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-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 new file mode 100644 index 000000000..52817babd --- /dev/null +++ b/NLP/UNIMO-2/README.md @@ -0,0 +1,177 @@ +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 | + + + + + +2 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 | + + + + + + + +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 +} diff --git a/NLP/UNIMO-2/images/cross-modal.graffle b/NLP/UNIMO-2/images/cross-modal.graffle new file mode 100644 index 000000000..69211001c Binary files /dev/null and b/NLP/UNIMO-2/images/cross-modal.graffle differ diff --git a/NLP/UNIMO-2/images/cross-modal.png b/NLP/UNIMO-2/images/cross-modal.png new file mode 100644 index 000000000..d289590a2 Binary files /dev/null and b/NLP/UNIMO-2/images/cross-modal.png differ diff --git a/NLP/UNIMO-2/images/paper.png b/NLP/UNIMO-2/images/paper.png new file mode 100644 index 000000000..7972dc761 Binary files /dev/null and b/NLP/UNIMO-2/images/paper.png differ diff --git a/NLP/UNIMO-2/images/textual.png b/NLP/UNIMO-2/images/textual.png new file mode 100644 index 000000000..6b8e6ab4a Binary files /dev/null and b/NLP/UNIMO-2/images/textual.png differ diff --git a/NLP/UNIMO-2/images/visual.png b/NLP/UNIMO-2/images/visual.png new file mode 100644 index 000000000..645eee8eb Binary files /dev/null and b/NLP/UNIMO-2/images/visual.png differ 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": 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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 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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 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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, "kind": 11031, "\u0120designated": 11032, "\u00c3\u00a4": 11033, "\u0120animation": 11034, "kee": 11035, "\u0120Introdu": 11036, "\u0120/>": 11037, "\u0120delayed": 11038, "\u0120tremend": 11039, "\u0120curious": 11040, "Use": 11041, "\u0120lect": 11042, "dam": 11043, "\u0120innovation": 11044, "\u0120Points": 11045, "\u0120loading": 11046, "\u0120dispute": 11047, "ctic": 11048, "irds": 11049, "\u0120BY": 11050, "\u0120nurs": 11051, "\u0120Value": 11052, "IONS": 11053, "\u0120Hum": 11054, "\u0120template": 11055, "mers": 11056, "\u0120appearances": 11057, "\u0120Entertainment": 11058, "\u0120translation": 11059, "\u0120sake": 11060, "\u0120beneath": 11061, "\u0120inhib": 11062, "\u0120euro": 11063, "abetes": 11064, "\u0120studying": 11065, "\u0120Mas": 11066, "\u0120perceived": 11067, "\u0120examined": 11068, "\u0120eager": 11069, "\u0120coaches": 11070, "\u0120imper": 11071, "chi": 11072, "\u0120produces": 11073, "\").": 11074, "\u0120Everyone": 11075, "\u0120municip": 11076, "\u0120girlfriend": 11077, "\u0120hire": 11078, "\u0120Vice": 11079, "\u0120suitable": 11080, "opy": 11081, "\u0120inequ": 11082, "\u0120Duke": 11083, "fish": 11084, "first": 11085, "\u0120Obs": 11086, "\u0120interior": 11087, "\u0120Bruce": 11088, "\u0120Ry": 11089, "\u0120analys": 11090, "\u0120considerable": 11091, "\u0120forecast": 11092, "\u0120fert": 11093, "orship": 11094, "\u0120Drug": 11095, "\u0120ALL": 11096, ":\"": 11097, "thur": 11098, "\u0120Mail": 11099, "\u0120ballot": 11100, "\u0120instantly": 11101, "\u0120Channel": 11102, "\u0120picks": 11103, "\u01201989": 11104, "\u0120tent": 11105, "oli": 11106, "\u0120civilian": 11107, "bling": 11108, "ello": 11109, "bu": 11110, "\u0120inch": 11111, "\u0120logo": 11112, "\u0120cooperation": 11113, "\u0120walks": 11114, "\u0120investments": 11115, "\u0120imprison": 11116, "\u0120Festival": 11117, "\u0120Ky": 11118, "\u0120legally": 11119, "\u0120gri": 11120, "charg": 11121, "Sl": 11122, "\u0120threatening": 11123, "duction": 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, "\u0120burst": 11173, "apolis": 11174, "\u0120Magazine": 11175, "\u0120Cra": 11176, "\u0120BR": 11177, "gged": 11178, "\u0120HE": 11179, "Mich": 11180, "bet": 11181, "\u0120Lady": 11182, "ylum": 11183, "erves": 11184, "\u0120meets": 11185, "white": 11186, "Log": 11187, "\u0120corresponding": 11188, "\u0120insisted": 11189, "GG": 11190, "\u0120surrounded": 11191, "\u0120tens": 11192, "\u0120lane": 11193, "\u0120coinc": 11194, "home": 11195, "\u0120existed": 11196, "ected": 11197, "\u0120Double": 11198, "lamm": 11199, "\u0120skept": 11200, "exp": 11201, "\u0120perception": 11202, "iev": 11203, "\u0120Being": 11204, "oft": 11205, "\u0120adopt": 11206, ".:": 11207, "];": 11208, "Windows": 11209, "\u0120satellite": 11210, "ASH": 11211, "\u0120infant": 11212, "description": 11213, "\u0120Meanwhile": 11214, "cm": 11215, "oca": 11216, "\u0120Treat": 11217, "actor": 11218, "\u0120tobacco": 11219, "\u0120Norm": 11220, "emption": 11221, "\u0120flesh": 11222, "\u0120je": 11223, "oop": 11224, "\u0120Heaven": 11225, "\u0120beating": 11226, "anim": 11227, "\u0120gathering": 11228, "\u0120cultiv": 11229, "GO": 11230, "abe": 11231, "\u0120Jonathan": 11232, "\u0120Safety": 11233, "\u0120badly": 11234, "prot": 11235, "\u0120choosing": 11236, "\u0120contacted": 11237, "\u0120quit": 11238, "\u0120distur": 11239, "\u0120stir": 11240, "\u0120token": 11241, "Det": 11242, "\u0120Pa": 11243, "\u0120functionality": 11244, "003": 11245, "some": 11246, "\u0120limitations": 11247, "\u0120meth": 11248, "build": 11249, "config": 11250, "NT": 11251, "rell": 11252, "blem": 11253, "\u0120Mom": 11254, "\u0120veterans": 11255, "\u0120Hu": 11256, "\u0120trends": 11257, "arer": 11258, "\u0120Given": 11259, "\u0120Caption": 11260, "may": 11261, "AST": 11262, "\u0120wondering": 11263, "\u0120Clark": 11264, "normal": 11265, "\u0120separated": 11266, "\u0120desp": 11267, "stic": 11268, "brew": 11269, "\u0120relating": 11270, "\u0120Nik": 11271, "\u0120Farm": 11272, "\u0120enthusi": 11273, "good": 11274, "deb": 11275, "\u0120activist": 11276, "\u0120mart": 11277, "\u0120explosion": 11278, "\u0120Economic": 11279, "Link": 11280, "\u0120insight": 11281, "\u0120convenient": 11282, "\u0120counterpart": 11283, "support": 11284, "\u0120Virt": 11285, "agen": 11286, "\u0120Tennessee": 11287, "\u0120Simon": 11288, "\u0120Award": 11289, "OCK": 11290, "\u0120Figure": 11291, "\u0120overseas": 11292, "\u0120pride": 11293, "\u0120Cas": 11294, "note": 11295, "mg": 11296, "Current": 11297, "\u0120displays": 11298, "content": 11299, "\u0120traveling": 11300, "\u0120hospitals": 11301, "\u0120Financial": 11302, "\u0120Past": 11303, "\u0120defendant": 11304, "\u0120streaming": 11305, "mble": 11306, "\u0120Berlin": 11307, "uki": 11308, "\u0120distribut": 11309, "\u0120antib": 11310, "\u0120chocolate": 11311, "\u0120Castle": 11312, "\u0120interrupt": 11313, "\u0120Row": 11314, "\u0120conversion": 11315, "\u0120bugs": 11316, "\u0120Rather": 11317, "liest": 11318, "LY": 11319, "\u0120Jean": 11320, "common": 11321, "akh": 11322, "\u0120130": 11323, "otton": 11324, "\u0120Dean": 11325, "\u0120amendment": 11326, "\u0120gameplay": 11327, "\u0120Warren": 11328, "oda": 11329, "\u0120highlights": 11330, "\u0120irre": 11331, "\u0120NATO": 11332, "\u0120balls": 11333, "\u0120demanding": 11334, "URE": 11335, "\u0120Luke": 11336, "Figure": 11337, "stop": 11338, "onia": 11339, "zone": 11340, "izers": 11341, "\u0120WR": 11342, "\u0120awarded": 11343, "\u0120regulatory": 11344, "\u0120Hart": 11345, "\u0120SN": 11346, "pling": 11347, "\u0120sour": 11348, "\u0120Pixel": 11349, "usive": 11350, "\u0120fet": 11351, "\u0120Sent": 11352, "\u0120automatic": 11353, "\u0120fer": 11354, "vernment": 11355, "\u0120Khan": 11356, "TON": 11357, "father": 11358, "\u0120extraordinary": 11359, "throp": 11360, "\u0120Python": 11361, "\u0120GPU": 11362, "\u0120sexually": 11363, "\u0120desktop": 11364, "itivity": 11365, "\u0120Antonio": 11366, "\u0120orient": 11367, "\u0120ears": 11368, "obby": 11369, "ouses": 11370, "vertisements": 11371, "\u0120manufacturers": 11372, "icient": 11373, "minute": 11374, "\u0120conviction": 11375, "\u0120garden": 11376, "public": 11377, "\u0120satisfied": 11378, "fold": 11379, "OK": 11380, "\u0120inhab": 11381, "\u0120Think": 11382, "\u0120programme": 11383, "\u0120stomach": 11384, "\u0120coordin": 11385, "\u0120holy": 11386, "\u0120threshold": 11387, "\u0120rhet": 11388, "\u0120serial": 11389, "\u0120employers": 11390, "\u0120Everything": 11391, "rah": 11392, "\u0120bother": 11393, "\u0120brands": 11394, "Value": 11395, "\u0120Ted": 11396, "\u0120Planet": 11397, "\u0120pink": 11398, "\u0120Furthermore": 11399, "sa": 11400, "PE": 11401, "reck": 11402, "\u0120USD": 11403, "otte": 11404, "\u0120&&": 11405, "\u0120landed": 11406, "gets": 11407, "\u0120producers": 11408, "\u0120healthcare": 11409, "\u0120dominant": 11410, "\u0120destro": 11411, "\u0120amended": 11412, "chron": 11413, "\u0120fits": 11414, "\u0120Syd": 11415, "\u0120Authority": 11416, "ATCH": 11417, "\u0120fights": 11418, "\u0120LLC": 11419, "\u0120---": 11420, "\u0120Corp": 11421, "\u0120toxic": 11422, "specific": 11423, "\u0120Corn": 11424, "\u0120Chel": 11425, "\u0120telephone": 11426, "\u0120Pant": 11427, "\u0120mysterious": 11428, "aunch": 11429, "odox": 11430, "media": 11431, "\u0120witnesses": 11432, "agu": 11433, "\u0120questioned": 11434, "\u0120Brexit": 11435, "\u0120Remember": 11436, "enez": 11437, "\u0120endorse": 11438, "iatric": 11439, "\u0120Ident": 11440, "\u0120ridiculous": 11441, "110": 11442, "\u0120prayer": 11443, "\u0120scientist": 11444, "\u01201950": 11445, "\u0120Aqu": 11446, "\u0120underground": 11447, "\u0120UFC": 11448, "mare": 11449, "\u0120Later": 11450, "wich": 11451, "\u0120subscrib": 11452, "\u0120hosts": 11453, "\u0120err": 11454, "\u0120grants": 11455, "antom": 11456, "\u0120summon": 11457, "early": 11458, "\u0120Clear": 11459, "\u0120Prim": 11460, "\u0120suspension": 11461, "\u0120guaranteed": 11462, "apper": 11463, "\u0120rice": 11464, "\u0120Sean": 11465, "\u0120Shin": 11466, "\u0120referendum": 11467, "\u0120fled": 11468, "rust": 11469, "\u0120360": 11470, "tery": 11471, "\u0120shocked": 11472, "BR": 11473, "\u0120Oil": 11474, "\u0120Allah": 11475, "\u0120partly": 11476, "\u0120ignor": 11477, "\u0120transmission": 11478, "\u0120homosexual": 11479, "iversal": 11480, "\u0120hopefully": 11481, "\u00e3\u0124\u00a4": 11482, "\u0120lesson": 11483, "Leg": 11484, "\u0120..": 11485, "Yet": 11486, "table": 11487, "appropri": 11488, "rett": 11489, "\u0120boards": 11490, "\u0120incorrect": 11491, "\u0120bacteria": 11492, "aru": 11493, "amac": 11494, "\u0120snap": 11495, ".'\"": 11496, "\u0120parad": 11497, "tem": 11498, "heart": 11499, "\u0120availability": 11500, "\u0120wisdom": 11501, "\u0120(+": 11502, "\u0120priest": 11503, "\u0120\u00c2\u0142\u0120\u00c2\u0142": 11504, "Open": 11505, "\u0120span": 11506, "\u0120parameter": 11507, "\u0120convince": 11508, "\u0120(%)": 11509, "rac": 11510, "\u0120fo": 11511, "\u0120safely": 11512, "\u0120converted": 11513, "\u0120Olympic": 11514, "\u0120reserve": 11515, 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"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, 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"\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": 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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, 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"\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, "\">\"": 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, ".": 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, "\u0120Veteran": 32733, "aughtered": 32734, "ingo": 32735, "\u0120uninsured": 32736, "CLOSE": 32737, "\u0120fuse": 32738, "\u0120erotic": 32739, "\u0120advertise": 32740, "raising": 32741, "Texture": 32742, "\u0120attends": 32743, "\u0120REAL": 32744, "uddled": 32745, "\u0120smoot": 32746, "\u0120305": 32747, "\u0120Willis": 32748, "\u0120blond": 32749, "Analysis": 32750, "\u0120VT": 32751, "onica": 32752, "\u0120stronghold": 32753, "RF": 32754, "NM": 32755, ".>>": 32756, "\u0120prosperous": 32757, "\u0120boasted": 32758, "292": 32759, "\u0120Manufacturing": 32760, "PRESS": 32761, "gren": 32762, "\u0120pharmacy": 32763, "\u0120Rockefeller": 32764, "kai": 32765, "\u0120thumbs": 32766, "\u0120Hut": 32767, "\u0120motherboard": 32768, "\u0120guardians": 32769, "\u0120Alter": 32770, "llular": 32771, "\u0120shack": 32772, "\u0120wisely": 32773, "\u0120backbone": 32774, "erva": 32775, "\u0120suicides": 32776, "\u0120McGregor": 32777, "ijah": 32778, "Emer": 32779, "\u0120Brav": 32780, "\u0120designate": 32781, "POST": 32782, "produced": 32783, "\u0120cleansing": 32784, "irlwind": 32785, "existent": 32786, "\u0120Humph": 32787, "\u0120Payne": 32788, "\u0120vested": 32789, "\u00c5\u00a1": 32790, "\u0120stringent": 32791, "iona": 32792, "\u0120unsub": 32793, "\u0120summed": 32794, "\u0120Hercules": 32795, "subject": 32796, "\u0120Ragnar": 32797, "\u0120Nos": 32798, "\u0120characterization": 32799, "\u0120savvy": 32800, "\u0120Dawson": 32801, "\u0120Casino": 32802, "\u0120fri": 32803, "\u0120Barrier": 32804, "\u0120misinformation": 32805, "\u0120insulation": 32806, "\u0120corridors": 32807, "\u0120airplanes": 32808, "\u0120Noct": 32809, "ahi": 32810, "\u01201916": 32811, "kb": 32812, "armac": 32813, "\u0120shun": 32814, "\u0120schema": 32815, "\u0120horrified": 32816, "\u0120239": 32817, "aunders": 32818, "NB": 32819, "iates": 32820, "erity": 32821, "\u0120Shard": 32822, "\u0120rarity": 32823, "\u0120grouped": 32824, "\u0120Ghana": 32825, "against": 32826, "\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, "\u0120interfering": 32874, "Builder": 32875, "\u0120overfl": 32876, "\u0120acne": 32877, "\u0120Mud": 32878, "\u0120Kerr": 32879, "flex": 32880, "\u0120Postal": 32881, "\u0120Baltic": 32882, "477": 32883, "\u0120Persons": 32884, "ourage": 32885, "HB": 32886, "\u0120Muse": 32887, "\u0120Immortal": 32888, "\u0120Driving": 32889, "\u0120petitions": 32890, "\u0120subscript": 32891, "\u0120sorce": 32892, "\u0120Processor": 32893, "uton": 32894, "Sony": 32895, "\u0120phon": 32896, "\u0120raced": 32897, "\u0120Anthrop": 32898, "\u0120daytime": 32899, "\u0120Exercise": 32900, "Adding": 32901, "\u0120engages": 32902, "\u0120Qualcomm": 32903, "\u0120miracles": 32904, "\u0120memes": 32905, "\u0120Drink": 32906, "\u0120Orioles": 32907, "\u0120hairs": 32908, "\u0120Polar": 32909, "athom": 32910, "\u0120slippery": 32911, "\u0120Remy": 32912, "\u0120caramel": 32913, "\u0120YEAR": 32914, "\u0120alk": 32915, "Ign": 32916, "aution": 32917, "\u0120Merlin": 32918, "\u0120Cran": 32919, "\u0120apologies": 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, ")[": 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": 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, "": 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": 49907, "acers": 49908, "\u0120fid": 49909, "angler": 49910, "\u0120CSI": 49911, "\u0120hereafter": 49912, "\u0120dissidents": 49913, "reporting": 49914, "iphany": 49915, "chev": 49916, "\u0120solitude": 49917, "\u0120lobe": 49918, "\u0120indis": 49919, "\u0120credential": 49920, "recent": 49921, "adult": 49922, "\u0120Nirvana": 49923, "\u0120Franchise": 49924, "Layer": 49925, "Hyp": 49926, "\u0120Berkshire": 49927, "\u0120wills": 49928, "tif": 49929, "\u0120totem": 49930, "\u0120Judah": 49931, "repair": 49932, "Instant": 49933, "548": 49934, "\u0120embassies": 49935, "\u0120bottleneck": 49936, "\u0120bount": 49937, "\u0120typew": 49938, "\u0120Alvin": 49939, "jing": 49940, "imilar": 49941, "Rush": 49942, "\u0120brim": 49943, "\u0120HELP": 49944, "Aim": 49945, "]'": 49946, "\u0120passively": 49947, "\u0120bounded": 49948, "\u0120Rated": 49949, "\u0120criminality": 49950, "\u0120biomark": 49951, "\u0120dispatcher": 49952, "\u0120Towards": 49953, "\u0120+++": 49954, "righteous": 49955, "frog": 49956, "\u0120Panc": 49957, "Carter": 49958, "032": 49959, "\u00e6\u00a9\u0141": 49960, "\u0120ultraviolet": 49961, "\u0120Licensed": 49962, "\u0120Tata": 49963, "\u0120Blessing": 49964, "\u0120GAM": 49965, "\u0120chemically": 49966, "\u0120Seaf": 49967, "\u0120RELE": 49968, "\u0120Mercenary": 49969, "capitalist": 49970, "\u0120formulations": 49971, "\u0120annihilation": 49972, "\u0120Verb": 49973, "\u0120Argon": 49974, "\u0120unloaded": 49975, "\u0120morphed": 49976, "\u0120conquering": 49977, "backer": 49978, "IELD": 49979, "\u0120thefts": 49980, "\u0120frontrunner": 49981, "\u0120Royale": 49982, "\u0120Fundamental": 49983, "elight": 49984, "Chip": 49985, "necessary": 49986, "ayn": 49987, "\u0120Slip": 49988, "\u0120448": 49989, "cerned": 49990, "Pause": 49991, "\u0120shockingly": 49992, "\u0120ABV": 49993, "\u0120composure": 49994, "733": 49995, "\u0120Motorsport": 49996, "ahime": 49997, "Murray": 49998, "Mach": 49999, "\u0120grids": 50000, "\u0120debian": 50001, "\u0120furthermore": 50002, "\u0120dexterity": 50003, "\u0120Collections": 50004, "oslov": 50005, "ilage": 50006, "bj": 50007, "\u0120Monteneg": 50008, "\u0120strutConnector": 50009, "\u0120massacres": 50010, "\u0120briefs": 50011, "fetched": 50012, "uvian": 50013, "olition": 50014, "Failure": 50015, "emonic": 50016, "\u0120flared": 50017, "\u0120claimant": 50018, "\u0120cures": 50019, 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+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 + and remove the and 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/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