diff --git a/__init__.py b/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/config.json b/config.json
new file mode 100644
index 0000000..5a155b3
--- /dev/null
+++ b/config.json
@@ -0,0 +1,8 @@
+{
+    "output_dir": "./",
+    "project_name": "testing",
+    "log_every_n_steps": 100,
+    "save_every_n_steps": 100,
+    "save_best_only": true,
+    "start_time": "2025-03-29 05:40:50"
+}
\ No newline at end of file
diff --git a/config.yaml b/config.yaml
new file mode 100644
index 0000000..c5f9d40
--- /dev/null
+++ b/config.yaml
@@ -0,0 +1,127 @@
+name: debug
+dump_dir: /workspace/AI-Uncomplicated/
+seed: 12
+grad_acc_steps: 1
+gc_collect_freq: 1000
+probe_freq: null
+steps: 3000
+data:
+  root_dir: /workspace/AI-Uncomplicated/artifact/pretraining_data/
+  sources:
+    TigerResearch: 100.0
+  batch_size: 32
+  seq_len: 1024
+  n_views: 2
+  seed: 42
+  add_bos: true
+  add_eos: true
+  load_async: true
+  prefetch_size: 64
+  fim_rate: 0.1
+  fim_type: document
+  tokenizer:
+    name: GI01-tokenizer-v0.1-en
+    path: /workspace/AI-Uncomplicated/artifact/tokenizer
+optim:
+  lr: 0.0003
+  weight_decay: 0.1
+  epsilon: 1.0e-08
+  beta1: 0.9
+  beta2: 0.95
+  clip: 10.0
+  scheduler: wsd
+  warmup: 2000
+  lr_min_ratio: 1.0e-06
+  cycle_length: 1.0
+  cosine_theta: 1.0
+  annealing_step: 1000
+  decay_fraction: 0.1
+  decay_type: cosine
+  exp_factor: 0.5
+model:
+  dim: 1024
+  n_layers: 16
+  head_dim: null
+  n_heads: 16
+  n_kv_heads: null
+  ffn_dim_multiplier: null
+  multiple_of: 256
+  norm_eps: 1.0e-05
+  rope_theta: 10000.0
+  init_base_std: null
+  init_std_factor: disabled
+  max_seqlen: 1024
+  seed: 42
+  n_future_head: 1
+  vocab_size: 52000
+  attn_impl: sdpa
+  mask: causal
+  sliding_window: null
+distributed:
+  dp_shard: 2
+  dp_replicate: 1
+  selective_activation_checkpointing: false
+  compile: true
+  fsdp_type: full_shard
+  model_dtype: bf16
+  float8_recipe: null
+  float8_filter: layers\.[0-9]+\.
+  matmul_allow_tf32: false
+  allow_bf16_reduced_precision_reduction: true
+  detect_anomaly: false
+  compile_cache_size_limit: 8
+  spawn_method: forkserver
+env:
+  MKL_SERVICE_FORCE_INTEL: GNU
+  OMP_NUM_THREADS: '1'
+  MKL_NUM_THREADS: '1'
+  ENABLE_INTRA_NODE_COMM: '1'
+  TORCH_NCCL_AVOID_RECORD_STREAMS: '1'
+  NCCL_IB_TIMEOUT: '22'
+  NCCL_DEBUG: INFO
+  TORCH_NCCL_ASYNC_ERROR_HANDLING: '1'
+checkpoint:
+  save_every:
+    step: 1000
+    limit: 0
+  eval_every:
+    step: 1000
+    limit: 0
+  path: /workspace/AI-Uncomplicated/
+  init_ckpt_path: null
+  continue_training_from_init: false
+profiling:
+  run: true
+  trace_folder: profiling
+  mem_warmup: 100
+  mem_steps: 2
+  profile_warmup: 102
+  profile_steps: 2
+logging:
+  freq: 1
+  acc_freq: null
+  wandb:
+    job_type: null
+    dir: /workspace/AI-Uncomplicated/wandb/
+    project: Pretrain
+    entity: vipinsaravana
+    tags: null
+    group: null
+    name: debug
+    notes: null
+    config_exclude_keys: null
+    config_include_keys: null
+    anonymous: null
+    mode: null
+    allow_val_change: null
+    resume: null
+    force: null
+    tensorboard: null
+    sync_tensorboard: null
+    monitor_gym: null
+    save_code: null
+    id: null
+    fork_from: null
+    resume_from: null
+async_eval_gpus: null
+eval: null
diff --git a/core/activations/__pycache__/gelu.cpython-311.pyc b/core/activations/__pycache__/gelu.cpython-311.pyc
deleted file mode 100644
index a5b0e08..0000000
Binary files a/core/activations/__pycache__/gelu.cpython-311.pyc and /dev/null differ
diff --git a/core/activations/__pycache__/test_gelu.cpython-311.pyc b/core/activations/__pycache__/test_gelu.cpython-311.pyc
deleted file mode 100644
index 5fbd9f7..0000000
Binary files a/core/activations/__pycache__/test_gelu.cpython-311.pyc and /dev/null differ
diff --git a/core/configurations/__pycache__/base.cpython-311.pyc b/core/configurations/__pycache__/base.cpython-311.pyc
deleted file mode 100644
index ebf2e0c..0000000
Binary files a/core/configurations/__pycache__/base.cpython-311.pyc and /dev/null differ
diff --git a/core/configurations/base.py b/core/configurations/base.py
index 99092ac..2e549ca 100644
--- a/core/configurations/base.py
+++ b/core/configurations/base.py
@@ -5,28 +5,29 @@ class BaseConfiguration(object):
     """
     base config for model architecture
     """
-    model_name: str = None
+    name: str | None = None
     num_layers: int = 1
     padding_id: int = 0
     hidden_dim: int = 512
     intermediate_dim: int = 3072
     max_positions: int = 2048
-    vocabulary_size: int = -1
+    vocab_size: int = -1
     layer_norm_eps: float = 1e-05
-    model_max_sequence: int = 2048
+    max_seq_len: int = 2048
     num_heads: int = 8
-    attention_dropout: int = 0.0
-    head_dim: int = None
+    attention_dropout: float = 0.0
+    head_dim: int | None = None
     use_rope: bool = True
     rope_base: float = 10000.0
     output_last_hidden_state: bool = False
+    seed: int = 42
 
     def __post_init__(self):
         if self.head_dim is None:
             assert self.hidden_dim % self.num_heads == 0
             self.head_dim = self.hidden_dim // self.num_heads
 
-        if self.vocabulary_size is None:
+        if self.vocab_size is None:
             raise ValueError("Vocabulary size should not be empty")
 
     def get_padding_token(self):
diff --git a/core/dataloaders/dataloader.py b/core/dataloaders/dataloader.py
deleted file mode 100644
index a56fbea..0000000
--- a/core/dataloaders/dataloader.py
+++ /dev/null
@@ -1,56 +0,0 @@
-"""
-Data Loader Module
-"""
-import torch
-
-from core.configurations.base import BaseConfiguration
-import sentencepiece as spm
-from torch.utils.data import Dataset, DataLoader
-
-class CustomDataset(Dataset):
-    def __init__(self, token_ids, config: BaseConfiguration):
-        self.input_ids = []
-        self.target_ids = []
-
-        for i in range(0, len(token_ids) - config.model_max_sequence, config.strides):
-            input_chunk = token_ids[i: i+config.model_max_sequence]
-            target_chunk = token_ids[i + 1 : i + config.model_max_sequence + 1]
-            self.input_ids.append(torch.tensor(input_chunk))
-            self.target_ids.append(torch.tensor(target_chunk))
-
-    def __len__(self):
-        return len(self.input_ids)
-
-    def __getitem__(self, idx):
-        return self.input_ids[idx], self.target_ids[idx]
-
-
-def load_tokenizer(tokenizer_path: str):
-    sp_model = spm.SentencePieceProcessor(model_file=tokenizer_path)
-    return sp_model
-
-def tokenize_text(tokenizer, text):
-    """Tokenize text using the provided tokenizer."""
-    return tokenizer.encode(text)
-
-def dataloader_v1(dataset, tokenizer, config: BaseConfiguration):
-    # Tokenize the text dataset
-    token_ids = []
-    for index, sample in enumerate(dataset):
-        token_ids.extend(tokenize_text(tokenizer, sample['content']))
-
-    # Create dataset
-    dataset = CustomDataset(token_ids, config)
-
-
-    # Create DataLoader
-    data_loader = DataLoader(
-        dataset,
-        batch_size=config.dataset_batch_size,
-        shuffle=config.dataset_shuffle,
-        num_workers=4
-    )
-
-    return data_loader
-
-
diff --git a/core/english_tokenizer/english_tokenizer.model b/core/english_tokenizer/english_tokenizer.model
deleted file mode 100644
index 1232992..0000000
Binary files a/core/english_tokenizer/english_tokenizer.model and /dev/null differ
diff --git a/core/english_tokenizer/english_tokenizer.vocab b/core/english_tokenizer/english_tokenizer.vocab
deleted file mode 100644
index 1932b14..0000000
--- a/core/english_tokenizer/english_tokenizer.vocab
+++ /dev/null
@@ -1,64496 +0,0 @@
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-mm	-2339
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-orrow	-2381
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-itch	-2385
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-ibly	-2388
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-hy	-2396
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-What	-2402
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-ruction	-2419
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-ED	-2429
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-onse	-2496
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-imum	-2508
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-lier	-2510
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-undred	-2530
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-IT	-2536
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-ING	-2559
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-esome	-2595
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-RE	-2616
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-ey	-2623
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-▁parts	-2729
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-abor	-3243
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-iend	-3252
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-terday	-3255
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-term	-3259
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-entially	-4075
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-ohol	-4109
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-atically	-4112
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-iation	-4116
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-tered	-4128
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-.....	-4146
-ulf	-4147
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-itute	-4149
-vis	-4150
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-oos	-4154
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-cket	-4165
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-isc	-4173
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-irty	-4215
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-erous	-4254
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-iable	-4300
-My	-4301
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-So	-4303
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-ev	-4305
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-orage	-4313
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-omb	-4317
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-ero	-4322
-icing	-4323
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-ooth	-4326
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-inct	-4359
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-ancing	-4389
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-ii	-4399
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-rary	-4694
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-ym	-4725
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-ushing	-4727
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-akers	-4730
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-porary	-4736
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-umn	-4753
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-illy	-4784
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-icians	-4790
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-Why	-4792
-odes	-4793
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-ij	-4797
-▁fairly	-4798
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-osen	-4815
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-alse	-4817
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-uce	-4843
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-reak	-4854
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-she	-4856
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-que	-4861
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-olic	-4867
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-ndom	-4873
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-owing	-4877
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-ida	-4888
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-kes	-4893
-izz	-4894
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-stood	-4899
-VER	-4900
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-ateful	-4907
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-▁losing	-4917
-▁Hot	-4918
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-IM	-4920
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-▁Techn	-4922
-iency	-4923
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-▁Group	-4925
-▁evil	-4926
-▁Mem	-4927
-▁core	-4928
-▁bread	-4929
-▁sky	-4930
-▁delicious	-4931
-▁teen	-4932
-▁criminal	-4933
-antic	-4934
-ading	-4935
-As	-4936
-▁accord	-4937
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-ship	-4939
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-▁bare	-4941
-▁stuck	-4942
-cel	-4943
-▁Jim	-4944
-▁loves	-4945
-▁thousand	-4946
-ibilities	-4947
-▁Come	-4948
-▁chat	-4949
-dr	-4950
-▁switch	-4951
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-▁ring	-4954
-▁orders	-4955
-icial	-4956
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-anging	-4958
-▁submit	-4959
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-▁explan	-4961
-▁Ev	-4962
-nel	-4963
-▁shit	-4964
-while	-4965
-outs	-4966
-▁uns	-4967
-▁News	-4968
-▁reply	-4969
-▁..	-4970
-▁gotten	-4971
-iche	-4972
-▁chapter	-4973
-▁proposed	-4974
-▁dedicated	-4975
-▁smooth	-4976
-agram	-4977
-▁proof	-4978
-▁approximately	-4979
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-▁south	-4981
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-▁courses	-4983
-▁manufacture	-4984
-▁Supp	-4985
-cipl	-4986
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-▁dance	-4991
-osis	-4992
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-▁Should	-4996
-▁situations	-4997
-▁sufficient	-4998
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-uine	-5004
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-US	-5007
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-iture	-5014
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-itect	-5018
-nding	-5019
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-rey	-5021
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-ula	-5023
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-orney	-5029
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-illing	-5031
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-▁Could	-5033
-▁random	-5034
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-ege	-5037
-▁everybody	-5038
-▁Loc	-5039
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-▁George	-5041
-▁Val	-5042
-▁dy	-5043
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-ocolate	-5057
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-erves	-5066
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-pan	-5072
-▁millions	-5073
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-▁ought	-5075
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-▁massive	-5078
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-▁Star	-5080
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-ilos	-5084
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-hand	-5090
-ya	-5091
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-UR	-5094
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-▁yeah	-5096
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-..."	-5098
-ivered	-5099
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-▁Green	-5102
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-ixt	-5104
-▁smell	-5105
-Wh	-5106
-gging	-5107
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-rage	-5114
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-iety	-5117
-▁iron	-5118
-▁Mrs	-5119
-semb	-5120
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-OD	-5122
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-▁maximum	-5125
-▁allowing	-5126
-cient	-5127
-chol	-5128
-▁chemical	-5129
-▁messages	-5130
-▁Indian	-5131
-eper	-5132
-▁fool	-5133
-▁outdo	-5134
-pends	-5135
-▁kiss	-5136
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-uclear	-5140
-▁quant	-5141
-▁somewhat	-5142
-▁unf	-5143
-▁element	-5144
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-ems	-5146
-▁magic	-5147
-▁dropped	-5148
-▁instructions	-5149
-▁facility	-5150
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-isted	-5152
-▁aside	-5153
-iveness	-5154
-point	-5155
-▁breast	-5156
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-resp	-5158
-Ch	-5159
-▁Port	-5160
-▁magaz	-5161
-▁parking	-5162
-cknow	-5163
-itate	-5164
-hem	-5165
-▁La	-5166
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-▁skill	-5168
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-▁Mex	-5170
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-▁Dist	-5172
-▁genuine	-5173
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-phas	-5176
-▁Second	-5177
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-▁grew	-5179
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-▁Sorry	-5184
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-olly	-5190
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-oking	-5198
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-▁Av	-5202
-▁hat	-5203
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-elines	-5208
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-?!	-5214
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-ta	-5216
-away	-5217
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-ingly	-5349
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-essor	-5388
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-ien	-5398
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-arge	-5410
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-istry	-5415
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-inate	-5422
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-bles	-5467
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-cking	-5486
-fe	-5487
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-▁involve	-5489
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-encing	-5492
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-ician	-5502
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-ipp	-5509
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-isters	-5515
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-teen	-5522
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-ective	-5526
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-abet	-5528
-ady	-5529
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-When	-5537
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-rate	-5541
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-reprene	-5551
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-aded	-5596
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-ena	-5632
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-inations	-5635
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-cluding	-5638
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-ography	-5681
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-vin	-5685
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-ashed	-5687
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-orable	-5709
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-anta	-5711
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-PS	-5713
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-ilty	-5717
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-rist	-5725
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-uration	-5727
-ores	-5728
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-▁Ret	-5732
-▁Sun	-5733
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-hors	-5735
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-▁AS	-5737
-▁Cle	-5738
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-▁architect	-5740
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-aped	-5744
-iat	-5745
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-TM	-5750
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-athy	-5753
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-uel	-5759
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-roid	-5778
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-orgeous	-5783
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-ilding	-5793
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-EM	-5810
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-IV	-5814
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-ials	-5817
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-pected	-5821
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-section	-5824
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-ef	-5828
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-itam	-5830
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-cont	-5833
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-ighter	-5838
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-ogue	-5843
-uting	-5844
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-urse	-5852
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-ias	-5863
-ga	-5864
-▁chart	-5865
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-ket	-5870
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-outhern	-5872
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-umber	-5874
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-oor	-5897
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-▁Real	-5903
-▁oh	-5904
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-▁Without	-5907
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-For	-5916
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-▁Fire	-5922
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-pret	-5932
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-elf	-5936
-hetic	-5937
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-gun	-5945
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-istan	-5947
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-urt	-5959
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-tra	-5964
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-rors	-5967
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-nda	-5994
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-ching	-5998
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-bered	-6005
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-eration	-6009
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-cribe	-6024
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-rence	-7056
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-uls	-7094
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-▁End	-7099
-▁Walk	-7100
-▁satisfaction	-7101
-▁weapon	-7102
-▁ocean	-7103
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-there	-7105
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-▁intense	-7113
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-gers	-7118
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-iece	-7141
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-uous	-7146
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-rete	-7150
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-▁Dra	-7152
-▁thirty	-7153
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-ipt	-7156
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-▁reduction	-7159
-atin	-7160
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-......	-7162
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-eries	-7166
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-▁fallen	-7169
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-▁objective	-7172
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-ensity	-7190
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-rid	-7194
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-uing	-7198
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-CC	-7200
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-inger	-7205
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-ita	-7213
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-ulty	-7217
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-verty	-7223
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-▁Chief	-7226
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-irts	-7230
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-▁WE	-7233
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-▁Fair	-7237
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-cling	-7248
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-eping	-7252
-igan	-7253
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-hour	-7255
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-lessly	-7263
-oted	-7264
-icate	-7265
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-ashing	-7270
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-lym	-7278
-OC	-7279
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-who	-7291
-uten	-7292
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-pel	-7294
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-erate	-7300
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-rated	-7304
-ifies	-7305
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-making	-7313
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-ultural	-7317
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-▁Ill	-7324
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-icks	-7327
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-ESS	-7333
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-ikes	-7340
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-Yeah	-7344
-▁sooner	-7345
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-▁ye	-7350
-actions	-7351
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-atre	-7354
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-oral	-7372
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-▁je	-7378
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-▁Ireland	-7380
-aste	-7381
-worth	-7382
-▁Sup	-7383
-▁Doctor	-7384
-▁Fig	-7385
-▁attempts	-7386
-▁crash	-7387
-▁diabetes	-7388
-▁rum	-7389
-▁ske	-7390
-▁math	-7391
-▁THAT	-7392
-▁remembered	-7393
-yes	-7394
-▁define	-7395
-▁tank	-7396
-▁employers	-7397
-▁phenomen	-7398
-▁Er	-7399
-Be	-7400
-▁rice	-7401
-▁Technology	-7402
-oston	-7403
-htt	-7404
-▁Richard	-7405
-▁fascinating	-7406
-atulations	-7407
-▁Ep	-7408
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-▁uncom	-7410
-▁certificate	-7411
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-▁involving	-7413
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-uality	-7416
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-gl	-7418
-amber	-7419
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-ensus	-7423
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-ographic	-7428
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-athered	-7436
-REE	-7437
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-ubs	-7442
-front	-7443
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-',	-7448
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-weet	-7461
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-ixture	-7465
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-On	-7467
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-pered	-7474
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-lla	-7476
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-adays	-7480
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-elve	-7494
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-jo	-7500
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-month	-7520
-▁Week	-7521
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-▁minister	-7529
-otes	-7530
-Then	-7531
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-plete	-7539
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-▁isol	-7547
-ican	-7548
-▁Cra	-7549
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-▁unfortunately	-7551
-▁hits	-7552
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-nings	-7556
-An	-7557
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-▁Ten	-7560
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-▁tong	-7563
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-▁surve	-7566
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-da	-7570
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-lements	-7577
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-eem	-7587
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-usions	-7589
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-gines	-7593
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-NA	-7597
-II	-7598
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-uana	-7600
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-ustration	-7602
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-ller	-7605
-ureau	-7606
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-erent	-7610
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-Ex	-7615
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-kins	-7618
-asty	-7619
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-VD	-7622
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-oes	-7625
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-friendly	-7628
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-▁reveal	-7635
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-▁statistics	-7640
-▁blessed	-7641
-ca	-7642
-▁permitted	-7643
-▁Whatever	-7644
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-bted	-7646
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-hot	-7648
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-▁mob	-7659
-eding	-7660
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-Our	-8538
-pire	-8539
-Of	-8540
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-acher	-8545
-inding	-8546
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-minute	-8583
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-▁describes	-8615
-▁Ra	-8616
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-▁compassion	-8619
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-▁frustrating	-8621
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-▁unp	-8629
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-▁protecting	-8633
-▁Wild	-8634
-▁motivation	-8635
-▁nowhere	-8636
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-._	-8639
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-apos	-8642
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-\\	-8651
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-▁Either	-8653
-oir	-8654
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-▁twelve	-8657
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-▁fruits	-8659
-▁ARE	-8660
-▁Certain	-8661
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-▁yoga	-8665
-▁inject	-8666
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-▁batt	-8669
-▁Pass	-8670
-▁aims	-8671
-▁exceptional	-8672
-▁Dark	-8673
-▁roughly	-8674
-never	-8675
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-▁gross	-8677
-▁editing	-8678
-rich	-8679
-▁survival	-8680
-▁tender	-8681
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-▁dies	-8683
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-▁pup	-8685
-▁baseball	-8686
-▁viewing	-8687
-▁landing	-8688
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-▁Est	-8691
-▁clubs	-8692
-▁Champ	-8693
-acon	-8694
-▁distingu	-8695
-Pro	-8696
-▁Prime	-8697
-▁skilled	-8698
-▁productivity	-8699
-▁neut	-8700
-▁integrity	-8701
-▁Prom	-8702
-urd	-8703
-▁Ron	-8704
-▁Bad	-8705
-▁attach	-8706
-▁copyright	-8707
-▁Jones	-8708
-▁resolved	-8709
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-zil	-8711
-utch	-8712
-▁handful	-8713
-▁boards	-8714
-▁chap	-8715
-▁unnecess	-8716
-▁Sea	-8717
-▁hack	-8718
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-▁targeted	-8721
-▁surge	-8722
-bi	-8723
-rolled	-8724
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-▁Kingdom	-8726
-etch	-8727
-world	-8728
-▁explaining	-8729
-▁Living	-8730
-Read	-8731
-▁wherever	-8732
-▁heal	-8733
-aughters	-8734
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-▁thy	-8736
-▁drives	-8737
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-▁interf	-8739
-▁occasions	-8740
-afe	-8741
-▁enthusi	-8742
-OK	-8743
-▁celebration	-8744
-▁inex	-8745
-▁museum	-8746
-▁poison	-8747
-▁announcement	-8748
-za	-8749
-▁Death	-8750
-▁motivated	-8751
-itis	-8752
-▁entertaining	-8753
-▁tub	-8754
-▁comparable	-8755
-▁photographs	-8756
-▁buff	-8757
-▁Mind	-8758
-▁Andrew	-8759
-▁Lind	-8760
-▁emphasis	-8761
-▁citizen	-8762
-▁speaks	-8763
-▁strengthen	-8764
-▁cha	-8765
-▁shadow	-8766
-▁Making	-8767
-▁trigger	-8768
-▁targets	-8769
-lined	-8770
-▁venue	-8771
-▁Cam	-8772
-▁worn	-8773
-▁screens	-8774
-mo	-8775
-▁FREE	-8776
-▁Got	-8777
-stone	-8778
-▁giveaway	-8779
-vity	-8780
-▁knee	-8781
-▁frequent	-8782
-ested	-8783
-▁architecture	-8784
-▁enabled	-8785
-▁girlfriend	-8786
-▁pricing	-8787
-▁Proper	-8788
-▁entrepreneurs	-8789
-▁niche	-8790
-▁dare	-8791
-▁Columb	-8792
-▁Brian	-8793
-▁dumpster	-8794
-▁Taking	-8795
-▁gentleman	-8796
-▁Fred	-8797
-▁entity	-8798
-▁minimal	-8799
-iser	-8800
-▁steady	-8801
-▁allerg	-8802
-ANT	-8803
-▁Training	-8804
-▁wanna	-8805
-hemat	-8806
-▁incom	-8807
-▁Sav	-8808
-etheless	-8809
-oooo	-8810
-▁processed	-8811
-▁ash	-8812
-▁dipl	-8813
-▁//	-8814
-▁Ref	-8815
-▁anxious	-8816
-▁Southern	-8817
-▁passes	-8818
-▁workout	-8819
-▁Space	-8820
-▁Obviously	-8821
-▁Tri	-8822
-▁gaming	-8823
-▁desperate	-8824
-▁lin	-8825
-▁warned	-8826
-▁Guide	-8827
-▁respective	-8828
-▁dealt	-8829
-▁moist	-8830
-ospel	-8831
-▁founded	-8832
-▁Stan	-8833
-▁semi	-8834
-▁temperatures	-8835
-▁fiscal	-8836
-▁liberal	-8837
-▁matches	-8838
-▁physician	-8839
-▁affects	-8840
-▁insights	-8841
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-HS	-8843
-▁diagnosis	-8844
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-▁suck	-8846
-▁Scotland	-8847
-▁neglect	-8848
-▁unto	-8849
-▁gently	-8850
-liers	-8851
-▁Ryan	-8852
-▁instances	-8853
-▁cin	-8854
-▁heating	-8855
-...)	-8856
-enth	-8857
-▁gotta	-8858
-▁Thanksgiving	-8859
-▁qualify	-8860
-▁Dog	-8861
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-▁perf	-8863
-▁knees	-8864
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-agine	-8868
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-iary	-8871
-asion	-8872
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-isd	-8874
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-iano	-8877
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-vation	-8883
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-▁criticism	-8886
-RI	-8887
-http	-8888
-ropri	-8889
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-▁representatives	-8892
-AB	-8893
-▁Sat	-8894
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-anes	-8897
-▁Less	-8898
-▁universities	-8899
-▁Clear	-8900
-▁tremendous	-8901
-▁lip	-8902
-▁Going	-8903
-▁Minist	-8904
-▁movements	-8905
-▁warming	-8906
-▁Fact	-8907
-▁grave	-8908
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-▁developer	-8910
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-urches	-8912
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-erted	-8919
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-aire	-8924
-RL	-8925
-▁Ide	-8926
-▁bom	-8927
-ends	-8928
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-▁Short	-8931
---"	-8932
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-urities	-8934
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-bie	-8939
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-▁EN	-8942
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-oween	-8945
-▁Ha	-8946
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-othe	-8948
-oding	-8949
-igation	-8950
-▁maxim	-8951
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-▁flexibility	-8953
-arer	-8954
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-▁Requ	-8956
-gain	-8957
-zer	-8958
-▁cure	-8959
-▁delivering	-8960
-▁Hand	-8961
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-▁tricks	-8963
-▁rescue	-8964
-▁shelter	-8965
-▁cousin	-8966
-▁stumbled	-8967
-▁hiding	-8968
-fire	-8969
-owers	-8970
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-▁Nations	-8972
-▁deny	-8973
-▁timely	-8974
-▁spray	-8975
-▁acquired	-8976
-▁prescription	-8977
-▁ignored	-8978
-▁magnific	-8979
-▁salad	-8980
-▁peak	-8981
-▁Apparently	-8982
-▁passengers	-8983
-ds	-8984
-▁recre	-8985
-▁Nick	-8986
-tery	-8987
-▁woods	-8988
-▁Johnson	-8989
-▁spelling	-8990
-sect	-8991
-▁investigate	-8992
-▁wand	-8993
-▁grocery	-8994
-▁cher	-8995
-▁western	-8996
-▁shocked	-8997
-▁wider	-8998
-▁capabilities	-8999
-▁Agency	-9000
-▁accidents	-9001
-▁chemicals	-9002
-▁speakers	-9003
-▁wage	-9004
-▁Years	-9005
-▁pizza	-9006
-▁inspire	-9007
-▁manufacturers	-9008
-star	-9009
-was	-9010
-▁nursing	-9011
-▁reasonably	-9012
-▁boyfriend	-9013
-UM	-9014
-▁overnight	-9015
-lay	-9016
-▁Protection	-9017
-when	-9018
-▁advised	-9019
-▁handled	-9020
-▁Five	-9021
-▁gossip	-9022
-▁intrig	-9023
-Go	-9024
-▁Eliz	-9025
-▁actor	-9026
-▁dramatic	-9027
-such	-9028
-▁acceler	-9029
-▁bump	-9030
-▁Lots	-9031
-▁Henry	-9032
-▁Irish	-9033
-▁insult	-9034
-▁cann	-9035
-▁confront	-9036
-▁Moreover	-9037
-▁attain	-9038
-iac	-9039
-▁subsection	-9040
-▁psychological	-9041
-▁Additionally	-9042
-▁harass	-9043
-▁Democratic	-9044
-▁failing	-9045
-▁cul	-9046
-▁hired	-9047
-▁evaluate	-9048
-▁denied	-9049
-▁references	-9050
-▁Due	-9051
-PM	-9052
-▁Prince	-9053
-?’	-9054
-atell	-9055
-organ	-9056
-▁Friends	-9057
-▁lens	-9058
-▁Assist	-9059
-▁initiatives	-9060
-▁appreciation	-9061
-about	-9062
-▁Northern	-9063
-▁sons	-9064
-▁Fort	-9065
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-▁Mess	-9067
-ags	-9068
-pany	-9069
-sole	-9070
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-ua	-9073
-▁attempted	-9074
-▁stab	-9075
-▁obligation	-9076
-▁Birth	-9077
-kward	-9078
-umer	-9079
-▁EVER	-9080
-▁Doc	-9081
-orious	-9082
-▁True	-9083
-▁decrease	-9084
-▁fantasy	-9085
-▁Join	-9086
-arry	-9087
-▁Almost	-9088
-rep	-9089
-▁Except	-9090
-▁Abs	-9091
-▁Site	-9092
-▁awarded	-9093
-five	-9094
-▁Ult	-9095
-▁cares	-9096
-uating	-9097
-▁jewel	-9098
-alog	-9099
-▁Within	-9100
-▁endless	-9101
-▁realistic	-9102
-▁retreat	-9103
-▁dirt	-9104
-▁fond	-9105
-uter	-9106
-▁Die	-9107
-ordan	-9108
-▁ugly	-9109
-▁diagnosed	-9110
-▁Cir	-9111
-▁virus	-9112
-requ	-9113
-▁regulatory	-9114
-▁glory	-9115
-▁lawsu	-9116
-▁friendship	-9117
-▁odds	-9118
-▁disabled	-9119
-▁bankruptcy	-9120
-▁Peace	-9121
-▁meantime	-9122
-cul	-9123
-▁intervention	-9124
-▁template	-9125
-▁reviewed	-9126
-▁container	-9127
-▁Works	-9128
-▁finishing	-9129
-abeth	-9130
-▁patch	-9131
-▁discipline	-9132
-▁hospitals	-9133
-▁outfit	-9134
-▁subscription	-9135
-▁Otherwise	-9136
-▁contemporary	-9137
-▁oral	-9138
-▁deciding	-9139
-▁resulted	-9140
-▁concluded	-9141
-▁fulfill	-9142
-▁renov	-9143
-bye	-9144
-▁Annual	-9145
-▁suspend	-9146
-▁dict	-9147
-▁pepper	-9148
-▁bullet	-9149
-ealous	-9150
-▁viewed	-9151
-▁Chair	-9152
-ership	-9153
-▁nic	-9154
-▁supporters	-9155
-▁relaxed	-9156
-Where	-9157
-▁abund	-9158
-gra	-9159
-▁gest	-9160
-iams	-9161
-▁royal	-9162
-▁pension	-9163
-Come	-9164
-▁Pop	-9165
-▁fifth	-9166
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-▁clue	-9168
-▁SC	-9169
-▁abst	-9170
-▁ly	-9171
-▁dread	-9172
-chers	-9173
-▁displ	-9174
-through	-9175
-▁centers	-9176
-mma	-9177
-▁Brazil	-9178
-▁fles	-9179
-▁irrit	-9180
-▁acquire	-9181
-▁gratitude	-9182
-week	-9183
-▁cooked	-9184
-▁Beautiful	-9185
-▁themes	-9186
-▁Ray	-9187
-thew	-9188
-▁medications	-9189
-▁waves	-9190
-▁nin	-9191
-▁execution	-9192
-▁slip	-9193
-▁Rather	-9194
-▁Charlie	-9195
-▁eleg	-9196
-▁Georg	-9197
-estly	-9198
-▁proportion	-9199
-▁Brother	-9200
-▁blend	-9201
-▁Run	-9202
-▁Break	-9203
-▁combine	-9204
-▁offense	-9205
-▁alleg	-9206
-▁disabilities	-9207
-▁notion	-9208
-▁invention	-9209
-▁suggesting	-9210
-▁exch	-9211
-erts	-9212
-▁ski	-9213
-▁alien	-9214
-▁alongside	-9215
-▁cooperation	-9216
-▁inspection	-9217
-▁rocks	-9218
-▁fate	-9219
-ionship	-9220
-your	-9221
-aware	-9222
-▁desp	-9223
-▁wings	-9224
-ects	-9225
-home	-9226
-▁kindness	-9227
-ailable	-9228
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-▁Daily	-9230
-▁licensed	-9231
-▁fears	-9232
-▁stranger	-9233
-▁accompanied	-9234
-IVE	-9235
-▁bands	-9236
-▁punishment	-9237
-mal	-9238
-▁actors	-9239
-uming	-9240
-▁assistant	-9241
-▁Member	-9242
-sex	-9243
-▁spectacular	-9244
-▁Jac	-9245
-▁Kevin	-9246
-▁Pack	-9247
-▁certified	-9248
-▁indicates	-9249
-▁developments	-9250
-▁wrapped	-9251
-▁favorites	-9252
-asm	-9253
-ommy	-9254
-▁anytime	-9255
-▁junk	-9256
-▁arrangement	-9257
-▁LE	-9258
-▁automatic	-9259
-▁Ice	-9260
-▁timing	-9261
-▁comic	-9262
-▁Pacific	-9263
-▁Pak	-9264
-▁fortune	-9265
-▁determination	-9266
-ura	-9267
-▁climb	-9268
-▁continuous	-9269
-▁bones	-9270
-▁LA	-9271
-▁Strateg	-9272
-▁leather	-9273
-▁captured	-9274
-▁Fa	-9275
-▁violation	-9276
-▁collective	-9277
-vol	-9278
-▁franch	-9279
-▁Ve	-9280
-finitely	-9281
-▁Isn	-9282
-ifully	-9283
-▁Save	-9284
-▁brush	-9285
-▁Tar	-9286
-Look	-9287
-▁Case	-9288
-▁WITH	-9289
-market	-9290
-▁CL	-9291
-▁embrace	-9292
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-oubtedly	-9448
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-▁HERE	-9470
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-▁Third	-9474
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-▁Liz	-9489
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-▁Williams	-9496
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-▁Major	-9499
-▁kil	-9500
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-otion	-9503
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-orship	-9590
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-igence	-9623
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-Ar	-9699
-IE	-9700
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-isy	-9774
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-apore	-9785
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-gments	-9890
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-imm	-10103
-▁memorable	-10104
-▁Laur	-10105
-▁ONE	-10106
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-▁prospects	-10109
-▁WILL	-10110
-▁Planning	-10111
-▁Quick	-10112
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-▁Ens	-10119
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-▁conj	-10125
-▁Figure	-10126
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-▁justify	-10136
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-uates	-10141
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-heast	-10148
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-▁artificial	-10150
-iosity	-10151
-▁soldier	-10152
-▁rape	-10153
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-▁́	-10164
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-▁assure	-10166
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-▁premises	-10168
-inator	-10169
-▁companion	-10170
-IGHT	-10171
-▁Nature	-10172
-▁viewers	-10173
-▁Fac	-10174
-▁mum	-10175
-▁lamp	-10176
-▁instinct	-10177
-olds	-10178
-▁accommodate	-10179
-▁swear	-10180
-▁facilitate	-10181
-▁legend	-10182
-▁teens	-10183
-▁vitamin	-10184
-▁UP	-10185
-arks	-10186
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-▁Kn	-10188
-▁reactions	-10189
-ulse	-10190
-▁utility	-10191
-▁Nevertheless	-10192
-▁placement	-10193
-▁submission	-10194
-intage	-10195
-▁blessing	-10196
-vironments	-10197
-▁stroke	-10198
-▁concentration	-10199
-▁Perfect	-10200
-proof	-10201
-▁exhibition	-10202
-▁Singapore	-10203
-▁punch	-10204
-▁surviv	-10205
-▁oxy	-10206
-▁awhile	-10207
-▁bol	-10208
-▁Impro	-10209
-▁basics	-10210
-▁Pers	-10211
-▁engineers	-10212
-▁kingdom	-10213
-▁Were	-10214
-)?	-10215
-▁keyboard	-10216
-itative	-10217
-▁radiation	-10218
-▁Others	-10219
-▁ax	-10220
-▁safer	-10221
-▁completing	-10222
-▁Bene	-10223
-▁striking	-10224
-▁donate	-10225
-achelor	-10226
-▁Governor	-10227
-▁Farm	-10228
-▁elabor	-10229
-▁idiot	-10230
-▁olive	-10231
-▁monster	-10232
-▁Convention	-10233
-▁devil	-10234
-▁traged	-10235
-▁fifteen	-10236
-▁Job	-10237
-▁CA	-10238
-iatric	-10239
-▁healthier	-10240
-high	-10241
-▁corruption	-10242
-▁RSS	-10243
-▁Unlike	-10244
-▁homeown	-10245
-▁boundaries	-10246
-OME	-10247
-▁Kar	-10248
-▁mud	-10249
-▁satell	-10250
-lihood	-10251
-ishop	-10252
-▁€	-10253
-▁Award	-10254
-▁commented	-10255
-link	-10256
-▁jer	-10257
-ping	-10258
-▁tomatoes	-10259
-▁poster	-10260
-▁sexy	-10261
-▁appet	-10262
-▁contacted	-10263
-LC	-10264
-▁exclus	-10265
-▁killer	-10266
-alo	-10267
-▁layers	-10268
-▁jumping	-10269
-▁supplements	-10270
-mod	-10271
-▁pause	-10272
-▁clarity	-10273
-▁distant	-10274
-▁vendors	-10275
-▁assert	-10276
-▁liable	-10277
-▁floors	-10278
-▁chuck	-10279
-▁transmission	-10280
-▁lover	-10281
-ambling	-10282
-▁Winter	-10283
-▁locally	-10284
-mg	-10285
-▁NFL	-10286
-▁Nic	-10287
-▁Pub	-10288
-▁complaining	-10289
-▁identifying	-10290
-irk	-10291
-▁mentally	-10292
-▁indu	-10293
-▁determining	-10294
-hr	-10295
-ribly	-10296
-▁thumb	-10297
-▁efficiently	-10298
-▁Independ	-10299
-▁disrupt	-10300
-▁happily	-10301
-ITY	-10302
-▁consequence	-10303
-hearted	-10304
-▁batteries	-10305
-▁excessive	-10306
-▁DNA	-10307
-▁Andy	-10308
-▁celebrating	-10309
-,--	-10310
-▁terrorist	-10311
-American	-10312
-PT	-10313
-▁Vi	-10314
-▁solved	-10315
-▁sheets	-10316
-▁jam	-10317
-▁demanded	-10318
-▁inhab	-10319
-▁succ	-10320
-▁athletes	-10321
-▁passenger	-10322
-▁servers	-10323
-▁requiring	-10324
-▁Po	-10325
-▁theater	-10326
-scale	-10327
-!'	-10328
-▁tower	-10329
-▁dressing	-10330
-ho	-10331
-▁lawn	-10332
-▁organize	-10333
-onic	-10334
-▁Month	-10335
-▁subtle	-10336
-▁necessity	-10337
-reedom	-10338
-▁arbit	-10339
-▁guilt	-10340
-▁Ce	-10341
-▁caps	-10342
-▁minority	-10343
-▁Application	-10344
-▁releases	-10345
-▁semin	-10346
-▁funded	-10347
-erable	-10348
-▁interpre	-10349
-ilipp	-10350
-▁Buff	-10351
-▁Java	-10352
-▁Truth	-10353
-▁occupied	-10354
-▁discretion	-10355
-▁governor	-10356
-▁theatre	-10357
-▁grief	-10358
-▁doub	-10359
-▁cultiv	-10360
-offs	-10361
-feed	-10362
-▁brut	-10363
-ieved	-10364
-▁incorrect	-10365
-▁amenities	-10366
-▁sore	-10367
-izer	-10368
-▁navigate	-10369
-▁Tips	-10370
-iti	-10371
-▁TIP	-10372
-▁spirits	-10373
-▁annually	-10374
-eks	-10375
-▁Kent	-10376
-▁closet	-10377
-▁finest	-10378
-▁partly	-10379
-▁countless	-10380
-▁frustrated	-10381
-▁cater	-10382
-▁environments	-10383
-▁convincing	-10384
-overs	-10385
-▁Fant	-10386
-▁kicked	-10387
-edge	-10388
-▁adventures	-10389
-▁abortion	-10390
-▁signature	-10391
-▁grants	-10392
-hesis	-10393
-▁cord	-10394
-occ	-10395
-▁HR	-10396
-▁midnight	-10397
-▁ancest	-10398
-quarters	-10399
-▁Move	-10400
-▁render	-10401
-▁perceived	-10402
-▁Relations	-10403
-▁FBI	-10404
-▁User	-10405
-▁perception	-10406
-▁loop	-10407
-▁intr	-10408
-aired	-10409
-▁Bless	-10410
-▁firmly	-10411
-print	-10412
-▁solo	-10413
-▁picks	-10414
-▁unfair	-10415
-especially	-10416
-▁spreading	-10417
-▁drain	-10418
-rape	-10419
-▁mathemat	-10420
-▁terrific	-10421
-▁breathe	-10422
-▁stating	-10423
-▁Georgia	-10424
-appropri	-10425
-blo	-10426
-▁wars	-10427
-▁workforce	-10428
-▁sentences	-10429
-▁meters	-10430
-▁presum	-10431
-▁expanded	-10432
-▁sac	-10433
-▁mattress	-10434
-▁variable	-10435
-▁composition	-10436
-▁defensive	-10437
-▁paintings	-10438
-▁grows	-10439
-conf	-10440
-▁careers	-10441
-▁agricultural	-10442
-▁Lew	-10443
-fortunate	-10444
-▁HAVE	-10445
-▁faithful	-10446
-▁Golden	-10447
-▁varied	-10448
-cellence	-10449
-▁Field	-10450
-▁feeds	-10451
-ogen	-10452
-▁bugs	-10453
-▁attendance	-10454
-iffs	-10455
-▁ethical	-10456
-▁Easter	-10457
-▁Stephen	-10458
-hew	-10459
-angle	-10460
-▁tennis	-10461
-▁bonds	-10462
-Some	-10463
-▁expanding	-10464
-▁stakeholders	-10465
-ogy	-10466
-ults	-10467
-▁Orig	-10468
-▁audit	-10469
-▁hazard	-10470
-▁oils	-10471
-▁imagined	-10472
-▁princess	-10473
-▁applicants	-10474
-iami	-10475
-▁attracted	-10476
-▁sew	-10477
-▁torn	-10478
-▁thoughtful	-10479
-▁entrepreneur	-10480
-▁branches	-10481
-▁gray	-10482
-▁nasty	-10483
-▁beating	-10484
-▁manifest	-10485
-▁vague	-10486
-▁clouds	-10487
-isode	-10488
-▁Cut	-10489
-pled	-10490
-▁Hou	-10491
-▁Capital	-10492
-▁Turkey	-10493
-▁Quality	-10494
-▁rewards	-10495
-▁newspapers	-10496
-▁admire	-10497
-store	-10498
-▁fever	-10499
-▁sequence	-10500
-▁accompany	-10501
-▁secured	-10502
-▁Future	-10503
-▁packaging	-10504
-▁forb	-10505
-MA	-10506
-▁inevitable	-10507
-▁counting	-10508
-▁iPad	-10509
-ishers	-10510
-uan	-10511
-▁Contract	-10512
-▁sevent	-10513
-▁tastes	-10514
-▁modified	-10515
-▁Assembly	-10516
-▁verify	-10517
-▁invitation	-10518
-▁configuration	-10519
-▁prominent	-10520
-olit	-10521
-▁Intell	-10522
-ifle	-10523
-annel	-10524
-▁Guy	-10525
-▁operated	-10526
-▁honour	-10527
-▁immigrants	-10528
-▁ranging	-10529
-onel	-10530
-reek	-10531
-▁AF	-10532
-▁Writing	-10533
-▁Pu	-10534
-lims	-10535
-▁algorith	-10536
-▁Cub	-10537
-oples	-10538
-▁Bang	-10539
-▁Kong	-10540
-▁bund	-10541
-▁Authority	-10542
-▁Sche	-10543
-▁departure	-10544
-▁stones	-10545
-▁Seattle	-10546
-▁cleaned	-10547
-▁briefly	-10548
-ushes	-10549
-▁advocate	-10550
-▁funeral	-10551
-▁Brad	-10552
-▁searched	-10553
-ifer	-10554
-▁pursuant	-10555
-▁**	-10556
-▁Bi	-10557
-▁Cru	-10558
-ockey	-10559
-▁hills	-10560
-▁Mobile	-10561
-▁jewelry	-10562
-▁strip	-10563
-CA	-10564
-ailing	-10565
-▁aston	-10566
-▁Depending	-10567
-▁genius	-10568
-although	-10569
-▁authentic	-10570
-Pe	-10571
-▁cosm	-10572
-Because	-10573
-▁Along	-10574
-▁Cancer	-10575
-aiting	-10576
-ocus	-10577
-▁earning	-10578
-▁beautifully	-10579
-▁BUT	-10580
-zona	-10581
-▁simult	-10582
-▁Body	-10583
-▁semes	-10584
-▁deduct	-10585
-▁contrad	-10586
-Here	-10587
-regon	-10588
-abling	-10589
-atched	-10590
-▁comedy	-10591
-▁hypot	-10592
-▁testimony	-10593
-bro	-10594
-▁clinic	-10595
-▁occasional	-10596
-▁understands	-10597
-▁reserves	-10598
-▁NEW	-10599
-▁demanding	-10600
-author	-10601
-▁convicted	-10602
-▁wow	-10603
-igious	-10604
-:)	-10605
-"?	-10606
-],	-10607
-hire	-10608
-imer	-10609
-▁subscrib	-10610
-▁breach	-10611
-▁lands	-10612
-lend	-10613
-▁frustration	-10614
-▁Smart	-10615
-▁apologize	-10616
-▁Ur	-10617
-▁moderate	-10618
-arency	-10619
-▁carries	-10620
-▁disadvant	-10621
-God	-10622
-▁bicy	-10623
-▁immense	-10624
-▁soap	-10625
-▁closest	-10626
-aza	-10627
-▁leaf	-10628
-▁screening	-10629
-▁stem	-10630
-▁intake	-10631
-▁technological	-10632
-▁boats	-10633
-▁Silver	-10634
-▁conducting	-10635
-▁rend	-10636
-cia	-10637
-cip	-10638
-▁avoided	-10639
-name	-10640
-early	-10641
-game	-10642
-▁lap	-10643
-De	-10644
-▁{	-10645
-▁pad	-10646
-▁cocon	-10647
-▁legacy	-10648
-Man	-10649
-▁portra	-10650
-▁lecture	-10651
-▁basement	-10652
-▁disgust	-10653
-▁Clark	-10654
-agra	-10655
-▁Compan	-10656
-aver	-10657
-stra	-10658
-▁shaking	-10659
-▁nearest	-10660
-▁landed	-10661
-▁representing	-10662
-third	-10663
-▁Affairs	-10664
-▁Private	-10665
-▁Success	-10666
-liest	-10667
-▁connecting	-10668
-aters	-10669
-▁intu	-10670
-▁Wars	-10671
-UC	-10672
-▁fart	-10673
-▁genre	-10674
-AST	-10675
-isp	-10676
-▁Bern	-10677
-▁quoted	-10678
-▁charming	-10679
-▁performances	-10680
-▁restricted	-10681
-▁Iron	-10682
-bet	-10683
-neath	-10684
-▁suspected	-10685
-igen	-10686
-▁shapes	-10687
-osc	-10688
-▁Las	-10689
-▁offence	-10690
-▁franchise	-10691
-check	-10692
-▁Rick	-10693
-oore	-10694
-▁Days	-10695
-▁Oil	-10696
-▁argued	-10697
-▁disposal	-10698
-▁slept	-10699
-▁Perm	-10700
-▁bulk	-10701
-▁certification	-10702
-▁Luke	-10703
-emption	-10704
-▁unre	-10705
-▁specialized	-10706
-eous	-10707
-▁Content	-10708
-▁tob	-10709
-▁tatt	-10710
-▁Taylor	-10711
-▁Soon	-10712
-▁awake	-10713
-chester	-10714
-▁snack	-10715
-cal	-10716
-▁Incre	-10717
-▁survived	-10718
-▁PRO	-10719
-▁Cab	-10720
-▁shade	-10721
-▁celebrated	-10722
-▁fatal	-10723
-stop	-10724
-▁shield	-10725
-▁cu	-10726
-▁Mach	-10727
-▁Shop	-10728
-istical	-10729
-▁tweet	-10730
-▁supplied	-10731
-ateral	-10732
-▁coaches	-10733
-▁barrel	-10734
-▁thou	-10735
-▁situated	-10736
-▁landl	-10737
-▁Currently	-10738
-▁tourists	-10739
-▁scrap	-10740
-▁enjoys	-10741
-▁touching	-10742
-▁progressive	-10743
-▁mit	-10744
-▁auction	-10745
-▁stressed	-10746
-▁believing	-10747
-▁Syria	-10748
-▁shelf	-10749
-▁avoiding	-10750
-▁Afghanistan	-10751
-▁incidents	-10752
-omeday	-10753
-▁Dise	-10754
-▁generic	-10755
-▁Line	-10756
-piracy	-10757
-▁flights	-10758
-▁spectrum	-10759
-unity	-10760
-▁booked	-10761
-hole	-10762
-▁biological	-10763
-▁fluid	-10764
-bies	-10765
-▁contempl	-10766
-▁Professional	-10767
-ptic	-10768
-▁cope	-10769
-▁treats	-10770
-enders	-10771
-▁camping	-10772
-▁gardens	-10773
-▁cookie	-10774
-▁entities	-10775
-▁prisoners	-10776
-EE	-10777
-▁Amazing	-10778
-beat	-10779
-backs	-10780
-▁mart	-10781
-▁unfortunate	-10782
-▁rewarding	-10783
-▁collapse	-10784
-▁tri	-10785
-▁cups	-10786
-▁Beh	-10787
-▁freel	-10788
-▁cruise	-10789
-TC	-10790
-▁fib	-10791
-▁mercy	-10792
-▁Square	-10793
-▁physics	-10794
-▁responding	-10795
-▁aunt	-10796
-▁affecting	-10797
-▁hosted	-10798
-▁Dun	-10799
-gi	-10800
-▁casino	-10801
-▁designing	-10802
-▁bargain	-10803
-▁keywords	-10804
-▁tuned	-10805
-atile	-10806
-▁Mountain	-10807
-▁contam	-10808
-screen	-10809
-▁Level	-10810
-▁cloth	-10811
-▁theories	-10812
-orneys	-10813
-▁ip	-10814
-▁colleges	-10815
-ilson	-10816
-lete	-10817
-▁versus	-10818
-izations	-10819
-Or	-10820
-▁Happ	-10821
-ousing	-10822
-▁Environmental	-10823
-▁grain	-10824
-▁Particip	-10825
-ierce	-10826
-▁pressing	-10827
-▁carrier	-10828
-▁remed	-10829
-osex	-10830
-▁Toronto	-10831
-▁corrupt	-10832
-▁hem	-10833
-ilation	-10834
-▁WHAT	-10835
-cor	-10836
-▁bay	-10837
-▁piano	-10838
-▁Process	-10839
-▁Broad	-10840
-▁significance	-10841
-▁Experience	-10842
-▁magazines	-10843
-▁calculated	-10844
-▁consulting	-10845
-▁executed	-10846
-adows	-10847
-▁Tele	-10848
-▁neutral	-10849
-▁liberty	-10850
-▁wounded	-10851
-atal	-10852
-kers	-10853
-▁vert	-10854
-imental	-10855
-▁defence	-10856
-▁reserved	-10857
-ainted	-10858
-▁preference	-10859
-▁meditation	-10860
-▁twist	-10861
-▁Niger	-10862
-soever	-10863
-▁preventing	-10864
-▁fatig	-10865
-▁gods	-10866
-▁licence	-10867
-▁mysterious	-10868
-▁sup	-10869
-▁Download	-10870
-▁envelop	-10871
-▁broker	-10872
-▁mall	-10873
-▁strikes	-10874
-▁Occ	-10875
-▁utilize	-10876
-esy	-10877
-anguage	-10878
-▁intimate	-10879
-▁smartphone	-10880
-▁cents	-10881
-▁oxygen	-10882
-oper	-10883
-▁gaining	-10884
-▁surprisingly	-10885
-coin	-10886
-▁Hong	-10887
-▁flip	-10888
-▁agriculture	-10889
-IDS	-10890
-aska	-10891
-uum	-10892
-▁curiosity	-10893
-throp	-10894
-▁Hum	-10895
-▁Oregon	-10896
-▁Privacy	-10897
-wind	-10898
-▁Ap	-10899
-Book	-10900
-▁lord	-10901
-▁intimid	-10902
-oons	-10903
-▁Bed	-10904
-alling	-10905
-▁Kelly	-10906
-▁panels	-10907
-▁Ram	-10908
-▁nap	-10909
-card	-10910
-▁weekends	-10911
-▁indication	-10912
-normal	-10913
-▁worrying	-10914
-▁formation	-10915
-▁buzz	-10916
-▁crop	-10917
-▁promising	-10918
-▁nonsense	-10919
-▁bio	-10920
-▁Amend	-10921
-By	-10922
-iries	-10923
-ester	-10924
-▁coconut	-10925
-▁aer	-10926
-▁Cover	-10927
-▁inflamm	-10928
-▁ties	-10929
-▁ramp	-10930
-iability	-10931
-ARD	-10932
-▁Arizona	-10933
-▁thereof	-10934
-aneous	-10935
-▁sadly	-10936
-HA	-10937
-▁phenomenon	-10938
-▁BBC	-10939
-itating	-10940
-▁mature	-10941
-▁modify	-10942
-▁amended	-10943
-▁continually	-10944
-▁overwhelmed	-10945
-piring	-10946
-▁wishing	-10947
-▁conviction	-10948
-Le	-10949
-▁Doug	-10950
-▁tobacco	-10951
-▁Altern	-10952
-▁MR	-10953
-▁Didn	-10954
-▁Yesterday	-10955
-▁competent	-10956
-▁carb	-10957
-▁caution	-10958
-▁Foot	-10959
-▁producers	-10960
-▁dated	-10961
-▁barriers	-10962
-▁exped	-10963
-▁MORE	-10964
-▁profitable	-10965
-▁associate	-10966
-▁laughter	-10967
-▁specialists	-10968
-ulpt	-10969
-isticated	-10970
-▁opponent	-10971
-▁apartments	-10972
-▁sunny	-10973
-ighters	-10974
-▁verse	-10975
-four	-10976
-▁Engineering	-10977
-▁grid	-10978
-holder	-10979
-▁Miami	-10980
-▁stressful	-10981
-▁Past	-10982
-▁describing	-10983
-▁HD	-10984
-▁trap	-10985
-▁Wales	-10986
-vard	-10987
-▁tube	-10988
-watch	-10989
-▁conqu	-10990
-▁Quest	-10991
-▁circuit	-10992
-ila	-10993
-▁Syd	-10994
-▁tid	-10995
-prof	-10996
-▁Anything	-10997
-▁reluct	-10998
-▁crown	-10999
-▁novels	-11000
-▁accordingly	-11001
-▁Bit	-11002
-▁interactions	-11003
-▁("	-11004
-▁Houston	-11005
-▁bree	-11006
-▁preferences	-11007
-▁declined	-11008
-▁ordering	-11009
-▁disclosure	-11010
-ACK	-11011
-ione	-11012
-▁Philipp	-11013
-▁recovered	-11014
-ounge	-11015
-▁conclusions	-11016
-▁negotiations	-11017
-Bl	-11018
-▁enroll	-11019
-▁assembly	-11020
-▁pant	-11021
-two	-11022
-have	-11023
-▁jar	-11024
-▁Labour	-11025
-▁Return	-11026
-ami	-11027
-▁thee	-11028
-▁cared	-11029
-▁solving	-11030
-▁tours	-11031
-▁Email	-11032
-▁blanket	-11033
-▁Imp	-11034
-▁banned	-11035
-▁Tal	-11036
-▁Similar	-11037
-▁ric	-11038
-▁Greg	-11039
-function	-11040
-Thanks	-11041
-grounds	-11042
-▁Regardless	-11043
-▁pose	-11044
-▁souls	-11045
-▁Judge	-11046
-worthy	-11047
-▁DI	-11048
-selling	-11049
-▁CS	-11050
-▁leaned	-11051
-▁dash	-11052
-▁Jenn	-11053
-▁brid	-11054
-▁laying	-11055
-▁leisure	-11056
-osit	-11057
-osity	-11058
-▁Standard	-11059
-intend	-11060
-▁owe	-11061
-▁acted	-11062
-▁secretary	-11063
-▁Barb	-11064
-▁Rule	-11065
-▁establishing	-11066
-orient	-11067
-▁Nation	-11068
-▁documentary	-11069
-▁straightforward	-11070
-These	-11071
-commerce	-11072
-▁Attorney	-11073
-▁province	-11074
-▁crimin	-11075
-▁digest	-11076
-▁incorporate	-11077
-▁Explore	-11078
-▁Simple	-11079
-▁tablets	-11080
-▁gam	-11081
-depth	-11082
-▁neighbour	-11083
-▁decides	-11084
-▁hated	-11085
-▁consume	-11086
-▁ED	-11087
-▁winds	-11088
-▁wonders	-11089
-▁Hur	-11090
-▁affection	-11091
-▁fellows	-11092
-▁Angel	-11093
-▁elegant	-11094
-▁hilar	-11095
-▁shoe	-11096
-▁dive	-11097
-▁intentions	-11098
-▁Palestin	-11099
-table	-11100
-▁temple	-11101
-▁Pick	-11102
-▁ups	-11103
-▁folk	-11104
-▁custody	-11105
-know	-11106
-think	-11107
-▁Harr	-11108
-▁dude	-11109
-▁beaches	-11110
-▁outdoors	-11111
-asted	-11112
-▁module	-11113
-▁constructed	-11114
-OO	-11115
-▁Josh	-11116
-fting	-11117
-▁acres	-11118
-▁aging	-11119
-▁parliament	-11120
-▁metab	-11121
-▁diamond	-11122
-▁conditioning	-11123
-orr	-11124
-▁appealing	-11125
-▁dentist	-11126
-▁reflected	-11127
-▁rings	-11128
-▁assemb	-11129
-▁vaccine	-11130
-▁identical	-11131
-▁hike	-11132
-ricane	-11133
-▁worlds	-11134
-napped	-11135
-▁overt	-11136
-▁revel	-11137
-▁insect	-11138
-▁analyze	-11139
-ASE	-11140
-▁lobby	-11141
-▁Side	-11142
-▁Camer	-11143
-▁substitute	-11144
-▁rounds	-11145
-▁crossing	-11146
-ustain	-11147
-▁essence	-11148
-▁paste	-11149
-▁Hun	-11150
-ciples	-11151
-▁slee	-11152
-▁aver	-11153
-▁wre	-11154
-▁font	-11155
-mate	-11156
-▁mandatory	-11157
-▁castle	-11158
-gets	-11159
-▁journalists	-11160
-▁dece	-11161
-▁Listen	-11162
-▁highway	-11163
-▁respected	-11164
-▁mole	-11165
-▁ruin	-11166
-▁Birthday	-11167
-▁influenced	-11168
-▁continuously	-11169
-▁threatening	-11170
-shirt	-11171
-arts	-11172
-Sp	-11173
-▁terrorism	-11174
-▁artistic	-11175
-▁Maria	-11176
-ba	-11177
-och	-11178
-▁Same	-11179
-▁Pray	-11180
-▁Import	-11181
-▁Episode	-11182
-▁programmes	-11183
-▁embra	-11184
-▁realised	-11185
-▁Systems	-11186
-▁squad	-11187
-▁grip	-11188
-▁costly	-11189
-▁Wonderful	-11190
-aka	-11191
-marked	-11192
-▁blown	-11193
-▁PDF	-11194
-▁equality	-11195
-▁tricky	-11196
-▁educate	-11197
-umbs	-11198
-▁toll	-11199
-▁infections	-11200
-adel	-11201
-illance	-11202
-▁VER	-11203
-▁tragedy	-11204
-▁knocked	-11205
-▁adj	-11206
-bridge	-11207
-▁notification	-11208
-▁rust	-11209
-▁Appro	-11210
-ja	-11211
-▁constra	-11212
-▁reaches	-11213
-ellow	-11214
-▁Blood	-11215
-nity	-11216
-▁Ca	-11217
-▁mechanical	-11218
-▁exclusively	-11219
-why	-11220
-Okay	-11221
-▁suspended	-11222
-▁Rain	-11223
-▁haunt	-11224
-▁emerged	-11225
-▁Different	-11226
-▁gaze	-11227
-▁Software	-11228
-▁charging	-11229
-▁concentrate	-11230
-▁torture	-11231
-▁acquisition	-11232
-mare	-11233
-▁dried	-11234
-▁climbing	-11235
-▁PA	-11236
-covery	-11237
-▁batch	-11238
-▁Ways	-11239
-▁sins	-11240
-▁marvel	-11241
-▁struggled	-11242
-▁circles	-11243
-▁whatsoever	-11244
-▁Victoria	-11245
-▁tendency	-11246
-▁Imm	-11247
-▁Diego	-11248
-burgh	-11249
-▁produces	-11250
-▁Woman	-11251
-▁Hit	-11252
-▁obtaining	-11253
-▁hiking	-11254
-")	-11255
-▁Fast	-11256
-▁divine	-11257
-▁insist	-11258
-▁steep	-11259
-▁angles	-11260
-▁infant	-11261
-▁ratings	-11262
-▁fet	-11263
-▁displays	-11264
-▁Notice	-11265
-working	-11266
-▁receives	-11267
-ixed	-11268
-iciency	-11269
-inson	-11270
-CL	-11271
-▁Sports	-11272
-▁imposed	-11273
-▁semester	-11274
-elect	-11275
-▁stere	-11276
-pool	-11277
-pensive	-11278
-▁WOR	-11279
-▁marketplace	-11280
-itual	-11281
-▁downloaded	-11282
-▁screaming	-11283
-▁indoor	-11284
-▁Jam	-11285
-sis	-11286
-▁Finance	-11287
-▁suited	-11288
-note	-11289
-▁Jay	-11290
-erals	-11291
-▁strictly	-11292
-▁Bat	-11293
-Me	-11294
-supp	-11295
-▁handsome	-11296
-▁angel	-11297
-▁purple	-11298
-▁Transport	-11299
-▁tempor	-11300
-▁fiber	-11301
-▁insisted	-11302
-▁contributing	-11303
-▁Awesome	-11304
-▁tourism	-11305
-azine	-11306
-▁clause	-11307
-▁Carl	-11308
-▁trace	-11309
-▁grandfather	-11310
-▁Iss	-11311
-▁selecting	-11312
-▁Deb	-11313
-cular	-11314
-▁Prior	-11315
-▁competing	-11316
-▁cheek	-11317
-▁lawsuit	-11318
-▁sticking	-11319
-▁mindset	-11320
-▁classified	-11321
-▁Radio	-11322
-▁priced	-11323
-▁Whenever	-11324
-▁descend	-11325
-▁floating	-11326
-osystem	-11327
-raine	-11328
-▁grades	-11329
-▁slave	-11330
-▁Susan	-11331
-ropical	-11332
-▁makers	-11333
-▁neuro	-11334
-▁Opp	-11335
-yr	-11336
-ividually	-11337
-leading	-11338
-▁brew	-11339
-▁Medicine	-11340
-▁wasted	-11341
-viet	-11342
-▁execut	-11343
-asses	-11344
-lan	-11345
-▁surre	-11346
-omin	-11347
-▁"'	-11348
-▁producer	-11349
-▁refriger	-11350
-▁queen	-11351
-▁parameters	-11352
-▁merc	-11353
-▁Includ	-11354
-▁Israeli	-11355
-storm	-11356
-▁plates	-11357
-▁Patrick	-11358
-▁loyalty	-11359
-▁declare	-11360
-▁trim	-11361
-▁Resources	-11362
-▁Rs	-11363
-▁deleted	-11364
-▁warri	-11365
-▁foolish	-11366
-arded	-11367
-▁shine	-11368
-effective	-11369
-▁founder	-11370
-▁operators	-11371
-around	-11372
-▁Stone	-11373
-▁pockets	-11374
-▁wides	-11375
-bound	-11376
-▁heritage	-11377
-▁wealthy	-11378
-levant	-11379
-SP	-11380
-low	-11381
-▁Hor	-11382
-ender	-11383
-▁Seems	-11384
-▁guided	-11385
-▁lonely	-11386
-▁consolid	-11387
-▁inclined	-11388
-▁%	-11389
-emic	-11390
-▁thats	-11391
-▁substances	-11392
-istol	-11393
-▁Valentine	-11394
-▁Lisa	-11395
-▁flew	-11396
-▁emotionally	-11397
-▁cabinet	-11398
-▁dys	-11399
-▁mos	-11400
-▁approve	-11401
-▁Property	-11402
-▁servant	-11403
-▁Script	-11404
-▁Ont	-11405
-▁caf	-11406
-▁Ven	-11407
-ader	-11408
-oting	-11409
-▁compromise	-11410
-▁seller	-11411
-▁wildlife	-11412
-dem	-11413
-▁hello	-11414
-▁Prep	-11415
-▁fitting	-11416
-▁obliged	-11417
-▁Simon	-11418
-▁dramatically	-11419
-▁OP	-11420
-▁strengths	-11421
-▁tum	-11422
-FF	-11423
-▁chip	-11424
-▁pp	-11425
-gree	-11426
-▁exhausted	-11427
-▁welcoming	-11428
-OUN	-11429
-pread	-11430
-▁fare	-11431
-▁contractor	-11432
-▁stain	-11433
-▁sophisticated	-11434
-▁Consult	-11435
-▁Revolution	-11436
-▁Wilson	-11437
-▁specify	-11438
-▁whereas	-11439
-flow	-11440
-▁Vietnam	-11441
-▁NOW	-11442
-open	-11443
-tha	-11444
-▁hardest	-11445
-▁overd	-11446
-▁Sydney	-11447
-▁divers	-11448
-▁quarters	-11449
-▁Generally	-11450
-▁AI	-11451
-▁Cool	-11452
-▁Forest	-11453
-▁sensible	-11454
-▁Books	-11455
-yer	-11456
-▁observations	-11457
-▁que	-11458
-▁rude	-11459
-▁Doesn	-11460
-▁chaos	-11461
-written	-11462
-▁economics	-11463
-sle	-11464
-▁indul	-11465
-▁curve	-11466
-▁inquiry	-11467
-▁snacks	-11468
-▁Var	-11469
-▁cotton	-11470
-▁rib	-11471
-▁infin	-11472
-▁functioning	-11473
-▁deadly	-11474
-▁paths	-11475
-▁conservation	-11476
-▁Ocean	-11477
-▁Nowadays	-11478
-▁jurisdiction	-11479
-▁(“	-11480
-▁finances	-11481
-▁valued	-11482
-▁SA	-11483
-blem	-11484
-▁consultant	-11485
-cur	-11486
-▁Columbia	-11487
-▁inherent	-11488
-▁rushed	-11489
-▁Empire	-11490
-itivity	-11491
-ceptions	-11492
-▁guessing	-11493
-▁nest	-11494
-bury	-11495
-berries	-11496
-▁pond	-11497
-▁gre	-11498
-▁ruled	-11499
-▁graduated	-11500
-▁Drive	-11501
-rup	-11502
-▁suite	-11503
-▁examined	-11504
-▁mistaken	-11505
-▁districts	-11506
-▁Snow	-11507
-▁Luckily	-11508
-▁peoples	-11509
-▁Sadly	-11510
-▁Hawai	-11511
-▁discoura	-11512
-ACT	-11513
-▁BB	-11514
-▁Gi	-11515
-▁Amy	-11516
-inois	-11517
-CD	-11518
-▁amendment	-11519
-▁hosts	-11520
-▁MO	-11521
-utical	-11522
-▁bent	-11523
-▁webpage	-11524
-▁Companies	-11525
-▁transparent	-11526
-▁Uk	-11527
-▁Rub	-11528
-▁bin	-11529
-▁propag	-11530
-oded	-11531
-▁Definitely	-11532
-▁Steven	-11533
-▁Vice	-11534
-▁financially	-11535
-▁indirect	-11536
-▁anticipated	-11537
-door	-11538
-▁remarks	-11539
-▁Recently	-11540
-END	-11541
-ji	-11542
-▁sewing	-11543
-abis	-11544
-▁texts	-11545
-▁Analy	-11546
-▁polite	-11547
-▁composed	-11548
-▁hal	-11549
-xx	-11550
-▁eternal	-11551
-▁misunder	-11552
-ools	-11553
-▁sincere	-11554
-▁balcon	-11555
-isons	-11556
-▁hints	-11557
-▁bun	-11558
-▁unbel	-11559
-▁nerve	-11560
-CO	-11561
-wan	-11562
-▁incon	-11563
-▁trauma	-11564
-▁cognitive	-11565
-▁revenues	-11566
-liness	-11567
-▁spacious	-11568
-▁Muslims	-11569
-▁arguing	-11570
-▁lyrics	-11571
-▁Thought	-11572
-▁robust	-11573
-▁Hig	-11574
-▁encouragement	-11575
-appropriate	-11576
-Have	-11577
-▁todd	-11578
-▁ranks	-11579
-▁fastest	-11580
-▁challenged	-11581
-hend	-11582
-▁embed	-11583
-▁login	-11584
-▁drum	-11585
-▁belongs	-11586
-▁Islamic	-11587
-dog	-11588
-▁audiences	-11589
-▁praying	-11590
-▁Grace	-11591
-▁Sell	-11592
-▁chapters	-11593
-plan	-11594
-▁brains	-11595
-▁consec	-11596
-▁breed	-11597
-▁trucks	-11598
-▁robot	-11599
-▁nurses	-11600
-▁musicians	-11601
-▁femin	-11602
-chair	-11603
-erge	-11604
-▁psychology	-11605
-▁parallel	-11606
-▁bail	-11607
-ouncing	-11608
-▁earthqu	-11609
-▁talents	-11610
-▁Brook	-11611
-▁dignity	-11612
-▁witnesses	-11613
-▁practicing	-11614
-▁bothered	-11615
-▁belly	-11616
-▁Luc	-11617
-▁Auto	-11618
-▁expressing	-11619
-▁noon	-11620
-arios	-11621
-▁sneak	-11622
-▁pills	-11623
-▁Soviet	-11624
-▁provin	-11625
-▁Interesting	-11626
-▁Davis	-11627
-▁rubber	-11628
-▁heck	-11629
-▁abstract	-11630
-▁Absolutely	-11631
-▁Pur	-11632
-▁Bridge	-11633
-▁Ang	-11634
-▁Cro	-11635
-iley	-11636
-▁Pict	-11637
-▁differ	-11638
-▁chef	-11639
-children	-11640
-▁breakdown	-11641
-▁prospective	-11642
-▁ware	-11643
-▁attorneys	-11644
-eman	-11645
-▁transf	-11646
-▁tasty	-11647
-pa	-11648
-▁vintage	-11649
-▁epic	-11650
-▁Linux	-11651
-▁facebook	-11652
-▁Tru	-11653
-▁premiums	-11654
-▁struggles	-11655
-▁mold	-11656
-▁pity	-11657
-▁Complete	-11658
-▁dawn	-11659
-▁Parents	-11660
-ipes	-11661
-▁transparency	-11662
-▁Provide	-11663
-”?	-11664
-ctic	-11665
-▁tire	-11666
-▁mush	-11667
-bally	-11668
-▁threads	-11669
-▁signals	-11670
-▁widespread	-11671
-▁individually	-11672
-▁Airport	-11673
-▁democratic	-11674
-DR	-11675
-▁absent	-11676
-▁waist	-11677
-icken	-11678
-lace	-11679
-▁seam	-11680
-▁Sound	-11681
-▁chamber	-11682
-▁therapist	-11683
-▁capability	-11684
-taining	-11685
-▁guards	-11686
-Ad	-11687
-▁Prop	-11688
-▁caut	-11689
-▁stiff	-11690
-▁Cry	-11691
-▁dull	-11692
-▁labels	-11693
-▁humble	-11694
-▁promptly	-11695
-▁disk	-11696
-▁repay	-11697
-▁Ord	-11698
-▁nutrients	-11699
-▁retailers	-11700
-▁rolls	-11701
-▁complexity	-11702
-onies	-11703
-▁Sweet	-11704
-▁enl	-11705
-▁reflects	-11706
-▁recognise	-11707
-MENT	-11708
-▁charm	-11709
-▁instructor	-11710
-▁automated	-11711
-▁Industry	-11712
-▁hug	-11713
-▁blocked	-11714
-▁consuming	-11715
-▁bloody	-11716
-▁complement	-11717
-▁Fre	-11718
-graduate	-11719
-▁Cheers	-11720
-▁horn	-11721
-▁backpack	-11722
-▁Legal	-11723
-▁Station	-11724
-▁Wil	-11725
-▁bubble	-11726
-▁satellite	-11727
-▁Naz	-11728
-▁strangers	-11729
-▁documented	-11730
-▁investigations	-11731
-▁heroes	-11732
-▁texture	-11733
-▁jokes	-11734
-▁Computer	-11735
-▁whoever	-11736
-Any	-11737
-▁smiles	-11738
-▁lighter	-11739
-lix	-11740
-sal	-11741
-▁poorly	-11742
-▁BL	-11743
-▁"...	-11744
-▁adjusted	-11745
-▁blond	-11746
-▁Modern	-11747
-▁Hillary	-11748
-▁revers	-11749
-▁BT	-11750
-▁dwell	-11751
-▁nowadays	-11752
-▁Navy	-11753
-▁presenting	-11754
-▁anch	-11755
-▁shru	-11756
-▁Miche	-11757
-prises	-11758
-▁outer	-11759
-▁Austin	-11760
-▁behaviors	-11761
-▁kidding	-11762
-olis	-11763
-ington	-11764
-▁controversial	-11765
-▁delayed	-11766
-▁soccer	-11767
-▁outlined	-11768
-▁leak	-11769
-▁retrie	-11770
-brid	-11771
-▁footage	-11772
-▁presentations	-11773
-▁Alice	-11774
-▁Miller	-11775
-▁curtain	-11776
-▁Lot	-11777
-▁utterly	-11778
-rot	-11779
-▁sadd	-11780
-▁addict	-11781
-▁travels	-11782
-▁horr	-11783
-▁lovers	-11784
-▁imprison	-11785
-—"	-11786
-▁sculpt	-11787
-▁bearing	-11788
-Get	-11789
-aga	-11790
-▁beaten	-11791
-▁onions	-11792
-▁Saud	-11793
-▁Individual	-11794
-vania	-11795
-▁tags	-11796
-▁Larry	-11797
-▁Freedom	-11798
-▁variables	-11799
-▁Bureau	-11800
-▁arriving	-11801
-▁exceptions	-11802
-archy	-11803
-▁packing	-11804
-▁dimin	-11805
-▁unhappy	-11806
-▁remarked	-11807
-ipedia	-11808
-▁valley	-11809
-▁glanced	-11810
-▁sacrific	-11811
-▁Method	-11812
-▁replacing	-11813
-▁traditions	-11814
-▁Ton	-11815
-▁fires	-11816
-▁Conserv	-11817
-▁harassment	-11818
-▁hurts	-11819
-▁corners	-11820
-impse	-11821
-▁seal	-11822
-isan	-11823
-mart	-11824
-▁fut	-11825
-▁commands	-11826
-Maybe	-11827
-inging	-11828
-held	-11829
-▁WW	-11830
-▁uncertainty	-11831
-▁Hug	-11832
-itations	-11833
-▁respectively	-11834
-▁leap	-11835
-▁pir	-11836
-pres	-11837
-▁underground	-11838
-aceutical	-11839
-▁Answer	-11840
-▁dar	-11841
-▁converted	-11842
-▁Half	-11843
-▁EVERY	-11844
-▁Laura	-11845
-Per	-11846
-▁junior	-11847
-▁scholarship	-11848
-inery	-11849
-▁Billy	-11850
-App	-11851
-▁Avenue	-11852
-▁jeans	-11853
-▁Faith	-11854
-▁accurately	-11855
-aws	-11856
-▁Cooper	-11857
-▁Century	-11858
-▁brick	-11859
-php	-11860
-esity	-11861
-▁Send	-11862
-▁vegan	-11863
-▁gluten	-11864
-ikipedia	-11865
-▁Might	-11866
-▁dairy	-11867
-▁deficit	-11868
-▁suspicion	-11869
-▁hobby	-11870
-▁ignorance	-11871
-▁protective	-11872
-formed	-11873
-▁experiments	-11874
-▁artwork	-11875
-iller	-11876
-▁Basically	-11877
-▁investor	-11878
-▁ink	-11879
-▁Meet	-11880
-▁qualifications	-11881
-▁publications	-11882
-▁Disease	-11883
-▁hilarious	-11884
-▁alright	-11885
-▁fleet	-11886
-comed	-11887
-IB	-11888
-▁Senator	-11889
-ationally	-11890
-▁KN	-11891
-▁absurd	-11892
-azing	-11893
-OUR	-11894
-plant	-11895
-core	-11896
-▁Pope	-11897
-▁outline	-11898
-▁scarce	-11899
-▁contractors	-11900
-Os	-11901
-▁Pra	-11902
-▁Comb	-11903
-▁crowded	-11904
-▁terminal	-11905
-▁barrier	-11906
-▁unlimited	-11907
-▁accountable	-11908
-oen	-11909
-▁EM	-11910
-▁BY	-11911
-▁clips	-11912
-icular	-11913
-▁orientation	-11914
-▁frankly	-11915
-▁Arm	-11916
-▁quilt	-11917
-▁smoothly	-11918
-▁conflicts	-11919
-oops	-11920
-▁portray	-11921
-thm	-11922
-▁urgent	-11923
-▁Mexican	-11924
-▁Grant	-11925
-▁thresh	-11926
-▁CN	-11927
-▁Term	-11928
-▁Atlantic	-11929
-▁bucks	-11930
-▁ritual	-11931
-conscious	-11932
-▁isolated	-11933
-▁faint	-11934
-fly	-11935
-▁desperately	-11936
-▁ensures	-11937
-▁positively	-11938
-▁refreshing	-11939
-ructive	-11940
-▁ranking	-11941
-▁syndrome	-11942
-▁scenarios	-11943
-idity	-11944
-▁lacking	-11945
-▁lightly	-11946
-fund	-11947
-▁bureau	-11948
-▁Left	-11949
-▁laser	-11950
-▁Pete	-11951
-▁slot	-11952
-actly	-11953
-▁doctr	-11954
-▁technically	-11955
-click	-11956
-▁CP	-11957
-▁depr	-11958
-▁Express	-11959
-▁seve	-11960
-INE	-11961
-▁Rachel	-11962
-▁“...	-11963
-▁perfection	-11964
-▁notify	-11965
-igs	-11966
-▁assumption	-11967
-▁Rand	-11968
-▁Rules	-11969
-building	-11970
-▁sustainability	-11971
-ickets	-11972
-▁Clin	-11973
-▁Bruce	-11974
-unct	-11975
-▁exploration	-11976
-▁Senior	-11977
-▁broader	-11978
-▁Lic	-11979
-▁dessert	-11980
-▁crypt	-11981
-▁distinction	-11982
-▁lasted	-11983
-▁refugees	-11984
-▁succeeded	-11985
-rous	-11986
-▁enqu	-11987
-▁dealer	-11988
-▁WAS	-11989
-▁pupils	-11990
-▁honored	-11991
-▁veterans	-11992
-▁appearing	-11993
-▁Xena	-11994
-▁intensity	-11995
-▁simultaneously	-11996
-▁slipped	-11997
-▁tires	-11998
-▁fatigue	-11999
-▁transit	-12000
-▁incorporated	-12001
-▁cited	-12002
-▁washed	-12003
-▁sustained	-12004
-▁vet	-12005
-▁Joy	-12006
-▁tens	-12007
-▁propose	-12008
-▁Respons	-12009
-uations	-12010
-ito	-12011
-▁scam	-12012
-▁Estate	-12013
-▁Karen	-12014
-▁cheapest	-12015
-dated	-12016
-▁gramm	-12017
-▁exempt	-12018
-▁console	-12019
-▁Lewis	-12020
-allas	-12021
-▁Robin	-12022
-awa	-12023
-▁lifting	-12024
-oln	-12025
-▁underneath	-12026
-umbling	-12027
-▁VERY	-12028
-▁homemade	-12029
-product	-12030
-▁matching	-12031
-▁theore	-12032
-▁bedrooms	-12033
-▁thereby	-12034
-▁procl	-12035
-▁Avoid	-12036
-▁Ted	-12037
-▁acknowledged	-12038
-itzer	-12039
-▁mailing	-12040
-▁suspension	-12041
-aints	-12042
-▁ecosystem	-12043
-boards	-12044
-▁iT	-12045
-▁tomato	-12046
-▁Ban	-12047
-▁Lad	-12048
-closure	-12049
-▁Bear	-12050
-▁betray	-12051
-love	-12052
-istically	-12053
-mary	-12054
-ylvania	-12055
-Qu	-12056
-▁literary	-12057
-▁restored	-12058
-▁Dutch	-12059
-▁prejud	-12060
-▁Kansas	-12061
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-▁conspiracy	-12063
-▁BO	-12064
-▁notified	-12065
-econom	-12066
-▁cub	-12067
-▁hunger	-12068
-▁farming	-12069
-▁ashamed	-12070
-ira	-12071
-▁enlight	-12072
-▁printer	-12073
-▁Challenge	-12074
-▁summar	-12075
-▁portable	-12076
-▁antibiot	-12077
-▁Yep	-12078
-GH	-12079
-▁Final	-12080
-▁populations	-12081
-▁forcing	-12082
-▁champion	-12083
-group	-12084
-▁Greece	-12085
-olester	-12086
-▁longest	-12087
-▁slic	-12088
-▁bias	-12089
-inf	-12090
-▁bears	-12091
-▁beast	-12092
-▁sheer	-12093
-▁metap	-12094
-owa	-12095
-▁heels	-12096
-peror	-12097
-▁derived	-12098
-▁vessels	-12099
-rix	-12100
-▁huh	-12101
-▁uncons	-12102
-▁Around	-12103
-▁anonymous	-12104
-▁sticks	-12105
-▁Illinois	-12106
-▁honesty	-12107
-▁hers	-12108
-url	-12109
-▁Os	-12110
-▁schemes	-12111
-axy	-12112
-iii	-12113
-▁aest	-12114
-▁shouted	-12115
-▁maj	-12116
-itants	-12117
-▁acute	-12118
-▁paused	-12119
-▁edges	-12120
-▁fer	-12121
-▁Organization	-12122
-arre	-12123
-▁Olymp	-12124
-▁Portug	-12125
-▁remedy	-12126
-▁quicker	-12127
-▁Jean	-12128
-▁Mail	-12129
-▁Girls	-12130
-▁participated	-12131
-▁Begin	-12132
-,,	-12133
-▁evolved	-12134
-▁cho	-12135
-▁Produ	-12136
-▁supervision	-12137
-▁prizes	-12138
-▁purely	-12139
-▁vacuum	-12140
-▁Together	-12141
-▁suspicious	-12142
-pson	-12143
-▁miracle	-12144
-▁inclusion	-12145
-▁Cloud	-12146
-▁viable	-12147
-fty	-12148
-▁tempted	-12149
-▁securities	-12150
-▁maker	-12151
-▁monetary	-12152
-▁measuring	-12153
-nected	-12154
-▁wheat	-12155
-▁Neither	-12156
-igenous	-12157
-▁Total	-12158
-▁confess	-12159
-▁haha	-12160
-▁resign	-12161
-CS	-12162
-▁Mayor	-12163
-osition	-12164
-▁legislative	-12165
-REAT	-12166
-▁pillow	-12167
-▁IV	-12168
-fish	-12169
-isible	-12170
-▁senses	-12171
-▁witnessed	-12172
-▁welcomed	-12173
-▁Sab	-12174
-▁sunshine	-12175
-▁seeks	-12176
-rations	-12177
-▁Assess	-12178
-▁Les	-12179
-▁foss	-12180
-▁reun	-12181
-▁booth	-12182
-▁Cand	-12183
-km	-12184
-▁exhibit	-12185
-cm	-12186
-▁separation	-12187
-▁ignorant	-12188
-pectives	-12189
-▁ma	-12190
-▁governance	-12191
-▁Ol	-12192
-▁racial	-12193
-▁vitamins	-12194
-EAR	-12195
-▁commod	-12196
-gh	-12197
-▁cooling	-12198
-ilitation	-12199
-▁dupl	-12200
-▁accus	-12201
-▁Py	-12202
-vious	-12203
-▁customs	-12204
-▁Partners	-12205
-▁attitudes	-12206
-▁assumptions	-12207
-▁conferences	-12208
-▁Hence	-12209
-▁Awards	-12210
-▁Practice	-12211
-▁attraction	-12212
-▁farmer	-12213
-tan	-12214
-breaking	-12215
-▁Amendment	-12216
-▁camps	-12217
-▁youngest	-12218
-▁opponents	-12219
-rating	-12220
-▁alph	-12221
-▁elite	-12222
-▁pursuing	-12223
-After	-12224
-▁reviewing	-12225
-udge	-12226
-▁pean	-12227
-▁Village	-12228
-▁shaped	-12229
-▁renewable	-12230
-▁controlling	-12231
-▁learnt	-12232
-▁households	-12233
-▁stepping	-12234
-▁NAS	-12235
-ounding	-12236
-▁organizational	-12237
-amous	-12238
-′′	-12239
-▁servants	-12240
-▁Advent	-12241
-▁autism	-12242
-enter	-12243
-▁Pain	-12244
-▁reasoning	-12245
-hill	-12246
-▁taxi	-12247
-white	-12248
-▁sends	-12249
-▁interrupted	-12250
-”)	-12251
-first	-12252
-▁strain	-12253
-▁veteran	-12254
-▁appointments	-12255
-▁Korean	-12256
-▁Mic	-12257
-▁Opt	-12258
-▁irr	-12259
-▁Tree	-12260
-▁Agr	-12261
-▁someday	-12262
-▁streaming	-12263
-oli	-12264
-▁BR	-12265
-▁kissed	-12266
-▁freak	-12267
-▁clarify	-12268
-▁RO	-12269
-▁miserable	-12270
-▁dimensions	-12271
-▁prohibited	-12272
-▁trains	-12273
-incoln	-12274
-▁bleeding	-12275
-▁counted	-12276
-fulness	-12277
-▁Ontario	-12278
-▁noble	-12279
-▁socks	-12280
-▁executives	-12281
-nsylvania	-12282
-▁disappointment	-12283
-▁Sa	-12284
-omer	-12285
-nut	-12286
-ARE	-12287
-▁libraries	-12288
-▁complications	-12289
-every	-12290
-▁cancelled	-12291
-▁borders	-12292
-▁phrases	-12293
-▁appli	-12294
-▁raises	-12295
-▁rated	-12296
-▁encourages	-12297
-▁variations	-12298
-▁puppy	-12299
-ivities	-12300
-▁execute	-12301
-▁beta	-12302
-▁Indians	-12303
-▁separately	-12304
-▁buddy	-12305
-▁rally	-12306
-roph	-12307
-▁optional	-12308
-people	-12309
-▁surveillance	-12310
-▁scientist	-12311
-▁wasting	-12312
-▁worthwhile	-12313
-Up	-12314
-ela	-12315
-▁predicted	-12316
-▁ethics	-12317
-▁congress	-12318
-▁wrest	-12319
-▁conclude	-12320
-▁preview	-12321
-▁surveys	-12322
-▁enabling	-12323
-▁hood	-12324
-▁Legisl	-12325
-▁essays	-12326
-▁restra	-12327
-▁ranked	-12328
-vable	-12329
-▁rect	-12330
-▁installing	-12331
-▁owns	-12332
-mercial	-12333
-▁Lyn	-12334
-▁allegations	-12335
-▁momentum	-12336
-▁Schools	-12337
-▁consumed	-12338
-isers	-12339
-▁chill	-12340
-▁destro	-12341
-▁Forum	-12342
-▁Ali	-12343
-girl	-12344
-▁bikes	-12345
-▁teaches	-12346
-▁cough	-12347
-▁complained	-12348
-achers	-12349
-▁vegetable	-12350
-▁Uncle	-12351
-▁partial	-12352
-cker	-12353
-▁Mov	-12354
-IME	-12355
-▁consensus	-12356
-▁Anna	-12357
-▁containers	-12358
-▁contra	-12359
-▁prolong	-12360
-▁accountability	-12361
-ANK	-12362
-lee	-12363
-▁revealing	-12364
-▁Holiday	-12365
-▁Host	-12366
-count	-12367
-▁browse	-12368
-mn	-12369
-▁epid	-12370
-▁obstacles	-12371
-▁teenagers	-12372
-▁holder	-12373
-▁inviting	-12374
-▁reporters	-12375
-▁Climate	-12376
-▁chemistry	-12377
-roit	-12378
-▁debts	-12379
-▁mm	-12380
-▁buck	-12381
-▁Fu	-12382
-ttes	-12383
-▁enhanced	-12384
-▁withdrawal	-12385
-plex	-12386
-arette	-12387
-▁Magic	-12388
-▁insured	-12389
-human	-12390
-▁medicines	-12391
-▁volumes	-12392
-ala	-12393
-▁Amanda	-12394
-▁les	-12395
-esis	-12396
-▁ML	-12397
-etts	-12398
-▁spoil	-12399
-▁vamp	-12400
-inar	-12401
-▁CB	-12402
-▁Located	-12403
-▁Been	-12404
-▁fairy	-12405
-▁erect	-12406
-▁pencil	-12407
-▁perceive	-12408
-▁Doing	-12409
-▁Eventually	-12410
-▁achievements	-12411
-▁crops	-12412
-▁gentlemen	-12413
-▁collections	-12414
-▁elementary	-12415
-▁consistency	-12416
-fare	-12417
-▁Nut	-12418
-▁bucket	-12419
-▁facial	-12420
-▁cardio	-12421
-▁glimpse	-12422
-▁NC	-12423
-▁surroundings	-12424
-▁vegg	-12425
-EP	-12426
-▁jet	-12427
-▁Dor	-12428
-▁Date	-12429
-▁corresponding	-12430
-▁constitutional	-12431
-▁whist	-12432
-llers	-12433
-▁charts	-12434
-terior	-12435
-▁chains	-12436
-▁updating	-12437
-▁selfish	-12438
-Very	-12439
-▁softly	-12440
-▁graduation	-12441
-▁rides	-12442
-IES	-12443
-▁attractions	-12444
-▁biz	-12445
-▁iTunes	-12446
-▁Commissioner	-12447
-▁PO	-12448
-,...	-12449
-isite	-12450
-▁traveled	-12451
-hma	-12452
-▁hi	-12453
-▁coin	-12454
-▁recognised	-12455
-▁MAN	-12456
-▁soda	-12457
-▁Reserve	-12458
-▁iOS	-12459
-writer	-12460
-TV	-12461
-thur	-12462
-▁Hop	-12463
-▁eyebr	-12464
-▁islands	-12465
-olesterol	-12466
-▁switching	-12467
-▁treas	-12468
-cus	-12469
-atis	-12470
-▁blowing	-12471
-▁Ox	-12472
-burn	-12473
-income	-12474
-▁Course	-12475
-▁Defense	-12476
-▁kilomet	-12477
-edu	-12478
-robe	-12479
-▁variation	-12480
-▁satisfying	-12481
-▁recruitment	-12482
-▁Exchange	-12483
-▁bride	-12484
-▁colleague	-12485
-▁actress	-12486
-▁melt	-12487
-▁partially	-12488
-▁planted	-12489
-▁reson	-12490
-▁titled	-12491
-achus	-12492
-▁twin	-12493
-▁Products	-12494
-▁descriptions	-12495
-▁encry	-12496
-▁undertaking	-12497
-▁ACT	-12498
-▁travelers	-12499
-▁organizing	-12500
-▁albums	-12501
-▁MA	-12502
-▁sacred	-12503
-▁Ensure	-12504
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-ontin	-12516
-▁Herm	-12517
-▁offerings	-12518
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-phia	-12521
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-text	-12523
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-▁Budd	-12532
-▁Mort	-12533
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-oom	-12542
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-enery	-12545
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-type	-12548
-▁trails	-12549
-▁ox	-12550
-oriented	-12551
-▁excellence	-12552
-▁palm	-12553
-▁snap	-12554
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-▁locals	-12557
-shit	-12558
-▁alternate	-12559
-▁chase	-12560
-▁puzzle	-12561
-▁reduces	-12562
-▁permits	-12563
-▁Certainly	-12564
-otte	-12565
-▁trapped	-12566
-▁criminals	-12567
-▁eastern	-12568
-shire	-12569
-▁Gary	-12570
-▁Magazine	-12571
-▁occupation	-12572
-▁ft	-12573
-ollar	-12574
-phant	-12575
-▁cooler	-12576
-????	-12577
-▁noting	-12578
----	-12579
-▁compelled	-12580
-¡¦	-12581
-▁Princess	-12582
-▁demo	-12583
-▁introducing	-12584
-ella	-12585
-▁sexually	-12586
-▁Pennsylvania	-12587
-.-	-12588
-▁apples	-12589
-▁embod	-12590
-▁doubts	-12591
-▁***	-12592
-▁Friend	-12593
-▁personalized	-12594
-▁shipped	-12595
-▁glorious	-12596
-ulin	-12597
-▁sucks	-12598
-▁racist	-12599
-urring	-12600
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-kel	-12602
-OULD	-12603
-▁patent	-12604
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-▁Dallas	-12607
-nedy	-12608
-▁Saudi	-12609
-oto	-12610
-▁gri	-12611
-▁penalties	-12612
-▁disturbing	-12613
-ixty	-12614
-▁openly	-12615
-▁curs	-12616
-▁costume	-12617
-▁pursuit	-12618
-ailed	-12619
-umbers	-12620
-▁modest	-12621
-rett	-12622
-▁arc	-12623
-ascular	-12624
-▁mechanisms	-12625
-▁Howard	-12626
-▁Subject	-12627
-UE	-12628
-nell	-12629
-ulator	-12630
-olved	-12631
-uther	-12632
-▁potato	-12633
-▁minimize	-12634
-▁generating	-12635
-▁Lincoln	-12636
-▁Band	-12637
-▁Gabrielle	-12638
-IK	-12639
-▁Song	-12640
-▁scroll	-12641
-trans	-12642
-▁mast	-12643
-▁nonprofit	-12644
-▁Mars	-12645
-▁Honestly	-12646
-▁III	-12647
-rative	-12648
-▁aggreg	-12649
-▁overly	-12650
-▁Knowing	-12651
-arity	-12652
-achusetts	-12653
-bitious	-12654
-iots	-12655
-▁outrage	-12656
-▁figuring	-12657
-▁proceeds	-12658
-▁explosion	-12659
-▁rope	-12660
-welling	-12661
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-stration	-12664
-unge	-12665
-▁adoles	-12666
-▁sixth	-12667
-▁sympathy	-12668
-▁AW	-12669
-▁mes	-12670
-▁stuffed	-12671
-▁ego	-12672
-▁prevented	-12673
-hol	-12674
-▁probability	-12675
-▁coupon	-12676
-▁Mine	-12677
-ati	-12678
-▁Univers	-12679
-▁unconscious	-12680
-▁GR	-12681
-▁advances	-12682
-▁submitting	-12683
-parents	-12684
-bean	-12685
-▁freeze	-12686
-▁permanently	-12687
-ICK	-12688
-▁Quotes	-12689
-▁murdered	-12690
-▁journalist	-12691
-▁battles	-12692
-▁propri	-12693
-news	-12694
-▁paperwork	-12695
-▁Cold	-12696
-▁obey	-12697
-cca	-12698
-▁sovere	-12699
-icop	-12700
-pless	-12701
-▁FL	-12702
-▁gambling	-12703
-▁counseling	-12704
-utic	-12705
-▁cleaner	-12706
-▁Quite	-12707
-adelphia	-12708
-▁Title	-12709
-▁Beyond	-12710
-▁searches	-12711
-urious	-12712
-healthy	-12713
-▁simpler	-12714
-ORT	-12715
-▁arrives	-12716
-▁supplier	-12717
-▁Barack	-12718
-▁reloc	-12719
-▁structured	-12720
-▁operates	-12721
-iens	-12722
-▁Laure	-12723
-.--	-12724
-▁maid	-12725
-▁tunnel	-12726
-▁startup	-12727
-▁mayor	-12728
-▁eh	-12729
-vised	-12730
-▁balcony	-12731
-▁comparing	-12732
-pert	-12733
-▁collaps	-12734
-▁conform	-12735
-▁scare	-12736
-▁leverage	-12737
-▁Sudden	-12738
-▁_____	-12739
-▁fixing	-12740
-stick	-12741
-▁wallet	-12742
-▁stats	-12743
-▁onion	-12744
-▁hast	-12745
-▁amuse	-12746
-service	-12747
-▁Innov	-12748
-▁justified	-12749
-▁promotional	-12750
-▁Ast	-12751
-▁voluntary	-12752
-▁Trib	-12753
-▁structural	-12754
-reach	-12755
-▁forehead	-12756
-▁wrist	-12757
-▁realizing	-12758
-▁newer	-12759
-▁speeds	-12760
-▁dangers	-12761
-▁upstairs	-12762
-▁zomb	-12763
-▁annoyed	-12764
-▁rival	-12765
-▁righteous	-12766
-▁independently	-12767
-▁Response	-12768
-▁promoted	-12769
-hist	-12770
-email	-12771
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-▁optimal	-12773
-roduction	-12774
-▁responsive	-12775
-▁willingness	-12776
-uscript	-12777
-▁Gulf	-12778
-▁recycling	-12779
-story	-12780
-▁recipient	-12781
-▁Mater	-12782
-▁backs	-12783
-pop	-12784
-▁accidentally	-12785
-commun	-12786
-▁Agricult	-12787
-▁unpre	-12788
-▁stimul	-12789
-▁dresses	-12790
-▁bitch	-12791
-▁Kennedy	-12792
-▁compact	-12793
-▁towel	-12794
-▁outlet	-12795
-▁realm	-12796
-▁agrees	-12797
-▁Exp	-12798
-▁LORD	-12799
-▁abandon	-12800
-▁dismissed	-12801
-▁switched	-12802
-▁disclose	-12803
-TR	-12804
-imens	-12805
-▁herbs	-12806
-Se	-12807
-assy	-12808
-▁query	-12809
-apa	-12810
-▁lasts	-12811
-▁Cash	-12812
-▁Fine	-12813
-More	-12814
-▁vibrant	-12815
-▁Anth	-12816
-▁interviewed	-12817
-▁lou	-12818
-tends	-12819
-ELL	-12820
-▁Oak	-12821
-▁rou	-12822
-▁Beat	-12823
-▁fundament	-12824
-▁Pros	-12825
-▁marine	-12826
-otive	-12827
-▁tragic	-12828
-▁Duke	-12829
-▁peek	-12830
-▁discom	-12831
-▁Finding	-12832
-▁reporter	-12833
-▁Linked	-12834
-▁profiles	-12835
-Ps	-12836
-EA	-12837
-▁laboratory	-12838
-▁terrorists	-12839
-▁sponsored	-12840
-▁institutional	-12841
-▁beg	-12842
-▁debr	-12843
-▁proves	-12844
-size	-12845
-▁bum	-12846
-▁Sale	-12847
-▁colored	-12848
-▁manually	-12849
-▁Tech	-12850
-▁Update	-12851
-▁credibility	-12852
-▁Hay	-12853
-▁gaps	-12854
-uity	-12855
-▁incentive	-12856
-▁investigating	-12857
-▁Youth	-12858
-keeping	-12859
-▁swift	-12860
-▁Allah	-12861
-▁Jimmy	-12862
-▁threshold	-12863
-▁Front	-12864
-▁graduates	-12865
-iscons	-12866
-▁inexpensive	-12867
-▁substantially	-12868
-▁nit	-12869
-▁clearing	-12870
-▁stall	-12871
-▁occurring	-12872
-▁cease	-12873
-▁intensive	-12874
-▁neglig	-12875
-ulously	-12876
-▁stew	-12877
-bourne	-12878
-▁Believe	-12879
-▁specially	-12880
-estone	-12881
-▁invisible	-12882
-▁delightful	-12883
-▁subscribers	-12884
-▁Large	-12885
-amic	-12886
-▁Drug	-12887
-second	-12888
-non	-12889
-▁accent	-12890
-▁uh	-12891
-▁penet	-12892
-▁width	-12893
-▁seniors	-12894
-▁Atlanta	-12895
-▁inclusive	-12896
-▁ingredient	-12897
-▁Mission	-12898
-isconsin	-12899
-▁teenage	-12900
-annah	-12901
-except	-12902
-opath	-12903
-inth	-12904
-town	-12905
-ritional	-12906
-range	-12907
-▁Risk	-12908
-▁Fer	-12909
-▁reign	-12910
-▁Purch	-12911
-▁slower	-12912
-Gu	-12913
-ufficient	-12914
-▁commentary	-12915
-hard	-12916
-▁Deep	-12917
-OCK	-12918
-▁Alan	-12919
-▁remotely	-12920
-ishment	-12921
-▁warn	-12922
-▁na	-12923
-▁Edward	-12924
-▁delicate	-12925
-▁united	-12926
-▁compatible	-12927
-▁mobility	-12928
-▁Questions	-12929
-▁researching	-12930
-▁scoring	-12931
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-▁unions	-12933
-▁Base	-12934
-▁Paper	-12935
-▁wicked	-12936
-outube	-12937
-▁waking	-12938
-vals	-12939
-leans	-12940
-▁sofa	-12941
-▁Toy	-12942
-▁failures	-12943
-ffe	-12944
-erer	-12945
-▁Than	-12946
-▁gloves	-12947
-▁Massachusetts	-12948
-▁Poor	-12949
-▁cottage	-12950
-▁measurement	-12951
-▁pasta	-12952
-▁activists	-12953
-▁discovering	-12954
-blog	-12955
-▁Analysis	-12956
-agle	-12957
-gger	-12958
-▁seated	-12959
-arations	-12960
-▁headache	-12961
-▁echo	-12962
-nesota	-12963
-▁Chairman	-12964
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-▁lod	-12966
-▁Iowa	-12967
-▁troll	-12968
-▁discon	-12969
-▁Haven	-12970
-▁sells	-12971
-fi	-12972
-▁violations	-12973
-▁obsessed	-12974
-Ind	-12975
-▁terribly	-12976
-▁spotted	-12977
-▁horizon	-12978
-green	-12979
-bes	-12980
-opt	-12981
-▁seasonal	-12982
-pages	-12983
-▁needle	-12984
-▁Sit	-12985
-▁appliances	-12986
-▁Directors	-12987
-▁gover	-12988
-▁unwanted	-12989
-bly	-12990
-oca	-12991
-▁flee	-12992
-▁renewed	-12993
-▁ignoring	-12994
-▁organised	-12995
-▁depressed	-12996
-▁Wish	-12997
-▁vendor	-12998
-▁enact	-12999
-▁racism	-13000
-▁porch	-13001
-▁endorse	-13002
-▁patri	-13003
-▁backing	-13004
-▁Guess	-13005
-▁insert	-13006
-▁inconven	-13007
-▁Ele	-13008
-▁NYC	-13009
-▁hassle	-13010
-▁Gun	-13011
-▁stroll	-13012
-▁escaped	-13013
-▁nonetheless	-13014
-▁baked	-13015
-▁earliest	-13016
-▁muse	-13017
-ifiable	-13018
-▁partnerships	-13019
-▁Bird	-13020
-ardon	-13021
-▁prints	-13022
-▁implies	-13023
-▁Du	-13024
-▁distracted	-13025
-▁fascinated	-13026
-▁Liter	-13027
-▁desirable	-13028
-▁blew	-13029
-▁tuition	-13030
-▁transformed	-13031
-atus	-13032
-▁unsure	-13033
-▁flowing	-13034
-dam	-13035
-▁replies	-13036
-▁bacon	-13037
-▁Clearly	-13038
-▁consecutive	-13039
-semble	-13040
-rise	-13041
-itably	-13042
-▁mixing	-13043
-▁yourselves	-13044
-▁shadows	-13045
-▁Blu	-13046
-▁attributes	-13047
-arettes	-13048
-▁cm	-13049
-Will	-13050
-▁highlighted	-13051
-▁nurt	-13052
-▁vocal	-13053
-▁Memorial	-13054
-▁ambitious	-13055
-issa	-13056
-▁Wolf	-13057
-▁outlets	-13058
-dge	-13059
-▁Emma	-13060
-▁mighty	-13061
-RC	-13062
-▁cattle	-13063
-▁Budget	-13064
-▁recession	-13065
-▁expectation	-13066
-▁autumn	-13067
-ammed	-13068
-▁durable	-13069
-▁rode	-13070
-▁targeting	-13071
-gered	-13072
-▁fraction	-13073
-▁whisper	-13074
-▁fog	-13075
-DER	-13076
-pen	-13077
-▁flows	-13078
-▁Growth	-13079
-▁turkey	-13080
-▁remembering	-13081
-▁considers	-13082
-▁Linda	-13083
-▁billions	-13084
-▁Wisconsin	-13085
-▁bru	-13086
-▁warmth	-13087
-▁distress	-13088
-▁stadium	-13089
-specific	-13090
-aned	-13091
-maybe	-13092
-▁catalog	-13093
-aby	-13094
-▁EV	-13095
-rons	-13096
-▁feas	-13097
-▁Appreci	-13098
-▁adjustments	-13099
-▁deliberately	-13100
-▁CC	-13101
-▁memor	-13102
-oline	-13103
-▁poker	-13104
-▁Beth	-13105
-▁aqu	-13106
-▁oun	-13107
-▁Anti	-13108
-▁Ba	-13109
-oker	-13110
-▁calculate	-13111
-▁lined	-13112
-▁resting	-13113
-▁dragon	-13114
-▁despair	-13115
-▁Cape	-13116
-driven	-13117
-▁portal	-13118
-oenix	-13119
-▁cops	-13120
-▁disturb	-13121
-ruit	-13122
-▁Inn	-13123
-Tw	-13124
-▁Veter	-13125
-▁edited	-13126
-▁metres	-13127
-▁backyard	-13128
-▁HOW	-13129
-ordon	-13130
-▁cholesterol	-13131
-▁cycl	-13132
-▁frames	-13133
-▁LOT	-13134
-▁crystal	-13135
-▁builds	-13136
-imination	-13137
-▁missions	-13138
-armed	-13139
-▁saves	-13140
-▁preval	-13141
-▁Dru	-13142
-▁Isa	-13143
-▁copper	-13144
-▁protests	-13145
-▁slice	-13146
-▁Marg	-13147
-▁bass	-13148
-▁locks	-13149
-▁Orange	-13150
-▁questioning	-13151
-▁underm	-13152
-▁cannabis	-13153
-▁interfere	-13154
-seen	-13155
-▁hatred	-13156
-▁appetite	-13157
-▁survivors	-13158
-sur	-13159
-▁Tea	-13160
-▁Available	-13161
-▁astron	-13162
-▁Gro	-13163
-▁pix	-13164
-something	-13165
-mers	-13166
-▁sentiment	-13167
-▁pulls	-13168
-▁Lat	-13169
-▁kidney	-13170
-▁tin	-13171
-TML	-13172
-jam	-13173
-HING	-13174
-▁Photos	-13175
-▁conscience	-13176
-▁donated	-13177
-▁incentives	-13178
-security	-13179
-▁defendant	-13180
-▁Christianity	-13181
-▁Compet	-13182
-▁farther	-13183
-▁Leon	-13184
-iated	-13185
-tech	-13186
-▁adjustment	-13187
-▁Fif	-13188
-▁allies	-13189
-▁hind	-13190
-morrow	-13191
-▁scandal	-13192
-▁advocates	-13193
-quo	-13194
-▁trium	-13195
-▁infring	-13196
-▁discharge	-13197
-▁Hunt	-13198
-▁fundraising	-13199
-▁Lie	-13200
-▁Surely	-13201
-▁catast	-13202
-pleasant	-13203
-acent	-13204
-▁trillion	-13205
-▁Fat	-13206
-▁ONLY	-13207
-▁temporarily	-13208
-▁subt	-13209
-▁surfing	-13210
-▁liking	-13211
-▁Indones	-13212
-▁Justin	-13213
-otherapy	-13214
-▁backwards	-13215
-▁enjoyment	-13216
-▁Yahoo	-13217
-▁ultra	-13218
-▁societies	-13219
-▁Rus	-13220
-utory	-13221
-▁fisher	-13222
-▁Philadelphia	-13223
-▁Kitchen	-13224
-▁Edition	-13225
-sea	-13226
-▁stove	-13227
-▁Coming	-13228
-ersion	-13229
-.:	-13230
-esc	-13231
-▁touches	-13232
-▁ank	-13233
-▁Detroit	-13234
-bird	-13235
-arma	-13236
-▁wont	-13237
-▁devastating	-13238
-▁perspectives	-13239
-▁arena	-13240
-vo	-13241
-obe	-13242
-▁shining	-13243
-▁electronics	-13244
-▁Minnesota	-13245
-▁Corporation	-13246
-▁assignments	-13247
-▁accred	-13248
-▁draws	-13249
-usalem	-13250
-▁upside	-13251
-▁salvation	-13252
-div	-13253
-▁Representatives	-13254
-Donald	-13255
-▁chairman	-13256
-▁splend	-13257
-▁relieved	-13258
-ista	-13259
-▁gospel	-13260
-▁Revenue	-13261
-▁arising	-13262
-▁elaborate	-13263
-neys	-13264
-ouver	-13265
-big	-13266
-▁alumin	-13267
-▁tv	-13268
-gmail	-13269
-▁Allow	-13270
-▁urged	-13271
-▁vertical	-13272
-▁boom	-13273
-ggy	-13274
-▁Sac	-13275
-▁lump	-13276
-▁pedest	-13277
-▁obesity	-13278
-▁conjunction	-13279
-▁outlook	-13280
-▁embarrassed	-13281
-▁backgrounds	-13282
-▁sunlight	-13283
-▁abundance	-13284
-erver	-13285
-criptions	-13286
-bul	-13287
-gling	-13288
-▁prophe	-13289
-▁wellness	-13290
-▁tailored	-13291
-▁Lucy	-13292
-▁hydro	-13293
-▁betting	-13294
-▁DON	-13295
-▁Orleans	-13296
-pol	-13297
-▁trainer	-13298
-▁infer	-13299
-▁trades	-13300
-▁CF	-13301
-▁fathers	-13302
-▁rip	-13303
-▁thrive	-13304
-rapeutic	-13305
-▁destinations	-13306
-start	-13307
-▁Copy	-13308
-▁centres	-13309
-▁Bry	-13310
-="	-13311
-rimin	-13312
-edia	-13313
-▁Emb	-13314
-▁greed	-13315
-▁halfway	-13316
-▁launching	-13317
-▁manuscript	-13318
-▁colorful	-13319
-▁generosity	-13320
-▁dual	-13321
-▁dos	-13322
-▁wounds	-13323
-▁infected	-13324
-Pal	-13325
-▁Focus	-13326
-▁um	-13327
-▁athe	-13328
-verted	-13329
-▁abused	-13330
-▁integrate	-13331
-▁Source	-13332
-▁TW	-13333
-▁eleven	-13334
-▁unbeliev	-13335
-▁virtue	-13336
-▁scrut	-13337
-▁lesser	-13338
-iblings	-13339
-▁parenting	-13340
-user	-13341
-▁Everybody	-13342
-sub	-13343
-▁deserved	-13344
-▁attacking	-13345
-▁fame	-13346
-keep	-13347
-▁Trek	-13348
-▁politically	-13349
-▁computing	-13350
-ostic	-13351
-ONG	-13352
-Su	-13353
-Day	-13354
-▁hockey	-13355
-▁pupp	-13356
-oler	-13357
-▁termin	-13358
-▁gene	-13359
-ancouver	-13360
-▁rul	-13361
-▁mounted	-13362
-▁Additional	-13363
-▁blade	-13364
-▁indicating	-13365
-▁embro	-13366
-olitan	-13367
-▁resil	-13368
-▁gravity	-13369
-▁denial	-13370
-ventions	-13371
-▁breeze	-13372
-▁OUT	-13373
-▁Moore	-13374
-irlines	-13375
-▁enthusiastic	-13376
-▁namely	-13377
-▁releasing	-13378
-▁Ow	-13379
-dig	-13380
-▁publishers	-13381
-▁delays	-13382
-cium	-13383
-▁Bowl	-13384
-▁SEC	-13385
-▁spo	-13386
-▁overl	-13387
-ido	-13388
-mbre	-13389
-▁deer	-13390
-cost	-13391
-▁quantities	-13392
-▁Sem	-13393
-▁dot	-13394
-▁exams	-13395
-▁lane	-13396
-TO	-13397
-▁candle	-13398
-▁EC	-13399
-eded	-13400
-▁prisoner	-13401
-▁notable	-13402
-▁Diet	-13403
-▁fierce	-13404
-▁tattoo	-13405
-▁communicating	-13406
-Mr	-13407
-chief	-13408
-▁Castle	-13409
-▁restoration	-13410
-▁vic	-13411
-▁Grad	-13412
-▁Inside	-13413
-▁discomfort	-13414
-▁undertaken	-13415
-avan	-13416
-▁lbs	-13417
-▁Berlin	-13418
-▁firing	-13419
-▁merchand	-13420
-cerpt	-13421
-▁kicking	-13422
-▁homeowners	-13423
-oard	-13424
-▁bang	-13425
-ioned	-13426
-anship	-13427
-▁tropical	-13428
-▁surprises	-13429
-▁automobile	-13430
-othy	-13431
-▁Ross	-13432
-▁Tower	-13433
-▁airline	-13434
-▁inappropriate	-13435
-▁sunset	-13436
-▁maximize	-13437
-web	-13438
-▁Manchester	-13439
-▁greenhouse	-13440
-▁translated	-13441
-▁pour	-13442
-▁inequ	-13443
-▁slim	-13444
-▁ster	-13445
-▁posters	-13446
-BM	-13447
-May	-13448
-OUT	-13449
-▁crisp	-13450
-▁negotiate	-13451
-▁toug	-13452
-ello	-13453
-pling	-13454
-▁pine	-13455
-▁hospitality	-13456
-cs	-13457
-▁severely	-13458
-▁Jerusalem	-13459
-▁economies	-13460
-!),	-13461
-▁fli	-13462
-▁Morgan	-13463
-▁likelihood	-13464
-▁collaborative	-13465
-bot	-13466
-closed	-13467
-▁traff	-13468
-▁stamps	-13469
-▁farms	-13470
-▁tribute	-13471
-half	-13472
-▁cycling	-13473
-▁Gene	-13474
-▁yog	-13475
-ifier	-13476
-▁Users	-13477
-▁Olympic	-13478
-▁Creative	-13479
-▁minerals	-13480
-▁civilization	-13481
-▁varying	-13482
-lights	-13483
-▁Roger	-13484
-▁Discuss	-13485
-too	-13486
-▁murm	-13487
-iaries	-13488
-▁additionally	-13489
-▁fossil	-13490
-▁Suddenly	-13491
-▁males	-13492
-▁considerations	-13493
-▁wax	-13494
-AKE	-13495
-maker	-13496
-▁panc	-13497
-▁slaves	-13498
-▁villages	-13499
-▁repet	-13500
-▁practitioners	-13501
-▁refresh	-13502
-▁constitu	-13503
-▁mentor	-13504
-▁comprehend	-13505
-▁bout	-13506
-▁typing	-13507
-▁expects	-13508
-▁Motor	-13509
-bn	-13510
-▁headquarters	-13511
-▁BS	-13512
-▁mates	-13513
-▁adapted	-13514
-▁Michelle	-13515
-▁Medicare	-13516
-▁Sep	-13517
-▁stub	-13518
-PP	-13519
-abs	-13520
-▁reset	-13521
-▁Allen	-13522
-▁remainder	-13523
-▁undertake	-13524
-▁optimization	-13525
-▁shrugged	-13526
-ancer	-13527
-▁angels	-13528
-▁binding	-13529
-▁mankind	-13530
-umbing	-13531
-▁constitute	-13532
-▁OFF	-13533
-▁hooked	-13534
-▁Nether	-13535
-▁Speaker	-13536
-▁invented	-13537
-accept	-13538
-▁assass	-13539
-woman	-13540
-eled	-13541
-▁Eat	-13542
-▁swap	-13543
-▁Maj	-13544
-▁lugg	-13545
-aria	-13546
-▁alot	-13547
-▁wherein	-13548
-river	-13549
-▁Portland	-13550
-▁Text	-13551
-▁Compl	-13552
-▁pairs	-13553
-rolling	-13554
-▁REALLY	-13555
-▁spends	-13556
-thrit	-13557
-▁shelves	-13558
-▁Marsh	-13559
-▁=)	-13560
-▁inability	-13561
-▁prone	-13562
-▁wipe	-13563
-hattan	-13564
-pace	-13565
-▁Balt	-13566
-▁Channel	-13567
-▁SD	-13568
-ector	-13569
-▁Citiz	-13570
-adequ	-13571
-▁Language	-13572
-▁coins	-13573
-workers	-13574
-encer	-13575
-▁stretched	-13576
-▁declaration	-13577
-▁carriage	-13578
-ige	-13579
-nals	-13580
-▁integral	-13581
-eek	-13582
-▁Prim	-13583
-speed	-13584
-▁Jerry	-13585
-inkle	-13586
-▁Jr	-13587
-sels	-13588
-▁Hus	-13589
-▁Nik	-13590
-▁acids	-13591
-perform	-13592
-▁inflammation	-13593
-utenant	-13594
-▁Electric	-13595
-▁defending	-13596
-▁passive	-13597
-▁enterprises	-13598
-▁Integr	-13599
-▁analytics	-13600
-▁Ready	-13601
-▁prompted	-13602
-▁cigarette	-13603
-▁sponsor	-13604
-atherine	-13605
-▁supposedly	-13606
-▁Tomorrow	-13607
-▁Agent	-13608
-▁Denver	-13609
-▁optimistic	-13610
-▁pharmaceutical	-13611
-▁schedules	-13612
-otype	-13613
-▁commence	-13614
-▁Film	-13615
-▁equation	-13616
-▁animation	-13617
-rose	-13618
-▁LED	-13619
-ologically	-13620
-bur	-13621
-Script	-13622
-▁Johnny	-13623
-▁Seven	-13624
-▁simplest	-13625
-▁serial	-13626
-ori	-13627
-▁Chem	-13628
-▁BM	-13629
-izard	-13630
-▁reimb	-13631
-▁Indiana	-13632
-IAL	-13633
-▁queries	-13634
-▁stylish	-13635
-▁lengthy	-13636
-▁congreg	-13637
-▁Specific	-13638
-track	-13639
-▁machinery	-13640
-▁rug	-13641
-▁Safe	-13642
-▁airpl	-13643
-▁exagger	-13644
-orie	-13645
-▁toes	-13646
-▁persist	-13647
-keys	-13648
-▁Marine	-13649
-▁goodbye	-13650
-▁sandwich	-13651
-▁rifle	-13652
-▁dietary	-13653
-▁unpleasant	-13654
-▁rack	-13655
-umns	-13656
-▁Pam	-13657
-▁Cher	-13658
-▁judicial	-13659
-▁invaluable	-13660
-▁resur	-13661
-▁reforms	-13662
-▁thesis	-13663
-▁Viol	-13664
-▁penny	-13665
-▁lively	-13666
-▁irrelevant	-13667
-▁prince	-13668
-▁Present	-13669
-▁forthcoming	-13670
-▁Trad	-13671
-▁Thinking	-13672
-▁Osc	-13673
-▁Colonel	-13674
-▁homosex	-13675
-ictionary	-13676
-▁lenses	-13677
-▁sentenced	-13678
-▁marketers	-13679
-ohn	-13680
-flix	-13681
-hero	-13682
-▁prest	-13683
-.).	-13684
-▁Scottish	-13685
-▁strongest	-13686
-▁Harvard	-13687
-▁Conditions	-13688
-▁Target	-13689
-full	-13690
-▁Sym	-13691
-aunting	-13692
-▁traits	-13693
-▁customized	-13694
-▁fract	-13695
-fox	-13696
-▁Makes	-13697
-▁stitch	-13698
-▁Mix	-13699
-▁ber	-13700
-pton	-13701
-▁comf	-13702
-▁autonom	-13703
-▁Volunt	-13704
-ivate	-13705
-▁priorit	-13706
-▁nationwide	-13707
-course	-13708
-▁lounge	-13709
-▁shifts	-13710
-▁helicop	-13711
-▁crowds	-13712
-▁Barbara	-13713
-inet	-13714
-▁masses	-13715
-▁surgeon	-13716
-▁marathon	-13717
-HER	-13718
-▁organs	-13719
-paper	-13720
-▁altered	-13721
-▁demonstration	-13722
-▁Temple	-13723
-entials	-13724
-▁pools	-13725
-▁budgets	-13726
-ouri	-13727
-▁Der	-13728
-▁Core	-13729
-▁pharmacy	-13730
-▁los	-13731
-▁bicycle	-13732
-avy	-13733
-bad	-13734
-▁definite	-13735
-ailand	-13736
-▁excuses	-13737
-▁luggage	-13738
-▁Celebr	-13739
-▁sla	-13740
-▁snapped	-13741
-▁surg	-13742
-his	-13743
-esters	-13744
-▁energ	-13745
-▁😀	-13746
-▁timeline	-13747
-▁Aunt	-13748
-▁Vancouver	-13749
-▁Hub	-13750
-▁manages	-13751
-▁reportedly	-13752
-▁dragged	-13753
-chain	-13754
-,''	-13755
-▁WHO	-13756
-▁scal	-13757
-▁aspiring	-13758
-▁delivers	-13759
-▁distinctive	-13760
-▁Tai	-13761
-▁prost	-13762
-▁DJ	-13763
-▁SW	-13764
-▁fulfilled	-13765
-▁assisted	-13766
-▁KNOW	-13767
-▁nutritional	-13768
-ateur	-13769
-▁bark	-13770
-iblical	-13771
-▁brutal	-13772
-▁controversy	-13773
-FC	-13774
-onge	-13775
-ussy	-13776
-▁paragraphs	-13777
-ika	-13778
-▁Sweden	-13779
-abul	-13780
-▁debris	-13781
-▁hurting	-13782
-kg	-13783
-AYS	-13784
-▁shortage	-13785
-▁processor	-13786
-stud	-13787
-certain	-13788
-&#	-13789
-oust	-13790
-▁assessments	-13791
-▁FO	-13792
-▁gates	-13793
-▁Bond	-13794
-words	-13795
-▁stare	-13796
-friends	-13797
-▁Double	-13798
-▁:(	-13799
-▁allegedly	-13800
-▁professionally	-13801
-▁analys	-13802
-▁popping	-13803
-elson	-13804
-▁Virt	-13805
-▁unclear	-13806
-igration	-13807
-▁NOTE	-13808
-ittees	-13809
-▁advisor	-13810
-▁NEVER	-13811
-▁moder	-13812
-.......	-13813
-▁rhet	-13814
-▁microw	-13815
-▁pumpkin	-13816
-angel	-13817
-▁fled	-13818
-▁Estab	-13819
-▁Nancy	-13820
-▁Treatment	-13821
-OUS	-13822
-anke	-13823
-rand	-13824
-▁Value	-13825
-▁hopeful	-13826
-▁statistical	-13827
-FA	-13828
-▁â	-13829
-pse	-13830
-tage	-13831
-igate	-13832
-▁Chuck	-13833
-▁flavors	-13834
-▁tanks	-13835
-▁Anthony	-13836
-▁associations	-13837
-▁fel	-13838
-▁density	-13839
-▁skept	-13840
-▁OM	-13841
-eu	-13842
-▁Editor	-13843
-▁diagram	-13844
-▁siblings	-13845
-▁Deal	-13846
-▁aspir	-13847
-▁Emergency	-13848
-▁gad	-13849
-▁Typ	-13850
-▁supper	-13851
-▁motorcycle	-13852
-▁Congrats	-13853
-lder	-13854
-▁Debt	-13855
-ement	-13856
-▁sporting	-13857
-▁HTML	-13858
-▁assurance	-13859
-▁apt	-13860
-▁Satan	-13861
-▁relaxation	-13862
-fold	-13863
-changing	-13864
-▁Throughout	-13865
-ani	-13866
-handed	-13867
-▁varieties	-13868
-▁Sean	-13869
-atar	-13870
-▁filters	-13871
-▁embarrassing	-13872
-▁chin	-13873
-▁amid	-13874
-▁medi	-13875
-▁vaccines	-13876
-inance	-13877
-▁enrolled	-13878
-▁shirts	-13879
-▁appropriately	-13880
-▁jaw	-13881
-▁impose	-13882
-▁editors	-13883
-▁subur	-13884
-TP	-13885
-▁py	-13886
-▁unst	-13887
-▁uncover	-13888
-shore	-13889
-▁surrender	-13890
-▁participant	-13891
-▁lungs	-13892
-▁venues	-13893
-▁pharmac	-13894
-Christ	-13895
-▁MUST	-13896
-▁shareholders	-13897
-pers	-13898
-▁cav	-13899
-matic	-13900
-▁vinegar	-13901
-▁Spot	-13902
-▁rhythm	-13903
-▁judgement	-13904
-▁Julie	-13905
-oft	-13906
-▁pony	-13907
-▁renewal	-13908
-▁crafts	-13909
-▁varies	-13910
-▁satur	-13911
-▁envelope	-13912
-▁assessed	-13913
-aturity	-13914
-▁dealers	-13915
-▁sch	-13916
-▁Philippines	-13917
-▁extrem	-13918
-▁projected	-13919
-▁skirt	-13920
-▁accessed	-13921
-telling	-13922
-▁vit	-13923
-astically	-13924
-▁Manhattan	-13925
-Fr	-13926
-▁Lt	-13927
-▁USB	-13928
-orate	-13929
-▁embark	-13930
-▁Registration	-13931
-▁pending	-13932
-▁apologies	-13933
-▁blessings	-13934
-▁pickup	-13935
-▁summit	-13936
-▁portrait	-13937
-▁shocking	-13938
-rator	-13939
-▁slavery	-13940
-via	-13941
-agen	-13942
-▁moisture	-13943
-gue	-13944
-amps	-13945
-brew	-13946
-mented	-13947
-▁Stew	-13948
-izable	-13949
-▁freezing	-13950
-▁prosecution	-13951
-▁subsequently	-13952
-▁Kyle	-13953
-▁pork	-13954
-▁Netherlands	-13955
-▁cens	-13956
-educ	-13957
-▁endure	-13958
-▁decorated	-13959
-PG	-13960
-▁Phone	-13961
-ols	-13962
-▁Bott	-13963
-▁purse	-13964
-▁Seeing	-13965
-▁absorb	-13966
-▁seized	-13967
-▁fried	-13968
-attering	-13969
-▁unity	-13970
-▁Morning	-13971
-▁measurements	-13972
-las	-13973
-▁iphone	-13974
-▁!!	-13975
-oken	-13976
-ali	-13977
-thod	-13978
-▁Arthur	-13979
-▁civilian	-13980
-▁Na	-13981
-anas	-13982
-▁comics	-13983
-iper	-13984
-million	-13985
-▁Discover	-13986
-▁utilizing	-13987
-▁MD	-13988
-▁championship	-13989
-▁cleans	-13990
-▁classical	-13991
-▁merit	-13992
-▁coding	-13993
-▁unlock	-13994
-▁stair	-13995
-▁courtesy	-13996
-▁lending	-13997
-▁correl	-13998
-▁relieve	-13999
-▁thunder	-14000
-▁Vin	-14001
-▁Charlotte	-14002
-▁Interview	-14003
-beit	-14004
-quet	-14005
-▁guessed	-14006
-▁sellers	-14007
-▁Trying	-14008
-▁scenery	-14009
-TF	-14010
-economic	-14011
-▁vain	-14012
-▁expose	-14013
-▁refrigerator	-14014
-▁internationally	-14015
-cha	-14016
-▁stealing	-14017
-▁Exactly	-14018
-▁escal	-14019
-rane	-14020
-ulative	-14021
-ographical	-14022
-▁rehe	-14023
-▁parked	-14024
-▁od	-14025
-▁pul	-14026
-▁viagra	-14027
-▁Lost	-14028
-Am	-14029
-dies	-14030
-dings	-14031
-▁impat	-14032
-▁Strong	-14033
-▁arrog	-14034
-▁slides	-14035
-▁Model	-14036
-gate	-14037
-▁Belg	-14038
-▁Housing	-14039
-▁informal	-14040
-▁Sony	-14041
-design	-14042
-▁Alliance	-14043
-▁celebrity	-14044
-▁manufactured	-14045
-▁theirs	-14046
-▁climbed	-14047
-rehens	-14048
-▁Sett	-14049
-▁smallest	-14050
-▁Mu	-14051
-▁governing	-14052
-thritis	-14053
-anim	-14054
-take	-14055
-▁frightened	-14056
-ieving	-14057
-Even	-14058
-▁Print	-14059
-▁polls	-14060
-▁Hawaii	-14061
-▁explanations	-14062
-unks	-14063
-▁Lay	-14064
-▁sealed	-14065
-▁respondents	-14066
-NS	-14067
-lu	-14068
-phew	-14069
-▁Soul	-14070
-▁distribute	-14071
-respect	-14072
-▁Terry	-14073
-▁elevator	-14074
-▁une	-14075
-opard	-14076
-▁Keeping	-14077
-▁incurred	-14078
-▁continent	-14079
-help	-14080
-▁Krist	-14081
-▁collar	-14082
-▁Phoenix	-14083
-▁powered	-14084
-▁rivers	-14085
-▁limiting	-14086
-▁RA	-14087
-▁squeeze	-14088
-▁Register	-14089
-▁Holl	-14090
-▁barn	-14091
-ko	-14092
-▁tops	-14093
-▁spokesman	-14094
-▁profess	-14095
-▁DR	-14096
-▁Regional	-14097
-▁visibility	-14098
-onia	-14099
-▁Obs	-14100
-▁advocacy	-14101
-▁GOP	-14102
-▁witch	-14103
-▁pains	-14104
-▁Thailand	-14105
-.—	-14106
-▁Coach	-14107
-▁appeals	-14108
-uters	-14109
-▁editorial	-14110
-▁Nonetheless	-14111
-▁Carib	-14112
-▁Gospel	-14113
-▁drawings	-14114
-▁retention	-14115
-riters	-14116
-▁grammar	-14117
-▁journalism	-14118
-bery	-14119
-ivia	-14120
-compass	-14121
-▁sights	-14122
-▁zip	-14123
-▁Treas	-14124
-opes	-14125
-▁Close	-14126
-▁payable	-14127
-▁irritating	-14128
-▁Client	-14129
-▁transfers	-14130
-▁influences	-14131
-▁KW	-14132
-▁Military	-14133
-▁Knowledge	-14134
-oria	-14135
-riages	-14136
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-▁thru	-14139
-fashion	-14140
-anne	-14141
-▁Performance	-14142
-▁hats	-14143
-erness	-14144
-asive	-14145
-▁revised	-14146
-▁Studio	-14147
-▁Mah	-14148
-erald	-14149
-▁plag	-14150
-▁Graham	-14151
-outheast	-14152
-▁headline	-14153
-▁reliability	-14154
-rawn	-14155
-▁nause	-14156
-▁templates	-14157
-▁recomm	-14158
-▁drill	-14159
-▁affirm	-14160
-▁prosperity	-14161
-▁Greet	-14162
-▁tales	-14163
-▁philosoph	-14164
-opping	-14165
-▁scattered	-14166
-▁sod	-14167
-▁Assistant	-14168
-▁distinguish	-14169
-▁Photo	-14170
-▁zones	-14171
-▁Pil	-14172
-oz	-14173
-▁corrid	-14174
-▁Aid	-14175
-please	-14176
-Pr	-14177
-▁damaging	-14178
-▁monsters	-14179
-▁ruined	-14180
-▁Earlier	-14181
-▁Prevention	-14182
-▁tribe	-14183
-▁Chrome	-14184
-▁circulation	-14185
-opher	-14186
-three	-14187
-▁RES	-14188
-▁formats	-14189
-▁sensation	-14190
-▁Craig	-14191
-▁yummy	-14192
-▁slo	-14193
-▁foam	-14194
-▁Fresh	-14195
-▁Matter	-14196
-▁Thankfully	-14197
-▁sufficiently	-14198
-▁spy	-14199
-▁certainty	-14200
-▁Universe	-14201
-▁sung	-14202
-▁Mental	-14203
-dro	-14204
-▁vag	-14205
-▁Pinterest	-14206
-▁palace	-14207
-▁Jacob	-14208
-▁holders	-14209
-▁steadily	-14210
-lie	-14211
-▁Rate	-14212
-▁doctrine	-14213
-▁gesture	-14214
-▁Jennifer	-14215
-▁risen	-14216
-▁Theatre	-14217
-▁Polit	-14218
-▁Normally	-14219
-▁inde	-14220
-▁spine	-14221
-▁diameter	-14222
-▁DS	-14223
-▁cargo	-14224
-▁emerge	-14225
-▁earthquake	-14226
-▁Dragon	-14227
-▁sciences	-14228
-bat	-14229
-▁feared	-14230
-▁proving	-14231
-▁counterpart	-14232
-drop	-14233
-''	-14234
-▁Carter	-14235
-shot	-14236
-▁Dest	-14237
-▁ladder	-14238
-▁intriguing	-14239
-▁arrow	-14240
-▁Bath	-14241
-aturally	-14242
-▁inhabitants	-14243
-▁nephew	-14244
-▁uncon	-14245
-▁intermedi	-14246
-▁Jake	-14247
-orical	-14248
-▁artic	-14249
-Sorry	-14250
-ridge	-14251
-▁Guardian	-14252
-▁correction	-14253
-▁lung	-14254
-▁Cass	-14255
-▁Battle	-14256
-▁christ	-14257
-▁shades	-14258
-▁verbal	-14259
-▁tenants	-14260
-ustomed	-14261
-▁Record	-14262
-▁Sciences	-14263
-▁fights	-14264
-▁prelim	-14265
-▁mant	-14266
-▁bend	-14267
-▁Chamber	-14268
-feeding	-14269
-▁ram	-14270
-▁Cars	-14271
-▁cozy	-14272
-regular	-14273
-▁dub	-14274
-▁Improve	-14275
-▁offline	-14276
-▁GPS	-14277
-▁awaken	-14278
-▁seating	-14279
-▁Pitt	-14280
-▁illnesses	-14281
-pher	-14282
-ugar	-14283
-rr	-14284
-▁athletic	-14285
-▁judging	-14286
-▁Intelligence	-14287
-▁streams	-14288
-▁Eight	-14289
-▁stole	-14290
-ylum	-14291
-▁LinkedIn	-14292
-assador	-14293
-▁shifted	-14294
-▁prevents	-14295
-▁tolerance	-14296
-▁Kid	-14297
-▁Margaret	-14298
-▁landlord	-14299
-ivic	-14300
-▁dwelling	-14301
-▁dimension	-14302
-CR	-14303
-iral	-14304
-▁asthma	-14305
-▁wholes	-14306
-▁feather	-14307
-▁prox	-14308
-▁attendees	-14309
-▁metabol	-14310
-▁lightning	-14311
-▁symbols	-14312
-▁mechanics	-14313
-▁photographers	-14314
-emen	-14315
-▁definitions	-14316
-▁intuitive	-14317
-▁Single	-14318
-▁forgiveness	-14319
-))	-14320
-ighing	-14321
-▁charter	-14322
-▁sensor	-14323
-ACE	-14324
-▁solic	-14325
-▁inspir	-14326
-▁reservations	-14327
-▁GDP	-14328
-▁awe	-14329
-▁Jere	-14330
-▁females	-14331
-▁inadequ	-14332
-thood	-14333
-▁nerves	-14334
-▁Gas	-14335
-▁groom	-14336
-▁patio	-14337
-▁indicators	-14338
-▁til	-14339
-▁commitments	-14340
-URE	-14341
-idency	-14342
-government	-14343
-▁appearances	-14344
-links	-14345
-▁premise	-14346
-▁vom	-14347
-▁Rh	-14348
-▁ABC	-14349
-▁padd	-14350
-▁influential	-14351
-▁masc	-14352
-amiliar	-14353
-▁tradem	-14354
-▁troubled	-14355
-▁modules	-14356
-azed	-14357
-zone	-14358
-▁Fantastic	-14359
-▁preliminary	-14360
-ancers	-14361
-▁contacting	-14362
-▁Nigeria	-14363
-▁Federation	-14364
-▁USD	-14365
-olves	-14366
-abel	-14367
-▁pier	-14368
-▁HP	-14369
-▁Hundred	-14370
-▁biology	-14371
-▁eliminated	-14372
-▁socially	-14373
-heimer	-14374
-▁bases	-14375
-▁columns	-14376
-▁forests	-14377
-▁specifications	-14378
-▁poems	-14379
-▁QU	-14380
-▁coalition	-14381
-psy	-14382
-▁tide	-14383
-▁jazz	-14384
-▁flies	-14385
-.''	-14386
-▁Saint	-14387
-idelines	-14388
-▁specialty	-14389
-▁taxpayers	-14390
-▁Gift	-14391
-▁administrator	-14392
-▁Gre	-14393
-▁watches	-14394
-▁bullshit	-14395
-naments	-14396
-▁Aside	-14397
-▁wives	-14398
-▁freezer	-14399
-▁Pent	-14400
-▁Weight	-14401
-▁detection	-14402
-▁evolve	-14403
-▁exercising	-14404
-ieves	-14405
-▁speculation	-14406
-ribution	-14407
-▁packs	-14408
-▁Julia	-14409
-▁headlines	-14410
-Gener	-14411
-▁evac	-14412
-▁Unit	-14413
-cled	-14414
-▁Aren	-14415
-▁shorts	-14416
-▁Culture	-14417
-▁Corn	-14418
-▁Subs	-14419
-▁Survey	-14420
-▁blocking	-14421
-visory	-14422
-▁Gordon	-14423
-▁smarter	-14424
-▁chew	-14425
-▁belonging	-14426
-prom	-14427
-▁warmer	-14428
-▁vou	-14429
-▁guarantees	-14430
-estab	-14431
-▁Netflix	-14432
-▁ol	-14433
-arten	-14434
-▁helmet	-14435
-▁sponsors	-14436
-▁consisting	-14437
-▁hormone	-14438
-▁sadness	-14439
-▁simplicity	-14440
-▁positioning	-14441
-▁dorm	-14442
-sk	-14443
-▁liv	-14444
-▁recruiting	-14445
-▁poured	-14446
-▁Ark	-14447
-▁Burn	-14448
-▁consultants	-14449
-▁grim	-14450
-▁careg	-14451
-▁deploy	-14452
-▁overhead	-14453
-▁BIG	-14454
-▁donors	-14455
-▁ic	-14456
-pired	-14457
-culiar	-14458
-▁sixty	-14459
-▁Enterprise	-14460
-▁bast	-14461
-▁deaf	-14462
-Somet	-14463
-▁Command	-14464
-cra	-14465
-▁bore	-14466
-▁darling	-14467
-▁lid	-14468
-icting	-14469
-▁laughs	-14470
-▁collision	-14471
-nor	-14472
-▁planes	-14473
-▁decreased	-14474
-bian	-14475
-▁disappointing	-14476
-▁banner	-14477
-▁cakes	-14478
-▁risky	-14479
-business	-14480
-▁Fle	-14481
-▁Used	-14482
-▁glow	-14483
-food	-14484
-▁FROM	-14485
-namon	-14486
-▁particles	-14487
-iro	-14488
-family	-14489
-▁allergies	-14490
-▁Communication	-14491
-▁Premier	-14492
-▁BC	-14493
-▁Thing	-14494
-▁remedies	-14495
-▁misleading	-14496
-▁overlooked	-14497
-QU	-14498
-▁gum	-14499
-othing	-14500
-▁coordination	-14501
-▁Urban	-14502
-▁stabil	-14503
-▁Missouri	-14504
-▁indicator	-14505
-▁proceeded	-14506
-From	-14507
-▁Fish	-14508
-vascular	-14509
-▁Alaska	-14510
-pub	-14511
-▁Extra	-14512
-SW	-14513
-▁exceeded	-14514
-▁migration	-14515
-▁carp	-14516
-▁lion	-14517
-▁flame	-14518
-▁Ru	-14519
-▁automation	-14520
-▁dominant	-14521
-mat	-14522
-anking	-14523
-▁recovering	-14524
-▁moms	-14525
-▁repeating	-14526
-▁certificates	-14527
-lies	-14528
-▁Construction	-14529
-mentioned	-14530
-▁rehabilitation	-14531
-▁Liver	-14532
-▁Considering	-14533
-giene	-14534
-▁brace	-14535
-▁shifting	-14536
-▁licensing	-14537
-▁scent	-14538
-BO	-14539
-▁showcase	-14540
-▁propaganda	-14541
-▁Remove	-14542
-▁::	-14543
-▁sanct	-14544
-appro	-14545
-mill	-14546
-▁Claim	-14547
-▁stance	-14548
-▁Eli	-14549
-▁---	-14550
-▁judged	-14551
-▁umbre	-14552
-▁Statement	-14553
-▁compounds	-14554
-▁radar	-14555
-▁cum	-14556
-▁gel	-14557
-▁blur	-14558
-▁ripped	-14559
-▁harmony	-14560
-aple	-14561
-idel	-14562
-▁crush	-14563
-gal	-14564
-omic	-14565
-vance	-14566
-▁Progress	-14567
-▁ER	-14568
-▁GOD	-14569
-▁Li	-14570
-▁reinforce	-14571
-▁pole	-14572
-ommon	-14573
-▁Crown	-14574
-ohy	-14575
-stock	-14576
-▁nominated	-14577
-▁Tan	-14578
-▁discrimin	-14579
-▁dialog	-14580
-iciary	-14581
-▁Flash	-14582
-filled	-14583
-▁toast	-14584
-▁viral	-14585
-▁Bol	-14586
-▁Breakfast	-14587
-thening	-14588
-▁bundle	-14589
-▁diving	-14590
-▁listings	-14591
-asonable	-14592
-reading	-14593
-▁bizarre	-14594
-▁SER	-14595
-▁grams	-14596
-▁peculiar	-14597
-▁Golf	-14598
-▁Wikipedia	-14599
-liver	-14600
-▁Maryland	-14601
-onda	-14602
-▁exterior	-14603
-▁neighborhoods	-14604
-keeper	-14605
-▁sensitivity	-14606
-▁buses	-14607
-mus	-14608
-rust	-14609
-▁warehouse	-14610
-▁Caribbean	-14611
-▁regulated	-14612
-information	-14613
-▁Kh	-14614
-ITE	-14615
-ounded	-14616
-immer	-14617
-reated	-14618
-▁revenge	-14619
-▁Brooklyn	-14620
-▁excluded	-14621
-▁void	-14622
-▁merchandise	-14623
-DAY	-14624
-▁Sue	-14625
-▁Buck	-14626
-▁ranges	-14627
-▁terrace	-14628
-▁sorted	-14629
-▁deposits	-14630
-iy	-14631
-▁frost	-14632
-▁mortality	-14633
-▁publicity	-14634
-▁bush	-14635
-▁gran	-14636
-▁cheeks	-14637
-GR	-14638
-▁spoon	-14639
-▁Tickets	-14640
-▁portions	-14641
-▁cancellation	-14642
-▁destroying	-14643
-▁din	-14644
-.(	-14645
-▁invasion	-14646
-Bay	-14647
-▁mineral	-14648
-itzerland	-14649
-▁Few	-14650
-LA	-14651
-iva	-14652
-▁eligibility	-14653
-▁DIY	-14654
-▁sounding	-14655
-▁controller	-14656
-▁Soph	-14657
-twitter	-14658
-cat	-14659
-▁popped	-14660
-▁substit	-14661
-▁Jessica	-14662
-▁inevitably	-14663
-IONS	-14664
-▁muttered	-14665
-▁EP	-14666
-▁Parent	-14667
-▁logged	-14668
-▁masters	-14669
-▁respects	-14670
-▁Sant	-14671
-▁Nearly	-14672
-▁hopeless	-14673
-▁embedded	-14674
-▁indo	-14675
-▁Hal	-14676
-▁lenders	-14677
-▁dislike	-14678
-▁veggies	-14679
-▁rankings	-14680
-▁Consumer	-14681
-▁Cos	-14682
-amine	-14683
-version	-14684
-▁Harris	-14685
-aki	-14686
-talk	-14687
-▁loses	-14688
-▁peanut	-14689
-▁defining	-14690
-▁rescu	-14691
-▁shiny	-14692
-▁exceptionally	-14693
-aux	-14694
-▁Coffee	-14695
-WH	-14696
-ennis	-14697
-▁virgin	-14698
-▁cycles	-14699
-▁punct	-14700
-▁demonstrates	-14701
-▁experimental	-14702
-avor	-14703
-▁FDA	-14704
-erman	-14705
-▁rewarded	-14706
-▁educators	-14707
-▁Merry	-14708
-ingle	-14709
-▁trunk	-14710
-nox	-14711
-idal	-14712
-▁Mand	-14713
-▁Feed	-14714
-▁inherit	-14715
-▁examining	-14716
-clusive	-14717
-▁renting	-14718
-obs	-14719
-▁chasing	-14720
-▁surgical	-14721
-▁:-	-14722
-▁circumstance	-14723
-▁;-)	-14724
-▁nick	-14725
-▁JavaScript	-14726
-▁Ak	-14727
-▁incap	-14728
-cedented	-14729
-▁customize	-14730
-direct	-14731
-eren	-14732
-▁cliff	-14733
-▁riders	-14734
-▁preceding	-14735
-▁Tob	-14736
-▁Melbourne	-14737
-▁Voice	-14738
-▁insurer	-14739
-▁outbreak	-14740
-▁ener	-14741
-itant	-14742
-▁Enc	-14743
-▁Todd	-14744
-▁civilians	-14745
-▁parade	-14746
-▁procure	-14747
-▁pipeline	-14748
-▁disgu	-14749
-▁aiming	-14750
-▁Boys	-14751
-▁Jobs	-14752
-▁Pink	-14753
-Us	-14754
-▁radi	-14755
-▁creator	-14756
-dict	-14757
-▁kits	-14758
-▁Fail	-14759
-ocation	-14760
-personal	-14761
-▁unfold	-14762
-▁Lock	-14763
-▁fats	-14764
-▁JO	-14765
-▁rocket	-14766
-▁meter	-14767
-▁Sha	-14768
-▁Rent	-14769
-itamin	-14770
-flu	-14771
-again	-14772
-▁fork	-14773
-▁unsu	-14774
-▁sanctions	-14775
-▁mock	-14776
-▁corrected	-14777
-▁TIME	-14778
-▁kills	-14779
-▁rod	-14780
-▁calcium	-14781
-▁Investment	-14782
-▁thumbs	-14783
-verages	-14784
-LEASE	-14785
-▁Records	-14786
-▁messaging	-14787
-▁picnic	-14788
-▁wardrobe	-14789
-▁illusion	-14790
-Te	-14791
-▁energet	-14792
-▁Cow	-14793
-▁hormones	-14794
-▁infinite	-14795
-▁bathrooms	-14796
-HD	-14797
-iches	-14798
-▁redeem	-14799
-▁slightest	-14800
-▁mentioning	-14801
-also	-14802
-▁messy	-14803
-▁compuls	-14804
-▁Olympics	-14805
-▁athlete	-14806
-charged	-14807
-▁tightly	-14808
-▁Sustain	-14809
-▁strings	-14810
-▁tutorials	-14811
-▁accustomed	-14812
-▁nost	-14813
-headed	-14814
-▁weaknesses	-14815
-▁robots	-14816
-mega	-14817
-▁museums	-14818
-▁sickness	-14819
-▁explicitly	-14820
-eenth	-14821
-redict	-14822
-cap	-14823
-▁digit	-14824
-▁beginners	-14825
-▁disciples	-14826
-agne	-14827
-▁Ye	-14828
-coll	-14829
-▁sorrow	-14830
-▁sang	-14831
-▁Ze	-14832
-▁journals	-14833
-▁Corporate	-14834
-▁resolutions	-14835
-▁Understanding	-14836
-▁Wy	-14837
-▁globally	-14838
-▁damp	-14839
-irming	-14840
-▁Hear	-14841
-▁rises	-14842
-▁Tonight	-14843
-▁Understand	-14844
-▁versatile	-14845
-▁therapeutic	-14846
-▁evenings	-14847
-ohydr	-14848
-▁allergic	-14849
-▁blink	-14850
-)...	-14851
-▁rendered	-14852
-great	-14853
-▁peppers	-14854
-▁remod	-14855
-▁rushing	-14856
-▁recreational	-14857
-▁GREAT	-14858
-bish	-14859
-▁genet	-14860
-efficient	-14861
-▁isolation	-14862
-▁triumph	-14863
-▁algorithm	-14864
-▁Beck	-14865
-▁seas	-14866
-▁Certific	-14867
-▁learners	-14868
-▁citizenship	-14869
-▁starter	-14870
-▁weaken	-14871
-▁Fro	-14872
-chant	-14873
-▁swept	-14874
-▁Criminal	-14875
-▁furnished	-14876
-▁hybrid	-14877
-▁knitting	-14878
-lict	-14879
-▁Explorer	-14880
-▁branding	-14881
-▁creepy	-14882
-risk	-14883
-▁Bee	-14884
-▁comfortably	-14885
-▁Object	-14886
-▁Physical	-14887
-▁mathematics	-14888
-▁mour	-14889
-▁Lex	-14890
-▁offset	-14891
-▁greeted	-14892
-▁evolving	-14893
-▁ancestors	-14894
-▁limitation	-14895
-:-	-14896
-▁ginger	-14897
-▁referral	-14898
-hyl	-14899
-arse	-14900
-▁reconst	-14901
-▁MM	-14902
-▁Against	-14903
-PER	-14904
-BER	-14905
-▁Ta	-14906
-▁Commerce	-14907
-▁amusement	-14908
-▁dick	-14909
-▁Funny	-14910
-abulary	-14911
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-apel	-14915
-▁smells	-14916
-▁volcan	-14917
-atching	-14918
-▁helpless	-14919
-▁musician	-14920
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-▁insightful	-14922
-uffy	-14923
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-▁Period	-14927
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-▁beginner	-14929
-urate	-14930
-Sure	-14931
-▁Rail	-14932
-▁Thor	-14933
-▁thanked	-14934
-Right	-14935
-lessness	-14936
-▁twins	-14937
-abama	-14938
-▁presently	-14939
-respons	-14940
-▁chunk	-14941
-ndy	-14942
-iably	-14943
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-prot	-14945
-century	-14946
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-▁nomination	-14948
-▁throws	-14949
-▁Independent	-14950
-▁throne	-14951
-▁explored	-14952
-Great	-14953
-ranean	-14954
-▁Anderson	-14955
-▁Strategy	-14956
-▁quil	-14957
-▁downs	-14958
-▁armor	-14959
-▁millenn	-14960
-▁mentions	-14961
-▁Higher	-14962
-▁grandparents	-14963
-▁Dance	-14964
-than	-14965
-▁spinning	-14966
-▁Switzerland	-14967
-▁static	-14968
-▁surpass	-14969
-▁census	-14970
-▁iconic	-14971
-era	-14972
-▁sher	-14973
-▁offended	-14974
-▁fulfilling	-14975
-has	-14976
-▁etern	-14977
-▁unaware	-14978
-▁CV	-14979
-▁constraints	-14980
-▁Nash	-14981
-▁condo	-14982
-uments	-14983
-▁cush	-14984
-▁lowered	-14985
-▁dreaming	-14986
-▁improves	-14987
-arming	-14988
-acre	-14989
-imore	-14990
-▁committing	-14991
-▁Football	-14992
-sim	-14993
-▁twisted	-14994
-▁Nev	-14995
-▁illustrate	-14996
-▁IRS	-14997
-▁wiped	-14998
-▁dysfunction	-14999
-tuce	-15000
-▁narc	-15001
-▁Tro	-15002
-▁Ghost	-15003
-▁Teacher	-15004
-▁foundations	-15005
-▁intro	-15006
-En	-15007
-▁curse	-15008
-▁outsour	-15009
-AME	-15010
-▁Dur	-15011
-▁Islands	-15012
-▁compiled	-15013
-GA	-15014
-▁Beauty	-15015
-▁impair	-15016
-thon	-15017
-Im	-15018
-▁GET	-15019
-resist	-15020
-▁interven	-15021
-▁Industrial	-15022
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-▁dense	-15024
-▁Bus	-15025
-▁anticipate	-15026
-▁Whilst	-15027
-▁verses	-15028
-▁satisfactory	-15029
-Take	-15030
-▁Hung	-15031
-▁aliens	-15032
-▁Ukraine	-15033
-▁cance	-15034
-▁statutory	-15035
-▁Native	-15036
-ayed	-15037
-▁adore	-15038
-▁Creating	-15039
-thy	-15040
-camp	-15041
-▁beats	-15042
-▁wander	-15043
-Would	-15044
-▁trump	-15045
-▁Events	-15046
-both	-15047
-▁Restaur	-15048
-inally	-15049
-▁apprent	-15050
-IFI	-15051
-▁Moving	-15052
-ldom	-15053
-▁Schedule	-15054
-▁deterior	-15055
-▁Walker	-15056
-mentation	-15057
-▁verification	-15058
-▁stalk	-15059
-▁cigarettes	-15060
-▁Technical	-15061
-▁marvelous	-15062
-▁orph	-15063
-auge	-15064
-▁complimentary	-15065
-GO	-15066
-Ne	-15067
-▁bog	-15068
-▁glue	-15069
-▁bullying	-15070
-▁promotions	-15071
-▁YES	-15072
-▁Letter	-15073
-▁Rab	-15074
-roductive	-15075
-▁amusing	-15076
-▁muff	-15077
-uv	-15078
-pring	-15079
-▁ample	-15080
-▁Advanced	-15081
-mentia	-15082
-!_	-15083
-BL	-15084
-▁fines	-15085
-rots	-15086
-▁commander	-15087
-▁inhib	-15088
-GBT	-15089
-nered	-15090
-▁Sams	-15091
-▁breasts	-15092
-▁excerpt	-15093
-▁headaches	-15094
-wid	-15095
-▁Ky	-15096
-lah	-15097
-▁Jonathan	-15098
-▁believers	-15099
-▁CIA	-15100
-▁neighbourhood	-15101
-▁cartoon	-15102
-▁Wouldn	-15103
-▁investigated	-15104
-▁vo	-15105
-▁Oxford	-15106
-▁Init	-15107
-QL	-15108
-points	-15109
-▁fatty	-15110
-▁downstairs	-15111
-▁justification	-15112
-▁predictions	-15113
-iping	-15114
-▁Turk	-15115
-shirts	-15116
-</	-15117
-▁grains	-15118
-▁disasters	-15119
-▁Andre	-15120
-▁Impact	-15121
-▁Ian	-15122
-▁Heat	-15123
-▁Walking	-15124
-▁Haha	-15125
-▁ponder	-15126
-▁Keith	-15127
-▁Kentucky	-15128
-▁disputes	-15129
-▁Boo	-15130
-▁railway	-15131
-▁trustworthy	-15132
-▁NHS	-15133
-index	-15134
-write	-15135
-▁utilized	-15136
-▁objection	-15137
-▁Palestinian	-15138
-Act	-15139
-develop	-15140
-▁attributed	-15141
-▁Veh	-15142
-▁yarn	-15143
-elling	-15144
-▁animated	-15145
-▁recalled	-15146
-ndum	-15147
-▁Nich	-15148
-▁unprecedented	-15149
-▁Bag	-15150
-▁flooding	-15151
-unless	-15152
-▁casting	-15153
-▁lag	-15154
-▁Autom	-15155
-▁doubled	-15156
-ivation	-15157
-▁stun	-15158
-▁workouts	-15159
-▁ling	-15160
-ometer	-15161
-▁Nelson	-15162
-▁planting	-15163
-▁grandchildren	-15164
-Never	-15165
-inyl	-15166
-▁pissed	-15167
-▁Poss	-15168
-▁Az	-15169
-▁Kings	-15170
-▁religions	-15171
-ulent	-15172
-Nothing	-15173
-ceptive	-15174
-▁diplom	-15175
-▁notices	-15176
-▁persistent	-15177
-▁demographic	-15178
-SC	-15179
-nia	-15180
-▁VA	-15181
-▁orche	-15182
-▁Example	-15183
-▁occurrence	-15184
-▁rant	-15185
-▁flaws	-15186
-ichever	-15187
-▁cautious	-15188
-▁Customers	-15189
-▁Depression	-15190
-▁stretching	-15191
-lied	-15192
-▁dile	-15193
-▁Luther	-15194
-▁insulin	-15195
-▁Potter	-15196
-▁Testament	-15197
-▁proposition	-15198
-orus	-15199
-eyed	-15200
-▁BTW	-15201
-▁preserved	-15202
-Ab	-15203
-▁cheating	-15204
-▁GP	-15205
-mile	-15206
-▁preferably	-15207
-▁defeated	-15208
-lad	-15209
-▁WA	-15210
-▁detected	-15211
-▁termination	-15212
-prising	-15213
-▁salaries	-15214
-▁Prize	-15215
-▁absorbed	-15216
-▁roller	-15217
-▁litigation	-15218
-▁Round	-15219
-veland	-15220
-▁Block	-15221
-▁extending	-15222
-▁(@	-15223
-▁lur	-15224
-▁mandate	-15225
-▁evaluated	-15226
-▁Leadership	-15227
-▁blonde	-15228
-▁sums	-15229
-▁Limited	-15230
-▁traders	-15231
-▁persuade	-15232
-▁Inv	-15233
-▁hypocr	-15234
-▁metals	-15235
-stage	-15236
-▁appra	-15237
-▁martial	-15238
-▁Democrat	-15239
-▁recommends	-15240
-▁Political	-15241
-▁toddler	-15242
-arians	-15243
-▁amendments	-15244
-ez	-15245
-rencies	-15246
-▁API	-15247
-social	-15248
-▁runner	-15249
-▁horizont	-15250
-cuse	-15251
-▁transcript	-15252
-▁proceeding	-15253
-▁interventions	-15254
-▁rows	-15255
-▁snaps	-15256
-did	-15257
-▁Fem	-15258
-▁rej	-15259
-uit	-15260
-▁wreck	-15261
-▁owning	-15262
-▁Bookmarked	-15263
-▁Phill	-15264
-bands	-15265
-▁HU	-15266
-▁Ho	-15267
-▁streak	-15268
-▁sailing	-15269
-!?	-15270
-▁Drop	-15271
-▁correspondence	-15272
-▁godd	-15273
-▁fuels	-15274
-▁receiver	-15275
-▁Healthy	-15276
-▁subjected	-15277
-▁probable	-15278
-▁soy	-15279
-apor	-15280
-▁Crow	-15281
-▁Costa	-15282
-▁Machine	-15283
-▁refuses	-15284
-▁unav	-15285
-▁Offer	-15286
-▁snake	-15287
-har	-15288
-▁Concer	-15289
-▁handles	-15290
-▁damned	-15291
-▁methodology	-15292
-rying	-15293
-▁Similarly	-15294
-▁utmost	-15295
-imen	-15296
-▁duck	-15297
-▁charitable	-15298
-▁Hills	-15299
-▁weights	-15300
-▁Utah	-15301
-amation	-15302
-▁seventh	-15303
-▁splendid	-15304
-bag	-15305
-▁NH	-15306
-▁``	-15307
-▁Anton	-15308
-▁grill	-15309
-▁Ped	-15310
-▁Uh	-15311
-▁READ	-15312
-▁mushrooms	-15313
-represent	-15314
-▁Arabia	-15315
-▁reluctant	-15316
-▁hammer	-15317
-▁Treasury	-15318
-▁cocktail	-15319
-▁refusing	-15320
-izon	-15321
-▁heir	-15322
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-▁requesting	-15324
-▁Poll	-15325
-▁cracked	-15326
-▁wandering	-15327
-▁bombs	-15328
-▁insects	-15329
-▁IC	-15330
-▁lining	-15331
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-ropolitan	-15333
-rah	-15334
-▁sober	-15335
-▁Somehow	-15336
-cuits	-15337
-▁mamm	-15338
-▁Adams	-15339
-▁distinguished	-15340
-emet	-15341
-▁Animal	-15342
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-▁clay	-15344
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-▁Row	-15346
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-Star	-15348
-▁gracious	-15349
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-▁Assessment	-15354
-Note	-15355
-▁maturity	-15356
-Dr	-15357
-▁Len	-15358
-▁administered	-15359
-▁abdom	-15360
-▁vivid	-15361
-Res	-15362
-▁harness	-15363
-Pl	-15364
-▁rented	-15365
-kyo	-15366
-▁Schol	-15367
-Spe	-15368
-▁Storm	-15369
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-▁Cole	-15372
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-▁Bachelor	-15374
-▁Communications	-15375
-▁deceased	-15376
-▁tenant	-15377
-▁likewise	-15378
-▁Regarding	-15379
-▁dug	-15380
-▁comed	-15381
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-▁scram	-15383
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-igma	-15385
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-ergarten	-15387
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-isle	-15393
-▁scholar	-15394
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-ensed	-15396
-▁Danny	-15397
-Tell	-15398
-▁unders	-15399
-▁voter	-15400
-▁tactic	-15401
-▁republic	-15402
-▁McG	-15403
-isive	-15404
-▁Canadians	-15405
-▁Jamie	-15406
-▁singles	-15407
-▁Opportun	-15408
-▁chatting	-15409
-▁Loved	-15410
-▁theoretical	-15411
-perfect	-15412
-▁skull	-15413
-▁tan	-15414
-▁clearer	-15415
-▁triple	-15416
-▁Alexander	-15417
-laws	-15418
-▁Kay	-15419
-▁matched	-15420
-▁Malaysia	-15421
-▁daylight	-15422
-▁notifications	-15423
-Part	-15424
-▁retained	-15425
-okes	-15426
-▁Alz	-15427
-nessee	-15428
-ountain	-15429
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-▁summon	-15431
-▁yearly	-15432
-▁rebuild	-15433
-▁rentals	-15434
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-▁Fear	-15439
-ername	-15440
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-.*	-15442
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-▁storms	-15444
-▁uk	-15445
-▁Ability	-15446
-borne	-15447
-ARY	-15448
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-▁relying	-15450
-iamond	-15451
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-▁cel	-15456
-patient	-15457
-ressive	-15458
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-ilight	-15460
-osures	-15461
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-rals	-15464
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-Ke	-15470
-det	-15471
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-sol	-15474
-▁pistol	-15475
-ilant	-15476
-▁Corps	-15477
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-▁warrior	-15481
-pin	-15482
-aco	-15483
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-elong	-15492
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-IDENT	-15506
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-ourage	-15509
-xit	-15510
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-▁seiz	-15521
-prov	-15522
-▁Tot	-15523
-inarily	-15524
-▁Million	-15525
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-▁hazardous	-15527
-▁illustration	-15528
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-empl	-15530
-udos	-15531
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-ancies	-15536
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-▁OUR	-15540
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-loor	-15544
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-atable	-15548
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-lif	-15559
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-▁unhealthy	-15564
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-▁Hearing	-15567
-▁clicked	-15568
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-FO	-15570
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-!!!!!	-15572
-▁Words	-15573
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-▁coastal	-15575
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-say	-15578
-▁Mit	-15579
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-▁MU	-15585
-hedral	-15586
-anor	-15587
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-▁towels	-15590
-▁underway	-15591
-▁screwed	-15592
-▁Dale	-15593
-▁benefited	-15594
-▁]	-15595
-marks	-15596
-▁chron	-15597
-▁wholly	-15598
-▁bonuses	-15599
-▁Sin	-15600
-▁Creek	-15601
-▁shallow	-15602
-▁sq	-15603
-▁Helen	-15604
-▁abrupt	-15605
-▁Determ	-15606
-▁Russell	-15607
-RY	-15608
-▁steak	-15609
-▁pled	-15610
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-▁haul	-15612
-▁stubborn	-15613
-falls	-15614
-▁cuis	-15615
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-/.	-15618
-▁ambul	-15619
-▁Fourth	-15620
-cott	-15621
-▁concentrated	-15622
-six	-15623
-▁impacted	-15624
-Ed	-15625
-▁retro	-15626
-Wor	-15627
-▁Amb	-15628
-▁Hair	-15629
-▁runners	-15630
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-▁Katie	-15632
-▁Neg	-15633
-▁validity	-15634
-▁neighbours	-15635
-▁revolutionary	-15636
-▁feast	-15637
-▁Argent	-15638
-▁energetic	-15639
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-▁Firefox	-15641
-▁MB	-15642
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-▁priests	-15644
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-INK	-15651
-erting	-15652
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-▁Regulations	-15657
-▁Edd	-15658
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-▁BEST	-15663
-▁Apply	-15664
-Like	-15665
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-▁Um	-15668
-▁Zone	-15669
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-unal	-15671
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-▁aluminum	-15679
-▁intentionally	-15680
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-▁clerk	-15682
-earl	-15683
-▁obed	-15684
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-▁Bishop	-15686
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-▁mosqu	-15690
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-▁Aaron	-15693
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-▁fuss	-15695
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-▁Crime	-15698
-▁abusive	-15699
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-▁drown	-15703
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-aph	-15705
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-ooo	-15807
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-cre	-15875
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-FT	-15907
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-venty	-15916
-▁acquiring	-15917
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-▁Tommy	-15929
-ivial	-15930
-olk	-15931
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-afood	-15936
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-irop	-15938
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-▁Indonesia	-15941
-▁McK	-15942
-▁Eval	-15943
-▁Yanke	-15944
-▁Expect	-15945
-▁yelled	-15946
-▁THEY	-15947
-▁tweets	-15948
-▁Cameron	-15949
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-▁reputable	-15951
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-ivery	-15954
-erto	-15955
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-▁enrich	-15963
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-IDE	-15976
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-inee	-15989
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-▁collaborate	-15993
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-kn	-15995
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-Mar	-15999
-▁Kath	-16000
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-▁metaphor	-16003
-▁scarcely	-16004
-▁vocabulary	-16005
-▁doses	-16006
-▁Multip	-16007
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-▁allowance	-16012
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-osterone	-16055
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-IZ	-16090
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-lings	-16093
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-ogram	-16111
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-izers	-16116
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-iere	-16120
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-elle	-16122
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-ACH	-16125
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-▁Merc	-16133
-▁almond	-16134
-▁observing	-16135
-▁notably	-16136
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-iropract	-16138
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-oks	-16141
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-instein	-16147
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-alph	-16149
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-zzy	-16152
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-loe	-16157
-ooting	-16158
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-cin	-16161
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-▁Anyways	-16168
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-umni	-16170
-die	-16171
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-igent	-16175
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-gone	-16177
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-role	-16183
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-▁Chel	-16188
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-FOR	-16191
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-▁Whole	-16193
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-bank	-16196
-▁Gay	-16197
-▁sob	-16198
-▁evaluating	-16199
-▁presumably	-16200
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-▁terrifying	-16203
-▁electro	-16204
-▁tangible	-16205
-acies	-16206
-▁carbohydr	-16207
-without	-16208
-▁Lastly	-16209
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-▁Norway	-16212
-▁plaint	-16213
-far	-16214
-▁conting	-16215
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-▁Lawrence	-16218
-▁Dak	-16219
-cession	-16220
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-▁concerts	-16222
-▁meth	-16223
-▁bachelor	-16224
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-gia	-16228
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-ussels	-16230
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-▁mph	-16233
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-letter	-16235
-review	-16236
-▁eru	-16237
-lining	-16238
-Perhaps	-16239
-ilateral	-16240
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-otional	-16243
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-aways	-16246
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-▁Inspect	-16249
-liter	-16250
-▁liquor	-16251
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-▁surviving	-16253
-liked	-16254
-eda	-16255
-General	-16256
-▁appar	-16257
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-visors	-16276
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-ippi	-16288
-XX	-16289
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-................	-16299
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-lando	-16303
-ivated	-16304
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-ighting	-16339
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-aky	-16352
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-zzle	-16385
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-licted	-16393
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-ENCE	-16413
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-eb	-16420
-ificant	-16421
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-icut	-16429
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-zo	-16432
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-ifi	-16438
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-icating	-16444
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-eches	-16448
-teenth	-16449
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-▁Wayne	-16454
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-olar	-16461
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-▁Diamond	-16476
-▁DIS	-16477
-▁Shar	-16478
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-▁Nathan	-16482
-▁Skills	-16483
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-astics	-16487
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-iencies	-16489
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-anity	-16492
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-fu	-16497
-▁chopped	-16498
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-ymph	-16501
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-gled	-16514
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-▁soooo	-16541
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-▁advertised	-16547
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-venant	-16550
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-▁Wat	-16557
-ieval	-16558
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-▁Dawn	-16586
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-erers	-16592
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-▁albeit	-16594
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-▁sworn	-16596
-▁Summary	-16597
-▁unbelievable	-16598
-▁plumbing	-16599
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-▁blu	-16605
-▁inequality	-16606
-▁Element	-16607
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-▁Hans	-16613
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-▁supermarket	-16615
-▁renowned	-16616
-▁thrill	-16617
-▁Into	-16618
-▁Candid	-16619
-▁Zero	-16620
-▁Kindle	-16621
-corn	-16622
-▁adaptation	-16623
-▁masterpiece	-16624
-ulates	-16625
-▁tremb	-16626
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-▁illustrated	-16628
-▁condemn	-16629
-▁touchdown	-16630
-itives	-16631
-▁Proced	-16632
-▁arises	-16633
-▁rehears	-16634
-▁Sherlock	-16635
-▁socio	-16636
-▁beverage	-16637
-▁retailer	-16638
-Most	-16639
-▁reimburse	-16640
-▁accompanying	-16641
-vit	-16642
-▁phases	-16643
-▁unreal	-16644
-▁analyzing	-16645
-▁privileges	-16646
-▁endeavor	-16647
-▁convent	-16648
-▁Evil	-16649
-▁jungle	-16650
-▁recordings	-16651
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-▁adopting	-16653
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-▁Vitamin	-16656
-▁tempting	-16657
-▁combining	-16658
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-uns	-16660
-▁litter	-16661
-▁sliding	-16662
-lon	-16663
-▁Fuck	-16664
-Int	-16665
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-▁Stewart	-16669
-▁balloon	-16670
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-▁Effect	-16672
-▁Liberal	-16673
-▁remembers	-16674
-inis	-16675
-▁refrain	-16676
-rates	-16677
-▁alley	-16678
-▁notebook	-16679
-▁Certificate	-16680
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-▁Iceland	-16683
-▁mindful	-16684
-bone	-16685
-▁pee	-16686
-▁Warri	-16687
-cending	-16688
-▁festivals	-16689
-▁VP	-16690
-...?	-16691
-▁Moscow	-16692
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-▁empowered	-16695
-▁Lower	-16696
-▁defic	-16697
-▁outdated	-16698
-wa	-16699
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-▁ES	-16704
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-▁supreme	-16708
-fat	-16709
-▁LLC	-16710
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-▁constitutes	-16715
-)!	-16716
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-ieces	-16722
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-ipper	-16725
-rer	-16726
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-▁hurricane	-16729
-del	-16730
-locked	-16731
-otta	-16732
-▁Speed	-16733
-▁Index	-16734
-htaking	-16735
-▁prolonged	-16736
-▁lifelong	-16737
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-would	-16740
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-otypes	-16743
-▁proudly	-16744
-▁Lol	-16745
-▁Summit	-16746
-▁Bul	-16747
-▁ankle	-16748
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-ALE	-16750
-▁THERE	-16751
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-▁helicopter	-16753
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-▁lakes	-16755
-▁sweater	-16756
-▁Eb	-16757
-▁didnt	-16758
-▁alignment	-16759
-▁predictable	-16760
-While	-16761
-▁formally	-16762
-▁BU	-16763
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-▁melted	-16765
-▁Firstly	-16766
-▁disgusting	-16767
-▁redund	-16768
-uctions	-16769
-▁Fisher	-16770
-▁loudly	-16771
-Co	-16772
-chell	-16773
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-should	-16777
-▁WR	-16778
-▁attracting	-16779
-▁insufficient	-16780
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-▁murmured	-16783
-▁acquainted	-16784
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-▁Nort	-16787
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-OSE	-16789
-▁owing	-16790
-lph	-16791
-Cont	-16792
-mith	-16793
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-akespe	-16796
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-▁Resear	-16798
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-icky	-16801
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-▁clearance	-16804
-▁positioned	-16805
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-hou	-16809
-▁Yay	-16810
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-▁hedge	-16814
-usually	-16815
-▁admits	-16816
-▁Patri	-16817
-▁firearms	-16818
-▁hay	-16819
-▁pitc	-16820
-condition	-16821
-Rem	-16822
-▁evidently	-16823
-▁exemption	-16824
-▁drone	-16825
-▁Needless	-16826
-fied	-16827
-▁Lif	-16828
-▁Moses	-16829
-▁depths	-16830
-infl	-16831
-▁scales	-16832
-▁Extreme	-16833
-▁clinics	-16834
-▁nationally	-16835
-ocur	-16836
-ummer	-16837
-▁Mini	-16838
-▁baggage	-16839
-mission	-16840
-▁crunch	-16841
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-▁Typically	-16843
-▁hath	-16844
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-▁OB	-16846
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-▁mechanic	-16849
-rays	-16850
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-▁vampire	-16853
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-▁deductible	-16865
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-ambers	-16867
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-▁pave	-16870
-▁tiles	-16871
-lit	-16872
-▁villain	-16873
-▁pam	-16874
-▁hydrogen	-16875
-▁overweight	-16876
-▁breakthrough	-16877
-GM	-16878
-ourable	-16879
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-▁Orlando	-16884
-▁begging	-16885
-":	-16886
-▁Guest	-16887
-rar	-16888
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-▁RT	-16898
-▁Pitts	-16899
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-_,	-16901
-Che	-16902
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-▁deputy	-16904
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-▁Newcastle	-16907
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-Sc	-16912
-ocaly	-16913
-▁Putin	-16914
-▁rotation	-16915
-umed	-16916
-▁whistle	-16917
-▁spotlight	-16918
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-THER	-16920
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-▁unacceptable	-16922
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-ijing	-16925
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-elp	-16927
-▁OMG	-16928
-▁Identify	-16929
-▁Thompson	-16930
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-▁weighed	-16933
-akespeare	-16934
-▁incomplete	-16935
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-obic	-16937
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-▁automotive	-16940
-turn	-16941
-▁Parad	-16942
-jack	-16943
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-▁cowork	-16949
-irus	-16950
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-tails	-16953
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-uki	-16959
-ellar	-16960
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-▁kissing	-16966
-----	-16967
-urdy	-16968
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-▁instructors	-16970
-▁enhancing	-16971
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-kill	-16975
-techn	-16976
-▁quo	-16977
-▁Option	-16978
-▁accepts	-16979
-▁cinnamon	-16980
-▁borrowed	-16981
-▁bullets	-16982
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-▁medal	-16984
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-▁polish	-16986
-▁singular	-16987
-omp	-16988
-online	-16989
-▁deepest	-16990
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-▁monkey	-16993
-▁Stage	-16994
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-tainment	-16998
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-estock	-17003
-san	-17004
-▁Visual	-17005
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-▁Virtual	-17010
-▁beard	-17011
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-▁Repe	-17014
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-▁Colomb	-17016
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-▁Changes	-17018
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-▁handing	-17021
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-▁extensions	-17029
-▁rested	-17030
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-tis	-17035
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-▁Transfer	-17037
-▁Cambridge	-17038
-▁Alcohol	-17039
-▁Arn	-17040
-THING	-17041
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-rill	-17043
-▁irony	-17044
-▁Appreciate	-17045
-▁Maur	-17046
-▁guiding	-17047
-▁Sold	-17048
-▁stainless	-17049
-▁waved	-17050
-▁wheelchair	-17051
-▁Mason	-17052
-▁Reply	-17053
-▁Mississippi	-17054
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-ettle	-17056
-▁Samsung	-17057
-▁rhetoric	-17058
-▁intentional	-17059
-▁pops	-17060
-▁Forget	-17061
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-Does	-17065
-▁Heather	-17066
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-ivan	-17078
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-His	-17089
-give	-17090
-()	-17091
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-▁Hu	-17095
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-▁recommending	-17098
-▁Choice	-17099
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-oots	-17102
-aimer	-17103
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-irteen	-17105
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-▁filming	-17110
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-idious	-17112
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-anked	-17118
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-▁Document	-17122
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-▁Connecticut	-17124
-comp	-17125
-▁lame	-17126
-▁frightening	-17127
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-▁incompet	-17129
-▁Perry	-17130
-▁villa	-17131
-uffed	-17132
-▁Task	-17133
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-▁secretly	-17135
-▁hackers	-17136
-map	-17137
-▁ranch	-17138
-▁Abraham	-17139
-▁Failure	-17140
-▁refusal	-17141
-▁Protect	-17142
-▁mathematical	-17143
-▁Alt	-17144
-▁Trip	-17145
-▁shuttle	-17146
-▁Lux	-17147
-▁Charter	-17148
-▁darn	-17149
-▁indirectly	-17150
-▁Hours	-17151
-▁lowering	-17152
-▁Size	-17153
-▁digestive	-17154
-▁Panel	-17155
-eight	-17156
-▁Economics	-17157
-▁professors	-17158
-▁Sel	-17159
-▁spice	-17160
-▁breathtaking	-17161
-balls	-17162
-mental	-17163
-▁hears	-17164
-▁Alph	-17165
-▁ner	-17166
-▁Theory	-17167
-▁Workers	-17168
-▁critically	-17169
-▁psychic	-17170
-DC	-17171
-▁owed	-17172
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-▁scrub	-17174
-▁twent	-17175
-etal	-17176
-ipher	-17177
-▁Beijing	-17178
-▁Kirk	-17179
-▁Verm	-17180
-▁dilemma	-17181
-quer	-17182
-▁negotiating	-17183
-squ	-17184
-▁Trail	-17185
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-▁Resource	-17187
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-▁queue	-17189
-▁corridor	-17190
-▁blah	-17191
-Sec	-17192
-▁secular	-17193
-▁flooring	-17194
-lation	-17195
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-imensional	-17197
-▁Sugar	-17198
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-▁artifact	-17200
-bb	-17201
-UCK	-17202
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-fig	-17204
-▁assessing	-17205
-regn	-17206
-▁Chip	-17207
-▁exploit	-17208
-▁pursued	-17209
-ceptible	-17210
-▁emphasize	-17211
-▁volunteering	-17212
-odies	-17213
-rained	-17214
-▁Eating	-17215
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-▁mins	-17217
-▁Batman	-17218
-▁Effective	-17219
-▁Lily	-17220
-import	-17221
-▁intrigued	-17222
-ibles	-17223
-▁Shin	-17224
-celona	-17225
-▁Kerry	-17226
-▁showc	-17227
-itals	-17228
-▁Stri	-17229
-▁prestigious	-17230
-▁Ott	-17231
-▁Alber	-17232
-▁Forces	-17233
-pathetic	-17234
-alone	-17235
-▁unrest	-17236
-▁Fashion	-17237
-isis	-17238
-▁Hem	-17239
-▁pars	-17240
-daughter	-17241
-▁interval	-17242
-▁subjective	-17243
-++	-17244
-uffs	-17245
-resses	-17246
-▁compromised	-17247
-▁sway	-17248
-question	-17249
-▁Players	-17250
-▁unve	-17251
-▁Spect	-17252
-▁Divine	-17253
-▁cooperative	-17254
-▁compassionate	-17255
-igo	-17256
-eps	-17257
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-▁architectural	-17259
-▁courte	-17260
-▁median	-17261
-▁minded	-17262
-▁traumatic	-17263
-code	-17264
-▁madness	-17265
-▁clut	-17266
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-ensitive	-17269
-▁heavens	-17270
-▁quantum	-17271
-▁drying	-17272
-▁spat	-17273
-▁Degree	-17274
-▁Conduct	-17275
-▁idiots	-17276
-▁Tel	-17277
-▁glucose	-17278
-▁disruption	-17279
-▁nausea	-17280
-▁celebrations	-17281
-https	-17282
-▁bunk	-17283
-▁Strange	-17284
-▁economically	-17285
-erie	-17286
-▁swallow	-17287
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-kee	-17390
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-TE	-17398
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-ieth	-17412
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-▁Marvel	-17443
-▁Princi	-17444
-▁suffers	-17445
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-▁Enough	-17447
-▁lookout	-17448
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-▁Optim	-17450
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-▁meanwhile	-17457
-Eng	-17458
-ISS	-17459
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-igm	-17462
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-▁Mos	-17487
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-▁Initi	-17490
-▁anyways	-17491
-ao	-17492
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-mel	-17494
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-▁Buying	-17497
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-opia	-17499
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-▁WAY	-17504
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-ogenic	-17511
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-rimp	-17515
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-ointed	-17522
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-▁Taiwan	-17550
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-IX	-17552
-▁Hamp	-17553
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-ni	-17555
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-▁Sara	-17574
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-Mart	-17579
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-ausible	-17584
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-ixon	-17597
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-uma	-17603
-▁unlawful	-17604
-Hi	-17605
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-anium	-17607
-▁mug	-17608
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-▁Hurricane	-17610
-ICH	-17611
-occup	-17612
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-▁comfy	-17616
-▁Wee	-17617
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-▁Honey	-17627
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-nded	-17629
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-MM	-17635
-usted	-17636
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-IO	-17638
-isen	-17639
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-▁eighth	-17642
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-Es	-17644
-oodles	-17645
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-atured	-17672
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-.”	-17675
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-otted	-17698
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-pherd	-17708
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-uddle	-17714
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-acking	-17719
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-asis	-17721
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-aman	-17726
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-urally	-17744
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-achment	-17752
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-reath	-17782
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-ocket	-17786
-stery	-17787
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-doing	-17791
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-***	-17796
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-rophic	-17804
-thodox	-17805
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-nesium	-17807
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-agles	-17809
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-▁Oz	-17813
-Free	-17814
-▁greeting	-17815
-▁rigorous	-17816
-mem	-17817
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-▁Chocolate	-17824
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-▁sovereign	-17826
-asha	-17827
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-acked	-17829
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-vency	-17833
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-IFE	-17841
-uffle	-17842
-venile	-17843
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-western	-17854
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-osph	-17862
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-▁menus	-17898
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-onial	-17904
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-atches	-17910
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-▁Arr	-17918
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-grow	-17921
-ripp	-17922
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-.),	-17928
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-imated	-17948
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-oodle	-17982
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-atson	-17995
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-olve	-17997
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-upunct	-18033
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-Ph	-18133
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-!...	-18136
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--$	-18147
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-orp	-18150
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-urous	-18193
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-inqu	-18205
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-yet	-18213
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-holding	-18218
-pract	-18219
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-▁prevalent	-18225
-▁MAD	-18226
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-▁unjust	-18228
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-ths	-18230
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-▁Mama	-18235
-▁denom	-18236
-▁blat	-18237
-▁congest	-18238
-▁interacting	-18239
-Wow	-18240
-▁UC	-18241
-inees	-18242
-▁Brew	-18243
-▁Eri	-18244
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-hesive	-18246
-▁theat	-18247
-▁improper	-18248
-▁triggers	-18249
-▁supernatural	-18250
-▁FIR	-18251
-iolet	-18252
-▁critique	-18253
-▁Spend	-18254
-▁naive	-18255
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-▁Cott	-18257
-enez	-18258
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-▁devotion	-18260
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-▁herbal	-18262
-▁extras	-18263
-▁startups	-18264
-fun	-18265
-▁oz	-18266
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-▁Turns	-18268
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-▁violate	-18272
-▁Increase	-18273
-clip	-18274
-▁lime	-18275
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-oused	-18278
-▁livestock	-18279
-▁Visa	-18280
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-)-	-18282
-▁bik	-18283
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-▁analog	-18285
-▁marking	-18286
-▁unsafe	-18287
-▁HUGE	-18288
-▁arbitrary	-18289
-▁framed	-18290
-▁rescued	-18291
-▁sampling	-18292
-INT	-18293
-▁Patient	-18294
-€™	-18295
-▁REC	-18296
-▁civic	-18297
-▁Romney	-18298
-▁calmly	-18299
-rians	-18300
-nothing	-18301
-▁dashboard	-18302
-▁Molly	-18303
-▁Antonio	-18304
-▁crafting	-18305
-▁seasoned	-18306
-rene	-18307
-▁shaping	-18308
-identally	-18309
-jamin	-18310
-RO	-18311
-▁marsh	-18312
-▁brakes	-18313
-▁zombie	-18314
-▁ringing	-18315
-▁Previous	-18316
-▁directing	-18317
-▁internally	-18318
-imming	-18319
-irical	-18320
-▁choir	-18321
-oven	-18322
-▁Pod	-18323
-▁Loan	-18324
-tor	-18325
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-▁pillows	-18330
-yan	-18331
-▁piv	-18332
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-ignant	-18334
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-OLD	-18338
-▁Adult	-18339
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-jour	-18341
-oped	-18342
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-▁Clay	-18344
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-▁Inj	-18346
-▁magnet	-18347
-▁precip	-18348
-▁connectivity	-18349
-topic	-18350
-▁toxins	-18351
-▁WORK	-18352
-▁Murphy	-18353
-▁inquired	-18354
-▁suic	-18355
-value	-18356
-▁Inqu	-18357
-▁Hannah	-18358
-▁intric	-18359
-▁Noah	-18360
-ardo	-18361
-▁compose	-18362
-▁lifes	-18363
-▁weighs	-18364
-▁Wire	-18365
-▁premier	-18366
-adder	-18367
-▁Carm	-18368
-▁Imperial	-18369
-▁Southeast	-18370
-▁instrumental	-18371
-▁noisy	-18372
-▁flavour	-18373
-▁freshly	-18374
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-▁Fan	-18377
-▁goo	-18378
-▁disable	-18379
-▁coating	-18380
-▁predomin	-18381
-▁trainers	-18382
-▁Require	-18383
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-▁SCH	-18385
-▁Cards	-18386
-▁mirrors	-18387
-▁●	-18388
-asper	-18389
-▁clown	-18390
-▁Evidence	-18391
-▁herd	-18392
-▁Edinburgh	-18393
-▁oak	-18394
-▁periodically	-18395
-NING	-18396
-▁Harper	-18397
-▁Russians	-18398
-▁announcements	-18399
-▁mines	-18400
-Since	-18401
-▁bible	-18402
-▁Alf	-18403
-▁sixteen	-18404
-▁Went	-18405
-▁Marshall	-18406
-▁Immedi	-18407
-▁hyster	-18408
-▁Millenn	-18409
-?...	-18410
-▁Hmmm	-18411
-▁finale	-18412
-▁TB	-18413
-washer	-18414
-▁galleries	-18415
-▁wig	-18416
-▁procurement	-18417
-▁unreasonable	-18418
-▁tee	-18419
-▁elevation	-18420
-riad	-18421
-▁Wake	-18422
-[/	-18423
-VED	-18424
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-ieur	-18427
-built	-18428
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-forcing	-18433
-▁naught	-18434
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-▁pussy	-18436
-manship	-18437
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-cro	-18439
-coun	-18440
-▁IBM	-18441
-names	-18442
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-obia	-18444
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-zech	-18446
-blems	-18447
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-▁disrespect	-18450
-▁Asked	-18451
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-icals	-18453
-▁felony	-18454
-▁Teen	-18455
-▁Greater	-18456
-▁Tab	-18457
-.?	-18458
-liv	-18459
-▁Ora	-18460
-growing	-18461
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-ABLE	-18465
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-late	-18470
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-aro	-18509
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-enz	-18519
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-ometric	-18521
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-call	-18530
-wart	-18531
-▁fourteen	-18532
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-abe	-18537
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-amn	-18539
-meal	-18540
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-ordable	-18543
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-▁ammunition	-18546
-ilia	-18547
-▁pinch	-18548
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-job	-18552
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-▁objections	-18555
-▁Wrong	-18556
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-▁feminine	-18558
-▁correlation	-18559
-▁Lets	-18560
-▁zoom	-18561
-▁promo	-18562
-▁surveyed	-18563
-exper	-18564
-▁ditch	-18565
-issance	-18566
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-▁Louisiana	-18568
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-▁gent	-18570
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-▁encounters	-18572
-WN	-18573
-▁Dennis	-18574
-▁Sexual	-18575
-ser	-18576
-ench	-18577
-▁hourly	-18578
-▁Hait	-18579
-blogspot	-18580
-▁Scholars	-18581
-▁spinal	-18582
-gles	-18583
-▁tubes	-18584
-▁chores	-18585
-▁illustrates	-18586
-▁counterparts	-18587
-ulu	-18588
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-▁Sister	-18590
-▁ceremon	-18591
-▁initiate	-18592
-▁pap	-18593
-▁Spike	-18594
-▁learns	-18595
-▁Crystal	-18596
-▁accelerate	-18597
-▁discourse	-18598
-▁Uber	-18599
-▁ABOUT	-18600
-▁OTHER	-18601
-▁screamed	-18602
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-▁Chad	-18605
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-▁storytelling	-18610
-national	-18611
-▁Spencer	-18612
-▁velocity	-18613
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-▁coping	-18616
-▁geared	-18617
-wild	-18618
-▁locker	-18619
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-eor	-18621
-may	-18622
-▁chuckled	-18623
-OWN	-18624
-contract	-18625
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-forcement	-18627
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-heric	-18630
-▁Murray	-18631
-inse	-18632
-▁buys	-18633
-Ob	-18634
-tsy	-18635
-▁helm	-18636
-nection	-18637
-▁clergy	-18638
-▁idle	-18639
-▁Flore	-18640
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-▁Lar	-18642
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-OSS	-18644
-mil	-18645
-orthy	-18646
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-insula	-18649
-▁cement	-18650
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-▁Nuclear	-18654
-▁Version	-18655
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-does	-18657
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-_.	-18660
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-criber	-18662
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-atories	-18664
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-YS	-18669
-▁Orders	-18670
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-▁prosecutors	-18674
->>	-18675
-▁homosexual	-18676
-▁Mas	-18677
-▁Affili	-18678
-▁symptom	-18679
-▁Bos	-18680
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-▁skinny	-18682
-▁wildly	-18683
-piration	-18684
-▁Hour	-18685
-▁factories	-18686
-fill	-18687
-▁stunt	-18688
-▁adrenal	-18689
-▁reproduce	-18690
-CP	-18691
-bows	-18692
-▁admired	-18693
-▁prophet	-18694
-FS	-18695
-▁issuing	-18696
-▁Workshop	-18697
-hots	-18698
-▁preach	-18699
-▁Sharon	-18700
-However	-18701
-▁Options	-18702
-produ	-18703
-park	-18704
-▁Clare	-18705
-▁faded	-18706
-▁dividends	-18707
-hu	-18708
-ento	-18709
-▁potent	-18710
-Keep	-18711
-▁cryst	-18712
-▁undergoing	-18713
-▁Cu	-18714
-▁altar	-18715
-▁Stephan	-18716
-▁shoppers	-18717
-▁rendering	-18718
-▁Derek	-18719
-▁tunes	-18720
-bath	-18721
-chet	-18722
-▁slam	-18723
-▁auditor	-18724
-▁decorate	-18725
-▁Commonwealth	-18726
-▁pins	-18727
-▁Poly	-18728
-tended	-18729
-▁Providing	-18730
-vell	-18731
-▁fres	-18732
-Once	-18733
-▁FUN	-18734
-itorial	-18735
-umps	-18736
-▁Multiple	-18737
-▁configure	-18738
-▁Brig	-18739
-▁ironic	-18740
-▁Christine	-18741
-▁encryption	-18742
-▁GB	-18743
-▁EST	-18744
-wheel	-18745
-▁exhibits	-18746
-▁Jama	-18747
-▁fantas	-18748
-arium	-18749
-▁Academic	-18750
-▁thickness	-18751
-nan	-18752
-▁sou	-18753
-▁admissions	-18754
-▁imaging	-18755
-▁imports	-18756
-▁continuity	-18757
-▁Favor	-18758
-Give	-18759
-▁Ladies	-18760
-▁vicious	-18761
-▁backward	-18762
-saving	-18763
-▁♥	-18764
-rium	-18765
-▁Donna	-18766
-▁unden	-18767
-▁squirrel	-18768
-▁vomiting	-18769
-▁gn	-18770
-established	-18771
-▁node	-18772
-▁Mitchell	-18773
-▁standpoint	-18774
-umm	-18775
-▁Mut	-18776
-youtube	-18777
-▁Shadow	-18778
-▁apparatus	-18779
-▁Scientific	-18780
-meaning	-18781
-▁researched	-18782
-▁Anyhow	-18783
-▁Streng	-18784
-▁broadly	-18785
-inea	-18786
-▁Stick	-18787
-▁waving	-18788
-funded	-18789
-▁Juan	-18790
-▁lesbian	-18791
-▁Barn	-18792
-▁expenditures	-18793
-hey	-18794
-▁wrestling	-18795
-▁dots	-18796
-▁alerts	-18797
-▁discern	-18798
-▁meanings	-18799
-▁occupational	-18800
-▁Map	-18801
-▁thieves	-18802
-fed	-18803
-nish	-18804
-▁Casino	-18805
-▁sleeves	-18806
-▁Robinson	-18807
-▁soothing	-18808
-perhaps	-18809
-▁drilling	-18810
-atics	-18811
-▁teammates	-18812
-▁tomb	-18813
-▁Aware	-18814
-▁slices	-18815
-▁enforced	-18816
-▁homepage	-18817
-ocaust	-18818
-▁ferry	-18819
-▁leaks	-18820
-creat	-18821
-▁unle	-18822
-▁Generation	-18823
-▁impaired	-18824
-▁insulation	-18825
-?:	-18826
-▁Armed	-18827
-▁Blake	-18828
-▁antique	-18829
-▁Cad	-18830
-▁COMM	-18831
-ioneer	-18832
-▁Brandon	-18833
-▁mutually	-18834
-▁Od	-18835
-▁Pull	-18836
-▁gosh	-18837
-▁savvy	-18838
-▁evangel	-18839
-▁murders	-18840
-▁Catherine	-18841
-▁Cob	-18842
-▁kern	-18843
-▁spoiled	-18844
-▁receipts	-18845
-▁disregard	-18846
-▁\\	-18847
-▁Directive	-18848
-▁ambitions	-18849
-▁groceries	-18850
-had	-18851
-▁saddle	-18852
-▁Setting	-18853
-▁supporter	-18854
-▁footsteps	-18855
-▁meaningless	-18856
-▁Premium	-18857
-▁whereby	-18858
-▁realizes	-18859
-▁regulators	-18860
-▁excurs	-18861
-▁___	-18862
-▁smoked	-18863
-▁Griff	-18864
-▁Exercise	-18865
-▁prostate	-18866
-▁meds	-18867
-▁presume	-18868
-▁evolutionary	-18869
-▁TM	-18870
-onal	-18871
-uese	-18872
-▁tracked	-18873
-▁polished	-18874
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-▁canal	-18876
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-▁Specifically	-18894
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-▁Goods	-18912
-▁Experts	-18913
-▁Benjamin	-18914
-▁Hat	-18915
-▁conception	-18916
-ELY	-18917
-▁dehy	-18918
-▁lawsuits	-18919
-▁resilience	-18920
-▁Strategic	-18921
-▁maneu	-18922
-▁Douglas	-18923
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-▁employing	-18925
-▁squash	-18926
-consuming	-18927
-▁technician	-18928
-▁similarities	-18929
-▁crafted	-18930
-▁Suz	-18931
-▁Marc	-18932
-▁Near	-18933
-▁norms	-18934
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-▁liberals	-18936
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-▁Nine	-18938
-▁grandma	-18939
-▁preaching	-18940
-▁Guidelines	-18941
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-▁Iranian	-18943
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-▁Kir	-18947
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-▁Berkeley	-18949
-▁Opening	-18950
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-▁Communist	-18953
-▁supplying	-18954
-eers	-18955
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-▁Clinical	-18957
-htm	-18958
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-▁efficacy	-18963
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-▁sausage	-18971
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-▁Monica	-18978
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-▁abide	-18983
-esus	-18984
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-▁commissions	-19001
-UP	-19002
-▁migrants	-19003
-▁accidental	-19004
-▁PART	-19005
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-▁weaker	-19007
-▁Stu	-19008
-▁Feeling	-19009
-▁prosecutor	-19010
-▁—	-19011
-▁flock	-19012
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-▁inconsistent	-19014
-▁endors	-19015
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-▁Likewise	-19017
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-▁pleasantly	-19024
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-angered	-19026
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-orce	-19031
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-usters	-19038
-▁Couldn	-19039
-▁Managing	-19040
-▁ml	-19041
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-ULL	-19043
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-OUGH	-19045
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-▁criticized	-19048
-DD	-19049
-▁turb	-19050
-itutional	-19051
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-▁Economy	-19054
-ML	-19055
-▁Snap	-19056
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-▁proprietary	-19058
-those	-19059
-▁voyage	-19060
-▁unsuccess	-19061
-▁Adventure	-19062
-esh	-19063
-▁ah	-19064
-▁anat	-19065
-▁corps	-19066
-▁optical	-19067
-vings	-19068
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-▁passions	-19070
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-viol	-19072
-▁Eddie	-19073
-insured	-19074
-rell	-19075
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-▁forgiven	-19077
-▁illegally	-19078
-▁quarterback	-19079
-ooh	-19080
-▁poisoning	-19081
-▁Jackie	-19082
-▁Watson	-19083
-▁marker	-19084
-▁Rush	-19085
-▁bubbles	-19086
-▁altitude	-19087
-▁configured	-19088
-▁immensely	-19089
-▁respectable	-19090
-nty	-19091
-▁Kr	-19092
-WAYS	-19093
-antern	-19094
-▁Parks	-19095
-▁coordinated	-19096
-mag	-19097
-▁Diana	-19098
-▁Shall	-19099
-▁bulbs	-19100
-▁Issues	-19101
-▁solemn	-19102
-▁berries	-19103
-▁balances	-19104
-▁„	-19105
-ENS	-19106
-essa	-19107
-▁inspections	-19108
-PDF	-19109
-▁DAY	-19110
-creen	-19111
-▁discouraged	-19112
-▁INTER	-19113
-▁Cruz	-19114
-▁rook	-19115
-▁dispose	-19116
-▁AA	-19117
-▁chees	-19118
-▁fasting	-19119
-▁Domestic	-19120
-▁stimulating	-19121
-haul	-19122
-stan	-19123
-▁sane	-19124
-alogue	-19125
-▁vanished	-19126
-▁uniforms	-19127
-▁experimenting	-19128
-brain	-19129
-▁aided	-19130
-▁amino	-19131
-▁noises	-19132
-▁Hampshire	-19133
-▁wal	-19134
-venth	-19135
-▁Cheap	-19136
-▁pigs	-19137
-▁analyzed	-19138
-rien	-19139
-▁kidn	-19140
-▁clutter	-19141
-cuts	-19142
-ylon	-19143
-▁ACC	-19144
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-▁installations	-19146
-▁myriad	-19147
-▁freshman	-19148
-▁Camb	-19149
-▁hamm	-19150
-▁proclaim	-19151
-▁Watching	-19152
-▁validation	-19153
-▁Style	-19154
-probably	-19155
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-▁favourites	-19157
-▁susceptible	-19158
-▁raped	-19159
-▁intoler	-19160
-▁sinking	-19161
-▁mitigate	-19162
-▁dominate	-19163
-▁Argentina	-19164
-▁saturated	-19165
-liant	-19166
-▁filmed	-19167
-▁gallons	-19168
-▁Educational	-19169
-▁HAS	-19170
-▁salon	-19171
-them	-19172
-▁timer	-19173
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-▁converting	-19175
-ophy	-19176
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-official	-19180
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-▁credited	-19182
-▁Stadium	-19183
-▁craving	-19184
-▁Choosing	-19185
-▁slid	-19186
-▁jewell	-19187
-▁incorporating	-19188
-Comm	-19189
-▁Gran	-19190
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-▁Totally	-19192
-▁gadgets	-19193
-▁affiliated	-19194
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-udding	-19197
-ierra	-19198
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-dad	-19200
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-▁dumped	-19203
-▁bombing	-19204
-paced	-19205
-▁crappy	-19206
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-▁withstand	-19208
-▁distractions	-19209
-▁Warm	-19210
-▁sque	-19211
-rators	-19212
-▁webinar	-19213
-▁downward	-19214
-▁Palestinians	-19215
-▁HT	-19216
-Many	-19217
-▁Annie	-19218
-▁nearer	-19219
-▁currencies	-19220
-invest	-19221
-▁Journey	-19222
-▁imperfect	-19223
-▁Lance	-19224
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-▁commissioned	-19226
-eur	-19227
-▁Chef	-19228
-▁bedtime	-19229
-ova	-19230
-alia	-19231
-▁chalk	-19232
-▁imagery	-19233
-▁advertisers	-19234
-ooter	-19235
-▁atro	-19236
-▁strap	-19237
-boarding	-19238
-▁accountant	-19239
-▁sympathetic	-19240
-blood	-19241
-ussion	-19242
-▁fraudulent	-19243
-▁Benn	-19244
-▁comr	-19245
-▁erad	-19246
-▁Lieutenant	-19247
-▁geographic	-19248
-▁Tun	-19249
-const	-19250
-▁Address	-19251
-▁Include	-19252
-▁cancers	-19253
-▁Dip	-19254
-▁Und	-19255
-▁Chase	-19256
-▁microphone	-19257
-▁perceptions	-19258
-ylan	-19259
-▁queer	-19260
-▁Original	-19261
-lol	-19262
-▁gems	-19263
-▁spicy	-19264
-▁Python	-19265
-▁conceived	-19266
-▁Jefferson	-19267
-▁Syn	-19268
-lower	-19269
-▁Granted	-19270
-▁(#	-19271
-retched	-19272
-▁winding	-19273
-▁inspires	-19274
-▁intermediate	-19275
-▁limbs	-19276
-▁overload	-19277
-▁Steam	-19278
-▁juices	-19279
-▁sorting	-19280
-▁prototype	-19281
-important	-19282
-▁mysteries	-19283
-▁elimination	-19284
-MP	-19285
-▁Fame	-19286
-felt	-19287
-▁detained	-19288
-▁EPA	-19289
-▁merchants	-19290
-▁Natal	-19291
-▁Caroline	-19292
-▁chemotherapy	-19293
-▁Fix	-19294
-founder	-19295
-▁literal	-19296
-vie	-19297
-▁culinary	-19298
-▁temp	-19299
-▁wiki	-19300
-▁Blues	-19301
-▁Ku	-19302
-▁Dian	-19303
-▁exclusion	-19304
-nies	-19305
-▁ratt	-19306
-▁Reality	-19307
-▁mapping	-19308
-▁coordinator	-19309
-▁mim	-19310
-▁mouths	-19311
-▁Putting	-19312
-▁Wo	-19313
-▁Recovery	-19314
-Em	-19315
-▁imperial	-19316
-▁Starbucks	-19317
-▁unexpectedly	-19318
-▁Neighb	-19319
-▁flooded	-19320
-▁Montana	-19321
-connected	-19322
-▁blockchain	-19323
-”).	-19324
-▁reper	-19325
-agged	-19326
-▁borne	-19327
-▁innocence	-19328
-▁Ker	-19329
-▁optimism	-19330
-▁ET	-19331
-NC	-19332
-agu	-19333
-ifty	-19334
-▁exqu	-19335
-length	-19336
-▁conceive	-19337
-▁heel	-19338
-illery	-19339
-▁Holidays	-19340
-▁Evans	-19341
-▁stray	-19342
-▁cereal	-19343
-aic	-19344
-bus	-19345
-▁diamonds	-19346
-▁chiropract	-19347
-▁kindergarten	-19348
-▁clarification	-19349
-gends	-19350
-▁gamers	-19351
-priced	-19352
-▁Partner	-19353
-aeda	-19354
-▁Hom	-19355
-▁Jesse	-19356
-▁Shell	-19357
-▁Photography	-19358
-shop	-19359
-▁Joel	-19360
-▁calculator	-19361
-▁blaming	-19362
-etta	-19363
-assium	-19364
-▁inheritance	-19365
-grand	-19366
-▁grounded	-19367
-▁Immigration	-19368
-▁louder	-19369
-▁insists	-19370
-▁aforementioned	-19371
-NO	-19372
-▁Mechan	-19373
-▁therapists	-19374
-▁nort	-19375
-▁grabs	-19376
-▁PE	-19377
-▁refine	-19378
-▁declaring	-19379
-▁apprentices	-19380
-▁condu	-19381
-process	-19382
-▁Wheel	-19383
-▁specializes	-19384
-▁Doll	-19385
-▁Colin	-19386
-▁Truly	-19387
-▁melting	-19388
-▁speeding	-19389
-▁Vienna	-19390
-▁cooker	-19391
-▁terminated	-19392
-▁gly	-19393
-▁princip	-19394
-▁reversed	-19395
-▁Penn	-19396
-▁Walmart	-19397
-▁stretches	-19398
-▁upgrading	-19399
-▁accommodating	-19400
-▁Bever	-19401
-▁excel	-19402
-▁Mario	-19403
-▁Miles	-19404
-▁striving	-19405
-▁discharged	-19406
-▁yields	-19407
-▁barrels	-19408
-▁fearful	-19409
-upuncture	-19410
-▁mentoring	-19411
-ORY	-19412
-▁pumps	-19413
-▁selections	-19414
-MB	-19415
-▁Waste	-19416
-FORE	-19417
-▁Pharm	-19418
-▁aspirations	-19419
-NY	-19420
-outed	-19421
-parent	-19422
-▁glitter	-19423
-▁Entreprene	-19424
-▁infectious	-19425
-yon	-19426
-▁Legislature	-19427
-▁Wear	-19428
-▁plausible	-19429
-?.	-19430
-starter	-19431
-▁Supply	-19432
-▁ounces	-19433
-▁sympath	-19434
-▁IMO	-19435
-III	-19436
-ximately	-19437
-▁tougher	-19438
-ramid	-19439
-▁packaged	-19440
-hett	-19441
-▁Banking	-19442
-▁resignation	-19443
-▁Decision	-19444
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-▁naming	-19446
-▁Shopping	-19447
-▁arrogant	-19448
-▁thirteen	-19449
-▁caregivers	-19450
-▁anten	-19451
-▁organizers	-19452
-itime	-19453
-guards	-19454
-▁Presidential	-19455
-mount	-19456
-small	-19457
-▁Multi	-19458
-▁Refer	-19459
-▁Quarter	-19460
-▁Introduction	-19461
-ania	-19462
-▁Hend	-19463
-▁hacking	-19464
-▁PRE	-19465
-▁establishments	-19466
-ltry	-19467
-▁alarming	-19468
-▁coherent	-19469
-▁concealed	-19470
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-▁Amsterdam	-19473
-▁Assistance	-19474
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-USE	-19476
-ignty	-19477
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-statement	-19480
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-▁Gaza	-19482
-▁whipped	-19483
-▁deprived	-19484
-▁Sustainable	-19485
-▁expressly	-19486
-▁sacrifices	-19487
-▁pear	-19488
-ounces	-19489
-▁enacted	-19490
-profits	-19491
-▁Mountains	-19492
-▁residency	-19493
-▁Ideally	-19494
-ante	-19495
-▁shells	-19496
-▁transforming	-19497
-ampa	-19498
-▁Pentagon	-19499
-▁Lang	-19500
-▁paired	-19501
-chi	-19502
-▁Bun	-19503
-inals	-19504
-aho	-19505
-chem	-19506
-logy	-19507
-inence	-19508
-▁Eagle	-19509
-▁rainbow	-19510
-Over	-19511
-▁Ches	-19512
-▁repro	-19513
-▁skate	-19514
-▁doomed	-19515
-▁dispens	-19516
-▁explode	-19517
-▁jugg	-19518
-▁Driver	-19519
-finished	-19520
-▁academy	-19521
-▁Lam	-19522
-enezuel	-19523
-▁messenger	-19524
-▁veins	-19525
-!.	-19526
-cool	-19527
-▁TWO	-19528
-▁esp	-19529
-▁oste	-19530
-▁citing	-19531
-▁descent	-19532
-▁Birmingham	-19533
-uties	-19534
-intendo	-19535
-▁cemetery	-19536
-▁inaccurate	-19537
-▁Minutes	-19538
-rophe	-19539
-▁Issue	-19540
-▁Shame	-19541
-▁moistur	-19542
-iological	-19543
-▁mistress	-19544
-▁disciplinary	-19545
-▁Retail	-19546
-▁contests	-19547
-▁robbery	-19548
-▁sunglasses	-19549
-▁Rat	-19550
-▁Lanka	-19551
-▁Status	-19552
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-▁concludes	-19554
-▁thankfully	-19555
-▁Wash	-19556
-▁Brussels	-19557
-▁priceless	-19558
-▁unauthorized	-19559
-▁Healthcare	-19560
-kshire	-19561
-▁simmer	-19562
-▁kilometers	-19563
-▁Jar	-19564
-▁signatures	-19565
-blue	-19566
-▁COMP	-19567
-▁plun	-19568
-▁bloom	-19569
-▁folders	-19570
-▁warriors	-19571
-controlled	-19572
-▁HS	-19573
-ioces	-19574
-sorry	-19575
-▁Casey	-19576
-▁contamination	-19577
-▁benchmark	-19578
-mighty	-19579
-▁Prayer	-19580
-▁Message	-19581
-▁praised	-19582
-▁podcasts	-19583
-eez	-19584
-▁GA	-19585
-▁sel	-19586
-▁fragment	-19587
-▁empowering	-19588
-▁contributors	-19589
-▁cass	-19590
-ATIONS	-19591
-▁Spider	-19592
-Two	-19593
-▁patiently	-19594
-PORT	-19595
-▁Roche	-19596
-▁Minimum	-19597
-▁Upper	-19598
-▁scarf	-19599
-olation	-19600
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-▁café	-19602
-▁cort	-19603
-▁recept	-19604
-inky	-19605
-angled	-19606
-utable	-19607
-▁commute	-19608
-▁Interestingly	-19609
-▁EE	-19610
-isal	-19611
-rings	-19612
-▁ally	-19613
-clipse	-19614
-▁Finland	-19615
-▁phenomenal	-19616
-fan	-19617
-▁unpaid	-19618
-▁discoveries	-19619
-haired	-19620
-▁milit	-19621
-atinum	-19622
-▁Lodge	-19623
-upp	-19624
-▁Brun	-19625
-▁Gods	-19626
-▁tribal	-19627
-▁analyses	-19628
-▁cracking	-19629
-▁Northwest	-19630
-▁burgl	-19631
-▁orbit	-19632
-UTC	-19633
-▁Rou	-19634
-▁Grandma	-19635
-▁Portuguese	-19636
-▁dishwasher	-19637
-▁Ruby	-19638
-▁Raven	-19639
-inflamm	-19640
-▁mansion	-19641
-▁Cany	-19642
-▁liar	-19643
-▁refurb	-19644
-▁shrimp	-19645
-▁embracing	-19646
-▁VIP	-19647
-▁Hudson	-19648
-▁Guarant	-19649
-▁Vermont	-19650
-exam	-19651
-▁Victorian	-19652
-AGE	-19653
-Was	-19654
-uu	-19655
-Big	-19656
-faced	-19657
-▁pertinent	-19658
-▁commercials	-19659
-portion	-19660
-▁Coalition	-19661
-▁HO	-19662
-ameless	-19663
-▁teasp	-19664
-▁pushes	-19665
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-iantly	-21043
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-ggie	-21800
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-ueless	-21907
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-Oct	-22032
-▁doom	-22033
-▁gymn	-22034
-▁saint	-22035
-▁Joshua	-22036
-▁scratching	-22037
-▁RF	-22038
-▁WHERE	-22039
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-▁Kindly	-22041
-▁daytime	-22042
-▁broccoli	-22043
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-tles	-22046
-▁Ferg	-22047
-▁Eagles	-22048
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-poke	-22050
-struct	-22051
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-ocal	-22053
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-▁astonished	-22055
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-mph	-22057
-▁cheated	-22058
-imag	-22059
-▁overlap	-22060
-▁Holocaust	-22061
-▁Thousands	-22062
-▁leftovers	-22063
-ISE	-22064
-olo	-22065
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-lr	-22072
-omas	-22073
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-otics	-22077
-smart	-22078
-▁Stir	-22079
-▁monet	-22080
-▁scissors	-22081
-▁synchron	-22082
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-▁malf	-22084
-▁bowel	-22085
-ivering	-22086
-▁Duncan	-22087
-▁reflex	-22088
-▁burdens	-22089
-▁flourish	-22090
-▁discarded	-22091
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-ordered	-22093
-▁danced	-22094
-▁Frankly	-22095
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-asionally	-22097
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-▁optimum	-22100
-▁staircase	-22101
-▁examinations	-22102
-▁Dw	-22103
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-▁breadth	-22105
-▁adjustable	-22106
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-▁endangered	-22109
-▁Aven	-22110
-▁spared	-22111
-organized	-22112
-▁Principal	-22113
-▁Administrator	-22114
-vement	-22115
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-operative	-22117
-▁trousers	-22118
-pical	-22119
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-▁cigar	-22121
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-▁floral	-22123
-▁gigantic	-22124
-▁Metropolitan	-22125
-cible	-22126
-▁omega	-22127
-▁taxable	-22128
-file	-22129
-▁underestimate	-22130
-otein	-22131
-▁majors	-22132
-▁trustee	-22133
-▁Creation	-22134
-▁detached	-22135
-▁contemplating	-22136
-ahu	-22137
-▁BET	-22138
-ourmet	-22139
-▁contr	-22140
-running	-22141
-▁Silicon	-22142
-▁employs	-22143
-▁flashed	-22144
-▁shotgun	-22145
-▁testimonials	-22146
-anti	-22147
-▁AOL	-22148
-▁Ranch	-22149
-▁merge	-22150
-▁aerial	-22151
-▁saints	-22152
-▁trendy	-22153
-▁Glasgow	-22154
-▁bladder	-22155
-▁plugged	-22156
-▁Historical	-22157
-▁lush	-22158
-▁extrav	-22159
-▁Cathedral	-22160
-▁Bangladesh	-22161
-elia	-22162
-need	-22163
-▁Beer	-22164
-▁Treaty	-22165
-▁kilometres	-22166
-quot	-22167
-acious	-22168
-istine	-22169
-▁summoned	-22170
-▁photographed	-22171
-▁redundant	-22172
-rag	-22173
-▁CONT	-22174
-▁offend	-22175
-▁outsourcing	-22176
-▁Et	-22177
-▁Comes	-22178
-▁wraps	-22179
-▁Cit	-22180
-▁Writers	-22181
-▁HAR	-22182
-▁Possibly	-22183
-▁prioritize	-22184
-▁subscribed	-22185
-▁indications	-22186
-▁Lights	-22187
-▁insecure	-22188
-▁artifacts	-22189
-▁disproportion	-22190
-▁."	-22191
-coal	-22192
-▁CALL	-22193
-▁glove	-22194
-▁ROI	-22195
-▁Gore	-22196
-?),	-22197
-xic	-22198
-anie	-22199
-▁Terr	-22200
-▁fingertips	-22201
-▁cultivation	-22202
-icts	-22203
-ariably	-22204
-▁intrins	-22205
-▁maneuver	-22206
-▁hatch	-22207
-▁Trial	-22208
-▁Ambassador	-22209
-▁reflective	-22210
-▁Shut	-22211
-▁Remind	-22212
-lett	-22213
-▁Leaf	-22214
-▁cafes	-22215
-▁replicate	-22216
-▁Reid	-22217
-▁Protest	-22218
-▁barbecue	-22219
-▁SY	-22220
-compet	-22221
-▁offspring	-22222
-▁membr	-22223
-▁screws	-22224
-▁slopes	-22225
-!!!!!!!!	-22226
-Black	-22227
-▁Carbon	-22228
-::	-22229
-iege	-22230
-isance	-22231
-▁leaked	-22232
-▁gatherings	-22233
-▁Vers	-22234
-▁compass	-22235
-▁garment	-22236
-▁garments	-22237
-▁confidently	-22238
-▁curry	-22239
-▁simplified	-22240
-▁complementary	-22241
-ursed	-22242
-markets	-22243
-▁payday	-22244
-▁placebo	-22245
-▁moreover	-22246
-▁FF	-22247
-▁mu	-22248
-▁AFTER	-22249
-▁Dollar	-22250
-▁Medium	-22251
-▁emperor	-22252
-▁overdue	-22253
-ophren	-22254
-▁Scout	-22255
-▁scalp	-22256
-▁dancer	-22257
-▁merger	-22258
-▁Mercury	-22259
-▁Respect	-22260
-▁strengthened	-22261
-▁spawn	-22262
-▁dearly	-22263
-gae	-22264
-braska	-22265
-ocking	-22266
-▁Dress	-22267
-▁Stuff	-22268
-▁privat	-22269
-▁Physics	-22270
-▁quitting	-22271
-▁spiritually	-22272
-▁Roth	-22273
-▁Elvis	-22274
-▁handic	-22275
-▁uploading	-22276
-roads	-22277
-▁crawling	-22278
-aires	-22279
-stract	-22280
-▁Mommy	-22281
-▁Bailey	-22282
-aternity	-22283
-▁differs	-22284
-posium	-22285
-▁genetically	-22286
-▁Woo	-22287
-▁Owner	-22288
-▁deport	-22289
-▁confisc	-22290
-▁editions	-22291
-▁designation	-22292
-▁persecution	-22293
-▁reconciliation	-22294
-▁democr	-22295
-▁posing	-22296
-grabbing	-22297
-▁turmoil	-22298
-▁marketed	-22299
-umpy	-22300
-▁Funds	-22301
-▁jealousy	-22302
-▁suitcase	-22303
-▁remodeling	-22304
-.;	-22305
-▁lb	-22306
-▁SHOULD	-22307
-▁jackets	-22308
-▁leaking	-22309
-▁unified	-22310
-▁Ya	-22311
-▁Kitty	-22312
-▁ropes	-22313
-▁scams	-22314
-▁diaper	-22315
-▁rotten	-22316
-▁MAKE	-22317
-ihad	-22318
-▁Elder	-22319
-▁Quote	-22320
-▁Ethics	-22321
-▁colonies	-22322
-▁pressured	-22323
-▁nas	-22324
-▁adulthood	-22325
-▁witnessing	-22326
-song	-22327
-things	-22328
-▁Yemen	-22329
-▁marching	-22330
-▁liquidity	-22331
-due	-22332
-▁).	-22333
-rylic	-22334
-balanced	-22335
-entric	-22336
-▁venge	-22337
-▁prohibit	-22338
-▁fundraiser	-22339
-;"	-22340
-obody	-22341
-▁Carn	-22342
-▁ISIS	-22343
-▁ecst	-22344
-▁plots	-22345
-▁Activity	-22346
-hetti	-22347
-▁Sessions	-22348
-hetical	-22349
-▁bracket	-22350
-▁preventive	-22351
-▁Jos	-22352
-▁unite	-22353
-▁hyperlink	-22354
-izophren	-22355
-▁guardians	-22356
-Ds	-22357
-▁IL	-22358
-▁PROV	-22359
-▁calming	-22360
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-▁vocals	-22362
-▁seriousness	-22363
-▁torch	-22364
-▁recipro	-22365
-▁demographics	-22366
-▁reconstruction	-22367
-▁confine	-22368
-▁enquiry	-22369
-▁happiest	-22370
-itious	-22371
-▁Downtown	-22372
-ilib	-22373
-▁CDs	-22374
-▁POL	-22375
-estyles	-22376
-▁Superb	-22377
-▁skeptic	-22378
-▁deliveries	-22379
-nest	-22380
-▁Nest	-22381
-▁naval	-22382
-▁wield	-22383
-▁Buzz	-22384
-▁sunk	-22385
-intern	-22386
-▁bumps	-22387
-▁dedicate	-22388
-▁programmed	-22389
-▁THEIR	-22390
-▁wiser	-22391
-▁mainland	-22392
-▁plaintiff	-22393
-▁standardized	-22394
-▁Ko	-22395
-aran	-22396
-▁Adults	-22397
-▁opioid	-22398
-▁cleaners	-22399
-▁obsolete	-22400
-▁stereotypes	-22401
-▁icing	-22402
-▁ventures	-22403
-▁antioxidants	-22404
-▁translations	-22405
-hours	-22406
-cir	-22407
-Last	-22408
-▁Ive	-22409
-▁brunch	-22410
-▁meme	-22411
-▁Gather	-22412
-▁Challeng	-22413
-▁informational	-22414
-▁LOOK	-22415
-▁mantra	-22416
-▁examines	-22417
-cream	-22418
-ricia	-22419
-▁Alas	-22420
-▁Maps	-22421
-▁chapel	-22422
-▁purity	-22423
-▁disappearing	-22424
-▁Audit	-22425
-▁STILL	-22426
-▁spamb	-22427
-practice	-22428
-▁"(	-22429
-▁Firm	-22430
-▁STEM	-22431
-ibling	-22432
-▁Gross	-22433
-▁biscuits	-22434
-▁resemble	-22435
-▁bookstore	-22436
-haz	-22437
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-▁AGAIN	-22439
-ocalypse	-22440
-▁emailing	-22441
-▁Democracy	-22442
-▁wordpress	-22443
-▁demise	-22444
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-▁borrowers	-22449
-▁Writer	-22450
-▁sucker	-22451
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-▁Claims	-22454
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-▁TX	-22457
-▁CDC	-22458
-▁Milk	-22459
-▁ache	-22460
-indust	-22461
-Remember	-22462
-▁mundane	-22463
-▁Behavior	-22464
-travel	-22465
-▁Pages	-22466
-▁Thirty	-22467
-▁gestures	-22468
-▁Kin	-22469
-▁Craw	-22470
-▁whiskey	-22471
-▁??	-22472
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-▁tion	-22474
-▁cubic	-22475
-▁Edwards	-22476
-▁jointly	-22477
-▁rebounds	-22478
-▁yr	-22479
-▁rumor	-22480
-▁Wizard	-22481
-▁Animals	-22482
-▁Certified	-22483
-ORS	-22484
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-agnetic	-22486
-amacare	-22487
-▁atheist	-22488
-mi	-22489
-▁BMW	-22490
-Could	-22491
-▁sprang	-22492
-▁Coordinator	-22493
-▁propriet	-22494
-▁digestion	-22495
-▁repairing	-22496
-▁pdf	-22497
-▁WORLD	-22498
-▁hugged	-22499
-▁nailed	-22500
-▁Alternative	-22501
-once	-22502
-▁Proof	-22503
-▁mould	-22504
-▁Repair	-22505
-▁atmospheric	-22506
-urope	-22507
-▁impending	-22508
-▁interfaces	-22509
-trad	-22510
-▁CAP	-22511
-option	-22512
-▁incumb	-22513
-▁presidents	-22514
-oro	-22515
-ungal	-22516
-▁Peru	-22517
-▁cleanup	-22518
-▁brightly	-22519
-▁specialised	-22520
-▁housed	-22521
-▁predicting	-22522
-▁bids	-22523
-▁physic	-22524
-▁inexper	-22525
-—”	-22526
-boro	-22527
-folk	-22528
-chlor	-22529
-ubscribe	-22530
-▁tart	-22531
-modern	-22532
-▁prophets	-22533
-▁Forward	-22534
-▁Wallace	-22535
-▁differing	-22536
-▁reflections	-22537
-aul	-22538
-esar	-22539
-▁puff	-22540
-laughs	-22541
-▁Rodney	-22542
-▁ceremonies	-22543
-abwe	-22544
-▁loft	-22545
-ithium	-22546
-▁marrying	-22547
-▁christmas	-22548
-▁Chancellor	-22549
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-▁STR	-22551
-acerb	-22552
-▁bins	-22553
-▁dang	-22554
-▁rall	-22555
-▁spike	-22556
-▁Danish	-22557
-▁Taliban	-22558
-Gr	-22559
-Post	-22560
-nesty	-22561
-▁Sites	-22562
-▁escaping	-22563
-▁Chart	-22564
-dia	-22565
-akin	-22566
-▁Pred	-22567
-▁Libya	-22568
-▁nightmares	-22569
-▁particulars	-22570
-▁Cuban	-22571
-▁Globe	-22572
-▁dismant	-22573
-▁stocked	-22574
-▁testament	-22575
-stro	-22576
-erick	-22577
-ishly	-22578
-program	-22579
-▁Dreams	-22580
-▁offenses	-22581
-oxide	-22582
-▁cursed	-22583
-▁refunds	-22584
-▁depicted	-22585
-yeah	-22586
-▁erase	-22587
-▁giants	-22588
-▁lantern	-22589
-ICS	-22590
-▁salv	-22591
-▁meteor	-22592
-▁shoved	-22593
-▁Anxiety	-22594
-▁_________	-22595
-▁oat	-22596
-ulsive	-22597
-▁agile	-22598
-▁endorsed	-22599
-▁UV	-22600
-▁carers	-22601
-▁pumped	-22602
-▁hardcore	-22603
-▁jurisdictions	-22604
-▁Mi	-22605
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-▁Veget	-22607
-ussions	-22608
-▁cabbage	-22609
-▁spambots	-22610
-▁congestion	-22611
-▁empowerment	-22612
-Wr	-22613
-aurs	-22614
-true	-22615
-▁vested	-22616
-▁preceded	-22617
-▁validate	-22618
-▁statistic	-22619
-]:	-22620
-COM	-22621
-hhh	-22622
-lynn	-22623
-▁Function	-22624
-vic	-22625
-▁Furn	-22626
-▁litt	-22627
-brance	-22628
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-▁postings	-22630
-bson	-22631
-▁bye	-22632
-▁sunscreen	-22633
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-team	-22635
-growth	-22636
-▁lunches	-22637
-▁mildly	-22638
-▁Classes	-22639
-▁nurturing	-22640
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-▁uncanny	-22644
-▁drowned	-22645
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-▁IRA	-22649
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-▁burgers	-22651
-▁drawers	-22652
-▁harbour	-22653
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-▁contag	-22656
-▁dances	-22657
-▁Bristol	-22658
-▁liberties	-22659
-▁cialis	-22660
-▁rainfall	-22661
-▁Knox	-22662
-▁whol	-22663
-▁parted	-22664
-▁maternal	-22665
-▁youthful	-22666
-▁Pul	-22667
-▁Bid	-22668
-▁binge	-22669
-▁ascend	-22670
-▁Anybody	-22671
-▁toddlers	-22672
-UTH	-22673
-▁topp	-22674
-▁Nepal	-22675
-▁Bernie	-22676
-▁bravery	-22677
-▁inspected	-22678
-present	-22679
-▁Purpose	-22680
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-pick	-22682
-▁vigor	-22683
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-▁stripes	-22685
-▁attendant	-22686
-▁shootings	-22687
-▁Else	-22688
-▁Arnold	-22689
-▁afforded	-22690
-▁disappro	-22691
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-imbabwe	-22693
-▁crushing	-22694
-ados	-22695
-▁stam	-22696
-▁Neuro	-22697
-▁irrig	-22698
-▁philosopher	-22699
-isha	-22700
-▁Raw	-22701
-evelop	-22702
-▁Lakes	-22703
-▁cylinder	-22704
-▁fruitful	-22705
-▁economist	-22706
-▁accumulate	-22707
-▁contemplate	-22708
-acting	-22709
-▁engra	-22710
-▁sect	-22711
-▁covenant	-22712
-▁Reporting	-22713
-▁Info	-22714
-▁Phillips	-22715
-▁averaged	-22716
-▁Profession	-22717
-▁aches	-22718
-▁lurking	-22719
-▁launches	-22720
-▁strawberry	-22721
-▁indifferent	-22722
-▁OT	-22723
-acet	-22724
-▁Wife	-22725
-▁wells	-22726
-▁outing	-22727
-▁Baseball	-22728
-▁brethren	-22729
-▁lem	-22730
-▁Duty	-22731
-▁uneasy	-22732
-opsy	-22733
-▁possesses	-22734
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-▁eman	-22736
-▁canoe	-22737
-▁prevailing	-22738
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-▁REG	-22741
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-▁Procedure	-22745
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-▁Monitor	-22750
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-▁catastrophe	-22753
-▁phosph	-22754
-▁biography	-22755
-▁outsourced	-22756
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-▁implicit	-22758
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-▁sulf	-22760
-▁Studios	-22761
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-▁revisions	-22763
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-▁persistence	-22765
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-rost	-22820
-▁WOULD	-22821
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-geon	-22823
-▁Diane	-22824
-▁Olivia	-22825
-▁Saying	-22826
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-▁cupboard	-22828
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-▁rationale	-22830
-▁activation	-22831
-▁notwithstanding	-22832
-thora	-22833
-▁Tall	-22834
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-▁Warning	-22836
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-▁exacerb	-22838
-▁Including	-22839
-leen	-22840
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-▁borrower	-22844
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-▁lieu	-22846
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-▁Etsy	-22860
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-▁Nixon	-22863
-▁encrypted	-22864
-Des	-22865
-odia	-22866
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-▁qualitative	-22869
-clean	-22870
-▁UPDATE	-22871
-▁Oracle	-22872
-▁washer	-22873
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-wire	-22875
-▁Vik	-22876
-▁cerv	-22877
-▁accomplishing	-22878
-▁prescriptions	-22879
-lb	-22880
-▁Contrib	-22881
-▁immortal	-22882
-aukee	-22883
-▁dizz	-22884
-▁needn	-22885
-▁sleek	-22886
-▁perplex	-22887
-▁muscular	-22888
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-▁Regards	-22891
-▁Constitutional	-22892
-DI	-22893
-issue	-22894
-▁Chances	-22895
-▁monopoly	-22896
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-▁highs	-22898
-▁Saddam	-22899
-▁courageous	-22900
-kok	-22901
-▁slugg	-22902
-ptember	-22903
-WE	-22904
-ATES	-22905
-▁Gad	-22906
-▁Quebec	-22907
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-▁Australians	-22909
-▁acknowledges	-22910
-▁concentrating	-22911
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-▁DVDs	-22913
-▁Olive	-22914
-▁outset	-22915
-▁emergence	-22916
-▁comparative	-22917
-▁investigative	-22918
-▁Beng	-22919
-▁Cindy	-22920
-▁clutch	-22921
-▁navigating	-22922
-UAL	-22923
-opl	-22924
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-▁Stuart	-22927
-▁virtues	-22928
-stead	-22929
-▁Bread	-22930
-▁decree	-22931
-▁indisp	-22932
-▁motors	-22933
-ais	-22934
-cum	-22935
-▁FS	-22936
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-aution	-22938
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-lain	-22941
-pict	-22942
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-▁decreases	-22944
-▁Electrical	-22945
-▁Increasing	-22946
-nine	-22947
-▁Shaw	-22948
-▁rituals	-22949
-▁irresponsible	-22950
-THE	-22951
-ois	-22952
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-▁paved	-22954
-▁inflammatory	-22955
-iker	-22956
-▁crab	-22957
-▁smashed	-22958
-▁skipping	-22959
-Rel	-22960
-itle	-22961
-▁dab	-22962
-▁geek	-22963
-enance	-22964
-▁suicidal	-22965
-▁RM	-22966
-▁Muh	-22967
-▁brig	-22968
-▁UNDER	-22969
-▁consolidated	-22970
-▁facilitating	-22971
-▁representations	-22972
-KS	-22973
-enary	-22974
-▁Nova	-22975
-▁witty	-22976
-▁Instant	-22977
-▁dictators	-22978
-▁strategically	-22979
-▁buds	-22980
-▁debated	-22981
-▁cruc	-22982
-▁prag	-22983
-▁plethora	-22984
-▁Kam	-22985
-bee	-22986
-▁powd	-22987
-effect	-22988
-▁flare	-22989
-▁overs	-22990
-▁gazing	-22991
-▁Katrina	-22992
-▁MIN	-22993
-▁cartoons	-22994
-▁victories	-22995
-▁resentment	-22996
-HE	-22997
-▁Kra	-22998
-▁baths	-22999
-enguin	-23000
-▁chefs	-23001
-▁Sakura	-23002
-▁perish	-23003
-▁scanner	-23004
-▁Airlines	-23005
-▁Province	-23006
-▁assaulted	-23007
-arin	-23008
-kens	-23009
-▁Brady	-23010
-▁curves	-23011
-analysis	-23012
-▁WP	-23013
-ATING	-23014
-▁trio	-23015
-stones	-23016
-▁ridge	-23017
-▁Defence	-23018
-▁Corinthians	-23019
-verbs	-23020
-▁SECTION	-23021
-▁Superior	-23022
-questionably	-23023
-▁distributor	-23024
-▁Stein	-23025
-▁shave	-23026
-minster	-23027
-▁quarrel	-23028
-▁disappears	-23029
-Col	-23030
-▁hrs	-23031
-rands	-23032
-▁repeal	-23033
-▁seizure	-23034
-▁ornaments	-23035
-▁Si	-23036
-▁Brill	-23037
-▁volley	-23038
-▁Combine	-23039
-▁bankers	-23040
-▁blended	-23041
-▁lingering	-23042
-▁QUEST	-23043
-▁accuse	-23044
-▁havoc	-23045
-▁Normal	-23046
-▁savage	-23047
-▁broadcasting	-23048
-little	-23049
-▁Massage	-23050
-▁chambers	-23051
-▁collects	-23052
-▁delegation	-23053
-▁Buffy	-23054
-▁Draco	-23055
-▁Arabic	-23056
-▁novice	-23057
-▁Intel	-23058
-istance	-23059
-▁toughest	-23060
-▁←	-23061
-▁Kil	-23062
-▁Lor	-23063
-opause	-23064
-private	-23065
-▁Camera	-23066
-▁canada	-23067
-▁skillet	-23068
-▁Led	-23069
-▁Natalie	-23070
-▁Package	-23071
-▁cupcakes	-23072
-▁delegate	-23073
-▁desperation	-23074
-cape	-23075
-▁shove	-23076
-▁gasped	-23077
-▁antibiotic	-23078
-▁programmers	-23079
-▁misrepresent	-23080
-▁UFO	-23081
-▁Zimbabwe	-23082
-▁bouncing	-23083
-▁ventured	-23084
-lot	-23085
-vor	-23086
-▁depreci	-23087
-▁Friendly	-23088
-▁Mercedes	-23089
-▁Spending	-23090
-▁nominations	-23091
-▁Ric	-23092
-astian	-23093
-▁Policies	-23094
-illas	-23095
-▁Crew	-23096
-▁forbid	-23097
-▁miniature	-23098
-▁Came	-23099
-▁centr	-23100
-facebook	-23101
-▁Timothy	-23102
-▁preferable	-23103
-ermined	-23104
-ilingual	-23105
-owl	-23106
-▁JS	-23107
-ulus	-23108
-▁trope	-23109
-raising	-23110
-▁grease	-23111
-▁drowning	-23112
-▁regiment	-23113
-▁reinforced	-23114
-remes	-23115
-▁Savior	-23116
-▁Rainbow	-23117
-▁brochure	-23118
-▁incorrectly	-23119
-▁HV	-23120
-▁Lis	-23121
-▁dazz	-23122
-elfare	-23123
-▁SHALL	-23124
-▁Fiction	-23125
-▁seizures	-23126
-rine	-23127
-▁Drugs	-23128
-▁capsule	-23129
-▁insomnia	-23130
-▁distributing	-23131
-PO	-23132
-Rec	-23133
-dead	-23134
-uras	-23135
-Books	-23136
-▁joys	-23137
-Mom	-23138
-▁Ts	-23139
-lists	-23140
-▁fusion	-23141
-▁Cookies	-23142
-▁Volunteer	-23143
-"--	-23144
-▁Apost	-23145
-▁poets	-23146
-▁interpersonal	-23147
-▁pint	-23148
-Listen	-23149
-▁incompetent	-23150
-▁Gotta	-23151
-▁pixel	-23152
-▁Advance	-23153
-▁Profile	-23154
-▁Rolling	-23155
-▁deception	-23156
-bral	-23157
-▁Pizza	-23158
-▁glare	-23159
-▁monaster	-23160
-▁restrictive	-23161
-oids	-23162
-went	-23163
-▁Rescue	-23164
-▁agreeable	-23165
-▁anonymity	-23166
-▁Organizations	-23167
-▁scientifically	-23168
-oyle	-23169
-▁equitable	-23170
-▁Jude	-23171
-▁Pale	-23172
-▁loung	-23173
-▁Territ	-23174
-▁ceramic	-23175
-▁Wildlife	-23176
-▁drafting	-23177
-▁Interviewer	-23178
-ASH	-23179
-▁campuses	-23180
-▁disliked	-23181
-▁disruptive	-23182
-▁Lions	-23183
-▁arter	-23184
-▁Trends	-23185
-▁paraly	-23186
-▁banners	-23187
-ingers	-23188
-funding	-23189
-▁Agreed	-23190
-▁ie	-23191
-clear	-23192
-▁openness	-23193
-▁municipalities	-23194
-▁lav	-23195
-▁Tablet	-23196
-▁Lessons	-23197
-▁capitalist	-23198
-▁supervisors	-23199
-▁confrontation	-23200
-▁pand	-23201
-▁rifles	-23202
-▁Published	-23203
-▁childcare	-23204
-▁sacrificed	-23205
-iph	-23206
-anting	-23207
-▁genocide	-23208
-▁distracting	-23209
-▁Clo	-23210
-▁Enh	-23211
-ivating	-23212
-Cause	-23213
-▁Alfred	-23214
-vs	-23215
-▁Kle	-23216
-▁epit	-23217
-▁Pride	-23218
-▁breeds	-23219
-▁relentless	-23220
-AW	-23221
-poss	-23222
-▁DEC	-23223
-ilings	-23224
-▁Clara	-23225
-purpose	-23226
-▁blinked	-23227
-▁Nebraska	-23228
-▁lieutenant	-23229
-▁homelessness	-23230
-▁squat	-23231
-content	-23232
-▁Courts	-23233
-▁exchanging	-23234
-▁submar	-23235
-Christian	-23236
-▁Cake	-23237
-▁Vent	-23238
-eredith	-23239
-▁Porter	-23240
-▁morals	-23241
-▁overdose	-23242
-brand	-23243
-▁Secure	-23244
-▁hastily	-23245
-▁persever	-23246
-ELF	-23247
-▁veil	-23248
-▁smash	-23249
-▁Selling	-23250
-▁prompts	-23251
-▁Material	-23252
-▁firmware	-23253
-▁postcard	-23254
-▁brightness	-23255
-ILE	-23256
-▁Laz	-23257
-▁sharks	-23258
-▁Calgary	-23259
-Miss	-23260
-assed	-23261
-▁pneumonia	-23262
-▁refreshed	-23263
-▁troublesh	-23264
-▁Thy	-23265
-▁chased	-23266
-▁debtor	-23267
-▁descriptive	-23268
-rance	-23269
-rights	-23270
-▁ballet	-23271
-▁Occasionally	-23272
-neg	-23273
-loving	-23274
-▁bamboo	-23275
-inspired	-23276
-▁glaring	-23277
-▁painfully	-23278
-▁Gem	-23279
-▁hah	-23280
-▁Older	-23281
-▁knots	-23282
-▁Hank	-23283
-▁Critical	-23284
-▁restless	-23285
-▁acupuncture	-23286
-FER	-23287
-▁richest	-23288
-▁overhaul	-23289
-▁warrants	-23290
-▁perpetual	-23291
-▁separating	-23292
-▁Bris	-23293
-▁zeal	-23294
-▁timet	-23295
-▁friction	-23296
-▁accelerated	-23297
-▁COUR	-23298
-▁pies	-23299
-▁hustle	-23300
-▁antagon	-23301
-▁Disability	-23302
-▁Wol	-23303
-▁Mead	-23304
-▁Rece	-23305
-▁fury	-23306
-▁lact	-23307
-▁classy	-23308
-▁organism	-23309
-Say	-23310
-▁Lately	-23311
-▁invoices	-23312
-▁GS	-23313
-▁sooo	-23314
-▁Principles	-23315
-▁furnishings	-23316
-▁Viv	-23317
-▁sings	-23318
-tically	-23319
-▁metallic	-23320
-▁teaspoon	-23321
-▁intricate	-23322
-▁Monitoring	-23323
-▁SG	-23324
-▁Acqu	-23325
-▁wiping	-23326
-▁insider	-23327
-ventional	-23328
-▁Laden	-23329
-▁Arsenal	-23330
-▁Jamaica	-23331
-▁Designer	-23332
-▁highways	-23333
-▁pursuits	-23334
-▁axe	-23335
-▁Cable	-23336
-▁childish	-23337
-▁Flying	-23338
-▁profoundly	-23339
-done	-23340
-▁POW	-23341
-ensable	-23342
-▁Cruise	-23343
-▁Leonard	-23344
-▁bothers	-23345
-▁rejuven	-23346
-▁textbooks	-23347
-▁cryptocurrency	-23348
-▁Rural	-23349
-▁needy	-23350
-elected	-23351
-▁pirates	-23352
-▁composer	-23353
-▁deserving	-23354
-▁Gy	-23355
-ptoms	-23356
-▁tighter	-23357
-▁Maintain	-23358
-▁exaggerated	-23359
-irie	-23360
-▁veto	-23361
-▁invariably	-23362
-▁disturbance	-23363
-omo	-23364
-▁Hire	-23365
-zel	-23366
-▁Cats	-23367
-▁coron	-23368
-atri	-23369
-prus	-23370
-apper	-23371
-amping	-23372
-oulder	-23373
-claimer	-23374
-▁sailed	-23375
-▁thrice	-23376
-▁mindfulness	-23377
-aments	-23378
-▁trailers	-23379
-▁composing	-23380
-wash	-23381
-▁hemp	-23382
-ugging	-23383
-vellous	-23384
-▁hashtag	-23385
-▁dealings	-23386
-▁specials	-23387
-▁Parkinson	-23388
-oT	-23389
-ussed	-23390
-▁Nina	-23391
-Master	-23392
-▁tiger	-23393
-▁Biblical	-23394
-▁Mortgage	-23395
-OPE	-23396
-▁oy	-23397
-▁slogan	-23398
-▁downhill	-23399
-▁organizer	-23400
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-▁gears	-23402
-▁bracelet	-23403
-▁repetition	-23404
-staff	-23405
-video	-23406
-▁Cere	-23407
-▁revise	-23408
-▁filtering	-23409
-▁shareholder	-23410
-▁Eug	-23411
-happy	-23412
-inton	-23413
-metic	-23414
-▁Replace	-23415
-▁Tag	-23416
-ochem	-23417
-▁Chin	-23418
-▁Pour	-23419
-▁Ride	-23420
-▁Managers	-23421
-▁exhibited	-23422
-▁autistic	-23423
-▁extinction	-23424
-▁overcoming	-23425
-▁subsidiary	-23426
-▁ruth	-23427
-▁hostage	-23428
-▁Accident	-23429
-▁Encourage	-23430
-▁Reference	-23431
-▁intending	-23432
-▁CE	-23433
-▁Nurse	-23434
-▁oddly	-23435
-▁slips	-23436
-▁tolerated	-23437
-▁investigator	-23438
-▁construed	-23439
-avia	-23440
-estead	-23441
-▁stabbed	-23442
-▁sequences	-23443
-▁cumulative	-23444
-▁prohibition	-23445
-namese	-23446
-▁coral	-23447
-▁antenna	-23448
-▁teasing	-23449
-▁Calendar	-23450
-▁delights	-23451
-▁celebrates	-23452
-▁precaution	-23453
-'?	-23454
-▁morale	-23455
-▁fid	-23456
-ggers	-23457
-▁retal	-23458
-▁discard	-23459
-▁Features	-23460
-umi	-23461
-isphere	-23462
-ricting	-23463
-▁Stream	-23464
-liferation	-23465
-▁authoring	-23466
-duty	-23467
-urers	-23468
-▁lick	-23469
-▁killers	-23470
-▁festivities	-23471
-tale	-23472
-gated	-23473
-endars	-23474
-▁caller	-23475
-▁Husband	-23476
-▁bipolar	-23477
-▁teamwork	-23478
-▁Associates	-23479
-▁lifestyles	-23480
-▁necessities	-23481
-▁Dell	-23482
-▁ammo	-23483
-overty	-23484
-▁Nazis	-23485
-▁Parts	-23486
-▁leasing	-23487
-▁reacted	-23488
-▁lessen	-23489
-▁Markets	-23490
-▁Surgery	-23491
-▁longtime	-23492
-ubric	-23493
-▁Troy	-23494
-▁Filip	-23495
-▁poultry	-23496
-▁Venezuela	-23497
-affe	-23498
-▁Omega	-23499
-▁Buddha	-23500
-deme	-23501
-loat	-23502
-ushi	-23503
-ESOME	-23504
-▁insur	-23505
-▁Complex	-23506
-▁comprise	-23507
-▁Monte	-23508
-▁terra	-23509
-▁steroids	-23510
-▁disconnected	-23511
-asa	-23512
-expl	-23513
-street	-23514
-▁Grove	-23515
-▁Witch	-23516
-angular	-23517
-▁frameworks	-23518
-▁simplistic	-23519
-▁Git	-23520
-▁WIN	-23521
-▁Mills	-23522
-▁tweak	-23523
-▁suprem	-23524
-▁proposes	-23525
-▁seventeen	-23526
-▁🙁	-23527
-utton	-23528
-▁Lanc	-23529
-▁womb	-23530
-▁Riley	-23531
-▁antiv	-23532
-cessions	-23533
-training	-23534
-▁Contest	-23535
-▁Happiness	-23536
-angs	-23537
-▁Kan	-23538
-▁Refuge	-23539
-▁swollen	-23540
-▁granddaughter	-23541
-▁Om	-23542
-cels	-23543
-▁rss	-23544
-categ	-23545
-▁Rise	-23546
-▁Reuters	-23547
-▁entitlement	-23548
-▁disappearance	-23549
-VEL	-23550
-▁Num	-23551
-noticed	-23552
-arnation	-23553
-▁colourful	-23554
-▁reh	-23555
-waukee	-23556
-▁Floor	-23557
-▁Palin	-23558
-▁carrot	-23559
-▁Factory	-23560
-▁Literature	-23561
-▁Accordingly	-23562
-fem	-23563
-▁Religious	-23564
-▁appraisal	-23565
-▁culturally	-23566
-▁assassination	-23567
-és	-23568
-▁Geoff	-23569
-▁atoms	-23570
-artments	-23571
-ertility	-23572
-▁Formula	-23573
-▁dissemin	-23574
-▁ethnicity	-23575
-▁sweetness	-23576
-▁historians	-23577
-Alright	-23578
-▁Scheme	-23579
-▁expires	-23580
-▁rejoice	-23581
-▁organising	-23582
-HC	-23583
-▁Attack	-23584
-ilibrium	-23585
-▁guideline	-23586
-▁Discussion	-23587
-weed	-23588
-▁spraw	-23589
-▁Citizen	-23590
-▁smokers	-23591
-▁customary	-23592
-▁acquisitions	-23593
-▁firefighters	-23594
-▁aka	-23595
-▁Boot	-23596
-▁Hungary	-23597
-▁thicker	-23598
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-▁Aurora	-23600
-▁dermat	-23601
-uddled	-23602
-▁Norweg	-23603
-▁Appeals	-23604
-▁behaviours	-23605
-▁“[	-23606
-▁divid	-23607
-▁Carlos	-23608
-▁measurable	-23609
-▁accumulation	-23610
-▁Conservatives	-23611
-▁GE	-23612
-▁ii	-23613
-▁clash	-23614
-▁Ticket	-23615
-▁Breaking	-23616
-▁pounding	-23617
-▁vibration	-23618
-▁swirl	-23619
-▁stalls	-23620
-estation	-23621
-▁shutter	-23622
-▁Responsible	-23623
-OY	-23624
-cock	-23625
-inted	-23626
-liable	-23627
-▁bleed	-23628
-▁sugars	-23629
-▁smelled	-23630
-$$	-23631
-tin	-23632
-▁Means	-23633
-▁Ninety	-23634
-▁postage	-23635
-▁unveiled	-23636
-▁multimedia	-23637
--"	-23638
-▁eater	-23639
-▁fibre	-23640
-▁uneven	-23641
-▁fortnight	-23642
-▁spirituality	-23643
-▁HM	-23644
-▁bib	-23645
-▁razor	-23646
-▁widget	-23647
-▁Applied	-23648
-▁Simpson	-23649
-▁Yorkshire	-23650
-roe	-23651
-yards	-23652
-▁Kurt	-23653
-▁Meredith	-23654
-▁kitchens	-23655
-▁cherished	-23656
-auer	-23657
-▁scrib	-23658
-▁Heroes	-23659
-▁Inform	-23660
-▁Governments	-23661
-▁subscriptions	-23662
-abil	-23663
-▁Scr	-23664
-▁lastly	-23665
-▁rotate	-23666
-▁territorial	-23667
-▁Pand	-23668
-▁banning	-23669
-▁Sergeant	-23670
-▁sergeant	-23671
-dar	-23672
-Other	-23673
-▁Quinn	-23674
-▁crave	-23675
-▁misdeme	-23676
-▁batting	-23677
-▁insignificant	-23678
-iances	-23679
-▁inward	-23680
-▁statutes	-23681
-▁Revelation	-23682
-▁waits	-23683
-▁sibling	-23684
-▁hardwood	-23685
-▁Southwest	-23686
-▁Bryan	-23687
-keepers	-23688
-▁Palestine	-23689
-uson	-23690
-▁Lob	-23691
-bearing	-23692
-▁Isaiah	-23693
-▁expectancy	-23694
-wives	-23695
-▁hefty	-23696
-▁Tribunal	-23697
-▁applause	-23698
-▁testified	-23699
-▁alarms	-23700
-▁curled	-23701
-▁communist	-23702
-▁paramount	-23703
-▁CG	-23704
-gently	-23705
-uci	-23706
-▁skirts	-23707
-▁Consulting	-23708
-▁Authorities	-23709
-▁disciplined	-23710
-▁Requirements	-23711
-▁babe	-23712
-finity	-23713
-▁china	-23714
-▁Clause	-23715
-▁turnout	-23716
-▁stitching	-23717
-▁Muscle	-23718
-▁harmed	-23719
-ashi	-23720
-▁turf	-23721
-▁yacht	-23722
-▁Compar	-23723
-▁FIF	-23724
-EMENT	-23725
-▁Mercy	-23726
-▁disob	-23727
-opening	-23728
-▁hypertension	-23729
-Lord	-23730
-▁Wid	-23731
-▁Catch	-23732
-▁asserted	-23733
-▁Consumers	-23734
-opsis	-23735
-▁caval	-23736
-▁theor	-23737
-▁bonding	-23738
-▁Hispanic	-23739
-▁cravings	-23740
-▁indefinitely	-23741
-June	-23742
-▁Joey	-23743
-▁mustn	-23744
-ocative	-23745
-▁bakery	-23746
-▁earrings	-23747
-▁averaging	-23748
-▁occupations	-23749
-▁predecessor	-23750
-▁Vel	-23751
-floor	-23752
-umper	-23753
-▁Wasn	-23754
-terness	-23755
-▁gamble	-23756
-▁revival	-23757
-placement	-23758
-▁flipping	-23759
-▁stocking	-23760
-▁astro	-23761
-▁orgasm	-23762
-▁summed	-23763
-▁coordinating	-23764
-▁disadvantaged	-23765
-▁LONG	-23766
-▁beet	-23767
-▁halluc	-23768
-▁ordeal	-23769
-Stud	-23770
-▁CRM	-23771
-▁leng	-23772
-▁pitches	-23773
-▁sailors	-23774
-paragraph	-23775
-▁tribunal	-23776
-▁tendencies	-23777
-iocese	-23778
-▁dependency	-23779
-▁Bend	-23780
-obiles	-23781
-▁dissent	-23782
-▁Katherine	-23783
-▁intercept	-23784
-ousy	-23785
-oorer	-23786
-▁psychiatrist	-23787
-▁superintendent	-23788
-rys	-23789
-▁LCD	-23790
-▁doubles	-23791
-▁downturn	-23792
-▁radically	-23793
-conditioned	-23794
-▁fier	-23795
-▁pope	-23796
-▁Convers	-23797
-▁summers	-23798
-▁pinpoint	-23799
-▁pretended	-23800
-▁Outstanding	-23801
-▁CLICK	-23802
-▁Recre	-23803
-▁surreal	-23804
-▁thanking	-23805
-▁insisting	-23806
-Trans	-23807
-▁foreseeable	-23808
-▁Cod	-23809
-either	-23810
-▁Demand	-23811
-▁Legend	-23812
-Mad	-23813
-dogs	-23814
-▁ASS	-23815
-▁PUR	-23816
-centric	-23817
-▁embassy	-23818
-▁traject	-23819
-send	-23820
-▁Goth	-23821
-▁Lemon	-23822
-▁caption	-23823
-▁packets	-23824
-▁strains	-23825
-▁Attention	-23826
-▁Residents	-23827
-▁scratched	-23828
-Aug	-23829
-onent	-23830
-▁tummy	-23831
-▁Sector	-23832
-▁directories	-23833
-▁irrespective	-23834
-umblr	-23835
-▁tying	-23836
-inges	-23837
-▁Vlad	-23838
-monary	-23839
-▁cellar	-23840
-▁thinner	-23841
-▁statistically	-23842
-▁palms	-23843
-▁Benefit	-23844
-▁slapped	-23845
-Gen	-23846
-War	-23847
-antha	-23848
-oland	-23849
-▁mixer	-23850
-▁imbalance	-23851
-▁turnaround	-23852
-▁Purs	-23853
-▁lure	-23854
-▁orth	-23855
-▁piled	-23856
-▁Midwest	-23857
-▁adolescent	-23858
-cheap	-23859
-▁continual	-23860
-▁manipulated	-23861
-▁rails	-23862
-▁bewild	-23863
-▁bursting	-23864
-▁Sed	-23865
-▁hash	-23866
-▁reuse	-23867
-▁vowed	-23868
-▁sophom	-23869
-OTHER	-23870
-phasis	-23871
-orative	-23872
-▁Letters	-23873
-]]	-23874
-arrant	-23875
-▁cooks	-23876
-▁ducks	-23877
-▁juicy	-23878
-▁pyramid	-23879
-▁careless	-23880
-▁inadvert	-23881
-▁Prest	-23882
-▁Adrian	-23883
-poons	-23884
-▁pinned	-23885
-▁bash	-23886
-▁caramel	-23887
-▁uninsured	-23888
-▁descendants	-23889
-▁resemblance	-23890
-large	-23891
-menting	-23892
-▁Danger	-23893
-▁NOTHING	-23894
-▁NS	-23895
-▁Gaz	-23896
-▁straps	-23897
-▁Bloomberg	-23898
--(	-23899
-hored	-23900
-▁pests	-23901
-▁cooled	-23902
-▁dubious	-23903
-▁Christina	-23904
-bud	-23905
-eton	-23906
-inants	-23907
-▁secrecy	-23908
-▁Rochester	-23909
-▁elephants	-23910
-▁Manage	-23911
-▁favored	-23912
-opa	-23913
-▁Dial	-23914
-▁Ellie	-23915
-urgical	-23916
-▁braces	-23917
-▁echoed	-23918
-▁pioneer	-23919
-▁violates	-23920
-▁Saturdays	-23921
-▁stimulation	-23922
-avil	-23923
-olin	-23924
-▁Mik	-23925
-▁youre	-23926
-▁losers	-23927
-▁raging	-23928
-▁Sullivan	-23929
-▁clueless	-23930
-▁blush	-23931
-▁Carrie	-23932
-▁bottled	-23933
-▁seeming	-23934
-▁Publishing	-23935
-ALK	-23936
-▁rim	-23937
-▁crate	-23938
-▁cannon	-23939
-▁ransom	-23940
-alin	-23941
-▁excav	-23942
-▁rehearsal	-23943
-LP	-23944
-▁Bin	-23945
-▁sauna	-23946
-▁Monthly	-23947
-▁Wellness	-23948
-▁declares	-23949
-▁proclaimed	-23950
-▁MPs	-23951
-▁rents	-23952
-▁defender	-23953
-▁practise	-23954
-▁civilized	-23955
-▁directive	-23956
-▁distinctly	-23957
-▁trilogy	-23958
-▁Blogging	-23959
-▁TC	-23960
-antom	-23961
-▁Admiral	-23962
-▁motivations	-23963
-draw	-23964
-feel	-23965
-connect	-23966
-▁brushes	-23967
-▁rewrite	-23968
-▁torrent	-23969
-▁tweeting	-23970
-▁Officials	-23971
-▁extr	-23972
-cycles	-23973
-▁Croat	-23974
-versely	-23975
-▁Cherry	-23976
-▁Slowly	-23977
-▁Pie	-23978
-▁nig	-23979
-▁HELP	-23980
-existent	-23981
-▁educator	-23982
-▁NSW	-23983
-▁Fans	-23984
-▁Herald	-23985
-▁indign	-23986
-▁violin	-23987
-▁UNIDENT	-23988
-▁persona	-23989
-ickers	-23990
-▁Hundreds	-23991
-▁Essentially	-23992
-zu	-23993
-heum	-23994
-comput	-23995
-▁Sonic	-23996
-▁rugged	-23997
-heastern	-23998
-▁restructuring	-23999
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-▁Flu	-24001
-▁degr	-24002
-▁ceilings	-24003
-▁defenses	-24004
-▁Exploring	-24005
-▁considerate	-24006
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-othes	-24008
-▁ferv	-24009
-▁tuna	-24010
-secret	-24011
-umping	-24012
-▁monks	-24013
-▁Damage	-24014
-▁selves	-24015
-▁Solomon	-24016
-▁truthful	-24017
-▁Obamacare	-24018
-▁thoughtfully	-24019
-Ok	-24020
-▁Ded	-24021
-cracy	-24022
-▁psycho	-24023
-approved	-24024
-▁spiders	-24025
-▁rotating	-24026
-▁adversely	-24027
-▁impartial	-24028
-wy	-24029
-VES	-24030
-oba	-24031
-▁amp	-24032
-essim	-24033
-▁Lesson	-24034
-▁Biology	-24035
-▁Concept	-24036
-▁weakened	-24037
-▁statewide	-24038
-▁parole	-24039
-▁balloons	-24040
-▁progressing	-24041
-short	-24042
-▁Lose	-24043
-▁wors	-24044
-▁summarize	-24045
-▁Cock	-24046
-▁Baron	-24047
-▁poised	-24048
-▁behaved	-24049
-▁influencing	-24050
-▁missionary	-24051
-▁unfinished	-24052
-▁vaccinated	-24053
-▁businessman	-24054
-▁Cred	-24055
-▁knowingly	-24056
-▁earthquakes	-24057
-▁Organisation	-24058
-▁sperm	-24059
-▁resides	-24060
-▁xxx	-24061
-▁Tiff	-24062
-▁enlar	-24063
-▁Sending	-24064
-▁attacker	-24065
-management	-24066
-▁distressed	-24067
-incinn	-24068
-▁rebuilt	-24069
-▁marketer	-24070
-▁reservoir	-24071
-cé	-24072
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-illes	-24074
-▁indem	-24075
-▁Pepper	-24076
-▁Dorothy	-24077
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-▁discreet	-24079
-▁erection	-24080
-▁Vietnamese	-24081
-▁prosecuted	-24082
-governmental	-24083
-vd	-24084
-▁Luna	-24085
-▁Charge	-24086
-▁stranded	-24087
-OTH	-24088
-aaaa	-24089
-▁Celt	-24090
-▁bolts	-24091
-▁Raiders	-24092
-▁generators	-24093
-▁Disabilities	-24094
-▁[[	-24095
-▁Ideal	-24096
-▁brink	-24097
-▁AWESOME	-24098
-▁obstruct	-24099
-▁Background	-24100
-▁engineered	-24101
-leness	-24102
-▁clasp	-24103
-▁Cyprus	-24104
-▁gadget	-24105
-▁whales	-24106
-▁multiply	-24107
-▁splitting	-24108
-▁Spread	-24109
-▁infilt	-24110
-▁unseen	-24111
-▁HOU	-24112
-▁hog	-24113
-▁vector	-24114
-▁alarmed	-24115
-oko	-24116
-▁sock	-24117
-▁Chapel	-24118
-▁errands	-24119
-heartedly	-24120
-▁Christie	-24121
-▁fantasies	-24122
-▁magically	-24123
-▁HIGH	-24124
-▁gloom	-24125
-▁tuning	-24126
-▁academia	-24127
-▁Yard	-24128
-atical	-24129
-▁Tracy	-24130
-▁Autism	-24131
-▁Determine	-24132
-ollo	-24133
-alion	-24134
-ippers	-24135
-▁fined	-24136
-▁Embassy	-24137
-▁fragrance	-24138
-dates	-24139
-dding	-24140
-inently	-24141
-▁astronaut	-24142
-untu	-24143
-▁hail	-24144
-▁onwards	-24145
-▁kidnapped	-24146
-▁insecurity	-24147
-luck	-24148
-sigh	-24149
-▁mushroom	-24150
-▁exploited	-24151
-▁conditioned	-24152
-▁pleasurable	-24153
-▁XD	-24154
-▁Gear	-24155
-▁gazed	-24156
-▁capita	-24157
-▁Historic	-24158
-Mo	-24159
-▁layouts	-24160
-▁capacities	-24161
-▁windshield	-24162
-▁accreditation	-24163
-death	-24164
-▁YEARS	-24165
-▁Helping	-24166
-▁dumping	-24167
-▁URLs	-24168
-sold	-24169
-▁tariff	-24170
-Whatever	-24171
-▁android	-24172
-▁dearest	-24173
-▁Engineers	-24174
-▁marvellous	-24175
-PRO	-24176
-find	-24177
-▁Nah	-24178
-frast	-24179
-▁Spin	-24180
-▁Weap	-24181
-▁Joyce	-24182
-▁plead	-24183
-▁sushi	-24184
-▁fading	-24185
-ws	-24186
-▁Mickey	-24187
-uble	-24188
-▁Elite	-24189
-▁minist	-24190
-▁Farmers	-24191
-▁glancing	-24192
-▁screened	-24193
-▁predominantly	-24194
-Whe	-24195
-▁Lil	-24196
-▁unin	-24197
-▁haste	-24198
-often	-24199
-▁gastro	-24200
-▁patriot	-24201
-▁dominance	-24202
-▁neo	-24203
-▁Burg	-24204
-▁surn	-24205
-general	-24206
-▁downtime	-24207
-FUL	-24208
-fic	-24209
-▁SHE	-24210
-iotics	-24211
-▁Reach	-24212
-▁personalised	-24213
-▁warns	-24214
-▁Kenn	-24215
-▁clam	-24216
-▁TODAY	-24217
-▁lobbying	-24218
-▁Excellence	-24219
-▁Warriors	-24220
-▁Volunteers	-24221
-▁alterations	-24222
-▁Duc	-24223
-▁WAT	-24224
-▁cue	-24225
-▁neon	-24226
-▁flashes	-24227
-▁metropolitan	-24228
-▁datas	-24229
-▁picky	-24230
-▁Dana	-24231
-custom	-24232
-▁Giant	-24233
-▁ubiqu	-24234
-▁Previously	-24235
-▁renovations	-24236
-▁BEC	-24237
-▁Bulgar	-24238
-▁favourable	-24239
-▁Gee	-24240
-▁Relief	-24241
-▁poorer	-24242
-▁Charity	-24243
-▁ensured	-24244
-incinnati	-24245
-▁dishonest	-24246
-▁speculate	-24247
-▁Superintendent	-24248
-▁–	-24249
-lean	-24250
-▁Tina	-24251
-▁culmin	-24252
-▁Idea	-24253
-iotic	-24254
-▁decks	-24255
-▁Wii	-24256
-▁lia	-24257
-▁cran	-24258
-▁syntax	-24259
-▁apparel	-24260
-oso	-24261
-uxe	-24262
-themed	-24263
-▁Shawn	-24264
-▁flung	-24265
-comings	-24266
-▁Advant	-24267
-andom	-24268
-perate	-24269
-▁Loving	-24270
-Hope	-24271
-▁seals	-24272
-▁shady	-24273
-▁cleanse	-24274
-▁historian	-24275
-owski	-24276
-▁Paula	-24277
-▁skins	-24278
-▁hinder	-24279
-▁prerequ	-24280
-▁contention	-24281
-▁approximate	-24282
-▁Hugs	-24283
-▁Buyer	-24284
-▁vampires	-24285
-isse	-24286
-▁Sty	-24287
-▁Sort	-24288
-▁riches	-24289
-▁listens	-24290
-▁billed	-24291
-▁avenues	-24292
-▁caregiver	-24293
-▁attachments	-24294
-▁nodding	-24295
-▁compressed	-24296
-▁explosives	-24297
-▁beneficiary	-24298
-▁Shield	-24299
-▁accusing	-24300
-▁cookbook	-24301
-▁deficiencies	-24302
-▁schizophren	-24303
-USH	-24304
-aba	-24305
-uding	-24306
-▁Apps	-24307
-▁Score	-24308
-▁Bass	-24309
-hopefully	-24310
-▁forwards	-24311
-▁TER	-24312
-▁Earn	-24313
-▁smir	-24314
-▁staged	-24315
-President	-24316
-▁nuisance	-24317
-▁annoyance	-24318
-arted	-24319
-▁deceived	-24320
-owser	-24321
-▁..."	-24322
-▁scout	-24323
-▁maiden	-24324
-avanaugh	-24325
-▁shortest	-24326
-▁intimidated	-24327
-▁acceleration	-24328
-cert	-24329
-▁spins	-24330
-billion	-24331
-▁granite	-24332
-▁lipstick	-24333
-everything	-24334
-▁IG	-24335
-▁Ethan	-24336
-▁Worse	-24337
-▁comrades	-24338
-▁Transform	-24339
-hee	-24340
-▁Vine	-24341
-▁rugby	-24342
-▁clocks	-24343
-▁Founded	-24344
-▁Mathematics	-24345
-▁OWN	-24346
-▁Loans	-24347
-▁shrug	-24348
-▁mortar	-24349
-▁senators	-24350
-▁Gam	-24351
-▁Beta	-24352
-▁Jessie	-24353
-▁starring	-24354
-▁bothersome	-24355
-▁GC	-24356
-▁Belle	-24357
-▁Calling	-24358
-gradation	-24359
-▁Position	-24360
-▁Messenger	-24361
-▁accusation	-24362
-▁Architecture	-24363
-Um	-24364
-Fine	-24365
-andel	-24366
-▁presumed	-24367
-▁bitterness	-24368
-Actually	-24369
-▁Marines	-24370
-avascript	-24371
-▁Paint	-24372
-unciation	-24373
-▁imperson	-24374
-▁killings	-24375
-▁Hate	-24376
-▁Brisbane	-24377
-▁Buddhist	-24378
-▁Shanghai	-24379
-▁arrogance	-24380
-▁adrenaline	-24381
-lishes	-24382
-▁Passion	-24383
-▁punched	-24384
-▁spelled	-24385
-▁EVERYTHING	-24386
-cup	-24387
-dev	-24388
-▁Ellis	-24389
-▁quilts	-24390
-▁intermitt	-24391
-vous	-24392
-onday	-24393
-▁Poke	-24394
-▁cowboy	-24395
-▁neural	-24396
-▁Display	-24397
-▁looming	-24398
-▁originality	-24399
-La	-24400
-rans	-24401
-▁Rid	-24402
-▁blatant	-24403
-▁novelty	-24404
-▁american	-24405
-▁Chemistry	-24406
-▁Listening	-24407
-▁mish	-24408
-▁synd	-24409
-backed	-24410
-▁Blind	-24411
-▁scrolling	-24412
-ESE	-24413
-▁doubted	-24414
-▁volcano	-24415
-▁superficial	-24416
-▁miscar	-24417
-odka	-24418
-enson	-24419
-isman	-24420
-▁Maya	-24421
-▁homeland	-24422
-▁Milwaukee	-24423
-▁MI	-24424
-▁Cance	-24425
-▁sabot	-24426
-▁pollut	-24427
-▁consultancy	-24428
-lson	-24429
-▁Movies	-24430
-▁Perman	-24431
-▁Submit	-24432
-▁camper	-24433
-▁cutest	-24434
-▁legends	-24435
-▁Almighty	-24436
-▁Orthodox	-24437
-▁Nad	-24438
-▁Abby	-24439
-▁outweigh	-24440
-▁Strategies	-24441
-VR	-24442
-ilk	-24443
-▁Bund	-24444
-▁capsules	-24445
-▁dues	-24446
-▁paced	-24447
-▁swore	-24448
-▁unravel	-24449
-▁impacting	-24450
-▁inconsist	-24451
-▁orb	-24452
-▁Subm	-24453
-▁Uganda	-24454
-▁fragments	-24455
-▁injections	-24456
-▁Lem	-24457
-▁gag	-24458
-icism	-24459
-walks	-24460
-▁Hyper	-24461
-▁BEL	-24462
-▁Appeal	-24463
-▁guarded	-24464
-▁poorest	-24465
-▁crystals	-24466
-uzz	-24467
-ingles	-24468
-▁Samantha	-24469
-▁whopping	-24470
-▁Communities	-24471
-▁=).	-24472
-▁jan	-24473
-▁hehe	-24474
-▁Parish	-24475
-▁sideways	-24476
-▁Approximately	-24477
-imi	-24478
-▁graphs	-24479
-▁Numbers	-24480
-▁framing	-24481
-▁communal	-24482
-▁interruption	-24483
-unky	-24484
-▁MOST	-24485
-another	-24486
-▁enlightened	-24487
-▁NCAA	-24488
-▁BD	-24489
-ethical	-24490
-▁stewards	-24491
-▁indispensable	-24492
-poon	-24493
-▁Cave	-24494
-payment	-24495
-▁Harold	-24496
-▁Hilton	-24497
-▁Launch	-24498
-▁Muhammad	-24499
-▁sluggish	-24500
-▁contingent	-24501
-▁evacuation	-24502
-FR	-24503
-lab	-24504
-▁cosy	-24505
-rapist	-24506
-▁muddy	-24507
-▁cosmetics	-24508
-▁pedestrians	-24509
-cf	-24510
-yson	-24511
-▁INS	-24512
-peace	-24513
-▁grapp	-24514
-istrate	-24515
-▁Defend	-24516
-▁booklet	-24517
-▁deductions	-24518
-▁substituted	-24519
-eston	-24520
-▁Lloyd	-24521
-▁drool	-24522
-▁overlapping	-24523
-▁MT	-24524
-incre	-24525
-▁hikes	-24526
-cipline	-24527
-solving	-24528
-▁mourning	-24529
-▁Britt	-24530
-▁metic	-24531
-▁Funding	-24532
-▁transcend	-24533
-▁misunderstand	-24534
-ovo	-24535
-phis	-24536
-▁Fees	-24537
-Intern	-24538
-▁succeeding	-24539
-▁coordinates	-24540
-▁foreclosure	-24541
-nik	-24542
-▁caves	-24543
-▁charism	-24544
-▁sourced	-24545
-▁Wireless	-24546
-▁mattered	-24547
-chen	-24548
-cheduled	-24549
-js	-24550
-LR	-24551
-▁DD	-24552
-▁Huff	-24553
-▁Myth	-24554
-rocket	-24555
-▁relay	-24556
-▁replying	-24557
-’?	-24558
-▁Tweet	-24559
-▁Scroll	-24560
-▁Savings	-24561
-▁pharmacist	-24562
-▁sentencing	-24563
-IBLE	-24564
-ilee	-24565
-language	-24566
-▁captive	-24567
-▁expresses	-24568
-▁intensely	-24569
-▁Sor	-24570
-▁Ginger	-24571
-▁Bennett	-24572
-▁elegance	-24573
-▁stationed	-24574
-▁Takes	-24575
-▁indec	-24576
-▁salsa	-24577
-eat	-24578
-stay	-24579
-▁glued	-24580
-Ins	-24581
-▁VM	-24582
-quin	-24583
-ranny	-24584
-▁genital	-24585
-▁indictment	-24586
-▁Investigation	-24587
-cd	-24588
-▁Drivers	-24589
-▁clauses	-24590
-▁insults	-24591
-▁generously	-24592
-Space	-24593
-▁Agents	-24594
-▁Spiritual	-24595
-gold	-24596
-▁gir	-24597
-▁medals	-24598
-▁Shannon	-24599
-▁unnoticed	-24600
-impro	-24601
-▁hiss	-24602
-▁Documents	-24603
-▁reorgan	-24604
-▁Admission	-24605
-▁Manufacturing	-24606
-aku	-24607
-colm	-24608
-▁Huh	-24609
-▁atom	-24610
-▁Symptoms	-24611
-▁trustees	-24612
-▁evidenced	-24613
-▁Emir	-24614
-▁Nike	-24615
-▁pige	-24616
-▁dusty	-24617
-▁Identity	-24618
-▁surgeons	-24619
-▁vengeance	-24620
-▁commanding	-24621
-▁relocation	-24622
-▁HAD	-24623
-▁Rental	-24624
-▁Travis	-24625
-gebra	-24626
-hesia	-24627
-▁Wave	-24628
-▁Hindu	-24629
-▁tease	-24630
-▁fleeing	-24631
-▁liquids	-24632
-▁orderly	-24633
-▁rethink	-24634
-▁Daughter	-24635
-▁congrats	-24636
-▁subscribing	-24637
-▁YA	-24638
-scient	-24639
-▁Hughes	-24640
-▁kitten	-24641
-▁interchange	-24642
-▁Bears	-24643
-▁Gloria	-24644
-▁unsett	-24645
-▁plagiar	-24646
-▁snapshot	-24647
-▁northeast	-24648
-▁spectacle	-24649
-▁twentieth	-24650
-▁dé	-24651
-egal	-24652
-itan	-24653
-sein	-24654
-ilated	-24655
-▁THINK	-24656
-▁scraps	-24657
-ahl	-24658
-****	-24659
-▁plastics	-24660
-▁implication	-24661
-▁experimentation	-24662
-andum	-24663
-umbles	-24664
-▁resembles	-24665
-▁milestones	-24666
-frastructure	-24667
-GL	-24668
-Brien	-24669
-Under	-24670
-orian	-24671
-▁slick	-24672
-▁Corner	-24673
-▁pleading	-24674
-▁reviewers	-24675
-otechnology	-24676
-▁Comprehensive	-24677
-olate	-24678
-iquette	-24679
-▁onboard	-24680
-▁counters	-24681
-▁gratifying	-24682
-odus	-24683
-▁snor	-24684
-carbon	-24685
-quarter	-24686
-▁Jensen	-24687
-▁beams	-24688
-▁illicit	-24689
-▁smartly	-24690
-▁Able	-24691
-center	-24692
-eteria	-24693
-▁Carry	-24694
-▁Prague	-24695
-ilitating	-24696
-▁Provider	-24697
-▁waterfall	-24698
-▁Nar	-24699
-▁Hamm	-24700
-▁Yale	-24701
-▁gren	-24702
-church	-24703
-▁Mouse	-24704
-▁thriller	-24705
-▁penetration	-24706
-mort	-24707
-▁Ace	-24708
-▁veg	-24709
-▁Podcast	-24710
-▁dragons	-24711
-▁filtered	-24712
-▁Population	-24713
-Tech	-24714
-▁Rising	-24715
-▁stares	-24716
-▁erosion	-24717
-▁frequencies	-24718
-▁awoke	-24719
-▁softer	-24720
-▁Outdoor	-24721
-▁affiliation	-24722
-▁organisational	-24723
-▁Fold	-24724
-▁pent	-24725
-▁tsun	-24726
-osexual	-24727
-▁diabetic	-24728
-▁influencers	-24729
-▁effortlessly	-24730
-IOUS	-24731
-intell	-24732
-▁stink	-24733
-▁stumbling	-24734
-▁stationary	-24735
-▁continental	-24736
-▁Asking	-24737
-▁sailor	-24738
-▁Drinking	-24739
-▁Scriptures	-24740
-▁biodiversity	-24741
-▁activism	-24742
-▁Ironically	-24743
-▁leash	-24744
-▁tasked	-24745
-▁drifted	-24746
-March	-24747
-▁Known	-24748
-▁Barnes	-24749
-▁Basket	-24750
-▁edible	-24751
-▁issuance	-24752
-▁Churchill	-24753
-▁courtroom	-24754
-▁grassroots	-24755
-▁scares	-24756
-▁bespoke	-24757
-▁pillars	-24758
-▁honoured	-24759
-▁policing	-24760
-UG	-24761
-▁Libr	-24762
-▁pastry	-24763
-▁dentists	-24764
-▁stuffing	-24765
-▁inconvenient	-24766
-July	-24767
-quest	-24768
-jected	-24769
-▁tunnels	-24770
-▁diminish	-24771
-▁PB	-24772
-▁Dro	-24773
-inars	-24774
-▁ants	-24775
-▁Forbes	-24776
-▁torque	-24777
-▁QB	-24778
-ooks	-24779
-....”	-24780
-▁frog	-24781
-▁pubs	-24782
-Europe	-24783
-▁hitch	-24784
-▁crackers	-24785
-▁Hotels	-24786
-▁bedding	-24787
-▁hormonal	-24788
-▁Bankruptcy	-24789
-opor	-24790
-▁Lords	-24791
-▁Billion	-24792
-chini	-24793
-▁Bali	-24794
-▁anon	-24795
-▁Comic	-24796
-▁bland	-24797
-▁scholarly	-24798
-?--	-24799
-▁Bent	-24800
-record	-24801
-▁coffin	-24802
-▁Ferguson	-24803
-▁partisan	-24804
-▁conformity	-24805
-dry	-24806
-tal	-24807
-ahah	-24808
-usable	-24809
-▁Bened	-24810
-rounded	-24811
-▁misfortune	-24812
-▁lions	-24813
-uttering	-24814
-▁Cincinnati	-24815
-ICA	-24816
-facing	-24817
-▁defenders	-24818
-▁demolition	-24819
-deep	-24820
-▁rabbits	-24821
-▁vouchers	-24822
-▁ultrasound	-24823
-sac	-24824
-▁mull	-24825
-omical	-24826
-▁rulers	-24827
-complete	-24828
-▁Counter	-24829
-▁loosely	-24830
-▁owes	-24831
-▁Yours	-24832
-▁Potential	-24833
-▁continents	-24834
-ggle	-24835
-adian	-24836
-▁Pric	-24837
-▁formulated	-24838
-emy	-24839
-▁Yun	-24840
-▁MANY	-24841
-▁Mystery	-24842
-▁histories	-24843
-▁concessions	-24844
-▁fascination	-24845
-Mat	-24846
-urst	-24847
-▁HVAC	-24848
-▁furry	-24849
-▁visas	-24850
-▁Powell	-24851
-▁chocol	-24852
-aples	-24853
-▁CHAR	-24854
-▁gust	-24855
-▁Fleet	-24856
-▁Flood	-24857
-▁anatomy	-24858
-▁altering	-24859
-▁apologise	-24860
-▁violently	-24861
-▁inclination	-24862
-times	-24863
-▁hairst	-24864
-▁plaque	-24865
-▁Methods	-24866
-▁eclipse	-24867
-▁Lone	-24868
-▁recruits	-24869
-▁settlers	-24870
-▁startling	-24871
-suit	-24872
-were	-24873
-▁vigorous	-24874
-▁honorable	-24875
-▁COD	-24876
-▁Brent	-24877
-▁Possible	-24878
-▁Straight	-24879
-▁interviewer	-24880
-▁uter	-24881
-isexual	-24882
-▁embell	-24883
-▁Opportunities	-24884
-▁WWII	-24885
-▁eagle	-24886
-▁Domain	-24887
-▁Employers	-24888
-▁Norwegian	-24889
-▁partnered	-24890
-traditional	-24891
-▁Philosophy	-24892
-ben	-24893
-Step	-24894
-▁advises	-24895
-▁Provided	-24896
-▁predators	-24897
-▁proficient	-24898
-▁Legislative	-24899
-ortium	-24900
-▁smack	-24901
-▁thrift	-24902
-▁tossing	-24903
-▁Neal	-24904
-▁pedal	-24905
-dration	-24906
-▁carpets	-24907
-miss	-24908
-▁Ghana	-24909
-covered	-24910
-pine	-24911
-▁Bod	-24912
-▁SHAR	-24913
-▁keynote	-24914
-▁oversee	-24915
-Plus	-24916
-▁Rum	-24917
-▁Device	-24918
-▁pristine	-24919
-▁laundering	-24920
-▁enlightening	-24921
-bene	-24922
-said	-24923
-Think	-24924
-▁Weird	-24925
-▁Bradley	-24926
-Bel	-24927
-itas	-24928
-▁Eco	-24929
-▁bour	-24930
-▁Armen	-24931
-inational	-24932
-▁hypocrisy	-24933
-▁nostalgia	-24934
-hrase	-24935
-▁maths	-24936
-married	-24937
-▁colonel	-24938
-dess	-24939
-▁bishops	-24940
-unched	-24941
-uously	-24942
-▁fringe	-24943
-▁miners	-24944
-▁Heights	-24945
-▁ib	-24946
-▁Matters	-24947
-▁Kee	-24948
-▁rogue	-24949
-aturing	-24950
-▁footing	-24951
-▁readable	-24952
-▁processors	-24953
-oother	-24954
-▁ratios	-24955
-▁reactor	-24956
-▁adapting	-24957
-▁constipation	-24958
-▁Notwithstanding	-24959
-▁THEN	-24960
-▁unsc	-24961
-cerity	-24962
-▁carcin	-24963
-▁pessim	-24964
-▁Cooking	-24965
-▁Shouldn	-24966
-▁rooftop	-24967
-nea	-24968
-▁icy	-24969
-▁ion	-24970
-Super	-24971
-forums	-24972
-▁PERSON	-24973
-▁enhances	-24974
-West	-24975
-imps	-24976
-olia	-24977
-▁GEN	-24978
-▁Pill	-24979
-▁roar	-24980
-▁robe	-24981
-picuous	-24982
-▁Sau	-24983
-notch	-24984
-▁PTSD	-24985
-▁Chand	-24986
-▁manga	-24987
-▁salty	-24988
-▁comedian	-24989
-▁Liability	-24990
-next	-24991
-irting	-24992
-▁Antar	-24993
-▁aspire	-24994
-▁redist	-24995
-▁zoning	-24996
-▁haircut	-24997
-aunted	-24998
-▁serum	-24999
-▁Darren	-25000
-▁suburb	-25001
-▁doubling	-25002
-▁contagious	-25003
-urer	-25004
-sexual	-25005
-▁Revised	-25006
-▁residing	-25007
-▁notor	-25008
-▁Mirror	-25009
-▁coughing	-25010
-▁abortions	-25011
-▁Continuing	-25012
-▁encountering	-25013
-aler	-25014
-event	-25015
-fires	-25016
-▁COUN	-25017
-▁topical	-25018
-▁deleting	-25019
-▁salesman	-25020
-▁giveaways	-25021
-hart	-25022
-▁Vote	-25023
-▁batht	-25024
-▁Introdu	-25025
-▁Childhood	-25026
-▁DB	-25027
-umbai	-25028
-▁Drake	-25029
-▁derive	-25030
-▁nutshell	-25031
-▁flyer	-25032
-▁socialism	-25033
-ARS	-25034
-▁Mull	-25035
-▁Zomb	-25036
-ueller	-25037
-▁kitty	-25038
-▁cheesy	-25039
-▁Chemical	-25040
-▁sanitation	-25041
-▁Yo	-25042
-▁oft	-25043
-▁lousy	-25044
-▁Adwords	-25045
-▁butcher	-25046
-ORK	-25047
-▁IND	-25048
-▁sage	-25049
-biased	-25050
-▁Berry	-25051
-▁roofs	-25052
-rington	-25053
-▁elbows	-25054
-▁lubric	-25055
-FI	-25056
-▁LP	-25057
-anding	-25058
-▁breaths	-25059
-▁masturb	-25060
-▁nominal	-25061
-ICES	-25062
-▁Jets	-25063
-▁Glory	-25064
-▁commits	-25065
-▁Detective	-25066
-▁admittedly	-25067
-▁Teams	-25068
-▁Sabbath	-25069
-▁artillery	-25070
-▁nucle	-25071
-igating	-25072
-▁turtle	-25073
-▁Welfare	-25074
-▁tipping	-25075
-▁hovering	-25076
-CLA	-25077
-▁Frost	-25078
-▁Topics	-25079
-emporary	-25080
-▁frontier	-25081
-▁welcomes	-25082
-▁cathedral	-25083
-▁negativity	-25084
-▁bureaucracy	-25085
-Web	-25086
-▁abdomen	-25087
-▁measles	-25088
-▁licensee	-25089
-▁villains	-25090
-▁respondent	-25091
-▁Nag	-25092
-▁Fuel	-25093
-▁Hogan	-25094
-culture	-25095
-▁hypnot	-25096
-▁Smoking	-25097
-▁Specialist	-25098
-▁mitigation	-25099
-Cs	-25100
-esame	-25101
-▁Cord	-25102
-▁Menu	-25103
-ooping	-25104
-protected	-25105
-▁Sacramento	-25106
-Card	-25107
-▁tails	-25108
-▁Teresa	-25109
-▁appell	-25110
-▁majesty	-25111
-▁Kris	-25112
-▁NGOs	-25113
-▁humane	-25114
-▁constructing	-25115
-wiki	-25116
-▁Lit	-25117
-▁omn	-25118
-▁Isle	-25119
-▁STOP	-25120
-utation	-25121
-▁Factors	-25122
-▁Patriots	-25123
-▁calendars	-25124
-▁COP	-25125
-▁Lynch	-25126
-▁feminism	-25127
-▁toxicity	-25128
-▁warranties	-25129
-▁contradiction	-25130
-▁psychologists	-25131
-athom	-25132
-tarian	-25133
-▁amidst	-25134
-▁insurg	-25135
-▁Knights	-25136
-▁statues	-25137
-heartedness	-25138
-▁BI	-25139
-▁Gig	-25140
-▁Bald	-25141
-▁Keys	-25142
-▁Mile	-25143
-▁unic	-25144
-▁bunny	-25145
-▁brightest	-25146
-▁systematically	-25147
-▁Bike	-25148
-▁deficits	-25149
-▁Queensland	-25150
-opular	-25151
-▁Rodrig	-25152
-▁Liberals	-25153
-▁Brilliant	-25154
-▁massively	-25155
-▁respectfully	-25156
-FP	-25157
-▁Meat	-25158
-esting	-25159
-paying	-25160
-▁deceive	-25161
-Put	-25162
-▁HB	-25163
-▁PG	-25164
-▁cartridge	-25165
-▁Peak	-25166
-▁deme	-25167
-▁Alone	-25168
-▁filthy	-25169
-▁Theresa	-25170
-▁chilling	-25171
-▁mandated	-25172
-▁Ministers	-25173
-▁awakening	-25174
-▁fostering	-25175
-▁competitiveness	-25176
-▁DW	-25177
-▁Sigh	-25178
-▁Cathy	-25179
-▁mourn	-25180
-actively	-25181
-▁equations	-25182
-▁transitions	-25183
-Play	-25184
-▁lib	-25185
-▁cube	-25186
-player	-25187
-▁hooks	-25188
-▁fortunes	-25189
-VEN	-25190
-cop	-25191
-▁rh	-25192
-▁FUCK	-25193
-immune	-25194
-▁Injury	-25195
-▁hither	-25196
-▁tractor	-25197
-▁specializing	-25198
-▁Saving	-25199
-▁lavender	-25200
-▁anticipating	-25201
-▁Priest	-25202
-▁Artists	-25203
-▁ailments	-25204
-▁smoother	-25205
-▁Retirement	-25206
-▁multiplayer	-25207
-refund	-25208
-planned	-25209
-▁ballots	-25210
-arella	-25211
-▁unintention	-25212
-▁comparatively	-25213
-ju	-25214
-ILD	-25215
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-▁Venus	-25218
-▁poking	-25219
-▁warmly	-25220
-etric	-25221
-▁plat	-25222
-renches	-25223
-▁burner	-25224
-products	-25225
-▁Evening	-25226
-▁Winston	-25227
-▁mosquit	-25228
-▁flashlight	-25229
-▁Westminster	-25230
-▁screenshots	-25231
-Bar	-25232
-▁gard	-25233
-▁hull	-25234
-▁pillar	-25235
-▁youths	-25236
-▁newbies	-25237
-▁dividing	-25238
-▁<@	-25239
-versa	-25240
-▁Adela	-25241
-getting	-25242
-▁quilting	-25243
-fs	-25244
-thouse	-25245
-▁Unity	-25246
-▁Napole	-25247
-▁lumber	-25248
-assuming	-25249
-▁bullied	-25250
-▁distorted	-25251
-▁Vo	-25252
-gall	-25253
-zero	-25254
-▁CMS	-25255
-▁discs	-25256
-▁fronts	-25257
-▁shortcomings	-25258
-ea	-25259
-heses	-25260
-▁brisk	-25261
-▁crock	-25262
-▁sinister	-25263
-sche	-25264
-▁boils	-25265
-▁Leading	-25266
-▁Warrior	-25267
-published	-25268
-▁locating	-25269
-▁postponed	-25270
-▁progresses	-25271
-▁perseverance	-25272
-Min	-25273
-gus	-25274
-▁Hip	-25275
-share	-25276
-▁Tibet	-25277
-▁elusive	-25278
-▁Aviation	-25279
-▁collaborating	-25280
-▁siege	-25281
-▁Michel	-25282
-▁olives	-25283
-▁recreate	-25284
-▁cultivated	-25285
-ayer	-25286
-▁PAY	-25287
-▁monk	-25288
-▁Brett	-25289
-▁Bangkok	-25290
-▁growled	-25291
-▁Facility	-25292
-▁massacre	-25293
-▁postpone	-25294
-▁residual	-25295
-yo	-25296
-evil	-25297
-ingham	-25298
-political	-25299
-▁Difference	-25300
-▁tablespoons	-25301
-▁vulnerabilities	-25302
-umen	-25303
-digit	-25304
-▁......	-25305
-▁Millions	-25306
-▁substantive	-25307
-Ear	-25308
-tim	-25309
-▁Mend	-25310
-▁caravan	-25311
-▁conveyed	-25312
-▁shrinking	-25313
-Box	-25314
-ILITY	-25315
-account	-25316
-▁neurological	-25317
-stown	-25318
-▁loops	-25319
-ucchini	-25320
-▁Monkey	-25321
-▁retains	-25322
-▁topping	-25323
-▁Savannah	-25324
-▁budgeting	-25325
-▁supermarkets	-25326
-▁DAM	-25327
-▁Decide	-25328
-▁Dwight	-25329
-▁innate	-25330
-▁financed	-25331
-▁curl	-25332
-▁papa	-25333
-ducted	-25334
-moving	-25335
-▁Ultra	-25336
-▁unfore	-25337
-▁horrified	-25338
-▁Connection	-25339
-Next	-25340
-▁Tol	-25341
-▁Eva	-25342
-April	-25343
-cloth	-25344
-▁extremes	-25345
-▁reconcile	-25346
-▁equilibrium	-25347
-▁heartbreaking	-25348
-OND	-25349
-▁ATT	-25350
-▁quaint	-25351
-▁southwest	-25352
-olph	-25353
-▁gums	-25354
-▁attire	-25355
-neapolis	-25356
-▁skeleton	-25357
-▁nineteenth	-25358
-▁hypothetical	-25359
-▁ot	-25360
-▁Birds	-25361
-▁arche	-25362
-▁Marina	-25363
-▁plight	-25364
-▁logically	-25365
-▁Challenges	-25366
-▁counselors	-25367
-▁imaginative	-25368
-yahoo	-25369
-▁POST	-25370
-assing	-25371
-▁Maker	-25372
-▁Window	-25373
-▁peeled	-25374
-▁stereo	-25375
-▁fixture	-25376
-▁footwear	-25377
-”;	-25378
-ariat	-25379
-forces	-25380
-▁wretched	-25381
-meas	-25382
-Though	-25383
-?!?!	-25384
-▁Caf	-25385
-performing	-25386
-trade	-25387
-▁Shirley	-25388
-▁culprit	-25389
-Ge	-25390
-sic	-25391
-wave	-25392
-eland	-25393
-▁behaving	-25394
-▁erg	-25395
-▁glaci	-25396
-▁Suppose	-25397
-▁baptism	-25398
-▁avoidance	-25399
-▁blindness	-25400
-▁harvesting	-25401
-abase	-25402
-liber	-25403
-▁Sick	-25404
-▁barbar	-25405
-▁cosmic	-25406
-▁acrylic	-25407
-▁formulas	-25408
-▁MIT	-25409
-▁Wise	-25410
-▁mell	-25411
-seller	-25412
-▁False	-25413
-▁bigot	-25414
-▁audits	-25415
-ugal	-25416
-actually	-25417
-▁grinning	-25418
-▁resonate	-25419
-▁installer	-25420
-horse	-25421
-▁Malcolm	-25422
-▁rampant	-25423
-▁reclaim	-25424
-▁Shepherd	-25425
-▁inexperienced	-25426
-▁Hoff	-25427
-Indeed	-25428
-▁Jeffrey	-25429
-▁consoles	-25430
-▁lifespan	-25431
-▁penetrate	-25432
-▁Rosa	-25433
-▁curved	-25434
-restrial	-25435
-▁oatmeal	-25436
-▁mischief	-25437
-▁Investors	-25438
-▁admirable	-25439
-▁suspicions	-25440
-Sub	-25441
-▁TOO	-25442
-▁Fruit	-25443
-▁morph	-25444
-▁fences	-25445
-▁wreath	-25446
-lord	-25447
-suite	-25448
-▁enzymes	-25449
-▁unavoid	-25450
-▁immersed	-25451
-▁wi	-25452
-ickr	-25453
-▁Colle	-25454
-▁hoard	-25455
-driving	-25456
-▁Export	-25457
-▁Increased	-25458
-▁specimens	-25459
-▁Broken	-25460
-▁horrors	-25461
-▁occupants	-25462
-anga	-25463
-▁coated	-25464
-▁stealth	-25465
-▁hostility	-25466
-▁Indianapolis	-25467
-abby	-25468
-omat	-25469
-arate	-25470
-▁Rust	-25471
-▁Noble	-25472
-▁sweats	-25473
-▁reconnect	-25474
-▁neighbouring	-25475
-ilot	-25476
-pport	-25477
-ractive	-25478
-▁Equity	-25479
-▁antiqu	-25480
-▁beware	-25481
-▁arsenal	-25482
-▁tighten	-25483
-▁allocate	-25484
-▁incorporates	-25485
-pill	-25486
-▁Aub	-25487
-▁Shot	-25488
-▁flawless	-25489
-identified	-25490
-▁Gand	-25491
-▁strang	-25492
-▁conserve	-25493
-▁enlisted	-25494
-▁Practical	-25495
-▁sarcastic	-25496
-MC	-25497
-▁Pig	-25498
-▁Rare	-25499
-rality	-25500
-▁oranges	-25501
-▁homicide	-25502
-▁timeframe	-25503
-▁treadmill	-25504
-Aut	-25505
-▁frig	-25506
-▁Negro	-25507
-antically	-25508
-"),	-25509
-tree	-25510
-▁ISS	-25511
-▁chimney	-25512
-▁corrupted	-25513
-▁documenting	-25514
-EX	-25515
-AMA	-25516
-▁audi	-25517
-▁affirmative	-25518
-ILY	-25519
-▁Huge	-25520
-▁Ther	-25521
-ocumented	-25522
-▁Practices	-25523
-▁scratches	-25524
-▁degradation	-25525
-mand	-25526
-▁Geor	-25527
-cities	-25528
-▁remun	-25529
-▁angrily	-25530
-▁brackets	-25531
-▁protesting	-25532
-▁Flo	-25533
-▁lows	-25534
-gotten	-25535
-▁exhaustive	-25536
-▁heightened	-25537
-ankind	-25538
-▁Ukrain	-25539
-▁Approach	-25540
-▁Subsection	-25541
-▁derivative	-25542
-▁owl	-25543
-▁figur	-25544
-▁Waters	-25545
-semination	-25546
-▁UPS	-25547
-ominium	-25548
-▁Gerald	-25549
-▁opting	-25550
-▁Afterwards	-25551
-cli	-25552
-iddy	-25553
-▁Sew	-25554
-▁Narr	-25555
-▁Seek	-25556
-▁Thou	-25557
-▁Vamp	-25558
-▁Milan	-25559
-romagnetic	-25560
-▁Compliance	-25561
-usp	-25562
-▁Bh	-25563
-▁Shane	-25564
-orating	-25565
-▁Marian	-25566
-▁Secrets	-25567
-▁boredom	-25568
-▁goodwill	-25569
-▁Sebastian	-25570
-▁superiority	-25571
-▁(£	-25572
-▁Belt	-25573
-▁worsh	-25574
-▁Marion	-25575
-ustralia	-25576
-▁meltdown	-25577
-production	-25578
-▁combustion	-25579
-▁inefficient	-25580
-amo	-25581
-arus	-25582
-members	-25583
-▁Attempt	-25584
-▁cruising	-25585
-eing	-25586
-Pacific	-25587
-kerchief	-25588
-▁Allison	-25589
-▁spectators	-25590
-▁motivational	-25591
-ODY	-25592
-▁Arms	-25593
-▁Scan	-25594
-▁ambient	-25595
-▁regulator	-25596
-▁Properties	-25597
-▁LIVE	-25598
-▁Papa	-25599
-bishop	-25600
-Certain	-25601
-▁fertile	-25602
-▁diplomacy	-25603
-uez	-25604
-▁Lect	-25605
-▁shack	-25606
-▁graves	-25607
-▁infinitely	-25608
-▁Cult	-25609
-parable	-25610
-▁Memphis	-25611
-▁Reflect	-25612
-▁enchant	-25613
-▁effected	-25614
-▁commenters	-25615
-▁regulating	-25616
-▁hect	-25617
-progress	-25618
-▁Lawyers	-25619
-▁pairing	-25620
-▁withdrew	-25621
-▁overwhelmingly	-25622
-KI	-25623
-▁Salv	-25624
-▁angu	-25625
-border	-25626
-▁Facts	-25627
-▁Pierre	-25628
-▁cursor	-25629
-▁sweaty	-25630
-▁humming	-25631
-secondary	-25632
-▁involunt	-25633
-▁anxiously	-25634
-▁occupancy	-25635
-lli	-25636
-TAIN	-25637
-▁maze	-25638
-▁booze	-25639
-▁Gilbert	-25640
-▁averages	-25641
-▁depended	-25642
-▁LET	-25643
-▁crept	-25644
-▁revive	-25645
-▁trolls	-25646
-▁resisted	-25647
-▁announces	-25648
-▁purchaser	-25649
-▁formulation	-25650
-Term	-25651
-enos	-25652
-▁drafts	-25653
-▁urgently	-25654
-▁mash	-25655
-pieces	-25656
-average	-25657
-▁visualize	-25658
-▁Agricultural	-25659
-legged	-25660
-▁chicks	-25661
-▁mosquito	-25662
-▁usefulness	-25663
-zi	-25664
-blind	-25665
-▁Beverly	-25666
-▁spokeswoman	-25667
-▁SR	-25668
-posure	-25669
-▁hacks	-25670
-▁extravag	-25671
-▁las	-25672
-▁scor	-25673
-▁Bills	-25674
-▁desks	-25675
-joining	-25676
-▁Spectrum	-25677
-▁Kavanaugh	-25678
-▁shortages	-25679
-▁estimation	-25680
-▁rag	-25681
-▁Goal	-25682
-▁bitten	-25683
-▁Staying	-25684
-▁immoral	-25685
-▁weighted	-25686
-fif	-25687
-▁Util	-25688
-▁Videos	-25689
-▁flushed	-25690
-marketing	-25691
-▁dinosaurs	-25692
-legraph	-25693
-▁mathem	-25694
-▁dripping	-25695
-▁synthesis	-25696
-▁Artificial	-25697
-ilyn	-25698
-▁composite	-25699
-▁contented	-25700
-▁ove	-25701
-▁Reyn	-25702
-▁Derby	-25703
-▁shone	-25704
-▁Bullet	-25705
-▁Sections	-25706
-▁preacher	-25707
-▁cautiously	-25708
-▁touchdowns	-25709
-▁HC	-25710
-forest	-25711
-▁abbre	-25712
-▁minimise	-25713
-▁northwest	-25714
-▁CER	-25715
-▁Claus	-25716
-▁Palmer	-25717
-▁puppet	-25718
-▁blindly	-25719
-▁borough	-25720
-▁councill	-25721
-▁mL	-25722
-rice	-25723
-▁influx	-25724
-▁vandal	-25725
-PH	-25726
-plants	-25727
-▁outlaw	-25728
-oporosis	-25729
-▁Hussein	-25730
-▁apologized	-25731
-ryn	-25732
-▁liz	-25733
-▁Cale	-25734
-▁Deer	-25735
-▁dome	-25736
-recogn	-25737
-▁Hoping	-25738
-▁nicest	-25739
-▁onsite	-25740
-▁bulletin	-25741
-Pay	-25742
-▁atop	-25743
-▁tits	-25744
-▁pacing	-25745
-▁smoothie	-25746
-▁Processing	-25747
-▁discriminate	-25748
-▁plent	-25749
-services	-25750
-▁Maximum	-25751
-▁incumbent	-25752
-▁Completely	-25753
-▁Bog	-25754
-odynam	-25755
-▁blinds	-25756
-▁Trustees	-25757
-▁Promotion	-25758
-▁Rehab	-25759
-▁Invent	-25760
-▁residue	-25761
-▁Payments	-25762
-von	-25763
-▁Pac	-25764
-price	-25765
-▁DOWN	-25766
-▁Vick	-25767
-▁eyed	-25768
-▁unat	-25769
-▁Holding	-25770
-.."	-25771
-▁EAR	-25772
-pperm	-25773
-▁Wiki	-25774
-▁Clerk	-25775
-▁edits	-25776
-▁INFORM	-25777
-▁Sisters	-25778
-▁bedside	-25779
-▁kidneys	-25780
-▁enforcing	-25781
-▁reproduced	-25782
-▁Tah	-25783
-iasis	-25784
-▁BEEN	-25785
-Person	-25786
-▁Houses	-25787
-▁obnox	-25788
-▁masculine	-25789
-▁WIFI	-25790
-▁faux	-25791
-ronics	-25792
-select	-25793
-▁extinct	-25794
-▁Secondary	-25795
-▁campaigning	-25796
-▁ana	-25797
-▁Depot	-25798
-▁pamph	-25799
-fitting	-25800
-▁detract	-25801
-▁Pit	-25802
-▁TRA	-25803
-▁Swan	-25804
-▁Abbey	-25805
-▁asses	-25806
-▁clone	-25807
-eworthy	-25808
-patible	-25809
-▁socket	-25810
-▁maritime	-25811
-▁specimen	-25812
-rared	-25813
-▁Boat	-25814
-▁dictator	-25815
-▁inflicted	-25816
-OLE	-25817
-inas	-25818
-▁unexpl	-25819
-PAR	-25820
-Sim	-25821
-trained	-25822
-▁Legion	-25823
-▁Prepar	-25824
-▁tokens	-25825
-reaching	-25826
-mine	-25827
-▁Messiah	-25828
-▁booming	-25829
-▁discontinued	-25830
-LO	-25831
-▁shalt	-25832
-▁monarch	-25833
-Americans	-25834
-▁irresist	-25835
-▁motorists	-25836
-▁possessing	-25837
-bod	-25838
-holm	-25839
-▁FEM	-25840
-▁Alison	-25841
-▁Months	-25842
-▁gossips	-25843
-▁sentimental	-25844
-iston	-25845
-▁EDIT	-25846
-igible	-25847
-▁Taken	-25848
-▁banker	-25849
-▁quickest	-25850
-▁misguided	-25851
-▁proficiency	-25852
-Tom	-25853
-yss	-25854
-izzard	-25855
-▁booster	-25856
-▁strained	-25857
-osom	-25858
-▁McCar	-25859
-▁inning	-25860
-▁pivotal	-25861
-▁charcoal	-25862
-▁partnering	-25863
-▁strives	-25864
-▁vitality	-25865
-▁FD	-25866
-Call	-25867
-Child	-25868
-▁coma	-25869
-▁noun	-25870
-▁Apollo	-25871
-▁Lounge	-25872
-▁remorse	-25873
-▁Peninsula	-25874
-▁Subscribe	-25875
-▁portfolios	-25876
-llo	-25877
-▁RED	-25878
-▁spilled	-25879
-▁prolific	-25880
-▁intolerance	-25881
-▁Hawk	-25882
-▁vows	-25883
-andals	-25884
-remember	-25885
-▁ripping	-25886
-▁ticking	-25887
-▁deferred	-25888
-▁needless	-25889
-▁induction	-25890
-▁monuments	-25891
-▁deprivation	-25892
-▁Sage	-25893
-iblity	-25894
-▁Worst	-25895
-Feb	-25896
-▁Constant	-25897
-▁Blessings	-25898
-▁Laboratory	-25899
-▁safeguards	-25900
-▁bookmarking	-25901
-biz	-25902
-▁.”	-25903
-dain	-25904
-ennial	-25905
-▁pointer	-25906
-▁Patricia	-25907
-▁irrigation	-25908
-▁trajectory	-25909
-▁Improvement	-25910
-▁girlfriends	-25911
-▁sor	-25912
-▁SPEC	-25913
-▁melody	-25914
-▁ranged	-25915
-▁circuits	-25916
-▁validated	-25917
-▁Kickstarter	-25918
-▁Agenda	-25919
-▁bitterly	-25920
-▁Farmer	-25921
-▁dubbed	-25922
-▁unwind	-25923
-▁waitress	-25924
-anya	-25925
-▁Buddy	-25926
-▁nicotine	-25927
-▁intelligently	-25928
-Isn	-25929
-▁idi	-25930
-▁Tigers	-25931
-▁darkest	-25932
-▁thirsty	-25933
-▁Unique	-25934
-▁creeping	-25935
-▁complying	-25936
-▁theatrical	-25937
-dra	-25938
-▁raced	-25939
-▁Illustr	-25940
-▁informs	-25941
-▁citation	-25942
-▁sophomore	-25943
-TMs	-25944
-Char	-25945
-▁Oops	-25946
-▁hotter	-25947
-▁unpack	-25948
-▁instability	-25949
-▁Duck	-25950
-▁Zack	-25951
-▁reacting	-25952
-ALTH	-25953
-sale	-25954
-▁MRI	-25955
-▁modular	-25956
-▁complied	-25957
-▁workings	-25958
-▁regretted	-25959
-▁seamlessly	-25960
-bike	-25961
-▁skim	-25962
-▁Bedroom	-25963
-▁conquered	-25964
-▁revelations	-25965
-▁Roof	-25966
-olving	-25967
-▁spans	-25968
-▁pitfalls	-25969
-▁stringent	-25970
-▁indicative	-25971
-▁millennials	-25972
-▁“‘	-25973
-gard	-25974
-▁Shows	-25975
-▁Factor	-25976
-▁Coaching	-25977
-▁dissolve	-25978
-▁entrusted	-25979
-groups	-25980
-▁frying	-25981
-▁Cer	-25982
-▁Paid	-25983
-▁devoid	-25984
-▁staging	-25985
-sky	-25986
-uber	-25987
-▁Cyn	-25988
-▁FULL	-25989
-▁Forms	-25990
-▁awaits	-25991
-▁vagina	-25992
-▁Frances	-25993
-▁Plastic	-25994
-▁grading	-25995
-▁variants	-25996
-White	-25997
-▁CARE	-25998
-▁Lois	-25999
-▁forge	-26000
-▁winters	-26001
-High	-26002
-▁tramp	-26003
-▁Guitar	-26004
-▁futile	-26005
-stuff	-26006
-refundable	-26007
-▁randomized	-26008
-▁annum	-26009
-▁unans	-26010
-▁whims	-26011
-▁Purple	-26012
-▁Flowers	-26013
-▁implying	-26014
-commercial	-26015
-▁PCs	-26016
-▁gangs	-26017
-▁corros	-26018
-▁Opinion	-26019
-▁plunged	-26020
-▁fishermen	-26021
-▁DT	-26022
-priv	-26023
-▁Mak	-26024
-▁THR	-26025
-hering	-26026
-▁Spons	-26027
-▁abusing	-26028
-▁suppressed	-26029
-▁Responsibility	-26030
-dark	-26031
-▁Eld	-26032
-cheon	-26033
-▁Mayo	-26034
-▁clen	-26035
-wordpress	-26036
-▁hindsight	-26037
-▁Minneapolis	-26038
-▁ITS	-26039
-▁evils	-26040
-▁hurdles	-26041
-len	-26042
-Help	-26043
-▁Fifty	-26044
-▁annex	-26045
-▁looph	-26046
-▁immers	-26047
-▁mattresses	-26048
-▁dads	-26049
-▁drills	-26050
-▁engages	-26051
-▁bicycles	-26052
-▁Featuring	-26053
-▁Ske	-26054
-▁births	-26055
-▁Morocco	-26056
-▁grieving	-26057
-▁bilateral	-26058
-▁achievable	-26059
-€œ	-26060
-music	-26061
-▁fueled	-26062
-▁actionable	-26063
-▁criticisms	-26064
-▁sacrificing	-26065
-notes	-26066
-▁kale	-26067
-▁Appendix	-26068
-▁cuz	-26069
-Should	-26070
-crimin	-26071
-English	-26072
-▁Arrange	-26073
-▁ecommerce	-26074
-▁overboard	-26075
-▁contestants	-26076
-comb	-26077
-▁Dame	-26078
-▁Chain	-26079
-▁disin	-26080
-▁cafeteria	-26081
-▁Gent	-26082
-▁rained	-26083
-▁sarcasm	-26084
-▁Entrepreneurs	-26085
-▁gin	-26086
-▁Flow	-26087
-▁prope	-26088
-▁taxed	-26089
-contact	-26090
-ustainable	-26091
-▁pawn	-26092
-▁Ethiopia	-26093
-▁Kai	-26094
-▁Burke	-26095
-▁grips	-26096
-▁lapse	-26097
-▁quizz	-26098
-▁injected	-26099
-▁meditate	-26100
-▁roadside	-26101
-▁translator	-26102
-▁Particularly	-26103
-Cons	-26104
-▁McM	-26105
-beaut	-26106
-▁Archives	-26107
-▁pineapple	-26108
-▁legitimacy	-26109
-yrs	-26110
-easy	-26111
-lude	-26112
-▁crus	-26113
-▁Selection	-26114
-==	-26115
-▁curly	-26116
-▁Vacation	-26117
-▁medically	-26118
-athe	-26119
-arcity	-26120
-▁cutter	-26121
-▁lodged	-26122
-▁Disclaimer	-26123
-SQL	-26124
-▁Ivy	-26125
-▁crad	-26126
-awning	-26127
-▁Fairy	-26128
-▁timed	-26129
-▁Serious	-26130
-▁Pressure	-26131
-▁shortcut	-26132
-?,	-26133
-gay	-26134
-Girl	-26135
-arious	-26136
-▁tipped	-26137
-▁gourmet	-26138
-▁reinvent	-26139
-▁Dot	-26140
-▁Shoot	-26141
-▁redef	-26142
-▁Aboriginal	-26143
-▁lac	-26144
-▁cath	-26145
-▁claw	-26146
-▁stag	-26147
-▁Qaeda	-26148
-medical	-26149
-▁Healing	-26150
-▁heroine	-26151
-▁nearing	-26152
-anto	-26153
-bout	-26154
-▁pits	-26155
-wright	-26156
-▁Burns	-26157
-▁Patrol	-26158
-▁addicts	-26159
-▁ornament	-26160
-ISA	-26161
-ICLE	-26162
-▁Ranger	-26163
-▁Chronic	-26164
-▁coaster	-26165
-sponsored	-26166
-▁physiological	-26167
-▁swamp	-26168
-▁uphill	-26169
-▁estates	-26170
-▁sincerity	-26171
-Cent	-26172
-▁Theme	-26173
-▁earthly	-26174
-▁Monetary	-26175
-▁offending	-26176
-rols	-26177
-▁Rosen	-26178
-▁uranium	-26179
-▁BJ	-26180
-igator	-26181
-▁finite	-26182
-▁dungeon	-26183
-▁surname	-26184
-▁Permanent	-26185
-▁Doors	-26186
-▁muster	-26187
-▁daycare	-26188
-▁scaling	-26189
-▁"[	-26190
-▁Liam	-26191
-▁jets	-26192
-▁Arena	-26193
-▁angst	-26194
-curricular	-26195
-▁unpopular	-26196
-▁retrospect	-26197
-▁compromising	-26198
-thodont	-26199
-▁disgusted	-26200
-▁Infrastructure	-26201
-▁Din	-26202
-close	-26203
-▁Straw	-26204
-▁temps	-26205
-▁manuals	-26206
-▁inserting	-26207
-posed	-26208
-▁forex	-26209
-▁Moment	-26210
-▁dispatch	-26211
-▁stalking	-26212
-▁Tournament	-26213
-▁ba	-26214
-trip	-26215
-jamas	-26216
-▁explosions	-26217
-acey	-26218
-birds	-26219
-earned	-26220
-▁Limit	-26221
-▁Guinea	-26222
-▁ankles	-26223
-▁Municipal	-26224
-▁shortcuts	-26225
-▁???	-26226
-asking	-26227
-▁Becky	-26228
-▁Invol	-26229
-ophobia	-26230
-▁Clarke	-26231
-▁Senators	-26232
-kal	-26233
-maid	-26234
-report	-26235
-ingling	-26236
-▁Threat	-26237
-▁wrists	-26238
-▁comforts	-26239
-▁inaugural	-26240
-▁Coke	-26241
-zzarella	-26242
-Neill	-26243
-assad	-26244
-▁Admin	-26245
-▁delve	-26246
-▁dwarf	-26247
-▁climax	-26248
-▁conditional	-26249
-,[	-26250
-boo	-26251
-rsquo	-26252
-▁forgiving	-26253
-erenn	-26254
-▁Evan	-26255
-▁Mumbai	-26256
-▁amazon	-26257
-▁Sunshine	-26258
-orf	-26259
-▁MAC	-26260
-▁vacancy	-26261
-▁filmmakers	-26262
-▁unreliable	-26263
-▁indifference	-26264
-Jack	-26265
-▁BOOK	-26266
-▁bleak	-26267
-▁solitude	-26268
-dit	-26269
-RECT	-26270
-▁Lan	-26271
-▁Ran	-26272
-▁Squad	-26273
-▁amazement	-26274
-▁alteration	-26275
-▁persuasive	-26276
-pie	-26277
-Full	-26278
-IFIC	-26279
-adata	-26280
-▁erotic	-26281
-isations	-26282
-smanship	-26283
-▁Planned	-26284
-▁Numerous	-26285
-▁villagers	-26286
-▁endowed	-26287
-community	-26288
-Val	-26289
-orde	-26290
-▁...”	-26291
-▁banging	-26292
-▁concurrent	-26293
-▁Intellectual	-26294
-▁(*	-26295
-▁translating	-26296
-▁elic	-26297
-▁cubes	-26298
-▁pseudo	-26299
-md	-26300
-imo	-26301
-▁Ferr	-26302
-rounds	-26303
-▁dentistry	-26304
-Fl	-26305
-idy	-26306
-▁Eh	-26307
-▁symm	-26308
-!!!!!!	-26309
-▁choked	-26310
-▁backlash	-26311
-stitutional	-26312
-GET	-26313
-try	-26314
-▁COR	-26315
-▁PET	-26316
-▁Bark	-26317
-▁motel	-26318
-▁Retrie	-26319
-▁tumors	-26320
-▁stroller	-26321
-▁qualifies	-26322
-NESS	-26323
-carb	-26324
-inged	-26325
-usterity	-26326
-▁automate	-26327
-▁Adventures	-26328
-▁unanimously	-26329
-uf	-26330
-▁LOC	-26331
-▁hex	-26332
-▁Rhod	-26333
-▁fitt	-26334
-▁soften	-26335
-▁backups	-26336
-▁refunded	-26337
-▁unnatural	-26338
-itars	-26339
-▁shuff	-26340
-▁bounty	-26341
-▁groaned	-26342
-▁Benedict	-26343
-▁Nigerian	-26344
-▁cellphone	-26345
-▁WS	-26346
-cler	-26347
-▁Monty	-26348
-▁panoramic	-26349
-▁reluctantly	-26350
-into	-26351
-▁Flynn	-26352
-uckland	-26353
-▁hunted	-26354
-?!"	-26355
-EVER	-26356
-ridges	-26357
-▁hides	-26358
-Section	-26359
-▁Losing	-26360
-raz	-26361
-sor	-26362
-psych	-26363
-▁smug	-26364
-reviewed	-26365
-▁Deposit	-26366
-▁sneakers	-26367
-▁Methodist	-26368
-Ms	-26369
-heet	-26370
-▁Ivan	-26371
-ensing	-26372
-▁Trave	-26373
-▁plugs	-26374
-▁locksmith	-26375
-▁volleyball	-26376
-▁hiat	-26377
-▁wastes	-26378
-▁Greatest	-26379
-▁methodologies	-26380
-norm	-26381
-▁nets	-26382
-reating	-26383
-▁cliffs	-26384
-▁empath	-26385
-▁mashed	-26386
-▁betrayal	-26387
-▁menstrual	-26388
-▁creatively	-26389
-Semit	-26390
-▁ESPN	-26391
-▁tilt	-26392
-itters	-26393
-▁rearr	-26394
-ittedly	-26395
-▁flagship	-26396
-▁authorize	-26397
-▁dizziness	-26398
-▁establishes	-26399
-▁ost	-26400
-▁Mong	-26401
-▁rept	-26402
-levard	-26403
-▁exile	-26404
-enguins	-26405
-▁plural	-26406
-▁funniest	-26407
-▁exemplary	-26408
-▁unbelievably	-26409
-,.	-26410
-▁&#	-26411
-▁Hep	-26412
-▁Portal	-26413
-▁bridal	-26414
-▁hating	-26415
-▁crooked	-26416
-mp	-26417
-ardi	-26418
-idav	-26419
-▁cone	-26420
-▁joked	-26421
-▁grouped	-26422
-▁citations	-26423
-▁reconstruct	-26424
-rc	-26425
-eger	-26426
-▁ICT	-26427
-▁****	-26428
-▁perimeter	-26429
-▁peripheral	-26430
-particularly	-26431
-▁GH	-26432
-▁coil	-26433
-▁inland	-26434
-▁rustic	-26435
-▁Expo	-26436
-▁Leeds	-26437
-▁pearls	-26438
-▁Tiffany	-26439
-Open	-26440
-hunt	-26441
-▁Echo	-26442
-▁oily	-26443
-▁Catal	-26444
-▁gravy	-26445
-▁fibers	-26446
-▁punches	-26447
-▁sprinkle	-26448
-asia	-26449
-▁overfl	-26450
-▁powerless	-26451
-▁recollection	-26452
-hang	-26453
-ubby	-26454
-▁Diagn	-26455
-inarian	-26456
-▁Calvin	-26457
-▁Flower	-26458
-▁Hidden	-26459
-▁turbul	-26460
-▁ignores	-26461
-▁amounted	-26462
-▁detector	-26463
-inders	-26464
-induced	-26465
-▁Cambodia	-26466
-▁TVs	-26467
-▁facet	-26468
-▁finely	-26469
-bringing	-26470
-▁Strength	-26471
-▁declines	-26472
-▁acclaimed	-26473
-▁clarified	-26474
-▁spaghetti	-26475
-▁commentators	-26476
-▁fluctuations	-26477
-▁multinational	-26478
-rob	-26479
-▁PIN	-26480
-Source	-26481
-▁Temper	-26482
-▁(+	-26483
-violent	-26484
-▁Called	-26485
-▁hacker	-26486
-▁risking	-26487
-developed	-26488
-▁southeast	-26489
-▁practicable	-26490
-▁Ul	-26491
-aining	-26492
-ducive	-26493
-▁Surve	-26494
-▁dipped	-26495
-▁flattering	-26496
-▁Residential	-26497
-▁understatement	-26498
-Sur	-26499
-hov	-26500
-▁Lun	-26501
-▁Toby	-26502
-leader	-26503
-pocket	-26504
-▁Wisdom	-26505
-▁undeniably	-26506
-▁Proceedings	-26507
-▁preventative	-26508
-prop	-26509
-natal	-26510
-igated	-26511
-▁Debtor	-26512
-▁gloomy	-26513
-▁sponge	-26514
-▁chilled	-26515
-▁holdings	-26516
-▁monastery	-26517
-afia	-26518
-clude	-26519
-loads	-26520
-▁Muse	-26521
-▁calf	-26522
-return	-26523
-▁Realt	-26524
-▁apron	-26525
-▁exported	-26526
-▁competencies	-26527
-aris	-26528
-fuel	-26529
-▁LAW	-26530
-▁Praise	-26531
-▁nationals	-26532
-▁transporting	-26533
-▁ll	-26534
-nice	-26535
-▁Maz	-26536
-▁Yea	-26537
-▁Auss	-26538
-distance	-26539
-▁conducive	-26540
-▁Distribution	-26541
-scenes	-26542
-▁vigil	-26543
-parallel	-26544
-appointed	-26545
-▁competency	-26546
-▁Jedi	-26547
-▁Mets	-26548
-presso	-26549
-▁Dining	-26550
-▁barred	-26551
-NELL	-26552
-omma	-26553
-blown	-26554
-▁Push	-26555
-▁Browse	-26556
-▁Surviv	-26557
-▁catchy	-26558
-▁Danielle	-26559
-▁Disneyland	-26560
-soon	-26561
-▁Cort	-26562
-▁Publ	-26563
-lectic	-26564
-▁leaps	-26565
-▁malls	-26566
-artisan	-26567
-▁Chiefs	-26568
-▁Willie	-26569
-▁fortress	-26570
-▁labeling	-26571
-▁Universities	-26572
-Law	-26573
-▁jewels	-26574
-▁Closing	-26575
-▁tranquil	-26576
-▁plentiful	-26577
-▁contentment	-26578
-▁customization	-26579
-ENC	-26580
-Mother	-26581
-flight	-26582
-▁knuck	-26583
-▁Gibson	-26584
-▁orchestr	-26585
-▁commonplace	-26586
-ablo	-26587
-▁carts	-26588
-▁Stupid	-26589
-▁bounced	-26590
-▁narrator	-26591
-▁diversion	-26592
-▁unforeseen	-26593
-Lo	-26594
-▁duel	-26595
-▁iter	-26596
-School	-26597
-▁custod	-26598
-▁facets	-26599
-▁malaria	-26600
-▁manifestation	-26601
-ieu	-26602
-sav	-26603
-▁Levi	-26604
-▁Offers	-26605
-▁imposs	-26606
-▁sweetest	-26607
-▁designate	-26608
-▁interpreter	-26609
-▁Pole	-26610
-▁Cycle	-26611
-▁handgun	-26612
-▁decentral	-26613
-▁Disclosure	-26614
-▁contradictory	-26615
-atra	-26616
-lass	-26617
-▁creams	-26618
-▁recourse	-26619
-▁ambiguous	-26620
-▁Renaissance	-26621
-eno	-26622
-▁muc	-26623
-▁Wade	-26624
-▁Equal	-26625
-▁hires	-26626
-secured	-26627
-▁Fridays	-26628
-▁reassure	-26629
-▁ubiquitous	-26630
-ibal	-26631
-▁Cox	-26632
-forth	-26633
-appreci	-26634
-▁impecc	-26635
-▁stamina	-26636
-▁sensational	-26637
-iliary	-26638
-ainting	-26639
-▁elevate	-26640
-▁muffins	-26641
-▁peanuts	-26642
-▁strides	-26643
-▁successive	-26644
-▁quieter	-26645
-▁sourcing	-26646
-▁brokerage	-26647
-▁ministries	-26648
-▁inadvertently	-26649
-AH	-26650
-▁enric	-26651
-▁unbear	-26652
-IAN	-26653
-▁Finn	-26654
-▁para	-26655
-▁denies	-26656
-▁fiance	-26657
-▁Dancing	-26658
-▁Receive	-26659
-▁Stevens	-26660
-▁melanch	-26661
-▁unavoidable	-26662
-Pat	-26663
-utt	-26664
-Live	-26665
-Three	-26666
-▁nerd	-26667
-rather	-26668
-education	-26669
-▁serviced	-26670
-▁signifies	-26671
-▁timetable	-26672
-▁xo	-26673
-▁INC	-26674
-mobile	-26675
-▁Claud	-26676
-▁Madonna	-26677
-▁tutoring	-26678
-▁nervously	-26679
-▁Petersburg	-26680
-▁animations	-26681
-▁interpreting	-26682
-luent	-26683
-icides	-26684
-▁prosecute	-26685
-string	-26686
-▁Chick	-26687
-founded	-26688
-▁halted	-26689
-▁hateful	-26690
-▁plagued	-26691
-▁roaming	-26692
-▁treason	-26693
-Still	-26694
-▁Tail	-26695
-▁ensl	-26696
-jection	-26697
-▁drifting	-26698
-▁shipments	-26699
-▁Mood	-26700
-▁Gifts	-26701
-▁separates	-26702
-:"	-26703
-▁TT	-26704
-cloud	-26705
-otions	-26706
-▁awakened	-26707
-▁chocolates	-26708
-▁satellites	-26709
-▁theological	-26710
-bsp	-26711
-peak	-26712
-ipples	-26713
-▁Damon	-26714
-▁chord	-26715
-▁surrend	-26716
-▁principals	-26717
-▁Loy	-26718
-folio	-26719
-▁CONTR	-26720
-▁ruining	-26721
-▁nominate	-26722
-▁Ebola	-26723
-▁acron	-26724
-▁boobs	-26725
-▁parting	-26726
-Certainly	-26727
-supported	-26728
-▁distrust	-26729
-ahn	-26730
-▁Yam	-26731
-▁reel	-26732
-▁calam	-26733
-▁potty	-26734
-October	-26735
-▁Mueller	-26736
-▁maximise	-26737
-▁smelling	-26738
-▁toothpaste	-26739
-neck	-26740
-▁IMF	-26741
-uning	-26742
-vices	-26743
-common	-26744
-▁Folks	-26745
-▁tongues	-26746
-▁nominees	-26747
-▁formulate	-26748
-eway	-26749
-ogens	-26750
-▁aide	-26751
-▁teased	-26752
-▁Harvest	-26753
-▁Miranda	-26754
-▁scriptures	-26755
-uer	-26756
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-▁Israelis	-26901
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-ailability	-26917
-orum	-26918
-below	-26919
-▁gays	-26920
-▁WHICH	-26921
-▁riots	-26922
-▁cohort	-26923
-▁tolerant	-26924
-▁yea	-26925
-▁Guid	-26926
-▁enlist	-26927
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-▁feasibility	-26929
-▁subsidiaries	-26930
-Coll	-26931
-games	-26932
-▁Oprah	-26933
-▁Remote	-26934
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-▁woe	-26936
-ciple	-26937
-▁Theo	-26938
-create	-26939
-▁backbone	-26940
-▁flavours	-26941
-▁newcomers	-26942
-▁patriotic	-26943
-▁Fo	-26944
-▁DEF	-26945
-▁dodge	-26946
-▁poetic	-26947
-▁Advisor	-26948
-▁Courses	-26949
-▁reviewer	-26950
-▁Paragraph	-26951
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-fruit	-26955
-▁Tess	-26956
-▁incub	-26957
-▁epidem	-26958
-▁barking	-26959
-▁Terminal	-26960
-▁miraculous	-26961
-ndas	-26962
-▁helmets	-26963
-▁Springfield	-26964
-▁constituted	-26965
-▁proliferation	-26966
-ulif	-26967
-▁snee	-26968
-rupted	-26969
-▁Files	-26970
-ATIONAL	-26971
-▁crispy	-26972
-▁crypto	-26973
-▁draining	-26974
-▁Industries	-26975
-onut	-26976
-sweet	-26977
-▁hiatus	-26978
-▁courier	-26979
-▁fearing	-26980
-▁sandals	-26981
-▁Abstract	-26982
-▁volcanic	-26983
-▁appalling	-26984
-ubar	-26985
-▁Darcy	-26986
-▁anthem	-26987
-▁lighted	-26988
-▁saucepan	-26989
-▁blueprint	-26990
-LES	-26991
-fired	-26992
-▁miser	-26993
-arrison	-26994
-▁prostitution	-26995
-kirts	-26996
-▁cess	-26997
-▁basin	-26998
-▁avatar	-26999
-▁bursts	-27000
-▁sweetie	-27001
-▁inventor	-27002
-▁prompting	-27003
-▁Recreation	-27004
-▁handkerchief	-27005
-▁recognizable	-27006
-aca	-27007
-Check	-27008
-▁Scouts	-27009
-▁hopping	-27010
-▁torment	-27011
-▁Meetings	-27012
-▁possiblity	-27013
-▁Prob	-27014
-▁biom	-27015
-▁saga	-27016
-casters	-27017
-▁mastery	-27018
-▁digitally	-27019
-▁obnoxious	-27020
-▁wholesome	-27021
-▁Wag	-27022
-▁shaky	-27023
-▁lashes	-27024
-▁refreshments	-27025
-▁Relationships	-27026
-cot	-27027
-▁Ooh	-27028
-▁coo	-27029
-▁Shit	-27030
-▁Rhode	-27031
-▁slender	-27032
-Af	-27033
-▁cd	-27034
-nosis	-27035
-ooped	-27036
-▁invaded	-27037
-▁provoke	-27038
-▁baptized	-27039
-▁Chronicle	-27040
-▁solicitor	-27041
-▁antioxidant	-27042
-▁percentages	-27043
-iri	-27044
-odor	-27045
-▁Disp	-27046
-▁benign	-27047
-▁remake	-27048
-▁Carroll	-27049
-▁awaited	-27050
-▁mammals	-27051
-▁Sterling	-27052
-▁genealog	-27053
-▁etiquette	-27054
-dent	-27055
-▁Aim	-27056
-▁Fool	-27057
-▁cros	-27058
-▁redd	-27059
-▁karma	-27060
-▁giggle	-27061
-▁signage	-27062
-▁skincare	-27063
-▁bartender	-27064
-▁sponsoring	-27065
-▁differential	-27066
-lost	-27067
-rers	-27068
-above	-27069
-opian	-27070
-▁bulky	-27071
-▁utens	-27072
-houette	-27073
-▁tyranny	-27074
-character	-27075
-▁twenties	-27076
-▁Apartment	-27077
-▁transcripts	-27078
-?",	-27079
-▁prow	-27080
-figure	-27081
-▁subsidy	-27082
-▁drawbacks	-27083
-▁emphasizes	-27084
-▁MH	-27085
-▁cues	-27086
-▁Sheet	-27087
-▁nickel	-27088
-▁Graphic	-27089
-▁solvent	-27090
-▁Snapchat	-27091
-▁spoilers	-27092
-▁versatility	-27093
-Phot	-27094
-▁Soup	-27095
-America	-27096
-▁Reynolds	-27097
-▁punctuation	-27098
-!!)	-27099
-▁Grass	-27100
-▁Fishing	-27101
-▁yielded	-27102
-?!”	-27103
-Nobody	-27104
-▁Cheney	-27105
-▁Provin	-27106
-▁podium	-27107
-▁pastors	-27108
-▁widened	-27109
-▁aesthetics	-27110
-oqu	-27111
-▁Bir	-27112
-caster	-27113
-▁slows	-27114
-▁replay	-27115
-▁disputed	-27116
-▁consolidate	-27117
-▁motorcycles	-27118
-AIL	-27119
-▁wil	-27120
-status	-27121
-▁Connor	-27122
-▁cabins	-27123
-▁Humanity	-27124
-▁moonlight	-27125
-▁Progressive	-27126
-KB	-27127
-▁inhal	-27128
-▁graders	-27129
-▁scrambled	-27130
-▁Sears	-27131
-▁fluor	-27132
-▁Mick	-27133
-▁Seller	-27134
-archment	-27135
-▁Comfort	-27136
-sensitive	-27137
-Fe	-27138
-suff	-27139
-given	-27140
-▁Cubs	-27141
-▁Lenn	-27142
-▁dusk	-27143
-▁oats	-27144
-ropolis	-27145
-▁bathtub	-27146
-▁shaving	-27147
-▁unsatis	-27148
-▁Champion	-27149
-▁modifying	-27150
-arf	-27151
-▁POS	-27152
-▁floss	-27153
-▁adjour	-27154
-▁Township	-27155
-▁fatalities	-27156
-ilda	-27157
-▁Tank	-27158
-▁baker	-27159
-▁funky	-27160
-▁respite	-27161
-▁upsetting	-27162
-▁WTF	-27163
-▁dre	-27164
-▁aroma	-27165
-▁revoked	-27166
-▁decidedly	-27167
-▁tightening	-27168
-UTION	-27169
-▁Rank	-27170
-choice	-27171
-▁Plain	-27172
-▁Rapid	-27173
-▁assim	-27174
-▁bytes	-27175
-▁Papers	-27176
-▁peaked	-27177
-▁Becoming	-27178
-▁terminals	-27179
-Dem	-27180
-▁Battery	-27181
-▁Networks	-27182
-▁lengthen	-27183
-▁excursion	-27184
-▁Implementation	-27185
-▁rav	-27186
-proper	-27187
-▁merged	-27188
-▁busiest	-27189
-▁expands	-27190
-▁inspectors	-27191
-▁appreciative	-27192
-thr	-27193
-quite	-27194
-alleys	-27195
-shadow	-27196
-volent	-27197
-▁overr	-27198
-▁groove	-27199
-▁malfunction	-27200
-▁KB	-27201
-ilet	-27202
-▁MED	-27203
-court	-27204
-▁bumped	-27205
-▁stride	-27206
-depending	-27207
-▁Northeast	-27208
-▁inventions	-27209
-▁humiliation	-27210
-wow	-27211
-▁ape	-27212
-▁pac	-27213
-▁uni	-27214
-▁cudd	-27215
-Father	-27216
-▁Dolph	-27217
-▁wipes	-27218
-▁Juliet	-27219
-▁Manning	-27220
-▁Lightning	-27221
-▁paranormal	-27222
-▁HI	-27223
-▁FIFA	-27224
-▁oxide	-27225
-▁wager	-27226
-▁oppressed	-27227
-▁summarized	-27228
-poor	-27229
-▁LOVED	-27230
-▁noses	-27231
-▁citrus	-27232
-essential	-27233
-▁McCarthy	-27234
-▁latitude	-27235
-▁handwriting	-27236
-udic	-27237
-atism	-27238
-Little	-27239
-▁clich	-27240
-▁relic	-27241
-igslist	-27242
-▁Grandpa	-27243
-▁Marilyn	-27244
-▁fencing	-27245
-▁regimes	-27246
-▁passionately	-27247
-odd	-27248
-▁rud	-27249
-brown	-27250
-etics	-27251
-▁Strip	-27252
-▁folly	-27253
-▁Investing	-27254
-▁eccentric	-27255
-▁derivatives	-27256
-▁Championships	-27257
-▁CONS	-27258
-▁scept	-27259
-▁Answers	-27260
-▁Raymond	-27261
-▁Wyoming	-27262
-▁pencils	-27263
-▁menopause	-27264
-▁whispering	-27265
-▁FC	-27266
-achi	-27267
-ukes	-27268
-▁Lean	-27269
-▁beck	-27270
-▁vodka	-27271
-▁oblivious	-27272
-▁smoot	-27273
-▁Garcia	-27274
-▁Greeks	-27275
-▁crawled	-27276
-▁Darkness	-27277
-▁outskirts	-27278
-▁Surprisingly	-27279
-▁NT	-27280
-▁Hoo	-27281
-▁synthes	-27282
-▁Gorgeous	-27283
-▁mythology	-27284
-▁deterioration	-27285
-imir	-27286
-ospace	-27287
-▁Disaster	-27288
-▁modelling	-27289
-▁pondering	-27290
-▁Supplement	-27291
-▁interfering	-27292
-▁mi	-27293
-stic	-27294
-▁Wet	-27295
-likes	-27296
-▁gamb	-27297
-▁Triple	-27298
-▁adapter	-27299
-▁perpetrators	-27300
-True	-27301
-▁earns	-27302
-▁treaties	-27303
-▁Buddh	-27304
-angible	-27305
-▁Regina	-27306
-▁enlarged	-27307
-▁whispers	-27308
-▁glamorous	-27309
-▁horrifying	-27310
-▁excessively	-27311
-▁undesirable	-27312
-')	-27313
-legal	-27314
-▁begg	-27315
-nation	-27316
-▁delet	-27317
-▁shouts	-27318
-▁Prospect	-27319
-▁clusters	-27320
-▁midfield	-27321
-bug	-27322
-uko	-27323
-irms	-27324
-olan	-27325
-thia	-27326
-▁Ner	-27327
-▁REQU	-27328
-inators	-27329
-▁busted	-27330
-▁replen	-27331
-▁velvet	-27332
-▁scooter	-27333
-▁earnestly	-27334
-sil	-27335
-▁AJ	-27336
-▁mills	-27337
-▁smirk	-27338
-▁corresponds	-27339
-mass	-27340
-train	-27341
-opedia	-27342
-▁Trace	-27343
-▁Salvation	-27344
-▁streamlined	-27345
-▁Novel	-27346
-▁Quiet	-27347
-▁presses	-27348
-▁relocate	-27349
-▁reminiscent	-27350
-▁Koh	-27351
-▁Lori	-27352
-▁Sophia	-27353
-▁horseback	-27354
-▁instagram	-27355
-/-	-27356
-▁CAM	-27357
-▁Breat	-27358
-▁modem	-27359
-▁skelet	-27360
-▁sloppy	-27361
-uliflower	-27362
-▁diligently	-27363
-▁noteworthy	-27364
-▁supervisory	-27365
-Bob	-27366
-warm	-27367
-▁Kara	-27368
-▁Lent	-27369
-▁TAKE	-27370
-▁barg	-27371
-campus	-27372
-▁Commit	-27373
-▁campsite	-27374
-▁attackers	-27375
-▁endlessly	-27376
-▁millionaire	-27377
-Hold	-27378
-▁END	-27379
-▁guru	-27380
-▁Conflict	-27381
-▁unbiased	-27382
-▁:/	-27383
-▁TD	-27384
-▁Xi	-27385
-oise	-27386
-bourg	-27387
-eware	-27388
-release	-27389
-▁devised	-27390
-▁ensemble	-27391
-▁unethical	-27392
-▁controllers	-27393
-istle	-27394
-▁Bren	-27395
-▁Hearts	-27396
-▁slated	-27397
-▁grooming	-27398
-▁surrounds	-27399
-▁tablespoon	-27400
-Pre	-27401
-cio	-27402
-▁Tak	-27403
-▁gor	-27404
-▁hamb	-27405
-▁slew	-27406
-▁Wings	-27407
-▁fists	-27408
-▁Missing	-27409
-▁omission	-27410
-▁corrective	-27411
-fav	-27412
-adic	-27413
-▁nug	-27414
-iator	-27415
-▁resc	-27416
-▁shudder	-27417
-▁outpatient	-27418
-▁replacements	-27419
-hog	-27420
-▁caus	-27421
-▁Bones	-27422
-▁reins	-27423
-▁trending	-27424
-▁harvested	-27425
-ITS	-27426
-grass	-27427
-▁warp	-27428
-▁Marqu	-27429
-▁Tours	-27430
-▁famine	-27431
-▁issuer	-27432
-▁cucumber	-27433
-ppe	-27434
-▁GPA	-27435
-▁UAE	-27436
-▁HOME	-27437
-▁Jade	-27438
-▁adam	-27439
-DONNELL	-27440
-▁hospice	-27441
-▁unanswered	-27442
-til	-27443
-▁Kl	-27444
-▁UT	-27445
-Know	-27446
-▁Naval	-27447
-▁Shock	-27448
-▁excell	-27449
-▁Percent	-27450
-▁Pom	-27451
-▁Sonny	-27452
-▁stacks	-27453
-▁scrapbook	-27454
-▁suppression	-27455
-▁ND	-27456
-illin	-27457
-upiter	-27458
-▁Laser	-27459
-▁soups	-27460
-▁urges	-27461
-▁sipping	-27462
-▁envelopes	-27463
-▁thunderstorm	-27464
-▁realistically	-27465
-uz	-27466
-horn	-27467
-▁Coup	-27468
-riment	-27469
-▁Promise	-27470
-▁fic	-27471
-stated	-27472
-▁Stamp	-27473
-▁blogged	-27474
-▁Registry	-27475
-wee	-27476
-ERAL	-27477
-▁hed	-27478
-▁epile	-27479
-itching	-27480
-▁Pension	-27481
-▁Accommod	-27482
-▁treacher	-27483
-▁Brotherhood	-27484
-Watch	-27485
-▁Epic	-27486
-warming	-27487
-▁Napoleon	-27488
-▁concession	-27489
-▁Institution	-27490
-▁“(	-27491
-eros	-27492
-▁begs	-27493
-umatic	-27494
-▁Zimmer	-27495
-▁ridden	-27496
-▁spatial	-27497
-igans	-27498
-▁dove	-27499
-urrence	-27500
-▁lotion	-27501
-religious	-27502
-▁Americas	-27503
-▁Guarantee	-27504
-▁enthusiast	-27505
-▁INFORMATION	-27506
-▁Certification	-27507
-▁McL	-27508
-▁bows	-27509
-▁loot	-27510
-▁Amount	-27511
-▁lithium	-27512
-Ty	-27513
-union	-27514
-ologic	-27515
-▁almonds	-27516
-▁isolate	-27517
-▁famously	-27518
-▁makeover	-27519
-▁redeemed	-27520
-NER	-27521
-▁USS	-27522
-▁foe	-27523
-▁Romance	-27524
-▁chiefly	-27525
-▁ACL	-27526
-▁mul	-27527
-▁liner	-27528
-▁anguish	-27529
-▁ANYTHING	-27530
-▁Hav	-27531
-▁Rex	-27532
-▁teas	-27533
-▁Civic	-27534
-assadors	-27535
-▁Drawing	-27536
-▁underpin	-27537
-▁Engagement	-27538
-▁fors	-27539
-ourcing	-27540
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-▁objected	-27543
-productive	-27544
-▁forged	-27545
-▁accents	-27546
-▁caliber	-27547
-▁zucchini	-27548
-▁nostalgic	-27549
-MI	-27550
-issy	-27551
-▁Adapt	-27552
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-▁verbally	-27554
-▁someplace	-27555
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-▁laboratories	-27557
-▁dwind	-27558
-▁snowy	-27559
-▁Evelyn	-27560
-▁Stones	-27561
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-▁Cay	-27565
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-inclusive	-27568
-bial	-27569
-▁tame	-27570
-▁Marco	-27571
-▁whisk	-27572
-▁flyers	-27573
-▁Ukrainian	-27574
-▁instructing	-27575
-~~	-27576
-:--	-27577
-ONEY	-27578
-▁Mitch	-27579
-,)	-27580
-iancé	-27581
-rofen	-27582
-number	-27583
-▁flirt	-27584
-▁Instru	-27585
-▁autobi	-27586
-▁ceases	-27587
-▁vulgar	-27588
-▁Emirates	-27589
-ikh	-27590
-▁Ny	-27591
-ESCO	-27592
-ecake	-27593
-▁Tier	-27594
-▁hydra	-27595
-▁carving	-27596
-▁scrutin	-27597
-▁approving	-27598
-BY	-27599
-Gs	-27600
-Face	-27601
-ograms	-27602
-▁emerges	-27603
-▁Apartments	-27604
-▁Charleston	-27605
-▁exponentially	-27606
-▁Cute	-27607
-sticks	-27608
-▁sinful	-27609
-▁tweaks	-27610
-nutrition	-27611
-▁resurrect	-27612
-▁liberation	-27613
-Ray	-27614
-▁leans	-27615
-available	-27616
-vir	-27617
-▁Gang	-27618
-▁Sixth	-27619
-▁Latino	-27620
-▁Laurie	-27621
-affected	-27622
-▁exasper	-27623
-▁tsunami	-27624
-provoking	-27625
-▁Agencies	-27626
-▁frosting	-27627
-▁pragmatic	-27628
-Another	-27629
-electric	-27630
-▁viewpoints	-27631
-)"	-27632
-▁SHOW	-27633
-▁ridd	-27634
-▁deterg	-27635
-▁salute	-27636
-▁brochures	-27637
-▁uplifting	-27638
-▁speculative	-27639
-▁roam	-27640
-ogical	-27641
-▁Pilot	-27642
-▁postcards	-27643
-▁everlasting	-27644
-▁contemplated	-27645
-anoia	-27646
-dling	-27647
-▁omit	-27648
-▁slash	-27649
-▁Mining	-27650
-▁Voting	-27651
-imentary	-27652
-▁geometry	-27653
-▁obsessive	-27654
-▁contingency	-27655
-▁FT	-27656
-▁Abe	-27657
-apsed	-27658
-▁CHAP	-27659
-▁quota	-27660
-▁domest	-27661
-▁reperc	-27662
-closures	-27663
-▁Pirates	-27664
-▁banquet	-27665
-▁engagements	-27666
-Link	-27667
-azer	-27668
-oshi	-27669
-▁agon	-27670
-▁fiercely	-27671
-▁SUCH	-27672
-▁bred	-27673
-▁claws	-27674
-▁expansive	-27675
-Dec	-27676
-▁wack	-27677
-▁inert	-27678
-▁Stalin	-27679
-▁creditor	-27680
-▁Interactive	-27681
-*,	-27682
-▁pian	-27683
-▁scler	-27684
-▁payout	-27685
-▁upbeat	-27686
-▁punching	-27687
-▁reliably	-27688
-▁retorted	-27689
-▁shepherd	-27690
-▁Gos	-27691
-olith	-27692
-urchase	-27693
-▁authoritative	-27694
-▁pH	-27695
-gomery	-27696
-▁bisexual	-27697
-▁infantry	-27698
-▁procedural	-27699
-▁compositions	-27700
-▁Yum	-27701
-ivism	-27702
-▁Rams	-27703
-▁glee	-27704
-▁hubs	-27705
-▁fiery	-27706
-urations	-27707
-▁Inspired	-27708
-▁twisting	-27709
-▁enormously	-27710
-▁faithfully	-27711
-▁remuneration	-27712
-▁misinformation	-27713
-brook	-27714
-iative	-27715
-▁dizzy	-27716
-▁Thousand	-27717
-▁austerity	-27718
-▁globalization	-27719
-IEW	-27720
-▁GIF	-27721
-▁Mau	-27722
-▁Nas	-27723
-▁moth	-27724
-ronting	-27725
-▁blends	-27726
-▁dehydr	-27727
-▁thrott	-27728
-zema	-27729
-▁WON	-27730
-aucus	-27731
-▁Poverty	-27732
-▁weeping	-27733
-▁swearing	-27734
-Mod	-27735
-iage	-27736
-▁crib	-27737
-worked	-27738
-▁Parade	-27739
-▁Sources	-27740
-▁destroys	-27741
-lat	-27742
-former	-27743
-▁stint	-27744
-▁cringe	-27745
-▁panties	-27746
-▁aquarium	-27747
-▁labelled	-27748
-▁restricting	-27749
-ND	-27750
-▁­	-27751
-ADY	-27752
-rences	-27753
-▁Hamas	-27754
-▁moron	-27755
-▁prick	-27756
-▁Murder	-27757
-▁decency	-27758
-▁importing	-27759
-▁sustaining	-27760
-RR	-27761
-▁cray	-27762
-▁gull	-27763
-▁Alert	-27764
-▁Rings	-27765
-▁facto	-27766
-▁Trevor	-27767
-▁canopy	-27768
-▁conductor	-27769
-▁allowances	-27770
-▁Tort	-27771
-▁pizz	-27772
-stress	-27773
-▁Fields	-27774
-▁allied	-27775
-▁extern	-27776
-▁Canal	-27777
-idepress	-27778
-▁chatted	-27779
-▁slippers	-27780
-▁trademarks	-27781
-▁circulating	-27782
-▁bir	-27783
-issan	-27784
-▁emph	-27785
-▁Posts	-27786
-▁Registrar	-27787
-▁believable	-27788
-▁intellectually	-27789
-...(	-27790
-Class	-27791
-theme	-27792
-▁arse	-27793
-▁Arist	-27794
-▁Horizon	-27795
-September	-27796
-▁EB	-27797
-mmmm	-27798
-▁xox	-27799
-House	-27800
-▁germs	-27801
-avilion	-27802
-▁scrape	-27803
-▁Lindsay	-27804
-▁Towards	-27805
-▁traitor	-27806
-▁enclosure	-27807
-▁Definition	-27808
-▁misdemeanor	-27809
-▁unconditional	-27810
-vak	-27811
-▁MET	-27812
-aurus	-27813
-eners	-27814
-washed	-27815
-▁Asset	-27816
-▁Ubuntu	-27817
-▁negatives	-27818
-▁hurricanes	-27819
-BLIC	-27820
-▁Fen	-27821
-▁Alexa	-27822
-▁Silence	-27823
-⠀⠀⠀⠀⠀⠀⠀⠀	-27824
-▁coronary	-27825
-▁Rodriguez	-27826
-Tis	-27827
-axis	-27828
-▁HOT	-27829
-▁mats	-27830
-amount	-27831
-▁Gmail	-27832
-success	-27833
-▁arcade	-27834
-▁Gregory	-27835
-▁Suicide	-27836
-▁unlucky	-27837
-▁influenza	-27838
-▁permissible	-27839
-▁Participation	-27840
-cig	-27841
-osi	-27842
-young	-27843
-▁isnt	-27844
-google	-27845
-▁grate	-27846
-▁Luxury	-27847
-▁thwart	-27848
-▁cervical	-27849
-▁acknowledgement	-27850
-▁LG	-27851
-▁McD	-27852
-▁Morr	-27853
-▁brag	-27854
-izards	-27855
-▁THREE	-27856
-▁cavity	-27857
-▁scaled	-27858
-olutions	-27859
-▁saddened	-27860
-▁Difficult	-27861
-▁Traveling	-27862
-▁adjoining	-27863
-cold	-27864
-▁WOM	-27865
-▁vaginal	-27866
-▁Monsieur	-27867
-▁apprenticeship	-27868
-Having	-27869
-▁ether	-27870
-▁doable	-27871
-▁voiced	-27872
-▁kittens	-27873
-▁indebted	-27874
-interested	-27875
-▁parchment	-27876
-▁Cooperation	-27877
-Hub	-27878
-lime	-27879
-▁NPR	-27880
-▁Voy	-27881
-▁whisky	-27882
-▁Nie	-27883
-▁omin	-27884
-▁perenn	-27885
-National	-27886
-▁tracker	-27887
-▁APP	-27888
-▁DUI	-27889
-▁bingo	-27890
-▁undue	-27891
-cutting	-27892
-▁Matrix	-27893
-▁condoms	-27894
-▁flatten	-27895
-▁obscene	-27896
-▁Improving	-27897
-▁dwellings	-27898
-inous	-27899
-▁Sask	-27900
-▁THAN	-27901
-adjust	-27902
-▁Incredible	-27903
-Top	-27904
-athed	-27905
-uitive	-27906
-▁hepat	-27907
-▁Sunset	-27908
-Learning	-27909
-▁agendas	-27910
-▁rejecting	-27911
-▁concluding	-27912
-▁disple	-27913
-▁delinqu	-27914
-▁erectile	-27915
-▁eliminates	-27916
-pod	-27917
-▁AH	-27918
-▁TOP	-27919
-▁revert	-27920
-▁prepaid	-27921
-Def	-27922
-▁Nex	-27923
-▁drip	-27924
-▁lull	-27925
-angles	-27926
-▁Brend	-27927
-▁Pokemon	-27928
-▁pollutants	-27929
-iona	-27930
-▁Dow	-27931
-▁Fay	-27932
-▁Taste	-27933
-▁clinging	-27934
-▁painless	-27935
-▁intimidation	-27936
-Cor	-27937
-▁LT	-27938
-antis	-27939
-▁Argu	-27940
-▁Providence	-27941
-▁allegation	-27942
-etus	-27943
-▁CPS	-27944
-▁Ware	-27945
-▁Soccer	-27946
-▁embarked	-27947
-▁poisoned	-27948
-▁coursework	-27949
-▁effortless	-27950
-jac	-27951
-afil	-27952
-Women	-27953
-▁timid	-27954
-▁Archer	-27955
-▁Profit	-27956
-▁skepticism	-27957
-Da	-27958
-▁STE	-27959
-▁USC	-27960
-▁Marty	-27961
-perture	-27962
-▁Escape	-27963
-▁blurred	-27964
-▁shameful	-27965
-▁Household	-27966
-▁absorbing	-27967
-▁irresistible	-27968
-▁Buch	-27969
-August	-27970
-ocular	-27971
-▁proportional	-27972
-oku	-27973
-rev	-27974
-zip	-27975
-itled	-27976
-▁bans	-27977
-estinal	-27978
-eeee	-27979
-▁Aww	-27980
-▁Tas	-27981
-▁Vern	-27982
-▁multif	-27983
-▁Newsletter	-27984
-olyn	-27985
-▁CLE	-27986
-▁Coca	-27987
-▁Wool	-27988
-buster	-27989
-▁amaze	-27990
-▁Tobacco	-27991
-▁salvage	-27992
-▁extracts	-27993
-▁Registered	-27994
-▁showcasing	-27995
-fing	-27996
-▁flora	-27997
-▁whore	-27998
-▁brewery	-27999
-▁falsely	-28000
-▁electorate	-28001
-▁condominium	-28002
-rica	-28003
-ulic	-28004
-▁Fry	-28005
-▁Leah	-28006
-▁Tanz	-28007
-▁astr	-28008
-▁Caval	-28009
-▁agility	-28010
-regulation	-28011
-▁Tiny	-28012
-▁ping	-28013
-▁dares	-28014
-▁Flickr	-28015
-▁Monroe	-28016
-▁termed	-28017
-▁despise	-28018
-▁piercing	-28019
-▁objectively	-28020
-guy	-28021
-▁Bone	-28022
-▁Cassie	-28023
-▁converts	-28024
-▁PRESIDENT	-28025
-▁warranted	-28026
-development	-28027
-STEM	-28028
-▁Ike	-28029
-▁Malta	-28030
-▁tilted	-28031
-▁Griffin	-28032
-▁cunning	-28033
-▁fingerprint	-28034
-Max	-28035
-okay	-28036
-assis	-28037
-▁Reds	-28038
-atemal	-28039
-▁ageing	-28040
-▁viability	-28041
-▁facilitated	-28042
-▁Contemporary	-28043
-uph	-28044
-▁NW	-28045
-INESS	-28046
-▁Whis	-28047
-▁whine	-28048
-▁errone	-28049
-▁steals	-28050
-▁starvation	-28051
-pc	-28052
-€	-28053
-▁GI	-28054
-▁GW	-28055
-actor	-28056
-▁Jung	-28057
-▁STATE	-28058
-▁swipe	-28059
-▁jailed	-28060
-▁Tribune	-28061
-▁hugging	-28062
-▁Infantry	-28063
-DO	-28064
-▁FCC	-28065
-▁paw	-28066
-▁Goes	-28067
-▁hone	-28068
-▁fluff	-28069
-▁hinted	-28070
-▁serene	-28071
-▁rumours	-28072
-dependent	-28073
-▁ruthless	-28074
-▁protector	-28075
-▁sidelines	-28076
-▁levy	-28077
-▁Scand	-28078
-▁reflux	-28079
-▁Everyday	-28080
-▁expelled	-28081
-▁persisted	-28082
-▁tiring	-28083
-▁unborn	-28084
-▁thereto	-28085
-▁Contractor	-28086
-culosis	-28087
-▁fluent	-28088
-▁moderately	-28089
-▁sensations	-28090
-▁dangerously	-28091
-LT	-28092
-▁Nem	-28093
-▁blending	-28094
-▁insanely	-28095
-:.	-28096
-▁adjud	-28097
-▁buckets	-28098
-▁Chambers	-28099
-nom	-28100
-▁RB	-28101
-▁MEM	-28102
-▁Rut	-28103
-Cloud	-28104
-retch	-28105
-wagon	-28106
-agogue	-28107
-▁Error	-28108
-▁weave	-28109
-▁armour	-28110
-▁boycott	-28111
-▁scarcity	-28112
-▁fisheries	-28113
-▁Resistance	-28114
-▁Era	-28115
-▁Angie	-28116
-▁perme	-28117
-▁Warrant	-28118
-▁Sach	-28119
-▁Smile	-28120
-▁mixes	-28121
-▁scold	-28122
-▁Conven	-28123
-research	-28124
-▁Fathers	-28125
-▁solicit	-28126
-▁massages	-28127
-▁recorder	-28128
-edo	-28129
-aith	-28130
-esan	-28131
-▁gob	-28132
-▁COULD	-28133
-▁curls	-28134
-▁eBook	-28135
-▁Celest	-28136
-▁starve	-28137
-▁Vanessa	-28138
-▁campers	-28139
-▁repentance	-28140
-▁brilliantly	-28141
-▁confinement	-28142
-▁Promote	-28143
-▁Contrary	-28144
-▁disclaimer	-28145
-▁responders	-28146
-▁waterfront	-28147
-▁Optimization	-28148
-▁Sr	-28149
-strom	-28150
-▁lava	-28151
-employ	-28152
-▁fandom	-28153
-▁Equality	-28154
-▁battered	-28155
-▁numerical	-28156
-▁kidnapping	-28157
-▁Nig	-28158
-▁Rib	-28159
-paralleled	-28160
-▁redevelop	-28161
-▁unintended	-28162
-▁LAST	-28163
-ukemia	-28164
-▁gravely	-28165
-▁outsider	-28166
-▁affordability	-28167
-Ac	-28168
-ews	-28169
-Blue	-28170
-▁Hole	-28171
-▁mango	-28172
-prehens	-28173
-▁referee	-28174
-▁Required	-28175
-▁obedient	-28176
-▁doubtless	-28177
-mult	-28178
-emerg	-28179
-ogene	-28180
-▁Serena	-28181
-▁frenzy	-28182
-PI	-28183
-phen	-28184
-▁Katy	-28185
-December	-28186
-▁GRE	-28187
-▁Isab	-28188
-usional	-28189
-▁snapping	-28190
-▁webmasters	-28191
-▁Wals	-28192
-ensible	-28193
-▁mystical	-28194
-▁resisting	-28195
-▁detectives	-28196
-Damn	-28197
-▁Bye	-28198
-▁Mia	-28199
-▁THESE	-28200
-▁Hopkins	-28201
-▁graphical	-28202
-▁FYI	-28203
-▁chemo	-28204
-▁Sample	-28205
-▁parody	-28206
-▁Goddess	-28207
-▁everytime	-28208
-▁blogosphere	-28209
-▁helicopters	-28210
-▁unnecessarily	-28211
-fy	-28212
-OTE	-28213
-harm	-28214
-▁melod	-28215
-Business	-28216
-▁rooting	-28217
-▁innovate	-28218
-fle	-28219
-▁Yang	-28220
-▁rheum	-28221
-▁artery	-28222
-CLUD	-28223
-▁outta	-28224
-▁exhilar	-28225
-▁Covenant	-28226
-▁paperback	-28227
-!*	-28228
-afety	-28229
-taker	-28230
-▁Vacc	-28231
-▁resh	-28232
-digital	-28233
-▁unconventional	-28234
-Jo	-28235
-uy	-28236
-iso	-28237
-▁wob	-28238
-▁Acid	-28239
-▁mandates	-28240
-▁domination	-28241
-▁unrestricted	-28242
-▁groundbreaking	-28243
-▁sar	-28244
-▁gimm	-28245
-▁arist	-28246
-▁hemorr	-28247
-▁textile	-28248
-▁purposely	-28249
-▁electromagnetic	-28250
-▁bac	-28251
-wrong	-28252
-aughing	-28253
-uprofen	-28254
-▁HEALTH	-28255
-▁Guatemal	-28256
-▁centralized	-28257
-IVER	-28258
-▁carve	-28259
-▁brutally	-28260
-▁managerial	-28261
-▁grav	-28262
-▁Poker	-28263
-▁Satis	-28264
-▁gamer	-28265
-serving	-28266
-▁lowers	-28267
-▁sufferers	-28268
-Nice	-28269
-ipers	-28270
-▁unres	-28271
-▁sprung	-28272
-▁Erik	-28273
-▁pund	-28274
-▁Xander	-28275
-▁pasture	-28276
-▁outraged	-28277
-▁coastline	-28278
-▁provoking	-28279
-▁stressing	-28280
-awl	-28281
-Save	-28282
-abad	-28283
-▁palp	-28284
-driver	-28285
-▁embar	-28286
-▁plaster	-28287
-▁adherence	-28288
-▁inhabited	-28289
-▁occupying	-28290
-▁photographing	-28291
-AKING	-28292
-▁Luis	-28293
-▁racks	-28294
-▁veggie	-28295
-▁workmanship	-28296
-CEPT	-28297
-anson	-28298
-▁Spice	-28299
-▁Confirm	-28300
-▁Password	-28301
-▁Websites	-28302
-▁eclectic	-28303
-▁gardener	-28304
-▁inflated	-28305
-▁surrendered	-28306
-▁coy	-28307
-▁Jeep	-28308
-umably	-28309
-▁Lines	-28310
-▁franc	-28311
-ordinary	-28312
-▁radicals	-28313
-▁smartest	-28314
-▁firsthand	-28315
-▁Montgomery	-28316
-▁LB	-28317
-enic	-28318
-▁Elig	-28319
-▁Songs	-28320
-icapped	-28321
-othermal	-28322
-▁peasant	-28323
-▁reluctance	-28324
-▁crowdfunding	-28325
-Cr	-28326
-▁enrol	-28327
-▁easing	-28328
-▁graceful	-28329
-▁secluded	-28330
-Real	-28331
-▁ERA	-28332
-celain	-28333
-▁Fully	-28334
-▁Inner	-28335
-▁hurdle	-28336
-▁Jupiter	-28337
-▁admiring	-28338
-▁airplanes	-28339
-▁tenderness	-28340
-▁Ecu	-28341
-orrows	-28342
-▁overwhelm	-28343
-▁stipulated	-28344
-_^	-28345
-Trump	-28346
-▁Crus	-28347
-▁vind	-28348
-▁Cairo	-28349
-▁Maple	-28350
-cycling	-28351
-▁crafty	-28352
-▁bouquet	-28353
-▁Developer	-28354
-AF	-28355
-umbo	-28356
-hagen	-28357
-▁Gina	-28358
-▁Kane	-28359
-▁ebay	-28360
-▁mare	-28361
-▁blaze	-28362
-▁Freddie	-28363
-awareness	-28364
-▁Included	-28365
-▁override	-28366
-▁mozzarella	-28367
-▁condemnation	-28368
-doctor	-28369
-ussian	-28370
-▁EXACT	-28371
-▁Curtis	-28372
-▁Alexand	-28373
-functional	-28374
-▁finalized	-28375
-▁conspicuous	-28376
-▁Castro	-28377
-▁revolving	-28378
-▁debilitating	-28379
-IH	-28380
-▁mp	-28381
-▁Henri	-28382
-crafted	-28383
-▁Savage	-28384
-November	-28385
-catching	-28386
-▁vacancies	-28387
-Aw	-28388
-trust	-28389
-together	-28390
-▁elaborating	-28391
-▁cybersecurity	-28392
-Foot	-28393
-Start	-28394
-▁Divide	-28395
-▁Likely	-28396
-▁relish	-28397
-▁steril	-28398
-▁affidav	-28399
-▁brighten	-28400
-▁cushions	-28401
-▁skyrocket	-28402
-▁enlightenment	-28403
-ti	-28404
-▁Audrey	-28405
-▁horrend	-28406
-▁Bringing	-28407
-▁smoothies	-28408
-▁commissioners	-28409
-pty	-28410
-▁Jewel	-28411
-▁shrubs	-28412
-▁parsley	-28413
-▁unfolding	-28414
-▁Pont	-28415
-▁Cotton	-28416
-▁longed	-28417
-▁resumes	-28418
-▁Capacity	-28419
-▁assaults	-28420
-▁exposures	-28421
-▁proverbial	-28422
-INS	-28423
-▁sap	-28424
-▁choking	-28425
-▁doctoral	-28426
-▁receptive	-28427
-▁untouched	-28428
-Page	-28429
-posts	-28430
-▁aura	-28431
-having	-28432
-▁crest	-28433
-aternal	-28434
-▁divert	-28435
-▁boating	-28436
-▁bundles	-28437
-▁witches	-28438
-▁estrogen	-28439
-▁Procedures	-28440
-▁bipartisan	-28441
-!!"	-28442
-irer	-28443
-▁Vul	-28444
-veyard	-28445
-▁Booking	-28446
-▁Emotional	-28447
-▁Pakistani	-28448
-tie	-28449
-▁Kap	-28450
-acted	-28451
-▁flea	-28452
-▁rusty	-28453
-▁Hosting	-28454
-▁broaden	-28455
-▁robotic	-28456
-▁handheld	-28457
-▁intently	-28458
-▁descending	-28459
-▁dissatisfied	-28460
-nz	-28461
-▁Lip	-28462
-▁Saul	-28463
-▁Vend	-28464
-heddar	-28465
-▁sinks	-28466
-▁electr	-28467
-▁turtles	-28468
-oux	-28469
-stri	-28470
-▁docs	-28471
-▁conceded	-28472
-▁guitarist	-28473
-▁unsubscribe	-28474
-move	-28475
-▁dudes	-28476
-▁Africans	-28477
-▁apprentice	-28478
-▁unquestionably	-28479
-UB	-28480
-kar	-28481
-▁ado	-28482
-imson	-28483
-▁Rabbi	-28484
-▁folds	-28485
-▁blurry	-28486
-▁radiant	-28487
-▁customizable	-28488
-▁depreciation	-28489
-▁transitional	-28490
-▁vaccinations	-28491
-▁Iv	-28492
-rowing	-28493
-▁Sunny	-28494
-▁hailed	-28495
-▁retard	-28496
-▁replica	-28497
-▁ignition	-28498
-▁Ans	-28499
-▁PPC	-28500
-forum	-28501
-iever	-28502
-▁Bagh	-28503
-▁Woah	-28504
-▁fanci	-28505
-▁Bonnie	-28506
-▁raffle	-28507
-▁bastards	-28508
-▁plotting	-28509
-▁capitalize	-28510
-▁nonprofits	-28511
-heit	-28512
-▁BAD	-28513
-▁Gap	-28514
-▁IoT	-28515
-▁Clos	-28516
-▁invade	-28517
-▁supplemental	-28518
-▁schizophrenia	-28519
-icone	-28520
-washing	-28521
-▁Stella	-28522
--,	-28523
-▁Percy	-28524
-▁bogus	-28525
-paragus	-28526
-▁engross	-28527
-▁unheard	-28528
-▁Midnight	-28529
-▁visualization	-28530
-▁civ	-28531
-▁sill	-28532
-factory	-28533
-▁roaring	-28534
-▁optimizing	-28535
-▁synonymous	-28536
-▁Incidentally	-28537
-hrs	-28538
-▁ICE	-28539
-Green	-28540
-▁spas	-28541
-▁worldly	-28542
-▁pulmonary	-28543
-▁unparalleled	-28544
-nn	-28545
-areth	-28546
-stores	-28547
-▁Responses	-28548
-▁Workplace	-28549
-▁jot	-28550
-allels	-28551
-▁Bride	-28552
-▁Comedy	-28553
-▁Comics	-28554
-▁Shared	-28555
-▁elites	-28556
-▁jargon	-28557
-▁predic	-28558
-▁constrained	-28559
-▁nanny	-28560
-▁shrie	-28561
-correct	-28562
-▁Babylon	-28563
-▁sprouts	-28564
-▁landfill	-28565
-▁leveraging	-28566
-▁mosquitoes	-28567
-URN	-28568
-▁Wang	-28569
-▁Patch	-28570
-▁spikes	-28571
-▁Parenthood	-28572
-▁Cul	-28573
-▁Meyer	-28574
-▁evapor	-28575
-▁knocks	-28576
-▁EVERYONE	-28577
-▁spanning	-28578
-▁subclass	-28579
-▁Territory	-28580
-▁disguised	-28581
-▁underwent	-28582
-Ev	-28583
-orting	-28584
-▁Shoes	-28585
-▁guitars	-28586
-▁honoring	-28587
-▁accelerating	-28588
-Talk	-28589
-▁Fah	-28590
-▁refin	-28591
-▁rockets	-28592
-thus	-28593
-arers	-28594
-▁Gonz	-28595
-▁mend	-28596
-▁hopped	-28597
-▁Mondays	-28598
-rov	-28599
-oxic	-28600
-▁gul	-28601
-▁Stark	-28602
-▁fares	-28603
-ferably	-28604
-umbered	-28605
-▁trench	-28606
-▁magnets	-28607
-▁dolphins	-28608
-wd	-28609
-enne	-28610
-▁EAS	-28611
-▁REM	-28612
-enzie	-28613
-anical	-28614
-▁Devon	-28615
-▁inbound	-28616
-▁Fighting	-28617
-GG	-28618
-▁((	-28619
-▁BAS	-28620
-▁Elli	-28621
-▁Nikon	-28622
-▁Teddy	-28623
-▁habitats	-28624
-▁governors	-28625
-Hon	-28626
-▁clad	-28627
-▁ench	-28628
-▁sands	-28629
-▁filler	-28630
-▁Kenneth	-28631
-▁infancy	-28632
-▁stereoty	-28633
-▁Il	-28634
-rosis	-28635
-▁Fiji	-28636
-▁Vince	-28637
-▁Cannot	-28638
-▁Harley	-28639
-▁cerebral	-28640
-▁distortion	-28641
-▁excursions	-28642
-save	-28643
-▁Elena	-28644
-▁Racing	-28645
-▁Hawaiian	-28646
-▁regression	-28647
-▁obstruction	-28648
-bnb	-28649
-▁Sie	-28650
-▁Names	-28651
-▁supra	-28652
-▁improv	-28653
-▁diagrams	-28654
-▁hardships	-28655
-▁astonishment	-28656
-OB	-28657
-OOL	-28658
-berculosis	-28659
-▁detecting	-28660
-▁benefiting	-28661
-▁regeneration	-28662
-▁CW	-28663
-▁fidd	-28664
-asmine	-28665
-▁Hunger	-28666
-▁Judging	-28667
-▁Visitor	-28668
-▁crochet	-28669
-▁adhesive	-28670
-▁allotted	-28671
-▁Furniture	-28672
-▁requisite	-28673
-▁steadfast	-28674
-▁toiletries	-28675
-enh	-28676
-▁SUN	-28677
-▁bere	-28678
-▁sighs	-28679
-▁Motors	-28680
-▁incess	-28681
-▁foliage	-28682
-▁urinary	-28683
-▁athletics	-28684
-▁melancholy	-28685
-▁undercover	-28686
-▁philosophers	-28687
-Rom	-28688
-▁NB	-28689
-Paul	-28690
-film	-28691
-▁Carp	-28692
-▁wits	-28693
-Follow	-28694
-cierge	-28695
-▁Fiscal	-28696
-▁Shower	-28697
-financial	-28698
-▁motivates	-28699
-▁narratives	-28700
-▁preparedness	-28701
-▁Tuc	-28702
-▁WELL	-28703
-▁mimic	-28704
-checked	-28705
-▁Persian	-28706
-▁childbirth	-28707
-hess	-28708
-▁tug	-28709
-heavy	-28710
-▁(...)	-28711
-▁Arabs	-28712
-▁Rough	-28713
-optional	-28714
-compliance	-28715
-▁criterion	-28716
-▁LIM	-28717
-organic	-28718
-▁dashed	-28719
-▁Depends	-28720
-▁Mothers	-28721
-▁disqual	-28722
-▁―	-28723
-ATA	-28724
-ithub	-28725
-▁Infl	-28726
-▁Belgian	-28727
-▁Created	-28728
-▁Croatia	-28729
-▁Divorce	-28730
-▁mocking	-28731
-▁responsibly	-28732
-▁authoritarian	-28733
-▁repercussions	-28734
-▁Cros	-28735
-▁Tory	-28736
-▁bots	-28737
-▁Units	-28738
-▁trout	-28739
-▁enzyme	-28740
-▁blazing	-28741
-▁weaving	-28742
-▁complexities	-28743
-chairs	-28744
-▁Bunny	-28745
-▁freaked	-28746
-▁lighten	-28747
-▁Colleges	-28748
-▁gratification	-28749
-kia	-28750
-▁Rig	-28751
-▁Thrones	-28752
-▁filmmaker	-28753
-communication	-28754
-▁theoretically	-28755
-bard	-28756
-isol	-28757
-▁Trent	-28758
-▁MIL	-28759
-anmar	-28760
-avers	-28761
-idian	-28762
-akings	-28763
-▁Paypal	-28764
-equipped	-28765
-▁progressively	-28766
-AUSE	-28767
-VING	-28768
-icus	-28769
-▁feud	-28770
-▁Jenna	-28771
-Welcome	-28772
-▁Terrific	-28773
-▁atrocities	-28774
-▁multicultural	-28775
-Long	-28776
-hhhh	-28777
-▁whoah	-28778
-inceton	-28779
-▁sugary	-28780
-▁Victory	-28781
-▁tackled	-28782
-▁prohibits	-28783
-rious	-28784
-▁Tribe	-28785
-▁brute	-28786
-▁disdain	-28787
-▁nineteen	-28788
-▁additives	-28789
-▁curiously	-28790
-▁pervasive	-28791
-▁Translation	-28792
-eto	-28793
-xton	-28794
-▁PLAY	-28795
-▁Winn	-28796
-▁disks	-28797
-▁lofty	-28798
-▁turbo	-28799
-▁vibes	-28800
-▁secondly	-28801
-▁transgress	-28802
-▁straightened	-28803
-▁GOT	-28804
-▁tet	-28805
-▁Caleb	-28806
-▁........	-28807
-▁commenter	-28808
-▁circumcision	-28809
-awi	-28810
-rea	-28811
-REAM	-28812
-azole	-28813
-▁Manit	-28814
-▁Polly	-28815
-▁Spart	-28816
-▁nasal	-28817
-▁biases	-28818
-▁erased	-28819
-▁preserv	-28820
-▁optimisation	-28821
-ETS	-28822
-ishi	-28823
-▁Ris	-28824
-▁XML	-28825
-▁york	-28826
-played	-28827
-▁Piper	-28828
-▁migraine	-28829
-kick	-28830
-ongs	-28831
-ovie	-28832
-▁stre	-28833
-manuel	-28834
-▁Pattern	-28835
-,”	-28836
-▁SV	-28837
-▁Heh	-28838
-▁SOM	-28839
-ibble	-28840
-visual	-28841
-▁encycl	-28842
-▁leases	-28843
-ographers	-28844
-▁membrane	-28845
-▁unanimous	-28846
-▁policymakers	-28847
-Leave	-28848
-▁voic	-28849
-atered	-28850
-olitical	-28851
-▁countdown	-28852
-▁exploding	-28853
-▁registers	-28854
-▁thermometer	-28855
-▁whereabouts	-28856
-/)	-28857
-quick	-28858
-within	-28859
-anguard	-28860
-▁helper	-28861
-▁kiddos	-28862
-▁selfie	-28863
-▁Morrison	-28864
-▁snatched	-28865
-accessible	-28866
-▁compressor	-28867
-▁osteoporosis	-28868
-gien	-28869
-allow	-28870
-▁tuck	-28871
-▁tally	-28872
-Without	-28873
-▁robber	-28874
-[...]...	-28875
-▁persuasion	-28876
-bia	-28877
-idis	-28878
-▁Gor	-28879
-▁Mul	-28880
-ennel	-28881
-odore	-28882
-eating	-28883
-pically	-28884
-▁glitch	-28885
-▁snorke	-28886
-▁roadmap	-28887
-▁Councill	-28888
-▁Overview	-28889
-King	-28890
-ifax	-28891
-▁ale	-28892
-▁Bash	-28893
-▁scoff	-28894
-▁spying	-28895
-▁trenches	-28896
-▁registrar	-28897
-▁remembrance	-28898
-umsy	-28899
-▁Bean	-28900
-▁CEOs	-28901
-▁Denise	-28902
-▁boldly	-28903
-▁utensils	-28904
-▁homestead	-28905
-▁Established	-28906
-oka	-28907
-▁EA	-28908
-▁Simpl	-28909
-▁Database	-28910
-▁currents	-28911
-FECT	-28912
-▁Sho	-28913
-ikers	-28914
-▁stale	-28915
-▁attest	-28916
-▁militia	-28917
-’)	-28918
-illions	-28919
-▁Resident	-28920
-▁interstate	-28921
-▁Kon	-28922
-negot	-28923
-Excuse	-28924
-▁Dirty	-28925
-▁Tears	-28926
-▁Phillip	-28927
-▁dresser	-28928
-adi	-28929
-ivol	-28930
-▁Ginny	-28931
-▁chiefs	-28932
-fighting	-28933
-▁dazzling	-28934
-▁shedding	-28935
-▁supervise	-28936
-▁correctness	-28937
-▁configurations	-28938
-each	-28939
-racks	-28940
-ysics	-28941
-▁Floyd	-28942
-▁bibli	-28943
-▁Ecuador	-28944
-▁sparkle	-28945
-▁brutality	-28946
-▁squirrels	-28947
-▁allegiance	-28948
-▁LD	-28949
-▁STUD	-28950
-▁drains	-28951
-intestinal	-28952
-▁Auth	-28953
-▁rhyme	-28954
-Station	-28955
-▁Hockey	-28956
-▁sender	-28957
-▁cavalry	-28958
-▁rivalry	-28959
-▁utilizes	-28960
-▁advancements	-28961
-▁ABS	-28962
-▁ENT	-28963
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-▁Bast	-28965
-▁Bros	-28966
-teacher	-28967
-▁Aikido	-28968
-▁Appoint	-28969
-▁plantation	-28970
-▁championships	-28971
-noons	-28972
-▁Maid	-28973
-▁Agile	-28974
-▁chast	-28975
-▁exits	-28976
-▁stren	-28977
-▁shrine	-28978
-▁smoker	-28979
-▁Spotify	-28980
-▁insulted	-28981
-▁Integrated	-28982
-▁workplaces	-28983
-gam	-28984
-▁Jin	-28985
-▁NOR	-28986
-▁Sanct	-28987
-▁décor	-28988
-▁Elaine	-28989
-▁expiry	-28990
-▁grasped	-28991
-▁realism	-28992
-▁Crawford	-28993
-▁metadata	-28994
-▁withheld	-28995
-area	-28996
-▁decip	-28997
-article	-28998
-▁fiancé	-28999
-▁safari	-29000
-▁Curious	-29001
-▁predicts	-29002
-▁counterfe	-29003
-▁MALE	-29004
-claimed	-29005
-▁charms	-29006
-▁valleys	-29007
-▁Highland	-29008
-▁conscient	-29009
-▁cartridges	-29010
-▁Reb	-29011
-▁ador	-29012
-▁Calif	-29013
-▁Cater	-29014
-HINGTON	-29015
-▁Plants	-29016
-▁psyche	-29017
-▁Brighton	-29018
-▁auditing	-29019
-▁prestige	-29020
-enta	-29021
-▁Kimber	-29022
-▁Desktop	-29023
-▁renters	-29024
-▁visibly	-29025
-▁harassed	-29026
-▁retarded	-29027
-ruce	-29028
-▁Bacon	-29029
-▁Weeks	-29030
-▁Singer	-29031
-▁pennies	-29032
-▁librarian	-29033
-▁petitions	-29034
-▁readership	-29035
-▁unbearable	-29036
-▁sightseeing	-29037
-▁chiropractic	-29038
-▁Tried	-29039
-uxiliary	-29040
-▁leisurely	-29041
-▁Protestant	-29042
-▁wholeheartedly	-29043
-borah	-29044
-▁ranc	-29045
-▁Tales	-29046
-percent	-29047
-▁UNESCO	-29048
-▁MacBook	-29049
-▁Elections	-29050
-▁paralyzed	-29051
-▁cheerfully	-29052
-acts	-29053
-▁nar	-29054
-claim	-29055
-crime	-29056
-▁bonded	-29057
-▁teamed	-29058
-▁Musical	-29059
-▁headset	-29060
-establish	-29061
-▁apprehens	-29062
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-▁Por	-29065
-achine	-29066
-▁entice	-29067
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-▁Holt	-29069
-▁Turt	-29070
-▁Raise	-29071
-▁heaps	-29072
-▁strat	-29073
-▁drummer	-29074
-▁MADD	-29075
-cooked	-29076
-idences	-29077
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-▁deepen	-29079
-▁flowed	-29080
-▁Situated	-29081
-▁reasoned	-29082
-▁advocated	-29083
-▁missionaries	-29084
-hig	-29085
-coins	-29086
-▁Paying	-29087
-▁aching	-29088
-▁loosen	-29089
-▁Baghdad	-29090
-▁migrate	-29091
-▁sinners	-29092
-▁deceptive	-29093
-South	-29094
-study	-29095
-Israel	-29096
-Mobile	-29097
-▁Carly	-29098
-▁Whitney	-29099
-▁_______	-29100
-▁blooming	-29101
-▁happenings	-29102
-▁vigorously	-29103
-FM	-29104
-tv	-29105
-).”	-29106
-isans	-29107
-antage	-29108
-▁badges	-29109
-▁sexist	-29110
-▁Heating	-29111
-▁External	-29112
-▁pumpkins	-29113
-▁interrogation	-29114
-▁Actions	-29115
-▁Regulatory	-29116
-▁Gosh	-29117
-▁Idol	-29118
-Connor	-29119
-runner	-29120
-▁gland	-29121
-▁dotted	-29122
-▁fetish	-29123
-▁summons	-29124
-uler	-29125
-▁Alien	-29126
-▁ludic	-29127
-charging	-29128
-▁retaliation	-29129
-STR	-29130
-utral	-29131
-idently	-29132
-itimate	-29133
-▁Gaming	-29134
-▁Winning	-29135
-terrorism	-29136
-▁Proposal	-29137
-▁immature	-29138
-▁blackmail	-29139
-ggles	-29140
-▁Tale	-29141
-▁HAPPY	-29142
-▁Wesley	-29143
-▁inexpl	-29144
-▁Bugscope	-29145
-▁tightened	-29146
-▁downloadable	-29147
-▁administering	-29148
-WW	-29149
-tre	-29150
-▁nob	-29151
-udging	-29152
-▁discer	-29153
-▁surrog	-29154
-▁Sod	-29155
-sense	-29156
-▁RSVP	-29157
-▁greasy	-29158
-▁grumpy	-29159
-▁Fernand	-29160
-▁Goldman	-29161
-▁stereotype	-29162
-▁pulp	-29163
-▁Wanna	-29164
-▁flair	-29165
-▁Cheryl	-29166
-▁squeezing	-29167
-appreciated	-29168
-▁undeniable	-29169
-▁Recommended	-29170
-Mag	-29171
-▁JJ	-29172
-rait	-29173
-grace	-29174
-▁Anch	-29175
-▁Mega	-29176
-▁rave	-29177
-▁renal	-29178
-▁Winner	-29179
-▁bruises	-29180
-▁youngster	-29181
-▁fertilizer	-29182
-▁similarity	-29183
-▁toothbrush	-29184
-aspx	-29185
-▁Barr	-29186
-▁Kard	-29187
-▁LIST	-29188
-▁Melanie	-29189
-▁nightly	-29190
-▁parasites	-29191
-▁reassured	-29192
-▁Performing	-29193
-azon	-29194
-advent	-29195
-▁Bryant	-29196
-▁cellul	-29197
-▁integer	-29198
-▁demolished	-29199
-▁microscope	-29200
-▁Underground	-29201
-▁convertible	-29202
-▁Hugo	-29203
-▁PROD	-29204
-credit	-29205
-▁Railway	-29206
-▁refining	-29207
-▁realising	-29208
-▁hospitalized	-29209
-▁homeschooling	-29210
-▁Spy	-29211
-▁Madam	-29212
-▁Astron	-29213
-▁joyous	-29214
-▁plumber	-29215
-▁Exhibition	-29216
-▁intermittent	-29217
-▁JF	-29218
-▁Fib	-29219
-model	-29220
-▁Cork	-29221
-Social	-29222
-posing	-29223
-▁sedan	-29224
-▁thugs	-29225
-▁rebound	-29226
-▁carnival	-29227
-▁drawback	-29228
-▁gripping	-29229
-▁Wellington	-29230
-▁standalone	-29231
-cases	-29232
-▁Gret	-29233
-▁peep	-29234
-Better	-29235
-▁shocks	-29236
-▁outputs	-29237
-▁Swimming	-29238
-▁withhold	-29239
-aney	-29240
-fuck	-29241
-▁yay	-29242
-idson	-29243
-▁robes	-29244
-▁ticks	-29245
-▁marrow	-29246
-▁majestic	-29247
-▁corridors	-29248
-▁Ore	-29249
-▁Nikki	-29250
-ouching	-29251
-▁PowerPoint	-29252
-▁Ri	-29253
-▁Pean	-29254
-▁slut	-29255
-ouncer	-29256
-▁Chang	-29257
-▁resin	-29258
-▁booths	-29259
-▁Cutting	-29260
-▁plummet	-29261
-▁arrivals	-29262
-▁auctions	-29263
-▁retrieved	-29264
-▁Frequently	-29265
-iaz	-29266
-pun	-29267
-▁fave	-29268
-▁Acting	-29269
-▁ironically	-29270
-asse	-29271
-▁Wik	-29272
-phalt	-29273
-Direct	-29274
-astered	-29275
-▁primer	-29276
-aculture	-29277
-▁ethanol	-29278
-▁jogging	-29279
-▁Vladimir	-29280
-▁rambling	-29281
-▁distressing	-29282
-▁contraception	-29283
-meg	-29284
-rums	-29285
-...).	-29286
-someone	-29287
-▁Beneath	-29288
-▁pleases	-29289
-etermined	-29290
-▁hepatitis	-29291
-oved	-29292
-▁slain	-29293
-▁Lisbon	-29294
-critical	-29295
-directed	-29296
-▁dipping	-29297
-▁revived	-29298
-▁remnants	-29299
-▁scandals	-29300
-▁eCommerce	-29301
-▁overthrow	-29302
-EEP	-29303
-pels	-29304
-▁Sah	-29305
-▁Deleg	-29306
-▁freeing	-29307
-bh	-29308
-▁HQ	-29309
-▁Osw	-29310
-Public	-29311
-▁Finish	-29312
-▁Plaint	-29313
-▁thumbn	-29314
-▁steward	-29315
-▁adaptive	-29316
-▁reactive	-29317
-▁outsiders	-29318
-▁complexion	-29319
-▁minimizing	-29320
-azi	-29321
-hate	-29322
-▁hoof	-29323
-▁Anglo	-29324
-▁Sharp	-29325
-▁strapped	-29326
-▁murdering	-29327
-▁manifested	-29328
-▁Millennials	-29329
-▁Sustainability	-29330
-Vs	-29331
-anded	-29332
-▁Origin	-29333
-▁Rocket	-29334
-▁Martine	-29335
-▁nagging	-29336
-according	-29337
-▁retirees	-29338
-▁reversal	-29339
-▁imaginable	-29340
-▁infertility	-29341
-same	-29342
-urai	-29343
-▁Café	-29344
-▁COMPL	-29345
-▁coerc	-29346
-▁Values	-29347
-▁Coconut	-29348
-▁unequiv	-29349
-▁afflicted	-29350
-▁shortened	-29351
-▁Supporting	-29352
-▁aggravated	-29353
-▁infographic	-29354
-▁CCT	-29355
-ffles	-29356
-▁Crist	-29357
-▁Habit	-29358
-▁stout	-29359
-▁Needed	-29360
-▁cockpit	-29361
-▁growers	-29362
-ographies	-29363
-▁autoimmune	-29364
-▁countenance	-29365
-ippy	-29366
-vacc	-29367
-▁Neo	-29368
-▁onc	-29369
-▁Copper	-29370
-▁donkey	-29371
-▁leased	-29372
-▁Mohammed	-29373
-▁Objective	-29374
-▁envisioned	-29375
-▁dissemination	-29376
-ORN	-29377
-fight	-29378
-▁Marks	-29379
-▁Andrews	-29380
-▁goddamn	-29381
-▁shields	-29382
-▁strands	-29383
-▁Avengers	-29384
-▁hectares	-29385
-added	-29386
-esque	-29387
-▁moods	-29388
-▁Incorpor	-29389
-▁handicap	-29390
-▁interconnected	-29391
-▁Root	-29392
-▁prett	-29393
-entimes	-29394
-▁Poetry	-29395
-▁Governance	-29396
-Wed	-29397
-▁Tough	-29398
-▁progn	-29399
-marking	-29400
-▁Cookie	-29401
-▁soaring	-29402
-▁heartily	-29403
-▁magician	-29404
-▁plaintiffs	-29405
-cco	-29406
-▁MG	-29407
-▁Hyp	-29408
-▁Heidi	-29409
-visible	-29410
-▁adored	-29411
-▁Kristen	-29412
-▁enriched	-29413
-▁treasury	-29414
-▁lunchtime	-29415
-CI	-29416
-itles	-29417
-▁ALSO	-29418
-▁madam	-29419
-▁horrid	-29420
-▁hideous	-29421
-▁ancestor	-29422
-▁deterrent	-29423
-▁landmarks	-29424
-▁submarine	-29425
-▁Toys	-29426
-▁deton	-29427
-redited	-29428
-stanbul	-29429
-▁garner	-29430
-▁Frederick	-29431
-▁Fut	-29432
-▁Indy	-29433
-cation	-29434
-▁Gonna	-29435
-▁cramps	-29436
-▁Louisville	-29437
-▁extremists	-29438
-▁upbringing	-29439
-▁forecasting	-29440
-▁ze	-29441
-oeuv	-29442
-▁Carmen	-29443
-▁benches	-29444
-▁succinct	-29445
-▁residences	-29446
-▁antidepress	-29447
-ruly	-29448
-▁Liv	-29449
-brainer	-29450
-▁revolves	-29451
-▁clientele	-29452
-▁Wu	-29453
-baugh	-29454
-mbuds	-29455
-aterial	-29456
-________	-29457
-▁immersion	-29458
-Pass	-29459
-inoa	-29460
-▁MSN	-29461
-sighted	-29462
-▁solves	-29463
-▁deputies	-29464
-▁sidewalks	-29465
-▁Presidents	-29466
-▁hysterical	-29467
-drug	-29468
-▁Rug	-29469
-truth	-29470
-▁Gwen	-29471
-▁fuse	-29472
-▁taps	-29473
-▁wrought	-29474
-▁procession	-29475
-▁dysfunctional	-29476
-cash	-29477
-▁Rita	-29478
-▁Cases	-29479
-▁tresp	-29480
-▁Brooke	-29481
-▁wrongly	-29482
-▁bustling	-29483
-▁dictated	-29484
-▁swapping	-29485
-▁contested	-29486
-▁Bes	-29487
-▁RESP	-29488
-sector	-29489
-uttered	-29490
-olicited	-29491
-▁Princeton	-29492
-▁foreigner	-29493
-▁veterinarian	-29494
-▁gh	-29495
-ahan	-29496
-▁Jae	-29497
-hthal	-29498
-▁corp	-29499
-▁feder	-29500
-▁Tucker	-29501
-▁fuller	-29502
-velength	-29503
-▁Basketball	-29504
-▁VC	-29505
-▁FAM	-29506
-▁Mam	-29507
-▁url	-29508
-urban	-29509
-▁Cain	-29510
-▁vomit	-29511
-▁Yankee	-29512
-▁ironing	-29513
-▁bragging	-29514
-▁undergrad	-29515
-▁subparagraph	-29516
-ée	-29517
-▁Rw	-29518
-Stay	-29519
-▁Sword	-29520
-▁camel	-29521
-▁dissip	-29522
-▁cohesive	-29523
-▁delusional	-29524
-▁impeccable	-29525
-▁Commissioners	-29526
-▁registrations	-29527
-▁Identification	-29528
-USA	-29529
-▁TED	-29530
-▁Grill	-29531
-▁patted	-29532
-▁shaved	-29533
-▁Scandin	-29534
-▁frantic	-29535
-▁remodel	-29536
-▁Austrian	-29537
-interesting	-29538
-▁knelt	-29539
-▁agitated	-29540
-▁graffiti	-29541
-▁Stockholm	-29542
-▁subcontract	-29543
-▁predecessors	-29544
-OOOO	-29545
-▁TIT	-29546
-▁Patt	-29547
-▁ital	-29548
-▁tavern	-29549
-▁Protein	-29550
-▁humbled	-29551
-▁Worldwide	-29552
-▁complains	-29553
-▁stabilize	-29554
-▁linguistic	-29555
-▁internships	-29556
-uned	-29557
-▁CAS	-29558
-odder	-29559
-▁EVEN	-29560
-▁Hiro	-29561
-▁exor	-29562
-▁Assad	-29563
-▁Curry	-29564
-▁Pedro	-29565
-▁scorn	-29566
-essment	-29567
-▁aroused	-29568
-▁routing	-29569
-▁likeness	-29570
-▁fad	-29571
-avier	-29572
-▁metaph	-29573
-▁starch	-29574
-▁espresso	-29575
-▁excerpts	-29576
-▁fleeting	-29577
-▁trailing	-29578
-▁marshmall	-29579
-▁presenter	-29580
-▁preventable	-29581
-mos	-29582
-▁Va	-29583
-▁zo	-29584
-▁EFF	-29585
-▁Alic	-29586
-▁vase	-29587
-▁hoops	-29588
-▁sexes	-29589
-▁lobster	-29590
-▁Conscious	-29591
-▁definately	-29592
-▁Destiny	-29593
-▁emulate	-29594
-▁locality	-29595
-▁untreated	-29596
-▁simultaneous	-29597
-▁KID	-29598
-facts	-29599
-▁Fasc	-29600
-▁snorted	-29601
-▁greetings	-29602
-▁intrusion	-29603
-▁ramifications	-29604
-▁archaeological	-29605
-▁Chrys	-29606
-▁Trees	-29607
-▁desir	-29608
-▁pores	-29609
-▁Burning	-29610
-▁Pumpkin	-29611
-▁Ki	-29612
-▁TOT	-29613
-▁Twe	-29614
-araoh	-29615
-▁Congo	-29616
-andering	-29617
-appiness	-29618
-ocalyptic	-29619
-▁Eph	-29620
-▁MLM	-29621
-▁yer	-29622
-Friday	-29623
-▁settles	-29624
-ALS	-29625
-▁Struct	-29626
-▁Happens	-29627
-▁welding	-29628
-▁Shinichi	-29629
-▁stakeholder	-29630
-Use	-29631
-came	-29632
-ousal	-29633
-▁slab	-29634
-▁snar	-29635
-summer	-29636
-▁fetus	-29637
-openhagen	-29638
-▁variance	-29639
-▁malpractice	-29640
-Fire	-29641
-▁Ski	-29642
-annie	-29643
-icine	-29644
-▁Chev	-29645
-▁dismay	-29646
-▁mumbled	-29647
-▁speechless	-29648
-nton	-29649
-▁Arrow	-29650
-▁annot	-29651
-▁Jacobs	-29652
-▁Dollars	-29653
-▁ridicule	-29654
-▁facilitates	-29655
-▁antics	-29656
-▁Transit	-29657
-▁poignant	-29658
-▁instituted	-29659
-▁commandments	-29660
-oi	-29661
-▁FI	-29662
-iago	-29663
-flowers	-29664
-▁gravit	-29665
-▁tangled	-29666
-▁cleanliness	-29667
-▁exaggeration	-29668
-EW	-29669
-ahaw	-29670
-▁Halo	-29671
-ulties	-29672
-▁obeyed	-29673
-▁sparks	-29674
-▁Myanmar	-29675
-▁Survival	-29676
-▁pioneers	-29677
-hun	-29678
-▁SET	-29679
-subject	-29680
-▁Shares	-29681
-▁Violet	-29682
-anything	-29683
-▁aquatic	-29684
-▁Boulevard	-29685
-▁specifying	-29686
-heid	-29687
-▁handc	-29688
-▁coarse	-29689
-▁finalists	-29690
-▁assertions	-29691
-▁correspondent	-29692
-iast	-29693
-▁Fate	-29694
-▁Dodge	-29695
-everyone	-29696
-▁ARTICLE	-29697
-▁dinosaur	-29698
-▁disagreed	-29699
-▁confronting	-29700
-▁provocative	-29701
-Ter	-29702
-imon	-29703
-▁EXT	-29704
-▁arteries	-29705
-▁fisherman	-29706
-▁resonates	-29707
-▁dehydration	-29708
-Ret	-29709
-hake	-29710
-▁vex	-29711
-▁Solo	-29712
-oscope	-29713
-▁spies	-29714
-▁Garage	-29715
-▁Munich	-29716
-▁reperto	-29717
-▁abolished	-29718
-▁enrichment	-29719
-jar	-29720
-▁Sail	-29721
-▁semic	-29722
-atellite	-29723
-▁Jenkins	-29724
-▁america	-29725
-▁buffalo	-29726
-▁bullies	-29727
-▁pregnancies	-29728
-Mail	-29729
-unit	-29730
-▁trop	-29731
-ledged	-29732
-▁Basil	-29733
-▁Lydia	-29734
-▁Punch	-29735
-▁snail	-29736
-▁monies	-29737
-▁Maxwell	-29738
-▁boarded	-29739
-▁iceberg	-29740
-▁overseeing	-29741
-▁appreciates	-29742
-▁cauliflower	-29743
-▁prescribing	-29744
-Run	-29745
-acqu	-29746
-croft	-29747
-ority	-29748
-▁Aber	-29749
-▁goofy	-29750
-▁clumsy	-29751
-▁herpes	-29752
-▁ergonom	-29753
-▁Socialist	-29754
-▁Celebration	-29755
-pees	-29756
-▁Butt	-29757
-▁Mate	-29758
-▁moons	-29759
-▁endings	-29760
-▁temporal	-29761
-▁Judiciary	-29762
-▁categorized	-29763
-▁Dh	-29764
-▁PJ	-29765
-▁Elliott	-29766
-▁softened	-29767
-▁anecdotes	-29768
-▁certifications	-29769
-xia	-29770
-Four	-29771
-double	-29772
-▁rever	-29773
-▁wedge	-29774
-▁idiotic	-29775
-▁trumpet	-29776
-▁reusable	-29777
-▁tweaking	-29778
-▁temperament	-29779
-rio	-29780
-Form	-29781
-imat	-29782
-▁Kod	-29783
-▁OLD	-29784
-Their	-29785
-▁Caps	-29786
-▁seaside	-29787
-▁persists	-29788
-▁misplaced	-29789
-▁Fellowship	-29790
-atorium	-29791
-▁intimately	-29792
-▁precipitation	-29793
-JECT	-29794
-bred	-29795
-lsen	-29796
-among	-29797
-burst	-29798
-▁Hull	-29799
-▁mason	-29800
-▁uterus	-29801
-▁reprint	-29802
-▁Congressman	-29803
-▁criticizing	-29804
-+.	-29805
-Bs	-29806
-▁KC	-29807
-...!	-29808
-uzzi	-29809
-▁CPA	-29810
-urger	-29811
-▁acet	-29812
-▁chats	-29813
-▁india	-29814
-uristic	-29815
-▁totals	-29816
-▁gearing	-29817
-interrupted	-29818
-▁devastation	-29819
-▁occurrences	-29820
-▁displacement	-29821
-natured	-29822
-▁Riding	-29823
-▁escapes	-29824
-▁sensing	-29825
-▁reverence	-29826
-▁afternoons	-29827
-▁anesthesia	-29828
-▁JC	-29829
-▁Abr	-29830
-▁slog	-29831
-▁typo	-29832
-▁Deborah	-29833
-▁Unknown	-29834
-▁cashier	-29835
-▁markings	-29836
-▁illusions	-29837
-▁Consultant	-29838
-▁anonymously	-29839
-▁reassurance	-29840
-Each	-29841
-ethyl	-29842
-rescent	-29843
-▁Abbott	-29844
-▁Franco	-29845
-▁Trouble	-29846
-▁deviation	-29847
-▁Indonesian	-29848
-▁Installation	-29849
-▁Lawyer	-29850
-▁quests	-29851
-▁untrue	-29852
-▁upheld	-29853
-▁Harbour	-29854
-▁perched	-29855
-▁flirting	-29856
-▁removable	-29857
-▁sheltered	-29858
-▁Continental	-29859
-nick	-29860
-▁Pars	-29861
-▁Stru	-29862
-▁Bronx	-29863
-▁bikini	-29864
-▁chrome	-29865
-▁nuances	-29866
-▁fruition	-29867
-▁som	-29868
-TMENT	-29869
-▁Meth	-29870
-imagin	-29871
-▁Flint	-29872
-▁movers	-29873
-▁Soldier	-29874
-▁assholes	-29875
-▁prevailed	-29876
-▁WASHINGTON	-29877
-▁Ain	-29878
-Looks	-29879
-▁Juda	-29880
-▁Ones	-29881
-▁sten	-29882
-▁Singh	-29883
-▁Taxes	-29884
-▁Hiring	-29885
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-ixties	-29926
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-Bre	-29930
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-iless	-29936
-liner	-29937
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-▁manipulating	-29957
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-renheit	-29965
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-phal	-29992
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-▁ROM	-29994
-▁Powerful	-29995
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-▁Apologies	-29997
-▁stimulated	-29998
-▁Kas	-29999
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-erencing	-30003
-▁weakest	-30004
-▁beauties	-30005
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-MY	-30008
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-abi	-30026
-Didn	-30027
-sure	-30028
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-▁adept	-30031
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-▁hauled	-30040
-▁thirds	-30041
-▁Archive	-30042
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-▁dictates	-30044
-▁powdered	-30045
-EF	-30046
-▁Roland	-30047
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-▁hardened	-30049
-▁Tac	-30050
-▁ply	-30051
-iesel	-30052
-▁pane	-30053
-athlon	-30054
-ueling	-30055
-▁Avatar	-30056
-▁bigotry	-30057
-▁collage	-30058
-▁jackpot	-30059
-▁Telephone	-30060
-▁compiling	-30061
-nin	-30062
-▁havin	-30063
-▁pouch	-30064
-▁dagger	-30065
-▁trimmed	-30066
-▁lovingly	-30067
-▁Agg	-30068
-▁yen	-30069
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-▁Jail	-30071
-▁ding	-30072
-▁Anders	-30073
-▁blossom	-30074
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-▁diversified	-30076
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-cery	-30078
-etti	-30079
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-▁START	-30081
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-fection	-30083
-▁capped	-30084
-president	-30085
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-▁MLB	-30089
-atche	-30090
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-▁IMPORT	-30093
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-▁receivers	-30096
-▁bustle	-30097
-▁clinicians	-30098
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-Cap	-30100
-eering	-30101
-▁slump	-30102
-mediate	-30103
-▁airing	-30104
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-▁abolition	-30107
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-▁royalties	-30109
-fo	-30110
-▁Obi	-30111
-▁Whoa	-30112
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-▁valu	-30114
-▁Rouge	-30115
-▁sensed	-30116
-▁servings	-30117
-▁scrambling	-30118
-▁「	-30119
-▁Ing	-30120
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-▁seams	-30122
-▁canyon	-30123
-▁Surface	-30124
-▁Cemetery	-30125
-▁escorted	-30126
-▁Economist	-30127
-▁Collective	-30128
-▁incompatible	-30129
-ECD	-30130
-few	-30131
-▁ms	-30132
-None	-30133
-▁Calc	-30134
-▁Newsp	-30135
-▁subpo	-30136
-▁Proven	-30137
-▁sweeter	-30138
-Everybody	-30139
-▁Veronica	-30140
-▁Admittedly	-30141
-hovah	-30142
-▁snatch	-30143
-▁valves	-30144
-▁enticing	-30145
-▁regained	-30146
-Ann	-30147
-▁ADV	-30148
-▁Heads	-30149
-ivalent	-30150
-▁nothin	-30151
-▁coveted	-30152
-▁shooters	-30153
-▁astronomy	-30154
-▁neutrality	-30155
-▁Integration	-30156
-Sam	-30157
-▁MF	-30158
-▁au	-30159
-farm	-30160
-▁grieve	-30161
-▁Copenhagen	-30162
-dle	-30163
-iates	-30164
-▁rugs	-30165
-▁sparse	-30166
-▁unbeat	-30167
-▁Extension	-30168
-▁Dictionary	-30169
-▁Networking	-30170
-▁segregation	-30171
-▁withholding	-30172
-▁unpl	-30173
-▁Cardinals	-30174
-▁clinically	-30175
-▁horrendous	-30176
-▁predicament	-30177
-runt	-30178
-▁Booth	-30179
-▁Burger	-30180
-▁glossy	-30181
-▁Auditor	-30182
-▁cheered	-30183
-▁conject	-30184
-▁Judicial	-30185
-▁troubleshooting	-30186
-DB	-30187
-▁Eston	-30188
-▁badass	-30189
-▁illiter	-30190
-▁unarmed	-30191
-▁whirlwind	-30192
-▁humanities	-30193
-andem	-30194
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-▁Seal	-30197
-January	-30198
-smoking	-30199
-▁compel	-30200
-regulated	-30201
-buck	-30202
-▁Told	-30203
-▁tofu	-30204
-▁ribbons	-30205
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-▁eradicate	-30207
-▁unsecured	-30208
-▁Ella	-30209
-▁Doyle	-30210
-▁checkbox	-30211
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-ICEF	-30213
-oyer	-30214
-▁Cec	-30215
-▁cape	-30216
-▁Blogs	-30217
-▁spooky	-30218
-▁audible	-30219
-▁tallest	-30220
-Has	-30221
-series	-30222
-▁appre	-30223
-▁Mister	-30224
-▁Learned	-30225
-▁paramed	-30226
-▁Platinum	-30227
-GC	-30228
-UMP	-30229
-▁VO	-30230
-▁shill	-30231
-earance	-30232
-▁Patent	-30233
-▁mosque	-30234
-▁junction	-30235
-▁sabotage	-30236
-▁wrongful	-30237
-▁Celebrate	-30238
-▁prejudices	-30239
-▁diminishing	-30240
-cats	-30241
-▁Cly	-30242
-▁emo	-30243
-least	-30244
-▁INTO	-30245
-raines	-30246
-▁peasants	-30247
-▁completes	-30248
-▁misconception	-30249
-kk	-30250
-▁Raz	-30251
-▁Zoe	-30252
-▁Hann	-30253
-▁parlor	-30254
-▁Forgive	-30255
-▁platinum	-30256
-▁overheard	-30257
-▁contentious	-30258
-Name	-30259
-itud	-30260
-▁Dems	-30261
-▁moss	-30262
-▁Config	-30263
-▁Tanzania	-30264
-▁infrared	-30265
-▁alliances	-30266
-▁Fav	-30267
-meter	-30268
-▁Cove	-30269
-▁avoids	-30270
-▁flakes	-30271
-▁polymer	-30272
-▁bearings	-30273
-▁graciously	-30274
-▁blueberries	-30275
-Chief	-30276
-▁SUPP	-30277
-▁glands	-30278
-▁Builder	-30279
-▁Kardash	-30280
-▁Seasons	-30281
-▁praises	-30282
-▁plagiarism	-30283
-▁instinctively	-30284
-▁individualized	-30285
-miah	-30286
-▁PBS	-30287
-hesion	-30288
-▁woken	-30289
-lightly	-30290
-▁litres	-30291
-▁haunting	-30292
-▁aluminium	-30293
-▁°	-30294
-tte	-30295
-wyn	-30296
-imble	-30297
-▁malt	-30298
-ophile	-30299
-▁Celtic	-30300
-▁sorely	-30301
-▁submer	-30302
-▁Cynthia	-30303
-▁artific	-30304
-▁Merchant	-30305
-▁orthodont	-30306
-major	-30307
-▁Connie	-30308
-▁onward	-30309
-▁rebate	-30310
-▁sweaters	-30311
-gies	-30312
-iqueness	-30313
-▁molecule	-30314
-▁ordained	-30315
-▁masterwork	-30316
-▁Rip	-30317
-▁NEXT	-30318
-config	-30319
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-▁Carnival	-30321
-RAT	-30322
-fax	-30323
-▁IB	-30324
-▁Hyde	-30325
-▁Twice	-30326
-▁floats	-30327
-▁Raising	-30328
-▁traumat	-30329
-▁outsource	-30330
-▁pioneering	-30331
-▁housekeeping	-30332
-▁pajamas	-30333
-▁upscale	-30334
-▁fearless	-30335
-rade	-30336
-▁ell	-30337
-▁nons	-30338
-aceted	-30339
-▁Giles	-30340
-▁condes	-30341
-ographically	-30342
-Ill	-30343
-▁mr	-30344
-▁Hort	-30345
-▁Kurd	-30346
-▁Maura	-30347
-▁sneaky	-30348
-mbudsman	-30349
-▁patriotism	-30350
-▁IA	-30351
-ders	-30352
-▁Sylvia	-30353
-technical	-30354
-▁collagen	-30355
-▁Ey	-30356
-▁MARK	-30357
-▁Oswald	-30358
-▁cramped	-30359
-▁partake	-30360
-▁tackles	-30361
-▁blinking	-30362
-▁carbohydrate	-30363
-ón	-30364
-▁Aly	-30365
-onomy	-30366
-▁Bran	-30367
-▁Hers	-30368
-inctions	-30369
-▁curated	-30370
-▁ancestry	-30371
-▁electrons	-30372
-▁externally	-30373
-▁diner	-30374
-▁lends	-30375
-▁reaff	-30376
-▁Gadget	-30377
-▁swallowing	-30378
-dou	-30379
-▁CI	-30380
-touch	-30381
-▁Boost	-30382
-▁Cisco	-30383
-▁whack	-30384
-▁Amelia	-30385
-▁Dragons	-30386
-▁reactors	-30387
-▁deficient	-30388
-sty	-30389
-HAHA	-30390
-rams	-30391
-▁Mug	-30392
-▁Rey	-30393
-▁partying	-30394
-cun	-30395
-jpg	-30396
-urge	-30397
-▁lax	-30398
-▁Bets	-30399
-Spirit	-30400
-▁delaying	-30401
-▁stockings	-30402
-girls	-30403
-▁bosom	-30404
-▁soothe	-30405
-acterial	-30406
-▁breaker	-30407
-▁Bulgaria	-30408
-industrial	-30409
-▁headlights	-30410
-male	-30411
-want	-30412
-▁HBO	-30413
-▁Von	-30414
-▁Pets	-30415
-▁yawn	-30416
-▁String	-30417
-▁bleach	-30418
-▁ecology	-30419
-▁grievances	-30420
-)”	-30421
-sq	-30422
-▁Gets	-30423
-▁defy	-30424
-partum	-30425
-▁scalable	-30426
-▁illuminated	-30427
-osl	-30428
-locks	-30429
-▁prank	-30430
-▁Milton	-30431
-▁Cottage	-30432
-▁algebra	-30433
-▁Employer	-30434
-Gold	-30435
-▁lig	-30436
-▁sails	-30437
-▁scoot	-30438
-▁ominous	-30439
-▁relocated	-30440
-▁wavelength	-30441
-▁AZ	-30442
-inel	-30443
-▁CAD	-30444
-▁^_^	-30445
-▁nove	-30446
-▁mesmer	-30447
-▁CAT	-30448
-elsius	-30449
-imester	-30450
-▁gutter	-30451
-▁Apprent	-30452
-▁Quickly	-30453
-▁Typical	-30454
-▁fallout	-30455
-▁appendix	-30456
-▁trolling	-30457
-▁conferred	-30458
-▁provisional	-30459
-gee	-30460
-bins	-30461
-▁EXP	-30462
-▁hmm	-30463
-▁sayin	-30464
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-▁kneeling	-30467
-▁Lifestyle	-30468
-▁peninsula	-30469
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-▁brilliance	-30471
-▁triggering	-30472
-▁dissolution	-30473
-▁bloke	-30474
-▁cords	-30475
-▁Burton	-30476
-▁giggled	-30477
-▁asteroid	-30478
-▁ace	-30479
-▁coax	-30480
-▁demos	-30481
-armesan	-30482
-▁casser	-30483
-▁scouts	-30484
-▁theres	-30485
-▁chassis	-30486
-▁directs	-30487
-▁screwing	-30488
-▁oppressive	-30489
-▁craftsmanship	-30490
-Set	-30491
-▁OC	-30492
-▁RN	-30493
-▁Kor	-30494
-▁Mol	-30495
-▁NGO	-30496
-lantic	-30497
-▁dismal	-30498
-▁lecturer	-30499
-▁detectors	-30500
-▁operative	-30501
-▁specifies	-30502
-▁influencer	-30503
-▁euth	-30504
-▁woes	-30505
-▁infusion	-30506
-▁Millennium	-30507
-▁dictatorship	-30508
-Mc	-30509
-▁oo	-30510
-finder	-30511
-▁Euros	-30512
-▁Hitch	-30513
-▁Woody	-30514
-hotmail	-30515
-setting	-30516
-▁Airbnb	-30517
-▁devise	-30518
-▁interns	-30519
-▁Combined	-30520
-▁steaming	-30521
-▁turbines	-30522
-▁initiation	-30523
-▁incompetence	-30524
-Vol	-30525
-▁JP	-30526
-ateurs	-30527
-▁Combat	-30528
-▁brownies	-30529
-▁oversized	-30530
-▁Everywhere	-30531
-,“	-30532
-▁knob	-30533
-oughed	-30534
-▁Naomi	-30535
-▁corro	-30536
-▁bungal	-30537
-articles	-30538
-▁impract	-30539
-▁spotting	-30540
-▁synopsis	-30541
-▁abandoning	-30542
-▁handicapped	-30543
-▁Bach	-30544
-▁Scorp	-30545
-imation	-30546
-loo	-30547
-▁Ji	-30548
-hare	-30549
-peer	-30550
-▁Anon	-30551
-▁WERE	-30552
-▁crumbs	-30553
-▁Carolyn	-30554
-▁Geographic	-30555
-End	-30556
-jury	-30557
-▁Via	-30558
-▁Guns	-30559
-▁Jehovah	-30560
-▁planetary	-30561
-tri	-30562
-OTUS	-30563
-Plan	-30564
-otto	-30565
-▁Seed	-30566
-aptain	-30567
-▁Nokia	-30568
-▁frogs	-30569
-▁havent	-30570
-European	-30571
-gre	-30572
-▁Notre	-30573
-literally	-30574
-▁centrally	-30575
-▁thermostat	-30576
-jet	-30577
-chez	-30578
-quel	-30579
-▁Shi	-30580
-▁CCTV	-30581
-▁asap	-30582
-fields	-30583
-▁Rosie	-30584
-▁blasp	-30585
-Besides	-30586
-▁psyched	-30587
-▁predictive	-30588
-▁transitioning	-30589
-anky	-30590
-chel	-30591
-▁CRA	-30592
-sleep	-30593
-▁Hamb	-30594
-iations	-30595
-▁township	-30596
-▁moderator	-30597
-▁Maintaining	-30598
-mis	-30599
-▁ISP	-30600
-▁Bout	-30601
-▁Mari	-30602
-▁Myers	-30603
-▁memes	-30604
-▁pesky	-30605
-▁sinus	-30606
-▁Genius	-30607
-▁subset	-30608
-▁projector	-30609
-▁rhetorical	-30610
-doms	-30611
-rists	-30612
-ophobic	-30613
-▁Updated	-30614
-▁bolster	-30615
-cellaneous	-30616
-▁mutations	-30617
-▁projecting	-30618
-▁billionaire	-30619
-▁HG	-30620
-▁taxis	-30621
-▁Philly	-30622
-erential	-30623
-▁intestinal	-30624
-▁illustrating	-30625
-dot	-30626
-▁REST	-30627
-▁grit	-30628
-▁rapt	-30629
-bodied	-30630
-▁Ahead	-30631
-▁allot	-30632
-▁hairy	-30633
-▁Thread	-30634
-▁indemn	-30635
-▁Leather	-30636
-▁alerted	-30637
-▁flaming	-30638
-▁unprepared	-30639
-▁presumption	-30640
-)."	-30641
-▁FOX	-30642
-▁Pony	-30643
-▁afar	-30644
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-largest	-30646
-▁Couple	-30647
-▁labore	-30648
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-▁waived	-30650
-▁Richardson	-30651
-▁commemorate	-30652
-▁receptionist	-30653
-bable	-30654
-▁Dont	-30655
-▁Hein	-30656
-ffield	-30657
-▁Ernest	-30658
-▁Johann	-30659
-▁boosts	-30660
-▁Gateway	-30661
-▁Kathryn	-30662
-▁deflect	-30663
-▁stalled	-30664
-▁delusion	-30665
-▁miserably	-30666
-▁evangelical	-30667
-▁impatiently	-30668
-▁SAY	-30669
-▁paws	-30670
-Unless	-30671
-ocusing	-30672
-pecting	-30673
-▁pollen	-30674
-▁hangover	-30675
-▁predator	-30676
-...'	-30677
-hones	-30678
-Things	-30679
-▁comma	-30680
-forming	-30681
-▁resale	-30682
-orations	-30683
-▁telecom	-30684
-▁Broadcast	-30685
-▁grievance	-30686
-▁proponents	-30687
-Ser	-30688
-ussia	-30689
-orated	-30690
-pretty	-30691
-▁LGBTQ	-30692
-▁Qatar	-30693
-▁itchy	-30694
-▁barren	-30695
-ensional	-30696
-▁robotics	-30697
-oki	-30698
-prim	-30699
-cence	-30700
-▁Lego	-30701
-▁STEP	-30702
-▁barley	-30703
-▁coined	-30704
-▁freeway	-30705
-▁sprayed	-30706
-▁counsell	-30707
-exec	-30708
-▁Yug	-30709
-▁Oral	-30710
-▁Pall	-30711
-▁emig	-30712
-▁flux	-30713
-▁Bliss	-30714
-▁jihad	-30715
-▁saver	-30716
-▁Postal	-30717
-▁senseless	-30718
-SD	-30719
-▁///	-30720
-▁HUN	-30721
-▁Mae	-30722
-▁OPEN	-30723
-▁coke	-30724
-strous	-30725
-▁Crypt	-30726
-▁Polar	-30727
-▁tacos	-30728
-▁Timber	-30729
-▁Judaism	-30730
-▁boosted	-30731
-▁WhatsApp	-30732
-▁whitening	-30733
-▁notoriously	-30734
-▁heterosexual	-30735
-▁ga	-30736
-▁torso	-30737
-watching	-30738
-▁smirked	-30739
-▁discrete	-30740
-▁eyesight	-30741
-Unfortunately	-30742
-▁MN	-30743
-▁LEA	-30744
-▁TIM	-30745
-▁PROP	-30746
-▁Negot	-30747
-▁eminent	-30748
-▁trainings	-30749
-▁deportation	-30750
-▁efficiencies	-30751
-▁collaborations	-30752
-▁von	-30753
-ascal	-30754
-Reilly	-30755
-quoise	-30756
-▁hanged	-30757
-▁whence	-30758
-▁Devices	-30759
-▁setback	-30760
-▁shopper	-30761
-▁Surprise	-30762
-▁commuting	-30763
-▁copyrighted	-30764
-Ask	-30765
-Hmm	-30766
-faith	-30767
-▁Shen	-30768
-▁iced	-30769
-▁Hayes	-30770
-▁Piano	-30771
-urances	-30772
-▁Strike	-30773
-▁rallies	-30774
-▁generals	-30775
-▁interiors	-30776
-▁abandonment	-30777
-Que	-30778
-thens	-30779
-▁Beau	-30780
-▁Reve	-30781
-▁Medal	-30782
-▁graft	-30783
-▁lovel	-30784
-▁salts	-30785
-Matthew	-30786
-▁Pocket	-30787
-▁Distance	-30788
-▁Treasurer	-30789
-▁organisers	-30790
-Try	-30791
-agh	-30792
-OUNT	-30793
-▁lunar	-30794
-▁Allows	-30795
-▁Archie	-30796
-▁Orient	-30797
-▁pierced	-30798
-iotherapy	-30799
-▁restroom	-30800
-▁salesperson	-30801
-▁chronological	-30802
-▁uninterrupted	-30803
-IW	-30804
-▁Esp	-30805
-▁SMEs	-30806
-▁dean	-30807
-▁vines	-30808
-▁forestry	-30809
-▁Riverside	-30810
-Sw	-30811
-lake	-30812
-plays	-30813
-▁admon	-30814
-ppermint	-30815
-▁succeeds	-30816
-▁survives	-30817
-▁extremist	-30818
-▁ludicrous	-30819
-▁murderers	-30820
-▁reimbursed	-30821
-aaS	-30822
-▁MW	-30823
-East	-30824
-OLOG	-30825
-▁Pond	-30826
-▁yahoo	-30827
-▁classify	-30828
-▁narrowly	-30829
-▁Hungarian	-30830
-▁Personality	-30831
-▁Rav	-30832
-▁HTTP	-30833
-▁gasp	-30834
-▁AMEND	-30835
-▁paces	-30836
-▁raids	-30837
-▁europe	-30838
-▁muffin	-30839
-▁pastoral	-30840
-▁Reduction	-30841
-▁feminists	-30842
-▁oval	-30843
-▁abound	-30844
-▁Married	-30845
-▁Negative	-30846
-▁aperture	-30847
-▁endanger	-30848
-▁communion	-30849
-▁tentative	-30850
-Rock	-30851
-Wind	-30852
-haven	-30853
-merse	-30854
-▁Hond	-30855
-▁Twain	-30856
-▁Aussie	-30857
-▁enumer	-30858
-▁frivol	-30859
-newsletter	-30860
-▁placements	-30861
-▁marginalized	-30862
-/#	-30863
-David	-30864
-▁Boeing	-30865
-▁affluent	-30866
-▁climates	-30867
-▁conquest	-30868
-▁exploits	-30869
-▁discriminatory	-30870
-NEY	-30871
-▁ANSW	-30872
-▁Shah	-30873
-▁trot	-30874
-▁troph	-30875
-imeters	-30876
-aturated	-30877
-▁downfall	-30878
-▁Nu	-30879
-▁snug	-30880
-▁COURT	-30881
-▁proofs	-30882
-▁raving	-30883
-▁curtail	-30884
-▁gripped	-30885
-▁Proverbs	-30886
-▁clenched	-30887
-▁totaling	-30888
-sufficient	-30889
-▁vertically	-30890
-FW	-30891
-▁Ninja	-30892
-▁Reich	-30893
-▁sulph	-30894
-blowing	-30895
-▁Evangel	-30896
-▁conspir	-30897
-▁grandpa	-30898
-▁Kathleen	-30899
-▁Salvador	-30900
-▁mastering	-30901
-▁militants	-30902
-▁franchises	-30903
-AI	-30904
-jay	-30905
-▁iii	-30906
-files	-30907
-uctor	-30908
-otonin	-30909
-▁Felix	-30910
-partisan	-30911
-▁Vampire	-30912
-▁restrain	-30913
-▁sanitary	-30914
-▁Wondering	-30915
-▁contender	-30916
-▁nightlife	-30917
-▁Supervisor	-30918
-▁entrenched	-30919
-HP	-30920
-wra	-30921
-cart	-30922
-icer	-30923
-PubMed	-30924
-▁Chevy	-30925
-▁eczema	-30926
-▁shabby	-30927
-▁Sherman	-30928
-▁watered	-30929
-▁Handbook	-30930
-▁replaces	-30931
-▁UM	-30932
-▁Kyoto	-30933
-▁bulls	-30934
-▁taped	-30935
-ithmetic	-30936
-▁Confederate	-30937
-▁disclosures	-30938
-ANY	-30939
-IPP	-30940
-Its	-30941
-ASON	-30942
-▁Lev	-30943
-anton	-30944
-▁ante	-30945
-▁chops	-30946
-▁Jasper	-30947
-▁fathom	-30948
-pressure	-30949
-▁guarding	-30950
-▁Meditation	-30951
-▁PI	-30952
-▁ht	-30953
-copy	-30954
-▁Pors	-30955
-▁Anita	-30956
-liction	-30957
-▁bailout	-30958
-▁departing	-30959
-▁dispersed	-30960
-▁elf	-30961
-▁Musk	-30962
-▁scree	-30963
-▁Miracle	-30964
-▁erupted	-30965
-▁wrecked	-30966
-righteous	-30967
-▁hashtags	-30968
-▁resembling	-30969
-▁dimensional	-30970
-▁documentaries	-30971
-yssey	-30972
-▁sled	-30973
-▁munch	-30974
-▁INCLUD	-30975
-▁condos	-30976
-▁disciple	-30977
-▁reunited	-30978
-▁Automatic	-30979
-▁Experiment	-30980
-▁__	-30981
-didn	-30982
-▁NRA	-30983
-▁jams	-30984
-▁crowns	-30985
-▁strife	-30986
-▁impover	-30987
-▁licking	-30988
-▁immersive	-30989
-▁Demonstrate	-30990
-▁prerequisite	-30991
-opus	-30992
-IENCE	-30993
-▁casc	-30994
-▁scant	-30995
-▁seren	-30996
-▁Scotia	-30997
-▁refill	-30998
-Fin	-30999
-Par	-31000
-LINE	-31001
-▁Gir	-31002
-▁dyn	-31003
-chery	-31004
-▁Angry	-31005
-▁Sprint	-31006
-▁repost	-31007
-▁WITHOUT	-31008
-▁serpent	-31009
-▁compiler	-31010
-▁punishing	-31011
-represented	-31012
-▁DP	-31013
-Such	-31014
-hydro	-31015
-ilate	-31016
-▁scuba	-31017
-▁Tennis	-31018
-▁breakup	-31019
-▁Frontier	-31020
-▁epilepsy	-31021
-▁luncheon	-31022
-▁webinars	-31023
-Air	-31024
-▁mute	-31025
-▁Tutor	-31026
-▁ammon	-31027
-▁Silent	-31028
-▁cupcake	-31029
-▁pronoun	-31030
-▁conducts	-31031
-▁asparagus	-31032
-mund	-31033
-odaf	-31034
-button	-31035
-▁Robot	-31036
-▁aiding	-31037
-▁teaser	-31038
-▁Herbert	-31039
-▁amending	-31040
-▁judiciary	-31041
-▁freelancer	-31042
-iwi	-31043
-ookie	-31044
-polit	-31045
-prised	-31046
-▁tumble	-31047
-▁Freeman	-31048
-▁stature	-31049
-▁juggling	-31050
-▁Guatemala	-31051
-▁anthology	-31052
-▁bureaucratic	-31053
-hl	-31054
-item	-31055
-▁Roo	-31056
-▁Drum	-31057
-▁ruff	-31058
-▁Atlas	-31059
-▁Walsh	-31060
-▁poked	-31061
-▁Revere	-31062
-▁Tucson	-31063
-▁entail	-31064
-▁Maurice	-31065
-▁Vintage	-31066
-▁baffled	-31067
-▁Temporary	-31068
-▁adaptable	-31069
-▁Programming	-31070
-ADE	-31071
-thro	-31072
-▁Gog	-31073
-match	-31074
-▁Folk	-31075
-▁Seen	-31076
-▁midday	-31077
-▁Amnesty	-31078
-▁harming	-31079
-▁hydraulic	-31080
-▁unthinkable	-31081
-▁Riv	-31082
-North	-31083
-▁Clubs	-31084
-morning	-31085
-▁Nights	-31086
-▁Prohib	-31087
-▁piracy	-31088
-▁tracing	-31089
-▁blatantly	-31090
-▁Ink	-31091
-▁cot	-31092
-opped	-31093
-▁Beef	-31094
-cludes	-31095
-▁Suites	-31096
-▁piping	-31097
-▁ensuing	-31098
-▁populace	-31099
-▁rut	-31100
-▁Flip	-31101
-▁Vera	-31102
-▁uphol	-31103
-▁Rhodes	-31104
-▁Authent	-31105
-▁budding	-31106
-▁unfairly	-31107
-▁Delicious	-31108
-▁disrupted	-31109
-▁Examination	-31110
-▁withdrawing	-31111
-▁Hag	-31112
-zilla	-31113
-client	-31114
-loaded	-31115
-▁Clock	-31116
-▁Brigade	-31117
-▁Concert	-31118
-▁licences	-31119
-▁porcelain	-31120
-▁unsolicited	-31121
-.’’	-31122
-▁Ree	-31123
-▁GIVE	-31124
-monton	-31125
-▁POWER	-31126
-▁mound	-31127
-▁trays	-31128
-▁widen	-31129
-▁Mandela	-31130
-▁vending	-31131
-▁multiplied	-31132
-▁supplementary	-31133
-CAR	-31134
-▁"_	-31135
-▁RP	-31136
-cise	-31137
-▁Eisen	-31138
-▁Result	-31139
-▁dramas	-31140
-▁peaches	-31141
-▁uniqueness	-31142
-▁wrongdoing	-31143
-▁HAY	-31144
-insert	-31145
-▁fastened	-31146
-▁mindless	-31147
-▁rattling	-31148
-▁consented	-31149
-▁overturned	-31150
-▁gist	-31151
-turned	-31152
-▁Homer	-31153
-▁Bought	-31154
-▁Pierce	-31155
-▁Winners	-31156
-▁Adoption	-31157
-▁handbook	-31158
-▁overflowing	-31159
-▁Institutions	-31160
-arta	-31161
-▁GST	-31162
-▁lyn	-31163
-Inter	-31164
-apart	-31165
-▁Conn	-31166
-▁Mann	-31167
-▁biochem	-31168
-▁undersc	-31169
-▁formations	-31170
-▁TY	-31171
-Both	-31172
-▁DNS	-31173
-extra	-31174
-▁BILL	-31175
-▁Salem	-31176
-▁ozone	-31177
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-husband	-31179
-excluding	-31180
-▁Reception	-31181
-▁Zimmerman	-31182
-▁miscarriage	-31183
-alls	-31184
-oters	-31185
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-▁denotes	-31188
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-▁exporting	-31190
-▁grandkids	-31191
-▁limitless	-31192
-▁vandalism	-31193
-▁assembling	-31194
-▁personable	-31195
-▁cavern	-31196
-▁Weapons	-31197
-▁Studying	-31198
-▁exclamation	-31199
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-▁mugs	-31201
-▁algae	-31202
-Someone	-31203
-▁Lindsey	-31204
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-▁faculties	-31206
-▁superstar	-31207
-▁libertarian	-31208
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-ermaid	-31210
-stakes	-31211
-▁Alleg	-31212
-channel	-31213
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-▁Statements	-31216
-▁Transition	-31217
-▁Instruction	-31218
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-?*	-31226
-nas	-31227
-▁Dj	-31228
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-▁Elf	-31230
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-▁ros	-31232
-▁Buyers	-31233
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-ifs	-31241
-▁Omn	-31242
-▁Sic	-31243
-▁CITY	-31244
-ifling	-31245
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-riculum	-31252
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-odiac	-31274
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-▁mantle	-31282
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-hythm	-31288
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-▁fittings	-31294
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-ITIES	-31302
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-isi	-31314
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-uminous	-31321
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-fifth	-31326
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-ustered	-31329
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-YING	-31335
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-▁curling	-31348
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-▁Chandler	-31350
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-▁Personnel	-31353
-Er	-31354
-RET	-31355
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-owment	-31378
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-abus	-31401
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-ifa	-31412
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-indle	-31415
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-▁Lah	-31425
-berty	-31426
-▁Ling	-31427
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-▁Deadline	-31430
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-academ	-31442
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-▁Colonial	-31447
-▁substitution	-31448
-NP	-31449
-Trust	-31450
-▁WEEK	-31451
-▁footh	-31452
-▁Killer	-31453
-▁Painting	-31454
-▁spraying	-31455
-Hy	-31456
-hs	-31457
-▁LL	-31458
-MENTS	-31459
-▁Carne	-31460
-▁hasty	-31461
-▁Worker	-31462
-▁Authors	-31463
-▁Pandora	-31464
-▁Checking	-31465
-▁rehashed	-31466
-▁magistrate	-31467
-lda	-31468
-▁FRI	-31469
-enium	-31470
-▁Mold	-31471
-▁Minute	-31472
-eligible	-31473
-▁Coleman	-31474
-▁anomaly	-31475
-▁drilled	-31476
-▁Neighborhood	-31477
-▁BF	-31478
-cends	-31479
-▁brim	-31480
-undown	-31481
-▁quint	-31482
-▁reread	-31483
-chemical	-31484
-▁theatres	-31485
-▁bandwagon	-31486
-▁outbreaks	-31487
-thms	-31488
-Smart	-31489
-▁Pike	-31490
-▁evap	-31491
-athing	-31492
-▁McCon	-31493
-history	-31494
-▁revolt	-31495
-▁Victims	-31496
-▁Yorkers	-31497
-▁annuity	-31498
-▁Presently	-31499
-▁inscription	-31500
-RIST	-31501
-aunch	-31502
-overe	-31503
-scopic	-31504
-▁Tests	-31505
-▁Mutual	-31506
-▁anarch	-31507
-▁astray	-31508
-▁chills	-31509
-▁climbs	-31510
-▁subord	-31511
-▁clothed	-31512
-▁reliant	-31513
-▁competed	-31514
-▁foll	-31515
-▁Brick	-31516
-▁Haley	-31517
-instead	-31518
-▁Hammer	-31519
-▁Towers	-31520
-▁siding	-31521
-▁orthodox	-31522
-▁carpenter	-31523
-▁christian	-31524
-▁evacuated	-31525
-▁notebooks	-31526
-OH	-31527
-Sm	-31528
-atha	-31529
-China	-31530
-▁famed	-31531
-arching	-31532
-▁Figures	-31533
-▁portals	-31534
-▁profiling	-31535
-▁conclusive	-31536
-▁expressive	-31537
-▁pg	-31538
-▁Raff	-31539
-▁sexism	-31540
-platform	-31541
-▁Feedback	-31542
-▁apostles	-31543
-▁Candidate	-31544
-▁Providers	-31545
-▁Confidence	-31546
-▁behavioural	-31547
-UTE	-31548
-▁),	-31549
-▁❤️	-31550
-Power	-31551
-▁Somalia	-31552
-▁neurons	-31553
-▁totaled	-31554
-▁energized	-31555
-▁attainment	-31556
-▁forwarding	-31557
-▁Rehabilitation	-31558
-CON	-31559
-▁OL	-31560
-rule	-31561
-thin	-31562
-▁PUBLIC	-31563
-▁bracelets	-31564
-▁seasoning	-31565
-▁authorisation	-31566
-▁REP	-31567
-effic	-31568
-▁Anat	-31569
-▁Pipe	-31570
-▁Shake	-31571
-umption	-31572
-▁showroom	-31573
-▁straighten	-31574
-▁Gn	-31575
-▁DET	-31576
-▁Rah	-31577
-▁Lara	-31578
-▁Calls	-31579
-▁Irene	-31580
-ilantro	-31581
-▁glimps	-31582
-▁injure	-31583
-▁piling	-31584
-▁assorted	-31585
-▁militant	-31586
-▁personalize	-31587
-▁spontaneously	-31588
-▁underestimated	-31589
-—“	-31590
-PTON	-31591
-▁Durham	-31592
-▁archived	-31593
-Port	-31594
-micro	-31595
-swick	-31596
-▁Skip	-31597
-▁sacks	-31598
-apanese	-31599
-▁CHAPTER	-31600
-▁audited	-31601
-▁infrequ	-31602
-▁demeanor	-31603
-▁ecstatic	-31604
-▁leukemia	-31605
-▁incarceration	-31606
-▁iod	-31607
-▁Mush	-31608
-isites	-31609
-▁creed	-31610
-▁Elliot	-31611
-▁Rotary	-31612
-▁reacts	-31613
-▁Distrib	-31614
-▁discont	-31615
-▁imitation	-31616
-chin	-31617
-itutes	-31618
-▁Gavin	-31619
-▁Topic	-31620
-▁idols	-31621
-▁Preston	-31622
-▁trainees	-31623
-▁USDA	-31624
-▁leve	-31625
-▁antit	-31626
-▁Lennon	-31627
-▁parach	-31628
-▁rapper	-31629
-▁Gazette	-31630
-▁layered	-31631
-▁chopping	-31632
-▁Structure	-31633
-▁agitation	-31634
-▁treasured	-31635
-▁unmarried	-31636
-▁existential	-31637
-grid	-31638
-urus	-31639
-▁Vas	-31640
-▁UCLA	-31641
-▁tyre	-31642
-▁subtract	-31643
-▁twilight	-31644
-construction	-31645
-Yet	-31646
-▁XL	-31647
-Again	-31648
-angar	-31649
-▁dart	-31650
-▁Unlimited	-31651
-▁detriment	-31652
-▁Appreciation	-31653
-▁distributions	-31654
-▁HARD	-31655
-▁spor	-31656
-▁Honest	-31657
-▁debunk	-31658
-▁donuts	-31659
-▁Auction	-31660
-▁diluted	-31661
-▁Malaysian	-31662
-▁deploying	-31663
-belt	-31664
-▁Emm	-31665
-▁Fri	-31666
-random	-31667
-▁Alive	-31668
-▁GOING	-31669
-▁Sched	-31670
-▁TITLE	-31671
-▁hered	-31672
-▁Albany	-31673
-▁lookup	-31674
-▁dormant	-31675
-▁grazing	-31676
-different	-31677
-▁esteemed	-31678
-▁takeaway	-31679
-iga	-31680
-▁BRE	-31681
-▁SOL	-31682
-▁ardu	-31683
-uciary	-31684
-▁Funeral	-31685
-▁indexed	-31686
-▁Billboard	-31687
-▁songwriter	-31688
-▁Lum	-31689
-▁Reven	-31690
-▁Scene	-31691
-▁Torah	-31692
-▁groan	-31693
-▁Rating	-31694
-▁chords	-31695
-▁spills	-31696
-▁Phantom	-31697
-▁cumbersome	-31698
-▁•	-31699
-ods	-31700
-Cola	-31701
-pull	-31702
-▁bard	-31703
-antics	-31704
-▁Heard	-31705
-▁Subsequ	-31706
-▁wallets	-31707
-▁Fabulous	-31708
-▁analysed	-31709
-▁confines	-31710
-▁painters	-31711
-▁softball	-31712
-▁quotations	-31713
-▁transformative	-31714
-chall	-31715
-ublished	-31716
-▁freakin	-31717
-▁Cognitive	-31718
-nc	-31719
-Fun	-31720
-iman	-31721
-▁Iris	-31722
-▁cites	-31723
-▁Thames	-31724
-▁fondly	-31725
-▁Fingers	-31726
-▁snoring	-31727
-knowledge	-31728
-▁strenuous	-31729
-▁directives	-31730
-OOD	-31731
-▁SEN	-31732
-orism	-31733
-▁Advantage	-31734
-opez	-31735
-shaw	-31736
-hurst	-31737
-▁Fanny	-31738
-▁horny	-31739
-▁Hazard	-31740
-▁Leices	-31741
-▁Streets	-31742
-▁castles	-31743
-▁affidavit	-31744
-Dan	-31745
-oya	-31746
-▁MVP	-31747
-▁Sou	-31748
-▁Hide	-31749
-▁ramb	-31750
-▁SOMET	-31751
-▁Eleven	-31752
-▁Garrett	-31753
-▁Automotive	-31754
-▁NR	-31755
-...’	-31756
-izzie	-31757
-▁disb	-31758
-ostics	-31759
-▁crane	-31760
-▁Falling	-31761
-▁sealing	-31762
-evolution	-31763
-▁surfaced	-31764
-▁trophies	-31765
-▁disagreements	-31766
-!:	-31767
-Dep	-31768
-▁Cody	-31769
-▁ejac	-31770
-▁phon	-31771
-▁Memor	-31772
-▁Easily	-31773
-▁outage	-31774
-▁Venture	-31775
-▁cyclist	-31776
-▁imitate	-31777
-▁standby	-31778
-▁panicked	-31779
-▁fermentation	-31780
-▁incorporation	-31781
-Sil	-31782
-sum	-31783
-vents	-31784
-▁vets	-31785
-▁Tunis	-31786
-▁arsen	-31787
-▁fumes	-31788
-▁quarant	-31789
-▁snippet	-31790
-▁thinker	-31791
-▁alcoholism	-31792
-Mrs	-31793
-hton	-31794
-▁Sket	-31795
-▁Mixed	-31796
-▁Oddly	-31797
-▁Causes	-31798
-▁Vikings	-31799
-▁addictions	-31800
-▁sanctioned	-31801
-▁affirmation	-31802
-▁(.	-31803
-iesta	-31804
-▁fend	-31805
-▁Klein	-31806
-▁Maxim	-31807
-▁Sammy	-31808
-▁deity	-31809
-▁scour	-31810
-▁lavish	-31811
-▁closets	-31812
-contained	-31813
-▁diverted	-31814
-▁hierarch	-31815
-▁silicone	-31816
-▁commencing	-31817
-▁PlayStation	-31818
-opal	-31819
-scape	-31820
-▁bage	-31821
-▁duke	-31822
-▁lakh	-31823
-▁Essex	-31824
-▁Permit	-31825
-▁Daniels	-31826
-▁coworker	-31827
-▁workspace	-31828
-▁cuff	-31829
-▁flax	-31830
-▁smear	-31831
-company	-31832
-▁blooms	-31833
-▁turbine	-31834
-▁Manitoba	-31835
-▁ambiance	-31836
-▁keyboards	-31837
-din	-31838
-reck	-31839
-▁YORK	-31840
-▁Ether	-31841
-▁envis	-31842
-▁worsen	-31843
-▁upstream	-31844
-▁mathematic	-31845
-pox	-31846
-▁Lut	-31847
-▁Webb	-31848
-▁guise	-31849
-▁guinea	-31850
-▁bruised	-31851
-▁occupies	-31852
-▁IMPORTANT	-31853
-▁surcharge	-31854
-▁pronunciation	-31855
-Walk	-31856
-loom	-31857
-stroke	-31858
-▁Criter	-31859
-▁cuddle	-31860
-▁pottery	-31861
-▁subdued	-31862
-▁corrosion	-31863
-▁visionary	-31864
-▁wastewater	-31865
-▁ole	-31866
-▁denim	-31867
-▁nifty	-31868
-historic	-31869
-▁Martial	-31870
-▁Newport	-31871
-▁crunchy	-31872
-▁noticeably	-31873
-▁pilgrimage	-31874
-▁waterfalls	-31875
-▁adolescence	-31876
-▁complication	-31877
-▁BEA	-31878
-▁COL	-31879
-▁Mai	-31880
-pants	-31881
-▁Drama	-31882
-▁Quran	-31883
-▁ethos	-31884
-▁bomber	-31885
-▁abbrevi	-31886
-▁smashing	-31887
-▁Decisions	-31888
-▁anew	-31889
-imates	-31890
-middle	-31891
-▁oasis	-31892
-▁styled	-31893
-▁Seventh	-31894
-▁scum	-31895
-▁shan	-31896
-▁dummy	-31897
-outside	-31898
-▁senate	-31899
-yourself	-31900
-▁tagging	-31901
-▁excitedly	-31902
-▁dominating	-31903
-▁obligatory	-31904
-mong	-31905
-aleigh	-31906
-▁slang	-31907
-▁Beware	-31908
-▁realms	-31909
-▁repaid	-31910
-▁Default	-31911
-▁propane	-31912
-▁Audience	-31913
-▁Detailed	-31914
-▁chlorine	-31915
-conditioning	-31916
-amba	-31917
-▁Fuk	-31918
-▁Bare	-31919
-▁cunt	-31920
-▁Fuller	-31921
-▁monopol	-31922
-▁unleash	-31923
-▁quantify	-31924
-▁Searching	-31925
-▁freelancers	-31926
-▁FTP	-31927
-flows	-31928
-illon	-31929
-▁Seem	-31930
-▁skys	-31931
-Always	-31932
-lavery	-31933
-months	-31934
-▁Steph	-31935
-▁fetal	-31936
-▁Myself	-31937
-▁banter	-31938
-▁pauses	-31939
-▁pastries	-31940
-▁Gathering	-31941
-▁trimester	-31942
-▁Craigslist	-31943
-▁auditorium	-31944
-mere	-31945
-tour	-31946
-owder	-31947
-▁Doom	-31948
-Figure	-31949
-▁Vegan	-31950
-▁Forums	-31951
-▁asphalt	-31952
-▁blasting	-31953
-▁precinct	-31954
-amin	-31955
-▁oxid	-31956
-▁polio	-31957
-athetic	-31958
-▁Hahaha	-31959
-▁dawned	-31960
-▁touted	-31961
-▁sophist	-31962
-▁detainees	-31963
-▁recruiter	-31964
-Find	-31965
-▁Bom	-31966
-budget	-31967
-▁leafy	-31968
-duction	-31969
-hundred	-31970
-▁Rabbit	-31971
-▁lateral	-31972
-▁fractures	-31973
-err	-31974
-▁Spani	-31975
-▁eased	-31976
-▁spree	-31977
-▁genome	-31978
-powering	-31979
-▁attendants	-31980
-▁""	-31981
-▁Athe	-31982
-etchup	-31983
-hesians	-31984
-▁Janice	-31985
-▁Latest	-31986
-▁fodder	-31987
-▁dieting	-31988
-▁farmhouse	-31989
-▁cheesecake	-31990
-▁civilizations	-31991
-▁QR	-31992
-Five	-31993
-aucas	-31994
-▁FORM	-31995
-▁jurors	-31996
-▁backlog	-31997
-▁overpriced	-31998
-▁proprietor	-31999
-▁Entrepreneur	-32000
-nk	-32001
-nee	-32002
-▁MER	-32003
-shoot	-32004
-▁hygien	-32005
-▁satire	-32006
-▁Borough	-32007
-▁sobbing	-32008
-▁closures	-32009
-▁grasping	-32010
-▁flowering	-32011
-▁psychologically	-32012
-Rob	-32013
-agi	-32014
-▁DL	-32015
-▁Ach	-32016
-▁REV	-32017
-▁AMER	-32018
-linked	-32019
-▁Lifetime	-32020
-▁mutation	-32021
-▁reproach	-32022
-▁textiles	-32023
-▁originals	-32024
-▁positivity	-32025
-inances	-32026
-▁Elijah	-32027
-▁faucet	-32028
-▁tandem	-32029
-▁resided	-32030
-▁Absolute	-32031
-▁chloride	-32032
-▁Augustine	-32033
-▁timelines	-32034
-▁warehouses	-32035
-▁tuberculosis	-32036
-▁hic	-32037
-Stand	-32038
-amera	-32039
-breed	-32040
-▁Eliza	-32041
-▁echoes	-32042
-▁Retreat	-32043
-▁Updates	-32044
-▁decimal	-32045
-▁darkened	-32046
-▁RR	-32047
-Jour	-32048
-▁Bri	-32049
-▁INT	-32050
-▁McN	-32051
-axies	-32052
-leigh	-32053
-▁Balk	-32054
-▁Mell	-32055
-▁tert	-32056
-gically	-32057
-▁anomal	-32058
-▁Approved	-32059
-▁prepping	-32060
-▁migraines	-32061
-▁interrupting	-32062
-spl	-32063
-Sign	-32064
-▁Oxy	-32065
-▁SAM	-32066
-Search	-32067
-▁stockp	-32068
-▁exiting	-32069
-▁platter	-32070
-▁throats	-32071
-▁invading	-32072
-▁captivity	-32073
-sted	-32074
-otent	-32075
-▁LESS	-32076
-▁Wald	-32077
-▁Peggy	-32078
-▁bribe	-32079
-▁verbs	-32080
-autical	-32081
-▁staffed	-32082
-▁insistence	-32083
-▁Scientology	-32084
--”	-32085
-▁KP	-32086
-Grand	-32087
-▁Alec	-32088
-▁headings	-32089
-▁repealed	-32090
-ckin	-32091
-▁DAR	-32092
-▁FAT	-32093
-▁cog	-32094
-▁HELL	-32095
-▁brun	-32096
-Second	-32097
-uesday	-32098
-▁HAYES	-32099
-▁perch	-32100
-▁Esther	-32101
-▁Reddit	-32102
-▁Landing	-32103
-▁redness	-32104
-intensive	-32105
-▁Conclusion	-32106
-▁Settlement	-32107
-▁maximizing	-32108
-▁Recruitment	-32109
-▁inauguration	-32110
-dozen	-32111
-dress	-32112
-▁flap	-32113
-▁Bound	-32114
-▁Simmons	-32115
-▁liaison	-32116
-▁whipping	-32117
-▁hemisphere	-32118
-▁businessmen	-32119
-▁appreciating	-32120
-hehe	-32121
-nate	-32122
-shed	-32123
-▁Fog	-32124
-▁unsus	-32125
-▁healer	-32126
-relations	-32127
-▁monstrous	-32128
-▁frantically	-32129
-▁contaminants	-32130
-seed	-32131
-elope	-32132
-fried	-32133
-unker	-32134
-▁Salad	-32135
-▁Scale	-32136
-▁mamma	-32137
-▁unman	-32138
-▁caveat	-32139
-▁pelvic	-32140
-▁Sheffield	-32141
-▁embarking	-32142
-exist	-32143
-▁FACT	-32144
-▁Titan	-32145
-▁Excess	-32146
-▁Gluten	-32147
-▁Irving	-32148
-▁ration	-32149
-▁Cowboys	-32150
-▁courthouse	-32151
-▁foundational	-32152
-▁UF	-32153
-▁Nit	-32154
-Daddy	-32155
-ahaha	-32156
-▁Luci	-32157
-▁latex	-32158
-lymouth	-32159
-▁Browns	-32160
-▁errand	-32161
-▁Grammar	-32162
-▁claimant	-32163
-▁whistles	-32164
-▁perfected	-32165
-▁companionship	-32166
-▁".	-32167
-▁Cla	-32168
-▁stump	-32169
-entious	-32170
-▁sauces	-32171
-illac	-32172
-today	-32173
-▁Exped	-32174
-▁mused	-32175
-▁undec	-32176
-▁Nissan	-32177
-▁beacon	-32178
-websites	-32179
-▁Valerie	-32180
-▁renewing	-32181
-▁stamping	-32182
-▁shameless	-32183
-▁compromises	-32184
-▁APPL	-32185
-▁swam	-32186
-▁bondage	-32187
-▁sizable	-32188
-▁Consolid	-32189
-▁scramble	-32190
-▁ibuprofen	-32191
-▁injunction	-32192
-▁specialties	-32193
-Fit	-32194
-nec	-32195
-sit	-32196
-▁Ez	-32197
-▁MSM	-32198
-▁SPE	-32199
-pathy	-32200
-▁Stro	-32201
-ggings	-32202
-▁Assets	-32203
-▁apocalypse	-32204
-▁Accommodation	-32205
-atta	-32206
-ifter	-32207
-famous	-32208
-▁amput	-32209
-▁locom	-32210
-▁intertw	-32211
-▁modeled	-32212
-▁nephews	-32213
-▁reiterated	-32214
-▁Revolutionary	-32215
-ete	-32216
-tot	-32217
-▁JD	-32218
-▁Corey	-32219
-believe	-32220
-▁Designs	-32221
-▁Niagara	-32222
-▁approvals	-32223
-▁disagrees	-32224
-arius	-32225
-▁venom	-32226
-▁Banner	-32227
-▁Zucker	-32228
-possibly	-32229
-▁Deliver	-32230
-▁Penguin	-32231
-▁Jeremiah	-32232
-▁Requests	-32233
-▁fingerprints	-32234
-EG	-32235
-▁Blank	-32236
-▁Offices	-32237
-▁Worship	-32238
-▁inpatient	-32239
-uddy	-32240
-zzled	-32241
-▁depicts	-32242
-According	-32243
-▁horizons	-32244
-▁tirelessly	-32245
-chal	-32246
-▁Zan	-32247
-▁Fiona	-32248
-▁append	-32249
-zan	-32250
-rout	-32251
-▁Nay	-32252
-venous	-32253
-▁Playa	-32254
-charges	-32255
-▁Computers	-32256
-▁relieving	-32257
-?]	-32258
-▁SAN	-32259
-ocent	-32260
-▁Silk	-32261
-▁Rider	-32262
-▁unsur	-32263
-brother	-32264
-▁Saturn	-32265
-▁Eternal	-32266
-▁Visiting	-32267
-▁assassin	-32268
-▁giggling	-32269
-▁turmeric	-32270
-▁ordinarily	-32271
-▁Electricity	-32272
-▁legitimately	-32273
-Ant	-32274
-toe	-32275
-▁Amish	-32276
-▁quasi	-32277
-odafone	-32278
-Probably	-32279
-independ	-32280
-▁cereals	-32281
-▁Regiment	-32282
-▁virtuous	-32283
-▁physiology	-32284
-▁MAG	-32285
-▁tru	-32286
-▁xen	-32287
-mania	-32288
-▁COME	-32289
-▁snag	-32290
-▁stal	-32291
-agency	-32292
-▁Shift	-32293
-▁Worry	-32294
-created	-32295
-▁pickle	-32296
-▁fanatic	-32297
-▁iPhones	-32298
-▁outings	-32299
-▁relapse	-32300
-▁toaster	-32301
-treatment	-32302
-resolution	-32303
-▁cessation	-32304
-▁unconsciously	-32305
-)—	-32306
-clin	-32307
-alted	-32308
-urbed	-32309
-▁nuns	-32310
-determ	-32311
-▁Enemy	-32312
-▁McKin	-32313
-▁fairies	-32314
-▁orphans	-32315
-▁Romanian	-32316
-▁ratified	-32317
-▁inventive	-32318
-▁collisions	-32319
-▁referencing	-32320
-insky	-32321
-unday	-32322
-▁amph	-32323
-▁eyew	-32324
-▁MONTH	-32325
-▁Wants	-32326
-▁Norfolk	-32327
-▁Printing	-32328
-▁crystall	-32329
-▁rendition	-32330
-▁showcases	-32331
-▁commanders	-32332
-▁intangible	-32333
-▁involuntary	-32334
-Ro	-32335
-▁HUM	-32336
-inant	-32337
-▁Feng	-32338
-ighted	-32339
-▁MySQL	-32340
-▁Judith	-32341
-▁Advocate	-32342
-▁adoptive	-32343
-▁blossoms	-32344
-▁Consumption	-32345
-▁Conversation	-32346
-▁‘‘	-32347
-▁yep	-32348
-itates	-32349
-▁aback	-32350
-▁Manila	-32351
-▁congen	-32352
-▁assures	-32353
-▁coroner	-32354
-▁widening	-32355
-publishing	-32356
-▁flattered	-32357
-BB	-32358
-Cong	-32359
-▁coz	-32360
-▁elves	-32361
-▁flute	-32362
-▁liars	-32363
-▁causal	-32364
-▁acronym	-32365
-▁escalate	-32366
-▁murderous	-32367
-▁subsidized	-32368
-▁restraining	-32369
-Supp	-32370
-llas	-32371
-▁nat	-32372
-▁unknow	-32373
-anguages	-32374
-▁focussed	-32375
-NN	-32376
-lore	-32377
-▁hoc	-32378
-▁Shark	-32379
-▁gritty	-32380
-▁Celsius	-32381
-▁vantage	-32382
-▁scouting	-32383
-▁assigning	-32384
-▁practising	-32385
-Pol	-32386
-▁CAL	-32387
-Young	-32388
-aptop	-32389
-▁Imag	-32390
-▁afloat	-32391
-▁concur	-32392
-▁equate	-32393
-▁adverts	-32394
-▁railing	-32395
-▁Edmonton	-32396
-▁daydream	-32397
-▁defaults	-32398
-▁backstory	-32399
-▁doctrines	-32400
-▁ingenious	-32401
-▁manifesto	-32402
-▁stewardship	-32403
-▁individuality	-32404
-Son	-32405
-▁YH	-32406
-▁POP	-32407
-Qaeda	-32408
-yrinth	-32409
-▁Bingo	-32410
-network	-32411
-▁Greene	-32412
-▁margar	-32413
-▁undone	-32414
-▁Ordinance	-32415
-▁discouraging	-32416
-.")	-32417
-Huh	-32418
-▁ECB	-32419
-▁HOL	-32420
-pires	-32421
-▁funk	-32422
-▁shuts	-32423
-▁Â	-32424
-▁Kot	-32425
-bians	-32426
-orman	-32427
-▁Lith	-32428
-▁decad	-32429
-▁latch	-32430
-enstein	-32431
-▁blouse	-32432
-omedical	-32433
-▁Suzanne	-32434
-▁SHARPTON	-32435
-▁memorize	-32436
-▁snippets	-32437
-▁Buildings	-32438
-▁graveyard	-32439
-▁vibrations	-32440
-▁hardworking	-32441
-Jim	-32442
-elfth	-32443
-▁cram	-32444
-▁wept	-32445
-introdu	-32446
-▁Closed	-32447
-▁peered	-32448
-location	-32449
-▁Removal	-32450
-iara	-32451
-ppel	-32452
-Works	-32453
-ulton	-32454
-▁Peer	-32455
-▁TRUE	-32456
-altern	-32457
-otheli	-32458
-teller	-32459
-▁Seoul	-32460
-▁hinges	-32461
-▁PERFECT	-32462
-▁tragedies	-32463
-▁incidental	-32464
-Hot	-32465
-ulses	-32466
-ureen	-32467
-▁Killing	-32468
-▁attends	-32469
-▁corpses	-32470
-▁spacing	-32471
-▁shingles	-32472
-▁Principle	-32473
-▁cultivating	-32474
-pi	-32475
-YOU	-32476
-▁mums	-32477
-weekly	-32478
-▁exclaim	-32479
-▁jeopardy	-32480
-▁exploiting	-32481
-▁Marketplace	-32482
-sent	-32483
-▁Kry	-32484
-istas	-32485
-▁brat	-32486
-▁Kinda	-32487
-▁cliché	-32488
-orescent	-32489
-▁Webster	-32490
-▁sparing	-32491
-▁breakout	-32492
-▁monsieur	-32493
-▁Computing	-32494
-▁mocked	-32495
-▁pigeon	-32496
-▁Tracking	-32497
-▁roasting	-32498
-▁astounded	-32499
-▁concerted	-32500
-▁SJ	-32501
-▁Ahh	-32502
-▁alloy	-32503
-▁laure	-32504
-▁soils	-32505
-▁Famous	-32506
-▁Concent	-32507
-▁Moments	-32508
-▁Pharaoh	-32509
-▁puberty	-32510
-▁Chrysler	-32511
-▁depleted	-32512
-▁garnered	-32513
-▁memberships	-32514
-▁Historically	-32515
-▁Gast	-32516
-▁Rein	-32517
-iberal	-32518
-▁lyric	-32519
-leaders	-32520
-▁cliche	-32521
-▁lemons	-32522
-▁deprive	-32523
-▁depicting	-32524
-▁safeguarding	-32525
-▁Psychological	-32526
-▁dissatisfaction	-32527
-Aid	-32528
-▁Bib	-32529
-▁Nile	-32530
-icycle	-32531
-▁Coral	-32532
-▁shrew	-32533
-▁diseng	-32534
-▁grumbled	-32535
-▁Preparing	-32536
-▁purported	-32537
-▁Electronics	-32538
-▁sophistication	-32539
---'	-32540
-▁CPR	-32541
-IVING	-32542
-▁Diss	-32543
-▁Slim	-32544
-gerald	-32545
-▁Hydro	-32546
-▁Boards	-32547
-▁Georget	-32548
-▁cheaply	-32549
-▁midwife	-32550
-▁precurs	-32551
-▁sterile	-32552
-▁entrants	-32553
-▁california	-32554
-SU	-32555
-▁RET	-32556
-▁Fake	-32557
-register	-32558
-motivated	-32559
-▁strengthens	-32560
-▁Manufacturers	-32561
-Mind	-32562
-Poor	-32563
-James	-32564
-shots	-32565
-target	-32566
-usting	-32567
-▁Salon	-32568
-▁sided	-32569
-▁Define	-32570
-▁Grammy	-32571
-▁trough	-32572
-▁captivating	-32573
-▁principally	-32574
-▁suitability	-32575
-UGH	-32576
-▁UX	-32577
-vict	-32578
-▁DAYS	-32579
-▁Dare	-32580
-▁Serge	-32581
-▁critters	-32582
-▁dignified	-32583
-▁sightings	-32584
-▁Interstate	-32585
-▁Journalism	-32586
-ECK	-32587
-▁Sag	-32588
-doors	-32589
-waves	-32590
-▁Hubby	-32591
-▁nudge	-32592
-▁martyr	-32593
-▁coached	-32594
-▁widgets	-32595
-▁telegram	-32596
-▁satisfies	-32597
-▁unsustainable	-32598
-Berry	-32599
-▁JOHN	-32600
-ppings	-32601
-ubilee	-32602
-▁DIRECT	-32603
-▁Sovere	-32604
-▁bunker	-32605
-▁paving	-32606
-▁skewed	-32607
-▁steroid	-32608
-▁grouping	-32609
-▁Hardy	-32610
-▁coldly	-32611
-▁crammed	-32612
-▁scarves	-32613
-▁Exposure	-32614
-▁falsehood	-32615
-▁horsepower	-32616
-▁proactively	-32617
-▁sow	-32618
-▁buoy	-32619
-▁rigor	-32620
-▁Stores	-32621
-▁humbly	-32622
-▁scents	-32623
-▁undead	-32624
-▁deported	-32625
-▁playback	-32626
-▁disparity	-32627
-hoo	-32628
-ortex	-32629
-▁Deck	-32630
-▁quer	-32631
-▁Yikes	-32632
-rupulous	-32633
-▁novices	-32634
-▁affirmed	-32635
-Camp	-32636
-▁SAL	-32637
-▁unen	-32638
-▁Norton	-32639
-▁Quantum	-32640
-▁Improved	-32641
-amus	-32642
-voice	-32643
-ptuous	-32644
-▁vicar	-32645
-▁Liquid	-32646
-▁Tables	-32647
-▁inquis	-32648
-▁laziness	-32649
-▁breakfasts	-32650
-▁victorious	-32651
-sie	-32652
-▁(-	-32653
-▁MAS	-32654
-▁SAP	-32655
-▁FEEL	-32656
-▁Grat	-32657
-▁Mons	-32658
-▁jaws	-32659
-ieties	-32660
-▁annih	-32661
-itudinal	-32662
-▁invoked	-32663
-▁benchmarks	-32664
-SM	-32665
-px	-32666
-Kat	-32667
-eese	-32668
-▁Dart	-32669
-▁perk	-32670
-ophone	-32671
-▁Album	-32672
-▁abode	-32673
-▁palate	-32674
-▁parcels	-32675
-▁walnuts	-32676
-▁overflow	-32677
-▁detergent	-32678
-▁bewildered	-32679
-▁collapsing	-32680
-▁impeachment	-32681
-▁PV	-32682
-ovan	-32683
-▁APR	-32684
-cibly	-32685
-▁Lopez	-32686
-▁exited	-32687
-▁Porsche	-32688
-▁eagerness	-32689
-▁Kem	-32690
-▁Clem	-32691
-fourth	-32692
-▁Canter	-32693
-▁executions	-32694
-▁phy	-32695
-▁Refr	-32696
-▁Alban	-32697
-▁Views	-32698
-▁cheddar	-32699
-▁anchored	-32700
-▁watermelon	-32701
-▁NPC	-32702
-▁PLUS	-32703
-▁irked	-32704
-▁Serbia	-32705
-▁bronch	-32706
-▁deodor	-32707
-▁rundown	-32708
-▁consortium	-32709
-▁incarcerated	-32710
-Cra	-32711
-Had	-32712
-ithe	-32713
-▁TAX	-32714
-▁WWE	-32715
-ptide	-32716
-attack	-32717
-▁ANSWER	-32718
-▁eyeing	-32719
-▁Seeking	-32720
-▁Heavenly	-32721
-▁PST	-32722
-▁cork	-32723
-▁butts	-32724
-▁snowfl	-32725
-▁syring	-32726
-atterson	-32727
-▁pharmacies	-32728
-▁Parliamentary	-32729
-TT	-32730
-▁Myr	-32731
-virus	-32732
-▁Jagu	-32733
-▁zest	-32734
-▁Module	-32735
-▁Morton	-32736
-▁Recipes	-32737
-..........	-32738
-▁Friendship	-32739
-ondo	-32740
-▁Fur	-32741
-ISION	-32742
-Through	-32743
-ishable	-32744
-killers	-32745
-▁menace	-32746
-▁Titanic	-32747
-▁runaway	-32748
-▁emphasizing	-32749
-kh	-32750
-Unt	-32751
-loid	-32752
-▁>>>	-32753
-▁MID	-32754
-▁PAC	-32755
-▁Yog	-32756
-▁parl	-32757
-▁rins	-32758
-▁Atkins	-32759
-military	-32760
-▁clutching	-32761
-▁persecuted	-32762
-▁installments	-32763
-ruck	-32764
-downs	-32765
-▁Dairy	-32766
-▁melts	-32767
-▁padding	-32768
-aughtered	-32769
-▁comforted	-32770
-▁cucumbers	-32771
-▁escalating	-32772
-▁precedence	-32773
-▁Hod	-32774
-igail	-32775
-Google	-32776
-inness	-32777
-▁Theft	-32778
-▁Verse	-32779
-▁amber	-32780
-▁giddy	-32781
-walking	-32782
-▁Fusion	-32783
-▁wrongs	-32784
-▁steamer	-32785
-▁Symposium	-32786
-ohan	-32787
-Guard	-32788
-▁COND	-32789
-▁Meta	-32790
-porter	-32791
-▁Frozen	-32792
-▁adultery	-32793
-▁interruptions	-32794
-onde	-32795
-▁Emmy	-32796
-▁Parr	-32797
-anasia	-32798
-▁erupt	-32799
-▁hazel	-32800
-▁scall	-32801
-▁Crimes	-32802
-▁revoke	-32803
-▁eloquent	-32804
-▁diagnoses	-32805
-▁unattended	-32806
-▁contraction	-32807
-▁incarnation	-32808
-▁Confidential	-32809
-':	-32810
-LLOW	-32811
-mans	-32812
-erella	-32813
-▁Roses	-32814
-▁antid	-32815
-▁broom	-32816
-▁perce	-32817
-▁Hollow	-32818
-▁ebooks	-32819
-▁goggles	-32820
-▁momentarily	-32821
-Non	-32822
-iong	-32823
-▁Voc	-32824
-▁rupt	-32825
-▁vene	-32826
-racial	-32827
-▁hunch	-32828
-▁telev	-32829
-▁Frankie	-32830
-▁Snowden	-32831
-▁praising	-32832
-▁incidentally	-32833
-▁😊	-32834
-!!”	-32835
-▁Overse	-32836
-▁nieces	-32837
-▁rubble	-32838
-▁villas	-32839
-February	-32840
-▁Patriot	-32841
-▁princes	-32842
-typically	-32843
-▁Martinez	-32844
-▁irrevers	-32845
-▁bloodstream	-32846
-ibe	-32847
-cake	-32848
-▁hops	-32849
-efined	-32850
-▁Scots	-32851
-▁cardi	-32852
-imposed	-32853
-▁canals	-32854
-▁braking	-32855
-▁emitted	-32856
-pretation	-32857
-▁Buddhism	-32858
-▁spilling	-32859
-▁stagnant	-32860
-▁pesticide	-32861
-▁Correction	-32862
-▁immortality	-32863
-Fore	-32864
-abol	-32865
-▁suing	-32866
-▁Entire	-32867
-▁filings	-32868
-▁spheres	-32869
-izational	-32870
-▁diversify	-32871
-▁Curriculum	-32872
-▁apprehension	-32873
-acam	-32874
-shock	-32875
-▁Eleanor	-32876
-▁freezes	-32877
-▁hauling	-32878
-▁peeling	-32879
-▁weekday	-32880
-▁Contracts	-32881
-▁Lancaster	-32882
-▁KING	-32883
-▁Lies	-32884
-uities	-32885
-▁Agnes	-32886
-▁Hazel	-32887
-▁inund	-32888
-▁Claudia	-32889
-▁pickles	-32890
-▁infringe	-32891
-▁portrayal	-32892
-▁broadcasts	-32893
-▁mistakenly	-32894
-▁¶	-32895
-hyde	-32896
-▁SUR	-32897
-▁kios	-32898
-▁plank	-32899
-▁vener	-32900
-▁artisan	-32901
-▁prominence	-32902
-▁councillors	-32903
-Bring	-32904
-▁HOUSE	-32905
-▁Snake	-32906
-▁derog	-32907
-▁fount	-32908
-▁elapsed	-32909
-▁pundits	-32910
-▁sighted	-32911
-▁tortilla	-32912
-▁Returning	-32913
-▁MK	-32914
-agua	-32915
-aise	-32916
-ibus	-32917
-estate	-32918
-▁Pirate	-32919
-▁striker	-32920
-▁gestured	-32921
-▁eighteenth	-32922
-▁disqualified	-32923
-chy	-32924
-boxes	-32925
-▁infos	-32926
-▁Kosovo	-32927
-▁Nordic	-32928
-▁breads	-32929
-▁EXACTLY	-32930
-▁crowned	-32931
-▁handbag	-32932
-▁summaries	-32933
-imony	-32934
-▁SAME	-32935
-Family	-32936
-▁metre	-32937
-▁halves	-32938
-▁SERVICE	-32939
-▁Applying	-32940
-▁Existing	-32941
-▁attaching	-32942
-▁Jacksonville	-32943
-fur	-32944
-Shut	-32945
-▁Coy	-32946
-▁Mao	-32947
-▁mex	-32948
-fters	-32949
-ickle	-32950
-quila	-32951
-bought	-32952
-▁Layer	-32953
-▁Alicia	-32954
-▁frugal	-32955
-▁reopen	-32956
-▁Lottery	-32957
-▁changer	-32958
-▁sprinkled	-32959
-▁compounded	-32960
-▁socioeconomic	-32961
-▁Au	-32962
-Lead	-32963
-▁meek	-32964
-▁soar	-32965
-▁Moral	-32966
-▁saith	-32967
-▁Rupert	-32968
-▁slider	-32969
-▁stylist	-32970
-intuitive	-32971
-▁retreats	-32972
-▁winnings	-32973
-▁lighthouse	-32974
-▁redundancy	-32975
-Str	-32976
-mac	-32977
-▁LR	-32978
-▁MEN	-32979
-▁Pear	-32980
-▁hymn	-32981
-▁Joker	-32982
-default	-32983
-▁Cinema	-32984
-▁cradle	-32985
-▁padded	-32986
-evaluate	-32987
-▁Someday	-32988
-▁realtor	-32989
-▁Kingston	-32990
-▁SERVICES	-32991
-▁defiance	-32992
-▁holiness	-32993
-;)	-32994
-Cre	-32995
-looks	-32996
-▁Cycl	-32997
-▁Alexis	-32998
-▁Yugosl	-32999
-▁fiddle	-33000
-▁Context	-33001
-▁Windsor	-33002
-▁belated	-33003
-▁Crossing	-33004
-▁merciful	-33005
-▁Terrorism	-33006
-▁indulging	-33007
-▁verifying	-33008
-feet	-33009
-itto	-33010
-▁NIV	-33011
-single	-33012
-▁Boots	-33013
-▁Naples	-33014
-▁buckle	-33015
-▁rigged	-33016
-▁stripping	-33017
-▁approachable	-33018
-AX	-33019
-fake	-33020
-none	-33021
-ovic	-33022
-▁AIM	-33023
-▁Sof	-33024
-▁UNC	-33025
-▁Nexus	-33026
-▁Gandhi	-33027
-▁scatter	-33028
-▁Wordpress	-33029
-▁criticised	-33030
-▁Accountability	-33031
-Po	-33032
-▁‘	-33033
-YES	-33034
-Line	-33035
-▁Rasp	-33036
-▁Tune	-33037
-▁sham	-33038
-▁wills	-33039
-▁Delete	-33040
-▁Gardner	-33041
-▁harshly	-33042
-▁trickle	-33043
-intention	-33044
-▁Integrity	-33045
-▁hydration	-33046
-▁Physicians	-33047
-▁exhilarating	-33048
-ruci	-33049
-▁buns	-33050
-▁hive	-33051
-▁canning	-33052
-▁skipper	-33053
-▁empowers	-33054
-▁understandably	-33055
-erey	-33056
-▁PNG	-33057
-boats	-33058
-▁Biden	-33059
-▁Luxem	-33060
-▁adage	-33061
-▁firstly	-33062
-▁leftist	-33063
-▁punitive	-33064
-▁declarations	-33065
-lia	-33066
-SELF	-33067
-obil	-33068
-▁Arbit	-33069
-▁opioids	-33070
-▁striped	-33071
-▁disheart	-33072
-▁smuggling	-33073
-▁advertiser	-33074
-esto	-33075
-itality	-33076
-omeness	-33077
-oriasis	-33078
-▁marina	-33079
-▁Duchess	-33080
-▁Leonardo	-33081
-▁cottages	-33082
-▁Robertson	-33083
-Far	-33084
-IPS	-33085
-aaa	-33086
-itte	-33087
-EMBER	-33088
-views	-33089
-▁fades	-33090
-Anything	-33091
-▁buildup	-33092
-▁columnist	-33093
-“.	-33094
-▁UW	-33095
-stat	-33096
-upus	-33097
-▁Teh	-33098
-pline	-33099
-▁Porn	-33100
-aucoma	-33101
-▁Rugby	-33102
-▁Played	-33103
-▁precon	-33104
-▁convict	-33105
-▁debuted	-33106
-▁Prepared	-33107
-▁Arlington	-33108
-pee	-33109
-▁SUB	-33110
-hands	-33111
-▁haze	-33112
-▁Doubt	-33113
-▁Barton	-33114
-▁Newman	-33115
-▁Englishman	-33116
-laces	-33117
-▁Tired	-33118
-▁hurtful	-33119
-▁teammate	-33120
-▁workable	-33121
-▁erroneous	-33122
-▁rectangle	-33123
-Sen	-33124
-IDEO	-33125
-▁Kum	-33126
-▁bead	-33127
-▁depl	-33128
-cester	-33129
-▁Creed	-33130
-▁vents	-33131
-▁emblem	-33132
-▁manure	-33133
-▁Hammond	-33134
-▁digress	-33135
-▁appalled	-33136
-▁elective	-33137
-▁scraping	-33138
-▁Conversely	-33139
-▁arithmetic	-33140
-▁pharmaceuticals	-33141
-▁Naked	-33142
-esigned	-33143
-▁Lizzie	-33144
-▁audiob	-33145
-▁disarm	-33146
-▁sinner	-33147
-▁merging	-33148
-▁vividly	-33149
-▁Plymouth	-33150
-▁clubhouse	-33151
-MIN	-33152
-----"	-33153
-▁Seat	-33154
-factor	-33155
-issive	-33156
-▁clung	-33157
-▁trance	-33158
-▁Younger	-33159
-▁staples	-33160
-▁revising	-33161
-▁Fitzgerald	-33162
-▁redirected	-33163
-▁thanksgiving	-33164
-,,,	-33165
-▁Lac	-33166
-cents	-33167
-▁cages	-33168
-▁drows	-33169
-▁plush	-33170
-▁SYSTEM	-33171
-▁septic	-33172
-▁wouldnt	-33173
-▁Lighting	-33174
-▁discredit	-33175
-▁gravitational	-33176
-anca	-33177
-deal	-33178
-▁Tat	-33179
-lated	-33180
-ruits	-33181
-▁MADE	-33182
-▁foes	-33183
-christ	-33184
-escent	-33185
-▁Levels	-33186
-▁petals	-33187
-▁Portfolio	-33188
-▁delegated	-33189
-▁downstream	-33190
-▁furthermore	-33191
-tta	-33192
-▁NV	-33193
-▁sly	-33194
-apest	-33195
-forms	-33196
-▁Hass	-33197
-Twitter	-33198
-burning	-33199
-▁Horses	-33200
-▁Worlds	-33201
-▁leaped	-33202
-▁Diseases	-33203
-▁relegated	-33204
-▁charismatic	-33205
-▁salespeople	-33206
-ongo	-33207
-Table	-33208
-yalgia	-33209
-▁tempo	-33210
-▁jumper	-33211
-▁electroly	-33212
-▁reiterate	-33213
-▁astronauts	-33214
-▁preoccupied	-33215
-▁uncertainties	-33216
-▁Tou	-33217
-citiz	-33218
-▁ethn	-33219
-Sounds	-33220
-▁sesame	-33221
-physical	-33222
-▁Dynamic	-33223
-▁Tropical	-33224
-▁apprehend	-33225
-▁materially	-33226
-▁perpetrator	-33227
-▁legalization	-33228
-Met	-33229
-anza	-33230
-llen	-33231
-▁Schne	-33232
-liberal	-33233
-▁Digest	-33234
-▁hahaha	-33235
-▁starred	-33236
-iscovered	-33237
-▁bargains	-33238
-▁clarifying	-33239
-Sah	-33240
-▁Pip	-33241
-▁shin	-33242
-▁Penal	-33243
-▁fudge	-33244
-▁pedag	-33245
-▁Blacks	-33246
-▁racket	-33247
-▁timley	-33248
-▁pondered	-33249
-▁indefinite	-33250
-FTA	-33251
-Got	-33252
-zar	-33253
-▁INV	-33254
-▁Yuk	-33255
-Level	-33256
-▁pomp	-33257
-igrant	-33258
-▁Juice	-33259
-▁PROGR	-33260
-▁Letting	-33261
-▁Panthers	-33262
-▁patented	-33263
-▁ingenuity	-33264
-▁serotonin	-33265
-▁Opposition	-33266
-▁contestant	-33267
-fet	-33268
-fail	-33269
-iera	-33270
-▁Nell	-33271
-▁govt	-33272
-female	-33273
-▁harms	-33274
-▁mower	-33275
-▁initials	-33276
-▁Sanctuary	-33277
-▁physicist	-33278
-▁tortillas	-33279
-▁AUTH	-33280
-▁ENTER	-33281
-▁mural	-33282
-▁metast	-33283
-▁quinoa	-33284
-▁wagons	-33285
-▁headers	-33286
-▁sensual	-33287
-▁unicorn	-33288
-▁footprints	-33289
-▁complainant	-33290
-▁contradictions	-33291
-fers	-33292
-▁Calm	-33293
-▁Joanna	-33294
-▁helpers	-33295
-▁unnamed	-33296
-▁Symphony	-33297
-▁unimportant	-33298
-▁alternatively	-33299
-!]	-33300
-zza	-33301
-▁strand	-33302
-▁mascara	-33303
-▁Addiction	-33304
-▁Electoral	-33305
-▁defeating	-33306
-▁narrowing	-33307
-▁slaughtered	-33308
-?”,	-33309
-Word	-33310
-anye	-33311
-oufl	-33312
-▁pas	-33313
-inism	-33314
-▁Lyme	-33315
-▁necks	-33316
-▁waive	-33317
-▁recycl	-33318
-▁accrued	-33319
-▁bashing	-33320
-▁instill	-33321
-Christmas	-33322
-▁newcomer	-33323
-exe	-33324
-▁bidder	-33325
-▁Belfast	-33326
-romyalgia	-33327
-▁Bathroom	-33328
-▁provoked	-33329
-▁socialize	-33330
-▁disturbances	-33331
-▁unauthorised	-33332
-ERT	-33333
-IRED	-33334
-hair	-33335
-ipeg	-33336
-ulge	-33337
-types	-33338
-▁Eggs	-33339
-▁Whew	-33340
-▁Nicar	-33341
-aculate	-33342
-▁Apache	-33343
-▁Helena	-33344
-▁Memories	-33345
-▁jab	-33346
-bells	-33347
-▁balm	-33348
-methyl	-33349
-despite	-33350
-▁solace	-33351
-▁Mozilla	-33352
-▁Carnegie	-33353
-▁rejoined	-33354
-▁Battalion	-33355
-▁Satellite	-33356
-▁contextual	-33357
-WR	-33358
-Bad	-33359
-GAN	-33360
-Har	-33361
-▁Ti	-33362
-avian	-33363
-▁Monk	-33364
-▁Quad	-33365
-parency	-33366
-▁arduous	-33367
-▁brigade	-33368
-▁inhibit	-33369
-▁impulses	-33370
-▁backpacks	-33371
-▁originating	-33372
-▁subordinate	-33373
-▁obe	-33374
-cliff	-33375
-▁Cait	-33376
-cancer	-33377
-▁Split	-33378
-▁Templ	-33379
-▁Zombie	-33380
-▁Notting	-33381
-▁motorist	-33382
-▁radiator	-33383
-▁rightful	-33384
-efficiency	-33385
-runs	-33386
-toxic	-33387
-▁Dish	-33388
-▁Brave	-33389
-▁camped	-33390
-▁diagon	-33391
-▁supers	-33392
-▁clasped	-33393
-▁Libraries	-33394
-▁supremacy	-33395
-▁Grandmother	-33396
-Boy	-33397
-▁mop	-33398
-otech	-33399
-▁OECD	-33400
-▁canv	-33401
-▁Overs	-33402
-▁Tories	-33403
-▁anemia	-33404
-▁Acceler	-33405
-▁catered	-33406
-▁shimmer	-33407
-othelioma	-33408
-▁Creativity	-33409
-eller	-33410
-▁Brock	-33411
-▁Samar	-33412
-▁swine	-33413
-▁Ronnie	-33414
-▁Worked	-33415
-▁butler	-33416
-▁cranky	-33417
-▁ticked	-33418
-▁cursing	-33419
-▁Residence	-33420
-▁unsettling	-33421
-▁cooperating	-33422
-INA	-33423
-doch	-33424
-vitra	-33425
-▁Gail	-33426
-▁tote	-33427
-▁Prophe	-33428
-▁barber	-33429
-▁sizing	-33430
-▁distill	-33431
-wikipedia	-33432
-▁Inspection	-33433
-▁supplemented	-33434
-magic	-33435
-▁doin	-33436
-▁bombard	-33437
-▁drizzle	-33438
-▁prettiest	-33439
-▁electrician	-33440
-Jew	-33441
-▁sped	-33442
-ISSION	-33443
-▁Devils	-33444
-▁Davidson	-33445
-inian	-33446
-▁Labs	-33447
-▁Blade	-33448
-▁Bonds	-33449
-▁Tanya	-33450
-▁Behold	-33451
-▁Titans	-33452
-▁Reducing	-33453
-▁Restrict	-33454
-▁barracks	-33455
-▁burglary	-33456
-▁nationalism	-33457
-▁translators	-33458
-▁distinctions	-33459
-▁Traditionally	-33460
-raud	-33461
-▁ppl	-33462
-▁viz	-33463
-▁lash	-33464
-▁uphe	-33465
-omever	-33466
-▁docks	-33467
-▁snows	-33468
-▁Buffett	-33469
-▁Showing	-33470
-▁lunatic	-33471
-▁renders	-33472
-▁Thankyou	-33473
-▁cilantro	-33474
-▁psoriasis	-33475
-▁Inspiration	-33476
-▁humiliating	-33477
-gano	-33478
-▁ACA	-33479
-▁html	-33480
-▁cutie	-33481
-▁lords	-33482
-serious	-33483
-▁Models	-33484
-▁steaks	-33485
-▁souvenir	-33486
-▁resonance	-33487
-▁conceivable	-33488
-▁malnutrition	-33489
-Del	-33490
-Way	-33491
-▁AU	-33492
-▁OCD	-33493
-Sense	-33494
-▁pact	-33495
-▁Byron	-33496
-avement	-33497
-▁Hebrews	-33498
-▁forgets	-33499
-▁hammock	-33500
-▁prowess	-33501
-▁toasted	-33502
-▁kayaking	-33503
-▁Dangerous	-33504
-▁Sya	-33505
-▁harb	-33506
-▁caste	-33507
-▁laden	-33508
-recious	-33509
-▁Seminar	-33510
-▁backlink	-33511
-▁hypnosis	-33512
-▁physique	-33513
-▁perennial	-33514
-▁retreated	-33515
-▁academically	-33516
-ctica	-33517
-reader	-33518
-▁engulf	-33519
-▁infirm	-33520
-balances	-33521
-▁chromos	-33522
-▁Matthews	-33523
-▁crippled	-33524
-▁Parenting	-33525
-▁raspberry	-33526
-▁transient	-33527
-▁inflatable	-33528
-▁Kindergarten	-33529
-▁diversification	-33530
-▁Macy	-33531
-▁golfers	-33532
-▁inaccur	-33533
-▁Californ	-33534
-▁Supplier	-33535
-▁scaffold	-33536
-▁uprising	-33537
-▁wasteful	-33538
-▁assemblies	-33539
-▁postseason	-33540
-▁proclamation	-33541
-Buy	-33542
-Hist	-33543
-events	-33544
-▁abyss	-33545
-▁chime	-33546
-Whether	-33547
-▁masked	-33548
-▁prized	-33549
-▁Picking	-33550
-▁deletion	-33551
-▁unleashed	-33552
-▁correlated	-33553
-tested	-33554
-▁Carey	-33555
-▁aides	-33556
-▁haters	-33557
-▁autopsy	-33558
-▁freshness	-33559
-▁groundwater	-33560
-ede	-33561
-ptu	-33562
-▁crem	-33563
-ensibly	-33564
-▁Enhance	-33565
-▁Telling	-33566
-▁eyelids	-33567
-▁Judgment	-33568
-▁assuring	-33569
-▁distaste	-33570
-▁unworthy	-33571
-▁extremism	-33572
-▁originate	-33573
-▁remission	-33574
-▁reinforces	-33575
-▁perpetrated	-33576
-▁LEG	-33577
-▁RPM	-33578
-frequ	-33579
-▁Boom	-33580
-▁Aging	-33581
-▁Freel	-33582
-▁grues	-33583
-▁Brands	-33584
-▁Manson	-33585
-▁unmist	-33586
-▁certify	-33587
-▁campfire	-33588
-▁Influence	-33589
-▁analysing	-33590
-▁offseason	-33591
-▁antibodies	-33592
-▁substitutes	-33593
-▁redevelopment	-33594
-kah	-33595
-▁CENT	-33596
-▁iMac	-33597
-▁puck	-33598
-▁Valid	-33599
-▁fella	-33600
-▁kudos	-33601
-Special	-33602
-▁scaven	-33603
-▁Emerson	-33604
-▁moaning	-33605
-▁rhythms	-33606
-▁robbers	-33607
-▁spanking	-33608
-▁impatience	-33609
-▁stimulates	-33610
-▁Sout	-33611
-▁blem	-33612
-▁kite	-33613
-▁Erika	-33614
-▁Feast	-33615
-▁Helps	-33616
-▁Nigel	-33617
-Donnell	-33618
-▁Atmosp	-33619
-▁crocod	-33620
-▁depict	-33621
-produced	-33622
-▁Syaoran	-33623
-▁bottoms	-33624
-ciliation	-33625
-▁hurriedly	-33626
-▁symposium	-33627
-▁Huffington	-33628
-idisciplinary	-33629
-^^	-33630
-azy	-33631
-Turn	-33632
-frey	-33633
-▁peb	-33634
-riety	-33635
-▁Lana	-33636
-▁WAIT	-33637
-▁dope	-33638
-▁PROVID	-33639
-▁THANKS	-33640
-▁transact	-33641
-▁Counseling	-33642
-▁identifier	-33643
-▁innovators	-33644
-txt	-33645
-▁hue	-33646
-▁Blow	-33647
-▁Roads	-33648
-▁heals	-33649
-▁Abigail	-33650
-▁fooling	-33651
-▁galaxies	-33652
-▁Brunswick	-33653
-▁undertook	-33654
-▁depressive	-33655
-▁recognises	-33656
-▁cancellations	-33657
-ldon	-33658
-Human	-33659
-sofar	-33660
-▁Pilgr	-33661
-▁Messages	-33662
-▁forcibly	-33663
-forestation	-33664
-▁Statistical	-33665
-▁DIF	-33666
-▁heres	-33667
-ittance	-33668
-▁Pamela	-33669
-▁Inquiry	-33670
-▁impactful	-33671
-▁confiscated	-33672
-▁incomprehens	-33673
-Ep	-33674
-argu	-33675
-pipe	-33676
-geois	-33677
-olfer	-33678
-▁glut	-33679
-bright	-33680
-▁Allerg	-33681
-▁Kuwait	-33682
-▁calves	-33683
-▁connot	-33684
-▁premie	-33685
-▁bystand	-33686
-▁brussels	-33687
-▁synagogue	-33688
-▁abundantly	-33689
-Rich	-33690
-been	-33691
-unta	-33692
-▁jig	-33693
-abolic	-33694
-▁wasnt	-33695
-▁solids	-33696
-▁texted	-33697
-▁webcam	-33698
-▁Graphics	-33699
-▁awkwardly	-33700
-▁estimating	-33701
-,'"	-33702
-zek	-33703
-drawn	-33704
-salms	-33705
-▁gals	-33706
-▁Paleo	-33707
-ishments	-33708
-▁dialect	-33709
-▁peering	-33710
-▁renamed	-33711
-▁fullness	-33712
-▁slamming	-33713
-▁bilingual	-33714
-▁nightclub	-33715
-▁discerning	-33716
-▁fabrication	-33717
-▁grammatical	-33718
-▁Pew	-33719
-leine	-33720
-Anyway	-33721
-▁Logic	-33722
-▁Noise	-33723
-picable	-33724
-▁HARRIS	-33725
-▁Insight	-33726
-▁glamour	-33727
-▁steamed	-33728
-▁Orchestra	-33729
-▁aerospace	-33730
-▁educative	-33731
-▁Javascript	-33732
-▁foreground	-33733
-▁purchasers	-33734
-▁transforms	-33735
-▁experimented	-33736
-ldquo	-33737
-▁????	-33738
-rifice	-33739
-udence	-33740
-▁fauna	-33741
-acamole	-33742
-▁malice	-33743
-▁multit	-33744
-download	-33745
-▁Cardiff	-33746
-▁Listing	-33747
-▁Aus	-33748
-▁Cav	-33749
-▁Lift	-33750
-▁dreamt	-33751
-▁immerse	-33752
-▁monoxide	-33753
-▁fiduciary	-33754
-pec	-33755
-rils	-33756
-inski	-33757
-▁Bree	-33758
-▁smith	-33759
-▁Tumblr	-33760
-▁facade	-33761
-▁Cricket	-33762
-▁Failing	-33763
-▁embodied	-33764
-▁Egyptians	-33765
-▁casserole	-33766
-▁insecurities	-33767
-▁Wig	-33768
-ndyke	-33769
-▁coer	-33770
-▁dens	-33771
-▁puls	-33772
-▁Manif	-33773
-▁Toilet	-33774
-▁Borders	-33775
-▁Raleigh	-33776
-▁trucking	-33777
-▁woodland	-33778
-▁symbolism	-33779
-▁Categories	-33780
-▁procrastinate	-33781
-▁CJ	-33782
-Spot	-33783
-phil	-33784
-ggling	-33785
-picked	-33786
-▁Diary	-33787
-▁assail	-33788
-▁neurot	-33789
-illusion	-33790
-▁defeats	-33791
-▁inhabit	-33792
-inspiring	-33793
-producing	-33794
-▁consumes	-33795
-▁gruesome	-33796
-▁turquoise	-33797
-▁voicemail	-33798
-▁geological	-33799
-▁prototypes	-33800
-▁Restoration	-33801
-▁intoxicated	-33802
-ugh	-33803
-▁Bie	-33804
-▁jug	-33805
-coach	-33806
-▁airy	-33807
-verbal	-33808
-▁accol	-33809
-▁faiths	-33810
-▁bombers	-33811
-▁orchard	-33812
-▁bombings	-33813
-▁eruption	-33814
-▁initiating	-33815
-▁stu	-33816
-Shall	-33817
-▁Junk	-33818
-▁cele	-33819
-website	-33820
-▁Athletic	-33821
-▁flavored	-33822
-▁constraint	-33823
-▁rightfully	-33824
-shift	-33825
-anyahu	-33826
-ursday	-33827
-▁Slack	-33828
-▁ivory	-33829
-▁mummy	-33830
-▁Surrey	-33831
-administ	-33832
-▁Hardware	-33833
-▁anecdote	-33834
-▁oversees	-33835
-▁harassing	-33836
-▁unresolved	-33837
-▁intervening	-33838
-▁transmitter	-33839
-Sex	-33840
-rub	-33841
-▁sis	-33842
-bones	-33843
-volume	-33844
-▁circa	-33845
-▁remix	-33846
-▁quizzes	-33847
-▁wrestle	-33848
-▁squarely	-33849
-conference	-33850
-▁Classical	-33851
-Att	-33852
-▁IX	-33853
-▁JR	-33854
-▁(%)	-33855
-▁Casa	-33856
-▁Koch	-33857
-berger	-33858
-▁Locke	-33859
-▁shawl	-33860
-ammable	-33861
-▁dissect	-33862
-▁reversing	-33863
-▁alternating	-33864
-▁dependencies	-33865
-hift	-33866
-▁dop	-33867
-daily	-33868
-fleet	-33869
-tender	-33870
-▁Spell	-33871
-▁Barney	-33872
-▁Elimin	-33873
-▁Laurel	-33874
-▁stormy	-33875
-▁Clothing	-33876
-▁Emerging	-33877
-▁presenters	-33878
-▁ambassadors	-33879
-ivo	-33880
-sad	-33881
-▁nun	-33882
-etine	-33883
-▁PROC	-33884
-▁biomass	-33885
-▁infused	-33886
-▁Thorndyke	-33887
-Lab	-33888
-▁pups	-33889
-▁slit	-33890
-isable	-33891
-tending	-33892
-▁Somers	-33893
-▁binder	-33894
-▁maniac	-33895
-▁absentee	-33896
-▁unfolded	-33897
-▁yearning	-33898
-▁improbable	-33899
-▁GIR	-33900
-▁Lap	-33901
-ourced	-33902
-▁egreg	-33903
-▁manly	-33904
-▁freaks	-33905
-▁clashes	-33906
-▁fraught	-33907
-▁philosophies	-33908
-Hard	-33909
-tick	-33910
-▁Jak	-33911
-▁Bess	-33912
-▁Sans	-33913
-▁uber	-33914
-▁marin	-33915
-▁crater	-33916
-▁discol	-33917
-▁licked	-33918
-▁parrot	-33919
-dropping	-33920
-▁Friedman	-33921
-▁replicated	-33922
-▁silhouette	-33923
-TB	-33924
-Doc	-33925
-nery	-33926
-▁Flet	-33927
-▁Orch	-33928
-▁hawk	-33929
-ammered	-33930
-regarded	-33931
-▁impetus	-33932
-▁Relevant	-33933
-▁auxiliary	-33934
-▁institutes	-33935
-▁FW	-33936
-object	-33937
-▁trifle	-33938
-▁uncles	-33939
-▁pretext	-33940
-▁redress	-33941
-▁Griffith	-33942
-▁craziness	-33943
-▁substituting	-33944
-abl	-33945
-Photo	-33946
-entary	-33947
-▁masse	-33948
-student	-33949
-▁Holder	-33950
-▁sniper	-33951
-▁adhered	-33952
-▁engraved	-33953
-▁trembled	-33954
-▁eternally	-33955
-▁vineyards	-33956
-▁Compassion	-33957
-ppa	-33958
-▁LM	-33959
-midt	-33960
-▁LAN	-33961
-tration	-33962
-▁Fulton	-33963
-▁Robbie	-33964
-▁clowns	-33965
-▁swayed	-33966
-▁tripped	-33967
-▁trekking	-33968
-▁Gentlemen	-33969
-▁pathology	-33970
-leaf	-33971
-▁nay	-33972
-▁FIND	-33973
-▁shun	-33974
-▁Hurry	-33975
-▁twink	-33976
-▁Nowhere	-33977
-▁pertains	-33978
-▁simulations	-33979
-▁manufactures	-33980
-▁manifestations	-33981
-▁technologically	-33982
-TERS	-33983
-▁Yan	-33984
-reast	-33985
-▁Semi	-33986
-▁boon	-33987
-▁Runner	-33988
-▁douche	-33989
-▁toured	-33990
-▁discrep	-33991
-▁nonfiction	-33992
-▁exponential	-33993
-axon	-33994
-hement	-33995
-▁Clyde	-33996
-▁outst	-33997
-▁Julius	-33998
-▁subtly	-33999
-▁Shelter	-34000
-▁Railroad	-34001
-▁hairstyle	-34002
-▁livelihoods	-34003
-▁Aya	-34004
-Bless	-34005
-▁Chak	-34006
-▁Xmas	-34007
-▁awes	-34008
-▁Paige	-34009
-▁marqu	-34010
-▁unatt	-34011
-▁wooded	-34012
-▁evolves	-34013
-▁Dominion	-34014
-▁Controller	-34015
-▁autobiography	-34016
-▁discretionary	-34017
-?".	-34018
-▁fag	-34019
-pharm	-34020
-▁Films	-34021
-▁ponds	-34022
-mounted	-34023
-▁Crafts	-34024
-▁Depend	-34025
-▁clerks	-34026
-▁sensit	-34027
-▁Saviour	-34028
-▁pausing	-34029
-▁unfolds	-34030
-▁powerhouse	-34031
-▁LC	-34032
-▁FRE	-34033
-owitz	-34034
-."..."	-34035
-▁WRONG	-34036
-▁Feature	-34037
-▁Sanchez	-34038
-▁politic	-34039
-▁Lutheran	-34040
-▁spirited	-34041
-▁bombarded	-34042
-▁souvenirs	-34043
-▁Prosecutor	-34044
-▁Enterprises	-34045
-▁impoverished	-34046
-▁lore	-34047
-▁popup	-34048
-▁battled	-34049
-▁Featured	-34050
-▁breached	-34051
-▁regulars	-34052
-▁stabbing	-34053
-Ben	-34054
-Food	-34055
-alys	-34056
-opin	-34057
-▁Piece	-34058
-▁fungi	-34059
-olithic	-34060
-▁Alumni	-34061
-▁flurry	-34062
-▁sitcom	-34063
-▁severed	-34064
-▁Renewable	-34065
-▁Valentines	-34066
-▁flourishing	-34067
-▁Gus	-34068
-etera	-34069
-prime	-34070
-separ	-34071
-▁Impl	-34072
-▁KEEP	-34073
-▁Tend	-34074
-iatrics	-34075
-▁pizzas	-34076
-▁upkeep	-34077
-▁exposes	-34078
-▁Presidency	-34079
-▁memorandum	-34080
-▁millennium	-34081
-▁summertime	-34082
-▁adaptations	-34083
-▁facilitator	-34084
-task	-34085
-▁FAA	-34086
-agent	-34087
-▁spar	-34088
-▁Mouth	-34089
-▁lawns	-34090
-▁jerked	-34091
-▁roared	-34092
-▁Mistress	-34093
-▁Languages	-34094
-▁uninstall	-34095
-▁Philippine	-34096
-▁housekeeper	-34097
-ymm	-34098
-▁OD	-34099
-▁WI	-34100
-hman	-34101
-▁Sons	-34102
-▁limo	-34103
-▁Lilly	-34104
-▁derail	-34105
-▁cluttered	-34106
-▁Investments	-34107
-▁liquidation	-34108
-▁inaccessible	-34109
-analy	-34110
-▁Hast	-34111
-▁gulf	-34112
-Friend	-34113
-Global	-34114
-▁abras	-34115
-▁Compos	-34116
-▁Guards	-34117
-▁Hilary	-34118
-▁embody	-34119
-▁afterlife	-34120
-▁stiffness	-34121
-▁redesigned	-34122
-▁contrasting	-34123
-▁Establishment	-34124
-wp	-34125
-guide	-34126
-▁Blend	-34127
-▁Pixel	-34128
-▁frail	-34129
-achable	-34130
-▁Mighty	-34131
-▁messes	-34132
-▁Praying	-34133
-▁boomers	-34134
-▁oysters	-34135
-▁frighten	-34136
-heets	-34137
-▁COST	-34138
-dehyde	-34139
-▁FOLLOW	-34140
-▁washes	-34141
-▁Impress	-34142
-▁starved	-34143
-▁balconies	-34144
-▁disinfect	-34145
-▁subtitles	-34146
-▁Developers	-34147
-?—	-34148
-AMP	-34149
-▁len	-34150
-ooray	-34151
-stice	-34152
-▁Jerome	-34153
-▁amazes	-34154
-▁richly	-34155
-▁rowing	-34156
-▁Dodgers	-34157
-▁puzzling	-34158
-▁Institutes	-34159
-▁exhibitors	-34160
-▁articulated	-34161
-▁›	-34162
-Money	-34163
-▁alkal	-34164
-▁Length	-34165
-▁Pelosi	-34166
-▁Rwanda	-34167
-▁Murdoch	-34168
-▁blister	-34169
-▁slumber	-34170
-▁Winnipeg	-34171
-▁countered	-34172
-▁unwelcome	-34173
-▁intermediary	-34174
-bach	-34175
-edit	-34176
-▁SOC	-34177
-▁Feet	-34178
-▁mayo	-34179
-▁punt	-34180
-▁Cabin	-34181
-▁Crash	-34182
-▁Fancy	-34183
-▁avert	-34184
-▁Format	-34185
-▁Scotty	-34186
-quartered	-34187
-▁circling	-34188
-▁composure	-34189
-▁Kardashian	-34190
-Det	-34191
-Win	-34192
-cie	-34193
-▁AIR	-34194
-▁Uni	-34195
-▁geo	-34196
-ittal	-34197
-▁Dates	-34198
-▁girly	-34199
-▁litre	-34200
-ranking	-34201
-▁Austen	-34202
-▁ascent	-34203
-▁occult	-34204
-istrates	-34205
-▁alluded	-34206
-▁Brittany	-34207
-▁willpower	-34208
-▁Alexandria	-34209
-▁intimidate	-34210
-▁condescending	-34211
-Quite	-34212
-▁plur	-34213
-▁volt	-34214
-▁Bowie	-34215
-▁inept	-34216
-▁reinc	-34217
-esville	-34218
-overning	-34219
-▁monolog	-34220
-urrection	-34221
-▁Cannabis	-34222
-▁nobility	-34223
-▁resonated	-34224
-▁shortfall	-34225
-▁distinguishing	-34226
-▁supplementation	-34227
-atoid	-34228
-▁Chess	-34229
-▁blurb	-34230
-▁Wagner	-34231
-ruciating	-34232
-▁underage	-34233
-▁cylinders	-34234
-TI	-34235
-▁hr	-34236
-Bill	-34237
-TING	-34238
-avez	-34239
-Techn	-34240
-▁Gren	-34241
-akable	-34242
-▁Useful	-34243
-▁Michele	-34244
-▁mischie	-34245
-▁Ordinary	-34246
-▁pitchers	-34247
-Mem	-34248
-dox	-34249
-fen	-34250
-▁XX	-34251
-▁NON	-34252
-▁osc	-34253
-▁Lars	-34254
-▁berth	-34255
-▁Richie	-34256
-▁skyline	-34257
-▁silenced	-34258
-▁distilled	-34259
-▁plagorism	-34260
-ICO	-34261
-imple	-34262
-▁Dropbox	-34263
-▁Insider	-34264
-▁Steelers	-34265
-▁lingerie	-34266
-▁concierge	-34267
-▁continuum	-34268
-▁ingrained	-34269
-▁signaling	-34270
-▁homosexuals	-34271
-tag	-34272
-▁GF	-34273
-▁Rox	-34274
-▁sores	-34275
-oxetine	-34276
-seeking	-34277
-▁Stacey	-34278
-▁dataset	-34279
-▁purposeful	-34280
-▁completeness	-34281
-gos	-34282
-tel	-34283
-▁TSA	-34284
-Water	-34285
-civil	-34286
-ollah	-34287
-▁COLL	-34288
-▁sewn	-34289
-asonic	-34290
-▁glaze	-34291
-creative	-34292
-▁Darling	-34293
-▁cowardly	-34294
-▁crumbling	-34295
-▁Committees	-34296
-▁detachment	-34297
-▁filtration	-34298
-▁logistical	-34299
-bab	-34300
-▁az	-34301
-ocra	-34302
-▁cull	-34303
-▁ANYONE	-34304
-▁Manuel	-34305
-▁revolve	-34306
-▁toolbar	-34307
-▁asserting	-34308
-▁bookstores	-34309
-▁endeavours	-34310
-▁undermined	-34311
-▁prohibiting	-34312
-▁digg	-34313
-▁Proud	-34314
-▁thorn	-34315
-▁Observ	-34316
-▁caters	-34317
-▁dreary	-34318
-▁oblige	-34319
-▁weekdays	-34320
-▁campground	-34321
-▁restraints	-34322
-▁Participate	-34323
-▁constituent	-34324
-▁procrastination	-34325
-itic	-34326
-hound	-34327
-▁Seas	-34328
-▁Socr	-34329
-▁VIII	-34330
-▁Gerry	-34331
-▁grudge	-34332
-▁abiding	-34333
-▁Reverend	-34334
-▁binocular	-34335
-▁geothermal	-34336
-▁characterize	-34337
-▁hypocritical	-34338
-▁stockholders	-34339
-▁Fi	-34340
-umph	-34341
-▁Trou	-34342
-▁pist	-34343
-▁unob	-34344
-valued	-34345
-▁catar	-34346
-▁acidic	-34347
-▁flashy	-34348
-▁chanting	-34349
-▁perplexed	-34350
-acea	-34351
-spir	-34352
-▁brandy	-34353
-▁saliva	-34354
-▁shrunk	-34355
-etically	-34356
-▁tainted	-34357
-▁Extended	-34358
-▁aversion	-34359
-▁cortisol	-34360
-▁telegraph	-34361
-▁repression	-34362
-%-	-34363
-ifix	-34364
-▁rink	-34365
-iguous	-34366
-▁ORDER	-34367
-▁Whose	-34368
-▁berry	-34369
-housing	-34370
-▁Mainly	-34371
-▁devils	-34372
-▁dreamy	-34373
-▁Maureen	-34374
-▁SPECIAL	-34375
-▁decipher	-34376
-▁swirling	-34377
-▁australia	-34378
-▁cinematic	-34379
-▁contempor	-34380
-▁sunflower	-34381
-▁hypotheses	-34382
-▁TL	-34383
-▁Lud	-34384
-nature	-34385
-▁hiber	-34386
-▁Assass	-34387
-▁Uniform	-34388
-▁sharpen	-34389
-▁churning	-34390
-▁hastened	-34391
-▁federally	-34392
-▁Headquarters	-34393
-Hell	-34394
-ULTZ	-34395
-fine	-34396
-▁ASK	-34397
-▁Meal	-34398
-▁Mole	-34399
-▁Twins	-34400
-▁pivot	-34401
-▁Longer	-34402
-▁Merlin	-34403
-▁phishing	-34404
-▁SOMETHING	-34405
-▁recalling	-34406
-▁Impossible	-34407
-▁healthiest	-34408
-▁specialise	-34409
-▁pediatrician	-34410
-▁VW	-34411
-Beaut	-34412
-hings	-34413
-▁Swim	-34414
-ecting	-34415
-images	-34416
-▁flagged	-34417
-▁Majority	-34418
-▁Maritime	-34419
-▁hopelessly	-34420
-▁ordinances	-34421
-▁Restaurants	-34422
-dose	-34423
-▁actu	-34424
-▁xoxo	-34425
-inting	-34426
-ionage	-34427
-▁Screw	-34428
-▁Trick	-34429
-▁dodgy	-34430
-▁Critics	-34431
-▁accomod	-34432
-▁leopard	-34433
-▁sublime	-34434
-▁Cinnamon	-34435
-▁airborne	-34436
-▁pancreat	-34437
-▁Netanyahu	-34438
-▁moisturizer	-34439
-▁predetermined	-34440
-.”)	-34441
-bill	-34442
-opted	-34443
-▁LITT	-34444
-▁Phen	-34445
-▁Fargo	-34446
-▁bathe	-34447
-▁BETTER	-34448
-▁Anthrop	-34449
-▁excludes	-34450
-▁chiropractor	-34451
-ERO	-34452
-▁JG	-34453
-Dark	-34454
-▁JFK	-34455
-diagn	-34456
-▁Guru	-34457
-▁unfav	-34458
-▁refute	-34459
-▁retract	-34460
-▁mitigating	-34461
-▁nervousness	-34462
-▁prematurely	-34463
-git	-34464
-▁pi	-34465
-habi	-34466
-▁Rover	-34467
-▁insin	-34468
-atorial	-34469
-▁savory	-34470
-mination	-34471
-▁Clayton	-34472
-organisms	-34473
-▁intersect	-34474
-▁Chronicles	-34475
-▁Legislation	-34476
-▁undermining	-34477
-inia	-34478
-byter	-34479
-riott	-34480
-▁Hawks	-34481
-▁Lever	-34482
-▁orally	-34483
-▁asserts	-34484
-▁hotline	-34485
-▁whatnot	-34486
-▁richness	-34487
-▁simulate	-34488
-▁vocation	-34489
-▁nighttime	-34490
-▁preseason	-34491
-▁gracefully	-34492
-▁moot	-34493
-▁obit	-34494
-▁Oblig	-34495
-▁Terri	-34496
-▁sling	-34497
-▁Emerald	-34498
-▁SCHULTZ	-34499
-▁solemnly	-34500
-▁fortified	-34501
-▁spectator	-34502
-▁Efficiency	-34503
-▁Zuckerberg	-34504
-▁fulfilment	-34505
-reas	-34506
-rone	-34507
-virt	-34508
-▁geop	-34509
-▁Deals	-34510
-▁Papua	-34511
-Captain	-34512
-▁Willis	-34513
-Republic	-34514
-▁Mention	-34515
-▁rejects	-34516
-▁Felicity	-34517
-▁Finished	-34518
-▁evacuate	-34519
-▁worsening	-34520
-engineering	-34521
-fts	-34522
-▁ont	-34523
-ppies	-34524
-▁Loop	-34525
-▁Quit	-34526
-▁sout	-34527
-Footnote	-34528
-▁abreast	-34529
-▁cordial	-34530
-▁prairie	-34531
-▁embraces	-34532
-▁hammered	-34533
-▁organically	-34534
-▁Kau	-34535
-▁Meh	-34536
-indeer	-34537
-ussing	-34538
-▁Dixon	-34539
-▁minis	-34540
-▁Budapest	-34541
-▁Tuesdays	-34542
-▁psychotic	-34543
-▁Luxembourg	-34544
-Av	-34545
-kun	-34546
-▁Abd	-34547
-▁Actor	-34548
-▁Freud	-34549
-▁Cornell	-34550
-▁clogged	-34551
-▁remediation	-34552
-▁breakthroughs	-34553
-JV	-34554
-IVES	-34555
-ibar	-34556
-▁mun	-34557
-slide	-34558
-▁Chal	-34559
-impact	-34560
-▁fuckin	-34561
-▁Clement	-34562
-▁waffles	-34563
-▁patriarch	-34564
-▁gymnastics	-34565
-▁motionless	-34566
-▁renewables	-34567
-▁disobedience	-34568
-Head	-34569
-▁Ole	-34570
-clair	-34571
-entin	-34572
-vakia	-34573
-ocally	-34574
-states	-34575
-▁purge	-34576
-▁Conrad	-34577
-▁nudity	-34578
-▁Hernand	-34579
-▁captains	-34580
-▁trimming	-34581
-▁stammered	-34582
-,(	-34583
-▁Elm	-34584
-Count	-34585
-▁harp	-34586
-▁naps	-34587
-▁Episc	-34588
-cension	-34589
-▁midway	-34590
-▁sooooo	-34591
-tersweet	-34592
-▁barrage	-34593
-▁tenancy	-34594
-investment	-34595
-▁caretaker	-34596
-▁gardeners	-34597
-▁Departments	-34598
-▁overarching	-34599
-▁unexplained	-34600
-▁lien	-34601
-▁Brits	-34602
-oppable	-34603
-▁Auburn	-34604
-▁grader	-34605
-▁hoarse	-34606
-▁eyelashes	-34607
-▁staggered	-34608
-slow	-34609
-absor	-34610
-agree	-34611
-vette	-34612
-ESSION	-34613
-▁dogma	-34614
-problem	-34615
-▁Vernon	-34616
-▁wearable	-34617
-▁notifying	-34618
-▁pathogens	-34619
-▁refinance	-34620
-▁Perspective	-34621
-USD	-34622
-▁Zo	-34623
-woods	-34624
-▁STAT	-34625
-Online	-34626
-Surely	-34627
-▁Rogue	-34628
-▁TRANS	-34629
-▁Necess	-34630
-▁levied	-34631
-▁Brendan	-34632
-▁THROUGH	-34633
-▁compute	-34634
-▁BMI	-34635
-▁Bold	-34636
-▁Rory	-34637
-▁dips	-34638
-▁boxed	-34639
-▁holog	-34640
-▁tidal	-34641
-▁rattle	-34642
-▁Chapman	-34643
-▁blushed	-34644
-▁lawfully	-34645
-▁Leicester	-34646
-▁Migration	-34647
-▁unintentionally	-34648
-MED	-34649
-alay	-34650
-Islam	-34651
-▁Amid	-34652
-▁Aval	-34653
-▁Skill	-34654
-▁Atomic	-34655
-▁Iraqis	-34656
-▁Rapids	-34657
-▁gunman	-34658
-▁stripe	-34659
-▁ketchup	-34660
-▁Atlantis	-34661
-▁Bradford	-34662
-▁Narrator	-34663
-▁prenatal	-34664
-▁vehement	-34665
-▁roommates	-34666
-▁whimsical	-34667
-▁groundwork	-34668
-IGHTS	-34669
-▁Neck	-34670
-▁Drain	-34671
-▁Volks	-34672
-▁Excell	-34673
-▁Subaru	-34674
-▁morons	-34675
-▁gastric	-34676
-▁nuanced	-34677
-▁pamphlet	-34678
-▁rosemary	-34679
-▁harmonious	-34680
-sort	-34681
-tiny	-34682
-▁Gut	-34683
-rosse	-34684
-▁Tatt	-34685
-▁yuan	-34686
-▁tumult	-34687
-▁Browser	-34688
-▁Serving	-34689
-▁fingern	-34690
-▁Rosemary	-34691
-▁notation	-34692
-▁weakening	-34693
-tm	-34694
-'...	-34695
-ISTR	-34696
-▁quid	-34697
-finance	-34698
-▁Guides	-34699
-▁extrad	-34700
-▁Hunting	-34701
-▁spitting	-34702
-▁introductions	-34703
-▁Pharmaceutical	-34704
-,’”	-34705
-Data	-34706
-Game	-34707
-avis	-34708
-othel	-34709
-wired	-34710
-Anyone	-34711
-▁dries	-34712
-▁Peanut	-34713
-▁sulfur	-34714
-starting	-34715
-▁Harmony	-34716
-▁manpower	-34717
-songwriter	-34718
-▁Preservation	-34719
-Ma	-34720
-POS	-34721
-auto	-34722
-▁(?)	-34723
-irties	-34724
-simple	-34725
-▁genders	-34726
-▁mockery	-34727
-▁nipples	-34728
-▁Monterey	-34729
-▁circumvent	-34730
-Baby	-34731
-▁HPV	-34732
-ompan	-34733
-sheet	-34734
-▁Actual	-34735
-▁cigars	-34736
-▁widest	-34737
-▁rehabil	-34738
-▁mythical	-34739
-▁somethin	-34740
-▁lobbyists	-34741
-▁momentary	-34742
-▁Chiropract	-34743
-▁variability	-34744
-▁imperfections	-34745
-€”	-34746
-iya	-34747
-HaHa	-34748
-▁Wra	-34749
-ambia	-34750
-▁Plot	-34751
-▁Synt	-34752
-▁Wrap	-34753
-▁seism	-34754
-▁wacky	-34755
-uropath	-34756
-▁Smooth	-34757
-▁Goodbye	-34758
-▁divisive	-34759
-▁examiner	-34760
-▁indicted	-34761
-▁intestine	-34762
-▁Antarctica	-34763
-▁reinforcing	-34764
-▁excruciating	-34765
-OX	-34766
-ADA	-34767
-pler	-34768
-▁BUY	-34769
-goers	-34770
-▁Meals	-34771
-lighten	-34772
-▁Adoles	-34773
-▁omissions	-34774
-▁prophetic	-34775
-▁discrepancy	-34776
-▁spreadsheets	-34777
-Sun	-34778
-▁il	-34779
-▁Biz	-34780
-▁Btw	-34781
-▁Least	-34782
-▁Strat	-34783
-▁apnea	-34784
-▁deems	-34785
-▁evade	-34786
-▁Enroll	-34787
-▁Wearing	-34788
-▁gathers	-34789
-▁Received	-34790
-▁hereafter	-34791
-▁assurances	-34792
-▁impractical	-34793
-vill	-34794
-▁Jol	-34795
-▁NYT	-34796
-▁Carpet	-34797
-▁barric	-34798
-▁unkind	-34799
-numbered	-34800
-▁adhering	-34801
-▁doubting	-34802
-▁hysteria	-34803
-▁Thursdays	-34804
-▁contrived	-34805
-ACA	-34806
-▁Bax	-34807
-▁Lamp	-34808
-▁oint	-34809
-vanced	-34810
-▁Cheer	-34811
-▁Jungle	-34812
-▁pharma	-34813
-▁spaced	-34814
-▁trunks	-34815
-ocracies	-34816
-▁Hampton	-34817
-▁garages	-34818
-▁mortals	-34819
-▁antiques	-34820
-▁despised	-34821
-▁impunity	-34822
-▁tertiary	-34823
-responsive	-34824
-▁borderline	-34825
-▁precarious	-34826
-▁Notification	-34827
-▁unsuspecting	-34828
-:—	-34829
-▁Sets	-34830
-▁Symb	-34831
-Design	-34832
-▁Knock	-34833
-▁Stack	-34834
-▁encro	-34835
-▁exfol	-34836
-▁saloon	-34837
-▁tingling	-34838
-▁ethically	-34839
-▁pipelines	-34840
-▁sleepless	-34841
-lots	-34842
-▁TON	-34843
-▁ker	-34844
-ranet	-34845
-league	-34846
-storey	-34847
-▁Terra	-34848
-▁Breath	-34849
-▁shenan	-34850
-▁adamant	-34851
-▁rumored	-34852
-preferred	-34853
-▁Disgrace	-34854
-▁Inventory	-34855
-▁palliative	-34856
-ibu	-34857
-vac	-34858
-Shirt	-34859
-usual	-34860
-▁mism	-34861
-iphany	-34862
-▁paleo	-34863
-▁colleg	-34864
-▁COMPLET	-34865
-▁allowable	-34866
-▁sociopath	-34867
-bog	-34868
-ucci	-34869
-▁ome	-34870
-▁shiver	-34871
-▁ulcers	-34872
-▁Patterson	-34873
-▁counterfeit	-34874
-Ver	-34875
-chemy	-34876
-geons	-34877
-itton	-34878
-▁flop	-34879
-▁mule	-34880
-▁rite	-34881
-Making	-34882
-▁Shape	-34883
-▁Bosnia	-34884
-▁Chavez	-34885
-▁extrap	-34886
-▁faintly	-34887
-▁ballroom	-34888
-▁backlinks	-34889
-examination	-34890
-▁Purchasing	-34891
-▁truthfully	-34892
-▁inco	-34893
-▁perc	-34894
-▁Seahaw	-34895
-▁ensued	-34896
-▁opaque	-34897
-▁payoff	-34898
-▁rupees	-34899
-▁Consent	-34900
-▁mislead	-34901
-▁frivolous	-34902
-▁sprawling	-34903
-▁Wednesdays	-34904
-▁sufferings	-34905
-▁Transaction	-34906
-ARP	-34907
-Cat	-34908
-INC	-34909
-bys	-34910
-borg	-34911
-crap	-34912
-▁APIs	-34913
-▁Amer	-34914
-▁Busy	-34915
-▁PROF	-34916
-▁Ahmed	-34917
-▁Humph	-34918
-▁Tammy	-34919
-▁Dawson	-34920
-▁meadow	-34921
-▁zipper	-34922
-▁microbi	-34923
-▁achieves	-34924
-▁toppings	-34925
-▁elevators	-34926
-▁outspoken	-34927
-▁dew	-34928
-Looking	-34929
-▁Exodus	-34930
-▁Invite	-34931
-▁Carrier	-34932
-▁comrade	-34933
-▁pendant	-34934
-▁polluted	-34935
-▁slideshow	-34936
-▁waterways	-34937
-▁underwriting	-34938
-▁Alps	-34939
-▁Marquis	-34940
-▁comical	-34941
-▁inhuman	-34942
-▁Pollution	-34943
-▁laughable	-34944
-▁meticulous	-34945
-▁relentlessly	-34946
-KK	-34947
-Elect	-34948
-▁Sina	-34949
-▁primal	-34950
-▁incapac	-34951
-▁sunsets	-34952
-▁heartbreak	-34953
-▁bureaucrats	-34954
-▁culmination	-34955
-▁recognising	-34956
-iol	-34957
-▁JM	-34958
-▁Uru	-34959
-osher	-34960
-▁aval	-34961
-▁riff	-34962
-▁Astro	-34963
-▁teddy	-34964
-conserv	-34965
-▁phased	-34966
-▁sacram	-34967
-▁tanning	-34968
-▁Bulletin	-34969
-▁monarchy	-34970
-▁KS	-34971
-inem	-34972
-▁Kur	-34973
-▁Stee	-34974
-hammer	-34975
-▁Barcl	-34976
-▁leapt	-34977
-▁Upload	-34978
-▁coloss	-34979
-▁phoned	-34980
-▁Managed	-34981
-▁legality	-34982
-▁vineyard	-34983
-▁communicates	-34984
-▁contemplation	-34985
-Bo	-34986
-prin	-34987
-ingen	-34988
-▁Dept	-34989
-▁Exit	-34990
-▁Oval	-34991
-▁jest	-34992
-▁THING	-34993
-▁ninja	-34994
-discipline	-34995
-▁diplomats	-34996
-▁Agreements	-34997
-▁relocating	-34998
-▁preservatives	-34999
-tz	-35000
-Holy	-35001
-Matt	-35002
-iree	-35003
-▁jal	-35004
-Death	-35005
-▁Tong	-35006
-opedic	-35007
-finding	-35008
-▁AUTHOR	-35009
-▁nourish	-35010
-▁constell	-35011
-▁submerged	-35012
-▁Behavioral	-35013
-▁astronomical	-35014
-▁transmitting	-35015
-▁questionnaires	-35016
-aja	-35017
-▁Vig	-35018
-▁Marj	-35019
-▁Tang	-35020
-▁Boris	-35021
-▁Charges	-35022
-▁Infinity	-35023
-▁Operator	-35024
-▁McConnell	-35025
-▁combating	-35026
-▁fractured	-35027
-▁indulgence	-35028
-"-	-35029
-Luke	-35030
-Mary	-35031
-thous	-35032
-▁Ming	-35033
-▁soaps	-35034
-benefit	-35035
-▁THINGS	-35036
-▁Leisure	-35037
-▁grossly	-35038
-▁Shooting	-35039
-▁dangling	-35040
-▁orphanage	-35041
-▁hydrochlor	-35042
-▁preferring	-35043
-clo	-35044
-actus	-35045
-cough	-35046
-itcher	-35047
-outhed	-35048
-▁yells	-35049
-estrian	-35050
-▁depress	-35051
-▁computed	-35052
-▁disrupting	-35053
-▁Bulls	-35054
-▁Chest	-35055
-▁Marin	-35056
-▁vouch	-35057
-slaught	-35058
-▁Banana	-35059
-▁Fraser	-35060
-▁Everest	-35061
-▁howling	-35062
-▁pitiful	-35063
-▁convened	-35064
-pray	-35065
-▁BIR	-35066
-▁Boyd	-35067
-▁chak	-35068
-arsity	-35069
-coffee	-35070
-▁Barbie	-35071
-▁oppress	-35072
-▁tremble	-35073
-▁Italians	-35074
-▁teleport	-35075
-▁Communion	-35076
-▁furiously	-35077
-afi	-35078
-▁GTA	-35079
-▁Noel	-35080
-▁Vert	-35081
-▁hass	-35082
-▁tubs	-35083
-icator	-35084
-Saharan	-35085
-▁Hardly	-35086
-▁devour	-35087
-▁endocr	-35088
-▁fishes	-35089
-▁morbid	-35090
-▁trusty	-35091
-▁abolish	-35092
-▁surveying	-35093
-▁BlackBerry	-35094
-▁transformations	-35095
-Team	-35096
-boot	-35097
-doesn	-35098
-▁Reno	-35099
-▁chal	-35100
-▁undis	-35101
-▁Caucas	-35102
-▁erratic	-35103
-▁minions	-35104
-▁Highness	-35105
-▁farmland	-35106
-VM	-35107
-guess	-35108
-▁wist	-35109
-corpor	-35110
-▁gated	-35111
-▁Kazakh	-35112
-▁jammed	-35113
-understand	-35114
-▁Georgetown	-35115
-▁BEN	-35116
-▁SSD	-35117
-Share	-35118
-logue	-35119
-ugged	-35120
-answer	-35121
-▁savor	-35122
-college	-35123
-inafter	-35124
-▁ripple	-35125
-▁Packers	-35126
-▁camoufl	-35127
-▁crackdown	-35128
-▁unloading	-35129
-rh	-35130
-atts	-35131
-hack	-35132
-thel	-35133
-▁pont	-35134
-enough	-35135
-▁bravely	-35136
-▁wildest	-35137
-▁overtake	-35138
-▁shutters	-35139
-▁watchful	-35140
-▁Episcopal	-35141
-▁emptiness	-35142
-▁Transformation	-35143
-mc	-35144
-User	-35145
-▁Sloven	-35146
-▁UNITED	-35147
-▁shroud	-35148
-▁Reserved	-35149
-undy	-35150
-▁FOUR	-35151
-▁TELL	-35152
-powers	-35153
-▁GOVER	-35154
-▁Freddy	-35155
-▁UNICEF	-35156
-▁blanks	-35157
-▁niches	-35158
-▁hearted	-35159
-▁Happened	-35160
-▁stemming	-35161
-▁Interface	-35162
-▁depiction	-35163
-Fs	-35164
-▁Dix	-35165
-▁Pair	-35166
-ensual	-35167
-▁Mandy	-35168
-▁reefs	-35169
-▁swarm	-35170
-library	-35171
-▁Wolver	-35172
-▁tights	-35173
-▁silicon	-35174
-▁walkers	-35175
-▁suitably	-35176
-▁strolling	-35177
-▁JB	-35178
-▁Toll	-35179
-▁morp	-35180
-▁Gibbs	-35181
-▁gmail	-35182
-▁Anytime	-35183
-▁selfies	-35184
-▁subpoena	-35185
-▁hamburger	-35186
-▁irritable	-35187
-▁selfishness	-35188
-▁unidentified	-35189
-▁appropriation	-35190
-veh	-35191
-ocon	-35192
-sson	-35193
-▁Lub	-35194
-▁Rocks	-35195
-Exactly	-35196
-aturday	-35197
-▁puddle	-35198
-▁diplomat	-35199
-▁leggings	-35200
-▁formatted	-35201
-▁metaphors	-35202
-▁liberating	-35203
-Conf	-35204
-URES	-35205
-clad	-35206
-▁CRE	-35207
-antan	-35208
-▁Yelp	-35209
-▁hoop	-35210
-policy	-35211
-▁BEING	-35212
-▁Recipe	-35213
-▁Remain	-35214
-▁improm	-35215
-▁mingle	-35216
-▁pasted	-35217
-▁ragged	-35218
-▁Giveaway	-35219
-▁Scientist	-35220
-▁entrances	-35221
-▁humankind	-35222
-▁lifecycle	-35223
-FSA	-35224
-ogo	-35225
-meat	-35226
-▁imb	-35227
-fresh	-35228
-vered	-35229
-▁Aviv	-35230
-▁DONE	-35231
-▁Load	-35232
-▁SURE	-35233
-testing	-35234
-▁nobles	-35235
-▁Vanguard	-35236
-▁Witnesses	-35237
-▁MJ	-35238
-!!!"	-35239
-▁Berm	-35240
-▁Mild	-35241
-▁Recon	-35242
-dressed	-35243
-▁Debate	-35244
-▁calmer	-35245
-▁cruises	-35246
-▁utilise	-35247
-▁practised	-35248
-##	-35249
-▁ANN	-35250
-▁rake	-35251
-urpose	-35252
-▁splits	-35253
-ographed	-35254
-▁Clothes	-35255
-▁Prairie	-35256
-▁biscuit	-35257
-▁abducted	-35258
-▁disabling	-35259
-▁shivering	-35260
-▁Intelligent	-35261
-▁inconsistencies	-35262
-Wan	-35263
-idas	-35264
-oder	-35265
-thal	-35266
-▁Kyl	-35267
-▁FIRE	-35268
-esthetic	-35269
-▁Jasmine	-35270
-▁Joining	-35271
-▁pledges	-35272
-▁desirous	-35273
-▁receptor	-35274
-▁Submitted	-35275
-▁ballistic	-35276
-▁liberated	-35277
-Bro	-35278
-▁MUS	-35279
-▁Deaf	-35280
-athons	-35281
-▁BLACK	-35282
-▁hamper	-35283
-▁behaves	-35284
-▁stormed	-35285
-▁Direction	-35286
-▁turbulent	-35287
-▁fraternity	-35288
-iba	-35289
-agna	-35290
-▁Sig	-35291
-▁ais	-35292
-crazy	-35293
-▁tarot	-35294
-fledged	-35295
-creation	-35296
-▁Mustang	-35297
-▁spurred	-35298
-▁Johannes	-35299
-▁intruder	-35300
-▁scammers	-35301
-▁imbalances	-35302
-▁unprotected	-35303
-Ref	-35304
-Chat	-35305
-idan	-35306
-ftime	-35307
-▁NAME	-35308
-▁Ports	-35309
-▁pears	-35310
-▁CHANGE	-35311
-▁Colour	-35312
-▁chewed	-35313
-▁risked	-35314
-▁hillside	-35315
-▁prostitute	-35316
-sand	-35317
-▁Laf	-35318
-▁HMRC	-35319
-▁KILL	-35320
-▁WARR	-35321
-▁olig	-35322
-▁libel	-35323
-▁lumps	-35324
-▁Casual	-35325
-▁potion	-35326
-atchewan	-35327
-certified	-35328
-▁Recognize	-35329
-▁necklaces	-35330
-▁retrieval	-35331
-▁Innovative	-35332
-▁Exceptional	-35333
-▁commentator	-35334
-tu	-35335
-icter	-35336
-▁Cran	-35337
-▁Grim	-35338
-▁linux	-35339
-▁bolted	-35340
-▁creeps	-35341
-▁spoons	-35342
-▁bluntly	-35343
-▁handler	-35344
-▁leakage	-35345
-▁wishful	-35346
-▁drooling	-35347
-▁reformed	-35348
-▁stitched	-35349
-scientific	-35350
-▁Retrieved	-35351
-▁polishing	-35352
-▁disparities	-35353
-▁shenanigans	-35354
-▁Bis	-35355
-▁SOU	-35356
-▁Fare	-35357
-▁Harri	-35358
-▁Spent	-35359
-▁chant	-35360
-▁churn	-35361
-▁Keller	-35362
-▁misled	-35363
-▁walker	-35364
-▁Covered	-35365
-▁hemorrh	-35366
-▁modesty	-35367
-▁prognosis	-35368
-▁accommodated	-35369
-▁Hm	-35370
-▁MOR	-35371
-▁godly	-35372
-violence	-35373
-▁Promoting	-35374
-▁Collaboration	-35375
-NT	-35376
-▁semb	-35377
-etting	-35378
-▁cones	-35379
-▁Damien	-35380
-▁Unified	-35381
-▁apostle	-35382
-▁Investor	-35383
-▁fracking	-35384
-▁inquiring	-35385
-▁unfounded	-35386
-▁complicate	-35387
-▁conjecture	-35388
-▁relativity	-35389
-Dig	-35390
-ako	-35391
-arat	-35392
-▁Dil	-35393
-▁Jod	-35394
-acons	-35395
-▁Siri	-35396
-Engine	-35397
-papers	-35398
-▁Judah	-35399
-anchise	-35400
-lesiast	-35401
-▁cloves	-35402
-ademoise	-35403
-▁Bahamas	-35404
-▁crumble	-35405
-▁debacle	-35406
-▁demonic	-35407
-▁divides	-35408
-▁cautioned	-35409
-▁guacamole	-35410
-▁impromptu	-35411
-▁Recommendations	-35412
-▁differentiation	-35413
-▁GMO	-35414
-▁wry	-35415
-▁TALK	-35416
-▁Tate	-35417
-▁Lotus	-35418
-▁warms	-35419
-▁watts	-35420
-imating	-35421
-▁Nurses	-35422
-▁shrewd	-35423
-▁Cadillac	-35424
-▁calculus	-35425
-▁fragrant	-35426
-▁Biological	-35427
-▁motherboard	-35428
-▁computational	-35429
-])	-35430
-atz	-35431
-AGES	-35432
-vote	-35433
-false	-35434
-▁Avon	-35435
-▁Outs	-35436
-▁chir	-35437
-▁keto	-35438
-albeit	-35439
-▁Stern	-35440
-▁fussy	-35441
-▁ulcer	-35442
-▁Ramsey	-35443
-▁penned	-35444
-▁Halifax	-35445
-▁Lithuan	-35446
-▁giggles	-35447
-▁rulings	-35448
-▁overcast	-35449
-▁plugging	-35450
-▁disparate	-35451
-▁intolerant	-35452
-ryer	-35453
-▁fift	-35454
-garden	-35455
-▁cheery	-35456
-Laughter	-35457
-▁Cinderella	-35458
-▁convergence	-35459
-▁HK	-35460
-LESS	-35461
-▁ICC	-35462
-wagen	-35463
-▁Dual	-35464
-versed	-35465
-▁Watts	-35466
-▁reels	-35467
-▁Buenos	-35468
-▁rarity	-35469
-▁discord	-35470
-▁parable	-35471
-▁Stafford	-35472
-▁antidote	-35473
-▁boasting	-35474
-▁delusions	-35475
-▁nourishment	-35476
-▁irreversible	-35477
-iffy	-35478
-▁Sle	-35479
-▁Fever	-35480
-▁Moody	-35481
-▁peeps	-35482
-▁hissed	-35483
-▁iodine	-35484
-▁keenly	-35485
-▁Islamist	-35486
-▁commuter	-35487
-▁grilling	-35488
-▁adversary	-35489
-▁genealogy	-35490
-▁onslaught	-35491
-▁acknowledgment	-35492
-'."	-35493
-▁/*	-35494
-▁Lens	-35495
-ologne	-35496
-▁Arbor	-35497
-▁EXPER	-35498
-▁Pulse	-35499
-▁eerie	-35500
-▁muted	-35501
-▁throb	-35502
-▁chauffe	-35503
-▁couldnt	-35504
-▁diction	-35505
-arez	-35506
-atsu	-35507
-uper	-35508
-opoly	-35509
-ylene	-35510
-▁Poké	-35511
-▁RUSH	-35512
-▁glimmer	-35513
-▁expended	-35514
-ademoiselle	-35515
-▁attainable	-35516
-▁dismissing	-35517
-▁participates	-35518
-Sn	-35519
-los	-35520
-▁FAC	-35521
-▁vid	-35522
-▁Rele	-35523
-▁Aires	-35524
-▁Jonah	-35525
-▁colds	-35526
-▁winery	-35527
-▁Bedford	-35528
-▁Fletcher	-35529
-▁budgetary	-35530
-▁Wonderland	-35531
-▁Colors	-35532
-▁Dexter	-35533
-▁lasers	-35534
-▁millig	-35535
-olerance	-35536
-▁tricked	-35537
-▁towering	-35538
-▁Practition	-35539
-▁dehydrated	-35540
-▁totalitarian	-35541
-phr	-35542
-oila	-35543
-▁Sne	-35544
-alore	-35545
-▁Anger	-35546
-▁BEAUT	-35547
-▁Outlet	-35548
-▁mayhem	-35549
-▁Enjoyed	-35550
-▁secretive	-35551
-▁complacent	-35552
-ANN	-35553
-ARDS	-35554
-▁Gel	-35555
-total	-35556
-▁Vall	-35557
-Season	-35558
-▁Shaun	-35559
-▁Voices	-35560
-▁napkin	-35561
-Semitism	-35562
-▁pinning	-35563
-▁pistols	-35564
-▁Triangle	-35565
-▁mediator	-35566
-▁narration	-35567
-▁bestselling	-35568
-▁manipulative	-35569
-ousse	-35570
-visit	-35571
-medicine	-35572
-▁Kashmir	-35573
-▁granola	-35574
-▁gunshot	-35575
-▁sixties	-35576
-▁democrat	-35577
-▁railways	-35578
-▁screenplay	-35579
-▁horizontally	-35580
-▁INST	-35581
-affili	-35582
-ranked	-35583
-against	-35584
-iscover	-35585
-located	-35586
-▁Edison	-35587
-▁glazed	-35588
-negative	-35589
-▁Startup	-35590
-▁fancied	-35591
-▁gutters	-35592
-▁mustache	-35593
-registration	-35594
-▁transferable	-35595
-▁retrospective	-35596
-hm	-35597
-ISM	-35598
-NAL	-35599
-▁Alto	-35600
-domain	-35601
-▁Wrote	-35602
-aggress	-35603
-etrical	-35604
-▁Continued	-35605
-▁absurdity	-35606
-▁strangest	-35607
-▁Humanities	-35608
-▁honourable	-35609
-▁meditating	-35610
-▁unequivocally	-35611
-"—	-35612
-."[	-35613
-flo	-35614
-▁IK	-35615
-BODY	-35616
-blank	-35617
-▁Held	-35618
-▁Lena	-35619
-ochond	-35620
-▁Breed	-35621
-▁nests	-35622
-▁phony	-35623
-▁toned	-35624
-▁Herman	-35625
-▁ovarian	-35626
-▁Marriott	-35627
-▁MasterCard	-35628
-▁futuristic	-35629
-▁('	-35630
-alis	-35631
-wald	-35632
-ayson	-35633
-orily	-35634
-▁rosy	-35635
-native	-35636
-thread	-35637
-helpful	-35638
-▁appease	-35639
-▁coercion	-35640
-▁Highlands	-35641
-▁condemning	-35642
-▁snorkeling	-35643
-▁televisions	-35644
-adan	-35645
-urgy	-35646
-terms	-35647
-dating	-35648
-▁antim	-35649
-▁amnesty	-35650
-▁dislikes	-35651
-▁preclude	-35652
-▁stricter	-35653
-▁commotion	-35654
-▁enrolling	-35655
-▁heartache	-35656
-▁superiors	-35657
-▁demonstrators	-35658
-bil	-35659
-gran	-35660
-mist	-35661
-▁Cube	-35662
-▁Sixty	-35663
-▁snuck	-35664
-itamins	-35665
-▁Hoover	-35666
-▁roadway	-35667
-▁Guinness	-35668
-▁lesbians	-35669
-▁imposition	-35670
-▁RUN	-35671
-Until	-35672
-▁lids	-35673
-▁Yunho	-35674
-▁boxer	-35675
-▁Plains	-35676
-▁resurg	-35677
-▁nuggets	-35678
-▁Romantic	-35679
-▁legalized	-35680
-▁rebellious	-35681
-vid	-35682
-kept	-35683
-▁Hulk	-35684
-▁amal	-35685
-▁dich	-35686
-▁snoo	-35687
-▁taco	-35688
-lander	-35689
-▁Siber	-35690
-enabled	-35691
-▁Sirius	-35692
-▁aesthetically	-35693
-Abs	-35694
-adia	-35695
-addle	-35696
-Canada	-35697
-finger	-35698
-▁Pears	-35699
-▁Pursu	-35700
-▁Ignore	-35701
-▁salted	-35702
-reatment	-35703
-▁Britney	-35704
-▁Heading	-35705
-▁Kendall	-35706
-▁Prayers	-35707
-▁overlay	-35708
-▁signify	-35709
-▁ushered	-35710
-▁Patience	-35711
-▁Suitable	-35712
-▁Hospitals	-35713
-▁preserves	-35714
-▁apologizing	-35715
-▁alphabetical	-35716
-osy	-35717
-▁Sole	-35718
-▁bona	-35719
-Sunday	-35720
-assemb	-35721
-pherds	-35722
-▁Ninth	-35723
-▁melan	-35724
-▁Salmon	-35725
-▁soared	-35726
-▁Cancell	-35727
-▁armchair	-35728
-▁dopamine	-35729
-▁lingered	-35730
-▁deflation	-35731
-▁aspiration	-35732
-▁intestines	-35733
-▁disruptions	-35734
-▁protagonists	-35735
-Light	-35736
-▁CHRIST	-35737
-▁sharper	-35738
-▁Presence	-35739
-▁numbness	-35740
-▁whomever	-35741
-▁classmate	-35742
-▁Southampton	-35743
-▁prostitutes	-35744
-heter	-35745
-▁Brush	-35746
-▁Stacy	-35747
-▁payer	-35748
-▁Acknow	-35749
-▁Occupy	-35750
-▁Utility	-35751
-installed	-35752
-▁Warranty	-35753
-▁desolate	-35754
-▁injuring	-35755
-▁sobriety	-35756
-▁Raspberry	-35757
-▁improperly	-35758
-▁Recognition	-35759
-/,	-35760
-▁(‘	-35761
-Soft	-35762
-stad	-35763
-encia	-35764
-▁Wheat	-35765
-▁Finals	-35766
-▁Khushi	-35767
-▁Orwell	-35768
-prepared	-35769
-▁Variety	-35770
-▁arsenic	-35771
-▁heinous	-35772
-▁unspoken	-35773
-▁Carpenter	-35774
-▁Developed	-35775
-▁accumulating	-35776
-Ten	-35777
-▁Marl	-35778
-▁Remy	-35779
-▁YHWH	-35780
-▁digs	-35781
-▁emit	-35782
-▁Chill	-35783
-▁Benson	-35784
-▁strata	-35785
-sounding	-35786
-▁reputed	-35787
-▁newfound	-35788
-ordination	-35789
-▁crippling	-35790
-▁heartburn	-35791
-▁negligible	-35792
-▁testimonial	-35793
-cate	-35794
-itory	-35795
-lycer	-35796
-▁Reef	-35797
-icably	-35798
-▁altru	-35799
-▁arent	-35800
-▁Myster	-35801
-▁cropped	-35802
-▁ascended	-35803
-▁fidelity	-35804
-▁converter	-35805
-▁benevolent	-35806
-▁breathless	-35807
-▁illuminate	-35808
-▁Investigators	-35809
-Cy	-35810
-!".	-35811
-bek	-35812
-anse	-35813
-▁Sult	-35814
-▁typh	-35815
-▁Panda	-35816
-▁inver	-35817
-▁adorned	-35818
-▁aerobic	-35819
-▁selfless	-35820
-▁enriching	-35821
-▁discontent	-35822
-Bra	-35823
-▁TP	-35824
-▁JES	-35825
-ATIVE	-35826
-Build	-35827
-▁Jury	-35828
-▁rhin	-35829
-▁Loves	-35830
-▁Sheep	-35831
-▁overe	-35832
-▁encaps	-35833
-▁seduct	-35834
-▁Pioneer	-35835
-▁raisins	-35836
-▁babysitter	-35837
-▁disclosing	-35838
-▁temptations	-35839
-▁Availability	-35840
-▁mesothelioma	-35841
-▁Bea	-35842
-▁TEC	-35843
-▁kara	-35844
-blocks	-35845
-▁junct	-35846
-▁Insert	-35847
-▁queens	-35848
-▁watery	-35849
-▁Contain	-35850
-▁rotting	-35851
-▁Midlands	-35852
-▁Warehouse	-35853
-▁flavorful	-35854
-▁infrastruct	-35855
-▁restitution	-35856
-▁ETF	-35857
-▁IVF	-35858
-nesia	-35859
-▁fleece	-35860
-▁marches	-35861
-▁supremac	-35862
-▁escalation	-35863
-▁increments	-35864
-▁unaffected	-35865
-▁Unemployment	-35866
-fac	-35867
-▁abom	-35868
-▁john	-35869
-▁Basin	-35870
-reported	-35871
-▁Reminds	-35872
-▁Suffice	-35873
-▁chained	-35874
-▁leaping	-35875
-▁outages	-35876
-▁freshmen	-35877
-▁countrymen	-35878
-▁insensitive	-35879
-▁anthropology	-35880
-tf	-35881
-▁soo	-35882
-rdquo	-35883
-▁pods	-35884
-aderie	-35885
-violet	-35886
-experts	-35887
-idental	-35888
-▁Borrow	-35889
-▁recite	-35890
-▁florida	-35891
-▁purification	-35892
-hp	-35893
-AMS	-35894
-nem	-35895
-▁NIC	-35896
-▁Coin	-35897
-▁Pell	-35898
-usively	-35899
-▁Fifteen	-35900
-▁atheism	-35901
-▁backend	-35902
-▁precept	-35903
-▁streamed	-35904
-▁commended	-35905
-▁fragmented	-35906
-▁handwritten	-35907
-▁imaginations	-35908
-▁journalistic	-35909
-WC	-35910
-elo	-35911
-lip	-35912
-...,	-35913
-▁CBC	-35914
-brids	-35915
-▁Sims	-35916
-▁lizard	-35917
-▁ranting	-35918
-▁Elephant	-35919
-▁Pharmacy	-35920
-▁bruising	-35921
-▁Evidently	-35922
-▁Landscape	-35923
-▁inference	-35924
-▁semblance	-35925
-▁experiential	-35926
-▁MOT	-35927
-▁Hert	-35928
-▁Otto	-35929
-▁idyll	-35930
-opausal	-35931
-▁Colony	-35932
-▁libido	-35933
-▁porter	-35934
-▁Trustee	-35935
-▁scanners	-35936
-▁attaining	-35937
-▁Protecting	-35938
-▁enchanting	-35939
-▁Tie	-35940
-▁CHIL	-35941
-▁Theore	-35942
-▁sunday	-35943
-▁Boeh	-35944
-▁Taxi	-35945
-▁Doris	-35946
-pressed	-35947
-▁LITTLE	-35948
-grateful	-35949
-▁tryiong	-35950
-▁Sometime	-35951
-▁approves	-35952
-▁hostages	-35953
-▁tiresome	-35954
-▁Guardians	-35955
-▁geometric	-35956
-’...	-35957
-▁Ging	-35958
-▁scur	-35959
-▁aunts	-35960
-▁geeks	-35961
-▁linens	-35962
-▁deserts	-35963
-▁grenade	-35964
-▁matured	-35965
-▁shoving	-35966
-▁Classroom	-35967
-▁Submission	-35968
-▁Secretariat	-35969
-▁embroidered	-35970
-▁religiously	-35971
-▁Corporations	-35972
-bots	-35973
-cass	-35974
-▁FAST	-35975
-▁Swing	-35976
-▁atone	-35977
-▁bumpy	-35978
-Children	-35979
-▁Obesity	-35980
-▁sunburn	-35981
-Roy	-35982
-oco	-35983
-rested	-35984
-chedule	-35985
-released	-35986
-▁Courage	-35987
-▁Wealthy	-35988
-▁propped	-35989
-▁tripping	-35990
-▁Presbyter	-35991
-▁sedentary	-35992
-▁relational	-35993
-▁pessimistic	-35994
-▁appropriated	-35995
-▁specialization	-35996
-▁Nana	-35997
-▁dyed	-35998
-uctible	-35999
-abortion	-36000
-ogeneous	-36001
-opolitan	-36002
-▁prophes	-36003
-▁trainee	-36004
-slaughter	-36005
-▁implicated	-36006
-▁physicists	-36007
-OTT	-36008
-Sit	-36009
-▁VS	-36010
-▁PVC	-36011
-ribute	-36012
-▁Login	-36013
-▁Troll	-36014
-▁evoke	-36015
-▁incite	-36016
-▁peruse	-36017
-traumatic	-36018
-▁Petroleum	-36019
-sch	-36020
-Must	-36021
-hurt	-36022
-mite	-36023
-▁VAL	-36024
-ontal	-36025
-▁Conv	-36026
-▁HOPE	-36027
-▁Weak	-36028
-▁THOSE	-36029
-▁shudd	-36030
-geoning	-36031
-▁STATES	-36032
-▁paypal	-36033
-▁indoctr	-36034
-▁Dolphins	-36035
-▁receptors	-36036
-▁longstanding	-36037
-tro	-36038
-rese	-36039
-▁$$$	-36040
-▁Tul	-36041
-▁ect	-36042
-Going	-36043
-iasco	-36044
-sound	-36045
-south	-36046
-▁Node	-36047
-▁vacu	-36048
-blower	-36049
-ctrine	-36050
-▁Joanne	-36051
-▁unwell	-36052
-▁burglar	-36053
-▁seizing	-36054
-currently	-36055
-▁librarians	-36056
-▁realisation	-36057
-▁philanthropic	-36058
-?”.	-36059
-’.”	-36060
-Inst	-36061
-behav	-36062
-quiet	-36063
-▁tides	-36064
-▁trolley	-36065
-▁Hernandez	-36066
-▁Winchester	-36067
-▁summarizes	-36068
-▁penetrating	-36069
-▁revolutions	-36070
-Brit	-36071
-ovies	-36072
-Doctor	-36073
-tenham	-36074
-▁Perez	-36075
-▁flank	-36076
-▁quarry	-36077
-▁stoked	-36078
-▁Terrace	-36079
-▁bumping	-36080
-▁grinder	-36081
-▁Fisheries	-36082
-▁glorified	-36083
-▁punishments	-36084
-acs	-36085
-kil	-36086
-▁CUR	-36087
-ename	-36088
-▁Phoe	-36089
-Getting	-36090
-araderie	-36091
-▁rallied	-36092
-▁Vodafone	-36093
-▁slapping	-36094
-▁strolled	-36095
-▁overshadow	-36096
-▁collaboratively	-36097
-Rad	-36098
-dal	-36099
-ucc	-36100
-▁MLS	-36101
-▁Coul	-36102
-▁Noon	-36103
-▁Phar	-36104
-▁bloc	-36105
-▁Rated	-36106
-assment	-36107
-▁Easier	-36108
-ractions	-36109
-▁Legends	-36110
-▁fascist	-36111
-▁Reaching	-36112
-▁insiders	-36113
-▁perished	-36114
-▁serenity	-36115
-▁yielding	-36116
-▁paraphrase	-36117
-▁familiarize	-36118
-▁mysteriously	-36119
-▁Defe	-36120
-▁Lauder	-36121
-▁methyl	-36122
-▁moaned	-36123
-▁murmur	-36124
-obviously	-36125
-▁Celebrity	-36126
-▁degrading	-36127
-▁localized	-36128
-▁characterised	-36129
-▁Sgt	-36130
-▁Quin	-36131
-oprost	-36132
-▁STAND	-36133
-▁chock	-36134
-▁grove	-36135
-operate	-36136
-▁Reward	-36137
-▁Tinder	-36138
-▁quirks	-36139
-▁unwise	-36140
-▁versed	-36141
-▁flatter	-36142
-▁lesions	-36143
-▁Approval	-36144
-▁Mandarin	-36145
-▁artisans	-36146
-▁stricken	-36147
-▁syllabus	-36148
-▁enactment	-36149
-▁Nottingham	-36150
-▁successors	-36151
-▁:-(	-36152
-▁sul	-36153
-▁Kens	-36154
-▁Jacqu	-36155
-▁craze	-36156
-▁plaza	-36157
-talking	-36158
-▁curing	-36159
-▁Peoples	-36160
-▁chemist	-36161
-▁amateurs	-36162
-▁ambience	-36163
-▁irritate	-36164
-▁resorting	-36165
-▁stillness	-36166
-▁unlocking	-36167
-▁journaling	-36168
-GP	-36169
-▁Font	-36170
-▁Alger	-36171
-▁Wolves	-36172
-▁graded	-36173
-▁suffoc	-36174
-▁Examine	-36175
-▁Dynamics	-36176
-▁vascular	-36177
-▁Exclusive	-36178
-▁Substance	-36179
-▁candidacy	-36180
-▁meatballs	-36181
-▁Characters	-36182
-▁Consultation	-36183
-BN	-36184
-ovich	-36185
-▁Lowe	-36186
-▁Lung	-36187
-▁coop	-36188
-cribes	-36189
-▁Tooth	-36190
-▁omnip	-36191
-emptive	-36192
-▁Bloody	-36193
-▁FAMILY	-36194
-▁detour	-36195
-▁Hamburg	-36196
-▁Planner	-36197
-▁Seniors	-36198
-LED	-36199
-ando	-36200
-▁Gru	-36201
-▁dup	-36202
-sever	-36203
-▁Mald	-36204
-▁Midd	-36205
-▁hens	-36206
-▁ploy	-36207
-cluded	-36208
-trying	-36209
-binding	-36210
-▁Pharma	-36211
-▁Tehran	-36212
-▁burrit	-36213
-▁burying	-36214
-▁grueling	-36215
-▁Recording	-36216
-▁Signature	-36217
-▁unprofessional	-36218
-Mal	-36219
-▁THC	-36220
-cised	-36221
-▁Bars	-36222
-▁quake	-36223
-Science	-36224
-favored	-36225
-▁Shelby	-36226
-▁Bridget	-36227
-▁mergers	-36228
-▁airtight	-36229
-▁bubbling	-36230
-▁suicides	-36231
-▁weaponry	-36232
-▁limestone	-36233
-isn	-36234
-kid	-36235
-lap	-36236
-meet	-36237
-tones	-36238
-▁Lark	-36239
-▁Betsy	-36240
-▁amends	-36241
-▁bombed	-36242
-▁fenced	-36243
-▁grated	-36244
-▁kinder	-36245
-▁manhood	-36246
-▁sighting	-36247
-▁egregious	-36248
-▁Significant	-36249
-▁Implementing	-36250
-▁Saskatchewan	-36251
-▁transcription	-36252
-▁YAY	-36253
-Drive	-36254
-atown	-36255
-cline	-36256
-mailed	-36257
-nesday	-36258
-volunt	-36259
-▁Getty	-36260
-▁eaves	-36261
-▁Happen	-36262
-▁Schwar	-36263
-▁uptake	-36264
-internet	-36265
-▁Realtor	-36266
-▁fossils	-36267
-▁cavities	-36268
-▁devoured	-36269
-▁eyeballs	-36270
-▁solicitors	-36271
-International	-36272
-▁collaborators	-36273
-▁impossibility	-36274
-hua	-36275
-▁Tub	-36276
-▁!!!!	-36277
-ridden	-36278
-▁typos	-36279
-Working	-36280
-extreme	-36281
-▁blasphe	-36282
-beautiful	-36283
-▁ballpark	-36284
-▁sausages	-36285
-processing	-36286
-▁despicable	-36287
-AGR	-36288
-▁JOB	-36289
-▁WTO	-36290
-hardt	-36291
-▁cams	-36292
-▁stung	-36293
-▁Ravens	-36294
-▁overre	-36295
-▁unison	-36296
-▁Coastal	-36297
-▁Defendant	-36298
-zene	-36299
-▁LEAR	-36300
-▁Moto	-36301
-▁luke	-36302
-irling	-36303
-▁BELIE	-36304
-▁Label	-36305
-▁Stain	-36306
-▁dunes	-36307
-▁saute	-36308
-▁Shades	-36309
-▁playable	-36310
-▁stroking	-36311
-▁formative	-36312
-▁duplication	-36313
-YY	-36314
-bos	-36315
-jer	-36316
-ski	-36317
-▁tul	-36318
-bring	-36319
-urnal	-36320
-▁Spir	-36321
-▁rags	-36322
-annies	-36323
-buying	-36324
-pillar	-36325
-▁satin	-36326
-▁Scream	-36327
-▁Summers	-36328
-▁evasion	-36329
-▁Focusing	-36330
-▁extracting	-36331
-si	-36332
-awk	-36333
-rox	-36334
-▁GMT	-36335
-▁EACH	-36336
-▁FEMA	-36337
-▁clap	-36338
-▁snob	-36339
-logist	-36340
-▁thyme	-36341
-▁Bronze	-36342
-▁Ingred	-36343
-▁gettin	-36344
-▁bandage	-36345
-▁fluoride	-36346
-▁Admissions	-36347
-▁Techniques	-36348
-▁unimaginable	-36349
-▁implementations	-36350
-Mer	-36351
-vil	-36352
-▁NUM	-36353
-Video	-36354
-▁Tear	-36355
-▁Vinci	-36356
-freedom	-36357
-▁sinned	-36358
-▁Natasha	-36359
-▁Allowing	-36360
-▁Insights	-36361
-▁favoured	-36362
-▁observes	-36363
-▁painkillers	-36364
-▁hospitalization	-36365
-safety	-36366
-▁monot	-36367
-▁Indoor	-36368
-▁Prompt	-36369
-▁humbling	-36370
-▁parasite	-36371
-▁crucified	-36372
-ych	-36373
-▁[+	-36374
-DREN	-36375
-pole	-36376
-plain	-36377
-▁elit	-36378
-▁euph	-36379
-Custom	-36380
-avering	-36381
-coaster	-36382
-consumer	-36383
-▁aptitude	-36384
-▁glitches	-36385
-▁colouring	-36386
-▁Comparison	-36387
-▁narcissistic	-36388
-!",	-36389
-▁ire	-36390
-▁Nolan	-36391
-▁Sachs	-36392
-invasive	-36393
-▁ecstasy	-36394
-▁midterm	-36395
-▁arresting	-36396
-▁substrate	-36397
-▁Presumably	-36398
-▁freshwater	-36399
-▁supervising	-36400
-olla	-36401
-tone	-36402
-▁hunts	-36403
-▁Granny	-36404
-▁Lakers	-36405
-▁Unable	-36406
-▁floppy	-36407
-coloured	-36408
-▁Scarlett	-36409
-▁cassette	-36410
-▁wreckage	-36411
-▁revisiting	-36412
-▁consequential	-36413
-Ent	-36414
-▁pea	-36415
-▁Bere	-36416
-▁Heya	-36417
-asions	-36418
-▁Kanye	-36419
-▁diced	-36420
-alsamic	-36421
-▁Indies	-36422
-icipated	-36423
-▁Changed	-36424
-▁Eclipse	-36425
-▁Shelley	-36426
-▁beaming	-36427
-▁pancake	-36428
-▁textual	-36429
-▁millennia	-36430
-international	-36431
-knit	-36432
-▁Hut	-36433
-▁Tut	-36434
-Music	-36435
-hello	-36436
-▁Kits	-36437
-▁sans	-36438
-ramids	-36439
-▁Quart	-36440
-awaited	-36441
-central	-36442
-▁Desire	-36443
-▁Fabric	-36444
-▁Baldwin	-36445
-▁Osborne	-36446
-respected	-36447
-▁footnote	-36448
-▁promoter	-36449
-▁collapses	-36450
-▁neglecting	-36451
-▁backpacking	-36452
-▁disillusion	-36453
-Hz	-36454
-Peace	-36455
-▁edgy	-36456
-staking	-36457
-▁Presley	-36458
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-▁encoding	-36460
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-▁millionaires	-36463
-.!	-36464
-img	-36465
-haus	-36466
-pain	-36467
-▁DOM	-36468
-▁Gob	-36469
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-▁Meghan	-36471
-▁Rarely	-36472
-▁devout	-36473
-▁Lebanese	-36474
-▁littered	-36475
-▁astrology	-36476
-▁reinstall	-36477
-▁Canterbury	-36478
-▁cancelling	-36479
-▁probiotics	-36480
-▁Identifying	-36481
-Sal	-36482
-▁Og	-36483
-cars	-36484
-imsy	-36485
-▁Dud	-36486
-▁Dang	-36487
-▁Mour	-36488
-Monday	-36489
-estial	-36490
-itties	-36491
-▁golfer	-36492
-▁Pilates	-36493
-▁abusers	-36494
-▁doctorate	-36495
-▁capitalists	-36496
-▁illuminating	-36497
-▁DF	-36498
-▁RH	-36499
-Prof	-36500
-▁Gaga	-36501
-finals	-36502
-▁Slide	-36503
-▁mounts	-36504
-▁sacked	-36505
-▁theyre	-36506
-iculture	-36507
-▁boilers	-36508
-▁sermons	-36509
-▁Stranger	-36510
-technology	-36511
-▁abduction	-36512
-▁malignant	-36513
-▁priesthood	-36514
-GN	-36515
-▁TLC	-36516
-nyder	-36517
-future	-36518
-▁Libby	-36519
-▁Truman	-36520
-▁shaded	-36521
-normally	-36522
-▁ANOTHER	-36523
-▁Grilled	-36524
-▁Duration	-36525
-▁Machines	-36526
-▁Blockchain	-36527
-▁homeopathy	-36528
-▁recurrence	-36529
-▁reorganization	-36530
-Aren	-36531
-▁lex	-36532
-savvy	-36533
-▁nook	-36534
-▁McCoy	-36535
-▁Madeleine	-36536
-environment	-36537
-▁repayments	-36538
-▁participatory	-36539
-yu	-36540
-IED	-36541
-▁NP	-36542
-anan	-36543
-helf	-36544
-▁Khal	-36545
-▁Surf	-36546
-▁airs	-36547
-Except	-36548
-▁Pence	-36549
-raining	-36550
-▁glimpses	-36551
-▁sympathize	-36552
-▁contractions	-36553
-▁Tx	-36554
-ITCH	-36555
-etimes	-36556
-▁Beyon	-36557
-▁carte	-36558
-▁cheats	-36559
-▁.......	-36560
-▁Monument	-36561
-▁cleverly	-36562
-▁flushing	-36563
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-▁wildfire	-36565
-▁ancestral	-36566
-psons	-36567
-▁Ebay	-36568
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-▁Moines	-36571
-▁clerical	-36572
-▁resorted	-36573
-▁roulette	-36574
-asso	-36575
-bler	-36576
-▁Goa	-36577
-▁Dund	-36578
-▁bends	-36579
-▁Royals	-36580
-▁biopsy	-36581
-▁impede	-36582
-▁halftime	-36583
-▁pandemic	-36584
-girlfriend	-36585
-▁undecided	-36586
-▁binoculars	-36587
-▁integrates	-36588
-▁infestation	-36589
-▁intertwined	-36590
-▁superheroes	-36591
-OF	-36592
-sam	-36593
-▁KM	-36594
-Away	-36595
-cans	-36596
-▁Dum	-36597
-▁Nak	-36598
-▁Mask	-36599
-▁SIGN	-36600
-▁peac	-36601
-issued	-36602
-▁Leone	-36603
-▁Sight	-36604
-pancies	-36605
-▁stools	-36606
-▁affords	-36607
-▁sayings	-36608
-▁Carefully	-36609
-▁recurrent	-36610
-▁standings	-36611
-▁stationery	-36612
-▁embodiments	-36613
-▁speculating	-36614
-idad	-36615
-sten	-36616
-▁Wim	-36617
-▁dum	-36618
-▁peg	-36619
-renew	-36620
-▁Nass	-36621
-▁Syra	-36622
-▁Wage	-36623
-▁Vicki	-36624
-▁bummed	-36625
-▁Achieve	-36626
-▁Estonia	-36627
-▁slumped	-36628
-▁Observer	-36629
-seem	-36630
-▁Lav	-36631
-▁Jonas	-36632
-▁aikido	-36633
-▁manned	-36634
-▁Camping	-36635
-experience	-36636
-▁proclaiming	-36637
-andi	-36638
-furt	-36639
-alach	-36640
-lding	-36641
-acency	-36642
-▁Edith	-36643
-▁Karma	-36644
-▁toolkit	-36645
-▁skeptics	-36646
-▁anxieties	-36647
-▁triumphant	-36648
-▁commandment	-36649
-Id	-36650
-▁mah	-36651
-orset	-36652
-▁gaug	-36653
-▁Hybrid	-36654
-▁golfing	-36655
-repancies	-36656
-▁Kimberly	-36657
-▁destabil	-36658
-▁straining	-36659
-▁deteriorated	-36660
-▁commissioning	-36661
-▁extracurricular	-36662
-Ext	-36663
-tip	-36664
-▁NET	-36665
-azaar	-36666
-▁Ahmad	-36667
-▁Omaha	-36668
-▁Phyll	-36669
-▁Belief	-36670
-▁Madden	-36671
-▁videot	-36672
-deserved	-36673
-▁loosing	-36674
-exclusive	-36675
-▁Stephens	-36676
-▁contenders	-36677
-▁resounding	-36678
-▁decentralized	-36679
-->	-36680
-pu	-36681
-IZE	-36682
-pora	-36683
-quez	-36684
-▁sax	-36685
-VELOP	-36686
-igsaw	-36687
-▁Explan	-36688
-▁recoup	-36689
-provided	-36690
-▁Madness	-36691
-▁funnier	-36692
-▁unequal	-36693
-▁remedial	-36694
-▁apprehensive	-36695
-▁illumination	-36696
-▁philanthropy	-36697
-▁Discrimination	-36698
-chip	-36699
-▁Ful	-36700
-helps	-36701
-votes	-36702
-▁HATE	-36703
-asmuch	-36704
-▁caric	-36705
-▁scape	-36706
-▁wreak	-36707
-adowing	-36708
-picious	-36709
-▁Archae	-36710
-▁Covent	-36711
-▁GOVERN	-36712
-▁prides	-36713
-▁worded	-36714
-▁Isabella	-36715
-▁lifeless	-36716
-▁observance	-36717
-▁blockbuster	-36718
-signed	-36719
-▁apathy	-36720
-▁fibres	-36721
-▁Panther	-36722
-▁dispense	-36723
-▁blogengine	-36724
-▁reconciled	-36725
-▁attribution	-36726
-cyl	-36727
-▁hp	-36728
-Nope	-36729
-otrop	-36730
-▁EDUC	-36731
-▁Hipp	-36732
-hetics	-36733
-▁esoph	-36734
-▁Newark	-36735
-▁prefix	-36736
-▁schema	-36737
-▁Apostle	-36738
-▁Literacy	-36739
-▁Survivor	-36740
-▁circulate	-36741
-▁deepening	-36742
-▁housework	-36743
-▁inequalities	-36744
-nets	-36745
-▁ACLU	-36746
-▁Bain	-36747
-UCTION	-36748
-▁Athen	-36749
-▁Dhabi	-36750
-▁hiked	-36751
-▁repell	-36752
-▁clapped	-36753
-▁puppets	-36754
-▁theolog	-36755
-▁eviction	-36756
-▁rescuing	-36757
-▁intensified	-36758
-▁dw	-36759
-cups	-36760
-laus	-36761
-▁Loud	-36762
-annual	-36763
-▁Tyson	-36764
-▁carer	-36765
-onnaise	-36766
-putting	-36767
-▁Styles	-36768
-▁lounging	-36769
-▁saturday	-36770
-▁treasurer	-36771
-▁resourceful	-36772
-▁refrigeration	-36773
-isin	-36774
-poll	-36775
-utri	-36776
-▁OEM	-36777
-▁nex	-36778
-oodoo	-36779
-▁Casc	-36780
-▁Cham	-36781
-▁Merch	-36782
-▁Rebel	-36783
-▁Warming	-36784
-▁vitally	-36785
-▁prettier	-36786
-▁postpartum	-36787
-▁camaraderie	-36788
-Joe	-36789
-▁LOTS	-36790
-▁Stam	-36791
-plings	-36792
-streng	-36793
-▁Beaver	-36794
-▁Oscars	-36795
-▁ambush	-36796
-▁oyster	-36797
-▁propel	-36798
-▁adjunct	-36799
-▁armored	-36800
-▁Reporter	-36801
-▁swimmers	-36802
-▁blueberry	-36803
-▁authorizing	-36804
-▁discontinue	-36805
-▁fue	-36806
-▁yum	-36807
-igmat	-36808
-itian	-36809
-▁Dumb	-36810
-▁Pest	-36811
-▁Ping	-36812
-▁encl	-36813
-▁slay	-36814
-gester	-36815
-▁Krish	-36816
-▁Rocke	-36817
-▁hasten	-36818
-Saturday	-36819
-▁Hubbard	-36820
-▁pissing	-36821
-▁knuckles	-36822
-▁motioned	-36823
-▁tumbling	-36824
-▁conveying	-36825
-▁flashback	-36826
-▁simulated	-36827
-▁Amendments	-36828
-▁upholstery	-36829
-▁geographically	-36830
-Bas	-36831
-Safe	-36832
-▁Gul	-36833
-▁ICO	-36834
-hower	-36835
-▁Solic	-36836
-calling	-36837
-skinned	-36838
-▁cockro	-36839
-▁crates	-36840
-assisted	-36841
-ickering	-36842
-▁FINALLY	-36843
-▁Jubilee	-36844
-▁averted	-36845
-▁testers	-36846
-▁Deciding	-36847
-▁Proposed	-36848
-▁equities	-36849
-▁Sovereign	-36850
-▁actresses	-36851
-▁volcanoes	-36852
-▁humiliated	-36853
-▁disseminate	-36854
-WD	-36855
-.”[	-36856
-cult	-36857
-tips	-36858
-vana	-36859
-▁CHO	-36860
-quets	-36861
-▁Cory	-36862
-▁Gala	-36863
-▁foyer	-36864
-▁wares	-36865
-▁Crosby	-36866
-▁Deluxe	-36867
-▁plucked	-36868
-▁swapped	-36869
-▁exempted	-36870
-▁gladness	-36871
-▁fluorescent	-36872
-▁ERP	-36873
-irled	-36874
-payer	-36875
-spect	-36876
-title	-36877
-▁Gust	-36878
-▁Lest	-36879
-▁Sang	-36880
-heated	-36881
-▁Kirby	-36882
-▁Gothic	-36883
-▁Obtain	-36884
-▁bubbly	-36885
-▁Dickens	-36886
-▁nesting	-36887
-▁nucleus	-36888
-▁wheeled	-36889
-▁blisters	-36890
-▁enchanted	-36891
-▁refresher	-36892
-▁nationalities	-36893
-rika	-36894
-ilian	-36895
-atitis	-36896
-emphasis	-36897
-▁chinese	-36898
-▁stroked	-36899
-existence	-36900
-▁billboard	-36901
-▁curricula	-36902
-▁wealthiest	-36903
-AAAA	-36904
-arah	-36905
-fant	-36906
-dwell	-36907
-viets	-36908
-▁sift	-36909
-ocrats	-36910
-▁Sauce	-36911
-busters	-36912
-▁APPLIC	-36913
-▁Lonely	-36914
-▁COMPANY	-36915
-▁Equally	-36916
-▁Illness	-36917
-▁freshen	-36918
-▁regulates	-36919
-▁relatable	-36920
-▁televised	-36921
-▁unchecked	-36922
-▁hereditary	-36923
-▁communicator	-36924
-ELD	-36925
-ELS	-36926
-omez	-36927
-▁Mimi	-36928
-ectomy	-36929
-▁Farms	-36930
-▁Mummy	-36931
-▁Destroy	-36932
-▁momentous	-36933
-▁Nationwide	-36934
-▁guaranteeing	-36935
-.",	-36936
-▁eb	-36937
-atan	-36938
-bolt	-36939
-▁Alv	-36940
-▁HAL	-36941
-▁Kol	-36942
-Laughs	-36943
-▁Hague	-36944
-popular	-36945
-▁Voters	-36946
-▁Hawkins	-36947
-▁restful	-36948
-▁Seahawks	-36949
-▁frowning	-36950
-▁mouthful	-36951
-▁watchdog	-36952
-▁implanted	-36953
-▁Strawberry	-36954
-▁sympathies	-36955
-enc	-36956
-amis	-36957
-icum	-36958
-▁Dunn	-36959
-enberg	-36960
-▁Brace	-36961
-▁Souls	-36962
-▁oscill	-36963
-▁parity	-36964
-▁thinly	-36965
-Facebook	-36966
-▁augment	-36967
-▁obliter	-36968
-gesterone	-36969
-▁blushing	-36970
-▁Conversion	-36971
-▁inhibitors	-36972
-▁Establishing	-36973
-▁meticulously	-36974
-▁postgraduate	-36975
-db	-36976
-▁SING	-36977
-▁Fiber	-36978
-▁Vaugh	-36979
-▁uneas	-36980
-▁GitHub	-36981
-▁Sherry	-36982
-▁brunette	-36983
-▁calamity	-36984
-▁glaucoma	-36985
-▁paternal	-36986
-▁Financing	-36987
-▁appetizer	-36988
-▁showcased	-36989
-▁captivated	-36990
-UES	-36991
-bub	-36992
-Rest	-36993
-ingled	-36994
-▁Conver	-36995
-▁annoys	-36996
-▁mosaic	-36997
-umerable	-36998
-▁tweaked	-36999
-▁Champagne	-37000
-▁ostensibly	-37001
-vii	-37002
-rack	-37003
-Speed	-37004
-▁USSR	-37005
-▁Adolf	-37006
-▁Sands	-37007
-▁foggy	-37008
-▁sonic	-37009
-▁Harlem	-37010
-▁bummer	-37011
-▁lookin	-37012
-▁Arabian	-37013
-lightenment	-37014
-▁exclusions	-37015
-Rs	-37016
-}}	-37017
-▁NEC	-37018
-ppard	-37019
-▁DSLR	-37020
-▁Huck	-37021
-▁Punk	-37022
-▁scow	-37023
-▁Avery	-37024
-▁palsy	-37025
-▁walnut	-37026
-ositories	-37027
-▁awarding	-37028
-▁reopened	-37029
-▁Aristotle	-37030
-▁cranberry	-37031
-▁handshake	-37032
-▁simulator	-37033
-▁Automation	-37034
-▁unsuitable	-37035
-▁ny	-37036
-▁DEM	-37037
-▁RAW	-37038
-▁Fury	-37039
-▁Lawn	-37040
-▁Lill	-37041
-▁Tact	-37042
-linear	-37043
-▁rhymes	-37044
-▁Pokémon	-37045
-▁Ceremony	-37046
-▁Nationals	-37047
-▁fountains	-37048
-▁Assignment	-37049
-▁portability	-37050
-told	-37051
-▁usa	-37052
-irsty	-37053
-▁FDIC	-37054
-▁JESUS	-37055
-▁nylon	-37056
-▁bowing	-37057
-incorpor	-37058
-▁Barrett	-37059
-▁Habitat	-37060
-▁karaoke	-37061
-▁Currency	-37062
-▁rounding	-37063
-▁Logistics	-37064
-▁proponent	-37065
-▁Correspond	-37066
-▁PF	-37067
-▁Jem	-37068
-Night	-37069
-ischer	-37070
-▁strode	-37071
-▁ammonia	-37072
-▁pastime	-37073
-▁Boutique	-37074
-▁befriend	-37075
-▁migrated	-37076
-▁congregations	-37077
-▁[]	-37078
-Alex	-37079
-atio	-37080
-imet	-37081
-Dream	-37082
-amics	-37083
-▁Hint	-37084
-yellow	-37085
-▁hives	-37086
-sustain	-37087
-erocious	-37088
-▁brownie	-37089
-▁Mistakes	-37090
-▁blizzard	-37091
-▁cohesion	-37092
-▁inaction	-37093
-▁Dedicated	-37094
-▁rainforest	-37095
-▁Politicians	-37096
-!—	-37097
-iku	-37098
-wink	-37099
-culus	-37100
-simpl	-37101
-liners	-37102
-unlike	-37103
-▁enthr	-37104
-Journal	-37105
-▁Allies	-37106
-▁Bottle	-37107
-▁COMMUN	-37108
-▁crotch	-37109
-▁relive	-37110
-▁cruiser	-37111
-▁dynasty	-37112
-▁bungalow	-37113
-▁corrobor	-37114
-▁obscured	-37115
-▁wondrous	-37116
-▁composers	-37117
-▁administrations	-37118
-iP	-37119
-▁😂	-37120
-vig	-37121
-cated	-37122
-▁Wins	-37123
-unders	-37124
-▁Reese	-37125
-▁Melody	-37126
-▁galvan	-37127
-▁hikers	-37128
-▁Shadows	-37129
-▁echoing	-37130
-▁passers	-37131
-▁fondness	-37132
-▁hairdress	-37133
-▁appetizers	-37134
-▁obtainable	-37135
-Bron	-37136
-rints	-37137
-volta	-37138
-ousine	-37139
-proven	-37140
-▁Jeanne	-37141
-▁Sawyer	-37142
-▁Sharks	-37143
-▁bathed	-37144
-▁tubing	-37145
-pictured	-37146
-unctions	-37147
-▁enforceable	-37148
-▁socializing	-37149
-▁departmental	-37150
-ANDS	-37151
-enting	-37152
-▁SHARE	-37153
-▁Susie	-37154
-▁Poison	-37155
-▁ledger	-37156
-▁rushes	-37157
-▁savior	-37158
-▁chandel	-37159
-▁handset	-37160
-▁Evaluate	-37161
-▁microbes	-37162
-▁flammable	-37163
-▁shillings	-37164
-determination	-37165
-RP	-37166
-bum	-37167
-▁PGA	-37168
-▁Lump	-37169
-▁SaaS	-37170
-▁crum	-37171
-▁gyms	-37172
-wonder	-37173
-▁Lists	-37174
-▁Silly	-37175
-▁Toast	-37176
-▁jerks	-37177
-runtled	-37178
-▁Enable	-37179
-▁Infect	-37180
-▁barring	-37181
-▁suction	-37182
-▁Fernando	-37183
-▁drinkers	-37184
-▁hypocrite	-37185
-▁unwitting	-37186
-▁illiterate	-37187
-▁Subscription	-37188
-▁suspiciously	-37189
-vre	-37190
-rable	-37191
-▁Mere	-37192
-▁Tray	-37193
-▁hush	-37194
-believ	-37195
-▁bouts	-37196
-▁depra	-37197
-▁nonex	-37198
-adjusted	-37199
-▁lactose	-37200
-▁nurtured	-37201
-▁grounding	-37202
-▁messengers	-37203
-▁psychiatry	-37204
-▁transpired	-37205
-▁bookkeeping	-37206
-▁abnormalities	-37207
-▁hym	-37208
-▁nib	-37209
-▁clom	-37210
-▁mars	-37211
-▁usur	-37212
-athlet	-37213
-fitted	-37214
-remlin	-37215
-ushima	-37216
-▁Named	-37217
-▁juggle	-37218
-▁tigers	-37219
-▁Odyssey	-37220
-▁geology	-37221
-▁leveled	-37222
-▁Anywhere	-37223
-▁bloating	-37224
-▁honorary	-37225
-worthiness	-37226
-▁Workforce	-37227
-▁bloodshed	-37228
-▁unmatched	-37229
-▁exposition	-37230
-▁firefighter	-37231
-oske	-37232
-▁Aha	-37233
-▁MOM	-37234
-▁Sexy	-37235
-▁Thin	-37236
-▁dams	-37237
-Change	-37238
-▁Crack	-37239
-heading	-37240
-▁Snyder	-37241
-▁Tomato	-37242
-▁raping	-37243
-▁rumour	-37244
-ompanied	-37245
-▁brittle	-37246
-▁snowing	-37247
-▁CHILDREN	-37248
-▁garrison	-37249
-▁Communicate	-37250
-SL	-37251
-▁LAS	-37252
-ancel	-37253
-oidal	-37254
-▁boun	-37255
-▁enam	-37256
-▁nope	-37257
-▁pinn	-37258
-▁iPads	-37259
-▁bearer	-37260
-▁enamel	-37261
-▁Consist	-37262
-odynamics	-37263
-▁colossal	-37264
-▁Preferred	-37265
-▁leveraged	-37266
-▁thumbnail	-37267
-▁Convenient	-37268
-▁Experienced	-37269
-saw	-37270
-oves	-37271
-▁Tus	-37272
-metal	-37273
-▁CLASS	-37274
-▁Codes	-37275
-▁protr	-37276
-▁Bolton	-37277
-▁Viking	-37278
-▁cosmos	-37279
-▁propensity	-37280
-▁painstaking	-37281
-▁subsections	-37282
-▁psychotherapy	-37283
-bol	-37284
-eme	-37285
-wig	-37286
-▁Umm	-37287
-▁RICH	-37288
-▁Uses	-37289
-▁valet	-37290
-▁Caring	-37291
-▁Trials	-37292
-▁fairer	-37293
-▁tumour	-37294
-▁Convert	-37295
-▁Fighter	-37296
-▁bribery	-37297
-▁catcher	-37298
-▁banished	-37299
-▁complexes	-37300
-▁Excellency	-37301
-.”.	-37302
-DRM	-37303
-oche	-37304
-▁Kab	-37305
-▁Owl	-37306
-▁Zur	-37307
-▁cac	-37308
-ammit	-37309
-▁abuser	-37310
-▁cocked	-37311
-▁honours	-37312
-▁reinvest	-37313
-▁fermented	-37314
-▁shoreline	-37315
-▁powerfully	-37316
-▁renovating	-37317
-▁mischievous	-37318
-▁purposefully	-37319
-▁deteriorating	-37320
-▁hallucinations	-37321
-án	-37322
-▁✓	-37323
-)).	-37324
-——"	-37325
-logs	-37326
-▁Rue	-37327
-sugar	-37328
-exempt	-37329
-thirty	-37330
-▁bogged	-37331
-▁croche	-37332
-▁dystop	-37333
-▁lethar	-37334
-tainable	-37335
-▁Pursuant	-37336
-▁Syracuse	-37337
-▁snowball	-37338
-▁spaceship	-37339
-▁unplanned	-37340
-▁Councillor	-37341
-▁Transparency	-37342
-huh	-37343
-Forg	-37344
-adas	-37345
-zers	-37346
-▁Gle	-37347
-maids	-37348
-▁Plug	-37349
-▁Orion	-37350
-▁silky	-37351
-▁huddled	-37352
-▁lentils	-37353
-▁hallways	-37354
-▁justices	-37355
-▁awesomeness	-37356
-▁solicitation	-37357
-▁probabilities	-37358
-▁μ	-37359
-CER	-37360
-rek	-37361
-▁CY	-37362
-pour	-37363
-laden	-37364
-smile	-37365
-▁Ouch	-37366
-ovable	-37367
-▁asymm	-37368
-▁basing	-37369
-▁seeker	-37370
-▁smelly	-37371
-Internet	-37372
-▁layoffs	-37373
-▁eggplant	-37374
-▁freebies	-37375
-▁intervie	-37376
-▁juncture	-37377
-▁landowners	-37378
-▁rehearsals	-37379
-▁sweeteners	-37380
-ibi	-37381
-ULAR	-37382
-▁Ares	-37383
-▁Borg	-37384
-▁hump	-37385
-▁pard	-37386
-Spring	-37387
-▁Goose	-37388
-▁Powder	-37389
-▁Lucifer	-37390
-▁sorrows	-37391
-▁totality	-37392
-▁semantics	-37393
-▁Citizenship	-37394
-▁manslaughter	-37395
-Mor	-37396
-▁PK	-37397
-▁IDE	-37398
-▁dvd	-37399
-iatry	-37400
-▁Gale	-37401
-camera	-37402
-rovers	-37403
-stered	-37404
-▁Droid	-37405
-▁TRUTH	-37406
-▁debug	-37407
-▁intel	-37408
-▁teller	-37409
-▁wither	-37410
-▁FUCKING	-37411
-▁embargo	-37412
-▁inverse	-37413
-▁sweatsh	-37414
-▁wrinkled	-37415
-▁Happening	-37416
-▁patronage	-37417
-▁shortness	-37418
-▁storefront	-37419
-▁Confirmation	-37420
-lys	-37421
-▁LEGO	-37422
-▁rumm	-37423
-ansion	-37424
-▁Sasha	-37425
-▁amort	-37426
-▁Incent	-37427
-▁Infinite	-37428
-▁oblivion	-37429
-▁nourishing	-37430
-▁Achievement	-37431
-▁biotechnology	-37432
-Mex	-37433
-tom	-37434
-ANTS	-37435
-▁Pup	-37436
-radio	-37437
-▁Herb	-37438
-▁Levy	-37439
-▁Wong	-37440
-▁pious	-37441
-▁rants	-37442
-▁elicit	-37443
-▁Leopard	-37444
-▁MySpace	-37445
-▁equates	-37446
-▁singled	-37447
-▁Socrates	-37448
-▁illogical	-37449
-▁justifies	-37450
-▁deposition	-37451
-▁dishonesty	-37452
-▁centerpiece	-37453
-▁deteriorate	-37454
-Sweet	-37455
-robes	-37456
-▁Lobby	-37457
-▁Salary	-37458
-▁kosher	-37459
-▁servic	-37460
-▁viscer	-37461
-▁DEVELOP	-37462
-▁insofar	-37463
-▁Pulitzer	-37464
-▁Nicaragua	-37465
-▁Recycling	-37466
-▁improvised	-37467
-▁bittersweet	-37468
-▁testimonies	-37469
-▁fide	-37470
-roidism	-37471
-▁Trainer	-37472
-▁inhaled	-37473
-▁revered	-37474
-▁gradient	-37475
-cigarettes	-37476
-▁surrogate	-37477
-▁Commitment	-37478
-▁Consultants	-37479
-ICI	-37480
-▁Prix	-37481
-▁saut	-37482
-riends	-37483
-▁Onion	-37484
-▁Spurs	-37485
-▁Filter	-37486
-▁artworks	-37487
-▁hairdryer	-37488
-▁marginally	-37489
-▁revocation	-37490
-▁disapproval	-37491
-asel	-37492
-owns	-37493
-▁Geo	-37494
-▁NAR	-37495
-minus	-37496
-▁Bret	-37497
-▁Dora	-37498
-▁Fres	-37499
-▁Ariel	-37500
-▁FedEx	-37501
-▁Bubble	-37502
-▁Feather	-37503
-▁swimmer	-37504
-▁cupboards	-37505
-▁Veterinary	-37506
-▁ascertained	-37507
-▁plantations	-37508
-▁tranquility	-37509
-’’	-37510
-gear	-37511
-▁Kub	-37512
-AWARE	-37513
-▁Melt	-37514
-orrect	-37515
-▁Milky	-37516
-▁Nairo	-37517
-▁Scotch	-37518
-▁ignite	-37519
-▁inhale	-37520
-▁outcry	-37521
-▁quotas	-37522
-▁Harriet	-37523
-▁pianist	-37524
-▁utilised	-37525
-▁disconcer	-37526
-▁lifetimes	-37527
-▁nationalist	-37528
-▁antidepressants	-37529
-dough	-37530
-ileen	-37531
-mills	-37532
-▁MANAG	-37533
-▁SMART	-37534
-▁Shack	-37535
-▁madame	-37536
-purchase	-37537
-▁WARNING	-37538
-▁bunnies	-37539
-▁candies	-37540
-▁Oriental	-37541
-▁laborers	-37542
-▁bookshelf	-37543
-▁inspecting	-37544
-▁mastermind	-37545
-▁moratorium	-37546
-▁underrated	-37547
-▁containment	-37548
-▁discrepancies	-37549
-▁discriminated	-37550
-▁miscellaneous	-37551
-▁kam	-37552
-clips	-37553
-hetto	-37554
-joyed	-37555
-▁Icon	-37556
-▁lique	-37557
-▁Infant	-37558
-▁francs	-37559
-▁hangar	-37560
-▁Encoura	-37561
-▁blunder	-37562
-▁scrapped	-37563
-▁symphony	-37564
-Government	-37565
-▁Minecraft	-37566
-▁Recession	-37567
-▁deceiving	-37568
-▁Surveillance	-37569
-▁superstition	-37570
-ggs	-37571
-▁*!	-37572
-ENDS	-37573
-acha	-37574
-reon	-37575
-uala	-37576
-lexia	-37577
-▁Erit	-37578
-▁Obst	-37579
-ENTION	-37580
-▁Bruno	-37581
-▁Feels	-37582
-▁Vital	-37583
-Product	-37584
-▁monday	-37585
-▁shrugs	-37586
-▁Prosper	-37587
-▁encrypt	-37588
-▁soluble	-37589
-▁Butterfly	-37590
-▁expedient	-37591
-▁loopholes	-37592
-▁chancellor	-37593
-▁saturation	-37594
-▁TJ	-37595
-subs	-37596
-▁AUD	-37597
-▁Hed	-37598
-ousel	-37599
-▁Perl	-37600
-machine	-37601
-▁aisles	-37602
-▁gaping	-37603
-▁hearth	-37604
-▁lagoon	-37605
-▁Empower	-37606
-▁fetched	-37607
-▁panting	-37608
-▁breweries	-37609
-▁ineligible	-37610
-fus	-37611
-onte	-37612
-▁ARC	-37613
-▁Eliot	-37614
-▁Myrtle	-37615
-▁liters	-37616
-▁circled	-37617
-▁dolphin	-37618
-seriously	-37619
-▁Refriger	-37620
-Bank	-37621
-keye	-37622
-wers	-37623
-Guess	-37624
-▁fray	-37625
-▁huts	-37626
-▁kins	-37627
-▁mmol	-37628
-▁Dover	-37629
-▁optic	-37630
-▁beamed	-37631
-▁Schmidt	-37632
-▁browned	-37633
-▁dryness	-37634
-▁irrevoc	-37635
-atography	-37636
-▁Thatcher	-37637
-▁occupant	-37638
-▁billboards	-37639
-▁credential	-37640
-▁spectacles	-37641
-,-	-37642
-’:	-37643
-ATS	-37644
-Tax	-37645
-hydr	-37646
-liqu	-37647
-achts	-37648
-baked	-37649
-omson	-37650
-▁nigh	-37651
-lusion	-37652
-▁Ethel	-37653
-▁Sigma	-37654
-▁shale	-37655
-▁teeny	-37656
-▁Bigger	-37657
-▁Marvin	-37658
-▁waffle	-37659
-▁hooking	-37660
-▁idyllic	-37661
-▁spawned	-37662
-▁thrives	-37663
-▁confided	-37664
-▁monetize	-37665
-▁expulsion	-37666
-▁euthanasia	-37667
-▁civilisation	-37668
-▁Recommendation	-37669
-ogly	-37670
-▁Gur	-37671
-▁>>>>	-37672
-▁Carb	-37673
-▁GAME	-37674
-▁Mang	-37675
-▁Rape	-37676
-▁surm	-37677
-▁Sofia	-37678
-▁Sidney	-37679
-▁Producer	-37680
-UU	-37681
-roof	-37682
-sing	-37683
-▁ark	-37684
-▁Roma	-37685
-▁polo	-37686
-ansing	-37687
-spring	-37688
-▁Graves	-37689
-watering	-37690
-▁copious	-37691
-▁robbing	-37692
-umberland	-37693
-▁Goodness	-37694
-▁equivalents	-37695
-:(	-37696
-▁POLIT	-37697
-▁matte	-37698
-ashions	-37699
-▁Baxter	-37700
-▁fiasco	-37701
-▁mowing	-37702
-▁Mansion	-37703
-mountable	-37704
-▁firewood	-37705
-▁optimise	-37706
-assessment	-37707
-▁Donations	-37708
-▁encompassing	-37709
-San	-37710
-during	-37711
-estine	-37712
-▁geese	-37713
-▁maids	-37714
-Project	-37715
-neutral	-37716
-▁caucus	-37717
-▁muzzle	-37718
-▁surged	-37719
-clerosis	-37720
-licensed	-37721
-olerable	-37722
-▁Perkins	-37723
-▁slashed	-37724
-▁walkway	-37725
-▁ferocious	-37726
-▁persevere	-37727
-▁promoters	-37728
-▁collaborated	-37729
-hom	-37730
-ixel	-37731
-nant	-37732
-▁zen	-37733
-oiled	-37734
-sellers	-37735
-▁Garlic	-37736
-▁cheeky	-37737
-▁grassy	-37738
-▁Soviets	-37739
-▁overturn	-37740
-▁reptiles	-37741
-▁impulsive	-37742
-▁primaries	-37743
-▁millennial	-37744
-▁Presbyterian	-37745
-..”	-37746
-APH	-37747
-Arch	-37748
-Clear	-37749
-rules	-37750
-▁Thee	-37751
-feller	-37752
-oscopy	-37753
-twenty	-37754
-▁swaps	-37755
-▁pallet	-37756
-▁stifle	-37757
-cleaning	-37758
-▁Airways	-37759
-▁Contents	-37760
-▁Hartford	-37761
-▁dependents	-37762
-▁fabricated	-37763
-▁exaggerating	-37764
-,’’	-37765
-hoot	-37766
-otti	-37767
-igning	-37768
-issues	-37769
-▁Weber	-37770
-justice	-37771
-▁Cannon	-37772
-▁Irvine	-37773
-▁busier	-37774
-▁Bridges	-37775
-▁Regions	-37776
-▁enquire	-37777
-▁imposes	-37778
-▁payouts	-37779
-▁reeling	-37780
-▁downsides	-37781
-▁petitioner	-37782
-▁accompanies	-37783
-▁denominator	-37784
-▁computerized	-37785
-umba	-37786
-▁ACE	-37787
-shall	-37788
-▁rename	-37789
-▁Advisors	-37790
-▁Somerset	-37791
-▁backstage	-37792
-▁predatory	-37793
-▁Christchurch	-37794
-BERS	-37795
-arty	-37796
-▁Hos	-37797
-olkien	-37798
-▁Conan	-37799
-▁Kling	-37800
-▁moose	-37801
-▁taper	-37802
-▁Lionel	-37803
-▁Wheels	-37804
-▁banged	-37805
-▁mellow	-37806
-▁Monarch	-37807
-▁backers	-37808
-▁stimuli	-37809
-▁Honduras	-37810
-▁applauded	-37811
-▁deliberation	-37812
-DM	-37813
-▁Cary	-37814
-annels	-37815
-motion	-37816
-▁OBAMA	-37817
-▁baron	-37818
-▁decon	-37819
-▁roost	-37820
-▁doubly	-37821
-category	-37822
-evidence	-37823
-▁Preview	-37824
-▁forfeit	-37825
-▁Geography	-37826
-▁analogous	-37827
-▁sixteenth	-37828
-▁unstoppable	-37829
-▁denomination	-37830
-▁Uz	-37831
-bang	-37832
-▁unab	-37833
-▁Jules	-37834
-▁Mabel	-37835
-▁autop	-37836
-▁Bengal	-37837
-▁deluxe	-37838
-▁hitter	-37839
-▁Concord	-37840
-▁overcame	-37841
-▁wetlands	-37842
-▁bourgeois	-37843
-▁forcefully	-37844
-▁interacted	-37845
-spec	-37846
-▁Clan	-37847
-▁fizz	-37848
-▁soot	-37849
-oratory	-37850
-▁Dustin	-37851
-▁callers	-37852
-▁coffees	-37853
-▁slogans	-37854
-▁sternly	-37855
-▁tantrum	-37856
-▁Passport	-37857
-▁polyester	-37858
-competitive	-37859
-▁refinement	-37860
-▁Organizational	-37861
-.**	-37862
-XXXX	-37863
-▁Geek	-37864
-▁anore	-37865
-ensical	-37866
-▁indent	-37867
-▁wiggle	-37868
-▁Wheeler	-37869
-▁unclean	-37870
-▁DELAWARE	-37871
-▁Portrait	-37872
-▁alleging	-37873
-▁⠀⠀⠀⠀⠀⠀⠀⠀	-37874
-▁amounting	-37875
-▁autograph	-37876
-▁peppermint	-37877
-▁Productions	-37878
-▁exacerbated	-37879
-▁mountainous	-37880
-▁treacherous	-37881
-▁multilateral	-37882
-epad	-37883
-▁ANG	-37884
-idated	-37885
-▁Bates	-37886
-▁Hedge	-37887
-▁pushy	-37888
-▁truce	-37889
-▁Fairly	-37890
-▁indist	-37891
-Therefore	-37892
-▁burdened	-37893
-▁previews	-37894
-▁actuality	-37895
-▁implicitly	-37896
-▁Replacement	-37897
-▁accelerator	-37898
-Yep	-37899
-▁$$	-37900
-▁bb	-37901
-▁Vit	-37902
-focus	-37903
-▁Renee	-37904
-▁farce	-37905
-▁furthe	-37906
-▁overst	-37907
-▁reprim	-37908
-▁untold	-37909
-rational	-37910
-▁Catalog	-37911
-▁wartime	-37912
-▁Alexandra	-37913
-▁receptions	-37914
-▁Resurrection	-37915
-▁emphatically	-37916
-Ra	-37917
-berra	-37918
-▁Erie	-37919
-▁Pats	-37920
-▁Unic	-37921
-picture	-37922
-▁Baltic	-37923
-▁arches	-37924
-curement	-37925
-▁backside	-37926
-▁glaciers	-37927
-▁burgeoning	-37928
-▁calibration	-37929
-★★	-37930
-Prom	-37931
-elic	-37932
-happ	-37933
-▁Byr	-37934
-▁RIS	-37935
-rolet	-37936
-▁Clim	-37937
-▁devs	-37938
-Perfect	-37939
-consult	-37940
-▁etched	-37941
-▁Hospice	-37942
-▁scaring	-37943
-▁RELATING	-37944
-▁handouts	-37945
-▁Colombian	-37946
-▁intervened	-37947
-▁perpetuate	-37948
-▁uncontrolled	-37949
-▁denominations	-37950
-APS	-37951
-▁EW	-37952
-▁Chu	-37953
-George	-37954
-▁Boxing	-37955
-▁mammal	-37956
-▁Forrest	-37957
-▁Passing	-37958
-▁desktops	-37959
-enny	-37960
-▁(!)	-37961
-iosis	-37962
-plane	-37963
-tions	-37964
-▁AWAY	-37965
-frames	-37966
-▁Scary	-37967
-filling	-37968
-finition	-37969
-▁sentient	-37970
-▁biometric	-37971
-▁counteract	-37972
-RM	-37973
-Fil	-37974
-▁Vib	-37975
-▁rpm	-37976
-▁Hash	-37977
-▁invo	-37978
-racing	-37979
-▁Maced	-37980
-▁dilute	-37981
-▁misman	-37982
-▁reused	-37983
-▁Entries	-37984
-▁hampered	-37985
-▁newborns	-37986
-▁assertive	-37987
-hoea	-37988
-▁dow	-37989
-Queen	-37990
-▁filth	-37991
-▁undet	-37992
-comment	-37993
-icators	-37994
-licting	-37995
-▁Coupon	-37996
-▁loathe	-37997
-▁quadru	-37998
-▁rodents	-37999
-▁shopped	-38000
-▁tumbled	-38001
-▁archival	-38002
-▁captions	-38003
-▁injecting	-38004
-ignon	-38005
-oiler	-38006
-▁Paso	-38007
-▁Rudy	-38008
-▁bade	-38009
-▁Azure	-38010
-▁CHRIS	-38011
-▁Rolls	-38012
-▁aptly	-38013
-▁dives	-38014
-loading	-38015
-▁Marcia	-38016
-▁Hawking	-38017
-▁blockade	-38018
-▁catalogs	-38019
-▁acquitted	-38020
-▁crossroads	-38021
-▁defensively	-38022
-voy	-38023
-▁Poppy	-38024
-▁kneel	-38025
-▁torped	-38026
-▁Nairobi	-38027
-▁Willing	-38028
-▁Appalach	-38029
-▁titanium	-38030
-▁Lexington	-38031
-▁Packaging	-38032
-▁celestial	-38033
-▁minimally	-38034
-▁Eisenhower	-38035
-▁metropolis	-38036
-▁phosphorus	-38037
-▁transplants	-38038
-sus	-38039
-Earth	-38040
-oored	-38041
-otomy	-38042
-▁aloe	-38043
-▁wade	-38044
-Summer	-38045
-invent	-38046
-period	-38047
-▁Pablo	-38048
-▁Wrest	-38049
-▁unacc	-38050
-▁Anglic	-38051
-▁Maddie	-38052
-▁strung	-38053
-▁invaders	-38054
-▁comedians	-38055
-▁Wer	-38056
-▁sab	-38057
-Enter	-38058
-assic	-38059
-whole	-38060
-▁Herr	-38061
-▁Sire	-38062
-▁Nicola	-38063
-▁Trophy	-38064
-▁Vivian	-38065
-▁Weston	-38066
-▁ardent	-38067
-irteenth	-38068
-▁AdWords	-38069
-▁Janeiro	-38070
-▁favours	-38071
-▁Heathrow	-38072
-▁carefree	-38073
-▁paddling	-38074
-reasonable	-38075
-▁Whichever	-38076
-▁debugging	-38077
-▁milligrams	-38078
-▁wickedness	-38079
-▁Competitive	-38080
-▁Conditioning	-38081
-zb	-38082
-nkx	-38083
-▁Rd	-38084
-abin	-38085
-▁Dyn	-38086
-▁LOW	-38087
-▁pry	-38088
-ascus	-38089
-beans	-38090
-idine	-38091
-ionic	-38092
-▁Panc	-38093
-▁Sung	-38094
-▁gala	-38095
-▁hoot	-38096
-▁Dolly	-38097
-▁Teens	-38098
-▁Willy	-38099
-▁dissu	-38100
-▁Circus	-38101
-▁Maddox	-38102
-▁Phyllis	-38103
-▁escorts	-38104
-▁muffled	-38105
-▁slander	-38106
-▁perverse	-38107
-▁Workshops	-38108
-▁propelled	-38109
-▁inactivity	-38110
-▁searchable	-38111
-▁innumerable	-38112
-nav	-38113
-▁agn	-38114
-▁Cull	-38115
-▁Mona	-38116
-▁hors	-38117
-▁Rally	-38118
-▁cedar	-38119
-▁coils	-38120
-▁crore	-38121
-cillary	-38122
-▁ranger	-38123
-▁sporty	-38124
-▁Dialogue	-38125
-▁Gentiles	-38126
-▁Theodore	-38127
-▁pretends	-38128
-▁sizeable	-38129
-▁foresight	-38130
-▁elasticity	-38131
-'-	-38132
-eder	-38133
-▁RECE	-38134
-▁wilt	-38135
-▁DEPAR	-38136
-blooded	-38137
-▁embryo	-38138
-▁indexes	-38139
-▁inserts	-38140
-▁snuggle	-38141
-Hopefully	-38142
-▁escalated	-38143
-▁manifests	-38144
-▁plurality	-38145
-=”	-38146
-▁Mud	-38147
-UALLY	-38148
-posal	-38149
-warts	-38150
-▁IDEA	-38151
-▁Mast	-38152
-▁Peps	-38153
-expert	-38154
-▁Tamil	-38155
-▁scrum	-38156
-▁Ethical	-38157
-▁glacier	-38158
-▁upheaval	-38159
-▁witchcraft	-38160
-UNK	-38161
-▁IPO	-38162
-▁LINK	-38163
-▁dunk	-38164
-▁hars	-38165
-gation	-38166
-killed	-38167
-▁iniqu	-38168
-▁soles	-38169
-▁uproar	-38170
-formerly	-38171
-▁patrols	-38172
-emotional	-38173
-▁Petition	-38174
-▁contrasts	-38175
-▁makeshift	-38176
-operability	-38177
-▁Guaranteed	-38178
-▁Volkswagen	-38179
-iq	-38180
-near	-38181
-▁CGI	-38182
-▁GUI	-38183
-▁Gri	-38184
-▁Bard	-38185
-▁Kale	-38186
-▁ford	-38187
-▁toil	-38188
-payers	-38189
-▁limbo	-38190
-▁towed	-38191
-managed	-38192
-▁Stevie	-38193
-▁winked	-38194
-▁Careful	-38195
-▁stables	-38196
-▁Withdraw	-38197
-▁justifying	-38198
-approximately	-38199
-▁refrigerated	-38200
-▁Consciousness	-38201
-WHO	-38202
-▁’’	-38203
-▁Poe	-38204
-rored	-38205
-sport	-38206
-▁donut	-38207
-▁rabid	-38208
-Friends	-38209
-privile	-38210
-▁Asians	-38211
-▁Finger	-38212
-▁infall	-38213
-▁mascot	-38214
-▁payers	-38215
-▁Jewelry	-38216
-▁Preheat	-38217
-▁carnage	-38218
-▁centred	-38219
-▁Councils	-38220
-▁Doctrine	-38221
-▁Screening	-38222
-▁venturing	-38223
-▁vigilance	-38224
-▁worksheet	-38225
-▁incoherent	-38226
-▁propositions	-38227
-MAIL	-38228
-▁Hak	-38229
-grees	-38230
-▁prod	-38231
-▁Kelley	-38232
-▁Signal	-38233
-▁Scratch	-38234
-▁roundup	-38235
-▁crucifix	-38236
-▁dominion	-38237
-▁inundated	-38238
-▁brainwashed	-38239
-optim	-38240
-▁Empty	-38241
-▁Outer	-38242
-▁Turks	-38243
-▁REPORT	-38244
-▁Sultan	-38245
-▁Texans	-38246
-director	-38247
-▁Randall	-38248
-▁Vanilla	-38249
-▁comedic	-38250
-▁Supplies	-38251
-▁dialysis	-38252
-▁Quarterly	-38253
-▁narcotics	-38254
-▁resembled	-38255
-▁Huntington	-38256
-essay	-38257
-▁Leigh	-38258
-▁fling	-38259
-concept	-38260
-▁quorum	-38261
-▁Pauline	-38262
-▁Scarlet	-38263
-▁additive	-38264
-▁blinding	-38265
-▁enslaved	-38266
-▁failings	-38267
-▁Situation	-38268
-▁speculated	-38269
-▁Intervention	-38270
-IPT	-38271
-tur	-38272
-▁mage	-38273
-oteric	-38274
-▁Gomez	-38275
-▁gorge	-38276
-▁nerds	-38277
-fathers	-38278
-▁blasts	-38279
-▁censor	-38280
-▁noodle	-38281
-▁Hoffman	-38282
-▁stalker	-38283
-▁palpable	-38284
-▁semicond	-38285
-▁Licensing	-38286
-▁Concerning	-38287
-▁judgements	-38288
-▁*/	-38289
-herty	-38290
-orean	-38291
-uphem	-38292
-▁FACE	-38293
-▁Gave	-38294
-▁Mara	-38295
-▁Pitch	-38296
-▁rabbi	-38297
-pelling	-38298
-▁Apples	-38299
-▁twitch	-38300
-▁amassed	-38301
-▁breeder	-38302
-▁routers	-38303
-▁theorists	-38304
-▁tolerable	-38305
-▁ideologies	-38306
-▁prophecies	-38307
-▁Fundamental	-38308
-▁POV	-38309
-▁PUT	-38310
-ocity	-38311
-▁FIVE	-38312
-ivists	-38313
-▁knead	-38314
-Despite	-38315
-▁fished	-38316
-▁foodie	-38317
-▁staffers	-38318
-Eh	-38319
-owdy	-38320
-arbon	-38321
-▁Keen	-38322
-▁arre	-38323
-▁Carlo	-38324
-minutes	-38325
-▁Victim	-38326
-▁Brennan	-38327
-▁Shopify	-38328
-▁cubicle	-38329
-▁fillings	-38330
-▁Apocalypse	-38331
-▁Hemisphere	-38332
-▁gingerbread	-38333
-▁unpublished	-38334
-▁Documentation	-38335
-▁Dew	-38336
-▁IEP	-38337
-▁Ashe	-38338
-▁Diaz	-38339
-▁Horm	-38340
-ambert	-38341
-▁beets	-38342
-feeling	-38343
-hunting	-38344
-ogenous	-38345
-▁Ending	-38346
-▁Havana	-38347
-▁pulpit	-38348
-▁purses	-38349
-▁tempered	-38350
-▁illustrator	-38351
-▁subsistence	-38352
-Week	-38353
-▁MAX	-38354
-spots	-38355
-▁Frog	-38356
-▁Yuki	-38357
-▁crux	-38358
-▁Petra	-38359
-▁Romeo	-38360
-▁invin	-38361
-▁Formal	-38362
-▁widows	-38363
-▁gunfire	-38364
-▁cynicism	-38365
-▁grandeur	-38366
-▁Applicant	-38367
-ku	-38368
-ANA	-38369
-lox	-38370
-phys	-38371
-▁Lease	-38372
-▁lured	-38373
-▁admins	-38374
-planning	-38375
-regation	-38376
-▁Analyst	-38377
-▁fueling	-38378
-▁Fountain	-38379
-▁Handling	-38380
-▁loophole	-38381
-Made	-38382
-mins	-38383
-onics	-38384
-sburg	-38385
-ureka	-38386
-▁Zika	-38387
-▁Boehner	-38388
-▁Smaller	-38389
-▁stencil	-38390
-▁Radiation	-38391
-▁extortion	-38392
-▁Corruption	-38393
-▁suggestive	-38394
-fas	-38395
-▁ADA	-38396
-▁Felt	-38397
-▁AVAIL	-38398
-▁Chero	-38399
-software	-38400
-Mike	-38401
-lust	-38402
-Smith	-38403
-biter	-38404
-ierre	-38405
-▁Minds	-38406
-▁gaunt	-38407
-▁Essays	-38408
-▁acutely	-38409
-▁bottlene	-38410
-▁squadron	-38411
-▁precursor	-38412
-▁sickening	-38413
-▁insurgents	-38414
-.&	-38415
-▁FX	-38416
-▁NL	-38417
-ISTER	-38418
-Value	-38419
-edged	-38420
-estry	-38421
-indul	-38422
-▁Spam	-38423
-▁groin	-38424
-▁herds	-38425
-▁latte	-38426
-▁fanatics	-38427
-individual	-38428
-▁anomalies	-38429
-▁judgmental	-38430
-ATT	-38431
-Vis	-38432
-▁Aj	-38433
-▁DG	-38434
-▁Elev	-38435
-▁Lama	-38436
-▁Toss	-38437
-▁Trin	-38438
-repair	-38439
-▁VIDEO	-38440
-▁booty	-38441
-▁Habits	-38442
-▁fungal	-38443
-▁yonder	-38444
-recorded	-38445
-▁GENERAL	-38446
-▁Tolkien	-38447
-▁molding	-38448
-▁Israelites	-38449
-▁affliction	-38450
-▁babysitting	-38451
-UX	-38452
-Fund	-38453
-▁imo	-38454
-greens	-38455
-orized	-38456
-▁Burma	-38457
-▁Chips	-38458
-aratory	-38459
-giveness	-38460
-▁Bishops	-38461
-▁Dawkins	-38462
-▁Incident	-38463
-▁Mexicans	-38464
-▁blackout	-38465
-▁decadent	-38466
-▁luxuries	-38467
-▁critiques	-38468
-▁delinquent	-38469
-ahs	-38470
-Thou	-38471
-bite	-38472
-rano	-38473
-▁Wolfe	-38474
-▁brewed	-38475
-transfer	-38476
-▁interst	-38477
-▁militar	-38478
-▁tripled	-38479
-▁indecent	-38480
-▁terraces	-38481
-▁migrating	-38482
-▁degeneration	-38483
-▁inexplicable	-38484
-!!!)	-38485
-Prov	-38486
-adal	-38487
-▁HUD	-38488
-inker	-38489
-▁Albu	-38490
-▁rubs	-38491
-▁Trish	-38492
-▁edged	-38493
-▁Ancest	-38494
-▁Asthma	-38495
-▁CANNOT	-38496
-▁Kidney	-38497
-▁Sunder	-38498
-▁astute	-38499
-▁stinks	-38500
-▁Tibetan	-38501
-▁nostrils	-38502
-▁outburst	-38503
-▁penchant	-38504
-▁Greenland	-38505
-▁Instructor	-38506
-▁terrestrial	-38507
-▁thunderstorms	-38508
-okers	-38509
-▁----	-38510
-▁Mesa	-38511
-▁sard	-38512
-▁runny	-38513
-farious	-38514
-▁Column	-38515
-▁Sketch	-38516
-▁talkin	-38517
-▁Sweetie	-38518
-▁Penguins	-38519
-▁overrated	-38520
-▁scripting	-38521
-▁Kazakhstan	-38522
-▁compulsion	-38523
-▁filmmaking	-38524
-▁attractiveness	-38525
-yang	-38526
-▁ASE	-38527
-▁Poo	-38528
-?????	-38529
-adult	-38530
-▁DEAD	-38531
-▁unner	-38532
-▁Vander	-38533
-▁bachel	-38534
-▁incarn	-38535
-▁preface	-38536
-▁scented	-38537
-▁enrolment	-38538
-Were	-38539
-▁Crowd	-38540
-▁syner	-38541
-▁usher	-38542
-strateg	-38543
-▁optics	-38544
-▁Happily	-38545
-▁soreness	-38546
-Type	-38547
-▁DLC	-38548
-▁tsp	-38549
-▁Tart	-38550
-career	-38551
-nished	-38552
-▁DEFIN	-38553
-▁abhor	-38554
-▁Hanson	-38555
-▁eaters	-38556
-▁boasted	-38557
-▁bourbon	-38558
-▁expanse	-38559
-adventure	-38560
-▁clapping	-38561
-▁extrater	-38562
-▁hijacked	-38563
-▁incentiv	-38564
-▁transist	-38565
-▁endearing	-38566
-▁parachute	-38567
-▁Acceptance	-38568
-▁infidelity	-38569
-▁disgruntled	-38570
-▁xD	-38571
-Educ	-38572
-Honey	-38573
-▁Dres	-38574
-▁rung	-38575
-▁Crane	-38576
-▁Smash	-38577
-▁Warsaw	-38578
-▁footer	-38579
-▁Cunning	-38580
-▁sampled	-38581
-▁blissful	-38582
-▁tiredness	-38583
-▁triangles	-38584
-▁seventeenth	-38585
-▁practicality	-38586
-▁Burr	-38587
-▁Dane	-38588
-▁wann	-38589
-United	-38590
-▁budge	-38591
-▁fleas	-38592
-▁Strict	-38593
-▁Charley	-38594
-▁Plaintiff	-38595
-▁hostilities	-38596
-▁miraculously	-38597
-ROM	-38598
-▁DV	-38599
-docs	-38600
-isty	-38601
-tors	-38602
-util	-38603
-▁Hes	-38604
-▁Tet	-38605
-OLOGY	-38606
-doubt	-38607
-▁Ally	-38608
-▁nast	-38609
-▁DREAM	-38610
-▁coven	-38611
-▁escap	-38612
-▁forks	-38613
-▁googled	-38614
-▁insides	-38615
-▁doorbell	-38616
-▁espionage	-38617
-▁quarantine	-38618
-▁congressman	-38619
-▁justifiable	-38620
-Age	-38621
-wish	-38622
-▁Buc	-38623
-shelf	-38624
-▁skis	-38625
-bidden	-38626
-uitous	-38627
-▁PLACE	-38628
-▁shrub	-38629
-▁Bieber	-38630
-renching	-38631
-▁Sinatra	-38632
-▁Portable	-38633
-▁Oversight	-38634
-▁grappling	-38635
-▁adjectives	-38636
-Lou	-38637
-▁́m	-38638
-Comp	-38639
-▁ASD	-38640
-▁Quiz	-38641
-▁Laugh	-38642
-▁Remark	-38643
-▁CONTROL	-38644
-▁fatally	-38645
-▁sketchy	-38646
-▁psychiatrists	-38647
-:-)	-38648
-▁CU	-38649
-Kids	-38650
-amen	-38651
-omla	-38652
-▁Coch	-38653
-▁Omar	-38654
-▁unde	-38655
-▁Asper	-38656
-▁bruise	-38657
-consider	-38658
-▁Rodgers	-38659
-▁Resolutions	-38660
-▁Collaborative	-38661
-Nay	-38662
-acid	-38663
-deen	-38664
-flav	-38665
-milk	-38666
-▁Coo	-38667
-▁ZIP	-38668
-wartz	-38669
-Wonder	-38670
-▁Cyrus	-38671
-▁gowns	-38672
-▁tacit	-38673
-▁thine	-38674
-▁Accord	-38675
-▁Hansen	-38676
-▁Toward	-38677
-▁incest	-38678
-readable	-38679
-▁Canberra	-38680
-▁Frenchman	-38681
-▁muttering	-38682
-grin	-38683
-icia	-38684
-▁:))	-38685
-▁Wan	-38686
-oward	-38687
-ugger	-38688
-matter	-38689
-server	-38690
-▁Loren	-38691
-industry	-38692
-▁Excited	-38693
-▁knitted	-38694
-▁napkins	-38695
-▁Pregnant	-38696
-▁deodorant	-38697
-▁grandchild	-38698
-▁mentorship	-38699
-▁refundable	-38700
-▁Proposition	-38701
-▁inquisitive	-38702
-▁transmissions	-38703
-'!	-38704
-ropy	-38705
-▁Lime	-38706
-▁watt	-38707
-▁retin	-38708
-querque	-38709
-▁fidget	-38710
-▁stoned	-38711
-▁plywood	-38712
-▁sparkly	-38713
-▁tidbits	-38714
-▁semantic	-38715
-▁touchscreen	-38716
-/"	-38717
-SB	-38718
-▁│	-38719
-ïve	-38720
-Orig	-38721
-aphne	-38722
-▁Orph	-38723
-elight	-38724
-inside	-38725
-▁Flame	-38726
-▁WATCH	-38727
-▁ailment	-38728
-▁fillers	-38729
-▁herring	-38730
-▁manoeuv	-38731
-▁fertilizers	-38732
-▁unhappiness	-38733
-FIG	-38734
-weh	-38735
-▁DK	-38736
-▁bc	-38737
-▁tat	-38738
-istro	-38739
-▁Hare	-38740
-checks	-38741
-▁savour	-38742
-▁straws	-38743
-laughter	-38744
-▁Brought	-38745
-▁conveys	-38746
-▁Geoffrey	-38747
-▁Hastings	-38748
-▁penguins	-38749
-▁thinning	-38750
-▁increment	-38751
-▁Percentage	-38752
-▁compulsive	-38753
-▁mutilation	-38754
-▁prospectus	-38755
-▁stronghold	-38756
-▁abstraction	-38757
-▁resurrected	-38758
-▁	-38759
-doi	-38760
-IFUL	-38761
-▁Shab	-38762
-▁hazy	-38763
-▁Fault	-38764
-besides	-38765
-▁haunts	-38766
-▁Embrace	-38767
-▁classed	-38768
-▁Publisher	-38769
-▁carelessly	-38770
-▁customised	-38771
-▁headquartered	-38772
-bole	-38773
-lery	-38774
-PERRY	-38775
-relim	-38776
-▁Slam	-38777
-ranged	-38778
-▁Debit	-38779
-▁comin	-38780
-▁mails	-38781
-▁Awaken	-38782
-▁Limits	-38783
-▁relics	-38784
-berspace	-38785
-▁earners	-38786
-▁showered	-38787
-▁replenish	-38788
-▁skeletons	-38789
-▁Functional	-38790
-▁unbalanced	-38791
-▁broadcasters	-38792
-▁×	-38793
-lis	-38794
-igue	-38795
-▁Nos	-38796
-▁OPT	-38797
-▁fas	-38798
-▁Peel	-38799
-▁ipad	-38800
-▁outp	-38801
-▁stoop	-38802
-▁Steele	-38803
-▁hermit	-38804
-▁runoff	-38805
-operated	-38806
-spirited	-38807
-▁Himalay	-38808
-▁masquer	-38809
-▁battalion	-38810
-▁wildfires	-38811
-▁disagreeable	-38812
-AKES	-38813
-PLOY	-38814
-hawk	-38815
-ifled	-38816
-▁Newt	-38817
-Author	-38818
-others	-38819
-wolves	-38820
-▁Risks	-38821
-▁clamp	-38822
-▁momma	-38823
-▁rapist	-38824
-▁tester	-38825
-▁Template	-38826
-▁greenery	-38827
-▁housewife	-38828
-▁inventing	-38829
-▁generalized	-38830
-NR	-38831
-sin	-38832
-Self	-38833
-ndon	-38834
-ujah	-38835
-▁ASP	-38836
-flash	-38837
-hotel	-38838
-▁Fatty	-38839
-▁Wyatt	-38840
-fishing	-38841
-ometers	-38842
-▁Afraid	-38843
-▁Phoebe	-38844
-▁Stiles	-38845
-▁Tracey	-38846
-▁hippie	-38847
-▁queues	-38848
-▁Donovan	-38849
-▁leaflet	-38850
-▁pounded	-38851
-▁savages	-38852
-▁procured	-38853
-▁paternity	-38854
-▁umbrellas	-38855
-▁consignment	-38856
-▁objectionable	-38857
-BUT	-38858
-Nah	-38859
-Want	-38860
-eryl	-38861
-▁Fla	-38862
-artic	-38863
-iuses	-38864
-worms	-38865
-▁Ahhh	-38866
-▁Weed	-38867
-▁ZERO	-38868
-killer	-38869
-walker	-38870
-▁Giuse	-38871
-▁bangs	-38872
-▁cores	-38873
-▁Walton	-38874
-▁vaping	-38875
-▁Couples	-38876
-▁Lecture	-38877
-▁chipped	-38878
-▁penguin	-38879
-▁reckoning	-38880
-▁ratification	-38881
-NI	-38882
-pd	-38883
-Six	-38884
-▁Qi	-38885
-Much	-38886
-elli	-38887
-▁Bak	-38888
-label	-38889
-ordes	-38890
-wrote	-38891
-▁JOUR	-38892
-▁nect	-38893
-Pretty	-38894
-appers	-38895
-▁Pepsi	-38896
-▁binds	-38897
-putable	-38898
-skilled	-38899
-▁hereto	-38900
-▁warped	-38901
-antanamo	-38902
-▁Bentley	-38903
-▁Melinda	-38904
-▁Criteria	-38905
-▁aligning	-38906
-▁degraded	-38907
-evaluation	-38908
-▁tinkering	-38909
-improvement	-38910
-▁rheumatoid	-38911
-▁tragically	-38912
-▁Prosecutors	-38913
-IOR	-38914
-buds	-38915
-▁pag	-38916
-▁ppm	-38917
-▁Herc	-38918
-behind	-38919
-▁Quint	-38920
-▁Gravity	-38921
-▁cheques	-38922
-▁derives	-38923
-▁dialing	-38924
-▁incense	-38925
-▁Chinatown	-38926
-▁neuroscience	-38927
-oC	-38928
-enna	-38929
-gang	-38930
-worn	-38931
-Files	-38932
-atham	-38933
-steen	-38934
-ucent	-38935
-▁drib	-38936
-▁Plato	-38937
-▁Scope	-38938
-▁WATER	-38939
-▁arson	-38940
-▁monog	-38941
-▁DIFFER	-38942
-▁Potato	-38943
-▁bonnet	-38944
-▁Meadows	-38945
-▁seismic	-38946
-▁Goodwill	-38947
-▁pilgrims	-38948
-▁collusion	-38949
-▁eloquently	-38950
-▁resurgence	-38951
-▁inconsistency	-38952
-tee	-38953
-▁NF	-38954
-Office	-38955
-medium	-38956
-utches	-38957
-▁Arnav	-38958
-▁Panic	-38959
-▁gleam	-38960
-▁granny	-38961
-▁Barrier	-38962
-▁Belarus	-38963
-▁Forestry	-38964
-▁aromatic	-38965
-▁Stevenson	-38966
-▁trillions	-38967
-▁appointing	-38968
-▁conflicted	-38969
-▁mayonnaise	-38970
-▁moderators	-38971
-▁Acquisition	-38972
-▁definitively	-38973
-;--	-38974
-Key	-38975
-▁CLA	-38976
-WHERE	-38977
-bella	-38978
-later	-38979
-▁quits	-38980
-▁rites	-38981
-▁Cardio	-38982
-▁Edmund	-38983
-▁Hayden	-38984
-▁drinker	-38985
-▁fosters	-38986
-▁powders	-38987
-▁sulphur	-38988
-naissance	-38989
-▁lamented	-38990
-▁ointment	-38991
-▁Construct	-38992
-▁Schneider	-38993
-▁eyewitness	-38994
-▁rationally	-38995
-▁Rockefeller	-38996
-▁intolerable	-38997
-▁Productivity	-38998
-▁compartments	-38999
-Ts	-39000
-eeds	-39001
-▁MIC	-39002
-iqued	-39003
-▁FOOD	-39004
-enaries	-39005
-▁clones	-39006
-▁Sunrise	-39007
-▁bounded	-39008
-▁fateful	-39009
-automatic	-39010
-▁Contains	-39011
-▁Separate	-39012
-▁finalize	-39013
-▁membranes	-39014
-▁parenthood	-39015
-▁generational	-39016
-▁deliberations	-39017
-▁RI	-39018
-guay	-39019
-▁WIP	-39020
-rican	-39021
-▁Oslo	-39022
-▁Tomb	-39023
-▁nond	-39024
-▁tamp	-39025
-▁Alarm	-39026
-▁Immun	-39027
-▁Puppy	-39028
-▁babes	-39029
-▁Announce	-39030
-▁sediment	-39031
-▁dwindling	-39032
-▁inspirations	-39033
-.<	-39034
-DVD	-39035
-Land	-39036
-eady	-39037
-Given	-39038
-hence	-39039
-▁Leap	-39040
-▁Turbo	-39041
-▁expat	-39042
-▁sauté	-39043
-otesque	-39044
-rapists	-39045
-▁convol	-39046
-▁picket	-39047
-▁python	-39048
-checking	-39049
-oooooooo	-39050
-religion	-39051
-▁Posting	-39052
-▁opposes	-39053
-▁scarred	-39054
-▁improvis	-39055
-▁insertion	-39056
-▁motivator	-39057
-▁oftentimes	-39058
-▁undermines	-39059
-▁objectivity	-39060
-▁Northwestern	-39061
-yah	-39062
-Prot	-39063
-▁PCB	-39064
-▁Ves	-39065
-▁Taco	-39066
-▁Wend	-39067
-drogen	-39068
-explan	-39069
-▁Sadie	-39070
-▁ovens	-39071
-▁privy	-39072
-omegran	-39073
-▁Simone	-39074
-▁overdo	-39075
-expected	-39076
-spiritual	-39077
-▁Castillo	-39078
-▁backseat	-39079
-▁Organized	-39080
-▁Commandments	-39081
-▁Reservations	-39082
-▁geopolitical	-39083
-▁privatization	-39084
-▁satisfactorily	-39085
-▁Vu	-39086
-dock	-39087
-ente	-39088
-▁IDs	-39089
-inform	-39090
-▁speck	-39091
-▁Cycling	-39092
-▁Robbins	-39093
-▁pruning	-39094
-▁Flexible	-39095
-▁motorway	-39096
-▁symmetry	-39097
-▁Designing	-39098
-▁boutiques	-39099
-▁splashing	-39100
-▁DEPARTMENT	-39101
-▁uneducated	-39102
-▁watercolor	-39103
-▁marshmallows	-39104
-▁mechanically	-39105
-swe	-39106
-cong	-39107
-eyes	-39108
-pots	-39109
-▁IMP	-39110
-▁Lia	-39111
-coast	-39112
-dance	-39113
-rette	-39114
-▁Maui	-39115
-gallon	-39116
-served	-39117
-ateness	-39118
-▁casing	-39119
-▁staffs	-39120
-bullying	-39121
-▁Armenia	-39122
-▁Tunisia	-39123
-▁atleast	-39124
-▁epitome	-39125
-▁lovable	-39126
-▁Damascus	-39127
-▁Ephesians	-39128
-▁labyrinth	-39129
-▁counsellor	-39130
-Fair	-39131
-itsu	-39132
-groom	-39133
-▁Azer	-39134
-▁airl	-39135
-▁egos	-39136
-Govern	-39137
-stable	-39138
-▁Mafia	-39139
-▁leaky	-39140
-builder	-39141
-▁Picard	-39142
-▁Bermuda	-39143
-▁Coaches	-39144
-▁Genetic	-39145
-▁Sponsor	-39146
-▁incline	-39147
-▁Journalists	-39148
-▁screwdriver	-39149
-▁►	-39150
-alez	-39151
-▁reds	-39152
-anyone	-39153
-collar	-39154
-▁Coron	-39155
-▁sidel	-39156
-▁tiers	-39157
-▁Predict	-39158
-▁Supposed	-39159
-▁culprits	-39160
-▁indexing	-39161
-▁Frankfurt	-39162
-▁Selecting	-39163
-▁commuters	-39164
-▁oxidation	-39165
-▁raspberries	-39166
-VO	-39167
-▁WoW	-39168
-Reply	-39169
-arget	-39170
-waste	-39171
-▁Mage	-39172
-▁pastel	-39173
-▁retina	-39174
-doctoral	-39175
-▁mammoth	-39176
-▁sockets	-39177
-▁launcher	-39178
-▁racially	-39179
-▁redefine	-39180
-▁sobering	-39181
-▁ascending	-39182
-▁rye	-39183
-▁vil	-39184
-▁Erica	-39185
-▁disen	-39186
-▁slime	-39187
-▁NASCAR	-39188
-▁Refugee	-39189
-▁grunted	-39190
-▁explorers	-39191
-▁frightful	-39192
-▁culminating	-39193
-fee	-39194
-olson	-39195
-speak	-39196
-Muslim	-39197
-▁Carla	-39198
-▁Shine	-39199
-▁Slate	-39200
-▁flask	-39201
-▁Sussex	-39202
-▁Verify	-39203
-▁briefs	-39204
-▁sprays	-39205
-▁probate	-39206
-▁twinkle	-39207
-▁grenades	-39208
-▁subsided	-39209
-▁thirties	-39210
-reliminary	-39211
-▁Gratitude	-39212
-▁Strangely	-39213
-Dar	-39214
-NAS	-39215
-ean	-39216
-...?”	-39217
-burger	-39218
-▁Alumin	-39219
-▁Turned	-39220
-▁chunky	-39221
-▁preten	-39222
-▁imprint	-39223
-▁keepers	-39224
-▁lineage	-39225
-▁Chapters	-39226
-▁breeders	-39227
-▁handbags	-39228
-▁kingdoms	-39229
-containing	-39230
-▁universes	-39231
-▁Capitalism	-39232
-▁prosecuting	-39233
-▁tentatively	-39234
-▁vegetarians	-39235
-▁Transactions	-39236
-nu	-39237
-▁Cho	-39238
-Found	-39239
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-iffel	-39241
-pects	-39242
-udeau	-39243
-▁LEAD	-39244
-▁flimsy	-39245
-▁Helpful	-39246
-▁Pharise	-39247
-▁Grateful	-39248
-▁bandages	-39249
-▁funerals	-39250
-▁Greenwich	-39251
-▁Wakefield	-39252
-independent	-39253
-▁segregated	-39254
-▁freelancing	-39255
-▁microscopic	-39256
-▁afterthought	-39257
-▁$.	-39258
-jerk	-39259
-▁Wyn	-39260
-homes	-39261
-▁THIR	-39262
-abilia	-39263
-▁Flora	-39264
-▁amiss	-39265
-angaroo	-39266
-▁scorer	-39267
-▁tyrant	-39268
-▁Massive	-39269
-▁Thomson	-39270
-▁oregano	-39271
-▁pertain	-39272
-▁Bloggers	-39273
-▁gullible	-39274
-proclaimed	-39275
-ARA	-39276
-Won	-39277
-till	-39278
-orect	-39279
-stack	-39280
-whose	-39281
-▁Modi	-39282
-▁Shan	-39283
-ayette	-39284
-▁OFFIC	-39285
-▁Torres	-39286
-▁eagles	-39287
-odynamic	-39288
-▁Literary	-39289
-▁adjourned	-39290
-▁unbeatable	-39291
-▁contradicts	-39292
-▁intellectuals	-39293
-nay	-39294
-zac	-39295
-▁BG	-39296
-▁cc	-39297
-Arthur	-39298
-antial	-39299
-▁Alley	-39300
-▁Skull	-39301
-▁Steak	-39302
-▁lowly	-39303
-Instead	-39304
-climate	-39305
-▁Sooner	-39306
-▁quartz	-39307
-▁sobbed	-39308
-▁conjure	-39309
-▁labelling	-39310
-▁Highlights	-39311
-▁boyfriends	-39312
-▁domestically	-39313
-?_	-39314
-aryn	-39315
-▁Soap	-39316
-▁bios	-39317
-▁Saved	-39318
-ustrious	-39319
-▁crusade	-39320
-▁Broadband	-39321
-▁Continent	-39322
-▁Fukushima	-39323
-▁botanical	-39324
-▁merciless	-39325
-▁flashbacks	-39326
-▁javascript	-39327
-▁ungrateful	-39328
-▁calculators	-39329
-▁replication	-39330
-▁Certificates	-39331
-▁unattractive	-39332
-jug	-39333
-damn	-39334
-▁Sas	-39335
-alers	-39336
-▁Lulu	-39337
-▁enum	-39338
-▁hort	-39339
-▁sizz	-39340
-▁Laptop	-39341
-▁staunch	-39342
-annuation	-39343
-sectional	-39344
-▁Guantanamo	-39345
-▁retribution	-39346
-▁unintentional	-39347
-Gl	-39348
-nai	-39349
-Pray	-39350
-▁Miy	-39351
-▁seep	-39352
-athere	-39353
-emeter	-39354
-▁hardy	-39355
-▁nipple	-39356
-▁vetted	-39357
-▁Penalty	-39358
-▁wrapper	-39359
-▁rallying	-39360
-▁scripted	-39361
-▁apartheid	-39362
-▁headphone	-39363
-▁shuddered	-39364
-▁Continuous	-39365
-▁convoluted	-39366
-▁songwriting	-39367
-▁unscrupulous	-39368
-Ag	-39369
-Hel	-39370
-LAND	-39371
-rrrr	-39372
-ALITY	-39373
-▁Rear	-39374
-▁Glend	-39375
-▁Spiel	-39376
-▁Vault	-39377
-▁molds	-39378
-▁Viagra	-39379
-▁Waterloo	-39380
-▁woodwork	-39381
-▁Addressing	-39382
-▁superpower	-39383
-▁Yellowstone	-39384
-inv	-39385
-pron	-39386
-▁Tee	-39387
-admin	-39388
-ipend	-39389
-▁PROM	-39390
-▁TARD	-39391
-▁axle	-39392
-▁grop	-39393
-▁Thorn	-39394
-▁sanit	-39395
-▁siren	-39396
-▁skates	-39397
-▁spoils	-39398
-uropathy	-39399
-▁Comcast	-39400
-▁diaries	-39401
-▁masking	-39402
-▁Giuseppe	-39403
-▁adjuster	-39404
-▁spoiling	-39405
-▁stadiums	-39406
-▁showering	-39407
-incorporated	-39408
-▁Comfortable	-39409
-▁extinguished	-39410
-Code	-39411
-eria	-39412
-repe	-39413
-▁JUD	-39414
-PRESS	-39415
-chart	-39416
-▁PROT	-39417
-uaries	-39418
-▁pluck	-39419
-▁raven	-39420
-▁degrade	-39421
-▁waivers	-39422
-▁wayside	-39423
-▁snowfall	-39424
-▁crossings	-39425
-▁Ministries	-39426
-▁Appropriate	-39427
-▁restorative	-39428
-ogyn	-39429
-▁ARM	-39430
-▁Pls	-39431
-igree	-39432
-prone	-39433
-▁poth	-39434
-▁Ferry	-39435
-▁plump	-39436
-▁torto	-39437
-▁Whites	-39438
-▁takers	-39439
-▁Addison	-39440
-▁Voyager	-39441
-▁bandits	-39442
-▁Monsters	-39443
-▁Assurance	-39444
-▁allergens	-39445
-▁nefarious	-39446
-▁connectors	-39447
-▁Observatory	-39448
-▁longitudinal	-39449
-byn	-39450
-cale	-39451
-wage	-39452
-▁Dys	-39453
-▁VIS	-39454
-▁Wax	-39455
-▁FAQs	-39456
-▁Ugly	-39457
-▁skimp	-39458
-▁Horton	-39459
-▁scapego	-39460
-▁steered	-39461
-▁turkeys	-39462
-▁Aircraft	-39463
-▁Removing	-39464
-▁standout	-39465
-▁metaphysical	-39466
-Ho	-39467
-MIT	-39468
-▁RU	-39469
-kiss	-39470
-▁AGM	-39471
-uchar	-39472
-▁Stre	-39473
-▁picn	-39474
-▁Teeth	-39475
-▁WARRANT	-39476
-▁splurge	-39477
-▁lineback	-39478
-▁fluctuate	-39479
-▁photoshop	-39480
-▁rearrange	-39481
-;.	-39482
-obo	-39483
-nson	-39484
-▁Oasis	-39485
-▁mains	-39486
-evident	-39487
-▁induct	-39488
-▁multic	-39489
-Personal	-39490
-handedly	-39491
-▁dusting	-39492
-▁emerald	-39493
-▁payback	-39494
-▁disburse	-39495
-▁sniffing	-39496
-▁uncontrollable	-39497
-Nor	-39498
-ogs	-39499
-rels	-39500
-▁Pag	-39501
-▁SAS	-39502
-Blood	-39503
-▁SALE	-39504
-▁whey	-39505
-reason	-39506
-▁Chili	-39507
-▁Anthem	-39508
-▁Facing	-39509
-▁goalie	-39510
-▁Scholar	-39511
-▁gasping	-39512
-▁Islanders	-39513
-▁accolades	-39514
-▁ergonomic	-39515
-▁Grandfather	-39516
-▁differentiated	-39517
-▁kh	-39518
-trial	-39519
-▁CASE	-39520
-▁ills	-39521
-utting	-39522
-▁Peach	-39523
-rically	-39524
-▁conund	-39525
-tracking	-39526
-▁hotspot	-39527
-▁pinched	-39528
-▁shading	-39529
-▁Homemade	-39530
-▁mirrored	-39531
-▁mobilize	-39532
-▁Inclusion	-39533
-▁insidious	-39534
-▁Sunderland	-39535
-▁evaporated	-39536
-▁regularity	-39537
-inx	-39538
-iza	-39539
-▁KY	-39540
-caps	-39541
-uren	-39542
-▁Elo	-39543
-▁Beet	-39544
-▁Hits	-39545
-▁congl	-39546
-▁murky	-39547
-▁locale	-39548
-▁snarky	-39549
-▁soundly	-39550
-▁Jennings	-39551
-▁populous	-39552
-▁Bangalore	-39553
-▁Nightmare	-39554
-▁Protective	-39555
-▁enumerated	-39556
-▁modalities	-39557
-▁fundraisers	-39558
-▁prohibitive	-39559
-▁disappointments	-39560
-Blog	-39561
-ENSE	-39562
-▁hee	-39563
-grove	-39564
-Create	-39565
-prison	-39566
-▁Lucia	-39567
-▁Reads	-39568
-▁Spray	-39569
-▁milks	-39570
-▁mulch	-39571
-similar	-39572
-▁Toledo	-39573
-▁dammit	-39574
-▁racked	-39575
-powerful	-39576
-Seriously	-39577
-▁tenderly	-39578
-revolution	-39579
-▁patterned	-39580
-▁chronically	-39581
-▁os	-39582
-asms	-39583
-▁BRA	-39584
-▁Taj	-39585
-xious	-39586
-abytes	-39587
-▁Europ	-39588
-▁NIGHT	-39589
-▁Realm	-39590
-▁Copies	-39591
-▁coasts	-39592
-▁diners	-39593
-▁fatigued	-39594
-▁Proposals	-39595
-▁gratefully	-39596
-▁illegitimate	-39597
-ahead	-39598
-makes	-39599
-▁Fors	-39600
-▁Hels	-39601
-▁Wich	-39602
-▁Yose	-39603
-▁Bucks	-39604
-▁Setup	-39605
-▁lucid	-39606
-reality	-39607
-▁Broker	-39608
-▁Lester	-39609
-▁Fucking	-39610
-▁gleaned	-39611
-▁dreading	-39612
-▁endowment	-39613
-▁interfered	-39614
-▁democracies	-39615
-▁unmistakable	-39616
-exc	-39617
-Shel	-39618
-este	-39619
-▁Crab	-39620
-▁Tape	-39621
-▁Zoom	-39622
-stupid	-39623
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-▁mined	-39625
-▁Cousin	-39626
-▁draped	-39627
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-ordinated	-39632
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-population	-39634
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-▁Ombudsman	-39637
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-ssa	-39639
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-alien	-39641
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-luence	-39643
-ticket	-39644
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-▁Jaime	-39647
-▁laund	-39648
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-▁Charlott	-39651
-omatherapy	-39652
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-▁Directions	-39655
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-appa	-39657
-▁Elk	-39658
-▁RIP	-39659
-Group	-39660
-Steve	-39661
-judge	-39662
-movie	-39663
-▁owls	-39664
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-▁hymns	-39666
-▁overest	-39667
-▁remnant	-39668
-▁Lorraine	-39669
-▁balsamic	-39670
-▁delicacy	-39671
-▁overdraft	-39672
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-▁orchestrated	-39674
-▁outstretched	-39675
-Sy	-39676
-lag	-39677
-xon	-39678
-▁hmmm	-39679
-▁Roots	-39680
-▁Weiss	-39681
-!!!!!!!	-39682
-Western	-39683
-picking	-39684
-▁Spaces	-39685
-students	-39686
-▁Bethany	-39687
-▁Extract	-39688
-▁Kristin	-39689
-ontinence	-39690
-▁genitals	-39691
-▁shuffled	-39692
-▁Allergies	-39693
-▁snapshots	-39694
-▁insurmountable	-39695
-▁↑	-39696
-Harry	-39697
-undai	-39698
-irvana	-39699
-▁Leafs	-39700
-▁dined	-39701
-olesale	-39702
-▁Barber	-39703
-▁Kaiser	-39704
-▁beggar	-39705
-▁Mobility	-39706
-▁Simpsons	-39707
-▁lukewarm	-39708
-▁thematic	-39709
-▁schematic	-39710
-▁bibliography	-39711
-Rev	-39712
-Edit	-39713
-▁doo	-39714
-ckett	-39715
-▁MSNBC	-39716
-▁brunt	-39717
-▁Reggie	-39718
-cylinder	-39719
-icrobial	-39720
-▁Choices	-39721
-▁Zionist	-39722
-▁tequila	-39723
-▁Plumbing	-39724
-▁closeness	-39725
-▁cookbooks	-39726
-▁inscribed	-39727
-▁motorbike	-39728
-▁Specialists	-39729
-▁dealerships	-39730
-mur	-39731
-▁DU	-39732
-▁KL	-39733
-AMES	-39734
-hosp	-39735
-llie	-39736
-zzly	-39737
-▁Hew	-39738
-▁RBI	-39739
-feres	-39740
-▁repo	-39741
-▁manor	-39742
-▁Backup	-39743
-▁WONDER	-39744
-▁Bolivia	-39745
-▁crayons	-39746
-boyfriend	-39747
-described	-39748
-▁nautical	-39749
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-▁downwards	-39751
-▁Interviews	-39752
-▁magnifying	-39753
-▁foolishness	-39754
-❤️	-39755
-lio	-39756
-Easy	-39757
-▁Zak	-39758
-▁Macs	-39759
-▁Bauer	-39760
-▁indig	-39761
-seekers	-39762
-▁mening	-39763
-▁rocker	-39764
-▁waxing	-39765
-▁busting	-39766
-▁eleventh	-39767
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-▁industrialized	-39770
-TRY	-39771
-▁HY	-39772
-agog	-39773
-satis	-39774
-ubarb	-39775
-▁FAIL	-39776
-▁Kiwi	-39777
-ichigan	-39778
-isively	-39779
-▁Tunnel	-39780
-▁violet	-39781
-▁gimmick	-39782
-▁superfl	-39783
-▁swaying	-39784
-▁Clicking	-39785
-▁edifying	-39786
-▁pavilion	-39787
-▁suitcases	-39788
-...........	-39789
-▁accompaniment	-39790
-yg	-39791
-___	-39792
-flex	-39793
-▁foray	-39794
-fetched	-39795
-▁Severe	-39796
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-▁creatives	-39798
-▁scrubbing	-39799
-';	-39800
-tx	-39801
-RIC	-39802
-oar	-39803
-phe	-39804
-▁roy	-39805
-Heart	-39806
-armin	-39807
-▁Moll	-39808
-▁gout	-39809
-amazon	-39810
-▁Ernie	-39811
-▁Retro	-39812
-▁Tesco	-39813
-▁Gallup	-39814
-▁goings	-39815
-▁pedals	-39816
-▁patched	-39817
-▁Ethernet	-39818
-▁Chevrolet	-39819
-▁sprinkler	-39820
-anticipated	-39821
-▁Publishers	-39822
-▁protracted	-39823
-▁concentrates	-39824
-▁responsiveness	-39825
-..)	-39826
-zos	-39827
-▁Kok	-39828
-▁Kun	-39829
-strings	-39830
-▁Ballet	-39831
-▁Gerard	-39832
-▁blower	-39833
-▁thingy	-39834
-▁Revenge	-39835
-▁Statute	-39836
-▁amplify	-39837
-▁dodging	-39838
-▁forearm	-39839
-▁overrun	-39840
-▁Terrible	-39841
-▁expertly	-39842
-▁rephrase	-39843
-▁biologist	-39844
-▁narcissist	-39845
-▁vehemently	-39846
-▁Temperature	-39847
-▁fibromyalgia	-39848
-cru	-39849
-Lady	-39850
-inho	-39851
-rite	-39852
-Peter	-39853
-licts	-39854
-mable	-39855
-▁Elon	-39856
-▁Gabe	-39857
-▁fishy	-39858
-▁Baking	-39859
-▁Daphne	-39860
-▁patting	-39861
-▁wineries	-39862
-▁BEAUTIFUL	-39863
-▁Blackberry	-39864
-▁workaround	-39865
-▁Hospitality	-39866
-▁displeasure	-39867
-▁enlargement	-39868
-▁dermatologist	-39869
-▁understandings	-39870
-ono	-39871
-▁JE	-39872
-▁VE	-39873
-Feed	-39874
-▁Ecc	-39875
-▁TRE	-39876
-banks	-39877
-▁Dion	-39878
-▁Susp	-39879
-bidity	-39880
-▁flops	-39881
-▁ACTION	-39882
-▁Larger	-39883
-▁cortex	-39884
-▁Compact	-39885
-▁Sellers	-39886
-▁Uruguay	-39887
-▁embryos	-39888
-▁glazing	-39889
-▁hostels	-39890
-▁referees	-39891
-▁succumbed	-39892
-Shop	-39893
-▁Goss	-39894
-▁McGu	-39895
-▁UFOs	-39896
-▁mong	-39897
-▁Grape	-39898
-▁growl	-39899
-▁august	-39900
-▁Carlson	-39901
-▁collars	-39902
-▁indices	-39903
-▁toolbox	-39904
-▁Wilderness	-39905
-▁overloaded	-39906
-▁Suggestions	-39907
-▁computation	-39908
-▁multiplication	-39909
-▁interchangeable	-39910
-®,	-39911
-ROW	-39912
-▁BH	-39913
-INCE	-39914
-Roll	-39915
-Wall	-39916
-▁BLOG	-39917
-▁Disk	-39918
-▁Merg	-39919
-▁whee	-39920
-▁Detect	-39921
-▁loaves	-39922
-▁Boomers	-39923
-▁bordering	-39924
-▁Complaints	-39925
-▁kitchenette	-39926
-MCA	-39927
-▁(:	-39928
-itos	-39929
-mini	-39930
-ogan	-39931
-tele	-39932
-▁Ket	-39933
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-sports	-39935
-▁Brass	-39936
-▁Miley	-39937
-▁Paulo	-39938
-▁crook	-39939
-Contact	-39940
-ielding	-39941
-ophilia	-39942
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-▁exuber	-39944
-▁undist	-39945
-hetamine	-39946
-▁Efforts	-39947
-▁Johnnie	-39948
-▁endemic	-39949
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-▁tiniest	-39951
-▁Cherokee	-39952
-▁Attorneys	-39953
-▁Physician	-39954
-▁Placement	-39955
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-▁strategist	-39957
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-Quest	-39959
-Quick	-39960
-hanna	-39961
-incal	-39962
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-ogenesis	-39967
-alcoholic	-39968
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-▁Implement	-39970
-▁Virtually	-39971
-▁dismantle	-39972
-▁Discipline	-39973
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-)]	-39975
-▁msn	-39976
-astly	-39977
-ocard	-39978
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-▁Pend	-39981
-▁Watt	-39982
-▁Colon	-39983
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-knowing	-39985
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-fortably	-39988
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-▁shadowy	-39991
-▁Entrance	-39992
-▁Schwartz	-39993
-▁populist	-39994
-▁tattooed	-39995
-▁fifteenth	-39996
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-▁Cage	-40004
-▁Oooh	-40005
-beyond	-40006
-onders	-40007
-winner	-40008
-▁Maths	-40009
-▁alias	-40010
-▁Becker	-40011
-▁Faster	-40012
-▁cashew	-40013
-▁nectar	-40014
-▁Placing	-40015
-▁midwives	-40016
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-▁exasperated	-40018
-▁refinancing	-40019
-▁instantaneous	-40020
-lesh	-40021
-leys	-40022
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-▁Napa	-40025
-raught	-40026
-▁Walls	-40027
-▁crows	-40028
-appoint	-40029
-▁Merkel	-40030
-▁Transl	-40031
-▁spoilt	-40032
-▁Koreans	-40033
-▁Founding	-40034
-▁exertion	-40035
-▁pancreas	-40036
-▁kilograms	-40037
-▁installers	-40038
-▁circumference	-40039
-eny	-40040
-byes	-40041
-needs	-40042
-▁bras	-40043
-▁dojo	-40044
-▁dyes	-40045
-▁snuff	-40046
-▁swoon	-40047
-▁Kenyan	-40048
-▁ducked	-40049
-▁intrep	-40050
-▁frontal	-40051
-emergency	-40052
-▁Licensed	-40053
-▁charters	-40054
-▁tenacity	-40055
-▁breakdowns	-40056
-▁Illustrated	-40057
-▁deductibles	-40058
-▁Scandinavian	-40059
-Own	-40060
-▁NK	-40061
-ells	-40062
-llah	-40063
-tank	-40064
-▁HTC	-40065
-▁ivy	-40066
-▁fiat	-40067
-rified	-40068
-▁dazed	-40069
-▁delir	-40070
-▁glist	-40071
-▁Eighth	-40072
-▁timers	-40073
-▁absorbs	-40074
-▁Afternoon	-40075
-▁blackjack	-40076
-▁workflows	-40077
-itts	-40078
-anine	-40079
-antes	-40080
-▁McKay	-40081
-▁crumb	-40082
-▁Looked	-40083
-▁Lambert	-40084
-▁ancillary	-40085
-▁elemental	-40086
-▁fi	-40087
-stab	-40088
-▁Oste	-40089
-▁Sutton	-40090
-▁fleets	-40091
-▁tenets	-40092
-▁potency	-40093
-emeteries	-40094
-▁solvents	-40095
-protection	-40096
-▁Antarctic	-40097
-▁Lauderdale	-40098
-▁defamation	-40099
-▁iterations	-40100
-▁operatives	-40101
-▁contemporaries	-40102
-▁EF	-40103
-baum	-40104
-oglo	-40105
-STAND	-40106
-hawks	-40107
-▁Myst	-40108
-▁muck	-40109
-example	-40110
-▁Dalton	-40111
-▁stunts	-40112
-marriage	-40113
-▁densely	-40114
-▁octopus	-40115
-oxicillin	-40116
-▁interacts	-40117
-▁monologue	-40118
-▁seventies	-40119
-▁Attendance	-40120
-▁♪	-40121
-Tim	-40122
-bai	-40123
-pac	-40124
-tec	-40125
-▁JK	-40126
-imely	-40127
-lique	-40128
-▁upto	-40129
-belief	-40130
-▁Virus	-40131
-▁polyg	-40132
-▁Alpine	-40133
-▁carniv	-40134
-Interest	-40135
-stricken	-40136
-▁PROGRAM	-40137
-▁cropping	-40138
-▁drenched	-40139
-▁favorably	-40140
-▁impairments	-40141
-▁Roe	-40142
-▁rue	-40143
-joint	-40144
-▁Crest	-40145
-▁EXCEPT	-40146
-▁Pieces	-40147
-▁reigns	-40148
-▁uplift	-40149
-culation	-40150
-▁Cheshire	-40151
-▁Thornton	-40152
-▁audacity	-40153
-▁Dashboard	-40154
-▁Technique	-40155
-▁denounced	-40156
-▁dependant	-40157
-▁posterity	-40158
-▁unsettled	-40159
-▁ceremonial	-40160
-▁congenital	-40161
-▁princesses	-40162
-▁Johannesburg	-40163
-▁endorsements	-40164
-ETY	-40165
-anz	-40166
-ombs	-40167
-▁LAT	-40168
-catch	-40169
-▁Epid	-40170
-▁naïve	-40171
-▁polym	-40172
-orectal	-40173
-▁Wicked	-40174
-▁haired	-40175
-▁justly	-40176
-▁Marxist	-40177
-▁cracker	-40178
-lite	-40179
-▁cis	-40180
-Obama	-40181
-Write	-40182
-onium	-40183
-▁HEAR	-40184
-▁WEBS	-40185
-hemian	-40186
-▁clots	-40187
-▁mince	-40188
-foreign	-40189
-▁junkie	-40190
-▁kennel	-40191
-▁Reverse	-40192
-▁hybrids	-40193
-▁sniffed	-40194
-▁Requires	-40195
-▁analyzes	-40196
-▁fashions	-40197
-▁briefcase	-40198
-▁Memorandum	-40199
-▁dispensing	-40200
-organization	-40201
-OTS	-40202
-Host	-40203
-▁Fow	-40204
-▁HEAD	-40205
-▁Mash	-40206
-▁Whom	-40207
-Jewish	-40208
-▁Hicks	-40209
-▁Louie	-40210
-▁hyped	-40211
-▁Suzuki	-40212
-▁Dominic	-40213
-▁Xfinity	-40214
-▁Stunning	-40215
-▁insuring	-40216
-▁checkpoint	-40217
-▁interferes	-40218
-▁diagnostics	-40219
-▁deforestation	-40220
-▁GG	-40221
-gift	-40222
-ourt	-40223
-URING	-40224
-ackle	-40225
-adena	-40226
-▁Brut	-40227
-▁Hoss	-40228
-▁Loki	-40229
-▁Tide	-40230
-enable	-40231
-▁Dying	-40232
-▁pours	-40233
-▁Forgot	-40234
-▁Leaves	-40235
-▁grimly	-40236
-▁Capture	-40237
-▁inertia	-40238
-▁corporal	-40239
-▁expedite	-40240
-▁inverted	-40241
-▁reindeer	-40242
-▁glittering	-40243
-▁researches	-40244
-▁Albuquerque	-40245
-▁unwillingness	-40246
-:”	-40247
-ZZ	-40248
-!!!”	-40249
-fear	-40250
-▁Hue	-40251
-▁JAN	-40252
-Learn	-40253
-olulu	-40254
-manent	-40255
-▁Lying	-40256
-▁negate	-40257
-▁techno	-40258
-▁Derrick	-40259
-▁Desmond	-40260
-▁stumped	-40261
-▁Gingrich	-40262
-▁grotesque	-40263
-▁unqualified	-40264
-’;	-40265
-utz	-40266
-▁Cig	-40267
-▁DHS	-40268
-▁MCC	-40269
-▁php	-40270
-dried	-40271
-humane	-40272
-▁moust	-40273
-▁Conway	-40274
-▁Makeup	-40275
-▁Subway	-40276
-▁hanger	-40277
-▁Breathe	-40278
-▁Rutgers	-40279
-▁Suffolk	-40280
-▁draught	-40281
-▁inquest	-40282
-▁bridging	-40283
-▁melodies	-40284
-▁claimants	-40285
-photography	-40286
-▁reciprocal	-40287
-▁refurbishment	-40288
-URY	-40289
-gey	-40290
-Send	-40291
-▁DeL	-40292
-▁Soy	-40293
-grain	-40294
-▁mids	-40295
-sister	-40296
-spread	-40297
-▁Coinc	-40298
-▁comet	-40299
-▁darts	-40300
-▁vegans	-40301
-▁bitches	-40302
-▁damning	-40303
-▁rebates	-40304
-▁squared	-40305
-▁Expenses	-40306
-▁vocalist	-40307
-▁Completed	-40308
-▁rewritten	-40309
-generational	-40310
-▁brotherhood	-40311
-▁countertops	-40312
-mv	-40313
-LEX	-40314
-owe	-40315
-▁BW	-40316
-letal	-40317
-▁Moose	-40318
-▁warts	-40319
-document	-40320
-▁Pearson	-40321
-▁legumes	-40322
-▁Apostles	-40323
-▁Builders	-40324
-▁dropdown	-40325
-▁accession	-40326
-▁heartless	-40327
-▁Authorization	-40328
-▁Contributions	-40329
-avo	-40330
-Mess	-40331
-▁gon	-40332
-System	-40333
-▁dumps	-40334
-▁epoch	-40335
-▁Zambia	-40336
-▁Anatomy	-40337
-▁Tourist	-40338
-▁angered	-40339
-▁gorilla	-40340
-▁kindred	-40341
-▁werewolf	-40342
-▁agonizing	-40343
-▁abstinence	-40344
-▁neurotrans	-40345
-▁typewriter	-40346
-▁adversaries	-40347
-▁disregarded	-40348
-▁provocation	-40349
-rera	-40350
-soul	-40351
-▁Moy	-40352
-▁Lite	-40353
-atcher	-40354
-▁Usage	-40355
-advised	-40356
-▁fanfic	-40357
-▁predis	-40358
-▁stuffy	-40359
-▁rattled	-40360
-▁Stepping	-40361
-▁purified	-40362
-▁Passenger	-40363
-▁assaulting	-40364
-▁pharmacists	-40365
-▁traumatized	-40366
-▁bodybuilding	-40367
-Op	-40368
-deb	-40369
-▁EMS	-40370
-▁Kia	-40371
-▁PAN	-40372
-perse	-40373
-▁Ours	-40374
-▁Tenn	-40375
-▁swan	-40376
-ancock	-40377
-▁Pompe	-40378
-▁Whale	-40379
-▁Spears	-40380
-▁biohaz	-40381
-▁reared	-40382
-▁valent	-40383
-▁unevent	-40384
-▁warmest	-40385
-frequency	-40386
-▁Precious	-40387
-▁auditory	-40388
-▁installs	-40389
-▁avalanche	-40390
-▁flattened	-40391
-▁Yugoslavia	-40392
-▁Supervision	-40393
-Rod	-40394
-flat	-40395
-kell	-40396
-▁CIS	-40397
-Party	-40398
-lehem	-40399
-▁Hick	-40400
-▁Piet	-40401
-▁Lagos	-40402
-▁brook	-40403
-▁crabs	-40404
-▁swoop	-40405
-▁texas	-40406
-▁Excerpt	-40407
-▁blurted	-40408
-▁cloning	-40409
-▁sequels	-40410
-▁Newfound	-40411
-▁Smithson	-40412
-▁spotless	-40413
-▁carriages	-40414
-▁hammering	-40415
-▁conferencing	-40416
-▁stereotypical	-40417
-aldo	-40418
-iton	-40419
-lady	-40420
-▁Chop	-40421
-▁MEAN	-40422
-▁Sapp	-40423
-▁slur	-40424
-▁gurus	-40425
-▁Camden	-40426
-▁Canvas	-40427
-▁polled	-40428
-▁dwellers	-40429
-▁Experiences	-40430
-“,	-40431
-adh	-40432
-ihu	-40433
-▁Ft	-40434
-olks	-40435
-atial	-40436
-▁Calv	-40437
-▁EASY	-40438
-▁Guer	-40439
-▁Moor	-40440
-▁unfl	-40441
-▁spong	-40442
-▁tonic	-40443
-▁Assume	-40444
-▁Eiffel	-40445
-▁abstain	-40446
-▁enraged	-40447
-▁scarlet	-40448
-▁romances	-40449
-▁indemnify	-40450
-▁formulating	-40451
-▁workstation	-40452
-▁Institutional	-40453
-anus	-40454
-nour	-40455
-▁Dup	-40456
-olera	-40457
-▁Coco	-40458
-▁Dock	-40459
-▁HAND	-40460
-secure	-40461
-▁Clive	-40462
-▁Downs	-40463
-hanging	-40464
-▁Subsid	-40465
-▁scones	-40466
-▁Marshal	-40467
-▁correlate	-40468
-▁introvert	-40469
-▁reinstated	-40470
-▁fingernails	-40471
-urf	-40472
-▁UL	-40473
-oley	-40474
-▁Bolt	-40475
-rarily	-40476
-▁Silva	-40477
-glasses	-40478
-▁Diesel	-40479
-▁latent	-40480
-oliberal	-40481
-▁deluded	-40482
-▁invests	-40483
-▁Outreach	-40484
-▁Passover	-40485
-▁hardness	-40486
-ensitivity	-40487
-▁presiding	-40488
-▁Communists	-40489
-▁complacency	-40490
-▁intuitively	-40491
-Doesn	-40492
-▁Camel	-40493
-Finally	-40494
-▁Fringe	-40495
-▁PROBLE	-40496
-▁expats	-40497
-▁Ramadan	-40498
-▁plunder	-40499
-▁smacked	-40500
-▁bronchitis	-40501
-▁composting	-40502
-▁Publication	-40503
-▁inventories	-40504
-▁nonsensical	-40505
-USS	-40506
-ENCY	-40507
-Oper	-40508
-bent	-40509
-cred	-40510
-loud	-40511
-joong	-40512
-phase	-40513
-▁MEET	-40514
-▁SAVE	-40515
-ummies	-40516
-▁feral	-40517
-▁Hector	-40518
-▁lockers	-40519
-▁wizards	-40520
-▁Daylight	-40521
-▁Advertise	-40522
-▁Googleing	-40523
-▁Ownership	-40524
-▁sceptical	-40525
-▁springing	-40526
-▁pancreatic	-40527
-▁progressives	-40528
-aq	-40529
-mmas	-40530
-rugs	-40531
-▁Gat	-40532
-▁Moo	-40533
-strap	-40534
-▁pudd	-40535
-preted	-40536
-▁Rican	-40537
-▁Braves	-40538
-▁darned	-40539
-▁mantel	-40540
-▁mister	-40541
-▁veiled	-40542
-▁Advocacy	-40543
-▁Optional	-40544
-▁Tasmania	-40545
-▁capitals	-40546
-apparently	-40547
-▁Highlight	-40548
-▁amplified	-40549
-▁disposing	-40550
-▁engrossed	-40551
-▁derogatory	-40552
-▁perplexing	-40553
-▁RD	-40554
-▁ay	-40555
-▁SON	-40556
-▁USED	-40557
-amines	-40558
-graded	-40559
-▁LEAST	-40560
-▁Lenin	-40561
-▁apric	-40562
-▁enshr	-40563
-▁madly	-40564
-▁moles	-40565
-▁toxin	-40566
-ophiles	-40567
-▁german	-40568
-▁hummus	-40569
-▁smokes	-40570
-▁Hearings	-40571
-▁Sprinkle	-40572
-▁Tomatoes	-40573
-▁hallmark	-40574
-▁portrays	-40575
-▁empathetic	-40576
-▁frequented	-40577
-▁grapefruit	-40578
-▁Procurement	-40579
-Ay	-40580
-.'”	-40581
-ryl	-40582
-uld	-40583
-mone	-40584
-▁Dos	-40585
-▁Hua	-40586
-ludge	-40587
-▁Mant	-40588
-▁Oaks	-40589
-▁balk	-40590
-buyers	-40591
-mainly	-40592
-▁TIMES	-40593
-▁kilos	-40594
-▁squid	-40595
-▁coldest	-40596
-▁selects	-40597
-▁Johnston	-40598
-▁inducted	-40599
-▁literate	-40600
-▁perfumes	-40601
-▁untimely	-40602
-▁Travelling	-40603
-▁undertakings	-40604
-etz	-40605
-bowl	-40606
-favor	-40607
-▁Logo	-40608
-▁latt	-40609
-▁Isles	-40610
-▁Payne	-40611
-▁decry	-40612
-ichrist	-40613
-▁Monaco	-40614
-▁markup	-40615
-▁memoirs	-40616
-▁rollers	-40617
-▁savored	-40618
-▁Randolph	-40619
-▁Slovenia	-40620
-▁foreseen	-40621
-▁lanterns	-40622
-▁cognition	-40623
-▁dynamically	-40624
-▁Intermediate	-40625
--.	-40626
-Sk	-40627
-wax	-40628
-haha	-40629
-vast	-40630
-▁Mord	-40631
-▁blot	-40632
-▁inse	-40633
-▁grins	-40634
-▁Norris	-40635
-▁TARDIS	-40636
-response	-40637
-▁forging	-40638
-▁criticise	-40639
-▁lightening	-40640
-▁Supplements	-40641
-▁masculinity	-40642
-▁Investigations	-40643
-mix	-40644
-ompl	-40645
-slee	-40646
-▁Nug	-40647
-▁Vet	-40648
-▁SUMM	-40649
-approx	-40650
-idders	-40651
-update	-40652
-▁Boise	-40653
-▁Seeds	-40654
-▁Tarot	-40655
-andered	-40656
-▁Turtle	-40657
-▁riddle	-40658
-▁Falcons	-40659
-▁Laundry	-40660
-▁pigeons	-40661
-▁mistrust	-40662
-▁pastures	-40663
-▁pinnacle	-40664
-▁seminary	-40665
-▁obscurity	-40666
-▁plastered	-40667
-▁Redemption	-40668
-▁gentleness	-40669
-▁Photographer	-40670
-▁preferential	-40671
-zier	-40672
-▁NDP	-40673
-enact	-40674
-▁racc	-40675
-▁stup	-40676
-▁miner	-40677
-iversal	-40678
-▁celiac	-40679
-▁decomp	-40680
-▁Pyramid	-40681
-▁misused	-40682
-▁Eligible	-40683
-▁eighties	-40684
-▁Governing	-40685
-▁penalized	-40686
-▁birthplace	-40687
-▁Destination	-40688
-▁superfluous	-40689
-▁unsatisfactory	-40690
-HM	-40691
-Adm	-40692
-ucket	-40693
-▁Stem	-40694
-▁arte	-40695
-▁idly	-40696
-▁vera	-40697
-ethnic	-40698
-▁Meant	-40699
-▁ramps	-40700
-▁Height	-40701
-▁bowels	-40702
-boggling	-40703
-▁detects	-40704
-▁slicing	-40705
-▁willful	-40706
-▁Pleasure	-40707
-▁charisma	-40708
-▁indulged	-40709
-▁outbound	-40710
-▁renovate	-40711
-▁spoonful	-40712
-▁Designers	-40713
-▁Paramount	-40714
-▁auditions	-40715
-▁overlooks	-40716
-▁rewriting	-40717
-▁semesters	-40718
-▁aquaculture	-40719
-▁pretentious	-40720
-kt	-40721
-ECH	-40722
-INAL	-40723
-▁DVR	-40724
-▁Zap	-40725
-▁pla	-40726
-ilden	-40727
-▁bays	-40728
-matics	-40729
-▁Vader	-40730
-▁Xavier	-40731
-creating	-40732
-▁emancip	-40733
-▁stalked	-40734
-▁vectors	-40735
-▁Angelina	-40736
-▁Steering	-40737
-▁disingen	-40738
-▁googling	-40739
-▁Recognizing	-40740
-▁Supervisors	-40741
-▁copywriting	-40742
-wal	-40743
-xie	-40744
-Burn	-40745
-▁Zig	-40746
-cheek	-40747
-▁DISC	-40748
-▁Rene	-40749
-▁axes	-40750
-▁juxt	-40751
-celebr	-40752
-▁Aston	-40753
-▁Anakin	-40754
-▁renter	-40755
-plicated	-40756
-▁Movable	-40757
-▁clipped	-40758
-▁condone	-40759
-discovery	-40760
-▁Entering	-40761
-▁pendulum	-40762
-▁parishion	-40763
-▁Motivation	-40764
-▁skateboard	-40765
-▁victimized	-40766
-▁billionaires	-40767
-fm	-40768
-Bull	-40769
-Coun	-40770
-odex	-40771
-ultz	-40772
-▁Lod	-40773
-▁RAD	-40774
-▁tux	-40775
-ophen	-40776
-▁Sham	-40777
-▁fins	-40778
-▁wast	-40779
-berman	-40780
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-essler	-40837
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-amom	-40853
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-ORGE	-40947
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-shima	-40973
-omorph	-40974
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-enjoy	-40991
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-utan	-41009
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-▁Owens	-41015
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-▁tacky	-41017
-▁Amongst	-41018
-▁napping	-41019
-dominated	-41020
-▁menacing	-41021
-regardless	-41022
-▁expectant	-41023
-▁numbering	-41024
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-ACP	-41028
-Lin	-41029
-pure	-41030
-▁Pey	-41031
-ilage	-41032
-sixth	-41033
-▁yoke	-41034
-French	-41035
-▁Glaci	-41036
-▁carol	-41037
-▁cocks	-41038
-monthly	-41039
-▁dispel	-41040
-▁revamp	-41041
-▁queried	-41042
-▁Analysts	-41043
-▁embodies	-41044
-▁skillful	-41045
-▁dominates	-41046
-▁shortening	-41047
-▁cheerleader	-41048
-▁indiscrimin	-41049
-avi	-41050
-hof	-41051
-▁DMV	-41052
-Small	-41053
-▁Dome	-41054
-▁HIST	-41055
-▁hare	-41056
-▁blitz	-41057
-▁recal	-41058
-▁escrow	-41059
-▁hooves	-41060
-▁rotary	-41061
-▁checker	-41062
-▁rebirth	-41063
-▁Refugees	-41064
-▁leaflets	-41065
-▁overkill	-41066
-▁Combining	-41067
-▁observant	-41068
-▁Enrollment	-41069
-▁homeopathic	-41070
-Phil	-41071
-hear	-41072
-ilts	-41073
-lang	-41074
-▁Dele	-41075
-▁Hath	-41076
-▁Hats	-41077
-edited	-41078
-ocrity	-41079
-▁Beacon	-41080
-▁SECOND	-41081
-tershire	-41082
-▁Bowling	-41083
-▁Centres	-41084
-▁Liberia	-41085
-▁commens	-41086
-▁drugged	-41087
-▁flicker	-41088
-▁overuse	-41089
-▁redhead	-41090
-▁Concrete	-41091
-▁colonists	-41092
-▁underside	-41093
-▁breastfeed	-41094
-▁impediment	-41095
-▁reputations	-41096
-▁underserved	-41097
-▁understated	-41098
-pei	-41099
-roo	-41100
-▁UCS	-41101
-azard	-41102
-▁Lola	-41103
-▁NaNo	-41104
-▁Sore	-41105
-hemoth	-41106
-▁ramble	-41107
-athering	-41108
-straight	-41109
-▁lagging	-41110
-▁racists	-41111
-▁sandbox	-41112
-▁Examiner	-41113
-▁credence	-41114
-▁Animation	-41115
-▁recollect	-41116
-▁ridiculed	-41117
-▁shuffling	-41118
-▁sketching	-41119
-▁Cunningham	-41120
-▁activating	-41121
-▁chemically	-41122
-▁excavation	-41123
-▁telephones	-41124
-IENT	-41125
-hung	-41126
-▁NIH	-41127
-▁Nim	-41128
-▁GOLD	-41129
-▁Allie	-41130
-▁semif	-41131
-▁perils	-41132
-confident	-41133
-▁Therapist	-41134
-▁rejoicing	-41135
-▁triathlon	-41136
-▁Controlled	-41137
-▁submissive	-41138
-▁foreclosures	-41139
-▁interestingly	-41140
-▁microorganisms	-41141
-awed	-41142
-▁AWS	-41143
-▁Miz	-41144
-acial	-41145
-plend	-41146
-regor	-41147
-▁GROU	-41148
-▁SEAL	-41149
-▁Sear	-41150
-▁Hutch	-41151
-▁gamma	-41152
-▁Liquor	-41153
-▁onerous	-41154
-▁submits	-41155
-▁Editorial	-41156
-▁Intensive	-41157
-▁Greensboro	-41158
-▁Ingredients	-41159
-▁pathological	-41160
-▁prioritizing	-41161
-▁Enlightenment	-41162
-▁affectionately	-41163
-POSE	-41164
-ombo	-41165
-▁PAP	-41166
-Cancel	-41167
-planet	-41168
-risome	-41169
-▁soggy	-41170
-▁Closer	-41171
-▁Marley	-41172
-▁passer	-41173
-relevant	-41174
-direction	-41175
-▁hoarding	-41176
-▁hurrying	-41177
-▁obituary	-41178
-determined	-41179
-▁boundless	-41180
-▁moderated	-41181
-▁sweetener	-41182
-▁veritable	-41183
-▁uninformed	-41184
-▁archaeology	-41185
-▁confessions	-41186
-▁perpetually	-41187
-▁rechargeable	-41188
-READ	-41189
-auce	-41190
-eroy	-41191
-fred	-41192
-▁DOT	-41193
-lause	-41194
-pilot	-41195
-▁Wick	-41196
-▁swat	-41197
-▁Quilt	-41198
-▁Sioux	-41199
-▁Trend	-41200
-▁accru	-41201
-▁piggy	-41202
-▁Belize	-41203
-▁acclaim	-41204
-▁juniors	-41205
-▁solidly	-41206
-▁Measuring	-41207
-▁Trafficking	-41208
-▁Publications	-41209
-WAY	-41210
-▁Pork	-41211
-ITIONS	-41212
-ezvous	-41213
-takers	-41214
-▁CHECK	-41215
-▁cameo	-41216
-▁lupus	-41217
-▁moody	-41218
-▁thnkx	-41219
-▁Aliens	-41220
-▁accrue	-41221
-▁blames	-41222
-▁spears	-41223
-▁Beloved	-41224
-▁WHEREAS	-41225
-▁latency	-41226
-▁poisons	-41227
-▁tenders	-41228
-▁redeeming	-41229
-▁teaspoons	-41230
-▁Comparative	-41231
-▁translucent	-41232
-▁conservatism	-41233
-OPS	-41234
-zels	-41235
-▁IRC	-41236
-Clean	-41237
-▁rigs	-41238
-philis	-41239
-▁Acute	-41240
-▁Hunts	-41241
-▁Starr	-41242
-▁WOMAN	-41243
-▁glyph	-41244
-▁bestow	-41245
-▁euphem	-41246
-▁Broncos	-41247
-▁Reduced	-41248
-▁repatri	-41249
-▁Sheridan	-41250
-▁forceful	-41251
-▁photocop	-41252
-▁visceral	-41253
-▁debatable	-41254
-▁unpacking	-41255
-▁heartwarming	-41256
-▁pornographic	-41257
-▁repositories	-41258
-”-	-41259
-mut	-41260
-rik	-41261
-udo	-41262
-hani	-41263
-▁ASC	-41264
-scene	-41265
-uders	-41266
-▁Hurt	-41267
-▁Vari	-41268
-▁mane	-41269
-▁Choir	-41270
-▁HOURS	-41271
-▁Toxic	-41272
-▁Levine	-41273
-▁deline	-41274
-▁Credits	-41275
-▁Rockies	-41276
-▁Trudeau	-41277
-▁salient	-41278
-▁shatter	-41279
-▁Achilles	-41280
-▁narrower	-41281
-▁strangle	-41282
-▁unrecogn	-41283
-▁democrats	-41284
-▁blacksmith	-41285
-▁pensioners	-41286
-▁playwright	-41287
-▁thresholds	-41288
-▁Reproductive	-41289
-▁contraceptive	-41290
-enza	-41291
-ITNESS	-41292
-▁flares	-41293
-▁signup	-41294
-external	-41295
-▁runtime	-41296
-▁Sheppard	-41297
-▁crouched	-41298
-▁furthest	-41299
-▁copyrights	-41300
-▁invincible	-41301
-▁proprietors	-41302
-▁proportionate	-41303
-▁HOR	-41304
-▁Sna	-41305
-▁Hemp	-41306
-▁ISPs	-41307
-▁halo	-41308
-hidden	-41309
-illary	-41310
-▁Dixie	-41311
-▁Elton	-41312
-▁abduct	-41313
-▁nuance	-41314
-▁Riviera	-41315
-▁archaic	-41316
-▁rotated	-41317
-▁syringe	-41318
-▁Limbaugh	-41319
-▁heaviest	-41320
-▁Lithuania	-41321
-▁roundabout	-41322
-▁concurrently	-41323
-aty	-41324
-▁Ai	-41325
-ildo	-41326
-▁Rin	-41327
-▁tir	-41328
-INING	-41329
-udges	-41330
-▁Frey	-41331
-▁shod	-41332
-orette	-41333
-▁fucks	-41334
-Article	-41335
-osocial	-41336
-▁madman	-41337
-▁thence	-41338
-fighters	-41339
-▁freebie	-41340
-▁stipend	-41341
-▁investigates	-41342
-▁Hin	-41343
-▁Dove	-41344
-▁SEND	-41345
-▁Reiki	-41346
-partner	-41347
-▁Weapon	-41348
-▁sitter	-41349
-▁admiral	-41350
-▁hassles	-41351
-▁prototy	-41352
-▁Pavilion	-41353
-▁faltered	-41354
-▁Bethlehem	-41355
-▁instalment	-41356
-Pop	-41357
-alon	-41358
-▁Kip	-41359
-▁Kru	-41360
-▁Pry	-41361
-Media	-41362
-aroni	-41363
-gings	-41364
-▁Toni	-41365
-▁lice	-41366
-urated	-41367
-▁lambs	-41368
-finally	-41369
-▁Checks	-41370
-▁Starts	-41371
-▁coolly	-41372
-▁dangle	-41373
-▁livery	-41374
-▁storey	-41375
-builders	-41376
-▁Latinos	-41377
-▁Teenage	-41378
-▁misogyn	-41379
-▁reddish	-41380
-▁Gambling	-41381
-▁fugitive	-41382
-▁needlessly	-41383
-Jul	-41384
-sed	-41385
-Pack	-41386
-crit	-41387
-dust	-41388
-asium	-41389
-iffin	-41390
-takes	-41391
-▁Ener	-41392
-Almost	-41393
-▁Pound	-41394
-▁mucus	-41395
-abetics	-41396
-▁gutted	-41397
-▁charmed	-41398
-▁pasting	-41399
-▁peeking	-41400
-▁Thorough	-41401
-▁tireless	-41402
-Absolutely	-41403
-▁Behaviour	-41404
-▁Cassandra	-41405
-▁appraiser	-41406
-▁blindfold	-41407
-▁endocrine	-41408
-▁polluting	-41409
-▁intercepted	-41410
-inox	-41411
-oohoo	-41412
-▁Fork	-41413
-▁NYPD	-41414
-▁Tori	-41415
-▁bile	-41416
-▁DOING	-41417
-▁Denny	-41418
-▁Velve	-41419
-density	-41420
-▁Abroad	-41421
-▁Covers	-41422
-▁Needle	-41423
-▁Tricks	-41424
-▁Mineral	-41425
-▁disband	-41426
-▁nonchal	-41427
-▁riddled	-41428
-▁breathes	-41429
-▁perilous	-41430
-▁Utilities	-41431
-▁bestseller	-41432
-▁exploratory	-41433
-▁preparatory	-41434
-▁uncomfortably	-41435
-SI	-41436
-Dead	-41437
-ERTY	-41438
-File	-41439
-IDAY	-41440
-pins	-41441
-ribe	-41442
-▁Etc	-41443
-▁HOA	-41444
-▁ugh	-41445
-logic	-41446
-▁Hehe	-41447
-▁Edwin	-41448
-▁poppy	-41449
-▁Ashton	-41450
-▁SECRET	-41451
-citizens	-41452
-▁Analyze	-41453
-▁Higgins	-41454
-▁Homework	-41455
-▁finalist	-41456
-▁inferred	-41457
-▁inflamed	-41458
-▁abstracts	-41459
-▁Consolidated	-41460
-▁conveniences	-41461
-TMt	-41462
-apps	-41463
-ivel	-41464
-▁ASU	-41465
-▁chi	-41466
-▁Dirk	-41467
-▁aber	-41468
-▁clog	-41469
-atonin	-41470
-▁LIGHT	-41471
-▁Thess	-41472
-▁feats	-41473
-▁Basics	-41474
-▁Joomla	-41475
-▁camels	-41476
-▁upsets	-41477
-▁workday	-41478
-intensity	-41479
-resources	-41480
-▁ceramics	-41481
-▁endpoint	-41482
-▁speedily	-41483
-▁preachers	-41484
-alternative	-41485
-▁Buckingham	-41486
-▁affections	-41487
-▁camouflage	-41488
-▁overriding	-41489
-▁unwavering	-41490
-▁adventurers	-41491
-ré	-41492
---”	-41493
-yla	-41494
-▁OW	-41495
-▁BDS	-41496
-▁Dob	-41497
-▁eras	-41498
-Access	-41499
-omsday	-41500
-▁Chern	-41501
-▁polka	-41502
-▁Psalms	-41503
-▁Jerseys	-41504
-▁steeped	-41505
-▁manifold	-41506
-▁prevails	-41507
-▁unloaded	-41508
-▁proofread	-41509
-▁reforming	-41510
-▁Liberation	-41511
-▁duplicated	-41512
-▁liberalism	-41513
-▁facilitation	-41514
-%;	-41515
-alom	-41516
-hook	-41517
-Break	-41518
-weeks	-41519
-▁Abel	-41520
-▁Mamm	-41521
-format	-41522
-formin	-41523
-raphic	-41524
-▁Yuuri	-41525
-▁adorn	-41526
-▁cumin	-41527
-▁flips	-41528
-▁nutty	-41529
-▁Buffet	-41530
-▁Finder	-41531
-▁corpus	-41532
-▁trumps	-41533
-▁Empress	-41534
-▁scraped	-41535
-▁Deputies	-41536
-▁Emphasis	-41537
-▁exporter	-41538
-▁powering	-41539
-▁fractions	-41540
-▁Attraction	-41541
-▁Connecting	-41542
-▁categorize	-41543
-▁underlined	-41544
-▁JA	-41545
-akis	-41546
-▁CVS	-41547
-▁Dul	-41548
-▁mpg	-41549
-ptune	-41550
-▁hues	-41551
-▁pimp	-41552
-Invest	-41553
-Secret	-41554
-▁Angus	-41555
-▁Bryce	-41556
-▁airst	-41557
-blogger	-41558
-▁convoy	-41559
-▁dialed	-41560
-▁matchup	-41561
-▁pageant	-41562
-▁parades	-41563
-▁Blizzard	-41564
-▁landline	-41565
-▁emanating	-41566
-▁frontline	-41567
-▁incessant	-41568
-▁worshipped	-41569
-▁perfectionist	-41570
-ZY	-41571
-wei	-41572
-▁Kw	-41573
-older	-41574
-▁Dunk	-41575
-▁Emil	-41576
-▁bode	-41577
-▁Micha	-41578
-▁Herbal	-41579
-▁Hunters	-41580
-▁Nielsen	-41581
-▁Seventy	-41582
-▁couches	-41583
-▁grapple	-41584
-▁Lavender	-41585
-▁importation	-41586
-;”	-41587
-▁NI	-41588
-mode	-41589
-▁cip	-41590
-affer	-41591
-isson	-41592
-▁Palo	-41593
-▁Pist	-41594
-▁goth	-41595
-▁jeep	-41596
-colour	-41597
-taught	-41598
-▁Kylie	-41599
-▁clans	-41600
-▁COURSE	-41601
-▁fruity	-41602
-▁taping	-41603
-clinical	-41604
-▁lyrical	-41605
-▁milling	-41606
-▁Disabled	-41607
-▁Packages	-41608
-▁traitors	-41609
-▁Gentleman	-41610
-▁Receiving	-41611
-▁hesitating	-41612
-▁imperialism	-41613
-▁sustainably	-41614
-▁observational	-41615
-SON	-41616
-Acts	-41617
-Test	-41618
-oing	-41619
-tons	-41620
-▁Avo	-41621
-▁Dess	-41622
-▁gore	-41623
-▁barge	-41624
-▁faked	-41625
-▁glide	-41626
-▁Lumpur	-41627
-renowned	-41628
-▁acidity	-41629
-▁tickled	-41630
-▁alluring	-41631
-▁datasets	-41632
-onzi	-41633
-roids	-41634
-▁Awww	-41635
-▁Fuji	-41636
-▁sash	-41637
-concer	-41638
-▁Abdul	-41639
-▁Grief	-41640
-▁chewy	-41641
-▁Canary	-41642
-▁Encycl	-41643
-▁Rivera	-41644
-▁freaky	-41645
-▁hordes	-41646
-employee	-41647
-reliance	-41648
-▁Garbage	-41649
-▁fostered	-41650
-▁sideline	-41651
-▁swimsuit	-41652
-oarthritis	-41653
-▁TrackBack	-41654
--'	-41655
-odox	-41656
-▁Lov	-41657
-▁Pav	-41658
-▁elk	-41659
-▁Ezra	-41660
-▁mono	-41661
-utters	-41662
-▁Valve	-41663
-▁Keynes	-41664
-▁curator	-41665
-▁Laughing	-41666
-▁Medieval	-41667
-▁factored	-41668
-▁snacking	-41669
-▁sporadic	-41670
-▁constructs	-41671
-▁unknowingly	-41672
-▁Newfoundland	-41673
-OVE	-41674
-eys	-41675
-auld	-41676
-iani	-41677
-ppet	-41678
-usher	-41679
-▁Burk	-41680
-▁ONCE	-41681
-▁awry	-41682
-▁curt	-41683
-▁dork	-41684
-▁Ambul	-41685
-▁Vicky	-41686
-▁laced	-41687
-▁misty	-41688
-▁aligns	-41689
-▁spruce	-41690
-namental	-41691
-▁Bahrain	-41692
-▁adjourn	-41693
-▁seaweed	-41694
-▁Jaejoong	-41695
-▁softness	-41696
-▁alienated	-41697
-▁crocodile	-41698
-▁estranged	-41699
-▁distraught	-41700
-conservative	-41701
-▁reinforcements	-41702
-▁subcontractors	-41703
-OA	-41704
-▁VT	-41705
-▁BFF	-41706
-▁SSH	-41707
-▁LINE	-41708
-▁Crush	-41709
-▁Ducks	-41710
-▁Facil	-41711
-▁genus	-41712
-authent	-41713
-▁HAPPEN	-41714
-▁Slavery	-41715
-▁rations	-41716
-▁Valencia	-41717
-▁explodes	-41718
-▁oncoming	-41719
-▁Newspaper	-41720
-▁chauffeur	-41721
-▁temperate	-41722
-▁Navigation	-41723
-▁impersonal	-41724
-▁Associations	-41725
-▁Investigator	-41726
-▁interpreters	-41727
-▁irregularities	-41728
-fab	-41729
-iom	-41730
-Dude	-41731
-Main	-41732
-▁Agu	-41733
-▁Nets	-41734
-▁Shoe	-41735
-Update	-41736
-onesome	-41737
-▁Tweets	-41738
-▁massac	-41739
-▁tracts	-41740
-▁valium	-41741
-▁divinity	-41742
-▁signaled	-41743
-▁Amazingly	-41744
-▁disservice	-41745
-▁executable	-41746
-▁recyclable	-41747
-▁reschedule	-41748
-▁microphones	-41749
-)/	-41750
-Pan	-41751
-vax	-41752
-Less	-41753
-uits	-41754
-▁OCT	-41755
-▁UBC	-41756
-▁gib	-41757
-Connect	-41758
-▁Jaguar	-41759
-▁Shinji	-41760
-▁Fairfax	-41761
-▁anchors	-41762
-▁looting	-41763
-▁cyclical	-41764
-▁gleaming	-41765
-▁revamped	-41766
-▁showdown	-41767
-▁trapping	-41768
-▁appellate	-41769
-▁clipboard	-41770
-▁overtaken	-41771
-▁Expression	-41772
-▁unilateral	-41773
-▁incessantly	-41774
-▁Transformers	-41775
-▁stabilization	-41776
-▁Pv	-41777
-▁ig	-41778
-▁Mmm	-41779
-▁TOM	-41780
-Short	-41781
-▁Bags	-41782
-▁Cana	-41783
-▁Tons	-41784
-▁java	-41785
-▁xbox	-41786
-centre	-41787
-▁Zhang	-41788
-▁dimly	-41789
-▁Stocks	-41790
-▁Jacques	-41791
-▁monoton	-41792
-▁workmen	-41793
-▁dilemmas	-41794
-▁inventors	-41795
-▁Corrections	-41796
-▁troubleshoot	-41797
-EV	-41798
-Aye	-41799
-dan	-41800
-Fest	-41801
-▁Sis	-41802
-▁CUST	-41803
-▁Cron	-41804
-▁Shed	-41805
-photos	-41806
-▁Freak	-41807
-▁Paddy	-41808
-▁Pants	-41809
-▁Stake	-41810
-▁epist	-41811
-▁infat	-41812
-chapter	-41813
-manager	-41814
-painted	-41815
-stories	-41816
-▁Raider	-41817
-▁Tattoo	-41818
-▁allure	-41819
-▁preset	-41820
-▁alleges	-41821
-▁cinemas	-41822
-▁conduit	-41823
-▁diffuse	-41824
-▁ophthal	-41825
-▁suckers	-41826
-▁Holdings	-41827
-▁Realtors	-41828
-▁Relating	-41829
-▁gymnasium	-41830
-▁immaculate	-41831
-▁percussion	-41832
-▁uneventful	-41833
-▁expeditions	-41834
-▁suppressing	-41835
-▁incontinence	-41836
-▁subordinates	-41837
-Ever	-41838
-ibia	-41839
-▁SUP	-41840
-)....	-41841
-amble	-41842
-cemic	-41843
-▁Bian	-41844
-▁Orac	-41845
-▁PHOT	-41846
-▁Rapp	-41847
-▁Thur	-41848
-▁ruby	-41849
-supply	-41850
-▁Dante	-41851
-▁Motel	-41852
-▁kiddo	-41853
-▁dosing	-41854
-▁hurled	-41855
-▁seduce	-41856
-▁outland	-41857
-▁fiddling	-41858
-▁Lafayette	-41859
-▁healthful	-41860
-▁Advantages	-41861
-▁abomination	-41862
-▁Scholarships	-41863
-tall	-41864
-influ	-41865
-▁DATA	-41866
-Living	-41867
-▁Sonia	-41868
-▁bould	-41869
-amatory	-41870
-nthesis	-41871
-▁Drinks	-41872
-▁Malawi	-41873
-▁arched	-41874
-▁Sheldon	-41875
-▁legalize	-41876
-deductible	-41877
-▁conditioners	-41878
-▁figuratively	-41879
-▁Participating	-41880
-nob	-41881
-Door	-41882
-kies	-41883
-ustin	-41884
-▁lewd	-41885
-▁Hatch	-41886
-▁karate	-41887
-▁smiley	-41888
-▁tendon	-41889
-▁Hancock	-41890
-▁Sabrina	-41891
-▁acquies	-41892
-▁caloric	-41893
-▁cryptic	-41894
-▁rescind	-41895
-▁Spaniards	-41896
-▁modernization	-41897
-Bit	-41898
-iard	-41899
-tune	-41900
-▁BON	-41901
-fuels	-41902
-▁WORD	-41903
-▁walt	-41904
-▁Paste	-41905
-▁chaps	-41906
-isecond	-41907
-▁Lawson	-41908
-▁Sahara	-41909
-▁disson	-41910
-▁macros	-41911
-▁Jacuzzi	-41912
-▁Sentinel	-41913
-▁diffusion	-41914
-▁sweetened	-41915
-▁overcrowded	-41916
-src	-41917
-Adam	-41918
-azes	-41919
-bomb	-41920
-▁FED	-41921
-▁SIR	-41922
-tings	-41923
-▁Aqua	-41924
-▁Lank	-41925
-bledon	-41926
-danger	-41927
-owning	-41928
-▁STORY	-41929
-▁motif	-41930
-mington	-41931
-▁Gotham	-41932
-▁kindle	-41933
-assemble	-41934
-selected	-41935
-▁Futures	-41936
-▁boulder	-41937
-▁Crescent	-41938
-▁Exciting	-41939
-▁Gonzalez	-41940
-▁Motorola	-41941
-▁modestly	-41942
-▁peroxide	-41943
-▁antiquity	-41944
-▁dismissive	-41945
-▁formalities	-41946
-consciousness	-41947
-▁fundamentalist	-41948
-goo	-41949
-CLUS	-41950
-WARD	-41951
-cute	-41952
-ocin	-41953
-▁Mog	-41954
-rador	-41955
-▁Bees	-41956
-▁perv	-41957
-▁tint	-41958
-artney	-41959
-cliffe	-41960
-mortar	-41961
-▁Volvo	-41962
-▁Flyers	-41963
-▁Timing	-41964
-▁Compass	-41965
-▁Goodman	-41966
-▁dashing	-41967
-▁headway	-41968
-▁scooped	-41969
-▁streaks	-41970
-▁Junction	-41971
-▁Dentistry	-41972
-▁Voluntary	-41973
-▁marathons	-41974
-▁pamphlets	-41975
-▁virginity	-41976
-▁standstill	-41977
-▁Eligibility	-41978
-▁unfavorable	-41979
-▁ug	-41980
-▁vu	-41981
-WARE	-41982
-▁DRA	-41983
-▁LIC	-41984
-▁Tau	-41985
-▁Tum	-41986
-▁Anzu	-41987
-▁myel	-41988
-▁nano	-41989
-▁Cosby	-41990
-▁fared	-41991
-▁nerdy	-41992
-▁Realty	-41993
-▁Tradem	-41994
-ometrics	-41995
-▁Editing	-41996
-▁Editors	-41997
-▁Feeding	-41998
-▁cutters	-41999
-▁mermaid	-42000
-▁desiring	-42001
-▁layering	-42002
-▁pretense	-42003
-▁forfeited	-42004
-▁cyberspace	-42005
-▁Explanation	-42006
-▁arbitrarily	-42007
-▁complements	-42008
-▁antibacterial	-42009
-ISON	-42010
-nard	-42011
-▁MBP	-42012
-▁Mys	-42013
-▁TRY	-42014
-onyms	-42015
-▁Crap	-42016
-▁snot	-42017
-launch	-42018
-▁React	-42019
-▁cocky	-42020
-History	-42021
-iculous	-42022
-killing	-42023
-▁FEMALE	-42024
-▁Zurich	-42025
-▁graces	-42026
-▁infuri	-42027
-▁debtors	-42028
-▁exorbit	-42029
-▁fainted	-42030
-▁phantom	-42031
-▁Carrying	-42032
-▁cannibal	-42033
-▁cashback	-42034
-▁geniuses	-42035
-application	-42036
-sufficiency	-42037
-▁soliciting	-42038
-▁discernment	-42039
-▁WD	-42040
-▁CSA	-42041
-bikes	-42042
-▁dred	-42043
-▁ions	-42044
-ubarak	-42045
-▁Adopt	-42046
-▁Talks	-42047
-▁sorce	-42048
-angling	-42049
-tarians	-42050
-zbollah	-42051
-▁Signed	-42052
-▁chests	-42053
-▁chubby	-42054
-▁emboss	-42055
-▁chickpe	-42056
-▁regroup	-42057
-▁zealous	-42058
-castically	-42059
-▁Prohibition	-42060
-▁hierarchical	-42061
-▁neighbourhoods	-42062
-▁Kro	-42063
-▁Shep	-42064
-imples	-42065
-legant	-42066
-▁Denis	-42067
-▁EMPLOY	-42068
-▁Meteor	-42069
-▁Nickel	-42070
-▁THOUGH	-42071
-▁bailed	-42072
-▁Beckham	-42073
-▁CONTACT	-42074
-▁anarchy	-42075
-▁ladders	-42076
-▁Goldberg	-42077
-▁farthest	-42078
-▁ingested	-42079
-▁sufferer	-42080
-▁chartered	-42081
-▁limousine	-42082
-▁turnovers	-42083
-tab	-42084
-Lost	-42085
-aggy	-42086
-beth	-42087
-▁XVI	-42088
-▁bil	-42089
-▁cay	-42090
-youtu	-42091
-▁Lyon	-42092
-▁RCMP	-42093
-▁Soil	-42094
-Wouldn	-42095
-▁Beans	-42096
-▁Fritz	-42097
-▁roused	-42098
-customer	-42099
-gradable	-42100
-▁Hanging	-42101
-▁Krishna	-42102
-▁Wichita	-42103
-▁Anglican	-42104
-▁heirloom	-42105
-▁musicals	-42106
-▁inclement	-42107
-▁resultant	-42108
-▁unveiling	-42109
-▁duplicates	-42110
-▁Satisfaction	-42111
-▁masturbation	-42112
-▁constructions	-42113
-▁superstitious	-42114
-▁UR	-42115
-Meet	-42116
-▁cools	-42117
-changes	-42118
-▁afield	-42119
-ultimate	-42120
-▁crimson	-42121
-▁Aberdeen	-42122
-▁censored	-42123
-▁eventful	-42124
-▁workbook	-42125
-▁overjoyed	-42126
-▁aggregated	-42127
-▁undertakes	-42128
-▁aggravating	-42129
-▁entitlements	-42130
-▁infrastructures	-42131
-▁DX	-42132
-▁sr	-42133
-Text	-42134
-oron	-42135
-▁Liu	-42136
-▁ops	-42137
-broad	-42138
-litre	-42139
-▁SUVs	-42140
-coated	-42141
-▁Ivory	-42142
-suggest	-42143
-▁ballad	-42144
-▁lizards	-42145
-▁minivan	-42146
-▁synergy	-42147
-▁thereon	-42148
-▁timeout	-42149
-▁worrisome	-42150
-destructive	-42151
-▁Provisions	-42152
-▁Transmission	-42153
-▁prosecutions	-42154
-▁snowboarding	-42155
-▁Pue	-42156
-▁Darn	-42157
-▁grub	-42158
-▁Keeps	-42159
-▁LOVES	-42160
-▁WHITE	-42161
-▁looms	-42162
-wearing	-42163
-▁Flames	-42164
-▁Hostel	-42165
-▁bribes	-42166
-▁eroded	-42167
-▁Genuine	-42168
-▁feeders	-42169
-▁ferment	-42170
-▁cleanser	-42171
-▁handlers	-42172
-▁appellant	-42173
-▁homophobic	-42174
-▁transports	-42175
-▁Celebrating	-42176
-appropriately	-42177
-▁Broadcasting	-42178
-▁Accessibility	-42179
-rax	-42180
-▁LOU	-42181
-ostal	-42182
-▁Garc	-42183
-▁Baton	-42184
-▁Moder	-42185
-Develop	-42186
-Semitic	-42187
-inistry	-42188
-nuclear	-42189
-▁Fallen	-42190
-▁Galile	-42191
-▁Serial	-42192
-▁dogged	-42193
-▁kicker	-42194
-vernight	-42195
-▁bracing	-42196
-▁adequacy	-42197
-▁Connected	-42198
-▁Hepatitis	-42199
-▁Sociology	-42200
-▁psychosis	-42201
-▁innocently	-42202
-▁sequential	-42203
-▁trampoline	-42204
-oque	-42205
-▁huff	-42206
-▁Razor	-42207
-▁Remem	-42208
-▁trink	-42209
-▁Asylum	-42210
-▁Hungry	-42211
-▁hoodie	-42212
-division	-42213
-▁Wilkins	-42214
-▁potions	-42215
-▁dismayed	-42216
-▁plumbers	-42217
-▁dialogues	-42218
-▁fortitude	-42219
-▁diagnosing	-42220
-▁fragrances	-42221
-▁Destruction	-42222
-ITA	-42223
-▁FP	-42224
-▁Ong	-42225
-▁Rag	-42226
-▁Vou	-42227
-iceps	-42228
-raise	-42229
-actual	-42230
-▁Wally	-42231
-Central	-42232
-▁jigsaw	-42233
-▁gallant	-42234
-▁Familiar	-42235
-▁digested	-42236
-▁personas	-42237
-▁devotional	-42238
-OE	-42239
-xxx	-42240
-helm	-42241
-▁YET	-42242
-Offic	-42243
-▁LOST	-42244
-▁Yuri	-42245
-London	-42246
-▁Boone	-42247
-▁Slave	-42248
-▁abate	-42249
-▁trove	-42250
-ificent	-42251
-leaning	-42252
-▁Donate	-42253
-▁Exeter	-42254
-▁INTERN	-42255
-▁Thirdly	-42256
-▁daresay	-42257
-▁governs	-42258
-▁adopters	-42259
-▁traverse	-42260
-▁cemeteries	-42261
-▁furnishing	-42262
-▁assassinated	-42263
-▁spectacularly	-42264
-MR	-42265
-)--	-42266
-punk	-42267
-▁FPS	-42268
-▁Ease	-42269
-▁Milo	-42270
-▁Stim	-42271
-▁Troop	-42272
-▁hoses	-42273
-▁primed	-42274
-▁racking	-42275
-▁Hercules	-42276
-▁presided	-42277
-▁Processes	-42278
-▁Stainless	-42279
-▁indemnity	-42280
-▁shellfish	-42281
-▁Supporters	-42282
-▁fictitious	-42283
-▁partitions	-42284
-▁Acupuncture	-42285
-▁Operational	-42286
-▁Partnerships	-42287
-ERC	-42288
-IFF	-42289
-aji	-42290
-kov	-42291
-▁Paw	-42292
-ndash	-42293
-nsics	-42294
-▁Geez	-42295
-▁Pate	-42296
-▁feta	-42297
-▁oops	-42298
-Enough	-42299
-▁Firms	-42300
-▁Uzbek	-42301
-▁fruct	-42302
-▁honed	-42303
-▁Fedora	-42304
-▁Miriam	-42305
-▁boxers	-42306
-▁scound	-42307
-inducing	-42308
-▁Augusta	-42309
-▁Smiling	-42310
-▁Parmesan	-42311
-▁Sinclair	-42312
-▁lordship	-42313
-▁Alongside	-42314
-▁blasphemy	-42315
-▁contraven	-42316
-▁Completion	-42317
-▁forfeiture	-42318
-▁biologically	-42319
-](	-42320
-clic	-42321
-pell	-42322
-▁Bac	-42323
-▁Doo	-42324
-▁pep	-42325
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-▁Rack	-42327
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-▁Malone	-42332
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-▁endorsing	-42340
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-▁flickering	-42342
-▁regenerate	-42343
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-.".	-42347
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-▁vign	-42351
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-▁Ethere	-42353
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-▁Slayer	-42355
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-guardian	-42358
-possible	-42359
-▁BELIEVE	-42360
-▁musings	-42361
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-▁firmness	-42363
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-▁Remembering	-42366
-▁consolidating	-42367
-▁transplantation	-42368
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-▁CIR	-42370
-▁Shr	-42371
-hemer	-42372
-queous	-42373
-▁tenor	-42374
-Neither	-42375
-promise	-42376
-▁Statue	-42377
-▁faking	-42378
-▁loaned	-42379
-▁snowed	-42380
-▁winced	-42381
-▁Freight	-42382
-▁Tuition	-42383
-▁Upgrade	-42384
-▁collide	-42385
-▁brimming	-42386
-▁outfield	-42387
-▁amplifier	-42388
-▁Vegetables	-42389
-▁Appointment	-42390
-▁disgraceful	-42391
-▁nonexistent	-42392
-OMM	-42393
-Hand	-42394
-ppin	-42395
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-coded	-42397
-▁Kung	-42398
-▁Marm	-42399
-▁sine	-42400
-▁irons	-42401
-▁opium	-42402
-▁skirm	-42403
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-▁Washing	-42408
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-▁bearded	-42410
-▁diocese	-42411
-▁stooped	-42412
-▁meddling	-42413
-▁sparking	-42414
-▁triumphs	-42415
-believable	-42416
-▁profanity	-42417
-▁tampering	-42418
-▁Salesforce	-42419
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-▁entertainers	-42421
-▁illustrative	-42422
-▁perpendicular	-42423
-..?	-42424
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-Lean	-42426
-▁HIP	-42427
-oneys	-42428
-orama	-42429
-nisone	-42430
-▁CHILD	-42431
-▁Cobra	-42432
-▁unamb	-42433
-▁Sicily	-42434
-▁barter	-42435
-▁SUPPORT	-42436
-▁pigment	-42437
-▁rollout	-42438
-▁Minority	-42439
-▁indignant	-42440
-▁Technician	-42441
-ija	-42442
-zie	-42443
-ITAL	-42444
-Side	-42445
-lack	-42446
-▁``(	-42447
-▁TEAM	-42448
-Posted	-42449
-▁Exxon	-42450
-▁Recru	-42451
-▁volts	-42452
-errilla	-42453
-▁Eighty	-42454
-▁feline	-42455
-▁meager	-42456
-arranted	-42457
-▁Delight	-42458
-▁Objects	-42459
-▁glorify	-42460
-▁skinned	-42461
-▁climatic	-42462
-▁inducing	-42463
-▁intranet	-42464
-▁Survivors	-42465
-▁emphasise	-42466
-▁extremity	-42467
-▁tornadoes	-42468
-▁Kensington	-42469
-▁biographies	-42470
-▁disingenuous	-42471
-▁disconcerting	-42472
-▁refrigerators	-42473
-▁TOS	-42474
-▁barb	-42475
-assies	-42476
-▁EARTH	-42477
-▁smoky	-42478
-nerable	-42479
-▁COMING	-42480
-▁planks	-42481
-▁ravine	-42482
-clusions	-42483
-whelming	-42484
-▁Produce	-42485
-▁deviate	-42486
-▁groomed	-42487
-necessary	-42488
-translate	-42489
-▁Iranians	-42490
-▁Sentence	-42491
-▁footpath	-42492
-▁syllable	-42493
-Especially	-42494
-▁takeaways	-42495
-▁townhouse	-42496
-▁sustenance	-42497
-▁Differences	-42498
-▁playgrounds	-42499
-▁overshadowed	-42500
-▁Classification	-42501
-EMS	-42502
-▁Iz	-42503
-▁»)	-42504
-anyl	-42505
-erma	-42506
-spin	-42507
-zine	-42508
-▁LNG	-42509
-▁Yao	-42510
-▁lug	-42511
-ilies	-42512
-▁KIND	-42513
-▁Repl	-42514
-▁sofas	-42515
-▁studs	-42516
-igators	-42517
-▁Fruits	-42518
-▁Mugabe	-42519
-▁Kremlin	-42520
-▁endorph	-42521
-▁spanish	-42522
-▁Homeless	-42523
-▁Slightly	-42524
-▁Functions	-42525
-▁jeopardize	-42526
-▁sharpening	-42527
-▁renaissance	-42528
-IQ	-42529
-iao	-42530
-kai	-42531
-rave	-42532
-▁vas	-42533
-Daily	-42534
-urved	-42535
-▁Kayla	-42536
-▁hinge	-42537
-▁toner	-42538
-▁Dozens	-42539
-▁insuff	-42540
-ioneered	-42541
-▁Hyundai	-42542
-▁commons	-42543
-▁exerted	-42544
-▁gelatin	-42545
-▁tagline	-42546
-▁Yosemite	-42547
-▁chaplain	-42548
-▁sedation	-42549
-▁custodian	-42550
-▁stressors	-42551
-▁promulgated	-42552
-?'"	-42553
-GIN	-42554
-ère	-42555
-enol	-42556
-▁Babe	-42557
-▁Goat	-42558
-▁figs	-42559
-▁jolt	-42560
-▁lube	-42561
-ptious	-42562
-tailed	-42563
-Believe	-42564
-causing	-42565
-▁Cowboy	-42566
-▁Yahweh	-42567
-▁darted	-42568
-▁sirens	-42569
-▁parlour	-42570
-▁Constance	-42571
-▁exporters	-42572
-▁Consortium	-42573
-▁alienation	-42574
-▁orthodontic	-42575
-hoc	-42576
-wns	-42577
-EDIT	-42578
-uked	-42579
-breat	-42580
-emies	-42581
-iothe	-42582
-▁Ritz	-42583
-▁prog	-42584
-▁jails	-42585
-▁labou	-42586
-▁spade	-42587
-▁fellas	-42588
-opportun	-42589
-▁memorized	-42590
-▁multiples	-42591
-▁complimented	-42592
-ATOR	-42593
-anth	-42594
-▁Sul	-42595
-▁unr	-42596
-agher	-42597
-invol	-42598
-▁culp	-42599
-▁thor	-42600
-▁thug	-42601
-▁Bloss	-42602
-▁Elise	-42603
-▁Mazda	-42604
-▁Batter	-42605
-▁Wizards	-42606
-▁anglers	-42607
-▁kinetic	-42608
-▁payload	-42609
-▁purport	-42610
-▁Catering	-42611
-▁inasmuch	-42612
-▁pampered	-42613
-▁Expensive	-42614
-▁intensify	-42615
-▁Permission	-42616
-▁overheating	-42617
-’!	-42618
-Ult	-42619
-▁CK	-42620
-diet	-42621
-▁apes	-42622
-▁wrig	-42623
-otence	-42624
-rabble	-42625
-▁Input	-42626
-▁MIGHT	-42627
-▁rapes	-42628
-▁Hobbit	-42629
-▁overha	-42630
-▁prerog	-42631
-▁resell	-42632
-▁resize	-42633
-▁sprout	-42634
-altering	-42635
-▁Renting	-42636
-▁orbital	-42637
-▁twelfth	-42638
-▁futility	-42639
-▁infested	-42640
-▁Immediate	-42641
-▁MacDonald	-42642
-▁diarrhoea	-42643
-▁Humanitarian	-42644
-uta	-42645
-▁WM	-42646
-▁“.	-42647
-adin	-42648
-rope	-42649
-scar	-42650
-▁SPR	-42651
-▁Sap	-42652
-▁whiff	-42653
-animate	-42654
-▁DESIGN	-42655
-▁Freder	-42656
-▁Prizes	-42657
-▁Ventura	-42658
-▁barbaric	-42659
-▁tantrums	-42660
-▁allotment	-42661
-▁Objectives	-42662
-▁Portsmouth	-42663
-▁onboarding	-42664
-▁Introducing	-42665
-▁subconsciously	-42666
-Aud	-42667
-ndo	-42668
-▁AE	-42669
-edic	-42670
-junk	-42671
-▁ere	-42672
-ggies	-42673
-ussis	-42674
-▁lily	-42675
-allerg	-42676
-▁coals	-42677
-oplasty	-42678
-▁Hassan	-42679
-▁brazen	-42680
-▁domino	-42681
-▁layman	-42682
-▁mutant	-42683
-▁squats	-42684
-▁Garland	-42685
-▁vetting	-42686
-▁Clearing	-42687
-▁Normandy	-42688
-▁informant	-42689
-▁limelight	-42690
-▁mediocrity	-42691
-▁multifaceted	-42692
-▁segmentation	-42693
-▁unresponsive	-42694
-▁incapacitated	-42695
-▁sensibilities	-42696
-ACC	-42697
-▁PW	-42698
-▁TG	-42699
-Lyle	-42700
-NOTE	-42701
-▁BAB	-42702
-▁MMA	-42703
-Local	-42704
-urist	-42705
-▁Sard	-42706
-▁alum	-42707
-feated	-42708
-▁Aspen	-42709
-▁Cecil	-42710
-▁forgo	-42711
-▁assent	-42712
-▁tangle	-42713
-Consider	-42714
-▁Playboy	-42715
-▁flannel	-42716
-▁lasagna	-42717
-▁Comparing	-42718
-▁Gradually	-42719
-▁landfills	-42720
-▁railroads	-42721
-▁Smithsonian	-42722
-▁prerequisites	-42723
-Pa	-42724
-oung	-42725
-▁Luk	-42726
-▁Knew	-42727
-Center	-42728
-Report	-42729
-urized	-42730
-▁Newly	-42731
-▁parse	-42732
-▁Caught	-42733
-▁adopts	-42734
-▁ellipt	-42735
-▁hypers	-42736
-▁Nichols	-42737
-▁PURPOSE	-42738
-▁WEBSITE	-42739
-▁avocados	-42740
-▁coatings	-42741
-Washington	-42742
-iversaries	-42743
-privileged	-42744
-▁conundrum	-42745
-▁factoring	-42746
-▁longitude	-42747
-▁Defendants	-42748
-▁Particular	-42749
-▁awkwardness	-42750
-▁controversies	-42751
-bore	-42752
-pony	-42753
-moral	-42754
-▁Torn	-42755
-razole	-42756
-▁thier	-42757
-▁vying	-42758
-▁harden	-42759
-▁hereof	-42760
-▁touchy	-42761
-▁drywall	-42762
-▁heroism	-42763
-▁numeric	-42764
-▁Goodreads	-42765
-▁Spotlight	-42766
-▁atrocious	-42767
-▁brightened	-42768
-▁politeness	-42769
-▁prioritise	-42770
-▁stagnation	-42771
-▁simplifying	-42772
-▁Organisations	-42773
-▁astonishingly	-42774
-giv	-42775
-kat	-42776
-scr	-42777
-wey	-42778
-▁BOY	-42779
-▁LTE	-42780
-▁ilk	-42781
-▁mow	-42782
-Query	-42783
-▁cyan	-42784
-▁inks	-42785
-▁Feder	-42786
-▁puree	-42787
-culator	-42788
-▁qualms	-42789
-▁tugging	-42790
-▁catheter	-42791
-▁chestnut	-42792
-▁markedly	-42793
-▁teething	-42794
-▁mercenary	-42795
-▁celebratory	-42796
-Requ	-42797
-anoi	-42798
-▁EXC	-42799
-▁LAR	-42800
-▁Sto	-42801
-grave	-42802
-itent	-42803
-▁Wast	-42804
-▁plut	-42805
-orange	-42806
-waters	-42807
-▁levers	-42808
-▁morrow	-42809
-▁promen	-42810
-▁raided	-42811
-▁garnish	-42812
-▁teaming	-42813
-▁Hezbollah	-42814
-▁drugstore	-42815
-▁playlists	-42816
-▁twinkling	-42817
-▁deliverables	-42818
-▁inaccuracies	-42819
-OSH	-42820
-▁Ape	-42821
-▁dan	-42822
-arton	-42823
-▁Niet	-42824
-▁byte	-42825
-nearly	-42826
-▁Botox	-42827
-▁sneer	-42828
-Current	-42829
-systems	-42830
-▁Recall	-42831
-▁Riders	-42832
-ceivable	-42833
-projects	-42834
-▁Ibrahim	-42835
-▁Paradox	-42836
-▁obeying	-42837
-▁thankyou	-42838
-▁shielding	-42839
-▁apologetic	-42840
-▁dismantling	-42841
-!?!	-42842
-Isa	-42843
-Kind	-42844
-Park	-42845
-▁CHA	-42846
-▁Kaw	-42847
-▁Ober	-42848
-▁Uran	-42849
-▁Pratt	-42850
-▁folic	-42851
-▁hound	-42852
-▁setups	-42853
-ondition	-42854
-teaching	-42855
-thousand	-42856
-▁dispers	-42857
-▁heather	-42858
-▁Slovakia	-42859
-▁Speakers	-42860
-▁alerting	-42861
-▁budgeted	-42862
-▁punctual	-42863
-▁benefitted	-42864
-▁Preliminary	-42865
-▁entertainer	-42866
-▁reactionary	-42867
-▁unwittingly	-42868
-▁intermediaries	-42869
-ен	-42870
-▁RAF	-42871
-akens	-42872
-▁Anto	-42873
-▁Pard	-42874
-▁VoIP	-42875
-▁barr	-42876
-▁rumble	-42877
-▁Everett	-42878
-▁issuers	-42879
-balancing	-42880
-naturally	-42881
-▁disclaim	-42882
-▁evergreen	-42883
-▁commendable	-42884
-▁intersections	-42885
-Ir	-42886
-nar	-42887
-WHAT	-42888
-alie	-42889
-oled	-42890
-snow	-42891
-Ready	-42892
-▁Phew	-42893
-mology	-42894
-▁Ryder	-42895
-▁biker	-42896
-▁puffy	-42897
-▁Domino	-42898
-▁Honour	-42899
-▁Console	-42900
-▁Pasadena	-42901
-▁foothold	-42902
-▁outlawed	-42903
-▁wishlist	-42904
-▁Allowance	-42905
-▁Discounts	-42906
-▁empathize	-42907
-▁mitochond	-42908
-▁scorching	-42909
-▁snowstorm	-42910
-▁unaccount	-42911
-▁helplessness	-42912
-JS	-42913
-.[/	-42914
-ajo	-42915
-fir	-42916
-▁Vy	-42917
-▁DOC	-42918
-▁Doe	-42919
-▁hav	-42920
-clubs	-42921
-▁Teal	-42922
-▁Pluto	-42923
-▁Winnie	-42924
-▁Harding	-42925
-▁Lending	-42926
-▁equator	-42927
-▁morphed	-42928
-promotion	-42929
-▁Argentine	-42930
-▁vanishing	-42931
-▁Accountant	-42932
-trl	-42933
-Wish	-42934
-mies	-42935
-▁Cum	-42936
-ellen	-42937
-▁Falk	-42938
-▁Para	-42939
-▁Roast	-42940
-▁taunt	-42941
-persons	-42942
-▁piston	-42943
-osterous	-42944
-▁Rockets	-42945
-▁Charming	-42946
-▁agnostic	-42947
-▁goldfish	-42948
-▁orphaned	-42949
-▁panicking	-42950
-▁premiered	-42951
-▁percentile	-42952
-▁mercenaries	-42953
-▁socialization	-42954
-▁constituencies	-42955
-""	-42956
-▁Yok	-42957
-▁omg	-42958
-tower	-42959
-▁Nuts	-42960
-▁retic	-42961
-▁downed	-42962
-▁rebuke	-42963
-pecified	-42964
-▁reaping	-42965
-▁torches	-42966
-▁emptying	-42967
-▁signings	-42968
-▁restricts	-42969
-▁observable	-42970
-▁stabilized	-42971
-▁cranberries	-42972
-▁guardianship	-42973
-LAN	-42974
-Resp	-42975
-acin	-42976
-emort	-42977
-lover	-42978
-▁Ikea	-42979
-▁enth	-42980
-Sports	-42981
-▁Invis	-42982
-▁Jorge	-42983
-▁TECHN	-42984
-▁WOMEN	-42985
-▁haught	-42986
-▁pisses	-42987
-▁spiked	-42988
-▁climbers	-42989
-▁Wimbledon	-42990
-▁dispensed	-42991
-▁handcuffs	-42992
-▁functionalities	-42993
-tex	-42994
-bond	-42995
-▁Urs	-42996
-robat	-42997
-▁ASEAN	-42998
-▁mites	-42999
-▁plaid	-43000
-▁Sevent	-43001
-▁ornate	-43002
-▁Signing	-43003
-▁intents	-43004
-▁trooper	-43005
-▁Juvenile	-43006
-▁Patterns	-43007
-▁clutched	-43008
-▁familial	-43009
-▁populate	-43010
-▁stinking	-43011
-affordable	-43012
-connection	-43013
-▁causation	-43014
-▁lecturers	-43015
-▁toughness	-43016
-▁injustices	-43017
-▁Beautifully	-43018
-▁classifications	-43019
-ophe	-43020
-serv	-43021
-▁AMC	-43022
-▁Gum	-43023
-▁MPH	-43024
-▁PSA	-43025
-utane	-43026
-▁Cate	-43027
-▁Kobe	-43028
-dining	-43029
-▁Trash	-43030
-▁spoof	-43031
-Shelton	-43032
-▁Claude	-43033
-▁MOTHER	-43034
-▁cranes	-43035
-▁pointy	-43036
-▁unplug	-43037
-spection	-43038
-▁Stanton	-43039
-▁mingled	-43040
-▁shaming	-43041
-▁Aquarium	-43042
-corruption	-43043
-▁carcinoma	-43044
-▁licensure	-43045
-▁Civilization	-43046
-▁choreography	-43047
-▁masterpieces	-43048
-▁preposterous	-43049
-ipel	-43050
-▁DEA	-43051
-▁Sut	-43052
-asher	-43053
-cakes	-43054
-▁NICE	-43055
-▁Tusc	-43056
-▁rick	-43057
-Travel	-43058
-flying	-43059
-locking	-43060
-▁Eileen	-43061
-▁barbed	-43062
-▁nonstop	-43063
-▁quartet	-43064
-▁reigned	-43065
-▁Honolulu	-43066
-▁Timeline	-43067
-▁scariest	-43068
-▁Advocates	-43069
-▁Chartered	-43070
-▁Collector	-43071
-▁inhalation	-43072
-▁Instruments	-43073
-Pet	-43074
-jin	-43075
-sha	-43076
-Hall	-43077
-surv	-43078
-▁EMP	-43079
-dryer	-43080
-ilers	-43081
-taste	-43082
-▁stee	-43083
-▁swag	-43084
-Winter	-43085
-▁Lunar	-43086
-▁Zelda	-43087
-▁Sesame	-43088
-▁overex	-43089
-▁resett	-43090
-▁thorns	-43091
-▁vortex	-43092
-feedback	-43093
-▁lounges	-43094
-▁rampage	-43095
-▁sanctity	-43096
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-ogia	-43178
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-▁Monsanto	-43222
-▁laminate	-43223
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-dl	-43226
-meth	-43227
-▁jac	-43228
-....)	-43229
-unals	-43230
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-angering	-43238
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-Blo	-43251
-Dave	-43252
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-erala	-43256
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-authored	-43263
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-▁remit	-43284
-▁immeas	-43285
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-▁upholding	-43295
-LyleShelton	-43296
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-onged	-43306
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-▁Holden	-43314
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-▁unhelpful	-43322
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-obyl	-43327
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-ommun	-43331
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-▁smitten	-43339
-▁snarled	-43340
-▁Engaging	-43341
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-▁molasses	-43343
-▁ponytail	-43344
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-▁musically	-43346
-continental	-43347
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-?!!	-43355
-hex	-43356
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-▁doss	-43358
-conian	-43359
-covers	-43360
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-ommission	-43367
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-▁duh	-43379
-award	-43380
-iander	-43381
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-▁Lowell	-43385
-▁MARKET	-43386
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-▁molten	-43388
-▁folklore	-43389
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-▁remodeled	-43392
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-▁dismantled	-43394
-▁landscaped	-43395
-▁outlandish	-43396
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-;&	-43399
-fc	-43400
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-ckers	-43404
-▁Berk	-43405
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-police	-43408
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-▁Hooray	-43411
-▁Regent	-43412
-▁adware	-43413
-▁bellig	-43414
-▁resusc	-43415
-▁Spirits	-43416
-▁milking	-43417
-▁Controls	-43418
-▁Pensions	-43419
-▁diagonal	-43420
-▁excesses	-43421
-▁plunging	-43422
-▁pioneered	-43423
-▁photovolta	-43424
-▁configuring	-43425
-▁capitalization	-43426
-▁redistribution	-43427
-Cath	-43428
-Vict	-43429
-▁MAJ	-43430
-▁Yar	-43431
-armac	-43432
-▁Blvd	-43433
-▁Mits	-43434
-▁Mori	-43435
-▁mana	-43436
-▁posh	-43437
-▁Milly	-43438
-awesome	-43439
-▁Lyrics	-43440
-▁eclips	-43441
-▁muslim	-43442
-▁Licence	-43443
-▁Pulling	-43444
-▁TOTALLY	-43445
-▁arousal	-43446
-▁kamagra	-43447
-▁pellets	-43448
-▁Organize	-43449
-▁antibody	-43450
-▁recovers	-43451
-▁Architects	-43452
-▁coefficient	-43453
-▁conservatory	-43454
-▁embellishments	-43455
-cz	-43456
-CELL	-43457
-▁lum	-43458
-aside	-43459
-▁beep	-43460
-▁Debra	-43461
-▁disem	-43462
-▁Duties	-43463
-▁Roller	-43464
-▁Norwich	-43465
-▁Zombies	-43466
-▁eyeball	-43467
-▁Directly	-43468
-sustaining	-43469
-▁Estimates	-43470
-▁coverages	-43471
-▁Installing	-43472
-▁legislator	-43473
-▁monotonous	-43474
-▁registrant	-43475
-▁hairdresser	-43476
-aida	-43477
-▁LDS	-43478
-amide	-43479
-endum	-43480
-mouse	-43481
-▁Abdu	-43482
-▁Hark	-43483
-▁Vold	-43484
-▁dere	-43485
-▁trep	-43486
-beauty	-43487
-editor	-43488
-▁Rails	-43489
-▁Seconds	-43490
-▁bullpen	-43491
-▁peptide	-43492
-▁sagging	-43493
-announced	-43494
-▁comedies	-43495
-▁drumming	-43496
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-▁wielding	-43498
-imar	-43499
-begin	-43500
-reted	-43501
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-ubstant	-43510
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-▁Renewal	-43516
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-▁meadows	-43518
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-▁innocuous	-43521
-Spiritcurse	-43522
-maintenance	-43523
-▁worksheets	-43524
-▁{{	-43525
-Feel	-43526
-fusc	-43527
-sick	-43528
-————	-43529
-icose	-43530
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-crisis	-43533
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-chanical	-43535
-proofing	-43536
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-amination	-43541
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-▁Berkshire	-43544
-▁Hiroshima	-43545
-▁Pediatric	-43546
-▁flawlessly	-43547
-▁helplessly	-43548
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-▁apprehended	-43550
-▁interception	-43551
-kj	-43552
-tool	-43553
-icion	-43554
-ocrat	-43555
-ooooo	-43556
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-▁MIND	-43559
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-▁clout	-43566
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-▁wisest	-43568
-▁yachts	-43569
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-▁catholic	-43571
-▁mixtures	-43572
-▁constable	-43573
-▁shortlist	-43574
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-▁Territories	-43576
-▁irritability	-43577
-?”	-43578
-ENG	-43579
-Lee	-43580
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-▁Ek	-43582
-ooky	-43583
-▁ACS	-43584
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-▁aph	-43586
-avage	-43587
-drops	-43588
-enged	-43589
-▁YMCA	-43590
-▁rift	-43591
-▁vial	-43592
-amorph	-43593
-▁LEARN	-43594
-▁Malibu	-43595
-▁abject	-43596
-▁bushel	-43597
-▁sullen	-43598
-▁Glacier	-43599
-▁divulge	-43600
-▁plotted	-43601
-▁backfire	-43602
-▁engulfed	-43603
-▁unusable	-43604
-▁Stability	-43605
-▁dormitory	-43606
-▁panelists	-43607
-▁severance	-43608
-▁uncomplicated	-43609
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-eca	-43611
-IDER	-43612
-odic	-43613
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-reeks	-43615
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-▁Plum	-43617
-▁Rash	-43618
-▁Rove	-43619
-static	-43620
-zzling	-43621
-Control	-43622
-▁Canton	-43623
-▁Expend	-43624
-▁Keegan	-43625
-▁Wishes	-43626
-▁barked	-43627
-behavior	-43628
-▁Assisted	-43629
-▁Released	-43630
-▁unreason	-43631
-▁Socialism	-43632
-▁coincides	-43633
-▁elaborated	-43634
-▁tumultuous	-43635
-▁shortlisted	-43636
-effectiveness	-43637
-bah	-43638
-▁Ryo	-43639
-▁Tao	-43640
-▁YUM	-43641
-atars	-43642
-▁Alma	-43643
-▁OSHA	-43644
-▁Wifi	-43645
-aceous	-43646
-Related	-43647
-▁nested	-43648
-▁nutmeg	-43649
-▁Neptune	-43650
-▁amiable	-43651
-▁angular	-43652
-▁forsake	-43653
-▁accorded	-43654
-▁adherent	-43655
-▁epiphany	-43656
-▁fixation	-43657
-▁branching	-43658
-▁uniformed	-43659
-▁formulations	-43660
-▁overpowering	-43661
-IER	-43662
-Kee	-43663
-Lib	-43664
-▁UE	-43665
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-pink	-43667
-▁Shay	-43668
-dinner	-43669
-multip	-43670
-▁Alter	-43671
-▁Armor	-43672
-▁Shout	-43673
-▁cuffs	-43674
-▁mania	-43675
-▁realy	-43676
-stained	-43677
-▁Coloss	-43678
-▁Weaver	-43679
-▁reggae	-43680
-▁snooze	-43681
-academic	-43682
-▁foreman	-43683
-▁Sandwich	-43684
-▁underdog	-43685
-▁indulgent	-43686
-▁Considered	-43687
-▁utilitarian	-43688
-▁affirmations	-43689
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-ACY	-43692
-▁Vs	-43693
-goal	-43694
-ycin	-43695
-▁CFO	-43696
-escap	-43697
-ooner	-43698
-▁toen	-43699
-▁Charm	-43700
-▁Drunk	-43701
-▁Elsie	-43702
-▁brist	-43703
-uspices	-43704
-▁nozzle	-43705
-▁probing	-43706
-▁moustache	-43707
-▁stubbornly	-43708
-▁selectively	-43709
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-▁Subsequently	-43711
-ía	-43712
-,..	-43713
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-grey	-43715
-houn	-43716
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-▁GED	-43718
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-▁Sax	-43720
-▁VHS	-43721
-▁bic	-43722
-▁Vale	-43723
-raiser	-43724
-rienne	-43725
-uffles	-43726
-▁antip	-43727
-▁duped	-43728
-▁gruff	-43729
-uminati	-43730
-▁Hamlet	-43731
-▁NUMBER	-43732
-▁chilli	-43733
-▁cogniz	-43734
-▁idiocy	-43735
-lifetime	-43736
-▁Azerbai	-43737
-▁Surveys	-43738
-▁Findings	-43739
-▁Penelope	-43740
-▁crutches	-43741
-▁excelled	-43742
-▁shivered	-43743
-▁EDUCATION	-43744
-▁Necessary	-43745
-▁bickering	-43746
-▁opposites	-43747
-▁exacerbate	-43748
-▁needlework	-43749
-▁competitively	-43750
-▁superannuation	-43751
-fro	-43752
-aday	-43753
-▁Hir	-43754
-▁Tian	-43755
-artner	-43756
-comple	-43757
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-▁Levin	-43759
-▁PARTY	-43760
-▁Twist	-43761
-▁absol	-43762
-▁catac	-43763
-▁sodas	-43764
-▁Oxygen	-43765
-▁deform	-43766
-tempered	-43767
-▁Fallout	-43768
-▁Specify	-43769
-▁thwarted	-43770
-▁Efficient	-43771
-▁Josephine	-43772
-▁complicit	-43773
-▁entangled	-43774
-▁nicknamed	-43775
-▁organizes	-43776
-▁turbulence	-43777
-APE	-43778
-Glad	-43779
-guns	-43780
-▁Rim	-43781
-azers	-43782
-owell	-43783
-▁Rage	-43784
-▁SOLD	-43785
-▁Zeus	-43786
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-▁derby	-43788
-minimum	-43789
-▁CUSTOM	-43790
-▁Guided	-43791
-▁bouncy	-43792
-▁racers	-43793
-▁Scorpio	-43794
-▁adjusts	-43795
-▁cowards	-43796
-▁Warcraft	-43797
-▁spamming	-43798
-▁untapped	-43799
-▁orthopedic	-43800
-▁streamlining	-43801
-▁precautionary	-43802
-RT	-43803
-▁◦	-43804
-rake	-43805
-▁Pee	-43806
-▁REF	-43807
-▁Beds	-43808
-▁Cair	-43809
-▁Heal	-43810
-▁Trap	-43811
-▁flin	-43812
-▁pepp	-43813
-utered	-43814
-▁Dwarf	-43815
-▁Mayan	-43816
-▁faves	-43817
-▁maize	-43818
-▁searc	-43819
-▁synth	-43820
-Speaking	-43821
-▁scolded	-43822
-▁negotiable	-43823
-▁prioritized	-43824
-HT	-43825
-gio	-43826
-▁Kre	-43827
-▁Died	-43828
-▁garg	-43829
-▁vern	-43830
-apters	-43831
-formal	-43832
-ulence	-43833
-▁Colby	-43834
-▁Curse	-43835
-▁racer	-43836
-treated	-43837
-▁Abrams	-43838
-▁lodges	-43839
-▁stalks	-43840
-▁Grayson	-43841
-▁Singles	-43842
-▁absences	-43843
-▁glossary	-43844
-▁inhumane	-43845
-▁elegantly	-43846
-▁reprinted	-43847
-▁vibrating	-43848
-▁Springsteen	-43849
-Hop	-43850
-▁Iâ	-43851
-crew	-43852
-cula	-43853
-▁Mih	-43854
-ASTIC	-43855
-▁Loch	-43856
-▁Pops	-43857
-▁Worm	-43858
-▁Boyle	-43859
-▁Hanoi	-43860
-▁aloof	-43861
-ippings	-43862
-▁Chosen	-43863
-▁Detail	-43864
-▁Wenger	-43865
-▁blazer	-43866
-▁weakly	-43867
-▁Fischer	-43868
-▁funders	-43869
-▁gifting	-43870
-▁halting	-43871
-▁jobless	-43872
-▁orgasms	-43873
-▁shelling	-43874
-▁depletion	-43875
-▁locomotive	-43876
-▁apocalyptic	-43877
-▁proofreading	-43878
-▁speculations	-43879
-)..	-43880
-HOD	-43881
-abuse	-43882
-erect	-43883
-▁Kand	-43884
-igraph	-43885
-▁mushy	-43886
-clothes	-43887
-▁Dealer	-43888
-▁excise	-43889
-▁Rowling	-43890
-▁Pipeline	-43891
-▁shelving	-43892
-▁intruders	-43893
-▁dermatitis	-43894
-▁shattering	-43895
-▁Implications	-43896
-▁Unquestionably	-43897
-irez	-43898
-▁Plu	-43899
-▁Psy	-43900
-patri	-43901
-upper	-43902
-▁PAGE	-43903
-▁enab	-43904
-▁Akron	-43905
-▁Josie	-43906
-▁techs	-43907
-▁Routine	-43908
-▁demoral	-43909
-▁tilting	-43910
-▁voicing	-43911
-▁waiters	-43912
-▁Complaint	-43913
-▁boardwalk	-43914
-▁inhibitor	-43915
-▁collegiate	-43916
-▁exorbitant	-43917
-▁sequencing	-43918
-▁specialises	-43919
-*)	-43920
-/~	-43921
-/...	-43922
-▁DOS	-43923
-▁Kne	-43924
-▁XXX	-43925
-wheat	-43926
-▁Docs	-43927
-aganza	-43928
-ratory	-43929
-▁Darth	-43930
-▁Rowan	-43931
-erville	-43932
-▁Mercer	-43933
-▁Themes	-43934
-▁clunky	-43935
-▁rabies	-43936
-particip	-43937
-▁Publish	-43938
-▁disenfr	-43939
-▁heathen	-43940
-▁ovation	-43941
-▁spyware	-43942
-▁Expected	-43943
-▁auspices	-43944
-▁blockers	-43945
-▁stallion	-43946
-▁earmarked	-43947
-▁uniformly	-43948
-▁policyholder	-43949
-▁INTERNATIONAL	-43950
-▁antimicrobial	-43951
-▁semiconductor	-43952
-VS	-43953
-izo	-43954
-Fall	-43955
-▁Eur	-43956
-▁Nun	-43957
-▁SME	-43958
-▁Tig	-43959
-Sleep	-43960
-▁loll	-43961
-▁tbsp	-43962
-▁Koran	-43963
-▁Manny	-43964
-▁Corbyn	-43965
-▁Knicks	-43966
-▁Tacoma	-43967
-▁muscul	-43968
-afternoon	-43969
-▁Contacts	-43970
-▁Signific	-43971
-▁stomping	-43972
-▁Companion	-43973
-▁outperform	-43974
-▁unwarranted	-43975
-!”.	-43976
-Jet	-43977
-ilo	-43978
-lica	-43979
-▁Gide	-43980
-▁ORGAN	-43981
-▁encir	-43982
-▁slant	-43983
-▁tonne	-43984
-ageddon	-43985
-ipelago	-43986
-▁Facial	-43987
-▁Oceans	-43988
-angelist	-43989
-▁Surgeon	-43990
-▁ignited	-43991
-▁scourge	-43992
-▁lymphoma	-43993
-▁disorient	-43994
-▁fledgling	-43995
-▁decisively	-43996
-jun	-43997
-phyl	-43998
-▁AMA	-43999
-▁SEL	-44000
-uvian	-44001
-▁Serm	-44002
-▁asian	-44003
-Company	-44004
-waiting	-44005
-▁Gently	-44006
-▁curfew	-44007
-▁pelvis	-44008
---------	-44009
-▁Diocese	-44010
-▁Lantern	-44011
-▁encoded	-44012
-▁Confeder	-44013
-▁cordless	-44014
-▁resolves	-44015
-▁marketable	-44016
-▁unspecified	-44017
-▁sociological	-44018
-▁LI	-44019
-iagn	-44020
-▁HEA	-44021
-▁VOL	-44022
-bench	-44023
-▁Cups	-44024
-▁Tens	-44025
-ayment	-44026
-▁Malik	-44027
-atiable	-44028
-▁Meadow	-44029
-▁Minnie	-44030
-▁Rubber	-44031
-▁recomp	-44032
-▁rodent	-44033
-▁Blanche	-44034
-▁Neville	-44035
-▁clouded	-44036
-▁flicked	-44037
-▁jarring	-44038
-▁spatula	-44039
-generally	-44040
-▁Founders	-44041
-▁Keystone	-44042
-▁defences	-44043
-▁worsened	-44044
-▁linebacker	-44045
-'?"	-44046
-lov	-44047
-zon	-44048
-▁Eg	-44049
-▁Ou	-44050
-anni	-44051
-hatu	-44052
-▁pls	-44053
-Place	-44054
-ateau	-44055
-iosyn	-44056
-plets	-44057
-▁Dive	-44058
-▁Fern	-44059
-▁Nail	-44060
-▁daft	-44061
-▁lass	-44062
-▁Ditto	-44063
-▁Donne	-44064
-▁Fewer	-44065
-▁Likes	-44066
-▁Lynne	-44067
-selfish	-44068
-▁DOCTOR	-44069
-▁shamed	-44070
-▁Loyalty	-44071
-▁occured	-44072
-▁Concerns	-44073
-▁Achieving	-44074
-inguishable	-44075
-▁propulsion	-44076
-▁publicized	-44077
-▁unfriendly	-44078
-▁Appreciating	-44079
-▁deregulation	-44080
-uku	-44081
-cken	-44082
-▁GPs	-44083
-▁Gaw	-44084
-▁Rit	-44085
-▁hep	-44086
-▁abys	-44087
-cients	-44088
-ercise	-44089
-▁Hmmmm	-44090
-▁freck	-44091
-▁unimp	-44092
-▁liners	-44093
-▁unveil	-44094
-▁Dynasty	-44095
-▁seperate	-44096
-▁stinging	-44097
-▁congested	-44098
-▁protestors	-44099
-▁ransomware	-44100
-▁unsuccessfully	-44101
-Nov	-44102
-tap	-44103
-▁ISA	-44104
-hoids	-44105
-▁SELL	-44106
-▁blob	-44107
-▁besee	-44108
-▁drier	-44109
-▁jaded	-44110
-▁Auntie	-44111
-▁stinky	-44112
-▁Adsense	-44113
-▁Brewing	-44114
-▁reunite	-44115
-▁Hispanics	-44116
-▁hyperbole	-44117
-grandfather	-44118
-intentioned	-44119
-ratulations	-44120
-▁Preferably	-44121
-▁flourished	-44122
-▁eradication	-44123
-viii	-44124
-stalk	-44125
-uffin	-44126
-▁Hanna	-44127
-▁Reyes	-44128
-▁brood	-44129
-▁convo	-44130
-▁Debian	-44131
-▁Keeper	-44132
-▁Anaheim	-44133
-▁Gospels	-44134
-▁evicted	-44135
-▁devoting	-44136
-▁Attendees	-44137
-▁expedited	-44138
-lesiastical	-44139
-▁rationalize	-44140
-Date	-44141
-▁Jal	-44142
-struck	-44143
-▁hotly	-44144
-entanyl	-44145
-▁Expand	-44146
-▁Gareth	-44147
-▁rashes	-44148
-▁videog	-44149
-▁Calcium	-44150
-▁Refresh	-44151
-▁Resorts	-44152
-▁archery	-44153
-▁webblog	-44154
-▁incision	-44155
-▁stairway	-44156
-▁McCartney	-44157
-▁Pharisees	-44158
-▁audiobook	-44159
-▁defamatory	-44160
-▁protectors	-44161
-▁Increasingly	-44162
-CF	-44163
-ás	-44164
-▁К	-44165
-wit	-44166
-zzo	-44167
-Cast	-44168
-agos	-44169
-beam	-44170
-▁Bam	-44171
-▁CSR	-44172
-▁DOJ	-44173
-asone	-44174
-▁Dirt	-44175
-▁Foss	-44176
-▁ques	-44177
-▁wigs	-44178
-▁pesos	-44179
-▁spout	-44180
-ciences	-44181
-landers	-44182
-undered	-44183
-▁Crunch	-44184
-▁Margin	-44185
-▁PARTIC	-44186
-▁casket	-44187
-▁plenary	-44188
-▁rafting	-44189
-▁smoothed	-44190
-▁Execution	-44191
-▁Icelandic	-44192
-▁carpeting	-44193
-▁landslide	-44194
-▁repressed	-44195
-▁Punishment	-44196
-▁roadblocks	-44197
-▁magistrates	-44198
-ño	-44199
-Hen	-44200
-rav	-44201
-bart	-44202
-join	-44203
-▁aft	-44204
-Force	-44205
-India	-44206
-suits	-44207
-▁KISS	-44208
-▁RAID	-44209
-▁SWAT	-44210
-▁Foley	-44211
-▁Patti	-44212
-▁hesit	-44213
-▁voila	-44214
-Chapter	-44215
-▁Cannes	-44216
-▁Killed	-44217
-▁Latter	-44218
-▁booted	-44219
-▁chimed	-44220
-blogging	-44221
-▁Jakarta	-44222
-▁Malware	-44223
-▁Tuscany	-44224
-▁checkup	-44225
-▁Ignoring	-44226
-▁Statutes	-44227
-▁Synopsis	-44228
-▁anorexia	-44229
-▁manicure	-44230
-▁textured	-44231
-maintained	-44232
-▁Nicholson	-44233
-▁breathable	-44234
-▁projectile	-44235
-Sl	-44236
-IFT	-44237
-mie	-44238
-woo	-44239
-▁;-	-44240
-Need	-44241
-▁AUT	-44242
-▁ETA	-44243
-▁FUT	-44244
-gable	-44245
-parts	-44246
-▁USPS	-44247
-▁boob	-44248
-ingway	-44249
-▁jalap	-44250
-▁Ethnic	-44251
-▁RIGHTS	-44252
-▁kidnap	-44253
-ferences	-44254
-itability	-44255
-▁calmness	-44256
-▁clutches	-44257
-▁rainbows	-44258
-▁splendor	-44259
-▁Introduce	-44260
-▁Spielberg	-44261
-▁Conspiracy	-44262
-▁copywriter	-44263
-▁checkpoints	-44264
-▁materialize	-44265
-▁Charlottesville	-44266
-Move	-44267
-azzy	-44268
-ulet	-44269
-▁ald	-44270
-oleum	-44271
-▁Cuts	-44272
-▁Lazy	-44273
-▁Snape	-44274
-▁avant	-44275
-▁tions	-44276
-▁Clever	-44277
-▁Vendor	-44278
-partners	-44279
-▁knowled	-44280
-▁wailing	-44281
-▁Carlisle	-44282
-▁narrated	-44283
-▁digesting	-44284
-▁Lighthouse	-44285
-▁bystanders	-44286
-▁Restrictions	-44287
-▁sarcastically	-44288
-cant	-44289
-kowski	-44290
-▁CREAT	-44291
-▁Lover	-44292
-▁Robyn	-44293
-▁Timmy	-44294
-▁ethyl	-44295
-mouthed	-44296
-▁Bellev	-44297
-▁Intent	-44298
-▁cadets	-44299
-▁chaper	-44300
-▁eyelid	-44301
-▁monsoon	-44302
-▁tabloid	-44303
-▁Assassin	-44304
-▁Channels	-44305
-▁Parental	-44306
-▁Spacious	-44307
-▁Subjects	-44308
-▁woefully	-44309
-▁consulate	-44310
-▁deadliest	-44311
-▁destitute	-44312
-▁sensitivities	-44313
-ña	-44314
-?’”	-44315
-ENN	-44316
-fal	-44317
-reve	-44318
-▁Bav	-44319
-▁ENG	-44320
-▁Tad	-44321
-▁UNT	-44322
-▁mugg	-44323
-▁pout	-44324
-phrase	-44325
-▁Moira	-44326
-▁TERMS	-44327
-▁dandy	-44328
-▁domic	-44329
-▁elong	-44330
-Content	-44331
-Russian	-44332
-amazing	-44333
-hosting	-44334
-▁Missed	-44335
-▁Resume	-44336
-▁waning	-44337
-▁Shuttle	-44338
-▁marries	-44339
-▁rigging	-44340
-oskeletal	-44341
-▁Stopping	-44342
-▁conversing	-44343
-▁tablecloth	-44344
-ROR	-44345
-sym	-44346
-▁IU	-44347
-itri	-44348
-maps	-44349
-▁Ada	-44350
-▁Kak	-44351
-▁slum	-44352
-▁Blast	-44353
-▁Jelly	-44354
-▁fluke	-44355
-▁CALLER	-44356
-▁winged	-44357
-▁bonfire	-44358
-▁bullish	-44359
-▁fresher	-44360
-▁wanders	-44361
-▁Georgian	-44362
-▁forsaken	-44363
-▁parishes	-44364
-▁Automated	-44365
-▁Nigerians	-44366
-▁Samaritan	-44367
-explanatory	-44368
-▁allocating	-44369
-▁exclusivity	-44370
-▁Cancellation	-44371
-JOY	-44372
-Bird	-44373
-STER	-44374
-acho	-44375
-itial	-44376
-refer	-44377
-rieve	-44378
-▁McCl	-44379
-arched	-44380
-render	-44381
-▁Bunch	-44382
-▁Chong	-44383
-▁Wooden	-44384
-▁defiant	-44385
-▁perjury	-44386
-▁widowed	-44387
-assembled	-44388
-▁PERSONAL	-44389
-▁cardigan	-44390
-▁Protected	-44391
-▁overgrown	-44392
-▁willfully	-44393
-▁arbitrator	-44394
-▁extradition	-44395
-▁interceptions	-44396
-ом	-44397
-ovy	-44398
-▁Shy	-44399
-batim	-44400
-etter	-44401
-▁Gras	-44402
-▁cusp	-44403
-▁haem	-44404
-German	-44405
-footed	-44406
-▁Niche	-44407
-▁hilly	-44408
-▁shipw	-44409
-dington	-44410
-privacy	-44411
-▁Agatha	-44412
-▁apiece	-44413
-▁artich	-44414
-▁Parkway	-44415
-▁sensibly	-44416
-▁sneezing	-44417
-▁stifling	-44418
-▁localities	-44419
-▁secondhand	-44420
-LEY	-44421
-ruth	-44422
-spam	-44423
-▁Faw	-44424
-▁Til	-44425
-drink	-44426
-esare	-44427
-taken	-44428
-vonne	-44429
-▁JACK	-44430
-▁Oils	-44431
-▁unsh	-44432
-mitted	-44433
-▁Kirst	-44434
-▁gamut	-44435
-▁tolls	-44436
-▁Euchar	-44437
-▁Partly	-44438
-▁mishaps	-44439
-▁rooster	-44440
-compliant	-44441
-▁Invisible	-44442
-▁cellulite	-44443
-▁restarted	-44444
-▁Vocational	-44445
-▁councillor	-44446
-▁randomness	-44447
-▁manifesting	-44448
-▁republicans	-44449
-▁degenerative	-44450
-vv	-44451
-Eff	-44452
-Fly	-44453
-cams	-44454
-weap	-44455
-▁GRA	-44456
-▁Lak	-44457
-▁PAL	-44458
-▁Ras	-44459
-▁Sli	-44460
-▁Amos	-44461
-▁Versa	-44462
-▁jaunt	-44463
-▁mover	-44464
-crapers	-44465
-▁Sailor	-44466
-▁elated	-44467
-▁Arriving	-44468
-▁baffling	-44469
-omegranate	-44470
-▁Voldemort	-44471
-▁affirming	-44472
-▁Advancement	-44473
-▁metaphorical	-44474
-Ve	-44475
-.):	-44476
-▁HF	-44477
-▁va	-44478
-HHHH	-44479
-Neil	-44480
-eree	-44481
-▁Cust	-44482
-▁Krug	-44483
-▁Riot	-44484
-▁TEST	-44485
-▁amps	-44486
-burner	-44487
-▁Tempt	-44488
-▁Mentor	-44489
-▁rekind	-44490
-ceptives	-44491
-▁Discord	-44492
-corporate	-44493
-▁Bulldogs	-44494
-▁decently	-44495
-▁campaigned	-44496
-▁Forgiveness	-44497
-▁refreshment	-44498
-▁disseminated	-44499
-▁transitioned	-44500
-▁disheartening	-44501
-Rent	-44502
-▁DJs	-44503
-READY	-44504
-funny	-44505
-▁Pret	-44506
-▁Anime	-44507
-▁Minim	-44508
-▁Stuck	-44509
-▁Tenth	-44510
-▁slabs	-44511
-ramatic	-44512
-▁Pledge	-44513
-▁routed	-44514
-▁cowboys	-44515
-▁platoon	-44516
-▁venting	-44517
-▁Avoiding	-44518
-▁Helsinki	-44519
-▁electing	-44520
-▁fatality	-44521
-▁defiantly	-44522
-▁Treatments	-44523
-▁financials	-44524
-▁infallible	-44525
-wag	-44526
-plot	-44527
-pred	-44528
-▁ILL	-44529
-▁Beam	-44530
-▁SAID	-44531
-▁Utop	-44532
-Choose	-44533
-onsequ	-44534
-▁Intro	-44535
-▁Rinse	-44536
-podcast	-44537
-▁Driven	-44538
-▁Santos	-44539
-▁doings	-44540
-▁exhale	-44541
-▁legion	-44542
-▁willow	-44543
-Honestly	-44544
-▁Annette	-44545
-▁cripple	-44546
-▁unbroken	-44547
-▁wrestler	-44548
-▁Afterward	-44549
-▁incurring	-44550
-▁softening	-44551
-▁statesman	-44552
-▁tailoring	-44553
-▁vaccinate	-44554
-▁biologists	-44555
-▁infringing	-44556
-▁endeavoured	-44557
-chu	-44558
-ilin	-44559
-ougar	-44560
-stall	-44561
-▁burg	-44562
-▁gale	-44563
-▁Belly	-44564
-▁Crohn	-44565
-▁foxes	-44566
-collect	-44567
-posting	-44568
-▁Ghosts	-44569
-▁Scenes	-44570
-▁Selena	-44571
-▁croiss	-44572
-required	-44573
-▁Observe	-44574
-▁Stretch	-44575
-▁profuse	-44576
-▁pedigree	-44577
-▁Preschool	-44578
-▁seclusion	-44579
-▁idealistic	-44580
-▁prejudiced	-44581
-▁unquestion	-44582
-▁distortions	-44583
-▁infographics	-44584
-Pad	-44585
-:...	-44586
-beds	-44587
-beach	-44588
-▁Gord	-44589
-gaming	-44590
-▁Renov	-44591
-▁nukes	-44592
-▁cetera	-44593
-▁Amateur	-44594
-▁Precise	-44595
-▁Realize	-44596
-▁Wanting	-44597
-▁cholera	-44598
-▁entropy	-44599
-▁marshal	-44600
-▁clipping	-44601
-▁patriots	-44602
-▁subtitle	-44603
-▁trespass	-44604
-▁chuckling	-44605
-▁treachery	-44606
-▁Dimensions	-44607
-▁criminally	-44608
-▁fireplaces	-44609
-▁narcissism	-44610
-▁thirteenth	-44611
-▁customizing	-44612
-▁deliverance	-44613
-▁indigestion	-44614
-▁instinctive	-44615
-▁propagation	-44616
-▁intoxicating	-44617
-iu	-44618
-loin	-44619
-▁UFC	-44620
-▁Cora	-44621
-▁Cumm	-44622
-▁drab	-44623
-▁expo	-44624
-▁guer	-44625
-hernal	-44626
-▁Mayer	-44627
-▁fiend	-44628
-chronic	-44629
-traffic	-44630
-typical	-44631
-▁mildew	-44632
-▁Conquer	-44633
-▁concoct	-44634
-▁convene	-44635
-ildenafil	-44636
-itatively	-44637
-▁chargers	-44638
-▁shielded	-44639
-▁Hazardous	-44640
-▁snowboard	-44641
-▁countertop	-44642
-▁storylines	-44643
-▁compounding	-44644
-▁Perspectives	-44645
-▁methodological	-44646
-dos	-44647
-epy	-44648
-hmm	-44649
-bron	-44650
-▁CHE	-44651
-▁CSV	-44652
-▁PIP	-44653
-atoon	-44654
-cings	-44655
-ernal	-44656
-herom	-44657
-▁Dong	-44658
-▁Gest	-44659
-▁Tahoe	-44660
-▁glean	-44661
-▁chival	-44662
-▁dexter	-44663
-▁onlook	-44664
-▁Groupon	-44665
-▁Origins	-44666
-▁flutter	-44667
-▁grieved	-44668
-▁mosques	-44669
-▁pallets	-44670
-▁swelled	-44671
-▁chipping	-44672
-▁cornered	-44673
-▁reigning	-44674
-▁Breathing	-44675
-▁Eliminate	-44676
-▁tightness	-44677
-▁Harassment	-44678
-Illustration	-44679
-▁Determining	-44680
-▁endangering	-44681
-▁rescheduled	-44682
-▁mathematician	-44683
-sta	-44684
-Shit	-44685
-aley	-44686
-bugs	-44687
-iden	-44688
-champ	-44689
-oblig	-44690
-▁Crom	-44691
-▁fuzz	-44692
-▁haph	-44693
-▁nois	-44694
-hunter	-44695
-ightly	-44696
-▁Jihad	-44697
-▁irrad	-44698
-Chinese	-44699
-healing	-44700
-▁flatly	-44701
-▁spasms	-44702
-▁wicket	-44703
-▁weeding	-44704
-▁Caldwell	-44705
-▁Corporal	-44706
-▁deepened	-44707
-▁assuredly	-44708
-▁Undeniably	-44709
-▁deplorable	-44710
-▁denominated	-44711
-▁nutritionist	-44712
-▁unproductive	-44713
-Item	-44714
-▁FAB	-44715
-▁Deut	-44716
-▁Dram	-44717
-▁moor	-44718
-▁unsp	-44719
-owness	-44720
-▁Freem	-44721
-▁McGee	-44722
-▁Schen	-44723
-▁antif	-44724
-▁timel	-44725
-changer	-44726
-▁Sculpt	-44727
-▁comfor	-44728
-▁pained	-44729
-▁scotch	-44730
-▁unruly	-44731
-▁wading	-44732
-▁werent	-44733
-▁Restore	-44734
-▁fervent	-44735
-expensive	-44736
-▁Distingu	-44737
-▁eateries	-44738
-▁Athletics	-44739
-▁dystopian	-44740
-▁rectified	-44741
-▁supposing	-44742
-▁situational	-44743
-▁walkthrough	-44744
-Dam	-44745
-Eat	-44746
-itia	-44747
-▁Kad	-44748
-▁Lea	-44749
-▁meh	-44750
-▁nip	-44751
-Psalm	-44752
-olver	-44753
-aughty	-44754
-intosh	-44755
-moment	-44756
-▁Adele	-44757
-▁piety	-44758
-▁Scotts	-44759
-▁Zodiac	-44760
-Atlantic	-44761
-regional	-44762
-▁Christy	-44763
-▁alienate	-44764
-▁puncture	-44765
-▁Anchorage	-44766
-▁Starfleet	-44767
-▁toothpick	-44768
-▁Perfection	-44769
-luc	-44770
-▁IDF	-44771
-▁Pec	-44772
-▁SPF	-44773
-elius	-44774
-▁onus	-44775
-▁reall	-44776
-▁relat	-44777
-▁repur	-44778
-▁Eureka	-44779
-▁Mohatu	-44780
-▁Taurus	-44781
-judgment	-44782
-▁Picasso	-44783
-▁excites	-44784
-▁skimmed	-44785
-recommend	-44786
-▁Diamonds	-44787
-▁Moroccan	-44788
-▁droughts	-44789
-▁sociable	-44790
-▁unmanned	-44791
-▁haphazard	-44792
-▁Instrument	-44793
-▁culminated	-44794
-▁dissonance	-44795
-▁fourteenth	-44796
-▁Extraordinary	-44797
-'"	-44798
-Ur	-44799
-cou	-44800
-gig	-44801
-▁XI	-44802
-ULTS	-44803
-▁CIT	-44804
-▁Ort	-44805
-weird	-44806
-▁COOL	-44807
-▁SNAP	-44808
-▁bled	-44809
-▁chag	-44810
-▁dept	-44811
-▁sown	-44812
-atious	-44813
-▁Dinos	-44814
-▁Flour	-44815
-▁Macro	-44816
-▁YOUNG	-44817
-▁fours	-44818
-▁sassy	-44819
-▁Bodies	-44820
-▁Gemini	-44821
-▁Thrive	-44822
-▁browns	-44823
-▁sweeps	-44824
-▁Colbert	-44825
-▁saddled	-44826
-▁Buchanan	-44827
-▁renounce	-44828
-▁cartilage	-44829
-▁commences	-44830
-▁disagreeing	-44831
-▁multilingual	-44832
-▁mismanagement	-44833
-arl	-44834
-pla	-44835
-Cost	-44836
-▁Zac	-44837
-gasus	-44838
-login	-44839
-▁Coff	-44840
-▁biode	-44841
-▁dwelt	-44842
-Richard	-44843
-herical	-44844
-innitus	-44845
-▁ghetto	-44846
-Thursday	-44847
-▁Betting	-44848
-▁Carlton	-44849
-▁Largest	-44850
-▁Reliable	-44851
-▁Macedonia	-44852
-▁downsizing	-44853
-▁sweatshirt	-44854
-▁Reformation	-44855
-▁inaugurated	-44856
-▁outnumbered	-44857
-▁constituting	-44858
-▁transnational	-44859
-Sad	-44860
-panz	-44861
-▁AFP	-44862
-▁GNU	-44863
-▁Vie	-44864
-▁umb	-44865
-▁scab	-44866
-▁Cyril	-44867
-▁Lenny	-44868
-▁rotor	-44869
-▁Grades	-44870
-▁Sharia	-44871
-▁Staten	-44872
-athletes	-44873
-internal	-44874
-▁hiccups	-44875
-▁kickoff	-44876
-▁lotions	-44877
-▁recieve	-44878
-▁seminal	-44879
-▁morphine	-44880
-▁tapestry	-44881
-▁windfall	-44882
-▁organiser	-44883
-▁reintrodu	-44884
-▁sharpness	-44885
-▁trepidation	-44886
-▁straightening	-44887
-Ang	-44888
-alla	-44889
-anty	-44890
-mean	-44891
-▁(((	-44892
-▁Kes	-44893
-ignor	-44894
-iators	-44895
-nobody	-44896
-▁yolks	-44897
-cracker	-44898
-▁Bruins	-44899
-▁Locate	-44900
-▁swears	-44901
-▁Hendrix	-44902
-▁JOURNAL	-44903
-▁minding	-44904
-▁Counting	-44905
-▁boulders	-44906
-▁lacrosse	-44907
-authorized	-44908
-▁dispenser	-44909
-▁fruitless	-44910
-▁triglycer	-44911
-▁accomplice	-44912
-▁uncovering	-44913
-▁rudimentary	-44914
-Whoa	-44915
-gown	-44916
-nbsp	-44917
-thor	-44918
-Haven	-44919
-▁Izzy	-44920
-▁tins	-44921
-▁Artie	-44922
-▁TRUMP	-44923
-▁TRUST	-44924
-▁aeros	-44925
-▁slums	-44926
-▁youve	-44927
-▁zebra	-44928
-▁Rarity	-44929
-▁easter	-44930
-▁subter	-44931
-▁victor	-44932
-▁waging	-44933
-▁admirer	-44934
-▁dreamer	-44935
-requisite	-44936
-▁Branding	-44937
-▁freshest	-44938
-▁lodgings	-44939
-▁Chernobyl	-44940
-▁unscathed	-44941
-▁Vegetarian	-44942
-▁colorectal	-44943
-▁meditations	-44944
-ardy	-44945
-imme	-44946
-izen	-44947
-▁\\_	-44948
-▁nab	-44949
-▁tha	-44950
-irted	-44951
-▁DIDN	-44952
-▁Rapt	-44953
-▁tees	-44954
-Dorian	-44955
-infect	-44956
-▁Zones	-44957
-▁longs	-44958
-cribers	-44959
-plastic	-44960
-reviews	-44961
-▁Harmon	-44962
-▁hounds	-44963
-Research	-44964
-hernalia	-44965
-▁airfare	-44966
-▁carcass	-44967
-▁lenient	-44968
-▁sequest	-44969
-▁Ethereum	-44970
-▁differed	-44971
-▁maturing	-44972
-▁Arthritis	-44973
-▁Reservation	-44974
-▁undisturbed	-44975
-▁coincidentally	-44976
-Kit	-44977
-UNE	-44978
-▁HH	-44979
-▁Duo	-44980
-Times	-44981
-ppage	-44982
-spons	-44983
-tenth	-44984
-▁Vest	-44985
-immers	-44986
-▁kiosk	-44987
-onomous	-44988
-▁amphib	-44989
-▁dryers	-44990
-▁puffed	-44991
-▁Peppers	-44992
-▁cayenne	-44993
-▁hitters	-44994
-democracy	-44995
-▁Weddings	-44996
-▁crowding	-44997
-▁phrasing	-44998
-▁announcer	-44999
-apocalyptic	-45000
-▁Organizing	-45001
-▁confection	-45002
-▁consequent	-45003
-▁outpouring	-45004
-▁Expectations	-45005
-▁intrinsically	-45006
-▁↩	-45007
-▁BED	-45008
-▁Dag	-45009
-rophes	-45010
-▁Judas	-45011
-▁Tamar	-45012
-▁Wilde	-45013
-versive	-45014
-▁avenge	-45015
-▁combos	-45016
-▁mangan	-45017
-▁molded	-45018
-▁Haunted	-45019
-▁Spartan	-45020
-▁expatri	-45021
-▁ghostly	-45022
-▁Cosmetic	-45023
-▁bitcoins	-45024
-▁Hemingway	-45025
-▁optimised	-45026
-▁checklists	-45027
-▁structuring	-45028
-▁Conversations	-45029
-dn	-45030
-CDC	-45031
-мен	-45032
-▁EQ	-45033
-mens	-45034
-▁SIP	-45035
-▁Flan	-45036
-▁Mats	-45037
-▁Tire	-45038
-▁hunk	-45039
-istant	-45040
-joined	-45041
-▁BUILD	-45042
-▁Horiz	-45043
-▁Scare	-45044
-carbons	-45045
-fitness	-45046
-wedding	-45047
-▁Cancel	-45048
-▁ONLINE	-45049
-▁Tanner	-45050
-▁bulldo	-45051
-capacity	-45052
-▁Trigger	-45053
-▁Michaels	-45054
-▁suffrage	-45055
-▁cancerous	-45056
-▁triangular	-45057
-▁TripAdvisor	-45058
-▁SELF	-45059
-▁nuke	-45060
-▁EVENT	-45061
-▁Grain	-45062
-▁Sloan	-45063
-▁inlet	-45064
-▁squab	-45065
-batical	-45066
-▁Louisa	-45067
-▁Malfoy	-45068
-▁REASON	-45069
-▁Volcan	-45070
-▁farmed	-45071
-▁ingest	-45072
-structed	-45073
-▁briskly	-45074
-▁hamster	-45075
-▁dungeons	-45076
-▁panorama	-45077
-▁splashed	-45078
-▁custodial	-45079
-▁fairytale	-45080
-▁strangled	-45081
-Information	-45082
-▁cautionary	-45083
-▁rediscover	-45084
-▁preconceived	-45085
-NB	-45086
-Mil	-45087
-dat	-45088
-fry	-45089
-hide	-45090
-iscu	-45091
-▁seq	-45092
-Apple	-45093
-▁Funk	-45094
-handle	-45095
-▁cabal	-45096
-▁expel	-45097
-▁______	-45098
-▁duplex	-45099
-▁eBooks	-45100
-iologist	-45101
-▁impregn	-45102
-▁Mongolia	-45103
-▁Overseas	-45104
-▁jokingly	-45105
-▁stomachs	-45106
-definition	-45107
-▁Generator	-45108
-▁plummeted	-45109
-▁Motorcycle	-45110
-▁Reasonable	-45111
-▁disorganized	-45112
-▁constellation	-45113
-cn	-45114
-Dee	-45115
-▁):	-45116
-oway	-45117
-▁Pia	-45118
-▁ISSN	-45119
-▁Temp	-45120
-▁gynec	-45121
-▁indel	-45122
-▁yearn	-45123
-▁phobia	-45124
-▁toying	-45125
-criptive	-45126
-▁Warfare	-45127
-▁boldness	-45128
-▁helpline	-45129
-▁passively	-45130
-▁intoxication	-45131
-ARK	-45132
-icz	-45133
-wis	-45134
-Hero	-45135
-▁Hee	-45136
-▁EXPL	-45137
-▁Tsar	-45138
-▁howl	-45139
-▁seab	-45140
-▁Modoc	-45141
-▁Thick	-45142
-▁jerky	-45143
-centred	-45144
-termilk	-45145
-▁Barker	-45146
-▁furrow	-45147
-▁silica	-45148
-▁Bourbon	-45149
-▁inescap	-45150
-▁realises	-45151
-▁contended	-45152
-▁Harrington	-45153
-▁Psychiatry	-45154
-▁apprenticeships	-45155
-(?)	-45156
-Boo	-45157
-aol	-45158
-ICAN	-45159
-▁Kiev	-45160
-Series	-45161
-atians	-45162
-conven	-45163
-▁METHOD	-45164
-▁crazed	-45165
-▁frauds	-45166
-▁incurs	-45167
-▁teleph	-45168
-▁unduly	-45169
-▁Assange	-45170
-▁florist	-45171
-▁overtly	-45172
-▁varsity	-45173
-▁Kindness	-45174
-▁conveyor	-45175
-▁manganese	-45176
-▁promenade	-45177
-▁stairwell	-45178
-▁weathered	-45179
-▁Endangered	-45180
-▁concealing	-45181
-▁Determination	-45182
-rt	-45183
-}.	-45184
-▁▪	-45185
-██	-45186
-NOW	-45187
-nier	-45188
-▁Thx	-45189
-omach	-45190
-optic	-45191
-▁mojo	-45192
-oliath	-45193
-▁Rusty	-45194
-▁april	-45195
-Imagine	-45196
-Thought	-45197
-▁Bitter	-45198
-▁cactus	-45199
-▁decked	-45200
-▁reposs	-45201
-▁biotech	-45202
-▁forgery	-45203
-▁hairsty	-45204
-▁masonry	-45205
-▁Surround	-45206
-▁Specifies	-45207
-▁foolishly	-45208
-▁multiplier	-45209
-▁Coordination	-45210
-zai	-45211
-ORTS	-45212
-ahem	-45213
-ease	-45214
-тари	-45215
-▁SNP	-45216
-▁Ком	-45217
-asaki	-45218
-▁ROOM	-45219
-▁Tran	-45220
-outlet	-45221
-▁sakes	-45222
-examine	-45223
-sitting	-45224
-ментари	-45225
-▁alleys	-45226
-▁flound	-45227
-▁intros	-45228
-▁theses	-45229
-teachers	-45230
-▁Dietary	-45231
-▁Utilize	-45232
-▁Walters	-45233
-▁CrossFit	-45234
-▁YOURSELF	-45235
-▁dynamite	-45236
-▁masterful	-45237
-▁regaining	-45238
-▁QuickBooks	-45239
-▁betterment	-45240
-▁abbreviated	-45241
-▁secretaries	-45242
-▁XV	-45243
-orna	-45244
-▁AMP	-45245
-▁feb	-45246
-▁MOVE	-45247
-▁slug	-45248
-diving	-45249
-▁Whist	-45250
-▁Widow	-45251
-▁tryin	-45252
-▁acclim	-45253
-▁acumen	-45254
-▁breezy	-45255
-▁todays	-45256
-▁bulging	-45257
-▁Citation	-45258
-▁deranged	-45259
-▁fearsome	-45260
-▁optimist	-45261
-▁Beginners	-45262
-▁insistent	-45263
-▁reserving	-45264
-▁whiteboard	-45265
-▁Комментари	-45266
-▁inconceivable	-45267
-▁reincarnation	-45268
-––	-45269
-erem	-45270
-uben	-45271
-▁faç	-45272
-▁Polo	-45273
-▁smog	-45274
-Street	-45275
-stands	-45276
-▁Alask	-45277
-▁Clash	-45278
-▁Welch	-45279
-▁extro	-45280
-▁remar	-45281
-▁stave	-45282
-Private	-45283
-feature	-45284
-▁bagels	-45285
-▁Mycroft	-45286
-▁fuelled	-45287
-▁spewing	-45288
-▁blockage	-45289
-▁diffuser	-45290
-▁trackers	-45291
-▁Presented	-45292
-▁Realizing	-45293
-▁Automobile	-45294
-▁homophobia	-45295
-▁snowflakes	-45296
-▁restructure	-45297
-▁synchronized	-45298
-Гў	-45299
-ГўВ	-45300
-olone	-45301
-udged	-45302
-▁Clif	-45303
-▁Elle	-45304
-▁Garn	-45305
-▁Nerv	-45306
-▁EMAIL	-45307
-▁bount	-45308
-ilating	-45309
-pattern	-45310
-▁Chiang	-45311
-▁Jennie	-45312
-▁beggars	-45313
-▁chaired	-45314
-▁fanfare	-45315
-▁tendons	-45316
-▁yoghurt	-45317
-▁analogue	-45318
-▁analytic	-45319
-▁artistry	-45320
-▁carpentry	-45321
-▁descendant	-45322
-▁succinctly	-45323
-hub	-45324
-AKER	-45325
-aras	-45326
-icas	-45327
-stim	-45328
-▁Oft	-45329
-▁TBR	-45330
-▁Kier	-45331
-▁Jeter	-45332
-▁peels	-45333
-▁purify	-45334
-▁sipped	-45335
-▁brainer	-45336
-commission	-45337
-▁supremely	-45338
-▁meningitis	-45339
-▁Комментарий	-45340
-▁anniversaries	-45341
-▁contravention	-45342
-▁involuntarily	-45343
-▁fb	-45344
-Fort	-45345
-▁BUR	-45346
-▁Gly	-45347
-▁GMOs	-45348
-▁Gert	-45349
-▁OPEC	-45350
-passed	-45351
-▁Balls	-45352
-▁Appell	-45353
-▁bowler	-45354
-▁greats	-45355
-▁sneeze	-45356
-manufact	-45357
-pharmacy	-45358
-▁HOWEVER	-45359
-▁Quentin	-45360
-▁rhubarb	-45361
-▁seduced	-45362
-▁birthing	-45363
-▁catapult	-45364
-▁shootout	-45365
-University	-45366
-▁bluetooth	-45367
-▁Apprentice	-45368
-▁impossibly	-45369
-▁retrieving	-45370
-▁Measurement	-45371
-▁psychedelic	-45372
-▁inappropriately	-45373
-HL	-45374
-▁TF	-45375
-dial	-45376
-trak	-45377
-▁ery	-45378
-dirty	-45379
-grams	-45380
-▁Brom	-45381
-▁Daly	-45382
-▁Divi	-45383
-▁Slip	-45384
-anders	-45385
-▁feces	-45386
-▁prima	-45387
-cturnal	-45388
-stances	-45389
-▁Indigo	-45390
-▁Pardon	-45391
-inarians	-45392
-▁Servant	-45393
-▁marquee	-45394
-▁tropics	-45395
-▁underworld	-45396
-UA	-45397
-ЂВ	-45398
-Sty	-45399
-uni	-45400
-enas	-45401
-reth	-45402
-▁BAM	-45403
-▁Tud	-45404
-Order	-45405
-innov	-45406
-ГўВЂВ	-45407
-▁Adri	-45408
-▁Pity	-45409
-▁Stur	-45410
-▁duff	-45411
-▁ANGEL	-45412
-▁READY	-45413
-▁barns	-45414
-addling	-45415
-tasking	-45416
-turning	-45417
-▁Beside	-45418
-▁Smiles	-45419
-▁disput	-45420
-Friendly	-45421
-includes	-45422
-▁Bicycle	-45423
-▁Marcell	-45424
-▁subside	-45425
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-▁sleepers	-45427
-▁trampled	-45428
-▁terminally	-45429
-mt	-45430
-Ele	-45431
-Eye	-45432
-Hol	-45433
-RED	-45434
-▁Ib	-45435
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-allion	-45440
-ithrom	-45441
-▁detach	-45442
-▁mishap	-45443
-▁Fleming	-45444
-▁colloqu	-45445
-▁melodic	-45446
-▁mittens	-45447
-▁montage	-45448
-Education	-45449
-▁Botanical	-45450
-▁mitigated	-45451
-▁emphasised	-45452
-▁unregulated	-45453
-),”	-45454
-?!)	-45455
-Ans	-45456
-vae	-45457
-▁QC	-45458
-▁DRM	-45459
-▁FDR	-45460
-▁GER	-45461
-▁pom	-45462
-▁Amar	-45463
-▁Fang	-45464
-▁Indo	-45465
-▁Trey	-45466
-▁tarp	-45467
-timers	-45468
-▁knobs	-45469
-several	-45470
-▁Berger	-45471
-▁Depart	-45472
-▁Redeem	-45473
-▁squish	-45474
-▁stashed	-45475
-▁wannabe	-45476
-▁Generate	-45477
-▁Launched	-45478
-▁grievous	-45479
-▁Assessing	-45480
-▁Perfectly	-45481
-▁combatants	-45482
-▁Inquisition	-45483
-▁terminating	-45484
-▁Verification	-45485
-▁mobilization	-45486
-wo	-45487
-igi	-45488
-▁Dre	-45489
-▁Gol	-45490
-▁UNH	-45491
-ITING	-45492
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-utives	-45495
-▁Boxes	-45496
-▁Shops	-45497
-▁emoji	-45498
-▁Boomer	-45499
-▁Concern	-45500
-▁planter	-45501
-▁illegals	-45502
-▁equipping	-45503
-▁infrequent	-45504
-▁Chamberlain	-45505
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-▁detoxification	-45507
-SEO	-45508
-fps	-45509
-pps	-45510
-usr	-45511
-▁WN	-45512
-Prin	-45513
-amol	-45514
-irut	-45515
-ragon	-45516
-▁Dign	-45517
-▁Mana	-45518
-▁pong	-45519
-workout	-45520
-▁Fowler	-45521
-▁Mozart	-45522
-▁flared	-45523
-▁monoch	-45524
-▁reborn	-45525
-▁Pilgrim	-45526
-▁blocker	-45527
-▁coolant	-45528
-▁ephemer	-45529
-reference	-45530
-wrenching	-45531
-▁INTEREST	-45532
-▁crumbled	-45533
-▁rooftops	-45534
-aggressive	-45535
-documented	-45536
-progressive	-45537
-▁unsupported	-45538
-jee	-45539
-tum	-45540
-▁Omg	-45541
-...),	-45542
-Eight	-45543
-fixed	-45544
-teens	-45545
-▁OPER	-45546
-▁Oven	-45547
-emaker	-45548
-▁Hobby	-45549
-▁Hyatt	-45550
-▁Vinyl	-45551
-▁unrel	-45552
-igmatic	-45553
-started	-45554
-▁Elders	-45555
-▁dodged	-45556
-▁firmer	-45557
-▁graced	-45558
-▁PROCESS	-45559
-▁Shields	-45560
-▁Skinner	-45561
-▁empires	-45562
-▁Hogwarts	-45563
-▁evaporation	-45564
-kim	-45565
-uin	-45566
-Disc	-45567
-▁ICU	-45568
-▁KEY	-45569
-▁LMS	-45570
-▁MAP	-45571
-Faith	-45572
-estro	-45573
-ogyny	-45574
-▁Zinc	-45575
-▁MUSIC	-45576
-▁Petty	-45577
-▁Trout	-45578
-▁capit	-45579
-▁dryly	-45580
-kitchen	-45581
-▁Alyssa	-45582
-▁Booker	-45583
-▁Camino	-45584
-▁MONTHS	-45585
-▁piqued	-45586
-▁Cartoon	-45587
-▁Grocery	-45588
-▁picnics	-45589
-▁valuing	-45590
-▁Beatrice	-45591
-▁Earnings	-45592
-▁Emotions	-45593
-▁recieved	-45594
-▁contraceptives	-45595
-▁WB	-45596
-Same	-45597
-▁McB	-45598
-▁Pes	-45599
-▁nil	-45600
-▁wad	-45601
-▁bure	-45602
-helves	-45603
-▁Nanny	-45604
-▁Ships	-45605
-▁gripe	-45606
-▁Crowley	-45607
-▁fainting	-45608
-▁veracity	-45609
-▁Referring	-45610
-▁citizenry	-45611
-▁holocaust	-45612
-▁Williamson	-45613
-▁nominating	-45614
-▁parishioners	-45615
-▁CCP	-45616
-▁Lup	-45617
-Uncle	-45618
-anova	-45619
-ombie	-45620
-▁Loft	-45621
-Coming	-45622
-studio	-45623
-▁Faced	-45624
-▁aerob	-45625
-herself	-45626
-restaur	-45627
-▁Commod	-45628
-▁Ribbon	-45629
-▁ailing	-45630
-▁beheld	-45631
-orthodox	-45632
-▁Pharmac	-45633
-▁cannons	-45634
-▁hinting	-45635
-▁nitrate	-45636
-Community	-45637
-questions	-45638
-▁Contrast	-45639
-▁loosened	-45640
-▁Collision	-45641
-▁Terrorist	-45642
-▁avoidable	-45643
-▁predictor	-45644
-▁beachfront	-45645
-▁furthering	-45646
-▁relinquish	-45647
-▁bridesmaids	-45648
-▁marketplaces	-45649
-▁persistently	-45650
-▁archaeologists	-45651
-Jun	-45652
-syn	-45653
-ipps	-45654
-uite	-45655
-▁GCSE	-45656
-▁Kush	-45657
-▁Pens	-45658
-▁Smok	-45659
-▁wimp	-45660
-memory	-45661
-▁horde	-45662
-undance	-45663
-▁Actors	-45664
-▁Bachel	-45665
-▁clomid	-45666
-▁fervor	-45667
-▁sickly	-45668
-▁Branson	-45669
-▁Cassidy	-45670
-▁Telecom	-45671
-▁coolers	-45672
-▁michael	-45673
-▁tidings	-45674
-▁varnish	-45675
-▁clearest	-45676
-▁stretcher	-45677
-▁Essentials	-45678
-▁SharePoint	-45679
-▁paraphernalia	-45680
-bz	-45681
-gil	-45682
-Hour	-45683
-▁Peb	-45684
-Exper	-45685
-trees	-45686
-▁Sith	-45687
-▁gait	-45688
-▁tipt	-45689
-ambles	-45690
-▁Cells	-45691
-▁Duane	-45692
-▁Snoop	-45693
-▁baton	-45694
-▁vices	-45695
-runners	-45696
-▁Kerala	-45697
-▁arouse	-45698
-▁elitist	-45699
-▁petting	-45700
-▁coasters	-45701
-▁fulfills	-45702
-▁roadways	-45703
-▁tractors	-45704
-▁doughnuts	-45705
-▁homicides	-45706
-▁Incredibly	-45707
-▁invalidate	-45708
-▁amoxicillin	-45709
-▁scrumptious	-45710
-▁facilitators	-45711
-▁unattainable	-45712
-RES	-45713
-Benz	-45714
-Body	-45715
-eeze	-45716
-loaf	-45717
-▁DIE	-45718
-Seems	-45719
-apply	-45720
-nance	-45721
-▁Axis	-45722
-▁Homo	-45723
-▁boll	-45724
-▁unbe	-45725
-▁crick	-45726
-▁tombs	-45727
-uscious	-45728
-▁allege	-45729
-▁exodus	-45730
-gendered	-45731
-▁Lenders	-45732
-▁bidders	-45733
-▁paraphr	-45734
-▁majoring	-45735
-▁Expansion	-45736
-▁evaluates	-45737
-▁professed	-45738
-▁insurgency	-45739
-BP	-45740
-Squ	-45741
-▁Pf	-45742
-▁SAR	-45743
-▁keg	-45744
-Girls	-45745
-vinced	-45746
-▁Prove	-45747
-planted	-45748
-▁Encoun	-45749
-▁PRODUC	-45750
-▁Celtics	-45751
-▁cursory	-45752
-▁cyclone	-45753
-▁entrust	-45754
-▁gastron	-45755
-▁rupture	-45756
-Financial	-45757
-▁HomeAway	-45758
-▁diseased	-45759
-▁japanese	-45760
-▁smallpox	-45761
-ilaterally	-45762
-▁diabetics	-45763
-▁kilometer	-45764
-▁penthouse	-45765
-▁Uzbekistan	-45766
-▁screeching	-45767
-▁undefeated	-45768
-)*	-45769
-deck	-45770
-ubes	-45771
-▁VOC	-45772
-▁wik	-45773
-wares	-45774
-Course	-45775
-craper	-45776
-rimony	-45777
-▁Sting	-45778
-▁ducts	-45779
-▁Pentec	-45780
-▁labors	-45781
-▁obfusc	-45782
-▁veneer	-45783
-▁ALREADY	-45784
-▁Balkans	-45785
-▁Enjoying	-45786
-▁melanoma	-45787
-▁prudence	-45788
-▁Extensive	-45789
-▁assassins	-45790
-▁twitching	-45791
-▁anesthetic	-45792
-Div	-45793
-...?"	-45794
-ideal	-45795
-▁Coat	-45796
-▁Purd	-45797
-▁puke	-45798
-▁MAJOR	-45799
-▁rodeo	-45800
-▁Dudley	-45801
-▁Hiking	-45802
-▁Tribal	-45803
-▁cervix	-45804
-▁pegged	-45805
-▁Printer	-45806
-andescent	-45807
-▁Emmanuel	-45808
-▁brooding	-45809
-▁tasteful	-45810
-▁Filipinos	-45811
-▁betraying	-45812
-▁covenants	-45813
-▁perverted	-45814
-▁pseudonym	-45815
-▁stimulant	-45816
-▁uniformity	-45817
-▁discredited	-45818
-▁Frankenstein	-45819
-cit	-45820
-onso	-45821
-vale	-45822
-▁HON	-45823
-▁Kah	-45824
-Creat	-45825
-▁Misc	-45826
-▁doth	-45827
-cutter	-45828
-zekiel	-45829
-▁GROUP	-45830
-▁Remed	-45831
-▁evalu	-45832
-▁gooey	-45833
-▁homer	-45834
-igrants	-45835
-storage	-45836
-▁Decree	-45837
-▁Filled	-45838
-▁Racism	-45839
-▁Velvet	-45840
-▁capitol	-45841
-▁collided	-45842
-▁convicts	-45843
-▁Calculate	-45844
-▁Worcester	-45845
-▁Graduation	-45846
-▁chandelier	-45847
-▁aftermarket	-45848
-Cry	-45849
-Might	-45850
-ickey	-45851
-ineas	-45852
-▁Cobb	-45853
-▁Hust	-45854
-animal	-45855
-suited	-45856
-▁Celia	-45857
-▁grime	-45858
-iliency	-45859
-▁Maurit	-45860
-▁Pounds	-45861
-▁minced	-45862
-▁coffers	-45863
-▁idiosyn	-45864
-▁scammed	-45865
-▁Athletes	-45866
-▁Labrador	-45867
-▁hotspots	-45868
-▁iniquity	-45869
-▁planters	-45870
-▁Ambulance	-45871
-▁Liberties	-45872
-▁obsessing	-45873
-▁repellent	-45874
-▁insatiable	-45875
-▁neoliberal	-45876
-▁DEVELOPMENT	-45877
-▁scaffolding	-45878
-KY	-45879
-ENTY	-45880
-ERGY	-45881
-agel	-45882
-heed	-45883
-▁EHR	-45884
-▁ESL	-45885
-▁Gio	-45886
-▁SEX	-45887
-sting	-45888
-trick	-45889
-▁Bugs	-45890
-▁FANT	-45891
-▁Hume	-45892
-▁Sour	-45893
-ducers	-45894
-icidal	-45895
-rowave	-45896
-▁Cocoa	-45897
-▁Grind	-45898
-▁PRICE	-45899
-▁clams	-45900
-▁whizz	-45901
-fighter	-45902
-▁Orphan	-45903
-▁nimble	-45904
-▁perfor	-45905
-▁tinted	-45906
-▁botched	-45907
-▁debrief	-45908
-▁outpost	-45909
-▁Thirteen	-45910
-occupation	-45911
-▁canceling	-45912
-▁finalised	-45913
-▁communists	-45914
-▁bf	-45915
-▁Ame	-45916
-▁Mee	-45917
-▁...)	-45918
-▁Axel	-45919
-▁Dahl	-45920
-▁Eddy	-45921
-▁Kool	-45922
-▁laud	-45923
-apples	-45924
-▁Emails	-45925
-▁Gibral	-45926
-▁ethere	-45927
-comments	-45928
-▁Dracula	-45929
-▁Firearms	-45930
-▁babbling	-45931
-▁INCLUDING	-45932
-▁EY	-45933
-Span	-45934
-pity	-45935
-rift	-45936
-▁TEN	-45937
-Speak	-45938
-ainty	-45939
-▁curv	-45940
-Econom	-45941
-▁Ashes	-45942
-▁GREEN	-45943
-▁stings	-45944
-▁surety	-45945
-▁AMERICA	-45946
-▁Pushing	-45947
-▁canteen	-45948
-▁pimples	-45949
-▁prefect	-45950
-▁prepped	-45951
-▁scrolls	-45952
-believers	-45953
-occurring	-45954
-▁december	-45955
-equivalent	-45956
-▁laborious	-45957
-▁probiotic	-45958
-▁delectable	-45959
-▁diminishes	-45960
-▁circumcised	-45961
-▁skyscrapers	-45962
-▁disinterested	-45963
-FX	-45964
-▁Ig	-45965
-hazi	-45966
-mast	-45967
-shut	-45968
-bless	-45969
-faces	-45970
-sharp	-45971
-▁Duff	-45972
-▁Edna	-45973
-▁Fulf	-45974
-▁Kern	-45975
-▁Kerr	-45976
-▁WALK	-45977
-▁Depos	-45978
-meeting	-45979
-▁Guilty	-45980
-▁fresco	-45981
-▁Grounds	-45982
-▁Roberto	-45983
-▁notepad	-45984
-▁buttocks	-45985
-▁cuddling	-45986
-▁sparring	-45987
-▁unifying	-45988
-▁Wholesale	-45989
-▁coordinators	-45990
-andan	-45991
-ibaba	-45992
-▁Demo	-45993
-▁MASS	-45994
-▁fron	-45995
-Review	-45996
-artist	-45997
-▁Whisk	-45998
-▁foreb	-45999
-▁Bakery	-46000
-▁Garmin	-46001
-▁Stroke	-46002
-Building	-46003
-▁MEMBERS	-46004
-▁cohorts	-46005
-▁healers	-46006
-▁marines	-46007
-▁Involved	-46008
-▁likeable	-46009
-▁verbatim	-46010
-▁watchers	-46011
-▁clinician	-46012
-▁engraving	-46013
-▁Mastercard	-46014
-▁displeased	-46015
-▁relegation	-46016
-▁confiscation	-46017
-▁legislatures	-46018
-RU	-46019
-Cut	-46020
-TPS	-46021
-pair	-46022
-▁LAM	-46023
-▁Ooo	-46024
-▁WMD	-46025
-etian	-46026
-▁Bray	-46027
-▁Noir	-46028
-▁SAFE	-46029
-▁puns	-46030
-▁Aster	-46031
-▁BOARD	-46032
-▁Rubio	-46033
-▁quirk	-46034
-viation	-46035
-▁Millie	-46036
-▁edging	-46037
-▁flicks	-46038
-▁grills	-46039
-▁shreds	-46040
-▁Attacks	-46041
-▁Profits	-46042
-▁cutlery	-46043
-▁kitties	-46044
-▁vacated	-46045
-▁Bernanke	-46046
-reditation	-46047
-▁campaigners	-46048
-▁unbelievers	-46049
-▁multitasking	-46050
-UNCH	-46051
-enia	-46052
-▁Ups	-46053
-▁Xin	-46054
-▁tbh	-46055
-oried	-46056
-▁Cane	-46057
-▁Detox	-46058
-▁Mines	-46059
-▁flaky	-46060
-▁roomy	-46061
-▁spied	-46062
-▁unful	-46063
-▁Horace	-46064
-▁Jarvis	-46065
-▁Treating	-46066
-▁coupling	-46067
-▁luminous	-46068
-▁resolute	-46069
-▁selenium	-46070
-▁clippings	-46071
-▁coherence	-46072
-▁Submissions	-46073
-▁capitalized	-46074
-▁infuriating	-46075
-▁Technological	-46076
-bait	-46077
-rath	-46078
-▁Erd	-46079
-▁aint	-46080
-▁Kumar	-46081
-▁Recip	-46082
-▁afoot	-46083
-▁vogue	-46084
-Forgive	-46085
-ittered	-46086
-▁OFFICE	-46087
-▁Zoning	-46088
-▁mement	-46089
-▁tanker	-46090
-inflicted	-46091
-▁Humphrey	-46092
-▁goodbyes	-46093
-▁lifeline	-46094
-▁sculptor	-46095
-▁Asheville	-46096
-▁Charities	-46097
-▁blackness	-46098
-▁undergraduates	-46099
-Wood	-46100
-icho	-46101
-▁(*)	-46102
-▁Ced	-46103
-odian	-46104
-pregn	-46105
-scopy	-46106
-▁AGRE	-46107
-▁Enqu	-46108
-▁JSON	-46109
-askets	-46110
-isbury	-46111
-ithing	-46112
-▁youll	-46113
-bladder	-46114
-▁deluge	-46115
-▁flinch	-46116
-▁honing	-46117
-▁mystic	-46118
-judicial	-46119
-▁calibre	-46120
-▁centrif	-46121
-▁contends	-46122
-▁converge	-46123
-▁coolness	-46124
-▁lessened	-46125
-▁Instantly	-46126
-▁downgrade	-46127
-▁habitable	-46128
-▁smoothing	-46129
-▁Homeowners	-46130
-▁validating	-46131
-▁Mademoiselle	-46132
-zh	-46133
-ANE	-46134
-Cool	-46135
-beer	-46136
-▁:-).	-46137
-myself	-46138
-ppelin	-46139
-rocess	-46140
-atering	-46141
-givings	-46142
-▁Assign	-46143
-▁Honors	-46144
-▁Strand	-46145
-▁encamp	-46146
-▁milder	-46147
-▁murals	-46148
-▁Neander	-46149
-▁Ronaldo	-46150
-▁Sparkle	-46151
-▁trashed	-46152
-▁dismount	-46153
-▁incrimin	-46154
-▁archiving	-46155
-▁paychecks	-46156
-▁sharpened	-46157
-amphetamine	-46158
-▁Hurricanes	-46159
-ABL	-46160
-POR	-46161
-VPN	-46162
-Fear	-46163
-azzi	-46164
-slip	-46165
-▁BAT	-46166
-kines	-46167
-▁Eren	-46168
-▁MESS	-46169
-▁Neon	-46170
-▁Xiao	-46171
-▁arid	-46172
-▁homo	-46173
-▁geeky	-46174
-▁matur	-46175
-▁scorp	-46176
-faceted	-46177
-welcome	-46178
-▁fender	-46179
-▁malign	-46180
-▁parson	-46181
-▁sewers	-46182
-▁HubSpot	-46183
-▁Patreon	-46184
-▁Sixteen	-46185
-▁Trailer	-46186
-▁uninhab	-46187
-▁Showcase	-46188
-▁Sporting	-46189
-▁dyslexia	-46190
-▁playbook	-46191
-▁reverber	-46192
-▁unsolved	-46193
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-▁Serg	-46970
-▁Saund	-46971
-▁empir	-46972
-▁FINANC	-46973
-▁Rafael	-46974
-▁caress	-46975
-▁retort	-46976
-▁steamy	-46977
-▁Broward	-46978
-▁angeles	-46979
-▁decreed	-46980
-▁hipster	-46981
-▁prequel	-46982
-▁stemmed	-46983
-▁Missions	-46984
-▁leftists	-46985
-▁parmesan	-46986
-▁stripper	-46987
-▁gesturing	-46988
-............	-46989
-;;	-46990
-YZ	-46991
-LER	-46992
-▁gf	-46993
-poly	-46994
-▁LOS	-46995
-▁Pou	-46996
-▁pew	-46997
-Early	-46998
-affod	-46999
-▁Torch	-47000
-imagine	-47001
-vaccine	-47002
-▁sheath	-47003
-▁sunken	-47004
-▁Actress	-47005
-▁MILLION	-47006
-▁Ramirez	-47007
-▁affront	-47008
-▁Gibraltar	-47009
-▁atonement	-47010
-▁notoriety	-47011
-▁aboriginal	-47012
-▁fiberglass	-47013
-▁Chairperson	-47014
-▁inescapable	-47015
-▁presentable	-47016
-▁specificity	-47017
-▁knowledgable	-47018
-cib	-47019
-Iron	-47020
-Upon	-47021
-pits	-47022
-▁Eps	-47023
-▁Nec	-47024
-▁noo	-47025
-icago	-47026
-▁Gian	-47027
-▁Morg	-47028
-Malley	-47029
-Russia	-47030
-▁Birch	-47031
-▁FOUND	-47032
-▁posit	-47033
-▁REVIEW	-47034
-▁boomer	-47035
-▁evokes	-47036
-▁nitric	-47037
-▁soiled	-47038
-▁Generic	-47039
-▁Recover	-47040
-▁SERIOUS	-47041
-▁SOMEONE	-47042
-▁Trayvon	-47043
-▁angelic	-47044
-▁blankly	-47045
-▁valiant	-47046
-▁gamblers	-47047
-▁knockout	-47048
-▁recounts	-47049
-▁grumbling	-47050
-▁maneuvers	-47051
-▁Consistent	-47052
-▁elliptical	-47053
-▁eradicated	-47054
-▁neutralize	-47055
-▁receivable	-47056
-▁Participant	-47057
-Mur	-47058
-bern	-47059
-blet	-47060
-seld	-47061
-▁Kom	-47062
-▁PEN	-47063
-▁SAF	-47064
-▁pau	-47065
-alypt	-47066
-basic	-47067
-quist	-47068
-▁Cops	-47069
-▁Katz	-47070
-▁Kraft	-47071
-▁gusts	-47072
-▁whiny	-47073
-editing	-47074
-▁Billie	-47075
-▁Cosmic	-47076
-▁Nicely	-47077
-▁chakra	-47078
-▁cuddly	-47079
-▁fitter	-47080
-▁thefts	-47081
-▁tremor	-47082
-▁Regency	-47083
-▁convuls	-47084
-▁wasteland	-47085
-▁Azerbaijan	-47086
-▁Separation	-47087
-▁cockroaches	-47088
-▁negotiators	-47089
-▁Contribution	-47090
-▁tribulations	-47091
-▁fragmentation	-47092
-WP	-47093
-Sch	-47094
-atti	-47095
-span	-47096
-Sarah	-47097
-iched	-47098
-▁hilt	-47099
-butter	-47100
-tuning	-47101
-▁Filed	-47102
-▁Misty	-47103
-ernacle	-47104
-reflect	-47105
-▁Clover	-47106
-▁bugged	-47107
-▁notary	-47108
-▁tumblr	-47109
-hospital	-47110
-▁Flaming	-47111
-▁Honesty	-47112
-▁affixed	-47113
-▁sampler	-47114
-▁thinkin	-47115
-▁Attached	-47116
-▁Bullying	-47117
-▁Coventry	-47118
-▁marinade	-47119
-▁wrestled	-47120
-▁Catholicism	-47121
-▁rationality	-47122
-▁electricians	-47123
-▁fantastically	-47124
-fork	-47125
-▁DNC	-47126
-▁IBS	-47127
-▁Nou	-47128
-▁Rhy	-47129
-Cross	-47130
-▁Jays	-47131
-Prince	-47132
-imilar	-47133
-ivided	-47134
-likely	-47135
-weeney	-47136
-▁Munch	-47137
-plusone	-47138
-readers	-47139
-▁Jenner	-47140
-▁reddit	-47141
-▁Amended	-47142
-▁paddock	-47143
-▁unsuper	-47144
-▁Forensic	-47145
-▁asterisk	-47146
-▁fielding	-47147
-▁nauseous	-47148
-▁Astronomy	-47149
-▁Mediation	-47150
-▁Salisbury	-47151
-▁maddening	-47152
-▁ovulation	-47153
-▁september	-47154
-▁Reflections	-47155
-▁Supernatural	-47156
-▁merchandising	-47157
-▁unincorporated	-47158
-▁☺	-47159
-,,,,	-47160
-Cart	-47161
-Lock	-47162
-ispr	-47163
-▁Jeb	-47164
-panic	-47165
-▁Feds	-47166
-▁Void	-47167
-method	-47168
-▁Lamar	-47169
-▁Mardi	-47170
-▁snort	-47171
-authors	-47172
-illegal	-47173
-protein	-47174
-▁Affirm	-47175
-▁Thesis	-47176
-▁averse	-47177
-▁helium	-47178
-▁Dolphin	-47179
-▁HUNDRED	-47180
-▁bounces	-47181
-▁linkage	-47182
-▁pebbles	-47183
-▁shunned	-47184
-▁Traveler	-47185
-▁lightbul	-47186
-▁subtlety	-47187
-▁Mechanics	-47188
-▁esophagus	-47189
-▁idiosyncr	-47190
-▁lecturing	-47191
-▁satirical	-47192
-▁syndicate	-47193
-▁placeholder	-47194
-uo	-47195
-kon	-47196
-apot	-47197
-brow	-47198
-▁CPC	-47199
-▁Mow	-47200
-▁pax	-47201
-▁cabs	-47202
-▁Beard	-47203
-▁forts	-47204
-▁proff	-47205
-Blessed	-47206
-ceiling	-47207
-ucalypt	-47208
-▁Vacuum	-47209
-▁Creates	-47210
-▁Endless	-47211
-▁Lithium	-47212
-▁Parsons	-47213
-▁Seymour	-47214
-▁Starter	-47215
-▁presupp	-47216
-▁torpedo	-47217
-▁Followed	-47218
-▁broadest	-47219
-▁cautions	-47220
-▁sadistic	-47221
-▁someones	-47222
-▁briefings	-47223
-▁deafening	-47224
-▁COMMISSION	-47225
-▁Determined	-47226
-▁reposition	-47227
-▁Flexibility	-47228
-▁prototyping	-47229
-▁surrendering	-47230
-▁unsupervised	-47231
-BU	-47232
-Gab	-47233
-cox	-47234
-uva	-47235
-IZED	-47236
-adone	-47237
-gates	-47238
-route	-47239
-shape	-47240
-▁Avoc	-47241
-intech	-47242
-▁pooch	-47243
-▁vials	-47244
-▁Gracie	-47245
-▁Marble	-47246
-▁dearth	-47247
-▁frosty	-47248
-▁nugget	-47249
-▁Ottoman	-47250
-▁customise	-47251
-▁recklessly	-47252
-▁linguistics	-47253
-▁mathematically	-47254
-ync	-47255
-▁Ng	-47256
-▁dh	-47257
-arro	-47258
-gray	-47259
-igon	-47260
-prep	-47261
-▁Lau	-47262
-▁atm	-47263
-▁gond	-47264
-▁hege	-47265
-▁trun	-47266
-Taking	-47267
-aphrag	-47268
-bering	-47269
-▁CLEAR	-47270
-▁Relay	-47271
-▁vitri	-47272
-eastern	-47273
-▁Contra	-47274
-▁FUTURE	-47275
-▁Latvia	-47276
-▁Persia	-47277
-▁Trails	-47278
-advanced	-47279
-▁Electro	-47280
-▁Furious	-47281
-▁forbids	-47282
-▁rescues	-47283
-▁Backpack	-47284
-▁Gretchen	-47285
-▁Redskins	-47286
-▁promiscu	-47287
-▁reflexes	-47288
-▁rejoiced	-47289
-▁remedied	-47290
-▁clarifies	-47291
-▁wealthier	-47292
-▁weirdness	-47293
-▁Structural	-47294
-▁burdensome	-47295
-specifically	-47296
-▁Directorate	-47297
-▁disinformation	-47298
-HF	-47299
-▁QE	-47300
-fying	-47301
-▁youd	-47302
-▁Gemma	-47303
-▁Yummy	-47304
-▁tiled	-47305
-iership	-47306
-ynchron	-47307
-▁Athena	-47308
-▁Dermat	-47309
-▁lauded	-47310
-Canadian	-47311
-▁Farming	-47312
-▁Seating	-47313
-▁Wilhelm	-47314
-▁movable	-47315
-▁plagues	-47316
-▁Stargate	-47317
-▁captives	-47318
-▁carousel	-47319
-▁emergent	-47320
-▁treatise	-47321
-▁unicorns	-47322
-▁Boyfriend	-47323
-▁popularly	-47324
-▁revisited	-47325
-▁Confession	-47326
-▁hairstyles	-47327
-▁superhuman	-47328
-Jess	-47329
-Thus	-47330
-john	-47331
-riel	-47332
-▁Ips	-47333
-▁PAS	-47334
-oling	-47335
-▁Garr	-47336
-▁Keny	-47337
-▁putt	-47338
-▁Tight	-47339
-▁fused	-47340
-missing	-47341
-veniles	-47342
-▁Diablo	-47343
-▁Gideon	-47344
-▁Racial	-47345
-▁SOCIAL	-47346
-▁Taipei	-47347
-▁fanboy	-47348
-▁hedges	-47349
-▁spores	-47350
-▁Brokers	-47351
-▁Haitian	-47352
-▁Krugman	-47353
-▁Schultz	-47354
-▁Watkins	-47355
-▁janitor	-47356
-construct	-47357
-suffering	-47358
-▁Bulgarian	-47359
-▁aggrieved	-47360
-▁munitions	-47361
-▁servitude	-47362
-▁elevations	-47363
-▁Magnificent	-47364
-▁aggregation	-47365
-▁communicative	-47366
-▁Reconstruction	-47367
-Fox	-47368
-apro	-47369
-balt	-47370
-▁Das	-47371
-▁Pis	-47372
-▁eer	-47373
-▁gab	-47374
-Cover	-47375
-Stars	-47376
-rooge	-47377
-udden	-47378
-linger	-47379
-▁Decre	-47380
-▁slimy	-47381
-Writing	-47382
-▁Explos	-47383
-▁recurs	-47384
-▁tinker	-47385
-▁tulips	-47386
-Japanese	-47387
-▁Degrees	-47388
-▁amnesia	-47389
-▁ravaged	-47390
-▁surging	-47391
-▁Moderate	-47392
-▁breather	-47393
-▁cleansed	-47394
-▁slashing	-47395
-▁whooping	-47396
-▁Educators	-47397
-▁adoration	-47398
-▁caricature	-47399
-▁altercation	-47400
-▁inexplicably	-47401
-ilus	-47402
-lins	-47403
-▁--"	-47404
-▁Alo	-47405
-▁teh	-47406
-▁CNBC	-47407
-▁Citi	-47408
-essori	-47409
-humans	-47410
-simply	-47411
-witness	-47412
-▁acoust	-47413
-▁recoil	-47414
-▁Olympus	-47415
-▁brevity	-47416
-▁inflate	-47417
-▁prophyl	-47418
-ordinator	-47419
-▁behemoth	-47420
-▁derailed	-47421
-▁autopilot	-47422
-▁Commenting	-47423
-▁discreetly	-47424
-▁Evangelical	-47425
-▁rollercoaster	-47426
-HI	-47427
-kie	-47428
-▁EG	-47429
-▁wo	-47430
-sens	-47431
-▁ooh	-47432
-▁seo	-47433
-"....	-47434
-▁Knot	-47435
-listen	-47436
-stocks	-47437
-▁Hilda	-47438
-▁Wills	-47439
-estruct	-47440
-▁ACCESS	-47441
-▁ENERGY	-47442
-▁Folder	-47443
-▁Mariah	-47444
-▁Trixie	-47445
-▁Waking	-47446
-▁reword	-47447
-▁shrank	-47448
-▁Dispute	-47449
-▁Fatigue	-47450
-▁HISTORY	-47451
-▁Negroes	-47452
-▁hangers	-47453
-▁recuper	-47454
-▁Miracles	-47455
-▁cultured	-47456
-▁renewals	-47457
-▁indescrib	-47458
-▁melatonin	-47459
-categorized	-47460
-▁Passengers	-47461
-▁filesystem	-47462
-▁unlawfully	-47463
-▁extinguisher	-47464
-▁Effectiveness	-47465
-ooch	-47466
-▁Fli	-47467
-▁Kob	-47468
-▁Ness	-47469
-▁mobs	-47470
-▁skid	-47471
-boiled	-47472
-sworth	-47473
-▁Garth	-47474
-▁Moved	-47475
-▁Spoon	-47476
-othered	-47477
-▁Binary	-47478
-▁Calder	-47479
-▁confid	-47480
-▁jagged	-47481
-▁Thermal	-47482
-▁Laurence	-47483
-▁Mainland	-47484
-▁airfield	-47485
-▁callback	-47486
-▁eyeliner	-47487
-▁readjust	-47488
-▁mistaking	-47489
-▁Components	-47490
-▁departures	-47491
-▁dreadfully	-47492
-▁northeastern	-47493
-▁psychosocial	-47494
-?“	-47495
-doll	-47496
-zzie	-47497
-▁CSI	-47498
-▁DEP	-47499
-▁Wen	-47500
-▁mog	-47501
-▁pip	-47502
-enton	-47503
-▁gulp	-47504
-itance	-47505
-▁Shira	-47506
-▁Vogue	-47507
-▁jumbo	-47508
-▁piped	-47509
-behaved	-47510
-▁seeded	-47511
-▁Educate	-47512
-▁Gilmore	-47513
-▁Namibia	-47514
-▁Reunion	-47515
-▁cleanly	-47516
-entertain	-47517
-▁baseless	-47518
-▁european	-47519
-university	-47520
-▁sourdough	-47521
-▁utilising	-47522
-▁Derbyshire	-47523
-▁Wilmington	-47524
-▁delicately	-47525
-▁disorderly	-47526
-▁foodstuffs	-47527
-▁transcends	-47528
-▁unilaterally	-47529
-OTC	-47530
-rows	-47531
-▁KDE	-47532
-▁OCC	-47533
-▁Yak	-47534
-▁ful	-47535
-▁Burt	-47536
-▁Frem	-47537
-▁gels	-47538
-rodite	-47539
-▁Resil	-47540
-▁tango	-47541
-Million	-47542
-details	-47543
-trusive	-47544
-▁CONTIN	-47545
-▁drivel	-47546
-▁quarre	-47547
-iciously	-47548
-▁astroph	-47549
-▁brewers	-47550
-▁caveats	-47551
-▁Modeling	-47552
-▁arterial	-47553
-▁inhaling	-47554
-▁Graduates	-47555
-▁depriving	-47556
-▁optimally	-47557
-▁treatable	-47558
-▁meditative	-47559
-▁Prospective	-47560
-▁extremities	-47561
-▁marshmallow	-47562
-▁superpowers	-47563
-aah	-47564
-▁?"	-47565
-MEDI	-47566
-rame	-47567
-▁EAT	-47568
-▁LAP	-47569
-▁SUS	-47570
-▁TOR	-47571
-rique	-47572
-▁Jell	-47573
-Giving	-47574
-raised	-47575
-▁ENJOY	-47576
-▁Gregg	-47577
-▁HTTPS	-47578
-▁Micah	-47579
-▁kinky	-47580
-▁redec	-47581
-command	-47582
-▁DURING	-47583
-▁Deploy	-47584
-▁Divers	-47585
-▁Europa	-47586
-▁Galway	-47587
-▁Saudis	-47588
-▁Stitch	-47589
-▁angled	-47590
-▁meetup	-47591
-▁nigger	-47592
-▁scrubs	-47593
-ethylene	-47594
-▁Crimson	-47595
-▁Whitman	-47596
-▁holster	-47597
-▁motherf	-47598
-▁Celestia	-47599
-▁divorces	-47600
-▁eurozone	-47601
-▁pretzels	-47602
-▁reappear	-47603
-▁threaded	-47604
-▁touristy	-47605
-▁troopers	-47606
-▁patchwork	-47607
-▁pedophile	-47608
-▁Preventing	-47609
-▁calculates	-47610
-▁patrolling	-47611
-▁disillusioned	-47612
-▁##	-47613
-inie	-47614
-fiber	-47615
-valid	-47616
-▁rips	-47617
-Select	-47618
-chloro	-47619
-dinand	-47620
-▁Braun	-47621
-▁Chant	-47622
-▁NEEDS	-47623
-▁Tango	-47624
-▁besie	-47625
-archive	-47626
-changed	-47627
-publish	-47628
-▁remiss	-47629
-▁zodiac	-47630
-▁Burnett	-47631
-▁Emotion	-47632
-▁Georgie	-47633
-▁decrees	-47634
-terrorist	-47635
-▁Dementia	-47636
-▁Preserve	-47637
-▁Bandwidth	-47638
-▁Taiwanese	-47639
-▁ceasefire	-47640
-▁interplay	-47641
-▁blueprints	-47642
-▁naturopath	-47643
-nit	-47644
-▁MX	-47645
-Beat	-47646
-azis	-47647
-▁Gau	-47648
-▁wan	-47649
-altry	-47650
-▁Hoot	-47651
-▁IIRC	-47652
-▁Nguy	-47653
-▁bemo	-47654
-▁bono	-47655
-▁dill	-47656
-outure	-47657
-▁PEACE	-47658
-▁Rhine	-47659
-iatures	-47660
-▁Jacket	-47661
-▁Twitch	-47662
-▁london	-47663
-▁Passage	-47664
-▁Terence	-47665
-▁afflict	-47666
-▁ejected	-47667
-▁renegot	-47668
-▁thrived	-47669
-stitution	-47670
-▁Attitude	-47671
-▁Stations	-47672
-▁Strictly	-47673
-▁faintest	-47674
-▁Chemicals	-47675
-▁Marketers	-47676
-▁Indicators	-47677
-▁chronicles	-47678
-▁endorphins	-47679
-▁insurances	-47680
-▁prednisone	-47681
-▁directional	-47682
-CRI	-47683
-uca	-47684
-▁TEM	-47685
-▁Wei	-47686
-engue	-47687
-enser	-47688
-▁Cric	-47689
-▁Eust	-47690
-▁duet	-47691
-Profit	-47692
-▁Herod	-47693
-▁freer	-47694
-▁Bamboo	-47695
-▁commas	-47696
-▁hydrop	-47697
-▁pierce	-47698
-▁welded	-47699
-▁Briefly	-47700
-▁Jumping	-47701
-difficult	-47702
-placeable	-47703
-rillation	-47704
-▁Beginner	-47705
-▁molested	-47706
-▁ranchers	-47707
-▁tabletop	-47708
-▁aimlessly	-47709
-▁normative	-47710
-hereinafter	-47711
-▁Plaintiffs	-47712
-▁broadening	-47713
-▁habitation	-47714
-▁colonization	-47715
-/$	-47716
-CES	-47717
-Low	-47718
-RAL	-47719
-chs	-47720
-ilde	-47721
-▁COU	-47722
-▁Vex	-47723
-Brown	-47724
-▁burs	-47725
-judice	-47726
-▁FAFSA	-47727
-▁Ollie	-47728
-▁Radar	-47729
-▁Waves	-47730
-▁wonky	-47731
-complex	-47732
-ificate	-47733
-▁Margar	-47734
-▁raking	-47735
-Applause	-47736
-▁alchemy	-47737
-▁Keyboard	-47738
-▁Peruvian	-47739
-▁airwaves	-47740
-▁vehicular	-47741
-▁unreasonably	-47742
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-ENDED	-47749
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-eminent	-47755
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-▁Sparrow	-47763
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-▁Thankful	-47765
-▁indented	-47766
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-▁hamburgers	-47770
-OMG	-47771
-▁OG	-47772
-▁ou	-47773
-adne	-47774
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-atry	-47776
-▁BPA	-47777
-ommel	-47778
-▁Chow	-47779
-▁Humb	-47780
-▁Jimi	-47781
-Twenty	-47782
-highly	-47783
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-▁Merid	-47785
-▁Missy	-47786
-▁Yacht	-47787
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-▁grail	-47789
-aseless	-47790
-iderman	-47791
-▁Amtrak	-47792
-▁Suther	-47793
-▁tropes	-47794
-approval	-47795
-▁Downing	-47796
-clamation	-47797
-▁Hannibal	-47798
-▁________	-47799
-▁nineties	-47800
-▁attrition	-47801
-▁diverting	-47802
-▁grandmothers	-47803
-—-	-47804
-▁±	-47805
-▁Civ	-47806
-Seven	-47807
-▁BANK	-47808
-▁Nath	-47809
-▁Nico	-47810
-▁Suck	-47811
-▁weir	-47812
-abelle	-47813
-aneity	-47814
-angler	-47815
-chance	-47816
-ologna	-47817
-panies	-47818
-▁Couch	-47819
-▁Debby	-47820
-▁Plane	-47821
-▁carot	-47822
-▁filib	-47823
-▁impen	-47824
-▁desist	-47825
-▁shafts	-47826
-Whenever	-47827
-▁Serbian	-47828
-shadowing	-47829
-▁misogyny	-47830
-▁infringed	-47831
-cestershire	-47832
-▁disapprove	-47833
-▁undetected	-47834
-▁architectures	-47835
-▁democratically	-47836
-▁HL	-47837
-aina	-47838
-rike	-47839
-ucha	-47840
-▁PAD	-47841
-▁ELSE	-47842
-▁Lyft	-47843
-Andrew	-47844
-racist	-47845
-voting	-47846
-▁Bells	-47847
-▁Gloss	-47848
-▁Knife	-47849
-▁Somali	-47850
-▁Squire	-47851
-▁indian	-47852
-criminal	-47853
-▁Ordered	-47854
-▁burrito	-47855
-▁chanced	-47856
-▁devious	-47857
-▁recited	-47858
-▁Ventures	-47859
-▁boosters	-47860
-▁groaning	-47861
-▁salesmen	-47862
-▁Awakening	-47863
-▁Middleton	-47864
-▁innocents	-47865
-▁pertussis	-47866
-▁Suspension	-47867
-▁conserving	-47868
-▁rejections	-47869
-▁socialists	-47870
-▁unspeakable	-47871
-▁hydrocarbons	-47872
-▁Reconciliation	-47873
-▁circumstantial	-47874
-mu	-47875
-Dog	-47876
-nah	-47877
-Ball	-47878
-▁Ley	-47879
-knock	-47880
-▁Stef	-47881
-▁Zane	-47882
-▁egot	-47883
-▁CHANG	-47884
-▁Camps	-47885
-▁denig	-47886
-▁airway	-47887
-▁cortic	-47888
-▁darkly	-47889
-▁unites	-47890
-▁Ayurved	-47891
-▁lonesome	-47892
-▁mellitus	-47893
-▁mustered	-47894
-▁oncology	-47895
-▁placenta	-47896
-▁polishes	-47897
-▁whirlpool	-47898
-▁Housewives	-47899
-▁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀	-47900
-▁transgressions	-47901
-Sky	-47902
-lav	-47903
-alex	-47904
-llis	-47905
-uran	-47906
-▁PCI	-47907
-▁PSP	-47908
-▁TPP	-47909
-▁WED	-47910
-cemia	-47911
-▁Bite	-47912
-▁dong	-47913
-Palest	-47914
-pecial	-47915
-phenyl	-47916
-silver	-47917
-simile	-47918
-window	-47919
-▁cafés	-47920
-▁ostra	-47921
-▁LISTEN	-47922
-▁Libyan	-47923
-▁MAKING	-47924
-▁Owning	-47925
-▁bolder	-47926
-▁pranks	-47927
-▁retake	-47928
-▁Michelin	-47929
-▁PROVIDED	-47930
-▁tampered	-47931
-Apparently	-47932
-▁Constable	-47933
-▁Excessive	-47934
-▁Overnight	-47935
-▁flustered	-47936
-▁physiothe	-47937
-▁scavenger	-47938
-▁windscreen	-47939
-▁reproducing	-47940
-▁connotations	-47941
-▁abbreviations	-47942
-▁à	-47943
-isch	-47944
-lage	-47945
-rops	-47946
-▁Jag	-47947
-▁kat	-47948
-mores	-47949
-▁Pose	-47950
-▁Sana	-47951
-▁Stub	-47952
-Either	-47953
-▁Olson	-47954
-▁Tract	-47955
-▁lipid	-47956
-olition	-47957
-verting	-47958
-▁Brewer	-47959
-▁snappy	-47960
-▁Packing	-47961
-▁Sanford	-47962
-▁rousing	-47963
-▁smeared	-47964
-▁Benghazi	-47965
-▁Bordeaux	-47966
-▁Squadron	-47967
-▁enamored	-47968
-▁Beethoven	-47969
-▁Gallagher	-47970
-▁Librarian	-47971
-▁byproduct	-47972
-▁frustrate	-47973
-%!	-47974
-lod	-47975
-abee	-47976
-llan	-47977
-▁DIV	-47978
-▁Nod	-47979
-▁Uta	-47980
-▁abl	-47981
-osite	-47982
-▁DAMN	-47983
-▁Tick	-47984
-▁spew	-47985
-▁Jolie	-47986
-▁Nicky	-47987
-▁eyesh	-47988
-forever	-47989
-isputed	-47990
-▁dandel	-47991
-▁denote	-47992
-▁Dentist	-47993
-▁Nicolas	-47994
-▁stifled	-47995
-▁Farewell	-47996
-▁gauntlet	-47997
-democratic	-47998
-▁Forecasts	-47999
-▁memorials	-48000
-▁ramblings	-48001
-▁Simplicity	-48002
-▁lackluster	-48003
-▁Interaction	-48004
-▁BN	-48005
-iose	-48006
-▁Muk	-48007
-Mommy	-48008
-garde	-48009
-mixed	-48010
-woven	-48011
-▁Boil	-48012
-▁Bryn	-48013
-▁Kept	-48014
-▁MAIN	-48015
-▁SOFT	-48016
-▁Suit	-48017
-▁Aband	-48018
-▁Brody	-48019
-▁lorry	-48020
-▁CERTAIN	-48021
-▁Siberia	-48022
-▁checkin	-48023
-▁washers	-48024
-mongering	-48025
-▁REQUIRED	-48026
-▁designee	-48027
-▁reprieve	-48028
-▁tortoise	-48029
-▁Fairfield	-48030
-▁immovable	-48031
-▁principled	-48032
-▁Manufacturer	-48033
-FD	-48034
-jp	-48035
-sg	-48036
-Rub	-48037
-cad	-48038
-fra	-48039
-lou	-48040
-▁Ago	-48041
-▁Zam	-48042
-▁umm	-48043
-Codex	-48044
-▁Dads	-48045
-▁deft	-48046
-▁Tasks	-48047
-▁repud	-48048
-Whoever	-48049
-▁Pascal	-48050
-▁Purdue	-48051
-▁Throne	-48052
-▁turret	-48053
-itiously	-48054
-politics	-48055
-▁Hygiene	-48056
-▁accuses	-48057
-▁bureaus	-48058
-▁netting	-48059
-▁pompous	-48060
-sacrifice	-48061
-▁Maldives	-48062
-▁unfollow	-48063
-▁firepower	-48064
-▁Restricted	-48065
-▁throughput	-48066
-▁handcrafted	-48067
-▁summarizing	-48068
-establishment	-48069
-▁structurally	-48070
-Lu	-48071
-esp	-48072
-gap	-48073
-ermo	-48074
-fors	-48075
-otel	-48076
-sets	-48077
-▁AFC	-48078
-▁Shu	-48079
-ameth	-48080
-itizer	-48081
-▁Bland	-48082
-▁wharf	-48083
-▁WITHIN	-48084
-▁crusty	-48085
-▁Diaries	-48086
-▁cramping	-48087
-▁dilution	-48088
-▁Operators	-48089
-▁Trademark	-48090
-▁assailant	-48091
-residential	-48092
-▁nonviolent	-48093
-▁Photographs	-48094
-€“	-48095
-IOD	-48096
-tow	-48097
-took	-48098
-▁ESS	-48099
-blade	-48100
-Pardon	-48101
-ingale	-48102
-slides	-48103
-▁envir	-48104
-▁rhino	-48105
-▁stard	-48106
-▁Carver	-48107
-▁airflow	-48108
-▁Ballroom	-48109
-▁Vertical	-48110
-▁confuses	-48111
-▁luscious	-48112
-▁DIFFERENT	-48113
-▁coriander	-48114
-▁gratified	-48115
-▁motorcycl	-48116
-▁unsightly	-48117
-▁exemplified	-48118
-!(	-48119
-daq	-48120
-Bear	-48121
-COME	-48122
-bock	-48123
-▁jim	-48124
-▁sig	-48125
-atist	-48126
-demon	-48127
-uterte	-48128
-▁Grave	-48129
-▁lipos	-48130
-branded	-48131
-brushes	-48132
-▁homers	-48133
-▁saline	-48134
-▁sleigh	-48135
-Nonsense	-48136
-▁hangout	-48137
-▁retweet	-48138
-▁strayed	-48139
-▁Counties	-48140
-▁Hathaway	-48141
-▁Laughter	-48142
-▁Premiere	-48143
-▁Rumsfeld	-48144
-▁Woodward	-48145
-▁admirers	-48146
-▁Vegetable	-48147
-▁WONDERFUL	-48148
-▁challenger	-48149
-▁reservoirs	-48150
-▁Councillors	-48151
-▁Discussions	-48152
-▁Therapeutic	-48153
-▁patriarchal	-48154
-▁incandescent	-48155
-Job	-48156
-▁<<	-48157
-▁Ary	-48158
-▁FAO	-48159
-▁Jax	-48160
-▁NYU	-48161
-▁aus	-48162
-Micro	-48163
-aried	-48164
-kerel	-48165
-▁fang	-48166
-▁pacif	-48167
-etrable	-48168
-▁Dundee	-48169
-▁Karate	-48170
-▁Sponge	-48171
-▁goodie	-48172
-▁charred	-48173
-▁shyness	-48174
-executive	-48175
-▁Reported	-48176
-▁humanoid	-48177
-▁landlady	-48178
-structured	-48179
-▁suspensions	-48180
-sr	-48181
-IDA	-48182
-Mir	-48183
-ucl	-48184
-Base	-48185
-busy	-48186
-▁CES	-48187
-▁Hok	-48188
-▁Ost	-48189
-▁Sug	-48190
-Radio	-48191
-▁Bhag	-48192
-▁Colt	-48193
-▁Laos	-48194
-▁Blink	-48195
-▁odour	-48196
-Amazing	-48197
-acteria	-48198
-▁defies	-48199
-zenegger	-48200
-▁FOREVER	-48201
-▁supersed	-48202
-supporting	-48203
-▁assistive	-48204
-▁frontiers	-48205
-▁resentful	-48206
-▁Subsequent	-48207
-▁chattering	-48208
-▁photoshoot	-48209
-▁surgically	-48210
-▁bookshelves	-48211
-▁transformer	-48212
-ppo	-48213
-xml	-48214
-▁HDD	-48215
-quant	-48216
-▁Kemp	-48217
-▁LAND	-48218
-▁Tile	-48219
-▁Ozone	-48220
-▁cults	-48221
-allowed	-48222
-estream	-48223
-iatrist	-48224
-▁Candle	-48225
-▁sledge	-48226
-▁throes	-48227
-▁defunct	-48228
-▁hippies	-48229
-▁likened	-48230
-▁Defender	-48231
-▁chimpanz	-48232
-▁fumbling	-48233
-▁Emissions	-48234
-▁Magnesium	-48235
-▁evidences	-48236
-▁forthwith	-48237
-▁inorganic	-48238
-▁resigning	-48239
-▁rotations	-48240
-▁sidelined	-48241
-▁readability	-48242
-▁vicariously	-48243
-▁Demonstrated	-48244
-Sea	-48245
-Sum	-48246
-uli	-48247
-▁NLP	-48248
-▁Pem	-48249
-Pages	-48250
-knife	-48251
-▁DEAR	-48252
-▁Jeez	-48253
-codone	-48254
-▁Kodak	-48255
-▁Mango	-48256
-▁Reset	-48257
-▁WHILE	-48258
-▁mired	-48259
-▁psalm	-48260
-▁upped	-48261
-Support	-48262
-erosene	-48263
-▁Denial	-48264
-▁Shelly	-48265
-▁fumble	-48266
-▁paltry	-48267
-▁warden	-48268
-catering	-48269
-▁Antique	-48270
-▁Courier	-48271
-▁McLaren	-48272
-▁Seafood	-48273
-▁Torture	-48274
-▁Vendors	-48275
-▁kindest	-48276
-▁flapping	-48277
-▁Travelers	-48278
-▁Arrangement	-48279
-▁Libertarian	-48280
-▁repetitions	-48281
-▁Temperatures	-48282
-▁opportunistic	-48283
-lc	-48284
-€	-48285
-Exam	-48286
-moor	-48287
-▁Naj	-48288
-▁sqm	-48289
-IVERS	-48290
-okane	-48291
-ordid	-48292
-▁Chey	-48293
-▁Volt	-48294
-▁Rainy	-48295
-▁SMALL	-48296
-▁Timor	-48297
-tinyurl	-48298
-▁alpine	-48299
-▁Shepard	-48300
-▁Bedrooms	-48301
-▁ascribed	-48302
-▁crescent	-48303
-▁rhythmic	-48304
-▁tributes	-48305
-▁unopened	-48306
-absolutely	-48307
-▁Ferdinand	-48308
-▁lubricant	-48309
-▁phosphate	-48310
-▁figurative	-48311
-▁footballer	-48312
-▁bombardment	-48313
-▁menstruation	-48314
-HAM	-48315
-Hum	-48316
-tat	-48317
-Bush	-48318
-atos	-48319
-iket	-48320
-▁BIM	-48321
-▁Gan	-48322
-▁LDL	-48323
-▁abd	-48324
-Third	-48325
-nette	-48326
-panel	-48327
-▁CARD	-48328
-▁CONN	-48329
-embark	-48330
-▁Osaka	-48331
-igraphy	-48332
-totally	-48333
-▁exhort	-48334
-▁pounce	-48335
-▁vacate	-48336
-▁Roofing	-48337
-▁bedrock	-48338
-▁cartels	-48339
-▁hedging	-48340
-excellent	-48341
-▁Circular	-48342
-▁Magnolia	-48343
-▁courting	-48344
-▁enviable	-48345
-▁reunions	-48346
-▁travesty	-48347
-▁Mackenzie	-48348
-▁nourished	-48349
-▁playfully	-48350
-▁ambulances	-48351
-▁prerogative	-48352
-▁substantiate	-48353
-ARC	-48354
-Cam	-48355
-Kay	-48356
-▁(&	-48357
-▁gan	-48358
-▁kms	-48359
-winds	-48360
-▁lair	-48361
-▁roug	-48362
-Gentle	-48363
-herson	-48364
-ituted	-48365
-▁Patsy	-48366
-▁Bucket	-48367
-▁Pinkie	-48368
-▁blight	-48369
-▁braced	-48370
-▁earthy	-48371
-▁forego	-48372
-▁squire	-48373
-magazine	-48374
-▁LOOKING	-48375
-▁burners	-48376
-▁eyelash	-48377
-▁inkling	-48378
-▁pickled	-48379
-▁Billings	-48380
-dependence	-48381
-▁Pyongyang	-48382
-▁coverings	-48383
-▁Impressive	-48384
-▁skillfully	-48385
-▁tastefully	-48386
-▁peacekeeping	-48387
-▁stumbledupon	-48388
-▁Developmental	-48389
-iak	-48390
-lev	-48391
-eure	-48392
-▁DEV	-48393
-comic	-48394
-honey	-48395
-omial	-48396
-pause	-48397
-▁Afro	-48398
-▁fris	-48399
-▁Cinem	-48400
-▁Hertz	-48401
-▁Nietz	-48402
-▁teary	-48403
-blowers	-48404
-players	-48405
-worldly	-48406
-▁Forced	-48407
-▁embold	-48408
-▁mayors	-48409
-▁peeing	-48410
-▁Alibaba	-48411
-▁Chilean	-48412
-▁Thieves	-48413
-▁ketosis	-48414
-▁photosy	-48415
-▁workman	-48416
-▁Chakotay	-48417
-successful	-48418
-▁Bucharest	-48419
-▁Woodstock	-48420
-▁academies	-48421
-▁dumplings	-48422
-▁undivided	-48423
-▁Oftentimes	-48424
-▁admissible	-48425
-▁surpassing	-48426
-▁Remembrance	-48427
-unfortunately	-48428
-▁synchronization	-48429
-apo	-48430
-xycy	-48431
-▁gaw	-48432
-▁upp	-48433
-▁SPIR	-48434
-ivably	-48435
-▁Bosch	-48436
-▁Humor	-48437
-▁Prius	-48438
-▁brine	-48439
-▁cland	-48440
-▁droid	-48441
-▁lingo	-48442
-hending	-48443
-seldorf	-48444
-▁decals	-48445
-▁moneys	-48446
-▁potted	-48447
-▁emphatic	-48448
-▁imagines	-48449
-▁scouring	-48450
-▁Wolverine	-48451
-▁dissipate	-48452
-▁stockpile	-48453
-▁restarting	-48454
-▁supremacist	-48455
-▁symptomatic	-48456
-▁Congregation	-48457
-▁augmentation	-48458
-▁Architectural	-48459
-▁argumentative	-48460
-Rule	-48461
-amax	-48462
-cand	-48463
-trig	-48464
-▁CUT	-48465
-▁LSD	-48466
-▁Flav	-48467
-▁toad	-48468
-▁ALLOW	-48469
-▁LEAVE	-48470
-▁Meets	-48471
-volence	-48472
-wrapped	-48473
-▁bicycl	-48474
-▁orbits	-48475
-ableness	-48476
-▁Kurdish	-48477
-▁Redmond	-48478
-▁wickets	-48479
-▁Bollywood	-48480
-▁civilised	-48481
-▁alcoholics	-48482
-▁reelection	-48483
-odo	-48484
-oyo	-48485
-Scan	-48486
-aryl	-48487
-liquid	-48488
-▁Dewey	-48489
-▁FAVOR	-48490
-▁Kabul	-48491
-▁brawl	-48492
-▁doggy	-48493
-▁dorms	-48494
-▁Refund	-48495
-▁shaman	-48496
-▁Rihanna	-48497
-▁hominem	-48498
-▁locales	-48499
-▁paprika	-48500
-▁Innocent	-48501
-▁Proceeds	-48502
-▁Zeppelin	-48503
-▁kerosene	-48504
-▁chickpeas	-48505
-▁fervently	-48506
-▁Venezuelan	-48507
-▁consummate	-48508
-▁discharges	-48509
-▁Experimental	-48510
-!?”	-48511
-Map	-48512
-▁IW	-48513
-INES	-48514
-push	-48515
-▁ABA	-48516
-▁Cot	-48517
-▁Cuc	-48518
-▁Neb	-48519
-▁SPA	-48520
-guest	-48521
-▁Isla	-48522
-▁Nano	-48523
-▁Succ	-48524
-▁gnaw	-48525
-▁mete	-48526
-▁rims	-48527
-address	-48528
-▁Damian	-48529
-▁Defect	-48530
-▁alight	-48531
-▁babble	-48532
-▁cutoff	-48533
-▁Emanuel	-48534
-▁Ezekiel	-48535
-▁Frequent	-48536
-▁Fundament	-48537
-▁antitrust	-48538
-▁lifesaver	-48539
-▁untrained	-48540
-▁Constantly	-48541
-▁Invisalign	-48542
-▁chairperson	-48543
-▁instalments	-48544
-▁independents	-48545
-▁authenticated	-48546
-▁introspection	-48547
-▁unaccompanied	-48548
-ERV	-48549
-Kill	-48550
-PRES	-48551
-▁Bev	-48552
-ammer	-48553
-egers	-48554
-▁Nish	-48555
-▁Pune	-48556
-casting	-48557
-▁Wilder	-48558
-▁Roswell	-48559
-xycycline	-48560
-▁Adrienne	-48561
-▁Beverage	-48562
-▁Parisian	-48563
-▁monotony	-48564
-▁gestation	-48565
-▁psychothe	-48566
-▁Girlfriend	-48567
-▁Supposedly	-48568
-▁neuropathy	-48569
-▁oncologist	-48570
-▁enterprising	-48571
-▁recollections	-48572
-qua	-48573
-fasc	-48574
-Henry	-48575
-yline	-48576
-▁Ding	-48577
-▁EPIC	-48578
-▁begr	-48579
-Energy	-48580
-▁Lexus	-48581
-▁PRACT	-48582
-▁esche	-48583
-▁LIVING	-48584
-▁crawls	-48585
-▁pherom	-48586
-supposed	-48587
-▁Albania	-48588
-▁amalgam	-48589
-▁despond	-48590
-▁reconna	-48591
-▁theorem	-48592
-▁sustains	-48593
-▁deceitful	-48594
-▁invoicing	-48595
-▁seniority	-48596
-▁underline	-48597
-functioning	-48598
-▁Challenger	-48599
-▁dissenting	-48600
-▁repressive	-48601
-prescription	-48602
-▁hibernation	-48603
-▁supplementing	-48604
-▁Rak	-48605
-▁SEA	-48606
-Sales	-48607
-apses	-48608
-veget	-48609
-▁UNTIL	-48610
-▁plums	-48611
-▁tapas	-48612
-jointed	-48613
-▁Sermon	-48614
-▁pooled	-48615
-▁Cameras	-48616
-▁aficion	-48617
-▁kinship	-48618
-▁prelude	-48619
-▁Marianne	-48620
-▁McKenzie	-48621
-▁debunked	-48622
-▁overseen	-48623
-▁Homestead	-48624
-▁microbial	-48625
-▁nightfall	-48626
-▁contrasted	-48627
-uces	-48628
-▁;).	-48629
-▁Nes	-48630
-Trade	-48631
-lifts	-48632
-▁Gael	-48633
-▁Bundy	-48634
-chamber	-48635
-▁Sodium	-48636
-▁distro	-48637
-▁mounds	-48638
-▁dossier	-48639
-▁hearsay	-48640
-▁ripples	-48641
-▁ACTUALLY	-48642
-▁cookware	-48643
-▁adherents	-48644
-▁apportion	-48645
-▁enveloped	-48646
-▁firewalls	-48647
-▁liberally	-48648
-▁massaging	-48649
-▁modernize	-48650
-▁posturing	-48651
-▁unnerving	-48652
-▁Diagnostic	-48653
-▁unjustified	-48654
-ei	-48655
-ADS	-48656
-ONY	-48657
-Germ	-48658
-auga	-48659
-cede	-48660
-uchi	-48661
-▁Glow	-48662
-▁Kart	-48663
-▁dias	-48664
-▁schm	-48665
-Object	-48666
-Return	-48667
-seated	-48668
-▁Careg	-48669
-▁Eaton	-48670
-▁Ibiza	-48671
-▁prune	-48672
-IZATION	-48673
-primary	-48674
-society	-48675
-obsessed	-48676
-▁Hoffner	-48677
-▁Inspire	-48678
-▁Ricardo	-48679
-▁intersp	-48680
-compassing	-48681
-▁Forbidden	-48682
-▁analogies	-48683
-▁Montessori	-48684
-▁Sutherland	-48685
-▁dedicating	-48686
-▁prosthetic	-48687
-▁wonderland	-48688
-▁associating	-48689
-▁uninteresting	-48690
-fam	-48691
-...:	-48692
-Corp	-48693
-auts	-48694
-yyyy	-48695
-▁Zar	-48696
-▁Kors	-48697
-▁Sikh	-48698
-▁Tone	-48699
-▁benz	-48700
-ypical	-48701
-▁DEATH	-48702
-▁mache	-48703
-iferous	-48704
-ineries	-48705
-olitics	-48706
-ributes	-48707
-▁Kramer	-48708
-▁docked	-48709
-▁pellet	-48710
-▁shaker	-48711
-▁sludge	-48712
-Tomorrow	-48713
-▁Britann	-48714
-▁Merrill	-48715
-▁gushing	-48716
-▁legible	-48717
-▁Faithful	-48718
-▁Potatoes	-48719
-▁Saunders	-48720
-▁Infection	-48721
-▁Sensitive	-48722
-▁ceaseless	-48723
-▁clustered	-48724
-▁autographs	-48725
-▁disqualify	-48726
-▁Documentary	-48727
-▁symmetrical	-48728
-Tur	-48729
-wet	-48730
-▁LW	-48731
-uria	-48732
-ciful	-48733
-▁DEAL	-48734
-▁Pied	-48735
-advice	-48736
-▁*****	-48737
-▁Cargo	-48738
-▁Porto	-48739
-▁husky	-48740
-▁memos	-48741
-▁panda	-48742
-▁scumb	-48743
-schools	-48744
-▁delish	-48745
-▁Butcher	-48746
-▁Dolores	-48747
-▁carpool	-48748
-▁cascade	-48749
-▁conglom	-48750
-▁fireman	-48751
-potential	-48752
-ucalyptus	-48753
-▁Confused	-48754
-▁Variable	-48755
-▁hygienic	-48756
-▁joyfully	-48757
-▁reviving	-48758
-▁trinkets	-48759
-▁periphery	-48760
-▁harnessing	-48761
-▁perceiving	-48762
-▁Preferences	-48763
-▁biochemical	-48764
-▁clandestine	-48765
-▁musculoskeletal	-48766
-CX	-48767
-cow	-48768
-▁([	-48769
-WEEN	-48770
-fton	-48771
-gues	-48772
-▁SCC	-48773
-▁ugl	-48774
-henge	-48775
-▁Chau	-48776
-▁FAIR	-48777
-indeed	-48778
-issary	-48779
-rendip	-48780
-▁Fires	-48781
-▁THIRD	-48782
-▁inane	-48783
-▁AROUND	-48784
-▁frowns	-48785
-▁heyday	-48786
-▁sowing	-48787
-moderate	-48788
-▁Adverse	-48789
-▁Brewery	-48790
-▁Caitlin	-48791
-▁penance	-48792
-▁Phillies	-48793
-▁Vineyard	-48794
-▁reliever	-48795
-▁unending	-48796
-▁unknowns	-48797
-▁forensics	-48798
-▁guerrilla	-48799
-▁Executives	-48800
-▁Unexpected	-48801
-▁drowsiness	-48802
-Cla	-48803
-ULES	-48804
-UTES	-48805
-▁Moe	-48806
-▁NES	-48807
-▁Wah	-48808
-ricks	-48809
-▁Feld	-48810
-▁Mali	-48811
-addafi	-48812
-▁invis	-48813
-▁thump	-48814
-figured	-48815
-tellers	-48816
-▁Bundle	-48817
-▁vistas	-48818
-american	-48819
-▁commune	-48820
-▁fancies	-48821
-▁harsher	-48822
-following	-48823
-▁Vitamins	-48824
-▁abrasive	-48825
-▁cataracts	-48826
-▁inconsequ	-48827
-▁parasitic	-48828
-▁refrained	-48829
-integration	-48830
-▁gratuitous	-48831
-▁regionally	-48832
-▁Definitions	-48833
-▁integrations	-48834
-ifu	-48835
-▁cf	-48836
-Mult	-48837
-bush	-48838
-plug	-48839
-quit	-48840
-▁AGW	-48841
-▁DeV	-48842
-▁SOP	-48843
-elman	-48844
-itans	-48845
-stance	-48846
-▁Abram	-48847
-▁Cruel	-48848
-▁Tessa	-48849
-derived	-48850
-ourably	-48851
-▁Grange	-48852
-▁mutter	-48853
-▁repose	-48854
-▁Arrived	-48855
-▁Glimpse	-48856
-▁Renault	-48857
-▁Trilogy	-48858
-▁martyrs	-48859
-▁tenuous	-48860
-▁Footnote	-48861
-▁Recorded	-48862
-▁Switching	-48863
-▁wrestlers	-48864
-▁cellphones	-48865
-▁innovating	-48866
-▁testifying	-48867
-▁predictably	-48868
-▁stabilizing	-48869
-▁indebtedness	-48870
-▁reconnaissance	-48871
-pas	-48872
-▁TI	-48873
-zhen	-48874
-▁Pru	-48875
-▁Sey	-48876
-Linux	-48877
-Scott	-48878
-belly	-48879
-ulele	-48880
-▁Keto	-48881
-Credit	-48882
-▁Huang	-48883
-▁Poles	-48884
-▁Stoke	-48885
-▁cohab	-48886
-isocial	-48887
-▁Hester	-48888
-▁Marcel	-48889
-▁crease	-48890
-▁peeked	-48891
-▁spades	-48892
-exchange	-48893
-▁Allowed	-48894
-▁Cologne	-48895
-▁PRODUCT	-48896
-▁Triumph	-48897
-▁martini	-48898
-▁suscept	-48899
-ISTRATION	-48900
-Wednesday	-48901
-immigrant	-48902
-performed	-48903
-▁Wardrobe	-48904
-▁FOLLOWING	-48905
-▁Utilizing	-48906
-▁alertness	-48907
-▁draconian	-48908
-▁tentacles	-48909
-▁Substances	-48910
-▁enclosures	-48911
-▁homogeneous	-48912
-▁sensibility	-48913
-▁impressively	-48914
-rm	-48915
-UFF	-48916
-ASED	-48917
-ILLE	-48918
-rama	-48919
-▁ATC	-48920
-▁Sly	-48921
-▁sep	-48922
-▁Chew	-48923
-▁Irma	-48924
-▁awed	-48925
-looked	-48926
-mortem	-48927
-retino	-48928
-▁ached	-48929
-▁vexed	-48930
-▁Ruther	-48931
-▁aprons	-48932
-▁clover	-48933
-▁snails	-48934
-▁Baptism	-48935
-▁Brewers	-48936
-▁Decided	-48937
-▁Schumer	-48938
-▁thicken	-48939
-▁ungodly	-48940
-▁Bookings	-48941
-▁Eurozone	-48942
-officially	-48943
-▁crossword	-48944
-▁ingesting	-48945
-▁shrugging	-48946
-▁immorality	-48947
-▁Encouraging	-48948
-NL	-48949
-UARY	-48950
-alyst	-48951
-flops	-48952
-▁Lips	-48953
-▁Ramb	-48954
-▁cuck	-48955
-▁glyc	-48956
-ifters	-48957
-▁BENEF	-48958
-▁Chees	-48959
-▁grads	-48960
-▁kilow	-48961
-▁nexus	-48962
-agascar	-48963
-agogues	-48964
-entered	-48965
-iocesan	-48966
-ophical	-48967
-ustroph	-48968
-▁Crimin	-48969
-▁Lesley	-48970
-▁strait	-48971
-payments	-48972
-▁Duterte	-48973
-▁fiancee	-48974
-▁genesis	-48975
-▁reshape	-48976
-▁spousal	-48977
-▁sulfate	-48978
-▁uploads	-48979
-activated	-48980
-extremely	-48981
-▁Isabelle	-48982
-▁hamstring	-48983
-▁soldering	-48984
-▁wholeness	-48985
-▁Antichrist	-48986
-▁Medication	-48987
-▁UNDERSTAND	-48988
-▁alienating	-48989
-▁deactivate	-48990
-▁dissolving	-48991
-▁perceptive	-48992
-▁podcasting	-48993
-▁dispensaries	-48994
-BG	-48995
-lus	-48996
-Mama	-48997
-WORK	-48998
-firm	-48999
-hoes	-49000
-jans	-49001
-oosh	-49002
-▁EPS	-49003
-▁MMR	-49004
-▁Org	-49005
-archs	-49006
-equal	-49007
-hyper	-49008
-kends	-49009
-undra	-49010
-▁Rite	-49011
-▁Synd	-49012
-▁lint	-49013
-▁noir	-49014
-▁uncl	-49015
-Golden	-49016
-azeera	-49017
-▁-----	-49018
-▁Diver	-49019
-▁Dyson	-49020
-▁Thumb	-49021
-ernames	-49022
-▁Aubrey	-49023
-▁FATHER	-49024
-▁equine	-49025
-▁hooded	-49026
-▁larvae	-49027
-▁Andreas	-49028
-▁Bargain	-49029
-▁Bubbles	-49030
-▁bigoted	-49031
-▁dissoci	-49032
-▁trotted	-49033
-▁vertigo	-49034
-▁Divinity	-49035
-▁absurdly	-49036
-▁spammers	-49037
-▁truffles	-49038
-▁socialized	-49039
-▁Fundraising	-49040
-▁particulate	-49041
-REM	-49042
-▁JT	-49043
-Prep	-49044
-nite	-49045
-roup	-49046
-▁HIT	-49047
-▁kim	-49048
-IONAL	-49049
-brief	-49050
-iland	-49051
-▁Clip	-49052
-▁Minh	-49053
-▁sapp	-49054
-▁teal	-49055
-export	-49056
-udible	-49057
-▁Gypsy	-49058
-▁Sheen	-49059
-▁atten	-49060
-▁cadet	-49061
-▁Viktor	-49062
-▁inclus	-49063
-▁tidbit	-49064
-▁Mocking	-49065
-▁Narciss	-49066
-▁Payroll	-49067
-▁Aluminum	-49068
-▁Monopoly	-49069
-▁Valuable	-49070
-▁rehearse	-49071
-▁alligator	-49072
-▁outweighs	-49073
-▁tenacious	-49074
-▁wrenching	-49075
-▁Mozambique	-49076
-▁calibrated	-49077
-▁invigorate	-49078
-Jon	-49079
-▁Cue	-49080
-▁RAC	-49081
-▁SIG	-49082
-▁Sop	-49083
-▁usb	-49084
-offer	-49085
-▁Angl	-49086
-▁SHIT	-49087
-Daniel	-49088
-▁Higgs	-49089
-▁Jarod	-49090
-▁curbs	-49091
-▁liens	-49092
-astical	-49093
-surgery	-49094
-updated	-49095
-windows	-49096
-▁Fallon	-49097
-▁Katara	-49098
-▁Landsc	-49099
-▁Splash	-49100
-▁Tracks	-49101
-▁hydrox	-49102
-lighting	-49103
-▁Mankind	-49104
-▁REQUIRE	-49105
-▁Senegal	-49106
-▁italics	-49107
-▁tucking	-49108
-▁weirdly	-49109
-riptyline	-49110
-▁Cameroon	-49111
-▁Feelings	-49112
-▁growling	-49113
-▁orchards	-49114
-▁Increases	-49115
-▁enshrined	-49116
-▁buttermilk	-49117
-▁unforgiving	-49118
-▁Biodiversity	-49119
-furn	-49120
-▁BTC	-49121
-▁MSG	-49122
-▁Pon	-49123
-▁bem	-49124
-itone	-49125
-▁Bono	-49126
-▁Soll	-49127
-▁crue	-49128
-▁fiss	-49129
-▁penn	-49130
-▁Angola	-49131
-▁Rhonda	-49132
-▁umpire	-49133
-Download	-49134
-▁Violent	-49135
-▁punters	-49136
-▁Blooming	-49137
-▁PROPERTY	-49138
-▁Stockton	-49139
-▁anterior	-49140
-▁demolish	-49141
-▁therefor	-49142
-▁ingestion	-49143
-▁publicize	-49144
-▁repulsive	-49145
-▁authenticate	-49146
-dc	-49147
-▁Ich	-49148
-▁OTC	-49149
-▁Ames	-49150
-▁Doha	-49151
-▁gour	-49152
-broker	-49153
-▁Groom	-49154
-▁Handy	-49155
-▁unmet	-49156
-whelmed	-49157
-▁Errors	-49158
-▁Sheesh	-49159
-▁baller	-49160
-▁inflex	-49161
-▁ACCOUNT	-49162
-▁Macbook	-49163
-▁mulling	-49164
-▁Properly	-49165
-▁biofuels	-49166
-▁mightily	-49167
-▁recesses	-49168
-electronic	-49169
-▁Ascension	-49170
-▁Precision	-49171
-▁constants	-49172
-▁perceives	-49173
-▁dispensary	-49174
-▁overstated	-49175
-▁silverware	-49176
-▁adjudication	-49177
-▁globalisation	-49178
->.	-49179
-ESH	-49180
-mao	-49181
-TERN	-49182
-bett	-49183
-Chuck	-49184
-itism	-49185
-otica	-49186
-ottie	-49187
-▁Zara	-49188
-arrows	-49189
-▁CONST	-49190
-▁Stats	-49191
-▁hoist	-49192
-session	-49193
-▁chases	-49194
-▁impuls	-49195
-cellular	-49196
-patients	-49197
-utations	-49198
-▁Belmont	-49199
-▁Lifting	-49200
-▁Midtown	-49201
-▁Survive	-49202
-▁baseman	-49203
-▁confide	-49204
-▁dropout	-49205
-▁surveyor	-49206
-▁Obtaining	-49207
-▁aggravate	-49208
-▁bleaching	-49209
-▁convinces	-49210
-▁reinstate	-49211
-▁signalling	-49212
-▁mathematicians	-49213
-▁Shim	-49214
-lovers	-49215
-relief	-49216
-▁Basel	-49217
-▁Nader	-49218
-▁SPACE	-49219
-cooking	-49220
-osoever	-49221
-▁Laguna	-49222
-▁Lucius	-49223
-▁Rebels	-49224
-▁eerily	-49225
-▁hubris	-49226
-▁sprawl	-49227
-▁Amazons	-49228
-▁BETWEEN	-49229
-▁Camille	-49230
-▁Spokane	-49231
-▁caching	-49232
-enhancing	-49233
-positions	-49234
-▁Sundance	-49235
-▁cemented	-49236
-▁devising	-49237
-▁displace	-49238
-▁precepts	-49239
-▁psychics	-49240
-▁rumbling	-49241
-▁unproven	-49242
-designated	-49243
-▁chronicle	-49244
-▁dressings	-49245
-▁intrigues	-49246
-▁Versailles	-49247
-▁metastatic	-49248
-▁resiliency	-49249
-▁sabbatical	-49250
-▁speciality	-49251
-▁Consistency	-49252
-▁heavyweight	-49253
-▁misfortunes	-49254
-▁transfusion	-49255
-▁Californians	-49256
-▁reconstructed	-49257
-awn	-49258
-▁WV	-49259
-appe	-49260
-bold	-49261
-calc	-49262
-cole	-49263
-lich	-49264
-Brain	-49265
-Title	-49266
-debit	-49267
-geist	-49268
-▁thre	-49269
-▁Basis	-49270
-▁Comet	-49271
-▁Hydra	-49272
-▁Malay	-49273
-▁Yukon	-49274
-▁cramp	-49275
-▁minut	-49276
-▁Stokes	-49277
-▁africa	-49278
-▁crutch	-49279
-▁groans	-49280
-▁rudely	-49281
-▁tanned	-49282
-▁Aspects	-49283
-▁Olympia	-49284
-▁tooling	-49285
-▁Hydrogen	-49286
-▁devolved	-49287
-▁enquired	-49288
-▁rustling	-49289
-▁trickier	-49290
-▁crunching	-49291
-▁smugglers	-49292
-▁Perception	-49293
-▁==	-49294
-====	-49295
-coni	-49296
-▁BAL	-49297
-alter	-49298
-apine	-49299
-brite	-49300
-olite	-49301
-osate	-49302
-rific	-49303
-▁Lila	-49304
-▁PARK	-49305
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-hungry	-49307
-▁Dread	-49308
-▁PERIOD	-49309
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-▁sojour	-49311
-▁spares	-49312
-▁vapour	-49313
-ustrated	-49314
-▁Eritrea	-49315
-▁Hershey	-49316
-▁dumbest	-49317
-Australia	-49318
-▁Capitals	-49319
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-▁walkways	-49323
-▁lightness	-49324
-▁magicians	-49325
-▁Contribute	-49326
-▁Nutritional	-49327
-▁Scandinavia	-49328
-▁substandard	-49329
-▁ecclesiastical	-49330
-Ap	-49331
-WOR	-49332
-]])	-49333
-eus	-49334
-sup	-49335
-hast	-49336
-limit	-49337
-▁Bulk	-49338
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-▁Helm	-49340
-▁NONE	-49341
-▁arra	-49342
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-▁gobl	-49344
-▁BELOW	-49345
-▁Waldo	-49346
-▁codec	-49347
-▁ensue	-49348
-▁Bianca	-49349
-▁marred	-49350
-▁tether	-49351
-shooting	-49352
-▁Goliath	-49353
-▁Scrooge	-49354
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-▁minibar	-49356
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-▁topless	-49358
-uteronomy	-49359
-▁Borrower	-49360
-▁REMEMBER	-49361
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-▁handguns	-49363
-▁shrouded	-49364
-▁storming	-49365
-▁Alternate	-49366
-▁Commodore	-49367
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-▁radiating	-49369
-▁fluttering	-49370
-▁uneasiness	-49371
-▁undiscovered	-49372
-▁VB	-49373
-ihil	-49374
-itha	-49375
-▁Rik	-49376
-eterm	-49377
-jured	-49378
-zzles	-49379
-apeake	-49380
-combat	-49381
-vester	-49382
-▁Ponzi	-49383
-▁beset	-49384
-▁yarns	-49385
-arction	-49386
-▁Benton	-49387
-▁Hacker	-49388
-▁Output	-49389
-▁Oyster	-49390
-▁beaver	-49391
-▁pecans	-49392
-▁sliver	-49393
-featured	-49394
-treating	-49395
-▁Blossom	-49396
-▁Mapping	-49397
-▁Traders	-49398
-▁forties	-49399
-▁travers	-49400
-▁Watchers	-49401
-▁Districts	-49402
-▁imitating	-49403
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-▁sacrament	-49405
-▁attentions	-49406
-▁glistening	-49407
-▁goalkeeper	-49408
-▁immaterial	-49409
-▁veterinarians	-49410
-▁virtualization	-49411
-Wri	-49412
-▁AX	-49413
-Anal	-49414
-Dist	-49415
-Gate	-49416
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-▁IPS	-49418
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-Marie	-49422
-aghan	-49423
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-▁Gilles	-49431
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-▁banish	-49433
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-▁Vaccine	-49435
-▁refuted	-49436
-▁remover	-49437
-▁rinsing	-49438
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-▁Inclusive	-49442
-▁Primarily	-49443
-▁groupings	-49444
-▁legislate	-49445
-▁championed	-49446
-▁insulating	-49447
-▁discharging	-49448
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-▁uncompromising	-49450
-aii	-49451
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-bees	-49453
-aylor	-49454
-beats	-49455
-aholic	-49456
-enters	-49457
-▁Clown	-49458
-▁Decor	-49459
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-▁Merit	-49461
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-▁emits	-49466
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-Students	-49470
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-▁conclusively	-49482
-▁explorations	-49483
-▁functionally	-49484
-▁interviewers	-49485
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-▁Confederation	-49487
-▁appropriateness	-49488
-.”(	-49489
-eni	-49490
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-iste	-49492
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-ENTAL	-49497
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-▁Kelvin	-49505
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-▁Delaney	-49509
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-▁reptile	-49514
-▁staffer	-49515
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-breakable	-49518
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-▁Springer	-49520
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-▁Schwarzenegger	-49534
-CM	-49535
-.\\	-49536
-api	-49537
-coe	-49538
-atly	-49539
-lorn	-49540
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-onson	-49543
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-athers	-49550
-donald	-49551
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-▁Daven	-49553
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-closing	-49558
-vernote	-49559
-▁Blonde	-49560
-▁Chanel	-49561
-▁FRIEND	-49562
-▁Retain	-49563
-▁Telesc	-49564
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-▁deduce	-49566
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-velation	-49568
-▁Algebra	-49569
-▁Elderly	-49570
-▁Profess	-49571
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-▁Landmark	-49574
-▁Leverage	-49575
-▁Parkland	-49576
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-▁shilling	-49578
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-▁footnotes	-49583
-▁tranquill	-49584
-▁depositing	-49585
-▁Imagination	-49586
-)[	-49587
-ZA	-49588
-AMI	-49589
-gru	-49590
-conv	-49591
-mium	-49592
-whom	-49593
-▁Zep	-49594
-▁sys	-49595
-arine	-49596
-igold	-49597
-ilded	-49598
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-thrown	-49603
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-esarean	-49605
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-▁hitched	-49611
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-isprudence	-49618
-retirement	-49619
-▁Regularly	-49620
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-▁profusely	-49623
-▁Auditorium	-49624
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-▁alleviation	-49626
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-▁Complimentary	-49628
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-jis	-49630
-rez	-49631
-umé	-49632
-▁RG	-49633
-ARCH	-49634
-ISTS	-49635
-achy	-49636
-pptx	-49637
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-ISHED	-49640
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-inflation	-49655
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-grandmother	-49661
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-Cas	-49664
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-tos	-49666
-lamp	-49667
-unda	-49668
-Major	-49669
-imper	-49670
-tough	-49671
-uccino	-49672
-▁Mikey	-49673
-▁Stere	-49674
-▁Tener	-49675
-▁Deacon	-49676
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-▁Jillian	-49678
-▁Offered	-49679
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-▁Steiger	-49681
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-▁shackles	-49684
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-DAQ	-49690
-itu	-49691
-▁JL	-49692
-alan	-49693
-tham	-49694
-▁TCP	-49695
-Magic	-49696
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-gallery	-49706
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-maximum	-49708
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-▁neonatal	-49711
-rification	-49712
-▁connoisse	-49713
-▁Scottsdale	-49714
-▁disarmament	-49715
-▁instantaneously	-49716
--“	-49717
-Bus	-49718
-CHA	-49719
-yre	-49720
-Dest	-49721
-chio	-49722
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-gross	-49726
-ovsky	-49727
-Number	-49728
-agents	-49729
-▁Delia	-49730
-▁Hubert	-49731
-▁Lender	-49732
-▁Nguyen	-49733
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-▁repris	-49736
-▁Minimal	-49737
-▁Zachary	-49738
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-▁phoenix	-49740
-aminophen	-49741
-▁Clippers	-49742
-▁Insurers	-49743
-▁ethereal	-49744
-▁facelift	-49745
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-▁spouting	-49747
-▁unjustly	-49748
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-▁blistering	-49754
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-▁Accreditation	-49758
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-lik	-49762
-arie	-49763
-fool	-49764
-olon	-49765
-▁DIA	-49766
-▁Moj	-49767
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-escal	-49769
-prove	-49770
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-▁Moves	-49776
-▁eject	-49777
-▁repug	-49778
-browser	-49779
-▁Alerts	-49780
-▁parale	-49781
-▁Succeed	-49782
-▁forgave	-49783
-▁natures	-49784
-▁nomadic	-49785
-▁relayed	-49786
-confirmed	-49787
-▁Chargers	-49788
-▁Seasonal	-49789
-▁blurring	-49790
-▁canister	-49791
-▁mainstay	-49792
-▁Nietzsche	-49793
-▁broadened	-49794
-▁enigmatic	-49795
-▁misdemean	-49796
-▁propagate	-49797
-▁forbidding	-49798
-▁periodical	-49799
-▁anticipates	-49800
-udy	-49801
-ulz	-49802
-aloo	-49803
-asey	-49804
-inds	-49805
-thru	-49806
-▁Ely	-49807
-▁Rie	-49808
-▁yup	-49809
-attan	-49810
-avans	-49811
-emate	-49812
-ertil	-49813
-▁PAUL	-49814
-▁bein	-49815
-enburg	-49816
-ittles	-49817
-opting	-49818
-▁Admit	-49819
-▁Rufus	-49820
-▁Verde	-49821
-chemist	-49822
-▁Fiesta	-49823
-▁Meyers	-49824
-▁Cluster	-49825
-▁Retired	-49826
-▁Winters	-49827
-▁hunched	-49828
-▁zooming	-49829
-▁Promoter	-49830
-▁orbiting	-49831
-▁Prisoners	-49832
-▁Rebellion	-49833
-▁Retailers	-49834
-▁unskilled	-49835
-▁whereupon	-49836
-▁decriminal	-49837
-▁nightclubs	-49838
-▁unlicensed	-49839
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-▁Investigate	-49841
-▁convincingly	-49842
-ür	-49843
-Wire	-49844
-▁rés	-49845
-▁Bras	-49846
-▁Dose	-49847
-▁Jody	-49848
-▁Perc	-49849
-▁earl	-49850
-▁kink	-49851
-▁vibr	-49852
-copter	-49853
-delete	-49854
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-▁NOTICE	-49859
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-▁goblins	-49864
-▁microch	-49865
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-researched	-49868
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-▁unrelenting	-49876
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-,*	-49878
-Cook	-49879
-rush	-49880
-amele	-49881
-inthe	-49882
-uking	-49883
-umers	-49884
-▁Leia	-49885
-▁oars	-49886
-▁whoa	-49887
-cheese	-49888
-theory	-49889
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-▁jacks	-49891
-▁lynch	-49892
-▁spank	-49893
-itators	-49894
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-▁sordid	-49896
-▁throng	-49897
-retching	-49898
-▁RESULTS	-49899
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-▁michigan	-49901
-▁pairings	-49902
-historical	-49903
-▁activates	-49904
-▁ombudsman	-49905
-▁roundtable	-49906
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-ENA	-49911
-gedy	-49912
-vado	-49913
-▁Fas	-49914
-▁LLP	-49915
-▁Govt	-49916
-▁etsy	-49917
-▁lain	-49918
-nesses	-49919
-soaked	-49920
-writes	-49921
-▁Ninet	-49922
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-patched	-49925
-ultures	-49926
-▁impert	-49927
-▁Cavalry	-49928
-▁Engines	-49929
-▁breezes	-49930
-dangerous	-49931
-reporting	-49932
-▁Catching	-49933
-▁bereaved	-49934
-▁exacting	-49935
-▁flossing	-49936
-▁linkages	-49937
-▁Eucharist	-49938
-▁FANTASTIC	-49939
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-▁snowflake	-49943
-▁bridesmaid	-49944
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-▁cheerleaders	-49947
-▁insurrection	-49948
-▁moisturizing	-49949
-responsibility	-49950
-EAH	-49951
-OTA	-49952
-Ord	-49953
-\\’	-49954
-ilah	-49955
-▁ATS	-49956
-▁Zel	-49957
-▁vie	-49958
-Price	-49959
-artan	-49960
-▁Rode	-49961
-▁Sync	-49962
-▁colt	-49963
-▁vids	-49964
-▁Asuka	-49965
-▁Mindy	-49966
-affeine	-49967
-otswana	-49968
-▁Enthus	-49969
-▁proton	-49970
-ptroller	-49971
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-▁basking	-49975
-▁consign	-49976
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-▁snagged	-49979
-▁unfaith	-49980
-umbledore	-49981
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-▁patently	-49983
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-categories	-49985
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-▁Henrietta	-49987
-▁Locations	-49988
-▁goodnight	-49989
-▁scribbled	-49990
-▁Greenpeace	-49991
-▁dissipated	-49992
-▁extinguish	-49993
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-ibo	-49995
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-DING	-49997
-rele	-49998
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-Event	-50000
-▁Goon	-50001
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-aganda	-50003
-▁Lynda	-50004
-▁Wanda	-50005
-▁Jewell	-50006
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-▁disputing	-50013
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-_;	-50018
-qa	-50019
-wi	-50020
-??)	-50021
-VAL	-50022
-frog	-50023
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-▁necr	-50026
-Couldn	-50027
-Notify	-50028
-actors	-50029
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-▁Chlor	-50032
-▁Gears	-50033
-▁Hawth	-50034
-▁THOUS	-50035
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-▁lefty	-50037
-▁tunic	-50038
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-▁jackass	-50045
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-▁Eastwood	-50047
-▁Estimate	-50048
-▁Seminary	-50049
-▁pedagogy	-50050
-▁seconded	-50051
-▁termites	-50052
-generating	-50053
-▁hemorrhoids	-50054
-▁observatory	-50055
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-▁localization	-50059
-▁perspiration	-50060
-?-	-50061
-kb	-50062
-▁Zy	-50063
-raut	-50064
-▁DOE	-50065
-Month	-50066
-▁Hager	-50067
-▁Innoc	-50068
-▁Insol	-50069
-▁waxed	-50070
-▁entang	-50071
-▁infuse	-50072
-ernation	-50073
-retinoin	-50074
-▁fairway	-50075
-▁vibrator	-50076
-▁Sentenced	-50077
-▁Geological	-50078
-▁Illuminati	-50079
-▁subversive	-50080
-▁interracial	-50081
-▁sacrificial	-50082
-Congratulations	-50083
-▁CUN	-50084
-▁KFC	-50085
-Touch	-50086
-lamps	-50087
-miles	-50088
-stuck	-50089
-▁PRAY	-50090
-▁brus	-50091
-▁neoc	-50092
-ighton	-50093
-unique	-50094
-▁Mamma	-50095
-▁Sweep	-50096
-▁Therm	-50097
-▁nemes	-50098
-▁Diving	-50099
-▁Lagoon	-50100
-▁Patton	-50101
-▁impass	-50102
-▁quiver	-50103
-traction	-50104
-▁Beckett	-50105
-▁Ecology	-50106
-▁Orioles	-50107
-▁Pradesh	-50108
-▁mussels	-50109
-▁soybean	-50110
-▁Evernote	-50111
-▁crockpot	-50112
-▁strikers	-50113
-▁unbeaten	-50114
-▁childlike	-50115
-▁embedding	-50116
-contractors	-50117
-▁Homecoming	-50118
-▁blackberry	-50119
-▁predefined	-50120
-▁sheepishly	-50121
-▁interstellar	-50122
-▁invigorating	-50123
-LET	-50124
-pta	-50125
-Gear	-50126
-Road	-50127
-Town	-50128
-drum	-50129
-fans	-50130
-geek	-50131
-kels	-50132
-WriMo	-50133
-▁Ench	-50134
-▁Gins	-50135
-▁HDMI	-50136
-▁WALL	-50137
-ething	-50138
-▁defra	-50139
-▁shits	-50140
-▁Anchor	-50141
-▁Jesuit	-50142
-▁Josiah	-50143
-drinking	-50144
-markable	-50145
-▁Outline	-50146
-▁chagrin	-50147
-▁yawning	-50148
-▁Supported	-50149
-▁innovator	-50150
-▁overdoses	-50151
-▁sumptuous	-50152
-▁tribunals	-50153
-▁Charitable	-50154
-▁herbicides	-50155
-▁scoreboard	-50156
-▁nothingness	-50157
-▁overpowered	-50158
-▁Neurological	-50159
-▁biodegradable	-50160
-Bey	-50161
-atu	-50162
-MORE	-50163
-magn	-50164
-oula	-50165
-▁Dai	-50166
-▁Nab	-50167
-aguay	-50168
-beard	-50169
-itech	-50170
-▁Acne	-50171
-▁Wand	-50172
-▁thud	-50173
-Minute	-50174
-reform	-50175
-rooted	-50176
-▁Aidan	-50177
-Brother	-50178
-icillin	-50179
-▁Attach	-50180
-▁Demons	-50181
-▁Pencil	-50182
-▁Warden	-50183
-breeding	-50184
-▁grumble	-50185
-▁jacuzzi	-50186
-▁muslims	-50187
-▁veneers	-50188
-▁webcast	-50189
-breakfast	-50190
-▁Dreaming	-50191
-▁blunders	-50192
-▁pinching	-50193
-▁tastings	-50194
-mindedness	-50195
-▁Authentic	-50196
-▁Producers	-50197
-▁eloquence	-50198
-▁guidebook	-50199
-▁opportune	-50200
-▁reprehens	-50201
-▁retailing	-50202
-▁unearthed	-50203
-▁Greenville	-50204
-▁apologised	-50205
-▁misgivings	-50206
-▁reappeared	-50207
-▁undisputed	-50208
-▁maneuvering	-50209
-▁skyrocketed	-50210
-▁abbreviation	-50211
-▁biochemistry	-50212
-arb	-50213
-▁LV	-50214
-lysm	-50215
-sexy	-50216
-twas	-50217
-▁Cali	-50218
-▁LUCK	-50219
-▁Poet	-50220
-Behold	-50221
-ilogue	-50222
-minton	-50223
-▁loooo	-50224
-▁shear	-50225
-comfort	-50226
-ivables	-50227
-▁Raised	-50228
-▁Slater	-50229
-▁Villas	-50230
-▁cystic	-50231
-▁willed	-50232
-▁Clarity	-50233
-▁samurai	-50234
-▁Croatian	-50235
-▁Accepting	-50236
-▁Believing	-50237
-▁Caucasian	-50238
-▁cardstock	-50239
-▁factually	-50240
-▁juveniles	-50241
-▁syllables	-50242
-▁patriarchy	-50243
-▁thumbnails	-50244
-▁prospecting	-50245
-▁unannounced	-50246
-▁coincidences	-50247
-▁rejuvenation	-50248
-▁Cardiovascular	-50249
-hz	-50250
-kr	-50251
-▁✔	-50252
-GMO	-50253
-Pot	-50254
-Sigh	-50255
-atee	-50256
-gasp	-50257
-▁enm	-50258
-amish	-50259
-steel	-50260
-▁Pixar	-50261
-▁Ramos	-50262
-▁Rubin	-50263
-▁Venue	-50264
-▁heady	-50265
-akistan	-50266
-prevent	-50267
-▁Mellon	-50268
-▁Salman	-50269
-▁Stripe	-50270
-▁birdie	-50271
-▁truest	-50272
-▁Heavens	-50273
-▁Ongoing	-50274
-▁attuned	-50275
-▁beseech	-50276
-▁virgins	-50277
-infection	-50278
-▁Venetian	-50279
-▁bookcase	-50280
-▁cataract	-50281
-▁scrubbed	-50282
-▁pessimism	-50283
-temperature	-50284
-▁Madagascar	-50285
-▁authorizes	-50286
-▁dissimilar	-50287
-▁bimatoprost	-50288
-▁reinsurance	-50289
-▁wholesalers	-50290
-▁worshipping	-50291
-▁improvisation	-50292
-▁antidepressant	-50293
-LI	-50294
-png	-50295
-ouls	-50296
-blest	-50297
-isley	-50298
-oseph	-50299
-▁MCAT	-50300
-▁cubs	-50301
-▁july	-50302
-▁pines	-50303
-▁sewed	-50304
-isanship	-50305
-▁Beaches	-50306
-▁Middles	-50307
-▁Natalia	-50308
-▁acreage	-50309
-▁advices	-50310
-▁animate	-50311
-▁barcode	-50312
-▁buttery	-50313
-▁tempest	-50314
-▁timings	-50315
-▁Landlord	-50316
-▁Struggle	-50317
-▁gateways	-50318
-▁outgrown	-50319
-▁reseller	-50320
-▁unmarked	-50321
-▁solicited	-50322
-▁Calculator	-50323
-▁negotiator	-50324
-▁speculators	-50325
-▁unfulfilled	-50326
-Il	-50327
-Hang	-50328
-IVEN	-50329
-ahua	-50330
-▁CST	-50331
-hosts	-50332
-noise	-50333
-▁Delo	-50334
-▁blip	-50335
-hander	-50336
-ockets	-50337
-▁buffs	-50338
-▁Arrest	-50339
-▁Failed	-50340
-▁Huawei	-50341
-▁Trains	-50342
-▁heaved	-50343
-▁savers	-50344
-▁Billing	-50345
-▁Peaches	-50346
-▁Samurai	-50347
-▁Bartlett	-50348
-▁Indicate	-50349
-▁Mohammad	-50350
-▁Newsweek	-50351
-▁Repeated	-50352
-▁namesake	-50353
-▁prologue	-50354
-▁Component	-50355
-▁Forgotten	-50356
-▁Navigator	-50357
-▁onlookers	-50358
-▁paramedic	-50359
-▁Chesapeake	-50360
-▁catalogues	-50361
-▁equipments	-50362
-conventional	-50363
-▁Representation	-50364
-▁determinations	-50365
-tog	-50366
-▁LF	-50367
-adda	-50368
-ibur	-50369
-iola	-50370
-▁Jas	-50371
-▁SDK	-50372
-audio	-50373
-▁Bamb	-50374
-▁Rath	-50375
-▁Trop	-50376
-▁Wren	-50377
-▁seaf	-50378
-namely	-50379
-riosis	-50380
-sheets	-50381
-▁faire	-50382
-booking	-50383
-▁Sewing	-50384
-▁rinsed	-50385
-▁zoomed	-50386
-aterials	-50387
-clicking	-50388
-mannered	-50389
-▁Mermaid	-50390
-▁ceasing	-50391
-▁endures	-50392
-▁thither	-50393
-▁Holiness	-50394
-▁Referral	-50395
-▁hypnotic	-50396
-▁newsroom	-50397
-▁selector	-50398
-▁tinnitus	-50399
-▁treading	-50400
-▁unintell	-50401
-▁cellulose	-50402
-▁fanatical	-50403
-▁freestyle	-50404
-▁trackback	-50405
-▁Discovering	-50406
-▁Arrangements	-50407
-">	-50408
-YG	-50409
-DOT	-50410
-Jer	-50411
-hur	-50412
-pus	-50413
-’?”	-50414
-Mass	-50415
-butt	-50416
-▁Wem	-50417
-Saxon	-50418
-hesda	-50419
-meyer	-50420
-prior	-50421
-shade	-50422
-plenty	-50423
-▁Punta	-50424
-▁stomp	-50425
-▁twine	-50426
-conform	-50427
-▁Durant	-50428
-▁scruff	-50429
-▁vigour	-50430
-ULATIONS	-50431
-▁Chennai	-50432
-▁fumbled	-50433
-▁gentler	-50434
-▁pooling	-50435
-▁Corvette	-50436
-▁Glendale	-50437
-▁NATIONAL	-50438
-▁droplets	-50439
-▁invasions	-50440
-▁squatting	-50441
-▁stepfather	-50442
-▁unoccupied	-50443
-▁adaptability	-50444
-▁hysterically	-50445
-TMAS	-50446
-aira	-50447
-evin	-50448
-omac	-50449
-▁amy	-50450
-▁JOIN	-50451
-▁Moff	-50452
-▁flot	-50453
-▁hypo	-50454
-▁pike	-50455
-isodes	-50456
-whinie	-50457
-▁Levit	-50458
-▁Lohan	-50459
-▁Maury	-50460
-▁Spare	-50461
-aligned	-50462
-▁adapts	-50463
-▁bagged	-50464
-▁decont	-50465
-▁threes	-50466
-oracious	-50467
-▁Accompl	-50468
-▁Dungeon	-50469
-▁Pleased	-50470
-▁lingers	-50471
-▁Googling	-50472
-▁Serenity	-50473
-▁bouquets	-50474
-▁engender	-50475
-▁unharmed	-50476
-▁washable	-50477
-▁Delegates	-50478
-▁wardrobes	-50479
-▁benefactor	-50480
-▁confounded	-50481
-▁optionally	-50482
-▁parentheses	-50483
-▁Anthropology	-50484
-Ess	-50485
-NEW	-50486
-OOO	-50487
-PCA	-50488
-iem	-50489
-▁JV	-50490
-Cash	-50491
-Sher	-50492
-zman	-50493
-▁ENV	-50494
-▁Oat	-50495
-▁Soo	-50496
-▁TTC	-50497
-▁bbb	-50498
-Leaks	-50499
-aphyl	-50500
-atsby	-50501
-timer	-50502
-▁Dugg	-50503
-▁Gott	-50504
-Mexico	-50505
-ilator	-50506
-quotes	-50507
-▁Encry	-50508
-▁covet	-50509
-▁erode	-50510
-▁hawks	-50511
-▁heath	-50512
-▁laces	-50513
-arounds	-50514
-▁Bowman	-50515
-▁Trades	-50516
-▁gunned	-50517
-▁subjug	-50518
-CRIPTION	-50519
-▁Coulter	-50520
-▁Elegant	-50521
-▁Kirsten	-50522
-▁Satanic	-50523
-▁Skilled	-50524
-▁beetles	-50525
-▁sloping	-50526
-▁Botswana	-50527
-▁Madeline	-50528
-▁restores	-50529
-presumably	-50530
-▁facsimile	-50531
-▁homegrown	-50532
-▁recapture	-50533
-▁seduction	-50534
-▁Rutherford	-50535
-▁Strengthen	-50536
-▁chromosomes	-50537
-▁standardization	-50538
-alb	-50539
-hao	-50540
--...	-50541
-Nazi	-50542
-cona	-50543
-▁SLE	-50544
-▁osm	-50545
-▁LOCK	-50546
-▁Medi	-50547
-▁Rafa	-50548
-▁sitt	-50549
-winded	-50550
-▁ester	-50551
-▁Cutter	-50552
-▁Medina	-50553
-▁Mosque	-50554
-▁Sedona	-50555
-▁antise	-50556
-▁batted	-50557
-▁gasket	-50558
-▁grooms	-50559
-▁kiosks	-50560
-▁premed	-50561
-ustering	-50562
-▁Langley	-50563
-▁Stampin	-50564
-▁deniers	-50565
-▁kernels	-50566
-▁sifting	-50567
-▁soulful	-50568
-▁Blessing	-50569
-▁amenable	-50570
-▁insulate	-50571
-▁onscreen	-50572
-▁retaliate	-50573
-▁Blackhawks	-50574
-▁claustroph	-50575
-▁seasonings	-50576
-▁amortization	-50577
-▁Qualifications	-50578
-GES	-50579
-Jane	-50580
-gots	-50581
-huge	-50582
-▁FORE	-50583
-▁Omni	-50584
-canada	-50585
-chwitz	-50586
-inaire	-50587
-▁GoPro	-50588
-▁criss	-50589
-▁dicks	-50590
-▁Mathew	-50591
-▁hookup	-50592
-▁operas	-50593
-▁plated	-50594
-▁stills	-50595
-▁Clifton	-50596
-▁PROBLEM	-50597
-▁Solving	-50598
-▁Tracker	-50599
-▁juicing	-50600
-▁legions	-50601
-▁tidying	-50602
-▁Galactic	-50603
-▁burglars	-50604
-▁checkups	-50605
-▁splinter	-50606
-▁lamenting	-50607
-▁obstinate	-50608
-▁uncharted	-50609
-▁Delivering	-50610
-encompassing	-50611
-▁Illustrator	-50612
-▁milliseconds	-50613
-▁workstations	-50614
-▁commemoration	-50615
-RD	-50616
-Hush	-50617
-▁CCC	-50618
-deeds	-50619
-metre	-50620
-▁Jaff	-50621
-▁Aiden	-50622
-▁Aries	-50623
-▁Ended	-50624
-▁Query	-50625
-▁pence	-50626
-▁skips	-50627
-▁Magnus	-50628
-▁Sensei	-50629
-▁damper	-50630
-▁gunmen	-50631
-▁lashed	-50632
-▁adoring	-50633
-▁bunches	-50634
-▁pollute	-50635
-▁weakens	-50636
-▁Covering	-50637
-▁Instinct	-50638
-▁assesses	-50639
-scientists	-50640
-▁Medicines	-50641
-▁Violation	-50642
-▁outbursts	-50643
-▁talkative	-50644
-▁Exceptions	-50645
-▁biohazards	-50646
-▁consecrated	-50647
-▁petitioners	-50648
-▁regrettable	-50649
-▁Personalized	-50650
-▁inconsequential	-50651
-KA	-50652
-SF	-50653
-▁😦	-50654
-Due	-50655
-Mid	-50656
-Sem	-50657
-ROOM	-50658
-▁Boh	-50659
-▁DSL	-50660
-▁Fax	-50661
-▁MEL	-50662
-▁jol	-50663
-rably	-50664
-rails	-50665
-wayne	-50666
-▁Pett	-50667
-arthen	-50668
-mining	-50669
-▁Climb	-50670
-▁Flags	-50671
-▁Viral	-50672
-Country	-50673
-mailing	-50674
-▁Dillon	-50675
-▁Rooney	-50676
-▁Towels	-50677
-▁Leaning	-50678
-▁annexed	-50679
-ustralian	-50680
-▁Stewards	-50681
-▁cuteness	-50682
-▁neurotic	-50683
-▁altitudes	-50684
-▁divergent	-50685
-▁foolproof	-50686
-▁obstetric	-50687
-educational	-50688
-▁resolutely	-50689
-"]	-50690
-Iss	-50691
-igms	-50692
-mire	-50693
-nova	-50694
-rude	-50695
-▁Bai	-50696
-▁eug	-50697
-▁gos	-50698
-Total	-50699
-stral	-50700
-▁Grap	-50701
-▁Iber	-50702
-▁Masc	-50703
-Police	-50704
-umbria	-50705
-▁SECUR	-50706
-▁unwra	-50707
-▁Beetle	-50708
-▁Closet	-50709
-▁biggie	-50710
-▁rework	-50711
-▁sundry	-50712
-othermia	-50713
-▁Hawkeye	-50714
-▁edifice	-50715
-▁grating	-50716
-▁Garcinia	-50717
-▁branched	-50718
-▁cleavage	-50719
-▁resented	-50720
-▁sardines	-50721
-protective	-50722
-▁Letterman	-50723
-▁entourage	-50724
-▁profiting	-50725
-Palestinian	-50726
-▁CONDITIONS	-50727
-ENE	-50728
-OLA	-50729
-sto	-50730
-cias	-50731
-jump	-50732
-▁BJP	-50733
-▁Fam	-50734
-achos	-50735
-outer	-50736
-paces	-50737
-▁Leop	-50738
-▁RISK	-50739
-Forget	-50740
-atonic	-50741
-opener	-50742
-shared	-50743
-starch	-50744
-▁Erick	-50745
-▁PRINT	-50746
-Genesis	-50747
-Muslims	-50748
-depress	-50749
-▁Memoir	-50750
-▁Primal	-50751
-▁arming	-50752
-▁nursed	-50753
-▁Angular	-50754
-▁Nirvana	-50755
-▁quibble	-50756
-▁ramping	-50757
-basically	-50758
-▁Displays	-50759
-▁Blackburn	-50760
-▁Mandatory	-50761
-▁elevating	-50762
-▁oversimpl	-50763
-▁strictest	-50764
-▁Thereafter	-50765
-▁complicity	-50766
-▁monopolies	-50767
-▁opinionated	-50768
-▁programmatic	-50769
-▁consternation	-50770
-afa	-50771
-VIEW	-50772
-▁CBT	-50773
-▁Orb	-50774
-▁fou	-50775
-ARANT	-50776
-ERING	-50777
-otsky	-50778
-ivitis	-50779
-▁Baked	-50780
-▁Hanks	-50781
-▁lumin	-50782
-▁Inspir	-50783
-▁Scores	-50784
-▁Snacks	-50785
-▁Tenant	-50786
-▁encore	-50787
-▁galley	-50788
-▁Myspace	-50789
-▁Oceanic	-50790
-▁resuming	-50791
-▁schooled	-50792
-▁squander	-50793
-▁Ecosystem	-50794
-▁ascension	-50795
-▁posterior	-50796
-▁Homeschool	-50797
-▁Terminator	-50798
-▁strikeouts	-50799
-▁Requirement	-50800
-▁alternately	-50801
-▁tribulation	-50802
-▁sterilization	-50803
-yi	-50804
-BIT	-50805
-▁Sz	-50806
-ismo	-50807
-▁Tok	-50808
-isals	-50809
-ooked	-50810
-Martin	-50811
-▁Peaks	-50812
-▁dildo	-50813
-▁spasm	-50814
-measure	-50815
-▁Quincy	-50816
-▁brewer	-50817
-▁coughs	-50818
-▁cranks	-50819
-▁starry	-50820
-flavored	-50821
-▁ensuite	-50822
-▁traumas	-50823
-▁tumours	-50824
-▁uterine	-50825
-▁atypical	-50826
-▁courtship	-50827
-▁nicknames	-50828
-▁authorship	-50829
-▁electrodes	-50830
-▁concussions	-50831
-▁daydreaming	-50832
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-Hm	-50835
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-▁Drill	-50849
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-roviral	-50854
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-▁Algorith	-50863
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-▁discernible	-50870
-▁fluctuating	-50871
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-▁prohibitions	-50873
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-jad	-50877
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-xxxx	-50879
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-elior	-50884
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-▁Haram	-50888
-▁Shire	-50889
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-IBILITY	-50892
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-▁Corrid	-50894
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-▁Ranking	-50897
-▁Wonders	-50898
-▁clocked	-50899
-▁gritted	-50900
-▁Bellevue	-50901
-▁Genetics	-50902
-▁Interpre	-50903
-▁descends	-50904
-▁keepsake	-50905
-▁reciting	-50906
-▁Mysteries	-50907
-▁barometer	-50908
-▁effecting	-50909
-▁timeshare	-50910
-▁Unbelievable	-50911
-UF	-50912
-heng	-50913
-hore	-50914
-▁nag	-50915
-pence	-50916
-▁MIDI	-50917
-▁PICT	-50918
-▁gory	-50919
-▁kilo	-50920
-marine	-50921
-▁Euras	-50922
-▁cello	-50923
-▁Avalon	-50924
-▁Capric	-50925
-▁Carney	-50926
-▁Oilers	-50927
-▁Plasma	-50928
-▁Reeves	-50929
-▁Titles	-50930
-▁smarts	-50931
-anchised	-50932
-esthesia	-50933
-▁Barnett	-50934
-▁Ellison	-50935
-▁Janeway	-50936
-▁Removed	-50937
-▁Revival	-50938
-▁jerking	-50939
-▁wagging	-50940
-▁Eleventh	-50941
-▁Paraguay	-50942
-▁Resolved	-50943
-▁Tenerife	-50944
-▁gimmicks	-50945
-▁demeaning	-50946
-▁unwritten	-50947
-▁rethinking	-50948
-▁stepmother	-50949
-▁scrutinized	-50950
-▁irreplaceable	-50951
-▁susceptibility	-50952
-dip	-50953
-bray	-50954
-olor	-50955
-reel	-50956
-wees	-50957
-▁Aki	-50958
-▁Vand	-50959
-estria	-50960
-perors	-50961
-▁Brides	-50962
-▁INSIDE	-50963
-▁methan	-50964
-▁wretch	-50965
-▁Catalan	-50966
-▁Nemesis	-50967
-▁disobey	-50968
-▁gravest	-50969
-▁wayward	-50970
-▁appended	-50971
-▁beckoned	-50972
-▁galactic	-50973
-▁Attending	-50974
-▁metformin	-50975
-▁chalkboard	-50976
-▁topography	-50977
-▁unassuming	-50978
-▁disparaging	-50979
-UDE	-50980
-iae	-50981
-nst	-50982
-▁RJ	-50983
-Arab	-50984
-aten	-50985
-erva	-50986
-gill	-50987
-▁BPM	-50988
-▁Hik	-50989
-▁Pax	-50990
-▁ard	-50991
-▁bif	-50992
-laugh	-50993
-▁Deco	-50994
-▁Muss	-50995
-▁Vega	-50996
-▁Etern	-50997
-▁LASIK	-50998
-▁Ratio	-50999
-▁cuppa	-51000
-▁frock	-51001
-Charlie	-51002
-itaries	-51003
-▁Marcos	-51004
-▁metall	-51005
-▁weasel	-51006
-colonial	-51007
-▁liturgy	-51008
-▁scathing	-51009
-completely	-51010
-▁Prospects	-51011
-▁impotence	-51012
-▁modernity	-51013
-▁Adaptation	-51014
-▁eyeglasses	-51015
-▁strikingly	-51016
-▁Medications	-51017
-▁integrative	-51018
-▁vacationing	-51019
-▁conspiracies	-51020
-▁unstructured	-51021
-alas	-51022
-oval	-51023
-▁MFA	-51024
-▁rew	-51025
-Pract	-51026
-ULOUS	-51027
-ideas	-51028
-ptons	-51029
-▁Bore	-51030
-▁Pawn	-51031
-▁Peck	-51032
-▁RPGs	-51033
-▁SUBS	-51034
-▁ripp	-51035
-GETHER	-51036
-infeld	-51037
-omethe	-51038
-ourses	-51039
-ranton	-51040
-throat	-51041
-▁BRING	-51042
-▁Clair	-51043
-▁MATER	-51044
-▁banjo	-51045
-athlete	-51046
-opsided	-51047
-▁Heller	-51048
-▁Quaker	-51049
-▁Resist	-51050
-▁STRONG	-51051
-▁Tribes	-51052
-▁Wester	-51053
-▁feisty	-51054
-▁gauges	-51055
-▁Gourmet	-51056
-▁Mistake	-51057
-▁coexist	-51058
-▁coyotes	-51059
-▁dosages	-51060
-▁rafters	-51061
-▁touting	-51062
-▁zillion	-51063
-▁Arguably	-51064
-▁Belgrade	-51065
-▁Magician	-51066
-▁Reaction	-51067
-▁Townsend	-51068
-▁logistic	-51069
-menopausal	-51070
-▁Everytime	-51071
-▁Replacing	-51072
-▁sprouting	-51073
-▁Appearance	-51074
-▁inconsider	-51075
-▁unsubstant	-51076
-▁Assessments	-51077
-▁reminiscing	-51078
-▁Supplemental	-51079
-eco	-51080
-▁yu	-51081
-cues	-51082
-asley	-51083
-▁Scal	-51084
-▁Toro	-51085
-▁Trot	-51086
-speech	-51087
-videos	-51088
-▁Patel	-51089
-▁Sushi	-51090
-▁Youre	-51091
-▁defen	-51092
-▁lakhs	-51093
-▁payee	-51094
-▁quadr	-51095
-▁robin	-51096
-▁scowl	-51097
-▁turnt	-51098
-▁Arrive	-51099
-▁Nearby	-51100
-▁Urgent	-51101
-▁Yamaha	-51102
-▁demean	-51103
-▁docket	-51104
-▁utopia	-51105
-carrying	-51106
-▁Dealers	-51107
-▁Granite	-51108
-▁McMahon	-51109
-▁hoisted	-51110
-▁puddles	-51111
-directory	-51112
-▁Nazareth	-51113
-▁Presents	-51114
-▁kilogram	-51115
-▁mansions	-51116
-▁sidekick	-51117
-▁sprawled	-51118
-▁Templates	-51119
-▁abnormally	-51120
-▁delicacies	-51121
-▁criminality	-51122
-▁crystalline	-51123
-▁theologians	-51124
-▁infiltration	-51125
-▁É	-51126
-Tre	-51127
-].”	-51128
-igy	-51129
-ocom	-51130
-otee	-51131
-▁Dri	-51132
-▁Wit	-51133
-▁sag	-51134
-Psych	-51135
-▁HOLY	-51136
-▁Kron	-51137
-▁TEAC	-51138
-▁unce	-51139
-▁COVER	-51140
-▁Fidel	-51141
-▁Gator	-51142
-▁Lorna	-51143
-▁Solve	-51144
-▁zeros	-51145
-▁Elohim	-51146
-▁Ponder	-51147
-▁skiers	-51148
-favorite	-51149
-▁flattery	-51150
-▁sorority	-51151
-obligation	-51152
-▁Desperate	-51153
-▁Dimension	-51154
-▁Incentive	-51155
-▁canonical	-51156
-▁proclaims	-51157
-▁revitalize	-51158
-▁gestational	-51159
-▁accommodates	-51160
-▁domesticated	-51161
-▁Strengthening	-51162
-▁whistleblower	-51163
-▁predictability	-51164
-WIN	-51165
-▁KR	-51166
-Guys	-51167
-inol	-51168
-usan	-51169
-Civil	-51170
-Style	-51171
-▁GUYS	-51172
-▁TODD	-51173
-▁Maori	-51174
-▁Ortiz	-51175
-▁slung	-51176
-▁Affair	-51177
-▁Bashar	-51178
-▁MASTER	-51179
-▁cached	-51180
-▁neared	-51181
-▁weirdo	-51182
-▁COUNTRY	-51183
-▁QUALITY	-51184
-▁Refunds	-51185
-▁Trusted	-51186
-▁analges	-51187
-▁cobbled	-51188
-▁immobil	-51189
-▁numbing	-51190
-▁tourney	-51191
-interview	-51192
-▁Learners	-51193
-▁Reserves	-51194
-▁crickets	-51195
-▁inciting	-51196
-▁wrappers	-51197
-▁Catalonia	-51198
-▁MacArthur	-51199
-▁digitized	-51200
-▁grandiose	-51201
-▁scoundrel	-51202
-▁symbolize	-51203
-▁bodyweight	-51204
-▁overbearing	-51205
-▁songwriters	-51206
-▁Lincolnshire	-51207
-Ns	-51208
-OV	-51209
-glo	-51210
-▁‘’	-51211
-▁MIS	-51212
-▁Dupl	-51213
-▁NOAA	-51214
-▁Oath	-51215
-▁Quay	-51216
-▁TURN	-51217
-▁aqua	-51218
-arazzi	-51219
-▁Hypot	-51220
-▁Petro	-51221
-▁clair	-51222
-▁puffs	-51223
-▁wasps	-51224
-▁whips	-51225
-▁Theres	-51226
-▁dainty	-51227
-▁cruelly	-51228
-▁stomped	-51229
-▁Compound	-51230
-▁Surgeons	-51231
-▁Surgical	-51232
-▁dandruff	-51233
-▁handyman	-51234
-▁headband	-51235
-▁Cornelius	-51236
-▁boardroom	-51237
-▁reachable	-51238
-▁vacuuming	-51239
-▁Inevitably	-51240
-▁depictions	-51241
-▁Constantine	-51242
-▁combustible	-51243
-▁reprehensible	-51244
-EMA	-51245
-cob	-51246
-Cold	-51247
-ongru	-51248
-▁Fist	-51249
-▁KNEW	-51250
-▁wets	-51251
-▁whiz	-51252
-wheels	-51253
-▁Fitch	-51254
-▁Loads	-51255
-▁RETURN	-51256
-▁Romero	-51257
-▁shards	-51258
-▁timest	-51259
-founders	-51260
-▁Jericho	-51261
-▁wearily	-51262
-▁acquires	-51263
-▁cruisers	-51264
-▁decaying	-51265
-▁Answering	-51266
-▁cognizant	-51267
-▁marinated	-51268
-▁conspiring	-51269
-▁humidifier	-51270
-▁stormwater	-51271
-▁stunningly	-51272
-▁connotation	-51273
-▁gallbladder	-51274
-▁itineraries	-51275
-HQ	-51276
-OWS	-51277
-▁ka	-51278
-Papa	-51279
-dled	-51280
-rare	-51281
-▁FAL	-51282
-▁SOR	-51283
-buyer	-51284
-moved	-51285
-▁CLEAN	-51286
-▁NORTH	-51287
-▁sects	-51288
-anxiety	-51289
-nicious	-51290
-▁ECONOM	-51291
-▁Wallet	-51292
-absorbed	-51293
-▁Lorenzo	-51294
-▁italian	-51295
-▁profane	-51296
-▁rebrand	-51297
-▁Hallmark	-51298
-▁Parallel	-51299
-▁reaffirm	-51300
-▁usernames	-51301
-enforcement	-51302
-estructible	-51303
-▁Applicable	-51304
-▁concoction	-51305
-▁contending	-51306
-▁caterpillar	-51307
-▁authentically	-51308
-▁intermittently	-51309
-MT	-51310
-Fix	-51311
-ooby	-51312
-▁BRI	-51313
-▁Eck	-51314
-▁TNT	-51315
-ANGER	-51316
-ulity	-51317
-▁Bump	-51318
-▁DIST	-51319
-▁Gram	-51320
-▁Gwyn	-51321
-▁Sark	-51322
-▁hogs	-51323
-govina	-51324
-▁Lyons	-51325
-▁diure	-51326
-▁flaps	-51327
-▁Buster	-51328
-▁COMPUT	-51329
-▁Spouse	-51330
-▁fatten	-51331
-▁Deficit	-51332
-▁Gartner	-51333
-▁Stripes	-51334
-▁outdone	-51335
-▁tremors	-51336
-▁utopian	-51337
-challenge	-51338
-▁acronyms	-51339
-▁emulator	-51340
-▁lightest	-51341
-▁ugliness	-51342
-▁withered	-51343
-▁Sacrifice	-51344
-▁halloween	-51345
-▁landowner	-51346
-comfortable	-51347
-▁incapacity	-51348
-▁syndicated	-51349
-▁Observation	-51350
-▁matchmaking	-51351
-▁sweepstakes	-51352
-▁unambiguous	-51353
-’).	-51354
-▁pt	-51355
-ONES	-51356
-▁BBB	-51357
-▁Ego	-51358
-▁Gah	-51359
-▁baw	-51360
-idium	-51361
-▁Gluc	-51362
-Thomas	-51363
-addock	-51364
-▁Fries	-51365
-▁Grown	-51366
-▁HIPAA	-51367
-▁Mecca	-51368
-▁Paran	-51369
-▁recur	-51370
-▁Darfur	-51371
-▁Mongol	-51372
-▁rewind	-51373
-iagnosed	-51374
-▁Ballard	-51375
-▁Exhaust	-51376
-▁Princes	-51377
-▁bonkers	-51378
-▁cashing	-51379
-▁defrost	-51380
-▁muddled	-51381
-▁origami	-51382
-▁Grammarly	-51383
-▁humiliate	-51384
-▁importers	-51385
-▁nurseries	-51386
-▁urination	-51387
-▁Conventions	-51388
-▁metaphysics	-51389
-').	-51390
-gum	-51391
-▁IH	-51392
-GDPR	-51393
-psis	-51394
-▁ACH	-51395
-▁CCS	-51396
-▁Atom	-51397
-▁HOLD	-51398
-▁ORIG	-51399
-Beauty	-51400
-couldn	-51401
-island	-51402
-itator	-51403
-▁Garry	-51404
-▁Maher	-51405
-▁Otter	-51406
-▁Wharf	-51407
-▁unfet	-51408
-leaving	-51409
-terated	-51410
-warning	-51411
-▁Raffle	-51412
-▁fliers	-51413
-▁oracle	-51414
-Optional	-51415
-epernick	-51416
-▁crazier	-51417
-▁erasing	-51418
-▁fringes	-51419
-▁scowled	-51420
-▁Sherwood	-51421
-impossible	-51422
-▁Formation	-51423
-▁precipice	-51424
-▁Continuity	-51425
-▁Millennial	-51426
-▁automakers	-51427
-▁meandering	-51428
-▁stimulants	-51429
-▁anaesthetic	-51430
-▁necessitate	-51431
-▁catastrophes	-51432
-▁overcrowding	-51433
-▁restlessness	-51434
-▁decomposition	-51435
-▁comprehensively	-51436
-NZ	-51437
-EET	-51438
-acus	-51439
-aimi	-51440
-uchs	-51441
-zzzz	-51442
-▁AAP	-51443
-crest	-51444
-▁WISH	-51445
-aggers	-51446
-rarian	-51447
-▁Finch	-51448
-▁Paine	-51449
-▁Pause	-51450
-▁louis	-51451
-atology	-51452
-▁Fresno	-51453
-▁supple	-51454
-▁tirade	-51455
-campaign	-51456
-football	-51457
-▁bunkers	-51458
-▁consumm	-51459
-▁Commonly	-51460
-▁filament	-51461
-▁reassess	-51462
-▁Specialty	-51463
-▁anarchist	-51464
-▁childrens	-51465
-▁shredding	-51466
-▁voracious	-51467
-▁myocardial	-51468
-▁dilapidated	-51469
-▁infiltrated	-51470
-▁Williamsburg	-51471
-▁screenwriter	-51472
-▁urbanization	-51473
-▁materialistic	-51474
-▁reinstatement	-51475
-Guy	-51476
-kas	-51477
-lop	-51478
-Club	-51479
-affy	-51480
-blow	-51481
-idea	-51482
-imia	-51483
-▁FUR	-51484
-▁GCC	-51485
-▁LTD	-51486
-▁MMO	-51487
-▁Seg	-51488
-Indian	-51489
-veless	-51490
-▁Bowen	-51491
-▁rouse	-51492
-▁rover	-51493
-episode	-51494
-▁Magdal	-51495
-▁extant	-51496
-▁folate	-51497
-identity	-51498
-▁Epstein	-51499
-▁Neutral	-51500
-▁Clarkson	-51501
-▁Godzilla	-51502
-▁Solstice	-51503
-▁CHRISTMAS	-51504
-▁examiners	-51505
-▁Courthouse	-51506
-▁Responsive	-51507
-▁shimmering	-51508
-▁interrogated	-51509
-hob	-51510
-▁si	-51511
-Dade	-51512
-esse	-51513
-ipop	-51514
-▁Bly	-51515
-▁GIS	-51516
-▁SOS	-51517
-herst	-51518
-▁Blas	-51519
-▁Bord	-51520
-▁alms	-51521
-▁tect	-51522
-caused	-51523
-rogate	-51524
-▁Duffy	-51525
-▁Loyal	-51526
-▁monst	-51527
-▁ramen	-51528
-▁spelt	-51529
-Rentals	-51530
-▁Briggs	-51531
-▁Nassau	-51532
-▁Pupils	-51533
-▁duress	-51534
-▁jQuery	-51535
-▁sprite	-51536
-▁squirt	-51537
-blocking	-51538
-handling	-51539
-▁Powered	-51540
-▁pursues	-51541
-▁Bethesda	-51542
-▁Deutsche	-51543
-▁blogroll	-51544
-▁fluently	-51545
-▁Suppliers	-51546
-▁contraind	-51547
-▁propeller	-51548
-▁Atmosphere	-51549
-▁Zealanders	-51550
-▁collectible	-51551
-▁wildflowers	-51552
-▁cooperatives	-51553
-▁designations	-51554
-▁Ci	-51555
-golf	-51556
-inus	-51557
-cumin	-51558
-iscus	-51559
-▁Gamb	-51560
-▁Runs	-51561
-▁ruse	-51562
-▁skew	-51563
-▁Explo	-51564
-▁Inbox	-51565
-▁licks	-51566
-▁megal	-51567
-▁polyn	-51568
-▁spurt	-51569
-entieth	-51570
-protect	-51571
-▁Ritual	-51572
-▁dailly	-51573
-▁enmity	-51574
-▁glowed	-51575
-▁ousted	-51576
-▁rudder	-51577
-▁Galilee	-51578
-▁stopper	-51579
-▁walmart	-51580
-▁Horrible	-51581
-▁Marbella	-51582
-▁chemists	-51583
-▁penitent	-51584
-osexuality	-51585
-▁Greenwood	-51586
-▁sprinting	-51587
-▁valentine	-51588
-▁Dumbledore	-51589
-▁mesmerized	-51590
-▁publically	-51591
-▁servicemen	-51592
-▁mesmerizing	-51593
-▁respiration	-51594
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-yx	-51727
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-zag	-51729
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-twice	-51734
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-Vision	-51737
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-▁secur	-51740
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-▁McCorm	-51742
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-eveloped	-51746
-▁affirms	-51747
-▁amelior	-51748
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-▁doughnut	-51752
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-foundation	-51755
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-▁gleefully	-51757
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-▁hydrochloride	-51762
-▁hypothyroidism	-51763
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-NES	-51765
-▁§§	-51766
-▁ANC	-51767
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-▁Ida	-51769
-Allah	-51770
-Movie	-51771
-dynam	-51772
-plugs	-51773
-schol	-51774
-▁Alam	-51775
-▁MacG	-51776
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-▁Abbot	-51780
-▁Clone	-51781
-▁Inver	-51782
-▁Jiang	-51783
-▁glint	-51784
-▁peeve	-51785
-hearing	-51786
-izabeth	-51787
-▁Dharma	-51788
-▁FRIDAY	-51789
-▁Picked	-51790
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-▁Buckley	-51792
-▁Insured	-51793
-▁photons	-51794
-▁retiree	-51795
-▁weirder	-51796
-▁Troubles	-51797
-California	-51798
-Christians	-51799
-▁Amenities	-51800
-▁Prejudice	-51801
-▁conforming	-51802
-▁divergence	-51803
-▁finalizing	-51804
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-▁reciprocity	-51807
-▁redesigning	-51808
-),"	-51809
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-Pont	-51811
-jord	-51812
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-odium	-51814
-usses	-51815
-▁Hasn	-51816
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-▁VIOL	-51818
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-azines	-51820
-comers	-51821
-rhosis	-51822
-▁MEANS	-51823
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-▁pamper	-51830
-▁ratify	-51831
-▁whined	-51832
-▁zipped	-51833
-aminated	-51834
-vastatin	-51835
-▁earshot	-51836
-▁grooves	-51837
-▁livable	-51838
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-▁Attempts	-51840
-▁bewilder	-51841
-▁downpour	-51842
-▁Auschwitz	-51843
-▁Manifesto	-51844
-▁follicles	-51845
-▁tasteless	-51846
-▁thrillers	-51847
-intelligent	-51848
-▁pernicious	-51849
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-▁pomegranate	-51851
-▁lighthearted	-51852
-▁categorically	-51853
-ERA	-51854
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-▁OJ	-51856
-▁BLM	-51857
-▁eul	-51858
-vegan	-51859
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-▁Kari	-51862
-▁Mayu	-51863
-cology	-51864
-▁FIGHT	-51865
-▁cadre	-51866
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-▁duvet	-51868
-▁prays	-51869
-porters	-51870
-▁Badger	-51871
-▁adores	-51872
-▁corral	-51873
-▁Montene	-51874
-▁Paulson	-51875
-▁Shapiro	-51876
-▁Strikes	-51877
-▁decking	-51878
-▁donkeys	-51879
-▁flopped	-51880
-▁sliders	-51881
-expanding	-51882
-▁Sponsors	-51883
-▁integers	-51884
-▁postures	-51885
-▁sculpted	-51886
-▁sprouted	-51887
-▁Countless	-51888
-▁Purchaser	-51889
-▁schoolers	-51890
-▁summarise	-51891
-▁undergoes	-51892
-▁evangelism	-51893
-▁Protestants	-51894
-▁cliffhanger	-51895
-▁requisition	-51896
-▁Developments	-51897
-PB	-51898
-FDA	-51899
-mys	-51900
-bras	-51901
-whis	-51902
-▁+/-	-51903
-opens	-51904
-▁SARS	-51905
-▁Sofa	-51906
-▁iris	-51907
-alogen	-51908
-pended	-51909
-reward	-51910
-▁Regan	-51911
-▁Trips	-51912
-parties	-51913
-▁Opened	-51914
-▁lapses	-51915
-▁locust	-51916
-▁silhou	-51917
-ocations	-51918
-▁Franken	-51919
-▁kiddies	-51920
-▁toppled	-51921
-economics	-51922
-galitarian	-51923
-▁hallmarks	-51924
-▁hijacking	-51925
-▁spiraling	-51926
-▁testicles	-51927
-▁channeling	-51928
-▁miniatures	-51929
-▁crucifixion	-51930
-▁irreparable	-51931
-Sat	-51932
-URS	-51933
-Mine	-51934
-uuuu	-51935
-▁HHS	-51936
-▁Haj	-51937
-▁IPA	-51938
-▁Zag	-51939
-aport	-51940
-knows	-51941
-▁(...	-51942
-▁BDSM	-51943
-▁Glee	-51944
-▁bele	-51945
-addled	-51946
-berish	-51947
-ileaks	-51948
-manage	-51949
-▁Jeans	-51950
-▁quell	-51951
-▁satan	-51952
-iddling	-51953
-▁Fannie	-51954
-▁forage	-51955
-????????	-51956
-ognition	-51957
-▁Arrival	-51958
-▁Daytona	-51959
-▁Essence	-51960
-▁Sweeney	-51961
-▁bailing	-51962
-▁glacial	-51963
-▁impasse	-51964
-▁runways	-51965
-Published	-51966
-cutaneous	-51967
-▁Limerick	-51968
-▁Spaniard	-51969
-▁Container	-51970
-▁Francesca	-51971
-▁Molecular	-51972
-▁Uncertain	-51973
-▁craftsman	-51974
-▁Armageddon	-51975
-▁Recruiting	-51976
-▁fanfiction	-51977
-▁molestation	-51978
-▁substantiated	-51979
-GT	-51980
-MAO	-51981
-hyp	-51982
-▁OU	-51983
-IVED	-51984
-amie	-51985
-▁CSU	-51986
-▁Gry	-51987
-UTELY	-51988
-energ	-51989
-▁Jove	-51990
-▁Komb	-51991
-▁Legs	-51992
-▁MART	-51993
-▁Yosh	-51994
-▁goug	-51995
-▁silo	-51996
-deprec	-51997
-▁Gnome	-51998
-▁beque	-51999
-▁dived	-52000
-▁rowdy	-52001
-▁Astros	-52002
-▁coyote	-52003
-▁rabbis	-52004
-measures	-52005
-▁Buttons	-52006
-▁Nervous	-52007
-▁captors	-52008
-▁nemesis	-52009
-inaudible	-52010
-▁Bitcoins	-52011
-▁Squirrel	-52012
-▁TOGETHER	-52013
-▁chromium	-52014
-▁glycemic	-52015
-▁overcoat	-52016
-▁driveways	-52017
-▁excepting	-52018
-▁subsystem	-52019
-▁archbishop	-52020
-▁correlates	-52021
-▁conceivably	-52022
-▁Distinguished	-52023
-eon	-52024
-ogg	-52025
-▁HW	-52026
-▁WT	-52027
-oldt	-52028
-antas	-52029
-asked	-52030
-▁Digg	-52031
-▁Mens	-52032
-▁Nemo	-52033
-▁SHOP	-52034
-▁saws	-52035
-appear	-52036
-rester	-52037
-▁Danes	-52038
-▁Hover	-52039
-▁LEGAL	-52040
-▁Plays	-52041
-▁Prote	-52042
-▁UNHCR	-52043
-▁brews	-52044
-▁harem	-52045
-▁spate	-52046
-▁truer	-52047
-inistic	-52048
-strange	-52049
-▁ENOUGH	-52050
-▁busily	-52051
-▁hamlet	-52052
-▁netted	-52053
-▁prolet	-52054
-▁Coroner	-52055
-▁macular	-52056
-fordshire	-52057
-▁atrocity	-52058
-▁idealism	-52059
-▁palettes	-52060
-▁admirably	-52061
-▁hindering	-52062
-▁summoning	-52063
-ominational	-52064
-▁antisocial	-52065
-▁punctuated	-52066
-▁preschoolers	-52067
-▁recognisable	-52068
-!")	-52069
-▁BK	-52070
-avir	-52071
-porn	-52072
-▁LON	-52073
-▁SLA	-52074
-anian	-52075
-avoid	-52076
-mandu	-52077
-▁Kira	-52078
-▁Mitz	-52079
-▁Popp	-52080
-▁Rift	-52081
-output	-52082
-▁bison	-52083
-▁oaths	-52084
-fellows	-52085
-iencing	-52086
-optimal	-52087
-▁McGill	-52088
-▁Pilots	-52089
-▁Gillian	-52090
-▁RELATED	-52091
-▁chopper	-52092
-▁pouches	-52093
-▁ruffled	-52094
-▁Alphabet	-52095
-▁revolved	-52096
-▁squashed	-52097
-negotiable	-52098
-▁Sacrament	-52099
-▁Underwood	-52100
-▁bargained	-52101
-▁outfitted	-52102
-▁outnumber	-52103
-▁regiments	-52104
-▁thrusting	-52105
-▁totalling	-52106
-▁Kaepernick	-52107
-▁MANAGEMENT	-52108
-▁degenerate	-52109
-▁detectable	-52110
-▁disjointed	-52111
-▁perishable	-52112
-▁alleviating	-52113
-▁candlelight	-52114
-▁incomparable	-52115
-▁Globalization	-52116
-Ly	-52117
-üs	-52118
-!?"	-52119
-Fat	-52120
-Pen	-52121
-abot	-52122
-▁Sai	-52123
-ITIVE	-52124
-alike	-52125
-umont	-52126
-▁Nove	-52127
-▁fowl	-52128
-▁visc	-52129
-▁BONUS	-52130
-▁BREAK	-52131
-▁Engel	-52132
-iscilla	-52133
-▁Bessie	-52134
-▁Cobalt	-52135
-▁Sonoma	-52136
-▁Stolen	-52137
-▁leaner	-52138
-▁plowed	-52139
-ractable	-52140
-sentence	-52141
-▁Federer	-52142
-▁cadence	-52143
-▁scrapes	-52144
-▁slimmer	-52145
-▁overused	-52146
-▁varicose	-52147
-▁Incorrect	-52148
-▁annotated	-52149
-▁eachother	-52150
-▁Initiatives	-52151
-▁concealment	-52152
-▁periodontal	-52153
-▁Northeastern	-52154
-ODE	-52155
-▁“’	-52156
-Boys	-52157
-Gene	-52158
-abre	-52159
-▁FGM	-52160
-▁Tir	-52161
-berta	-52162
-▁Aman	-52163
-▁Farr	-52164
-▁Peas	-52165
-▁VERS	-52166
-borrow	-52167
-styled	-52168
-▁BASIC	-52169
-▁Moran	-52170
-▁Perse	-52171
-▁Stall	-52172
-▁flipp	-52173
-▁vista	-52174
-Quality	-52175
-▁Draper	-52176
-▁Gatsby	-52177
-▁Makers	-52178
-▁potter	-52179
-▁spleen	-52180
-▁surges	-52181
-crossing	-52182
-password	-52183
-▁Cousins	-52184
-▁barbeque	-52185
-▁highness	-52186
-▁disgraced	-52187
-▁eminently	-52188
-▁secretion	-52189
-▁confessing	-52190
-▁inferiority	-52191
-▁subcommittee	-52192
-▁commemorating	-52193
-FY	-52194
-Ain	-52195
-Joy	-52196
-Mot	-52197
-▁EZ	-52198
-▁NU	-52199
-▁dj	-52200
-iacs	-52201
-imov	-52202
-loan	-52203
-synt	-52204
-▁Gon	-52205
-▁alk	-52206
-broke	-52207
-theft	-52208
-▁Hive	-52209
-▁Judd	-52210
-▁Oman	-52211
-▁VISA	-52212
-▁mink	-52213
-▁zoos	-52214
-apagos	-52215
-ceased	-52216
-strich	-52217
-▁Andes	-52218
-▁Cypri	-52219
-▁FAITH	-52220
-▁Texan	-52221
-Further	-52222
-▁Bridal	-52223
-▁blinks	-52224
-benefits	-52225
-▁Inflamm	-52226
-▁Mastery	-52227
-▁Raphael	-52228
-▁piecing	-52229
-▁purging	-52230
-▁warring	-52231
-offensive	-52232
-▁bathrobe	-52233
-▁fanciful	-52234
-▁audacious	-52235
-▁comforter	-52236
-▁Litigation	-52237
-▁unorthodox	-52238
-▁neurologist	-52239
-▁mistreatment	-52240
-▁communicators	-52241
-▁indoctrination	-52242
-▁revolutionized	-52243
-ysm	-52244
-,--"	-52245
-SHIP	-52246
-Wise	-52247
-awan	-52248
-beta	-52249
-hiba	-52250
-omme	-52251
-weds	-52252
-▁Lal	-52253
-Angel	-52254
-omend	-52255
-▁Guth	-52256
-▁STDs	-52257
-ocated	-52258
-ungles	-52259
-▁Guilt	-52260
-▁Inaug	-52261
-▁Rhino	-52262
-▁curvy	-52263
-▁gasps	-52264
-▁skunk	-52265
-therapy	-52266
-▁Cullen	-52267
-▁Metall	-52268
-▁Woohoo	-52269
-▁bakers	-52270
-▁extric	-52271
-▁falter	-52272
-▁ironed	-52273
-▁rubles	-52274
-saturated	-52275
-▁crossbow	-52276
-▁rudeness	-52277
-▁sedative	-52278
-▁wheezing	-52279
-▁Hilarious	-52280
-▁curvature	-52281
-▁hardening	-52282
-▁scapegoat	-52283
-▁simplifies	-52284
-▁archipelago	-52285
-▁consultative	-52286
-▁constitutions	-52287
-▁interchangeably	-52288
-TSA	-52289
-zzi	-52290
-Emer	-52291
-Pick	-52292
-▁Awe	-52293
-▁TAB	-52294
-▁mak	-52295
-adish	-52296
-▁furs	-52297
-▁icky	-52298
-switch	-52299
-▁Gains	-52300
-▁Viola	-52301
-▁cobbl	-52302
-▁obscen	-52303
-▁shriek	-52304
-▁tacked	-52305
-▁voodoo	-52306
-continue	-52307
-solution	-52308
-▁Hotline	-52309
-▁deviant	-52310
-▁faraway	-52311
-▁terrors	-52312
-▁vibrate	-52313
-▁weighty	-52314
-▁Sudanese	-52315
-▁Trafford	-52316
-▁contours	-52317
-▁patching	-52318
-▁theorist	-52319
-▁disembark	-52320
-▁repealing	-52321
-▁sandstone	-52322
-▁Assistants	-52323
-▁reaffirmed	-52324
-▁deactivated	-52325
-▁😍	-52326
-ATO	-52327
-ANGU	-52328
-TIME	-52329
-Ware	-52330
-aura	-52331
-bilt	-52332
-kine	-52333
-mson	-52334
-oxin	-52335
-▁FHA	-52336
-▁HUR	-52337
-etown	-52338
-juice	-52339
-▁fics	-52340
-losing	-52341
-▁DOESN	-52342
-▁olden	-52343
-▁Chains	-52344
-▁Gossip	-52345
-▁juries	-52346
-▁quench	-52347
-▁thrash	-52348
-▁hypogly	-52349
-▁offends	-52350
-▁AMERICAN	-52351
-▁Defining	-52352
-▁Philippe	-52353
-▁Sapphire	-52354
-▁deformed	-52355
-▁fentanyl	-52356
-▁munching	-52357
-▁reverent	-52358
-▁feathered	-52359
-▁migratory	-52360
-▁stupidest	-52361
-▁surpasses	-52362
-▁recreating	-52363
-▁brainwashing	-52364
-hap	-52365
-ñoz	-52366
-alyn	-52367
-▁Yel	-52368
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-issory	-52375
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-alienable	-52389
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-iann	-52399
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-allergenic	-52417
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-▁FINANCIAL	-52419
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-▁plutonium	-52421
-▁resetting	-52422
-▁Struggling	-52423
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-▁repeatable	-52425
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-▁synthesize	-52427
-▁Correctional	-52428
-▁radiotherapy	-52429
-▁wp	-52430
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-▁sop	-52432
-fraud	-52433
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-▁gong	-52435
-murder	-52436
-▁Embed	-52437
-▁Sheer	-52438
-▁Silas	-52439
-▁Stras	-52440
-▁envoy	-52441
-▁Bannon	-52442
-▁Layout	-52443
-▁bigots	-52444
-▁meddle	-52445
-▁squint	-52446
-▁Cascade	-52447
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-▁blowout	-52449
-▁forlorn	-52450
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-▁Wildcats	-52452
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-▁subprime	-52454
-▁Precisely	-52455
-▁Teenagers	-52456
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-inka	-52468
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-MeToo	-52471
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-ihuahua	-52479
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-operable	-52484
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-▁narcotic	-52489
-▁Davenport	-52490
-▁Elisabeth	-52491
-▁Finishing	-52492
-▁blossomed	-52493
-▁breakouts	-52494
-▁naysayers	-52495
-▁radiology	-52496
-▁Counselling	-52497
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-▁offensively	-52499
-▁coincidental	-52500
-▁contradicting	-52501
-▁postsecondary	-52502
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-army	-52508
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-▁EMT	-52511
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-▁Yen	-52513
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-▁lobe	-52516
-▁maxi	-52517
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-iality	-52519
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-▁prism	-52524
-▁riffs	-52525
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-trading	-52527
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-▁Trusts	-52529
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-▁Disagree	-52532
-▁compatri	-52533
-▁diaspora	-52534
-▁enacting	-52535
-▁faceless	-52536
-▁rollover	-52537
-▁Traumatic	-52538
-▁WikiLeaks	-52539
-▁memorizing	-52540
-▁articulation	-52541
-▁materialized	-52542
-▁indescribable	-52543
-▁generalizations	-52544
-▁unsubstantiated	-52545
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-Ken	-52547
-hay	-52548
-▁Yi	-52549
-abit	-52550
-hora	-52551
-▁FLY	-52552
-▁Gag	-52553
-ECTED	-52554
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-omsky	-52558
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-unknown	-52568
-▁Topper	-52569
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-▁GUARANT	-52571
-▁Matilda	-52572
-▁OnePlus	-52573
-▁heaving	-52574
-authentic	-52575
-▁Explicit	-52576
-▁Whistler	-52577
-▁ancients	-52578
-▁unfathom	-52579
-▁authorise	-52580
-▁breaching	-52581
-▁instructs	-52582
-▁Attempting	-52583
-▁Stonehenge	-52584
-▁randomised	-52585
-▁counsellors	-52586
-▁monasteries	-52587
-▁magnificence	-52588
-rak	-52589
-cove	-52590
-indy	-52591
-jama	-52592
-▁DIN	-52593
-▁Paz	-52594
-▁WAL	-52595
-▁bor	-52596
-▁exe	-52597
-etted	-52598
-Modern	-52599
-sealed	-52600
-▁Annoy	-52601
-▁Bower	-52602
-▁PRESS	-52603
-▁Param	-52604
-▁vowel	-52605
-▁Donkey	-52606
-▁Macron	-52607
-▁Magnet	-52608
-▁Mystic	-52609
-▁Skinny	-52610
-▁chants	-52611
-▁quarts	-52612
-Congress	-52613
-▁WITNESS	-52614
-▁adheres	-52615
-▁blacked	-52616
-▁climber	-52617
-▁ineffic	-52618
-▁feverish	-52619
-▁Synthetic	-52620
-▁mutilated	-52621
-▁Adjustment	-52622
-▁Mainstream	-52623
-▁Waterfront	-52624
-▁tyrannical	-52625
-▁werewolves	-52626
-▁Accountants	-52627
-▁indisputable	-52628
-▁intermission	-52629
--[	-52630
-xa	-52631
-▁UH	-52632
-Xena	-52633
-▁CFL	-52634
-▁POT	-52635
-ANCES	-52636
-attie	-52637
-igham	-52638
-oreal	-52639
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-▁ENGL	-52641
-▁Oreo	-52642
-▁Pals	-52643
-▁mont	-52644
-▁peri	-52645
-▁reed	-52646
-▁yolk	-52647
-Employ	-52648
-brates	-52649
-▁Flesh	-52650
-▁Token	-52651
-▁Volta	-52652
-▁omnis	-52653
-▁sheen	-52654
-chicken	-52655
-iscally	-52656
-▁Chorus	-52657
-▁Donnie	-52658
-▁Navajo	-52659
-▁slings	-52660
-▁Erdogan	-52661
-▁Patagon	-52662
-▁Tablets	-52663
-▁mangled	-52664
-▁plaques	-52665
-▁puffing	-52666
-▁incongru	-52667
-▁Dartmouth	-52668
-▁Reputation	-52669
-▁liturgical	-52670
-▁Outsourcing	-52671
-Luc	-52672
-amon	-52673
-dise	-52674
-▁PDT	-52675
-▁Qui	-52676
-▁RNA	-52677
-▁fps	-52678
-brick	-52679
-ilted	-52680
-means	-52681
-▁Byrd	-52682
-▁PAID	-52683
-▁Yuan	-52684
-▁puss	-52685
-forget	-52686
-▁Mobil	-52687
-▁Schro	-52688
-▁aloft	-52689
-▁cower	-52690
-exactly	-52691
-gathere	-52692
-listing	-52693
-▁Armour	-52694
-▁promos	-52695
-▁sleazy	-52696
-pointers	-52697
-somewhat	-52698
-▁Balloon	-52699
-▁Blender	-52700
-▁Cecilia	-52701
-▁batters	-52702
-▁blaring	-52703
-▁pragmat	-52704
-▁Peaceful	-52705
-▁Scranton	-52706
-▁competes	-52707
-▁condemns	-52708
-▁scheming	-52709
-▁Flagstaff	-52710
-▁paperless	-52711
-▁Practicing	-52712
-▁microbiome	-52713
-▁stipulation	-52714
-▁Practitioners	-52715
-:]	-52716
-Ya	-52717
-Lie	-52718
-oza	-52719
-▁MOV	-52720
-▁VAR	-52721
-▁WSJ	-52722
-othal	-52723
-▁Chil	-52724
-▁Malk	-52725
-▁PCOS	-52726
-▁chum	-52727
-▁whet	-52728
-ausage	-52729
-istice	-52730
-oopers	-52731
-▁Herbs	-52732
-▁Liked	-52733
-▁Tudor	-52734
-▁Wages	-52735
-▁indef	-52736
-▁taker	-52737
-▁CREDIT	-52738
-▁Mosaic	-52739
-▁Onions	-52740
-▁bandit	-52741
-▁gothic	-52742
-▁scrimm	-52743
-▁subdue	-52744
-▁whores	-52745
-indrical	-52746
-▁Monique	-52747
-▁Smarter	-52748
-▁hammers	-52749
-▁smother	-52750
-operating	-52751
-▁breakage	-52752
-▁derelict	-52753
-▁executor	-52754
-▁furrowed	-52755
-▁hypothes	-52756
-▁spawning	-52757
-▁superbly	-52758
-▁taunting	-52759
-▁Galatians	-52760
-▁Injection	-52761
-▁Mentoring	-52762
-▁NaNoWriMo	-52763
-▁reopening	-52764
-itriptyline	-52765
-▁astronomer	-52766
-▁audiobooks	-52767
-▁refineries	-52768
-▁torrential	-52769
-▁trafficked	-52770
-.............	-52771
-▁uninterested	-52772
-▁KA	-52773
-phon	-52774
-pump	-52775
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-▁Sok	-52777
-mberg	-52778
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-▁mite	-52780
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-▁pett	-52782
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-denial	-52785
-itaire	-52786
-lipped	-52787
-▁Greed	-52788
-▁Muñoz	-52789
-▁Tells	-52790
-▁WORDS	-52791
-▁hater	-52792
-▁waltz	-52793
-lighter	-52794
-▁muddle	-52795
-▁Celeste	-52796
-▁breadcr	-52797
-▁exhaled	-52798
-▁phrased	-52799
-▁pistach	-52800
-universal	-52801
-▁Profiles	-52802
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-▁headsets	-52804
-▁whiskers	-52805
-▁Aerospace	-52806
-▁Clearance	-52807
-▁Positions	-52808
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-▁hereunder	-52810
-▁picturing	-52811
-effectively	-52812
-▁Physically	-52813
-▁Reflection	-52814
-▁imperialist	-52815
-▁interconnect	-52816
-▁Unsurprisingly	-52817
-!}	-52818
-▁Eno	-52819
-▁GBP	-52820
-holds	-52821
-uracy	-52822
-▁LOSS	-52823
-▁Unch	-52824
-▁anus	-52825
-▁sept	-52826
-battle	-52827
-elujah	-52828
-▁Jonny	-52829
-▁maxed	-52830
-PRESENT	-52831
-atchers	-52832
-rollers	-52833
-▁Clouds	-52834
-▁Gregor	-52835
-▁buzzed	-52836
-▁celebs	-52837
-▁errant	-52838
-▁Bearing	-52839
-▁airbags	-52840
-▁firemen	-52841
-▁Invasion	-52842
-▁PRODUCTS	-52843
-▁beatings	-52844
-▁shrieked	-52845
-▁actuarial	-52846
-▁Adolescent	-52847
-▁protruding	-52848
-▁rehearsing	-52849
-▁retrograde	-52850
-▁inefficiency	-52851
-▁commemorative	-52852
-Cur	-52853
-oum	-52854
-Exec	-52855
-Tree	-52856
-pter	-52857
-▁hoe	-52858
-anter	-52859
-esian	-52860
-▁Luca	-52861
-▁Slee	-52862
-▁Waco	-52863
-▁Blond	-52864
-▁crass	-52865
-idazole	-52866
-▁Brunch	-52867
-▁Shrine	-52868
-▁Static	-52869
-involved	-52870
-▁Roasted	-52871
-▁defying	-52872
-▁turnkey	-52873
-ceptively	-52874
-▁amicable	-52875
-▁followup	-52876
-▁rearview	-52877
-▁Countdown	-52878
-▁Inspiring	-52879
-▁harnessed	-52880
-▁untenable	-52881
-▁antiseptic	-52882
-▁pedagogical	-52883
-▁thoughtless	-52884
-▁sponsorships	-52885
-▁developmentally	-52886
-Gov	-52887
-▁ng	-52888
-Sett	-52889
-THIS	-52890
-unas	-52891
-▁aaa	-52892
-lofen	-52893
-oloft	-52894
-▁Brat	-52895
-▁KPIs	-52896
-▁Nant	-52897
-▁Polk	-52898
-▁gras	-52899
-casual	-52900
-llable	-52901
-ulking	-52902
-▁Asher	-52903
-▁Bluff	-52904
-▁Shank	-52905
-▁annul	-52906
-▁antis	-52907
-Overall	-52908
-Success	-52909
-akedown	-52910
-▁Bergen	-52911
-▁Volley	-52912
-substant	-52913
-▁Threats	-52914
-▁malaise	-52915
-▁snorkel	-52916
-▁truffle	-52917
-certainly	-52918
-▁Airborne	-52919
-▁MATERIAL	-52920
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-▁expiring	-52922
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-▁sketched	-52924
-▁vanishes	-52925
-definitely	-52926
-▁Checklist	-52927
-▁Consulate	-52928
-▁Sincerely	-52929
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-▁learnings	-52931
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-▁Coordinate	-52933
-▁fractional	-52934
-▁colonialism	-52935
-▁rehabilitate	-52936
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-▁triumphantly	-52938
-?..	-52939
-vik	-52940
-Band	-52941
-herly	-52942
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-▁TYPE	-52944
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-vacation	-52956
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-apoxetine	-52962
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-▁misdiagn	-52969
-▁normalcy	-52970
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-▁elongated	-52973
-▁overdrive	-52974
-▁reformers	-52975
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-▁constructively	-52977
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-enics	-52985
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-ilical	-52988
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-unting	-52991
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-ITATION	-52998
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-▁Animated	-53005
-▁Browning	-53006
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-▁cleanest	-53008
-▁monstros	-53009
-▁potholes	-53010
-▁proudest	-53011
-▁Knoxville	-53012
-▁Watergate	-53013
-▁basements	-53014
-▁blissfully	-53015
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-▁multimillion	-53017
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-Gar	-53019
-Sep	-53020
-anu	-53021
-bic	-53022
-▁YO	-53023
-IVID	-53024
-burd	-53025
-mend	-53026
-▁WEB	-53027
-nosed	-53028
-uckle	-53029
-▁Demi	-53030
-▁Mina	-53031
-▁dood	-53032
-▁peat	-53033
-▁Ambro	-53034
-▁Boxer	-53035
-▁Saras	-53036
-▁Swamp	-53037
-▁handi	-53038
-▁lurch	-53039
-▁macho	-53040
-▁navel	-53041
-stocked	-53042
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-▁Boring	-53044
-▁Quartz	-53045
-▁bridle	-53046
-▁havens	-53047
-▁Burgess	-53048
-▁bravado	-53049
-▁butters	-53050
-▁laurels	-53051
-▁undress	-53052
-▁Genocide	-53053
-▁dogmatic	-53054
-▁heroines	-53055
-▁ladyship	-53056
-▁pigments	-53057
-▁destroyer	-53058
-▁mortified	-53059
-▁precincts	-53060
-▁recreated	-53061
-▁auspicious	-53062
-▁infarction	-53063
-▁ordination	-53064
-▁Ambassadors	-53065
-▁rediscovered	-53066
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-/).	-53068
-Dick	-53069
-Hint	-53070
-IRON	-53071
-Twas	-53072
-ahas	-53073
-▁Lys	-53074
-▁ken	-53075
-▁Diva	-53076
-▁Gigi	-53077
-▁Odin	-53078
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-▁jade	-53080
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-▁Idiot	-53082
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-▁mirth	-53087
-imetres	-53088
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-▁motifs	-53090
-▁stupor	-53091
-▁sultry	-53092
-ominable	-53093
-▁greased	-53094
-▁Globally	-53095
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-▁fretting	-53098
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-inette	-53127
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-▁sexiest	-53136
-realistic	-53137
-▁Minerals	-53138
-▁Stamford	-53139
-▁bordered	-53140
-▁heralded	-53141
-▁loathing	-53142
-▁playtime	-53143
-▁Shiraishi	-53144
-▁butternut	-53145
-▁reloading	-53146
-▁shoveling	-53147
-▁unselfish	-53148
-▁Detectives	-53149
-▁crocheting	-53150
-▁forthright	-53151
-▁Californian	-53152
-▁delinquency	-53153
-▁transcribed	-53154
-▁Conveniently	-53155
-▁Communicating	-53156
-▁underprivileged	-53157
-QA	-53158
-Bur	-53159
-eri	-53160
-zep	-53161
-Mean	-53162
-▁Ars	-53163
-▁Vax	-53164
-drama	-53165
-imoto	-53166
-trend	-53167
-▁Cola	-53168
-▁FIGS	-53169
-▁INSP	-53170
-▁Scha	-53171
-▁Tant	-53172
-ashire	-53173
-▁Inher	-53174
-▁REALT	-53175
-▁Ramon	-53176
-federal	-53177
-reliant	-53178
-▁Breaks	-53179
-▁Darryl	-53180
-▁Tarant	-53181
-▁cloned	-53182
-▁groves	-53183
-▁stowed	-53184
-▁teapot	-53185
-▁Compton	-53186
-▁grossed	-53187
-▁restate	-53188
-attention	-53189
-organised	-53190
-▁Augustus	-53191
-▁Fourteen	-53192
-▁Garfield	-53193
-▁SOFTWARE	-53194
-▁clogging	-53195
-▁daybreak	-53196
-▁fetching	-53197
-▁Annapolis	-53198
-▁Fireworks	-53199
-▁embroiled	-53200
-▁secession	-53201
-▁CarRentals	-53202
-▁Reflecting	-53203
-▁Westerners	-53204
-righteousness	-53205
-▁benchmarking	-53206
-▁inconclusive	-53207
-rups	-53208
-▁JOY	-53209
-▁Tsu	-53210
-▁sms	-53211
-icist	-53212
-▁scru	-53213
-▁stap	-53214
-▁Abyss	-53215
-mitters	-53216
-vincing	-53217
-▁Belong	-53218
-▁Jurisd	-53219
-▁Nellie	-53220
-▁glycer	-53221
-▁graham	-53222
-▁Runners	-53223
-▁Webinar	-53224
-▁boyhood	-53225
-▁giraffe	-53226
-▁mediate	-53227
-▁peacock	-53228
-▁posthum	-53229
-▁ADDITION	-53230
-▁Donation	-53231
-▁Equipped	-53232
-▁Immortal	-53233
-▁Lordship	-53234
-▁carvings	-53235
-▁idolatry	-53236
-▁midterms	-53237
-▁pledging	-53238
-▁Friedrich	-53239
-▁Galapagos	-53240
-▁lethargic	-53241
-▁wistfully	-53242
-▁ABSOLUTELY	-53243
-▁Nominations	-53244
-▁unification	-53245
-▁transplanted	-53246
-▁individualism	-53247
-',"	-53248
-HOL	-53249
-aam	-53250
-Rain	-53251
-▁doe	-53252
-▁tai	-53253
-DRESS	-53254
-Saint	-53255
-Woman	-53256
-insic	-53257
-oulos	-53258
-ustic	-53259
-▁FEAR	-53260
-ashore	-53261
-ernaut	-53262
-jacket	-53263
-lifted	-53264
-▁Bubba	-53265
-▁Fleur	-53266
-▁Promo	-53267
-▁Saves	-53268
-▁nears	-53269
-Suppose	-53270
-▁CREATE	-53271
-▁Coyote	-53272
-▁verily	-53273
-adically	-53274
-destruct	-53275
-universe	-53276
-▁Colombo	-53277
-▁alimony	-53278
-▁ketones	-53279
-▁limping	-53280
-▁surmise	-53281
-Anonymous	-53282
-▁conforms	-53283
-▁Retention	-53284
-▁barrister	-53285
-▁catalysts	-53286
-▁disbanded	-53287
-▁dissolves	-53288
-▁pointedly	-53289
-▁videotape	-53290
-▁breathlessly	-53291
-▁malfunctions	-53292
-▁painstakingly	-53293
-mr	-53294
-ème	-53295
-▁JH	-53296
-▁KU	-53297
-Hara	-53298
-Wake	-53299
-imed	-53300
-umab	-53301
-▁JAM	-53302
-▁Wom	-53303
-▁dmv	-53304
-▁lor	-53305
-Broad	-53306
-zburg	-53307
-▁Bale	-53308
-▁Depp	-53309
-▁deco	-53310
-twater	-53311
-ursing	-53312
-▁Beech	-53313
-▁Pussy	-53314
-African	-53315
-Usually	-53316
-convers	-53317
-pendium	-53318
-rugated	-53319
-▁Blaine	-53320
-▁Dammit	-53321
-▁Ledger	-53322
-▁POLICY	-53323
-▁Punjab	-53324
-▁Puzzle	-53325
-▁Voyage	-53326
-▁afresh	-53327
-▁dwells	-53328
-▁indisc	-53329
-▁lactic	-53330
-▁vermin	-53331
-▁accuser	-53332
-▁faucets	-53333
-▁grimace	-53334
-▁hissing	-53335
-▁entryway	-53336
-▁inextric	-53337
-▁sizzling	-53338
-reflection	-53339
-relatively	-53340
-ribusiness	-53341
-▁Forrester	-53342
-▁devouring	-53343
-▁euphemism	-53344
-▁remarried	-53345
-▁succulent	-53346
-▁Cumberland	-53347
-▁Transplant	-53348
-▁introverted	-53349
-▁tantalizing	-53350
-▁bewilderment	-53351
-▁disheartened	-53352
-▁simplification	-53353
-TW	-53354
-INO	-53355
-wwww	-53356
-▁Ard	-53357
-▁CPD	-53358
-▁DOD	-53359
-▁SCR	-53360
-Track	-53361
-multi	-53362
-▁Okin	-53363
-▁Sami	-53364
-Indust	-53365
-▁Cakes	-53366
-▁Jacks	-53367
-▁Jolly	-53368
-▁Poems	-53369
-▁forte	-53370
-classes	-53371
-failure	-53372
-loxacin	-53373
-outdoor	-53374
-▁Insect	-53375
-▁Ridley	-53376
-▁buzzer	-53377
-▁dampen	-53378
-▁juicer	-53379
-▁photon	-53380
-▁tamper	-53381
-currency	-53382
-▁Decades	-53383
-▁Halfway	-53384
-▁Vinegar	-53385
-▁refract	-53386
-▁sundown	-53387
-▁Courtesy	-53388
-▁headlong	-53389
-▁soulmate	-53390
-▁undercut	-53391
-▁Mansfield	-53392
-▁dictating	-53393
-▁Preference	-53394
-▁bottleneck	-53395
-▁infraction	-53396
-▁inordinate	-53397
-▁disoriented	-53398
-!..	-53399
-ANI	-53400
-Haw	-53401
-Leg	-53402
-egg	-53403
-ulp	-53404
-Drop	-53405
-▁ESA	-53406
-iards	-53407
-psies	-53408
-▁isle	-53409
-mitter	-53410
-ohnson	-53411
-ouston	-53412
-▁Elias	-53413
-▁RULES	-53414
-▁solos	-53415
-distant	-53416
-▁Crying	-53417
-▁Marcie	-53418
-▁Stages	-53419
-▁anoint	-53420
-▁bowled	-53421
-▁hitchh	-53422
-▁whiter	-53423
-strapped	-53424
-▁blemish	-53425
-▁takeout	-53426
-▁Optimize	-53427
-▁landfall	-53428
-▁metering	-53429
-▁methanol	-53430
-▁realtors	-53431
-▁Godfather	-53432
-▁crackling	-53433
-▁altruistic	-53434
-▁inflection	-53435
-▁squandered	-53436
-▁tantamount	-53437
-▁spontaneity	-53438
-IMP	-53439
-abo	-53440
-▁dd	-53441
-."--	-53442
-Zero	-53443
-dash	-53444
-enet	-53445
-▁INF	-53446
-▁XII	-53447
-▁duc	-53448
-arnia	-53449
-▁Maud	-53450
-▁iffy	-53451
-▁pico	-53452
-▁Bally	-53453
-▁FINAL	-53454
-▁USING	-53455
-▁pubic	-53456
-ORGEOUS	-53457
-uelling	-53458
-▁Bazaar	-53459
-▁faggot	-53460
-▁manger	-53461
-▁motels	-53462
-▁Provost	-53463
-▁Giuliani	-53464
-▁primates	-53465
-▁clamoring	-53466
-▁clockwise	-53467
-▁jailbreak	-53468
-▁wherewith	-53469
-▁aberration	-53470
-▁Archdiocese	-53471
-▁undeveloped	-53472
-▁compensating	-53473
-▁interrelated	-53474
-▁regenerative	-53475
-cko	-53476
-zig	-53477
-Cert	-53478
-FULL	-53479
-Zone	-53480
-flag	-53481
-rank	-53482
-▁Dia	-53483
-▁LAB	-53484
-▁PPI	-53485
-truck	-53486
-▁EBIT	-53487
-▁Ramp	-53488
-▁pore	-53489
-▁seag	-53490
-Import	-53491
-Within	-53492
-▁Appra	-53493
-▁Argos	-53494
-▁Coins	-53495
-▁Novak	-53496
-▁Stalk	-53497
-▁Umbre	-53498
-▁islam	-53499
-express	-53500
-▁ACCEPT	-53501
-▁Corpus	-53502
-▁Reaper	-53503
-▁flaunt	-53504
-▁keypad	-53505
-▁Clooney	-53506
-▁Screens	-53507
-▁british	-53508
-▁preside	-53509
-▁Gonzales	-53510
-▁belittle	-53511
-▁juxtapos	-53512
-▁sprained	-53513
-▁Defensive	-53514
-▁confesses	-53515
-▁conspired	-53516
-▁laminated	-53517
-▁Economists	-53518
-▁egalitarian	-53519
-▁Alternatives	-53520
-Vo	-53521
-rn	-53522
-Occ	-53523
-jes	-53524
-wah	-53525
-yay	-53526
-loose	-53527
-ombre	-53528
-▁DOOR	-53529
-▁Este	-53530
-▁Gaia	-53531
-▁Neur	-53532
-▁PHYS	-53533
-▁shes	-53534
-▁spac	-53535
-▁Angle	-53536
-▁Marse	-53537
-▁Pinot	-53538
-▁poise	-53539
-guarant	-53540
-sterect	-53541
-▁Bolshe	-53542
-▁DAMMIT	-53543
-▁Edmond	-53544
-▁Effort	-53545
-▁Remedy	-53546
-▁cannab	-53547
-▁capric	-53548
-▁fucker	-53549
-▁thyself	-53550
-picuously	-53551
-▁besieged	-53552
-▁feasting	-53553
-▁piloting	-53554
-▁bicycling	-53555
-▁pinterest	-53556
-▁saltwater	-53557
-▁disordered	-53558
-▁encampment	-53559
-▁recounting	-53560
-▁assimilation	-53561
-▁unsurprisingly	-53562
-▁‐	-53563
-▁♦	-53564
-COP	-53565
-▁AED	-53566
-▁Rei	-53567
-ecito	-53568
-inaig	-53569
-tooth	-53570
-▁Lass	-53571
-▁shoo	-53572
-Silver	-53573
-achron	-53574
-rofoam	-53575
-uitary	-53576
-▁Reuben	-53577
-▁adrift	-53578
-▁tosses	-53579
-▁Pegasus	-53580
-▁chanted	-53581
-▁sauteed	-53582
-forceable	-53583
-ownership	-53584
-▁Carleton	-53585
-▁Cellular	-53586
-▁executes	-53587
-▁graphite	-53588
-▁caressing	-53589
-▁circuitry	-53590
-▁caregiving	-53591
-▁hemorrhage	-53592
-▁Playstation	-53593
-▁Municipality	-53594
-▁accomplishes	-53595
-▁epidemiological	-53596
-MER	-53597
-USB	-53598
-bio	-53599
-Snow	-53600
-clos	-53601
-cosm	-53602
-hive	-53603
-▁JOH	-53604
-▁Mim	-53605
-▁SHO	-53606
-▁ety	-53607
-ampoo	-53608
-▁Chim	-53609
-▁Deed	-53610
-▁PICK	-53611
-▁THER	-53612
-▁hern	-53613
-engage	-53614
-lating	-53615
-schild	-53616
-▁whoop	-53617
-Reading	-53618
-▁Helped	-53619
-▁Ransom	-53620
-▁Ripper	-53621
-▁Rookie	-53622
-▁Tamara	-53623
-assified	-53624
-▁Swansea	-53625
-▁citrate	-53626
-▁kinderg	-53627
-▁widower	-53628
-▁BIRTHDAY	-53629
-▁disarray	-53630
-▁warships	-53631
-pronounced	-53632
-▁Campaigns	-53633
-▁Dickinson	-53634
-▁cremation	-53635
-▁equestrian	-53636
-▁wallpapers	-53637
-▁Commissions	-53638
-:[	-53639
-ACS	-53640
-tong	-53641
-▁Ryu	-53642
-rials	-53643
-stool	-53644
-▁Cler	-53645
-▁Joke	-53646
-▁Maru	-53647
-▁Suns	-53648
-▁Sven	-53649
-propos	-53650
-quoted	-53651
-▁flake	-53652
-sembles	-53653
-▁Hubble	-53654
-▁Rohing	-53655
-▁Stayed	-53656
-▁Troops	-53657
-▁decode	-53658
-conflict	-53659
-domestic	-53660
-▁Hartley	-53661
-▁Pretend	-53662
-▁Servers	-53663
-▁Severus	-53664
-▁Vaughan	-53665
-▁birding	-53666
-matically	-53667
-▁amphithe	-53668
-▁tattered	-53669
-▁twitched	-53670
-▁segmented	-53671
-▁applesauce	-53672
-▁conductors	-53673
-▁contiguous	-53674
-▁timeliness	-53675
-▁irrevocable	-53676
-▁machinations	-53677
-FN	-53678
-Ws	-53679
-Van	-53680
-soc	-53681
-’,”	-53682
-▁(«	-53683
-▁rx	-53684
-▁“”	-53685
-mask	-53686
-oree	-53687
-ssss	-53688
-▁Yon	-53689
-▁VICT	-53690
-▁aden	-53691
-▁nike	-53692
-▁slob	-53693
-Beyond	-53694
-estown	-53695
-▁Buick	-53696
-▁gypsy	-53697
-▁macar	-53698
-Between	-53699
-strokes	-53700
-uscular	-53701
-▁jitter	-53702
-▁musing	-53703
-▁skater	-53704
-▁Christi	-53705
-▁Jealous	-53706
-▁Newtown	-53707
-▁Orville	-53708
-▁Squares	-53709
-▁fleshed	-53710
-▁jungles	-53711
-▁Figuring	-53712
-▁ditching	-53713
-▁november	-53714
-▁pathogen	-53715
-▁profiled	-53716
-▁Kathmandu	-53717
-▁QUESTIONS	-53718
-▁Fourteenth	-53719
-▁driverless	-53720
-▁overtaking	-53721
-▁Elimination	-53722
-▁graphically	-53723
-▁aristocratic	-53724
-▁conglomerate	-53725
-▁chiropractors	-53726
-▁generalization	-53727
-Pg	-53728
-Cir	-53729
-OLS	-53730
-JUNK	-53731
-hows	-53732
-owan	-53733
-▁BOM	-53734
-▁Kep	-53735
-Enjoy	-53736
-ITELY	-53737
-abyte	-53738
-ifers	-53739
-onian	-53740
-ptive	-53741
-uette	-53742
-▁Boca	-53743
-▁Cllr	-53744
-▁Inca	-53745
-▁Lust	-53746
-▁Tuna	-53747
-▁Boliv	-53748
-▁Impal	-53749
-▁Malin	-53750
-▁greek	-53751
-▁jesus	-53752
-numbers	-53753
-strably	-53754
-▁Iphone	-53755
-▁daffod	-53756
-▁squeal	-53757
-▁Lettuce	-53758
-▁PROJECT	-53759
-▁Ritchie	-53760
-▁haughty	-53761
-osteroids	-53762
-▁Adorable	-53763
-▁booklets	-53764
-▁ligament	-53765
-▁headlight	-53766
-▁lessening	-53767
-▁tarnished	-53768
-▁Plantation	-53769
-▁devolution	-53770
-▁rearranged	-53771
-▁Limitations	-53772
-▁autographed	-53773
-▁resettlement	-53774
-▁uncontrollably	-53775
-▁methamphetamine	-53776
-BTW	-53777
-OSP	-53778
-▁KK	-53779
-▁ti	-53780
-Fred	-53781
-Marc	-53782
-▁Coh	-53783
-▁HSE	-53784
-▁ISN	-53785
-▁VPS	-53786
-Phone	-53787
-ameda	-53788
-lared	-53789
-rived	-53790
-tweet	-53791
-ussen	-53792
-▁IAEA	-53793
-▁Jodi	-53794
-▁Nerd	-53795
-▁Oliv	-53796
-▁Ying	-53797
-▁moll	-53798
-▁xmas	-53799
-cerned	-53800
-gating	-53801
-plasia	-53802
-virgin	-53803
-▁Appet	-53804
-▁Deter	-53805
-▁Olsen	-53806
-▁gnome	-53807
-▁slaps	-53808
-▁soapy	-53809
-assured	-53810
-▁Comply	-53811
-▁ESTABL	-53812
-▁Merely	-53813
-▁Verily	-53814
-▁Vessel	-53815
-▁nibble	-53816
-▁rapids	-53817
-allowing	-53818
-▁Maynard	-53819
-▁Shaking	-53820
-▁bravest	-53821
-▁entrant	-53822
-▁hellish	-53823
-▁vaulted	-53824
-cigarette	-53825
-residents	-53826
-▁GORGEOUS	-53827
-▁Kristina	-53828
-▁Offshore	-53829
-▁chastise	-53830
-▁precedes	-53831
-▁Bernstein	-53832
-▁judicious	-53833
-▁petrified	-53834
-▁Internship	-53835
-▁vindictive	-53836
-▁interjected	-53837
-▁undiagnosed	-53838
-▁Appointments	-53839
-▁perpetuating	-53840
-Fan	-53841
-bom	-53842
-wif	-53843
-▁(_	-53844
-LOAD	-53845
-PROP	-53846
-cube	-53847
-▁Cuz	-53848
-▁Ved	-53849
-Assad	-53850
-berth	-53851
-▁Amir	-53852
-▁Hess	-53853
-▁Raid	-53854
-▁Rats	-53855
-victim	-53856
-▁flint	-53857
-▁papal	-53858
-▁slugs	-53859
-▁Downey	-53860
-▁Hosted	-53861
-▁enchil	-53862
-▁feller	-53863
-▁goddam	-53864
-▁spotty	-53865
-▁Amherst	-53866
-▁Laurent	-53867
-▁Wembley	-53868
-▁beacons	-53869
-▁merrily	-53870
-▁rookies	-53871
-▁typhoon	-53872
-collection	-53873
-▁detonated	-53874
-▁labourers	-53875
-▁coalitions	-53876
-▁countering	-53877
-▁discarding	-53878
-▁providence	-53879
-▁transistor	-53880
-▁dumbfounded	-53881
-▁indestructible	-53882
-.–	-53883
-Gi	-53884
-PF	-53885
-YA	-53886
-BERT	-53887
-Moon	-53888
-lawn	-53889
-▁SBC	-53890
-▁TUR	-53891
-▁Thu	-53892
-ansen	-53893
-billy	-53894
-▁Bits	-53895
-▁KIDS	-53896
-▁Unle	-53897
-▁cany	-53898
-▁orbs	-53899
-rapnel	-53900
-▁ADMIN	-53901
-▁Persu	-53902
-▁Vicar	-53903
-▁david	-53904
-▁modus	-53905
-rasonic	-53906
-thirsty	-53907
-ulently	-53908
-▁Blocks	-53909
-▁wheelb	-53910
-▁Morales	-53911
-▁catfish	-53912
-▁combing	-53913
-▁epistem	-53914
-▁lobbied	-53915
-▁misread	-53916
-▁trimmer	-53917
-continent	-53918
-▁Corridor	-53919
-▁Purposes	-53920
-▁accutane	-53921
-▁kangaroo	-53922
-▁Detention	-53923
-▁auctioned	-53924
-▁grandsons	-53925
-▁methadone	-53926
-▁Watermelon	-53927
-▁Conclusions	-53928
-▁miscarriages	-53929
-YL	-53930
-CMS	-53931
-Ron	-53932
-mint	-53933
-▁Fir	-53934
-Print	-53935
-laced	-53936
-obias	-53937
-ukkah	-53938
-▁Aang	-53939
-▁Aure	-53940
-▁Ravi	-53941
-▁Tuck	-53942
-▁gins	-53943
-▁tout	-53944
-▁Bucky	-53945
-▁Linus	-53946
-▁ahold	-53947
-▁unsav	-53948
-drivers	-53949
-matched	-53950
-pillars	-53951
-▁aswell	-53952
-▁consul	-53953
-▁gaggle	-53954
-▁grainy	-53955
-▁hernia	-53956
-▁rectum	-53957
-▁roamed	-53958
-▁smacks	-53959
-▁Bigfoot	-53960
-▁Marlene	-53961
-▁McGuire	-53962
-▁availed	-53963
-▁oversaw	-53964
-▁tearful	-53965
-▁Asbestos	-53966
-▁Cavalier	-53967
-▁Culinary	-53968
-▁flailing	-53969
-▁rebelled	-53970
-▁receding	-53971
-▁whistled	-53972
-▁Aphrodite	-53973
-▁Expanding	-53974
-▁piercings	-53975
-▁Systematic	-53976
-▁handsomely	-53977
-▁masturbate	-53978
-▁occasioned	-53979
-▁contingencies	-53980
-▁thoughtfulness	-53981
-pn	-53982
-Beh	-53983
-Inc	-53984
-Leod	-53985
-▁MIA	-53986
-▁Yue	-53987
-tocin	-53988
-▁Benz	-53989
-▁Karn	-53990
-▁tric	-53991
-armony	-53992
-olated	-53993
-▁Linen	-53994
-▁Pills	-53995
-▁Shred	-53996
-▁Tomas	-53997
-▁fates	-53998
-▁thanx	-53999
-▁Cubans	-54000
-▁halved	-54001
-▁looser	-54002
-▁mulled	-54003
-▁unholy	-54004
-▁Candace	-54005
-▁SUCCESS	-54006
-▁hinders	-54007
-▁hydrate	-54008
-▁Acoustic	-54009
-▁unapolog	-54010
-restaurant	-54011
-▁Ignorance	-54012
-▁Mauritius	-54013
-▁metamorph	-54014
-▁workloads	-54015
-▁Scheduling	-54016
-▁calligraphy	-54017
-▁circulatory	-54018
-▁signatories	-54019
-environmental	-54020
-▁ecologically	-54021
-▁Circumstances	-54022
-▁employability	-54023
-▁Authentication	-54024
-Bot	-54025
-UMB	-54026
-fib	-54027
-▁</	-54028
-\\\\	-54029
-dumb	-54030
-wake	-54031
-▁Blen	-54032
-▁YEAH	-54033
-▁jost	-54034
-Letter	-54035
-inists	-54036
-ipsych	-54037
-omencl	-54038
-veston	-54039
-▁Bezos	-54040
-▁Gabby	-54041
-▁aneur	-54042
-▁enfor	-54043
-▁grout	-54044
-▁holed	-54045
-▁Bibles	-54046
-▁Grease	-54047
-▁Larson	-54048
-▁cometh	-54049
-▁heaped	-54050
-▁Malaria	-54051
-▁hapless	-54052
-▁presets	-54053
-▁stiffly	-54054
-conceived	-54055
-▁Addition	-54056
-▁FAVORITE	-54057
-▁[...]...	-54058
-▁depraved	-54059
-▁dizzying	-54060
-▁Maternity	-54061
-▁Suggested	-54062
-▁crucially	-54063
-▁harmonies	-54064
-▁initiates	-54065
-▁Programmes	-54066
-▁femininity	-54067
-▁epidemiology	-54068
-!:)	-54069
-Hit	-54070
-ODC	-54071
-OIL	-54072
-′′]	-54073
-▁.@	-54074
-Stat	-54075
-azoo	-54076
-▁LSU	-54077
-Brand	-54078
-ENCES	-54079
-IMATE	-54080
-Story	-54081
-hakes	-54082
-▁BLUE	-54083
-▁Crop	-54084
-▁Kohl	-54085
-▁Ummm	-54086
-▁Yoko	-54087
-▁camc	-54088
-▁Whirl	-54089
-▁abbey	-54090
-▁cacao	-54091
-▁heist	-54092
-▁lance	-54093
-▁trite	-54094
-▁Epidem	-54095
-▁Picnic	-54096
-▁Sabres	-54097
-▁concil	-54098
-Computer	-54099
-▁Balcony	-54100
-▁Casting	-54101
-▁Godfrey	-54102
-▁Worried	-54103
-▁bassist	-54104
-▁booties	-54105
-▁marshes	-54106
-▁patties	-54107
-▁replete	-54108
-▁skaters	-54109
-▁terrier	-54110
-▁dejected	-54111
-▁overtook	-54112
-▁Spaghetti	-54113
-▁handiwork	-54114
-▁thickened	-54115
-▁utterance	-54116
-▁impervious	-54117
-▁unfaithful	-54118
-▁retroactive	-54119
-▁philanthropist	-54120
-ouf	-54121
-Copy	-54122
-URCH	-54123
-rece	-54124
-volt	-54125
-▁ARR	-54126
-▁CMA	-54127
-▁McP	-54128
-▁AREA	-54129
-▁SNOW	-54130
-▁flic	-54131
-ormous	-54132
-▁Corin	-54133
-▁Gould	-54134
-▁Pines	-54135
-▁Squid	-54136
-▁addon	-54137
-▁decap	-54138
-willing	-54139
-▁Canaan	-54140
-▁Durban	-54141
-▁arcane	-54142
-▁vaults	-54143
-▁winger	-54144
-exercise	-54145
-majority	-54146
-programs	-54147
-▁Ambrose	-54148
-▁Midland	-54149
-▁PERFORM	-54150
-▁Spoiler	-54151
-▁flanked	-54152
-▁labours	-54153
-customers	-54154
-▁Andersen	-54155
-▁Promised	-54156
-▁mediated	-54157
-▁showings	-54158
-▁swimwear	-54159
-▁unsteady	-54160
-vaccinated	-54161
-▁Leviticus	-54162
-▁capacitor	-54163
-▁channeled	-54164
-▁inversion	-54165
-▁iterative	-54166
-▁navigator	-54167
-▁thrashing	-54168
-▁contemplative	-54169
-▁correspondents	-54170
-Fem	-54171
-Gra	-54172
-kow	-54173
-qus	-54174
-nski	-54175
-▁OTH	-54176
-▁Peg	-54177
-▁RIA	-54178
-▁lan	-54179
-Final	-54180
-itize	-54181
-▁Dict	-54182
-▁Glue	-54183
-▁INFO	-54184
-▁ISBN	-54185
-▁Mids	-54186
-▁Pays	-54187
-▁UPON	-54188
-▁flab	-54189
-▁oxym	-54190
-injury	-54191
-inning	-54192
-▁Antig	-54193
-▁Monet	-54194
-▁Regis	-54195
-▁SHORT	-54196
-Morning	-54197
-▁VMware	-54198
-▁Artemis	-54199
-▁Potomac	-54200
-▁Tribute	-54201
-▁critter	-54202
-▁groping	-54203
-▁riddles	-54204
-▁scammer	-54205
-addressed	-54206
-▁creatine	-54207
-▁fiscally	-54208
-▁salaried	-54209
-Management	-54210
-discovered	-54211
-▁Delivered	-54212
-▁Priscilla	-54213
-▁deterrence	-54214
-▁dispersion	-54215
-▁obstructed	-54216
-▁accelerates	-54217
-▁enchantment	-54218
-▁stipulations	-54219
-▁misrepresented	-54220
-€”	-54221
-Mmm	-54222
-figs	-54223
-ympt	-54224
-▁PIC	-54225
-Round	-54226
-fated	-54227
-romat	-54228
-▁Aids	-54229
-▁IMPL	-54230
-Monkey	-54231
-Simple	-54232
-etrics	-54233
-hinged	-54234
-movies	-54235
-orrhea	-54236
-purple	-54237
-▁Elves	-54238
-▁chimes	-54239
-▁jacked	-54240
-▁legged	-54241
-▁Lansing	-54242
-▁Serpent	-54243
-▁defraud	-54244
-▁sponges	-54245
-▁syncing	-54246
-▁Cromwell	-54247
-▁arrhythm	-54248
-▁conjured	-54249
-▁cranking	-54250
-▁felonies	-54251
-▁Sollecito	-54252
-▁lymphatic	-54253
-▁photocopy	-54254
-▁condiments	-54255
-▁guesthouse	-54256
-▁projectors	-54257
-▁popularized	-54258
-▁suffocating	-54259
-▁undervalued	-54260
-▁polarization	-54261
-▁justifications	-54262
-▁predisposition	-54263
-Ali	-54264
-cab	-54265
-nip	-54266
-Priv	-54267
-scan	-54268
-zeal	-54269
-▁BIT	-54270
-▁Hib	-54271
-oreau	-54272
-orice	-54273
-▁Cens	-54274
-▁Faye	-54275
-▁yank	-54276
-phrine	-54277
-▁OFFER	-54278
-▁Royce	-54279
-▁Denton	-54280
-▁Dorset	-54281
-▁Gamble	-54282
-▁choppy	-54283
-▁gazebo	-54284
-▁inclin	-54285
-▁solder	-54286
-▁swells	-54287
-▁topple	-54288
-Services	-54289
-▁Collier	-54290
-▁MICHAEL	-54291
-▁PROVIDE	-54292
-▁cartons	-54293
-▁curbing	-54294
-▁dehuman	-54295
-▁finesse	-54296
-▁Argument	-54297
-▁Lieberman	-54298
-▁bachelors	-54299
-▁bystander	-54300
-▁cathartic	-54301
-▁fallacies	-54302
-▁gangsters	-54303
-▁immutable	-54304
-▁orthodoxy	-54305
-▁outwardly	-54306
-▁Professors	-54307
-▁hypothermia	-54308
-▁obliterated	-54309
-▁sidetracked	-54310
-▁Refrigerator	-54311
-▁masturbating	-54312
-▁romantically	-54313
-▁sporadically	-54314
-'--	-54315
-Sing	-54316
-aber	-54317
-arde	-54318
-▁Lid	-54319
-▁SLR	-54320
-▁Tox	-54321
-▁USP	-54322
-▁hap	-54323
-Sever	-54324
-shops	-54325
-stamp	-54326
-▁Amph	-54327
-▁Apex	-54328
-▁Aria	-54329
-▁Lacy	-54330
-▁fain	-54331
-region	-54332
-ridged	-54333
-▁goody	-54334
-Madison	-54335
-▁Utopia	-54336
-▁dimmed	-54337
-▁minion	-54338
-▁oozing	-54339
-▁sepsis	-54340
-▁verand	-54341
-intended	-54342
-▁Monkeys	-54343
-▁Watched	-54344
-▁dilated	-54345
-▁hatchet	-54346
-▁lashing	-54347
-▁outlier	-54348
-▁secures	-54349
-▁sinuses	-54350
-▁whimper	-54351
-▁Promethe	-54352
-▁polymers	-54353
-▁scallops	-54354
-▁addicting	-54355
-▁erections	-54356
-▁falsified	-54357
-▁hygienist	-54358
-▁minefield	-54359
-▁Priorities	-54360
-▁calamities	-54361
-▁receptacle	-54362
-▁sildenafil	-54363
-▁irrevocably	-54364
-▁penultimate	-54365
-▁collaborator	-54366
-▁emancipation	-54367
-▁redistribute	-54368
-▁malfunctioning	-54369
-ERG	-54370
-▁WO	-54371
-EEEE	-54372
-IMUM	-54373
-Rank	-54374
-buff	-54375
-sion	-54376
-▁Bem	-54377
-▁Rej	-54378
-▁TMZ	-54379
-▁Umb	-54380
-▁dir	-54381
-arina	-54382
-witch	-54383
-▁hagg	-54384
-▁kiln	-54385
-▁meld	-54386
-France	-54387
-dealer	-54388
-▁LANGU	-54389
-▁Reign	-54390
-▁basal	-54391
-▁suede	-54392
-neither	-54393
-packing	-54394
-▁Barlow	-54395
-▁clumps	-54396
-▁doping	-54397
-▁rockin	-54398
-▁scribe	-54399
-▁Burbank	-54400
-▁Cleaner	-54401
-▁GETTING	-54402
-▁SCIENCE	-54403
-▁Strauss	-54404
-▁clamped	-54405
-▁fittest	-54406
-▁untruth	-54407
-▁Aquarius	-54408
-▁COMPLETE	-54409
-▁boroughs	-54410
-▁destruct	-54411
-▁proofing	-54412
-▁Consensus	-54413
-▁Locksmith	-54414
-▁Righteous	-54415
-▁Statutory	-54416
-▁Telescope	-54417
-▁appraised	-54418
-▁childless	-54419
-▁unabashed	-54420
-▁Solidarity	-54421
-▁superseded	-54422
-▁Specialized	-54423
-▁accumulates	-54424
-▁perpetuated	-54425
-▁inscriptions	-54426
-▁anthropogenic	-54427
-aqu	-54428
-laz	-54429
-obl	-54430
-▁CQ	-54431
-CEED	-54432
-▁AFA	-54433
-Train	-54434
-alian	-54435
-isoft	-54436
-▁Gunn	-54437
-▁Pigs	-54438
-▁SPAM	-54439
-▁sous	-54440
-▁INCRE	-54441
-▁Myths	-54442
-▁awash	-54443
-▁unify	-54444
-immings	-54445
-nemouth	-54446
-▁DISCLA	-54447
-▁Intrig	-54448
-▁Testim	-54449
-▁musket	-54450
-▁placid	-54451
-▁pliers	-54452
-▁rammed	-54453
-▁slacks	-54454
-Advanced	-54455
-multiple	-54456
-▁Partial	-54457
-▁airship	-54458
-▁duality	-54459
-▁junkies	-54460
-▁makings	-54461
-▁subpart	-54462
-▁countess	-54463
-Technology	-54464
-inaigrette	-54465
-nerability	-54466
-profession	-54467
-▁fragility	-54468
-▁sanitizer	-54469
-▁Lancashire	-54470
-▁Officially	-54471
-▁paediatric	-54472
-▁Promotional	-54473
-▁wavelengths	-54474
-▁unfathomable	-54475
-▁confrontational	-54476
-FB	-54477
-▁(>	-54478
-Dean	-54479
-Diff	-54480
-olls	-54481
-▁Ane	-54482
-▁Kag	-54483
-▁Bans	-54484
-▁Bose	-54485
-▁EXAM	-54486
-▁Rend	-54487
-erobic	-54488
-owsing	-54489
-potism	-54490
-▁Chalk	-54491
-▁epoxy	-54492
-▁gents	-54493
-▁nappy	-54494
-embered	-54495
-▁Advise	-54496
-▁Midway	-54497
-▁combed	-54498
-▁gulped	-54499
-dressing	-54500
-modified	-54501
-promised	-54502
-▁mutters	-54503
-▁rioting	-54504
-documents	-54505
-▁assessor	-54506
-pertension	-54507
-▁abatement	-54508
-▁barbarian	-54509
-▁strollers	-54510
-▁Accessible	-54511
-▁Firefighters	-54512
-,&	-54513
-JA	-54514
-."]	-54515
-IFY	-54516
-eck	-54517
-Pict	-54518
-Risk	-54519
-euro	-54520
-illi	-54521
-▁HDR	-54522
-▁HEN	-54523
-▁WWW	-54524
-ERSON	-54525
-VILLE	-54526
-sniff	-54527
-tales	-54528
-▁EVIL	-54529
-▁Scam	-54530
-▁Seab	-54531
-▁Witt	-54532
-▁baht	-54533
-▁beau	-54534
-babies	-54535
-▁Curve	-54536
-▁Enron	-54537
-▁Gayle	-54538
-partame	-54539
-▁Kendra	-54540
-▁Nikola	-54541
-▁Werner	-54542
-▁medics	-54543
-▁wanton	-54544
-▁Caravan	-54545
-▁Focused	-54546
-▁crushes	-54547
-▁fiancée	-54548
-▁mourned	-54549
-▁Catalyst	-54550
-▁backdoor	-54551
-integrated	-54552
-leadership	-54553
-▁affording	-54554
-▁crouching	-54555
-▁curtailed	-54556
-▁DEFINITELY	-54557
-▁Preventive	-54558
-▁redefining	-54559
-▁ethnicities	-54560
-▁inexperience	-54561
-▁perfectionism	-54562
-▁nanotechnology	-54563
-]"	-54564
-cb	-54565
-Sha	-54566
-aye	-54567
-▁WL	-54568
-Lots	-54569
-iane	-54570
-▁Adj	-54571
-▁FOM	-54572
-▁OSU	-54573
-▁STA	-54574
-▁Soc	-54575
-▁XIV	-54576
-▁nec	-54577
-plans	-54578
-vests	-54579
-▁COPY	-54580
-▁Foxy	-54581
-▁PATH	-54582
-▁SHER	-54583
-angelo	-54584
-rexate	-54585
-▁Adder	-54586
-▁Magna	-54587
-▁Spine	-54588
-▁alibi	-54589
-▁graze	-54590
-pointer	-54591
-sources	-54592
-▁Deaths	-54593
-▁Drives	-54594
-▁Merger	-54595
-▁Nasdaq	-54596
-▁awning	-54597
-▁superl	-54598
-▁vestib	-54599
-▁Anymore	-54600
-▁Firefly	-54601
-▁Pontiac	-54602
-▁cobwebs	-54603
-▁prickly	-54604
-▁Dungeons	-54605
-▁McKinsey	-54606
-▁Turnbull	-54607
-▁electors	-54608
-▁recliner	-54609
-▁scorched	-54610
-▁synonyms	-54611
-▁AGREEMENT	-54612
-▁Machinery	-54613
-▁Wilkinson	-54614
-▁campsites	-54615
-▁gibberish	-54616
-▁licensees	-54617
-▁glyphosate	-54618
-▁electrolyte	-54619
-▁preservative	-54620
-▁stubbornness	-54621
-▁hyperactivity	-54622
-▁pronouncements	-54623
--’	-54624
-RF	-54625
-ALT	-54626
-nat	-54627
-ouk	-54628
-hine	-54629
-legs	-54630
-mese	-54631
-oupe	-54632
-prey	-54633
-▁Caj	-54634
-▁DoD	-54635
-▁FIX	-54636
-▁Zem	-54637
-ocate	-54638
-Burner	-54639
-atomic	-54640
-▁uninv	-54641
-graders	-54642
-▁Pebble	-54643
-▁Reveal	-54644
-▁Spence	-54645
-▁sherry	-54646
-assembly	-54647
-limiting	-54648
-▁Atheist	-54649
-▁Pursuit	-54650
-▁floored	-54651
-▁illumin	-54652
-▁parsing	-54653
-Gentlemen	-54654
-▁Honorary	-54655
-▁Narrative	-54656
-▁hatchback	-54657
-▁appraisals	-54658
-▁barbarians	-54659
-▁monogamous	-54660
-▁subjecting	-54661
-▁Confederacy	-54662
-▁cylindrical	-54663
-▁defenseless	-54664
-▁metaphorically	-54665
-eo	-54666
-RON	-54667
-▁Ik	-54668
-Aunt	-54669
-Deep	-54670
-menu	-54671
-Field	-54672
-asset	-54673
-iverr	-54674
-oprop	-54675
-shown	-54676
-▁Lush	-54677
-▁chow	-54678
-device	-54679
-skills	-54680
-▁Boats	-54681
-▁Condo	-54682
-▁fable	-54683
-Spanish	-54684
-ackling	-54685
-ahahaha	-54686
-▁Mutant	-54687
-▁caster	-54688
-packaged	-54689
-▁Klingon	-54690
-▁Obvious	-54691
-▁oblique	-54692
-▁saluted	-54693
-▁shivers	-54694
-▁statins	-54695
-▁Accurate	-54696
-▁Marigold	-54697
-▁flicking	-54698
-recognized	-54699
-▁dumbbells	-54700
-▁eyeshadow	-54701
-▁Altogether	-54702
-▁newsworthy	-54703
-▁Innovations	-54704
-▁franchisees	-54705
-▁implausible	-54706
-▁visualizing	-54707
-VIS	-54708
-▁Pt	-54709
-Jobs	-54710
-imee	-54711
-orsi	-54712
-otom	-54713
-▁Zed	-54714
-Panel	-54715
-abras	-54716
-ghans	-54717
-▁Fost	-54718
-▁Inch	-54719
-▁REMO	-54720
-▁bony	-54721
-ramine	-54722
-▁BEGIN	-54723
-▁Bored	-54724
-▁Brigg	-54725
-▁Cider	-54726
-▁Kurds	-54727
-▁WORKS	-54728
-▁physi	-54729
-ivalled	-54730
-tasting	-54731
-▁Breeze	-54732
-▁Immune	-54733
-▁Packed	-54734
-▁Respir	-54735
-▁chatty	-54736
-▁fewest	-54737
-▁pebble	-54738
-▁toning	-54739
-abolical	-54740
-ilations	-54741
-▁INDIVID	-54742
-▁Moldova	-54743
-▁Wharton	-54744
-▁browsed	-54745
-▁ontario	-54746
-▁searing	-54747
-Marketing	-54748
-▁monastic	-54749
-▁ofcourse	-54750
-▁overdone	-54751
-▁windmill	-54752
-ynchronous	-54753
-▁digestible	-54754
-▁ventilated	-54755
-▁conceptions	-54756
-▁horticulture	-54757
-▁inefficiencies	-54758
-jon	-54759
-▁bs	-54760
-NERS	-54761
-usha	-54762
-▁Hai	-54763
-▁MGM	-54764
-▁mam	-54765
-freak	-54766
-▁Boll	-54767
-▁Roan	-54768
-▁Vass	-54769
-▁plod	-54770
-▁thes	-54771
-appies	-54772
-fixing	-54773
-▁goons	-54774
-cookies	-54775
-courses	-54776
-obvious	-54777
-reports	-54778
-▁MATTER	-54779
-▁Passed	-54780
-▁strove	-54781
-petition	-54782
-premises	-54783
-▁McBride	-54784
-▁Balanced	-54785
-▁Caffeine	-54786
-▁Humboldt	-54787
-▁caravans	-54788
-▁dialects	-54789
-▁Expertise	-54790
-▁Wellbeing	-54791
-▁pressuring	-54792
-.);	-54793
-ESA	-54794
-Pod	-54795
-Grow	-54796
-blah	-54797
-▁FIT	-54798
-▁Kyr	-54799
-▁dic	-54800
-RATOR	-54801
-skill	-54802
-vidia	-54803
-▁Gund	-54804
-decade	-54805
-ertile	-54806
-ittled	-54807
-naming	-54808
-auldron	-54809
-uploads	-54810
-▁Caller	-54811
-▁McCall	-54812
-▁McLean	-54813
-▁Visits	-54814
-▁atrium	-54815
-▁noxious	-54816
-▁Quantity	-54817
-▁Spelling	-54818
-▁adapters	-54819
-▁curators	-54820
-▁knickers	-54821
-▁modelled	-54822
-▁rumoured	-54823
-▁Batteries	-54824
-▁REPRESENT	-54825
-▁snuggling	-54826
-▁Transcript	-54827
-▁Truthfully	-54828
-▁distillery	-54829
-▁possessive	-54830
-▁Thunderbird	-54831
-▁brightening	-54832
-▁centralised	-54833
-((	-54834
-Imm	-54835
-NIV	-54836
-cee	-54837
-MOST	-54838
-capt	-54839
-onne	-54840
-▁wtf	-54841
-minor	-54842
-onaut	-54843
-raint	-54844
-resso	-54845
-ureth	-54846
-▁AARP	-54847
-▁Jord	-54848
-▁bris	-54849
-retail	-54850
-▁Zumba	-54851
-▁raved	-54852
-▁smelt	-54853
-▁snore	-54854
-▁stoic	-54855
-▁wince	-54856
-▁Howell	-54857
-▁Maxine	-54858
-▁Pardew	-54859
-▁Ripple	-54860
-▁Battles	-54861
-▁ducking	-54862
-▁encased	-54863
-▁Holistic	-54864
-▁Meridian	-54865
-▁Roulette	-54866
-▁confetti	-54867
-▁Reservoir	-54868
-▁Translate	-54869
-▁affective	-54870
-▁flickered	-54871
-▁Adolescents	-54872
-▁contaminate	-54873
-ZE	-54874
-▁KO	-54875
-uket	-54876
-▁BOB	-54877
-▁Lep	-54878
-advoc	-54879
-olism	-54880
-reeze	-54881
-▁Kahn	-54882
-▁bacc	-54883
-omping	-54884
-ureate	-54885
-▁Walks	-54886
-▁fakes	-54887
-Parents	-54888
-hopping	-54889
-oeuvres	-54890
-radical	-54891
-▁Barnet	-54892
-▁EXCELL	-54893
-▁Speaks	-54894
-▁Spinal	-54895
-▁Virgil	-54896
-▁cobalt	-54897
-shifting	-54898
-▁Barclay	-54899
-▁Canucks	-54900
-▁Regents	-54901
-▁Shabbat	-54902
-▁cliches	-54903
-▁retires	-54904
-▁twofold	-54905
-▁expropri	-54906
-▁overalls	-54907
-▁salvaged	-54908
-▁schooner	-54909
-installing	-54910
-▁Producing	-54911
-▁martyrdom	-54912
-▁PROFESSION	-54913
-▁blossoming	-54914
-▁dispatcher	-54915
-▁filibuster	-54916
-▁wrongfully	-54917
-▁synthesized	-54918
-▁underscored	-54919
-▁Preparedness	-54920
-ARM	-54921
-Sov	-54922
-atro	-54923
-bara	-54924
-osse	-54925
-▁Sao	-54926
-▁pyr	-54927
-Funny	-54928
-idone	-54929
-irond	-54930
-▁Shad	-54931
-neighb	-54932
-▁bushy	-54933
-▁roman	-54934
-▁wring	-54935
-Climate	-54936
-▁Afford	-54937
-▁Blooms	-54938
-▁jingle	-54939
-▁poodle	-54940
-▁rarest	-54941
-▁sentry	-54942
-▁truely	-54943
-argument	-54944
-▁Invoice	-54945
-▁modicum	-54946
-financing	-54947
-▁Mariners	-54948
-▁clairvoy	-54949
-▁heighten	-54950
-▁indelible	-54951
-▁retelling	-54952
-▁sleepover	-54953
-▁assailants	-54954
-▁downgraded	-54955
-▁inhibition	-54956
-▁schoolwork	-54957
-▁contraption	-54958
-▁enumeration	-54959
-▁inclinations	-54960
-▁disseminating	-54961
-▁inevitability	-54962
-wC	-54963
-Bah	-54964
-viv	-54965
-coup	-54966
-▁GIB	-54967
-Staff	-54968
-astes	-54969
-rally	-54970
-▁Hier	-54971
-▁Tull	-54972
-▁wank	-54973
-roying	-54974
-▁Daryl	-54975
-▁Donor	-54976
-▁Ernst	-54977
-▁LARGE	-54978
-▁Loser	-54979
-▁Lucie	-54980
-▁Remus	-54981
-▁SEVEN	-54982
-▁Stony	-54983
-▁idiom	-54984
-▁mumps	-54985
-nington	-54986
-▁POLICE	-54987
-▁Propri	-54988
-▁aghast	-54989
-▁corals	-54990
-▁daemon	-54991
-▁levies	-54992
-▁swivel	-54993
-▁wicker	-54994
-▁Princip	-54995
-▁matures	-54996
-▁scribes	-54997
-solutions	-54998
-▁FaceBook	-54999
-▁Informed	-55000
-▁existent	-55001
-▁lectured	-55002
-motivation	-55003
-▁loosening	-55004
-▁splendour	-55005
-▁apparition	-55006
-▁benefitting	-55007
-▁hydrocarbon	-55008
-▁demonstrably	-55009
-▁interviewees	-55010
-▁confrontations	-55011
-▁😆	-55012
-arly	-55013
-iflu	-55014
-llll	-55015
-opol	-55016
-ribs	-55017
-▁ssh	-55018
-Sport	-55019
-coats	-55020
-▁Raul	-55021
-▁dutch	-55022
-results	-55023
-retired	-55024
-▁Hayley	-55025
-▁Landon	-55026
-▁Pueblo	-55027
-▁canola	-55028
-▁hyphen	-55029
-▁tartar	-55030
-▁Capsule	-55031
-▁Krypton	-55032
-▁Marissa	-55033
-▁ravages	-55034
-▁seeding	-55035
-▁tampons	-55036
-▁Navigate	-55037
-▁attested	-55038
-▁crowning	-55039
-▁impotent	-55040
-▁lethargy	-55041
-▁shambles	-55042
-▁surefire	-55043
-▁Himalayas	-55044
-▁Purchases	-55045
-▁Seventeen	-55046
-▁causality	-55047
-▁chestnuts	-55048
-▁doctrinal	-55049
-▁yellowish	-55050
-▁Extensions	-55051
-▁apologists	-55052
-▁convection	-55053
-▁multitudes	-55054
-▁prophesied	-55055
-▁schizophrenic	-55056
-qs	-55057
-..!	-55058
-IRT	-55059
-▁EK	-55060
-▁CIV	-55061
-▁MOU	-55062
-▁UTC	-55063
-▁sut	-55064
-▁Ctrl	-55065
-▁Funn	-55066
-▁HOST	-55067
-▁Laun	-55068
-▁Shat	-55069
-▁UKIP	-55070
-▁diva	-55071
-▁swel	-55072
-boring	-55073
-▁Atari	-55074
-▁Libra	-55075
-▁Lymph	-55076
-▁Socio	-55077
-▁WILLI	-55078
-▁amalg	-55079
-▁Buffer	-55080
-▁COUNTY	-55081
-▁Cialis	-55082
-▁Donuts	-55083
-▁Kettle	-55084
-▁Tarzan	-55085
-▁crayon	-55086
-▁eluded	-55087
-▁imbued	-55088
-▁orator	-55089
-▁pieced	-55090
-stopping	-55091
-surprise	-55092
-▁Bankers	-55093
-▁Chatham	-55094
-▁Forests	-55095
-▁Googled	-55096
-▁Jeannie	-55097
-▁dwarves	-55098
-▁flocked	-55099
-▁lioness	-55100
-Wonderful	-55101
-▁Colonies	-55102
-▁Periodic	-55103
-▁Suburban	-55104
-▁boneless	-55105
-▁inverter	-55106
-▁wavering	-55107
-▁Reporters	-55108
-▁margarine	-55109
-▁Cartwright	-55110
-▁inflexible	-55111
-▁interprets	-55112
-availability	-55113
-▁audiovisual	-55114
-▁serviceable	-55115
-▁Volunteering	-55116
-▁discipleship	-55117
-▁thermodynamics	-55118
-ieri	-55119
-oden	-55120
-▁ATP	-55121
-▁DOG	-55122
-▁MSU	-55123
-▁gur	-55124
-▁vat	-55125
-,....	-55126
-▁Adel	-55127
-▁NPCs	-55128
-▁Quot	-55129
-▁Rowe	-55130
-▁SUBM	-55131
-▁romp	-55132
-▁wasp	-55133
-Isaiah	-55134
-heaven	-55135
-strike	-55136
-▁Conce	-55137
-▁Ianto	-55138
-▁Meter	-55139
-▁exten	-55140
-▁noose	-55141
-▁Sagitt	-55142
-▁loomed	-55143
-▁stanza	-55144
-isterous	-55145
-▁Diagram	-55146
-▁empress	-55147
-▁entrees	-55148
-▁scoured	-55149
-▁Firewall	-55150
-▁Floating	-55151
-▁candidly	-55152
-▁downplay	-55153
-▁downsize	-55154
-▁implored	-55155
-▁Patagonia	-55156
-▁conversely	-55157
-▁cornstarch	-55158
-▁repurchase	-55159
-▁doxycycline	-55160
-▁replicating	-55161
-▁Governmental	-55162
-▁orientations	-55163
-▁jurisprudence	-55164
-▁differentiating	-55165
-DN	-55166
-Jay	-55167
-poo	-55168
-ubb	-55169
-EDOM	-55170
-HEAD	-55171
-▁PRC	-55172
-▁WWI	-55173
-amers	-55174
-lucan	-55175
-▁FUND	-55176
-▁GIFs	-55177
-▁Kare	-55178
-▁bunt	-55179
-▁flak	-55180
-Regard	-55181
-capped	-55182
-cented	-55183
-▁Absor	-55184
-▁Chico	-55185
-▁Hosts	-55186
-▁Reykj	-55187
-▁Synod	-55188
-▁dingy	-55189
-Charles	-55190
-Connell	-55191
-sourced	-55192
-▁Brings	-55193
-▁Krista	-55194
-▁archer	-55195
-▁chalet	-55196
-▁cocoon	-55197
-▁endear	-55198
-▁relays	-55199
-Governor	-55200
-recovery	-55201
-▁Lobster	-55202
-▁Twelfth	-55203
-▁batsman	-55204
-▁beheaded	-55205
-▁clarinet	-55206
-▁demented	-55207
-▁electroc	-55208
-▁exclaims	-55209
-▁hairline	-55210
-▁inhibits	-55211
-▁pedicure	-55212
-engineered	-55213
-▁Predators	-55214
-▁cirrhosis	-55215
-▁publicist	-55216
-▁signatory	-55217
-▁Simulation	-55218
-▁ambivalent	-55219
-▁holographic	-55220
-▁obsessively	-55221
-▁rejuvenated	-55222
-▁Testosterone	-55223
-▁underpinning	-55224
-▁peculiarities	-55225
-Ren	-55226
-▁EH	-55227
-▁mt	-55228
-Alas	-55229
-emap	-55230
-iiii	-55231
-soil	-55232
-terr	-55233
-▁AFF	-55234
-▁BCE	-55235
-▁Byz	-55236
-▁Phr	-55237
-▁Sib	-55238
-Crazy	-55239
-Tools	-55240
-▁Fris	-55241
-▁Taka	-55242
-▁Yoda	-55243
-▁pram	-55244
-adding	-55245
-ochial	-55246
-▁STUFF	-55247
-▁eases	-55248
-▁execs	-55249
-▁fryer	-55250
-▁kimon	-55251
-Message	-55252
-suicide	-55253
-▁Manner	-55254
-▁Marino	-55255
-▁carpal	-55256
-▁stumps	-55257
-evolving	-55258
-▁Adviser	-55259
-▁Goodwin	-55260
-▁Rapture	-55261
-▁Deposits	-55262
-▁allergen	-55263
-▁neckline	-55264
-▁radiance	-55265
-department	-55266
-▁Safeguard	-55267
-▁antiviral	-55268
-▁archetype	-55269
-▁loveliest	-55270
-▁recorders	-55271
-▁reprogram	-55272
-▁Approaches	-55273
-▁confluence	-55274
-▁fascinates	-55275
-▁assassinate	-55276
-▁unequivocal	-55277
-▁carcinogenic	-55278
-▁privatisation	-55279
-▁−	-55280
-Cli	-55281
-NYC	-55282
-gren	-55283
-▁IPv	-55284
-▁Ler	-55285
-▁idk	-55286
-Texas	-55287
-lodge	-55288
-olini	-55289
-unded	-55290
-▁Boko	-55291
-▁MacK	-55292
-▁Toad	-55293
-▁Vail	-55294
-▁bums	-55295
-▁cyto	-55296
-▁Khmer	-55297
-▁PHOTO	-55298
-▁facie	-55299
-▁gusto	-55300
-bending	-55301
-english	-55302
-ishness	-55303
-▁Paraly	-55304
-▁Whoops	-55305
-▁candor	-55306
-priority	-55307
-▁Calvary	-55308
-▁Foreman	-55309
-▁Permits	-55310
-▁Tylenol	-55311
-▁airways	-55312
-▁entitle	-55313
-▁silvery	-55314
-▁swagger	-55315
-▁absently	-55316
-▁topology	-55317
-▁trifling	-55318
-adventures	-55319
-▁forgetful	-55320
-▁manicured	-55321
-▁pandering	-55322
-▁quadruple	-55323
-▁waistcoat	-55324
-▁Montenegro	-55325
-▁Herzegovina	-55326
-▁benevolence	-55327
-▁extrapolate	-55328
-▁uncharacter	-55329
-▁immunizations	-55330
-Ice	-55331
-▁(=	-55332
-▁Mm	-55333
-CENT	-55334
-▁Düs	-55335
-▁Eun	-55336
-Agent	-55337
-Block	-55338
-▁KORN	-55339
-▁EXTRA	-55340
-▁Snack	-55341
-Clearly	-55342
-display	-55343
-fantasy	-55344
-▁Affect	-55345
-▁Bounty	-55346
-▁Compat	-55347
-▁Moreno	-55348
-▁Patron	-55349
-▁bleeds	-55350
-▁loader	-55351
-appeared	-55352
-ciplined	-55353
-▁herding	-55354
-▁Eighteen	-55355
-▁Massacre	-55356
-▁Provence	-55357
-▁lawmaker	-55358
-▁modality	-55359
-▁Artillery	-55360
-▁Platforms	-55361
-▁bluegrass	-55362
-▁sorrowful	-55363
-▁Spirituality	-55364
-▁interactivity	-55365
-ín	-55366
-Cos	-55367
-▁Md	-55368
-iret	-55369
-ooga	-55370
-▁ASA	-55371
-▁dul	-55372
-Kevin	-55373
-addin	-55374
-teach	-55375
-utary	-55376
-▁Nook	-55377
-▁jock	-55378
-dragon	-55379
-itized	-55380
-rapeut	-55381
-▁Blitz	-55382
-▁Chrom	-55383
-▁Marla	-55384
-▁Numer	-55385
-▁Soooo	-55386
-▁Spicy	-55387
-▁VOICE	-55388
-▁irref	-55389
-▁ische	-55390
-▁tween	-55391
-letting	-55392
-seventh	-55393
-▁Barely	-55394
-▁Outfit	-55395
-▁Swords	-55396
-▁Tagged	-55397
-▁polyam	-55398
-▁stingy	-55399
-▁tiered	-55400
-▁Confuci	-55401
-▁Dismiss	-55402
-▁deduced	-55403
-▁queuing	-55404
-▁storied	-55405
-▁STUDENTS	-55406
-▁Stratford	-55407
-▁Streaming	-55408
-▁imprinted	-55409
-▁preponder	-55410
-▁sincerest	-55411
-▁ultimatum	-55412
-▁caretakers	-55413
-▁friendlier	-55414
-▁singularly	-55415
-▁preoccupation	-55416
-GI	-55417
-âte	-55418
-Tony	-55419
-kong	-55420
-oslo	-55421
-▁GHG	-55422
-▁SKY	-55423
-helia	-55424
-iffed	-55425
-rains	-55426
-▁NICU	-55427
-▁geez	-55428
-▁husk	-55429
-Corpor	-55430
-warned	-55431
-▁Lough	-55432
-▁MARCH	-55433
-▁Norma	-55434
-▁Patio	-55435
-▁STUDY	-55436
-▁Skate	-55437
-▁clove	-55438
-▁pecan	-55439
-▁Flores	-55440
-▁Lyndon	-55441
-▁Prints	-55442
-▁Wither	-55443
-movement	-55444
-▁Corbett	-55445
-▁Insulin	-55446
-▁hurling	-55447
-▁wastage	-55448
-gathering	-55449
-▁Predator	-55450
-▁buzzword	-55451
-▁cuttings	-55452
-▁regimens	-55453
-▁shuttles	-55454
-▁Cambodian	-55455
-▁Endowment	-55456
-▁Transfers	-55457
-▁abhorrent	-55458
-▁kilometre	-55459
-▁stalemate	-55460
-▁contraband	-55461
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-▁dissidents	-55463
-▁inferences	-55464
-▁Notifications	-55465
-▁authorizations	-55466
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-pha	-55468
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-avinia	-55477
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-Eastern	-55488
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-▁Bylaws	-55490
-▁Padres	-55491
-▁accost	-55492
-▁sneaks	-55493
-▁Hitting	-55494
-▁Pottery	-55495
-▁binders	-55496
-▁godsend	-55497
-▁paralys	-55498
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-▁phasing	-55500
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-forgotten	-55502
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-▁forklift	-55504
-▁nobleman	-55505
-inationals	-55506
-▁Capricorn	-55507
-▁depravity	-55508
-▁sculpting	-55509
-▁Neutrality	-55510
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-▁galvanized	-55512
-▁legalizing	-55513
-▁deceptively	-55514
-▁Irrespective	-55515
-▁Mathematical	-55516
-▁diversifying	-55517
-▁immeasurable	-55518
-▁togetherness	-55519
-▁differentiates	-55520
-'(	-55521
-UV	-55522
-qi	-55523
-URL	-55524
-▁db	-55525
-▁tx	-55526
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-etto	-55528
-opot	-55529
-▁EFT	-55530
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-amboy	-55535
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-egiate	-55540
-ollary	-55541
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-popping	-55546
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-▁Merchand	-55559
-▁REGISTER	-55560
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-▁lifeblood	-55563
-▁Authorized	-55564
-▁Insolvency	-55565
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-▁postmodern	-55567
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-▁indiscriminate	-55570
-Je	-55571
-mw	-55572
-zia	-55573
-▁"-	-55574
-▁mb	-55575
-Neal	-55576
-Tour	-55577
-▁DBS	-55578
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-teries	-55585
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-bridled	-55590
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-▁Gladys	-55592
-▁Sheryl	-55593
-▁unread	-55594
-▁Impacts	-55595
-▁Optical	-55596
-▁Spinach	-55597
-▁Stomach	-55598
-▁cancels	-55599
-▁outlaws	-55600
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-lifestyle	-55602
-tolerance	-55603
-ulnerable	-55604
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-▁Redeemer	-55606
-▁Remedies	-55607
-▁antihist	-55608
-▁scrolled	-55609
-▁washroom	-55610
-▁outweighed	-55611
-▁semifinals	-55612
-▁patronizing	-55613
-▁reinventing	-55614
-▁retardation	-55615
-▁underpinned	-55616
-▁triglycerides	-55617
-▁Oj	-55618
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-umann	-55621
-urion	-55622
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-▁Elin	-55624
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-otland	-55628
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-▁nymph	-55630
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-▁zoned	-55632
-Knowing	-55633
-ablanca	-55634
-ahassee	-55635
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-▁Visible	-55640
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-▁mercies	-55642
-▁pecking	-55643
-▁plowing	-55644
-▁shelved	-55645
-IFICATION	-55646
-▁trumpets	-55647
-acceptance	-55648
-▁Examining	-55649
-▁clickable	-55650
-▁diaphragm	-55651
-▁saxophone	-55652
-▁Attachment	-55653
-▁cooperated	-55654
-▁extraneous	-55655
-▁minimising	-55656
-▁adversarial	-55657
-▁appreciable	-55658
-▁technologists	-55659
-jj	-55660
-Ber	-55661
-CLE	-55662
-WOW	-55663
-ieg	-55664
-ndi	-55665
-▁XS	-55666
-Poss	-55667
-enga	-55668
-mbal	-55669
-▁Hym	-55670
-▁Naw	-55671
-▁Yin	-55672
-Japan	-55673
-ucous	-55674
-▁Hays	-55675
-▁Olaf	-55676
-▁sips	-55677
-Church	-55678
-Coffee	-55679
-biting	-55680
-urably	-55681
-▁Acres	-55682
-▁Luigi	-55683
-▁Smoky	-55684
-▁doves	-55685
-▁hemat	-55686
-ittling	-55687
-▁CHURCH	-55688
-▁SIMPLE	-55689
-▁behest	-55690
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-▁Bologna	-55692
-▁Helicop	-55693
-▁Stopped	-55694
-▁cupping	-55695
-▁mutants	-55696
-▁Prostate	-55697
-▁Enchanted	-55698
-▁barricade	-55699
-▁infirmary	-55700
-▁quantified	-55701
-introduction	-55702
-▁paradoxical	-55703
-▁presumptuous	-55704
-▁standardised	-55705
-SN	-55706
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-Mill	-55708
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-▁AST	-55710
-Louis	-55711
-ORROW	-55712
-affes	-55713
-asmus	-55714
-gedly	-55715
-uggle	-55716
-▁DEST	-55717
-▁Hola	-55718
-▁TONS	-55719
-▁bane	-55720
-▁sump	-55721
-finish	-55722
-▁DAVID	-55723
-▁Elmer	-55724
-▁Syrup	-55725
-▁hiker	-55726
-▁remin	-55727
-Israeli	-55728
-poverty	-55729
-stering	-55730
-▁Goblin	-55731
-▁Scared	-55732
-▁purvey	-55733
-utilized	-55734
-▁KORNACK	-55735
-▁regress	-55736
-buildings	-55737
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-▁lockdown	-55739
-▁quadrant	-55740
-▁Mushrooms	-55741
-▁Webmaster	-55742
-▁microsoft	-55743
-▁Düsseldorf	-55744
-▁Missionary	-55745
-▁theologian	-55746
-▁strategists	-55747
-▁fibrillation	-55748
-▁outrageously	-55749
-▁juxtaposition	-55750
-KC	-55751
-MW	-55752
-EES	-55753
-Laws	-55754
-OBAL	-55755
-erol	-55756
-ikin	-55757
-isto	-55758
-▁OTO	-55759
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-▁Riy	-55761
-▁SSA	-55762
-▁TRU	-55763
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-alogy	-55765
-▁Sidd	-55766
-▁Zill	-55767
-▁eons	-55768
-▁moat	-55769
-Intell	-55770
-ethers	-55771
-hounds	-55772
-▁Epsom	-55773
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-▁Roles	-55775
-▁gauze	-55776
-erkraut	-55777
-heating	-55778
-trailer	-55779
-▁Assemb	-55780
-▁Dwayne	-55781
-▁Linden	-55782
-▁burrow	-55783
-▁lessee	-55784
-▁lumped	-55785
-▁mutiny	-55786
-▁prawns	-55787
-Original	-55788
-▁Brianna	-55789
-▁Naughty	-55790
-▁colitis	-55791
-▁ricotta	-55792
-▁stardom	-55793
-anonymous	-55794
-▁Adequate	-55795
-▁Highways	-55796
-▁canoeing	-55797
-▁mammogram	-55798
-▁pollutant	-55799
-▁threesome	-55800
-▁unplugged	-55801
-▁Affiliates	-55802
-▁Structures	-55803
-▁Volleyball	-55804
-▁birthright	-55805
-▁Deuteronomy	-55806
-▁indignantly	-55807
-’-	-55808
-cfm	-55809
-▁Ic	-55810
-▁hm	-55811
-HTML	-55812
-▁Soho	-55813
-▁lags	-55814
-▁mudd	-55815
-▁Poole	-55816
-▁Russo	-55817
-▁Shiro	-55818
-▁remot	-55819
-▁willy	-55820
-Members	-55821
-michael	-55822
-surface	-55823
-▁Approx	-55824
-▁Bender	-55825
-▁Doming	-55826
-▁Layers	-55827
-▁Reilly	-55828
-▁biceps	-55829
-▁craved	-55830
-▁mimics	-55831
-▁toasty	-55832
-pointing	-55833
-rimonial	-55834
-▁Ceiling	-55835
-▁Chomsky	-55836
-▁Costume	-55837
-▁WORKING	-55838
-▁weaning	-55839
-▁darlings	-55840
-▁Avalanche	-55841
-▁Judgement	-55842
-▁Macintosh	-55843
-▁apathetic	-55844
-▁cosmology	-55845
-▁earphones	-55846
-▁weighting	-55847
-description	-55848
-▁ethnically	-55849
-▁excellency	-55850
-▁vindicated	-55851
-▁disassemble	-55852
-▁intractable	-55853
-▁Incorporated	-55854
-▁communicable	-55855
-▁quantifiable	-55856
-▁incriminating	-55857
-OUD	-55858
-▁nd	-55859
-cust	-55860
-glad	-55861
-lion	-55862
-wrap	-55863
-imacy	-55864
-lless	-55865
-▁Grig	-55866
-▁Mish	-55867
-▁STRE	-55868
-▁UNIX	-55869
-Ground	-55870
-models	-55871
-▁Crowe	-55872
-▁Intra	-55873
-▁Pryor	-55874
-▁Viper	-55875
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-INATION	-55877
-animity	-55878
-impress	-55879
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-▁synerg	-55882
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-▁phoning	-55884
-▁thrifty	-55885
-steroidal	-55886
-▁Commence	-55887
-▁Scrabble	-55888
-▁Blackpool	-55889
-▁Buddhists	-55890
-▁Subverted	-55891
-▁aeroplane	-55892
-▁dutifully	-55893
-▁scrimmage	-55894
-▁Clearwater	-55895
-▁overthrown	-55896
-▁washington	-55897
-tc	-55898
-Expl	-55899
-ivot	-55900
-vier	-55901
-wasn	-55902
-▁Ayn	-55903
-▁WWF	-55904
-▁joc	-55905
-rolls	-55906
-▁orgy	-55907
-▁Nadia	-55908
-▁PRINC	-55909
-▁plaus	-55910
-cusable	-55911
-▁Perpet	-55912
-▁Puppet	-55913
-▁Tinker	-55914
-▁goblin	-55915
-▁ported	-55916
-▁solver	-55917
-Breaking	-55918
-▁Curtain	-55919
-▁Jaguars	-55920
-▁Milford	-55921
-▁Shelton	-55922
-▁booting	-55923
-▁departs	-55924
-▁grizzly	-55925
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-▁summits	-55927
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-alaureate	-55929
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-▁Cookbook	-55931
-▁Seinfeld	-55932
-▁beholder	-55933
-▁foraging	-55934
-▁hijacker	-55935
-▁waterway	-55936
-resistance	-55937
-▁magnetism	-55938
-▁normalize	-55939
-▁Attributes	-55940
-▁Gettysburg	-55941
-▁financiers	-55942
-▁misspelled	-55943
-▁elaboration	-55944
-▁empirically	-55945
-▁revolutionaries	-55946
-Na	-55947
-rx	-55948
-~!	-55949
-lls	-55950
-Anti	-55951
-▁MIR	-55952
-▁Osm	-55953
-▁RCA	-55954
-▁Viz	-55955
-▁sna	-55956
-rices	-55957
-stake	-55958
-▁Huss	-55959
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-▁hurl	-55961
-ICIANS	-55962
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-iastic	-55964
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-▁moans	-55966
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-scoring	-55969
-▁Arafat	-55970
-▁Pillow	-55971
-▁euthan	-55972
-▁gander	-55973
-▁suffix	-55974
-familiar	-55975
-▁Swallow	-55976
-▁lawless	-55977
-▁rappers	-55978
-▁cavalier	-55979
-▁clotting	-55980
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-▁ethernet	-55982
-▁imparted	-55983
-▁scolding	-55984
-▁toenails	-55985
-▁volition	-55986
-▁Committed	-55987
-▁Downloads	-55988
-▁roadblock	-55989
-▁Invitation	-55990
-▁Resilience	-55991
-▁restocking	-55992
-▁receivables	-55993
-▁mythological	-55994
-▁superstitions	-55995
-BOX	-55996
-Bal	-55997
-Bat	-55998
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-tied	-56000
-▁PTO	-56001
-▁pts	-56002
-amino	-56003
-▁BULL	-56004
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-astery	-56007
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-▁Ignat	-56009
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-▁pangs	-56011
-▁shite	-56012
-Manager	-56013
-evening	-56014
-mentary	-56015
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-▁clamps	-56020
-▁morsel	-56021
-▁repack	-56022
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-▁tensed	-56024
-▁unsubs	-56025
-▁vetoed	-56026
-▁winded	-56027
-adequate	-56028
-asteride	-56029
-discount	-56030
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-▁Fayette	-56032
-▁Guthrie	-56033
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-▁midwest	-56037
-▁tankers	-56038
-▁KORNACKI	-56039
-▁Turmeric	-56040
-▁deriving	-56041
-▁epidural	-56042
-▁mumbling	-56043
-▁rigidity	-56044
-▁unsigned	-56045
-Department	-56046
-restricted	-56047
-▁recharged	-56048
-▁Presenting	-56049
-▁qualifiers	-56050
-▁stipulates	-56051
-▁rearranging	-56052
-▁Conservatory	-56053
-▁artistically	-56054
-▁impartiality	-56055
-▁intercession	-56056
-▁unenforceable	-56057
-).[	-56058
-CAP	-56059
-sat	-56060
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-adul	-56062
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-idges	-56067
-imals	-56068
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-▁ICANN	-56074
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-▁brash	-56076
-▁liven	-56077
-▁plume	-56078
-ularity	-56079
-▁Bumble	-56080
-▁donned	-56081
-▁enigma	-56082
-accident	-56083
-shoulder	-56084
-▁Alameda	-56085
-▁Citadel	-56086
-▁Fasting	-56087
-▁Frazier	-56088
-▁MINUTES	-56089
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-▁denials	-56091
-▁merrier	-56092
-▁rotates	-56093
-▁tuesday	-56094
-▁chivalry	-56095
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-▁tombstone	-56099
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-addy	-56109
-agic	-56110
-ulia	-56111
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-adine	-56118
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-▁smel	-56124
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-▁Norse	-56128
-▁Salle	-56129
-▁Wiley	-56130
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-▁gallo	-56132
-▁infra	-56133
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-emplate	-56135
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-layered	-56137
-uddenly	-56138
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-e	-56142
-t	-56143
-o	-56144
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-i	-56147
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-h	-56150
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-.	-56162
-,	-56163
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-T	-56167
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-’	-56172
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-"	-56176
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-:	-56189
-)	-56190
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-(	-56194
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-;	-56205
-1	-56206
-2	-56207
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-5	-56210
-0	-56211
-4	-56212
-6	-56213
-8	-56214
-$	-56215
-—	-56216
-7	-56217
-]	-56218
-[	-56219
-%	-56220
-9	-56221
-_	-56222
-‘	-56223
-&	-56224
-Q	-56225
-*	-56226
-X	-56227
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->	-56230
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-`	-56232
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-•	-56234
-­	-56235
-=	-56236
-£	-56237
-🙂	-56238
-<	-56239
-é	-56240
-~	-56241
-^	-56242
-»	-56243
-\	-56244
-€	-56245
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-’	-56247
-·	-56248
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-}	-56250
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-😉	-56252
-°	-56253
-⠀	-56254
-§	-56255
-”	-56256
-{	-56257
-«	-56258
-¦	-56259
-“	-56260
-®	-56261
-́	-56262
-¡	-56263
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-⁄	-56265
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-️	-56269
-—	-56270
-‐	-56271
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-❤	-56274
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-―	-56292
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-■	-56297
-😂	-56298
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-😊	-56302
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-「	-56305
-😛	-56306
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-œ	-56319
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-‍	-56322
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-█	-56327
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-►	-56331
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-±	-56336
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-↩	-56339
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-−	-56341
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-』	-56382
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-🎄	-56384
-🙏	-56385
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-Б	-56391
-♀	-56392
-💜	-56393
-ę	-56394
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-🙌	-56396
-─	-56397
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-😃	-56402
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-Ü	-56452
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-😈	-56464
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-【	-56468
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-Ó	-56470
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-😜	-56473
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-Æ	-56498
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-📷	-56500
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-🏾	-56504
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-✈	-56506
-	-56507
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-প	-56513
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-😮	-56517
-্	-56518
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-ˇ	-56525
-⦁	-56526
-อ	-56527
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-🌟	-56529
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-😋	-56531
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-њ	-56533
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-の	-56535
-🎶	-56536
-✌	-56537
-︎	-56538
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-👉	-56541
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-🎅	-56543
-🤷	-56544
-ƒ	-56545
-د	-56546
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-ย	-56555
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-ạ	-56564
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-💞	-56578
-。	-56579
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-б	-56584
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-้	-56587
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-😳	-56592
-̊	-56593
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-⨂	-56595
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-💯	-56597
-◼	-56598
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-˜	-56600
-ż	-56601
-、	-56602
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-🖤	-56607
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-😕	-56614
-😡	-56615
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-🌸	-56617
-➡	-56618
-ト	-56619
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-‒	-56622
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-※	-56628
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-◾	-56636
-✍	-56637
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-	-56646
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-▼	-56650
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-Ž	-56661
-ˈ	-56662
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-ď	-56664
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-🇸	-56666
-🇺	-56667
-😏	-56668
-ư	-56669
-Δ	-56670
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-☕	-56675
-☐	-56676
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-▫	-56679
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-👀	-56681
-노	-56682
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-🌲	-56686
-💀	-56687
-Í	-56688
-💃	-56689
-😒	-56690
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-❖	-56692
-카	-56693
-À	-56694
-Μ	-56695
-П	-56696
-😔	-56697
-Ł	-56698
-ت	-56699
-▸	-56700
-☃	-56701
-グ	-56702
-以	-56703
-る	-56704
-õ	-56705
-็	-56706
-Ω	-56707
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-Ÿ	-56709
-✦	-56710
-〜	-56711
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-̧	-56713
-η	-56714
-บ	-56715
-더	-56716
-🤦	-56717
-Þ	-56718
-ク	-56719
-💫	-56720
-Č	-56721
-不	-56722
-	-56723
-ั	-56724
-☘	-56725
-⚡	-56726
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-😞	-56728
-̋	-56729
-ข	-56730
-⃣	-56731
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-È	-56733
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-✘	-56736
-👨	-56737
-🚀	-56738
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-◄	-56740
-か	-56741
-イ	-56742
-🐾	-56743
-💟	-56744
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-🥂	-56746
-ف	-56747
-ส	-56748
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-	-56750
-👊	-56751
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-❏	-56753
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-🦄	-56759
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-🌺	-56766
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-🐶	-56769
-ə	-56770
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-鈥	-56772
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-시	-56774
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-🌊	-56776
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-🌎	-56778
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-ら	-56782
-ラ	-56783
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-ю	-56795
-⚠	-56796
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-🔊	-56798
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-🍷	-56800
-Ò	-56801
-Ι	-56802
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-❶	-56810
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-	-56812
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-␊	-56814
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-ộ	-56826
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-킹	-56828
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-🤓	-56830
-χ	-56831
-พ	-56832
-😙	-56833
-🤩	-56834
-ת	-56835
-ไ	-56836
-《	-56837
-🌞	-56838
-🎥	-56839
-ל	-56840
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-》	-56842
-👎	-56843
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-て	-56847
-全	-56848
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-🌿	-56851
-💎	-56852
-ą	-56853
-์	-56854
-➤	-56855
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-	-56857
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-দ	-56859
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-在	-56861
-	-56862
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-😲	-56864
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-🎨	-56870
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-ত	-56878
-⍝	-56879
-❓	-56880
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-🎈	-56882
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-✊	-56889
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-›	-56892
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-🚨	-56900
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-	-56907
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-⁠	-56912
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-ィ	-56915
-	-56916
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-	-56954
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-щ	-56965
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-⛄	-57030
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-ニ	-57035
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-ả	-57067
-▻	-57068
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-❌	-57073
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-🎵	-57075
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-◯	-57082
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-🌍	-57086
-📘	-57087
-ė	-57088
-Α	-57089
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-ी	-57092
-ṣ	-57093
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-了	-57095
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-🎤	-57101
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-😵	-57103
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-🌏	-57110
-💧	-57111
-📚	-57112
-Ê	-57113
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-语	-57119
-👸	-57120
-🔔	-57121
-😖	-57122
-Ε	-57123
-И	-57124
-Н	-57125
-ش	-57126
-ู	-57127
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-☞	-57129
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-最	-57132
-爱	-57133
-쿠	-57134
-폰	-57135
-🌼	-57136
-🍕	-57137
-🏠	-57138
-😨	-57139
-ุ	-57140
-⇧	-57141
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-者	-57143
-芒	-57144
-뀐	-57145
-	-57146
-💻	-57147
-😗	-57148
-ĥ	-57149
-ح	-57150
-ط	-57151
-়	-57152
-ะ	-57153
-₱	-57154
-∗	-57155
-▬	-57156
-➦	-57157
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-	-57306
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-Ë	-57308
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-Ӏ	-57312
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-ọ	-57315
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-✽	-57353
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-	-57362
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-থ	-57372
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-	-57388
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-З	-57397
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-َ	-57399
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-❝	-57404
-❞	-57405
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-	-57422
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-	-57424
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-🎭	-57430
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-ェ	-57737
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-央	-57802
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-ろ	-57883
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-🍟	-57918
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-🎸	-57921
-📈	-57922
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-ǧ	-57927
-Ƿ	-57928
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-	-57984
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-ű	-58003
-ǂ	-58004
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-̵	-58006
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-ּ	-58013
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-己	-58050
-巴	-58051
-很	-58052
-必	-58053
-房	-58054
-指	-58055
-揥	-58056
-支	-58057
-标	-58058
-秒	-58059
-空	-58060
-舗	-58061
-莊	-58062
-葉	-58063
-覧	-58064
-话	-58065
-长	-58066
-问	-58067
-限	-58068
-電	-58069
-香	-58070
-국	-58071
-리	-58072
-	-58073
-	-58074
-	-58075
-	-58076
-	-58077
-	-58078
-🍣	-58079
-🏖	-58080
-🏫	-58081
-🐤	-58082
-👭	-58083
-💠	-58084
-💣	-58085
-📩	-58086
-🔪	-58087
-🗺	-58088
-😧	-58089
-🙆	-58090
-🚩	-58091
-🤵	-58092
-🥇	-58093
-🧚	-58094
-ņ	-58095
-ŏ	-58096
-ɒ	-58097
-̯	-58098
-Ц	-58099
-ӏ	-58100
-ա	-58101
-ָ	-58102
-ߋ	-58103
-ড	-58104
-ญ	-58105
-ศ	-58106
-๊	-58107
-ე	-58108
-ợ	-58109
-‽	-58110
-₩	-58111
-∀	-58112
-≦	-58113
-⊙	-58114
-♩	-58115
-✱	-58116
-❈	-58117
-❋	-58118
-じ	-58119
-ず	-58120
-元	-58121
-区	-58122
-卡	-58123
-及	-58124
-古	-58125
-吗	-58126
-商	-58127
-娘	-58128
-木	-58129
-样	-58130
-楽	-58131
-機	-58132
-江	-58133
-治	-58134
-现	-58135
-电	-58136
-福	-58137
-竄	-58138
-站	-58139
-章	-58140
-輝	-58141
-过	-58142
-马	-58143
-魔	-58144
-공	-58145
-우	-58146
-튀	-58147
-	-58148
-	-58149
-	-58150
-🌐	-58151
-🌮	-58152
-🌶	-58153
-🏁	-58154
-🐘	-58155
-🐥	-58156
-🐩	-58157
-👐	-58158
-👠	-58159
-👱	-58160
-💆	-58161
-💇	-58162
-💒	-58163
-💵	-58164
-💿	-58165
-🔱	-58166
-🕷	-58167
-🤸	-58168
-	-58169
-ħ	-58170
-ʋ	-58171
-͛	-58172
-Ь	-58173
-ё	-58174
-ґ	-58175
-Ԁ	-58176
-ڈ	-58177
-స	-58178
-መ	-58179
-ር	-58180
-ና	-58181
-ṃ	-58182
-ề	-58183
-₵	-58184
-⇑	-58185
-⇩	-58186
-⌒	-58187
-└	-58188
-☄	-58189
-☇	-58190
-☒	-58191
-♞	-58192
-⚾	-58193
-⛪	-58194
-✆	-58195
-✉	-58196
-✸	-58197
-❦	-58198
-ⲣ	-58199
-ぐ	-58200
-ゆ	-58201
-わ	-58202
-ペ	-58203
-也	-58204
-五	-58205
-介	-58206
-共	-58207
-兴	-58208
-千	-58209
-协	-58210
-取	-58211
-吧	-58212
-基	-58213
-報	-58214
-妈	-58215
-师	-58216
-引	-58217
-总	-58218
-拉	-58219
-提	-58220
-揑	-58221
-料	-58222
-无	-58223
-易	-58224
-望	-58225
-東	-58226
-檛	-58227
-母	-58228
-比	-58229
-流	-58230
-港	-58231
-牛	-58232
-私	-58233
-类	-58234
-經	-58235
-線	-58236
-船	-58237
-若	-58238
-被	-58239
-让	-58240
-讲	-58241
-贝	-58242
-还	-58243
-進	-58244
-銀	-58245
-龙	-58246
-대	-58247
-라	-58248
-소	-58249
-타	-58250
-터	-58251
-	-58252
-	-58253
-	-58254
-	-58255
-	-58256
-🇴	-58257
-🇿	-58258
-🌀	-58259
-🌄	-58260
-🍞	-58261
-🍧	-58262
-🍨	-58263
-🍭	-58264
-🍮	-58265
-🏍	-58266
-🏴	-58267
-🐉	-58268
-🐑	-58269
-🐔	-58270
-👷	-58271
-💲	-58272
-📀	-58273
-📹	-58274
-📽	-58275
-🔗	-58276
-🔫	-58277
-🖌	-58278
-🖕	-58279
-🙉	-58280
-🚌	-58281
-🚓	-58282
-🛀	-58283
-🤫	-58284
-🥶	-58285
-|	-58286
-ǐ	-58287
-̅	-58288
-ִ	-58289
-ַ	-58290
-ष	-58291
-న	-58292
-మ	-58293
-ა	-58294
-ል	-58295
-ት	-58296
-ኣ	-58297
-ᚢ	-58298
-ᚨ	-58299
-ᚱ	-58300
-ᛏ	-58301
-ᴡ	-58302
-Ḳ	-58303
-ử	-58304
-ữ	-58305
-ự	-58306
-ỳ	-58307
-↥	-58308
-▣	-58309
-♭	-58310
-⚙	-58311
-✡	-58312
-➔	-58313
-ボ	-58314
-企	-58315
-休	-58316
-八	-58317
-制	-58318
-助	-58319
-印	-58320
-反	-58321
-受	-58322
-号	-58323
-君	-58324
-因	-58325
-奇	-58326
-島	-58327
-已	-58328
-帮	-58329
-常	-58330
-当	-58331
-後	-58332
-才	-58333
-旅	-58334
-机	-58335
-权	-58336
-条	-58337
-條	-58338
-油	-58339
-然	-58340
-版	-58341
-玩	-58342
-珍	-58343
-眼	-58344
-研	-58345
-秘	-58346
-等	-58347
-米	-58348
-编	-58349
-茶	-58350
-血	-58351
-觉	-58352
-貸	-58353
-輪	-58354
-銆	-58355
-锘	-58356
-頭	-58357
-馬	-58358
-검	-58359
-경	-58360
-나	-58361
-느	-58362
-동	-58363
-미	-58364
-바	-58365
-서	-58366
-세	-58367
-수	-58368
-전	-58369
-정	-58370
-제	-58371
-즈	-58372
-팬	-58373
-화	-58374
-	-58375
-	-58376
-	-58377
-	-58378
-	-58379
-	-58380
-	-58381
-	-58382
-	-58383
-	-58384
-🌾	-58385
-🍅	-58386
-🍏	-58387
-🍗	-58388
-🍯	-58389
-🎮	-58390
-🎺	-58391
-🏘	-58392
-🐀	-58393
-🐡	-58394
-🐦	-58395
-🐧	-58396
-🐬	-58397
-🐳	-58398
-🐵	-58399
-🐹	-58400
-👄	-58401
-👬	-58402
-👾	-58403
-💉	-58404
-🔧	-58405
-🔨	-58406
-🖖	-58407
-🗻	-58408
-🚁	-58409
-🛁	-58410
-🛸	-58411
-🤡	-58412
-🥃	-58413
-Ž	-58414
-Û	-58415
-ɐ	-58416
-ɘ	-58417
-̇	-58418
-̨	-58419
-ҝ	-58420
-ԛ	-58421
-ّ	-58422
-۱	-58423
-۲	-58424
-घ	-58425
-চ	-58426
-ভ	-58427
-చ	-58428
-త	-58429
-ద	-58430
-ฃ	-58431
-ษ	-58432
-ለ	-58433
-ሓ	-58434
-ᥙ	-58435
-ậ	-58436
-ỹ	-58437
-⁃	-58438
-⏩	-58439
-▉	-58440
-▾	-58441
-♔	-58442
-⛓	-58443
-⛔	-58444
-✂	-58445
-✎	-58446
-✮	-58447
-➥	-58448
-⫽	-58449
-゚	-58450
-ヤ	-58451
-九	-58452
-产	-58453
-京	-58454
-住	-58455
-保	-58456
-写	-58457
-别	-58458
-口	-58459
-向	-58460
-問	-58461
-堂	-58462
-夢	-58463
-妮	-58464
-它	-58465
-宗	-58466
-实	-58467
-将	-58468
-居	-58469
-屋	-58470
-展	-58471
-府	-58472
-康	-58473
-建	-58474
-志	-58475
-找	-58476
-择	-58477
-晚	-58478
-曾	-58479
-服	-58480
-束	-58481
-楼	-58482
-横	-58483
-气	-58484
-淡	-58485
-源	-58486
-熟	-58487
-玉	-58488
-球	-58489
-痴	-58490
-相	-58491
-省	-58492
-票	-58493
-精	-58494
-约	-58495
-给	-58496
-習	-58497
-華	-58498
-虎	-58499
-複	-58500
-见	-58501
-設	-58502
-論	-58503
-认	-58504
-议	-58505
-许	-58506
-评	-58507
-象	-58508
-貨	-58509
-资	-58510
-走	-58511
-身	-58512
-返	-58513
-遥	-58514
-邃	-58515
-郎	-58516
-酒	-58517
-队	-58518
-阿	-58519
-除	-58520
-順	-58521
-飛	-58522
-食	-58523
-餐	-58524
-鬼	-58525
-黃	-58526
-구	-58527
-메	-58528
-물	-58529
-민	-58530
-방	-58531
-심	-58532
-야	-58533
-에	-58534
-은	-58535
-증	-58536
-진	-58537
-집	-58538
-포	-58539
-	-58540
-	-58541
-	-58542
-	-58543
-	-58544
-	-58545
-🇻	-58546
-🇾	-58547
-🌝	-58548
-🍌	-58549
-🍜	-58550
-🍥	-58551
-🎹	-58552
-🏞	-58553
-🐚	-58554
-🐷	-58555
-👔	-58556
-👢	-58557
-💂	-58558
-📢	-58559
-📧	-58560
-🔆	-58561
-🔬	-58562
-🚙	-58563
-🚜	-58564
-🤝	-58565
-🤭	-58566
-🤮	-58567
-🥊	-58568
-🥺	-58569
-Ġ	-58570
-ľ	-58571
-ʌ	-58572
-̪	-58573
-̷	-58574
-Ξ	-58575
-ә	-58576
-ז	-58577
-ך	-58578
-ץ	-58579
-أ	-58580
-ؤ	-58581
-۹	-58582
-इ	-58583
-ॉ	-58584
-ప	-58585
-ლ	-58586
-ብ	-58587
-ተ	-58588
-ፍ	-58589
-ᴠ	-58590
-ṉ	-58591
-Ṭ	-58592
-ỏ	-58593
-ἰ	-58594
-ῖ	-58595
-⁨	-58596
-↔	-58597
-↪	-58598
-∃	-58599
-∇	-58600
-⋆	-58601
-⋘	-58602
-⋙	-58603
-⌚	-58604
-⌣	-58605
-⏱	-58606
-◉	-58607
-◳	-58608
-☭	-58609
-♐	-58610
-♢	-58611
-♯	-58612
-⛈	-58613
-❅	-58614
-❆	-58615
-➕	-58616
-⟨	-58617
-⫹	-58618
-〇	-58619
-专	-58620
-久	-58621
-书	-58622
-亚	-58623
-付	-58624
-位	-58625
-俱	-58626
-個	-58627
-候	-58628
-傭	-58629
-六	-58630
-兰	-58631
-具	-58632
-册	-58633
-冒	-58634
-列	-58635
-劉	-58636
-効	-58637
-務	-58638
-區	-58639
-双	-58640
-吉	-58641
-吳	-58642
-告	-58643
-員	-58644
-單	-58645
-园	-58646
-圣	-58647
-声	-58648
-头	-58649
-婚	-58650
-師	-58651
-弹	-58652
-强	-58653
-彩	-58654
-彼	-58655
-快	-58656
-战	-58657
-扁	-58658
-揂	-58659
-放	-58660
-故	-58661
-於	-58662
-族	-58663
-普	-58664
-曜	-58665
-梨	-58666
-楊	-58667
-止	-58668
-此	-58669
-步	-58670
-洋	-58671
-湖	-58672
-演	-58673
-潮	-58674
-發	-58675
-秀	-58676
-秋	-58677
-科	-58678
-究	-58679
-素	-58680
-経	-58681
-結	-58682
-级	-58683
-纪	-58684
-线	-58685
-罗	-58686
-股	-58687
-节	-58688
-話	-58689
-謝	-58690
-警	-58691
-速	-58692
-遇	-58693
-運	-58694
-選	-58695
-鈧	-58696
-録	-58697
-隆	-58698
-震	-58699
-面	-58700
-題	-58701
-首	-58702
-김	-58703
-네	-58704
-년	-58705
-놀	-58706
-디	-58707
-떻	-58708
-백	-58709
-빠	-58710
-상	-58711
-식	-58712
-저	-58713
-종	-58714
-주	-58715
-치	-58716
-	-58717
-	-58718
-	-58719
-	-58720
-	-58721
-	-58722
-	-58723
-	-58724
-	-58725
-	-58726
-🇯	-58727
-🇶	-58728
-🇼	-58729
-🌇	-58730
-🌋	-58731
-🌚	-58732
-🍇	-58733
-🍤	-58734
-🍬	-58735
-🍲	-58736
-🎒	-58737
-🏇	-58738
-🏌	-58739
-🏥	-58740
-🏩	-58741
-🏪	-58742
-🏰	-58743
-🐜	-58744
-🐞	-58745
-🐭	-58746
-👂	-58747
-👅	-58748
-👖	-58749
-👗	-58750
-💑	-58751
-💮	-58752
-💼	-58753
-📊	-58754
-📑	-58755
-📻	-58756
-🔎	-58757
-🔷	-58758
-🕎	-58759
-🕵	-58760
-🕸	-58761
-🗝	-58762
-🗹	-58763
-🚑	-58764
-🚽	-58765
-🛩	-58766
-🛫	-58767
-🤰	-58768
-🤱	-58769
-🦆	-58770
-🦊	-58771
-🦞	-58772
-󠁧	-58773
-‡	-58774
-Ċ	-58775
-ċ	-58776
-ĕ	-58777
-Ğ	-58778
-Ī	-58779
-ĭ	-58780
-Ū	-58781
-ų	-58782
-ƴ	-58783
-ǒ	-58784
-ǵ	-58785
-̀	-58786
-̲	-58787
-̳	-58788
-̽	-58789
-ϳ	-58790
-ъ	-58791
-џ	-58792
-ӓ	-58793
-ө	-58794
-ӽ	-58795
-ֵ	-58796
-ֶ	-58797
-ׁ	-58798
-ף	-58799
-إ	-58800
-۪	-58801
-ँ	-58802
-ण	-58803
-ध	-58804
-த	-58805
-ன	-58806
-ఆ	-58807
-డ	-58808
-ల	-58809
-ే	-58810
-฀	-58811
-ฏ	-58812
-გ	-58813
-თ	-58814
-ს	-58815
-ᅮ	-58816
-ም	-58817
-ሳ	-58818
-ስ	-58819
-በ	-58820
-እ	-58821
-ያ	-58822
-ይ	-58823
-ᵻ	-58824
-ṁ	-58825
-ṟ	-58826
-ỗ	-58827
-ἄ	-58828
-ἱ	-58829
-ῆ	-58830
-ῦ	-58831
-	-58832
-⇏	-58833
-⇣	-58834
-⌛	-58835
-⎛	-58836
-╘	-58837
-◐	-58838
-◡	-58839
-☚	-58840
-♻	-58841
-⚕	-58842
-⛺	-58843
-✇	-58844
-❚	-58845
-❢	-58846
-❷	-58847
-➼	-58848
-⟩	-58849
-⭓	-58850
-⭕	-58851
-〉	-58852
-げ	-58853
-ご	-58854
-ざ	-58855
-び	-58856
-ふ	-58857
-ほ	-58858
-ょ	-58859
-ギ	-58860
-两	-58861
-仁	-58862
-仲	-58863
-份	-58864
-众	-58865
-优	-58866
-侠	-58867
-們	-58868
-值	-58869
-健	-58870
-免	-58871
-典	-58872
-军	-58873
-农	-58874
-初	-58875
-功	-58876
-勝	-58877
-半	-58878
-協	-58879
-单	-58880
-博	-58881
-叔	-58882
-变	-58883
-史	-58884
-吸	-58885
-周	-58886
-唱	-58887
-啤	-58888
-営	-58889
-器	-58890
-址	-58891
-坛	-58892
-変	-58893
-夕	-58894
-失	-58895
-姑	-58896
-威	-58897
-娜	-58898
-孩	-58899
-守	-58900
-官	-58901
-少	-58902
-尔	-58903
-屁	-58904
-峰	-58905
-川	-58906
-差	-58907
-希	-58908
-床	-58909
-座	-58910
-弯	-58911
-怎	-58912
-恋	-58913
-戦	-58914
-戰	-58915
-托	-58916
-択	-58917
-拢	-58918
-按	-58919
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-擁	-58921
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-	-62408
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-	-62416
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-	-62424
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-	-62427
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-	-62429
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diff --git a/core/layers/__pycache__/point_wise_projection.cpython-311.pyc b/core/layers/__pycache__/point_wise_projection.cpython-311.pyc
deleted file mode 100644
index 2c35e9c..0000000
Binary files a/core/layers/__pycache__/point_wise_projection.cpython-311.pyc and /dev/null differ
diff --git a/core/layers/__pycache__/test_point_wise_projection.cpython-311.pyc b/core/layers/__pycache__/test_point_wise_projection.cpython-311.pyc
deleted file mode 100644
index 23ea690..0000000
Binary files a/core/layers/__pycache__/test_point_wise_projection.cpython-311.pyc and /dev/null differ
diff --git a/core/layers/attention.py b/core/layers/attention.py
index fefb74b..f82f878 100644
--- a/core/layers/attention.py
+++ b/core/layers/attention.py
@@ -8,8 +8,64 @@
 
 import math
 
+from xformers.ops import fmha, AttentionBias
 
-class RopeAttention(nn.Module):
+
+from torch.nn.attention.flex_attention import (
+    BlockMask,
+    flex_attention
+)
+
+flex_attention_comp = torch.compile(flex_attention)
+
+
+
+def reshape_for_broadcast(frequency_cis: torch.Tensor, x: torch.Tensor, seq_dim: int):
+    """
+    Reshape frequency tensor for broadcasting it with another tensor.
+
+    This function reshapes the frequency tensor to have the same shape as the target tensor 'x'
+    for the purpose of broadcasting the frequency tensor during element-wise operations.
+
+    Args:
+        frequency_cis (torch.Tensor): Frequency tensor to be reshaped.
+        x (torch.Tensor): Target tensor for broadcasting compatibility.
+        seq_dim (int): Sequence dimension index.
+
+    Returns:
+        torch.Tensor: Reshaped frequency tensor.
+    """
+    ndim = x.ndim
+    assert 0 <= seq_dim < ndim
+    assert frequency_cis.shape == (
+        x.shape[seq_dim],
+        x.shape[-3],
+        2,
+        2,
+    ), f"frequency_cis vs x: {(frequency_cis.shape, x.shape)}"
+    shape = [
+        d if i == seq_dim or i == ndim - 3 else 1 for i, d in enumerate(x.shape[:-2])
+    ] + [2, 2]
+    return frequency_cis.view(*shape)
+
+
+def apply_rotary_emb(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    seq_dim: int,
+    frequency_cis: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    xq_ = xq.reshape(*xq.shape[:-1], -1, 1, 2)  # B S H D -> B S H D/2 1 2
+    xk_ = xk.reshape(*xk.shape[:-1], -1, 1, 2)  # B S H D -> B S H D/2 1 2
+    frequency_cis = reshape_for_broadcast(
+        frequency_cis, xq_, seq_dim
+    ).float()  # S D/2 2 2 -> 1 S 1 D/2 2 2
+    xq_out = (xq_ * frequency_cis).sum(5).flatten(3)
+    xk_out = (xk_ * frequency_cis).sum(5).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+class AttentionBlock(nn.Module):
     """
     Implements attention mechanism with Rotary Position Embedding (RoPE).
     """
@@ -30,15 +86,6 @@ def __init__(self, config: BaseConfiguration):
         self.head_dim = config.head_dim
         self.scaling = 1 / math.sqrt(config.head_dim)
 
-        # Initialize RoPE if enabled
-        self.use_rope = config.use_rope
-        if self.use_rope:
-            self.rope_position_projection = RopePositionEmbedding(
-                hidden_dim=config.head_dim,
-                max_positions=config.max_positions,
-                base=config.rope_base
-            )
-
         # Initialize projection layers
         self.qkv_projection = nn.Linear(
             config.hidden_dim,
@@ -56,7 +103,8 @@ def forward(
         self,
         input_tensor: torch.Tensor,
         attention_mask: Optional[torch.Tensor] = None,
-        positions: Optional[torch.Tensor] = None,
+        frequency_cis: Optional[torch.Tensor] = None,
+        token_idx: int = None,
         output_attentions: bool = False
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
         """
@@ -81,13 +129,11 @@ def forward(
         qkv = qkv.view(batch_size, seq_length, 3, self.num_heads, self.head_dim)
         qkv = qkv.permute(2, 0, 3, 1, 4)  # [3, batch_size, num_heads, seq_len, head_dim]
         query_states, key_states, value_states = qkv
-
         # Apply RoPE if enabled
-        if self.use_rope:
-            cos, sin = self.rope_position_projection(query_states)
-            query_states, key_states = apply_positional_embedding(
-                query_states, key_states, cos, sin
-            )
+        query_states, key_states = apply_rotary_emb(query_states, key_states, 2 ,frequency_cis[0:seq_length])
+
+        if hasattr(self, "kv_cache"):
+            key_states, value_states = self.kv_cache.update(key_states, value_states, token_idx)
 
         # Compute attention scores with improved numerical stability
         attn_weights = torch.matmul(query_states, key_states.transpose(-2, -1))
@@ -135,3 +181,149 @@ def forward(
         attn_output = self.output_projection(attn_output)
 
         return attn_output, (attn_weights if output_attentions else None)
+
+    def reset_parameters(self, init_std=None, factor=1.0):
+        init_std = init_std or (self.dim ** (-0.5))
+
+        nn.init.trunc_normal_(
+            self.qkv_projection.weight,
+            mean=0.0,
+            std=init_std,
+            a=-3 * init_std,
+            b=3 * init_std,
+        )
+
+        nn.init.trunc_normal_(
+            self.output_projection.weight,
+            mean=0.0,
+            std=init_std / factor,
+            a=-3 * init_std,
+            b=3 * init_std,
+        )
+
+
+
+
+class MultiTypeAttentionBlock(nn.Module):
+    """
+    Implements attention mechanism with Rotary Position Embedding (RoPE).
+    """
+
+    def __init__(self, config: BaseConfiguration):
+        super().__init__()
+        assert config.head_dim is not None
+        if config.hidden_dim % config.head_dim != 0:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.config = config
+        self.attention_dropout = config.attention_dropout
+        self.hidden_dim = config.hidden_dim
+        self.num_heads = config.num_heads
+        self.head_dim = config.head_dim
+        self.scaling = 1 / math.sqrt(config.head_dim)
+
+        # Initialize projection layers
+        self.qkv_projection = nn.Linear(
+            config.hidden_dim,
+            3 * config.hidden_dim,
+            bias=False
+        )
+
+        self.output_projection = nn.Linear(
+            config.hidden_dim,
+            config.hidden_dim,
+            bias=False
+        )
+
+    def forward(
+        self,
+        input_tensor: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        frequency_cis: Optional[torch.Tensor] = None,
+        token_idx: int = None,
+        attn_impl: str = "sdpa",
+        output_attentions: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        """
+        Forward pass for RoPE attention.
+
+        Args:
+            input_tensor: Input tensor [batch_size, seq_len, hidden_dim]
+            attention_mask: Optional attention mask [batch_size, seq_len]
+            positions: Optional position indices
+            output_attentions: Whether to return attention weights
+
+        Returns:
+            Tuple of (output tensor, optional attention weights)
+        """
+        if input_tensor.dim() != 3:
+            raise ValueError(f"Expected 3D input, got {input_tensor.dim()}D")
+
+        batch_size, seq_length, _ = input_tensor.shape
+        output_shape = input_tensor.shape
+        # Fused QKV projection
+        qkv = self.qkv_projection(input_tensor)
+        qkv = qkv.view(batch_size, seq_length, 3, self.num_heads, self.head_dim)
+        qkv = qkv.permute(2, 0, 1, 3, 4)  # [3, batch_size, seq_len, num_heads,  head_dim]
+
+
+        query_states, key_states, value_states = qkv
+        # Apply RoPE if enabled
+        query_states, key_states = apply_rotary_emb(query_states, key_states, 1 ,frequency_cis)
+
+        if hasattr(self, "kv_cache"):
+            key_states, value_states = self.kv_cache.update(key_states, value_states, token_idx)
+
+        if attn_impl == "flex_attention":
+            assert attention_mask is None or isinstance(attention_mask, BlockMask)
+            query_states, key_states, value_states = map(lambda e: e.transpose(1, 2), (query_states, key_states, value_states))
+            output = flex_attention_comp(query_states, key_states, value_states, block_mask=attention_mask)
+            output = output.transpose(1, 2).contiguous()  # B H S D -> B S H D
+
+        elif attn_impl == "fmha":
+            assert attention_mask is None or isinstance(attention_mask, AttentionBias)
+            output = fmha.memory_efficient_attention(xq, xk, xv, attn_bias=attention_mask)
+            # This uses B S H D instead of B H S D of pytorch
+
+        elif attn_impl == "sdpa":
+            query_states, key_states, value_states = map(lambda e: e.transpose(1, 2), (query_states, key_states, value_states))
+            assert attention_mask is None or isinstance(attention_mask, (str, torch.Tensor))
+            is_causal = (attention_mask == "causal") if isinstance(attention_mask, str) else False
+            attention_mask = attention_mask if isinstance(attention_mask, torch.Tensor) else None
+            output = F.scaled_dot_product_attention(
+                query_states,
+                key_states,
+                value_states,
+                is_causal=is_causal,
+                attn_mask=attention_mask,
+            )
+            output = output.transpose(1, 2).contiguous()  # B H S D -> B S H D
+        else:
+            raise NotImplementedError(
+                f"Attention implementation {attn_impl} not supported"
+            )
+
+        attn_output = self.output_projection(output.reshape(output_shape))
+        return attn_output, None
+
+    def reset_parameters(self, init_std=None, factor=1.0):
+        init_std = init_std or (self.hidden_dim ** (-0.5))
+
+        nn.init.trunc_normal_(
+            self.qkv_projection.weight,
+            mean=0.0,
+            std=init_std,
+            a=-3 * init_std,
+            b=3 * init_std,
+        )
+
+        nn.init.trunc_normal_(
+            self.output_projection.weight,
+            mean=0.0,
+            std=init_std / factor,
+            a=-3 * init_std,
+            b=3 * init_std,
+        )
diff --git a/core/layers/layer_norm.py b/core/layers/layer_norm.py
deleted file mode 100644
index 260d6ee..0000000
--- a/core/layers/layer_norm.py
+++ /dev/null
@@ -1,30 +0,0 @@
-"""Implementing Layer Norm"""
-
-import numbers
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-class LayerNorm(nn.Module):
-    """
-    Class the contains LayerNorm
-    This is same as transformers implementation
-    """
-    def __init__(self, model_dimension, epsilon=0.00001):
-        super(LayerNorm, self).__init__()
-        if isinstance(model_dimension, numbers.Integral):
-            # mypy error: incompatible types in assignment
-            model_dimension = (model_dimension,)
-        self.model_dimension = model_dimension
-        self.gamma = nn.Parameter(torch.ones(model_dimension))
-        self.beta = nn.Parameter(torch.zeros(model_dimension))
-        self.epsilon = epsilon
-
-
-    def forward(self, x):
-        if isinstance(x, tuple):
-            x = x[0]
-        return F.layer_norm(
-            x, self.model_dimension, self.gamma, self.beta, self.epsilon
-        )
-
diff --git a/core/layers/norms.py b/core/layers/norms.py
new file mode 100644
index 0000000..b910073
--- /dev/null
+++ b/core/layers/norms.py
@@ -0,0 +1,63 @@
+"""Implementing Layer Norm"""
+
+import numbers
+import torch
+from torch import nn
+import torch.nn.functional as F
+from core.trainer import probe
+
+
+class LayerNorm(nn.Module):
+    """
+    Class the contains LayerNorm
+    This is same as transformers implementation
+    """
+    def __init__(self, model_dimension, epsilon=0.00001):
+        super(LayerNorm, self).__init__()
+        if isinstance(model_dimension, numbers.Integral):
+            # mypy error: incompatible types in assignment
+            model_dimension = (model_dimension,)
+        self.model_dimension = model_dimension
+        self.gamma = nn.Parameter(torch.ones(model_dimension))
+        self.beta = nn.Parameter(torch.zeros(model_dimension))
+        self.epsilon = epsilon
+
+
+    def forward(self, x):
+        if isinstance(x, tuple):
+            x = x[0]
+        return F.layer_norm(
+            x, self.model_dimension, self.gamma, self.beta, self.epsilon
+        )
+
+
+
+class RMSNorm(nn.Module):
+    """
+    Initialize the RMSNorm normalization layer.
+
+    Args:
+        dim (int): The dimension of the input tensor.
+        eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+
+    Attributes:
+        eps (float): A small value added to the denominator for numerical stability.
+        weight (nn.Parameter): Learnable scaling parameter.
+
+    """
+
+    def __init__(self, model_dimension: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(model_dimension))
+
+    def _norm(self, x: torch.Tensor):
+        return x * torch.rsqrt((x * x).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x: torch.Tensor):
+        x = probe.log_stats(x, "resid")
+        output = self._norm(x.float())
+        return (output * self.weight.float()).type_as(x)
+
+    def reset_parameters(self):
+        torch.nn.init.ones_(self.weight)  # type: ignore
\ No newline at end of file
diff --git a/core/layers/point_wise_projection.py b/core/layers/point_wise_projection.py
index 39d4f89..684053c 100644
--- a/core/layers/point_wise_projection.py
+++ b/core/layers/point_wise_projection.py
@@ -3,7 +3,7 @@
 from core.configurations.base import BaseConfiguration
 
 from core.activations.gelu import PytorchGELUTanh
-
+import torch.nn.functional as F
 
 class PointWiseProjection(nn.Module):
     """
@@ -13,15 +13,40 @@ class PointWiseProjection(nn.Module):
     """
     def __init__(self, config: BaseConfiguration):
         super().__init__()
+        self.hidden_dim = config.hidden_dim
+        self.intermediate_size = config.intermediate_dim
+
         self.up_projection = nn.Linear(config.hidden_dim, config.intermediate_dim, bias=False)
         self.down_projection = nn.Linear(config.intermediate_dim, config.hidden_dim, bias=False)
-        self.act_func = PytorchGELUTanh()
+        # self.act_func = PytorchGELUTanh()
 
 
     def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
-        return self.down_projection(self.act_func(self.up_projection(input_tensor)))
-    
-    
+        return self.down_projection(F.silu(self.up_projection(input_tensor)))
+
+    def reset_parameters(self, init_std=None, factor=1.0):
+        in_init_std = init_std or (self.hidden_dim ** (-0.5))
+        out_init_std = init_std or (self.intermediate_size ** (-0.5))
+        in_init_std = in_init_std
+        out_init_std = out_init_std / factor
+
+        nn.init.trunc_normal_(
+            self.up_projection.weight,
+            mean=0.0,
+            std=in_init_std,
+            a=-3 * in_init_std,
+            b=3 * in_init_std,
+        )
+
+        nn.init.trunc_normal_(
+            self.down_projection.weight,
+            mean=0.0,
+            std=out_init_std,
+            a=-3 * out_init_std,
+            b=3 * out_init_std,
+        )
+
+
 
 class PointWiseGatedProjection(nn.Module):
     """
@@ -32,15 +57,44 @@ class PointWiseGatedProjection(nn.Module):
 
     def __init__(self, config: BaseConfiguration):
         super().__init__()
-        self.intermediate_size = config.intermediate_dim
+        self.hidden_dim = config.hidden_dim
+
+        intermediate_dim = int(2 * config.intermediate_dim / 3)
+        if config.ffn_dim_multiplier is not None:
+            intermediate_dim = int(config.ffn_dim_multiplier * intermediate_dim)
+        intermediate_dim = config.multiple_of * ((intermediate_dim + config.multiple_of - 1) // config.multiple_of)
+        self.intermediate_dim = intermediate_dim
+
         # print(f"## config hidden dim {config.hidden_dim}")
         # print(f"## config intermediate dim {config.intermediate_dim}")
         self.gate_projection = nn.Linear(config.hidden_dim, config.intermediate_dim, bias=False)
         self.up_projection = nn.Linear(config.hidden_dim, config.intermediate_dim, bias=False)
         self.down_projection = nn.Linear(config.intermediate_dim, config.hidden_dim, bias=False)
-        
-        self.act_func = PytorchGELUTanh()
+
+        # self.act_func = PytorchGELUTanh()
 
     def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
-        return self.down_projection(self.act_func(self.gate_projection(input_tensor)) * self.up_projection(input_tensor))
+        return self.down_projection(F.silu(self.gate_projection(input_tensor)) * self.up_projection(input_tensor))
+
+
+    def reset_parameters(self, init_std=None, factor=1.0):
+        in_init_std = init_std or (self.hidden_dim ** (-0.5))
+        out_init_std = init_std or (self.intermediate_dim ** (-0.5))
+        in_init_std = in_init_std
 
+        out_init_std = out_init_std / factor
+        for w in [self.up_projection, self.gate_projection]:
+            nn.init.trunc_normal_(
+                w.weight,
+                mean=0.0,
+                std=in_init_std,
+                a=-3 * in_init_std,
+                b=3 * in_init_std,
+            )
+        nn.init.trunc_normal_(
+            self.down_projection.weight,
+            mean=0.0,
+            std=out_init_std,
+            a=-3 * out_init_std,
+            b=3 * out_init_std,
+        )
\ No newline at end of file
diff --git a/core/layers/positional_embedding/__pycache__/rope_projector.cpython-311.pyc b/core/layers/positional_embedding/__pycache__/rope_projector.cpython-311.pyc
deleted file mode 100644
index 6d98ba1..0000000
Binary files a/core/layers/positional_embedding/__pycache__/rope_projector.cpython-311.pyc and /dev/null differ
diff --git a/core/layers/positional_embedding/__pycache__/test_rope_projector.cpython-311.pyc b/core/layers/positional_embedding/__pycache__/test_rope_projector.cpython-311.pyc
deleted file mode 100644
index 7415903..0000000
Binary files a/core/layers/positional_embedding/__pycache__/test_rope_projector.cpython-311.pyc and /dev/null differ
diff --git a/core/layers/positional_embedding/rope_projector.py b/core/layers/positional_embedding/rope_projector.py
index aff703d..312fc3c 100644
--- a/core/layers/positional_embedding/rope_projector.py
+++ b/core/layers/positional_embedding/rope_projector.py
@@ -1,6 +1,9 @@
 import torch
 from torch import nn
+import logging
+from typing import Optional
 
+logger = logging.getLogger(__name__)
 
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
@@ -20,54 +23,132 @@ def apply_positional_embedding(q, k, cos, sin):
     return q, k
 
 
-class RopePositionEmbedding(nn.Module):
-    def __init__(self, hidden_dim, max_positions, base):
-        super().__init__()
-        self.hidden_dim = hidden_dim
-        self.max_positions = max_positions
-        self.base = base
-
-        rotatory_matrix = self._get_cache_rotatory_matrix(
-            max_positions=self.max_positions,
-            hidden_dim=self.hidden_dim,
-            base=self.base
-        )
-        self.register_buffer("rotatory_matrix", tensor=rotatory_matrix, persistent=False)
+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
+    """
+    Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
 
+    This function calculates a frequency tensor with complex exponentials using the given dimension 'dim'
+    and the end index 'end'. The 'theta' parameter scales the frequencies.
+    The returned tensor contains complex values in complex64 data type.
 
-    def _get_cache_rotatory_matrix(self, max_positions,
-                             hidden_dim,
-                             base: int = None):
+    Args:
+        dim (int): Dimension of the frequency tensor.
+        end (int): End index for precomputing frequencies.
+        theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0.
 
-        if base is None:
-            base = 10000
+    Returns:
+        torch.Tensor: Precomputed frequency tensor with complex exponentials.
+    """
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    print(f"===== Freqs device is {freqs.device} ======")
+    t = torch.arange(end, device=freqs.device)
+    freqs = torch.outer(t, freqs).float()
 
-        positions = torch.arange(max_positions, dtype=torch.float32)
-        angular_freq = 1.0 / (
-                base ** (torch.arange(0, hidden_dim, 2, dtype=torch.int64).float() / hidden_dim)
-        )
-        angular_freq = angular_freq.float().unsqueeze(0)  # 1 x D/2
-        positions = positions.unsqueeze(1)  # S x 1
-        rotatory_matrix = positions @ angular_freq  # S x D/2
-        return rotatory_matrix
+    cos, sin = freqs.cos(), freqs.sin()
 
+    return torch.stack((cos, -sin, sin, cos), dim=-1).view(*freqs.size(), 2, 2)
 
+class RopePositionEmbedding(torch.nn.Module):
+    """
+    RotaryEmbedding Module
+    """
 
-    @torch.no_grad()
-    def forward(self, x):
-        batch_size, _, sequence_length, dim = x.shape  # B x H x S x D
-        assert self.hidden_dim == dim  # 64
-        device_type = x.device
+    def __init__(self, hidden_dim, max_positions, base):
+        super().__init__()
 
-        if self.rotatory_matrix.device != device_type:
-            self.rotatory_matrix.device.to(device_type)
+        self.theta = base
+        self.head_dim = hidden_dim
+        self.max_seqlen = max_positions
 
-        freqs = self.rotatory_matrix[:sequence_length, :]  # S x D / 2
-        freqs = freqs[None, :, :].expand(batch_size, -1, -1)  # 1 x S x D/ 2 -> B x s x D/ 2
+        self.register_buffer(
+            "freqs_cis",
+            precompute_freqs_cis(dim=self.head_dim, end=self.max_seqlen, theta=self.theta),
+            persistent=False,
+        )
 
-        with torch.autocast(device_type=device_type.type, enabled=False):
-            emb = torch.cat((freqs, freqs), dim=-1)
+    def reset_parameters(self):
+        self.freqs_cis[...] = precompute_freqs_cis(
+            dim=self.head_dim, end=self.max_seqlen, theta=self.theta
+        )
 
-            cos = emb.cos()  # B x S x D
-            sin = emb.sin()  # B x S x D
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)  # B x S x D
+    def forward(
+        self, seqlen: Optional[int] = None, token_idx: Optional[torch.Tensor] = None
+    ):
+        """
+        Return freqs_cis corresponding to consecutive seqlen positions or the corresponding tok_idx positions
+        Args:
+            seqlen (int): Contiguous sequence length
+            tok_idx (torch.Tensor[int]): Position indices of each token this overrides seqlen
+
+        Returns:
+            Tuple(torch.Tensor, torch.Tensor): Embedded input tensor and freqs_cis
+        """
+        test = (seqlen is not None) or (token_idx is not None)
+        assert test, "Should provide atleast seqlen or tok_idx"
+        if token_idx is not None:
+            return self.freqs_cis[token_idx]
+        elif seqlen is not None:
+            return self.freqs_cis[0:seqlen]
+
+# class RopePositionEmbedding(nn.Module):
+#     def __init__(self, hidden_dim, max_positions, base):
+#         super().__init__()
+#         self.hidden_dim = hidden_dim
+#         self.max_positions = max_positions
+#         self.base = base
+
+#         frequency_cis = self._get_cache_rotatory_matrix(
+#             max_positions=self.max_positions,
+#             hidden_dim=self.hidden_dim,
+#             base=self.base
+#         )
+#         self.register_buffer("frequency_cis", tensor=frequency_cis, persistent=False)
+
+
+#     def _get_cache_rotatory_matrix(self, max_positions,
+#                              hidden_dim,
+#                              base: int = None):
+
+#         if base is None:
+#             base = 10000
+
+#         positions = torch.arange(max_positions, dtype=torch.float32)
+#         angular_freq = 1.0 / (
+#                 base ** (torch.arange(0, hidden_dim, 2, dtype=torch.int64).float() / hidden_dim)
+#         )
+#         angular_freq = angular_freq.float() # .unsqueeze(0)  # 1 x D/2
+#         # positions = positions.unsqueeze(1)  # S x 1
+#         rotatory_matrix = torch.outer(positions, angular_freq).float() # S x D/2
+#         cos, sin = rotatory_matrix.cos(), rotatory_matrix.sin()
+#         return torch.stack((cos, -sin, sin, cos), dim=-1).view(*rotatory_matrix.size(), 2, 2) # S x D/2 x 2 x 2 for each dimension we have 2x2 matrix which is [[cos -sin], [sin cos]]
+
+
+
+#     @torch.no_grad()
+#     def forward(self, input_tensor, token_idx=None):
+#         assert input_tensor.ndim in [2, 3], "Input tensor should be of shape (B x S) or (B x S x D)"
+#         sequence_length = input_tensor.shape[1] if token_idx is None else None
+
+#         assert (sequence_length is not None) or (token_idx is not None), "Should provide atleast sequence_length or token_idx"
+
+#         device_type = input_tensor.device
+
+#         if self.frequency_cis.device != device_type:
+#             logger.info("Changing frequency_cis device to match input tensor")
+#             self.frequency_cis = self.frequency_cis.to(device_type)
+#         else:
+#             logger.info("Loaded properly")
+
+#         if token_idx is not None:
+#             return self.frequency_cis[token_idx]
+#         elif sequence_length is not None:
+#             return self.frequency_cis[0:sequence_length]
+
+
+#     def reset_parameters(self):
+#         frequency_cis = self._get_cache_rotatory_matrix(
+#             max_positions=self.max_positions,
+#             hidden_dim=self.hidden_dim,
+#             base=self.base
+#         )
+#         self.register_buffer("frequency_cis", tensor=frequency_cis, persistent=False)
diff --git a/core/layers/test_layer_norm.py b/core/layers/test_layer_norm.py
index 02cf1ec..a8fdf8e 100644
--- a/core/layers/test_layer_norm.py
+++ b/core/layers/test_layer_norm.py
@@ -7,7 +7,7 @@
 import torch
 import unittest
 
-from core.layers.layer_norm import LayerNorm
+from core.layers.norms import LayerNorm
 
 
 class TestLayerNorm(unittest.TestCase):
@@ -42,7 +42,7 @@ def test_gamma_and_beta(self):
         # print(f"output tensor is {output_tensor[0]}")
         mean = input_tensor[0].mean()
         variance = input_tensor[0].var(unbiased=False)
-        
+
         normalized_input = (
                                    input_tensor[0] - mean
                            ) / torch.sqrt(variance + self.layer_norm.epsilon)
diff --git a/core/models/GI_01/config.yaml b/core/models/GI_01/config.yaml
new file mode 100644
index 0000000..f2bdb02
--- /dev/null
+++ b/core/models/GI_01/config.yaml
@@ -0,0 +1,140 @@
+name: debug
+dump_dir: /workspace/AI-Uncomplicated/artifacts/runs/GI-01-kickstart-0.1
+seed: 12
+grad_acc_steps: 1
+gc_collect_freq: 1000
+probe_freq: null
+steps: 3000
+
+data:
+  root_dir: /workspace/AI-Uncomplicated/artifact/pretraining_data/
+  sources:
+    TigerResearch: 1.0
+  batch_size: 2
+  seq_len: 32
+  n_views: 2
+  seed: 42
+  add_bos: true
+  add_eos: true
+  load_async: true
+  prefetch_size: 64
+  fim_rate: 0.1
+  fim_type: document
+  tokenizer:
+    name: GI01-tokenizer-v0.1-en
+    path: /workspace/AI-Uncomplicated/artifact/tokenizer
+
+optim:
+  lr: 0.0003
+  weight_decay: 0.1
+  epsilon: 1.0e-08
+  beta1: 0.9
+  beta2: 0.95
+  clip: 10.0
+  scheduler: wsd
+  warmup: 2000
+  lr_min_ratio: 1.0e-06
+  cycle_length: 1.0
+  cosine_theta: 1.0
+  annealing_step: 1000
+  decay_fraction: 0.1
+  exp_factor: 0.5
+  decay_type: cosine
+
+model:
+  name: null
+  num_layers: 1
+  padding_id: 0
+  hidden_dim: 64
+  intermediate_dim: 128
+  max_positions: 256
+  vocab_size: -1
+  layer_norm_eps: 1.0e-05
+  max_seq_len: 256
+  num_heads: 2
+  attention_dropout: 0.0
+  head_dim: null
+  use_rope: true
+  rope_base: 10000.0
+  output_last_hidden_state: false
+  seed: 42
+  weight_tying: false
+  ffn_dim_multiplier: null
+  multiple_of: 256
+  init_base_std: null
+  init_std_factor: "disabled"
+
+
+distributed:
+  dp_shard: 2
+  dp_replicate: 1
+  selective_activation_checkpointing: false
+  compile: true
+  fsdp_type: full_shard
+  model_dtype: bf16
+  float8_recipe: null
+  float8_filter: layers\.[0-9]+\.
+  matmul_allow_tf32: false
+  detect_anomaly: false
+  compile_cache_size_limit: 8
+  spawn_method: forkserver
+  allow_bf16_reduced_precision_reduction: true
+
+env:
+  MKL_SERVICE_FORCE_INTEL: GNU
+  OMP_NUM_THREADS: '1'
+  MKL_NUM_THREADS: '1'
+  ENABLE_INTRA_NODE_COMM: '1'
+  TORCH_NCCL_AVOID_RECORD_STREAMS: '1'
+  NCCL_IB_TIMEOUT: '22'
+  NCCL_DEBUG: INFO
+  TORCH_NCCL_ASYNC_ERROR_HANDLING: '1'
+
+checkpoint:
+  save_every:
+    step: 1000
+    limit: 0
+  eval_every:
+    step: 1000
+    limit: 0
+  path: null
+  init_ckpt_path: null
+  continue_training_from_init: false
+
+profiling:
+  run: true
+  trace_folder: profiling
+  mem_warmup: 100
+  mem_steps: 2
+  profile_warmup: 102
+  profile_steps: 2
+
+logging:
+  freq: 1
+  acc_freq: null
+  wandb:
+    job_type: null
+    dir: /workspace/AI-Uncomplicated/wandb/
+    project: Pretrain
+    entity: vipinsaravana
+    tags: null
+    group: null
+    name: null
+    notes: null
+    config_exclude_keys: null
+    config_include_keys: null
+    anonymous: null
+    mode: null
+    allow_val_change: null
+    resume: null
+    force: null
+    tensorboard: null
+    sync_tensorboard: null
+    monitor_gym: null
+    save_code: null
+    id: null
+    fork_from: null
+    resume_from: null
+
+async_eval_gpus: null
+eval: null
diff --git a/core/models/GI_01/distributed_trainer.py b/core/models/GI_01/distributed_trainer.py
new file mode 100644
index 0000000..68db98a
--- /dev/null
+++ b/core/models/GI_01/distributed_trainer.py
@@ -0,0 +1,328 @@
+import functools
+import math
+import os
+from typing import List, Optional
+import torch
+import torch.distributed as dist
+from torch.distributed.fsdp import (
+    FullyShardedDataParallel as FSDP
+)
+import numbers
+from torch.distributed.fsdp.wrap import (
+    transformer_auto_wrap_policy,
+    size_based_auto_wrap_policy,
+    enable_wrap,
+    wrap,
+)
+
+from core.models.GI_01.main.config import DatasetConfig, ModelConfig, TrainingConfig
+from core.models.GI_01.main.model import ConstrueAutoRegressiveModel
+from core.models.GI_01.main.decoder import ConstrueDecoderLayer
+from core.tokenizer.tokenizer_loader import SPMTokenizer
+from core.models.GI_01.main.data_loader import EntoPTDataSet, NextTokenPredictionCollator
+
+from torch.utils.data.distributed import DistributedSampler
+from torch.utils.data import Dataset, DataLoader
+
+from datasets import Dataset, load_from_disk
+import kagglehub
+
+from core.trainer.logger.local_logger import TrainingLogger
+from core.trainer.loss import cross_entropy_loss
+from core.trainer.schedulers import WarmupCosineScheduler
+from torch.optim import AdamW
+
+
+def setup(rank, world_size):
+    # os.environ['MASTER_ADDR'] = "localhost"
+    # os.environ['MASTER_PORT'] = "12355"
+
+    dist.init_process_group("nccl", rank=rank, world_size=world_size)
+    print("complete init")
+
+def cleanup():
+    dist.destroy_process_group()
+
+
+def worker_init_fn(worker_id):
+    torch.manual_seed(torch.initial_seed() + worker_id)
+
+
+def load_dataset(tokenizer, batch_size):
+    # Download latest version
+    rank = int(os.environ["LOCAL_RANK"])
+    world_size = int(os.environ["WORLD_SIZE"])
+
+    path = kagglehub.dataset_download("dhruvildave/en-fr-translation-dataset")
+
+    print("Path to dataset files:", path)
+
+    try:
+        dataset = Dataset.from_csv(os.path.join(path, "en-fr.csv"))
+        dataset = dataset.take(3000000)
+        dataset = dataset.filter(lambda d: d["en"] is not None and d["fr"] is not None)
+        dataset = dataset.filter(lambda x: len(tokenizer.encode(x["en"] + x["fr"], return_type=None)["input_ids"][0]) < 500)
+        dataset = dataset.train_test_split(test_size=0.2)
+        dataset.save_to_disk("filterd_dataset")
+    except NameError:
+        dataset = load_from_disk("filterd_dataset")
+
+
+    train_examples_pt = EntoPTDataSet(dataset["train"])
+    val_examples_pt = EntoPTDataSet(dataset["test"])
+
+    collate_fn = NextTokenPredictionCollator(tokenizer=tokenizer)
+
+    sampler_1 = DistributedSampler(train_examples_pt, rank=rank, num_replicas= world_size, shuffle=True)
+    train_dataloader = DataLoader(
+        dataset=train_examples_pt,
+        batch_size=batch_size,
+        sampler=sampler_1,
+        num_workers=2,
+        collate_fn=collate_fn,
+        pin_memory=True,
+        drop_last=False,
+        shuffle=False
+        # worker_init_fn=worker_init_fn
+    )
+
+    return train_dataloader, sampler_1
+
+
+def cal_performance(pred, gold, trg_pad_idx=-100):
+    ''' Apply label smoothing if needed '''
+
+    loss = cross_entropy_loss(pred, gold, trg_pad_idx)
+
+    pred = pred.max(-1)[1].view(-1)
+    gold = gold.contiguous().view(-1)
+    non_pad_mask = gold.ne(trg_pad_idx)
+    n_correct = pred.eq(gold).masked_select(non_pad_mask).sum().item()
+    n_word = non_pad_mask.sum().item()
+
+    return loss, n_correct, n_word
+
+
+def create_optimizer_and_scheduler(
+    model: torch.nn.Module,
+    num_training_steps: int,
+    learning_rate: float = 5e-5,
+    weight_decay: float = 0.01,
+    warmup_ratio: float = 0.1,
+    min_lr_ratio: float = 0.1,
+    beta1: float = 0.9,
+    beta2: float = 0.999,
+    eps: float = 1e-8,
+    no_decay_params: Optional[List[str]] = None
+):
+    # Default params that should not have weight decay
+    if no_decay_params is None:
+        no_decay_params = ['bias', 'LayerNorm.weight', 'layer_norm.weight']
+
+    # Separate parameters that should and should not have weight decay
+    optimizer_grouped_parameters = [
+        {
+            "params": [
+                p for n, p in model.named_parameters()
+                if not any(nd in n for nd in no_decay_params)
+            ],
+            "weight_decay": weight_decay,
+        },
+        {
+            "params": [
+                p for n, p in model.named_parameters()
+                if any(nd in n for nd in no_decay_params)
+            ],
+            "weight_decay": 0.0,
+        },
+    ]
+
+    # Create AdamW optimizer
+    optimizer = AdamW(
+        optimizer_grouped_parameters,
+        lr=learning_rate,
+        betas=(beta1, beta2),
+        eps=eps
+    )
+
+    # Create scheduler with linear warmup and cosine decay
+    print(f"NUM Training Step :: {num_training_steps}")
+
+    warmup_steps = int(num_training_steps * warmup_ratio)
+    print(f"warmup_steps :: {warmup_steps}")
+
+    scheduler = WarmupCosineScheduler(
+        optimizer=optimizer,
+        warmup_steps=warmup_steps,
+        total_steps=num_training_steps,
+        min_lr_ratio=min_lr_ratio
+    )
+
+    return optimizer, scheduler, warmup_steps
+
+
+def calculate_training_steps(
+    num_examples: int,
+    num_epochs: int,
+    batch_size: int,
+    gradient_accumulation_steps: int = 1
+) -> int:
+    """
+    Calculate the total number of training steps.
+
+    Args:
+        num_examples: Total number of training examples
+        num_epochs: Number of epochs to train for
+        batch_size: Batch size per forward pass
+        gradient_accumulation_steps: Number of steps to accumulate gradients
+
+    Returns:
+        int: Total number of optimizer update steps
+    """
+
+    update_steps_per_epoch = math.ceil(num_examples / gradient_accumulation_steps)
+
+    total_training_steps = update_steps_per_epoch * num_epochs
+
+    return total_training_steps
+
+
+import tqdm
+
+def train_step(epoch, model, train_loader, optimizer, sampler, train_size):
+    # forward
+    model.train()
+    rank = int(os.environ["RANK"])
+    fsdp_loss = torch.zeros(2).to("cuda")
+    if sampler:
+        sampler.set_epoch(epoch)
+
+    if rank==0:
+        inner_pbar = tqdm.tqdm(
+            total=train_size, colour="blue", desc="r0 Training Epoch"
+        )
+
+    for batch in train_loader:
+        for key in batch.keys():
+            batch[key] = batch[key].to("cuda")
+
+        labels = batch["labels"]
+
+        optimizer.zero_grad()
+        output = model(input_ids=batch["input_ids"], attention_mask=batch["attention_mask"])
+        logits = output["logits"]
+        loss = cross_entropy_loss(logits, labels, -100)
+
+        loss.backward()
+        optimizer.step()
+
+        fsdp_loss[0] += loss.item()
+        fsdp_loss[1] += len(batch)
+
+        if rank==0:
+            inner_pbar.update(1)
+
+    dist.all_reduce(fsdp_loss, op=dist.ReduceOp.SUM)
+    train_accuracy = fsdp_loss[0] / fsdp_loss[1]
+
+    if rank == 0:
+        inner_pbar.close()
+        print(
+                f"Train Epoch: \t{epoch}, Loss: \t{train_accuracy:.4f}"
+            )
+    return train_accuracy
+    # return None
+
+
+def main():
+    rank = int(os.environ["LOCAL_RANK"])
+    world_size = int(os.environ["WORLD_SIZE"])
+
+    print("rank ", rank)
+    setup(rank=rank, world_size=world_size)
+    torch.cuda.set_device(rank)
+
+    sample_input = torch.randint(0, 100, (4, 10))
+    sample_input = sample_input.to(dtype=torch.long, device="cuda")
+
+
+    model_config = ModelConfig(model_name="Construe", num_layers = 2,
+                                padding_id = 0,
+                                hidden_dim = 512,
+                                intermediate_dim = 3072,
+                                max_positions = 2048,
+                                layer_norm_eps = 1e-05,
+                                model_max_sequence = 2048,
+                                num_heads = 8,
+                                attention_dropout = 0.1)
+
+    dataset_config = DatasetConfig(dataset_path=".artifacts/dataset", dataset_shuffle=True, batch_size=2)
+    training_config = TrainingConfig(tokenizer_path="/workspace/AI-Uncomplicated/core/models/GI_01/tokenzier/en_fr_combined_tokenizer/", warm_up=4000, logging_steps=747)
+
+    ## Load model and tokenizer
+    tokenizer = SPMTokenizer(training_config.tokenizer_path)
+
+    model_config.vocabulary_size = tokenizer.vocab_size
+
+    model = ConstrueAutoRegressiveModel(model_config)
+    model.to("cuda")
+
+
+    train_dataloader, sampler_1 = load_dataset(tokenizer=tokenizer, batch_size=dataset_config.batch_size)
+
+    training_size = len(train_dataloader)
+
+    # for data in train_dataloader:
+    #     break
+    # print("============== Dataset stats ===================")
+    # print(data["input_ids"].size())
+    # print(data["attention_mask"].size())
+
+    # print(model(input_ids=data["input_ids"].to('cuda'),
+    #             attention_mask=data["attention_mask"].to('cuda'))["logits"].size())
+    fsdp_model = FSDP(model, device_id=torch.cuda.current_device(),
+        auto_wrap_policy=functools.partial(
+        transformer_auto_wrap_policy,
+        transformer_layer_cls={
+            ConstrueDecoderLayer,
+        },
+    ))
+    print(fsdp_model)
+    # fsdp_model.train()
+    print(fsdp_model(sample_input)["logits"].size())
+
+
+    num_training_steps = calculate_training_steps(
+        len(train_dataloader),
+        num_epochs=2,
+        batch_size=dataset_config.batch_size,
+        gradient_accumulation_steps=1
+    )
+
+    optimizer, scheduler, warmup_steps = create_optimizer_and_scheduler(
+        model=fsdp_model,
+        num_training_steps=num_training_steps,
+        learning_rate=0.0001,
+        warmup_ratio=0.1,
+        weight_decay=0.0
+    )
+
+    train_logger = TrainingLogger(
+        project_name="testing",
+        output_dir="./",
+        log_every_n_steps=100,
+        save_every_n_steps=100,
+    )
+
+    print("all set to run")
+    for epoch in range(2):
+        print(f"Running Epoch :: {epoch}")
+        train_step(epoch=epoch, model=fsdp_model, train_loader=train_dataloader, optimizer=optimizer, sampler=sampler_1, train_size=training_size)
+        scheduler.step()
+
+    dist.barrier()
+    cleanup()
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/core/models/GI_01/eval.py b/core/models/GI_01/eval.py
new file mode 100644
index 0000000..c8384bd
--- /dev/null
+++ b/core/models/GI_01/eval.py
@@ -0,0 +1,330 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+from collections import defaultdict
+from dataclasses import asdict, dataclass, field
+from datetime import datetime
+import json
+import logging
+import os
+from pathlib import Path
+from lm_eval.api.instance import Instance
+from lm_eval.api.model import LM
+from typing import Any, List, Optional, Tuple, Union
+from lm_eval import simple_evaluate
+from omegaconf import OmegaConf
+import torch
+
+from core.models.GI_01.generation import PackedCausalTransformerGenerator, PackedCausalTransformerGeneratorArgs, load_consolidated_model_and_tokenizer
+from core.models.GI_01.main.model import ConstrueAutoRegressiveModel, GI01ModelArgs
+from core.trainer.args import dump_config
+from core.trainer.checkpointer import CONSOLIDATE_FOLDER, consolidate_checkpoints
+from core.trainer.dataloader import init_choice_state, setup_sources
+from core.trainer.distributed import DistributedArgs, dist_mean_dict, get_global_rank, get_world_size, setup_torch_distributed
+
+
+EVAL_FOLDER_NAME = "Eval_{:010d}"
+
+logger = logging.getLogger()
+
+
+@dataclass
+class LMHarnessArgs:
+    tasks: Optional[List[Any]] = None
+    num_fewshot: Optional[int] = None
+    device: Optional[str] = None
+    use_cache: Optional[str] = None
+    cache_requests: bool = False
+    rewrite_requests_cache: bool = False
+    delete_requests_cache: bool = False
+    limit: Optional[Union[int, float]] = None
+    bootstrap_iters: int = 100000
+    check_integrity: bool = False
+    write_out: bool = False
+    log_samples: bool = True
+    system_instruction: Optional[str] = None
+    apply_chat_template: Union[bool, str] = False
+    fewshot_as_multiturn: bool = False
+    gen_kwargs: Optional[str] = None
+    verbostiy: str = "INFO"
+    predict_only: bool = False
+    random_seed: int = 0
+    numpy_random_seed: int = 1234
+    torch_random_seed: int = 1234
+    fewshot_random_seed: int = 1234
+
+@dataclass
+class ValidationArgs:
+    max_steps: Optional[int] = None # If None the whole validation file is used -> /!\ This number of steps is gpu dependent (100 max steps on 8 gpus = 800 steps on 1 gpu)
+    use_val_from_train_src: bool = True # Use the validation set from training sources
+    root_dir: str = ""
+    sources: List[str] = field(default_factory=list) # Other sources to eval on
+
+@dataclass
+class EvalArgs:
+    name: str = "evals"
+    dump_dir: Optional[str] = None
+    metric_log_dir: Optional[str] = None
+    ckpt_dir: str = ""
+    generator: PackedCausalTransformerGeneratorArgs = field(
+        default_factory=PackedCausalTransformerGeneratorArgs
+    )
+    harness: Optional[LMHarnessArgs] = field(default_factory=LMHarnessArgs)
+    validation: Optional[ValidationArgs] = field(default_factory=ValidationArgs)
+
+    wandb: Optional[Any] = None
+
+    global_step: Optional[int] = None  # for in-training evaluation
+
+
+def all_dicts_same(dict_list):
+    if not dict_list:  # Check if the list is empty
+        return True
+
+    # Compare each dictionary to the first one
+    first_dict = dict_list[0]
+    return all(d == first_dict for d in dict_list)
+
+
+class MockAccelerator:
+    def gather(self, tensor):
+        l = [torch.zeros_like(tensor) for _ in range(get_world_size())]
+        torch.distributed.all_gather(l, tensor)
+        return torch.stack(l)
+
+    def wait_for_everyone(self):
+        torch.distributed.barrier()
+
+
+# Light wrapper around generator for lm-eval harness
+class EvalHarnessLM(LM):
+    def __init__(self, generator):
+        super().__init__()
+        self.generator = generator
+        self.accelerator = MockAccelerator()
+        self._rank = get_global_rank()
+        self._world_size = get_world_size()
+        self.device = generator.device
+
+    def generate_until(self, requests: List[Instance]) -> List[str]:
+        prompts, gen_args = zip(*[req.args for req in requests])
+        assert all_dicts_same(gen_args), "Doesn't support different gen args for now"
+        gen_args = gen_args[0]
+        temperature = gen_args.get("temperature", 0.0)
+        top_p = gen_args.get("top_p", None)
+        top_k = gen_args.get("top_k", None)
+        until = gen_args.get("until", [])
+
+        self.generator.temperature = temperature
+        self.generator.top_p = top_p
+        self.generator.top_k = top_k
+        self.generator.until = until
+        generations, _, _ = self.generator.generate(prompts)
+        filtered_gen = []
+        for g in generations:
+            for e in until:
+                g = g.replace(e, "")
+            filtered_gen.append(g)
+        return filtered_gen
+
+    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
+        prompts, continuations = zip(*[req.args for req in requests])
+        inputs = [req.args[0] + req.args[1] for req in requests]
+        max_gen_len = self.generator.max_gen_len
+        # We temporarily lower max gen len
+        self.generator.max_gen_len = 1
+        _, lls, greedy = self.generator.generate(inputs)
+        results = []
+        for p, ll, gr in zip(prompts, lls, greedy):
+            p_len = len(self.generator.tokenizer.encode(p, add_bos=False, add_eos=False))
+            results.append((ll[p_len:].sum().item(), gr[p_len:].all().item()))
+
+        self.generator.max_gen_len = max_gen_len
+        return results
+
+    def loglikelihood_rolling(self, requests: List[Instance]) -> List[float]:
+        prompts = [req.args[0] for req in requests]
+        max_gen_len = self.generator.max_gen_len
+        # We temporarily lower max gen len
+        self.generator.max_gen_len = 1
+        _, lls, _ = self.generator.generate(prompts)
+        results = []
+        for ll in lls:
+            results.append((ll.sum().item(),))
+        self.generator.max_gen_len = max_gen_len
+
+        return results
+
+
+def eval_on_val(generator, val_args: ValidationArgs, train_cfg):
+    srcs = {}
+    for src in val_args.sources:
+        path = os.path.join(val_args.root_dir, src)
+        srcs[path] = 1.0
+    for src in train_cfg.data.sources:
+        path = os.path.join(train_cfg.data.root_dir, src)
+        srcs[path] = 1.0
+
+    multi_state = init_choice_state("", srcs, 0, get_global_rank(), get_world_size(), "*.val.jsonl")
+    path_to_iter = setup_sources(multi_state)
+
+    max_gen_len = generator.max_gen_len
+    # We temporarily lower max gen len
+    generator.max_gen_len = 1
+
+    all_val_metrics = {}
+    for src in path_to_iter:
+        jsonl_iterator = path_to_iter[src]
+        texts = []
+        logger.info(f"Running validation on {src}...")
+        for step, (content, state) in enumerate(jsonl_iterator):
+            if state['current_iter'] > 0 or (val_args.max_steps is not None and step >= val_args.max_steps):
+                break
+            content_key = "text" if ("text" in content) else "content"
+            texts.append(content[content_key])
+
+        _, loglikelihood, _ = generator.generate(texts)
+
+        metrics = defaultdict(list)
+        for i, ll in enumerate(loglikelihood):
+            tmp = ll.sum().item()
+            metrics['nll'].append(tmp)
+            metrics['nll_per_token'].append(tmp / len(ll))
+            metrics['nll_per_char'].append(tmp / len(texts[i]))
+
+            metrics['avg_seqlen'].append(len(ll))
+
+        for m in metrics:
+            metrics[m] = sum(metrics[m]) / len(metrics[m])
+        metrics.update(dist_mean_dict(metrics))
+        logger.info(f"Validation on {src} done. Metrics: {metrics}")
+
+        name = os.path.basename(src)
+        if name in all_val_metrics:
+            logger.warning(f"Duplicate source name {name}, path {src} in validation sources, renaming to {name}_1")
+            name = f"{name}_1"
+        all_val_metrics[name] = metrics
+
+    generator.max_gen_len = max_gen_len
+
+    return all_val_metrics
+
+def launch_eval(cfg: EvalArgs):
+    if not torch.distributed.is_initialized():
+        setup_torch_distributed(DistributedArgs())
+    if (
+        Path(cfg.ckpt_dir).exists()
+        and (Path(cfg.ckpt_dir) / "params.json").exists()
+        and next(Path(cfg.ckpt_dir).glob("*.pth"), None) is not None
+    ):
+        consolidate_path = Path(cfg.ckpt_dir)
+    else:
+        consolidate_path = Path(cfg.ckpt_dir) / CONSOLIDATE_FOLDER
+        if not consolidate_path.exists() and get_global_rank() == 0:
+            consolidate_path = consolidate_checkpoints(cfg.ckpt_dir)
+
+    Path(cfg.dump_dir).mkdir(parents=True, exist_ok=True)
+    dump_config(cfg, Path(cfg.dump_dir) / "config.yaml", log_config=False)
+
+    consolidate_path = str(consolidate_path)
+    torch.distributed.barrier()
+    logger.info("Loading model")
+    model, tokenizer, train_cfg = load_consolidated_model_and_tokenizer(
+        consolidate_path,
+        model_cls=ConstrueAutoRegressiveModel,
+        model_args_cls=GI01ModelArgs,
+    )
+    logger.info("Model loaded")
+    model.eval()
+    generator = PackedCausalTransformerGenerator(cfg.generator, model, tokenizer)
+
+    wrap = EvalHarnessLM(generator)
+    results = simple_evaluate(wrap, **asdict(cfg.harness))
+    val_results =  None
+    if cfg.validation:
+        val_results = eval_on_val(generator, cfg.validation, train_cfg)
+    if get_global_rank() == 0:
+        with open(Path(cfg.dump_dir) / "results.json", "w") as f:
+            f.write(json.dumps(results))
+        logger.info(f"All evaluation results: {results['results']}")
+        if val_results is not None:
+            with open(Path(cfg.dump_dir) / "validation.json", "w") as f:
+                f.write(json.dumps(val_results))
+            logger.info(f"All validation results: {val_results}")
+    if cfg.metric_log_dir and get_global_rank() == 0:
+        metric_log_path = Path(cfg.metric_log_dir) / "metrics.eval.jsonl"
+
+        logger.info(f"Writing metric logs to {metric_log_path}")
+        timestamp = {
+            "created_at": datetime.utcnow().isoformat(),
+        }
+        if cfg.global_step is not None:
+            timestamp["global_step"] = cfg.global_step
+        print(
+            json.dumps(timestamp | results["results"]),
+            file=open(metric_log_path, mode="a"),
+            flush=True,
+        )
+
+        val_log_path = Path(cfg.metric_log_dir) / "metrics.validation.jsonl"
+        if val_results is not None:
+            print(
+                json.dumps(timestamp | val_results),
+                file=open(val_log_path, mode="a"),
+                flush=True,
+            )
+
+    del generator
+
+
+def main():
+    """
+    The command line interface here uses OmegaConf https://omegaconf.readthedocs.io/en/2.3_branch/usage.html#from-command-line-arguments
+    This accepts arguments as a dot list
+    So if the dataclass looks like
+
+    @dataclass
+    class DummyArgs:
+        name: str
+        model: LMTransformerArgsgs
+
+    @dataclass
+    class LMTransformerArgsgs:
+        dim: int
+
+    Then you can pass model.dim=32 to change values in LMTransformerArgsgs
+    or just name=tictac for top level attributes.
+
+    The behavior here is as follows:
+    1. We instantiate EvalArgs with its default values
+    2. We override those default values with the ones in the provided config file
+    3. We override the result with the additional arguments provided through command line
+
+    For example, if the config is the following
+
+    model:
+        dim: 128
+        n_layers: 4
+
+    and you call eval.py with eval.py model.dim=64
+
+    Then the final TrainArgs will have
+
+    model:
+        dim: 64
+        n_layers: 4
+
+    Plus all the default values in EvalArgs dataclass.
+    """
+    cli_args = OmegaConf.from_cli()
+    file_cfg = OmegaConf.load(cli_args.config)
+    # We remove 'config' attribute from config as the underlying DataClass does not have it
+    del cli_args.config
+
+    default_cfg = OmegaConf.structured(EvalArgs())
+    cfg = OmegaConf.merge(default_cfg, file_cfg, cli_args)
+    cfg = OmegaConf.to_object(cfg)
+    launch_eval(cfg)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/core/models/GI_01/generation.py b/core/models/GI_01/generation.py
new file mode 100644
index 0000000..5d72d1c
--- /dev/null
+++ b/core/models/GI_01/generation.py
@@ -0,0 +1,542 @@
+from dataclasses import dataclass, field
+from pathlib import Path
+import time
+from typing import List, Optional
+from omegaconf import OmegaConf
+from torch import nn
+from core.layers.attention import AttentionBlock, MultiTypeAttentionBlock
+import torch
+from torch.nn.attention.flex_attention import create_block_mask
+import torch.nn.functional as F
+
+from torch.nn.attention.flex_attention import (
+    BlockMask,
+    flex_attention,
+    _mask_mod_signature,
+)
+
+from core.models.GI_01.main.model import ConstrueAutoRegressiveModel, GI01ModelArgs
+from core.trainer.args import dataclass_from_dict
+from core.trainer.checkpointer import CONSOLIDATE_NAME, consolidate_checkpoints
+from core.trainer.dataloader import build_tokenizer
+
+
+
+@dataclass
+class PackedCausalTransformerGeneratorArgs:
+    temperature: float = 0.0
+    top_p: Optional[float] = None
+    top_k: Optional[float] = None
+    max_gen_len: int = 512  # Maximum number of tokens to generate
+    max_tokens: int = 1024  # Maximum number of tokens that can go through the model
+    max_prompt_len: Optional[int] = None
+    until: List[str] = field(default_factory=list)
+    compile_prefilling: bool = False
+    reduce_generation_overhead: bool = False
+    show_progress: bool = False
+    dtype: Optional[str] = "bf16"
+    device: Optional[str] = "cuda"
+
+
+
+def causal_mask(b, h, q_idx, kv_idx):
+    return q_idx >= kv_idx
+
+
+def lengths_to_start_ids(lengths):
+    doc_start = lengths.cumsum(0)
+    doc_start = doc_start.roll(1)
+    doc_start[0] = 0
+    return doc_start
+
+
+def lengths_to_local_ids(lengths):
+    assert lengths.ndim == 1
+    nb_seqs = lengths.size(0)
+    total_seqlen = lengths.sum()
+    # This gives the document id of each token
+    doc_id = torch.repeat_interleave(lengths)
+    # Compute document start for each document
+    doc_start = lengths_to_start_ids(lengths)
+    # Compute document start for each token
+    doc_start = doc_start[doc_id]
+    # Compute the position of each token within each document
+    tok_id = torch.arange(total_seqlen, device=lengths.device) - doc_start
+
+    return doc_id, tok_id
+
+
+def generate_doc_mask_mod(
+    mask_mod: _mask_mod_signature,
+    lengths: torch.Tensor,
+    kv_lengths: Optional[torch.Tensor] = None,
+) -> _mask_mod_signature:
+    """Generates mask mods that apply to inputs to flex attention in the sequence stacked
+    format.
+
+    Args:
+        mask_mod: The mask mod to apply to the documents
+        lengths: Lengths of each document
+
+    Note:
+        What is the sequence stacked format? When assembling batches of inputs, we
+        take multiple sequences and stack them together to form 1 large sequence. We then
+        use masking to ensure that the attention scores are only applied to tokens within
+        the same document.
+
+    Example:
+
+    - Square mask
+      doc_mask         lengths
+      a a b b b c c    2 3 2
+    a 1 0 0 0 0 0 0
+    a 1 1 0 0 0 0 0
+    b 0 0 1 0 0 0 0
+    b 0 0 1 1 0 0 0
+    b 0 0 1 1 1 0 0
+    c 0 0 0 0 0 1 0
+    c 0 0 0 0 0 1 1
+
+    """
+    kv_lengths = kv_lengths if kv_lengths is not None else lengths
+    q_document_id, q_token_id = lengths_to_local_ids(lengths)
+    kv_document_id, kv_token_id = lengths_to_local_ids(kv_lengths)
+    q_max_idx = lengths.sum() - 1
+    kv_max_idx = kv_lengths.sum() - 1
+
+    def doc_mask_mod(b, h, q_idx, kv_idx):
+        q_idx_cap = torch.minimum(q_max_idx, q_idx)
+        kv_idx_cap = torch.minimum(kv_max_idx, kv_idx)
+        valid_idx = (q_idx <= q_max_idx) & (kv_idx <= kv_max_idx)
+        same_doc = q_document_id[q_idx_cap] == kv_document_id[kv_idx_cap]
+        q_logical = q_token_id[q_idx_cap]
+        kv_logical = kv_token_id[kv_idx_cap]
+        inner_mask = mask_mod(b, h, q_logical, kv_logical)
+        return same_doc & inner_mask & valid_idx
+
+    return doc_mask_mod
+
+def sample_top_p(probs: torch.Tensor, p: float) -> torch.Tensor:
+    probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
+    probs_sum = torch.cumsum(probs_sort, dim=-1)
+    mask = probs_sum - probs_sort > p
+    probs_sort[mask] = 0.0
+    next_token = torch.multinomial(probs_sort, num_samples=1)
+    next_token = torch.gather(probs_idx, -1, next_token)
+    return next_token
+
+
+def sample_top_k(probs, k):
+    topk_value, _ = torch.topk(probs, k)  # batch_sz x topk
+    min_value_top_k = topk_value[:, [-1]]
+    probs[probs < min_value_top_k] = 0.0
+    probs.div_(probs.sum(dim=-1, keepdim=True))
+    next_token = torch.multinomial(probs, num_samples=1)
+    return next_token
+
+
+def sample_tokens(logits, temperature=0.0, top_p=None, top_k=None):
+    shape = logits.shape
+    logits = logits.flatten(end_dim=-2)
+    if temperature > 0.0:
+        probs = torch.softmax(logits / temperature, dim=-1)
+
+        if top_p is not None:
+            next_token = sample_top_p(probs, top_p)
+        elif top_k is not None:
+            next_token = sample_top_k(probs, top_k)
+        else:
+            next_token = torch.multinomial(probs, num_samples=1)
+    else:
+        next_token = torch.argmax(logits, dim=-1)
+    return next_token.view(shape[:-1])
+
+
+def pack_prompts(prompts: List[int]):
+    res = []
+    lengths = []
+    for i, p in enumerate(prompts):
+        p = torch.tensor(p, dtype=torch.long)
+        l = p.size(0)
+        res.append(p)
+        lengths.append(l)
+    lengths = torch.tensor(lengths, dtype=torch.long)
+    res = torch.cat(res)
+    return res, lengths
+
+
+def batch_prompts(prompts, max_elements, lengths=None):
+    batches = []
+    current_batch = []
+    current_count = 0
+
+    for i in range(len(prompts)):
+        prt = prompts[i]
+        prompt_size = len(prt) if lengths is None else lengths[i]
+        if current_count + prompt_size <= max_elements:
+            current_batch.append(prt)
+            current_count += prompt_size
+        else:
+            if current_batch:  # Add the current batch to batches
+                batches.append(current_batch)
+            # Start a new batch with the current prompt
+            current_batch = [prt]
+            current_count = prompt_size
+
+    # Add the last batch if it contains any prompts
+    if current_batch:
+        batches.append(current_batch)
+
+    return batches
+
+
+class KVCache(nn.Module):
+    def __init__(self, bsz, seqlen, n_heads, head_dim, dtype, device):
+        super().__init__()
+        shape = (bsz, seqlen, n_heads, head_dim)
+        self.register_buffer("k_cache", torch.zeros(shape, dtype=dtype, device=device))
+        self.register_buffer("v_cache", torch.zeros(shape, dtype=dtype, device=device))
+        self.offset = 0
+
+    def reset(self):
+        self.k_cache.zero_()
+        self.v_cache.zero_()
+        self.offset = 0
+
+    def update(self, k_val, v_val, tok_idx):
+        # input_pos: [B], k_val: [B, S, H, D]
+        self.k_cache.index_copy_(1, self.offset + tok_idx, k_val)
+        self.v_cache.index_copy_(1, self.offset + tok_idx, v_val)
+        return self.k_cache, self.v_cache
+
+class PackedCausalTransformerGenerator:
+    def __init__(
+        self,
+        cfg: PackedCausalTransformerGeneratorArgs,
+        model: nn.Module,
+        tokenizer: nn.Module,
+    ):
+        """
+        This class wraps a causal transformer model with its corresponding tokenizer
+        and provides an efficient way to pack prompts together and do generation on
+        the packed sequence.
+
+        For example, if we had the prompts "Hello, I am a " and "Initiating calibration "
+        Then this class will concatenate those sequence (pack them together)
+        "Hello, I am a Initiating calibration"
+        And make the necessary attention masks such that a sequence only attends to itself
+        during prefilling and generation.
+
+        This class creates a fixed size cache of size max_tokens or sum of prompt sizes
+        + the max number of generated tokens per sequence.
+        """
+        self.model = model
+        self.tokenizer = tokenizer
+        self.temperature = cfg.temperature
+        self.top_p = cfg.top_p
+        self.top_k = cfg.top_k
+
+        self.max_gen_len = cfg.max_gen_len
+        self.max_tokens = cfg.max_tokens
+        self.max_prompt_len = cfg.max_prompt_len
+        self.until = cfg.until
+        self.max_until_size = max([len(e) for e in self.until]) if self.until else 1
+        self.device = cfg.device
+
+        # Compile if necessary
+        self.prefill = torch.compile(self.prefill, disable=not cfg.compile_prefilling)
+        self.generate_next_token = torch.compile(
+            self.generate_next_token,
+            mode="reduce-overhead",
+            disable=not cfg.reduce_generation_overhead,
+        )
+
+        self.show_progress = cfg.show_progress
+        self.dtype = dict(fp32=torch.float32, bf16=torch.bfloat16)[cfg.dtype]
+
+        self.prefill_doc_id, self.prefill_tok_id = None, None
+        self.padded_doc_id, self.padded_tok_id = None, None
+        self.current_doc_id, self.current_tok_id = None, None
+        self.padded_doc_start = None
+        self.prefill_mask = None
+
+    def clear_cache(self, offset):
+        for module in self.model.modules():
+            if isinstance(module, MultiTypeAttentionBlock):
+                if not hasattr(module, "kv_cache"):
+                    module.kv_cache = KVCache(
+                        1,
+                        self.max_tokens,
+                        module.num_heads,
+                        module.head_dim,
+                        self.dtype,
+                        self.device,
+                    )
+                module.kv_cache.offset = offset
+
+    @torch.compiler.disable
+    def setup_prefilling(self, lengths: torch.Tensor):
+        # The KV cache is a fixed size tensor of size max_tokens that we need
+        # to update in order to do correct autoregressive generation.
+
+        # Here we will generate token by token but on multiple sequences
+        # at once. To do so, we need to have an attention mask that makes
+        # each sequence independent.
+
+        # Each sequence will write to its allocated space in the KV Cache.
+        # We allocate len(seq) + max_gen_len to each sequence in the cache.
+
+        # We will generate max_gen_len for each document
+        padded_lengths = lengths + self.max_gen_len
+        max_tokens = self.max_tokens or padded_lengths.sum().item()
+        # The last document might have more padding to fill up to max_tokens
+        padded_lengths[-1] += max_tokens - padded_lengths.sum()
+
+        # This is the start index in the cache for each document
+        self.padded_doc_start = lengths_to_start_ids(padded_lengths)
+        # For example with ab--123--cdef--
+        # this would be 0, 4, 9 if max_gen_len is 2
+
+        # We repeat interleave to align with tokens for prefilling
+        # Ex: ab--123--cdef--
+        #     000044444999999
+        prefill_offset = torch.repeat_interleave(self.padded_doc_start, lengths)
+        # This offset will make sure the tokens are written to the
+        # correct positions in the cache during prefilling
+
+        # We either init the cache or clear it by resetting the offset to prefill_offset
+        self.clear_cache(prefill_offset)
+
+        # The prefilling mask looks like the following for
+        # the two packed sequences ab and 123 : ab123
+        # Where spaces are empty cache positions
+        #                 keys
+        #                ab---123---
+        #   queries    a 10000000000
+        #              b 11000000000
+        #              1 00000100000
+        #              2 00000110000
+        #              3 00000111000
+        # We make sure to skip the empty cache positions
+        # and only attend to positions within the same sequence
+        doc_mask_mod = generate_doc_mask_mod(causal_mask, lengths, padded_lengths)
+        self.prefill_mask = create_block_mask(
+            doc_mask_mod, 1, None, lengths.sum(), max_tokens
+        )
+
+        # This creates the prefilling token ids which look like
+        # the following for the packed sequence abcdefg1234
+        # abcdefg1234
+        # 01234560123
+        # The token id gives us the position within each sequence
+        # This is used to compute ROPE and to update the cache
+        # At each forward pass the current tokens are written to
+        # offset + tok_id
+        self.prefill_doc_id, self.prefill_tok_id = lengths_to_local_ids(lengths)
+
+        # This creates the padded token and document ids
+        # which look like the following for the packed sequence ab123
+        #               ab---123---               ab---123---
+        # padded_doc_id 00000111111 padded_tok_id 01234012345
+        # This will later be useful for the attention mask at generation
+        self.padded_doc_id, self.padded_tok_id = lengths_to_local_ids(padded_lengths)
+
+    @torch.compiler.disable
+    def setup_generation(self, lengths):
+        # KV Cache offset is set to the start of the padded documents
+        for module in self.model.modules():
+            if isinstance(module, MultiTypeAttentionBlock):
+                module.kv_cache.offset = self.padded_doc_start
+        # The token ids during generations correspond to the lengths of each doc
+        # current_tok_id will be incremented during generation
+        self.current_tok_id = lengths.clone()
+        # Since we're generating one token per document
+        # the document id is just an arange
+        self.current_doc_id = torch.arange(lengths.size(0), device=lengths.device)
+
+    # From here on some methods for generation
+    def prefill(self, tokens: torch.Tensor, lengths: torch.Tensor):
+        # Prefilling is done by taking multiple packed sequences and
+        # doing block diagonal attention on them so they remain independent
+        self.setup_prefilling(lengths=lengths)
+        prefill_out = self.model.forward(
+            tokens,
+            token_idx=self.prefill_tok_id,
+            attention_mask=self.prefill_mask,
+            attn_impl="flex_attention",
+        )
+        self.setup_generation(lengths=lengths)
+        return prefill_out
+
+    def generate_next_token(self, current_token):
+        # Since we're doing generation with multiple sequences at once
+        # we need to ignore tokens and cache entries from other sequences
+        # or in the future.
+        # Example mask :
+        #                  keys
+        #                abc--1234--
+        #   queries    c 11100000000
+        #              4 00000111100
+
+        # mask shape : (n_seqs, cache_size)
+        doc_mask = self.current_doc_id.unsqueeze(1) == self.padded_doc_id.unsqueeze(0)
+        caus_mask = self.current_tok_id.unsqueeze(1) >= self.padded_tok_id.unsqueeze(0)
+        mask = doc_mask & caus_mask
+        out = self.model.forward(
+            current_token,
+            token_idx=self.current_tok_id,  # n_seqs
+            attention_mask=mask,
+            attn_impl="sdpa",
+        )
+        self.current_tok_id += 1
+        return out
+
+    @torch.inference_mode()
+    def generate(self, prompts):
+        # Tokenize
+        prompts = [
+            self.tokenizer.encode(p, return_type=None)["input_ids"][0] for p in prompts
+        ]
+        # Truncate
+        max_seqlen = (
+            self.max_tokens
+            if not hasattr(self.model, "max_seqlen")
+            else self.model.max_seqlen
+        )
+        max_prompt_len = self.max_prompt_len or min(
+            max_seqlen - self.max_gen_len, self.max_tokens - self.max_gen_len
+        )
+        prompts = [p[-max_prompt_len:] for p in prompts]
+        # Account for the generation in lengths
+        padded_lengths = [len(p) + self.max_gen_len for p in prompts]
+        generation = []
+        loglikelihood = []
+        greedy = []
+        it = batch_prompts(prompts, self.max_tokens, lengths=padded_lengths)
+        if self.show_progress:
+            it = tqdm(it)
+        for batch in it:
+            n_seqs = len(batch)
+            generated_tokens = [[] for _ in range(n_seqs)]
+            is_done = [False for _ in range(n_seqs)]
+            packed_batch, lengths = pack_prompts(batch)
+            packed_batch, lengths = packed_batch.cuda(), lengths.cuda()
+            n_seqs = lengths.size(0)
+
+            # Prefilling cache
+            prompt_logits = self.prefill(packed_batch.unsqueeze(0), lengths)
+            # Selecting last token in each prompt
+            all_tokens = sample_tokens(
+                prompt_logits["logits"], self.temperature, self.top_p, self.top_k
+            )
+            start_token = all_tokens[:, lengths.cumsum(0) - 1]
+
+            for seq_id, tok in enumerate(start_token.squeeze(0).tolist()):
+                generated_tokens[seq_id].append(tok)
+
+            current_token = start_token
+            for i in range(1, self.max_gen_len):
+
+                next_logits = self.generate_next_token(current_token)
+                next_token = sample_tokens(
+                    next_logits["logits"].clone(), self.temperature, self.top_p, self.top_k
+                )
+
+                for seq_id, tok in enumerate(next_token.squeeze(0).tolist()):
+                    if not is_done[seq_id]:
+                        generated_tokens[seq_id].append(tok)
+                        current_end_str = self.tokenizer.decode(
+                            generated_tokens[seq_id][-self.max_until_size :]
+                        )
+                        contains_end_string = any(
+                            [e in current_end_str for e in self.until]
+                        )
+                        is_done[seq_id] = (
+                            contains_end_string or tok == self.tokenizer.end_token_idx
+                        )
+                if all(is_done):
+                    break
+
+                current_token = next_token
+
+            generation.extend([self.tokenizer.decode(g) for g in generated_tokens])
+
+            for p, logit in zip(
+                batch, prompt_logits["logits"].squeeze(0).split(lengths.tolist())
+            ):
+                x = logit[:-1]
+                y = torch.tensor(p[1:], device=x.device)
+                loglikelihood.append(-F.cross_entropy(x, y, reduction="none").cpu())
+                greedy.append((x.argmax(dim=-1) == y).cpu())
+
+        return generation, loglikelihood, greedy
+
+
+
+def load_consolidated_model_and_tokenizer(
+    consolidated_path,
+    model_cls=ConstrueAutoRegressiveModel,
+    model_args_cls=GI01ModelArgs,
+):
+    ckpt_path = Path(consolidated_path)
+    config = ckpt_path / "params.json"
+    config = OmegaConf.load(config)
+
+    param_dtype = dict(fp32=torch.float32, fp16=torch.float16, bf16=torch.bfloat16)[
+        config.distributed.model_dtype
+    ]
+    model_args = dataclass_from_dict(model_args_cls, config.model, strict=False)
+    tokenizer = build_tokenizer(config.data.tokenizer.name, config.data.tokenizer.path)
+    model = model_cls(model_args)
+
+    consolidate_path = ckpt_path / CONSOLIDATE_NAME
+    if not consolidate_path.exists():
+        consolidate_path = consolidate_checkpoints(ckpt_path)
+
+    st_dict = torch.load(consolidate_path / CONSOLIDATE_NAME, weights_only=True)
+    model.load_state_dict(st_dict["model"])
+    model = model.cuda().eval()
+    for param in model.parameters():
+        param.data = param.data.to(dtype=param_dtype)
+    return model, tokenizer, config
+
+
+def main():
+    # Load CLI arguments (overrides) and combine with a YAML config
+    cfg = OmegaConf.from_cli()
+    gen_cfg = dataclass_from_dict(
+        PackedCausalTransformerGeneratorArgs, cfg, strict=False
+    )
+    print(cfg)
+
+    model, tokenizer, _ = load_consolidated_model_and_tokenizer(cfg.ckpt)
+
+    generator = PackedCausalTransformerGenerator(gen_cfg, model, tokenizer)
+
+    # Allow multiple prompts
+    prompts = []
+    while True:
+        prompt = input("Enter a prompt (or press enter to finish): ")
+        if not prompt:
+            break
+        prompts.append(prompt)
+
+    # Start generation
+    start_time = time.time()
+    generation, loglikelihood, greedy = generator.generate(prompts)
+    end_time = time.time()
+
+    # Calculate tokens per second
+    total_tokens = sum(len(tokenizer.encode(gen)) for gen in generation)
+    tokens_per_second = total_tokens / (end_time - start_time)
+
+    # Display the results
+    for i, gen in enumerate(generation):
+        print(f"\nPrompt {i+1}: {prompts[i]}")
+        print(f"Generated Text: {gen}")
+
+    print(f"\nTokens per second: {tokens_per_second:.2f}")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/core/models/translator/config.py b/core/models/GI_01/main/config.py
similarity index 96%
rename from core/models/translator/config.py
rename to core/models/GI_01/main/config.py
index 7d6d63f..79214e5 100644
--- a/core/models/translator/config.py
+++ b/core/models/GI_01/main/config.py
@@ -12,6 +12,7 @@ class TrainingConfig:
     warm_up: int = 0
     learning_rate: float = 2e-5
     num_epochs: int = 4
+    logging_steps: int = -1
     eval_frequency: int = 1
     eval_iter: int = 1
     optimzer = "adam"
diff --git a/core/models/GI_01/main/data_loader.py b/core/models/GI_01/main/data_loader.py
new file mode 100644
index 0000000..1594b94
--- /dev/null
+++ b/core/models/GI_01/main/data_loader.py
@@ -0,0 +1,108 @@
+from torch.utils.data import Dataset, DataLoader
+from torch.utils.data.distributed import DistributedSampler
+import torch
+from torch.utils.data.sampler import RandomSampler, SequentialSampler
+from typing import Optional, Callable
+
+
+class EntoPTDataSet(Dataset):
+    def __init__(self, tensorflow_dataset):
+        self.dataset = tensorflow_dataset
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, index):
+        return self.dataset[index]
+
+
+
+def worker_init_fn(worker_id):
+    torch.manual_seed(torch.initial_seed() + worker_id)
+
+def create_data_loader(
+    dataset,
+    batch_size: int = 32,
+    shuffle: bool = True,
+    num_workers: int = 4,
+    pin_memory: bool = True,
+    collate_fn: Optional[Callable] = None,
+    drop_last: bool = False,
+    generator: Optional[torch.Generator] = None,
+    is_distributed_env=False,
+    distributed_sampler_args=None
+) -> DataLoader:
+    """
+    Create a PyTorch DataLoader with optimized settings.
+    """
+    if collate_fn is None:
+        raise ValueError("collate_fn function not provided")
+
+    if is_distributed_env:
+        sampler = DistributedSampler(dataset, **distributed_sampler_args)
+    elif shuffle:
+        sampler = RandomSampler(dataset, generator=generator)
+    else:
+        sampler = SequentialSampler(dataset)
+
+    loader = DataLoader(
+        dataset=dataset,
+        batch_size=batch_size,
+        sampler=sampler,
+        num_workers=num_workers,
+        collate_fn=collate_fn,
+        pin_memory=pin_memory,
+        drop_last=drop_last,
+        worker_init_fn=worker_init_fn
+    )
+
+    return loader
+
+
+
+class NextTokenPredictionCollator:
+    def __init__(self, tokenizer):
+        self.tokenizer = tokenizer
+
+        self.start = tokenizer.model.piece_to_id("<s>")
+        self.end = tokenizer.model.piece_to_id("</s>")
+
+        self.en_start = tokenizer.model.piece_to_id("<lang_en>")
+        self.en_end = tokenizer.model.piece_to_id("</lang_en>")
+        self.pt_start = tokenizer.model.piece_to_id("<lang_pt>")
+        self.pt_end = tokenizer.model.piece_to_id("</lang_pt>")
+        self.pad_token_idx = tokenizer.model.PieceToId("<pad>")
+
+    def __call__(self, batch):
+        input_ids = []
+        labels = []
+
+        for item in batch:
+            french, english = item["fr"], item["en"]
+
+            english_encoded = self.tokenizer.encode(
+                english, return_type=None, add_special_tokens=False
+            )["input_ids"][0]
+
+            french_encoded = self.tokenizer.encode(
+                french, return_type=None, add_special_tokens=False
+            )["input_ids"][0]
+
+
+            english_encoded = [self.start] + [self.en_start] +  english_encoded +  [self.en_end]
+            french_encoded = [self.pt_start] + french_encoded + [self.pt_end] + [self.end]
+
+            input_ids.append(
+                english_encoded + french_encoded
+            )
+            labels.append(
+                (english_encoded + french_encoded)[1::] + [self.pad_token_idx]
+            )
+
+        paddded_tokens = torch.nn.utils.rnn.pad_sequence([torch.tensor(p) for p in input_ids], batch_first=True, padding_value=self.pad_token_idx).long()
+        attention_mask = (paddded_tokens != self.pad_token_idx).to(torch.int32)
+
+        target = torch.nn.utils.rnn.pad_sequence([torch.tensor(p) for p in labels], batch_first=True, padding_value=self.pad_token_idx).long()
+        target = torch.where(attention_mask == 0, -100, target)
+
+        return {"input_ids": paddded_tokens, "attention_mask": attention_mask, "labels": target}
\ No newline at end of file
diff --git a/core/models/translator/decoder.py b/core/models/GI_01/main/decoder.py
similarity index 66%
rename from core/models/translator/decoder.py
rename to core/models/GI_01/main/decoder.py
index 8649f66..b473507 100644
--- a/core/models/translator/decoder.py
+++ b/core/models/GI_01/main/decoder.py
@@ -3,8 +3,8 @@
 from torch import nn
 
 from core.configurations.base import BaseConfiguration
-from core.layers.attention import RopeAttention
-from core.layers.layer_norm import LayerNorm
+from core.layers.attention import AttentionBlock, MultiTypeAttentionBlock
+from core.layers.norms import RMSNorm
 from core.layers.point_wise_projection import PointWiseGatedProjection
 
 
@@ -12,18 +12,18 @@ class ConstrueDecoderLayer(nn.Module):
     def __init__(self, base_cfg: BaseConfiguration):
         super().__init__()
 
-        self.input_norm = LayerNorm(model_dimension=base_cfg.hidden_dim)
-        self.self_attn = RopeAttention(
+        self.input_norm = RMSNorm(model_dimension=base_cfg.hidden_dim)
+        self.self_attn = MultiTypeAttentionBlock(
             config=base_cfg
         )
         self.attention_dropout = nn.Dropout(p=base_cfg.attention_dropout)
 
-        self.post_attention_norm = LayerNorm(model_dimension=base_cfg.hidden_dim)
+        self.post_attention_norm = RMSNorm(model_dimension=base_cfg.hidden_dim)
         self.mlp = PointWiseGatedProjection(config=base_cfg)
         self.dropout2 = nn.Dropout(p=base_cfg.attention_dropout)
 
 
-    def forward(self, hidden_state, attention_mask, output_attentions=False):
+    def forward(self, hidden_state, attention_mask, frequency_cis, token_idx=None, attn_impl: str = "sdpa", output_attentions=False):
         """
          https://arxiv.org/pdf/2002.04745 (PRE-Norm)
 
@@ -44,6 +44,9 @@ def forward(self, hidden_state, attention_mask, output_attentions=False):
         hidden_state, self_attn_weights = self.self_attn(
             input_tensor=hidden_state,
             attention_mask=attention_mask,
+            frequency_cis=frequency_cis,
+            token_idx=token_idx,
+            attn_impl=attn_impl,
             output_attentions=output_attentions,
         )
         hidden_state = self.attention_dropout(hidden_state)
@@ -58,3 +61,10 @@ def forward(self, hidden_state, attention_mask, output_attentions=False):
 
         return hidden_state, self_attn_weights
 
+
+    def init_weights(self, init_std=None, factor=1.0):
+        self.self_attn.reset_parameters(init_std, factor)
+        self.input_norm.reset_parameters()
+
+        self.mlp.reset_parameters(init_std, factor)
+        self.post_attention_norm.reset_parameters()
\ No newline at end of file
diff --git a/core/models/GI_01/main/model.py b/core/models/GI_01/main/model.py
new file mode 100644
index 0000000..5359424
--- /dev/null
+++ b/core/models/GI_01/main/model.py
@@ -0,0 +1,206 @@
+from dataclasses import dataclass
+from enum import Enum
+from typing import List, Optional, Tuple
+
+import torch
+from torch.nn import functional as F
+
+from core.configurations.base import BaseConfiguration
+from core.layers.positional_embedding.rope_projector import RopePositionEmbedding
+from core.models.GI_01.main.decoder import ConstrueDecoderLayer
+from core.layers.norms import RMSNorm
+from core.utils.masks import create_causal_mask, create_multi_type_causal_mask
+
+from torch import nn
+
+class InitStdFactor(Enum):
+    DISABLED = "disabled"  # Init std is divided by 1.0
+    GLOBAL_DEPTH = "global_depth"  # Init std is divided by sqrt(2*n_layers)
+    CURRENT_DEPTH = "current_depth"  # Init std is divided by sqrt(2*depth)
+    DIM_RATIO = "dim_ratio"  # Init std is divided by model_dim/4096
+
+
+class TiedLinear(nn.Module):
+    def __init__(self, tied_module: nn.Module) -> None:
+        super().__init__()
+        self.tied_module = tied_module
+        if not hasattr(tied_module, "weight"):
+            raise AttributeError(
+                "Provided module does not have attribute 'weight'. Please check your tied_module."
+            )
+
+    def __call__(self, x: torch.Tensor) -> torch.Tensor:
+        return F.linear(x, self.tied_module.weight)
+
+@dataclass
+class GI01ModelArgs(BaseConfiguration):
+    weight_tying: bool = False
+    ffn_dim_multiplier: Optional[float] = None
+    multiple_of: int = 256
+    init_base_std: Optional[float] = None
+    init_std_factor: str = "disabled"
+
+
+def cross_entropy(pred, target, **kwargs):
+    loss = F.nll_loss(
+        F.log_softmax(pred.flatten(end_dim=-2).float(), -1),
+        target.flatten(end_dim=-1),
+        **kwargs,
+    )
+    return loss
+
+
+class ConstrueModel(nn.Module):
+    def __init__(self, config: BaseConfiguration):
+        super().__init__()
+
+        self.config = config
+        self.hidden_dim = config.hidden_dim
+        self.init_base_std = config.init_base_std
+        self.init_std_factor = InitStdFactor(config.init_std_factor)
+
+        self.token_embeddings = nn.Embedding(
+            num_embeddings=config.vocab_size,
+            embedding_dim=config.hidden_dim,
+            padding_idx=config.padding_id
+        )
+        self.rope_embeddings = RopePositionEmbedding(
+                hidden_dim=config.head_dim,
+                max_positions=config.max_positions,
+                base=config.rope_base
+            )
+
+        # Decoder layer stack
+        self.decoder_layers = nn.ModuleList([
+            ConstrueDecoderLayer(
+                config
+            )
+            for _ in range(config.num_layers)
+        ])
+
+        # Layer Norm initialization
+        self.final_layer_norm = RMSNorm(
+            model_dimension=config.hidden_dim
+        )
+
+        self.weight_tying = config.weight_tying
+        if config.weight_tying:
+            self.llm_head = TiedLinear(self.token_embeddings)
+        else:
+            self.llm_head = nn.Linear(
+                config.hidden_dim,
+                config.vocab_size,
+                bias=False,
+            )
+
+
+    def reset_parameters(self):
+        # Either use fixed base std or sqrt model dim
+        self.rope_embeddings.reset_parameters()
+
+
+    def init_weights(self, init_std=None):
+        self.reset_parameters()
+
+        init_std = init_std or (self.hidden_dim ** (-0.5))
+        self.final_layer_norm.reset_parameters()
+
+        nn.init.trunc_normal_(
+            self.token_embeddings.weight,
+            mean=0.0,
+            std=init_std,
+            a=-3 * init_std,
+            b=3 * init_std,
+        )
+
+        if not self.weight_tying:
+            nn.init.trunc_normal_(
+                self.llm_head.weight,
+                mean=0.0,
+                std=init_std,
+                a=-3 * init_std,
+                b=3 * init_std,
+            )
+
+
+        for depth, layer in enumerate(self.decoder_layers):
+            factor = {
+                InitStdFactor.CURRENT_DEPTH: (2 * (depth + 1)) ** 0.5,
+                InitStdFactor.GLOBAL_DEPTH: (2 * (len(self.decoder_layers) + 1)) ** 0.5,
+                InitStdFactor.DIM_RATIO: self.hidden_dim / 4096,
+                InitStdFactor.DISABLED: 1.0,
+            }[self.init_std_factor]
+
+            layer.init_weights(self.init_base_std, factor)
+
+
+
+
+    def forward(self, input_ids, attention_mask=None, target=None, token_idx=None, attn_impl="sdpa", output_attentions=False, output_hidden_states=False):
+
+        bsz, seqlen = input_ids.shape
+
+        # if attn_mask is None:
+        #     attn_mask = torch.ones_like(input_tensor)
+
+        hidden_states = self.token_embeddings(input_ids)
+
+
+        positional_frequency = self.rope_embeddings(seqlen=seqlen, token_idx=token_idx)
+
+        # causal_mask = create_causal_mask(
+        #     attention_mask=attn_mask,
+        #     shape=input_tensor.shape,
+        #     dtype=hidden_states.dtype,
+        #     device=hidden_states.device
+        # )
+
+        causal_mask = (
+            attention_mask
+            if attention_mask is not None
+            else create_multi_type_causal_mask(seqlen, attn_impl)
+        )
+
+        output_attentions_weights = (hidden_states)
+        layers_hidden_states = ()
+        for decoder_layer in self.decoder_layers:
+            hidden_states, attention_weight = decoder_layer(
+                hidden_states,
+                causal_mask,
+                frequency_cis=positional_frequency,
+                token_idx=token_idx,
+                attn_impl=attn_impl,
+                output_attentions=output_attentions
+            )
+            if output_hidden_states:
+                layers_hidden_states += (hidden_states,)
+            if output_attentions:
+                output_attentions_weights += (attention_weight,)
+
+        hidden_states = self.final_layer_norm(hidden_states)
+
+        logits = self.llm_head(hidden_states)
+        output = {
+            "logits": logits,
+            "last_hidden_state": hidden_states,
+            "attention_map": output_attentions_weights if output_attentions else None,
+            "hidden_states": hidden_states if output_hidden_states else None
+        }
+        if target is not None:
+            output["loss"] = cross_entropy(logits, target)
+        return output
+
+
+
+def build_fsdp_grouping_plan(model_args: GI01ModelArgs) -> List[Tuple[str, bool]]:
+    group_plan: Tuple[int, bool] = []
+
+    # Grouping and output seperately
+    group_plan.append(("token_embeddings", False))
+
+    # Grouping by layers
+    for i in range(model_args.num_layers):
+        group_plan.append((f"decoder_layers.{i}", False))
+
+    group_plan.append((f"llm_head", False))
+    return group_plan
\ No newline at end of file
diff --git a/core/models/GI_01/notebooks/.ipynb_checkpoints/trainer-checkpoint.ipynb b/core/models/GI_01/notebooks/.ipynb_checkpoints/trainer-checkpoint.ipynb
new file mode 100644
index 0000000..a5b884d
--- /dev/null
+++ b/core/models/GI_01/notebooks/.ipynb_checkpoints/trainer-checkpoint.ipynb
@@ -0,0 +1,1481 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "ROOT_PROJECT_PATH = \"/root/AI-Uncomplicated\"\n",
+    "# Add the root directory to the sys.path\n",
+    "sys.path.insert(0, ROOT_PROJECT_PATH)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "WORKSPACE = os.path.join(ROOT_PROJECT_PATH, \"core/models/translator\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from core.models.translator.config import ModelConfig, DatasetConfig, TrainingConfig\n",
+    "from core.dataloaders.dataloader import load_tokenizer\n",
+    "\n",
+    "## Initialize configurations\n",
+    "model_config = ModelConfig(model_name=\"Construe\",\n",
+    "                           num_layers = 2,\n",
+    "                            padding_id = 0,\n",
+    "                            hidden_dim = 128,\n",
+    "                            intermediate_dim = 1024,\n",
+    "                            max_positions = 2048,\n",
+    "                            layer_norm_eps = 1e-05,\n",
+    "                            model_max_sequence = 2048,\n",
+    "                            num_heads = 8,\n",
+    "                            attention_dropout = 0.1)\n",
+    "\n",
+    "dataset_config = DatasetConfig(dataset_path=\"./dataset\",\n",
+    "                               dataset_shuffle=True)\n",
+    "training_config = TrainingConfig(tokenizer_path=\"/root/AI-Uncomplicated/core/models/translator/tokenzier/european_tokenizer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Translation Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Path to dataset files: /root/.cache/kagglehub/datasets/dhruvildave/en-fr-translation-dataset/versions/2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# import kagglehub\n",
+    "\n",
+    "# # Download latest version\n",
+    "# path = kagglehub.dataset_download(\"dhruvildave/en-fr-translation-dataset\")\n",
+    "\n",
+    "# print(\"Path to dataset files:\", path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train your tokenizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DATASET_DOWNLOAD_PATH = os.path.join(WORKSPACE, \"dataset\", \"cc_100_en_fr\")\n",
+    "\n",
+    "# if not os.path.exists(DATASET_DOWNLOAD_PATH):\n",
+    "#     os.makedirs(DATASET_DOWNLOAD_PATH)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TOKENIZER_TRAIN_DATASET_NAME = \"statmt/cc100\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DATA_DOWNLAD_SCRIPT_PATH =  os.path.join(ROOT_PROJECT_PATH, \"scripts/hf_data_downloader.py\")\n",
+    "\n",
+    "# !python $DATA_DOWNLAD_SCRIPT_PATH --dataset $TOKENIZER_TRAIN_DATASET_NAME --working_dir=$DATASET_DOWNLOAD_PATH --allowed_pattern=\"en/**/*.parquet,fr/**/*.parquet\" --revision=convert/parquet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DATASET_DOWNLOAD_PATH"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DATA_PROCESS_SCRIPT =  os.path.join(ROOT_PROJECT_PATH, \"data_processing/parquet/beam_text_writer.py\")\n",
+    "# OUTPUT_PATH = os.path.join(DATASET_DOWNLOAD_PATH, \"processed_path\")\n",
+    "# INPUT_PATH = os.path.join(DATASET_DOWNLOAD_PATH, \"statmt/cc100\")\n",
+    "\n",
+    "\n",
+    "# !python $DATA_PROCESS_SCRIPT --input_path=$INPUT_PATH --chunk_size=100000 --output_path=$OUTPUT_PATH --languages=en,fr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TOKENIZER_TRAINING_SCRIPT = os.path.join(ROOT_PROJECT_PATH, \"core/tokenizer/trainer.py\")\n",
+    "# MODEL_NAME = \"en_fr_combined_tokenizer\"\n",
+    "# TOKENIZER_SAVED_PATH = os.path.join(ROOT_PROJECT_PATH, \"core/models/translator/tokenzier/\")\n",
+    "\n",
+    "# !python $TOKENIZER_TRAINING_SCRIPT --data_dir=$OUTPUT_PATH --vocab_size=60000 --model_name=$MODEL_NAME --character_coverage=1.0 --num_threads=100 --output_dir=$TOKENIZER_SAVED_PATH --yaml_file_path=\"/root/AI-Uncomplicated/core/models/translator/tokenzier/config.yml\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run below command from project root directory to convert the smp format to custom format we are going to use for training\n",
+    "\n",
+    "```bash\n",
+    "python -m core.tokenizer.setencepiece_to_tokenizer --model_path=core/models/translator/tokenzier/en_pt_combined_tokenizer.model --save_path=core/models/translator/tokenzier\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize Model and tokenizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from core.tokenizer import SPMTokenizer\n",
+    "from core.models.translator.construe import ConstrueAutoRegressiveModel\n",
+    "\n",
+    "training_config.tokenizer_path = \"/root/AI-Uncomplicated/core/models/translator/tokenzier/en_fr_combined_tokenizer\"\n",
+    "\n",
+    "tokenizer = SPMTokenizer(training_config.tokenizer_path)\n",
+    "\n",
+    "model_config.vocabulary_size = tokenizer.vocab_size\n",
+    "model = ConstrueAutoRegressiveModel(config=model_config)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import Dataset, DataLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class EntoPTDataSet(Dataset):\n",
+    "    def __init__(self, tensorflow_dataset):\n",
+    "        self.dataset = tensorflow_dataset\n",
+    "        \n",
+    "    def __len__(self):\n",
+    "        return len(self.dataset)\n",
+    "    \n",
+    "    def __getitem__(self, index):\n",
+    "        return self.dataset[index]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import Dataset, load_from_disk\n",
+    "\n",
+    "try:\n",
+    "    dataset = Dataset.from_csv(os.path.join(path, \"en-fr.csv\"))\n",
+    "    dataset = dataset.take(3000000)\n",
+    "    dataset = dataset.filter(lambda d: d[\"en\"] is not None and d[\"fr\"] is not None)\n",
+    "    dataset = dataset.filter(lambda x: len(tokenizer.encode(x[\"en\"] + x[\"fr\"], return_type=None)[\"input_ids\"][0]) < 500)\n",
+    "    dataset = dataset.train_test_split(test_size=0.2)\n",
+    "    dataset.save_to_disk(\"filterd_dataset\")\n",
+    "except NameError:\n",
+    "    dataset = load_from_disk(\"filterd_dataset\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['en', 'fr'],\n",
+       "        num_rows: 2393178\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['en', 'fr'],\n",
+       "        num_rows: 598295\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_examples_pt = EntoPTDataSet(dataset[\"train\"])\n",
+    "val_examples_pt = EntoPTDataSet(dataset[\"test\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torch.utils.data.sampler import RandomSampler, SequentialSampler\n",
+    "from typing import Optional, Callable\n",
+    "\n",
+    "def create_data_loader(\n",
+    "    dataset,\n",
+    "    batch_size: int = 32,\n",
+    "    shuffle: bool = True,\n",
+    "    num_workers: int = 4,\n",
+    "    pin_memory: bool = True,\n",
+    "    collate_fn: Optional[Callable] = None,\n",
+    "    drop_last: bool = False,\n",
+    "    generator: Optional[torch.Generator] = None\n",
+    ") -> DataLoader:\n",
+    "    \"\"\"\n",
+    "    Create a PyTorch DataLoader with optimized settings.\n",
+    "    \n",
+    "    Args:\n",
+    "        dataset: PyTorch Dataset object\n",
+    "        batch_size: Number of samples per batch\n",
+    "        shuffle: Whether to shuffle the data\n",
+    "        num_workers: Number of subprocesses for data loading\n",
+    "        pin_memory: Whether to pin memory in GPU training\n",
+    "        collate_fn: Custom collate function for batching\n",
+    "        drop_last: Whether to drop the last incomplete batch\n",
+    "        generator: Random number generator for reproducibility\n",
+    "    \n",
+    "    Returns:\n",
+    "        DataLoader: Configured PyTorch DataLoader\n",
+    "    \"\"\"\n",
+    "    \n",
+    "\n",
+    "    if collate_fn is None:\n",
+    "        raise ValueError(\"collator function not provided\")\n",
+    "\n",
+    "    # Choose sampler based on shuffle parameter\n",
+    "    if shuffle:\n",
+    "        sampler = RandomSampler(dataset, generator=generator)\n",
+    "    else:\n",
+    "        sampler = SequentialSampler(dataset)\n",
+    "    \n",
+    "    # Create DataLoader with optimized settings\n",
+    "    loader = DataLoader(\n",
+    "        dataset=dataset,\n",
+    "        batch_size=batch_size,\n",
+    "        sampler=sampler,\n",
+    "        num_workers=num_workers,\n",
+    "        collate_fn=collate_fn,\n",
+    "        pin_memory=pin_memory,\n",
+    "        drop_last=drop_last,\n",
+    "        # Worker init function for reproducibility\n",
+    "        worker_init_fn=lambda worker_id: torch.manual_seed(torch.initial_seed() + worker_id)\n",
+    "    )\n",
+    "    \n",
+    "    return loader\n",
+    "\n",
+    "\n",
+    "class NextTokenPredictionCollator:\n",
+    "    def __init__(self, tokenizer):\n",
+    "        self.tokenizer = tokenizer\n",
+    "        \n",
+    "        self.start = tokenizer.model.piece_to_id(\"<s>\")\n",
+    "        self.end = tokenizer.model.piece_to_id(\"</s>\")\n",
+    "        \n",
+    "        self.en_start = tokenizer.model.piece_to_id(\"<lang_en>\")\n",
+    "        self.en_end = tokenizer.model.piece_to_id(\"</lang_en>\")\n",
+    "        self.pt_start = tokenizer.model.piece_to_id(\"<lang_pt>\")\n",
+    "        self.pt_end = tokenizer.model.piece_to_id(\"</lang_pt>\")\n",
+    "        self.pad_token_idx = tokenizer.model.PieceToId(\"<pad>\")\n",
+    "        \n",
+    "    def __call__(self, batch):\n",
+    "        input_ids = []\n",
+    "        labels = []\n",
+    "        \n",
+    "        for item in batch:\n",
+    "            french, english = item[\"fr\"], item[\"en\"]\n",
+    "            \n",
+    "            english_encoded = tokenizer.encode(\n",
+    "                english, return_type=None, add_special_tokens=False\n",
+    "            )[\"input_ids\"][0]\n",
+    "            \n",
+    "            french_encoded = tokenizer.encode(\n",
+    "                french, return_type=None, add_special_tokens=False\n",
+    "            )[\"input_ids\"][0]\n",
+    "            \n",
+    "            \n",
+    "            english_encoded = [self.start] + [self.en_start] +  english_encoded +  [self.en_end]\n",
+    "            french_encoded = [self.pt_start] + french_encoded + [self.pt_end] + [self.end]\n",
+    "            \n",
+    "            input_ids.append(\n",
+    "                english_encoded + french_encoded\n",
+    "            )\n",
+    "            labels.append(\n",
+    "                (english_encoded + french_encoded)[1::] + [self.pad_token_idx]\n",
+    "            )\n",
+    "        \n",
+    "        paddded_tokens = torch.nn.utils.rnn.pad_sequence([torch.tensor(p) for p in input_ids], batch_first=True, padding_value=self.pad_token_idx).long()\n",
+    "        attention_mask = (paddded_tokens != self.pad_token_idx).to(torch.int32)\n",
+    "        \n",
+    "        target = torch.nn.utils.rnn.pad_sequence([torch.tensor(p) for p in labels], batch_first=True, padding_value=self.pad_token_idx).long()\n",
+    "        target = torch.where(attention_mask == 0, -100, target)\n",
+    "        \n",
+    "        return {\"input_ids\": paddded_tokens, \"attention_mask\": attention_mask, \"labels\": target}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "collate_fn = NextTokenPredictionCollator(tokenizer=tokenizer)\n",
+    "\n",
+    "train_dataloader = create_data_loader(train_examples_pt, collate_fn=collate_fn, batch_size=dataset_config.batch_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for batch in train_dataloader:\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 -> 6\n",
+      "6 -> 21743\n",
+      "21743 -> 3458\n",
+      "3458 -> 9345\n",
+      "9345 -> 1420\n",
+      "1420 -> 2132\n",
+      "2132 -> 2701\n",
+      "2701 -> 8304\n",
+      "8304 -> 6921\n",
+      "6921 -> 27895\n",
+      "27895 -> 1424\n",
+      "1424 -> 13901\n",
+      "13901 -> 52597\n",
+      "52597 -> 7\n",
+      "7 -> 8\n",
+      "8 -> 2564\n",
+      "2564 -> 9881\n",
+      "9881 -> 38594\n",
+      "38594 -> 1512\n",
+      "1512 -> 6923\n",
+      "6923 -> 8992\n",
+      "8992 -> 24439\n",
+      "24439 -> 30594\n",
+      "30594 -> 1426\n",
+      "1426 -> 10541\n",
+      "10541 -> 1390\n",
+      "1390 -> 37336\n",
+      "37336 -> 4745\n",
+      "4745 -> 1440\n",
+      "1440 -> 1535\n",
+      "1535 -> 19083\n",
+      "19083 -> 29380\n",
+      "29380 -> 52597\n",
+      "52597 -> 9\n",
+      "9 -> 2\n",
+      "2 -> 0\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n"
+     ]
+    }
+   ],
+   "source": [
+    "for l, i in zip(batch[\"labels\"][0].numpy().tolist(), batch[\"input_ids\"][0].numpy().tolist()):\n",
+    "    print(f\"{i} -> {l}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "sns.heatmap(batch[\"attention_mask\"].numpy().tolist())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "training_config.warm_up = 4000\n",
+    "training_config.logging_steps = 747"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch = {key: value.to(\"cpu\") for key, value in batch.items()}\n",
+    "pred = model(input_ids=batch[\"input_ids\"], attention_mask = batch[\"attention_mask\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch.nn.functional as F\n",
+    "\n",
+    "def cal_loss(logits, gold, trg_pad_idx, smoothing=False):\n",
+    "    ''' Calculate cross entropy loss, apply label smoothing if needed. '''\n",
+    "    n_classes = logits.shape[-1]\n",
+    "    logits = logits.view(-1, n_classes)\n",
+    "    gold = gold.unsqueeze(-1).view(-1)\n",
+    "    loss = F.cross_entropy(logits, gold)\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([32, 119])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred[\"logits\"].max(-1)[1].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ConstrueAutoRegressiveModel(\n",
+       "  (model): ConstrueModel(\n",
+       "    (token_embeddings): Embedding(60000, 128, padding_idx=0)\n",
+       "    (decoder_layers): ModuleList(\n",
+       "      (0-1): 2 x ConstrueDecoderLayer(\n",
+       "        (input_norm): LayerNorm()\n",
+       "        (self_attn): RopeAttention(\n",
+       "          (rope_position_projection): RopePositionEmbedding()\n",
+       "          (qkv_projection): Linear(in_features=128, out_features=384, bias=False)\n",
+       "          (output_projection): Linear(in_features=128, out_features=128, bias=False)\n",
+       "        )\n",
+       "        (attention_dropout): Dropout(p=0.1, inplace=False)\n",
+       "        (post_attention_norm): LayerNorm()\n",
+       "        (mlp): PointWiseGatedProjection(\n",
+       "          (gate_projection): Linear(in_features=128, out_features=1024, bias=False)\n",
+       "          (up_projection): Linear(in_features=128, out_features=1024, bias=False)\n",
+       "          (down_projection): Linear(in_features=1024, out_features=128, bias=False)\n",
+       "          (act_func): PytorchGELUTanh()\n",
+       "        )\n",
+       "        (dropout2): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "    )\n",
+       "    (final_layer_norm): LayerNorm()\n",
+       "  )\n",
+       "  (lm_head): Linear(in_features=128, out_features=60000, bias=False)\n",
+       ")"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.to(\"cuda\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cal_performance(pred, gold, trg_pad_idx=-100):\n",
+    "    ''' Apply label smoothing if needed '''\n",
+    "\n",
+    "    loss = cal_loss(pred, gold, trg_pad_idx)\n",
+    "\n",
+    "    pred = pred.max(-1)[1].view(-1)\n",
+    "    gold = gold.contiguous().view(-1)\n",
+    "    non_pad_mask = gold.ne(trg_pad_idx)\n",
+    "    n_correct = pred.eq(gold).masked_select(non_pad_mask).sum().item()\n",
+    "    n_word = non_pad_mask.sum().item()\n",
+    "\n",
+    "    return loss, n_correct, n_word"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torch.optim import AdamW\n",
+    "from torch.optim.lr_scheduler import LambdaLR\n",
+    "import math\n",
+    "from typing import Optional, List\n",
+    "\n",
+    "class WarmupCosineScheduler(LambdaLR):\n",
+    "    \"\"\"Linear warmup and cosine decay scheduler.\"\"\"\n",
+    "    \n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        optimizer: AdamW,\n",
+    "        warmup_steps: int,\n",
+    "        total_steps: int,\n",
+    "        min_lr_ratio: float = 0.1,\n",
+    "        last_epoch: int = -1\n",
+    "    ):\n",
+    "        \"\"\"\n",
+    "        Initialize warmup and decay scheduler.\n",
+    "        \n",
+    "        Args:\n",
+    "            optimizer: AdamW optimizer\n",
+    "            warmup_steps: Number of warmup steps\n",
+    "            total_steps: Total number of training steps\n",
+    "            min_lr_ratio: Minimum learning rate ratio compared to initial lr\n",
+    "            last_epoch: The index of the last epoch\n",
+    "        \"\"\"\n",
+    "        self.warmup_steps = warmup_steps\n",
+    "        self.total_steps = total_steps\n",
+    "        self.min_lr_ratio = min_lr_ratio\n",
+    "        super().__init__(optimizer, self.lr_lambda, last_epoch)\n",
+    "    \n",
+    "    def lr_lambda(self, current_step: int) -> float:\n",
+    "        \"\"\"Calculate lr multiplier based on current step.\"\"\"\n",
+    "        if current_step < self.warmup_steps:\n",
+    "            # Linear warmup\n",
+    "            return float(current_step) / float(max(1, self.warmup_steps))\n",
+    "        \n",
+    "        # Cosine decay\n",
+    "        progress = float(current_step - self.warmup_steps) / \\\n",
+    "            float(max(1, self.total_steps - self.warmup_steps))\n",
+    "        decay = 0.5 * (1.0 + math.cos(math.pi * progress))\n",
+    "        # Scale decay to min_lr_ratio\n",
+    "        decay = self.min_lr_ratio + (1.0 - self.min_lr_ratio) * decay\n",
+    "        return decay\n",
+    "\n",
+    "def create_optimizer_and_scheduler(\n",
+    "    model: torch.nn.Module,\n",
+    "    num_training_steps: int,\n",
+    "    learning_rate: float = 5e-5,\n",
+    "    weight_decay: float = 0.01,\n",
+    "    warmup_ratio: float = 0.1,\n",
+    "    min_lr_ratio: float = 0.1,\n",
+    "    beta1: float = 0.9,\n",
+    "    beta2: float = 0.999,\n",
+    "    eps: float = 1e-8,\n",
+    "    no_decay_params: Optional[List[str]] = None\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Create AdamW optimizer and warmup scheduler.\n",
+    "    \n",
+    "    Args:\n",
+    "        model: PyTorch model\n",
+    "        num_training_steps: Total number of training steps\n",
+    "        learning_rate: Maximum learning rate after warmup\n",
+    "        weight_decay: Weight decay coefficient\n",
+    "        warmup_ratio: Ratio of warmup steps to total steps\n",
+    "        min_lr_ratio: Minimum learning rate ratio compared to max lr\n",
+    "        beta1: AdamW beta1 parameter\n",
+    "        beta2: AdamW beta2 parameter\n",
+    "        eps: AdamW epsilon parameter\n",
+    "        correct_bias: Whether to correct bias in AdamW\n",
+    "        no_decay_params: List of parameter names that should not have weight decay\n",
+    "    \n",
+    "    Returns:\n",
+    "        tuple: (optimizer, scheduler)\n",
+    "    \"\"\"\n",
+    "    # Default params that should not have weight decay\n",
+    "    if no_decay_params is None:\n",
+    "        no_decay_params = ['bias', 'LayerNorm.weight', 'layer_norm.weight']\n",
+    "    \n",
+    "    # Separate parameters that should and should not have weight decay\n",
+    "    optimizer_grouped_parameters = [\n",
+    "        {\n",
+    "            \"params\": [\n",
+    "                p for n, p in model.named_parameters()\n",
+    "                if not any(nd in n for nd in no_decay_params)\n",
+    "            ],\n",
+    "            \"weight_decay\": weight_decay,\n",
+    "        },\n",
+    "        {\n",
+    "            \"params\": [\n",
+    "                p for n, p in model.named_parameters()\n",
+    "                if any(nd in n for nd in no_decay_params)\n",
+    "            ],\n",
+    "            \"weight_decay\": 0.0,\n",
+    "        },\n",
+    "    ]\n",
+    "    \n",
+    "    # Create AdamW optimizer\n",
+    "    optimizer = AdamW(\n",
+    "        optimizer_grouped_parameters,\n",
+    "        lr=learning_rate,\n",
+    "        betas=(beta1, beta2),\n",
+    "        eps=eps\n",
+    "    )\n",
+    "    \n",
+    "    # Create scheduler with linear warmup and cosine decay\n",
+    "    warmup_steps = int(num_training_steps * warmup_ratio)\n",
+    "    \n",
+    "    scheduler = WarmupCosineScheduler(\n",
+    "        optimizer=optimizer,\n",
+    "        warmup_steps=warmup_steps,\n",
+    "        total_steps=num_training_steps,\n",
+    "        min_lr_ratio=min_lr_ratio\n",
+    "    )\n",
+    "    \n",
+    "    return optimizer, scheduler, warmup_steps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "def train_step(model, batch, optimizer, scheduler, device, logger=None, gradient_accumulation_steps=1):\n",
+    "    # forward\n",
+    "    labels = batch[\"labels\"].to(device)\n",
+    "    optimizer.zero_grad()\n",
+    "    pred = model(input_ids=batch[\"input_ids\"].to(device), attention_mask=batch[\"attention_mask\"].to(device))\n",
+    "    logits = pred[\"logits\"]\n",
+    "    # backward and update parameters\n",
+    "    loss, n_correct, n_word = cal_performance(logits, labels) \n",
+    "    loss = loss / gradient_accumulation_steps\n",
+    "    \n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "    scheduler.step()\n",
+    "    # if (optimizer.step_count + 1) % gradient_accumulation_steps == 0:\n",
+    "    #     torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+    "    #     optimizer.step()\n",
+    "    #     scheduler.step()\n",
+    "    #     optimizer.zero_grad()\n",
+    "    \n",
+    "    return loss.item() * gradient_accumulation_steps, n_correct, n_word\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "def train_epoch(epoch, model, training_data, optimizer, scheduler, device, logging_steps=-1, logger=None, gradient_accumulation_steps=1):\n",
+    "    ''' Epoch operation in training phase'''\n",
+    "    model.train()\n",
+    "    total_loss, n_word_total, n_word_correct = 0, 0, 0 \n",
+    "\n",
+    "    desc = '  - (Training)   '\n",
+    "    for step, batch in tqdm(enumerate(training_data), mininterval=2, desc=desc, leave=False, total=len(train_dataloader)):\n",
+    "        # forward\n",
+    "        labels = batch[\"labels\"].to(device)\n",
+    "        optimizer.zero_grad()\n",
+    "        pred = model(input_ids=batch[\"input_ids\"].to(device), attention_mask=batch[\"attention_mask\"].to(device))\n",
+    "        logits = pred[\"logits\"]\n",
+    "        # backward and update parameters\n",
+    "        loss, n_correct, n_word = cal_performance(logits, labels) \n",
+    "        \n",
+    "        loss.backward()\n",
+    "        if (optimizer.step_count + 1) % gradient_accumulation_steps == 0:\n",
+    "            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+    "            optimizer.step()\n",
+    "            scheduler.step()\n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "        # note keeping\n",
+    "        n_word_total += n_word\n",
+    "        n_word_correct += n_correct\n",
+    "        total_loss += loss.item()\n",
+    "        \n",
+    "        logger.log_metrics(metrics={\n",
+    "            'loss': loss.item(),\n",
+    "            'epoch': epoch,\n",
+    "            \"n_correct\": n_correct,\n",
+    "            \"n_word\": n_word,\n",
+    "            'learning_rate': sched.get_current_learning_rate()\n",
+    "        }, step=step)\n",
+    "\n",
+    "    loss_per_word = total_loss/n_word_total\n",
+    "    accuracy = n_word_correct/n_word_total\n",
+    "    return loss_per_word, accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_training_steps(\n",
+    "    num_examples: int,\n",
+    "    num_epochs: int,\n",
+    "    batch_size: int,\n",
+    "    gradient_accumulation_steps: int = 1\n",
+    ") -> int:\n",
+    "    \"\"\"\n",
+    "    Calculate the total number of training steps.\n",
+    "    \n",
+    "    Args:\n",
+    "        num_examples: Total number of training examples\n",
+    "        num_epochs: Number of epochs to train for\n",
+    "        batch_size: Batch size per forward pass\n",
+    "        gradient_accumulation_steps: Number of steps to accumulate gradients\n",
+    "    \n",
+    "    Returns:\n",
+    "        int: Total number of optimizer update steps\n",
+    "    \"\"\"\n",
+    "    steps_per_epoch = math.ceil(num_examples / batch_size)\n",
+    "    \n",
+    "    update_steps_per_epoch = math.ceil(steps_per_epoch / gradient_accumulation_steps)\n",
+    "    \n",
+    "    total_training_steps = update_steps_per_epoch * num_epochs\n",
+    "    \n",
+    "    return total_training_steps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import logging\n",
+    "import time\n",
+    "from pathlib import Path\n",
+    "import json\n",
+    "from typing import Dict, Any, Optional\n",
+    "from datetime import datetime\n",
+    "\n",
+    "class TrainingLogger:\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        output_dir: str,\n",
+    "        project_name: str,\n",
+    "        log_every_n_steps: int = 100,\n",
+    "        save_every_n_steps: int = 1000,\n",
+    "        save_best_only: bool = True\n",
+    "    ):\n",
+    "        \"\"\"\n",
+    "        Initialize training logger with various logging options.\n",
+    "        \n",
+    "        Args:\n",
+    "            output_dir: Directory to save checkpoints and logs\n",
+    "            project_name: Name of the project\n",
+    "            use_wandb: Whether to use Weights & Biases logging\n",
+    "            log_every_n_steps: How often to log metrics\n",
+    "            save_every_n_steps: How often to save checkpoints\n",
+    "            save_best_only: Whether to save only the best model\n",
+    "        \"\"\"\n",
+    "        self.output_dir = Path(output_dir)\n",
+    "        self.output_dir.mkdir(parents=True, exist_ok=True)\n",
+    "        \n",
+    "        # Initialize logging\n",
+    "        self.log_file = self.output_dir / 'training.log'\n",
+    "        self.setup_logging()\n",
+    "        \n",
+    "        # Training state\n",
+    "        self.global_step = 0\n",
+    "        self.best_loss = float('inf')\n",
+    "        self.start_time = time.time()\n",
+    "        self.last_log_time = self.start_time\n",
+    "        \n",
+    "        # Configuration\n",
+    "        self.log_every_n_steps = log_every_n_steps\n",
+    "        self.save_every_n_steps = save_every_n_steps\n",
+    "            \n",
+    "        # Save configuration\n",
+    "        self.save_config({\n",
+    "            'output_dir': str(output_dir),\n",
+    "            'project_name': project_name,\n",
+    "            'log_every_n_steps': log_every_n_steps,\n",
+    "            'save_every_n_steps': save_every_n_steps,\n",
+    "            'save_best_only': save_best_only,\n",
+    "            'start_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n",
+    "        })\n",
+    "    \n",
+    "    def setup_logging(self):\n",
+    "        \"\"\"Setup logging configuration.\"\"\"\n",
+    "        logging.basicConfig(\n",
+    "            level=logging.INFO,\n",
+    "            format='%(asctime)s - %(levelname)s - %(message)s',\n",
+    "            handlers=[\n",
+    "                logging.FileHandler(self.log_file),\n",
+    "                logging.StreamHandler()\n",
+    "            ]\n",
+    "        )\n",
+    "    \n",
+    "    def save_config(self, config: Dict[str, Any]):\n",
+    "        \"\"\"Save configuration to JSON file.\"\"\"\n",
+    "        config_path = self.output_dir / 'config.json'\n",
+    "        with open(config_path, 'w') as f:\n",
+    "            json.dump(config, f, indent=4)\n",
+    "    \n",
+    "    def log_metrics(\n",
+    "        self,\n",
+    "        metrics: Dict[str, float],\n",
+    "        step: Optional[int] = None,\n",
+    "        force_log: bool = False\n",
+    "    ):\n",
+    "        \"\"\"\n",
+    "        Log metrics to all configured outputs.\n",
+    "        \n",
+    "        Args:\n",
+    "            metrics: Dictionary of metric names and values\n",
+    "            step: Optional step number (uses global_step if not provided)\n",
+    "            force_log: Whether to log regardless of log_every_n_steps\n",
+    "        \"\"\"\n",
+    "        if step is not None:\n",
+    "            self.global_step = step\n",
+    "        \n",
+    "        # Check if we should log\n",
+    "        if not force_log and self.global_step % self.log_every_n_steps != 0:\n",
+    "            return\n",
+    "        \n",
+    "        # Calculate time statistics\n",
+    "        current_time = time.time()\n",
+    "        elapsed = current_time - self.start_time\n",
+    "        elapsed_since_last = current_time - self.last_log_time\n",
+    "        steps_since_last = self.log_every_n_steps\n",
+    "        steps_per_second = steps_since_last / elapsed_since_last\n",
+    "        \n",
+    "        # Add timing metrics\n",
+    "        metrics.update({\n",
+    "            'elapsed_time': elapsed,\n",
+    "            'steps_per_second': steps_per_second\n",
+    "        })\n",
+    "        \n",
+    "        # Log to terminal and file\n",
+    "        log_str = f'Step {self.global_step}: ' + ', '.join(\n",
+    "            f'{k}: {v:.4f}' for k, v in metrics.items()\n",
+    "        )\n",
+    "        logging.info(log_str)\n",
+    "        \n",
+    "        self.last_log_time = current_time\n",
+    "    \n",
+    "    def save_checkpoint(\n",
+    "        self,\n",
+    "        model: torch.nn.Module,\n",
+    "        optimizer: torch.optim.Optimizer,\n",
+    "        loss: float,\n",
+    "        extra_data: Optional[Dict[str, Any]] = None,\n",
+    "        force_save: bool = False\n",
+    "    ):\n",
+    "        \"\"\"\n",
+    "        Save model checkpoint.\n",
+    "        \n",
+    "        Args:\n",
+    "            model: PyTorch model\n",
+    "            optimizer: PyTorch optimizer\n",
+    "            loss: Current loss value\n",
+    "            extra_data: Additional data to save in checkpoint\n",
+    "            force_save: Whether to save regardless of save_every_n_steps\n",
+    "        \"\"\"\n",
+    "        # Check if we should save\n",
+    "        should_save = (\n",
+    "            force_save or\n",
+    "            self.global_step % self.save_every_n_steps == 0 or\n",
+    "            (self.save_best_only and loss < self.best_loss)\n",
+    "        )\n",
+    "        \n",
+    "        if not should_save:\n",
+    "            return\n",
+    "        \n",
+    "        # Update best loss if needed\n",
+    "        if loss < self.best_loss:\n",
+    "            self.best_loss = loss\n",
+    "        \n",
+    "        # Prepare checkpoint data\n",
+    "        checkpoint = {\n",
+    "            'model_state_dict': model.state_dict(),\n",
+    "            'optimizer_state_dict': optimizer.state_dict(),\n",
+    "            'global_step': self.global_step,\n",
+    "            'loss': loss,\n",
+    "            'best_loss': self.best_loss\n",
+    "        }\n",
+    "        \n",
+    "        if extra_data:\n",
+    "            checkpoint.update(extra_data)\n",
+    "        \n",
+    "        # Save checkpoint\n",
+    "        checkpoint_path = self.output_dir / f'checkpoint_{self.global_step}.pt'\n",
+    "        torch.save(checkpoint, checkpoint_path)\n",
+    "        \n",
+    "        logging.info(f'Saved checkpoint at step {self.global_step}')\n",
+    "    \n",
+    "    def finish(self):\n",
+    "        \"\"\"Cleanup and final logging.\"\"\"\n",
+    "        total_time = time.time() - self.start_time\n",
+    "        logging.info(f'Training finished. Total time: {total_time:.2f}s')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train(experimentation_name,\n",
+    "          output_dir,\n",
+    "          logging_steps,\n",
+    "          save_steps,\n",
+    "          num_epochs,\n",
+    "          train_dataset,\n",
+    "          collate_fn,\n",
+    "          batch_size,\n",
+    "          gradient_accumulation_steps=1, # not yet implemented\n",
+    "          learning_rate=5e-5,\n",
+    "          warmup_ratio=0.1,\n",
+    "          weight_decay=0.01,\n",
+    "          device=\"cuda\"):\n",
+    "    \n",
+    "    # prepare model\n",
+    "    model.train()\n",
+    "    model.to(device)\n",
+    "    \n",
+    "    train_dataloader = create_data_loader(train_dataset, collate_fn=collate_fn, batch_size=batch_size)\n",
+    "    \n",
+    "    num_training_steps = calculate_training_steps(len(train_dataloader), num_epochs=num_epochs, batch_size=batch_size, gradient_accumulation_steps=gradient_accumulation_steps)\n",
+    "\n",
+    "    # Create optimizer and scheduler\n",
+    "    optimizer, scheduler, warmup_steps = create_optimizer_and_scheduler(\n",
+    "        model=model,\n",
+    "        num_training_steps=num_training_steps,\n",
+    "        learning_rate=learning_rate,\n",
+    "        warmup_ratio=warmup_ratio,\n",
+    "        weight_decay=weight_decay\n",
+    "    )\n",
+    "    \n",
+    "    train_logger = TrainingLogger(\n",
+    "        project_name=experimentation_name,\n",
+    "        output_dir=output_dir,\n",
+    "        log_every_n_steps=logging_steps,\n",
+    "        save_every_n_steps=save_steps,\n",
+    "    )\n",
+    "\n",
+    "    for epoch in range(num_epochs):\n",
+    "        desc = f'  - (Training)   Epoch {epoch}'\n",
+    "        n_word_total, n_word_correct, total_loss = 0, 0, 0\n",
+    "        for step, batch in tqdm(enumerate(train_dataloader), mininterval=2, desc=desc, leave=False, total=len(train_dataloader)):\n",
+    "            loss, n_correct, n_word = train_step(\n",
+    "                model=model,\n",
+    "                batch=batch,\n",
+    "                optimizer=optimizer,\n",
+    "                scheduler=scheduler,\n",
+    "                device=device,\n",
+    "                gradient_accumulation_steps=gradient_accumulation_steps,\n",
+    "            )\n",
+    "            \n",
+    "            \n",
+    "            n_word_total += n_word\n",
+    "            n_word_correct += n_correct\n",
+    "            total_loss += loss\n",
+    "            \n",
+    "            current_lr = scheduler.get_last_lr()[0]\n",
+    "            \n",
+    "            train_logger.log_metrics(metrics={\n",
+    "                'loss': loss,\n",
+    "                'epoch': epoch,\n",
+    "                \"n_correct\": n_correct,\n",
+    "                \"n_word\": n_word,\n",
+    "                'learning_rate': current_lr,\n",
+    "                \"warmup_steps\": warmup_steps\n",
+    "            }, step=step)\n",
+    "        print()\n",
+    "        print(f\"Epoch {epoch} :: loss per word :: {total_loss/n_word_total}\")\n",
+    "    train_logger.finish()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "077dbbb07a714b029e1d089a7654adf3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  - (Training)   Epoch 0:   0%|          | 0/74787 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-01-19 08:13:20,512 - INFO - Step 0: loss: 11.1261, epoch: 0.0000, n_correct: 0.0000, n_word: 2181.0000, learning_rate: 0.0000, elapsed_time: 0.3608, steps_per_second: 2771.5616\n",
+      "2025-01-19 08:15:19,346 - INFO - Step 1000: loss: 7.5842, epoch: 0.0000, n_correct: 300.0000, n_word: 2404.0000, learning_rate: 0.0000, elapsed_time: 119.1949, steps_per_second: 8.4151\n",
+      "2025-01-19 08:17:19,041 - INFO - Step 2000: loss: 6.5283, epoch: 0.0000, n_correct: 385.0000, n_word: 2198.0000, learning_rate: 0.0000, elapsed_time: 238.8901, steps_per_second: 8.3546\n",
+      "2025-01-19 08:19:19,875 - INFO - Step 3000: loss: 6.1710, epoch: 0.0000, n_correct: 406.0000, n_word: 2173.0000, learning_rate: 0.0000, elapsed_time: 359.7244, steps_per_second: 8.2758\n",
+      "2025-01-19 08:21:17,619 - INFO - Step 4000: loss: 6.1138, epoch: 0.0000, n_correct: 438.0000, n_word: 2346.0000, learning_rate: 0.0000, elapsed_time: 477.4685, steps_per_second: 8.4930\n",
+      "2025-01-19 08:23:12,649 - INFO - Step 5000: loss: 5.7809, epoch: 0.0000, n_correct: 497.0000, n_word: 2590.0000, learning_rate: 0.0000, elapsed_time: 592.4976, steps_per_second: 8.6935\n",
+      "2025-01-19 08:25:10,540 - INFO - Step 6000: loss: 5.6213, epoch: 0.0000, n_correct: 441.0000, n_word: 1969.0000, learning_rate: 0.0000, elapsed_time: 710.3888, steps_per_second: 8.4824\n",
+      "2025-01-19 08:27:08,897 - INFO - Step 7000: loss: 5.7631, epoch: 0.0000, n_correct: 522.0000, n_word: 2570.0000, learning_rate: 0.0000, elapsed_time: 828.7459, steps_per_second: 8.4490\n",
+      "2025-01-19 08:29:10,558 - INFO - Step 8000: loss: 5.7749, epoch: 0.0000, n_correct: 501.0000, n_word: 2592.0000, learning_rate: 0.0000, elapsed_time: 950.4069, steps_per_second: 8.2196\n",
+      "2025-01-19 08:31:09,879 - INFO - Step 9000: loss: 5.4365, epoch: 0.0000, n_correct: 458.0000, n_word: 2054.0000, learning_rate: 0.0000, elapsed_time: 1069.7279, steps_per_second: 8.3807\n",
+      "2025-01-19 08:33:11,153 - INFO - Step 10000: loss: 5.5730, epoch: 0.0000, n_correct: 489.0000, n_word: 2419.0000, learning_rate: 0.0000, elapsed_time: 1191.0021, steps_per_second: 8.2458\n",
+      "2025-01-19 08:35:10,677 - INFO - Step 11000: loss: 5.6693, epoch: 0.0000, n_correct: 436.0000, n_word: 1958.0000, learning_rate: 0.0000, elapsed_time: 1310.5263, steps_per_second: 8.3665\n",
+      "2025-01-19 08:37:08,904 - INFO - Step 12000: loss: 5.8997, epoch: 0.0000, n_correct: 482.0000, n_word: 2482.0000, learning_rate: 0.0000, elapsed_time: 1428.7536, steps_per_second: 8.4583\n",
+      "2025-01-19 08:39:07,226 - INFO - Step 13000: loss: 5.3159, epoch: 0.0000, n_correct: 552.0000, n_word: 2424.0000, learning_rate: 0.0000, elapsed_time: 1547.0753, steps_per_second: 8.4515\n",
+      "2025-01-19 08:41:05,106 - INFO - Step 14000: loss: 5.5746, epoch: 0.0000, n_correct: 529.0000, n_word: 2461.0000, learning_rate: 0.0000, elapsed_time: 1664.9554, steps_per_second: 8.4832\n",
+      "2025-01-19 08:43:04,549 - INFO - Step 15000: loss: 5.0682, epoch: 0.0000, n_correct: 518.0000, n_word: 2142.0000, learning_rate: 0.0000, elapsed_time: 1784.3984, steps_per_second: 8.3722\n",
+      "2025-01-19 08:45:02,475 - INFO - Step 16000: loss: 5.0798, epoch: 0.0000, n_correct: 515.0000, n_word: 1999.0000, learning_rate: 0.0000, elapsed_time: 1902.3244, steps_per_second: 8.4799\n",
+      "2025-01-19 08:47:04,547 - INFO - Step 17000: loss: 5.2511, epoch: 0.0000, n_correct: 548.0000, n_word: 2278.0000, learning_rate: 0.0000, elapsed_time: 2024.3961, steps_per_second: 8.1919\n",
+      "2025-01-19 08:49:03,932 - INFO - Step 18000: loss: 5.3163, epoch: 0.0000, n_correct: 538.0000, n_word: 2286.0000, learning_rate: 0.0000, elapsed_time: 2143.7816, steps_per_second: 8.3762\n",
+      "2025-01-19 08:50:59,916 - INFO - Step 19000: loss: 4.9038, epoch: 0.0000, n_correct: 520.0000, n_word: 2084.0000, learning_rate: 0.0000, elapsed_time: 2259.7652, steps_per_second: 8.6219\n",
+      "2025-01-19 08:52:58,381 - INFO - Step 20000: loss: 5.2682, epoch: 0.0000, n_correct: 548.0000, n_word: 2210.0000, learning_rate: 0.0000, elapsed_time: 2378.2304, steps_per_second: 8.4413\n",
+      "2025-01-19 08:54:56,018 - INFO - Step 21000: loss: 5.0877, epoch: 0.0000, n_correct: 555.0000, n_word: 2267.0000, learning_rate: 0.0000, elapsed_time: 2495.8667, steps_per_second: 8.5008\n",
+      "2025-01-19 08:56:53,590 - INFO - Step 22000: loss: 4.7005, epoch: 0.0000, n_correct: 565.0000, n_word: 2194.0000, learning_rate: 0.0000, elapsed_time: 2613.4394, steps_per_second: 8.5054\n",
+      "2025-01-19 08:58:54,527 - INFO - Step 23000: loss: 4.8690, epoch: 0.0000, n_correct: 594.0000, n_word: 2335.0000, learning_rate: 0.0000, elapsed_time: 2734.3763, steps_per_second: 8.2688\n",
+      "2025-01-19 09:00:53,560 - INFO - Step 24000: loss: 4.9957, epoch: 0.0000, n_correct: 558.0000, n_word: 2124.0000, learning_rate: 0.0000, elapsed_time: 2853.4095, steps_per_second: 8.4010\n",
+      "2025-01-19 09:02:52,331 - INFO - Step 25000: loss: 5.2211, epoch: 0.0000, n_correct: 527.0000, n_word: 2309.0000, learning_rate: 0.0000, elapsed_time: 2972.1803, steps_per_second: 8.4196\n",
+      "2025-01-19 09:04:51,743 - INFO - Step 26000: loss: 4.8525, epoch: 0.0000, n_correct: 588.0000, n_word: 2239.0000, learning_rate: 0.0000, elapsed_time: 3091.5918, steps_per_second: 8.3744\n",
+      "2025-01-19 09:06:48,430 - INFO - Step 27000: loss: 4.7996, epoch: 0.0000, n_correct: 542.0000, n_word: 1887.0000, learning_rate: 0.0000, elapsed_time: 3208.2786, steps_per_second: 8.5700\n",
+      "2025-01-19 09:08:49,877 - INFO - Step 28000: loss: 4.9380, epoch: 0.0000, n_correct: 525.0000, n_word: 1998.0000, learning_rate: 0.0000, elapsed_time: 3329.7257, steps_per_second: 8.2340\n",
+      "2025-01-19 09:10:47,592 - INFO - Step 29000: loss: 4.5870, epoch: 0.0000, n_correct: 697.0000, n_word: 2564.0000, learning_rate: 0.0000, elapsed_time: 3447.4413, steps_per_second: 8.4950\n",
+      "2025-01-19 09:12:46,997 - INFO - Step 30000: loss: 4.6909, epoch: 0.0000, n_correct: 638.0000, n_word: 2416.0000, learning_rate: 0.0000, elapsed_time: 3566.8460, steps_per_second: 8.3749\n",
+      "2025-01-19 09:14:44,832 - INFO - Step 31000: loss: 4.6856, epoch: 0.0000, n_correct: 598.0000, n_word: 2117.0000, learning_rate: 0.0000, elapsed_time: 3684.6813, steps_per_second: 8.4864\n",
+      "2025-01-19 09:16:43,832 - INFO - Step 32000: loss: 4.7878, epoch: 0.0000, n_correct: 614.0000, n_word: 2441.0000, learning_rate: 0.0000, elapsed_time: 3803.6815, steps_per_second: 8.4033\n",
+      "2025-01-19 09:18:42,339 - INFO - Step 33000: loss: 4.7256, epoch: 0.0000, n_correct: 550.0000, n_word: 2152.0000, learning_rate: 0.0000, elapsed_time: 3922.1878, steps_per_second: 8.4384\n",
+      "2025-01-19 09:20:41,716 - INFO - Step 34000: loss: 4.5742, epoch: 0.0000, n_correct: 630.0000, n_word: 2228.0000, learning_rate: 0.0000, elapsed_time: 4041.5647, steps_per_second: 8.3768\n",
+      "2025-01-19 09:22:42,838 - INFO - Step 35000: loss: 4.6395, epoch: 0.0000, n_correct: 601.0000, n_word: 2171.0000, learning_rate: 0.0000, elapsed_time: 4162.6873, steps_per_second: 8.2561\n",
+      "2025-01-19 09:24:41,681 - INFO - Step 36000: loss: 4.7001, epoch: 0.0000, n_correct: 556.0000, n_word: 1870.0000, learning_rate: 0.0000, elapsed_time: 4281.5304, steps_per_second: 8.4145\n",
+      "2025-01-19 09:26:36,792 - INFO - Step 37000: loss: 4.6198, epoch: 0.0000, n_correct: 580.0000, n_word: 2114.0000, learning_rate: 0.0000, elapsed_time: 4396.6407, steps_per_second: 8.6873\n",
+      "2025-01-19 09:28:36,464 - INFO - Step 38000: loss: 4.4665, epoch: 0.0000, n_correct: 713.0000, n_word: 2488.0000, learning_rate: 0.0000, elapsed_time: 4516.3134, steps_per_second: 8.3561\n",
+      "2025-01-19 09:30:34,744 - INFO - Step 39000: loss: 4.8569, epoch: 0.0000, n_correct: 513.0000, n_word: 2119.0000, learning_rate: 0.0000, elapsed_time: 4634.5934, steps_per_second: 8.4545\n",
+      "2025-01-19 09:32:34,614 - INFO - Step 40000: loss: 4.8627, epoch: 0.0000, n_correct: 623.0000, n_word: 2462.0000, learning_rate: 0.0000, elapsed_time: 4754.4630, steps_per_second: 8.3424\n",
+      "2025-01-19 09:34:34,280 - INFO - Step 41000: loss: 4.5318, epoch: 0.0000, n_correct: 696.0000, n_word: 2319.0000, learning_rate: 0.0000, elapsed_time: 4874.1288, steps_per_second: 8.3566\n",
+      "2025-01-19 09:36:32,602 - INFO - Step 42000: loss: 4.4519, epoch: 0.0000, n_correct: 767.0000, n_word: 2524.0000, learning_rate: 0.0000, elapsed_time: 4992.4507, steps_per_second: 8.4515\n",
+      "2025-01-19 09:38:30,482 - INFO - Step 43000: loss: 4.5382, epoch: 0.0000, n_correct: 676.0000, n_word: 2332.0000, learning_rate: 0.0000, elapsed_time: 5110.3313, steps_per_second: 8.4832\n",
+      "2025-01-19 09:40:26,110 - INFO - Step 44000: loss: 4.7279, epoch: 0.0000, n_correct: 611.0000, n_word: 2198.0000, learning_rate: 0.0000, elapsed_time: 5225.9591, steps_per_second: 8.6484\n",
+      "2025-01-19 09:42:24,551 - INFO - Step 45000: loss: 4.3221, epoch: 0.0000, n_correct: 591.0000, n_word: 1965.0000, learning_rate: 0.0000, elapsed_time: 5344.4002, steps_per_second: 8.4430\n",
+      "2025-01-19 09:44:22,009 - INFO - Step 46000: loss: 4.5642, epoch: 0.0000, n_correct: 607.0000, n_word: 2194.0000, learning_rate: 0.0000, elapsed_time: 5461.8584, steps_per_second: 8.5137\n",
+      "2025-01-19 09:46:21,529 - INFO - Step 47000: loss: 4.6846, epoch: 0.0000, n_correct: 606.0000, n_word: 2175.0000, learning_rate: 0.0000, elapsed_time: 5581.3776, steps_per_second: 8.3669\n",
+      "2025-01-19 09:48:20,629 - INFO - Step 48000: loss: 4.4548, epoch: 0.0000, n_correct: 915.0000, n_word: 2980.0000, learning_rate: 0.0000, elapsed_time: 5700.4785, steps_per_second: 8.3962\n",
+      "2025-01-19 09:50:18,551 - INFO - Step 49000: loss: 4.5768, epoch: 0.0000, n_correct: 628.0000, n_word: 2341.0000, learning_rate: 0.0000, elapsed_time: 5818.4001, steps_per_second: 8.4802\n",
+      "2025-01-19 09:52:15,601 - INFO - Step 50000: loss: 4.4300, epoch: 0.0000, n_correct: 633.0000, n_word: 2220.0000, learning_rate: 0.0000, elapsed_time: 5935.4499, steps_per_second: 8.5434\n",
+      "2025-01-19 09:54:14,602 - INFO - Step 51000: loss: 4.9783, epoch: 0.0000, n_correct: 556.0000, n_word: 2218.0000, learning_rate: 0.0000, elapsed_time: 6054.4516, steps_per_second: 8.4032\n",
+      "2025-01-19 09:56:12,764 - INFO - Step 52000: loss: 4.7508, epoch: 0.0000, n_correct: 582.0000, n_word: 2112.0000, learning_rate: 0.0000, elapsed_time: 6172.6128, steps_per_second: 8.4630\n",
+      "2025-01-19 09:58:10,789 - INFO - Step 53000: loss: 4.1543, epoch: 0.0000, n_correct: 643.0000, n_word: 1894.0000, learning_rate: 0.0000, elapsed_time: 6290.6382, steps_per_second: 8.4728\n",
+      "2025-01-19 10:00:09,106 - INFO - Step 54000: loss: 4.4759, epoch: 0.0000, n_correct: 553.0000, n_word: 1956.0000, learning_rate: 0.0000, elapsed_time: 6408.9547, steps_per_second: 8.4519\n",
+      "2025-01-19 10:02:06,961 - INFO - Step 55000: loss: 4.6134, epoch: 0.0000, n_correct: 640.0000, n_word: 2215.0000, learning_rate: 0.0000, elapsed_time: 6526.8097, steps_per_second: 8.4850\n",
+      "2025-01-19 10:04:04,121 - INFO - Step 56000: loss: 4.3529, epoch: 0.0000, n_correct: 589.0000, n_word: 2056.0000, learning_rate: 0.0000, elapsed_time: 6643.9698, steps_per_second: 8.5353\n",
+      "2025-01-19 10:06:01,464 - INFO - Step 57000: loss: 4.2794, epoch: 0.0000, n_correct: 684.0000, n_word: 2338.0000, learning_rate: 0.0000, elapsed_time: 6761.3133, steps_per_second: 8.5220\n",
+      "2025-01-19 10:08:01,105 - INFO - Step 58000: loss: 4.0324, epoch: 0.0000, n_correct: 823.0000, n_word: 2502.0000, learning_rate: 0.0000, elapsed_time: 6880.9546, steps_per_second: 8.3583\n",
+      "2025-01-19 10:10:01,963 - INFO - Step 59000: loss: 4.3218, epoch: 0.0000, n_correct: 713.0000, n_word: 2329.0000, learning_rate: 0.0000, elapsed_time: 7001.8124, steps_per_second: 8.2742\n",
+      "2025-01-19 10:12:00,612 - INFO - Step 60000: loss: 4.4752, epoch: 0.0000, n_correct: 680.0000, n_word: 2355.0000, learning_rate: 0.0000, elapsed_time: 7120.4607, steps_per_second: 8.4283\n",
+      "2025-01-19 10:13:59,503 - INFO - Step 61000: loss: 4.6459, epoch: 0.0000, n_correct: 679.0000, n_word: 2431.0000, learning_rate: 0.0000, elapsed_time: 7239.3523, steps_per_second: 8.4110\n",
+      "2025-01-19 10:15:56,894 - INFO - Step 62000: loss: 4.4396, epoch: 0.0000, n_correct: 614.0000, n_word: 2092.0000, learning_rate: 0.0000, elapsed_time: 7356.7429, steps_per_second: 8.5186\n",
+      "2025-01-19 10:17:54,905 - INFO - Step 63000: loss: 4.5069, epoch: 0.0000, n_correct: 660.0000, n_word: 2269.0000, learning_rate: 0.0000, elapsed_time: 7474.7537, steps_per_second: 8.4738\n",
+      "2025-01-19 10:19:54,803 - INFO - Step 64000: loss: 4.5851, epoch: 0.0000, n_correct: 663.0000, n_word: 2401.0000, learning_rate: 0.0000, elapsed_time: 7594.6518, steps_per_second: 8.3404\n",
+      "2025-01-19 10:21:55,094 - INFO - Step 65000: loss: 4.4775, epoch: 0.0000, n_correct: 669.0000, n_word: 2271.0000, learning_rate: 0.0000, elapsed_time: 7714.9427, steps_per_second: 8.3132\n",
+      "2025-01-19 10:23:55,967 - INFO - Step 66000: loss: 4.4011, epoch: 0.0000, n_correct: 649.0000, n_word: 2097.0000, learning_rate: 0.0000, elapsed_time: 7835.8158, steps_per_second: 8.2731\n",
+      "2025-01-19 10:25:54,174 - INFO - Step 67000: loss: 4.2620, epoch: 0.0000, n_correct: 848.0000, n_word: 2782.0000, learning_rate: 0.0000, elapsed_time: 7954.0233, steps_per_second: 8.4597\n",
+      "2025-01-19 10:27:53,147 - INFO - Step 68000: loss: 4.3724, epoch: 0.0000, n_correct: 773.0000, n_word: 2591.0000, learning_rate: 0.0000, elapsed_time: 8072.9958, steps_per_second: 8.4053\n",
+      "2025-01-19 10:29:52,588 - INFO - Step 69000: loss: 4.4732, epoch: 0.0000, n_correct: 672.0000, n_word: 2331.0000, learning_rate: 0.0000, elapsed_time: 8192.4374, steps_per_second: 8.3723\n",
+      "2025-01-19 10:31:51,881 - INFO - Step 70000: loss: 4.3063, epoch: 0.0000, n_correct: 688.0000, n_word: 2196.0000, learning_rate: 0.0000, elapsed_time: 8311.7305, steps_per_second: 8.3827\n",
+      "2025-01-19 10:33:49,709 - INFO - Step 71000: loss: 4.4756, epoch: 0.0000, n_correct: 610.0000, n_word: 2066.0000, learning_rate: 0.0000, elapsed_time: 8429.5582, steps_per_second: 8.4870\n",
+      "2025-01-19 10:35:49,076 - INFO - Step 72000: loss: 4.3445, epoch: 0.0000, n_correct: 630.0000, n_word: 2054.0000, learning_rate: 0.0000, elapsed_time: 8548.9250, steps_per_second: 8.3775\n",
+      "2025-01-19 10:37:48,103 - INFO - Step 73000: loss: 4.3034, epoch: 0.0000, n_correct: 616.0000, n_word: 2052.0000, learning_rate: 0.0000, elapsed_time: 8667.9519, steps_per_second: 8.4015\n",
+      "2025-01-19 10:39:47,754 - INFO - Step 74000: loss: 4.2784, epoch: 0.0000, n_correct: 722.0000, n_word: 2473.0000, learning_rate: 0.0000, elapsed_time: 8787.6030, steps_per_second: 8.3576\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Epoch 0 :: loss per word :: 0.0021391541398447806\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "caa00de8692845068b2448994fa6b7b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  - (Training)   Epoch 1:   0%|          | 0/74787 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-01-19 10:41:40,785 - INFO - Step 0: loss: 4.4777, epoch: 1.0000, n_correct: 653.0000, n_word: 2231.0000, learning_rate: 0.0000, elapsed_time: 8900.6342, steps_per_second: 8.8471\n",
+      "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x740b75b56fc0>\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/torch/utils/data/dataloader.py\", line 1604, in __del__\n",
+      "    self._shutdown_workers()\n",
+      "  File \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/torch/utils/data/dataloader.py\", line 1587, in _shutdown_workers\n",
+      "    if w.is_alive():\n",
+      "       ^^^^^^^^^^^^\n",
+      "  File \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/multiprocessing/process.py\", line 160, in is_alive\n",
+      "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "AssertionError: can only test a child process\n",
+      "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x740b75b56fc0>\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/torch/utils/data/dataloader.py\", line 1604, in __del__\n",
+      "    self._shutdown_workers()\n",
+      "  File \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/torch/utils/data/dataloader.py\", line 1587, in _shutdown_workers\n",
+      "    if w.is_alive():\n",
+      "       ^^^^^^^^^^^^\n",
+      "  File \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/multiprocessing/process.py\", line 160, in is_alive\n",
+      "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "AssertionError: can only test a child process\n",
+      "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x740b75b56fc0>\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/torch/utils/data/dataloader.py\", line 1604, in __del__\n",
+      "    self._shutdown_workers()\n",
+      "  File \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/torch/utils/data/dataloader.py\", line 1587, in _shutdown_workers\n",
+      "    if w.is_alive():\n",
+      "       ^^^^^^^^^^^^\n",
+      "  File \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/multiprocessing/process.py\", line 160, in is_alive\n",
+      "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "AssertionError: can only test a child process\n",
+      "2025-01-19 10:43:37,693 - INFO - Step 1000: loss: 4.4594, epoch: 1.0000, n_correct: 612.0000, n_word: 2141.0000, learning_rate: 0.0000, elapsed_time: 9017.5421, steps_per_second: 8.5537\n",
+      "2025-01-19 10:45:33,910 - INFO - Step 2000: loss: 4.2591, epoch: 1.0000, n_correct: 660.0000, n_word: 2005.0000, learning_rate: 0.0000, elapsed_time: 9133.7593, steps_per_second: 8.6046\n",
+      "2025-01-19 10:47:33,659 - INFO - Step 3000: loss: 4.0903, epoch: 1.0000, n_correct: 694.0000, n_word: 2166.0000, learning_rate: 0.0000, elapsed_time: 9253.5080, steps_per_second: 8.3508\n",
+      "2025-01-19 10:49:32,509 - INFO - Step 4000: loss: 4.2696, epoch: 1.0000, n_correct: 715.0000, n_word: 2295.0000, learning_rate: 0.0000, elapsed_time: 9372.3584, steps_per_second: 8.4139\n",
+      "2025-01-19 10:51:31,800 - INFO - Step 5000: loss: 4.3528, epoch: 1.0000, n_correct: 705.0000, n_word: 2270.0000, learning_rate: 0.0000, elapsed_time: 9491.6491, steps_per_second: 8.3829\n",
+      "2025-01-19 10:53:30,982 - INFO - Step 6000: loss: 4.4004, epoch: 1.0000, n_correct: 620.0000, n_word: 2045.0000, learning_rate: 0.0000, elapsed_time: 9610.8312, steps_per_second: 8.3905\n",
+      "2025-01-19 10:55:29,210 - INFO - Step 7000: loss: 4.2450, epoch: 1.0000, n_correct: 903.0000, n_word: 2920.0000, learning_rate: 0.0000, elapsed_time: 9729.0593, steps_per_second: 8.4582\n",
+      "2025-01-19 10:57:25,573 - INFO - Step 8000: loss: 4.3420, epoch: 1.0000, n_correct: 643.0000, n_word: 2145.0000, learning_rate: 0.0000, elapsed_time: 9845.4219, steps_per_second: 8.5938\n",
+      "2025-01-19 10:59:23,058 - INFO - Step 9000: loss: 4.1204, epoch: 1.0000, n_correct: 737.0000, n_word: 2301.0000, learning_rate: 0.0000, elapsed_time: 9962.9069, steps_per_second: 8.5117\n",
+      "2025-01-19 11:01:21,236 - INFO - Step 10000: loss: 4.4733, epoch: 1.0000, n_correct: 652.0000, n_word: 2299.0000, learning_rate: 0.0000, elapsed_time: 10081.0850, steps_per_second: 8.4618\n",
+      "2025-01-19 11:03:20,285 - INFO - Step 11000: loss: 4.2279, epoch: 1.0000, n_correct: 645.0000, n_word: 2040.0000, learning_rate: 0.0000, elapsed_time: 10200.1336, steps_per_second: 8.3999\n",
+      "2025-01-19 11:05:18,243 - INFO - Step 12000: loss: 4.3885, epoch: 1.0000, n_correct: 700.0000, n_word: 2374.0000, learning_rate: 0.0000, elapsed_time: 10318.0919, steps_per_second: 8.4776\n",
+      "2025-01-19 11:07:14,496 - INFO - Step 13000: loss: 3.9267, epoch: 1.0000, n_correct: 770.0000, n_word: 2194.0000, learning_rate: 0.0000, elapsed_time: 10434.3450, steps_per_second: 8.6019\n",
+      "2025-01-19 11:09:13,357 - INFO - Step 14000: loss: 4.2879, epoch: 1.0000, n_correct: 642.0000, n_word: 2222.0000, learning_rate: 0.0000, elapsed_time: 10553.2058, steps_per_second: 8.4132\n",
+      "2025-01-19 11:11:12,007 - INFO - Step 15000: loss: 4.2024, epoch: 1.0000, n_correct: 656.0000, n_word: 2048.0000, learning_rate: 0.0000, elapsed_time: 10671.8559, steps_per_second: 8.4281\n",
+      "2025-01-19 11:13:12,128 - INFO - Step 16000: loss: 4.2026, epoch: 1.0000, n_correct: 559.0000, n_word: 1782.0000, learning_rate: 0.0000, elapsed_time: 10791.9773, steps_per_second: 8.3249\n",
+      "2025-01-19 11:15:11,018 - INFO - Step 17000: loss: 4.3297, epoch: 1.0000, n_correct: 797.0000, n_word: 2634.0000, learning_rate: 0.0000, elapsed_time: 10910.8667, steps_per_second: 8.4112\n",
+      "2025-01-19 11:17:11,885 - INFO - Step 18000: loss: 4.5067, epoch: 1.0000, n_correct: 674.0000, n_word: 2251.0000, learning_rate: 0.0000, elapsed_time: 11031.7344, steps_per_second: 8.2735\n",
+      "2025-01-19 11:19:10,198 - INFO - Step 19000: loss: 4.3180, epoch: 1.0000, n_correct: 609.0000, n_word: 1914.0000, learning_rate: 0.0000, elapsed_time: 11150.0475, steps_per_second: 8.4521\n",
+      "2025-01-19 11:21:11,437 - INFO - Step 20000: loss: 4.2169, epoch: 1.0000, n_correct: 880.0000, n_word: 2835.0000, learning_rate: 0.0000, elapsed_time: 11271.2859, steps_per_second: 8.2482\n",
+      "2025-01-19 11:23:10,262 - INFO - Step 21000: loss: 4.0214, epoch: 1.0000, n_correct: 680.0000, n_word: 1998.0000, learning_rate: 0.0000, elapsed_time: 11390.1114, steps_per_second: 8.4157\n",
+      "2025-01-19 11:25:08,655 - INFO - Step 22000: loss: 4.5519, epoch: 1.0000, n_correct: 685.0000, n_word: 2388.0000, learning_rate: 0.0000, elapsed_time: 11508.5038, steps_per_second: 8.4465\n",
+      "2025-01-19 11:27:04,967 - INFO - Step 23000: loss: 4.1960, epoch: 1.0000, n_correct: 702.0000, n_word: 2253.0000, learning_rate: 0.0000, elapsed_time: 11624.8164, steps_per_second: 8.5975\n",
+      "2025-01-19 11:29:03,302 - INFO - Step 24000: loss: 4.3910, epoch: 1.0000, n_correct: 764.0000, n_word: 2493.0000, learning_rate: 0.0000, elapsed_time: 11743.1513, steps_per_second: 8.4506\n",
+      "2025-01-19 11:30:59,734 - INFO - Step 25000: loss: 4.2227, epoch: 1.0000, n_correct: 820.0000, n_word: 2701.0000, learning_rate: 0.0000, elapsed_time: 11859.5828, steps_per_second: 8.5887\n",
+      "2025-01-19 11:32:58,424 - INFO - Step 26000: loss: 4.2673, epoch: 1.0000, n_correct: 781.0000, n_word: 2574.0000, learning_rate: 0.0000, elapsed_time: 11978.2726, steps_per_second: 8.4253\n",
+      "2025-01-19 11:34:58,605 - INFO - Step 27000: loss: 4.3264, epoch: 1.0000, n_correct: 790.0000, n_word: 2524.0000, learning_rate: 0.0000, elapsed_time: 12098.4542, steps_per_second: 8.3207\n",
+      "2025-01-19 11:36:58,964 - INFO - Step 28000: loss: 4.1725, epoch: 1.0000, n_correct: 726.0000, n_word: 2258.0000, learning_rate: 0.0000, elapsed_time: 12218.8131, steps_per_second: 8.3085\n",
+      "2025-01-19 11:38:56,668 - INFO - Step 29000: loss: 4.2002, epoch: 1.0000, n_correct: 657.0000, n_word: 2109.0000, learning_rate: 0.0000, elapsed_time: 12336.5175, steps_per_second: 8.4959\n",
+      "2025-01-19 11:40:56,659 - INFO - Step 30000: loss: 4.0469, epoch: 1.0000, n_correct: 746.0000, n_word: 2317.0000, learning_rate: 0.0000, elapsed_time: 12456.5082, steps_per_second: 8.3340\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset_config.batch_size\n",
+    "\n",
+    "\n",
+    "collate_fn = NextTokenPredictionCollator(tokenizer=tokenizer)\n",
+    "\n",
+    "training_config.logging_steps = 1000\n",
+    "training_config.warm_up = 4000\n",
+    "\n",
+    "train(\n",
+    "    experimentation_name=training_config.experimentation_name,\n",
+    "    output_dir=training_config.save_path,\n",
+    "    logging_steps=training_config.logging_steps,\n",
+    "    save_steps=training_config.logging_steps,\n",
+    "    num_epochs=training_config.num_epochs,\n",
+    "    train_dataset=train_examples_pt,\n",
+    "    collate_fn=collate_fn,\n",
+    "    batch_size=dataset_config.batch_size\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"weights.pt\", \"wb\") as handler:\n",
+    "    torch.save(model.state_dict(), handler)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/models/GI_01/notebooks/trainer.ipynb b/core/models/GI_01/notebooks/trainer.ipynb
new file mode 100644
index 0000000..4c7f951
--- /dev/null
+++ b/core/models/GI_01/notebooks/trainer.ipynb
@@ -0,0 +1,15245 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"3\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "ROOT_PROJECT_PATH = \"/root/AI-Uncomplicated\"\n",
+    "# Add the root directory to the sys.path\n",
+    "sys.path.insert(0, ROOT_PROJECT_PATH)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "WORKSPACE = os.path.join(ROOT_PROJECT_PATH, \"core/models/GI_01/artifacts\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from core.models.GI_01.main.config import ModelConfig, DatasetConfig, TrainingConfig\n",
+    "from core.dataloaders.dataloader import load_tokenizer\n",
+    "\n",
+    "## Initialize configurations\n",
+    "model_config = ModelConfig(model_name=\"Construe\",\n",
+    "                           num_layers = 2,\n",
+    "                            padding_id = 0,\n",
+    "                            hidden_dim = 128,\n",
+    "                            intermediate_dim = 1024,\n",
+    "                            max_positions = 2048,\n",
+    "                            layer_norm_eps = 1e-05,\n",
+    "                            model_max_sequence = 2048,\n",
+    "                            num_heads = 8,\n",
+    "                            attention_dropout = 0.1)\n",
+    "\n",
+    "dataset_config = DatasetConfig(dataset_path=\"./dataset\",\n",
+    "                               dataset_shuffle=True)\n",
+    "training_config = TrainingConfig(tokenizer_path=\"/root/AI-Uncomplicated/core/models/translator/tokenzier/european_tokenizer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Translation Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Path to dataset files: /root/.cache/kagglehub/datasets/dhruvildave/en-fr-translation-dataset/versions/2\n"
+     ]
+    }
+   ],
+   "source": [
+    "import kagglehub\n",
+    "\n",
+    "# Download latest version\n",
+    "path = kagglehub.dataset_download(\"dhruvildave/en-fr-translation-dataset\")\n",
+    "\n",
+    "print(\"Path to dataset files:\", path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train your tokenizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATASET_DOWNLOAD_PATH = os.path.join(WORKSPACE, \"dataset\", \"cc_100_en_fr\")\n",
+    "\n",
+    "if not os.path.exists(DATASET_DOWNLOAD_PATH):\n",
+    "    os.makedirs(DATASET_DOWNLOAD_PATH)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TOKENIZER_TRAIN_DATASET_NAME = \"statmt/cc100\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating source directory /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/statmt/cc100\n",
+      "Downloading dataset from statmt/cc100...\n",
+      "/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+      "  warnings.warn(\n",
+      "Fetching 20 files:   0%|                                 | 0/20 [00:00<?, ?it/s]\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
+      "\n",
+      "\n",
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+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
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+      "\n",
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+      "0005.parquet:   7%|█▋                       | 21.0M/311M [00:05<01:05, 4.41MB/s]\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
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+      "\n",
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+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
+      "\n",
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+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "0003.parquet:  17%|████▏                    | 52.4M/311M [00:05<00:30, 8.49MB/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
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+      "\n",
+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
+      "\n",
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+      "\n",
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+      "\n",
+      "\n",
+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
+      "\n",
+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
+      "\n",
+      "\n",
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+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
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+      "\n",
+      "\n",
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+      "\n",
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+      "\n",
+      "\n",
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+      "\n",
+      "\n",
+      "\n",
+      "\n",
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+      "\n",
+      "\n",
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+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
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+      "Dataset downloaded to /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/statmt/cc100\n"
+     ]
+    }
+   ],
+   "source": [
+    "DATA_DOWNLAD_SCRIPT_PATH =  os.path.join(ROOT_PROJECT_PATH, \"scripts/hf_data_downloader.py\")\n",
+    "\n",
+    "!python $DATA_DOWNLAD_SCRIPT_PATH --dataset $TOKENIZER_TRAIN_DATASET_NAME --working_dir=$DATASET_DOWNLOAD_PATH --allowed_pattern=\"en/**/*.parquet,fr/**/*.parquet\" --revision=convert/parquet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "DATASET_DOWNLOAD_PATH"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "\n",
+      "=== Starting Pipeline Execution ===\n",
+      "\n",
+      "Starting pipeline at 2025-03-11 06:59:02.695372\n",
+      "Number of CPU cores available: 48\n",
+      "Processing languages: en, fr\n",
+      "Processing started for Language : en\n",
+      "Processing started for Language : fr\n",
+      "WARNING:apache_beam.options.pipeline_options:Discarding unparseable args: ['--input_path=/root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/statmt/cc100', '--chunk_size=100000', '--output_path=/root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path', '--languages=en,fr']\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676343   nanos: 889441490 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676343   nanos: 891354084 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676343   nanos: 897391080 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676343   nanos: 903532028 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676343   nanos: 914941072 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676343   nanos: 916559934 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676343   nanos: 914121389 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676343   nanos: 919186353 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676343   nanos: 946364164 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676343   nanos: 948085069 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 7015943 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 8929967 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 31889200 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 35634279 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 40381193 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 42304992 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 53123474 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 54937601 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 56306600 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 58146953 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 107034444 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 108761787 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 133270025 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 135111093 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 138708114 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 140700817 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 160092115 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 161909341 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 167811155 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 169716596 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 173334360 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 175180912 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 173628330 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 175564527 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 180592060 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 182155370 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 179122686 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 181121826 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 208727121 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 210584402 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 267073631 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 268815517 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 299898862 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 301890373 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 370651960 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 371908426 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 371908426 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 373903512 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 373303413 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 375122785 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 386596679 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 388386487 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 449302434 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 451098442 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 468954086 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 470751285 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 469738245 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 471269845 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 473229169 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 474872589 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 472056150 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 473896741 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 472754716 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 474686145 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 484112977 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 485355615 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 491903066 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 493581056 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 512604475 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 514409065 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 518037080 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 519340991 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 520485162 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 520109653 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 521749496 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 521427869 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 523532390 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 524960756 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 531153202 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 532341957 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 535124063 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 536417245 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 536695003 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 537937641 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 538490056 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 539826631 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "Failed to import GCSFileSystem; loading of this filesystem will be skipped. Error details: cannot import name 'storage' from 'google.cloud' (unknown location)\n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 555927753 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 557132005 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 562017679 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 563247442 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 566290378 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 567456960 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 594912528 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 596093893 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 617019176 } message: \"No semi_persistent_directory found: Functions defined in __main__ (interactive session) may fail.\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/runners/worker/sdk_worker_main.py:361\" thread: \"MainThread\" \n",
+      "WARNING:apache_beam.runners.portability.local_job_service:Worker: severity: WARN timestamp {   seconds: 1741676344   nanos: 618192195 } message: \"Discarding unparseable args: [\\'--direct_runner_use_stacked_bundle\\', \\'--pipeline_type_check\\']\" log_location: \"/root/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/apache_beam/options/pipeline_options.py:394\" thread: \"MainThread\" \n",
+      "\n",
+      "==== Ending Excecution ======\n",
+      "Time taken to complet ::  31.02\n"
+     ]
+    }
+   ],
+   "source": [
+    "DATA_PROCESS_SCRIPT =  os.path.join(ROOT_PROJECT_PATH, \"data_processing/parquet/beam_text_writer.py\")\n",
+    "OUTPUT_PATH = os.path.join(DATASET_DOWNLOAD_PATH, \"processed_path\")\n",
+    "INPUT_PATH = os.path.join(DATASET_DOWNLOAD_PATH, \"statmt/cc100\")\n",
+    "\n",
+    "\n",
+    "!python $DATA_PROCESS_SCRIPT --input_path=$INPUT_PATH --chunk_size=100000 --output_path=$OUTPUT_PATH --languages=en,fr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==== TOKENIZER TRAINING STARTED =====\n",
+      "\n",
+      "Total files found: 644\n",
+      "========= Training Tokenizer with Given YML File symbols ==========\n",
+      "Starting tokenizer training...\n",
+      "sentencepiece_trainer.cc(77) LOG(INFO) Starts training with : \n",
+      "trainer_spec {\n",
+      "  input: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00063-of-00324-processed.txt\n",
+      "  input: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00216-of-00324-processed.txt\n",
+      "  input: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00045-of-00324-processed.txt\n",
+      "  input: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00012-of-00324-processed.txt\n",
+      "  input: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00064-of-00324-processed.txt\n",
+      "  input: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00135-of-00324-processed.txt\n",
+      "  input: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00047-of-00324-processed.txt\n",
+      "  input: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00112-of-00324-processed.txt\n",
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+      "  user_defined_symbols: 997\n",
+      "  user_defined_symbols: 998\n",
+      "  user_defined_symbols: 999\n",
+      "  required_chars: \n",
+      "  byte_fallback: 1\n",
+      "  vocabulary_output_piece_score: 1\n",
+      "  train_extremely_large_corpus: 1\n",
+      "  hard_vocab_limit: 1\n",
+      "  use_all_vocab: 0\n",
+      "  unk_id: 0\n",
+      "  bos_id: 1\n",
+      "  eos_id: 2\n",
+      "  pad_id: -1\n",
+      "  unk_piece: <unk>\n",
+      "  bos_piece: <s>\n",
+      "  eos_piece: </s>\n",
+      "  pad_piece: <pad>\n",
+      "  unk_surface:  ⁇ \n",
+      "  enable_differential_privacy: 0\n",
+      "  differential_privacy_noise_level: 0\n",
+      "  differential_privacy_clipping_threshold: 0\n",
+      "}\n",
+      "normalizer_spec {\n",
+      "  name: nmt_nfkc\n",
+      "  add_dummy_prefix: 1\n",
+      "  remove_extra_whitespaces: 1\n",
+      "  escape_whitespaces: 1\n",
+      "  normalization_rule_tsv: \n",
+      "}\n",
+      "denormalizer_spec {}\n",
+      "trainer_interface.cc(351) LOG(INFO) SentenceIterator is not specified. Using MultiFileSentenceIterator.\n",
+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00063-of-00324-processed.txt\n",
+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00216-of-00324-processed.txt\n",
+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00045-of-00324-processed.txt\n",
+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00012-of-00324-processed.txt\n",
+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00064-of-00324-processed.txt\n",
+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00135-of-00324-processed.txt\n",
+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00047-of-00324-processed.txt\n",
+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00112-of-00324-processed.txt\n",
+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00094-of-00324-processed.txt\n",
+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00268-of-00324-processed.txt\n",
+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00198-of-00324-processed.txt\n",
+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00200-of-00324-processed.txt\n",
+      "trainer_interface.cc(145) LOG(INFO) Loaded 1000000 lines\n",
+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00126-of-00324-processed.txt\n",
+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00099-of-00324-processed.txt\n",
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+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00011-of-00324-processed.txt\n",
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+      "trainer_interface.cc(145) LOG(INFO) Loaded 2000000 lines\n",
+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00256-of-00324-processed.txt\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trainer_interface.cc(389) LOG(INFO) Reserved chars are found. Skipped: In the navigation bar, at the previous trial Electronics installers and repairers $30,928 2,100 1 You up with your own insurance company based on our site: city-data From getting into a 15-year age gap, but doesn't feel like family By state law tort claim Best-quotes - call 623-581-0102 ▂▃▅▆ ▆▅▃▂ keywords: insurance auto insurance .\n",
+      "trainer_interface.cc(183) LOG(INFO) Loading corpus: /root/AI-Uncomplicated/core/models/GI_01/artifacts/dataset/cc_100_en_fr/processed_path/en/data-00319-of-00324-processed.txt\n",
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+      "trainer_interface.cc(124) LOG(WARNING) Consider using --input_sentence_size=<size> and --shuffle_input_sentence=true.\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <unused29>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <unused30>\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 223\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 524\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 818\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 819\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 820\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 821\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 822\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 823\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 824\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 825\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 826\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 827\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 828\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 829\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 830\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 831\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 832\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 833\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 834\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 835\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 836\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 837\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 838\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 839\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 840\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 841\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 842\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 843\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 844\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 848\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 850\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 853\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 860\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 861\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 862\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 863\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 864\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 865\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 866\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 867\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 868\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 869\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 870\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 871\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 872\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 873\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 874\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 875\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 876\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 877\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 878\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 879\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 880\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 881\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 882\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 883\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 884\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 885\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 886\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 887\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 888\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 889\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 890\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 891\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 892\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 893\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 894\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 895\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 896\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 897\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 898\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 899\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 900\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 904\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 905\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 906\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 907\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 908\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 909\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 910\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 911\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 912\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 913\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 914\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 915\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 916\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 917\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 918\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 919\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 920\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 921\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 922\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 923\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 924\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 925\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 926\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 927\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 928\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 929\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 930\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 931\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 932\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 933\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 934\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 935\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 936\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 937\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 938\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 939\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 940\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 941\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 942\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 943\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 944\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 945\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 946\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 947\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 948\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 949\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 950\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 951\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 952\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 953\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 954\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 955\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 956\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 957\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 958\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 959\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 960\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 961\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 962\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 963\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 964\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 965\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 966\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 967\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 968\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 969\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 970\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 972\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 973\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 974\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 975\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 976\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 977\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 978\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 979\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 980\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 982\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 983\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 986\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 987\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 988\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 989\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 991\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 992\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 995\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: 996\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x00>\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x0A>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x0B>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x0C>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x0D>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x0E>\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x15>\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x1A>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x1B>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x1C>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x1D>\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x2A>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x2B>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x2C>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x2D>\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x3A>\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x4A>\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x5B>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x5C>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x5D>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x5E>\n",
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+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x64>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x65>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x66>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x67>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x68>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x69>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x6A>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x6B>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x6C>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x6D>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x6E>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x6F>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x70>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x71>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x72>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x73>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x74>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x75>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x76>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x77>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x78>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x79>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x7A>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x7B>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x7C>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x7D>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x7E>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x7F>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x80>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x81>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x82>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x83>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x84>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x85>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x86>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x87>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x88>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x89>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x8A>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x8B>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x8C>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x8D>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x8E>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x8F>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x90>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x91>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x92>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x93>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x94>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x95>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x96>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x97>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x98>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x99>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x9A>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x9B>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x9C>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x9D>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x9E>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0x9F>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xA0>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xA1>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xA2>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xA3>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xA4>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xA5>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xA6>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xA7>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xA8>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xA9>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xAA>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xAB>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xAC>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xAD>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xAE>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xAF>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xB0>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xB1>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xB2>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xB3>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xB4>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xB5>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xB6>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xB7>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xB8>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xB9>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xBA>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xBB>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xBC>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xBD>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xBE>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xBF>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xC0>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xC1>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xC2>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xC3>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xC4>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xC5>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xC6>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xC7>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xC8>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xC9>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xCA>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xCB>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xCC>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xCD>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xCE>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xCF>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xD0>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xD1>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xD2>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xD3>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xD4>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xD5>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xD6>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xD7>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xD8>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xD9>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xDA>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xDB>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xDC>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xDD>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xDE>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xDF>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xE0>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xE1>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xE2>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xE3>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xE4>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xE5>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xE6>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xE7>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xE8>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xE9>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xEA>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xEB>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xEC>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xED>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xEE>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xEF>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xF0>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xF1>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xF2>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xF3>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xF4>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xF5>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xF6>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xF7>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xF8>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xF9>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xFA>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xFB>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xFC>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xFD>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xFE>\n",
+      "trainer_interface.cc(423) LOG(INFO) Adding meta_piece: <0xFF>\n",
+      "trainer_interface.cc(428) LOG(INFO) Normalizing sentences...\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(522) LOG(INFO) Found null character. The corpus must be encoded in utf-8.\n",
+      "trainer_interface.cc(537) LOG(INFO) all chars count=8819701464\n",
+      "trainer_interface.cc(548) LOG(INFO) Done: 100% characters are covered.\n",
+      "trainer_interface.cc(558) LOG(INFO) Alphabet size=7421\n",
+      "trainer_interface.cc(559) LOG(INFO) Final character coverage=1\n",
+      "trainer_interface.cc(591) LOG(INFO) Done! preprocessed 58451911 sentences.\n",
+      "trainer_interface.cc(597) LOG(INFO) Tokenizing input sentences with whitespace: 58451911\n",
+      "trainer_interface.cc(608) LOG(INFO) Done! 13969998\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=129510222 min_freq=1078\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=50892100 size=20 all=59563 active=5844 piece=at\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=28511563 size=40 all=62923 active=9204 piece=al\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18488018 size=60 all=66067 active=12348 piece=est\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13905122 size=80 all=70031 active=16312 piece=▁à\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10228149 size=100 all=73785 active=20066 piece=▁com\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=10206466 min_freq=62112\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9051695 size=120 all=77770 active=7587 piece=▁that\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7493725 size=140 all=81197 active=11014 piece=▁st\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6316324 size=160 all=85700 active=15517 piece=ne\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5578850 size=180 all=90050 active=19867 piece=ac\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4960107 size=200 all=94250 active=24067 piece=ite\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4957577 min_freq=66646\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4432208 size=220 all=98470 active=8615 piece=▁this\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3903661 size=240 all=103781 active=13926 piece=pl\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3457537 size=260 all=108804 active=18949 piece=▁av\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3070086 size=280 all=113734 active=23879 piece=ction\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2882899 size=300 all=119047 active=29192 piece=og\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2821964 min_freq=51720\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2648827 size=320 all=123881 active=10341 piece=ille\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2514047 size=340 all=129771 active=16231 piece=ough\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2284909 size=360 all=135355 active=21815 piece=▁int\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2178141 size=380 all=139711 active=26171 piece=ult\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2057426 size=400 all=145599 active=32059 piece=ord\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2050207 min_freq=40254\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=1930122 size=420 all=151559 active=12800 piece=form\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=1834794 size=440 all=156848 active=18089 piece=aut\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=1772958 size=460 all=162296 active=23537 piece=▁mar\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=1667877 size=480 all=166590 active=27831 piece=so\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=1550014 size=500 all=170451 active=31692 piece=cr\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=1549841 min_freq=32462\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=1409308 size=520 all=173789 active=11310 piece=ru\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=1342362 size=540 all=180152 active=17673 piece=▁other\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=1283042 size=560 all=184458 active=21979 piece=▁need\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=1222581 size=580 all=188599 active=26120 piece=▁bec\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=1163120 size=600 all=194366 active=31887 piece=▁trav\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=1160078 min_freq=27289\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=1105566 size=620 all=198857 active=14156 piece=aires\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=1077459 size=640 all=202235 active=17534 piece=▁déc\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=1052951 size=660 all=206459 active=21758 piece=▁did\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=1013602 size=680 all=210272 active=25571 piece=▁back\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=983300 size=700 all=214030 active=29329 piece=▁cas\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=983246 min_freq=23402\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=953154 size=720 all=216924 active=13489 piece=elf\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=924150 size=740 all=219718 active=16283 piece=util\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=881280 size=760 all=224308 active=20873 piece=▁even\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=859572 size=780 all=227962 active=24527 piece=▁met\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=825898 size=800 all=232502 active=29067 piece=▁mal\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=824773 min_freq=20611\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=802213 size=820 all=236322 active=15277 piece=tt\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=779247 size=840 all=240452 active=19407 piece=▁donc\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=759776 size=860 all=243854 active=22809 piece=▁che\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=744431 size=880 all=246849 active=25804 piece=erv\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=730524 size=900 all=251826 active=30781 piece=orte\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=728805 min_freq=18270\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=705136 size=920 all=256939 active=17493 piece=▁dur\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=678649 size=940 all=260657 active=21211 piece=we\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=658876 size=960 all=265394 active=25948 piece=ann\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=641390 size=980 all=268898 active=29452 piece=con\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=627381 size=1000 all=273448 active=34002 piece=ém\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=627192 min_freq=15872\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=611995 size=1020 all=277374 active=17087 piece=▁question\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=599651 size=1040 all=281913 active=21626 piece=thing\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=583217 size=1060 all=284568 active=24281 piece=erci\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=570941 size=1080 all=287125 active=26838 piece=▁They\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=561661 size=1100 all=290487 active=30200 piece=▁got\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=561531 min_freq=14577\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=554056 size=1120 all=294777 active=18752 piece=▁op\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=541348 size=1140 all=298602 active=22577 piece=▁sem\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=531134 size=1160 all=301689 active=25664 piece=aque\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=521057 size=1180 all=305773 active=29748 piece=▁ext\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=510461 size=1200 all=308418 active=32393 piece=ret\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=510050 min_freq=13200\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=498686 size=1220 all=312230 active=18685 piece=▁always\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=486140 size=1240 all=315663 active=22118 piece=▁profession\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=477634 size=1260 all=318064 active=24519 piece=▁dép\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=465366 size=1280 all=322130 active=28585 piece=nement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=456455 size=1300 all=325186 active=31641 piece=era\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=456416 min_freq=12073\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=450801 size=1320 all=327433 active=17982 piece=aî\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=440099 size=1340 all=331108 active=21657 piece=▁nouve\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=432166 size=1360 all=334688 active=25237 piece=ruct\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=424404 size=1380 all=339605 active=30154 piece=▁sour\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=413658 size=1400 all=344026 active=34575 piece=eau\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=413605 min_freq=11083\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=405597 size=1420 all=347519 active=20518 piece=▁belie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=396433 size=1440 all=350959 active=23958 piece=▁sk\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=387416 size=1460 all=353940 active=26939 piece=ON\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=381297 size=1480 all=357203 active=30202 piece=▁journ\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=374295 size=1500 all=359323 active=32322 piece=▁underst\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=373972 min_freq=10347\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=370829 size=1520 all=361121 active=19725 piece=til\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=364347 size=1540 all=365124 active=23728 piece=isme\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=355983 size=1560 all=366963 active=25567 piece=▁since\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=350924 size=1580 all=369577 active=28181 piece=▁mor\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=345430 size=1600 all=372229 active=30833 piece=▁suff\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=345366 min_freq=9762\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=340355 size=1620 all=375652 active=21979 piece=rain\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=334561 size=1640 all=378529 active=24856 piece=cript\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=327844 size=1660 all=382258 active=28585 piece=soci\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=324191 size=1680 all=387238 active=33565 piece=gl\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=318873 size=1700 all=390031 active=36358 piece=▁premier\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=318579 min_freq=9068\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=313810 size=1720 all=393445 active=22896 piece=tement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=309128 size=1740 all=394657 active=24108 piece=pression\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=304708 size=1760 all=396787 active=26238 piece=▁happen\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=300332 size=1780 all=400313 active=29764 piece=▁parce\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=297044 size=1800 all=403011 active=32462 piece=iez\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=296960 min_freq=8574\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=291866 size=1820 all=406249 active=22865 piece=ished\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=288514 size=1840 all=410952 active=27568 piece=amment\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=286039 size=1860 all=414999 active=31615 piece=ister\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=283401 size=1880 all=418579 active=35195 piece=fe\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=279919 size=1900 all=423067 active=39683 piece=anche\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=279817 min_freq=7915\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=276820 size=1920 all=425976 active=23882 piece=▁ren\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=273800 size=1940 all=428824 active=26730 piece=▁fac\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=271320 size=1960 all=432764 active=30670 piece=▁effet\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=267187 size=1980 all=434629 active=32535 piece=ograph\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=262938 size=2000 all=437723 active=35629 piece=▁difficult\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=262797 min_freq=7521\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=259527 size=2020 all=441401 active=25540 piece=▁vér\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=256608 size=2040 all=444421 active=28560 piece=▁points\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=254161 size=2060 all=446904 active=31043 piece=▁often\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=251438 size=2080 all=449984 active=34123 piece=▁ven\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=247821 size=2100 all=453847 active=37986 piece=ending\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=247259 min_freq=7081\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=244327 size=2120 all=456872 active=25536 piece=▁shall\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=242185 size=2140 all=459639 active=28303 piece=▁façon\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=239550 size=2160 all=462279 active=30943 piece=entr\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=236710 size=2180 all=465229 active=33893 piece=▁exact\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=234747 size=2200 all=466396 active=35060 piece=quer\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=234406 min_freq=6771\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=232511 size=2220 all=469800 active=26390 piece=▁terr\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=228949 size=2240 all=472669 active=29259 piece=alf\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=226628 size=2260 all=474675 active=31265 piece=▁women\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=224110 size=2280 all=479394 active=35984 piece=▁exist\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=221500 size=2300 all=481329 active=37919 piece=ium\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=221485 min_freq=6436\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=218321 size=2320 all=484507 active=26798 piece=ump\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=215717 size=2340 all=487522 active=29813 piece=▁semaine\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=213515 size=2360 all=489272 active=31563 piece=ones\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=211469 size=2380 all=492790 active=35081 piece=usement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=209473 size=2400 all=495027 active=37318 piece=jout\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=209377 min_freq=6112\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=208226 size=2420 all=496647 active=26294 piece=▁benef\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=206392 size=2440 all=499777 active=29424 piece=▁princip\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=204901 size=2460 all=503020 active=32667 piece=▁X\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=202857 size=2480 all=505405 active=35052 piece=▁students\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=200509 size=2500 all=508553 active=38200 piece=âce\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=200277 min_freq=5836\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=198335 size=2520 all=509870 active=26722 piece=▁notamment\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=196040 size=2540 all=512728 active=29580 piece=▁cadre\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=193658 size=2560 all=516132 active=32984 piece=▁mid\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=192141 size=2580 all=517883 active=34735 piece=oogle\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=190165 size=2600 all=520164 active=37016 piece=▁lect\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=190068 min_freq=5627\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=187928 size=2620 all=523292 active=29087 piece=▁Que\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=186571 size=2640 all=527573 active=33368 piece=ront\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=184875 size=2660 all=529461 active=35256 piece=▁mer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=183332 size=2680 all=532752 active=38547 piece=▁press\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=180994 size=2700 all=534084 active=39879 piece=▁envie\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=180786 min_freq=5339\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=179438 size=2720 all=536828 active=29444 piece=▁eyes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=177398 size=2740 all=540466 active=33082 piece=▁Me\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=175519 size=2760 all=545714 active=38330 piece=▁éch\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=174399 size=2780 all=548000 active=40616 piece=▁univers\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=173064 size=2800 all=550186 active=42802 piece=▁six\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=172997 min_freq=5053\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=171270 size=2820 all=552108 active=29383 piece=iding\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=169751 size=2840 all=554959 active=32234 piece=▁music\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=168404 size=2860 all=556851 active=34126 piece=▁distr\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=166856 size=2880 all=560449 active=37724 piece=though\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=165579 size=2900 all=562950 active=40225 piece=▁wasn\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=165558 min_freq=4864\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=164676 size=2920 all=565406 active=30592 piece=AS\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=163825 size=2940 all=569436 active=34622 piece=▁!!!\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=162292 size=2960 all=572342 active=37528 piece=▁rent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=160619 size=2980 all=574551 active=39737 piece=▁glob\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=159337 size=3000 all=576265 active=41451 piece=▁Col\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=159321 min_freq=4673\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=157466 size=3020 all=578821 active=31094 piece=▁Acc\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=156377 size=3040 all=580546 active=32819 piece=▁price\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=155161 size=3060 all=583174 active=35447 piece=qué\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=154020 size=3080 all=584765 active=37038 piece=▁vir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=152839 size=3100 all=586815 active=39088 piece=éb\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=152838 min_freq=4529\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=151623 size=3120 all=589735 active=31812 piece=▁!!\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=150368 size=3140 all=591545 active=33622 piece=▁Est\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=149678 size=3160 all=593671 active=35748 piece=▁ouv\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=148245 size=3180 all=595615 active=37692 piece=▁via\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=146717 size=3200 all=598030 active=40107 piece=itting\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=146635 min_freq=4393\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=145256 size=3220 all=600244 active=31956 piece=go\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=144071 size=3240 all=602944 active=34656 piece=▁scient\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=142650 size=3260 all=606232 active=37944 piece=oyer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=141045 size=3280 all=608328 active=40040 piece=onnes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=139975 size=3300 all=612272 active=43984 piece=enf\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=139863 min_freq=4213\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=139121 size=3320 all=614304 active=32170 piece=▁results\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=138160 size=3340 all=616254 active=34120 piece=▁study\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=137110 size=3360 all=618296 active=36162 piece=uellement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=136074 size=3380 all=619579 active=37445 piece=▁laisse\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=134941 size=3400 all=621451 active=39317 piece=▁tool\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=134930 min_freq=4114\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=133949 size=3420 all=623147 active=32724 piece=▁mother\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=132963 size=3440 all=626952 active=36529 piece=▁mars\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=131673 size=3460 all=628645 active=38222 piece=▁industry\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=130825 size=3480 all=629797 active=39374 piece=ural\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=129526 size=3500 all=632364 active=41941 piece=▁general\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=129484 min_freq=3981\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=128705 size=3520 all=634359 active=33568 piece=▁box\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=127924 size=3540 all=637132 active=36341 piece=▁haven\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=126384 size=3560 all=638994 active=38203 piece=▁Fe\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=125581 size=3580 all=641559 active=40768 piece=▁cuis\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=124695 size=3600 all=643072 active=42281 piece=▁arrive\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=124693 min_freq=3870\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=124046 size=3620 all=644724 active=33792 piece=éric\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=123090 size=3640 all=646985 active=36053 piece=orry\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=121962 size=3660 all=648346 active=37414 piece=▁grands\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=121084 size=3680 all=649923 active=38991 piece=▁fair\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=120241 size=3700 all=651738 active=40806 piece=▁comfort\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=120229 min_freq=3777\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=119568 size=3720 all=653882 active=34698 piece=▁listen\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=118574 size=3740 all=655526 active=36342 piece=yl\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=118220 size=3760 all=658162 active=38978 piece=▁sleep\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=117329 size=3780 all=659931 active=40747 piece=▁cart\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=116495 size=3800 all=661866 active=42682 piece=oid\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=116472 min_freq=3665\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=115941 size=3820 all=664511 active=35359 piece=▁paper\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=115233 size=3840 all=667287 active=38135 piece=rée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=114563 size=3860 all=669523 active=40371 piece=▁according\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=113713 size=3880 all=671593 active=42441 piece=▁host\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=112643 size=3900 all=674226 active=45074 piece=▁adult\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=112502 min_freq=3562\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=111658 size=3920 all=675751 active=35185 piece=▁sometimes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=110859 size=3940 all=677616 active=37050 piece=onst\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=110109 size=3960 all=678591 active=38025 piece=▁events\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=109424 size=3980 all=679988 active=39422 piece=co\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=108677 size=4000 all=683017 active=42451 piece=▁heav\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=108653 min_freq=3491\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=108160 size=4020 all=684828 active=35940 piece=▁din\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=107485 size=4040 all=688607 active=39719 piece=▁drive\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=106835 size=4060 all=691608 active=42720 piece=▁hit\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=106202 size=4080 all=693627 active=44739 piece=▁apply\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=105477 size=4100 all=695410 active=46522 piece=▁hair\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=105410 min_freq=3390\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=104713 size=4120 all=696417 active=35721 piece=issements\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=104059 size=4140 all=697765 active=37069 piece=▁private\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=103485 size=4160 all=700048 active=39352 piece=▁prest\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=102733 size=4180 all=701814 active=41118 piece=▁pouvait\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=102182 size=4200 all=705480 active=44784 piece=dom\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=102134 min_freq=3299\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=101462 size=4220 all=708613 active=37961 piece=▁weekend\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=100930 size=4240 all=710122 active=39470 piece=▁luck\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=100268 size=4260 all=713873 active=43221 piece=OS\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=99759 size=4280 all=715856 active=45204 piece=▁Bar\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=99357 size=4300 all=718035 active=47383 piece=▁commission\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=99320 min_freq=3199\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=98660 size=4320 all=718658 active=36506 piece=▁sociale\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=98340 size=4340 all=719778 active=37626 piece=▁truly\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=97769 size=4360 all=722047 active=39895 piece=urr\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=96885 size=4380 all=726143 active=43991 piece=▁Sam\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=96372 size=4400 all=728584 active=46432 piece=▁First\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=96163 min_freq=3106\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=95459 size=4420 all=730873 active=38652 piece=ana\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=94744 size=4440 all=732511 active=40290 piece=▁items\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=94266 size=4460 all=733853 active=41632 piece=▁stru\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=93778 size=4480 all=736674 active=44453 piece=▁request\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=93192 size=4500 all=737864 active=45643 piece=▁Bonjour\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=93190 min_freq=3035\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=92570 size=4520 all=739668 active=38670 piece=▁chiff\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=91894 size=4540 all=741299 active=40301 piece=▁brand\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=91378 size=4560 all=743960 active=42962 piece=isode\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=90858 size=4580 all=745394 active=44396 piece=▁blanc\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=90266 size=4600 all=746788 active=45790 piece=▁dollars\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=90214 min_freq=2977\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=89688 size=4620 all=748648 active=39188 piece=état\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=88807 size=4640 all=751319 active=41859 piece=▁manger\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=88351 size=4660 all=752256 active=42796 piece=ara\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=87814 size=4680 all=755162 active=45702 piece=▁Sy\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=87419 size=4700 all=756917 active=47457 piece=comm\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=87400 min_freq=2901\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=86955 size=4720 all=758837 active=39204 piece=▁instant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=86595 size=4740 all=760707 active=41074 piece=oms\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=85932 size=4760 all=763526 active=43893 piece=▁doubt\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=85438 size=4780 all=765208 active=45575 piece=▁provides\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=84990 size=4800 all=766299 active=46666 piece=▁celebr\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=84981 min_freq=2838\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=84561 size=4820 all=768890 active=40881 piece=rim\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=83907 size=4840 all=771686 active=43677 piece=▁ap\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=83335 size=4860 all=773407 active=45398 piece=▁understanding\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=83025 size=4880 all=774852 active=46843 piece=usse\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=82569 size=4900 all=776181 active=48172 piece=ras\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=82565 min_freq=2767\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=82115 size=4920 all=778949 active=41013 piece=▁restaurant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=81603 size=4940 all=780094 active=42158 piece=▁premiers\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=81275 size=4960 all=781265 active=43329 piece=▁cru\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=80866 size=4980 all=782511 active=44575 piece=adem\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=80470 size=5000 all=785083 active=47147 piece=▁Tod\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=80470 min_freq=2713\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=80006 size=5020 all=788863 active=42966 piece=▁breat\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=79514 size=5040 all=791150 active=45253 piece=▁né\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=78973 size=5060 all=792445 active=46548 piece=osing\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=78558 size=5080 all=794451 active=48554 piece=▁somme\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=78136 size=5100 all=798241 active=52344 piece=intérieur\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=78075 min_freq=2629\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=77716 size=5120 all=801279 active=42935 piece=atory\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=77348 size=5140 all=803070 active=44726 piece=▁responsable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=76900 size=5160 all=804272 active=45928 piece=▁maladie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=76643 size=5180 all=805526 active=47182 piece=ieuse\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=76340 size=5200 all=807271 active=48927 piece=▁load\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=76321 min_freq=2580\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=75998 size=5220 all=808622 active=41668 piece=ufact\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=75638 size=5240 all=809436 active=42482 piece=UE\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=75237 size=5260 all=810892 active=43938 piece=▁helped\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=74802 size=5280 all=813449 active=46495 piece=▁Brit\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=74521 size=5300 all=814589 active=47635 piece=!!!!\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=74514 min_freq=2534\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=74065 size=5320 all=817095 active=43094 piece=ctober\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=73776 size=5340 all=818832 active=44831 piece=▁Très\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=73451 size=5360 all=820160 active=46159 piece=▁parag\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=73097 size=5380 all=822395 active=48394 piece=▁applications\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=72722 size=5400 all=824220 active=50219 piece=▁essayer\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=72698 min_freq=2485\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=72319 size=5420 all=825633 active=42613 piece=▁professionnelle\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=71865 size=5440 all=827180 active=44160 piece=▁statement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=71504 size=5460 all=828864 active=45844 piece=orer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=71154 size=5480 all=831381 active=48361 piece=léch\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=70863 size=5500 all=832178 active=49158 piece=▁enjoyed\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=70862 min_freq=2440\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=70409 size=5520 all=834545 active=43974 piece=rows\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=70117 size=5540 all=836458 active=45887 piece=▁aime\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=69667 size=5560 all=838361 active=47790 piece=▁network\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=69438 size=5580 all=839280 active=48709 piece=▁creating\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=69199 size=5600 all=840037 active=49466 piece=▁interd\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=69189 min_freq=2397\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=68909 size=5620 all=841761 active=43685 piece=▁mental\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=68481 size=5640 all=843498 active=45422 piece=▁corpor\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=68001 size=5660 all=846571 active=48495 piece=▁décr\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=67644 size=5680 all=849358 active=51282 piece=▁states\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=67310 size=5700 all=851655 active=53579 piece=ardi\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=67301 min_freq=2333\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=66995 size=5720 all=852310 active=43005 piece=▁apport\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=66613 size=5740 all=853887 active=44582 piece=▁paiement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=66306 size=5760 all=855153 active=45848 piece=▁banc\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=66006 size=5780 all=856347 active=47042 piece=oved\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=65775 size=5800 all=857223 active=47918 piece=▁slight\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=65773 min_freq=2305\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=65502 size=5820 all=858641 active=44270 piece=▁LE\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=65289 size=5840 all=859821 active=45450 piece=you\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=64976 size=5860 all=862349 active=47978 piece=▁scène\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=64676 size=5880 all=863479 active=49108 piece=association\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=64333 size=5900 all=864985 active=50614 piece=uie\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=64328 min_freq=2267\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=64017 size=5920 all=866644 active=44829 piece=▁apprendre\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=63710 size=5940 all=867749 active=45934 piece=▁military\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=63400 size=5960 all=870978 active=49163 piece=▁attrib\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=63127 size=5980 all=873090 active=51275 piece=▁followed\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=62720 size=6000 all=875964 active=54149 piece=▁leading\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=62708 min_freq=2214\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=62358 size=6020 all=877283 active=45111 piece=▁generally\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=61991 size=6040 all=879205 active=47033 piece=▁dirige\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=61752 size=6060 all=880948 active=48776 piece=▁Code\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=61524 size=6080 all=882878 active=50706 piece=▁aimé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=61298 size=6100 all=883796 active=51624 piece=▁Gra\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=61294 min_freq=2179\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=61033 size=6120 all=885641 active=45800 piece=▁États\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=60670 size=6140 all=886581 active=46740 piece=▁sympa\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=60413 size=6160 all=888778 active=48937 piece=▁tests\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=60005 size=6180 all=890508 active=50667 piece=essions\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=59694 size=6200 all=891674 active=51833 piece=▁crime\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=59653 min_freq=2147\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=59401 size=6220 all=893804 active=46705 piece=▁stick\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=59218 size=6240 all=895422 active=48323 piece=▁workers\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=58938 size=6260 all=896245 active=49146 piece=▁leader\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=58755 size=6280 all=899610 active=52511 piece=▁manner\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=58542 size=6300 all=901610 active=54511 piece=sociation\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=58537 min_freq=2106\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=58251 size=6320 all=902931 active=46376 piece=▁capit\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=58008 size=6340 all=905492 active=48937 piece=▁tomber\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=57715 size=6360 all=906332 active=49777 piece=▁PS\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=57525 size=6380 all=908132 active=51577 piece=ATION\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=57318 size=6400 all=910683 active=54128 piece=amélior\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=57290 min_freq=2069\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=56916 size=6420 all=913090 active=47904 piece=▁reprodu\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=56692 size=6440 all=915801 active=50615 piece=▁cloth\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=56412 size=6460 all=918178 active=52992 piece=▁Journ\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=56184 size=6480 all=919325 active=54139 piece=▁indispens\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=55930 size=6500 all=921481 active=56295 piece=▁IT\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=55928 min_freq=2024\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=55714 size=6520 all=922336 active=46820 piece=▁citiz\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=55486 size=6540 all=923318 active=47802 piece=▁Republic\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=55255 size=6560 all=924800 active=49284 piece=▁voulait\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=55058 size=6580 all=927138 active=51622 piece=▁permettra\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=54799 size=6600 all=929191 active=53675 piece=▁possibly\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=54786 min_freq=1994\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=54602 size=6620 all=931149 active=48414 piece=▁apporter\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=54443 size=6640 all=934006 active=51271 piece=onstr\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=54183 size=6660 all=935924 active=53189 piece=arde\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=53991 size=6680 all=938095 active=55360 piece=▁winter\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=53713 size=6700 all=939419 active=56684 piece=igration\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=53702 min_freq=1952\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=53553 size=6720 all=941672 active=49161 piece=▁federal\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=53331 size=6740 all=943218 active=50707 piece=▁sea\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=53045 size=6760 all=945327 active=52816 piece=▁fid\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=52840 size=6780 all=946642 active=54131 piece=▁précise\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=52614 size=6800 all=947144 active=54633 piece=▁hall\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=52601 min_freq=1920\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=52445 size=6820 all=948287 active=48447 piece=▁measure\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=52282 size=6840 all=949325 active=49485 piece=▁suivante\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=52062 size=6860 all=950152 active=50312 piece=▁handle\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=51716 size=6880 all=952226 active=52386 piece=▁instance\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=51528 size=6900 all=953217 active=53377 piece=▁uses\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=51524 min_freq=1895\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=51349 size=6920 all=955845 active=50280 piece=▁exciting\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=51148 size=6940 all=957394 active=51829 piece=▁guarante\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=50978 size=6960 all=959259 active=53694 piece=noon\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=50782 size=6980 all=961028 active=55463 piece=▁Franc\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=50579 size=7000 all=963660 active=58095 piece=▁ajouter\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=50554 min_freq=1858\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=50333 size=7020 all=965401 active=49913 piece=▁Flor\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=50131 size=7040 all=966470 active=50982 piece=▁potenti\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=49885 size=7060 all=967507 active=52019 piece=▁policies\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=49668 size=7080 all=968786 active=53298 piece=▁freed\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=49530 size=7100 all=970909 active=55421 piece=▁Entre\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=49530 min_freq=1829\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=49331 size=7120 all=972493 active=50074 piece=îte\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=49179 size=7140 all=973974 active=51555 piece=oule\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=48974 size=7160 all=976679 active=54260 piece=▁entend\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=48796 size=7180 all=978101 active=55682 piece=▁privil\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=48581 size=7200 all=980341 active=57922 piece=▁prat\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=48571 min_freq=1796\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=48396 size=7220 all=981757 active=50388 piece=▁hundred\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=48157 size=7240 all=983057 active=51688 piece=▁domicile\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=47992 size=7260 all=985132 active=53763 piece=▁usage\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=47737 size=7280 all=986637 active=55268 piece=▁propriété\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=47535 size=7300 all=987428 active=56059 piece=▁réduction\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=47519 min_freq=1769\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=47309 size=7320 all=988580 active=50518 piece=▁starts\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=47094 size=7340 all=990116 active=52054 piece=▁broken\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=46934 size=7360 all=992374 active=54312 piece=▁neighbor\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=46745 size=7380 all=993537 active=55475 piece=▁eventually\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=46574 size=7400 all=995461 active=57399 piece=▁boulot\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=46574 min_freq=1741\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=46402 size=7420 all=997258 active=51558 piece=▁advent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=46214 size=7440 all=998801 active=53101 piece=▁réception\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=46032 size=7460 all=1001862 active=56162 piece=ipped\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=45808 size=7480 all=1002824 active=57124 piece=▁dispositif\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=45653 size=7500 all=1003676 active=57976 piece=▁battle\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=45645 min_freq=1715\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=45408 size=7520 all=1005570 active=52043 piece=My\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=45262 size=7540 all=1009494 active=55967 piece=ommod\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=45127 size=7560 all=1011416 active=57889 piece=▁motor\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=44990 size=7580 all=1013597 active=60070 piece=▁East\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=44838 size=7600 all=1014400 active=60873 piece=izon\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=44821 min_freq=1678\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=44638 size=7620 all=1016136 active=52305 piece=pet\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=44452 size=7640 all=1018588 active=54757 piece=▁obst\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=44194 size=7660 all=1020222 active=56391 piece=▁listed\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=44007 size=7680 all=1021398 active=57567 piece=▁profond\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=43854 size=7700 all=1022319 active=58488 piece=▁flat\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=43798 min_freq=1654\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=43660 size=7720 all=1024201 active=52937 piece=▁leadership\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=43535 size=7740 all=1025962 active=54698 piece=▁partis\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=43371 size=7760 all=1028048 active=56784 piece=▁Mark\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=43194 size=7780 all=1030000 active=58736 piece=▁ouverte\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=43065 size=7800 all=1033096 active=61832 piece=andis\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=43065 min_freq=1623\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=42913 size=7820 all=1034748 active=53205 piece=▁appeared\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=42710 size=7840 all=1036629 active=55086 piece=▁panne\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=42563 size=7860 all=1038495 active=56952 piece=▁Whether\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=42418 size=7880 all=1039741 active=58198 piece=▁parad\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=42279 size=7900 all=1040429 active=58886 piece=▁compon\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=42264 min_freq=1599\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=42158 size=7920 all=1041630 active=53210 piece=▁Instead\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=41923 size=7940 all=1042838 active=54418 piece=▁Again\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=41728 size=7960 all=1043626 active=55206 piece=▁indispensable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=41570 size=7980 all=1045178 active=56758 piece=lé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=41500 size=8000 all=1046494 active=58074 piece=▁Section\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=41497 min_freq=1583\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=41369 size=8020 all=1047311 active=53128 piece=▁fi\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=41229 size=8040 all=1048867 active=54684 piece=▁ge\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=41109 size=8060 all=1050415 active=56232 piece=roits\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=40982 size=8080 all=1052167 active=57984 piece=▁entour\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=40870 size=8100 all=1054275 active=60092 piece=▁chasse\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=40866 min_freq=1561\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=40748 size=8120 all=1055235 active=53664 piece=▁hasn\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=40542 size=8140 all=1056086 active=54515 piece=▁perfectly\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=40407 size=8160 all=1057814 active=56243 piece=▁feelings\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=40305 size=8180 all=1058945 active=57374 piece=▁recommended\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=40173 size=8200 all=1060094 active=58523 piece=ups\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=40168 min_freq=1542\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=40021 size=8220 all=1061567 active=54165 piece=▁signs\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=39845 size=8240 all=1063525 active=56123 piece=▁writer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=39674 size=8260 all=1064681 active=57279 piece=▁Person\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=39517 size=8280 all=1065919 active=58517 piece=▁simples\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=39386 size=8300 all=1067450 active=60048 piece=▁contempor\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=39380 min_freq=1522\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=39239 size=8320 all=1068731 active=54625 piece=▁envers\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=39137 size=8340 all=1070093 active=55987 piece=▁lesquelles\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=38992 size=8360 all=1071899 active=57793 piece=▁generation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=38823 size=8380 all=1073344 active=59238 piece=▁Fait\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=38687 size=8400 all=1074348 active=60242 piece=▁storage\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=38670 min_freq=1501\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=38512 size=8420 all=1075868 active=55229 piece=Ex\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=38317 size=8440 all=1078781 active=58142 piece=▁milliers\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=38216 size=8460 all=1079197 active=58558 piece=affaire\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=38109 size=8480 all=1081341 active=60702 piece=umeur\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=37984 size=8500 all=1083758 active=63119 piece=istry\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=37983 min_freq=1477\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=37893 size=8520 all=1086196 active=56548 piece=assin\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=37769 size=8540 all=1086894 active=57246 piece=▁incredible\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=37653 size=8560 all=1089591 active=59943 piece=othing\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=37500 size=8580 all=1091430 active=61782 piece=▁Yet\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=37411 size=8600 all=1093321 active=63673 piece=▁bonus\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=37411 min_freq=1453\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=37241 size=8620 all=1094089 active=55426 piece=▁famous\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=37112 size=8640 all=1095357 active=56694 piece=▁edge\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=36991 size=8660 all=1096421 active=57758 piece=aques\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=36850 size=8680 all=1098936 active=60273 piece=▁Ger\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=36759 size=8700 all=1099680 active=61017 piece=▁keeps\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=36758 min_freq=1436\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=36642 size=8720 all=1100359 active=55654 piece=▁sensible\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=36468 size=8740 all=1101774 active=57069 piece=▁auront\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=36372 size=8760 all=1103630 active=58925 piece=▁library\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=36271 size=8780 all=1105620 active=60915 piece=ja\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=36161 size=8800 all=1108140 active=63435 piece=▁principaux\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=36152 min_freq=1414\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=36051 size=8820 all=1109413 active=56680 piece=▁hôtel\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=35939 size=8840 all=1110563 active=57830 piece=▁authent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=35833 size=8860 all=1112129 active=59396 piece=uble\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=35730 size=8880 all=1113028 active=60295 piece=illing\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=35636 size=8900 all=1114458 active=61725 piece=environ\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=35630 min_freq=1399\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=35536 size=8920 all=1116081 active=57294 piece=▁writers\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=35435 size=8940 all=1117223 active=58436 piece=▁dure\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=35307 size=8960 all=1118940 active=60153 piece=▁Disc\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=35196 size=8980 all=1120438 active=61651 piece=▁Terre\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=35029 size=9000 all=1121627 active=62840 piece=SA\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=35009 min_freq=1380\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=34965 size=9020 all=1122714 active=56842 piece=imately\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=34891 size=9040 all=1123866 active=57994 piece=▁Roy\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=34757 size=9060 all=1125556 active=59684 piece=▁Cond\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=34632 size=9080 all=1126343 active=60471 piece=▁presentation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=34515 size=9100 all=1127776 active=61904 piece=▁Client\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=34512 min_freq=1366\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=34362 size=9120 all=1128787 active=57373 piece=▁réussir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=34283 size=9140 all=1130250 active=58836 piece=▁calm\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=34160 size=9160 all=1131494 active=60080 piece=▁AR\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=34087 size=9180 all=1133011 active=61597 piece=▁pouvais\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=33942 size=9200 all=1134393 active=62979 piece=▁effectuer\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=33938 min_freq=1352\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=33814 size=9220 all=1135869 active=58187 piece=▁profile\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=33741 size=9240 all=1136806 active=59124 piece=away\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=33637 size=9260 all=1137712 active=60030 piece=▁monst\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=33526 size=9280 all=1138922 active=61240 piece=▁ordre\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=33416 size=9300 all=1140607 active=62925 piece=▁pâte\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=33398 min_freq=1337\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=33308 size=9320 all=1141691 active=58107 piece=itz\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=33224 size=9340 all=1143633 active=60049 piece=▁médecins\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=33139 size=9360 all=1146731 active=63147 piece=▁coûte\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=33015 size=9380 all=1148478 active=64894 piece=▁toucher\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=32932 size=9400 all=1149513 active=65929 piece=lands\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=32931 min_freq=1315\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=32837 size=9420 all=1151274 active=58927 piece=▁noms\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=32744 size=9440 all=1152811 active=60464 piece=▁nine\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=32670 size=9460 all=1154249 active=61902 piece=▁sommeil\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=32559 size=9480 all=1157246 active=64899 piece=▁inspired\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=32446 size=9500 all=1157986 active=65639 piece=▁acknow\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=32445 min_freq=1297\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=32359 size=9520 all=1159283 active=59187 piece=▁mourir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=32274 size=9540 all=1160309 active=60213 piece=▁accéder\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=32175 size=9560 all=1161840 active=61744 piece=▁nations\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=32010 size=9580 all=1162385 active=62289 piece=▁supporting\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=31905 size=9600 all=1164285 active=64189 piece=naires\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=31902 min_freq=1283\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=31819 size=9620 all=1165123 active=58996 piece='.\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=31743 size=9640 all=1166086 active=59959 piece=▁auraient\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=31658 size=9660 all=1167640 active=61513 piece=▁prepar\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=31583 size=9680 all=1169727 active=63600 piece=impos\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=31460 size=9700 all=1170984 active=64857 piece=▁III\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=31459 min_freq=1269\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=31394 size=9720 all=1171538 active=59083 piece=▁quels\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=31302 size=9740 all=1172440 active=59985 piece=▁neighborhood\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=31195 size=9760 all=1174187 active=61732 piece=▁énorme\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=31113 size=9780 all=1174899 active=62444 piece=▁destruction\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=31045 size=9800 all=1176148 active=63693 piece=▁Ess\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=31041 min_freq=1257\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=30974 size=9820 all=1177579 active=60080 piece=▁accurate\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=30880 size=9840 all=1179225 active=61726 piece=▁everywhere\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=30806 size=9860 all=1179798 active=62299 piece=ubles\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=30716 size=9880 all=1180310 active=62811 piece=▁Phili\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=30637 size=9900 all=1181307 active=63808 piece=▁payments\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=30635 min_freq=1246\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=30547 size=9920 all=1182284 active=60037 piece=▁couns\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=30429 size=9940 all=1183498 active=61251 piece=▁dict\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=30327 size=9960 all=1185334 active=63087 piece=▁concerning\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=30238 size=9980 all=1186368 active=64121 piece=▁périod\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=30118 size=10000 all=1187883 active=65636 piece=enquête\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=30118 min_freq=1233\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=30029 size=10020 all=1189142 active=60639 piece=▁bullet\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=29931 size=10040 all=1190845 active=62342 piece=ERS\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=29851 size=10060 all=1191897 active=63394 piece=▁interv\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=29774 size=10080 all=1193021 active=64518 piece=impôt\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=29690 size=10100 all=1194419 active=65916 piece=▁everyday\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=29685 min_freq=1220\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=29612 size=10120 all=1197105 active=62398 piece=œuvre\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=29528 size=10140 all=1199508 active=64801 piece=occuper\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=29462 size=10160 all=1201408 active=66701 piece=▁bru\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=29385 size=10180 all=1203252 active=68545 piece=▁Down\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=29341 size=10200 all=1204729 active=70022 piece=▁dollar\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=29339 min_freq=1200\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=29270 size=10220 all=1205891 active=61374 piece=▁fundament\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=29164 size=10240 all=1207592 active=63075 piece=▁Everything\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=29089 size=10260 all=1209687 active=65170 piece=▁entit\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=29020 size=10280 all=1210666 active=66149 piece=▁renforcer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=28903 size=10300 all=1211722 active=67205 piece=écrire\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=28903 min_freq=1185\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=28822 size=10320 all=1212235 active=61082 piece=▁odd\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=28741 size=10340 all=1215108 active=63955 piece=▁;-)\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=28662 size=10360 all=1216549 active=65396 piece=azz\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=28601 size=10380 all=1218019 active=66866 piece=▁énergétique\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=28521 size=10400 all=1219803 active=68650 piece=▁affir\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=28520 min_freq=1169\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=28436 size=10420 all=1221963 active=63144 piece=▁argent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=28376 size=10440 all=1222675 active=63856 piece=▁crucial\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=28275 size=10460 all=1223485 active=64666 piece=▁processing\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=28207 size=10480 all=1226240 active=67421 piece=▁rental\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=28125 size=10500 all=1227139 active=68320 piece=▁MO\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=28123 min_freq=1156\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=28060 size=10520 all=1228934 active=63057 piece=mans\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=28009 size=10540 all=1230934 active=65057 piece=▁decent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=27906 size=10560 all=1232276 active=66399 piece=ignon\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=27808 size=10580 all=1234019 active=68142 piece=▁interact\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=27745 size=10600 all=1235447 active=69570 piece=ographe\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=27743 min_freq=1140\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=27649 size=10620 all=1237309 active=63506 piece=itage\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=27572 size=10640 all=1237989 active=64186 piece=▁différente\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=27512 size=10660 all=1239190 active=65387 piece=▁souhaiter\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=27453 size=10680 all=1240709 active=66906 piece=▁severe\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=27398 size=10700 all=1241587 active=67784 piece=▁métiers\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=27395 min_freq=1128\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=27333 size=10720 all=1243115 active=63605 piece=▁Water\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=27271 size=10740 all=1243712 active=64202 piece=▁logements\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=27192 size=10760 all=1244621 active=65111 piece=aped\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=27126 size=10780 all=1246651 active=67141 piece=▁electronic\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=27015 size=10800 all=1248845 active=69335 piece=ems\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=27013 min_freq=1114\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=26951 size=10820 all=1250858 active=64221 piece=ements\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=26869 size=10840 all=1252344 active=65707 piece=▁émotions\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=26796 size=10860 all=1252794 active=66157 piece=▁désolé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=26743 size=10880 all=1253758 active=67121 piece=▁smiled\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=26655 size=10900 all=1254805 active=68168 piece=▁prenez\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=26653 min_freq=1104\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=26574 size=10920 all=1255680 active=63614 piece=NE\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=26463 size=10940 all=1257332 active=65266 piece=▁innovation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=26369 size=10960 all=1258745 active=66679 piece=▁insight\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=26307 size=10980 all=1259773 active=67707 piece=TR\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=26242 size=11000 all=1261643 active=69577 piece=▁meals\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=26239 min_freq=1091\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=26164 size=11020 all=1263895 active=65334 piece=▁robot\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=26086 size=11040 all=1266175 active=67614 piece=▁referr\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=26030 size=11060 all=1267113 active=68552 piece=▁retrouvé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=25942 size=11080 all=1268288 active=69727 piece=▁?\"\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=25878 size=11100 all=1269489 active=70928 piece=▁ital\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=25876 min_freq=1076\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=25811 size=11120 all=1270882 active=64838 piece=crit\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=25741 size=11140 all=1272676 active=66632 piece=atoes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=25670 size=11160 all=1273670 active=67626 piece=▁SA\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=25598 size=11180 all=1275087 active=69043 piece=▁Ry\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=25531 size=11200 all=1276120 active=70076 piece=▁Découvrez\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=25528 min_freq=1065\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=25456 size=11220 all=1277001 active=64687 piece=▁prévoir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=25382 size=11240 all=1277691 active=65377 piece=▁cuisson\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=25308 size=11260 all=1279452 active=67138 piece=▁connaissent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=25230 size=11280 all=1280230 active=67916 piece=▁accum\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=25180 size=11300 all=1281152 active=68838 piece=▁affili\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=25179 min_freq=1056\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=25095 size=11320 all=1282820 active=65710 piece=▁interrog\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=25028 size=11340 all=1283428 active=66318 piece=▁grace\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=24933 size=11360 all=1284429 active=67319 piece=▁finger\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=24850 size=11380 all=1285811 active=68701 piece=▁vrais\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=24778 size=11400 all=1288311 active=71201 piece=▁pit\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=24778 min_freq=1043\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=24706 size=11420 all=1289284 active=65270 piece=▁wishes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=24646 size=11440 all=1290297 active=66283 piece=▁dépôt\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=24579 size=11460 all=1291710 active=67696 piece=▁entretien\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=24507 size=11480 all=1292324 active=68310 piece=▁Jusqu\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=24470 size=11500 all=1292864 active=68850 piece=▁directions\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=24468 min_freq=1035\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=24417 size=11520 all=1294383 active=66161 piece=GE\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=24357 size=11540 all=1295898 active=67676 piece=BS\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=24299 size=11560 all=1298481 active=70259 piece=▁Daniel\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=24231 size=11580 all=1298967 active=70745 piece=▁fraî\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=24186 size=11600 all=1300081 active=71859 piece=▁astu\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=24183 min_freq=1023\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=24119 size=11620 all=1301141 active=66061 piece=irts\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=24049 size=11640 all=1302468 active=67388 piece=▁airport\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23994 size=11660 all=1303947 active=68867 piece=▁bibliothèque\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23947 size=11680 all=1305460 active=70380 piece=▁sanction\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23890 size=11700 all=1306494 active=71414 piece=▁transmettre\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=23889 min_freq=1014\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23821 size=11720 all=1308066 active=66893 piece=▁bisous\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23742 size=11740 all=1309582 active=68409 piece=▁frères\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23684 size=11760 all=1310508 active=69335 piece=▁connaissez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23635 size=11780 all=1311320 active=70147 piece=▁chaussures\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23589 size=11800 all=1312690 active=71517 piece=▁Cher\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=23588 min_freq=1004\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23529 size=11820 all=1314455 active=67195 piece=▁Michel\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23459 size=11840 all=1316683 active=69423 piece=aste\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23398 size=11860 all=1318070 active=70810 piece=▁evol\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23338 size=11880 all=1319076 active=71816 piece=▁Celle\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23295 size=11900 all=1320743 active=73483 piece=▁Fun\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=23288 min_freq=992\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23233 size=11920 all=1322529 active=67642 piece=▁Sarah\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23171 size=11940 all=1324222 active=69335 piece=▁Pos\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23105 size=11960 all=1325172 active=70285 piece=▁installé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=23036 size=11980 all=1326342 active=71455 piece=umbled\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22978 size=12000 all=1327242 active=72355 piece=▁agenda\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=22973 min_freq=982\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22920 size=12020 all=1328562 active=67679 piece=▁personnellement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22861 size=12040 all=1329137 active=68254 piece=▁collected\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22774 size=12060 all=1330792 active=69909 piece=▁critiques\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22700 size=12080 all=1332717 active=71834 piece=van\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22638 size=12100 all=1334696 active=73813 piece=▁juris\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=22633 min_freq=970\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22587 size=12120 all=1335477 active=67502 piece=▁habitudes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22519 size=12140 all=1337168 active=69193 piece=▁détaill\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22447 size=12160 all=1337998 active=70023 piece=▁NY\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22383 size=12180 all=1339428 active=71453 piece=▁tested\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22322 size=12200 all=1340130 active=72155 piece=▁uniform\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=22316 min_freq=962\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22268 size=12220 all=1340634 active=67470 piece=▁avenir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22230 size=12240 all=1341885 active=68721 piece=▁Game\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22169 size=12260 all=1342379 active=69215 piece=▁Cat\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22130 size=12280 all=1343615 active=70451 piece=▁managing\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22097 size=12300 all=1344514 active=71350 piece=▁Protection\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=22096 min_freq=954\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=22030 size=12320 all=1346701 active=69400 piece=▁Pap\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21973 size=12340 all=1347948 active=70647 piece=▁réglementation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21917 size=12360 all=1348609 active=71308 piece=▁channel\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21844 size=12380 all=1350784 active=73483 piece=icite\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21784 size=12400 all=1351538 active=74237 piece=▁Media\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=21778 min_freq=944\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21689 size=12420 all=1352954 active=68918 piece=▁doigt\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21644 size=12440 all=1354024 active=69988 piece=endance\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21579 size=12460 all=1354951 active=70915 piece=▁insult\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21518 size=12480 all=1355520 active=71484 piece=about\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21460 size=12500 all=1356963 active=72927 piece=▁Arts\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=21455 min_freq=936\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21401 size=12520 all=1357861 active=68682 piece=humour\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21374 size=12540 all=1359632 active=70453 piece=▁dessert\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21321 size=12560 all=1361241 active=72062 piece=▁persist\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21277 size=12580 all=1362763 active=73584 piece=▁voisin\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21233 size=12600 all=1365044 active=75865 piece=▁ouvrage\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=21231 min_freq=925\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21203 size=12620 all=1367068 active=70274 piece=▁ferais\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21146 size=12640 all=1367560 active=70766 piece=▁élimin\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21083 size=12660 all=1368733 active=71939 piece=▁devais\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=21048 size=12680 all=1370291 active=73497 piece=▁élabor\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20975 size=12700 all=1371176 active=74382 piece=▁Britain\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=20974 min_freq=916\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20923 size=12720 all=1372518 active=69895 piece=▁réagir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20883 size=12740 all=1373171 active=70548 piece=▁.....\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20841 size=12760 all=1374751 active=72128 piece=avion\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20756 size=12780 all=1375556 active=72933 piece=▁duties\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20714 size=12800 all=1375904 active=73281 piece=▁hook\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=20714 min_freq=908\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20673 size=12820 all=1376397 active=69254 piece=inquiète\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20632 size=12840 all=1377312 active=70169 piece=▁raw\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20570 size=12860 all=1378272 active=71129 piece=▁Kim\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20526 size=12880 all=1379273 active=72130 piece=▁settled\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20472 size=12900 all=1380129 active=72986 piece=▁smoking\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=20466 min_freq=902\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20398 size=12920 all=1381151 active=70019 piece=▁organisée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20357 size=12940 all=1382153 active=71021 piece=sans\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20321 size=12960 all=1383344 active=72212 piece=▁sne\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20281 size=12980 all=1384033 active=72901 piece=▁Mun\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20232 size=13000 all=1385314 active=74182 piece=▁antib\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=20231 min_freq=895\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20201 size=13020 all=1386936 active=70829 piece=▁permanence\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20164 size=13040 all=1388830 active=72723 piece=▁pomp\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20102 size=13060 all=1390668 active=74561 piece=▁Kh\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20061 size=13080 all=1391811 active=75704 piece=cole\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=20010 size=13100 all=1392913 active=76806 piece=avier\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=20010 min_freq=885\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19975 size=13120 all=1393836 active=70468 piece=▁parlant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19919 size=13140 all=1395306 active=71938 piece=▁européennes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19870 size=13160 all=1396940 active=73572 piece=▁poverty\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19835 size=13180 all=1398109 active=74741 piece=▁stimul\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19786 size=13200 all=1399278 active=75910 piece=▁Valley\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=19783 min_freq=878\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19736 size=13220 all=1400388 active=71048 piece=▁lap\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19702 size=13240 all=1401121 active=71781 piece=▁cheaper\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19658 size=13260 all=1401980 active=72640 piece=▁luxe\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19614 size=13280 all=1403423 active=74083 piece=▁quête\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19565 size=13300 all=1404568 active=75228 piece=▁réactions\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=19562 min_freq=870\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19514 size=13320 all=1405761 active=71421 piece=mat\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19483 size=13340 all=1407942 active=73602 piece=▁responses\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19428 size=13360 all=1409572 active=75232 piece=▁pizza\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19392 size=13380 all=1411865 active=77525 piece=▁voting\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19321 size=13400 all=1413044 active=78704 piece=▁rénovation\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=19314 min_freq=859\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19280 size=13420 all=1414096 active=71701 piece=▁adore\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19232 size=13440 all=1416209 active=73814 piece=amber\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19188 size=13460 all=1417897 active=75502 piece=appliquer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19147 size=13480 all=1418761 active=76366 piece=▁adhér\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19101 size=13500 all=1419628 active=77233 piece=allait\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=19100 min_freq=851\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19064 size=13520 all=1420781 active=72077 piece=▁Control\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=19013 size=13540 all=1422055 active=73351 piece=onstration\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18976 size=13560 all=1423444 active=74740 piece=▁expense\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18931 size=13580 all=1424803 active=76099 piece=▁passés\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18889 size=13600 all=1425685 active=76981 piece=émique\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=18889 min_freq=843\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18854 size=13620 all=1426601 active=72138 piece=▁Ident\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18828 size=13640 all=1427651 active=73188 piece=▁pratiquer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18795 size=13660 all=1428226 active=73763 piece=▁grocer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18760 size=13680 all=1429325 active=74862 piece=▁Someone\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18708 size=13700 all=1430115 active=75652 piece=▁ly\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=18704 min_freq=837\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18671 size=13720 all=1431441 active=72717 piece=month\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18622 size=13740 all=1432389 active=73665 piece=▁interaction\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18582 size=13760 all=1433103 active=74379 piece=▁Key\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18534 size=13780 all=1434982 active=76258 piece=▁settle\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18474 size=13800 all=1435640 active=76916 piece=▁preferred\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=18473 min_freq=830\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18435 size=13820 all=1437229 active=73371 piece=new\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18392 size=13840 all=1439095 active=75237 piece=▁representative\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18337 size=13860 all=1441242 active=77384 piece=▁grounds\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18288 size=13880 all=1442624 active=78766 piece=▁Luck\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18242 size=13900 all=1443873 active=80015 piece=▁ain\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=18237 min_freq=820\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18193 size=13920 all=1444409 active=72701 piece=▁Fond\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18163 size=13940 all=1445818 active=74110 piece=▁spam\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18123 size=13960 all=1447649 active=75941 piece=▁Philippe\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18073 size=13980 all=1449365 active=77657 piece=▁Samedi\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=18022 size=14000 all=1450028 active=78320 piece=▁voyager\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=18020 min_freq=813\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17987 size=14020 all=1451653 active=74118 piece=▁CG\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17935 size=14040 all=1453438 active=75903 piece=▁emm\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17885 size=14060 all=1455057 active=77522 piece=▁buyers\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17852 size=14080 all=1455886 active=78351 piece=▁arrangements\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17801 size=14100 all=1456711 active=79176 piece=icked\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=17800 min_freq=805\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17754 size=14120 all=1457944 active=73947 piece=EP\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17717 size=14140 all=1459573 active=75576 piece=vie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17676 size=14160 all=1461820 active=77823 piece=▁Webs\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17644 size=14180 all=1462433 active=78436 piece=atrices\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17615 size=14200 all=1464058 active=80061 piece=▁eh\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=17615 min_freq=797\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17579 size=14220 all=1465540 active=74628 piece=▁shel\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17538 size=14240 all=1467066 active=76154 piece=▁mirror\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17498 size=14260 all=1468050 active=77138 piece=▁fonctionnaires\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17470 size=14280 all=1469855 active=78943 piece=▁lighting\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17437 size=14300 all=1471760 active=80848 piece=▁domest\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=17437 min_freq=789\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17387 size=14320 all=1472529 active=74335 piece=▁profondément\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17345 size=14340 all=1473977 active=75783 piece=▁identité\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17312 size=14360 all=1475330 active=77136 piece=ags\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17276 size=14380 all=1476111 active=77917 piece=▁ministres\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17258 size=14400 all=1476516 active=78322 piece=▁cérémonie\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=17255 min_freq=783\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17234 size=14420 all=1476959 active=74267 piece=▁flaw\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17199 size=14440 all=1479122 active=76430 piece=▁nail\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17162 size=14460 all=1480774 active=78082 piece=▁formidable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17111 size=14480 all=1481840 active=79148 piece=▁équipé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17073 size=14500 all=1483372 active=80680 piece=▁lentement\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=17071 min_freq=776\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17044 size=14520 all=1483835 active=74631 piece=vation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=17018 size=14540 all=1484792 active=75588 piece=▁bét\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16983 size=14560 all=1485639 active=76435 piece=▁filling\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16964 size=14580 all=1487154 active=77950 piece=othéra\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16906 size=14600 all=1488990 active=79786 piece=▁observation\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=16902 min_freq=770\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16863 size=14620 all=1490042 active=75495 piece=▁Programme\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16827 size=14640 all=1490910 active=76363 piece=war\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16801 size=14660 all=1492066 active=77519 piece=▁Parc\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16761 size=14680 all=1493206 active=78659 piece=mony\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16715 size=14700 all=1493993 active=79446 piece=▁intit\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=16715 min_freq=765\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16696 size=14720 all=1494915 active=75604 piece=utable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16653 size=14740 all=1495397 active=76086 piece=▁historic\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16623 size=14760 all=1496142 active=76831 piece=▁costume\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16586 size=14780 all=1498085 active=78774 piece=▁tentative\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16560 size=14800 all=1499880 active=80569 piece=▁Glad\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=16558 min_freq=758\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16525 size=14820 all=1500386 active=75437 piece=HS\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16495 size=14840 all=1501289 active=76340 piece=▁mixture\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16458 size=14860 all=1502543 active=77594 piece=onymous\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16442 size=14880 all=1503327 active=78378 piece=assure\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16405 size=14900 all=1504363 active=79414 piece=erai\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=16404 min_freq=752\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16386 size=14920 all=1506807 active=77250 piece=▁Carolina\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16346 size=14940 all=1507676 active=78119 piece=Allemagne\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16300 size=14960 all=1508438 active=78881 piece=▁frontière\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16271 size=14980 all=1509347 active=79790 piece=▁combine\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16241 size=15000 all=1510074 active=80517 piece=▁aimez\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=16239 min_freq=746\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16200 size=15020 all=1512323 active=77751 piece=istré\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16158 size=15040 all=1513948 active=79376 piece=▁magistr\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16127 size=15060 all=1514787 active=80215 piece=▁preced\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16076 size=15080 all=1515081 active=80509 piece=▁fed\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16046 size=15100 all=1515560 active=80988 piece=roniques\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=16046 min_freq=740\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=16022 size=15120 all=1516664 active=76834 piece=▁difficulties\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15986 size=15140 all=1517435 active=77605 piece=▁plages\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15957 size=15160 all=1518958 active=79128 piece=▁offrant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15938 size=15180 all=1519897 active=80067 piece=▁Ur\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15914 size=15200 all=1520697 active=80867 piece=▁Virginia\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=15912 min_freq=735\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15886 size=15220 all=1521235 active=76570 piece=▁religions\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15857 size=15240 all=1522813 active=78148 piece=▁Store\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15832 size=15260 all=1523939 active=79274 piece=▁cub\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15793 size=15280 all=1525141 active=80476 piece=▁survie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15763 size=15300 all=1525409 active=80744 piece=▁gentleman\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=15762 min_freq=729\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15729 size=15320 all=1526579 active=77432 piece=▁recruit\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15688 size=15340 all=1528414 active=79267 piece=tery\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15660 size=15360 all=1529382 active=80235 piece=▁actively\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15639 size=15380 all=1530390 active=81243 piece=▁downtown\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15616 size=15400 all=1531309 active=82162 piece=▁veiller\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=15615 min_freq=724\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15589 size=15420 all=1531691 active=76937 piece=ituer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15556 size=15440 all=1532749 active=77995 piece=▁gren\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15527 size=15460 all=1534160 active=79406 piece=cific\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15494 size=15480 all=1535679 active=80925 piece=▁verrez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15450 size=15500 all=1536543 active=81789 piece=▁shadow\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=15449 min_freq=719\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15421 size=15520 all=1538000 active=78238 piece=▁Retr\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15403 size=15540 all=1538947 active=79185 piece=▁Deb\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15381 size=15560 all=1540247 active=80485 piece=▁Maria\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15349 size=15580 all=1541449 active=81687 piece=▁arrête\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15316 size=15600 all=1542261 active=82499 piece=▁dite\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=15312 min_freq=713\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15278 size=15620 all=1542562 active=77412 piece=cile\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15236 size=15640 all=1544970 active=79820 piece=▁Vegas\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15188 size=15660 all=1545965 active=80815 piece=▁emissions\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15154 size=15680 all=1547836 active=82686 piece=▁chante\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15121 size=15700 all=1548960 active=83810 piece=▁donation\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=15121 min_freq=707\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15101 size=15720 all=1550080 active=78563 piece=section\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15084 size=15740 all=1551915 active=80398 piece=▁virtually\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15063 size=15760 all=1553613 active=82096 piece=▁represented\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15041 size=15780 all=1554107 active=82590 piece=▁guaranteed\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=15016 size=15800 all=1554961 active=83444 piece=▁amazed\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=15016 min_freq=701\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14987 size=15820 all=1556394 active=79181 piece=joux\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14964 size=15840 all=1558283 active=81070 piece=▁Exc\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14942 size=15860 all=1559744 active=82531 piece=▁fiches\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14913 size=15880 all=1560198 active=82985 piece=▁integrity\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14882 size=15900 all=1560605 active=83392 piece=chester\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=14875 min_freq=696\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14843 size=15920 all=1561530 active=78860 piece=upe\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14822 size=15940 all=1562585 active=79915 piece=PC\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14804 size=15960 all=1563644 active=80974 piece=▁contribu\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14769 size=15980 all=1564668 active=81998 piece=▁primord\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14742 size=16000 all=1566214 active=83544 piece=▁announcement\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=14741 min_freq=691\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14720 size=16020 all=1567998 active=80094 piece=▁knee\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14695 size=16040 all=1568739 active=80835 piece=▁irre\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14678 size=16060 all=1569989 active=82085 piece=▁provenant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14655 size=16080 all=1571248 active=83344 piece=▁publiée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14633 size=16100 all=1573013 active=85109 piece=▁dumpster\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=14633 min_freq=685\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14610 size=16120 all=1575400 active=81036 piece=▁Salon\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14580 size=16140 all=1576135 active=81771 piece=▁subi\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14560 size=16160 all=1577102 active=82738 piece=▁anxious\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14538 size=16180 all=1578307 active=83943 piece=▁suspic\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14508 size=16200 all=1579812 active=85448 piece=accéder\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=14508 min_freq=679\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14480 size=16220 all=1580846 active=80009 piece=oreille\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14456 size=16240 all=1582438 active=81601 piece=▁cohér\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14438 size=16260 all=1583520 active=82683 piece=▁Elizabeth\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14403 size=16280 all=1584015 active=83178 piece=▁assumed\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14373 size=16300 all=1584910 active=84073 piece=agerie\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=14371 min_freq=675\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14351 size=16320 all=1585924 active=80217 piece=aze\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14314 size=16340 all=1587206 active=81499 piece=▁warming\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14285 size=16360 all=1587572 active=81865 piece=▁dependent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14255 size=16380 all=1589172 active=83465 piece=intr\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14231 size=16400 all=1590893 active=85186 piece=▁serre\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=14230 min_freq=670\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14205 size=16420 all=1591631 active=80274 piece=▁Nation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14178 size=16440 all=1592353 active=80996 piece=MI\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14153 size=16460 all=1592985 active=81628 piece=▁nursing\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14133 size=16480 all=1594824 active=83467 piece=▁Apparently\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14114 size=16500 all=1595515 active=84158 piece=▁lens\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=14114 min_freq=665\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14102 size=16520 all=1596037 active=80270 piece=▁MAR\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14069 size=16540 all=1596744 active=80977 piece=▁Due\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14038 size=16560 all=1598020 active=82253 piece=▁Foot\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=14011 size=16580 all=1598831 active=83064 piece=▁Ach\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13992 size=16600 all=1599646 active=83879 piece=▁Pacific\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=13990 min_freq=662\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13957 size=16620 all=1600719 active=81034 piece=▁purchases\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13918 size=16640 all=1601564 active=81879 piece=▁rigueur\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13888 size=16660 all=1602649 active=82964 piece=Come\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13853 size=16680 all=1603697 active=84012 piece=▁Relations\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13831 size=16700 all=1604704 active=85019 piece=▁ador\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=13830 min_freq=657\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13801 size=16720 all=1605623 active=81101 piece=▁Staff\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13782 size=16740 all=1606332 active=81810 piece=▁envoie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13758 size=16760 all=1606889 active=82367 piece=▁pepper\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13733 size=16780 all=1607309 active=82787 piece=▁spark\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13707 size=16800 all=1607965 active=83443 piece=PP\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=13707 min_freq=654\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13686 size=16820 all=1609387 active=81406 piece=▁themes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13661 size=16840 all=1610445 active=82464 piece=▁Produ\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13622 size=16860 all=1611369 active=83388 piece=▁disons\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13590 size=16880 all=1613004 active=85023 piece=▁environs\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13547 size=16900 all=1613986 active=86005 piece=high\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=13545 min_freq=648\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13523 size=16920 all=1615330 active=81821 piece=▁livrer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13489 size=16940 all=1616741 active=83232 piece=irable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13472 size=16960 all=1617641 active=84132 piece=▁exit\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13448 size=16980 all=1618891 active=85382 piece=merie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13417 size=17000 all=1619862 active=86353 piece=▁emball\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=13416 min_freq=643\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13394 size=17020 all=1620476 active=81589 piece=▁bones\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13362 size=17040 all=1622560 active=83673 piece=▁sentait\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13341 size=17060 all=1624134 active=85247 piece=thur\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13321 size=17080 all=1624990 active=86103 piece=▁interro\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13300 size=17100 all=1626920 active=88033 piece=▁optimal\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=13298 min_freq=638\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13276 size=17120 all=1627775 active=82183 piece=▁croix\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13249 size=17140 all=1628445 active=82853 piece=affir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13230 size=17160 all=1629522 active=83930 piece=▁homeless\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13212 size=17180 all=1631958 active=86366 piece=▁mince\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13191 size=17200 all=1633144 active=87552 piece=▁Compl\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=13191 min_freq=633\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13154 size=17220 all=1633804 active=82251 piece=▁Obs\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13129 size=17240 all=1634910 active=83357 piece=▁statue\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13104 size=17260 all=1637121 active=85568 piece=▁semin\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13084 size=17280 all=1638080 active=86527 piece=▁réfugiés\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13064 size=17300 all=1639585 active=88032 piece=xts\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=13064 min_freq=628\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=13014 size=17320 all=1640892 active=83249 piece=▁Seriously\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12991 size=17340 all=1641694 active=84051 piece=aved\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12970 size=17360 all=1642514 active=84871 piece=▁affl\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12945 size=17380 all=1643401 active=85758 piece=xi\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12924 size=17400 all=1644363 active=86720 piece=best\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=12922 min_freq=623\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12899 size=17420 all=1645764 active=83280 piece=▁Leave\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12878 size=17440 all=1646088 active=83604 piece=product\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12859 size=17460 all=1647398 active=84914 piece=▁TVA\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12839 size=17480 all=1648179 active=85695 piece=▁humour\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12820 size=17500 all=1648864 active=86380 piece=▁casserole\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=12820 min_freq=619\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12806 size=17520 all=1649859 active=83438 piece=▁desktop\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12773 size=17540 all=1650810 active=84389 piece=▁educated\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12738 size=17560 all=1651215 active=84794 piece=▁pénale\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12718 size=17580 all=1651979 active=85558 piece=▁Cool\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12694 size=17600 all=1652501 active=86080 piece=Did\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=12690 min_freq=616\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12670 size=17620 all=1654098 active=84126 piece=bu\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12648 size=17640 all=1655548 active=85576 piece=▁legally\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12627 size=17660 all=1656436 active=86464 piece=▁Line\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12610 size=17680 all=1658413 active=88441 piece=infos\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12590 size=17700 all=1659621 active=89649 piece=worthy\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=12588 min_freq=611\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12566 size=17720 all=1660483 active=83684 piece=▁suffisante\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12545 size=17740 all=1661830 active=85031 piece=▁Id\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12525 size=17760 all=1663355 active=86556 piece=▁adop\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12500 size=17780 all=1664875 active=88076 piece=school\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12472 size=17800 all=1666154 active=89355 piece=▁poison\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=12472 min_freq=606\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12455 size=17820 all=1666488 active=83629 piece=ington\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12430 size=17840 all=1667423 active=84564 piece=,--\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12403 size=17860 all=1669627 active=86768 piece=▁dépression\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12377 size=17880 all=1670248 active=87389 piece=▁fines\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12355 size=17900 all=1671251 active=88392 piece=ighte\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=12352 min_freq=602\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12337 size=17920 all=1671812 active=84053 piece=édia\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12316 size=17940 all=1673127 active=85368 piece=▁claiming\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12302 size=17960 all=1673482 active=85723 piece=▁ACT\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12283 size=17980 all=1674822 active=87063 piece=▁Study\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12264 size=18000 all=1675869 active=88110 piece=▁CM\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=12264 min_freq=598\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12245 size=18020 all=1677066 active=84846 piece=▁venues\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12222 size=18040 all=1677678 active=85458 piece=▁folder\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12204 size=18060 all=1678392 active=86172 piece=osion\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12182 size=18080 all=1679017 active=86797 piece=▁œil\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12162 size=18100 all=1679824 active=87604 piece=▁croiser\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=12154 min_freq=595\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12132 size=18120 all=1680527 active=84682 piece=▁Laurent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12110 size=18140 all=1681624 active=85779 piece=▁invested\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12079 size=18160 all=1682215 active=86370 piece=▁priorities\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12059 size=18180 all=1682611 active=86766 piece=▁photographe\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12032 size=18200 all=1683276 active=87431 piece=▁Pretty\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=12032 min_freq=591\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=12018 size=18220 all=1684015 active=84880 piece=▁reportage\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11996 size=18240 all=1685307 active=86172 piece=▁écart\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11981 size=18260 all=1686790 active=87655 piece=âche\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11968 size=18280 all=1687522 active=88387 piece=▁Création\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11944 size=18300 all=1688940 active=89805 piece=▁parcourir\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=11942 min_freq=587\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11924 size=18320 all=1690341 active=85847 piece=▁Bless\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11908 size=18340 all=1690702 active=86208 piece=▁estimates\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11888 size=18360 all=1691557 active=87063 piece=▁boucl\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11873 size=18380 all=1692452 active=87958 piece=▁usages\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11854 size=18400 all=1693097 active=88603 piece=▁semblerait\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=11851 min_freq=584\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11836 size=18420 all=1693936 active=85494 piece=▁ignorance\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11819 size=18440 all=1694812 active=86370 piece=▁pots\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11800 size=18460 all=1695557 active=87115 piece=▁régulier\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11785 size=18480 all=1697332 active=88890 piece=▁dozens\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11769 size=18500 all=1697634 active=89192 piece=▁Restaur\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=11768 min_freq=581\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11758 size=18520 all=1698463 active=85685 piece=▁analyser\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11738 size=18540 all=1699839 active=87061 piece=▁jazz\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11715 size=18560 all=1700857 active=88079 piece=▁wheels\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11683 size=18580 all=1701129 active=88351 piece=▁JavaScript\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11658 size=18600 all=1702199 active=89421 piece=▁méchant\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=11653 min_freq=577\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11635 size=18620 all=1703484 active=86389 piece=▁artificial\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11626 size=18640 all=1704059 active=86964 piece=atmosph\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11607 size=18660 all=1705102 active=88007 piece=▁kindly\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11593 size=18680 all=1705872 active=88777 piece=▁migration\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11576 size=18700 all=1707302 active=90207 piece=ologists\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=11576 min_freq=573\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11558 size=18720 all=1708538 active=86415 piece=▁gau\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11542 size=18740 all=1708953 active=86830 piece=▁poils\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11520 size=18760 all=1709822 active=87699 piece=▁Princess\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11499 size=18780 all=1710095 active=87972 piece=▁tutor\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11476 size=18800 all=1711419 active=89296 piece=ounge\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=11475 min_freq=571\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11455 size=18820 all=1712498 active=86596 piece=▁rod\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11441 size=18840 all=1713114 active=87212 piece=▁completing\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11421 size=18860 all=1714067 active=88165 piece=▁Hollande\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11389 size=18880 all=1715147 active=89245 piece=▁Contract\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11368 size=18900 all=1716163 active=90261 piece=▁Janvier\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=11367 min_freq=567\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11353 size=18920 all=1717146 active=86791 piece=▁shamp\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11336 size=18940 all=1717769 active=87414 piece=natural\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11312 size=18960 all=1718937 active=88582 piece=internet\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11297 size=18980 all=1719462 active=89107 piece=▁identifying\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11278 size=19000 all=1719795 active=89440 piece=▁Sydney\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=11277 min_freq=564\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11254 size=19020 all=1720159 active=86341 piece=▁chapeau\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11239 size=19040 all=1720675 active=86857 piece=apte\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11223 size=19060 all=1723649 active=89831 piece=▁Empire\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11206 size=19080 all=1724769 active=90951 piece=onely\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11185 size=19100 all=1725312 active=91494 piece=▁Quality\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=11182 min_freq=560\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11157 size=19120 all=1726664 active=87602 piece=▁Portugal\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11138 size=19140 all=1727750 active=88688 piece=lee\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11119 size=19160 all=1728873 active=89811 piece=▁countless\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11103 size=19180 all=1729457 active=90395 piece=▁abortion\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11084 size=19200 all=1730086 active=91024 piece=▁picks\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=11084 min_freq=557\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11065 size=19220 all=1730608 active=87025 piece=▁dissim\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11055 size=19240 all=1732599 active=89016 piece=especially\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11033 size=19260 all=1734311 active=90728 piece=allez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11017 size=19280 all=1735253 active=91670 piece=▁naï\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=11000 size=19300 all=1736183 active=92600 piece=▁intitulé\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=10999 min_freq=552\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10985 size=19320 all=1737317 active=87940 piece=▁silh\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10974 size=19340 all=1737759 active=88382 piece=▁cherchant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10959 size=19360 all=1738615 active=89238 piece=ÉR\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10949 size=19380 all=1739728 active=90351 piece=animité\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10935 size=19400 all=1740366 active=90989 piece=▁courants\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=10935 min_freq=549\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10917 size=19420 all=1741144 active=87795 piece=▁messieurs\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10900 size=19440 all=1741853 active=88504 piece=êtement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10884 size=19460 all=1744081 active=90732 piece=pol\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10856 size=19480 all=1745512 active=92163 piece=▁récompense\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10833 size=19500 all=1746333 active=92984 piece=▁Fast\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=10832 min_freq=545\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10815 size=19520 all=1747320 active=88169 piece=▁stér\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10794 size=19540 all=1748090 active=88939 piece=▁dépendance\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10778 size=19560 all=1748973 active=89822 piece=▁junior\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10758 size=19580 all=1749257 active=90106 piece=nait\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10746 size=19600 all=1750760 active=91609 piece=▁Arizona\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=10746 min_freq=542\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10733 size=19620 all=1751107 active=87882 piece=indique\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10721 size=19640 all=1751418 active=88193 piece=▁sexuel\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10710 size=19660 all=1752504 active=89279 piece=▁uncover\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10690 size=19680 all=1752817 active=89592 piece=▁dignité\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10672 size=19700 all=1753865 active=90640 piece=ocaly\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=10672 min_freq=539\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10658 size=19720 all=1754560 active=88329 piece=▁promesses\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10638 size=19740 all=1754920 active=88689 piece=chi\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10618 size=19760 all=1756099 active=89868 piece=▁continuité\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10602 size=19780 all=1756631 active=90400 piece=▁congrat\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10590 size=19800 all=1757179 active=90948 piece=▁specialized\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=10589 min_freq=536\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10570 size=19820 all=1758745 active=89424 piece=ateral\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10559 size=19840 all=1759934 active=90613 piece=▁tenues\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10532 size=19860 all=1760569 active=91248 piece=onic\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10520 size=19880 all=1761961 active=92640 piece=▁analyz\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10504 size=19900 all=1762680 active=93359 piece=▁soumises\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=10504 min_freq=533\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10485 size=19920 all=1763334 active=88788 piece=▁Défense\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10467 size=19940 all=1765172 active=90626 piece=▁ménages\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10452 size=19960 all=1766935 active=92389 piece=▁Vend\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10441 size=19980 all=1768098 active=93552 piece=▁MORE\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10420 size=20000 all=1768908 active=94362 piece=▁\".\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=10420 min_freq=529\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10403 size=20020 all=1769666 active=89192 piece=▁frapper\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10375 size=20040 all=1770197 active=89723 piece=▁tienne\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10351 size=20060 all=1771223 active=90749 piece=▁ER\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10337 size=20080 all=1771681 active=91207 piece=▁Billy\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10319 size=20100 all=1773182 active=92708 piece=▁oxygen\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=10317 min_freq=526\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10301 size=20120 all=1773669 active=89132 piece=▁Chapitre\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10282 size=20140 all=1775278 active=90741 piece=▁Décembre\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10270 size=20160 all=1775937 active=91400 piece=▁Syrie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10257 size=20180 all=1777989 active=93452 piece=▁exped\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10249 size=20200 all=1778880 active=94343 piece=▁phenomenon\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=10248 min_freq=522\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10230 size=20220 all=1779918 active=89979 piece=▁reli\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10210 size=20240 all=1781487 active=91548 piece=▁Nantes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10196 size=20260 all=1782332 active=92393 piece=▁cheminée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10171 size=20280 all=1783213 active=93274 piece=inging\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10155 size=20300 all=1784403 active=94464 piece=étrie\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=10155 min_freq=518\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10145 size=20320 all=1784967 active=89747 piece=▁Dall\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10131 size=20340 all=1785935 active=90715 piece=▁déterminée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10120 size=20360 all=1786554 active=91334 piece=▁thee\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10105 size=20380 all=1788089 active=92869 piece=▁ego\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10084 size=20400 all=1788921 active=93701 piece=▁leisure\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=10084 min_freq=515\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10068 size=20420 all=1789575 active=90095 piece=▁pâtes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10055 size=20440 all=1790305 active=90825 piece=▁Magic\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10032 size=20460 all=1792023 active=92543 piece=▁hated\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=10014 size=20480 all=1793462 active=93982 piece=Mon\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9998 size=20500 all=1795459 active=95979 piece=▁skept\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=9996 min_freq=511\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9982 size=20520 all=1796150 active=90450 piece=mate\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9972 size=20540 all=1797521 active=91821 piece=▁Pict\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9955 size=20560 all=1798468 active=92768 piece=▁Sachez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9928 size=20580 all=1799977 active=94277 piece=acceptable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9913 size=20600 all=1800747 active=95047 piece=▁Avril\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=9909 min_freq=508\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9896 size=20620 all=1801314 active=90600 piece=▁seller\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9883 size=20640 all=1802135 active=91421 piece=▁hostile\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9871 size=20660 all=1802555 active=91841 piece=▁retreat\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9853 size=20680 all=1803525 active=92811 piece=▁extérieurs\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9834 size=20700 all=1803929 active=93215 piece=▁charging\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=9834 min_freq=505\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9817 size=20720 all=1804452 active=90719 piece=▁PO\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9806 size=20740 all=1805309 active=91576 piece=▁whatsoever\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9792 size=20760 all=1807335 active=93602 piece=osystem\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9783 size=20780 all=1807983 active=94250 piece=▁dépenser\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9768 size=20800 all=1808614 active=94881 piece=▁lifting\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=9765 min_freq=502\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9751 size=20820 all=1809368 active=91183 piece=▁isolation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9739 size=20840 all=1811544 active=93359 piece=▁musicale\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9720 size=20860 all=1811938 active=93753 piece=”?\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9704 size=20880 all=1813672 active=95487 piece=syl\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9688 size=20900 all=1814503 active=96318 piece=▁nam\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=9684 min_freq=499\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9670 size=20920 all=1815497 active=91640 piece=onnent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9652 size=20940 all=1817114 active=93257 piece=▁existants\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9641 size=20960 all=1818040 active=94183 piece=▁volontiers\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9627 size=20980 all=1818595 active=94738 piece=▁guided\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9612 size=21000 all=1819420 active=95563 piece=▁Introdu\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=9609 min_freq=496\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9599 size=21020 all=1820681 active=92208 piece=accueillir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9583 size=21040 all=1821068 active=92595 piece=▁Yep\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9568 size=21060 all=1821725 active=93252 piece=▁welcoming\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9554 size=21080 all=1822708 active=94235 piece=orts\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9543 size=21100 all=1823482 active=95009 piece=abiliser\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=9543 min_freq=493\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9528 size=21120 all=1823848 active=91490 piece=▁interromp\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9515 size=21140 all=1824700 active=92342 piece=▁malheureux\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9497 size=21160 all=1825236 active=92878 piece=▁gymn\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9482 size=21180 all=1826411 active=94053 piece=▁mutation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9464 size=21200 all=1827075 active=94717 piece=▁sortit\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=9463 min_freq=491\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9453 size=21220 all=1829018 active=93296 piece=▁::\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9437 size=21240 all=1830007 active=94285 piece=▁finis\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9425 size=21260 all=1830748 active=95026 piece=▁détermine\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9412 size=21280 all=1832167 active=96445 piece=▁polite\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9398 size=21300 all=1832592 active=96870 piece=▁brevet\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=9397 min_freq=488\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9382 size=21320 all=1833411 active=92426 piece=▁ranks\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9367 size=21340 all=1834887 active=93902 piece=▁bâ\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9357 size=21360 all=1835515 active=94530 piece=▁Send\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9345 size=21380 all=1835966 active=94981 piece=▁fatigué\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9334 size=21400 all=1837588 active=96603 piece=▁amène\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=9334 min_freq=485\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9320 size=21420 all=1838554 active=92845 piece=▁Croix\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9307 size=21440 all=1839442 active=93733 piece=▁Left\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9290 size=21460 all=1840469 active=94760 piece=▁paisible\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9275 size=21480 all=1841787 active=96078 piece=▁parlons\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9257 size=21500 all=1843094 active=97385 piece=▁wifi\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=9257 min_freq=481\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9245 size=21520 all=1844077 active=93133 piece=▁immeubles\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9237 size=21540 all=1844874 active=93930 piece=▁obé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9223 size=21560 all=1847000 active=96056 piece=graduate\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9208 size=21580 all=1847677 active=96733 piece=▁instaur\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9195 size=21600 all=1848075 active=97131 piece=▁respirer\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=9194 min_freq=479\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9184 size=21620 all=1849765 active=94086 piece=▁caress\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9169 size=21640 all=1850233 active=94554 piece=archy\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9157 size=21660 all=1850892 active=95213 piece=▁adversaires\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9142 size=21680 all=1851490 active=95811 piece=▁presentations\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9129 size=21700 all=1852410 active=96731 piece=▁Inv\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=9128 min_freq=476\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9116 size=21720 all=1853659 active=93787 piece=▁extensions\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9103 size=21740 all=1854628 active=94756 piece=▁créés\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9095 size=21760 all=1855183 active=95311 piece=▁tolérance\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9076 size=21780 all=1856999 active=97127 piece=ibilités\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9064 size=21800 all=1857366 active=97494 piece=▁forcing\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=9063 min_freq=474\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9049 size=21820 all=1857732 active=93235 piece=▁respectively\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9033 size=21840 all=1858474 active=93977 piece=▁leap\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9017 size=21860 all=1859371 active=94874 piece=ustain\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=9007 size=21880 all=1860075 active=95578 piece=▁curieuse\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8997 size=21900 all=1860828 active=96331 piece=▁sien\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8997 min_freq=472\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8984 size=21920 all=1861284 active=93494 piece=▁hilarious\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8970 size=21940 all=1861917 active=94127 piece=▁créature\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8951 size=21960 all=1862257 active=94467 piece=▁manifester\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8935 size=21980 all=1862842 active=95052 piece=▁sticks\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8922 size=22000 all=1863487 active=95697 piece=▁aunt\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8922 min_freq=470\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8913 size=22020 all=1864660 active=94339 piece=written\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8897 size=22040 all=1865816 active=95495 piece=étit\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8886 size=22060 all=1866503 active=96182 piece=▁attentif\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8872 size=22080 all=1867161 active=96840 piece=▁sourcils\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8855 size=22100 all=1867909 active=97588 piece=prom\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8855 min_freq=467\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8841 size=22120 all=1869568 active=94731 piece=upport\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8833 size=22140 all=1870533 active=95696 piece=▁hunger\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8815 size=22160 all=1871683 active=96846 piece=▁Din\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8802 size=22180 all=1872807 active=97970 piece=▁Yan\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8788 size=22200 all=1873507 active=98670 piece=▁acknowledged\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8786 min_freq=464\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8776 size=22220 all=1874412 active=94581 piece=avérer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8762 size=22240 all=1875255 active=95424 piece=▁restored\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8751 size=22260 all=1876406 active=96575 piece=▁Linda\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8743 size=22280 all=1877185 active=97354 piece=▁Philippines\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8732 size=22300 all=1878031 active=98200 piece=▁seal\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8732 min_freq=462\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8720 size=22320 all=1879145 active=94993 piece=parer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8703 size=22340 all=1881148 active=96996 piece=▁Cord\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8693 size=22360 all=1882378 active=98226 piece=▁Michelle\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8680 size=22380 all=1882910 active=98758 piece=▁mû\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8668 size=22400 all=1884104 active=99952 piece=▁aesth\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8668 min_freq=459\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8659 size=22420 all=1884538 active=94630 piece=stick\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8645 size=22440 all=1886086 active=96178 piece=▁désirs\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8633 size=22460 all=1886988 active=97080 piece=acity\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8621 size=22480 all=1887690 active=97782 piece=▁conçus\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8612 size=22500 all=1888454 active=98546 piece=▁hood\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8612 min_freq=457\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8603 size=22520 all=1889874 active=95819 piece=quettes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8589 size=22540 all=1890778 active=96723 piece=▁momentum\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8573 size=22560 all=1891805 active=97750 piece=▁recense\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8563 size=22580 all=1893097 active=99042 piece=▁prestataire\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8545 size=22600 all=1893448 active=99393 piece=▁GU\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8545 min_freq=454\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8534 size=22620 all=1895931 active=97051 piece=▁verres\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8522 size=22640 all=1896305 active=97425 piece=▁reservation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8509 size=22660 all=1896571 active=97691 piece=▁someday\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8501 size=22680 all=1897150 active=98270 piece=▁clarify\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8494 size=22700 all=1897917 active=99037 piece=iscons\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8491 min_freq=451\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8483 size=22720 all=1898878 active=95817 piece=▁déception\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8474 size=22740 all=1899469 active=96408 piece=▁validé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8462 size=22760 all=1899849 active=96788 piece=▁wasting\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8453 size=22780 all=1900745 active=97684 piece=▁récents\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8440 size=22800 all=1901732 active=98671 piece=▁allegations\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8439 min_freq=449\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8427 size=22820 all=1902356 active=95709 piece=ilés\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8416 size=22840 all=1902544 active=95897 piece=▁revealing\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8397 size=22860 all=1903833 active=97186 piece=airie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8389 size=22880 all=1905252 active=98605 piece=hill\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8375 size=22900 all=1906509 active=99862 piece=▁fuss\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8375 min_freq=447\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8361 size=22920 all=1907454 active=96245 piece=▁fassent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8349 size=22940 all=1908008 active=96799 piece=Very\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8336 size=22960 all=1909193 active=97984 piece=pton\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8325 size=22980 all=1910202 active=98993 piece=▁figurent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8314 size=23000 all=1911166 active=99957 piece=▁polyval\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8313 min_freq=444\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8301 size=23020 all=1911670 active=96053 piece=▁allocation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8288 size=23040 all=1912547 active=96930 piece=▁baie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8275 size=23060 all=1912859 active=97242 piece=▁Beyond\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8263 size=23080 all=1913203 active=97586 piece=▁rapproche\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8251 size=23100 all=1914132 active=98515 piece=▁abandonner\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8250 min_freq=443\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8238 size=23120 all=1914650 active=96215 piece=.—\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8222 size=23140 all=1915302 active=96867 piece=▁levé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8211 size=23160 all=1915684 active=97249 piece=▁Extra\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8200 size=23180 all=1916477 active=98042 piece=start\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8188 size=23200 all=1917959 active=99524 piece=▁anges\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8187 min_freq=440\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8180 size=23220 all=1918866 active=96804 piece=▁superficie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8169 size=23240 all=1919708 active=97646 piece=▁pige\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8157 size=23260 all=1920491 active=98429 piece=▁Jake\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8147 size=23280 all=1921654 active=99592 piece=▁sexually\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8136 size=23300 all=1922407 active=100345 piece=▁fitted\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8136 min_freq=438\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8128 size=23320 all=1923319 active=97032 piece=▁imprimer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8118 size=23340 all=1923647 active=97360 piece=▁conneries\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8108 size=23360 all=1924637 active=98350 piece=AME\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8092 size=23380 all=1925348 active=99061 piece=leans\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8078 size=23400 all=1926402 active=100115 piece=▁constituée\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8077 min_freq=436\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8067 size=23420 all=1926859 active=96778 piece=▁jure\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8057 size=23440 all=1927871 active=97790 piece=▁craint\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8049 size=23460 all=1928592 active=98511 piece=▁prevented\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8036 size=23480 all=1930537 active=100456 piece=▁sob\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8027 size=23500 all=1931501 active=101420 piece=▁paperwork\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=8026 min_freq=434\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8014 size=23520 all=1932786 active=97860 piece=issantes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=8002 size=23540 all=1934872 active=99946 piece=▁LORD\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7991 size=23560 all=1935327 active=100401 piece=umine\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7981 size=23580 all=1936253 active=101327 piece=▁équipés\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7964 size=23600 all=1937334 active=102408 piece=Comment\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7963 min_freq=431\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7954 size=23620 all=1938642 active=98090 piece=▁comptent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7941 size=23640 all=1939163 active=98611 piece=▁Lev\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7933 size=23660 all=1939843 active=99291 piece=habitat\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7919 size=23680 all=1940446 active=99894 piece=)!\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7912 size=23700 all=1941561 active=101009 piece=▁Finding\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7912 min_freq=429\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7904 size=23720 all=1942313 active=97826 piece=▁interviewed\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7893 size=23740 all=1942804 active=98317 piece=▁périph\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7884 size=23760 all=1943273 active=98786 piece=actory\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7874 size=23780 all=1943591 active=99104 piece=▁nails\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7865 size=23800 all=1944157 active=99670 piece=raham\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7865 min_freq=427\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7853 size=23820 all=1945163 active=98162 piece=▁stagiaires\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7843 size=23840 all=1945777 active=98776 piece=obé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7828 size=23860 all=1946563 active=99562 piece=woman\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7818 size=23880 all=1947762 active=100761 piece=▁blocage\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7802 size=23900 all=1948865 active=101864 piece=▁Lamb\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7802 min_freq=425\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7790 size=23920 all=1950221 active=98672 piece=▁activists\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7778 size=23940 all=1951202 active=99653 piece=▁seated\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7765 size=23960 all=1951801 active=100252 piece=tête\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7751 size=23980 all=1952674 active=101125 piece=▁Directors\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7741 size=24000 all=1953056 active=101507 piece=ologne\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7739 min_freq=423\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7733 size=24020 all=1953286 active=97859 piece=▁anonyme\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7722 size=24040 all=1954122 active=98695 piece=▁lourdes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7712 size=24060 all=1954991 active=99564 piece=▁trades\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7705 size=24080 all=1955540 active=100113 piece=effond\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7698 size=24100 all=1955943 active=100516 piece=aibl\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7698 min_freq=421\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7684 size=24120 all=1956538 active=98348 piece=▁Safe\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7675 size=24140 all=1957483 active=99293 piece=▁SL\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7664 size=24160 all=1957963 active=99773 piece=▁Vac\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7653 size=24180 all=1958646 active=100456 piece=▁WiFi\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7643 size=24200 all=1959327 active=101137 piece=▁freelance\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7642 min_freq=419\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7630 size=24220 all=1959816 active=98450 piece=▁utilité\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7622 size=24240 all=1960138 active=98772 piece=▁officiers\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7608 size=24260 all=1960813 active=99447 piece=TF\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7601 size=24280 all=1961755 active=100389 piece=▁survivors\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7588 size=24300 all=1962370 active=101004 piece=▁vache\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7587 min_freq=418\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7579 size=24320 all=1962978 active=98726 piece=▁Stage\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7570 size=24340 all=1963795 active=99543 piece=▁débattre\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7554 size=24360 all=1965402 active=101150 piece=▁defendant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7543 size=24380 all=1965746 active=101494 piece=▁psychiatr\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7533 size=24400 all=1966528 active=102276 piece=▁temporarily\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7532 min_freq=415\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7526 size=24420 all=1967878 active=99677 piece=▁wont\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7515 size=24440 all=1968491 active=100290 piece=▁chlor\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7504 size=24460 all=1969604 active=101403 piece=▁embro\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7495 size=24480 all=1970598 active=102397 piece=▁Deliver\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7479 size=24500 all=1971827 active=103626 piece=▁Pitt\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7479 min_freq=413\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7470 size=24520 all=1972398 active=99093 piece=▁obesity\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7462 size=24540 all=1973274 active=99969 piece=▁alla\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7450 size=24560 all=1974289 active=100984 piece=cinq\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7439 size=24580 all=1975297 active=101992 piece=▁racisme\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7428 size=24600 all=1975651 active=102346 piece=▁colorful\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7427 min_freq=412\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7417 size=24620 all=1976324 active=99454 piece=▁programmer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7410 size=24640 all=1977397 active=100527 piece=anthrop\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7404 size=24660 all=1978188 active=101318 piece=ston\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7395 size=24680 all=1979009 active=102139 piece=▁Progress\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7386 size=24700 all=1979990 active=103120 piece=▁^^)\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7384 min_freq=409\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7375 size=24720 all=1981652 active=100652 piece=▁indicating\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7369 size=24740 all=1982136 active=101136 piece=▁Thail\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7358 size=24760 all=1982739 active=101739 piece=▁trek\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7349 size=24780 all=1983227 active=102227 piece=▁prisoner\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7340 size=24800 all=1984406 active=103406 piece=▁Notez\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7339 min_freq=407\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7328 size=24820 all=1985364 active=100179 piece=▁dépens\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7320 size=24840 all=1985968 active=100783 piece=▁Beat\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7315 size=24860 all=1986849 active=101664 piece=▁negotiate\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7306 size=24880 all=1987283 active=102098 piece=▁Map\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7299 size=24900 all=1988194 active=103009 piece=fac\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7299 min_freq=405\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7290 size=24920 all=1989823 active=100767 piece=▁pérenn\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7280 size=24940 all=1990402 active=101346 piece=▁souveraineté\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7270 size=24960 all=1991740 active=102684 piece=▁(-\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7262 size=24980 all=1993056 active=104000 piece=▁Mans\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7256 size=25000 all=1993728 active=104672 piece=▁undertake\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7255 min_freq=403\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7247 size=25020 all=1994300 active=100259 piece=▁reçue\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7237 size=25040 all=1994932 active=100891 piece=▁visiteur\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7225 size=25060 all=1995688 active=101647 piece=▁hygien\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7215 size=25080 all=1997125 active=103084 piece=▁combustion\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7204 size=25100 all=1997841 active=103800 piece=▁développeurs\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7203 min_freq=401\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7195 size=25120 all=1998526 active=100577 piece=alarme\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7186 size=25140 all=1999923 active=101974 piece=▁schedules\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7176 size=25160 all=2001160 active=103211 piece=▁queries\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7168 size=25180 all=2001871 active=103922 piece=▁Specific\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7158 size=25200 all=2002584 active=104635 piece=▁reimb\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7158 min_freq=399\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7152 size=25220 all=2003421 active=100962 piece=▁respectivement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7143 size=25240 all=2004388 active=101929 piece=▁thesis\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7133 size=25260 all=2005056 active=102597 piece=▁seigneur\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7121 size=25280 all=2006534 active=104075 piece=▁blocs\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7112 size=25300 all=2006728 active=104269 piece=▁judicieux\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7112 min_freq=397\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7104 size=25320 all=2008163 active=101772 piece=▁surgeon\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7096 size=25340 all=2008382 active=101991 piece=▁bénéficiez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7086 size=25360 all=2009660 active=103269 piece=indemnisation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7071 size=25380 all=2010919 active=104528 piece=▁Membre\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7058 size=25400 all=2011892 active=105501 piece=▁laissée\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7057 min_freq=395\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7048 size=25420 all=2012824 active=101527 piece=▁reflex\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7038 size=25440 all=2013628 active=102331 piece=▁Joh\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7028 size=25460 all=2013859 active=102562 piece=▁végétaux\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7019 size=25480 all=2014078 active=102781 piece=▁reviendrai\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7009 size=25500 all=2014686 active=103389 piece=▁vieil\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=7009 min_freq=393\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=7001 size=25520 all=2015017 active=101060 piece=▁compétents\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6994 size=25540 all=2015482 active=101525 piece=▁pires\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6987 size=25560 all=2015837 active=101880 piece=▁Melbourne\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6980 size=25580 all=2016637 active=102680 piece=▁anticipation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6972 size=25600 all=2017240 active=103283 piece=▁conservé\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6971 min_freq=392\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6958 size=25620 all=2018276 active=101898 piece=▁hauss\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6950 size=25640 all=2018877 active=102499 piece=▁Throughout\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6938 size=25660 all=2019711 active=103333 piece=▁Chamber\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6931 size=25680 all=2020562 active=104184 piece=▁Candid\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6923 size=25700 all=2021414 active=105036 piece=▁Communications\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6922 min_freq=390\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6916 size=25720 all=2022482 active=102138 piece=▁envelope\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6906 size=25740 all=2023658 active=103314 piece=▁Bos\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6893 size=25760 all=2024499 active=104155 piece=▁Carte\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6886 size=25780 all=2025332 active=104988 piece=▁slavery\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6879 size=25800 all=2025706 active=105362 piece=▁tablettes\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6877 min_freq=388\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6869 size=25820 all=2026469 active=102048 piece=▁plombier\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6863 size=25840 all=2028362 active=103941 piece=▁figurer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6854 size=25860 all=2029679 active=105258 piece=▁apost\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6848 size=25880 all=2030602 active=106181 piece=▁Hôtel\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6835 size=25900 all=2031031 active=106610 piece=▁industrie\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6834 min_freq=386\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6825 size=25920 all=2031580 active=102100 piece=▁autorise\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6813 size=25940 all=2032392 active=102912 piece=▁charitable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6804 size=25960 all=2032928 active=103448 piece=▁manufactured\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6796 size=25980 all=2033455 active=103975 piece=explosion\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6790 size=26000 all=2034452 active=104972 piece=▁collar\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6790 min_freq=384\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6783 size=26020 all=2035454 active=102721 piece=retour\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6776 size=26040 all=2035888 active=103155 piece=▁mamans\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6767 size=26060 all=2036342 active=103609 piece=▁Bac\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6760 size=26080 all=2037819 active=105086 piece=▁reprocher\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6752 size=26100 all=2038923 active=106190 piece=endence\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6752 min_freq=382\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6744 size=26120 all=2039218 active=102211 piece=▁gentils\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6736 size=26140 all=2039800 active=102793 piece=▁capr\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6725 size=26160 all=2040511 active=103504 piece=▁spécialisées\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6714 size=26180 all=2040944 active=103937 piece=▁levure\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6703 size=26200 all=2041831 active=104824 piece=▁Nad\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6703 min_freq=380\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6693 size=26220 all=2042632 active=102732 piece=▁fiers\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6684 size=26240 all=2043133 active=103233 piece=▁embroider\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6675 size=26260 all=2043557 active=103657 piece=Star\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6670 size=26280 all=2044720 active=104820 piece=▁REL\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6661 size=26300 all=2045369 active=105469 piece=nature\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6661 min_freq=379\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6652 size=26320 all=2046191 active=102948 piece=▁bâtir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6640 size=26340 all=2047494 active=104251 piece=▁glow\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6631 size=26360 all=2048348 active=105105 piece=▁Brain\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6621 size=26380 all=2048701 active=105458 piece=▁démons\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6615 size=26400 all=2049159 active=105916 piece=▁oiseau\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6615 min_freq=377\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6603 size=26420 all=2049470 active=102767 piece=▁déposée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6597 size=26440 all=2050444 active=103741 piece=▁épaisse\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6588 size=26460 all=2051985 active=105282 piece=idency\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6582 size=26480 all=2053754 active=107051 piece=▁Bobby\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6576 size=26500 all=2054910 active=108207 piece=▁Eye\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6576 min_freq=375\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6572 size=26520 all=2056149 active=103906 piece=▁obscure\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6564 size=26540 all=2056550 active=104307 piece=▁administrator\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6555 size=26560 all=2057628 active=105385 piece=▁Dj\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6549 size=26580 all=2058707 active=106464 piece=▁Crown\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6541 size=26600 all=2059457 active=107214 piece=ublin\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6541 min_freq=373\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6532 size=26620 all=2060230 active=103718 piece=Ils\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6522 size=26640 all=2061237 active=104725 piece=▁périp\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6518 size=26660 all=2061766 active=105254 piece=▁simplicity\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6511 size=26680 all=2062663 active=106151 piece=▁positioning\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6502 size=26700 all=2063284 active=106772 piece=▁autorisations\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6501 min_freq=371\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6492 size=26720 all=2064064 active=103945 piece=▁serviettes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6484 size=26740 all=2064462 active=104343 piece=▁disappointing\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6471 size=26760 all=2064658 active=104539 piece=atant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6465 size=26780 all=2065633 active=105514 piece=▁licensing\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6459 size=26800 all=2066346 active=106227 piece=▁prononcée\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6456 min_freq=370\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6450 size=26820 all=2067407 active=104378 piece=▁unders\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6442 size=26840 all=2068149 active=105120 piece=abama\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6433 size=26860 all=2068613 active=105584 piece=▁Against\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6425 size=26880 all=2069942 active=106913 piece=▁ripped\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6414 size=26900 all=2070405 active=107376 piece=write\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6414 min_freq=368\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6404 size=26920 all=2071074 active=104037 piece=ocy\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6392 size=26940 all=2072500 active=105463 piece=▁:).\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6380 size=26960 all=2073117 active=106080 piece=▁Maxim\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6372 size=26980 all=2074285 active=107248 piece=▁draps\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6366 size=27000 all=2074878 active=107841 piece=▁mondialisation\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6365 min_freq=367\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6357 size=27020 all=2075521 active=104387 piece=▁JAM\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6349 size=27040 all=2076119 active=104985 piece=▁Tommy\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6341 size=27060 all=2076754 active=105620 piece=again\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6333 size=27080 all=2077387 active=106253 piece=▁persuade\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6322 size=27100 all=2078819 active=107685 piece=▁réflexe\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6322 min_freq=365\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6314 size=27120 all=2079620 active=104742 piece=▁impliqués\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6306 size=27140 all=2080933 active=106055 piece=▁considérées\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6296 size=27160 all=2081776 active=106898 piece=éenne\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6289 size=27180 all=2082608 active=107730 piece=Col\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6282 size=27200 all=2083706 active=108828 piece=▁voyance\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6281 min_freq=363\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6277 size=27220 all=2084595 active=105071 piece=▁surpass\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6270 size=27240 all=2085139 active=105615 piece=▁infinite\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6263 size=27260 all=2085729 active=106205 piece=▁lança\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6255 size=27280 all=2086600 active=107076 piece=▁Understanding\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6248 size=27300 all=2087665 active=108141 piece=▁colonnes\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6248 min_freq=362\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6240 size=27320 all=2088418 active=105137 piece=▁Crédit\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6235 size=27340 all=2088874 active=105593 piece=▁Expert\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6227 size=27360 all=2089458 active=106177 piece=▁Million\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6219 size=27380 all=2090152 active=106871 piece=▁Marvel\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6212 size=27400 all=2090517 active=107236 piece=▁WOW\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6212 min_freq=360\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6204 size=27420 all=2091467 active=105445 piece=adulte\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6198 size=27440 all=2092488 active=106466 piece=▁knitting\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6194 size=27460 all=2094041 active=108019 piece=▁swept\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6187 size=27480 all=2094356 active=108334 piece=▁Accès\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6180 size=27500 all=2094663 active=108641 piece=tique\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6180 min_freq=359\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6173 size=27520 all=2096293 active=106291 piece=▁dentaire\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6167 size=27540 all=2097124 active=107122 piece=▁scolar\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6158 size=27560 all=2098482 active=108480 piece=▁throne\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6150 size=27580 all=2099242 active=109240 piece=▁renfer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6141 size=27600 all=2099779 active=109777 piece=▁bibli\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6141 min_freq=357\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6134 size=27620 all=2100808 active=105992 piece=▁subord\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6127 size=27640 all=2102071 active=107255 piece=▁filtres\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6120 size=27660 all=2102866 active=108050 piece=▁guerr\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6114 size=27680 all=2103664 active=108848 piece=▁dysfunction\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6105 size=27700 all=2104720 active=109904 piece=▁contes\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6105 min_freq=355\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6098 size=27720 all=2105799 active=106315 piece=MC\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6093 size=27740 all=2106933 active=107449 piece=énerve\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6082 size=27760 all=2107292 active=107808 piece=▁abr\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6076 size=27780 all=2109032 active=109548 piece=▁sortira\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6067 size=27800 all=2110374 active=110890 piece=▁referendum\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6066 min_freq=353\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6057 size=27820 all=2110946 active=106091 piece=▁comptez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6051 size=27840 all=2112338 active=107483 piece=▁rescu\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6045 size=27860 all=2113244 active=108389 piece=▁baskets\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6038 size=27880 all=2113563 active=108708 piece=Pourquoi\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6033 size=27900 all=2114718 active=109863 piece=▁récapit\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=6033 min_freq=351\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6024 size=27920 all=2115489 active=106493 piece=accompagn\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6015 size=27940 all=2115832 active=106836 piece=yrénées\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6009 size=27960 all=2116589 active=107593 piece=▁complets\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=6002 size=27980 all=2116839 active=107843 piece=▁grandchildren\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5993 size=28000 all=2117402 active=108406 piece=▁culmin\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5993 min_freq=350\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5988 size=28020 all=2118165 active=106610 piece=▁félicite\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5979 size=28040 all=2118605 active=107050 piece=▁Tch\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5973 size=28060 all=2119543 active=107988 piece=▁litigation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5962 size=28080 all=2120623 active=109068 piece=▁transmet\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5956 size=28100 all=2121107 active=109552 piece=rencies\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5956 min_freq=348\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5951 size=28120 all=2121912 active=106856 piece=▁rows\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5944 size=28140 all=2123439 active=108383 piece=▁fuels\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5936 size=28160 all=2124047 active=108991 piece=▁rud\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5930 size=28180 all=2125495 active=110439 piece=ilde\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5924 size=28200 all=2126453 active=111397 piece=issaient\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5924 min_freq=346\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5916 size=28220 all=2127373 active=107058 piece=▁rois\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5908 size=28240 all=2127829 active=107514 piece=▁Alabama\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5903 size=28260 all=2128456 active=108141 piece=▁Choisir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5897 size=28280 all=2129183 active=108868 piece=▁déroulent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5889 size=28300 all=2130398 active=110083 piece=▁bridges\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5889 min_freq=345\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5882 size=28320 all=2131036 active=107155 piece=incap\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5874 size=28340 all=2131573 active=107692 piece=▁chagrin\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5867 size=28360 all=2132007 active=108126 piece=▁deceased\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5858 size=28380 all=2132374 active=108493 piece=▁Rappelons\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5850 size=28400 all=2133373 active=109492 piece=▁égoï\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5850 min_freq=344\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5842 size=28420 all=2134267 active=107556 piece=▁chatting\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5834 size=28440 all=2134621 active=107910 piece=▁Commune\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5828 size=28460 all=2135623 active=108912 piece=▁Bella\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5819 size=28480 all=2136253 active=109542 piece=▁chevalier\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5809 size=28500 all=2137608 active=110897 piece=▁destructive\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5808 min_freq=342\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5802 size=28520 all=2138213 active=107486 piece=▁wears\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5795 size=28540 all=2139208 active=108481 piece=▁candles\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5790 size=28560 all=2139827 active=109100 piece=▁galax\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5784 size=28580 all=2140273 active=109546 piece=▁Daddy\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5779 size=28600 all=2141153 active=110426 piece=▁Budd\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5779 min_freq=341\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5773 size=28620 all=2141751 active=107627 piece=UI\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5766 size=28640 all=2143324 active=109200 piece=▁finesse\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5760 size=28660 all=2143739 active=109615 piece=▁acne\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5755 size=28680 all=2144976 active=110852 piece=▁chasseur\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5752 size=28700 all=2145382 active=111258 piece=▁rebelles\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5752 min_freq=340\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5745 size=28720 all=2145929 active=107817 piece=▁resent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5738 size=28740 all=2146349 active=108237 piece=vens\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5730 size=28760 all=2147264 active=109152 piece=▁gestionnaires\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5723 size=28780 all=2148144 active=110032 piece=assage\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5714 size=28800 all=2148666 active=110554 piece=\";\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5714 min_freq=339\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5708 size=28820 all=2149343 active=108056 piece=▁Palestine\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5702 size=28840 all=2149973 active=108686 piece=▁scénar\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5695 size=28860 all=2150481 active=109194 piece=▁Trésor\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5687 size=28880 all=2151702 active=110415 piece=oil\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5682 size=28900 all=2152570 active=111283 piece=▁collector\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5682 min_freq=337\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5675 size=28920 all=2152952 active=108005 piece=▁Kos\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5670 size=28940 all=2154087 active=109140 piece=▁vole\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5663 size=28960 all=2154947 active=110000 piece=ullition\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5654 size=28980 all=2156690 active=111743 piece=▁ron\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5646 size=29000 all=2157406 active=112459 piece=▁Damn\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5646 min_freq=335\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5641 size=29020 all=2158015 active=108457 piece=dling\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5635 size=29040 all=2158567 active=109009 piece=▁bulletins\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5629 size=29060 all=2159165 active=109607 piece=▁weddings\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5621 size=29080 all=2159778 active=110220 piece=▁Masters\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5614 size=29100 all=2160472 active=110914 piece=_^\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5614 min_freq=334\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5608 size=29120 all=2161299 active=108786 piece=▁revelation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5600 size=29140 all=2161988 active=109475 piece=▁unlock\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5595 size=29160 all=2162400 active=109887 piece=▁Address\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5590 size=29180 all=2163500 active=110987 piece=▁sentit\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5585 size=29200 all=2163948 active=111435 piece=▁reminding\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5585 min_freq=333\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5578 size=29220 all=2164329 active=108577 piece=▁victoires\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5570 size=29240 all=2164836 active=109084 piece=▁chômeurs\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5564 size=29260 all=2165650 active=109898 piece=*,\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5559 size=29280 all=2166446 active=110694 piece=étudiants\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5555 size=29300 all=2166813 active=111061 piece=▁certificats\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5554 min_freq=332\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5546 size=29320 all=2167307 active=108834 piece=▁repère\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5538 size=29340 all=2167729 active=109256 piece=▁fonctionné\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5533 size=29360 all=2168698 active=110225 piece=▁paysans\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5529 size=29380 all=2169625 active=111152 piece=▁rappelons\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5523 size=29400 all=2170054 active=111581 piece=▁acquiring\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5522 min_freq=331\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5516 size=29420 all=2170462 active=108911 piece=▁satisfaisant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5509 size=29440 all=2171413 active=109862 piece=▁sonores\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5502 size=29460 all=2172395 active=110844 piece=▁maigrir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5497 size=29480 all=2173219 active=111668 piece=▁reputable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5487 size=29500 all=2174560 active=113009 piece=▁outright\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5487 min_freq=329\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5483 size=29520 all=2175138 active=109305 piece=endettement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5476 size=29540 all=2175666 active=109833 piece=traitance\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5470 size=29560 all=2176005 active=110172 piece=entretenir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5465 size=29580 all=2176430 active=110597 piece=▁vocabulary\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5456 size=29600 all=2176854 active=111021 piece=▁demandés\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5455 min_freq=328\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5447 size=29620 all=2177120 active=109109 piece=▁maid\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5441 size=29640 all=2178224 active=110213 piece=▁emprunter\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5433 size=29660 all=2178912 active=110901 piece=▁socialistes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5425 size=29680 all=2179266 active=111255 piece=▁rencontrées\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5421 size=29700 all=2180023 active=112012 piece=▁paramètre\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5421 min_freq=327\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5413 size=29720 all=2180412 active=109387 piece=▁unlaw\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5409 size=29740 all=2181664 active=110639 piece=▁considérations\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5401 size=29760 all=2182050 active=111025 piece=!’\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5395 size=29780 all=2182686 active=111661 piece=▁représentés\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5388 size=29800 all=2183360 active=112335 piece=▁incertain\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5388 min_freq=326\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5382 size=29820 all=2183979 active=109781 piece=topic\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5377 size=29840 all=2184822 active=110624 piece=▁Chelsea\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5371 size=29860 all=2186080 active=111882 piece=▁rubbish\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5364 size=29880 all=2186898 active=112700 piece=without\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5357 size=29900 all=2187875 active=113677 piece=▁LLC\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5357 min_freq=324\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5352 size=29920 all=2188558 active=110069 piece=▁fabuleux\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5346 size=29940 all=2188825 active=110336 piece=asa\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5341 size=29960 all=2189583 active=111094 piece=▁bâton\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5333 size=29980 all=2190735 active=112246 piece=▁aggrav\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5327 size=30000 all=2191188 active=112699 piece=▁playground\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5326 min_freq=323\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5321 size=30020 all=2191914 active=110283 piece=ielding\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5316 size=30040 all=2192480 active=110849 piece=▁recueillies\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5310 size=30060 all=2193727 active=112096 piece=▁Collège\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5303 size=30080 all=2194378 active=112747 piece=▁probation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5298 size=30100 all=2195029 active=113398 piece=▁driveway\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5298 min_freq=322\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5292 size=30120 all=2195484 active=110207 piece=▁orgas\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5282 size=30140 all=2196519 active=111242 piece=▁arrogant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5276 size=30160 all=2196602 active=111325 piece=▁soie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5270 size=30180 all=2198008 active=112731 piece=▁mins\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5265 size=30200 all=2198671 active=113394 piece=▁ministers\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5264 min_freq=321\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5260 size=30220 all=2199477 active=110737 piece=arras\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5255 size=30240 all=2200181 active=111441 piece=▁sap\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5249 size=30260 all=2200768 active=112028 piece=uthanas\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5242 size=30280 all=2201629 active=112889 piece=▁piqu\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5236 size=30300 all=2202243 active=113503 piece=aigu\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5236 min_freq=319\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5229 size=30320 all=2202889 active=110698 piece=avement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5224 size=30340 all=2203655 active=111464 piece=▁facilit\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5220 size=30360 all=2204386 active=112195 piece=▁chiot\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5214 size=30380 all=2205026 active=112835 piece=▁Sakura\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5209 size=30400 all=2205738 active=113547 piece=▁THERE\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5209 min_freq=318\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5204 size=30420 all=2206487 active=111019 piece=▁redém\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5200 size=30440 all=2206893 active=111425 piece=▁jumps\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5194 size=30460 all=2208570 active=113102 piece=▁actives\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5186 size=30480 all=2209458 active=113990 piece=▁breeding\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5181 size=30500 all=2209872 active=114404 piece=▁RC\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5181 min_freq=317\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5174 size=30520 all=2210646 active=111177 piece=▁Presse\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5169 size=30540 all=2211701 active=112232 piece=▁résidences\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5163 size=30560 all=2212282 active=112813 piece=▁meurent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5154 size=30580 all=2212417 active=112948 piece=▁Justement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5149 size=30600 all=2213548 active=114079 piece=raux\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5149 min_freq=315\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5143 size=30620 all=2214526 active=111589 piece=▁Transfer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5137 size=30640 all=2214798 active=111861 piece=....\"\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5133 size=30660 all=2215815 active=112878 piece=▁législative\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5126 size=30680 all=2217120 active=114183 piece=façon\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5120 size=30700 all=2217386 active=114449 piece=▁mater\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5120 min_freq=314\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5115 size=30720 all=2218041 active=111497 piece=▁cheminement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5109 size=30740 all=2218444 active=111900 piece=ènera\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5106 size=30760 all=2219095 active=112551 piece=▁attributions\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5102 size=30780 all=2220438 active=113894 piece=▁brigade\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5096 size=30800 all=2221451 active=114907 piece=▁voudra\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5096 min_freq=313\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5092 size=30820 all=2221783 active=111400 piece=▁empowered\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5087 size=30840 all=2222371 active=111988 piece=▁laissés\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5081 size=30860 all=2223178 active=112795 piece=▁hurricane\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5075 size=30880 all=2223972 active=113589 piece=▁epidemic\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5069 size=30900 all=2224773 active=114390 piece=phém\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5069 min_freq=312\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5064 size=30920 all=2226017 active=112447 piece=auguration\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5057 size=30940 all=2227216 active=113646 piece=▁Jou\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5052 size=30960 all=2229182 active=115612 piece=usp\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5049 size=30980 all=2229427 active=115857 piece=▁Camille\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5044 size=31000 all=2229829 active=116259 piece=▁Picture\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5043 min_freq=310\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5039 size=31020 all=2231019 active=112649 piece=▁confisc\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5033 size=31040 all=2232024 active=113654 piece=▁RO\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5028 size=31060 all=2232638 active=114268 piece=hero\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5024 size=31080 all=2233309 active=114939 piece=▁risqué\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5019 size=31100 all=2234166 active=115796 piece=▁penis\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=5019 min_freq=309\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5015 size=31120 all=2235175 active=112707 piece=▁chooses\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5011 size=31140 all=2235661 active=113193 piece=▁Shipping\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5007 size=31160 all=2236213 active=113745 piece=▁Economics\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=5003 size=31180 all=2236757 active=114289 piece=▁verrai\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4996 size=31200 all=2237284 active=114816 piece=▁renforcé\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4996 min_freq=308\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4988 size=31220 all=2237730 active=112310 piece=▁BMW\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4982 size=31240 all=2238387 active=112967 piece=▁plongé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4977 size=31260 all=2238677 active=113257 piece=▁deputy\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4973 size=31280 all=2239105 active=113685 piece=▁graphisme\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4968 size=31300 all=2239864 active=114444 piece=▁douanes\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4968 min_freq=307\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4960 size=31320 all=2240612 active=112741 piece=▁aviation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4954 size=31340 all=2241429 active=113558 piece=▁immersion\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4949 size=31360 all=2242172 active=114301 piece=▁aire\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4943 size=31380 all=2242890 active=115019 piece=etical\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4939 size=31400 all=2243359 active=115488 piece=▁accusation\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4938 min_freq=306\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4934 size=31420 all=2243660 active=112469 piece=▁livest\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4928 size=31440 all=2244007 active=112816 piece=▁Sais\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4923 size=31460 all=2244596 active=113405 piece=▁vagin\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4918 size=31480 all=2245051 active=113860 piece=LL\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4912 size=31500 all=2245602 active=114411 piece=ovo\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4912 min_freq=305\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4907 size=31520 all=2246330 active=112829 piece=▁Fried\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4903 size=31540 all=2246524 active=113023 piece=▁corrigé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4897 size=31560 all=2247185 active=113684 piece=▁opérer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4893 size=31580 all=2247489 active=113988 piece=▁Charter\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4886 size=31600 all=2247710 active=114209 piece=▁bitcoin\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4886 min_freq=304\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4879 size=31620 all=2248318 active=112981 piece=▁nager\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4872 size=31640 all=2248847 active=113510 piece=▁slope\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4867 size=31660 all=2249203 active=113866 piece=THER\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4863 size=31680 all=2250183 active=114846 piece=▁lawmakers\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4856 size=31700 all=2251459 active=116122 piece=exact\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4856 min_freq=303\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4849 size=31720 all=2251976 active=113060 piece=▁Honda\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4845 size=31740 all=2252336 active=113420 piece=▁instantanément\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4838 size=31760 all=2252995 active=114079 piece=▁Ralph\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4832 size=31780 all=2253417 active=114501 piece=▁hears\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4826 size=31800 all=2254335 active=115419 piece=UTC\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4826 min_freq=302\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4820 size=31820 all=2254941 active=113306 piece=▁Eating\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4811 size=31840 all=2255348 active=113713 piece=▁Tag\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4806 size=31860 all=2256041 active=114406 piece=▁mete\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4800 size=31880 all=2256273 active=114638 piece=▁router\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4797 size=31900 all=2256859 active=115224 piece=▁prenantes\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4797 min_freq=301\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4793 size=31920 all=2257049 active=113033 piece=▁placées\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4789 size=31940 all=2258124 active=114108 piece=imposent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4784 size=31960 all=2258502 active=114486 piece=wi\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4780 size=31980 all=2259957 active=115941 piece=▁drying\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4776 size=32000 all=2260292 active=116276 piece=▁bordure\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4776 min_freq=300\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4771 size=32020 all=2260872 active=113593 piece=ellows\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4766 size=32040 all=2261661 active=114382 piece=▁dimanches\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4759 size=32060 all=2262701 active=115422 piece=▁hereby\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4755 size=32080 all=2263606 active=116327 piece=▁Employees\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4751 size=32100 all=2264409 active=117130 piece=▁variant\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4750 min_freq=299\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4746 size=32120 all=2264937 active=113747 piece=▁canadiens\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4741 size=32140 all=2265498 active=114308 piece=outch\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4736 size=32160 all=2266121 active=114931 piece=▁Evolution\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4730 size=32180 all=2266296 active=115106 piece=etz\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4727 size=32200 all=2267103 active=115913 piece=▁Hurricane\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4727 min_freq=298\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4723 size=32220 all=2267921 active=114169 piece=▁Faculté\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4717 size=32240 all=2268366 active=114614 piece=▁dryer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4712 size=32260 all=2269752 active=116000 piece=▁potager\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4708 size=32280 all=2270059 active=116307 piece=élation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4705 size=32300 all=2270609 active=116857 piece=▁thérapeutiques\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4704 min_freq=296\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4701 size=32320 all=2271957 active=114879 piece=▁Alb\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4696 size=32340 all=2272576 active=115498 piece=▁manners\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4692 size=32360 all=2273065 active=115987 piece=▁insérer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4687 size=32380 all=2273900 active=116822 piece=▁permettez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4682 size=32400 all=2274121 active=117043 piece=▁synthetic\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4682 min_freq=295\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4678 size=32420 all=2274589 active=114173 piece=▁electro\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4675 size=32440 all=2275267 active=114851 piece=▁propagation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4670 size=32460 all=2275769 active=115353 piece=▁messed\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4666 size=32480 all=2276754 active=116338 piece=▁TTC\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4663 size=32500 all=2277022 active=116606 piece=▁satisfaisante\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4662 min_freq=294\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4655 size=32520 all=2277500 active=114330 piece=▁déplacé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4650 size=32540 all=2278067 active=114897 piece=▁affecter\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4644 size=32560 all=2278686 active=115516 piece=▁Vista\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4639 size=32580 all=2279702 active=116532 piece=▁prostitu\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4635 size=32600 all=2280811 active=117641 piece=▁Bai\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4635 min_freq=293\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4627 size=32620 all=2281386 active=114559 piece=▁palli\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4623 size=32640 all=2282809 active=115982 piece=▁balancing\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4617 size=32660 all=2283558 active=116731 piece=▁Steps\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4613 size=32680 all=2283822 active=116995 piece=▁slowing\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4609 size=32700 all=2284455 active=117628 piece=▁correspondants\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4608 min_freq=292\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4604 size=32720 all=2285598 active=115366 piece=▁passation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4597 size=32740 all=2286437 active=116205 piece=élég\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4591 size=32760 all=2287099 active=116867 piece=▁incite\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4586 size=32780 all=2287683 active=117451 piece=▁yo\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4579 size=32800 all=2288428 active=118196 piece=▁gosses\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4579 min_freq=291\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4576 size=32820 all=2289268 active=115260 piece=appelait\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4573 size=32840 all=2290197 active=116189 piece=▁gât\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4570 size=32860 all=2290920 active=116912 piece=▁turnover\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4564 size=32880 all=2291594 active=117586 piece=vency\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4559 size=32900 all=2292340 active=118332 piece=▁Hamps\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4559 min_freq=290\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4555 size=32920 all=2293141 active=115408 piece=▁ballet\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4550 size=32940 all=2293866 active=116133 piece=ensable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4546 size=32960 all=2295167 active=117434 piece=▁bonté\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4543 size=32980 all=2296425 active=118692 piece=▁contributes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4538 size=33000 all=2297150 active=119417 piece=olat\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4538 min_freq=288\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4534 size=33020 all=2297907 active=115476 piece=▁Guests\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4529 size=33040 all=2298379 active=115948 piece=avascript\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4523 size=33060 all=2298842 active=116411 piece=▁kios\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4519 size=33080 all=2299156 active=116725 piece=▁animales\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4515 size=33100 all=2299504 active=117073 piece=▁briefing\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4514 min_freq=288\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4511 size=33120 all=2299855 active=115325 piece=▁galeries\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4506 size=33140 all=2300729 active=116199 piece=▁vitale\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4504 size=33160 all=2301507 active=116977 piece=▁envoyant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4500 size=33180 all=2302237 active=117707 piece=▁philosophical\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4495 size=33200 all=2302963 active=118433 piece=▁Adult\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4495 min_freq=287\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4491 size=33220 all=2303702 active=115855 piece=▁silently\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4485 size=33240 all=2304230 active=116383 piece=issy\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4482 size=33260 all=2304925 active=117078 piece=▁souvenez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4478 size=33280 all=2306769 active=118922 piece=▁reun\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4474 size=33300 all=2307740 active=119893 piece=▁faction\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4474 min_freq=285\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4471 size=33320 all=2307824 active=115460 piece=▁donnerait\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4467 size=33340 all=2308507 active=116143 piece=▁Hebrew\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4462 size=33360 all=2308982 active=116618 piece=▁weary\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4454 size=33380 all=2309826 active=117462 piece=▁Juge\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4450 size=33400 all=2310299 active=117935 piece=▁Austria\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4449 min_freq=284\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4446 size=33420 all=2310687 active=115903 piece=▁downloads\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4442 size=33440 all=2311389 active=116605 piece=▁Chase\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4437 size=33460 all=2311957 active=117173 piece=endix\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4433 size=33480 all=2312910 active=118126 piece=▁avancés\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4427 size=33500 all=2313395 active=118611 piece=▁fundamentally\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4426 min_freq=283\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4421 size=33520 all=2313903 active=116178 piece=▁arguably\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4413 size=33540 all=2314644 active=116919 piece=▁nuance\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4409 size=33560 all=2314981 active=117256 piece=▁dragging\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4403 size=33580 all=2315329 active=117604 piece=▁trottoir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4400 size=33600 all=2315967 active=118242 piece=▁CBD\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4400 min_freq=282\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4392 size=33620 all=2316795 active=116611 piece=▁Stu\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4387 size=33640 all=2317248 active=117064 piece=cette\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4381 size=33660 all=2318278 active=118094 piece=ainted\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4377 size=33680 all=2319390 active=119206 piece=▁ouvertement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4374 size=33700 all=2321015 active=120831 piece=▁identifiant\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4373 min_freq=281\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4368 size=33720 all=2322267 active=117303 piece=▁herbal\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4362 size=33740 all=2322848 active=117884 piece=▁soucieux\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4358 size=33760 all=2323062 active=118098 piece=▁Corinth\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4353 size=33780 all=2323908 active=118944 piece=icienne\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4349 size=33800 all=2324228 active=119264 piece=▁stables\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4349 min_freq=280\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4344 size=33820 all=2324938 active=116922 piece=▁fâche\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4339 size=33840 all=2325451 active=117435 piece=▁Colombie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4332 size=33860 all=2326378 active=118362 piece=PH\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4329 size=33880 all=2327319 active=119303 piece=▁Immigration\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4322 size=33900 all=2327803 active=119787 piece=▁Seth\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4322 min_freq=279\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4319 size=33920 all=2328624 active=117170 piece=▁généré\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4313 size=33940 all=2329002 active=117548 piece=▁oak\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4309 size=33960 all=2329579 active=118125 piece=▁classée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4305 size=33980 all=2330470 active=119016 piece=ignante\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4300 size=34000 all=2330869 active=119415 piece=▁penn\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4300 min_freq=278\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4296 size=34020 all=2331300 active=116916 piece=▁Cycl\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4293 size=34040 all=2331916 active=117532 piece=▁vacations\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4290 size=34060 all=2332517 active=118133 piece=▁_________\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4285 size=34080 all=2333005 active=118621 piece=▁Arc\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4280 size=34100 all=2333497 active=119113 piece=▁noe\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4280 min_freq=277\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4276 size=34120 all=2333858 active=117021 piece=▁compatibilité\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4271 size=34140 all=2334204 active=117367 piece=▁solenn\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4267 size=34160 all=2334963 active=118126 piece=▁Sage\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4261 size=34180 all=2335794 active=118957 piece=▁servis\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4257 size=34200 all=2337155 active=120318 piece=▁rémunérations\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4256 min_freq=276\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4251 size=34220 all=2337908 active=117611 piece=▁Kil\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4248 size=34240 all=2338527 active=118230 piece=▁contingent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4244 size=34260 all=2338974 active=118677 piece=▁disparus\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4240 size=34280 all=2339622 active=119325 piece=exercices\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4234 size=34300 all=2340105 active=119808 piece=▁oneself\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4234 min_freq=275\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4231 size=34320 all=2340919 active=117820 piece=▁vicinity\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4227 size=34340 all=2341596 active=118497 piece=▁Plaza\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4223 size=34360 all=2342679 active=119580 piece=▁Kab\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4219 size=34380 all=2343898 active=120799 piece=▁ideally\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4215 size=34400 all=2344633 active=121534 piece=▁idle\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4215 min_freq=274\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4212 size=34420 all=2344694 active=117287 piece=▁Utilisation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4209 size=34440 all=2344875 active=117468 piece=▁buys\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4206 size=34460 all=2346039 active=118632 piece=▁laun\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4203 size=34480 all=2346237 active=118830 piece=essais\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4200 size=34500 all=2346694 active=119287 piece=▁Alfred\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4200 min_freq=273\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4197 size=34520 all=2347582 active=118208 piece=▁VIH\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4192 size=34540 all=2348032 active=118658 piece=▁rubbed\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4186 size=34560 all=2348705 active=119331 piece=depuis\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4181 size=34580 all=2349525 active=120151 piece=▁Retail\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4179 size=34600 all=2350025 active=120651 piece=▁assurément\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4178 min_freq=272\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4173 size=34620 all=2351237 active=118714 piece=▁CHAP\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4168 size=34640 all=2351639 active=119116 piece=kit\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4163 size=34660 all=2352483 active=119960 piece=▁déterminés\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4159 size=34680 all=2353030 active=120507 piece=▁Excellence\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4156 size=34700 all=2353348 active=120825 piece=▁prévisible\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4155 min_freq=271\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4152 size=34720 all=2354555 active=118875 piece=ulence\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4149 size=34740 all=2355211 active=119531 piece=baye\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4146 size=34760 all=2356424 active=120744 piece=▁diverg\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4141 size=34780 all=2356981 active=121301 piece=],[\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4138 size=34800 all=2357410 active=121730 piece=▁célibataires\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4137 min_freq=270\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4134 size=34820 all=2357736 active=118196 piece=▁Eg\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4132 size=34840 all=2358218 active=118678 piece=▁cherchons\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4128 size=34860 all=2358713 active=119173 piece=▁Divers\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4125 size=34880 all=2360147 active=120607 piece=wordpress\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4121 size=34900 all=2360612 active=121072 piece=▁Sie\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4121 min_freq=269\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4117 size=34920 all=2361285 active=118622 piece=▁enforced\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4112 size=34940 all=2361792 active=119129 piece=▁Diabetes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4109 size=34960 all=2362136 active=119473 piece=▁tch\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4106 size=34980 all=2363222 active=120559 piece=▁brume\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4102 size=35000 all=2363762 active=121099 piece=▁Fuk\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4102 min_freq=268\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4098 size=35020 all=2364045 active=118408 piece=▁dévers\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4095 size=35040 all=2364423 active=118786 piece=▁complétée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4091 size=35060 all=2365990 active=120353 piece=▁popcorn\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4089 size=35080 all=2366872 active=121235 piece=insuffisance\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4083 size=35100 all=2367309 active=121672 piece=▁appuyé\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4083 min_freq=267\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4079 size=35120 all=2368255 active=119312 piece=▁herd\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4075 size=35140 all=2369044 active=120101 piece=accroître\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4070 size=35160 all=2369755 active=120812 piece=avid\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4067 size=35180 all=2370801 active=121858 piece=▁commém\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4064 size=35200 all=2371859 active=122916 piece=▁Lorraine\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4064 min_freq=266\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4060 size=35220 all=2372826 active=119552 piece=▁Frais\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4056 size=35240 all=2373703 active=120429 piece=▁vitres\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4052 size=35260 all=2373887 active=120613 piece=estimate\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4048 size=35280 all=2374270 active=120996 piece=▁empreinte\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4044 size=35300 all=2374698 active=121424 piece=▁convenable\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4044 min_freq=265\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4039 size=35320 all=2375487 active=119523 piece=▁redoutable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4036 size=35340 all=2376350 active=120386 piece=ELS\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4034 size=35360 all=2377300 active=121336 piece=▁punition\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4030 size=35380 all=2378221 active=122257 piece=▁feminist\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4023 size=35400 all=2378410 active=122446 piece=indust\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4023 min_freq=264\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4020 size=35420 all=2379009 active=119434 piece=▁batailles\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4014 size=35440 all=2379702 active=120127 piece=▁catastrophique\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4009 size=35460 all=2380015 active=120440 piece=▁légaux\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4007 size=35480 all=2380736 active=121161 piece=▁conventionnelle\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4003 size=35500 all=2381501 active=121926 piece=▁natives\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=4003 min_freq=263\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=4000 size=35520 all=2382051 active=119626 piece=West\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3997 size=35540 all=2382415 active=119990 piece=▁experimenting\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3992 size=35560 all=2382833 active=120408 piece=▁Relax\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3989 size=35580 all=2383309 active=120884 piece=inscrivent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3985 size=35600 all=2383601 active=121176 piece=▁raft\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3985 min_freq=263\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3981 size=35620 all=2384318 active=119887 piece=▁crypto\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3976 size=35640 all=2384589 active=120158 piece=::\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3973 size=35660 all=2385122 active=120691 piece=onnait\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3970 size=35680 all=2386153 active=121722 piece=▁pizzas\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3968 size=35700 all=2386505 active=122074 piece=▁Activités\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3968 min_freq=262\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3964 size=35720 all=2386877 active=119698 piece=opathic\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3960 size=35740 all=2387557 active=120378 piece=▁Fitz\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3957 size=35760 all=2388305 active=121126 piece=▁Informatique\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3953 size=35780 all=2389123 active=121944 piece=▁Vladimir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3949 size=35800 all=2389547 active=122368 piece=export\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3949 min_freq=261\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3946 size=35820 all=2390162 active=120054 piece=▁déséquilib\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3942 size=35840 all=2391592 active=121484 piece=illants\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3939 size=35860 all=2392758 active=122650 piece=▁culinary\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3935 size=35880 all=2393742 active=123634 piece=▁rigide\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3931 size=35900 all=2394121 active=124013 piece=▁recouvre\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3931 min_freq=260\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3928 size=35920 all=2394527 active=120108 piece=▁naming\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3923 size=35940 all=2395279 active=120860 piece=▁Shame\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3920 size=35960 all=2395911 active=121492 piece=▁Yourself\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3916 size=35980 all=2397764 active=123345 piece=osevelt\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3912 size=36000 all=2398313 active=123894 piece=▁forfaits\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3912 min_freq=259\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3908 size=36020 all=2398925 active=120528 piece=▁livrée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3905 size=36040 all=2399698 active=121301 piece=▁cristall\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3901 size=36060 all=2400547 active=122150 piece=▁Figaro\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3898 size=36080 all=2401804 active=123407 piece=performance\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3894 size=36100 all=2402710 active=124313 piece=presque\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3894 min_freq=258\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3892 size=36120 all=2402990 active=120406 piece=▁préventive\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3888 size=36140 all=2403666 active=121082 piece=▁indien\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3884 size=36160 all=2404111 active=121527 piece=pourquoi\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3881 size=36180 all=2405091 active=122507 piece=▁galaxy\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3878 size=36200 all=2405419 active=122835 piece=motiv\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3878 min_freq=257\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3875 size=36220 all=2406181 active=120961 piece=▁bais\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3870 size=36240 all=2406953 active=121733 piece=▁filets\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3866 size=36260 all=2407285 active=122065 piece=▁Venise\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3863 size=36280 all=2408208 active=122988 piece=onnell\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3861 size=36300 all=2408730 active=123510 piece=▁catalyst\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3861 min_freq=256\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3856 size=36320 all=2409094 active=120800 piece=▁empois\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3853 size=36340 all=2409393 active=121099 piece=honnêteté\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3847 size=36360 all=2410173 active=121879 piece=SL\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3842 size=36380 all=2410965 active=122671 piece=▁AMAZ\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3839 size=36400 all=2411181 active=122887 piece=▁frustrations\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3838 min_freq=255\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3836 size=36420 all=2411823 active=121202 piece=▁cracking\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3832 size=36440 all=2412937 active=122316 piece=▁départementales\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3828 size=36460 all=2413175 active=122554 piece=▁décour\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3825 size=36480 all=2413702 active=123081 piece=ometry\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3822 size=36500 all=2414720 active=124099 piece=▁épaisseur\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3821 min_freq=254\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3818 size=36520 all=2415768 active=121784 piece=▁Interestingly\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3815 size=36540 all=2416794 active=122810 piece=▁écoutez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3812 size=36560 all=2416976 active=122992 piece=▁marchent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3808 size=36580 all=2417161 active=123177 piece=▁téléchargé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3804 size=36600 all=2417997 active=124013 piece=▁teasp\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3804 min_freq=253\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3800 size=36620 all=2418528 active=121428 piece=▁pointers\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3796 size=36640 all=2419351 active=122251 piece=▁variée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3794 size=36660 all=2420237 active=123137 piece=▁versements\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3791 size=36680 all=2420966 active=123866 piece=▁Richards\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3788 size=36700 all=2421721 active=124621 piece=▁Faculty\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3788 min_freq=252\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3785 size=36720 all=2422216 active=121578 piece=▁lignée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3782 size=36740 all=2422657 active=122019 piece=▁insuffisante\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3779 size=36760 all=2422844 active=122206 piece=▁naît\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3775 size=36780 all=2423033 active=122395 piece=$.\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3771 size=36800 all=2423839 active=123201 piece=ificial\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3771 min_freq=252\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3767 size=36820 all=2424954 active=122282 piece=oner\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3765 size=36840 all=2425630 active=122958 piece=▁GRAND\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3761 size=36860 all=2426068 active=123396 piece=▁Dijon\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3756 size=36880 all=2426159 active=123487 piece=aner\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3752 size=36900 all=2427250 active=124578 piece=Mo\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3752 min_freq=251\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3750 size=36920 all=2428195 active=121965 piece=▁drafted\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3747 size=36940 all=2428648 active=122418 piece=péries\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3743 size=36960 all=2429159 active=122929 piece=▁inégal\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3739 size=36980 all=2429598 active=123368 piece=▁Yankees\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3735 size=37000 all=2429934 active=123704 piece=▁Membres\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3735 min_freq=250\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3732 size=37020 all=2430284 active=121847 piece=ouvrent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3728 size=37040 all=2430933 active=122496 piece=incarnation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3724 size=37060 all=2431576 active=123139 piece=sided\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3721 size=37080 all=2432361 active=123924 piece=EMBRE\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3718 size=37100 all=2433230 active=124793 piece=▁SOME\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3718 min_freq=249\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3715 size=37120 all=2434051 active=122464 piece=▁caresser\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3711 size=37140 all=2435645 active=124058 piece=▁Yun\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3707 size=37160 all=2435884 active=124297 piece=▁Kee\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3705 size=37180 all=2436875 active=125288 piece=▁Bund\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3703 size=37200 all=2437162 active=125575 piece=▁Apprenez\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3703 min_freq=248\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3699 size=37220 all=2437713 active=122410 piece=▁chirurgicale\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3695 size=37240 all=2438227 active=122924 piece=▁riverains\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3691 size=37260 all=2438570 active=123267 piece=▁rivals\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3687 size=37280 all=2438993 active=123690 piece=ivage\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3685 size=37300 all=2439872 active=124569 piece=▁reprennent\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3685 min_freq=248\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3681 size=37320 all=2440564 active=122686 piece=▁therein\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3679 size=37340 all=2441128 active=123250 piece=▁sponsorship\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3676 size=37360 all=2441822 active=123944 piece=chlor\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3673 size=37380 all=2442621 active=124743 piece=▁escapade\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3669 size=37400 all=2442930 active=125052 piece=rt\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3669 min_freq=247\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3665 size=37420 all=2444771 active=123466 piece=▁fonts\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3661 size=37440 all=2445100 active=123795 piece=▁pumping\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3658 size=37460 all=2445761 active=124456 piece=etit\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3655 size=37480 all=2446825 active=125520 piece=demand\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3651 size=37500 all=2447521 active=126216 piece=▁Wik\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3651 min_freq=246\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3649 size=37520 all=2448492 active=123240 piece=▁contention\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3647 size=37540 all=2449214 active=123962 piece=▁récidive\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3644 size=37560 all=2449826 active=124574 piece=avortement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3640 size=37580 all=2450761 active=125509 piece=▁Butler\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3637 size=37600 all=2451220 active=125968 piece=▁représentatif\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3636 min_freq=245\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3633 size=37620 all=2452500 active=123841 piece=▁brides\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3629 size=37640 all=2453137 active=124478 piece=▁aiguë\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3626 size=37660 all=2453395 active=124736 piece=▁innocents\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3622 size=37680 all=2454192 active=125533 piece=borough\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3619 size=37700 all=2455252 active=126593 piece=▁Pauv\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3619 min_freq=244\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3617 size=37720 all=2455661 active=123163 piece=▁Richmond\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3613 size=37740 all=2456348 active=123850 piece=▁Trinity\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3609 size=37760 all=2456882 active=124384 piece=▁ciment\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3606 size=37780 all=2457433 active=124935 piece=▁toiles\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3602 size=37800 all=2457673 active=125175 piece=▁masks\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3602 min_freq=243\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3599 size=37820 all=2458733 active=123943 piece=▁inert\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3596 size=37840 all=2459152 active=124362 piece=▁congén\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3593 size=37860 all=2459586 active=124796 piece=▁crever\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3589 size=37880 all=2460183 active=125393 piece=▁Wealth\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3587 size=37900 all=2461066 active=126276 piece=▁navy\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3587 min_freq=242\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3584 size=37920 all=2461677 active=123659 piece=▁Nig\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3581 size=37940 all=2462106 active=124088 piece=▁entrés\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3578 size=37960 all=2463130 active=125112 piece=▁circulent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3575 size=37980 all=2463544 active=125526 piece=▁disastrous\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3572 size=38000 all=2464010 active=125992 piece=ominations\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3571 min_freq=242\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3569 size=38020 all=2464676 active=123855 piece=▁Conception\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3566 size=38040 all=2465105 active=124284 piece=▁endeavors\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3562 size=38060 all=2465837 active=125016 piece=▁diagnostics\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3558 size=38080 all=2466393 active=125572 piece=▁canada\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3556 size=38100 all=2466842 active=126021 piece=▁interchange\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3555 min_freq=241\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3553 size=38120 all=2467494 active=123985 piece=mph\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3550 size=38140 all=2468232 active=124723 piece=▁stupidity\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3545 size=38160 all=2468727 active=125218 piece=▁weeds\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3543 size=38180 all=2469210 active=125701 piece=▁gestation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3539 size=38200 all=2469542 active=126033 piece=▁arranger\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3539 min_freq=240\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3534 size=38220 all=2470335 active=124263 piece=▁aéroports\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3531 size=38240 all=2471092 active=125020 piece=▁mutuelles\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3528 size=38260 all=2471908 active=125836 piece=▁optez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3525 size=38280 all=2472458 active=126386 piece=weigh\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3522 size=38300 all=2473202 active=127130 piece=aumes\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3522 min_freq=239\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3518 size=38320 all=2473739 active=124152 piece=▁Pul\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3515 size=38340 all=2474634 active=125047 piece=▁Chevalier\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3512 size=38360 all=2475502 active=125915 piece=wash\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3510 size=38380 all=2476095 active=126508 piece=▁appréciés\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3505 size=38400 all=2476513 active=126926 piece=emprunte\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3505 min_freq=239\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3503 size=38420 all=2476823 active=124129 piece=▁Agence\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3500 size=38440 all=2478012 active=125318 piece=histoires\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3497 size=38460 all=2478619 active=125925 piece=encour\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3495 size=38480 all=2478989 active=126295 piece=▁ustens\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3492 size=38500 all=2479694 active=127000 piece=conform\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3492 min_freq=238\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3490 size=38520 all=2479996 active=124243 piece=▁trophy\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3487 size=38540 all=2481120 active=125367 piece=▁Colombia\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3484 size=38560 all=2481780 active=126027 piece=endurance\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3481 size=38580 all=2482769 active=127016 piece=▁Refuge\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3477 size=38600 all=2483154 active=127401 piece=OPLE\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3477 min_freq=237\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3474 size=38620 all=2484153 active=125137 piece=▁Selected\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3470 size=38640 all=2484741 active=125725 piece=chais\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3467 size=38660 all=2485282 active=126266 piece=▁empreintes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3463 size=38680 all=2485535 active=126519 piece=▁dine\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3461 size=38700 all=2485860 active=126844 piece=▁appealed\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3461 min_freq=237\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3456 size=38720 all=2486491 active=124924 piece=▁FI\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3453 size=38740 all=2486869 active=125302 piece=▁sovereignty\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3449 size=38760 all=2487560 active=125993 piece=▁STOP\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3447 size=38780 all=2488059 active=126492 piece=▁prometteur\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3443 size=38800 all=2489180 active=127613 piece=▁Agents\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3443 min_freq=236\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3438 size=38820 all=2489642 active=124919 piece=▁blu\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3436 size=38840 all=2490181 active=125458 piece=▁échangé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3434 size=38860 all=2490749 active=126026 piece=▁graine\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3430 size=38880 all=2491714 active=126991 piece=▁Forever\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3428 size=38900 all=2492444 active=127721 piece=▁retiring\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3427 min_freq=235\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3424 size=38920 all=2492742 active=124920 piece=employed\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3420 size=38940 all=2493333 active=125511 piece=▁Wid\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3418 size=38960 all=2493722 active=125900 piece=▁solitary\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3415 size=38980 all=2494215 active=126393 piece=mét\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3413 size=39000 all=2494892 active=127070 piece=▁bounds\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3413 min_freq=234\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3409 size=39020 all=2494987 active=124840 piece=▁exting\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3406 size=39040 all=2495459 active=125312 piece=-\"\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3404 size=39060 all=2496381 active=126234 piece=▁OVER\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3400 size=39080 all=2496729 active=126582 piece=▁curé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3398 size=39100 all=2497026 active=126879 piece=accusation\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3398 min_freq=234\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3395 size=39120 all=2497635 active=125460 piece=▁câlin\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3392 size=39140 all=2498260 active=126085 piece=Cloud\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3389 size=39160 all=2499087 active=126912 piece=▁whale\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3387 size=39180 all=2499931 active=127756 piece=▁aimant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3385 size=39200 all=2500265 active=128090 piece=▁pasteur\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3384 min_freq=233\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3381 size=39220 all=2500886 active=125607 piece=▁demeurant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3378 size=39240 all=2501360 active=126081 piece=▁surnature\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3375 size=39260 all=2501529 active=126250 piece=▁Operation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3372 size=39280 all=2502557 active=127278 piece=▁abandonnés\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3367 size=39300 all=2503299 active=128020 piece=ichy\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3367 min_freq=232\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3365 size=39320 all=2503788 active=125606 piece=▁dudit\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3362 size=39340 all=2504857 active=126675 piece=▁hoo\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3358 size=39360 all=2505642 active=127460 piece=tenez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3355 size=39380 all=2505854 active=127672 piece=▁Mayotte\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3353 size=39400 all=2506812 active=128630 piece=utilisent\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3353 min_freq=232\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3349 size=39420 all=2507596 active=126121 piece=▁Nem\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3346 size=39440 all=2508185 active=126710 piece=▁renseigné\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3343 size=39460 all=2508899 active=127424 piece=▁daté\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3340 size=39480 all=2509273 active=127798 piece=▁TEC\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3337 size=39500 all=2509637 active=128162 piece=kens\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3337 min_freq=231\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3335 size=39520 all=2510637 active=126354 piece=▁ruelles\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3331 size=39540 all=2511426 active=127143 piece=▁Km\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3328 size=39560 all=2512202 active=127919 piece=▁monast\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3326 size=39580 all=2512904 active=128621 piece=▁criticize\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3321 size=39600 all=2513634 active=129351 piece=▁Hip\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3321 min_freq=230\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3319 size=39620 all=2514582 active=126562 piece=▁saurai\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3316 size=39640 all=2515121 active=127101 piece=▁pledged\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3313 size=39660 all=2515526 active=127506 piece=▁Natalie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3310 size=39680 all=2516353 active=128333 piece=▁Bernie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3308 size=39700 all=2516933 active=128913 piece=▁eldest\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3308 min_freq=229\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3305 size=39720 all=2517630 active=126544 piece=pteurs\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3302 size=39740 all=2518451 active=127365 piece=▁hangar\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3300 size=39760 all=2518894 active=127808 piece=▁dégagé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3298 size=39780 all=2518991 active=127905 piece=treat\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3295 size=39800 all=2519761 active=128675 piece=wy\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3295 min_freq=228\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3293 size=39820 all=2520820 active=126618 piece=▁spécialisation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3290 size=39840 all=2521286 active=127084 piece=▁lambda\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3286 size=39860 all=2522339 active=128137 piece=▁polym\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3282 size=39880 all=2523208 active=129006 piece=▁BCE\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3280 size=39900 all=2523962 active=129760 piece=▁hectic\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3280 min_freq=228\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3278 size=39920 all=2524190 active=126426 piece=▁dominance\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3275 size=39940 all=2524915 active=127151 piece=▁énervé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3272 size=39960 all=2525148 active=127384 piece=▁corpse\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3269 size=39980 all=2525830 active=128066 piece=▁songer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3266 size=40000 all=2526233 active=128469 piece=▁Définir\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3266 min_freq=227\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3263 size=40020 all=2526510 active=126589 piece=▁lavande\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3260 size=40040 all=2527317 active=127396 piece=▁casualties\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3257 size=40060 all=2527534 active=127613 piece=▁Tém\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3253 size=40080 all=2528052 active=128131 piece=pomp\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3250 size=40100 all=2528385 active=128464 piece=▁Enreg\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3250 min_freq=226\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3248 size=40120 all=2529371 active=127397 piece=▁usable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3245 size=40140 all=2529801 active=127827 piece=▁Tort\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3242 size=40160 all=2530859 active=128885 piece=▁wiring\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3240 size=40180 all=2530936 active=128962 piece=▁brancher\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3238 size=40200 all=2531578 active=129604 piece=▁removes\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3238 min_freq=226\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3235 size=40220 all=2531943 active=126944 piece=▁hearty\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3233 size=40240 all=2532491 active=127492 piece=▁integrating\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3229 size=40260 all=2533185 active=128186 piece=▁Zomb\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3226 size=40280 all=2533798 active=128799 piece=▁birthdays\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3223 size=40300 all=2534152 active=129153 piece=Reg\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3223 min_freq=225\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3221 size=40320 all=2534440 active=126753 piece=▁proportionnelle\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3219 size=40340 all=2534809 active=127122 piece=addition\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3215 size=40360 all=2535508 active=127821 piece=▁Cogn\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3213 size=40380 all=2536207 active=128520 piece=▁antennes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3210 size=40400 all=2536692 active=129005 piece=▁garantissent\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3209 min_freq=224\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3205 size=40420 all=2537248 active=127390 piece=▁scrib\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3203 size=40440 all=2537915 active=128057 piece=▁Husse\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3201 size=40460 all=2538409 active=128551 piece=▁racistes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3199 size=40480 all=2539153 active=129295 piece=▁dentelle\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3195 size=40500 all=2539498 active=129640 piece=ulatory\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3195 min_freq=224\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3192 size=40520 all=2540039 active=127487 piece=uait\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3190 size=40540 all=2540556 active=128004 piece=▁mund\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3188 size=40560 all=2540938 active=128386 piece=▁affichées\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3186 size=40580 all=2541395 active=128843 piece=▁paranoid\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3183 size=40600 all=2541584 active=129032 piece=kar\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3183 min_freq=223\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3180 size=40620 all=2542585 active=127552 piece=▁séc\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3177 size=40640 all=2543042 active=128009 piece=▁Firm\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3175 size=40660 all=2543631 active=128598 piece=▁déplacés\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3172 size=40680 all=2543939 active=128906 piece=bol\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3170 size=40700 all=2545227 active=130194 piece=▁croisée\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3170 min_freq=222\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3167 size=40720 all=2545773 active=127808 piece=coins\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3165 size=40740 all=2546254 active=128289 piece=▁Polynésie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3162 size=40760 all=2546661 active=128696 piece=photos\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3160 size=40780 all=2547497 active=129532 piece=▁Ancient\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3157 size=40800 all=2547684 active=129719 piece=rail\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3157 min_freq=222\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3153 size=40820 all=2548510 active=127986 piece=▁Tun\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3151 size=40840 all=2549411 active=128887 piece=▁skating\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3148 size=40860 all=2549608 active=129084 piece=▁stained\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3146 size=40880 all=2549816 active=129292 piece=▁superhero\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3143 size=40900 all=2550244 active=129720 piece=▁Appuyez\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3143 min_freq=221\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3141 size=40920 all=2550813 active=128082 piece=▁Coaching\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3137 size=40940 all=2551256 active=128525 piece=▁roulants\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3134 size=40960 all=2551640 active=128909 piece=▁wager\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3129 size=40980 all=2552017 active=129286 piece=apest\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3126 size=41000 all=2552525 active=129794 piece=ivois\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3126 min_freq=220\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3124 size=41020 all=2553281 active=128336 piece=▁achètent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3121 size=41040 all=2553872 active=128927 piece=▁majest\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3117 size=41060 all=2554633 active=129688 piece=register\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3114 size=41080 all=2554813 active=129868 piece=▁progressed\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3111 size=41100 all=2554985 active=130040 piece=▁:))\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3111 min_freq=220\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3109 size=41120 all=2555233 active=127987 piece=▁rendements\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3106 size=41140 all=2556367 active=129121 piece=▁grimace\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3103 size=41160 all=2556599 active=129353 piece=Imm\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3101 size=41180 all=2557151 active=129905 piece=▁renouvelle\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3097 size=41200 all=2557379 active=130133 piece=▁Else\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3097 min_freq=219\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3095 size=41220 all=2557461 active=127938 piece=▁scissors\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3092 size=41240 all=2557826 active=128303 piece=▁Proof\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3091 size=41260 all=2558265 active=128742 piece=▁aimerais\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3089 size=41280 all=2558581 active=129058 piece=▁adjustable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3086 size=41300 all=2559132 active=129609 piece=▁corpus\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3086 min_freq=219\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3083 size=41320 all=2559526 active=128347 piece=▁présidentiel\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3080 size=41340 all=2560084 active=128905 piece=▁rejetée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3077 size=41360 all=2560389 active=129210 piece=▁arracher\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3075 size=41380 all=2560999 active=129820 piece=▁redundant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3072 size=41400 all=2561665 active=130486 piece=▁basiques\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3072 min_freq=218\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3069 size=41420 all=2561938 active=128355 piece=hs\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3066 size=41440 all=2562938 active=129355 piece=▁Laf\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3064 size=41460 all=2563426 active=129843 piece=▁négligence\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3060 size=41480 all=2564051 active=130468 piece=▁Guinée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3057 size=41500 all=2564433 active=130850 piece=rales\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3057 min_freq=218\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3055 size=41520 all=2565590 active=129342 piece=▁Wife\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3053 size=41540 all=2565914 active=129666 piece=oudres\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3050 size=41560 all=2565988 active=129740 piece=Say\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3049 size=41580 all=2566675 active=130427 piece=▁dialect\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3047 size=41600 all=2567019 active=130771 piece=▁remodeling\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3047 min_freq=217\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3045 size=41620 all=2568036 active=129367 piece=▁jackets\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3042 size=41640 all=2568227 active=129558 piece=▁differs\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3040 size=41660 all=2568747 active=130078 piece=▁complices\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3037 size=41680 all=2569243 active=130574 piece=▁gérée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3034 size=41700 all=2570140 active=131471 piece=▁Ded\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3034 min_freq=216\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3033 size=41720 all=2570375 active=128633 piece=▁liquidity\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3028 size=41740 all=2571030 active=129288 piece=$$\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3026 size=41760 all=2571562 active=129820 piece=▁précipitation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3024 size=41780 all=2572128 active=130386 piece=▁protègent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3022 size=41800 all=2572694 active=130952 piece=▁arômes\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3022 min_freq=216\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3020 size=41820 all=2573156 active=129095 piece=▁gala\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3017 size=41840 all=2573287 active=129226 piece=Ray\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3015 size=41860 all=2573708 active=129647 piece=▁THEIR\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3013 size=41880 all=2574007 active=129946 piece=▁goof\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3011 size=41900 all=2574990 active=130929 piece=▁Acquisition\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=3010 min_freq=215\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3008 size=41920 all=2576210 active=129968 piece=▁Cuban\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3005 size=41940 all=2576365 active=130123 piece=élus\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3003 size=41960 all=2577138 active=130896 piece=énage\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=3001 size=41980 all=2577361 active=131119 piece=▁Venus\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2999 size=42000 all=2577959 active=131717 piece=autel\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2999 min_freq=215\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2997 size=42020 all=2578894 active=129817 piece=▁critiqué\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2995 size=42040 all=2579566 active=130489 piece=▁Warning\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2992 size=42060 all=2579990 active=130913 piece=▁adhés\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2991 size=42080 all=2580117 active=131040 piece=▁désespérément\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2989 size=42100 all=2581266 active=132189 piece=▁spambots\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2989 min_freq=214\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2986 size=42120 all=2581862 active=129660 piece=▁Noble\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2983 size=42140 all=2582387 active=130185 piece=▁audible\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2980 size=42160 all=2583439 active=131237 piece=▁oat\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2978 size=42180 all=2583889 active=131687 piece=Before\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2976 size=42200 all=2584517 active=132315 piece=▁escaping\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2976 min_freq=213\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2974 size=42220 all=2584775 active=129484 piece=▁danses\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2971 size=42240 all=2585460 active=130169 piece=phis\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2969 size=42260 all=2586047 active=130756 piece=Engl\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2968 size=42280 all=2586721 active=131430 piece=▁endorsed\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2965 size=42300 all=2587491 active=132200 piece=othèque\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2965 min_freq=213\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2962 size=42320 all=2588071 active=129819 piece=▁baissa\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2959 size=42340 all=2588152 active=129900 piece=Happy\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2957 size=42360 all=2588882 active=130630 piece=▁flirt\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2955 size=42380 all=2589356 active=131104 piece=▁taqu\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2953 size=42400 all=2589569 active=131317 piece=allons\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2953 min_freq=212\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2951 size=42420 all=2590285 active=130166 piece=▁bravery\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2948 size=42440 all=2590834 active=130715 piece=Something\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2946 size=42460 all=2591749 active=131630 piece=▁barreaux\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2944 size=42480 all=2592193 active=132074 piece=▁bios\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2943 size=42500 all=2593159 active=133040 piece=▁fossé\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2943 min_freq=211\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2940 size=42520 all=2593338 active=129837 piece=▁galop\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2937 size=42540 all=2593933 active=130432 piece=vaire\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2936 size=42560 all=2594533 active=131032 piece=▁typiques\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2933 size=42580 all=2595209 active=131708 piece=ascension\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2931 size=42600 all=2595504 active=132003 piece=assés\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2931 min_freq=211\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2930 size=42620 all=2596012 active=130217 piece=▁consultative\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2926 size=42640 all=2596885 active=131090 piece=uges\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2923 size=42660 all=2597524 active=131729 piece=▁UNDER\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2921 size=42680 all=2598098 active=132303 piece=▁tirages\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2918 size=42700 all=2598516 active=132721 piece=▁Cere\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2918 min_freq=210\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2915 size=42720 all=2598678 active=130048 piece=andaise\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2913 size=42740 all=2599253 active=130623 piece=reuse\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2911 size=42760 all=2599766 active=131136 piece=achever\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2909 size=42780 all=2600270 active=131640 piece=▁capot\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2906 size=42800 all=2600596 active=131966 piece=▁censorship\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2906 min_freq=210\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2904 size=42820 all=2600806 active=130239 piece=▁rencontrée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2901 size=42840 all=2601978 active=131411 piece=▁perish\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2899 size=42860 all=2602322 active=131755 piece=▁Piscine\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2897 size=42880 all=2602499 active=131932 piece=▁concentrating\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2895 size=42900 all=2602850 active=132283 piece=▁representations\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2894 min_freq=209\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2891 size=42920 all=2603430 active=130723 piece=▁vile\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2889 size=42940 all=2603775 active=131068 piece=▁inflammatory\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2886 size=42960 all=2604482 active=131775 piece=ype\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2884 size=42980 all=2605063 active=132356 piece=▁idéales\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2881 size=43000 all=2605546 active=132839 piece=accr\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2881 min_freq=208\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2880 size=43020 all=2605974 active=130671 piece=▁récept\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2877 size=43040 all=2606341 active=131038 piece=▁restituer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2874 size=43060 all=2606535 active=131232 piece=▁havoc\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2872 size=43080 all=2606752 active=131449 piece=▁Wellness\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2868 size=43100 all=2607255 active=131952 piece=enaient\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2868 min_freq=208\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2866 size=43120 all=2607968 active=131057 piece=▁Cum\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2865 size=43140 all=2608950 active=132039 piece=▁Spending\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2863 size=43160 all=2609706 active=132795 piece=▁Pension\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2861 size=43180 all=2609999 active=133088 piece=▁déboucher\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2858 size=43200 all=2611154 active=134243 piece=▁Powell\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2858 min_freq=207\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2856 size=43220 all=2611489 active=130888 piece=▁distributing\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2853 size=43240 all=2611810 active=131209 piece=▁FTP\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2850 size=43260 all=2612411 active=131810 piece=▁veil\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2848 size=43280 all=2612957 active=132356 piece=▁glare\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2846 size=43300 all=2613360 active=132759 piece=▁anonymity\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2846 min_freq=207\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2844 size=43320 all=2613792 active=131100 piece=▁caller\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2842 size=43340 all=2614722 active=132030 piece=▁oubliez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2840 size=43360 all=2615164 active=132472 piece=▁statique\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2837 size=43380 all=2615736 active=133044 piece=iago\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2835 size=43400 all=2616609 active=133917 piece=▁breeds\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2835 min_freq=206\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2833 size=43420 all=2617166 active=131388 piece=▁éloigne\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2831 size=43440 all=2617403 active=131625 piece=▁knots\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2828 size=43460 all=2617730 active=131952 piece=▁Chang\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2826 size=43480 all=2618167 active=132389 piece=▁restless\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2823 size=43500 all=2618703 active=132925 piece=gées\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2823 min_freq=206\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2822 size=43520 all=2619261 active=131459 piece=▁suicides\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2819 size=43540 all=2620340 active=132538 piece=Sam\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2817 size=43560 all=2621427 active=133625 piece=sale\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2814 size=43580 all=2622469 active=134667 piece=▁tint\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2812 size=43600 all=2622907 active=135105 piece=▁Yale\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2812 min_freq=205\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2810 size=43620 all=2623533 active=131762 piece=▁Hongrie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2808 size=43640 all=2623914 active=132143 piece=▁specials\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2805 size=43660 all=2624567 active=132796 piece=▁Frost\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2804 size=43680 all=2625282 active=133511 piece=▁aveugles\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2802 size=43700 all=2625946 active=134175 piece=▁losers\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2802 min_freq=204\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2800 size=43720 all=2626105 active=131457 piece=▁rebondissements\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2797 size=43740 all=2627113 active=132465 piece=▁dissent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2794 size=43760 all=2628049 active=133401 piece=JECT\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2792 size=43780 all=2628905 active=134257 piece=▁halte\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2790 size=43800 all=2629121 active=134473 piece=▁téles\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2790 min_freq=204\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2788 size=43820 all=2629709 active=132038 piece=▁tolerated\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2785 size=43840 all=2630140 active=132469 piece=▁martiaux\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2782 size=43860 all=2631441 active=133770 piece=▁HOT\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2780 size=43880 all=2631700 active=134029 piece=Conn\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2778 size=43900 all=2632132 active=134461 piece=▁crackers\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2778 min_freq=203\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2775 size=43920 all=2632295 active=131769 piece=PLE\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2773 size=43940 all=2633119 active=132593 piece=irons\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2771 size=43960 all=2633648 active=133122 piece=▁entitlement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2769 size=43980 all=2634810 active=134284 piece=▁disappearance\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2766 size=44000 all=2635129 active=134603 piece=uggish\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2766 min_freq=202\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2764 size=44020 all=2635362 active=131979 piece=▁ripp\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2762 size=44040 all=2636235 active=132852 piece=mation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2760 size=44060 all=2636818 active=133435 piece=▁shutter\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2758 size=44080 all=2637360 active=133977 piece=▁Miranda\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2755 size=44100 all=2637484 active=134101 piece=▁énoncés\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2755 min_freq=202\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2753 size=44120 all=2637813 active=132204 piece=▁bourré\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2751 size=44140 all=2638193 active=132584 piece=▁frequ\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2749 size=44160 all=2639056 active=133447 piece=habitant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2747 size=44180 all=2639410 active=133801 piece=▁Savannah\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2745 size=44200 all=2639907 active=134298 piece=▁pluriann\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2745 min_freq=201\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2742 size=44220 all=2640639 active=132722 piece=▁misdeme\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2739 size=44240 all=2640862 active=132945 piece=▁yen\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2738 size=44260 all=2641436 active=133519 piece=▁transitoire\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2736 size=44280 all=2642012 active=134095 piece=▁électrom\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2734 size=44300 all=2642646 active=134729 piece=▁misog\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2734 min_freq=201\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2732 size=44320 all=2642838 active=132316 piece=▁potions\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2730 size=44340 all=2643164 active=132642 piece=▁earrings\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2728 size=44360 all=2643749 active=133227 piece=▁retrace\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2726 size=44380 all=2644090 active=133568 piece=▁pressée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2724 size=44400 all=2644582 active=134060 piece=▁répartie\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2724 min_freq=200\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2723 size=44420 all=2645259 active=132907 piece=▁dependency\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2721 size=44440 all=2646401 active=134049 piece=accordent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2719 size=44460 all=2647181 active=134829 piece=▁Outstanding\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2716 size=44480 all=2647755 active=135403 piece=▁Thou\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2713 size=44500 all=2648325 active=135973 piece=,(\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2713 min_freq=200\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2711 size=44520 all=2648863 active=132920 piece=▁quorum\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2709 size=44540 all=2649328 active=133385 piece=▁Laugh\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2707 size=44560 all=2649580 active=133637 piece=Rem\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2705 size=44580 all=2649932 active=133989 piece=▁éveillé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2703 size=44600 all=2650999 active=135056 piece=▁Merde\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2703 min_freq=199\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2702 size=44620 all=2651205 active=132751 piece=▁processeur\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2698 size=44640 all=2652229 active=133775 piece=▁Ange\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2696 size=44660 all=2652530 active=134076 piece=▁PROGRAM\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2694 size=44680 all=2652860 active=134406 piece=▁pests\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2693 size=44700 all=2653488 active=135034 piece=▁erection\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2693 min_freq=199\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2690 size=44720 all=2653757 active=132943 piece=UU\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2688 size=44740 all=2655030 active=134216 piece=config\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2686 size=44760 all=2655352 active=134538 piece=zip\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2685 size=44780 all=2656205 active=135391 piece=▁civilized\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2682 size=44800 all=2656822 active=136008 piece=inav\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2682 min_freq=198\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2680 size=44820 all=2657259 active=133220 piece=▁VDM\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2678 size=44840 all=2657589 active=133550 piece=▁Sortir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2676 size=44860 all=2657952 active=133913 piece=▁neon\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2674 size=44880 all=2658956 active=134917 piece=▁dens\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2672 size=44900 all=2659419 active=135380 piece=afil\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2672 min_freq=198\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2671 size=44920 all=2660171 active=133612 piece=▁balloons\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2669 size=44940 all=2660422 active=133863 piece=▁ecommerce\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2667 size=44960 all=2660732 active=134173 piece=▁knowingly\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2665 size=44980 all=2661144 active=134585 piece=arisation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2663 size=45000 all=2661412 active=134853 piece=▁Vietnamese\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2663 min_freq=197\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2661 size=45020 all=2661703 active=133362 piece=▁adéquates\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2658 size=45040 all=2662624 active=134283 piece=▁initier\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2657 size=45060 all=2663270 active=134929 piece=▁durement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2655 size=45080 all=2664184 active=135843 piece=▁reporté\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2653 size=45100 all=2664532 active=136191 piece=▁précitée\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2653 min_freq=196\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2651 size=45120 all=2664833 active=133527 piece=▁Pupp\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2650 size=45140 all=2665397 active=134091 piece=▁régression\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2648 size=45160 all=2666002 active=134696 piece=imaginable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2645 size=45180 all=2666412 active=135106 piece=inflammatoires\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2643 size=45200 all=2667114 active=135808 piece=▁indirecte\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2643 min_freq=196\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2641 size=45220 all=2667504 active=133746 piece=ravail\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2639 size=45240 all=2667778 active=134020 piece=▁Saviez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2637 size=45260 all=2668053 active=134295 piece=▁thrift\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2635 size=45280 all=2668913 active=135155 piece=itanie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2633 size=45300 all=2669334 active=135576 piece=▁Hogan\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2633 min_freq=195\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2631 size=45320 all=2669598 active=133731 piece=▁ADN\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2629 size=45340 all=2669777 active=133910 piece=▁‘‘\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2626 size=45360 all=2670164 active=134297 piece=account\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2624 size=45380 all=2670661 active=134794 piece=▁Espagnol\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2621 size=45400 all=2671121 active=135254 piece=)[\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2621 min_freq=195\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2619 size=45420 all=2671721 active=134135 piece=Neill\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2617 size=45440 all=2672120 active=134534 piece=billion\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2615 size=45460 all=2672682 active=135096 piece=▁Buddy\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2612 size=45480 all=2673064 active=135478 piece=aspx\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2610 size=45500 all=2673727 active=136141 piece=ISA\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2610 min_freq=195\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2609 size=45520 all=2674798 active=134626 piece=▁SAF\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2608 size=45540 all=2675386 active=135214 piece=Class\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2606 size=45560 all=2675880 active=135708 piece=▁senators\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2603 size=45580 all=2676433 active=136261 piece=backed\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2602 size=45600 all=2676741 active=136569 piece=▁roches\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2602 min_freq=194\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2600 size=45620 all=2677039 active=134136 piece=ibiot\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2599 size=45640 all=2677391 active=134488 piece=▁certitudes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2597 size=45660 all=2677866 active=134963 piece=▁originality\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2595 size=45680 all=2678249 active=135346 piece=emprunteur\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2592 size=45700 all=2679007 active=136104 piece=alla\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2592 min_freq=194\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2590 size=45720 all=2679645 active=134294 piece=!:\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2588 size=45740 all=2680615 active=135264 piece=▁Haïti\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2587 size=45760 all=2680902 active=135551 piece=▁impacting\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2584 size=45780 all=2681494 active=136143 piece=gebra\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2583 size=45800 all=2681815 active=136464 piece=▁impératifs\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2583 min_freq=193\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2580 size=45820 all=2682636 active=134912 piece=▁EA\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2578 size=45840 all=2683261 active=135537 piece=▁bof\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2576 size=45860 all=2683769 active=136045 piece=▁muddy\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2575 size=45880 all=2683988 active=136264 piece=▁pedestrians\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2573 size=45900 all=2684600 active=136876 piece=▁foreclosure\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2573 min_freq=193\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2570 size=45920 all=2684924 active=134553 piece=URSS\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2569 size=45940 all=2686114 active=135743 piece=▁mattered\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2567 size=45960 all=2686536 active=136165 piece=▁trustees\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2564 size=45980 all=2687376 active=137005 piece=▁zo\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2562 size=46000 all=2687941 active=137570 piece=▁alias\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2562 min_freq=192\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2561 size=46020 all=2688156 active=134605 piece=▁servitude\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2559 size=46040 all=2689176 active=135625 piece=▁kitty\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2557 size=46060 all=2690175 active=136624 piece=download\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2555 size=46080 all=2690778 active=137227 piece=▁addicts\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2553 size=46100 all=2691276 active=137725 piece=▁commanding\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2552 min_freq=191\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2551 size=46120 all=2691590 active=134878 piece=▁garderie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2549 size=46140 all=2691925 active=135213 piece=▁veterin\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2547 size=46160 all=2692579 active=135867 piece=▁bland\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2545 size=46180 all=2693209 active=136497 piece=▁pedal\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2542 size=46200 all=2693668 active=136956 piece=Jul\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2542 min_freq=191\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2540 size=46220 all=2694094 active=135020 piece=Express\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2538 size=46240 all=2694429 active=135355 piece=▁Rogue\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2537 size=46260 all=2694552 active=135478 piece=▁serpents\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2535 size=46280 all=2695054 active=135980 piece=▁Ginny\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2534 size=46300 all=2695161 active=136087 piece=▁policing\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2534 min_freq=191\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2532 size=46320 all=2695457 active=135055 piece=▁éloignées\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2530 size=46340 all=2696112 active=135710 piece=▁Rach\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2529 size=46360 all=2696797 active=136395 piece=▁basilic\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2527 size=46380 all=2697601 active=137199 piece=▁caféine\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2526 size=46400 all=2698333 active=137931 piece=▁Opportunities\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2525 min_freq=190\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2524 size=46420 all=2698919 active=135500 piece=▁Bankruptcy\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2521 size=46440 all=2699502 active=136083 piece=border\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2519 size=46460 all=2699970 active=136551 piece=owl\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2517 size=46480 all=2700449 active=137030 piece=▁enrichi\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2515 size=46500 all=2701670 active=138251 piece=▁Broken\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2515 min_freq=189\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2514 size=46520 all=2701945 active=135341 piece=gouvernement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2511 size=46540 all=2702389 active=135785 piece=undis\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2509 size=46560 all=2702938 active=136334 piece=▁Abbey\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2507 size=46580 all=2703509 active=136905 piece=▁startling\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2504 size=46600 all=2704182 active=137578 piece=▁Fasc\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2504 min_freq=189\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2503 size=46620 all=2704533 active=135525 piece=▁Indianapolis\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2501 size=46640 all=2705674 active=136666 piece=▁iranien\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2498 size=46660 all=2706306 active=137298 piece=▁Laurie\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2496 size=46680 all=2706567 active=137559 piece=▁promène\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2494 size=46700 all=2706968 active=137960 piece=▁hypocrisy\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2494 min_freq=188\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2492 size=46720 all=2707658 active=136039 piece=▁drap\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2490 size=46740 all=2708013 active=136394 piece=▁Artists\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2488 size=46760 all=2708583 active=136964 piece=▁Shouldn\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2486 size=46780 all=2709201 active=137582 piece=ysique\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2484 size=46800 all=2709948 active=138329 piece=notch\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2484 min_freq=188\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2483 size=46820 all=2710479 active=136021 piece=animations\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2481 size=46840 all=2710768 active=136310 piece=▁avérée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2480 size=46860 all=2711518 active=137060 piece=▁déménagé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2478 size=46880 all=2711718 active=137260 piece=▁Properties\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2475 size=46900 all=2712248 active=137790 piece=▁détenues\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2475 min_freq=187\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2473 size=46920 all=2712716 active=136081 piece=biased\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2471 size=46940 all=2713524 active=136889 piece=▁breaths\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2469 size=46960 all=2714247 active=137612 piece=▁Trent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2467 size=46980 all=2715118 active=138483 piece=gros\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2466 size=47000 all=2715632 active=138997 piece=▁stérile\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2466 min_freq=187\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2464 size=47020 all=2716106 active=136256 piece=▁STE\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2463 size=47040 all=2716612 active=136762 piece=▁contactant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2461 size=47060 all=2717010 active=137160 piece=▁protégeant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2459 size=47080 all=2717400 active=137550 piece=ouvertes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2458 size=47100 all=2717632 active=137782 piece=▁survenus\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2458 min_freq=186\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2456 size=47120 all=2718913 active=137163 piece=▁drô\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2454 size=47140 all=2719518 active=137768 piece=▁cuites\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2453 size=47160 all=2719683 active=137933 piece=▁lavender\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2451 size=47180 all=2719971 active=138221 piece=National\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2449 size=47200 all=2720402 active=138652 piece=EA\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2449 min_freq=186\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2448 size=47220 all=2721132 active=136566 piece=▁ailments\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2446 size=47240 all=2721608 active=137042 piece=▁warmly\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2444 size=47260 all=2721880 active=137314 piece=▁baux\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2442 size=47280 all=2722529 active=137963 piece=Vol\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2441 size=47300 all=2723142 active=138576 piece=▁homosexuel\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2440 min_freq=185\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2438 size=47320 all=2723385 active=136400 piece=▁drafts\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2437 size=47340 all=2723448 active=136463 piece=▁collaborating\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2435 size=47360 all=2724215 active=137230 piece=▁inefficient\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2432 size=47380 all=2724635 active=137650 piece=APH\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2430 size=47400 all=2725080 active=138095 piece=▁hen\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2430 min_freq=185\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2428 size=47420 all=2726211 active=137334 piece=ranes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2427 size=47440 all=2726689 active=137812 piece=▁kidnapping\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2424 size=47460 all=2727162 active=138285 piece=nl\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2423 size=47480 all=2728143 active=139266 piece=▁chassis\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2421 size=47500 all=2728698 active=139821 piece=▁Gel\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2421 min_freq=184\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2420 size=47520 all=2729149 active=136723 piece=▁Patrol\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2418 size=47540 all=2729756 active=137330 piece=▁PCs\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2417 size=47560 all=2730620 active=138194 piece=▁Jasper\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2416 size=47580 all=2730882 active=138456 piece=▁gouvernemental\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2413 size=47600 all=2731520 active=139094 piece=ologic\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2413 min_freq=184\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2412 size=47620 all=2732164 active=137096 piece=▁effectuez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2410 size=47640 all=2732673 active=137605 piece=azole\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2409 size=47660 all=2732860 active=137792 piece=▁corridors\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2407 size=47680 all=2733828 active=138760 piece=éliens\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2405 size=47700 all=2734533 active=139465 piece=▁Mueller\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2405 min_freq=183\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2403 size=47720 all=2735004 active=137195 piece=▁owl\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2402 size=47740 all=2735661 active=137852 piece=▁indésirable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2399 size=47760 all=2735933 active=138124 piece=▁Apartment\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2397 size=47780 all=2736586 active=138777 piece=▁backups\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2395 size=47800 all=2736927 active=139118 piece=▁Resident\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2395 min_freq=183\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2393 size=47820 all=2737169 active=137079 piece=▁postés\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2391 size=47840 all=2737772 active=137682 piece=▁miscar\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2389 size=47860 all=2738238 active=138148 piece=ahan\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2388 size=47880 all=2738711 active=138621 piece=close\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2386 size=47900 all=2739056 active=138966 piece=▁CCT\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2386 min_freq=182\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2384 size=47920 all=2739670 active=137539 piece=▁Advisor\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2382 size=47940 all=2740218 active=138087 piece=▁chicks\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2380 size=47960 all=2740519 active=138388 piece=▁Poul\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2379 size=47980 all=2741182 active=139051 piece=évènements\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2378 size=48000 all=2741943 active=139812 piece=▁Disclaimer\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2377 min_freq=182\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2376 size=48020 all=2742223 active=137374 piece=▁Documentation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2374 size=48040 all=2742675 active=137826 piece=▁Truck\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2372 size=48060 all=2743088 active=138239 piece=▁munir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2371 size=48080 all=2743577 active=138728 piece=▁excédent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2369 size=48100 all=2744260 active=139411 piece=famille\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2369 min_freq=181\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2367 size=48120 all=2744769 active=137674 piece=▁Verts\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2365 size=48140 all=2745303 active=138208 piece=▁Archer\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2363 size=48160 all=2745620 active=138525 piece=▁nettoyé\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2360 size=48180 all=2746415 active=139320 piece=WW\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2359 size=48200 all=2747014 active=139919 piece=▁Completely\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2359 min_freq=181\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2357 size=48220 all=2747517 active=137853 piece=▁Peterson\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2355 size=48240 all=2747826 active=138162 piece=ablanca\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2354 size=48260 all=2747970 active=138306 piece=▁téléchargeable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2351 size=48280 all=2748667 active=139003 piece=apons\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2349 size=48300 all=2749318 active=139654 piece=CAR\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2349 min_freq=180\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2348 size=48320 all=2750265 active=138222 piece=▁booming\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2345 size=48340 all=2750847 active=138804 piece=▁Lour\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2344 size=48360 all=2752215 active=140172 piece=▁Souvenez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2342 size=48380 all=2752573 active=140530 piece=▁vitality\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2340 size=48400 all=2752853 active=140810 piece=FD\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2340 min_freq=180\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2338 size=48420 all=2753661 active=138215 piece=▁remorse\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2337 size=48440 all=2754079 active=138633 piece=▁occurrences\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2334 size=48460 all=2754354 active=138908 piece=kun\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2333 size=48480 all=2755296 active=139850 piece=▁brisée\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2331 size=48500 all=2755925 active=140479 piece=▁devrons\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2331 min_freq=179\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2329 size=48520 all=2756250 active=138122 piece=▁euphor\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2327 size=48540 all=2756535 active=138407 piece=▁creeping\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2325 size=48560 all=2756677 active=138549 piece=▁Myanmar\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2323 size=48580 all=2757145 active=139017 piece=▁complied\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2321 size=48600 all=2757495 active=139367 piece=▁Sunset\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2321 min_freq=179\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2319 size=48620 all=2757651 active=138028 piece=ikh\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2318 size=48640 all=2758477 active=138854 piece=▁succèdent\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2315 size=48660 all=2759338 active=139715 piece=▁pète\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2313 size=48680 all=2759642 active=140019 piece=▁Lub\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2312 size=48700 all=2760124 active=140501 piece=▁Chronic\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2312 min_freq=178\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2310 size=48720 all=2760499 active=138368 piece=▁worsh\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2309 size=48740 all=2760951 active=138820 piece=▁pseudos\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2307 size=48760 all=2761657 active=139526 piece=tries\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2306 size=48780 all=2762228 active=140097 piece=▁achievable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2304 size=48800 all=2763436 active=141305 piece=▁melon\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2304 min_freq=178\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2303 size=48820 all=2763736 active=138460 piece=▁saisissant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2300 size=48840 all=2764888 active=139612 piece=▁rained\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2299 size=48860 all=2765152 active=139876 piece=professionnel\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2297 size=48880 all=2765597 active=140321 piece=▁meditate\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2295 size=48900 all=2765820 active=140544 piece=Application\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2294 min_freq=178\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2293 size=48920 all=2766531 active=138964 piece=▁natal\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2291 size=48940 all=2766631 active=139064 piece=▁prédis\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2289 size=48960 all=2767216 active=139649 piece=paralle\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2288 size=48980 all=2767491 active=139924 piece=▁palestinienne\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2286 size=49000 all=2767776 active=140209 piece=▁sincerity\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2286 min_freq=177\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2284 size=49020 all=2768550 active=139163 piece=▁Cater\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2282 size=49040 all=2769565 active=140178 piece=▁passeront\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2280 size=49060 all=2770344 active=140957 piece=REM\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2279 size=49080 all=2770996 active=141609 piece=▁Editions\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2277 size=49100 all=2771161 active=141774 piece=▁perturbe\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2277 min_freq=177\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2275 size=49120 all=2771766 active=139164 piece=?*\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2273 size=49140 all=2772261 active=139659 piece=osexual\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2271 size=49160 all=2772519 active=139917 piece=▁diffamation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2269 size=49180 all=2773032 active=140430 piece=▁unreliable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2267 size=49200 all=2773246 active=140644 piece=▁intimidation\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2267 min_freq=176\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2265 size=49220 all=2773638 active=139055 piece=AW\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2264 size=49240 all=2775025 active=140442 piece=▁banging\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2262 size=49260 all=2775862 active=141279 piece=▁Miche\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2260 size=49280 all=2776334 active=141751 piece=▁Posez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2258 size=49300 all=2777066 active=142483 piece=▁Fields\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2258 min_freq=176\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2257 size=49320 all=2777284 active=139066 piece=▁hospice\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2255 size=49340 all=2777559 active=139341 piece=▁Hak\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2254 size=49360 all=2778284 active=140066 piece=▁hunted\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2252 size=49380 all=2778779 active=140561 piece=▁enveloppes\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2250 size=49400 all=2779007 active=140789 piece=▁contesté\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2250 min_freq=175\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2249 size=49420 all=2779590 active=139534 piece=▁médiatiques\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2247 size=49440 all=2780085 active=140029 piece=▁grouped\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2245 size=49460 all=2780687 active=140631 piece=▁RAT\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2244 size=49480 all=2781029 active=140973 piece=▁inland\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2242 size=49500 all=2781875 active=141819 piece=▁phén\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2242 min_freq=175\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2241 size=49520 all=2782391 active=139565 piece=▁expulsion\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2239 size=49540 all=2782907 active=140081 piece=▁oily\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2238 size=49560 all=2783559 active=140733 piece=▁brouillon\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2236 size=49580 all=2784172 active=141346 piece=rame\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2235 size=49600 all=2784721 active=141895 piece=▁Chandler\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2235 min_freq=174\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2233 size=49620 all=2785096 active=139607 piece=connect\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2232 size=49640 all=2785957 active=140468 piece=▁recess\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2230 size=49660 all=2786235 active=140746 piece=arrison\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2229 size=49680 all=2786899 active=141410 piece=▁Seller\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2228 size=49700 all=2786966 active=141477 piece=▁competency\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2227 min_freq=174\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2226 size=49720 all=2787307 active=139689 piece=▁prothèses\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2224 size=49740 all=2787851 active=140233 piece=▁Meetings\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2222 size=49760 all=2788101 active=140483 piece=▁pue\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2221 size=49780 all=2789032 active=141414 piece=▁Hou\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2220 size=49800 all=2789491 active=141873 piece=initiatives\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2220 min_freq=174\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2218 size=49820 all=2790052 active=140026 piece=▁formées\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2216 size=49840 all=2790701 active=140675 piece=▁tapant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2214 size=49860 all=2791054 active=141028 piece=▁Remote\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2212 size=49880 all=2791452 active=141426 piece=Angl\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2211 size=49900 all=2791738 active=141712 piece=cn\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2211 min_freq=173\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2210 size=49920 all=2792945 active=140406 piece=▁Vichy\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2209 size=49940 all=2793589 active=141050 piece=▁treason\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2207 size=49960 all=2794175 active=141636 piece=▁Ecole\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2206 size=49980 all=2794417 active=141878 piece=▁separates\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2204 size=50000 all=2794762 active=142223 piece=▁scell\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2204 min_freq=173\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2203 size=50020 all=2794955 active=139912 piece=▁photoshop\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2201 size=50040 all=2795699 active=140656 piece=▁Nak\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2199 size=50060 all=2796344 active=141301 piece=▁calf\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2198 size=50080 all=2797012 active=141969 piece=▁Paragraph\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2196 size=50100 all=2797620 active=142577 piece=▁Areas\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2196 min_freq=172\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2195 size=50120 all=2797857 active=140118 piece=▁Recreation\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2193 size=50140 all=2798369 active=140630 piece=strom\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2192 size=50160 all=2799301 active=141562 piece=▁dérout\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2190 size=50180 all=2799781 active=142042 piece=Jo\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2189 size=50200 all=2800323 active=142584 piece=▁recrutements\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2188 min_freq=172\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2187 size=50220 all=2800861 active=140554 piece=▁tendrement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2185 size=50240 all=2801362 active=141055 piece=fur\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2183 size=50260 all=2802240 active=141933 piece=▁IoT\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2182 size=50280 all=2802783 active=142476 piece=▁kWh\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2181 size=50300 all=2803397 active=143090 piece=▁sneaking\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2181 min_freq=171\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2179 size=50320 all=2803943 active=140715 piece=▁OCT\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2178 size=50340 all=2804637 active=141409 piece=▁Cure\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2177 size=50360 all=2805026 active=141798 piece=▁consultable\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2175 size=50380 all=2805529 active=142301 piece=▁Basque\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2173 size=50400 all=2805817 active=142589 piece=▁JR\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2173 min_freq=171\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2171 size=50420 all=2806107 active=140519 piece=glut\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2170 size=50440 all=2806822 active=141234 piece=▁symposium\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2169 size=50460 all=2807674 active=142086 piece=▁validés\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2168 size=50480 all=2808093 active=142505 piece=▁nettoyant\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2166 size=50500 all=2808694 active=143106 piece=▁abrog\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2166 min_freq=171\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2165 size=50520 all=2808865 active=140589 piece=▁handkerchief\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2163 size=50540 all=2809431 active=141155 piece=▁éponge\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2162 size=50560 all=2809784 active=141508 piece=▁prédictions\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2161 size=50580 all=2810031 active=141755 piece=▁profiterez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2159 size=50600 all=2810895 active=142619 piece=▁Hitch\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2159 min_freq=170\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2158 size=50620 all=2810990 active=140611 piece=▁baker\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2157 size=50640 all=2811237 active=140858 piece=▁charpente\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2155 size=50660 all=2811744 active=141365 piece=LEX\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2154 size=50680 all=2812742 active=142363 piece=▁WHICH\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2152 size=50700 all=2813120 active=142741 piece=▁Zamb\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2152 min_freq=170\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2151 size=50720 all=2813746 active=141223 piece=▁warp\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2149 size=50740 all=2814179 active=141656 piece=▁brav\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2147 size=50760 all=2814350 active=141827 piece=▁oxide\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2146 size=50780 all=2814608 active=142085 piece=▁pesticide\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2144 size=50800 all=2815514 active=142991 piece=▁inbound\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2144 min_freq=169\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2143 size=50820 all=2815677 active=140934 piece=▁Remplissez\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2141 size=50840 all=2816081 active=141338 piece=▁catégor\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2140 size=50860 all=2816415 active=141672 piece=▁antérieurement\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2138 size=50880 all=2817061 active=142318 piece=▁Posts\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2137 size=50900 all=2817734 active=142991 piece=parleurs\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2137 min_freq=169\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2136 size=50920 all=2817935 active=141086 piece=▁oppressed\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2134 size=50940 all=2818721 active=141872 piece=Votre\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2132 size=50960 all=2818999 active=142150 piece=▁deterioration\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2130 size=50980 all=2819487 active=142638 piece=tun\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2129 size=51000 all=2820111 active=143262 piece=▁fabuleuse\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2129 min_freq=169\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2127 size=51020 all=2820709 active=141603 piece=▁bows\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2126 size=51040 all=2821641 active=142535 piece=▁rasoir\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2125 size=51060 all=2822041 active=142935 piece=▁lisais\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2124 size=51080 all=2822467 active=143361 piece=▁replacements\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2122 size=51100 all=2823033 active=143927 piece=▁earnestly\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2122 min_freq=168\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2120 size=51120 all=2823472 active=141591 piece=▁majuscule\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2118 size=51140 all=2823609 active=141728 piece=AUD\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2117 size=51160 all=2824597 active=142716 piece=▁Heidi\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2116 size=51180 all=2824916 active=143035 piece=▁souhaiteraient\n",
+      "bpe_model_trainer.cc(268) LOG(INFO) Added: freq=2114 size=51200 all=2825459 active=143578 piece=aquarium\n",
+      "bpe_model_trainer.cc(159) LOG(INFO) Updating active symbols. max_freq=2114 min_freq=168\n",
+      "trainer_interface.cc(686) LOG(INFO) Saving model: /root/AI-Uncomplicated/core/models/GI_01/tokenzier/en_fr_combined_tokenizer.model\n",
+      "trainer_interface.cc(698) LOG(INFO) Saving vocabs: /root/AI-Uncomplicated/core/models/GI_01/tokenzier/en_fr_combined_tokenizer.vocab\n",
+      "trainer_interface.cc(706) LOG(WARNING) The piece [\n",
+      "] contains escaped characters that break the format of /root/AI-Uncomplicated/core/models/GI_01/tokenzier/en_fr_combined_tokenizer.vocab\n",
+      "trainer_interface.cc(706) LOG(WARNING) The piece [\n",
+      "\n",
+      "] contains escaped characters that break the format of /root/AI-Uncomplicated/core/models/GI_01/tokenzier/en_fr_combined_tokenizer.vocab\n",
+      "\n",
+      "Tokenizer trained successfully!\n",
+      "Model saved at: /root/AI-Uncomplicated/core/models/GI_01/tokenzier/en_fr_combined_tokenizer.model\n",
+      "Vocab saved at: /root/AI-Uncomplicated/core/models/GI_01/tokenzier/en_fr_combined_tokenizer.vocab\n",
+      "Time taken to complete ::  1491.68\n",
+      "==== TOKENIZER TRAINED SUCCESSFULLY =====\n"
+     ]
+    }
+   ],
+   "source": [
+    "TOKENIZER_TRAINING_SCRIPT = os.path.join(ROOT_PROJECT_PATH, \"core/tokenizer/trainer.py\")\n",
+    "MODEL_NAME = \"en_fr_combined_tokenizer\"\n",
+    "TOKENIZER_SAVED_PATH = os.path.join(ROOT_PROJECT_PATH, \"core/models/GI_01/tokenzier/\")\n",
+    "\n",
+    "!python $TOKENIZER_TRAINING_SCRIPT --data_dir=$OUTPUT_PATH --vocab_size=60000 --model_name=$MODEL_NAME --character_coverage=1.0 --num_threads=100 --output_dir=$TOKENIZER_SAVED_PATH --yaml_file_path=\"/root/AI-Uncomplicated/core/models/GI_01/tokenzier/config.yml\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run below command from project root directory to convert the smp format to custom format we are going to use for training\n",
+    "\n",
+    "```bash\n",
+    "python -m core.tokenizer.setencepiece_to_tokenizer --model_path=core/models/translator/tokenzier/en_pt_combined_tokenizer.model --save_path=core/models/translator/tokenzier\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize Model and tokenizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "OSError",
+     "evalue": "Not found: \"/root/AI-Uncomplicated/core/models/translator/tokenzier/en_fr_combined_tokenizer/spm_buffer.model\": No such file or directory Error #2",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[13], line 6\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mGI_01\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmain\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ConstrueAutoRegressiveModel\n\u001b[1;32m      4\u001b[0m training_config\u001b[38;5;241m.\u001b[39mtokenizer_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/root/AI-Uncomplicated/core/models/translator/tokenzier/en_fr_combined_tokenizer\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 6\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m \u001b[43mSPMTokenizer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtokenizer_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m model_config\u001b[38;5;241m.\u001b[39mvocabulary_size \u001b[38;5;241m=\u001b[39m tokenizer\u001b[38;5;241m.\u001b[39mvocab_size\n\u001b[1;32m      9\u001b[0m model \u001b[38;5;241m=\u001b[39m ConstrueAutoRegressiveModel(config\u001b[38;5;241m=\u001b[39mmodel_config)\n",
+      "File \u001b[0;32m~/AI-Uncomplicated/core/tokenizer/tokenizer_loader.py:10\u001b[0m, in \u001b[0;36mSPMTokenizer.__init__\u001b[0;34m(self, tokenizer_path, bos_peice, eos_peice, padding_peice)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, tokenizer_path, bos_peice\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m<s>\u001b[39m\u001b[38;5;124m\"\u001b[39m, eos_peice\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m</s>\u001b[39m\u001b[38;5;124m\"\u001b[39m, padding_peice\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m<pad>\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m      9\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpath \u001b[38;5;241m=\u001b[39m tokenizer_path\n\u001b[0;32m---> 10\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m=\u001b[39m \u001b[43mspm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSentencePieceProcessor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtokenizer_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mspm_buffer.model\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     12\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(tokenizer_path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtokenizer.config\u001b[39m\u001b[38;5;124m\"\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m handler:\n\u001b[1;32m     13\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(handler)\n",
+      "File \u001b[0;32m~/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/sentencepiece/__init__.py:447\u001b[0m, in \u001b[0;36mSentencePieceProcessor.Init\u001b[0;34m(self, model_file, model_proto, out_type, add_bos, add_eos, reverse, emit_unk_piece, enable_sampling, nbest_size, alpha, num_threads)\u001b[0m\n\u001b[1;32m    445\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_threads \u001b[38;5;241m=\u001b[39m num_threads\n\u001b[1;32m    446\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model_file \u001b[38;5;129;01mor\u001b[39;00m model_proto:\n\u001b[0;32m--> 447\u001b[0m   \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mLoad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_proto\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_proto\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/sentencepiece/__init__.py:905\u001b[0m, in \u001b[0;36mSentencePieceProcessor.Load\u001b[0;34m(self, model_file, model_proto)\u001b[0m\n\u001b[1;32m    903\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model_proto:\n\u001b[1;32m    904\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mLoadFromSerializedProto(model_proto)\n\u001b[0;32m--> 905\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mLoadFromFile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_file\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/ai_experimentation_env/lib/python3.11/site-packages/sentencepiece/__init__.py:310\u001b[0m, in \u001b[0;36mSentencePieceProcessor.LoadFromFile\u001b[0;34m(self, arg)\u001b[0m\n\u001b[1;32m    309\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mLoadFromFile\u001b[39m(\u001b[38;5;28mself\u001b[39m, arg):\n\u001b[0;32m--> 310\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_sentencepiece\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSentencePieceProcessor_LoadFromFile\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mOSError\u001b[0m: Not found: \"/root/AI-Uncomplicated/core/models/translator/tokenzier/en_fr_combined_tokenizer/spm_buffer.model\": No such file or directory Error #2"
+     ]
+    }
+   ],
+   "source": [
+    "from core.tokenizer import SPMTokenizer\n",
+    "from core.models.GI_01.main.model import ConstrueAutoRegressiveModel\n",
+    "\n",
+    "training_config.tokenizer_path = \"/root/AI-Uncomplicated/core/models/translator/tokenzier/en_fr_combined_tokenizer\"\n",
+    "\n",
+    "tokenizer = SPMTokenizer(training_config.tokenizer_path)\n",
+    "\n",
+    "model_config.vocabulary_size = tokenizer.vocab_size\n",
+    "model = ConstrueAutoRegressiveModel(config=model_config)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import Dataset, DataLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class EntoPTDataSet(Dataset):\n",
+    "    def __init__(self, tensorflow_dataset):\n",
+    "        self.dataset = tensorflow_dataset\n",
+    "        \n",
+    "    def __len__(self):\n",
+    "        return len(self.dataset)\n",
+    "    \n",
+    "    def __getitem__(self, index):\n",
+    "        return self.dataset[index]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import Dataset, load_from_disk\n",
+    "\n",
+    "try:\n",
+    "    dataset = Dataset.from_csv(os.path.join(path, \"en-fr.csv\"))\n",
+    "    dataset = dataset.take(3000000)\n",
+    "    dataset = dataset.filter(lambda d: d[\"en\"] is not None and d[\"fr\"] is not None)\n",
+    "    dataset = dataset.filter(lambda x: len(tokenizer.encode(x[\"en\"] + x[\"fr\"], return_type=None)[\"input_ids\"][0]) < 500)\n",
+    "    dataset = dataset.train_test_split(test_size=0.2)\n",
+    "    dataset.save_to_disk(\"filterd_dataset\")\n",
+    "except NameError:\n",
+    "    dataset = load_from_disk(\"filterd_dataset\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['en', 'fr'],\n",
+       "        num_rows: 2393178\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['en', 'fr'],\n",
+       "        num_rows: 598295\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_examples_pt = EntoPTDataSet(dataset[\"train\"])\n",
+    "val_examples_pt = EntoPTDataSet(dataset[\"test\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torch.utils.data.sampler import RandomSampler, SequentialSampler\n",
+    "from typing import Optional, Callable\n",
+    "\n",
+    "def create_data_loader(\n",
+    "    dataset,\n",
+    "    batch_size: int = 32,\n",
+    "    shuffle: bool = True,\n",
+    "    num_workers: int = 4,\n",
+    "    pin_memory: bool = True,\n",
+    "    collate_fn: Optional[Callable] = None,\n",
+    "    drop_last: bool = False,\n",
+    "    generator: Optional[torch.Generator] = None\n",
+    ") -> DataLoader:\n",
+    "    \"\"\"\n",
+    "    Create a PyTorch DataLoader with optimized settings.\n",
+    "    \n",
+    "    Args:\n",
+    "        dataset: PyTorch Dataset object\n",
+    "        batch_size: Number of samples per batch\n",
+    "        shuffle: Whether to shuffle the data\n",
+    "        num_workers: Number of subprocesses for data loading\n",
+    "        pin_memory: Whether to pin memory in GPU training\n",
+    "        collate_fn: Custom collate function for batching\n",
+    "        drop_last: Whether to drop the last incomplete batch\n",
+    "        generator: Random number generator for reproducibility\n",
+    "    \n",
+    "    Returns:\n",
+    "        DataLoader: Configured PyTorch DataLoader\n",
+    "    \"\"\"\n",
+    "    \n",
+    "\n",
+    "    if collate_fn is None:\n",
+    "        raise ValueError(\"collator function not provided\")\n",
+    "\n",
+    "    # Choose sampler based on shuffle parameter\n",
+    "    if shuffle:\n",
+    "        sampler = RandomSampler(dataset, generator=generator)\n",
+    "    else:\n",
+    "        sampler = SequentialSampler(dataset)\n",
+    "    \n",
+    "    # Create DataLoader with optimized settings\n",
+    "    loader = DataLoader(\n",
+    "        dataset=dataset,\n",
+    "        batch_size=batch_size,\n",
+    "        sampler=sampler,\n",
+    "        num_workers=num_workers,\n",
+    "        collate_fn=collate_fn,\n",
+    "        pin_memory=pin_memory,\n",
+    "        drop_last=drop_last,\n",
+    "        # Worker init function for reproducibility\n",
+    "        worker_init_fn=lambda worker_id: torch.manual_seed(torch.initial_seed() + worker_id)\n",
+    "    )\n",
+    "    \n",
+    "    return loader\n",
+    "\n",
+    "\n",
+    "class NextTokenPredictionCollator:\n",
+    "    def __init__(self, tokenizer):\n",
+    "        self.tokenizer = tokenizer\n",
+    "        \n",
+    "        self.start = tokenizer.model.piece_to_id(\"<s>\")\n",
+    "        self.end = tokenizer.model.piece_to_id(\"</s>\")\n",
+    "        \n",
+    "        self.en_start = tokenizer.model.piece_to_id(\"<lang_en>\")\n",
+    "        self.en_end = tokenizer.model.piece_to_id(\"</lang_en>\")\n",
+    "        self.pt_start = tokenizer.model.piece_to_id(\"<lang_pt>\")\n",
+    "        self.pt_end = tokenizer.model.piece_to_id(\"</lang_pt>\")\n",
+    "        self.pad_token_idx = tokenizer.model.PieceToId(\"<pad>\")\n",
+    "        \n",
+    "    def __call__(self, batch):\n",
+    "        input_ids = []\n",
+    "        labels = []\n",
+    "        \n",
+    "        for item in batch:\n",
+    "            french, english = item[\"fr\"], item[\"en\"]\n",
+    "            \n",
+    "            english_encoded = tokenizer.encode(\n",
+    "                english, return_type=None, add_special_tokens=False\n",
+    "            )[\"input_ids\"][0]\n",
+    "            \n",
+    "            french_encoded = tokenizer.encode(\n",
+    "                french, return_type=None, add_special_tokens=False\n",
+    "            )[\"input_ids\"][0]\n",
+    "            \n",
+    "            \n",
+    "            english_encoded = [self.start] + [self.en_start] +  english_encoded +  [self.en_end]\n",
+    "            french_encoded = [self.pt_start] + french_encoded + [self.pt_end] + [self.end]\n",
+    "            \n",
+    "            input_ids.append(\n",
+    "                english_encoded + french_encoded\n",
+    "            )\n",
+    "            labels.append(\n",
+    "                (english_encoded + french_encoded)[1::] + [self.pad_token_idx]\n",
+    "            )\n",
+    "        \n",
+    "        paddded_tokens = torch.nn.utils.rnn.pad_sequence([torch.tensor(p) for p in input_ids], batch_first=True, padding_value=self.pad_token_idx).long()\n",
+    "        attention_mask = (paddded_tokens != self.pad_token_idx).to(torch.int32)\n",
+    "        \n",
+    "        target = torch.nn.utils.rnn.pad_sequence([torch.tensor(p) for p in labels], batch_first=True, padding_value=self.pad_token_idx).long()\n",
+    "        target = torch.where(attention_mask == 0, -100, target)\n",
+    "        \n",
+    "        return {\"input_ids\": paddded_tokens, \"attention_mask\": attention_mask, \"labels\": target}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "collate_fn = NextTokenPredictionCollator(tokenizer=tokenizer)\n",
+    "\n",
+    "train_dataloader = create_data_loader(train_examples_pt, collate_fn=collate_fn, batch_size=dataset_config.batch_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for batch in train_dataloader:\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 -> 6\n",
+      "6 -> 4949\n",
+      "4949 -> 17156\n",
+      "17156 -> 6363\n",
+      "6363 -> 43056\n",
+      "43056 -> 30723\n",
+      "30723 -> 5396\n",
+      "5396 -> 6710\n",
+      "6710 -> 6138\n",
+      "6138 -> 9621\n",
+      "9621 -> 21213\n",
+      "21213 -> 7747\n",
+      "7747 -> 52644\n",
+      "52644 -> 52615\n",
+      "52615 -> 3946\n",
+      "3946 -> 28302\n",
+      "28302 -> 20596\n",
+      "20596 -> 52636\n",
+      "52636 -> 7168\n",
+      "7168 -> 9128\n",
+      "9128 -> 1468\n",
+      "1468 -> 52597\n",
+      "52597 -> 2120\n",
+      "2120 -> 2254\n",
+      "2254 -> 1371\n",
+      "1371 -> 41641\n",
+      "41641 -> 17237\n",
+      "17237 -> 6218\n",
+      "6218 -> 36010\n",
+      "36010 -> 1424\n",
+      "1424 -> 19439\n",
+      "19439 -> 52601\n",
+      "52601 -> 7560\n",
+      "7560 -> 1420\n",
+      "1420 -> 7253\n",
+      "7253 -> 5119\n",
+      "5119 -> 52601\n",
+      "52601 -> 6320\n",
+      "6320 -> 1420\n",
+      "1420 -> 41101\n",
+      "41101 -> 7\n",
+      "7 -> 8\n",
+      "8 -> 7187\n",
+      "7187 -> 30723\n",
+      "30723 -> 52715\n",
+      "52715 -> 4681\n",
+      "4681 -> 17156\n",
+      "17156 -> 6363\n",
+      "6363 -> 14897\n",
+      "14897 -> 1528\n",
+      "1528 -> 52607\n",
+      "52607 -> 19565\n",
+      "19565 -> 52676\n",
+      "52676 -> 21213\n",
+      "21213 -> 7747\n",
+      "7747 -> 7876\n",
+      "7876 -> 4635\n",
+      "4635 -> 1459\n",
+      "1459 -> 3946\n",
+      "3946 -> 20422\n",
+      "20422 -> 3257\n",
+      "3257 -> 30182\n",
+      "30182 -> 52676\n",
+      "52676 -> 9128\n",
+      "9128 -> 1468\n",
+      "1468 -> 52597\n",
+      "52597 -> 2120\n",
+      "2120 -> 2254\n",
+      "2254 -> 1371\n",
+      "1371 -> 17237\n",
+      "17237 -> 52611\n",
+      "52611 -> 19545\n",
+      "19545 -> 16949\n",
+      "16949 -> 1447\n",
+      "1447 -> 1379\n",
+      "1379 -> 52607\n",
+      "52607 -> 7882\n",
+      "7882 -> 1440\n",
+      "1440 -> 11000\n",
+      "11000 -> 1561\n",
+      "1561 -> 7671\n",
+      "7671 -> 1390\n",
+      "1390 -> 43570\n",
+      "43570 -> 6320\n",
+      "6320 -> 1420\n",
+      "1420 -> 41101\n",
+      "41101 -> 9\n",
+      "9 -> 2\n",
+      "2 -> 0\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n",
+      "0 -> -100\n"
+     ]
+    }
+   ],
+   "source": [
+    "for l, i in zip(batch[\"labels\"][0].numpy().tolist(), batch[\"input_ids\"][0].numpy().tolist()):\n",
+    "    print(f\"{i} -> {l}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "sns.heatmap(batch[\"attention_mask\"].numpy().tolist())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "training_config.warm_up = 4000\n",
+    "training_config.logging_steps = 747"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch = {key: value.to(\"cpu\") for key, value in batch.items()}\n",
+    "pred = model(input_ids=batch[\"input_ids\"], attention_mask = batch[\"attention_mask\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch.nn.functional as F\n",
+    "\n",
+    "def cal_loss(logits, gold, trg_pad_idx, smoothing=False):\n",
+    "    ''' Calculate cross entropy loss, apply label smoothing if needed. '''\n",
+    "    n_classes = logits.shape[-1]\n",
+    "    logits = logits.view(-1, n_classes)\n",
+    "    gold = gold.unsqueeze(-1).view(-1)\n",
+    "    loss = F.cross_entropy(logits, gold)\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([32, 402])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred[\"logits\"].max(-1)[1].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ConstrueAutoRegressiveModel(\n",
+       "  (model): ConstrueModel(\n",
+       "    (token_embeddings): Embedding(60000, 128, padding_idx=0)\n",
+       "    (decoder_layers): ModuleList(\n",
+       "      (0-1): 2 x ConstrueDecoderLayer(\n",
+       "        (input_norm): LayerNorm()\n",
+       "        (self_attn): RopeAttention(\n",
+       "          (rope_position_projection): RopePositionEmbedding()\n",
+       "          (qkv_projection): Linear(in_features=128, out_features=384, bias=False)\n",
+       "          (output_projection): Linear(in_features=128, out_features=128, bias=False)\n",
+       "        )\n",
+       "        (attention_dropout): Dropout(p=0.1, inplace=False)\n",
+       "        (post_attention_norm): LayerNorm()\n",
+       "        (mlp): PointWiseGatedProjection(\n",
+       "          (gate_projection): Linear(in_features=128, out_features=1024, bias=False)\n",
+       "          (up_projection): Linear(in_features=128, out_features=1024, bias=False)\n",
+       "          (down_projection): Linear(in_features=1024, out_features=128, bias=False)\n",
+       "          (act_func): PytorchGELUTanh()\n",
+       "        )\n",
+       "        (dropout2): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "    )\n",
+       "    (final_layer_norm): LayerNorm()\n",
+       "  )\n",
+       "  (lm_head): Linear(in_features=128, out_features=60000, bias=False)\n",
+       ")"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.to(\"cuda\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cal_performance(pred, gold, trg_pad_idx=-100):\n",
+    "    ''' Apply label smoothing if needed '''\n",
+    "\n",
+    "    loss = cal_loss(pred, gold, trg_pad_idx)\n",
+    "\n",
+    "    pred = pred.max(-1)[1].view(-1)\n",
+    "    gold = gold.contiguous().view(-1)\n",
+    "    non_pad_mask = gold.ne(trg_pad_idx)\n",
+    "    n_correct = pred.eq(gold).masked_select(non_pad_mask).sum().item()\n",
+    "    n_word = non_pad_mask.sum().item()\n",
+    "\n",
+    "    return loss, n_correct, n_word"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torch.optim import AdamW\n",
+    "from torch.optim.lr_scheduler import LambdaLR\n",
+    "import math\n",
+    "from typing import Optional, List\n",
+    "\n",
+    "class WarmupCosineScheduler(LambdaLR):\n",
+    "    \"\"\"Linear warmup and cosine decay scheduler.\"\"\"\n",
+    "    \n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        optimizer: AdamW,\n",
+    "        warmup_steps: int,\n",
+    "        total_steps: int,\n",
+    "        min_lr_ratio: float = 0.1,\n",
+    "        last_epoch: int = -1\n",
+    "    ):\n",
+    "        \"\"\"\n",
+    "        Initialize warmup and decay scheduler.\n",
+    "        \n",
+    "        Args:\n",
+    "            optimizer: AdamW optimizer\n",
+    "            warmup_steps: Number of warmup steps\n",
+    "            total_steps: Total number of training steps\n",
+    "            min_lr_ratio: Minimum learning rate ratio compared to initial lr\n",
+    "            last_epoch: The index of the last epoch\n",
+    "        \"\"\"\n",
+    "        self.warmup_steps = warmup_steps\n",
+    "        self.total_steps = total_steps\n",
+    "        self.min_lr_ratio = min_lr_ratio\n",
+    "        super().__init__(optimizer, self.lr_lambda, last_epoch)\n",
+    "    \n",
+    "    def lr_lambda(self, current_step: int) -> float:\n",
+    "        \"\"\"Calculate lr multiplier based on current step.\"\"\"\n",
+    "        if current_step < self.warmup_steps:\n",
+    "            # Linear warmup\n",
+    "            return float(current_step) / float(max(1, self.warmup_steps))\n",
+    "        \n",
+    "        # Cosine decay\n",
+    "        progress = float(current_step - self.warmup_steps) / \\\n",
+    "            float(max(1, self.total_steps - self.warmup_steps))\n",
+    "        decay = 0.5 * (1.0 + math.cos(math.pi * progress))\n",
+    "        # Scale decay to min_lr_ratio\n",
+    "        decay = self.min_lr_ratio + (1.0 - self.min_lr_ratio) * decay\n",
+    "        return decay\n",
+    "\n",
+    "def create_optimizer_and_scheduler(\n",
+    "    model: torch.nn.Module,\n",
+    "    num_training_steps: int,\n",
+    "    learning_rate: float = 5e-5,\n",
+    "    weight_decay: float = 0.01,\n",
+    "    warmup_ratio: float = 0.1,\n",
+    "    min_lr_ratio: float = 0.1,\n",
+    "    beta1: float = 0.9,\n",
+    "    beta2: float = 0.999,\n",
+    "    eps: float = 1e-8,\n",
+    "    no_decay_params: Optional[List[str]] = None\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Create AdamW optimizer and warmup scheduler.\n",
+    "    \n",
+    "    Args:\n",
+    "        model: PyTorch model\n",
+    "        num_training_steps: Total number of training steps\n",
+    "        learning_rate: Maximum learning rate after warmup\n",
+    "        weight_decay: Weight decay coefficient\n",
+    "        warmup_ratio: Ratio of warmup steps to total steps\n",
+    "        min_lr_ratio: Minimum learning rate ratio compared to max lr\n",
+    "        beta1: AdamW beta1 parameter\n",
+    "        beta2: AdamW beta2 parameter\n",
+    "        eps: AdamW epsilon parameter\n",
+    "        correct_bias: Whether to correct bias in AdamW\n",
+    "        no_decay_params: List of parameter names that should not have weight decay\n",
+    "    \n",
+    "    Returns:\n",
+    "        tuple: (optimizer, scheduler)\n",
+    "    \"\"\"\n",
+    "    # Default params that should not have weight decay\n",
+    "    if no_decay_params is None:\n",
+    "        no_decay_params = ['bias', 'LayerNorm.weight', 'layer_norm.weight']\n",
+    "    \n",
+    "    # Separate parameters that should and should not have weight decay\n",
+    "    optimizer_grouped_parameters = [\n",
+    "        {\n",
+    "            \"params\": [\n",
+    "                p for n, p in model.named_parameters()\n",
+    "                if not any(nd in n for nd in no_decay_params)\n",
+    "            ],\n",
+    "            \"weight_decay\": weight_decay,\n",
+    "        },\n",
+    "        {\n",
+    "            \"params\": [\n",
+    "                p for n, p in model.named_parameters()\n",
+    "                if any(nd in n for nd in no_decay_params)\n",
+    "            ],\n",
+    "            \"weight_decay\": 0.0,\n",
+    "        },\n",
+    "    ]\n",
+    "    \n",
+    "    # Create AdamW optimizer\n",
+    "    optimizer = AdamW(\n",
+    "        optimizer_grouped_parameters,\n",
+    "        lr=learning_rate,\n",
+    "        betas=(beta1, beta2),\n",
+    "        eps=eps\n",
+    "    )\n",
+    "    \n",
+    "    # Create scheduler with linear warmup and cosine decay\n",
+    "    print(f\"NUM Training Step :: {num_training_steps}\")\n",
+    "    \n",
+    "    warmup_steps = int(num_training_steps * warmup_ratio)\n",
+    "    print(f\"warmup_steps :: {warmup_steps}\")\n",
+    "    \n",
+    "    scheduler = WarmupCosineScheduler(\n",
+    "        optimizer=optimizer,\n",
+    "        warmup_steps=warmup_steps,\n",
+    "        total_steps=num_training_steps,\n",
+    "        min_lr_ratio=min_lr_ratio\n",
+    "    )\n",
+    "    \n",
+    "    return optimizer, scheduler, warmup_steps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "def train_step(model, batch, optimizer, scheduler, device, logger=None, gradient_accumulation_steps=1):\n",
+    "    # forward\n",
+    "    labels = batch[\"labels\"].to(device)\n",
+    "    optimizer.zero_grad()\n",
+    "    pred = model(input_ids=batch[\"input_ids\"].to(device), attention_mask=batch[\"attention_mask\"].to(device))\n",
+    "    logits = pred[\"logits\"]\n",
+    "    # backward and update parameters\n",
+    "    loss, n_correct, n_word = cal_performance(logits, labels) \n",
+    "    loss = loss / gradient_accumulation_steps\n",
+    "    \n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "    scheduler.step()\n",
+    "    # if (optimizer.step_count + 1) % gradient_accumulation_steps == 0:\n",
+    "    #     torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+    "    #     optimizer.step()\n",
+    "    #     scheduler.step()\n",
+    "    #     optimizer.zero_grad()\n",
+    "    \n",
+    "    return loss.item() * gradient_accumulation_steps, n_correct, n_word\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "def train_epoch(epoch, model, training_data, optimizer, scheduler, device, logging_steps=-1, logger=None, gradient_accumulation_steps=1):\n",
+    "    ''' Epoch operation in training phase'''\n",
+    "    model.train()\n",
+    "    total_loss, n_word_total, n_word_correct = 0, 0, 0 \n",
+    "\n",
+    "    desc = '  - (Training)   '\n",
+    "    for step, batch in tqdm(enumerate(training_data), mininterval=2, desc=desc, leave=False, total=len(train_dataloader)):\n",
+    "        # forward\n",
+    "        labels = batch[\"labels\"].to(device)\n",
+    "        optimizer.zero_grad()\n",
+    "        pred = model(input_ids=batch[\"input_ids\"].to(device), attention_mask=batch[\"attention_mask\"].to(device))\n",
+    "        logits = pred[\"logits\"]\n",
+    "        # backward and update parameters\n",
+    "        loss, n_correct, n_word = cal_performance(logits, labels) \n",
+    "        \n",
+    "        loss.backward()\n",
+    "        if (optimizer.step_count + 1) % gradient_accumulation_steps == 0:\n",
+    "            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+    "            optimizer.step()\n",
+    "            scheduler.step()\n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "        # note keeping\n",
+    "        n_word_total += n_word\n",
+    "        n_word_correct += n_correct\n",
+    "        total_loss += loss.item()\n",
+    "        \n",
+    "        logger.log_metrics(metrics={\n",
+    "            'loss': loss.item(),\n",
+    "            'epoch': epoch,\n",
+    "            \"n_correct\": n_correct,\n",
+    "            \"n_word\": n_word,\n",
+    "            'learning_rate': sched.get_current_learning_rate()\n",
+    "        }, step=step)\n",
+    "\n",
+    "    loss_per_word = total_loss/n_word_total\n",
+    "    accuracy = n_word_correct/n_word_total\n",
+    "    return loss_per_word, accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_training_steps(\n",
+    "    num_examples: int,\n",
+    "    num_epochs: int,\n",
+    "    batch_size: int,\n",
+    "    gradient_accumulation_steps: int = 1\n",
+    ") -> int:\n",
+    "    \"\"\"\n",
+    "    Calculate the total number of training steps.\n",
+    "    \n",
+    "    Args:\n",
+    "        num_examples: Total number of training examples\n",
+    "        num_epochs: Number of epochs to train for\n",
+    "        batch_size: Batch size per forward pass\n",
+    "        gradient_accumulation_steps: Number of steps to accumulate gradients\n",
+    "    \n",
+    "    Returns:\n",
+    "        int: Total number of optimizer update steps\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    update_steps_per_epoch = math.ceil(num_examples / gradient_accumulation_steps)\n",
+    "    \n",
+    "    total_training_steps = update_steps_per_epoch * num_epochs\n",
+    "    \n",
+    "    return total_training_steps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import logging\n",
+    "import time\n",
+    "from pathlib import Path\n",
+    "import json\n",
+    "from typing import Dict, Any, Optional\n",
+    "from datetime import datetime\n",
+    "\n",
+    "class TrainingLogger:\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        output_dir: str,\n",
+    "        project_name: str,\n",
+    "        log_every_n_steps: int = 100,\n",
+    "        save_every_n_steps: int = 1000,\n",
+    "        save_best_only: bool = True\n",
+    "    ):\n",
+    "        \"\"\"\n",
+    "        Initialize training logger with various logging options.\n",
+    "        \n",
+    "        Args:\n",
+    "            output_dir: Directory to save checkpoints and logs\n",
+    "            project_name: Name of the project\n",
+    "            use_wandb: Whether to use Weights & Biases logging\n",
+    "            log_every_n_steps: How often to log metrics\n",
+    "            save_every_n_steps: How often to save checkpoints\n",
+    "            save_best_only: Whether to save only the best model\n",
+    "        \"\"\"\n",
+    "        self.output_dir = Path(output_dir)\n",
+    "        self.output_dir.mkdir(parents=True, exist_ok=True)\n",
+    "        \n",
+    "        # Initialize logging\n",
+    "        self.log_file = self.output_dir / 'training.log'\n",
+    "        self.setup_logging()\n",
+    "        \n",
+    "        # Training state\n",
+    "        self.global_step = 0\n",
+    "        self.best_loss = float('inf')\n",
+    "        self.start_time = time.time()\n",
+    "        self.last_log_time = self.start_time\n",
+    "        \n",
+    "        # Configuration\n",
+    "        self.log_every_n_steps = log_every_n_steps\n",
+    "        self.save_every_n_steps = save_every_n_steps\n",
+    "            \n",
+    "        # Save configuration\n",
+    "        self.save_config({\n",
+    "            'output_dir': str(output_dir),\n",
+    "            'project_name': project_name,\n",
+    "            'log_every_n_steps': log_every_n_steps,\n",
+    "            'save_every_n_steps': save_every_n_steps,\n",
+    "            'save_best_only': save_best_only,\n",
+    "            'start_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n",
+    "        })\n",
+    "    \n",
+    "    def setup_logging(self):\n",
+    "        \"\"\"Setup logging configuration.\"\"\"\n",
+    "        logging.basicConfig(\n",
+    "            level=logging.INFO,\n",
+    "            format='%(asctime)s - %(levelname)s - %(message)s',\n",
+    "            handlers=[\n",
+    "                logging.FileHandler(self.log_file),\n",
+    "                logging.StreamHandler()\n",
+    "            ]\n",
+    "        )\n",
+    "    \n",
+    "    def save_config(self, config: Dict[str, Any]):\n",
+    "        \"\"\"Save configuration to JSON file.\"\"\"\n",
+    "        config_path = self.output_dir / 'config.json'\n",
+    "        with open(config_path, 'w') as f:\n",
+    "            json.dump(config, f, indent=4)\n",
+    "    \n",
+    "    def log_metrics(\n",
+    "        self,\n",
+    "        metrics: Dict[str, float],\n",
+    "        step: Optional[int] = None,\n",
+    "        force_log: bool = False\n",
+    "    ):\n",
+    "        \"\"\"\n",
+    "        Log metrics to all configured outputs.\n",
+    "        \n",
+    "        Args:\n",
+    "            metrics: Dictionary of metric names and values\n",
+    "            step: Optional step number (uses global_step if not provided)\n",
+    "            force_log: Whether to log regardless of log_every_n_steps\n",
+    "        \"\"\"\n",
+    "        if step is not None:\n",
+    "            self.global_step = step\n",
+    "        \n",
+    "        # Check if we should log\n",
+    "        if not force_log and self.global_step % self.log_every_n_steps != 0:\n",
+    "            return\n",
+    "        \n",
+    "        # Calculate time statistics\n",
+    "        current_time = time.time()\n",
+    "        elapsed = current_time - self.start_time\n",
+    "        elapsed_since_last = current_time - self.last_log_time\n",
+    "        steps_since_last = self.log_every_n_steps\n",
+    "        steps_per_second = steps_since_last / elapsed_since_last\n",
+    "        \n",
+    "        # Add timing metrics\n",
+    "        metrics.update({\n",
+    "            'elapsed_time': elapsed,\n",
+    "            'steps_per_second': steps_per_second\n",
+    "        })\n",
+    "        \n",
+    "        # Log to terminal and file\n",
+    "        log_str = f'Step {self.global_step}: ' + ', '.join(\n",
+    "            f'{k}: {v}' for k, v in metrics.items()\n",
+    "        )\n",
+    "        logging.info(log_str)\n",
+    "        \n",
+    "        self.last_log_time = current_time\n",
+    "    \n",
+    "    def save_checkpoint(\n",
+    "        self,\n",
+    "        model: torch.nn.Module,\n",
+    "        optimizer: torch.optim.Optimizer,\n",
+    "        loss: float,\n",
+    "        extra_data: Optional[Dict[str, Any]] = None,\n",
+    "        force_save: bool = False\n",
+    "    ):\n",
+    "        \"\"\"\n",
+    "        Save model checkpoint.\n",
+    "        \n",
+    "        Args:\n",
+    "            model: PyTorch model\n",
+    "            optimizer: PyTorch optimizer\n",
+    "            loss: Current loss value\n",
+    "            extra_data: Additional data to save in checkpoint\n",
+    "            force_save: Whether to save regardless of save_every_n_steps\n",
+    "        \"\"\"\n",
+    "        # Check if we should save\n",
+    "        should_save = (\n",
+    "            force_save or\n",
+    "            self.global_step % self.save_every_n_steps == 0 or\n",
+    "            (self.save_best_only and loss < self.best_loss)\n",
+    "        )\n",
+    "        \n",
+    "        if not should_save:\n",
+    "            return\n",
+    "        \n",
+    "        # Update best loss if needed\n",
+    "        if loss < self.best_loss:\n",
+    "            self.best_loss = loss\n",
+    "        \n",
+    "        # Prepare checkpoint data\n",
+    "        checkpoint = {\n",
+    "            'model_state_dict': model.state_dict(),\n",
+    "            'optimizer_state_dict': optimizer.state_dict(),\n",
+    "            'global_step': self.global_step,\n",
+    "            'loss': loss,\n",
+    "            'best_loss': self.best_loss\n",
+    "        }\n",
+    "        \n",
+    "        if extra_data:\n",
+    "            checkpoint.update(extra_data)\n",
+    "        \n",
+    "        # Save checkpoint\n",
+    "        checkpoint_path = self.output_dir / f'checkpoint_{self.global_step}.pt'\n",
+    "        torch.save(checkpoint, checkpoint_path)\n",
+    "        \n",
+    "        logging.info(f'Saved checkpoint at step {self.global_step}')\n",
+    "    \n",
+    "    def finish(self):\n",
+    "        \"\"\"Cleanup and final logging.\"\"\"\n",
+    "        total_time = time.time() - self.start_time\n",
+    "        logging.info(f'Training finished. Total time: {total_time:.2f}s')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train(experimentation_name,\n",
+    "          output_dir,\n",
+    "          logging_steps,\n",
+    "          save_steps,\n",
+    "          num_epochs,\n",
+    "          train_dataset,\n",
+    "          collate_fn,\n",
+    "          batch_size,\n",
+    "          gradient_accumulation_steps=1, # not yet implemented\n",
+    "          learning_rate=5e-5,\n",
+    "          warmup_ratio=0.2,\n",
+    "          weight_decay=0.0,\n",
+    "          device=\"cuda\"):\n",
+    "    \n",
+    "    # prepare model\n",
+    "    model.train()\n",
+    "    model.to(device)\n",
+    "    \n",
+    "    train_dataloader = create_data_loader(train_dataset, collate_fn=collate_fn, batch_size=batch_size)\n",
+    "    \n",
+    "    num_training_steps = calculate_training_steps(len(train_dataloader), num_epochs=num_epochs, batch_size=batch_size, gradient_accumulation_steps=gradient_accumulation_steps)\n",
+    "\n",
+    "    # Create optimizer and scheduler\n",
+    "    optimizer, scheduler, warmup_steps = create_optimizer_and_scheduler(\n",
+    "        model=model,\n",
+    "        num_training_steps=num_training_steps,\n",
+    "        learning_rate=learning_rate,\n",
+    "        warmup_ratio=warmup_ratio,\n",
+    "        weight_decay=weight_decay\n",
+    "    )\n",
+    "    \n",
+    "    train_logger = TrainingLogger(\n",
+    "        project_name=experimentation_name,\n",
+    "        output_dir=output_dir,\n",
+    "        log_every_n_steps=logging_steps,\n",
+    "        save_every_n_steps=save_steps,\n",
+    "    )\n",
+    "\n",
+    "    for epoch in range(num_epochs):\n",
+    "        desc = f'  - (Training)   Epoch {epoch}'\n",
+    "        n_word_total, n_word_correct, total_loss = 0, 0, 0\n",
+    "        for step, batch in tqdm(enumerate(train_dataloader), mininterval=2, desc=desc, leave=False, total=len(train_dataloader)):\n",
+    "            loss, n_correct, n_word = train_step(\n",
+    "                model=model,\n",
+    "                batch=batch,\n",
+    "                optimizer=optimizer,\n",
+    "                scheduler=scheduler,\n",
+    "                device=device,\n",
+    "                gradient_accumulation_steps=gradient_accumulation_steps,\n",
+    "            )\n",
+    "            \n",
+    "            \n",
+    "            n_word_total += n_word\n",
+    "            n_word_correct += n_correct\n",
+    "            total_loss += loss\n",
+    "            \n",
+    "            current_lr = scheduler.get_last_lr()[0]\n",
+    "            \n",
+    "            train_logger.log_metrics(metrics={\n",
+    "                'loss': loss,\n",
+    "                'epoch': epoch,\n",
+    "                \"n_correct\": n_correct,\n",
+    "                \"n_word\": n_word,\n",
+    "                'learning_rate': current_lr,\n",
+    "                \"warmup_steps\": warmup_steps\n",
+    "            }, step=step)\n",
+    "        print()\n",
+    "        print(f\"Epoch {epoch} :: loss per word :: {total_loss/n_word_total}\")\n",
+    "    train_logger.finish()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NUM Training Step :: 149574\n",
+      "warmup_steps :: 14957\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b3c410d160a401b9d1626f45fe11adc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  - (Training)   Epoch 0:   0%|          | 0/74787 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-01-19 13:05:47,320 - INFO - Step 0: loss: 11.160490989685059, epoch: 0, n_correct: 0, n_word: 2685, learning_rate: 2.0057498161396e-08, warmup_steps: 14957, elapsed_time: 0.6764383316040039, steps_per_second: 1478.3313618977663\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset_config.batch_size\n",
+    "\n",
+    "\n",
+    "collate_fn = NextTokenPredictionCollator(tokenizer=tokenizer)\n",
+    "\n",
+    "training_config.logging_steps = 1000\n",
+    "training_config.warm_up = 4000\n",
+    "\n",
+    "train(\n",
+    "    experimentation_name=training_config.experimentation_name,\n",
+    "    output_dir=training_config.save_path,\n",
+    "    logging_steps=training_config.logging_steps,\n",
+    "    save_steps=training_config.logging_steps,\n",
+    "    num_epochs= 2, # training_config.num_epochs,\n",
+    "    train_dataset=train_examples_pt,\n",
+    "    collate_fn=collate_fn,\n",
+    "    batch_size=dataset_config.batch_size,\n",
+    "    learning_rate=3e-4,\n",
+    "    warmup_ratio=0.1\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"weights.pt\", \"wb\") as handler:\n",
+    "    torch.save(model.state_dict(), handler)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_390491/2733062557.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  state_dict = torch.load(handler)\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"weights.pt\", \"rb\") as handler:\n",
+    "    state_dict = torch.load(handler)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.load_state_dict(state_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 487,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"how to enable zia's email intelligence features in zoho crm\"\n",
+    "\n",
+    "input_ids = tokenizer.encode(text, add_special_tokens=False, return_type=None)[\"input_ids\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 488,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_ids = [tokenizer.model.PieceToId(\"<s>\")] + [tokenizer.model.PieceToId(\"<lang_en>\")] +  input_ids[0] + [tokenizer.model.PieceToId(\"</lang_en>\")] + [tokenizer.model.PieceToId(\"<lang_pt>\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 489,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model.to(\"cuda\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 490,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_ids = torch.as_tensor([input_ids])\n",
+    "# input_ids = input_ids.unsqueeze(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 491,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[    1,     6,  1895,  1409,  9256,  3073,  1966, 52605, 52583,  3980,\n",
+       "         10368,  6032,  1413,  3073,  3248, 52586,  1842, 52593,     7,     8]])"
+      ]
+     },
+     "execution_count": 491,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_ids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 653,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[\"how to enable zia's email intelligence features in zoho crm Comment faire preuve de l'information sur le savoir-faire de l'information sur les produits de l'information\"]"
+      ]
+     },
+     "execution_count": 653,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenizer.decode(input_ids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 654,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with torch.no_grad():\n",
+    "    outputs = model(input_ids.to(\"cuda\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 655,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next_token_logits = outputs[\"logits\"][:, -1, :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 656,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-17.3053, -13.8722, -12.7217,  ..., -13.8961, -13.6052, -13.5213]],\n",
+       "       device='cuda:0')"
+      ]
+     },
+     "execution_count": 656,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next_token_logits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 657,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(9, device='cuda:0')"
+      ]
+     },
+     "execution_count": 657,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.argmax(next_token_logits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 658,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'</lang_pt>'"
+      ]
+     },
+     "execution_count": 658,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenizer.model.id_to_piece(torch.argmax(next_token_logits).item())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 659,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_ids = torch.hstack([input_ids, torch.as_tensor([[torch.argmax(next_token_logits).item()]])])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ai_experimentation_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/models/GI_01/tokenizer_config.yml b/core/models/GI_01/tokenizer_config.yml
new file mode 100644
index 0000000..7542263
--- /dev/null
+++ b/core/models/GI_01/tokenizer_config.yml
@@ -0,0 +1,1766 @@
+user_defined_symbols:
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+- "\u2581\u2581"
+- "\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581"
+- "\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581"
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+  \u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\
+  \u2581\u2581\u2581"
+- <table>
+- <caption>
+- <thead>
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+- <tfoot>
+- <tr>
+- <th>
+- <td>
+- </table>
+- </caption>
+- </thead>
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+- </tr>
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+- <h1>
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+- <h6>
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+- <strong>
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+- </b>
+- </i>
+- </u>
+- </s>
+- </sub>
+- </sup>
+- </code>
+- "\t"
+- <mask>
+- <2mass>
+- '[@BOS@]'
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+control_symbols:
+- <start_of_turn>
+- <end_of_turn>
+- <system_prompt>
+- <language>
+- </language>
diff --git a/core/models/GI_01/train.py b/core/models/GI_01/train.py
new file mode 100644
index 0000000..81e57c1
--- /dev/null
+++ b/core/models/GI_01/train.py
@@ -0,0 +1,643 @@
+import datetime
+import pprint
+import random
+import time
+from copy import deepcopy
+import gc
+import logging
+import sys
+from contextlib import ExitStack
+
+import numpy as np
+from dataclasses import asdict, dataclass, field
+from timeit import default_timer as timer
+
+from omegaconf import OmegaConf
+import torch
+import torch.distributed
+import xformers.profiler
+from torch.optim import lr_scheduler
+from torch.distributed._tensor import DTensor
+import os
+from pathlib import Path
+from typing import Dict, Any, Iterator, Optional, TypedDict
+from torch.distributed.checkpoint.stateful import Stateful
+from core.trainer.distributed import (
+    DistributedArgs,
+    EnvironmentArgs,
+    init_signal_handler,
+    dist_mean_dict,
+    get_device_mesh,
+    get_is_master,
+    get_world_size,
+    parallelize_model,
+    setup_env,
+    setup_torch_distributed,
+    clean_env,
+    requeue_slurm_job,
+    check_model_value_range,
+)
+from core.trainer.args import dataclass_from_dict, dump_config, flatten_dict
+from core.trainer.checkpointer import CheckpointArgs, CheckpointManager, load_from_checkpoint
+from core.trainer.dataloader import (
+    DataArgs,
+    PackTokensState,
+    build_dataloader_from_args,
+    init_dataloader_state_from_args,
+)
+from core.trainer.logger import init_logger
+from core.trainer.metrics import (
+    GPUMemoryMonitor,
+    LoggingArgs,
+    MetricLogger,
+    get_num_params,
+)
+from core.trainer.optim import OptimArgs, build_optimizer
+from core.trainer.profiling import ProfilerArgs, maybe_run_profiler
+from core.trainer.dataloader import build_tokenizer
+
+from core.trainer.probe import AutoProbeD
+from core.trainer.stool import StoolArgs, launch_job
+from core.models.GI_01.main.model import ConstrueModel, GI01ModelArgs, build_fsdp_grouping_plan
+from core.configurations.base import BaseConfiguration
+
+
+import wandb
+
+from core.trainer.utils import get_initializer, initialize_model
+
+logger = logging.getLogger()
+
+
+def attention_flops_per_token(n_layers, seq_len, dim, causal):
+    # Formula from https://github.com/Dao-AILab/flash-attention/blob/main/benchmarks/benchmark_flash_attention.py#L27-L30
+    return 3.5 * (4 * n_layers * seq_len * dim // (2 if causal else 1))
+
+
+def get_num_flop_per_token(
+    num_non_embed_params: int, n_layers: int, dim: int, seq_len: int
+) -> int:
+    return 6 * num_non_embed_params + attention_flops_per_token(
+        n_layers, seq_len, dim, True
+    )
+
+
+@dataclass
+class TrainArgs:
+    name: str = "lingua_zoho"
+    dump_dir: str = "/workspace/AI-Uncomplicated/"
+
+    seed: int = 42
+
+    # Number of gradient accumulation steps
+    # Total batch size is batch_size*grad_acc_steps
+    grad_acc_steps: int = 1
+
+    gc_collect_freq: int = 1000
+    probe_freq: Optional[int] = None
+
+    # Nb optimizer steps to take
+    steps: int = 1000
+
+    data: DataArgs = field(default_factory=DataArgs)
+    optim: OptimArgs = field(default_factory=OptimArgs)
+    model: GI01ModelArgs = field(default_factory=GI01ModelArgs)
+    distributed: DistributedArgs = field(default_factory=DistributedArgs)
+    env: EnvironmentArgs = field(default_factory=EnvironmentArgs)
+
+    checkpoint: CheckpointArgs = field(default_factory=CheckpointArgs)
+    profiling: ProfilerArgs = field(default_factory=ProfilerArgs)
+    logging: LoggingArgs = field(default_factory=LoggingArgs)
+
+    # If set to None, eval is run locally otherwise it launches a new job with the given number of gpus
+    async_eval_gpus: Optional[int] = None
+    eval: Optional[Any] = None
+
+
+
+@dataclass
+class TrainState(Stateful):
+    step: int  # Nb of steps taken by the optimizer
+    acc_step: int  # Nb of accumulation steps done since last optimizer step
+    scheduler: lr_scheduler.LambdaLR
+    data_loader_state: PackTokensState
+
+    def state_dict(self) -> Dict[str, Any]:
+        return {
+            "step": self.step,
+            "acc_step": self.acc_step,
+            "data_loader_state": self.data_loader_state,
+            "scheduler": self.scheduler.state_dict(),
+        }
+
+    def load_state_dict(self, state_dict):
+        self.step = state_dict["step"]
+        self.acc_step = state_dict["acc_step"]
+        self.data_loader_state = PackTokensState(**state_dict["data_loader_state"])
+        self.scheduler.load_state_dict(state_dict["scheduler"])
+
+
+
+def validate_train_args(args: TrainArgs, output_size: int):
+    if args.model.vocab_size < 0:
+        logger.info(f"Setting model output size to {output_size}")
+        args.model.vocab_size = output_size
+    assert (
+        args.model.vocab_size == output_size
+    ), "Vocab size should be the same as output size"
+
+    assert args.dump_dir, "Dump dir not set"
+
+    if args.checkpoint.path is None:
+        logger.info(f"=============args is {args}==============")
+        logger.info(f"Setting checkpoint path to {str(Path(args.dump_dir) / 'checkpoints')}")
+        args.checkpoint.path = str(Path(args.dump_dir) / "checkpoints")
+
+    for source in args.data.sources:
+        data_path = os.path.join(args.data.root_dir, source)
+        assert os.path.exists(data_path), f"{data_path} doesn't exist"
+
+    if (
+        args.distributed.dp_replicate
+        * args.distributed.dp_shard
+        != get_world_size()
+    ):
+        assert get_world_size() % args.distributed.dp_shard == 0
+        args.distributed.dp_replicate = get_world_size() // args.distributed.dp_shard
+
+        logger.warning(
+            f"Setting Data Parallel size to {args.distributed.dp_replicate * args.distributed.dp_shard}"
+        )
+        assert (
+            args.distributed.dp_replicate
+            * args.distributed.dp_shard
+            == get_world_size()
+        )
+
+        if args.distributed.fsdp_type == "no_shard":
+            assert (
+                args.distributed.dp_shard == 1
+                and args.distributed.dp_replicate == get_world_size()
+            )
+
+    args.model.max_seq_len = args.data.seq_len
+
+    assert (
+        args.probe_freq != args.profiling.mem_steps
+    ), "Don't profile during probe step"
+    assert (
+        args.probe_freq != args.profiling.profile_steps
+    ), "Don't profile during probe step"
+    if args.logging.wandb is not None:
+        args.logging.wandb.name = args.name
+
+    if args.probe_freq is not None:
+        assert (
+            args.distributed.selective_activation_checkpointing is False
+        ), "Probing not supported with selective activation checkpointing"
+
+
+    if args.model.name is None:
+        args.model.name = args.name + f"_{round(time.time() * 1000)}"
+
+
+preemption_flag = dict(flag=False)
+
+def set_preemption_flag(signum, frame):
+    logger.warning("Signal handler called with signal " + str(signum))
+    logger.warning("Preemption ! checkpointing asap and exiting.")
+    preemption_flag["flag"] = True
+
+
+def every_n_steps(train_state, freq, acc_step=None, acc_freq=None):
+    test = train_state.step % freq == 0
+    if acc_step is not None:
+        test = test and (train_state.acc_step == acc_step)
+    elif acc_freq is not None:
+        test = test and ((train_state.acc_step % acc_freq) == 0)
+    return test
+
+
+def seed_everything(seed=42):
+    random.seed(seed)
+    os.environ['PYTHONHASHSEED'] = str(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def train(args):
+    with ExitStack() as context_stack:
+        tokenizer = build_tokenizer(args.data.tokenizer.name, args.data.tokenizer.path)
+
+        n_words = tokenizer.vocab_size
+        print(f"========Num words is {n_words}=======")
+
+        validate_train_args(
+            args,
+            n_words,
+        )
+
+        if get_is_master():
+            os.makedirs(args.dump_dir, exist_ok=True)
+            dump_config(args, Path(args.dump_dir) / "config.yaml")
+
+        init_logger(Path(args.dump_dir) / "train.log")
+        init_signal_handler(set_preemption_flag)  # For handling preemption signals.
+
+        setup_env(args.env)
+        setup_torch_distributed(args.distributed)
+        world_mesh = get_device_mesh(args.distributed)
+
+        logger.info(f"Starting job: {args.name}")
+
+        # build dataloader
+        # need dp world size and rank
+        dp_mesh = world_mesh["dp_replicate"]
+        dp_degree = dp_mesh.size()
+        dp_rank = dp_mesh.get_local_rank()
+        if args.distributed.dp_shard > 1:
+            dp_rank = dp_rank * world_mesh["dp_shard"].size() + world_mesh["dp_shard"].get_local_rank()
+            dp_degree *= world_mesh["dp_shard"].size()
+
+        logger.info(f"Running on dp rank : {dp_rank}")
+        logger.info(f"Running on dp size : {dp_degree}")
+
+        torch.manual_seed(args.seed)
+        seed_everything(args.seed)
+        logger.info(f"Building model")
+
+        with torch.device("meta"):
+            model = ConstrueModel(args.model)
+        logger.info(f"Model is built !")
+
+        model_param_count = get_num_params(model)
+        logger.info(f"No of params : {model_param_count}")
+
+        model = parallelize_model(
+            model,
+            world_mesh,
+            args.distributed,
+            fsdp_grouping_plan=build_fsdp_grouping_plan(args.model),
+            no_recompute_ops=None,
+        )
+
+        # Once we shard the model on different gpus we can actually initialize the model
+        # First we create empty tensors of the correct shapes
+        model = model.to_empty(device="cuda")
+        # Then we init the model. Please make sure this function initializes *ALL* parameters
+        # and buffers, otherwise you will have random values in the unitialized tensors
+        # which will silently fail (give nan gradients for example)
+
+        if args.checkpoint.init_ckpt_path:
+            logger.info(f"Loading initial model from {args.checkpoint.init_ckpt_path}")
+            load_from_checkpoint(args.checkpoint.init_ckpt_path, model, model_key="model")  # Put model_key="" if its directly the model checkpoint
+            model.rope_embeddings.reset_parameters()  # For RoPe initialization since it's a buffer it might not be loaded
+        else:
+            with torch.random.fork_rng(devices=[torch.cuda.current_device()]):
+                torch.manual_seed(args.model.seed)
+                model.init_weights()
+
+        if get_is_master():
+            check_model_value_range(model, range=10.0, std=1.0)
+
+        logger.info(f"Model size: {model_param_count:,} total parameters")
+
+        gpu_memory_monitor = GPUMemoryMonitor("cuda")
+        logger.info(
+            f"GPU capacity: {gpu_memory_monitor.device_name} ({gpu_memory_monitor.device_index}) "
+            f"with {gpu_memory_monitor.device_capacity_gib:.2f}GiB memory"
+        )
+        logger.info(f"GPU memory usage: {gpu_memory_monitor}")
+
+        # build optimizer after apply parallelisms to the model
+        optimizer, scheduler = build_optimizer(model, args.optim, args.steps)
+        data_loader_state = init_dataloader_state_from_args(
+            args.data, dp_rank, dp_degree
+        )
+
+        train_state = TrainState(
+            step=0,
+            acc_step=0,
+            data_loader_state=data_loader_state,
+            scheduler=scheduler,
+        )
+
+        # Either load from latest checkpoint or start from scratch
+        checkpoint = CheckpointManager.instantiate_and_make_dir(args.checkpoint)
+        checkpoint.load(model, optimizer, train_state, world_mesh)
+
+        if args.probe_freq is not None:
+            if get_is_master():
+                os.makedirs(Path(args.dump_dir) / "probe", exist_ok=True)
+            torch.distributed.barrier()
+            probe = AutoProbeD(
+                model,
+                (
+                    Path(args.dump_dir) / "probe" / f"probe.{dp_rank}.jsonl"
+                    if (dp_rank % 128 == 0)
+                    else None
+                ),
+            )
+
+        gc.disable()
+
+        # train loop
+        model.train()
+        metric_logger = context_stack.enter_context(
+            MetricLogger(Path(args.dump_dir) / "metrics.jsonl", args)
+        )
+        data_loader = context_stack.enter_context(
+            build_dataloader_from_args(
+                args.data,
+                state=train_state.data_loader_state,
+            )
+        )
+        torch_profiler = context_stack.enter_context(
+            maybe_run_profiler(args.dump_dir, model, args.profiling)
+        )
+
+        nwords_since_last_log = 0
+        time_last_log = timer()
+        gc.collect()
+        while train_state.step < args.steps:
+            # We constrain train_state.acc_step to be in range 0 to args.grad_acc_steps - 1
+            train_state.acc_step += 1
+            train_state.acc_step = train_state.acc_step % args.grad_acc_steps
+
+            # get batch
+            curr_lr = float(optimizer.param_groups[0]["lr"])
+            data_load_start = timer()
+            batch, train_state.data_loader_state = next(data_loader)
+            batch = torch.tensor(
+                batch,
+                dtype=torch.long,
+            )
+
+            if every_n_steps(train_state, args.gc_collect_freq, acc_step=0):
+                logger.info("garbage collection")
+                # we do garbage collection manually otherwise different processes
+                # run the GC at different times so they slow down the whole pipeline
+                gc.collect()
+
+            input_ids = batch[:, :, 0].cuda()
+            labels = batch[:, :, 1:].cuda()
+            data_load_time = round(timer() - data_load_start, 4)
+            nwords_since_last_log += input_ids.numel()
+
+            bsz, seqlen, _ = labels.shape
+
+            # forward
+            start_timer = torch.cuda.Event(enable_timing=True)
+            end_timer = torch.cuda.Event(enable_timing=True)
+            start_timer.record()
+
+            # This is an automatic probe that will compute statistics
+            # of all linears' inputs, weights and outputs
+            # along with attention logits and entropy
+            # both in forward and backward pass
+            if (args.probe_freq is not None) and every_n_steps(
+                train_state, args.probe_freq, acc_step=1 % args.grad_acc_steps
+            ):
+                # Here we do a fake forward and backward pass on a smaller
+                # batch size to avoid OOM
+                # This assumes the model has no stateful layers (batch norm..)
+                assert (
+                    next(model.parameters()).grad is None
+                ), "Can't probe model if grads are not reset"
+
+                with probe:
+                    probe.metadata = {
+                        "it": train_state.step,
+                        "global_step": train_state.step,
+                        "loop": "lingua",
+                    }
+                    # Non compiled model uses roughly 2x memory in our exps
+                    # So we divide bsz by 2 or seqlen by 2
+                    probe_bsz = max(1, bsz // 2)
+                    probe_seq = seqlen if (bsz // 2 >= 1) else (seqlen // 2)
+                    probe_loss = model(
+                        input_ids[:probe_bsz, :probe_seq],
+                        labels[:probe_bsz, :probe_seq],
+                    )
+                    sum(probe_loss).backward()
+                    # We zero grads to cancel this fake step
+                    optimizer.zero_grad()
+
+                assert (
+                    next(model.parameters()).grad is None
+                ), "Probe model shouldn't have grads at this point"
+
+            output = model(input_ids=input_ids, target=labels)
+            loss = output["loss"]
+
+            if args.grad_acc_steps > 1:
+                model.set_requires_gradient_sync(train_state.acc_step == 0)
+
+            # We scale loss with grad_acc_steps so the gradient is the same
+            # regardless of grad_acc_steps
+            loss = loss / args.grad_acc_steps
+            # backward on scaled loss to create scaled gradients
+            loss.backward()
+            # For logging we undo that scaling
+            loss = loss.detach() * args.grad_acc_steps
+
+            # optimizer step
+            grad_norm = -1.0
+            if train_state.acc_step == 0:
+                grad_norm = torch.nn.utils.clip_grad_norm_(
+                    model.parameters(), max_norm=args.optim.clip, foreach=True
+                )
+
+                grad_norm = (
+                    grad_norm.full_tensor() if isinstance(grad_norm, DTensor) else grad_norm
+                ).item()
+
+                optimizer.step()
+                scheduler.step()
+                optimizer.zero_grad()
+                train_state.step += 1
+
+            # updates the scale for next iteration
+            # training iteration complete
+            end_timer.record()
+
+            torch.cuda.synchronize()
+
+            curr_iter_time = round(start_timer.elapsed_time(end_timer) * 1e-3, 4)
+
+            # if profiler is active
+            if torch_profiler:
+                xformers.profiler.step()
+
+            # log metrics
+            if every_n_steps(
+                train_state,
+                args.logging.freq,
+                acc_step=None if args.logging.acc_freq else 0,
+                acc_freq=args.logging.acc_freq,
+            ):
+                time_delta = timer() - time_last_log
+                wps = nwords_since_last_log / time_delta
+
+                gpu_mem_stats = gpu_memory_monitor.get_peak_stats()
+
+                total_acc_steps = (
+                    args.grad_acc_steps * train_state.step + train_state.acc_step
+                )
+                tokens_per_gpu = (
+                    total_acc_steps * args.data.batch_size * args.data.seq_len
+                )
+                total_tokens = dp_degree * tokens_per_gpu
+                # This is an estimate and the correct values may change
+                # if you change the architecture
+                # Use xformer's analyze profile trace to get actual measurement
+                FLOPS = (
+                    get_num_flop_per_token(
+                        model_param_count - args.model.vocab_size * args.model.hidden_dim,
+                        args.model.num_layers,
+                        args.model.hidden_dim,
+                        args.data.seq_len,
+                    )
+                    * wps
+                )
+                metrics = flatten_dict(
+                    {
+                        "global_step": train_state.step,
+                        "acc_step": train_state.acc_step,
+                        "speed": {
+                            "wps": wps,
+                            "FLOPS": FLOPS,
+                            "curr_iter_time": curr_iter_time,
+                            "data_load_time": data_load_time,
+                        },
+                        "optim": {
+                            "grad_norm": grad_norm,
+                            "lr": curr_lr,
+                            "total_tokens": total_tokens,
+                        },
+                        "memory": gpu_mem_stats._asdict(),
+                    },
+                    sep="/",
+                )
+
+                to_sync = {}
+                to_sync["loss/out"] = loss.item()
+                metrics.update(dist_mean_dict(to_sync))
+
+                if get_is_master():
+                    metric_logger.log(metrics)
+
+                gpu_memory_monitor.reset_peak_stats()
+                nwords_since_last_log = 0
+                time_last_log = timer()
+                logger.info(
+                    f"step: {train_state.step}"
+                    f"  acc: {train_state.acc_step}"
+                    f"  loss: {round(loss.item(),4):>7}"
+                    f"  grad: {grad_norm:.2e}"
+                    f"  flops: {FLOPS:.2e}"
+                    f"  wps: {wps:.2e}"
+                    f"  iter: {curr_iter_time:>7}"
+                    f"  data: {data_load_time:>5}"
+                    f"  lr: {curr_lr:.2e}"
+                    f"  mem: {gpu_mem_stats.max_active_pct:.0f}%"
+                    f"  pow: {gpu_mem_stats.power_draw/1000} W"
+                )
+
+            saved = False
+            if every_n_steps(
+                train_state, args.checkpoint.save_every.step, acc_step=0
+            ) or every_n_steps(train_state, args.checkpoint.eval_every.step, acc_step=0):
+                saved = checkpoint.save(
+                    model,
+                    optimizer,
+                    train_state,
+                    args,
+                    device_mesh=world_mesh,
+                )
+
+
+            if args.eval is not None and every_n_steps(
+                train_state, args.checkpoint.eval_every.step, acc_step=0
+            ):
+                from core.models.GI_01.eval import (
+                    launch_eval,
+                    EVAL_FOLDER_NAME,
+                    EvalArgs,
+                )
+
+                eval_args = dataclass_from_dict(EvalArgs, args.eval)
+
+                eval_args.global_step = train_state.step
+                eval_args.ckpt_dir = str(checkpoint.existing_saves[-1])
+                eval_args.dump_dir = str(
+                    os.path.join(
+                        args.dump_dir,
+                        "evals",
+                        EVAL_FOLDER_NAME.format(train_state.step),
+                    )
+                )
+                eval_args.metric_log_dir = args.dump_dir
+                if args.async_eval_gpus is None:
+                    launch_eval(eval_args)
+                elif get_is_master():
+                    if wandb.run is not None and args.logging.wandb is not None:
+                        eval_args.wandb = deepcopy(args.logging.wandb)
+                    assert args.async_eval_gpus > 0
+                    logger.info(f"Launching evals on {args.async_eval_gpus} gpus")
+                    with clean_env():
+                        launch_job(
+                            StoolArgs(
+                                asdict(eval_args),
+                                script="core.models.GI_01.eval",
+                                copy_code=False,
+                                nodes=args.async_eval_gpus // 8,
+                                qos="lowest",
+                            )
+                        )
+
+            if preemption_flag["flag"]:
+                if not saved:
+                    checkpoint.save(
+                        model,
+                        optimizer,
+                        train_state,
+                        args,
+                        device_mesh=world_mesh,
+                    )
+                requeue_slurm_job()
+                sys.exit(0)
+
+    if not saved:
+        checkpoint.save(
+            model,
+            optimizer,
+            train_state,
+            args,
+            device_mesh=world_mesh,
+        )
+    gc.collect()
+
+
+
+
+
+
+def main():
+    cli_args = OmegaConf.from_cli()
+    file_cfg = OmegaConf.load(cli_args.config)
+
+    del cli_args.config
+
+    default_cfg = OmegaConf.structured(TrainArgs())
+
+    cfg = OmegaConf.merge(default_cfg, file_cfg, cli_args)
+    cfg = OmegaConf.to_object(cfg)
+
+    train(cfg)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/core/models/translator/construe.py b/core/models/translator/construe.py
deleted file mode 100644
index 71e4b74..0000000
--- a/core/models/translator/construe.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import torch
-import torch.nn.init
-
-from core.configurations.base import BaseConfiguration
-from core.models.translator.decoder import ConstrueDecoderLayer
-from core.layers.layer_norm import LayerNorm
-from core.utils.masks import create_causal_mask
-from core.layers.positional_embedding.rope_projector import RopePositionEmbedding
-
-from torch import nn
-
-
-class ConstrueModel(nn.Module):
-    def __init__(self, config: BaseConfiguration):
-        super().__init__()
-
-        self.config = config
-
-        self.token_embeddings = nn.Embedding(
-            num_embeddings=config.vocabulary_size,
-            embedding_dim=config.hidden_dim,
-            padding_idx=config.padding_id
-        )
-
-        # Decoder layer stack
-        self.decoder_layers = nn.ModuleList([
-            ConstrueDecoderLayer(
-                config
-            )
-            for _ in range(config.num_layers)
-        ])
-
-        # Layer Norm initialization
-        self.final_layer_norm = LayerNorm(
-            model_dimension=config.hidden_dim
-        )
-
-    def forward(self, input_tensor, attn_mask=None, output_attentions=False, output_hidden_states=False):
-        if attn_mask is None:
-            attn_mask = torch.ones_like(input_tensor)
-
-        hidden_states = self.token_embeddings(input_tensor)
-        causal_mask = create_causal_mask(
-            attention_mask=attn_mask,
-            shape=input_tensor.shape,
-            dtype=hidden_states.dtype,
-            device=hidden_states.device
-        )
-
-        output_attentions_weights = (hidden_states)
-        layers_hidden_states = ()
-        for decoder_layer in self.decoder_layers:
-            hidden_states, attention_weight = decoder_layer(
-                hidden_states,
-                causal_mask,
-                output_attentions=output_attentions
-            )
-            if output_hidden_states:
-                layers_hidden_states += (hidden_states, )
-            if output_attentions:
-                output_attentions_weights += (attention_weight,)
-
-        hidden_states = self.final_layer_norm(hidden_states)
-
-        return hidden_states, (output_attentions_weights if output_attentions else None), (layers_hidden_states if output_hidden_states else None)
-
-
-
-class ConstrueAutoRegressiveModel(nn.Module):
-    def __init__(self, config: BaseConfiguration):
-        super().__init__()
-        self.config = config
-        self.model = ConstrueModel(config)
-        self.lm_head = nn.Linear(
-            in_features=config.hidden_dim,
-            out_features=config.vocabulary_size,
-            bias=False
-        )
-
-
-    def forward(self, input_tensor, attn_mask=None, output_attentions=False, output_hidden_states=False):
-        last_hidden_state, output_attention_weights, hidden_states = self.model(input_tensor, attn_mask)
-        logits = self.lm_head(last_hidden_state)
-
-        return {
-            "logits": logits,
-            "last_hidden_state": last_hidden_state,
-            "attention_map": output_attention_weights if output_attentions else None,
-            "hidden_states": hidden_states if output_hidden_states else None
-        }
diff --git a/core/models/translator/notebooks/trainer.ipynb b/core/models/translator/notebooks/trainer.ipynb
deleted file mode 100644
index eaecaa0..0000000
--- a/core/models/translator/notebooks/trainer.ipynb
+++ /dev/null
@@ -1,277 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "import os\n",
-    "\n",
-    "# Add the root directory to the sys.path\n",
-    "sys.path.insert(0, \"/root/AI-Uncomplicated\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from core.models.translator.config import ModelConfig, DatasetConfig, TrainingConfig\n",
-    "from core.dataloaders.dataloader import load_tokenizer\n",
-    "\n",
-    "## Initialize configurations\n",
-    "model_config = ModelConfig(model_name=\"Construe\",\n",
-    "                           num_layers = 2,\n",
-    "                            padding_id = 0,\n",
-    "                            hidden_dim = 512,\n",
-    "                            intermediate_dim = 3072,\n",
-    "                            max_positions = 2048,\n",
-    "                            layer_norm_eps = 1e-05,\n",
-    "                            model_max_sequence = 2048,\n",
-    "                            num_heads = 8,\n",
-    "                            attention_dropout = 0.1)\n",
-    "\n",
-    "dataset_config = DatasetConfig(dataset_path=\"./dataset\",\n",
-    "                               dataset_shuffle=True)\n",
-    "training_config = TrainingConfig(tokenizer_path=\"/root/AI-Uncomplicated/core/models/translator/tokenzier/european_tokenizer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import regex as re\n",
-    "import sentencepiece as spm\n",
-    "import core.utils.sentencepiece_model_pb2 as model_loader\n",
-    "import torch\n",
-    "import json\n",
-    "\n",
-    "class SPMTokenizer:\n",
-    "    def __init__(self, tokenizer_path, bos_peice=\"<s>\", eos_peice=\"</s>\", padding_peice=\"<pad>\"):\n",
-    "        self.path = tokenizer_path\n",
-    "        self.model = spm.SentencePieceProcessor(model_file=os.path.join(tokenizer_path, \"spm_buffer.model\"))\n",
-    "        \n",
-    "        with open(os.path.join(tokenizer_path, \"tokenizer.config\"), \"r\") as handler:\n",
-    "            self.config = json.load(handler)\n",
-    "            \n",
-    "        self.peices = [self.model.id_to_piece(id) for id in range(self.model.get_piece_size())]\n",
-    "        self.unused_token_pattern = \"<unused\\d+>\"\n",
-    "        \n",
-    "        self.token_slots = self.config[\"available_unused_slots\"]\n",
-    "        self.filled_slots = 0\n",
-    "\n",
-    "        self.special_tokens = self.config[\"special_tokens\"]\n",
-    "        self.__special_token_slot_map = {}\n",
-    "        \n",
-    "        self.start_token_idx = self.model.PieceToId(bos_peice)\n",
-    "        self.end_token_idx = self.model.PieceToId(eos_peice)\n",
-    "        self.pad_token_idx = self.model.PieceToId(padding_peice)\n",
-    "    \n",
-    "    # def add_special_token(self, token_value):\n",
-    "    #     if self.filled_slots < self.token_slots:\n",
-    "    #         self.special_tokens.append(token_value)\n",
-    "    #         self.__special_token_slot_map[f\"<unused{self.filled_slots}>\"] = token_value\n",
-    "    #         self.filled_slots += 1\n",
-    "    #     raise ValueError(\"Slot full\")\n",
-    "        \n",
-    "    \n",
-    "    @property\n",
-    "    def available_special_token_slots(self):\n",
-    "        return self.token_slots\n",
-    "    \n",
-    "    \n",
-    "    def get_unused_peices(self):\n",
-    "        unused_peices = []\n",
-    "        for peice in self.peices:\n",
-    "            if re.match(self.unused_token_pattern, peice):\n",
-    "                unused_peices.append(peice)\n",
-    "        return unused_peices\n",
-    "    \n",
-    "    @property\n",
-    "    def vocab_size(self):\n",
-    "        return self.model.vocab_size()\n",
-    "\n",
-    "    @vocab_size.setter\n",
-    "    def vocab_size(self, value):\n",
-    "        raise ValueError(\"Assigning value not supported\")\n",
-    "    \n",
-    "    def encode(self, inputs, add_special_tokens=True, return_type=\"pt\"):\n",
-    "        if isinstance(inputs, str):\n",
-    "            inputs = [inputs]\n",
-    "        encoded_tokens = self.model.Encode(inputs)\n",
-    "        if add_special_tokens:\n",
-    "            for idx in range(len(encoded_tokens)):\n",
-    "                encoded_tokens[idx] = [self.start_token_idx] + encoded_tokens[idx] + [self.end_token_idx]\n",
-    "        \n",
-    "        if return_type is None:\n",
-    "            return {\"input_ids\": encoded_tokens, \"attention_mask\": None}\n",
-    "        elif return_type == \"pt\":\n",
-    "            paddded_tokens = torch.nn.utils.rnn.pad_sequence([torch.tensor(p) for p in encoded_tokens], batch_first=True, padding_value=self.pad_token_idx).long()\n",
-    "            attention_mask = (paddded_tokens != self.pad_token_idx).to(torch.int32)\n",
-    "            return {\"input_ids\": paddded_tokens, \"attention_maks\": attention_mask}\n",
-    "        else:\n",
-    "            raise ValueError(\"unsupported return type\")\n",
-    "    \n",
-    "    def decode(self, tokens):\n",
-    "        if isinstance(tokens, torch.Tensor):\n",
-    "            tokens = tokens.numpy().tolist()\n",
-    "        return self.model.Decode(tokens)\n",
-    "    \n",
-    "    # def save_to_folder(self, folder_path, name=\"model\"):\n",
-    "    #     m = model_loader.ModelProto()\n",
-    "    #     m.ParseFromString(open(self.path , 'rb').read())\n",
-    "    \n",
-    "    #     fillable_peices = list(self.__special_token_slot_map.keys())\n",
-    "    #     for p in m.pieces:\n",
-    "    #         if p.piece in fillable_peices:\n",
-    "    #             p.piece = self.__special_token_slot_map[p.piece]\n",
-    "        \n",
-    "        \n",
-    "    #     with open(os.path.join(folder_path, f\"spm_buffer.model\"), 'wb') as f:\n",
-    "    #         f.write(m.SerializeToString())\n",
-    "        \n",
-    "    #     with open(os.path.join(folder_path, f\"tokenizer.config\"), \"w\") as f:\n",
-    "    #         config = {\n",
-    "    #             \"vocab_size\": self.vocab_size,\n",
-    "    #             \"available_unused_slots\": self.available_special_token_slots,\n",
-    "    #             \"special_tokens\": self.special_tokens,\n",
-    "    #         }\n",
-    "    #         json.dump(config, f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "token = SPMTokenizer(training_config.tokenizer_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['<s>', 'what', 'is', 'zo', 'ho', 'cr', 'm']"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[token.model.decode(i) for i in  [token.model.Encode(\"<s>\")[1]] + token.model.Encode(\"what is zoho crm\")]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[     1,   3027,   1670,   2465,   1840,   2418, 108127,      2],\n",
-       "        [     1,   2222,   1670,   2031,   1482,      2,      0,      0]])"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "token.encode([\"what is zoho crm\", \"this is jagan\"])[\"input_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "token.add_special_token()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "token.model.PieceToId(\"<pad>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for p in m.pieces:\n",
-    "    if p.piece == \" ⁇\":\n",
-    "        print(p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ai_experimentation_env",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/core/models/translator/tokenzier/european_tokenizer/spm_buffer.model b/core/models/translator/tokenzier/european_tokenizer/spm_buffer.model
deleted file mode 100644
index 12f27ab..0000000
Binary files a/core/models/translator/tokenzier/european_tokenizer/spm_buffer.model and /dev/null differ
diff --git a/core/models/translator/tokenzier/european_tokenizer/tokenizer.config b/core/models/translator/tokenzier/european_tokenizer/tokenizer.config
deleted file mode 100644
index b6b606c..0000000
--- a/core/models/translator/tokenzier/european_tokenizer/tokenizer.config
+++ /dev/null
@@ -1 +0,0 @@
-{"vocab_size": 120000, "available_unused_slots": 94, "special_tokens": ["<lang_en>", "</lang_en>", "<lang_fr>", "</lang_fr>"]}
\ No newline at end of file
diff --git a/core/models/translator/trainer.py b/core/models/translator/trainer.py
deleted file mode 100644
index d8fb4c9..0000000
--- a/core/models/translator/trainer.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from core.models.translator.config import ModelConfig, DatasetConfig, TrainingConfig
-from core.dataloaders.dataloader import load_tokenizer
-
-## Initialize configurations
-model_config = ModelConfig(model_name="Construe",
-                           num_layers = 2,
-                            padding_id = 0,
-                            hidden_dim = 512,
-                            intermediate_dim = 3072,
-                            max_positions = 2048,
-                            layer_norm_eps = 1e-05,
-                            model_max_sequence = 2048,
-                            num_heads = 8,
-                            attention_dropout = 0.1)
-
-dataset_config = DatasetConfig(dataset_path="./dataset",
-                               dataset_shuffle=True)
-training_config = TrainingConfig(tokenizer_path="/root/AI-Uncomplicated/core/tokenizer/bpe/pre_trained/europian_ml")
-
-## Load model and tokenizer
-
-tokenizer =  load_tokenizer(training_config.tokenizer_path)
-
-
diff --git a/core/tokenizer/setencepiece_to_tokenizer.py b/core/tokenizer/setencepiece_to_tokenizer.py
index 8ad17cc..8a04899 100644
--- a/core/tokenizer/setencepiece_to_tokenizer.py
+++ b/core/tokenizer/setencepiece_to_tokenizer.py
@@ -25,7 +25,6 @@ def convert(model_path, special_tokens, save_path):
 
     model = spm.SentencePieceProcessor(model_file=model_path)
     unused_slots, peices = get_unused_peices(model)
-
     available_unused_slots = len(unused_slots) - 1
     if len(special_tokens) > available_unused_slots:
         raise ValueError(f"Avialble special token slots {available_unused_slots} but given {len(special_tokens)}")
diff --git a/core/tokenizer/tokenizer_loader.py b/core/tokenizer/tokenizer_loader.py
index febab3f..1198901 100644
--- a/core/tokenizer/tokenizer_loader.py
+++ b/core/tokenizer/tokenizer_loader.py
@@ -1,9 +1,19 @@
+from typing import Optional
+
+from dataclasses import dataclass
+
 import regex as re
 import sentencepiece as spm
 import torch
 import json
 import os
 
+
+@dataclass
+class TokenizerArgs:
+    name: str = "bytes"
+    path: Optional[str] = None
+
 class SPMTokenizer:
     def __init__(self, tokenizer_path, bos_peice="<s>", eos_peice="</s>", padding_peice="<pad>"):
         self.path = tokenizer_path
diff --git a/core/tokenizer/trainer.py b/core/tokenizer/trainer.py
index 18d9e83..bfe96e4 100644
--- a/core/tokenizer/trainer.py
+++ b/core/tokenizer/trainer.py
@@ -29,8 +29,8 @@ def __init__(self,
 
 
     def get_training_files(self, allowed_pattern) -> str:
+        path = os.path.join(self.data_path, allowed_pattern)
         try:
-            path = os.path.join(self.data_path, allowed_pattern)
             training_files = glob.glob(path)
 
             if not training_files:
@@ -40,7 +40,8 @@ def get_training_files(self, allowed_pattern) -> str:
 
             return ",".join(training_files)
         except Exception as e:
-            print(f"Error collecting training files: {str(e)}")
+            print(f"Error collecting training files: {str(e)} :: path :: {path}")
+            raise ValueError(e)
 
     def get_yaml_list(self):
         """Read the yaml and get the user fefined symbol and control symbol"""
diff --git a/core/train.py b/core/train.py
index 5d27d8a..b3c4321 100644
--- a/core/train.py
+++ b/core/train.py
@@ -11,7 +11,7 @@
 from typing import Optional, Dict, Any
 
 from core.configurations.base import BaseConfiguration
-from core.dataloaders.dataloader import dataloader_v1, load_tokenizer
+from core.trainer.dataloader import dataloader_v1, load_tokenizer
 from core.models.model import LLM
 
 run = wandb.init()# Init wandb
@@ -146,7 +146,7 @@ def save_object(obj, filename):
 
 if __name__ == "__main__":
     config = BaseConfiguration(model_name="small_lm", num_layers=2, hidden_dim=128, intermediate_dim=512,
-                               max_positions=256, vocabulary_size=64000, num_heads=2, attention_dropout=0.05,
+                               max_positions=256, vocab_size=64000, num_heads=2, attention_dropout=0.05,
                                batch_size=8, weight_decay=0.01,
                                learning_rate=5e-4,
                                tokenizer_path="/workspace/vipin_g6/personal/pretraining/english_tokenizer/english_tokenizer.model",
diff --git a/core/trainer/__init__.py b/core/trainer/__init__.py
index e69de29..ad75d60 100644
--- a/core/trainer/__init__.py
+++ b/core/trainer/__init__.py
@@ -0,0 +1,86 @@
+import torch
+from torch.optim import AdamW
+from typing import Optional, List
+
+from core.trainer.schedulers import WarmupCosineScheduler
+
+
+# todo: yet to be optimizer for multiple scheduler choice and optimizer choice
+def get_optimzer_with_scheduler(
+    model: torch.nn.Module,
+    num_training_steps: int,
+    learning_rate: float = 5e-5,
+    weight_decay: float = 0.01,
+    warmup_ratio: float = 0.1,
+    min_lr_ratio: float = 0.1,
+    beta1: float = 0.9,
+    beta2: float = 0.999,
+    eps: float = 1e-8,
+    no_decay_params: Optional[List[str]] = None
+):
+    """
+    Create AdamW optimizer and warmup scheduler.
+
+    Args:
+        model: PyTorch model
+        num_training_steps: Total number of training steps
+        learning_rate: Maximum learning rate after warmup
+        weight_decay: Weight decay coefficient
+        warmup_ratio: Ratio of warmup steps to total steps
+        min_lr_ratio: Minimum learning rate ratio compared to max lr
+        beta1: AdamW beta1 parameter
+        beta2: AdamW beta2 parameter
+        eps: AdamW epsilon parameter
+        correct_bias: Whether to correct bias in AdamW
+        no_decay_params: List of parameter names that should not have weight decay
+
+    Returns:
+        tuple: (optimizer, scheduler)
+    """
+    # Default params that should not have weight decay
+    if no_decay_params is None:
+        no_decay_params = ['bias', 'LayerNorm.weight', 'layer_norm.weight']
+
+    # Separate parameters that should and should not have weight decay
+    optimizer_grouped_parameters = [
+        {
+            "params": [
+                p for n, p in model.named_parameters()
+                if not any(nd in n for nd in no_decay_params)
+            ],
+            "weight_decay": weight_decay,
+        },
+        {
+            "params": [
+                p for n, p in model.named_parameters()
+                if any(nd in n for nd in no_decay_params)
+            ],
+            "weight_decay": 0.0,
+        },
+    ]
+
+    # Create AdamW optimizer
+    optimizer = AdamW(
+        optimizer_grouped_parameters,
+        lr=learning_rate,
+        betas=(beta1, beta2),
+        eps=eps
+    )
+
+    # Create scheduler with linear warmup and cosine decay
+    print(f"NUM Training Step :: {num_training_steps}")
+
+    warmup_ratio = None
+    warmup_steps = -1
+    if warmup_ratio > 0:
+        warmup_steps = int(num_training_steps * warmup_ratio)
+        print(f"warmup_steps :: {warmup_steps}")
+
+        scheduler = WarmupCosineScheduler(
+            optimizer=optimizer,
+            warmup_steps=warmup_steps,
+            total_steps=num_training_steps,
+            min_lr_ratio=min_lr_ratio
+        )
+
+    return optimizer, scheduler, warmup_steps
\ No newline at end of file
diff --git a/core/trainer/args.py b/core/trainer/args.py
new file mode 100644
index 0000000..e3ec1f5
--- /dev/null
+++ b/core/trainer/args.py
@@ -0,0 +1,71 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+import argparse
+import logging
+from omegaconf import OmegaConf, DictConfig, ListConfig
+from typing import Type, TypeVar
+
+logger = logging.getLogger()
+
+T = TypeVar("T")
+
+def set_struct_recursively(cfg, strict: bool = True):
+    # Set struct mode for the current level
+    OmegaConf.set_struct(cfg, strict)
+
+    # Traverse through nested dictionaries and lists
+    if isinstance(cfg, DictConfig):
+        for key, value in cfg.items():
+            if isinstance(value, (DictConfig, ListConfig)):
+                set_struct_recursively(value, strict)
+    elif isinstance(cfg, ListConfig):
+        for item in cfg:
+            if isinstance(item, (DictConfig, ListConfig)):
+                set_struct_recursively(item, strict)
+
+
+def flatten_dict(d, parent_key="", sep="_"):
+    items = []
+    for k, v in d.items():
+        new_key = f"{parent_key}{sep}{k}" if parent_key else k
+        if isinstance(v, dict):
+            items.extend(flatten_dict(v, new_key, sep=sep).items())
+        else:
+            items.append((new_key, v))
+    return dict(items)
+
+
+def dataclass_from_dict(cls: Type[T], data: dict, strict: bool = True) -> T:
+    """
+    Converts a dictionary to a dataclass instance, recursively for nested structures.
+    """
+    base = OmegaConf.structured(cls())
+    OmegaConf.set_struct(base, strict)
+    override = OmegaConf.create(data)
+    return OmegaConf.to_object(OmegaConf.merge(base, override))
+
+
+def dataclass_to_dict(dataclass_instance: T) -> dict:
+    """
+    Converts a dataclass instance to a dictionary, recursively for nested structures.
+    """
+    if isinstance(dataclass_instance, dict):
+        return dataclass_instance
+
+    return OmegaConf.to_container(
+        OmegaConf.structured(dataclass_instance), resolve=True
+    )
+
+
+def load_config_file(config_file, dataclass_cls: Type[T]) -> T:
+    config = OmegaConf.to_container(OmegaConf.load(config_file), resolve=True)
+    return dataclass_from_dict(dataclass_cls, config)
+
+
+def dump_config(config, path, log_config=True):
+    yaml_dump = OmegaConf.to_yaml(OmegaConf.structured(config))
+    with open(path, "w") as f:
+        if log_config:
+            logger.info("Using the following config for this run:")
+            logger.info(yaml_dump)
+        f.write(yaml_dump)
\ No newline at end of file
diff --git a/core/trainer/checkpointer.py b/core/trainer/checkpointer.py
new file mode 100644
index 0000000..1a3b2a9
--- /dev/null
+++ b/core/trainer/checkpointer.py
@@ -0,0 +1,324 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
+
+from dataclasses import field, dataclass
+import json
+import os
+from pathlib import Path
+import re
+from typing import List, Optional, Tuple
+from omegaconf import OmegaConf
+import torch.distributed as dist
+from torch.distributed._tensor import DeviceMesh
+import torch
+from core.trainer.distributed import get_is_master
+
+
+import torch.nn as nn
+
+import torch.distributed.checkpoint as dcp
+from torch.distributed.checkpoint.state_dict import (
+    get_state_dict,
+    set_state_dict
+)
+
+from torch.distributed.checkpoint.format_utils import dcp_to_torch_save
+
+import logging
+
+logger = logging.getLogger(__name__)
+
+# todo: did some changes in name, have to verify
+DUMP_FOLDER_NAME = "checkpoint_{:010d}" # Formatted as ten digit number padded with zeros in front (eg., 0000000001)
+EVAL_FOLDER_NAME = "eval_{:010d}"
+RE_FOLDER = r"(?:checkpoint|eval)_\d{10}"
+
+CONSOLIDATE_FOLDER = "consolidated"
+CONSOLIDATE_NAME = "consolidated.pth"
+
+CONFIG_NAME = "params.json"
+TRAIN_STATE_NAME = "train_state_{:05d}.json"
+
+
+RE_DIGIT = r"\d+"
+
+
+# @dataclass
+# class SaveEvery:
+#     every: int = 1000
+#     keep: int = 0
+
+
+# @dataclass
+# class CheckpointArgs:
+#     dump: SaveEvery = field(default_factory=SaveEvery)
+#     eval: SaveEvery = field(default_factory=SaveEvery)
+#     path: Optional[str] = None
+#     init_ckpt_path: Optional[str] = None
+#     continue_training_from_init: bool = False
+
+def consolidate_checkpoints(ckpt_dir: str):
+    """
+    Consolidates all FSDP checkpoints in a directory to a single file
+    Consolidate checkpoint is saved in a subdirectory of ckpt_dir
+
+    Parameters:
+        ckpt_dir: str - path to the directory containing the checkpoints
+
+    Returns the path to the consolidated checkpoint
+    """
+    consolidate_path = Path(ckpt_dir) / CONSOLIDATE_FOLDER
+    if not (consolidate_path / CONSOLIDATE_NAME).exists():
+        consolidate_path.mkdir(exist_ok=True)
+        logger.info(f"Consolidating to: {str(consolidate_path)}")
+        dcp_to_torch_save(ckpt_dir, str(consolidate_path / CONSOLIDATE_NAME))
+        (consolidate_path / CONFIG_NAME).write_text(
+            (Path(ckpt_dir) / CONFIG_NAME).read_text()
+        )
+        logger.info("Consolidated !")
+    return consolidate_path
+
+
+@dataclass
+class SaveEvery:
+    step: int = 1000
+    limit: int = 0
+
+
+@dataclass
+class CheckpointArgs:
+    save_every: SaveEvery = field(default_factory=SaveEvery)
+    eval_every: SaveEvery = field(default_factory=SaveEvery)
+    path: Optional[str] = None
+    init_ckpt_path: Optional[str] = None
+    continue_training_from_init: bool = False
+
+
+def _get_key_step(name: str):
+    return int(re.findall(RE_DIGIT, name)[-1])
+
+def load_from_checkpoint(ckpt_dir: str, model: nn.Module, optimizer: Optional[torch.optim.Optimizer] = None, model_key: str = "model", optim_key: str = "optim"):
+    if not (Path(ckpt_dir) / '.metadata').exists():
+        raise ValueError(f"Please convert the checkpoint distcp format using `torch.distributed.checkpoint.format_utils.torch_save_to_dcp` before loading it")
+
+    state_dict = {}
+    if optimizer is not None:
+        state_dict[model_key], state_dict[optim_key] = get_state_dict(model, optimizer)
+    else:
+        state_dict[model_key] = get_model_state_dict(model)
+        if model_key == "": # If only loading a model directly, the key should be empty
+            state_dict = state_dict.pop(model_key)
+
+    dcp.load(state_dict, checkpoint_id=ckpt_dir)
+
+
+class CheckpointManager:
+    def __init__(self, args: CheckpointArgs):
+        self.path = args.path
+        self.save_every = args.save_every
+        self.eval_every = args.eval_every
+        self.init_ckpt_path = args.init_ckpt_path
+        self.continue_training_from_init = args.continue_training_from_init
+
+        assert self.path and os.path.exists(self.path), f"Path {self.path} does not exist and needs to be created before using CheckpointManager (use instantiate_and_make_dir)"
+
+        self.existing_saves = self.get_existing_saves()
+
+    def get_existing_saves(self) -> List[Path]:
+        folders = [
+            p
+            for p in Path(self.path).iterdir()
+            if p.is_dir() and re.match(RE_FOLDER, p.name)
+        ]
+        folders.sort(key=lambda p: _get_key_step(p.name))
+        return folders
+
+    def clean_up(self):
+        logger.info("Cleaning up checkpoints...")
+        dump_folders = []
+        eval_folders = []
+        other_folders = []
+        for p in self.existing_saves:
+            is_dump = p.name.startswith("checkpoint")
+            is_eval = p.name.startswith("eval")
+            if is_dump:
+                dump_folders.append(p)
+            if is_eval:
+                eval_folders.append(p)
+            if not (is_dump or is_eval):
+                other_folders.append(p)
+
+        logger.info(f"Dump folders: {dump_folders}")
+        logger.info(f"Eval folders: {eval_folders}")
+        logger.info(f"Other folders: {other_folders}")
+
+        if self.save_every.limit > 0:
+            dump_folders = dump_folders[-self.save_every.limit :]
+        if self.eval_every.limit > 0:
+            eval_folders = eval_folders[-self.eval_every.limit :]
+
+        folder_to_keep = set(other_folders + dump_folders + eval_folders)
+        folder_to_remove = set(self.existing_saves) - folder_to_keep
+
+        logger.info(f"Removing folders: {folder_to_remove}")
+
+        if dist.get_rank() == 0:
+            for folder in folder_to_remove:
+                for file in folder.iterdir():
+                    if file.is_file():
+                        file.unlink()
+                    elif file.is_dir():
+                        assert file.name in [CONSOLIDATE_FOLDER]
+                        for f in file.iterdir():
+                            f.unlink()
+                        file.rmdir()
+                folder.rmdir()
+
+        dist.barrier()
+
+        self.existing_saves = list(folder_to_keep)
+        self.existing_saves.sort(key=lambda p: _get_key_step(p.name))
+
+
+    @classmethod
+    def instantiate_and_make_dir(cls, args: CheckpointArgs):
+        if get_is_master(): # todo: have to implement in dist folder
+            os.makedirs(args.path, exist_ok=True)
+        dist.barrier()
+
+        return cls(args)
+
+    def _create_folder(self, base_path: Path, folder_name: str) -> Path:
+        folder = base_path / folder_name
+        if get_is_master(): # todo: have to implement in dist folder
+            folder.mkdir(parents=False, exist_ok=True)
+        if dist.is_initialized():
+            dist.barrier()
+        return folder
+
+
+    def _get_dp_mesh(
+        self, device_mesh: Optional[DeviceMesh] = None
+    ) -> Tuple[int, int]:
+        dp_rank = 0
+        if device_mesh is not None:
+            if "dp_replicate" in device_mesh.mesh_dim_names:
+                dp_rank = device_mesh.get_local_rank("dp_replicate")
+                if "dp_shard" in device_mesh.mesh_dim_names:
+                    dp_rank = dp_rank * device_mesh["dp_shard"].size() + device_mesh.get_local_rank("dp_shard")
+        return dp_rank
+
+
+    @torch.no_grad()
+    def get_state_dict(
+        self,
+        model,
+        optimizer,
+    ):
+        model_sd, optim_sd = get_state_dict(model, optimizer)
+        return {"model": model_sd, "optim": optim_sd}
+
+
+    def save(
+        self,
+        model,
+        optimizer,
+        train_state,
+        config,
+        device_mesh: Optional[DeviceMesh] = None,
+    ) -> bool:
+
+        # When creating directory check if only rank0 or is there other solution
+        path = Path(self.path)
+        curr_save_dir = self._create_folder(path, DUMP_FOLDER_NAME.format(train_state.step))
+        logger.info(f"Saving to: {str(curr_save_dir)}")
+
+        if dist.is_initialized():
+            dist.barrier()
+
+        logger.info("Saving...")
+        state_dict = self.get_state_dict(model, optimizer)
+        dcp.save(state_dict, checkpoint_id=curr_save_dir)
+        logger.info("State dict saved!")
+
+        if dist.is_initialized():
+            dist.barrier()
+
+        if get_is_master(): # todo: have to implement in dist folder
+            with open(curr_save_dir / CONFIG_NAME, "w") as f:
+                json.dump(
+                    OmegaConf.to_container(OmegaConf.structured(config), resolve=True),
+                    f,
+                )
+
+        # Add json dump here
+        dp_rank = self._get_dp_mesh(device_mesh)
+
+        train_state_name = TRAIN_STATE_NAME.format(dp_rank)
+        logger.info(
+            f"Saving train state to: {str(curr_save_dir / train_state_name)}"
+        )
+        with open(curr_save_dir / train_state_name, "w") as f:
+            json.dump(train_state.state_dict(), f)
+        logger.info("Train state saved !")
+
+        self.existing_saves.append(curr_save_dir)
+
+        self.clean_up()
+
+        if dist.is_initialized():
+            dist.barrier()
+        return True
+
+
+    def get_last_step_path(self, dp_rank: int = 0) -> Optional[Path]:
+        path = None
+        for p in reversed(self.existing_saves):
+            if (p / TRAIN_STATE_NAME.format(dp_rank)).is_file():
+                path = p
+                break
+        return path
+
+
+    @torch.no_grad()
+    def load(
+        self,
+        model: nn.Module,
+        optimizer,
+        train_state,
+        device_mesh: DeviceMesh,
+        path: Optional[Path] = None,
+    ):
+        dp_rank = self._get_dp_mesh(device_mesh)
+        # Loading tries to load the provided path, if not available the last saved step and finally from the init path
+        path = path or self.get_last_step_path(dp_rank=dp_rank)
+        # If none of those are available don't do anything
+        if path is None:
+            # If no checkpoints exist do nothing
+            return
+
+        # Only load train state if it's provided, the files exist and we're not loading from init path
+        train_state_name = TRAIN_STATE_NAME.format(dp_rank)
+        logger.info("Reloading train state")
+        with open(path / train_state_name, "r") as f:
+            train_state_dict = json.load(f)
+        train_state.load_state_dict(train_state_dict)
+        logger.info("Train state reloaded")
+
+        logger.info(f"Loading from: {str(path)}")
+        state_dict = self.get_state_dict(
+            model=model,
+            optimizer=optimizer,
+        )
+        dcp.load(state_dict, checkpoint_id=path)
+        logger.info("State dict loaded.")
+
+        logger.info("Reloading model and optim")
+
+        set_state_dict(
+            model,
+            optimizer,
+            model_state_dict=state_dict["model"],
+            optim_state_dict=state_dict["optim"],
+        )
+        logger.info("Model and optim reloaded")
\ No newline at end of file
diff --git a/core/trainer/dataloader.py b/core/trainer/dataloader.py
new file mode 100644
index 0000000..4ac9149
--- /dev/null
+++ b/core/trainer/dataloader.py
@@ -0,0 +1,1027 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
+
+"""
+Data Loader Module
+
+taken from : https://github.com/facebookresearch/lingua/blob/main/lingua/data.py#L245
+
+Added FIM process along with that
+"""
+from asyncio import Queue
+import contextlib
+from copy import deepcopy
+import functools
+from multiprocessing import Process
+from multiprocessing import Process, Queue, Event
+from queue import Full, Empty
+from multiprocessing.synchronize import Event as EventClass
+
+import json
+from functools import partial
+import os
+from pathlib import Path
+import re
+
+import numpy as np
+from dataclasses import field, dataclass
+from typing import Callable, Iterator, Optional, Dict, Any, TypedDict
+import torch
+from torch.utils.data.sampler import RandomSampler, SequentialSampler
+
+from core.configurations.base import BaseConfiguration
+import sentencepiece as spm
+from torch.utils.data import Dataset, DataLoader
+
+from core.tokenizer.tokenizer_loader import SPMTokenizer, TokenizerArgs
+
+import logging
+
+logger = logging.getLogger(__name__)
+
+class JSONLState(TypedDict):
+    """Represents the current state of a JSON line reader.
+
+    Attributes:
+        content (Dict): The JSON content of the line.
+        file_path (str): The path to the JSONL file.
+        position (int): The file position after reading the line (in bytes).
+        window (int): The window size used for iteration.
+        offset (int): The offset used for iteration.
+        current_iter (Optional[int]): Number of iterations over the jsonl file (for infinite iteration).
+    """
+
+    file_path: str
+    position: int
+    block_size: int
+    offset: int
+    current_iter: int
+
+
+class PrefetchState(TypedDict):
+    """Represents the current state of a prefetching iterator.
+
+    Attributes:
+        prefetch_buffer: numpy array to store prefetched data
+        seq_idx: int index of the current sequence to resume from
+        rng_state: dict numpy bit generator state used to resume rng
+    """
+
+    it_state: Any
+    seq_idx: int
+    rng_state: Dict[str, Any]
+    prefetch_size: int
+    batch_size: int
+
+
+class MultiChoiceState(TypedDict):
+    """Represents the current state of a Multi choice iterator.
+
+    Attributes:
+        root_dir: path to dataset root directory
+        sources Dict[str, float]: Dict from subdirectory to the weight used for sampling
+        source_states: Dict[str, Any] Dict from source to iterator state
+        rng_state: dict numpy bit generator state used to resume rng
+    """
+
+    root_dir: str
+    sources: Dict[str, float]
+    source_to_state: Dict[str, Any]
+    rng_state: Dict[str, Any]
+
+
+class TokenizerState(TypedDict):
+    it_state: Any
+    name: str
+    add_bos: bool
+    add_eos: bool
+    rng_state: Dict[str, Any]
+    path: Optional[str]
+    fim_rate: float
+
+
+@dataclass
+class DataArgs:
+    root_dir: Optional[str] = None
+    sources: Dict[str, float] = field(default_factory=dict)
+    batch_size: int = 2
+    seq_len: int = 2048
+    n_views: int = 2
+    seed: int = 42
+    add_bos: bool = True
+    add_eos: bool = True
+    load_async: bool = True
+    prefetch_size: int = 64
+    fim_rate: float = 0.0
+    fim_type: str = "content"
+    tokenizer: TokenizerArgs = field(default_factory=TokenizerArgs)
+
+
+class PackTokensState(TypedDict):
+    """Represents the current state of a packing iterator.
+
+    Attributes:
+        start_token: int index to start reading from in the current sequence
+        output_seq_len: int Length of sequences to output
+        n_views: dict int Number of views to output. Each view is the same sequence but shifted by 1 from the previous
+    """
+
+    start_token: int
+    it_state: Any
+    output_seq_len: int
+    n_views: int
+    seq_len: int
+
+
+def find_and_sanitize_chunks(dataset_path: str, world_size: int, file_pattern: str = "*.chunk.*.jsonl"):
+    dataset_chunks = [str(p) for p in Path(dataset_path).glob(file_pattern)]
+    n_chunks = len(dataset_chunks)
+
+    if n_chunks > world_size:
+        n_discard = n_chunks - world_size
+        dataset_chunks = dataset_chunks[:world_size]
+    else:
+        assert (
+                world_size % n_chunks == 0
+        ), "World size should be a multiple of number of chunks"
+
+    assert n_chunks > 0, f"No valid chunks in {dataset_path}"
+
+    return dataset_chunks
+
+
+def distribute_data_to_rank(dataset_path: str, rank: int, world_size: int, file_pattern: str):
+    """
+    Distributes the chunk files in a dataset path to each worker.
+    If world_size is smaller than the number of chunks, the extra chunks are discarded.
+    Otherwise, world_size is assumed to be a multiple of number of chunks.
+    In that case there are world_size//nb_chunks workers on each chunk file, reading with different offsets.
+    """
+    dataset_chunks = find_and_sanitize_chunks(dataset_path, world_size, file_pattern)
+    n_ranks_per_chunk = world_size // len(dataset_chunks)
+    rank_to_jsonl_iterator_params = []
+    for chunk_path in dataset_chunks:
+        for i in range(n_ranks_per_chunk):
+            rank_to_jsonl_iterator_params.append(
+                JSONLState(
+                    file_path=chunk_path,
+                    position=0,
+                    block_size=n_ranks_per_chunk,
+                    offset=i,
+                    current_iter=0,
+                )
+            )
+
+    return rank_to_jsonl_iterator_params[rank]
+
+
+def init_choice_state(
+        root_dir: str,
+        sources: Dict[str, float],
+        seed: int,
+        rank: int,
+        world_size: int,
+        file_pattern: str,
+):
+    data_path_to_jsonl_state = dict()
+    for dataset_path in sources:
+        jsonl_state = distribute_data_to_rank(
+            os.path.join(root_dir, dataset_path), rank, world_size, file_pattern
+        )
+        data_path_to_jsonl_state[dataset_path] = jsonl_state
+
+    multi_rng_state = np.random.default_rng(
+        (seed, rank, world_size)
+    ).bit_generator.state
+
+    multi_choice_state = MultiChoiceState(
+        root_dir=root_dir,
+        sources=sources,
+        source_to_state=data_path_to_jsonl_state,
+        rng_state=multi_rng_state,
+    )
+    return multi_choice_state
+
+
+def init_state(
+        root_dir: str,
+        sources: Dict[str, float],
+        batch_size: int,
+        prefetch_size: int,
+        seq_len: int,
+        n_views: int,
+        seed: int,
+        rank: int,
+        world_size: int,
+        add_bos: bool,
+        add_eos: bool,
+        fim_rate: float,
+        tokenizer_name: str,
+        tokenizer_path: Optional[str] = None,
+        file_pattern: str = "*.chunk.*.jsonl"
+):
+    multi_choice_state = init_choice_state(
+        root_dir=root_dir, sources=sources, seed=seed, rank=rank, world_size=world_size, file_pattern=file_pattern
+    )
+    tokenizer_state = TokenizerState(
+        it_state=multi_choice_state,
+        add_bos=add_bos,
+        add_eos=add_eos,
+        name=tokenizer_name,
+        path=tokenizer_path,
+        rng_state=np.random.default_rng((seed, rank, world_size)).bit_generator.state,
+        fim_rate=fim_rate
+    )
+    pack_state = PackTokensState(
+        start_token=0,
+        it_state=tokenizer_state,
+        output_seq_len=seq_len,
+        n_views=n_views,
+        seq_len=0,
+    )
+    prefetch_rng_state = np.random.default_rng(
+        (seed + 1, rank, world_size)
+    ).bit_generator.state
+
+    return PrefetchState(
+        it_state=pack_state,
+        seq_idx=0,
+        rng_state=prefetch_rng_state,
+        batch_size=batch_size,
+        prefetch_size=prefetch_size,
+    )
+
+
+def init_dataloader_state_from_args(
+        args: DataArgs,
+        rank: int,
+        world_size: int,
+):
+    return init_state(
+        root_dir=args.root_dir,
+        sources=args.sources,
+        seq_len=args.seq_len,
+        batch_size=args.batch_size,
+        prefetch_size=args.prefetch_size,
+        n_views=args.n_views,
+        seed=args.seed,
+        rank=rank,
+        world_size=world_size,
+        fim_rate=args.fim_rate,
+        tokenizer_name=args.tokenizer.name,
+        tokenizer_path=args.tokenizer.path,
+        add_bos=args.add_bos,
+        add_eos=args.add_eos,
+    )
+
+
+def read_jsonl(
+    file_path: str,
+    position: int,
+    block_size: int,
+    offset: int,
+    current_iter: int,
+):
+    """Iterates over a JSON Lines file, yielding a line every `block_size` lines with an offset
+
+    Example : If block_size = 3, offset = 1, iterator will yield lines 1 4 7 10 ...
+    Example : If block_size = 2, offset = 0, iterator will yield lines 0 2 4 6 ...
+
+    Args:
+        file_path (str): Path to the JSONL file.
+        position (int): The file position (in bytes) from which to start reading.
+        block_size (int): The number of lines to skip between yields
+        offset (int): The initial number of lines skiped
+
+    Yields:
+        JSONLState: Represents the state of each line read according to window and offset.
+    """
+    if (offset < 0) or (offset >= block_size):
+        raise RuntimeError(f"JSONL iterator offset value is invalid")
+    # We assume the start position is either 0 or given by the last line yielded
+    # Therefore the current line is right after the offset (modulo block_size)
+    current_line = offset + 1 if position > 0 else 0
+
+    state = JSONLState(
+        file_path=file_path,
+        position=position,
+        block_size=block_size,
+        offset=offset,
+        current_iter=current_iter,
+    )
+    with open(file_path, "r") as file:
+        file.seek(position)
+        while line := file.readline():
+            current_line += 1
+            if (current_line - 1) % block_size == offset:
+                # We return state that will allow resuming from this position
+                # We update state for next position
+                state = JSONLState(
+                    file_path=file_path,
+                    position=file.tell(),
+                    block_size=block_size,
+                    offset=offset,
+                    current_iter=current_iter,
+                )
+                yield json.loads(line), state
+
+
+def loop_on_jsonl(
+    file_path: str,
+    position: int,
+    block_size: int,
+    offset: int,
+    current_iter: int,
+):
+    """Makes the block jsonl iterator infinite and updates n_iter counter"""
+    try:
+        while True:
+            it = read_jsonl(file_path, position, block_size, offset, current_iter)
+            for content, jsonl_state in it:
+                yield content, jsonl_state
+            current_iter += 1
+            position = 0
+    finally:
+        it.close()
+
+def setup_sources(multi_state):
+    path_to_iter = dict()
+    for source in multi_state["sources"]:
+        jsonl_state = multi_state["source_to_state"][source]
+        path_to_iter[source] = loop_on_jsonl(
+            jsonl_state["file_path"],
+            jsonl_state["position"],
+            jsonl_state["block_size"],
+            jsonl_state["offset"],
+            jsonl_state["current_iter"],
+        )
+
+    return path_to_iter
+
+
+def choose_source(
+    source_to_iterator: Dict[str, Iterator],
+    source_to_state: Dict[str, Any],
+    root_dir: str,
+    sources: Dict[str, float],
+    rng_state: Dict[str, Any],
+):
+    """
+    Iterates over multiple data sources, selecting sequences based on weighted random choice.
+
+    Parameters:
+    - source_to_iterator (Dict[str, Iterator]): Dict from source paths to their iterators.
+    - source_to_state (Dict[str, State]): Initial state for each source, allowing state tracking.
+    - root_dir str: Root dir of data sources
+    - sources Dict[str, float]: Dict from subdirectory to the weight used for sampling
+    - rng_state (dict): State of the random number generator for reproducibility.
+
+    Yields:
+    - Tuple of (seq, multi_choice_state) where `seq` is the next sequence from the chosen source,
+    and `multi_choice_state` includes the current state of all sources and the RNG.
+
+    This function ensures that sequences are chosen from the provided sources based on the specified weights,
+    maintaining state information for each source and the RNG to allow for reproducible iteration.
+    """
+    n_sources = len(sources)
+    possible_sources = list(sources.keys())
+    weights = list(sources.values())
+    # We create the rng and set its state
+    rng = np.random.default_rng()
+    rng.bit_generator.state = rng_state
+    while True:
+        # We save the rng state before sampling to be able to yield the same sequence on reload
+        norm_weights = np.array(weights) / np.array(weights).sum()
+        source_choice = possible_sources[rng.choice(n_sources, p=norm_weights)]
+        seq, state = next(source_to_iterator[source_choice])
+        source_to_state = {**source_to_state, source_choice: state}
+        # We update the corresponding source state
+        multi_choice_state = MultiChoiceState(
+            root_dir=root_dir,
+            sources=sources,
+            source_to_state=source_to_state,
+            rng_state=rng.bit_generator.state,
+        )
+        yield seq, multi_choice_state
+
+
+def get_empty_buffer_state(
+    start_token,
+    states,
+):
+    """
+    Calculates the state to resume iteration after the buffer is cleared.
+
+    This function determines the starting point for resuming iteration by rewinding `n_views` from the `end_token`.
+    It handles cases where the rewind goes beyond the current sequence, adjusting the starting sequence and token index accordingly.
+    """
+    # We rewind n_views
+    # This index can be negative if we go beyond the current sample
+    # In that case we go back to find which sequence to start from
+    # And the correct token index to start from
+    seq_to_resume_from = -1
+    while start_token < 0:
+        seq_to_resume_from -= 1
+        start_token += states[seq_to_resume_from]["seq_len"]
+    resume_state = deepcopy(states[seq_to_resume_from])
+    resume_state["start_token"] = start_token
+    # When resuming, the iterator will then correctly fill the buffer
+    del states[:seq_to_resume_from]
+    if "seq_len" in resume_state:
+        del resume_state["seq_len"]
+
+    return resume_state
+
+
+def apply_fill_in_sampling(tokens):
+    return tokens
+
+
+
+def split_by_end_token(tokens, start_token_idx=0, end_token_idx=1):
+    """More robust implementation that handles sequences not starting with start token."""
+    # Find positions of all tokens
+    end_positions = np.where(tokens == end_token_idx)[0]
+    start_positions = np.where(tokens == start_token_idx)[0]
+    if len(end_positions) == 0:
+        # No end tokens found, return entire sequence as one chunk
+        return [tokens]
+    chunks = []
+    chunk_start = 0  # Initialize to beginning of sequence
+    # Handle tokens before the first explicit start token
+    if len(start_positions) == 0 or start_positions[0] > 0:
+        # Find the first end token
+        first_end = end_positions[0] if len(end_positions) > 0 else len(tokens)
+        # If there are tokens before the first end token
+        if first_end > 0:
+            chunks.append(tokens[:first_end + 1])
+            chunk_start = first_end + 1
+    # Process remaining chunks
+    for i, end_pos in enumerate(end_positions):
+        # Find preceding start token for this end token
+        preceding_starts = start_positions[start_positions < end_pos]
+        if len(preceding_starts) > 0:
+            # Get the closest start token
+            closest_start = preceding_starts[-1]
+            # Ensure we don't duplicate chunks or skip tokens
+            if closest_start >= chunk_start:
+                chunks.append(tokens[closest_start:end_pos + 1])
+                chunk_start = end_pos + 1
+        # If there's no start token before this end token but we're not at the beginning
+        elif chunk_start <= end_pos:
+            # Include all tokens from previous position to this end token
+            chunks.append(tokens[chunk_start:end_pos + 1])
+            chunk_start = end_pos + 1
+    # Handle any remaining tokens after the last end token
+    if chunk_start < len(tokens):
+        chunks.append(tokens[chunk_start:])
+    return chunks
+
+def pack_tokens(
+    iterator: Iterator,
+    empty_buffer_state: PackTokensState,
+    do_fim: bool = True
+):
+    """
+    Iterates over tokens, packing them into chunks.
+
+    This function aggregates tokens into a buffer and yields fixed-size chunks with dimensions `(output_seq_len, n_views)`,
+    where each column represents shifted sequences of tokens. It ensures continuity in token sequences across chunks,
+    preventing boundary effects and maintaining consistency regardless of `n_views`.
+
+    Parameters:
+    - iterator: An iterator that yields pairs of (tokens, state), where tokens is a 1D sequence of tokens and state contains all necessary information to resume iterator from current position.
+    - it_state: State of the iterator currently.
+    - start_token (int): The index of the first token to start reading from for the first sequence.
+    - output_seq_len (int): The length of the output sequences to be generated.
+    - n_views (int): The number of shifted views to include in each output chunk.
+
+    Yields:
+    - numpy.ndarray: An array of shape `(output_seq_len, n_views)` containing the packed tokens.
+    - PackTokensState: The state required to resume packing tokens from where the last returned chunk.
+
+    The function handles the complexity of determining the correct state for resuming iteration after the buffer is cleared, ensuring seamless continuation of token sequences.
+    """
+    tokenizer_state = empty_buffer_state["it_state"]
+    tokenizer = get_tokenizer(name=tokenizer_state["name"], path=tokenizer_state["path"])
+
+
+    buffer = []
+    states = []
+
+    output_seq_len = empty_buffer_state["output_seq_len"]
+    n_views = empty_buffer_state["n_views"]
+    start_token = empty_buffer_state["start_token"]
+    previous_state = empty_buffer_state["it_state"]
+    fim_rate = previous_state["fim_rate"]
+
+    rng = np.random.default_rng()
+    rng.bit_generator.state = previous_state["rng_state"]
+
+    buffer_size = output_seq_len + n_views - 1
+    for i, (tokens, state) in enumerate(iterator):
+        end_token = start_token
+        sample_is_read = False
+        while not sample_is_read:
+            assert start_token < len(
+                tokens
+            ), f"Start token index {start_token} bigger than sequence {len(tokens)}"
+            free_space = buffer_size - len(buffer)
+            seq_len = min(free_space, len(tokens) - start_token)
+            end_token = start_token + seq_len
+            buffer.extend(tokens[start_token:end_token])
+            start_token = end_token
+
+            states.append(
+                PackTokensState(
+                    start_token=start_token,
+                    seq_len=seq_len,
+                    it_state=previous_state,
+                    output_seq_len=output_seq_len,
+                    n_views=n_views,
+                )
+            )
+            assert len(buffer) <= buffer_size, "Buffer overflow"
+
+            if len(buffer) == buffer_size:
+                out = np.array(buffer)
+                if do_fim:
+                    document_tokens = split_by_end_token(out, start_token_idx=tokenizer.start_token_idx, end_token_idx=tokenizer.end_token_idx)
+                    processed_documents = []
+                    for chunk in document_tokens:
+                        if do_fim and rng.binomial(1, fim_rate):
+                            chunk = apply_fim(chunk, tokenizer, rng, truncate=True)
+                        processed_documents.append(chunk)
+                    out = np.concatenate(processed_documents)
+
+                assert out.ndim == 1, "Iterator should return 1D sequences"
+                out = np.lib.stride_tricks.sliding_window_view(
+                    out, n_views, axis=0
+                )  # (output_seq_len, n_views)
+
+                # We rewind by n_views to account for the last tokens not having their targets
+                rewinded_idx = start_token - (n_views - 1)
+                empty_buffer_state = get_empty_buffer_state(rewinded_idx, states)
+
+                buffer = buffer[output_seq_len:]
+                assert len(buffer) == (n_views - 1)
+                yield out, empty_buffer_state
+
+            if start_token == len(tokens):
+                start_token = 0
+                sample_is_read = True
+                previous_state = state
+
+
+def batch_and_shuffle_prefetched_sequences(
+    data_loader: Iterator,
+    batch_size: int,
+    prefetch_size: int,
+    seq_len: int,
+    n_views: int,
+    state: PrefetchState,
+):
+    """
+    Prepare batch in advance and shuffle them to reduce correlation inside batches (for ex when very long document is encountered).
+
+    This function aggregates batches into a buffer and yields fixed-size batch size and seqlen with dimensions `(batch_size, seqlen, n_views)`,
+
+    It uses a prefetch buffer to store batches in advance and shuffles them, the prefetch buffer is similar to `reservoir sampling`,
+    but by block to preserve a smooth, easy and deterministic reloading. To ensure more uniform sequence sampling -> prefetch_size * batch_size * seq_len >> max_document_seqlength.
+
+    Parameters:
+    - iterator: An iterator that yields pairs of (sequence, state), where is a random sequence sampled from a corpus (as done by pack_tokens for example).
+    - batch_size: The desired batch size.
+    - prefetch_size: The number of batches to prefetch in advance.
+    - seq_len (int): The length of the output sequences to be generated.
+    - n_views (int): The number of shifted views to include in each output chunk.
+
+    Yields:
+    - numpy.ndarray: An array of shape `(batch_size, seq_len, n_views)` containing the packed tokens.
+    - PrefetchState: The state required to resume prefetched batch. Contains also the internal of iterator.
+    """
+    prefetch_buffer = -1 * np.ones(
+        (prefetch_size * batch_size, seq_len, n_views), dtype=int
+    )
+    rng = np.random.default_rng()
+    rng.bit_generator.state = state["rng_state"]
+
+    # Rewind the iterator to the correct position by skipping seq_idx sequences to roll the buffer accordingly
+    seq_idx = state["seq_idx"]
+    assert (
+        seq_idx >= 0 and seq_idx < prefetch_size
+    ), "Prefetch state seq_idx should be in 0 <= seq_idx < prefetch_size."
+
+    _rng_state = state["rng_state"]
+    _it_state = state["it_state"]
+
+    for i in range(prefetch_size * batch_size):
+        sample, next_it_state = next(data_loader)
+
+
+        if sample.shape != (seq_len, n_views):
+            raise ValueError(f"Sample at index {i} has shape {sample.shape}, expected ({seq_len}, {n_views})")
+
+        prefetch_buffer[i] = sample
+
+    rng.shuffle(prefetch_buffer, axis=0)
+    for i in range(seq_idx * batch_size):
+        prefetch_buffer[i], _ = next(data_loader)
+
+    idx = seq_idx
+    while True:
+        if idx == prefetch_size - 1:
+            _it_state = next_it_state
+            _rng_state = rng.bit_generator.state
+
+        state = PrefetchState(
+            it_state=_it_state,
+            seq_idx=(idx + 1) % prefetch_size,
+            rng_state=_rng_state,
+            batch_size=batch_size,
+            prefetch_size=prefetch_size,
+        )
+
+        yield prefetch_buffer[idx * batch_size : (idx + 1) * batch_size].copy(), state
+
+        for i in range(batch_size):
+            prefetch_buffer[idx * batch_size + i], pack_state = next(data_loader)
+
+        if idx == prefetch_size - 1:
+            next_it_state = pack_state
+            rng.shuffle(prefetch_buffer, axis=0)
+
+        idx = (idx + 1) % prefetch_size
+
+
+
+@functools.lru_cache(maxsize=None)
+def get_tokenizer(name, path):
+    return build_tokenizer(name=name, tokenizer_path=path)
+
+
+@functools.lru_cache(maxsize=None)
+def get_fim_token_ids(tokenizer):
+    try:
+        FIM_PREFIX, FIM_MIDDLE, FIM_SUFFIX = "<FIL_PRE>", "<FIL_MID>", "<FIL_SUF>"
+        suffix_tok_id, prefix_tok_id, middle_tok_id = (
+            tokenizer.model.PieceToId(tok) for tok in [FIM_SUFFIX, FIM_PREFIX, FIM_MIDDLE]
+        )
+    except KeyError:
+        suffix_tok_id, prefix_tok_id, middle_tok_id = None, None, None
+    return suffix_tok_id, prefix_tok_id, middle_tok_id
+
+def apply_fim(tokens, tokenizer, rng, truncate=False):
+    add_st = False
+    add_et = False
+    if len(tokens) < 3:
+        return tokens
+    if tokens[0] == tokenizer.start_token_idx:
+        tokens = tokens[1:]
+        add_st = True
+
+    if tokens[-1] == tokenizer.end_token_idx:
+        tokens = tokens[:-1]
+        add_et = True
+
+    boundaries = list(rng.integers(low=0, high=len(tokens) + 1, size=2))
+    boundaries.sort()
+
+    prefix = np.array(tokens[: boundaries[0]], dtype=np.int64)
+    middle = np.array(tokens[boundaries[0] : boundaries[1]], dtype=np.int64)
+    suffix = np.array(tokens[boundaries[1] :], dtype=np.int64)
+
+    if truncate:
+        new_length = suffix.shape[0] + prefix.shape[0] + middle.shape[0] + 3
+        diff = new_length - len(tokens)
+        if diff > 0:
+            if suffix.shape[0] <= diff:
+
+                tokens_carrier = []
+                if add_st:
+                    tokens_carrier.append([tokenizer.start_token_idx])
+                tokens_carrier.append(tokens)
+                if add_et:
+                    tokens_carrier.append( [tokenizer.end_token_idx])
+                tokens = np.concatenate(tokens_carrier)
+
+                return tokens
+            suffix = suffix[: suffix.shape[0] - diff]
+        elif diff < 0:
+            raise ValueError("This should not happen")
+
+    (
+    suffix_tok_id,
+    prefix_tok_id,
+    middle_tok_id,
+    ) = get_fim_token_ids(tokenizer)
+
+    tokens_carrier = []
+    if add_st:
+        tokens_carrier.append([tokenizer.start_token_idx])
+
+    tokens_carrier.extend([[prefix_tok_id],
+            prefix,
+            [suffix_tok_id],
+            suffix,
+            [middle_tok_id],
+            middle])
+    if add_et:
+        tokens_carrier.append( [tokenizer.end_token_idx])
+    tokens = np.concatenate(tokens_carrier)
+    return tokens
+
+def tokenize(
+    iterator: Iterator,
+    add_bos: bool,
+    add_eos: bool,
+    tokenizer_type: str,
+    rng_state: Dict[str, Any],
+    tokenizer_path: Optional[str] = None,
+    fim_rate: float = 0.0,
+    do_fim: bool =False
+):
+    """
+    Tokenizes text from an iterator of content-state pairs using a specified tokenizer.
+
+    Parameters:
+    - iterator: An iterable of (content, state) pairs where content is a dict with a 'text' or 'content' key.
+    - tokenizer: Tokenizer object with an `encode` method to convert text to tokens, supporting `add_bos` and `add_eos`.
+    - add_bos (bool): Flag to add a beginning-of-sequence token.
+    - add_eos (bool): Flag to add an end-of-sequence token.
+
+    Yields:
+    - (tokens, state) pairs, where `tokens` is a list of tokenized text, and `state` is the original state from the iterator.
+    """
+    tokenizer = get_tokenizer(name=tokenizer_type, path=tokenizer_path)
+    rng = np.random.default_rng()
+    rng.bit_generator.state = rng_state
+
+    for content, state in iterator:
+        assert (
+            "text" in content or "content" in content
+        ), "JSON line must contain either text or content key"
+        content_key = "text" if ("text" in content) else "content"
+        text = content[content_key]
+        # fall back process to remove special token before tokenizer
+        text = re.sub("<s>|<\/s>", "", text)
+        # tokens = tokenizer.encode(text, add_bos=add_bos, add_eos=add_eos)
+
+        if do_fim and rng.binomial(1, fim_rate):
+            tokens = tokenizer.encode(text, add_special_tokens=True, return_type=None)["input_ids"][0]
+            tokens = apply_fim(tokens, tokenizer, rng)
+        else:
+            tokens = tokenizer.encode(text, add_special_tokens=True, return_type=None)["input_ids"][0]
+
+        yield tokens, TokenizerState(
+            it_state=state,
+            add_bos=add_bos,
+            add_eos=add_eos,
+            name=tokenizer_type,
+            path=tokenizer_path,
+            fim_rate=fim_rate,
+            rng_state=rng.bit_generator.state
+        )
+
+
+@contextlib.contextmanager
+def build_dataloader(
+    state: PrefetchState,
+    fim_type=None
+):
+    assert fim_type in ["document", "content"] or fim_type is None, "Unrecognized fim type"
+
+    pack_state = state["it_state"]
+    tokenizer_state = pack_state["it_state"]
+    multi_state = tokenizer_state["it_state"]
+
+    path_to_iter = setup_sources(multi_state)
+    data_it = choose_source(
+        source_to_iterator=path_to_iter,
+        source_to_state=multi_state["source_to_state"],
+        root_dir=multi_state["root_dir"],
+        sources=multi_state["sources"],
+        rng_state=multi_state["rng_state"],
+    )
+    data_it = tokenize(
+        data_it,
+        tokenizer_state["add_bos"],
+        tokenizer_state["add_eos"],
+        tokenizer_state["name"],
+        rng_state=tokenizer_state["rng_state"],
+        tokenizer_path=tokenizer_state["path"],
+        fim_rate=tokenizer_state["fim_rate"],
+        do_fim=True if fim_type is None or fim_type == "document" else False
+    )
+
+    data_it = pack_tokens(
+        data_it,
+        pack_state,
+        do_fim=True if fim_type and fim_type == "content" else False
+    )
+
+    data_it = batch_and_shuffle_prefetched_sequences(
+        data_loader=data_it,
+        seq_len=pack_state["output_seq_len"],
+        n_views=pack_state["n_views"],
+        batch_size=state["batch_size"],
+        prefetch_size=state["prefetch_size"],
+        state=state,
+    )
+    yield data_it
+    for it in path_to_iter.values():
+        it.close()
+    data_it.close()
+
+
+def feed_buffer(queue: Queue, stop_event: EventClass, iterator_builder):
+    """
+    Producer function to fetch data from an iterable dataset and put it into a queue.
+    Incorporates timeout management to avoid hanging on queue.put() when the queue is full.
+    """
+    with iterator_builder() as iterator:
+        for item in iterator:
+            while not stop_event.is_set():
+                try:
+                    queue.put(
+                        item, timeout=0.1
+                    )  # Attempts to put item into the queue with a timeout
+                    break  # On successful put, breaks out of the while loop
+                except Full:
+                    pass
+            if stop_event.is_set():
+                break
+
+
+def consume_buffer(producer: Process, queue: Queue):
+    """
+    Consumer function to process items from the queue.
+    Handles cases where the queue might be empty by implementing timeouts on queue.get().
+    """
+    while producer.exitcode is None:
+        try:
+            item = queue.get(
+                timeout=0.1
+            )  # Tries to get an item from the queue with a timeout
+            yield item
+        except Empty:
+            pass
+
+    raise RuntimeError(
+        "Data loader quit unexpectedly, real error has been raised previously"
+    )
+
+
+@contextlib.contextmanager
+def async_iterator(buffer_size: int, iterator_builder):
+    """
+    Context manager to setup and manage asynchronous iteration with producer-consumer model.
+    """
+    queue = Queue(maxsize=buffer_size)
+    stop_event = Event()
+    producer = Process(target=feed_buffer, args=(queue, stop_event, iterator_builder))
+    logger.info("Async dataloader started")
+    producer.start()
+
+    consumer = consume_buffer(producer, queue)
+    try:
+        yield consumer
+    finally:
+        stop_event.set()  # Ensures the stop event is signaled
+        consumer.close()
+        producer.join(timeout=0.2)  # Waits for the producer to finish
+        if producer.exitcode is None:
+            logger.info(f"Killing async data process {producer.pid} ...")
+            producer.kill()
+        else:
+            logger.info(
+                f"Async data process {producer.pid} exited with code {producer.exitcode}"
+            )
+        logger.info("Async dataloader cleaned up")
+
+
+def build_dataloader_from_args(
+    args: DataArgs,
+    state: Optional[PrefetchState] = None,
+):
+    data_builder = partial(build_dataloader, state, args.fim_type)
+    if args.load_async:
+        return async_iterator(args.prefetch_size, data_builder)
+    else:
+        return data_builder()
+
+
+
+class CustomDataset(Dataset):
+    def __init__(self, token_ids, config: BaseConfiguration):
+        self.input_ids = []
+        self.target_ids = []
+
+        for i in range(0, len(token_ids) - config.model_max_sequence, config.strides):
+            input_chunk = token_ids[i: i + config.model_max_sequence]
+            target_chunk = token_ids[i + 1: i + config.model_max_sequence + 1]
+            self.input_ids.append(torch.tensor(input_chunk))
+            self.target_ids.append(torch.tensor(target_chunk))
+
+    def __len__(self):
+        return len(self.input_ids)
+
+    def __getitem__(self, idx):
+        return self.input_ids[idx], self.target_ids[idx]
+
+
+def build_tokenizer(name: str, tokenizer_path: str):
+    return SPMTokenizer(os.path.join(tokenizer_path, name))
+
+
+def tokenize_text(tokenizer, text):
+    """Tokenize text using the provided tokenizer."""
+    return tokenizer.encode(text)
+
+
+def dataloader_v1(dataset, tokenizer, config: BaseConfiguration):
+    # Tokenize the text dataset
+    token_ids = []
+    for index, sample in enumerate(dataset):
+        token_ids.extend(tokenize_text(tokenizer, sample['content']))
+
+    # Create dataset
+    dataset = CustomDataset(token_ids, config)
+
+    # Create DataLoader
+    data_loader = DataLoader(
+        dataset,
+        batch_size=config.dataset_batch_size,
+        shuffle=config.dataset_shuffle,
+        num_workers=4
+    )
+
+    return data_loader
+
+
+def create_data_loader(
+        dataset,
+        batch_size: int = 32,
+        shuffle: bool = True,
+        num_workers: int = 4,
+        pin_memory: bool = True,
+        collate_fn: Optional[Callable] = None,
+        drop_last: bool = False,
+        generator: Optional[torch.Generator] = None
+) -> DataLoader:
+    """
+    Create a PyTorch DataLoader with optimized settings.
+
+    Args:
+        dataset: PyTorch Dataset object
+        batch_size: Number of samples per batch
+        shuffle: Whether to shuffle the data
+        num_workers: Number of subprocesses for data loading
+        pin_memory: Whether to pin memory in GPU training
+        collate_fn: Custom collate function for batching
+        drop_last: Whether to drop the last incomplete batch
+        generator: Random number generator for reproducibility
+
+    Returns:
+        DataLoader: Configured PyTorch DataLoader
+    """
+
+    if collate_fn is None:
+        raise ValueError("collator function not provided")
+
+    # Choose sampler based on shuffle parameter
+    if shuffle:
+        sampler = RandomSampler(dataset, generator=generator)
+    else:
+        sampler = SequentialSampler(dataset)
+
+    # Create DataLoader with optimized settings
+    loader = DataLoader(
+        dataset=dataset,
+        batch_size=batch_size,
+        sampler=sampler,
+        num_workers=num_workers,
+        collate_fn=collate_fn,
+        pin_memory=pin_memory,
+        drop_last=drop_last,
+        # Worker init function for reproducibility
+        worker_init_fn=lambda worker_id: torch.manual_seed(torch.initial_seed() + worker_id)
+    )
+
+    return loader
+
+
+if __name__ == "__main__":
+
+    args = DataArgs(root_dir="/workspace/AI-Uncomplicated/artifact/pretraining_data",
+                    sources={"TigerResearch": 1.0},
+                    tokenizer=TokenizerArgs(name="GI01-tokenizer-v0.1-en", path="/workspace/AI-Uncomplicated/artifact/tokenizer"),
+                    fim_rate=1.0,
+                    fim_type="document")
+    states = init_dataloader_state_from_args(args, 1, 2)
+    dataloader = build_dataloader_from_args(args, states)
+    with dataloader as loader:
+        for data in loader:
+            # print(data)
+            # break
+            break
\ No newline at end of file
diff --git a/core/trainer/debug.yaml b/core/trainer/debug.yaml
new file mode 100644
index 0000000..fae026a
--- /dev/null
+++ b/core/trainer/debug.yaml
@@ -0,0 +1,52 @@
+# This is where Meta Lingua will store anything related to the experiment.
+dump_dir: /workspace/AI-Uncomplicated/
+name: "debug"
+steps: 3000
+
+seed: 12
+
+optim:
+    lr: 3e-4
+    warmup: 2000
+    lr_min_ratio: 0.000001
+    clip: 10.0
+
+distributed:
+    fsdp_type: full_shard
+    compile: true
+    dp_shard: 2
+    dp_replicate: 1
+    selective_activation_checkpointing: false
+
+model:
+    dim: 1024
+    n_layers: 16
+    n_heads: 16
+
+checkpoint:
+    path: /workspace/AI-Uncomplicated/
+
+profiling:
+    run: true
+    trace_folder: profiling
+
+logging:
+    freq: 1
+    wandb:
+        entity: vipinsaravana
+        project: Pretrain
+        name: lingua_model_base_run
+        dir: /workspace/AI-Uncomplicated/wandb/
+
+data:
+    root_dir: /workspace/AI-Uncomplicated/artifact/pretraining_data/
+    sources:
+      TigerResearch: 100.0
+    batch_size: 32
+    seq_len: 1024
+    load_async: true
+    fim_type: document
+    fim_rate: 0.1
+    tokenizer:
+        name: GI01-tokenizer-v0.1-en
+        path: /workspace/AI-Uncomplicated/artifact/tokenizer
\ No newline at end of file
diff --git a/core/trainer/distributed.py b/core/trainer/distributed.py
new file mode 100644
index 0000000..07e1ca0
--- /dev/null
+++ b/core/trainer/distributed.py
@@ -0,0 +1,457 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+import atexit
+import contextlib
+from itertools import chain
+import logging
+import multiprocessing as mp
+import os
+import random
+import shutil
+import signal
+import socket
+import subprocess
+import sys
+import tempfile
+from dataclasses import asdict, dataclass
+from functools import lru_cache, partial, reduce
+from typing import List, Optional, Tuple, Union
+
+import torch
+from torch.distributed import ReduceOp
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch import distributed as dist
+from torch.distributed._tensor import DTensor
+from torch.distributed._composable.fsdp import MixedPrecisionPolicy, fully_shard
+from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
+    checkpoint_wrapper,
+)
+from torch.utils.checkpoint import (
+    create_selective_checkpoint_contexts,
+    CheckpointPolicy,
+)
+from torch.distributed.device_mesh import DeviceMesh, init_device_mesh
+
+# for no recompute ops
+import xformers.ops
+
+from core.trainer.float8 import convert_linears_to_fp8
+
+logger = logging.getLogger()
+
+# for selective AC
+default_no_recompute_ops = {
+    torch.ops.aten.mm.default,
+    torch.ops.aten._scaled_mm.default,
+    torch.ops.aten._scaled_dot_product_efficient_attention.default,
+    torch.ops.aten._scaled_dot_product_flash_attention.default,
+    torch.ops.c10d_functional.reduce_scatter_tensor.default,
+    torch.ops.xformers_flash.flash_fwd.default,
+    torch.ops.xformers.efficient_attention_forward_cutlass.default,
+}
+
+
+@dataclass
+class DistributedArgs:
+    dp_shard: int = (
+        4  # In how many shard to split the model weight. Typically number gpu in a node.
+    )
+    dp_replicate: int = (
+        4  # How many times to replicate the model weight. Typically number of nodes.
+    )
+    selective_activation_checkpointing: bool = False
+    compile: bool = False
+    fsdp_type: str = "full_shard" # full_shard
+    model_dtype: str = "bf16"
+    float8_recipe: Optional[str] = None
+    float8_filter: str = r"layers\.[0-9]+\."
+
+    matmul_allow_tf32: bool = False
+    allow_bf16_reduced_precision_reduction: bool = True
+    detect_anomaly: bool = False
+
+    compile_cache_size_limit: int = 8
+
+    spawn_method: str = "forkserver"
+
+
+@dataclass
+class EnvironmentArgs:
+    # Use GNU openMP (GOMP) instead of Intel OpenMP [Intel Math Kernel Library (MKL)]
+    MKL_SERVICE_FORCE_INTEL: str = "GNU"
+    OMP_NUM_THREADS: str = "1"
+    MKL_NUM_THREADS: str = "1"
+    # faster intra-node collectives, seems to be a cluster specific flag
+    ENABLE_INTRA_NODE_COMM: str = "1"
+    # avoids OOMs with long context
+    TORCH_NCCL_AVOID_RECORD_STREAMS: str = "1"
+    # increasing NCCL timeout time before having some NCCL error 22 should give a 16s timeout
+    NCCL_IB_TIMEOUT: str = "22"
+    NCCL_DEBUG: str = "INFO"
+    TORCH_NCCL_ASYNC_ERROR_HANDLING: str = "1"
+
+
+def get_device_mesh(distributed_args: DistributedArgs):
+    dp_replicate = distributed_args.dp_replicate
+    dp_shard = distributed_args.dp_shard
+
+    assert (
+        dp_replicate * dp_shard == get_world_size()
+    ), f"dp_replicate * dp_shard * tp_size ({dp_replicate} * {dp_shard}) != world_size ({get_world_size()})"
+
+    dims = []
+    names = []
+    if dp_replicate >= 1:
+        dims.append(dp_replicate)
+        names.append("dp_replicate")
+    if dp_shard > 1 or distributed_args.fsdp_type == "no_shard":
+        dims.append(dp_shard)
+        names.append("dp_shard")
+
+    dims = tuple(dims)
+    names = tuple(names)
+
+    return init_device_mesh("cuda", mesh_shape=dims, mesh_dim_names=names)
+
+
+def dist_max(x: Union[int, float], mesh: DeviceMesh = None):
+    tensor = torch.tensor(x).cuda()
+    dist.all_reduce(tensor, op=ReduceOp.MAX, group=mesh.get_group() if mesh else None)
+    return tensor
+
+
+def dist_mean(x: Union[int, float], mesh: DeviceMesh = None):
+    tensor = torch.tensor(x).cuda()
+    dist.all_reduce(tensor, op=ReduceOp.AVG, group=mesh.get_group() if mesh else None)
+    return tensor
+
+
+def dist_mean_dict(x):
+    r = dict()
+    for k in x:
+        r[k] = dist_mean(x[k])
+        r[k] = r[k].item() if (r[k].dim() == 0) else r[k].tolist()
+    return r
+
+
+@lru_cache()
+def get_is_torch_run() -> bool:
+    return os.environ.get("LOCAL_RANK") is not None
+
+
+@lru_cache()
+def get_is_slurm_job() -> bool:
+    return "SLURM_JOB_ID" in os.environ and not get_is_torch_run()
+
+
+@lru_cache()
+def get_global_rank() -> int:
+    if get_is_torch_run():
+        return int(os.environ["RANK"])
+    elif get_is_slurm_job():
+        return int(os.environ["SLURM_PROCID"])
+    else:
+        return 0
+
+
+@lru_cache()
+def get_local_rank() -> int:
+    if get_is_torch_run():
+        return int(os.environ["LOCAL_RANK"])
+    elif get_is_slurm_job():
+        return int(os.environ["SLURM_LOCALID"])
+    else:
+        return 0
+
+
+@lru_cache()
+def get_world_size() -> int:
+    if get_is_torch_run():
+        return int(os.environ["WORLD_SIZE"])
+    elif get_is_slurm_job():
+        return int(os.environ["SLURM_NTASKS"])
+    else:
+        return 1
+
+
+@lru_cache()
+def get_is_master() -> bool:
+    return get_global_rank() == 0
+
+
+@lru_cache()
+def get_master_port(job_id: int) -> int:
+    if get_is_torch_run():
+        return int(os.environ["MASTER_PORT"])
+    else:
+        MIN_MASTER_PORT, MAX_MASTER_PORT = (20000, 60000)
+        rng = random.Random(job_id)
+        return rng.randint(MIN_MASTER_PORT, MAX_MASTER_PORT)
+
+
+@lru_cache()
+def get_master_addr() -> str:
+    if get_is_torch_run():
+        return os.environ["MASTER_ADDR"]
+    elif get_is_slurm_job():
+        hostnames = subprocess.check_output(
+            ["scontrol", "show", "hostnames", os.environ["SLURM_JOB_NODELIST"]]
+        )
+        return hostnames.split()[0].decode("utf-8")
+    else:
+        return "127.0.0.1"
+
+
+def setup_env(env_args):
+    env_vars = asdict(env_args)
+
+    # When using Triton, it attempts to locate prebuilt kernels in a cache
+    # located at ~/.triton/cache, but when that's backed by NFS this can fail
+    # with a "OSError: [Errno 116] Stale file handle" error. If we were to set
+    # it to a local directory it would belong to the first user who created it
+    # and it would fail for the job of any other successive user assigned to
+    # that machine. To avoid all this mess we use a temporary per-process cache.
+    triton_cache_dir = tempfile.mkdtemp()
+    atexit.register(shutil.rmtree, triton_cache_dir, ignore_errors=True)
+    env_vars["TRITON_CACHE_DIR"] = triton_cache_dir
+
+    # We change the tmp dir to /scratch in case it's slurm job
+    # This avoids filling up the host's usually limited tmpfs
+    # A full tmpfs leads to very slow creation of processes and weird bugs
+    if get_is_slurm_job():
+        new_tmp = f"/scratch/slurm_tmpdir/{os.environ['SLURM_JOB_ID']}"
+        if os.path.exists(new_tmp):
+            env_vars["TMP_DIR"] = new_tmp
+
+    for name, value in env_vars.items():
+        if os.environ.get(name) != str(value):
+            os.environ[name] = str(value)
+            logger.warning(f"WARNING: Setting {name} to {value}")
+
+
+def setup_torch_distributed(dist_args):
+    """
+    Handle single and multi-GPU / multi-node / SLURM jobs.
+    Initialize the following variables:
+        - global_rank
+        - world_size
+    """
+    mp.set_start_method(dist_args.spawn_method)
+    with mp.Manager():
+        pass
+
+    local_rank = get_local_rank()
+
+    os.environ["RANK"] = str(get_global_rank())
+    os.environ["WORLD_SIZE"] = str(get_world_size())
+    os.environ["MASTER_ADDR"] = get_master_addr()
+    os.environ["MASTER_PORT"] = str(
+        get_master_port(job_id=int(os.environ.get("SLURM_JOB_ID", -1)))
+    )
+
+    if get_is_torch_run():
+        logger.info(f"Run launched with torchrun, local rank: {local_rank}")
+    elif get_is_slurm_job():
+        logger.info(f"Run launched with slurm, local rank: {local_rank}")
+    else:
+        logger.info("Single GPU job")
+
+    logger.info(f"ENV: {os.environ}")
+
+    # set GPU device
+    assert 0 <= local_rank < 8
+    if dist_args.matmul_allow_tf32:
+        torch.backends.cuda.matmul.allow_tf32 = True
+        logger.warning(
+            f"WARNING: Setting torch.backends.matmul.allow_tf32 to True. This is faster but less accurate."
+        )
+    torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = (
+        dist_args.allow_bf16_reduced_precision_reduction
+    )
+    if torch.cuda.device_count() > 1:
+        torch.cuda.set_device(local_rank)
+    torch.distributed.init_process_group(init_method="env://", backend="nccl")
+    torch.autograd.set_detect_anomaly(dist_args.detect_anomaly)
+
+
+def get_module(module, access_string):
+    names = access_string.split(sep=".")
+    return reduce(getattr, names, module)
+
+
+def set_module(module, access_string, value):
+    names = access_string.split(sep=".")
+    parent = reduce(getattr, names[:-1], module)
+    setattr(parent, names[-1], value)
+
+
+def default_fsdp_grouping_plan(n_layers: int) -> List[Tuple[str, bool]]:
+    return [(f"layers.{i}", i < n_layers - 1) for i in range(n_layers)]
+
+
+def get_default_policy(no_recompute_ops=None):
+    no_recompute_ops = no_recompute_ops or default_no_recompute_ops
+
+    def default_policy(ctx, func, *args, **kwargs):
+        return (
+            CheckpointPolicy.MUST_SAVE
+            if func in no_recompute_ops
+            else CheckpointPolicy.PREFER_RECOMPUTE
+        )
+
+    return default_policy
+
+
+@torch.no_grad()
+def check_model_value_range(
+    model: torch.nn.Module, range: float = 1e3, std: float = 1e3
+):
+    for name, param in chain(model.named_parameters(), model.named_buffers()):
+        if isinstance(param, DTensor):
+            param = param.to_local()
+
+        if param.numel() == 0:
+            logger.warning(f"Model parameter {name} is empty, probably because of FSDP sharding")
+            continue
+
+        if torch.isnan(param).any() or torch.isinf(param).any():
+            logger.warning(f"Model parameter {name} contains NaN or Inf")
+
+        param_range = param.max() - param.min()
+        param_std = param.std()
+
+        if param_range > range:
+            logger.warning(
+                f"Model parameter {name} has a suspiciously large range ({param_range}): please check initialization and init_weights is defined and called"
+            )
+        if param_std > std:
+            logger.warning(
+                f"Model parameter {name} has a suspiciously large standard deviation ({param_std}): please check initialization and init_weights is defined and called"
+            )
+        if (param == 0).all():
+            logger.warning(
+                f"Model parameter {name} is all zeros: it might be because of a missing initialization"
+            )
+
+
+def init_signal_handler(callable):
+    """
+    Handle signals sent by SLURM for time limit / pre-emption.
+    """
+    signal.signal(signal.SIGUSR2, callable)
+    logger.warning("Signal handler installed.")
+
+
+def requeue_slurm_job():
+    prod_id = int(os.environ["SLURM_PROCID"])
+    logger.warning("Host: %s - Global rank: %i" % (socket.gethostname(), prod_id))
+    if prod_id == 0 and os.environ.get("LAUNCH_WITH", "") != "DORA":
+        logger.warning("Requeuing job " + os.environ["SLURM_JOB_ID"])
+        os.system("scontrol requeue " + os.environ["SLURM_JOB_ID"])
+    else:
+        logger.warning("Not the master process, no need to requeue.")
+    sys.exit(0)
+
+
+@contextlib.contextmanager
+def clean_env():
+    distrib_names = (
+        "MASTER_ADDR",
+        "MASTER_PORT",
+        "RANK",
+        "WORLD_SIZE",
+        "LOCAL_RANK",
+        "LOCAL_WORLD_SIZE",
+        "TORCHELASTIC_RUN_ID",
+        "DORA_FORCE_DISTRIB",
+    )
+    cluster_env = {
+        x: os.environ.pop(x)
+        for x in os.environ
+        if x.startswith(
+            ("SLURM_", "SLURMD_", "SRUN_", "SBATCH_", "SUBMITIT_", "WANDB_")
+        )
+        or x in distrib_names
+    }
+    try:
+        yield
+    finally:
+        os.environ.update(cluster_env)
+
+
+def parallelize_model(
+    model,
+    device_mesh,
+    distributed_args: DistributedArgs,
+    fsdp_grouping_plan: Optional[List[Tuple[str, bool]]] = None,
+    no_recompute_ops=None,
+):
+    if distributed_args.float8_recipe is not None:
+        model = convert_linears_to_fp8(
+            model, distributed_args.float8_recipe, distributed_args.float8_filter
+        )
+
+    param_dtype = dict(fp32=torch.float32, fp16=torch.float16, bf16=torch.bfloat16)[
+        distributed_args.model_dtype
+    ]
+    if (
+        distributed_args.fsdp_type == "full_shard"
+        or distributed_args.fsdp_type == "no_shard"
+    ):
+        if distributed_args.fsdp_type == "no_shard":
+            assert (
+                distributed_args.dp_shard == 1
+            ), "dp_shard must be 1 for no_shard fsdp_type"
+            assert (
+                device_mesh["dp_shard"].size() == 1
+            ), "dp_shard must be 1 for no_shard fsdp_type"
+
+        fsdp_config = dict(
+            mp_policy=(
+                MixedPrecisionPolicy(
+                    param_dtype=param_dtype,
+                    reduce_dtype=torch.float32,
+                )
+            ),
+            mesh=(
+                device_mesh["dp_replicate", "dp_shard"]
+                if distributed_args.dp_shard > 1
+                or distributed_args.fsdp_type == "no_shard"
+                else device_mesh["dp_replicate"]
+            ),
+        )
+
+        if fsdp_grouping_plan is None:
+            # Assume that the model has list of layers and group around it
+            fsdp_grouping_plan = default_fsdp_grouping_plan(len(model.layers))
+
+        for path, reshard_after_forward in fsdp_grouping_plan:
+            module = get_module(model, path)
+            set_module(
+                model,
+                path,
+                fully_shard(
+                    module, **fsdp_config, reshard_after_forward=reshard_after_forward
+                ),
+            )
+
+        model = fully_shard(model, **fsdp_config, reshard_after_forward=True)
+    else:
+        raise ValueError(f"Invalid fsdp_type: {distributed_args.fsdp_type}")
+
+    if distributed_args.selective_activation_checkpointing:
+        model = checkpoint_wrapper(
+            model,
+            context_fn=partial(
+                create_selective_checkpoint_contexts,
+                get_default_policy(no_recompute_ops),
+            ),
+        )
+
+    if distributed_args.compile:
+        torch._dynamo.config.cache_size_limit = (
+            distributed_args.compile_cache_size_limit
+        )
+        model.compile()
+
+    return model
\ No newline at end of file
diff --git a/core/trainer/distributed/config.py b/core/trainer/distributed/config.py
new file mode 100644
index 0000000..9a4400d
--- /dev/null
+++ b/core/trainer/distributed/config.py
@@ -0,0 +1,27 @@
+from dataclasses import dataclass
+from typing import Optional
+
+
+@dataclass
+class DistributedArgs:
+    dp_shard: int = (
+        1
+    )
+    dp_replicate: int = (
+        1  # How many times to replicate the model weight. Typically number of nodes.
+    )
+    tp_size: int = 1
+    selective_activation_checkpointing: bool = False
+    compile: bool = False
+    fsdp_type: str = "no_shard"
+    model_dtype: str = "bf16"
+    float8_recipe: Optional[str] = None
+    float8_filter: str = r"layers\.[0-9]+\."
+
+    matmul_allow_tf32: bool = False
+    allow_bf16_reduced_precision_reduction = True
+    detect_anomaly: bool = False
+
+    compile_cache_size_limit: int = 8
+
+    spawn_method: str = "forkserver"
\ No newline at end of file
diff --git a/core/trainer/env.sh b/core/trainer/env.sh
new file mode 100644
index 0000000..b8c63f3
--- /dev/null
+++ b/core/trainer/env.sh
@@ -0,0 +1,49 @@
+#!/bin/bash
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+#SBATCH --job-name=env_creation
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --gres=gpu:8
+#SBATCH --exclusive
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=128
+#SBATCH --mem=0
+#SBATCH --time=01:00:00
+
+# Exit immediately if a command exits with a non-zero status
+set -e
+
+# Start timer
+start_time=$(date +%s)
+
+# Get the current date
+current_date=$(date +%y%m%d)
+
+# Create environment name with the current date
+env_prefix=lingua_$current_date
+
+# Create the conda environment
+
+source $CONDA_ROOT/opt/conda/etc/profile.d/conda.sh
+conda create -n $env_prefix python=3.11 -y -c anaconda
+conda activate $env_prefix
+
+echo "Currently in env $(which python)"
+
+# Install packages
+pip install torch==2.5.0 xformers --index-url https://download.pytorch.org/whl/cu121
+pip install ninja
+pip install --requirement requirements.txt
+
+# End timer
+end_time=$(date +%s)
+
+# Calculate elapsed time in seconds
+elapsed_time=$((end_time - start_time))
+
+# Convert elapsed time to minutes
+elapsed_minutes=$((elapsed_time / 60))
+
+echo "Environment $env_prefix created and all packages installed successfully in $elapsed_minutes minutes!"
+
diff --git a/core/trainer/float8.py b/core/trainer/float8.py
new file mode 100644
index 0000000..b69505a
--- /dev/null
+++ b/core/trainer/float8.py
@@ -0,0 +1,315 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+import re
+import warnings
+from typing import Callable
+
+import torch
+from torch.distributed.tensor import DTensor, Partial, Shard
+
+# avoid division by zero when calculating scale
+EPS = 1e-12
+
+
+def get_splitk(t):
+    # When tensor parallelism splits the operands along the reduction dim, it's
+    # more natural (and efficient, and accurate) to do sub-row-wise scaling, so
+    # that each rank can compute its own scales independently.
+    if isinstance(t, DTensor) and t.placements == (Shard(dim=1),):
+        return t.device_mesh.size()
+    else:
+        return 1
+
+
+def mul_tiled(a, *bs):
+    # If b is m x n, divide a into m x n chunks and multiply each by an element of b
+    for b in bs:
+        a = a.unflatten(0, (b.shape[0], -1)).unflatten(-1, (b.shape[-1], -1))
+        a = a * b[:, None, :, None]
+        a = a.flatten(end_dim=1).flatten(start_dim=-2)
+    return a
+
+
+def apply_to_partial(fn, t, *args, **kwargs):
+    # With tensor parallelism, _scaled_mm returns a "partial" result, but we do
+    # manual (post-)scaling which we want to apply to each partial term
+    # separately, thus we do this hack to "unpack" the DTensors.
+    if isinstance(t, DTensor) and t.placements == (Partial(),):
+        return torch.distributed.tensor.experimental.local_map(fn, [*t.placements])(t, *args, **kwargs)
+    else:
+        return fn(t, *args, **kwargs)
+
+
+def scale(t, amax_t):
+    max_v = torch.finfo(torch.float8_e4m3fn).max
+    scale_t = torch.clamp(amax_t.float(), min=EPS) / max_v
+    t_fp8 = mul_tiled(t, scale_t.reciprocal()).to(torch.float8_e4m3fn)
+    return t_fp8, scale_t
+
+
+def matmul(first, amax_first, second_t, amax_second_t, bias, use_fast_accum):
+    first_fp8, scale_first = scale(first, amax_first)
+    second_t_fp8, scale_second_t = scale(second_t, amax_second_t)
+
+    # PyTorch's row-wise scaled matmul kernel is based on CUTLASS and is quite
+    # slow when fast_accum is disabled. Hence we fall back to an "unscaled"
+    # matmul, which uses cuBLAS, and apply the scale manually afterwards.
+    post_scales = []
+    post_bias = None
+    if not use_fast_accum:
+        post_scales = [scale_first, scale_second_t.t()]
+        scale_first = scale_first.new_ones((1, 1))
+        scale_second_t = scale_second_t.t().new_ones((1, 1))
+        post_bias, bias = bias, None
+
+    res = torch._scaled_mm(
+        first_fp8,
+        second_t_fp8.t(),
+        scale_a=scale_first,
+        scale_b=scale_second_t.t(),
+        bias=bias,
+        out_dtype=torch.bfloat16,
+        use_fast_accum=use_fast_accum,
+    )
+
+    res = apply_to_partial(mul_tiled, res, *post_scales).to(torch.bfloat16)
+    if post_bias is not None:
+        res += post_bias
+
+    return res
+
+
+@torch.compiler.allow_in_graph
+class Fp8LinearFn(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, a, b_t, bias):
+        amax_a = a.abs().unflatten(-1, (get_splitk(a), -1)).amax(dim=-1)
+        amax_b_t = b_t.abs().unflatten(-1, (get_splitk(b_t), -1)).amax(dim=-1)
+        out = matmul(a, amax_a, b_t, amax_b_t, bias, use_fast_accum=True)
+
+        ctx.a_requires_grad = a.requires_grad
+        ctx.b_requires_grad = b_t.requires_grad
+        ctx.bias_requires_grad = bias.requires_grad if bias is not None else False
+
+        ctx.save_for_backward(a, b_t, amax_b_t)
+
+        return out
+
+    @staticmethod
+    def backward(ctx, grad_out):
+        a, b_t, amax_b_t = ctx.saved_tensors
+
+        # Workaround for https://github.com/pytorch/pytorch/issues/141881.
+        # The partitioner would pre-compute the transposed scaling of the weight
+        # in the forward (as it's most efficient, but it actually uses too much
+        # memory). We prevent that by making the scaling depend on the gradient
+        # in a way that has no effect and will be optimized away later.
+        # Care is needed to support tensor parallelism and circumvent bugs.
+        b_t = b_t + grad_out[:1, :, None].squeeze(0) * 0
+
+        if ctx.a_requires_grad:
+            b = b_t.t().contiguous()
+            amax_grad_out = (
+                grad_out.abs().unflatten(-1, (get_splitk(grad_out), -1)).amax(dim=-1)
+            )
+            amax_b = amax_b_t.t().unflatten(-1, (get_splitk(b), -1)).amax(dim=-1)
+            amax_b = amax_b.repeat_interleave(
+                b.shape[0] // amax_b.shape[0], dim=0, output_size=b.shape[0]
+            )
+            grad_a = matmul(grad_out, amax_grad_out, b, amax_b, None, use_fast_accum=False)
+        else:
+            grad_a = None
+        if ctx.b_requires_grad:
+            grad_b = grad_out.t() @ a
+        else:
+            grad_b = None
+        if ctx.bias_requires_grad:
+            grad_bias = grad_out.sum(dim=0)
+        else:
+            grad_bias = None
+
+        return grad_a, grad_b, grad_bias
+
+
+class Fp8Linear(torch.nn.Linear):
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        out = Fp8LinearFn.apply(input.flatten(end_dim=-2), self.weight, self.bias)
+        out = out.unflatten(0, input.shape[:-1])
+        return out
+
+
+def named_replace(fn: Callable[[torch.nn.Module, str], torch.nn.Module], module: torch.nn.Module, name="") -> torch.nn.Module:
+    for child_name, child_module in list(module.named_children()):
+        full_name = f"{name}.{child_name}" if name else child_name
+        new_child_module = named_replace(fn, child_module, full_name)
+        setattr(module, child_name, new_child_module)
+    module = fn(module, name)
+    return module
+
+
+def convert_linears_to_fp8(root_module: torch.nn.Module, recipe: str, filter: str) -> torch.nn.Module:
+    if recipe not in ["rowwise"]:
+        raise RuntimeError(f"Unknown float8 recipe {recipe!r}")
+
+    if recipe == "rowwise" and torch.__version__ < "2.5":
+        # We need https://github.com/pytorch/pytorch/pull/134781.
+        warnings.warn("Float8 row-wise scaling is slow in PyTorch prior to v2.5.0")
+
+    # Multi-kernel makes Inductor auto-tune between a regular "streaming"-based
+    # reduction kernel and a "persistent" reduction kernel. Since fp8 has some
+    # multi-pass steps (e.g., first get amax, then scale), persistent kernels
+    # should perform better.
+    torch._inductor.config.triton.multi_kernel = 1
+
+    filter_re = re.compile(filter)
+    def replace(module: torch.nn.Module, name: str) -> torch.nn.Module:
+        if not isinstance(module, torch.nn.Linear) or not filter_re.search(name):
+            return module
+        if type(module) == torch.nn.Linear:
+            if recipe == "rowwise":
+                new_module = Fp8Linear(
+                    in_features=module.in_features,
+                    out_features=module.out_features,
+                    bias=module.bias is not None,
+                    dtype=module.weight.dtype,
+                    device=module.weight.device,
+                )
+                new_module.weight = module.weight
+                new_module.bias = module.bias
+            else:
+                assert False, recipe
+        else:
+            assert False, str(type(module))
+        return new_module
+    out = named_replace(replace, root_module)
+
+    # Force re-compile everything
+    torch._dynamo.reset_code_caches()
+    from torch._inductor.cudagraph_trees import reset_cudagraph_trees
+    reset_cudagraph_trees()
+
+    return out
+
+
+# We need some upstream PyTorch fixes which are only present in v2.7+ or in
+# nightlies starting from January 7, 2025. For earlier versions, we copy-pasted
+# the relevant pieces of code below.
+if torch.__version__ < "2.7.0.dev20250107":
+    from torch.distributed.device_mesh import DeviceMesh
+    from torch.distributed.tensor._dtensor_spec import DTensorSpec
+    from torch.distributed.tensor._op_schema import (
+        OpSchema,
+        OpStrategy,
+        PlacementStrategy,
+        RuntimeSchemaInfo,
+    )
+    from torch.distributed.tensor._ops._einsum_strategy import gen_einsum_strategies
+    from torch.distributed.tensor._ops._math_ops import (
+        _infer_reduction_dims,
+        common_reduction_strategy,
+    )
+    from torch.distributed.tensor._ops.utils import (
+        generate_redistribute_costs,
+        is_tensor_shardable,
+        prod,
+        register_op_strategy,
+    )
+    from torch.distributed.tensor.placement_types import Replicate
+
+    # Cherry-pick of https://github.com/pytorch/pytorch/pull/143747
+
+    LINEAR_REDUCTION_OP_MAP = {
+        torch.ops.aten.amax.default: "max",
+        torch.ops.aten.amin.default: "min",
+    }
+
+    @register_op_strategy(
+        list(LINEAR_REDUCTION_OP_MAP.keys()), schema_info=RuntimeSchemaInfo(1)
+    )
+    def linear_reduction_strategy(mesh: DeviceMesh, op_schema: OpSchema) -> OpStrategy:
+        args_schema = op_schema.args_schema
+        input_strategy = args_schema[0]
+        assert isinstance(input_strategy, OpStrategy)
+        dims = None
+        if len(op_schema.args_schema) > 1:
+            dims = _infer_reduction_dims(args_schema[1], input_strategy.ndim)
+
+        reduce_dims = list(range(input_strategy.ndim)) if dims is None else dims
+
+        keep_dim = len(op_schema.args_schema) > 2 and bool(op_schema.args_schema[2])
+        reduction_op = LINEAR_REDUCTION_OP_MAP[op_schema.op]
+        return common_reduction_strategy(
+            mesh,
+            input_strategy,
+            reduce_dims,
+            keep_dim=keep_dim,
+            reduction_linear=True,
+            reduction_op=reduction_op,
+        )
+
+    # Cherry-pick of https://github.com/pytorch/pytorch/pull/143760
+
+    def _mm_like_strategy(
+        mm_equation: str, mesh: DeviceMesh, op_schema: OpSchema
+    ) -> OpStrategy:
+        (
+            self_strategy,
+            mat2_strategy,
+            scale_self_strategy,
+            scale_mat2_strategy,
+            bias_strategy,
+            scale_result_strategy,
+            *_,
+        ) = op_schema.args_schema
+        assert isinstance(self_strategy, OpStrategy)
+        assert isinstance(mat2_strategy, OpStrategy)
+        assert isinstance(scale_self_strategy, OpStrategy)
+        assert isinstance(scale_mat2_strategy, OpStrategy)
+        assert bias_strategy is None
+        assert scale_result_strategy is None
+        # generate all possible strategies for mm
+        mm_strategy = gen_einsum_strategies(mm_equation, mesh)
+        assert isinstance(mm_strategy, OpStrategy)
+        # filter out invalid strategies and associate costs
+        strategies = mm_strategy.strategies
+        filtered_strategies = []
+        for strtg in strategies:
+            assert isinstance(strtg, PlacementStrategy)
+            assert strtg.input_specs is not None
+            self_spec = strtg.input_specs[0]
+            mat2_spec = strtg.input_specs[1]
+            assert isinstance(self_spec, DTensorSpec)
+            assert isinstance(mat2_spec, DTensorSpec)
+            scale_self_spec = (
+                DTensorSpec(self_spec.mesh, (Replicate(),))
+                if prod(scale_self_strategy.shape) == 1
+                else self_spec
+            )
+            scale_mat2_spec = (
+                DTensorSpec(mat2_spec.mesh, (Replicate(),))
+                if prod(scale_mat2_strategy.shape) == 1
+                else mat2_spec
+            )
+            strtg.input_specs.extend([scale_self_spec, scale_mat2_spec])
+            if (
+                is_tensor_shardable(self_strategy.shape, self_spec)
+                and is_tensor_shardable(mat2_strategy.shape, mat2_spec)
+                and is_tensor_shardable(scale_self_strategy.shape, scale_self_spec)
+                and is_tensor_shardable(scale_mat2_strategy.shape, scale_mat2_spec)
+            ):
+                redistribute_cost = [
+                    generate_redistribute_costs(self_strategy, self_spec),
+                    generate_redistribute_costs(mat2_strategy, mat2_spec),
+                    generate_redistribute_costs(scale_self_strategy, scale_self_spec),
+                    generate_redistribute_costs(scale_mat2_strategy, scale_mat2_spec),
+                ]
+                strtg.redistribute_cost = redistribute_cost
+                filtered_strategies.append(strtg)
+
+        mm_strategy.strategies = filtered_strategies
+
+        return mm_strategy
+
+    @register_op_strategy(torch.ops.aten._scaled_mm.default)
+    def mm_strategy(mesh: DeviceMesh, op_schema: OpSchema) -> OpStrategy:
+        return _mm_like_strategy("mk,kn->mn", mesh, op_schema)
\ No newline at end of file
diff --git a/core/trainer/logger.py b/core/trainer/logger.py
new file mode 100644
index 0000000..e95c2cf
--- /dev/null
+++ b/core/trainer/logger.py
@@ -0,0 +1,129 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+from datetime import timedelta
+import logging
+import math
+import sys
+import time
+
+from core.trainer.distributed import get_global_rank, get_is_slurm_job
+
+
+class LogFormatter(logging.Formatter):
+    """
+    Custom logger for distributed jobs, displaying rank
+    and preserving indent from the custom prefix format.
+    """
+
+    def __init__(self):
+        self.start_time = time.time()
+        self.rank = get_global_rank()
+        self.show_rank = not get_is_slurm_job()  # srun has --label
+
+    def formatTime(self, record):
+        subsecond, seconds = math.modf(record.created)
+        curr_date = (
+            time.strftime("%y-%m-%d %H:%M:%S", time.localtime(seconds))
+            + f".{int(subsecond * 1_000_000):06d}"
+        )
+        delta = timedelta(seconds=round(record.created - self.start_time))
+        return f"{curr_date} - {delta}"
+
+    def formatPrefix(self, record):
+        fmt_time = self.formatTime(record)
+        if self.show_rank:
+            return f"{self.rank}: {record.levelname:<7} {fmt_time} - "
+        else:
+            return f"{record.levelname:<7} {fmt_time} - "
+
+    def formatMessage(self, record, indent: str):
+        content = record.getMessage()
+        content = content.replace("\n", "\n" + indent)
+        # Exception handling as in the default formatter, albeit with indenting
+        # according to our custom prefix
+        if record.exc_info:
+            # Cache the traceback text to avoid converting it multiple times
+            # (it's constant anyway)
+            if not record.exc_text:
+                record.exc_text = self.formatException(record.exc_info)
+        if record.exc_text:
+            if content[-1:] != "\n":
+                content = content + "\n" + indent
+            content = content + indent.join(
+                [l + "\n" for l in record.exc_text.splitlines()]
+            )
+            if content[-1:] == "\n":
+                content = content[:-1]
+        if record.stack_info:
+            if content[-1:] != "\n":
+                content = content + "\n" + indent
+            stack_text = self.formatStack(record.stack_info)
+            content = content + indent.join([l + "\n" for l in stack_text.splitlines()])
+            if content[-1:] == "\n":
+                content = content[:-1]
+
+        return content
+
+    def format(self, record):
+        prefix = self.formatPrefix(record)
+        indent = " " * len(prefix)
+        content = self.formatMessage(record, indent)
+        return prefix + content
+
+
+def set_root_log_level(log_level: str):
+    logger = logging.getLogger()
+    level: int | str = log_level.upper()
+    try:
+        level = int(log_level)
+    except ValueError:
+        pass
+    try:
+        logger.setLevel(level)  # type: ignore
+    except Exception:
+        logger.warning(
+            f"Failed to set logging level to {log_level}, using default 'NOTSET'"
+        )
+        logger.setLevel(logging.NOTSET)
+
+
+def init_logger(
+    log_file: str | None = None,
+    *,
+    name: str | None = None,
+    level: str = "NOTSET",
+):
+    """
+    Setup logging.
+
+    Args:
+        log_file: A file name to save file logs to.
+        name: The name of the logger to configure, by default the root logger.
+        level: The logging level to use.
+    """
+    set_root_log_level(level)
+    logger = logging.getLogger(name)
+
+    # stdout: everything
+    stdout_handler = logging.StreamHandler(sys.stdout)
+    stdout_handler.setLevel(logging.NOTSET)
+    stdout_handler.setFormatter(LogFormatter())
+
+    # stderr: warnings / errors and above
+    stderr_handler = logging.StreamHandler(sys.stderr)
+    stderr_handler.setLevel(logging.WARNING)
+    stderr_handler.setFormatter(LogFormatter())
+
+    # set stream handlers
+    logger.handlers.clear()
+    logger.handlers.append(stdout_handler)
+    logger.handlers.append(stderr_handler)
+
+    if log_file is not None and get_global_rank() == 0:
+        # build file handler
+        file_handler = logging.FileHandler(log_file, "a")
+        file_handler.setLevel(logging.NOTSET)
+        file_handler.setFormatter(LogFormatter())
+        # update logger
+        logger = logging.getLogger()
+        logger.addHandler(file_handler)
\ No newline at end of file
diff --git a/core/trainer/logger/local_logger.py b/core/trainer/logger/local_logger.py
new file mode 100644
index 0000000..d825cbc
--- /dev/null
+++ b/core/trainer/logger/local_logger.py
@@ -0,0 +1,168 @@
+import torch
+import logging
+import time
+from pathlib import Path
+import json
+from typing import Dict, Any, Optional
+from datetime import datetime
+
+class TrainingLogger:
+    def __init__(
+        self,
+        output_dir: str,
+        project_name: str,
+        log_every_n_steps: int = 100,
+        save_every_n_steps: int = 1000,
+        save_best_only: bool = True
+    ):
+        """
+        Initialize training logger with various logging options.
+
+        Args:
+            output_dir: Directory to save checkpoints and logs
+            project_name: Name of the project
+            use_wandb: Whether to use Weights & Biases logging
+            log_every_n_steps: How often to log metrics
+            save_every_n_steps: How often to save checkpoints
+            save_best_only: Whether to save only the best model
+        """
+        self.output_dir = Path(output_dir)
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+
+        # Initialize logging
+        self.log_file = self.output_dir / 'training.log'
+        self.setup_logging()
+
+        # Training state
+        self.global_step = 0
+        self.best_loss = float('inf')
+        self.start_time = time.time()
+        self.last_log_time = self.start_time
+
+        # Configuration
+        self.log_every_n_steps = log_every_n_steps
+        self.save_every_n_steps = save_every_n_steps
+
+        # Save configuration
+        self.save_config({
+            'output_dir': str(output_dir),
+            'project_name': project_name,
+            'log_every_n_steps': log_every_n_steps,
+            'save_every_n_steps': save_every_n_steps,
+            'save_best_only': save_best_only,
+            'start_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+        })
+
+    def setup_logging(self):
+        """Setup logging configuration."""
+        logging.basicConfig(
+            level=logging.INFO,
+            format='%(asctime)s - %(levelname)s - %(message)s',
+            handlers=[
+                logging.FileHandler(self.log_file),
+                logging.StreamHandler()
+            ]
+        )
+
+    def save_config(self, config: Dict[str, Any]):
+        """Save configuration to JSON file."""
+        config_path = self.output_dir / 'config.json'
+        with open(config_path, 'w') as f:
+            json.dump(config, f, indent=4)
+
+    def log_metrics(
+        self,
+        metrics: Dict[str, float],
+        step: Optional[int] = None,
+        force_log: bool = False
+    ):
+        """
+        Log metrics to all configured outputs.
+
+        Args:
+            metrics: Dictionary of metric names and values
+            step: Optional step number (uses global_step if not provided)
+            force_log: Whether to log regardless of log_every_n_steps
+        """
+        if step is not None:
+            self.global_step = step
+
+        # Check if we should log
+        if not force_log and self.global_step % self.log_every_n_steps != 0:
+            return
+
+        # Calculate time statistics
+        current_time = time.time()
+        elapsed = current_time - self.start_time
+        elapsed_since_last = current_time - self.last_log_time
+        steps_since_last = self.log_every_n_steps
+        steps_per_second = steps_since_last / elapsed_since_last
+
+        # Add timing metrics
+        metrics.update({
+            'elapsed_time': elapsed,
+            'steps_per_second': steps_per_second
+        })
+
+        # Log to terminal and file
+        log_str = f'Step {self.global_step}: ' + ', '.join(
+            f'{k}: {v}' for k, v in metrics.items()
+        )
+        logging.info(log_str)
+
+        self.last_log_time = current_time
+
+    def save_checkpoint(
+        self,
+        model: torch.nn.Module,
+        optimizer: torch.optim.Optimizer,
+        loss: float,
+        extra_data: Optional[Dict[str, Any]] = None,
+        force_save: bool = False
+    ):
+        """
+        Save model checkpoint.
+
+        Args:
+            model: PyTorch model
+            optimizer: PyTorch optimizer
+            loss: Current loss value
+            extra_data: Additional data to save in checkpoint
+            force_save: Whether to save regardless of save_every_n_steps
+        """
+        # Check if we should save
+        should_save = (
+            force_save or
+            self.global_step % self.save_every_n_steps == 0 or
+            (self.save_best_only and loss < self.best_loss)
+        )
+
+        if not should_save:
+            return
+
+        # Update best loss if needed
+        if loss < self.best_loss:
+            self.best_loss = loss
+
+        # Prepare checkpoint data
+        checkpoint = {
+            'model_state_dict': model.state_dict(),
+            'optimizer_state_dict': optimizer.state_dict(),
+            'global_step': self.global_step,
+            'loss': loss,
+            'best_loss': self.best_loss
+        }
+
+        if extra_data:
+            checkpoint.update(extra_data)
+
+        # Save checkpoint
+        checkpoint_path = self.output_dir / f'checkpoint_{self.global_step}.pt'
+        torch.save(checkpoint, checkpoint_path)
+
+        logging.info(f'Saved checkpoint at step {self.global_step}')
+
+    def finish(self):
+        """Cleanup and final logging."""
+        total_time = time.time() - self.start_time
+        logging.info(f'Training finished. Total time: {total_time:.2f}s')
\ No newline at end of file
diff --git a/core/trainer/loss.py b/core/trainer/loss.py
new file mode 100644
index 0000000..0d707ca
--- /dev/null
+++ b/core/trainer/loss.py
@@ -0,0 +1,240 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
+
+import torch.nn.functional as F
+import logging
+from collections import namedtuple
+from dataclasses import dataclass, asdict
+from typing import Dict, Any, List, Optional, Union
+from pathlib import Path
+import json
+from datetime import datetime, timezone
+
+import torch
+import torch.nn as nn
+
+import wandb
+
+logger = logging.getLogger()
+
+
+def cross_entropy_loss(logits, gold, trg_pad_idx):
+    ''' Calculate cross entropy loss'''
+    n_classes = logits.shape[-1]
+
+    logits = logits.view(-1, n_classes)
+    gold = gold.unsqueeze(-1).view(-1)
+    loss = F.cross_entropy(logits, gold, ignore_index=trg_pad_idx)
+    return loss
+
+
+@dataclass
+class WandbArgs:
+    job_type: Optional[str] = None
+    dir: Optional[str] = None
+    project: Optional[str] = None
+    entity: Optional[str] = None
+    tags: Optional[List] = None
+    group: Optional[str] = None
+    name: Optional[str] = None
+    notes: Optional[str] = None
+    config_exclude_keys: Optional[List[str]] = None
+    config_include_keys: Optional[List[str]] = None
+    anonymous: Optional[str] = None
+    mode: Optional[str] = None
+    allow_val_change: Optional[bool] = None
+    resume: Optional[Union[bool, str]] = None
+    force: Optional[bool] = None
+    tensorboard: Optional[bool] = None
+    sync_tensorboard: Optional[bool] = None
+    monitor_gym: Optional[bool] = None
+    save_code: Optional[bool] = None
+    id: Optional[str] = None
+    fork_from: Optional[str] = None
+    resume_from: Optional[str] = None
+
+
+@dataclass
+class LoggingArgs:
+    freq: int = 10  # Log every freq optimizer steps
+    acc_freq: Optional[int] = None  # Log every acc_freq gradient accumulation steps
+
+    wandb: Optional[WandbArgs] = None
+
+
+class MetricLogger:
+    def __init__(self, outdir: Path, args: Optional[Any] = None):
+        self.outdir = outdir
+        self.jsonl_writer = None
+        self.args = args
+
+    def open(self):
+        if self.jsonl_writer is None:
+            self.jsonl_writer = open(self.outdir, "a")
+        if (
+            self.args is not None
+            and self.args.logging.wandb is not None
+            and get_is_master()
+        ):
+            run = wandb.init(
+                config=asdict(self.args),
+                **asdict(self.args.logging.wandb),
+            )
+
+    def log(self, metrics: Dict[str, Any]):
+        if (
+            self.args is not None
+            and self.args.logging.wandb is not None
+            and (wandb.run is not None)
+        ):
+            wandb.log(metrics, step=metrics["global_step"])
+
+        metrics.update({"created_at": datetime.now(timezone.utc).isoformat()})
+        print(json.dumps(metrics), file=self.jsonl_writer, flush=True)
+
+    def close(self):
+        if self.jsonl_writer is not None:
+            self.jsonl_writer.close()
+            self.jsonl_writer = None
+
+    def __enter__(self):
+        self.open()
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.close()
+
+    def __del__(self):
+        self.close()
+
+
+GPUMemStats = namedtuple(
+    "GPUMemStats",
+    [
+        "max_active_gib",
+        "max_active_pct",
+        "max_reserved_gib",
+        "max_reserved_pct",
+        "num_alloc_retries",
+        "num_ooms",
+        "power_draw",
+    ],
+)
+
+
+class GPUMemoryMonitor:
+    """
+    Class to monitor GPU memory usage
+    """
+
+    def __init__(self, device: str = "cuda:0"):
+        self.device = torch.device(device)  # device object
+        self.device_name = torch.cuda.get_device_name(self.device)
+        self.device_index = torch.cuda.current_device()
+        self.device_capacity = torch.cuda.get_device_properties(
+            self.device
+        ).total_memory
+        self.device_capacity_gib = self._to_gib(self.device_capacity)
+
+        # reset stats, clear cache
+        torch.cuda.reset_peak_memory_stats()
+        torch.cuda.empty_cache()
+
+    def _to_gib(self, memory_in_bytes):
+        # NOTE: GiB (gibibyte) is 1024, vs GB is 1000
+        _gib_in_bytes = 1024 * 1024 * 1024
+        memory_in_gib = memory_in_bytes / _gib_in_bytes
+        return memory_in_gib
+
+    def _to_pct(self, memory):
+        return 100 * memory / self.device_capacity
+
+    def get_peak_stats(self):
+        cuda_info = torch.cuda.memory_stats(self.device)
+
+        max_active = cuda_info["active_bytes.all.peak"]
+        max_active_gib = self._to_gib(max_active)
+        max_active_pct = self._to_pct(max_active)
+
+        max_reserved = cuda_info["reserved_bytes.all.peak"]
+        max_reserved_gib = self._to_gib(max_reserved)
+        max_reserved_pct = self._to_pct(max_reserved)
+
+        num_retries = cuda_info["num_alloc_retries"]
+        num_ooms = cuda_info["num_ooms"]
+        power_draw = torch.cuda.power_draw()
+
+        if num_retries > 0:
+            logger.warning(f"{num_retries} CUDA memory allocation retries.")
+        if num_ooms > 0:
+            logger.warning(f"{num_ooms} CUDA OOM errors thrown.")
+
+        return GPUMemStats(
+            max_active_gib,
+            max_active_pct,
+            max_reserved_gib,
+            max_reserved_pct,
+            num_retries,
+            num_ooms,
+            power_draw,
+        )
+
+    def reset_peak_stats(self):
+        torch.cuda.reset_peak_memory_stats()
+        torch.cuda.reset_accumulated_memory_stats()
+
+    def __str__(self):
+        mem_stats = self.get_peak_stats()
+        display_str = f"{self.device_name} ({self.device_index}): {self.device_capacity_gib} GiB capacity, "
+        display_str += (
+            f"{mem_stats.max_reserved_gib} GiB peak, {mem_stats.max_reserved_pct}% peak"
+        )
+        return f"{display_str}"
+
+
+def upload_train_to_wandb(
+    ckpt_dir, project="GI_01", entity="good-to-achieve", train=True, eval=True
+):
+    import wandb
+    from omegaconf import OmegaConf
+    import json
+    from pathlib import Path
+
+    cfg = OmegaConf.load(Path(ckpt_dir) / "config.yaml")
+    cfg = OmegaConf.to_container(cfg)
+
+    if train:
+        wandb.init(config=cfg, name=cfg["name"], project=project, entity=entity)
+
+        with open(Path(ckpt_dir) / "metrics.jsonl") as f:
+            for l in f:
+                m = json.loads(l)
+                wandb.log(m, step=m["global_step"])
+
+        wandb.finish()
+
+    if eval:
+        wandb.init(config=cfg, name=cfg["name"], project=project, entity=entity)
+
+        with open(Path(ckpt_dir) / "metrics.eval.jsonl") as f:
+            for l in f:
+                m = json.loads(l)
+                wandb.log(
+                    {
+                        f"evals/{name.replace('/','.')}": value
+                        for name, value in m.items()
+                        if "/" in name
+                    },
+                    step=m["global_step"],
+                )
+
+        wandb.finish()
+
+
+def get_num_params(model: nn.Module) -> int:
+    """
+    Get the total model params
+    Args : only_trainable: whether to only count trainable params
+    """
+    numel = {n: p.numel() for n, p in model.named_parameters()}
+    return sum(numel.values())
\ No newline at end of file
diff --git a/core/trainer/metrics.py b/core/trainer/metrics.py
new file mode 100644
index 0000000..e585453
--- /dev/null
+++ b/core/trainer/metrics.py
@@ -0,0 +1,230 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
+
+import logging
+from collections import namedtuple
+from dataclasses import dataclass, asdict
+from typing import Dict, Any, List, Optional, Union
+from pathlib import Path
+import json
+from datetime import datetime, timezone
+from core.trainer.distributed import get_is_master
+
+import torch
+import torch.nn as nn
+
+import wandb
+
+logger = logging.getLogger()
+
+
+@dataclass
+class WandbArgs:
+    job_type: Optional[str] = None
+    dir: Optional[str] = None
+    project: Optional[str] = None
+    entity: Optional[str] = None
+    tags: Optional[List] = None
+    group: Optional[str] = None
+    name: Optional[str] = None
+    notes: Optional[str] = None
+    config_exclude_keys: Optional[List[str]] = None
+    config_include_keys: Optional[List[str]] = None
+    anonymous: Optional[str] = None
+    mode: Optional[str] = None
+    allow_val_change: Optional[bool] = None
+    resume: Optional[Union[bool, str]] = None
+    force: Optional[bool] = None
+    tensorboard: Optional[bool] = None
+    sync_tensorboard: Optional[bool] = None
+    monitor_gym: Optional[bool] = None
+    save_code: Optional[bool] = None
+    id: Optional[str] = None
+    fork_from: Optional[str] = None
+    resume_from: Optional[str] = None
+
+
+@dataclass
+class LoggingArgs:
+    freq: int = 10  # Log every freq optimizer steps
+    acc_freq: Optional[int] = None  # Log every acc_freq gradient accumulation steps
+
+    wandb: Optional[WandbArgs] = None
+
+
+class MetricLogger:
+    def __init__(self, outdir: Path, args: Optional[Any] = None):
+        self.outdir = outdir
+        self.jsonl_writer = None
+        self.args = args
+
+    def open(self):
+        if self.jsonl_writer is None:
+            self.jsonl_writer = open(self.outdir, "a")
+        if (
+            self.args is not None
+            and self.args.logging.wandb is not None
+            and get_is_master()
+        ):
+            run = wandb.init(
+                config=asdict(self.args),
+                **asdict(self.args.logging.wandb),
+            )
+
+    def log(self, metrics: Dict[str, Any]):
+        if (
+            self.args is not None
+            and self.args.logging.wandb is not None
+            and (wandb.run is not None)
+        ):
+            wandb.log(metrics, step=metrics["global_step"])
+
+        metrics.update({"created_at": datetime.now(timezone.utc).isoformat()})
+        print(json.dumps(metrics), file=self.jsonl_writer, flush=True)
+
+    def close(self):
+        if self.jsonl_writer is not None:
+            self.jsonl_writer.close()
+            self.jsonl_writer = None
+
+    def __enter__(self):
+        self.open()
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.close()
+
+    def __del__(self):
+        self.close()
+
+
+GPUMemStats = namedtuple(
+    "GPUMemStats",
+    [
+        "max_active_gib",
+        "max_active_pct",
+        "max_reserved_gib",
+        "max_reserved_pct",
+        "num_alloc_retries",
+        "num_ooms",
+        "power_draw",
+    ],
+)
+
+
+class GPUMemoryMonitor:
+    """
+    Class to monitor GPU memory usage
+    """
+
+    def __init__(self, device: str = "cuda:0"):
+        self.device = torch.device(device)  # device object
+        self.device_name = torch.cuda.get_device_name(self.device)
+        self.device_index = torch.cuda.current_device()
+        self.device_capacity = torch.cuda.get_device_properties(
+            self.device
+        ).total_memory
+        self.device_capacity_gib = self._to_gib(self.device_capacity)
+
+        # reset stats, clear cache
+        torch.cuda.reset_peak_memory_stats()
+        torch.cuda.empty_cache()
+
+    def _to_gib(self, memory_in_bytes):
+        # NOTE: GiB (gibibyte) is 1024, vs GB is 1000
+        _gib_in_bytes = 1024 * 1024 * 1024
+        memory_in_gib = memory_in_bytes / _gib_in_bytes
+        return memory_in_gib
+
+    def _to_pct(self, memory):
+        return 100 * memory / self.device_capacity
+
+    def get_peak_stats(self):
+        cuda_info = torch.cuda.memory_stats(self.device)
+
+        max_active = cuda_info["active_bytes.all.peak"]
+        max_active_gib = self._to_gib(max_active)
+        max_active_pct = self._to_pct(max_active)
+
+        max_reserved = cuda_info["reserved_bytes.all.peak"]
+        max_reserved_gib = self._to_gib(max_reserved)
+        max_reserved_pct = self._to_pct(max_reserved)
+
+        num_retries = cuda_info["num_alloc_retries"]
+        num_ooms = cuda_info["num_ooms"]
+        power_draw = torch.cuda.power_draw()
+
+        if num_retries > 0:
+            logger.warning(f"{num_retries} CUDA memory allocation retries.")
+        if num_ooms > 0:
+            logger.warning(f"{num_ooms} CUDA OOM errors thrown.")
+
+        return GPUMemStats(
+            max_active_gib,
+            max_active_pct,
+            max_reserved_gib,
+            max_reserved_pct,
+            num_retries,
+            num_ooms,
+            power_draw,
+        )
+
+    def reset_peak_stats(self):
+        torch.cuda.reset_peak_memory_stats()
+        torch.cuda.reset_accumulated_memory_stats()
+
+    def __str__(self):
+        mem_stats = self.get_peak_stats()
+        display_str = f"{self.device_name} ({self.device_index}): {self.device_capacity_gib} GiB capacity, "
+        display_str += (
+            f"{mem_stats.max_reserved_gib} GiB peak, {mem_stats.max_reserved_pct}% peak"
+        )
+        return f"{display_str}"
+
+
+def upload_train_to_wandb(
+    ckpt_dir, project="lingua", entity="codegen-team", train=True, eval=True
+):
+    import wandb
+    from omegaconf import OmegaConf
+    import json
+    from pathlib import Path
+
+    cfg = OmegaConf.load(Path(ckpt_dir) / "config.yaml")
+    cfg = OmegaConf.to_container(cfg)
+
+    if train:
+        wandb.init(config=cfg, name=cfg["name"], project=project, entity=entity)
+
+        with open(Path(ckpt_dir) / "metrics.jsonl") as f:
+            for l in f:
+                m = json.loads(l)
+                wandb.log(m, step=m["global_step"])
+
+        wandb.finish()
+
+    if eval:
+        wandb.init(config=cfg, name=cfg["name"], project=project, entity=entity)
+
+        with open(Path(ckpt_dir) / "metrics.eval.jsonl") as f:
+            for l in f:
+                m = json.loads(l)
+                wandb.log(
+                    {
+                        f"evals/{name.replace('/','.')}": value
+                        for name, value in m.items()
+                        if "/" in name
+                    },
+                    step=m["global_step"],
+                )
+
+        wandb.finish()
+
+
+def get_num_params(model: nn.Module) -> int:
+    """
+    Get the total model params
+    Args : only_trainable: whether to only count trainable params
+    """
+    numel = {n: p.numel() for n, p in model.named_parameters()}
+    return sum(numel.values())
\ No newline at end of file
diff --git a/core/trainer/optim.py b/core/trainer/optim.py
new file mode 100644
index 0000000..74e7e83
--- /dev/null
+++ b/core/trainer/optim.py
@@ -0,0 +1,180 @@
+
+
+from dataclasses import dataclass
+from functools import partial
+import math
+
+import logging
+from torch import nn
+from torch.optim import AdamW, lr_scheduler
+
+logger = logging.getLogger()
+
+
+@dataclass
+class OptimArgs:
+    lr: float = 3e-4
+    weight_decay: float = 0.1
+    epsilon: float = 1e-8
+    beta1: float = 0.9
+    beta2: float = 0.95
+    clip: float = 1.0
+
+    scheduler: str = "wsd"
+    warmup: int = 2000
+    lr_min_ratio: float = 0.1
+    cycle_length: float = 1.0
+    cosine_theta: float = 1.0
+    annealing_step: int = 1000
+    decay_fraction: float = 0.1
+    decay_type: str = "cosine"
+
+    exp_factor: float = 0.5
+
+
+def lr_linear(step: int, warmup: int, n_steps: int, min_ratio: float) -> float:
+    if step < warmup:
+        lr = float(step) / warmup
+    elif step <= n_steps:
+        s = float(step - warmup) / (n_steps - warmup)
+        lr = s * min_ratio + (1 - s)
+    else:
+        lr = min_ratio
+    return lr
+
+
+def lr_inv_sqrt(step: int, warmup: int, exp_factor: float, min_ratio: float) -> float:
+    if step < warmup:
+        lr = float(step) / warmup
+    else:
+        lr = max((warmup**exp_factor) / (step**exp_factor), min_ratio)
+    return lr
+
+
+def lr_cosine(
+    step: int,
+    warmup: int,
+    n_steps: int,
+    cycle_length: float,
+    theta: float,
+    min_ratio: float,
+) -> float:
+    sign = ((step // (n_steps*cycle_length)) % 2) * -2 + 1
+    if step < warmup:
+        lr = float(step) / warmup
+    elif step <= n_steps:
+        s = float(step - warmup) / (n_steps - warmup)
+        lr = min_ratio + 0.5 * (1 - min_ratio) * (
+            sign * math.cos(math.pi * s**theta / cycle_length) + 1
+        )
+    else:
+        lr = min_ratio
+    return lr
+
+def lr_wsd(
+    step: int,
+    warmup: int,
+    n_steps: int,
+    decay_fraction: float,
+    cycle_length: float,
+    min_ratio: float,
+    theta: float,
+    decay_type: str = "inverse_linear"
+) -> float:
+    """
+    UNDERSTANDING WARMUP-STABLE-DECAY LEARNING RATES: A RIVER VALLEY LOSS LANDSCAPE PERSPECTIVE
+    https://arxiv.org/pdf/2410.05192
+    """
+    cycle_num = step // int(n_steps * cycle_length) + 1
+    curr_n_steps = int(n_steps * cycle_length) * cycle_num
+    decay_length = int(curr_n_steps * decay_fraction)
+    if step == n_steps:
+        cycle_num -= 1
+        curr_n_steps = n_steps
+
+    if step < warmup:
+        lr = float(step) / warmup
+    elif step <= curr_n_steps - decay_length:
+        lr = 1.0
+    elif step > curr_n_steps - decay_length and step <= curr_n_steps:
+        # Linear interpolation gives similar results
+        # slope = -(1.0 - min_ratio) / decay_length
+        # intercept = min_ratio + ((1.0 - min_ratio) * curr_n_steps) / decay_length
+        # lr = slope * step + intercept
+        step_in_decay = step - (curr_n_steps - decay_length)
+        progress = step_in_decay / decay_length
+        if decay_type == "inverse_linear":
+            lr = 1 / (progress * (1/min_ratio) + (1 - progress))
+        elif decay_type == "cosine":
+            lr = lr_cosine(step_in_decay, 0.0, decay_length, cycle_length, theta, min_ratio)
+            # sign = ((step_in_decay // (decay_length * cycle_length)) % 2) * -2 + 1
+            # lr = min_ratio + 0.5 * (1 - min_ratio) * (
+            #         sign * math.cos(math.pi * progress ** theta / cycle_length) + 1
+            # )
+        else:
+            raise NotImplementedError(f"wds decay type not implemented for `{decay_type}`")
+    else:
+        lr = min_ratio
+
+    return lr
+
+
+def build_lr_fn(args: OptimArgs, n_steps: int):
+    if args.scheduler == "constant":
+        lr_fn = lambda x: 1.0
+    elif args.scheduler == "linear":
+        lr_fn = partial(
+            lr_linear, warmup=args.warmup, n_steps=n_steps, min_ratio=args.lr_min_ratio
+        )
+    elif args.scheduler == "inv_sqrt":
+        lr_fn = partial(
+            lr_inv_sqrt,
+            warmup=args.warmup,
+            exp_factor=args.exp_factor,
+            min_ratio=args.lr_min_ratio,
+        )
+    elif args.scheduler == "cosine":
+        lr_fn = partial(
+            lr_cosine,
+            warmup=args.warmup,
+            n_steps=n_steps,
+            cycle_length=args.cycle_length,
+            theta=args.cosine_theta,
+            min_ratio=args.lr_min_ratio,
+        )
+    elif args.scheduler == "wsd":
+        assert args.decay_fraction < args.cycle_length
+        lr_fn = partial(
+            lr_wsd,
+            warmup=args.warmup,
+            n_steps=n_steps,
+            decay_fraction=args.decay_fraction,
+            cycle_length=args.cycle_length,
+            min_ratio=args.lr_min_ratio,
+            theta=args.cosine_theta,
+            decay_type=args.decay_type,
+        )
+    else:
+        raise NotImplementedError(f"Unknown scheduler: {args.scheduler}")
+    return lr_fn
+
+
+def build_optimizer(model: nn.Module, args: OptimArgs, n_steps: int):
+    logger.info("Starting build of optimizer...")
+    optimizer = AdamW(
+        model.parameters(),
+        lr=args.lr,
+        betas=(args.beta1, args.beta2),
+        weight_decay=args.weight_decay,
+        eps=args.epsilon,
+        fused=True,  # Faster optim.step but can throw errors
+    )
+
+    # scheduler
+    lr_fn = build_lr_fn(args, n_steps)
+    scheduler = lr_scheduler.LambdaLR(
+        optimizer, lr_fn
+    )  # lr_scheduler.LambdaLR(optimizer, lr_fn)
+
+    logger.info("Done with build of optimizer.")
+    return optimizer, scheduler
\ No newline at end of file
diff --git a/core/trainer/optimizers.py b/core/trainer/optimizers.py
new file mode 100644
index 0000000..946539d
--- /dev/null
+++ b/core/trainer/optimizers.py
@@ -0,0 +1,81 @@
+from dataclasses import dataclass
+from functools import partial
+import math
+
+import logging
+from torch import nn
+from torch.optim import AdamW, lr_scheduler
+
+from core.trainer.schedulers import WarmUpLinearScheduler, WarmupCosineScheduler, WSDScheduler
+
+logger = logging.getLogger()
+
+
+@dataclass
+class OptimArgs:
+    lr: float = 3e-4
+    weight_decay: float = 0.1
+    epsilon: float = 1e-8
+    beta1: float = 0.9
+    beta2: float = 0.95
+    clip: float = 1.0
+
+    scheduler: str = "cosine"
+    warmup: int = 2000
+    min_lr: float = 0.1
+    cycle_length: float = 1.0
+    cosine_theta: float = 1.0
+    annealing_step: int = 1000
+    decay_fraction: float = 0.1
+
+    exp_factor: float = 0.5
+
+
+def build_scheduler_fn(optimizer, args, n_steps):
+    if args.scheduler == "Constant":
+        lr_fn = lambda x: args.lr
+    elif args.scheduler == "WrLinear":
+        lr_fn = WarmUpLinearScheduler(
+            optimizer=optimizer,
+            warmup_step=args.warmup,
+            total_steps=args.n_steps,
+            min_lr=args.min_lr,
+        )
+    elif args.scheduler == "cosine":
+        lr_fn = WarmupCosineScheduler(
+            optimizer=optimizer,
+            warmup_steps=args.warmup,
+            total_steps=n_steps,
+            cycle_length=args.cycle_length,
+            theta=args.cosine_theta,
+            min_lr=args.min_lr,
+        )
+    elif args.scheduler == "wsd":
+        lr_fn = WSDScheduler(
+            optimizer=optimizer,
+            warmup_step=args.warmup,
+            total_steps=n_steps,
+            cycle_length=args.cycle_length,
+            decay_fraction=args.decay_fraction,
+            max_lr=args.lr,
+            min_lr=args.min_lr,
+        )
+    else:
+        raise ValueError(f"un-recognized scheduler type `{args.scheduler}`")
+    return lr_fn
+
+
+def build_optimizer(model: nn.Module, args: OptimArgs, n_steps: int):
+    logger.info("Starting build of optimizer...")
+    optimizer = AdamW(
+        model.parameters(),
+        lr=args.lr,
+        betas=(args.beta1, args.beta2),
+        weight_decay=args.weight_decay,
+        eps=args.epsilon,
+        fused=True,
+    )
+    # scheduler
+    scheduler = build_scheduler_fn(optimizer, args, n_steps)
+    logger.info("Done with build of optimizer.")
+    return optimizer, scheduler
diff --git a/core/trainer/probe.py b/core/trainer/probe.py
new file mode 100644
index 0000000..84916cd
--- /dev/null
+++ b/core/trainer/probe.py
@@ -0,0 +1,630 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+# This file from the xFormers repo is just a example of how to implement
+# probing of the activations of a model, without changing anything.
+# By default, the linear inputs/outputs/gradients are logged, as well as
+# the attention logits+entropy. It is possible to log an additional tensor, eg:
+# x = log_stats(x, "name")
+#
+# Known limitations:
+# * Only a subset of the attention biases is supported
+# * Torch-compile is disabled automatically when this is enabled
+# * Only tested with bf16/f16/f32 datatypes
+
+import os
+import uuid
+import contextlib
+import functools
+import json
+import math
+from enum import Enum
+from collections import defaultdict
+from typing import Any, Dict, List, Optional, Tuple
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
+    CheckpointImpl,
+    checkpoint_wrapper,
+)
+from torch.utils._python_dispatch import TorchDispatchMode
+from torch.utils._pytree import tree_map
+from torch.utils.module_tracker import ModuleTracker
+from torch.fx.operator_schemas import normalize_function
+from torch.nn.attention import sdpa_kernel, SDPBackend
+
+from xformers.ops import fmha
+
+
+@torch.library.custom_op("torchprobe::log", mutates_args=(), device_types=None)
+def _log(x: torch.Tensor, name: str, uid: str) -> None:
+    pass
+
+
+@_log.register_fake
+def _log_fake(x: torch.Tensor, name: str, uid: str) -> None:
+    pass
+
+
+class _LogStats(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: torch.Tensor, name: str):
+        uid = str(uuid.uuid4())
+        torch.ops.torchprobe.log(x, name, uid)
+        ctx.name = name
+        ctx.uid = uid
+        return x
+
+    @staticmethod
+    def backward(ctx, grad: torch.Tensor):
+        torch.ops.torchprobe.log(grad, f"{ctx.name}.g", ctx.uid)
+        return grad, None
+
+
+_PROBING_ENABLED = False
+
+
+def log_stats(x: torch.Tensor, name: str) -> torch.Tensor:
+    if not _PROBING_ENABLED:
+        return x
+    return _LogStats.apply(x, name)
+
+
+QUANTILES = [
+    0.0000001, 0.000001, 0.00001, 0.0001, 0.001, 0.01,
+    0.05, 0.1, 0.3, 0.5, 0.7, 0.9, 0.95,
+    0.99, 0.999, 0.9999, 0.99999, 0.999999, 0.9999999,
+]
+
+
+@functools.cache
+def _get_quantiles(device: torch.device, dtype) -> torch.Tensor:
+    return torch.tensor(QUANTILES, device=device, dtype=dtype)
+
+
+def _get_stats(x_: torch.Tensor, remove_inf=False) -> Dict[str, Any]:
+    if x_.dtype not in [torch.float, torch.double, torch.float16, torch.bfloat16]:
+        return {}
+    x = x_.flatten()
+    if remove_inf:
+        x = x[x.abs() < float("inf")]
+    if x.dtype is not torch.double:
+        x = x.float()
+    xabs = x.abs()
+    quantiles = _get_quantiles(x.device, x.dtype)
+    mean = x.mean()
+    std = x.std()
+    return {
+        "shape": tuple(x_.shape),
+        "mean": mean,
+        "std": std,
+        "skew": (((x - mean) / std) ** 3).double().mean(),
+        "kurtosis": (((x - mean) / std) ** 4).double().mean(),
+        "abs.mean": xabs.mean(),
+        "max": x.max(),
+        "min": x.min(),
+        # Note: `quantile` takes at most 2**24 elements, see
+        # https://github.com/pytorch/pytorch/issues/64947
+        "quantiles": torch.quantile(x[: 2**24], quantiles),
+    }
+
+
+def _mask_attn_causal_inplace(logits: torch.Tensor, q_idx, q_len, kv_len) -> None:
+    assert logits.ndim == 4
+    logits[:, :, :, q_idx + kv_len - q_len + 1 :] = -math.inf
+
+
+def _mask_attn_logits(
+    logits: torch.Tensor,
+    q_idx: List[int],
+    *,
+    causal: bool,
+    cu_seqlens_q: Optional[torch.Tensor] = None,
+    cu_seqlens_k: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    assert logits.dtype is torch.float32
+    # Handle BlockDiagonalMask
+    if cu_seqlens_q is not None:
+        assert cu_seqlens_k is not None
+        # Expect BHMqMkv
+        assert logits.ndim == 4, logits.shape
+        qs = cu_seqlens_q.tolist()
+        ks = cu_seqlens_k.tolist()
+        q_batchid = []
+        k_batchid = [-2] * logits.shape[-1]
+        q_idx_i = 0
+        for bid, (q0, q1, k0, k1) in enumerate(zip(qs, qs[1:], ks, ks[1:])):
+            for k in range(k0, k1):
+                k_batchid[k] = bid
+            while q_idx_i < len(q_idx) and q_idx[q_idx_i] < q1:
+                q_batchid.append(bid)
+                if causal:
+                    _mask_attn_causal_inplace(
+                        logits[:, :, q_idx_i : q_idx_i + 1, k0:k1],
+                        q_idx[q_idx_i] - q0,
+                        q1 - q0,
+                        k1 - k0,
+                    )
+                q_idx_i += 1
+        mask_out = (
+            torch.tensor(q_batchid, device=logits.device)[None, None, :, None]
+            != torch.tensor(k_batchid, device=logits.device)[None, None, None, :]
+        )
+        logits[mask_out.expand_as(logits)] = -math.inf
+        assert q_idx_i == len(q_idx)
+    elif causal:
+        for q_idx_i in range(len(q_idx)):
+            _mask_attn_causal_inplace(
+                logits[:, :, q_idx_i : q_idx_i + 1, :],
+                q_idx[q_idx_i],
+                logits.shape[2],
+                logits.shape[3],
+            )
+    return logits
+
+
+def _attn_queries_subset(num_queries: int) -> List[int]:
+    return list(range(0, num_queries, max(1, num_queries // 128)))
+
+
+@torch.no_grad()
+def _compute_attn_stats_sdpa(
+    probe,
+    path: str,
+    # supports arguments both cudnn + flash backends
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attn_mask=None,
+    attn_bias=None,
+    dropout_p=0.0,
+    is_causal=False,
+    scale=None,
+    compute_log_sumexp=True,
+    return_debug_mask=False,
+    **kwargs,
+):
+    if scale is None:
+        scale = 1 / (query.shape[-1] ** 0.5)
+    # Filter-out not supported cases
+    if attn_mask is not None or attn_bias is not None or dropout_p != 0.0 or kwargs:
+        probe.store[f"{path}::attn"] = {
+            "query.shape": tuple(query.shape),
+            "key.shape": tuple(key.shape),
+            "value.shape": tuple(value.shape),
+            "attn_mask": attn_mask.shape if attn_mask is not None else None,
+            "dropout_p": dropout_p,
+            "is_causal": is_causal,
+            "scale": scale,
+            "unk_kwargs": list(kwargs.keys()),
+        }
+        return
+    # Take a subset of the queries and compute the logits
+    query_s = _attn_queries_subset(query.shape[-2])
+    logits = query[:, :, query_s] @ key.transpose(-1, -2) * scale
+    logits = _mask_attn_logits(logits.float(), query_s, causal=is_causal)
+    p = logits.float().softmax(-1)
+    masked_logsoft = logits.log_softmax(-1).where((logits > -math.inf), torch.zeros_like(logits))
+    entropy = -(p * masked_logsoft).sum(-1)
+    probe.log_tensor(f"{path}::attn_entropy", entropy)
+    probe.log_tensor(f"{path}::attn_logits", logits, remove_inf=True)
+
+
+@torch.no_grad()
+def _compute_attn_stats_flash(
+    probe,
+    path: str,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    cu_seqlens_q: Optional[torch.Tensor],
+    cu_seqlens_k: Optional[torch.Tensor],
+    seqused_k: Optional[torch.Tensor],
+    max_seqlen_q: int,
+    max_seqlen_k: int,
+    p: float,
+    softmax_scale: float,
+    is_causal: bool,
+    window_left: int,
+    window_right: int,
+    return_softmax: bool,
+    block_tables: Optional[torch.Tensor],
+    unpadded_lse: bool = False,
+) -> None:
+    # Filter-out not supported cases
+    if seqused_k is not None or p != 0.0 or window_left >= 0 or window_right >= 0 or block_tables is not None:
+        probe.store[f"{path}::attn"] = {
+            "query.shape": tuple(query.shape),
+            "key.shape": tuple(key.shape),
+            "value.shape": tuple(value.shape),
+            "op": "flash",
+        }
+        return
+
+    if cu_seqlens_q is not None:
+        assert query.ndim == 3, query.shape
+        query, key, value = query[None], key[None], value[None]
+    assert query.ndim == 4, query.shape
+
+    # Take a subset of the queries and compute the logits
+    query_s = _attn_queries_subset(query.shape[1])
+    logits = query[:, query_s].transpose(1, 2) @ key.transpose(1, 2).transpose(-1, -2) * softmax_scale
+    logits = _mask_attn_logits(
+        logits.float(),
+        query_s,
+        cu_seqlens_q=cu_seqlens_q,
+        cu_seqlens_k=cu_seqlens_k,
+        causal=is_causal,
+    )
+    p = logits.float().softmax(-1)
+    masked_logsoft = logits.log_softmax(-1).where((logits > -math.inf), torch.zeros_like(logits))
+    entropy = -(p * masked_logsoft).sum(-1)
+    probe.log_tensor(f"{path}::attn_entropy", entropy)
+    probe.log_tensor(f"{path}::attn_logits", logits, remove_inf=True)
+
+
+def _tensors_to_python(x):
+    if not isinstance(x, torch.Tensor):
+        return x
+    return x.tolist()
+
+
+# class syntax
+class LinearBwType(Enum):
+    DW = 1
+    DX = 2
+    UNKNOWN = 3
+
+
+class AutoProbeD(TorchDispatchMode):
+    def __init__(self, module: nn.Module, write_file: Optional[str] = None) -> None:
+        self.write_file = Path(write_file) if write_file is not None else None
+        self.write_tensors_tmpdir: Optional[Path] = None
+        self.compile_disabler = TorchCompileDisabler(module)
+        self.mod_tracker = ModuleTracker()
+        self.count_per_path: Dict[str, int] = defaultdict(int)
+        self.store: Dict[str, Dict[str, Any]] = {}
+        self.linear_data: Dict[str, Tuple[Any, Any, Any, Any, Any]] = {}
+        self.uid_to_path: Dict[str, str] = {}
+        self.metadata: Any = None
+        self.enabled = False
+        self.verbose = bool(int(os.environ.get("PROBE_VERBOSE", "0")))
+
+    def __enter__(self):
+        global _PROBING_ENABLED
+        assert not self.enabled, "Entered probe twice"
+        self.compile_disabler.__enter__()
+        self.mod_tracker.__enter__()
+        super().__enter__()
+        self.enabled = True
+        _PROBING_ENABLED = True
+        # self._setup_tensors_logging()
+        return self
+
+    def __exit__(self, *args) -> None:
+        global _PROBING_ENABLED
+        assert self.enabled, "Exiting probe without entering it"
+        super().__exit__(*args)
+        self.mod_tracker.__exit__(*args)
+        self.compile_disabler.__exit__(*args)
+        self._flush_and_clear()
+        _PROBING_ENABLED = False
+        self.enabled = False
+
+    def _setup_tensors_logging(self):
+        if self.write_file is not None:
+            self.write_file.parent.mkdir(exist_ok=True)
+            self.write_tensors_tmpdir = self.write_file.parent / f"{self.write_file.name}-tmp-{str(uuid.uuid4())[:8]}"
+            self.write_tensors_tmpdir.mkdir(exist_ok=True)
+
+    def _flush_and_clear(self) -> None:
+        if self.write_file is not None:
+            dump_data = tree_map(_tensors_to_python, self.store)
+            with self.write_file.open("a") as fd:
+                json.dump(
+                    {
+                        "data": dump_data,
+                        "meta": self.metadata,
+                        "version": 2,
+                        "quantiles": QUANTILES,
+                    },
+                    fd,
+                )
+                fd.write("\n")
+        if self.write_tensors_tmpdir is not None:
+            assert self.write_file is not None
+            dump_dir = self.write_tensors_tmpdir.parent / f"{self.write_file.name}-dump"
+            dump_dir.mkdir(exist_ok=True)
+            dir_name = ""
+            if "it" in self.metadata:
+                dir_name = f"it{int(self.metadata['it']):010}"
+            if dir_name == "" or (dump_dir / dir_name).exists():
+                num_files = len(list(dump_dir.glob(f"{dir_name}v*")))
+                dir_name = f"{dir_name}v{num_files}"
+            dump_dir = dump_dir / dir_name
+            assert not dump_dir.exists()
+            self.write_tensors_tmpdir.rename(dump_dir)
+            self.write_tensors_tmpdir = None
+        self.store.clear()
+        self.count_per_path.clear()
+        self.uid_to_path.clear()
+
+    def _find_bw_path_and_type(self, path: str, out: torch.Tensor, args) -> Tuple[str, LinearBwType]:
+        """
+        We are in the BW pass, and process a GEMM.
+        Let's figure out:
+        (1) The path for the FW pass (might differ in case of ModuleTracker bug)
+        (2) The type of BW pass (eg `dw` or `dx`)
+        """
+
+        def _is_path_correct_dw(path: str) -> bool:
+            # dW.t = dY.t @ X
+            in_shape, w_shape, out_shape, input_sm, weight_sm = self.linear_data[path]
+            return out.shape == (w_shape[1], w_shape[0]) and torch.allclose(input_sm, args[1][:4, :4])
+
+        def _is_path_correct_dx(path: str) -> bool:
+            # dX = dY @ W.t
+            in_shape, w_shape, out_shape, input_sm, weight_sm = self.linear_data[path]
+            return out.shape == in_shape and torch.allclose(weight_sm, args[1][:4, :4])
+
+        if path in self.linear_data:
+            if _is_path_correct_dw(path):
+                return path, LinearBwType.DW
+            if _is_path_correct_dx(path):
+                return path, LinearBwType.DX
+        for candidate_path in self.mod_tracker.parents:
+            if candidate_path not in self.linear_data:
+                continue
+            if _is_path_correct_dw(candidate_path):
+                return candidate_path, LinearBwType.DW
+            if _is_path_correct_dx(candidate_path):
+                return candidate_path, LinearBwType.DX
+        return path, LinearBwType.UNKNOWN
+
+    def log_tensor(self, name: str, x: torch.Tensor, **kwargs) -> None:
+        self.store[name] = _get_stats(x, **kwargs)
+        if self.write_tensors_tmpdir is not None:
+            name_safe = name.replace("::", "__").replace("/", "")
+            torch.save(x, self.write_tensors_tmpdir / f"{name_safe}.pkl")
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        kwargs = kwargs if kwargs else {}
+        path = None
+        # Find longest path
+        for p in self.mod_tracker.parents:
+            if p == "Global":
+                continue
+            if path is None or len(p) > len(path):
+                path = p
+        if path is None:
+            path = "Global"
+        path = path.replace("._checkpoint_wrapped_module", "")
+        out = func(*args, **kwargs)
+
+        # Handle linear layers
+        if func._overloadpacket in [torch.ops.aten.addmm, torch.ops.aten.mm]:
+            weight: torch.Tensor
+            input: torch.Tensor
+            if not self.mod_tracker.is_bw:
+                # (technically, weight is transposed)
+                if func._overloadpacket == torch.ops.aten.addmm:
+                    _bias, input, weight = args[:3]
+                else:
+                    assert func._overloadpacket == torch.ops.aten.mm
+                    input, weight = args[:2]
+                self.log_tensor(f"{path}::in", input)
+                self.log_tensor(f"{path}::w", weight)
+                self.log_tensor(f"{path}::out", out)
+                self.linear_data[path] = (
+                    input.shape,
+                    weight.shape,
+                    out.shape,
+                    input[:4, :4].clone(),
+                    weight[:4, :4].T.clone(),
+                )
+            elif func._overloadpacket == torch.ops.aten.mm:
+                # XXX: Try to find the actual path for the linear layer
+                # This is messed with with Francisco's FSDP sometimes
+                new_path, bwtype = self._find_bw_path_and_type(path, out, args)
+                if new_path != path:
+                    if self.verbose:
+                        print(f"E: Fixing path `{path}` -> `{new_path}")
+                    path = new_path
+
+                if bwtype == LinearBwType.DW:
+                    # dW.t = dY.t @ X
+                    self.log_tensor(f"{path}::w.g", out)
+                elif bwtype == LinearBwType.DX:
+                    # dX = dY @ W.t
+                    self.log_tensor(f"{path}::in.g", out)
+                    self.log_tensor(f"{path}::out.g", args[0])
+        elif func._overloadpacket in [
+            torch.ops.aten._scaled_dot_product_flash_attention,
+            torch.ops.aten._scaled_dot_product_cudnn_attention,
+        ]:
+            _, kwargs = normalize_function(func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True)
+            _compute_attn_stats_sdpa(self, path, **kwargs)
+        elif func._overloadpacket == fmha.flash.FwOp.OPERATOR:
+            _, kwargs = normalize_function(func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True)
+            _compute_attn_stats_flash(self, path, **kwargs)
+        elif func._overloadpacket == torch.ops.torchprobe.log:
+            uid = args[2]
+            path = self.uid_to_path.setdefault(uid, path)
+            self.log_tensor(f"{path}::{args[1]}", args[0])
+        if self.verbose:
+            print(f"{'[BW]' if self.mod_tracker.is_bw else '[FW]'} `{path}`: {func}")
+        return out
+
+
+def _find_all_submodules_compiled(out: List[nn.Module], module: nn.Module) -> None:
+    if module._compiled_call_impl is not None:
+        out.append(module)
+    for c in module.children():
+        _find_all_submodules_compiled(out, module=c)
+
+
+class TorchCompileDisabler:
+    def __init__(self, module: nn.Module) -> None:
+        self.module = module
+        self.submodules_compiled: List[nn.Module] = []
+        self.compiled_call_impl: List[Any] = []
+        self.disable_compile = torch.compiler.disable()
+        torch._dynamo.config.raise_on_ctx_manager_usage = False  # type: ignore
+
+    def __enter__(self) -> None:
+        # Remove all `_compiled_call_impl` attributes to effectively
+        # "undo" compilation
+        self.submodules_compiled.clear()
+        _find_all_submodules_compiled(self.submodules_compiled, self.module)
+        self.compiled_call_impl = [m._compiled_call_impl for m in self.submodules_compiled]
+        for m in self.submodules_compiled:
+            m._compiled_call_impl = None
+        self.disable_compile.__enter__()  # type: ignore
+
+    def __exit__(self, *args) -> None:
+        self.disable_compile.__exit__(*args)  # type: ignore
+        for m, c_impl in zip(self.submodules_compiled, self.compiled_call_impl):
+            m._compiled_call_impl = c_impl
+        self.compiled_call_impl = []
+
+
+Probe = AutoProbeD
+
+# EXAMPLE USAGE
+d = 512
+seqlen = 4
+bs = 2
+
+
+class Attention1(nn.Module):
+    def forward(self, x):
+        attn_bias = fmha.attn_bias.LowerTriangularFromBottomRightMask()
+        return fmha.memory_efficient_attention(x, x, x, attn_bias=attn_bias).reshape([x.shape[0], seqlen, -1])
+
+
+class Attention2(nn.Module):
+    def forward(self, x):
+        attn_bias = fmha.attn_bias.BlockDiagonalMask.from_seqlens([seqlen] * bs).make_causal()
+        xr = x.reshape([1, 2 * seqlen, x.shape[2], x.shape[3]])
+        return fmha.memory_efficient_attention(xr, xr, xr, attn_bias=attn_bias).reshape([x.shape[0], seqlen, -1])
+
+
+class AttentionSDPA(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.wo = nn.Linear(d, d)
+
+    def forward(self, x):
+        x = x.transpose(1, 2)
+        return self.wo(F.scaled_dot_product_attention(x, x, x).transpose(1, 2).reshape([x.shape[0], seqlen, -1]))
+
+
+class AttentionSDPAFlash(AttentionSDPA):
+    def forward(self, x):
+        x = x.transpose(1, 2)
+        with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
+            return self.wo(F.scaled_dot_product_attention(x, x, x).transpose(1, 2).reshape([x.shape[0], seqlen, -1]))
+
+
+class Model(nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.head = nn.Linear(d, 16)
+        self.trunk = nn.Sequential(
+            nn.Linear(d, d),
+            nn.Linear(d, d),
+        )
+        self.q_proj = nn.Linear(d, d, bias=False)
+        self.trunk.compile()
+        self.attn1 = Attention1()
+        self.attn2 = Attention2()
+        self.attnSDPA = AttentionSDPA()
+        self.attnSDPAflash = AttentionSDPAFlash()
+
+    def forward(self, x):
+        B, nHeads, D = x.shape[0], d // 64, 64
+        x = self.q_proj(x).reshape([B, seqlen, nHeads, D])
+        x = self.attn1(x) + self.attn2(x) + self.attnSDPA(x) + self.attnSDPAflash(x)
+        x = log_stats(x, "attns_out")
+        return self.head(self.trunk(x))
+
+
+def test_masking() -> None:
+    q_seqlen = [1, 1, 14, 12]
+    kv_seqlen = [2, 2, 14, 18]
+    attn_bias = fmha.attn_bias.BlockDiagonalCausalMask.from_seqlens(q_seqlen, kv_seqlen).make_causal_from_bottomright()
+    logits = torch.randn([1, 1, sum(q_seqlen), sum(kv_seqlen)], dtype=torch.float32, device="cuda")
+    bias = attn_bias.materialize(logits.shape, dtype=logits.dtype, device=logits.device)
+    logits_masked = logits.clone()
+    _mask_attn_logits(
+        logits_masked,
+        list(range(logits.shape[2])),
+        causal=True,
+        cu_seqlens_q=attn_bias.q_seqinfo.seqstart,
+        cu_seqlens_k=attn_bias.k_seqinfo.seqstart,
+    )
+    assert (logits + bias == logits_masked).all().item()
+
+
+def test_toy_model() -> None:
+    # Test masking
+    kw = dict(device="cuda", dtype=torch.float16)
+    x = torch.randn([bs, seqlen, d], **kw)
+    m = Model()
+    m.head = checkpoint_wrapper(m.head, checkpoint_impl=CheckpointImpl.NO_REENTRANT, preserve_rng_state=False)
+    m.to(**kw)
+    m.compile()
+    optim = torch.optim.SGD(m.parameters(), lr=0.0)
+    probe = AutoProbeD(m, "./probe.json")
+
+    for i in range(4):
+        with contextlib.ExitStack() as stack:
+            print(f"########### STEP {i}")
+            if i % 4 == 1:
+                stack.enter_context(probe)
+                probe.metadata = {"it": i}
+            y = m(x)
+            g = torch.randn_like(y)
+            y.backward(g)
+            if i % 4 == 1:
+                assert probe.enabled
+                # Make sure we registered all linears
+                print(list(probe.store.keys()))
+                for key in [
+                    "Model::attns_out",
+                    "Model::attns_out.g",
+                    "Model.attn1::attn_logits",
+                    "Model.attn2::attn_logits",
+                    "Model.attnSDPA::attn_logits",
+                    "Model.attnSDPAflash::attn_logits",
+                    "Model.head::w",
+                    "Model.head::w.g",
+                    "Model.head::in",
+                    "Model.head::in.g",
+                    "Model.head::out",
+                    "Model.head::out.g",
+                    "Model.trunk.0::in",
+                    "Model.trunk.1::in",
+                ]:
+                    assert key in probe.store, f"Missing key: '{key}'"
+                # .. and that the values are correct
+                for key, tensor in [
+                    ("Model.head::w", m.head.weight),
+                    ("Model.head::w.g", m.head.weight.grad),
+                    ("Model.q_proj::in", x),
+                    ("Model.q_proj::w.g", m.q_proj.weight.grad),
+                    ("Model.head::out", y),
+                    ("Model.head::out.g", g),
+                ]:
+                    assert key in probe.store, f"Missing key: '{key}'"
+                    assert torch.allclose(
+                        probe.store[key]["abs.mean"], tensor.float().abs().mean()
+                    ), f"'{key}' mismatches"
+                # Check we don't have `nans`
+                for key, value in probe.store.items():
+                    if "abs.mean" in value:
+                        assert math.isfinite(value["abs.mean"].item()), f"Inf/Nan for {key}"
+            optim.step()
+            optim.zero_grad()
\ No newline at end of file
diff --git a/core/trainer/profiling.py b/core/trainer/profiling.py
new file mode 100644
index 0000000..a27dce8
--- /dev/null
+++ b/core/trainer/profiling.py
@@ -0,0 +1,137 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
+
+import contextlib
+from dataclasses import dataclass
+import os
+from pathlib import Path
+import torch.distributed
+import logging
+
+from torch.profiler.profiler import profile
+import xformers.profiler
+from xformers.profiler import (
+    MemSnapshotsProfiler,
+    PyTorchProfiler,
+)
+
+from core.trainer.distributed import get_is_master
+
+import wandb
+
+
+@dataclass
+class ProfilerArgs:
+    run: bool = False
+    trace_folder: str = "profiling"
+    mem_warmup: int = 100
+    mem_steps: int = 2
+    profile_warmup: int = 102
+    profile_steps: int = 2
+
+
+logger = logging.getLogger()
+
+
+def perfetto_to_html(json_file, html_file):
+    import viztracer
+    import gzip
+    import string
+
+    root = os.path.dirname(viztracer.__file__)
+    sub = {}
+    json_file = gzip.open(json_file) if ".gz" in str(json_file) else open(json_file)
+    with open(
+        os.path.join(root, "html/trace_viewer_embedder.html"), encoding="utf-8"
+    ) as f:
+        tmpl = f.read()
+    with open(os.path.join(root, "html/trace_viewer_full.html"), encoding="utf-8") as f:
+        sub["trace_viewer_full"] = f.read()
+    with json_file as j:
+        content = j.read()
+        if isinstance(content, bytes):
+            content = content.decode("utf-8")
+        sub["json_data"] = content.replace("</script>", "<\\/script>")  # type: ignore
+    with open(html_file, "w+", encoding="utf-8") as output_file:
+        output_file.write(string.Template(tmpl).substitute(sub))
+
+
+class PyTorchProfilerWandb(PyTorchProfiler):
+    def __init__(self, main_profiler) -> None:
+        self.main_profiler = main_profiler
+        self.num_steps = 0
+        self.pytorch_profiler = torch.profiler.profile(
+            on_trace_ready=self._on_trace,
+            profile_memory=True,
+            record_shapes=True,
+            # With stack gives huge profile traces
+            # and bugs out because of some non ascii
+            # character somewhere in pytorch
+            with_stack=False,
+            with_flops=True,
+            activities=self.ACTIVITIES,
+        )
+
+    def _analyze_trace(self, prof: profile):
+        logger.info("Begin analyze trace")
+        super()._analyze_trace(prof)
+        logger.info("End analyze trace")
+
+    def _on_trace(self, prof: torch.profiler.profiler.profile) -> None:
+        super()._on_trace(prof)
+        if get_is_master() and wandb.run is not None:
+            filename = list(
+                Path(self.main_profiler.output_dir).glob(
+                    "profile_CPU_CUDA*/*.pt.trace.json*"
+                )
+            )[0]
+            html_path = str(filename).replace(".json", ".html")
+            perfetto_to_html(filename, html_path)
+            wandb.log({"profile_trace": wandb.Html(html_path)})
+
+
+class MemSnapshotsProfilerWandb(MemSnapshotsProfiler):
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        super().__exit__(exc_type, exc_val, exc_tb)
+        if get_is_master() and wandb.run is not None:
+            filename = list(
+                Path(self.main_profiler.output_dir).glob("memory_trace_plot/*.html")
+            )[0]
+            wandb.log({"memory_trace": wandb.Html(open(filename), inject=False)})
+
+
+@contextlib.contextmanager
+def maybe_run_profiler(dump_dir, module, config: ProfilerArgs):
+    # get user defined profiler settings
+
+    if config.run:
+        trace_dir = os.path.join(dump_dir, config.trace_folder)
+
+        logger.info(f"Profiling active.  Traces will be saved at {trace_dir}")
+
+        if get_is_master() and not os.path.exists(trace_dir):
+            os.makedirs(trace_dir)
+        if torch.distributed.is_initialized():
+            torch.distributed.barrier()
+
+        with xformers.profiler.profile(
+            output_dir=trace_dir,
+            module=module,
+            schedule=[
+                (
+                    MemSnapshotsProfilerWandb,
+                    config.mem_warmup,
+                    config.mem_warmup + config.mem_steps,
+                ),
+                (
+                    PyTorchProfilerWandb,
+                    config.profile_warmup,
+                    config.profile_warmup + config.profile_steps,
+                ),
+            ],
+        ) as profiler:
+            yield profiler
+
+    else:
+        torch_profiler = contextlib.nullcontext()
+        yield None
\ No newline at end of file
diff --git a/core/trainer/schedulers.py b/core/trainer/schedulers.py
new file mode 100644
index 0000000..340d3f0
--- /dev/null
+++ b/core/trainer/schedulers.py
@@ -0,0 +1,106 @@
+from torch.optim import AdamW, Optimizer
+from torch.optim.lr_scheduler import LambdaLR
+import math
+
+class WarmupCosineScheduler(LambdaLR):
+    """Linear warmup and cosine decay scheduler."""
+
+    def __init__(
+        self,
+        optimizer: AdamW,
+        warmup_steps: int,
+        cycle_length: int,
+        theta: int,
+        total_steps: int,
+        min_lr: float = 0.1,
+        last_epoch: int = -1
+    ):
+        """
+        Initialize warmup and decay scheduler.
+
+        Args:
+            optimizer: AdamW optimizer
+            warmup_steps: Number of warmup steps
+            total_steps: Total number of training steps
+            min_lr_ratio: Minimum learning rate ratio compared to initial lr
+            last_epoch: The index of the last epoch
+        """
+        self.cycle_length = cycle_length
+        self.theta = theta
+        self.warmup_steps = warmup_steps
+        self.total_steps = total_steps
+        self.min_lr = min_lr
+        super().__init__(optimizer, self.lr_lambda, last_epoch)
+
+    def lr_lambda(self, current_step: int) -> float:
+        """Calculate lr multiplier based on current step."""
+        sign = ((current_step // (self.total_steps * self.cycle_length)) % 2) * -2 + 1
+        if current_step < self.warmup_steps:
+            return float(current_step) / self.warmup_steps
+        elif current_step <= self.total_steps:
+            s = float(current_step - self.warmup_steps) / (self.total_steps - self.warmup_steps)
+            return self.min_lr + 0.5 * (1 - self.min_lr) * (sign * math.cos(math.pi * s ** self.theta / self.cycle_length) + 1)
+
+        else:
+            return self.min_lr
+
+
+
+class WarmUpLinearScheduler(LambdaLR):
+    def __init__(self, optimizer: Optimizer, warmup_step: int, total_steps: int, min_lr: int = 0.1, last_epoch: int = -1):
+        self.warmup_step = warmup_step
+        self.total_steps = total_steps
+        self.min_lr = min_lr
+        self.last_epoch = last_epoch
+        super().__init__(optimizer, self.lr_lambda, last_epoch)
+
+    def lr_lambda(self, current_step: int) -> float:
+        if current_step < self.warmup_step:
+            return float(current_step) / self.warmup_step
+        elif self.warmup_step <= current_step <= self.total_steps:
+            value = float(current_step - self.warmup_step) / (self.total_steps - self.warmup_step)
+            return value * self.min_lr + (1 - value)
+        else:
+            return self.min_lr
+
+
+
+class WSDScheduler(LambdaLR):
+    def __init__(self, optimizer: Optimizer,
+                 warmup_step: int,
+                 total_steps: int,
+                 cycle_length: int,
+                 decay_fraction: float,
+                 max_lr: float,
+                 min_lr: int = 0.1,
+                 last_epoch: int = -1):
+        self.warmup_step = warmup_step
+        self.total_steps = total_steps
+        self.cycle_length = cycle_length
+        self.decay_fraction = decay_fraction
+        self.min_lr = min_lr
+        self.max_lr = max_lr
+        self.last_epoch = last_epoch
+        super().__init__(optimizer, self.lr_lambda, last_epoch)
+
+    def lr_lambda(self, current_step: int) -> float:
+        cycle_num = current_step // int(self.total_steps * self.cycle_length) + 1
+        curr_n_steps = int(self.total_steps * self.cycle_length) * cycle_num
+        decay_length = int(curr_n_steps * self.decay_fraction)
+        if current_step == self.total_steps:
+            cycle_num -= 1
+            curr_n_steps = self.total_steps
+
+        if current_step < self.warmup_step:
+            lr = self.max_lr * (float(current_step) / self.warmup_step)
+        elif current_step <= curr_n_steps - decay_length:
+            lr = self.max_lr
+        elif curr_n_steps - decay_length < current_step <= curr_n_steps:
+            step_in_decay = current_step - (curr_n_steps - decay_length)
+            progress = step_in_decay / decay_length
+            lr = 1 / ((progress * (1 / self.min_lr)) + ((1 - progress) * (1 / self.max_lr)))
+        else:
+            lr = self.min_lr
+        return lr
+
+
diff --git a/core/trainer/stool.py b/core/trainer/stool.py
new file mode 100644
index 0000000..444c6b7
--- /dev/null
+++ b/core/trainer/stool.py
@@ -0,0 +1,238 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+from dataclasses import dataclass
+import json
+import os
+import shutil
+import subprocess
+from typing import Dict, Any
+
+from omegaconf import OmegaConf
+
+from core.trainer.args import dataclass_from_dict
+
+
+@dataclass
+class StoolArgs:
+    config: Any = None
+    launcher: str = "sbatch"  # Can be sbatch or bash if already in salloc
+    script: str = "apps.main.train"  # The script to run.
+    copy_code: bool = True  # Wether to copy code to dump dir
+    dirs_exists_ok: bool = (
+        False  # Wether to copy new code and config and run regardless that dir exists
+    )
+    override: bool = False  # Wether to delete dump dir and restart
+    nodes: int = -1  # The number of nodes to run the job on.
+    ngpu: int = 8  # The number of GPUs required per node.
+    ncpu: int = 16  # The number of CPUs allocated per GPU.
+    mem: str = ""  # The amount of memory to allocate.
+    anaconda: str = "default"  # The path to the anaconda environment.
+    constraint: str = ""  # The constraint on the nodes.
+    exclude: str = ""  # The nodes to exclude.
+    time: int = -1  # The time limit of the job (in minutes).
+    account: str = ""
+    qos: str = ""
+    partition: str = "learn"
+    stdout: bool = False
+
+
+SBATCH_COMMAND = """#!/bin/bash
+
+{exclude}
+{qos}
+{account}
+{constraint}
+#SBATCH --job-name={name}
+#SBATCH --nodes={nodes}
+#SBATCH --gres=gpu:{ngpus}
+#SBATCH --cpus-per-gpu={ncpu}
+#SBATCH --time={time}
+#SBATCH --partition={partition}
+#SBATCH --mem={mem}
+
+#SBATCH --output={dump_dir}/logs/%j/%j.stdout
+#SBATCH --error={dump_dir}/logs/%j/%j.stderr
+
+#SBATCH --open-mode=append
+#SBATCH --signal=USR2@120
+#SBATCH --distribution=block
+
+# Mimic the effect of "conda init", which doesn't work for scripts
+eval "$({conda_exe} shell.bash hook)"
+source activate {conda_env_path}
+
+{go_to_code_dir}
+
+export OMP_NUM_THREADS=1
+export LAUNCH_WITH="SBATCH"
+export DUMP_DIR={dump_dir}
+srun {log_output} -n {tasks} -N {nodes_per_run} python -u -m {script} config=$DUMP_DIR/base_config.yaml
+"""
+
+
+def copy_dir(input_dir: str, output_dir: str) -> None:
+    print(f"Copying : {input_dir}\n" f"to      : {output_dir} ...")
+    assert os.path.isdir(input_dir), f"{input_dir} is not a directory"
+    assert os.path.isdir(output_dir), f"{output_dir} is not a directory"
+    rsync_cmd = (
+        f"rsync -arm --copy-links "
+        f"--include '**/' "
+        f"--include '*.py' "
+        f"--exclude='*' "
+        f"{input_dir}/ {output_dir}"
+    )
+    print(f"Copying command: {rsync_cmd}")
+    subprocess.call([rsync_cmd], shell=True)
+    print("Copy done.")
+
+
+def retrieve_max_time_per_partition() -> Dict[str, int]:
+    # retrieve partition max times (a bit slow)
+
+    sinfo = json.loads(subprocess.check_output("sinfo --json", shell=True))["sinfo"]
+    max_times: Dict[str, int] = {}
+
+    for info in sinfo:
+        if info["partition"]["maximums"]["time"]["infinite"]:
+            max_times[info["partition"]["name"]] = 14 * 24 * 60  # 14 days
+        else:
+            max_times[info["partition"]["name"]] = info["partition"]["maximums"][
+                "time"
+            ][
+                "number"
+            ]  # in minutes
+
+    return max_times
+
+
+def validate_args(args) -> None:
+    # Set maximum time limit if not specified
+    if args.time == -1:
+        max_times = retrieve_max_time_per_partition()
+        args.time = max_times.get(
+            args.partition, 3 * 24 * 60
+        )  # Default to 3 days if not found
+        print(
+            f"No time limit specified, using max time for partitions: {args.time} minutes"
+        )
+
+    if args.constraint:
+        args.constraint = f"#SBATCH --constraint={args.constraint}"
+
+    if args.account:
+        args.account = f"#SBATCH  --account={args.account}"
+
+    if args.qos:
+        args.qos = f"#SBATCH --qos={args.qos}"
+
+    if getattr(args, "exclude", ""):
+        args.exclude = f"#SBATCH --exclude={args.exclude}"
+
+    if hasattr(args, "anaconda") and args.anaconda:
+        if args.anaconda == "default":
+            args.anaconda = (
+                subprocess.check_output("which python", shell=True)
+                .decode("ascii")
+                .strip()
+            )
+        else:
+            args.anaconda = f"{args.anaconda}/bin/python"
+        assert os.path.isfile(args.anaconda)
+
+    args.mem = args.mem or "0"
+
+    assert args.partition
+    assert args.ngpu > 0
+    assert args.ncpu > 0
+    assert args.nodes > 0
+    assert args.time > 0
+    assert args.partition
+
+
+def launch_job(args: StoolArgs):
+    # Set up args default and validate them depending on the cluster or partition requested
+    validate_args(args)
+    dump_dir = args.config["dump_dir"]
+    job_name = args.config["name"]
+    print("Creating directories...")
+    os.makedirs(dump_dir, exist_ok=args.dirs_exists_ok or args.override)
+    if args.override:
+        confirm = input(
+            f"Are you sure you want to delete the directory '{dump_dir}'? This action cannot be undone. (yes/no): "
+        )
+        if confirm.lower() == "yes":
+            shutil.rmtree(dump_dir)
+            print(f"Directory '{dump_dir}' has been deleted.")
+        else:
+            print("Operation cancelled.")
+            return
+    if args.copy_code:
+        os.makedirs(f"{dump_dir}/code", exist_ok=args.dirs_exists_ok)
+        print("Copying code ...")
+        copy_dir(os.getcwd(), f"{dump_dir}/code")
+
+    print("Saving config file ...")
+    with open(f"{dump_dir}/base_config.yaml", "w") as cfg:
+        cfg.write(OmegaConf.to_yaml(args.config))
+
+    conda_exe = os.environ.get("CONDA_EXE", "conda")
+    conda_env_path = os.path.dirname(os.path.dirname(args.anaconda))
+    log_output = (
+        "-o $DUMP_DIR/logs/%j/%j_%t.out -e $DUMP_DIR/logs/%j/%j_%t.err"
+        if not args.stdout
+        else ""
+    )
+    sbatch = SBATCH_COMMAND.format(
+        name=job_name,
+        script=args.script,
+        dump_dir=dump_dir,
+        nodes=args.nodes,
+        tasks=args.nodes * args.ngpu,
+        nodes_per_run=args.nodes,
+        ngpus=args.ngpu,
+        ncpu=args.ncpu,
+        mem=args.mem,
+        qos=args.qos,
+        account=args.account,
+        constraint=args.constraint,
+        exclude=args.exclude,
+        time=args.time,
+        partition=args.partition,
+        conda_exe=conda_exe,
+        conda_env_path=conda_env_path,
+        log_output=log_output,
+        go_to_code_dir=f"cd {dump_dir}/code/" if args.copy_code else "",
+    )
+
+    print("Writing sbatch command ...")
+    with open(f"{dump_dir}/submit.slurm", "w") as f:
+        f.write(sbatch)
+
+    print("Submitting job ...")
+    os.system(f"{args.launcher} {dump_dir}/submit.slurm")
+
+    print("Done.")
+
+
+if __name__ == "__main__":
+    """
+    The command line interface here uses OmegaConf https://omegaconf.readthedocs.io/en/2.3_branch/usage.html#from-command-line-arguments
+    This accepts arguments as a dot list
+    So if the dataclass looks like
+
+    @dataclass
+    class DummyArgs:
+        name: str
+        mode: LMTransformerArgs
+
+    @dataclass
+    class LMTransformerArgs:
+        dim: int
+
+    Then you can pass model.dim=32 to change values in LMTransformerArgs
+    or just name=tictac for top level attributes.
+    """
+    args = OmegaConf.from_cli()
+    args.config = OmegaConf.load(args.config)
+    args = dataclass_from_dict(StoolArgs, args)
+    launch_job(args)
\ No newline at end of file
diff --git a/core/trainer/tokenizer.py b/core/trainer/tokenizer.py
new file mode 100644
index 0000000..a3c2499
--- /dev/null
+++ b/core/trainer/tokenizer.py
@@ -0,0 +1,203 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+import abc
+from copy import copy
+from dataclasses import dataclass
+from pathlib import Path
+from typing import List, Optional, Tuple
+import logging
+import os
+
+from sentencepiece import SentencePieceProcessor
+import tiktoken
+from tiktoken.load import load_tiktoken_bpe
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class TokenizerArgs:
+    name: str = "bytes"
+    path: Optional[str] = None
+
+
+class Tokenizer(abc.ABC):
+    @abc.abstractmethod
+    def encode(self, tokens, add_bos, add_eos):
+        pass
+
+    @abc.abstractmethod
+    def decode(self, tokens):
+        pass
+
+    @abc.abstractmethod
+    def get_token_offsets(
+        self, text: str, tokens: Optional[List[int]] = None
+    ) -> Tuple[List[str], List[int]]:
+        """Return the offsets of the tokens in the original text. Only used for evaluation."""
+        pass
+
+
+class MockTokenizer(Tokenizer):
+    n_words: int = 256
+
+    def encode(self, tokens, add_bos, add_eos):
+        return tokens
+
+
+class ByteTokenizer(Tokenizer):
+    def __init__(self):
+        self.bos_id = 256
+        self.eos_id = 257
+        self.n_words = 258
+
+    def encode(self, s: str, add_bos: bool = False, add_eos: bool = False):
+        tokens = [self.bos_id] * add_bos + list(s.encode()) + [self.eos_id] * add_eos
+        return tokens
+
+    def decode(self, tokens: List[int]):
+        byte_tokens = bytes([t for t in tokens if t < 256])
+        return byte_tokens.decode("utf-8", errors="backslashreplace")
+
+    def get_token_offsets(
+        self, text: str, tokens: Optional[List[int]] = None
+    ) -> Tuple[List[str], List[int]]:
+        if tokens is None:
+            tokens = self.encode(text)
+
+        decoded_chars, offsets = [], []
+        byte_pos = 0
+        for token in tokens:
+            if token < 256:
+                char = bytes([token]).decode("utf-8", errors="ignore")
+                if char:
+                    decoded_chars.append(char)
+                    offsets.append(byte_pos)
+                byte_pos += len(char.encode("utf-8"))
+
+        return decoded_chars, offsets
+
+
+class SentencePieceTokenizer(Tokenizer):
+    def __init__(self, model_path: str) -> None:
+        assert os.path.isfile(model_path), model_path
+        self.sp_model = SentencePieceProcessor(model_file=model_path)
+
+        logger.info(f"Reloaded SentencePiece model from {model_path}")
+
+        # BOS / EOS token IDs
+        self.n_words: int = self.sp_model.vocab_size()
+        self.bos_id: int = self.sp_model.bos_id()
+        self.eos_id: int = self.sp_model.eos_id()
+        self.pad_id: int = self.sp_model.pad_id()
+        logger.info(
+            f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}"
+        )
+        assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
+
+    def vocab_size(self):
+        return self.n_words
+
+    def encode(self, s: str, add_bos: bool, add_eos: bool):
+        assert type(s) is str
+        tokens = (
+            [self.bos_id] * add_bos + self.sp_model.encode(s) + [self.eos_id] * add_eos
+        )
+        return tokens
+
+    def decode(self, tokens: List[int]):
+        return self.sp_model.decode(tokens)
+
+    def get_token_offsets(
+        self, text: str, tokens: Optional[List[int]] = None
+    ) -> Tuple[List[str], List[int]]:
+        pieces = self.sp_model.encode_as_immutable_proto(text).pieces
+        substrs = [p.surface for p in pieces]
+        offsets = [p.begin for p in pieces]
+        return substrs, offsets
+
+
+DEFAULT_TIKTOKEN_PATTERN = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
+DEFAULT_TIKTOKEN_SPECIAL_TOKENS = {
+    "<|begin_of_text|>": 0,
+    "<|end_of_text|>": 1,
+    "<|fim_prefix|>": 2,
+    "<|fim_middle|>": 3,
+    "<|fim_end_fill|>": 253,
+    "<|fim_pad|>": 254,
+    "<|fim_suffix|>": 255,
+}
+TIKTOKEN_MAX_ENCODE_CHARS = 400_000
+
+
+class TikTokenTokenizer(Tokenizer):
+
+    def __init__(self, model_path: str) -> None:
+        mergeable_ranks = load_tiktoken_bpe(model_path)
+        all_special_tokens_with_ids = copy(DEFAULT_TIKTOKEN_SPECIAL_TOKENS)
+        missing_ids = set(range(256)) - set(all_special_tokens_with_ids.values())
+        for id in missing_ids:
+            all_special_tokens_with_ids[f"<|reserved_special_token_{id}|>"] = id
+        for name in all_special_tokens_with_ids:
+            all_special_tokens_with_ids[name] += len(mergeable_ranks)
+
+        self.tkt_model = tiktoken.core.Encoding(
+            name=Path(model_path).stem,
+            pat_str=DEFAULT_TIKTOKEN_PATTERN,
+            mergeable_ranks=mergeable_ranks,
+            special_tokens=all_special_tokens_with_ids,
+        )
+
+        self.bos_id: int = self.tkt_model.encode_single_token("<|begin_of_text|>")
+        self.eos_id: int = self.tkt_model.encode_single_token("<|end_of_text|>")
+
+        self.n_words: int = self.tkt_model.n_vocab
+
+        logger.info(
+            f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}"
+        )
+
+    def encode(self, s: str, add_bos: bool, add_eos: bool):
+        assert isinstance(s, str)
+
+        subs = []
+        for i in range(0, len(s), TIKTOKEN_MAX_ENCODE_CHARS):
+            subs.append(s[i : i + TIKTOKEN_MAX_ENCODE_CHARS])
+        return (
+            [self.bos_id] * add_bos
+            + sum(self.tkt_model.encode_ordinary_batch(subs), start=[])
+            + [self.eos_id] * add_eos
+        )
+
+    def decode(self, tokens: List[int]):
+        return self.tkt_model.decode(tokens)
+
+    def get_token_offsets(
+        self, text: str, tokens: Optional[List[int]] = None
+    ) -> Tuple[List[str], List[int]]:
+        if tokens is not None:
+            token_bytes = self.tkt_model.decode_tokens_bytes(tokens)
+        else:
+            token_bytes = self.tkt_model.decode_tokens_bytes(
+                self.tkt_model.encode(text, allowed_special="all")
+            )
+
+        text_len, offsets = 0, []
+        for token in token_bytes:
+            offsets.append(max(0, text_len - (0x80 <= token[0] < 0xC0)))
+            text_len += sum(1 for c in token if not 0x80 <= c < 0xC0)
+        substrs = [text[s:e] for s, e in zip(offsets, offsets[1:] + [None])]
+        return substrs, offsets
+
+
+def build_tokenizer(name: str, path: Optional[str] = None) -> Tokenizer:
+    if name == "bytes":
+        return ByteTokenizer()
+    elif name == "mock":
+        return MockTokenizer()
+    elif name == "sp":
+        return SentencePieceTokenizer(path)
+    elif name == "tiktoken":
+        return TikTokenTokenizer(path)
+    else:
+        raise NotImplementedError(f"{name} tokenizer type is not implemented")
\ No newline at end of file
diff --git a/core/trainer/train.py b/core/trainer/train.py
new file mode 100644
index 0000000..2ace94d
--- /dev/null
+++ b/core/trainer/train.py
@@ -0,0 +1,663 @@
+import contextlib
+from copy import deepcopy
+from functools import partial
+import json
+from dataclasses import dataclass, field
+from copy import deepcopy
+import gc
+import logging
+import os
+import sys
+import time
+from contextlib import ExitStack
+from dataclasses import asdict, dataclass, field
+from pathlib import Path
+from timeit import default_timer as timer
+from typing import Any, Dict, List, Optional
+
+import numpy as np
+from omegaconf import OmegaConf
+import torch
+import torch.distributed
+import torch.nn.functional as F
+import xformers.profiler
+from torch.optim import lr_scheduler
+from torch.distributed.checkpoint.stateful import Stateful
+from torch.distributed._tensor import DTensor
+from multiprocessing import Process, Queue, Event
+from queue import Full, Empty
+from multiprocessing.synchronize import Event as EventClass
+import os
+from pathlib import Path
+from queue import Full
+from typing import Dict, Any, Iterator, Optional, TypedDict
+from core.trainer.transformer import LMMTPArgs
+from torch.distributed.checkpoint.stateful import Stateful
+from core.trainer.distributed import (
+    DistributedArgs,
+    EnvironmentArgs,
+    init_signal_handler,
+    dist_mean_dict,
+    get_device_mesh,
+    get_is_master,
+    get_world_size,
+    parallelize_model,
+    setup_env,
+    setup_torch_distributed,
+    clean_env,
+    requeue_slurm_job,
+    check_model_value_range,
+)
+from core.trainer.args import dataclass_from_dict, dump_config, flatten_dict
+from core.trainer.checkpointer import CheckpointArgs, CheckpointManager, load_from_checkpoint
+from core.trainer.dataloader import (
+    DataArgs,
+    PackTokensState,
+    build_dataloader_from_args,
+    init_dataloader_state_from_args,
+)
+from core.trainer.logger import init_logger
+from core.trainer.metrics import (
+    GPUMemoryMonitor,
+    LoggingArgs,
+    MetricLogger,
+    get_num_params,
+)
+from core.trainer.optim import OptimArgs, build_optimizer
+from core.trainer.profiling import ProfilerArgs, maybe_run_profiler
+from core.trainer.dataloader import build_tokenizer
+from core.trainer.transformer import (
+    LMMTPArgs,
+    LMTransformer,
+    get_num_flop_per_token,
+    build_fsdp_grouping_plan,
+)
+from core.trainer.probe import AutoProbeD
+from core.trainer.stool import StoolArgs, launch_job
+
+
+import wandb
+
+logger = logging.getLogger()
+
+
+@dataclass
+class TrainArgs:
+    name: str = "lingua_zoho"
+    dump_dir: str = "/workspace/AI-Uncomplicated/"
+
+    seed: int = 42
+
+    # Number of gradient accumulation steps
+    # Total batch size is batch_size*grad_acc_steps
+    grad_acc_steps: int = 1
+
+    gc_collect_freq: int = 1000
+    probe_freq: Optional[int] = None
+
+    # Nb optimizer steps to take
+    steps: int = 1000
+
+    data: DataArgs = field(default_factory=DataArgs)
+    optim: OptimArgs = field(default_factory=OptimArgs)
+    model: LMMTPArgs = field(default_factory=LMMTPArgs)
+    distributed: DistributedArgs = field(default_factory=DistributedArgs)
+    env: EnvironmentArgs = field(default_factory=EnvironmentArgs)
+
+    checkpoint: CheckpointArgs = field(default_factory=CheckpointArgs)
+    profiling: ProfilerArgs = field(default_factory=ProfilerArgs)
+    logging: LoggingArgs = field(default_factory=LoggingArgs)
+
+    # If set to None, eval is run locally otherwise it launches a new job with the given number of gpus
+    async_eval_gpus: Optional[int] = None
+    eval: Optional[Any] = None
+
+
+@dataclass
+class TrainState(Stateful):
+    step: int  # Nb of steps taken by the optimizer
+    acc_step: int  # Nb of accumulation steps done since last optimizer step
+    scheduler: lr_scheduler.LambdaLR
+    data_loader_state: PackTokensState
+
+    def state_dict(self) -> Dict[str, Any]:
+        return {
+            "step": self.step,
+            "acc_step": self.acc_step,
+            "data_loader_state": self.data_loader_state,
+            "scheduler": self.scheduler.state_dict(),
+        }
+
+    def load_state_dict(self, state_dict):
+        self.step = state_dict["step"]
+        self.acc_step = state_dict["acc_step"]
+        self.data_loader_state = PackTokensState(**state_dict["data_loader_state"])
+        self.scheduler.load_state_dict(state_dict["scheduler"])
+
+
+def validate_train_args(args: TrainArgs, output_size: int):
+    if args.model.vocab_size < 0:
+        logger.info(f"Setting model output size to {output_size}")
+        args.model.vocab_size = output_size
+    assert (
+        args.model.vocab_size == output_size
+    ), "Vocab size should be the same as output size"
+
+    assert args.dump_dir, "Dump dir not set"
+
+    if args.checkpoint.path is None:
+        logger.info(f"=============args is {args}==============")
+        # logger.info(f"Setting checkpoint path to {str(Path(args.dump_dir) / "checkpoints")}")
+        args.checkpoint.path = str(Path(args.dump_dir) / "checkpoints")
+
+    for source in args.data.sources:
+        data_path = os.path.join(args.data.root_dir, source)
+        assert os.path.exists(data_path), f"{data_path} doesn't exist"
+
+    if (
+        args.distributed.dp_replicate
+        * args.distributed.dp_shard
+        != get_world_size()
+    ):
+        assert get_world_size() % args.distributed.dp_shard == 0
+        args.distributed.dp_replicate = get_world_size() // args.distributed.dp_shard
+
+        logger.warning(
+            f"Setting Data Parallel size to {args.distributed.dp_replicate * args.distributed.dp_shard}"
+        )
+        assert (
+            args.distributed.dp_replicate
+            * args.distributed.dp_shard
+            == get_world_size()
+        )
+
+        if args.distributed.fsdp_type == "no_shard":
+            assert (
+                args.distributed.dp_shard == 1
+                and args.distributed.dp_replicate == get_world_size()
+            )
+
+    args.model.max_seqlen = args.data.seq_len
+
+
+    assert (
+        args.probe_freq != args.profiling.mem_steps
+    ), "Don't profile during probe step"
+    assert (
+        args.probe_freq != args.profiling.profile_steps
+    ), "Don't profile during probe step"
+    if args.logging.wandb is not None:
+        args.logging.wandb.name = args.name
+
+    if args.probe_freq is not None:
+        assert (
+            args.distributed.selective_activation_checkpointing is False
+        ), "Probing not supported with selective activation checkpointing"
+
+    assert args.model.n_future_head == args.data.n_views - 1
+
+
+preemption_flag = dict(flag=False)
+
+
+def set_preemption_flag(signum, frame):
+    logger.warning("Signal handler called with signal " + str(signum))
+    logger.warning("Preemption ! checkpointing asap and exiting.")
+    preemption_flag["flag"] = True
+
+
+def every_n_steps(train_state, freq, acc_step=None, acc_freq=None):
+    test = train_state.step % freq == 0
+    if acc_step is not None:
+        test = test and (train_state.acc_step == acc_step)
+    elif acc_freq is not None:
+        test = test and ((train_state.acc_step % acc_freq) == 0)
+    return test
+
+
+def train(args: TrainArgs):
+    with ExitStack() as context_stack:
+        tokenizer = build_tokenizer(args.data.tokenizer.name, args.data.tokenizer.path)
+
+        n_words = tokenizer.vocab_size
+        print(f"========Num words is {n_words}=======")
+
+        validate_train_args(
+            args,
+            n_words,
+        )
+        if get_is_master():
+            os.makedirs(args.dump_dir, exist_ok=True)
+            dump_config(args, Path(args.dump_dir) / "config.yaml")
+        init_logger(Path(args.dump_dir) / "train.log")
+        init_signal_handler(set_preemption_flag)  # For handling preemption signals.
+        # setup_env(args.env)
+        setup_torch_distributed(args.distributed)
+        world_mesh = get_device_mesh(args.distributed)
+        logger.info(f"Starting job: {args.name}")
+
+        # build dataloader
+        # need dp world size and rank
+        dp_mesh = world_mesh["dp_replicate"]
+        dp_degree = dp_mesh.size()
+        dp_rank = dp_mesh.get_local_rank()
+        if args.distributed.dp_shard > 1:
+            dp_rank = dp_rank * world_mesh["dp_shard"].size() + world_mesh["dp_shard"].get_local_rank()
+            dp_degree *= world_mesh["dp_shard"].size()
+
+        logger.info(f"Running on dp rank : {dp_rank}")
+        logger.info(f"Running on dp size : {dp_degree}")
+
+        torch.manual_seed(args.seed)
+        logger.info(f"Building model")
+
+        # Initializing Model in meta device allows us to initialize models much bigger than 1 gpu's memory
+        with torch.device("meta"):
+            model = LMTransformer(args.model)
+        logger.info(f"Model is built !")
+
+        model_param_count = get_num_params(model)
+
+        model = parallelize_model(
+            model,
+            world_mesh,
+            args.distributed,
+            fsdp_grouping_plan=build_fsdp_grouping_plan(args.model),
+            no_recompute_ops=None,
+        )
+
+        # Once we shard the model on different gpus we can actually initialize the model
+        # First we create empty tensors of the correct shapes
+        model = model.to_empty(device="cuda")
+        # Then we init the model. Please make sure this function initializes *ALL* parameters
+        # and buffers, otherwise you will have random values in the unitialized tensors
+        # which will silently fail (give nan gradients for example)
+
+        if args.checkpoint.init_ckpt_path:
+            logger.info(f"Loading initial model from {args.checkpoint.init_ckpt_path}")
+            load_from_checkpoint(args.checkpoint.init_ckpt_path, model, model_key="model") # Put model_key="" if its directly the model checkpoint
+            model.rope_embeddings.reset_parameters() # For RoPe initialization since it's a buffer it might not be loaded
+        else:
+            with torch.random.fork_rng(devices=[torch.cuda.current_device()]):
+                torch.manual_seed(args.model.seed)
+                model.init_weights()
+        check_model_value_range(model, range=10.0, std=1.0)
+
+        # log model size
+
+        logger.info(f"Model size: {model_param_count:,} total parameters")
+
+        gpu_memory_monitor = GPUMemoryMonitor("cuda")
+        logger.info(
+            f"GPU capacity: {gpu_memory_monitor.device_name} ({gpu_memory_monitor.device_index}) "
+            f"with {gpu_memory_monitor.device_capacity_gib:.2f}GiB memory"
+        )
+        logger.info(f"GPU memory usage: {gpu_memory_monitor}")
+
+        # build optimizer after apply parallelisms to the model
+        optimizer, scheduler = build_optimizer(model, args.optim, args.steps)
+        data_loader_state = init_dataloader_state_from_args(
+            args.data, dp_rank, dp_degree
+        )
+
+        train_state = TrainState(
+            step=0,
+            acc_step=0,
+            data_loader_state=data_loader_state,
+            scheduler=scheduler,
+        )
+
+        checkpoint = CheckpointManager.instantiate_and_make_dir(args.checkpoint)
+        checkpoint.load(model, optimizer, train_state, world_mesh)
+        # Either load from latest checkpoint or start from scratch
+        if args.probe_freq is not None:
+            if get_is_master():
+                os.makedirs(Path(args.dump_dir) / "probe", exist_ok=True)
+            torch.distributed.barrier()
+            probe = AutoProbeD(
+                model,
+                (
+                    Path(args.dump_dir) / "probe" / f"probe.{dp_rank}.jsonl"
+                    if (dp_rank % 128 == 0)
+                    else None
+                ),
+            )
+
+        gc.disable()
+
+        # train loop
+        model.train()
+        metric_logger = context_stack.enter_context(
+            MetricLogger(Path(args.dump_dir) / "metrics.jsonl", args)
+        )
+        data_loader = context_stack.enter_context(
+            build_dataloader_from_args(
+                args.data,
+                state=train_state.data_loader_state,
+            )
+        )
+        torch_profiler = context_stack.enter_context(
+            maybe_run_profiler(args.dump_dir, model, args.profiling)
+        )
+
+        nwords_since_last_log = 0
+        time_last_log = timer()
+        gc.collect()
+        while train_state.step < args.steps:
+            # We constrain train_state.acc_step to be in range 0 to args.grad_acc_steps - 1
+            train_state.acc_step += 1
+            train_state.acc_step = train_state.acc_step % args.grad_acc_steps
+
+            # get batch
+            curr_lr = float(optimizer.param_groups[0]["lr"])
+            data_load_start = timer()
+            batch, train_state.data_loader_state = next(data_loader)
+            batch = torch.tensor(
+                batch,
+                dtype=torch.long,
+            )
+
+            if every_n_steps(train_state, args.gc_collect_freq, acc_step=0):
+                logger.info("garbage collection")
+                # we do garbage collection manually otherwise different processes
+                # run the GC at different times so they slow down the whole pipeline
+                gc.collect()
+
+            input_ids = batch[:, :, 0].cuda()
+            labels = batch[:, :, 1:].cuda()
+            data_load_time = round(timer() - data_load_start, 4)
+            nwords_since_last_log += input_ids.numel()
+
+            bsz, seqlen, _ = labels.shape
+
+            # forward
+            start_timer = torch.cuda.Event(enable_timing=True)
+            end_timer = torch.cuda.Event(enable_timing=True)
+            start_timer.record()
+
+            # This is an automatic probe that will compute statistics
+            # of all linears' inputs, weights and outputs
+            # along with attention logits and entropy
+            # both in forward and backward pass
+            if (args.probe_freq is not None) and every_n_steps(
+                train_state, args.probe_freq, acc_step=1 % args.grad_acc_steps
+            ):
+                # Here we do a fake forward and backward pass on a smaller
+                # batch size to avoid OOM
+                # This assumes the model has no stateful layers (batch norm..)
+                assert (
+                    next(model.parameters()).grad is None
+                ), "Can't probe model if grads are not reset"
+
+                with probe:
+                    probe.metadata = {
+                        "it": train_state.step,
+                        "global_step": train_state.step,
+                        "loop": "lingua",
+                    }
+                    # Non compiled model uses roughly 2x memory in our exps
+                    # So we divide bsz by 2 or seqlen by 2
+                    probe_bsz = max(1, bsz // 2)
+                    probe_seq = seqlen if (bsz // 2 >= 1) else (seqlen // 2)
+                    probe_loss = model(
+                        input_ids[:probe_bsz, :probe_seq],
+                        labels[:probe_bsz, :probe_seq],
+                    )
+                    sum(probe_loss).backward()
+                    # We zero grads to cancel this fake step
+                    optimizer.zero_grad()
+
+                assert (
+                    next(model.parameters()).grad is None
+                ), "Probe model shouldn't have grads at this point"
+
+            losses = model(input_ids, labels)
+
+            if args.grad_acc_steps > 1:
+                model.set_requires_gradient_sync(train_state.acc_step == 0)
+
+            # We scale loss with grad_acc_steps so the gradient is the same
+            # regardless of grad_acc_steps
+            loss = sum(losses) / args.grad_acc_steps
+            # backward on scaled loss to create scaled gradients
+            loss.backward()
+            # For logging we undo that scaling
+            loss = loss.detach() * args.grad_acc_steps
+
+            # optimizer step
+            grad_norm = -1.0
+            if train_state.acc_step == 0:
+                grad_norm = torch.nn.utils.clip_grad_norm_(
+                    model.parameters(), max_norm=args.optim.clip, foreach=True
+                )
+
+                grad_norm = (
+                    grad_norm.full_tensor() if isinstance(grad_norm, DTensor) else grad_norm
+                ).item()
+
+                optimizer.step()
+                scheduler.step()
+                optimizer.zero_grad()
+                train_state.step += 1
+
+            # updates the scale for next iteration
+            # training iteration complete
+            end_timer.record()
+
+            torch.cuda.synchronize()
+
+            curr_iter_time = round(start_timer.elapsed_time(end_timer) * 1e-3, 4)
+
+            # if profiler is active
+            if torch_profiler:
+                xformers.profiler.step()
+
+            # log metrics
+            if every_n_steps(
+                train_state,
+                args.logging.freq,
+                acc_step=None if args.logging.acc_freq else 0,
+                acc_freq=args.logging.acc_freq,
+            ):
+                time_delta = timer() - time_last_log
+                wps = nwords_since_last_log / (time_delta)
+
+                gpu_mem_stats = gpu_memory_monitor.get_peak_stats()
+
+                total_acc_steps = (
+                    args.grad_acc_steps * train_state.step + train_state.acc_step
+                )
+                tokens_per_gpu = (
+                    total_acc_steps * args.data.batch_size * args.data.seq_len
+                )
+                total_tokens = dp_degree * tokens_per_gpu
+                # This is an estimate and the correct values may change
+                # if you change the architecture
+                # Use xformer's analyze profile trace to get actual measurement
+                FLOPS = (
+                    get_num_flop_per_token(
+                        model_param_count - args.model.vocab_size * args.model.dim,
+                        args.model.n_layers,
+                        args.model.dim,
+                        args.data.seq_len,
+                    )
+                    * wps
+                )
+                metrics = flatten_dict(
+                    {
+                        "global_step": train_state.step,
+                        "acc_step": train_state.acc_step,
+                        "speed": {
+                            "wps": wps,
+                            "FLOPS": FLOPS,
+                            "curr_iter_time": curr_iter_time,
+                            "data_load_time": data_load_time,
+                        },
+                        "optim": {
+                            "grad_norm": grad_norm,
+                            "lr": curr_lr,
+                            "total_tokens": total_tokens,
+                        },
+                        "memory": gpu_mem_stats._asdict(),
+                    },
+                    sep="/",
+                )
+
+                to_sync = {}
+                for head_idx, l in enumerate(losses):
+                    to_sync[f"loss/head_{head_idx}"] = l.item()
+
+                to_sync["loss/out"] = loss.item()
+                metrics.update(dist_mean_dict(to_sync))
+
+                if get_is_master():
+                    metric_logger.log(metrics)
+
+                gpu_memory_monitor.reset_peak_stats()
+                nwords_since_last_log = 0
+                time_last_log = timer()
+                logger.info(
+                    f"step: {train_state.step}"
+                    f"  acc: {train_state.acc_step}"
+                    f"  loss: {round(loss.item(),4):>7}"
+                    f"  grad: {grad_norm:.2e}"
+                    f"  flops: {FLOPS:.2e}"
+                    f"  wps: {wps:.2e}"
+                    f"  iter: {curr_iter_time:>7}"
+                    f"  data: {data_load_time:>5}"
+                    f"  lr: {curr_lr:.2e}"
+                    f"  mem: {gpu_mem_stats.max_active_pct:.0f}%"
+                    f"  pow: {gpu_mem_stats.power_draw/1000} W"
+                )
+
+            saved = False
+            if every_n_steps(
+                train_state, args.checkpoint.save_every.every, acc_step=0
+            ) or every_n_steps(train_state, args.checkpoint.eval_every.every, acc_step=0):
+                saved = checkpoint.save(
+                    model,
+                    optimizer,
+                    train_state,
+                    args,
+                    device_mesh=world_mesh,
+                )
+
+            if args.eval is not None and every_n_steps(
+                train_state, args.checkpoint.eval_every.every, acc_step=0
+            ):
+                from apps.mtp.eval import (
+                    launch_eval,
+                    EVAL_FOLDER_NAME,
+                    EvalArgs,
+                )
+
+                eval_args = dataclass_from_dict(EvalArgs, args.eval)
+
+                eval_args.global_step = train_state.step
+                eval_args.ckpt_dir = str(checkpoint.existing_saves[-1])
+                eval_args.dump_dir = str(
+                    os.path.join(
+                        args.dump_dir,
+                        "evals",
+                        EVAL_FOLDER_NAME.format(train_state.step),
+                    )
+                )
+                eval_args.metric_log_dir = args.dump_dir
+                if args.async_eval_gpus is None:
+                    launch_eval(eval_args)
+                elif get_is_master():
+                    if wandb.run is not None and args.logging.wandb is not None:
+                        eval_args.wandb = deepcopy(args.logging.wandb)
+                    assert args.async_eval_gpus > 0
+                    logger.info(f"Launching evals on {args.async_eval_gpus} gpus")
+                    with clean_env():
+                        launch_job(
+                            StoolArgs(
+                                asdict(eval_args),
+                                script="apps.mtp.eval",
+                                copy_code=False,
+                                nodes=args.async_eval_gpus // 8,
+                                qos="lowest",
+                            )
+                        )
+
+            if preemption_flag["flag"]:
+                if not saved:
+                    checkpoint.save(
+                        model,
+                        optimizer,
+                        train_state,
+                        args,
+                        device_mesh=world_mesh,
+                    )
+                requeue_slurm_job()
+                sys.exit(0)
+
+    if not saved:
+        checkpoint.save(
+            model,
+            optimizer,
+            train_state,
+            args,
+            device_mesh=world_mesh,
+        )
+    gc.collect()
+
+
+def main():
+    """
+    The command line interface here uses OmegaConf https://omegaconf.readthedocs.io/en/2.3_branch/usage.html#from-command-line-arguments
+    This accepts arguments as a dot list
+    So if the dataclass looks like
+
+    @dataclass
+    class DummyArgs:
+        name: str
+        mode: LMMTPArgs
+
+    @dataclass
+    class LMMTPArgs:
+        dim: int
+
+    Then you can pass model.dim=32 to change values in LMMTPArgs
+    or just name=tictac for top level attributes.
+
+    The behavior here is as follows:
+    1. We instantiate TrainArgs with its default values
+    2. We override those default values with the ones in the provided config file
+    3. We override the result with the additional arguments provided through command line
+
+    For example, if the config is the following
+
+    model:
+        dim: 128
+        n_layers: 4
+
+    and you call train.py with train.py model.dim=64
+
+    Then the final TrainArgs will have
+
+    model:
+        dim: 64
+        n_layers: 4
+
+    Plus all the default values in TrainArgs dataclass.
+    """
+    cli_args = OmegaConf.from_cli()
+    file_cfg = OmegaConf.load(cli_args.config)
+    print(file_cfg)
+    # We remove 'config' attribute from config as the underlying DataClass does not have it
+    del cli_args.config
+
+    default_cfg = OmegaConf.structured(TrainArgs())
+
+    cfg = OmegaConf.merge(default_cfg, file_cfg, cli_args)
+    cfg = OmegaConf.to_object(cfg)
+
+    print(cfg)
+
+    train(cfg)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/core/trainer/transformer.py b/core/trainer/transformer.py
new file mode 100644
index 0000000..ebc0438
--- /dev/null
+++ b/core/trainer/transformer.py
@@ -0,0 +1,761 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+from dataclasses import dataclass
+from enum import Enum
+from typing import Optional, Union, Tuple
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+from xformers.ops import fmha, AttentionBias
+from torch.nn.attention.flex_attention import (
+    BlockMask,
+    flex_attention,
+    _mask_mod_signature,
+)
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from torch.nn.attention.flex_attention import create_block_mask, BlockMask
+
+import torch.utils.checkpoint
+from xformers.ops import fmha, AttentionBias
+
+from core.trainer import probe
+
+flex_attention_comp = torch.compile(flex_attention)
+
+
+class InitStdFactor(Enum):
+    DISABLED = "disabled"  # Init std is divided by 1.0
+    GLOBAL_DEPTH = "global_depth"  # Init std is divided by sqrt(2*n_layers)
+    CURRENT_DEPTH = "current_depth"  # Init std is divided by sqrt(2*depth)
+    DIM_RATIO = "dim_ratio"  # Init std is divided by model_dim/4096
+
+
+@dataclass
+class BaseTransformerArgs:
+    dim: int = 512
+    n_layers: int = 8
+    head_dim: Optional[int] = None
+    n_heads: Optional[int] = None
+    n_kv_heads: Optional[int] = None
+
+    ffn_dim_multiplier: Optional[float] = None
+
+    multiple_of: int = 256
+
+    norm_eps: float = 1e-5
+
+    rope_theta: float = 10000.0
+
+    init_base_std: Optional[float] = None
+    init_std_factor: str = "disabled"
+
+    max_seqlen: int = 1024
+
+
+def cross_entropy(pred, target, **kwargs):
+    return F.nll_loss(
+        F.log_softmax(pred.flatten(end_dim=-2).float(), -1),
+        target.flatten(end_dim=-1),
+        **kwargs,
+    )
+
+
+def repeat_kv(x: torch.Tensor, n_rep: int, dim: int) -> torch.Tensor:
+    """torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
+    assert dim == 2, "Only dim=2 is supported. Check the implementation for other dims."
+    bs, slen, n_kv_heads, head_dim = x.shape
+    if n_rep == 1:
+        return x
+    return (
+        x[:, :, :, None, :]
+        .expand(bs, slen, n_kv_heads, n_rep, head_dim)
+        .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
+    )
+
+
+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
+    """
+    Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
+
+    This function calculates a frequency tensor with complex exponentials using the given dimension 'dim'
+    and the end index 'end'. The 'theta' parameter scales the frequencies.
+    The returned tensor contains complex values in complex64 data type.
+
+    Args:
+        dim (int): Dimension of the frequency tensor.
+        end (int): End index for precomputing frequencies.
+        theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0.
+
+    Returns:
+        torch.Tensor: Precomputed frequency tensor with complex exponentials.
+    """
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)
+    freqs = torch.outer(t, freqs).float()
+
+    cos, sin = freqs.cos(), freqs.sin()
+
+    return torch.stack((cos, -sin, sin, cos), dim=-1).view(*freqs.size(), 2, 2)
+
+
+def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor, seq_dim: int):
+    """
+    Reshape frequency tensor for broadcasting it with another tensor.
+
+    This function reshapes the frequency tensor to have the same shape as the target tensor 'x'
+    for the purpose of broadcasting the frequency tensor during element-wise operations.
+
+    Args:
+        freqs_cis (torch.Tensor): Frequency tensor to be reshaped.
+        x (torch.Tensor): Target tensor for broadcasting compatibility.
+        seq_dim (int): Sequence dimension index.
+
+    Returns:
+        torch.Tensor: Reshaped frequency tensor.
+    """
+    ndim = x.ndim
+    assert 0 <= seq_dim < ndim
+    assert freqs_cis.shape == (
+        x.shape[seq_dim],
+        x.shape[-3],
+        2,
+        2,
+    ), f"freqs_cis vs x: {(freqs_cis.shape, x.shape)}"
+    shape = [
+        d if i == seq_dim or i == ndim - 3 else 1 for i, d in enumerate(x.shape[:-2])
+    ] + [2, 2]
+    return freqs_cis.view(*shape)
+
+
+def apply_rotary_emb(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    seq_dim: int,
+    freqs_cis: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    xq_ = xq.reshape(*xq.shape[:-1], -1, 1, 2)  # B S H D -> B S H D/2 1 2
+    xk_ = xk.reshape(*xk.shape[:-1], -1, 1, 2)  # B S H D -> B S H D/2 1 2
+    freqs_cis = reshape_for_broadcast(
+        freqs_cis, xq_, seq_dim
+    ).float()  # S D/2 2 2 -> 1 S 1 D/2 2 2
+    xq_out = (xq_ * freqs_cis).sum(5).flatten(3)
+    xk_out = (xk_ * freqs_cis).sum(5).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+def causal_mask(b, h, q_idx, kv_idx):
+    return q_idx >= kv_idx
+
+
+def lengths_to_start_ids(lengths):
+    doc_start = lengths.cumsum(0)
+    doc_start = doc_start.roll(1)
+    doc_start[0] = 0
+    return doc_start
+
+
+def lengths_to_local_ids(lengths):
+    assert lengths.ndim == 1
+    nb_seqs = lengths.size(0)
+    total_seqlen = lengths.sum()
+    # This gives the document id of each token
+    doc_id = torch.repeat_interleave(lengths)
+    # Compute document start for each document
+    doc_start = lengths_to_start_ids(lengths)
+    # Compute document start for each token
+    doc_start = doc_start[doc_id]
+    # Compute the position of each token within each document
+    tok_id = torch.arange(total_seqlen, device=lengths.device) - doc_start
+
+    return doc_id, tok_id
+
+
+def generate_doc_mask_mod(
+    mask_mod: _mask_mod_signature,
+    lengths: torch.Tensor,
+    kv_lengths: Optional[torch.Tensor] = None,
+) -> _mask_mod_signature:
+    """Generates mask mods that apply to inputs to flex attention in the sequence stacked
+    format.
+
+    Args:
+        mask_mod: The mask mod to apply to the documents
+        lengths: Lengths of each document
+
+    Note:
+        What is the sequence stacked format? When assembling batches of inputs, we
+        take multiple sequences and stack them together to form 1 large sequence. We then
+        use masking to ensure that the attention scores are only applied to tokens within
+        the same document.
+
+    Example:
+
+    - Square mask
+      doc_mask         lengths
+      a a b b b c c    2 3 2
+    a 1 0 0 0 0 0 0
+    a 1 1 0 0 0 0 0
+    b 0 0 1 0 0 0 0
+    b 0 0 1 1 0 0 0
+    b 0 0 1 1 1 0 0
+    c 0 0 0 0 0 1 0
+    c 0 0 0 0 0 1 1
+
+    """
+    kv_lengths = kv_lengths if kv_lengths is not None else lengths
+    q_document_id, q_token_id = lengths_to_local_ids(lengths)
+    kv_document_id, kv_token_id = lengths_to_local_ids(kv_lengths)
+    q_max_idx = lengths.sum() - 1
+    kv_max_idx = kv_lengths.sum() - 1
+
+    def doc_mask_mod(b, h, q_idx, kv_idx):
+        q_idx_cap = torch.minimum(q_max_idx, q_idx)
+        kv_idx_cap = torch.minimum(kv_max_idx, kv_idx)
+        valid_idx = (q_idx <= q_max_idx) & (kv_idx <= kv_max_idx)
+        same_doc = q_document_id[q_idx_cap] == kv_document_id[kv_idx_cap]
+        q_logical = q_token_id[q_idx_cap]
+        kv_logical = kv_token_id[kv_idx_cap]
+        inner_mask = mask_mod(b, h, q_logical, kv_logical)
+        return same_doc & inner_mask & valid_idx
+
+    return doc_mask_mod
+
+
+# Rotary embedding as in xformer, see if torchtrain implementation is not better. Also might be usefull to make it work with batch*seqlen collapsed.
+class RotaryEmbedding(torch.nn.Module):
+    """
+    RotaryEmbedding Module
+    """
+
+    def __init__(self, theta: float, head_dim: int, max_seqlen: int = 1024):
+        super().__init__()
+
+        self.theta = theta
+        self.head_dim = head_dim
+        self.max_seqlen = max_seqlen
+
+        self.register_buffer(
+            "freqs_cis",
+            precompute_freqs_cis(dim=head_dim, end=max_seqlen, theta=theta),
+            persistent=False,
+        )
+
+    def reset_parameters(self):
+        self.freqs_cis[...] = precompute_freqs_cis(
+            dim=self.head_dim, end=self.max_seqlen, theta=self.theta
+        )
+
+    def forward(
+        self, seqlen: Optional[int] = None, tok_idx: Optional[torch.Tensor] = None
+    ):
+        """
+        Return freqs_cis corresponding to consecutive seqlen positions or the corresponding tok_idx positions
+        Args:
+            seqlen (int): Contiguous sequence length
+            tok_idx (torch.Tensor[int]): Position indices of each token this overrides seqlen
+
+        Returns:
+            Tuple(torch.Tensor, torch.Tensor): Embedded input tensor and freqs_cis
+        """
+        test = (seqlen is not None) or (tok_idx is not None)
+        assert test, "Should provide atleast seqlen or tok_idx"
+        if tok_idx is not None:
+            return self.freqs_cis[tok_idx]
+        elif seqlen is not None:
+            return self.freqs_cis[0:seqlen]
+
+
+class RMSNorm(nn.Module):
+    """
+    Initialize the RMSNorm normalization layer.
+
+    Args:
+        dim (int): The dimension of the input tensor.
+        eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+
+    Attributes:
+        eps (float): A small value added to the denominator for numerical stability.
+        weight (nn.Parameter): Learnable scaling parameter.
+
+    """
+
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x: torch.Tensor):
+        return x * torch.rsqrt((x * x).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x: torch.Tensor):
+        x = probe.log_stats(x, "resid")
+        output = self._norm(x.float())
+        return (output * self.weight.float()).type_as(x)
+
+    def reset_parameters(self):
+        torch.nn.init.ones_(self.weight)  # type: ignore
+
+class TiedLinear(nn.Module):
+    def __init__(self, tied_module: nn.Module) -> None:
+        super().__init__()
+        self.tied_module = tied_module
+        if not hasattr(tied_module, "weight"):
+            raise AttributeError(
+                "Provided module does not have attribute 'weight'. Please check your tied_module."
+            )
+
+    def __call__(self, x: torch.Tensor) -> torch.Tensor:
+        return F.linear(x, self.tied_module.weight)
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        head_dim: int,
+        n_heads: int,
+        n_kv_heads: int,
+        rope_theta: float,
+    ):
+        super().__init__()
+
+        self.dim = dim
+        self.head_dim = head_dim
+        self.rope_theta = rope_theta
+
+        self.n_heads = n_heads
+        self.n_kv_heads = n_kv_heads
+        self.heads_per_group = self.n_heads // self.n_kv_heads
+
+        self.wq = nn.Linear(
+            dim,
+            n_heads * head_dim,
+            bias=False,
+        )
+        self.wk = nn.Linear(
+            dim,
+            n_kv_heads * head_dim,
+            bias=False,
+        )
+        self.wv = nn.Linear(
+            dim,
+            n_kv_heads * head_dim,
+            bias=False,
+        )
+
+        self.wo = nn.Linear(
+            n_heads * head_dim,
+            dim,
+            bias=False,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        freq_cis: torch.Tensor,
+        tok_idx: Optional[torch.Tensor] = None,
+        mask: Optional[Union[BlockMask, AttentionBias, str]] = None,
+        attn_impl: str = "sdpa",
+    ) -> torch.Tensor:
+        # B S D
+        bsz, seq_len, dim = x.shape
+        xq = self.wq(x.view_as(x))
+        xk = self.wk(x.view_as(x))
+        xv = self.wv(x.view_as(x))
+
+        output_shape = xq.shape
+        # B S D -> B S H D
+        xq = xq.view(bsz, seq_len, self.n_heads, self.head_dim)
+        xk = xk.view(bsz, seq_len, self.n_kv_heads, self.head_dim)
+        xv = xv.view(bsz, seq_len, self.n_kv_heads, self.head_dim)
+
+        xq, xk = apply_rotary_emb(xq, xk, 1, freq_cis[0:seq_len])
+
+        # This condition helps us be easily compatible
+        # with inference by adding a pluggable KVCache
+        if hasattr(self, "kv_cache"):
+            xk, xv = self.kv_cache.update(xk, xv, tok_idx)
+
+        xk = repeat_kv(xk, self.heads_per_group, dim=2)
+        xv = repeat_kv(xv, self.heads_per_group, dim=2)
+
+        if attn_impl == "flex_attention":
+            assert mask is None or isinstance(mask, BlockMask)
+            xq, xk, xv = map(lambda e: e.transpose(1, 2), (xq, xk, xv))
+            output = flex_attention_comp(xq, xk, xv, block_mask=mask)
+            output = output.transpose(1, 2).contiguous()  # B H S D -> B S H D
+
+        elif attn_impl == "fmha":
+            assert mask is None or isinstance(mask, AttentionBias)
+            output = fmha.memory_efficient_attention(xq, xk, xv, attn_bias=mask)
+            # This uses B S H D instead of B H S D of pytorch
+
+        elif attn_impl == "sdpa":
+            xq, xk, xv = map(lambda e: e.transpose(1, 2), (xq, xk, xv))
+            assert mask is None or isinstance(mask, (str, torch.Tensor))
+            is_causal = (mask == "causal") if isinstance(mask, str) else False
+            mask = mask if isinstance(mask, torch.Tensor) else None
+            output = F.scaled_dot_product_attention(
+                xq,
+                xk,
+                xv,
+                is_causal=is_causal,
+                attn_mask=mask,
+            )
+            output = output.transpose(1, 2).contiguous()  # B H S D -> B S H D
+        else:
+            raise NotImplementedError(
+                f"Attention implementation {attn_impl} not supported"
+            )
+
+        output = self.wo(output.reshape(output_shape))
+
+        return output
+
+    def reset_parameters(self, init_std=None, factor=1.0):
+        init_std = init_std or (self.dim ** (-0.5))
+
+        for w in [self.wq, self.wk, self.wv]:
+            nn.init.trunc_normal_(
+                w.weight,
+                mean=0.0,
+                std=init_std,
+                a=-3 * init_std,
+                b=3 * init_std,
+            )
+
+        nn.init.trunc_normal_(
+            self.wo.weight,
+            mean=0.0,
+            std=init_std / factor,
+            a=-3 * init_std,
+            b=3 * init_std,
+        )
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        multiple_of: int,
+        ffn_dim_multiplier: Optional[float],
+        mp_size: int = 1,
+    ):
+        super().__init__()
+
+        hidden_dim = int(2 * hidden_dim / 3)
+        if ffn_dim_multiplier is not None:
+            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+        assert hidden_dim % mp_size == 0
+
+        self.dim = dim
+        self.hidden_dim = hidden_dim
+
+        self.w1 = nn.Linear(
+            dim,
+            hidden_dim,
+            bias=False,
+        )
+        self.w3 = nn.Linear(
+            dim,
+            hidden_dim,
+            bias=False,
+        )
+        self.w2 = nn.Linear(
+            hidden_dim,
+            dim,
+            bias=False,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # B S D
+        x1 = self.w1(x.view_as(x))
+        x3 = self.w3(x.view_as(x))
+        output = self.w2(F.silu(x1) * x3)
+        return output
+
+    def reset_parameters(self, init_std=None, factor=1.0):
+        in_init_std = init_std or (self.dim ** (-0.5))
+        out_init_std = init_std or (self.hidden_dim ** (-0.5))
+        in_init_std = in_init_std
+        out_init_std = out_init_std / factor
+        for w in [self.w1, self.w3]:
+            nn.init.trunc_normal_(
+                w.weight,
+                mean=0.0,
+                std=in_init_std,
+                a=-3 * in_init_std,
+                b=3 * in_init_std,
+            )
+        nn.init.trunc_normal_(
+            self.w2.weight,
+            mean=0.0,
+            std=out_init_std,
+            a=-3 * out_init_std,
+            b=3 * out_init_std,
+        )
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, args: BaseTransformerArgs):
+        super().__init__()
+
+        assert (args.head_dim is not None) or (
+            args.n_heads is not None
+        ), "Should specify at least head_dim or n_heads"
+        self.head_dim = args.head_dim or args.dim // args.n_heads
+        self.n_heads = args.n_heads or args.dim // args.head_dim
+        self.n_kv_heads = args.n_kv_heads or self.n_heads
+
+        assert args.n_heads % self.n_kv_heads == 0
+        assert args.dim % args.n_heads == 0
+
+        self.attention = Attention(
+            dim=args.dim,
+            head_dim=self.head_dim,
+            n_heads=self.n_heads,
+            n_kv_heads=self.n_kv_heads,
+            rope_theta=args.rope_theta,
+        )
+        self.feed_forward = FeedForward(
+            dim=args.dim,
+            hidden_dim=4 * args.dim,
+            multiple_of=args.multiple_of,
+            ffn_dim_multiplier=args.ffn_dim_multiplier,
+        )
+        self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
+        self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        freq_cis: torch.Tensor,
+        tok_idx: Optional[torch.Tensor] = None,
+        mask: Optional[Union[BlockMask, AttentionBias, str]] = None,
+        attn_impl: str = "sdpa",
+    ) -> torch.Tensor:
+
+        h = x + self.attention(
+            self.attention_norm(x),
+            freq_cis,
+            tok_idx=tok_idx,
+            mask=mask,
+            attn_impl=attn_impl,
+        )
+        out = h + self.feed_forward(self.ffn_norm(h))
+        return out
+
+    def init_weights(self, init_std=None, factor=1.0):
+        self.attention.reset_parameters(init_std, factor)
+        self.attention_norm.reset_parameters()
+
+        self.feed_forward.reset_parameters(init_std, factor)
+        self.ffn_norm.reset_parameters()
+
+
+class BaseTransformer(nn.Module):
+    def __init__(self, args: BaseTransformerArgs):
+        super().__init__()
+        self.dim = args.dim
+        self.init_base_std = args.init_base_std
+        self.init_std_factor = InitStdFactor(args.init_std_factor)
+        self.max_seqlen = args.max_seqlen
+        self.rope_embeddings = RotaryEmbedding(
+            theta=args.rope_theta,
+            head_dim=args.head_dim or args.dim // args.n_heads,
+            max_seqlen=args.max_seqlen,
+        )
+
+        self.layers = nn.ModuleList()
+        for _ in range(args.n_layers):
+            self.layers.append(TransformerBlock(args))
+
+    def forward(
+        self,
+        h,
+        tok_idx: Optional[torch.Tensor] = None,
+        mask: Optional[Union[BlockMask, AttentionBias, str]] = None,
+        attn_impl: str = "sdpa",
+    ):
+
+        freq_cis = self.rope_embeddings(seqlen=self.max_seqlen, tok_idx=tok_idx)
+
+        for i, layer in enumerate(self.layers):
+            h = layer(h, freq_cis, tok_idx=tok_idx, mask=mask, attn_impl=attn_impl)
+        return h
+
+    def reset_parameters(self):
+        # Either use fixed base std or sqrt model dim
+        self.rope_embeddings.reset_parameters()
+
+    def init_weights(self):
+        self.reset_parameters()
+        for depth, layer in enumerate(self.layers):
+            factor = {
+                InitStdFactor.CURRENT_DEPTH: (2 * (depth + 1)) ** 0.5,
+                InitStdFactor.GLOBAL_DEPTH: (2 * (len(self.layers) + 1)) ** 0.5,
+                InitStdFactor.DIM_RATIO: self.dim / 4096,
+                InitStdFactor.DISABLED: 1.0,
+            }[self.init_std_factor]
+
+            layer.init_weights(self.init_base_std, factor)
+
+
+
+
+def create_causal_mask(seqlen, attn_impl, sliding_window):
+    if sliding_window is not None and attn_impl == "xformers":
+        return fmha.attn_bias.LocalAttentionFromBottomRightMask(
+            window_left=sliding_window - 1, window_right=0
+        )
+    elif attn_impl == "xformers":
+        return fmha.attn_bias.LowerTriangularMask()
+    elif attn_impl == "sdpa":
+        return "causal"
+    elif attn_impl == "flex_attention":
+        return create_block_mask(causal_mask, None, None, seqlen, seqlen)
+    else:
+        raise NotImplementedError(
+            f"Attention {attn_impl} with {sliding_window} sliding window not implemented"
+        )
+
+
+def attention_flops_per_token(n_layers, seq_len, dim, causal):
+    # Formula from https://github.com/Dao-AILab/flash-attention/blob/main/benchmarks/benchmark_flash_attention.py#L27-L30
+    return 3.5 * (4 * n_layers * seq_len * dim // (2 if causal else 1))
+
+
+def get_num_flop_per_token(
+    num_non_embed_params: int, n_layers: int, dim: int, seq_len: int
+) -> int:
+    return 6 * num_non_embed_params + attention_flops_per_token(
+        n_layers, seq_len, dim, True
+    )
+
+
+def causal_mask(b, h, q_idx, kv_idx):
+    return q_idx >= kv_idx
+
+
+@dataclass
+class LMMTPArgs(BaseTransformerArgs):
+
+    seed: int = 42
+    n_future_head: int = 1
+
+    vocab_size: int = -1
+
+    attn_impl: str = "sdpa"
+    mask: str = "causal"
+    sliding_window: Optional[int] = None
+
+
+class LMTransformer(BaseTransformer):
+    def __init__(self, args: LMMTPArgs):
+        super().__init__(args)
+        self.sliding_window = args.sliding_window
+        self.mask = args.mask
+        self.attn_impl = args.attn_impl
+
+        self.n_future_head = args.n_future_head
+
+        assert self.n_future_head >= 1
+        assert args.vocab_size > 0
+
+        self.tok_embeddings = torch.nn.Embedding(args.vocab_size, args.dim)
+
+        self.norm = RMSNorm(args.dim, eps=args.norm_eps)
+
+        self.heads = nn.ModuleList()
+        for _ in range(self.n_future_head):
+            self.heads.append(
+                nn.Linear(
+                    args.dim,
+                    args.vocab_size,
+                    bias=False,
+                )
+            )
+
+    def forward(
+        self,
+        token_values: torch.Tensor,
+        target: Optional[List[torch.Tensor]] = None,
+        tok_idx: Optional[torch.Tensor] = None,
+        mask: Optional[Union[BlockMask, AttentionBias, torch.Tensor, str]] = None,
+        attn_impl: str = "sdpa",
+    ):
+        bsz, seqlen = token_values.shape
+
+        h = self.tok_embeddings(token_values)
+
+        mask = (
+            mask
+            if mask is not None
+            else create_causal_mask(seqlen, self.attn_impl, self.sliding_window)
+        )
+
+        h = super().forward(h, tok_idx=tok_idx, mask=mask, attn_impl=attn_impl)
+
+        norm_h = self.norm(h)
+        if target is not None:
+            if self.training:
+                ce = []
+                for i, head in enumerate(self.heads):
+                    logits = torch.utils.checkpoint.checkpoint(
+                        head,
+                        norm_h,
+                        use_reentrant=False,
+                        preserve_rng_state=False,
+                    )
+                    ce.append(cross_entropy(logits, target[..., i]))
+            else:
+                head = self.heads[0]
+                logits = head(norm_h)
+                ce = cross_entropy(logits, target)
+            return ce
+        else:
+            return self.heads[0](norm_h)
+
+    def reset_parameters(self, init_std=None):
+        # Either use fixed base std or sqrt model dim
+        super().reset_parameters()
+        init_std = init_std or (self.dim ** (-0.5))
+        self.norm.reset_parameters()
+        nn.init.trunc_normal_(
+            self.tok_embeddings.weight,
+            mean=0.0,
+            std=init_std,
+            a=-3 * init_std,
+            b=3 * init_std,
+        )
+
+        for head in self.heads:
+            nn.init.trunc_normal_(
+                head.weight,
+                mean=0.0,
+                std=init_std,
+                a=-3 * init_std,
+                b=3 * init_std,
+            )
+
+    def init_weights(self):
+        super().init_weights()
+
+
+def build_fsdp_grouping_plan(model_args: LMMTPArgs) -> List[Tuple[str, bool]]:
+    group_plan: Tuple[int, bool] = []
+
+    # Grouping and output seperately
+    group_plan.append(("tok_embeddings", False))
+
+    # Grouping by layers
+    for i in range(model_args.n_layers):
+        group_plan.append((f"layers.{i}", False))
+
+    return group_plan
\ No newline at end of file
diff --git a/core/trainer/utils.py b/core/trainer/utils.py
index 80df7d7..c3dee18 100644
--- a/core/trainer/utils.py
+++ b/core/trainer/utils.py
@@ -3,6 +3,10 @@
 import torch
 import torch.nn as nn
 
+from core.layers.norms import RMSNorm
+from core.layers.positional_embedding.rope_projector import RopePositionEmbedding
+
+
 
 def calculate_gelu_gain():
     """
@@ -85,6 +89,10 @@ def xavier_init(m: nn.Module) -> None:
                 nn.init.zeros_(m.bias)
         elif isinstance(m, nn.Embedding) and embedding_init:
             init_embedding_weights(m, embedding_init)
+        elif isinstance(m, RMSNorm):
+            m.init_weights()
+        elif isinstance(m, RopePositionEmbedding):
+            m.init_weights()
 
 
     def kaiming_init(m: nn.Module) -> None:
@@ -97,6 +105,10 @@ def kaiming_init(m: nn.Module) -> None:
                 nn.init.zeros_(m.bias)
         elif isinstance(m, nn.Embedding) and embedding_init:
             init_embedding_weights(m, embedding_init)
+        elif isinstance(m, RMSNorm):
+            m.init_weights()
+        elif isinstance(m, RopePositionEmbedding):
+            m.init_weights()
 
     def normal_init(m: nn.Module) -> None:
         if isinstance(m, (nn.Linear, nn.Conv2d)):
@@ -108,6 +120,10 @@ def normal_init(m: nn.Module) -> None:
                 nn.init.zeros_(m.bias)
         elif isinstance(m, nn.Embedding) and embedding_init:
             init_embedding_weights(m, embedding_init)
+        elif isinstance(m, RMSNorm):
+            m.init_weights()
+        elif isinstance(m, RopePositionEmbedding):
+            m.init_weights()
 
     def uniform_init(m: nn.Module) -> None:
         if isinstance(m, (nn.Linear, nn.Conv2d)):
@@ -119,6 +135,10 @@ def uniform_init(m: nn.Module) -> None:
                 nn.init.zeros_(m.bias)
         elif isinstance(m, nn.Embedding) and embedding_init:
             init_embedding_weights(m, embedding_init)
+        elif isinstance(m, RMSNorm):
+            m.init_weights()
+        elif isinstance(m, RopePositionEmbedding):
+            m.init_weights()
 
     def orthogonal_init(m: nn.Module) -> None:
         if isinstance(m, (nn.Linear, nn.Conv2d)):
@@ -129,6 +149,10 @@ def orthogonal_init(m: nn.Module) -> None:
                 nn.init.zeros_(m.bias)
         elif isinstance(m, nn.Embedding) and embedding_init:
             init_embedding_weights(m, embedding_init)
+        elif isinstance(m, RMSNorm):
+            m.init_weights()
+        elif isinstance(m, RopePositionEmbedding):
+            m.init_weights()
 
     initializers: Dict[str, Callable] = {
         'xavier': xavier_init,
@@ -143,3 +167,12 @@ def orthogonal_init(m: nn.Module) -> None:
                         f"Choose from {list(initializers.keys())}")
 
     return initializers[init_type]
+
+
+def initialize_model(model: nn.Module,
+                    init_type: str,
+                    activation: Optional[str] = None,
+                    embedding_init: Optional[str] = None
+                    ):
+    initializer = get_initializer(init_type=init_type, activation=activation, embedding_init=embedding_init)
+    model.apply(initializer)
diff --git a/core/trainer/validator.py b/core/trainer/validator.py
index b7e610b..1e04d70 100644
--- a/core/trainer/validator.py
+++ b/core/trainer/validator.py
@@ -1,9 +1,11 @@
+from itertools import chain
 import torch
 import torch.nn as nn
 import numpy as np
 from typing import Dict, Any
 import matplotlib.pyplot as plt
-from core.trainer.utils import get_initializer
+from torch.distributed._tensor import DTensor
+
 
 torch.autograd.detect_anomaly(True)
 
@@ -96,7 +98,7 @@ def detect_nan_hook(module, input, output):
 
     # Forward pass
     with torch.no_grad():
-        model(x)
+        model(x.to(model.device))
 
     # Compute variance of activations
     variances = {name: act.var().item() for name, act in activations.items()}
@@ -129,20 +131,55 @@ def validate_model_initial_states(model, config, input_size, n_samples):
         print(f"{layer}: {var:.6f}")
 
 
-from core.models.translator.construe import ConstrueAutoRegressiveModel
-from core.configurations.base import BaseConfiguration
-
-config = BaseConfiguration(model_name="small_lm", num_layers=6, hidden_dim=32, intermediate_dim=512,
-                               max_positions=256, vocabulary_size=64000, num_heads=8, attention_dropout=0.05,
-                               batch_size=8, weight_decay=0.01,
-                               learning_rate=5e-4,
-                               tokenizer_path="/workspace/vipin_g6/personal/pretraining/english_tokenizer/english_tokenizer.model",
-                               dataset_batch_size=16, dataset_shuffle=True, num_epochs=2, eval_frequency=1,
-                               eval_iter=10,
-                               model_max_sequence=256)
-
-model = ConstrueAutoRegressiveModel(config=config)
-initalizer = get_initializer(init_type="xavier", activation="gelu", embedding_init="xavier", embedding_padding_idx=config.padding_id)
 
-model.apply(initalizer)
-validate_model_initial_states(model, config, (10, ), n_samples=1000)
\ No newline at end of file
+@torch.no_grad()
+def check_model_value_range(
+    model: torch.nn.Module, range: float = 1e3, std: float = 1e3
+):
+    for name, param in chain(model.named_parameters(), model.named_buffers()):
+        if isinstance(param, DTensor):
+            param = param.to_local()
+
+        if param.numel() == 0:
+            print(f"WARNING: Model parameter {name} is empty, probably because of FSDP sharding")
+            continue
+
+        if torch.isnan(param).any() or torch.isinf(param).any():
+            print(f"WARNING: Model parameter {name} contains NaN or Inf")
+
+        param_range = param.max() - param.min()
+        param_std = param.std()
+
+        if param_range > range:
+            print(
+                f"WARNING: Model parameter {name} has a suspiciously large range ({param_range}): please check initialization and init_weights is defined and called"
+            )
+        if param_std > std:
+            print(
+                f"WARNING: Model parameter {name} has a suspiciously large standard deviation ({param_std}): please check initialization and init_weights is defined and called"
+            )
+        if (param == 0).all():
+            print(
+                f"WARNING: Model parameter {name} is all zeros: it might be because of a missing initialization"
+            )
+
+
+if __name__ == "__main__":
+    from core.trainer.utils import get_initializer
+    from core.models.GI_01.main.model import ConstrueAutoRegressiveModel
+    from core.configurations.base import BaseConfiguration
+
+    config = BaseConfiguration(model_name="small_lm", num_layers=6, hidden_dim=32, intermediate_dim=512,
+                                max_positions=256, vocabulary_size=64000, num_heads=8, attention_dropout=0.05,
+                                batch_size=8, weight_decay=0.01,
+                                learning_rate=5e-4,
+                                tokenizer_path="/workspace/vipin_g6/personal/pretraining/english_tokenizer/english_tokenizer.model",
+                                dataset_batch_size=16, dataset_shuffle=True, num_epochs=2, eval_frequency=1,
+                                eval_iter=10,
+                                model_max_sequence=256)
+
+    model = ConstrueAutoRegressiveModel(config=config)
+    initalizer = get_initializer(init_type="xavier", activation="gelu", embedding_init="xavier", embedding_padding_idx=config.padding_id)
+
+    model.apply(initalizer)
+    validate_model_initial_states(model, config, (10, ), n_samples=1000)
\ No newline at end of file
diff --git a/core/utils/__pycache__/masks.cpython-311.pyc b/core/utils/__pycache__/masks.cpython-311.pyc
deleted file mode 100644
index f24e1b9..0000000
Binary files a/core/utils/__pycache__/masks.cpython-311.pyc and /dev/null differ
diff --git a/core/utils/__pycache__/test_mask.cpython-311.pyc b/core/utils/__pycache__/test_mask.cpython-311.pyc
deleted file mode 100644
index 75701d5..0000000
Binary files a/core/utils/__pycache__/test_mask.cpython-311.pyc and /dev/null differ
diff --git a/core/utils/masks.py b/core/utils/masks.py
index 1ef4e65..27db958 100644
--- a/core/utils/masks.py
+++ b/core/utils/masks.py
@@ -1,6 +1,9 @@
 from typing import Optional, Tuple
 import torch
 from torch import Tensor
+from xformers.ops import fmha
+from torch.nn.attention.flex_attention import create_block_mask, BlockMask
+
 
 
 def create_causal_mask(
@@ -36,4 +39,22 @@ def create_causal_mask(
 
         mask_value = torch.finfo(dtype).min
         causal_mask = causal_mask.masked_fill(attention_mask == 0, mask_value)
-    return causal_mask
\ No newline at end of file
+    return causal_mask
+
+
+
+def causal_mask(b, h, q_idx, kv_idx):
+    return q_idx >= kv_idx
+
+
+def create_multi_type_causal_mask(seqlen, attn_impl):
+    if attn_impl == "xformers":
+        return fmha.attn_bias.LowerTriangularMask()
+    elif attn_impl == "sdpa":
+        return "causal"
+    elif attn_impl == "flex_attention":
+        return create_block_mask(causal_mask, None, None, seqlen, seqlen)
+    else:
+        raise NotImplementedError(
+            f"Attention {attn_impl} not implemented"
+        )
\ No newline at end of file
diff --git a/data_processing/parquet/convertors.py b/data_processing/parquet/convertors.py
new file mode 100644
index 0000000..93e0439
--- /dev/null
+++ b/data_processing/parquet/convertors.py
@@ -0,0 +1,28 @@
+import os
+
+
+def parquet_to_jsonl(dataset, work_dir, src_dir, tgt_dir, ntasks=64):
+    from datatrove.executor import LocalPipelineExecutor
+    from datatrove.pipeline.readers import ParquetReader
+    from datatrove.pipeline.writers import JsonlWriter
+    dataset_name = dataset.split("/", 1)[0]
+
+    pipeline_exec = LocalPipelineExecutor(
+        pipeline=[
+            ParquetReader(
+                str(src_dir),
+                file_progress=True,
+                doc_progress=True,
+                text_key="content",
+                glob_pattern="**/*.parquet",
+            ),
+            JsonlWriter(
+                str(tgt_dir),
+                output_filename=dataset_name + ".chunk.${rank}.jsonl",
+                compression=None,
+            ),
+        ],
+        tasks=ntasks,
+        logging_dir=os.path.join(work_dir, "datatrove"),
+    )
+    pipeline_exec.run()
\ No newline at end of file
diff --git a/data_processing/parquet/sentence_sampler.py b/data_processing/parquet/sentence_sampler.py
new file mode 100644
index 0000000..c87a585
--- /dev/null
+++ b/data_processing/parquet/sentence_sampler.py
@@ -0,0 +1,62 @@
+import argparse
+import random
+import os
+
+import apache_beam as beam
+from apache_beam.io import parquetio
+from apache_beam.options.pipeline_options import PipelineOptions
+
+import nltk
+from nltk.tokenize import sent_tokenize, word_tokenize
+
+nltk.download('punkt_tab')
+
+
+# Function to extract the sentence from a record.
+# Change 'text' to the appropriate key if your Parquet files use a different field name.
+def extract_sentence(record):
+    # Expecting record is a dict with a 'text' field.
+    text = record.get('content')
+    sentences = sent_tokenize(text)
+    return [sent.strip().strip("\n") for sent in sentences]
+
+def filter_short_sentence(record):
+    return len(word_tokenize(record.strip())) >= 5 and record.count("\n") == 0
+
+def sample_by_percentage(element, percentage=10):
+    return random.random() < (percentage / 100.0)
+
+def run(args):
+    # Define your pipeline options if needed.
+    options = PipelineOptions()
+
+    with beam.Pipeline(options=options) as p:
+        sampled_sentences = (
+            p
+            # Read from multiple parquet files; adjust the glob pattern as needed.
+            | 'ReadParquetFiles' >> parquetio.ReadFromParquet(args.input_path)
+            # Extract the sentence from each record.
+            | 'ExtractSentence' >> beam.FlatMap(extract_sentence)
+            # Filter out records that did not have a valid sentence.
+            | 'FilterShortSentences' >> beam.Filter(filter_short_sentence)
+            | 'Sample' >> beam.Filter(sample_by_percentage, 20)
+            # | 'FlattenSample' >> beam.FlatMap(lambda x: x)
+            # # (Optional) Write the sampled sentences to a text file.
+            | 'WriteToText' >> beam.io.WriteToText(file_path_prefix=os.path.join(args.output_path, "data"),
+                                file_name_suffix="-sampled.txt",
+                                append_trailing_newlines=True,
+                                max_records_per_shard=100000,
+                                skip_if_empty=True)
+        )
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_path", type=str)
+    parser.add_argument("--output_path", type=str)
+    args = parser.parse_args()
+
+    import time
+
+    start_time = time.time()
+    run(args)
+    print(f"Completed run in {time.time() - start_time}")
diff --git a/data_processing/terashuf/setup.py b/data_processing/terashuf/setup.py
new file mode 100644
index 0000000..bdf3972
--- /dev/null
+++ b/data_processing/terashuf/setup.py
@@ -0,0 +1,31 @@
+import os
+import subprocess
+
+
+def run_command(command):
+    print(f"Running: {command}")
+    subprocess.run(command, shell=True, check=True)
+
+
+def setup_terashuf(work_dir):
+    terashuf_dir = os.path.join(work_dir, "terashuf")
+    terashuf_executable = os.path.join(terashuf_dir, "terashuf")
+
+    if os.path.exists(terashuf_executable):
+        print("terashuf executable already exists. Skipping setup.")
+        return terashuf_dir
+
+    print("Setting up terashuf...")
+    run_command(f"git clone https://github.com/alexandres/terashuf {terashuf_dir}")
+    run_command(f"make -C {terashuf_dir}")
+    return terashuf_dir
+
+
+def execute_terashuf(terashuf_dir, src_dir, orig_extension, cat_command, nchunks, out_dir, suffix, prefix):
+    terashuf_executable = os.path.join(terashuf_dir, "terashuf")
+    run_command(
+        f"ulimit -n 100000 && "
+        f"find {src_dir} -type f -name '*{orig_extension}' -print0 | xargs -0 -I {{}} sh -c '{cat_command}' | {terashuf_executable} | "
+        f"split -n r/{nchunks} -d --suffix-length 2 --additional-suffix {suffix} - {out_dir}/{prefix}"
+        # "; trap 'echo \"Caught signal 13, exiting with code 1\"; exit 1' SIGPIPE;"
+    )
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index fa7517a..4d22e0b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,34 +7,45 @@ requires-python = ">=3.11"
 dependencies = [
     "blobfile>=3.0.0",
     "datasets~=3.2.0",
-    "fsspec>=2024.9.0",
+    "datatrove>=0.4.0",
+    "fsspec>=2024.6.1",
     "huggingface-hub~=0.26.2",
-    "lm-eval>=0.4.7",
+    "lm-eval>=0.4.8",
     "matplotlib==3.9.2",
-    "msgspec>=0.18.6",
+    "msgspec>=0.19.0",
     "multiprocess==0.70.16",
     "omegaconf>=2.3.0",
+    "orjson>=3.10.16",
     "pynvml>=12.0.0",
     "pyyaml~=6.0",
     "randomname>=0.2.1",
     "regex~=2024.11.6",
     "requests~=2.32.3",
     "rouge-score>=0.1.2",
-    "sacrebleu>=2.4.3",
-    "scipy>=1.14.1",
+    "sacrebleu>=2.5.1",
+    "scipy>=1.15.2",
     "seaborn==0.13.2",
     "sentencepiece~=0.1.99",
-    "tiktoken>=0.8.0",
-    "tokenizers>=0.21.0",
-    "torch>=2.5.1",
+    "tiktoken>=0.9.0",
+    "tokenizers>=0.21.1",
+    "torch==2.5.0+cu121",
     "tqdm~=4.67.0",
-    "transformers>=4.47.1",
-    "uv>=0.5.11",
-    "viztracer>=1.0.0",
-    "wandb>=0.19.1",
+    "transformers>=4.50.3",
+    "uv>=0.6.16",
+    "viztracer>=1.0.3",
+    "wandb>=0.19.10",
+    "xformers>=0.0.28.post2",
 ]
 
 [dependency-groups]
 ci = [
     "pre-commit>=4.0.1",
 ]
+
+[tool.uv.sources]
+torch = { index = "pytorch-cu121" }
+
+[[tool.uv.index]]
+name = "pytorch-cu121"
+url = "https://download.pytorch.org/whl/cu121"
+explicit = true
diff --git a/requirements-ci.txt b/requirements-ci.txt
index d3f6df6..f260492 100644
--- a/requirements-ci.txt
+++ b/requirements-ci.txt
@@ -2,10 +2,10 @@
 #    uv export --no-hashes --frozen --only-group=ci --output-file=requirements-ci.txt
 cfgv==3.4.0
 distlib==0.3.9
-filelock==3.16.1
-identify==2.6.3
+filelock==3.13.1
+identify==2.6.10
 nodeenv==1.9.1
-platformdirs==4.3.6
-pre-commit==4.0.1
+platformdirs==4.3.7
+pre-commit==4.2.0
 pyyaml==6.0.2
-virtualenv==20.28.0
+virtualenv==20.30.0
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 37eb1aa..4bae115 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,129 +1,136 @@
 # This file was autogenerated by uv via the following command:
 #    uv export --no-hashes --frozen --output-file=requirements-dev.txt
-absl-py==2.1.0
-accelerate==1.2.1
-aiohappyeyeballs==2.4.4
-aiohttp==3.11.11
+absl-py==2.2.2
+accelerate==1.6.0
+aiohappyeyeballs==2.6.1
+aiohttp==3.11.18
 aiosignal==1.3.2
 annotated-types==0.7.0
 antlr4-python3-runtime==4.9.3
-attrs==24.3.0
+attrs==25.3.0
 blobfile==3.0.0
-certifi==2024.12.14
+certifi==2025.1.31
 cffi==1.17.1 ; platform_python_implementation == 'PyPy'
 chardet==5.2.0
-charset-normalizer==3.4.0
+charset-normalizer==3.4.1
 click==8.1.8
 colorama==0.4.6
-contourpy==1.3.1
+contourpy==1.3.2
 cycler==0.12.1
-dataproperty==1.0.1
+dataproperty==1.1.0
 datasets==3.2.0
+datatrove==0.4.0
 dill==0.3.8
 docker-pycreds==0.4.0
 evaluate==0.4.3
-filelock==3.16.1
+filelock==3.13.1
 fire==0.7.0
-fonttools==4.55.3
-frozenlist==1.5.0
-fsspec==2024.9.0
-gitdb==4.0.11
-gitpython==3.1.43
+fonttools==4.57.0
+frozenlist==1.6.0
+fsspec==2024.6.1
+gitdb==4.0.12
+gitpython==3.1.44
 huggingface-hub==0.26.5
+humanize==4.12.2
 idna==3.10
-jinja2==3.1.5
+jinja2==3.1.4
 joblib==1.4.2
 jsonlines==4.0.0
-kiwisolver==1.4.7
-lm-eval==0.4.7
-lxml==5.3.0
+kiwisolver==1.4.8
+lm-eval==0.4.8
+loguru==0.7.3
+lxml==5.4.0
 markupsafe==3.0.2
 matplotlib==3.9.2
-mbstrdecoder==1.1.3
-more-itertools==10.5.0
+mbstrdecoder==1.1.4
+more-itertools==10.7.0
 mpmath==1.3.0
-msgspec==0.18.6
-multidict==6.1.0
+msgspec==0.19.0
+multidict==6.4.3
 multiprocess==0.70.16
-networkx==3.4.2
+networkx==3.3
 nltk==3.9.1
 numexpr==2.10.2
-numpy==2.2.1
-nvidia-cublas-cu12==12.4.5.8 ; platform_machine == 'x86_64' and sys_platform == 'linux'
-nvidia-cuda-cupti-cu12==12.4.127 ; platform_machine == 'x86_64' and sys_platform == 'linux'
-nvidia-cuda-nvrtc-cu12==12.4.127 ; platform_machine == 'x86_64' and sys_platform == 'linux'
-nvidia-cuda-runtime-cu12==12.4.127 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+numpy==2.2.5
+nvidia-cublas-cu12==12.1.3.1 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+nvidia-cuda-cupti-cu12==12.1.105 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+nvidia-cuda-nvrtc-cu12==12.1.105 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+nvidia-cuda-runtime-cu12==12.1.105 ; platform_machine == 'x86_64' and sys_platform == 'linux'
 nvidia-cudnn-cu12==9.1.0.70 ; platform_machine == 'x86_64' and sys_platform == 'linux'
-nvidia-cufft-cu12==11.2.1.3 ; platform_machine == 'x86_64' and sys_platform == 'linux'
-nvidia-curand-cu12==10.3.5.147 ; platform_machine == 'x86_64' and sys_platform == 'linux'
-nvidia-cusolver-cu12==11.6.1.9 ; platform_machine == 'x86_64' and sys_platform == 'linux'
-nvidia-cusparse-cu12==12.3.1.170 ; platform_machine == 'x86_64' and sys_platform == 'linux'
-nvidia-ml-py==12.560.30
+nvidia-cufft-cu12==11.0.2.54 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+nvidia-curand-cu12==10.3.2.106 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+nvidia-cusolver-cu12==11.4.5.107 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+nvidia-cusparse-cu12==12.1.0.106 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+nvidia-ml-py==12.570.86
 nvidia-nccl-cu12==2.21.5 ; platform_machine == 'x86_64' and sys_platform == 'linux'
-nvidia-nvjitlink-cu12==12.4.127 ; platform_machine == 'x86_64' and sys_platform == 'linux'
-nvidia-nvtx-cu12==12.4.127 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+nvidia-nvjitlink-cu12==12.1.105 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+nvidia-nvtx-cu12==12.1.105 ; platform_machine == 'x86_64' and sys_platform == 'linux'
 objprint==0.3.0
 omegaconf==2.3.0
-packaging==24.2
+orjson==3.10.16
+packaging==25.0
 pandas==2.2.3
-pathvalidate==3.2.1
-peft==0.14.0
-pillow==11.0.0
-platformdirs==4.3.6
-portalocker==3.0.0
-propcache==0.2.1
-protobuf==5.29.2
-psutil==6.1.1
-pyarrow==18.1.0
+pathvalidate==3.2.3
+peft==0.15.2
+pillow==11.2.1
+platformdirs==4.3.7
+portalocker==3.1.1
+propcache==0.3.1
+protobuf==6.30.2
+psutil==7.0.0
+pyarrow==19.0.1
 pybind11==2.13.6
 pycparser==2.22 ; platform_python_implementation == 'PyPy'
-pycryptodomex==3.21.0
-pydantic==2.10.4
-pydantic-core==2.27.2
+pycryptodomex==3.22.0
+pydantic==2.11.3
+pydantic-core==2.33.1
 pynvml==12.0.0
-pyparsing==3.2.0
-pytablewriter==1.2.0
+pyparsing==3.2.3
+pytablewriter==1.2.1
 python-dateutil==2.9.0.post0
-pytz==2024.2
-pywin32==308 ; sys_platform == 'win32'
+pytz==2025.2
+pywin32==310 ; sys_platform == 'win32'
 pyyaml==6.0.2
 randomname==0.2.1
 regex==2024.11.6
 requests==2.32.3
 rouge-score==0.1.2
-sacrebleu==2.4.3
-safetensors==0.4.5
-scikit-learn==1.6.0
-scipy==1.14.1
+sacrebleu==2.5.1
+safetensors==0.5.3
+scikit-learn==1.6.1
+scipy==1.15.2
 seaborn==0.13.2
 sentencepiece==0.1.99
-sentry-sdk==2.19.2
-setproctitle==1.3.4
-setuptools==75.6.0
+sentry-sdk==2.26.1
+setproctitle==1.3.5
+setuptools==70.2.0
 six==1.17.0
-smmap==5.0.1
+smmap==5.0.2
 sqlitedict==2.1.0
 sympy==1.13.1
-tabledata==1.3.3
+tabledata==1.3.4
 tabulate==0.9.0
-tcolorpy==0.1.6
-termcolor==2.5.0
-threadpoolctl==3.5.0
-tiktoken==0.8.0
-tokenizers==0.21.0
-torch==2.5.1
+tcolorpy==0.1.7
+termcolor==3.0.1
+threadpoolctl==3.6.0
+tiktoken==0.9.0
+tokenizers==0.21.1
+torch==2.5.0+cu121
 tqdm==4.67.1
 tqdm-multiprocess==0.0.11
-transformers==4.47.1
+transformers==4.50.3
 triton==3.1.0 ; python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'linux'
-typepy==1.3.2
+typepy==1.3.4
 typing-extensions==4.12.2
-tzdata==2024.2
-urllib3==2.3.0
-uv==0.5.11
-viztracer==1.0.0
-wandb==0.19.1
+typing-inspection==0.4.0
+tzdata==2025.2
+urllib3==2.4.0
+uv==0.6.16
+viztracer==1.0.3
+wandb==0.19.10
+win32-setctime==1.2.0 ; sys_platform == 'win32'
 word2number==1.1
+xformers==0.0.28.post2
 xxhash==3.5.0
-yarl==1.18.3
+yarl==1.20.0
 zstandard==0.23.0
diff --git a/scripts/download_and_prepare_pre_training_dataset.py b/scripts/download_and_prepare_pre_training_dataset.py
new file mode 100644
index 0000000..0c3442d
--- /dev/null
+++ b/scripts/download_and_prepare_pre_training_dataset.py
@@ -0,0 +1,98 @@
+import argparse
+import os
+from pathlib import Path
+
+from data_processing.parquet.convertors import parquet_to_jsonl
+from data_processing.terashuf.setup import setup_terashuf, execute_terashuf, run_command
+from scripts.hf_data_downloader import download_dataset_from_huggingface
+
+
+def get_sample_file(path):
+    for path, subdirs, files in os.walk(path):
+        for name in files:
+            return os.path.join(path, name)
+
+
+def get_extension(file):
+    filename, file_extension = os.path.splitext(file)
+    return file_extension
+
+
+def main(args):
+    working_dir = args.data_dir
+    if working_dir is None:
+        working_dir = ""
+
+    working_dir = Path(working_dir)
+    src_dir = working_dir / args.dataset
+
+    if not src_dir.exists():
+        print(f"Creating source directory {src_dir}")
+        src_dir.mkdir(parents=True)
+
+    # download dataset
+    download_dataset_from_huggingface(
+        repo_id=args.dataset,
+        local_dir=str(src_dir),
+        allow_patterns=args.allowed_pattern,
+        num_workers=args.num_workers,
+        revision=args.revision
+    )
+
+    out_dir = args.output_dir
+    os.makedirs(out_dir, exist_ok=True)
+    work_dir = src_dir  # Directory of this Python file
+    dataset_name = args.dataset.split("/", 1)[0]
+    prefix = f"{dataset_name}.chunk."
+
+    sample_file = get_sample_file(work_dir)
+    orig_extension = get_extension(sample_file)
+
+    is_parquet = False
+    if "parquet" in orig_extension:
+        is_parquet = True
+        orig_extension = ".jsonl"
+    elif orig_extension == ".jsonl":
+        orig_extension = ".jsonl"
+    else:
+        raise NotImplementedError(f"Not implemented extension type {orig_extension}")
+
+    cat_command = "cat {}"
+    suffix = ".jsonl"
+    k_validation = 10000
+
+    if is_parquet:
+        print("Running parquet to jsonl.....")
+        parquet_to_jsonl(args.dataset, work_dir, src_dir, src_dir)
+
+    terashuf_dir = setup_terashuf(work_dir)
+    os.environ["MEMORY"] = f"{args.memory}"
+    os.environ["SEED"] = f"{args.seed}"
+
+    execute_terashuf(terashuf_dir, str(src_dir), orig_extension, cat_command, args.nchunks, str(out_dir), suffix, prefix)
+
+    validation_file = f"{out_dir}/{dataset_name}.val{suffix}"
+    for i in range(args.nchunks):
+        chunk_file = f"{out_dir}/{prefix}{i:02d}{suffix}"
+        run_command(f"head -n {k_validation} {chunk_file} >> {validation_file}")
+        run_command(f"sed -i '1,{k_validation}d' {chunk_file}")
+
+    print("All tasks completed successfully!")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset", type=str)
+    parser.add_argument("--allowed_pattern", type=str, default=None,
+                        help="multiple patterns can be provided as comma seperated format")
+    parser.add_argument("--revision", type=str, default=None)
+    parser.add_argument("--num_workers", type=int, default=16)
+    parser.add_argument("--memory", type=float, default=8)
+    parser.add_argument("--data_dir", type=str, default="data")
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--nchunks", type=int, default=32)
+    parser.add_argument("--output_dir", type=str, default="data")
+
+    args = parser.parse_args()
+
+    main(args)
diff --git a/scripts/hf_data_downloader.py b/scripts/hf_data_downloader.py
index 65ab481..08667e9 100644
--- a/scripts/hf_data_downloader.py
+++ b/scripts/hf_data_downloader.py
@@ -13,19 +13,19 @@ def download_dataset_from_huggingface(repo_id, revision, local_dir, allow_patter
         local_dir (str): destination folder to store files
         allow_patterns (str): file patter
         num_workers (str): number of workers to download files
-        
+
     Eg:
     If hf repo contains files in parquer format use pattern like
         `.parquet`
     If in sub-folder of parquet format
         `**.parquet`
-    
+
     Increase the num workers to enjoy the parallelism
     """
-    
+
     if allow_patterns is not None:
         allow_patterns = allow_patterns.split(",")
-        
+
     print(f"Downloading dataset from {repo_id}...")
     max_retries = 5
     retry_delay = 10  # seconds
@@ -48,23 +48,23 @@ def download_dataset_from_huggingface(repo_id, revision, local_dir, allow_patter
             else:
                 raise
     print(f"Dataset downloaded to {local_dir}")
-    
 
-    
-    
+
+
+
 def main(args, seed=42):
     # prepare workspace
     working_dir = args.working_dir
     if working_dir is None:
         working_dir = ""
-    
+
     working_dir = Path(working_dir)
     src_dir = working_dir / args.dataset
-    
+
     if not src_dir.exists():
         print(f"Creating source directory {src_dir}")
         src_dir.mkdir(parents=True)
-    
+
     # download dataset
     download_dataset_from_huggingface(
         repo_id=args.dataset,
@@ -74,7 +74,7 @@ def main(args, seed=42):
         revision=args.revision
     )
 
-        
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--dataset", type=str)
@@ -86,4 +86,4 @@ def main(args, seed=42):
 
     args = parser.parse_args()
 
-    main(args, args.seed)
\ No newline at end of file
+    main(args, args.seed)
diff --git a/scripts/setup_env.sh b/scripts/setup_env.sh
index 019f977..b90804b 100644
--- a/scripts/setup_env.sh
+++ b/scripts/setup_env.sh
@@ -72,9 +72,9 @@ conda activate $env_prefix
 conda install git -y
 
 # Install packages
-pip install ninja
-pip install --requirement requirements-dev.txt
+pip install --requirement requirements-dev.txt --extra-index-url https://download.pytorch.org/whl/cu121
 pip install --requirement requirements-ci.txt
+pip install ninja
 pre-commit install
 
 
diff --git a/study/dataloader_analysis.ipynb b/study/dataloader_analysis.ipynb
new file mode 100644
index 0000000..7d638c7
--- /dev/null
+++ b/study/dataloader_analysis.ipynb
@@ -0,0 +1,173 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "\n",
+    "sys.path.append('/workspace/AI-Uncomplicated')\n",
+    "\n",
+    "from core.tokenizer.tokenizer_loader import TokenizerArgs\n",
+    "from core.trainer.dataloader import DataArgs\n",
+    "\n",
+    "\n",
+    "args = DataArgs(root_dir=\"/workspace/AI-Uncomplicated/artifact/pretraining_data\",\n",
+    "                    sources={\"TigerResearch\": 1.0},\n",
+    "                    tokenizer=TokenizerArgs(name=\"GI01-tokenizer-v0.1-en\", path=\"/workspace/AI-Uncomplicated/artifact/tokenizer\"),\n",
+    "                    fim_rate=0.1,\n",
+    "                    fim_type=\"content\",\n",
+    "                    batch_size=8,\n",
+    "                    load_async=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from core.tokenizer.tokenizer_loader import SPMTokenizer, TokenizerArgs\n",
+    "\n",
+    "tokenizer = SPMTokenizer(os.path.join(args.tokenizer.path, args.tokenizer.name))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from core.trainer.dataloader import init_dataloader_state_from_args\n",
+    "\n",
+    "\n",
+    "states = init_dataloader_state_from_args(args, 1, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from core.trainer.dataloader import build_dataloader_from_args\n",
+    "\n",
+    "\n",
+    "dataloader = build_dataloader_from_args(args, states)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with dataloader as loader:\n",
+    "    for data in loader:\n",
+    "        # print(data)\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = data[0][:, :, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<FIL_PRE>. Consider using multiple folds or unusual shapes. Don't forget to include the important information—such as the date, time, telephone number, and place (fig. 1-20<FIL_SUF> Cahoon, is a terrific way to get your name out into the industry. Add your name and e-mail on the back for<FIL_MID>).\n",
+      "\n",
+      "Fig. 1–20: This lovely 3-D pop-up card, designed by Fran</s><s> _R. N. Shaw's Leyswood in Sussex, England, designed 1866 and executed 1868–69. Mostly demolished, this house was reproduced in periodicals of the day and had a broad influence on English and American architects in the early 1870s_. (Illustration Credit 2.1)\n",
+      "\n",
+      "When McKim reached France, he began a ten-day tour of Normandy, the northern French region so popular with the court of Louis Napoléon. Along the Channel coast, summer visitors now flocked to what had once been a quiet agricultural region. Tourists had come to Normandy from the 1830s onward, and English architects sketched the giant vernacular farm buildings and the manor houses. Deauville and Trouville became resorts. The trip to these villages had been arduous until the railroad pushed through from Paris to Rouen, and again in 1854 to the coastal towns. Visitors found cool breezes, picturesque vistas, and great fishing in the region in the early days. The area especially attracted the newly made men of wealth in industrial France who did not have the loyalty to their home districts of the landed aristocratic class. They would build commodious, wooden-framed summer villas along the coast from the mid-century forward. The new houses often used elements of the vernacular buildings of the region, such as brick infilling between timbers and decorative woodwork, to create a harmonious appearance and reflect the older buildings nearby. Such coastal houses were also being built in northern Germany. The new villas appear taller and more vertical in contrast to the horizontal appearance of the older farm complexes and manors of the region, which sprawl heavier and lower to the land.</s><s> So the answer to the question 'what did they know about the Nazis' crimes?' is, frustratingly, 'not very much'. There were a few indications that – in retrospect – hinted at dark deeds, not least Martin Bormann's vindictive, high-handed behaviour, but beyond that little of any substance. We must conclude – paradoxically perhaps – that the gilded cage of the Berghof, or of Hitler's entourage, was not the best vantage point from which to view the true murderous nature of Nazism.\n",
+      "\n",
+      "Indeed it was Martin Bormann who – as much as Hitler – played a key role in the lives and careers of the Berghof staff. As Hitler's personal secretary, Bormann assumed responsibility for the day-to-day running of the entire Berghof area, as well as overseeing the building's expansion in 1936. In this capacity, he became known as the 'Lord of the Obersalzberg', and sought to exercise authority over the Berghof staff, and was responsible for the dismissal of Anna Plaim in 1943, considering her unsuitable for service because of her parents' religious faith. Herbert Döhring also got on the wrong side of Bormann, and only earned the latter's respect through his robust self-defence.</s><s> Those cold blue eyes glued my tongue. I sat without moving.\n",
+      "\n",
+      "\"From the very beginning of my marriage to Jill, Leigh seemed to hate me. She could never forgive me for taking her mother away from her father. She adored her father. I tried to win her affections. She wanted none of that. I didn't do a thing to harm her, and eventually I stopped trying to win her over. I knew she blamed me for her father's desperate unhappiness.\n",
+      "\n",
+      "\"I came home from my long honeymoon with Jill disillusioned. Horribly disillusioned. I tried not to let anyone see it. Jill isn't capable of loving anyone more than she loves herself and her everlasting youthful image. My God, how that woman loves to look in mirrors!\n",
+      "\n",
+      "\"I grew disgusted seeing the way she had to have every hair in place all the time, always glancing to check on the shine on her nose, checking for lipstick smudges.\"\n",
+      "\n",
+      "His smile was crooked, bitter. \"And so I came to realize too late that despite all the beauty Jill possessed, no man could love Jill for anything other than her facade. Jill has no depths. She's just a shell of a woman. Everything sweet, and thoughtful and kind went into her daughter. I began to be more aware of Leigh in a room than I was of her mother. Soon I was noticing a lovely adolescent girl who seldom glanced in any mirror. A girl who loved to wear simple, loose garments that fluttered when she moved, and her hair was long and loose and straight. Leigh waited on Troy, with pleasure and joy she waited on Troy. I loved and admired her for doing that.</s><s><FIL_PRE> Someone grabs me. The chair comes out of my hands and I fall. It is the principal and Ms. Dana. I am going ape-shit because I need to tell Gloria not to go. I need to tell her to come help me escape but Ms. Dana pulls me down and puts me on the floor. She is on top of<FIL_SUF>. Now I'm on the floor again next to a book rack with Julie of the Wolves and Island of the Blue Dolphins. My eyes want to cry but they can't because my breath is catching and catching and I can't breathe. I see Ms. Dana and Mrs. Lomos and Mrs. Wake and the librarian and now it feels like I'm under water or a blanket and then everything is dark.12\n",
+      "\n",
+      "EXACTLY 3:31 IN THE AFTERNOON, TUESDAY, SEPTEMBER<FIL_MID> me so I can't get up. I kick and fight. I bite her in the arm. She yells and lets go.\n",
+      "\n",
+      "\"Ginny!\" I hear someone say. \"Ginny!\"\n",
+      "\n",
+      "It is Mrs. Lomos. I see her feet.\n",
+      "\n",
+      "I stand. \"It—\" I say. \"It was exa—\" But the words don't come and then the principal grabs me from behind. I am falling but as I go down I look out the window and see the Green Car driving away</s><s> Irenaeus makes many interesting generalizations about the types of people advancing the \"gnosis falsely so-called\"; they appeared slippery, cocky, and at times strangely insecure. The preface refers to the heretics' \"plausible system.\" This plausibility he proceeds very cleverly and often wittily to undermine, unafraid to go into detailed exposition of Gnostic theosophical superstructures, even to the point of boredom—part of his method for undermining the grip of the Gnostics' plausibility. One thing he wants his readers to recognize is that \"Their language resembles ours, while their sentiments are very different.\" This is still a resonant observation and subverts the notion, often held today, that, _well,_ they were all _Christians,_ weren't they? Not in the view of Irenaeus! They were outsiders to the communion of the apostolic faithful and should repent of their blasphemies before seeking reentry to the fold. The orthodox majority, under such leaders as Irenaeus, would never allow an esoteric branch of the faith with superior tendencies to thrive: too much like competition, to say the very least.</s><s> Introduction\n",
+      "The Roman Superhighway or Bataan Provincial Highway, formerly known as the Bataan Provincial Expressway, is a , two- to four-lane major highway that connects the municipality of Dinalupihan to the municipality of Mariveles in Bataan, Philippines. The entire road forms part of National Route 301 of the Philippine highway network.\n",
+      "\n",
+      "Etymology\n",
+      "Roman Superhighway is named after Pablo Roman Sr., the former representative of Bataan who is the acknowledged father of the export processing zone known as Bataan Export Processing Zone (BEPZ; now known as Freeport Area of Bataan (FAB) since June 30, 2010).\n",
+      " History \n",
+      "Construction of the Roman Superhighway began on April 7, 1973 during the Martial Law period and completed on July 16, 1977. The project was implemented by President Ferdinand Marcos. It was originally intended to be an expressway to serve BEPZ in Mariveles, Bataan, but it later became an at-grade highway when local residents built houses and businesses along it.\n",
+      "The fully concrete road has an effective width of , although some portions measured up to maximum. Phase 1 of the total project covered from Dinalupihan to Alauli Junction in Pilar and it measured long and complemented with 14 steel-concrete bridges. Phase 2 is measured and has 12 bridges.\n",
+      "Construction Development Corporation of the Philippines (CDCP) and Monark International worked on the project. CDCP accomplished its task in three years and three months. Phase 2 was completed by Monark in two years and 11 months. The Department of Public Works and Highways (DPWH) designed and supervised the construction of the road project. Phase 1 costs , while was spent for Phase 2. The total amount includes the payment for the right-of-way of former agricultural lands. Some of the farmlots were even donated by the owners.\n",
+      "Throughout its existence, rehabilitation works were made on the highway such as applying asphalt overlay either on an existing concrete pavement or both the concrete pavement and the asphalt used on its shoulders and replacing an old pavement with a new one. Recently, some of its portions were widened to accommodate more\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tokenizer.decode(sample[0].tolist()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "217 in sample[0]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ai_experimentation_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/study/rope_positional_encoding.ipynb b/study/rope_positional_encoding.ipynb
index d74b0c1..205093a 100644
--- a/study/rope_positional_encoding.ipynb
+++ b/study/rope_positional_encoding.ipynb
@@ -463,7 +463,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ai_experimentation_env",
    "language": "python",
    "name": "python3"
   },
@@ -477,7 +477,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,
diff --git a/uv.lock b/uv.lock
index df46287..f1dd31d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,4 +1,5 @@
 version = 1
+revision = 2
 requires-python = ">=3.11"
 resolution-markers = [
     "python_full_version >= '3.12' and sys_platform == 'linux'",
@@ -9,16 +10,16 @@ resolution-markers = [
 
 [[package]]
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+    { url = "https://files.pythonhosted.org/packages/f6/d4/349f7f4bd5ea92dab34f5bb0fe31775ef6c311427a14d5a5b31ecb442341/absl_py-2.2.2-py3-none-any.whl", hash = "sha256:e5797bc6abe45f64fd95dc06394ca3f2bedf3b5d895e9da691c9ee3397d70092", size = 135565, upload_time = "2025-04-03T12:41:03.172Z" },
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 name = "accelerate"
-version = "1.2.1"
+version = "1.6.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
@@ -29,9 +30,9 @@ dependencies = [
     { name = "safetensors" },
     { name = "torch" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/8a/6e/c29a1dcde7db07f47870ed63e5124086b11874ad52ccd533dc1ca2c799da/accelerate-1.6.0.tar.gz", hash = "sha256:28c1ef1846e690944f98b68dc7b8bb6c51d032d45e85dcbb3adb0c8b99dffb32", size = 363804, upload_time = "2025-04-01T11:53:03.037Z" }
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+    { url = "https://files.pythonhosted.org/packages/63/b1/8198e3cdd11a426b1df2912e3381018c4a4a55368f6d0857ba3ca418ef93/accelerate-1.6.0-py3-none-any.whl", hash = "sha256:1aee717d3d3735ad6d09710a7c26990ee4652b79b4e93df46551551b5227c2aa", size = 354748, upload_time = "2025-04-01T11:53:01.298Z" },
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 [[package]]
@@ -41,6 +42,7 @@ source = { virtual = "." }
 dependencies = [
     { name = "blobfile" },
     { name = "datasets" },
+    { name = "datatrove" },
     { name = "fsspec" },
     { name = "huggingface-hub" },
     { name = "lm-eval" },
@@ -48,6 +50,7 @@ dependencies = [
     { name = "msgspec" },
     { name = "multiprocess" },
     { name = "omegaconf" },
+    { name = "orjson" },
     { name = "pynvml" },
     { name = "pyyaml" },
     { name = "randomname" },
@@ -66,6 +69,7 @@ dependencies = [
     { name = "uv" },
     { name = "viztracer" },
     { name = "wandb" },
+    { name = "xformers" },
 ]
 
 [package.dev-dependencies]
@@ -77,31 +81,34 @@ ci = [
 requires-dist = [
     { name = "blobfile", specifier = ">=3.0.0" },
     { name = "datasets", specifier = "~=3.2.0" },
-    { name = "fsspec", specifier = ">=2024.9.0" },
+    { name = "datatrove", specifier = ">=0.4.0" },
+    { name = "fsspec", specifier = ">=2024.6.1" },
     { name = "huggingface-hub", specifier = "~=0.26.2" },
-    { name = "lm-eval", specifier = ">=0.4.7" },
+    { name = "lm-eval", specifier = ">=0.4.8" },
     { name = "matplotlib", specifier = "==3.9.2" },
-    { name = "msgspec", specifier = ">=0.18.6" },
+    { name = "msgspec", specifier = ">=0.19.0" },
     { name = "multiprocess", specifier = "==0.70.16" },
     { name = "omegaconf", specifier = ">=2.3.0" },
+    { name = "orjson", specifier = ">=3.10.16" },
     { name = "pynvml", specifier = ">=12.0.0" },
     { name = "pyyaml", specifier = "~=6.0" },
     { name = "randomname", specifier = ">=0.2.1" },
     { name = "regex", specifier = "~=2024.11.6" },
     { name = "requests", specifier = "~=2.32.3" },
     { name = "rouge-score", specifier = ">=0.1.2" },
-    { name = "sacrebleu", specifier = ">=2.4.3" },
-    { name = "scipy", specifier = ">=1.14.1" },
+    { name = "sacrebleu", specifier = ">=2.5.1" },
+    { name = "scipy", specifier = ">=1.15.2" },
     { name = "seaborn", specifier = "==0.13.2" },
     { name = "sentencepiece", specifier = "~=0.1.99" },
-    { name = "tiktoken", specifier = ">=0.8.0" },
-    { name = "tokenizers", specifier = ">=0.21.0" },
-    { name = "torch", specifier = ">=2.5.1" },
+    { name = "tiktoken", specifier = ">=0.9.0" },
+    { name = "tokenizers", specifier = ">=0.21.1" },
+    { name = "torch", specifier = "==2.5.0+cu121", index = "https://download.pytorch.org/whl/cu121" },
     { name = "tqdm", specifier = "~=4.67.0" },
-    { name = "transformers", specifier = ">=4.47.1" },
-    { name = "uv", specifier = ">=0.5.11" },
-    { name = "viztracer", specifier = ">=1.0.0" },
-    { name = "wandb", specifier = ">=0.19.1" },
+    { name = "transformers", specifier = ">=4.50.3" },
+    { name = "uv", specifier = ">=0.6.16" },
+    { name = "viztracer", specifier = ">=1.0.3" },
+    { name = "wandb", specifier = ">=0.19.10" },
+    { name = "xformers", specifier = ">=0.0.28.post2" },
 ]
 
 [package.metadata.requires-dev]
@@ -109,16 +116,16 @@ ci = [{ name = "pre-commit", specifier = ">=4.0.1" }]
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.4"
+version = "2.6.1"
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+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload_time = "2025-03-12T01:42:47.083Z" },
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-version = "3.11.11"
+version = "3.11.18"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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