346, "\u0120Vick": 33347, "\u0120conveyed": 33348, "Tab": 33349, "peer": 33350, "Scan": 33351, "\u0120defensively": 33352, "vae": 33353, "\u0120approving": 33354, "\u0120tiers": 33355, "\u0120Via": 33356, "querade": 33357, "\u0120Saudis": 33358, "\u0120demolished": 33359, "\u0120Prophe": 33360, "\u0120mono": 33361, "\u0120hospitality": 33362, "HAM": 33363, "\u0120Ariel": 33364, "MOD": 33365, "\u0120Torah": 33366, "\u0120blah": 33367, "\u0120Belarus": 33368, "erential": 33369, "\u0120Tuc": 33370, "\u0120banker": 33371, "397": 33372, "\u0120mosquit": 33373, "\u0120Scientist": 33374, "\u0120Musical": 33375, "\u0120hust": 33376, "Shift": 33377, "\u0120torment": 33378, "\u0120standoff": 33379, "Educ": 33380, "\u0120Fog": 33381, "\u0120amplifier": 33382, "Shape": 33383, "Instance": 33384, "\u0120Critics": 33385, "\u0120daemon": 33386, "Houston": 33387, "\u0120mattress": 33388, "\u0120IDF": 33389, "\u0120obscene": 33390, "\u0120Amer": 33391, "hetti": 33392, "\u0120compiling": 33393, "352": 33394, "verett": 33395, "\u0120Reduction": 33396, "istration": 33397, "\u0120Blessed": 33398, "\u0120Bachelor": 33399, "316": 33400, "\u0120prank": 33401, "\u0120Vulcan": 33402, "dding": 33403, "\u0120mourning": 33404, "\u0120Quint": 33405, "\u0120Blaster": 33406, "testing": 33407, "\u0120sediment": 33408, ">>>": 33409, "\u0120Eternity": 33410, "\u0120WHERE": 33411, "\u0120Maze": 33412, "\u0120reacting": 33413, "\u0120Alv": 33414, "omsday": 33415, "\u0120CRA": 33416, "\u0120translator": 33417, "\u0120bogus": 33418, "atu": 33419, "Website": 33420, "olls": 33421, "\u0120baptism": 33422, "\u0120sibling": 33423, "\u0120Autumn": 33424, "vez": 33425, "\u00e3\u0123\u00ae\u00e9": 33426, "guards": 33427, "Georg": 33428, "assadors": 33429, "\u0120Freud": 33430, "\u0120continents": 33431, "\u0120Registry": 33432, "Bernie": 33433, "\u0138\u013c\u00e5\u00a3\u00ab": 33434, "\u0120tolerant": 33435, "\u0120UW": 33436, "\u0120horribly": 33437, "995": 33438, "\u0120MIDI": 33439, "\u0120impatient": 33440, "ocado": 33441, "eri": 33442, "\u0120Worst": 33443, "\u0120Norris": 33444, "\u0120Talking": 33445, "\u0120defends": 33446, "ensable": 33447, "\u01202021": 33448, "\u0120anatomy": 33449, "Lew": 33450, "\u0120drawer": 33451, "\u0120Canberra": 33452, "\u0120patriotic": 33453, "\u00e9\u00be\u012f\u00e5\u0138\u013c\u00e5\u00a3\u00ab": 33454, "\u0120Avg": 33455, "ARM": 33456, "\u0120undisclosed": 33457, "\u0120farewell": 33458, "459": 33459, "bable": 33460, "\u0120Allison": 33461, "OLOG": 33462, "\u0120conco": 33463, "tight": 33464, "\u0120ACPI": 33465, "\u0120Mines": 33466, "lich": 33467, "\u0120\u00e2\u0136\u013e": 33468, "represented": 33469, "200000": 33470, "\u0120enthusiast": 33471, "OTS": 33472, "bil": 33473, "\u0120Ingredients": 33474, "\u0120inventor": 33475, "\u0120MySQL": 33476, "\u00c2\u0142\u00c2\u0142\u00c2\u0142": 33477, "\u0120ABOUT": 33478, "within": 33479, "\u0120mk": 33480, "Bul": 33481, "\u0120Fake": 33482, "\u0120draconian": 33483, "Wa": 33484, "helm": 33485, "\u0120Terran": 33486, "erville": 33487, "\u0120commonplace": 33488, "SIZE": 33489, "\u0120\"<": 33490, "replace": 33491, "ographs": 33492, "\u0120SELECT": 33493, "incible": 33494, "\u0120Mostly": 33495, "\u0120Sheffield": 33496, "\u0120IDE": 33497, "uggle": 33498, "\u0120citations": 33499, "hurst": 33500, "\u0120Unix": 33501, "\u0120unleash": 33502, "\u0120Piper": 33503, "\u0120Nano": 33504, "\u0120succumb": 33505, "\u0120reluctance": 33506, "\u01202500": 33507, "\u0120Merchant": 33508, "\u0120wiret": 33509, "\u0120combos": 33510, "\u0120Birthday": 33511, "\u0120charcoal": 33512, "\u0120UPS": 33513, "\u0120Fairfax": 33514, "\u0120driveway": 33515, "\u0120Tek": 33516, "\u0120Pitch": 33517, "overe": 33518, "\u0120technicians": 33519, "\u0120Actual": 33520, "flation": 33521, "\u0120Fiscal": 33522, "\u0120Empty": 33523, "anamo": 33524, "\u0120magnesium": 33525, "\u0120slut": 33526, "\u0120growers": 33527, "Investigators": 33528, "():": 33529, "\u0120Satellite": 33530, "\u0120Keynes": 33531, "missive": 33532, "lane": 33533, "\u0120borough": 33534, "344": 33535, "\u0120TEAM": 33536, "\u0120Bethesda": 33537, "CV": 33538, "hower": 33539, "\u0120RAD": 33540, "\u0120chant": 33541, "\u0120Riy": 33542, "\u0120compositions": 33543, "\u0120mildly": 33544, "\u0120meddling": 33545, "\u0120agility": 33546, "aneers": 33547, "501": 33548, "\u0120synth": 33549, "linger": 33550, "291": 33551, "\u0120exclaimed": 33552, "Party": 33553, "\u0120contamin": 33554, "\u0120Manor": 33555, "\u0120Respond": 33556, "\u0120praising": 33557, "\u0120manners": 33558, "fleet": 33559, "Summer": 33560, "\u0120Lynd": 33561, "\u0120Definitely": 33562, "grim": 33563, "\u0120bowling": 33564, "stri": 33565, "\u00e7\u013d": 33566, "ynt": 33567, "\u0120mandates": 33568, "DIV": 33569, "\u0120reconcile": 33570, "views": 33571, "\u0120Damon": 33572, "vette": 33573, "Flo": 33574, "\u0120Greatest": 33575, "ilon": 33576, "icia": 33577, "\u0120portrayal": 33578, "\u0120cushion": 33579, "504": 33580, "1979": 33581, "ossal": 33582, "Applic": 33583, "scription": 33584, "\u0120mitigation": 33585, "ATS": 33586, "pac": 33587, "\u0120erased": 33588, "\u0120deficiencies": 33589, "\u0120Hollande": 33590, "\u0120Xu": 33591, "\u0120bred": 33592, "\u0120pregnancies": 33593, "femin": 33594, "\u0120emph": 33595, "\u0120planners": 33596, "\u0120outper": 33597, "uttering": 33598, "\u0120perpetrator": 33599, "\u0120motto": 33600, "\u0120Ellison": 33601, "\u0120NEVER": 33602, "\u0120admittedly": 33603, "ARI": 33604, "\u0120Azerbaijan": 33605, "\u0120millisec": 33606, "\u0120combustion": 33607, "\u0120Bottle": 33608, "\u0120Lund": 33609, "\u0120Ps": 33610, "\u0120Dress": 33611, "\u0120fabricated": 33612, "\u0120battered": 33613, "\u0120sidel": 33614, "\u0120Notting": 33615, "Foreign": 33616, "\u0120Jerome": 33617, "020": 33618, "\u0120Arbit": 33619, "\u0120knots": 33620, "\u0120RIGHT": 33621, "Moving": 33622, "\u00e3\u0123\u013b": 33623, "\u0120surgeries": 33624, "\u0120courthouse": 33625, "\u0120mastered": 33626, "\u0120hovering": 33627, "\u0120Bran": 33628, "\u0120Alison": 33629, "\u0120safest": 33630, "military": 33631, "\u0120bullied": 33632, "\u0120barrage": 33633, "Reader": 33634, "ESE": 33635, "\u0120Geographic": 33636, "Tools": 33637, "314": 33638, "\u0120Geek": 33639, "roth": 33640, "glers": 33641, "\u0120FIN": 33642, "\u00cf\u0123": 33643, "\u0120Aston": 33644, "altern": 33645, "488": 33646, "\u0120veterin": 33647, "Gamer": 33648, "\u0120intel": 33649, "renches": 33650, "Shield": 33651, "\u0120amnesty": 33652, "\u0120Bhar": 33653, "\u0120piled": 33654, "\u0120honorable": 33655, "\u0120Institutes": 33656, "\u0120soaked": 33657, "\u0120coma": 33658, "\u0120EFF": 33659, "341": 33660, "bytes": 33661, "\u0120Gmail": 33662, "lein": 33663, "\u0120Canadiens": 33664, "material": 33665, "Il": 33666, "\u0120instructors": 33667, "\u0120KY": 33668, "\u0120conceive": 33669, "ubb": 33670, "\u0120Possible": 33671, "\u0120easing": 33672, "\u0120Christina": 33673, "\u0120caric": 33674, "\u0120HDR": 33675, "ROM": 33676, "\u0120shovel": 33677, "delete": 33678, "\u0120puff": 33679, "\u0120Changing": 33680, "\u0120seamlessly": 33681, "Attribute": 33682, "\u0120acquisitions": 33683, "akery": 33684, "\u0120EF": 33685, "\u0120autistic": 33686, "\u0120Takes": 33687, "\u0120Powder": 33688, "\u0120Stir": 33689, "510": 33690, "\u0120Bubble": 33691, "settings": 33692, "\u0120Fowler": 33693, "\u0120mustard": 33694, "\u0120moreover": 33695, "\u0120copyrighted": 33696, "\u0120LEDs": 33697, "1500": 33698, "\u00e6\u012b": 33699, "\u0120HIS": 33700, "enf": 33701, "\u0120custod": 33702, "\u0120Huck": 33703, "Gi": 33704, "\u0120img": 33705, "Answer": 33706, "Ct": 33707, "jay": 33708, "\u0120Infrastructure": 33709, "\u0120federally": 33710, "Loc": 33711, "\u0120microbes": 33712, "\u0120overrun": 33713, "dds": 33714, "otent": 33715, "adiator": 33716, ">>>>>>>>": 33717, "\u0120tornado": 33718, "\u0120adjud": 33719, "\u0120intrigued": 33720, "\u0120si": 33721, "\u0120Revelation": 33722, "progress": 33723, "\u0120burglary": 33724, "\u0120Saiyan": 33725, "\u0120Kathy": 33726, "\u0120serpent": 33727, "\u0120Andreas": 33728, "\u0120compel": 33729, "essler": 33730, "\u0120Plastic": 33731, "\u0120Advent": 33732, "\u0120Positive": 33733, "\u0120Qt": 33734, "\u0120Hindus": 33735, "registered": 33736, "ularity": 33737, "\u0120righteousness": 33738, "\u0120demonic": 33739, "uitive": 33740, "\u0120BDS": 33741, "\u0120Gregg": 33742, "cia": 33743, "\u0120Crusade": 33744, "\u0120Sinai": 33745, "WARE": 33746, "+(": 33747, "\u0120mell": 33748, "\u0120derail": 33749, "yards": 33750, "Ast": 33751, "\u0120noticeably": 33752, "\u0120Ober": 33753, "Ram": 33754, "\u0120unnoticed": 33755, "\u0120seq": 33756, "avage": 33757, "Ts": 33758, "\u0120640": 33759, "\u0120concede": 33760, "\u0120])": 33761, "Fill": 33762, "\u0120captivity": 33763, "\u0120Improvement": 33764, "\u0120Crusader": 33765, "araoh": 33766, "MAP": 33767, "\u00e6\u0139": 33768, "\u0120stride": 33769, "always": 33770, "Fly": 33771, "Nit": 33772, "\u0120algae": 33773, "\u0120Cooking": 33774, "\u0120Doors": 33775, "Malley": 33776, "\u0120policemen": 33777, "\u00e3\u0123\u012f": 33778, "\u0120astronaut": 33779, "accessible": 33780, "495": 33781, "\u0120RAW": 33782, "cliffe": 33783, "udicrous": 33784, "\u0120depended": 33785, "alach": 33786, "\u0120ventures": 33787, "rake": 33788, "\u0120tits": 33789, "\u0120Hou": 33790, "\u0120condom": 33791, "ormonal": 33792, "\u0120indent": 33793, "\u0120uploading": 33794, "Footnote": 33795, "Important": 33796, "\u0120271": 33797, "\u0120mindful": 33798, "\u0120contends": 33799, "Cra": 33800, "\u0120calibr": 33801, "\u0120OECD": 33802, "plugin": 33803, "Fat": 33804, "\u0120ISS": 33805, "\u0120Dynamics": 33806, "ansen": 33807, "686": 33808, "'),": 33809, "\u0120sprite": 33810, "\u0120handheld": 33811, "\u0120Hipp": 33812, "=~=~": 33813, "Trust": 33814, "\u0120semantics": 33815, "\u0120Bundes": 33816, "\u0120Reno": 33817, "\u0120Literature": 33818, "sense": 33819, "Gary": 33820, "\u0120Aeg": 33821, "\u0120Trin": 33822, "EEK": 33823, "\u0120cleric": 33824, "\u0120SSH": 33825, "\u0120christ": 33826, "\u0120invading": 33827, "ibu": 33828, "\u0120enum": 33829, "aura": 33830, "\u0120allege": 33831, "\u0120Incredible": 33832, "BBC": 33833, "\u0120thru": 33834, "\u0120sailed": 33835, "\u0120emulate": 33836, "\u0120insecurity": 33837, "\u0120crou": 33838, "\u0120accommodations": 33839, "\u0120incompetent": 33840, "\u0120slips": 33841, "\u0120Earthqu": 33842, "sama": 33843, "ILLE": 33844, "\u0120iPhones": 33845, "asaki": 33846, "\u0120bye": 33847, "\u0120ard": 33848, "\u0120extras": 33849, "\u0120slaughtered": 33850, "\u0120crowdfunding": 33851, "resso": 33852, "\u0120filib": 33853, "\u0120ERROR": 33854, "\u0120TLS": 33855, "egg": 33856, "\u0120Ital": 33857, "\u0120enlist": 33858, "\u0120Catalonia": 33859, "\u0120Scots": 33860, "\u0120sergeant": 33861, "\u0120dissolve": 33862, "NH": 33863, "\u0120standings": 33864, "rique": 33865, "IQ": 33866, "\u0120beneficiary": 33867, "\u0120aquarium": 33868, "YouTube": 33869, "\u0120PowerShell": 33870, "\u0120brightest": 33871, "\u0120Warrant": 33872, "Sold": 33873, "Writing": 33874, "\u0120beginnings": 33875, "\u0120Reserved": 33876, "\u0120Latinos": 33877, "heading": 33878, "\u0120440": 33879, "\u0120rooftop": 33880, "ATING": 33881, "\u0120390": 33882, "VPN": 33883, "Gs": 33884, "kernel": 33885, "turned": 33886, "\u0120preferable": 33887, "\u0120turnovers": 33888, "\u0120Hels": 33889, "Sa": 33890, "\u0120Shinji": 33891, "veh": 33892, "\u0120MODULE": 33893, "Viol": 33894, "\u0120exiting": 33895, "\u0120jab": 33896, "\u0120Vanilla": 33897, "\u0120acron": 33898, "\u0120Gap": 33899, "bern": 33900, "Ak": 33901, "\u0120McGu": 33902, "\u0120endlessly": 33903, "\u0120Farage": 33904, "\u0120Noel": 33905, "Va": 33906, "MK": 33907, "\u0120brute": 33908, "\u0120Kru": 33909, "\u0120ESV": 33910, "\u0120Olivia": 33911, "\u00e2\u0122\u0142": 33912, "\u0120Kaf": 33913, "\u0120trusting": 33914, "\u0120hots": 33915, "324": 33916, "\u0120malaria": 33917, "\u0120json": 33918, "\u0120pounding": 33919, "ortment": 33920, "Country": 33921, "\u0120postponed": 33922, "\u0120unequiv": 33923, "?),": 33924, "\u0120Rooney": 33925, "udding": 33926, "\u0120Leap": 33927, "urrence": 33928, "shapeshifter": 33929, "\u0120HAS": 33930, "osate": 33931, "\u0120cavern": 33932, "\u0120conservatism": 33933, "\u0120BAD": 33934, "\u0120mileage": 33935, "\u0120arresting": 33936, "Vaults": 33937, "\u0120mixer": 33938, "Democratic": 33939, "\u0120Benson": 33940, "\u0120authored": 33941, "8000": 33942, "\u0120proactive": 33943, "\u0120Spiritual": 33944, "tre": 33945, "\u0120incarcerated": 33946, "\u0120Sort": 33947, "\u0120peaked": 33948, "\u0120wielding": 33949, "reciation": 33950, "\u00d7\u013b\u00d7": 33951, "Patch": 33952, "\u0120Emmy": 33953, "\u0120exqu": 33954, "tto": 33955, "\u0120Ratio": 33956, "\u0120Picks": 33957, "\u0120Gry": 33958, "phant": 33959, "\u0120fret": 33960, "\u0120ethn": 33961, "\u0120archived": 33962, "%-": 33963, "cases": 33964, "\u0120Blaze": 33965, "\u0120imb": 33966, "cv": 33967, "yss": 33968, "imony": 33969, "\u0120countdown": 33970, "\u0120awakening": 33971, "\u0120Tunisia": 33972, "\u0120Refer": 33973, "\u0120MJ": 33974, "\u0120unnatural": 33975, "\u0120Carnegie": 33976, "izen": 33977, "\u0120Nuggets": 33978, "hess": 33979, "\u0120evils": 33980, "647": 33981, "\u0120introductory": 33982, "loving": 33983, "\u0120McMahon": 33984, "\u0120ambiguity": 33985, "Label": 33986, "\u0120Almighty": 33987, "\u0120coloring": 33988, "\u0120Claus": 33989, "setting": 33990, "NULL": 33991, "\u0120Favorite": 33992, "\u0120SIG": 33993, ">(": 33994, "\u0120Shiva": 33995, "\u0120Mayer": 33996, "\u0120stormed": 33997, "\u0120Coverage": 33998, "weapons": 33999, "igham": 34000, "\u0120unanswered": 34001, "\u0120leve": 34002, "\u0120coy": 34003, "cas": 34004, "bags": 34005, "asured": 34006, "Seattle": 34007, "\u0120Santorum": 34008, "serious": 34009, "\u0120courageous": 34010, "\u0120Soup": 34011, "\u0120confiscated": 34012, "\u0120///": 34013, "\u0120unconventional": 34014, "\u0120moms": 34015, "\u0120Rohingya": 34016, "\u0120Orchestra": 34017, "\u0120Potion": 34018, "\u0120discredit": 34019, "\u0120FIL": 34020, "fixed": 34021, "\u0120Deer": 34022, "doi": 34023, "\u0120Dimension": 34024, "\u0120bureaucrats": 34025, "eteen": 34026, "\u0120actionGroup": 34027, "ohm": 34028, "\u0120bumps": 34029, "\u0120Utility": 34030, "\u0120submarines": 34031, "renheit": 34032, "research": 34033, "\u0120Shapiro": 34034, "\u0120sketches": 34035, "\u0120deceptive": 34036, "\u0120Vil": 34037, "esame": 34038, "\u0120Essentially": 34039, "\u0120rampage": 34040, "isky": 34041, "\u0120muttered": 34042, "thritis": 34043, "\u0120236": 34044, "fet": 34045, "bars": 34046, "\u0120pupil": 34047, "\u0120Thou": 34048, "oS": 34049, "song": 34050, "\u0120fractured": 34051, "\u0120revert": 34052, "picture": 34053, "\u0120criterion": 34054, "usher": 34055, "\u0120repercussions": 34056, "\u0120Vintage": 34057, "\u0120Superintendent": 34058, "Officers": 34059, "\u0120flagged": 34060, "\u0120blames": 34061, "\u0120inverse": 34062, "ographers": 34063, "\u0120makeshift": 34064, "\u0120devoid": 34065, "\u0120fossils": 34066, "\u0120Aristotle": 34067, "\u0120Funds": 34068, "\u0120depleted": 34069, "\u0120Flu": 34070, "\u0120Yuan": 34071, "\u0120woes": 34072, "\u0120lipid": 34073, "\u0120situ": 34074, "requisites": 34075, "\u0120furnish": 34076, "\u0120Samar": 34077, "\u0120shameful": 34078, "\u0120adversely": 34079, "\u0120adept": 34080, "\u0120remorse": 34081, "\u0120murderous": 34082, "uckles": 34083, "\u0120ESL": 34084, "\u0120314": 34085, "sent": 34086, "\u0120redef": 34087, "\u0120Cache": 34088, "\u0120Purs": 34089, "igans": 34090, "\u0120460": 34091, "\u0120prescriptions": 34092, "\u0120fres": 34093, "Fuck": 34094, "ocrates": 34095, "Twenty": 34096, "\u0120Weird": 34097, "\u0120Toggle": 34098, "\u0120Called": 34099, "itizens": 34100, "\u0120poultry": 34101, "\u0120harvesting": 34102, "\u00e3\u0124\u00a6\u00e3\u0124\u00b9": 34103, "Bottom": 34104, "\u0120cautioned": 34105, "tn": 34106, "396": 34107, "\u0120Nikki": 34108, "\u0120evaluations": 34109, "\u0120harassing": 34110, "\u0120bindings": 34111, "\u0120Monetary": 34112, "\u0120hitters": 34113, "\u0120adversary": 34114, "unts": 34115, "\u0120setback": 34116, "\u0120encrypt": 34117, "\u0120Cait": 34118, "\u0120lows": 34119, "enges": 34120, "\u0120Norn": 34121, "\u0120bulbs": 34122, "\u0120bottled": 34123, "\u0120Voyager": 34124, "317": 34125, "\u0120spheres": 34126, "politics": 34127, "\u0120subtract": 34128, "\u0120sensations": 34129, "\u0120appalling": 34130, "\u0120316": 34131, "\u0120environmentally": 34132, "\u0120STEM": 34133, "\u0120publishes": 34134, "560": 34135, "\u0120diligence": 34136, "484": 34137, "\u0120advises": 34138, "\u0120petrol": 34139, "\u0120imagining": 34140, "\u0120patrols": 34141, "\u0120Integer": 34142, "\u0120Ashes": 34143, "actus": 34144, "\u0120Radiant": 34145, "\u0120LT": 34146, "itability": 34147, "htaking": 34148, "Setting": 34149, "\u0120nuanced": 34150, "\u0120Reef": 34151, "\u0120Developers": 34152, "Ni": 34153, "pieces": 34154, "990": 34155, "License": 34156, "\u0120lowers": 34157, "\u0120Ottoman": 34158, "327": 34159, "ooo": 34160, "\u0120quitting": 34161, "markets": 34162, "Behind": 34163, "\u0120basin": 34164, "\u0120docs": 34165, "anie": 34166, "flash": 34167, "ctl": 34168, "\u0120civilized": 34169, "\u0120Fukushima": 34170, "\"],\"": 34171, "\u0120KS": 34172, "\u0120Honestly": 34173, "arat": 34174, "\u0120constructs": 34175, "\u0120Lans": 34176, "\u0120Dire": 34177, "\u0120LIKE": 34178, "\u0120Trouble": 34179, "\u0120withholding": 34180, "\u0120Oblivion": 34181, "\u0120sanity": 34182, "anya": 34183, "Const": 34184, "\u0120grocer": 34185, "\u0120Celsius": 34186, "\u0120recounted": 34187, "\u0120Wife": 34188, "Border": 34189, "atered": 34190, "happy": 34191, "\u0120spoiler": 34192, "\u0120logically": 34193, "Hall": 34194, "\u0120succeeding": 34195, "\u0120polymorph": 34196, "\u0120axes": 34197, "\u0120Shotgun": 34198, "\u0120Slim": 34199, "\u0120Principles": 34200, "\u0120Leth": 34201, "arta": 34202, "\u0120scor": 34203, "Screenshot": 34204, "\u0120relaxation": 34205, "#$#$": 34206, "\u0120deterrent": 34207, "iddy": 34208, "\u0120powerless": 34209, "\u0120lesbians": 34210, "\u0120chords": 34211, "\u0120Edited": 34212, "selected": 34213, "\u0120separatists": 34214, "0002": 34215, "\u0120airspace": 34216, "\u0120turnaround": 34217, "\u0120cunning": 34218, "PATH": 34219, "Poly": 34220, "\u0120bombed": 34221, "\u0120tion": 34222, "xs": 34223, "\u0120withhold": 34224, "\u0120waged": 34225, "\u0120Liberties": 34226, "Flag": 34227, "\u0120comforting": 34228, "454": 34229, "\u0120Iris": 34230, "arers": 34231, "\u0120rag": 34232, "\u0120relocated": 34233, "\u0120Guarant": 34234, "\u0120strategically": 34235, "\u0120gamma": 34236, "uberty": 34237, "\u0120Lockheed": 34238, "gres": 34239, "\u0120grilled": 34240, "\u0120Lowe": 34241, "stats": 34242, "\u0120Rocks": 34243, "\u0120sensing": 34244, "\u0120renting": 34245, "\u0120Geological": 34246, "\u00d8\u00a7\u00d8": 34247, "otrop": 34248, "\u0120sew": 34249, "\u0120improperly": 34250, "486": 34251, "\u0120\u00e2\u0138\u0142": 34252, "\u0120starving": 34253, "\u0120Bj": 34254, "Discussion": 34255, "328": 34256, "\u0120Combo": 34257, "\u0120Fixes": 34258, "NAT": 34259, "\u0120striving": 34260, "thora": 34261, "\u0120harvested": 34262, "\u0120Ping": 34263, "\u0120playful": 34264, "\u0120avenues": 34265, "\u0120occupational": 34266, "\u0120wakes": 34267, "\u0120Courier": 34268, "\u0120drummer": 34269, "\u0120Browser": 34270, "\u0120Houth": 34271, "itu": 34272, "\u0120apparel": 34273, "paste": 34274, "\u0120hunted": 34275, "\u0120Secondly": 34276, "lain": 34277, "XY": 34278, "\u0120PIN": 34279, "icons": 34280, "\u0120cocktails": 34281, "\u0120sizable": 34282, "\u0120hurdles": 34283, "estinal": 34284, "\u0120Recreation": 34285, "\u0120eco": 34286, "648": 34287, "\u0120Died": 34288, "mint": 34289, "\u0120fingerprints": 34290, "\u0120dispose": 34291, "\u0120Bosnia": 34292, "tsy": 34293, "2200": 34294, "\u0120inspected": 34295, "\u0120Fou": 34296, "\u0120fuss": 34297, "\u0120ambush": 34298, "\u0120Rak": 34299, "\u0120manifested": 34300, "Prosecut": 34301, "\u0120suffice": 34302, "rences": 34303, "\u0120compensated": 34304, "\u0120Cyrus": 34305, "\u0120genus": 34306, "\u0120Wolverine": 34307, "\u0120Trends": 34308, "\u0120hikes": 34309, "\u0120Seen": 34310, "\u0120enrol": 34311, "Cold": 34312, "\u0120politely": 34313, "\u0120Slav": 34314, "\u0120Rupert": 34315, "\u0120eyewitness": 34316, "\u0120Alto": 34317, "\u0120uncomp": 34318, "\u0120posterior": 34319, "Must": 34320, "\u0120Herz": 34321, "\u0120progressively": 34322, "\u0120234": 34323, "\u0120indifference": 34324, "\u0120Cunningham": 34325, "\u0120academia": 34326, "\u0120sewer": 34327, "\u0120astounding": 34328, "\u0120AES": 34329, "rather": 34330, "\u0120eldest": 34331, "\u0120climbs": 34332, "\u0120Adds": 34333, "\u0120outcry": 34334, "\u0120contag": 34335, "\u0120Houses": 34336, "\u0120pept": 34337, "\u0120Melania": 34338, "interested": 34339, "\u0120UCH": 34340, "\u0120Roots": 34341, "\u0120Hubbard": 34342, "\u0120TBD": 34343, "\u0120Romanian": 34344, "filename": 34345, "Stone": 34346, "\u0120Impl": 34347, "\u0120chromosome": 34348, "Cle": 34349, "dx": 34350, "\u0120scrambled": 34351, "\u0120Pt": 34352, "\u0120242": 34353, "OPLE": 34354, "\u0120tremendously": 34355, "Street": 34356, "\u0120craving": 34357, "\u0120bundled": 34358, "\u0120RG": 34359, "pipe": 34360, "\u0120injuring": 34361, "\u0120arcane": 34362, "Particip": 34363, "\u0120Heroic": 34364, "sty": 34365, "\u0120topping": 34366, "\u0120Tempest": 34367, "rentices": 34368, "bh": 34369, "\u0120paranoia": 34370, "\u0120Unicode": 34371, "\u0120egregious": 34372, "\u0120\\'": 34373, "\u0120Oswald": 34374, "\u0120gravel": 34375, "\u0120Simpsons": 34376, "\u0120bland": 34377, "\u0120Guantanamo": 34378, "Writer": 34379, "liners": 34380, "\u0120Dice": 34381, "JC": 34382, "\u0120parity": 34383, "\u0120sided": 34384, "\u0120237": 34385, "\u0120Pyrrha": 34386, "atters": 34387, "dk": 34388, "Fine": 34389, "compan": 34390, "\u0120formulated": 34391, "\u0120Idol": 34392, "ilers": 34393, "hemoth": 34394, "\u0120Fav": 34395, "\u0120intrusion": 34396, "\u0120carrots": 34397, "\u0120Layer": 34398, "\u0120Hacker": 34399, "\u0120----------------": 34400, "\u0120moderation": 34401, "\u00e9\u0123": 34402, "ococ": 34403, "\u0120characterize": 34404, "\u0120Teresa": 34405, "\u0120socioeconomic": 34406, "\u0120perk": 34407, "\u0120Participation": 34408, "training": 34409, "\u0120Paulo": 34410, "phys": 34411, "\u0120trustworthy": 34412, "\u0120embodied": 34413, "\u0120Merch": 34414, "currency": 34415, "\u0120Priority": 34416, "\u0120teasing": 34417, "\u0120absorbing": 34418, "\u0120unfinished": 34419, "\u0120Comparison": 34420, "\u0120disple": 34421, "writers": 34422, "\u0120professions": 34423, "\u0120Penguin": 34424, "\u0120angrily": 34425, "\u0120LINK": 34426, "688": 34427, "\u0120Correspond": 34428, "\u0120prevailed": 34429, "\u0120cartel": 34430, "lp": 34431, "asms": 34432, "\u0120Redemption": 34433, "\u0120Islamists": 34434, "effects": 34435, "dose": 34436, "\u0120Latter": 34437, "\u0120Halifax": 34438, "\u0120vas": 34439, "\u0120Topics": 34440, "\u0120Named": 34441, "advertising": 34442, "zza": 34443, "ICES": 34444, "\u0120retarded": 34445, "achable": 34446, "\u0120Puppet": 34447, "\u0120ItemLevel": 34448, "\u0120retract": 34449, "\u0120identifiable": 34450, "Aaron": 34451, "\u0120Buster": 34452, "sol": 34453, "helle": 34454, "assemb": 34455, "Hope": 34456, "ranged": 34457, "Ba": 34458, "\u0120Purch": 34459, "\u00e9\u0122": 34460, "\u0120Siri": 34461, "\u0120arrivals": 34462, "\u01201912": 34463, "\u0120shortened": 34464, "\u0120312": 34465, "\u0120discrepancy": 34466, "\u0120Temperature": 34467, "\u0120Walton": 34468, "\u0120kinderg": 34469, "polit": 34470, "\u0120remix": 34471, "\u0120connectors": 34472, "\u00e3\u0125\u013a\u00e3\u0125\u00a9": 34473, "\u0120Kazakhstan": 34474, "dominated": 34475, "\u0120sugars": 34476, "imble": 34477, "\u0120Panic": 34478, "\u0120Demand": 34479, "\u0120Colony": 34480, "onen": 34481, "\u0120MER": 34482, "775": 34483, "uria": 34484, "azaar": 34485, "\u0120Degree": 34486, "Pri": 34487, "\u0120sunshine": 34488, "\u0120251": 34489, "\u0120psychedelic": 34490, "\u0120digitally": 34491, "\u0120Braun": 34492, "\u0120shimmer": 34493, "\u0120shave": 34494, "\u0120Telesc": 34495, "\u0120Astral": 34496, "\u0120Venezuelan": 34497, "\u0120OG": 34498, "\u0120crawling": 34499, "Integ": 34500, "\u0120Feather": 34501, "\u0120unfolding": 34502, "\u0120appropriation": 34503, "\u0120\u00e8\u00a3\u0131\u00e8": 34504, "\u0120Mobility": 34505, "\u0120Ney": 34506, "-.": 34507, "bilt": 34508, "LIN": 34509, "\u0120Tube": 34510, "\u0120Conversely": 34511, "\u0120keyboards": 34512, "\u0120Cao": 34513, "\u0120overth": 34514, "\u0120laure": 34515, ">>\\": 34516, "\u0120Viper": 34517, "acha": 34518, "Offset": 34519, "\u0120Raleigh": 34520, "\u0120Jae": 34521, "Jordan": 34522, "jp": 34523, "\u0120totalitarian": 34524, "Connector": 34525, "\u0120observes": 34526, "\u0120Spartan": 34527, "\u0120Immediately": 34528, "\u0120Scal": 34529, "Cool": 34530, "\u0120taps": 34531, "\u0120roar": 34532, "Past": 34533, "\u0120chars": 34534, "\u0120Bender": 34535, "\u0120Sheldon": 34536, "\u0120painter": 34537, "\u0120beacon": 34538, "\u0120Creatures": 34539, "\u0120downturn": 34540, "\u0120hinder": 34541, "\u0120Andromeda": 34542, "\u00c3\u013d": 34543, "ccoli": 34544, "\u0120Fitness": 34545, "etrical": 34546, "\u0120utilizes": 34547, "\u0120senate": 34548, "\u0120ensemble": 34549, "\u0120cheers": 34550, "TW": 34551, "\u0120affluent": 34552, "kil": 34553, "rylic": 34554, "ordering": 34555, "Computer": 34556, "\u0120gruesome": 34557, "ostics": 34558, "\u0120Ubisoft": 34559, "\u0120Kelley": 34560, "\u0120wrench": 34561, "\u0120bourgeoisie": 34562, "IBLE": 34563, "\u0120Preston": 34564, "worn": 34565, "arist": 34566, "reating": 34567, "\u0120stained": 34568, "arine": 34569, "\u0120slime": 34570, "ENN": 34571, "\u0120chests": 34572, "\u0120groundwater": 34573, "annot": 34574, "\u0120Tray": 34575, "\u0120Locke": 34576, "\u0120CTR": 34577, "\u0120dudes": 34578, "\u0120External": 34579, "\u0120Decoder": 34580, "\u0120paramed": 34581, "\u0120Medline": 34582, "809": 34583, "\u0120Dinner": 34584, "rupal": 34585, "gz": 34586, "\u0120Gum": 34587, "\u0120Demo": 34588, "jee": 34589, "\u0120dh": 34590, "berman": 34591, "archs": 34592, "\u0120enqu": 34593, "\u0120Epstein": 34594, "\u0120devastation": 34595, "\u0120friendships": 34596, "\u0120Ard": 34597, "\u0120231": 34598, "\u0120Rubin": 34599, "\u0120Distance": 34600, "\u0120spurred": 34601, "\u0120dossier": 34602, "\u0120overlooking": 34603, "\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\": 34604, "Forest": 34605, "\u0120Comes": 34606, "\\\",": 34607, "\u0120Iranians": 34608, "\u0120fixtures": 34609, "Laughs": 34610, "\u0120curry": 34611, "\u0120Kingston": 34612, "\u0120squash": 34613, "\u0120catalogue": 34614, "\u0120abnormalities": 34615, "\u0120digestive": 34616, ".........": 34617, "\u0120subordinate": 34618, "ogly": 34619, "\u0120249": 34620, "Middle": 34621, "\u0120massac": 34622, "\u0120burgers": 34623, "\u0120downstairs": 34624, "\u01201931": 34625, "394": 34626, "\u0120VG": 34627, "\u0120lasers": 34628, "\u0120Sikh": 34629, "\u0120Alexa": 34630, "derived": 34631, "\u0120cyclist": 34632, "\u00e3\u0123\u00ae\u00e9\u0143\u0136": 34633, "oneliness": 34634, "!!!!!!!!": 34635, "\u0120buffs": 34636, "legate": 34637, "\u0120raping": 34638, "\u0120recommending": 34639, "rored": 34640, "\u0120multicultural": 34641, "unique": 34642, "\u0120businessmen": 34643, "\u0120uneasy": 34644, "\u0120MAP": 34645, "\u0120dispersed": 34646, "cipline": 34647, "Jess": 34648, "\u0120Kerala": 34649, "\u00e5\u00a7": 34650, "\u0120abstraction": 34651, "Surv": 34652, "Uh": 34653, "\u0120printers": 34654, "ija": 34655, "owder": 34656, "\u0120analogous": 34657, "\u0120ASP": 34658, "afer": 34659, "\u0120unfolded": 34660, "\u0120leveling": 34661, "\u0120breached": 34662, "\u0120Hearing": 34663, "\u0120nat": 34664, "\u0120translating": 34665, "critical": 34666, "\u0120antagonist": 34667, "\u0120Yesterday": 34668, "\u0120fuzzy": 34669, "wash": 34670, "mere": 34671, "\u0120bewild": 34672, "\u0120Mae": 34673, "Virgin": 34674, "phrase": 34675, "\u0120signaled": 34676, "\u0120HIGH": 34677, "\u0120protester": 34678, "\u0120garner": 34679, "unknown": 34680, "\u0120kay": 34681, "\u0120abducted": 34682, "\u0120stalking": 34683, "amn": 34684, "\u0120deserving": 34685, "\u0120Riv": 34686, "\u0120Jorge": 34687, "\u0120scratching": 34688, "\u0120Saving": 34689, "iping": 34690, "\u0120tease": 34691, "\u0120missionary": 34692, "\u0120Morrow": 34693, "TIME": 34694, "Present": 34695, "\u0120chemotherapy": 34696, "terness": 34697, "\u0120Homes": 34698, "\u0120Purdue": 34699, "\u0120staunch": 34700, "\u0120Whitney": 34701, "\u0120THERE": 34702, "\u00ce\u00bc": 34703, "iatus": 34704, "\u0120Ernest": 34705, "\u0120Deploy": 34706, "\u0120coveted": 34707, "FML": 34708, "\u0120Dialogue": 34709, "\u0120exited": 34710, "fruit": 34711, "\u0120nerd": 34712, "\":\"\",\"": 34713, "\u0120vivo": 34714, "ruly": 34715, "460": 34716, "\u0120Amen": 34717, "rehensible": 34718, "\u0120\u00e2\u013a": 34719, "DIR": 34720, "\u0120adherence": 34721, "\u0120chew": 34722, "\u0120Coke": 34723, "\u0120Sergei": 34724, "digital": 34725, "\u0120Neck": 34726, "gently": 34727, "enthal": 34728, "/)": 34729, "\u0120weary": 34730, "\u0120guise": 34731, "\u0120Concord": 34732, "\u0120Onion": 34733, "atcher": 34734, "\u0120binge": 34735, "\u0120Directive": 34736, "\u0120manned": 34737, "ansk": 34738, "\u0120illusions": 34739, "\u0120billionaires": 34740, "383": 34741, "olyn": 34742, "odynamic": 34743, "\u0120Wheat": 34744, "\u0120Alic": 34745, "\u0120coloured": 34746, "\u0120NAFTA": 34747, "abo": 34748, "\u0120macros": 34749, "independent": 34750, "sweet": 34751, "\u0120spac": 34752, "\u0120Kabul": 34753, "\u0120\u00c4": 34754, "eme": 34755, "\u0120dictated": 34756, "\u0120shouts": 34757, "={": 34758, "\u0120ripping": 34759, "\u0120Shay": 34760, "\u0120Cricket": 34761, "directed": 34762, "\u0120analysed": 34763, "\u0120WARRANT": 34764, "agons": 34765, "\u0120Blazers": 34766, "\u0120cheered": 34767, "\u0120arithmetic": 34768, "\u0120Tanz": 34769, "373": 34770, "\u0120Flags": 34771, "\u0120295": 34772, "\u0120witches": 34773, "\u0120Included": 34774, "\u0120Gained": 34775, "\u0120Blades": 34776, "Gam": 34777, "\u0120Samantha": 34778, "\u0120Atlantis": 34779, "\u0120Pratt": 34780, "\u0120spoiled": 34781, "\u0120IB": 34782, "\u0120Ramirez": 34783, "Probably": 34784, "rero": 34785, "\u0120Ng": 34786, "\u0120Warlock": 34787, "tp": 34788, "\u0120overhe": 34789, "\u0120administrations": 34790, "\u0120tint": 34791, "\u0120regiment": 34792, "\u0120pistols": 34793, "\u0120blankets": 34794, "\u0120epist": 34795, "\u0120bowls": 34796, "\u0120hydraulic": 34797, "\u0120dean": 34798, "\u0120jung": 34799, "\u0120ascend": 34800, "705": 34801, "\u0120Santiago": 34802, "\u00c3\u00ae": 34803, "\u0120unavoid": 34804, "\u0120Shaman": 34805, "reb": 34806, "\u0120stemming": 34807, "998": 34808, "\u0120MG": 34809, "sticks": 34810, "esthesia": 34811, "ERO": 34812, "\u0120morbid": 34813, "\u0120Grill": 34814, "\u0120Poe": 34815, "anyl": 34816, "\u0120deleting": 34817, "\u0120Surveillance": 34818, "\u0120directives": 34819, "\u0120iterations": 34820, "\u0120Rox": 34821, "\u0120Milky": 34822, "Father": 34823, "\u0120patented": 34824, "447": 34825, "\u0120precursor": 34826, "\u0120maiden": 34827, "\u0120Phen": 34828, "\u0120Vegan": 34829, "\u0120Patent": 34830, "Kelly": 34831, "Redditor": 34832, "\u0120nods": 34833, "\u0120ventilation": 34834, "\u0120Schwarz": 34835, "\u0120wizards": 34836, "\u0120ominous": 34837, "\u0120Heads": 34838, "\u0120BG": 34839, "\u0120lumber": 34840, "\u0120Spiel": 34841, "\u0120isEnabled": 34842, "\u0120ancestral": 34843, "\u0120Ships": 34844, "\u0120wrestler": 34845, "phi": 34846, "\u0120yuan": 34847, "\u0120Rebellion": 34848, "\u0120iceberg": 34849, "\u0120magically": 34850, "\u0120diversion": 34851, "arro": 34852, "ythm": 34853, "\u0120Riders": 34854, "\u0120Robbie": 34855, "\u0120Kara": 34856, "\u0120Maintenance": 34857, "\u0120Herb": 34858, "\u0120harms": 34859, "packed": 34860, "\u0120Feinstein": 34861, "\u0120marrying": 34862, "\u0120blending": 34863, "\u0120Rates": 34864, "\u01201880": 34865, "\u0120wrink": 34866, "\u0120Unch": 34867, "\u0120Torch": 34868, "described": 34869, "\u0120humanoid": 34870, "ilitating": 34871, "\u0120Conv": 34872, "\u0120Feld": 34873, "IGHTS": 34874, "\u0120whistleblower": 34875, "ortmund": 34876, "etsy": 34877, "arrett": 34878, "\u0120Mono": 34879, "\u0120Ike": 34880, "\u0120CNBC": 34881, "\u0120WAY": 34882, "\u0120MDMA": 34883, "\u0120Individuals": 34884, "\u0120supplemental": 34885, "\u0120powerhouse": 34886, "\u0120Stru": 34887, "Focus": 34888, "aphael": 34889, "\u0120Colleg": 34890, "atti": 34891, "ZA": 34892, "\u0120perenn": 34893, "\u0120Signature": 34894, "\u0120Rodney": 34895, "\u0120cubes": 34896, "iddled": 34897, "\u0120Dante": 34898, "\u0120INV": 34899, "ilingual": 34900, "\u0120Cth": 34901, "\u0120sofa": 34902, "\u0120intimidate": 34903, "\u0120Roe": 34904, "\u0120Diplom": 34905, "\u0120Countries": 34906, "ayson": 34907, "\u0120extradition": 34908, "\u0120disabling": 34909, "\u0120Cardiff": 34910, "\u0120memorandum": 34911, "\u0120Trace": 34912, "\u0120???": 34913, "sector": 34914, "\u0120Rouhani": 34915, "\u0120Yates": 34916, "\u0120Freeze": 34917, "\u0120bladder": 34918, "Motor": 34919, "\u0120Promise": 34920, "antasy": 34921, "\u0120foreseeable": 34922, "\u0120Cologne": 34923, "container": 34924, "\u0120Trees": 34925, "\u0120Gors": 34926, "\u0120Sinclair": 34927, "\u0120barring": 34928, "keye": 34929, "\u0120slashed": 34930, "\u0120Statistical": 34931, "\u00e9\u0129": 34932, "\u0120\u00e2\u0138\u00ba": 34933, "Allows": 34934, "\u0120humility": 34935, "\u0120drilled": 34936, "\u0120Furn": 34937, "443": 34938, "\u0120sewage": 34939, "\u0120homepage": 34940, "\u0120courtyard": 34941, "\u0120vile": 34942, "\u0120subsidiaries": 34943, "ajo": 34944, "directory": 34945, "\u0120ammon": 34946, "Vers": 34947, "charges": 34948, "\u0120}}": 34949, "\u0120Chains": 34950, "\u0120246": 34951, "nob": 34952, "\u0120percept": 34953, "\u0120grit": 34954, "\u0120fishermen": 34955, "\u0120Iraqis": 34956, "\u0120DISTR": 34957, "\u0120FULL": 34958, "\u0120Evaluation": 34959, "graph": 34960, "atial": 34961, "\u0120cooperating": 34962, "\u0120melan": 34963, "\u0120enlightened": 34964, "\u0120ali": 34965, "tailed": 34966, "\u0120salute": 34967, "\u0120weakest": 34968, "\u0120Bulldogs": 34969, "UA": 34970, "\u0120Alloy": 34971, "\u0120semen": 34972, "ocene": 34973, "\u0120Williamson": 34974, "spr": 34975, ",\u00e2\u0122\u0136": 34976, "\u0120GF": 34977, "ittens": 34978, "Beat": 34979, "\u0120Junk": 34980, "iphate": 34981, "\u0120Farmers": 34982, "\u0120Bitcoins": 34983, "igers": 34984, "dh": 34985, "\u0120Loyal": 34986, "payer": 34987, "\u0120entertained": 34988, "\u0120penned": 34989, "\u0120coupon": 34990, "Queue": 34991, "\u0120weakening": 34992, "carry": 34993, "\u0120underestimate": 34994, "\u0120shootout": 34995, "\u0120charismatic": 34996, "\u0120Procedure": 34997, "\u0120prudent": 34998, "inances": 34999, "\u0120riches": 35000, "\u0120cortical": 35001, "\u0120strides": 35002, "\u0120drib": 35003, "\u0120Oilers": 35004, "540": 35005, "\u0120Perform": 35006, "\u0120Bangkok": 35007, "\u0120euth": 35008, "SER": 35009, "\u0120simplistic": 35010, "tops": 35011, "campaign": 35012, "Quality": 35013, "\u0120impoverished": 35014, "\u0120Eisenhower": 35015, "\u0120augment": 35016, "\u0120Harden": 35017, "\u0120intervened": 35018, "\u0120listens": 35019, "\u0120Kok": 35020, "\u0120sage": 35021, "\u0120rubbish": 35022, "\u0120Ded": 35023, "\u0120mull": 35024, "pelling": 35025, "\u0120videot": 35026, "Production": 35027, "DJ": 35028, "miah": 35029, "\u0120adaptations": 35030, "\u0120medically": 35031, "\u0120boarded": 35032, "\u0120arrogance": 35033, "\u0120scrapped": 35034, "\u0120oppress": 35035, "FORMATION": 35036, "\u0120junction": 35037, "415": 35038, "EEEE": 35039, "Skill": 35040, "\u0120subdu": 35041, "\u0120Suggest": 35042, "\u0120Pett": 35043, "\u0120lett": 35044, "\u0120Manip": 35045, "\u0120Caf": 35046, "\u0120Cooperation": 35047, "Ther": 35048, "\u0120regained": 35049, "\u00b6\u00e6": 35050, "reflect": 35051, "\u0120thugs": 35052, "\u0120Shelby": 35053, "\u0120dictates": 35054, "\u0120Weiner": 35055, "\u0120Hale": 35056, "\u0120battleground": 35057, "schild": 35058, "\u0120condol": 35059, "hunt": 35060, "ositories": 35061, "\u0120accuses": 35062, "Filename": 35063, "\u0120shri": 35064, "\u0120motivate": 35065, "\u0120reflections": 35066, "Null": 35067, "\u0120Lobby": 35068, "\u00a5\u00b5": 35069, "\u0120SATA": 35070, "\u0120Backup": 35071, "\u00d1\u0125": 35072, "nin": 35073, "\u0120Correction": 35074, "\u0120juicy": 35075, "utra": 35076, "\u0120Pric": 35077, "\u0120restraining": 35078, "\u0120Airbnb": 35079, "\u0120Arrest": 35080, "\u0120appropriations": 35081, "\u0120slopes": 35082, "\u0120manslaughter": 35083, "\u0120workings": 35084, "\u0120Huss": 35085, "\u0120Frey": 35086, "Leave": 35087, "\u0120Harmony": 35088, "\u0120Feder": 35089, "\u0120430": 35090, "\u0120trench": 35091, "\u0120gladly": 35092, "\u0120bullpen": 35093, "\u0120Gau": 35094, "bones": 35095, "\u0120groove": 35096, "\u0120pretext": 35097, "\u00e3\u0127\u012d": 35098, "\u0120transmitter": 35099, "\u0120Component": 35100, "\u0120underage": 35101, "\u0120Empires": 35102, "Tile": 35103, "\u0120oy": 35104, "\u0120Marvin": 35105, "\u0120CAS": 35106, "\u0120bloss": 35107, "\u0120replicated": 35108, "\u0120Mariners": 35109, "Marcus": 35110, "\u0120Blocks": 35111, "\u0120liberated": 35112, "\u0120butterfly": 35113, "Feel": 35114, "\u0120fermentation": 35115, "\u0120youtube": 35116, "\u0120offend": 35117, "\u0120Term": 35118, "resist": 35119, "\u0120cessation": 35120, "\u0120insurgency": 35121, "\u0120bir": 35122, "\u0120Raise": 35123, "595": 35124, "\u0120hypotheses": 35125, "502": 35126, "\u0120plaque": 35127, "ocrat": 35128, "\u0120jackets": 35129, "\u0120HuffPost": 35130, "among": 35131, "\u0120confer": 35132, "487": 35133, "\u0120Lilly": 35134, "\u0120adapting": 35135, "\u0120Fay": 35136, "\u0120shoved": 35137, "vec": 35138, "\u0120refine": 35139, "\u0120gon": 35140, "\u0120gunmen": 35141, "zai": 35142, "\u0120Shuttle": 35143, "\u0120Izan": 35144, "\u01201913": 35145, "\u0120plethora": 35146, "\u00c2\u00b7\u00c2\u00b7": 35147, "\u0120510": 35148, "\u0120puberty": 35149, "\u0120241": 35150, "\u0120Wealth": 35151, "\u0120Alma": 35152, "\u0120MEM": 35153, "\u0120Adults": 35154, "Cas": 35155, "prison": 35156, "Race": 35157, "\u0120waterproof": 35158, "\u0120athleticism": 35159, "\u0120capitalize": 35160, "\u0120Juice": 35161, "\u0120illuminated": 35162, "\u0120Pascal": 35163, "\u0120irritation": 35164, "\u0120Witnesses": 35165, "adle": 35166, "\u0120Astro": 35167, "\u0120fax": 35168, "\u0120Elvis": 35169, "Primary": 35170, "\u0120Lich": 35171, "\u0120Elves": 35172, "\u0120residing": 35173, "\u0120stumble": 35174, "319": 35175, "\u0120PKK": 35176, "\u0120adversaries": 35177, "DOS": 35178, "\u0120Ritual": 35179, "\u0120smear": 35180, "\u0120arson": 35181, "idental": 35182, "\u0120scant": 35183, "\u0120monarchy": 35184, "\u0120halftime": 35185, "\u0120residue": 35186, "\u0120indign": 35187, "\u0120Shaun": 35188, "\u0120Elm": 35189, "auri": 35190, "Aff": 35191, "WATCH": 35192, "\u0120Lyon": 35193, "helps": 35194, "361": 35195, "\u0120lobbyist": 35196, "\u0120diminishing": 35197, "\u0120outbreaks": 35198, "\u0120goats": 35199, "favorite": 35200, "\u0120Nah": 35201, "sonian": 35202, "\u0120Booster": 35203, "\u0120sandbox": 35204, "\u0120Fare": 35205, "\u0120Malta": 35206, "\u0120attRot": 35207, "\u0120MOR": 35208, "lde": 35209, "\u0120navigating": 35210, "Touch": 35211, "\u0120untrue": 35212, "\u0120Disaster": 35213, "\u0120ludicrous": 35214, "Password": 35215, "\u0120JFK": 35216, "blogspot": 35217, "416": 35218, "\u0120UNDER": 35219, "ernal": 35220, "\u0120delaying": 35221, "TOP": 35222, "\u0120implants": 35223, "\u0120AVG": 35224, "\u0120Huge": 35225, "attr": 35226, "\u0120journalistic": 35227, "\u0120Peyton": 35228, "\u0120IA": 35229, "Rap": 35230, "goal": 35231, "\u0120Programme": 35232, "\u0120smashing": 35233, "wives": 35234, "println": 35235, "\u0120Plague": 35236, "inus": 35237, "EEP": 35238, "\u0120cruiser": 35239, "\u0120Parish": 35240, "uminium": 35241, "\u0120occupants": 35242, "\u0120Jihad": 35243, "mop": 35244, "\u0120pint": 35245, "\u0120hect": 35246, "\u0120Mecca": 35247, "director": 35248, "\u0120Funding": 35249, "\u0120Mixed": 35250, "\u0120stag": 35251, "Tier": 35252, "\u0120gust": 35253, "\u0120brightly": 35254, "orsi": 35255, "\u0120uphill": 35256, "RD": 35257, "\u0120lesions": 35258, "\u0120Bundy": 35259, "livious": 35260, "\u0120biologist": 35261, "\u0120Faculty": 35262, "\u0120Authorization": 35263, "\u0120244": 35264, "Allow": 35265, "\u00ef\u00b8": 35266, "\u0120Giul": 35267, "\u0120pertinent": 35268, "otaur": 35269, "esse": 35270, "\u0120Roof": 35271, "\u0120unmanned": 35272, "351": 35273, "\u0120Shak": 35274, "\u0120Orient": 35275, "\u0120endanger": 35276, "Dir": 35277, "\u0120replen": 35278, "edient": 35279, "\u0120tailor": 35280, "\u0120gadgets": 35281, "\u0120audible": 35282, "\u00e2\u013a\u0128": 35283, "Nice": 35284, "\u0120bombard": 35285, "\u0120Rape": 35286, "\u0120defiance": 35287, "\u0120TWO": 35288, "\u0120Filipino": 35289, "\u0120unaffected": 35290, "ervatives": 35291, "\u0120soared": 35292, "\u0120Bolton": 35293, "\u0120compromising": 35294, "\u0120Brewers": 35295, "RAL": 35296, "\u0120AHL": 35297, "icycle": 35298, "\u0120vampires": 35299, "\u0120dipped": 35300, "oyer": 35301, "\u0120XIII": 35302, "\u0120sideways": 35303, "\u0120Waste": 35304, "\u0120Diss": 35305, "\u0120\u00e2\u0136\u013e\u00e2\u0136\u0122\u00e2\u0136\u0122": 35306, "$.": 35307, "\u0120habitats": 35308, "\u0120Beef": 35309, "truth": 35310, "trained": 35311, "split": 35312, "Rus": 35313, "Andy": 35314, "\u0120Bram": 35315, "REP": 35316, "pid": 35317, "\u00e8\u00a3\u0127": 35318, "\u0120Mutant": 35319, "Anim": 35320, "\u0120Marina": 35321, "\u0120futile": 35322, "highest": 35323, "frequency": 35324, "\u0120epilepsy": 35325, "\u0120coping": 35326, "\u0120concise": 35327, "\u0120tracing": 35328, "\u0120SUN": 35329, "panel": 35330, "\u0120Sophie": 35331, "\u0120Crowley": 35332, "\u0120Adolf": 35333, "\u0120Shooter": 35334, "\u0120shaky": 35335, "\u0120IG": 35336, "\u0120Lies": 35337, "\u0120Barber": 35338, "pkg": 35339, "\u0120uptake": 35340, "\u0120predatory": 35341, "ULTS": 35342, "/**": 35343, "\u0120intoxicated": 35344, "\u0120Westbrook": 35345, "odder": 35346, "hement": 35347, "\u0120baseman": 35348, "APD": 35349, "storage": 35350, "\u0120Fifty": 35351, "editor": 35352, "GEN": 35353, "UTION": 35354, "irting": 35355, "\u0120sewing": 35356, "rift": 35357, "\u0120agony": 35358, "\u0120Sands": 35359, "\u0120254": 35360, "Cash": 35361, "\u0120lodge": 35362, "\u0120punt": 35363, "Natural": 35364, "\u0120Ideas": 35365, "\u0120erroneous": 35366, "\u0120Sensor": 35367, "\u0120Hannity": 35368, "\u01201921": 35369, "\u0120mould": 35370, "\u0120Gon": 35371, "kaya": 35372, "\u0120anonymously": 35373, "\u0120KEY": 35374, "\u0120simulator": 35375, "Winter": 35376, "\u0120streamed": 35377, "507": 35378, "?\",": 35379, "\u0120teased": 35380, "\u0120coefficient": 35381, "\u0120wartime": 35382, "\u0120THR": 35383, "''.": 35384, "\u0120Banking": 35385, "mpire": 35386, "\u0120fandom": 35387, "\u0120lia": 35388, "Ga": 35389, "\u0120downhill": 35390, "\u0120interpreting": 35391, "Individual": 35392, "Norm": 35393, "\u0120jealousy": 35394, "bitcoin": 35395, "\u0120pleasures": 35396, "\u0120Toys": 35397, "\u0120Chevrolet": 35398, "\u0120Advisor": 35399, "IZE": 35400, "\u0120receptions": 35401, "706": 35402, "Cro": 35403, "\u0120262": 35404, "\u0120citrus": 35405, "iru": 35406, "Reviewer": 35407, "jected": 35408, "UES": 35409, "anz": 35410, "1981": 35411, "\u0120Worker": 35412, "\u0120complied": 35413, "orescent": 35414, "continental": 35415, "Ton": 35416, "\u0120Prism": 35417, "\u0120Sheep": 35418, "\u0120288": 35419, "nox": 35420, "\u0120Vog": 35421, "Ord": 35422, "\u0120realms": 35423, "tek": 35424, "\u0120irrigation": 35425, "\u0120bicycles": 35426, "\u0120electronically": 35427, "poly": 35428, "tall": 35429, "());": 35430, "\u0120aesthetics": 35431, "\u0120Integrated": 35432, "Explore": 35433, "\u0120dunk": 35434, "476": 35435, "pain": 35436, "\u0120Jacques": 35437, "\u0120Dmit": 35438, "Frames": 35439, "\u0120reunited": 35440, "\u0120humid": 35441, "Dro": 35442, "Political": 35443, "\u0120youthful": 35444, "\u0120entails": 35445, "\u0120mosquito": 35446, "363": 35447, "species": 35448, "\u0120coordinating": 35449, "\u0120Mayhem": 35450, "\u0120Magnus": 35451, "Mount": 35452, "Improved": 35453, "\u0120STATE": 35454, "ATTLE": 35455, "\u0120flowed": 35456, "\u0120tackled": 35457, "\u0120fashioned": 35458, "\u0120reorgan": 35459, "ivari": 35460, "finger": 35461, "\u0120reluctantly": 35462, "etting": 35463, "\u0120Vand": 35464, "young": 35465, "\u0120Garland": 35466, "\u0120presumption": 35467, "\u0120amenities": 35468, "\u0120Pleasant": 35469, "onential": 35470, "\u0120Oxy": 35471, "\u0120morals": 35472, "\u0120Yah": 35473, "Ready": 35474, "Simon": 35475, "Enh": 35476, "Demon": 35477, "\u0120clich": 35478, "Monitor": 35479, "\u0120DU": 35480, "\u0120welcomes": 35481, "\u0120standout": 35482, "\u0120dreadful": 35483, "\u0120bananas": 35484, "\u0120balloons": 35485, "hooting": 35486, "basic": 35487, "\u0120suffix": 35488, "\u0120duly": 35489, "cano": 35490, "Chain": 35491, "atos": 35492, "\u0120geopolitical": 35493, "\u0120(&": 35494, "\u0120Gemini": 35495, "\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124\u00c3\u0125\u00c3\u0124": 35496, "\u0120acquitted": 35497, "Luck": 35498, "protect": 35499, "1024": 35500, "\u0120scarcity": 35501, "\u0120mindfulness": 35502, "ecided": 35503, "DN": 35504, "prime": 35505, "\u0120Presidents": 35506, "\u0120VIDEO": 35507, "\u0120(\u00e2\u012a\u0134": 35508, "addock": 35509, "NOR": 35510, "\u0120Pru": 35511, "pun": 35512, "\u0120LOL": 35513, "))))": 35514, "\u0120Liqu": 35515, "\u0120SAS": 35516, "\u0120styling": 35517, "\u0120punishments": 35518, "\u0120numb": 35519, "\u0120ascertain": 35520, "\u0120Rockies": 35521, "flu": 35522, "Thumbnail": 35523, "\u0120perpetrated": 35524, "\u0120Semi": 35525, "\u0120disarm": 35526, "\u0120Older": 35527, "\u0120Exception": 35528, "\u0120exponentially": 35529, "\u0120Communities": 35530, "\u0120abolish": 35531, "\u0120Partner": 35532, "ptoms": 35533, "\u0120777": 35534, "\u0120Foley": 35535, "\u0120Cases": 35536, "\u0120grease": 35537, "\u0120Rebirth": 35538, "Ground": 35539, "\u0120;)": 35540, "\u0120Doctrine": 35541, "ikini": 35542, "Ye": 35543, "\u0120Blossom": 35544, "\u0120persists": 35545, "bill": 35546, "\u0120infusion": 35547, "\u0120buddies": 35548, "911": 35549, "\u0120Patient": 35550, "\u0120demos": 35551, "\u0120acquaintance": 35552, "\u0120Paw": 35553, "atari": 35554, "\u0120xml": 35555, "\u0120fascination": 35556, "\u0120Serve": 35557, "\u00cf\u0124": 35558, "branded": 35559, "\u0120az": 35560, "Returns": 35561, "\u0120overshadow": 35562, "\u0120roam": 35563, "\u0120speedy": 35564, "numbered": 35565, "helial": 35566, "\u0120disciple": 35567, "\u0120assurances": 35568, "given": 35569, "pecting": 35570, "\u0120Natalie": 35571, "\u00e7\u0136\u00b0": 35572, "\u0120mosquitoes": 35573, "rotein": 35574, "\u0120numeric": 35575, "\u0120independents": 35576, "\u0120transitional": 35577, "\u0120reactionary": 35578, "\u0120Mechdragon": 35579, "doctor": 35580, "\u0120shortest": 35581, "\u0120sequential": 35582, "\u0120Bac": 35583, "\u0120Accounts": 35584, "\u00e3\u0123\u012e": 35585, "achy": 35586, "ractive": 35587, "\u0120Regiment": 35588, "\u0120breathtaking": 35589, "fficiency": 35590, "\u0120Bates": 35591, "\u0120311": 35592, "\u0120wardrobe": 35593, "fts": 35594, "\u0120Berk": 35595, "Simply": 35596, "\u0120Riverside": 35597, "ivering": 35598, "idential": 35599, "lucent": 35600, "\u0120enriched": 35601, "\u0120Conver": 35602, "\u0120Giving": 35603, "\u00e3\u0125\u013b": 35604, "\u0120legalize": 35605, "\u0120FTC": 35606, "\u0120freaking": 35607, "Mix": 35608, "\u0120terrestrial": 35609, "esian": 35610, "cients": 35611, "Wing": 35612, "LOAD": 35613, "\u0120ledge": 35614, "\u0120Violent": 35615, "\u0120Metall": 35616, "\u0120308": 35617, "\u0120southeastern": 35618, "hetto": 35619, "Meat": 35620, "\u0120slowdown": 35621, "\u0120retreated": 35622, "Jeremy": 35623, "endas": 35624, "*****": 35625, "eric": 35626, "\u0120reins": 35627, "oppable": 35628, "\u0120Humanity": 35629, "earances": 35630, "rigan": 35631, "Camera": 35632, "\u0120waivers": 35633, "soc": 35634, "\u0120alteration": 35635, "transform": 35636, "\u0120Cemetery": 35637, "506": 35638, "\u0120indefinite": 35639, "\u0120stimulating": 35640, "yg": 35641, "603": 35642, "\u0120Sop": 35643, "\u0120descriptive": 35644, "Phase": 35645, "\u0120Edmund": 35646, "\u0120pneumonia": 35647, "ventus": 35648, "Amb": 35649, "\u0120laboratories": 35650, "\u0120Exclusive": 35651, "ugar": 35652, "Were": 35653, "\u0120malfunction": 35654, "\u0120homosexuals": 35655, "\u0120-------": 35656, "uni": 35657, "\u0120turbines": 35658, "\u0120Equity": 35659, "Du": 35660, "\u0120minded": 35661, "\u0120RH": 35662, "\u0120Blackhawks": 35663, "\u0120feats": 35664, "\u01201700": 35665, "repl": 35666, "362": 35667, "laden": 35668, "\u0120indispensable": 35669, "lyss": 35670, "tti": 35671, "\u0120reel": 35672, "\u0120diverted": 35673, "\u0120likeness": 35674, "\u0120subscriptions": 35675, "\u0120fingert": 35676, "\u0120filthy": 35677, "destruct": 35678, "draft": 35679, "\u0120Bernardino": 35680, "launch": 35681, "\u0120perplex": 35682, "\u0120SUM": 35683, "carb": 35684, "\u0120sweater": 35685, "\u0120Venture": 35686, "\u0120Jag": 35687, "\u0120Celeb": 35688, "\u0120Voters": 35689, "\u0120steadfast": 35690, "\u0120athletics": 35691, "\u0120Hanson": 35692, "\u0120Drac": 35693, "Tracker": 35694, "\u0120commend": 35695, "\u0120Presidency": 35696, "\u0120DID": 35697, "informed": 35698, "\u0120webpage": 35699, "Pretty": 35700, "\u0120forcefully": 35701, "\u00e3\u0125\u0125\u00e3\u0124\u00af": 35702, "\u0120relocation": 35703, "\u0120satire": 35704, "\u00e2\u012b": 35705, "\u0120Sunderland": 35706, "\u00e6\u0126": 35707, "Voice": 35708, "????????": 35709, "\u0120informant": 35710, "\u0120bowel": 35711, "\u0120Uniform": 35712, "\u0120...\"": 35713, "\u0120purge": 35714, "\u0120picnic": 35715, "\u0120Umb": 35716, "\u0120UPDATE": 35717, "\u0120Sapphire": 35718, "\u0120Stall": 35719, "learn": 35720, "\u0120objectively": 35721, "\u0120obliter": 35722, "\u0120loophole": 35723, "\u0120journeys": 35724, "\u0120omission": 35725, "Pros": 35726, "\u0120Sidney": 35727, "ploma": 35728, "\u0120sprayed": 35729, "\u0120guru": 35730, "\u0120traitor": 35731, "\u0120timet": 35732, "\u0120snapping": 35733, "\u0120Sevent": 35734, "urnal": 35735, "\u0120Ukip": 35736, "\u0120bowed": 35737, "poral": 35738, "liberal": 35739, "Ros": 35740, "Questions": 35741, "iOS": 35742, "\u0120summarize": 35743, "STAT": 35744, "\u01201850": 35745, "apest": 35746, "\u0120lender": 35747, "\u0120Variable": 35748, "bringing": 35749, "\u0120LORD": 35750, ",)": 35751, "\u0120collapses": 35752, "xiety": 35753, "\u0120Ned": 35754, "YD": 35755, "\u0120Scha": 35756, "\u0120antibody": 35757, "\u0120disband": 35758, "yre": 35759, "illusion": 35760, "\u0120rover": 35761, "shed": 35762, "\u0120Hirosh": 35763, "cci": 35764, "\u0120calam": 35765, "\u0120Morton": 35766, "Pinterest": 35767, "\u01201928": 35768, "\u0120Euras": 35769, "ordes": 35770, "\u0120fences": 35771, "\u0120Inventory": 35772, "\u0120Valencia": 35773, "\u0120Ud": 35774, "\u0120Tiff": 35775, "\u0120sque": 35776, "\u0120quotation": 35777, "\u0120troublesome": 35778, "erker": 35779, "QUEST": 35780, "\u0120Kingdoms": 35781, "south": 35782, "\u0120levy": 35783, "Prince": 35784, "\u0120Sting": 35785, "\u0120nicknamed": 35786, "\u0120appe": 35787, "\u0120photographic": 35788, "\u0120corpus": 35789, "reference": 35790, "\u0120Trog": 35791, "Unt": 35792, ")=(": 35793, "\u0120Latvia": 35794, "\u0120activating": 35795, "\u0120licensee": 35796, "\u0120disparities": 35797, "\u0120Newsletter": 35798, "\u00e3\u0125\u0125\u00e3\u0125\u012a": 35799, "\u0120freeing": 35800, "\u0120Jeep": 35801, "\u0120Perception": 35802, "insk": 35803, "\u0120silicone": 35804, "\u0120Hayden": 35805, "Lean": 35806, "\u0120Suzuki": 35807, "ibrarian": 35808, "668": 35809, "\u0120spor": 35810, "\u0120correlations": 35811, "aghetti": 35812, "\u0120tuber": 35813, "\u0120IPCC": 35814, "ilus": 35815, "\u0120Vu": 35816, "\u0120wealthiest": 35817, "\u0120Carbuncle": 35818, "anza": 35819, "\u0120fooled": 35820, "\u0120Zur": 35821, "\u0120daddy": 35822, "rano": 35823, "ilian": 35824, "\u0120knockout": 35825, "fman": 35826, "required": 35827, "\u0120Wikileaks": 35828, "\u0120Duffy": 35829, "ONT": 35830, "\u0120insol": 35831, "\u0120Objects": 35832, "\u0120bou": 35833, "\u0120Nordic": 35834, "\u0120Insert": 35835, "scan": 35836, "\u0120dancers": 35837, "\u0120idiots": 35838, "majority": 35839, "\u0120Neville": 35840, "\u0120FreeBSD": 35841, "\u0120tart": 35842, "panic": 35843, "690": 35844, "\u0120cocoa": 35845, "\u0120sampled": 35846, "\u0120lookup": 35847, "Indust": 35848, "\u0120injections": 35849, "genre": 35850, "\u0120au": 35851, "\u0120roadway": 35852, "\u0120genitals": 35853, "Kind": 35854, "\u0120Examiner": 35855, "\u0120Yaz": 35856, "Fresh": 35857, "\u0120paralysis": 35858, "\u0120Aluminum": 35859, "\u0120reap": 35860, "ok\u00c3\u00a9": 35861, "\u0120sloppy": 35862, "\u0120Tunnel": 35863, "posium": 35864, "nery": 35865, "enic": 35866, "\u0120herbal": 35867, "\u0120Outer": 35868, "\u0120Builder": 35869, "\u0120incur": 35870, "\u0120ideologies": 35871, "\u0120backups": 35872, "consuming": 35873, "\u0120Detect": 35874, "deck": 35875, "\u0120KNOW": 35876, "\u0120Gret": 35877, "\u0120MIC": 35878, "\u0120toughness": 35879, "\u0120Exhibit": 35880, "\u0120hive": 35881, "Les": 35882, "\u0120SCHOOL": 35883, "\u0120Atari": 35884, "alde": 35885, "\u0120Null": 35886, "andestine": 35887, "mouse": 35888, "\u0120brigade": 35889, "489": 35890, "\u0120revol": 35891, "\u0120Lawson": 35892, "\u0120Wah": 35893, "opoly": 35894, "ebted": 35895, "\u0120Saunders": 35896, "\u0120313": 35897, "\u0120Winc": 35898, "\u0120taboo": 35899, "\u0120Helmet": 35900, "\u0120wedge": 35901, "chip": 35902, "\u0120Tina": 35903, "bg": 35904, "\u0120infuri": 35905, "rn": 35906, "\u0120anomalies": 35907, "\u0120Sync": 35908, "\u0120Exam": 35909, "\u0120Commit": 35910, "\u0120Diary": 35911, "\u0120ALSO": 35912, "\u0120Debor": 35913, "omedical": 35914, "\u0120comprehension": 35915, "655": 35916, "\u0120empowering": 35917, "\u0120ire": 35918, "\u0120juices": 35919, "\u0120ETH": 35920, "\u0120Boxing": 35921, "=\"/": 35922, "\u0120facilitated": 35923, "poke": 35924, "\u0120Parsons": 35925, "\u0120Moder": 35926, "travel": 35927, "\u0120civilizations": 35928, "\u0120libertarians": 35929, "\u0120rune": 35930, "\u0120Clarks": 35931, "athed": 35932, "\u0120campaigners": 35933, "\u0120Dispatch": 35934, "\u0120Fahrenheit": 35935, "\u0120Capcom": 35936, "----------": 35937, "\u0120lace": 35938, "\u0120draining": 35939, "\u0120liner": 35940, "\u0120Artificial": 35941, "\u00c3\u00a9n": 35942, "task": 35943, "]).": 35944, "\u0120GMO": 35945, "\u0120Operator": 35946, "ordinary": 35947, "\u0120Influence": 35948, "\u0120Ups": 35949, "\u0120potency": 35950, "ussen": 35951, "ospons": 35952, "\u0120Swim": 35953, "\u0120Deadline": 35954, "Unity": 35955, "\u0120culinary": 35956, "\u0120enlightenment": 35957, "\u0120wearer": 35958, "\u0120mined": 35959, "\u0120ply": 35960, "\u0120incest": 35961, "\u0120DVDs": 35962, "Walk": 35963, "BTC": 35964, "Trade": 35965, "\u0120deval": 35966, "iband": 35967, "\u0120Oversight": 35968, "Palestinian": 35969, "\u0120dart": 35970, "\u0120mul": 35971, "LR": 35972, "\u0120removable": 35973, "\u0120Realms": 35974, "\u00ec\u013f": 35975, "\u0120miscar": 35976, "\u0120Vulkan": 35977, "685": 35978, "\u00c3\u00a8re": 35979, "\u0120Sap": 35980, "\u0120merging": 35981, "\u0120Carly": 35982, "chester": 35983, "\u0120brisk": 35984, "\u0120luxurious": 35985, "\u0120Generator": 35986, "\u0120bitterness": 35987, "\u0120edible": 35988, "\u0120243": 35989, "TG": 35990, "\u0120rectangle": 35991, "WithNo": 35992, "below": 35993, "Jenn": 35994, "\u0120darkest": 35995, "\u0120hitch": 35996, "\u0120dosage": 35997, "\u0120scaven": 35998, "\u0120Keller": 35999, "\u0120Illustrated": 36000, "Certainly": 36001, "\u0120Mavericks": 36002, "Marginal": 36003, "\u0120diarrhea": 36004, "\u0120enormously": 36005, "\u0120999": 36006, "shr": 36007, "quart": 36008, "\u0120adamant": 36009, "\u0120Mew": 36010, "\u0120renovation": 36011, "\u0120cervical": 36012, "\u0120Percentage": 36013, "eners": 36014, "\u0120Kimber": 36015, "\u0120floats": 36016, "\u0120dex": 36017, "\u0120Witcher": 36018, "\u0120Swansea": 36019, "dm": 36020, "\u0120salty": 36021, "yellow": 36022, "\u0120cape": 36023, "\u0120Drain": 36024, "\u0120Paula": 36025, "\u0120Toledo": 36026, "lesi": 36027, "Magazine": 36028, "\u0120Wick": 36029, "\u0120Mn": 36030, "\u0120Ack": 36031, "\u0120Riding": 36032, "ASON": 36033, "\u0120homophobic": 36034, "ARP": 36035, "\u0120wandered": 36036, "CPU": 36037, "oodoo": 36038, "\u0120Pipe": 36039, "\u0120tightening": 36040, "\u0120Butt": 36041, "318": 36042, "\u0120deserted": 36043, "Session": 36044, "\u0120facilitating": 36045, "Jump": 36046, "\u0120emergencies": 36047, "OWER": 36048, "\u0120exhaustive": 36049, "\u0120AFTER": 36050, "\u0120heartbeat": 36051, "\u0120Label": 36052, "acky": 36053, "\u0120Certified": 36054, "iltration": 36055, "Ze": 36056, "\u0120Utt": 36057, "\u01201300": 36058, "\u0120presume": 36059, "\u0120Disp": 36060, "\u0120surged": 36061, "\u0120dolls": 36062, "Columb": 36063, "\u0120chimpan": 36064, "\u0120Razor": 36065, "\u0120ticks": 36066, "\u0120councillor": 36067, "\u0120pilgrimage": 36068, "\u0120Rebels": 36069, "\u0120QC": 36070, "\u0120Auction": 36071, "xia": 36072, "ikk": 36073, "bred": 36074, "\u0120insertion": 36075, "\u0120coarse": 36076, "dB": 36077, "SEE": 36078, "\u0120Zap": 36079, "\u0120Foo": 36080, "\u0120contempor": 36081, "\u0120Quarterly": 36082, "otions": 36083, "\u0120Alchemist": 36084, "\u0120Trey": 36085, "\u0120Duo": 36086, "Sweet": 36087, "804": 36088, "\u0120Giov": 36089, "\u0120funn": 36090, "Nin": 36091, "hoff": 36092, "\u0120ramifications": 36093, "\u01201922": 36094, "\u0120Experts": 36095, "azes": 36096, "\u0120garments": 36097, "arial": 36098, "\u0120Nab": 36099, "\u0120257": 36100, "\u0120Ved": 36101, "\u0120humorous": 36102, "\u0120Pompe": 36103, "\u0120nylon": 36104, "\u0120lurking": 36105, "\u0120Sergey": 36106, "\u0120Mattis": 36107, "\u0120misogyny": 36108, "\u0120Components": 36109, "\u0120Watching": 36110, "\u0120Folk": 36111, "ractical": 36112, "Bush": 36113, "\u0120taped": 36114, "\u0120grouping": 36115, "\u0120beads": 36116, "\u01202048": 36117, "\u0120condu": 36118, "querque": 36119, "Reading": 36120, "\u0120grievances": 36121, "Ultra": 36122, "\u0120endpoint": 36123, "Hig": 36124, "\u0120Static": 36125, "\u0120Scarborough": 36126, "Lua": 36127, "\u0120Messi": 36128, "aqu": 36129, "\u0120PsyNet": 36130, "\u0120Rudd": 36131, "\u0120avenue": 36132, "vp": 36133, "Jer": 36134, "\u0120shady": 36135, "\u0120Resist": 36136, "\u0120Artemis": 36137, "\u0120careless": 36138, "\u0120brokers": 36139, "\u0120temperament": 36140, "\u0120520": 36141, "Tags": 36142, "\u0120Turning": 36143, "\u0120uttered": 36144, "\u0120pedd": 36145, "\u0120improvised": 36146, "\u0120:(": 36147, "\u0120tabl": 36148, "\u0120plains": 36149, "1600": 36150, "pressure": 36151, "\u0120Essence": 36152, "margin": 36153, "friends": 36154, "\u0120Restoration": 36155, "\u0120pollut": 36156, "\u0120Poker": 36157, "\u0120Augustine": 36158, "\u0120CIS": 36159, "\u0120SEAL": 36160, "orama": 36161, "\u0120thwart": 36162, "seek": 36163, "\u0120pagan": 36164, "\u00c2\u00ba": 36165, "cpu": 36166, "\u0120garn": 36167, "\u0120assortment": 36168, "\u0120ILCS": 36169, "tower": 36170, "Recommended": 36171, "\u0120unborn": 36172, "\u0120RandomRedditor": 36173, "\u0120RandomRedditorWithNo": 36174, "\u0120paralyzed": 36175, "\u0120eruption": 36176, "\u0120intersect": 36177, "\u0120Stoke": 36178, "\u0120Sco": 36179, "Bind": 36180, "\u00e5\u00be": 36181, "\u0120PNG": 36182, "\u0120Negative": 36183, "\u0120NOAA": 36184, "Leon": 36185, "\u0120alloy": 36186, "\u0120Lama": 36187, "\u0120Diversity": 36188, "575": 36189, "\u0120underestimated": 36190, "\u0120Scor": 36191, "\u0120mural": 36192, "\u0120busted": 36193, "soon": 36194, "lif": 36195, "\u0120nonex": 36196, "\u0120allergy": 36197, "\u0120Underworld": 36198, "\u0120Rays": 36199, "\u0120Blasio": 36200, "\u0120hrs": 36201, "\u0120Dir": 36202, "\u0120327": 36203, "byter": 36204, "\u0120replacements": 36205, "\u0120activates": 36206, "rived": 36207, "MH": 36208, "\u0120pans": 36209, "\u0120HI": 36210, "\u0120longitudinal": 36211, "\u0120nuisance": 36212, "aler": 36213, "\u0120swell": 36214, "\u0120Signed": 36215, "sci": 36216, "\u0120Isles": 36217, "\u0120AGA": 36218, "\u0120defiant": 36219, "\u0120sonic": 36220, "ocon": 36221, "KC": 36222, "\u0120Aim": 36223, "tie": 36224, "ahah": 36225, "\u0120mL": 36226, "DX": 36227, "\u0120bisc": 36228, "\u0120Billboard": 36229, "\u0120SYSTEM": 36230, "NEY": 36231, "gaard": 36232, "\u0120distressed": 36233, "formerly": 36234, "Alan": 36235, "\u0120chefs": 36236, "\u0120optics": 36237, "\u0120Comet": 36238, "\u0120AMC": 36239, "\u0120redesigned": 36240, "irmation": 36241, "\u0120sightings": 36242, "382": 36243, "311": 36244, "\u0120WB": 36245, "\u0120contraction": 36246, "\u0120TOTAL": 36247, "Dual": 36248, "\u0120startled": 36249, "\u0120understandably": 36250, "\u0120sunglasses": 36251, "ETHOD": 36252, "\u0120docker": 36253, "\u0120surfing": 36254, "\u0120HEL": 36255, "\u0120Slack": 36256, "tones": 36257, "\u0120shalt": 36258, "Visual": 36259, "498": 36260, "Department": 36261, "cussion": 36262, "\u0120unrestricted": 36263, "\u0120tad": 36264, "\u0120rename": 36265, "employed": 36266, "\u0120educating": 36267, "\u0120grinned": 36268, "bedroom": 36269, "\u0120Activities": 36270, "\u0120Velvet": 36271, "\u0120SWAT": 36272, "\u0120shuffle": 36273, "igor": 36274, "\u0120saturation": 36275, "Finding": 36276, "cream": 36277, "icter": 36278, "\u0120vodka": 36279, "tracking": 36280, "tec": 36281, "\u0120foreground": 36282, "iesta": 36283, "\u0120vehement": 36284, "\u0120ECB": 36285, "\u0120Tie": 36286, "Ey": 36287, "\u0120turtles": 36288, "\u0120Railroad": 36289, "\u0120Katz": 36290, "\u0120Frames": 36291, "\u0120menace": 36292, "\u0120Fellowship": 36293, "\u0120Essential": 36294, "uggish": 36295, "\u0120drip": 36296, "chwitz": 36297, "\u0120Kyoto": 36298, "sb": 36299, "\u0120Nina": 36300, "Parameter": 36301, "\u0120alarms": 36302, "\u0120Claud": 36303, "\u0120pioneering": 36304, "\u0120chiefly": 36305, "\u0120Scream": 36306, "Collection": 36307, "\u0120thankfully": 36308, "\u0120Ronaldo": 36309, "\u00e5\u0143\u0132": 36310, "strip": 36311, "\u0120Disneyland": 36312, "commercial": 36313, "Seeing": 36314, "Soul": 36315, "\u0120evacuate": 36316, "\u0120civ": 36317, "\u0120Ashe": 36318, "\u0120divides": 36319, "\u0120Dagger": 36320, "rehensive": 36321, "\u0120berries": 36322, "\u0120DF": 36323, "\u0120sushi": 36324, "\u0120plurality": 36325, "WI": 36326, "\u0120disadvantaged": 36327, "\u0120battalion": 36328, "obiles": 36329, "451": 36330, "\u0120cling": 36331, "\u0120undeniable": 36332, "\u0120Lounge": 36333, "\u0120haunt": 36334, "phe": 36335, "\u0120quantify": 36336, "\u0120differed": 36337, "\u0120[*]": 36338, "\u0120Viz": 36339, "cum": 36340, "slave": 36341, "\u0120videog": 36342, "\u0120quar": 36343, "\u0120bundles": 36344, "\u0120Alonso": 36345, "tackle": 36346, "\u0120neuronal": 36347, "\u0120landslide": 36348, "confirmed": 36349, "\u0120Depth": 36350, "\u0120renewables": 36351, "Bear": 36352, "\u0120Macedonia": 36353, "\u0120jerseys": 36354, "\u0120bunk": 36355, "\u0120Spawn": 36356, "\u0120Controls": 36357, "\u0120Buchanan": 36358, "\u0120robotics": 36359, "\u0120emphasizing": 36360, "\u0120Tutorial": 36361, "hyp": 36362, "iston": 36363, "\u0120monumental": 36364, "\u00e6\u00b0": 36365, "\u0120Carry": 36366, "\u0120tbsp": 36367, "enance": 36368, "Hill": 36369, "arthed": 36370, "\u0120rotten": 36371, "Dean": 36372, "\u0120twisting": 36373, "\u0120goodwill": 36374, "\u0120immersion": 36375, "Living": 36376, "\u0120brushes": 36377, "\u0120CGI": 36378, "\u0120Atk": 36379, "traditional": 36380, "\u0120phantom": 36381, "\u0120Stamina": 36382, "\u0120expansions": 36383, "\u0120Marin": 36384, "\u0120embarked": 36385, "\u0120Eg": 36386, "intestinal": 36387, "\u0120PEOPLE": 36388, "\u0120Booth": 36389, "\u0120Appalach": 36390, "\u0120relegated": 36391, "VT": 36392, "MIT": 36393, "\u0120muster": 36394, "\u0120withdrawing": 36395, "\u0120microscope": 36396, "\u0120Gathering": 36397, "\u0120Crescent": 36398, "\u0120Argentine": 36399, "\u0120Decre": 36400, "\u0120Dominic": 36401, "\u0120buds": 36402, "antage": 36403, "\u0120Ion": 36404, "\u0120widened": 36405, "ONSORED": 36406, "\u0120Gloves": 36407, "iannopoulos": 36408, "razen": 36409, "feel": 36410, "\u0120repayment": 36411, "\u0120hindsight": 36412, "\u0120REALLY": 36413, "\u0120Pistol": 36414, "\u0120Brah": 36415, "\u0120watts": 36416, "\u0120survives": 36417, "\u0120flurry": 36418, "issy": 36419, "Alert": 36420, "\u0120Uruguay": 36421, "Phoenix": 36422, "Slow": 36423, "\u0120Grave": 36424, "\u0120Fir": 36425, "\u0120manageable": 36426, "\u0120tariff": 36427, "\u0120UDP": 36428, "\u0120Pistons": 36429, "\u0120Nigerian": 36430, "\u0120strikeouts": 36431, "\u0120cosmetics": 36432, "whelming": 36433, "fab": 36434, "cape": 36435, "proxy": 36436, "\u0120rethink": 36437, "\u0120overcoming": 36438, "simple": 36439, "\u0120woo": 36440, "\u0120distracting": 36441, "\u0120Stanton": 36442, "\u0120Tulsa": 36443, "\u0120Dock": 36444, "659": 36445, "\u0120discord": 36446, "\u0120Emacs": 36447, "\u0120Ves": 36448, "\u0120ROB": 36449, "\u0120reassuring": 36450, "\u0120consortium": 36451, "Muslims": 36452, "321": 36453, "\u0120prompts": 36454, "sei": 36455, "\u0120Hitch": 36456, "imposed": 36457, "\u0120Fool": 36458, "\u0120indiscrim": 36459, "wrong": 36460, "buquerque": 36461, "Davis": 36462, "!]": 36463, "\u0120timeless": 36464, "\u0120NEED": 36465, "\u0120pesticide": 36466, "\u0120rallying": 36467, "\u0120Calder": 36468, "\u0120\u00e5\u00a4": 36469, "\u0120xp": 36470, "\u0120Unle": 36471, "\u0120Export": 36472, "luaj": 36473, "Buff": 36474, ")</": 36475, "Boot": 36476, "\u0120Chrysler": 36477, "orative": 36478, "Mess": 36479, "\u0120negligible": 36480, "ertodd": 36481, "\u0120Mushroom": 36482, "\u0120Gale": 36483, "gc": 36484, "\u0120Cosby": 36485, "\u0120Rural": 36486, "ritical": 36487, "Bell": 36488, "\u0120turbine": 36489, "00200000": 36490, "\u0120legitimately": 36491, "\u0120Animated": 36492, "TED": 36493, "\u0120Theodore": 36494, "conduct": 36495, "\u0120Hier": 36496, "\u0120counterfeit": 36497, "\u0120Algeria": 36498, "\u0120unbeat": 36499, "controller": 36500, "\u0120unres": 36501, "\u0120scrambling": 36502, "\u0120Fallon": 36503, "Tes": 36504, "\u0120amber": 36505, "\u0120royalties": 36506, "\u0120Shelter": 36507, "\u0120Lester": 36508, "\u0120classify": 36509, "Remote": 36510, "\u0120unheard": 36511, "\u0120controversies": 36512, "\u0120enrichment": 36513, "\u0120Yankee": 36514, "gamer": 36515, "\u0120platinum": 36516, "\u0120ecology": 36517, "\u0120Sark": 36518, "\u0120untouched": 36519, "\u0120supervisors": 36520, "\u0120\"%": 36521, "\u0120footh": 36522, "\u0120commons": 36523, "\u0120narcotics": 36524, "\u0120indices": 36525, "\u0120Ply": 36526, "\u0120additionally": 36527, "\u0120Gawker": 36528, "\u0120EQ": 36529, "Playing": 36530, "\u0120caveat": 36531, "\u0120Absolute": 36532, "ossus": 36533, "Baby": 36534, "\u0120ration": 36535, "\u0120resin": 36536, "\u0120calibration": 36537, "\u0120Newport": 36538, "\u0120knocks": 36539, "vt": 36540, "\u0120compost": 36541, "Scene": 36542, "\u0120sarcast": 36543, "\u0120kisses": 36544, "\u0120ns": 36545, "alli": 36546, "\u0120Marcel": 36547, "\u0120Piet": 36548, "iatrics": 36549, "\u0120surrounds": 36550, "\u0120Reprodu": 36551, "\u0120Phillies": 36552, "\u0120uncertainties": 36553, "\u0120Eur": 36554, "\u0120Romance": 36555, "\u0120Hath": 36556, "\u0120Needs": 36557, "\u0120Cloak": 36558, "\u0120crem": 36559, "queue": 36560, "\u0120355": 36561, "\u0120upfront": 36562, "]);": 36563, "\u0120reciproc": 36564, "\u01201927": 36565, "\u01201100": 36566, "utsu": 36567, "\u0120depressive": 36568, "owment": 36569, "Fans": 36570, "\u0120mech": 36571, "\u0120annihil": 36572, "\u0120counterterrorism": 36573, "\u0120Figures": 36574, "bold": 36575, "\u0120Moines": 36576, "\u0120Drivers": 36577, "\u0120manuscripts": 36578, "\u0120Crypto": 36579, "\u0120hypnot": 36580, "reddits": 36581, "\u0120prosecutions": 36582, "\u0120divert": 36583, "CRIP": 36584, "\u0120Bene": 36585, "\u0120Reggie": 36586, "\u0120taxing": 36587, "\u0120Morales": 36588, "enting": 36589, "tur": 36590, "significant": 36591, "\u0120PROV": 36592, "\u0120strands": 36593, "\u0120pouch": 36594, "\u0120Rookie": 36595, "\u00bb\u0134": 36596, "\u0120nicer": 36597, "hemy": 36598, "hw": 36599, "ECA": 36600, "\u0120intimidated": 36601, "\u0120stricter": 36602, "\u0120microbial": 36603, "details": 36604, "\u0120vows": 36605, "\u0120quake": 36606, "hhhh": 36607, "\u0120reinvent": 36608, "Ub": 36609, "\u0120relinqu": 36610, "\u0120Buffett": 36611, "licensed": 36612, "ittered": 36613, "\u0120Picard": 36614, "\u0120chewing": 36615, "ucl": 36616, "organic": 36617, "\u0120localized": 36618, "\u0120Economist": 36619, "\u0120acquainted": 36620, "Definition": 36621, "sed": 36622, "Critics": 36623, "\u0120cc": 36624, "453": 36625, "381": 36626, "\u0120fellows": 36627, "\u0120checkpoints": 36628, "025": 36629, "\u0120reelection": 36630, "\u0120mediated": 36631, "\u0120KDE": 36632, "\u0120hurdle": 36633, "\u0120texting": 36634, "Perfect": 36635, "\u0120trustees": 36636, "fecture": 36637, "\u0120dich": 36638, "monary": 36639, "\u0120distinctions": 36640, "\u01201400": 36641, "\u0120usher": 36642, "\u0120parasites": 36643, "\u0120Sharing": 36644, "\u0120Vim": 36645, "\u0120barbecue": 36646, "\u0120Ministers": 36647, "erella": 36648, "\u0120eb": 36649, "\u0120mc": 36650, "\u0120Somehow": 36651, "\u0120Insect": 36652, "changes": 36653, "broad": 36654, "\u0120Byz": 36655, "\u0120grapes": 36656, "669": 36657, "\u0120=================": 36658, "\u0120assimil": 36659, "\u0120haunting": 36660, "\u0120firepower": 36661, "\u0120defamation": 36662, "emphasis": 36663, "\u0120compose": 36664, "\u0120allergies": 36665, "\u0120strang": 36666, "rollers": 36667, "bang": 36668, "\u0120brewers": 36669, "rongh": 36670, "riot": 36671, "poor": 36672, "cold": 36673, "Sample": 36674, "\u0120buoy": 36675, "040": 36676, "\u0120Courtney": 36677, "\u0120268": 36678, "\u0120Wedding": 36679, "702": 36680, "\u0120obsessive": 36681, "\u0120braking": 36682, "\u0120Lal": 36683, "anical": 36684, "\u00e5\u00a6": 36685, "aten": 36686, "Construction": 36687, "\u0120clinically": 36688, "iership": 36689, "Names": 36690, "\u0120Discuss": 36691, "\u0120Ramos": 36692, "\u0120locale": 36693, "\u0120Agricultural": 36694, "Enable": 36695, "\u0120horsepower": 36696, "enture": 36697, "Pref": 36698, "Court": 36699, "\u0120staffing": 36700, "\u0120futuristic": 36701, "drivers": 36702, "\u0120Marketplace": 36703, "\u00e6\u012a\u00a6": 36704, "Friends": 36705, "\u0120damning": 36706, "\u0120Customers": 36707, "\u0120weeds": 36708, "\u0120Mai": 36709, "\u0120agile": 36710, "\u0120Tatt": 36711, "icent": 36712, "Ranked": 36713, "croft": 36714, "\u0120Katy": 36715, "Extreme": 36716, "\u0120carve": 36717, "\u0120Rover": 36718, "\u0120Byron": 36719, "372": 36720, "\u0120conducts": 36721, "ratch": 36722, "itia": 36723, "\u0120Pumpkin": 36724, "Sadly": 36725, "Reloaded": 36726, "Policy": 36727, "\u0120lick": 36728, "peak": 36729, "isks": 36730, "\u0120CDs": 36731, "\u0120Encyclopedia": 36732, "initial": 36733, "Cos": 36734, "\u0120Awareness": 36735, "\u0120Dram": 36736, "$$$$": 36737, "\u0120riff": 36738, "\u0120scripture": 36739, "runners": 36740, "\u0120boiler": 36741, "onson": 36742, "oin": 36743, "\u0120hamstring": 36744, "\u0120cataly": 36745, "\u0120Archbishop": 36746, "chall": 36747, "\u0120faux": 36748, "okin": 36749, "localhost": 36750, "\u0120NAME": 36751, "adobe": 36752, "SAN": 36753, "amate": 36754, "\u0120scramble": 36755, "\u0120carc": 36756, "\u0120Manifest": 36757, "\u0120Cedar": 36758, "\u0120Sergio": 36759, "later": 36760, "ffer": 36761, "\u0120grappling": 36762, "\u0120Deutsche": 36763, "agonists": 36764, "\u0120Newsp": 36765, "\u0120pretended": 36766, "archment": 36767, "\u0120curated": 36768, "\u0120headphone": 36769, "\u0120Uncommon": 36770, "\u0120SIGN": 36771, "Agent": 36772, "\u0120deadlines": 36773, "\u0120horizontally": 36774, "\u0120MAT": 36775, "\u0120Summers": 36776, "\u0120ordained": 36777, "\u0120Lastly": 36778, "\u0120Kendall": 36779, "\u0120frig": 36780, "\u0120Machina": 36781, "\u0120Waterloo": 36782, "\u0120Mexicans": 36783, "\u0120protector": 36784, "\u0120glare": 36785, "}\"": 36786, "Premium": 36787, "\u0120rift": 36788, "\u0120Telescope": 36789, "Metal": 36790, "\u0120recapt": 36791, "\u0120;;": 36792, "\u0120inclination": 36793, "\u0120imposes": 36794, "ingen": 36795, "^{": 36796, "\u0120haste": 36797, "\u0120dolphins": 36798, "\u0120commuters": 36799, "planned": 36800, "cong": 36801, "mx": 36802, "\u0120Upload": 36803, "\u0120extrap": 36804, "\u0120Tucson": 36805, "\u0120Exploration": 36806, "efeated": 36807, "\u0120slender": 36808, "703": 36809, "\u0120Buk": 36810, "isel": 36811, "\u0120competitiveness": 36812, "chlor": 36813, "\u0120Permanent": 36814, "\u0120Everett": 36815, "\u0120Specialist": 36816, "\u0120SOL": 36817, "\u0120cyan": 36818, "\u0120Exactly": 36819, "UF": 36820, "\u0120LIFE": 36821, "aryl": 36822, "onet": 36823, "\u0120Employee": 36824, "awed": 36825, "\u0120Ratings": 36826, "\u0120extravag": 36827, "ulhu": 36828, "\u0120Plane": 36829, "\u0120elevate": 36830, "\u0120Coordinator": 36831, "\u0120Watkins": 36832, "\u0120excludes": 36833, "\u0120sentient": 36834, "\u0120epoch": 36835, "\u0120alloc": 36836, "Previously": 36837, "\u0120Shy": 36838, "\u0120Slovakia": 36839, "LOCK": 36840, "\u0120markedly": 36841, "\u0120knob": 36842, "\u0120adventurers": 36843, "\u0120Been": 36844, "\u0120Costs": 36845, "ammers": 36846, "\u0120onslaught": 36847, "\u0120Supported": 36848, "\u0120Tau": 36849, "ikarp": 36850, "\u0120Sovere": 36851, "\u0120Hampton": 36852, "\u00e3\u0124\u012b": 36853, "Prev": 36854, "\u0120Worse": 36855, "\u0120cottage": 36856, "\u0120Hades": 36857, "lez": 36858, "bowl": 36859, "\u0120fragrance": 36860, "\u0120Lok": 36861, "EMOTE": 36862, "\u0120Petro": 36863, "\u01201925": 36864, "\u0120Pend": 36865, "producing": 36866, "\u0120relocate": 36867, "vati": 36868, "pole": 36869, "\u0120semin": 36870, "\u0120NUM": 36871, "\u0120rocked": 36872, "buff": 36873, "bly": 36874, "Reply": 36875, "\u0120Hai": 36876, "\u0120articulated": 36877, "\u0120Islamabad": 36878, "665": 36879, "\u0120Claims": 36880, "Desktop": 36881, "\u0120trustee": 36882, "\u0120scripting": 36883, "\u0120Sob": 36884, "\u0120Asylum": 36885, "STDOUT": 36886, "\u0120Clown": 36887, "\u0120Dortmund": 36888, "\u0120Devon": 36889, "lite": 36890, "\u0120Marble": 36891, "\u0120bunker": 36892, "\u0120crest": 36893, "\u0120arousal": 36894, "\u0120Sears": 36895, "\u0120Buddy": 36896, "eredith": 36897, "\u0120Polly": 36898, "\u0120decode": 36899, "\u0120Vish": 36900, "\u0120Reflect": 36901, "anon": 36902, "\u0120refunds": 36903, "immers": 36904, "HM": 36905, "\u0120wiping": 36906, "\u0120puzzled": 36907, "\u0120matte": 36908, "uno": 36909, "Pierre": 36910, ")),": 36911, "\u0120tainted": 36912, "\u0120symbolism": 36913, "\u0120Fraz": 36914, "\u0120protestors": 36915, "etheus": 36916, "%%%%": 36917, "Wra": 36918, "\u0120lax": 36919, "adem": 36920, "aturation": 36921, "\u00e3\u0125\u0135": 36922, "\u0120Trailer": 36923, "\u0120ENG": 36924, "\u0120Bowser": 36925, "\u0120attm": 36926, "Dur": 36927, "807": 36928, "\u0120sidx": 36929, "\u0120cider": 36930, "\u0120Affect": 36931, "\u0120woven": 36932, "\u0120Barker": 36933, "benef": 36934, "\u0120dstg": 36935, "\u0120Ryu": 36936, ">[": 36937, "\u0120sqor": 36938, "Saudi": 36939, "\u0120istg": 36940, "\u0120indulge": 36941, "proc": 36942, "\u0120disgusted": 36943, "\u0120compounded": 36944, "\u0120nem": 36945, "\u0120schooling": 36946, "\u0120Cure": 36947, "processing": 36948, "Sol": 36949, "\u0120proverb": 36950, "itized": 36951, "\u0120Alvarez": 36952, "\u0120scarf": 36953, "\u0120rectangular": 36954, "reve": 36955, "\u0120hormonal": 36956, "\u0120Stress": 36957, "itizen": 36958, "\u0120425": 36959, "girls": 36960, "\u0120Noir": 36961, "\u0120Rapp": 36962, "\u0120marches": 36963, "church": 36964, "\u0120Uses": 36965, "\u0120405": 36966, "\u0120Berm": 36967, "\u0120ordinances": 36968, "\u0120Judgment": 36969, "Charges": 36970, "\u0120Zin": 36971, "\u0120dusty": 36972, "\u0120strawberries": 36973, "\u0120perce": 36974, "\u0120Thur": 36975, "\u0120Deborah": 36976, "netflix": 36977, "\u0120Lambert": 36978, "\u0120amused": 36979, "\u0120Guang": 36980, "YOU": 36981, "RGB": 36982, "\u0120CCTV": 36983, "\u0120fiat": 36984, "rang": 36985, "\u0120federation": 36986, "\u0120Mant": 36987, "\u0120Bust": 36988, "\u0120Mare": 36989, "respective": 36990, "\u0120Migration": 36991, "\u0120BIT": 36992, "590": 36993, "\u0120patriotism": 36994, "\u0120outlining": 36995, "region": 36996, "\u0120Jos\u00c3\u00a9": 36997, "\u0120blasting": 36998, "\u0120Ezra": 36999, "Bs": 37000, "\u0120undermines": 37001, "\u0120Smooth": 37002, "\u0120clashed": 37003, "radio": 37004, "\u0120transitioning": 37005, "\u0120Buccaneers": 37006, "\u0120Owl": 37007, "\u0120plugs": 37008, "\u0120hiatus": 37009, "\u0120Pinball": 37010, "\u0120mig": 37011, "\u0120Nutr": 37012, "\u0120Wolfe": 37013, "\u0120integers": 37014, "\u0120orbits": 37015, "\u0120Edwin": 37016, "\u0120DirectX": 37017, "bite": 37018, "\u0120blazing": 37019, "vr": 37020, "Edge": 37021, "\u0120PID": 37022, "exit": 37023, "\u0120Comed": 37024, "\u0120Pathfinder": 37025, "\u0120Guid": 37026, "\u0120Signs": 37027, "\u0120Zer": 37028, "\u0120Agenda": 37029, "\u0120reimbursement": 37030, "Mesh": 37031, "iPhone": 37032, "\u0120Marcos": 37033, "\u0120Sites": 37034, "hate": 37035, "enburg": 37036, "\u0120sockets": 37037, "pend": 37038, "Batman": 37039, "vir": 37040, "\u0120SHOW": 37041, "\u0120provisional": 37042, "conn": 37043, "\u0120Deaths": 37044, "ATIVE": 37045, "Profile": 37046, "sym": 37047, "JA": 37048, "\u0120ninja": 37049, "installed": 37050, "idates": 37051, "ebra": 37052, "\u0120Omaha": 37053, "\u0120seizing": 37054, "\u0120Beasts": 37055, "\u0120salts": 37056, "Mission": 37057, "Generally": 37058, "\u0120Trilogy": 37059, "heon": 37060, "legates": 37061, "\u0120dime": 37062, "\u0120faire": 37063, "parable": 37064, "Graph": 37065, "\u0120totaling": 37066, "\u0120diagrams": 37067, "\u0120Yanuk": 37068, "plet": 37069, "\u0120Meh": 37070, "\u0120mythical": 37071, "\u0120Stephens": 37072, "autical": 37073, "ochemistry": 37074, "\u0120kilograms": 37075, "\u0120elbows": 37076, "ancock": 37077, "\u0120BCE": 37078, "\u0120Prague": 37079, "\u0120improv": 37080, "\u0120Devin": 37081, "\u0120\"\\": 37082, "paralle": 37083, "\u0120supremacists": 37084, "\u0120Billion": 37085, "\u0120regimen": 37086, "innacle": 37087, "\u0120requisite": 37088, "angan": 37089, "\u0120Burlington": 37090, "ainment": 37091, "\u0120Objective": 37092, "omsky": 37093, "GV": 37094, "\u0120unilateral": 37095, "\u0120tc": 37096, "\u0120hires": 37097, "mental": 37098, "\u0120involuntary": 37099, "\u0120transpl": 37100, "\u0120ASCII": 37101, "\u00c2\u00a8": 37102, "Events": 37103, "\u0120doubted": 37104, "\u0120Kaplan": 37105, "\u0120Courage": 37106, "igon": 37107, "\u0120Managing": 37108, "\u0120Tart": 37109, "\u0120falsehood": 37110, "\u0120Violet": 37111, "\u0120airs": 37112, "\u0120fertilizer": 37113, "Britain": 37114, "\u0120aquatic": 37115, "ouf": 37116, "Words": 37117, "\u0120Hartford": 37118, "\u0120evenings": 37119, "\u0120Vengeance": 37120, "quite": 37121, "Gall": 37122, "\u0120Pret": 37123, "\u0120pdf": 37124, "\u0120LM": 37125, "\u0120Sochi": 37126, "\u0120Intercept": 37127, "920": 37128, "\u0120profitability": 37129, "\u0120Idle": 37130, "\u0120MacDonald": 37131, "\u0120Establishment": 37132, "umsy": 37133, "\u0120gatherings": 37134, "\u0120Naj": 37135, "Charlie": 37136, "\u0120ascent": 37137, "\u0120Protector": 37138, "\u0120algebra": 37139, "\u0120bios": 37140, "forums": 37141, "ELS": 37142, "Introduced": 37143, "\u0120335": 37144, "\u0120astronomy": 37145, "Contribut": 37146, "\u0120Polic": 37147, "Platform": 37148, "\u0120containment": 37149, "wrap": 37150, "\u0120coronary": 37151, "\u0120Jelly": 37152, "manager": 37153, "\u0120heartbreaking": 37154, "cair": 37155, "\u0120Chero": 37156, "cgi": 37157, "Medical": 37158, "\u0120Accountability": 37159, "!!\"": 37160, "ophile": 37161, "\u0120psychotic": 37162, "\u0120Restrict": 37163, "\u0120equitable": 37164, "issues": 37165, "\u01201905": 37166, "\u0120Nek": 37167, "cised": 37168, "\u0120Tracking": 37169, "\u0120ozone": 37170, "\u0120cooker": 37171, "rosis": 37172, "\u0120reopen": 37173, "\u0120infinity": 37174, "\u0120Pharmaceutical": 37175, "ensional": 37176, "Attempt": 37177, "\u0120Rory": 37178, "Marco": 37179, "\u0120awaits": 37180, "HOW": 37181, "treated": 37182, "\u0120bolst": 37183, "\u0120revered": 37184, "\u0120pods": 37185, "oppers": 37186, "0010": 37187, "\u0120amplitude": 37188, "rican": 37189, "SPONSORED": 37190, "\u0120trousers": 37191, "\u0120halves": 37192, "\u0120Kaine": 37193, "\u0120Cutler": 37194, "\u0120AUTH": 37195, "\u0120splendid": 37196, "\u0120preventive": 37197, "\u0120Dudley": 37198, "ifacts": 37199, "uminati": 37200, "\u0120Yin": 37201, "\u0120admon": 37202, "\u0120Vag": 37203, "\u0120inverted": 37204, "\u0120hastily": 37205, "\u0120Hague": 37206, "Lyn": 37207, "\u0120ledger": 37208, "\u0120astronomical": 37209, "getting": 37210, "\u0120circa": 37211, "\u0120Cic": 37212, "\u0120Tennis": 37213, "Limited": 37214, "\u0120dru": 37215, "\u0120BYU": 37216, "\u0120travellers": 37217, "\u0120pane": 37218, "\u0120Intro": 37219, "\u0120patiently": 37220, "\u0120aiding": 37221, "\u0120loos": 37222, "\u0120Tough": 37223, "\u0120293": 37224, "\u0120consumes": 37225, "SourceFile": 37226, "\u0120\"\"\"": 37227, "\u0120bonding": 37228, "\u0120tilted": 37229, "\u0120menstrual": 37230, "\u0120Celestial": 37231, "ULAR": 37232, "Plugin": 37233, "\u0120risking": 37234, "Naz": 37235, "\u0120Riyadh": 37236, "\u0120accredited": 37237, "\u0120skirm": 37238, "\u00e9\u013d": 37239, "\u0120examiner": 37240, "\u0120messing": 37241, "\u0120nearing": 37242, "\u0120Chern": 37243, "\u0120Beckham": 37244, "\u0120swapped": 37245, "\u0120goose": 37246, "Kay": 37247, "\u0120lofty": 37248, "\u0120Wallet": 37249, "\u0120['": 37250, "\u0120apocalypse": 37251, "\u0120bamboo": 37252, "\u0120SPACE": 37253, "\u0120Elena": 37254, "\u0120306": 37255, "acons": 37256, "\u0120tightened": 37257, "\u0120adolescence": 37258, "\u0120rainy": 37259, "\u0120vandalism": 37260, "\u0120Newtown": 37261, "\u0120conject": 37262, "cakes": 37263, "\u0120cheated": 37264, "\u0120moderators": 37265, "params": 37266, "EFF": 37267, "\u0120deceit": 37268, "\u0120STL": 37269, "\u0120Tanzania": 37270, "\u0120RI": 37271, "\u01201923": 37272, "\u0120Exile": 37273, "thel": 37274, "\u0120theolog": 37275, "\u0120quirky": 37276, "\u0120Irvine": 37277, "\u0120needy": 37278, "oris": 37279, "Um": 37280, "Ka": 37281, "\u0120mailbox": 37282, "322": 37283, "\u0120bos": 37284, "\u0120Petra": 37285, "KING": 37286, "\u0120enlarged": 37287, "Often": 37288, "\u0120badass": 37289, "\u0120343": 37290, "\u0120Places": 37291, "\u0120CAD": 37292, "\u0120pristine": 37293, "\u0120intervening": 37294, "direction": 37295, "\u0120laz": 37296, "\u0120DSM": 37297, "\u0120projecting": 37298, "\u0120Funk": 37299, "agog": 37300, "payment": 37301, "nov": 37302, "\u0120chatter": 37303, "ARB": 37304, "\u0120examinations": 37305, "\u0120Household": 37306, "\u0120Gus": 37307, "Ford": 37308, "414": 37309, "Boss": 37310, "\u0120mystic": 37311, "\u0120leaps": 37312, "\u0120Bav": 37313, "ulz": 37314, "budget": 37315, "Football": 37316, "\u0120subsidized": 37317, "\u0120firsthand": 37318, "\u0120coincide": 37319, "ocular": 37320, "Conn": 37321, "\u0120Collabor": 37322, "\u0120fools": 37323, "amura": 37324, "ahar": 37325, "rists": 37326, "\u0120swollen": 37327, "\u0120expended": 37328, "\u0120Pau": 37329, "sup": 37330, "\u0120spar": 37331, "\u0120keynote": 37332, "suff": 37333, "\u0120unequal": 37334, "\u0120progressing": 37335, "strings": 37336, "\u0120Gamergate": 37337, "Disney": 37338, "\u0120Eleven": 37339, "omnia": 37340, "\u0120scripted": 37341, "\u0120earners": 37342, "brother": 37343, "\u0120Enabled": 37344, "\u00e6\u00b3": 37345, "\u0120larvae": 37346, "\u0120LOC": 37347, "mess": 37348, "Wilson": 37349, "\u0120Template": 37350, "successfully": 37351, "\u0120paramount": 37352, "\u0120camouflage": 37353, "\u0120binds": 37354, "\u0120Quiet": 37355, "\u0120Shutterstock": 37356, "rush": 37357, "\u0120mascot": 37358, "fortune": 37359, "\u0120Colt": 37360, "\u0120Beyon": 37361, "habi": 37362, "\u0120hairc": 37363, "\u0120267": 37364, "\u0120Deus": 37365, "\u0120twitch": 37366, "\u0120concentrating": 37367, "\u0120nipples": 37368, "cible": 37369, "\u0120gir": 37370, "NZ": 37371, "Math": 37372, "nih": 37373, "Required": 37374, "\u0120ponder": 37375, "\u0120SAN": 37376, "\u0120weddings": 37377, "\u0120loneliness": 37378, "NES": 37379, "\u0120Mahjong": 37380, "695": 37381, "addle": 37382, "\u0120Garner": 37383, "\u0120COUR": 37384, "Bridge": 37385, "\u0120spree": 37386, "\u0120Caldwell": 37387, "\u0120bribery": 37388, "\u0120\u00ef\u00bf\u00bd\u00ef\u00bf\u00bd\u00ef\u00bf\u00bd\u00ef\u00bf\u00bd\u00ef\u00bf\u00bd\u00ef\u00bf\u00bd\u00ef\u00bf\u00bd\u00ef\u00bf\u00bd": 37389, "plugins": 37390, "\u0120racket": 37391, "\u0120champagne": 37392, "versible": 37393, "Vote": 37394, "\u0120modifiers": 37395, "Mayor": 37396, "680": 37397, "\u0120assemblies": 37398, "\u0120Sultan": 37399, "\u0120Ning": 37400, "\u0120Ladies": 37401, "\u0120sulfur": 37402, "\u0120orbs": 37403, "\u0120-----": 37404, "_______": 37405, "\u0120Journalism": 37406, "\u0120esports": 37407, "\u0120lush": 37408, "\u0120hue": 37409, "\u0120spectral": 37410, "Honest": 37411, "\u00e3\u0125\u0131": 37412, "\u0120bushes": 37413, "\u0120reinforcement": 37414, "\u0120reopened": 37415, "\u0120Wheels": 37416, "\u0120Morg": 37417, "rieving": 37418, "\u0120auxiliary": 37419, "\u0120jQuery": 37420, "\u0120BAT": 37421, "tesque": 37422, "\u0120vertex": 37423, "pure": 37424, "frey": 37425, "\u00e3\u0124\u00ba": 37426, "dos": 37427, "\u0120typh": 37428, "\u0120cull": 37429, "\u0120eq": 37430, "\u0120decon": 37431, "\u0120tossing": 37432, "\u0120disparate": 37433, "\u0120Brigham": 37434, "printf": 37435, "ledged": 37436, "\u0120sund": 37437, "\u0120cozy": 37438, "\u0120hepatitis": 37439, "performing": 37440, "\u0120aval": 37441, "\u0120GG": 37442, "future": 37443, "\u0120petertodd": 37444, "\u0120Kosovo": 37445, "\u0120magnets": 37446, "Already": 37447, "\u0120Edison": 37448, "\u0120Ceres": 37449, "\u0120RAID": 37450, "\u0120brilliance": 37451, "576": 37452, "\u0120derives": 37453, "\u0120hypertension": 37454, "\u0120\u00ce\u0136": 37455, "\u0120lambda": 37456, "\u0120flair": 37457, "\u0120missionaries": 37458, "\u0120rapes": 37459, "\u0120Starter": 37460, "\u0120Months": 37461, "\u0120defy": 37462, "\u0120seismic": 37463, "\u0120Raphael": 37464, "\u0120eurozone": 37465, "656": 37466, "zsche": 37467, "\u0120scratched": 37468, "\u0120bows": 37469, "\u0120Lennon": 37470, "\u0120Gaia": 37471, "\u0120dripping": 37472, "facts": 37473, "Ale": 37474, "\u0120frogs": 37475, "\u0120Breast": 37476, "ogeneity": 37477, "\u0120Prosecutor": 37478, "\u0120amplified": 37479, "\u0120Hodg": 37480, "\u0120Fn": 37481, "Thousands": 37482, "\u0120NIH": 37483, "\u0120Monitoring": 37484, "FTWARE": 37485, "\u0120Priebus": 37486, "\u0120Growing": 37487, "hunter": 37488, "\u0120diagnose": 37489, "\u0120Mald": 37490, "\u0120LR": 37491, "\u0120crowned": 37492, "\u0120bursting": 37493, "\u0120dissolution": 37494, "javascript": 37495, "\u0120usefulness": 37496, "\u0120Execution": 37497, ":(": 37498, "\u0120Ivory": 37499, "aah": 37500, "\u0120persecuted": 37501, "violence": 37502, "istas": 37503, "\u0120Crate": 37504, "\u0120impulses": 37505, "\u0120Spani": 37506, "edes": 37507, "Handle": 37508, "\u0120Zerg": 37509, "thinkable": 37510, "Lastly": 37511, "\u0120spontaneously": 37512, "\u0120inconvenient": 37513, "\u0120dismissing": 37514, "\u0120plotted": 37515, "\u0120eighty": 37516, "\u0120737": 37517, "rish": 37518, "\u0120Thornton": 37519, "atham": 37520, "\u0120sitcom": 37521, "Ven": 37522, "Recipe": 37523, "tel": 37524, "lund": 37525, "\u0120clears": 37526, "\u0120Sasuke": 37527, "\u0120258": 37528, "\u0120opting": 37529, "\u0120enraged": 37530, "esthetic": 37531, "\u0120Ae": 37532, "uchs": 37533, "Prep": 37534, "Flow": 37535, "\u0120runoff": 37536, "\u0120Eating": 37537, "\u0120Giles": 37538, "\u0120Acting": 37539, "resources": 37540, "ibaba": 37541, "\u0120rpm": 37542, "\u0120skewed": 37543, "\u0120Blanc": 37544, "\u0120Sakuya": 37545, "\u0120hotter": 37546, "\u01201924": 37547, "opian": 37548, "cko": 37549, "\u0120crumbling": 37550, "\u0120captains": 37551, "\u0120Appropriations": 37552, "leaders": 37553, "dropping": 37554, "anuts": 37555, "\u0120reversing": 37556, "\u0120Pose": 37557, "\u0120Sek": 37558, "Scot": 37559, "\u0120Idea": 37560, "cise": 37561, "\u0120Slovenia": 37562, "\u0120317": 37563, "Doctor": 37564, "\u0120crocod": 37565, "aldi": 37566, "Sea": 37567, "\u0120Farrell": 37568, "\u0120mercenaries": 37569, "\u0120RNC": 37570, "\u0120Guess": 37571, "\u0120pacing": 37572, "Machine": 37573, "StreamerBot": 37574, "\u0120Charity": 37575, "\u0120298": 37576, "\u0120cannons": 37577, "\u0120Toby": 37578, "TPPStreamerBot": 37579, "\u0120Passion": 37580, "cfg": 37581, "Thom": 37582, "\u0120badges": 37583, "\u0120Bernstein": 37584, ".\u00e2\u0122\u0135": 37585, "\u0120POP": 37586, "\u0120Conj": 37587, "\u0120initialization": 37588, "\u0120biodiversity": 37589, "Dub": 37590, "\u0120feudal": 37591, "\u0120disclaimer": 37592, "\u0120crow": 37593, "\u0120ignition": 37594, "arf": 37595, "SHA": 37596, "\u0120kHz": 37597, "hazard": 37598, "\u0120Artists": 37599, "oeuv": 37600, "679": 37601, "\u0120Rudy": 37602, "Nine": 37603, "\u0120Ramadan": 37604, "\u00e5\u00bd": 37605, "itto": 37606, "\u0120adrenaline": 37607, "Cert": 37608, "\u0120smelled": 37609, "\u0120impunity": 37610, "\u0120agendas": 37611, "\u0120Reborn": 37612, "\u0120Concent": 37613, "\u0120Seems": 37614, "\u0120omega": 37615, "\u0120Dustin": 37616, "\u0120backer": 37617, "\u0120Sauce": 37618, "\u0120Boyle": 37619, "WIN": 37620, "\u0120spins": 37621, "\u0120pauses": 37622, "upt": 37623, "\u0120shredded": 37624, "\u0120strapped": 37625, "\u0120Corruption": 37626, "\u0120scratches": 37627, "\u0120ni": 37628, "\u0120attire": 37629, "\u0120SAF": 37630, "FactoryReloaded": 37631, "\u0120IPS": 37632, "\u0120(%": 37633, "\u0120seminar": 37634, "focus": 37635, "civil": 37636, "\u01201860": 37637, "intosh": 37638, "\u0120continual": 37639, "\u0120abbrevi": 37640, "\u0120Sok": 37641, "ocobo": 37642, "XM": 37643, "\u0120frantic": 37644, "\u0120unavoidable": 37645, "\u0120artery": 37646, "\u0120annotations": 37647, "bath": 37648, "Climate": 37649, "\u0120dors": 37650, "\u0120Slide": 37651, "coord": 37652, "\u0120Reload": 37653, "\u0120LDL": 37654, "\u0120Lovecraft": 37655, "\u0120unimagin": 37656, "\u0120resembled": 37657, "\u0120barracks": 37658, "np": 37659, "\u0120surrogate": 37660, "\u0120categorized": 37661, "\u00e3\u0124\u00a9": 37662, "\u0120vaccinated": 37663, "\u0120drainage": 37664, "\u0120indist": 37665, "\u0120WhatsApp": 37666, "\u01201870": 37667, "olerance": 37668, "invoke": 37669, "amorph": 37670, "\u0120reconnect": 37671, "\u0120emanc": 37672, "\u0120blindness": 37673, "\u01201280": 37674, "internet": 37675, "collar": 37676, "\u0120altru": 37677, "\u0120abyss": 37678, "\u0120TRI": 37679, "657": 37680, "\u0120infused": 37681, "HEAD": 37682, "\u0120forestry": 37683, "\u0120Woody": 37684, "\u0120Ci": 37685, "wi": 37686, "sam": 37687, "784": 37688, "holiday": 37689, "\u0120mogul": 37690, "\u0120Fees": 37691, "\u0120DEN": 37692, "Internal": 37693, "urbed": 37694, "fusc": 37695, "atom": 37696, "\u0120Illusion": 37697, "\u0120polled": 37698, "\u0120flap": 37699, "\u0120coax": 37700, "LGBT": 37701, "Analy": 37702, "\u0120Sections": 37703, "\u0120Californ": 37704, "emn": 37705, "\u0120hither": 37706, "\u0120NIGHT": 37707, "\u0120nailed": 37708, "\u0120Pipeline": 37709, "391": 37710, "oof": 37711, "\u0120Primal": 37712, "verend": 37713, "\u0120slashing": 37714, "\u0120retri": 37715, "aviour": 37716, "\u0120departing": 37717, "gil": 37718, "ISC": 37719, "\u0120midway": 37720, "\u0120ultrasound": 37721, "\u0120behaving": 37722, "\u0120Tara": 37723, "classes": 37724, "Virtual": 37725, "\u0120Colonial": 37726, "\u0120stripping": 37727, "\u0120orchestrated": 37728, "\u0120Graves": 37729, "452": 37730, "\u0120Ironically": 37731, "\u0120Writers": 37732, "\u0120lends": 37733, "\u0120Manz": 37734, "\u0120raven": 37735, "\u0120oxidative": 37736, "\u0120266": 37737, "ELF": 37738, "actually": 37739, "ascar": 37740, "Draft": 37741, "\u0120favourable": 37742, "\u0120humiliating": 37743, "\u0120fidelity": 37744, "\u0120Hof": 37745, "\u0120Xuan": 37746, "496": 37747, "\u0120layered": 37748, "atis": 37749, "790": 37750, "\u0120paycheck": 37751, "iton": 37752, "Kar": 37753, "\u0120VMware": 37754, "\u0120Farmer": 37755, "\u0120servic": 37756, "glomer": 37757, "\u0120slump": 37758, "\u0120Fabric": 37759, "\u0120DOC": 37760, "esting": 37761, "\u0120reassure": 37762, "\u0120phyl": 37763, "volt": 37764, "itory": 37765, "Rules": 37766, "\u0120oxidation": 37767, "\u0120prized": 37768, "\u0120mistress": 37769, "\u0120Django": 37770, "WARN": 37771, "\u00e5\u0133": 37772, "\u0120encode": 37773, "\u0120Feedback": 37774, "\u0120stupidity": 37775, "Ian": 37776, "\u0120Yugoslavia": 37777, "\u00d7\u00a8": 37778, "acl": 37779, "UTE": 37780, "1977": 37781, "\u0120qualifies": 37782, "\u0120pulses": 37783, "pretty": 37784, "\u0120froze": 37785, "\u0120ss": 37786, "Iterator": 37787, "\u0120urgently": 37788, "\u0120mailed": 37789, "\u0120Cham": 37790, "\u0120sustaining": 37791, "\u0120basil": 37792, "\u0120puppies": 37793, "ilant": 37794, "\u0120PLEASE": 37795, "lap": 37796, "aceous": 37797, "Fear": 37798, "\u0120Mastery": 37799, "automatic": 37800, "\u0120TAG": 37801, "\u0120antim": 37802, "agles": 37803, "473": 37804, "frames": 37805, "\u0120whispers": 37806, "\u0120Whoever": 37807, "\u0120bravery": 37808, "\u0120UKIP": 37809, "ractions": 37810, "\"\"\"": 37811, "\u0120tame": 37812, "\u0120parted": 37813, "everything": 37814, "CONT": 37815, "\u0120indebted": 37816, "\u0120addr": 37817, "rek": 37818, "IRED": 37819, "\u0120eminent": 37820, "clinton": 37821, "\u0120ousted": 37822, "\u0120reviewer": 37823, "\u0120meltdown": 37824, "\u0120rearr": 37825, "\u0120Yao": 37826, "thereal": 37827, "abyte": 37828, "\u0120stumbling": 37829, "\u0120batches": 37830, "\u0120259": 37831, "\u0120contraceptive": 37832, "\u0120prostitute": 37833, "ensis": 37834, "Decl": 37835, "\u0120Strikes": 37836, "Military": 37837, "\u0120Oath": 37838, "vacc": 37839, "ppings": 37840, "052": 37841, "\u0120partName": 37842, "amping": 37843, "Reports": 37844, "KI": 37845, "CHR": 37846, "\u0120subtly": 37847, "swers": 37848, "Blake": 37849, "usual": 37850, "\u0120contestants": 37851, "\u0120cartridges": 37852, "\u0120GREAT": 37853, "\u0120blush": 37854, "\u0120\u00e2\u0122\u00ba": 37855, "472": 37856, "\u0120reasoned": 37857, "\u00e3\u0125\u00a4": 37858, "paralleled": 37859, "\u0120dyn": 37860, "agate": 37861, "\u0120nightly": 37862, "\u00e5\u0128": 37863, "556": 37864, "\u0120semantic": 37865, "\u0120Advoc": 37866, "\u0120!!": 37867, "\u0120disagrees": 37868, "\u0120BW": 37869, "Veh": 37870, "\u0120harming": 37871, "\u0120embraces": 37872, "\u0120strives": 37873, "\u0120inland": 37874, "\u0120Kard": 37875, "\u0120heats": 37876, "\u0120Ginny": 37877, "utan": 37878, "ernaut": 37879, "ylene": 37880, "\u0120Elev": 37881, "JD": 37882, "\u0120hars": 37883, "\u0120Starr": 37884, "\u0120skysc": 37885, "\u0120collaborators": 37886, "Usually": 37887, "\u0120revolutions": 37888, "\u0120STATS": 37889, "\u0120dismantle": 37890, "\u0120confidently": 37891, "\u0120kinetic": 37892, "Ali": 37893, "\u0120percentile": 37894, "\u0120extracting": 37895, "illian": 37896, "estead": 37897, "\u0120physicists": 37898, "\u0120Marshal": 37899, "\u0120fellowship": 37900, "\u0120dashed": 37901, "\u0120UR": 37902, "\u0120Sioux": 37903, "\u0120Compact": 37904, "amide": 37905, "Python": 37906, "\u0120Leigh": 37907, "\u0120Pharmac": 37908, "istrates": 37909, "herical": 37910, "\u0120fue": 37911, "\u0120Emin": 37912, "\u0120({": 37913, "\u0120Neighborhood": 37914, "\u0120disrupting": 37915, "\u0120Dup": 37916, "\u0120gland": 37917, "\u0120Sev": 37918, "\u0120Marian": 37919, "argon": 37920, "\u0120Dund": 37921, "\u0120<!--": 37922, "\u0120strand": 37923, "\u0120stadiums": 37924, "zos": 37925, "\u0120psychosis": 37926, "\u0120Rack": 37927, "\u0120brilliantly": 37928, "\u00ef\u00b8\u0131": 37929, "\u0120submerged": 37930, "\u0120Instit": 37931, "\u0120Chow": 37932, "\u0120cages": 37933, "\u0120Hats": 37934, "\u0120Urs": 37935, "\u0120diluted": 37936, "usat": 37937, "ienne": 37938, "\u0120Membership": 37939, "\u0120Burk": 37940, "\u0120ie": 37941, "\u0120archetype": 37942, "Drug": 37943, "ulton": 37944, "\u0120Spock": 37945, "\u0120McKay": 37946, "\u0120Depend": 37947, "Featured": 37948, "Soc": 37949, "1978": 37950, "\u0120Bere": 37951, "\u0120relentlessly": 37952, "\u0120crippling": 37953, "\u0120arthritis": 37954, "\u00e7\u0136\u0141": 37955, "\u0120Tropical": 37956, "\u0120Bulg": 37957, "\u0120Cheryl": 37958, "\u0120admirable": 37959, "\u0120subtitle": 37960, "Override": 37961, "\u0120originating": 37962, "\u0120CCP": 37963, "\u0120swore": 37964, "\u0120Sole": 37965, "\u0120Disorders": 37966, "329": 37967, "\u0120procession": 37968, "\u0120refurb": 37969, "\u0120immersed": 37970, "requently": 37971, "\u0120skeptics": 37972, "\u0120ceramic": 37973, "mitter": 37974, "enstein": 37975, "belt": 37976, "\u0120TIT": 37977, "bidden": 37978, "\u0120fir": 37979, "mist": 37980, ">]": 37981, "\u0120weave": 37982, "\u0120Paradox": 37983, "\u0120entrusted": 37984, "\u0120Barclays": 37985, "\u0120novelist": 37986, "ogie": 37987, "806": 37988, "\u0120ninety": 37989, "\u0120disagreements": 37990, "@@@@@@@@": 37991, "\u0120Auschwitz": 37992, "cars": 37993, "\u0120LET": 37994, "tub": 37995, "arantine": 37996, "POS": 37997, "\u0120backstory": 37998, "\u0120cheerful": 37999, "\u0120Rag": 38000, "eka": 38001, "biased": 38002, "\u0120inexperienced": 38003, "akra": 38004, "\u0120Witt": 38005, "tan": 38006, "\u0120rapist": 38007, "\u0120plateau": 38008, "chal": 38009, "\u0120Inquis": 38010, "expression": 38011, "\u0120cipher": 38012, "\u0120shaving": 38013, "adden": 38014, "rely": 38015, "(\\": 38016, "isma": 38017, "\u0120Regulatory": 38018, "CHAR": 38019, "ilyn": 38020, "NVIDIA": 38021, "GU": 38022, "\u0120murm": 38023, "laus": 38024, "Christopher": 38025, "\u0120contractual": 38026, "\u0120Proxy": 38027, "\u0120Jaime": 38028, "\u0120Methodist": 38029, "\u0120stewards": 38030, "sta": 38031, "peria": 38032, "\u0120physiology": 38033, "\u0120bumped": 38034, "\u0120fructose": 38035, "Australian": 38036, "\u0120Metallic": 38037, "\u0120Masquerade": 38038, "arb": 38039, "\u0120promul": 38040, "\u0120downfall": 38041, "\u0120butcher": 38042, "\u0120bour": 38043, "\u0120INFORMATION": 38044, "\u0120Bis": 38045, "pects": 38046, "adena": 38047, "\u0120contemplating": 38048, "aroo": 38049, "centered": 38050, "\u0120Peaks": 38051, "Used": 38052, "\u0120modem": 38053, "\u0120genders": 38054, "\u01208000": 38055, "371": 38056, "\u0120maternity": 38057, "\u0120Raz": 38058, "\u0120rocking": 38059, "\u0120handguns": 38060, "\u0120DACA": 38061, "Autom": 38062, "\u0120Nile": 38063, "\u0120tumult": 38064, "\u0120Benefit": 38065, "\u0120Approach": 38066, "workshop": 38067, "\u0120Leaving": 38068, "Ger": 38069, "instead": 38070, "\u0120vibrations": 38071, "\u0120repositories": 38072, "497": 38073, "\u0120Aunt": 38074, "\u0120Jub": 38075, "\u0120Expedition": 38076, "Alpha": 38077, "\u0120sans": 38078, "\u0120overdue": 38079, "\u0120overcrowd": 38080, "\u0120legislatures": 38081, "\u0120paternal": 38082, "\u0120Leonardo": 38083, "\u0120expressive": 38084, "\u0120distractions": 38085, "\u0120silenced": 38086, "trust": 38087, "\u0120biking": 38088, "\u0120560": 38089, "\u0120propriet": 38090, "\u0120imposition": 38091, "\u0120conglomer": 38092, "\u0120=================================================================": 38093, "\u0120Teaching": 38094, "\u0120Yose": 38095, "intensive": 38096, "Town": 38097, "\u0120trolling": 38098, "\u0120Grac": 38099, "\u0120ASUS": 38100, "Yo": 38101, "\u0120specials": 38102, "\u0120Neph": 38103, "\u0120Godzilla": 38104, "Database": 38105, "\u0120Hegel": 38106, "\u0120272": 38107, "1976": 38108, "\u0120Gloria": 38109, "\u0120disemb": 38110, "\u0120Investigations": 38111, "\u0120Bane": 38112, "agements": 38113, "Strange": 38114, "\u0120treasury": 38115, "\u0120Plays": 38116, "\u0120undesirable": 38117, "\u0120widening": 38118, "\u0120verbally": 38119, "\u0120infancy": 38120, "\u0120cutter": 38121, "fml": 38122, "\u01202100": 38123, "prototype": 38124, "fine": 38125, "\u0120decriminal": 38126, "\u0120dysfunctional": 38127, "\u0120besie": 38128, "\u0120Ernst": 38129, "zeb": 38130, "\u0120northeastern": 38131, "\u0120aust": 38132, "porate": 38133, "\u0120Marlins": 38134, "\u0120segregated": 38135, "eworld": 38136, "\u0120Maher": 38137, "\u0120traverse": 38138, "\u0120monastery": 38139, "urgy": 38140, "Gear": 38141, "sand": 38142, "Compl": 38143, "\u0120EMP": 38144, "\u0120plent": 38145, "\u0120Mercer": 38146, "\u0120276": 38147, "TABLE": 38148, "Configuration": 38149, "Hundreds": 38150, "\u0120pric": 38151, "\u0120collaborating": 38152, "\u0120Paramount": 38153, "\u0120Cummings": 38154, "\u0120(<": 38155, "\u0120recorder": 38156, "\u0120flats": 38157, "\u0120416": 38158, "whose": 38159, "FontSize": 38160, "\u0120Orbit": 38161, "YR": 38162, "\u0120wrists": 38163, "\u0120bakery": 38164, ")}": 38165, "\u0120Bounty": 38166, "\u0120Lancaster": 38167, "\u0120endings": 38168, "according": 38169, "\u0120Salam": 38170, "easy": 38171, "755": 38172, "\u0120Burr": 38173, "\u0120Barnett": 38174, "onomous": 38175, "Union": 38176, "\u0120precedence": 38177, "\u0120Scholarship": 38178, "\u0120UX": 38179, "\u0120rollout": 38180, "\u0120boon": 38181, "alm": 38182, "\u0120Canter": 38183, "\u00e6\u00b5": 38184, "\u0120rounding": 38185, "\u0120clad": 38186, "\u0120vap": 38187, "\u0120Featured": 38188, "isations": 38189, "\u0120540": 38190, "police": 38191, "\u0120unsettling": 38192, "\u0120drifting": 38193, "\u0120Lumia": 38194, "\u0120ObamaCare": 38195, "\u0120Favor": 38196, "Hyper": 38197, "\u0120Rothschild": 38198, "\u0120Miliband": 38199, "analy": 38200, "\u0120Juliet": 38201, "Hu": 38202, "\u0120recalling": 38203, "ahead": 38204, "696": 38205, "\u0120unfavorable": 38206, "\u0120dances": 38207, "Ox": 38208, "\u0120legality": 38209, "\u0120403": 38210, "romancer": 38211, "\u0120inquire": 38212, "\u0120Moves": 38213, "\\\">": 38214, "\u0120Variant": 38215, "\u0120Messiah": 38216, "\u0120LCS": 38217, "\u0120Bah\u00c3\u00a1": 38218, "756": 38219, "\u0120eyebrow": 38220, "\u0120\u00c2\u00a5": 38221, "\u0120McF": 38222, "\u0120Forty": 38223, "Mas": 38224, "\u0120panicked": 38225, "\u0120transformations": 38226, "qq": 38227, "\u0120revolves": 38228, "ringe": 38229, "\u0120Ai": 38230, "axe": 38231, "\u0120onward": 38232, "\u0120CFR": 38233, "\u0120Bare": 38234, "login": 38235, "\u0120liquids": 38236, "\u0120decomp": 38237, "secondary": 38238, "ilan": 38239, "\u0120Convert": 38240, "amiya": 38241, "\u0120prosecuting": 38242, "\u0120\u00e2\u012b\u00a1": 38243, "\u0120Yorkers": 38244, "\u0120Byrne": 38245, "slow": 38246, "awei": 38247, "Jean": 38248, "\u0120269": 38249, "\u0120Skydragon": 38250, "\u0120\u00c3\u00a9": 38251, "\u0120Nicaragua": 38252, "\u0120Huckabee": 38253, "\u0120Highly": 38254, "\u0120amphib": 38255, "\u0120Pastor": 38256, "\u0120Lets": 38257, "\u0120blurred": 38258, "\u0120visceral": 38259, "\u0120CBO": 38260, "\u0120collaborated": 38261, "zig": 38262, "Legal": 38263, "\u0120apartheid": 38264, "\u0120brid": 38265, "\u0120preset": 38266, "\u0120DET": 38267, "\u0120AMA": 38268, "\u00d7\u0136": 38269, "arching": 38270, "aucuses": 38271, "builder": 38272, "\u0120poetic": 38273, "\u0120emulator": 38274, "\u0120Molecular": 38275, "\u0120honoring": 38276, "iseum": 38277, "\u0120tractor": 38278, "\u0120Cluster": 38279, "\u0120Calm": 38280, "aredevil": 38281, "\u0120sidewalks": 38282, "\u0120violin": 38283, "\u0120generalized": 38284, "\u0120Alec": 38285, "\u0120embargo": 38286, "\u0120fastball": 38287, "\u0120HTTPS": 38288, "\u0120Lack": 38289, "\u0120Chill": 38290, "river": 38291, "Chel": 38292, "\u0120Swarm": 38293, "\u0120Levine": 38294, "roying": 38295, "Launch": 38296, "\u0120kicker": 38297, "\u0120additive": 38298, "\u0120Deals": 38299, "Widget": 38300, "containing": 38301, "\u0120escalate": 38302, "\u0120OPEN": 38303, "\u0120tweaked": 38304, "\u0120stash": 38305, "\u0120sparks": 38306, "\u0120Essex": 38307, "\u0120Ecc": 38308, "\u0120convict": 38309, "\u0120blogging": 38310, "IER": 38311, "\u0120HL": 38312, "\u0120murderers": 38313, "759": 38314, "\u0120Hib": 38315, "\u0120depl": 38316, "\u0120Jord": 38317, "Sac": 38318, "\u0120dissect": 38319, "\u0120Howe": 38320, "osher": 38321, "\u0120customizable": 38322, "\u0120Franz": 38323, "\u0120atro": 38324, "\u00c4\u0129": 38325, "\u01200004": 38326, "\u0120outpost": 38327, "Ross": 38328, "\u0120glyphosate": 38329, "\u0120Hastings": 38330, "\u0120BEFORE": 38331, "\u0120shove": 38332, "opped": 38333, "\u0120Scala": 38334, "\u0120amulet": 38335, "anian": 38336, "\u0120exacerbated": 38337, "\u0120eater": 38338, "471": 38339, "UME": 38340, "\u0120pulp": 38341, "izontal": 38342, "\u0120Zam": 38343, "\u0120ATI": 38344, "immune": 38345, "abytes": 38346, "\u0120unnecessarily": 38347, "\u0120CAT": 38348, "\u0120Axis": 38349, "\u0120visualize": 38350, "\u00c3\u012b": 38351, "\u0120Radical": 38352, "fm": 38353, "Documents": 38354, "\u0120Forrest": 38355, "\u0120contextual": 38356, "\u0120Symbol": 38357, "\u0120tentative": 38358, "\u0120DOES": 38359, "\u0120Goods": 38360, "\u0120intermittent": 38361, "}:": 38362, "mediated": 38363, "\u0120ridicule": 38364, "\u0120atheism": 38365, "\u0120pathogens": 38366, "\u0120Mum": 38367, "\u0120reintrodu": 38368, "\u0120307": 38369, "iHUD": 38370, "\u0120flashlight": 38371, "\u0120swearing": 38372, "\u0120pengu": 38373, "Bu": 38374, "\u0120rotated": 38375, "\u0120Crane": 38376, "\u0120());": 38377, "\u0120fashionable": 38378, "\u0120endorsing": 38379, "463": 38380, ")[": 38381, "\u0120ingestion": 38382, "\u0120cooks": 38383, "\u0120950": 38384, "otomy": 38385, "\u0120Imam": 38386, "\u0120ka": 38387, "\u0120teaser": 38388, "\u0120Ghosts": 38389, "\u0120\u00e3\u0124\u00b5": 38390, "1969": 38391, "\u00cf\u0125": 38392, "ubby": 38393, "\u0120converter": 38394, "zanne": 38395, "ende": 38396, "\u0120Prepar": 38397, "\u0120Nickel": 38398, "\u0120Chimera": 38399, "him": 38400, "\u0120Tyrann": 38401, "\u0120Sabbath": 38402, "\u0120Nichols": 38403, "\u0120rapt": 38404, "ihar": 38405, "\u0120shelling": 38406, "\u0120illuminate": 38407, "\u0120dentist": 38408, "utor": 38409, "\u0120Integration": 38410, "\u0120whims": 38411, "\u0120Literary": 38412, "Beaut": 38413, "\u0120parchment": 38414, "agara": 38415, "Brand": 38416, "\u0120derog": 38417, "\u00e2\u0122\u00a6)": 38418, "\u0120Norse": 38419, "\u0120unwitting": 38420, "\u0120cuc": 38421, "\u0120borderline": 38422, "\u0120upsetting": 38423, "\u0120recourse": 38424, "\u0120draped": 38425, "\u0120Radar": 38426, "\u0120colder": 38427, "\u0120Pepsi": 38428, "iminary": 38429, "],[": 38430, "658": 38431, "Vi": 38432, "\u0120Frem": 38433, "\u0120Pes": 38434, "\u0120veterinary": 38435, "\u0120TED": 38436, "\u0120Epidem": 38437, "nova": 38438, "kid": 38439, "\u0120devout": 38440, "oct": 38441, "jad": 38442, "Moh": 38443, "\u0120PAY": 38444, "\u0120geometric": 38445, "\u0120323": 38446, "\u0120circumference": 38447, "ichick": 38448, "1975": 38449, "\u0120Yuri": 38450, "\u0120Shall": 38451, "\u0120Hover": 38452, "unin": 38453, "Spr": 38454, "\u0120graft": 38455, "\u0120Happiness": 38456, "\u0120disadvantages": 38457, "attacks": 38458, "\u0120hubs": 38459, "\u0120StarCraft": 38460, "\u00e9\u0138": 38461, "\u0120galleries": 38462, "\u0120Korra": 38463, "\u0120groceries": 38464, "\u0120Gorsuch": 38465, "\u0120rapists": 38466, "\u0120fungi": 38467, "\u0120Typhoon": 38468, "Vector": 38469, "\u0120Empress": 38470, "battle": 38471, "468": 38472, "\u0120parasite": 38473, "\u0120Bomber": 38474, "SG": 38475, "exist": 38476, "\u0120Pf": 38477, "\u0120unse": 38478, "\u0120surgeons": 38479, "Birth": 38480, "\u0120Unsure": 38481, "\u0120Printed": 38482, "\u0120Behavioral": 38483, "\u0120Aster": 38484, "Pakistan": 38485, "\u0120unethical": 38486, "\u0120sv": 38487, "\u0120IoT": 38488, "\u0120layouts": 38489, "Pain": 38490, "\u0120constants": 38491, "\u0120LW": 38492, "\u0120Bake": 38493, "\u0120towels": 38494, "\u0120deterioration": 38495, "\u0120Bolivia": 38496, "\u0120blinded": 38497, "\u0120Warden": 38498, "\u0120Mistress": 38499, "\u0120onstage": 38500, "\u0120clans": 38501, "\u0120BEST": 38502, "1960": 38503, "\u0120antique": 38504, "\u0120rhetorical": 38505, "\u0120Percy": 38506, "\u0120Rwanda": 38507, ",.": 38508, "Bruce": 38509, "\u0120traumat": 38510, "\u0120Parliamentary": 38511, "\u0120footnote": 38512, "idia": 38513, "\u0120Learned": 38514, "seeking": 38515, "genic": 38516, "\u0120dimensional": 38517, "Hide": 38518, "\u00e8\u0122\u0127": 38519, "\u0120intrigue": 38520, "inse": 38521, "\u0120leases": 38522, "\u0120apprentices": 38523, "washing": 38524, "\u01201926": 38525, "VILLE": 38526, "\u0120swoop": 38527, "scl": 38528, "\u0120bedrooms": 38529, "onics": 38530, "\u0120Crunch": 38531, "compatible": 38532, "\u0120incapac": 38533, "\u0120Yemeni": 38534, "ashtra": 38535, "zhou": 38536, "danger": 38537, "\u0120manifestations": 38538, "\u0120Demons": 38539, "AAF": 38540, "Secretary": 38541, "ACTED": 38542, "LOD": 38543, "\u0120amy": 38544, "raper": 38545, "ethnic": 38546, "417": 38547, "\u0120positives": 38548, "\u0120273": 38549, "\u0120Refugees": 38550, "\u0120usb": 38551, "\u0120Vald": 38552, "oddy": 38553, "\u0120Mahmoud": 38554, "Asia": 38555, "\u0120skulls": 38556, "\u0120Exodus": 38557, "\u0120Compet": 38558, "\u0120LIC": 38559, "\u0120Mansion": 38560, "\u0120Ame": 38561, "\u0120consolidate": 38562, "storms": 38563, "ontent": 38564, "996": 38565, "\u0120clen": 38566, "\u0120mummy": 38567, "flat": 38568, "758": 38569, "\u0120VOL": 38570, "oteric": 38571, "nen": 38572, "\u0120Minute": 38573, "Sov": 38574, "\u0120finer": 38575, "Rh": 38576, "lycer": 38577, "\u0120reinforcements": 38578, "\u0120Johannes": 38579, "\u0120Gallagher": 38580, "\u0120gymn": 38581, "Suddenly": 38582, "\u0120extortion": 38583, "kr": 38584, "iator": 38585, "Ta": 38586, "\u0120hippocampus": 38587, "NPR": 38588, "\u0120Computing": 38589, "\u0120squarely": 38590, "\u0120modelling": 38591, "\u0120Forums": 38592, "\u0120Lisp": 38593, "\u0120Krishna": 38594, "\u0120324": 38595, "\u0120rushes": 38596, "\u0120ensued": 38597, "\u0120creeping": 38598, "onte": 38599, "nai": 38600, "ilater": 38601, "\u0120Hornets": 38602, "\u0120oblivious": 38603, "INST": 38604, "559": 38605, "\u0120jeopardy": 38606, "\u0120distinguishing": 38607, "jured": 38608, "\u0120begs": 38609, "similar": 38610, "phot": 38611, "530": 38612, "\u0120Parkway": 38613, "\u0120sinks": 38614, "\u0120Hearthstone": 38615, "ibur": 38616, "\u0120Baton": 38617, "Avoid": 38618, "\u0120dancer": 38619, "\u0120magistrate": 38620, "aryn": 38621, "\u0120disturbances": 38622, "\u0120Romero": 38623, "\u0120paraph": 38624, "\u0120mischief": 38625, "\u00e2\u0138\u0135": 38626, "\u0120Sharia": 38627, "\u0120urinary": 38628, "route": 38629, "ivas": 38630, "fitted": 38631, "\u0120ejected": 38632, "\u0120Albuquerque": 38633, "\u0120470": 38634, "\u0120irritated": 38635, "\u0120Zip": 38636, "\u0120Biol": 38637, "\u00c3\u012f": 38638, "\u0120denounce": 38639, "\u0120binaries": 38640, "\u0120Verse": 38641, "\u0120oppos": 38642, "\u0120Kendrick": 38643, "\u0120GPL": 38644, "\u0120spew": 38645, "\u0120Elijah": 38646, "\u0120Eas": 38647, "\u0120drifted": 38648, "sofar": 38649, "\u0120annoyance": 38650, "\u0120BET": 38651, "474": 38652, "\u0120Strongh": 38653, "itates": 38654, "\u0120Cognitive": 38655, "ophone": 38656, "\u0120Identification": 38657, "ocrine": 38658, "connection": 38659, "\u0120boxer": 38660, "\u0120ASD": 38661, "\u0120Areas": 38662, "Yang": 38663, "tch": 38664, "ullah": 38665, "\u0120deceive": 38666, "Combat": 38667, "episode": 38668, "crete": 38669, "Witness": 38670, "\u0120condolences": 38671, "htar": 38672, "\u0120heals": 38673, "\u0120buckets": 38674, "\u0120LAW": 38675, "Blu": 38676, "\u0120slab": 38677, "\u0120ORDER": 38678, "ocl": 38679, "atton": 38680, "\u0120Stevenson": 38681, "\u0120Ginger": 38682, "\u0120Friendly": 38683, "\u0120Vanderbilt": 38684, "spirit": 38685, "igl": 38686, "\u0120Regarding": 38687, "\u0120PROG": 38688, "\u0120sealing": 38689, "starting": 38690, "\u0120cardinal": 38691, "\u0120Vec": 38692, "\u0120Beir": 38693, "\u0120milliseconds": 38694, "weak": 38695, "perse": 38696, "\u0120sterile": 38697, "\u0120Contemporary": 38698, "\u0120Phant": 38699, "\u0120Clo": 38700, "\u0120outp": 38701, "\u0120exiled": 38702, "\u0120277": 38703, "\u0120selfie": 38704, "\u0120manic": 38705, "\u0120nano": 38706, "terms": 38707, "Alexander": 38708, "\u0120resolves": 38709, "\u0120millennia": 38710, "\u0120explodes": 38711, "\u0120constellation": 38712, "\u0120adultery": 38713, "motion": 38714, "DOC": 38715, "\u0120broadcasters": 38716, "\u0120kindergarten": 38717, "\u0120Mayweather": 38718, "\u0120Eco": 38719, "icho": 38720, "\u0120287": 38721, "laun": 38722, "\u0120mute": 38723, "\u0120discreet": 38724, "\u0120preschool": 38725, "\u0120preempt": 38726, "Delete": 38727, "\u0120Freed": 38728, "Pi": 38729, "HK": 38730, "\u0120blocker": 38731, "\u0120Cumber": 38732, "\u0120wrought": 38733, "dating": 38734, "\u0120insurer": 38735, "\u0120quotas": 38736, "\u0120preached": 38737, "\u0120eviction": 38738, "\u0120Regina": 38739, "\u0120Pens": 38740, "\u0120seventeen": 38741, "\u0120Nass": 38742, "Dick": 38743, "\u0120folds": 38744, "\u0120dotted": 38745, "\u0120Aad": 38746, "Universal": 38747, "\u0120pizz": 38748, "\u0120Guru": 38749, "\u0120soils": 38750, "\u0120novice": 38751, "\u0120Neander": 38752, "\u0120stool": 38753, "\u0120detonated": 38754, "\u0120Pikachu": 38755, "\u0120Massive": 38756, "IVER": 38757, "\u0120Abdel": 38758, "\u0120subdued": 38759, "\u0120tallest": 38760, "\u0120precarious": 38761, "\u0120ay": 38762, "rification": 38763, "\u0120Obj": 38764, "cale": 38765, "\u0120unquestion": 38766, "culosis": 38767, "adas": 38768, "igrated": 38769, "Days": 38770, "\u0120queens": 38771, "\u0120Gazette": 38772, "\u0120Colour": 38773, "\u0120Bowman": 38774, "\u0120JJ": 38775, "\u00c3\u00afve": 38776, "\u0120dominates": 38777, "Student": 38778, "\u0120mu": 38779, "\u0120backlog": 38780, "\u0120Electro": 38781, "Truth": 38782, "483": 38783, "\u0120condensed": 38784, "rules": 38785, "\u0120Conspiracy": 38786, "\u0120acronym": 38787, "handled": 38788, "\u0120Matte": 38789, "jri": 38790, "\u0120Impossible": 38791, "lude": 38792, "creation": 38793, "\u0120warmed": 38794, "\u0120Slave": 38795, "\u0120misled": 38796, "\u0120ferment": 38797, "\u0120Kah": 38798, "inki": 38799, "keleton": 38800, "cyl": 38801, "\u0120Karin": 38802, "Hunter": 38803, "Register": 38804, "\u0120Surrey": 38805, "\u0120stares": 38806, "\u0120Width": 38807, "\u0120Nay": 38808, "\u0120Ski": 38809, "\u0120blacklist": 38810, "ucket": 38811, "\u0120expulsion": 38812, "imet": 38813, "\u0120retweet": 38814, "vantage": 38815, "Feature": 38816, "\u0120troopers": 38817, "\u0120homers": 38818, "969": 38819, "\u0120contingency": 38820, "\u0120WTC": 38821, "\u0120Brewer": 38822, "foreign": 38823, "Ware": 38824, "Solar": 38825, "\u0120undue": 38826, "REC": 38827, "ulnerable": 38828, "pathic": 38829, "\u0120Boise": 38830, "\u0120322": 38831, "\u0120aroused": 38832, "\u0120Ying": 38833, "\u00e4\u00b8\u012f": 38834, "ueless": 38835, "\u0120pas": 38836, "\u0120morp": 38837, "\u0120floral": 38838, "Express": 38839, "udging": 38840, "kB": 38841, "\u0120Granted": 38842, "\u00d8\u00af": 38843, "\u0120Micha": 38844, "\u0120Gothic": 38845, "\u0120SPECIAL": 38846, "\u0120Ricardo": 38847, "Fran": 38848, "\u0120administering": 38849, "620": 38850, "pora": 38851, "\u0120\u00c2\u00ae": 38852, "\u0120compromises": 38853, "\u0120bitten": 38854, "Accept": 38855, "Thirty": 38856, "\u00d0\u00b2": 38857, "\u0120materially": 38858, "\u0120Terr": 38859, "igmatic": 38860, "chains": 38861, "\u0120dove": 38862, "stadt": 38863, "Marvel": 38864, "FAULT": 38865, "\u0120windshield": 38866, "\u0120336": 38867, "adier": 38868, "\u0120swapping": 38869, "\u0120flawless": 38870, "\u0120Predator": 38871, "\u0120Michele": 38872, "\u0120propulsion": 38873, "\u0120Psychic": 38874, "\u0120assigning": 38875, "\u0120fabrication": 38876, "\u0120barley": 38877, "lust": 38878, "\u0120towering": 38879, "\u0120altercation": 38880, "\u0120Bentley": 38881, "Sphere": 38882, "\u0120tuna": 38883, "\u0120Classes": 38884, "Freedom": 38885, "uner": 38886, "Lady": 38887, "voice": 38888, "\u0120coolest": 38889, "orr": 38890, "\u0120palp": 38891, "${": 38892, "\u0120hysteria": 38893, "\u0120Metatron": 38894, "pants": 38895, "\u0120spawning": 38896, "Experts": 38897, "\u0120Investors": 38898, "\u0120Anarchy": 38899, "\u0120shrunk": 38900, "\u0120Victim": 38901, "\u0120289": 38902, "\u0120ecstasy": 38903, "\u0120Binding": 38904, "585": 38905, "\u0120Melody": 38906, "578": 38907, "otally": 38908, "\u0120Etsy": 38909, "liga": 38910, "\u0120applauded": 38911, "\u0120sweating": 38912, "\u0120redistributed": 38913, "\u0120popcorn": 38914, "\u0120seminal": 38915, "fur": 38916, "\u0120Neuroscience": 38917, "Rand": 38918, "\u0120Ost": 38919, "\u0120Madden": 38920, "\u0120Increasing": 38921, "\u0120Dawkins": 38922, "\u0120Subway": 38923, "\u0120arsen": 38924, "conserv": 38925, "BUR": 38926, "\u0120spiked": 38927, "\u0120Lyft": 38928, "\u0120Imperium": 38929, "\u0120Dropbox": 38930, "\u0120favoured": 38931, "\u0120encompasses": 38932, "ghost": 38933, "\u0120inspires": 38934, "\u0120burgeoning": 38935, "\u0120Yoshi": 38936, "\u0120Vertical": 38937, "\u0120Auditor": 38938, "\u0120intending": 38939, "\u0120filibuster": 38940, "Bloom": 38941, "fac": 38942, "\u0120Cavs": 38943, "igning": 38944, "\u0120coworkers": 38945, "\u0120Barbarian": 38946, "remember": 38947, "FLAG": 38948, "\u0120auditory": 38949, "asonry": 38950, "College": 38951, "\u0120muted": 38952, "gemony": 38953, "obin": 38954, "\u0120Psycho": 38955, "968": 38956, "\u0120lavish": 38957, "\u0120hierarchical": 38958, "\u0120Drone": 38959, "ouk": 38960, "\u0120crippled": 38961, "\u0120Maxim": 38962, "Slot": 38963, "\u0120quiz": 38964, "\u0120Vid": 38965, "ifling": 38966, "\u0120archaeologists": 38967, "\u0120abandonment": 38968, "dial": 38969, "leon": 38970, "\u0120Fas": 38971, "Ted": 38972, "\u0120raspberry": 38973, "\u0120maneuvers": 38974, "\u0120behaviours": 38975, "\u0120insure": 38976, "\u0120remod": 38977, "Switch": 38978, "hoe": 38979, "\u0120spaced": 38980, "\u0120affordability": 38981, "\u0120Fern": 38982, "notation": 38983, "\u0120Balanced": 38984, "\u0120occupies": 38985, "environment": 38986, "\u0120necklace": 38987, "\u0120sedan": 38988, "FU": 38989, "\u0120Bravo": 38990, "\u0120abusers": 38991, "\u0120Anita": 38992, "metadata": 38993, "\u0120Github": 38994, "aito": 38995, "\u0120Faster": 38996, "\u0120Wasserman": 38997, "\u0120Flesh": 38998, "\u0120thorn": 38999, "rarily": 39000, "\u0120Merry": 39001, "wine": 39002, "\u0120populace": 39003, "\u0120Lann": 39004, "\u0120repairing": 39005, "\u0120psyche": 39006, "\u0120modulation": 39007, "awaru": 39008, "\u00e2\u0122\u012d\u00e2\u0122\u012d": 39009, "arij": 39010, "\u0120decorations": 39011, "\u0120apologise": 39012, "\u0120Garg": 39013, "apply": 39014, "\u0120giveaway": 39015, "\u0120Flan": 39016, "\u0120Wyatt": 39017, "Uber": 39018, "\u0120authorised": 39019, "\u0120Moral": 39020, "HAHAHAHA": 39021, "activate": 39022, "\u0120torpedo": 39023, "\u0120FAR": 39024, "\u0120amassed": 39025, "\u0120Aram": 39026, "arkin": 39027, "\u0120Victims": 39028, "stab": 39029, "\u0120om": 39030, "\u0120ECO": 39031, "\u0120opioids": 39032, "\u0120purposely": 39033, "\u0120Vest": 39034, "\u0120erg": 39035, "atan": 39036, "\u0120Surgery": 39037, "\u0120correcting": 39038, "\u0120Ortiz": 39039, "\u0120Beet": 39040, "\u0120revoke": 39041, "\u0120freeway": 39042, "\u0120Higgins": 39043, "Fail": 39044, "\u0120Farms": 39045, "\u0120ATP": 39046, "hound": 39047, "\u0120poking": 39048, "\u0120Communists": 39049, "monster": 39050, "imentary": 39051, "\u0120unlocking": 39052, "\u0120unfit": 39053, "weed": 39054, "enario": 39055, "atical": 39056, "\u0120Enlightenment": 39057, "\u0120NG": 39058, "\u0120Compensation": 39059, "deen": 39060, "\u0120Widow": 39061, "\u0120Cindy": 39062, "\u0120Afterwards": 39063, "\u01206000": 39064, "ikhail": 39065, "agically": 39066, "\u0120ratified": 39067, "\u0120casualty": 39068, "HOME": 39069, "psey": 39070, "fee": 39071, "\u0120sparkling": 39072, "\u0120d\u00c3\u00a9": 39073, "\u0120concerted": 39074, "Catal": 39075, "\u0120complying": 39076, "\u0120Ares": 39077, "\u0120Dent": 39078, "Shut": 39079, "\u0120skim": 39080, "administ": 39081, "\u0120hostilities": 39082, "\u0120Gins": 39083, "\u0120608": 39084, "\u0120muddy": 39085, "\u0120McInt": 39086, "\u0120Decay": 39087, "525": 39088, "\u0120conspicuous": 39089, "\u0120Exposure": 39090, "\u0120rescind": 39091, "\u0120wearable": 39092, "\u0120328": 39093, "ourmet": 39094, "ahs": 39095, "\u0120Robots": 39096, "\u0120eclips": 39097, "instance": 39098, "\u0120REPORT": 39099, "\u0120Appl": 39100, "030": 39101, "\u0120Skies": 39102, "0100": 39103, "\u0120fallacy": 39104, "Socket": 39105, "\u0120Receiver": 39106, "\u0120solves": 39107, "\u0120Butterfly": 39108, "\u0120Shopping": 39109, "\u0120FIRE": 39110, "654": 39111, "Medic": 39112, "\u0120singers": 39113, "\u0120Needless": 39114, "''''": 39115, "ishers": 39116, "\u0120Dive": 39117, "588": 39118, "\u0120selectively": 39119, "\u0120clumsy": 39120, "889": 39121, "\u0120purchaser": 39122, "earned": 39123, "ardy": 39124, "\u0120benefiting": 39125, "english": 39126, "\u0120yielding": 39127, "\u0120Pour": 39128, "\u0120spinach": 39129, "\u0120delve": 39130, "\u0120Crom": 39131, "610": 39132, "\u0120exporting": 39133, "\u0120MAKE": 39134, "\u0120263": 39135, "\u0120grop": 39136, "\u0120envoy": 39137, "\u0120Inquiry": 39138, "\u0120Luigi": 39139, "dry": 39140, "\u0120Turing": 39141, "ThumbnailImage": 39142, "\u0120Variety": 39143, "\u0120facet": 39144, "\u0120fluffy": 39145, "\u0120excerpts": 39146, "\u0120shorth": 39147, "\u0120Olsen": 39148, "CLUD": 39149, "\u0120reliant": 39150, "\u0120UNC": 39151, "Tour": 39152, "\u0120bathing": 39153, "Company": 39154, "\u0120globalization": 39155, "Pred": 39156, "\u0120Malfoy": 39157, "\u0120hoc": 39158, "jam": 39159, "crafted": 39160, "\u0120Bonds": 39161, "\u0120Kissinger": 39162, "England": 39163, "\u0120orderly": 39164, "catentry": 39165, "\u0120261": 39166, "\u0120exchanging": 39167, "\u0120Intent": 39168, "\u0120Amendments": 39169, "DOM": 39170, "\u0120stout": 39171, "\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142\u00c2\u0142": 39172, "\u0120Airbus": 39173, "\u0120278": 39174, "hyde": 39175, "Poll": 39176, "ItemThumbnailImage": 39177, "\u0120loopholes": 39178, "\u0120Pillar": 39179, "\u0120explor": 39180, "Stretch": 39181, "Apart": 39182, "\u0120unmarried": 39183, "Limit": 39184, "\u0120Transformers": 39185, "\u0120intellectually": 39186, "uncture": 39187, "1800": 39188, "\u0120darn": 39189, "Brazil": 39190, "\u0120leftover": 39191, "berus": 39192, "fred": 39193, "Minecraft": 39194, "326": 39195, "\u0120Forms": 39196, "\u0120proofs": 39197, "\u0120Designed": 39198, "\u0120indexes": 39199, "\u0120Suppose": 39200, "EMS": 39201, "\u0120Loving": 39202, "\u0120Bonnie": 39203, "imating": 39204, "OTUS": 39205, "\u0120conductor": 39206, "\u0120behaved": 39207, "\u0120Fren": 39208, "\u0120synerg": 39209, "\u0120millennium": 39210, "\u0120catering": 39211, "\u0120Lauder": 39212, "Wr": 39213, "\u0120Yiannopoulos": 39214, "\u0120ATF": 39215, "\u0120enslaved": 39216, "\u0120awakened": 39217, "DVD": 39218, "\u0120EDITION": 39219, "\u0120Concert": 39220, "\u0120Challenger": 39221, "\u0120Haku": 39222, "umeric": 39223, "\u0120deprecated": 39224, "\u0120SHAR": 39225, "412": 39226, "\u0120dystop": 39227, "\u0120trembling": 39228, "\u0120dreaded": 39229, "\u0120Spac": 39230, "padding": 39231, "Repl": 39232, "\u0120Garrison": 39233, "Mini": 39234, "\u0120unparalleled": 39235, "amar": 39236, "URRENT": 39237, "wreck": 39238, "certain": 39239, "tal": 39240, "\u0120CLS": 39241, "appings": 39242, "\u0120sensed": 39243, "\u0120fencing": 39244, "\u0120Paso": 39245, "\u0120Desk": 39246, "\u0120scoff": 39247, "\u0120contemplate": 39248, "\u0120Liga": 39249, "liquid": 39250, "757": 39251, "\u0120apprentice": 39252, "\u0120UCHIJ": 39253, "570": 39254, "\u0120Thousand": 39255, "\u0120Illum": 39256, "\u0120championed": 39257, "\u00e3\u0124\u012e": 39258, "\u0120electors": 39259, "\u0120398": 39260, "\u0120Hancock": 39261, "rounded": 39262, "\u0120JOHN": 39263, "\u0120unsatisf": 39264, "\u0120qualifier": 39265, "\u0120Gadget": 39266, "ENE": 39267, "\u0120deadliest": 39268, "\u0120Plants": 39269, "\u0120ions": 39270, "\u0120accents": 39271, "\u0120tweaking": 39272, "\u0120shaved": 39273, "FREE": 39274, "\u0120Chaser": 39275, "Against": 39276, "960": 39277, "\u0120methamphetamine": 39278, "\u0120normalized": 39279, "\u0120$\\": 39280, "\u0120Precision": 39281, "\u0120Guam": 39282, "\u0120choked": 39283, "\u0120XII": 39284, "\u0120Casting": 39285, "Torrent": 39286, "\u0120scalp": 39287, "\u0120Jaguar": 39288, "wit": 39289, "\u0120semic": 39290, "ixie": 39291, "\u0120Gould": 39292, "\u0120confines": 39293, "Nusra": 39294, "\u0120Lon": 39295, "\u0120Jugg": 39296, "ycle": 39297, "\u0120Codec": 39298, "Egypt": 39299, "\u0120restrain": 39300, "\u0120Aliens": 39301, "\u0120choking": 39302, "\u0120Dunk": 39303, "\u0120Bella": 39304, "abc": 39305, "\u0120slang": 39306, "\u0120neurotrans": 39307, "sav": 39308, "\u0120empowerment": 39309, "\u00e2\u0128\u0134": 39310, "\u0120climbers": 39311, "\u0120Mim": 39312, "\u0120Fra": 39313, "rosse": 39314, "Capital": 39315, "\u0120Cthulhu": 39316, "Interface": 39317, "\u0120proficient": 39318, "\u0120INTO": 39319, "\u0120318": 39320, "rontal": 39321, "580": 39322, "\u0120Despair": 39323, "Kenn": 39324, "\u0120scrimmage": 39325, "\u0120Coat": 39326, "asions": 39327, "\u0120wallpaper": 39328, "\u0120Jol": 39329, "\u0120resurgence": 39330, "\u0120antiv": 39331, "\u0120Balls": 39332, "\u00b2\u00be": 39333, "\u0120buffers": 39334, "\u0120subsystem": 39335, "\u0120Stellar": 39336, "\u0120Lung": 39337, "AIDS": 39338, "\u0120eradicate": 39339, "\u0120blatantly": 39340, "\u0120behaves": 39341, "\u0120Nun": 39342, "\u0120antics": 39343, "export": 39344, "DEV": 39345, "wb": 39346, "\u0120php": 39347, "\u0120Integrity": 39348, "\u0120explorer": 39349, "\u0120revolving": 39350, "authored": 39351, "gans": 39352, "\u0120bask": 39353, "\u0120asynchronous": 39354, "\u00e5\u012f": 39355, "THING": 39356, "698": 39357, "Gene": 39358, "\u0120Racer": 39359, "\u0120Nico": 39360, "issued": 39361, "\u0120sermon": 39362, "possibly": 39363, "\u0120sizeof": 39364, "\u0120entrepreneurial": 39365, "oxin": 39366, "\u0120Minerva": 39367, "\u0120platoon": 39368, "nos": 39369, "riks": 39370, "AUT": 39371, "\u0120Avalanche": 39372, "\u0120Desc": 39373, "\u0133\u00e5\u00a3\u00ab": 39374, "\u0120Poc": 39375, "\u0120conferred": 39376, "\u00ce\u00bb": 39377, "\u0120patched": 39378, "FBI": 39379, "662": 39380, "\u0120fractures": 39381, "\u0120detects": 39382, "\u0120dedicate": 39383, "\u0120constituent": 39384, "\u0120cosmos": 39385, "WT": 39386, "\u0120sweats": 39387, "\u0120sprung": 39388, "bara": 39389, "solid": 39390, "\u0120unsus": 39391, "\u0120bulky": 39392, "\u0120Philippe": 39393, "\u0120Fenrir": 39394, "\u0120therapists": 39395, "oreal": 39396, "^^^^": 39397, "\u0120totaled": 39398, "\u0120booze": 39399, "\u0120RPC": 39400, "Prosecutors": 39401, "\u0120diseng": 39402, "\u0120Shared": 39403, "\u0120motorcycles": 39404, "\u0120inventions": 39405, "\u0120lettuce": 39406, "\u0120Merge": 39407, "\u0120JC": 39408, "\u0120spirituality": 39409, "\u0120WARNING": 39410, "\u0120unlucky": 39411, "\u0120Tess": 39412, "\u0120tongues": 39413, "\u0120DUI": 39414, "Tumblr": 39415, "\u0120leans": 39416, "\u0120invaders": 39417, "\u0120canopy": 39418, "\u0120Hurricanes": 39419, "\u0120Bret": 39420, "\u0120APPLIC": 39421, "idine": 39422, "ickle": 39423, "Regarding": 39424, "\u0120veggies": 39425, "\u0120ejac": 39426, "juven": 39427, "Fish": 39428, "DEM": 39429, "\u0120Dino": 39430, "Throw": 39431, "\u0120Checking": 39432, "beard": 39433, "(&": 39434, "\u0120jails": 39435, "\u0120hr": 39436, "transfer": 39437, "ivating": 39438, "\u0120fleets": 39439, "\u0120Imag": 39440, "\u0120McDonnell": 39441, "\u0120snippet": 39442, "Isa": 39443, "\u0120Chatt": 39444, "\u0120Stain": 39445, "\u0120SetFontSize": 39446, "\u0120Oy": 39447, "\u0120Mathematics": 39448, "494": 39449, "\u0120electroly": 39450, "\u0120Gott": 39451, "\u0120Bras": 39452, "BOOK": 39453, "\u0120Finger": 39454, "dump": 39455, "\u0120mutants": 39456, "\u0120rentals": 39457, "\u0120intertw": 39458, "\u0120creek": 39459, "aila": 39460, "Brother": 39461, "\u0120Discord": 39462, "pee": 39463, "rawler": 39464, "\u0120carp": 39465, "\u0120279": 39466, "\u00e3\u0124\u00b7\u00e3\u0125\u00a3": 39467, "relations": 39468, "\u0120contrasts": 39469, "Column": 39470, "\u0120reconnaissance": 39471, "\u0120unknow": 39472, "\u0120looting": 39473, "\u0120regulates": 39474, "\u0120optimum": 39475, "\u0120Cherokee": 39476, "\u0120Ary": 39477, "Latest": 39478, "\u0120roadside": 39479, "\u0120danced": 39480, "\u0120Unicorn": 39481, "Acknowled": 39482, "\u0120uncontroll": 39483, "\u0120MUS": 39484, "atio": 39485, "chance": 39486, "haven": 39487, "VALUE": 39488, "\u0120favourites": 39489, "\u0120ceremonial": 39490, "binary": 39491, "peed": 39492, "woods": 39493, "EMP": 39494, "\u0120vascular": 39495, "\u0120contemplated": 39496, "\u0120barren": 39497, "\u0120LIST": 39498, "Yellow": 39499, "osponsors": 39500, "\u0120whisky": 39501, "\u0120Mamm": 39502, "\u0120DeVos": 39503, "minimum": 39504, "Hung": 39505, "442": 39506, "Pic": 39507, "\u0120Snapdragon": 39508, "776": 39509, "\u0120carving": 39510, "\u0120undecided": 39511, "\u0120advantageous": 39512, "\u0120palms": 39513, "\u0120AQ": 39514, "\u0120starch": 39515, "Loop": 39516, "\u0120paddle": 39517, "\u0120flaming": 39518, "\u0120Horizons": 39519, "Animation": 39520, "boost": 39521, "\u0120probabilities": 39522, "\u0120Mish": 39523, "\u0120exodus": 39524, "\u0120Editorial": 39525, "\u0120fungus": 39526, "\u0120dissenting": 39527, "\u0120Delicious": 39528, "rogram": 39529, "\u0120Dyn": 39530, "disk": 39531, "tom": 39532, "\u0120fabrics": 39533, "\u0120Cove": 39534, "\u0120Bans": 39535, "\u0120soften": 39536, "\u0120CONS": 39537, "\u0120ineligible": 39538, "\u0120estimating": 39539, "\u0120Lexington": 39540, "practice": 39541, "ofi": 39542, "\u0120shedding": 39543, "\u0120Nope": 39544, "\u0120breathed": 39545, "\u0120Corinthians": 39546, "yne": 39547, "eki": 39548, "Bull": 39549, "\u0120attaching": 39550, "reenshots": 39551, "\u0120analyse": 39552, "\u0120Kappa": 39553, "\u0120unsustainable": 39554, "\u0120interpol": 39555, "anky": 39556, "hemer": 39557, "\u0120protagonists": 39558, "\u0120formatted": 39559, "\u0120Bryce": 39560, "\u0120Achilles": 39561, "\u0120Abedin": 39562, "shock": 39563, "\u0120bum": 39564, "bos": 39565, "qua": 39566, "\u0120Warn": 39567, "qt": 39568, "\u0120Diabetes": 39569, "864": 39570, "\u0120Invisible": 39571, "\u0120vanish": 39572, "\u0120transmitting": 39573, "\u0120murky": 39574, "\u0120Fei": 39575, "\u0120awaited": 39576, "\u0120Jurassic": 39577, "ummies": 39578, "\u0120menacing": 39579, "gall": 39580, "Cath": 39581, "Built": 39582, "ildo": 39583, "\u0120Votes": 39584, "\u0120ont": 39585, "\u0120munitions": 39586, "\u0120Freem": 39587, "\u00c3\u0143n": 39588, "\u0120decency": 39589, "lopp": 39590, "ieved": 39591, "\u0120Gord": 39592, "\u0120unthinkable": 39593, "\u0120Newsweek": 39594, "\u0120321": 39595, "Heat": 39596, "\u0120presenter": 39597, "jiang": 39598, "\u0120plank": 39599, "\u0120Avalon": 39600, "\u0120benz": 39601, "\u0120Rout": 39602, "\u0120slamming": 39603, "\u0120Dai": 39604, "outer": 39605, "\u0120Cookie": 39606, "\u0120Alicia": 39607, "gey": 39608, "\u0120vanity": 39609, "\u0120owl": 39610, "\u00e1\u00b5": 39611, "tested": 39612, "\u0120Awakens": 39613, "\u0120canv": 39614, "\u0120blindly": 39615, "\u0120Ridley": 39616, "\u0120Emails": 39617, "Requires": 39618, "\u0120Serbian": 39619, "ographed": 39620, "iframe": 39621, "eteria": 39622, "\u0120alternating": 39623, "quiet": 39624, "\u0120sociology": 39625, "\u0120Unlock": 39626, "\u0120Communism": 39627, "\u0120ops": 39628, "\u0120attribution": 39629, "\u0120abduction": 39630, "\u0120Abram": 39631, "\u0120sidelined": 39632, "\u0120BOOK": 39633, "\u0120refining": 39634, "\u0120Feeling": 39635, "\u0120Oslo": 39636, "\u0120Pruitt": 39637, "rack": 39638, "angible": 39639, "\u0120cautiously": 39640, "\u0120MARK": 39641, "eeds": 39642, "Mouse": 39643, "\u0120Steph": 39644, "\u0120Pair": 39645, "Sab": 39646, "997": 39647, "\u0120Baal": 39648, "Bec": 39649, "\u0120comma": 39650, "\u0120Pall": 39651, "\u0120Gael": 39652, "\u0120misunderstand": 39653, "\u0120Pesh": 39654, "Orderable": 39655, "\u0120dismal": 39656, "\u0120Shiny": 39657, "%\"": 39658, "\u0120realistically": 39659, "\u0120patio": 39660, "\u0120Gw": 39661, "\u0120Virtue": 39662, "\u0120exhausting": 39663, "whatever": 39664, "ophys": 39665, "yip": 39666, "418": 39667, "Adjust": 39668, "\u0120Waiting": 39669, "esson": 39670, "\u0120Mazda": 39671, "\u0120Dozens": 39672, "\u0120streamlined": 39673, "\u0120incompetence": 39674, "\u0120Meth": 39675, "\u0120ethos": 39676, "ONES": 39677, "\u0120incentiv": 39678, "\u0120gritty": 39679, "\u0120Butcher": 39680, "Header": 39681, "\u0120exponential": 39682, "\u00c3\u0141": 39683, "\u0120correlate": 39684, "\u0120consensual": 39685, "sounding": 39686, "Ring": 39687, "Origin": 39688, "\u0120conclusive": 39689, "feet": 39690, "acly": 39691, "\u0120Fernandez": 39692, "Buyable": 39693, "\u0120ducks": 39694, "auntlets": 39695, "\u0120elong": 39696, "\u0120286": 39697, "\u0120simul": 39698, "Gas": 39699, "\u0120Kirst": 39700, "\u0120protr": 39701, "\u0120Robo": 39702, "\u0120AoE": 39703, "opol": 39704, "\u0120psychologically": 39705, "spin": 39706, "ilaterally": 39707, "\u0120Conrad": 39708, "Wave": 39709, "441": 39710, "\u0120Advertisement": 39711, "\u0120Harmon": 39712, "\u0120Oriental": 39713, "isSpecial": 39714, "\u0120presumptive": 39715, "\u0120wil": 39716, "\u0120Kier": 39717, "nea": 39718, "\u0120ppm": 39719, "\u0120harbour": 39720, "\u0120Wired": 39721, "company": 39722, "\u0120coroner": 39723, "aturdays": 39724, "\u0120Proud": 39725, "\u0120NEXT": 39726, "\u0120Flake": 39727, "valued": 39728, "ceiver": 39729, "\u0120fraught": 39730, "\u0120casing": 39731, "\u0120runaway": 39732, "\u0120gin": 39733, "\u0120Laurent": 39734, "\u0120Harlem": 39735, "\u0120Curiosity": 39736, "quished": 39737, "\u0120neuroscience": 39738, "\u0120Hulu": 39739, "\u0120borrower": 39740, "\u0120petitioner": 39741, "\u0120Cooldown": 39742, "WARD": 39743, "\u0120invoking": 39744, "confidence": 39745, "Forward": 39746, "\u0120sts": 39747, "population": 39748, "DeliveryDate": 39749, "Film": 39750, "\u0120Cov": 39751, "quickShip": 39752, "quickShipAvailable": 39753, "primary": 39754, "isSpecialOrderable": 39755, "inventoryQuantity": 39756, "channelAvailability": 39757, "BOX": 39758, "\u0120Multiplayer": 39759, "\u0120Jenner": 39760, "778": 39761, "\u0120Md": 39762, "\u0120~/.": 39763, "MN": 39764, "\u0120childish": 39765, "\u0120antioxidant": 39766, "\u0120Chromebook": 39767, "\u0120274": 39768, "\u0120screenplay": 39769, "\u0120adventurous": 39770, "\u0120Relationship": 39771, "responsive": 39772, "mington": 39773, "\u0120cornerstone": 39774, "\u0120Fey": 39775, "FIR": 39776, "\u0120rookies": 39777, "\u0120Featuring": 39778, "\u0120originate": 39779, "\u0120electrodes": 39780, "antes": 39781, "\u0120scriptures": 39782, "\u0120glued": 39783, "\u0120discontent": 39784, "\u0120afflicted": 39785, "layout": 39786, "Brave": 39787, "\u0120mosa": 39788, "\u0120Quantity": 39789, "\u0120Hik": 39790, "winner": 39791, "Hours": 39792, "\u0120entail": 39793, "\u0120Cells": 39794, "ologue": 39795, "\u0120vil": 39796, "\u0120preacher": 39797, "\u0120decorative": 39798, "different": 39799, "\u0120prejudices": 39800, "\u0120Smoking": 39801, "\u0120Nottingham": 39802, "soType": 39803, "\u0120rhythms": 39804, "\u0120Alph": 39805, "blast": 39806, "Steel": 39807, "\u0120Danielle": 39808, "\u0120strife": 39809, "\u0120rematch": 39810, "soDeliveryDate": 39811, "\u0120Fork": 39812, "trip": 39813, "olulu": 39814, "heses": 39815, "CG": 39816, "\u0120POLITICO": 39817, "osta": 39818, "\u0120Drift": 39819, "\u00e9\u00be\u012f\u00e5\u00a5": 39820, "\u00e9\u00be\u012f\u00e5\u00a5\u0133\u00e5\u00a3\u00ab": 39821, "\u0120vetting": 39822, "\u0120Jinping": 39823, "\u0120Recession": 39824, "Minor": 39825, "\u0120Fraud": 39826, "enfranch": 39827, "\u0120convened": 39828, "\u0120NAACP": 39829, "\u0120Millions": 39830, "\u0120Farming": 39831, "\u0120Woo": 39832, "\u0120Flare": 39833, "rito": 39834, "immigrant": 39835, "\u0120vacancy": 39836, "\u0120HEAD": 39837, "\u0120Vaj": 39838, "egal": 39839, "\u0120Vigil": 39840, "Study": 39841, "\u0120ruining": 39842, "\u0120racks": 39843, "\u0120heater": 39844, "\u0120Randolph": 39845, "\u0120Brush": 39846, "\u0120Tir": 39847, "\u00d8\u00a8": 39848, "\u0120cov": 39849, "%]": 39850, "\u0120recounts": 39851, "\u0120OPT": 39852, "\u0120Melt": 39853, "\u0120truce": 39854, "\u0120casinos": 39855, "\u0120crusade": 39856, "\u0120carnage": 39857, "\u0120stripe": 39858, "\u0120Kyl": 39859, "Textures": 39860, "\u0120698": 39861, "\u0120proclamation": 39862, "\u0120goodies": 39863, "\u0120..........": 39864, "proclaimed": 39865, "Polit": 39866, "\u0120topical": 39867, "\u0120specialize": 39868, "\u0120Amin": 39869, "gm": 39870, "\u0120anchored": 39871, "\u0120bearings": 39872, "sample": 39873, "\u0120Highland": 39874, "\u0120Autism": 39875, "\u0120mercenary": 39876, "\u0120interviewer": 39877, "LER": 39878, "\u0120Somers": 39879, "\u0120embryo": 39880, "\u0120Assy": 39881, "\u0120281": 39882, "\u0120Editing": 39883, "\u0120Chosen": 39884, "660": 39885, "\u0120pci": 39886, "\u0120Thunderbolt": 39887, "BILL": 39888, "\u0120chuckled": 39889, "jriwal": 39890, "hof": 39891, "\u0120earthly": 39892, "(){": 39893, "independence": 39894, "\u0120dispers": 39895, "\u0120Vendor": 39896, "\u0120Gareth": 39897, "\u0120pals": 39898, "Penn": 39899, "\u0120Submit": 39900, "icum": 39901, "Thu": 39902, "\u0120clandestine": 39903, "\u0120cannibal": 39904, "\u0120Clerk": 39905, "EStream": 39906, "galitarian": 39907, "\u00e2\u013b\u00a5": 39908, "gew": 39909, "\u0120horrend": 39910, "\u0120Lov": 39911, "\u0120Reaction": 39912, "ocrin": 39913, "Classic": 39914, "\u0120echoing": 39915, "\u0120disclosing": 39916, "\u0120Insight": 39917, "ogun": 39918, "\u0120Incarn": 39919, "uploads": 39920, "pperc": 39921, "guyen": 39922, "\u01201901": 39923, "\u0120Bars": 39924, "687": 39925, "\u0120bribes": 39926, "\u0120Fresno": 39927, "urat": 39928, "\u0120Reese": 39929, "\u0120intrusive": 39930, "\u0120gripping": 39931, "\u0120Blueprint": 39932, "\u0120Rasm": 39933, "unia": 39934, "managed": 39935, "\u0120Hebdo": 39936, "\u0120345": 39937, "\u0120decoding": 39938, "\u0120poets": 39939, "\u0120jaws": 39940, "\u0120FIGHT": 39941, "ameless": 39942, "\u0120Meadows": 39943, "\u0120Harbaugh": 39944, "Interview": 39945, "\u0120Hosp": 39946, "\u0120BRA": 39947, "\u0120deletion": 39948, "mob": 39949, "Walker": 39950, "\u0120Moonlight": 39951, "\u0120Jed": 39952, "\u0120Sophia": 39953, "\u0120usur": 39954, "\u0120fortunately": 39955, "\u0120Putting": 39956, "\u0120Fold": 39957, "\u0120sanitation": 39958, "\u0120partisans": 39959, "ISON": 39960, "Bow": 39961, "\u0120CONC": 39962, "\u0120Reduced": 39963, "\u0120Sutton": 39964, "\u0120touchscreen": 39965, "\u0120embryos": 39966, "\u00e2\u0122\u00a2\u00e2\u0122\u00a2\u00e2\u0122\u00a2\u00e2\u0122\u00a2": 39967, "\u0120Krug": 39968, "combat": 39969, "\u0120Petroleum": 39970, "\u0120amd": 39971, "\u0120Cosmos": 39972, "\u0120prescribing": 39973, "\u0120conformity": 39974, "ourses": 39975, "\u0120plentiful": 39976, "\u0120disillusion": 39977, "\u0120Ecology": 39978, "ittal": 39979, "\u0120fanc": 39980, "\u0120assassinated": 39981, "regnancy": 39982, "\u0120perennial": 39983, "\u0120Bullets": 39984, "\u0120stale": 39985, "\u0120cached": 39986, "\u0120Judith": 39987, "\u0120Diseases": 39988, "Allen": 39989, "\u0120las": 39990, "\u0120shards": 39991, "\u0120Suarez": 39992, "\u0120Friendship": 39993, "interface": 39994, "\u0120Supporters": 39995, "addons": 39996, "462": 39997, "\u0120Imran": 39998, "\u0120Wim": 39999, "\u0120newfound": 40000, "\u0120Mb": 40001, "Animal": 40002, "\u0120darling": 40003, "ande": 40004, "\u0120rhy": 40005, "\u0120Twisted": 40006, "posal": 40007, "ynski": 40008, "Various": 40009, "\u00d7\u013e": 40010, "\u0120Kiw": 40011, "uyomi": 40012, "\u0120wellbeing": 40013, "\u0120Lau": 40014, "anos": 40015, "\u0120unmist": 40016, "\u0120macOS": 40017, "\u0120restroom": 40018, "\u0120Oliv": 40019, "\u0120Airways": 40020, "\u0120timetable": 40021, "980": 40022, "\u0120radios": 40023, "voy": 40024, "iasco": 40025, "\u0120cloudy": 40026, "\u0120Drawing": 40027, "Anything": 40028, "Syria": 40029, "\u0120Hert": 40030, "staking": 40031, "\u0120unchecked": 40032, "\u0120brazen": 40033, "\u0120NRS": 40034, "697": 40035, "onomic": 40036, "establish": 40037, "\u0120leng": 40038, "\u0120diagonal": 40039, "\u0120Fior": 40040, "Lair": 40041, "\u0120Stard": 40042, "\u0120deficient": 40043, "joining": 40044, "beam": 40045, "\u0120omnip": 40046, "\u0120blender": 40047, "\u0120sunrise": 40048, "Moore": 40049, "\u0120Fault": 40050, "\u0120Costume": 40051, "\u0120Mub": 40052, "Flags": 40053, "anse": 40054, "\u0120payout": 40055, "\u0120Governors": 40056, "\u0120Dillon": 40057, "\u0120Banana": 40058, "Nar": 40059, "\u0120trailed": 40060, "\u0120imperialist": 40061, "umann": 40062, "atsuki": 40063, "435": 40064, "\u0120Roads": 40065, "\u0120slur": 40066, "\u0120Ideally": 40067, "\u0120trenches": 40068, "Ctrl": 40069, "\u0120mirrored": 40070, "\u0120Zel": 40071, "\u0120Crest": 40072, "Compat": 40073, "\u0120Rolls": 40074, "scrib": 40075, "\u0120Trails": 40076, "ometers": 40077, "winter": 40078, "\u0120immortality": 40079, "ilated": 40080, "\u0120contradicts": 40081, "universal": 40082, "illions": 40083, "\u0120Mama": 40084, "optim": 40085, "ATURE": 40086, "\u0120geo": 40087, "etter": 40088, "\u0120Carlo": 40089, "424": 40090, "\u0120canonical": 40091, "\u0120Stronghold": 40092, "near": 40093, "\u0120perfume": 40094, "\u0120orchestra": 40095, "odiac": 40096, "\u0120uphe": 40097, "\u0120reigning": 40098, "versive": 40099, "\u0120caucuses": 40100, "\u0120DEM": 40101, "\u0120insulted": 40102, "\u0120------": 40103, "\u0120Crush": 40104, "\u0120rooting": 40105, "\u0120Wraith": 40106, "\u0120whore": 40107, "\u0120tofu": 40108, "Cmd": 40109, "\u0120Bree": 40110, "\u0120$_": 40111, "\u0120rive": 40112, "\u0120Advertising": 40113, "\u0120watt": 40114, "\u0120HO": 40115, "\u0120persuasive": 40116, "\u0120Parameters": 40117, "\u0120observational": 40118, "\u0120NCT": 40119, "\u0120Moj": 40120, "\u0120Salon": 40121, "\u0120trunc": 40122, "\u0120exquisite": 40123, "\u0120Mara": 40124, "\u0120poop": 40125, "\u0120ANN": 40126, "Exc": 40127, "\u0120Wonderful": 40128, "\u0120Taco": 40129, "\u0120homeowner": 40130, "\u0120Smithsonian": 40131, "orporated": 40132, "mmmm": 40133, "\u0120loaf": 40134, "\u0120Yamato": 40135, "\u0120Indo": 40136, "\u0120clinging": 40137, "\u00c3\u00a1s": 40138, "\u0120immutable": 40139, "hub": 40140, "Orange": 40141, "\u0120fingertips": 40142, "\u0120Wooden": 40143, "\u0120Kidd": 40144, "\u0120JPM": 40145, "\u0120Damn": 40146, "Cow": 40147, "codes": 40148, "482": 40149, "\u0120initiating": 40150, "\u0120Elk": 40151, "\u0120Cutting": 40152, "\u0120absentee": 40153, "\u0120Vance": 40154, "\u0120Lilith": 40155, "GUI": 40156, "\u0120obscured": 40157, "\u0120dwarves": 40158, "\u0120Chop": 40159, "\u0120Boko": 40160, "Values": 40161, "\u0120multimedia": 40162, "\u0120brewed": 40163, "Regular": 40164, "CRIPTION": 40165, "\u0120Mortal": 40166, "\u0120apex": 40167, "\u0120traveler": 40168, "\u0120boils": 40169, "\u0120spraying": 40170, "Represent": 40171, "\u0120Starship": 40172, "428": 40173, "\u0120disapproval": 40174, "\u0120shadowy": 40175, "\u0120lamented": 40176, "\u0120Replace": 40177, "\u0120Fran\u00c3\u00a7": 40178, "677": 40179, "dor": 40180, "\u0120unstoppable": 40181, "\u0120cohorts": 40182, "gyn": 40183, "\u0120Classics": 40184, "\u0120Amph": 40185, "\u0120sluggish": 40186, "\u0120Addiction": 40187, "\u0120Padres": 40188, "\u0120inscription": 40189, "\u0120inhuman": 40190, "minus": 40191, "\u0120Jeremiah": 40192, "atars": 40193, "Terror": 40194, "\u0120Tos": 40195, "\u0120Sharma": 40196, "asta": 40197, "catch": 40198, "\u0120plumbing": 40199, "\u0120Timbers": 40200, "Shar": 40201, "Hal": 40202, "\u0120Osc": 40203, "\u0120coupling": 40204, "humans": 40205, "\u0120sponge": 40206, "\u0120idols": 40207, "\u0120Spa": 40208, "\u0120Advocate": 40209, "\u0120Beats": 40210, "lua": 40211, "\u0120ticking": 40212, "\u0120loader": 40213, "\u0120Gron": 40214, "810": 40215, "\u0120stimulated": 40216, "\u0120sidebar": 40217, "\u0120Manufacturer": 40218, "oreAnd": 40219, "1973": 40220, "\u0120praises": 40221, "\u0120Flores": 40222, "disable": 40223, "\u0120Electrical": 40224, "raise": 40225, "Eth": 40226, "\u0120migrated": 40227, "\u0120lecturer": 40228, "Kids": 40229, "\u0120Cavern": 40230, "\u0120kettle": 40231, "\u0120glyc": 40232, "\u0120Mandela": 40233, "\u0120Fully": 40234, "\u00e5\u00a7\u00ab": 40235, "FINEST": 40236, "\u0120squeezing": 40237, "\u0120Ryder": 40238, "ampoo": 40239, "oreAndOnline": 40240, "InstoreAndOnline": 40241, "BuyableInstoreAndOnline": 40242, "\u0120commemorate": 40243, "\u0120Rampage": 40244, "Austin": 40245, "\u0120Shroud": 40246, "\u0120Ruins": 40247, "915": 40248, "\u0120KH": 40249, "\u0120waterfront": 40250, "\u0120ESC": 40251, "baby": 40252, "\u0120Cout": 40253, "\u0120Emblem": 40254, "\u0120equivalents": 40255, "492": 40256, "Unique": 40257, "\u0120Nietzsche": 40258, "browser": 40259, "\u0120imitation": 40260, "\u0120Werewolf": 40261, "\u0120Kirin": 40262, "acas": 40263, "',\"": 40264, "\u0120\u00c3\u00be": 40265, "Reviewed": 40266, "\u0120cunt": 40267, "\u0120voic": 40268, "\u0120Lenovo": 40269, "\u0120bonded": 40270, "481": 40271, "\u0120inhibitors": 40272, "\u0120endeavors": 40273, "\u0120Havana": 40274, "\u0120Stout": 40275, "\u0120Jolly": 40276, "Actor": 40277, "*/(": 40278, "\u0120occurrences": 40279, "\u0120Tens": 40280, "Increased": 40281, "\u0120ACTION": 40282, "\u0120\u00e3\u0122\u012e": 40283, "\u0120Rankings": 40284, "\u0120Breat": 40285, "\u0120309": 40286, "Dou": 40287, "\u0120impacting": 40288, "\u0120Duchess": 40289, "prefix": 40290, "QB": 40291, "\u0120summoning": 40292, "\u0120bestowed": 40293, "\u0120Kepler": 40294, "\u0120POWER": 40295, "cube": 40296, "\u0120Kits": 40297, "\u0120Grip": 40298, "\u0120opium": 40299, "\u0120reputable": 40300, "toc": 40301, "ichael": 40302, "\u0120Ripple": 40303, "\u0120caf\u00c3\u00a9": 40304, "\u0120Zoom": 40305, "\u0120Burma": 40306, "\u0120waive": 40307, "\u0120stalls": 40308, "\u0120demeanor": 40309, "incerity": 40310, "\u0120fluoride": 40311, "\u0120SHOULD": 40312, "Paris": 40313, "\u0120longing": 40314, "\u0120plat": 40315, "\u0120grossly": 40316, "\u0120bulls": 40317, "\u0120showcasing": 40318, "expected": 40319, "\u0120Gaddafi": 40320, "engineering": 40321, "Repeat": 40322, "\u0120Kut": 40323, "\u0120conceivable": 40324, "\u0120trimmed": 40325, "oscope": 40326, "\u0120Candidate": 40327, "\u0120Tears": 40328, "rolog": 40329, "Lewis": 40330, "SUP": 40331, "\u0120roadmap": 40332, "\u0120saliva": 40333, "\u0120trumpet": 40334, "Jimmy": 40335, "\u0120miraculous": 40336, "\u0120colonization": 40337, "\u0120amput": 40338, "\u0120GNOME": 40339, "atech": 40340, "Different": 40341, "\u0120ELE": 40342, "\u0120Governments": 40343, "\u0120Ahead": 40344, "\u00e3\u0127\u012d\u00e3\u0127\u012d": 40345, "wordpress": 40346, "LIB": 40347, "\u0120Include": 40348, "\u0120Dorothy": 40349, "045": 40350, "\u0120Colombian": 40351, "\u0120leased": 40352, "884": 40353, "\u0120degrading": 40354, "\u0120Daisy": 40355, "iations": 40356, "\u0120baptized": 40357, "\u0120surname": 40358, "cox": 40359, "\u0120blinked": 40360, "\u00e3\u0125\u00a2": 40361, "\u0120pollen": 40362, "\u0120dermat": 40363, "\u0120regex": 40364, "\u0120Nicholson": 40365, "\u0120Eater": 40366, "\u00e7\u013e": 40367, "rador": 40368, "\u0120narrower": 40369, "\u0120hurricanes": 40370, "\u0120hallucinations": 40371, "ridden": 40372, "ISSION": 40373, "\u0120Firefly": 40374, "\u0120attainment": 40375, "\u0120nominate": 40376, "\u0120avocado": 40377, "\u0120Meredith": 40378, "\u0120ts": 40379, "\u0120reverence": 40380, "\u0120euph": 40381, "\u0120crates": 40382, "\u0120TEXT": 40383, "\u0120443": 40384, "\u0120319": 40385, "JSON": 40386, "iquette": 40387, "\u0120shortstop": 40388, "ickey": 40389, "\u0120propelled": 40390, "\u0120api": 40391, "\u0120Thieves": 40392, "779": 40393, "\u0120oversaw": 40394, "\u0120coli": 40395, "\u0120Nicola": 40396, "\u0120overcl": 40397, "ikawa": 40398, "\u0120Cyr": 40399, "\u0120384": 40400, "789": 40401, "\u0120Allows": 40402, "1027": 40403, "Detroit": 40404, "TRY": 40405, "setup": 40406, "\u0120Socialism": 40407, "Soviet": 40408, "susp": 40409, "\u0120APR": 40410, "\u0120Shutdown": 40411, "\u0120aluminium": 40412, "zbek": 40413, "\u0120Lover": 40414, "GGGGGGGG": 40415, "\u0120democracies": 40416, "\u01201908": 40417, "\u0120Merrill": 40418, "\u0120Francois": 40419, "gdala": 40420, "\u0120traffickers": 40421, "\u0120Til": 40422, "\u0120Goat": 40423, "\u0120sped": 40424, "\u0120Reserv": 40425, "\u0120prod": 40426, "552": 40427, "\u0120cac": 40428, "\u0120Univ": 40429, "\u0120Schwe": 40430, "\u0120swirling": 40431, "\u0120Wilderness": 40432, "\u0120Eggs": 40433, "\u0120saddened": 40434, "\u0120archaic": 40435, "Hyd": 40436, "\u0120excessively": 40437, "BRE": 40438, "\u0120aerospace": 40439, "\u0120Voices": 40440, "Craig": 40441, "\u0120ignited": 40442, "Initially": 40443, "\u0120McA": 40444, "\u0120handset": 40445, "\u0120reforming": 40446, "\u0120frustrations": 40447, "\u0120Deadpool": 40448, "\u0120Belichick": 40449, "ractor": 40450, "\u0120Ragnarok": 40451, "\u0120Drupal": 40452, "\u0120Approximately": 40453, "1920": 40454, "\u0120Hubble": 40455, "armor": 40456, "\u0120Saras": 40457, "\u0120Jonas": 40458, "\u0120nostalgic": 40459, "\u0120feasibility": 40460, "Saharan": 40461, "\u0120orbiting": 40462, "\u0120970": 40463, "Ru": 40464, "\u0120shin": 40465, "\u0120Investigators": 40466, "\u0120inconsistencies": 40467, "\u0120PAN": 40468, "BG": 40469, "\u0120grazing": 40470, "\u0120detectors": 40471, "\u0120Startup": 40472, "\u0120Funny": 40473, "\u0120Naomi": 40474, "Considering": 40475, "\u0120hog": 40476, "utf": 40477, "cemic": 40478, "\u0120fortified": 40479, "\u0120Functions": 40480, "\u0120codec": 40481, "nutrition": 40482, "Hat": 40483, "\"!": 40484, "microsoft": 40485, "558": 40486, "\u0120Thin": 40487, "\u0120ACE": 40488, "Alias": 40489, "\u0120OPS": 40490, "papers": 40491, "PK": 40492, "\u00e3\u0122\u0130": 40493, "\u0120improbable": 40494, "Northern": 40495, "equal": 40496, "\u0120lookout": 40497, "\u0120tyres": 40498, "\u0120Modified": 40499, "\u0120Kop": 40500, "Absolutely": 40501, "\u0120buildup": 40502, "silver": 40503, "\u0120audi": 40504, "\u0120grotesque": 40505, "\u0120Saber": 40506, "\u0120Presbyter": 40507, "ONY": 40508, "\u0120glaciers": 40509, "\u0120Shoals": 40510, "\u0120Kass": 40511, "\u0120HRC": 40512, "\u0120Nicol": 40513, "\u0120Lunch": 40514, "\u0120Foss": 40515, "\u00e2\u0138\u0134": 40516, "ADRA": 40517, "\u0120OnePlus": 40518, "oing": 40519, "grounds": 40520, "\u0120incidental": 40521, "\u0120datasets": 40522, "689": 40523, "\u0120Clarkson": 40524, "\u0120assembling": 40525, "\u0120Corrections": 40526, "\u0120drinkers": 40527, "\u0120qualifiers": 40528, "\u0120leash": 40529, "\u0120unfounded": 40530, "\u0120Hundred": 40531, "\u0120kickoff": 40532, "Ti": 40533, "\u0120reconcil": 40534, "\u0120Grants": 40535, "\u0120Compliance": 40536, "\u0120Dexterity": 40537, "\u01201906": 40538, "warn": 40539, "Dallas": 40540, "Maximum": 40541, "nard": 40542, "avia": 40543, "beaut": 40544, "ensitivity": 40545, "trace": 40546, "\u0120pioneers": 40547, "\u0120Fract": 40548, "\u00e3\u0122\u0131": 40549, "\u0120precept": 40550, "\u0120glossy": 40551, "\u0120IEEE": 40552, "Across": 40553, "\u0120680": 40554, "Sleep": 40555, "cheon": 40556, "\u0120satirical": 40557, "\u0120Minotaur": 40558, "\u0120Claude": 40559, "\u0120r\u00c3\u00a9": 40560, "apego": 40561, "\u0120carrot": 40562, "\u0120Semin": 40563, "inoa": 40564, "\u0120zo": 40565, "Independent": 40566, "\u0120diagnoses": 40567, "\u0120Cue": 40568, "MAR": 40569, "\u0120rendition": 40570, "\u0120Kik": 40571, "\u0120pathology": 40572, "\u0120selects": 40573, "LinkedIn": 40574, "\u0120assay": 40575, "\u0120Dres": 40576, "\u0120textual": 40577, "posted": 40578, "ITAL": 40579, "\u0120Maul": 40580, "Neal": 40581, "\u0120interconnected": 40582, "\u0120erratic": 40583, "\u0120Virus": 40584, "\u0120530": 40585, "\u0120environmentalists": 40586, "\u0120Phelps": 40587, "\u0120engagements": 40588, "\u0120INST": 40589, "\u0120economical": 40590, "noxious": 40591, "\u0120gearing": 40592, "izzy": 40593, "\u0120favorably": 40594, "\u0120McGill": 40595, "Term": 40596, "\u0120hanged": 40597, "\u0120ballpark": 40598, "\u0120Reyes": 40599, "\u0120beware": 40600, "\u0120Psal": 40601, "\u0120Massacre": 40602, "qi": 40603, "\u0120inaccessible": 40604, "aclysm": 40605, "\u0120fray": 40606, "illac": 40607, "\u0120bitterly": 40608, "\u0120Certification": 40609, "Michigan": 40610, "\u0120irrespective": 40611, "alore": 40612, "Empty": 40613, "\u0120endorsements": 40614, "\u0120undet": 40615, "fg": 40616, "equipped": 40617, "\u0120merciless": 40618, "\u0120Cust": 40619, "\u0120immature": 40620, "\u0120voucher": 40621, "\u0120Blackwell": 40622, "\u00d1\u0131": 40623, "hawk": 40624, "disciplinary": 40625, "ilee": 40626, "\u0120Makoto": 40627, "\u0120Dude": 40628, "\u00e3\u0125\u0129\u00e3\u0124\u00a3": 40629, "Years": 40630, "\u0120inver": 40631, "\u0120shaman": 40632, "\u0120Yong": 40633, "ipel": 40634, "ellen": 40635, "\u0120Cathy": 40636, "brids": 40637, "\u0120sarc": 40638, "651": 40639, "Near": 40640, "\u0120groundwork": 40641, "\u0120amaz": 40642, "\u0120415": 40643, "\u0120Huntington": 40644, "hews": 40645, "\u0120Bung": 40646, "\u0120arbitrarily": 40647, "\u0120Wit": 40648, "\u0120Alberto": 40649, "\u0120disqualified": 40650, "bestos": 40651, "461": 40652, "\u0120pc": 40653, "\u0120284": 40654, "robat": 40655, "Robin": 40656, "\u0120hugs": 40657, "\u0120Transition": 40658, "\u0120Occasionally": 40659, "\u0120326": 40660, "\u0120Whilst": 40661, "\u0120Ley": 40662, "\u0120spaceship": 40663, "csv": 40664, "\u0120unsuccessfully": 40665, "\u0120Au": 40666, "leck": 40667, "\u0120Winged": 40668, "\u0120Grizzlies": 40669, ".\u00ef\u00bf\u00bd": 40670, "\u0120nearer": 40671, "\u0120Sorceress": 40672, "\u0120Indigo": 40673, "Else": 40674, "840": 40675, "letes": 40676, "Coach": 40677, "\u0120upbringing": 40678, "\u0120Kes": 40679, "\u0120separatist": 40680, "\u0120racists": 40681, "\u0120chained": 40682, "\u0120abstinence": 40683, "learning": 40684, "\u0120reinstated": 40685, "\u0120symmetry": 40686, "\u0120reminders": 40687, "\u0120Chevy": 40688, "\u0120mont": 40689, "\u0120exemplary": 40690, "\u0120TOR": 40691, "ZX": 40692, "\u0120qualitative": 40693, "\u0120Stamp": 40694, "\u0120Savannah": 40695, "\u0120Rossi": 40696, "\u0120paed": 40697, "\u0120dispensaries": 40698, "\u0120Walls": 40699, "\u0120Chronic": 40700, "\u0120complimentary": 40701, "\u0120Beirut": 40702, "\u0120+---": 40703, "igslist": 40704, "\u0120cryptographic": 40705, "masters": 40706, "\u0120Capitals": 40707, "\u0120maximal": 40708, "\u0120entropy": 40709, "Points": 40710, "\u0120combatants": 40711, "lip": 40712, "\u0120Glob": 40713, "\u0120BMC": 40714, "phase": 40715, "thank": 40716, "HTTP": 40717, "\u0120commuter": 40718, "\u0120\\(\\": 40719, "../": 40720, "\u0120Regener": 40721, "\u0120DOI": 40722, "\u0120Activision": 40723, "\u0120slit": 40724, "osal": 40725, "REM": 40726, "\u0120chants": 40727, "Yu": 40728, "Keys": 40729, "Brexit": 40730, "\u0120Forced": 40731, "Arizona": 40732, "\u0120squadron": 40733, "ISO": 40734, "\u0120Malone": 40735, "\u0120338": 40736, "\u0120contrasting": 40737, "\u0120tidal": 40738, "\u0120libel": 40739, "\u0120implanted": 40740, "\u0120uproar": 40741, "\u0120Cater": 40742, "\u0120propositions": 40743, "Manchester": 40744, "\u0120Euros": 40745, "itamin": 40746, "Gil": 40747, "\u0120Elven": 40748, "\u0120Seek": 40749, "\u0120Bai": 40750, "\u0120redevelopment": 40751, "\u0120Towns": 40752, "\u0120Lub": 40753, "!\",": 40754, "alon": 40755, "Krist": 40756, "\u0120measurable": 40757, "\u0120imaginable": 40758, "\u0120apostles": 40759, "YN": 40760, "760": 40761, "\u0120steroid": 40762, "\u0120specificity": 40763, "\u0120Located": 40764, "\u0120Becker": 40765, "\u0120Edu": 40766, "\u0120Dietary": 40767, "utsch": 40768, "\u0120Marilyn": 40769, "\u0120blister": 40770, "\u0120MEP": 40771, "\u0120Koz": 40772, "\u0120CMS": 40773, "yahoo": 40774, "\u0120Carney": 40775, "\u0120boasting": 40776, "\u0120Caleb": 40777, "Byte": 40778, "reads": 40779, "aden": 40780, "Problem": 40781, "\u0120Woodward": 40782, "Swe": 40783, "Sup": 40784, "\u0120KGB": 40785, "Setup": 40786, "\u0120tacit": 40787, "\u0120retribution": 40788, "\u0120dues": 40789, "\u0120M\u00c3\u00bc": 40790, ".?": 40791, "\u00e4\u00b8\u0143": 40792, "pots": 40793, "\u0120cameo": 40794, "\u0120PAL": 40795, "education": 40796, "Amy": 40797, "likely": 40798, "gling": 40799, "\u0120constitutionally": 40800, "\u0120Hamm": 40801, "\u0120Speak": 40802, "\u0120widgets": 40803, "brate": 40804, "\u0120crappy": 40805, "\u0120Iter": 40806, "\u0120anticipating": 40807, "\u0120Bout": 40808, "Pixel": 40809, "\u0120Yep": 40810, "\u0120Laurie": 40811, "\u0120hut": 40812, "\u0120bulletin": 40813, "\u0120Salvation": 40814, "\u0120chats": 40815, "earable": 40816, "Honestly": 40817, "ALTH": 40818, "onsequ": 40819, "cult": 40820, "iscovery": 40821, "ovych": 40822, "\u0120selves": 40823, "\u0120Satoshi": 40824, "Sounds": 40825, "\u0120convergence": 40826, "\u0120Rosenberg": 40827, "1974": 40828, "\u0120nasal": 40829, "\u0120fullest": 40830, "\u0120ferocious": 40831, "xus": 40832, "iste": 40833, "AMS": 40834, "\u0120lobbied": 40835, "\u0120soothing": 40836, "\u0120Gunn": 40837, "today": 40838, "024": 40839, "\u0120inspirational": 40840, "\u0120NBN": 40841, "pb": 40842, "gewater": 40843, "orah": 40844, "allowed": 40845, "\u0120Coliseum": 40846, "\u0120specializing": 40847, "\u0120insanely": 40848, "\u0120Tape": 40849, "delay": 40850, "\u0120tarn": 40851, "\u0120Pound": 40852, "\u0120melanch": 40853, "\u0120deployments": 40854, "iland": 40855, "\u0120lessen": 40856, "\u0120furry": 40857, "\u0120UEFA": 40858, "\u0120bloodshed": 40859, "\u0120Meier": 40860, "ithering": 40861, "\u0120heirs": 40862, "\u0120Jaw": 40863, "axter": 40864, "\u0120Publications": 40865, "\u0120alters": 40866, "intention": 40867, "\u0120Winchester": 40868, "determination": 40869, "\u0120Lifetime": 40870, "thin": 40871, "Monster": 40872, "780": 40873, "\u0120approximation": 40874, "\u0120supermarkets": 40875, "\u0120Seconds": 40876, "oros": 40877, "huge": 40878, "\u0120bribe": 40879, "\u0120LIMITED": 40880, "uned": 40881, "\u0120misinterpret": 40882, "\u0120Injury": 40883, "\u0120367": 40884, "\u0120thresholds": 40885, "\u0120Carnival": 40886, "\u0120gastrointestinal": 40887, "\u0120guideline": 40888, "\u0120deceived": 40889, "features": 40890, "\u0120purportedly": 40891, "\u0120Ronnie": 40892, "\u0120Newt": 40893, "\u0120spacious": 40894, "asus": 40895, "\u0120superheroes": 40896, "\u0120Cynthia": 40897, "legged": 40898, "kamp": 40899, "chio": 40900, "\u0120thumbnail": 40901, "\u0120Shirley": 40902, "illation": 40903, "\u0120sheds": 40904, "\u0120Zy": 40905, "EPA": 40906, "\u0120dams": 40907, "\u0120yawn": 40908, "nah": 40909, "\u0120Peggy": 40910, "\u0120Erie": 40911, "\u0120Juventus": 40912, "\u0120Fountain": 40913, "rx": 40914, "donald": 40915, "album": 40916, "\u0120Comprehensive": 40917, "\u0120caching": 40918, "\u0120Uz": 40919, "ulnerability": 40920, "\u0120Principle": 40921, "\u0120Jian": 40922, "ingers": 40923, "casts": 40924, "\u0120Osiris": 40925, "chart": 40926, "tile": 40927, "\u0120Tiffany": 40928, "\u0120Patton": 40929, "\u0120Whip": 40930, "\u0120oversized": 40931, "Je": 40932, "\u0120Cinderella": 40933, "\u0120Borders": 40934, "\u0120Daesh": 40935, "Mah": 40936, "\u0120dogma": 40937, "\u0120communists": 40938, "vu": 40939, "Council": 40940, "\u0120freshwater": 40941, "\u0120wounding": 40942, "\u0120debacle": 40943, "\u0120youngster": 40944, "\u0120threaded": 40945, "\u0120Bots": 40946, "\u0120Savings": 40947, "\u00e3\u0123\u0124": 40948, "oling": 40949, "oho": 40950, "\u0120illumination": 40951, "MRI": 40952, "\u0120loosen": 40953, "trump": 40954, "agency": 40955, "urion": 40956, "\u0120momentarily": 40957, "\u0120Chun": 40958, "\u0120Budapest": 40959, "\u0120Alley": 40960, "Disk": 40961, "\u0120astonished": 40962, "\u0120Conquer": 40963, "\u0120Accounting": 40964, "having": 40965, "\u0120Wein": 40966, "\u0120Alright": 40967, "\u0120revolver": 40968, "\u0120delusion": 40969, "\u0120relics": 40970, "\u0120adherent": 40971, "quant": 40972, "\u0120handmade": 40973, "orio": 40974, "\u0120combating": 40975, "coded": 40976, "\u0120quadru": 40977, "reth": 40978, "Nik": 40979, "\u0120Tribal": 40980, "\u0120Mysterious": 40981, "\u0120inhal": 40982, "\u0120Winning": 40983, "\u0120Classification": 40984, "changed": 40985, "\u0120unab": 40986, "\u0120scorn": 40987, "icipated": 40988, "wl": 40989, "onductor": 40990, "\u0120reinforcing": 40991, "\u0120Childhood": 40992, "anova": 40993, "\u0120adventurer": 40994, "\u0120doctoral": 40995, "\u0120Strategies": 40996, "\u0120engulfed": 40997, "\u0120Encounter": 40998, "\u0120lashes": 40999, "Critical": 41000, "ricular": 41001, "\u0120UTF": 41002, "ociation": 41003, "checking": 41004, "\u0120Consulting": 41005, "Runtime": 41006, "period": 41007, "\u0120Asgard": 41008, "\u0120distilled": 41009, "\u0120Pasadena": 41010, "\u0120Dying": 41011, "\u0120COUNTY": 41012, "\u0120granite": 41013, "\u0120smack": 41014, "\u0120parachute": 41015, "\u0120SUR": 41016, "Virginia": 41017, "\u0120Furious": 41018, "787": 41019, "\u0120Okin": 41020, "\u0120camel": 41021, "\u0120Mbps": 41022, "1972": 41023, "\u0120Chao": 41024, "\u0120Cyan": 41025, "joice": 41026, "efer": 41027, "\u0120Wrap": 41028, "\u0120Debate": 41029, "Seg": 41030, "\u0120forearm": 41031, "\u0120Ignore": 41032, "\u0120timestamp": 41033, "\u0120probing": 41034, "\u0120Noon": 41035, "\u0120Grail": 41036, "fen": 41037, "\u0120dormant": 41038, "\u0120Firstly": 41039, "\u0120Eighth": 41040, "\u0120HUN": 41041, "\u0120Desire": 41042, "oras": 41043, "Girls": 41044, "\u0120Desmond": 41045, "zar": 41046, "amines": 41047, "OAD": 41048, "execute": 41049, "\u0120boobs": 41050, "\u0120ATL": 41051, "_(": 41052, "Chelsea": 41053, "\u0120masturbation": 41054, "\u0120CoC": 41055, "\u0120destroyer": 41056, "\u0120Chomsky": 41057, "\u0120scatter": 41058, "\u0120Assets": 41059, "796": 41060, "\u0120Cargo": 41061, "\u0120receptive": 41062, "\u0120Scope": 41063, "\u0120marketers": 41064, "\u0120launchers": 41065, "\u0120axle": 41066, "\u0120SEA": 41067, "seq": 41068, "\u0120Moff": 41069, "finding": 41070, "\u0120Gibbs": 41071, "Georgia": 41072, "extremely": 41073, "NJ": 41074, "\u0120laborers": 41075, "stals": 41076, "\u0120mediation": 41077, "\u0120Hedge": 41078, "atown": 41079, "\u0120iod": 41080, "despite": 41081, "vill": 41082, "Jane": 41083, "existence": 41084, "\u0120coincided": 41085, "\u0120Utilities": 41086, "\u0120Cheap": 41087, "\u0120logistical": 41088, "\u0120culmination": 41089, "\u0120Nicotine": 41090, "pak": 41091, "Folder": 41092, "\u0120rodents": 41093, "stuff": 41094, "\u0120lawfully": 41095, "\u0120reperto": 41096, "ioch": 41097, "jj": 41098, "Dialogue": 41099, "HHHH": 41100, "liction": 41101, "Looks": 41102, "\u0120297": 41103, "\u0120turrets": 41104, "\u0120Abandon": 41105, "\u0120incess": 41106, "\u0120Trafford": 41107, "\u0120curled": 41108, "\u0120preferring": 41109, "\u0120privatization": 41110, "\u0120irresist": 41111, "\u0120Panda": 41112, "\u0120Shake": 41113, "\u0120McGr": 41114, "\u00e3\u0125\u0126": 41115, "unders": 41116, "\u0120discriminated": 41117, "\u0120bartender": 41118, "ILE": 41119, "Atlantic": 41120, "\u0120propensity": 41121, "\u0120Wiz": 41122, "\u0120Gim": 41123, "conference": 41124, "\u0120reinforces": 41125, "Gh": 41126, "wagon": 41127, "\u0120eerie": 41128, "Fal": 41129, "\u0120hugged": 41130, "racist": 41131, "RIC": 41132, "Fu": 41133, "\u0120filler": 41134, "\u0120Stub": 41135, "\u0120engraved": 41136, "\u0120Wrestle": 41137, "\u0120imaginative": 41138, "\u0120Peer": 41139, "\u0120Factors": 41140, "anus": 41141, "\u0120Dracula": 41142, "monitor": 41143, "\u0120routers": 41144, "ibia": 41145, "\u0120Boolean": 41146, "endale": 41147, "\u0120Slaughter": 41148, "\u0120Shack": 41149, "RFC": 41150, "\u0120Spielberg": 41151, "Sax": 41152, "\u0120PHOTO": 41153, "\u0120Clover": 41154, "\u0120Rae": 41155, "Depending": 41156, "\u0120Memor": 41157, "aram": 41158, "\u0120pierced": 41159, "\u0120curtains": 41160, "vale": 41161, "\u0120Inquisition": 41162, "\u0120Poke": 41163, "\u0120forecasting": 41164, "\u0120complains": 41165, "Sense": 41166, "\u0120Hermes": 41167, "iscovered": 41168, "\u0120bible": 41169, "\u0120Morph": 41170, "\u0120germ": 41171, "785": 41172, "DON": 41173, "\u0120congen": 41174, "\u0120crane": 41175, "\u0120DPR": 41176, "\u0120respectfully": 41177, "Room": 41178, "\u0120Naw": 41179, "\u0120Dalai": 41180, "reason": 41181, "\u0120Angus": 41182, "Education": 41183, "\u0120Titanic": 41184, "\u00cb\u013e": 41185, "\u0120oval": 41186, "united": 41187, "\u0120thirds": 41188, "\u0120moistur": 41189, "\u0120CPC": 41190, "Miami": 41191, "\u0120tentacles": 41192, "\u0120Polaris": 41193, "exc": 41194, "exclusive": 41195, "\u0120Prairie": 41196, "\u0120colossal": 41197, "\u0120Blend": 41198, "surprisingly": 41199, "\u00c3\u0143s": 41200, "\u0120indoctr": 41201, "\u0120basal": 41202, "\u0120MPEG": 41203, "undo": 41204, "Split": 41205, "Development": 41206, "\u0120lantern": 41207, "1971": 41208, "\u0120provocation": 41209, "\u0120anguish": 41210, "\u0120Bind": 41211, "\u0120Leia": 41212, "ducers": 41213, "ippy": 41214, "conservancy": 41215, "\u0120initialize": 41216, "\u0120Twice": 41217, "\u0120Suk": 41218, "\u0120predic": 41219, "\u0120diploma": 41220, "\u0120sociop": 41221, "Ingredients": 41222, "\u0120hammered": 41223, "\u0120Irma": 41224, "Qaida": 41225, "\u0120glimps": 41226, "\u0120Bian": 41227, "\u0120stacking": 41228, "\u0120fend": 41229, "govtrack": 41230, "\u0120unn": 41231, "democratic": 41232, "igree": 41233, "\u0120580": 41234, "\u0120294": 41235, "\u0120strawberry": 41236, "IDER": 41237, "\u0120cherished": 41238, "\u0120Hots": 41239, "\u0120inferred": 41240, "\u0120808": 41241, "\u0120Socrates": 41242, "Oregon": 41243, "\u0120Roses": 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"texture": 41293, "Spider": 41294, "freedom": 41295, "cipled": 41296, "\u0120TAMADRA": 41297, "\u00e2\u013b\u00a6": 41298, "authent": 41299, "\u0120WANT": 41300, "rified": 41301, "\u0120rites": 41302, "\u0120uterus": 41303, "kiss": 41304, "\u0120\u00e2\u012b\u00a4": 41305, "\u0120skillet": 41306, "\u0120disenfranch": 41307, "\u0120Gaal": 41308, "Compan": 41309, "\u0120ageing": 41310, "guide": 41311, "Balt": 41312, "\u0120iterator": 41313, "\u0120discretionary": 41314, "tips": 41315, "\u0120primates": 41316, "\u0120Technique": 41317, "\u0120Payments": 41318, "azel": 41319, "\u0120ROCK": 41320, "stantial": 41321, "060": 41322, "\u0120dmg": 41323, "\u0120Jackets": 41324, "\u0120Playoff": 41325, "\u0120nursery": 41326, "\u0120Symb": 41327, "arton": 41328, "\u0120annexation": 41329, "Colorado": 41330, "\u0120coils": 41331, "\u0120Shoes": 41332, "\u00e2\u0126\u00a2:": 41333, "\u0120Roz": 41334, "COMPLE": 41335, "\u0120Everest": 41336, "\u0120Triumph": 41337, "Joy": 41338, "Grid": 41339, "\u00e0\u00bc": 41340, "processor": 41341, "\u0120Prosper": 41342, "\u0120Severus": 41343, "\u0120Selected": 41344, "rg": 41345, "\u0120Tayyip": 41346, "Stra": 41347, "\u0120skiing": 41348, "\u0120?)": 41349, "\u0120peg": 41350, "Tesla": 41351, "\u0120timeframe": 41352, "\u0120mastermind": 41353, "\u0120NB": 41354, "scientific": 41355, "\u0120Shit": 41356, "generic": 41357, "INTER": 41358, "NUM": 41359, "\u0120stroll": 41360, "\u0120Enix": 41361, "\u0120MMR": 41362, "\u0120EMS": 41363, "movie": 41364, "\u0124\u00aa": 41365, "\u0120minimizing": 41366, "iddling": 41367, "\u0120illegitimate": 41368, "\u0120prototyp": 41369, "\u0120prematurely": 41370, "\u0120manuals": 41371, "obbies": 41372, "\u0120Cassidy": 41373, "DEC": 41374, "desktop": 41375, "\u0120aeros": 41376, "\u0120screenings": 41377, "\u0120debilitating": 41378, "\u0120Grind": 41379, "natureconservancy": 41380, "\u0120fades": 41381, "termination": 41382, "assetsadobe": 41383, "Factor": 41384, "\u0120definitively": 41385, "Pok\u00c3\u00a9": 41386, "apult": 41387, "\u0120Lafayette": 41388, "Corn": 41389, "\u0120Coral": 41390, "\u0120stagnant": 41391, "Tue": 41392, "\u0120dissatisfaction": 41393, "Gender": 41394, "\u0120kidneys": 41395, "\u0120Gow": 41396, "\u0120Defeat": 41397, "\u0120Ashton": 41398, "\u0120cartels": 41399, "\u0120foreclosure": 41400, "\u0120Explore": 41401, "strength": 41402, "otin": 41403, "\u0120veterinarian": 41404, "\u0120fumble": 41405, "\u0120parap": 41406, "\u0120Strait": 41407, "rils": 41408, "\u0120prick": 41409, "\u0120Bermuda": 41410, "\u0120Ammunition": 41411, "skinned": 41412, "\u0120abound": 41413, "\u0120Braz": 41414, "\u0120sharper": 41415, "\u0120Ascension": 41416, "\u0120978": 41417, "\u0120previews": 41418, "\u0120communion": 41419, "\u0120XY": 41420, "\u0120phony": 41421, "\u0120newcomer": 41422, "\u0120332": 41423, ".\",\"": 41424, "\u0120redistribution": 41425, "Protect": 41426, "\u0120Sof": 41427, "Kal": 41428, "\u0120lipstick": 41429, "worst": 41430, "\u0120tangled": 41431, "\u0120retrospective": 41432, "integer": 41433, "\u0120volunteering": 41434, "\u01201907": 41435, "\u0120--------------------": 41436, "ichen": 41437, "\u0120unveiling": 41438, "\u0120senseless": 41439, "\u0120fisheries": 41440, "\\-": 41441, "\u0120hinges": 41442, "\u0120calculus": 41443, "Myth": 41444, "\u0120undefeated": 41445, "\u0120optimizations": 41446, "\u0120depress": 41447, "\u0120billboard": 41448, "\u0120Yad": 41449, "\u0120Pyramid": 41450, "Isn": 41451, "Ide": 41452, "\u0120legion": 41453, "\u0120Kramer": 41454, "entanyl": 41455, "\u0120penetrating": 41456, "\u0120Hawth": 41457, "\u0120PRODUCT": 41458, "\u0120Gerard": 41459, "\u0120Pact": 41460, "\u0120Including": 41461, "\u0120Elias": 41462, "\u0120Elaine": 41463, "visual": 41464, "\u0120humming": 41465, "\u0120condesc": 41466, "\u0120Fasc": 41467, "\u00e4\u00b8\u012c": 41468, "\u0120egalitarian": 41469, "\u0120devs": 41470, "\u0120Dahl": 41471, "Ops": 41472, "DH": 41473, "\u0120Bounce": 41474, "idated": 41475, "aldo": 41476, "\u0120republican": 41477, "\u0120hamb": 41478, "\u0120Sett": 41479, "ographies": 41480, "CHAPTER": 41481, "\u0120transsexual": 41482, "\u0120skyrocket": 41483, "answer": 41484, "\u0120markup": 41485, "\u00d8\u00aa": 41486, "\u0120heroine": 41487, "Compare": 41488, "\u0120Tav": 41489, "Beast": 41490, "\u0120successors": 41491, "\u0120na\u00c3\u00afve": 41492, "\u0120Buckley": 41493, "stress": 41494, "meat": 41495, "\u0120downloadable": 41496, "\u0120indexed": 41497, "\u0120scaff": 41498, "\u0120Lump": 41499, "\u0120Homo": 41500, "Studio": 41501, "Insp": 41502, "\u0120racked": 41503, "farious": 41504, "\u0120Petty": 41505, "External": 41506, "\u01201909": 41507, "Wars": 41508, "commit": 41509, "puters": 41510, "\u0120unob": 41511, "\u0120Err": 41512, "\u0120EG": 41513, "\u0120Alam": 41514, "\u0120Siberia": 41515, "\u0120Atmospheric": 41516, "ISTER": 41517, "\u0120Satanic": 41518, "translation": 41519, "\u0120Loud": 41520, "traumatic": 41521, "lique": 41522, "\u0120resonate": 41523, "\u0120Welch": 41524, "\u0120sparking": 41525, "\u0120TOM": 41526, "tone": 41527, "\u0120outl": 41528, "\u0120handcuffed": 41529, "\u0120Serie": 41530, "801": 41531, "\u0120landmarks": 41532, "\u0120Reeves": 41533, "\u0120softened": 41534, "\u0120dazzling": 41535, "\u0120Wanted": 41536, "months": 41537, "Magikarp": 41538, "\u0120untreated": 41539, "\u0120Bedford": 41540, "Mi": 41541, "\u0120Dynamo": 41542, "Ore": 41543, "795": 41544, "\u0120wrongful": 41545, "\u0120lured": 41546, "\u0120cortisol": 41547, "\u0120vex": 41548, "drawn": 41549, "ilet": 41550, "Downloadha": 41551, "\u0120Faction": 41552, "\u0120labyrinth": 41553, "\u0120hijacked": 41554, "waters": 41555, "erick": 41556, "\u0120superiors": 41557, "\u0120Rowling": 41558, "\u0120Guinness": 41559, "\u0120td": 41560, "992": 41561, "\u0120unearthed": 41562, "\u0120centrif": 41563, "\u0120shameless": 41564, "Pod": 41565, "\u0120Fib": 41566, "\u0120icing": 41567, "\u0120predictor": 41568, "\u0120292": 41569, "forestation": 41570, "construct": 41571, "Cand": 41572, "@#": 41573, "\u0120agitated": 41574, "\u0120repr": 41575, "OVA": 41576, "\u0120knitting": 41577, "\u0120Lima": 41578, "\u0120fodder": 41579, "684": 41580, "\u0120Persona": 41581, "kl": 41582, "701": 41583, "\u0120breakup": 41584, "\u00e1\u00b8": 41585, "\u0120appalled": 41586, "\u0120antidepressants": 41587, "\u0120Sussex": 41588, "Harris": 41589, "\u0120Thermal": 41590, "eeee": 41591, "Upload": 41592, "\u0120gulf": 41593, "\u0120doorstep": 41594, "\u0120Shank": 41595, "LU": 41596, "\u0120MEN": 41597, "\u0120Pond": 41598, "sorry": 41599, "\u0120misfortune": 41600, "nance": 41601, "\u0120bona": 41602, "Mut": 41603, "\u0120degraded": 41604, "\u0120LOG": 41605, "\u0120Ness": 41606, "animal": 41607, "\u0120aversion": 41608, "undown": 41609, "\u0120supplemented": 41610, "\u0120Cups": 41611, "\u0120504": 41612, "\u0120deprive": 41613, "\u0120Sparkle": 41614, "\u00c5\u0124": 41615, "\u0120Meditation": 41616, "authors": 41617, "\u0120Saban": 41618, "\u0120Naked": 41619, "aird": 41620, "\u0120Mandarin": 41621, "\u0120Scriptures": 41622, "\u0120Personnel": 41623, "\u0120Maharashtra": 41624, "\u01201903": 41625, "\u0120Pai": 41626, "\u0120Mirage": 41627, "ombat": 41628, "Accessory": 41629, "\u0120fragmented": 41630, "Together": 41631, "\u0120believable": 41632, "\u0120Gladiator": 41633, "aligned": 41634, "\u0120Slug": 41635, "MAT": 41636, "\u0120convertible": 41637, "\u0120Bourbon": 41638, "ameron": 41639, "\u0120Rehab": 41640, "ntax": 41641, "\u0120powdered": 41642, "pillar": 41643, "\u0120smoker": 41644, "\u0120Manson": 41645, "\u0120BF": 41646, "511": 41647, "\u0120Goodell": 41648, "\u0120DAR": 41649, "mud": 41650, "gart": 41651, "\u0120obedient": 41652, "\u0120Transmission": 41653, "\u0120Donation": 41654, "880": 41655, "\u0120bothering": 41656, "Materials": 41657, "\u00e3\u0124\u00b1": 41658, "destroy": 41659, "\u0120foregoing": 41660, "\u0120anarchism": 41661, "\u0120Kry": 41662, "iceps": 41663, "\u0120littered": 41664, "\u0120Schiff": 41665, "\u0120anecdotal": 41666, "units": 41667, "\u0120fian": 41668, "\u0120Stim": 41669, "\u0120SOME": 41670, "\u0120Invaders": 41671, "\u0120behavioural": 41672, "\u0120Ventures": 41673, "\u0120sublime": 41674, "\u0120fruition": 41675, "\u0120Penalty": 41676, "\u0120corrosion": 41677, "\u00b6\u0127": 41678, "\u0120likened": 41679, "\u0120besieged": 41680, "weeney": 41681, "\u0120Creep": 41682, "\u0120linemen": 41683, "multi": 41684, "icably": 41685, "udder": 41686, "\u0120vitality": 41687, "\u0120shortfall": 41688, "\u0120Pants": 41689, "apist": 41690, "Hidden": 41691, "\u0120Drops": 41692, "medical": 41693, "\u0120pronunciation": 41694, "\u0120NRL": 41695, "\u0120insightful": 41696, "JV": 41697, "\u0120Beard": 41698, "\u0120Chou": 41699, "\u0120charms": 41700, "\u0120bins": 41701, "\u0120ambassadors": 41702, "\u0120Saturdays": 41703, "\u0120inhibitor": 41704, "\u0120Franch": 41705, "601": 41706, "','": 41707, "\u0120Conor": 41708, "artney": 41709, "\u0120Xperia": 41710, "grave": 41711, "bees": 41712, "\u0120Protestants": 41713, "\u0120soaking": 41714, "\u0120Mandal": 41715, "\u0120phased": 41716, "\u0120660": 41717, "\u0120scams": 41718, "\u0120buzzing": 41719, "\u0120Italians": 41720, "\u0120Lorenzo": 41721, "\u0120JA": 41722, "\u0120hesitated": 41723, "\u0120cliffs": 41724, "\u0120GOT": 41725, "inguishable": 41726, "\u0120ko": 41727, "\u0120interruption": 41728, "Zip": 41729, "Learning": 41730, "\u0120underscores": 41731, "\u0120Blink": 41732, "Ku": 41733, "579": 41734, "\u0120Autob": 41735, "IRE": 41736, "\u0120watering": 41737, "\u0120pastry": 41738, "820": 41739, "\u0120visionary": 41740, "\u0120Templar": 41741, "awaited": 41742, "\u0120piston": 41743, "\u0120antid": 41744, "currently": 41745, "\u0120pard": 41746, "\u0120waging": 41747, "\u0120nobility": 41748, "\u0120Yus": 41749, "\u0120injecting": 41750, "faith": 41751, "\u0120PASS": 41752, "\u00e5\u00ba": 41753, "\u0120retake": 41754, "\u0120PROC": 41755, "\u0120cathedral": 41756, "bash": 41757, "\u0120wrestlers": 41758, "\u0120partnering": 41759, "\u0120noses": 41760, "\u0120358": 41761, "Transform": 41762, "amen": 41763, "\u0120bouts": 41764, "\u0120Ideal": 41765, "\u0120Constantin": 41766, "\u0120sep": 41767, "\u0120Monarch": 41768, "atten": 41769, "\u0120Peoples": 41770, "modified": 41771, "\u0120moratorium": 41772, "\u0120penchant": 41773, "\u0120offensively": 41774, "\u0120proxies": 41775, "okane": 41776, "\u0120Taiwanese": 41777, "\u0120Poo": 41778, "\u0120HOME": 41779, "usional": 41780, "\u0120verbs": 41781, "\u0120Oman": 41782, "visory": 41783, "\u0120persuasion": 41784, "\u0120multit": 41785, "\u0120scissors": 41786, "Gay": 41787, "oway": 41788, "ophysical": 41789, "lus": 41790, "gnu": 41791, "\u0120apocalyptic": 41792, "\u0120absurdity": 41793, "\u0120playbook": 41794, "\u0120autobiography": 41795, "IUM": 41796, "\u0120sneaking": 41797, "\u0120Simulation": 41798, "pps": 41799, "ellery": 41800, "Planet": 41801, "\u0120rightfully": 41802, "\u0120niece": 41803, "\u0120NEC": 41804, "\u0120IPO": 41805, "\u0120Disclosure": 41806, "leanor": 41807, "ousy": 41808, "STER": 41809, "\u0120282": 41810, "Cruz": 41811, "Chall": 41812, "643": 41813, "\u0120Survive": 41814, "\u0120Fatal": 41815, "\u0120Amid": 41816, "apo": 41817, "Weapons": 41818, "DEN": 41819, "770": 41820, "\u0120Greenwald": 41821, "\u0120linen": 41822, "alos": 41823, "\u0120pollutants": 41824, "\u0120PCIe": 41825, "kat": 41826, "\u0120paw": 41827, "\u0120Kraft": 41828, "Chem": 41829, "\u0120Terminator": 41830, "\u0120reincarn": 41831, "\u0120][": 41832, "\u0120Seeds": 41833, "\u0120silhouette": 41834, "\u0120Stores": 41835, "\u0120grooming": 41836, "\u0120Direction": 41837, "\u0120Isabel": 41838, "\u0120Bridges": 41839, "\u00f0\u0141\u0133": 41840, "EED": 41841, "\u0120Morsi": 41842, "\u0120valves": 41843, "\u0120Ranked": 41844, "\u0120Pharma": 41845, "\u0120Organizations": 41846, "\u0120penetrated": 41847, "\u0120Rodham": 41848, "\u0120Protoss": 41849, "\u0120overest": 41850, "\u0120exasper": 41851, "\u0120TJ": 41852, "\u0120000000": 41853, "\u0120trickle": 41854, "\u0120bourbon": 41855, "WHO": 41856, "\u0120wretched": 41857, "\u0120microscopic": 41858, "\u0120checklist": 41859, "\u0120adorned": 41860, "Royal": 41861, "Administ": 41862, "\u0120Retirement": 41863, "\u0120Highest": 41864, "Weather": 41865, "ilege": 41866, "\u0120increments": 41867, "\u0120Cosponsors": 41868, "\u0120masse": 41869, "\u0120Sinn": 41870, "rf": 41871, "\u0120hordes": 41872, "assembly": 41873, "754": 41874, "\u0120Natasha": 41875, "\u0120TYPE": 41876, "\u0120GENERAL": 41877, "\u0120arranging": 41878, "\u0120407": 41879, "lator": 41880, "\u0120glean": 41881, "\u0120discredited": 41882, "\u0120clinicians": 41883, "UNE": 41884, "\u0120achieves": 41885, "\u0120Emerson": 41886, "complex": 41887, "=[": 41888, "\u0120principally": 41889, "\u0120frail": 41890, "picked": 41891, "\u0120thanking": 41892, "\u0120recl": 41893, "\u0120LAST": 41894, "\u0120suppressing": 41895, "ilic": 41896, "\u0120antidepressant": 41897, "\u0120Lisbon": 41898, "\u0120thor": 41899, "\u0120spa": 41900, "\u0120kingdoms": 41901, "\u0120Pearce": 41902, "emo": 41903, "\u0120plung": 41904, "\u0120divest": 41905, "\u0120********************************": 41906, "bis": 41907, "ospels": 41908, "adr": 41909, "Spirit": 41910, "halla": 41911, "Pink": 41912, "endez": 41913, "\u0120resurrected": 41914, "escape": 41915, "\u0120Rosenstein": 41916, "\u0120geological": 41917, "\u0120necessities": 41918, "\u0120carniv": 41919, "\u0120Elys": 41920, "\u0120Barney": 41921, "\u0120296": 41922, "digy": 41923, "STON": 41924, "DOWN": 41925, "\u0120milestones": 41926, "\u0120ker": 41927, "\u0120dismantling": 41928, "\u0120reprim": 41929, "\u0120crossings": 41930, "1945": 41931, "\u0120patriarchy": 41932, "\u0120blasphemy": 41933, "\u0120359": 41934, "metry": 41935, "\u0120Obesity": 41936, "\u0120Differences": 41937, "blocking": 41938, "\u00e3\u0125\u0137\u00e3\u0124\u00a1": 41939, "ichita": 41940, "\u0120Sabha": 41941, "phalt": 41942, "\u0120Colo": 41943, "uala": 41944, "efficients": 41945, "\u0120Medina": 41946, "console": 41947, "557": 41948, "\u0120Hannibal": 41949, "\u0120Habit": 41950, "\u0120Fever": 41951, "\u0120thence": 41952, "\u0120synagogue": 41953, "\u0120essentials": 41954, "\u0120wink": 41955, "\u0120Trader": 41956, "IDA": 41957, "\u0120Spoiler": 41958, "\u0120Icelandic": 41959, "\u0120Hayward": 41960, "\u0120peac": 41961, "\u0120malice": 41962, "\u0120flashback": 41963, "\u0120thw": 41964, "\u0120layoffs": 41965, "Liquid": 41966, "\u0120trooper": 41967, "\u0120hinge": 41968, "\u0120Readers": 41969, "Phill": 41970, "\u0120Bauer": 41971, "Created": 41972, "\u0120audits": 41973, "accompan": 41974, "\u0120unsuspecting": 41975, "iera": 41976, "66666666": 41977, "\u0120broch": 41978, "\u0120apprehended": 41979, "\u0120Malk": 41980, "cerning": 41981, "\u0120Codex": 41982, "OVER": 41983, "Marsh": 41984, "\u0120Deng": 41985, "\u0120Expression": 41986, "\u0120disrespectful": 41987, "\u0120ascending": 41988, "tests": 41989, "\u0120Plaintiff": 41990, "stery": 41991, "\u0120Alibaba": 41992, "dinand": 41993, "\u0120Dempsey": 41994, "Applications": 41995, "moral": 41996, "\u0120throughput": 41997, "\u0120quarrel": 41998, "\u0120mills": 41999, "\u0120hemor": 42000, "\u0120CASE": 42001, "terrorist": 42002, "stim": 42003, "ifestyle": 42004, "rozen": 42005, "CEPT": 42006, "Ark": 42007, "uci": 42008, "lectic": 42009, "\u0120irritating": 42010, "sheets": 42011, "Ay": 42012, "\u0120redeemed": 42013, "\u0120horny": 42014, "\u0120Teach": 42015, "\u0120Sear": 42016, "democracy": 42017, "465": 42018, "\u0120Restore": 42019, "\u0120standby": 42020, "\u0120Pis": 42021, "iffin": 42022, "\u0120sleepy": 42023, "\u0120extrater": 42024, "\u0120compliments": 42025, "Frameworks": 42026, "\u0120installs": 42027, "\u0120banging": 42028, "surface": 42029, "foundland": 42030, "\u0120metaphysical": 42031, "\u0120283": 42032, "ouls": 42033, "devices": 42034, "Args": 42035, "\u0120Sacrifice": 42036, "\u0120McCorm": 42037, "eson": 42038, "Conservative": 42039, "\u0120Mikhail": 42040, "seeing": 42041, "isively": 42042, "\u0120Rooms": 42043, "\u0120Generic": 42044, "\u0120enthusiastically": 42045, "\u0120gripped": 42046, "\u0120comedic": 42047, "\u0120Electricity": 42048, "\u0120guerrilla": 42049, "\u0120decoration": 42050, "\u0120Perspective": 42051, "\u0120consultations": 42052, "\u0120unamb": 42053, "\u0120plagiar": 42054, "\u0120magician": 42055, "\u0120erection": 42056, "\u0120Tourism": 42057, "oried": 42058, "roxy": 42059, "1100": 42060, "Tam": 42061, "\u012a\u00e8": 42062, "\u00ce\u00b3": 42063, "\u00d7\u00aa": 42064, "\u0120Predators": 42065, "Nitrome": 42066, "\u0120telescopes": 42067, "projects": 42068, "\u0120unprotected": 42069, "\u0120stocked": 42070, "\u0120Entreprene": 42071, "nexpected": 42072, "\u0120wastewater": 42073, "Vill": 42074, "\u0120intimately": 42075, "\u0120iCloud": 42076, "\u0120Constable": 42077, "\u0120spoof": 42078, "\u0120nefarious": 42079, "\u0120fins": 42080, "\u0120censor": 42081, "\u0120Modes": 42082, "\u0120Esper": 42083, "arbon": 42084, "\u0120intersections": 42085, "\u0120lauded": 42086, "\u0120physi": 42087, "\u0120generously": 42088, "\u0120TheNitrome": 42089, "\u0120TheNitromeFan": 42090, "\u0120arisen": 42091, "\u0120\u00d9\u012a": 42092, "\u0120glands": 42093, "\u0120Pavilion": 42094, "\u0120Gupta": 42095, "\u0120uniformly": 42096, "\u0120ramps": 42097, "riet": 42098, "\u0120WHEN": 42099, "\u0120Vanessa": 42100, "\u0120routed": 42101, "\u0120limp": 42102, "\u0120CPI": 42103, "pter": 42104, "intuitive": 42105, "\u0120vaping": 42106, "\u0120experimented": 42107, "\u0120Olympus": 42108, "\u0120Amon": 42109, "\u0120sighting": 42110, "\u0120infiltrate": 42111, "\u0120Gentleman": 42112, "\u0120signings": 42113, "\u0120Meow": 42114, "\u0120Navigation": 42115, "checks": 42116, "433": 42117, "\u0120elapsed": 42118, "\u0120Bulgarian": 42119, "espie": 42120, "\u0120SOM": 42121, "during": 42122, "\u0120spills": 42123, "anca": 42124, "\u0120Plymouth": 42125, "MAL": 42126, "\u0120domestically": 42127, "\u0120Watergate": 42128, "\u0120FAM": 42129, "killed": 42130, "edited": 42131, "\u0120Yourself": 42132, "\u0120synchronization": 42133, "\u0120Practices": 42134, "STEP": 42135, "\u0120genomes": 42136, "\u0120QR": 42137, "notice": 42138, "\u0120locating": 42139, "zin": 42140, "\u0120329": 42141, "alcohol": 42142, "\u0120kitten": 42143, "Vo": 42144, "\u0120rinse": 42145, "\u0120grapple": 42146, "\u0120Screw": 42147, "\u0120Dul": 42148, "AIR": 42149, "\u0120leasing": 42150, "\u0120Caf\u00c3\u00a9": 42151, "\u0120roses": 42152, "\u0120Respect": 42153, "\u0120mislead": 42154, "\u0120perfected": 42155, "\u0120nudity": 42156, "\u0120nonpartisan": 42157, "\u0120Consumption": 42158, "Reporting": 42159, "\u0120nuances": 42160, "\u0120deductible": 42161, "\u0120Shots": 42162, "\u0120377": 42163, "\u0120\u00e6\u013e": 42164, "anooga": 42165, "Benef": 42166, "\u0120Bam": 42167, "\u0120Samp": 42168, "ifix": 42169, "\u0120galvan": 42170, "\u0120Medals": 42171, "radius": 42172, "\u0120nobles": 42173, "\u0120eaves": 42174, "igrate": 42175, "KT": 42176, "\u0120Harbour": 42177, "uers": 42178, "\u0120risked": 42179, "req": 42180, "\u0120neurot": 42181, "gettable": 42182, "aina": 42183, "Romney": 42184, "\u0120underpin": 42185, "\u0120loft": 42186, "\u0120Subcommittee": 42187, "\u0120Mongol": 42188, "biz": 42189, "\u0120manifests": 42190, "assisted": 42191, "\u0120Gaga": 42192, "\u0120synergy": 42193, "\u0120religiously": 42194, "\u0120Pref": 42195, "\u0120Gerry": 42196, "TAG": 42197, "\u0120Choi": 42198, "466": 42199, "behind": 42200, "\u0120Ou": 42201, "GoldMagikarp": 42202, "\u0120hemorrh": 42203, "River": 42204, "\u0120tendon": 42205, "\u0120injure": 42206, "\u0120Fiona": 42207, "\u0120pag": 42208, "\u0120agitation": 42209, "||||": 42210, "uran": 42211, "\u0120ESA": 42212, "\u0120esteem": 42213, "\u0120dodging": 42214, "\u0120412": 42215, "rss": 42216, "\u0120ceases": 42217, "excluding": 42218, "\u0120intakes": 42219, "\u0120inserts": 42220, "\u0120embold": 42221, "\u0120Oral": 42222, "upuncture": 42223, "411": 42224, "\u0120Unified": 42225, "\u0120Dele": 42226, "\u0120furnace": 42227, "\u0120Coyotes": 42228, "\u0120Brach": 42229, "Labor": 42230, "\u0120handshake": 42231, "\u0120bruises": 42232, "Grade": 42233, "\u00e9\u0139\u013a": 42234, "\u0120Grammy": 42235, "ileen": 42236, "States": 42237, "\u0120Scandinavian": 42238, "\u0120Kardash": 42239, "866": 42240, "\u0120effortlessly": 42241, "\u0120DIRECT": 42242, "\u0120THEN": 42243, "\u0120Mei": 42244, "ertation": 42245, "1968": 42246, "\u0120groin": 42247, "witch": 42248, "Requirements": 42249, "985": 42250, "\u0120roofs": 42251, "\u0120estates": 42252, "\u0120HF": 42253, "\u0120haha": 42254, "\u0120densely": 42255, "\u0120OCT": 42256, "\u0120plastics": 42257, "\u0120incidentally": 42258, "\u0120Tracks": 42259, "\u0120Taxes": 42260, "\u0120chanted": 42261, "\u0120forceful": 42262, "\u0120Bieber": 42263, "\u0120Kahn": 42264, "Kent": 42265, "\u0120Cot": 42266, "licts": 42267, "Fed": 42268, "\u0120hideous": 42269, "\u0120Verd": 42270, "\u0120Syndicate": 42271, "\u0120Illegal": 42272, "Jet": 42273, "\u0120DAV": 42274, "reasonable": 42275, "crew": 42276, "\u0120fundamentalist": 42277, "\u0120truthful": 42278, "\u0120Jing": 42279, "\u0120lil": 42280, "\u0120downed": 42281, "\u0120enchanted": 42282, "\u0120Policies": 42283, "\u0120McMaster": 42284, "\u0120Hare": 42285, "ideshow": 42286, "\u0120params": 42287, "encers": 42288, "gorithm": 42289, "\u0120allowances": 42290, "\u0120turbulent": 42291, "\u0120complexities": 42292, "\u0120KT": 42293, "\u0120337": 42294, "\u0120Genetic": 42295, "FUN": 42296, "Doug": 42297, "tick": 42298, "\u0120gigs": 42299, "umenthal": 42300, "\u0120patriarchal": 42301, "\u0120calc": 42302, ",...": 42303, "\u0120cout": 42304, "\u0120Guan": 42305, "\u0120pathological": 42306, "\u0120Rivals": 42307, "\u0120underrated": 42308, "\u0120fluorescent": 42309, "\u0120Jiu": 42310, "arnaev": 42311, "\u0120Quan": 42312, "\u0120429": 42313, "\u0120\u00e0\u00a8": 42314, "Mario": 42315, "Construct": 42316, "\u0120Citation": 42317, "\u0120Racial": 42318, "\u0120RSA": 42319, "\u0120Fidel": 42320, "\u0120395": 42321, "Personally": 42322, "Cause": 42323, "\u00c3\u00bb": 42324, "radical": 42325, "inen": 42326, "\u0120vehemently": 42327, "\u0120Papa": 42328, "\u0120internship": 42329, "\u0120flakes": 42330, "\u0120Reck": 42331, "Luckily": 42332, "Bra": 42333, "2020": 42334, "ravings": 42335, "RN": 42336, "Wonder": 42337, "Seriously": 42338, "\u0120reusable": 42339, "\u0120polluted": 42340, "\u0120Peng": 42341, "leigh": 42342, "indle": 42343, "\u0120circuitry": 42344, "\u0120Madonna": 42345, "\u0120BART": 42346, "Residents": 42347, "attribute": 42348, "Philadelphia": 42349, "Club": 42350, "\u0120planner": 42351, "\u0120frantically": 42352, "\u0120faithfully": 42353, "\u0120Territories": 42354, "\u0120LAT": 42355, "\u0120Andersen": 42356, "anu": 42357, "\u0120PARK": 42358, "\u0120Sora": 42359, "iage": 42360, "\u0120Playoffs": 42361, "\u0120GCC": 42362, "427": 42363, "\u0120abnorm": 42364, "\u0120Lever": 42365, "\u0120disobedience": 42366, "Async": 42367, "\u0120Shea": 42368, "Vert": 42369, "\u0120skirts": 42370, "\u0120Sawyer": 42371, "xp": 42372, "\u0120worsening": 42373, "\u0120scapego": 42374, "\u0120Angle": 42375, "othal": 42376, "\u0120trove": 42377, "\u0120Sty": 42378, "\u0120Nguyen": 42379, "marine": 42380, "ideon": 42381, "Depths": 42382, "Blog": 42383, "\u0120Illuminati": 42384, "\u0120tracts": 42385, "\u0120organise": 42386, "\u0120ostr": 42387, "Fs": 42388, "\u0120leveraging": 42389, "\u0120Daredevil": 42390, "asar": 42391, "\u0120lang": 42392, "\u0120extermin": 42393, "ursions": 42394, "\u0120Romo": 42395, "\u00e3\u0124\u00a4\u00e3\u0125\u012a": 42396, "\u0120contended": 42397, "\u0120encountering": 42398, "\u0120Tablet": 42399, "\u0120Alternate": 42400, "skill": 42401, "\u0120sweets": 42402, "\u0120cohesive": 42403, "capacity": 42404, "\u0120repud": 42405, "\u0120lizard": 42406, "roo": 42407, "\u0120pilgrims": 42408, "\u0120Ruff": 42409, "\u0120Instrument": 42410, "\u0120Logo": 42411, "uitous": 42412, "EH": 42413, "\u0120salesman": 42414, "\u0120ankles": 42415, "Led": 42416, "\u0120Patty": 42417, "udos": 42418, "Owner": 42419, "\u0120discrepancies": 42420, "kj": 42421, "MU": 42422, "\u0120unconditional": 42423, "DragonMagazine": 42424, "iard": 42425, "Oak": 42426, "\u0120Conversation": 42427, "beer": 42428, "\u0120Osaka": 42429, "Delta": 42430, "usky": 42431, "\u0120secretion": 42432, "\u0120plaza": 42433, "\u0120ming": 42434, "\u0120depletion": 42435, "\u0120Mous": 42436, "\u0120ITS": 42437, "\u0120Himal": 42438, "\u0120Fleming": 42439, "\u0120cytok": 42440, "\u0120Hick": 42441, "\u0120batters": 42442, "\u0120Intellectual": 42443, "675": 42444, "\u00c3\u00a9r": 42445, "ISION": 42446, "\u0120Quentin": 42447, "\u0120Chapters": 42448, "ihadi": 42449, "\u0120coaster": 42450, "WAYS": 42451, "\u0120Lizard": 42452, "\u0120Yor": 42453, "andering": 42454, "Skin": 42455, "haust": 42456, "abby": 42457, "\u0120portraying": 42458, "\u0120wielded": 42459, "dash": 42460, "\u0120proponent": 42461, "\u0120ripple": 42462, "\u0120graphene": 42463, "\u0120flyer": 42464, "\u0120recurrent": 42465, "\u0120devils": 42466, "\u0120waterfall": 42467, "\u00e6\u013a\u00af": 42468, "goo": 42469, "TextColor": 42470, "\u0120tampering": 42471, "IVES": 42472, "TRUMP": 42473, "\u0120Abel": 42474, "\u0120SAL": 42475, "\u0120Hendricks": 42476, "\u0120Lucius": 42477, "bots": 42478, "\u01204096": 42479, "ISTORY": 42480, "Guest": 42481, "\u0120NX": 42482, "inant": 42483, "Benz": 42484, "\u0120Loaded": 42485, "\u0120Clever": 42486, "treatment": 42487, "\u0120tavern": 42488, "\u0120339": 42489, "\u0120TNT": 42490, "ificantly": 42491, "Temperature": 42492, "Fel": 42493, "\u0120underworld": 42494, "\u0120Judges": 42495, "\u0120<+": 42496, "\u0120stump": 42497, "\u0120occupancy": 42498, "\u0120aber": 42499, "\u0120Finder": 42500, ")\",": 42501, "\u0120Nunes": 42502, "reset": 42503, "inet": 42504, "ectomy": 42505, "\u0120wellness": 42506, "\u0120Peb": 42507, "quartered": 42508, "andan": 42509, "\u0120negatives": 42510, "\u0120Thiel": 42511, "\u0120Clip": 42512, "\u0120LTD": 42513, "\u0120blight": 42514, "\u0120repertoire": 42515, "Kyle": 42516, "\u0120quer": 42517, "\u0120Ces": 42518, "\u0120hapl": 42519, "989": 42520, "\u0120Thames": 42521, "iscopal": 42522, "Desk": 42523, "ivariate": 42524, "\u0120Excellence": 42525, "foundation": 42526, "\u0120\u00e2\u0129": 42527, "Xi": 42528, "\u0120mysteriously": 42529, "estyles": 42530, "\u0120perish": 42531, "\u0120Engels": 42532, "\u0120DEAD": 42533, "090": 42534, "}}}": 42535, "\u0120Unreal": 42536, "\u0120restless": 42537, "IDES": 42538, "orthodox": 42539, "\u0120Intermediate": 42540, "\u0120dinners": 42541, "\u0120Trout": 42542, "\u0120Seym": 42543, "\u0120Halls": 42544, "ogged": 42545, "\u0120tragedies": 42546, "\u0120didnt": 42547, "676": 42548, "\u0120ailments": 42549, "\u0120observable": 42550, "\u0120Vide": 42551, "adapt": 42552, "\u0120Dusk": 42553, "\u0120professionalism": 42554, "\u0120Prescott": 42555, "\u0120Indies": 42556, "pox": 42557, "\u0120Mehran": 42558, "Wide": 42559, "\u0120endemic": 42560, "\u0120Paran": 42561, "Bird": 42562, "\u0120pedals": 42563, "\u0120IU": 42564, "\u0120Adamant": 42565, "\u0120Hurt": 42566, "\u0120correlates": 42567, "urden": 42568, "\u0120sponsoring": 42569, "climate": 42570, "\u0120Universities": 42571, "\u0120Knot": 42572, "ennes": 42573, "\u0120Damian": 42574, "\u0120Axel": 42575, "Sport": 42576, "\u0120barb": 42577, "\u0120Sno": 42578, "shown": 42579, "steen": 42580, "udence": 42581, "\u0120nonviolent": 42582, "\u0120homophobia": 42583, "\u0120biomass": 42584, "\u0120Detail": 42585, "\u0120srfN": 42586, "\u0120Tune": 42587, "accompanied": 42588, "IENCE": 42589, "Albert": 42590, "\u0120Mongo": 42591, "zx": 42592, "\u0120Cerberus": 42593, "orbit": 42594, "cens": 42595, "\u0120slay": 42596, "SHARE": 42597, "HY": 42598, "\u0120brawl": 42599, "\u0120Probe": 42600, "\u0120nonexistent": 42601, "\u0120Clarence": 42602, "\u0120Blackburn": 42603, "\u0120portals": 42604, "\u0120Rita": 42605, "\u0120Remain": 42606, "\u0120Levant": 42607, "\u0120tricked": 42608, "\u0120Ferry": 42609, "avering": 42610, "\u0120Strawberry": 42611, "\u0120Answers": 42612, "\u0120horrendous": 42613, "\u0120Aman": 42614, "Supplement": 42615, "\u0120Toad": 42616, "\u0120peeled": 42617, "\u0120manoeuv": 42618, "\u0120Uzbek": 42619, "monds": 42620, "\u0120Hector": 42621, "\u0120402": 42622, "pees": 42623, "fixes": 42624, "\u0120dj": 42625, "\u0120resumes": 42626, "\u0120accountant": 42627, "\u0120adversity": 42628, "\u0120hampered": 42629, "\u0120Larson": 42630, "\u0120doping": 42631, "parts": 42632, "Hur": 42633, "\u0120bearded": 42634, "\u0120yr": 42635, "\u0120Plugin": 42636, "\u00e5\u00a5\u00b3": 42637, "\u0120/**": 42638, "rolley": 42639, "\u0120watershed": 42640, "\u0120Submission": 42641, "iflower": 42642, "ASC": 42643, "\u0120choir": 42644, "\u0120sculptures": 42645, "mA": 42646, "increasing": 42647, "aii": 42648, "\u0120sneakers": 42649, "\u0120confronts": 42650, "\u0120Elephant": 42651, "\u0120Elixir": 42652, "\u0120recal": 42653, "\u0120TTL": 42654, "widget": 42655, "\u0120Wax": 42656, "\u0120Grayson": 42657, "\u0120hairst": 42658, "\u0120humiliated": 42659, "\u0120WARN": 42660, "appiness": 42661, "\u0120TTC": 42662, "Fuel": 42663, "\u0120polio": 42664, "\u0120complexes": 42665, "\u0120babe": 42666, "\u0120XIV": 42667, "PF": 42668, ").[": 42669, "Parts": 42670, "\u0120435": 42671, "Meg": 42672, "\u0120Yards": 42673, "\u0120ALP": 42674, "\u0120yells": 42675, "\u0120princes": 42676, "\u0120bullies": 42677, "\u0120Capitalism": 42678, "exempt": 42679, "FAQ": 42680, "\u0120Sponge": 42681, "\u0120Ala": 42682, "\u0120pleasantly": 42683, "\u0120buf": 42684, "\u0120denote": 42685, "\u0120unpublished": 42686, "\u0120kneeling": 42687, "asca": 42688, "\u0120lapse": 42689, "alien": 42690, "994": 42691, "\u0120referees": 42692, "\u0120Lawyers": 42693, "Santa": 42694, "\u0120puzzling": 42695, "\u0120Prometheus": 42696, "\u0120Pharaoh": 42697, "\u0120Delay": 42698, "\u0120facilitates": 42699, "\u0120CES": 42700, "\u0120jewels": 42701, "\u0120booklet": 42702, "onding": 42703, "\u0120polarization": 42704, "\u0120Moran": 42705, "\u0120Salad": 42706, "\u0120SOS": 42707, "\u0120Advice": 42708, "PHOTOS": 42709, "ICAN": 42710, "iatures": 42711, "express": 42712, "\u0120Wonderland": 42713, "\u0120CODE": 42714, "\u0120CLASS": 42715, "975": 42716, "\u0120grep": 42717, "\u0120Diesel": 42718, "\u0120Glac": 42719, "!?\"": 42720, "\u0120rm": 42721, "oine": 42722, "discrimination": 42723, "\u0120Nurse": 42724, "mallow": 42725, "\u0120vortex": 42726, "\u0120Consortium": 42727, "\u0120largeDownload": 42728, "straight": 42729, "aughlin": 42730, "Grad": 42731, "\u0120publicized": 42732, "\u0120Waves": 42733, "\u0120Redd": 42734, "\u0120festivities": 42735, "\u0120Mane": 42736, "arov": 42737, "\u0120fleeting": 42738, "\u0120Drunk": 42739, "ugen": 42740, "Cele": 42741, "\u0120chromosomes": 42742, "\u0120DOT": 42743, "-+-+-+-+": 42744, "\u0120busiest": 42745, "\u0120Beaver": 42746, "Syrian": 42747, "\u0120Kyr": 42748, "kas": 42749, "\u0120CrossRef": 42750, "1950": 42751, "7601": 42752, "\u0120repealing": 42753, "\u0120Winners": 42754, "\u0120Macro": 42755, "\u0120DOD": 42756, "blance": 42757, "Sort": 42758, "641": 42759, "\u0120metre": 42760, "\u0120Dirk": 42761, "\u0120goggles": 42762, "\u0120drawbacks": 42763, "\u0120complainant": 42764, "\u0120authorizing": 42765, "\u0120antitrust": 42766, "operated": 42767, "\u0120mah": 42768, "\u0120exaggeration": 42769, "Amazing": 42770, "\u0120Seraph": 42771, "\u0120haze": 42772, "wow": 42773, "\u0120extinguished": 42774, "\u0120canyon": 42775, "\u0120Bosh": 42776, "\u0120vents": 42777, "\u0120scrape": 42778, "Correct": 42779, "426": 42780, "\u0120avg": 42781, "Demand": 42782, "\u0120\u00e2\u012a\u00bc": 42783, "\u0120microbiota": 42784, "\"}],\"": 42785, "\u0120Stev": 42786, "Bio": 42787, "\u0120Planes": 42788, "\u0120suggestive": 42789, "\u0120decipher": 42790, "\u0120Refugee": 42791, "\u0120Kejriwal": 42792, "\u0120Greenpeace": 42793, "\u0120declass": 42794, "\u0120Sounders": 42795, "\u0120tho": 42796, "\u0120decrypt": 42797, "\u0120brushing": 42798, "\u0120Janeiro": 42799, "ipop": 42800, "Si": 42801, "877": 42802, "\u0120Geoffrey": 42803, "\u0120cpu": 42804, "\u0120Hazel": 42805, "\u0120viewpoints": 42806, "\u0120crispy": 42807, "\u0120Notification": 42808, "\u0120solder": 42809, "\u0120Modest": 42810, "\u0120Hemisphere": 42811, "\u0120cassette": 42812, "includes": 42813, "\u0120identifiers": 42814, "\u0120CALL": 42815, "incent": 42816, "Todd": 42817, "\u0120Sweep": 42818, "\u0120334": 42819, "boss": 42820, "\u0120smir": 42821, "ginx": 42822, "\u0120township": 42823, "\u0120grieving": 42824, "\u0120Mosque": 42825, "Netflix": 42826, "ASED": 42827, "\u0120Millennials": 42828, "ocom": 42829, "1967": 42830, "\u0120boldly": 42831, "sleep": 42832, "\u0120esche": 42833, "arijuana": 42834, "\u0120swirl": 42835, "\u0120Penal": 42836, "\u0120negligent": 42837, "\u0120Stephenson": 42838, "KER": 42839, "\u0120Zoro": 42840, "risis": 42841, "\u0120localization": 42842, "\u0120Seymour": 42843, "\u0120Anglic": 42844, "reditation": 42845, "protection": 42846, "\u0120Paige": 42847, "\u0120omit": 42848, "\u0120Rousse": 42849, "\u0120Tub": 42850, "\u0120invitations": 42851, "tty": 42852, "\u0120moss": 42853, "physical": 42854, "Credits": 42855, "\u0120anarchy": 42856, "\u0120childcare": 42857, "\u0120lull": 42858, "\u0120Mek": 42859, "\u0120Languages": 42860, "latest": 42861, "\u0120Sanford": 42862, "\u0120usability": 42863, "\u0120diffuse": 42864, "\u0120DATA": 42865, "\u0120sprites": 42866, "\u0120Vegeta": 42867, "\u0120Promotion": 42868, "\u00e3\u0125\u00bc\u00e3\u0124\u00af": 42869, "ricting": 42870, "zee": 42871, "Turkish": 42872, "\u0120TDs": 42873, "proven": 42874, "571": 42875, "\u0120smugglers": 42876, "70710": 42877, "\u0120reformed": 42878, "\u0120Lois": 42879, "\u0120unfl": 42880, "\u0120WITHOUT": 42881, "\u0120Returning": 42882, "annie": 42883, "\u0120Tomas": 42884, "Franc": 42885, "\u0120Profit": 42886, "\u0120SERV": 42887, "\u0120Rumble": 42888, "ikuman": 42889, "esan": 42890, "\u0120testers": 42891, "\u0120gadget": 42892, "\u0120bracelet": 42893, "\u0120FSA": 42894, "component": 42895, "\u0120paramedics": 42896, "\u0120jan": 42897, "\u0120Remem": 42898, "\u0120Skinner": 42899, "\u0120lov": 42900, "\u0120Quake": 42901, "roma": 42902, "\u0120flask": 42903, "Princ": 42904, "\u0120overpower": 42905, "\u0120lodging": 42906, "\u0120KKK": 42907, "rette": 42908, "\u0120absorbs": 42909, "wrote": 42910, "\u0120,\"": 42911, "Kings": 42912, "\u0120Hail": 42913, "\u0120Falling": 42914, "xtap": 42915, "\u0120Helena": 42916, "irens": 42917, "Larry": 42918, "\u0120pamphlet": 42919, "\u0120CPR": 42920, "Gro": 42921, "\u0120Hiroshima": 42922, "\u0120holistic": 42923, "\".[": 42924, "\u0120detachment": 42925, "\u0120aspire": 42926, "\u0120complicit": 42927, "\u0120Greenwood": 42928, "\u0120respawn": 42929, "\u0120Stupid": 42930, "\u0120Finished": 42931, "fal": 42932, "bass": 42933, "\u0120abhor": 42934, "\u0120mockery": 42935, "\u0120Feast": 42936, "VIDEO": 42937, "\u0120consec": 42938, "\u0120Hungry": 42939, "Pull": 42940, "\u0120Hust": 42941, "itance": 42942, "?\u00e3\u0122\u012f": 42943, ")--": 42944, "\u0120Parallel": 42945, "conv": 42946, "469": 42947, "haar": 42948, "want": 42949, "Paper": 42950, "mins": 42951, "\u0120Toro": 42952, "\u0120TRUMP": 42953, "\u0120Rai": 42954, "DW": 42955, "\u0120Wicked": 42956, "\u0120Lep": 42957, "\u0120funky": 42958, "\u0120detriment": 42959, "iosis": 42960, "achev": 42961, "\u0120degrade": 42962, "imilation": 42963, "\u0120retard": 42964, "\u0120fragmentation": 42965, "\u0120cowboy": 42966, "\u0120YPG": 42967, "\u0120HAL": 42968, "Parents": 42969, "\u0120Sieg": 42970, "\u0120Strauss": 42971, "\u0120Rubber": 42972, "\u00d7\u0132": 42973, "Frag": 42974, "\u0120pt": 42975, "\u0120optionally": 42976, "\u0120ZIP": 42977, "\u0120Transcript": 42978, "\u0120Dwell": 42979, "882": 42980, "Merc": 42981, "\u0120MOT": 42982, "\u00e3\u0125\u00af\u00e3\u0125\u00b3": 42983, "\u0120hunts": 42984, "\u0120executes": 42985, "Includes": 42986, "\u0120acidic": 42987, "\u0120Responsibility": 42988, "\u0120Dumb": 42989, "wei": 42990, "Anderson": 42991, "\u0120Jasper": 42992, "ighton": 42993, "absolutely": 42994, "Adult": 42995, "\u0120plunder": 42996, "Morning": 42997, "\u0120Tours": 42998, "\u0120Dane": 42999, "\u00ce\u00ba": 43000, "\u0120TEST": 43001, "\u0120Gina": 43002, "\u0120canine": 43003, "awan": 43004, "\u0120socialists": 43005, "\u0120Soda": 43006, "\u0120impetus": 43007, "\u0120Supplementary": 43008, "oliath": 43009, "\u0120Kinnikuman": 43010, "mittedly": 43011, "seconds": 43012, "\u0120organisers": 43013, "\u0120documentaries": 43014, "Variable": 43015, "GREEN": 43016, "\u0120resorts": 43017, "\u0120bragging": 43018, "\u0120368": 43019, "Artist": 43020, "wk": 43021, "blers": 43022, "Uncommon": 43023, "\u0120Retrieved": 43024, "\u0120hectares": 43025, "\u0120toxin": 43026, "rank": 43027, "\u0120faiths": 43028, "\u0120Graphic": 43029, "\u0120vec": 43030, "\u0120LIA": 43031, "African": 43032, "\u0120ardent": 43033, "endiary": 43034, "Lake": 43035, "\u0120DOS": 43036, "cientious": 43037, "\u0120Okawaru": 43038, "\u0120Ally": 43039, "\u0120Timeline": 43040, "Dash": 43041, "\u0120Ic": 43042, "continue": 43043, "\u0120tidy": 43044, "\u0120instinctively": 43045, "\u0120Possibly": 43046, "\u0120Outdoor": 43047, "\u0120Wouldn": 43048, "\u0120lich": 43049, "\u0120Bray": 43050, "\u0120AX": 43051, "\u0120\u00c3\u012b": 43052, "\u0120+#": 43053, "\\'": 43054, "Directory": 43055, "abiding": 43056, "\u0120feral": 43057, "icative": 43058, "butt": 43059, "\u0120perverse": 43060, "Salt": 43061, "\u0120warped": 43062, "\u0120nineteen": 43063, "\u0120cabinets": 43064, "\u0120srfAttach": 43065, "\u0120Sloan": 43066, "\u0120powering": 43067, "regation": 43068, "Flight": 43069, "severe": 43070, "\u0120stren": 43071, "\u0120cog": 43072, "apache": 43073, "\u0120\u00e2\u013f": 43074, "\u0120cafeteria": 43075, "paces": 43076, "\u0120Grimoire": 43077, "utonium": 43078, "\u0120raining": 43079, "\u0120circling": 43080, "\u0120linebackers": 43081, "credit": 43082, "\u0120repatri": 43083, "\u0120Camden": 43084, "license": 43085, "\u0120lyric": 43086, "\u0120descriptor": 43087, "\u0120valleys": 43088, "\u0120req": 43089, "\u0120backstage": 43090, "\u0120Prohibition": 43091, "\u0120Ket": 43092, "Opening": 43093, "Sym": 43094, "\u00e6\u0138\u00b9": 43095, "\u0120servings": 43096, "\u0120overseen": 43097, "\u0120asteroids": 43098, "\u0120Mods": 43099, "\u0120Springer": 43100, "\u0120Container": 43101, "\u00e8\u00bb": 43102, "\u0120Mens": 43103, "\u0120multim": 43104, "\u0120firefighter": 43105, "pec": 43106, "\u0120chlorine": 43107, "\u00d0\u00bc": 43108, "endi": 43109, "\u0120sparing": 43110, "\u0120polygamy": 43111, "\u0120RN": 43112, "\u0120Pell": 43113, "\u0120tigers": 43114, "\u0120flashy": 43115, "\u0120Madame": 43116, "Sword": 43117, "\u0120prefrontal": 43118, "\u0120prerequisite": 43119, "uca": 43120, "\u0120wifi": 43121, "\u0120misconception": 43122, "\u0120harshly": 43123, "\u0120Streaming": 43124, "otom": 43125, "\u0120Giuliani": 43126, "footed": 43127, "\u0120tubing": 43128, "individual": 43129, "zek": 43130, "nuclear": 43131, "mol": 43132, "\u0120rightful": 43133, "493": 43134, "\u0120specialization": 43135, "\u0120passionately": 43136, "\u0120Velocity": 43137, "\u0120Availability": 43138, "Tenn": 43139, "\u0120latch": 43140, "\u0120Somebody": 43141, "\u0120helium": 43142, "claw": 43143, "\u0120dipping": 43144, "XXX": 43145, "\u0120interpersonal": 43146, "710": 43147, "\u0120subter": 43148, "\u0120biologists": 43149, "\u0120Lighting": 43150, "\u0120optic": 43151, "\u0120denim": 43152, "endon": 43153, "\u0120Corm": 43154, "\u0120341": 43155, "\u0120Coup": 43156, "\u0120fearless": 43157, "\u0120alot": 43158, "\u0120Clifford": 43159, "\u0120Runtime": 43160, "\u0120Provision": 43161, "updated": 43162, "leneck": 43163, "\u0120neuron": 43164, "\u0120grading": 43165, "\u0120Ct": 43166, "sequence": 43167, "inia": 43168, "concept": 43169, "\u0120roaring": 43170, "rival": 43171, "\u0120Caucasian": 43172, "\u0120monog": 43173, "keyes": 43174, "\u0120appellate": 43175, "\u0120liaison": 43176, "EStreamFrame": 43177, "\u0120Plum": 43178, "!.": 43179, "\u0120spherical": 43180, "\u0120perished": 43181, "\u0120blot": 43182, "\u0120benches": 43183, "\u0120411": 43184, "\u0120pioneered": 43185, "\u0120hurled": 43186, "Jennifer": 43187, "\u0120Yosemite": 43188, "Chair": 43189, "\u0120reefs": 43190, "\u0120elector": 43191, "\u0120Anthem": 43192, "652": 43193, "\u0120uninstall": 43194, "\u0120impede": 43195, "\u0120blinking": 43196, "\u0120goto": 43197, "Decre": 43198, "Aren": 43199, "\u0120stabilization": 43200, "\u0120Disabled": 43201, "\u0120Yanukovych": 43202, "\u0120outlawed": 43203, "\u0120Ventura": 43204, "teness": 43205, "\u0120plantation": 43206, "\u0120yacht": 43207, "\u0120Huawei": 43208, "\u0120solvent": 43209, "\u0120gracious": 43210, "\u0120curiously": 43211, "\u0120capacitor": 43212, "\u0120cx": 43213, "\u0120Reflex": 43214, "Phys": 43215, "\u0120Cf": 43216, "ptin": 43217, "conservative": 43218, "\u0120invocation": 43219, "cour": 43220, "FN": 43221, "\u0120Newly": 43222, "Hour": 43223, "Asian": 43224, "\u0120Leading": 43225, "\u0120Aerospace": 43226, "Anne": 43227, "\u0120prenatal": 43228, "\u0120deteriorating": 43229, "HCR": 43230, "\u0120Normandy": 43231, "olini": 43232, "\u0120Ambro": 43233, "910": 43234, "\u0120setbacks": 43235, "\u0120TRE": 43236, "\u0120sig": 43237, "\u0120Scourge": 43238, "597": 43239, "798": 43240, "Gameplay": 43241, "\u0120msec": 43242, "MX": 43243, "\u0120pricey": 43244, "\u0120LLP": 43245, "akeru": 43246, "\u0120overarching": 43247, "\u0120Bale": 43248, "\u0120worldly": 43249, "Clark": 43250, "\u0120scenic": 43251, "\u0120disliked": 43252, "\u0120Controlled": 43253, "Tickets": 43254, "\u0120EW": 43255, "abies": 43256, "\u0120Plenty": 43257, "Nonetheless": 43258, "\u0120artisan": 43259, "Transfer": 43260, "\u0120Famous": 43261, "\u0120infield": 43262, "bley": 43263, "\u0120unresolved": 43264, "\u0120MLA": 43265, "\u00e3\u0124\u0124": 43266, "Correction": 43267, "\u0120democrat": 43268, "\u0120Moreno": 43269, "rocal": 43270, "ilings": 43271, "\u0120sailor": 43272, "\u0120rife": 43273, "hung": 43274, "\u0120tropes": 43275, "\u0120snatched": 43276, "\u0120LIN": 43277, "\u0120Bib": 43278, "ESA": 43279, "\u0120Prev": 43280, "\u0120Camel": 43281, "runtime": 43282, "\u0120obnoxious": 43283, "437": 43284, "\u0120summers": 43285, "\u0120unexplained": 43286, "\u0120Walters": 43287, "caliber": 43288, "\u0120gull": 43289, "\u0120Endurance": 43290, "\u00e4\u00bd\u013e": 43291, "\u0120347": 43292, "Irish": 43293, "\u0120aerobic": 43294, "\u0120cramped": 43295, "\u0120Honolulu": 43296, "\u00e0\u00a9": 43297, "userc": 43298, "ecast": 43299, "ACY": 43300, "\u0120Query": 43301, "\u00e3\u0124\u00b9\u00e3\u0125\u012a": 43302, "Beta": 43303, "\u0120susceptibility": 43304, "\u0120Shiv": 43305, "\u0120Limbaugh": 43306, "\u0120\u00c3\u0138": 43307, "\u0120NXT": 43308, "\u0120Muss": 43309, "\u0120Britons": 43310, "ESCO": 43311, "EGIN": 43312, "\u0120%%": 43313, "\u0120secession": 43314, "\u0120Patron": 43315, "\u0120Lua": 43316, "naires": 43317, "\u0120JPMorgan": 43318, "usb": 43319, "ocyte": 43320, "\u0120councillors": 43321, "\u0120Liang": 43322, "farm": 43323, "\u0120nervously": 43324, "\u0120attractiveness": 43325, "\u0120Kov": 43326, "jump": 43327, "Plot": 43328, "\u0120stains": 43329, "\u0120Statue": 43330, "\u0120Apostles": 43331, "heter": 43332, "\u0120SUPPORT": 43333, "\u0120overwhelm": 43334, "YES": 43335, "\u0120291": 43336, "density": 43337, "\u0120trapping": 43338, "Mit": 43339, "\u0120fide": 43340, "\u0120Pamela": 43341, "atlantic": 43342, "Damn": 43343, "\u0120pts": 43344, "OPA": 43345, "\u0120servicing": 43346, "\u0120overflowing": 43347, "ulo": 43348, "\u0120Erit": 43349, "ticket": 43350, "lighting": 43351, "\u0120Hmm": 43352, "\u00e3\u0125\u00bc\u00e3\u0125\u00ab": 43353, "imoto": 43354, "\u0120chuckle": 43355, "423": 43356, "\u00e3\u0123\u0137": 43357, "shape": 43358, "\u0120queues": 43359, "\u0120anchors": 43360, "\u00e3\u0124\u00bc\u00e3\u0124\u00a6\u00e3\u0124\u00b9": 43361, "Fer": 43362, "\u0120awoke": 43363, "\u0120666": 43364, "hands": 43365, "\u0120divergence": 43366, "\u0120505": 43367, "Tips": 43368, "\u0120depot": 43369, "\u0120skew": 43370, "\u0120Deliver": 43371, "opot": 43372, "\u0120divul": 43373, "\u0120EB": 43374, "unsigned": 43375, "\u0120Uni": 43376, "Xbox": 43377, "\u0120forks": 43378, "\u0120702": 43379, "\u00e5\u00af": 43380, "\u0120promoters": 43381, "\u0120Vapor": 43382, "\u0120levied": 43383, "slot": 43384, "\u0120pigment": 43385, "\u0120cylinders": 43386, "CRE": 43387, "\u0120snatch": 43388, "\u0120perpetually": 43389, "\u0120licking": 43390, "\u0120Feet": 43391, "\u0120Kraken": 43392, "\u0120Holden": 43393, "\u0120CLSID": 43394, "mr": 43395, "\u0120projector": 43396, "\u0120denotes": 43397, "\u0120chapel": 43398, "\u0120Torrent": 43399, "bler": 43400, "Route": 43401, "\u0120Defendant": 43402, "\u0120Publishers": 43403, "\u0120Males": 43404, "\u0120Innov": 43405, "\u0120Agility": 43406, "riter": 43407, "tymology": 43408, "stores": 43409, "Lind": 43410, "\u0120folly": 43411, "\u0120Zurich": 43412, "Ble": 43413, "\u0120nurture": 43414, "\u0120coastline": 43415, "uchin": 43416, "Domin": 43417, "\u0120frivol": 43418, "\u0120Consolid": 43419, "results": 43420, "MJ": 43421, "\u0120phylogen": 43422, "\u0120hauled": 43423, "\u0120Wiley": 43424, "\u0120Jessie": 43425, "\u0120Prepare": 43426, "\u0120Eps": 43427, "\u0120treasurer": 43428, "IAS": 43429, "\u0120colonists": 43430, "\u0120inund": 43431, "\u0120WWF": 43432, "\u0120Converted": 43433, "6000": 43434, "outside": 43435, "\u0120Appearance": 43436, "\u0120Relic": 43437, "\u0120Mister": 43438, "saw": 43439, "\u0120resultant": 43440, "\u0120adjective": 43441, "\u0120Laurel": 43442, "\u0120Hindi": 43443, "bda": 43444, "Peace": 43445, "\u0120rebirth": 43446, "\u0120membranes": 43447, "\u0120forwarding": 43448, "\u0120collided": 43449, "\u0120Carolyn": 43450, "Kansas": 43451, "599": 43452, "\u0120SolidGoldMagikarp": 43453, "Beck": 43454, "\u0120stressing": 43455, "\u0120Goo": 43456, "\u0120Cooperative": 43457, "\u0120fs": 43458, "\u0120Archie": 43459, "Liter": 43460, "\u0120Klopp": 43461, "Jerry": 43462, "\u0120footwear": 43463, "Warren": 43464, "\u0120scree": 43465, "hare": 43466, "Understanding": 43467, "Ped": 43468, "\u0120anthology": 43469, "\u0120Announce": 43470, "Mega": 43471, "\u0120fluent": 43472, "\u0120bondage": 43473, "\u0120Discount": 43474, "ilial": 43475, "Cart": 43476, "\u0120Nightmares": 43477, "Sham": 43478, "\u0120Boll": 43479, "ussie": 43480, "Http": 43481, "Atlanta": 43482, "\u0120unrecogn": 43483, "\u0120Bid": 43484, "\u0120undergrad": 43485, "\u0120forgiving": 43486, "\u0120Glover": 43487, "AAAAAAAA": 43488, "445": 43489, "VG": 43490, "paio": 43491, "killers": 43492, "\u0120responsibly": 43493, "\u0120mobilize": 43494, "\u0120effected": 43495, "\u0120Lumin": 43496, "\u0120kale": 43497, "\u0120infringing": 43498, "announced": 43499, "\u0120fitt": 43500, "batch": 43501, "\u0120Tackle": 43502, "\u0120Lime": 43503, "\u0120APP": 43504, "ukemia": 43505, "\u0120ruby": 43506, "\u0120exoner": 43507, "\u0120Casual": 43508, "070": 43509, "\u0120pelvic": 43510, "\u0120automate": 43511, "\u0120Kear": 43512, "\u0120Coastal": 43513, "\u0120creed": 43514, "\u0120boredom": 43515, "\u0120Stun": 43516, "riott": 43517, "\u0124\u0130": 43518, "\u0120regenerate": 43519, "\u0120comedians": 43520, "\u0120OPER": 43521, "Spons": 43522, "idium": 43523, "onis": 43524, "Located": 43525, "057": 43526, "\u0120suspense": 43527, "\u0120Dating": 43528, "Cass": 43529, "\u0120neocons": 43530, "\u0120Shinzo": 43531, "\u0120awoken": 43532, "christ": 43533, "\u0120Messages": 43534, "attled": 43535, "\u0120Spray": 43536, "\u0120Spice": 43537, "CW": 43538, "\u0120shielding": 43539, "\u0120Gaul": 43540, "Amid": 43541, "\u0120paramilitary": 43542, "\u0120multif": 43543, "\u0120Tanner": 43544, "ilk": 43545, "\u0120goddamn": 43546, "gements": 43547, "\u0120befriend": 43548, "mobi": 43549, "\u0120388": 43550, "folder": 43551, "acca": 43552, "\u0120insin": 43553, "gap": 43554, "Nev": 43555, "fifth": 43556, "\u0120psychiatry": 43557, "banks": 43558, "THIS": 43559, "\u0120harb": 43560, "acqu": 43561, "\u0120facade": 43562, "\u0120PowerPoint": 43563, "803": 43564, "\u0120bluff": 43565, "Shares": 43566, "\u0120favoring": 43567, "Elizabeth": 43568, "\u00c3\u012f\u00c3\u012f": 43569, "\u0120ranger": 43570, "772": 43571, "\u0120Arche": 43572, "hak": 43573, "\u0120Genetics": 43574, "\u0120FEMA": 43575, "\u0120evolves": 43576, "\u0120este": 43577, "\u0120Pets": 43578, "\u0120M\u00c3\u00a9": 43579, "\u0120Interesting": 43580, "\u0120Canterbury": 43581, "chapter": 43582, "\u0120Starfleet": 43583, "Spanish": 43584, "\u0120drawback": 43585, "\u0120Norwich": 43586, "970": 43587, "north": 43588, "aganda": 43589, "\u0120transformative": 43590, "ramids": 43591, "biology": 43592, "aday": 43593, "\u0120propagation": 43594, "\u0120Gamma": 43595, "\u0120Denise": 43596, "\u0120Calculator": 43597, "entimes": 43598, "\u0120Bett": 43599, "\u0120appendix": 43600, "\u0120HDD": 43601, "AKING": 43602, "\u0120stigmat": 43603, "\u0120holster": 43604, "\u0120ordinarily": 43605, "Chance": 43606, "\u0120Contrary": 43607, "\u0120adhesive": 43608, "\u0120gathers": 43609, "612": 43610, "reau": 43611, "onyms": 43612, "eways": 43613, "\u0120induces": 43614, "\u0120interchangeable": 43615, "sem": 43616, "Whit": 43617, "\u0120trance": 43618, "\u0120incorporation": 43619, "\u0120Extras": 43620, "Financial": 43621, "\u0120awkwardly": 43622, "\u0120Sturgeon": 43623, "\u0120HY": 43624, "Normally": 43625, "\u0120Ending": 43626, "\u0120Assist": 43627, "encrypted": 43628, "\u0120subjug": 43629, "\u0120nos": 43630, "\u0120fanatic": 43631, "Cub": 43632, "CU": 43633, "?\".": 43634, "\u0120irreversible": 43635, "\u00e5\u0124": 43636, "031": 43637, "\u0120HAR": 43638, "spread": 43639, "ulia": 43640, "=$": 43641, "Scope": 43642, "Lots": 43643, "\u0120lifestyles": 43644, "olon": 43645, "\u0120feds": 43646, "\u0120congratulate": 43647, "webkit": 43648, "\u0120indistinguishable": 43649, "\u0120Swing": 43650, "\u0120commandments": 43651, "quila": 43652, "abella": 43653, "methyl": 43654, "annabin": 43655, "\u0120overe": 43656, "\u0120lobster": 43657, "\u0120QUEST": 43658, "\u0120CONTIN": 43659, "bernatorial": 43660, "::::::::": 43661, "\u0120Trave": 43662, "\u0120Samoa": 43663, "ANI": 43664, "752": 43665, "\u00d0\u00b4": 43666, "usercontent": 43667, "\u0120Moderate": 43668, "yeah": 43669, "\u0120Kitt": 43670, "\u0120wee": 43671, "\u0120stuffing": 43672, "\u0120Intervention": 43673, "\u0120Dign": 43674, "\u0120warehouses": 43675, "\u0120Fiji": 43676, "\u0120pellets": 43677, "\u0120takeaway": 43678, "\u0120TABLE": 43679, "\u0120Classical": 43680, "collection": 43681, "\u0120landfall": 43682, "\u0120Muscle": 43683, "\u0120settles": 43684, "\u0120ADV": 43685, "\u0120344": 43686, "Laura": 43687, "\u0120fared": 43688, "\u0120Partial": 43689, "436": 43690, "ossibility": 43691, "\u0120Daly": 43692, "\u0120Tarant": 43693, "\u0120Fuji": 43694, "aml": 43695, "cence": 43696, "551": 43697, "\u0120Procedures": 43698, "\u0120OCD": 43699, "\u0120UD": 43700, "tin": 43701, "QUI": 43702, "acho": 43703, "438": 43704, "\u0120glitches": 43705, "\u0120enchantment": 43706, "\u0120calculates": 43707, "IRO": 43708, "\u0120Hua": 43709, "alyses": 43710, "\u0120Lift": 43711, "umo": 43712, "\u0120leapt": 43713, "\u0120hypothesized": 43714, "\u0120Gustav": 43715, "itans": 43716, "VERSION": 43717, "\u00e6\u0142": 43718, "Roger": 43719, "\u0120rand": 43720, "\u0120Adapter": 43721, "\u0120331": 43722, "\u0120Petition": 43723, "kies": 43724, "Mars": 43725, "\u0120undercut": 43726, "zees": 43727, "\u0120Lyons": 43728, "\u0120DHCP": 43729, "Missing": 43730, "\u0120retirees": 43731, "\u0120insidious": 43732, "eli": 43733, ">)": 43734, ".\u00e3\u0122\u012f": 43735, "\u0120finalists": 43736, "\u0120Aure": 43737, "\u0120accuser": 43738, "\u0120wastes": 43739, "\u0120Ys": 43740, "\u0120Lori": 43741, "\u0120constituencies": 43742, "\u0120supper": 43743, "\u0120mayhem": 43744, "orange": 43745, "\u0120misplaced": 43746, "\u0120managerial": 43747, "\u0120exce": 43748, "\u0120CLI": 43749, "\u0120primal": 43750, "\u0120Lent": 43751, "Crystal": 43752, "hover": 43753, "\u0120NTS": 43754, "endum": 43755, "\u0120dw": 43756, "\u0120Alc": 43757, "nostic": 43758, "\u0120preserves": 43759, "\u0120Tsarnaev": 43760, "\u0120tripled": 43761, "relative": 43762, "Arcade": 43763, "killing": 43764, "\u0120WEEK": 43765, "\u0120Hanna": 43766, "Dust": 43767, "Completed": 43768, "\u0123\u00ab": 43769, "\u0120approves": 43770, "\u0120Surf": 43771, "\u0120Lutheran": 43772, "venants": 43773, "\u0120robberies": 43774, "weights": 43775, "software": 43776, "atana": 43777, "ugal": 43778, "\u0120gravy": 43779, "\u0120Cance": 43780, "OLOGY": 43781, "lyak": 43782, "Tonight": 43783, "\u0120unveil": 43784, "\u01201904": 43785, "\u0120Minion": 43786, "entious": 43787, "stice": 43788, "packages": 43789, "\u0120GEAR": 43790, "\u0120gol": 43791, "\u0120Hutchinson": 43792, "\u0120Profession": 43793, "\u0120GUN": 43794, "\u0120Difference": 43795, "\u0120Tsukuyomi": 43796, "\u0120Lesbian": 43797, "670": 43798, "\u0120fugitive": 43799, "\u0120Planetary": 43800, "--------------------------------------------------------": 43801, "\u0120accrued": 43802, "\u0120chicks": 43803, "\u0120stopp": 43804, "\u0120blockers": 43805, "Cod": 43806, "\u0120commenters": 43807, "\u0120Somewhere": 43808, "\u0120Photographer": 43809, "theme": 43810, "\u0120mayoral": 43811, "wu": 43812, "\u0120antennas": 43813, "\u0120revamped": 43814, "\u0120Subjects": 43815, "it\u00c3\u00a9": 43816, "imura": 43817, "\u0120entrances": 43818, "literally": 43819, "\u0120tenets": 43820, "\u0120OMG": 43821, "\u0120MPH": 43822, "\u0120Donkey": 43823, "\u0120Offense": 43824, "\u0120\"+": 43825, "Snap": 43826, "\u0120AFB": 43827, "\u0120animate": 43828, "\u0120Sod": 43829, "Hispanic": 43830, "\u0120inconsistency": 43831, "Db": 43832, "FY": 43833, "Export": 43834, "\u0120ape": 43835, "\u0120pearl": 43836, "ibel": 43837, "\u0120PACs": 43838, "\u0120{\\": 43839, "\u0120actu": 43840, "\u0120HSBC": 43841, "campus": 43842, "\u0120payoff": 43843, "\u0120deities": 43844, "\u0120Nato": 43845, "ouple": 43846, "\u0120censored": 43847, "\u0120Clojure": 43848, "\u0120confounding": 43849, "eni": 43850, "\u0120reckon": 43851, "ophe": 43852, "\u0120spotting": 43853, "\u0120signifies": 43854, "\u0120propel": 43855, "\u0120festive": 43856, "Suggest": 43857, "\u0120pledging": 43858, "\u0120Berman": 43859, "\u0120rebellious": 43860, "\u0120overshadowed": 43861, "\u0120infiltrated": 43862, "jobs": 43863, "672": 43864, "\u0120scalable": 43865, "\u0120dominion": 43866, "\u0120Newfoundland": 43867, "\u0120Meadow": 43868, "\u0120partitions": 43869, "AMI": 43870, "\u0120supplementary": 43871, "strument": 43872, "\u0120hairy": 43873, "\u0120perpetuate": 43874, "\u0120nutshell": 43875, "\u0120Potato": 43876, "\u0120Hobbit": 43877, "\u0120curses": 43878, "Float": 43879, "\u0120quieter": 43880, "\u0120fueling": 43881, "\u0120capsules": 43882, "\u0120Lust": 43883, "\u0120Haunted": 43884, "Executive": 43885, "\u0120childbirth": 43886, "Gre": 43887, "\u0120radiant": 43888, "\u00e5\u0130": 43889, "\u0120malls": 43890, "\u0120inept": 43891, "\u0120Warranty": 43892, "\u0120spectator": 43893, "Eh": 43894, "thens": 43895, "\u0120culminating": 43896, "\u00e6\u00a9": 43897, "arya": 43898, "\u00e3\u0124\u00ae": 43899, "ilitarian": 43900, "\u0120ORIG": 43901, "\u0120Spending": 43902, "ptives": 43903, "\u0120Siren": 43904, "\u0120Recording": 43905, "ayne": 43906, "\u0120vim": 43907, "\u0120sprang": 43908, "Tang": 43909, "\u0120MFT": 43910, "morning": 43911, "\u0120Weed": 43912, "mpeg": 43913, "cession": 43914, "\u0120Chung": 43915, "730": 43916, "warning": 43917, "562": 43918, "handedly": 43919, "Poor": 43920, "Politics": 43921, ":#": 43922, "\u0120pian": 43923, "\u0120feces": 43924, "\u0120Documentation": 43925, "\u0120banished": 43926, "\u0120399": 43927, "\u0120ARC": 43928, "\u0120heinous": 43929, "Jake": 43930, "\u0120Amir": 43931, "wayne": 43932, "vre": 43933, "oshenko": 43934, "\u0120notebooks": 43935, "\u0120foundational": 43936, "\u0120marvelous": 43937, "ixtape": 43938, "\u0120withdrawals": 43939, "\u0120horde": 43940, "\u0120Dhabi": 43941, "isable": 43942, "\u0120KD": 43943, "\u0120contagious": 43944, "\u0120Dip": 43945, "\u0120Arrows": 43946, "\u0120pronouns": 43947, "\u0120morphine": 43948, "\u0120BUS": 43949, "682": 43950, "\u0120kosher": 43951, "finished": 43952, "\u0120Instruments": 43953, "\u0120fused": 43954, "yden": 43955, "\u0120Salmon": 43956, "Fab": 43957, "affected": 43958, "KEN": 43959, "CENT": 43960, "Domain": 43961, "\u0120pokemon": 43962, "\u0120Drinking": 43963, "Growing": 43964, "\u0120Investigative": 43965, "\u0120Aether": 43966, "emi": 43967, "\u0120tabloid": 43968, "\u0120repro": 43969, "\u0120Notwithstanding": 43970, "\u0120Berserker": 43971, "\u0120dramas": 43972, "\u0120clich\u00c3\u00a9": 43973, "\u0120bung": 43974, "\u0120URI": 43975, "\u0120Dos": 43976, "044": 43977, "\u0120pastors": 43978, "\u0120ls": 43979, "\u0120acrylic": 43980, "aunts": 43981, "Edward": 43982, "\u0120majorities": 43983, "Bang": 43984, "\u0120fielding": 43985, "\u0120Replacement": 43986, "\u0120Alchemy": 43987, "ppard": 43988, "\u0120Romeo": 43989, "\u0120Sanct": 43990, "\u0120Lavrov": 43991, "ibble": 43992, "Instruct": 43993, "\u0120impractical": 43994, "\u0120Playboy": 43995, "cephal": 43996, "\u0120swaps": 43997, "\u0120kan": 43998, "\u0120Theo": 43999, "\u0120illustrating": 44000, "\u0120dismantled": 44001, "\u0120Transgender": 44002, "\u0120Guth": 44003, "UGH": 44004, "\u0120triumphant": 44005, "\u0120encompass": 44006, "\u0120bookmark": 44007, "uddin": 44008, "jer": 44009, "\u0120predicate": 44010, "ESH": 44011, "\u0120whence": 44012, "\u0120ABE": 44013, "\u0120nonprofits": 44014, "Sequ": 44015, "\u0120diabetic": 44016, "\u0120pend": 44017, "\u0120heartfelt": 44018, "shi": 44019, "\u0120interacts": 44020, "\u0120Telecom": 44021, "\u0120bombardment": 44022, "depending": 44023, "\u0120Lowry": 44024, "\u0120Admission": 44025, "\u0120Blooming": 44026, "ustration": 44027, "enegger": 44028, "Brew": 44029, "\u0120molten": 44030, "\u0120Nerd": 44031, "PIN": 44032, "\u00e2\u0138\u0122": 44033, "avement": 44034, "\u0120toured": 44035, "\u0120coefficients": 44036, "\u0120Trayvon": 44037, "ansson": 44038, "\u0120sandy": 44039, "told": 44040, "flows": 44041, "\u0120populous": 44042, "\u0120Tinder": 44043, "\u0120Bliss": 44044, "Rachel": 44045, "Minimum": 44046, "\u0120contestant": 44047, "\u0120Reduce": 44048, "\u0120Morse": 44049, "\u0120Grassley": 44050, "\u0120Clicker": 44051, "\u0120expr": 44052, "\u0120sincerity": 44053, "\u0120marqu": 44054, "\u0120elicit": 44055, "\u0120Proposition": 44056, "\u0120Demonic": 44057, "\u0120tacos": 44058, "Greek": 44059, "\u0120postwar": 44060, "\u0120insofar": 44061, "\u0120Pork": 44062, "\u0120352": 44063, "doctoral": 44064, "walking": 44065, "\u0120midterm": 44066, "\u0120Sammy": 44067, "sighted": 44068, "\u0120TRANS": 44069, "ici": 44070, "ALD": 44071, "\u0120USL": 44072, "\u0120FISA": 44073, "\u0120Ampl": 44074, "\u0120Alexandra": 44075, "inelli": 44076, "Train": 44077, "\u0120signify": 44078, "\u0120Versus": 44079, "\u0120obfusc": 44080, "\u0120kh": 44081, "\u0120aggro": 44082, "\u0120Renault": 44083, "\u0120348": 44084, "518": 44085, "oxicity": 44086, "022": 44087, "\u0120Twist": 44088, "\u0120goofy": 44089, "Dynamic": 44090, "\u0120briefings": 44091, "might": 44092, "899": 44093, "\u0120derogatory": 44094, "Tro": 44095, "\u0120forging": 44096, "\u0120Koran": 44097, "\u0120Married": 44098, "\u0120Bucs": 44099, "\u0120palate": 44100, "\u0120Conversion": 44101, "mable": 44102, "413": 44103, "\u0120(_": 44104, "\u0120siph": 44105, "\u0120NEO": 44106, "college": 44107, "\u0120marginally": 44108, "\u0120flirt": 44109, "\u0120Traps": 44110, "\u0120Pace": 44111, "\u00e9\u00bb\u0134": 44112, "\u0120goaltender": 44113, "\u0120forbids": 44114, "\u0120clerks": 44115, "\u0120Tant": 44116, "\u0120Robbins": 44117, "\u0120Printing": 44118, "\u0120premiered": 44119, "\u0120magnification": 44120, "\u0120TG": 44121, "\u0120Rouse": 44122, "\u0120Mock": 44123, "odynamics": 44124, "\u0120preclude": 44125, "ismo": 44126, "\u0120Pulitzer": 44127, "\u0120avalanche": 44128, "\u0120Kodi": 44129, "ribune": 44130, "\u0120Lena": 44131, "Electric": 44132, "\u0120refinery": 44133, "\u0120endowed": 44134, "\u0120counselors": 44135, "\u0120dolphin": 44136, "\u0120Mith": 44137, "\u0120armoured": 44138, "hibited": 44139, "Begin": 44140, "\u0120PW": 44141, "Oil": 44142, "\u0120Vor": 44143, "\u0120Sharif": 44144, "\u0120Frazier": 44145, "estate": 44146, "\u0120jams": 44147, "Proxy": 44148, "\u0120bandits": 44149, "\u0120Presbyterian": 44150, "\u0120Premiere": 44151, "tiny": 44152, "\u0120Cruel": 44153, "Testing": 44154, "\u0120homer": 44155, "\u0120VERS": 44156, "\u0120Prol": 44157, "\u0120Deposit": 44158, "\u0120Coffin": 44159, "\u0120seminars": 44160, "\u0120sql": 44161, "\u0120Defendants": 44162, "Alternatively": 44163, "\u0120Rats": 44164, "\u00e7\u00ab": 44165, "ethyst": 44166, "'>": 44167, "\u0120issuer": 44168, "589": 44169, "\u0120chaired": 44170, "\u0120Accessories": 44171, "manent": 44172, "\u0120marrow": 44173, "\u0120Primordial": 44174, "CN": 44175, "\u0120limitless": 44176, "\u0120Carnage": 44177, "\u0120undrafted": 44178, "qv": 44179, "INESS": 44180, "onew": 44181, "\u0120cohesion": 44182, "987": 44183, "\u0120necks": 44184, "\u0120footballer": 44185, "\u0120GER": 44186, "\u0120detectable": 44187, "\u0120Supporting": 44188, "\u0120CSV": 44189, "ocally": 44190, "kHz": 44191, "\u0120unde": 44192, "\u0120shone": 44193, "\u0120budding": 44194, "trak": 44195, "Standing": 44196, "\u0120Starcraft": 44197, "\u0120Kemp": 44198, "Bench": 44199, "\u0120thwarted": 44200, "\u0120Grounds": 44201, "athi": 44202, "Lisa": 44203, "Dialog": 44204, "\u0120SX": 44205, "Vision": 44206, "\u0120ingenious": 44207, "\u00d9\u0132": 44208, "\u0120fostering": 44209, "\u0120Za": 44210, "\u0120Ingram": 44211, "\u0120\"@": 44212, "Naturally": 44213, "616": 44214, "035": 44215, "\u0120FAC": 44216, "Hmm": 44217, "554": 44218, "\u0120accelerator": 44219, "\u0120Vend": 44220, "\u0120sunscreen": 44221, "\u0120tuberculosis": 44222, "raviolet": 44223, "\u0120Functional": 44224, "\u0120Errors": 44225, "edar": 44226, "1966": 44227, "\u0120Spectre": 44228, "\u0120Recipes": 44229, "885": 44230, "\u0120Mankind": 44231, "Liverpool": 44232, "\u0120|--": 44233, "\u0120substitutes": 44234, "\u0120XT": 44235, "wired": 44236, "\u0120inco": 44237, "\u0120Afgh": 44238, "Eva": 44239, "icc": 44240, "Song": 44241, "Knight": 44242, "\u0120diligently": 44243, "\u0120Broadcast": 44244, "Aid": 44245, "\u0120afar": 44246, "\u0120HMS": 44247, "atonin": 44248, "\u0120Grateful": 44249, "\u0120fireplace": 44250, "\u0120Omni": 44251, "euro": 44252, "\u0120FRE": 44253, "\u0120Shib": 44254, "\u0120Digest": 44255, "toggle": 44256, "\u0120headsets": 44257, "\u0120diffusion": 44258, "\u0120Squirrel": 44259, "\u0120FN": 44260, "\u0120darkened": 44261, "outher": 44262, "\u0120sleeps": 44263, "\u0120Xer": 44264, "guns": 44265, "\u0120setups": 44266, "\u0120parsed": 44267, "\u0120mammoth": 44268, "\u0120Curious": 44269, "gob": 44270, "\u0120Fitzpatrick": 44271, "\u0120Emil": 44272, "imov": 44273, ".............": 44274, "\u0120Benny": 44275, "Secondly": 44276, "\u0120hearty": 44277, "\u0120conson": 44278, "stained": 44279, "\u0120galactic": 44280, "clave": 44281, "\u0120plummeted": 44282, "\u0120pests": 44283, "\u0120swat": 44284, "\u0120referrals": 44285, "\u0120Lionel": 44286, "holy": 44287, "\u0120underdog": 44288, "\u0120Slater": 44289, "\u0120Provide": 44290, "\u0120Amar": 44291, "ressor": 44292, "\u00e5\u012e": 44293, "onga": 44294, "\u0120timid": 44295, "\u0120piety": 44296, "\u0120Dek": 44297, "\u0120surging": 44298, "azo": 44299, "\u0120610": 44300, "\u0120desks": 44301, "\u0120Spokane": 44302, "\u0120Anfield": 44303, "\u0120warships": 44304, "\u0120Cobra": 44305, "\u0120arming": 44306, "clusively": 44307, "\u0120Badge": 44308, "agascar": 44309, "\u0120PRESS": 44310, "\u0120McKenzie": 44311, "\u0120Ferdinand": 44312, "burning": 44313, "Afee": 44314, "\u0120tyrann": 44315, "\u0120Iw": 44316, "\u0120Boone": 44317, "1007": 44318, "\u0120Rept": 44319, "\u010a\u00c2\u0142": 44320, "\u0120caravan": 44321, "\u0120Dill": 44322, "\u0120Bundesliga": 44323, "Chuck": 44324, "\u0120healer": 44325, "\u00e3\u0125\u00bc\u00e3\u0125\u0128": 44326, "\u0120Hobby": 44327, "\u0120negate": 44328, "\u0120critiques": 44329, "sectional": 44330, "mopolitan": 44331, "\u0120dx": 44332, "\u0120outsourcing": 44333, "\u0120Cipher": 44334, "tap": 44335, "Sharp": 44336, "\u0120upbeat": 44337, "\u0120hangar": 44338, "\u0120cruising": 44339, "\u0120Niagara": 44340, "\u0120342": 44341, "illus": 44342, "\u0120Sv": 44343, "\u0120subtitles": 44344, "\u0120squared": 44345, "\u0120bookstore": 44346, "\u0120revolutionaries": 44347, "\u0120Carlton": 44348, "abal": 44349, "Utah": 44350, "\u0120despise": 44351, "\u0120UM": 44352, "consider": 44353, "aido": 44354, "\u0120carts": 44355, "\u0120Turtles": 44356, "Training": 44357, "\u0120honorary": 44358, "\u00c2\u00a2": 44359, "\u0120triangles": 44360, "422": 44361, "\u0120reprinted": 44362, "\u0120graceful": 44363, "\u0120Mongolia": 44364, "\u0120disruptions": 44365, "\u0120Boh": 44366, "\u0120349": 44367, "\u0120drains": 44368, "\u0120consulate": 44369, "\u0120bends": 44370, "\u0120mafia": 44371, "uron": 44372, "\u0120Fulton": 44373, "misc": 44374, "\u0120renal": 44375, "\u0120inaction": 44376, "cking": 44377, "\u0120photons": 44378, "\u0120bruised": 44379, "\u0120Codes": 44380, "ogi": 44381, "\u0120nests": 44382, "\u0120Lovely": 44383, "\u0120Libre": 44384, "\u0120Daryl": 44385, "\u0120###": 44386, "Sys": 44387, ".,\"": 44388, "\u0120freezes": 44389, "establishment": 44390, "andowski": 44391, "\u0120cumbers": 44392, "\u0120Starg": 44393, "\u0120Bombs": 44394, "\u0120legions": 44395, "\u0120handwriting": 44396, "\u0120grun": 44397, "\u0120Cah": 44398, "sequent": 44399, "\u0120moth": 44400, "\u0120MSM": 44401, "Insert": 44402, "Fif": 44403, "\u0120motel": 44404, "\u0120dexter": 44405, "\u0120Bild": 44406, "heartedly": 44407, "\u0120prope": 44408, "\u0120Texture": 44409, "\u0120Junction": 44410, "ynthesis": 44411, "ocard": 44412, "\u0120Vera": 44413, "\u0120Barth": 44414, "\u0120\u00ce\u00bcg": 44415, "\u0120lashed": 44416, "\u0120351": 44417, "\u0120Zamb": 44418, "\u0120Staples": 44419, "\u0120Cortex": 44420, "\u0120Corker": 44421, "\u0120continuum": 44422, "\u0120WRITE": 44423, "unta": 44424, "ridor": 44425, "\u0120deems": 44426, "033": 44427, "\u0120GOLD": 44428, "pas": 44429, "\u0120repressive": 44430, "\u00e3\u0125\u0128\u00e3\u0124\u00a3": 44431, "\u0120baffled": 44432, "Scar": 44433, "\u0120crave": 44434, "\u0120______": 44435, "\u0120entrepreneurship": 44436, "\u0120Directorate": 44437, "\u0120'[": 44438, "\u0120vines": 44439, "\u0120ascended": 44440, "\u0120GROUP": 44441, "\u0120Goodbye": 44442, "\u0120dogged": 44443, "\u00e3\u0125\u00b4\u00e3\u0124\u00a1": 44444, "Manufact": 44445, "\u0120unimaginable": 44446, "riots": 44447, "ierrez": 44448, "\u0120relativity": 44449, "\u0120Crafting": 44450, "raught": 44451, "uden": 44452, "cookie": 44453, "\u0120assassins": 44454, "\u0120dissatisfied": 44455, "acci": 44456, "\u0120conduit": 44457, "Spread": 44458, "\u0120Rican": 44459, "nice": 44460, "izzle": 44461, "\u0120scares": 44462, "\u0120WHY": 44463, "phans": 44464, "535": 44465, "\u0120protracted": 44466, "\u0120Kristen": 44467, "536": 44468, "\u0120Scrib": 44469, "\u0120Neh": 44470, "\u0120twenties": 44471, "\u0120predicament": 44472, "\u0120handcuffs": 44473, "\u0120fruitful": 44474, "\u0120UL": 44475, "\u0120Ludwig": 44476, "\u0120attest": 44477, "\u0120Breaker": 44478, "\u0120biologically": 44479, "\u0120Dealer": 44480, "\u0120renovations": 44481, "fw": 44482, "essen": 44483, "Alice": 44484, "\u0120Henri": 44485, "\u0120unilaterally": 44486, "\u0120Sidd": 44487, "hai": 44488, "\u0120Stretch": 44489, "Sales": 44490, "\u0120cumbersome": 44491, "\u0120Javier": 44492, "\u0120trendy": 44493, "\u0120rotting": 44494, "\u0120Challenges": 44495, "\u0120scraps": 44496, "\u0120facets": 44497, "\u0120Veronica": 44498, "\u0120Verge": 44499, "\u0120Sana": 44500, "Alien": 44501, "\u0120Rih": 44502, "\u0120radial": 44503, "ectar": 44504, "\u0120630": 44505, "cli": 44506, "Marie": 44507, "\u0120wildfire": 44508, "\u0120Cato": 44509, "hander": 44510, "\u0120waitress": 44511, "\u0120chops": 44512, "\u0120SECTION": 44513, "\u0120bluntly": 44514, "\u0120Catalog": 44515, "nian": 44516, "study": 44517, "\u0120patrolling": 44518, "\u0120Tenth": 44519, "nexus": 44520, "\u0120NON": 44521, "opsy": 44522, "\u0120scathing": 44523, "sie": 44524, "\u0120deteriorated": 44525, "VB": 44526, "Nazis": 44527, "\u0120depictions": 44528, "\u0120authenticated": 44529, "\u0120Conce": 44530, "krit": 44531, "\u0120promulg": 44532, "\u0120LONG": 44533, "UFC": 44534, "\u0120Visitors": 44535, "\u0120Recall": 44536, "\u0120rehabilit": 44537, "\u0120SLI": 44538, "\u0120glacier": 44539, "\u0120Bite": 44540, "\u0120503": 44541, "\u0120vomit": 44542, "\u0120fermented": 44543, "\u0120Khalid": 44544, "\u0120graded": 44545, "\u0120Magicka": 44546, "\u0120Ichigo": 44547, "powerful": 44548, "icators": 44549, "753": 44550, "\u0120shrew": 44551, "\u0120356": 44552, "\u0120legalizing": 44553, "\u0120allotted": 44554, "\u0120Archdemon": 44555, "ithing": 44556, "iggurat": 44557, "VOL": 44558, "Leod": 44559, "\u0120oily": 44560, "\u0120inducing": 44561, "\u0120amygdala": 44562, "\u0120admins": 44563, "\u0120Acquisition": 44564, "CAN": 44565, "\u0120schematic": 44566, "\u0120moan": 44567, "\u0120Cameroon": 44568, "\u0120tink": 44569, "\u0120merry": 44570, "\u0120butterflies": 44571, "\u0120Goff": 44572, "\u0120workspace": 44573, "\u0120Corona": 44574, "\u0120javascript": 44575, "\u0120Dolphin": 44576, "\u0120Cantor": 44577, "464": 44578, "toe": 44579, "APS": 44580, "\u0120Aging": 44581, "\u0120padded": 44582, "\u0120Zheng": 44583, "\u0120Held": 44584, "\u0120estranged": 44585, "\u0120770": 44586, ".}": 44587, "\u0120Dunham": 44588, "\u0120smokes": 44589, "\u0120capitals": 44590, "undai": 44591, "Shin": 44592, "\u0120Founding": 44593, "\u0120entitle": 44594, "\u0120centerpiece": 44595, "Discover": 44596, "\u0120thereto": 44597, "alert": 44598, "\u0120Nou": 44599, "\u0120Analyst": 44600, "lc": 44601, "FH": 44602, "FIELD": 44603, "\u0120POV": 44604, "gray": 44605, "\u0120arcs": 44606, "\u0120HOT": 44607, "\u0120rs": 44608, "\u0120obligatory": 44609, "\u0120Architects": 44610, "\u0120Sven": 44611, "\u0120FEC": 44612, "0200": 44613, "Christmas": 44614, "\u0120Albania": 44615, "ratom": 44616, "587": 44617, "\u0120hardships": 44618, "\u0120autos": 44619, "\u0120Charges": 44620, "\u0120apes": 44621, "\u0120376": 44622, "wallet": 44623, "\u0120intoxication": 44624, "\u0120goblin": 44625, "\u0120570": 44626, "++++++++++++++++": 44627, "\u0120Yelp": 44628, "\u0120Magnetic": 44629, "\u0120Briggs": 44630, "Rail": 44631, "\u0120spawns": 44632, "\u0120Wiggins": 44633, "\u0120showcased": 44634, "\u0120resorted": 44635, "uben": 44636, "\u0120whipping": 44637, "\u0120imitate": 44638, "\u0120digestion": 44639, "\u0120USPS": 44640, "\u0120Gest": 44641, "\u0120yea": 44642, "\u0120Tight": 44643, "indal": 44644, "icas": 44645, "`.": 44646, "CAST": 44647, "'';": 44648, "\u0120Fet": 44649, "opathic": 44650, "Invalid": 44651, "\u0120regretted": 44652, "\u0120broccoli": 44653, "\u0120Scores": 44654, "eve": 44655, "\u0120postings": 44656, "\u0120accumulating": 44657, "\u0120needless": 44658, "elfth": 44659, "\u0120mayors": 44660, "\u0120scrib": 44661, "\u0120anecdotes": 44662, "\u0120botched": 44663, "\u0120Ribbon": 44664, "\u0120Constantine": 44665, "iuses": 44666, "esses": 44667, "\u0120devise": 44668, "Compared": 44669, "\u0120pudding": 44670, "\u0120garg": 44671, "\u0120evoke": 44672, "797": 44673, "\u0120detox": 44674, "909": 44675, "\u0120Pieces": 44676, "\u0120McCartney": 44677, "\u0120metast": 44678, "\u0120Krypt": 44679, "POR": 44680, "\u0120tending": 44681, "\u0120Merchants": 44682, "Proof": 44683, "\u0120Varg": 44684, "\u0120Portable": 44685, "\u00e3\u0125\u00bc\u00e3\u0125\u0128\u00e3\u0124\u00a3": 44686, "Brain": 44687, "2500": 44688, "\u0120foliage": 44689, "\u00d8\u00b9": 44690, "\u0120mentors": 44691, "\u0120Aires": 44692, "\u0120minimalist": 44693, "\u0120ingested": 44694, "\u0120Trojan": 44695, "\u0120Qian": 44696, "involved": 44697, "027": 44698, "\u0120eroded": 44699, "RAFT": 44700, "\u0120blurry": 44701, "Mob": 44702, "\u0120buffet": 44703, "\u0120Fnatic": 44704, "aea": 44705, "KNOWN": 44706, "\u0120Init": 44707, "safety": 44708, "enum": 44709, "ACTION": 44710, "\u0120Crusher": 44711, "\u0120Dates": 44712, "\u0120................": 44713, "calling": 44714, "akov": 44715, "\u0120ventured": 44716, "\u0120555": 44717, "auga": 44718, "Hart": 44719, "\u0120Aero": 44720, "MAC": 44721, "\u0120thinly": 44722, "\u0120arra": 44723, "STATE": 44724, "ilde": 44725, "\u0120Jacqu": 44726, "\u0120Females": 44727, "\u0120theorem": 44728, "\u0120346": 44729, "\u0120smartest": 44730, "\u0120PUBLIC": 44731, "\u0120Kron": 44732, "\u0120Bits": 44733, "\u0120Vessel": 44734, "\u0120Telephone": 44735, "\u0120decap": 44736, "\u0120adjunct": 44737, "\u0120SEN": 44738, "merga": 44739, "\u0120redacted": 44740, "\u0120prehistoric": 44741, "\u0120explanatory": 44742, "\u0120Runs": 44743, "\u0120Uttar": 44744, "\u0120Manny": 44745, "\u0120AUTHOR": 44746, "\u0120Unleashed": 44747, "\u0120Bowling": 44748, "beans": 44749, "793": 44750, "\u0120universes": 44751, "\u0120sensit": 44752, "\u0120Kung": 44753, "repeat": 44754, "ctrl": 44755, "\u0120paced": 44756, "\u0120fuller": 44757, "Clock": 44758, "\u0120recomb": 44759, "\u0120Faul": 44760, "\u0120Bunker": 44761, "\u0120pooled": 44762, "\u0120ana": 44763, "\u0120Mouth": 44764, "LLOW": 44765, "humane": 44766, "\u0120bulldo": 44767, "\u0120Michaels": 44768, "fam": 44769, "\u0120wrecked": 44770, "\u0120portrays": 44771, "\u0120Whale": 44772, "\u0120Hes": 44773, "\u0120guesses": 44774, "\u0120Browse": 44775, "\u0120LAPD": 44776, "\u0120consequential": 44777, "\u0120Innocent": 44778, "\u0120DRAG": 44779, "\u0120transgress": 44780, "\u0120Oaks": 44781, "\u0120trivia": 44782, "\u0120Reson": 44783, "\u0120ADS": 44784, "--+": 44785, "\u0120Toll": 44786, "\u0120grasping": 44787, "\u0120THEM": 44788, "\u0120Tags": 44789, "\u0120Conclusion": 44790, "\u0120practicable": 44791, "\u0120hoop": 44792, "\u0120unintentionally": 44793, "\u0120ignite": 44794, "\u0120Mov": 44795, "urized": 44796, "lehem": 44797, "Termin": 44798, "\u0120colourful": 44799, "\u0120Linear": 44800, "\u0120Ellie": 44801, "Gy": 44802, "\u0120manpower": 44803, "\u0120js": 44804, "\u0120emoji": 44805, "\u0120SHARES": 44806, "_.": 44807, "00007": 44808, "\u0120sophistication": 44809, "\u0120underscore": 44810, "\u0120practise": 44811, "\u0120blob": 44812, "opens": 44813, "Ukraine": 44814, "Keeping": 44815, "YC": 44816, "JR": 44817, "ultimate": 44818, "Claim": 44819, "\u0120automobiles": 44820, "993": 44821, "steel": 44822, "\u0120parting": 44823, "\u0120Lank": 44824, "...?": 44825, "\u0120385": 44826, "\u0120remembrance": 44827, "\u0120eased": 44828, "\u0120covari": 44829, "\u0120Sind": 44830, "Effective": 44831, "\u0120dissemination": 44832, "\u0120Moose": 44833, "\u0120Clapper": 44834, "brates": 44835, "Apply": 44836, "\u0120invis": 44837, "\u0120worsened": 44838, "\u00e2\u0122\u0136-": 44839, "\u0120legislator": 44840, "\u0120Lol": 44841, "\u0120Rowe": 44842, "\u0120dealership": 44843, "umar": 44844, "idences": 44845, "\u0120investigates": 44846, "\u0120cascade": 44847, "\u0120bidder": 44848, "\u0120BEN": 44849, "Ironically": 44850, "\u0120presiding": 44851, "\u0120ding": 44852, "\u0120contradicted": 44853, "\u0120shuts": 44854, "\u0120FIX": 44855, "\u0120366": 44856, "District": 44857, "\u0120sinful": 44858, "\u0120Charisma": 44859, "oops": 44860, "\u0120totality": 44861, "\u0120restitution": 44862, "\u0120Optimus": 44863, "\u0120Dah": 44864, "\u0120clueless": 44865, "urned": 44866, "\u0120nutrit": 44867, "\u0120landowners": 44868, "\u0120flushed": 44869, "\u0120broaden": 44870, "mie": 44871, "\u0120println": 44872, "\u0120nig": 44873, "\u0120Corpus": 44874, "Jen": 44875, "\u0120proto": 44876, "\u0120Wikimedia": 44877, "\u0120Palo": 44878, "COR": 44879, "\u0120storylines": 44880, "\u0120evangelicals": 44881, "\u0120Darrell": 44882, "\u0120rotor": 44883, "\u0120HW": 44884, "skilled": 44885, "eryl": 44886, "\u0120begg": 44887, "\u0120Blumenthal": 44888, "\u0120weaving": 44889, "\u0120downwards": 44890, "\u0120Jacket": 44891, "\u0120ANGEL": 44892, "Technology": 44893, "\u0120esoteric": 44894, "aldehyde": 44895, "\u0120furiously": 44896, "\u0120foreigner": 44897, "Weak": 44898, "CHO": 44899, "\u0120Hound": 44900, "Experience": 44901, "\u0120Playstation": 44902, "\u0120MIA": 44903, "\u0120Ung": 44904, "cloth": 44905, "agall": 44906, "\u0120calming": 44907, "izens": 44908, "Struct": 44909, "\u0120Witches": 44910, "\u0120Celebration": 44911, "\u0120..............": 44912, "ptroller": 44913, "\u0120TCU": 44914, "\u0120bunny": 44915, "\u00e3\u0125\u012f": 44916, "utorial": 44917, "\u0120upscale": 44918, "\u0120Sta": 44919, "\u0120Colossus": 44920, "\u0120chloride": 44921, "\u0120Zac": 44922, "\u0120Reasons": 44923, "\u0120Brookings": 44924, "\u0120WHITE": 44925, "][/": 44926, "\u0120Lose": 44927, "905": 44928, "\u0120underside": 44929, "ernels": 44930, "\u0120vape": 44931, "dozen": 44932, "uppet": 44933, "\u0120STOP": 44934, "matical": 44935, "\u0120Statements": 44936, "heddar": 44937, "PAC": 44938, "Customer": 44939, "\u0120memos": 44940, "\u0120PJ": 44941, "endars": 44942, "\u0120Limits": 44943, "laugh": 44944, "\u0120stabilized": 44945, "\u0120ALEC": 44946, "YA": 44947, "Upgrade": 44948, "alam": 44949, "\u0120techno": 44950, "\u0120anew": 44951, "foreseen": 44952, "\u0120collegiate": 44953, "\u0120Pyro": 44954, "\u0120Dism": 44955, "\u0120frontline": 44956, "\u0120ammonia": 44957, "IU": 44958, "Quite": 44959, "Johnny": 44960, "assin": 44961, "GOP": 44962, "\u0120Styles": 44963, "\u0120Sovereign": 44964, "acterial": 44965, "549": 44966, "\u0120RIP": 44967, "\u0120Lists": 44968, "\u0120364": 44969, "\u0120Recep": 44970, "socket": 44971, "\u0120Byrd": 44972, "\u0120Candle": 44973, "Ancient": 44974, "\u0120appellant": 44975, "enforcement": 44976, "acea": 44977, "anski": 44978, "\u0120olds": 44979, "886": 44980, "\u0120slurs": 44981, "\u0120empires": 44982, "\u0120buckle": 44983, "\u0120alienation": 44984, "\u0120Aberdeen": 44985, "\u0120unicorn": 44986, "\u0120overriding": 44987, "\u0120LX": 44988, "ppa": 44989, "\u0120despised": 44990, "\u0120Bugs": 44991, "\u0120BST": 44992, "Southern": 44993, "533": 44994, "\u0120hallmark": 44995, "\u0120Poster": 44996, "\u0120stemmed": 44997, "\u0120principals": 44998, "\u0120TECH": 44999, "\u0120Sandwich": 45000, "Italy": 45001, "\u0120cheesy": 45002, "\u0120SetTextColor": 45003, "\u0120Protective": 45004, "\u0120Cohn": 45005, "JO": 45006, "aptop": 45007, "Reason": 45008, "Leader": 45009, "\u0120Understand": 45010, "\u0120Fridays": 45011, "\u0120Continuous": 45012, "\u0120clipping": 45013, "\u0120Rye": 45014, "\u0120berth": 45015, "timer": 45016, "annis": 45017, "react": 45018, "\u0120buffalo": 45019, "\u0120Paras": 45020, "\u0120655": 45021, "\u0120presided": 45022, "\u0120Sunrise": 45023, "\u0120vets": 45024, "\u0120cloves": 45025, "\u0120McCull": 45026, "Strength": 45027, "GAN": 45028, "\u0120illiter": 45029, "\u0120Pricing": 45030, "l\u00c3\u00a9": 45031, "\u0120resistor": 45032, "\u0120brun": 45033, "\u0120Suffolk": 45034, "\u00d1\u012d": 45035, "\u0120Liver": 45036, "Released": 45037, "\u0120whats": 45038, "860": 45039, "\u0120Measures": 45040, "\u0120denouncing": 45041, "\u0120Ryzen": 45042, "\u0120souven": 45043, "\u0120caregivers": 45044, "chini": 45045, "\u0120Scarlett": 45046, "\u0120trough": 45047, "Congratulations": 45048, "\u0120taxis": 45049, "\u0120Tradition": 45050, "jit": 45051, "\u0120tabletop": 45052, "\u0120hitherto": 45053, "\u0120disinformation": 45054, "offensive": 45055, "hra": 45056, "\u0120DISTRICT": 45057, "\u0120complicate": 45058, "chenko": 45059, "\u0120Reconstruction": 45060, "\u0120palpable": 45061, "\u0120ausp": 45062, "\u0120428": 45063, "\u0120showcases": 45064, "\u0120Publication": 45065, "knowledge": 45066, "innon": 45067, "419": 45068, "\u0120retrieval": 45069, "anders": 45070, "\u0120refute": 45071, "\u0120inquired": 45072, "gur": 45073, "\u0120negativity": 45074, "\u0120conserve": 45075, "\u0120afterlife": 45076, "\u0120presupp": 45077, "\u0120Gillespie": 45078, "\u0120mt": 45079, "\u0120DN": 45080, "Tap": 45081, "\u0120perpend": 45082, "\u0120Smy": 45083, "doesn": 45084, "\u0120spilling": 45085, "\u0120hypers": 45086, "Kate": 45087, "\u00c2\u00ae,": 45088, "kept": 45089, "\u0120Powered": 45090, "\u0120ja": 45091, "\u0120Klux": 45092, "arde": 45093, "aban": 45094, "\u0120444": 45095, "\u0120flattened": 45096, "\u0120Improvements": 45097, "urga": 45098, "\u0120Kund": 45099, "\u0120inscribed": 45100, "\u0120facult": 45101, "\u0120unprepared": 45102, "\u0120Consumers": 45103, "\u0120satisfies": 45104, "\u0120pulmonary": 45105, "\u0120infiltration": 45106, "\u0120externally": 45107, "\u0120congratulations": 45108, "aghan": 45109, "\u0120airliner": 45110, "\u0120flung": 45111, "\u0120flyers": 45112, "GD": 45113, "\u0120snippets": 45114, "\u0120recursive": 45115, "\u0120mastering": 45116, "Lex": 45117, "\u0120overtly": 45118, "vg": 45119, "\u0120luckily": 45120, "\u0120encro": 45121, "\u0120Lancet": 45122, "\u0120Abyssal": 45123, "functional": 45124, "\u0120sow": 45125, "\u0120squid": 45126, "\u0120narration": 45127, "\u0120naughty": 45128, "\u0120Honour": 45129, "\u0120Spartans": 45130, "\u0120shatter": 45131, "\u0120Tacoma": 45132, "\u0120Calories": 45133, "\u0120Races": 45134, "Submit": 45135, "\u0120purposefully": 45136, "wav": 45137, "\u0120Yok": 45138, "Fest": 45139, "\u0120Gerr": 45140, "Metro": 45141, "\u0120itiner": 45142, "famous": 45143, "\u0120\"{": 45144, "inline": 45145, "washer": 45146, "Issue": 45147, "\u0120CLIENT": 45148, "ozo": 45149, "Versions": 45150, "725": 45151, "\u0120Glock": 45152, "\u0120shielded": 45153, "\u0120PCR": 45154, "ENCY": 45155, "\u0120Weld": 45156, "\u0120Simpl": 45157, "\u0120redirected": 45158, "\u0120Kham": 45159, "\u0120(>": 45160, "\u0120labou": 45161, "\u0120diapers": 45162, "ssl": 45163, "\u0120cellar": 45164, "organisms": 45165, "oresc": 45166, "\u0120Berks": 45167, "didn": 45168, "Shipping": 45169, "Chest": 45170, "\u0120undone": 45171, "\u0120millionaire": 45172, "\u0120cords": 45173, "\u0120Younger": 45174, "appropriately": 45175, "\u0120sequels": 45176, "uve": 45177, "anticipated": 45178, "\u0120lewd": 45179, "\u0120Shirt": 45180, "\u0120Dmitry": 45181, "Veter": 45182, "\u0120slaying": 45183, "\u0120Yar": 45184, "\u0120complication": 45185, "Iowa": 45186, "\u0120Erica": 45187, "\u0120BLM": 45188, "girlfriend": 45189, "bodied": 45190, "626": 45191, "1963": 45192, "\u0120intermediary": 45193, "\u0120consolation": 45194, "Mask": 45195, "\u0120Siem": 45196, "owan": 45197, "Beginning": 45198, "\u0120fixme": 45199, "\u0120culminated": 45200, "\u0120conduc": 45201, "\u0120Volunteer": 45202, "\u0120positional": 45203, "\u0120greets": 45204, "\u0120Definitions": 45205, "\u0120thinker": 45206, "\u0120ingenuity": 45207, "\u0120freshmen": 45208, "\u0120Moments": 45209, "\u0120357": 45210, "ateurs": 45211, "\u0120FedEx": 45212, "sg": 45213, "694": 45214, "\u0120dwindling": 45215, "\u0120BOX": 45216, "selage": 45217, "\u0120tmp": 45218, "\u0120sten": 45219, "\u0120Sut": 45220, "\u0120neighbourhoods": 45221, "\u0120classmate": 45222, "fledged": 45223, "\u0120leftists": 45224, "\u0120climates": 45225, "ATHER": 45226, "\u0120Scythe": 45227, "uliffe": 45228, "\u0120sag": 45229, "\u0120hopped": 45230, "\u0120Ft": 45231, "\u0120Eck": 45232, "\u0120CK": 45233, "\u0120Doomsday": 45234, "kids": 45235, "\u0120gasped": 45236, "\u0120moniker": 45237, "\u0120Lod": 45238, "\u0120CFL": 45239, "tions": 45240, "rums": 45241, "folios": 45242, "\u0120md": 45243, "\u0120uncanny": 45244, "\u0120transports": 45245, "\u0120Labrador": 45246, "\u0120railways": 45247, "\u0120appliance": 45248, "\u0120CTRL": 45249, "\u00e6\u0122": 45250, "Population": 45251, "\u0120Confederacy": 45252, "\u0120unbearable": 45253, "\u0120dorsal": 45254, "\u0120Inform": 45255, "opted": 45256, "\u0120KILL": 45257, "Marx": 45258, "\u0120hypocritical": 45259, "qus": 45260, "\u0120Numerous": 45261, "\u0120Georgian": 45262, "\u0120Ambrose": 45263, "\u0120Loch": 45264, "\u0120gubernatorial": 45265, "\u0120Xeon": 45266, "\u0120Supports": 45267, "enser": 45268, "eely": 45269, "\u0120Avenger": 45270, "1965": 45271, "Army": 45272, "\u0120juxtap": 45273, "\u0120chopping": 45274, "\u0120Splash": 45275, "\u0120Sustainable": 45276, "\u0120Finch": 45277, "\u01201861": 45278, "ictive": 45279, "atmeal": 45280, "\u0120Gohan": 45281, "\u0120lightsaber": 45282, "\u0120GPA": 45283, "ugu": 45284, "\u0120REPL": 45285, "variable": 45286, "\u0120herpes": 45287, "\u0120deserts": 45288, "aciously": 45289, "\u0120situational": 45290, "weekly": 45291, "obl": 45292, "\u0120textile": 45293, "\u0120Cornwall": 45294, "\u0120contraceptives": 45295, "\u0120Ake": 45296, "]-": 45297, 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"\u0120suitcase": 45391, "dayName": 45392, "\u0120wasteful": 45393, "marriage": 45394, "\u0120McCabe": 45395, "itech": 45396, "\u0120Oss": 45397, "Closure": 45398, "\u0120Treasurer": 45399, "\u0120indecent": 45400, "\u0120Dull": 45401, "\u0120residences": 45402, "1959": 45403, "\u0120Settlement": 45404, "Hamilton": 45405, "\u0120selfies": 45406, "\u0120Ranking": 45407, "\u0120Barkley": 45408, "\u0120Bore": 45409, "\u0120WCS": 45410, "\u0120Maritime": 45411, "\u0120Huh": 45412, "\u0120Forestry": 45413, "\u0120cultivating": 45414, "\u0120Ballard": 45415, "\u0120garrison": 45416, "\u0120SDL": 45417, "930": 45418, "\u0120nascent": 45419, "\u0120irresistible": 45420, "\u0120awfully": 45421, "\\/\\/": 45422, "\u0120equate": 45423, "\u0120anthropology": 45424, "\u0120Sylvia": 45425, "\u0120intestine": 45426, "\u0120innocuous": 45427, "cessive": 45428, "agra": 45429, "\u0120Metroid": 45430, "Grant": 45431, "855": 45432, "\u0123\u0138": 45433, "\u0120\"_": 45434, "\u00e3\u0125\u0125\u00e3\u0125\u012b": 45435, "\u0120appraisal": 45436, "\u0120Freddy": 45437, "046": 45438, "\u0120406": 45439, "\u01201830": 45440, "\u0120docking": 45441, "Static": 45442, "\u0120pont": 45443, "\u0120Voltage": 45444, "\u0120Stead": 45445, "\u0120Mortgage": 45446, "\u0120Jonah": 45447, "YL": 45448, "CLASSIFIED": 45449, "\u0120asbestos": 45450, "nikov": 45451, "\u0120collagen": 45452, "\u0120Orbital": 45453, "Pocket": 45454, "799": 45455, "\u0120hybrids": 45456, "inches": 45457, "\u0120invoice": 45458, "undy": 45459, "\u0120inequalities": 45460, "Trend": 45461, "washed": 45462, "BALL": 45463, "\u0120lucid": 45464, "\u0120Commentary": 45465, "\u0120witty": 45466, "Brandon": 45467, "\u0120bruising": 45468, "\u0120620": 45469, "escent": 45470, "boxing": 45471, "POL": 45472, "\u0120378": 45473, "Rect": 45474, "\u0120licences": 45475, "\u0120McGee": 45476, "pressed": 45477, "Danny": 45478, "\u0120jammed": 45479, "ordinate": 45480, "\u0120leth": 45481, "\u0120distinguishes": 45482, "\u0120Yamaha": 45483, "ILS": 45484, "\u0120Hume": 45485, "\u0120Categories": 45486, "Roberts": 45487, "Chart": 45488, "\u0120beetle": 45489, "\u0120Graveyard": 45490, "\u0120($)": 45491, "o\u00c4\u0141": 45492, "\u0120twilight": 45493, "arella": 45494, "\u00e1\u00bd": 45495, "\u0120booths": 45496, "\u0120HHS": 45497, "\u0120Feldman": 45498, "\u0120excavation": 45499, "\u0120philosophies": 45500, "atography": 45501, "\u0120Garage": 45502, "technology": 45503, "\u0120unforgettable": 45504, "\u0120verifying": 45505, "\u0120subordinates": 45506, "Els": 45507, "\u0120neb": 45508, "Gaming": 45509, "ENA": 45510, "\u0120Achievement": 45511, "itters": 45512, "\u0120Gabe": 45513, "\u0120dumps": 45514, "forcer": 45515, "\u0120poignant": 45516, "\u0120MBA": 45517, "\u0120Heidi": 45518, "imei": 45519, "\u0120mages": 45520, "\u0120liberate": 45521, "\u0120circumcised": 45522, "\u0120Mermaid": 45523, "\u0120Matth": 45524, "together": 45525, "\u0120Wichita": 45526, 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45567, "\u0120heaviest": 45568, "\u0120grasped": 45569, "URA": 45570, "\u0120crooked": 45571, "Jessica": 45572, "problem": 45573, "\u0120predetermined": 45574, "\u0120maniac": 45575, "\u0120breaths": 45576, "\u0120Lauderdale": 45577, "\u0120hobbies": 45578, "yz": 45579, "Crime": 45580, "\u0120charisma": 45581, "dL": 45582, "\u0120leaping": 45583, "\u0120kittens": 45584, "Angelo": 45585, "\u0120JACK": 45586, "\u0120Suzanne": 45587, "\u0120halting": 45588, "ENTION": 45589, "\u0120swallowing": 45590, "\u0120Earthquake": 45591, "\u0120eighteenth": 45592, "\u0120NIC": 45593, "\u0120INF": 45594, "\u0120Conscious": 45595, "\u0120particulars": 45596, "circle": 45597, "740": 45598, "\u0120benevolent": 45599, "\u0120747": 45600, "\u0120490": 45601, "\u0120rundown": 45602, "\u0120Valerie": 45603, "\u0120BUR": 45604, "\u0120civilisation": 45605, "\u0120Schn": 45606, "WB": 45607, "otide": 45608, "international": 45609, "\u0120john": 45610, "\u01201902": 45611, "\u0120peanuts": 45612, "\u0120flavored": 45613, "kus": 45614, "\u0120roared": 45615, "\u0120cutoff": 45616, "\u00e9\u00a3": 45617, "\u0120ornament": 45618, "\u0120architectures": 45619, "\u0120369": 45620, "olor": 45621, "\u0120Wilde": 45622, "\u0120CRC": 45623, "\u0120Adjusted": 45624, "\u0120provoking": 45625, "landish": 45626, "\u0120rationality": 45627, "\u0120justifies": 45628, "\u0120dispel": 45629, "\u0120americ": 45630, "\u0120Poles": 45631, "\u00d8\u00a9": 45632, "\u0120envis": 45633, "\u0120Doodle": 45634, "\u00e4\u00bd\u00bf": 45635, "igsaw": 45636, "auldron": 45637, "Technical": 45638, "Teen": 45639, "uphem": 45640, "\u0120Xiang": 45641, "\u0120detractors": 45642, "\u0120Zi": 45643, "\u0120Journalists": 45644, "\u0120conducive": 45645, "\u0120Volunteers": 45646, "\u0120sd": 45647, "Knowing": 45648, "\u0120transmissions": 45649, "\u0120PLAN": 45650, "\u0120LIB": 45651, "\u0120alluded": 45652, "\u0120obe": 45653, "\u0120dope": 45654, "\u0120Goldstein": 45655, "\u0120wavelengths": 45656, "\u0120Destination": 45657, "nda": 45658, "ugi": 45659, "\u0120attentive": 45660, "\u0120Lean": 45661, "raltar": 45662, "\u0120mang": 45663, "mbuds": 45664, "akings": 45665, "bender": 45666, "\u0120accol": 45667, "\u0120crawled": 45668, "NOW": 45669, "Minnesota": 45670, "\u0120flourished": 45671, "\u0120Zup": 45672, "\u0120Supervisor": 45673, "\u0120Olivier": 45674, "Excellent": 45675, "\u0120widen": 45676, "Done": 45677, "\u0120wig": 45678, "\u0120misconceptions": 45679, "Corp": 45680, "Wan": 45681, "\u0120venerable": 45682, "\u0120Notably": 45683, "\u0120Klingon": 45684, "animate": 45685, "Boost": 45686, "\u0120SAY": 45687, "missing": 45688, "ibliography": 45689, "melon": 45690, "\u0120payday": 45691, "\u00d8\u00b3": 45692, "bole": 45693, "\u0120veiled": 45694, "\u0120Alphabet": 45695, "Italian": 45696, "\u0120everlasting": 45697, "\u0120RIS": 45698, "\u0120Cree": 45699, "rompt": 45700, "\u0120hating": 45701, "\u0120grinning": 45702, "\u0120geographically": 45703, "OSH": 45704, "\u0120weeping": 45705, "\u0120\u00c2\u0142\u0120\u00c2\u0142\u0120\u00c2\u0142\u0120\u00c2\u0142\u0120\u00c2\u0142\u0120\u00c2\u0142\u0120\u00c2\u0142\u0120\u00c2\u0142": 45706, "\u0120impecc": 45707, "Letter": 45708, "\u0120bloated": 45709, "PLA": 45710, "\u0120Fein": 45711, "\u0120persever": 45712, "Thunder": 45713, "\u0120aur": 45714, "\u0120RL": 45715, "\u0120pitfalls": 45716, "\u00e2\u0138\u00ba": 45717, "\u0120predominant": 45718, "\u0120525": 45719, "718": 45720, "APE": 45721, "714": 45722, "\u0120farmland": 45723, "\u0120Qiao": 45724, "\u0120violet": 45725, "\u0120Bahamas": 45726, "\u0120inflicting": 45727, "\u0120Efficiency": 45728, "\u0120homebrew": 45729, "\u0120undertook": 45730, "\u0120curly": 45731, "\u0120Harding": 45732, "mania": 45733, "596": 45734, "\u0120tempered": 45735, "\u0120harrowing": 45736, "\u0120Pledge": 45737, "\u0120Frankenstein": 45738, "\u00e8\u00aa": 45739, "Motion": 45740, "\u0120predictably": 45741, "\u0120Explosion": 45742, "ocusing": 45743, "erd": 45744, "colo": 45745, "FFER": 45746, "\u0120backfield": 45747, "\u0120VIDE": 45748, "uebl": 45749, "Narr": 45750, "\u0120Argument": 45751, "\u0120genomic": 45752, "\u0120boutique": 45753, "\u0120batted": 45754, "\u0120Binary": 45755, "\u0120gamb": 45756, "\u0120Rhythm": 45757, "673": 45758, "\u0120afloat": 45759, "\u0120Olympia": 45760, "YING": 45761, "\u0120endif": 45762, "isin": 45763, "\u0120winters": 45764, "\u0120scattering": 45765, "Iv": 45766, "Distance": 45767, "\u0120tru": 45768, "\u0120Comfort": 45769, "\u0120nexus": 45770, "\u0120airflow": 45771, "\u0120Byzantine": 45772, "payers": 45773, "coni": 45774, "\u0120Betsy": 45775, "Deal": 45776, "\u0120Nug": 45777, "\u0120Continent": 45778, "redibly": 45779, "\u0120optimizing": 45780, "albeit": 45781, "\u0120ecstatic": 45782, "\u0120Proto": 45783, "\u00e7\u00b7": 45784, "ivot": 45785, "\u00e2\u0138\u0126": 45786, "emp": 45787, "rounder": 45788, "\u0120clout": 45789, "\u0120IST": 45790, "663": 45791, "\u0120Dollars": 45792, "\u0120DAC": 45793, "\u0120subscribed": 45794, "\u0120rehearsal": 45795, "\u0120amps": 45796, "\u0120Shang": 45797, "esm": 45798, "\u0120sprinkle": 45799, "\u0120assailant": 45800, "\u0120Oo": 45801, "\u0120Coinbase": 45802, "Tact": 45803, "\u0120retina": 45804, "\u0120nuns": 45805, "RON": 45806, "atto": 45807, "\u0120jug": 45808, "\u0120SVG": 45809, "\u0120bikini": 45810, "\u0120FILE": 45811, "\u0120Founders": 45812, "eport": 45813, "\u0120KP": 45814, "\u0120restores": 45815, "\u0120Thick": 45816, "\u0120ashore": 45817, "\u0120approvals": 45818, "Render": 45819, "MAG": 45820, "Graham": 45821, "\u0120Cortana": 45822, "\u00e3\u0125\u00b3\u00e3\u0124\u00b8": 45823, "ssh": 45824, "orians": 45825, "arsity": 45826, "\u0120Inspired": 45827, "upper": 45828, "\u0120signalling": 45829, "\u0120rebuke": 45830, "\u0120flares": 45831, "\u0120downtime": 45832, "Studies": 45833, "\u0120stagnation": 45834, "\u0120Sequence": 45835, "\u0120grunt": 45836, "\u0120assures": 45837, "\u0120PLA": 45838, "592": 45839, "\u0120intraven": 45840, "depend": 45841, "Susan": 45842, "\u0120Manziel": 45843, "Mania": 45844, "Contract": 45845, "\u0120slams": 45846, "\u0120cultured": 45847, "\u0120creditor": 45848, "LIST": 45849, "\u0120HUM": 45850, "\u0120Chattanooga": 45851, "served": 45852, "\u0120cloaked": 45853, "\u0120FTP": 45854, "powder": 45855, "\u0120Stella": 45856, "uctive": 45857, "\u0120cheaply": 45858, "\u0120MUCH": 45859, "\u0120Galileo": 45860, "\u0120suites": 45861, "speech": 45862, "\u0120deliberations": 45863, "\u0120Chips": 45864, "\u00ab\u013a": 45865, "Balance": 45866, "\u0120Wynne": 45867, "\u0120Akron": 45868, "Asset": 45869, "\u0120honoured": 45870, "\u0120edged": 45871, "Likewise": 45872, "animous": 45873, "\u0120Wage": 45874, "\u0120Ezek": 45875, "advertisement": 45876, "\u0120RTX": 45877, "\u0120MAD": 45878, "\u0120migrating": 45879, "\u0120SQU": 45880, "\u0120475": 45881, "Edited": 45882, "\u0120shorthand": 45883, "\u0120Basics": 45884, "\u0120crotch": 45885, "\u0120EVEN": 45886, "\u0120vm": 45887, "efficiency": 45888, "\u0120calves": 45889, "\u0120Frie": 45890, "\u0120Brilliant": 45891, "\u0120strikers": 45892, "\u0120repentance": 45893, "\u0120arteries": 45894, "rl": 45895, "Bed": 45896, "hap": 45897, "\u0120cryptography": 45898, "\u0120Sabres": 45899, "\u0120414": 45900, "viks": 45901, "ihara": 45902, "apses": 45903, "Talking": 45904, "\u0120intertwined": 45905, "\u0120docks": 45906, "\u0120allele": 45907, "\u0120Artifact": 45908, "\u0120HIM": 45909, "torn": 45910, "\u00e7\u0137": 45911, "\u0120opacity": 45912, "\u0120Ely": 45913, "osuke": 45914, "\u0120nipple": 45915, "\u0120handwritten": 45916, "\u0120VK": 45917, "\u0120Chamberlain": 45918, "\u0120Laos": 45919, "igraph": 45920, "grow": 45921, "\u0120trillions": 45922, "\u0120descendant": 45923, "\u0120Sailor": 45924, "asuring": 45925, "\u0120ceilings": 45926, "\u0120Warehouse": 45927, "flying": 45928, "\u0120Glow": 45929, "\u0120nont": 45930, "\u0120miscarriage": 45931, "\u0120rigs": 45932, "\u0120ministries": 45933, "\u0120elaborated": 45934, "\u0120delusional": 45935, "\u0120Humane": 45936, "\u0120379": 45937, "nets": 45938, "\u0120blackout": 45939, "adders": 45940, "\u0120np": 45941, "\u0120Tire": 45942, "rosc": 45943, "\u0120subdiv": 45944, "\u0120linkage": 45945, "\u0120chronological": 45946, "\u0120HERO": 45947, "\u0120resettlement": 45948, "\u0120Vinyl": 45949, "\u0120pastoral": 45950, "\u0120Mobil": 45951, "\u0120Barbar": 45952, "Cooldown": 45953, "\u0120Fritz": 45954, "criminal": 45955, "repe": 45956, "\u0120bellig": 45957, "\u0120Breed": 45958, "\u0120418": 45959, "\u0120semblance": 45960, "ijk": 45961, "\u0120curtail": 45962, "\u0120clinch": 45963, "contained": 45964, "\u0120Prompt": 45965, "aston": 45966, "\u0120wi": 45967, "\u0120pursuits": 45968, "515": 45969, "\u0120Gloss": 45970, "\u0120flips": 45971, "\u0120coupons": 45972, "\u0120cloning": 45973, "\u0120Likely": 45974, "Removed": 45975, "\u0120Quartz": 45976, "rices": 45977, "\u0120Spears": 45978, "\u0120pious": 45979, "\u0120depreciation": 45980, "\u0120Dare": 45981, "ounces": 45982, "amaz": 45983, "Ont": 45984, "\u0120pinnacle": 45985, "docker": 45986, "026": 45987, "\u0120Wyr": 45988, "\u0120Proper": 45989, "\u00cb\u012a": 45990, "nil": 45991, "Bytes": 45992, "\u0120seeker": 45993, "trial": 45994, "\u0120unfolds": 45995, "\u0120Marse": 45996, "\u0120extravagant": 45997, "\u0120Survivors": 45998, "REDACTED": 45999, "\u0120Speedway": 46000, "\u0120Craigslist": 46001, "submit": 46002, "\u0120Generations": 46003, "\u0120upholding": 46004, "\u0120bloodstream": 46005, "\u0120Missions": 46006, "\u0120Lawn": 46007, "\u0120limbo": 46008, "enei": 46009, "Huh": 46010, "\u0120Wildcats": 46011, "prep": 46012, "\u0120Markus": 46013, "\u0120Forbidden": 46014, "ritic": 46015, "INO": 46016, "\u0120exhibiting": 46017, "requent": 46018, "chuk": 46019, "\u0120habitual": 46020, "\u0120Compatibility": 46021, "Drag": 46022, "RIPT": 46023, "ujah": 46024, "GROUND": 46025, "\u0120delinquent": 46026, "\u0120burner": 46027, "\u0120contemporaries": 46028, "\u0120gimmick": 46029, "loads": 46030, "\u0120nozzle": 46031, "podcast": 46032, "\u0120Wak": 46033, "\u0120Staten": 46034, "\u0120Kuh": 46035, "\u00e3\u0123\u0135": 46036, "interrupted": 46037, "\u0120invincible": 46038, "\u0120Burnett": 46039, "cigarette": 46040, "\u0120Pebble": 46041, "\u0120Temporary": 46042, "\u0120Marino": 46043, "582": 46044, "\u0120wasteland": 46045, "idently": 46046, "Tx": 46047, "\u0120rite": 46048, "\u0120Panasonic": 46049, "\u0120Middles": 46050, "\u0120Horton": 46051, "aeus": 46052, "\u0120curing": 46053, "\u0120mats": 46054, "\u0120adjourn": 46055, "\u0120fearsome": 46056, "pez": 46057, "boats": 46058, "\u0120propell": 46059, "\u0120conflicted": 46060, "\u0120Anger": 46061, "\u0120insurgent": 46062, "Karl": 46063, "\u0120coales": 46064, "\u0120southwestern": 46065, "\u0120dissu": 46066, "\u0120Overt": 46067, "************": 46068, "\u0120boxed": 46069, "\u0120Brune": 46070, "aaa": 46071, "\u0120gardening": 46072, "\u0120Engel": 46073, "tracks": 46074, "\u0120purified": 46075, "\u0120placeholder": 46076, "\u0120Likes": 46077, "\u0120dan": 46078, "Gab": 46079, "\u0120ect": 46080, "\u0120Faw": 46081, "\u0120Eliot": 46082, "\u0120',": 46083, "otropic": 46084, "\u0120Ruin": 46085, "hedon": 46086, "\u0120caul": 46087, "\u0120aft": 46088, "\u0120Cadillac": 46089, "gha": 46090, "assian": 46091, "udeb": 46092, "\u0120Tick": 46093, "\u0120adjusts": 46094, "ARGET": 46095, "537": 46096, "ische": 46097, "anty": 46098, "\u0120Friedrich": 46099, "\u0120Blizz": 46100, "\u0120AOL": 46101, "Campaign": 46102, "\u0120mammal": 46103, "\u0120Veil": 46104, "\u0120Kev": 46105, "\u0120Maurit": 46106, "\u0120Damien": 46107, "Nation": 46108, "Eastern": 46109, "\u0120{:": 46110, "\u0120=================================": 46111, "\u0120stereotypical": 46112, "\u0120attic": 46113, "\u0120Cyborg": 46114, "require": 46115, "\u0120awarding": 46116, "\u0120Papua": 46117, "btn": 46118, "bent": 46119, "Boo": 46120, "\u0120(=": 46121, "\u0120Xander": 46122, "\u0120Somerset": 46123, "\u0120catchy": 46124, "\u0120certify": 46125, "STRUCT": 46126, "\u0120ital": 46127, "\u0120tides": 46128, "\u0120Brands": 46129, "Gray": 46130, "competitive": 46131, "\u0120curator": 46132, "\u0120DG": 46133, "ominium": 46134, "\u0120GMOs": 46135, "ciating": 46136, "\u0120Carmen": 46137, "oward": 46138, "Baltimore": 46139, "\u0120rgb": 46140, "Cu": 46141, "\u0120wipes": 46142, "spell": 46143, "ITNESS": 46144, "\u0120summarizes": 46145, "\u0120Revis": 46146, "\u0120whistleblowers": 46147, "\u0120Breach": 46148, "\u0120crochet": 46149, "kos": 46150, "ewski": 46151, "\u0120repet": 46152, "\u0120crimson": 46153, "\u0120Karachi": 46154, "readable": 46155, "dimension": 46156, "\u0120Igor": 46157, "ilded": 46158, "\u0120Zed": 46159, "\u0120Keane": 46160, "\u0120Cosmetic": 46161, "DEP": 46162, "\u0120retreating": 46163, "\u0120UA": 46164, "ensical": 46165, "\u0120dusk": 46166, "\u0120Dickens": 46167, "\u0120arenas": 46168, "\u0120Passage": 46169, "levels": 46170, "\u0120curv": 46171, "Pope": 46172, "\u0120chores": 46173, "\u0120Elise": 46174, "\u0120Compass": 46175, "bub": 46176, "\u0120mammalian": 46177, "\u0120Sanskrit": 46178, "\u0120ANC": 46179, "\u0120Crack": 46180, "Qual": 46181, "Laun": 46182, "ampunk": 46183, "\u0120learners": 46184, "\u0120glamorous": 46185, "\u0120furthe": 46186, "ermott": 46187, "cand": 46188, "Generic": 46189, "\u0120narrated": 46190, "\u0120disorderly": 46191, "\u0120Transactions": 46192, "\u0120Detention": 46193, "\u0120Roku": 46194, "\u00c4\u012f": 46195, "\u0120understatement": 46196, "\u0120Saur": 46197, "\u0120Rodrigo": 46198, "\u0120ASAP": 46199, "Sin": 46200, "\u0120rejoice": 46201, "Methods": 46202, "\u0120electrode": 46203, "\u0120worshipped": 46204, "\u0120idi": 46205, "\u0120Physicians": 46206, "\u0120popup": 46207, "\u0120deft": 46208, "\u0120Removal": 46209, "\u0120Buenos": 46210, "verbs": 46211, "\u0120funk": 46212, "usha": 46213, "riction": 46214, "orea": 46215, "\u0120Bangalore": 46216, "\u0120Kenobi": 46217, "zzi": 46218, "\u0120normative": 46219, "\u0120goblins": 46220, "\u0120cafes": 46221, "\u0120UNCLASSIFIED": 46222, "\u0120Fired": 46223, "SIGN": 46224, "\u0120sclerosis": 46225, "\u0120Voter": 46226, "\u0120Sonny": 46227, "\u0120Extend": 46228, "\u0120EVs": 46229, "Arsenal": 46230, "\u0120psi": 46231, "\u0120widest": 46232, "\u0120Tus": 46233, "\u0120looms": 46234, "\u0120justifying": 46235, "\u0120Granger": 46236, "\u00e8\u00af": 46237, "Refer": 46238, "583": 46239, "\u0120flourishing": 46240, "abre": 46241, "\u0120rave": 46242, "\u0120Contra": 46243, "\u01201898": 46244, "Adds": 46245, "\u0120ful": 46246, "\u0120Cooke": 46247, "someone": 46248, "=#": 46249, "671": 46250, "\u0120yak": 46251, "\u0120arte": 46252, "\u0120Miscellaneous": 46253, "\u0120Detection": 46254, "\u0120Clancy": 46255, "\u00e2\u0123": 46256, "assies": 46257, "\u0120valiant": 46258, "\u0120Feminist": 46259, "corruption": 46260, "Vel": 46261, "Pear": 46262, "\u0120succinct": 46263, "\u0120quickest": 46264, "kw": 46265, "\u0120spitting": 46266, "\u0120Libraries": 46267, "\u00e5\u0127\u012b": 46268, "antz": 46269, "Dad": 46270, "\u0120Specifications": 46271, "rupulous": 46272, "andr": 46273, "RESULTS": 46274, "\u0120snowball": 46275, "\u0120predis": 46276, "\u0120Baxter": 46277, "\u0120Nursing": 46278, "\u0120Chaff": 46279, "swe": 46280, "\u0120outage": 46281, "\u0120nesting": 46282, "\u0120notoriety": 46283, "trigger": 46284, "onite": 46285, "jon": 46286, "\u0120fou": 46287, "ooked": 46288, "\u0120Celebrity": 46289, "reality": 46290, "\u0120fatig": 46291, "\u0120hugging": 46292, "\u0120bothers": 46293, "\u0120Panzer": 46294, "\u0120Chandra": 46295, "figured": 46296, "\u0120volts": 46297, "\u0120Clouds": 46298, "\u0120feeble": 46299, "\u0120Curve": 46300, "\u0120Asus": 46301, "786": 46302, "absor": 46303, "\u0120VICE": 46304, "\u0120Hess": 46305, "\u0120manufactures": 46306, "\u0120grizz": 46307, "\u0120Powerful": 46308, "acid": 46309, "\u0120subsections": 46310, "\u0120Krugman": 46311, "\u0120Alps": 46312, "isu": 46313, "\u0120sequest": 46314, "\u0120Ultron": 46315, "\u0120Tinker": 46316, "\u0120Goose": 46317, "\u0120mismatch": 46318, "Attorney": 46319, "\u0120morphology": 46320, "\u0120Sixers": 46321, "uttered": 46322, "\u0120ELECT": 46323, "gran": 46324, "Russell": 46325, "\u0120GSL": 46326, "\u0120fortnight": 46327, "\u0120.)": 46328, "\u0120apostle": 46329, "prone": 46330, "elist": 46331, "Untitled": 46332, "\u0120Implementation": 46333, "istors": 46334, "\u0120tanker": 46335, "\u0120plush": 46336, "\u0120attendants": 46337, "\u0120Tik": 46338, "\u0120Greenwich": 46339, "\u0120Yon": 46340, "\u0120SPL": 46341, "cells": 46342, "untled": 46343, "Solution": 46344, "\u0120Qu\u00c3\u00a9": 46345, "\u0120vacated": 46346, "\u0120uptick": 46347, "\u0120Meridian": 46348, "\u00e6\u0125": 46349, "\u0120Drill": 46350, "925": 46351, "584": 46352, "\u0120renovated": 46353, "\u0120Kubrick": 46354, "zyk": 46355, "\u0120lousy": 46356, "ppel": 46357, "ohydrate": 46358, "\u0120Izzy": 46359, "lesiastical": 46360, "CCC": 46361, "\u0120Ajax": 46362, "\u0120adapters": 46363, "\u0120Petraeus": 46364, "\u0120affirmation": 46365, "\u0120STOR": 46366, "lems": 46367, "adoes": 46368, "\u0120Constantinople": 46369, "\u0120ponies": 46370, "\u0120lighthouse": 46371, "\u0120adherents": 46372, "\u0120Brees": 46373, "omorphic": 46374, "Fighting": 46375, "\u0120plaster": 46376, "\u0120PVC": 46377, "\u0120Obst": 46378, "\u0120dearly": 46379, "\u0120Tooth": 46380, "ickson": 46381, "\u0120shaming": 46382, "Plex": 46383, "Agg": 46384, "\u0120\u00e2\u0122\u00a6\"": 46385, "\u0120subreddits": 46386, "\u0120pigeon": 46387, "\u0120Residential": 46388, "\u0120Passing": 46389, "\u0120lum": 46390, "\u0120Pension": 46391, "\u0120pessimistic": 46392, "\u0120432": 46393, "zinski": 46394, "cade": 46395, "075": 46396, "\u0120apologised": 46397, "iyah": 46398, "Putting": 46399, "\u0120gloomy": 46400, "\u0120Lyme": 46401, "=-=-=-=-=-=-=-=-": 46402, "\u0120Tome": 46403, "\u0120Psychiatric": 46404, "\u0120HIT": 46405, "cms": 46406, "apolog": 46407, "\u0120breaker": 46408, "\u0120deepen": 46409, "\u0120theorist": 46410, "\u0120Highlands": 46411, "\u0120baker": 46412, "\u0120staples": 46413, "\u0120interfered": 46414, "\u0120Abortion": 46415, "joined": 46416, "chu": 46417, "\u0120formulate": 46418, "\u0120vaccinations": 46419, "\u0120banter": 46420, "pheus": 46421, "\u0120outfielder": 46422, "\u0120Meter": 46423, "\u0120#####": 46424, "\u01201895": 46425, "\u0120narrowing": 46426, "\u0120STORY": 46427, "fp": 46428, "\u0120CST": 46429, "ignore": 46430, "\u0120proclaiming": 46431, "\u0120RU": 46432, "\u0120BALL": 46433, "yna": 46434, "653": 46435, "\u0120posit": 46436, "PRE": 46437, "594": 46438, "\u0120Registrar": 46439, "\u0120Pilgrim": 46440, "icio": 46441, "\u0120prett": 46442, "\u0120lifeless": 46443, "\u0120___": 46444, "Neigh": 46445, "\u0120Churches": 46446, "orno": 46447, "\u0120orcs": 46448, "\u0120kindred": 46449, "\u0120Audit": 46450, "\u0120millennial": 46451, "\u0120Persia": 46452, "gravity": 46453, "\u0120Disability": 46454, "\u0120DARK": 46455, "Ws": 46456, "odon": 46457, "\u0120granddaughter": 46458, "\u0120Brooke": 46459, "\u0120ADA": 46460, "ERA": 46461, "\u0120pickups": 46462, "\u0120Wilkinson": 46463, "\u0120Shards": 46464, "\u0120NK": 46465, "\u0120expel": 46466, "\u0120Kislyak": 46467, "\u0120jargon": 46468, "\u0120polarized": 46469, "iane": 46470, "Publisher": 46471, "\u0120rebutt": 46472, "\u0120apprehension": 46473, "\u0120Kessler": 46474, "\u0120prism": 46475, "FUL": 46476, "1964": 46477, "\u0120Loll": 46478, "\u00e4\u00bf": 46479, "lethal": 46480, "\u00c5\u0141": 46481, "\u0120ghetto": 46482, "\u0120boulder": 46483, "\u0120Slowly": 46484, "\u0120Oscars": 46485, "\u0120Instruction": 46486, "\u0120Ultr": 46487, "\u0120Moe": 46488, "Nich": 46489, "\u0120PATH": 46490, "(*": 46491, "\u0120RELEASE": 46492, "uning": 46493, "rouse": 46494, "eneg": 46495, "\u0120reimb": 46496, "\u0120Detected": 46497, "DoS": 46498, "\u0120sterling": 46499, "\u0120aggregation": 46500, "\u0120Lonely": 46501, "\u0120Attend": 46502, "higher": 46503, "\u0120airstrike": 46504, "kson": 46505, "SELECT": 46506, "\u0120deflation": 46507, "\u0120Herrera": 46508, "Cole": 46509, "ritch": 46510, "\u0120advisable": 46511, "Fax": 46512, "\u0120workaround": 46513, "\u0120pid": 46514, "mortem": 46515, "ersen": 46516, "\u0120typo": 46517, "\u0120alum": 46518, "782": 46519, "\u0120Jamal": 46520, "scripts": 46521, "\u0120captives": 46522, "\u0120Presence": 46523, "\u0120Lieberman": 46524, "angelo": 46525, "\u0120alcoholism": 46526, "assi": 46527, "\u0120recite": 46528, "\u0120gaping": 46529, "\u0120baskets": 46530, "\u0120Gou": 46531, "Browser": 46532, "neau": 46533, "\u0120corrective": 46534, "unda": 46535, "scoring": 46536, "\u0120XD": 46537, "\u0120filament": 46538, "\u0120deepening": 46539, "\u0120Stainless": 46540, "Integer": 46541, "\u0120buggy": 46542, "\u0120tenancy": 46543, "\u0120Mubarak": 46544, "\u0120tuple": 46545, "\u0120Droid": 46546, "\u0120Sitting": 46547, "\u0120forfeit": 46548, "\u0120Rasmussen": 46549, "ixties": 46550, "esi": 46551, "\u0120Kimmel": 46552, "\u0120meticulously": 46553, "\u0120apopt": 46554, "\u0120Seller": 46555, "088": 46556, "ecake": 46557, "hematically": 46558, "TN": 46559, "\u0120mindless": 46560, "\u0120digs": 46561, "\u0120Accord": 46562, "onsense": 46563, "eming": 46564, "brace": 46565, "\u0120eBook": 46566, "\u0120Distribut": 46567, "\u0120Investments": 46568, "wt": 46569, "]),": 46570, "behavior": 46571, "563": 46572, "\u0120blinding": 46573, "\u0120Protesters": 46574, "topia": 46575, "\u0120reborn": 46576, "\u0120Kelvin": 46577, "\u0120Dover": 46578, "\u0120Dairy": 46579, "\u0120Outs": 46580, "\u0120[/": 46581, "\u00cf\u0122": 46582, "bp": 46583, "\u0120Vanity": 46584, "\u0120Recap": 46585, "\u0120HOUSE": 46586, "\u0120FACE": 46587, "\u0120422": 46588, "692": 46589, "\u0120Antioch": 46590, "cooked": 46591, "\u0120collide": 46592, "\u0120apr": 46593, "\u0120sleeper": 46594, "\u0120Jarvis": 46595, "\u0120alternatively": 46596, "\u0120Leaves": 46597, "\u0120Maw": 46598, "\u0120antiquity": 46599, "\u0120Adinida": 46600, "\u0120abuser": 46601, "Pok\u00c3\u00a9mon": 46602, "\u0120assorted": 46603, "\u0120Revision": 46604, "\u0120Piano": 46605, "\u0120Gideon": 46606, "Ocean": 46607, "\u0120salon": 46608, "\u0120bustling": 46609, "ognitive": 46610, "\u0120Rahman": 46611, "\u0120waiter": 46612, "\u0120presets": 46613, "\u0120Osh": 46614, "\u0120GHC": 46615, "operator": 46616, "\u0120reptiles": 46617, "\u0120413": 46618, "\u0120Garr": 46619, "\u0120Chak": 46620, "\u0120hashes": 46621, "\u0120failings": 46622, "\u0120folklore": 46623, "\u0120abl": 46624, "\u0120Cena": 46625, "\u0120MacArthur": 46626, "\u0120COURT": 46627, "\u0120periphery": 46628, "appers": 46629, "\u0120reckoned": 46630, "\u0120Influ": 46631, "\u0120CET": 46632, "\u0120372": 46633, "\u0120Definitive": 46634, "assault": 46635, "421": 46636, "\u0120reservoirs": 46637, "\u0120dives": 46638, "\u0120Coil": 46639, "DAQ": 46640, "\u0120vividly": 46641, "\u0120RJ": 46642, "\u0120Bellev": 46643, "\u0120eclectic": 46644, "\u0120Showdown": 46645, "\u0120KM": 46646, "iped": 46647, "reetings": 46648, "\u0120Asuka": 46649, "Liberal": 46650, "\u0120\u00cf\u0126": 46651, "\u0120bystanders": 46652, "\u0120Goodwin": 46653, "ukong": 46654, "Sit": 46655, "\u0120Trem": 46656, "\u0120criminally": 46657, "\u0120Circus": 46658, "chrome": 46659, "887": 46660, "\u0120nanop": 46661, "\u0120Obi": 46662, "\u0120LOW": 46663, "ogh": 46664, "\u0120Authors": 46665, "obyl": 46666, "Urban": 46667, "\u0120ti": 46668, "\u0120Weir": 46669, "trap": 46670, "agy": 46671, "\u0120parentheses": 46672, "\u0120outnumbered": 46673, "\u0120counterproductive": 46674, "\u0120Tobias": 46675, "ubis": 46676, "Parser": 46677, "STAR": 46678, "\u0120synaptic": 46679, "\u0120Gears": 46680, "\u0120hiber": 46681, "\u0120debunked": 46682, "\u0120exalted": 46683, "awatts": 46684, "HOU": 46685, "Church": 46686, "\u0120Pixie": 46687, "\u0120Uri": 46688, "\u0120Formation": 46689, "\u0120Prediction": 46690, "CEO": 46691, "\u0120thrott": 46692, "\u0120Britann": 46693, "\u0120Madagascar": 46694, "\u00eb\u012d": 46695, "\u0120billboards": 46696, "\u0120RPGs": 46697, "\u0120Bees": 46698, "completely": 46699, "FIL": 46700, "\u0120doesnt": 46701, "\u0120Greenberg": 46702, "reys": 46703, "\u0120sling": 46704, "\u0120emptied": 46705, "\u0120Pixar": 46706, "\u0120Dharma": 46707, "luck": 46708, "inguished": 46709, "\u0120endot": 46710, "\u0120babys": 46711, "059": 46712, "chest": 46713, "rats": 46714, "\u0120ridden": 46715, "\u0120beetles": 46716, "\u0120illuminating": 46717, "\u0120fictitious": 46718, "\u0120Provincial": 46719, "\u0120768": 46720, "\u0120shepherd": 46721, "\u0120Render": 46722, "\u01201896": 46723, "Crew": 46724, "\u0120molded": 46725, "\u0120Xiaomi": 46726, "\u0120Spiral": 46727, "\u0120delim": 46728, "\u0120organising": 46729, "\u0120hoops": 46730, "\u0120Bei": 46731, "zhen": 46732, "\u0120fuckin": 46733, "\u0120decad": 46734, "\u0120unbiased": 46735, "ammy": 46736, "swing": 46737, "\u0120smuggled": 46738, "\u0120kios": 46739, "\u0120PERSON": 46740, "\u0120Inquisitor": 46741, "\u0120snowy": 46742, "\u0120scraping": 46743, "\u0120Burgess": 46744, "Ptr": 46745, "agame": 46746, "RW": 46747, "\u0120droid": 46748, "\u0120Lys": 46749, "\u0120Cassandra": 46750, "Jacob": 46751, "\u0120354": 46752, "\u0120pasture": 46753, "\u0120franc": 46754, "\u0120Scotch": 46755, "\u0120Ends": 46756, "\u0120IGF": 46757, "definition": 46758, "\u0120hysterical": 46759, "\u0120Browne": 46760, "771": 46761, "\u0120mobilization": 46762, "\u00e6\u0137": 46763, "iqueness": 46764, "Thor": 46765, "\u0120spearheaded": 46766, "\u0120embroiled": 46767, "\u0120conjecture": 46768, "judicial": 46769, "Choice": 46770, "\u0120paperback": 46771, "Pir": 46772, "\u0120recovers": 46773, "\u0120Surge": 46774, "\u0120Shogun": 46775, "\u0120Pediatrics": 46776, "\u00e3\u0123\u0142": 46777, "\u0120sweeps": 46778, "\u0120Laboratories": 46779, "\u0120Packs": 46780, "alus": 46781, "addin": 46782, "\u0120headlights": 46783, "gra": 46784, "Evidence": 46785, "COLOR": 46786, "Admin": 46787, "\u012c\u00b1": 46788, "\u0120concoct": 46789, "sufficient": 46790, "\u0120unmarked": 46791, "\u0120richness": 46792, "\u0120dissertation": 46793, "\u0120seasoning": 46794, "\u0120gib": 46795, "\u0120Mages": 46796, "unctions": 46797, "\u0120Nid": 46798, "cheat": 46799, "\u0120TMZ": 46800, "citizens": 46801, "\u0120Catholicism": 46802, "nb": 46803, "\u0120disembark": 46804, "\u0120PROGRAM": 46805, "aques": 46806, "Tyler": 46807, "Org": 46808, "\u0120Slay": 46809, "\u0120Nero": 46810, "\u0120Townsend": 46811, "INTON": 46812, "tele": 46813, "\u0120mesmer": 46814, "901": 46815, "\u0120fireball": 46816, "evidence": 46817, "affiliated": 46818, "\u0120Frenchman": 46819, "\u0120Augusta": 46820, "021": 46821, "\u0120sled": 46822, "\u0120reused": 46823, "\u0120Immunity": 46824, "\u0120wrestle": 46825, "assembled": 46826, "Maria": 46827, "\u0120gunshots": 46828, "\u0120Barbie": 46829, "\u0120cannabinoids": 46830, "\u0120Toast": 46831, "\u0120Kinder": 46832, "IRD": 46833, "\u0120rejuven": 46834, "\u0120gore": 46835, "\u0120rupture": 46836, "\u0120breaching": 46837, "\u0120Cartoon": 46838, "\u0120455": 46839, "\u0120Paleo": 46840, "614": 46841, "\u0120spears": 46842, "\u0120Ames": 46843, "abus": 46844, "Madison": 46845, "GROUP": 46846, "\u0120aborted": 46847, "yah": 46848, "\u0120felon": 46849, "\u0120causation": 46850, "\u0120prepaid": 46851, "\u0120pitted": 46852, "oplan": 46853, "\u0120Shelley": 46854, "\u0120Russo": 46855, "\u0120Pagan": 46856, "\u0120willfully": 46857, "\u0120Canaver": 46858, "undrum": 46859, "\u0120Salary": 46860, "\u0120Arpaio": 46861, "reader": 46862, "\u0120Rational": 46863, "\u0120Overse": 46864, "\u0120Causes": 46865, "\u0120*.": 46866, "\u0120wob": 46867, "Keith": 46868, "\u0120Consent": 46869, "manac": 46870, "773": 46871, "623": 46872, "\u0120fateful": 46873, "etimes": 46874, "\u0120spirited": 46875, "\u0120Dys": 46876, "\u0120hegemony": 46877, "\u0120boycot": 46878, "\u0120Enrique": 46879, "emouth": 46880, "\u0120timelines": 46881, "\u0120Sahara": 46882, "\u0120Relax": 46883, "\u0120Quincy": 46884, "\u0120Lessons": 46885, "\u0120EQU": 46886, "SEA": 46887, "NK": 46888, "\u0120Costco": 46889, "Increase": 46890, "\u0120motivating": 46891, "\u0120Chong": 46892, "amaru": 46893, "\u0120Divide": 46894, "\u0120pedigree": 46895, "\u0120Tasmania": 46896, "\u0120Prelude": 46897, "Las": 46898, "940": 46899, "574": 46900, "\u0120chau": 46901, "\u0120Spiegel": 46902, "unic": 46903, "-->": 46904, "\u0120Philips": 46905, "\u0120Kafka": 46906, "\u0120upheaval": 46907, "\u0120sentimental": 46908, "\u0120sax": 46909, "\u0120Akira": 46910, "serial": 46911, "Matrix": 46912, "\u0120electing": 46913, "\u0120commenter": 46914, "\u0120Nebula": 46915, "plets": 46916, "\u0120Nadu": 46917, "\u0120Adren": 46918, "\u0120enshr": 46919, "\u0120RAND": 46920, "financial": 46921, "\u0120Clyde": 46922, "utherford": 46923, "\u0120signage": 46924, "\u0120deline": 46925, "\u0120phosphate": 46926, "roversial": 46927, "fascist": 46928, "\u0120Vall": 46929, "\u0120Bethlehem": 46930, "\u0120fors": 46931, "\u0120english": 46932, "Solid": 46933, "Nature": 46934, "\u0120va": 46935, "\u0120Guests": 46936, "\u0120tantal": 46937, "\u0120autoimmune": 46938, ";;;;;;;;;;;;": 46939, "\u0120Totally": 46940, "\u0120Ov": 46941, "\u0120defences": 46942, "\u0120Coconut": 46943, "\u0120tranquil": 46944, "\u0120ploy": 46945, "\u0120flavours": 46946, "\u0120Flask": 46947, "\u00e3\u0124\u00a8\u00e3\u0125\u00ab": 46948, "\u0120Weston": 46949, "\u0120Volvo": 46950, "870": 46951, "\u0120microphones": 46952, "verbal": 46953, "RPG": 46954, "\u0120iii": 46955, ";}": 46956, "028": 46957, "\u0120headlined": 46958, "\u0120primed": 46959, "\u0120hoard": 46960, "\u0120Shad": 46961, "\u0120ENTER": 46962, "\u0120triangular": 46963, "\u0120capit": 46964, "lik": 46965, "\u0120Ancients": 46966, "\u0120lash": 46967, "\u0120convol": 46968, "\u0120colonel": 46969, "enemy": 46970, "Gra": 46971, "\u0120pubs": 46972, "utters": 46973, "\u0120assigns": 46974, "\u0120Penet": 46975, "\u0120Monstrous": 46976, "\u0120Bowen": 46977, "ilver": 46978, "Haunted": 46979, "\u0120Ding": 46980, "started": 46981, "plin": 46982, "\u0120contaminants": 46983, "\u0120DOE": 46984, "ffen": 46985, "\u0120Technician": 46986, "Ry": 46987, "\u0120robbers": 46988, "\u0120hotline": 46989, "\u0120Guardiola": 46990, "\u0120Kaufman": 46991, "rower": 46992, "\u0120Dresden": 46993, "\u0120Alpine": 46994, "Elf": 46995, "\u0120fmt": 46996, "\u0120Sard": 46997, "urses": 46998, "gpu": 46999, "Unix": 47000, "\u0120unequivocally": 47001, "\u0120Citizenship": 47002, "quad": 47003, "mire": 47004, "\u0120Sweeney": 47005, "Battery": 47006, "615": 47007, "\u0120pancakes": 47008, "\u0120oats": 47009, "Maps": 47010, "\u0120Contrast": 47011, "mbudsman": 47012, "\u0120EPS": 47013, "\u0120subcommittee": 47014, "\u0120sourcing": 47015, "\u0120sizing": 47016, "\u0120Buffer": 47017, "\u0120Mandatory": 47018, "\u0120moderates": 47019, "\u0120Patterns": 47020, "\u0120Chocobo": 47021, "\u0120Zan": 47022, "\u0120STATES": 47023, "\u0120Judging": 47024, "\u0120Inher": 47025, "*:": 47026, "\u0120bil": 47027, "\u0120Yen": 47028, "\u0120exhilar": 47029, "ollower": 47030, "zers": 47031, "\u0120snug": 47032, "maximum": 47033, "\u0120despicable": 47034, "\u0120PACK": 47035, "\u0120Annex": 47036, "\u0120sarcastic": 47037, "\u0120latex": 47038, "\u0120tamp": 47039, "\u0120Sao": 47040, "bah": 47041, "\u0120Reverend": 47042, "\u0120Chinatown": 47043, "\u0120AUT": 47044, "documented": 47045, "\u0120GABA": 47046, "\u0120Canaan": 47047, "\u0120\u00d9\u0127": 47048, "\u0120governs": 47049, "prev": 47050, "Esc": 47051, "\u0120Estimates": 47052, "OSP": 47053, "\u0120endeavour": 47054, "\u0120Closing": 47055, "ometime": 47056, "everyone": 47057, "\u0120worsen": 47058, "\u0120scanners": 47059, "\u0120deviations": 47060, "\u0120Robotics": 47061, "\u0120Compton": 47062, "\u0120sorcerer": 47063, "\u0120endogenous": 47064, "\u0120emulation": 47065, "\u0120Piercing": 47066, "\u0120Aph": 47067, "\u0120Socket": 47068, "\u0120bould": 47069, "\u0120OU": 47070, "\u0120Borderlands": 47071, "\u01201863": 47072, "Gordon": 47073, "\u0120WTO": 47074, "\u0120restricts": 47075, "\u0120mosaic": 47076, "\u0120melodies": 47077, "\u00e7\u0126": 47078, "Tar": 47079, "\u0120disson": 47080, "\u0120Provides": 47081, "\u0120......": 47082, "bek": 47083, "FIX": 47084, "\u0120broom": 47085, "anship": 47086, "Doctors": 47087, "\u0120nerds": 47088, "\u0120Regions": 47089, "naissance": 47090, "\u0120mete": 47091, "\u0120crept": 47092, "plings": 47093, "\u0120girlfriends": 47094, "knit": 47095, "igent": 47096, "owe": 47097, "\u0120ushered": 47098, "\u0120Baz": 47099, "Mobil": 47100, "434": 47101, "\u0120Presents": 47102, "origin": 47103, "\u0120insomnia": 47104, "\u0120Aux": 47105, "439": 47106, "\u0120Chili": 47107, "irsch": 47108, "GAME": 47109, "\u0120gestation": 47110, "algia": 47111, "romising": 47112, "$,": 47113, "crow": 47114, "\u0120Inspection": 47115, "atomic": 47116, "Relations": 47117, "JOHN": 47118, "roman": 47119, "\u0120Clockwork": 47120, "\u0120Bakr": 47121, "mone": 47122, "MET": 47123, "\u0120thirsty": 47124, "\u0120bc": 47125, "\u0120faculties": 47126, "Rum": 47127, "\u0120nuance": 47128, "\u0120Darius": 47129, "pleting": 47130, "fters": 47131, "etchup": 47132, "Registration": 47133, "\u0120KE": 47134, "Rah": 47135, "\u0120preferential": 47136, "\u0120Lash": 47137, "\u0120HH": 47138, "Valid": 47139, "\u0120NAV": 47140, "\u0120starve": 47141, "\u0120Gong": 47142, "zynski": 47143, "\u0120Actress": 47144, "\u0120wik": 47145, "\u0120unaccompanied": 47146, "lvl": 47147, "Bride": 47148, "ADS": 47149, "\u0120Commando": 47150, "\u0120Vaughn": 47151, "Wallet": 47152, "\u0120hopping": 47153, "\u0120Vie": 47154, "\u0120caveats": 47155, "\u0120alas": 47156, "ifled": 47157, "abuse": 47158, "661": 47159, "\u0120ibn": 47160, "\u0120gul": 47161, "\u0120robbing": 47162, "til": 47163, "ILA": 47164, "\u0120mitigating": 47165, "\u0120aptly": 47166, "\u0120tyrant": 47167, "\u0120midday": 47168, "\u0120Gilmore": 47169, "\u0120Decker": 47170, "\u0120\u00c2\u00a7\u00c2\u00a7": 47171, "partial": 47172, "Exactly": 47173, "\u0120phenotype": 47174, "\u0120[+]": 47175, "\u0120Plex": 47176, "\u0120Ips": 47177, "versions": 47178, "\u0120ebook": 47179, "\u0120chic": 47180, "gross": 47181, "\":\"\"},{\"": 47182, "\u0120Surprisingly": 47183, "Morgan": 47184, "\u0120residues": 47185, "\u0120Confederation": 47186, "infeld": 47187, "\u0120lyr": 47188, "moderate": 47189, "\u0120perpendicular": 47190, "VK": 47191, "\u0120synchronized": 47192, "\u0120refreshed": 47193, "\u0120adore": 47194, "\u0120Torment": 47195, "olina": 47196, "\u01202600": 47197, "ItemTracker": 47198, "\u0120pies": 47199, "\u0120FAT": 47200, "\u0120RHP": 47201, "048": 47202, "\u0120RESP": 47203, "\u0120BJ": 47204, "allows": 47205, "Pand": 47206, "\u0120unwelcome": 47207, "\u0120Voc": 47208, "\u0120Bastard": 47209, "\u0120OW": 47210, "\u0120LAR": 47211, "\u0120Healer": 47212, "Environmental": 47213, "\u0120Kenyan": 47214, "\u0120Trance": 47215, "\u0120Pats": 47216, "\u0120aliases": 47217, "\u0120Garfield": 47218, "\u0120campaigner": 47219, "\u0120advancements": 47220, "\u0120Okinawa": 47221, "\u0120Coh": 47222, "owsky": 47223, "\u0120starved": 47224, "\u0120sizeable": 47225, "\u0120:-)": 47226, "\u0120mRNA": 47227, "\u0120suspensions": 47228, "istar": 47229, "Scotland": 47230, "Prin": 47231, "------------------------------------------------": 47232, "\u0120502": 47233, "\u0120teaspoons": 47234, "\u01201050": 47235, "\u0120coercive": 47236, "\u0120Masonic": 47237, "edded": 47238, "\u0120Passenger": 47239, "\u0120latt": 47240, "\u0120braces": 47241, "\u0120Steal": 47242, "\u0120NYT": 47243, "\u0120Kats": 47244, "\u0120Celest": 47245, "aez": 47246, "Tu": 47247, "\u0120Coulter": 47248, "\u00f0\u0141\u013a": 47249, "Flickr": 47250, "\u0120Wilmington": 47251, "iths": 47252, "++;": 47253, "\u0120vending": 47254, "\u0120negro": 47255, "\u0120Phi": 47256, "\u0120Yellowstone": 47257, "Callback": 47258, "\u0120shampoo": 47259, "\u0120Shades": 47260, "wat": 47261, "\u0120superhuman": 47262, "\u0120ridiculed": 47263, "\u0120holiest": 47264, "ombo": 47265, "\u0120interns": 47266, "\u0120hone": 47267, "\u0120Paragu": 47268, "URI": 47269, "\u0120dangling": 47270, "\u00e3\u0124\u00bb": 47271, "sov": 47272, "ictional": 47273, "availability": 47274, "\u0120revocation": 47275, "\u0120dow": 47276, "inic": 47277, "\u0120THEIR": 47278, "\u0120iso": 47279, "\u0120outings": 47280, "\u0120Lethal": 47281, "\u0120)))": 47282, "\u0120inaccur": 47283, "\u0120outlandish": 47284, "\u0120anus": 47285, "letico": 47286, "idon": 47287, "lol": 47288, "\u0120unregulated": 47289, "\u0120succumbed": 47290, "\u0120cuff": 47291, "\u0120Wasteland": 47292, "letal": 47293, "\u0120substr": 47294, "\u0120coffers": 47295, "\u0120automakers": 47296, "ovi": 47297, "\u0120Xue": 47298, "\u0120Daytona": 47299, "\u0120jarring": 47300, "\u0120fumes": 47301, "\u0120disbanded": 47302, "zik": 47303, "itton": 47304, "\u0120strikingly": 47305, "\u0120spores": 47306, "Adapter": 47307, ".):": 47308, "\u0120Lyndon": 47309, "ivalry": 47310, "\u0120orally": 47311, "\u0120tumultuous": 47312, "\u0120displeasure": 47313, "\u0120cones": 47314, "orrect": 47315, "\u0120appease": 47316, "\u0120derby": 47317, "\u0120Tripoli": 47318, "\u0120Aless": 47319, "\u0120poked": 47320, "\u0120Guilty": 47321, "vP": 47322, "Enough": 47323, "\u0120originals": 47324, "699": 47325, "\u0120rabbi": 47326, "\u0120proverbial": 47327, "\u0120postpone": 47328, "elope": 47329, "\u0120Misty": 47330, "\u0120staffed": 47331, "\u0120Unemployment": 47332, "reditary": 47333, "\u0120diligent": 47334, "recomm": 47335, "measures": 47336, "asin": 47337, "825": 47338, "\u0120ponds": 47339, "\u0120mmol": 47340, "\u0120SAR": 47341, "\u0120CARE": 47342, "\u0120371": 47343, "\u0120clenched": 47344, "\u0120Corsair": 47345, "\u0120caricature": 47346, "zn": 47347, "attach": 47348, "\u0120Schro": 47349, "speak": 47350, "painted": 47351, "\u0120Suc": 47352, "\u0120ENT": 47353, "\u0120cellul": 47354, "\u0120Paid": 47355, "diagn": 47356, "WHERE": 47357, "\u0120texted": 47358, "Barn": 47359, "\u0120retracted": 47360, "\u0120Referred": 47361, "Sav": 47362, "\u0120upkeep": 47363, "\u0120workplaces": 47364, "\u0120Tokens": 47365, "\u0120amplify": 47366, "clinical": 47367, "\u0120multic": 47368, "mberg": 47369, "\u0120convoluted": 47370, "Region": 47371, "565": 47372, "\u0120Topic": 47373, "\u0120snail": 47374, "\u0120saline": 47375, "\u0120insurrection": 47376, "\u0120Petr": 47377, "forts": 47378, "BAT": 47379, "\u0120Navajo": 47380, "\u0120rudimentary": 47381, "\u0120Laksh": 47382, "ONDON": 47383, "Measure": 47384, "\u0120transformer": 47385, "\u0120Goddard": 47386, "\u0120coincides": 47387, "irin": 47388, "Rex": 47389, "\u0120Bok": 47390, "quit": 47391, "\u0120shotguns": 47392, "\u0120proletarian": 47393, "\u0120scorp": 47394, "\u0120Ada": 47395, "514": 47396, "\u0120slander": 47397, "recorded": 47398, "\u0120embell": 47399, "risome": 47400, "\u0120apologizing": 47401, "\u0120Mulcair": 47402, "\u0120Gibraltar": 47403, "Cla": 47404, "\u0120allot": 47405, "\u0120Attention": 47406, "\u0120433": 47407, "leave": 47408, "\u0120whine": 47409, "\u0120Issa": 47410, "\u0120Faust": 47411, "\u0120Barron": 47412, "heny": 47413, "\u0120victimized": 47414, "Jews": 47415, "\u0120nurturing": 47416, "ettel": 47417, "Winged": 47418, "\u0120Subtle": 47419, "\u0120flavorful": 47420, "\u0120Reps": 47421, "enged": 47422, "callback": 47423, "\u0120directional": 47424, "\u0120clasp": 47425, "\u0120Directions": 47426, "planet": 47427, "iculture": 47428, "Helper": 47429, "icion": 47430, "acia": 47431, "\u0120\u00e7\u00a5\u0140": 47432, "\u0120surges": 47433, "\u0120canoe": 47434, "\u0120Premiership": 47435, "been": 47436, "\u0120defied": 47437, "\u0120Trooper": 47438, "\u0120tripod": 47439, "\u0120gasp": 47440, "\u0120Euph": 47441, "\u0120Ads": 47442, "vernight": 47443, "highly": 47444, "Role": 47445, "\u0120entangled": 47446, "\u0120Zeit": 47447, "618": 47448, "\u0120Rusty": 47449, "\u0120havens": 47450, "\u0120Vaughan": 47451, "HAEL": 47452, "\u0120SERVICE": 47453, "/,": 47454, "\u0120stricken": 47455, "\u0120delusions": 47456, "\u0120bis": 47457, "\u0120Haf": 47458, "\u0120gratification": 47459, "\u0120enticing": 47460, "UNCH": 47461, "Adams": 47462, "\u0120OLED": 47463, "\u0120Beetle": 47464, "\u01201899": 47465, "\u0120SOFTWARE": 47466, "ategor": 47467, "VL": 47468, "\u0120Totem": 47469, "\u0120Gators": 47470, "ATURES": 47471, "\u0120impedance": 47472, "Registered": 47473, "\u0120Cary": 47474, "\u0120Aerial": 47475, "onne": 47476, "enium": 47477, "\u0120dred": 47478, "\u0120Beg": 47479, "\u0120concurrently": 47480, "\u0120superpower": 47481, "\u0120Xan": 47482, "jew": 47483, "imester": 47484, "\u0120Dickinson": 47485, "\u00e2\u0136\u0123": 47486, "Fla": 47487, "\u0120pree": 47488, "\u0120Rollins": 47489, "\u00a9\u00b6\u00e6": 47490, "\u0120denomination": 47491, "\u0120Lana": 47492, "516": 47493, "\u0120inciting": 47494, "scribed": 47495, "juries": 47496, "\u0120Wonders": 47497, "approximately": 47498, "\u0120suspending": 47499, "\u0120mountainous": 47500, "\u0120Laugh": 47501, "oidal": 47502, "Ns": 47503, "Detect": 47504, ")=": 47505, "\u0120Luthor": 47506, "\u0120Schwarzenegger": 47507, "\u0120Muller": 47508, "\u0120Devi": 47509, "ecycle": 47510, "Jar": 47511, "613": 47512, "\u0120Longh": 47513, "Bah": 47514, "\u0120SPORTS": 47515, "nw": 47516, "\u0120refinement": 47517, "\u0120waterways": 47518, "\u0120diner": 47519, "Blade": 47520, "683": 47521, "Fac": 47522, "\u0120initials": 47523, "\u0120rog": 47524, "\u0120paranormal": 47525, "BUT": 47526, "\u0120[(": 47527, "\u0120Swanson": 47528, "\u0120Mesh": 47529, "\u00e2\u0138\u00ac": 47530, "Improve": 47531, "\u0120Radiation": 47532, "\u0120Esther": 47533, "\u0120Esk": 47534, "\u0120Aly": 47535, "iky": 47536, "\u0120irrad": 47537, "\u0120Buckingham": 47538, "\u0120refill": 47539, "\u0120._": 47540, "Repe": 47541, "CONCLUS": 47542, "\u0120differentiated": 47543, "\u0120chirop": 47544, "\u0120Atkins": 47545, "Pattern": 47546, "\u0120excise": 47547, "\u0120cabal": 47548, "NSA": 47549, "\u0120STA": 47550, "\u0120SIL": 47551, "\u0120Paraly": 47552, "\u0120rye": 47553, "\u0120Howell": 47554, "\u0120Countdown": 47555, "nesses": 47556, "alysed": 47557, "\u0120resize": 47558, "\u00e3\u0124\u00bd": 47559, "\u0120budgetary": 47560, "\u0120Stras": 47561, "wang": 47562, "\u0120apiece": 47563, "\u0120precincts": 47564, "\u0120peach": 47565, "\u0120skyline": 47566, "\u0120353": 47567, "popular": 47568, "Appearances": 47569, "\u0120Mechanics": 47570, "\u0120DevOnline": 47571, "Sullivan": 47572, "Zen": 47573, "\u0120pu": 47574, "opolis": 47575, "544": 47576, "\u0120deform": 47577, "\u0120counteract": 47578, "\u0120Lange": 47579, "\u0120417": 47580, "Console": 47581, "774": 47582, "\u0120nodding": 47583, "\u0120populism": 47584, "\u0120hep": 47585, "\u0120counselling": 47586, "compliance": 47587, "UFF": 47588, "\u0120undeniably": 47589, "\u0120railing": 47590, "\u0120Horowitz": 47591, "\u0120Simone": 47592, "\u0120Bungie": 47593, "\u0120ak": 47594, "\u0120Talks": 47595, "xff": 47596, "flake": 47597, "Crash": 47598, "\u0120sweaty": 47599, "\u0120banquet": 47600, "\u0120OFFIC": 47601, "\u0120inventive": 47602, "\u0120astronomer": 47603, "\u0120Stamford": 47604, "\u0120Scare": 47605, "\u0120GREEN": 47606, "olicited": 47607, "\u0120rusher": 47608, "\u0120centrist": 47609, "ighting": 47610, "\u0120subclass": 47611, "\u0120disav": 47612, "\u0120defund": 47613, "\u0120Nanto": 47614, "ociate": 47615, "mast": 47616, "\u0120pacif": 47617, "\u0120mend": 47618, "eers": 47619, "immigration": 47620, "ESSION": 47621, "\u0120numbering": 47622, "\u0120laughable": 47623, "\u0120Ended": 47624, "viation": 47625, "emark": 47626, "Pitt": 47627, "\u0120meticulous": 47628, "\u0120LF": 47629, "\u0120congratulated": 47630, "\u0120Birch": 47631, "\u0120swayed": 47632, "\u0120semifinals": 47633, "\u0120humankind": 47634, "matter": 47635, "\u0120Equip": 47636, "opausal": 47637, "Said": 47638, "\u0120Layout": 47639, "\u0120voicing": 47640, "\u0120thug": 47641, "\u0120pornographic": 47642, "IPS": 47643, "\u0120moaning": 47644, "\u0120grievance": 47645, "\u0120confessions": 47646, "escal": 47647, "TEXTURE": 47648, "Authent": 47649, "osaurus": 47650, "Purchase": 47651, "\u0120relegation": 47652, "alter": 47653, "\u0120\u00c2\u0142\u00c2\u0142": 47654, "\u0120riddled": 47655, "\u0120ogre": 47656, "\u0120Lowell": 47657, "Occup": 47658, "Eat": 47659, "\u0120Hyder": 47660, "\u0120Adviser": 47661, "Commerce": 47662, "Hunt": 47663, "\u0120Orth": 47664, "\u0120Competitive": 47665, "\u0120CLA": 47666, "CDC": 47667, "\u0120salads": 47668, "Fle": 47669, "\u0120industrialized": 47670, "`,": 47671, "\u0120OWN": 47672, "\u0120beck": 47673, "\u0120Particularly": 47674, "oubt": 47675, "\u0120mM": 47676, "\u0120Hussain": 47677, "\u0120Chennai": 47678, "\u0120920": 47679, "\u0120appointing": 47680, "\u0120Cullen": 47681, ",,,,,,,,": 47682, "\u0120pores": 47683, "verified": 47684, "\u0120biochemical": 47685, "emate": 47686, "\u0120cowardly": 47687, "\u0120Helsinki": 47688, "\u0120Ethiopian": 47689, "SOURCE": 47690, "ERC": 47691, "estro": 47692, "\u0120biotech": 47693, "\u0120Sour": 47694, "\u0120brewer": 47695, "Bloomberg": 47696, "\u0120intensify": 47697, "Glass": 47698, "anco": 47699, "\u0120FDR": 47700, "greSQL": 47701, "\u0120Fires": 47702, "\u00a9\u00b6\u00e6\u00a5\u00b5": 47703, "eco": 47704, "1001": 47705, "\u0120Homeless": 47706, "\u0120instantaneous": 47707, "\u0120Haste": 47708, "igel": 47709, "Diamond": 47710, "\u0120paving": 47711, "\u0120landfill": 47712, "\u0120dads": 47713, "houn": 47714, ":]": 47715, "\u0120incendiary": 47716, "\u0120Livingston": 47717, "\u0120Hilbert": 47718, "\u0120Checks": 47719, "styles": 47720, "inators": 47721, "\u0120Clive": 47722, "phrine": 47723, "\u0120chimpanzees": 47724, "\u0120pall": 47725, "\u0120JM": 47726, "\u0120Aadhaar": 47727, "\u00f0\u013f": 47728, "\u0120achievable": 47729, "disabled": 47730, "PET": 47731, "OOOOOOOO": 47732, "Mot": 47733, "\u0120intangible": 47734, "\u0120ballet": 47735, "\u0120Webs": 47736, "\u0120Estimated": 47737, "Effects": 47738, "\u0120bailed": 47739, "Joshua": 47740, "\u0120turbulence": 47741, "\u0120occupant": 47742, "\u0120Daylight": 47743, "\u0120361": 47744, "meet": 47745, "\u0120statically": 47746, "\u0120onlook": 47747, "\u0120ki": 47748, "illegal": 47749, "\u0120velvet": 47750, "\u0120dehydration": 47751, "\u0120acquies": 47752, "\u0120Rez": 47753, "akura": 47754, "\u0120Upton": 47755, "atro": 47756, "\u0120incomprehensible": 47757, "\u0120backdoor": 47758, "\u0120Rhino": 47759, "727": 47760, "\u0120maths": 47761, ")+": 47762, "\u0120heresy": 47763, "\u0120df": 47764, "\u0120Roche": 47765, "\u0120Lydia": 47766, "\u0120pancreat": 47767, "reply": 47768, "arrell": 47769, "\u0120solicitation": 47770, "\u0120circadian": 47771, "BIP": 47772, "\u0120foray": 47773, "\u0120cryptic": 47774, "izu": 47775, "imeo": 47776, "\u0120Tomato": 47777, "\u0120Homs": 47778, "examination": 47779, "\u0120quarry": 47780, "\u0120Valiant": 47781, "\u0120Jericho": 47782, "\u0120INCLUD": 47783, "\u01201840": 47784, "519": 47785, "\u0120resists": 47786, "\u0120snapshots": 47787, "\u0120Spur": 47788, "\u0120Antiqu": 47789, "Login": 47790, "\u0120bestselling": 47791, "\u0120antic": 47792, "\u0120Sutherland": 47793, "\u00e3\u0124\u00a2\u00e3\u0125\u00ab": 47794, "\u0120~/": 47795, "\u0120Parm": 47796, "\u00e8\u0125": 47797, "Pages": 47798, "intensity": 47799, "\u0120immobil": 47800, "\u01201865": 47801, "zzo": 47802, "\u0120nifty": 47803, "\u0120fentanyl": 47804, "\u0120Preservation": 47805, "ophen": 47806, "\u0120darts": 47807, "\u0120Dinosaur": 47808, "pointers": 47809, "\u0120Rite": 47810, "suggest": 47811, "awareness": 47812, "\u0120Sheridan": 47813, "\u0120stances": 47814, "\u0120sorcery": 47815, "\u0120perjury": 47816, "\u0120Nikola": 47817, "iever": 47818, "\u0120fiance": 47819, "\u0120Jordanian": 47820, "\u0120Balloon": 47821, "\u0120nab": 47822, "\u0120kb": 47823, "\u0120humanities": 47824, "\u0120Tanaka": 47825, "hillary": 47826, "\u0120consultancy": 47827, "\u0120Zub": 47828, "\u0120remission": 47829, "\u0120confid": 47830, "CHQ": 47831, "\u0120Fug": 47832, "\u0120improvis": 47833, "Yep": 47834, "/_": 47835, "\u0120unwillingness": 47836, "\u0120portfolios": 47837, "055": 47838, "\u0120Instructor": 47839, "aiman": 47840, "\u0120claimants": 47841, "Mbps": 47842, "\u0120Bye": 47843, "received": 47844, "Tweet": 47845, "\u0120indemn": 47846, "riz": 47847, "amara": 47848, "Nat": 47849, "\u0120evaluates": 47850, "\u0120Lur": 47851, "epad": 47852, "FOX": 47853, "\u0120Thro": 47854, "\u0120rusty": 47855, "\u0120bedrock": 47856, "\u0120Oprah": 47857, "JB": 47858, "\u0120manipulative": 47859, "\u0120willful": 47860, "\u0120relapse": 47861, "\u0120extant": 47862, "Theme": 47863, "Sensor": 47864, "\u0120Stability": 47865, "govern": 47866, "\u0120poppy": 47867, "\u0120knack": 47868, "\u0120insulated": 47869, "\u0120Tile": 47870, "\u0120Extrem": 47871, "\u0120untold": 47872, "\u0120converge": 47873, "\u0120refuel": 47874, "igroup": 47875, "\u0120distortions": 47876, "\u0120ravaged": 47877, "\u0120mechanically": 47878, "\u0120Reilly": 47879, "\u0120Nose": 47880, "\u0120Incarnation": 47881, "\u0120Becky": 47882, "abbling": 47883, "\u0120taco": 47884, "\u0120rake": 47885, "\u0120melancholy": 47886, "\u0120illustrious": 47887, "\u0120Dartmouth": 47888, "Guide": 47889, "\u0120Razer": 47890, "\u0120Benz": 47891, "Ultimate": 47892, "\u0120Surprise": 47893, "\u0120pageant": 47894, "offer": 47895, "Whoever": 47896, "\u0120wiser": 47897, "\u0120chemist": 47898, "\u0120HELL": 47899, "\u0120Bulk": 47900, "\u0120plutonium": 47901, "\u0120COVER": 47902, "\u00d6\u00bc": 47903, "failed": 47904, "\u0120tirelessly": 47905, "\u0120infertility": 47906, "\u0120Trident": 47907, "\u0120Showtime": 47908, "\u0120Civ": 47909, "Vice": 47910, "requires": 47911, "ittance": 47912, "\u0120uncontrolled": 47913, "interesting": 47914, "561": 47915, "\u0120innovate": 47916, "ategic": 47917, "Lie": 47918, "\u0120Selling": 47919, "Ul": 47920, "\u0120savior": 47921, "\u0120Tosh": 47922, "\u0120swast": 47923, "PASS": 47924, "\u0120rink": 47925, "\u0120cardio": 47926, "\u0120Iro": 47927, "udi": 47928, "\u0120vantage": 47929, "\u0120vans": 47930, "\u0120Ni\u00c3\u00b1o": 47931, "+=": 47932, "\u0120propagate": 47933, "<?": 47934, "\u0120methodological": 47935, "20439": 47936, "\u0120triglycer": 47937, "\u0120ingrained": 47938, "\u0120Annotations": 47939, "arranted": 47940, "617": 47941, "\u0120Sodium": 47942, "\u0120AAC": 47943, "technical": 47944, "multipl": 47945, "\u0120373": 47946, "\u00e5\u012d": 47947, "\u0120decisively": 47948, "\u0120boosters": 47949, "\u0120desserts": 47950, "\u0120Grenade": 47951, "\u0120testifying": 47952, "\u0120Scully": 47953, "IDs": 47954, "\u0120lockdown": 47955, "\u0120Scher": 47956, "\u0120R\u00c3\u00a9": 47957, "\u0120Whitman": 47958, "\u0120Ramsay": 47959, "remote": 47960, "\u0120hikers": 47961, "\u0120Hyundai": 47962, "\u0120conscientious": 47963, "\u0120clerics": 47964, "\u0120Siberian": 47965, "uti": 47966, "isbury": 47967, "\u0120relayed": 47968, "\u0120quartz": 47969, "\u0120CBI": 47970, "seekers": 47971, "ulla": 47972, "\u0120welding": 47973, "\u0120Shal": 47974, "bleacher": 47975, "Tai": 47976, "\u0120Samson": 47977, "\u0120tumble": 47978, "\u0120Investor": 47979, "\u0120subcontract": 47980, "\u0120Shinra": 47981, "owicz": 47982, "jandro": 47983, "dad": 47984, "\u0120terminating": 47985, "\u0120Neural": 47986, "\u00e4\u00bb\u00a3": 47987, "\u0120leakage": 47988, "\u0120Midlands": 47989, "\u0120Caucasus": 47990, "\u00ed\u0137": 47991, "cit": 47992, "llan": 47993, "ivably": 47994, "\u0120Albion": 47995, "\u0120457": 47996, "\u0120registrations": 47997, "\u0120comrade": 47998, "\u0120clipboard": 47999, "047": 48000, "\u0120discouraging": 48001, "\u0120Oops": 48002, "Adapt": 48003, "\u0120empath": 48004, "nv": 48005, "\u0120PROT": 48006, "\u0120Donn": 48007, "\u0120Pax": 48008, "\u0120Bayer": 48009, "tis": 48010, "Square": 48011, "\u0120footprints": 48012, "particip": 48013, "\u0120Chilean": 48014, "Brend": 48015, "inducing": 48016, "Magn": 48017, "\u0120clubhouse": 48018, "\u0120Magnum": 48019, "\u0120encamp": 48020, "\u0120Ethnic": 48021, "ucha": 48022, "erey": 48023, "\u0120watered": 48024, "\u0120Calais": 48025, "\u0120complexion": 48026, "\u0120sects": 48027, "\u0120renters": 48028, "\u0120bras": 48029, "o\u00c4\u0141an": 48030, "Timeout": 48031, "Management": 48032, "\u0120infographic": 48033, "Pokemon": 48034, "Clar": 48035, "\u0120locality": 48036, "\u0120flora": 48037, "asel": 48038, "Pont": 48039, "\u0120populate": 48040, "\u0120Ong": 48041, "\u0120subsistence": 48042, "\u0120auctions": 48043, "\u0120McAuliffe": 48044, "\u0120LOOK": 48045, "bringer": 48046, "\u0120titan": 48047, "\u0120manifold": 48048, "\u0120\u00e2\u0139\u0131": 48049, "\u0120calibrated": 48050, "\u0120caliphate": 48051, "\u0120SHE": 48052, "\u0120Commissioners": 48053, "ceivable": 48054, "jc": 48055, "Winner": 48056, "524": 48057, "\u0120condone": 48058, "Otherwise": 48059, "\u0120piling": 48060, "\u0120embody": 48061, "\u0120Crimean": 48062, "utics": 48063, "\u0120Exhibition": 48064, "\u0120426": 48065, "eering": 48066, "\u0120vying": 48067, "\u0120HUGE": 48068, "*=-": 48069, "\u0120principled": 48070, "\u00e0\u00a6": 48071, "\u0120quirks": 48072, "\u0120Editors": 48073, "puting": 48074, "GES": 48075, "\u0120FTA": 48076, "\u00e0\u00a4\u00be": 48077, "addon": 48078, "\u0120HAM": 48079, "\u0120Frieza": 48080, "Woman": 48081, ".$": 48082, "\u0120crib": 48083, "\u0120Herod": 48084, "\u0120timers": 48085, "\u0120Spaces": 48086, "\u0120Macintosh": 48087, "ataka": 48088, "\u0120glide": 48089, "\u0120smelling": 48090, "\u0120BAL": 48091, "\u0120unsu": 48092, "\u0120condos": 48093, "\u0120bicycl": 48094, "\u0120Revival": 48095, "553": 48096, "\u0120juggling": 48097, "Hug": 48098, "\u0120Kardashian": 48099, "\u0120Balkans": 48100, "multiple": 48101, "\u0120nutritious": 48102, "ocry": 48103, "1900": 48104, "\u0120integrates": 48105, "\u0120adjoining": 48106, "\u0120Folder": 48107, "rollment": 48108, "venient": 48109, "\u0120uber": 48110, "yi": 48111, "\u0120whiff": 48112, "\u0120Juven": 48113, "\u0120Borough": 48114, "nette": 48115, "\u0120bilingual": 48116, "\u0120Sparks": 48117, "phthal": 48118, "manufact": 48119, "\u0120touting": 48120, "\u0120PHI": 48121, "Keefe": 48122, "Reward": 48123, "\u0120infall": 48124, "\u0120Temper": 48125, "typically": 48126, "\u0120Nikol": 48127, "\u0120regulars": 48128, "\u0120pseudonym": 48129, "\u0120exhibitions": 48130, "\u0120blaster": 48131, "\u0120409": 48132, "warming": 48133, "\u0120reverber": 48134, "\u0120reciprocal": 48135, "\u0120670": 48136, "ipient": 48137, "bett": 48138, "\u0120Begins": 48139, "\u0120itching": 48140, "\u0120Phar": 48141, "Assuming": 48142, "\u0120emitting": 48143, "\u0120MLG": 48144, "\u0120birthplace": 48145, "\u0120taunt": 48146, "\u0120Luffy": 48147, "\u0120Amit": 48148, "\u0120circled": 48149, "\u0120Nost": 48150, "ennett": 48151, "\u0120deforestation": 48152, "\u0120Historically": 48153, "\u0120Everyday": 48154, "\u0120overtake": 48155, "792": 48156, "\u0120nun": 48157, "\u0120Lucia": 48158, "\u0120accompanies": 48159, "\u0120Seeking": 48160, "\u0120Trash": 48161, "anism": 48162, "Rogue": 48163, "\u0120northwestern": 48164, "\u0120Supplemental": 48165, "\u0120NYU": 48166, "\u0120FRI": 48167, "\u0120Satisf": 48168, "xes": 48169, "517": 48170, "\u0120reassured": 48171, "\u0120sporadic": 48172, "\u0120701": 48173, "\u0120medial": 48174, "\u0120cannabinoid": 48175, "\u0120barbaric": 48176, "\u0120epis": 48177, "\u0120Explosive": 48178, "\u0120Dough": 48179, "\u0120unsolved": 48180, "Supported": 48181, "\u0120acknowledgment": 48182, "spawn": 48183, "\u0120kitchens": 48184, "\u0120-=": 48185, "talking": 48186, "icist": 48187, "\u0120Pegasus": 48188, "\u0120PSU": 48189, "\u0120photon": 48190, "\u0120Authentication": 48191, "RG": 48192, "@#&": 48193, "762": 48194, "\u0120Clair": 48195, "\u0120diaper": 48196, "\u0120brist": 48197, "\u0120Prosecutors": 48198, "\u0120Jem": 48199, "628": 48200, "\u0120Everywhere": 48201, "\u0120Jeanne": 48202, "equality": 48203, "\u00e3\u0125\u00a9\u00e3\u0125\u00b3": 48204, "objects": 48205, "\u0120Pelicans": 48206, "\u0120392": 48207, "\u0120blu": 48208, "bys": 48209, "\u0120Ago": 48210, "\u0120instructional": 48211, "\u0120discriminating": 48212, "\u0120TRAN": 48213, "\u0120Cornel": 48214, "agos": 48215, "\u0120tyre": 48216, "\u0120aspiration": 48217, "\u0120Bridgewater": 48218, "\":-": 48219, "!\".": 48220, "\u0120Ens": 48221, "\u0120Coco": 48222, "Pie": 48223, "\u0120detach": 48224, "\u0120Couch": 48225, "\u0120physique": 48226, "\u0120Occupations": 48227, "oscopic": 48228, "enough": 48229, "Buzz": 48230, "Appearance": 48231, "YP": 48232, "\u0120racer": 48233, "\u0120complicity": 48234, "rpm": 48235, "Toy": 48236, "\u0120interrupts": 48237, "\u0120Catalyst": 48238, "\u0120utilitarian": 48239, "impact": 48240, "\u0120spaghetti": 48241, "\u0120porous": 48242, "\u0120esteemed": 48243, "\u0120inciner": 48244, "\u0120IOC": 48245, "748": 48246, "\u0120espresso": 48247, "\u0120Smile": 48248, "abilia": 48249, "635": 48250, "\u0120mathematician": 48251, "\u0120424": 48252, "\u0120KL": 48253, "\u0120HIP": 48254, "\u0120overheard": 48255, "\u0120Tud": 48256, "\u0120Tec": 48257, "\u0120quizz": 48258, "\u0120flattering": 48259, "\u0120conn": 48260, "\u00e2\u0122\u0130": 48261, "\u0120attaches": 48262, "\u0120ROS": 48263, "\u0120ACS": 48264, "\u0120tcp": 48265, "\u0120Shame": 48266, "skip": 48267, "respected": 48268, "\u0120Trinidad": 48269, "grain": 48270, "\u0120foothold": 48271, "\u0120Uncharted": 48272, "\u0120Julio": 48273, "zl": 48274, "avored": 48275, "\u0120Anxiety": 48276, "errors": 48277, "\u0120Centauri": 48278, "itsch": 48279, "Daddy": 48280, "\u0120clutching": 48281, "\u0120Implement": 48282, "\u0120Gutierrez": 48283, "\u0120760": 48284, "\u0120teleportation": 48285, "endra": 48286, "\u0120reversible": 48287, "stros": 48288, "Adventure": 48289, "083": 48290, "\u0120liberating": 48291, "\u0120asphalt": 48292, "\u0120Spend": 48293, "ARDS": 48294, "imsy": 48295, "PRES": 48296, "\u0120Emerging": 48297, "\u0120wildfires": 48298, "\u0120technologically": 48299, "\u0120emits": 48300, "\u0120ARTICLE": 48301, "\u0120irregularities": 48302, "\u0120cherish": 48303, "\u00e7\u012b\u012a": 48304, "\u0120stink": 48305, "\u0120Rost": 48306, "Economic": 48307, "\u0120coughing": 48308, "\u0120McCann": 48309, "properties": 48310, "ilantro": 48311, "\u0120renegoti": 48312, "Translation": 48313, "\u0120inquest": 48314, "\u0120Grape": 48315, "ooters": 48316, "gui": 48317, "\u0120Swordsman": 48318, "aceae": 48319, "hitting": 48320, "\u0120rc": 48321, "\u0120exerted": 48322, "\u0120SAP": 48323, "itent": 48324, "\u0120perilous": 48325, "\u0120obscurity": 48326, "\u0120assassinate": 48327, "\u0120aboriginal": 48328, "\u0120rescuing": 48329, "\u0120Shattered": 48330, "locking": 48331, "allion": 48332, "Changing": 48333, "\u0120Harrington": 48334, "\u0120Bord": 48335, "\u0120Afghans": 48336, "Jamie": 48337, "aretz": 48338, "\u0120Augustus": 48339, "\u0120386": 48340, "830": 48341, "\u0120jog": 48342, "okingly": 48343, "Trigger": 48344, "\u0120HOR": 48345, "Statistics": 48346, "\u0120viewership": 48347, "\u0120additives": 48348, "hur": 48349, "\u0120maximizing": 48350, "\u0120Rove": 48351, "\u0120Louie": 48352, "\u0120Bucket": 48353, "\u0120CHRIST": 48354, "ousel": 48355, "\u0120streaks": 48356, "irted": 48357, "\u0120tert": 48358, "\u0120colonialism": 48359, "\u0120burying": 48360, "yk": 48361, "Condition": 48362, "\u0120DPRK": 48363, "ById": 48364, "751": 48365, "\u00e2\u0139\u00bc": 48366, "\u0120worrisome": 48367, "\u0120vocational": 48368, "slice": 48369, "\u0120sails": 48370, "\u0120Correctional": 48371, "954": 48372, "\u0120tul": 48373, "Kid": 48374, "luster": 48375, "\u0120familial": 48376, "\u0120Spit": 48377, "\u0120Episcopal": 48378, "Specifically": 48379, "\u0120Volcano": 48380, "runs": 48381, "qs": 48382, "\u0120vetted": 48383, "\u0120crammed": 48384, "trop": 48385, "herer": 48386, "Thankfully": 48387, "\u0120percussion": 48388, "\u0120oranges": 48389, "\u0120roundup": 48390, "\u0120499": 48391, "xious": 48392, "Characters": 48393, "\u0120Zionism": 48394, "\u0120Rao": 48395, "\u00c3\u013d\u00c3\u013d": 48396, "WF": 48397, "\u0120unintentional": 48398, "ONEY": 48399, "Grab": 48400, "Commercial": 48401, "\u0120glutamate": 48402, "\u0120McKenna": 48403, "ruciating": 48404, "nington": 48405, "ihu": 48406, "Chan": 48407, "\u0120Swap": 48408, "\u0120leaflets": 48409, "\u0120functionally": 48410, "erous": 48411, "Farm": 48412, "\u0120caloric": 48413, "\u0120Literally": 48414, "concert": 48415, "\u0120shenan": 48416, "\u0120repaid": 48417, "eyes": 48418, "\u0120bashing": 48419, "\u0120Gorge": 48420, "\u0120collaborations": 48421, "\u0120unaccount": 48422, "itchie": 48423, "\u0120teamwork": 48424, "ppelin": 48425, "\u0120piping": 48426, "\u0120minced": 48427, "\u0120diam": 48428, "rieg": 48429, "\u0120mascara": 48430, "\u0120sucker": 48431, "\u0120Moons": 48432, "Apps": 48433, "\u0120Peck": 48434, "\u0120perv": 48435, "\u0120Float": 48436, "oley": 48437, "\u0120Nish": 48438, "imize": 48439, "\u0120aromatic": 48440, "uin": 48441, "endish": 48442, "!/": 48443, "\u0120Bicycle": 48444, "\u0120ASIC": 48445, "ileged": 48446, "\u0120Quadro": 48447, "iosyn": 48448, "\u0120lockout": 48449, "\u0120Wink": 48450, "SPEC": 48451, "Attempts": 48452, "\u0120seeded": 48453, "redo": 48454, "iasis": 48455, "\u0120snag": 48456, "\u00e3\u0125\u0137\u00e3\u0124\u00a9": 48457, "\u00e3\u0124\u00b6": 48458, "\u0120grounding": 48459, "\u0120reliever": 48460, "\u0120frivolous": 48461, "\u0120Gifts": 48462, "\u0120Faces": 48463, "Especially": 48464, "\u0120microbiome": 48465, "imag": 48466, "\u0120Schl": 48467, "\u0120Ples": 48468, "\u0120Bleach": 48469, "\u0120Irwin": 48470, "\u0120Eaton": 48471, "\u0120Disciple": 48472, "\u0120multiplication": 48473, "\u0120coerced": 48474, "\u0120419": 48475, "sth": 48476, "Evil": 48477, "Bomb": 48478, "\u0120exorc": 48479, "\u0120staggered": 48480, "LESS": 48481, "\u0120inertia": 48482, "\u0120EDIT": 48483, "\u0120gob": 48484, "Traditional": 48485, "\u0120classy": 48486, "Leary": 48487, "\u0120PAGE": 48488, "yrs": 48489, "\u0120transporter": 48490, "\u0120matured": 48491, "\u0120hijab": 48492, "\u0120biome": 48493, "Whereas": 48494, "\u0120extermination": 48495, "\u0120Tues": 48496, "\u0120Takeru": 48497, "\u0120Audrey": 48498, "erial": 48499, "\u0120Aden": 48500, "affles": 48501, "\u0120narcissistic": 48502, "\u0120Baird": 48503, "UTF": 48504, "Ire": 48505, "\u0120Connie": 48506, "Champ": 48507, "\u0120whispering": 48508, "\u0120Hatt": 48509, "DK": 48510, "\u0120disinfect": 48511, "\u0120deducted": 48512, "\u0120partake": 48513, "\u0120downgrade": 48514, "\u0120Esports": 48515, "\u0120Continuing": 48516, "\u0120democratically": 48517, "icrobial": 48518, "itta": 48519, "\u0120limestone": 48520, "\u0120exempted": 48521, "\u0120Frenzy": 48522, "Herm": 48523, "728": 48524, "\u0120fledgling": 48525, "Meta": 48526, "76561": 48527, "693": 48528, "%:": 48529, "wake": 48530, "526": 48531, "\u0120Discipline": 48532, "\u0120virginity": 48533, "\u0120Legions": 48534, "\u0120Frankie": 48535, "intent": 48536, "\u0120restrooms": 48537, "\u0120Router": 48538, "daq": 48539, "\u0120objectionable": 48540, "\u00e2\u0128\u0133": 48541, "wark": 48542, "\u0120Rahul": 48543, "gain": 48544, "activation": 48545, "absolute": 48546, "\u0120Accessed": 48547, "\u01202400": 48548, "oggles": 48549, "\u0120secondly": 48550, "\u0120DEFENSE": 48551, "\u0120postage": 48552, "wrapper": 48553, "sharp": 48554, "729": 48555, "\u0120communicates": 48556, "\u0120addon": 48557, "\u0120Militia": 48558, "Hong": 48559, "\u0120slumped": 48560, "\u0120JPEG": 48561, "\u0120Icar": 48562, "adish": 48563, "681": 48564, "\u0120majesty": 48565, "\u0120Wolfgang": 48566, "\u0120Elastic": 48567, "uper": 48568, "\u0120viz": 48569, "\u0120unconsciously": 48570, "\u0120STD": 48571, "\u0120Sass": 48572, "\u0120flowering": 48573, "\u0120Helic": 48574, "\u0120Draper": 48575, "\u0120Amateur": 48576, "\u0120manure": 48577, "\u0120disingen": 48578, "\u0120Lei": 48579, "bring": 48580, "949": 48581, "\u0120inhibited": 48582, "\u0120headquartered": 48583, "\u0120enigmatic": 48584, "\u00ef\u00bf\u00bd\u00ef\u00bf\u00bd\u00ef\u00bf\u00bd": 48585, "\u0120redress": 48586, "RH": 48587, "\u0120rattled": 48588, "\u0120diction": 48589, "lio": 48590, "\u0120TBA": 48591, "\u0120SNAP": 48592, "Calling": 48593, "\u0120fascists": 48594, "\u0120Dove": 48595, "iewicz": 48596, "036": 48597, "\u0120coasts": 48598, "\u0120Rect": 48599, "\u0120)]": 48600, "Lot": 48601, "629": 48602, "\u0120SEM": 48603, "\u0120Petersen": 48604, "\u0120Explain": 48605, "\u0120Boards": 48606, "\u0120Bezos": 48607, "\u0120Journals": 48608, "\u01202024": 48609, "parser": 48610, "\u0120mistrust": 48611, "\u0120grate": 48612, "\u0120Locked": 48613, "boa": 48614, "Saint": 48615, "gaming": 48616, "\u0120vowel": 48617, "inately": 48618, "blow": 48619, "Allah": 48620, "\u0120unmatched": 48621, "\u0120bordering": 48622, "\u0120Expend": 48623, "nr": 48624, "Oracle": 48625, "rouch": 48626, "\u0120contiguous": 48627, "acus": 48628, "\u0120distraught": 48629, "581": 48630, "\u0120anatomical": 48631, "OX": 48632, "apixel": 48633, "833": 48634, "\u0120PLUS": 48635, "\u0120resusc": 48636, "\u0120abiding": 48637, "573": 48638, "\u0120vacancies": 48639, "Emily": 48640, "\u0120hypothal": 48641, "\u0120Werner": 48642, "\u0120Wee": 48643, "\u0120DJs": 48644, "513": 48645, "\u0120witchcraft": 48646, "\u0120acupuncture": 48647, "entary": 48648, "benefit": 48649, "Products": 48650, "\u0120PSP": 48651, "\u0120MPG": 48652, "\u0120Jinn": 48653, "\u0120Jarrett": 48654, "\u0120445": 48655, "\u0120Imaging": 48656, "\u0120Pyth": 48657, "Finish": 48658, "\u0120tex": 48659, "\u0120juveniles": 48660, "\u0120heroism": 48661, "\u0120doubtless": 48662, "\u0120Aki": 48663, "\u0120Tend": 48664, "\u0120Patriarch": 48665, "\u0120bitters": 48666, "\u0120Telecommunications": 48667, "itatively": 48668, "agna": 48669, "\u0120rg": 48670, "\u0120SOLD": 48671, "\u0120compulsion": 48672, "\u0120Nasa": 48673, "\u0120Kathryn": 48674, "\u0120millionaires": 48675, "\u0120intrinsically": 48676, "\u0120bolstered": 48677, "timeout": 48678, "flo": 48679, "\u0120tutor": 48680, "pour": 48681, "Statement": 48682, "\u0120{*": 48683, "\u0120Rudolph": 48684, "\u0120Kimberly": 48685, "rogens": 48686, "adiq": 48687, "]+": 48688, "\u0120indignation": 48689, "\u0120fracturing": 48690, "\u0120Releases": 48691, "\u0120Grain": 48692, "protein": 48693, "Lago": 48694, "\u0120vacations": 48695, "\u0120booted": 48696, "\u0120THREE": 48697, "\u0120HG": 48698, "orescence": 48699, "\u0120tf": 48700, "\u0120soar": 48701, "iosyncr": 48702, "\u0120glances": 48703, "\u0120Spoon": 48704, "\u0120Jury": 48705, "\u0120Cowboy": 48706, "\u0120creatively": 48707, "Higher": 48708, "\u0120solicitor": 48709, "\u0120hawk": 48710, "acio": 48711, "896": 48712, "\u0120superflu": 48713, "\u0120bombshell": 48714, "cture": 48715, "\u0120brokerage": 48716, "\u0120raiding": 48717, "\u0120french": 48718, "\u0120angled": 48719, "Transaction": 48720, "\u0120Genocide": 48721, "upe": 48722, "\u0120Haitian": 48723, "572": 48724, "!:": 48725, "\u0120unwittingly": 48726, "iterator": 48727, "scroll": 48728, "\u0120tallied": 48729, "\u0120biomedical": 48730, "\u0120CARD": 48731, "\u0120euphem": 48732, "\u0120brainstorm": 48733, "aquin": 48734, "Ko": 48735, "Michelle": 48736, "\u0120Runes": 48737, "\u0120Ballistic": 48738, "uders": 48739, "\u0120modesty": 48740, "\u0120iPads": 48741, "\u0120Ezekiel": 48742, "YE": 48743, "\u0120starship": 48744, "\u0120powerfully": 48745, "\u0120perl": 48746, "\u0120Shade": 48747, "\u0120Quart": 48748, "\u0120EEG": 48749, "\u0120fisherman": 48750, "OSED": 48751, "\u0120Typical": 48752, "dfx": 48753, "\u0120meshes": 48754, "\u0120etched": 48755, "worthiness": 48756, "\u0120toppled": 48757, "\u0120396": 48758, "orius": 48759, "Weiss": 48760, "\u0120mysql": 48761, "\u0120Valhalla": 48762, "\u00d9\u0134": 48763, "leasing": 48764, "\u0120recomp": 48765, "rapnel": 48766, "Sel": 48767, "043": 48768, "\u0120derailed": 48769, "\u0120Guides": 48770, "IRT": 48771, "\u0120dehuman": 48772, "\u0120Brittany": 48773, "\"))": 48774, "\u0120exclaim": 48775, "\u0120balk": 48776, "\u0120840": 48777, "CLAIM": 48778, "intel": 48779, "LAB": 48780, "\u0120pegged": 48781, "\u0120astroph": 48782, "smoking": 48783, "\u0120rigging": 48784, "\u0120fixation": 48785, "\u0120catapult": 48786, "inside": 48787, "\u0120Cascade": 48788, "\u0120Bolshevik": 48789, "Gaza": 48790, "Depth": 48791, "\u0120loudspe": 48792, "\u0120almonds": 48793, "meyer": 48794, "leness": 48795, "jen": 48796, "fresh": 48797, "\u0120unbeaten": 48798, "\u0120Squid": 48799, "\u0120Presumably": 48800, "Timer": 48801, "BW": 48802, "\u0120rosters": 48803, "\u0120ellipt": 48804, "\u0120Harriet": 48805, "database": 48806, "\u0120Mutual": 48807, "\u0120Commodore": 48808, "uked": 48809, "knife": 48810, "\u0120COMMUN": 48811, "hya": 48812, "\u0120melts": 48813, "archives": 48814, "\u0120ratification": 48815, "\u0120multiplying": 48816, "\u0120interoper": 48817, "\u0120ascert": 48818, "wings": 48819, "verting": 48820, "\u0120Scorpion": 48821, "aye": 48822, "\u0120Portsmouth": 48823, "\u0120MTA": 48824, "nit": 48825, "iazep": 48826, "\u0120quarantine": 48827, "\u0120slideshow": 48828, "\u0120centimeters": 48829, "\u0120synopsis": 48830, "\u0120spate": 48831, "thirst": 48832, "\u0120nominating": 48833, "\u0120Melvin": 48834, "Preview": 48835, "\u0120throb": 48836, "\u0120generational": 48837, "\u0120Radius": 48838, "restling": 48839, "putable": 48840, "awar": 48841, "NECT": 48842, "\u0120unlawfully": 48843, "\u0120Revelations": 48844, "Wikipedia": 48845, "surv": 48846, "\u0120eyeing": 48847, "ijn": 48848, "\u0120FW": 48849, "\u0120brunt": 48850, "\u0120interstellar": 48851, "\u0120clitor": 48852, "\u0120Croatian": 48853, "\u0120Chic": 48854, "eva": 48855, "\u0120Disapp": 48856, "\u0120Akin": 48857, "ineries": 48858, "dust": 48859, "Interested": 48860, "\u0120genesis": 48861, "\u0120Eucl": 48862, "\u00c3\u00b6n": 48863, "picking": 48864, "\u0120mutated": 48865, "\u0120disapprove": 48866, "\u0120HDL": 48867, "\u0120625": 48868, "\u00cc\u00b6": 48869, "cancer": 48870, "\u0120squats": 48871, "\u0120levers": 48872, "Discuss": 48873, "=]": 48874, "Dex": 48875, "\u0120VIDEOS": 48876, "AUD": 48877, "\u0120transact": 48878, "\u0120Kinect": 48879, "\u0120Kuala": 48880, "\u0120Cyp": 48881, "747": 48882, "\u0120shattering": 48883, "\u0120arsenic": 48884, "\u0120Intake": 48885, "\u0120Angelo": 48886, "\u0120Quit": 48887, "\u0120Khe": 48888, "\u01201893": 48889, "Maker": 48890, "029": 48891, "\u0120Painting": 48892, "Disable": 48893, "916": 48894, "\u0120analges": 48895, "\u0120tactile": 48896, "\u0120prophes": 48897, "\u0120diced": 48898, "\u0120Travels": 48899, "\u0120Header": 48900, "\u0120Clubs": 48901, "Assistant": 48902, "\u0120incrim": 48903, "\u0120dips": 48904, "\u0120crucifix": 48905, "\u0120Shanahan": 48906, "\u0120Interpret": 48907, "\u01204090": 48908, "alogy": 48909, "abba": 48910, "\u0120simulac": 48911, "husband": 48912, "SIM": 48913, "\u0120recycle": 48914, "ucer": 48915, "edged": 48916, "\u0120renaissance": 48917, "\u0120Bombay": 48918, "Catholic": 48919, "\u0120LINE": 48920, "\u0120Clothing": 48921, "reports": 48922, "\u0120plaus": 48923, "\u0120dag": 48924, "\u0120Mace": 48925, "ZI": 48926, "\u0120intruder": 48927, "\u0120Veterinary": 48928, "gru": 48929, "\u0120sneaky": 48930, "\u0120Sie": 48931, "\u0120Cinnamon": 48932, "POSE": 48933, "\u0120courier": 48934, "\u0120CNS": 48935, "\u0120emancipation": 48936, "sit": 48937, "\u0120playthrough": 48938, "\u0120Facilities": 48939, "virt": 48940, "\u0120Gauntlet": 48941, "Thompson": 48942, "\u0120unbelievably": 48943, "Parameters": 48944, "\u0120stitching": 48945, "igne": 48946, "\u0120THESE": 48947, "Privacy": 48948, "\u0120shenanigans": 48949, "\u0120vitri": 48950, "\u0120Valid": 48951, "591": 48952, "\u0143\u00b7": 48953, "\u0120Prototype": 48954, "inka": 48955, "SCP": 48956, "\u0120Tid": 48957, "\u00e8\u012a": 48958, "olded": 48959, "\u0120individuality": 48960, "\u0120barking": 48961, "\u0120mars": 48962, "\u0120WD": 48963, "\u0120820": 48964, "\u0120tir": 48965, "\u0120slapping": 48966, "\u0120disgruntled": 48967, "\u0120Angola": 48968, "rius": 48969, "\u0120Tornado": 48970, "\u0120Thurs": 48971, "\u0120captcha": 48972, "\u0120angst": 48973, "\u0120Pog": 48974, "\u0120Assassins": 48975, "\u0120Adidas": 48976, "\u0120joyful": 48977, "\u0120whining": 48978, "Emergency": 48979, "\u0120phosphorus": 48980, "\u0120attrition": 48981, "ophon": 48982, "\u0120Timberwolves": 48983, "\u0120Jah": 48984, "\u0120Bringing": 48985, "\u0120Wad": 48986, "\u0120Ensure": 48987, "ohl": 48988, "\u0120Xie": 48989, "ommel": 48990, "cmp": 48991, "\u0120zipper": 48992, "\u0120relat": 48993, "\u0120Corridor": 48994, "milo": 48995, "TING": 48996, "Avg": 48997, "\u0120cropped": 48998, "]}": 48999, "\u0120raged": 49000, "\u0120Lumpur": 49001, "\u0120Guerrero": 49002, "ourke": 49003, "Nut": 49004, "\u0120offsets": 49005, "oglu": 49006, "drm": 49007, "\u0120mortals": 49008, "latable": 49009, "\u0120dismissive": 49010, "\u00e4\u00b8\u012b": 49011, "\u0120throats": 49012, "\u0120chipset": 49013, "\u0120Spotlight": 49014, "Catalog": 49015, "artist": 49016, "Gb": 49017, "\u0120chilly": 49018, "\u0120stoked": 49019, "\u0120374": 49020, "Ward": 49021, "Latin": 49022, "\u0120fiasco": 49023, "\u0120bleach": 49024, "\u0120brav": 49025, "Enhanced": 49026, "\u0120inoc": 49027, "\u0120Fiorina": 49028, "_>": 49029, "\u0120leukemia": 49030, "\u0120eluc": 49031, "\u0120announcer": 49032, "\u0120Lithuan": 49033, "\u0120Armageddon": 49034, "\u00e5\u0129": 49035, "Lenin": 49036, "\u0120Ruk": 49037, "\u0120pepp": 49038, "\u0120Romantic": 49039, "\u0120PIT": 49040, "\u0120Interstellar": 49041, "\u0120Atkinson": 49042, "Raid": 49043, "Js": 49044, "Goal": 49045, "Course": 49046, "\u0120vanishing": 49047, "esley": 49048, "\u0120Rounds": 49049, "Elsa": 49050, "593": 49051, "\u0120redundancy": 49052, "\u0120STAND": 49053, "\u0120prophetic": 49054, "\u0120habitable": 49055, "ryu": 49056, "\u0120faintly": 49057, "MODE": 49058, "\u0120flanked": 49059, "IRC": 49060, "Awesome": 49061, "\u0120spurious": 49062, "\u0120Zah": 49063, "\u0120MSG": 49064, "\u0120shading": 49065, "\u0120motivational": 49066, "\u0120Santana": 49067, "\u0120SPR": 49068, "\u0120excruciating": 49069, "omial": 49070, "\u0120Miko": 49071, "\u0120Leopard": 49072, "Abyss": 49073, "\u0120[|": 49074, "dirty": 49075, "\u0120baths": 49076, "\u0120demoral": 49077, "andre": 49078, "PB": 49079, "\u0120unification": 49080, "\u0120sacrament": 49081, "\u0120[&": 49082, "\u0120priceless": 49083, "\u0120gelatin": 49084, "\u0120emanating": 49085, "\u0120Allaah": 49086, "986": 49087, "\u0120outburst": 49088, "\u0120eras": 49089, "\u0120XVI": 49090, "\u0120SPI": 49091, "Ott": 49092, "\u0120Lazarus": 49093, "PLIED": 49094, "Flying": 49095, "blogs": 49096, "Wisconsin": 49097, "Raven": 49098, "\u0120rebate": 49099, "\u0120creeps": 49100, "\u0120Span": 49101, "\u0120Painter": 49102, "\u0120Kira": 49103, "\u0120Amos": 49104, "\u0120Corvette": 49105, "Consumer": 49106, "\u0120Recover": 49107, "cki": 49108, "\u0120pesky": 49109, "\u0120Invention": 49110, "Companies": 49111, "\u0120challengers": 49112, "ademic": 49113, "\u0120Ukrainians": 49114, "\u0120Neurolog": 49115, "\u0120Forsaken": 49116, "\u0120entrants": 49117, "\u0120embattled": 49118, "\u0120defunct": 49119, "\u0120Glacier": 49120, "\u0120poisons": 49121, "\u0120Horses": 49122, "makes": 49123, "\u0120Dirt": 49124, "\u0120423": 49125, "hhh": 49126, "\u0120Transformation": 49127, "QUIRE": 49128, "..................": 49129, "\u0120traveller": 49130, "\u0120Sexy": 49131, "\u0120Kern": 49132, "ipolar": 49133, "\u0120ransomware": 49134, "oooooooooooooooo": 49135, "Ec": 49136, "ruby": 49137, "Professional": 49138, "\u0120Outbreak": 49139, "argument": 49140, "Grey": 49141, "\u0120Fifa": 49142, "\u0120CHO": 49143, "\u0120FORM": 49144, "\u0120Amtrak": 49145, "-[": 49146, "\u0120cradle": 49147, "\u0120antioxidants": 49148, "\u00e3\u0123\u00ae\u00e5\u00ae": 49149, "736": 49150, "\u0120NASL": 49151, "\u0120Contributions": 49152, "Indiana": 49153, "\u0120STEP": 49154, "CSS": 49155, "\u0120salient": 49156, "\u0120allocations": 49157, "yrights": 49158, "\u0120mashed": 49159, "\u0120Cutter": 49160, "Sexual": 49161, "\u0120pounded": 49162, "\u0120fanbase": 49163, "\u0120casc": 49164, "\u0120Transparency": 49165, "\u0120analytic": 49166, "\u0120Summoner": 49167, "\u00d7\u0140": 49168, "\u0120ADC": 49169, "detail": 49170, "\u0120vanquished": 49171, "\u0120crabs": 49172, "arie": 49173, "Destroy": 49174, "\u0120Sack": 49175, "\u0120transistor": 49176, "Alabama": 49177, "\u0120Koen": 49178, "\u0120Fisheries": 49179, "cone": 49180, "\u0120annexed": 49181, "\u0120MGM": 49182, "esa": 49183, "\u0120faked": 49184, "\u0120Congratulations": 49185, "\u0120hindered": 49186, "\u0120correctional": 49187, "\u0120ITV": 49188, "leeve": 49189, "\u0120inappropriately": 49190, "licks": 49191, "\u0120trespass": 49192, "\u0120paws": 49193, "\u0120negotiator": 49194, "\u0120Christensen": 49195, "limits": 49196, "\u0120Dianne": 49197, "\u0120elegance": 49198, "\u0120Contracts": 49199, "anke": 49200, "Obj": 49201, "\u0120vigilance": 49202, "\u0120castles": 49203, "\u0120NAD": 49204, "\u0120Holo": 49205, "\u0120emphatically": 49206, "\u0120Titus": 49207, "\u0120Serving": 49208, "\u0120Richie": 49209, "\u0120Pigs": 49210, "568": 49211, "\u0120animosity": 49212, "\u0120Attributes": 49213, "\u0120Uriel": 49214, "MQ": 49215, "myra": 49216, "\u0120Applicant": 49217, "\u0120psychiatrists": 49218, "\u0120Vij": 49219, "\u0120Abby": 49220, "agree": 49221, "Push": 49222, "\u0120kWh": 49223, "hiba": 49224, "\u0120incite": 49225, "\u0120Weasley": 49226, "\u0120Taxi": 49227, "ministic": 49228, "hyper": 49229, "\u0120Farn": 49230, "\u0120601": 49231, "\u0120Nationwide": 49232, "Fake": 49233, "952": 49234, "\u0120maize": 49235, "\u0120interacted": 49236, "\u0120transitioned": 49237, "\u0120parasitic": 49238, "\u0120harmonic": 49239, "\u0120decaying": 49240, "\u0120baseless": 49241, "nsics": 49242, "\u0120transpired": 49243, "\u0120abundantly": 49244, "\u0120Forensic": 49245, "\u0120treadmill": 49246, "\u0120Jav": 49247, "aband": 49248, "\u0120sshd": 49249, "\u0120frontman": 49250, "\u0120Jakarta": 49251, "oller": 49252, "drops": 49253, "\u0120SERVICES": 49254, "romptu": 49255, "ophical": 49256, "hospital": 49257, "bledon": 49258, "645": 49259, "\u0120midrange": 49260, "\u0120EVENT": 49261, "culated": 49262, "rawled": 49263, "\u0120perched": 49264, "\u0120overboard": 49265, "\u0120Peel": 49266, "\u0120Pwr": 49267, "\u0120Carth": 49268, "\u0120COMPLE": 49269, "coe": 49270, "shall": 49271, "\u0120deterrence": 49272, "METHOD": 49273, "\u0120Absent": 49274, "MEN": 49275, "\u0120sill": 49276, "\u0120LEVEL": 49277, "York": 49278, "\u0120sinners": 49279, "\u0120OPEC": 49280, "\u0120Nur": 49281, "\u0120Designs": 49282, "selection": 49283, "\u0120unworthy": 49284, "CHA": 49285, "\u0120strengthens": 49286, "883": 49287, "edly": 49288, "\u0120slicing": 49289, "\u0120malnutrition": 49290, "\u0120filmmaking": 49291, "\u0120Polk": 49292, "urated": 49293, "\u0120421": 49294, "breakers": 49295, "!'\"": 49296, "\u0120wetlands": 49297, "\u0120Discrimination": 49298, "\u0120allowable": 49299, "\u0120steered": 49300, "\u0120Sicily": 49301, "SAM": 49302, "\u0120mustache": 49303, "\u0120mids": 49304, "\u0120clipped": 49305, "\u0120circulate": 49306, "\u0120brittle": 49307, "\u0120Buildings": 49308, "raised": 49309, "\u0120Roundup": 49310, "\u0120wealthier": 49311, "\u0120overwrite": 49312, "\u0120overpowered": 49313, "\u0120Gerrard": 49314, "sites": 49315, "PDATED": 49316, "\u0120acutely": 49317, "\u0120Gamble": 49318, "\u0120pim": 49319, "\u0120Kus": 49320, "Typically": 49321, "Deploy": 49322, "\u0120Moroccan": 49323, "potion": 49324, "combe": 49325, "\u0120vigilante": 49326, "\u0120363": 49327, "Stew": 49328, "\u0120Bagg": 49329, "\u0120resided": 49330, "\u0120Spo": 49331, "\u0120remnant": 49332, "\u0120emptiness": 49333, "brainer": 49334, "\u0120outpatient": 49335, "priority": 49336, "\u0120leptin": 49337, "\u0120Payton": 49338, "\u0120Gleaming": 49339, "\u0120Shed": 49340, "\u0120Polo": 49341, "\u0120Mormonism": 49342, "restricted": 49343, "arlane": 49344, "wx": 49345, "\u0120creatine": 49346, "\u0120Anon": 49347, "\u0120STUD": 49348, "\u0120JUL": 49349, "\u0120Tee": 49350, "528": 49351, "089": 49352, "\u0120hatched": 49353, "Dispatch": 49354, "\u0120Composite": 49355, "\u0120451": 49356, "puff": 49357, "\u0120XCOM": 49358, "\u0120Orn": 49359, "\u0120THANK": 49360, "ENDED": 49361, "\u0120Asheville": 49362, "\u0120\u00c3\u013e": 49363, "\u0120mango": 49364, "\u0120Slightly": 49365, "worldly": 49366, "\u0120Wander": 49367, "\u0120Expand": 49368, "\u0120Chr": 49369, "Mist": 49370, "\u0120orthodoxy": 49371, "\u0120UNESCO": 49372, "regate": 49373, "Elsewhere": 49374, "kie": 49375, "irled": 49376, "\u0120topple": 49377, "\u0120adoptive": 49378, "\u0120Legs": 49379, "dress": 49380, "\u0120Sagan": 49381, "bare": 49382, "\u0120Glou": 49383, "Crunch": 49384, "\u0120helpers": 49385, "\u0120chronically": 49386, "\u0120Huma": 49387, "10000": 49388, "\u0120accommodating": 49389, "\u00e4\u00ba\u0136": 49390, "\u0120wrinkles": 49391, "\u0120dodged": 49392, "fourth": 49393, "\u0120precon": 49394, "\u0120compressor": 49395, "\u0120Kare": 49396, "\u0120evict": 49397, "\u0120Warwick": 49398, "imar": 49399, "\u0120modernization": 49400, "\u0120bandwagon": 49401, "\u0120refuted": 49402, "\u0120netted": 49403, "\u0120Naples": 49404, "\u0120Genie": 49405, "perors": 49406, "\u0120fielded": 49407, "\u0120dere": 49408, "\u0120Parables": 49409, "lees": 49410, "\u0120trout": 49411, "aspers": 49412, "\u0120nihil": 49413, "\u0120happiest": 49414, "\u0120floppy": 49415, "\u0120Loft": 49416, "\u0120Heard": 49417, "\u0120unison": 49418, "\u0120lug": 49419, "\u0120Redmond": 49420, "classic": 49421, "Supporters": 49422, "SHIP": 49423, "GMT": 49424, "\u0120fuelled": 49425, "\u00e7\u0132": 49426, "\u0120dd": 49427, "\u0120Eminem": 49428, "\u01201897": 49429, "NYSE": 49430, "\u0120secretaries": 49431, "\u0120FIA": 49432, "\u0120Canaveral": 49433, "Favorite": 49434, "\u0120pomp": 49435, "\u0120detainee": 49436, "ership": 49437, "aimon": 49438, "iour": 49439, "\u0120Apex": 49440, "\u0120plantations": 49441, "amia": 49442, "acion": 49443, "Rust": 49444, "\u0120towed": 49445, "\u0120Truly": 49446, "577": 49447, "\u0120sheltered": 49448, "rider": 49449, "Wo": 49450, "\u0120lair": 49451, "\u0120Intelligent": 49452, "improve": 49453, "matically": 49454, "\u0120etiquette": 49455, "adra": 49456, "allo": 49457, "\u0120Juno": 49458, "anything": 49459, "\u0120Struggle": 49460, "\u0120Predict": 49461, "\u0120Grimes": 49462, "\u0120AMERICA": 49463, "ctx": 49464, "\u0120Situation": 49465, "WOOD": 49466, "\u0120soluble": 49467, "meier": 49468, "\u0120intolerable": 49469, "angering": 49470, "\u0120uninterrupted": 49471, "\u0120tooltip": 49472, "\u0120interrogated": 49473, "\u0120gunned": 49474, "\u0120Sneak": 49475, "\u00e6\u0143\u00a6": 49476, "\u0120tether": 49477, "\u0120crumble": 49478, "Lens": 49479, "\u0120clustered": 49480, "\u0120Syl": 49481, "\u0120Hasan": 49482, "\u0120dystopian": 49483, "wana": 49484, "\u0120joystick": 49485, "\u0120Thib": 49486, "ammu": 49487, "Tomorrow": 49488, "546": 49489, "\u0120overcame": 49490, "\u0120minimized": 49491, "ceptor": 49492, "Runner": 49493, "ENGTH": 49494, "\u0120Brenda": 49495, "\u0120Achievements": 49496, "\u0120torches": 49497, "\u0120rapport": 49498, "\u0120Investigator": 49499, "\u0120Handling": 49500, "relation": 49501, "grey": 49502, "815": 49503, "\u0120kcal": 49504, "\u0120Commands": 49505, "dq": 49506, "\u0120curls": 49507, "\u0120bearer": 49508, "\u0120cynicism": 49509, "itri": 49510, "\u0120Useful": 49511, "Bee": 49512, "DCS": 49513, "\u0120abras": 49514, "Pract": 49515, "BILITIES": 49516, "712": 49517, "\u0120debugger": 49518, "\u0120debtor": 49519, "\u0120Lia": 49520, "\u0120Kers": 49521, "\u0120exacerbate": 49522, "\u0120Stacy": 49523, "\u0120Bland": 49524, "\u0120Scenes": 49525, "\u0120branching": 49526, "\u00e2\u0138\u012a\u00e2\u0138\u012a\u00e2\u0138\u012a\u00e2\u0138\u012a\u00e2\u0138\u012a\u00e2\u0138\u012a\u00e2\u0138\u012a\u00e2\u0138\u012a": 49527, "apeake": 49528, "\u0120salsa": 49529, "\u0120mishand": 49530, "\u0120Konami": 49531, "\u0120Nib": 49532, "\u0120anecdote": 49533, "\u0120agreeable": 49534, "\u00cf\u012b": 49535, "\u0120Nathaniel": 49536, "\u0120Heisman": 49537, "\u0120Beware": 49538, "\u01201886": 49539, "spective": 49540, "691": 49541, "522": 49542, "\u0120inhibits": 49543, "\u0120hashing": 49544, "\u01201889": 49545, "\u00e5\u00b0\u0128": 49546, "vich": 49547, "Pure": 49548, "\u0120solidly": 49549, "\u0120aspirin": 49550, "imaru": 49551, "\u0120streetcar": 49552, "\u0120UCS": 49553, "\u0120Judd": 49554, "\u0120flashbacks": 49555, "pins": 49556, "\u01201440": 49557, "\u0120UNHCR": 49558, "\u0120Symptoms": 49559, "TIT": 49560, "538": 49561, "Fra": 49562, "%);": 49563, "\u0120ooz": 49564, "\u0120curfew": 49565, "\u0120calmed": 49566, "\u0120participates": 49567, "TeX": 49568, "\u0120nonsensical": 49569, "\u0120fullback": 49570, "\u0120DeL": 49571, "monkey": 49572, "hari": 49573, "\u0120metabolites": 49574, "\u0120looted": 49575, "\u0120ALWAYS": 49576, "\u0120BCC": 49577, "Lt": 49578, "ochet": 49579, "Bone": 49580, "\u0120vetoed": 49581, "\u0120gcc": 49582, "\u0120CLICK": 49583, "\u01201888": 49584, "saf": 49585, "\u0120stiffness": 49586, "\u0120lowly": 49587, "\u0120Geh": 49588, "verson": 49589, "orset": 49590, "\u0120unforeseen": 49591, "\u0120anesthesia": 49592, "\u0120Optical": 49593, "\u0120reconstructed": 49594, "\u0120Tup": 49595, "shows": 49596, "NEWS": 49597, "\u0120Newspaper": 49598, "\u0120ASA": 49599, "tera": 49600, "Numbers": 49601, "\u0120inexplicable": 49602, "\u00d7\u0133": 49603, "\u0120hardness": 49604, "untarily": 49605, "\u0120Acer": 49606, "gradient": 49607, "ARDIS": 49608, "\u0120woodland": 49609, "\u0120metaphors": 49610, "\u0120Wembley": 49611, "\u0120Pavel": 49612, "philis": 49613, "\u0120rewriting": 49614, "\u0120perceptual": 49615, "\u01201070": 49616, "worms": 49617, "\u0120Downs": 49618, "\u0120unsurprisingly": 49619, "\u0120tagging": 49620, "flame": 49621, "\u0120litres": 49622, "\u0120bounces": 49623, "\u0120Babe": 49624, "shut": 49625, "\u0120overdoses": 49626, "\u0120Sheila": 49627, "\u0120Chau": 49628, "\u0120Bless": 49629, "Capture": 49630, "\u0120Significant": 49631, "\u0120Scion": 49632, "\u0120389": 49633, "\u0120McH": 49634, "\u0120Titanium": 49635, "\u0120Meal": 49636, "ameda": 49637, "agents": 49638, "aggressive": 49639, "Billy": 49640, "763": 49641, "\u0120Saying": 49642, "DERR": 49643, "itone": 49644, "Collins": 49645, "Bound": 49646, "\u0120bolted": 49647, "\u0120DMCA": 49648, "953": 49649, "\u0120uniqueness": 49650, "\u0120epigen": 49651, "unci": 49652, "antam": 49653, "\u0120reckoning": 49654, "chairs": 49655, "OGR": 49656, "\u0120Senegal": 49657, "\u01201862": 49658, "relevant": 49659, "\u0120\u00c2\u00af": 49660, "\u0120pharmacies": 49661, "\u0120Geral": 49662, "vier": 49663, "Yan": 49664, "ORPG": 49665, "\u0120rabid": 49666, "bending": 49667, "\u0120UNITED": 49668, "\u0120465": 49669, "Assembly": 49670, "\u0120weep": 49671, "\u0120behest": 49672, "\u0120Mothers": 49673, "\u0120Jace": 49674, "hid": 49675, "\u0120whirlwind": 49676, "\u0120UNIVERS": 49677, "\u0120utopian": 49678, "\u0120kidnap": 49679, "Philipp": 49680, "Kin": 49681, "893": 49682, "\u0120livestream": 49683, "\u0120MISS": 49684, "\u0120subversive": 49685, "\u0120Techniques": 49686, "\u0120JUSTICE": 49687, "\u0120BASE": 49688, "\u0120387": 49689, "\u0120assailants": 49690, "\u0120Hardcore": 49691, "\u0120sprinkled": 49692, "\u0120Pse": 49693, "\u00e9\u013c": 49694, "printed": 49695, "\u0120Hau": 49696, "ORGE": 49697, "\u0120TOUR": 49698, "\u0120laced": 49699, "\u0120itch": 49700, "Giving": 49701, "\u0120ported": 49702, "781": 49703, "////////////////////////////////": 49704, "breeding": 49705, "\u0120logger": 49706, "\u0120HOL": 49707, "innie": 49708, "Firstly": 49709, "\u0120embryonic": 49710, "\u0120delegated": 49711, "pai": 49712, "OIL": 49713, "\u0120centrally": 49714, "\u0120Rx": 49715, "\u0120Scouting": 49716, "Dutch": 49717, "\u0120hereditary": 49718, "\u0120Cruiser": 49719, "sat": 49720, "529": 49721, "\u0120Marriott": 49722, "othermal": 49723, "\u0120prohibitions": 49724, "Earn": 49725, "\u0120Stab": 49726, "\u0120Colleges": 49727, "\u0120Belief": 49728, "stretched": 49729, "\u0120LH": 49730, "\u0120EntityItem": 49731, "CIA": 49732, "\u0120unrem": 49733, "\u0120laureate": 49734, "\u0120denominations": 49735, "summary": 49736, "hler": 49737, "Spect": 49738, "\u0120Klaus": 49739, "\u0120Beans": 49740, "\u0120insur": 49741, "\u0120PAX": 49742, "\u0120fielder": 49743, "\u0120Vet": 49744, "\u0120Sparrow": 49745, "zie": 49746, "\u0120SQ": 49747, "\u0120Mondays": 49748, "\u0120Offline": 49749, "\u0120Lerner": 49750, "\u0120Extensions": 49751, "Ireland": 49752, "\u0120patronage": 49753, "\u0120contrasted": 49754, "\u0120Mania": 49755, "hirt": 49756, "Moscow": 49757, "\u0120condemns": 49758, "\u0120Ange": 49759, "\u0120composing": 49760, "\u0120Pepe": 49761, "\u0120Paddock": 49762, "\u0120heterogeneity": 49763, "\u0120ideologically": 49764, "\u0120fishes": 49765, "\u0120cursing": 49766, "\u0120Rutherford": 49767, "\u0120Floating": 49768, "\u0120Amelia": 49769, "Tea": 49770, "Synopsis": 49771, "\u0120stunts": 49772, "\u0120bead": 49773, "\u0120stocking": 49774, "\u0120MILL": 49775, "obook": 49776, "massive": 49777, "\\<": 49778, "\u0120hump": 49779, "\u0120Preferences": 49780, "EngineDebug": 49781, "geist": 49782, "\u0120Nieto": 49783, "omever": 49784, "ishy": 49785, "evaluate": 49786, "colonial": 49787, "Alternative": 49788, "\u0120GoPro": 49789, "\u0120Vortex": 49790, "\u0120NETWORK": 49791, "ansky": 49792, "Secure": 49793, "\u0120Thrust": 49794, "Snake": 49795, "\u0120parcels": 49796, "\u0120samurai": 49797, "\u0120actresses": 49798, "Nap": 49799, "MF": 49800, "iferation": 49801, "Beer": 49802, "523": 49803, "\u0120Ily": 49804, "ointment": 49805, "Ping": 49806, "\u0120striped": 49807, "\u0120Mellon": 49808, "ossession": 49809, "\u0120neutron": 49810, "endium": 49811, "\u0120aph": 49812, "\u0120Flavoring": 49813, "\u0120383": 49814, "\u0120responsiveness": 49815, "\u0120Jindal": 49816, "\u0120Hitchcock": 49817, "Denver": 49818, "\u0120DRAGON": 49819, "smanship": 49820, "\u0120Dupl": 49821, "\u0120sly": 49822, "\u0120webcam": 49823, "\u0120Twain": 49824, "\u0120Darling": 49825, "iliate": 49826, "consumer": 49827, "DIT": 49828, "\u0120namesake": 49829, "\u0120unorthodox": 49830, "\u0120funer": 49831, "\u0120PLoS": 49832, "\u0120CONTROL": 49833, "ozyg": 49834, "oglobin": 49835, "FACE": 49836, "ERG": 49837, "\u0120Dia": 49838, "\u0120Fiesta": 49839, "cele": 49840, "034": 49841, "\u0120enclave": 49842, "\u00e2\u0138\u00ac\u00e2\u0138\u00ac": 49843, "onement": 49844, "alist": 49845, "Mand": 49846, "\u0120homegrown": 49847, "\u0120Fancy": 49848, "\u0120conceptions": 49849, "\u0120Contains": 49850, "ureen": 49851, "\u0120reiterate": 49852, "\u0120meager": 49853, "\u0120installments": 49854, "Spawn": 49855, "627": 49856, "\u0120photoc": 49857, "\u0120Cabrera": 49858, "\u0120Rosenthal": 49859, "\u0120Lansing": 49860, "isner": 49861, "\u0120invests": 49862, "\u0120UFOs": 49863, "EXP": 49864, "Hardware": 49865, "\u0120tragically": 49866, "\u0120concedes": 49867, "ieft": 49868, "cham": 49869, "borgh": 49870, "\u0120Schr": 49871, "\u0120Melanie": 49872, "\u0120Hoy": 49873, "\u0120visitation": 49874, "\u0120idiosyncr": 49875, "\u0120fractions": 49876, "\u0120foreskin": 49877, "obos": 49878, "\u0120poaching": 49879, "\u0120VIEW": 49880, "\u0120stimulates": 49881, "\u0120Gork": 49882, "canon": 49883, "MIC": 49884, "\u0120Nemesis": 49885, "\u0120Indra": 49886, "\u0120DMV": 49887, "\u0120529": 49888, "\u0120inspecting": 49889, "\u0120grandma": 49890, "\u0120Whedon": 49891, "\u0120Shant": 49892, "\u0120Purg": 49893, "ikan": 49894, "\u0120Teg": 49895, "\u0120CLR": 49896, "zac": 49897, "Victoria": 49898, "\u0120Verify": 49899, "ionics": 49900, "\u0120partying": 49901, "\u0120Mou": 49902, "colour": 49903, "\u0120testimonies": 49904, "lations": 49905, "\u0120pressuring": 49906, "hiro": 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\ No newline at end of file
diff --git a/NLP/UNIMO-2/model_files/dict/roberta_base_en.vocab.bpe b/NLP/UNIMO-2/model_files/dict/roberta_base_en.vocab.bpe
new file mode 100644
index 000000000..226b0752c
--- /dev/null
+++ b/NLP/UNIMO-2/model_files/dict/roberta_base_en.vocab.bpe
@@ -0,0 +1,50001 @@
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diff --git a/NLP/UNIMO-2/model_files/dict/roberta_base_en.vocab.txt b/NLP/UNIMO-2/model_files/dict/roberta_base_en.vocab.txt
new file mode 100644
index 000000000..ac1beb67d
--- /dev/null
+++ b/NLP/UNIMO-2/model_files/dict/roberta_base_en.vocab.txt
@@ -0,0 +1,50265 @@
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diff --git a/NLP/UNIMO-2/requirements.txt b/NLP/UNIMO-2/requirements.txt
new file mode 100644
index 000000000..fa1026403
--- /dev/null
+++ b/NLP/UNIMO-2/requirements.txt
@@ -0,0 +1,60 @@
+astor==0.8.1
+astroid==2.11.2
+black==22.3.0
+cached-property==1.5.2
+certifi==2020.12.5
+chardet==4.0.0
+click==8.1.1
+colorama==0.4.4
+colorlog==5.0.1
+cycler==0.10.0
+decorator==4.4.2
+dill==0.3.4
+gast==0.4.0
+h5py==3.3.0
+idna==2.10
+imageio==2.9.0
+importlib-metadata==4.11.3
+isort==5.10.1
+jieba==0.42.1
+joblib==1.0.1
+kiwisolver==1.3.1
+lazy-object-proxy==1.7.1
+matplotlib==3.4.2
+mccabe==0.7.0
+multiprocess==0.70.12.2
+mypy-extensions==0.4.3
+networkx==2.5.1
+numpy==1.20.2
+opencv-python==4.5.2.52
+paddlecloudplatform==3.0.13
+paddlenlp==2.0.7
+paddlepaddle-gpu==2.1.2
+pathspec==0.9.0
+Pillow==8.2.0
+platformdirs==2.5.1
+protobuf==3.15.8
+pylint==2.13.2
+pyparsing==2.4.7
+pyrouge==0.1.3
+python-dateutil==2.8.1
+PyWavelets==1.1.1
+regex==2021.4.4
+requests==2.25.1
+scikit-image==0.18.1
+scikit-learn==0.24.2
+scipy==1.7.0
+sentencepiece==0.1.96
+seqeval==1.2.2
+six==1.15.0
+termcolor==1.1.0
+threadpoolctl==2.2.0
+tifffile==2021.6.14
+tomli==2.0.1
+typed-ast==1.5.2
+typing-extensions==4.1.1
+urllib3==1.26.4
+wrapt==1.14.0
+xlrd==1.2.0
+xlwt==1.2.0
+zipp==3.7.0
diff --git a/NLP/UNIMO-2/script/classification-grounded/MNLI-AX/model_conf b/NLP/UNIMO-2/script/classification-grounded/MNLI-AX/model_conf
new file mode 100644
index 000000000..c7f190418
--- /dev/null
+++ b/NLP/UNIMO-2/script/classification-grounded/MNLI-AX/model_conf
@@ -0,0 +1,58 @@
+output_name="classification-grounded"
+
+# env
+source ./script/${output_name}/method_conf
+
+## CONTROL
+task=MNLI-AX
+
+
+## hyper param
+lr_scheduler="linear_warmup_decay"
+use_fp16="False"
+loss_scaling=12800
+do_train="True"
+do_val="True"
+do_val_hard="True"
+do_test="False"
+do_test_hard="False"
+do_pred="False"
+do_pred_hard="False"
+do_diagnostic="False"
+num_labels=3
+weight_decay=0
+max_len=512
+warmup_ratio=0.06
+do_lower_case="True"
+in_tokens="False"
+weight_sharing="True"
+save_checkpoints="False"
+save_steps=200000
+validation_steps=20000
+skip_steps=100
+eval_mertrics=simple_accuracy
+
+num_codebook=2048
+grounding_method='topk'
+topk_value=100
+
+model_type="grounded"
+# "grounded" "baseline"
+with_grounding_projection=False
+
+with_grounding_pos=False
+text_enc_layers='0,1,2,3,4,5,6,7,8,9,10,11'
+grounding_enc_layers='0,1,2,3,4,5,6,7,8,9,10,11'
+
+#adam optimizer
+beta1=0.9
+beta2=0.98
+epsilon=1e-06
+
+EPOCH=("20")
+BATCH_SIZE=("16")
+LR_RATE=("1e-5" "2e-5" "3e-5")
+DD_RAND_SEED=("1" "2" "3" "4" "5")
+
+CONFIG_PATH="./model_files/config/grounded_base_en.json"
+init_model="./model_files/UNIMO-2"
diff --git a/NLP/UNIMO-2/script/classification-grounded/MNLI-AX/run.sh b/NLP/UNIMO-2/script/classification-grounded/MNLI-AX/run.sh
new file mode 100644
index 000000000..cc3f2046d
--- /dev/null
+++ b/NLP/UNIMO-2/script/classification-grounded/MNLI-AX/run.sh
@@ -0,0 +1,132 @@
+#!/usr/bin/env bash
+set -eux
+R_DIR=`dirname $0`; MYDIR=`cd $R_DIR;pwd`
+cd ${MYDIR}/../../../
+# config env
+source ${MYDIR}/model_conf
+
+source ./env_local/env.sh
+source ./env_local/utils.sh
+
+set -eu
+output_dir=./output/${task}
+log_dir=./output/${task}/log
+rm -rf $output_dir
+save_model_dir=$output_dir/save_model
+mkdir -p $output_dir $log_dir $save_model_dir
+
+e_executor=$(echo ${use_experimental_executor:-'True'} | tr '[A-Z]' '[a-z]')
+
+use_fuse=$(echo ${use_fuse:-'False'} | tr '[A-Z]' '[a-z]')
+if [[ ${use_fuse} == "true" ]]; then
+    #MB
+    export FLAGS_fuse_parameter_memory_size=64
+fi
+
+# check
+check_iplist
+
+if [[ ${do_pred} == "True" ]]; then
+    pred_save_prefix="${output_dir}/predict"
+    mkdir -p $pred_save_prefix
+fi
+
+for seed in "${DD_RAND_SEED[@]}"; do
+  echo "seed "$seed
+  for epoch in "${EPOCH[@]}"; do
+    echo "epoch "$epoch
+    for lr in "${LR_RATE[@]}"; do
+      echo "learning rate "$lr
+      for bs in "${BATCH_SIZE[@]}"; do
+        echo "batch_size "$bs
+        log_prefix=$seed"_"$epoch"_"$lr"_"$bs"."
+        if [[ ${do_pred} == "True" ]]; then
+            pred_save="${pred_save_prefix}/test.${seed}.${epoch}.${lr}.${bs}"
+        fi
+
+        if [[ ${save_checkpoints} == "True" ]]; then
+            save_model_dir="${save_model_dir}/params.${seed}.${epoch}.${lr}.${bs}"
+            mkdir -p $save_model_dir
+        fi
+
+        if [[ ${bs} == "32" ]]; then
+            validation_steps=10000
+        fi
+
+        distributed_args="--node_ips ${PADDLE_TRAINERS} \
+                --node_id ${PADDLE_TRAINER_ID} \
+                --current_node_ip ${POD_IP} \
+                --selected_gpus 0,1,2,3,4,5,6,7 \
+                --split_log_path $log_dir \
+                --log_prefix $log_prefix \
+                --nproc_per_node 8"
+
+        python3 -u ./src/launch.py ${distributed_args} \
+            ./src/run_classifier_grounded.py --use_cuda "True" \
+                   --is_distributed ${is_distributed:-"True"} \
+                   --weight_sharing ${weight_sharing:-"True"} \
+                   --use_fast_executor ${e_executor:-"true"} \
+                   --use_fp16 ${use_fp16:-"false"} \
+                   --nccl_comm_num ${nccl_comm_num:-1} \
+                   --use_hierarchical_allreduce ${use_hierarchical_allreduce:-"False"} \
+                   --in_tokens ${in_tokens:-"false"} \
+                   --use_dynamic_loss_scaling ${use_fp16} \
+                   --use_modal_tag ${use_modal_tag:-"false"} \
+                   --init_loss_scaling ${loss_scaling:-128} \
+                   --beta1 ${beta1:-0.9} \
+                   --beta2 ${beta2:-0.98} \
+                   --epsilon ${epsilon:-1e-06} \
+                   --verbose true \
+                   --do_train ${do_train:-"True"} \
+                   --do_val ${do_val:-"True"} \
+                   --do_val_hard ${do_val_hard:-"False"} \
+                   --do_test ${do_test:-"True"} \
+                   --do_test_hard ${do_test_hard:-"False"} \
+                   --do_pred ${do_pred:-"True"} \
+                   --do_pred_hard ${do_pred_hard:-"False"} \
+                   --do_diagnostic ${do_diagnostic:-"True"} \
+                   --pred_save ${pred_save:-"./output/predict/test"} \
+                   --batch_size ${bs:-16} \
+                   --init_pretraining_params ${init_model:-""} \
+                   --train_set ./data/$task/train.tsv \
+                   --dev_set ./data/$task/m/dev.tsv \
+                   --dev_hard_set ./data/$task/mm/dev.tsv \
+                   --test_set ./data/$task/m/test.tsv \
+                   --test_hard_set ./data/$task/mm/test.tsv \
+                   --diagnostic_set ./data/$task/diagnostic.tsv \
+                   --checkpoints $save_model_dir \
+                   --save_checkpoints ${save_checkpoints:-"True"} \
+                   --save_steps ${save_steps:-1000} \
+                   --weight_decay ${weight_decay:-"0.1"} \
+                   --warmup_proportion ${warmup_ratio:-"0.06"} \
+                   --validation_steps ${validation_steps:-"100"} \
+                   --epoch $epoch \
+                   --max_seq_len ${max_len:-512} \
+                   --learning_rate ${lr:-"5e-5"} \
+                   --lr_scheduler ${lr_scheduler} \
+                   --skip_steps ${skip_steps:-"10"} \
+                   --num_iteration_per_drop_scope 10 \
+                   --num_labels ${num_labels:-2} \
+                   --roberta_vocab_file ${vocab_file} \
+                   --encoder_json_file ${bpe_json} \
+                   --vocab_bpe_file ${bpe_file} \
+                   --vl_config_path ${CONFIG_PATH} \
+                   --eval_mertrics ${eval_mertrics:-"simple_accuracy"} \
+                   --model_type ${model_type:-"fleet"} \
+                   --num_codebook ${num_codebook:-2048} \
+                   --grounding_method ${grounding_method:-"topk"} \
+                   --topk_value ${topk_value:-100} \
+                   --with_grounding_projection ${with_grounding_projection:-"False"} \
+                   --with_grounding_pos ${with_grounding_pos:-"False"} \
+                   --text_enc_layers ${text_enc_layers:-'0,1,2,3,4,5'} \
+                   --grounding_enc_layers ${grounding_enc_layers:-'6,7,8,9,10,11'} \
+                   --random_seed ${seed:-1} >> $log_dir/${log_prefix}lanch.log 2>&1
+      done
+    done
+  done
+done
+
+python ./src/utils/stat_res.py --log_dir=$log_dir --key_words='job.log' --line_prefix="Best validation result:" --final_res_file="final_res.m.txt"
+python ./src/utils/stat_res.py --log_dir=$log_dir --key_words='job.log' --line_prefix="Best validation_hard result:" --final_res_file="final_res.mm.txt"
+
+exit 0
diff --git a/NLP/UNIMO-2/script/classification-grounded/method_conf b/NLP/UNIMO-2/script/classification-grounded/method_conf
new file mode 100644
index 000000000..55dccb504
--- /dev/null
+++ b/NLP/UNIMO-2/script/classification-grounded/method_conf
@@ -0,0 +1,5 @@
+source ./env_local/utils.sh
+
+vocab_file="./model_files/dict/roberta_base_en.vocab.txt"
+bpe_json="./model_files/dict/roberta_base_en.encoder.json"
+bpe_file="./model_files/dict/roberta_base_en.vocab.bpe"
diff --git a/NLP/UNIMO-2/script/img2txt-grounded/coco-oscar/model_conf b/NLP/UNIMO-2/script/img2txt-grounded/coco-oscar/model_conf
new file mode 100644
index 000000000..68b595ef3
--- /dev/null
+++ b/NLP/UNIMO-2/script/img2txt-grounded/coco-oscar/model_conf
@@ -0,0 +1,80 @@
+output_name="img2txt-grounded"
+
+# env
+source ./script/${output_name}/method_conf
+
+## CONTROL
+task=coco-oscar
+
+# hyper param
+lr_scheduler="linear_warmup_decay"
+use_fp16="False"
+# Merge the ALLReduce times of a layer
+use_fuse="True"
+use_hierarchical_allreduce="True"
+loss_scaling=12800
+
+skip_steps=100
+save_steps=100000
+validation_steps=2000
+label_smooth=0.1
+weight_decay=0.01
+max_seq_len=50
+random_seed=666
+
+#decoding params
+do_decode="true"
+max_obj_len=100
+max_tgt_len=50
+max_out_len=50
+min_out_len=5
+beam_size=5
+length_penalty=0.6
+block_trigram="False"
+use_multi_gpu_test="True"
+
+#adam optimizer
+beta1=0.9
+beta2=0.98
+epsilon=1e-06
+
+#dataset
+data_path="./data/coco"
+train_filelist="train_filelist"
+valid_filelist="valid_filelist"
+test_filelist="test_filelist"
+do_train="true"
+do_val="false"
+do_test="true"
+do_pred="false"
+
+#evaluate
+eval_script="bash ./src/eval/tasks/coco/eval.sh"
+eval_mertrics="Bleu_1,Bleu_2,Bleu_3,Bleu_4,METEOR,ROUGE_L,CIDEr,SPICE"
+
+## turning params
+pred_batch_size=32
+epoch=10
+BATCH_SIZE=("32")
+LR_RATE=("1e-5")
+DD_RAND_SEED=("1")
+WARMUP_PROP=("0.06")
+
+use_recompute=True
+resolution=16
+image_size=384
+num_codebook=2048
+grounding_method='topk'
+topk_value=100
+
+model_type="grounded"
+# "grounded" "baseline"
+with_grounding_projection=False
+
+with_grounding_pos=False
+text_enc_layers='0,1,2,3,4,5,6,7,8,9,10,11'
+grounding_enc_layers='0,1,2,3,4,5,6,7,8,9,10,11'
+
+config_path="./model_files/config/grounded_base_en.json"
+object_file_local_path="coco_object_0.35_tot.ids"
+init_model="./model_files/UNIMO-2"
diff --git a/NLP/UNIMO-2/script/img2txt-grounded/coco-oscar/run.sh b/NLP/UNIMO-2/script/img2txt-grounded/coco-oscar/run.sh
new file mode 100644
index 000000000..acdf6b1af
--- /dev/null
+++ b/NLP/UNIMO-2/script/img2txt-grounded/coco-oscar/run.sh
@@ -0,0 +1,124 @@
+#!/usr/bin/env bash
+set -eux
+R_DIR=`dirname $0`; MYDIR=`cd $R_DIR;pwd`
+cd ${MYDIR}/../../../
+# config env
+source ${MYDIR}/model_conf
+
+source ./env_local/env.sh
+source ./env_local/utils.sh
+
+timestamp=`date "+%Y%m%d-%H%M%S"`
+echo $timestamp
+#put_local_code $iplist $submitter $timestamp
+
+# check
+check_iplist
+
+set -eu
+output_dir=./output/${task}
+log_dir=./output/${task}/log
+rm -rf $output_dir
+mkdir -p $output_dir $log_dir
+
+e_executor=$(echo ${use_experimental_executor-'True'} | tr '[A-Z]' '[a-z]')
+
+use_fuse=$(echo ${use_fuse-'False'} | tr '[A-Z]' '[a-z]')
+if [[ ${use_fuse} == "true" ]]; then
+    #MB
+    export FLAGS_fuse_parameter_memory_size=64
+fi
+
+export EVAL_SCRIPT_LOG=${MYDIR}/../../../${output_dir}/eval.log
+export TASK_DATA_PATH="./data/coco"
+
+
+for random_seed in "${DD_RAND_SEED[@]}"; do
+    echo "random_seed "${random_seed}
+    for batch_size in "${BATCH_SIZE[@]}"; do
+        echo "batch_size "${batch_size}
+        for warmup_proportion in "${WARMUP_PROP[@]}"; do
+            echo "warmup_proportion "${warmup_proportion}
+            for learning_rate in "${LR_RATE[@]}"; do
+                echo "learning rate "${learning_rate}
+
+                distributed_args="--ips $(hostname -i) \
+                                --gpus 0,1,2,3 \
+                                --log_dir $log_dir"
+
+                python3 -m paddle.distributed.launch ${distributed_args} \
+                    ./src/run_img2txt_oscar.py --use_cuda "True" \
+                       --is_distributed "True" \
+                       --use_multi_gpu_test ${use_multi_gpu_test:-"True"} \
+                       --use_fp16 ${use_fp16:-"False"} \
+                       --use_dynamic_loss_scaling ${use_fp16} \
+                       --init_loss_scaling ${loss_scaling:-128} \
+                       --use_fast_executor ${e_executor:-"True"} \
+                       --use_fuse ${use_fuse:-"False"} \
+                       --nccl_comm_num ${nccl_comm_num:-1} \
+                       --use_hierarchical_allreduce ${use_hierarchical_allreduce:-"False"} \
+                       --do_train ${do_train:-"true"} \
+                       --do_val ${do_val:-"false"} \
+                       --do_test ${do_test:-"true"} \
+                       --do_pred ${do_pred:-"false"} \
+                       --do_decode ${do_decode:-"True"} \
+                       --train_filelist ./data/coco/${train_filelist:-""} \
+                       --valid_filelist ./data/coco/${valid_filelist:-""} \
+                       --test_filelist ./data/coco/${test_filelist:-""} \
+                       --epoch ${epoch} \
+                       --task_type ${task_type:-"img2txt"} \
+                       --max_seq_len ${max_seq_len} \
+                       --max_tgt_len ${max_tgt_len} \
+                       --max_out_len ${max_out_len} \
+                       --min_out_len ${min_out_len} \
+                       --max_obj_len ${max_obj_len} \
+                       --object_file ./data/coco/${object_file_local_path:-""} \
+                       --block_trigram ${block_trigram:-"True"} \
+                       --beam_size ${beam_size:-5}  \
+                       --length_penalty ${length_penalty:-0.6} \
+                       --hidden_dropout_prob ${hidden_dropout_prob:-0.1} \
+                       --attention_probs_dropout_prob ${attention_probs_dropout_prob:-0.1} \
+                       --beta1 ${beta1:-0.9} \
+                       --beta2 ${beta2:-0.98} \
+                       --epsilon ${epsilon:-1e-06} \
+                       --tgt_type_id ${tgt_type_id:-1}\
+                       --batch_size ${batch_size} \
+                       --pred_batch_size ${pred_batch_size} \
+                       --learning_rate ${learning_rate} \
+                       --lr_scheduler ${lr_scheduler:-"linear_warmup_decay"} \
+                       --warmup_proportion ${warmup_proportion:-0.02} \
+                       --weight_decay ${weight_decay:-0.01} \
+                       --weight_sharing ${weight_sharing:-"True"} \
+                       --label_smooth ${label_smooth:-0.1} \
+                       --init_pretraining_params ${init_model:-""} \
+                       --roberta_vocab_file ${vocab_file} \
+                       --encoder_json_file ${bpe_json} \
+                       --vocab_bpe_file ${bpe_file} \
+                       --vl_config_path ${config_path} \
+                       --checkpoints $output_dir \
+                       --save_steps ${save_steps:-10000} \
+                       --validation_steps ${validation_steps:-10000} \
+                       --skip_steps ${skip_steps:-10} \
+                       --save_and_valid_by_epoch ${save_and_valid_by_epoch:-"False"} \
+                       --eval_script ${eval_script:-""} \
+                       --eval_mertrics ${eval_mertrics:-""} \
+                       --resolution ${resolution:-16} \
+                       --image_size ${image_size:-224} \
+                       --num_codebook ${num_codebook:-2048} \
+                       --model_type ${model_type:-"grounded"} \
+                       --grounding_method ${grounding_method:-"topk"} \
+                       --topk_value ${topk_value:-100} \
+                       --with_grounding_projection ${with_grounding_projection:-"False"} \
+                       --with_grounding_pos ${with_grounding_pos:-"False"} \
+                       --text_enc_layers ${text_enc_layers:-'0,1,2,3,4,5'} \
+                       --grounding_enc_layers ${grounding_enc_layers:-'6,7,8,9,10,11'} \
+                       --use_recompute ${use_recompute:-'False'} \
+                       --random_seed ${random_seed:-"1"} >> $log_dir/lanch.log 2>&1
+            done
+        done
+    done
+done
+
+python3 ./src/utils/extract_eval_res.py --log_dir=$log_dir --file_name="workerlog.0"
+#put_local_dir $iplist $submitter $timestamp $log_dir
+exit 0
\ No newline at end of file
diff --git a/NLP/UNIMO-2/script/img2txt-grounded/method_conf b/NLP/UNIMO-2/script/img2txt-grounded/method_conf
new file mode 100644
index 000000000..f02baefe0
--- /dev/null
+++ b/NLP/UNIMO-2/script/img2txt-grounded/method_conf
@@ -0,0 +1,5 @@
+source ./env_local/utils.sh
+
+vocab_file="./model_files/dict/roberta_base_en.vocab.txt"
+bpe_json="./model_files/dict/roberta_base_en.encoder.json"
+bpe_file="./model_files/dict/roberta_base_en.vocab.bpe"
\ No newline at end of file
diff --git a/NLP/UNIMO-2/script/retrieval-grounded/Flickr30k-fleet/model_conf b/NLP/UNIMO-2/script/retrieval-grounded/Flickr30k-fleet/model_conf
new file mode 100644
index 000000000..af7cc2424
--- /dev/null
+++ b/NLP/UNIMO-2/script/retrieval-grounded/Flickr30k-fleet/model_conf
@@ -0,0 +1,62 @@
+output_name="retrieval-grounded"
+
+# env
+source ./script/${output_name}/method_conf
+
+## CONTROL
+task=Flickr30k-fleet
+
+
+## hyper param
+epoch=40
+is_distributed=True
+nccl_comm_num=2
+use_fp16="False"
+loss_scaling=12800
+use_fuse="True"
+do_train="True"
+do_val="True"
+do_test="False"
+save_checkpoints="False"
+save_steps=10000
+validation_steps=10000
+skip_steps=100
+
+seed=1
+batch_size=2
+test_batch_size=128
+lr=5e-6
+lr_scheduler="scale_by_epoch_decay"
+learning_rate_scale=0.1
+learning_rate_decay_epoch1=24
+learning_rate_decay_epoch2=32
+warmup_step=1
+weight_decay=0.01
+max_len=128
+use_sigmoid=False
+eval_mertrics="recall@k"
+
+samples_num=20
+zero_shot="True"
+
+use_recompute=True
+resolution=16
+image_size=384
+num_codebook=2048
+grounding_method='topk'
+topk_value=100
+
+model_type="grounded"
+# "grounded" "baseline"
+with_cmcl=True
+with_grounding_projection=False
+with_cmcl_projection=False
+cmcl_share_parameters=False
+cmcl_score_weight=0.3
+
+with_grounding_pos=False
+text_enc_layers='0,1,2,3,4,5,6,7,8,9,10,11'
+grounding_enc_layers='0,1,2,3,4,5,6,7,8,9,10,11'
+
+CONFIG_PATH="./model_files/config/grounded_base_en.json"
+init_model="./model_files/UNIMO-2"
diff --git a/NLP/UNIMO-2/script/retrieval-grounded/Flickr30k-fleet/run.sh b/NLP/UNIMO-2/script/retrieval-grounded/Flickr30k-fleet/run.sh
new file mode 100644
index 000000000..3e2a8f0c0
--- /dev/null
+++ b/NLP/UNIMO-2/script/retrieval-grounded/Flickr30k-fleet/run.sh
@@ -0,0 +1,127 @@
+#!/usr/bin/env bash
+set -eux
+R_DIR=`dirname $0`; MYDIR=`cd $R_DIR;pwd`
+cd ${MYDIR}/../../../
+# config env
+source ${MYDIR}/model_conf
+
+source ./env_local/env.sh
+source ./env_local/utils.sh
+
+set -x
+export PADDLE_WITH_GLOO=0
+export GLOG_v=0
+export NCCL_DEBUG=INFO
+export FLAGS_call_stack_level=2
+#export FLAGS_allocator_strategy=naive_best_fit
+unset CUDA_VISIBLE_DEVICES
+
+timestamp=`date "+%Y%m%d-%H%M%S"`
+echo $timestamp
+
+# check
+check_iplist
+
+set -eu
+output_dir=./output/${task}
+log_dir=./output/${task}/log
+rm -rf $output_dir
+save_model_base_dir=$output_dir/save_model
+mkdir -p $output_dir $log_dir $save_model_base_dir
+
+e_executor=$(echo ${use_experimental_executor:-'True'} | tr '[A-Z]' '[a-z]')
+use_fuse=$(echo ${use_fuse:-'False'} | tr '[A-Z]' '[a-z]')
+if [[ ${use_fuse} == "true" ]]; then
+    #MB
+    export FLAGS_fuse_parameter_memory_size=64
+fi
+
+log_prefix=$seed"_"$epoch"_"$lr"_"$batch_size"."
+eval_dir="${output_dir}/tmp/params.${seed}.${epoch}.${lr}.${batch_size}"
+mkdir -p $eval_dir
+
+if [[ ${save_checkpoints} == "True" ]]; then
+    save_model_dir="${save_model_base_dir}/params.${seed}.${epoch}.${lr}.${batch_size}"
+    mkdir -p $save_model_dir
+fi
+
+distributed_args="--ips $(hostname -i) \
+                --gpus 0,1,2,3,4,5,6,7 \
+                --log_dir $log_dir"
+
+python3 -m paddle.distributed.launch ${distributed_args} \
+    ./src/run_retrieval_grounded_fleet.py \
+    --use_cuda "True" \
+    --is_distributed ${is_distributed:-"True"} \
+    --weight_sharing ${weight_sharing:-"True"} \
+    --use_fuse ${use_fuse:-"True"} \
+    --use_fast_executor ${e_executor:-"true"} \
+    --use_fp16 ${use_fp16:-"false"} \
+    --nccl_comm_num ${nccl_comm_num:-1} \
+    --use_hierarchical_allreduce ${use_hierarchical_allreduce:-"False"} \
+    --use_dynamic_loss_scaling ${use_fp16:-"False"} \
+    --use_sigmoid ${use_sigmoid:-"False"} \
+    --init_loss_scaling ${loss_scaling:-12800} \
+    --beta1 ${beta1:-0.9} \
+    --beta2 ${beta2:-0.98} \
+    --epsilon ${epsilon:-1e-06} \
+    --scale_circle ${scale_circle:-1.0} \
+    --margin ${margin:-0.2} \
+    --verbose true \
+    --samples_num ${samples_num:-20} \
+    --run_random ${run_random:-"False"} \
+    --do_train ${do_train:-"True"} \
+    --do_val ${do_val:-"True"} \
+    --do_test ${do_test:-"True"} \
+    --batch_size ${batch_size:-16} \
+    --test_batch_size ${test_batch_size:-96} \
+    --init_pretraining_params ${init_model:-""} \
+    --train_image_caption ./data/Flickr30k/flickr30k-textids/train.ids \
+    --dev_image_caption ./data/Flickr30k/flickr30k-textids/val.all.ids \
+    --test_image_caption ./data/Flickr30k/flickr30k-textids/test.all.ids \
+    --checkpoints ${save_model_dir:-""} \
+    --save_checkpoints ${save_checkpoints:-"True"} \
+    --save_steps ${save_steps:-1000} \
+    --weight_decay ${weight_decay:-"0.1"} \
+    --warmup_step ${warmup_step:-"1"} \
+    --validation_steps ${validation_steps:-"100"} \
+    --epoch $epoch \
+    --max_seq_len ${max_len:-512} \
+    --learning_rate ${lr:-"5e-6"} \
+    --learning_rate_scale ${learning_rate_scale:-0.1} \
+    --learning_rate_decay_epoch1 ${learning_rate_decay_epoch1:-24} \
+    --learning_rate_decay_epoch2 ${learning_rate_decay_epoch2:-32} \
+    --lr_scheduler ${lr_scheduler:-"scale_by_epoch_decay"} \
+    --skip_steps ${skip_steps:-"50"} \
+    --num_iteration_per_drop_scope 10 \
+    --unimo_vocab_file ${vocab_file} \
+    --encoder_json_file ${bpe_json} \
+    --vocab_bpe_file ${bpe_file} \
+    --unimo_config_path ${CONFIG_PATH} \
+    --eval_mertrics ${eval_mertrics:-"recall@k"} \
+    --eval_dir $eval_dir \
+    --random_seed ${seed:-1} \
+    --resolution ${resolution:-16} \
+    --image_size ${image_size:-224} \
+    --num_codebook ${num_codebook:-2048} \
+    --grounding_method ${grounding_method:-'normal'} \
+    --topk_value ${topk_value:-100} \
+    --model_type ${model_type:-"grounded"} \
+    --with_cmcl ${with_cmcl:-"True"} \
+    --with_grounding_projection ${with_grounding_projection:-"True"} \
+    --cmcl_share_parameters ${cmcl_share_parameters:-"True"} \
+    --with_cmcl_projection ${with_cmcl_projection:-"True"} \
+    --cmcl_score_weight ${cmcl_score_weight:-0.1} \
+    --with_grounding_pos ${with_grounding_pos:-"False"} \
+    --text_enc_layers ${text_enc_layers:-'0,1,2,3,4,5'} \
+    --grounding_enc_layers ${grounding_enc_layers:-'6,7,8,9,10,11'} \
+    --use_recompute ${use_recompute:-'False'} \
+    >> $log_dir/${log_prefix}lanch.log 2>&1
+
+if [[ $? -ne 0 ]]; then
+    echo "run failed"
+    exit 1
+fi
+
+python3 ./src/utils/new_stat_res.py --log_dir=$log_dir --key_words='workerlog.0'
+exit 0
diff --git a/NLP/UNIMO-2/script/retrieval-grounded/method_conf b/NLP/UNIMO-2/script/retrieval-grounded/method_conf
new file mode 100644
index 000000000..55dccb504
--- /dev/null
+++ b/NLP/UNIMO-2/script/retrieval-grounded/method_conf
@@ -0,0 +1,5 @@
+source ./env_local/utils.sh
+
+vocab_file="./model_files/dict/roberta_base_en.vocab.txt"
+bpe_json="./model_files/dict/roberta_base_en.encoder.json"
+bpe_file="./model_files/dict/roberta_base_en.vocab.bpe"
diff --git a/NLP/UNIMO-2/setup.cfg b/NLP/UNIMO-2/setup.cfg
new file mode 100644
index 000000000..f51af81c9
--- /dev/null
+++ b/NLP/UNIMO-2/setup.cfg
@@ -0,0 +1,80 @@
+# Here's a link about setup.cfg
+# https://setuptools.readthedocs.io/en/latest/setuptools.html#configuring-setup-using-setup-cfg-files
+[metadata]
+# Project name, the project name will be used while publishing and installing
+name = UNIMO2-Open
+# Author's name and email address
+author = liwei85
+author_email = liwei85@baidu.com
+# Project version, versions only above than 1.0 will assumed as a released version.
+# When modifying project version to above than 1.0, here's the rules should be followed.
+# http://wiki.baidu.com/pages/viewpage.action?pageId=469686381
+version = 0.1.0
+# A brief introduction about the project, ANY NON-ENGLISH CHARACTER IS NOT SUPPORTED!
+description = project descriptions here
+# A longer version of introduction abouth the project, you can also include readme, change log, etc. .md or rst file is recommended.
+long_description = file: README.md, CHANGELOG.md
+long_description_content_type = text/markdown
+# Main page of the project, usually the project's icode page, you can set to its wiki or other documents url instead.
+home_page = http://icode.baidu.com/repos/baidu/personal-code/UNIMO2-Open/tree/master
+# License, you can ignore this if the project is not going to open source to the public.
+license = MIT
+# Project type, you can ignore this if the project is not going to open source to the public.
+# Choose the right field to fulfill from PyPI's official list.
+# https://pypi.org/pypi?%3Aaction=list_classifiers
+classifier =
+    Private :: Do Not Upload
+    Programming Language :: Python
+    Programming Language :: Python :: 2
+    Programming Language :: Python :: 2.7
+# keywords, used for indexing, easier to search for other users if they are interested of your project.
+#keywords =
+#    baidu
+#    demo
+
+[options]
+# Package name. find means search automatically, you also can have detailed configuration in options.packages.find
+packages = find:
+# Dependency management, all project's dependency is needed here.
+# Every single line for a specified dependency, only the dependency is need, you don't have to consider the hierarchy dependency
+# Versions here should be as abstract as possible, usually only specific a version range including minimum and maximum version.
+#install_requires =
+#    six >= 1.10
+
+# Test dependencies, all dependencies for tests here. The format is align to install_requires.
+# You can use the internal unittest, or the simplier framework such as pytest or nose.
+# python3 has a mock library with itself, but it's not exist in python 2, add as you need.
+#tests_require =
+#    pytest
+#    mock
+
+# directory for unit test
+test_suite = unimo2_open.tests
+# add all data files controled by git
+include_package_data = True
+# You can run zip source code for plain python project
+zip_safe = False
+
+# You can set this configuration to let users run directly the main entrance function
+#[options.entry_points]
+#console_scripts =
+#    UNIMO2-Open = unimo2_open.cmdline:main
+
+# You can add conf/data directory into package, the following directory will be installed under site-package
+# Only file is supported, but you can use wildcard instead.
+#[options.package_data]
+#unimo2_open =
+#    conf/*
+#    data/*
+
+[sdist]
+dist_dir = output/dist
+
+[bdist_wheel]
+# set universal=1 if this project can both run in python 2 or 3 environment.
+#universal=1
+dist_dir = output/dist
+
+[easy_install]
+# using baidu's official pip source
+index_url = http://pip.baidu.com/root/baidu/+simple/
diff --git a/NLP/UNIMO-2/setup.py b/NLP/UNIMO-2/setup.py
new file mode 100755
index 000000000..c41f875b6
--- /dev/null
+++ b/NLP/UNIMO-2/setup.py
@@ -0,0 +1,18 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+################################################################################
+#
+# Copyright (c) 2022 Baidu.com, Inc. All Rights Reserved
+#
+################################################################################
+"""
+Setup script.
+
+Authors: liwei85(liwei85@baidu.com)
+Date:    2022/05/05 10:57:51
+"""
+
+import setuptools
+
+setuptools.setup()
+
diff --git a/NLP/UNIMO-2/src/__init__.py b/NLP/UNIMO-2/src/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/NLP/UNIMO-2/src/__main__.py b/NLP/UNIMO-2/src/__main__.py
new file mode 100644
index 000000000..56c355d4f
--- /dev/null
+++ b/NLP/UNIMO-2/src/__main__.py
@@ -0,0 +1,21 @@
+# -*- coding: UTF-8 -*-
+################################################################################
+#
+# Copyright (c) 2021 Baidu.com, Inc. All Rights Reserved
+#
+################################################################################
+"""
+本文件允许模块包以python -m unimo2方式直接执行。
+
+Authors: liwei85(liwei85@baidu.com)
+Date:    2021/04/15 10:40:40
+"""
+
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import unicode_literals
+
+
+import sys
+from unimo2.cmdline import main
+sys.exit(main())
diff --git a/NLP/UNIMO-2/src/args/__init__.py b/NLP/UNIMO-2/src/args/__init__.py
new file mode 100644
index 000000000..955e892fb
--- /dev/null
+++ b/NLP/UNIMO-2/src/args/__init__.py
@@ -0,0 +1,12 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2019 Baidu.com, Inc. All Rights Reserved
+#
+
+"""
+File: __init__.py.py
+Author: liwei(liwei85@baidu.com)
+Date: 2020-07-08 15:26
+Desc:
+"""
\ No newline at end of file
diff --git a/NLP/UNIMO-2/src/args/classification_grounded_args.py b/NLP/UNIMO-2/src/args/classification_grounded_args.py
new file mode 100644
index 000000000..5bdc5e4b5
--- /dev/null
+++ b/NLP/UNIMO-2/src/args/classification_grounded_args.py
@@ -0,0 +1,130 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: classification_grounded_args.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-09-15 14:23
+Desc: classification arguments
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+from utils.args import ArgumentGroup
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+model_g = ArgumentGroup(parser, "model", "model configuration and paths.")
+model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.")
+model_g.add_arg("init_pretraining_params", str, None,
+                "Init pre-training params which preforms fine-tuning from. If the "
+                "arg 'init_checkpoint' has been set, this argument wouldn't be valid.")
+model_g.add_arg("checkpoints", str, "checkpoints", "Path to save checkpoints.")
+model_g.add_arg("save_checkpoints", bool, True, "Whether to save checkpoints")
+model_g.add_arg("weight_sharing", bool, True, "If set, share weights between word embedding and masked lm.")
+model_g.add_arg("roberta_vocab_file", str, './roberta_config/vocab.txt', "roberta vocab")
+model_g.add_arg("encoder_json_file", str, './roberta_config/encoder.json', 'bpt map')
+model_g.add_arg("vocab_bpe_file", str, './roberta_config/vocab.bpe', "vocab bpe")
+model_g.add_arg("vl_config_path", str, "./roberta_config/roberta_config.json",
+                "The file to save roberta configuration.")
+
+train_g = ArgumentGroup(parser, "training", "training options.")
+train_g.add_arg("epoch", int, 3, "Number of epoches for fine-tuning.")
+train_g.add_arg("learning_rate", float, 5e-5, "Learning rate used to train with warmup.")
+train_g.add_arg("lr_scheduler", str, "linear_warmup_decay",
+                "scheduler of learning rate.", choices=['linear_warmup_decay', 'noam_decay'])
+train_g.add_arg("weight_decay", float, 0.01, "Weight decay rate for L2 regularizer.")
+train_g.add_arg("warmup_proportion", float, 0.1,
+                "Proportion of training steps to perform linear learning rate warmup for.")
+train_g.add_arg("save_steps", int, 10000, "The steps interval to save checkpoints.")
+train_g.add_arg("validation_steps", int, 1000, "The steps interval to evaluate model performance.")
+train_g.add_arg("nccl_comm_num", int, 1, "NCCL comm num.")
+train_g.add_arg("hierarchical_allreduce_inter_nranks", int, 8, "Hierarchical allreduce inter ranks.")
+train_g.add_arg("use_hierarchical_allreduce", bool, False, "Use hierarchical allreduce or not.")
+train_g.add_arg("use_fp16", bool, False, "Whether to use fp16 mixed precision training.")
+train_g.add_arg("use_dynamic_loss_scaling", bool, False, "Whether to use dynamic loss scaling.")
+train_g.add_arg("init_loss_scaling", float, 1.0,
+                "Loss scaling factor for mixed precision training, only valid when use_fp16 is enabled.")
+train_g.add_arg("incr_every_n_steps", int, 100, "Increases loss scaling every n consecutive.")
+train_g.add_arg("decr_every_n_nan_or_inf", int, 2,
+                "Decreases loss scaling every n accumulated steps with nan or inf gradients.")
+train_g.add_arg("use_modal_tag", bool, False, "Whether to use modal tag.")
+train_g.add_arg("incr_ratio", float, 2.0,
+                "The multiplier to use when increasing the loss scaling.")
+train_g.add_arg("decr_ratio", float, 0.8,
+                "The less-than-one-multiplier to use when decreasing.")
+# args for adam optimizer
+train_g.add_arg("beta1", float, 0.9, "beta1 for adam")
+train_g.add_arg("beta2", float, 0.98, "beta2 for adam.")
+train_g.add_arg("epsilon", float, 1e-06, "epsilon for adam.")
+
+log_g = ArgumentGroup(parser, "logging", "logging related.")
+log_g.add_arg("skip_steps", int, 10, "The steps interval to print loss.")
+log_g.add_arg("verbose", bool, False, "Whether to output verbose log.")
+
+data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options")
+data_g.add_arg("train_set", str, None, "Path to training data.")
+data_g.add_arg("test_set", str, None, "Path to test data.")
+data_g.add_arg("test_hard_set", str, None, "Path to test_hard data.")
+data_g.add_arg("dev_set", str, None, "Path to validation data.")
+data_g.add_arg("dev_hard_set", str, None, "Path to validation_hard data.")
+data_g.add_arg("diagnostic_set", str, None, "Path to diagnostic data.")
+data_g.add_arg("max_seq_len", int, 512, "Number of words of the longest seqence.")
+data_g.add_arg("batch_size", int, 32, "Total examples' number in batch for training. see also --in_tokens.")
+data_g.add_arg("in_tokens", bool, False,
+               "If set, the batch size will be the maximum number of tokens in one batch. "
+               "Otherwise, it will be the maximum number of examples in one batch.")
+data_g.add_arg("do_lower_case", bool, True,
+               "Whether to lower case the input text. Should be True for uncased models and False for cased models.")
+data_g.add_arg("random_seed", int, 0, "Random seed.")
+data_g.add_arg("num_labels", int, 2, "label number")
+
+data_g.add_arg("max_query_length", int, 64, "Max query length.")
+data_g.add_arg("max_answer_length", int, 100, "Max answer length.")
+
+run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
+run_type_g.add_arg("visualdl_log", bool, False, "If set, use visualdl_log on paddlecloud.")
+run_type_g.add_arg("use_cuda", bool, True, "If set, use GPU for training.")
+run_type_g.add_arg("is_distributed", bool, False, "If set, then start distributed training.")
+run_type_g.add_arg("use_fast_executor", bool, False, "If set, use fast parallel executor (in experiment).")
+run_type_g.add_arg("num_iteration_per_drop_scope", int, 10, "Iteration intervals to drop scope.")
+run_type_g.add_arg("do_train", bool, False, "Whether to perform training.")
+run_type_g.add_arg("do_val", bool, False, "Whether to perform evaluation on dev data set.")
+run_type_g.add_arg("do_val_hard", bool, False, "Whether to perform evaluation on dev hard data set.")
+run_type_g.add_arg("do_test", bool, False, "Whether to perform evaluation on test data set.")
+run_type_g.add_arg("do_test_hard", bool, False, "Whether to perform evaluation on test hard data set.")
+run_type_g.add_arg("do_pred", bool, False, "Whether to predict on test data set.")
+run_type_g.add_arg("do_pred_hard", bool, False, "Whether to predict on test hard data set.")
+run_type_g.add_arg("do_diagnostic", bool, False, "Whether to predict on diagnostic data set.")
+run_type_g.add_arg("pred_save", str, "./output/predict/test", "Whether to predict on test data set.")
+run_type_g.add_arg("use_multi_gpu_test", bool, False, "Whether to perform evaluation using multiple gpu cards")
+run_type_g.add_arg("eval_mertrics", str, "simple_accuracy", "eval_mertrics")
+run_type_g.add_arg("base_mnli", bool, False, "must be set True when train STS-B/MRPC/RTE model")
+
+run_type_g.add_arg("num_codebook", int, 2048, "size of image codebook")
+run_type_g.add_arg("model_type", str, "grounded",
+                   "type of base model", choices=['grounded', 'baseline'])
+run_type_g.add_arg("grounding_method", str, "topk",
+                   "type of grounding method", choices=['normal', 'topk', 'optimal'])
+run_type_g.add_arg("topk_value", int, 100, "pamater for topk grounding")
+run_type_g.add_arg("with_grounding_projection", bool, False, "Whether to use linear projection before cmcl")
+model_g.add_arg("with_grounding_pos", bool, False, "Whether to use pos_emb for grounding tokens")
+model_g.add_arg("text_enc_layers", str, '0,1,2,3,4,5', "text encoder layers")
+model_g.add_arg("grounding_enc_layers", str, '6,7,8,9,10,11', "grounding encoder layers")
+# yapf: enable
diff --git a/NLP/UNIMO-2/src/args/img2txt_oscar_args.py b/NLP/UNIMO-2/src/args/img2txt_oscar_args.py
new file mode 100644
index 000000000..a180691f5
--- /dev/null
+++ b/NLP/UNIMO-2/src/args/img2txt_oscar_args.py
@@ -0,0 +1,146 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: img2txt_oscar_args.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-10-25 16:08
+Desc: arguments for img2txt
+"""
+
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import time
+import argparse
+from utils.args import ArgumentGroup
+
+
+class CustomAction(argparse.Action):
+    def __call__(self, parser, namespace, values, option_string=None):
+        setattr(namespace, self.dest, " ".join(values))
+
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+model_g = ArgumentGroup(parser, "model", "model configuration and paths.")
+model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.")
+model_g.add_arg("init_pretraining_params", str, None,
+                "Init pre-training params which preforms fine-tuning from. If the "
+                "arg 'init_checkpoint' has been set, this argument wouldn't be valid.")
+model_g.add_arg("checkpoints", str, "checkpoints", "Path to save checkpoints.")
+model_g.add_arg("weight_sharing", bool, True, "If set, share weights between word embedding and masked lm.")
+model_g.add_arg("roberta_vocab_file", str, './model_files/dict/roberta_base_en.vocab.txt', "roberta vocab")
+model_g.add_arg("encoder_json_file", str, './model_files/dict/roberta_base_en.encoder.json', 'bpt map')
+model_g.add_arg("vocab_bpe_file", str, './model_files/dict/roberta_base_en.vocab.bpe', "vocab bpe")
+model_g.add_arg("vl_config_path", str, "./model_files/config/vit_base_en.json",
+                "The file to save roberta configuration.")
+
+train_g = ArgumentGroup(parser, "training", "training options.")
+train_g.add_arg("epoch", int, 50, "Number of epoches for fine-tuning.")
+train_g.add_arg("learning_rate", float, 4e-5, "Learning rate used to train with warmup.")
+train_g.add_arg("lr_scheduler", str, "linear_warmup_decay",
+                "scheduler of learning rate.", choices=['linear_warmup_decay', 'noam_decay'])
+train_g.add_arg("weight_decay", float, 0.01, "Weight decay rate for L2 regularizer.")
+train_g.add_arg("warmup_proportion", float, 0.02,
+                "Proportion of training steps to perform linear learning rate warmup for.")
+train_g.add_arg("save_steps", int, 100000, "The steps interval to save checkpoints.")
+train_g.add_arg("validation_steps", int, 100000, "The steps interval to evaluate model performance.")
+train_g.add_arg("use_fuse", bool, False, "Whether to use fuse_allreduce_ops.")
+train_g.add_arg("nccl_comm_num", int, 1, "NCCL comm num.")
+train_g.add_arg("hierarchical_allreduce_inter_nranks", int, 8, "Hierarchical allreduce inter ranks.")
+train_g.add_arg("use_hierarchical_allreduce", bool, False, "Use hierarchical allreduce or not.")
+train_g.add_arg("use_fp16", bool, False, "Whether to use fp16 mixed precision training.")
+train_g.add_arg("use_dynamic_loss_scaling", bool, False, "Whether to use dynamic loss scaling.")
+train_g.add_arg("init_loss_scaling", float, 128.0,
+                "Loss scaling factor for mixed precision training, only valid when use_fp16 is enabled.")
+train_g.add_arg("incr_every_n_steps", int, 100, "Increases loss scaling every n consecutive.")
+train_g.add_arg("decr_every_n_nan_or_inf", int, 2,
+                "Decreases loss scaling every n accumulated steps with nan or inf gradients.")
+train_g.add_arg("incr_ratio", float, 2.0,
+                "The multiplier to use when increasing the loss scaling.")
+train_g.add_arg("decr_ratio", float, 0.8,
+                "The less-than-one-multiplier to use when decreasing.")
+# args for adam optimizer
+train_g.add_arg("beta1", float, 0.9, "beta1 for adam")
+train_g.add_arg("beta2", float, 0.98, "beta2 for adam.")
+train_g.add_arg("epsilon", float, 1e-06, "epsilon for adam.")
+
+train_g.add_arg("tgt_type_id", int, 1, "for seq2seq task.")
+train_g.add_arg("do_decode", bool, False, "for seq2seq task.")
+train_g.add_arg("label_smooth", float, 0.1, "label smooth")
+train_g.add_arg("hidden_dropout_prob", float, 0.1, "hidden_dropout_prob")
+train_g.add_arg("attention_probs_dropout_prob", float, 0.1, "attention_probs_dropout_prob")
+
+log_g = ArgumentGroup(parser, "logging", "logging related.")
+log_g.add_arg("skip_steps", int, 100, "The steps interval to print loss.")
+log_g.add_arg("verbose", bool, True, "Whether to output verbose log.")
+
+data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options")
+data_g.add_arg("task_type", str, "normal", "is task type")
+data_g.add_arg("train_filelist", str, None, "Path to training data.")
+data_g.add_arg("test_filelist", str, None, "Path to test data.")
+data_g.add_arg("valid_filelist", str, None, "Path to validation data.")
+data_g.add_arg("max_seq_len", int, 512, "Number of words of the longest seqence.")
+data_g.add_arg("max_tgt_len", int, 512, "for seq2seq task.")
+data_g.add_arg("max_out_len", int, 512, "for seq2seq task.")
+data_g.add_arg("min_out_len", int, 20, "for seq2seq task.")
+data_g.add_arg("block_trigram", bool, True, "utilize trigram blocking during beam search")
+data_g.add_arg("beam_size", int, 5, "for seq2seq task.")
+data_g.add_arg("batch_size", int, 32, "Total examples' number in batch for training.")
+data_g.add_arg("pred_batch_size", int, 0, "Total examples' number in batch for training.")
+data_g.add_arg("do_lower_case", bool, True,
+               "Whether to lower case the input text. Should be True for uncased models and False for cased models.")
+data_g.add_arg("length_penalty", float, 0.6, "length_penalty")
+
+run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
+run_type_g.add_arg("use_cuda", bool, True, "If set, use GPU for training.")
+run_type_g.add_arg("visualdl_log", bool, False, "If set, use visualdl_log on paddlecloud.")
+run_type_g.add_arg("is_distributed", bool, True, "If set, then start distributed training.")
+run_type_g.add_arg("use_fast_executor", bool, True, "If set, use fast parallel executor (in experiment).")
+run_type_g.add_arg("num_iteration_per_drop_scope", int, 1, "Iteration intervals to drop scope.")
+run_type_g.add_arg("do_train", bool, True, "Whether to perform training.")
+run_type_g.add_arg("do_val", bool, True, "Whether to perform evaluation on dev data set.")
+run_type_g.add_arg("do_test", bool, True, "Whether to perform evaluation on test data set.")
+run_type_g.add_arg("do_pred", bool, True, "Whether to perform evaluation on pred data set.")
+run_type_g.add_arg("use_multi_gpu_test", bool, True, "Whether to perform evaluation using multiple gpu cards")
+run_type_g.add_arg("save_and_valid_by_epoch", bool, False, "save_and_valid_by_epoch")
+run_type_g.add_arg("eval_script", action=CustomAction, type=str, nargs='+', help="eval_script", default=None)
+run_type_g.add_arg("eval_mertrics", str, "", "eval_mertrics")
+run_type_g.add_arg("random_seed", int, 0, "Random seed.")
+
+# new add args
+image_g = ArgumentGroup(parser, "image", "image configuration options")
+image_g.add_arg("resolution", int, 16, "used for VIT model")
+image_g.add_arg("image_size", int, 224, "image size")
+image_g.add_arg("num_codebook", int, 2048, "size of image codebook")
+image_g.add_arg("model_type", str, "grounded",
+                "type of base model", choices=['grounded', 'baseline'])
+image_g.add_arg("grounding_method", str, "topk",
+                "type of grounding method", choices=['normal', 'topk', 'optimal'])
+image_g.add_arg("topk_value", int, 100, "pamater for topk grounding")
+image_g.add_arg("with_grounding_projection", bool, False, "Whether to use linear projection before cmcl")
+image_g.add_arg("with_grounding_pos", bool, False, "Whether to use pos_emb for grounding tokens")
+model_g.add_arg("text_enc_layers", str, '0,1,2,3,4,5', "text encoder layers")
+model_g.add_arg("grounding_enc_layers", str, '6,7,8,9,10,11', "grounding encoder layers")
+image_g.add_arg("use_recompute", bool, False, "Whether to use use_recompute")
+
+image_g.add_arg("max_obj_len", int, 100, "max num of object size.")
+model_g.add_arg("object_file", str, "./data/coco_object_0.35_tot.ids", "The object file for image bounding boxes.")
+# yapf: enable
diff --git a/NLP/UNIMO-2/src/args/retrieval_grounded_args.py b/NLP/UNIMO-2/src/args/retrieval_grounded_args.py
new file mode 100644
index 000000000..37c4c548b
--- /dev/null
+++ b/NLP/UNIMO-2/src/args/retrieval_grounded_args.py
@@ -0,0 +1,134 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: retrieval_grounded_args.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-08-23 14:28
+Desc: args for retrieval task
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+from utils.args import ArgumentGroup
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+model_g = ArgumentGroup(parser, "model", "model configuration and paths.")
+model_g.add_arg("run_random", bool, False, "run model with random params")
+model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.")
+model_g.add_arg("init_pretraining_params", str, None,
+                "Init pre-training params which preforms fine-tuning from. If the "
+                "arg 'init_checkpoint' has been set, this argument wouldn't be valid.")
+model_g.add_arg("checkpoints", str, "checkpoints", "Path to save checkpoints.")
+model_g.add_arg("save_checkpoints", bool, True, "Whether to save checkpoints")
+model_g.add_arg("weight_sharing", bool, True, "If set, share weights between word embedding and masked lm.")
+model_g.add_arg("unimo_vocab_file", str, './model_files/dict/unimo_en.vocab.txt', "unimo vocab")
+model_g.add_arg("encoder_json_file", str, './model_files/dict/unimo_en.encoder.json', 'bpt map')
+model_g.add_arg("vocab_bpe_file", str, './model_files/dict/unimo_en.vocab.bpe', "vocab bpe")
+model_g.add_arg("unimo_config_path", str, "./model_files/config/unimo_base_en.json",
+                "The file to save unimo configuration.")
+train_g = ArgumentGroup(parser, "training", "training options.")
+train_g.add_arg("epoch", int, 3, "Number of epoches for fine-tuning.")
+train_g.add_arg("learning_rate", float, 5e-5, "Learning rate used to train.")
+train_g.add_arg("learning_rate_scale", float, 0.1, "Learning rate decay scale.")
+train_g.add_arg("lr_scheduler", str, "scale_by_epoch_decay",
+                "scheduler of learning rate.", choices=['linear_warmup_decay', 'noam_decay', 'scale_by_epoch_decay'])
+train_g.add_arg("learning_rate_decay_epoch1", int, 24, "Learning rate decay epoch1.")
+train_g.add_arg("learning_rate_decay_epoch2", int, 32, "Learning rate decay epoch2.")
+train_g.add_arg("weight_decay", float, 0.01, "Weight decay rate for L2 regularizer.")
+train_g.add_arg("warmup_step", int, 1, "warmup_step, 1 for scale_by_epoch_decay, 0 for others")
+
+train_g.add_arg("save_steps", int, 10000, "The steps interval to save checkpoints.")
+train_g.add_arg("validation_steps", int, 1000, "The steps interval to evaluate model performance.")
+train_g.add_arg("nccl_comm_num", int, 1, "NCCL comm num.")
+train_g.add_arg("hierarchical_allreduce_inter_nranks", int, 8, "Hierarchical allreduce inter ranks.")
+train_g.add_arg("use_hierarchical_allreduce", bool, False, "Use hierarchical allreduce or not.")
+train_g.add_arg("use_fp16", bool, False, "Whether to use fp16 mixed precision training.")
+train_g.add_arg("use_dynamic_loss_scaling", bool, False, "Whether to use dynamic loss scaling.")
+train_g.add_arg("use_sigmoid", bool, True, "Whether to use sigmoid before loss")
+train_g.add_arg("init_loss_scaling", float, 1.0,
+                "Loss scaling factor for mixed precision training, only valid when use_fp16 is enabled.")
+train_g.add_arg("incr_every_n_steps", int, 100, "Increases loss scaling every n consecutive.")
+train_g.add_arg("decr_every_n_nan_or_inf", int, 2,
+                "Decreases loss scaling every n accumulated steps with nan or inf gradients.")
+train_g.add_arg("incr_ratio", float, 2.0,
+                "The multiplier to use when increasing the loss scaling.")
+train_g.add_arg("decr_ratio", float, 0.8,
+                "The less-than-one-multiplier to use when decreasing.")
+
+# args for adam optimizer
+train_g.add_arg("beta1", float, 0.9, "beta1 for adam")
+train_g.add_arg("beta2", float, 0.98, "beta2 for adam.")
+train_g.add_arg("epsilon", float, 1e-06, "epsilon for adam.")
+train_g.add_arg("use_fuse", bool, False, "Whether to use fuse_allreduce_ops.")
+
+log_g = ArgumentGroup(parser, "logging", "logging related.")
+log_g.add_arg("skip_steps", int, 10, "The steps interval to print loss.")
+log_g.add_arg("verbose", bool, False, "Whether to output verbose log.")
+log_g.add_arg("eval_dir", str, "", "eval_dir to save tmp data")
+
+data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options")
+data_g.add_arg("samples_num", int, 20, "neg sample num.")
+data_g.add_arg("train_image_caption", str, None, "Path to training data.")
+data_g.add_arg("test_image_caption", str, None, "Path to test data.")
+data_g.add_arg("dev_image_caption", str, None, "Path to validation data.")
+data_g.add_arg("max_seq_len", int, 512, "Number of words of the longest seqence.")
+data_g.add_arg("batch_size", int, 32, "Total examples' number in batch for training. see also --in_tokens.")
+data_g.add_arg("test_batch_size", int, 24, "Total examples' number in batch for testing.")
+data_g.add_arg("do_lower_case", bool, True,
+               "Whether to lower case the input text. Should be True for uncased models and False for cased models.")
+data_g.add_arg("random_seed", int, 0, "Random seed.")
+data_g.add_arg("max_img_len", int, 37, "Image feature size==2048.")
+data_g.add_arg("scale_circle", float, "1.0", "The scale factor in circle loss function, only use in circle loss mode")
+data_g.add_arg("margin", float, "0.2", "The margin value in loss function")
+data_g.add_arg("max_neg_cap_num", int, 0, "max_neg_cap_num")
+
+run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
+run_type_g.add_arg("use_cuda", bool, True, "If set, use GPU for training.")
+run_type_g.add_arg("is_distributed", bool, False, "If set, then start distributed training.")
+run_type_g.add_arg("use_fast_executor", bool, False, "If set, use fast parallel executor (in experiment).")
+run_type_g.add_arg("num_iteration_per_drop_scope", int, 10, "Iteration intervals to drop scope.")
+run_type_g.add_arg("do_train", bool, True, "Whether to perform training.")
+run_type_g.add_arg("do_val", bool, True, "Whether to perform evaluation on dev data set.")
+run_type_g.add_arg("do_test", bool, True, "Whether to perform evaluation on test data set.")
+run_type_g.add_arg("use_multi_gpu_test", bool, False, "Whether to perform evaluation using multiple gpu cards")
+run_type_g.add_arg("eval_mertrics", str, "recall@k", "eval_mertrics")
+run_type_g.add_arg("zero_shot", bool, True, "Whether to do zero shot.")
+
+# new add args
+image_g = ArgumentGroup(parser, "image", "image configuration options")
+image_g.add_arg("resolution", int, 16, "used for VIT model")
+image_g.add_arg("image_size", int, 224, "image size")
+image_g.add_arg("num_codebook", int, 2048, "size of image codebook")
+image_g.add_arg("grounding_method", str, "topk",
+                "type of grounding method", choices=['normal', 'topk', 'optimal'])
+image_g.add_arg("model_type", str, "grounded",
+                "type of base model", choices=['grounded', 'baseline'])
+image_g.add_arg("topk_value", int, 100, "pamater for topk grounding")
+image_g.add_arg("with_cmcl", bool, True, "Whether to use CMCL")
+image_g.add_arg("with_cmcl_projection", bool, True, "Whether to use linear projection for cmcl")
+image_g.add_arg("with_grounding_projection", bool, True, "Whether to use linear projection before cmcl")
+image_g.add_arg("cmcl_share_parameters", bool, True, "Whether to share linear projection for cmcl and img-text matching")
+image_g.add_arg("cmcl_score_weight", float, "0.1", "The weight for cmcl score")
+image_g.add_arg("with_grounding_pos", bool, False, "Whether to use pos_emb for grounding tokens")
+model_g.add_arg("text_enc_layers", str, '0,1,2,3,4,5', "text encoder layers")
+model_g.add_arg("grounding_enc_layers", str, '6,7,8,9,10,11', "grounding encoder layers")
+image_g.add_arg("use_recompute", bool, False, "Whether to use use_recompute")
+# yapf: enable
diff --git a/NLP/UNIMO-2/src/eval/__init__.py b/NLP/UNIMO-2/src/eval/__init__.py
new file mode 100644
index 000000000..bb20007af
--- /dev/null
+++ b/NLP/UNIMO-2/src/eval/__init__.py
@@ -0,0 +1,12 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2019 Baidu.com, Inc. All Rights Reserved
+#
+
+"""
+File: __init__.py.py
+Author: liwei(liwei85@baidu.com)
+Date: 2020-07-13 13:39
+Desc:
+"""
\ No newline at end of file
diff --git a/NLP/UNIMO-2/src/eval/gen_eval.py b/NLP/UNIMO-2/src/eval/gen_eval.py
new file mode 100644
index 000000000..170ea3a3b
--- /dev/null
+++ b/NLP/UNIMO-2/src/eval/gen_eval.py
@@ -0,0 +1,131 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. 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 help and eval functions for text generation."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import json
+import math
+import subprocess
+from model.roberta_tokenization import GptBpeTokenizer, BasicTokenizer
+
+
+class GenerationEval(object):
+    """GenerationEval"""
+
+    def __init__(self, args):
+        self.eval_script = args.eval_script.split(" ")
+        self.eval_mertrics = args.eval_mertrics.split(",") if args.eval_mertrics else []
+        self.basic_tokenizer = BasicTokenizer(do_lower_case=True)
+        self.roberta_tokenizer = GptBpeTokenizer(vocab_file=args.roberta_vocab_file,
+                                                 encoder_json_file=args.encoder_json_file,
+                                                 vocab_bpe_file=args.vocab_bpe_file,
+                                                 do_lower_case=True)
+
+    def eval(self, output_file, phase="", features=None):
+        """run eval"""
+        eval_res = {}
+        if self.eval_script:
+            eval_res = subprocess.check_output(self.eval_script + [output_file, phase])
+            eval_res = json.loads(eval_res)
+        else:
+            preds = []
+            for line in open(output_file):
+                preds.append(self.basic_tokenizer.tokenize(line.strip()))
+
+            refs = []
+            for id in sorted(features.keys()):
+                ref_str = self.roberta_tokenizer.gptbpe_tokenizer.decode(features[id].tgt.split(" "))
+                refs.append([self.basic_tokenizer.tokenize(ref_str)])
+
+            for mertric in self.eval_mertrics:
+                eval_func = getattr(self, mertric, None)
+                if eval_func:
+                    eval_res[mertric] = eval_func(refs, preds)
+
+        ret = []
+        for mertric in self.eval_mertrics:
+            mertric_res = eval_res.get(mertric, None)
+            if mertric_res is None:
+                raise Exception("Eval mertric: %s is not supported" % mertric)
+            ret.append("%s: %f" % (mertric, mertric_res))
+
+        return ", ".join(ret)
+
+    def bleu(self, refs, preds):
+        """bleu mertric"""
+        return _compute_bleu(refs, preds, max_order=4)[0]
+
+
+def _get_ngrams(segment, max_order):
+    ngram_counts = collections.Counter()
+    for order in range(1, max_order + 1):
+        for i in range(0, len(segment) - order + 1):
+            ngram = tuple(segment[i: i + order])
+            ngram_counts[ngram] += 1
+    return ngram_counts
+
+
+def _compute_bleu(reference_corpus, translation_corpus, max_order=4, smooth=False):
+    matches_by_order = [0] * max_order
+    possible_matches_by_order = [0] * max_order
+    reference_length = 0
+    translation_length = 0
+    for (references, translation) in zip(reference_corpus, translation_corpus):
+        reference_length += min(len(r) for r in references)
+        translation_length += len(translation)
+
+        merged_ref_ngram_counts = collections.Counter()
+        for reference in references:
+            merged_ref_ngram_counts |= _get_ngrams(reference, max_order)
+        translation_ngram_counts = _get_ngrams(translation, max_order)
+        overlap = translation_ngram_counts & merged_ref_ngram_counts
+        for ngram in overlap:
+            matches_by_order[len(ngram) - 1] += overlap[ngram]
+        for order in range(1, max_order + 1):
+            possible_matches = len(translation) - order + 1
+            if possible_matches > 0:
+                possible_matches_by_order[order - 1] += possible_matches
+
+    precisions = [0] * max_order
+    for i in range(0, max_order):
+        if smooth:
+            precisions[i] = ((matches_by_order[i] + 1.) /
+                             (possible_matches_by_order[i] + 1.))
+        else:
+            if possible_matches_by_order[i] > 0:
+                precisions[i] = (float(matches_by_order[i]) /
+                                 possible_matches_by_order[i])
+            else:
+                precisions[i] = 0.0
+
+    if min(precisions) > 0:
+        p_log_sum = sum((1. / max_order) * math.log(p) for p in precisions)
+        geo_mean = math.exp(p_log_sum)
+    else:
+        geo_mean = 0
+
+    ratio = float(translation_length) / reference_length
+
+    if ratio > 1.0:
+        bp = 1.
+    else:
+        bp = math.exp(1 - 1. / (ratio + 1e-4))
+
+    bleu = geo_mean * bp
+    ret = [bleu, precisions, bp, ratio, translation_length, reference_length]
+    return ret
diff --git a/NLP/UNIMO-2/src/finetune/__init__.py b/NLP/UNIMO-2/src/finetune/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/NLP/UNIMO-2/src/finetune/classifier_grounded.py b/NLP/UNIMO-2/src/finetune/classifier_grounded.py
new file mode 100644
index 000000000..43ab3c944
--- /dev/null
+++ b/NLP/UNIMO-2/src/finetune/classifier_grounded.py
@@ -0,0 +1,201 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: classifier_grounded.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-09-15 14:22
+Desc: Model for classifier.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import time
+import numpy as np
+
+import paddle
+import paddle.fluid as fluid
+from finetune import glue_eval
+from collections import OrderedDict
+from utils.utils import print_eval_log
+
+from model.unimo_grounded import VlModel as GroundingModel
+from model.unimo_grounded_baseline import VlModel as BaselineModel
+
+
+def create_model(args, pyreader_name, vl_config, is_prediction=False):
+    stype = 'int64'
+    pyreader = fluid.layers.py_reader(
+        capacity=50,
+        shapes=[[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1],
+                [-1, args.max_seq_len, 1], [-1, 1, args.max_seq_len], [-1, 1],
+                [-1, 1]],
+        dtypes=[stype, stype, stype, 'float32', stype, stype],
+        lod_levels=[0, 0, 0, 0, 0, 0],
+        name=pyreader_name,
+        use_double_buffer=True)
+
+    (src_ids, sent_ids, pos_ids, text_mask, labels, qids) = fluid.layers.read_file(pyreader)
+    text_input = {"text.word_embedding": src_ids, "text.sent_embedding": sent_ids, "text.pos_embedding": pos_ids}
+
+    text_enc_layers = [int(i) for i in args.text_enc_layers.split(',')]
+    grounding_enc_layers = [int(i) for i in args.grounding_enc_layers.split(',')]
+
+    if args.model_type == "grounded":
+        model = GroundingModel(text_input=text_input,
+                               text_mask=text_mask,
+                               config=vl_config,
+                               weight_sharing=args.weight_sharing,
+                               grounding_method=args.grounding_method,
+                               topk_value=args.topk_value,
+                               with_grounding_projection=args.with_grounding_projection,
+                               with_grounding_pos=args.with_grounding_pos,
+                               text_enc_layers=text_enc_layers,
+                               grounding_enc_layers=grounding_enc_layers)
+    elif args.model_type == "baseline":
+        model = BaselineModel(text_input=text_input,
+                              text_mask=text_mask,
+                              config=vl_config,
+                              weight_sharing=args.weight_sharing,
+                              text_enc_layers=text_enc_layers,
+                              grounding_enc_layers=grounding_enc_layers)
+    else:
+        raise ValueError("The model_type is invalid!!!")
+
+    cls_feats = model.get_pooled_txt_output()
+    cls_feats = fluid.layers.dropout(
+        x=cls_feats,
+        dropout_prob=0.1,
+        dropout_implementation="upscale_in_train")
+
+    if args.base_mnli:
+        cls_params_name = ["cls_out_new_w", "cls_out_new_b"]
+    else:
+        cls_params_name = ["cls_out_w", "cls_out_b"]
+
+    logits = fluid.layers.fc(
+        input=cls_feats,
+        size=args.num_labels,
+        param_attr=fluid.ParamAttr(
+            name=cls_params_name[0],
+            initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
+        bias_attr=fluid.ParamAttr(
+            name=cls_params_name[1], initializer=fluid.initializer.Constant(0.)))
+
+    if is_prediction:
+        probs = fluid.layers.softmax(logits)
+        feed_targets_name = [
+            src_ids.name, pos_ids.name, sent_ids.name, text_mask.name
+        ]
+        return pyreader, probs, feed_targets_name
+
+    ce_loss, probs = fluid.layers.softmax_with_cross_entropy(
+        logits=logits, label=labels, return_softmax=True)
+
+    loss = fluid.layers.mean(x=ce_loss)
+
+    num_seqs = fluid.layers.create_tensor(dtype='int64')
+    accuracy = fluid.layers.accuracy(input=probs, label=labels, total=num_seqs)
+
+    graph_vars = {
+        "loss": loss,
+        "probs": probs,
+        "accuracy": accuracy,
+        "labels": labels,
+        "num_seqs": num_seqs,
+        "qids": qids
+    }
+
+    # for k, v in graph_vars.items():
+    # v.persistable = True
+
+    return pyreader, graph_vars
+
+
+def predict(exe, test_program, test_pyreader, graph_vars, dev_count=1):
+    test_pyreader.start()
+    qids, scores, probs, preds = [], [], [], []
+    fetch_list = [graph_vars["probs"].name, graph_vars["qids"].name]
+    while True:
+        try:
+            if dev_count == 1:
+                np_probs, np_qids = exe.run(program=test_program, fetch_list=fetch_list)
+            else:
+                np_probs, np_qids = exe.run(fetch_list=fetch_list)
+
+            qids.extend(np_qids.reshape(-1).tolist())
+            np_preds = np.argmax(np_probs, axis=1).astype(np.float32)
+            preds.extend(np_preds)
+            probs.append(np_probs)
+
+        except fluid.core.EOFException:
+            test_pyreader.reset()
+            break
+
+    probs = np.concatenate(probs, axis=0).reshape([len(qids), -1])
+    return qids, preds, probs
+
+
+def evaluate(args, exe, test_program, test_pyreader, graph_vars, eval_phase):
+    total_cost, total_num_seqs = 0.0, 0.0
+    qids, labels, scores, preds = [], [], [], []
+
+    test_pyreader.start()
+    time_begin = time.time()
+
+    fetch_list = [
+        graph_vars["loss"].name,
+        graph_vars["probs"].name, graph_vars["labels"].name,
+        graph_vars["num_seqs"].name, graph_vars["qids"].name
+    ]
+    while True:
+        try:
+            np_loss, np_probs, np_labels, np_num_seqs, np_qids = exe.run(
+                program=test_program, fetch_list=fetch_list) \
+                if not args.use_multi_gpu_test else exe.run(fetch_list=fetch_list)
+            total_cost += np.sum(np_loss * np_num_seqs)
+            total_num_seqs += np.sum(np_num_seqs)
+            labels.extend(np_labels.reshape((-1)).tolist())
+            if np_qids is not None:
+                qids.extend(np_qids.reshape(-1).tolist())
+            scores.extend(np_probs[:, 1].reshape(-1).tolist())
+            np_preds = list(np.argmax(np_probs, axis=1).astype(np.float32))
+            preds.extend([float(val) for val in np_preds])
+        except fluid.core.EOFException:
+            test_pyreader.reset()
+            break
+    time_end = time.time()
+    ret = OrderedDict()
+    ret['phase'] = eval_phase
+    ret['loss'] = round(total_cost / total_num_seqs, 4)
+    ret['data_num'] = total_num_seqs
+    ret['used_time'] = round(time_end - time_begin, 4)
+
+    metrics = OrderedDict()
+    metrics["acc_and_f1"] = glue_eval.acc_and_f1
+    metrics["simple_accuracy"] = glue_eval.simple_accuracy
+    metrics["matthews_corrcoef"] = glue_eval.matthews_corrcoef
+
+    if args.eval_mertrics in metrics:
+        ret_metric = metrics[args.eval_mertrics](preds, labels)
+        ret.update(ret_metric)
+        print_eval_log(ret)
+    else:
+        raise ValueError('unsupported metric {}'.format(args.eval_mertrics))
+
+    return ret
diff --git a/NLP/UNIMO-2/src/finetune/glue_eval.py b/NLP/UNIMO-2/src/finetune/glue_eval.py
new file mode 100644
index 000000000..67998cedd
--- /dev/null
+++ b/NLP/UNIMO-2/src/finetune/glue_eval.py
@@ -0,0 +1,222 @@
+#   Copyright (c) 2019 PaddlePaddle Authors. 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.
+"""ultis help and eval functions for glue ."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import time
+import numpy as np
+
+from scipy.stats import pearsonr, spearmanr
+from six.moves import xrange
+import paddle
+from functools import partial
+from collections import OrderedDict
+
+
+def scale_l2(x, norm_length):
+    """
+    # copy lines from here https://github.com/tensorflow/models/blob/master/research/adversarial_text/adversarial_losses.py#L190
+    # shape(x) = (batch, num_timesteps, d)
+    # Divide x by max(abs(x)) for a numerically stable L2 norm.
+    # 2norm(x) = a * 2norm(x/a)
+    # Scale over the full sequence, dims (1, 2)
+    """
+
+    alpha = paddle.max(x=paddle.abs(x), axis=1, keepdim=True) + 1e-12
+    l2_norm = alpha * paddle.sqrt(
+        paddle.sum(x=paddle.pow(x=x / alpha), axis=1, keepdim=True) + 1e-6)
+    x_unit = x / l2_norm
+    return norm_length * x_unit
+
+
+def pgd_loss(ernie, labels, loss, task_fc_fn, epsilon=0.25):
+    """ refer code from
+    https://github.com/tensorflow/models/blob/master/research/adversarial_text/adversarial_losses.py#L145
+    but we didn't use the vat loss for now
+    """
+
+    # TODO any difference with fleet_main_program or ParallelProgram or TrainProgram?
+    program = paddle.static.default_main_program()
+
+    param_grads = paddle.static.append_backward(loss, parameter_list=[ernie._word_emb_name])
+
+    # in the VAT paper code, the d is draw from a norm distribution, what's the advantage? why not use the
+    # gradient of the emb directly?
+    # d = fluid.layers.random_normal(shape=emb.shape)
+    d = filter(lambda p: p[0].name == ernie._word_emb_name, param_grads)[0][1]
+    emb = program.block(0).var(ernie._word_emb_name)
+
+    # for _ in range(args.K_iteration):
+    K_iteration = 8
+    small_constant_for_finite_diff = 1e-5
+    emb_hat = emb
+
+    d = paddle.fluid.layers.gaussian_random(emb.shape)
+
+    # it seems it can be implemented by the while loop
+    for _ in range(K_iteration):
+        # d = xi * utils_tf.l2_batch_normalize(d)
+        d = scale_l2(d, small_constant_for_finite_diff)
+        # logits_d = model.get_logits(x + d)
+        # kl = utils_tf.kl_with_logits(logits, logits_d)
+
+        emb_hat = emb_hat + d
+        ernie._build_model(emb=emb_hat)
+        graph_vars = task_fc_fn(ernie, labels)
+
+        gradient = filter(lambda p: p[0].name == ernie._word_emb_name, param_grads)[0][1]
+        gradient.stop_gradient = True
+        d = gradient
+        # Hd = tf.gradients(kl, d)[0]
+        # d = tf.stop_gradient(Hd)
+
+    d = scale_l2(d, small_constant_for_finite_diff)
+    emb_hat = emb_hat + d
+    ernie._build_model(emb=emb_hat)
+    graph_vars = task_fc_fn(ernie, labels)
+
+    return graph_vars['loss']
+
+
+def matthews_corrcoef(preds, labels):
+    """matthews_corrcoef"""
+    preds = np.array(preds)
+    labels = np.array(labels)
+    tp = np.sum((labels == 1) & (preds == 1))
+    tn = np.sum((labels == 0) & (preds == 0))
+    fp = np.sum((labels == 0) & (preds == 1))
+    fn = np.sum((labels == 1) & (preds == 0))
+
+    mcc = ((tp * tn) - (fp * fn)) / np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
+    ret = OrderedDict()
+    ret['mat_cor'] = mcc
+    ret['key_eval'] = "mat_cor"
+    return ret
+
+
+def f1_score(preds, labels):
+    """f1_score"""
+    preds = np.array(preds)
+    labels = np.array(labels)
+
+    tp = np.sum((labels == 1) & (preds == 1))
+    tn = np.sum((labels == 0) & (preds == 0))
+    fp = np.sum((labels == 0) & (preds == 1))
+    fn = np.sum((labels == 1) & (preds == 0))
+    p = tp / (tp + fp)
+    r = tp / (tp + fn)
+    f1 = (2 * p * r) / (p + r + 1e-8)
+    ret = OrderedDict()
+    ret['f1'] = f1
+    ret['key_eval'] = "f1"
+    return ret
+
+
+def pearson_and_spearman(preds, labels):
+    """pearson_and_spearman"""
+    preds = np.array(preds)
+    labels = np.array(labels)
+
+    pearson_corr = pearsonr(preds, labels)[0]
+    spearman_corr = spearmanr(preds, labels)[0]
+    ret = OrderedDict()
+    ret['pearson'] = pearson_corr
+    ret['spearmanr'] = spearman_corr
+    ret['p_and_sp'] = (pearson_corr + spearman_corr) / 2
+    ret['key_eval'] = "p_and_sp"
+    return ret
+
+
+def acc_and_f1(preds, labels):
+    """acc_and_f1"""
+    preds = np.array(preds)
+    labels = np.array(labels)
+
+    acc = simple_accuracy(preds, labels)['acc']
+    f1 = f1_score(preds, labels)['f1']
+
+    ret = OrderedDict()
+    ret['acc'] = acc
+    ret['f1'] = f1
+    ret['acc_and_f1'] = (acc + f1) / 2
+    ret['key_eval'] = "acc_and_f1"
+    return ret
+
+
+def simple_accuracy(preds, labels):
+    """simple_accuracy"""
+    preds = np.array(preds)
+    labels = np.array(labels)
+    acc = (preds == labels).mean()
+    ret = OrderedDict()
+    ret['acc'] = acc
+    ret['key_eval'] = "acc"
+    return ret
+
+
+def evaluate_mrr(preds):
+    """evaluate_mrr"""
+    last_qid = None
+    total_mrr = 0.0
+    qnum = 0.0
+    rank = 0.0
+    correct = False
+    for qid, score, label in preds:
+        if qid != last_qid:
+            rank = 0.0
+            qnum += 1
+            correct = False
+            last_qid = qid
+
+        rank += 1
+        if not correct and label != 0:
+            total_mrr += 1.0 / rank
+            correct = True
+
+    return total_mrr / qnum
+
+
+def evaluate_map(preds):
+    """evaluate_map"""
+
+    def singe_map(st, en):
+        """singe_map"""
+        total_p = 0.0
+        correct_num = 0.0
+        for index in xrange(st, en):
+            if int(preds[index][2]) != 0:
+                correct_num += 1
+                total_p += correct_num / (index - st + 1)
+        if int(correct_num) == 0:
+            return 0.0
+        return total_p / correct_num
+
+    last_qid = None
+    total_map = 0.0
+    qnum = 0.0
+    st = 0
+    for i in xrange(len(preds)):
+        qid = preds[i][0]
+        if qid != last_qid:
+            qnum += 1
+            if last_qid is not None:
+                total_map += singe_map(st, i)
+            st = i
+            last_qid = qid
+
+    total_map += singe_map(st, len(preds))
+    return total_map / qnum
diff --git a/NLP/UNIMO-2/src/finetune/img2txt_oscar.py b/NLP/UNIMO-2/src/finetune/img2txt_oscar.py
new file mode 100644
index 000000000..71f515de3
--- /dev/null
+++ b/NLP/UNIMO-2/src/finetune/img2txt_oscar.py
@@ -0,0 +1,1690 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: img2txt_oscar.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-10-25 16:06
+Desc: img to text generation
+"""
+
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import re
+import time
+import numpy as np
+import glob
+import json
+import codecs
+
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+from eval.gen_eval import GenerationEval
+from finetune.trigram_blocking import TrigramBlocking
+
+from model.transformer_encoder import encoder as grounded_encoder
+from model.transformer_encoder import pre_process_layer as grounded_pre_process_layer
+from model.transformer_encoder import encoder as text_encoder
+from model.transformer_encoder import pre_process_layer as text_pre_process_layer
+from model.vision_transformer_encoder import encoder as vit_encoder
+from model.vision_transformer_encoder import pre_process_layer as vit_pre_process_layer
+from utils.pos_emb_interpolate import interpolate_pos_embed
+
+
+class Img2Txt(object):
+    def __init__(self, args, vl_config, tokenizer):
+        self.vl_config = vl_config
+        self.weight_sharing = args.weight_sharing
+        self.max_seq_len = args.max_seq_len
+        self.max_obj_len = args.max_obj_len
+        self.label_smooth = args.label_smooth
+        self.tgt_type_id = args.tgt_type_id
+        self.tokenizer = tokenizer
+        self.vocab_size = vl_config["text_vocab_size"]
+        self._emb_dtype = "float32"
+
+        # for beam_search decoding
+        self.do_decode = args.do_decode
+        self.length_penalty = args.length_penalty
+        self.max_out_len = args.max_out_len
+        self.min_out_len = args.min_out_len
+        self.block_trigram = args.block_trigram
+        self.beam_size = args.beam_size
+
+        self.patch_seq_len = self.vl_config['image_size'] * self.vl_config['image_size'] // \
+                             (self.vl_config['resolution'] * self.vl_config['resolution'])
+        # directly utilize Conv2d to extract path and linear transforming
+        self.patch_emb_size = self.vl_config['resolution'] * self.vl_config['resolution'] * 3
+
+        self.bos_id = tokenizer.cls_token_id
+        self.eos_id = tokenizer.sep_token_id
+        self.evaluator = GenerationEval(args)
+        self.task_type = "img2txt"
+
+        self.model_type = args.model_type
+        self.grounding_method = args.grounding_method
+        self.topk_value = args.topk_value
+        self.with_grounding_projection = args.with_grounding_projection
+        self.with_grounding_pos = args.with_grounding_pos
+
+        self.text_enc_layers = [int(i) for i in args.text_enc_layers.split(',')]
+        self.grounding_enc_layers = [int(i) for i in args.grounding_enc_layers.split(',')]
+
+    def cal_logit(self, enc_out, tgt_pos):
+        enc_out = fluid.layers.reshape(x=enc_out,
+                                       shape=[-1, self.vl_config["hidden_size"]])
+        if tgt_pos:
+            tgt_pos = fluid.layers.cast(x=tgt_pos, dtype='int32')
+            tgt_feat = fluid.layers.gather(input=enc_out, index=tgt_pos)
+        else:
+            tgt_feat = enc_out
+
+        tgt_trans_feat = fluid.layers.fc(
+            input=tgt_feat,
+            size=self.vl_config["hidden_size"],
+            act=self.vl_config["hidden_act"],
+            param_attr=fluid.ParamAttr(
+                name="grounded.mask_lm_trans_fc.w_0",
+                initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
+            bias_attr=fluid.ParamAttr(
+                name="grounded.mask_lm_trans_fc.b_0",
+                initializer=fluid.initializer.Constant(0.)))
+
+        tgt_trans_feat = fluid.layers.layer_norm(
+            tgt_trans_feat,
+            begin_norm_axis=len(tgt_trans_feat.shape) - 1,
+            param_attr=fluid.ParamAttr(
+                name='grounded.mask_lm_trans_layer_norm_scale',
+                initializer=fluid.initializer.Constant(1.)),
+            bias_attr=fluid.ParamAttr(
+                name='grounded.mask_lm_trans_layer_norm_bias',
+                initializer=fluid.initializer.Constant(1.)))
+
+        seq2seq_out_bias_attr = fluid.ParamAttr(
+            name="grounded.mask_lm_out_fc.b_0",
+            initializer=fluid.initializer.Constant(value=0.0))
+
+        if self.weight_sharing:
+            fc_out = fluid.layers.matmul(
+                x=tgt_trans_feat,
+                y=fluid.default_main_program().global_block().var(
+                    "text.word_embedding"),
+                transpose_y=True)
+            fc_out += fluid.layers.create_parameter(
+                shape=[self.vl_config['text_vocab_size']],
+                dtype="float32",
+                attr=seq2seq_out_bias_attr,
+                is_bias=True)
+        else:
+            out_size = self.vl_config['text_vocab_size']
+            fc_out = fluid.layers.fc(input=tgt_trans_feat,
+                                     size=out_size,
+                                     param_attr=fluid.ParamAttr(
+                                         name="grounded.mask_lm_out_fc.w_0",
+                                         initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
+                                     bias_attr=seq2seq_out_bias_attr)
+
+        return fc_out
+
+    def to_tensor(self, shapes, dtypes, lod_levels):
+        return [fluid.layers.data(name="placeholder_" + str(i), shape=shapes[i], dtype=dtypes[i],
+                                  lod_level=lod_levels[i]) for i in range(len(shapes))]
+
+    def create_model(self, decoding=False):
+        """create model"""
+        if decoding:
+            return self.fast_decode()
+
+        img_input_shapes = [[-1, self.vl_config['image_size'], self.vl_config['image_size'], 3],  # image_pixel_input
+                            [-1, 1, self.patch_seq_len + 1]]  # image_mask
+        img_input_dtypes = ['float32', 'float32']
+        img_input_lod_levels = [0, 0]
+
+        emb_num = 3
+        text_input_shapes = [[-1, self.max_seq_len, 1]] * emb_num + \
+                            [[-1, self.max_seq_len, self.max_seq_len], [-1, 1], [-1, 1]]
+        text_input_dtypes = ['int64'] * emb_num + ['float32', 'int64', 'int64']
+        text_input_lod_levels = [0] * emb_num + [0, 0, 0]
+
+        shapes = img_input_shapes + text_input_shapes
+        dtypes = img_input_dtypes + text_input_dtypes
+        lod_levels = img_input_lod_levels + text_input_lod_levels
+
+        inputs = self.to_tensor(shapes, dtypes, lod_levels)
+        pyreader = fluid.io.DataLoader.from_generator(feed_list=inputs, capacity=70, iterable=False)
+
+        image_input = {}
+        text_input = {}
+        image_input["pixel_embedding"], image_mask, text_input["text.word_embedding"], \
+        text_input["text.sent_embedding"], text_input["text.pos_embedding"], text_mask, tgt_labels, tgt_pos = inputs
+
+        if self.model_type == "grounded":
+            model = GroundingModelForImg2Txt(text_input=text_input,
+                                             text_mask=text_mask,
+                                             image_input=image_input,
+                                             image_mask=image_mask,
+                                             config=self.vl_config,
+                                             weight_sharing=self.weight_sharing,
+                                             grounding_method=self.grounding_method,
+                                             topk_value=self.topk_value,
+                                             with_grounding_projection=self.with_grounding_projection,
+                                             with_grounding_pos=self.with_grounding_pos,
+                                             text_enc_layers=self.text_enc_layers,
+                                             grounding_enc_layers=self.grounding_enc_layers)
+        elif self.model_type == "baseline":
+            model = BaselineForImg2Txt(text_input=text_input,
+                                       text_mask=text_mask,
+                                       image_input=image_input,
+                                       image_mask=image_mask,
+                                       config=self.vl_config,
+                                       weight_sharing=self.weight_sharing,
+                                       text_enc_layers=self.text_enc_layers,
+                                       grounding_enc_layers=self.grounding_enc_layers)
+        else:
+            raise ValueError("The model_type is invalid!!!")
+
+        enc_out = model.get_text_sequence_output()
+        fc_out = self.cal_logit(enc_out, tgt_pos)
+
+        if self.label_smooth:
+            out_size = self.vl_config['text_vocab_size']
+            labels = fluid.layers.label_smooth(
+                label=fluid.layers.one_hot(
+                    input=tgt_labels, depth=out_size),
+                epsilon=self.label_smooth)
+
+            ce_loss = layers.softmax_with_cross_entropy(
+                logits=fc_out, label=labels, soft_label=True)
+        else:
+            ce_loss, probs = fluid.layers.softmax_with_cross_entropy(
+                logits=fc_out, label=tgt_labels, return_softmax=True)
+
+        loss = fluid.layers.mean(x=ce_loss)
+        graph_vars = {"loss": loss}
+        for k, v in graph_vars.items():
+            v.persistable = True
+
+        return pyreader, graph_vars, model.get_checkpoints()
+
+    def fast_decode(self):
+        input_shapes = [[-1, self.vl_config['image_size'], self.vl_config['image_size'], 3],  # image_pixel_input
+                        [-1, 1, self.patch_seq_len + 1],  # image_mask
+                        [-1, 1],  # image_id
+                        [-1, self.max_obj_len, 1],  # padded_obj_token_id
+                        [-1, self.max_obj_len, 1],  # padded_obj_sent_ids
+                        [-1, self.max_obj_len, 1],  # padded_obj_pos_ids
+                        [-1, self.max_obj_len, self.max_obj_len]]  # obj_mask
+        input_dtypes = ['float32', 'float32', 'int32', 'int64', 'int64', 'int64', 'float32']
+        input_lod_levels = [0, 0, 0, 0, 0, 0, 0]
+
+        shapes = input_shapes + [[-1, 1, 1], [-1, 1, 1], [-1, 1], [-1], [-1, 1, self.max_obj_len]]
+        dtypes = input_dtypes + ['int64', 'int64', 'float32', 'int32', 'float32']
+        lod_levels = input_lod_levels + [2, 2, 2, 0, 0]
+
+        inputs = self.to_tensor(shapes, dtypes, lod_levels)
+        pyreader = fluid.io.DataLoader.from_generator(feed_list=inputs, capacity=70, iterable=False)
+
+        image_input = {}
+        obj_input = {}
+        image_input["pixel_embedding"], image_mask, image_ids, \
+        obj_input["text.word_embedding"], obj_input["text.sent_embedding"], obj_input["text.pos_embedding"], obj_mask,\
+        tgt_ids, tgt_pos, init_scores, parent_idx, tgt_input_mask = inputs
+
+        if self.model_type == "grounded":
+            model = GroundingModelForImg2Txt(text_input=obj_input,
+                                             text_mask=obj_mask,
+                                             image_input=image_input,
+                                             image_mask=image_mask,
+                                             config=self.vl_config,
+                                             weight_sharing=self.weight_sharing,
+                                             decoding=True,
+                                             gather_idx=parent_idx,
+                                             grounding_method=self.grounding_method,
+                                             topk_value=self.topk_value,
+                                             with_grounding_projection=self.with_grounding_projection,
+                                             with_grounding_pos=self.with_grounding_pos,
+                                             text_enc_layers=self.text_enc_layers,
+                                             grounding_enc_layers=self.grounding_enc_layers)
+        elif self.model_type == "baseline":
+            model = BaselineForImg2Txt(text_input=obj_input,
+                                       text_mask=obj_mask,
+                                       image_input=image_input,
+                                       image_mask=image_mask,
+                                       config=self.vl_config,
+                                       weight_sharing=self.weight_sharing,
+                                       decoding=True,
+                                       gather_idx=parent_idx,
+                                       text_enc_layers=self.text_enc_layers,
+                                       grounding_enc_layers=self.grounding_enc_layers)
+        else:
+            raise ValueError("The model_type is invalid!!!")
+
+        max_len = layers.fill_constant(
+            shape=[1], dtype=tgt_ids.dtype, value=self.max_out_len, force_cpu=True)
+        min_len = layers.fill_constant(
+            shape=[1], dtype=tgt_ids.dtype, value=self.min_out_len, force_cpu=True)
+        neg_inf = layers.fill_constant(
+            shape=[1], dtype='float32', value=-1e18)
+        step_idx = layers.fill_constant(
+            shape=[1], dtype=tgt_ids.dtype, value=0, force_cpu=True)
+        step_next_idx = layers.fill_constant(
+            shape=[1], dtype=tgt_ids.dtype, value=1, force_cpu=True)
+        cond = layers.less_than(x=step_idx, y=max_len)
+        while_op = layers.While(cond)
+
+        ids = layers.array_write(layers.reshape(tgt_ids, (-1, 1)), step_idx)
+        # pos_biases = layers.array_write(layers.reshape(tgt_pos, (-1, 1)), step_idx)
+        pos_biases = layers.array_write(tgt_pos, step_idx)
+        scores = layers.array_write(init_scores, step_idx)
+
+        batch_size = paddle.shape(tgt_ids)[0]
+        grounding_mask = paddle.ones(shape=[batch_size, 1, model.out_seq_len], dtype=self._emb_dtype)
+        grounding_masks = layers.array_write(grounding_mask, step_idx)
+
+        tgt_masks = layers.array_write(tgt_input_mask, step_idx)
+        trigram_blocking = TrigramBlocking(tgt_ids, self.tokenizer, beam_size=self.beam_size)
+
+        with while_op.block():
+            pre_ids = layers.array_read(array=ids, i=step_idx)
+            pre_ids = layers.reshape(pre_ids, (-1, 1, 1), inplace=True)
+            pre_scores = layers.array_read(array=scores, i=step_idx)
+            pos_bias = layers.array_read(array=pos_biases, i=step_idx)
+            pos_bias = layers.gather(input=pos_bias, index=parent_idx)
+
+            def gen_batch_like(value, dtype="int64", shape=[-1, 1, 1], is_scalar=True):
+                if is_scalar:
+                    return layers.fill_constant_batch_size_like(
+                        input=parent_idx, value=value, shape=shape, dtype=dtype)
+                else:
+                    return layers.elementwise_mul(
+                        x=layers.fill_constant_batch_size_like(
+                            input=parent_idx, value=1, shape=shape, dtype=dtype),
+                        y=value, axis=0)
+
+            tmp_grounding_mask = layers.array_read(grounding_masks, i=step_idx)
+            tmp_grounding_mask = layers.gather(input=tmp_grounding_mask, index=parent_idx)
+            append_1_mask = gen_batch_like(1.0, dtype=tmp_grounding_mask.dtype)
+            pre_grounding_mask = layers.concat([tmp_grounding_mask, append_1_mask], axis=2)
+
+            tmp_text_mask = layers.array_read(tgt_masks, i=step_idx)
+            tmp_text_mask = layers.gather(input=tmp_text_mask, index=parent_idx)
+            append_1_mask = gen_batch_like(1.0, dtype=tmp_text_mask.dtype)
+            pre_text_mask = layers.concat([tmp_text_mask, append_1_mask], axis=2)
+
+            pre_pos = gen_batch_like(step_idx, is_scalar=False)
+            pre_pos = pre_pos + pos_bias  ####################### pos start from 2
+            pre_sent = gen_batch_like(self.tgt_type_id, dtype=pre_ids.dtype)
+
+            dec_emb_ids = {"text.word_embedding": pre_ids, "text.sent_embedding": pre_sent,
+                           "text.pos_embedding": pre_pos}
+            dec_out, _ = model.encode(text_input=dec_emb_ids,
+                                      text_mask=pre_text_mask,
+                                      gather_idx=parent_idx,
+                                      decoding_step=True,
+                                      grounded_decoding_mask=pre_grounding_mask)
+            fc_out = self.cal_logit(dec_out, None)
+
+            # prevent generating end token if length less than min_out_len
+            eos_index = layers.fill_constant(shape=[layers.shape(fc_out)[0]],
+                                             dtype='int64',
+                                             value=self.eos_id)
+            eos_index = fluid.one_hot(eos_index, depth=self.vocab_size)
+            less_cond = layers.cast(layers.less_than(x=step_idx, y=min_len), dtype='float32')
+            less_val = layers.elementwise_mul(less_cond, neg_inf)
+            eos_val = layers.elementwise_mul(eos_index, less_val, axis=0)
+            revised_logits = layers.elementwise_add(fc_out, eos_val, axis=0)
+
+            # topK reduction across beams, also contain special handle of
+            # end beams and end sentences(batch reduction)
+            topk_scores, topk_indices = layers.topk(
+                input=layers.softmax(revised_logits), k=self.beam_size)
+
+            # Roll-Back previous-scores for length-penalty
+            # previous-scores has been length-penaltied, before this timestep length-penalty, need roll-back
+            # because of doing this, we need store the length-penaltied score in `scores`
+            # while calculating use the un-penaltied score
+            # -> safe for step_idx == 0 (initialization state), because previous-score == 0
+            pre_timestep_length_penalty = fluid.layers.pow(
+                ((5.0 + fluid.layers.cast(step_idx, pre_scores.dtype)) / 6.0), self.length_penalty)
+            pre_scores_wo_len_penalty = fluid.layers.elementwise_mul(pre_scores, pre_timestep_length_penalty)
+
+            # calc trigram-blocking delta scores for current alive sequence
+            if self.block_trigram:
+                trigram_blocking.update_seq(pre_ids, parent_idx)
+                trigram_blocking.expand_cand_seq(topk_indices)
+                fluid.layers.py_func(func=trigram_blocking.blocking_forward,
+                                     x=[trigram_blocking.cand_seq,
+                                        trigram_blocking.id2is_full_token],
+                                     out=trigram_blocking.delta_score_out,
+                                     backward_func=None)
+                pre_scores_wo_len_penalty = fluid.layers.elementwise_add(x=trigram_blocking.delta_score_out,
+                                                                         y=pre_scores_wo_len_penalty,
+                                                                         axis=0)
+            # => [N, topk]
+            accu_scores = layers.elementwise_add(
+                x=layers.log(topk_scores), y=pre_scores_wo_len_penalty, axis=0)
+
+            cur_timestep_length_penalty = layers.pow(((5.0 + layers.cast(step_next_idx, accu_scores.dtype)) / 6.0),
+                                                     self.length_penalty)
+            curr_scores = layers.elementwise_div(accu_scores, cur_timestep_length_penalty)
+
+            # beam_search op uses lod to differentiate branches.
+            curr_scores = layers.lod_reset(curr_scores, pre_ids)
+            topk_indices = layers.lod_reset(topk_indices, pre_ids)
+            selected_ids, selected_scores, gather_idx = layers.beam_search(
+                pre_ids=pre_ids,
+                pre_scores=pre_scores,
+                ids=topk_indices,
+                scores=curr_scores,
+                beam_size=self.beam_size,
+                end_id=self.eos_id,
+                return_parent_idx=True)
+
+            layers.increment(x=step_idx, value=1.0, in_place=True)
+            layers.increment(x=step_next_idx, value=1.0, in_place=True)
+            # cell states(caches) have been updated in wrap_decoder,
+            # only need to update beam search states here.
+            layers.array_write(selected_ids, i=step_idx, array=ids)
+            layers.array_write(selected_scores, i=step_idx, array=scores)
+            layers.array_write(pre_text_mask, i=step_idx, array=tgt_masks)
+            layers.array_write(pre_grounding_mask, i=step_idx, array=grounding_masks)
+            layers.array_write(pos_bias, i=step_idx, array=pos_biases)
+            layers.assign(gather_idx, parent_idx)
+
+            length_cond = layers.less_than(x=step_idx, y=max_len)
+            finish_cond = layers.logical_not(layers.is_empty(x=selected_ids))
+            layers.logical_and(x=length_cond, y=finish_cond, out=cond)
+
+        finished_ids, finished_scores = layers.beam_search_decode(
+            ids, scores, beam_size=self.beam_size, end_id=self.eos_id)
+
+        graph_vars = {
+            "finished_ids": finished_ids,
+            "finished_scores": finished_scores,
+            "image_ids": image_ids
+        }
+
+        for k, v in graph_vars.items():
+            v.persistable = True
+
+        return pyreader, graph_vars
+
+    def post_process_seq(self, seq):
+        """
+        Post-process the beam-search decoded sequence. Truncate from the first
+        <eos> and remove the <bos> and <eos> tokens currently.
+        """
+        eos_pos = len(seq)
+        for i, idx in enumerate(seq):
+            if idx == self.eos_id:
+                eos_pos = i
+                break
+        seq = seq[1:eos_pos]
+        return seq
+
+    def remove_special_tokens(self, seq, special_tokens):
+        """Remove special tokens from output sequence"""
+        seq = [idx for idx in seq if idx not in special_tokens]
+        return seq
+
+    def evaluate(self, resource, eval_phase, graph_vars, features=None,
+                 output_path=None, dev_count=1, gpu_id=0):
+        exe, program, pyreader = resource["exe"], resource["program"], resource["pyreader"]
+
+        if eval_phase == "train":
+            fetch_list = [graph_vars["loss"].name]
+            outputs = exe.run(fetch_list=fetch_list)
+            np_loss = outputs[0]
+            ret = {"loss": np.mean(np_loss), "ppl": np.exp(np.mean(np_loss))}
+            return ret
+
+        if self.do_decode:
+            return_numpy = False
+            outfile = output_path + "/" + eval_phase
+            outfile_part = outfile + ".part" + str(gpu_id)
+            writer = codecs.open(outfile_part, "w", encoding='utf-8')
+            fetch_keys = ["finished_ids", "finished_scores", "image_ids"]
+            special_tokens = [self.tokenizer.cls_token_id,
+                              self.tokenizer.sep_token_id,
+                              self.tokenizer.mask_token_id,
+                              self.tokenizer.pad_token_id,
+                              self.tokenizer.unk_token_id]
+        else:
+            steps = 0
+            cost = 0.0
+            return_numpy = True
+            fetch_keys = ["loss"]
+
+        fetch_list = [graph_vars[key].name for key in fetch_keys]
+
+        time_begin = time.time()
+        pyreader.start()
+        while True:
+            try:
+                outputs = exe.run(program=program,
+                                  fetch_list=fetch_list,
+                                  return_numpy=return_numpy)
+                if not self.do_decode:
+                    np_loss = outputs[0]
+                    cost += np.mean(np_loss)
+                    steps += 1
+                else:
+                    seq_ids, seq_scores, image_ids = outputs
+                    seq_ids_list, seq_scores_list = [seq_ids], [seq_scores] \
+                        if isinstance(seq_ids, paddle.fluid.core.LoDTensor) else (seq_ids, seq_scores)
+
+                    image_ids = np.array(image_ids).reshape(-1).tolist()
+                    data_idx = 0
+
+                    for seq_ids, seq_scores in zip(seq_ids_list, seq_scores_list):
+                        # How to parse the results:
+                        #   Suppose the lod of seq_ids is:
+                        #     [[0, 3, 6], [0, 12, 24, 40, 54, 67, 82]]
+                        #   then from lod[0]:
+                        #     there are 2 source sentences, beam width is 3.
+                        #   from lod[1]:
+                        #     the first source sentence has 3 hyps; the lengths are 12, 12, 16
+                        #     the second source sentence has 3 hyps; the lengths are 14, 13, 15
+                        # hyps = [[] for i in range(len(seq_ids.lod()[0]) - 1)]
+                        # scores = [[] for i in range(len(seq_scores.lod()[0]) - 1)]
+                        for i in range(len(seq_ids.lod()[0]) - 1):  # for each source sentence
+                            start = seq_ids.lod()[0][i]
+                            end = seq_ids.lod()[0][i + 1]
+                            max_cand = None
+                            for j in range(end - start):  # for each candidate
+                                sub_start = seq_ids.lod()[1][start + j]
+                                sub_end = seq_ids.lod()[1][start + j + 1]
+                                token_ids = [int(idx) for idx in self.post_process_seq(
+                                    np.array(seq_ids)[sub_start:sub_end])]
+                                # print(len(token_ids))
+
+                                hyp_ids = self.remove_special_tokens(token_ids, special_tokens)
+                                hyp_tokens = self.tokenizer.convert_ids_to_tokens(hyp_ids)
+
+                                hyp_str = self.tokenizer.gptbpe_tokenizer.decode(hyp_tokens)
+                                # hyp_str_tokens = []
+                                # for hyp_token in hyp_tokens:
+                                #     hyp_str_tokens.append(self.tokenizer.gptbpe_tokenizer.decode_token(hyp_token))
+                                # hyp_str = ' '.join(hyp_str_tokens)
+
+                                hyp_str = re.sub('\\s+', ' ', hyp_str)
+                                # print(hyp_str)
+
+                                score = np.array(seq_scores)[sub_end - 1]
+                                if (not max_cand) or score > max_cand[1]:
+                                    max_cand = (hyp_str, score)
+
+                            image_id = image_ids[data_idx]
+                            data_idx += 1
+                            pred = max_cand[0]
+                            writer.write("%d\t%s\n" % (image_id, pred))
+
+            except fluid.core.EOFException:
+                pyreader.reset()
+                break
+
+        time_end = time.time()
+        if not self.do_decode:
+            eval_result = "loss: %f, ppl: %f" % (cost / steps, np.exp(cost / steps))
+            print("[%s evaluation] %s, elapsed time: %f s"
+                  % (eval_phase, eval_result, time_end - time_begin))
+        else:
+            writer.close()
+            tmp_writer = open("%s/%s_dec_finish.%d" % (output_path, eval_phase, gpu_id), "w")
+            tmp_writer.close()
+            if gpu_id != 0:
+                return
+
+            while True:
+                ret = os.popen('find %s -maxdepth 1 -name "%s_dec_finish.*"' %
+                               (output_path, eval_phase)).readlines()
+                if len(ret) != dev_count:
+                    time.sleep(1)
+                    continue
+                else:
+                    break
+
+            all_outfiles = glob.glob("%s.part*" % outfile)
+            img_caption_res = []
+            unique_image_ids = []
+            for cur_file in all_outfiles:
+                for line in open(cur_file, 'r'):
+                    parts = line.strip().split('\t')
+                    if len(parts) != 2:
+                        print("Warning: invalid output %s " % line.strip())
+                        continue
+
+                    image_id, caption = parts
+
+                    if image_id in unique_image_ids:
+                        print("Warning: Repeated image_id %s" % str(image_id))
+                        continue
+                    unique_image_ids.append(image_id)
+                    img_caption_res.append({"image_id": int(image_id), "caption": caption})
+
+            fout = open(outfile, 'w')
+            fout.write(json.dumps(img_caption_res))
+            fout.close()
+            os.system("rm %s.part*" % outfile)
+            os.system("rm %s/%s_dec_finish.*" % (output_path, eval_phase))
+
+            eval_result = self.evaluator.eval(outfile,
+                                              phase=eval_phase.split("_")[0], features=features)
+            print("[%s evaluation] %s, elapsed time: %f s"
+                  % (eval_phase, eval_result, time_end - time_begin))
+
+
+class VlConfig(object):
+    def __init__(self, config_path):
+        self._config_dict = self._parse(config_path)
+
+    def _parse(self, config_path):
+        try:
+            with open(config_path) as json_file:
+                config_dict = json.load(json_file)
+        except Exception:
+            raise IOError("Error in parsing VL model config file '%s'" %
+                          config_path)
+        else:
+            return config_dict
+
+    def __getitem__(self, key):
+        return self._config_dict.get(key, None)
+
+    def __setitem__(self, key, value):
+        self._config_dict[key] = value
+
+    def print_config(self):
+        for arg, value in self._config_dict.items():
+            print('%s: %s' % (arg, value))
+        print('------------------------------------------------')
+
+
+class GroundingModelForImg2Txt(object):
+    def __init__(self,
+                 image_input=None,
+                 image_mask=None,
+                 text_input=None,
+                 text_mask=None,
+                 config=None,
+                 weight_sharing=True,
+                 decoding=False,
+                 gather_idx=None,
+                 grounded_encoder_trainable=True,
+                 vit_encoder_trainable=True,
+                 text_encoder_trainable=True,
+                 grounding_method='topk',
+                 topk_value=100,
+                 with_grounding_projection=False,
+                 with_grounding_pos=False,
+                 text_enc_layers=None,
+                 grounding_enc_layers=None):
+
+        # for grounded cross-modal encoder
+        self._n_layer = config['num_hidden_layers']
+        self._n_head = config['num_attention_heads']
+        self._max_position_seq_len = config['max_position_embeddings']
+        self._hidden_act = config['hidden_act']
+        self._emb_size = config['hidden_size']
+        self._prepostprocess_dropout = config['hidden_dropout_prob']
+        self._attention_dropout = config['attention_probs_dropout_prob']
+        self._emb_dtype = "float32"
+        self._sent_types = config['type_vocab_size']
+
+        # for text encoder
+        self._text_n_layer = config['text_num_hidden_layers']
+        self._text_n_head = config['text_num_attention_heads']
+        self._text_voc_size = config['text_vocab_size']
+        self._text_max_position_seq_len = config['text_max_position_embeddings']
+        self._text_hidden_act = config['text_hidden_act']
+        self._text_prepostprocess_dropout = config['text_hidden_dropout_prob']
+        self._text_attention_dropout = config['text_attention_probs_dropout_prob']
+        self._text_emb_vocab_size = {"text.word_embedding": self._text_voc_size,
+                                     "text.pos_embedding": self._text_max_position_seq_len}
+
+        # for vit encoder
+        self._vit_n_layer = config['vit_num_hidden_layers']
+        self._vit_n_head = config['vit_num_attention_heads']
+        self._vit_hidden_act = config['vit_hidden_act']
+        self._vit_prepostprocess_dropout = config['vit_hidden_dropout_prob']
+        self._vit_attention_dropout = config['vit_attention_probs_dropout_prob']
+        self._vit_layer_norm_eps = config['vit_layer_norm_eps']
+
+        self._weight_sharing = weight_sharing
+        self._grounding_method = grounding_method
+        self._K = topk_value
+        self._grounded_encoder_trainable = grounded_encoder_trainable
+        self._vit_encoder_trainable = vit_encoder_trainable
+        self._text_encoder_trainable = text_encoder_trainable
+        self._with_grounding_projection = with_grounding_projection
+        self.with_grounding_pos = with_grounding_pos
+
+        # Initialize all weigths by truncated normal initializer, and all biases
+        # will be initialized by constant zero by default.
+        self._param_initializer = paddle.fluid.initializer.TruncatedNormalInitializer(scale=config['initializer_range'])
+        self._bias_initializer = paddle.fluid.initializer.ConstantInitializer(value=0.0)
+
+        assert text_input is not None or image_input is not None, "text_input and image_input cannot both be None"
+        self.image_size = config['image_size']
+        self.num_codebook = config['num_codebook']
+        self.resolution = config['resolution']
+        self.width = self.image_size // config['resolution']
+        self.patch_seq_len = self.image_size * self.image_size // (config['resolution'] * config['resolution'])
+        self.patch_emb_size = config['resolution'] * config['resolution'] * 3
+
+        if text_enc_layers is None:
+            text_enc_layers = list(range(self._text_n_layer))
+        if grounding_enc_layers is None:
+            grounding_enc_layers = list(range(self._n_layer))
+
+        print("text_enc_layers", text_enc_layers)
+        print("grounding_enc_layers", grounding_enc_layers)
+        self.text_enc_layers = text_enc_layers
+        self.grounding_enc_layers = grounding_enc_layers
+
+        # vector codebook
+        self.vq_emb = paddle.static.create_parameter(
+            shape=[self.num_codebook, self._emb_size],
+            dtype=self._emb_dtype,
+            attr=paddle.ParamAttr(
+                name="vq_emb",
+                initializer=paddle.fluid.initializer.UniformInitializer(
+                    low=-1 / self.num_codebook, high=1 / self.num_codebook)))
+
+        self.topk_temperature = paddle.static.create_parameter(
+            shape=[1],
+            dtype=self._emb_dtype,
+            attr=paddle.ParamAttr(
+                name="topk_temperature",
+                initializer=paddle.fluid.initializer.ConstantInitializer(value=100)))
+
+        if decoding:
+            self.grounded_caches = [{
+                "k":
+                    paddle.fluid.layers.fill_constant_batch_size_like(
+                        input=text_input["text.word_embedding"] if text_input is not None else image_input[
+                            "pixel_embedding"],
+                        shape=[-1, 0, self._emb_size],
+                        dtype=self._emb_dtype,  # float32,
+                        value=0),
+                "v":
+                    paddle.fluid.layers.fill_constant_batch_size_like(
+                        input=text_input["text.word_embedding"] if text_input is not None else image_input[
+                            "pixel_embedding"],
+                        shape=[-1, 0, self._emb_size],
+                        dtype=self._emb_dtype,  # float32,
+                        value=0),
+            } for i in range(self._n_layer)]
+
+            self.text_caches = [{
+                "k":
+                    paddle.fluid.layers.fill_constant_batch_size_like(
+                        input=text_input["text.word_embedding"] if text_input is not None else image_input[
+                            "pixel_embedding"],
+                        shape=[-1, 0, self._emb_size],
+                        dtype=self._emb_dtype,  # float32,
+                        value=0),
+                "v":
+                    paddle.fluid.layers.fill_constant_batch_size_like(
+                        input=text_input["text.word_embedding"] if text_input is not None else image_input[
+                            "pixel_embedding"],
+                        shape=[-1, 0, self._emb_size],
+                        dtype=self._emb_dtype,  # float32,
+                        value=0),
+            } for i in range(self._text_n_layer)]
+        else:
+            self.grounded_caches = None
+            self.text_caches = None
+
+        self._build_model(text_input=text_input,
+                          text_mask=text_mask,
+                          gather_idx=gather_idx,
+                          image_input=image_input,
+                          image_mask=image_mask)
+
+    def _build_model(self, text_input=None, text_mask=None, gather_idx=None, image_input=None, image_mask=None):
+        if text_input is None and image_input is not None:  # for img2txt inference
+            self._enc_v_out, self._enc_g_out, self.all_checkpoints, self.out_seq_len = \
+                self.encode(image_input=image_input,
+                            image_mask=image_mask,
+                            gather_idx=gather_idx)
+        elif text_input is not None and image_input is None:  # for img2txt step-by-step decoding
+            self._enc_g_out, self._enc_l_out, self.all_checkpoints = self.encode(text_input=text_input,
+                                                                                 text_mask=text_mask,
+                                                                                 gather_idx=gather_idx)
+        else:  # for img2txt training
+            self._enc_v_out, self._enc_g_out, self._enc_l_out, self.all_checkpoints, self.out_seq_len = \
+                self.encode(text_input=text_input,
+                            text_mask=text_mask,
+                            gather_idx=gather_idx,
+                            image_input=image_input,
+                            image_mask=image_mask)
+
+    def encode(self, text_input=None, text_mask=None, gather_idx=None,
+               image_input=None, image_mask=None, decoding_step=False, grounded_decoding_mask=None):
+        all_checkpoints = []
+        # padding id in vocabulary must be set to 0
+        if text_input is None and image_input is not None:  # for img2txt inference
+            emb_v_out, emb_g_out, v_seq_len, g_seq_len, n_head_self_attn_mask, _checkpoints = \
+                self._gen_input(image_input=image_input, image_mask=image_mask, gather_idx=gather_idx)
+            all_checkpoints.extend(_checkpoints)
+
+            if self.with_grounding_pos:
+                pos_range = paddle.unsqueeze(paddle.arange(start=2, end=g_seq_len + 2, dtype='int64'), axis=0)
+                # (batch_size, g_seq_len, 1)
+                batch_pos_range = paddle.unsqueeze(
+                    paddle.tile(pos_range, repeat_times=[paddle.shape(emb_g_out)[0], 1]), axis=-1)
+                g_pos_emb = paddle.static.nn.embedding(
+                    input=batch_pos_range,
+                    size=[self._text_emb_vocab_size["text.pos_embedding"], self._emb_size],
+                    dtype=self._emb_dtype,
+                    param_attr=paddle.ParamAttr(
+                        name='text.pos_embedding',
+                        trainable=self._text_encoder_trainable,
+                        initializer=self._param_initializer))
+                g_pos_emb = paddle.squeeze(g_pos_emb, axis=2)  # (batch_size, g_seq_len, emb_dim)
+                emb_g_out = emb_g_out + g_pos_emb
+
+            emb_g_out = grounded_pre_process_layer(
+                emb_g_out, 'nd', self._prepostprocess_dropout,
+                name="grounded.pre_encoder", trainable=self._grounded_encoder_trainable)
+            emb_vg_out = paddle.concat([emb_v_out, emb_g_out], axis=1)
+
+            enc_vg_out, grounding_checkpoints = grounded_encoder(
+                enc_input=emb_vg_out,
+                attn_bias=n_head_self_attn_mask,
+                enc_layers=self.grounding_enc_layers,
+                n_head=self._n_head,
+                d_key=self._emb_size // self._n_head,
+                d_value=self._emb_size // self._n_head,
+                d_model=self._emb_size,
+                d_inner_hid=self._emb_size * 4,
+                prepostprocess_dropout=self._prepostprocess_dropout,
+                attention_dropout=self._attention_dropout,
+                relu_dropout=0,
+                hidden_act=self._hidden_act,
+                preprocess_cmd="",
+                postprocess_cmd="dan",
+                param_initializer=self._param_initializer,
+                name='grounded.encoder',
+                caches=self.grounded_caches,
+                gather_idx=gather_idx,
+                trainable=self._grounded_encoder_trainable)
+            all_checkpoints.extend(grounding_checkpoints)
+
+            vg_seq_len = v_seq_len + g_seq_len
+            enc_v_out = paddle.slice(
+                input=enc_vg_out, axes=[1], starts=[0], ends=[v_seq_len])
+            enc_g_out = paddle.slice(
+                input=enc_vg_out, axes=[1], starts=[v_seq_len], ends=[v_seq_len + g_seq_len])
+
+            return enc_v_out, enc_g_out, all_checkpoints, vg_seq_len
+
+        elif image_input is None and text_input is not None:  # for img2txt step-by-step decoding
+            assert decoding_step is True, "decoding_step must be set True"
+
+            emb_l_out, l_seq_len, n_head_self_attn_mask, _checkpoints = \
+                self._gen_input(text_input=text_input, text_mask=text_mask, gather_idx=gather_idx,
+                                decoding_step=True, grounded_decoding_mask=grounded_decoding_mask)
+            all_checkpoints.extend(_checkpoints)
+
+            enc_l_out, grounding_checkpoints = grounded_encoder(
+                enc_input=emb_l_out,
+                attn_bias=n_head_self_attn_mask,
+                enc_layers=self.grounding_enc_layers,
+                n_head=self._n_head,
+                d_key=self._emb_size // self._n_head,
+                d_value=self._emb_size // self._n_head,
+                d_model=self._emb_size,
+                d_inner_hid=self._emb_size * 4,
+                prepostprocess_dropout=self._prepostprocess_dropout,
+                attention_dropout=self._attention_dropout,
+                relu_dropout=0,
+                hidden_act=self._hidden_act,
+                preprocess_cmd="",
+                postprocess_cmd="dan",
+                param_initializer=self._param_initializer,
+                name='grounded.encoder',
+                caches=self.grounded_caches,
+                gather_idx=gather_idx,
+                trainable=self._grounded_encoder_trainable)
+            all_checkpoints.extend(grounding_checkpoints)
+
+            return enc_l_out, all_checkpoints
+
+        elif image_input is not None and text_input is not None:  # for img2txt training
+            emb_v_out, emb_g_out, emb_l_out, v_seq_len, g_seq_len, l_seq_len, n_head_self_attn_mask, _checkpoints = \
+                self._gen_input(image_input=image_input, image_mask=image_mask,
+                                text_input=text_input, text_mask=text_mask, gather_idx=gather_idx)
+            all_checkpoints.extend(_checkpoints)
+
+            emb_g_out = grounded_pre_process_layer(
+                emb_g_out, 'nd', self._prepostprocess_dropout,
+                name="grounded.pre_encoder", trainable=self._grounded_encoder_trainable)
+            emb_vgl_out = paddle.concat([emb_v_out, emb_g_out, emb_l_out], axis=1)
+
+            enc_vgl_out, grounding_checkpoints = grounded_encoder(
+                enc_input=emb_vgl_out,
+                attn_bias=n_head_self_attn_mask,
+                enc_layers=self.grounding_enc_layers,
+                n_head=self._n_head,
+                d_key=self._emb_size // self._n_head,
+                d_value=self._emb_size // self._n_head,
+                d_model=self._emb_size,
+                d_inner_hid=self._emb_size * 4,
+                prepostprocess_dropout=self._prepostprocess_dropout,
+                attention_dropout=self._attention_dropout,
+                relu_dropout=0,
+                hidden_act=self._hidden_act,
+                preprocess_cmd="",
+                postprocess_cmd="dan",
+                param_initializer=self._param_initializer,
+                name='grounded.encoder',
+                caches=self.grounded_caches,
+                gather_idx=gather_idx,
+                trainable=self._grounded_encoder_trainable)
+            all_checkpoints.extend(grounding_checkpoints)
+
+            vgl_seq_len = v_seq_len + g_seq_len + l_seq_len
+            enc_v_out = paddle.slice(
+                input=enc_vgl_out, axes=[1], starts=[0], ends=[v_seq_len])
+            enc_g_out = paddle.slice(
+                input=enc_vgl_out, axes=[1], starts=[v_seq_len], ends=[v_seq_len + g_seq_len])
+            enc_l_out = paddle.slice(
+                input=enc_vgl_out, axes=[1], starts=[v_seq_len + g_seq_len], ends=[v_seq_len + g_seq_len + l_seq_len])
+            return enc_v_out, enc_g_out, enc_l_out, all_checkpoints, vgl_seq_len
+        else:
+            raise ValueError("The input is invalid")
+
+    def vit_encode(self, image_input, image_mask):
+        """encode image by pre-trained ViT"""
+        assert image_mask is not None, "text_mask should not be none"
+        image_self_attn_mask = paddle.matmul(x=paddle.transpose(image_mask, perm=[0, 2, 1]), y=image_mask)
+        self_attn_mask = paddle.scale(
+            x=image_self_attn_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+        n_head_self_attn_mask = paddle.stack(
+            x=[self_attn_mask] * self._vit_n_head, axis=1)
+        n_head_self_attn_mask.stop_gradient = True
+
+        pixel_embeddings = paddle.static.nn.conv2d(
+            input=image_input['pixel_embedding'],
+            num_filters=self._emb_size,
+            filter_size=self.resolution,
+            stride=self.resolution,
+            padding=(self.resolution - 1) // 2,
+            param_attr=paddle.ParamAttr(
+                name="vit.patch_embeddings_projection_weight",
+                trainable=self._vit_encoder_trainable),
+            bias_attr=paddle.ParamAttr(
+                name="vit.patch_embeddings_projection_bias",
+                trainable=self._vit_encoder_trainable),
+            data_format="NHWC")
+        # paddle.static.Print(paddle.shape(pixel_embeddings), message="pixel_embeddings", summarize=-1)
+        pixel_embeddings = paddle.reshape(pixel_embeddings, shape=[-1, self.patch_seq_len, self._emb_size])
+
+        cls_token_emb = paddle.static.create_parameter(
+            shape=[1, 1, self._emb_size],
+            dtype=self._emb_dtype,
+            attr=paddle.ParamAttr(
+                name="vit.cls_token_embeddings",
+                trainable=self._vit_encoder_trainable,
+                initializer=self._param_initializer))
+        cls_token_emb = paddle.expand(x=cls_token_emb,
+                                      shape=[paddle.shape(pixel_embeddings)[0], 1, self._emb_size])
+
+        # cpncate global [CLS] token with image patches
+        # (batch_size, patch_seq_len + 1, emb_dim)
+        all_pixel_embeddings = paddle.concat(x=[cls_token_emb, pixel_embeddings], axis=1)
+
+        # default image_size=224, resolution=16, patch_seq_len=196
+        pixel_pos_emb = paddle.static.create_parameter(
+            shape=[1, 197, self._emb_size],
+            dtype=self._emb_dtype,
+            attr=paddle.ParamAttr(name="vit.position_embeddings",
+                                  trainable=self._vit_encoder_trainable,
+                                  initializer=self._param_initializer))
+        # paddle.static.Print(paddle.shape(pixel_pos_emb), message="pixel_pos_emb", summarize=-1)
+        if self.patch_seq_len > 196:  # when image_size > 224
+            pixel_pos_emb = interpolate_pos_embed(pixel_pos_emb, self.patch_seq_len)
+
+        emb_v_out = all_pixel_embeddings + pixel_pos_emb
+        emb_v_out = vit_pre_process_layer(
+            emb_v_out, 'd', self._vit_prepostprocess_dropout, name='vit.pre_encoder',
+            trainable=self._vit_encoder_trainable)
+
+        vit_enc_out, checkpoints = vit_encoder(
+            enc_input=emb_v_out,
+            attn_bias=n_head_self_attn_mask,
+            n_layer=self._vit_n_layer,
+            n_head=self._vit_n_head,
+            d_key=self._emb_size // self._vit_n_head,
+            d_value=self._emb_size // self._vit_n_head,
+            d_model=self._emb_size,
+            d_inner_hid=self._emb_size * 4,
+            prepostprocess_dropout=self._vit_prepostprocess_dropout,
+            attention_dropout=self._vit_attention_dropout,
+            relu_dropout=0,
+            hidden_act=self._vit_hidden_act,
+            preprocess_cmd="n",
+            postprocess_cmd="da",
+            param_initializer=self._param_initializer,
+            name='vit.encoder',
+            trainable=self._vit_encoder_trainable)
+        vit_seq_len = paddle.shape(vit_enc_out)[1]  # patch_seq_len + 1
+
+        return vit_enc_out, vit_seq_len, image_self_attn_mask, checkpoints
+
+    def text_encode(self, text_input, text_mask, gather_idx=None):
+        assert text_mask is not None, "text_mask should not be none"
+        text_self_attn_mask = text_mask
+        self_attn_mask = paddle.scale(
+            x=text_self_attn_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+        n_head_self_attn_mask = paddle.stack(
+            x=[self_attn_mask] * self._n_head, axis=1)
+        n_head_self_attn_mask.stop_gradient = True
+
+        # text part
+        text_emb = paddle.static.nn.embedding(
+            input=text_input["text.word_embedding"],
+            size=[self._text_emb_vocab_size["text.word_embedding"], self._emb_size],
+            dtype=self._emb_dtype,
+            param_attr=paddle.ParamAttr(
+                name='text.word_embedding',
+                trainable=self._text_encoder_trainable,
+                initializer=self._param_initializer))
+        text_emb = paddle.squeeze(text_emb, axis=2)  # (batch_size, seq_len, emb_dim)
+
+        pos_emb = paddle.static.nn.embedding(
+            input=text_input["text.pos_embedding"],
+            size=[self._text_emb_vocab_size["text.pos_embedding"], self._emb_size],
+            dtype=self._emb_dtype,
+            param_attr=paddle.ParamAttr(
+                name='text.pos_embedding',
+                trainable=self._text_encoder_trainable,
+                initializer=self._param_initializer))
+        pos_emb = paddle.squeeze(pos_emb, axis=2)  # (batch_size, seq_len, emb_dim)
+        emb_out = text_emb + pos_emb
+        emb_out = text_pre_process_layer(
+            emb_out, 'nd', self._text_prepostprocess_dropout,
+            name="text.pre_encoder", trainable=self._text_encoder_trainable)
+
+        text_enc_out, checkpoints = text_encoder(
+            enc_input=emb_out,
+            attn_bias=n_head_self_attn_mask,
+            enc_layers=self.text_enc_layers,
+            n_head=self._text_n_head,
+            d_key=self._emb_size // self._text_n_head,
+            d_value=self._emb_size // self._text_n_head,
+            d_model=self._emb_size,
+            d_inner_hid=self._emb_size * 4,
+            prepostprocess_dropout=self._text_prepostprocess_dropout,
+            attention_dropout=self._text_attention_dropout,
+            relu_dropout=0,
+            hidden_act=self._text_hidden_act,
+            preprocess_cmd="",
+            postprocess_cmd="dan",
+            param_initializer=self._param_initializer,
+            name='text.encoder',
+            caches=self.text_caches,
+            gather_idx=gather_idx,
+            trainable=self._text_encoder_trainable)
+
+        text_seq_len = paddle.shape(text_enc_out)[1]
+        return text_enc_out, text_seq_len, text_self_attn_mask, checkpoints
+
+    def vector_quantizer(self, inputs):
+        """inputs: (batch_size, seq_len, emb_dim)"""
+        input_shape = paddle.shape(inputs)
+        # Flatten input (batch_size * seq_len, emb_dim)
+        flat_input = paddle.reshape(inputs, shape=[-1, self._emb_size])
+
+        # Calculate distances (batch_size * seq_len, num_codebook)
+        distances = (paddle.sum(paddle.pow(flat_input, 2), axis=1, keepdim=True)
+                     + paddle.unsqueeze(x=paddle.sum(paddle.pow(self.vq_emb, 2), axis=1), axis=0)
+                     - 2 * paddle.matmul(flat_input, paddle.transpose(self.vq_emb, perm=[1, 0])))
+
+        # Encoding (batch_size * seq_len, 1)
+        encoding_indices = paddle.unsqueeze(x=paddle.argmin(distances, axis=1), axis=1)
+        # paddle.static.Print(encoding_indices, message="encoding_indices", summarize=1000)
+        size_range = paddle.unsqueeze(x=paddle.arange(paddle.shape(encoding_indices)[0], dtype='int64'), axis=1)
+        # (batch_size * seq_len, 2)
+        index = paddle.concat([size_range, encoding_indices], axis=1)
+
+        # (batch_size * seq_len, num_codebook)
+        out_shape = [paddle.shape(encoding_indices)[0], self.num_codebook]
+        # (batch_size * seq_len)
+        updates = paddle.ones(shape=[paddle.shape(encoding_indices)[0]], dtype='float32')
+        # (batch_size * seq_len, num_codebook)
+        encodings = paddle.scatter_nd(index=index, updates=updates, shape=out_shape)
+        # paddle.static.Print(encodings, message="encodings", summarize=1000)
+
+        # Quantize and unflatten (batch_size, seq_len, emb_dim)
+        quantized = paddle.reshape(x=paddle.matmul(encodings, self.vq_emb),
+                                   shape=[input_shape[0], input_shape[1], self._emb_size])
+        # paddle.static.Print(quantized, message="quantized", summarize=1000)
+        encoding_indices_reshaped = paddle.reshape(encoding_indices, shape=[input_shape[0], input_shape[1]])
+
+        return quantized, encoding_indices_reshaped
+
+    def topk_vector_quantizer(self, inputs, K=100):
+        """inputs: (batch_size, seq_len, emb_dim)"""
+        self.topk_temperature = paddle.clip(self.topk_temperature, min=1.0, max=10000.0)
+
+        input_shape = paddle.shape(inputs)
+        # Flatten input (batch_size * seq_len, emb_dim)
+        flat_input = paddle.reshape(inputs, shape=[-1, self._emb_size])
+
+        # Calculate distances (batch_size * seq_len, num_codebook)
+        # distances = (paddle.sum(paddle.pow(flat_input, 2), axis=1, keepdim=True)
+        #              + paddle.unsqueeze(x=paddle.sum(paddle.pow(self.vq_emb, 2), axis=1), axis=0)
+        #              - 2 * paddle.matmul(flat_input, paddle.transpose(self.vq_emb, perm=[1, 0])))
+
+        distances = paddle.nn.functional.sigmoid(self.topk_temperature * \
+                                                 paddle.matmul(flat_input, paddle.transpose(self.vq_emb, perm=[1, 0])))
+        # paddle.static.Print(distances, message="distances", summarize=2048)
+
+        # (batch_size, num_codebook)
+        cumulated_score = paddle.sum(
+            paddle.reshape(distances, shape=[input_shape[0], input_shape[1], self.num_codebook]), axis=1)
+        # paddle.static.Print(cumulated_score, message="cumulated_score", summarize=2048)
+
+        # (batch_size, K)
+        topk_value, topk_indices = paddle.topk(x=cumulated_score, k=K, axis=1, largest=True)
+        topk_indices = paddle.reshape(topk_indices, shape=[-1, 1])
+        # paddle.static.Print(topk_indices, message="topk_indices", summarize=100)
+
+        size_range = paddle.unsqueeze(x=paddle.arange(paddle.shape(topk_indices)[0], dtype='int64'), axis=1)
+        # (batch_size * K, 2)
+        index = paddle.concat([size_range, topk_indices], axis=1)
+        # paddle.static.Print(index, message="index", summarize=2000)
+
+        # (batch_size * K, num_codebook)
+        out_shape = [paddle.shape(topk_indices)[0], self.num_codebook]
+        # (batch_size * K)
+        updates = paddle.ones(shape=[paddle.shape(topk_indices)[0]], dtype='float32')
+        # (batch_size * K, num_codebook)
+        encodings = paddle.scatter_nd(index=index, updates=updates, shape=out_shape)
+        # paddle.static.Print(encodings, message="encodings", summarize=2048)
+
+        # Quantize and unflatten (batch_size, K, emb_dim)
+        quantized = paddle.reshape(x=paddle.matmul(encodings, self.vq_emb),
+                                   shape=[input_shape[0], K, self._emb_size])
+        # paddle.static.Print(quantized, message="quantized", summarize=1000)
+        encoding_indices_reshaped = paddle.reshape(topk_indices, shape=[input_shape[0], K])
+
+        return quantized, encoding_indices_reshaped
+
+    def _gen_input(self, text_input=None, text_mask=None,
+                   image_input=None, image_mask=None, gather_idx=None, decoding_step=False,
+                   grounded_decoding_mask=None):
+        """encode images and texts independently by Vit and RoBERTa, get the optimal grounded tokens"""
+        _checkpoints = []
+
+        if image_input is not None:
+            # visual part
+            self.vit_enc_out, vit_seq_len, image_self_attn_mask, vit_checkpoints = \
+                self.vit_encode(image_input, image_mask)
+            _checkpoints.extend(vit_checkpoints)
+
+            # (batch_size, 1, emb_dim)
+            patch_cls_embeddings = paddle.slice(input=self.vit_enc_out, axes=[1], starts=[0], ends=[1])
+            # (batch_size, patch_seq_len, emb_dim)
+            patch_embeddings = paddle.slice(input=self.vit_enc_out, axes=[1], starts=[1], ends=[vit_seq_len])
+
+            if self._with_grounding_projection:
+                patch_embeddings = paddle.static.nn.fc(
+                    x=patch_embeddings,
+                    size=self._emb_size,
+                    num_flatten_dims=2,
+                    activation="relu",
+                    weight_attr=paddle.ParamAttr(
+                        name="grounded.grounding_patch_projection.w_0",
+                        initializer=self._param_initializer),
+                    bias_attr="grounded.grounding_patch_projection.b_0")
+                patch_embeddings = paddle.nn.functional.normalize(patch_embeddings, axis=-1)
+
+        if text_input is not None:
+            # textual part
+            self.text_enc_out, text_seq_len, text_self_attn_mask, text_checkpoints = \
+                self.text_encode(text_input, text_mask, gather_idx=gather_idx)
+            _checkpoints.extend(text_checkpoints)
+
+        if image_input is not None and text_input is not None:  # for img2txt training
+            if self._grounding_method == "normal":
+                self.grounded_enc_out, self.grounded_encoding_indices = \
+                    self.vector_quantizer(patch_embeddings)
+            elif self._grounding_method == "topk":
+                self.grounded_enc_out, self.grounded_encoding_indices = \
+                    self.topk_vector_quantizer(patch_embeddings, self._K)
+            elif self._grounding_method == "optimal":
+                self.grounded_enc_out, self.grounded_encoding_indices = \
+                    self.vector_quantizer(patch_embeddings)
+            else:
+                raise ValueError("%s is not supported!!!" % self._grounding_method)
+
+            batch_size = paddle.shape(self.grounded_enc_out)[0]
+            g_seq_len = paddle.shape(self.grounded_enc_out)[1]
+
+            # (batch_size, v_seq_len, g_seq_len)
+            img_grounded_attn_mask = paddle.ones(shape=[batch_size, vit_seq_len, g_seq_len], dtype='float32')
+            # (batch_size, v_seq_len, l_seq_len)
+            img_text_attn_mask = paddle.zeros(shape=[batch_size, vit_seq_len, text_seq_len], dtype='float32')
+            # (batch_size, g_seq_len, v_seq_len)
+            grounded_img_attn_mask = paddle.ones(shape=[batch_size, g_seq_len, vit_seq_len], dtype='float32')
+            # (batch_size, g_seq_len, g_seq_len)
+            grounded_self_attn_mask = paddle.ones(shape=[batch_size, g_seq_len, g_seq_len], dtype='float32')
+            # (batch_size, g_seq_len, l_seq_len)
+            grounded_text_attn_mask = paddle.zeros(shape=[batch_size, g_seq_len, text_seq_len], dtype='float32')
+            # (batch_size, l_seq_len, v_seq_len)
+            text_img_attn_mask = paddle.ones(shape=[batch_size, text_seq_len, vit_seq_len], dtype='float32')
+            # (batch_size, l_seq_len, g_seq_len)
+            text_grounded_attn_mask = paddle.ones(shape=[batch_size, text_seq_len, g_seq_len], dtype='float32')
+
+            image_row = paddle.concat([image_self_attn_mask, img_grounded_attn_mask, img_text_attn_mask], axis=2)
+            grounded_row = paddle.concat([grounded_img_attn_mask, grounded_self_attn_mask, grounded_text_attn_mask],
+                                         axis=2)
+            text_row = paddle.concat([text_img_attn_mask, text_grounded_attn_mask, text_mask], axis=2)
+            vgl_self_attn_mask = paddle.concat([image_row, grounded_row, text_row], axis=1)
+            vgl_self_attn_mask = paddle.scale(
+                x=vgl_self_attn_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+            n_head_self_attn_mask = paddle.stack(
+                x=[vgl_self_attn_mask] * self._n_head, axis=1)
+            n_head_self_attn_mask.stop_gradient = True
+
+            return self.vit_enc_out, self.grounded_enc_out, self.text_enc_out, \
+                   vit_seq_len, g_seq_len, text_seq_len, n_head_self_attn_mask, _checkpoints
+
+        elif image_input is not None and text_input is None:  # for img2txt inference
+            if self._grounding_method == "normal":
+                self.grounded_enc_out, self.grounded_encoding_indices = \
+                    self.vector_quantizer(patch_embeddings)
+            elif self._grounding_method == "topk":
+                self.grounded_enc_out, self.grounded_encoding_indices = \
+                    self.topk_vector_quantizer(patch_embeddings, self._K)
+            elif self._grounding_method == "optimal":
+                self.grounded_enc_out, self.grounded_encoding_indices = \
+                    self.vector_quantizer(patch_embeddings)
+            else:
+                raise ValueError("%s is not supported!!!" % self._grounding_method)
+
+            batch_size = paddle.shape(self.grounded_enc_out)[0]
+            g_seq_len = paddle.shape(self.grounded_enc_out)[1]
+
+            # (batch_size, 1, g_seq_len)
+            grounded_mask = paddle.ones(shape=[batch_size, 1, g_seq_len])
+            vg_mask = paddle.concat([image_mask, grounded_mask], axis=2)
+            vg_self_attn_mask = paddle.matmul(paddle.transpose(vg_mask, perm=[0, 2, 1]), vg_mask)
+            vg_self_attn_mask = paddle.scale(
+                x=vg_self_attn_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+            n_head_self_attn_mask = paddle.stack(
+                x=[vg_self_attn_mask] * self._n_head, axis=1)
+            n_head_self_attn_mask.stop_gradient = True
+
+            return self.vit_enc_out, self.grounded_enc_out, vit_seq_len, g_seq_len, n_head_self_attn_mask, _checkpoints
+
+        elif image_input is None and text_input is not None:  # for img2txt step-by-step decoding
+            assert decoding_step is True, "decoding_step must be True"
+            assert grounded_decoding_mask is not None, "grounded_decoding_mask should not be none"
+            self_attn_mask = paddle.scale(
+                x=grounded_decoding_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+            n_head_self_attn_mask = paddle.stack(
+                x=[self_attn_mask] * self._n_head, axis=1)
+            n_head_self_attn_mask.stop_gradient = True
+
+            return self.text_enc_out, text_seq_len, n_head_self_attn_mask, _checkpoints
+
+    def get_checkpoints(self):
+        return self.all_checkpoints
+
+    def get_text_sequence_output(self):
+        return self._enc_l_out
+
+
+class BaselineForImg2Txt(object):
+    def __init__(self,
+                 image_input=None,
+                 image_mask=None,
+                 text_input=None,
+                 text_mask=None,
+                 config=None,
+                 weight_sharing=True,
+                 decoding=False,
+                 gather_idx=None,
+                 grounded_encoder_trainable=True,
+                 vit_encoder_trainable=True,
+                 text_encoder_trainable=True,
+                 text_enc_layers=None,
+                 grounding_enc_layers=None):
+
+        # for grounded cross-modal encoder
+        self._n_layer = config['num_hidden_layers']
+        self._n_head = config['num_attention_heads']
+        self._max_position_seq_len = config['max_position_embeddings']
+        self._hidden_act = config['hidden_act']
+        self._emb_size = config['hidden_size']
+        self._prepostprocess_dropout = config['hidden_dropout_prob']
+        self._attention_dropout = config['attention_probs_dropout_prob']
+        self._emb_dtype = "float32"
+        self._sent_types = config['type_vocab_size']
+
+        # for text encoder
+        self._text_n_layer = config['text_num_hidden_layers']
+        self._text_n_head = config['text_num_attention_heads']
+        self._text_voc_size = config['text_vocab_size']
+        self._text_max_position_seq_len = config['text_max_position_embeddings']
+        self._text_hidden_act = config['text_hidden_act']
+        self._text_prepostprocess_dropout = config['text_hidden_dropout_prob']
+        self._text_attention_dropout = config['text_attention_probs_dropout_prob']
+        self._text_emb_vocab_size = {"text.word_embedding": self._text_voc_size,
+                                     "text.pos_embedding": self._text_max_position_seq_len}
+
+        # for vit encoder
+        self._vit_n_layer = config['vit_num_hidden_layers']
+        self._vit_n_head = config['vit_num_attention_heads']
+        self._vit_hidden_act = config['vit_hidden_act']
+        self._vit_prepostprocess_dropout = config['vit_hidden_dropout_prob']
+        self._vit_attention_dropout = config['vit_attention_probs_dropout_prob']
+        self._vit_layer_norm_eps = config['vit_layer_norm_eps']
+
+        self._weight_sharing = weight_sharing
+        self._grounded_encoder_trainable = grounded_encoder_trainable
+        self._vit_encoder_trainable = vit_encoder_trainable
+        self._text_encoder_trainable = text_encoder_trainable
+
+        # Initialize all weigths by truncated normal initializer, and all biases
+        # will be initialized by constant zero by default.
+        self._param_initializer = paddle.fluid.initializer.TruncatedNormalInitializer(scale=config['initializer_range'])
+        self._bias_initializer = paddle.fluid.initializer.ConstantInitializer(value=0.0)
+
+        assert text_input is not None or image_input is not None, "text_input and image_input cannot both be None"
+
+        self.image_size = config['image_size']
+        self.num_codebook = config['num_codebook']
+        self.resolution = config['resolution']
+        self.width = self.image_size // config['resolution']
+        self.patch_seq_len = self.image_size * self.image_size // (config['resolution'] * config['resolution'])
+        self.patch_emb_size = config['resolution'] * config['resolution'] * 3
+
+        if text_enc_layers is None:
+            text_enc_layers = list(range(self._text_n_layer))
+        if grounding_enc_layers is None:
+            grounding_enc_layers = list(range(self._n_layer))
+
+        print("text_enc_layers", text_enc_layers)
+        print("grounding_enc_layers", grounding_enc_layers)
+        self.text_enc_layers = text_enc_layers
+        self.grounding_enc_layers = grounding_enc_layers
+
+        if decoding:
+            self.grounded_caches = [{
+                "k":
+                    paddle.fluid.layers.fill_constant_batch_size_like(
+                        input=text_input["text.word_embedding"] if text_input is not None else image_input[
+                            "pixel_embedding"],
+                        shape=[-1, 0, self._emb_size],
+                        dtype=self._emb_dtype,  # float32,
+                        value=0),
+                "v":
+                    paddle.fluid.layers.fill_constant_batch_size_like(
+                        input=text_input["text.word_embedding"] if text_input is not None else image_input[
+                            "pixel_embedding"],
+                        shape=[-1, 0, self._emb_size],
+                        dtype=self._emb_dtype,  # float32,
+                        value=0),
+            } for i in range(self._n_layer)]
+
+            self.text_caches = [{
+                "k":
+                    paddle.fluid.layers.fill_constant_batch_size_like(
+                        input=text_input["text.word_embedding"] if text_input is not None else image_input[
+                            "pixel_embedding"],
+                        shape=[-1, 0, self._emb_size],
+                        dtype=self._emb_dtype,  # float32,
+                        value=0),
+                "v":
+                    paddle.fluid.layers.fill_constant_batch_size_like(
+                        input=text_input["text.word_embedding"] if text_input is not None else image_input[
+                            "pixel_embedding"],
+                        shape=[-1, 0, self._emb_size],
+                        dtype=self._emb_dtype,  # float32,
+                        value=0),
+            } for i in range(self._text_n_layer)]
+        else:
+            self.grounded_caches = None
+            self.text_caches = None
+
+        self._build_model(text_input=text_input,
+                          text_mask=text_mask,
+                          gather_idx=gather_idx,
+                          image_input=image_input,
+                          image_mask=image_mask)
+
+    def _build_model(self, text_input=None, text_mask=None, gather_idx=None, image_input=None, image_mask=None):
+        if text_input is None and image_input is not None:  # for img2txt inference
+            self._enc_v_out, self.all_checkpoints, self.out_seq_len = self.encode(image_input=image_input,
+                                                                                  image_mask=image_mask,
+                                                                                  gather_idx=gather_idx)
+        elif text_input is not None and image_input is None:  # for img2txt step-by-step decoding
+            self._enc_l_out, self.all_checkpoints = self.encode(text_input=text_input,
+                                                                text_mask=text_mask,
+                                                                gather_idx=gather_idx)
+        else:  # for img2txt training
+            self._enc_v_out, self._enc_l_out, self.all_checkpoints, self.out_seq_len = \
+                self.encode(text_input=text_input,
+                            text_mask=text_mask,
+                            gather_idx=gather_idx,
+                            image_input=image_input,
+                            image_mask=image_mask)
+
+    def encode(self, text_input=None, text_mask=None, gather_idx=None,
+               image_input=None, image_mask=None, decoding_step=False, grounded_decoding_mask=None):
+        all_checkpoints = []
+        # padding id in vocabulary must be set to 0
+        if text_input is None and image_input is not None:  # for img2txt inference
+            emb_v_out, v_seq_len, n_head_self_attn_mask, _checkpoints = \
+                self._gen_input(image_input=image_input, image_mask=image_mask, gather_idx=gather_idx)
+            all_checkpoints.extend(_checkpoints)
+
+            enc_v_out, grounding_checkpoints = grounded_encoder(
+                enc_input=emb_v_out,
+                attn_bias=n_head_self_attn_mask,
+                enc_layers=self.grounding_enc_layers,
+                n_head=self._n_head,
+                d_key=self._emb_size // self._n_head,
+                d_value=self._emb_size // self._n_head,
+                d_model=self._emb_size,
+                d_inner_hid=self._emb_size * 4,
+                prepostprocess_dropout=self._prepostprocess_dropout,
+                attention_dropout=self._attention_dropout,
+                relu_dropout=0,
+                hidden_act=self._hidden_act,
+                preprocess_cmd="",
+                postprocess_cmd="dan",
+                param_initializer=self._param_initializer,
+                name='grounded.encoder',
+                caches=self.grounded_caches,
+                gather_idx=gather_idx,
+                trainable=self._grounded_encoder_trainable)
+            all_checkpoints.extend(grounding_checkpoints)
+            return enc_v_out, all_checkpoints, v_seq_len
+
+        elif image_input is None and text_input is not None:  # for img2txt step-by-step decoding
+            assert decoding_step is True, 'decoding_step must be True'
+            emb_l_out, l_seq_len, n_head_self_attn_mask, _checkpoints = \
+                self._gen_input(text_input=text_input, text_mask=text_mask, gather_idx=gather_idx,
+                                decoding_step=True, grounded_decoding_mask=grounded_decoding_mask)
+            all_checkpoints.extend(_checkpoints)
+
+            enc_l_out, grounding_checkpoints = grounded_encoder(
+                enc_input=emb_l_out,
+                attn_bias=n_head_self_attn_mask,
+                enc_layers=self.grounding_enc_layers,
+                n_head=self._n_head,
+                d_key=self._emb_size // self._n_head,
+                d_value=self._emb_size // self._n_head,
+                d_model=self._emb_size,
+                d_inner_hid=self._emb_size * 4,
+                prepostprocess_dropout=self._prepostprocess_dropout,
+                attention_dropout=self._attention_dropout,
+                relu_dropout=0,
+                hidden_act=self._hidden_act,
+                preprocess_cmd="",
+                postprocess_cmd="dan",
+                param_initializer=self._param_initializer,
+                name='grounded.encoder',
+                caches=self.grounded_caches,
+                gather_idx=gather_idx,
+                trainable=self._grounded_encoder_trainable)
+            all_checkpoints.extend(grounding_checkpoints)
+            return enc_l_out, all_checkpoints
+
+        elif image_input is not None and text_input is not None:  # for img2txt training
+            emb_v_out, emb_l_out, v_seq_len, l_seq_len, n_head_self_attn_mask, _checkpoints = \
+                self._gen_input(image_input=image_input, image_mask=image_mask,
+                                text_input=text_input, text_mask=text_mask, gather_idx=gather_idx)
+            all_checkpoints.extend(_checkpoints)
+            emb_vl_out = paddle.concat([emb_v_out, emb_l_out], axis=1)
+
+            enc_vl_out, grounding_checkpoints = grounded_encoder(
+                enc_input=emb_vl_out,
+                attn_bias=n_head_self_attn_mask,
+                enc_layers=self.grounding_enc_layers,
+                n_head=self._n_head,
+                d_key=self._emb_size // self._n_head,
+                d_value=self._emb_size // self._n_head,
+                d_model=self._emb_size,
+                d_inner_hid=self._emb_size * 4,
+                prepostprocess_dropout=self._prepostprocess_dropout,
+                attention_dropout=self._attention_dropout,
+                relu_dropout=0,
+                hidden_act=self._hidden_act,
+                preprocess_cmd="",
+                postprocess_cmd="dan",
+                param_initializer=self._param_initializer,
+                name='grounded.encoder',
+                caches=self.grounded_caches,
+                gather_idx=gather_idx,
+                trainable=self._grounded_encoder_trainable)
+            all_checkpoints.extend(grounding_checkpoints)
+
+            vl_seq_len = v_seq_len + l_seq_len
+            enc_v_out = paddle.slice(
+                input=enc_vl_out, axes=[1], starts=[0], ends=[v_seq_len])
+            enc_l_out = paddle.slice(
+                input=enc_vl_out, axes=[1], starts=[v_seq_len], ends=[v_seq_len + l_seq_len])
+            return enc_v_out, enc_l_out, all_checkpoints, vl_seq_len
+        else:
+            raise ValueError("The input is invalid")
+
+    def vit_encode(self, image_input, image_mask):
+        """encode image by pre-trained ViT"""
+        assert image_mask is not None, "text_mask should not be none"
+        image_self_attn_mask = paddle.matmul(x=paddle.transpose(image_mask, perm=[0, 2, 1]), y=image_mask)
+        self_attn_mask = paddle.scale(
+            x=image_self_attn_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+        n_head_self_attn_mask = paddle.stack(
+            x=[self_attn_mask] * self._vit_n_head, axis=1)
+        n_head_self_attn_mask.stop_gradient = True
+
+        pixel_embeddings = paddle.static.nn.conv2d(
+            input=image_input['pixel_embedding'],
+            num_filters=self._emb_size,
+            filter_size=self.resolution,
+            stride=self.resolution,
+            padding=(self.resolution - 1) // 2,
+            param_attr=paddle.ParamAttr(
+                name="vit.patch_embeddings_projection_weight",
+                trainable=self._vit_encoder_trainable),
+            bias_attr=paddle.ParamAttr(
+                name="vit.patch_embeddings_projection_bias",
+                trainable=self._vit_encoder_trainable),
+            data_format="NHWC")
+        # paddle.static.Print(paddle.shape(pixel_embeddings), message="pixel_embeddings", summarize=-1)
+        pixel_embeddings = paddle.reshape(pixel_embeddings, shape=[-1, self.patch_seq_len, self._emb_size])
+
+        cls_token_emb = paddle.static.create_parameter(
+            shape=[1, 1, self._emb_size],
+            dtype=self._emb_dtype,
+            attr=paddle.ParamAttr(
+                name="vit.cls_token_embeddings",
+                trainable=self._vit_encoder_trainable,
+                initializer=self._param_initializer))
+        cls_token_emb = paddle.expand(x=cls_token_emb,
+                                      shape=[paddle.shape(pixel_embeddings)[0], 1, self._emb_size])
+
+        # cpncate global [CLS] token with image patches
+        # (batch_size, patch_seq_len + 1, emb_dim)
+        all_pixel_embeddings = paddle.concat(x=[cls_token_emb, pixel_embeddings], axis=1)
+
+        # default image_size=224, resolution=16, patch_seq_len=196
+        pixel_pos_emb = paddle.static.create_parameter(
+            shape=[1, 197, self._emb_size],
+            dtype=self._emb_dtype,
+            attr=paddle.ParamAttr(name="vit.position_embeddings",
+                                  trainable=self._vit_encoder_trainable,
+                                  initializer=self._param_initializer))
+        # paddle.static.Print(paddle.shape(pixel_pos_emb), message="pixel_pos_emb", summarize=-1)
+        if self.patch_seq_len > 196:  # when image_size > 224
+            pixel_pos_emb = interpolate_pos_embed(pixel_pos_emb, self.patch_seq_len)
+
+        emb_v_out = all_pixel_embeddings + pixel_pos_emb
+        emb_v_out = vit_pre_process_layer(
+            emb_v_out, 'd', self._vit_prepostprocess_dropout, name='vit.pre_encoder',
+            trainable=self._vit_encoder_trainable)
+
+        vit_enc_out, checkpoints = vit_encoder(
+            enc_input=emb_v_out,
+            attn_bias=n_head_self_attn_mask,
+            n_layer=self._vit_n_layer,
+            n_head=self._vit_n_head,
+            d_key=self._emb_size // self._vit_n_head,
+            d_value=self._emb_size // self._vit_n_head,
+            d_model=self._emb_size,
+            d_inner_hid=self._emb_size * 4,
+            prepostprocess_dropout=self._vit_prepostprocess_dropout,
+            attention_dropout=self._vit_attention_dropout,
+            relu_dropout=0,
+            hidden_act=self._vit_hidden_act,
+            preprocess_cmd="n",
+            postprocess_cmd="da",
+            param_initializer=self._param_initializer,
+            name='vit.encoder',
+            trainable=self._vit_encoder_trainable)
+        vit_seq_len = paddle.shape(vit_enc_out)[1]  # patch_seq_len + 1
+
+        return vit_enc_out, vit_seq_len, n_head_self_attn_mask, checkpoints
+
+    def text_encode(self, text_input, text_mask, gather_idx=None):
+        assert text_mask is not None, "text_mask should not be none"
+        text_self_attn_mask = text_mask
+        self_attn_mask = paddle.scale(
+            x=text_self_attn_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+        n_head_self_attn_mask = paddle.stack(
+            x=[self_attn_mask] * self._n_head, axis=1)
+        n_head_self_attn_mask.stop_gradient = True
+
+        # text part
+        text_emb = paddle.static.nn.embedding(
+            input=text_input["text.word_embedding"],
+            size=[self._text_emb_vocab_size["text.word_embedding"], self._emb_size],
+            dtype=self._emb_dtype,
+            param_attr=paddle.ParamAttr(
+                name='text.word_embedding',
+                trainable=self._text_encoder_trainable,
+                initializer=self._param_initializer))
+        text_emb = paddle.squeeze(text_emb, axis=2)  # (batch_size, seq_len, emb_dim)
+
+        pos_emb = paddle.static.nn.embedding(
+            input=text_input["text.pos_embedding"],
+            size=[self._text_emb_vocab_size["text.pos_embedding"], self._emb_size],
+            dtype=self._emb_dtype,
+            param_attr=paddle.ParamAttr(
+                name='text.pos_embedding',
+                trainable=self._text_encoder_trainable,
+                initializer=self._param_initializer))
+        pos_emb = paddle.squeeze(pos_emb, axis=2)  # (batch_size, seq_len, emb_dim)
+
+        emb_out = text_emb + pos_emb
+        emb_out = text_pre_process_layer(
+            emb_out, 'nd', self._text_prepostprocess_dropout,
+            name="text.pre_encoder", trainable=self._text_encoder_trainable)
+
+        text_enc_out, checkpoints = text_encoder(
+            enc_input=emb_out,
+            attn_bias=n_head_self_attn_mask,
+            enc_layers=self.text_enc_layers,
+            n_head=self._text_n_head,
+            d_key=self._emb_size // self._text_n_head,
+            d_value=self._emb_size // self._text_n_head,
+            d_model=self._emb_size,
+            d_inner_hid=self._emb_size * 4,
+            prepostprocess_dropout=self._text_prepostprocess_dropout,
+            attention_dropout=self._text_attention_dropout,
+            relu_dropout=0,
+            hidden_act=self._text_hidden_act,
+            preprocess_cmd="",
+            postprocess_cmd="dan",
+            param_initializer=self._param_initializer,
+            name='text.encoder',
+            caches=self.text_caches,
+            gather_idx=gather_idx,
+            trainable=self._text_encoder_trainable)
+
+        text_seq_len = paddle.shape(text_enc_out)[1]
+        return text_enc_out, text_seq_len, n_head_self_attn_mask, checkpoints
+
+    def _gen_input(self, text_input=None, text_mask=None,
+                   image_input=None, image_mask=None, gather_idx=None, decoding_step=False,
+                   grounded_decoding_mask=None):
+        _checkpoints = []
+        """encode images and texts independently by Vit and RoBERTa, get the optimal grounded tokens"""
+        if image_input is not None:
+            # visual part
+            self.vit_enc_out, vit_seq_len, nhead_image_self_attn_mask, vit_checkpoints = self.vit_encode(image_input,
+                                                                                                         image_mask)
+            _checkpoints.extend(vit_checkpoints)
+
+        if text_input is not None:
+            # textual part
+            self.text_enc_out, text_seq_len, _, text_checkpoints = self.text_encode(text_input,
+                                                                                    text_mask,
+                                                                                    gather_idx=gather_idx)
+            _checkpoints.extend(text_checkpoints)
+
+        if image_input is not None and text_input is not None:
+            batch_size = paddle.shape(self.text_enc_out)[0]
+            # (batch_size, v_seq_len, v_seq_len)
+            image_self_attn_mask = paddle.matmul(x=paddle.transpose(image_mask, perm=[0, 2, 1]), y=image_mask)
+            # (batch_size, v_seq_len, l_seq_len)
+            img_text_attn_mask = paddle.zeros(shape=[batch_size, vit_seq_len, text_seq_len], dtype='float32')
+            # (batch_size, l_seq_len, v_seq_len)
+            text_img_attn_mask = paddle.ones(shape=[batch_size, text_seq_len, vit_seq_len], dtype='float32')
+
+            image_row = paddle.concat([image_self_attn_mask, img_text_attn_mask], axis=2)
+            text_row = paddle.concat([text_img_attn_mask, text_mask], axis=2)
+            vl_self_attn_mask = paddle.concat([image_row, text_row], axis=1)
+            vl_self_attn_mask = paddle.scale(
+                x=vl_self_attn_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+            n_head_self_attn_mask = paddle.stack(
+                x=[vl_self_attn_mask] * self._n_head, axis=1)
+            n_head_self_attn_mask.stop_gradient = True
+
+            return self.vit_enc_out, self.text_enc_out, vit_seq_len, text_seq_len, n_head_self_attn_mask, _checkpoints
+
+        elif image_input is not None and text_input is None:
+            return self.vit_enc_out, vit_seq_len, nhead_image_self_attn_mask, _checkpoints
+
+        elif image_input is None and text_input is not None:
+            assert decoding_step is True, "decoding_step must be True"
+            assert grounded_decoding_mask is not None, "grounded_decoding_mask should not be none"
+            self_attn_mask = paddle.scale(
+                x=grounded_decoding_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+            n_head_self_attn_mask = paddle.stack(
+                x=[self_attn_mask] * self._n_head, axis=1)
+            n_head_self_attn_mask.stop_gradient = True
+
+            return self.text_enc_out, text_seq_len, n_head_self_attn_mask, _checkpoints
+
+    def get_checkpoints(self):
+        return self.all_checkpoints
+
+    def get_text_sequence_output(self):
+        return self._enc_l_out
diff --git a/NLP/UNIMO-2/src/finetune/img_eval.py b/NLP/UNIMO-2/src/finetune/img_eval.py
new file mode 100644
index 000000000..a9c723af2
--- /dev/null
+++ b/NLP/UNIMO-2/src/finetune/img_eval.py
@@ -0,0 +1,218 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+from collections import OrderedDict
+
+
+def recall_at_k(score_matrix, text2img, img2texts):
+    assert score_matrix.shape[0] == len(text2img) * len(img2texts)
+    cur_img, cur_cap = score_matrix[:, 1], score_matrix[:, 2]
+    img_len, cap_len = len(np.unique(cur_img)), len(np.unique(cur_cap))
+    
+    cur_img_sort = np.reshape(np.argsort(cur_img), [-1, cap_len])
+    cur_cap_sort = np.reshape(np.argsort(cur_cap), [-1, img_len])
+    i2c = np.take(score_matrix, cur_img_sort, axis=0) # img_len x cap_len x 3
+    c2i = np.take(score_matrix, cur_cap_sort, axis=0) # cap_len x img_len x 3
+
+    def get_recall_k(scores, idx, label_dict):
+        '''
+        scores: sample x len x 5
+        idx: 1 means text retrieval(i2c), 2 means image retrieval(c2i)
+        '''
+        cand_idx_dict = {1: 2, 2: 1}
+        cand_idx = cand_idx_dict[idx]
+        tot = scores.shape[0]
+        r1, r5, r10, rank_tot = 0, 0, 0, 0
+
+        for i in range(tot):
+            score_mat = scores[i]
+            cur_ids = score_mat[0][idx]
+            ans_ids = label_dict[cur_ids] # when idx is 1, type is list. idx is 2, type is int
+
+            score = score_mat[:, 0]
+            score_sort = np.argsort(score)[::-1]
+            cand_ans = np.take(score_mat[:, cand_idx], score_sort, axis=0)
+            cand_ans = cand_ans.astype(np.int64)
+
+            if isinstance(ans_ids, list):
+                rank = min([np.where(cand_ans == ans)[0] for ans in ans_ids])
+            elif isinstance(ans_ids, int):
+                rank = np.where(cand_ans == ans_ids)[0]
+            else:
+                raise ValueError('type error')
+            if rank < 1:
+                r1 += 1.0
+            if rank < 5:
+                r5 += 1.0
+            if rank < 10:
+                r10 += 1.0
+            rank_tot += (rank + 1)
+        ret = {
+                'recall@1': float(r1)/tot,
+                'recall@5': float(r5)/tot,
+                'recall@10': float(r10)/tot,
+                'avg_rank': float(rank_tot)/tot
+              }
+        return ret
+
+    cap_retrieval_recall = get_recall_k(i2c, 1, img2texts)
+    img_retrieval_recall = get_recall_k(c2i, 2, text2img)
+
+    ret = OrderedDict()
+    ret['img_avg_rank'] = img_retrieval_recall['avg_rank']
+    ret['cap_avg_rank'] = cap_retrieval_recall['avg_rank']
+
+    ret['img_recall@1'] = img_retrieval_recall['recall@1']
+    ret['img_recall@5'] = img_retrieval_recall['recall@5']
+    ret['img_recall@10'] = img_retrieval_recall['recall@10']
+
+    ret['cap_recall@1'] = cap_retrieval_recall['recall@1']
+    ret['cap_recall@5'] = cap_retrieval_recall['recall@5']
+    ret['cap_recall@10'] = cap_retrieval_recall['recall@10']
+
+    ret['avg_img_recall'] = (img_retrieval_recall['recall@1'] + img_retrieval_recall['recall@5'] + img_retrieval_recall['recall@10']) /3
+    ret['avg_cap_recall'] = (cap_retrieval_recall['recall@1'] + cap_retrieval_recall['recall@5'] + cap_retrieval_recall['recall@10']) /3
+
+    ret['avg_recall@1'] = (img_retrieval_recall['recall@1'] + cap_retrieval_recall['recall@1']) /2
+    ret['avg_recall@5'] = (img_retrieval_recall['recall@5'] + cap_retrieval_recall['recall@5']) /2
+    ret['avg_recall@10'] = (img_retrieval_recall['recall@10'] + cap_retrieval_recall['recall@10']) /2
+
+    ret['key_eval'] = "avg_recall@1"
+    return ret
+
+
+def recall_at_k_caption(score_matrix, img2texts, cap_len=100):
+    cur_img = score_matrix[:, 1]
+    cur_img_sort = np.reshape(np.argsort(cur_img), [-1, cap_len])
+    i2c = np.take(score_matrix, cur_img_sort, axis=0) # img_len x cap_len x 3
+
+    def get_recall_k(scores, idx, label_dict):
+        '''
+        scores: sample x len x 5
+        idx: 1 means text retrieval(i2c), 2 means image retrieval(c2i)
+        '''
+        cand_idx_dict = {1: 2, 2: 1}
+        cand_idx = cand_idx_dict[idx]
+        tot = scores.shape[0]
+        r1, r5, r10, rank_tot = 0, 0, 0, 0
+
+        for i in range(tot):
+            score_mat = scores[i]
+            cur_ids = score_mat[0][idx]
+            ans_ids = label_dict[cur_ids] # when idx is 1, type is list. idx is 2, type is int
+
+            score = score_mat[:, 0]
+            score_sort = np.argsort(score)[::-1]
+            cand_ans = np.take(score_mat[:, cand_idx], score_sort, axis=0)
+            cand_ans = cand_ans.astype(np.int64)
+
+            if isinstance(ans_ids, list):
+                rank = min([np.where(cand_ans == ans)[0] for ans in ans_ids])
+            elif isinstance(ans_ids, int):
+                rank = np.where(cand_ans == ans_ids)[0]
+            else:
+                raise ValueError('type error')
+            if rank < 1:
+                r1 += 1.0
+            if rank < 5:
+                r5 += 1.0
+            if rank < 10:
+                r10 += 1.0
+            rank_tot += (rank + 1)
+        ret = {
+                'recall@1': float(r1)/tot,
+                'recall@5': float(r5)/tot,
+                'recall@10': float(r10)/tot,
+                'avg_rank': float(rank_tot)/tot
+              }
+        return ret
+
+    cap_retrieval_recall = get_recall_k(i2c, 1, img2texts)
+
+    ret = OrderedDict()
+    ret['cap_avg_rank'] = cap_retrieval_recall['avg_rank']
+    ret['cap_recall@1'] = cap_retrieval_recall['recall@1']
+    ret['cap_recall@5'] = cap_retrieval_recall['recall@5']
+    ret['cap_recall@10'] = cap_retrieval_recall['recall@10']
+    ret['avg_cap_recall'] = (cap_retrieval_recall['recall@1'] + cap_retrieval_recall['recall@5'] + cap_retrieval_recall['recall@10']) /3
+    ret['key_eval'] = "cap_recall@1"
+    return ret
+
+
+def rank_acc(score_matrix, tot_sample, candidate_len):
+    """
+    shape is (tot_sample * candidate_len, 4)
+    score, ids, real_label, fake_label
+    """
+    assert score_matrix.shape[1] == 4
+    # rank by ids
+    score_matrix = np.take(score_matrix, np.argsort(score_matrix[:, 1]), axis=0)
+    score_matrix = np.reshape(score_matrix, [tot_sample, candidate_len, 4])
+    acc = 0
+    for i in range(tot_sample):
+        score_mat = score_matrix[i]
+        top_idx = np.argsort(score_mat[:, 0])[::-1][0]
+        acc = acc + int(score_mat[top_idx][2] == score_mat[top_idx][3])
+    ret = OrderedDict()
+    ret['rank_acc'] = round(float(acc/tot_sample), 4)
+    ret['key_eval'] = "rank_acc"
+    return ret
+
+def test_recall_at_k():
+    for i in range(3):
+        for img_len in [3, 4, 5]:
+            cap_ids = list(range(img_len * 5))
+            text_len = len(cap_ids)
+            img2texts, text2img = {}, {}
+            for img in range(img_len):
+                texts =  cap_ids[img*5 : (img+1)*5]
+                img2texts[img] =  texts
+                for text in texts:
+                    text2img[text] = img
+            score_mat = np.random.rand(img_len * img_len * 5, 1)
+            imgs, texts = list(img2texts.keys()), list(text2img.keys())
+            img_text = []
+            for i in range(img_len):
+                for j in range(text_len):
+                    mat = [imgs[i], texts[j]]
+                    img_text.append(mat)
+            img_text = np.array(img_text)
+            score_mat = np.concatenate([score_mat, img_text], axis=1)
+
+            #print(img2texts)
+            #print(text2img)
+            #print(score_mat)
+            ret = recall_at_k(score_mat, text2img, img2texts)
+            for k, v in ret.items():
+                print(k, v)
+            print('---------------')
+
+def test_rank_acc():
+    for k in range(10):
+        tot_sample, candidate_len, acc = 100, 1000, 0
+        score_matrix = None
+        real_label = list(range(candidate_len))
+        for i in range(tot_sample):
+            score_mat = np.random.random((candidate_len, 4))
+            fake_label = np.array([np.random.randint(2) for i in range(candidate_len)]) * real_label
+            score_mat[:, 1] = i
+            score_mat[:, 2] = real_label
+            score_mat[:, 3] = fake_label
+            score_matrix = score_mat if score_matrix is None else np.concatenate([score_matrix, score_mat], axis=0)
+            top_idx = np.argsort(score_mat[:, 0])[::-1][0]
+            acc = acc + int(score_mat[top_idx][2] == score_mat[top_idx][3])
+        np.random.shuffle(score_matrix)
+        # print(score_matrix)
+        ret = rank_acc(score_matrix, tot_sample, candidate_len)
+        cul_acc = ret[ret['key_eval']]
+        acc = round(float(acc/tot_sample), 4)
+        assert cul_acc == acc
+        print(cul_acc, acc)
+
+
+if __name__ == "__main__":
+    # test_recall_at_k()
+    test_rank_acc()
+
diff --git a/NLP/UNIMO-2/src/finetune/retrieval_grounded_fleet.py b/NLP/UNIMO-2/src/finetune/retrieval_grounded_fleet.py
new file mode 100644
index 000000000..ba2192a8e
--- /dev/null
+++ b/NLP/UNIMO-2/src/finetune/retrieval_grounded_fleet.py
@@ -0,0 +1,256 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: retrieval_grounded_fleet.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-10-25 10:50
+Desc: Model for retrieval.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import glob
+import time
+import codecs
+import numpy as np
+
+import paddle
+import paddle.fluid as fluid
+from finetune import img_eval
+from collections import OrderedDict
+from utils.utils import print_eval_log
+from model.unimo_grounded import VlModel as GroundedModel
+from model.unimo_grounded_baseline import VlModel as BaselineModel
+
+
+def circle_loss(sp, sn, m, scale):
+    """
+    sp: score list of positive samples, shape [B * L]
+    sn: score list of negative samples, shape [B * K]
+    m: relaxation factor in circle loss function
+    scale:  scale factor in circle loss function
+
+    return: circle loss value, shape [1]
+    """
+    op = 1. + m
+    on = 0. - m
+
+    delta_p = 1 - m
+    delta_n = m
+
+    ap = fluid.layers.relu(op - sp)
+    ap.stop_gradient = True
+    an = fluid.layers.relu(sn - on)
+    an.stop_gradient = True
+
+    logit_p = ap * (sp - delta_p)
+    logit_p = -1. * scale * logit_p
+    logit_p = fluid.layers.cast(x=logit_p, dtype=np.float64)
+    loss_p = fluid.layers.reduce_sum(fluid.layers.exp(logit_p), dim=1, keep_dim=False)
+
+    logit_n = an * (sn - delta_n)
+    logit_n = scale * logit_n
+    logit_n = fluid.layers.cast(x=logit_n, dtype=np.float64)
+    loss_n = fluid.layers.reduce_sum(fluid.layers.exp(logit_n), dim=1, keep_dim=False)
+
+    circle_loss = fluid.layers.log(1 + loss_n * loss_p)
+    circle_loss = fluid.layers.cast(x=circle_loss, dtype=np.float32)
+    return fluid.layers.mean(circle_loss)
+
+
+def create_model(args, phase, vl_config, samples_num):
+    """"create_model"""
+    emb_size = vl_config['hidden_size']
+    patch_seq_len = vl_config['image_size'] * vl_config['image_size'] // \
+                    (vl_config['resolution'] * vl_config['resolution'])
+
+    src_ids = fluid.layers.data(name='src_ids', shape=[-1, args.max_seq_len, 1], dtype='int64')
+    pos_ids = fluid.layers.data(name='pos_ids', shape=[-1, args.max_seq_len, 1], dtype='int64')
+    sent_ids = fluid.layers.data(name='sent_ids', shape=[-1, args.max_seq_len, 1], dtype='int64')
+    text_mask = fluid.layers.data(name='text_mask', shape=[-1, 1, args.max_seq_len], dtype='float32')
+    image_pixel_input = fluid.layers.data(name='image_pixel_input',
+                                          shape=[-1, vl_config['image_size'], vl_config['image_size'], 3],
+                                          dtype='float32')
+    image_mask = fluid.layers.data(name='image_mask', shape=[-1, 1, patch_seq_len + 1], dtype='float32')
+    labels = fluid.layers.data(name='labels', shape=[-1, 1], dtype='int64')
+    ids = fluid.layers.data(name='ids', shape=[-1, 2], dtype='int64')
+
+    drop_last = True if phase == 'train' else False
+    feed_list = [src_ids, pos_ids, sent_ids, text_mask, image_pixel_input, image_mask, labels, ids]
+    pyreader = fluid.io.DataLoader.from_generator(
+        feed_list=feed_list,
+        capacity=70,
+        use_double_buffer=True,
+        iterable=False,
+        drop_last=drop_last)
+
+    text_input = {"text.word_embedding": src_ids, "text.sent_embedding": sent_ids, "text.pos_embedding": pos_ids}
+    image_input = {"pixel_embedding": image_pixel_input}
+
+    text_enc_layers = [int(i) for i in args.text_enc_layers.split(',')]
+    grounding_enc_layers = [int(i) for i in args.grounding_enc_layers.split(',')]
+
+    if args.model_type == 'grounded':
+        model = GroundedModel(
+            text_input=text_input,
+            text_mask=text_mask,
+            image_input=image_input,
+            image_mask=image_mask,
+            config=vl_config,
+            weight_sharing=args.weight_sharing,
+            grounding_method=args.grounding_method,
+            topk_value=args.topk_value,
+            with_grounding_projection=args.with_grounding_projection,
+            with_cmcl_projection=args.with_cmcl_projection,
+            cmcl_share_parameters=args.cmcl_share_parameters,
+            with_grounding_pos=args.with_grounding_pos,
+            text_enc_layers=text_enc_layers,
+            grounding_enc_layers=grounding_enc_layers)
+
+        if args.with_cmcl:
+            cmcl_score = model.get_cmcl_scores()
+
+    elif args.model_type == 'baseline':
+        model = BaselineModel(
+            text_input=text_input,
+            text_mask=text_mask,
+            image_input=image_input,
+            image_mask=image_mask,
+            config=vl_config,
+            weight_sharing=args.weight_sharing,
+            with_cmcl_projection=args.with_cmcl_projection,
+            text_enc_layers=text_enc_layers,
+            grounding_enc_layers=grounding_enc_layers)
+
+        if args.with_cmcl:
+            cmcl_score = model.get_cmcl_scores()
+
+    else:
+        raise ValueError("The model_type is invalid!!!")
+
+    text, image = model.get_pooled_output()
+    score = model.get_match_output(text, image, mode="mul")
+    # if args.zero_shot:
+    logits = fluid.layers.fc(
+        input=score,
+        size=2,
+        param_attr=fluid.ParamAttr(
+            name="grounded.img_text_matching_fc.w_0",
+            initializer=fluid.initializer.Xavier()),
+        bias_attr=fluid.ParamAttr(
+            name="grounded.img_text_matching_fc.b_0",
+            initializer=fluid.initializer.Constant(0.)))
+    itm_score = fluid.layers.slice(logits, axes=[1], starts=[1], ends=[2])
+
+    if args.with_cmcl:
+        score = itm_score + args.cmcl_score_weight * cmcl_score
+    else:
+        score = itm_score
+
+    score = fluid.layers.reshape(score, [-1, samples_num])
+    if phase == 'train':
+        if args.use_sigmoid:
+            score = fluid.layers.sigmoid(score)
+        positive_score = score[:, 0]
+        image_neg_score = score[:, 1:int((samples_num + 1) / 2)]
+        caption_neg_score = score[:, int((samples_num + 1) / 2):]
+        acc = fluid.layers.accuracy(score, labels, k=1)
+
+        positive_score = fluid.layers.reshape(x=positive_score, shape=[-1, 1])
+        loss_c = circle_loss(positive_score, caption_neg_score, args.margin, args.scale_circle)
+        loss_i = circle_loss(positive_score, image_neg_score, args.margin, args.scale_circle)
+        total_loss = (loss_c + loss_i) / 2
+    else:
+        assert samples_num == 1
+        total_loss = fluid.layers.cross_entropy(input=score, label=labels)
+        total_loss = fluid.layers.mean(x=total_loss)
+        acc = fluid.layers.zeros_like(total_loss)
+    graph_vars = {"loss": total_loss, "acc": acc, "score": score, "label": labels, "ids": ids}
+    return pyreader, graph_vars, model.get_checkpoints()
+
+
+def evaluate(args, exe, test_program, test_pyreader, graph_vars, eval_phase, dev_count=1, gpu_id=0, data_reader=None):
+    """evaluate"""
+    test_pyreader.start()
+    time_begin = time.time()
+    all_mat = None
+    fetch_list = [graph_vars["score"].name, graph_vars["ids"].name]
+    while True:
+        try:
+            score, ids = exe.run(fetch_list=fetch_list)
+            mat = np.concatenate([score, ids], axis=1)
+            if all_mat is None:
+                all_mat = mat
+            else:
+                all_mat = np.concatenate([all_mat, mat], axis=0)
+        except fluid.core.EOFException:
+            test_pyreader.reset()
+            break
+    time_end = time.time()
+
+    save_file = "%s/%s.trainers_%d.part_%d.npy" % (args.eval_dir, eval_phase, dev_count, gpu_id)
+    np.save(save_file, all_mat)
+    tmp_file = "%s/%s.trainers_%d.part_%d.finish" % (args.eval_dir, eval_phase, dev_count, gpu_id)
+    tmp_writer = codecs.open(tmp_file, "w", 'utf-8')
+    tmp_writer.close()
+
+    if gpu_id == 0:
+        while True:
+            ret = os.popen('find %s -maxdepth 1 -name "%s.trainers_%d.part_*.finish"' %
+                           (args.eval_dir, eval_phase, dev_count)).readlines()
+            if len(ret) != dev_count:
+                time.sleep(1)
+                continue
+            else:
+                break
+
+        all_mat = None
+        save_files = glob.glob("%s/%s.trainers_%d.part_*.npy" % (args.eval_dir, eval_phase, dev_count))
+        for cur_save_file in save_files:
+            mat = np.load(cur_save_file)
+            if all_mat is None:
+                all_mat = mat
+            else:
+                all_mat = np.concatenate([all_mat, mat], axis=0)
+
+        cur_time = str(int(time.time()))
+        os.system("mkdir %s/%s" % (args.eval_dir, cur_time))
+        os.system("mv %s/%s.trainers_%d.* %s/%s" % (args.eval_dir, eval_phase, dev_count, args.eval_dir, cur_time))
+
+        assert data_reader is not None
+        text2img = {text_id: item[-1] for text_id, item in data_reader._caption_ids_dict.items()}
+        img2texts = data_reader._image_sent_map
+
+        ret = OrderedDict()
+        ret['phase'] = eval_phase
+        ret['loss'] = -1
+        ret['data_num'] = all_mat.shape[0]
+        ret['used_time'] = round(time_end - time_begin, 4)
+        metrics = OrderedDict()
+        metrics["recall@k"] = img_eval.recall_at_k
+        if args.eval_mertrics in metrics:
+            ret_metric = metrics[args.eval_mertrics](all_mat, text2img, img2texts)
+            ret.update(ret_metric)
+            print_eval_log(ret)
+        else:
+            raise ValueError('unsupported metric {}'.format(args.eval_mertrics))
+        return ret
+    else:
+        return None
diff --git a/NLP/UNIMO-2/src/finetune/trigram_blocking.py b/NLP/UNIMO-2/src/finetune/trigram_blocking.py
new file mode 100644
index 000000000..780b51348
--- /dev/null
+++ b/NLP/UNIMO-2/src/finetune/trigram_blocking.py
@@ -0,0 +1,175 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: trigram_blocking.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-06-22 19:35
+Desc: trigram_blocking for sequence generation
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import numpy as np
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+
+
+class TrigramBlocking(object):
+    """trigram blocking check data holder
+    """
+
+    def __init__(self, init_token, roberta_tokenizer, beam_size, use_fp16=False):
+        """use tokenizer to generate the real-tokens from sub-token ids.
+        but we can't pass the tokenizer to network, so we need make a trick
+        """
+        # => [N, T==0, 1]
+        self._alive_seq = fluid.layers.fill_constant_batch_size_like(
+            input=init_token,
+            shape=[-1, 0, 1],
+            dtype=init_token.dtype,
+            value=0)
+        self._cand_seq = fluid.layers.fill_constant_batch_size_like(
+            input=init_token,
+            shape=[-1, 0, beam_size],
+            dtype=init_token.dtype,
+            value=0)
+
+        self.beam_size = beam_size
+
+        self._dtype = "float32" if not use_fp16 else "float16"
+        _SHAPE_PLACEHOLDER = [10, beam_size]
+        self._delta_score_out = fluid.layers.create_parameter(shape=_SHAPE_PLACEHOLDER, dtype=self._dtype,
+                                                              name="duplicated_trigram_blocking_delta_score_out")
+        self.tokenizer = roberta_tokenizer
+        id2is_full_token = self._build_id2is_full_token(self.tokenizer, self._dtype)
+        self._id2is_full_token = fluid.layers.create_parameter(shape=id2is_full_token.shape, dtype=self._dtype,
+                                                               name="duplicated_trigram_blocking_id2is_full_token",
+                                                               default_initializer=fluid.initializer.NumpyArrayInitializer(
+                                                                   id2is_full_token))
+
+    def update_seq(self, new_step_id, gather_idx):
+        """update alive sequence. need pre-gather the inner seq then concat the new step id"""
+        # new_step_id = fluid.layers.unsqueeze(new_step_id, axes=[1])
+        alive_seq = fluid.layers.gather(self._alive_seq, gather_idx)
+        # => [N, T==1, 1]
+        alive_seq = fluid.layers.concat([alive_seq, new_step_id], axis=1)
+        fluid.layers.assign(alive_seq, self._alive_seq)
+        return self._alive_seq
+
+    def expand_cand_seq(self, new_topk_indx):
+        """expand the alive seq by concatenating the topk candidates"""
+        new_topk_indx = fluid.layers.unsqueeze(new_topk_indx, axes=[1])  # (batch_size, 1, beam_size)
+        cand_seq = fluid.layers.expand(self._alive_seq, expand_times=[1, 1, self.beam_size])
+        # => [N, T+1, beam_size]
+        expand_cand_seq = fluid.layers.concat([cand_seq, new_topk_indx], axis=1)
+        fluid.layers.assign(expand_cand_seq, self._cand_seq)
+        return self._cand_seq
+
+    @property
+    def alive_seq(self):
+        """alive seq"""
+        return self._alive_seq
+
+    @property
+    def cand_seq(self):
+        """candidate seq"""
+        return self._cand_seq
+
+    @property
+    def delta_score_out(self):
+        """delta score out"""
+        return self._delta_score_out
+
+    @property
+    def id2is_full_token(self):
+        """id->isfulltoken"""
+        return self._id2is_full_token
+
+    @staticmethod
+    def blocking_forward(cand_seq, id2is_full_token):
+        """py_func can't be member function
+        run the trigram-blocking logic. return `delta-score` for every sequence.
+            for seq which has duplicated trigram, set delta-score = -inf,
+            else set delta-score = 0
+        in the outer, should do the `seq-score + delta-score` logic
+
+        alive_seq: shape = [N, T, 1]
+
+        Returns
+        ---------
+        np.array, shape = [N, 1]
+        """
+        _BLOCKING_DELTA = -65000.0  # -65500.0 is the min value of float16
+        _KEEP_DELTA = 0.0
+        cand_seq = np.array(cand_seq)  # (batch_size, dec_len, beam_size)
+        cand_seq = np.transpose(cand_seq, axes=(0, 2, 1))  # (batch_size, beam_size, dec_len)
+        id2is_full_token = np.array(id2is_full_token)
+
+        def _sub_token_id2full_tokens(sub_token_ids):
+            full_tokens = []
+            for sub_token_id in sub_token_ids:
+                is_full_token = bool(id2is_full_token[sub_token_id])
+
+                if is_full_token or not full_tokens:
+                    full_tokens.append([sub_token_id])
+                else:
+                    pre_full_token = full_tokens[-1]
+                    pre_full_token.append(sub_token_id)
+
+            full_tokens = ["-".join(map(str, full_token)) for full_token in full_tokens]
+            return full_tokens
+
+        _make_trigram_str = lambda trigram_tokens: "_".join(trigram_tokens)
+        delta_list = []
+        for beam_cand_ids in cand_seq:
+            delta_score = []
+            for one_seq_ids in beam_cand_ids:
+                sub_token_ids = one_seq_ids.reshape(-1)
+                tokens = _sub_token_id2full_tokens(sub_token_ids)
+                if len(tokens) <= 3:
+                    delta_score.append(_KEEP_DELTA)
+                    continue
+                # don't include the last trigram(checking self)!
+                trigrams = [_make_trigram_str(tokens[end - 3: end]) for end in range(3, len(tokens))]
+                trigrams_set = set(trigrams)
+                last_trigram = _make_trigram_str(tokens[-3:])
+                if last_trigram in trigrams_set:
+                    # duplicated
+                    delta_score.append(_BLOCKING_DELTA)
+                else:
+                    delta_score.append(_KEEP_DELTA)
+            delta_list.append(delta_score)
+
+        return np.array(delta_list, dtype=id2is_full_token.dtype).reshape(cand_seq.shape[0], cand_seq.shape[1])
+
+    @staticmethod
+    def blocking_backward(*args):
+        """blocking backward"""
+        raise ValueError("Impossible call backward.")
+
+    def _build_id2is_full_token(self, tokenizer, dtype):
+        vocab_sz = tokenizer.vocab_size()
+        is_full_token = [0.0] * vocab_sz
+        for token_id in range(vocab_sz):
+            token = tokenizer.convert_id_to_token(token_id)
+            token_str = tokenizer.gptbpe_tokenizer.decode_token(token)
+            if token_str.startswith(' '):
+                is_full_token[token_id] = 1.0
+
+        return np.array(is_full_token, dtype=dtype)
+
diff --git a/NLP/UNIMO-2/src/launch.py b/NLP/UNIMO-2/src/launch.py
new file mode 100644
index 000000000..63e73b023
--- /dev/null
+++ b/NLP/UNIMO-2/src/launch.py
@@ -0,0 +1,137 @@
+"""
+launch for multi process training
+"""
+import sys
+import subprocess
+import os
+import copy
+import argparse
+
+from utils.args import ArgumentGroup, print_arguments
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+multip_g = ArgumentGroup(parser, "multiprocessing",
+                         "start paddle training using multi-processing mode.")
+multip_g.add_arg("node_ips", str, None,
+                 "paddle trainer ips")
+multip_g.add_arg("node_id", int, None,
+                 "the trainer id of the node for multi-node distributed training.")
+multip_g.add_arg("print_config", bool, True,
+                 "print the config of multi-processing mode.")
+multip_g.add_arg("current_node_ip", str, None,
+                 "the ip of current node.")
+multip_g.add_arg("split_log_path", str, "log",
+                 "log path for each trainer.")
+multip_g.add_arg("log_prefix", str, "",
+                 "the prefix name of job log.")
+multip_g.add_arg("nproc_per_node", int, 8,
+                 "the number of process to use on each node.")
+multip_g.add_arg("selected_gpus", str, "0,1,2,3,4,5,6,7",
+                 "the gpus selected to use.")
+multip_g.add_arg("training_script", str, None, "the program/script to be lauched "
+                                               "in parallel followed by all the arguments", positional_arg=True)
+multip_g.add_arg("training_script_args", str, None,
+                 "training script args", positional_arg=True, nargs=argparse.REMAINDER)
+
+
+# yapf: enable
+
+
+def start_procs(args):
+    """
+        start_procs
+    """
+    default_env = os.environ.copy()
+
+    node_id = args.node_id
+    print(args.node_ips)
+    node_ips = [x.strip() for x in args.node_ips.split(',')]
+    current_ip = args.current_node_ip
+    num_nodes = len(node_ips)
+    selected_gpus = [x.strip() for x in args.selected_gpus.split(',')]
+    selected_gpu_num = len(selected_gpus)
+    start_port = int(default_env['PADDLE_PORT'])
+    all_trainer_endpoints = ""
+    for ip in node_ips:
+        cur_port = start_port + 1
+        for i in range(args.nproc_per_node):
+            cur_port += 1
+            if all_trainer_endpoints != "":
+                all_trainer_endpoints += ","
+            all_trainer_endpoints += "%s:%d" % (ip, cur_port)
+
+    nranks = num_nodes * args.nproc_per_node
+    gpus_per_proc = args.nproc_per_node % selected_gpu_num
+    if gpus_per_proc == 0:
+        gpus_per_proc = selected_gpu_num // args.nproc_per_node
+    else:
+        gpus_per_proc = selected_gpu_num // args.nproc_per_node + 1
+
+    selected_gpus_per_proc = [selected_gpus[i:i + gpus_per_proc]
+                              for i in range(0, len(selected_gpus), gpus_per_proc)]
+
+    if args.print_config:
+        print("all_trainer_endpoints: ", all_trainer_endpoints,
+              ", node_id: ", node_id,
+              ", current_ip: ", current_ip,
+              ", num_nodes: ", num_nodes,
+              ", node_ips: ", node_ips,
+              ", gpus_per_proc: ", gpus_per_proc,
+              ", selected_gpus_per_proc: ", selected_gpus_per_proc,
+              ", nranks: ", nranks)
+
+    current_env = copy.copy(default_env)
+    procs = []
+    cmds = []
+    log_fns = []
+    cur_port = start_port + 1
+    for i in range(0, args.nproc_per_node):
+        trainer_id = node_id * args.nproc_per_node + i
+        cur_port += 1
+        current_env.update({
+            "FLAGS_selected_gpus": "%s" % ",".join([str(s) for s in selected_gpus_per_proc[i]]),
+            "PADDLE_TRAINER_ID": "%d" % trainer_id,
+            "PADDLE_CURRENT_ENDPOINT": "%s:%d" % (current_ip, cur_port),
+            "PADDLE_TRAINERS_NUM": "%d" % nranks,
+            "PADDLE_TRAINER_ENDPOINTS": all_trainer_endpoints,
+            "PADDLE_NODES_NUM": "%d" % num_nodes
+        })
+
+        cmd = [sys.executable, "-u",
+               args.training_script] + args.training_script_args
+        cmds.append(cmd)
+
+        if args.split_log_path:
+            fn = open("%s/%sjob.log.%d" % (args.split_log_path, args.log_prefix, trainer_id), "a")
+            log_fns.append(fn)
+            process = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn)
+        else:
+            process = subprocess.Popen(cmd, env=current_env)
+        procs.append(process)
+
+    for i in range(len(procs)):
+        proc = procs[i]
+        proc.wait()
+        if len(log_fns) > 0:
+            log_fns[i].close()
+        if proc.returncode != 0:
+            raise subprocess.CalledProcessError(returncode=procs[i].returncode,
+                                                cmd=cmds[i])
+        else:
+            print("proc %d finsh" % i)
+    print("run success")
+
+
+def main(args):
+    """
+        main_func
+    """
+    if args.print_config:
+        print_arguments(args)
+    start_procs(args)
+
+
+if __name__ == "__main__":
+    lanch_args = parser.parse_args()
+    main(lanch_args)
diff --git a/NLP/UNIMO-2/src/model/__init__.py b/NLP/UNIMO-2/src/model/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/NLP/UNIMO-2/src/model/roberta_tokenization.py b/NLP/UNIMO-2/src/model/roberta_tokenization.py
new file mode 100644
index 000000000..e377442ea
--- /dev/null
+++ b/NLP/UNIMO-2/src/model/roberta_tokenization.py
@@ -0,0 +1,633 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2019 Baidu.com, Inc. All Rights Reserved
+#
+
+"""
+File: main.py
+Author: liwei(liwei85@baidu.com)
+Date: 2019-12-01 16:21
+Desc: Tokenization classes.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import unicodedata
+import six
+import os
+import json
+import regex as re
+from functools import lru_cache
+import numpy as np
+
+
+def convert_to_unicode(text):
+    """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
+    if six.PY3:
+        if isinstance(text, str):
+            return text
+        elif isinstance(text, bytes):
+            try:
+                return text.decode("utf-8", "ignore")
+            except:
+                return text.decode("gb18030", "ignore")
+        else:
+            raise ValueError("Unsupported string type: %s" % (type(text)))
+    elif six.PY2:
+        if isinstance(text, str):
+            try:
+                return text.decode("utf-8", "ignore")
+            except:
+                return text.decode("gb18030", "ignore")
+        elif isinstance(text, unicode):
+            return text
+        else:
+            raise ValueError("Unsupported string type: %s" % (type(text)))
+    else:
+        raise ValueError("Not running on Python2 or Python 3?")
+
+
+def printable_text(text):
+    """Returns text encoded in a way suitable for print or `tf.logging`."""
+
+    # These functions want `str` for both Python2 and Python3, but in one case
+    # it's a Unicode string and in the other it's a byte string.
+    if six.PY3:
+        if isinstance(text, str):
+            return text
+        elif isinstance(text, bytes):
+            return text.decode("utf-8", "ignore")
+        else:
+            raise ValueError("Unsupported string type: %s" % (type(text)))
+    elif six.PY2:
+        if isinstance(text, str):
+            return text
+        elif isinstance(text, unicode):
+            return text.encode("utf-8")
+        else:
+            raise ValueError("Unsupported string type: %s" % (type(text)))
+    else:
+        raise ValueError("Not running on Python2 or Python 3?")
+
+
+def load_vocab(vocab_file):
+    """Loads a vocabulary file into a dictionary."""
+    vocab = collections.OrderedDict()
+    fin = open(vocab_file)
+    for num, line in enumerate(fin):
+        items = convert_to_unicode(line.strip()).split("\t")
+        if len(items) > 2:
+            break
+        token = items[0]
+        index = items[1] if len(items) == 2 else num
+        token = token.strip()
+        vocab[token] = int(index)
+    return vocab
+
+
+def convert_by_vocab(vocab, items):
+    """Converts a sequence of [tokens|ids] using the vocab."""
+    output = []
+    for item in items:
+        output.append(vocab[item])
+    return output
+
+
+def convert_tokens_to_ids(vocab, tokens):
+    """convert tokens to vocab ids"""
+    return convert_by_vocab(vocab, tokens)
+
+
+def convert_ids_to_tokens(inv_vocab, ids):
+    """convert vocab ids to tokens"""
+    return convert_by_vocab(inv_vocab, ids)
+
+
+def whitespace_tokenize(text):
+    """Runs basic whitespace cleaning and splitting on a peice of text."""
+    text = text.strip()
+    if not text:
+        return []
+    tokens = text.split()
+    return tokens
+
+
+@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.
+    """
+    bs = list(range(33, 126 + 1)) + list(range(161, 172 + 1)) + list(range(174, 255 + 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 Tokenizer(object):
+    """RoBERTa Tokenizer"""
+    def __init__(self, encoder, bpe_merges, errors='replace'):
+        self.encoder = encoder
+        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()}
+        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.int_token = re.compile(r"^[0-9]+$")
+
+    def bpe(self, token):
+        """bpe tokenizing"""
+        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 encode(self, text):
+        """bpe encoding"""
+        bpe_tokens = []
+        for token in re.findall(self.pat, text):
+            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
+            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
+        return bpe_tokens
+
+    def decode(self, tokens):
+        """bpe decoding"""
+        decoded_tokens = []
+        for token in tokens:
+            if self.int_token.match(str(token)):
+                decoded_tokens.append(self.decoder[int(token)])
+            else:
+                decoded_tokens.append(str(token))
+        text = ''.join(decoded_tokens)
+        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
+        return text
+
+    def decode_token(self, token):
+        if self.int_token.match(str(token)):
+            token = self.decoder[int(token)]
+        else:
+            token = str(token)
+        text = bytearray([self.byte_decoder[c] for c in token]).decode('utf-8', errors=self.errors)
+        return text
+
+
+class GptBpeTokenizer(object):
+    """GptBpeTokenizer"""
+    def __init__(self, vocab_file=None, encoder_json_file=None, vocab_bpe_file=None, do_lower_case=True):
+        if vocab_file is None:
+            vocab_file = "./model_files/dict/roberta_base_en.vocab.txt"
+        if encoder_json_file is None:
+            encoder_json_file = "./model_files/dict/roberta_base_en.encoder.json"
+        if vocab_bpe_file is None:
+            vocab_bpe_file = "./model_files/dict/roberta_base_en.vocab.bpe"
+
+        with open(encoder_json_file, 'r') as f:
+            encoder = json.load(f)
+        with open(vocab_bpe_file, 'r', encoding="utf-8") as f:
+            bpe_data = f.read()
+
+        bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split('\n')[1:-1]]
+        self.gptbpe_tokenizer = Tokenizer(encoder=encoder, bpe_merges=bpe_merges)
+        self.vocab = load_vocab(vocab_file)
+        self.inv_vocab = {v: k for k, v in self.vocab.items()}
+
+        self.cls_token = '[CLS]'
+        self.pad_token = '[PAD]'
+        self.sep_token = '[SEP]'
+        self.unk_token = '[UNK]'
+        self.mask_token = '[MASK]'
+        self.IMG_token = 'madeupword0000'
+
+        self.cls_token_id = self.vocab['[CLS]']
+        self.pad_token_id = self.vocab['[PAD]']
+        self.sep_token_id = self.vocab['[SEP]']
+        self.unk_token_id = self.vocab['[UNK]']
+        self.mask_token_id = self.vocab['[MASK]']
+        self.IMG_token_id = self.vocab['madeupword0000']
+
+    def tokenize(self, text):
+        """tokenize text to a list of tokens"""
+        return [str(token) for token in self.gptbpe_tokenizer.encode(text)]
+
+    def convert_tokens_to_ids(self, tokens):
+        """convert tokens to vocab ids"""
+        return convert_by_vocab(self.vocab, tokens)
+
+    def convert_token_to_id(self, token):
+        """convert token to vocab id"""
+        return self.vocab[token]
+
+    def convert_ids_to_tokens(self, ids):
+        """convert vocab ids to tokens"""
+        return convert_by_vocab(self.inv_vocab, ids)
+
+    def convert_id_to_token(self, id):
+        """convert vocab id to token"""
+        return self.inv_vocab[id]
+
+    def vocab_size(self):
+        """get the vocab size"""
+        return len(self.vocab)
+
+
+class FullTokenizer(object):
+    """Runs end-to-end tokenziation."""
+
+    def __init__(self, vocab_file, do_lower_case=True):
+        self.vocab = load_vocab(vocab_file)
+        self.inv_vocab = {v: k for k, v in self.vocab.items()}
+        self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
+        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
+
+    def tokenize(self, text):
+        """tokenize the text"""
+        split_tokens = []
+        for token in self.basic_tokenizer.tokenize(text):
+            for sub_token in self.wordpiece_tokenizer.tokenize(token):
+                split_tokens.append(sub_token)
+
+        return split_tokens
+
+    def convert_tokens_to_ids(self, tokens):
+        """convert tokens to vocab ids"""
+        return convert_by_vocab(self.vocab, tokens)
+
+    def convert_ids_to_tokens(self, ids):
+        """convert vocab ids to tokens"""
+        return convert_by_vocab(self.inv_vocab, ids)
+
+    def merge_subword(self, tokens):
+        ret = []
+        for token in tokens:
+            if token.startswith("##"):
+                real_token = token[2:]
+                if len(ret):
+                    ret[-1] += real_token
+                else:
+                    ret.append(real_token)
+            else:
+                ret.append(token)
+
+        return ret
+
+
+class CharTokenizer(object):
+    """Runs end-to-end tokenziation."""
+
+    def __init__(self, vocab_file, do_lower_case=True):
+        self.vocab = load_vocab(vocab_file)
+        self.inv_vocab = {v: k for k, v in self.vocab.items()}
+        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
+
+    def tokenize(self, text):
+        """tokenize the text"""
+        split_tokens = []
+        for token in text.lower().split(" "):
+            for sub_token in self.wordpiece_tokenizer.tokenize(token):
+                split_tokens.append(sub_token)
+
+        return split_tokens
+
+    def convert_tokens_to_ids(self, tokens):
+        """convert tokens to vocab ids"""
+        return convert_by_vocab(self.vocab, tokens)
+
+    def convert_ids_to_tokens(self, ids):
+        """convert vocab ids to tokens"""
+        return convert_by_vocab(self.inv_vocab, ids)
+
+
+class BasicTokenizer(object):
+    """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
+
+    def __init__(self, do_lower_case=True):
+        """Constructs a BasicTokenizer.
+
+        Args:
+            do_lower_case: Whether to lower case the input.
+        """
+        self.do_lower_case = do_lower_case
+
+    def tokenize(self, text):
+        """Tokenizes a piece of text."""
+        text = convert_to_unicode(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:
+                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."""
+        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 tokenziation."""
+
+    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.
+        """
+
+        text = convert_to_unicode(text)
+
+        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
+
+
+def test_gpt_bpe_tokenizer():
+    """test the tokenizer"""
+    tokenizer = GptBpeTokenizer()
+
+    text = "Membership gives the ICC jurisdiction over alleged crimes committed " \
+           "in Palestinian territories since last June . Israel and the United States " \
+           "opposed the move , which could open the door to war crimes investigations against Israelis ."
+    print(1, text)
+    text = convert_to_unicode(text)
+    tokens = tokenizer.tokenize(text)
+    # tokens = ["[UNK]", "[PAD]", "[CLS]", "[SEP]"]
+    print(2, tokens)
+    token_ids = tokenizer.convert_tokens_to_ids(tokens)
+    print(3, token_ids)
+    tokens = tokenizer.convert_ids_to_tokens(token_ids)
+    print(4, tokens)
+    tokens = tokenizer.gptbpe_tokenizer.decode(tokens)
+    print(5, tokens)
+    tokens = tokenizer.gptbpe_tokenizer.encode(text)
+    print(6, tokens)
+
+    str_tokens = []
+    for token in tokens:
+        str_tokens.append(tokenizer.gptbpe_tokenizer.decode_token(token))
+    print(7, str_tokens)
+
+    tokens = tokenizer.gptbpe_tokenizer.decode(tokens)
+    print(8, tokens)
+
+
+def build_id2is_full_token(tokenizer, dtype="float32"):
+    vocab_sz = tokenizer.vocab_size()
+    is_full_token = [0.0] * vocab_sz
+    token_strs = []
+
+    for token_id in range(vocab_sz):
+        token = tokenizer.convert_id_to_token(token_id)
+        token_str = tokenizer.gptbpe_tokenizer.decode_token(token)
+        token_strs.append(token_str)
+        if token_str.startswith(' '):
+            is_full_token[token_id] = 1.0
+
+    is_full_token = np.array(is_full_token, dtype=dtype)
+    print(is_full_token[500:520])
+    print(token_strs[500:520])
+
+
+if __name__ == "__main__":
+    test_gpt_bpe_tokenizer()
+    # tokenizer = GptBpeTokenizer()
+    # build_id2is_full_token(tokenizer)
diff --git a/NLP/UNIMO-2/src/model/transformer_encoder.py b/NLP/UNIMO-2/src/model/transformer_encoder.py
new file mode 100644
index 000000000..363b20257
--- /dev/null
+++ b/NLP/UNIMO-2/src/model/transformer_encoder.py
@@ -0,0 +1,400 @@
+#   Copyright (c) 2019 PaddlePaddle Authors. 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 encoder."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from functools import partial
+import paddle
+
+
+def multi_head_attention(queries,
+                         keys,
+                         values,
+                         attn_bias,
+                         d_key,
+                         d_value,
+                         d_model,
+                         n_head=1,
+                         dropout_rate=0.,
+                         cache=None,
+                         gather_idx=None,
+                         param_initializer=None,
+                         name='multi_head_att',
+                         trainable=True):
+    """
+    Multi-Head Attention. Note that attn_bias is added to the logit before
+    computing softmax activiation to mask certain selected positions so that
+    they will not considered in attention weights.
+    """
+    keys = queries if keys is None else keys
+    values = keys if values is None else values
+
+    if not (len(queries.shape) == len(keys.shape) == len(values.shape) == 3):
+        raise ValueError(
+            "Inputs: quries, keys and values should all be 3-D tensors.")
+
+    def __compute_qkv(queries, keys, values, n_head, d_key, d_value):
+        """
+        Add linear projection to queries, keys, and values.
+        """
+        q = paddle.static.nn.fc(x=queries,
+                                size=d_key * n_head,
+                                num_flatten_dims=2,
+                                weight_attr=paddle.ParamAttr(
+                                    name=name + '_query_fc.w_0',
+                                    trainable=trainable,
+                                    initializer=param_initializer),
+                                bias_attr=paddle.ParamAttr(
+                                    name=name + '_query_fc.b_0',
+                                    trainable=trainable))
+        k = paddle.static.nn.fc(x=keys,
+                                size=d_key * n_head,
+                                num_flatten_dims=2,
+                                weight_attr=paddle.ParamAttr(
+                                    name=name + '_key_fc.w_0',
+                                    trainable=trainable,
+                                    initializer=param_initializer),
+                                bias_attr=paddle.ParamAttr(
+                                    name=name + '_key_fc.b_0',
+                                    trainable=trainable))
+        v = paddle.static.nn.fc(x=values,
+                                size=d_value * n_head,
+                                num_flatten_dims=2,
+                                weight_attr=paddle.ParamAttr(
+                                    name=name + '_value_fc.w_0',
+                                    trainable=trainable,
+                                    initializer=param_initializer),
+                                bias_attr=paddle.ParamAttr(
+                                    name=name + '_value_fc.b_0',
+                                    trainable=trainable))
+        return q, k, v
+
+    def __split_heads(x, n_head):
+        """
+        Reshape the last dimension of inpunt tensor x so that it becomes two
+        dimensions and then transpose. Specifically, input a tensor with shape
+        [bs, max_sequence_length, n_head * hidden_dim] then output a tensor
+        with shape [bs, n_head, max_sequence_length, hidden_dim].
+        """
+        hidden_size = x.shape[-1]
+        # The value 0 in shape attr means copying the corresponding dimension
+        # size of the input as the output dimension size.
+        reshaped = paddle.reshape(
+            x=x, shape=[0, 0, n_head, hidden_size // n_head])
+
+        # permuate the dimensions into:
+        # [batch_size, n_head, max_sequence_len, hidden_size_per_head]
+        return paddle.transpose(x=reshaped, perm=[0, 2, 1, 3])
+
+    def __combine_heads(x):
+        """
+        Transpose and then reshape the last two dimensions of inpunt tensor x
+        so that it becomes one dimension, which is reverse to __split_heads.
+        """
+        if len(x.shape) == 3: return x
+        if len(x.shape) != 4:
+            raise ValueError("Input(x) should be a 4-D Tensor.")
+
+        trans_x = paddle.transpose(x, perm=[0, 2, 1, 3])
+        # The value 0 in shape attr means copying the corresponding dimension
+        # size of the input as the output dimension size.
+        return paddle.reshape(
+            x=trans_x,
+            shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]])
+
+    def scaled_dot_product_attention(q, k, v, attn_bias, d_key, dropout_rate):
+        """
+        Scaled Dot-Product Attention
+        """
+        scaled_q = paddle.scale(x=q, scale=d_key ** -0.5)
+        product = paddle.matmul(x=scaled_q, y=k, transpose_y=True)
+        if attn_bias:
+            product += attn_bias
+        weights = paddle.nn.functional.softmax(x=product)
+        if dropout_rate:
+            weights = paddle.fluid.layers.nn.dropout(
+                weights,
+                dropout_prob=dropout_rate,
+                dropout_implementation="upscale_in_train",
+                is_test=False)
+        out = paddle.matmul(x=weights, y=v)
+        return out
+
+    q, k, v = __compute_qkv(queries, keys, values, n_head, d_key, d_value)
+
+    if cache is not None:  # use cache and concat time steps
+        # Since the inplace reshape in __split_heads changes the shape of k and
+        # v, which is the cache input for next time step, reshape the cache
+        # input from the previous time step first.
+
+        cache_k, cache_v = cache["k"], cache["v"]
+        select_k = paddle.gather(x=cache_k, index=gather_idx)
+        select_v = paddle.gather(x=cache_v, index=gather_idx)
+        select_k = paddle.reshape(x=select_k, shape=[0, 0, d_model])
+        select_v = paddle.reshape(x=select_v, shape=[0, 0, d_model])
+        k = paddle.concat(x=[select_k, k], axis=1)
+        v = paddle.concat(x=[select_v, v], axis=1)
+        paddle.assign(k, cache["k"])
+        paddle.assign(v, cache["v"])
+
+    q = __split_heads(q, n_head)
+    k = __split_heads(k, n_head)
+    v = __split_heads(v, n_head)
+
+    ctx_multiheads = scaled_dot_product_attention(q, k, v, attn_bias, d_key,
+                                                  dropout_rate)
+
+    out = __combine_heads(ctx_multiheads)
+
+    # Project back to the model size.
+    proj_out = paddle.static.nn.fc(x=out,
+                                   size=d_model,
+                                   num_flatten_dims=2,
+                                   weight_attr=paddle.ParamAttr(
+                                       name=name + '_output_fc.w_0',
+                                       trainable=trainable,
+                                       initializer=param_initializer),
+                                   bias_attr=paddle.ParamAttr(
+                                       name=name + '_output_fc.b_0',
+                                       trainable=trainable))
+    return proj_out
+
+
+def positionwise_feed_forward(x,
+                              d_inner_hid,
+                              d_hid,
+                              dropout_rate,
+                              hidden_act,
+                              param_initializer=None,
+                              name='ffn',
+                              trainable=True):
+    """
+    Position-wise Feed-Forward Networks.
+    This module consists of two linear transformations with a ReLU activation
+    in between, which is applied to each position separately and identically.
+    """
+    hidden = paddle.static.nn.fc(x=x,
+                                 size=d_inner_hid,
+                                 num_flatten_dims=2,
+                                 activation=hidden_act,
+                                 weight_attr=paddle.ParamAttr(
+                                     name=name + '_fc_0.w_0',
+                                     trainable=trainable,
+                                     initializer=param_initializer),
+                                 bias_attr=paddle.ParamAttr(
+                                     name=name + '_fc_0.b_0',
+                                     trainable=trainable))
+    if dropout_rate:
+        hidden = paddle.fluid.layers.nn.dropout(
+            hidden,
+            dropout_prob=dropout_rate,
+            dropout_implementation="upscale_in_train",
+            is_test=False)
+
+    out = paddle.static.nn.fc(x=hidden,
+                              size=d_hid,
+                              num_flatten_dims=2,
+                              weight_attr=paddle.ParamAttr(
+                                  name=name + '_fc_1.w_0',
+                                  trainable=trainable,
+                                  initializer=param_initializer),
+                              bias_attr=paddle.ParamAttr(
+                                  name=name + '_fc_1.b_0',
+                                  trainable=trainable))
+    return out
+
+
+def pre_post_process_layer(prev_out, out, process_cmd, dropout_rate=0., name='', trainable=True):
+    """
+    Add residual connection, layer normalization and droput to the out tensor
+    optionally according to the value of process_cmd.
+    This will be used before or after multi-head attention and position-wise
+    feed-forward networks.
+    """
+    for cmd in process_cmd:
+        if cmd == "a":  # add residual connection
+            out = out + prev_out if prev_out else out
+        elif cmd == "n":  # add layer normalization
+            out = paddle.static.nn.layer_norm(
+                out,
+                begin_norm_axis=len(out.shape) - 1,
+                param_attr=paddle.ParamAttr(
+                    name=name + '_layer_norm_scale',
+                    trainable=trainable,
+                    initializer=paddle.nn.initializer.Constant(value=1.)),
+                bias_attr=paddle.ParamAttr(
+                    name=name + '_layer_norm_bias',
+                    trainable=trainable,
+                    initializer=paddle.nn.initializer.Constant(value=0.)))
+        elif cmd == "d":  # add dropout
+            if dropout_rate:
+                out = paddle.fluid.layers.nn.dropout(
+                    out,
+                    dropout_prob=dropout_rate,
+                    dropout_implementation="upscale_in_train",
+                    is_test=False)
+    return out
+
+
+pre_process_layer = partial(pre_post_process_layer, None)
+post_process_layer = pre_post_process_layer
+
+
+def encoder_layer(query_input,
+                  key_input,
+                  attn_bias,
+                  n_head,
+                  d_key,
+                  d_value,
+                  d_model,
+                  d_inner_hid,
+                  prepostprocess_dropout,
+                  attention_dropout,
+                  relu_dropout,
+                  hidden_act,
+                  preprocess_cmd="n",
+                  postprocess_cmd="da",
+                  param_initializer=None,
+                  name='',
+                  cache=None,
+                  gather_idx=None,
+                  trainable=True):
+    """The encoder layers that can be stacked to form a deep encoder.
+    This module consits of a multi-head (self) attention followed by
+    position-wise feed-forward networks and both the two components companied
+    with the post_process_layer to add residual connection, layer normalization
+    and droput.
+    """
+    key_input = pre_process_layer(
+        key_input,
+        preprocess_cmd,
+        prepostprocess_dropout,
+        name=name + '_pre_att',
+        trainable=trainable) if key_input else None
+    value_input = key_input if key_input else None
+
+    attn_output = multi_head_attention(
+        pre_process_layer(
+            query_input,
+            preprocess_cmd,
+            prepostprocess_dropout,
+            name=name + '_pre_att',
+            trainable=trainable),
+        key_input,
+        value_input,
+        attn_bias,
+        d_key,
+        d_value,
+        d_model,
+        n_head,
+        attention_dropout,
+        param_initializer=param_initializer,
+        name=name + '_multi_head_att',
+        cache=cache,
+        gather_idx=gather_idx,
+        trainable=trainable)
+    attn_output = post_process_layer(
+        query_input,
+        attn_output,
+        postprocess_cmd,
+        prepostprocess_dropout,
+        name=name + '_post_att',
+        trainable=trainable)
+    ffd_output = positionwise_feed_forward(
+        pre_process_layer(
+            attn_output,
+            preprocess_cmd,
+            prepostprocess_dropout,
+            name=name + '_pre_ffn',
+            trainable=trainable),
+        d_inner_hid,
+        d_model,
+        relu_dropout,
+        hidden_act,
+        param_initializer=param_initializer,
+        name=name + '_ffn',
+        trainable=trainable)
+    return (post_process_layer(
+        attn_output,
+        ffd_output,
+        postprocess_cmd,
+        prepostprocess_dropout,
+        name=name + '_post_ffn',
+        trainable=trainable), ffd_output)
+
+
+def encoder(enc_input,
+            attn_bias=None,
+            enc_layers=None,
+            n_layer=None,
+            n_head=None,
+            d_key=None,
+            d_value=None,
+            d_model=None,
+            d_inner_hid=None,
+            prepostprocess_dropout=None,
+            attention_dropout=None,
+            relu_dropout=None,
+            hidden_act=None,
+            preprocess_cmd="n",
+            postprocess_cmd="da",
+            param_initializer=None,
+            name='',
+            caches=None,
+            gather_idx=None,
+            trainable=True):
+    """
+    The encoder is composed of a stack of identical layers returned by calling
+    encoder_layer.
+    """
+    if n_layer is None:
+        assert enc_layers is not None
+    else:
+        assert enc_layers is None
+
+    if enc_layers is None:
+        enc_layers = list(range(n_layer))
+
+    checkpoints = []
+    for i in enc_layers:
+        enc_output, cp = encoder_layer(
+            enc_input,
+            None,
+            attn_bias,
+            n_head,
+            d_key,
+            d_value,
+            d_model,
+            d_inner_hid,
+            prepostprocess_dropout,
+            attention_dropout,
+            relu_dropout,
+            hidden_act,
+            preprocess_cmd,
+            postprocess_cmd,
+            param_initializer=param_initializer,
+            name=name + '_layer_' + str(i),
+            cache=caches[i] if caches is not None else None,
+            gather_idx=gather_idx,
+            trainable=trainable)
+        enc_input = enc_output
+        checkpoints.append(cp.name)
+
+    enc_output = pre_process_layer(
+        enc_output, preprocess_cmd, prepostprocess_dropout, name="post_encoder", trainable=trainable)
+
+    return enc_output, checkpoints
diff --git a/NLP/UNIMO-2/src/model/unimo_grounded.py b/NLP/UNIMO-2/src/model/unimo_grounded.py
new file mode 100644
index 000000000..dd19a45bc
--- /dev/null
+++ b/NLP/UNIMO-2/src/model/unimo_grounded.py
@@ -0,0 +1,1494 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: unimo_grounded.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-09-23 15:41
+Desc: RoBERTa + ViT + Grounded Transformer
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import json
+import six
+import paddle
+import paddle.distributed.fleet as fleet
+from model.transformer_encoder import encoder as grounded_encoder
+from model.transformer_encoder import pre_process_layer as grounded_pre_process_layer
+from model.transformer_encoder import encoder as text_encoder
+from model.transformer_encoder import pre_process_layer as text_pre_process_layer
+from model.vision_transformer_encoder import encoder as vit_encoder
+from model.vision_transformer_encoder import pre_process_layer as vit_pre_process_layer
+from utils.pos_emb_interpolate import interpolate_pos_embed
+
+
+class VlConfig(object):
+    def __init__(self, config_path):
+        self._config_dict = self._parse(config_path)
+
+    def _parse(self, config_path):
+        try:
+            with open(config_path) as json_file:
+                config_dict = json.load(json_file)
+        except Exception:
+            raise IOError("Error in parsing VL model config file '%s'" %
+                          config_path)
+        else:
+            return config_dict
+
+    def __getitem__(self, key):
+        return self._config_dict.get(key, None)
+
+    def __setitem__(self, key, value):
+        self._config_dict[key] = value
+
+    def print_config(self):
+        for arg, value in self._config_dict.items():
+            print('%s: %s' % (arg, value))
+        print('------------------------------------------------')
+
+
+class VlModel(object):
+    def __init__(self,
+                 image_input=None,
+                 image_mask=None,
+                 text_input=None,
+                 text_mask=None,
+                 config=None,
+                 weight_sharing=True,
+                 task_type="normal",
+                 decoding=False,
+                 gather_idx=None,
+                 grounded_encoder_trainable=True,
+                 vit_encoder_trainable=True,
+                 text_encoder_trainable=True,
+                 alpha=1.0,
+                 beta=0.25,
+                 grounding_method='topk',
+                 topk_value=100,
+                 with_grounding_projection=False,
+                 with_cmcl_projection=False,
+                 cmcl_share_parameters=False,
+                 with_grounding_mask=False,
+                 grounding_mask_ratio=0.15,
+                 with_grounding_pos=False,
+                 text_enc_layers=None,
+                 grounding_enc_layers=None):
+
+        # for grounded cross-modal encoder
+        self._n_layer = config['num_hidden_layers']
+        self._n_head = config['num_attention_heads']
+        self._max_position_seq_len = config['max_position_embeddings']
+        self._hidden_act = config['hidden_act']
+        self._emb_size = config['hidden_size']
+        self._prepostprocess_dropout = config['hidden_dropout_prob']
+        self._attention_dropout = config['attention_probs_dropout_prob']
+        self._emb_dtype = "float32"
+        self._sent_types = config['type_vocab_size']
+
+        # for text encoder
+        self._text_n_layer = config['text_num_hidden_layers']
+        self._text_n_head = config['text_num_attention_heads']
+        self._text_voc_size = config['text_vocab_size']
+        self._text_max_position_seq_len = config['text_max_position_embeddings']
+        self._text_hidden_act = config['text_hidden_act']
+        self._text_prepostprocess_dropout = config['text_hidden_dropout_prob']
+        self._text_attention_dropout = config['text_attention_probs_dropout_prob']
+        self._text_emb_vocab_size = {"text.word_embedding": self._text_voc_size,
+                                     "text.pos_embedding": self._text_max_position_seq_len}
+
+        # for vit encoder
+        self._vit_n_layer = config['vit_num_hidden_layers']
+        self._vit_n_head = config['vit_num_attention_heads']
+        self._vit_hidden_act = config['vit_hidden_act']
+        self._vit_prepostprocess_dropout = config['vit_hidden_dropout_prob']
+        self._vit_attention_dropout = config['vit_attention_probs_dropout_prob']
+        self._vit_layer_norm_eps = config['vit_layer_norm_eps']
+
+        self._weight_sharing = weight_sharing
+        self._alpha = alpha
+        self._beta = beta
+        self._grounding_method = grounding_method
+        self._K = topk_value
+        self._grounded_encoder_trainable = grounded_encoder_trainable
+        self._vit_encoder_trainable = vit_encoder_trainable
+        self._text_encoder_trainable = text_encoder_trainable
+        self._with_grounding_projection = with_grounding_projection
+        self._cmcl_share_parameters = cmcl_share_parameters
+        self._with_cmcl_projection = with_cmcl_projection
+
+        # Initialize all weigths by truncated normal initializer, and all biases
+        # will be initialized by constant zero by default.
+        self._param_initializer = paddle.fluid.initializer.TruncatedNormalInitializer(scale=config['initializer_range'])
+        self._bias_initializer = paddle.fluid.initializer.ConstantInitializer(value=0.0)
+
+        assert text_input is not None or image_input is not None, "text_input and image_input cannot be both None"
+        self._task_type = task_type
+        self._is_img2txt_task = (task_type == "img2txt")
+        self._is_multimodal_task = (image_input is not None)
+
+        self.image_size = config['image_size']
+        self.num_codebook = config['num_codebook']
+        self.resolution = config['resolution']
+        self.width = self.image_size // config['resolution']
+        self.patch_seq_len = self.image_size * self.image_size // (config['resolution'] * config['resolution'])
+        self.patch_emb_size = config['resolution'] * config['resolution'] * 3
+
+        self.with_grounding_mask = with_grounding_mask
+        self.grounding_mask_ratio = grounding_mask_ratio
+        self.with_grounding_pos = with_grounding_pos
+
+        if text_enc_layers is None:
+            text_enc_layers = list(range(self._text_n_layer))
+        if grounding_enc_layers is None:
+            grounding_enc_layers = list(range(self._n_layer))
+
+        print("text_enc_layers", text_enc_layers)
+        print("grounding_enc_layers", grounding_enc_layers)
+        self.text_enc_layers = text_enc_layers
+        self.grounding_enc_layers = grounding_enc_layers
+
+        # vector codebook
+        self.vq_emb = paddle.static.create_parameter(
+            shape=[self.num_codebook, self._emb_size],
+            dtype=self._emb_dtype,
+            attr=paddle.ParamAttr(
+                name="vq_emb",
+                initializer=paddle.fluid.initializer.UniformInitializer(
+                    low=-1 / self.num_codebook, high=1 / self.num_codebook)))
+
+        self.cmcl_temperature = paddle.static.create_parameter(
+            shape=[1],
+            dtype=self._emb_dtype,
+            attr=paddle.ParamAttr(
+                name="cmcl_temperature",
+                initializer=paddle.fluid.initializer.ConstantInitializer(value=0.07)))
+
+        self.topk_temperature = paddle.static.create_parameter(
+            shape=[1],
+            dtype=self._emb_dtype,
+            attr=paddle.ParamAttr(
+                name="topk_temperature",
+                initializer=paddle.fluid.initializer.ConstantInitializer(value=100)))
+
+        if decoding:
+            self.grounded_caches = [{
+                "k":
+                    paddle.fluid.layers.fill_constant_batch_size_like(
+                        input=text_input["text.word_embedding"] if text_input is not None else image_input[
+                            "pixel_embedding"],
+                        shape=[-1, 0, self._emb_size],
+                        dtype=self._emb_dtype,  # float32,
+                        value=0),
+                "v":
+                    paddle.fluid.layers.fill_constant_batch_size_like(
+                        input=text_input["text.word_embedding"] if text_input is not None else image_input[
+                            "pixel_embedding"],
+                        shape=[-1, 0, self._emb_size],
+                        dtype=self._emb_dtype,  # float32,
+                        value=0),
+            } for i in range(self._n_layer)]
+
+            self.text_caches = [{
+                "k":
+                    paddle.fluid.layers.fill_constant_batch_size_like(
+                        input=text_input["text.word_embedding"] if text_input is not None else image_input[
+                            "pixel_embedding"],
+                        shape=[-1, 0, self._emb_size],
+                        dtype=self._emb_dtype,  # float32,
+                        value=0),
+                "v":
+                    paddle.fluid.layers.fill_constant_batch_size_like(
+                        input=text_input["text.word_embedding"] if text_input is not None else image_input[
+                            "pixel_embedding"],
+                        shape=[-1, 0, self._emb_size],
+                        dtype=self._emb_dtype,  # float32,
+                        value=0),
+            } for i in range(self._text_n_layer)]
+        else:
+            self.grounded_caches = None
+            self.text_caches = None
+
+        self._build_model(text_input=text_input,
+                          text_mask=text_mask,
+                          gather_idx=gather_idx,
+                          image_input=image_input,
+                          image_mask=image_mask)
+
+    def _build_model(self, text_input=None, text_mask=None, gather_idx=None, image_input=None, image_mask=None):
+        if text_input is None and image_input is not None:  # for img2txt when decoding or image tasks
+            self._enc_v_out, self._enc_g_out, self.all_checkpoints = self.encode(image_input=image_input,
+                                                                                 image_mask=image_mask,
+                                                                                 gather_idx=gather_idx)
+        elif text_input is not None and image_input is None:  # for textual tasks
+            self._enc_g_out, self._enc_l_out, self.all_checkpoints = self.encode(text_input=text_input,
+                                                                                 text_mask=text_mask,
+                                                                                 gather_idx=gather_idx)
+        else:  # for multi-modal tasks
+            self._enc_v_out, self._enc_g_out, self._enc_l_out, self.all_checkpoints = \
+                self.encode(text_input=text_input,
+                            text_mask=text_mask,
+                            gather_idx=gather_idx,
+                            image_input=image_input,
+                            image_mask=image_mask)
+
+    def encode(self, text_input=None, text_mask=None, gather_idx=None,
+               image_input=None, image_mask=None, decoding_step=False, grounded_decoding_mask=None):
+        all_checkpoints = []
+        # padding id in vocabulary must be set to 0
+        if text_input is None and image_input is not None:  # for img2txt task when decoding or image tasks
+            emb_v_out, emb_g_out, v_seq_len, g_seq_len, n_head_self_attn_mask, _checkpoints = \
+                self._gen_input(image_input=image_input, image_mask=image_mask, gather_idx=gather_idx)
+            all_checkpoints.extend(_checkpoints)
+
+            if self.with_grounding_mask:
+                grounded_seq_embed = paddle.reshape(emb_g_out, shape=[-1, self._emb_size])
+                grounded_seq_len = paddle.shape(grounded_seq_embed)[0]
+                probs = paddle.full(shape=[grounded_seq_len], fill_value=self.grounding_mask_ratio, dtype="float32")
+                is_mask = paddle.bernoulli(probs)
+                masked_grounded_seq_embed = grounded_seq_embed * (1 - paddle.unsqueeze(is_mask, axis=-1))
+                emb_g_out = paddle.reshape(masked_grounded_seq_embed, shape=[-1, g_seq_len, self._emb_size])
+                is_mask_condition = paddle.cast(is_mask, 'bool')
+                mask_pos = paddle.fluid.layers.where(is_mask_condition)
+                self.grounding_mask_pos = paddle.cast(x=mask_pos, dtype='int32')
+
+            if self.with_grounding_pos:
+                pos_range = paddle.unsqueeze(paddle.arange(start=2, end=g_seq_len + 2, dtype='int64'), axis=0)
+                # (batch_size, g_seq_len, 1)
+                batch_pos_range = paddle.unsqueeze(
+                    paddle.tile(pos_range, repeat_times=[paddle.shape(emb_g_out)[0], 1]), axis=-1)
+                g_pos_emb = paddle.static.nn.embedding(
+                    input=batch_pos_range,
+                    size=[self._text_emb_vocab_size["text.pos_embedding"], self._emb_size],
+                    dtype=self._emb_dtype,
+                    param_attr=paddle.ParamAttr(
+                        name='text.pos_embedding',
+                        trainable=self._text_encoder_trainable,
+                        initializer=self._param_initializer))
+                g_pos_emb = paddle.squeeze(g_pos_emb, axis=2)  # (batch_size, seq_len, emb_dim)
+                emb_g_out = emb_g_out + g_pos_emb
+
+            emb_g_out = grounded_pre_process_layer(
+                emb_g_out, 'nd', self._prepostprocess_dropout,
+                name="grounded.pre_encoder", trainable=self._grounded_encoder_trainable)
+            emb_vg_out = paddle.concat([emb_v_out, emb_g_out], axis=1)
+
+            enc_vg_out, grounding_checkpoints = grounded_encoder(
+                enc_input=emb_vg_out,
+                attn_bias=n_head_self_attn_mask,
+                enc_layers=self.grounding_enc_layers,
+                n_head=self._n_head,
+                d_key=self._emb_size // self._n_head,
+                d_value=self._emb_size // self._n_head,
+                d_model=self._emb_size,
+                d_inner_hid=self._emb_size * 4,
+                prepostprocess_dropout=self._prepostprocess_dropout,
+                attention_dropout=self._attention_dropout,
+                relu_dropout=0,
+                hidden_act=self._hidden_act,
+                preprocess_cmd="",
+                postprocess_cmd="dan",
+                param_initializer=self._param_initializer,
+                name='grounded.encoder',
+                caches=self.grounded_caches,
+                gather_idx=gather_idx,
+                trainable=self._grounded_encoder_trainable)
+            all_checkpoints.extend(grounding_checkpoints)
+
+            enc_v_out = paddle.slice(
+                input=enc_vg_out, axes=[1], starts=[0], ends=[v_seq_len])
+            enc_g_out = paddle.slice(
+                input=enc_vg_out, axes=[1], starts=[v_seq_len], ends=[v_seq_len + g_seq_len])
+
+            return enc_v_out, enc_g_out, all_checkpoints
+
+        elif image_input is None and text_input is not None:  # for textual task
+            if decoding_step:  # for step-by-step textual or img2txt generation during decoding
+                emb_l_out, l_seq_len, n_head_self_attn_mask, _checkpoints = \
+                    self._gen_input(text_input=text_input, text_mask=text_mask, gather_idx=gather_idx,
+                                    decoding_step=True, grounded_decoding_mask=grounded_decoding_mask)
+                all_checkpoints.extend(_checkpoints)
+
+                enc_l_out, grounding_checkpoints = grounded_encoder(
+                    enc_input=emb_l_out,
+                    attn_bias=n_head_self_attn_mask,
+                    enc_layers=self.grounding_enc_layers,
+                    n_head=self._n_head,
+                    d_key=self._emb_size // self._n_head,
+                    d_value=self._emb_size // self._n_head,
+                    d_model=self._emb_size,
+                    d_inner_hid=self._emb_size * 4,
+                    prepostprocess_dropout=self._prepostprocess_dropout,
+                    attention_dropout=self._attention_dropout,
+                    relu_dropout=0,
+                    hidden_act=self._hidden_act,
+                    preprocess_cmd="",
+                    postprocess_cmd="dan",
+                    param_initializer=self._param_initializer,
+                    name='grounded.encoder',
+                    caches=self.grounded_caches,
+                    gather_idx=gather_idx,
+                    trainable=self._grounded_encoder_trainable)
+                all_checkpoints.extend(grounding_checkpoints)
+
+                return enc_l_out, all_checkpoints
+
+            else:
+                emb_g_out, emb_l_out, g_seq_len, l_seq_len, n_head_self_attn_mask, _checkpoints = \
+                    self._gen_input(text_input=text_input, text_mask=text_mask, gather_idx=gather_idx)
+                all_checkpoints.extend(_checkpoints)
+
+                if self.with_grounding_mask:
+                    grounded_seq_embed = paddle.reshape(emb_g_out, shape=[-1, self._emb_size])
+                    grounded_seq_len = paddle.shape(grounded_seq_embed)[0]
+                    probs = paddle.full(shape=[grounded_seq_len], fill_value=self.grounding_mask_ratio, dtype="float32")
+                    is_mask = paddle.bernoulli(probs)
+                    masked_grounded_seq_embed = grounded_seq_embed * (1 - paddle.unsqueeze(is_mask, axis=-1))
+                    emb_g_out = paddle.reshape(masked_grounded_seq_embed, shape=[-1, g_seq_len, self._emb_size])
+                    is_mask_condition = paddle.cast(is_mask, 'bool')
+                    mask_pos = paddle.fluid.layers.where(is_mask_condition)
+                    self.grounding_mask_pos = paddle.cast(x=mask_pos, dtype='int32')
+
+                if self.with_grounding_pos:
+                    pos_range = paddle.unsqueeze(paddle.arange(start=2, end=g_seq_len + 2, dtype='int64'), axis=0)
+                    # (batch_size, g_seq_len, 1)
+                    batch_pos_range = paddle.unsqueeze(
+                        paddle.tile(pos_range, repeat_times=[paddle.shape(emb_g_out)[0], 1]), axis=-1)
+                    g_pos_emb = paddle.static.nn.embedding(
+                        input=batch_pos_range,
+                        size=[self._text_emb_vocab_size["text.pos_embedding"], self._emb_size],
+                        dtype=self._emb_dtype,
+                        param_attr=paddle.ParamAttr(
+                            name='text.pos_embedding',
+                            trainable=self._text_encoder_trainable,
+                            initializer=self._param_initializer))
+                    g_pos_emb = paddle.squeeze(g_pos_emb, axis=2)  # (batch_size, seq_len, emb_dim)
+                    emb_g_out = emb_g_out + g_pos_emb
+
+                emb_g_out = grounded_pre_process_layer(
+                    emb_g_out, 'nd', self._prepostprocess_dropout,
+                    name="grounded.pre_encoder", trainable=self._grounded_encoder_trainable)
+                emb_gl_out = paddle.concat([emb_g_out, emb_l_out], axis=1)
+
+                enc_gl_out, grounding_checkpoints = grounded_encoder(
+                    enc_input=emb_gl_out,
+                    attn_bias=n_head_self_attn_mask,
+                    enc_layers=self.grounding_enc_layers,
+                    n_head=self._n_head,
+                    d_key=self._emb_size // self._n_head,
+                    d_value=self._emb_size // self._n_head,
+                    d_model=self._emb_size,
+                    d_inner_hid=self._emb_size * 4,
+                    prepostprocess_dropout=self._prepostprocess_dropout,
+                    attention_dropout=self._attention_dropout,
+                    relu_dropout=0,
+                    hidden_act=self._hidden_act,
+                    preprocess_cmd="",
+                    postprocess_cmd="dan",
+                    param_initializer=self._param_initializer,
+                    name='grounded.encoder',
+                    caches=self.grounded_caches,
+                    gather_idx=gather_idx,
+                    trainable=self._grounded_encoder_trainable)
+                all_checkpoints.extend(grounding_checkpoints)
+
+                enc_g_out = paddle.slice(
+                    input=enc_gl_out, axes=[1], starts=[0], ends=[g_seq_len])
+                enc_l_out = paddle.slice(
+                    input=enc_gl_out, axes=[1], starts=[g_seq_len], ends=[g_seq_len + l_seq_len])
+
+                return enc_g_out, enc_l_out, all_checkpoints
+
+        elif image_input is not None and text_input is not None:  # for multi-modal task
+            emb_v_out, emb_g_out, emb_l_out, v_seq_len, g_seq_len, l_seq_len, n_head_self_attn_mask, _checkpoints = \
+                self._gen_input(image_input=image_input, image_mask=image_mask,
+                                text_input=text_input, text_mask=text_mask, gather_idx=gather_idx)
+            all_checkpoints.extend(_checkpoints)
+
+            if self.with_grounding_mask:
+                grounded_seq_embed = paddle.reshape(emb_g_out, shape=[-1, self._emb_size])
+                grounded_seq_len = paddle.shape(grounded_seq_embed)[0]
+                probs = paddle.full(shape=[grounded_seq_len], fill_value=self.grounding_mask_ratio, dtype="float32")
+                is_mask = paddle.bernoulli(probs)
+                masked_grounded_seq_embed = grounded_seq_embed * (1 - paddle.unsqueeze(is_mask, axis=-1))
+                emb_g_out = paddle.reshape(masked_grounded_seq_embed, shape=[-1, g_seq_len, self._emb_size])
+                is_mask_condition = paddle.cast(is_mask, 'bool')
+                mask_pos = paddle.fluid.layers.where(is_mask_condition)
+                self.grounding_mask_pos = paddle.cast(x=mask_pos, dtype='int32')
+
+            if self.with_grounding_pos:
+                pos_range = paddle.unsqueeze(paddle.arange(start=2, end=g_seq_len + 2, dtype='int64'), axis=0)
+                # (batch_size, g_seq_len, 1)
+                batch_pos_range = paddle.unsqueeze(
+                    paddle.tile(pos_range, repeat_times=[paddle.shape(emb_g_out)[0], 1]), axis=-1)
+                g_pos_emb = paddle.static.nn.embedding(
+                    input=batch_pos_range,
+                    size=[self._text_emb_vocab_size["text.pos_embedding"], self._emb_size],
+                    dtype=self._emb_dtype,
+                    param_attr=paddle.ParamAttr(
+                        name='text.pos_embedding',
+                        trainable=self._text_encoder_trainable,
+                        initializer=self._param_initializer))
+                g_pos_emb = paddle.squeeze(g_pos_emb, axis=2)  # (batch_size, seq_len, emb_dim)
+                emb_g_out = emb_g_out + g_pos_emb
+
+            emb_g_out = grounded_pre_process_layer(
+                emb_g_out, 'nd', self._prepostprocess_dropout,
+                name="grounded.pre_encoder", trainable=self._grounded_encoder_trainable)
+            emb_vgl_out = paddle.concat([emb_v_out, emb_g_out, emb_l_out], axis=1)
+
+            enc_vgl_out, grounding_checkpoints = grounded_encoder(
+                enc_input=emb_vgl_out,
+                attn_bias=n_head_self_attn_mask,
+                enc_layers=self.grounding_enc_layers,
+                n_head=self._n_head,
+                d_key=self._emb_size // self._n_head,
+                d_value=self._emb_size // self._n_head,
+                d_model=self._emb_size,
+                d_inner_hid=self._emb_size * 4,
+                prepostprocess_dropout=self._prepostprocess_dropout,
+                attention_dropout=self._attention_dropout,
+                relu_dropout=0,
+                hidden_act=self._hidden_act,
+                preprocess_cmd="",
+                postprocess_cmd="dan",
+                param_initializer=self._param_initializer,
+                name='grounded.encoder',
+                caches=self.grounded_caches,
+                gather_idx=gather_idx,
+                trainable=self._grounded_encoder_trainable)
+            all_checkpoints.extend(grounding_checkpoints)
+
+            enc_v_out = paddle.slice(
+                input=enc_vgl_out, axes=[1], starts=[0], ends=[v_seq_len])
+            enc_g_out = paddle.slice(
+                input=enc_vgl_out, axes=[1], starts=[v_seq_len], ends=[v_seq_len + g_seq_len])
+            enc_l_out = paddle.slice(
+                input=enc_vgl_out, axes=[1], starts=[v_seq_len + g_seq_len], ends=[v_seq_len + g_seq_len + l_seq_len])
+            return enc_v_out, enc_g_out, enc_l_out, all_checkpoints
+        else:
+            raise ValueError("The input is invalid")
+
+    def vit_encode(self, image_input, image_mask):
+        """encode image by pre-trained ViT"""
+        assert image_mask is not None, "text_mask should not be none"
+        image_self_attn_mask = paddle.matmul(x=paddle.transpose(image_mask, perm=[0, 2, 1]), y=image_mask)
+        self_attn_mask = paddle.scale(
+            x=image_self_attn_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+        n_head_self_attn_mask = paddle.stack(
+            x=[self_attn_mask] * self._vit_n_head, axis=1)
+        n_head_self_attn_mask.stop_gradient = True
+
+        pixel_embeddings = paddle.static.nn.conv2d(
+            input=image_input['pixel_embedding'],
+            num_filters=self._emb_size,
+            filter_size=self.resolution,
+            stride=self.resolution,
+            padding=(self.resolution - 1) // 2,
+            param_attr=paddle.ParamAttr(
+                name="vit.patch_embeddings_projection_weight",
+                trainable=self._vit_encoder_trainable),
+            bias_attr=paddle.ParamAttr(
+                name="vit.patch_embeddings_projection_bias",
+                trainable=self._vit_encoder_trainable),
+            data_format="NHWC")
+        # paddle.static.Print(paddle.shape(pixel_embeddings), message="pixel_embeddings", summarize=-1)
+        pixel_embeddings = paddle.reshape(pixel_embeddings, shape=[-1, self.patch_seq_len, self._emb_size])
+
+        cls_token_emb = paddle.static.create_parameter(
+            shape=[1, 1, self._emb_size],
+            dtype=self._emb_dtype,
+            attr=paddle.ParamAttr(
+                name="vit.cls_token_embeddings",
+                trainable=self._vit_encoder_trainable,
+                initializer=self._param_initializer))
+        cls_token_emb = paddle.expand(x=cls_token_emb,
+                                      shape=[paddle.shape(pixel_embeddings)[0], 1, self._emb_size])
+
+        # cpncate global [CLS] token with image patches
+        # (batch_size, patch_seq_len + 1, emb_dim)
+        all_pixel_embeddings = paddle.concat(x=[cls_token_emb, pixel_embeddings], axis=1)
+
+        # default image_size=224, resolution=16, patch_seq_len=196
+        pixel_pos_emb = paddle.static.create_parameter(
+            shape=[1, 197, self._emb_size],
+            dtype=self._emb_dtype,
+            attr=paddle.ParamAttr(name="vit.position_embeddings",
+                                  trainable=self._vit_encoder_trainable,
+                                  initializer=self._param_initializer))
+        # paddle.static.Print(paddle.shape(pixel_pos_emb), message="pixel_pos_emb", summarize=-1)
+        if self.patch_seq_len > 196:  # when image_size > 224
+            pixel_pos_emb = interpolate_pos_embed(pixel_pos_emb, self.patch_seq_len)
+
+        emb_v_out = all_pixel_embeddings + pixel_pos_emb
+        emb_v_out = vit_pre_process_layer(
+            emb_v_out, 'd', self._vit_prepostprocess_dropout, name='vit.pre_encoder',
+            trainable=self._vit_encoder_trainable)
+
+        vit_enc_out, checkpoints = vit_encoder(
+            enc_input=emb_v_out,
+            attn_bias=n_head_self_attn_mask,
+            n_layer=self._vit_n_layer,
+            n_head=self._vit_n_head,
+            d_key=self._emb_size // self._vit_n_head,
+            d_value=self._emb_size // self._vit_n_head,
+            d_model=self._emb_size,
+            d_inner_hid=self._emb_size * 4,
+            prepostprocess_dropout=self._vit_prepostprocess_dropout,
+            attention_dropout=self._vit_attention_dropout,
+            relu_dropout=0,
+            hidden_act=self._vit_hidden_act,
+            preprocess_cmd="n",
+            postprocess_cmd="da",
+            param_initializer=self._param_initializer,
+            name='vit.encoder',
+            trainable=self._vit_encoder_trainable)
+        vit_seq_len = paddle.shape(vit_enc_out)[1]  # patch_seq_len + 1
+
+        return vit_enc_out, vit_seq_len, image_self_attn_mask, checkpoints
+
+    def text_encode(self, text_input, text_mask, gather_idx=None, decoding_step=False):
+        assert text_mask is not None, "text_mask should not be none"
+        if decoding_step or self._is_img2txt_task:  # for img2txt tasks or step-by-step decoding
+            text_self_attn_mask = text_mask
+        else:
+            text_self_attn_mask = paddle.matmul(x=paddle.transpose(text_mask, perm=[0, 2, 1]), y=text_mask)
+
+        self_attn_mask = paddle.scale(
+            x=text_self_attn_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+        n_head_self_attn_mask = paddle.stack(
+            x=[self_attn_mask] * self._n_head, axis=1)
+        n_head_self_attn_mask.stop_gradient = True
+
+        # text part
+        text_emb = paddle.static.nn.embedding(
+            input=text_input["text.word_embedding"],
+            size=[self._text_emb_vocab_size["text.word_embedding"], self._emb_size],
+            dtype=self._emb_dtype,
+            param_attr=paddle.ParamAttr(
+                name='text.word_embedding',
+                trainable=self._text_encoder_trainable,
+                initializer=self._param_initializer))
+        text_emb = paddle.squeeze(text_emb, axis=2)  # (batch_size, seq_len, emb_dim)
+
+        pos_emb = paddle.static.nn.embedding(
+            input=text_input["text.pos_embedding"],
+            size=[self._text_emb_vocab_size["text.pos_embedding"], self._emb_size],
+            dtype=self._emb_dtype,
+            param_attr=paddle.ParamAttr(
+                name='text.pos_embedding',
+                trainable=self._text_encoder_trainable,
+                initializer=self._param_initializer))
+        pos_emb = paddle.squeeze(pos_emb, axis=2)  # (batch_size, seq_len, emb_dim)
+
+        emb_out = text_emb + pos_emb
+        emb_out = text_pre_process_layer(
+            emb_out, 'nd', self._text_prepostprocess_dropout,
+            name="text.pre_encoder", trainable=self._text_encoder_trainable)
+
+        text_enc_out, checkpoints = text_encoder(
+            enc_input=emb_out,
+            attn_bias=n_head_self_attn_mask,
+            enc_layers=self.text_enc_layers,
+            n_head=self._text_n_head,
+            d_key=self._emb_size // self._text_n_head,
+            d_value=self._emb_size // self._text_n_head,
+            d_model=self._emb_size,
+            d_inner_hid=self._emb_size * 4,
+            prepostprocess_dropout=self._text_prepostprocess_dropout,
+            attention_dropout=self._text_attention_dropout,
+            relu_dropout=0,
+            hidden_act=self._text_hidden_act,
+            preprocess_cmd="",
+            postprocess_cmd="dan",
+            param_initializer=self._param_initializer,
+            name='text.encoder',
+            caches=self.text_caches,
+            gather_idx=gather_idx,
+            trainable=self._text_encoder_trainable)
+
+        text_seq_len = paddle.shape(text_enc_out)[1]
+        return text_enc_out, text_seq_len, text_self_attn_mask, checkpoints
+
+    def vector_quantizer(self, inputs):
+        """inputs: (batch_size, seq_len, emb_dim)"""
+        input_shape = paddle.shape(inputs)
+        # Flatten input (batch_size * seq_len, emb_dim)
+        flat_input = paddle.reshape(inputs, shape=[-1, self._emb_size])
+
+        # Calculate distances (batch_size * seq_len, num_codebook)
+        distances = (paddle.sum(paddle.pow(flat_input, 2), axis=1, keepdim=True)
+                     + paddle.unsqueeze(x=paddle.sum(paddle.pow(self.vq_emb, 2), axis=1), axis=0)
+                     - 2 * paddle.matmul(flat_input, paddle.transpose(self.vq_emb, perm=[1, 0])))
+
+        # Encoding (batch_size * seq_len, 1)
+        encoding_indices = paddle.unsqueeze(x=paddle.argmin(distances, axis=1), axis=1)
+        # paddle.static.Print(encoding_indices, message="encoding_indices", summarize=1000)
+        size_range = paddle.unsqueeze(x=paddle.arange(paddle.shape(encoding_indices)[0], dtype='int64'), axis=1)
+        # (batch_size * seq_len, 2)
+        index = paddle.concat([size_range, encoding_indices], axis=1)
+
+        # (batch_size * seq_len, num_codebook)
+        out_shape = [paddle.shape(encoding_indices)[0], self.num_codebook]
+        # (batch_size * seq_len)
+        updates = paddle.ones(shape=[paddle.shape(encoding_indices)[0]], dtype='float32')
+        # (batch_size * seq_len, num_codebook)
+        encodings = paddle.scatter_nd(index=index, updates=updates, shape=out_shape)
+        # paddle.static.Print(encodings, message="encodings", summarize=1000)
+
+        # Quantize and unflatten (batch_size, seq_len, emb_dim)
+        quantized = paddle.reshape(x=paddle.matmul(encodings, self.vq_emb),
+                                   shape=[input_shape[0], input_shape[1], self._emb_size])
+        # paddle.static.Print(quantized, message="quantized", summarize=1000)
+        encoding_indices_reshaped = paddle.reshape(encoding_indices, shape=[input_shape[0], input_shape[1]])
+
+        constant_zeros = paddle.zeros_like(x=inputs)
+        quantized_detach = paddle.add(constant_zeros, quantized)
+        quantized_detach.stop_gradient = True
+        inputs_detach = paddle.add(constant_zeros, inputs)
+        inputs_detach.stop_gradient = True
+
+        # Loss
+        e_latent_loss = paddle.nn.functional.mse_loss(input=quantized_detach, label=inputs)
+        # paddle.static.Print(e_latent_loss, message="e_latent_loss", summarize=1000)
+        q_latent_loss = paddle.nn.functional.mse_loss(input=quantized, label=inputs_detach)
+        # paddle.static.Print(q_latent_loss, message="q_latent_loss", summarize=1000)
+        loss = self._alpha * q_latent_loss + self._beta * e_latent_loss
+
+        # Straight Through Estimator
+        # sg_quantized = paddle.subtract(x=quantized, y=inputs)
+        # sg_quantized.stop_gradient = True
+        # quantized_emb = paddle.add(x=inputs, y=sg_quantized)
+        # paddle.static.Print(quantized_emb, message="quantized_emb", summarize=1000)
+        return loss, quantized, encoding_indices_reshaped
+
+    def topk_vector_quantizer(self, inputs, K=100):
+        """inputs: (batch_size, seq_len, emb_dim)"""
+        self.topk_temperature = paddle.clip(self.topk_temperature, min=1.0, max=10000.0)
+
+        input_shape = paddle.shape(inputs)
+        # Flatten input (batch_size * seq_len, emb_dim)
+        flat_input = paddle.reshape(inputs, shape=[-1, self._emb_size])
+
+        # Calculate distances (batch_size * seq_len, num_codebook)
+        # distances = (paddle.sum(paddle.pow(flat_input, 2), axis=1, keepdim=True)
+        #              + paddle.unsqueeze(x=paddle.sum(paddle.pow(self.vq_emb, 2), axis=1), axis=0)
+        #              - 2 * paddle.matmul(flat_input, paddle.transpose(self.vq_emb, perm=[1, 0])))
+
+        distances = paddle.nn.functional.sigmoid(self.topk_temperature * \
+                                                 paddle.matmul(flat_input, paddle.transpose(self.vq_emb, perm=[1, 0])))
+        # paddle.static.Print(distances, message="distances", summarize=2048)
+
+        # (batch_size, num_codebook)
+        cumulated_score = paddle.sum(
+            paddle.reshape(distances, shape=[input_shape[0], input_shape[1], self.num_codebook]), axis=1)
+        # paddle.static.Print(cumulated_score, message="cumulated_score", summarize=2048)
+
+        # (batch_size, K)
+        topk_value, topk_indices = paddle.topk(x=cumulated_score, k=K, axis=1, largest=True)
+        topk_indices = paddle.reshape(topk_indices, shape=[-1, 1])
+        # paddle.static.Print(topk_indices, message="topk_indices", summarize=100)
+
+        size_range = paddle.unsqueeze(x=paddle.arange(paddle.shape(topk_indices)[0], dtype='int64'), axis=1)
+        # (batch_size * K, 2)
+        index = paddle.concat([size_range, topk_indices], axis=1)
+        # paddle.static.Print(index, message="index", summarize=2000)
+
+        # (batch_size * K, num_codebook)
+        out_shape = [paddle.shape(topk_indices)[0], self.num_codebook]
+        # (batch_size * K)
+        updates = paddle.ones(shape=[paddle.shape(topk_indices)[0]], dtype='float32')
+        # (batch_size * K, num_codebook)
+        encodings = paddle.scatter_nd(index=index, updates=updates, shape=out_shape)
+        # paddle.static.Print(encodings, message="encodings", summarize=2048)
+
+        # Quantize and unflatten (batch_size, K, emb_dim)
+        quantized = paddle.reshape(x=paddle.matmul(encodings, self.vq_emb),
+                                   shape=[input_shape[0], K, self._emb_size])
+        # paddle.static.Print(quantized, message="quantized", summarize=1000)
+        encoding_indices_reshaped = paddle.reshape(topk_indices, shape=[input_shape[0], K])
+
+        quantized_constant_zeros = paddle.zeros_like(x=quantized)
+        quantized_detach = paddle.add(quantized_constant_zeros, quantized)
+        quantized_detach.stop_gradient = True
+        input_constant_zeros = paddle.zeros_like(x=inputs)
+        inputs_detach = paddle.add(input_constant_zeros, inputs)
+        inputs_detach.stop_gradient = True
+
+        # (batch_size*num_codebook, seq_len)
+        distances_trans = paddle.reshape(
+            paddle.transpose(paddle.reshape(distances, shape=[input_shape[0], input_shape[1], self.num_codebook]),
+                             perm=[0, 2, 1]),
+            shape=[-1, input_shape[1]])
+        # (batch_size, 1)
+        batchsize_range = paddle.unsqueeze(x=paddle.arange(input_shape[0], dtype='int64'), axis=1) * self.num_codebook
+        # paddle.static.Print(batchsize_range, message="batchsize_range", summarize=-1)
+        # (batch_size, K)
+        topk_indices = paddle.reshape(topk_indices, shape=[-1, K])
+        # (batch_size * K)
+        gather_index = paddle.reshape(x=(batchsize_range + topk_indices), shape=[-1])
+        # paddle.static.Print(gather_index, message="gather_index", summarize=2000)
+        # (batch_size * K, seq_len)
+        distance_gather = paddle.gather(distances_trans, index=gather_index)
+        # paddle.static.Print(distance_gather, message="distance_gather", summarize=245)
+        # (batch_size, K, seq_len)
+        distance_gather = paddle.reshape(distance_gather, shape=[input_shape[0], K, input_shape[1]])
+
+        # (batch_size, K, seq_len)
+        e_distance_gather_normalized = paddle.divide(
+            x=distance_gather,
+            y=paddle.reshape(topk_value + 1e-18, shape=[input_shape[0], K, 1]))
+        # paddle.static.Print(e_distance_gather_normalized, message="e_distance_gather_normalized", summarize=245)
+
+        # (batch_size, K, emb_dim)
+        e_latent_value = paddle.matmul(e_distance_gather_normalized, inputs)
+        e_latent_loss = paddle.nn.functional.mse_loss(input=quantized_detach, label=e_latent_value)
+        # paddle.static.Print(e_latent_loss, message="e_latent_loss", summarize=1000)
+
+        # (batch_size, seq_len, K)
+        q_distance_gather_normailzed = paddle.divide(
+            x=paddle.transpose(x=distance_gather, perm=[0, 2, 1]),
+            y=paddle.unsqueeze(paddle.sum(x=distance_gather, axis=1) + 1e-18, axis=2))
+        # paddle.static.Print(q_distance_gather_normailzed, message="q_distance_gather_normailzed", summarize=100)
+
+        # (batch_size, seq_len, emb_dim)
+        q_latent_value = paddle.matmul(q_distance_gather_normailzed, quantized)
+        q_latent_loss = paddle.nn.functional.mse_loss(input=inputs_detach, label=q_latent_value)
+        # paddle.static.Print(q_latent_loss, message="q_latent_loss", summarize=1000)
+
+        loss = self._alpha * q_latent_loss + self._beta * e_latent_loss
+        return loss, quantized, encoding_indices_reshaped
+
+    def _gen_input(self, text_input=None, text_mask=None,
+                   image_input=None, image_mask=None, gather_idx=None, decoding_step=False,
+                   grounded_decoding_mask=None):
+        """encode images and texts independently by Vit and RoBERTa, get the optimal grounded tokens"""
+        _checkpoints = []
+
+        if image_input is not None:
+            # visual part
+            self.vit_enc_out, vit_seq_len, image_self_attn_mask, vit_checkpoints = \
+                self.vit_encode(image_input, image_mask)
+            _checkpoints.extend(vit_checkpoints)
+
+            # (batch_size, 1, emb_dim)
+            patch_cls_embeddings = paddle.slice(input=self.vit_enc_out, axes=[1], starts=[0], ends=[1])
+            # (batch_size, patch_seq_len, emb_dim)
+            patch_embeddings = paddle.slice(input=self.vit_enc_out, axes=[1], starts=[1], ends=[vit_seq_len])
+
+            if self._with_grounding_projection:
+                patch_embeddings = paddle.static.nn.fc(
+                    x=patch_embeddings,
+                    size=self._emb_size,
+                    num_flatten_dims=2,
+                    activation="relu",
+                    weight_attr=paddle.ParamAttr(
+                        name="grounded.grounding_patch_projection.w_0",
+                        initializer=self._param_initializer),
+                    bias_attr="grounded.grounding_patch_projection.b_0")
+                patch_embeddings = paddle.nn.functional.normalize(patch_embeddings, axis=-1)
+
+        if text_input is not None:
+            # textual part
+            self.text_enc_out, text_seq_len, text_self_attn_mask, text_checkpoints = \
+                self.text_encode(text_input, text_mask, gather_idx=gather_idx, decoding_step=decoding_step)
+            _checkpoints.extend(text_checkpoints)
+
+            # (batch_size, 1, emb_dim)
+            text_cls_embeddings = paddle.slice(input=self.text_enc_out, axes=[1], starts=[0], ends=[1])
+            # (batch_size, text_seq_len - 1, emb_dim)
+            token_embeddings = paddle.slice(input=self.text_enc_out, axes=[1], starts=[1], ends=[text_seq_len])
+
+            if self._with_grounding_projection:
+                token_embeddings = paddle.static.nn.fc(
+                    x=token_embeddings,
+                    size=self._emb_size,
+                    num_flatten_dims=2,
+                    activation="relu",
+                    weight_attr=paddle.ParamAttr(
+                        name="grounded.grounding_token_projection.w_0",
+                        initializer=self._param_initializer),
+                    bias_attr="grounded.grounding_token_projection.b_0")
+                token_embeddings = paddle.nn.functional.normalize(token_embeddings, axis=-1)
+
+        if image_input is not None and text_input is not None:
+            vit_text_enc_out = paddle.concat([patch_embeddings, token_embeddings], axis=1)
+
+            if self._grounding_method == "normal":
+                self.vq_loss, self.grounded_enc_out, self.grounded_encoding_indices = \
+                    self.vector_quantizer(vit_text_enc_out)
+            elif self._grounding_method == "topk":
+                self.vq_loss, self.grounded_enc_out, self.grounded_encoding_indices = \
+                    self.topk_vector_quantizer(vit_text_enc_out, self._K)
+            elif self._grounding_method == "optimal":
+                self.vq_loss, self.grounded_enc_out, self.grounded_encoding_indices = \
+                    self.vector_quantizer(vit_text_enc_out)
+            else:
+                raise ValueError("%s is not supported!!!" % self._grounding_method)
+
+            batch_size = paddle.shape(self.grounded_enc_out)[0]
+            g_seq_len = paddle.shape(self.grounded_enc_out)[1]
+
+            # (batch_size, 1, g_seq_len)
+            grounded_mask = paddle.ones(shape=[batch_size, 1, g_seq_len])
+            # (batch_size, g_seq_len, g_seq_len)
+            grounded_self_attn_mask = paddle.matmul(paddle.transpose(grounded_mask, perm=[0, 2, 1]), grounded_mask)
+
+            # (batch_size, v_seq_len, g_seq_len)
+            img_grounded_attn_mask = paddle.matmul(paddle.transpose(image_mask, perm=[0, 2, 1]), grounded_mask)
+            # (batch_size, v_seq_len, l_seq_len)
+            img_text_attn_mask = paddle.zeros(shape=[batch_size, vit_seq_len, text_seq_len], dtype='float32')
+
+            grounded_img_attn_mask = paddle.matmul(paddle.transpose(grounded_mask, perm=[0, 2, 1]), image_mask)
+            grounded_text_attn_mask = paddle.matmul(paddle.transpose(grounded_mask, perm=[0, 2, 1]), text_mask)
+
+            text_img_attn_mask = paddle.zeros(shape=[batch_size, text_seq_len, vit_seq_len], dtype='float32')
+            text_grounded_attn_mask = paddle.matmul(paddle.transpose(text_mask, perm=[0, 2, 1]), grounded_mask)
+
+            image_row = paddle.concat([image_self_attn_mask, img_grounded_attn_mask, img_text_attn_mask], axis=2)
+            grounded_row = paddle.concat([grounded_img_attn_mask, grounded_self_attn_mask, grounded_text_attn_mask],
+                                         axis=2)
+            text_row = paddle.concat([text_img_attn_mask, text_grounded_attn_mask, text_self_attn_mask], axis=2)
+            vgl_self_attn_mask = paddle.concat([image_row, grounded_row, text_row], axis=1)
+            vgl_self_attn_mask = paddle.scale(
+                x=vgl_self_attn_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+            n_head_self_attn_mask = paddle.stack(
+                x=[vgl_self_attn_mask] * self._n_head, axis=1)
+            n_head_self_attn_mask.stop_gradient = True
+
+            return self.vit_enc_out, self.grounded_enc_out, self.text_enc_out, \
+                   vit_seq_len, g_seq_len, text_seq_len, n_head_self_attn_mask, _checkpoints
+
+        elif image_input is not None and text_input is None:
+            if self._grounding_method == "normal":
+                self.vq_loss, self.grounded_enc_out, self.grounded_encoding_indices = \
+                    self.vector_quantizer(patch_embeddings)
+            elif self._grounding_method == "topk":
+                self.vq_loss, self.grounded_enc_out, self.grounded_encoding_indices = \
+                    self.topk_vector_quantizer(patch_embeddings, self._K)
+            elif self._grounding_method == "optimal":
+                self.vq_loss, self.grounded_enc_out, self.grounded_encoding_indices = \
+                    self.vector_quantizer(patch_embeddings)
+            else:
+                raise ValueError("%s is not supported!!!" % self._grounding_method)
+
+            batch_size = paddle.shape(self.grounded_enc_out)[0]
+            g_seq_len = paddle.shape(self.grounded_enc_out)[1]
+
+            # (batch_size, 1, g_seq_len)
+            grounded_mask = paddle.ones(shape=[batch_size, 1, g_seq_len])
+            vg_mask = paddle.concat([image_mask, grounded_mask], axis=2)
+            vg_self_attn_mask = paddle.matmul(paddle.transpose(vg_mask, perm=[0, 2, 1]), vg_mask)
+            vg_self_attn_mask = paddle.scale(
+                x=vg_self_attn_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+            n_head_self_attn_mask = paddle.stack(
+                x=[vg_self_attn_mask] * self._n_head, axis=1)
+            n_head_self_attn_mask.stop_gradient = True
+
+            return self.vit_enc_out, self.grounded_enc_out, vit_seq_len, g_seq_len, n_head_self_attn_mask, _checkpoints
+
+        elif image_input is None and text_input is not None:
+            if decoding_step:
+                assert grounded_decoding_mask is not None, "grounded_decoding_mask should not be none"
+                self_attn_mask = paddle.scale(
+                    x=grounded_decoding_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+                n_head_self_attn_mask = paddle.stack(
+                    x=[self_attn_mask] * self._n_head, axis=1)
+                n_head_self_attn_mask.stop_gradient = True
+
+                return self.text_enc_out, text_seq_len, n_head_self_attn_mask, _checkpoints
+            else:
+                if self._grounding_method == "normal":
+                    self.vq_loss, self.grounded_enc_out, self.grounded_encoding_indices = \
+                        self.vector_quantizer(token_embeddings)
+                elif self._grounding_method == "topk":
+                    self.vq_loss, self.grounded_enc_out, self.grounded_encoding_indices = \
+                        self.topk_vector_quantizer(token_embeddings, self._K)
+                elif self._grounding_method == "optimal":
+                    self.vq_loss, self.grounded_enc_out, self.grounded_encoding_indices = \
+                        self.vector_quantizer(token_embeddings)
+                else:
+                    raise ValueError("%s is not supported!!!" % self._grounding_method)
+
+                batch_size = paddle.shape(self.grounded_enc_out)[0]
+                g_seq_len = paddle.shape(self.grounded_enc_out)[1]
+
+                # (batch_size, 1, g_seq_len)
+                grounded_mask = paddle.ones(shape=[batch_size, 1, g_seq_len])
+
+                gl_mask = paddle.concat([grounded_mask, text_mask], axis=2)
+                gl_self_attn_mask = paddle.matmul(paddle.transpose(gl_mask, perm=[0, 2, 1]), gl_mask)
+                gl_self_attn_mask = paddle.scale(
+                    x=gl_self_attn_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+                n_head_self_attn_mask = paddle.stack(
+                    x=[gl_self_attn_mask] * self._n_head, axis=1)
+                n_head_self_attn_mask.stop_gradient = True
+
+                return self.grounded_enc_out, self.text_enc_out, g_seq_len, text_seq_len, n_head_self_attn_mask, _checkpoints
+
+    def get_checkpoints(self):
+        return self.all_checkpoints
+
+    def get_text_encoder_output(self):
+        return self.text_enc_out
+
+    def get_vit_encoder_output(self):
+        return self.vit_enc_out
+
+    def get_grounded_output(self):
+        return self.grounded_enc_out
+
+    def get_text_sequence_output(self):
+        return self._enc_l_out
+
+    def get_pooled_output(self):
+        """Get the first feature of each sequence for classification"""
+        next_sent_feat = paddle.slice(
+            input=self._enc_l_out, axes=[1], starts=[0], ends=[1])
+        next_sent_feat = paddle.reshape(
+            x=next_sent_feat, shape=[-1, self._emb_size])
+        next_sent_feat = paddle.static.nn.fc(
+            x=next_sent_feat,
+            size=self._emb_size,
+            activation="relu",
+            weight_attr=paddle.ParamAttr(
+                name="grounded.pooled_fc_text.w_0", initializer=self._param_initializer),
+            bias_attr="grounded.pooled_fc_text.b_0")
+
+        next_sent_v_feat = paddle.slice(
+            input=self._enc_v_out, axes=[1], starts=[0], ends=[1])
+        next_sent_v_feat = paddle.reshape(
+            x=next_sent_v_feat, shape=[-1, self._emb_size])
+        next_sent_v_feat = paddle.static.nn.fc(
+            x=next_sent_v_feat,
+            size=self._emb_size,
+            activation="relu",
+            weight_attr=paddle.ParamAttr(
+                name="grounded.pooled_fc_image.w_0", initializer=self._param_initializer),
+            bias_attr="grounded.pooled_fc_image.b_0")
+        return next_sent_feat, next_sent_v_feat
+
+    def get_pooled_txt_output(self):
+        """Get the first feature of each sequence for classification"""
+        next_sent_feat = paddle.slice(
+            input=self._enc_l_out, axes=[1], starts=[0], ends=[1])
+        next_sent_feat = paddle.reshape(
+            x=next_sent_feat, shape=[-1, self._emb_size])
+        next_sent_feat = paddle.static.nn.fc(
+            x=next_sent_feat,
+            size=self._emb_size,
+            activation="relu",
+            weight_attr=paddle.ParamAttr(
+                name="grounded.pooled_fc_text.w_0", initializer=self._param_initializer),
+            bias_attr="grounded.pooled_fc_text.b_0")
+
+        return next_sent_feat
+
+    def get_pooled_img_output(self):
+        """Get the first feature of each sequence for classification"""
+        next_sent_v_feat = paddle.slice(
+            input=self._enc_v_out, axes=[1], starts=[0], ends=[1])
+        next_sent_v_feat = paddle.reshape(
+            x=next_sent_v_feat, shape=[-1, self._emb_size])
+        next_sent_v_feat = paddle.static.nn.fc(
+            x=next_sent_v_feat,
+            size=self._emb_size,
+            activation="relu",
+            weight_attr=paddle.ParamAttr(
+                name="grounded.pooled_fc_image.w_0", initializer=self._param_initializer),
+            bias_attr="grounded.pooled_fc_image.b_0")
+        return next_sent_v_feat
+
+    def get_match_output(self, text, image, mode="mul"):
+        if mode == "sum":
+            emb_fuse = text + image
+        elif mode == "mul":
+            emb_fuse = text * image
+        else:
+            print("current mode %s is not supported" % mode)
+            return
+        emb_fuse = paddle.fluid.layers.nn.dropout(emb_fuse,
+                                                  self._attention_dropout,
+                                                  dropout_implementation="upscale_in_train")
+        return emb_fuse
+
+    def get_img_txt_matching_output(self, labels, is_multimodal=None, num_labels=2, fusion_method="sum"):
+        print('-----img_txt_matching------')
+        self.next_sent_feat, self.next_sent_v_feat = self.get_pooled_output()
+        next_feat = self.get_match_output(self.next_sent_feat,
+                                          self.next_sent_v_feat, fusion_method)
+        # paddle.static.Print(next_feat, message='next_feat', summarize=-1)
+
+        matching_fc_out = paddle.static.nn.fc(
+            x=next_feat,
+            size=num_labels,
+            weight_attr=paddle.ParamAttr(
+                name="grounded.img_text_matching_fc.w_0",
+                initializer=self._param_initializer),
+            bias_attr="grounded.img_text_matching_fc.b_0")
+        # paddle.static.Print(matching_fc_out, message='matching_fc_out', summarize=-1)
+
+        matching_loss, matching_softmax = paddle.nn.functional.softmax_with_cross_entropy(
+            logits=matching_fc_out, label=labels, return_softmax=True)
+        # paddle.static.Print(matching_loss, message='matching_loss', summarize=-1)
+        # paddle.static.Print(matching_softmax, message='matching_softmax', summarize=-1)
+
+        image_match_weight = paddle.cast(is_multimodal, dtype="float32")
+        matching_loss = paddle.multiply(x=matching_loss, y=image_match_weight)  # exclude only text input
+        # paddle.static.Print(matching_loss, message='matching_loss_2', summarize=-1)
+        avg_img_num = paddle.mean(x=image_match_weight)
+        # paddle.static.Print(avg_img_num, message='avg_img_num', summarize=-1)
+        mean_matching_loss = paddle.mean(x=matching_loss) / (avg_img_num + 1e-18)
+        # paddle.static.Print(mean_matching_loss, message='mean_matching_loss', summarize=-1)
+
+        valid_condition = paddle.cast(paddle.reshape(x=is_multimodal, shape=[-1]), 'bool')
+        valid_img2txt_ind = paddle.fluid.layers.where(valid_condition)
+        res = paddle.is_empty(x=valid_img2txt_ind)
+
+        def false_func():
+            valid_matching_softmax = paddle.gather(x=matching_softmax, index=valid_img2txt_ind)
+            valid_labels = paddle.gather(x=labels, index=valid_img2txt_ind)
+            valid_labels = paddle.reshape(x=valid_labels, shape=[-1, 1])
+            matching_acc = paddle.metric.accuracy(input=valid_matching_softmax, label=valid_labels)
+            return matching_acc
+
+        def true_func():
+            return paddle.zeros(shape=[1], dtype='float32')
+
+        matching_acc = paddle.static.nn.cond(res, true_fn=true_func, false_fn=false_func)
+        return mean_matching_loss, matching_acc
+
+    def get_pretraining_txt_output(self, mask_label, mask_pos, lm_loss_flag,
+                                   with_text=None, is_multimodal=None, mm_lr_weight=1.0):
+        """Get the loss & accuracy for pretraining language"""
+        mask_pos = paddle.cast(x=mask_pos, dtype='int32')
+        reshaped_emb_out = paddle.reshape(x=self._enc_l_out, shape=[-1, self._emb_size])
+        # extract masked tokens' feature
+        mask_feat = paddle.gather(x=reshaped_emb_out, index=mask_pos)
+
+        with_text = paddle.fluid.layers.expand(with_text,  # [batch_size, max_seq_len]
+                                               expand_times=[1, paddle.shape(self._enc_l_out)[1]])
+        with_text = paddle.reshape(x=with_text, shape=[-1])  # [batch_size * max_seq_len]
+        with_text = paddle.gather(x=with_text, index=mask_pos)
+        with_text = paddle.reshape(x=with_text, shape=[-1, 1])  # [batch_size * max_seq_len, 1]
+        with_text.stop_gradient = True
+
+        is_multimodal = paddle.fluid.layers.expand(is_multimodal,  # [batch_size, max_seq_len]
+                                                   expand_times=[1, paddle.shape(self._enc_l_out)[1]])
+        is_multimodal = paddle.reshape(x=is_multimodal, shape=[-1])  # [batch_size * max_seq_len]
+        is_multimodal = paddle.gather(x=is_multimodal, index=mask_pos)
+        mm_loss_weight = is_multimodal * mm_lr_weight + (1 - is_multimodal)
+        mm_loss_weight.stop_gradient = True
+
+        # transform: fc
+        mask_trans_feat = paddle.static.nn.fc(
+            x=mask_feat,
+            size=self._emb_size,
+            activation=self._hidden_act,
+            weight_attr=paddle.ParamAttr(
+                name='grounded.mask_lm_trans_fc.w_0',
+                trainable=self._grounded_encoder_trainable,
+                initializer=self._param_initializer),
+            bias_attr=paddle.ParamAttr(
+                name='grounded.mask_lm_trans_fc.b_0',
+                trainable=self._grounded_encoder_trainable,
+                initializer=paddle.nn.initializer.Constant(value=0.)))
+
+        # transform: layer norm
+        mask_trans_feat = paddle.static.nn.layer_norm(
+            mask_trans_feat,
+            begin_norm_axis=len(mask_trans_feat.shape) - 1,
+            param_attr=paddle.ParamAttr(
+                name='grounded.mask_lm_trans_layer_norm_scale',
+                trainable=self._grounded_encoder_trainable,
+                initializer=paddle.nn.initializer.Constant(value=1.)),
+            bias_attr=paddle.ParamAttr(
+                name='grounded.mask_lm_trans_layer_norm_bias',
+                trainable=self._grounded_encoder_trainable,
+                initializer=paddle.nn.initializer.Constant(value=0.)))
+
+        mask_lm_out_bias_attr = paddle.ParamAttr(
+            name="grounded.mask_lm_out_fc.b_0",
+            trainable=self._grounded_encoder_trainable,
+            initializer=paddle.nn.initializer.Constant(value=0.0))
+
+        if self._weight_sharing:
+            fc_out = paddle.matmul(
+                x=mask_trans_feat,
+                y=paddle.static.default_main_program().global_block().var("text.word_embedding"),
+                transpose_y=True)
+            fc_out += paddle.create_parameter(
+                shape=[self._text_voc_size],
+                dtype=self._emb_dtype,
+                attr=mask_lm_out_bias_attr,
+                is_bias=True)
+        else:
+            fc_out = paddle.static.nn.fc(x=mask_trans_feat,
+                                         size=self._text_voc_size,
+                                         weight_attr=paddle.ParamAttr(
+                                             name="grounded.mask_lm_out_fc.w_0",
+                                             trainable=self._grounded_encoder_trainable,
+                                             initializer=self._param_initializer),
+                                         bias_attr=mask_lm_out_bias_attr)
+
+        mask_lm_loss = paddle.nn.functional.softmax_with_cross_entropy(
+            logits=fc_out, label=mask_label)
+        mask_lm_loss = paddle.multiply(x=mask_lm_loss, y=with_text)
+        mask_lm_loss = paddle.sum(x=mask_lm_loss, axis=1, keepdim=True)
+        mask_lm_loss = paddle.multiply(x=mask_lm_loss, y=mm_loss_weight)
+        mean_mask_lm_loss = paddle.mean(paddle.multiply(x=mask_lm_loss, y=lm_loss_flag))
+        num_lm_loss = paddle.mean(paddle.multiply(x=lm_loss_flag, y=with_text))
+        mean_mask_lm_loss = mean_mask_lm_loss / (num_lm_loss + 1e-18)
+        return mean_mask_lm_loss
+
+    def get_pretraining_cmcl_output_within_batch(self, labels, with_image, with_text, is_multimodal):
+        """Get the loss & accuracy for cross modal contrastive learning"""
+        self.cmcl_temperature = paddle.clip(self.cmcl_temperature, min=0.001, max=0.5)
+
+        print('-----img_txt_infonce------')
+        text_feat = paddle.slice(input=self.text_enc_out, axes=[1], starts=[0], ends=[1])
+        text_feat = paddle.reshape(x=text_feat, shape=[-1, self._emb_size])
+        if self._with_cmcl_projection:
+            if self._cmcl_share_parameters:
+                text_feat = paddle.static.nn.fc(
+                    x=text_feat,
+                    size=self._emb_size,
+                    activation="relu",
+                    weight_attr=paddle.ParamAttr(
+                        name="grounded.grounding_token_projection.w_0",
+                        initializer=self._param_initializer),
+                    bias_attr="grounded.grounding_token_projection.b_0")
+            else:
+                text_feat = paddle.static.nn.fc(
+                    x=text_feat,
+                    size=self._emb_size,
+                    activation="relu",
+                    weight_attr=paddle.ParamAttr(
+                        name="grounded.cmcl_pooled_fc_text.w_0", initializer=self._param_initializer),
+                    bias_attr="grounded.cmcl_pooled_fc_text.b_0")
+            text_feat = paddle.nn.functional.normalize(text_feat, axis=-1)
+
+        img_feat = paddle.slice(input=self.vit_enc_out, axes=[1], starts=[0], ends=[1])
+        img_feat = paddle.reshape(x=img_feat, shape=[-1, self._emb_size])
+        if self._with_cmcl_projection:
+            if self._cmcl_share_parameters:
+                img_feat = paddle.static.nn.fc(
+                    x=img_feat,
+                    size=self._emb_size,
+                    activation="relu",
+                    weight_attr=paddle.ParamAttr(
+                        name="grounded.grounding_patch_projection.w_0",
+                        initializer=self._param_initializer),
+                    bias_attr="grounded.grounding_patch_projection.b_0")
+            else:
+                img_feat = paddle.static.nn.fc(
+                    x=img_feat,
+                    size=self._emb_size,
+                    activation="relu",
+                    weight_attr=paddle.ParamAttr(
+                        name="grounded.cmcl_pooled_fc_image.w_0", initializer=self._param_initializer),
+                    bias_attr="grounded.cmcl_pooled_fc_image.b_0")
+        img_feat = paddle.nn.functional.normalize(img_feat, axis=-1)
+
+        labels = paddle.reshape(paddle.cast(labels, dtype='float32'), shape=[-1])
+        # paddle.static.Print(labels, message='labels', summarize=-1)
+        is_multimodal = paddle.reshape(paddle.cast(is_multimodal, dtype='float32'), shape=[-1])
+        # paddle.static.Print(is_multimodal, message='is_multimodal', summarize=-1)
+
+        """compute infoNCE loss"""
+        # (batch, batch)
+        all_img2txt_scores = paddle.matmul(img_feat, paddle.transpose(text_feat, perm=[1, 0]))
+        all_img2txt_scores = paddle.divide(x=all_img2txt_scores, y=self.cmcl_temperature)
+        # paddle.static.Print(all_img2txt_scores, message='all_img2txt_scores', summarize=-1)
+
+        pos_labels = paddle.arange(start=0, end=paddle.shape(text_feat)[0], dtype='int64')
+        # paddle.static.Print(pos_labels, message='pos_labels', summarize=-1)
+
+        img2txt_loss, img2txt_softmax = paddle.nn.functional.softmax_with_cross_entropy(
+            logits=all_img2txt_scores, label=paddle.unsqueeze(pos_labels, axis=1), return_softmax=True, axis=1)
+        img2txt_loss = paddle.squeeze(img2txt_loss)
+        # paddle.static.Print(img2txt_loss, message='img2txt_loss', summarize=-1)
+        # paddle.static.Print(img2txt_softmax, message='img2txt_softmax', summarize=-1)
+
+        txt2img_loss, txt2img_softmax = paddle.nn.functional.softmax_with_cross_entropy(
+            logits=all_img2txt_scores, label=paddle.unsqueeze(pos_labels, axis=0), return_softmax=True, axis=0)
+        txt2img_loss = paddle.squeeze(txt2img_loss)
+        # paddle.static.Print(txt2img_loss, message='txt2img_loss', summarize=-1)
+        # paddle.static.Print(txt2img_softmax, message='txt2img_softmax', summarize=-1)
+
+        total_loss = (img2txt_loss + txt2img_loss) / 2.0
+        total_loss = paddle.multiply(total_loss, labels)
+        total_loss = paddle.multiply(total_loss, is_multimodal)
+        # paddle.static.Print(total_loss, message='total_loss', summarize=-1)
+        loss = paddle.mean(total_loss)
+
+        # compute accuracy
+        valid_condition = paddle.cast(is_multimodal * labels, 'bool')
+        valid_img2txt_ind = paddle.fluid.layers.where(valid_condition)
+        # paddle.static.Print(valid_img2txt_ind, message='valid_img2txt_ind', summarize=-1)
+        res = paddle.is_empty(x=valid_img2txt_ind)
+
+        def false_func():
+            valid_img2txt_softmax = paddle.gather(x=img2txt_softmax, index=valid_img2txt_ind)
+            valid_txt2img_softmax = paddle.gather(x=txt2img_softmax, index=valid_img2txt_ind)
+            valid_labels = paddle.gather(x=pos_labels, index=valid_img2txt_ind)
+            valid_labels = paddle.reshape(x=valid_labels, shape=[-1, 1])
+            img2txt_acc = paddle.metric.accuracy(input=valid_img2txt_softmax, label=valid_labels, k=5)
+            txt2img_acc = paddle.metric.accuracy(input=valid_txt2img_softmax, label=valid_labels, k=5)
+            return img2txt_acc, txt2img_acc
+
+        def true_func():
+            return paddle.zeros(shape=[1], dtype='float32'), paddle.zeros(shape=[1], dtype='float32')
+
+        img2txt_acc, txt2img_acc = paddle.static.nn.cond(res, true_fn=true_func, false_fn=false_func)
+        return loss, img2txt_acc, txt2img_acc
+
+    def get_pretraining_cmcl_output_cross_batch(self, labels, with_image, with_text, is_multimodal, batch_size=None):
+        """Get the loss & accuracy for cross modal contrastive learning"""
+        self.cmcl_temperature = paddle.clip(self.cmcl_temperature, min=0.001, max=0.5)
+
+        print('-----img_txt_infonce------')
+        text_feat = paddle.slice(input=self.text_enc_out, axes=[1], starts=[0], ends=[1])
+        text_feat = paddle.reshape(x=text_feat, shape=[batch_size, self._emb_size])
+        if self._with_cmcl_projection:
+            if self._cmcl_share_parameters:
+                text_feat = paddle.static.nn.fc(
+                    x=text_feat,
+                    size=self._emb_size,
+                    activation="relu",
+                    weight_attr=paddle.ParamAttr(
+                        name="grounded.grounding_token_projection.w_0",
+                        initializer=self._param_initializer),
+                    bias_attr="grounded.grounding_token_projection.b_0")
+            else:
+                text_feat = paddle.static.nn.fc(
+                    x=text_feat,
+                    size=self._emb_size,
+                    activation="relu",
+                    weight_attr=paddle.ParamAttr(
+                        name="grounded.cmcl_pooled_fc_text.w_0", initializer=self._param_initializer),
+                    bias_attr="grounded.cmcl_pooled_fc_text.b_0")
+            text_feat = paddle.nn.functional.normalize(text_feat, axis=-1)
+
+        img_feat = paddle.slice(input=self.vit_enc_out, axes=[1], starts=[0], ends=[1])
+        img_feat = paddle.reshape(x=img_feat, shape=[batch_size, self._emb_size])
+        if self._with_cmcl_projection:
+            if self._cmcl_share_parameters:
+                img_feat = paddle.static.nn.fc(
+                    x=img_feat,
+                    size=self._emb_size,
+                    activation="relu",
+                    weight_attr=paddle.ParamAttr(
+                        name="grounded.grounding_patch_projection.w_0",
+                        initializer=self._param_initializer),
+                    bias_attr="grounded.grounding_patch_projection.b_0")
+            else:
+                img_feat = paddle.static.nn.fc(
+                    x=img_feat,
+                    size=self._emb_size,
+                    activation="relu",
+                    weight_attr=paddle.ParamAttr(
+                        name="grounded.cmcl_pooled_fc_image.w_0", initializer=self._param_initializer),
+                    bias_attr="grounded.cmcl_pooled_fc_image.b_0")
+            img_feat = paddle.nn.functional.normalize(img_feat, axis=-1)
+
+        worker_num = fleet.worker_num()
+        print("worker num is: {}".format(fleet.worker_num()))
+        print("worker index is: {}".format(fleet.worker_index()))
+        # obtain cross batch data
+        tot_text_feat = paddle.fluid.layers.collective._c_allgather(
+            text_feat, fleet.worker_num(), use_calc_stream=True)  # (fake_tot_batch x self._emb_size)
+        tot_img_feat = paddle.fluid.layers.collective._c_allgather(
+            img_feat, fleet.worker_num(), use_calc_stream=True)  # (fake_tot_batch x self._emb_size)
+        tot_labels = paddle.fluid.layers.collective._c_allgather(
+            labels, fleet.worker_num(), use_calc_stream=True)  # (fake_tot_batch x 1)
+        tot_is_multimodal = paddle.fluid.layers.collective._c_allgather(
+            is_multimodal, fleet.worker_num(), use_calc_stream=True)  # (fake_tot_batch x 1)
+
+        tot_labels = paddle.reshape(paddle.cast(tot_labels, dtype='float32'),
+                                    shape=[batch_size * worker_num])
+        # paddle.static.Print(tot_labels, message='tot_labels', summarize=-1)
+        tot_is_multimodal = paddle.reshape(paddle.cast(tot_is_multimodal, dtype='float32'),
+                                           shape=[batch_size * worker_num])
+        # paddle.static.Print(tot_is_multimodal, message='tot_is_multimodal', summarize=-1)
+
+        """compute infoNCE loss"""
+        # (batch, batch)
+        all_img2txt_scores = paddle.matmul(tot_img_feat, paddle.transpose(tot_text_feat, perm=[1, 0]))
+        all_img2txt_scores = paddle.divide(x=all_img2txt_scores, y=self.cmcl_temperature)
+        # paddle.static.Print(all_img2txt_scores, message='all_img2txt_scores', summarize=-1)
+
+        pos_labels = paddle.arange(start=0, end=(batch_size * worker_num), dtype='int64')
+        # paddle.static.Print(pos_labels, message='pos_labels', summarize=-1)
+
+        img2txt_loss, img2txt_softmax = paddle.nn.functional.softmax_with_cross_entropy(
+            logits=all_img2txt_scores, label=paddle.unsqueeze(pos_labels, axis=1), return_softmax=True, axis=1)
+        img2txt_loss = paddle.squeeze(img2txt_loss)
+        # paddle.static.Print(img2txt_loss, message='img2txt_loss', summarize=-1)
+        # paddle.static.Print(img2txt_softmax, message='img2txt_softmax', summarize=-1)
+
+        txt2img_loss, txt2img_softmax = paddle.nn.functional.softmax_with_cross_entropy(
+            logits=all_img2txt_scores, label=paddle.unsqueeze(pos_labels, axis=0), return_softmax=True, axis=0)
+        txt2img_loss = paddle.squeeze(txt2img_loss)
+        # paddle.static.Print(txt2img_loss, message='txt2img_loss', summarize=-1)
+        # paddle.static.Print(txt2img_softmax, message='txt2img_softmax', summarize=-1)
+
+        total_loss = (img2txt_loss + txt2img_loss) / 2.0
+        total_loss = paddle.multiply(total_loss, tot_labels)
+        total_loss = paddle.multiply(total_loss, tot_is_multimodal)
+        # paddle.static.Print(total_loss, message='total_loss', summarize=-1)
+        loss = paddle.mean(total_loss)
+
+        # compute accuracy
+        valid_condition = paddle.cast(tot_is_multimodal * tot_labels, 'bool')
+        valid_img2txt_ind = paddle.fluid.layers.where(valid_condition)
+        # paddle.static.Print(valid_img2txt_ind, message='valid_img2txt_ind', summarize=-1)
+        res = paddle.is_empty(x=valid_img2txt_ind)
+
+        def false_func():
+            valid_img2txt_softmax = paddle.gather(x=img2txt_softmax, index=valid_img2txt_ind)
+            valid_txt2img_softmax = paddle.gather(x=txt2img_softmax, index=valid_img2txt_ind)
+            valid_labels = paddle.gather(x=pos_labels, index=valid_img2txt_ind)
+            valid_labels = paddle.reshape(x=valid_labels, shape=[-1, 1])
+            img2txt_acc = paddle.metric.accuracy(input=valid_img2txt_softmax, label=valid_labels, k=5)
+            txt2img_acc = paddle.metric.accuracy(input=valid_txt2img_softmax, label=valid_labels, k=5)
+            return img2txt_acc, txt2img_acc
+
+        def true_func():
+            return paddle.zeros(shape=[1], dtype='float32'), paddle.zeros(shape=[1], dtype='float32')
+
+        img2txt_acc, txt2img_acc = paddle.static.nn.cond(res, true_fn=true_func, false_fn=false_func)
+        return loss, img2txt_acc, txt2img_acc
+
+    def get_cmcl_scores(self):
+        """Get the score for img-text pairs"""
+        text_feat = paddle.slice(input=self.text_enc_out, axes=[1], starts=[0], ends=[1])
+        text_feat = paddle.reshape(x=text_feat, shape=[-1, self._emb_size])
+        if self._with_cmcl_projection:
+            if self._cmcl_share_parameters:
+                text_feat = paddle.static.nn.fc(
+                    x=text_feat,
+                    size=self._emb_size,
+                    activation="relu",
+                    weight_attr=paddle.ParamAttr(
+                        name="grounded.grounding_token_projection.w_0",
+                        initializer=self._param_initializer),
+                    bias_attr="grounded.grounding_token_projection.b_0")
+            else:
+                text_feat = paddle.static.nn.fc(
+                    x=text_feat,
+                    size=self._emb_size,
+                    activation="relu",
+                    weight_attr=paddle.ParamAttr(
+                        name="grounded.cmcl_pooled_fc_text.w_0", initializer=self._param_initializer),
+                    bias_attr="grounded.cmcl_pooled_fc_text.b_0")
+            text_feat = paddle.nn.functional.normalize(text_feat, axis=-1)
+
+        img_feat = paddle.slice(input=self.vit_enc_out, axes=[1], starts=[0], ends=[1])
+        img_feat = paddle.reshape(x=img_feat, shape=[-1, self._emb_size])
+        if self._with_cmcl_projection:
+            if self._cmcl_share_parameters:
+                img_feat = paddle.static.nn.fc(
+                    x=img_feat,
+                    size=self._emb_size,
+                    activation="relu",
+                    weight_attr=paddle.ParamAttr(
+                        name="grounded.grounding_patch_projection.w_0",
+                        initializer=self._param_initializer),
+                    bias_attr="grounded.grounding_patch_projection.b_0")
+            else:
+                img_feat = paddle.static.nn.fc(
+                    x=img_feat,
+                    size=self._emb_size,
+                    activation="relu",
+                    weight_attr=paddle.ParamAttr(
+                        name="grounded.cmcl_pooled_fc_image.w_0", initializer=self._param_initializer),
+                    bias_attr="grounded.cmcl_pooled_fc_image.b_0")
+        img_feat = paddle.nn.functional.normalize(img_feat, axis=-1)
+
+        """compute matching score"""
+        # (batch, 1)
+        img2txt_scores = paddle.sum(img_feat * text_feat, axis=1, keepdim=True)
+        return img2txt_scores
+
+    def get_pretraining_mask_grounding_output(self):
+        """Get the loss & accuracy for pretraining language"""
+        grounded_seq_label = paddle.reshape(self.grounded_encoding_indices, shape=[-1, 1])
+        grounding_mask_label = paddle.gather(grounded_seq_label, index=self.grounding_mask_pos)
+
+        # extract masked tokens' feature
+        reshaped_grounding_feat = paddle.reshape(self._enc_g_out, shape=[-1, self._emb_size])
+        mask_feat = paddle.gather(x=reshaped_grounding_feat, index=self.grounding_mask_pos)
+
+        # transform: fc
+        mask_trans_feat = paddle.static.nn.fc(
+            x=mask_feat,
+            size=self._emb_size,
+            activation=self._hidden_act,
+            weight_attr=paddle.ParamAttr(
+                name='grounded.mask_g_trans_fc.w_0',
+                trainable=self._grounded_encoder_trainable,
+                initializer=self._param_initializer),
+            bias_attr=paddle.ParamAttr(
+                name='grounded.mask_g_trans_fc.b_0',
+                trainable=self._grounded_encoder_trainable,
+                initializer=paddle.nn.initializer.Constant(value=0.)))
+
+        # transform: layer norm
+        mask_trans_feat = paddle.static.nn.layer_norm(
+            mask_trans_feat,
+            begin_norm_axis=len(mask_trans_feat.shape) - 1,
+            param_attr=paddle.ParamAttr(
+                name='grounded.mask_g_trans_layer_norm_scale',
+                trainable=self._grounded_encoder_trainable,
+                initializer=paddle.nn.initializer.Constant(value=1.)),
+            bias_attr=paddle.ParamAttr(
+                name='grounded.mask_g_trans_layer_norm_bias',
+                trainable=self._grounded_encoder_trainable,
+                initializer=paddle.nn.initializer.Constant(value=0.)))
+
+        mask_lm_out_bias_attr = paddle.ParamAttr(
+            name="grounded.mask_g_out_fc.b_0",
+            trainable=self._grounded_encoder_trainable,
+            initializer=paddle.nn.initializer.Constant(value=0.0))
+
+        if self._weight_sharing:
+            fc_out = paddle.matmul(
+                x=mask_trans_feat,
+                y=paddle.static.default_main_program().global_block().var("vq_emb"),
+                transpose_y=True)
+            fc_out += paddle.create_parameter(
+                shape=[self.num_codebook],
+                dtype=self._emb_dtype,
+                attr=mask_lm_out_bias_attr,
+                is_bias=True)
+        else:
+            fc_out = paddle.static.nn.fc(x=mask_trans_feat,
+                                         size=self.num_codebook,
+                                         weight_attr=paddle.ParamAttr(
+                                             name="grounded.mask_g_out_fc.w_0",
+                                             trainable=self._grounded_encoder_trainable,
+                                             initializer=self._param_initializer),
+                                         bias_attr=mask_lm_out_bias_attr)
+
+        mask_g_loss = paddle.nn.functional.softmax_with_cross_entropy(
+            logits=fc_out, label=grounding_mask_label)
+        mask_g_loss = paddle.sum(x=mask_g_loss, axis=1, keepdim=True)
+        mean_mask_g_loss = paddle.mean(mask_g_loss)
+        return mean_mask_g_loss
diff --git a/NLP/UNIMO-2/src/model/unimo_grounded_baseline.py b/NLP/UNIMO-2/src/model/unimo_grounded_baseline.py
new file mode 100644
index 000000000..41b1e0637
--- /dev/null
+++ b/NLP/UNIMO-2/src/model/unimo_grounded_baseline.py
@@ -0,0 +1,948 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: unimo_grounded_baseline.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-08-31 20:46
+Desc: RoBERTa + ViT + Grounded Transformer
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import json
+import six
+import paddle
+import paddle.distributed.fleet as fleet
+from model.transformer_encoder import encoder as grounded_encoder
+from model.transformer_encoder import encoder as text_encoder
+from model.transformer_encoder import pre_process_layer as text_pre_process_layer
+from model.vision_transformer_encoder import encoder as vit_encoder
+from model.vision_transformer_encoder import pre_process_layer as vit_pre_process_layer
+from utils.pos_emb_interpolate import interpolate_pos_embed
+
+
+class VlConfig(object):
+    def __init__(self, config_path):
+        self._config_dict = self._parse(config_path)
+
+    def _parse(self, config_path):
+        try:
+            with open(config_path) as json_file:
+                config_dict = json.load(json_file)
+        except Exception:
+            raise IOError("Error in parsing VL model config file '%s'" %
+                          config_path)
+        else:
+            return config_dict
+
+    def __getitem__(self, key):
+        return self._config_dict.get(key, None)
+
+    def __setitem__(self, key, value):
+        self._config_dict[key] = value
+
+    def print_config(self):
+        for arg, value in self._config_dict.items():
+            print('%s: %s' % (arg, value))
+        print('------------------------------------------------')
+
+
+class VlModel(object):
+    def __init__(self,
+                 image_input=None,
+                 image_mask=None,
+                 text_input=None,
+                 text_mask=None,
+                 config=None,
+                 weight_sharing=True,
+                 task_type="normal",
+                 decoding=False,
+                 gather_idx=None,
+                 grounded_encoder_trainable=True,
+                 vit_encoder_trainable=True,
+                 text_encoder_trainable=True,
+                 with_cmcl_projection=True,
+                 text_enc_layers=None,
+                 grounding_enc_layers=None):
+
+        # for grounded cross-modal encoder
+        self._n_layer = config['num_hidden_layers']
+        self._n_head = config['num_attention_heads']
+        self._max_position_seq_len = config['max_position_embeddings']
+        self._hidden_act = config['hidden_act']
+        self._emb_size = config['hidden_size']
+        self._prepostprocess_dropout = config['hidden_dropout_prob']
+        self._attention_dropout = config['attention_probs_dropout_prob']
+        self._emb_dtype = "float32"
+        self._sent_types = config['type_vocab_size']
+
+        # for text encoder
+        self._text_n_layer = config['text_num_hidden_layers']
+        self._text_n_head = config['text_num_attention_heads']
+        self._text_voc_size = config['text_vocab_size']
+        self._text_max_position_seq_len = config['text_max_position_embeddings']
+        self._text_hidden_act = config['text_hidden_act']
+        self._text_prepostprocess_dropout = config['text_hidden_dropout_prob']
+        self._text_attention_dropout = config['text_attention_probs_dropout_prob']
+        self._text_emb_vocab_size = {"text.word_embedding": self._text_voc_size,
+                                     "text.pos_embedding": self._text_max_position_seq_len}
+
+        # for vit encoder
+        self._vit_n_layer = config['vit_num_hidden_layers']
+        self._vit_n_head = config['vit_num_attention_heads']
+        self._vit_hidden_act = config['vit_hidden_act']
+        self._vit_prepostprocess_dropout = config['vit_hidden_dropout_prob']
+        self._vit_attention_dropout = config['vit_attention_probs_dropout_prob']
+        self._vit_layer_norm_eps = config['vit_layer_norm_eps']
+
+        self._weight_sharing = weight_sharing
+        self._grounded_encoder_trainable = grounded_encoder_trainable
+        self._vit_encoder_trainable = vit_encoder_trainable
+        self._text_encoder_trainable = text_encoder_trainable
+        self._with_cmcl_projection = with_cmcl_projection
+
+        # Initialize all weigths by truncated normal initializer, and all biases
+        # will be initialized by constant zero by default.
+        self._param_initializer = paddle.fluid.initializer.TruncatedNormalInitializer(scale=config['initializer_range'])
+        self._bias_initializer = paddle.fluid.initializer.ConstantInitializer(value=0.0)
+
+        assert text_input is not None or image_input is not None, "text_input and image_input cannot be both None"
+        self._task_type = task_type
+        self._is_img2txt_task = (task_type == "img2txt")
+        self._is_multimodal_task = (image_input is not None)
+
+        self.image_size = config['image_size']
+        self.num_codebook = config['num_codebook']
+        self.resolution = config['resolution']
+        self.width = self.image_size // config['resolution']
+        self.patch_seq_len = self.image_size * self.image_size // (config['resolution'] * config['resolution'])
+        self.patch_emb_size = config['resolution'] * config['resolution'] * 3
+
+        if text_enc_layers is None:
+            text_enc_layers = list(range(self._text_n_layer))
+        if grounding_enc_layers is None:
+            grounding_enc_layers = list(range(self._n_layer))
+
+        print("text_enc_layers", text_enc_layers)
+        print("grounding_enc_layers", grounding_enc_layers)
+        self.text_enc_layers = text_enc_layers
+        self.grounding_enc_layers = grounding_enc_layers
+
+        self.cmcl_temperature = paddle.static.create_parameter(
+            shape=[1],
+            dtype=self._emb_dtype,
+            attr=paddle.ParamAttr(
+                name="cmcl_temperature",
+                initializer=paddle.fluid.initializer.ConstantInitializer(value=0.07)))
+
+        if decoding:
+            self.grounded_caches = [{
+                "k":
+                    paddle.fluid.layers.fill_constant_batch_size_like(
+                        input=text_input["text.word_embedding"] if text_input is not None else image_input[
+                            "pixel_embedding"],
+                        shape=[-1, 0, self._emb_size],
+                        dtype=self._emb_dtype,  # float32,
+                        value=0),
+                "v":
+                    paddle.fluid.layers.fill_constant_batch_size_like(
+                        input=text_input["text.word_embedding"] if text_input is not None else image_input[
+                            "pixel_embedding"],
+                        shape=[-1, 0, self._emb_size],
+                        dtype=self._emb_dtype,  # float32,
+                        value=0),
+            } for i in range(self._n_layer)]
+
+            self.text_caches = [{
+                "k":
+                    paddle.fluid.layers.fill_constant_batch_size_like(
+                        input=text_input["text.word_embedding"] if text_input is not None else image_input[
+                            "pixel_embedding"],
+                        shape=[-1, 0, self._emb_size],
+                        dtype=self._emb_dtype,  # float32,
+                        value=0),
+                "v":
+                    paddle.fluid.layers.fill_constant_batch_size_like(
+                        input=text_input["text.word_embedding"] if text_input is not None else image_input[
+                            "pixel_embedding"],
+                        shape=[-1, 0, self._emb_size],
+                        dtype=self._emb_dtype,  # float32,
+                        value=0),
+            } for i in range(self._text_n_layer)]
+        else:
+            self.grounded_caches = None
+            self.text_caches = None
+
+        self._build_model(text_input=text_input,
+                          text_mask=text_mask,
+                          gather_idx=gather_idx,
+                          image_input=image_input,
+                          image_mask=image_mask)
+
+    def _build_model(self, text_input=None, text_mask=None, gather_idx=None, image_input=None, image_mask=None):
+        if text_input is None and image_input is not None:  # for img2txt when decoding or image tasks
+            self._enc_v_out, self.all_checkpoints = self.encode(image_input=image_input,
+                                                                image_mask=image_mask,
+                                                                gather_idx=gather_idx)
+        elif text_input is not None and image_input is None:  # for textual tasks
+            self._enc_l_out, self.all_checkpoints = self.encode(text_input=text_input,
+                                                                text_mask=text_mask,
+                                                                gather_idx=gather_idx)
+        else:  # for multi-modal tasks
+            self._enc_v_out, self._enc_l_out, self.all_checkpoints = \
+                self.encode(text_input=text_input,
+                            text_mask=text_mask,
+                            gather_idx=gather_idx,
+                            image_input=image_input,
+                            image_mask=image_mask)
+
+    def encode(self, text_input=None, text_mask=None, gather_idx=None,
+               image_input=None, image_mask=None, decoding_step=False, grounded_decoding_mask=None):
+        all_checkpoints = []
+        # padding id in vocabulary must be set to 0
+        if text_input is None and image_input is not None:  # for img2txt task when decoding or image tasks
+            emb_v_out, v_seq_len, n_head_self_attn_mask, _checkpoints = \
+                self._gen_input(image_input=image_input, image_mask=image_mask, gather_idx=gather_idx)
+            all_checkpoints.extend(_checkpoints)
+
+            enc_v_out, grounding_checkpoints = grounded_encoder(
+                enc_input=emb_v_out,
+                attn_bias=n_head_self_attn_mask,
+                enc_layers=self.grounding_enc_layers,
+                n_head=self._n_head,
+                d_key=self._emb_size // self._n_head,
+                d_value=self._emb_size // self._n_head,
+                d_model=self._emb_size,
+                d_inner_hid=self._emb_size * 4,
+                prepostprocess_dropout=self._prepostprocess_dropout,
+                attention_dropout=self._attention_dropout,
+                relu_dropout=0,
+                hidden_act=self._hidden_act,
+                preprocess_cmd="",
+                postprocess_cmd="dan",
+                param_initializer=self._param_initializer,
+                name='grounded.encoder',
+                caches=self.grounded_caches,
+                gather_idx=gather_idx,
+                trainable=self._grounded_encoder_trainable)
+            all_checkpoints.extend(grounding_checkpoints)
+            return enc_v_out, all_checkpoints
+
+        elif image_input is None and text_input is not None:  # for textual task
+            if decoding_step:  # for step-by-step generation during decoding
+                emb_l_out, l_seq_len, n_head_self_attn_mask, _checkpoints = \
+                    self._gen_input(text_input=text_input, text_mask=text_mask, gather_idx=gather_idx,
+                                    decoding_step=True, grounded_decoding_mask=grounded_decoding_mask)
+                all_checkpoints.extend(_checkpoints)
+
+                enc_l_out, grounding_checkpoints = grounded_encoder(
+                    enc_input=emb_l_out,
+                    attn_bias=n_head_self_attn_mask,
+                    enc_layers=self.grounding_enc_layers,
+                    n_head=self._n_head,
+                    d_key=self._emb_size // self._n_head,
+                    d_value=self._emb_size // self._n_head,
+                    d_model=self._emb_size,
+                    d_inner_hid=self._emb_size * 4,
+                    prepostprocess_dropout=self._prepostprocess_dropout,
+                    attention_dropout=self._attention_dropout,
+                    relu_dropout=0,
+                    hidden_act=self._hidden_act,
+                    preprocess_cmd="",
+                    postprocess_cmd="dan",
+                    param_initializer=self._param_initializer,
+                    name='grounded.encoder',
+                    caches=self.grounded_caches,
+                    gather_idx=gather_idx,
+                    trainable=self._grounded_encoder_trainable)
+                all_checkpoints.extend(grounding_checkpoints)
+                return enc_l_out, all_checkpoints
+
+            else:
+                emb_l_out, l_seq_len, n_head_self_attn_mask, _checkpoints = \
+                    self._gen_input(text_input=text_input, text_mask=text_mask, gather_idx=gather_idx)
+                all_checkpoints.extend(_checkpoints)
+
+                enc_l_out, grounding_checkpoints = grounded_encoder(
+                    enc_input=emb_l_out,
+                    attn_bias=n_head_self_attn_mask,
+                    enc_layers=self.grounding_enc_layers,
+                    n_head=self._n_head,
+                    d_key=self._emb_size // self._n_head,
+                    d_value=self._emb_size // self._n_head,
+                    d_model=self._emb_size,
+                    d_inner_hid=self._emb_size * 4,
+                    prepostprocess_dropout=self._prepostprocess_dropout,
+                    attention_dropout=self._attention_dropout,
+                    relu_dropout=0,
+                    hidden_act=self._hidden_act,
+                    preprocess_cmd="",
+                    postprocess_cmd="dan",
+                    param_initializer=self._param_initializer,
+                    name='grounded.encoder',
+                    caches=self.grounded_caches,
+                    gather_idx=gather_idx,
+                    trainable=self._grounded_encoder_trainable)
+                all_checkpoints.extend(grounding_checkpoints)
+                return enc_l_out, all_checkpoints
+
+        elif image_input is not None and text_input is not None:  # for multi-modal task
+            emb_v_out, emb_l_out, v_seq_len, l_seq_len, n_head_self_attn_mask, _checkpoints = \
+                self._gen_input(image_input=image_input, image_mask=image_mask,
+                                text_input=text_input, text_mask=text_mask, gather_idx=gather_idx)
+            all_checkpoints.extend(_checkpoints)
+            emb_vl_out = paddle.concat([emb_v_out, emb_l_out], axis=1)
+
+            enc_vl_out, grounding_checkpoints = grounded_encoder(
+                enc_input=emb_vl_out,
+                attn_bias=n_head_self_attn_mask,
+                enc_layers=self.grounding_enc_layers,
+                n_head=self._n_head,
+                d_key=self._emb_size // self._n_head,
+                d_value=self._emb_size // self._n_head,
+                d_model=self._emb_size,
+                d_inner_hid=self._emb_size * 4,
+                prepostprocess_dropout=self._prepostprocess_dropout,
+                attention_dropout=self._attention_dropout,
+                relu_dropout=0,
+                hidden_act=self._hidden_act,
+                preprocess_cmd="",
+                postprocess_cmd="dan",
+                param_initializer=self._param_initializer,
+                name='grounded.encoder',
+                caches=self.grounded_caches,
+                gather_idx=gather_idx,
+                trainable=self._grounded_encoder_trainable)
+            all_checkpoints.extend(grounding_checkpoints)
+            enc_v_out = paddle.slice(
+                input=enc_vl_out, axes=[1], starts=[0], ends=[v_seq_len])
+            enc_l_out = paddle.slice(
+                input=enc_vl_out, axes=[1], starts=[v_seq_len], ends=[v_seq_len + l_seq_len])
+            return enc_v_out, enc_l_out, all_checkpoints
+        else:
+            raise ValueError("The input is invalid")
+
+    def vit_encode(self, image_input, image_mask):
+        """encode image by pre-trained ViT"""
+        assert image_mask is not None, "text_mask should not be none"
+        image_self_attn_mask = paddle.matmul(x=paddle.transpose(image_mask, perm=[0, 2, 1]), y=image_mask)
+        self_attn_mask = paddle.scale(
+            x=image_self_attn_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+        n_head_self_attn_mask = paddle.stack(
+            x=[self_attn_mask] * self._vit_n_head, axis=1)
+        n_head_self_attn_mask.stop_gradient = True
+
+        pixel_embeddings = paddle.static.nn.conv2d(
+            input=image_input['pixel_embedding'],
+            num_filters=self._emb_size,
+            filter_size=self.resolution,
+            stride=self.resolution,
+            padding=(self.resolution - 1) // 2,
+            param_attr=paddle.ParamAttr(
+                name="vit.patch_embeddings_projection_weight",
+                trainable=self._vit_encoder_trainable),
+            bias_attr=paddle.ParamAttr(
+                name="vit.patch_embeddings_projection_bias",
+                trainable=self._vit_encoder_trainable),
+            data_format="NHWC")
+        # paddle.static.Print(paddle.shape(pixel_embeddings), message="pixel_embeddings", summarize=-1)
+        pixel_embeddings = paddle.reshape(pixel_embeddings, shape=[-1, self.patch_seq_len, self._emb_size])
+
+        cls_token_emb = paddle.static.create_parameter(
+            shape=[1, 1, self._emb_size],
+            dtype=self._emb_dtype,
+            attr=paddle.ParamAttr(
+                name="vit.cls_token_embeddings",
+                trainable=self._vit_encoder_trainable,
+                initializer=self._param_initializer))
+        cls_token_emb = paddle.expand(x=cls_token_emb,
+                                      shape=[paddle.shape(pixel_embeddings)[0], 1, self._emb_size])
+
+        # cpncate global [CLS] token with image patches
+        # (batch_size, patch_seq_len + 1, emb_dim)
+        all_pixel_embeddings = paddle.concat(x=[cls_token_emb, pixel_embeddings], axis=1)
+
+        # default image_size=224, resolution=16, patch_seq_len=196
+        pixel_pos_emb = paddle.static.create_parameter(
+            shape=[1, 197, self._emb_size],
+            dtype=self._emb_dtype,
+            attr=paddle.ParamAttr(name="vit.position_embeddings",
+                                  trainable=self._vit_encoder_trainable,
+                                  initializer=self._param_initializer))
+        # paddle.static.Print(paddle.shape(pixel_pos_emb), message="pixel_pos_emb", summarize=-1)
+        if self.patch_seq_len > 196:  # when image_size > 224
+            pixel_pos_emb = interpolate_pos_embed(pixel_pos_emb, self.patch_seq_len)
+
+        emb_v_out = all_pixel_embeddings + pixel_pos_emb
+        emb_v_out = vit_pre_process_layer(
+            emb_v_out, 'd', self._vit_prepostprocess_dropout, name='vit.pre_encoder',
+            trainable=self._vit_encoder_trainable)
+
+        vit_enc_out, checkpoints = vit_encoder(
+            enc_input=emb_v_out,
+            attn_bias=n_head_self_attn_mask,
+            n_layer=self._vit_n_layer,
+            n_head=self._vit_n_head,
+            d_key=self._emb_size // self._vit_n_head,
+            d_value=self._emb_size // self._vit_n_head,
+            d_model=self._emb_size,
+            d_inner_hid=self._emb_size * 4,
+            prepostprocess_dropout=self._vit_prepostprocess_dropout,
+            attention_dropout=self._vit_attention_dropout,
+            relu_dropout=0,
+            hidden_act=self._vit_hidden_act,
+            preprocess_cmd="n",
+            postprocess_cmd="da",
+            param_initializer=self._param_initializer,
+            name='vit.encoder',
+            trainable=self._vit_encoder_trainable)
+        vit_seq_len = paddle.shape(vit_enc_out)[1]  # patch_seq_len + 1
+
+        return vit_enc_out, vit_seq_len, n_head_self_attn_mask, checkpoints
+
+    def text_encode(self, text_input, text_mask, gather_idx=None, decoding_step=False):
+        assert text_mask is not None, "text_mask should not be none"
+        if decoding_step:
+            text_self_attn_mask = text_mask
+        else:
+            text_self_attn_mask = paddle.matmul(x=paddle.transpose(text_mask, perm=[0, 2, 1]), y=text_mask)
+
+        self_attn_mask = paddle.scale(
+            x=text_self_attn_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+        n_head_self_attn_mask = paddle.stack(
+            x=[self_attn_mask] * self._n_head, axis=1)
+        n_head_self_attn_mask.stop_gradient = True
+
+        # text part
+        text_emb = paddle.static.nn.embedding(
+            input=text_input["text.word_embedding"],
+            size=[self._text_emb_vocab_size["text.word_embedding"], self._emb_size],
+            dtype=self._emb_dtype,
+            param_attr=paddle.ParamAttr(
+                name='text.word_embedding',
+                trainable=self._text_encoder_trainable,
+                initializer=self._param_initializer))
+        text_emb = paddle.squeeze(text_emb, axis=2)  # (batch_size, seq_len, emb_dim)
+
+        pos_emb = paddle.static.nn.embedding(
+            input=text_input["text.pos_embedding"],
+            size=[self._text_emb_vocab_size["text.pos_embedding"], self._emb_size],
+            dtype=self._emb_dtype,
+            param_attr=paddle.ParamAttr(
+                name='text.pos_embedding',
+                trainable=self._text_encoder_trainable,
+                initializer=self._param_initializer))
+        pos_emb = paddle.squeeze(pos_emb, axis=2)  # (batch_size, seq_len, emb_dim)
+
+        emb_out = text_emb + pos_emb
+        emb_out = text_pre_process_layer(
+            emb_out, 'nd', self._text_prepostprocess_dropout,
+            name="text.pre_encoder", trainable=self._text_encoder_trainable)
+
+        text_enc_out, checkpoints = text_encoder(
+            enc_input=emb_out,
+            attn_bias=n_head_self_attn_mask,
+            enc_layers=self.text_enc_layers,
+            n_head=self._text_n_head,
+            d_key=self._emb_size // self._text_n_head,
+            d_value=self._emb_size // self._text_n_head,
+            d_model=self._emb_size,
+            d_inner_hid=self._emb_size * 4,
+            prepostprocess_dropout=self._text_prepostprocess_dropout,
+            attention_dropout=self._text_attention_dropout,
+            relu_dropout=0,
+            hidden_act=self._text_hidden_act,
+            preprocess_cmd="",
+            postprocess_cmd="dan",
+            param_initializer=self._param_initializer,
+            name='text.encoder',
+            caches=self.text_caches,
+            gather_idx=gather_idx,
+            trainable=self._text_encoder_trainable)
+
+        text_seq_len = paddle.shape(text_enc_out)[1]
+        return text_enc_out, text_seq_len, n_head_self_attn_mask, checkpoints
+
+    def _gen_input(self, text_input=None, text_mask=None,
+                   image_input=None, image_mask=None, gather_idx=None, decoding_step=False,
+                   grounded_decoding_mask=None):
+        _checkpoints = []
+        """encode images and texts independently by Vit and RoBERTa, get the optimal grounded tokens"""
+        if image_input is not None:
+            # visual part
+            self.vit_enc_out, vit_seq_len, image_self_attn_mask, vit_checkpoints = self.vit_encode(image_input,
+                                                                                                   image_mask)
+            _checkpoints.extend(vit_checkpoints)
+
+        if text_input is not None:
+            # textual part
+            self.text_enc_out, text_seq_len, text_self_attn_mask, text_checkpoints = self.text_encode(text_input,
+                                                                                                      text_mask,
+                                                                                                      gather_idx=gather_idx,
+                                                                                                      decoding_step=decoding_step)
+            _checkpoints.extend(text_checkpoints)
+
+        if image_input is not None and text_input is not None:
+            vl_mask = paddle.concat([image_mask, text_mask], axis=2)
+            vl_self_attn_mask = paddle.matmul(paddle.transpose(vl_mask, perm=[0, 2, 1]), vl_mask)
+            vl_self_attn_mask = paddle.scale(
+                x=vl_self_attn_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+            n_head_self_attn_mask = paddle.stack(
+                x=[vl_self_attn_mask] * self._n_head, axis=1)
+            n_head_self_attn_mask.stop_gradient = True
+
+            return self.vit_enc_out, self.text_enc_out, vit_seq_len, text_seq_len, n_head_self_attn_mask, _checkpoints
+
+        elif image_input is not None and text_input is None:
+            return self.vit_enc_out, vit_seq_len, image_self_attn_mask, _checkpoints
+
+        elif image_input is None and text_input is not None:
+            if decoding_step:
+                assert grounded_decoding_mask is not None, "grounded_decoding_mask should not be none"
+                self_attn_mask = paddle.scale(
+                    x=grounded_decoding_mask, scale=1e4, bias=-1.0, bias_after_scale=False)
+                n_head_self_attn_mask = paddle.stack(
+                    x=[self_attn_mask] * self._n_head, axis=1)
+                n_head_self_attn_mask.stop_gradient = True
+
+                return self.text_enc_out, text_seq_len, n_head_self_attn_mask, _checkpoints
+            else:
+                return self.text_enc_out, text_seq_len, text_self_attn_mask, _checkpoints
+
+    def get_checkpoints(self):
+        return self.all_checkpoints
+
+    def get_text_encoder_output(self):
+        return self.text_enc_out
+
+    def get_vit_encoder_output(self):
+        return self.vit_enc_out
+
+    def get_text_sequence_output(self):
+        return self._enc_l_out
+
+    def get_pooled_output(self):
+        """Get the first feature of each sequence for classification"""
+        next_sent_feat = paddle.slice(
+            input=self._enc_l_out, axes=[1], starts=[0], ends=[1])
+        next_sent_feat = paddle.reshape(
+            x=next_sent_feat, shape=[-1, self._emb_size])
+        next_sent_feat = paddle.static.nn.fc(
+            x=next_sent_feat,
+            size=self._emb_size,
+            activation="relu",
+            weight_attr=paddle.ParamAttr(
+                name="grounded.pooled_fc_text.w_0", initializer=self._param_initializer),
+            bias_attr="grounded.pooled_fc_text.b_0")
+
+        next_sent_v_feat = paddle.slice(
+            input=self._enc_v_out, axes=[1], starts=[0], ends=[1])
+        next_sent_v_feat = paddle.reshape(
+            x=next_sent_v_feat, shape=[-1, self._emb_size])
+        next_sent_v_feat = paddle.static.nn.fc(
+            x=next_sent_v_feat,
+            size=self._emb_size,
+            activation="relu",
+            weight_attr=paddle.ParamAttr(
+                name="grounded.pooled_fc_image.w_0", initializer=self._param_initializer),
+            bias_attr="grounded.pooled_fc_image.b_0")
+        return next_sent_feat, next_sent_v_feat
+
+    def get_pooled_txt_output(self):
+        """Get the first feature of each sequence for classification"""
+        next_sent_feat = paddle.slice(
+            input=self._enc_l_out, axes=[1], starts=[0], ends=[1])
+        next_sent_feat = paddle.reshape(
+            x=next_sent_feat, shape=[-1, self._emb_size])
+        next_sent_feat = paddle.static.nn.fc(
+            x=next_sent_feat,
+            size=self._emb_size,
+            activation="relu",
+            weight_attr=paddle.ParamAttr(
+                name="grounded.pooled_fc_text.w_0", initializer=self._param_initializer),
+            bias_attr="grounded.pooled_fc_text.b_0")
+
+        return next_sent_feat
+
+    def get_pooled_img_output(self):
+        """Get the first feature of each sequence for classification"""
+        next_sent_v_feat = paddle.slice(
+            input=self._enc_v_out, axes=[1], starts=[0], ends=[1])
+        next_sent_v_feat = paddle.reshape(
+            x=next_sent_v_feat, shape=[-1, self._emb_size])
+        next_sent_v_feat = paddle.static.nn.fc(
+            x=next_sent_v_feat,
+            size=self._emb_size,
+            activation="relu",
+            weight_attr=paddle.ParamAttr(
+                name="grounded.pooled_fc_image.w_0", initializer=self._param_initializer),
+            bias_attr="grounded.pooled_fc_image.b_0")
+        return next_sent_v_feat
+
+    def get_match_output(self, text, image, mode="mul"):
+        if mode == "sum":
+            emb_fuse = text + image
+        elif mode == "mul":
+            emb_fuse = text * image
+        else:
+            print("current mode %s is not supported" % mode)
+            return
+        emb_fuse = paddle.fluid.layers.nn.dropout(emb_fuse,
+                                                  self._attention_dropout,
+                                                  dropout_implementation="upscale_in_train")
+        return emb_fuse
+
+    def get_img_txt_matching_output(self, labels, is_multimodal=None, num_labels=2, fusion_method="mul"):
+        print('-----img_txt_matching------')
+        self.next_sent_feat, self.next_sent_v_feat = self.get_pooled_output()
+        next_feat = self.get_match_output(self.next_sent_feat,
+                                          self.next_sent_v_feat, fusion_method)
+        # paddle.static.Print(next_feat, message='next_feat', summarize=-1)
+
+        matching_fc_out = paddle.static.nn.fc(
+            x=next_feat,
+            size=num_labels,
+            weight_attr=paddle.ParamAttr(
+                name="grounded.img_text_matching_fc.w_0",
+                initializer=self._param_initializer),
+            bias_attr="grounded.img_text_matching_fc.b_0")
+        # paddle.static.Print(matching_fc_out, message='matching_fc_out', summarize=-1)
+
+        matching_loss, matching_softmax = paddle.nn.functional.softmax_with_cross_entropy(
+            logits=matching_fc_out, label=labels, return_softmax=True)
+        # paddle.static.Print(matching_loss, message='matching_loss', summarize=-1)
+        # paddle.static.Print(matching_softmax, message='matching_softmax', summarize=-1)
+
+        image_match_weight = paddle.cast(is_multimodal, dtype="float32")
+        matching_loss = paddle.multiply(x=matching_loss, y=image_match_weight)  # exclude only text input
+        # paddle.static.Print(matching_loss, message='matching_loss_2', summarize=-1)
+        avg_img_num = paddle.mean(x=image_match_weight)
+        # paddle.static.Print(avg_img_num, message='avg_img_num', summarize=-1)
+        mean_matching_loss = paddle.mean(x=matching_loss) / (avg_img_num + 1e-18)
+        # paddle.static.Print(mean_matching_loss, message='mean_matching_loss', summarize=-1)
+
+        valid_condition = paddle.cast(paddle.reshape(x=is_multimodal, shape=[-1]), 'bool')
+        valid_img2txt_ind = paddle.fluid.layers.where(valid_condition)
+        res = paddle.is_empty(x=valid_img2txt_ind)
+
+        def false_func():
+            valid_matching_softmax = paddle.gather(x=matching_softmax, index=valid_img2txt_ind)
+            valid_labels = paddle.gather(x=labels, index=valid_img2txt_ind)
+            valid_labels = paddle.reshape(x=valid_labels, shape=[-1, 1])
+            matching_acc = paddle.metric.accuracy(input=valid_matching_softmax, label=valid_labels)
+            return matching_acc
+
+        def true_func():
+            return paddle.zeros(shape=[1], dtype='float32')
+
+        matching_acc = paddle.static.nn.cond(res, true_fn=true_func, false_fn=false_func)
+        return mean_matching_loss, matching_acc
+
+    def get_pretraining_txt_output(self, mask_label, mask_pos, lm_loss_flag,
+                                   with_text=None, is_multimodal=None, mm_lr_weight=1.0):
+        """Get the loss & accuracy for pretraining language"""
+        mask_pos = paddle.cast(x=mask_pos, dtype='int32')
+        reshaped_emb_out = paddle.reshape(x=self._enc_l_out, shape=[-1, self._emb_size])
+        # extract masked tokens' feature
+        mask_feat = paddle.gather(x=reshaped_emb_out, index=mask_pos)
+
+        with_text = paddle.fluid.layers.expand(with_text,  # [batch_size, max_seq_len]
+                                               expand_times=[1, paddle.shape(self._enc_l_out)[1]])
+        with_text = paddle.reshape(x=with_text, shape=[-1])  # [batch_size * max_seq_len]
+        with_text = paddle.gather(x=with_text, index=mask_pos)
+        with_text = paddle.reshape(x=with_text, shape=[-1, 1])  # [batch_size * max_seq_len, 1]
+        with_text.stop_gradient = True
+
+        is_multimodal = paddle.fluid.layers.expand(is_multimodal,  # [batch_size, max_seq_len]
+                                                   expand_times=[1, paddle.shape(self._enc_l_out)[1]])
+        is_multimodal = paddle.reshape(x=is_multimodal, shape=[-1])  # [batch_size * max_seq_len]
+        is_multimodal = paddle.gather(x=is_multimodal, index=mask_pos)
+        mm_loss_weight = is_multimodal * mm_lr_weight + (1 - is_multimodal)
+        mm_loss_weight.stop_gradient = True
+
+        # transform: fc
+        mask_trans_feat = paddle.static.nn.fc(
+            x=mask_feat,
+            size=self._emb_size,
+            activation=self._hidden_act,
+            weight_attr=paddle.ParamAttr(
+                name='grounded.mask_lm_trans_fc.w_0',
+                trainable=self._grounded_encoder_trainable,
+                initializer=self._param_initializer),
+            bias_attr=paddle.ParamAttr(
+                name='grounded.mask_lm_trans_fc.b_0',
+                trainable=self._grounded_encoder_trainable,
+                initializer=paddle.nn.initializer.Constant(value=0.)))
+
+        # transform: layer norm
+        mask_trans_feat = paddle.static.nn.layer_norm(
+            mask_trans_feat,
+            begin_norm_axis=len(mask_trans_feat.shape) - 1,
+            param_attr=paddle.ParamAttr(
+                name='grounded.mask_lm_trans_layer_norm_scale',
+                trainable=self._grounded_encoder_trainable,
+                initializer=paddle.nn.initializer.Constant(value=1.)),
+            bias_attr=paddle.ParamAttr(
+                name='grounded.mask_lm_trans_layer_norm_bias',
+                trainable=self._grounded_encoder_trainable,
+                initializer=paddle.nn.initializer.Constant(value=0.)))
+
+        mask_lm_out_bias_attr = paddle.ParamAttr(
+            name="grounded.mask_lm_out_fc.b_0",
+            trainable=self._grounded_encoder_trainable,
+            initializer=paddle.nn.initializer.Constant(value=0.0))
+
+        if self._weight_sharing:
+            fc_out = paddle.matmul(
+                x=mask_trans_feat,
+                y=paddle.static.default_main_program().global_block().var("text.word_embedding"),
+                transpose_y=True)
+            fc_out += paddle.create_parameter(
+                shape=[self._text_voc_size],
+                dtype=self._emb_dtype,
+                attr=mask_lm_out_bias_attr,
+                is_bias=True)
+        else:
+            fc_out = paddle.static.nn.fc(x=mask_trans_feat,
+                                         size=self._text_voc_size,
+                                         weight_attr=paddle.ParamAttr(
+                                             name="grounded.mask_lm_out_fc.w_0",
+                                             trainable=self._grounded_encoder_trainable,
+                                             initializer=self._param_initializer),
+                                         bias_attr=mask_lm_out_bias_attr)
+
+        mask_lm_loss = paddle.nn.functional.softmax_with_cross_entropy(
+            logits=fc_out, label=mask_label)
+        mask_lm_loss = paddle.multiply(x=mask_lm_loss, y=with_text)
+        mask_lm_loss = paddle.sum(x=mask_lm_loss, axis=1, keepdim=True)
+        mask_lm_loss = paddle.multiply(x=mask_lm_loss, y=mm_loss_weight)
+        mean_mask_lm_loss = paddle.mean(paddle.multiply(x=mask_lm_loss, y=lm_loss_flag))
+        num_lm_loss = paddle.mean(paddle.multiply(x=lm_loss_flag, y=with_text))
+        mean_mask_lm_loss = mean_mask_lm_loss / (num_lm_loss + 1e-18)
+        return mean_mask_lm_loss
+
+    def get_pretraining_cmcl_output_within_batch(self, labels, with_image, with_text, is_multimodal):
+        """Get the loss & accuracy for cross modal contrastive learning"""
+        self.cmcl_temperature = paddle.clip(self.cmcl_temperature, min=0.001, max=0.5)
+
+        print('-----img_txt_infonce------')
+        text_feat = paddle.slice(input=self.text_enc_out, axes=[1], starts=[0], ends=[1])
+        text_feat = paddle.reshape(x=text_feat, shape=[-1, self._emb_size])
+        if self._with_cmcl_projection:
+            text_feat = paddle.static.nn.fc(
+                x=text_feat,
+                size=self._emb_size,
+                activation="relu",
+                weight_attr=paddle.ParamAttr(
+                    name="grounded.cmcl_pooled_fc_text.w_0", initializer=self._param_initializer),
+                bias_attr="grounded.cmcl_pooled_fc_text.b_0")
+            text_feat = paddle.nn.functional.normalize(text_feat, axis=-1)
+
+        img_feat = paddle.slice(input=self.vit_enc_out, axes=[1], starts=[0], ends=[1])
+        img_feat = paddle.reshape(x=img_feat, shape=[-1, self._emb_size])
+        if self._with_cmcl_projection:
+            img_feat = paddle.static.nn.fc(
+                x=img_feat,
+                size=self._emb_size,
+                activation="relu",
+                weight_attr=paddle.ParamAttr(
+                    name="grounded.cmcl_pooled_fc_image.w_0", initializer=self._param_initializer),
+                bias_attr="grounded.cmcl_pooled_fc_image.b_0")
+            img_feat = paddle.nn.functional.normalize(img_feat, axis=-1)
+
+        labels = paddle.reshape(paddle.cast(labels, dtype='float32'), shape=[-1])
+        # paddle.static.Print(labels, message='labels', summarize=-1)
+        is_multimodal = paddle.reshape(paddle.cast(is_multimodal, dtype='float32'), shape=[-1])
+        # paddle.static.Print(is_multimodal, message='is_multimodal', summarize=-1)
+
+        """compute infoNCE loss"""
+        # (batch, batch)
+        all_img2txt_scores = paddle.matmul(img_feat, paddle.transpose(text_feat, perm=[1, 0]))
+        all_img2txt_scores = paddle.divide(x=all_img2txt_scores, y=self.cmcl_temperature)
+        # paddle.static.Print(all_img2txt_scores, message='all_img2txt_scores', summarize=-1)
+
+        pos_labels = paddle.arange(start=0, end=paddle.shape(text_feat)[0], dtype='int64')
+        # paddle.static.Print(pos_labels, message='pos_labels', summarize=-1)
+
+        img2txt_loss, img2txt_softmax = paddle.nn.functional.softmax_with_cross_entropy(
+            logits=all_img2txt_scores, label=paddle.unsqueeze(pos_labels, axis=1), return_softmax=True, axis=1)
+        img2txt_loss = paddle.squeeze(img2txt_loss)
+        # paddle.static.Print(img2txt_loss, message='img2txt_loss', summarize=-1)
+        # paddle.static.Print(img2txt_softmax, message='img2txt_softmax', summarize=-1)
+
+        txt2img_loss, txt2img_softmax = paddle.nn.functional.softmax_with_cross_entropy(
+            logits=all_img2txt_scores, label=paddle.unsqueeze(pos_labels, axis=0), return_softmax=True, axis=0)
+        txt2img_loss = paddle.squeeze(txt2img_loss)
+        # paddle.static.Print(txt2img_loss, message='txt2img_loss', summarize=-1)
+        # paddle.static.Print(txt2img_softmax, message='txt2img_softmax', summarize=-1)
+
+        total_loss = (img2txt_loss + txt2img_loss) / 2.0
+        total_loss = paddle.multiply(total_loss, labels)
+        total_loss = paddle.multiply(total_loss, is_multimodal)
+        # paddle.static.Print(total_loss, message='total_loss', summarize=-1)
+        loss = paddle.mean(total_loss)
+
+        # compute accuracy
+        valid_condition = paddle.cast(is_multimodal * labels, 'bool')
+        valid_img2txt_ind = paddle.fluid.layers.where(valid_condition)
+        # paddle.static.Print(valid_img2txt_ind, message='valid_img2txt_ind', summarize=-1)
+        res = paddle.is_empty(x=valid_img2txt_ind)
+
+        def false_func():
+            valid_img2txt_softmax = paddle.gather(x=img2txt_softmax, index=valid_img2txt_ind)
+            valid_txt2img_softmax = paddle.gather(x=txt2img_softmax, index=valid_img2txt_ind)
+            valid_labels = paddle.gather(x=pos_labels, index=valid_img2txt_ind)
+            valid_labels = paddle.reshape(x=valid_labels, shape=[-1, 1])
+            img2txt_acc = paddle.metric.accuracy(input=valid_img2txt_softmax, label=valid_labels, k=5)
+            txt2img_acc = paddle.metric.accuracy(input=valid_txt2img_softmax, label=valid_labels, k=5)
+            return img2txt_acc, txt2img_acc
+
+        def true_func():
+            return paddle.zeros(shape=[1], dtype='float32'), paddle.zeros(shape=[1], dtype='float32')
+
+        img2txt_acc, txt2img_acc = paddle.static.nn.cond(res, true_fn=true_func, false_fn=false_func)
+        return loss, img2txt_acc, txt2img_acc
+
+    def get_pretraining_cmcl_output_cross_batch(self, labels, with_image, with_text, is_multimodal, batch_size=None):
+        """Get the loss & accuracy for cross modal contrastive learning"""
+        self.cmcl_temperature = paddle.clip(self.cmcl_temperature, min=0.001, max=0.5)
+
+        print('-----img_txt_infonce------')
+        text_feat = paddle.slice(input=self.text_enc_out, axes=[1], starts=[0], ends=[1])
+        text_feat = paddle.reshape(x=text_feat, shape=[batch_size, self._emb_size])
+        if self._with_cmcl_projection:
+            text_feat = paddle.static.nn.fc(
+                x=text_feat,
+                size=self._emb_size,
+                activation="relu",
+                weight_attr=paddle.ParamAttr(
+                    name="grounded.cmcl_pooled_fc_text.w_0", initializer=self._param_initializer),
+                bias_attr="grounded.cmcl_pooled_fc_text.b_0")
+            text_feat = paddle.nn.functional.normalize(text_feat, axis=-1)
+
+        img_feat = paddle.slice(input=self.vit_enc_out, axes=[1], starts=[0], ends=[1])
+        img_feat = paddle.reshape(x=img_feat, shape=[batch_size, self._emb_size])
+        if self._with_cmcl_projection:
+            img_feat = paddle.static.nn.fc(
+                x=img_feat,
+                size=self._emb_size,
+                activation="relu",
+                weight_attr=paddle.ParamAttr(
+                    name="grounded.cmcl_pooled_fc_image.w_0", initializer=self._param_initializer),
+                bias_attr="grounded.cmcl_pooled_fc_image.b_0")
+            img_feat = paddle.nn.functional.normalize(img_feat, axis=-1)
+
+        worker_num = fleet.worker_num()
+        print("worker num is: {}".format(fleet.worker_num()))
+        print("worker index is: {}".format(fleet.worker_index()))
+        # obtain cross batch data
+        tot_text_feat = paddle.fluid.layers.collective._c_allgather(
+            text_feat, fleet.worker_num(), use_calc_stream=True)  # (fake_tot_batch x self._emb_size)
+        tot_img_feat = paddle.fluid.layers.collective._c_allgather(
+            img_feat, fleet.worker_num(), use_calc_stream=True)  # (fake_tot_batch x self._emb_size)
+        tot_labels = paddle.fluid.layers.collective._c_allgather(
+            labels, fleet.worker_num(), use_calc_stream=True)  # (fake_tot_batch x 1)
+        tot_is_multimodal = paddle.fluid.layers.collective._c_allgather(
+            is_multimodal, fleet.worker_num(), use_calc_stream=True)  # (fake_tot_batch x 1)
+
+        tot_labels = paddle.reshape(paddle.cast(tot_labels, dtype='float32'),
+                                    shape=[batch_size * worker_num])
+        # paddle.static.Print(tot_labels, message='tot_labels', summarize=-1)
+        tot_is_multimodal = paddle.reshape(paddle.cast(tot_is_multimodal, dtype='float32'),
+                                           shape=[batch_size * worker_num])
+        # paddle.static.Print(tot_is_multimodal, message='tot_is_multimodal', summarize=-1)
+
+        """compute infoNCE loss"""
+        # (batch, batch)
+        all_img2txt_scores = paddle.matmul(tot_img_feat, paddle.transpose(tot_text_feat, perm=[1, 0]))
+        all_img2txt_scores = paddle.divide(x=all_img2txt_scores, y=self.cmcl_temperature)
+        # paddle.static.Print(all_img2txt_scores, message='all_img2txt_scores', summarize=-1)
+
+        pos_labels = paddle.arange(start=0, end=(batch_size * worker_num), dtype='int64')
+        # paddle.static.Print(pos_labels, message='pos_labels', summarize=-1)
+
+        img2txt_loss, img2txt_softmax = paddle.nn.functional.softmax_with_cross_entropy(
+            logits=all_img2txt_scores, label=paddle.unsqueeze(pos_labels, axis=1), return_softmax=True, axis=1)
+        img2txt_loss = paddle.squeeze(img2txt_loss)
+        # paddle.static.Print(img2txt_loss, message='img2txt_loss', summarize=-1)
+        # paddle.static.Print(img2txt_softmax, message='img2txt_softmax', summarize=-1)
+
+        txt2img_loss, txt2img_softmax = paddle.nn.functional.softmax_with_cross_entropy(
+            logits=all_img2txt_scores, label=paddle.unsqueeze(pos_labels, axis=0), return_softmax=True, axis=0)
+        txt2img_loss = paddle.squeeze(txt2img_loss)
+        # paddle.static.Print(txt2img_loss, message='txt2img_loss', summarize=-1)
+        # paddle.static.Print(txt2img_softmax, message='txt2img_softmax', summarize=-1)
+
+        total_loss = (img2txt_loss + txt2img_loss) / 2.0
+        total_loss = paddle.multiply(total_loss, tot_labels)
+        total_loss = paddle.multiply(total_loss, tot_is_multimodal)
+        # paddle.static.Print(total_loss, message='total_loss', summarize=-1)
+        loss = paddle.mean(total_loss)
+
+        # compute accuracy
+        valid_condition = paddle.cast(tot_is_multimodal * tot_labels, 'bool')
+        valid_img2txt_ind = paddle.fluid.layers.where(valid_condition)
+        # paddle.static.Print(valid_img2txt_ind, message='valid_img2txt_ind', summarize=-1)
+        res = paddle.is_empty(x=valid_img2txt_ind)
+
+        def false_func():
+            valid_img2txt_softmax = paddle.gather(x=img2txt_softmax, index=valid_img2txt_ind)
+            valid_txt2img_softmax = paddle.gather(x=txt2img_softmax, index=valid_img2txt_ind)
+            valid_labels = paddle.gather(x=pos_labels, index=valid_img2txt_ind)
+            valid_labels = paddle.reshape(x=valid_labels, shape=[-1, 1])
+            img2txt_acc = paddle.metric.accuracy(input=valid_img2txt_softmax, label=valid_labels, k=5)
+            txt2img_acc = paddle.metric.accuracy(input=valid_txt2img_softmax, label=valid_labels, k=5)
+            return img2txt_acc, txt2img_acc
+
+        def true_func():
+            return paddle.zeros(shape=[1], dtype='float32'), paddle.zeros(shape=[1], dtype='float32')
+
+        img2txt_acc, txt2img_acc = paddle.static.nn.cond(res, true_fn=true_func, false_fn=false_func)
+        return loss, img2txt_acc, txt2img_acc
+
+    def get_cmcl_scores(self):
+        """Get the score for img-text pairs"""
+        text_feat = paddle.slice(input=self.text_enc_out, axes=[1], starts=[0], ends=[1])
+        text_feat = paddle.reshape(x=text_feat, shape=[-1, self._emb_size])
+        if self._with_cmcl_projection:
+            text_feat = paddle.static.nn.fc(
+                x=text_feat,
+                size=self._emb_size,
+                activation="relu",
+                weight_attr=paddle.ParamAttr(
+                    name="grounded.cmcl_pooled_fc_text.w_0", initializer=self._param_initializer),
+                bias_attr="grounded.cmcl_pooled_fc_text.b_0")
+            text_feat = paddle.nn.functional.normalize(text_feat, axis=-1)
+
+        img_feat = paddle.slice(input=self.vit_enc_out, axes=[1], starts=[0], ends=[1])
+        img_feat = paddle.reshape(x=img_feat, shape=[-1, self._emb_size])
+        if self._with_cmcl_projection:
+            img_feat = paddle.static.nn.fc(
+                x=img_feat,
+                size=self._emb_size,
+                activation="relu",
+                weight_attr=paddle.ParamAttr(
+                    name="grounded.cmcl_pooled_fc_image.w_0", initializer=self._param_initializer),
+                bias_attr="grounded.cmcl_pooled_fc_image.b_0")
+        img_feat = paddle.nn.functional.normalize(img_feat, axis=-1)
+
+        """compute matching score"""
+        # (batch, 1)
+        img2txt_scores = paddle.sum(img_feat * text_feat, axis=1, keepdim=True)
+        return img2txt_scores
diff --git a/NLP/UNIMO-2/src/model/vision_transformer_encoder.py b/NLP/UNIMO-2/src/model/vision_transformer_encoder.py
new file mode 100644
index 000000000..345c99306
--- /dev/null
+++ b/NLP/UNIMO-2/src/model/vision_transformer_encoder.py
@@ -0,0 +1,400 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: vision_transformer_encoder.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-07-20 20:46
+Desc: Vision Transformer encoder.
+"""
+
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from functools import partial
+import paddle
+
+
+def multi_head_attention(queries,
+                         keys,
+                         values,
+                         attn_bias,
+                         d_key,
+                         d_value,
+                         d_model,
+                         n_head=1,
+                         dropout_rate=0.,
+                         cache=None,
+                         gather_idx=None,
+                         param_initializer=None,
+                         name='multi_head_att',
+                         trainable=True):
+    """
+    Multi-Head Attention. Note that attn_bias is added to the logit before
+    computing softmax activiation to mask certain selected positions so that
+    they will not considered in attention weights.
+    """
+    keys = queries if keys is None else keys
+    values = keys if values is None else values
+
+    if not (len(queries.shape) == len(keys.shape) == len(values.shape) == 3):
+        raise ValueError(
+            "Inputs: quries, keys and values should all be 3-D tensors.")
+
+    def __compute_qkv(queries, keys, values, n_head, d_key, d_value):
+        """
+        Add linear projection to queries, keys, and values.
+        """
+        q = paddle.static.nn.fc(x=queries,
+                                size=d_key * n_head,
+                                num_flatten_dims=2,
+                                weight_attr=paddle.ParamAttr(
+                                    name=name + '_query_fc.w_0',
+                                    trainable=trainable,
+                                    initializer=param_initializer),
+                                bias_attr=paddle.ParamAttr(
+                                    name=name + '_query_fc.b_0',
+                                    trainable=trainable))
+        k = paddle.static.nn.fc(x=keys,
+                                size=d_key * n_head,
+                                num_flatten_dims=2,
+                                weight_attr=paddle.ParamAttr(
+                                    name=name + '_key_fc.w_0',
+                                    trainable=trainable,
+                                    initializer=param_initializer),
+                                bias_attr=paddle.ParamAttr(
+                                    name=name + '_key_fc.b_0',
+                                    trainable=trainable))
+        v = paddle.static.nn.fc(x=values,
+                                size=d_value * n_head,
+                                num_flatten_dims=2,
+                                weight_attr=paddle.ParamAttr(
+                                    name=name + '_value_fc.w_0',
+                                    trainable=trainable,
+                                    initializer=param_initializer),
+                                bias_attr=paddle.ParamAttr(
+                                    name=name + '_value_fc.b_0',
+                                    trainable=trainable))
+        return q, k, v
+
+    def __split_heads(x, n_head):
+        """
+        Reshape the last dimension of inpunt tensor x so that it becomes two
+        dimensions and then transpose. Specifically, input a tensor with shape
+        [bs, max_sequence_length, n_head * hidden_dim] then output a tensor
+        with shape [bs, n_head, max_sequence_length, hidden_dim].
+        """
+        hidden_size = x.shape[-1]
+        # The value 0 in shape attr means copying the corresponding dimension
+        # size of the input as the output dimension size.
+        reshaped = paddle.reshape(
+            x=x, shape=[0, 0, n_head, hidden_size // n_head])
+
+        # permuate the dimensions into:
+        # [batch_size, n_head, max_sequence_len, hidden_size_per_head]
+        return paddle.transpose(x=reshaped, perm=[0, 2, 1, 3])
+
+    def __combine_heads(x):
+        """
+        Transpose and then reshape the last two dimensions of inpunt tensor x
+        so that it becomes one dimension, which is reverse to __split_heads.
+        """
+        if len(x.shape) == 3: return x
+        if len(x.shape) != 4:
+            raise ValueError("Input(x) should be a 4-D Tensor.")
+
+        trans_x = paddle.transpose(x, perm=[0, 2, 1, 3])
+        # The value 0 in shape attr means copying the corresponding dimension
+        # size of the input as the output dimension size.
+        return paddle.reshape(
+            x=trans_x,
+            shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]])
+
+    def scaled_dot_product_attention(q, k, v, attn_bias, d_key, dropout_rate):
+        """
+        Scaled Dot-Product Attention
+        """
+        scaled_q = paddle.scale(x=q, scale=d_key ** -0.5)
+        product = paddle.matmul(x=scaled_q, y=k, transpose_y=True)
+        if attn_bias:
+            product += attn_bias
+        weights = paddle.nn.functional.softmax(x=product)
+        if dropout_rate:
+            weights = paddle.fluid.layers.nn.dropout(
+                weights,
+                dropout_prob=dropout_rate,
+                dropout_implementation="upscale_in_train",
+                is_test=False)
+        out = paddle.matmul(x=weights, y=v)
+        return out
+
+    q, k, v = __compute_qkv(queries, keys, values, n_head, d_key, d_value)
+
+    if cache is not None:  # use cache and concat time steps
+        # Since the inplace reshape in __split_heads changes the shape of k and
+        # v, which is the cache input for next time step, reshape the cache
+        # input from the previous time step first.
+
+        cache_k, cache_v = cache["k"], cache["v"]
+        select_k = paddle.gather(x=cache_k, index=gather_idx)
+        select_v = paddle.gather(x=cache_v, index=gather_idx)
+        select_k = paddle.reshape(x=select_k, shape=[0, 0, d_model])
+        select_v = paddle.reshape(x=select_v, shape=[0, 0, d_model])
+        k = paddle.concat(x=[select_k, k], axis=1)
+        v = paddle.concat(x=[select_v, v], axis=1)
+        paddle.assign(k, cache["k"])
+        paddle.assign(v, cache["v"])
+
+    q = __split_heads(q, n_head)
+    k = __split_heads(k, n_head)
+    v = __split_heads(v, n_head)
+
+    ctx_multiheads = scaled_dot_product_attention(q, k, v, attn_bias, d_key,
+                                                  dropout_rate)
+
+    out = __combine_heads(ctx_multiheads)
+
+    # Project back to the model size.
+    proj_out = paddle.static.nn.fc(x=out,
+                                   size=d_model,
+                                   num_flatten_dims=2,
+                                   weight_attr=paddle.ParamAttr(
+                                       name=name + '_output_fc.w_0',
+                                       trainable=trainable,
+                                       initializer=param_initializer),
+                                   bias_attr=paddle.ParamAttr(
+                                       name=name + '_output_fc.b_0',
+                                       trainable=trainable))
+    return proj_out
+
+
+def positionwise_feed_forward(x,
+                              d_inner_hid,
+                              d_hid,
+                              dropout_rate,
+                              hidden_act,
+                              param_initializer=None,
+                              name='ffn',
+                              trainable=True):
+    """
+    Position-wise Feed-Forward Networks.
+    This module consists of two linear transformations with a ReLU activation
+    in between, which is applied to each position separately and identically.
+    """
+    hidden = paddle.static.nn.fc(x=x,
+                                 size=d_inner_hid,
+                                 num_flatten_dims=2,
+                                 activation=hidden_act,
+                                 weight_attr=paddle.ParamAttr(
+                                     name=name + '_fc_0.w_0',
+                                     trainable=trainable,
+                                     initializer=param_initializer),
+                                 bias_attr=paddle.ParamAttr(
+                                     name=name + '_fc_0.b_0',
+                                     trainable=trainable))
+    if dropout_rate:
+        hidden = paddle.fluid.layers.nn.dropout(
+            hidden,
+            dropout_prob=dropout_rate,
+            dropout_implementation="upscale_in_train",
+            is_test=False)
+    out = paddle.static.nn.fc(x=hidden,
+                              size=d_hid,
+                              num_flatten_dims=2,
+                              weight_attr=paddle.ParamAttr(
+                                  name=name + '_fc_1.w_0',
+                                  trainable=trainable,
+                                  initializer=param_initializer),
+                              bias_attr=paddle.ParamAttr(
+                                  name=name + '_fc_1.b_0',
+                                  trainable=trainable))
+    return out
+
+
+def pre_post_process_layer(prev_out, out, process_cmd, dropout_rate=0., name='', trainable=True):
+    """
+    Add residual connection, layer normalization and droput to the out tensor
+    optionally according to the value of process_cmd.
+    This will be used before or after multi-head attention and position-wise
+    feed-forward networks.
+    """
+    for cmd in process_cmd:
+        if cmd == "a":  # add residual connection
+            out = out + prev_out if prev_out else out
+        elif cmd == "n":  # add layer normalization
+            out = paddle.static.nn.layer_norm(
+                out,
+                begin_norm_axis=len(out.shape) - 1,
+                epsilon=1e-12,
+                param_attr=paddle.ParamAttr(
+                    name=name + '_layer_norm_scale',
+                    trainable=trainable,
+                    initializer=paddle.nn.initializer.Constant(value=1.)),
+                bias_attr=paddle.ParamAttr(
+                    name=name + '_layer_norm_bias',
+                    trainable=trainable,
+                    initializer=paddle.nn.initializer.Constant(value=0.)))
+        elif cmd == "d":  # add dropout
+            if dropout_rate:
+                out = paddle.fluid.layers.nn.dropout(
+                    out,
+                    dropout_prob=dropout_rate,
+                    dropout_implementation="upscale_in_train",
+                    is_test=False)
+    return out
+
+
+pre_process_layer = partial(pre_post_process_layer, None)
+post_process_layer = pre_post_process_layer
+
+
+def encoder_layer(query_input,
+                  key_input,
+                  attn_bias,
+                  n_head,
+                  d_key,
+                  d_value,
+                  d_model,
+                  d_inner_hid,
+                  prepostprocess_dropout,
+                  attention_dropout,
+                  relu_dropout,
+                  hidden_act,
+                  preprocess_cmd="n",
+                  postprocess_cmd="da",
+                  param_initializer=None,
+                  name='',
+                  cache=None,
+                  gather_idx=None,
+                  trainable=True):
+    """The encoder layers that can be stacked to form a deep encoder.
+    This module consits of a multi-head (self) attention followed by
+    position-wise feed-forward networks and both the two components companied
+    with the post_process_layer to add residual connection, layer normalization
+    and droput.
+    """
+    key_input = pre_process_layer(
+        key_input,
+        preprocess_cmd,
+        prepostprocess_dropout,
+        name=name + '_pre_att',
+        trainable=trainable) if key_input else None
+    value_input = key_input if key_input else None
+
+    attn_output = multi_head_attention(
+        pre_process_layer(
+            query_input,
+            preprocess_cmd,
+            prepostprocess_dropout,
+            name=name + '_pre_att',
+            trainable=trainable),
+        key_input,
+        value_input,
+        attn_bias,
+        d_key,
+        d_value,
+        d_model,
+        n_head,
+        attention_dropout,
+        param_initializer=param_initializer,
+        name=name + '_multi_head_att',
+        cache=cache,
+        gather_idx=gather_idx,
+        trainable=trainable)
+    attn_output = post_process_layer(
+        query_input,
+        attn_output,
+        postprocess_cmd,
+        prepostprocess_dropout,
+        name=name + '_post_att',
+        trainable=trainable)
+    ffd_output = positionwise_feed_forward(
+        pre_process_layer(
+            attn_output,
+            preprocess_cmd,
+            prepostprocess_dropout,
+            name=name + '_pre_ffn',
+            trainable=trainable),
+        d_inner_hid,
+        d_model,
+        relu_dropout,
+        hidden_act,
+        param_initializer=param_initializer,
+        name=name + '_ffn',
+        trainable=trainable)
+    return (post_process_layer(
+        attn_output,
+        ffd_output,
+        postprocess_cmd,
+        prepostprocess_dropout,
+        name=name + '_post_ffn',
+        trainable=trainable), ffd_output)
+
+
+def encoder(enc_input,
+            attn_bias,
+            n_layer,
+            n_head,
+            d_key,
+            d_value,
+            d_model,
+            d_inner_hid,
+            prepostprocess_dropout,
+            attention_dropout,
+            relu_dropout,
+            hidden_act,
+            preprocess_cmd="n",
+            postprocess_cmd="da",
+            param_initializer=None,
+            name='vit.encoder',
+            caches=None,
+            gather_idx=None,
+            trainable=True):
+    """
+    The encoder is composed of a stack of identical layers returned by calling
+    encoder_layer.
+    """
+    checkpoints = []
+    for i in range(n_layer):
+        enc_output, cp = encoder_layer(
+            enc_input,
+            None,
+            attn_bias,
+            n_head,
+            d_key,
+            d_value,
+            d_model,
+            d_inner_hid,
+            prepostprocess_dropout,
+            attention_dropout,
+            relu_dropout,
+            hidden_act,
+            preprocess_cmd,
+            postprocess_cmd,
+            param_initializer=param_initializer,
+            name=name + '_layer_' + str(i),
+            cache=caches[i] if caches is not None else None,
+            gather_idx=gather_idx,
+            trainable=trainable)
+        enc_input = enc_output
+        checkpoints.append(cp.name)
+
+    enc_output = pre_process_layer(
+        enc_output, preprocess_cmd, prepostprocess_dropout, name="vit.post_encoder", trainable=trainable)
+
+    return enc_output, checkpoints
diff --git a/NLP/UNIMO-2/src/reader/__init__.py b/NLP/UNIMO-2/src/reader/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/NLP/UNIMO-2/src/reader/__pycache__/__init__.cpython-37.pyc b/NLP/UNIMO-2/src/reader/__pycache__/__init__.cpython-37.pyc
new file mode 100644
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diff --git a/NLP/UNIMO-2/src/reader/classification_grounded_reader.py b/NLP/UNIMO-2/src/reader/classification_grounded_reader.py
new file mode 100644
index 000000000..66ad4abff
--- /dev/null
+++ b/NLP/UNIMO-2/src/reader/classification_grounded_reader.py
@@ -0,0 +1,252 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: classification_grounded_reader.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-09-15 14:23
+Desc: Classifier Data Reader
+"""
+
+import os
+import csv
+import json
+import numpy as np
+from collections import namedtuple
+
+from model import roberta_tokenization as tokenization
+from reader.unimo_grounded_batching import pad_batch_data
+import paddle.fluid as fluid
+
+
+class ClassifyReader(object):
+    def __init__(self, tokenizer, args):
+        self.tokenizer = tokenizer
+        self.pad_id = tokenizer.pad_token_id
+        self.cls_id = tokenizer.cls_token_id
+        self.sep_id = tokenizer.sep_token_id
+        self.mask_id = tokenizer.mask_token_id
+
+        self.max_seq_len = args.max_seq_len
+        self.in_tokens = args.in_tokens
+
+        self.random_seed = 0
+        self.global_rng = np.random.RandomState(self.random_seed)
+
+        self.trainer_id = 0
+        self.trainer_nums = 1
+        if os.getenv("PADDLE_TRAINER_ID"):
+            self.trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
+        if os.getenv("PADDLE_NODES_NUM"):
+            self.trainer_nums = int(os.getenv("PADDLE_TRAINERS_NUM"))
+
+        self.current_example = 0
+        self.current_epoch = 0
+        self.num_examples = 0
+
+    def get_train_progress(self):
+        """Gets progress for training phase."""
+        return self.current_example, self.current_epoch
+
+    def _read_tsv(self, input_file, quotechar=None):
+        """Reads a tab separated value file."""
+        with open(input_file, "r") as f:
+            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
+            headers = next(reader)
+            text_indices = [
+                index for index, h in enumerate(headers) if h != "label"
+            ]
+            Example = namedtuple('Example', headers)
+
+            examples = []
+            for line in reader:
+                # for index, text in enumerate(line):
+                # if index in text_indices:
+                # line[index] = text.replace(' ', '')
+                example = Example(*line)
+                examples.append(example)
+            return examples
+
+    def _pad_batch_records(self, batch_records):
+        batch_token_ids = [record.token_ids for record in batch_records]
+        batch_text_type_ids = [record.text_type_ids for record in batch_records]
+        batch_position_ids = [record.position_ids for record in batch_records]
+        batch_labels = [record.label_id for record in batch_records]
+        batch_labels = np.array(batch_labels).astype('int64').reshape([-1, 1])
+
+        if batch_records[0].qid:
+            batch_qids = [record.qid for record in batch_records]
+            batch_qids = np.array(batch_qids).astype('int64').reshape([-1, 1])
+        else:
+            batch_qids = np.array([]).astype('int64').reshape([-1, 1])
+
+        # padding
+        padded_token_ids, input_mask = pad_batch_data(
+            batch_token_ids, pretraining_task='nlu', pad_idx=self.pad_id, return_input_mask=True)
+        padded_text_type_ids = pad_batch_data(
+            batch_text_type_ids, pretraining_task='nlu', pad_idx=self.pad_id)
+        padded_position_ids = pad_batch_data(
+            batch_position_ids, pretraining_task='nlu', pad_idx=self.pad_id)
+
+        return_list = [
+            padded_token_ids, padded_text_type_ids, padded_position_ids,
+            input_mask, batch_labels, batch_qids
+        ]
+
+        return return_list
+
+    def _truncate_seq_pair(self, tokens_a, tokens_b, max_length):
+        """Truncates a sequence pair in place to the maximum length."""
+        # This is a simple heuristic which will always truncate the longer sequence
+        # one token at a time. This makes more sense than truncating an equal percent
+        # of tokens from each, since if one sequence is very short then each token
+        # that's truncated likely contains more information than a longer sequence.
+        while True:
+            total_length = len(tokens_a) + len(tokens_b)
+            if total_length <= max_length:
+                break
+            if len(tokens_a) > len(tokens_b):
+                tokens_a.pop()
+            else:
+                tokens_b.pop()
+
+    def _convert_example_to_record(self, example, max_seq_length, tokenizer):
+        """Converts a single `Example` into a single `Record`."""
+
+        text_a = tokenization.convert_to_unicode(example.text_a)
+        tokens_a = tokenizer.tokenize(text_a)
+        tokens_b = None
+        if "text_b" in example._fields:
+            text_b = tokenization.convert_to_unicode(example.text_b)
+            tokens_b = tokenizer.tokenize(text_b)
+
+        if tokens_b:
+            # Modifies `tokens_a` and `tokens_b` in place so that the total
+            # length is less than the specified length.
+            # Account for [CLS], [SEP], [SEP] with "- 3"
+            self._truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
+        else:
+            # Account for [CLS] and [SEP] with "- 2"
+            if len(tokens_a) > max_seq_length - 2:
+                tokens_a = tokens_a[0:(max_seq_length - 2)]
+
+        tokens = []
+        text_type_ids = []
+        tokens.append("[CLS]")
+        text_type_ids.append(0)
+        for token in tokens_a:
+            tokens.append(token)
+            text_type_ids.append(0)
+        tokens.append("[SEP]")
+        text_type_ids.append(0)
+
+        if tokens_b:
+            for token in tokens_b:
+                tokens.append(token)
+                text_type_ids.append(1)
+            tokens.append("[SEP]")
+            text_type_ids.append(1)
+
+        token_ids = tokenizer.convert_tokens_to_ids(tokens)
+        position_ids = list(range(2, len(token_ids) + 2))
+        label_id = example.label
+
+        Record = namedtuple(
+            'Record',
+            ['token_ids', 'text_type_ids', 'position_ids', 'label_id', 'qid'])
+
+        qid = None
+        if "qid" in example._fields:
+            qid = example.qid
+
+        record = Record(
+            token_ids=token_ids,
+            text_type_ids=text_type_ids,
+            position_ids=position_ids,
+            label_id=label_id,
+            qid=qid)
+        return record
+
+    def _prepare_batch_data(self, examples, batch_size, phase=None):
+        """generate batch records"""
+        batch_records, max_len = [], 0
+        for index, example in enumerate(examples):
+            if phase == "train":
+                self.current_example = index
+            record = self._convert_example_to_record(example, self.max_seq_len,
+                                                     self.tokenizer)
+            max_len = max(max_len, len(record.token_ids))
+            if self.in_tokens:
+                to_append = (len(batch_records) + 1) * max_len <= batch_size
+            else:
+                to_append = len(batch_records) < batch_size
+            if to_append:
+                batch_records.append(record)
+            else:
+                yield self._pad_batch_records(batch_records)
+                batch_records, max_len = [record], len(record.token_ids)
+
+        if batch_records:
+            yield self._pad_batch_records(batch_records)
+
+    def get_num_examples(self, input_file):
+        examples = self._read_tsv(input_file)
+        return len(examples)
+
+    def data_generator(self,
+                       input_file,
+                       batch_size,
+                       epoch,
+                       dev_count=1,
+                       shuffle=True,
+                       phase=None):
+        examples = self._read_tsv(input_file)
+
+        def wrapper():
+            all_dev_batches = []
+            trainer_id = 0
+            for epoch_index in range(epoch):
+                if phase == "train":
+                    self.current_example = 0
+                    self.current_epoch = epoch_index
+                    self.random_seed = epoch_index
+                    self.global_rng = np.random.RandomState(self.random_seed)
+                    trainer_id = self.trainer_id
+                else:
+                    # dev/test
+                    trainer_id = 0
+                    assert dev_count == 1, "only supports 1 GPU while prediction"
+                current_examples = [ins for ins in examples]
+                if shuffle:
+                    self.global_rng.shuffle(current_examples)
+                    # pass
+                # if phase == "train" and self.trainer_nums > 1:
+                #    current_examples = [ins for index, ins in enumerate(current_examples)
+                #                if index % self.trainer_nums == self.trainer_id]
+                for batch_data in self._prepare_batch_data(
+                        current_examples, batch_size, phase=phase):
+                    if len(all_dev_batches) < dev_count:
+                        all_dev_batches.append(batch_data)
+                    if len(all_dev_batches) == dev_count:
+                        # trick: handle batch inconsistency caused by data sharding for each trainer
+                        yield all_dev_batches[trainer_id]
+                        all_dev_batches = []
+
+        return wrapper
+
+
+if __name__ == '__main__':
+    pass
diff --git a/NLP/UNIMO-2/src/reader/img2txt_oscar_reader.py b/NLP/UNIMO-2/src/reader/img2txt_oscar_reader.py
new file mode 100644
index 000000000..7c7f46d62
--- /dev/null
+++ b/NLP/UNIMO-2/src/reader/img2txt_oscar_reader.py
@@ -0,0 +1,404 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: img2txt_oscar_reader.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-10-25 16:07
+Desc: img2txt reader
+"""
+
+
+import csv
+csv.field_size_limit(1024 * 1024)
+import numpy as np
+from collections import namedtuple
+import base64
+import os
+import gzip
+import six
+import time
+import sys
+
+import paddle.fluid as fluid
+import functools
+import model.roberta_tokenization as tokenization
+from reader.unimo_grounded_batching import pad_batch_data
+from utils.image_utils import process_image
+
+
+def get_time():
+    res = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
+    return res
+
+
+class Img2TxtReader(object):
+    def __init__(self, tokenizer, args, image_size=224, resolution=16):
+        self.tokenizer = tokenizer
+        self.pad_id = tokenizer.pad_token_id
+        self.cls_id = tokenizer.cls_token_id
+        self.sep_id = tokenizer.sep_token_id
+        self.mask_id = tokenizer.mask_token_id
+
+        self.max_obj_len = args.max_obj_len
+        self.tgt_type_id = args.tgt_type_id
+        self.max_tgt_len = args.max_tgt_len
+        self.max_out_len = args.max_out_len
+        self.obj_dict = self.load_obj_file(args.object_file)
+
+        # random_seed must be set for data slicing when using multi-gpu
+        if args.random_seed:
+            np.random.seed(args.random_seed)
+        else:
+            np.random.seed(0)
+
+        self.trainer_id = 0
+        self.trainer_nums = 1
+        if os.getenv("PADDLE_TRAINER_ID"):
+            self.trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
+        if os.getenv("PADDLE_TRAINERS_NUM"):
+            self.trainer_nums = int(os.getenv("PADDLE_TRAINERS_NUM"))
+
+        self.current_example = 0
+        self.current_epoch = 0
+        self.num_examples = 0
+
+        self.features = {}
+
+        self.image_size = image_size
+        assert resolution in {14, 16}
+        self.patch_seq_len = self.image_size * self.image_size // (resolution * resolution)
+        self.patch_emb_size = resolution * resolution * 3
+
+    def get_progress(self):
+        """return current progress of traning data
+        """
+        return self.current_epoch, self.current_file_index, self.total_file, self.current_file
+
+    def get_num_examples(self, filelist):
+        num_exp = 0
+        files = open(filelist).readlines()
+        for index, file_ in enumerate(files):
+            print("%s - reading %d/%d" % (get_time(), index, len(files)))
+            file_ = file_.strip()
+            if file_.endswith('.gz'):
+                with gzip.open(file_, "rt") as f:
+                    for line in f:
+                        if line is None:
+                            continue
+                        num_exp += 1
+            else:
+                with open(file_, "r") as f:
+                    for line in f:
+                        if line is None:
+                            continue
+                        num_exp += 1
+        return num_exp
+
+    def parse_line(self, line):
+        """ parse one line to token_ids, sentence_ids, pos_ids, label
+        """
+        line = line.strip('\r\n').split(";")
+
+        if len(line) == 8:
+            image_id, image_path, caption_id, token_ids, sent_ids, pos_ids, seg_labels, label = line
+            image_id = image_id[9:]
+        else:
+            raise ValueError("One sample have %d fields!" % len(line))
+
+        image_id, image_pixel = process_image(image_id=image_id,
+                                              image_path=image_path,
+                                              target_shape_h=self.image_size,
+                                              target_shape_w=self.image_size,
+                                              data_format="channels_last",
+                                              dtype='float32')
+        h, w, c = image_pixel.shape
+        assert h == w == self.image_size and c == 3
+
+        obj_token_ids, obj_sent_ids, obj_pos_ids = self.obj_dict[image_id]
+        obj_token_ids = [int(token) for token in obj_token_ids.split(" ")]
+        obj_sent_ids = [int(token) for token in obj_sent_ids.split(" ")]
+        obj_pos_ids = [int(token) for token in obj_pos_ids.split(" ")]
+        assert len(obj_token_ids) == len(obj_sent_ids) == len(obj_pos_ids), \
+            "[Must be true]len(obj_token_ids) == len(obj_sent_ids) == len(obj_pos_ids)"
+
+        if len(obj_token_ids) > self.max_obj_len:
+            obj_token_ids = obj_token_ids[:self.max_obj_len]
+            obj_sent_ids = obj_sent_ids[:self.max_obj_len]
+            obj_pos_ids = obj_pos_ids[:self.max_obj_len]
+
+        if token_ids != '':
+            token_ids = [int(token) for token in token_ids.split(" ")]
+            sent_ids = [int(token) for token in sent_ids.split(" ")]
+            pos_ids = [int(token) for token in pos_ids.split(" ")]
+            seg_labels = [int(seg_label) for seg_label in seg_labels.split(" ")]
+
+            if len(token_ids) > self.max_tgt_len:
+                token_ids = token_ids[:self.max_tgt_len - 1] + [self.sep_id]
+                sent_ids = sent_ids[:self.max_tgt_len]
+                pos_ids = pos_ids[:self.max_tgt_len]
+                seg_labels = seg_labels[:self.max_tgt_len - 1] + [-1]
+
+            assert len(token_ids) == len(sent_ids) == len(pos_ids) == len(seg_labels), \
+                "[Must be true]len(token_ids) == len(sent_ids) == len(pos_ids) == len(seg_labels)"
+
+            Record = namedtuple(
+                'Record',
+                ['image_pixel', 'image_id', 'obj_token_ids', 'obj_sent_ids', 'obj_pos_ids',
+                 'token_ids', 'sent_ids', 'pos_ids', 'seg_labels'])
+
+            record = Record(
+                image_pixel=image_pixel,
+                image_id=int(image_id),
+                obj_token_ids=obj_token_ids,
+                obj_sent_ids=obj_sent_ids,
+                obj_pos_ids=obj_pos_ids,
+                token_ids=token_ids,
+                sent_ids=sent_ids,
+                pos_ids=pos_ids,
+                seg_labels=seg_labels)
+        else:
+            Record = namedtuple(
+                'Record',
+                ['image_pixel', 'image_id', 'obj_token_ids', 'obj_sent_ids', 'obj_pos_ids'])
+
+            record = Record(
+                image_pixel=image_pixel,
+                image_id=int(image_id),
+                obj_token_ids=obj_token_ids,
+                obj_sent_ids=obj_sent_ids,
+                obj_pos_ids=obj_pos_ids)
+
+        return record
+
+    def load_obj_file(self, obj_file):
+        if not obj_file:
+            print("obj_file is None")
+            return None
+        _dict = {}
+        for line in open(obj_file):
+            line = line.strip('\r\n').split(';')
+            assert len(line) == 4, "the object file should only contain 4 fields!!!"
+            image_id, obj_token_ids, obj_sent_ids, obj_pos_ids = line
+            _dict[image_id] = [obj_token_ids, obj_sent_ids, obj_pos_ids]
+        print('obj_dict size is ', len(_dict))
+        return _dict
+
+    def read_file(self, file):
+        if file.endswith('.gz'):
+            with gzip.open(file, "rt") as f:
+                for line in f:
+                    parsed_line = self.parse_line(line)
+                    if parsed_line is None:
+                        continue
+                    yield parsed_line
+        else:
+            with open(file, "r") as f:
+                for line in f:
+                    parsed_line = self.parse_line(line)
+                    if parsed_line is None:
+                        continue
+                    yield parsed_line
+
+    def shuffle_samples(self, sample_generator, buffer=1000):
+        samples = []
+        try:
+            while True:
+                while len(samples) < buffer:
+                    sample = next(sample_generator)
+                    samples.append(sample)
+                np.random.shuffle(samples)
+                for sample in samples:
+                    yield sample
+                samples = []
+        except StopIteration:
+            print("stopiteration: reach end of file")
+            if len(samples) == 0:
+                yield None
+            else:
+                np.random.shuffle(samples)
+                for sample in samples:
+                    yield sample
+
+    def _prepare_batch_data(self, sample_generator, batch_size, phase=None, do_decode=False, place=None):
+        """generate batch records"""
+        batch, index = [], 0
+        for sample in sample_generator:
+            if sample is None:
+                continue
+            self.current_example = index
+            index += 1
+
+            to_append = len(batch) < batch_size
+            if to_append:
+                batch.append(sample)
+            else:
+                yield self._pad_batch_records(batch, do_decode, place)
+                batch = [sample]
+
+        if batch:
+            yield self._pad_batch_records(batch, do_decode, place)
+
+    def data_generator(self,
+                       filelist,
+                       batch_size,
+                       epoch,
+                       dev_count=1,
+                       shuffle=True,
+                       phase=None,
+                       do_decode=False,
+                       place=None):
+        files = open(filelist).readlines()
+        self.total_file = len(files)
+        print("total_file: ", self.total_file)
+
+        def wrapper():
+            all_dev_batches = []
+            trainer_id = self.trainer_id
+            for epoch_index in range(epoch):
+                self.current_file_index = 0
+                self.current_epoch = epoch_index
+
+                if phase == "train":  # shuffle file list
+                    np.random.shuffle(files)
+
+                for index, file_ in enumerate(files):
+                    file_ = file_.strip()
+                    self.current_file_index = index + 1
+                    self.current_file = file_
+
+                    sample_generator = self.read_file(file_)
+                    if phase == "train":  # shuffle buffered sample
+                        sample_generator = self.shuffle_samples(sample_generator)
+
+                    for batch_data in self._prepare_batch_data(
+                            sample_generator, batch_size, phase=phase, do_decode=do_decode, place=place):
+                        if len(all_dev_batches) < dev_count:
+                            all_dev_batches.append(batch_data)
+                        if len(all_dev_batches) == dev_count:
+                            yield all_dev_batches[trainer_id]
+                            all_dev_batches = []
+
+            if phase != "train":
+                if trainer_id < len(all_dev_batches):
+                    yield all_dev_batches[trainer_id]
+
+        return wrapper
+
+    def _to_lodtensor(self, data, place, lod=None):
+        data_tensor = fluid.LoDTensor()
+        data_tensor.set(data, place)
+        if lod is not None:
+            data_tensor.set_lod(lod)
+        return data_tensor
+
+    def _pad_batch_records(self, batch_records, do_decode, place):
+        # visual image part
+        batch_image_pixel = [record.image_pixel for record in batch_records]
+
+        # image pixels, include the global image token
+        image_mask = np.ones(shape=[len(batch_records), 1, self.patch_seq_len + 1], dtype="float32")
+        image_pixel_input = np.array(batch_image_pixel, dtype='float32')
+
+        batch_obj_token_ids = [record.obj_token_ids for record in batch_records]
+        batch_obj_sent_ids = [record.obj_sent_ids for record in batch_records]
+        batch_obj_pos_ids = [record.obj_pos_ids for record in batch_records]
+
+        batch_size = len(batch_image_pixel)
+        if do_decode:
+            batch_image_id = [record.image_id for record in batch_records]
+            image_id = np.array(batch_image_id, dtype='int32').reshape((-1, 1))
+
+            tgt_word = np.array([[self.cls_id]] * batch_size,
+                                dtype="int64").reshape([-1, 1, 1])
+            tgt_pos_id = np.full_like(tgt_word, 2, dtype="int64").reshape(
+                [-1, 1, 1])  ####################### pos start from 2
+            init_score = np.zeros_like(tgt_word, dtype="float32").reshape([-1, 1])
+
+            lods = [range(tgt_word.shape[0] + 1)] * 2
+            init_score = self._to_lodtensor(init_score, place, lods)
+            tgt_word = self._to_lodtensor(tgt_word, place, lods)
+            tgt_pos_id = self._to_lodtensor(tgt_pos_id, place, lods)
+            init_idx = np.array(range(batch_size), dtype="int32")
+
+            padded_obj_token_ids, obj_token_mask = pad_batch_data(
+                batch_obj_token_ids, pretraining_task='nlu', pad_idx=self.pad_id, return_input_mask=True)
+            padded_obj_sent_ids = pad_batch_data(
+                batch_obj_sent_ids, pretraining_task='nlu', pad_idx=self.pad_id)
+            padded_obj_pos_ids = pad_batch_data(
+                batch_obj_pos_ids, pretraining_task='nlu', pad_idx=self.pad_id)
+
+            # (batch_size, max_obj_len, max_obj_len)
+            obj_mask = np.matmul(np.transpose(obj_token_mask, (0, 2, 1)), obj_token_mask)
+
+            return_list = [image_pixel_input, image_mask, image_id,
+                           padded_obj_token_ids, padded_obj_sent_ids, padded_obj_pos_ids, obj_mask,
+                           tgt_word, tgt_pos_id, init_score, init_idx, obj_token_mask]
+
+        else:
+            batch_token_ids = [record.token_ids for record in batch_records]
+            batch_sent_ids = [record.sent_ids for record in batch_records]
+            batch_position_ids = [record.pos_ids for record in batch_records]
+
+            input_token_ids = []
+            input_sent_ids = []
+            input_pos_ids = []
+            for obj_token_ids, token_ids in zip(batch_obj_token_ids, batch_token_ids):
+                input_token_ids.append(obj_token_ids + token_ids)
+
+            for obj_sent_ids, sent_ids in zip(batch_obj_sent_ids, batch_sent_ids):
+                input_sent_ids.append(obj_sent_ids + sent_ids)
+
+            for obj_pos_ids, pos_ids in zip(batch_obj_pos_ids, batch_position_ids):
+                input_pos_ids.append(obj_pos_ids + pos_ids)
+
+            token_ids = pad_batch_data(input_token_ids, pad_idx=self.pad_id)
+            sent_ids = pad_batch_data(input_sent_ids, pad_idx=self.pad_id)
+            position_ids = pad_batch_data(input_pos_ids, pad_idx=self.pad_id)
+
+            max_len = token_ids.shape[1]
+            tgt_label = []
+            for i in range(len(batch_token_ids)):
+                tgt_idxs = range(1, len(batch_token_ids[i]))
+                tgt_label.extend(batch_token_ids[i][idx] for idx in tgt_idxs)
+            tgt_label = np.array(tgt_label).astype("int64").reshape([-1, 1])
+
+            tgt_pos = sum(list(map(lambda i: list(range(max_len * i + len(batch_obj_token_ids[i]),
+                                                        max_len * i + len(batch_obj_token_ids[i])
+                                                        + len(batch_token_ids[i]) - 1)),
+                                   range(batch_size))), [])
+            tgt_pos = np.array(tgt_pos).reshape([-1, 1]).astype('int64')
+
+            # This is used to avoid attention on paddings and subsequent words.
+            token_seq_mask_data = np.zeros((batch_size, max_len, max_len))
+            for index, mask_data in enumerate(token_seq_mask_data):
+                start = len(batch_obj_token_ids[index])
+                end = len(input_token_ids[index])
+                mask_data[:end, :start] = 1
+
+                # Generate the lower triangular matrix using the slice of matrix
+                b = np.tril(np.ones([end - start, end - start]), 0)
+                mask_data[start:end, start:end] = b
+            token_seq_mask = token_seq_mask_data.astype("float32")
+
+            return_list = [image_pixel_input, image_mask,
+                           token_ids, sent_ids, position_ids, token_seq_mask, tgt_label, tgt_pos]
+
+        return return_list
+
+
+if __name__ == '__main__':
+    pass
diff --git a/NLP/UNIMO-2/src/reader/retrieval_grounded_reader.py b/NLP/UNIMO-2/src/reader/retrieval_grounded_reader.py
new file mode 100644
index 000000000..3a377462e
--- /dev/null
+++ b/NLP/UNIMO-2/src/reader/retrieval_grounded_reader.py
@@ -0,0 +1,399 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: retrieval_grounded_reader.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-08-23 14:26
+Desc: data reader for image-text retrieval tasks
+"""
+
+import os
+import pickle
+import base64
+import codecs
+import numpy as np
+from collections import namedtuple
+from reader.unimo_grounded_batching import pad_batch_data
+from utils.image_utils import process_image_with_multi_proc, process_image
+from functools import partial
+
+
+class RetrievalTrainReader(object):
+    """RetrievalTrainReader"""
+
+    def __init__(self, tokenizer, args, image_caption, image_size, resolution):
+        self.epoch = args.epoch
+        self.batch_size = args.batch_size
+        self.tokenizer = tokenizer
+        self.pad_id = tokenizer.pad_token_id
+        self.cls_id = tokenizer.cls_token_id
+        self.sep_id = tokenizer.sep_token_id
+        self.mask_id = tokenizer.mask_token_id
+        self.max_seq_len = args.max_seq_len
+
+        self.trainer_id = int(os.getenv("PADDLE_TRAINER_ID", 0))
+        self.trainers_num = int(os.getenv("PADDLE_TRAINERS_NUM", 1))
+
+        self.image_size = image_size
+        assert resolution in {14, 16}
+        self.patch_seq_len = self.image_size * self.image_size // (resolution * resolution)
+        self.patch_emb_size = resolution * resolution * 3
+
+        self.current_example = 0
+        self.current_epoch = 0
+        self._load_caption_image_dict(image_caption)
+
+        if args.samples_num == 20:
+            self._negative_schema = ['ei'] * 10 + ['ec'] * 10
+            self.outs = len(self._negative_schema) + 1
+        else:
+            raise ValueError('dont support')
+
+    def _load_caption_image_dict(self, image_caption, proc_num=8):
+        '''parse dataset_flickr30k.json which is made by karpathy'''
+        self._caption_ids_dict = {}
+        self._image_sent_map = {}
+
+        with codecs.open(image_caption, 'r', encoding='utf-8') as f:
+            for line in f:
+                line = line.strip().split(";")
+                token_ids, sent_ids, pos_ids, image_id, sent_id = line
+                token_ids = [int(token) for token in token_ids.split(" ")]
+                sent_ids = [int(token) for token in sent_ids.split(" ")]
+                pos_ids = [int(token) for token in pos_ids.split(" ")]
+                if len(token_ids) > self.max_seq_len:
+                    token_ids = [token_ids[0]] + token_ids[1:self.max_seq_len - 1] + [token_ids[-1]]
+                    sent_ids = sent_ids[:self.max_seq_len]
+                    pos_ids = pos_ids[:self.max_seq_len]
+                assert len(token_ids) <= self.max_seq_len, \
+                    "token length must be less than max_seq_len"
+                assert len(token_ids) == len(sent_ids) == len(pos_ids), \
+                    "[Must be true]len(token_ids) == len(sent_ids) == len(pos_ids)"
+
+                self._caption_ids_dict[int(sent_id)] = \
+                    [token_ids, sent_ids, pos_ids, int(image_id)]
+                self._image_sent_map.setdefault(int(image_id), [])
+                self._image_sent_map[int(image_id)].append(int(sent_id))
+
+        self._train_caption_ids = list(self._caption_ids_dict.keys())
+        self._train_image_list = list(self._image_sent_map.keys())
+
+        image_base_dir = './data/Flickr30k/flickr30k-images'
+        image_id_paths = [(image_id, os.path.join(image_base_dir, str(image_id) + '.jpg'))
+                          for image_id in self._train_image_list]
+        cur_process_image = partial(process_image,
+                                    target_shape_h=self.image_size,
+                                    target_shape_w=self.image_size)
+        image_id_pixels_list = process_image_with_multi_proc(image_id_paths, proc_num, cur_process_image)
+        self._image_id_pixels_dict = dict(image_id_pixels_list)
+
+    def get_train_progress(self):
+        """Gets progress for training phase."""
+        return self.current_example, self.current_epoch
+
+    def _prepare_batch_data(self, insts):
+        """generate batch and pad"""
+        batch_src_ids = [inst["token_ids"][out] for inst in insts for out in range(self.outs)]
+        batch_sent_ids = [inst["sent_ids"][out] for inst in insts for out in range(self.outs)]
+        batch_pos_ids = [inst["pos_ids"][out] for inst in insts for out in range(self.outs)]
+        batch_image_pixel = [inst["image_pixel_inputs"][out] for inst in insts for out in range(self.outs)]
+
+        batch_size = int(len(batch_src_ids) / self.outs)
+        label = np.array([[0]] * batch_size, dtype="int64")
+        ids = np.array([[0, 0]] * batch_size, dtype="int64")
+
+        padded_token_ids, token_mask = pad_batch_data(
+            batch_src_ids, pretraining_task='nlu', pad_idx=self.pad_id, return_input_mask=True)
+        padded_sent_ids = pad_batch_data(
+            batch_sent_ids, pretraining_task='nlu', pad_idx=self.pad_id)
+        padded_pos_ids = pad_batch_data(
+            batch_pos_ids, pretraining_task='nlu', pad_idx=self.pad_id)
+
+        # image pixels, include the global image token
+        image_mask = np.ones(shape=[len(batch_image_pixel), 1, self.patch_seq_len + 1], dtype="float32")
+        image_pixel_input = np.array(batch_image_pixel, dtype='float32')
+
+        return_list = [
+            padded_token_ids, padded_pos_ids, padded_sent_ids, token_mask,
+            image_pixel_input, image_mask, label, ids
+        ]
+        return return_list
+
+    def get_num_examples(self):
+        """get_num_examples"""
+        cap_len = len(self._train_caption_ids)
+        img_len = len(self._train_image_list)
+        total_samples = cap_len
+        return total_samples, cap_len, img_len
+
+    def process_vl(self, sent_id):
+        """trans the orgin tokens to the wanted tokens"""
+        token_ids, sent_ids, pos_ids, image_id = self._caption_ids_dict[sent_id]
+        image_pixel_input = self._image_id_pixels_dict[image_id]
+
+        images = [image_pixel_input]
+        captions = [[token_ids, sent_ids, pos_ids]]
+
+        for item in self._negative_schema:
+            if item[0] == "e":
+                while True:
+                    image_id_neg = self.neg_rng.choice(self._train_image_list)
+                    if image_id_neg != image_id:
+                        break
+            else:
+                print("error negative schema")
+                exit()
+
+            if item[1] == "i":
+                image_pixel_input_neg = self._image_id_pixels_dict[image_id_neg]
+                captions.append(self._caption_ids_dict[sent_id][:-1])
+                images.append(image_pixel_input_neg)
+            elif item[1] == "c":
+                sent_id_neg = self.neg_rng.choice(self._image_sent_map[image_id_neg])
+                captions.append(self._caption_ids_dict[sent_id_neg][:-1])
+                images.append(image_pixel_input)
+            else:
+                print("error negative schema")
+                exit()
+
+        token_ids_list, sent_ids_list, pos_ids_list = zip(*captions)
+        image_pixel_input_list = images
+
+        sample_json = {
+            "token_ids": token_ids_list,
+            "sent_ids": sent_ids_list,
+            "pos_ids": pos_ids_list,
+            "image_pixel_inputs": image_pixel_input_list
+        }
+        return sample_json
+
+    def read_caption_id(self):
+        """read_caption_id"""
+        self.global_rng.shuffle(self._train_caption_ids)
+        for index, item in enumerate(self._train_caption_ids):
+            if index % self.trainers_num != self.trainer_id:
+                continue
+            yield self.process_vl(item)
+
+    def shuffle_samples(self, sample_generator, buffer=128):
+        """shuffle_samples"""
+        samples = []
+        try:
+            while True:
+                while len(samples) < buffer:
+                    sample = next(sample_generator)
+                    samples.append(sample)
+                for sample in samples:
+                    yield sample
+                samples = []
+        except StopIteration:
+            if len(samples) == 0:
+                yield None
+            else:
+                for sample in samples:
+                    yield sample
+
+    def data_generator(self):
+        """data_generator"""
+
+        def wrapper():
+            """wrapper"""
+            for epoch_index in range(self.epoch):
+                self.global_rng = np.random.RandomState(epoch_index)
+                self.neg_rng = np.random.RandomState(epoch_index)
+                self.current_epoch = epoch_index
+                batch_records = []
+                self.current_example = 0
+                for sample in self.shuffle_samples(self.read_caption_id()):
+                    self.current_example = self.current_example + 1
+                    if len(batch_records) < self.batch_size:
+                        batch_records.append(sample)
+                    if len(batch_records) == self.batch_size:
+                        yield self._prepare_batch_data(batch_records)
+                        batch_records = []
+                if batch_records:
+                    yield self._prepare_batch_data(batch_records)
+
+        return wrapper
+
+
+class RetrievalTestReader(object):
+    """RetrievalTrainReader"""
+
+    def __init__(self, tokenizer, args, image_caption, image_size, resolution):
+        self.batch_size = args.test_batch_size
+        self.tokenizer = tokenizer
+        self.pad_id = tokenizer.pad_token_id
+        self.cls_id = tokenizer.cls_token_id
+        self.sep_id = tokenizer.sep_token_id
+        self.mask_id = tokenizer.mask_token_id
+        self.max_seq_len = args.max_seq_len
+        self.trainer_id = int(os.getenv("PADDLE_TRAINER_ID", 0))
+        self.trainers_num = int(os.getenv("PADDLE_TRAINERS_NUM", "1"))
+        self.current_example = 0
+
+        self.image_size = image_size
+        assert resolution in {14, 16}
+        self.patch_seq_len = self.image_size * self.image_size // (resolution * resolution)
+        self.patch_emb_size = resolution * resolution * 3
+
+        self._load_caption_image_dict(image_caption)
+
+    def _load_caption_image_dict(self, image_caption, proc_num=8):
+        '''parse dataset_flickr30k.json which is made by karpathy'''
+        self._caption_ids_dict = {}
+        self._image_sent_map = {}
+
+        with codecs.open(image_caption, 'r', encoding='utf-8') as f:
+            cnt = 0
+            for line in f:
+                line = line.strip().split(";")
+                token_ids, sent_ids, pos_ids, image_id, sent_id = line
+                token_ids = [int(token) for token in token_ids.split(" ")]
+                sent_ids = [int(token) for token in sent_ids.split(" ")]
+                pos_ids = [int(token) for token in pos_ids.split(" ")]
+                if len(token_ids) > self.max_seq_len:
+                    token_ids = [token_ids[0]] + token_ids[1:self.max_seq_len - 1] + [token_ids[-1]]
+                    sent_ids = sent_ids[:self.max_seq_len]
+                    pos_ids = pos_ids[:self.max_seq_len]
+                assert len(token_ids) <= self.max_seq_len, \
+                    "token length must be less than max_seq_len"
+                assert len(token_ids) == len(sent_ids) == len(pos_ids), \
+                    "[Must be true]len(token_ids) == len(sent_ids) == len(pos_ids)"
+
+                self._caption_ids_dict[int(sent_id)] = \
+                    [token_ids, sent_ids, pos_ids, int(image_id)]
+                self._image_sent_map.setdefault(int(image_id), [])
+                self._image_sent_map[int(image_id)].append(int(sent_id))
+
+        self._train_caption_ids = list(self._caption_ids_dict.keys())
+        self._train_image_list = list(self._image_sent_map.keys())
+
+        image_base_dir = './data/Flickr30k/flickr30k-images'
+        image_id_paths = [(image_id, os.path.join(image_base_dir, str(image_id) + '.jpg'))
+                          for image_id in self._train_image_list]
+        cur_process_image = partial(process_image,
+                                    target_shape_h=self.image_size,
+                                    target_shape_w=self.image_size)
+        image_id_pixels_list = process_image_with_multi_proc(image_id_paths, proc_num, cur_process_image)
+        self._image_id_pixels_dict = dict(image_id_pixels_list)
+
+    def _prepare_batch_data(self, insts):
+        """generate batch and pad"""
+        batch_src_ids = [inst["token_ids"] for inst in insts]
+        batch_sent_ids = [inst["sent_ids"] for inst in insts]
+        batch_pos_ids = [inst["pos_ids"] for inst in insts]
+        batch_image_pixel = [inst["image_pixel_input"] for inst in insts]
+        batch_ids = [inst["cur_ids"] for inst in insts]
+        batch_labels = [[0]] * len(insts)
+
+        padded_token_ids, token_mask = pad_batch_data(
+            batch_src_ids, pretraining_task='nlu', pad_idx=self.pad_id, return_input_mask=True)
+        padded_sent_ids = pad_batch_data(
+            batch_sent_ids, pretraining_task='nlu', pad_idx=self.pad_id)
+        padded_pos_ids = pad_batch_data(
+            batch_pos_ids, pretraining_task='nlu', pad_idx=self.pad_id)
+
+        # image pixels, include the global image token
+        image_mask = np.ones(shape=[len(batch_image_pixel), 1, self.patch_seq_len + 1], dtype="float32")
+        image_pixel_input = np.array(batch_image_pixel, dtype='float32')
+
+        ids = np.array(batch_ids, dtype="int64")
+        label = np.array(batch_labels, dtype="int64")
+
+        return_list = [
+            padded_token_ids, padded_pos_ids, padded_sent_ids, token_mask,
+            image_pixel_input, image_mask, label, ids
+        ]
+        return return_list
+
+    def get_num_examples(self):
+        """get_num_examples"""
+        cap_len = len(self._train_caption_ids)
+        img_len = len(self._train_image_list)
+        total_samples = cap_len
+        return total_samples, cap_len, img_len
+
+    def read_caption_id(self):
+        """read_caption_id"""
+        for sent_id in self._train_caption_ids:
+            token_ids, sent_ids, pos_ids, _ = self._caption_ids_dict[sent_id]
+
+            for cur_img_id in self._train_image_list:
+                cur_image_pixel_input = self._image_id_pixels_dict[cur_img_id]
+                sample_json = {
+                    "token_ids": token_ids,
+                    "sent_ids": sent_ids,
+                    "pos_ids": pos_ids,
+                    "image_pixel_input": cur_image_pixel_input,
+                    "cur_ids": [cur_img_id, sent_id]
+                }
+                yield sample_json
+
+    def shuffle_samples(self, sample_generator, buffer=128):
+        """shuffle_samples"""
+        samples = []
+        try:
+            while True:
+                while len(samples) < buffer:
+                    sample = next(sample_generator)
+                    samples.append(sample)
+                for sample in samples:
+                    yield sample
+                samples = []
+        except StopIteration:
+            if len(samples) == 0:
+                yield None
+            else:
+                for sample in samples:
+                    yield sample
+
+    def data_generator(self):
+        """data_generator"""
+
+        def wrapper():
+            """"wrapper"""
+
+            def batch_reader():
+                """batch_reader"""
+                batch_records = []
+                self.current_example = 0
+                for sample in self.shuffle_samples(self.read_caption_id()):
+                    self.current_example = self.current_example + 1
+                    if len(batch_records) < self.batch_size:
+                        batch_records.append(sample)
+                    if len(batch_records) == self.batch_size:
+                        yield self._prepare_batch_data(batch_records)
+                        batch_records = []
+                if batch_records:
+                    yield self._prepare_batch_data(batch_records)
+
+            all_dev_batches = []
+            for batch_data in batch_reader():
+                if len(all_dev_batches) < self.trainers_num:
+                    all_dev_batches.append(batch_data)
+                if len(all_dev_batches) == self.trainers_num:
+                    yield all_dev_batches[self.trainer_id]
+                    all_dev_batches = []
+            if self.trainer_id < len(all_dev_batches):
+                yield all_dev_batches[self.trainer_id]
+
+        return wrapper
+
+
+if __name__ == '__main__':
+    pass
diff --git a/NLP/UNIMO-2/src/reader/unimo_grounded_batching.py b/NLP/UNIMO-2/src/reader/unimo_grounded_batching.py
new file mode 100644
index 000000000..019632d9c
--- /dev/null
+++ b/NLP/UNIMO-2/src/reader/unimo_grounded_batching.py
@@ -0,0 +1,552 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: unimo_grounded_batching.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-09-23 15:43
+Desc: Mask, padding and batching.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import copy
+import random
+import math
+
+
+def mask(batch_tokens,
+         seg_labels,
+         total_token_num,
+         txt_mask_ratio=0.15,
+         pair_labels=None,
+         vocab_size=0,
+         pretraining_task='seq2seq',
+         CLS=0,
+         SEP=2,
+         MASK=50264,
+         sent_b_starts=None,
+         del_pos=None,
+         geometric_p=0.2,
+         span_lower=0,
+         span_upper=10,
+         batch_can_mask=None):
+    """
+    Add mask for batch_tokens, return out, mask_label, mask_pos;
+    Note: mask_pos responding the batch_tokens after padded;
+    mask_token_pair_label responding the label of the pair where this token is from
+    """
+    max_len = max([len(sent) for sent in batch_tokens])
+    mask_label = []
+    mask_pos = []
+    mask_token_pair_label = []
+
+    if pretraining_task is 'seq2seq':
+        assert sent_b_starts is not None, \
+            "[FATAL] For seq2seq language model loss," \
+            " sent_b_starts should not be None"
+        for sent_index, sent in enumerate(batch_tokens):
+            sent_b_index = sent_b_starts[sent_index]
+            mask_label_cur = sent[sent_b_index + 1:]  # from the second target word until the end
+            mask_pos_cur = [
+                sent_index * max_len + sent_b_index + i
+                for i in range(len(sent[sent_b_index:]))
+            ]  # from target word index to the end
+
+            if del_pos:  # remove the index of [cls_id] between target spans
+                map(lambda i: mask_label_cur.pop(i), del_pos[sent_index])
+                map(lambda i: mask_pos_cur.pop(i - 1), del_pos[sent_index])
+
+            # the output labels for the masked positions
+            mask_label.extend(mask_label_cur)
+            # traditional generation: the masked index of context stream,
+            # which use the current token to predict the next token
+            mask_pos.extend(mask_pos_cur[:-1])
+            mask_token_pair_label.extend([pair_labels[sent_index]] * len(mask_label_cur))
+
+        assert len(mask_pos) == len(mask_label) == len(mask_token_pair_label), "the mask len is invalid"
+        mask_label = np.array(mask_label).astype('int64').reshape([-1, 1])
+        mask_pos = np.array(mask_pos).astype('int64').reshape([-1, 1])
+        mask_token_pair_label = np.array(mask_token_pair_label).astype("float32").reshape([-1, 1])
+        return batch_tokens, mask_label, mask_pos, mask_token_pair_label
+
+    assert geometric_p > 0, "geometric_p must be larger than 0."
+    span_lens = list(range(span_lower, span_upper + 1))
+    len_distrib = [geometric_p * (1 - geometric_p) ** (i - span_lower) for i in span_lens]
+    len_distrib = [x / (sum(len_distrib)) for x in len_distrib]
+
+    pre_sent_len = 0
+    prob_index = 0
+    new_batch_tokens = []
+    for sent_index, sent in enumerate(batch_tokens):
+        sent = copy.deepcopy(sent)
+        prob_index += pre_sent_len
+
+        if batch_can_mask is not None and batch_can_mask[sent_index]:
+            sentence, sent_mask_label, sent_mask_pos = mask_span(sent, seg_labels[sent_index], txt_mask_ratio,
+                                                                 vocab_size=vocab_size, CLS=CLS, SEP=SEP, MASK=MASK,
+                                                                 span_lens=span_lens, len_distrib=len_distrib)
+            batch_tokens[sent_index] = sentence
+            mask_label.extend(sent_mask_label)
+            for pos_index in range(len(sent_mask_pos)):
+                mask_pos.append(sent_mask_pos[pos_index] + sent_index * max_len)
+            mask_token_pair_label.extend([pair_labels[sent_index]] * len(sent_mask_label))
+
+        pre_sent_len = len(sent)
+        new_batch_tokens.append(sent)
+
+    assert (prob_index + pre_sent_len) == total_token_num, 'the total_token_num is incorrect'
+    while len(mask_pos) < 2:
+        rand_index = np.random.randint(low=1, high=total_token_num)
+        pre_seq_len = 0
+        for sent_index, sent in enumerate(batch_tokens):
+            if rand_index < pre_seq_len + len(sent):
+                mask_pos.append(rand_index - pre_seq_len + sent_index * max_len)
+                mask_label.append(sent[rand_index - pre_seq_len])
+                mask_token_pair_label.append(0)
+                break
+            pre_seq_len += len(sent)
+
+    assert len(mask_pos) == len(mask_label) == len(mask_token_pair_label), "the mask len is invalid"
+    mask_label = np.array(mask_label).astype('int64').reshape([-1, 1])
+    mask_pos = np.array(mask_pos).astype('int64').reshape([-1, 1])
+    mask_token_pair_label = np.array(mask_token_pair_label).astype("float32").reshape([-1, 1])
+    return new_batch_tokens, mask_label, mask_pos, mask_token_pair_label
+
+
+def mask_span(sentence,
+              seg_label,
+              txt_mask_ratio=0.15,
+              vocab_size=0,
+              CLS=0,
+              SEP=2,
+              MASK=50264,
+              span_lens=None,
+              len_distrib=None):
+    """
+    Add mask for batch_tokens, return out, mask_label, mask_pos;
+    Note: mask_pos responding the batch_tokens after padded;
+    mask_token_pair_label responding the label of the pair where this token is from
+    """
+    sent_length = len(sentence)
+    mask_num = math.ceil(sent_length * txt_mask_ratio)
+    mask_pos = set()
+    spans = []
+    max_iteration = 0
+    while len(mask_pos) < mask_num and sent_length > 2:
+        max_iteration += 1
+        if max_iteration > 100 * mask_num:
+            break
+
+        span_len = np.random.choice(span_lens, p=len_distrib)
+        anchor = np.random.choice(sent_length)
+        if anchor in mask_pos or seg_label[anchor] == -1:
+            continue
+
+        # find word start, end
+        left1, right1 = get_word_start(anchor, seg_label, sent_length), \
+                        get_word_end(anchor, seg_label, sent_length)
+        spans.append([left1, left1])
+
+        for i in range(left1, right1):
+            if len(mask_pos) >= mask_num:
+                break
+            mask_pos.add(i)
+            spans[-1][-1] = i
+
+        num_words = 1
+        right2 = right1
+        while num_words < span_len and right2 < len(sentence) and len(mask_pos) < mask_num:
+            # complete current word
+            left2 = right2
+            right2 = get_word_end(right2, seg_label, sent_length)
+            num_words += 1
+            for i in range(left2, right2):
+                if len(mask_pos) >= mask_num:
+                    break
+                mask_pos.add(i)
+                spans[-1][-1] = i
+
+    sentence, mask_label, ordered_mask_pos = span_masking(sentence, spans, vocab_size,
+                                                          mask_id=MASK, mask_pos=mask_pos)
+    return sentence, mask_label, ordered_mask_pos
+
+
+def span_masking(sentence, spans, vocab_size, mask_id=50264, mask_pos=None):
+    sent_length = len(sentence)
+    replace_ids = np.random.randint(4, high=vocab_size, size=sent_length)  # exclude CLS, PAD, SEP and MASK
+    spans = merge_intervals(spans)
+    assert len(mask_pos) == sum([e - s + 1 for s, e in spans])
+    mask_label = []
+    ordered_mask_pos = []
+    for start, end in spans:
+        for i in range(start, end + 1):
+            rand = np.random.random()
+            assert i in mask_pos
+            if rand < 0.8:
+                ordered_mask_pos.append(i)
+                mask_label.append(sentence[i])
+                sentence[i] = mask_id
+            elif rand < 0.9:
+                ordered_mask_pos.append(i)
+                mask_label.append(sentence[i])
+                # sample random token according to input distribution
+                sentence[i] = replace_ids[i]
+            else:
+                ordered_mask_pos.append(i)
+                mask_label.append(sentence[i])
+    assert len(mask_label) == len(ordered_mask_pos), 'the length of mask_label and mask_pos are not equal!'
+    return sentence, mask_label, ordered_mask_pos
+
+
+def merge_intervals(intervals):
+    intervals = sorted(intervals, key=lambda x: x[0])
+    merged = []
+    for interval in intervals:
+        # if the list of merged intervals is empty or if the current
+        # interval does not overlap with the previous, simply append it.
+        if not merged or merged[-1][1] + 1 < interval[0]:
+            merged.append(interval)
+        else:
+            # otherwise, there is overlap, so we merge the current and previous
+            # intervals.
+            merged[-1][1] = max(merged[-1][1], interval[1])
+
+    return merged
+
+
+def get_word_start(anchor, seg_label, sent_length):
+    if seg_label[anchor] == 0:
+        return anchor
+    else:
+        start = anchor
+        while start >= 0 and seg_label[start] == 1:
+            start -= 1
+
+        return max(start, 0)
+
+
+def get_word_end(anchor, seg_label, sent_length):
+    if seg_label[anchor] == 0:
+        if anchor + 1 >= sent_length:
+            return anchor + 1
+        if seg_label[anchor + 1] == 0 or seg_label[anchor + 1] == -1:
+            return anchor + 1
+        else:
+            end = anchor + 1
+            while end < sent_length and seg_label[end] == 1:
+                end += 1
+
+            return end
+    else:
+        end = anchor
+        while end < sent_length and seg_label[end] == 1:
+            end += 1
+
+        return end
+
+
+def _except(sent_index, batch_del_pos, batch_src_ids, batch_pos_ids,
+            seg_labels, batch_sent_ids, sent_b_starts, mask_id, cls_id, sep_id):
+    """making all src_ids as target
+    src_ids: [CLS] [SEP] [CLS] t1 t2 ... tn
+    pos_ids: [0, 1, 0, 1, ..., n-1]
+    seg_labels: [-1, -1, -1, 0, 0, ...,0]
+    sent_ids: [0, 0, 0, 0, ..., 0]
+    sent_b_starts: [2]
+    """
+    batch_del_pos.append([])
+    batch_src_ids[sent_index] = [cls_id, sep_id] + batch_src_ids[sent_index][:510]
+    batch_pos_ids[sent_index] = [0, 1] + list(range(0, len(batch_src_ids[sent_index]) - 2))
+    seg_labels[sent_index] = [-1, -1] + seg_labels[sent_index][:510]
+    batch_sent_ids[sent_index] = [0] * len(seg_labels[sent_index])
+    sent_b_starts.append(2)
+
+
+def prepare_batch_data(insts,
+                       total_token_num,
+                       txt_mask_ratio=0.15,
+                       vl_mask_ratio=0.15,
+                       voc_size=0,
+                       pad_id=None,
+                       cls_id=None,
+                       sep_id=None,
+                       mask_id=None,
+                       patch_seq_len=196,
+                       patch_emb_size=768):
+    # text part
+    batch_src_ids = [inst[1] for inst in insts]
+    batch_sent_ids = [inst[2] for inst in insts]
+    batch_pos_ids = [inst[3] for inst in insts]
+    labels = [inst[4] for inst in insts]
+    labels = np.array(labels).astype("int64").reshape([-1, 1])
+    pair_labels = labels[:, 0]
+    seg_labels = [inst[5] for inst in insts]
+    batch_can_mask = [inst[6] for inst in insts]
+
+    sent_b_starts = []
+    batch_del_pos = []
+
+    # pretraining_task = ['mlm', 'seq2seq']
+    # pretraining_prob = [0.5, 0.5]
+    # # choose which pretraining task to use
+    # sample_task = np.random.choice(pretraining_task, p=pretraining_prob)
+
+    sample_task = "mlm"
+    if sample_task is 'seq2seq':
+        for sent_index, sent in enumerate(batch_src_ids):
+            sample_info = [
+                {"en_slice_min": 3, "en_slice_max": 12, "de_slice_min": 1, "de_slice_max": 4},
+                {"en_slice_min": 12, "en_slice_max": 96, "de_slice_min": 4, "de_slice_max": 32},
+            ]
+            sample_prob = [0.4, 0.6]
+
+            loop_max = 1000
+            loop_stop = False
+            pos_start = 2
+            if len(sent) >= sample_info[0]["de_slice_min"] + sample_info[0]["en_slice_min"] + 2:
+                loop_cnt = 0
+                split_idx = [1]
+                start_choice = np.random.choice(2)  # mask encoder part or decoder part
+                while len(split_idx) < 2 or (len(split_idx) == 2 and split_idx[-1] == len(sent)):
+                    if loop_cnt >= loop_max:
+                        loop_stop = True
+                        break
+                    loop_cnt += 1
+                    mass_len, seg_id = 0, -1
+                    split_idx = [1]
+
+                    # choose which sample_info to use
+                    sample = np.random.choice(sample_info, p=sample_prob)
+
+                    # segment length to be masked
+                    if start_choice == 0:
+                        seg_len = np.random.choice(range(sample['en_slice_min'], sample['en_slice_max'] + 1))
+                    else:
+                        seg_len = np.random.choice(range(sample['de_slice_min'], sample['de_slice_max'] + 1))
+
+                    # don't include the first [CLS] token and the last [SEP] token
+                    while len(sent) - split_idx[-1] > seg_len:
+                        split_idx.append(seg_len + split_idx[-1])
+                        seg_id += 1
+                        sample = np.random.choice(sample_info, p=sample_prob)
+                        if seg_id % 2 == start_choice:
+                            seg_len = np.random.choice(range(sample['de_slice_min'], sample['de_slice_max'] + 1))
+                        else:
+                            seg_len = np.random.choice(range(sample['en_slice_min'], sample['en_slice_max'] + 1))
+
+                # include the last [SEP] token
+                if split_idx[-1] != len(sent):
+                    split_idx.append(len(sent))
+
+                if loop_stop:
+                    _except(sent_index, batch_del_pos, batch_src_ids, batch_pos_ids,
+                            seg_labels, batch_sent_ids, sent_b_starts, mask_id, cls_id, sep_id)
+                    print("loop stop")
+                    continue
+
+                en_src_ids, de_src_ids = [cls_id], []
+                en_pos_ids, de_pos_ids = [pos_start], []
+                en_seg_labels, de_seg_labels = [-1], []
+
+                """en_src_ids: [CLS] span1 span2 span3 ... [SEP]
+                   de_src_ids: [CLS] span1 [CLS] span2 [CLS] span3 ...
+                """
+                for i in range(1, len(split_idx)):
+                    if i % 2 != start_choice:
+                        en_src_ids.extend(batch_src_ids[sent_index][split_idx[i - 1]:split_idx[i]])
+                        en_pos_ids.extend(batch_pos_ids[sent_index][split_idx[i - 1]:split_idx[i]])
+                        en_seg_labels.extend(seg_labels[sent_index][split_idx[i - 1]:split_idx[i]])
+                    else:
+                        de_src_ids.append([cls_id] + batch_src_ids[sent_index][split_idx[i - 1]:split_idx[i]])
+                        de_pos_ids.append(batch_pos_ids[sent_index][split_idx[i - 1] - 1:split_idx[i]])
+                        de_seg_labels.append([-1] + seg_labels[sent_index][split_idx[i - 1]:split_idx[i]])
+
+                # include the last [SEP] token
+                en_src_ids.append(sep_id)
+                en_pos_ids.append(batch_pos_ids[sent_index][-1])
+                en_seg_labels.append(-1)
+                de_src_ids, de_pos_ids, de_seg_labels = sum(de_src_ids, []), sum(de_pos_ids, []), sum(de_seg_labels, [])
+
+                del_pos = []
+                # remove the index of [cls_id] from mask_pos except for the first one
+                for i, src_id in enumerate(de_src_ids):
+                    if i and src_id == 1:
+                        del_pos.append(i - len(de_src_ids))
+
+                batch_del_pos.append(del_pos)
+                batch_src_ids[sent_index] = en_src_ids + de_src_ids
+                batch_pos_ids[sent_index] = en_pos_ids + de_pos_ids
+                batch_sent_ids[sent_index] = [0] * (len(en_src_ids) + len(de_src_ids))
+                seg_labels[sent_index] = en_seg_labels + de_seg_labels
+                sent_b_starts.append(len(en_src_ids))
+            else:
+                _except(sent_index, batch_del_pos, batch_src_ids, batch_pos_ids,
+                        seg_labels, batch_sent_ids, sent_b_starts, mask_id, cls_id, sep_id)
+
+    # First step: do mask without padding
+    assert mask_id >= 0, "[FATAL] mask_id must >= 0"
+    out, mask_label, mask_pos, mask_token_pair_label = mask(
+        batch_src_ids,
+        seg_labels,
+        total_token_num,
+        txt_mask_ratio=txt_mask_ratio,
+        pair_labels=pair_labels,
+        vocab_size=voc_size,
+        pretraining_task=sample_task,
+        CLS=cls_id,
+        SEP=sep_id,
+        MASK=mask_id,
+        sent_b_starts=sent_b_starts,
+        del_pos=batch_del_pos,
+        batch_can_mask=batch_can_mask)
+
+    # Second step: padding
+    """self_input_mask: all tokens can attend src part, each tgt word only attend its own 
+    and previous tgt words, used for word-by-word generation"""
+    src_id, text_mask = pad_batch_data(
+        out,
+        pretraining_task=sample_task,
+        sent_b_starts=sent_b_starts,
+        pad_idx=pad_id,
+        return_input_mask=True)
+
+    pos_id = pad_batch_data(batch_pos_ids, pad_idx=pad_id)
+    sent_id = pad_batch_data(batch_sent_ids, pad_idx=pad_id)
+
+    # visual part
+    batch_image_pixel = [inst[8] for inst in insts]
+    batch_image_pixel_pos = [inst[9] for inst in insts]
+    batch_with_image = [inst[11] for inst in insts]
+    batch_with_text = [inst[12] for inst in insts]
+    batch_is_multimodal = [inst[13] for inst in insts]
+    total_img_token_num = patch_seq_len * len(batch_image_pixel)  # don't include the global image token
+
+    with_image = np.array(batch_with_image, dtype='float32')
+    with_image = with_image.reshape((-1, 1))
+    with_text = np.array(batch_with_text, dtype='float32')
+    with_text = with_text.reshape((-1, 1))
+    is_multimodal = np.array(batch_is_multimodal, dtype='float32')
+    is_multimodal = is_multimodal.reshape((-1, 1))
+
+    # image pixels, include the global image token
+    image_mask = np.ones(shape=[len(insts), 1, patch_seq_len + 1], dtype="float32")
+    image_pixel_input = np.array(batch_image_pixel, dtype='float32')
+
+    text_batch = [src_id, pos_id, sent_id, text_mask, mask_label, mask_pos, mask_token_pair_label]
+    image_batch = [image_pixel_input, image_mask, with_image, with_text, is_multimodal, labels]
+    return_list = text_batch + image_batch
+
+    return return_list
+
+
+def pad_batch_data(insts,
+                   pretraining_task='seq2seq',
+                   pad_idx=1,
+                   sent_b_starts=None,
+                   return_pos=False,
+                   return_input_mask=False,
+                   return_max_len=False,
+                   return_num_token=False,
+                   return_seq_lens=False):
+    """
+    Pad the instances to the max sequence length in batch, and generate the
+    corresponding position data and attention bias.
+    """
+    return_list = []
+    max_len = max(len(inst) for inst in insts)
+    # Any token included in dict can be used to pad, since the paddings' loss
+    # will be masked out by weights and make no effect on parameter gradients.
+
+    inst_data = np.array(
+        [inst + list([pad_idx] * (max_len - len(inst))) for inst in insts])
+    return_list += [inst_data.astype('int64').reshape([-1, max_len, 1])]
+
+    # position data
+    if return_pos:
+        inst_pos = np.array([
+            list(range(0, len(inst))) + [pad_idx] * (max_len - len(inst))
+            for inst in insts
+        ])
+
+        return_list += [inst_pos.astype('int64').reshape([-1, max_len, 1])]
+
+    if return_input_mask:
+        if pretraining_task is 'seq2seq':
+            assert sent_b_starts is not None, \
+                "[FATAL] For seq2seq lanugae model loss," \
+                " sent_b_starts should not be None"
+            # This is used to avoid attention on paddings and subsequent words.
+            input_mask_data = np.zeros((inst_data.shape[0], max_len, max_len))
+            for index, mask_data in enumerate(input_mask_data):
+                start = sent_b_starts[index]
+                end = len(insts[index])
+                mask_data[:end, :start] = 1.0
+                # Generate the lower triangular matrix using the slice of matrix
+                b = np.tril(np.ones([end - start, end - start]), 0)
+                mask_data[start:end, start:end] = b
+            input_mask_data = np.array(input_mask_data).reshape([-1, max_len, max_len])
+        else:
+            # This is used to avoid attention on paddings.
+            input_mask_data = np.array([[1] * len(inst) + [0] *
+                                        (max_len - len(inst)) for inst in insts])
+            input_mask_data = np.expand_dims(input_mask_data, axis=1)
+            # input_mask_data = np.matmul(input_mask_data, np.transpose(input_mask_data, (0, 2, 1)))
+        return_list += [input_mask_data.astype("float32")]
+
+    if return_max_len:
+        return_list += [max_len]
+
+    if return_num_token:
+        num_token = 0
+        for inst in insts:
+            num_token += len(inst)
+        return_list += [num_token]
+
+    if return_seq_lens:
+        seq_lens = np.array([len(inst) for inst in insts])
+        return_list += [seq_lens.astype('int64').reshape([-1, 1])]
+
+    return return_list if len(return_list) > 1 else return_list[0]
+
+
+def gen_seq2seq_mask(insts, batch_src_lens=None, sent_b_starts=None):
+    """
+    generate input mask for seq2seq
+    """
+    max_len = max(len(inst) for inst in insts)
+    input_mask_data = np.zeros((len(insts), max_len, max_len))
+    for index, mask_data in enumerate(input_mask_data):
+        start = sent_b_starts[index]
+        end = len(insts[index])
+        if batch_src_lens:
+            src_len = batch_src_lens[index]
+            mask_data[:end, :src_len] = 1.0
+            mask_data[src_len:start, :] = 0.0
+        else:
+            mask_data[:end, :start] = 1.0
+        # Generate the lower triangular matrix using the slice of matrix
+        b = np.tril(np.ones([end - start, end - start]), 0)
+        mask_data[start:end, start:end] = b
+    input_mask_data = np.array(input_mask_data, dtype='float32').reshape([-1, max_len, max_len])
+    return input_mask_data
+
+
+if __name__ == "__main__":
+    pass
diff --git a/NLP/UNIMO-2/src/run_classifier_grounded.py b/NLP/UNIMO-2/src/run_classifier_grounded.py
new file mode 100644
index 000000000..80c61a5a1
--- /dev/null
+++ b/NLP/UNIMO-2/src/run_classifier_grounded.py
@@ -0,0 +1,515 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: run_classifier_grounded.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-09-15 14:22
+Desc: Finetuning on classification tasks.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import time
+import multiprocessing
+
+import paddle
+paddle.enable_static()
+import paddle.fluid as fluid
+import numpy as np
+
+from reader.classification_grounded_reader import ClassifyReader
+from model.unimo_grounded import VlConfig
+from model.roberta_tokenization import GptBpeTokenizer
+from finetune.classifier_grounded import create_model, evaluate, predict
+from utils.optimization import optimization
+from utils.utils import visualdl_log, get_time
+from utils.args import print_arguments
+from utils.init import init_pretraining_params, init_checkpoint
+from args.classification_grounded_args import parser
+
+args = parser.parse_args()
+
+
+def main(args):
+    vl_config = VlConfig(args.vl_config_path)
+    vl_config['num_codebook'] = args.num_codebook
+    vl_config.print_config()
+
+    gpu_id = 0
+    gpus = fluid.core.get_cuda_device_count()
+    if args.is_distributed and os.getenv("FLAGS_selected_gpus") is not None:
+        gpu_list = os.getenv("FLAGS_selected_gpus").split(",")
+        gpus = len(gpu_list)
+        gpu_id = int(gpu_list[0])
+
+    if args.use_cuda:
+        place = fluid.CUDAPlace(gpu_id)
+        dev_count = gpus
+    else:
+        place = fluid.CPUPlace()
+        dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+
+    tokenizer = GptBpeTokenizer(vocab_file=args.roberta_vocab_file,
+                                encoder_json_file=args.encoder_json_file,
+                                vocab_bpe_file=args.vocab_bpe_file,
+                                do_lower_case=args.do_lower_case)
+
+    data_reader = ClassifyReader(tokenizer, args)
+
+    if not (args.do_train or args.do_val or args.do_val_hard or
+            args.do_test or args.do_test_hard or args.do_diagnostic):
+        raise ValueError(
+            "For args `do_train`, `do_val`, `do_val_hard`, `do_test`, `do_test_hard` and `do_diagnostic`, at "
+            "least one of them must be True.")
+
+    startup_prog = fluid.Program()
+    if args.random_seed is not None:
+        startup_prog.random_seed = args.random_seed
+
+    if args.do_train:
+        trainers_num = int(os.getenv("PADDLE_TRAINERS_NUM"))
+        train_data_generator = data_reader.data_generator(
+            input_file=args.train_set,
+            batch_size=args.batch_size,
+            epoch=args.epoch,
+            dev_count=trainers_num,
+            shuffle=True,
+            phase="train")
+
+        num_train_examples = data_reader.get_num_examples(args.train_set)
+
+        if args.in_tokens:
+            max_train_steps = args.epoch * num_train_examples // (
+                    args.batch_size // args.max_seq_len) // trainers_num
+        else:
+            max_train_steps = args.epoch * num_train_examples // args.batch_size // trainers_num
+
+        warmup_steps = int(max_train_steps * args.warmup_proportion)
+        print("Device count: %d, gpu_id: %d" % (dev_count, gpu_id))
+        print("Num train examples: %d" % num_train_examples)
+        print("Max train steps: %d" % max_train_steps)
+        print("Num warmup steps: %d" % warmup_steps)
+
+        train_program = fluid.Program()
+
+        with fluid.program_guard(train_program, startup_prog):
+            with fluid.unique_name.guard():
+                train_pyreader, graph_vars = create_model(
+                    args,
+                    pyreader_name='train_reader',
+                    vl_config=vl_config)
+                scheduled_lr, loss_scaling = optimization(
+                    loss=graph_vars["loss"],
+                    warmup_steps=warmup_steps,
+                    num_train_steps=max_train_steps,
+                    learning_rate=args.learning_rate,
+                    train_program=train_program,
+                    weight_decay=args.weight_decay,
+                    scheduler=args.lr_scheduler,
+                    use_fp16=args.use_fp16,
+                    use_dynamic_loss_scaling=args.use_dynamic_loss_scaling,
+                    init_loss_scaling=args.init_loss_scaling,
+                    beta1=args.beta1,
+                    beta2=args.beta2,
+                    epsilon=args.epsilon)
+
+    if args.do_val or args.do_val_hard or args.do_test or args.do_test_hard or \
+            args.do_pred or args.do_pred_hard or args.do_diagnostic:
+        test_prog = fluid.Program()
+        with fluid.program_guard(test_prog, startup_prog):
+            with fluid.unique_name.guard():
+                test_pyreader, graph_vars = create_model(
+                    args,
+                    pyreader_name='test_reader',
+                    vl_config=vl_config)
+
+        test_prog = test_prog.clone(for_test=True)
+
+    nccl2_num_trainers = 1
+    nccl2_trainer_id = 0
+    print("args.is_distributed:", args.is_distributed)
+    if args.is_distributed:
+        trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
+        worker_endpoints_env = os.getenv("PADDLE_TRAINER_ENDPOINTS")
+        current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT")
+        worker_endpoints = worker_endpoints_env.split(",")
+        trainers_num = len(worker_endpoints)
+
+        print("worker_endpoints:{} trainers_num:{} current_endpoint:{} \
+              trainer_id:{}".format(worker_endpoints, trainers_num,
+                                    current_endpoint, trainer_id))
+
+        # prepare nccl2 env.
+        config = fluid.DistributeTranspilerConfig()
+        config.mode = "nccl2"
+        if args.nccl_comm_num > 1:
+            config.nccl_comm_num = args.nccl_comm_num
+        if args.use_hierarchical_allreduce and trainers_num > args.hierarchical_allreduce_inter_nranks:
+            config.use_hierarchical_allreduce = args.use_hierarchical_allreduce
+            config.hierarchical_allreduce_inter_nranks = args.hierarchical_allreduce_inter_nranks
+
+            assert config.hierarchical_allreduce_inter_nranks > 1
+            assert trainers_num % config.hierarchical_allreduce_inter_nranks == 0
+
+            config.hierarchical_allreduce_exter_nranks = \
+                trainers_num / config.hierarchical_allreduce_inter_nranks
+
+        t = fluid.DistributeTranspiler(config=config)
+        t.transpile(
+            trainer_id,
+            trainers=worker_endpoints_env,
+            current_endpoint=current_endpoint,
+            program=train_program if args.do_train else test_prog,
+            startup_program=startup_prog)
+        nccl2_num_trainers = trainers_num
+        nccl2_trainer_id = trainer_id
+
+    exe = fluid.Executor(place)
+    exe.run(startup_prog)
+
+    if args.do_train:
+        if args.init_checkpoint and args.init_pretraining_params:
+            print(
+                "WARNING: args 'init_checkpoint' and 'init_pretraining_params' "
+                "both are set! Only arg 'init_checkpoint' is made valid.")
+        if args.init_checkpoint:
+            init_checkpoint(
+                exe,
+                args.init_checkpoint,
+                main_program=train_program)
+        elif args.init_pretraining_params:
+            init_pretraining_params(
+                exe,
+                args.init_pretraining_params,
+                main_program=train_program)
+    elif args.do_val or args.do_val_hard or args.do_test or args.do_test_hard or \
+            args.do_pred or args.do_pred_hard or args.do_diagnostic:
+        if not args.init_checkpoint:
+            raise ValueError("args 'init_checkpoint' should be set if"
+                             "only doing validation or testing!")
+        init_checkpoint(
+            exe,
+            args.init_checkpoint,
+            main_program=startup_prog)
+
+    if args.do_train:
+        exec_strategy = fluid.ExecutionStrategy()
+        if args.use_fast_executor:
+            exec_strategy.use_experimental_executor = True
+        exec_strategy.num_threads = dev_count
+        exec_strategy.num_iteration_per_drop_scope = args.num_iteration_per_drop_scope
+
+        train_exe = fluid.ParallelExecutor(
+            use_cuda=args.use_cuda,
+            loss_name=graph_vars["loss"].name,
+            exec_strategy=exec_strategy,
+            main_program=train_program,
+            num_trainers=nccl2_num_trainers,
+            trainer_id=nccl2_trainer_id)
+
+        train_pyreader.decorate_tensor_provider(train_data_generator)
+    else:
+        train_exe = None
+
+    test_exe = exe
+    if args.do_val or args.do_val_hard or args.do_test or args.do_test_hard or args.do_pred \
+            or args.do_pred_hard or args.do_diagnostic:
+        if args.use_multi_gpu_test:
+            test_exe = fluid.ParallelExecutor(
+                use_cuda=args.use_cuda,
+                main_program=test_prog,
+                share_vars_from=train_exe)
+
+    dev_ret_history = []  # (steps, key_eval, eval)
+    dev_hard_ret_history = []  # (steps, key_eval, eval)
+    test_ret_history = []  # (steps, key_eval, eval)
+    test_hard_ret_history = []  # (steps, key_eval, eval)
+    if args.do_train:
+        train_pyreader.start()
+        steps = 0
+        if warmup_steps > 0:
+            graph_vars["learning_rate"] = scheduled_lr
+
+        time_begin = time.time()
+
+        skip_steps = args.skip_steps
+        while True:
+            try:
+                steps += 1
+
+                if steps % skip_steps == 0:
+                    train_fetch_list = [
+                        graph_vars["loss"].name,
+                        graph_vars["accuracy"].name,
+                        graph_vars["num_seqs"].name
+                    ]
+                    if "learning_rate" in graph_vars:
+                        train_fetch_list.append(graph_vars["learning_rate"].name)
+                    res = train_exe.run(fetch_list=train_fetch_list)
+
+                    outputs = {"loss": np.mean(res[0])}
+                    if "learning_rate" in graph_vars:
+                        outputs["learning_rate"] = float(res[3][0])
+
+                    if args.verbose:
+                        verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size(
+                        )
+                        verbose += "learning rate: %f" % (
+                            outputs["learning_rate"]
+                            if warmup_steps > 0 else args.learning_rate)
+                        print(verbose)
+
+                    current_example, current_epoch = data_reader.get_train_progress()
+                    time_end = time.time()
+                    used_time = time_end - time_begin
+                    print("%s - epoch: %d, progress: %d/%d, step: %d, ave loss: %f, speed: %f steps/s" % \
+                          (get_time(), current_epoch, current_example, num_train_examples, steps, outputs["loss"],
+                           args.skip_steps / used_time))
+                    time_begin = time.time()
+                else:
+                    train_exe.run(fetch_list=[])
+
+                if nccl2_trainer_id == 0:
+                    if steps % args.save_steps == 0 and args.save_checkpoints:
+                        save_path = os.path.join(args.checkpoints,
+                                                 "step_" + str(steps))
+                        fluid.io.save_persistables(exe, save_path, train_program)
+
+                    if steps % args.validation_steps == 0:
+                        # evaluate dev set
+                        if args.do_val:
+                            test_pyreader.decorate_tensor_provider(
+                                data_reader.data_generator(
+                                    args.dev_set,
+                                    batch_size=args.batch_size,
+                                    epoch=1,
+                                    dev_count=1,
+                                    shuffle=False))
+                            outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "dev")
+                            dev_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']]))
+
+                        # evaluate dev_hard set
+                        if args.do_val_hard:
+                            test_pyreader.decorate_tensor_provider(
+                                data_reader.data_generator(
+                                    args.dev_hard_set,
+                                    batch_size=args.batch_size,
+                                    epoch=1,
+                                    dev_count=1,
+                                    shuffle=False))
+                            outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "dev_hard")
+                            dev_hard_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']]))
+
+                        # evaluate test set
+                        if args.do_test:
+                            test_pyreader.decorate_tensor_provider(
+                                data_reader.data_generator(
+                                    args.test_set,
+                                    batch_size=args.batch_size,
+                                    epoch=1,
+                                    dev_count=1,
+                                    shuffle=False))
+                            outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "test")
+                            test_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']]))
+
+                        # evaluate test_hard set
+                        if args.do_test_hard:
+                            test_pyreader.decorate_tensor_provider(
+                                data_reader.data_generator(
+                                    args.test_hard_set,
+                                    batch_size=args.batch_size,
+                                    epoch=1,
+                                    dev_count=1,
+                                    shuffle=False))
+                            outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "test_hard")
+                            test_hard_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']]))
+
+                        # pred diagnostic set
+                        if args.do_diagnostic:
+                            test_pyreader.decorate_tensor_provider(
+                                data_reader.data_generator(
+                                    args.diagnostic_set,
+                                    batch_size=args.batch_size,
+                                    epoch=1,
+                                    dev_count=1,
+                                    shuffle=False))
+                            qids, preds, probs = predict(test_exe, test_prog, test_pyreader, graph_vars, dev_count=1)
+                            save_path = args.pred_save + '.diagnostic.' + str(steps) + '.txt'
+                            print("testing {}, save to {}".format(args.diagnostic_set, save_path))
+                            with open(save_path, 'w') as f:
+                                for id, s, p in zip(qids, preds, probs):
+                                    f.write('{}\t{}\t{}\n'.format(id, s, p))
+
+                        # pred test set
+                        if args.do_pred:
+                            test_pyreader.decorate_tensor_provider(
+                                data_reader.data_generator(
+                                    args.test_set,
+                                    batch_size=args.batch_size,
+                                    epoch=1,
+                                    dev_count=1,
+                                    shuffle=False))
+                            qids, preds, probs = predict(test_exe, test_prog, test_pyreader, graph_vars, dev_count=1)
+                            save_path = args.pred_save + '.test.' + str(steps) + '.txt'
+                            print("testing {}, save to {}".format(args.test_set, save_path))
+                            with open(save_path, 'w') as f:
+                                for id, s, p in zip(qids, preds, probs):
+                                    f.write('{}\t{}\t{}\n'.format(id, s, p))
+
+                        # pred test hard set
+                        if args.do_pred_hard:
+                            test_pyreader.decorate_tensor_provider(
+                                data_reader.data_generator(
+                                    args.test_hard_set,
+                                    batch_size=args.batch_size,
+                                    epoch=1,
+                                    dev_count=1,
+                                    shuffle=False))
+                            qids, preds, probs = predict(test_exe, test_prog, test_pyreader, graph_vars, dev_count=1)
+                            save_path = args.pred_save + '.test_hard.' + str(steps) + '.txt'
+                            print("testing {}, save to {}".format(args.test_hard_set, save_path))
+                            with open(save_path, 'w') as f:
+                                for id, s, p in zip(qids, preds, probs):
+                                    f.write('{}\t{}\t{}\n'.format(id, s, p))
+
+            except fluid.core.EOFException:
+                if args.save_checkpoints:
+                    save_path = os.path.join(args.checkpoints, "step_" + str(steps))
+                    fluid.io.save_persistables(exe, save_path, train_program)
+                train_pyreader.reset()
+                break
+
+    if nccl2_trainer_id == 0:
+        # final pred on diagnostic set
+        if args.do_diagnostic:
+            test_pyreader.decorate_tensor_provider(
+                data_reader.data_generator(
+                    args.diagnostic_set,
+                    batch_size=args.batch_size,
+                    epoch=1,
+                    dev_count=1,
+                    shuffle=False))
+            qids, preds, probs = predict(test_exe, test_prog, test_pyreader, graph_vars, dev_count=1)
+            save_path = args.pred_save + '.diagnostic.' + str(steps) + '.txt'
+            print("testing {}, save to {}".format(args.diagnostic_set, save_path))
+            with open(save_path, 'w') as f:
+                for id, s, p in zip(qids, preds, probs):
+                    f.write('{}\t{}\t{}\n'.format(id, s, p))
+
+        # final pred on test set
+        if args.do_pred:
+            test_pyreader.decorate_tensor_provider(
+                data_reader.data_generator(
+                    args.test_set,
+                    batch_size=args.batch_size,
+                    epoch=1,
+                    dev_count=1,
+                    shuffle=False))
+            qids, preds, probs = predict(test_exe, test_prog, test_pyreader, graph_vars, dev_count=1)
+            save_path = args.pred_save + '.test.' + str(steps) + '.txt'
+            print("testing {}, save to {}".format(args.test_set, save_path))
+            with open(save_path, 'w') as f:
+                for id, s, p in zip(qids, preds, probs):
+                    f.write('{}\t{}\t{}\n'.format(id, s, p))
+
+        # final pred on test_hard set
+        if args.do_pred_hard:
+            test_pyreader.decorate_tensor_provider(
+                data_reader.data_generator(
+                    args.test_hard_set,
+                    batch_size=args.batch_size,
+                    epoch=1,
+                    dev_count=1,
+                    shuffle=False))
+            qids, preds, probs = predict(test_exe, test_prog, test_pyreader, graph_vars, dev_count=1)
+            save_path = args.pred_save + '.test_hard.' + str(steps) + '.txt'
+            print("testing {}, save to {}".format(args.test_hard_set, save_path))
+            with open(save_path, 'w') as f:
+                for id, s, p in zip(qids, preds, probs):
+                    f.write('{}\t{}\t{}\n'.format(id, s, p))
+
+        # final eval on test set
+        if args.do_test:
+            test_pyreader.decorate_tensor_provider(
+                data_reader.data_generator(
+                    args.test_set,
+                    batch_size=args.batch_size,
+                    epoch=1,
+                    dev_count=1,
+                    shuffle=False))
+            print("Final test result:")
+            outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "test")
+            test_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']]))
+            test_ret_history = sorted(test_ret_history, key=lambda a: a[2], reverse=True)
+            print("Best testing result: step %d %s %f" % (
+                test_ret_history[0][0], test_ret_history[0][1], test_ret_history[0][2]))
+
+        # final eval on test set
+        if args.do_test_hard:
+            test_pyreader.decorate_tensor_provider(
+                data_reader.data_generator(
+                    args.test_hard_set,
+                    batch_size=args.batch_size,
+                    epoch=1,
+                    dev_count=1,
+                    shuffle=False))
+            print("Final test_hard result:")
+            outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "test_hard")
+            test_hard_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']]))
+            test_hard_ret_history = sorted(test_hard_ret_history, key=lambda a: a[2], reverse=True)
+            print("Best testing hard result: step %d %s %f" % (
+                test_hard_ret_history[0][0], test_hard_ret_history[0][1], test_hard_ret_history[0][2]))
+
+        # final eval on dev set
+        if args.do_val:
+            test_pyreader.decorate_tensor_provider(
+                data_reader.data_generator(
+                    args.dev_set,
+                    batch_size=args.batch_size,
+                    epoch=1,
+                    dev_count=1,
+                    shuffle=False))
+            print("Final validation result:")
+            outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "dev")
+            dev_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']]))
+            dev_ret_history = sorted(dev_ret_history, key=lambda a: a[2], reverse=True)
+            print("Best validation result: step %d %s %f" % (
+                dev_ret_history[0][0], dev_ret_history[0][1], dev_ret_history[0][2]))
+
+        # final eval on dev set
+        if args.do_val_hard:
+            test_pyreader.decorate_tensor_provider(
+                data_reader.data_generator(
+                    args.dev_hard_set,
+                    batch_size=args.batch_size,
+                    epoch=1,
+                    dev_count=1,
+                    shuffle=False))
+            print("Final validation_hard result:")
+            outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "dev_hard")
+            dev_hard_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']]))
+            dev_hard_ret_history = sorted(dev_hard_ret_history, key=lambda a: a[2], reverse=True)
+            print("Best validation_hard result: step %d %s %f" % (
+                dev_hard_ret_history[0][0], dev_hard_ret_history[0][1], dev_hard_ret_history[0][2]))
+
+
+if __name__ == '__main__':
+    print_arguments(args)
+    main(args)
diff --git a/NLP/UNIMO-2/src/run_img2txt_oscar.py b/NLP/UNIMO-2/src/run_img2txt_oscar.py
new file mode 100644
index 000000000..cdff1dcc6
--- /dev/null
+++ b/NLP/UNIMO-2/src/run_img2txt_oscar.py
@@ -0,0 +1,337 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: run_img2txt_oscar.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-10-25 15:41
+Desc: Finetuning on image-to-text generation tasks.
+"""
+
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import time
+import multiprocessing
+
+os.environ['FLAGS_fraction_of_gpu_memory_to_use'] = "0.98"
+os.environ['FLAGS_sync_nccl_allreduce'] = "1"
+os.environ['FLAGS_eager_delete_tensor_gb'] = "0"
+os.environ['FLAGS_fuse_parameter_memory_size'] = "32"
+os.environ['FLAGS_fuse_parameter_groups_size'] = "50"
+os.environ['FLAGS_check_nan_inf'] = "0"
+
+import paddle
+paddle.enable_static()
+import paddle.distributed.fleet as fleet
+import paddle.distributed.fleet.base.role_maker as role_maker
+import numpy as np
+import random
+
+from reader.img2txt_oscar_reader import Img2TxtReader
+from model.roberta_tokenization import GptBpeTokenizer
+from model.unimo_grounded import VlConfig
+from utils.optimization_fleet import optimization
+from utils.init import init_model, check_pretraining_params
+from utils.args import print_arguments
+from utils.utils import visualdl_log
+from finetune.img2txt_oscar import Img2Txt
+from args.img2txt_oscar_args import parser
+from functools import partial
+from collections import OrderedDict
+
+args = parser.parse_args()
+
+
+def get_time():
+    res = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
+    return res
+
+
+def evaluate_datasets(pyreader, reader, eval_func, data_generator,
+                      do_pred=False, suffix="out"):
+    def evaluate_dataset(phase, path):
+        pyreader.set_batch_generator(data_generator(filelist=path, phase=phase))
+        eval_func(eval_phase="%s_%s" % (phase, suffix))
+
+    if args.do_val:
+        evaluate_dataset("dev", args.valid_filelist)
+    if args.do_test:
+        evaluate_dataset("test", args.test_filelist)
+    if args.do_pred and do_pred:
+        evaluate_dataset("pred", args.test_filelist)
+
+
+def save_checkpoint(program, exe, suffix):
+    save_path = os.path.join(args.checkpoints, suffix)
+    paddle.fluid.io.save_persistables(exe, save_path, program)
+
+
+def main(args):
+    vl_config = VlConfig(args.vl_config_path)
+    if args.hidden_dropout_prob >= 0:
+        vl_config["hidden_dropout_prob"] = args.hidden_dropout_prob
+    if args.attention_probs_dropout_prob >= 0:
+        vl_config["attention_probs_dropout_prob"] = args.attention_probs_dropout_prob
+
+    vl_config['image_size'] = args.image_size
+    vl_config["resolution"] = args.resolution
+    vl_config['num_codebook'] = args.num_codebook
+    vl_config.print_config()
+
+    if args.pred_batch_size <= 0:
+        args.pred_batch_size = args.batch_size
+
+    print("FLAGS_selected_gpus:", os.environ.get('FLAGS_selected_gpus'))
+    place = paddle.CUDAPlace(int(os.environ.get('FLAGS_selected_gpus', 0)))
+
+    dist_strategy = paddle.distributed.fleet.DistributedStrategy()
+    # dist_strategy.without_graph_optimization = True
+    dist_strategy.fuse_all_reduce_ops = True
+
+    exec_strategy = paddle.static.ExecutionStrategy()
+    exec_strategy.num_threads = 4 if args.use_fp16 else 2  # 2 for fp32 4 for fp16
+    exec_strategy.num_iteration_per_drop_scope = min(args.num_iteration_per_drop_scope, args.skip_steps)
+    # exec_strategy.num_iteration_per_run = 10
+    dist_strategy.execution_strategy = exec_strategy
+
+    build_strategy = paddle.static.BuildStrategy()
+    dist_strategy.build_strategy = build_strategy
+
+    if args.use_fp16:
+        dist_strategy.amp = True
+        dist_strategy.amp_configs = {
+            "init_loss_scaling": args.init_loss_scaling,
+            "use_dynamic_loss_scaling": args.use_dynamic_loss_scaling,
+            "custom_black_list": ['layer_norm', 'arg_max', 'argmax'],
+            "custom_black_varnames": ["loss"]
+        }
+
+    if args.use_recompute:
+        print("using recompute.")
+        dist_strategy.recompute = True
+        build_strategy.enable_sequential_execution = True
+
+    role = role_maker.PaddleCloudRoleMaker(is_collective=True)
+    fleet.init(role_maker=role, strategy=dist_strategy)
+
+    worker_num = fleet.worker_num()
+    worker_index = fleet.worker_index()
+    print("worker_num %d" % worker_num)
+    print("worker_index %d" % worker_index)
+
+    """load vocabulary"""
+    tokenizer = GptBpeTokenizer(vocab_file=args.roberta_vocab_file,
+                                encoder_json_file=args.encoder_json_file,
+                                vocab_bpe_file=args.vocab_bpe_file,
+                                do_lower_case=True)
+
+    reader = Img2TxtReader(tokenizer=tokenizer,
+                           args=args,
+                           image_size=args.image_size,
+                           resolution=args.resolution)
+    img2txt = Img2Txt(args, vl_config, tokenizer)
+
+    if not (args.do_train or args.do_val or args.do_test or args.do_pred):
+        raise ValueError("For args `do_train`, `do_val` and `do_test`, at "
+                         "least one of them must be True.")
+
+    startup_prog = paddle.static.Program()
+    if args.random_seed is not None:
+        startup_prog.random_seed = args.random_seed
+        random.seed(args.random_seed)
+        np.random.seed(args.random_seed)
+        paddle.seed(args.random_seed)
+
+    if args.do_train:
+        trainers_num = int(os.getenv("PADDLE_TRAINERS_NUM", 1))
+        train_data_generator = reader.data_generator(
+            filelist=args.train_filelist,
+            batch_size=args.batch_size,
+            epoch=args.epoch,
+            dev_count=trainers_num,
+            shuffle=True,
+            phase="train")
+
+        num_train_examples = 566747  # reader.get_num_examples(args.train_filelist)
+        max_train_steps = args.epoch * num_train_examples // args.batch_size // trainers_num
+
+        warmup_steps = int(max_train_steps * args.warmup_proportion)
+        print("Num train examples: %d" % num_train_examples)
+        print("Max train steps: %d" % max_train_steps)
+        print("Num warmup steps: %d" % warmup_steps)
+
+        train_program = paddle.static.Program()
+        with paddle.static.program_guard(train_program, startup_prog):
+            train_pyreader, graph_vars, checkpoints = img2txt.create_model()
+
+            if args.use_recompute:
+                dist_strategy.recompute_configs = {"checkpoints": checkpoints}
+
+            scheduled_lr = optimization(
+                loss=graph_vars["loss"],
+                warmup_steps=warmup_steps,
+                num_train_steps=max_train_steps,
+                learning_rate=args.learning_rate,
+                train_program=train_program,
+                weight_decay=args.weight_decay,
+                scheduler=args.lr_scheduler,
+                beta1=args.beta1,
+                beta2=args.beta2,
+                epsilon=args.epsilon,
+                dist_strategy=dist_strategy)
+
+    if args.do_val or args.do_test or args.do_pred:
+        test_prog = paddle.static.Program()
+        with paddle.static.program_guard(test_prog, startup_prog):
+            test_pyreader, test_graph_vars = img2txt.create_model(decoding=args.do_decode)
+        test_prog = test_prog.clone(for_test=True)
+
+    exe = paddle.static.Executor(place)
+    exe.run(startup_prog)
+
+    if args.init_checkpoint:
+        check_pretraining_params(args.init_checkpoint, train_program if args.do_train else test_prog)
+    elif args.init_pretraining_params:
+        check_pretraining_params(args.init_pretraining_params, train_program if args.do_train else test_prog)
+    else:
+        print("Note!!!!!!!!!!!!!!")
+        print("No pretraining_params are setted. Run from random params....")
+
+    init_model(args, exe, train_program if args.do_train else test_prog)
+
+    if args.do_train:
+        train_exe = paddle.static.ParallelExecutor(
+            use_cuda=args.use_cuda,
+            loss_name=graph_vars["loss"].name,
+            build_strategy=build_strategy,
+            exec_strategy=exec_strategy,
+            main_program=train_program,
+            num_trainers=worker_num,
+            trainer_id=worker_index)
+        train_pyreader.set_batch_generator(train_data_generator)
+        train_resource = {"exe": train_exe,
+                          "program": train_program,
+                          "pyreader": train_pyreader}
+        save_model = partial(save_checkpoint, program=train_program, exe=exe)
+
+    test_dev_count = 1
+    if args.do_val or args.do_test or args.do_pred:
+        test_exe = exe
+        if args.use_multi_gpu_test:
+            test_dev_count = worker_num
+        test_resource = {"exe": test_exe,
+                         "program": test_prog,
+                         "pyreader": test_pyreader}
+        eval_data_generator = partial(reader.data_generator, batch_size=args.pred_batch_size,
+                                      epoch=1, dev_count=test_dev_count, shuffle=False, do_decode=args.do_decode,
+                                      place=place)
+        eval_func = partial(img2txt.evaluate, resource=test_resource, graph_vars=test_graph_vars,
+                            dev_count=test_dev_count, output_path=args.checkpoints, gpu_id=worker_index)
+        evaluate = partial(evaluate_datasets, pyreader=test_pyreader, reader=reader,
+                           eval_func=eval_func, data_generator=eval_data_generator)
+
+    if args.do_train:
+        train_pyreader.start()
+        steps = 0
+        last_epoch = 0
+        time_begin = time.time()
+        skip_steps = args.skip_steps
+        while True:
+            try:
+                steps += 1
+                scheduled_lr.step(steps - 1)
+                if args.save_and_valid_by_epoch:
+                    suffix = "epoch_" + str(last_epoch)
+                else:
+                    suffix = "step_" + str(steps)
+
+                if steps % skip_steps == 0:
+                    outputs = img2txt.evaluate(train_resource, "train", graph_vars)
+                    if args.verbose:
+                        verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size()
+                        verbose += "learning rate: %.8f" % (float(scheduled_lr.get_lr()))
+                        print(verbose)
+
+                    current_epoch = steps * args.batch_size * trainers_num // num_train_examples
+                    current_example = steps * args.batch_size * trainers_num % num_train_examples
+
+                    time_end = time.time()
+                    used_time = time_end - time_begin
+                    print("%s - epoch: %d, progress: %d/%d, step: %d, loss: %f, "
+                          "ppl: %f, speed: %f steps/s"
+                          % (get_time(), current_epoch, current_example, num_train_examples,
+                             steps, outputs["loss"], outputs["ppl"],
+                             args.skip_steps / used_time))
+                    time_begin = time.time()
+
+                    if args.visualdl_log and worker_index == 0:
+                        visuallog_dict = OrderedDict()
+                        visuallog_dict["ppl"] = outputs["ppl"]
+                        visualdl_log(visuallog_dict, outputs["ppl"], steps, phase='train')
+                else:
+                    train_exe.run(fetch_list=[])
+
+                if worker_index >= test_dev_count:
+                    continue
+
+                do_save = False
+                do_eval = False
+                if not args.save_and_valid_by_epoch:
+                    if steps % args.save_steps == 0 and worker_index == 0:
+                        do_save = True
+                    if steps % args.validation_steps == 0:
+                        do_eval = True
+                else:
+                    current_epoch = steps * args.batch_size * trainers_num // num_train_examples
+                    if current_epoch != last_epoch:
+                        if worker_index == 0:
+                            do_save = True
+                        do_eval = True
+
+                if do_save:
+                    save_model(suffix=suffix)
+                if do_eval:
+                    evaluate(suffix=suffix)
+
+                if args.save_and_valid_by_epoch:
+                    last_epoch = current_epoch
+
+            except paddle.fluid.core.EOFException:
+                save_model(suffix=suffix)
+                train_pyreader.reset()
+                break
+
+    if worker_index >= test_dev_count:
+        return
+
+    if args.do_val or args.do_test or args.do_pred:
+        suffix = "output"
+        if args.do_train:
+            if not args.save_and_valid_by_epoch:
+                suffix = "step_" + str(steps)
+            else:
+                suffix = "epoch_" + str(last_epoch)
+
+        evaluate(suffix=suffix, do_pred=True)
+
+
+if __name__ == '__main__':
+    print_arguments(args)
+    main(args)
diff --git a/NLP/UNIMO-2/src/run_retrieval_grounded_fleet.py b/NLP/UNIMO-2/src/run_retrieval_grounded_fleet.py
new file mode 100644
index 000000000..533f38540
--- /dev/null
+++ b/NLP/UNIMO-2/src/run_retrieval_grounded_fleet.py
@@ -0,0 +1,326 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: run_retrieval_grounded_fleet.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-10-22 14:26
+Desc: finetuning for image / text retrieval
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import time
+import multiprocessing
+
+os.environ['FLAGS_fraction_of_gpu_memory_to_use'] = "0.98"
+os.environ['FLAGS_sync_nccl_allreduce'] = "1"
+os.environ['FLAGS_eager_delete_tensor_gb'] = "0"
+os.environ['FLAGS_fuse_parameter_memory_size'] = "32"
+os.environ['FLAGS_fuse_parameter_groups_size'] = "50"
+os.environ['FLAGS_check_nan_inf'] = "0"
+
+import paddle
+
+paddle.enable_static()
+import paddle.distributed.fleet as fleet
+import paddle.distributed.fleet.base.role_maker as role_maker
+import numpy as np
+import random
+
+from reader.retrieval_grounded_reader import RetrievalTrainReader, RetrievalTestReader
+from model.unimo_grounded import VlConfig
+from model.roberta_tokenization import GptBpeTokenizer
+from finetune.retrieval_grounded_fleet import create_model, evaluate
+from utils.optimization_fleet import optimization
+from utils.args import print_arguments
+from utils.utils import get_time
+from utils.init import init_pretraining_params, init_checkpoint, check_pretraining_params
+from args.retrieval_grounded_args import parser
+
+args = parser.parse_args()
+
+
+def main(args):
+    """main"""
+    model_config = VlConfig(args.unimo_config_path)
+    model_config['image_size'] = args.image_size
+    model_config["resolution"] = args.resolution
+    model_config['num_codebook'] = args.num_codebook
+    model_config.print_config()
+
+    print("FLAGS_selected_gpus:", os.environ.get('FLAGS_selected_gpus'))
+    place = paddle.CUDAPlace(int(os.environ.get('FLAGS_selected_gpus', 0)))
+
+    dist_strategy = paddle.distributed.fleet.DistributedStrategy()
+    # dist_strategy.without_graph_optimization = True
+    dist_strategy.fuse_all_reduce_ops = True
+
+    exec_strategy = paddle.static.ExecutionStrategy()
+    exec_strategy.num_threads = 4 if args.use_fp16 else 2  # 2 for fp32 4 for fp16
+    exec_strategy.num_iteration_per_drop_scope = min(args.num_iteration_per_drop_scope, args.skip_steps)
+    # exec_strategy.num_iteration_per_run = 10
+    dist_strategy.execution_strategy = exec_strategy
+
+    build_strategy = paddle.static.BuildStrategy()
+    dist_strategy.build_strategy = build_strategy
+
+    if args.use_fp16:
+        dist_strategy.amp = True
+        dist_strategy.amp_configs = {
+            "init_loss_scaling": args.init_loss_scaling,
+            "use_dynamic_loss_scaling": args.use_dynamic_loss_scaling,
+            "custom_black_list": ['layer_norm', 'arg_max', 'argmax'],
+            "custom_black_varnames": ["loss"]
+        }
+
+    if args.use_recompute:
+        print("using recompute.")
+        dist_strategy.recompute = True
+        build_strategy.enable_sequential_execution = True
+
+    role = role_maker.PaddleCloudRoleMaker(is_collective=True)
+    fleet.init(role_maker=role, strategy=dist_strategy)
+
+    worker_num = fleet.worker_num()
+    worker_index = fleet.worker_index()
+    print("worker_num %d" % worker_num)
+    print("worker_index %d" % worker_index)
+
+    tokenizer = GptBpeTokenizer(vocab_file=args.unimo_vocab_file,
+                                encoder_json_file=args.encoder_json_file,
+                                vocab_bpe_file=args.vocab_bpe_file,
+                                do_lower_case=args.do_lower_case)
+
+    if not (args.do_train or args.do_val or args.do_test):
+        raise ValueError("For args `do_train`, `do_val`, `do_test`, at "
+                         "least one of them must be True.")
+
+    startup_prog = paddle.static.Program()
+    if args.random_seed is not None:
+        startup_prog.random_seed = args.random_seed
+        random.seed(args.random_seed)
+        np.random.seed(args.random_seed)
+        paddle.seed(args.random_seed)
+
+    if args.do_train:
+        train_data_reader = RetrievalTrainReader(tokenizer=tokenizer,
+                                                 args=args,
+                                                 image_caption=args.train_image_caption,
+                                                 image_size=args.image_size,
+                                                 resolution=args.resolution)
+        train_data_generator = train_data_reader.data_generator()
+        num_train_examples, captions_num, image_num = train_data_reader.get_num_examples()
+        step_num_per_epoch = num_train_examples // args.batch_size // worker_num
+        max_train_steps = args.epoch * step_num_per_epoch
+        args.learning_rate_decay_step1 = args.learning_rate_decay_epoch1 * step_num_per_epoch
+        args.learning_rate_decay_step2 = args.learning_rate_decay_epoch2 * step_num_per_epoch
+
+        print("Num train examples: %d" % num_train_examples)
+        print("Max train steps: %d" % max_train_steps)
+
+        train_program = paddle.static.Program()
+        lr_boundaries = [args.learning_rate_decay_step1, args.learning_rate_decay_step2]
+        lr_value = [args.learning_rate * args.learning_rate_scale ** i for i in range(len(lr_boundaries) + 1)]
+
+        with paddle.static.program_guard(train_program, startup_prog):
+            train_pyreader, graph_vars, checkpoints = create_model(
+                args,
+                phase='train',
+                vl_config=model_config,
+                samples_num=args.samples_num + 1)
+
+            if args.use_recompute:
+                dist_strategy.recompute_configs = {"checkpoints": checkpoints}
+
+            scheduled_lr = optimization(
+                loss=graph_vars["loss"],
+                warmup_steps=args.warmup_step,
+                num_train_steps=max_train_steps,
+                learning_rate=args.learning_rate,
+                train_program=train_program,
+                weight_decay=args.weight_decay,
+                scheduler=args.lr_scheduler,
+                beta1=args.beta1,
+                beta2=args.beta2,
+                epsilon=args.epsilon,
+                dist_strategy=dist_strategy,
+                boundaries=lr_boundaries,
+                values=lr_value)
+
+    if args.do_val or args.do_test:
+        test_prog = paddle.static.Program()
+        with paddle.static.program_guard(test_prog, startup_prog):
+            test_pyreader, test_graph_vars, _ = create_model(
+                args,
+                phase='dev',
+                vl_config=model_config,
+                samples_num=1)
+        test_prog = test_prog.clone(for_test=True)
+
+        if args.do_val:
+            dev_data_reader = RetrievalTestReader(tokenizer=tokenizer,
+                                                  args=args,
+                                                  image_caption=args.dev_image_caption,
+                                                  image_size=args.image_size,
+                                                  resolution=args.resolution)
+            dev_data_generator = dev_data_reader.data_generator()
+        if args.do_test:
+            test_data_reader = RetrievalTestReader(tokenizer=tokenizer,
+                                                   args=args,
+                                                   image_caption=args.dev_image_caption,
+                                                   image_size=args.image_size,
+                                                   resolution=args.resolution)
+            test_data_generator = test_data_reader.data_generator()
+
+    exe = paddle.static.Executor(place)
+    exe.run(startup_prog)
+
+    if args.do_train:
+        if not args.run_random:
+            if args.init_checkpoint and args.init_pretraining_params:
+                print(
+                    "WARNING: args 'init_checkpoint' and 'init_pretraining_params' "
+                    "both are set! Only arg 'init_checkpoint' is made valid.")
+            if args.init_checkpoint:
+                check_pretraining_params(args.init_checkpoint, train_program)
+                init_checkpoint(
+                    exe,
+                    args.init_checkpoint,
+                    main_program=train_program)
+            elif args.init_pretraining_params:
+                check_pretraining_params(args.init_pretraining_params, train_program)
+                init_pretraining_params(
+                    exe,
+                    args.init_pretraining_params,
+                    main_program=train_program)
+    elif args.do_val or args.do_test:
+        args.init_checkpoint = args.init_pretraining_params
+        if not args.init_checkpoint:
+            raise ValueError("args 'init_checkpoint' should be set if"
+                             "only doing validation or testing!")
+        check_pretraining_params(args.init_checkpoint, test_prog)
+        init_checkpoint(
+            exe,
+            args.init_checkpoint,
+            main_program=test_prog)
+
+    if args.do_train:
+        train_exe = paddle.static.ParallelExecutor(
+            use_cuda=args.use_cuda,
+            loss_name=graph_vars["loss"].name,
+            build_strategy=build_strategy,
+            exec_strategy=exec_strategy,
+            main_program=train_program,
+            num_trainers=worker_num,
+            trainer_id=worker_index)
+    else:
+        train_exe = None
+
+    if args.do_val or args.do_test:
+        test_exe = paddle.static.ParallelExecutor(use_cuda=args.use_cuda,
+                                                  main_program=test_prog,
+                                                  share_vars_from=train_exe)
+
+    dev_ret_history = []  # (steps, key_eval, eval)
+    test_ret_history = []  # (steps, key_eval, eval)
+    steps = 0
+    if args.do_train:
+        train_pyreader.set_batch_generator(train_data_generator, places=place)
+        train_pyreader.start()
+        time_begin = time.time()
+        skip_steps = args.skip_steps
+        while True:
+            try:
+                scheduled_lr.step(steps)
+                if steps % skip_steps == 0:
+                    train_fetch_list = [graph_vars["loss"].name]
+                    res = train_exe.run(fetch_list=train_fetch_list)
+                    outputs = {"loss": np.mean(res[0]), 'learning_rate': float(scheduled_lr.get_lr())}
+                    if args.verbose:
+                        verbose = "train pyreader queue size: %d, learning_rate: %.10f" % \
+                                  (train_pyreader.queue.size(), outputs['learning_rate'])
+                        print(verbose)
+                    current_example, current_epoch = train_data_reader.get_train_progress()
+                    time_end = time.time()
+                    used_time = time_end - time_begin
+                    print("%s - epoch: %d, progress: %d/%d, step: %d, ave loss: %f, speed: %f steps/s" %
+                          (get_time(), current_epoch, current_example, num_train_examples,
+                           steps, outputs["loss"], args.skip_steps / used_time))
+                    time_begin = time.time()
+                else:
+                    train_exe.run(fetch_list=[])
+
+                if worker_index == 0:
+                    if steps % args.save_steps == 0 and args.save_checkpoints and steps > 0:
+                        save_path = os.path.join(args.checkpoints, "step_" + str(steps))
+                        paddle.fluid.io.save_persistables(exe, save_path, train_program)
+
+                if steps % args.validation_steps == 0:
+                    # evaluate dev set
+                    if args.do_val:
+                        test_pyreader.set_batch_generator(dev_data_generator, places=place)
+                        outputs = evaluate(args, test_exe, test_prog, test_pyreader, test_graph_vars, "dev",
+                                           worker_num, worker_index, data_reader=dev_data_reader)
+                        if worker_index == 0:
+                            dev_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']]))
+
+                    # evaluate test set
+                    if args.do_test:
+                        test_pyreader.set_batch_generator(test_data_generator, places=place)
+                        outputs = evaluate(args, test_exe, test_prog, test_pyreader, test_graph_vars, "test",
+                                           worker_num, worker_index, data_reader=test_data_reader)
+                        if worker_index == 0:
+                            test_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']]))
+
+                steps += 1
+
+            except paddle.fluid.core.EOFException:
+                if args.save_checkpoints:
+                    save_path = os.path.join(args.checkpoints, "step_" + str(steps))
+                    paddle.fluid.io.save_persistables(exe, save_path, train_program)
+                train_pyreader.reset()
+                break
+
+    # final eval on dev set
+    if args.do_val:
+        test_pyreader.set_batch_generator(dev_data_generator, places=place)
+        if worker_index == 0:
+            print("Final validation result:")
+        outputs = evaluate(args, test_exe, test_prog, test_pyreader, test_graph_vars, "dev",
+                           worker_num, worker_index, data_reader=dev_data_reader)
+
+        if worker_index == 0:
+            dev_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']]))
+            dev_ret_history = sorted(dev_ret_history, key=lambda a: a[2], reverse=True)
+            print("Best validation result: step %d %s %f" %
+                  (dev_ret_history[0][0], dev_ret_history[0][1], dev_ret_history[0][2]))
+
+    # final eval on test set
+    if args.do_test:
+        test_pyreader.set_batch_generator(test_data_generator, places=place)
+        if worker_index == 0:
+            print("Final test result:")
+        outputs = evaluate(args, test_exe, test_prog, test_pyreader, test_graph_vars, "test",
+                           worker_num, worker_index, data_reader=test_data_reader)
+
+
+if __name__ == '__main__':
+    print_arguments(args)
+    main(args)
diff --git a/NLP/UNIMO-2/src/utils/__init__.py b/NLP/UNIMO-2/src/utils/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/NLP/UNIMO-2/src/utils/args.py b/NLP/UNIMO-2/src/utils/args.py
new file mode 100644
index 000000000..b875279ca
--- /dev/null
+++ b/NLP/UNIMO-2/src/utils/args.py
@@ -0,0 +1,57 @@
+#   Copyright (c) 2019 PaddlePaddle Authors. 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.
+"""Arguments for configuration."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import six
+import argparse
+
+
+def str2bool(v):
+    # because argparse does not support to parse "true, False" as python
+    # boolean directly
+    return v.lower() in ("true", "t", "1")
+
+
+class ArgumentGroup(object):
+    def __init__(self, parser, title, des):
+        self._group = parser.add_argument_group(title=title, description=des)
+
+    def add_arg(self, name, type, default, help, positional_arg=False, **kwargs):
+        prefix = "" if positional_arg else "--"
+        type = str2bool if type == bool else type
+        self._group.add_argument(
+            prefix + name,
+            default=default,
+            type=type,
+            help=help + ' Default: %(default)s.',
+            **kwargs)
+
+
+def print_arguments(args):
+    print('-----------  Configuration Arguments -----------')
+    for arg, value in vars(args).items():
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------------')
+
+
+def inv_arguments(args):
+    print('[Warning] Only keyword argument type is supported.')
+    args_list = []
+    for arg, value in vars(args).items():
+        args_list.extend(['--' + str(arg), str(value)])
+    return args_list
diff --git a/NLP/UNIMO-2/src/utils/bleu.py b/NLP/UNIMO-2/src/utils/bleu.py
new file mode 100644
index 000000000..f834483cb
--- /dev/null
+++ b/NLP/UNIMO-2/src/utils/bleu.py
@@ -0,0 +1,107 @@
+# Copyright 2017 Google Inc. 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.
+# ==============================================================================
+
+"""Python implementation of BLEU and smooth-BLEU.
+This module provides a Python implementation of BLEU and smooth-BLEU.
+Smooth BLEU is computed following the method outlined in the paper:
+Chin-Yew Lin, Franz Josef Och. ORANGE: a method for evaluating automatic
+evaluation metrics for machine translation. COLING 2004.
+"""
+
+import collections
+import math
+
+
+def _get_ngrams(segment, max_order):
+  """Extracts all n-grams upto a given maximum order from an input segment.
+  Args:
+    segment: text segment from which n-grams will be extracted.
+    max_order: maximum length in tokens of the n-grams returned by this
+        methods.
+  Returns:
+    The Counter containing all n-grams upto max_order in segment
+    with a count of how many times each n-gram occurred.
+  """
+  ngram_counts = collections.Counter()
+  for order in range(1, max_order + 1):
+    for i in range(0, len(segment) - order + 1):
+      ngram = tuple(segment[i:i+order])
+      ngram_counts[ngram] += 1
+  return ngram_counts
+
+
+def compute_bleu(reference_corpus, translation_corpus, max_order=4,
+                 smooth=False):
+  """Computes BLEU score of translated segments against one or more references.
+  Args:
+    reference_corpus: list of lists of references for each translation. Each
+        reference should be tokenized into a list of tokens.
+    translation_corpus: list of translations to score. Each translation
+        should be tokenized into a list of tokens.
+    max_order: Maximum n-gram order to use when computing BLEU score.
+    smooth: Whether or not to apply Lin et al. 2004 smoothing.
+  Returns:
+    3-Tuple with the BLEU score, n-gram precisions, geometric mean of n-gram
+    precisions and brevity penalty.
+  """
+  matches_by_order = [0] * max_order
+  possible_matches_by_order = [0] * max_order
+  reference_length = 0
+  translation_length = 0
+  for (references, translation) in zip(reference_corpus,
+                                       translation_corpus):
+    reference_length += min(len(r) for r in references)
+    translation_length += len(translation)
+
+    merged_ref_ngram_counts = collections.Counter()
+    for reference in references:
+      merged_ref_ngram_counts |= _get_ngrams(reference, max_order)
+    translation_ngram_counts = _get_ngrams(translation, max_order)
+    overlap = translation_ngram_counts & merged_ref_ngram_counts
+    for ngram in overlap:
+      matches_by_order[len(ngram)-1] += overlap[ngram]
+    for order in range(1, max_order+1):
+      possible_matches = len(translation) - order + 1
+      if possible_matches > 0:
+        possible_matches_by_order[order-1] += possible_matches
+
+  precisions = [0] * max_order
+  for i in range(0, max_order):
+    if smooth:
+      precisions[i] = ((matches_by_order[i] + 1.) /
+                       (possible_matches_by_order[i] + 1.))
+    else:
+      if possible_matches_by_order[i] > 0:
+        precisions[i] = (float(matches_by_order[i]) /
+                         possible_matches_by_order[i])
+      else:
+        precisions[i] = 0.0
+
+  if min(precisions) > 0:
+    p_log_sum = sum((1. / max_order) * math.log(p) for p in precisions)
+    geo_mean = math.exp(p_log_sum)
+  else:
+    geo_mean = 0
+
+  ratio = float(translation_length) / reference_length
+
+  if ratio > 1.0:
+    bp = 1.
+  else:
+    bp = math.exp(1 - 1. / (ratio + 1e-4))
+
+  bleu = geo_mean * bp
+
+  return (bleu, precisions, bp, ratio, translation_length, reference_length)
diff --git a/NLP/UNIMO-2/src/utils/cmrc2018_eval.py b/NLP/UNIMO-2/src/utils/cmrc2018_eval.py
new file mode 100644
index 000000000..f9b07dbf5
--- /dev/null
+++ b/NLP/UNIMO-2/src/utils/cmrc2018_eval.py
@@ -0,0 +1,153 @@
+# -*- coding: utf-8 -*-
+'''
+Evaluation script for CMRC 2018
+version: v5
+Note: 
+v5 formatted output, add usage description
+v4 fixed segmentation issues
+'''
+
+
+from collections import Counter, OrderedDict
+import string
+import re
+import argparse
+import json
+import sys
+import nltk
+import pdb
+
+
+# split Chinese with English
+def mixed_segmentation(in_str, rm_punc=False):
+    in_str = str(in_str).decode('utf-8').lower().strip()
+    segs_out = []
+    temp_str = ""
+    sp_char = [
+        '-', ':', '_', '*', '^', '/', '\\', '~', '`', '+', '=', '，', '。', '：',
+        '？', '！', '“', '”', '；', '’', '《', '》', '……', '·', '、', '「', '」', '（',
+        '）', '－', '～', '『', '』'
+    ]
+    for char in in_str:
+        if rm_punc and char in sp_char:
+            continue
+        if re.search(ur'[\u4e00-\u9fa5]', char) or char in sp_char:
+            if temp_str != "":
+                ss = nltk.word_tokenize(temp_str)
+                segs_out.extend(ss)
+                temp_str = ""
+            segs_out.append(char)
+        else:
+            temp_str += char
+
+    #handling last part
+    if temp_str != "":
+        ss = nltk.word_tokenize(temp_str)
+        segs_out.extend(ss)
+
+    return segs_out
+
+
+# remove punctuation
+def remove_punctuation(in_str):
+    in_str = str(in_str).decode('utf-8').lower().strip()
+    sp_char = [
+        '-', ':', '_', '*', '^', '/', '\\', '~', '`', '+', '=', '，', '。', '：',
+        '？', '！', '“', '”', '；', '’', '《', '》', '……', '·', '、', '「', '」', '（',
+        '）', '－', '～', '『', '』'
+    ]
+    out_segs = []
+    for char in in_str:
+        if char in sp_char:
+            continue
+        else:
+            out_segs.append(char)
+    return ''.join(out_segs)
+
+
+# find longest common string
+def find_lcs(s1, s2):
+    m = [[0 for i in range(len(s2) + 1)] for j in range(len(s1) + 1)]
+    mmax = 0
+    p = 0
+    for i in range(len(s1)):
+        for j in range(len(s2)):
+            if s1[i] == s2[j]:
+                m[i + 1][j + 1] = m[i][j] + 1
+                if m[i + 1][j + 1] > mmax:
+                    mmax = m[i + 1][j + 1]
+                    p = i + 1
+    return s1[p - mmax:p], mmax
+
+
+#
+def evaluate(ground_truth_file, prediction_file):
+    f1 = 0
+    em = 0
+    total_count = 0
+    skip_count = 0
+    for instances in ground_truth_file["data"]:
+        for instance in instances["paragraphs"]:
+            context_text = instance['context'].strip()
+            for qas in instance['qas']:
+                total_count += 1
+                query_id = qas['id'].strip()
+                query_text = qas['question'].strip()
+                answers = [ans["text"] for ans in qas["answers"]]
+
+                if query_id not in prediction_file:
+                    sys.stderr.write('Unanswered question: {}\n'.format(
+                        query_id))
+                    skip_count += 1
+                    continue
+
+                prediction = str(prediction_file[query_id])
+                f1 += calc_f1_score(answers, prediction)
+                em += calc_em_score(answers, prediction)
+
+    f1_score = 100.0 * f1 / total_count
+    em_score = 100.0 * em / total_count
+    return f1_score, em_score, total_count, skip_count
+
+
+def calc_f1_score(answers, prediction):
+    f1_scores = []
+    for ans in answers:
+        ans_segs = mixed_segmentation(ans, rm_punc=True)
+        prediction_segs = mixed_segmentation(prediction, rm_punc=True)
+        lcs, lcs_len = find_lcs(ans_segs, prediction_segs)
+        if lcs_len == 0:
+            f1_scores.append(0)
+            continue
+        precision = 1.0 * lcs_len / len(prediction_segs)
+        recall = 1.0 * lcs_len / len(ans_segs)
+        f1 = (2 * precision * recall) / (precision + recall)
+        f1_scores.append(f1)
+    return max(f1_scores)
+
+
+def calc_em_score(answers, prediction):
+    em = 0
+    for ans in answers:
+        ans_ = remove_punctuation(ans)
+        prediction_ = remove_punctuation(prediction)
+        if ans_ == prediction_:
+            em = 1
+            break
+    return em
+
+
+def eval_file(dataset_file, prediction_file):
+    ground_truth_file = json.load(open(dataset_file, 'rb'))
+    prediction_file = json.load(open(prediction_file, 'rb'))
+    F1, EM, TOTAL, SKIP = evaluate(ground_truth_file, prediction_file)
+    AVG = (EM + F1) * 0.5
+    return EM, F1, AVG, TOTAL
+
+
+if __name__ == '__main__':
+    EM, F1, AVG, TOTAL = eval_file(sys.argv[1], sys.argv[2])
+    print(EM)
+    print(F1)
+    print(TOTAL)
+
diff --git a/NLP/UNIMO-2/src/utils/extract_eval_res.py b/NLP/UNIMO-2/src/utils/extract_eval_res.py
new file mode 100644
index 000000000..bccc1f55d
--- /dev/null
+++ b/NLP/UNIMO-2/src/utils/extract_eval_res.py
@@ -0,0 +1,64 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2019 Baidu.com, Inc. All Rights Reserved
+#
+
+"""
+File: extract_eval_res.py
+Author: liwei(liwei85@baidu.com)
+Date: 2020-07-26 16:34
+Desc:
+"""
+
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import sys
+import os
+import re
+import argparse
+from args import ArgumentGroup
+
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+model_g = ArgumentGroup(parser, "stat", "stat configuration")
+model_g.add_arg("log_dir", str, None, "stat log dir")
+model_g.add_arg("file_name", str, "job.log.0", "key words indentify log file")
+model_g.add_arg("final_res_file", str, "final_res.txt", "the file to save final stat score")
+args = parser.parse_args()
+
+
+def extract_res(infile):
+    res = []
+    with open(infile) as fr:
+        for line in fr.readlines():
+            line = line.strip('\r\n')
+            pattern = re.compile(r'\[\w+_step_\d+ evaluation\]')  # 用于匹配至少一个数字
+            if pattern.match(line):  # 查找头部，没有匹配
+                res.append(line)
+    return res
+
+
+eval_res = {}
+log_file = os.path.join(args.log_dir, args.file_name)
+if os.path.exists(log_file):
+    eval_res[args.file_name] = extract_res(log_file)
+else:
+    for sub_dir in os.listdir(args.log_dir):
+        cur_log_dir = os.path.join(args.log_dir, sub_dir)
+        log_file = os.path.join(cur_log_dir, args.file_name)
+        if os.path.exists(log_file):
+            res = extract_res(log_file)
+            eval_res[sub_dir] = res
+
+
+sys.stdout = open(os.path.join(args.log_dir, args.final_res_file), 'w')
+for name, all_res in eval_res.items():
+    print(name)
+    for val in all_res:
+        print(val)
+    print('\n')
diff --git a/NLP/UNIMO-2/src/utils/fp16.py b/NLP/UNIMO-2/src/utils/fp16.py
new file mode 100644
index 000000000..29598c320
--- /dev/null
+++ b/NLP/UNIMO-2/src/utils/fp16.py
@@ -0,0 +1,204 @@
+#   Copyright (c) 2019 PaddlePaddle Authors. 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 __future__ import print_function
+import paddle
+
+def append_cast_op(i, o, prog):
+    """
+    Append a cast op in a given Program to cast input `i` to data type `o.dtype`.
+    Args:
+        i (Variable): The input Variable.
+        o (Variable): The output Variable.
+        prog (Program): The Program to append cast op.
+    """
+    prog.global_block().append_op(
+        type="cast",
+        inputs={"X": i},
+        outputs={"Out": o},
+        attrs={"in_dtype": i.dtype,
+               "out_dtype": o.dtype})
+
+
+def copy_to_master_param(p, block):
+    v = block.vars.get(p.name, None)
+    if v is None:
+        raise ValueError("no param name %s found!" % p.name)
+    new_p = paddle.fluid.framework.Parameter(
+        block=block,
+        shape=v.shape,
+        dtype=paddle.fluid.core.VarDesc.VarType.FP32,
+        type=v.type,
+        lod_level=v.lod_level,
+        stop_gradient=p.stop_gradient,
+        trainable=p.trainable,
+        optimize_attr=p.optimize_attr,
+        regularizer=p.regularizer,
+        gradient_clip_attr=p.gradient_clip_attr,
+        error_clip=p.error_clip,
+        name=v.name + ".master")
+    return new_p
+
+
+def apply_dynamic_loss_scaling(loss_scaling, master_params_grads,
+                               incr_every_n_steps, decr_every_n_nan_or_inf,
+                               incr_ratio, decr_ratio):
+    _incr_every_n_steps = paddle.full(
+        shape=[1], fill_value=incr_every_n_steps)
+    _decr_every_n_nan_or_inf = paddle.full(
+        shape=[1], fill_value=decr_every_n_nan_or_inf)
+
+    _num_good_steps = paddle.fluid.layers.create_global_var(
+        name=paddle.utils.unique_name.generate("num_good_steps"),
+        shape=[1],
+        value=0,
+        dtype='int32',
+        persistable=True)
+    _num_bad_steps = paddle.fluid.layers.create_global_var(
+        name=paddle.utils.unique_name.generate("num_bad_steps"),
+        shape=[1],
+        value=0,
+        dtype='int32',
+        persistable=True)
+
+    grads = [paddle.sum(x=g) for [_, g] in master_params_grads]
+    all_grads = paddle.concat(x=grads)
+    all_grads_sum = paddle.sum(x=all_grads)
+    is_overall_finite = paddle.isfinite(all_grads_sum)
+
+    update_loss_scaling(is_overall_finite, loss_scaling, _num_good_steps,
+                        _num_bad_steps, _incr_every_n_steps,
+                        _decr_every_n_nan_or_inf, incr_ratio, decr_ratio)
+
+    # apply_gradient append all ops in global block, thus we shouldn't
+    # apply gradient in the switch branch.
+    with paddle.fluid.layers.Switch() as switch:
+        with switch.case(is_overall_finite):
+            pass
+        with switch.default():
+            for _, g in master_params_grads:
+                paddle.assign(paddle.zeros_like(x=g), g)
+
+
+def create_master_params_grads(params_grads, main_prog, startup_prog,
+                               loss_scaling):
+    master_params_grads = []
+    for p, g in params_grads:
+        with main_prog._optimized_guard([p, g]):
+        # create master parameters
+            # cast fp16->fp32 in main_prog
+            master_param = copy_to_master_param(p, main_prog.global_block())
+            startup_master_param = startup_prog.global_block()._clone_variable(
+                master_param)
+            startup_p = startup_prog.global_block().var(p.name)
+            # cast fp16->fp32 in startup_prog
+            append_cast_op(startup_p, startup_master_param, startup_prog)
+            # cast fp16 gradients to fp32 before apply gradients
+            if g.name.find("layer_norm") > -1:
+                scaled_g = g / loss_scaling
+                master_params_grads.append([p, scaled_g])
+                continue
+            master_grad = paddle.cast(g, "float32")
+            master_grad = master_grad / loss_scaling
+            master_params_grads.append([master_param, master_grad])
+
+    return master_params_grads
+
+
+def master_param_to_train_param(master_params_grads, params_grads, main_prog):
+    for idx, m_p_g in enumerate(master_params_grads):
+        train_p, _ = params_grads[idx]
+        #if train_p.name.find("layer_norm") > -1:
+        #    continue
+        with main_prog._optimized_guard([m_p_g[0], m_p_g[1]]):
+            #append_cast_op(m_p_g[0], train_p, main_prog)
+            if train_p.name.find("layer_norm") > -1:
+                paddle.assign(m_p_g[0], train_p)
+            else:
+                append_cast_op(m_p_g[0], train_p, main_prog)
+
+
+def update_loss_scaling(is_overall_finite, prev_loss_scaling, num_good_steps,
+                        num_bad_steps, incr_every_n_steps,
+                        decr_every_n_nan_or_inf, incr_ratio, decr_ratio):
+    """
+    Update loss scaling according to overall gradients. If all gradients is 
+    finite after incr_every_n_steps, loss scaling will increase by incr_ratio. 
+    Otherwisw, loss scaling will decrease by decr_ratio after 
+    decr_every_n_nan_or_inf steps and each step some gradients are infinite.
+    Args:
+        is_overall_finite (Variable): A boolean variable indicates whether 
+                                     all gradients are finite.
+        prev_loss_scaling (Variable): Previous loss scaling.
+        num_good_steps (Variable): A variable accumulates good steps in which 
+                                   all gradients are finite.
+        num_bad_steps (Variable): A variable accumulates bad steps in which 
+                                  some gradients are infinite.
+        incr_every_n_steps (Variable): A variable represents increasing loss 
+                                       scaling every n consecutive steps with 
+                                       finite gradients.
+        decr_every_n_nan_or_inf (Variable): A variable represents decreasing 
+                                            loss scaling every n accumulated 
+                                            steps with nan or inf gradients.
+        incr_ratio(float): The multiplier to use when increasing the loss 
+                           scaling.
+        decr_ratio(float): The less-than-one-multiplier to use when decreasing 
+                           loss scaling.
+    """
+    zero_steps = paddle.full(shape=[1], fill_value=0)
+    with paddle.fluid.layers.Switch() as switch:
+        with switch.case(is_overall_finite):
+            should_incr_loss_scaling = paddle.less_than(
+                x=incr_every_n_steps, y=num_good_steps + 1)
+            with paddle.fluid.layers.Switch() as switch1:
+                with switch1.case(should_incr_loss_scaling):
+                    new_loss_scaling = prev_loss_scaling * incr_ratio
+                    loss_scaling_is_finite = paddle.isfinite(
+                        new_loss_scaling)
+                    with paddle.fluid.layers.Switch() as switch2:
+                        with switch2.case(loss_scaling_is_finite):
+                            paddle.assign(new_loss_scaling,
+                                                prev_loss_scaling)
+                        with switch2.default():
+                            pass
+                    paddle.assign(zero_steps, num_good_steps)
+                    paddle.assign(zero_steps, num_bad_steps)
+
+                with switch1.default():
+                    paddle.increment(x=num_good_steps)
+                    paddle.assign(zero_steps, num_bad_steps)
+
+        with switch.default():
+            should_decr_loss_scaling = paddle.less_than(
+                x=decr_every_n_nan_or_inf, y=num_bad_steps + 1)
+            with paddle.fluid.layers.Switch() as switch3:
+                with switch3.case(should_decr_loss_scaling):
+                    new_loss_scaling = prev_loss_scaling * decr_ratio
+                    static_loss_scaling = \
+                        paddle.full(shape=[1],
+                                             fill_value=1.0)
+                    less_than_one = paddle.less_than(x=new_loss_scaling,
+                                                           y=static_loss_scaling)
+                    with paddle.fluid.layers.Switch() as switch4:
+                        with switch4.case(less_than_one):
+                            paddle.assign(static_loss_scaling,
+                                                prev_loss_scaling)
+                        with switch4.default():
+                            paddle.assign(new_loss_scaling,
+                                                prev_loss_scaling)
+                    paddle.assign(zero_steps, num_good_steps)
+                    paddle.assign(zero_steps, num_bad_steps)
+                with switch3.default():
+                    paddle.assign(zero_steps, num_good_steps)
+                    paddle.increment(x=num_bad_steps)
diff --git a/NLP/UNIMO-2/src/utils/image_utils.py b/NLP/UNIMO-2/src/utils/image_utils.py
new file mode 100644
index 000000000..b0c72e879
--- /dev/null
+++ b/NLP/UNIMO-2/src/utils/image_utils.py
@@ -0,0 +1,240 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: img2txt_reader.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-06-21 19:27
+Desc: image file reader and normalization
+"""
+
+import cv2
+import numpy as np
+import random
+import paddle
+from PIL import Image, ImageFile, UnidentifiedImageError
+from multiprocessing.pool import ThreadPool
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+
+class ImageReader(object):
+    def __init__(self,
+                 target_shape_h=224,
+                 target_shape_w=224,
+                 data_format="channels_last",
+                 dtype='float32',
+                 mean_value=None,
+                 std_value=None,
+                 with_random_crop=False,
+                 with_random_flip=False):
+        if std_value is None:
+            std_value = [0.5, 0.5, 0.5]
+        if mean_value is None:
+            mean_value = [0.5, 0.5, 0.5]
+
+        if target_shape_h and target_shape_w:
+            self.target_size = (target_shape_h, target_shape_w)
+        else:
+            self.target_size = None
+        self.data_format = data_format
+        self.dtype = dtype
+        assert data_format in {'channels_first', 'channels_last'}
+
+        self.with_random_crop = with_random_crop
+        self.with_random_clip = with_random_flip
+
+        self.mean = np.array(mean_value).astype(self.dtype)
+        self.std = np.array(std_value).astype(self.dtype)
+
+    def _load_img(self, path):
+        '''
+        :param path: img path
+        :return: PIL image, (w, h)
+        '''
+        try:
+            img = Image.open(path)  # (w, h)
+        except UnidentifiedImageError:
+            print("UnidentifiedImageError: ", path)
+            return None
+        except:
+            print("Image.open Fail: ", path)
+            img = cv2.imread(path)
+            img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+
+        if img.mode != 'RGB':
+            img = img.convert('RGB')
+
+        if self.with_random_crop:
+            img = self.random_crop(img, self.target_size)
+
+        if self.with_random_clip:
+            img = self.random_flip(img)
+
+        if self.target_size:
+            if img.size != self.target_size:
+                img = img.resize(self.target_size)
+        return img
+
+    def _img_to_array(self, img):
+        '''
+        :param img: PIL image, (w, h)
+        :return: numpy array, (c, h, w) or (h, w, c)
+        '''
+        x = np.asarray(img, dtype=self.dtype)  # (h, w, c)
+        assert len(x.shape) == 3
+        if self.data_format == 'channels_first':
+            x = x.transpose(2, 0, 1)
+        return x
+
+    def process_image(self, image_path):
+        """Given an image, process it and return the array."""
+        # Load the image.
+        image = self._load_img(image_path)
+        if image is None:
+            return None
+        img_arr = self._img_to_array(image)
+        x = (img_arr / 255.).astype('float32')  #
+        x = (x - self.mean) / self.std  # normalize images
+        return x
+
+    def random_crop(self, img, target_size, p=0.5):
+        """random crop image"""
+        v1 = random.random()
+        if v1 < p:
+            return img
+
+        w, h = img.size
+        th, tw = target_size
+        if w > tw and h > th:
+            x1 = random.randint(0, w - tw)
+            y1 = random.randint(0, h - th)
+            return img.crop((x1, y1, x1 + tw, y1 + th))
+        elif w > tw:
+            x1 = np.random.randint(0, w - tw)
+            return img.crop((x1, 0, x1 + tw, h))
+        elif h > th:
+            y1 = random.randint(0, h - th)
+            return img.crop((0, y1, w, y1 + th))
+        else:
+            return img
+
+    def random_flip(self, img, p=0.5):
+        """
+        :param method: One of :py:data:`PIL.Image.FLIP_LEFT_RIGHT`,
+          :py:data:`PIL.Image.FLIP_TOP_BOTTOM`, :py:data:`PIL.Image.ROTATE_90`,
+          :py:data:`PIL.Image.ROTATE_180`, :py:data:`PIL.Image.ROTATE_270`.
+        """
+        v1 = random.random()
+        if v1 < p:
+            v2 = random.random()
+            if v2 < 0.2:
+                return img.transpose(Image.FLIP_LEFT_RIGHT)
+            elif v2 < 0.4:
+                return img.transpose(Image.FLIP_TOP_BOTTOM)
+            elif v2 < 0.6:
+                return img.transpose(Image.ROTATE_90)
+            elif v2 < 0.8:
+                return img.transpose(Image.ROTATE_180)
+            else:
+                return img.transpose(Image.ROTATE_270)
+        else:
+            return img
+
+
+def process_image(image_id,
+                  image_path,
+                  target_shape_h=224,
+                  target_shape_w=224,
+                  data_format="channels_last",
+                  dtype='float32',
+                  with_random_crop=False,
+                  with_random_flip=False):
+    img_reader = ImageReader(target_shape_h=target_shape_h,
+                             target_shape_w=target_shape_w,
+                             data_format=data_format,
+                             dtype=dtype,
+                             with_random_crop=with_random_crop,
+                             with_random_flip=with_random_flip)
+    x = img_reader.process_image(image_path)
+    return (image_id, x)
+
+
+def process_image_with_multi_proc(images_list, proc_num, func):
+    pool = ThreadPool(processes=proc_num)
+    thread_list = []
+    for image_id, img_path in images_list:
+        out = pool.apply_async(func=func, args=(image_id, img_path,))
+        thread_list.append(out)
+    pool.close()
+    pool.join()
+    results = []
+    for p in thread_list:
+        result = p.get()
+        results.append(result)
+    return results
+
+
+def test_img_reader():
+    img_path = "999999"
+    img_reader = ImageReader()
+    x = img_reader.process_image(img_path)
+    print(x.shape, x)
+
+
+def test_process_image():
+    img_path = "999999"
+    x = process_image(1, img_path)
+    print(x[1].shape, x)
+
+
+def test_process_image_with_multi_proc(img_paths):
+    img_arrs = process_image_with_multi_proc(img_paths, 3, process_image)
+    for i in range(len(img_arrs)):
+        x = img_arrs[i]
+        print(x[1].shape, x)
+        h, w, c = x[1].shape
+        print(x[1].reshape(h * w // (16 * 16), -1).shape)
+        print(h, w, c)
+
+
+def parse_line(line):
+    """ parse one line to token_ids, sentence_ids, pos_ids, label
+    """
+    line = line.strip('\r\n').split(";")
+    (image_id, image_path, caption_id, token_ids, sent_ids, pos_ids, seg_labels, label, image_label) = line
+    return image_id, image_path
+
+
+def read_file(file):
+    image_paths = []
+    with open(file, "r") as f:
+        for line in f:
+            img_id, img_path = parse_line(line)
+            image_paths.append((img_id, img_path))
+
+            if len(image_paths) == 100:
+                process_image_with_multi_proc(image_paths, 20, process_image)
+                image_paths = []
+
+
+if __name__ == "__main__":
+    img_filelist = "data/images-prompt/image_filelist.imagenet21k_openimages"
+    img_files = open(img_filelist).readlines()
+    for img_file in img_files:
+        img_file = img_file.strip()
+        print(img_file)
+        read_file(img_file)
diff --git a/NLP/UNIMO-2/src/utils/init.py b/NLP/UNIMO-2/src/utils/init.py
new file mode 100644
index 000000000..f6bf990b8
--- /dev/null
+++ b/NLP/UNIMO-2/src/utils/init.py
@@ -0,0 +1,141 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. 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 __future__ import print_function
+
+import os
+import six
+import ast
+import copy
+
+import numpy as np
+import paddle
+
+
+def cast_fp32_to_fp16(exe, main_program):
+    print("Cast parameters to float16 data format.")
+    for param in main_program.global_block().all_parameters():
+        if not param.name.endswith(".master"):
+            #load fp16
+            param_t = paddle.static.global_scope().find_var(param.name).get_tensor()
+            data = np.array(param_t)
+            if param.name.startswith("encoder_layer") \
+                    and "layer_norm" not in param.name:
+                print(param.name)
+                param_t.set(np.float16(data).view(np.uint16), exe.place)
+            
+            #load fp32
+            master_param_var = paddle.static.global_scope().find_var(param.name + 
+                    ".master")
+            if master_param_var is not None:
+                master_param_var.get_tensor().set(data, exe.place)
+
+
+def init_checkpoint(exe, init_checkpoint_path, main_program):
+    assert os.path.exists(init_checkpoint_path), \
+        "[%s] cann't be found." % init_checkpoint_path
+    
+    def existed_persitables(var):
+        if not paddle.fluid.io.is_persistable(var):
+            return False
+        return os.path.exists(os.path.join(init_checkpoint_path, var.name))
+
+    paddle.static.load_vars(
+        exe,
+        init_checkpoint_path,
+        main_program=main_program,
+        predicate=existed_persitables)
+    print("Load model from {}".format(init_checkpoint_path))
+
+
+def init_pretraining_params(exe,
+                            pretraining_params_path,
+                            main_program):
+    assert os.path.exists(pretraining_params_path), \
+        "[%s] cann't be found." % pretraining_params_path
+
+    def existed_params(var):
+        if not isinstance(var, paddle.fluid.framework.Parameter):
+            return False
+        return os.path.exists(os.path.join(pretraining_params_path, var.name))
+
+    paddle.static.load_vars(
+        exe,
+        pretraining_params_path,
+        main_program=main_program,
+        predicate=existed_params)
+    print("Load pretraining parameters from {}.".format(
+        pretraining_params_path))
+
+
+def check_pretraining_params(pretraining_params_path, main_program):
+    assert os.path.exists(pretraining_params_path), \
+        "[%s] cann't be found." % pretraining_params_path
+ 
+    vars = main_program.list_vars()
+    param_files = os.listdir(pretraining_params_path)
+    fout = open("check_params.txt", 'w')
+
+    var_names = []
+    # fout.write("All vars:\n")
+    for var in vars:
+        if isinstance(var, paddle.fluid.framework.Parameter):
+            var_names.append(var.name)
+            # fout.write(var.name + '\n')
+    
+    # fout.write("All files:\n")
+    # for filename in param_files:
+    #     fout.write(filename + '\n')
+
+    fout.write("Check vars:\n")
+    for var_name in var_names:
+        if not os.path.exists(os.path.join(pretraining_params_path, var_name)):
+            fout.write("%s is not included by %s \n" % (var_name, pretraining_params_path))
+    fout.write("\n")
+
+    fout.write("Check param_files: \n")
+    for filename in param_files:
+        is_used = False
+        for var_name in var_names:
+            if var_name == filename:
+                is_used = True
+                break
+
+        if not is_used:
+            fout.write("%s is not utilized \n" % filename)
+    fout.close()
+
+
+def init_model(args, exe, startup_prog):
+    init_func, init_path = None, None
+    if args.do_train:
+        if args.init_checkpoint and args.init_pretraining_params:
+            print(
+                "WARNING: args 'init_checkpoint' and 'init_pretraining_params' "
+                "both are set! Only arg 'init_checkpoint' is made valid.")
+        if args.init_checkpoint:
+            init_func = init_checkpoint
+            init_path = args.init_checkpoint
+        elif args.init_pretraining_params:
+            init_func = init_pretraining_params
+            init_path = args.init_pretraining_params
+
+    elif args.do_val or args.do_test or args.do_pred:
+        init_path = args.init_checkpoint or args.init_pretraining_params
+        if not init_path:
+            raise ValueError("args 'init_checkpoint' should be set if"
+                             "only doing validation or testing!")
+        init_func = init_checkpoint
+    if init_path:
+        init_func(exe, init_path, main_program=startup_prog)
diff --git a/NLP/UNIMO-2/src/utils/new_stat_res.py b/NLP/UNIMO-2/src/utils/new_stat_res.py
new file mode 100644
index 000000000..eec74bc70
--- /dev/null
+++ b/NLP/UNIMO-2/src/utils/new_stat_res.py
@@ -0,0 +1,99 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: new_stat_res.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-08-15 15:34
+Desc: eval results statistics
+"""
+
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import sys
+import os
+import numpy as np
+import argparse
+from args import ArgumentGroup
+import codecs
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+model_g = ArgumentGroup(parser, "stat", "stat configuration")
+model_g.add_arg("log_dir", str, None, "stat log dir")
+model_g.add_arg("score_name", str, "avg_recall@1", "random slot in log file")
+model_g.add_arg("key_words", str, "job.log.0", "key words indentify log file")
+model_g.add_arg("line_prefix", str, "[dev evaluation]", "key words indentify scores to stat")
+model_g.add_arg("score_slot", int, -1, "score slot in stat line")
+model_g.add_arg("final_res_file", str, "final_res.txt", "the file to save final stat score")
+
+args = parser.parse_args()
+
+
+def get_all_res(infile, score_name):
+    scores = []
+    step = 0
+    with codecs.open(infile, 'r', encoding='utf-8') as fr:
+        for line in fr.readlines():
+            line = line.strip('\r\n')
+
+            if 'progress:' in line:
+                step = int(line.split(', ')[2].split(': ')[1])
+            elif line.startswith(args.line_prefix):
+                res_list = line.split(', ')
+                for res in res_list:
+                    key, value = res.split(': ')
+                    if key == score_name:
+                        scores.append((step, float(value)))
+                        break
+    return scores
+
+
+def print_stat(score_files):
+    nums = len(score_files)
+    max_score_file, (max_score_step, max_score) = score_files[0]
+    min_score_file, (min_score_step, min_score) = score_files[-1]
+    median_score_file, (median_score_step, median_score) = score_files[int(nums / 2)]
+    mean_score = np.average([score for file, (step, score) in score_files])
+
+    log = '%d\nmax_score %f max_file %s max_score_step %d\nmin_score %f min_file %s min_score_step %d\n' \
+          'median_score %f median_file %s median_score_step %d\navg_score %f' % \
+          (nums, max_score, max_score_file, max_score_step, min_score, min_score_file, min_score_step,
+           median_score, median_score_file, median_score_step, mean_score)
+    print(log)
+
+
+score_file = {}
+for file in os.listdir(args.log_dir):
+    if args.key_words in file:
+        scores = get_all_res(os.path.join(args.log_dir, file), args.score_name)
+        score_file[file] = scores
+
+best_score_file = []
+for file, scores in score_file.items():
+    sorted_scores = sorted(scores, key=lambda a: a[1], reverse=True)
+    if len(sorted_scores) > 0:
+        best_score_file.append((file, sorted_scores[0]))
+    if len(sorted_scores) > 1:
+        best_score_file.append((file, sorted_scores[1]))
+
+best_score_file = sorted(best_score_file, key=lambda a: a[1][1], reverse=True)
+
+sys.stdout = codecs.open(os.path.join(args.log_dir, args.final_res_file), 'w', encoding='utf-8')
+print_stat(best_score_file)
diff --git a/NLP/UNIMO-2/src/utils/optimization.py b/NLP/UNIMO-2/src/utils/optimization.py
new file mode 100644
index 000000000..b11268d0d
--- /dev/null
+++ b/NLP/UNIMO-2/src/utils/optimization.py
@@ -0,0 +1,136 @@
+#   Copyright (c) 2019 PaddlePaddle Authors. 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.
+"""Optimization and learning rate scheduling."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle.fluid as fluid
+
+
+def linear_warmup_decay(learning_rate, warmup_steps, num_train_steps):
+    """ Applies linear warmup of learning rate from 0 and decay to 0."""
+    with fluid.default_main_program()._lr_schedule_guard():
+        lr = fluid.layers.tensor.create_global_var(
+            shape=[1],
+            value=0.0,
+            dtype='float32',
+            persistable=True,
+            name="scheduled_learning_rate")
+
+        global_step = fluid.layers.learning_rate_scheduler._decay_step_counter()
+
+        with fluid.layers.control_flow.Switch() as switch:
+            with switch.case(global_step < warmup_steps):
+                warmup_lr = learning_rate * (global_step / warmup_steps)
+                fluid.layers.tensor.assign(warmup_lr, lr)
+            with switch.default():
+                decayed_lr = fluid.layers.learning_rate_scheduler.polynomial_decay(
+                    learning_rate=learning_rate,
+                    decay_steps=num_train_steps,
+                    end_learning_rate=0.0,
+                    power=1.0,
+                    cycle=False)
+                fluid.layers.tensor.assign(decayed_lr, lr)
+
+        return lr
+
+
+def optimization(loss,
+                 warmup_steps,
+                 num_train_steps,
+                 learning_rate,
+                 train_program,
+                 weight_decay,
+                 scheduler='linear_warmup_decay',
+                 use_fp16=False,
+                 use_dynamic_loss_scaling=False,
+                 init_loss_scaling=1.0,
+                 beta1=0.9,
+                 beta2=0.98,
+                 epsilon=1e-06,
+                 boundaries=None,
+                 values=None):
+    def exclude_from_weight_decay(name):
+        name = name.rstrip('.master')
+        if name.find("layer_norm") > -1:
+            return True
+        bias_suffix = ["_bias", "_b", ".b_0"]
+        for suffix in bias_suffix:
+            if name.endswith(suffix):
+                return True
+        return False
+
+    if warmup_steps > 0:
+        if scheduler == 'noam_decay':
+            scheduled_lr = fluid.layers.learning_rate_scheduler \
+                .noam_decay(1 / (warmup_steps * (learning_rate ** 2)),
+                            warmup_steps)
+        elif scheduler == 'linear_warmup_decay':
+            scheduled_lr = linear_warmup_decay(learning_rate, warmup_steps,
+                                               num_train_steps)
+        elif scheduler == 'scale_by_epoch_decay':
+            if boundaries is None:
+                boundaries = [10000, 20000]
+            if values is None:
+                values = [5e-6, 5e-7, 5e-8]
+            scheduled_lr = fluid.layers.piecewise_decay(boundaries=boundaries, values=values)
+        else:
+            raise ValueError("Unkown learning rate scheduler, should be "
+                             "'noam_decay' or 'linear_warmup_decay'")
+        optimizer = fluid.optimizer.Adam(learning_rate=scheduled_lr, beta1=beta1, beta2=beta2, epsilon=epsilon)
+    else:
+        scheduled_lr = fluid.layers.create_global_var(
+            name=fluid.unique_name.generate("learning_rate"),
+            shape=[1],
+            value=learning_rate,
+            dtype='float32',
+            persistable=True)
+        optimizer = fluid.optimizer.Adam(learning_rate=scheduled_lr, beta1=beta1, beta2=beta2, epsilon=epsilon)
+        optimizer._learning_rate_map[fluid.default_main_program()] = scheduled_lr
+
+    fluid.clip.set_gradient_clip(
+        clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=1.0))
+
+    if use_fp16:
+        optimizer = fluid.contrib.mixed_precision.decorator.decorate(optimizer,
+                                                                     amp_lists=fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
+                                                                         custom_black_varnames={'loss'},
+                                                                         custom_black_list={'layer_norm', 'arg_max', 'argmax'}),
+                                                                     init_loss_scaling=init_loss_scaling,
+                                                                     use_dynamic_loss_scaling=use_dynamic_loss_scaling)
+        _, param_grads = optimizer.minimize(loss)
+        loss_scaling = optimizer.get_loss_scaling()
+    else:
+        _, param_grads = optimizer.minimize(loss)
+        loss_scaling = None
+
+    param_list = dict()
+
+    for param in train_program.global_block().all_parameters():
+        param_list[param.name] = param * 1.0
+        param_list[param.name].stop_gradient = True
+
+    if weight_decay > 0:
+        for param, grad in param_grads:
+            if exclude_from_weight_decay(param.name):
+                continue
+            with param.block.program._optimized_guard(
+                    [param, grad]), fluid.framework.name_scope("weight_decay"):
+                updated_param = param - param_list[
+                    param.name] * weight_decay * scheduled_lr
+                fluid.layers.assign(output=param, input=updated_param)
+
+    return scheduled_lr, loss_scaling
diff --git a/NLP/UNIMO-2/src/utils/optimization_fleet.py b/NLP/UNIMO-2/src/utils/optimization_fleet.py
new file mode 100644
index 000000000..90b0e5cc4
--- /dev/null
+++ b/NLP/UNIMO-2/src/utils/optimization_fleet.py
@@ -0,0 +1,106 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: optimization_fleet.py.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-10-25 11:25
+Desc: Optimization and learning rate scheduling.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import paddle
+import paddle.distributed.fleet as fleet
+
+
+def optimization(loss,
+                 warmup_steps,
+                 num_train_steps,
+                 learning_rate,
+                 train_program,
+                 weight_decay,
+                 scheduler='linear_warmup_decay',
+                 beta1=0.9,
+                 beta2=0.98,
+                 epsilon=1e-06,
+                 boundaries=None,
+                 values=None,
+                 dist_strategy=None):
+    if warmup_steps > 0:
+        if scheduler == 'noam_decay':
+            scheduled_lr = paddle.optimizer.lr.NoamDecay(
+                1 / (warmup_steps * (learning_rate ** 2)), warmup_steps)
+        elif scheduler == 'linear_warmup_decay':
+            scheduled_lr = paddle.optimizer.lr.LinearWarmup(
+                learning_rate=paddle.optimizer.lr.PolynomialDecay(learning_rate=learning_rate,
+                                                                  decay_steps=num_train_steps,
+                                                                  end_lr=0.0,
+                                                                  power=1.0,
+                                                                  cycle=False),
+                warmup_steps=warmup_steps,
+                start_lr=0,
+                end_lr=learning_rate)
+        elif scheduler == 'scale_by_epoch_decay':
+            if boundaries is None:
+                boundaries = [10000, 20000]
+            if values is None:
+                values = [5e-6, 5e-7, 5e-8]
+            scheduled_lr = paddle.optimizer.lr.PiecewiseDecay(boundaries=boundaries, values=values)
+        else:
+            raise ValueError("Unkown learning rate scheduler, should be "
+                             "'noam_decay' or 'linear_warmup_decay'")
+    else:
+        scheduled_lr = paddle.optimizer.lr.LRScheduler(learning_rate=learning_rate)
+
+    # FIXME: added here
+    def exclude_from_weight_decay(name):
+        name = name.rstrip('.master')
+        if name.find("layer_norm") > -1:
+            return False
+        bias_suffix = ["_bias", "_b", ".b_0"]
+        for suffix in bias_suffix:
+            if name.endswith(suffix):
+                return False
+        return True
+
+    optimizer = paddle.fluid.optimizer.Adam(learning_rate=scheduled_lr,
+                                            beta1=beta1,
+                                            beta2=beta2,
+                                            epsilon=epsilon,
+                                            grad_clip=paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0))
+
+    optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy)
+
+    # param_list = dict()
+    # for param in train_program.global_block().all_parameters():
+    #     param_list[param.name] = param * 1.0
+    #     param_list[param.name].stop_gradient = True
+
+    _, param_grads = optimizer.minimize(loss, paddle.fluid.default_startup_program())
+
+    # if weight_decay > 0:
+    #     for param, grad in param_grads:
+    #         if exclude_from_weight_decay(param.name):
+    #             continue
+    #         with param.block.program._optimized_guard(
+    #                 [param, grad]), paddle.fluid.framework.name_scope("weight_decay"):
+    #             updated_param = param - param_list[
+    #                 param.name] * weight_decay * scheduled_lr.get_lr()
+    #             paddle.fluid.layers.assign(output=param, input=updated_param)
+
+    return scheduled_lr
diff --git a/NLP/UNIMO-2/src/utils/optimization_fleet_new.py b/NLP/UNIMO-2/src/utils/optimization_fleet_new.py
new file mode 100644
index 000000000..7d55a6b8d
--- /dev/null
+++ b/NLP/UNIMO-2/src/utils/optimization_fleet_new.py
@@ -0,0 +1,92 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: optimization_fleet_new.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-06-02 20:16
+Desc: Optimization and learning rate scheduling.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import paddle
+import paddle.distributed.fleet as fleet
+
+
+def optimization(loss,
+                 warmup_steps,
+                 num_train_steps,
+                 learning_rate,
+                 train_program,
+                 weight_decay,
+                 scheduler='linear_warmup_decay',
+                 beta1=0.9,
+                 beta2=0.98,
+                 epsilon=1e-06,
+                 boundaries=None,
+                 values=None,
+                 dist_strategy=None):
+
+    if warmup_steps > 0:
+        if scheduler == 'noam_decay':
+            scheduled_lr = paddle.optimizer.lr.NoamDecay(
+                1 / (warmup_steps * (learning_rate ** 2)), warmup_steps)
+        elif scheduler == 'linear_warmup_decay':
+            scheduled_lr = paddle.optimizer.lr.LinearWarmup(
+                learning_rate=paddle.optimizer.lr.PolynomialDecay(learning_rate=learning_rate,
+                                                                  decay_steps=num_train_steps,
+                                                                  end_lr=0.0,
+                                                                  power=1.0,
+                                                                  cycle=False),
+                warmup_steps=warmup_steps,
+                start_lr=0,
+                end_lr=learning_rate)
+        elif scheduler == 'scale_by_epoch_decay':
+            if boundaries is None:
+                boundaries = [10000, 20000]
+            if values is None:
+                values = [5e-6, 5e-7, 5e-8]
+            scheduled_lr = paddle.optimizer.lr.PiecewiseDecay(boundaries=boundaries, values=values)
+        else:
+            raise ValueError("Unkown learning rate scheduler, should be "
+                             "'noam_decay' or 'linear_warmup_decay'")
+    else:
+        scheduled_lr = paddle.optimizer.lr.LRScheduler(learning_rate=learning_rate)
+
+    # FIXME: added here
+    def exclude_from_weight_decay(name):
+        name = name.rstrip('.master')
+        if name.find("layer_norm") > -1:
+            return False
+        bias_suffix = ["_bias", "_b", ".b_0"]
+        for suffix in bias_suffix:
+            if name.endswith(suffix):
+                return False
+        return True
+
+    optimizer = paddle.optimizer.AdamW(learning_rate=scheduled_lr,
+                                       beta1=beta1,
+                                       beta2=beta2,
+                                       epsilon=epsilon,
+                                       grad_clip=paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0),
+                                       weight_decay=weight_decay,
+                                       apply_decay_param_fun=exclude_from_weight_decay)
+
+    optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy)
+    optimizer.minimize(loss)
+    return scheduled_lr
diff --git a/NLP/UNIMO-2/src/utils/pos_emb_interpolate.py b/NLP/UNIMO-2/src/utils/pos_emb_interpolate.py
new file mode 100644
index 000000000..e497a18a3
--- /dev/null
+++ b/NLP/UNIMO-2/src/utils/pos_emb_interpolate.py
@@ -0,0 +1,50 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2021 PaddlePaddle Authors. 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.
+
+"""
+File: pos_emb_interpolate.py
+Author: liwei(liwei85@baidu.com)
+Date: 2021-10-15 15:02
+Desc:
+"""
+import paddle
+
+
+def interpolate_pos_embed(pos_embed_checkpoint, num_patches):
+    # interpolate position embedding
+    embedding_size = pos_embed_checkpoint.shape[-1]
+    # height (== width) for the checkpoint position embedding
+    orig_size = int((pos_embed_checkpoint.shape[-2] - 1) ** 0.5)
+    # height (== width) for the new position embedding
+    new_size = int(num_patches ** 0.5)
+
+    if orig_size != new_size:
+        # class_token are kept unchanged
+        extra_tokens = pos_embed_checkpoint[:, :1, :]
+        # only the position tokens are interpolated
+        pos_tokens = pos_embed_checkpoint[:, 1:, :]
+        pos_tokens = paddle.reshape(pos_tokens, shape=[-1, orig_size, orig_size, embedding_size])
+        pos_tokens = paddle.transpose(pos_tokens, perm=[0, 3, 1, 2])
+        pos_tokens = paddle.nn.functional.interpolate(
+            pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
+        pos_tokens = paddle.transpose(pos_tokens, perm=[0, 2, 3, 1])
+        pos_tokens = paddle.reshape(pos_tokens, shape=[-1, new_size*new_size, embedding_size])
+        new_pos_embed = paddle.concat((extra_tokens, pos_tokens), axis=1)
+        print('reshape position embedding from %d to %d' % (orig_size ** 2, new_size ** 2))
+
+        return new_pos_embed
+    else:
+        return pos_embed_checkpoint
diff --git a/NLP/UNIMO-2/src/utils/stat_res.py b/NLP/UNIMO-2/src/utils/stat_res.py
new file mode 100644
index 000000000..402643a53
--- /dev/null
+++ b/NLP/UNIMO-2/src/utils/stat_res.py
@@ -0,0 +1,72 @@
+#!/usr/bin/python
+# -*- coding=utf-8 -*-
+"""
+# @Author : Gao Can
+# @Created Time : 2020-07-17 10:14:13
+# @Description : 
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import sys
+import os
+import numpy as np
+import argparse
+from args import ArgumentGroup
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+model_g = ArgumentGroup(parser, "stat", "stat configuration")
+model_g.add_arg("log_dir", str, None, "stat log dir")
+model_g.add_arg("random_slot", int, 2, "random slot in log file")
+model_g.add_arg("key_words", str, "lanch.log", "key words indentify log file")
+model_g.add_arg("line_prefix", str, "Best validation result:", "key words indentify final score to stat")
+model_g.add_arg("score_slot", int, -1, "score slot in stat line")
+model_g.add_arg("final_res_file", str, "final_res.txt", "the file to save final stat score")
+
+args = parser.parse_args()
+
+
+def get_res(infile):
+    acc = 0
+    with open(infile) as fr:
+        for line in fr.readlines():
+            line = line.strip('\r\n')
+            if line.startswith(args.line_prefix):
+                acc = float(line.split(' ')[args.score_slot])
+    return acc
+
+
+def print_stat(score_files):
+    nums = len(score_files)
+    max_score, max_score_file = score_files[0]
+    min_score, min_score_file = score_files[-1]
+    median_score, median_score_file = score_files[int(nums / 2)]
+    mean_score = np.average([s for s, f in score_files])
+
+    log = 'tot_random_seed %d\nmax_score %f max_file %s\nmin_score %f min_file %s\nmedian_score %f median_file %s\navg_score %f' % \
+          (nums, max_score, max_score_file, min_score, min_score_file, median_score, median_score_file, mean_score)
+    print(log)
+
+
+score_file = {}
+for file in os.listdir(args.log_dir):
+    if args.key_words in file:
+        randint = file.split('_')[args.random_slot]
+        acc = get_res(os.path.join(args.log_dir, file))
+        if randint in score_file:
+            score_file[randint].append((acc, file))
+        else:
+            score_file[randint] = [(acc, file)]
+
+best_randint_score_file = []
+for randint, s_f in score_file.items():
+    sorted_s_f = sorted(s_f, key=lambda a: a[0], reverse=True)
+    best_randint_score_file.append(sorted_s_f[0])
+
+best_randint_score_file = sorted(best_randint_score_file, key=lambda a: a[0], reverse=True)
+
+sys.stdout = open(os.path.join(args.log_dir, args.final_res_file), 'w')
+print_stat(best_randint_score_file)
diff --git a/NLP/UNIMO-2/src/utils/utils.py b/NLP/UNIMO-2/src/utils/utils.py
new file mode 100644
index 000000000..b850d5bc4
--- /dev/null
+++ b/NLP/UNIMO-2/src/utils/utils.py
@@ -0,0 +1,47 @@
+#!/usr/bin/python
+# -*- coding=utf-8 -*-
+"""
+# @Author : Gao Can
+# @Created Time : 2020-07-17 16:56:09
+# @Description : 
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import time
+
+
+def visualdl_log(metrics_output, train_loss, steps, phase):
+    """log可视化，仅限于paddlecloud 平台任务
+    """
+    print("{phase} log: steps {steps}, loss {loss}, metrics: {metrics}".format(phase=phase, steps=steps, loss=train_loss, metrics=metrics_output))
+    try:
+        if metrics_output and len(metrics_output) != 0:
+            import paddlecloud.visual_util as visualdl
+            x_dic = {"x_name": "step", "x_value": steps}
+            y_ls = []
+            for key, value in metrics_output.items():
+                y = {}
+                y["y_name"] = key
+                y["y_value"] = value
+                y_ls.append(y)
+            visualdl.show_fluid_trend(x_dic, y_ls, tag="train")
+    except Exception:
+        print("import paddlecloud.visual_util failed")
+
+
+def print_eval_log(ret):
+    prefix_log = "[%s evaluation] ave loss: %.4f," % (ret['phase'], ret['loss'])
+    postfix_log = "data_num: %d, elapsed time: %.4f s" % (ret['data_num'], ret['used_time'])
+    mid_log = " "
+    for k, v in ret.items():
+        if k not in ['phase', 'loss', 'data_num', 'used_time', 'key_eval']:
+            mid_log = mid_log + "%s: %.4f, " % (k, round(v, 4))
+    log = prefix_log + mid_log + postfix_log
+    print(log)
+
+def get_time():
+    res = time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))
+    return res

From 97f2f30a30415128d4a36cd3777e1c3d61c36107 Mon Sep 17 00:00:00 2001
From: weili-nlp <weili.ucas.ict@gmail.com>
Date: Fri, 20 May 2022 18:04:15 +0800
Subject: [PATCH 2/4] open release code for UNIMO-2

---
 NLP/UNIMO-2/README-md-bak | 216 ++++++++++++++++++++++++++++++++++++++
 NLP/UNIMO-2/README.md     |  41 +-------
 2 files changed, 217 insertions(+), 40 deletions(-)
 create mode 100644 NLP/UNIMO-2/README-md-bak

diff --git a/NLP/UNIMO-2/README-md-bak b/NLP/UNIMO-2/README-md-bak
new file mode 100644
index 000000000..18ec6f583
--- /dev/null
+++ b/NLP/UNIMO-2/README-md-bak
@@ -0,0 +1,216 @@
+UNIMO
+====
+Code for the findings of ACL2022 long paper [UNIMO-2: End-to-End Unified Vision-Language Grounded Learning](https://arxiv.org/pdf/2203.09067.pdf)
+
+
+Abstract
+---
+
+Vision-Language Pre-training (VLP) has achieved impressive performance on various cross-modal downstream tasks. 
+However, most existing methods can only learn from aligned image-caption data and rely heavily on expensive regional 
+features, which greatly limits their scalability and performance. In this paper, we propose an end-to-end unified-modal 
+pre-training framework, namely UNIMO-2, for joint learning on both aligned image-caption data and unaligned image-only 
+and text-only corpus. We build a unified Transformer model to jointly learn visual representations, textual 
+representations and semantic alignment between images and texts. In particular, we propose to conduct grounded learning 
+on both images and texts via a sharing grounded space, which helps bridge unaligned images and texts, and align the 
+visual and textual semantic spaces on different types of corpora. The experiments show that our grounded learning 
+method can improve textual and visual semantic alignment for improving performance on various cross-modal tasks. 
+Moreover, benefiting from effective joint modeling of different types of corpora, our model also achieves impressive 
+performance on single-modal visual and textual tasks. Our code and models are public at the UNIMO project page 
+\url{https://unimo-ptm.github.io}.
+
+![UNIMO-2](images/paper.png#pic_center)
+
+
+
+Dependencies
+---
+python3.7.4\
+cuda-10.1\
+cudnn_v7.6\
+nccl2.4.2\
+java1.8
+paddlepaddle-gpu==2.1.2\
+pyrouge==0.1.3
+
+
+Pre-trained Models
+---
+Similar to UNIMO, UNIMO-2 adopts large-scale text corpus, image collections and image-text aligned datasets as the pre-training data. 
+We provide pre-trained UNIMO-2 models:
+
+```
+cd /path/to/model_files
+wget --no-check-certificate -q https://unimo-2.bj.bcebos.com/model/UNIMO-2.tar.gz
+tar -zxf UNIMO-2.tar.gz
+```
+
+
+Experiments
+---
+
+Our fine-tuning experiments are carried on V100 GPU. Here are the results from the UNIMO-2 model:
+
+
+1 Cross-Modal  Tasks
+---
+
+
+### (1) Image-Text Retrieval
+
+#### Download Flickr30k dataset:
+
+```
+cd /path/to/data
+wget --no-check-certificate -q https://unimo-2.bj.bcebos.com/data/Flickr30k.tar.gz
+tar -zxf Flickr30k.tar.gz
+```
+
+#### Run the following common to train and evaluate on the Flickr30k dataset:
+
+```
+bash ./script/retrieval-grounded/Flickr30k-fleet/run.sh
+```
+
+#### Evaluation Results:
+
+Results of Image Retrieval task on Flickr30k dataset
+
+|   Model   | R@1 |  R@5  |  R@10  |
+| ----------- | ------- | ------- | ------- |
+|   UNIMO-2 (zero-shot)  |  72.70 | 91.18 | 94.60  |
+|   UNIMO-2 (finetuned)  |  80.14 | 95.58 | 97.75  |
+
+Results of Text Retrieval task on Flickr30k dataset
+
+|   Model   |  R@1  |  R@5  |  R@10  |
+| ----------- | ------- | ------- | ------- |
+|   UNIMO-2 (zero-shot)  |  88.46 | 96.84 | 98.92  |
+|   UNIMO-2 (finetuned) | 92.01 | 99.31 | 99.51 |
+
+
+
+### (2) Image Caption Generation
+
+#### Download COCO Caption dataset:
+
+```
+cd /path/to/data
+wget --no-check-certificate -q https://unimo-2.bj.bcebos.com/data/coco.tar.gz
+tar -zxf coco.tar.gz
+```
+
+#### Download evaluation script:
+
+```
+mkdir src/eval/tasks
+cd src/eval/tasks
+wget --no-check-certificate -q https://unimo.bj.bcebos.com/eval_script/coco.tar.gz
+tar -zxf coco.tar.gz
+```
+
+#### Run the following common to train and evaluate on the COCO Caption dataset:
+
+```
+bash ./script/img2txt-grounded/coco-oscar/run.sh
+```
+
+
+#### Evaluation Results:
+
+|   Model   | BLUE4 | CIDEr |
+| ----------- | ------- | ------- |
+|   UNIMO-2 |  39.7  |  131.2  |
+
+
+
+### (3) Visual Entailment
+####todo
+
+
+
+### (4) Visual Question Answering (VQA)
+####todo
+
+
+
+
+
+2 Visual Tasks
+---
+
+### (1) Image Classification
+####todo
+
+### (2) Zero-shot Image Classification
+####todo
+
+
+
+3 Textual Tasks
+---
+
+### (1) Natural Language Inference
+
+#### Download MNLI-AX dataset:
+```
+cd /path/to/data
+wget --no-check-certificate -q https://unimo-2.bj.bcebos.com/data/MNLI-AX.tar.gz
+tar -zxf MNLI-AX.tar.gz
+```
+
+#### Run the following common to train and evaluate on the MNLI-AX dataset:
+
+```
+bash ./script/classification/MNLI-AX/run.sh
+```
+
+
+#### Evaluation Results:
+
+|   Model   | Acc-(m/mm) |
+| ----------- | ------- |
+|   UNIMO-2  |  87.5/87.5 |
+
+
+
+
+### (2) Sentiment Classification
+####todo
+
+
+
+
+
+### (3) Similarity Tasks
+####todo
+
+
+
+
+
+### (4) Linguistic Acceptability Judgments
+####todo
+
+
+
+
+
+Citation
+---
+If you find our paper and code useful, please cite the following paper:
+```
+@article{li2022unimo,
+  title={UNIMO-2: End-to-End Unified Vision-Language Grounded Learning},
+  author={Li, Wei and Gao, Can and Niu, Guocheng and Xiao, Xinyan and Liu, Hao and Liu, Jiachen and Wu, Hua and Wang, Haifeng},
+  journal={arXiv preprint arXiv:2203.09067},
+  year={2022}
+}
+```
+
+Contact information
+---
+
+For help or issues using UNIMO-2, please submit a GitHub issue.
+
+For personal communication related to UNIMO, please contact Wei Li (liwei85@baidu.com), Can Gao (gaocan01@baidu.com), Guocheng Niu (niuguocheng@baidu.com).
diff --git a/NLP/UNIMO-2/README.md b/NLP/UNIMO-2/README.md
index 18ec6f583..52817babd 100644
--- a/NLP/UNIMO-2/README.md
+++ b/NLP/UNIMO-2/README.md
@@ -124,30 +124,9 @@ bash ./script/img2txt-grounded/coco-oscar/run.sh
 
 
 
-### (3) Visual Entailment
-####todo
 
 
-
-### (4) Visual Question Answering (VQA)
-####todo
-
-
-
-
-
-2 Visual Tasks
----
-
-### (1) Image Classification
-####todo
-
-### (2) Zero-shot Image Classification
-####todo
-
-
-
-3 Textual Tasks
+2 Textual Tasks
 ---
 
 ### (1) Natural Language Inference
@@ -175,24 +154,6 @@ bash ./script/classification/MNLI-AX/run.sh
 
 
 
-### (2) Sentiment Classification
-####todo
-
-
-
-
-
-### (3) Similarity Tasks
-####todo
-
-
-
-
-
-### (4) Linguistic Acceptability Judgments
-####todo
-
-
 
 
 

From a960f050b6be9268e7f6323a4dfd36b5d4cde656 Mon Sep 17 00:00:00 2001
From: weili-nlp <weili.ucas.ict@gmail.com>
Date: Fri, 20 May 2022 18:18:06 +0800
Subject: [PATCH 3/4] open release code for UNIMO-2

---
 NLP/UNIMO-2/.idea/UNIMO2-Open.iml | 12 -----
 NLP/UNIMO-2/.idea/misc.xml        |  4 --
 NLP/UNIMO-2/.idea/modules.xml     |  8 ---
 NLP/UNIMO-2/.idea/vcs.xml         |  6 ---
 NLP/UNIMO-2/.idea/workspace.xml   | 90 -------------------------------
 5 files changed, 120 deletions(-)
 delete mode 100644 NLP/UNIMO-2/.idea/UNIMO2-Open.iml
 delete mode 100644 NLP/UNIMO-2/.idea/misc.xml
 delete mode 100644 NLP/UNIMO-2/.idea/modules.xml
 delete mode 100644 NLP/UNIMO-2/.idea/vcs.xml
 delete mode 100644 NLP/UNIMO-2/.idea/workspace.xml

diff --git a/NLP/UNIMO-2/.idea/UNIMO2-Open.iml b/NLP/UNIMO-2/.idea/UNIMO2-Open.iml
deleted file mode 100644
index 7c9d48f0f..000000000
--- a/NLP/UNIMO-2/.idea/UNIMO2-Open.iml
+++ /dev/null
@@ -1,12 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
-    <orderEntry type="sourceFolder" forTests="false" />
-  </component>
-  <component name="TestRunnerService">
-    <option name="projectConfiguration" value="pytest" />
-    <option name="PROJECT_TEST_RUNNER" value="pytest" />
-  </component>
-</module>
\ No newline at end of file
diff --git a/NLP/UNIMO-2/.idea/misc.xml b/NLP/UNIMO-2/.idea/misc.xml
deleted file mode 100644
index fdf575236..000000000
--- a/NLP/UNIMO-2/.idea/misc.xml
+++ /dev/null
@@ -1,4 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 2.7 (multi_gpus)" project-jdk-type="Python SDK" />
-</project>
\ No newline at end of file
diff --git a/NLP/UNIMO-2/.idea/modules.xml b/NLP/UNIMO-2/.idea/modules.xml
deleted file mode 100644
index aa8af3339..000000000
--- a/NLP/UNIMO-2/.idea/modules.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/UNIMO2-Open.iml" filepath="$PROJECT_DIR$/.idea/UNIMO2-Open.iml" />
-    </modules>
-  </component>
-</project>
\ No newline at end of file
diff --git a/NLP/UNIMO-2/.idea/vcs.xml b/NLP/UNIMO-2/.idea/vcs.xml
deleted file mode 100644
index 94a25f7f4..000000000
--- a/NLP/UNIMO-2/.idea/vcs.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="VcsDirectoryMappings">
-    <mapping directory="$PROJECT_DIR$" vcs="Git" />
-  </component>
-</project>
\ No newline at end of file
diff --git a/NLP/UNIMO-2/.idea/workspace.xml b/NLP/UNIMO-2/.idea/workspace.xml
deleted file mode 100644
index 89213821b..000000000
--- a/NLP/UNIMO-2/.idea/workspace.xml
+++ /dev/null
@@ -1,90 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ChangeListManager">
-    <list default="true" id="dce0a8ac-138e-4944-b6c2-395663ae3585" name="Default Changelist" comment="" />
-    <option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" />
-    <option name="SHOW_DIALOG" value="false" />
-    <option name="HIGHLIGHT_CONFLICTS" value="true" />
-    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
-    <option name="LAST_RESOLUTION" value="IGNORE" />
-  </component>
-  <component name="Git.Settings">
-    <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
-  </component>
-  <component name="ProjectFrameBounds" fullScreen="true">
-    <option name="x" value="-1920" />
-    <option name="y" value="-180" />
-    <option name="width" value="1920" />
-    <option name="height" value="1080" />
-  </component>
-  <component name="ProjectView">
-    <navigator proportions="" version="1">
-      <foldersAlwaysOnTop value="true" />
-    </navigator>
-    <panes>
-      <pane id="Scope" />
-      <pane id="ProjectPane">
-        <subPane>
-          <expand>
-            <path>
-              <item name="UNIMO2-Open" type="b2602c69:ProjectViewProjectNode" />
-              <item name="UNIMO2-Open" type="462c0819:PsiDirectoryNode" />
-            </path>
-          </expand>
-          <select />
-        </subPane>
-      </pane>
-    </panes>
-  </component>
-  <component name="PropertiesComponent">
-    <property name="last_opened_file_path" value="$PROJECT_DIR$" />
-    <property name="settings.editor.selected.configurable" value="fileTemplates" />
-  </component>
-  <component name="RunDashboard">
-    <option name="ruleStates">
-      <list>
-        <RuleState>
-          <option name="name" value="ConfigurationTypeDashboardGroupingRule" />
-        </RuleState>
-        <RuleState>
-          <option name="name" value="StatusDashboardGroupingRule" />
-        </RuleState>
-      </list>
-    </option>
-  </component>
-  <component name="SvnConfiguration">
-    <configuration />
-  </component>
-  <component name="TaskManager">
-    <task active="true" id="Default" summary="Default task">
-      <changelist id="dce0a8ac-138e-4944-b6c2-395663ae3585" name="Default Changelist" comment="" />
-      <created>1652167313560</created>
-      <option name="number" value="Default" />
-      <option name="presentableId" value="Default" />
-      <updated>1652167313560</updated>
-    </task>
-    <servers />
-  </component>
-  <component name="ToolWindowManager">
-    <frame x="-1920" y="-180" width="1920" height="1080" extended-state="0" />
-    <layout>
-      <window_info id="Favorites" side_tool="true" />
-      <window_info active="true" content_ui="combo" id="Project" order="0" visible="true" weight="0.24960506" />
-      <window_info id="Structure" order="1" side_tool="true" weight="0.25" />
-      <window_info anchor="bottom" id="Version Control" />
-      <window_info anchor="bottom" id="Python Console" />
-      <window_info anchor="bottom" id="Terminal" />
-      <window_info anchor="bottom" id="Event Log" side_tool="true" />
-      <window_info anchor="bottom" id="Message" order="0" />
-      <window_info anchor="bottom" id="Find" order="1" />
-      <window_info anchor="bottom" id="Run" order="2" />
-      <window_info anchor="bottom" id="Debug" order="3" weight="0.4" />
-      <window_info anchor="bottom" id="Cvs" order="4" weight="0.25" />
-      <window_info anchor="bottom" id="Inspection" order="5" weight="0.4" />
-      <window_info anchor="bottom" id="TODO" order="6" />
-      <window_info anchor="right" id="Commander" internal_type="SLIDING" order="0" type="SLIDING" weight="0.4" />
-      <window_info anchor="right" id="Ant Build" order="1" weight="0.25" />
-      <window_info anchor="right" content_ui="combo" id="Hierarchy" order="2" weight="0.25" />
-    </layout>
-  </component>
-</project>
\ No newline at end of file

From f3932a934404899249f80098134df4b2d24e7d4e Mon Sep 17 00:00:00 2001
From: weili-nlp <weili.ucas.ict@gmail.com>
Date: Fri, 20 May 2022 18:21:57 +0800
Subject: [PATCH 4/4] clean code

---
 .../reader/__pycache__/__init__.cpython-37.pyc    | Bin 174 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 NLP/UNIMO-2/src/reader/__pycache__/__init__.cpython-37.pyc

diff --git a/NLP/UNIMO-2/src/reader/__pycache__/__init__.cpython-37.pyc b/NLP/UNIMO-2/src/reader/__pycache__/__init__.cpython-37.pyc
deleted file mode 100644
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