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.idea/misc.xml/0

<?xml version="1.0" encoding="UTF-8"?> <project version="4"> <component name="Black"> <option name="sdkName" value="LLaMA-Factory" /> </component> <component name="ProjectRootManager" version="2" project-jdk-name="LLaMA-Factory" project-jdk-type="Python SDK" /> </project>

LLaMA-Factory/.idea/misc.xml/0

{ "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "gradient_accumulation_steps": "auto", "gradient_clipping": "auto", "zero_allow_untested_optimizer": true, "fp16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 }, "bf16": { "enabled": "auto" }, "zero_optimization": { "stage": 3, "overlap_comm": true, "contiguous_gradients": true, "sub_group_size": 1000000000.0, "reduce_bucket_size": "auto", "stage3_prefetch_bucket_size": "auto", "stage3_param_persistence_threshold": "auto", "stage3_max_live_parameters": 1000000000.0, "stage3_max_reuse_distance": 1000000000.0, "stage3_gather_16bit_weights_on_model_save": true } }

LLaMA-Factory/cache/ds_z3_config.json/0

{ "agronomy": { "name": "农学", "category": "Other" }, "anatomy": { "name": "解剖学", "category": "STEM" }, "ancient_chinese": { "name": "古汉语", "category": "Social Sciences" }, "arts": { "name": "艺术学", "category": "Humanities" }, "astronomy": { "name": "天文学", "category": "STEM" }, "business_ethics": { "name": "商业伦理", "category": "Social Sciences" }, "chinese_civil_service_exam": { "name": "中国公务员考试", "category": "Social Sciences" }, "chinese_driving_rule": { "name": "中国驾驶规则", "category": "Other" }, "chinese_food_culture": { "name": "中国饮食文化", "category": "Social Sciences" }, "chinese_foreign_policy": { "name": "中国外交政策", "category": "Social Sciences" }, "chinese_history": { "name": "中国历史", "category": "Humanities" }, "chinese_literature": { "name": "中国文学", "category": "Humanities" }, "chinese_teacher_qualification": { "name": "中国教师资格", "category": "Social Sciences" }, "college_actuarial_science": { "name": "大学精算学", "category": "STEM" }, "college_education": { "name": "大学教育学", "category": "Social Sciences" }, "college_engineering_hydrology": { "name": "大学工程水文学", "category": "STEM" }, "college_law": { "name": "大学法律", "category": "Humanities" }, "college_mathematics": { "name": "大学数学", "category": "STEM" }, "college_medical_statistics": { "name": "大学医学统计", "category": "STEM" }, "clinical_knowledge": { "name": "临床知识", "category": "Other" }, "college_medicine": { "name": "大学医学", "category": "Other" }, "computer_science": { "name": "计算机科学", "category": "STEM" }, "computer_security": { "name": "计算机安全", "category": "Other" }, "conceptual_physics": { "name": "概念物理学", "category": "STEM" }, "construction_project_management": { "name": "建设工程管理", "category": "Other" }, "economics": { "name": "经济学", "category": "Social Sciences" }, "education": { "name": "教育学", "category": "Social Sciences" }, "elementary_chinese": { "name": "小学语文", "category": "Social Sciences" }, "elementary_commonsense": { "name": "小学常识", "category": "Other" }, "elementary_information_and_technology": { "name": "小学信息技术", "category": "Other" }, "electrical_engineering": { "name": "电气工程", "category": "STEM" }, "elementary_mathematics": { "name": "初等数学", "category": "STEM" }, "ethnology": { "name": "民族学", "category": "Social Sciences" }, "food_science": { "name": "食品科学", "category": "Other" }, "genetics": { "name": "遗传学", "category": "STEM" }, "global_facts": { "name": "全球事实", "category": "Humanities" }, "high_school_biology": { "name": "高中生物", "category": "STEM" }, "high_school_chemistry": { "name": "高中化学", "category": "STEM" }, "high_school_geography": { "name": "高中地理", "category": "Social Sciences" }, "high_school_mathematics": { "name": "高中数学", "category": "STEM" }, "high_school_physics": { "name": "高中物理学", "category": "STEM" }, "high_school_politics": { "name": "高中政治", "category": "Social Sciences" }, "human_sexuality": { "name": "人类性行为", "category": "Other" }, "international_law": { "name": "国际法学", "category": "Humanities" }, "journalism": { "name": "新闻学", "category": "Social Sciences" }, "jurisprudence": { "name": "法理学", "category": "Humanities" }, "legal_and_moral_basis": { "name": "法律与道德基础", "category": "Other" }, "logical": { "name": "逻辑学", "category": "Humanities" }, "machine_learning": { "name": "机器学习", "category": "STEM" }, "management": { "name": "管理学", "category": "Social Sciences" }, "marketing": { "name": "市场营销", "category": "Social Sciences" }, "marxist_theory": { "name": "马克思主义理论", "category": "Humanities" }, "modern_chinese": { "name": "现代汉语", "category": "Social Sciences" }, "nutrition": { "name": "营养学", "category": "Other" }, "philosophy": { "name": "哲学", "category": "Humanities" }, "professional_accounting": { "name": "专业会计", "category": "Social Sciences" }, "professional_law": { "name": "专业法学", "category": "Humanities" }, "professional_medicine": { "name": "专业医学", "category": "Other" }, "professional_psychology": { "name": "专业心理学", "category": "Social Sciences" }, "public_relations": { "name": "公共关系", "category": "Social Sciences" }, "security_study": { "name": "安全研究", "category": "Social Sciences" }, "sociology": { "name": "社会学", "category": "Social Sciences" }, "sports_science": { "name": "体育学", "category": "Other" }, "traditional_chinese_medicine": { "name": "中医中药", "category": "Other" }, "virology": { "name": "病毒学", "category": "STEM" }, "world_history": { "name": "世界历史", "category": "Humanities" }, "world_religions": { "name": "世界宗教", "category": "Humanities" } }

LLaMA-Factory/evaluation/cmmlu/mapping.json/0

### model model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct ### method stage: sft do_train: true finetuning_type: full use_galore: true galore_layerwise: true galore_target: mlp,self_attn galore_rank: 128 galore_scale: 2.0 ### dataset dataset: identity,alpaca_en_demo template: llama3 cutoff_len: 1024 max_samples: 1000 overwrite_cache: true preprocessing_num_workers: 16 ### output output_dir: saves/llama3-8b/full/sft logging_steps: 10 save_steps: 500 plot_loss: true overwrite_output_dir: true ### train per_device_train_batch_size: 1 gradient_accumulation_steps: 1 learning_rate: 1.0e-4 num_train_epochs: 3.0 lr_scheduler_type: cosine warmup_ratio: 0.1 pure_bf16: true ### eval val_size: 0.1 per_device_eval_batch_size: 1 eval_strategy: steps eval_steps: 500

LLaMA-Factory/examples/extras/galore/llama3_full_sft.yaml/0

### model model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct reward_model: saves/llama3-8b/lora/reward ### method stage: ppo do_train: true finetuning_type: lora lora_target: all ### dataset dataset: identity,alpaca_en_demo template: llama3 cutoff_len: 1024 max_samples: 1000 overwrite_cache: true preprocessing_num_workers: 16 ### output output_dir: saves/llama3-8b/lora/ppo logging_steps: 10 save_steps: 500 plot_loss: true overwrite_output_dir: true ### train per_device_train_batch_size: 1 gradient_accumulation_steps: 8 learning_rate: 1.0e-5 num_train_epochs: 3.0 lr_scheduler_type: cosine warmup_ratio: 0.1 fp16: true ddp_timeout: 180000000 ### generate max_new_tokens: 512 top_k: 0 top_p: 0.9

LLaMA-Factory/examples/train_lora/llama3_lora_ppo.yaml/0

# Copyright 2024 the LlamaFactory team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import concurrent.futures import os from threading import Thread from typing import TYPE_CHECKING, Any, AsyncGenerator, Callable, Dict, List, Optional, Sequence, Tuple, Union import torch from transformers import GenerationConfig, TextIteratorStreamer from ..data import get_template_and_fix_tokenizer from ..extras.logging import get_logger from ..extras.misc import get_logits_processor from ..model import load_model, load_tokenizer from .base_engine import BaseEngine, Response if TYPE_CHECKING: from numpy.typing import NDArray from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin from transformers.image_processing_utils import BaseImageProcessor from trl import PreTrainedModelWrapper from ..data import Template from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments logger = get_logger(__name__) class HuggingfaceEngine(BaseEngine): def __init__( self, model_args: "ModelArguments", data_args: "DataArguments", finetuning_args: "FinetuningArguments", generating_args: "GeneratingArguments", ) -> None: self.can_generate = finetuning_args.stage == "sft" tokenizer_module = load_tokenizer(model_args) self.tokenizer = tokenizer_module["tokenizer"] self.processor = tokenizer_module["processor"] self.tokenizer.padding_side = "left" if self.can_generate else "right" self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template, data_args.tool_format) self.model = load_model( self.tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=(not self.can_generate) ) # must after fixing tokenizer to resize vocab self.generating_args = generating_args.to_dict() try: asyncio.get_event_loop() except RuntimeError: logger.warning("There is no current event loop, creating a new one.") loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) self.semaphore = asyncio.Semaphore(int(os.environ.get("MAX_CONCURRENT", "1"))) @staticmethod def _process_args( model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer", processor: Optional["ProcessorMixin"], template: "Template", generating_args: Dict[str, Any], messages: Sequence[Dict[str, str]], system: Optional[str] = None, tools: Optional[str] = None, image: Optional["NDArray"] = None, input_kwargs: Optional[Dict[str, Any]] = {}, ) -> Tuple[Dict[str, Any], int]: if ( processor is not None and image is not None and not hasattr(processor, "image_seq_length") and template.image_token not in messages[0]["content"] ): # llava-like models messages[0]["content"] = template.image_token + messages[0]["content"] paired_messages = messages + [{"role": "assistant", "content": ""}] system = system or generating_args["default_system"] pixel_values = None prompt_ids, _ = template.encode_oneturn( tokenizer=tokenizer, messages=paired_messages, system=system, tools=tools ) if processor is not None and image is not None: # add image features image_processor: "BaseImageProcessor" = getattr(processor, "image_processor") batch_feature = image_processor(image, return_tensors="pt") pixel_values = batch_feature.to(model.device)["pixel_values"] # shape (B, C, H, W) if hasattr(processor, "image_seq_length"): # paligemma models image_token_id = tokenizer.convert_tokens_to_ids(template.image_token) prompt_ids = [image_token_id] * getattr(processor, "image_seq_length") + prompt_ids prompt_length = len(prompt_ids) inputs = torch.tensor([prompt_ids], device=model.device) attention_mask = torch.ones_like(inputs, dtype=torch.bool) do_sample: Optional[bool] = input_kwargs.pop("do_sample", None) temperature: Optional[float] = input_kwargs.pop("temperature", None) top_p: Optional[float] = input_kwargs.pop("top_p", None) top_k: Optional[float] = input_kwargs.pop("top_k", None) num_return_sequences: int = input_kwargs.pop("num_return_sequences", 1) repetition_penalty: Optional[float] = input_kwargs.pop("repetition_penalty", None) length_penalty: Optional[float] = input_kwargs.pop("length_penalty", None) max_length: Optional[int] = input_kwargs.pop("max_length", None) max_new_tokens: Optional[int] = input_kwargs.pop("max_new_tokens", None) stop: Optional[Union[str, List[str]]] = input_kwargs.pop("stop", None) if stop is not None: logger.warning("Stop parameter is not supported in Huggingface engine yet.") generating_args = generating_args.copy() generating_args.update( dict( do_sample=do_sample if do_sample is not None else generating_args["do_sample"], temperature=temperature if temperature is not None else generating_args["temperature"], top_p=top_p if top_p is not None else generating_args["top_p"], top_k=top_k if top_k is not None else generating_args["top_k"], num_return_sequences=num_return_sequences, repetition_penalty=repetition_penalty if repetition_penalty is not None else generating_args["repetition_penalty"], length_penalty=length_penalty if length_penalty is not None else generating_args["length_penalty"], eos_token_id=[tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids, pad_token_id=tokenizer.pad_token_id, ) ) if isinstance(num_return_sequences, int) and num_return_sequences > 1: # do_sample needs temperature > 0 generating_args["do_sample"] = True generating_args["temperature"] = generating_args["temperature"] or 1.0 if not generating_args["temperature"]: generating_args["do_sample"] = False if not generating_args["do_sample"]: generating_args.pop("temperature", None) generating_args.pop("top_p", None) if max_length: generating_args.pop("max_new_tokens", None) generating_args["max_length"] = max_length if max_new_tokens: generating_args.pop("max_length", None) generating_args["max_new_tokens"] = max_new_tokens gen_kwargs = dict( inputs=inputs, attention_mask=attention_mask, generation_config=GenerationConfig(**generating_args), logits_processor=get_logits_processor(), ) if pixel_values is not None: gen_kwargs["pixel_values"] = pixel_values return gen_kwargs, prompt_length @staticmethod @torch.inference_mode() def _chat( model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer", processor: Optional["ProcessorMixin"], template: "Template", generating_args: Dict[str, Any], messages: Sequence[Dict[str, str]], system: Optional[str] = None, tools: Optional[str] = None, image: Optional["NDArray"] = None, input_kwargs: Optional[Dict[str, Any]] = {}, ) -> List["Response"]: gen_kwargs, prompt_length = HuggingfaceEngine._process_args( model, tokenizer, processor, template, generating_args, messages, system, tools, image, input_kwargs ) generate_output = model.generate(**gen_kwargs) response_ids = generate_output[:, prompt_length:] response = tokenizer.batch_decode(response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True) results = [] for i in range(len(response)): eos_index = (response_ids[i] == tokenizer.eos_token_id).nonzero() response_length = (eos_index[0].item() + 1) if len(eos_index) else len(response_ids[i]) results.append( Response( response_text=response[i], response_length=response_length, prompt_length=prompt_length, finish_reason="stop" if len(eos_index) else "length", ) ) return results @staticmethod @torch.inference_mode() def _stream_chat( model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer", processor: Optional["ProcessorMixin"], template: "Template", generating_args: Dict[str, Any], messages: Sequence[Dict[str, str]], system: Optional[str] = None, tools: Optional[str] = None, image: Optional["NDArray"] = None, input_kwargs: Optional[Dict[str, Any]] = {}, ) -> Callable[[], str]: gen_kwargs, _ = HuggingfaceEngine._process_args( model, tokenizer, processor, template, generating_args, messages, system, tools, image, input_kwargs ) streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) gen_kwargs["streamer"] = streamer thread = Thread(target=model.generate, kwargs=gen_kwargs, daemon=True) thread.start() def stream(): try: return streamer.__next__() except StopIteration: raise StopAsyncIteration() return stream @staticmethod @torch.inference_mode() def _get_scores( model: "PreTrainedModelWrapper", tokenizer: "PreTrainedTokenizer", batch_input: List[str], input_kwargs: Optional[Dict[str, Any]] = {}, ) -> List[float]: max_length = input_kwargs.pop("max_length", None) device = getattr(model.pretrained_model, "device", "cuda") inputs = tokenizer( batch_input, padding=True, truncation=True, max_length=max_length or getattr(model.config, "max_position_embeddings", 1024), return_tensors="pt", add_special_tokens=True, ).to(device) input_ids: torch.Tensor = inputs["input_ids"] _, _, values = model(**inputs, output_hidden_states=True, return_dict=True) if getattr(model.config, "model_type", None) == "chatglm": values = torch.transpose(values, 0, 1) scores = [] for i in range(input_ids.size(0)): end_indexes = (input_ids[i] != tokenizer.pad_token_id).nonzero() end_index = end_indexes[-1].item() if len(end_indexes) else 0 scores.append(values[i, end_index].nan_to_num().item()) return scores async def chat( self, messages: Sequence[Dict[str, str]], system: Optional[str] = None, tools: Optional[str] = None, image: Optional["NDArray"] = None, **input_kwargs, ) -> List["Response"]: if not self.can_generate: raise ValueError("The current model does not support `chat`.") loop = asyncio.get_running_loop() input_args = ( self.model, self.tokenizer, self.processor, self.template, self.generating_args, messages, system, tools, image, input_kwargs, ) async with self.semaphore: with concurrent.futures.ThreadPoolExecutor() as pool: return await loop.run_in_executor(pool, self._chat, *input_args) async def stream_chat( self, messages: Sequence[Dict[str, str]], system: Optional[str] = None, tools: Optional[str] = None, image: Optional["NDArray"] = None, **input_kwargs, ) -> AsyncGenerator[str, None]: if not self.can_generate: raise ValueError("The current model does not support `stream_chat`.") loop = asyncio.get_running_loop() input_args = ( self.model, self.tokenizer, self.processor, self.template, self.generating_args, messages, system, tools, image, input_kwargs, ) async with self.semaphore: with concurrent.futures.ThreadPoolExecutor() as pool: stream = self._stream_chat(*input_args) while True: try: yield await loop.run_in_executor(pool, stream) except StopAsyncIteration: break async def get_scores( self, batch_input: List[str], **input_kwargs, ) -> List[float]: if self.can_generate: raise ValueError("Cannot get scores using an auto-regressive model.") loop = asyncio.get_running_loop() input_args = (self.model, self.tokenizer, batch_input, input_kwargs) async with self.semaphore: with concurrent.futures.ThreadPoolExecutor() as pool: return await loop.run_in_executor(pool, self._get_scores, *input_args)

LLaMA-Factory/src/llamafactory/chat/hf_engine.py/0

# Copyright 2024 the LlamaFactory team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import re from abc import ABC, abstractmethod from dataclasses import dataclass, field from typing import Any, Dict, List, Literal, Optional, Sequence, Set, Tuple, Union SLOTS = Sequence[Union[str, Set[str], Dict[str, str]]] DEFAULT_TOOL_PROMPT = ( "You have access to the following tools:\n{tool_text}" "Use the following format if using a tool:\n" "```\n" "Action: tool name (one of [{tool_names}]).\n" "Action Input: the input to the tool, in a JSON format representing the kwargs " """(e.g. ```{{"input": "hello world", "num_beams": 5}}```).\n""" "```\n" ) GLM4_TOOL_PROMPT = ( "你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，" "你的任务是针对用户的问题和要求提供适当的答复和支持。# 可用工具{tool_text}" ) def default_tool_formatter(tools: List[Dict[str, Any]]) -> str: tool_text = "" tool_names = [] for tool in tools: param_text = "" for name, param in tool["parameters"]["properties"].items(): required = ", required" if name in tool["parameters"].get("required", []) else "" enum = ", should be one of [{}]".format(", ".join(param["enum"])) if param.get("enum", None) else "" items = ( ", where each item should be {}".format(param["items"].get("type", "")) if param.get("items") else "" ) param_text += " - {name} ({type}{required}): {desc}{enum}{items}\n".format( name=name, type=param.get("type", ""), required=required, desc=param.get("description", ""), enum=enum, items=items, ) tool_text += "> Tool Name: {name}\nTool Description: {desc}\nTool Args:\n{args}\n".format( name=tool["name"], desc=tool.get("description", ""), args=param_text ) tool_names.append(tool["name"]) return DEFAULT_TOOL_PROMPT.format(tool_text=tool_text, tool_names=", ".join(tool_names)) def default_tool_extractor(content: str) -> Union[str, List[Tuple[str, str]]]: regex = re.compile(r"Action:\s*([a-zA-Z0-9_]+)\s*Action Input:\s*(.+?)(?=\s*Action:|\s*$)", re.DOTALL) action_match: List[Tuple[str, str]] = re.findall(regex, content) if not action_match: return content results = [] for match in action_match: tool_name = match[0].strip() tool_input = match[1].strip().strip('"').strip("```") try: arguments = json.loads(tool_input) results.append((tool_name, json.dumps(arguments, ensure_ascii=False))) except json.JSONDecodeError: return content return results def glm4_tool_formatter(tools: List[Dict[str, Any]]) -> str: tool_text = "" for tool in tools: tool_text += "\n\n## {name}\n\n{body}\n在调用上述函数时，请使用 Json 格式表示调用的参数。".format( name=tool["name"], body=json.dumps(tool, indent=4, ensure_ascii=False) ) return GLM4_TOOL_PROMPT.format(tool_text=tool_text) def glm4_tool_extractor(content: str) -> Union[str, List[Tuple[str, str]]]: if "\n" not in content: return content tool_name, tool_input = content.split("\n", maxsplit=1) try: arguments = json.loads(tool_input) except json.JSONDecodeError: return content return [(tool_name, json.dumps(arguments, ensure_ascii=False))] @dataclass class Formatter(ABC): slots: SLOTS = field(default_factory=list) tool_format: Optional[Literal["default", "glm4"]] = None @abstractmethod def apply(self, **kwargs) -> SLOTS: ... def extract(self, content: str) -> Union[str, List[Tuple[str, str]]]: raise NotImplementedError @dataclass class EmptyFormatter(Formatter): def __post_init__(self): has_placeholder = False for slot in filter(lambda s: isinstance(s, str), self.slots): if re.search(r"\{\{[a-zA-Z_][a-zA-Z0-9_]*\}\}", slot): has_placeholder = True if has_placeholder: raise ValueError("Empty formatter should not contain any placeholder.") def apply(self, **kwargs) -> SLOTS: return self.slots @dataclass class StringFormatter(Formatter): def __post_init__(self): has_placeholder = False for slot in filter(lambda s: isinstance(s, str), self.slots): if re.search(r"\{\{[a-zA-Z_][a-zA-Z0-9_]*\}\}", slot): has_placeholder = True if not has_placeholder: raise ValueError("A placeholder is required in the string formatter.") def apply(self, **kwargs) -> SLOTS: elements = [] for slot in self.slots: if isinstance(slot, str): for name, value in kwargs.items(): if not isinstance(value, str): raise RuntimeError("Expected a string, got {}".format(value)) slot = slot.replace("{{" + name + "}}", value, 1) elements.append(slot) elif isinstance(slot, (dict, set)): elements.append(slot) else: raise RuntimeError("Input must be string, set[str] or dict[str, str], got {}".format(type(slot))) return elements @dataclass class FunctionFormatter(Formatter): def __post_init__(self): has_name, has_args = False, False for slot in filter(lambda s: isinstance(s, str), self.slots): if "{{name}}" in slot: has_name = True if "{{arguments}}" in slot: has_args = True if not has_name or not has_args: raise ValueError("Name and arguments placeholders are required in the function formatter.") def apply(self, **kwargs) -> SLOTS: content = kwargs.pop("content") functions: List[Tuple[str, str]] = [] try: tool_calls = json.loads(content) if not isinstance(tool_calls, list): # parallel function call tool_calls = [tool_calls] for tool_call in tool_calls: functions.append((tool_call["name"], json.dumps(tool_call["arguments"], ensure_ascii=False))) except json.JSONDecodeError: functions = [] elements = [] for name, arguments in functions: for slot in self.slots: if isinstance(slot, str): slot = slot.replace("{{name}}", name).replace("{{arguments}}", arguments) elements.append(slot) elif isinstance(slot, (dict, set)): elements.append(slot) else: raise RuntimeError("Input must be string, set[str] or dict[str, str], got {}".format(type(slot))) return elements @dataclass class ToolFormatter(Formatter): def __post_init__(self): if self.tool_format == "default": self._tool_formatter = default_tool_formatter self._tool_extractor = default_tool_extractor elif self.tool_format == "glm4": self._tool_formatter = glm4_tool_formatter self._tool_extractor = glm4_tool_extractor else: raise NotImplementedError("Tool format {} was not found.".format(self.tool_format)) def apply(self, **kwargs) -> SLOTS: content = kwargs.pop("content") try: tools = json.loads(content) return [self._tool_formatter(tools) if len(tools) != 0 else ""] except json.JSONDecodeError: return [""] def extract(self, content: str) -> Union[str, List[Tuple[str, str]]]: return self._tool_extractor(content)

LLaMA-Factory/src/llamafactory/data/formatter.py/0

# Copyright 2024 the LlamaFactory team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from collections import defaultdict from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple from ...extras.constants import IGNORE_INDEX from ...extras.logging import get_logger from .processor_utils import get_paligemma_token_type_ids, get_pixel_values, greedy_knapsack if TYPE_CHECKING: from transformers import PreTrainedTokenizer, ProcessorMixin from ...hparams import DataArguments from ..template import Template logger = get_logger(__name__) def _encode_supervised_example( prompt: Sequence[Dict[str, str]], response: Sequence[Dict[str, str]], system: Optional[str], tools: Optional[str], template: "Template", tokenizer: "PreTrainedTokenizer", processor: Optional["ProcessorMixin"], data_args: "DataArguments", ) -> Tuple[List[int], List[int]]: if processor is not None and not hasattr(processor, "image_seq_length"): # llava-like models prompt[0]["content"] = template.image_token + prompt[0]["content"] messages = prompt + response input_ids, labels = [], [] if processor is not None and hasattr(processor, "image_seq_length"): # paligemma models image_token_id = tokenizer.convert_tokens_to_ids(template.image_token) input_ids += [image_token_id] * getattr(processor, "image_seq_length") labels += [IGNORE_INDEX] * getattr(processor, "image_seq_length") encoded_pairs = template.encode_multiturn( tokenizer, messages, system, tools, data_args.cutoff_len, data_args.reserved_label_len ) for turn_idx, (source_ids, target_ids) in enumerate(encoded_pairs): if data_args.train_on_prompt: source_mask = source_ids elif turn_idx != 0 and template.efficient_eos: source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1) else: source_mask = [IGNORE_INDEX] * len(source_ids) input_ids += source_ids + target_ids labels += source_mask + target_ids if template.efficient_eos: input_ids += [tokenizer.eos_token_id] labels += [tokenizer.eos_token_id] return input_ids, labels def preprocess_supervised_dataset( examples: Dict[str, List[Any]], template: "Template", tokenizer: "PreTrainedTokenizer", processor: Optional["ProcessorMixin"], data_args: "DataArguments", ) -> Dict[str, List[List[int]]]: # build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>` # for multiturn examples, we only mask the prompt part in each prompt-response pair. model_inputs = {"input_ids": [], "attention_mask": [], "labels": []} if processor is not None: model_inputs["pixel_values"] = [] if hasattr(processor, "image_seq_length"): # paligemma models model_inputs["token_type_ids"] = [] for i in range(len(examples["prompt"])): if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1: logger.warning("Dropped invalid example: {}".format(examples["prompt"][i] + examples["response"][i])) continue input_ids, labels = _encode_supervised_example( prompt=examples["prompt"][i], response=examples["response"][i], system=examples["system"][i], tools=examples["tools"][i], template=template, tokenizer=tokenizer, processor=processor, data_args=data_args, ) model_inputs["input_ids"].append(input_ids) model_inputs["attention_mask"].append([1] * len(input_ids)) model_inputs["labels"].append(labels) if processor is not None: model_inputs["pixel_values"].append(get_pixel_values(examples["images"][i], processor)) if hasattr(processor, "image_seq_length"): # paligemma models model_inputs["token_type_ids"].append(get_paligemma_token_type_ids(len(input_ids), processor)) return model_inputs def preprocess_packed_supervised_dataset( examples: Dict[str, List[Any]], template: "Template", tokenizer: "PreTrainedTokenizer", data_args: "DataArguments", ) -> Dict[str, List[List[int]]]: # build inputs with format `<bos> X1 Y1 <eos> <bos> X2 Y2 <eos>` # and labels with format `<ignore> ... <ignore> Y1 <eos> <ignore> ... <ignore> Y2 <eos>` valid_num = 0 batch_input_ids, batch_labels = [], [] lengths = [] length2indexes = defaultdict(list) for i in range(len(examples["prompt"])): if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1: logger.warning("Dropped invalid example: {}".format(examples["prompt"][i] + examples["response"][i])) continue input_ids, labels = _encode_supervised_example( prompt=examples["prompt"][i], response=examples["response"][i], system=examples["system"][i], tools=examples["tools"][i], template=template, tokenizer=tokenizer, processor=None, data_args=data_args, ) length = len(input_ids) if length > data_args.cutoff_len: logger.warning("Dropped lengthy example with length {} > {}.".format(length, data_args.cutoff_len)) else: lengths.append(length) length2indexes[length].append(valid_num) batch_input_ids.append(input_ids) batch_labels.append(labels) valid_num += 1 model_inputs = {"input_ids": [], "attention_mask": [], "labels": []} knapsacks = greedy_knapsack(lengths, data_args.cutoff_len) for knapsack in knapsacks: packed_input_ids, packed_labels = [], [] for length in knapsack: index = length2indexes[length].pop() packed_input_ids += batch_input_ids[index] packed_labels += batch_labels[index] if len(packed_input_ids) < data_args.cutoff_len: pad_length = data_args.cutoff_len - len(packed_input_ids) packed_input_ids += [tokenizer.pad_token_id] * pad_length packed_labels += [IGNORE_INDEX] * pad_length if len(packed_input_ids) != data_args.cutoff_len: raise ValueError("The length of packed example should be identical to the cutoff length.") model_inputs["input_ids"].append(packed_input_ids) model_inputs["attention_mask"].append([1] * data_args.cutoff_len) model_inputs["labels"].append(packed_labels) return model_inputs def print_supervised_dataset_example(example: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None: valid_labels = list(filter(lambda x: x != IGNORE_INDEX, example["labels"])) print("input_ids:\n{}".format(example["input_ids"])) print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False))) print("label_ids:\n{}".format(example["labels"])) print("labels:\n{}".format(tokenizer.decode(valid_labels, skip_special_tokens=False)))

LLaMA-Factory/src/llamafactory/data/processors/supervised.py/0

# Copyright 2024 EleutherAI, HuggingFace Inc., Yukang Chen, and the LlamaFactory team. # # This code is based on the EleutherAI's GPT-NeoX and the HuggingFace's Transformers libraries. # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llama/modeling_llama.py # This code is also inspired by the original LongLoRA implementation. # https://github.com/dvlab-research/LongLoRA/blob/main/llama_attn_replace.py # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math from typing import TYPE_CHECKING, Optional, Tuple import torch import torch.nn as nn from transformers.models.llama.modeling_llama import ( Cache, LlamaAttention, LlamaFlashAttention2, LlamaSdpaAttention, apply_rotary_pos_emb, repeat_kv, ) from transformers.utils import logging from transformers.utils.versions import require_version from ...extras.constants import SUPPORTED_CLASS_FOR_S2ATTN from ...extras.logging import get_logger if TYPE_CHECKING: from transformers import PretrainedConfig from ...hparams import ModelArguments logger = logging.get_logger(__name__) # Modified from: # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llama/modeling_llama.py def llama_attention_forward( self: "LlamaAttention", hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional["Cache"] = None, output_attentions: bool = False, cache_position: Optional[torch.LongTensor] = None, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() query_states: "torch.Tensor" = self.q_proj(hidden_states) key_states: "torch.Tensor" = self.k_proj(hidden_states) value_states: "torch.Tensor" = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) cos, sin = self.rotary_emb(value_states, position_ids) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) past_key_value = getattr(self, "past_key_value", past_key_value) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) if getattr(self.config, "group_size_ratio", None) and self.training: # shift groupsz = int(q_len * getattr(self.config, "group_size_ratio")) assert q_len % groupsz == 0, "q_len {} should be divisible by group size {}.".format(q_len, groupsz) num_groups = q_len // groupsz def shift(state: torch.Tensor) -> torch.Tensor: state = state.transpose(1, 2) # output: (bsz, seq_len, n_heads, head_dim) state = torch.cat( (state[:, :, : self.num_heads // 2], state[:, :, self.num_heads // 2 :].roll(-groupsz // 2, dims=1)), dim=2, ) return state.reshape(bsz * num_groups, groupsz, self.num_heads, self.head_dim).transpose(1, 2) query_states, key_states, value_states = shift(query_states), shift(key_states), shift(value_states) if attention_mask is not None: attention_mask = attention_mask[:, :, :groupsz, :groupsz].repeat(num_groups, 1, 1, 1) attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) if attention_mask is not None: # no matter the length, we just slice it causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask # upcast attention to fp32 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) attn_output = torch.matmul(attn_weights, value_states) # (bsz, :, seq_len, :) or (bsz * n_group, :, groupsz, :) attn_output = attn_output.transpose(1, 2).contiguous() if getattr(self.config, "group_size_ratio", None) and self.training: # shift back attn_output.reshape(bsz, q_len, self.num_heads, self.head_dim) attn_output = torch.cat( ( attn_output[:, :, : self.num_heads // 2], attn_output[:, :, self.num_heads // 2 :].roll(groupsz // 2, dims=1), ), dim=2, ) attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value # Modified from: # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llama/modeling_llama.py def llama_flash_attention_2_forward( self: "LlamaFlashAttention2", hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional["Cache"] = None, output_attentions: bool = False, cache_position: Optional[torch.LongTensor] = None, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: # LlamaFlashAttention2 attention does not support output_attentions output_attentions = False bsz, q_len, _ = hidden_states.size() query_states: "torch.Tensor" = self.q_proj(hidden_states) key_states: "torch.Tensor" = self.k_proj(hidden_states) value_states: "torch.Tensor" = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) cos, sin = self.rotary_emb(value_states, position_ids) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) past_key_value = getattr(self, "past_key_value", past_key_value) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) # FlashAttention requires the input to have the shape (bsz, seq_len, n_heads, head_dim) query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) dropout_rate = self.attention_dropout if self.training else 0.0 input_dtype = query_states.dtype if input_dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.q_proj.weight.dtype logger.warning_once("The input hidden states seems to be silently casted in float32.") query_states = query_states.to(target_dtype) key_states = key_states.to(target_dtype) value_states = value_states.to(target_dtype) if getattr(self.config, "group_size_ratio", None) and self.training: # shift groupsz = int(q_len * getattr(self.config, "group_size_ratio")) assert q_len % groupsz == 0, "q_len {} should be divisible by group size {}.".format(q_len, groupsz) num_groups = q_len // groupsz def shift(state: torch.Tensor) -> torch.Tensor: state = torch.cat( (state[:, :, : self.num_heads // 2], state[:, :, self.num_heads // 2 :].roll(-groupsz // 2, dims=1)), dim=2, ) return state.reshape(bsz * num_groups, groupsz, self.num_heads, self.head_dim) query_states, key_states, value_states = shift(query_states), shift(key_states), shift(value_states) if attention_mask is not None: attention_mask = attention_mask[:, :groupsz].repeat(num_groups, 1) attn_output: torch.Tensor = self._flash_attention_forward( query_states, key_states, value_states, attention_mask, query_states.size(1), dropout=dropout_rate ) if getattr(self.config, "group_size_ratio", None) and self.training: # shift back attn_output.reshape(bsz, q_len, self.num_heads, self.head_dim) attn_output = torch.cat( ( attn_output[:, :, : self.num_heads // 2], attn_output[:, :, self.num_heads // 2 :].roll(groupsz // 2, dims=1), ), dim=2, ) attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value # Modified from: # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llama/modeling_llama.py def llama_sdpa_attention_forward( self: "LlamaSdpaAttention", hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional["Cache"] = None, output_attentions: bool = False, cache_position: Optional[torch.LongTensor] = None, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: if output_attentions: logger.warning_once("SDPA does not support `output_attentions=True`. Falling back to the vanilla attention") return llama_attention_forward( self, hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, cache_position=cache_position, **kwargs, ) bsz, q_len, _ = hidden_states.size() query_states: "torch.Tensor" = self.q_proj(hidden_states) key_states: "torch.Tensor" = self.k_proj(hidden_states) value_states: "torch.Tensor" = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) cos, sin = self.rotary_emb(value_states, position_ids) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) if getattr(self.config, "group_size_ratio", None) and self.training: # shift groupsz = int(q_len * getattr(self.config, "group_size_ratio")) assert q_len % groupsz == 0, "q_len {} should be divisible by group size {}.".format(q_len, groupsz) num_groups = q_len // groupsz def shift(state: torch.Tensor) -> torch.Tensor: state = state.transpose(1, 2) # output: (bsz, seq_len, n_heads, head_dim) state = torch.cat( (state[:, :, : self.num_heads // 2], state[:, :, self.num_heads // 2 :].roll(-groupsz // 2, dims=1)), dim=2, ) return state.reshape(bsz * num_groups, groupsz, self.num_heads, self.head_dim).transpose(1, 2) query_states, key_states, value_states = shift(query_states), shift(key_states), shift(value_states) if attention_mask is not None: attention_mask = attention_mask[:, :, :groupsz, :groupsz].repeat(num_groups, 1, 1, 1) causal_mask = attention_mask if attention_mask is not None: causal_mask = causal_mask[:, :, :, : key_states.shape[-2]] if query_states.device.type == "cuda" and causal_mask is not None: query_states = query_states.contiguous() key_states = key_states.contiguous() value_states = value_states.contiguous() attn_output = torch.nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=causal_mask, dropout_p=self.attention_dropout if self.training else 0.0, is_causal=causal_mask is None and q_len > 1, ) attn_output = attn_output.transpose(1, 2).contiguous() if getattr(self.config, "group_size_ratio", None) and self.training: # shift back attn_output.reshape(bsz, q_len, self.num_heads, self.head_dim) attn_output = torch.cat( ( attn_output[:, :, : self.num_heads // 2], attn_output[:, :, self.num_heads // 2 :].roll(groupsz // 2, dims=1), ), dim=2, ) attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) attn_output = self.o_proj(attn_output) return attn_output, None, past_key_value def _apply_llama_patch() -> None: require_version("transformers==4.41.2", "To fix: pip install transformers==4.41.2") LlamaAttention.forward = llama_attention_forward LlamaFlashAttention2.forward = llama_flash_attention_2_forward LlamaSdpaAttention.forward = llama_sdpa_attention_forward def configure_longlora(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None: if not is_trainable or not model_args.shift_attn: return logger = get_logger(__name__) if getattr(config, "model_type", None) in SUPPORTED_CLASS_FOR_S2ATTN: setattr(config, "group_size_ratio", 0.25) _apply_llama_patch() logger.info("Using shift short attention with group_size_ratio=1/4.") else: logger.warning("Current model does not support shift short attention.")

LLaMA-Factory/src/llamafactory/model/model_utils/longlora.py/0

# Copyright 2024 HuggingFace Inc. and the LlamaFactory team. # # This code is inspired by the HuggingFace's TRL library. # https://github.com/huggingface/trl/blob/v0.8.0/trl/trainer/ppo_trainer.py # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import os import sys import warnings from types import MethodType from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple import torch from accelerate.utils import DistributedDataParallelKwargs from tqdm import tqdm from transformers import GenerationConfig, Trainer, TrainerControl, TrainerState from transformers.optimization import get_scheduler from transformers.trainer_pt_utils import remove_dummy_checkpoint from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR from transformers.utils import SAFE_WEIGHTS_NAME, WEIGHTS_NAME from trl import PPOConfig, PPOTrainer from trl.core import PPODecorators, logprobs_from_logits from trl.models.utils import unwrap_model_for_generation from ...extras.callbacks import FixValueHeadModelCallback, LogCallback from ...extras.logging import get_logger from ...extras.misc import AverageMeter, count_parameters, get_current_device, get_logits_processor from ..trainer_utils import create_custom_optimzer, create_custom_scheduler from .ppo_utils import dump_layernorm, get_rewards_from_server, replace_model, restore_layernorm if TYPE_CHECKING: from datasets import Dataset from transformers import ( DataCollatorWithPadding, PreTrainedTokenizer, ProcessorMixin, Seq2SeqTrainingArguments, TrainerCallback, ) from trl import AutoModelForCausalLMWithValueHead from ...hparams import FinetuningArguments, GeneratingArguments, ModelArguments logger = get_logger(__name__) class CustomPPOTrainer(PPOTrainer, Trainer): r""" Inherits PPOTrainer. """ def __init__( self, model_args: "ModelArguments", training_args: "Seq2SeqTrainingArguments", finetuning_args: "FinetuningArguments", generating_args: "GeneratingArguments", callbacks: List["TrainerCallback"], model: "AutoModelForCausalLMWithValueHead", reward_model: Optional["AutoModelForCausalLMWithValueHead"], ref_model: Optional["AutoModelForCausalLMWithValueHead"], tokenizer: "PreTrainedTokenizer", processor: Optional["ProcessorMixin"], dataset: "Dataset", data_collator: "DataCollatorWithPadding", ): backward_batch_size = training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps ppo_config = PPOConfig( model_name=model_args.model_name_or_path, learning_rate=training_args.learning_rate, mini_batch_size=training_args.per_device_train_batch_size, batch_size=backward_batch_size * finetuning_args.ppo_buffer_size, gradient_accumulation_steps=training_args.gradient_accumulation_steps, ppo_epochs=finetuning_args.ppo_epochs, max_grad_norm=training_args.max_grad_norm, seed=training_args.seed, optimize_device_cache=True, target=finetuning_args.ppo_target, use_score_scaling=finetuning_args.ppo_score_norm, use_score_norm=finetuning_args.ppo_score_norm, whiten_rewards=finetuning_args.ppo_whiten_rewards, accelerator_kwargs={"step_scheduler_with_optimizer": False}, log_with=training_args.report_to[0] if training_args.report_to else None, project_kwargs={"logging_dir": training_args.logging_dir}, ) # Add deepspeed config if training_args.deepspeed_plugin is not None: ppo_config.accelerator_kwargs["kwargs_handlers"] = [ DistributedDataParallelKwargs(find_unused_parameters=training_args.ddp_find_unused_parameters) ] ppo_config.accelerator_kwargs["deepspeed_plugin"] = training_args.deepspeed_plugin # Create optimizer and scheduler if training_args.max_steps > 0: num_training_steps = training_args.max_steps else: total_train_batch_size = backward_batch_size * finetuning_args.ppo_buffer_size * training_args.world_size num_training_steps = training_args.num_train_epochs * math.ceil(len(dataset) / total_train_batch_size) optimizer = self.create_optimizer(model, training_args, finetuning_args) scheduler = self.create_scheduler(training_args, num_training_steps, optimizer) PPOTrainer.__init__( self, config=ppo_config, model=model, ref_model=ref_model, tokenizer=tokenizer, dataset=dataset, data_collator=data_collator, lr_scheduler=scheduler, ) self.args = training_args self.model_args = model_args self.finetuning_args = finetuning_args self.reward_model = reward_model self.current_device = get_current_device() # patch for deepspeed training self.processor = processor self.generation_config = GenerationConfig( pad_token_id=self.tokenizer.pad_token_id, eos_token_id=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids, **generating_args.to_dict(), ) self.state = TrainerState() self.control = TrainerControl() self.is_deepspeed_enabled = getattr(self.accelerator.state, "deepspeed_plugin", None) is not None self.is_fsdp_enabled = getattr(self.accelerator.state, "fsdp_plugin", None) is not None self.log_callback, self.save_callback = callbacks[0], callbacks[1] assert isinstance(self.log_callback, LogCallback) and isinstance(self.save_callback, FixValueHeadModelCallback) if self.args.max_steps > 0: logger.info("max_steps is given, it will override any value given in num_train_epochs") unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model) self.is_chatglm_model = getattr(unwrapped_model.config, "model_type", None) == "chatglm" self.amp_context = torch.autocast(self.current_device.type, dtype=self.model_args.compute_dtype) warnings.simplefilter("ignore") # remove gc warnings on ref model if finetuning_args.reward_model_type == "full": if self.is_deepspeed_enabled: if not ( getattr(reward_model.pretrained_model, "is_loaded_in_8bit", False) or getattr(reward_model.pretrained_model, "is_loaded_in_4bit", False) ): # quantized models are already set on the correct device self.reward_model = self._prepare_deepspeed(self.reward_model) else: self.reward_model = self.accelerator.prepare_model(self.reward_model, evaluation_mode=True) if finetuning_args.use_badam: from badam import BAdamCallback, clip_grad_norm_old_version self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator) self.callback_handler.add_callback(BAdamCallback) def ppo_train(self, resume_from_checkpoint: Optional[str] = None) -> None: r""" Implements training loop for the PPO stage, like _inner_training_loop() in Huggingface's Trainer. """ if resume_from_checkpoint is not None: raise ValueError("`resume_from_checkpoint` will be supported in the future version.") total_train_batch_size = ( self.args.per_device_train_batch_size * self.args.gradient_accumulation_steps * self.finetuning_args.ppo_buffer_size * self.args.world_size ) if self.args.max_steps > 0: num_examples = total_train_batch_size * self.args.max_steps num_train_epochs = sys.maxsize max_steps = self.args.max_steps steps_in_epoch = self.args.max_steps else: len_dataloader = len(self.dataloader) num_examples = len(self.dataset) num_train_epochs = self.args.num_train_epochs max_steps = math.ceil(num_train_epochs * len_dataloader) steps_in_epoch = len_dataloader self.state.max_steps = max_steps self.state.num_train_epochs = num_train_epochs self.state.is_local_process_zero = self.is_local_process_zero() self.state.is_world_process_zero = self.is_world_process_zero() if self.is_world_process_zero(): logger.info("***** Running training *****") logger.info(" Num examples = {:,}".format(num_examples)) logger.info(" Num Epochs = {:,}".format(num_train_epochs)) logger.info(" Instantaneous batch size per device = {:,}".format(self.args.per_device_train_batch_size)) logger.info( " Total train batch size (w. parallel, buffer, distributed & accumulation) = {:,}".format( total_train_batch_size ) ) logger.info(" Gradient Accumulation steps = {:,}".format(self.args.gradient_accumulation_steps)) logger.info(" Num optimization epochs per batch = {:,}".format(self.finetuning_args.ppo_epochs)) logger.info(" Total training steps = {:,}".format(max_steps)) logger.info(" Number of trainable parameters = {:,}".format(count_parameters(self.model)[0])) dataiter = iter(self.dataloader) loss_meter = AverageMeter() reward_meter = AverageMeter() self.log_callback.on_train_begin(self.args, self.state, self.control) for step in tqdm(range(max_steps), disable=not self.is_local_process_zero()): try: batch = next(dataiter) except StopIteration: dataiter = iter(self.dataloader) batch = next(dataiter) # Get inputs self.model.eval() self.tokenizer.padding_side = "right" # change padding side queries, responses, rewards = [], [], [] for idx in range(0, self.config.batch_size, self.config.mini_batch_size): mini_batch_queries, mini_batch_responses = self.get_inputs( batch[idx : idx + self.config.mini_batch_size] ) mini_batch_rewards = self.get_rewards(mini_batch_queries, mini_batch_responses) queries.extend(mini_batch_queries) responses.extend(mini_batch_responses) rewards.extend(mini_batch_rewards) # Run PPO step self.model.train() stats = self.step(queries, responses, rewards) self.tokenizer.padding_side = "left" # restore padding side loss_meter.update(float(stats["ppo/loss/total"]), n=len(rewards)) reward_meter.update(torch.stack(rewards).mean().item(), n=len(rewards)) if self.config.log_with is not None: try: batch["query"] = self.tokenizer.batch_decode(queries, skip_special_tokens=True) batch["response"] = self.tokenizer.batch_decode(responses, skip_special_tokens=True) self.log_stats(stats, batch, rewards) except Exception: logger.warning("Failed to save stats due to unknown errors.") self.state.global_step += 1 self.log_callback.on_step_end(self.args, self.state, self.control) if self.is_local_process_zero() and (step + 1) % self.args.logging_steps == 0: logs = dict( loss=round(loss_meter.avg, 4), reward=round(reward_meter.avg, 4), learning_rate=stats["ppo/learning_rate"], epoch=round(step / steps_in_epoch, 2), ) tqdm.write(str(logs)) logs["step"] = step self.state.log_history.append(logs) self.log_callback.on_log(self.args, self.state, self.control) loss_meter.reset() reward_meter.reset() if (step + 1) % self.args.save_steps == 0: # save checkpoint self.save_model( os.path.join(self.args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, self.state.global_step)) ) self.save_callback.on_save( self.args, self.state, self.control, model=self.accelerator.unwrap_model(self.model) ) if self.control.should_epoch_stop or self.control.should_training_stop: break self.log_callback.on_train_end(self.args, self.state, self.control) self.save_callback.on_train_end( self.args, self.state, self.control, model=self.accelerator.unwrap_model(self.model) ) def create_optimizer( self, model: "AutoModelForCausalLMWithValueHead", training_args: "Seq2SeqTrainingArguments", finetuning_args: "FinetuningArguments", ) -> "torch.optim.Optimizer": optimizer = create_custom_optimzer(model, training_args, finetuning_args) if optimizer is None: decay_params, nodecay_params = [], [] decay_param_names = self.get_decay_parameter_names(model) for name, param in model.named_parameters(): if param.requires_grad: if name in decay_param_names: decay_params.append(param) else: nodecay_params.append(param) optim_class, optim_kwargs = Trainer.get_optimizer_cls_and_kwargs(training_args) param_groups = [ dict(params=nodecay_params), dict(params=decay_params, weight_decay=training_args.weight_decay), ] optimizer = optim_class(param_groups, **optim_kwargs) return optimizer def create_scheduler( self, training_args: "Seq2SeqTrainingArguments", num_training_steps: int, optimizer: "torch.optim.Optimizer" ) -> "torch.optim.lr_scheduler.LRScheduler": create_custom_scheduler(training_args, num_training_steps, optimizer) lr_scheduler = get_scheduler( training_args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=training_args.get_warmup_steps(num_training_steps), num_training_steps=num_training_steps, ) return lr_scheduler @torch.no_grad() def get_inputs(self, batch: Dict[str, "torch.Tensor"]) -> Tuple[List["torch.Tensor"], List["torch.Tensor"]]: r""" Generates model's responses given queries. """ if batch["input_ids"].size(0) == 1: # handle llama2 ppo with gradient accumulation > 1 start_index = (batch["input_ids"][0] != self.tokenizer.pad_token_id).nonzero()[0].item() for k, v in batch.items(): batch[k] = v[:, start_index:] with unwrap_model_for_generation(self.model, self.accelerator) as unwrapped_model: unwrapped_model = self.accelerator.unwrap_model(self.model) # issue in trl v0.8.6 if self.model_args.upcast_layernorm: layernorm_params = dump_layernorm(unwrapped_model) generate_output: torch.Tensor = unwrapped_model.generate( generation_config=self.generation_config, logits_processor=get_logits_processor(), **batch ) if self.model_args.upcast_layernorm: restore_layernorm(unwrapped_model, layernorm_params) query = batch["input_ids"].detach().cpu() response = generate_output[:, batch["input_ids"].size(-1) :].detach().cpu() queries, responses = [], [] for i in range(len(query)): query_start_index = (query[i] != self.tokenizer.pad_token_id).nonzero()[0].item() response_index = (response[i] != self.tokenizer.pad_token_id).nonzero() if len(response_index) == 0: response_length = 1 # allow empty response else: response_length = response_index[-1].item() + 1 queries.append(query[i, query_start_index:]) # remove padding from left responses.append(response[i, :response_length]) # remove padding from right return queries, responses @torch.no_grad() def get_rewards( self, queries: List["torch.Tensor"], responses: List["torch.Tensor"], ) -> List["torch.Tensor"]: r""" Computes scores using given reward model. Both inputs and outputs are put on CPU. """ if self.finetuning_args.reward_model_type == "api": token_ids = [torch.cat((q, r), dim=-1).tolist() for q, r in zip(queries, responses)] messages = self.tokenizer.batch_decode(token_ids, skip_special_tokens=True) return get_rewards_from_server(self.reward_model, messages) batch = self.prepare_model_inputs(queries, responses) unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model) if self.finetuning_args.reward_model_type == "lora": replace_model(unwrapped_model, target="reward") reward_model = self.model else: reward_model = self.reward_model with unwrap_model_for_generation(reward_model, self.accelerator), self.amp_context: # support bf16 _, _, values = reward_model(**batch, output_hidden_states=True, return_dict=True, use_cache=False) if self.finetuning_args.reward_model_type == "lora": replace_model(unwrapped_model, target="default") if self.is_chatglm_model: # assume same architecture values = torch.transpose(values, 0, 1) rewards = [] for i in range(values.size(0)): end_indexes = (batch["input_ids"][i] != self.tokenizer.pad_token_id).nonzero() end_index = end_indexes[-1].item() if len(end_indexes) else 0 rewards.append(values[i, end_index].float().detach().cpu()) # use fp32 type return rewards @PPODecorators.empty_device_cache() def batched_forward_pass( self, model: "AutoModelForCausalLMWithValueHead", queries: "torch.Tensor", responses: "torch.Tensor", model_inputs: Dict[str, Any], return_logits: bool = False, response_masks: Optional["torch.Tensor"] = None, ) -> Tuple["torch.Tensor", Optional["torch.Tensor"], "torch.Tensor", "torch.Tensor"]: r""" Calculates model outputs in multiple batches. Subclass and override to inject custom behavior. """ bs = len(queries) fbs = self.config.mini_batch_size all_logprobs = [] all_logits = [] all_masks = [] all_values = [] for i in range(math.ceil(bs / fbs)): input_kwargs = {key: value[i * fbs : (i + 1) * fbs] for key, value in model_inputs.items()} query_batch = queries[i * fbs : (i + 1) * fbs] response_batch = responses[i * fbs : (i + 1) * fbs] if response_masks is not None: response_masks_batch = response_masks[i * fbs : (i + 1) * fbs] input_ids = input_kwargs["input_ids"] attention_mask = input_kwargs["attention_mask"] with self.amp_context: # support bf16 logits, _, values = model(**input_kwargs) if self.is_chatglm_model: values = torch.transpose(values, 0, 1) logprobs = logprobs_from_logits(logits[:, :-1, :], input_ids[:, 1:]) masks = torch.zeros_like(attention_mask) masks[:, :-1] = attention_mask[:, 1:] for j in range(len(query_batch)): start = len(query_batch[j]) - 1 if attention_mask[j, 0] == 0: # offset left padding start += attention_mask[j, :].nonzero()[0].item() end = start + len(response_batch[j]) if response_masks is not None: response_masks_batch = torch.cat((torch.zeros_like(query_batch[j]), response_masks_batch[j]))[1:] masks[j, :start] = 0 masks[j, end:] = 0 if response_masks is not None: masks[j, start:end] = masks[j, start:end] * response_masks_batch[j][start:end] if return_logits: all_logits.append(logits) else: del logits all_values.append(values) all_logprobs.append(logprobs) all_masks.append(masks) return ( torch.cat(all_logprobs), torch.cat(all_logits)[:, :-1] if return_logits else None, torch.cat(all_values)[:, :-1], torch.cat(all_masks)[:, :-1], ) def save_model(self, output_dir: Optional[str] = None) -> None: r""" Saves model checkpoint. Subclass and override to inject custom behavior. """ if output_dir is None: output_dir = self.args.output_dir if self.is_fsdp_enabled or self.is_deepspeed_enabled: try: state_dict = self.accelerator.get_state_dict(self.model) # must be called at all ranks if self.args.should_save: self._save(output_dir, state_dict=state_dict) except ValueError: logger.warning( " stage3_gather_16bit_weights_on_model_save=false. Saving the full checkpoint instead," " use zero_to_fp32.py to recover weights" ) if self.args.should_save: self._save(output_dir, state_dict={}) # remove the dummy state_dict remove_dummy_checkpoint(self.args.should_save, output_dir, [WEIGHTS_NAME, SAFE_WEIGHTS_NAME]) self.model.save_checkpoint(output_dir) elif self.args.should_save: self._save(output_dir) if self.processor is not None and self.args.should_save: output_dir = output_dir if output_dir is not None else self.args.output_dir getattr(self.processor, "image_processor").save_pretrained(output_dir)

LLaMA-Factory/src/llamafactory/train/ppo/trainer.py/0

# Copyright 2024 the LlamaFactory team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING, Dict, Tuple from ...data import Role from ...extras.packages import is_gradio_available from ..utils import check_json_schema if is_gradio_available(): import gradio as gr if TYPE_CHECKING: from gradio.components import Component from ..engine import Engine def create_chat_box( engine: "Engine", visible: bool = False ) -> Tuple["Component", "Component", Dict[str, "Component"]]: with gr.Column(visible=visible) as chat_box: chatbot = gr.Chatbot(show_copy_button=True) messages = gr.State([]) with gr.Row(): with gr.Column(scale=4): with gr.Row(): with gr.Column(): role = gr.Dropdown(choices=[Role.USER.value, Role.OBSERVATION.value], value=Role.USER.value) system = gr.Textbox(show_label=False) tools = gr.Textbox(show_label=False, lines=3) with gr.Column() as image_box: image = gr.Image(sources=["upload"], type="numpy") query = gr.Textbox(show_label=False, lines=8) submit_btn = gr.Button(variant="primary") with gr.Column(scale=1): max_new_tokens = gr.Slider(minimum=8, maximum=4096, value=512, step=1) top_p = gr.Slider(minimum=0.01, maximum=1.0, value=0.7, step=0.01) temperature = gr.Slider(minimum=0.01, maximum=1.5, value=0.95, step=0.01) clear_btn = gr.Button() tools.input(check_json_schema, inputs=[tools, engine.manager.get_elem_by_id("top.lang")]) submit_btn.click( engine.chatter.append, [chatbot, messages, role, query], [chatbot, messages, query], ).then( engine.chatter.stream, [chatbot, messages, system, tools, image, max_new_tokens, top_p, temperature], [chatbot, messages], ) clear_btn.click(lambda: ([], []), outputs=[chatbot, messages]) return ( chatbot, messages, dict( chat_box=chat_box, role=role, system=system, tools=tools, image_box=image_box, image=image, query=query, submit_btn=submit_btn, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature, clear_btn=clear_btn, ), )

LLaMA-Factory/src/llamafactory/webui/components/chatbot.py/0

# Copyright 2024 the LlamaFactory team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json from llamafactory.data.formatter import EmptyFormatter, FunctionFormatter, StringFormatter, ToolFormatter def test_empty_formatter(): formatter = EmptyFormatter(slots=["\n"]) assert formatter.apply() == ["\n"] def test_string_formatter(): formatter = StringFormatter(slots=["<s>", "Human: {{content}}\nAssistant:"]) assert formatter.apply(content="Hi") == ["<s>", "Human: Hi\nAssistant:"] def test_function_formatter(): formatter = FunctionFormatter(slots=["Action: {{name}}\nAction Input: {{arguments}}\n"]) tool_calls = json.dumps({"name": "tool_name", "arguments": {"foo": "bar", "size": 10}}) assert formatter.apply(content=tool_calls) == [ """Action: tool_name\nAction Input: {\"foo\": \"bar\", \"size\": 10}\n""" ] def test_multi_function_formatter(): formatter = FunctionFormatter(slots=["Action: {{name}}\nAction Input: {{arguments}}\n"]) tool_calls = json.dumps([{"name": "tool_name", "arguments": {"foo": "bar", "size": 10}}] * 2) assert formatter.apply(content=tool_calls) == [ """Action: tool_name\nAction Input: {\"foo\": \"bar\", \"size\": 10}\n""", """Action: tool_name\nAction Input: {\"foo\": \"bar\", \"size\": 10}\n""", ] def test_default_tool_formatter(): formatter = ToolFormatter(tool_format="default") tools = [ { "name": "test_tool", "description": "tool_desc", "parameters": { "type": "object", "properties": { "foo": {"type": "string", "description": "foo_desc"}, "bar": {"type": "number", "description": "bar_desc"}, }, "required": ["foo"], }, } ] assert formatter.apply(content=json.dumps(tools)) == [ "You have access to the following tools:\n" "> Tool Name: test_tool\n" "Tool Description: tool_desc\n" "Tool Args:\n" " - foo (string, required): foo_desc\n" " - bar (number): bar_desc\n\n" "Use the following format if using a tool:\n" "```\n" "Action: tool name (one of [test_tool]).\n" "Action Input: the input to the tool, in a JSON format representing the kwargs " """(e.g. ```{"input": "hello world", "num_beams": 5}```).\n""" "```\n" ] def test_default_tool_extractor(): formatter = ToolFormatter(tool_format="default") result = """Action: test_tool\nAction Input: {"foo": "bar", "size": 10}\n""" assert formatter.extract(result) == [("test_tool", """{"foo": "bar", "size": 10}""")] def test_default_multi_tool_extractor(): formatter = ToolFormatter(tool_format="default") result = ( """Action: test_tool\nAction Input: {"foo": "bar", "size": 10}\n""" """Action: another_tool\nAction Input: {"foo": "job", "size": 2}\n""" ) assert formatter.extract(result) == [ ("test_tool", """{"foo": "bar", "size": 10}"""), ("another_tool", """{"foo": "job", "size": 2}"""), ] def test_glm4_tool_formatter(): formatter = ToolFormatter(tool_format="glm4") tools = [ { "name": "test_tool", "description": "tool_desc", "parameters": { "type": "object", "properties": { "foo": {"type": "string", "description": "foo_desc"}, "bar": {"type": "number", "description": "bar_desc"}, }, "required": ["foo"], }, } ] assert formatter.apply(content=json.dumps(tools)) == [ "你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，" "你的任务是针对用户的问题和要求提供适当的答复和支持。# 可用工具\n\n" "## test_tool\n\n{}\n在调用上述函数时，请使用 Json 格式表示调用的参数。".format(json.dumps(tools[0], indent=4)) ] def test_glm4_tool_extractor(): formatter = ToolFormatter(tool_format="glm4") result = """test_tool\n{"foo": "bar", "size": 10}\n""" assert formatter.extract(result) == [("test_tool", """{"foo": "bar", "size": 10}""")]

LLaMA-Factory/tests/data/test_formatter.py/0

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PaddleDetection/.idea/workspace.xml/0

English | [简体中文](README_cn.md) # CenterNet (CenterNet: Objects as Points) ## Table of Contents - [Introduction](#Introduction) - [Model Zoo](#Model_Zoo) - [Citations](#Citations) ## Introduction [CenterNet](http://arxiv.org/abs/1904.07850) is an Anchor Free detector, which model an object as a single point -- the center point of its bounding box. The detector uses keypoint estimation to find center points and regresses to all other object properties. The center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors. ## Model Zoo ### CenterNet Results on COCO-val 2017 | backbone | input shape | mAP | FPS | download | config | | :--------------| :------- | :----: | :------: | :----: |:-----: | | DLA-34(paper) | 512x512 | 37.4 | - | - | - | | DLA-34 | 512x512 | 37.6 | - | [model](https://bj.bcebos.com/v1/paddledet/models/centernet_dla34_140e_coco.pdparams) | [config](./centernet_dla34_140e_coco.yml) | | ResNet50 + DLAUp | 512x512 | 38.9 | - | [model](https://bj.bcebos.com/v1/paddledet/models/centernet_r50_140e_coco.pdparams) | [config](./centernet_r50_140e_coco.yml) | | MobileNetV1 + DLAUp | 512x512 | 28.2 | - | [model](https://bj.bcebos.com/v1/paddledet/models/centernet_mbv1_140e_coco.pdparams) | [config](./centernet_mbv1_140e_coco.yml) | | MobileNetV3_small + DLAUp | 512x512 | 17 | - | [model](https://bj.bcebos.com/v1/paddledet/models/centernet_mbv3_small_140e_coco.pdparams) | [config](./centernet_mbv3_small_140e_coco.yml) | | MobileNetV3_large + DLAUp | 512x512 | 27.1 | - | [model](https://bj.bcebos.com/v1/paddledet/models/centernet_mbv3_large_140e_coco.pdparams) | [config](./centernet_mbv3_large_140e_coco.yml) | | ShuffleNetV2 + DLAUp | 512x512 | 23.8 | - | [model](https://bj.bcebos.com/v1/paddledet/models/centernet_shufflenetv2_140e_coco.pdparams) | [config](./centernet_shufflenetv2_140e_coco.yml) | ## Citations ``` @article{zhou2019objects, title={Objects as points}, author={Zhou, Xingyi and Wang, Dequan and Kr{\"a}henb{\"u}hl, Philipp}, journal={arXiv preprint arXiv:1904.07850}, year={2019} } ```

PaddleDetection/configs/centernet/README.md/0

epoch: 15 snapshot_epoch: 5 LearningRate: base_lr: 0.6e-3 schedulers: - !CosineDecay max_epochs: 15 use_warmup: False OptimizerBuilder: regularizer: False optimizer: type: AdamW

PaddleDetection/configs/clrnet/_base_/optimizer_1x.yml/0

metric: RBOX num_classes: 9 TrainDataset: !COCODataSet image_dir: images anno_path: annotations/train.json dataset_dir: dataset/spine_coco data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd', 'gt_poly'] EvalDataset: !COCODataSet image_dir: images anno_path: annotations/valid.json dataset_dir: dataset/spine_coco data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd', 'gt_poly'] TestDataset: !ImageFolder anno_path: annotations/valid.json dataset_dir: dataset/spine_coco

PaddleDetection/configs/datasets/spine_coco.yml/0

# Deformable DETR ## Introduction Deformable DETR is an object detection model based on DETR. We reproduced the model of the paper. ## Model Zoo | Backbone | Model | Images/GPU | Epochs | Box AP | Config | Log | Download | |:--------:|:---------------:|:----------:|:------:|:------:|:------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------:| | R-50 | Deformable DETR | 2 | 50 | 44.5 | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/deformable_detr/deformable_detr_r50_1x_coco.yml) | [log](https://bj.bcebos.com/v1/paddledet/logs/deformable_detr_r50_1x_coco_44.5.log) | [model](https://paddledet.bj.bcebos.com/models/deformable_detr_r50_1x_coco.pdparams) | **Notes:** - Deformable DETR is trained on COCO train2017 dataset and evaluated on val2017 results of `mAP(IoU=0.5:0.95)`. - Deformable DETR uses 8GPU to train 50 epochs. GPU multi-card training ```bash export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/deformable_detr/deformable_detr_r50_1x_coco.yml --fleet ``` ## Citations ``` @inproceedings{ zhu2021deformable, title={Deformable DETR: Deformable Transformers for End-to-End Object Detection}, author={Xizhou Zhu and Weijie Su and Lewei Lu and Bin Li and Xiaogang Wang and Jifeng Dai}, booktitle={International Conference on Learning Representations}, year={2021}, url={https://openreview.net/forum?id=gZ9hCDWe6ke} } ```

PaddleDetection/configs/deformable_detr/README.md/0

_BASE_: [ 'faster_rcnn_r50_1x_coco.yml', ] pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_pretrained.pdparams weights: output/faster_rcnn_r50_vd_1x_coco/model_final ResNet: # index 0 stands for res2 depth: 50 variant: d norm_type: bn freeze_at: 0 return_idx: [2] num_stages: 3

PaddleDetection/configs/faster_rcnn/faster_rcnn_r50_vd_1x_coco.yml/0

_BASE_: [ '../datasets/coco_detection.yml', '../runtime.yml', '_base_/fcos_r50_fpn.yml', '_base_/optimizer_1x.yml', '_base_/fcos_reader.yml', ] weights: output/fcos_r50_fpn_iou_multiscale_2x_coco_010/model_final TrainReader: sample_transforms: - Decode: {} - RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], keep_ratio: True, interp: 1} - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True} - RandomFlip: {} batch_transforms: - Permute: {} - PadBatch: {pad_to_stride: 32} - Gt2FCOSTarget: object_sizes_boundary: [64, 128, 256, 512] center_sampling_radius: 1.5 downsample_ratios: [8, 16, 32, 64, 128] norm_reg_targets: True batch_size: 2 shuffle: True drop_last: True use_shared_memory: True EvalReader: sample_transforms: - Decode: {} - Resize: {target_size: [800, 1333], keep_ratio: True, interp: 1} - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True} - Permute: {} batch_transforms: - PadBatch: {pad_to_stride: 32} batch_size: 1 TestReader: sample_transforms: - Decode: {} - Resize: {target_size: [800, 1333], keep_ratio: True, interp: 1} - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True} - Permute: {} batch_transforms: - PadBatch: {pad_to_stride: 32} batch_size: 1 fuse_normalize: True epoch: 24 LearningRate: base_lr: 0.01 schedulers: - !PiecewiseDecay gamma: 0.1 milestones: [16, 22] - !LinearWarmup start_factor: 0.001 steps: 1000 FCOSHead: fcos_feat: name: FCOSFeat feat_in: 256 feat_out: 256 num_convs: 4 norm_type: "gn" use_dcn: False fpn_stride: [8, 16, 32, 64, 128] prior_prob: 0.01 norm_reg_targets: True centerness_on_reg: True fcos_loss: name: FCOSLoss loss_alpha: 0.25 loss_gamma: 2.0 iou_loss_type: "giou" reg_weights: 1.0 quality: "iou" # default 'centerness' nms: name: MultiClassNMS nms_top_k: 1000 keep_top_k: 100 score_threshold: 0.025 nms_threshold: 0.6

PaddleDetection/configs/fcos/fcos_r50_fpn_iou_multiscale_2x_coco.yml/0

architecture: GFL pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_cos_pretrained.pdparams GFL: backbone: ResNet neck: FPN head: GFLHead ResNet: depth: 50 variant: b norm_type: bn freeze_at: 0 return_idx: [1,2,3] num_stages: 4 FPN: out_channel: 256 spatial_scales: [0.125, 0.0625, 0.03125] extra_stage: 2 has_extra_convs: true use_c5: false GFLHead: conv_feat: name: FCOSFeat feat_in: 256 feat_out: 256 num_convs: 4 norm_type: "gn" use_dcn: false fpn_stride: [8, 16, 32, 64, 128] prior_prob: 0.01 reg_max: 16 loss_class: name: QualityFocalLoss use_sigmoid: True beta: 2.0 loss_weight: 1.0 loss_dfl: name: DistributionFocalLoss loss_weight: 0.25 loss_bbox: name: GIoULoss loss_weight: 2.0 nms: name: MultiClassNMS nms_top_k: 1000 keep_top_k: 100 score_threshold: 0.025 nms_threshold: 0.6

PaddleDetection/configs/gfl/_base_/gfl_r50_fpn.yml/0

_BASE_: [ 'mask_rcnn_r50_fpn_1x_coco.yml', ] pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_pretrained.pdparams weights: output/mask_rcnn_r50_vd_fpn_1x_coco/model_final ResNet: # index 0 stands for res2 depth: 50 variant: d norm_type: bn freeze_at: 0 return_idx: [0,1,2,3] num_stages: 4

PaddleDetection/configs/mask_rcnn/mask_rcnn_r50_vd_fpn_1x_coco.yml/0

简体中文 | [English](README.md) # FairMOT (FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking) ## 内容 - [简介](#简介) - [模型库](#模型库) - [快速开始](#快速开始) - [引用](#引用) ## 内容 [FairMOT](https://arxiv.org/abs/2004.01888)以Anchor Free的CenterNet检测器为基础，克服了Anchor-Based的检测框架中anchor和特征不对齐问题，深浅层特征融合使得检测和ReID任务各自获得所需要的特征，并且使用低维度ReID特征，提出了一种由两个同质分支组成的简单baseline来预测像素级目标得分和ReID特征，实现了两个任务之间的公平性，并获得了更高水平的实时多目标跟踪精度。 ### PP-Tracking 实时多目标跟踪系统 此外，PaddleDetection还提供了[PP-Tracking](../../../deploy/pptracking/README.md)实时多目标跟踪系统。PP-Tracking是基于PaddlePaddle深度学习框架的业界首个开源的实时多目标跟踪系统，具有模型丰富、应用广泛和部署高效三大优势。 PP-Tracking支持单镜头跟踪(MOT)和跨镜头跟踪(MTMCT)两种模式，针对实际业务的难点和痛点，提供了行人跟踪、车辆跟踪、多类别跟踪、小目标跟踪、流量统计以及跨镜头跟踪等各种多目标跟踪功能和应用，部署方式支持API调用和GUI可视化界面，部署语言支持Python和C++，部署平台环境支持Linux、NVIDIA Jetson等。 ### AI Studio公开项目案例 PP-Tracking 提供了AI Studio公开项目案例，教程请参考[PP-Tracking之手把手玩转多目标跟踪](https://aistudio.baidu.com/aistudio/projectdetail/3022582)。 ## 模型库 ### FairMOT在MOT-16 Training Set上结果 | 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 | | :--------------| :------- | :----: | :----: | :---: | :----: | :---: | :------: | :----: |:----: | | DLA-34(paper) | 1088x608 | 83.3 | 81.9 | 544 | 3822 | 14095 | - | - | - | | DLA-34 | 1088x608 | 83.2 | 83.1 | 499 | 3861 | 14223 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608.yml) | | DLA-34 | 864x480 | 80.8 | 81.1 | 561 | 3643 | 16967 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_864x480.pdparams) | [配置文件](./fairmot_dla34_30e_864x480.yml) | | DLA-34 | 576x320 | 74.0 | 76.1 | 640 | 4989 | 23034 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_576x320.pdparams) | [配置文件](./fairmot_dla34_30e_576x320.yml) | ### FairMOT在MOT-16 Test Set上结果 | 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 | | :--------------| :------- | :----: | :----: | :----: | :----: | :----: |:-------: | :----: | :----: | | DLA-34(paper) | 1088x608 | 74.9 | 72.8 | 1074 | - | - | 25.9 | - | - | | DLA-34 | 1088x608 | 75.0 | 74.7 | 919 | 7934 | 36747 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608.yml) | | DLA-34 | 864x480 | 73.0 | 72.6 | 977 | 7578 | 40601 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_864x480.pdparams) | [配置文件](./fairmot_dla34_30e_864x480.yml) | | DLA-34 | 576x320 | 69.9 | 70.2 | 1044 | 8869 | 44898 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_576x320.pdparams) | [配置文件](./fairmot_dla34_30e_576x320.yml) | **注意:** - FairMOT DLA-34均使用2个GPU进行训练，每个GPU上batch size为6，训练30个epoch。 ### FairMOT enhance模型 ### 在MOT-16 Test Set上结果 | 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 | | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: | | DLA-34 | 1088x608 | 75.9 | 74.7 | 1021 | 11425 | 31475 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_60e_1088x608.pdparams) | [配置文件](./fairmot_enhance_dla34_60e_1088x608.yml) | | HarDNet-85 | 1088x608 | 75.0 | 70.0 | 1050 | 11837 | 32774 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [配置文件](./fairmot_enhance_hardnet85_30e_1088x608.yml) | ### 在MOT-17 Test Set上结果 | 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 | | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: | | DLA-34 | 1088x608 | 75.3 | 74.2 | 3270 | 29112 | 106749 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_60e_1088x608.pdparams) | [配置文件](./fairmot_enhance_dla34_60e_1088x608.yml) | | HarDNet-85 | 1088x608 | 74.7 | 70.7 | 3210 | 29790 | 109914 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [配置文件](./fairmot_enhance_hardnet85_30e_1088x608.yml) | **注意:** - FairMOT enhance模型均使用8个GPU进行训练，训练集中加入了crowdhuman数据集一起参与训练。 - FairMOT enhance DLA-34 每个GPU上batch size为16，训练60个epoch。 - FairMOT enhance HarDNet-85 每个GPU上batch size为10，训练30个epoch。 ### FairMOT轻量级模型 ### 在MOT-16 Test Set上结果 | 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 | | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: | | HRNetV2-W18 | 1088x608 | 71.7 | 66.6 | 1340 | 8642 | 41592 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_1088x608.pdparams) | [配置文件](./fairmot_hrnetv2_w18_dlafpn_30e_1088x608.yml) | ### 在MOT-17 Test Set上结果 | 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 | | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: | | HRNetV2-W18 | 1088x608 | 70.7 | 65.7 | 4281 | 22485 | 138468 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_1088x608.pdparams) | [配置文件](./fairmot_hrnetv2_w18_dlafpn_30e_1088x608.yml) | | HRNetV2-W18 | 864x480 | 70.3 | 65.8 | 4056 | 18927 | 144486 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_864x480.pdparams) | [配置文件](./fairmot_hrnetv2_w18_dlafpn_30e_864x480.yml) | | HRNetV2-W18 | 576x320 | 65.3 | 64.8 | 4137 | 28860 | 163017 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.pdparams) | [配置文件](./fairmot_hrnetv2_w18_dlafpn_30e_576x320.yml) | **注意:** - FairMOT HRNetV2-W18均使用8个GPU进行训练，每个GPU上batch size为4，训练30个epoch，使用的ImageNet预训练，优化器策略采用的是Momentum，并且训练集中加入了crowdhuman数据集一起参与训练。 ### FairMOT + BYTETracker ### 在MOT-17 Half上结果 | 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 | | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: | | DLA-34 | 1088x608 | 69.1 | 72.8 | 299 | 1957 | 14412 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608.yml) | | DLA-34 + BYTETracker| 1088x608 | 70.3 | 73.2 | 234 | 2176 | 13598 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_bytetracker.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_bytetracker.yml) | **注意:** - FairMOT模型此处是ablation study的配置，使用的训练集是原先MIX的5个数据集(Caltech,CUHKSYSU,PRW,Cityscapes,ETHZ)加上MOT17 Train的前一半，且使用是预训练权重是CenterNet的COCO预训练权重，验证是在MOT17 Train的后一半上测的。 - BYTETracker应用到PaddleDetection的其他FairMOT模型，只需要更改对应的config文件里的tracker部分为如下所示： ``` JDETracker: use_byte: True match_thres: 0.8 conf_thres: 0.4 low_conf_thres: 0.2 ``` ### FairMOT迁移学习模型 ### 在GMOT-40的airplane子集上的结果 | 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 | | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: | | DLA-34 | 1088x608 | 96.6 | 94.7 | 19 | 300 | 466 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_airplane.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_airplane.yml) | **注意:** - 此模型数据集是GMOT-40的airplane类别抽离出来的子集，PaddleDetection团队整理后的下载链接为: ```wget https://bj.bcebos.com/v1/paddledet/data/mot/airplane.zip```，下载解压存放于 ```dataset/mot```目录下，并将其中的```airplane.train```复制存放于```dataset/mot/image_lists```。 - FairMOT模型此处训练是采用行人FairMOT训好的模型作为预训练权重，使用的训练集是airplane全集共4个视频序列，验证也是在全集上测的。 - 应用到其他物体的跟踪，需要更改对应的config文件里的tracker部分的```min_box_area```和```vertical_ratio```，如下所示： ``` JDETracker: conf_thres: 0.4 tracked_thresh: 0.4 metric_type: cosine min_box_area: 0 # 200 for pedestrian vertical_ratio: 0 # 1.6 for pedestrian ``` ## 快速开始 ### 1. 训练 使用2个GPU通过如下命令一键式启动训练 ```bash python -m paddle.distributed.launch --log_dir=./fairmot_dla34_30e_1088x608/ --gpus 0,1 tools/train.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml ``` ### 2. 评估 使用单张GPU通过如下命令一键式启动评估 ```bash # 使用PaddleDetection发布的权重 CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams # 使用训练保存的checkpoint CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=output/fairmot_dla34_30e_1088x608/model_final.pdparams ``` **注意:** - 默认评估的是MOT-16 Train Set数据集, 如需换评估数据集可参照以下代码修改`configs/datasets/mot.yml`： ``` EvalMOTDataset: !MOTImageFolder dataset_dir: dataset/mot data_root: MOT17/images/train keep_ori_im: False # set True if save visualization images or video ``` - 跟踪结果会存于`{output_dir}/mot_results/`中，里面每个视频序列对应一个txt，每个txt文件每行信息是`frame,id,x1,y1,w,h,score,-1,-1,-1`, 此外`{output_dir}`可通过`--output_dir`设置。 ### 3. 预测 使用单个GPU通过如下命令预测一个视频，并保存为视频 ```bash # 预测一个视频 CUDA_VISIBLE_DEVICES=0 python tools/infer_mot.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams --video_file={your video name}.mp4 --save_videos ``` **注意:** - 请先确保已经安装了[ffmpeg](https://ffmpeg.org/ffmpeg.html), Linux(Ubuntu)平台可以直接用以下命令安装：`apt-get update && apt-get install -y ffmpeg`。 ### 4. 导出预测模型 ```bash CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams ``` ### 5. 用导出的模型基于Python去预测 ```bash python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --save_mot_txts ``` **注意:** - 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`表示保存跟踪结果的txt文件，或`--save_images`表示保存跟踪结果可视化图片。 - 跟踪结果txt文件每行信息是`frame,id,x1,y1,w,h,score,-1,-1,-1`。 ### 6. 用导出的跟踪和关键点模型Python联合预测 ```bash python deploy/python/mot_keypoint_unite_infer.py --mot_model_dir=output_inference/fairmot_dla34_30e_1088x608/ --keypoint_model_dir=output_inference/higherhrnet_hrnet_w32_512/ --video_file={your video name}.mp4 --device=GPU ``` **注意:** - 关键点模型导出教程请参考`configs/keypoint/README.md`。 ## 引用 ``` @article{zhang2020fair, title={FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking}, author={Zhang, Yifu and Wang, Chunyu and Wang, Xinggang and Zeng, Wenjun and Liu, Wenyu}, journal={arXiv preprint arXiv:2004.01888}, year={2020} } @article{shao2018crowdhuman, title={CrowdHuman: A Benchmark for Detecting Human in a Crowd}, author={Shao, Shuai and Zhao, Zijian and Li, Boxun and Xiao, Tete and Yu, Gang and Zhang, Xiangyu and Sun, Jian}, journal={arXiv preprint arXiv:1805.00123}, year={2018} } ```

PaddleDetection/configs/mot/fairmot/README_cn.md/0

# This config is an assembled config for ByteTrack MOT, used as eval/infer mode for MOT. _BASE_: [ '../bytetrack/detector/yolox_x_24e_800x1440_mix_det.yml', '../bytetrack/_base_/mix_det.yml', '../bytetrack/_base_/yolox_mot_reader_800x1440.yml' ] weights: output/ocsort_yolox/model_final log_iter: 20 snapshot_epoch: 2 metric: MOT # eval/infer mode num_classes: 1 architecture: ByteTrack pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/yolox_x_300e_coco.pdparams ByteTrack: detector: YOLOX reid: None tracker: OCSORTTracker det_weights: https://bj.bcebos.com/v1/paddledet/models/mot/yolox_x_24e_800x1440_mix_mot_ch.pdparams reid_weights: None depth_mult: 1.33 width_mult: 1.25 YOLOX: backbone: CSPDarkNet neck: YOLOCSPPAN head: YOLOXHead input_size: [800, 1440] size_stride: 32 size_range: [18, 22] # multi-scale range [576*1024 ~ 800*1440], w/h ratio=1.8 CSPDarkNet: arch: "X" return_idx: [2, 3, 4] depthwise: False YOLOCSPPAN: depthwise: False # Tracking requires higher quality boxes, so NMS score_threshold will be higher YOLOXHead: l1_epoch: 20 depthwise: False loss_weight: {cls: 1.0, obj: 1.0, iou: 5.0, l1: 1.0} assigner: name: SimOTAAssigner candidate_topk: 10 use_vfl: False nms: name: MultiClassNMS nms_top_k: 1000 keep_top_k: 100 score_threshold: 0.1 nms_threshold: 0.7 # For speed while keep high mAP, you can modify 'nms_top_k' to 1000 and 'keep_top_k' to 100, the mAP will drop about 0.1%. # For high speed demo, you can modify 'score_threshold' to 0.25 and 'nms_threshold' to 0.45, but the mAP will drop a lot. OCSORTTracker: det_thresh: 0.6 max_age: 30 min_hits: 3 iou_threshold: 0.3 delta_t: 3 inertia: 0.2 vertical_ratio: 1.6 min_box_area: 100 use_byte: False # MOTDataset for MOT evaluation and inference EvalMOTDataset: !MOTImageFolder dataset_dir: dataset/mot data_root: MOT17/images/half keep_ori_im: True # set as True in DeepSORT and ByteTrack TestMOTDataset: !MOTImageFolder dataset_dir: dataset/mot keep_ori_im: True # set True if save visualization images or video

PaddleDetection/configs/mot/ocsort/ocsort_yolox.yml/0

epoch: 300 LearningRate: base_lr: 0.15 schedulers: - !CosineDecay max_epochs: 300 - !LinearWarmup start_factor: 1.0 steps: 34350 OptimizerBuilder: optimizer: momentum: 0.9 type: Momentum regularizer: factor: 0.00004 type: L2

PaddleDetection/configs/picodet/legacy_model/pruner/optimizer_300e_pruner.yml/0

_BASE_: [ '../datasets/coco_detection.yml', '../runtime.yml', '../ppyoloe/_base_/optimizer_300e.yml', '../ppyoloe/_base_/ppyoloe_plus_crn_tiny_auxhead.yml', '../ppyoloe/_base_/ppyoloe_plus_reader_320.yml', ] log_iter: 100 snapshot_epoch: 4 weights: output/ppyoloe_plus_crn_t_auxhead_320_60e_pphuman/model_final pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_t_auxhead_300e_coco.pdparams # 640*640 COCO mAP 39.7 depth_mult: 0.33 width_mult: 0.375 num_classes: 1 TrainDataset: !COCODataSet image_dir: "" anno_path: annotations/train.json dataset_dir: dataset/pphuman data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd'] EvalDataset: !COCODataSet image_dir: "" anno_path: annotations/val.json dataset_dir: dataset/pphuman TestDataset: !ImageFolder anno_path: annotations/val.json dataset_dir: dataset/pphuman TrainReader: batch_size: 8 epoch: 60 LearningRate: base_lr: 0.001 schedulers: - !CosineDecay max_epochs: 72 - !LinearWarmup start_factor: 0. epochs: 1 PPYOLOEHead: static_assigner_epoch: -1 nms: name: MultiClassNMS nms_top_k: 1000 keep_top_k: 300 score_threshold: 0.01 nms_threshold: 0.7

PaddleDetection/configs/pphuman/ppyoloe_plus_crn_t_auxhead_320_60e_pphuman.yml/0

{ "images": [], "annotations": [], "categories": [ { "supercategory": "component", "id": 1, "name": "car" }, { "supercategory": "component", "id": 2, "name": "truck" }, { "supercategory": "component", "id": 3, "name": "bus" }, { "supercategory": "component", "id": 4, "name": "motorbike" }, { "supercategory": "component", "id": 5, "name": "tricycle" }, { "supercategory": "component", "id": 6, "name": "carplate" } ] }

PaddleDetection/configs/ppvehicle/vehicle_yolov3/vehicle.json/0

epoch: 300 LearningRate: base_lr: 0.01 schedulers: - name: CosineDecay max_epochs: 360 - name: LinearWarmup start_factor: 0. epochs: 5 OptimizerBuilder: optimizer: momentum: 0.9 type: Momentum regularizer: factor: 0.0005 type: L2

PaddleDetection/configs/ppyoloe/_base_/optimizer_300e.yml/0

_BASE_: [ './_base_/exdark_detection.yml', '../../runtime.yml', '../_base_/optimizer_80e.yml', '../_base_/ppyoloe_crn.yml', '../_base_/ppyoloe_reader.yml', ] log_iter: 100 snapshot_epoch: 5 weights: output/ppyoloe_crn_m_80e_exdark/model_final pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams depth_mult: 0.67 width_mult: 0.75

PaddleDetection/configs/ppyoloe/application/ppyoloe_crn_m_80e_exdark.yml/0

_BASE_: [ '../ppyoloe_plus_crn_s_80e_coco.yml', ] for_distill: True architecture: PPYOLOE PPYOLOE: backbone: CSPResNet neck: CustomCSPPAN yolo_head: PPYOLOEHead post_process: ~ worker_num: 4 TrainReader: sample_transforms: - Decode: {} - RandomDistort: {} - RandomExpand: {fill_value: [123.675, 116.28, 103.53]} - RandomCrop: {} - RandomFlip: {} batch_transforms: - BatchRandomResize: {target_size: [640], random_size: True, random_interp: True, keep_ratio: False} - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none} - Permute: {} - PadGT: {} batch_size: 8 shuffle: True drop_last: True use_shared_memory: True collate_batch: True log_iter: 100 snapshot_epoch: 5 weights: output/ppyoloe_plus_crn_s_80e_coco_distill/model_final pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_s_obj365_pretrained.pdparams depth_mult: 0.33 width_mult: 0.50

PaddleDetection/configs/ppyoloe/distill/ppyoloe_plus_crn_s_80e_coco_distill.yml/0

_BASE_: [ '../datasets/coco_detection.yml', '../runtime.yml', './_base_/optimizer_300e.yml', './_base_/ppyoloe_plus_crn_tiny_auxhead.yml', './_base_/ppyoloe_plus_reader_320.yml', ] log_iter: 100 snapshot_epoch: 10 weights: output/ppyoloe_plus_crn_t_auxhead_320_300e_coco/model_final pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_t_pretrained.pdparams depth_mult: 0.33 width_mult: 0.375

PaddleDetection/configs/ppyoloe/ppyoloe_plus_crn_t_auxhead_320_300e_coco.yml/0

worker_num: 2 TrainReader: sample_transforms: - Decode: {} - AutoAugment: {autoaug_type: v1} - RandomResize: {target_size: [[384,1000], [416,1000], [448,1000], [480,1000], [512,1000], [544,1000], [576,1000], [608,1000], [640,1000], [672,1000]], interp: 2, keep_ratio: True} - RandomFlip: {prob: 0.5} - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]} - Permute: {} batch_transforms: - PadBatch: {pad_to_stride: 32} batch_size: 2 shuffle: true drop_last: true collate_batch: false use_shared_memory: true EvalReader: sample_transforms: - Decode: {} - Resize: {interp: 2, target_size: [640, 640], keep_ratio: True} - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]} - Permute: {} batch_transforms: - PadBatch: {pad_to_stride: 32} batch_size: 1 shuffle: false drop_last: false TestReader: sample_transforms: - Decode: {} - Resize: {interp: 2, target_size: [640, 640], keep_ratio: True} - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]} - Permute: {} batch_transforms: - PadBatch: {pad_to_stride: 32} batch_size: 1 shuffle: false drop_last: false

PaddleDetection/configs/rcnn_enhance/_base_/faster_rcnn_enhance_reader.yml/0

简体中文 | [English](README_en.md) # 旋转框检测 ## 内容 - [简介](#简介) - [模型库](#模型库) - [数据准备](#数据准备) - [安装依赖](#安装依赖) ## 简介 旋转框常用于检测带有角度信息的矩形框，即矩形框的宽和高不再与图像坐标轴平行。相较于水平矩形框，旋转矩形框一般包括更少的背景信息。旋转框检测常用于遥感等场景中。 ## 模型库 | 模型 | mAP | 学习率策略 | 角度表示 | 数据增广 | GPU数目 | 每GPU图片数目 | 模型下载 | 配置文件 | |:---:|:----:|:---------:|:-----:|:--------:|:-----:|:------------:|:-------:|:------:| | [S2ANet](./s2anet/README.md) | 73.84 | 2x | le135 | - | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/s2anet/s2anet_alignconv_2x_dota.yml) | | [FCOSR](./fcosr/README.md) | 76.62 | 3x | oc | RR | 4 | 4 | [model](https://paddledet.bj.bcebos.com/models/fcosr_x50_3x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/fcosr/fcosr_x50_3x_dota.yml) | | [PP-YOLOE-R-s](./ppyoloe_r/README.md) | 73.82 | 3x | oc | RR | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_r_crn_s_3x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/ppyoloe_r/ppyoloe_r_crn_s_3x_dota.yml) | | [PP-YOLOE-R-s](./ppyoloe_r/README.md) | 79.42 | 3x | oc | MS+RR | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_r_crn_s_3x_dota_ms.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/ppyoloe_r/ppyoloe_r_crn_s_3x_dota_ms.yml) | | [PP-YOLOE-R-m](./ppyoloe_r/README.md) | 77.64 | 3x | oc | RR | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_r_crn_m_3x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/ppyoloe_r/ppyoloe_r_crn_m_3x_dota.yml) | | [PP-YOLOE-R-m](./ppyoloe_r/README.md) | 79.71 | 3x | oc | MS+RR | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_r_crn_m_3x_dota_ms.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/ppyoloe_r/ppyoloe_r_crn_m_3x_dota_ms.yml) | | [PP-YOLOE-R-l](./ppyoloe_r/README.md) | 78.14 | 3x | oc | RR | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_r_crn_l_3x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/ppyoloe_r/ppyoloe_r_crn_l_3x_dota.yml) | | [PP-YOLOE-R-l](./ppyoloe_r/README.md) | 80.02 | 3x | oc | MS+RR | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_r_crn_l_3x_dota_ms.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/ppyoloe_r/ppyoloe_r_crn_l_3x_dota_ms.yml) | | [PP-YOLOE-R-x](./ppyoloe_r/README.md) | 78.28 | 3x | oc | RR | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_r_crn_x_3x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/ppyoloe_r/ppyoloe_r_crn_x_3x_dota.yml) | | [PP-YOLOE-R-x](./ppyoloe_r/README.md) | 80.73 | 3x | oc | MS+RR | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_r_crn_x_3x_dota_ms.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/ppyoloe_r/ppyoloe_r_crn_x_3x_dota_ms.yml) | **注意:** - 如果**GPU卡数**或者**batch size**发生了改变，你需要按照公式 **lr_new = lr_default * (batch_size_new * GPU_number_new) / (batch_size_default * GPU_number_default)** 调整学习率。 - 模型库中的模型默认使用单尺度训练单尺度测试。如果数据增广一栏标明MS，意味着使用多尺度训练和多尺度测试。如果数据增广一栏标明RR，意味着使用RandomRotate数据增广进行训练。 ## 数据准备 ### DOTA数据准备 DOTA数据集是一个大规模的遥感图像数据集，包含旋转框和水平框的标注。可以从[DOTA数据集官网](https://captain-whu.github.io/DOTA/)下载数据集并解压，解压后的数据集目录结构如下所示： ``` ${DOTA_ROOT} ├── test │ └── images ├── train │ ├── images │ └── labelTxt └── val ├── images └── labelTxt ``` 对于有标注的数据，每一张图片会对应一个同名的txt文件，文件中每一行为一个旋转框的标注，其格式如下： ``` x1 y1 x2 y2 x3 y3 x4 y4 class_name difficult ``` #### 单尺度切图 DOTA数据集分辨率较高，因此一般在训练和测试之前对图像进行离线切图，使用单尺度进行切图可以使用以下命令： ``` bash # 对于有标注的数据进行切图 python configs/rotate/tools/prepare_data.py \ --input_dirs ${DOTA_ROOT}/train/ ${DOTA_ROOT}/val/ \ --output_dir ${OUTPUT_DIR}/trainval1024/ \ --coco_json_file DOTA_trainval1024.json \ --subsize 1024 \ --gap 200 \ --rates 1.0 # 对于无标注的数据进行切图需要设置--image_only python configs/rotate/tools/prepare_data.py \ --input_dirs ${DOTA_ROOT}/test/ \ --output_dir ${OUTPUT_DIR}/test1024/ \ --coco_json_file DOTA_test1024.json \ --subsize 1024 \ --gap 200 \ --rates 1.0 \ --image_only ``` #### 多尺度切图 使用多尺度进行切图可以使用以下命令： ``` bash # 对于有标注的数据进行切图 python configs/rotate/tools/prepare_data.py \ --input_dirs ${DOTA_ROOT}/train/ ${DOTA_ROOT}/val/ \ --output_dir ${OUTPUT_DIR}/trainval/ \ --coco_json_file DOTA_trainval1024.json \ --subsize 1024 \ --gap 500 \ --rates 0.5 1.0 1.5 # 对于无标注的数据进行切图需要设置--image_only python configs/rotate/tools/prepare_data.py \ --input_dirs ${DOTA_ROOT}/test/ \ --output_dir ${OUTPUT_DIR}/test1024/ \ --coco_json_file DOTA_test1024.json \ --subsize 1024 \ --gap 500 \ --rates 0.5 1.0 1.5 \ --image_only ``` ### 自定义数据集 旋转框使用标准COCO数据格式，你可以将你的数据集转换成COCO格式以训练模型。COCO标准数据格式的标注信息中包含以下信息： ``` python 'annotations': [ { 'id': 2083, 'category_id': 9, 'image_id': 9008, 'bbox': [x, y, w, h], # 水平框标注 'segmentation': [[x1, y1, x2, y2, x3, y3, x4, y4]], # 旋转框标注 ... } ... ] ``` **需要注意的是`bbox`的标注是水平框标注，`segmentation`为旋转框四个点的标注(顺时针或逆时针均可)。在旋转框训练时`bbox`是可以缺省，一般推荐根据旋转框标注`segmentation`生成。** 在PaddleDetection 2.4及之前的版本，`bbox`为旋转框标注[x, y, w, h, angle]，`segmentation`缺省，**目前该格式已不再支持，请下载最新数据集或者转换成标准COCO格式**。 ## 安装依赖 旋转框检测模型需要依赖外部算子进行训练，评估等。Linux环境下，你可以执行以下命令进行编译安装 ``` cd ppdet/ext_op python setup.py install ``` Windows环境请按照如下步骤安装： （1）准备Visual Studio (版本需要>=Visual Studio 2015 update3)，这里以VS2017为例； （2）点击开始-->Visual Studio 2017-->适用于 VS 2017 的x64本机工具命令提示； （3）设置环境变量：`set DISTUTILS_USE_SDK=1` （4）进入`PaddleDetection/ppdet/ext_op`目录，通过`python setup.py install`命令进行安装。 安装完成后，可以执行`ppdet/ext_op/unittest`下的单测验证外部op是否正确安装

PaddleDetection/configs/rotate/README.md/0

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys import six import glob import time import yaml import argparse import cv2 import numpy as np import paddle import paddle.version as paddle_version from paddle.inference import Config, create_predictor, PrecisionType, get_trt_runtime_version TUNED_TRT_DYNAMIC_MODELS = {'DETR'} def check_version(version='2.2'): err = "PaddlePaddle version {} or higher is required, " \ "or a suitable develop version is satisfied as well. \n" \ "Please make sure the version is good with your code.".format(version) version_installed = [ paddle_version.major, paddle_version.minor, paddle_version.patch, paddle_version.rc ] if version_installed == ['0', '0', '0', '0']: return if version == 'develop': raise Exception("PaddlePaddle develop version is required!") version_split = version.split('.') length = min(len(version_installed), len(version_split)) for i in six.moves.range(length): if version_installed[i] > version_split[i]: return if version_installed[i] < version_split[i]: raise Exception(err) def check_trt_version(version='8.2'): err = "TensorRT version {} or higher is required," \ "Please make sure the version is good with your code.".format(version) version_split = list(map(int, version.split('.'))) version_installed = get_trt_runtime_version() length = min(len(version_installed), len(version_split)) for i in six.moves.range(length): if version_installed[i] > version_split[i]: return if version_installed[i] < version_split[i]: raise Exception(err) # preprocess ops def decode_image(im_file, im_info): if isinstance(im_file, str): with open(im_file, 'rb') as f: im_read = f.read() data = np.frombuffer(im_read, dtype='uint8') im = cv2.imdecode(data, 1) # BGR mode, but need RGB mode im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) else: im = im_file im_info['im_shape'] = np.array(im.shape[:2], dtype=np.float32) im_info['scale_factor'] = np.array([1., 1.], dtype=np.float32) return im, im_info class Resize(object): def __init__(self, target_size, keep_ratio=True, interp=cv2.INTER_LINEAR): if isinstance(target_size, int): target_size = [target_size, target_size] self.target_size = target_size self.keep_ratio = keep_ratio self.interp = interp def __call__(self, im, im_info): assert len(self.target_size) == 2 assert self.target_size[0] > 0 and self.target_size[1] > 0 im_channel = im.shape[2] im_scale_y, im_scale_x = self.generate_scale(im) im = cv2.resize( im, None, None, fx=im_scale_x, fy=im_scale_y, interpolation=self.interp) im_info['im_shape'] = np.array(im.shape[:2]).astype('float32') im_info['scale_factor'] = np.array( [im_scale_y, im_scale_x]).astype('float32') return im, im_info def generate_scale(self, im): origin_shape = im.shape[:2] im_c = im.shape[2] if self.keep_ratio: im_size_min = np.min(origin_shape) im_size_max = np.max(origin_shape) target_size_min = np.min(self.target_size) target_size_max = np.max(self.target_size) im_scale = float(target_size_min) / float(im_size_min) if np.round(im_scale * im_size_max) > target_size_max: im_scale = float(target_size_max) / float(im_size_max) im_scale_x = im_scale im_scale_y = im_scale else: resize_h, resize_w = self.target_size im_scale_y = resize_h / float(origin_shape[0]) im_scale_x = resize_w / float(origin_shape[1]) return im_scale_y, im_scale_x class Permute(object): def __init__(self, ): super(Permute, self).__init__() def __call__(self, im, im_info): im = im.transpose((2, 0, 1)) return im, im_info class NormalizeImage(object): def __init__(self, mean, std, is_scale=True, norm_type='mean_std'): self.mean = mean self.std = std self.is_scale = is_scale self.norm_type = norm_type def __call__(self, im, im_info): im = im.astype(np.float32, copy=False) if self.is_scale: scale = 1.0 / 255.0 im *= scale if self.norm_type == 'mean_std': mean = np.array(self.mean)[np.newaxis, np.newaxis, :] std = np.array(self.std)[np.newaxis, np.newaxis, :] im -= mean im /= std return im, im_info class PadStride(object): def __init__(self, stride=0): self.coarsest_stride = stride def __call__(self, im, im_info): coarsest_stride = self.coarsest_stride if coarsest_stride <= 0: return im, im_info im_c, im_h, im_w = im.shape pad_h = int(np.ceil(float(im_h) / coarsest_stride) * coarsest_stride) pad_w = int(np.ceil(float(im_w) / coarsest_stride) * coarsest_stride) padding_im = np.zeros((im_c, pad_h, pad_w), dtype=np.float32) padding_im[:, :im_h, :im_w] = im return padding_im, im_info def preprocess(im, preprocess_ops): # process image by preprocess_ops im_info = { 'scale_factor': np.array( [1., 1.], dtype=np.float32), 'im_shape': None, } im, im_info = decode_image(im, im_info) for operator in preprocess_ops: im, im_info = operator(im, im_info) return im, im_info def parse_args(): parser = argparse.ArgumentParser() parser.add_argument( '--model_dir', type=str, help='directory of inference model') parser.add_argument( '--run_mode', type=str, default='paddle', help='running mode') parser.add_argument('--batch_size', type=int, default=1, help='batch size') parser.add_argument( '--image_dir', type=str, default='/paddle/data/DOTA_1024_ss/test1024/images', help='directory of test images') parser.add_argument( '--warmup_iter', type=int, default=5, help='num of warmup iters') parser.add_argument( '--total_iter', type=int, default=2000, help='num of total iters') parser.add_argument( '--log_iter', type=int, default=50, help='num of log interval') parser.add_argument( '--tuned_trt_shape_file', type=str, default='shape_range_info.pbtxt', help='dynamic shape range info') args = parser.parse_args() return args def init_predictor(FLAGS): model_dir, run_mode, batch_size = FLAGS.model_dir, FLAGS.run_mode, FLAGS.batch_size yaml_file = os.path.join(model_dir, 'infer_cfg.yml') with open(yaml_file) as f: yml_conf = yaml.safe_load(f) config = Config( os.path.join(model_dir, 'model.pdmodel'), os.path.join(model_dir, 'model.pdiparams')) # initial GPU memory(M), device ID config.enable_use_gpu(200, 0) # optimize graph and fuse op config.switch_ir_optim(True) precision_map = { 'trt_int8': Config.Precision.Int8, 'trt_fp32': Config.Precision.Float32, 'trt_fp16': Config.Precision.Half } arch = yml_conf['arch'] tuned_trt_shape_file = os.path.join(model_dir, FLAGS.tuned_trt_shape_file) if run_mode in precision_map.keys(): if arch in TUNED_TRT_DYNAMIC_MODELS and not os.path.exists( tuned_trt_shape_file): print( 'dynamic shape range info is saved in {}. After that, rerun the code'. format(tuned_trt_shape_file)) config.collect_shape_range_info(tuned_trt_shape_file) config.enable_tensorrt_engine( workspace_size=(1 << 25) * batch_size, max_batch_size=batch_size, min_subgraph_size=yml_conf['min_subgraph_size'], precision_mode=precision_map[run_mode], use_static=True, use_calib_mode=False) if yml_conf['use_dynamic_shape']: if arch in TUNED_TRT_DYNAMIC_MODELS and os.path.exists( tuned_trt_shape_file): config.enable_tuned_tensorrt_dynamic_shape(tuned_trt_shape_file, True) else: min_input_shape = { 'image': [batch_size, 3, 640, 640], 'scale_factor': [batch_size, 2] } max_input_shape = { 'image': [batch_size, 3, 1280, 1280], 'scale_factor': [batch_size, 2] } opt_input_shape = { 'image': [batch_size, 3, 1024, 1024], 'scale_factor': [batch_size, 2] } config.set_trt_dynamic_shape_info( min_input_shape, max_input_shape, opt_input_shape) # disable print log when predict config.disable_glog_info() # enable shared memory config.enable_memory_optim() # disable feed, fetch OP, needed by zero_copy_run config.switch_use_feed_fetch_ops(False) predictor = create_predictor(config) return predictor, yml_conf def create_preprocess_ops(yml_conf): preprocess_ops = [] for op_info in yml_conf['Preprocess']: new_op_info = op_info.copy() op_type = new_op_info.pop('type') preprocess_ops.append(eval(op_type)(**new_op_info)) return preprocess_ops def get_test_images(image_dir): images = set() infer_dir = os.path.abspath(image_dir) exts = ['jpg', 'jpeg', 'png', 'bmp'] exts += [ext.upper() for ext in exts] for ext in exts: images.update(glob.glob('{}/*.{}'.format(infer_dir, ext))) images = list(images) return images def create_inputs(image_files, preprocess_ops): inputs = dict() im_list, im_info_list = [], [] for im_path in image_files: im, im_info = preprocess(im_path, preprocess_ops) im_list.append(im) im_info_list.append(im_info) inputs['im_shape'] = np.stack( [e['im_shape'] for e in im_info_list], axis=0).astype('float32') inputs['scale_factor'] = np.stack( [e['scale_factor'] for e in im_info_list], axis=0).astype('float32') inputs['image'] = np.stack(im_list, axis=0).astype('float32') return inputs def measure_speed(FLAGS): predictor, yml_conf = init_predictor(FLAGS) input_names = predictor.get_input_names() preprocess_ops = create_preprocess_ops(yml_conf) image_files = get_test_images(FLAGS.image_dir) batch_size = FLAGS.batch_size warmup_iter, log_iter, total_iter = FLAGS.warmup_iter, FLAGS.log_iter, FLAGS.total_iter total_time = 0 fps = 0 for i in range(0, total_iter, batch_size): # make data ready inputs = create_inputs(image_files[i:i + batch_size], preprocess_ops) for name in input_names: input_tensor = predictor.get_input_handle(name) input_tensor.copy_from_cpu(inputs[name]) paddle.device.cuda.synchronize() # start running start_time = time.perf_counter() predictor.run() paddle.device.cuda.synchronize() if i >= warmup_iter: total_time += time.perf_counter() - start_time if (i + 1) % log_iter == 0: fps = (i + 1 - warmup_iter) / total_time print( f'Done image [{i + 1:<3}/ {total_iter}], ' f'fps: {fps:.1f} img / s, ' f'times per image: {1000 / fps:.1f} ms / img', flush=True) if (i + 1) == total_iter: fps = (i + 1 - warmup_iter) / total_time print( f'Overall fps: {fps:.1f} img / s, ' f'times per image: {1000 / fps:.1f} ms / img', flush=True) break if __name__ == '__main__': FLAGS = parse_args() if 'trt' in FLAGS.run_mode: check_version('develop') check_trt_version('8.2') else: check_version('2.4') measure_speed(FLAGS)

PaddleDetection/configs/rotate/tools/inference_benchmark.py/0

_BASE_: [ '../datasets/coco_detection.yml', '../runtime.yml', '_base_/optimizer_6x.yml', '_base_/rtdetr_r50vd.yml', '_base_/rtdetr_reader.yml', ] weights: output/rtdetr_swin_L_384_3x_coco/model_final find_unused_parameters: True log_iter: 100 snapshot_epoch: 2 pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/dino_swin_large_384_4scale_3x_coco.pdparams DETR: backbone: SwinTransformer neck: HybridEncoder transformer: RTDETRTransformer detr_head: DINOHead post_process: DETRPostProcess SwinTransformer: arch: 'swin_L_384' # ['swin_T_224', 'swin_S_224', 'swin_B_224', 'swin_L_224', 'swin_B_384', 'swin_L_384'] ape: false drop_path_rate: 0.2 patch_norm: true out_indices: [1, 2, 3] HybridEncoder: hidden_dim: 256 use_encoder_idx: [2] num_encoder_layers: 6 # encoder_layer: name: TransformerLayer d_model: 256 nhead: 8 dim_feedforward: 2048 # dropout: 0. activation: 'gelu' expansion: 1.0 RTDETRTransformer: num_queries: 300 position_embed_type: sine feat_strides: [8, 16, 32] num_levels: 3 nhead: 8 num_decoder_layers: 6 dim_feedforward: 2048 # dropout: 0.0 activation: relu num_denoising: 100 label_noise_ratio: 0.5 box_noise_scale: 1.0 learnt_init_query: False DINOHead: loss: name: DINOLoss loss_coeff: {class: 1, bbox: 5, giou: 2} aux_loss: True use_vfl: True matcher: name: HungarianMatcher matcher_coeff: {class: 2, bbox: 5, giou: 2} DETRPostProcess: num_top_queries: 300 epoch: 36 LearningRate: base_lr: 0.0001 schedulers: - !PiecewiseDecay gamma: 0.1 milestones: [36] use_warmup: false OptimizerBuilder: clip_grad_by_norm: 0.1 regularizer: false optimizer: type: AdamW weight_decay: 0.0001 param_groups: - params: ['absolute_pos_embed', 'relative_position_bias_table', 'norm'] weight_decay: 0.0

PaddleDetection/configs/rtdetr/rtdetr_swin_L_384_3x_coco.yml/0

简体中文 | [English](README_en.md) # Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection ## FCOS模型库 | 模型 | 监督数据比例 | Sup Baseline | Sup Epochs (Iters) | Sup mAP<sup>val<br>0.5:0.95 | Semi mAP<sup>val<br>0.5:0.95 | Semi Epochs (Iters) | 模型下载 | 配置文件 | | :------------: | :---------: | :---------------------: | :---------------------: |:---------------------------: |:----------------------------: | :------------------: |:--------: |:----------: | | DenseTeacher-FCOS | 5% | [sup_config](../baseline/fcos_r50_fpn_2x_coco_sup005.yml) | 24 (8712) | 21.3 | **30.6** | 240 (87120) | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi005.pdparams) | [config](./denseteacher_fcos_r50_fpn_coco_semi005.yml) | | DenseTeacher-FCOS | 10% | [sup_config](../baseline/fcos_r50_fpn_2x_coco_sup010.yml) | 24 (17424) | 26.3 | **35.1** | 240 (174240) | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010.pdparams) | [config](./denseteacher_fcos_r50_fpn_coco_semi010.yml) | | DenseTeacher-FCOS(LSJ)| 10% | [sup_config](../baseline/fcos_r50_fpn_2x_coco_sup010.yml) | 24 (17424) | 26.3 | **37.1(LSJ)** | 240 (174240) | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010_lsj.pdparams) | [config](./denseteacher_fcos_r50_fpn_coco_semi010_lsj.yml) | | DenseTeacher-FCOS |100%(full)| [sup_config](../../fcos/fcos_r50_fpn_iou_multiscale_2x_coco.ymll) | 24 (175896) | 42.6 | **44.2** | 24 (175896)| [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_full.pdparams) | [config](./denseteacher_fcos_r50_fpn_coco_full.yml) | **注意:** - 以上模型训练默认使用8 GPUs，监督数据总batch_size默认为16，无监督数据总batch_size默认也为16，默认初始学习率为0.01。如果改动了总batch_size，请按线性比例相应地调整学习率； - **监督数据比例**是指使用的有标签COCO数据集占 COCO train2017 全量训练集的百分比，使用的无标签COCO数据集一般也是相同比例，但具体图片和有标签数据的图片不重合； - `Semi Epochs (Iters)`表示**半监督训练**的模型的 Epochs (Iters)，如果使用**自定义数据集**，需自行根据Iters换算到对应的Epochs调整，最好保证总Iters 和COCO数据集的设置较为接近； - `Sup mAP`是**只使用有监督数据训练**的模型的精度，请参照**基础检测器的配置文件** 和 [baseline](../baseline)； - `Semi mAP`是**半监督训练**的模型的精度，模型下载和配置文件的链接均为**半监督模型**； - `LSJ`表示 **large-scale jittering**，表示使用更大范围的多尺度训练，可进一步提升精度，但训练速度也会变慢； - 半监督检测的配置讲解，请参照[文档](../README.md/#半监督检测配置)； - `Dense Teacher`原文使用`R50-va-caffe`预训练，PaddleDetection中默认使用`R50-vb`预训练，如果使用`R50-vd`结合[SSLD](../../../docs/feature_models/SSLD_PRETRAINED_MODEL.md)的预训练模型，可进一步显著提升检测精度，同时backbone部分配置也需要做出相应更改，如： ```python pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams ResNet: depth: 50 variant: d norm_type: bn freeze_at: 0 return_idx: [1, 2, 3] num_stages: 4 lr_mult_list: [0.05, 0.05, 0.1, 0.15] ``` ## PPYOLOE+ 模型库 | 模型 | 监督数据比例 | Sup Baseline | Sup Epochs (Iters) | Sup mAP<sup>val<br>0.5:0.95 | Semi mAP<sup>val<br>0.5:0.95 | Semi Epochs (Iters) | 模型下载 | 配置文件 | | :------------: | :---------: | :---------------------: | :---------------------: |:---------------------------: |:----------------------------: | :------------------: |:--------: |:----------: | | DenseTeacher-PPYOLOE+_s | 5% | [sup_config](../baseline/ppyoloe_plus_crn_s_80e_coco_sup005.yml) | 80 (14480) | 32.8 | **34.0** | 200 (36200) | [download](https://paddledet.bj.bcebos.com/models/denseteacher_ppyoloe_plus_crn_s_coco_semi005.pdparams) | [config](./denseteacher_ppyoloe_plus_crn_s_coco_semi005.yml) | | DenseTeacher-PPYOLOE+_s | 10% | [sup_config](../baseline/ppyoloe_plus_crn_s_80e_coco_sup010.yml) | 80 (14480) | 35.3 | **37.5** | 200 (36200) | [download](https://paddledet.bj.bcebos.com/models/denseteacher_ppyoloe_plus_crn_s_coco_semi010.pdparams) | [config](./denseteacher_ppyoloe_plus_crn_s_coco_semi010.yml) | | DenseTeacher-PPYOLOE+_l | 5% | [sup_config](../baseline/ppyoloe_plus_crn_s_80e_coco_sup005.yml) | 80 (14480) | 42.9 | **45.4** | 200 (36200) | [download](https://paddledet.bj.bcebos.com/models/denseteacher_ppyoloe_plus_crn_l_coco_semi005.pdparams) | [config](./denseteacher_ppyoloe_plus_crn_l_coco_semi005.yml) | | DenseTeacher-PPYOLOE+_l | 10% | [sup_config](../baseline/ppyoloe_plus_crn_l_80e_coco_sup010.yml) | 80 (14480) | 45.7 | **47.4** | 200 (36200) | [download](https://paddledet.bj.bcebos.com/models/denseteacher_ppyoloe_plus_crn_l_coco_semi010.pdparams) | [config](./denseteacher_ppyoloe_plus_crn_l_coco_semi010.yml) | ## 使用说明 仅训练时必须使用半监督检测的配置文件去训练，评估、预测、部署也可以按基础检测器的配置文件去执行。 ### 训练 ```bash # 单卡训练 (不推荐，需按线性比例相应地调整学习率) CUDA_VISIBLE_DEVICES=0 python tools/train.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml --eval # 多卡训练 python -m paddle.distributed.launch --log_dir=denseteacher_fcos_semi010/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml --eval ``` ### 评估 ```bash CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=output/denseteacher_fcos_r50_fpn_coco_semi010/model_final.pdparams ``` ### 预测 ```bash CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=output/denseteacher_fcos_r50_fpn_coco_semi010/model_final.pdparams --infer_img=demo/000000014439.jpg ``` ### 部署 部署可以使用半监督检测配置文件，也可以使用基础检测器的配置文件去部署和使用。 ```bash # 导出模型 CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010.pdparams # 导出权重预测 CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/denseteacher_fcos_r50_fpn_coco_semi010 --image_file=demo/000000014439_640x640.jpg --device=GPU # 部署测速 CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/denseteacher_fcos_r50_fpn_coco_semi010 --image_file=demo/000000014439_640x640.jpg --device=GPU --run_benchmark=True # --run_mode=trt_fp16 # 导出ONNX paddle2onnx --model_dir output_inference/denseteacher_fcos_r50_fpn_coco_semi010/ --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 12 --save_file denseteacher_fcos_r50_fpn_coco_semi010.onnx ``` ## 引用 ``` @article{denseteacher2022, title={Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection}, author={Hongyu Zhou, Zheng Ge, Songtao Liu, Weixin Mao, Zeming Li, Haiyan Yu, Jian Sun}, journal={arXiv preprint arXiv:2207.02541}, year={2022} } ```

PaddleDetection/configs/semi_det/denseteacher/README.md/0

pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams slim: QAT QAT: quant_config: { 'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max', 'weight_bits': 8, 'activation_bits': 8, 'dtype': 'int8', 'window_size': 10000, 'moving_rate': 0.99, 'quantizable_layer_type': ['Conv2D', 'Linear']} print_model: True PPYOLOFPN: in_channels: [160, 368] coord_conv: true conv_block_num: 0 spp: true drop_block: false

PaddleDetection/configs/slim/quant/ppyolo_mbv3_large_qat.yml/0

_BASE_: [ './_base_/DOTA_sliced_500_025_detection.yml', '../runtime.yml', '../ppyoloe/_base_/optimizer_300e.yml', '../ppyoloe/_base_/ppyoloe_crn.yml', '../ppyoloe/_base_/ppyoloe_reader.yml', ] log_iter: 100 snapshot_epoch: 10 weights: output/ppyoloe_p2_crn_l_80e_sliced_DOTA_500_025/model_final pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams depth_mult: 1.0 width_mult: 1.0 CSPResNet: return_idx: [0, 1, 2, 3] use_alpha: True CustomCSPPAN: out_channels: [768, 384, 192, 64] TrainReader: batch_size: 4 EvalReader: batch_size: 1 TestReader: batch_size: 1 fuse_normalize: True epoch: 80 LearningRate: base_lr: 0.01 schedulers: - !CosineDecay max_epochs: 96 - !LinearWarmup start_factor: 0. epochs: 1 PPYOLOEHead: fpn_strides: [32, 16, 8, 4] static_assigner_epoch: -1 nms: name: MultiClassNMS nms_top_k: 10000 keep_top_k: 500 score_threshold: 0.01 nms_threshold: 0.6

PaddleDetection/configs/smalldet/ppyoloe_p2_crn_l_80e_sliced_DOTA_500_025.yml/0

# SSD: Single Shot MultiBox Detector ## Model Zoo ### SSD on Pascal VOC | 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | 下载 | 配置文件 | | :-------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: | | VGG | SSD | 8 | 240e | ---- | 77.8 | [下载链接](https://paddledet.bj.bcebos.com/models/ssd_vgg16_300_240e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ssd/ssd_vgg16_300_240e_voc.yml) | | MobileNet v1 | SSD | 32 | 120e | ---- | 73.8 | [下载链接](https://paddledet.bj.bcebos.com/models/ssd_mobilenet_v1_300_120e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml) | **注意：** SSD-VGG使用4GPU在总batch size为32下训练240个epoch。SSD-MobileNetv1使用2GPU在总batch size为64下训练120周期。 ## Citations ``` @article{Liu_2016, title={SSD: Single Shot MultiBox Detector}, journal={ECCV}, author={Liu, Wei and Anguelov, Dragomir and Erhan, Dumitru and Szegedy, Christian and Reed, Scott and Fu, Cheng-Yang and Berg, Alexander C.}, year={2016}, } ```

PaddleDetection/configs/ssd/README.md/0

# 1. TTFNet ## 简介 TTFNet是一种用于实时目标检测且对训练时间友好的网络，对CenterNet收敛速度慢的问题进行改进，提出了利用高斯核生成训练样本的新方法，有效的消除了anchor-free head中存在的模糊性。同时简单轻量化的网络结构也易于进行任务扩展。 **特点:** 结构简单，仅需要两个head检测目标位置和大小，并且去除了耗时的后处理操作 训练时间短，基于DarkNet53的骨干网路，V100 8卡仅需要训练2个小时即可达到较好的模型效果 ## Model Zoo | 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | 下载 | 配置文件 | | :-------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: | | DarkNet53 | TTFNet | 12 | 1x | ---- | 33.5 | [下载链接](https://paddledet.bj.bcebos.com/models/ttfnet_darknet53_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ttfnet/ttfnet_darknet53_1x_coco.yml) | # 2. PAFNet ## 简介 PAFNet（Paddle Anchor Free）是PaddleDetection基于TTFNet的优化模型，精度达到anchor free领域SOTA水平，同时产出移动端轻量级模型PAFNet-Lite PAFNet系列模型从如下方面优化TTFNet模型： - [CutMix](https://arxiv.org/abs/1905.04899) - 更优的骨干网络: ResNet50vd-DCN - 更大的训练batch size: 8 GPUs，每GPU batch_size=18 - Synchronized Batch Normalization - [Deformable Convolution](https://arxiv.org/abs/1703.06211) - [Exponential Moving Average](https://www.investopedia.com/terms/e/ema.asp) - 更优的预训练模型 ## 模型库 | 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | 下载 | 配置文件 | | :-------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: | | ResNet50vd | PAFNet | 18 | 10x | ---- | 39.8 | [下载链接](https://paddledet.bj.bcebos.com/models/pafnet_10x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ttfnet/pafnet_10x_coco.yml) | ### PAFNet-Lite | 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 | Box AP | 麒麟990延时（ms） | 体积（M） | 下载 | 配置文件 | | :-------------- | :------------- | :-----: | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: | | MobileNetv3 | PAFNet-Lite | 12 | 20x | 23.9 | 26.00 | 14 | [下载链接](https://paddledet.bj.bcebos.com/models/pafnet_lite_mobilenet_v3_20x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ttfnet/pafnet_lite_mobilenet_v3_20x_coco.yml) | **注意：** 由于动态图框架整体升级，PAFNet的PaddleDetection发布的权重模型评估时需要添加--bias字段, 例如 ```bash # 使用PaddleDetection发布的权重 CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyolo/pafnet_10x_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/pafnet_10x_coco.pdparams --bias ``` ## Citations ``` @article{liu2019training, title = {Training-Time-Friendly Network for Real-Time Object Detection}, author = {Zili Liu, Tu Zheng, Guodong Xu, Zheng Yang, Haifeng Liu, Deng Cai}, journal = {arXiv preprint arXiv:1909.00700}, year = {2019} } ```

PaddleDetection/configs/ttfnet/README.md/0

_BASE_: [ '../datasets/coco_detection.yml', '../runtime.yml', '_base_/optimizer_270e.yml', '_base_/yolov3_mobilenet_v1.yml', '_base_/yolov3_reader.yml', ] snapshot_epoch: 5 pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/MobileNetV1_ssld_pretrained.pdparams weights: output/yolov3_mobilenet_v1_ssld_270e_coco/model_final

PaddleDetection/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_coco.yml/0

# PaddleDetection 预测部署 PaddleDetection提供了Paddle Inference、Paddle Serving、Paddle-Lite多种部署形式，支持服务端、移动端、嵌入式等多种平台，提供了完善的Python和C++部署方案。 ## PaddleDetection支持的部署形式说明 |形式|语言|教程|设备/平台| |-|-|-|-| |Paddle Inference|Python|已完善|Linux(ARM\X86)、Windows |Paddle Inference|C++|已完善|Linux(ARM\X86)、Windows| |Paddle Serving|Python|已完善|Linux(ARM\X86)、Windows| |Paddle-Lite|C++|已完善|Android、IOS、FPGA、RK... ## 1.Paddle Inference部署 ### 1.1 导出模型 使用`tools/export_model.py`脚本导出模型以及部署时使用的配置文件，配置文件名字为`infer_cfg.yml`。模型导出脚本如下： ```bash # 导出YOLOv3模型 python tools/export_model.py -c configs/yolov3/yolov3_mobilenet_v1_roadsign.yml -o weights=output/yolov3_mobilenet_v1_roadsign/best_model.pdparams ``` 预测模型会导出到`output_inference/yolov3_mobilenet_v1_roadsign`目录下，分别为`infer_cfg.yml`, `model.pdiparams`, `model.pdiparams.info`, `model.pdmodel`。 模型导出具体请参考文档[PaddleDetection模型导出教程](EXPORT_MODEL.md)。 ### 1.2 使用PaddleInference进行预测 * Python部署 支持`CPU`、`GPU`和`XPU`环境，支持，windows、linux系统，支持NV Jetson嵌入式设备上部署。参考文档[python部署](python/README.md) * C++部署 支持`CPU`、`GPU`和`XPU`环境，支持，windows、linux系统，支持NV Jetson嵌入式设备上部署。参考文档[C++部署](cpp/README.md) * PaddleDetection支持TensorRT加速,相关文档请参考[TensorRT预测部署教程](TENSOR_RT.md) **注意:** Paddle预测库版本需要>=2.1，batch_size>1仅支持YOLOv3和PP-YOLO。 ## 2.PaddleServing部署 ### 2.1 导出模型 如果需要导出`PaddleServing`格式的模型，需要设置`export_serving_model=True`: ```buildoutcfg python tools/export_model.py -c configs/yolov3/yolov3_mobilenet_v1_roadsign.yml -o weights=output/yolov3_mobilenet_v1_roadsign/best_model.pdparams --export_serving_model=True ``` 预测模型会导出到`output_inference/yolov3_darknet53_270e_coco`目录下，分别为`infer_cfg.yml`, `model.pdiparams`, `model.pdiparams.info`, `model.pdmodel`, `serving_client/`文件夹, `serving_server/`文件夹。 模型导出具体请参考文档[PaddleDetection模型导出教程](EXPORT_MODEL.md)。 ### 2.2 使用PaddleServing进行预测 * [安装PaddleServing](https://github.com/PaddlePaddle/Serving/blob/develop/README.md#installation) * [使用PaddleServing](./serving/README.md) ## 3.PaddleLite部署 - [使用PaddleLite部署PaddleDetection模型](./lite/README.md) - 详细案例请参考[Paddle-Lite-Demo](https://github.com/PaddlePaddle/Paddle-Lite-Demo)部署。更多内容，请参考[Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) ## 4.第三方部署（MNN、NCNN、Openvino） - 第三方部署提供PicoDet、TinyPose案例，其他模型请参考修改 - TinyPose部署推荐工具：Intel CPU端推荐使用Openvino，GPU端推荐使用PaddleInference，ARM/ANDROID端推荐使用PaddleLite或者MNN | Third_Engine | MNN | NCNN | OPENVINO | | ------------ | ---- | ----- | ---------- | | PicoDet | [PicoDet_MNN](./third_engine/demo_mnn/README.md) | [PicoDet_NCNN](./third_engine/demo_ncnn/README.md) | [PicoDet_OPENVINO](./third_engine/demo_openvino/README.md) | | TinyPose | [TinyPose_MNN](./third_engine/demo_mnn_kpts/README.md) | - | [TinyPose_OPENVINO](./third_engine/demo_openvino_kpts/README.md) | ## 5.Benchmark测试 - 使用导出的模型，运行Benchmark批量测试脚本： ```shell sh deploy/benchmark/benchmark.sh {model_dir} {model_name} ``` **注意** 如果是量化模型，请使用`deploy/benchmark/benchmark_quant.sh`脚本。 - 将测试结果log导出至Excel中： ``` python deploy/benchmark/log_parser_excel.py --log_path=./output_pipeline --output_name=benchmark_excel.xlsx ``` ## 6.常见问题QA - 1、`Paddle 1.8.4`训练的模型，可以用`Paddle2.0`部署吗？ Paddle 2.0是兼容Paddle 1.8.4的，因此是可以的。但是部分模型(如SOLOv2)使用到了Paddle 2.0中新增OP，这类模型不可以。 - 2、Windows编译时，预测库是VS2015编译的，选择VS2017或VS2019会有问题吗？ 关于VS兼容性问题请参考：[C++Visual Studio 2015、2017和2019之间的二进制兼容性](https://docs.microsoft.com/zh-cn/cpp/porting/binary-compat-2015-2017?view=msvc-160) - 3、cuDNN 8.0.4连续预测会发生内存泄漏吗？ 经QA测试，发现cuDNN 8系列连续预测时都有内存泄漏问题，且cuDNN 8性能差于cuDNN 7，推荐使用CUDA + cuDNN7.6.4的方式进行部署。

PaddleDetection/deploy/README.md/0

metric: COCO num_classes: 80 # Datset configuration TrainDataset: !COCODataSet image_dir: train2017 anno_path: annotations/instances_train2017.json dataset_dir: dataset/coco/ EvalDataset: !COCODataSet image_dir: val2017 anno_path: annotations/instances_val2017.json dataset_dir: dataset/coco/ worker_num: 0 # preprocess reader in test EvalReader: sample_transforms: - Decode: {} - Resize: {target_size: [640, 640], keep_ratio: False, interp: 2} - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True} - Permute: {} batch_size: 4

PaddleDetection/deploy/auto_compression/configs/ppyoloe_reader.yml/0

# All rights `PaddleDetection` reserved #!/bin/bash model_dir=$1 model_name=$2 export img_dir="demo" export log_path="output_pipeline" echo "model_dir : ${model_dir}" echo "img_dir: ${img_dir}" # TODO: support batch size>1 for use_mkldnn in "True" "False"; do for threads in "1" "6"; do echo "${model_name} ${model_dir}, use_mkldnn: ${use_mkldnn} threads: ${threads}" python deploy/python/infer.py \ --model_dir=${model_dir} \ --run_benchmark True \ --enable_mkldnn=${use_mkldnn} \ --device=CPU \ --cpu_threads=${threads} \ --image_dir=${img_dir} 2>&1 | tee ${log_path}/${model_name}_cpu_usemkldnn_${use_mkldnn}_cputhreads_${threads}_bs1_infer.log done done for run_mode in "fluid" "trt_fp32" "trt_fp16"; do echo "${model_name} ${model_dir}, run_mode: ${run_mode}" python deploy/python/infer.py \ --model_dir=${model_dir} \ --run_benchmark=True \ --device=GPU \ --run_mode=${run_mode} \ --image_dir=${img_dir} 2>&1 | tee ${log_path}/${model_name}_gpu_runmode_${run_mode}_bs1_infer.log done

PaddleDetection/deploy/benchmark/benchmark.sh/0

// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #pragma once #include <glog/logging.h> #include <yaml-cpp/yaml.h> #include <iostream> #include <memory> #include <string> #include <unordered_map> #include <utility> #include <vector> #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> namespace PaddleDetection { // Object for storing all preprocessed data class ImageBlob { public: // image width and height std::vector<float> im_shape_; // Buffer for image data after preprocessing std::vector<float> im_data_; // in net data shape(after pad) std::vector<float> in_net_shape_; // Evaluation image width and height // std::vector<float> eval_im_size_f_; // Scale factor for image size to origin image size std::vector<float> scale_factor_; // in net image after preprocessing cv::Mat in_net_im_; }; // Abstraction of preprocessing opration class class PreprocessOp { public: virtual void Init(const YAML::Node& item) = 0; virtual void Run(cv::Mat* im, ImageBlob* data) = 0; }; class InitInfo : public PreprocessOp { public: virtual void Init(const YAML::Node& item) {} virtual void Run(cv::Mat* im, ImageBlob* data); }; class NormalizeImage : public PreprocessOp { public: virtual void Init(const YAML::Node& item) { mean_ = item["mean"].as<std::vector<float>>(); scale_ = item["std"].as<std::vector<float>>(); if (item["is_scale"]) is_scale_ = item["is_scale"].as<bool>(); if (item["norm_type"]) norm_type_ = item["norm_type"].as<std::string>(); } virtual void Run(cv::Mat* im, ImageBlob* data); private: // CHW or HWC std::vector<float> mean_; std::vector<float> scale_; bool is_scale_ = true; std::string norm_type_ = "mean_std"; }; class Permute : public PreprocessOp { public: virtual void Init(const YAML::Node& item) {} virtual void Run(cv::Mat* im, ImageBlob* data); }; class Resize : public PreprocessOp { public: virtual void Init(const YAML::Node& item) { interp_ = item["interp"].as<int>(); keep_ratio_ = item["keep_ratio"].as<bool>(); target_size_ = item["target_size"].as<std::vector<int>>(); } // Compute best resize scale for x-dimension, y-dimension std::pair<float, float> GenerateScale(const cv::Mat& im); virtual void Run(cv::Mat* im, ImageBlob* data); private: int interp_; bool keep_ratio_; std::vector<int> target_size_; std::vector<int> in_net_shape_; }; class LetterBoxResize : public PreprocessOp { public: virtual void Init(const YAML::Node& item) { target_size_ = item["target_size"].as<std::vector<int>>(); } float GenerateScale(const cv::Mat& im); virtual void Run(cv::Mat* im, ImageBlob* data); private: std::vector<int> target_size_; std::vector<int> in_net_shape_; }; // Models with FPN need input shape % stride == 0 class PadStride : public PreprocessOp { public: virtual void Init(const YAML::Node& item) { stride_ = item["stride"].as<int>(); } virtual void Run(cv::Mat* im, ImageBlob* data); private: int stride_; }; class TopDownEvalAffine : public PreprocessOp { public: virtual void Init(const YAML::Node& item) { trainsize_ = item["trainsize"].as<std::vector<int>>(); } virtual void Run(cv::Mat* im, ImageBlob* data); private: int interp_ = 1; std::vector<int> trainsize_; }; class WarpAffine : public PreprocessOp { public: virtual void Init(const YAML::Node& item) { input_h_ = item["input_h"].as<int>(); input_w_ = item["input_w"].as<int>(); keep_res_ = item["keep_res"].as<bool>(); } virtual void Run(cv::Mat* im, ImageBlob* data); private: int input_h_; int input_w_; int interp_ = 1; bool keep_res_ = true; int pad_ = 31; }; class Pad : public PreprocessOp { public: virtual void Init(const YAML::Node& item) { size_ = item["size"].as<std::vector<int>>(); fill_value_ = item["fill_value"].as<std::vector<float>>(); } virtual void Run(cv::Mat* im, ImageBlob* data); private: std::vector<int> size_; std::vector<float> fill_value_; }; void CropImg(cv::Mat& img, cv::Mat& crop_img, std::vector<int>& area, std::vector<float>& center, std::vector<float>& scale, float expandratio = 0.15); // check whether the input size is dynamic bool CheckDynamicInput(const std::vector<cv::Mat>& imgs); // Pad images in batch std::vector<cv::Mat> PadBatch(const std::vector<cv::Mat>& imgs); class Preprocessor { public: void Init(const YAML::Node& config_node) { // initialize image info at first ops_["InitInfo"] = std::make_shared<InitInfo>(); for (const auto& item : config_node) { auto op_name = item["type"].as<std::string>(); ops_[op_name] = CreateOp(op_name); ops_[op_name]->Init(item); } } std::shared_ptr<PreprocessOp> CreateOp(const std::string& name) { if (name == "Resize") { return std::make_shared<Resize>(); } else if (name == "LetterBoxResize") { return std::make_shared<LetterBoxResize>(); } else if (name == "Permute") { return std::make_shared<Permute>(); } else if (name == "NormalizeImage") { return std::make_shared<NormalizeImage>(); } else if (name == "PadStride") { // use PadStride instead of PadBatch return std::make_shared<PadStride>(); } else if (name == "TopDownEvalAffine") { return std::make_shared<TopDownEvalAffine>(); } else if (name == "WarpAffine") { return std::make_shared<WarpAffine>(); }else if (name == "Pad") { return std::make_shared<Pad>(); } std::cerr << "can not find function of OP: " << name << " and return: nullptr" << std::endl; return nullptr; } void Run(cv::Mat* im, ImageBlob* data); public: static const std::vector<std::string> RUN_ORDER; private: std::unordered_map<std::string, std::shared_ptr<PreprocessOp>> ops_; }; } // namespace PaddleDetection

PaddleDetection/deploy/cpp/include/preprocess_op.h/0

[English](README.md) | 简体中文 # PaddleDetection Ascend Python部署示例 本目录下提供`infer.py`快速完成PPYOLOE在华为昇腾上部署的示例。 ## 1. 部署环境准备 在部署前，需自行编译基于华为昇腾NPU的FastDeploy python wheel包并安装，参考文档[华为昇腾NPU部署环境编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装) ## 2. 部署模型准备 在部署前，请准备好您所需要运行的推理模型，你可以选择使用[预导出的推理模型](../README.md)或者[自行导出PaddleDetection部署模型](../README.md)。 ## 3. 运行部署示例 ```bash # 下载部署示例代码 git clone https://github.com/PaddlePaddle/PaddleDetection.git cd PaddleDetection/deploy/fastdeploy/ascend/python # 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到develop分支 # git checkout develop # 下载模型文件和测试图片 wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg tar xvf ppyoloe_crn_l_300e_coco.tgz # 华为昇腾推理 python infer.py --model_dir ppyoloe_crn_l_300e_coco --image_file 000000014439.jpg ``` 运行完成可视化结果如下图所示 <div align="center"> <img src="https://user-images.githubusercontent.com/16222477/191712880-91ae128d-247a-43e0-b1e3-cafae78431e0.jpg", width=512px, height=256px /> </div> ## 4. 更多指南 - [PaddleDetection Python API文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/object_detection.html) - [FastDeploy部署PaddleDetection模型概览](../../) - [C++部署](../cpp) ## 5. 常见问题 - [如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md) - [Intel GPU(独立显卡/集成显卡)的使用](https://github.com/PaddlePaddle/FastDeploy/blob/develop/tutorials/intel_gpu/README.md) - [编译CPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/cpu.md) - [编译GPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/gpu.md) - [编译Jetson部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/jetson.md)

PaddleDetection/deploy/fastdeploy/ascend/python/README.md/0

// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #include "fastdeploy/vision.h" void ONNXInfer(const std::string& model_dir, const std::string& image_file) { std::string model_file = model_dir + "/ppyoloe_plus_crn_s_80e_coco.onnx"; std::string params_file; std::string config_file = model_dir + "/infer_cfg.yml"; auto option = fastdeploy::RuntimeOption(); option.UseCpu(); auto format = fastdeploy::ModelFormat::ONNX; auto model = fastdeploy::vision::detection::PPYOLOE( model_file, params_file, config_file, option, format); fastdeploy::TimeCounter tc; tc.Start(); auto im = cv::imread(image_file); fastdeploy::vision::DetectionResult res; if (!model.Predict(im, &res)) { std::cerr << "Failed to predict." << std::endl; return; } auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5); tc.End(); tc.PrintInfo("PPDet in ONNX"); std::cout << res.Str() << std::endl; cv::imwrite("infer_onnx.jpg", vis_im); std::cout << "Visualized result saved in ./infer_onnx.jpg" << std::endl; } void RKNPU2Infer(const std::string& model_dir, const std::string& image_file) { auto model_file = model_dir + "/ppyoloe_plus_crn_s_80e_coco_rk3588_quantized.rknn"; auto params_file = ""; auto config_file = model_dir + "/infer_cfg.yml"; auto option = fastdeploy::RuntimeOption(); option.UseRKNPU2(); auto format = fastdeploy::ModelFormat::RKNN; auto model = fastdeploy::vision::detection::PPYOLOE( model_file, params_file, config_file, option, format); model.GetPreprocessor().DisablePermute(); model.GetPreprocessor().DisableNormalize(); model.GetPostprocessor().ApplyNMS(); auto im = cv::imread(image_file); fastdeploy::vision::DetectionResult res; fastdeploy::TimeCounter tc; tc.Start(); if (!model.Predict(&im, &res)) { std::cerr << "Failed to predict." << std::endl; return; } tc.End(); tc.PrintInfo("PPDet in RKNPU2"); std::cout << res.Str() << std::endl; auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5); cv::imwrite("infer_rknpu2.jpg", vis_im); std::cout << "Visualized result saved in ./infer_rknpu2.jpg" << std::endl; } int main(int argc, char* argv[]) { if (argc < 4) { std::cout << "Usage: infer_demo path/to/model_dir path/to/image run_option, " "e.g ./infer_demo ./model_dir ./test.jpeg" << std::endl; return -1; } if (std::atoi(argv[3]) == 0) { ONNXInfer(argv[1], argv[2]); } else if (std::atoi(argv[3]) == 1) { RKNPU2Infer(argv[1], argv[2]); } return 0; }

PaddleDetection/deploy/fastdeploy/rockchip/rknpu2/cpp/infer.cc/0

# Paddle-Lite端侧部署 [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite)是飞桨轻量化推理引擎，为手机、IOT端提供高效推理能力，并广泛整合跨平台硬件，为端侧部署及应用落地问题提供轻量化的部署方案。 本目录提供了PaddleDetection中主要模型在Paddle-Lite上的端到端部署代码。用户可以通过本教程了解如何使用该部分代码，基于Paddle-Lite实现在移动端部署PaddleDetection模型。 ## 1. 准备环境 ### 运行准备 - 电脑（编译Paddle Lite） - 安卓手机（armv7或armv8） ### 1.1 准备交叉编译环境 交叉编译环境用于编译 Paddle Lite 和 PaddleDetection 的C++ demo。 支持多种开发环境，不同开发环境的编译流程请参考对应文档，请确保安装完成Java jdk、Android NDK(R17 < version < R21，其他版本以上未做测试)。 设置NDK_ROOT命令： ```shell export NDK_ROOT=[YOUR_NDK_PATH]/android-ndk-r17c ``` 1. [Docker](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#docker) 2. [Linux](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#linux) 3. [MAC OS](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#mac-os) ### 1.2 准备预测库 预测库有两种获取方式： 1. [**建议**]直接从[Paddle-Lite Release](https://github.com/PaddlePaddle/Paddle-Lite/releases)中, 根据设备类型与架构选择对应的预编译库，请注意使用模型FP32/16版本需要与库相对应，库文件的说明请参考[官方文档](https://paddle-lite.readthedocs.io/zh/latest/quick_start/release_lib.html#android-toolchain-gcc)。 **注意**：（1） 如果是从 Paddle-Lite [官方文档](https://paddle-lite.readthedocs.io/zh/latest/quick_start/release_lib.html#android-toolchain-gcc)下载的预测库，注意选择`with_extra=ON，with_cv=ON`的下载链接。2. 目前只提供Android端demo，IOS端demo可以参考[Paddle-Lite IOS demo](https://github.com/PaddlePaddle/Paddle-Lite-Demo/tree/master/PaddleLite-ios-demo) （2）PP-PicoDet部署需要Paddle Lite 2.11以上版本。 2. 编译Paddle-Lite得到预测库，Paddle-Lite的编译方式如下(Lite库在不断更新，如若下列命令无效，请以Lite官方repo为主)： ```shell git clone https://github.com/PaddlePaddle/Paddle-Lite.git cd Paddle-Lite # 如果使用编译方式，建议使用develop分支编译预测库 git checkout develop # FP32 ./lite/tools/build_android.sh --arch=armv8 --toolchain=clang --with_cv=ON --with_extra=ON # FP16 ./lite/tools/build_android.sh --arch=armv8 --toolchain=clang --with_cv=ON --with_extra=ON --with_arm82_fp16=ON ``` **注意**：编译Paddle-Lite获得预测库时，需要打开`--with_cv=ON --with_extra=ON`两个选项，`--arch`表示`arm`版本，这里指定为armv8，更多编译命令介绍请参考[链接](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_options.html)。 直接下载预测库并解压后，可以得到`inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/`文件夹，通过编译Paddle-Lite得到的预测库位于`Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/`文件夹下。 预测库的文件目录如下： ``` inference_lite_lib.android.armv8/ |-- cxx C++ 预测库和头文件 | |-- include C++ 头文件 | | |-- paddle_api.h | | |-- paddle_image_preprocess.h | | |-- paddle_lite_factory_helper.h | | |-- paddle_place.h | | |-- paddle_use_kernels.h | | |-- paddle_use_ops.h | | `-- paddle_use_passes.h | `-- lib C++预测库 | |-- libpaddle_api_light_bundled.a C++静态库 | `-- libpaddle_light_api_shared.so C++动态库 |-- java Java预测库 | |-- jar | | `-- PaddlePredictor.jar | |-- so | | `-- libpaddle_lite_jni.so | `-- src |-- demo C++和Java示例代码 | |-- cxx C++ 预测库demo, 请将本文档目录下的PaddleDetection相关代码拷贝至该文件夹下执行交叉编译。 | `-- java Java 预测库demo ``` ## 2 开始运行 ### 2.1 模型转换 Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括量化、子图融合、混合调度、Kernel优选等方法，使用Paddle-Lite的`opt`工具可以自动对inference模型进行优化，并转换为推理所使用的文件格式。目前支持两种优化方式，优化后的模型更轻量，模型运行速度更快。 **注意**：如果已经准备好了 `.nb` 结尾的模型文件，可以跳过此步骤。 #### 2.1.1 安装paddle_lite_opt工具 安装`paddle_lite_opt`工具有如下两种方法, **请注意**，无论使用哪种方法，请尽量保证`paddle_lite_opt`工具和预测库的版本一致，以避免未知的Bug。 1. [**建议**]pip安装paddlelite并进行转换 ```shell pip install paddlelite ``` 2. 源码编译Paddle-Lite生成`paddle_lite_opt`工具 模型优化需要Paddle-Lite的`opt`可执行文件，可以通过编译Paddle-Lite源码获得，编译步骤如下： ```shell # 如果准备环境时已经clone了Paddle-Lite，则不用重新clone Paddle-Lite git clone https://github.com/PaddlePaddle/Paddle-Lite.git cd Paddle-Lite git checkout develop # 启动编译 ./lite/tools/build.sh build_optimize_tool ``` 编译完成后，`opt`文件位于`build.opt/lite/api/`下，可通过如下方式查看`opt`的运行选项和使用方式； ```shell cd build.opt/lite/api/ ./opt ``` `opt`的使用方式与参数与上面的`paddle_lite_opt`完全一致。 之后使用`paddle_lite_opt`工具可以进行inference模型的转换。`paddle_lite_opt`的部分参数如下： |选项|说明| |-|-| |--model_file|待优化的PaddlePaddle模型（combined形式）的网络结构文件路径| |--param_file|待优化的PaddlePaddle模型（combined形式）的权重文件路径| |--optimize_out_type|输出模型类型，目前支持两种类型：protobuf和naive_buffer，其中naive_buffer是一种更轻量级的序列化/反序列化实现，默认为naive_buffer| |--optimize_out|优化模型的输出路径| |--valid_targets|指定模型可执行的backend，默认为arm。目前可支持x86、arm、opencl、npu、xpu，可以同时指定多个backend(以空格分隔)，Model Optimize Tool将会自动选择最佳方式。如果需要支持华为NPU（Kirin 810/990 Soc搭载的达芬奇架构NPU），应当设置为npu, arm| | --enable_fp16| true/false，是否使用fp16进行推理。如果开启，需要使用对应fp16的预测库| 更详细的`paddle_lite_opt`工具使用说明请参考[使用opt转化模型文档](https://paddle-lite.readthedocs.io/zh/latest/user_guides/opt/opt_bin.html) `--model_file`表示inference模型的model文件地址，`--param_file`表示inference模型的param文件地址；`optimize_out`用于指定输出文件的名称（不需要添加`.nb`的后缀）。直接在命令行中运行`paddle_lite_opt`，也可以查看所有参数及其说明。 #### 2.1.2 转换示例 下面以PaddleDetection中的 `PicoDet` 模型为例，介绍使用`paddle_lite_opt`完成预训练模型到inference模型，再到Paddle-Lite优化模型的转换。 ```shell # 进入PaddleDetection根目录 cd PaddleDetection_root_path # 将预训练模型导出为inference模型 python tools/export_model.py -c configs/picodet/picodet_s_320_coco.yml \ -o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams --output_dir=output_inference # 将inference模型转化为Paddle-Lite优化模型 # FP32 paddle_lite_opt --valid_targets=arm --model_file=output_inference/picodet_s_320_coco/model.pdmodel --param_file=output_inference/picodet_s_320_coco/model.pdiparams --optimize_out=output_inference/picodet_s_320_coco/model # FP16 paddle_lite_opt --valid_targets=arm --model_file=output_inference/picodet_s_320_coco/model.pdmodel --param_file=output_inference/picodet_s_320_coco/model.pdiparams --optimize_out=output_inference/picodet_s_320_coco/model --enable_fp16=true # 将inference模型配置转化为json格式 python deploy/lite/convert_yml_to_json.py output_inference/picodet_s_320_coco/infer_cfg.yml ``` 最终在output_inference/picodet_s_320_coco/文件夹下生成`model.nb` 和 `infer_cfg.json`的文件。 **注意**：`--optimize_out` 参数为优化后模型的保存路径，无需加后缀`.nb`；`--model_file` 参数为模型结构信息文件的路径，`--param_file` 参数为模型权重信息文件的路径，请注意文件名。 ### 2.2 与手机联调 首先需要进行一些准备工作。 1. 准备一台arm8的安卓手机，如果编译的预测库是armv7，则需要arm7的手机，并修改Makefile中`ARM_ABI=arm7`。 2. 电脑上安装ADB工具，用于调试。 ADB安装方式如下： 2.1. MAC电脑安装ADB: ```shell brew cask install android-platform-tools ``` 2.2. Linux安装ADB ```shell sudo apt update sudo apt install -y wget adb ``` 2.3. Window安装ADB win上安装需要去谷歌的安卓平台下载ADB软件包进行安装：[链接](https://developer.android.com/studio) 3. 手机连接电脑后，开启手机`USB调试`选项，选择`文件传输`模式，在电脑终端中输入： ```shell adb devices ``` 如果有device输出，则表示安装成功，如下所示： ``` List of devices attached 744be294 device ``` 4. 编译lite部署代码生成移动端可执行文件 ```shell cd {PadddleDetection_Root} cd deploy/lite/ inference_lite_path=/{lite prediction library path}/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.with_cv/ mkdir $inference_lite_path/demo/cxx/lite cp -r Makefile src/ include/ *runtime_config.json $inference_lite_path/demo/cxx/lite cd $inference_lite_path/demo/cxx/lite # 执行编译，等待完成后得到可执行文件main make ARM_ABI=arm8 #如果是arm7，则执行 make ARM_ABI = arm7 (或者在Makefile中修改该项) ``` 5. 准备优化后的模型、预测库文件、测试图像。 ```shell mkdir deploy cp main *runtime_config.json deploy/ cd deploy mkdir model_det mkdir model_keypoint # 将优化后的模型、预测库文件、测试图像放置在预测库中的demo/cxx/detection文件夹下 cp {PadddleDetection_Root}/output_inference/picodet_s_320_coco/model.nb ./model_det/ cp {PadddleDetection_Root}/output_inference/picodet_s_320_coco/infer_cfg.json ./model_det/ # 如果需要关键点模型，则只需操作： cp {PadddleDetection_Root}/output_inference/hrnet_w32_256x192/model.nb ./model_keypoint/ cp {PadddleDetection_Root}/output_inference/hrnet_w32_256x192/infer_cfg.json ./model_keypoint/ # 将测试图像复制到deploy文件夹中 cp [your_test_img].jpg ./demo.jpg # 将C++预测动态库so文件复制到deploy文件夹中 cp ../../../cxx/lib/libpaddle_light_api_shared.so ./ ``` 执行完成后，deploy文件夹下将有如下文件格式： ``` deploy/ |-- model_det/ | |--model.nb 优化后的检测模型文件 | |--infer_cfg.json 检测器模型配置文件 |-- model_keypoint/ | |--model.nb 优化后的关键点模型文件 | |--infer_cfg.json 关键点模型配置文件 |-- main 生成的移动端执行文件 |-- det_runtime_config.json 目标检测执行时参数配置文件 |-- keypoint_runtime_config.json 关键点检测执行时参数配置文件 |-- libpaddle_light_api_shared.so Paddle-Lite库文件 ``` **注意：** * `det_runtime_config.json` 包含了目标检测的超参数，请按需进行修改： ```shell { "model_dir_det": "./model_det/", #检测器模型路径 "batch_size_det": 1, #检测预测时batchsize "threshold_det": 0.5, #检测器输出阈值 "image_file": "demo.jpg", #测试图片 "image_dir": "", #测试图片文件夹 "run_benchmark": true, #性能测试开关 "cpu_threads": 4 #线程数 } ``` * `keypoint_runtime_config.json` 同时包含了目标检测和关键点检测的超参数，支持Top-Down方案的推理流程，请按需进行修改： ```shell { "model_dir_det": "./model_det/", #检测模型路径 "batch_size_det": 1, #检测模型预测时batchsize, 存在关键点模型时只能为1 "threshold_det": 0.5, #检测器输出阈值 "model_dir_keypoint": "./model_keypoint/", #关键点模型路径（不使用需为空字符） "batch_size_keypoint": 8, #关键点预测时batchsize "threshold_keypoint": 0.5, #关键点输出阈值 "image_file": "demo.jpg", #测试图片 "image_dir": "", #测试图片文件夹 "run_benchmark": true, #性能测试开关 "cpu_threads": 4 #线程数 "use_dark_decode": true #是否使用DARK解码关键点坐标 } ``` 6. 启动调试，上述步骤完成后就可以使用ADB将文件夹 `deploy/` push到手机上运行，步骤如下： ```shell # 将上述deploy文件夹push到手机上 adb push deploy /data/local/tmp/ adb shell cd /data/local/tmp/deploy export LD_LIBRARY_PATH=/data/local/tmp/deploy:$LD_LIBRARY_PATH # 修改权限为可执行 chmod 777 main # 以检测为例，执行程序 ./main det_runtime_config.json ``` 如果对代码做了修改，则需要重新编译并push到手机上。 运行效果如下： <div align="center"> <img src="../../docs/images/lite_demo.jpg" width="600"> </div> ## FAQ Q1：如果想更换模型怎么办，需要重新按照流程走一遍吗？ A1：如果已经走通了上述步骤，更换模型只需要替换 `.nb` 模型文件及其对应模型配置文件`infer_cfg.json`，同时要注意修改下配置文件中的 `.nb` 文件路径以及类别映射文件（如有必要）。 Q2：换一个图测试怎么做？ A2：替换 deploy 下的测试图像为你想要测试的图像，使用 ADB 再次 push 到手机上即可。

PaddleDetection/deploy/lite/README.md/0

# config of tracker for MOT SDE Detector, use 'OCSORTTracker' as default, 'JDETracker' here is just BYTETracker. # The tracker of MOT JDE Detector (such as FairMOT) is exported together with the model. # Here 'min_box_area' and 'vertical_ratio' are set for pedestrian, you can modify for other objects tracking. type: BOTSORTTracker # choose one tracker in ['JDETracker', 'OCSORTTracker', 'DeepSORTTracker','BOTSORTTracker'] # When using for MTMCT(Multi-Target Multi-Camera Tracking), you should modify to 'DeepSORTTracker' # just as BYTETracker, used for FairMOT in PP-Tracking project and for ByteTrack in PP-Humanv1 project JDETracker: use_byte: True det_thresh: 0.3 conf_thres: 0.6 low_conf_thres: 0.1 match_thres: 0.9 min_box_area: 0 vertical_ratio: 0 # 1.6 for pedestrian # used for OC-SORT in PP-Humanv2 project and PP-Vehicle project OCSORTTracker: det_thresh: 0.4 max_age: 30 min_hits: 3 iou_threshold: 0.3 delta_t: 3 inertia: 0.2 min_box_area: 0 vertical_ratio: 0 use_byte: False use_angle_cost: False # used for DeepSORT and MTMCT in PP-Tracking project DeepSORTTracker: input_size: [64, 192] # An unique operation to scale the sub-image of the selected detected boxes to a fixed size min_box_area: 0 vertical_ratio: -1 budget: 100 max_age: 70 n_init: 3 metric_type: cosine matching_threshold: 0.2 max_iou_distance: 0.9 BOTSORTTracker: track_high_thresh: 0.3 track_low_thresh: 0.2 new_track_thresh: 0.4 match_thresh: 0.7 track_buffer: 30 min_box_area: 0 camera_motion: False cmc_method: 'sparseOptFlow' # only camera_motion is True, # sparseOptFlow | files (Vidstab GMC) | orb | ecc

PaddleDetection/deploy/pipeline/config/tracker_config.yml/0

[English](ppvehicle_attribute_en.md) | 简体中文 # PP-Vehicle属性识别模块 车辆属性识别在智慧城市，智慧交通等方向具有广泛应用。在PP-Vehicle中，集成了车辆属性识别模块，可识别车辆颜色及车型属性的识别。 | 任务 | 算法 | 精度 | 预测速度 | 下载链接| |-----------|------|-----------|----------|---------------| | 车辆检测/跟踪 | PP-YOLOE | mAP 63.9 | 38.67ms | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | | 车辆属性识别 | PPLCNet | 90.81 | 7.31 ms | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) | 注意： 1. 属性模型预测速度是基于NVIDIA T4, 开启TensorRT FP16得到。模型预测速度包含数据预处理、模型预测、后处理部分。 2. 关于PP-LCNet的介绍可以参考[PP-LCNet](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNet.md)介绍，相关论文可以查阅[PP-LCNet paper](https://arxiv.org/abs/2109.15099)。 3. 属性模型的训练和精度测试均基于[VeRi数据集](https://www.v7labs.com/open-datasets/veri-dataset)。 - 当前提供的预训练模型支持识别10种车辆颜色及9种车型，同VeRi数据集，具体如下： ```yaml # 车辆颜色 - "yellow" - "orange" - "green" - "gray" - "red" - "blue" - "white" - "golden" - "brown" - "black" # 车型 - "sedan" - "suv" - "van" - "hatchback" - "mpv" - "pickup" - "bus" - "truck" - "estate" ``` ## 使用方法 ### 配置项说明 [配置文件](../../config/infer_cfg_ppvehicle.yml)中与属性相关的参数如下： ``` VEHICLE_ATTR: model_dir: output_inference/vehicle_attribute_infer/ # 车辆属性模型调用路径 batch_size: 8 # 模型预测时的batch_size大小 color_threshold: 0.5 # 颜色属性阈值，需要置信度达到此阈值才会确定具体颜色，否则为'Unknown‘ type_threshold: 0.5 # 车型属性阈值，需要置信度达到此阈值才会确定具体属性，否则为'Unknown‘ enable: False # 是否开启该功能 ``` ### 使用命令 1. 从模型库下载`车辆检测/跟踪`, `车辆属性识别`两个预测部署模型并解压到`./output_inference`路径下；默认会自动下载模型，如果手动下载，需要修改模型文件夹为模型存放路径。 2. 修改配置文件中`VEHICLE_ATTR`项的`enable: True`，以启用该功能。 3. 图片输入时，启动命令如下(更多命令参数说明，请参考[快速开始-参数说明](./PPVehicle_QUICK_STARTED.md))： ```bash # 预测单张图片文件 python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \ --image_file=test_image.jpg \ --device=gpu # 预测包含一张或多张图片的文件夹 python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \ --image_dir=images/ \ --device=gpu ``` 4. 视频输入时，启动命令如下： ```bash #预测单个视频文件 python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \ --video_file=test_video.mp4 \ --device=gpu #预测包含一个或多个视频的文件夹 python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \ --video_dir=test_videos/ \ --device=gpu ``` 5. 若修改模型路径，有以下两种方式： - 方法一：`./deploy/pipeline/config/infer_cfg_ppvehicle.yml`下可以配置不同模型路径，属性识别模型修改`VEHICLE_ATTR`字段下配置 - 方法二：直接在命令行中增加`-o`，以覆盖配置文件中的默认模型路径： ```bash python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \ --video_file=test_video.mp4 \ --device=gpu \ -o VEHICLE_ATTR.model_dir=output_inference/vehicle_attribute_infer ``` 测试效果如下： <div width="600" align="center"> <img src="https://user-images.githubusercontent.com/22989727/205599146-56abd72f-6e0a-4a21-bd11-f8bb421f2887.gif"/> </div> ## 方案说明 车辆属性识别模型使用了[PaddleClas](https://github.com/PaddlePaddle/PaddleClas) 的超轻量图像分类方案(PULC，Practical Ultra Lightweight image Classification)。关于该模型的数据准备、训练、测试等详细内容，请见[PULC 车辆属性识别模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/PULC/PULC_vehicle_attribute.md). 车辆属性识别模型选用了轻量级、高精度的PPLCNet。并在该模型的基础上，进一步使用了以下优化方案： - 使用SSLD预训练模型，在不改变推理速度的前提下，精度可以提升约0.5个百分点 - 融合EDA数据增强策略，精度可以再提升0.52个百分点 - 使用SKL-UGI知识蒸馏, 精度可以继续提升0.23个百分点

PaddleDetection/deploy/pipeline/docs/tutorials/ppvehicle_attribute.md/0

# coding: utf8 # Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import glob import os.path import argparse import warnings def parse_args(): parser = argparse.ArgumentParser( description='PaddleSeg generate file list on cityscapes or your customized dataset.' ) parser.add_argument('dataset_root', help='dataset root directory', type=str) parser.add_argument( '--type', help='dataset type: \n' '- cityscapes \n' '- custom(default)', default="custom", type=str) parser.add_argument( '--separator', dest='separator', help='file list separator', default=" ", type=str) parser.add_argument( '--folder', help='the folder names of images and labels', type=str, nargs=2, default=['images', 'labels']) parser.add_argument( '--second_folder', help='the second-level folder names of train set, validation set, test set', type=str, nargs='*', default=['train', 'val', 'test']) parser.add_argument( '--format', help='data format of images and labels, e.g. jpg or png.', type=str, nargs=2, default=['jpg', 'png']) parser.add_argument( '--postfix', help='postfix of images or labels', type=str, nargs=2, default=['', '']) return parser.parse_args() def get_files(image_or_label, dataset_split, args): dataset_root = args.dataset_root postfix = args.postfix format = args.format folder = args.folder pattern = '*%s.%s' % (postfix[image_or_label], format[image_or_label]) search_files = os.path.join(dataset_root, folder[image_or_label], dataset_split, pattern) search_files2 = os.path.join(dataset_root, folder[image_or_label], dataset_split, "*", pattern) # 包含子目录 search_files3 = os.path.join(dataset_root, folder[image_or_label], dataset_split, "*", "*", pattern) # 包含三级目录 search_files4 = os.path.join(dataset_root, folder[image_or_label], dataset_split, "*", "*", "*", pattern) # 包含四级目录 search_files5 = os.path.join(dataset_root, folder[image_or_label], dataset_split, "*", "*", "*", "*", pattern) # 包含五级目录 filenames = glob.glob(search_files) filenames2 = glob.glob(search_files2) filenames3 = glob.glob(search_files3) filenames4 = glob.glob(search_files4) filenames5 = glob.glob(search_files5) filenames = filenames + filenames2 + filenames3 + filenames4 + filenames5 return sorted(filenames) def generate_list(args): dataset_root = args.dataset_root separator = args.separator for dataset_split in args.second_folder: print("Creating {}.txt...".format(dataset_split)) image_files = get_files(0, dataset_split, args) label_files = get_files(1, dataset_split, args) if not image_files: img_dir = os.path.join(dataset_root, args.folder[0], dataset_split) warnings.warn("No images in {} !!!".format(img_dir)) num_images = len(image_files) if not label_files: label_dir = os.path.join(dataset_root, args.folder[1], dataset_split) warnings.warn("No labels in {} !!!".format(label_dir)) num_label = len(label_files) if num_images != num_label and num_label > 0: raise Exception( "Number of images = {} number of labels = {} \n" "Either number of images is equal to number of labels, " "or number of labels is equal to 0.\n" "Please check your dataset!".format(num_images, num_label)) file_list = os.path.join(dataset_root, dataset_split + '.txt') with open(file_list, "w") as f: for item in range(num_images): left = image_files[item].replace(dataset_root, '', 1) if left[0] == os.path.sep: left = left.lstrip(os.path.sep) try: right = label_files[item].replace(dataset_root, '', 1) if right[0] == os.path.sep: right = right.lstrip(os.path.sep) line = left + separator + right + '\n' except: line = left + '\n' f.write(line) print(line) if __name__ == '__main__': args = parse_args() generate_list(args)

PaddleDetection/deploy/pipeline/tools/create_dataset_list.py/0

# PP-Tracking Python端预测部署 ## 内容 - [简介](#简介) - [1-FairMOT模型导出和预测](#1-FairMOT模型导出和预测) - [2-DeepSORT模型导出和预测](#2-DeepSORT模型导出和预测) - [3-ByteTrack和OC_SORT模型导出和预测](#3-ByteTrack和OC_SORT模型导出和预测) - [4-车辆跨镜头跟踪模型导出和预测](#4-车辆跨镜头跟踪模型导出和预测) - [5-参数说明](#5-参数说明) ## 简介 在PaddlePaddle中预测引擎和训练引擎底层有着不同的优化方法, 预测引擎使用了AnalysisPredictor，专门针对推理进行了优化，是基于[C++预测库](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/native_infer.html)的Python接口，该引擎可以对模型进行多项图优化，减少不必要的内存拷贝。如果用户在部署已训练模型的过程中对性能有较高的要求，我们提供了独立于PaddleDetection的预测脚本，方便用户直接集成部署。 主要包含两个步骤： - 导出预测模型 - 基于Python进行预测 PaddleDetection在训练过程包括网络的前向和优化器相关参数，而在部署过程中，我们只需要前向参数，具体参考:[导出模型](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/deploy/EXPORT_MODEL.md) 导出后目录下，包括`infer_cfg.yml`, `model.pdiparams`, `model.pdiparams.info`, `model.pdmodel`四个文件。 PP-Tracking也提供了AI Studio公开项目案例，教程请参考[PP-Tracking之手把手玩转多目标跟踪](https://aistudio.baidu.com/aistudio/projectdetail/3022582)。 ## 1-FairMOT模型导出和预测 ### 1.1 导出预测模型 ```bash # 命令行导出PaddleDetection发布的权重 CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/fairmot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.pdparams # 命令行导出训完保存的checkpoint权重 CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/fairmot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.yml -o weights=output/fairmot_hrnetv2_w18_dlafpn_30e_576x320/model_final.pdparams # 或下载PaddleDetection发布的已导出的模型 wget https://bj.bcebos.com/v1/paddledet/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.tar tar -xvf fairmot_hrnetv2_w18_dlafpn_30e_576x320.tar ``` **注意:** 导出的模型默认会保存在`output_inference`目录下，如新下载请存放于对应目录下。 ### 1.2 用导出的模型基于Python去预测 ```bash # 下载行人跟踪demo视频： wget https://bj.bcebos.com/v1/paddledet/data/mot/demo/mot17_demo.mp4 # Python预测视频 python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video_file=mot17_demo.mp4 --device=GPU --threshold=0.5 --save_mot_txts --save_images ``` ### 1.3 用导出的模型基于Python去预测，以及进行流量计数、出入口统计和绘制跟踪轨迹等 ```bash # 下载出入口统计demo视频： wget https://bj.bcebos.com/v1/paddledet/data/mot/demo/entrance_count_demo.mp4 # Python预测视频 python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video_file=entrance_count_demo.mp4 --device=GPU --do_entrance_counting --draw_center_traj ``` **注意:** - 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`表示保存跟踪结果的txt文件，或`--save_images`表示保存跟踪结果可视化图片。 - 跟踪结果txt文件每行信息是`frame,id,x1,y1,w,h,score,-1,-1,-1`。 - `--threshold`表示结果可视化的置信度阈值，默认为0.5，低于该阈值的结果会被过滤掉，为了可视化效果更佳，可根据实际情况自行修改。 - `--do_entrance_counting`表示是否统计出入口流量，默认为False，`--draw_center_traj`表示是否绘制跟踪轨迹，默认为False。注意绘制跟踪轨迹的测试视频最好是静止摄像头拍摄的。 - 对于多类别或车辆的FairMOT模型的导出和Python预测只需更改相应的config和模型权重即可。如： ```bash job_name=mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone model_type=mot/mcfairmot config=configs/${model_type}/${job_name}.yml # 命令行导出模型 CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=https://paddledet.bj.bcebos.com/models/mot/${job_name}.pdparams # Python预测视频 python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/${job_name} --video_file={your video name}.mp4 --device=GPU --threshold=0.5 --save_mot_txts --save_images ``` - 多类别跟踪结果txt文件每行信息是`frame,id,x1,y1,w,h,score,cls_id,-1,-1`。 - visdrone多类别跟踪demo视频可从此链接下载：`wget https://bj.bcebos.com/v1/paddledet/data/mot/demo/visdrone_demo.mp4` - bdd100k车辆跟踪和多类别demo视频可从此链接下载：`wget https://bj.bcebos.com/v1/paddledet/data/mot/demo/bdd100k_demo.mp4` ## 2-DeepSORT模型导出和预测 ### 2.1 导出预测模型 Step 1：导出检测模型 ```bash # 导出PPYOLOe行人检测模型 CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/detector/ppyoloe_crn_l_36e_640x640_mot17half.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/deepsort/ppyoloe_crn_l_36e_640x640_mot17half.pdparams ``` Step 2：导出行人ReID模型 ```bash # 导出PCB Pyramid ReID模型 CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/reid/deepsort_pcb_pyramid_r101.yml -o reid_weights=https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pcb_pyramid_r101.pdparams # 或者导出PPLCNet ReID模型 CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/reid/deepsort_pplcnet.yml -o reid_weights=https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pplcnet.pdparams ``` ### 2.2 用导出的模型基于Python去预测行人跟踪 ```bash # 下载行人跟踪demo视频： wget https://bj.bcebos.com/v1/paddledet/data/mot/demo/mot17_demo.mp4 # 用导出的PPYOLOE行人检测模型和PPLCNet ReID模型 python3.7 deploy/pptracking/python/mot_sde_infer.py --model_dir=output_inference/ppyoloe_crn_l_36e_640x640_mot17half/ --reid_model_dir=output_inference/deepsort_pplcnet/ --tracker_config=deploy/pptracking/python/tracker_config.yml --video_file=mot17_demo.mp4 --device=GPU --save_mot_txts --threshold=0.5 ``` ### 2.3 用导出的模型基于Python去预测车辆跟踪 ```bash # 下载车辆demo视频 wget https://bj.bcebos.com/v1/paddledet/data/mot/demo/bdd100k_demo.mp4 # 下载车辆检测PPYOLOE导出的模型： wget https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip unzip mot_ppyoloe_l_36e_ppvehicle.zip # 下载车辆ReID导出的模型： wget https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pplcnet_vehicle.tar tar -xvf deepsort_pplcnet_vehicle.tar # 用导出的PPYOLOE车辆检测模型和PPLCNet车辆ReID模型 python deploy/pptracking/python/mot_sde_infer.py --model_dir=mot_ppyoloe_l_36e_ppvehicle/ --reid_model_dir=deepsort_pplcnet_vehicle/ --tracker_config=deploy/pptracking/python/tracker_config.yml --device=GPU --threshold=0.5 --video_file=bdd100k_demo.mp4 --save_mot_txts --save_images ``` **注意:** - 运行前需要手动修改`tracker_config.yml`的跟踪器类型为`type: DeepSORTTracker`。 - 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`(对每个视频保存一个txt)或`--save_images`表示保存跟踪结果可视化图片。 - 跟踪结果txt文件每行信息是`frame,id,x1,y1,w,h,score,-1,-1,-1`。 - `--threshold`表示结果可视化的置信度阈值，默认为0.5，低于该阈值的结果会被过滤掉，为了可视化效果更佳，可根据实际情况自行修改。 - DeepSORT算法不支持多类别跟踪，只支持单类别跟踪，且ReID模型最好是与检测模型同一类别的物体训练过的，比如行人跟踪最好使用行人ReID模型，车辆跟踪最好使用车辆ReID模型。 ## 3-ByteTrack和OC_SORT模型导出和预测 ### 3.1 导出预测模型 ```bash # 导出PPYOLOe行人检测模型 CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/bytetrack/detector/ppyoloe_crn_l_36e_640x640_mot17half.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/ppyoloe_crn_l_36e_640x640_mot17half.pdparams ``` ### 3.2 用导出的模型基于Python去预测行人跟踪 ```bash # 下载行人跟踪demo视频： wget https://bj.bcebos.com/v1/paddledet/data/mot/demo/mot17_demo.mp4 # 用导出的PPYOLOe行人检测模型 python deploy/pptracking/python/mot_sde_infer.py --model_dir=output_inference/ppyoloe_crn_l_36e_640x640_mot17half/ --tracker_config=deploy/pptracking/python/tracker_config.yml --video_file=mot17_demo.mp4 --device=GPU --save_mot_txts # 用导出的PPYOLOe行人检测模型和PPLCNet ReID模型 python deploy/pptracking/python/mot_sde_infer.py --model_dir=output_inference/ppyoloe_crn_l_36e_640x640_mot17half/ --reid_model_dir=output_inference/deepsort_pplcnet/ --tracker_config=deploy/pptracking/python/tracker_config.yml --video_file=mot17_demo.mp4 --device=GPU --threshold=0.5 --save_mot_txts --save_images ``` **注意:** - 运行ByteTrack模型需要确认`tracker_config.yml`的跟踪器类型为`type: JDETracker`。 - 可切换`tracker_config.yml`的跟踪器类型为`type: OCSORTTracker`运行OC_SORT模型。 - ByteTrack模型是加载导出的检测器和单独配置的`--tracker_config`文件运行的，为了实时跟踪所以不需要reid模型，`--reid_model_dir`表示reid导出模型的路径，默认为空，加不加具体视效果而定； - 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`(对每个视频保存一个txt)或`--save_images`表示保存跟踪结果可视化图片。 - 跟踪结果txt文件每行信息是`frame,id,x1,y1,w,h,score,-1,-1,-1`。 - `--threshold`表示结果可视化的置信度阈值，默认为0.5，低于该阈值的结果会被过滤掉，为了可视化效果更佳，可根据实际情况自行修改。 ## 4-车辆跨镜头跟踪模型导出和预测 ### 4.1 导出预测模型 Step 1：下载导出的检测模型 ```bash # 下载车辆检测PPYOLOE导出的模型： wget https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip unzip mot_ppyoloe_l_36e_ppvehicle.zip ``` Step 2：下载导出的ReID模型 ```bash wget https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pplcnet_vehicle.tar tar -xvf deepsort_pplcnet_vehicle.tar ``` ### 4.2 用导出的模型基于Python去做跨镜头跟踪 ```bash # 下载demo测试视频 wget https://paddledet.bj.bcebos.com/data/mot/demo/mtmct-demo.tar tar -xvf mtmct-demo.tar # 用导出的PPYOLOE车辆检测模型和PPLCNet车辆ReID模型 python deploy/pptracking/python/mot_sde_infer.py --model_dir=mot_ppyoloe_l_36e_ppvehicle/ --reid_model_dir=deepsort_pplcnet_vehicle/ --tracker_config=deploy/pptracking/python/tracker_config.yml --mtmct_dir=mtmct-demo --mtmct_cfg=deploy/pptracking/python/mtmct_cfg.yml --device=GPU --threshold=0.5 --save_mot_txts --save_images ``` **注意:** - 运行前需要手动修改`tracker_config.yml`的跟踪器类型为`type: DeepSORTTracker`，跨镜头跟踪仅支持DeepSORT。 - 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`(对每个视频保存一个txt)，或`--save_images`表示保存跟踪结果可视化图片。 - 跨镜头跟踪结果txt文件每行信息是`camera_id,frame,id,x1,y1,w,h,-1,-1`。 - `--threshold`表示结果可视化的置信度阈值，默认为0.5，低于该阈值的结果会被过滤掉，为了可视化效果更佳，可根据实际情况自行修改。 - DeepSORT算法不支持多类别跟踪，只支持单类别跟踪，且ReID模型最好是与检测模型同一类别的物体训练过的，比如行人跟踪最好使用行人ReID模型，车辆跟踪最好使用车辆ReID模型。 - `--mtmct_dir`是MTMCT预测的某个场景的文件夹名字，里面包含该场景不同摄像头拍摄视频的图片文件夹，其数量至少为两个。 - `--mtmct_cfg`是MTMCT预测的某个场景的配置文件，里面包含该一些trick操作的开关和该场景摄像头相关设置的文件路径，用户可以自行更改相关路径以及设置某些操作是否启用。 ## 5-参数说明 | 参数 | 是否必须|含义 | |-------|-------|----------| | --model_dir | Yes| 上述导出的模型路径 | | --reid_model_dir | Option| ReID导出的模型路径 | | --image_file | Option | 需要预测的图片 | | --image_dir | Option | 要预测的图片文件夹路径 | | --video_file | Option | 需要预测的视频 | | --camera_id | Option | 用来预测的摄像头ID，默认为-1(表示不使用摄像头预测，可设置为：0 - (摄像头数目-1) )，预测过程中在可视化界面按`q`退出输出预测结果到：output/output.mp4| | --device | Option | 运行时的设备，可选择`CPU/GPU/XPU`，默认为`CPU`| | --run_mode | Option |使用GPU时，默认为paddle, 可选（paddle/trt_fp32/trt_fp16/trt_int8）| | --batch_size | Option |预测时的batch size，在指定`image_dir`时有效，默认为1 | | --threshold | Option|预测得分的阈值，默认为0.5| | --output_dir | Option|可视化结果保存的根目录，默认为output/| | --run_benchmark | Option| 是否运行benchmark，同时需指定`--image_file`或`--image_dir`，默认为False | | --enable_mkldnn | Option | CPU预测中是否开启MKLDNN加速，默认为False | | --cpu_threads | Option| 设置cpu线程数，默认为1 | | --trt_calib_mode | Option| TensorRT是否使用校准功能，默认为False。使用TensorRT的int8功能时，需设置为True，使用PaddleSlim量化后的模型时需要设置为False | | --save_mot_txts | Option | 跟踪任务是否保存txt结果文件，默认为False | | --save_images | Option | 跟踪任务是否保存视频的可视化图片，默认为False | | --do_entrance_counting | Option | 跟踪任务是否统计出入口流量，默认为False | | --draw_center_traj | Option | 跟踪任务是否绘制跟踪轨迹，默认为False | | --mtmct_dir | Option | 需要进行MTMCT跨境头跟踪预测的图片文件夹路径，默认为None | | --mtmct_cfg | Option | 需要进行MTMCT跨境头跟踪预测的配置文件路径，默认为None | 说明： - 参数优先级顺序：`camera_id` > `video_file` > `image_dir` > `image_file`。 - run_mode：paddle代表使用AnalysisPredictor，精度float32来推理，其他参数指用AnalysisPredictor，TensorRT不同精度来推理。 - 如果安装的PaddlePaddle不支持基于TensorRT进行预测，需要自行编译，详细可参考[预测库编译教程](https://paddleinference.paddlepaddle.org.cn/user_guides/source_compile.html)。 - --run_benchmark如果设置为True，则需要安装依赖`pip install pynvml psutil GPUtil`。

PaddleDetection/deploy/pptracking/python/README.md/0

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This code is based on https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/tracker/matching.py """ try: import lap except: print( 'Warning: Unable to use JDE/FairMOT/ByteTrack, please install lap, for example: `pip install lap`, see https://github.com/gatagat/lap' ) pass import scipy import numpy as np from scipy.spatial.distance import cdist from ..motion import kalman_filter import warnings warnings.filterwarnings("ignore") __all__ = [ 'merge_matches', 'linear_assignment', 'bbox_ious', 'iou_distance', 'embedding_distance', 'fuse_motion', ] def merge_matches(m1, m2, shape): O, P, Q = shape m1 = np.asarray(m1) m2 = np.asarray(m2) M1 = scipy.sparse.coo_matrix( (np.ones(len(m1)), (m1[:, 0], m1[:, 1])), shape=(O, P)) M2 = scipy.sparse.coo_matrix( (np.ones(len(m2)), (m2[:, 0], m2[:, 1])), shape=(P, Q)) mask = M1 * M2 match = mask.nonzero() match = list(zip(match[0], match[1])) unmatched_O = tuple(set(range(O)) - set([i for i, j in match])) unmatched_Q = tuple(set(range(Q)) - set([j for i, j in match])) return match, unmatched_O, unmatched_Q def linear_assignment(cost_matrix, thresh): try: import lap except Exception as e: raise RuntimeError( 'Unable to use JDE/FairMOT/ByteTrack, please install lap, for example: `pip install lap`, see https://github.com/gatagat/lap' ) if cost_matrix.size == 0: return np.empty( (0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple( range(cost_matrix.shape[1])) matches, unmatched_a, unmatched_b = [], [], [] cost, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh) for ix, mx in enumerate(x): if mx >= 0: matches.append([ix, mx]) unmatched_a = np.where(x < 0)[0] unmatched_b = np.where(y < 0)[0] matches = np.asarray(matches) return matches, unmatched_a, unmatched_b def bbox_ious(atlbrs, btlbrs): boxes = np.ascontiguousarray(atlbrs, dtype=np.float32) query_boxes = np.ascontiguousarray(btlbrs, dtype=np.float32) N = boxes.shape[0] K = query_boxes.shape[0] ious = np.zeros((N, K), dtype=boxes.dtype) if N * K == 0: return ious for k in range(K): box_area = ((query_boxes[k, 2] - query_boxes[k, 0] + 1) * (query_boxes[k, 3] - query_boxes[k, 1] + 1)) for n in range(N): iw = (min(boxes[n, 2], query_boxes[k, 2]) - max( boxes[n, 0], query_boxes[k, 0]) + 1) if iw > 0: ih = (min(boxes[n, 3], query_boxes[k, 3]) - max( boxes[n, 1], query_boxes[k, 1]) + 1) if ih > 0: ua = float((boxes[n, 2] - boxes[n, 0] + 1) * (boxes[ n, 3] - boxes[n, 1] + 1) + box_area - iw * ih) ious[n, k] = iw * ih / ua return ious def iou_distance(atracks, btracks): """ Compute cost based on IoU between two list[STrack]. """ if (len(atracks) > 0 and isinstance(atracks[0], np.ndarray)) or ( len(btracks) > 0 and isinstance(btracks[0], np.ndarray)): atlbrs = atracks btlbrs = btracks else: atlbrs = [track.tlbr for track in atracks] btlbrs = [track.tlbr for track in btracks] _ious = bbox_ious(atlbrs, btlbrs) cost_matrix = 1 - _ious return cost_matrix def embedding_distance(tracks, detections, metric='euclidean'): """ Compute cost based on features between two list[STrack]. """ cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float32) if cost_matrix.size == 0: return cost_matrix det_features = np.asarray( [track.curr_feat for track in detections], dtype=np.float32) track_features = np.asarray( [track.smooth_feat for track in tracks], dtype=np.float32) cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features return cost_matrix def fuse_motion(kf, cost_matrix, tracks, detections, only_position=False, lambda_=0.98): if cost_matrix.size == 0: return cost_matrix gating_dim = 2 if only_position else 4 gating_threshold = kalman_filter.chi2inv95[gating_dim] measurements = np.asarray([det.to_xyah() for det in detections]) for row, track in enumerate(tracks): gating_distance = kf.gating_distance( track.mean, track.covariance, measurements, only_position, metric='maha') cost_matrix[row, gating_distance > gating_threshold] = np.inf cost_matrix[row] = lambda_ * cost_matrix[row] + (1 - lambda_ ) * gating_distance return cost_matrix

PaddleDetection/deploy/pptracking/python/mot/matching/jde_matching.py/0

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np from scipy.special import softmax def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200): """ Args: box_scores (N, 5): boxes in corner-form and probabilities. iou_threshold: intersection over union threshold. top_k: keep top_k results. If k <= 0, keep all the results. candidate_size: only consider the candidates with the highest scores. Returns: picked: a list of indexes of the kept boxes """ scores = box_scores[:, -1] boxes = box_scores[:, :-1] picked = [] indexes = np.argsort(scores) indexes = indexes[-candidate_size:] while len(indexes) > 0: current = indexes[-1] picked.append(current) if 0 < top_k == len(picked) or len(indexes) == 1: break current_box = boxes[current, :] indexes = indexes[:-1] rest_boxes = boxes[indexes, :] iou = iou_of( rest_boxes, np.expand_dims( current_box, axis=0), ) indexes = indexes[iou <= iou_threshold] return box_scores[picked, :] def iou_of(boxes0, boxes1, eps=1e-5): """Return intersection-over-union (Jaccard index) of boxes. Args: boxes0 (N, 4): ground truth boxes. boxes1 (N or 1, 4): predicted boxes. eps: a small number to avoid 0 as denominator. Returns: iou (N): IoU values. """ overlap_left_top = np.maximum(boxes0[..., :2], boxes1[..., :2]) overlap_right_bottom = np.minimum(boxes0[..., 2:], boxes1[..., 2:]) overlap_area = area_of(overlap_left_top, overlap_right_bottom) area0 = area_of(boxes0[..., :2], boxes0[..., 2:]) area1 = area_of(boxes1[..., :2], boxes1[..., 2:]) return overlap_area / (area0 + area1 - overlap_area + eps) def area_of(left_top, right_bottom): """Compute the areas of rectangles given two corners. Args: left_top (N, 2): left top corner. right_bottom (N, 2): right bottom corner. Returns: area (N): return the area. """ hw = np.clip(right_bottom - left_top, 0.0, None) return hw[..., 0] * hw[..., 1] class PicoDetPostProcess(object): """ Args: input_shape (int): network input image size ori_shape (int): ori image shape of before padding scale_factor (float): scale factor of ori image enable_mkldnn (bool): whether to open MKLDNN """ def __init__(self, input_shape, ori_shape, scale_factor, strides=[8, 16, 32, 64], score_threshold=0.4, nms_threshold=0.5, nms_top_k=1000, keep_top_k=100): self.ori_shape = ori_shape self.input_shape = input_shape self.scale_factor = scale_factor self.strides = strides self.score_threshold = score_threshold self.nms_threshold = nms_threshold self.nms_top_k = nms_top_k self.keep_top_k = keep_top_k def warp_boxes(self, boxes, ori_shape): """Apply transform to boxes """ width, height = ori_shape[1], ori_shape[0] n = len(boxes) if n: # warp points xy = np.ones((n * 4, 3)) xy[:, :2] = boxes[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape( n * 4, 2) # x1y1, x2y2, x1y2, x2y1 # xy = xy @ M.T # transform xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8) # rescale # create new boxes x = xy[:, [0, 2, 4, 6]] y = xy[:, [1, 3, 5, 7]] xy = np.concatenate( (x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T # clip boxes xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width) xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height) return xy.astype(np.float32) else: return boxes def __call__(self, scores, raw_boxes): batch_size = raw_boxes[0].shape[0] reg_max = int(raw_boxes[0].shape[-1] / 4 - 1) out_boxes_num = [] out_boxes_list = [] for batch_id in range(batch_size): # generate centers decode_boxes = [] select_scores = [] for stride, box_distribute, score in zip(self.strides, raw_boxes, scores): box_distribute = box_distribute[batch_id] score = score[batch_id] # centers fm_h = self.input_shape[0] / stride fm_w = self.input_shape[1] / stride h_range = np.arange(fm_h) w_range = np.arange(fm_w) ww, hh = np.meshgrid(w_range, h_range) ct_row = (hh.flatten() + 0.5) * stride ct_col = (ww.flatten() + 0.5) * stride center = np.stack((ct_col, ct_row, ct_col, ct_row), axis=1) # box distribution to distance reg_range = np.arange(reg_max + 1) box_distance = box_distribute.reshape((-1, reg_max + 1)) box_distance = softmax(box_distance, axis=1) box_distance = box_distance * np.expand_dims(reg_range, axis=0) box_distance = np.sum(box_distance, axis=1).reshape((-1, 4)) box_distance = box_distance * stride # top K candidate topk_idx = np.argsort(score.max(axis=1))[::-1] topk_idx = topk_idx[:self.nms_top_k] center = center[topk_idx] score = score[topk_idx] box_distance = box_distance[topk_idx] # decode box decode_box = center + [-1, -1, 1, 1] * box_distance select_scores.append(score) decode_boxes.append(decode_box) # nms bboxes = np.concatenate(decode_boxes, axis=0) confidences = np.concatenate(select_scores, axis=0) picked_box_probs = [] picked_labels = [] for class_index in range(0, confidences.shape[1]): probs = confidences[:, class_index] mask = probs > self.score_threshold probs = probs[mask] if probs.shape[0] == 0: continue subset_boxes = bboxes[mask, :] box_probs = np.concatenate( [subset_boxes, probs.reshape(-1, 1)], axis=1) box_probs = hard_nms( box_probs, iou_threshold=self.nms_threshold, top_k=self.keep_top_k, ) picked_box_probs.append(box_probs) picked_labels.extend([class_index] * box_probs.shape[0]) if len(picked_box_probs) == 0: out_boxes_list.append(np.empty((0, 4))) out_boxes_num.append(0) else: picked_box_probs = np.concatenate(picked_box_probs) # resize output boxes picked_box_probs[:, :4] = self.warp_boxes( picked_box_probs[:, :4], self.ori_shape[batch_id]) im_scale = np.concatenate([ self.scale_factor[batch_id][::-1], self.scale_factor[batch_id][::-1] ]) picked_box_probs[:, :4] /= im_scale # clas score box out_boxes_list.append( np.concatenate( [ np.expand_dims( np.array(picked_labels), axis=-1), np.expand_dims( picked_box_probs[:, 4], axis=-1), picked_box_probs[:, :4] ], axis=1)) out_boxes_num.append(len(picked_labels)) out_boxes_list = np.concatenate(out_boxes_list, axis=0) out_boxes_num = np.asarray(out_boxes_num).astype(np.int32) return out_boxes_list, out_boxes_num

PaddleDetection/deploy/pptracking/python/picodet_postprocess.py/0

// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #include "core/general-server/op/tinypose_128x96.h" #include "core/predictor/framework/infer.h" #include "core/predictor/framework/memory.h" #include "core/predictor/framework/resource.h" #include "core/util/include/timer.h" #include <algorithm> #include <iostream> #include <memory> #include <sstream> namespace baidu { namespace paddle_serving { namespace serving { using baidu::paddle_serving::Timer; using baidu::paddle_serving::predictor::InferManager; using baidu::paddle_serving::predictor::MempoolWrapper; using baidu::paddle_serving::predictor::PaddleGeneralModelConfig; using baidu::paddle_serving::predictor::general_model::Request; using baidu::paddle_serving::predictor::general_model::Response; using baidu::paddle_serving::predictor::general_model::Tensor; int tinypose_128x96::inference() { VLOG(2) << "Going to run inference"; const std::vector<std::string> pre_node_names = pre_names(); if (pre_node_names.size() != 1) { LOG(ERROR) << "This op(" << op_name() << ") can only have one predecessor op, but received " << pre_node_names.size(); return -1; } const std::string pre_name = pre_node_names[0]; const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name); if (!input_blob) { LOG(ERROR) << "input_blob is nullptr,error"; return -1; } uint64_t log_id = input_blob->GetLogId(); VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name; GeneralBlob *output_blob = mutable_data<GeneralBlob>(); if (!output_blob) { LOG(ERROR) << "output_blob is nullptr,error"; return -1; } output_blob->SetLogId(log_id); if (!input_blob) { LOG(ERROR) << "(logid=" << log_id << ") Failed mutable depended argument, op:" << pre_name; return -1; } const TensorVector *in = &input_blob->tensor_vector; TensorVector *out = &output_blob->tensor_vector; int batch_size = input_blob->_batch_size; output_blob->_batch_size = batch_size; VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size; Timer timeline; int64_t start = timeline.TimeStampUS(); timeline.Start(); // only support string type char *total_input_ptr = static_cast<char *>(in->at(0).data.data()); std::string base64str = total_input_ptr; cv::Mat img = Base2Mat(base64str); cv::cvtColor(img, img, cv::COLOR_BGR2RGB); // preprocess std::vector<float> input(1 * 3 * im_shape_h * im_shape_w, 0.0f); preprocess_det(img, input.data(), scale_factor_h, scale_factor_w, im_shape_h, im_shape_w, mean_, scale_, is_scale_); // create real_in TensorVector *real_in = new TensorVector(); if (!real_in) { LOG(ERROR) << "real_in is nullptr,error"; return -1; } int in_num = 0; size_t databuf_size = 0; void *databuf_data = NULL; char *databuf_char = NULL; // image in_num = 1 * 3 * im_shape_h * im_shape_w; databuf_size = in_num * sizeof(float); databuf_data = MempoolWrapper::instance().malloc(databuf_size); if (!databuf_data) { LOG(ERROR) << "Malloc failed, size: " << databuf_size; return -1; } memcpy(databuf_data, input.data(), databuf_size); databuf_char = reinterpret_cast<char *>(databuf_data); paddle::PaddleBuf paddleBuf(databuf_char, databuf_size); paddle::PaddleTensor tensor_in; tensor_in.name = "image"; tensor_in.dtype = paddle::PaddleDType::FLOAT32; tensor_in.shape = {1, 3, im_shape_h, im_shape_w}; tensor_in.lod = in->at(0).lod; tensor_in.data = paddleBuf; real_in->push_back(tensor_in); if (InferManager::instance().infer(engine_name().c_str(), real_in, out, batch_size)) { LOG(ERROR) << "(logid=" << log_id << ") Failed do infer in fluid model: " << engine_name().c_str(); return -1; } int64_t end = timeline.TimeStampUS(); CopyBlobInfo(input_blob, output_blob); AddBlobInfo(output_blob, start); AddBlobInfo(output_blob, end); return 0; } void tinypose_128x96::preprocess_det(const cv::Mat &img, float *data, float &scale_factor_h, float &scale_factor_w, int im_shape_h, int im_shape_w, const std::vector<float> &mean, const std::vector<float> &scale, const bool is_scale) { // Resize cv::Mat resize_img; cv::resize(img, resize_img, cv::Size(im_shape_w, im_shape_h), 0, 0, 1); // Normalize double e = 1.0; if (is_scale) { e /= 255.0; } cv::Mat img_fp; (resize_img).convertTo(img_fp, CV_32FC3, e); for (int h = 0; h < im_shape_h; h++) { for (int w = 0; w < im_shape_w; w++) { img_fp.at<cv::Vec3f>(h, w)[0] = (img_fp.at<cv::Vec3f>(h, w)[0] - mean[0]) / scale[0]; img_fp.at<cv::Vec3f>(h, w)[1] = (img_fp.at<cv::Vec3f>(h, w)[1] - mean[1]) / scale[1]; img_fp.at<cv::Vec3f>(h, w)[2] = (img_fp.at<cv::Vec3f>(h, w)[2] - mean[2]) / scale[2]; } } // Permute int rh = img_fp.rows; int rw = img_fp.cols; int rc = img_fp.channels(); for (int i = 0; i < rc; ++i) { cv::extractChannel(img_fp, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i); } } cv::Mat tinypose_128x96::Base2Mat(std::string &base64_data) { cv::Mat img; std::string s_mat; s_mat = base64Decode(base64_data.data(), base64_data.size()); std::vector<char> base64_img(s_mat.begin(), s_mat.end()); img = cv::imdecode(base64_img, cv::IMREAD_COLOR); // CV_LOAD_IMAGE_COLOR return img; } std::string tinypose_128x96::base64Decode(const char *Data, int DataByte) { const char DecodeTable[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 62, // '+' 0, 0, 0, 63, // '/' 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, // '0'-'9' 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, // 'A'-'Z' 0, 0, 0, 0, 0, 0, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, // 'a'-'z' }; std::string strDecode; int nValue; int i = 0; while (i < DataByte) { if (*Data != '\r' && *Data != '\n') { nValue = DecodeTable[*Data++] << 18; nValue += DecodeTable[*Data++] << 12; strDecode += (nValue & 0x00FF0000) >> 16; if (*Data != '=') { nValue += DecodeTable[*Data++] << 6; strDecode += (nValue & 0x0000FF00) >> 8; if (*Data != '=') { nValue += DecodeTable[*Data++]; strDecode += nValue & 0x000000FF; } } i += 4; } else // 回车换行,跳过 { Data++; i++; } } return strDecode; } DEFINE_OP(tinypose_128x96); } // namespace serving } // namespace paddle_serving } // namespace baidu

PaddleDetection/deploy/serving/cpp/preprocess/tinypose_128x96.cpp/0

<!--- Licensed to the Apache Software Foundation (ASF) under one --> <!--- or more contributor license agreements. See the NOTICE file --> <!--- distributed with this work for additional information --> <!--- regarding copyright ownership. The ASF licenses this file --> <!--- to you under the Apache License, Version 2.0 (the --> <!--- "License"); you may not use this file except in compliance --> <!--- with the License. You may obtain a copy of the License at --> <!--- http://www.apache.org/licenses/LICENSE-2.0 --> <!--- Unless required by applicable law or agreed to in writing, --> <!--- software distributed under the License is distributed on an --> <!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --> <!--- KIND, either express or implied. See the License for the --> <!--- specific language governing permissions and limitations --> <!--- under the License. --> Running PP-PicoDet object detection model on bare metal Arm(R) Cortex(R)-M55 CPU using Arm Virtual Hardware ====================================================================== This folder contains an example of how to run a PP-PicoDet model on bare metal [Cortex(R)-M55 CPU](https://www.arm.com/products/silicon-ip-cpu/cortex-m/cortex-m55) using [Arm Virtual Hardware](https://www.arm.com/products/development-tools/simulation/virtual-hardware). Running environment and prerequisites ------------- Case 1: If the demo is run in Arm Virtual Hardware Amazon Machine Image(AMI) instance hosted by [AWS](https://aws.amazon.com/marketplace/pp/prodview-urbpq7yo5va7g?sr=0-1&ref_=beagle&applicationId=AWSMPContessa)/[AWS China](https://awsmarketplace.amazonaws.cn/marketplace/pp/prodview-2y7nefntbmybu), the following software will be installed through [configure_avh.sh](./configure_avh.sh) script. It will install automatically when you run the application through [run_demo.sh](./run_demo.sh) script. You can refer to this [guide](https://arm-software.github.io/AVH/main/examples/html/MicroSpeech.html#amilaunch) to launch an Arm Virtual Hardware AMI instance. Case 2: If the demo is run in the [ci_cpu Docker container](https://github.com/apache/tvm/blob/main/docker/Dockerfile.ci_cpu) provided with [TVM](https://github.com/apache/tvm), then the following software will already be installed. Case 3: If the demo is not run in the ci_cpu Docker container, then you will need the following: - Software required to build and run the demo (These can all be installed by running tvm/docker/install/ubuntu_install_ethosu_driver_stack.sh.) - [Fixed Virtual Platform (FVP) based on Arm(R) Corstone(TM)-300 software](https://developer.arm.com/tools-and-software/open-source-software/arm-platforms-software/arm-ecosystem-fvps) - [cmake 3.19.5](https://github.com/Kitware/CMake/releases/) - [GCC toolchain from Arm(R)](https://developer.arm.com/-/media/Files/downloads/gnu-rm/10-2020q4/gcc-arm-none-eabi-10-2020-q4-major-x86_64-linux.tar.bz2) - [Arm(R) Ethos(TM)-U NPU driver stack](https://review.mlplatform.org) - [CMSIS](https://github.com/ARM-software/CMSIS_5) - The python libraries listed in the requirements.txt of this directory - These can be installed by running the following from the current directory: ```bash pip install -r ./requirements.txt ``` In case2 and case3: You will need to update your PATH environment variable to include the path to cmake 3.19.5 and the FVP. For example if you've installed these in ```/opt/arm``` , then you would do the following: ```bash export PATH=/opt/arm/FVP_Corstone_SSE-300/models/Linux64_GCC-6.4:/opt/arm/cmake/bin:$PATH ``` You will also need TVM which can either be: - Installed from TLCPack(see [TLCPack](https://tlcpack.ai/)) - Built from source (see [Install from Source](https://tvm.apache.org/docs/install/from_source.html)) - When building from source, the following need to be set in config.cmake: - set(USE_CMSISNN ON) - set(USE_MICRO ON) - set(USE_LLVM ON) Running the demo application ---------------------------- Type the following command to run the bare metal text recognition application ([src/demo_bare_metal.c](./src/demo_bare_metal.c)): ```bash ./run_demo.sh ``` If you are not able to use Arm Virtual Hardware Amazon Machine Image(AMI) instance hosted by AWS/AWS China, specify argument --enable_FVP to 1 to make the application run on local Fixed Virtual Platforms (FVPs) executables. ```bash ./run_demo.sh --enable_FVP 1 ``` If the Ethos(TM)-U platform and/or CMSIS have not been installed in /opt/arm/ethosu then the locations for these can be specified as arguments to run_demo.sh, for example: ```bash ./run_demo.sh --cmsis_path /home/tvm-user/cmsis \ --ethosu_platform_path /home/tvm-user/ethosu/core_platform ``` With [run_demo.sh](./run_demo.sh) to run the demo application, it will: - Set up running environment by installing the required prerequisites automatically if running in Arm Virtual Hardware Amazon AMI instance(not specify --enable_FVP to 1) - Download a PP-PicoDet model - Use tvmc to compile the text recognition model for Cortex(R)-M55 CPU and CMSIS-NN - Create a C header file inputs.c containing the image data as a C array - Create a C header file outputs.c containing a C array where the output of inference will be stored - Build the demo application - Run the demo application on a Arm Virtual Hardware based on Arm(R) Corstone(TM)-300 software - The application will report the text on the image and the corresponding score. Using your own image -------------------- The create_image.py script takes a single argument on the command line which is the path of the image to be converted into an array of bytes for consumption by the model. The demo can be modified to use an image of your choice by changing the following line in run_demo.sh ```bash python3 ./convert_image.py path/to/image ``` Model description ----------------- In this demo, the model we used is based on [PP-PicoDet](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/picodet). Because of the excellent performance, PP-PicoDet are very suitable for deployment on mobile or CPU. And it is released by [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection).

PaddleDetection/deploy/third_engine/demo_avh/README.md/0

// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #ifndef __PicoDet_H__ #define __PicoDet_H__ #pragma once #include "Interpreter.hpp" #include "ImageProcess.hpp" #include "MNNDefine.h" #include "Tensor.hpp" #include <algorithm> #include <chrono> #include <iostream> #include <memory> #include <opencv2/opencv.hpp> #include <string> #include <vector> typedef struct NonPostProcessHeadInfo_ { std::string cls_layer; std::string dis_layer; int stride; } NonPostProcessHeadInfo; typedef struct BoxInfo_ { float x1; float y1; float x2; float y2; float score; int label; } BoxInfo; class PicoDet { public: PicoDet(const std::string &mnn_path, int input_width, int input_length, int num_thread_ = 4, float score_threshold_ = 0.5, float nms_threshold_ = 0.3); ~PicoDet(); int detect(cv::Mat &img, std::vector<BoxInfo> &result_list, bool has_postprocess); private: void decode_infer(MNN::Tensor *cls_pred, MNN::Tensor *dis_pred, int stride, float threshold, std::vector<std::vector<BoxInfo>> &results); BoxInfo disPred2Bbox(const float *&dfl_det, int label, float score, int x, int y, int stride); void nms(std::vector<BoxInfo> &input_boxes, float NMS_THRESH); private: std::shared_ptr<MNN::Interpreter> PicoDet_interpreter; MNN::Session *PicoDet_session = nullptr; MNN::Tensor *input_tensor = nullptr; int num_thread; int image_w; int image_h; int in_w = 320; int in_h = 320; float score_threshold; float nms_threshold; const float mean_vals[3] = {103.53f, 116.28f, 123.675f}; const float norm_vals[3] = {0.017429f, 0.017507f, 0.017125f}; const int num_class = 80; const int reg_max = 7; std::vector<float> bbox_output_data_; std::vector<float> class_output_data_; std::vector<std::string> nms_heads_info{"tmp_16", "concat_4.tmp_0"}; // If not export post-process, will use non_postprocess_heads_info std::vector<NonPostProcessHeadInfo> non_postprocess_heads_info{ // cls_pred|dis_pred|stride {"transpose_0.tmp_0", "transpose_1.tmp_0", 8}, {"transpose_2.tmp_0", "transpose_3.tmp_0", 16}, {"transpose_4.tmp_0", "transpose_5.tmp_0", 32}, {"transpose_6.tmp_0", "transpose_7.tmp_0", 64}, }; }; template <typename _Tp> int activation_function_softmax(const _Tp *src, _Tp *dst, int length); inline float fast_exp(float x); inline float sigmoid(float x); #endif

PaddleDetection/deploy/third_engine/demo_mnn/picodet_mnn.hpp/0

# PP-YOLOE 转ONNX-TRT教程 本教程内容为：使用PP-YOLOE模型导出转换为ONNX格式，并定制化修改网络，使用[EfficientNMS_TRT](https://github.com/NVIDIA/TensorRT/tree/main/plugin/efficientNMSPlugin) OP， 可成功运行在[TensorRT](https://github.com/NVIDIA/TensorRT)上，示例仅供参考 ## 1. 环境依赖 CUDA 10.2 + [cudnn 8.2.1](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html) + [TensorRT 8.2](https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-821/install-guide/index.htm) ```commandline onnx onnxruntime paddle2onnx ``` ## 2. Paddle模型导出 ```commandline python tools/export_model.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams trt=True exclude_nms=True ``` ## 3. ONNX模型转换 + 定制化修改EfficientNMS_TRT ```commandline python deploy/third_engine/demo_onnx_trt/onnx_custom.py --onnx_file=output_inference/ppyoloe_crn_l_300e_coco/ppyoloe_crn_l_300e_coco.onnx --model_dir=output_inference/ppyoloe_crn_l_300e_coco/ --opset_version=11 ``` ## 4. TensorRT Engine ```commandline trtexec --onnx=output_inference/ppyoloe_crn_l_300e_coco/ppyoloe_crn_l_300e_coco.onnx --saveEngine=ppyoloe_crn_l_300e_coco.engine ``` **注意**：若运行报错，可尝试添加`--tacticSources=-cublasLt,+cublas`参数解决 ## 5. 运行TensorRT推理 ```commandline python deploy/third_engine/demo_onnx_trt/trt_infer.py --infer_cfg=output_inference/ppyoloe_crn_l_300e_coco/infer_cfg.yml --trt_engine=ppyoloe_crn_l_300e_coco.engine --image_file=demo/000000014439.jpg ```

PaddleDetection/deploy/third_engine/demo_onnx_trt/README.md/0

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import cv2 import numpy as np import time import argparse from scipy.special import softmax from openvino.runtime import Core def image_preprocess(img_path, re_shape): img = cv2.imread(img_path) img = cv2.resize( img, (re_shape, re_shape), interpolation=cv2.INTER_LANCZOS4) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = np.transpose(img, [2, 0, 1]) / 255 img = np.expand_dims(img, 0) img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1)) img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1)) img -= img_mean img /= img_std return img.astype(np.float32) def draw_box(img, results, class_label, scale_x, scale_y): label_list = list( map(lambda x: x.strip(), open(class_label, 'r').readlines())) for i in range(len(results)): print(label_list[int(results[i][0])], ':', results[i][1]) bbox = results[i, 2:] label_id = int(results[i, 0]) score = results[i, 1] if (score > 0.20): xmin, ymin, xmax, ymax = [ int(bbox[0] * scale_x), int(bbox[1] * scale_y), int(bbox[2] * scale_x), int(bbox[3] * scale_y) ] cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 3) font = cv2.FONT_HERSHEY_SIMPLEX label_text = label_list[label_id] cv2.rectangle(img, (xmin, ymin), (xmax, ymin - 60), (0, 255, 0), -1) cv2.putText(img, "#" + label_text, (xmin, ymin - 10), font, 1, (255, 255, 255), 2, cv2.LINE_AA) cv2.putText(img, str(round(score, 3)), (xmin, ymin - 40), font, 0.8, (255, 255, 255), 2, cv2.LINE_AA) return img def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200): """ Args: box_scores (N, 5): boxes in corner-form and probabilities. iou_threshold: intersection over union threshold. top_k: keep top_k results. If k <= 0, keep all the results. candidate_size: only consider the candidates with the highest scores. Returns: picked: a list of indexes of the kept boxes """ scores = box_scores[:, -1] boxes = box_scores[:, :-1] picked = [] indexes = np.argsort(scores) indexes = indexes[-candidate_size:] while len(indexes) > 0: current = indexes[-1] picked.append(current) if 0 < top_k == len(picked) or len(indexes) == 1: break current_box = boxes[current, :] indexes = indexes[:-1] rest_boxes = boxes[indexes, :] iou = iou_of( rest_boxes, np.expand_dims( current_box, axis=0), ) indexes = indexes[iou <= iou_threshold] return box_scores[picked, :] def iou_of(boxes0, boxes1, eps=1e-5): """Return intersection-over-union (Jaccard index) of boxes. Args: boxes0 (N, 4): ground truth boxes. boxes1 (N or 1, 4): predicted boxes. eps: a small number to avoid 0 as denominator. Returns: iou (N): IoU values. """ overlap_left_top = np.maximum(boxes0[..., :2], boxes1[..., :2]) overlap_right_bottom = np.minimum(boxes0[..., 2:], boxes1[..., 2:]) overlap_area = area_of(overlap_left_top, overlap_right_bottom) area0 = area_of(boxes0[..., :2], boxes0[..., 2:]) area1 = area_of(boxes1[..., :2], boxes1[..., 2:]) return overlap_area / (area0 + area1 - overlap_area + eps) def area_of(left_top, right_bottom): """Compute the areas of rectangles given two corners. Args: left_top (N, 2): left top corner. right_bottom (N, 2): right bottom corner. Returns: area (N): return the area. """ hw = np.clip(right_bottom - left_top, 0.0, None) return hw[..., 0] * hw[..., 1] class PicoDetPostProcess(object): """ Args: input_shape (int): network input image size ori_shape (int): ori image shape of before padding scale_factor (float): scale factor of ori image enable_mkldnn (bool): whether to open MKLDNN """ def __init__(self, input_shape, ori_shape, scale_factor, strides=[8, 16, 32, 64], score_threshold=0.4, nms_threshold=0.5, nms_top_k=1000, keep_top_k=100): self.ori_shape = ori_shape self.input_shape = input_shape self.scale_factor = scale_factor self.strides = strides self.score_threshold = score_threshold self.nms_threshold = nms_threshold self.nms_top_k = nms_top_k self.keep_top_k = keep_top_k def warp_boxes(self, boxes, ori_shape): """Apply transform to boxes """ width, height = ori_shape[1], ori_shape[0] n = len(boxes) if n: # warp points xy = np.ones((n * 4, 3)) xy[:, :2] = boxes[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape( n * 4, 2) # x1y1, x2y2, x1y2, x2y1 # xy = xy @ M.T # transform xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8) # rescale # create new boxes x = xy[:, [0, 2, 4, 6]] y = xy[:, [1, 3, 5, 7]] xy = np.concatenate( (x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T # clip boxes xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width) xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height) return xy.astype(np.float32) else: return boxes def __call__(self, scores, raw_boxes): batch_size = raw_boxes[0].shape[0] reg_max = int(raw_boxes[0].shape[-1] / 4 - 1) out_boxes_num = [] out_boxes_list = [] for batch_id in range(batch_size): # generate centers decode_boxes = [] select_scores = [] for stride, box_distribute, score in zip(self.strides, raw_boxes, scores): box_distribute = box_distribute[batch_id] score = score[batch_id] # centers fm_h = self.input_shape[0] / stride fm_w = self.input_shape[1] / stride h_range = np.arange(fm_h) w_range = np.arange(fm_w) ww, hh = np.meshgrid(w_range, h_range) ct_row = (hh.flatten() + 0.5) * stride ct_col = (ww.flatten() + 0.5) * stride center = np.stack((ct_col, ct_row, ct_col, ct_row), axis=1) # box distribution to distance reg_range = np.arange(reg_max + 1) box_distance = box_distribute.reshape((-1, reg_max + 1)) box_distance = softmax(box_distance, axis=1) box_distance = box_distance * np.expand_dims(reg_range, axis=0) box_distance = np.sum(box_distance, axis=1).reshape((-1, 4)) box_distance = box_distance * stride # top K candidate topk_idx = np.argsort(score.max(axis=1))[::-1] topk_idx = topk_idx[:self.nms_top_k] center = center[topk_idx] score = score[topk_idx] box_distance = box_distance[topk_idx] # decode box decode_box = center + [-1, -1, 1, 1] * box_distance select_scores.append(score) decode_boxes.append(decode_box) # nms bboxes = np.concatenate(decode_boxes, axis=0) confidences = np.concatenate(select_scores, axis=0) picked_box_probs = [] picked_labels = [] for class_index in range(0, confidences.shape[1]): probs = confidences[:, class_index] mask = probs > self.score_threshold probs = probs[mask] if probs.shape[0] == 0: continue subset_boxes = bboxes[mask, :] box_probs = np.concatenate( [subset_boxes, probs.reshape(-1, 1)], axis=1) box_probs = hard_nms( box_probs, iou_threshold=self.nms_threshold, top_k=self.keep_top_k, ) picked_box_probs.append(box_probs) picked_labels.extend([class_index] * box_probs.shape[0]) if len(picked_box_probs) == 0: out_boxes_list.append(np.empty((0, 4))) out_boxes_num.append(0) else: picked_box_probs = np.concatenate(picked_box_probs) # resize output boxes picked_box_probs[:, :4] = self.warp_boxes( picked_box_probs[:, :4], self.ori_shape[batch_id]) im_scale = np.concatenate([ self.scale_factor[batch_id][::-1], self.scale_factor[batch_id][::-1] ]) picked_box_probs[:, :4] /= im_scale # clas score box out_boxes_list.append( np.concatenate( [ np.expand_dims( np.array(picked_labels), axis=-1), np.expand_dims( picked_box_probs[:, 4], axis=-1), picked_box_probs[:, :4] ], axis=1)) out_boxes_num.append(len(picked_labels)) out_boxes_list = np.concatenate(out_boxes_list, axis=0) out_boxes_num = np.asarray(out_boxes_num).astype(np.int32) return out_boxes_list, out_boxes_num def detect(img_file, compiled_model, re_shape, class_label): output = compiled_model.infer_new_request({0: test_image}) result_ie = list(output.values()) #[0] test_im_shape = np.array([[re_shape, re_shape]]).astype('float32') test_scale_factor = np.array([[1, 1]]).astype('float32') np_score_list = [] np_boxes_list = [] num_outs = int(len(result_ie) / 2) for out_idx in range(num_outs): np_score_list.append(result_ie[out_idx]) np_boxes_list.append(result_ie[out_idx + num_outs]) postprocess = PicoDetPostProcess(test_image.shape[2:], test_im_shape, test_scale_factor) np_boxes, np_boxes_num = postprocess(np_score_list, np_boxes_list) image = cv2.imread(img_file, 1) scale_x = image.shape[1] / test_image.shape[3] scale_y = image.shape[0] / test_image.shape[2] res_image = draw_box(image, np_boxes, class_label, scale_x, scale_y) cv2.imwrite('res.jpg', res_image) cv2.imshow("res", res_image) cv2.waitKey() def benchmark(test_image, compiled_model): # benchmark loop_num = 100 warm_up = 8 timeall = 0 time_min = float("inf") time_max = float('-inf') for i in range(loop_num + warm_up): time0 = time.time() #perform the inference step output = compiled_model.infer_new_request({0: test_image}) time1 = time.time() timed = time1 - time0 if i >= warm_up: timeall = timeall + timed time_min = min(time_min, timed) time_max = max(time_max, timed) time_avg = timeall / loop_num print('inference_time(ms): min={}, max={}, avg={}'.format( round(time_min * 1000, 2), round(time_max * 1000, 1), round(time_avg * 1000, 1))) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( '--benchmark', type=int, default=1, help="0:detect; 1:benchmark") parser.add_argument( '--img_path', type=str, default='../../../../demo/000000014439.jpg', help="image path") parser.add_argument( '--onnx_path', type=str, default='out_onnxsim/picodet_s_320_processed.onnx', help="onnx filepath") parser.add_argument('--in_shape', type=int, default=320, help="input_size") parser.add_argument( '--class_label', type=str, default='coco_label.txt', help="class label file") args = parser.parse_args() ie = Core() net = ie.read_model(args.onnx_path) test_image = image_preprocess(args.img_path, args.in_shape) compiled_model = ie.compile_model(net, 'CPU') if args.benchmark == 0: detect(args.img_path, compiled_model, args.in_shape, args.class_label) if args.benchmark == 1: benchmark(test_image, compiled_model)

PaddleDetection/deploy/third_engine/demo_openvino/python/openvino_benchmark.py/0

English | [简体中文](./CHANGELOG.md) # Version Update Information ## Last Version Information ### 2.6(02.15/2023) - Featured model - Release rotated object detector PP-YOLOE-R：SOTA Anchor-free rotated object detection model with high accuracy and efficiency. It has a series of models, named s/m/l/x, for cloud and edge devices and avoids using special operators to be deployed friendly with TensorRT. - Release small object detector PP-YOLOE-SOD: End-to-end detection pipeline based on sliced images and SOTA model on VisDrone based on original images. - Release crowded object detector: Crowded object detection model with top accuracy on SKU dataset. - Functions in different scenarios - Release real-time object detection model on edge device in PP-Human v2. The model reaches 45.7mAP and 80FPS on Jetson AGX - Release real-time object detection model on edge device in PP-Vehicle. The model reaches 53.5mAP and 80FPS on Jetson AGX - Support multi-stream deployment in PP-Human v2 and PP-Vehicle. Achieved 20FPS in 4-stream deployment on Jetson AGX - Support retrograde and press line detection in PP-Vehicle - Cutting-edge algorithms - Release YOLOv8 and YOLOv6 3.0 in YOLO Family - Release object detection algorithm DINO, YOLOF - Rich ViTDet series including PP-YOLOE+ViT_base, Mask RCNN + ViT_base, Mask RCNN + ViT_large - Release MOT algorithm CenterTrack - Release oriented object detection algorithm FCOSR - Release instance segmentation algorithm QueryInst - Release 3D keypoint detection algorithm Metro3d - Release distillation algorithm FGD，LD，CWD and PP-YOLOE+ distillation with improvement of 1.1+ mAP - Release SSOD algorithm DenseTeacher and adapt for PP-YOLOE+ - Release few shot finetuning algorithm, including Co-tuning and Contrastive learning - Framework capabilities - New functions - Release Grad-CAM for heatmap visualization. Support Faster RCNN, Mask RCNN, PP-YOLOE, BlazeFace, SSD, RetinaNet. - Improvement and fixes - Support python 3.10 - Fix EMA for no-grad parameters - Simplify PP-YOLOE architecture - Support AdamW for Paddle 2.4.1 ### 2.5(08.26/2022) - Featured model - PP-YOLOE+： - Released PP-YOLOE+ model, with a 0.7%-2.4% mAP improvement on COCO test2017. 3.75 times faster model training convergence rate and 1.73-2.3 times faster end-to-end inference speed - Released pre-trained models for smart agriculture, night security detection, and industrial quality inspection with 1.3%-8.1% mAP accuracy improvement - supports 10 high-performance training deployment capabilities, including distributed training, online quantization, and serving deployment. We also provide more than five new deployment demos, such as C++/Python Serving, TRT native inference, and ONNX Runtime - PP-PicoDet： - Release the PicoDet-NPU model to support full quantization of model deployment - Add PicoDet layout analysis model with 0.5% mAP accuracy improvement due to FGD distillation algorithm - PP-TinyPose - Release PP-TinyPose Plus with 9.1% end-to-end AP improvement for business data sets such as physical exercises, dance, and other scenarios - Covers unconventional movements such as turning to one side, lying down, jumping, high lift - Add stabilization module (via filter) to significantly improve the stability at key points - Functions in different scenarios - PP-Human v2 - Release PP-Human v2, which supports four industrial features: behavioral recognition case zoo for multiple solutions, human attribute recognition, human traffic detection and trajectory retention, as well as high precision multi-camera tracking - Upgraded underlying algorithm capabilities: 1.5% mAP improvement in pedestrian detection accuracy; 10.2% MOTA improvement in pedestrian tracking accuracy, 34% speed improvement in the lightweight model; 0.6% ma improvement in attribute recognition accuracy, 62.5% speed improvement in the lightweight model - Provides comprehensive tutorials covering data collection and annotation, model training optimization and prediction deployment, and post-processing code modification in the pipeline - Supports online video streaming input - Become more user-friendly with a one-line code execution function that automates the process determination and model download - PP-Vehicle - Launch PP-Vehicle, which supports four core functions for traffic application: license plate recognition, attribute recognition, traffic flow statistics, and violation detection - License plate recognition supports a lightweight model based on PP-OCR v3 - Vehicle attribute recognition supports a multi-label classification model based on PP-LCNet - Compatible with various data input formats such as pictures, videos and online video streaming - Become more user-friendly with a one-line code execution function that automates the process determination and model download - Cutting-edge algorithms - YOLO Family - Release the full range of YOLO family models covering the cutting-edge detection algorithms YOLOv5, YOLOv6 and YOLOv7 - Based on the ConvNext backbone network, YOLO's algorithm training periods are reduced by 5-8 times with accuracy generally improving by 1%-5% mAP; Thanks to the model compression strategy, its speed increased by over 30% with no loss of precision. - Newly add high precision detection model based on [ViT](configs/vitdet) backbone network, with a 55.7% mAP accuracy on the COCO dataset - Newly add multi-object tracking model [OC-SORT](configs/mot/ocsort) - Newly add [ConvNeXt](configs/convnext) backbone network. - Industrial application - Intelligent physical exercise recognition based on PP-TinyPose Plus - Fighting recognition based on PP-Human - Business hall visitor analysis based on PP-Human - Vehicle structuring analysis based on PP-Vehicle - PCB board defect detection based on PP-YOLOE+ - Framework capabilities - New functions - Release auto-compression tools and demos, 0.3% mAP accuracy loss for PP-YOLOE l version, while 13% speed increase for V100 - Release PaddleServing python/C++ and ONNXRuntime deployment demos - Release PP-YOLOE end-to-end TensorRT deployment demo - Release FGC distillation algorithm with RetinaNet accuracy improved by 3.3% - Release distributed training documentation - Improvement and fixes - Fix compilation problem with Windows c++ deployment - Fix problems when saving results of inference data in VOC format - Fix the detection box output of FairMOT c++ deployment - Rotating frame detection model S2ANet supports batch size>1 deployment ### 2.4(03.24/2022) - PP-YOLOE： - Release PP-YOLOE object detection models, achieve mAP as 51.6% on COCO test dataset and 78.1 FPS on Nvidia V100 by PP-YOLOE-l, reach SOTA performance for object detection on GPU`` - Release series models: s/m/l/x, and support deployment base on TensorRT & ONNX - Spport AMP training and training speed is 33% faster than PP-YOLOv2 - PP-PicoDet: - Release enhanced models of PP-PicoDet, mAP promoted ~2% on COCO and inference speed accelerated 63% on CPU - Release PP-PicoDet-XS model with 0.7M parameters - Post-processing integrated into the network to optimize deployment pipeline - PP-Human： - Release PP-Human human analysis pipeline，including pedestrian detection, attribute recognition, human tracking, multi-camera tracking, human statistics, action recognition. Supporting deployment with TensorRT - Release StrongBaseline model for attribute recognition - Release Centroid model for ReID - Release ST-GCN model for falldown action recognition - Model richness: - Publish YOLOX object detection model, release series models: nano/tiny/s/m/l/x, and YOLOX-x achieves mAP as 51.8% on COCO val2017 dataset - Function Optimize： - Optimize 20% training speed when training with EMA, improve saving method of EMA weights - Support saving inference results in COCO format - Deployment Optimize： - Support export ONNX model by Paddle2ONNX for all RCNN models - Supoort export model with fused decode OP for SSD models to enhance inference speed in edge side - Support export NMS to TensorRT model, optmize inference speed on TensorRT ### 2.3(11.03/2021) - Feature models: - Object detection: The lightweight object detection model PP-PicoDet, performace and inference speed reaches SOTA on mobile side - Keypoint detection: The lightweight keypoint detection model PP-TinyPose for mobile side - Model richness: - Object detection: - Publish Swin-Transformer object detection model - Publish TOOD(Task-aligned One-stage Object Detection) model - Publish GFL(Generalized Focal Loss) object detection model - Publish Sniper optimization method for tiny object detection, supporting Faster RCNN and PP-YOLO series models - Publish PP-YOLO optimized model PP-YOLO-EB for EdgeBoard - Multi-object tracking: - Publish Real-time tracking system PP-Tracking - Publish high-precision, small-scale and lightweight model based on FairMot - Publish real-time tracking model zoo for pedestrian, head and vehicle tracking, including scenarios such as aerial surveillance, autonomous driving, dense crowds, and tiny object tracking - DeepSort support PP-YOLO, PP-PicoDet as object detector - Keypoint detection: - Publish Lite HRNet model - Inference deployment: - Support NPU deployment for YOLOv3 series - Support C++ deployment for FairMot - Support C++ and PaddleLite deployment for keypoint detection series model - Documents: - Add series English documents ### 2.2(08.10/2021) - Model richness: - Publish the Transformer test model: DETR, Deformable DETR, Sparse RCNN - Key point test new Dark model, release Dark HRNet model - Publish the MPII dataset HRNet keypoint detection model - Release head and vehicle tracking vertical model - Model optimization: - AlignConv optimization model was released by S2ANet, and DOTA dataset mAP was optimized to 74.0 - Inference deployment - Mainstream models support batch size>1 predictive deployment, including YOLOv3, PP-YOLO, Faster RCNN, SSD, TTFNet, FCOS - New addition of target tracking models (JDE, Fair Mot, Deep Sort) Python side prediction deployment support, and support for TensorRT prediction - FairMot joint key point detection model deployment Python side predictive deployment support - Added support for key point detection model combined with PP-YOLO prediction deployment - Documents: - New TensorRT version notes to Windows Predictive Deployment documentation - FAQ documents are updated - Bug fixes: - Fixed PP-YOLO series model training convergence problem - Fixed the problem of no label data training when batch_size > 1 ### 2.1(05.20/2021) - Model richness enhancement: - Key point model: HRNet, HigherHRNet - Publish the multi-target tracking model: DeepSort, FairMot, JDE - Basic framework Capabilities: - Supports training without labels - Forecast deployment: - Paddle Inference YOLOv3 series model support batch_size>1 prediction - Rotating frame detection S2ANet model prediction deployment is open - Incremental quantization model benchmark - Add dynamic graph model and static graph model: Paddle-Lite demo - Detection model compression: - Release PP-YOLO series model compression model - Documents: - Update quick start, forecast deployment and other tutorial documentation - Added ONNX model export tutorial - Added the mobile deployment document ### 2.0(04.15/2021) **Description:** Since version 2.0, dynamic graphs are used as the default version of Paddle Detection, the original `dygraph` directory is switched to the root directory, and the original static graph implementation is moved to the `static` directory. - Enhancement of dynamic graph model richness: - PP-YOLOv2 and PP-YOLO tiny models were published. The accuracy of PP-YOLOv2 COCO Test dataset reached 49.5%, and the prediction speed of V100 reached 68.9 FPS - Release the rotary frame detection model S2ANet - Release the two-phase utility model PSS-Det - Publish the face detection model Blazeface - New basic module: - Added SENet, GhostNet, and Res2Net backbone networks - Added VisualDL training visualization support - Added single precision calculation and PR curve drawing function - The YOLO models support THE NHWC data format - Forecast deployment: - Publish forecast benchmark data for major models - Adaptive to TensorRT6, support TensorRT dynamic size input, support TensorRT int8 quantitative prediction - 7 types of models including PP-YOLO, YOLOv3, SSD, TTFNet, FCOS, Faster RCNN are deployed in Python/CPP/TRT prediction on Linux, Windows and NV Jetson platforms - Detection model compression: - Distillation: Added dynamic map distillation support and released YOLOv3-MobileNetV1 distillation model - Joint strategy: new dynamic graph prunning + distillation joint strategy compression scheme, and release YOLOv3-MobileNetV1 prunning + distillation compression model - Problem fix: Fixed dynamic graph quantization model export problem - Documents: - New English document of dynamic graph: including homepage document, getting started, quick start, model algorithm, new dataset, etc - Added both English and Chinese installation documents of dynamic diagrams - Added configuration file templates and description documents of dynamic graph RCNN series and YOLO series ## Historical Version Information ### 2.0-rc(02.23/2021) - Enhancement of dynamic graph model richness: - Optimize networking and training mode of RCNN models, and improve accuracy of RCNN series models (depending on Paddle Develop or version 2.0.1) - Added support for SSDLite, FCOS, TTFNet, SOLOv2 series models - Added pedestrian and vehicle vertical object detection models - New dynamic graph basic module: - Added MobileNetV3 and HRNet backbone networks - Improved roi-align calculation logic for RCNN series models (depending on Paddle Develop or version 2.0.1) - Added support for Synchronized Batch Norm - Added support for Modulated Deformable Convolution - Forecast deployment: - Publish dynamic diagrams in python, C++, and Serving deployment solution and documentation. Support Faster RCNN, Mask RCNN, YOLOv3, PPYOLO, SSD, TTFNet, FCOS, SOLOv2 and other models to predict deployment - Dynamic graph prediction deployment supports TensorRT mode FP32, FP16 inference acceleration - Detection model compression: - Prunning: Added dynamic graph prunning support, and released YOLOv3-MobileNetV1 prunning model - Quantization: Added quantization support of dynamic graph, and released quantization models of YOLOv3-MobileNetV1 and YOLOv3-MobileNetV3 - Documents: - New Dynamic Diagram tutorial documentation: includes installation instructions, quick start, data preparation, and training/evaluation/prediction process documentation - New advanced tutorial documentation for dynamic diagrams: includes documentation for model compression and inference deployment - Added dynamic graph model library documentation ### v2.0-beta(12.20/2020) - Dynamic graph support: - Support for Faster-RCNN, Mask-RCNN, FPN, Cascade Faster/Mask RCNN, YOLOv3 and SSD models, trial version. - Model upgrade: - Updated PP-YOLO Mobile-Netv3 large and small models with improved accuracy, and added prunning and distillation models. - New features: - Support VisualDL visual data preprocessing pictures. - Bug fix: - Fix Blaze Face keypoint prediction bug. ### v0.5.0(11/2020) - Model richness enhancement: - SOLOv2 series models were released, in which the SOLOv2-Light-R50-VD-DCN-FPN model achieved 38.6 FPS on a single gpu V100, accelerating by 24%, and the accuracy of COCO verification set reached 38.8%, improving by 2.4 absolute percentage points. - Added Android mobile terminal detection demo, including SSD, YOLO series model, can directly scan code installation experience. - Mobile terminal model optimization: - Added to PACT's new quantization strategy, YOLOv3 Mobilenetv3 is 0.7% better than normal quantization on COCO datasets. - Ease of use and functional components: - Enhance the function of generate_proposal_labels operator to avoid nan risk of the model. - Fixed several problems with deploy python and C++ prediction. - Unified COCO and VOC datasets under the evaluation process, support the output of a single class of AP and P-R curves. - PP-YOLO supports rectangular input images. - Documents: - Added object detection whole process tutorial, added Jetson platform deployment tutorial. ### v0.4.0(07/2020) - Model richness enhancement: - The PPYOLO model was released. The accuracy of COCO dataset reached 45.2%, and the prediction speed of single gpu V100 reached 72.9 FPS, which was better than that of YOL Ov4 model. - New TTFNet model, base version aligned with competing products, COCO dataset accuracy up to 32.9%. - New HTC model, base version aligned with competing products, COCO dataset accuracy up to 42.2%. - BlazeFace key point detection model was added, with an accuracy of 85.2% in Wider-Face's Easy-Set. - ACFPN model was added, and the accuracy of COCO dataset reached 39.6%. - General object detection model (including 676 classes) on the publisher side. On the COCO dataset with the same strategy, when V100 is 19.5FPS, the COCO mAP can reach 49.4%. - Mobile terminal model optimization: - Added SSD Lite series optimization models, including Ghost Net Backbone, FPN components, etc., with accuracy improved by 0.5% and 1.5%. - Ease of use and functional components: - Add GridMask, Random Erasing data enhancement method. - Added support for Matrix NMS. - EMA(Exponential Moving Average) training support. - The new multi-machine training method, the average acceleration ratio of two machines to single machine is 80%, multi-machine training support needs to be further verified. ### v0.3.0(05/2020) - Model richness enhancement: - Efficientdet-D0 model added, speed and accuracy is better than competing products. - Added YOLOv4 prediction model, precision aligned with competing products; Added YOLOv4 fine tuning training on Pascal VOC datasets with accuracy of 85.5%. - YOLOv3 added MobileNetV3 backbone network, COCO dataset accuracy reached 31.6%. - Add Anchor-free model FCOS, the accuracy is better than competing products. - Anchor-free model Cornernet Squeeze was added, the accuracy was better than competing products, and the accuracy of COCO dataset of optimized model was 38.2% and +3.7%, 5% faster than YOL Ov3 Darknet53. - The CascadeRCNN-ResNet50vd model, which is a practical object detection model on the server side, is added, and its speed and accuracy are better than that of the competitive EfficientDet. - Mobile terminal launched three models: - SSSDLite model: SSDLite-Mobilenetv3 small/large model, with better accuracy than competitors. - YOLOv3 Mobile solution: The YOLOv3-MobileNetv3 model accelerates 3.5 times after compression, which is faster and more accurate than the SSD Lite model of competing products. - RCNN Mobile terminal scheme: CascadeRCNN-MobileNetv3, after series optimization, launched models with input images of 320x320 and 640x640 respectively, with high cost performance for speed and accuracy. - Anticipate deployment refactoring: - New Python prediction deployment process, support for RCNN, YOLO, SSD, Retina Net, face models, support for video prediction. - Refactoring C++ predictive deployment to improve ease of use. - Ease of use and functional components: - Added Auto Augment data enhancement. - Upgrade the detection library document structure. - Support shape matching automatically by transfer learning. - Optimize memory footprint during mask branch evaluation. ### v0.2.0(02/2020) - The new model: - Added CBResNet model. - Added LibraRCNN model. - The accuracy of YOLOv3 model was further improved, and the accuracy based on COCO data reached 43.2%, 1.4% higher than the previous version. - New Basic module: - Trunk network: CBResNet is added. - Loss module: Loss of YOLOv3 supports fine-grained OP combinations. - Regular module: Added the Drop Block module. - Function optimization and improvement: - Accelerate YOLOv3 data preprocessing and increase the overall training speed by 40%. - Optimize data preprocessing logic to improve ease of use. - dd face detection prediction benchmark data. - Added C++ prediction engine Python API prediction example. - Detection model compression: - prunning: Release MobileNet-YOLOv3 prunning scheme and model, based on VOC data FLOPs 69.6%, mAP + 1.4%, based on COCO DATA FLOPS 28.8%, mAP + 0.9%; Release ResNet50vd-DCN-YOLOv3 clipped solution and model based on COCO datasets 18.4%, mAP + 0.8%. - Distillation: Release MobileNet-YOLOv3 distillation scheme and model, based on VOC data mAP + 2.8%, COCO data mAP + 2.1%. - Quantification: Release quantification models of YOLOv3 Mobile Net and Blaze Face. - Prunning + distillation: release MobileNet-YOLOv3 prunning + distillation solution and model, 69.6% based on COCO DATA FLOPS, 64.5% based on TensorRT prediction acceleration, 0.3% mAP; Release ResNet50vd-DCN-YOLOv3 tailoring + distillation solution and model, 43.7% based on COCO Data FLOPS, 24.0% based on TensorRT prediction acceleration, mAP + 0.6%. - Search: Open source Blaze Face Nas complete search solution. - Predict deployment: - Integrated TensorRT, support FP16, FP32, INT8 quantitative inference acceleration. - Document: - Add detailed data preprocessing module to introduce documents and implement custom data Reader documents. - Added documentation on how to add algorithm models. - Document deployment to the web site: https://paddledetection.readthedocs.io ### 12/2019 - Add Res2Net model. - Add HRNet model. - Add GIOU loss and DIOU loss。 ### 21/11/2019 - Add CascadeClsAware RCNN model. - Add CBNet, ResNet200 and Non-local model. - Add SoftNMS. - Add Open Image V5 dataset and Objects365 dataset model ### 10/2019 - Added enhanced YOLOv3 model with accuracy up to 41.4%. - Added Face detection models BlazeFace and Faceboxes. - Rich COCO based models, accuracy up to 51.9%. - Added CA-Cascade-RCNN, one of the best single models to win on Objects365 2019 Challenge. - Add pedestrian detection and vehicle detection pre-training models. - Support FP16 training. - Added cross-platform C++ inference deployment scheme. - Add model compression examples. ### 2/9/2019 - Add GroupNorm model. - Add CascadeRCNN+Mask model. ### 5/8/2019 - Add Modulated Deformable Convolution series model ### 29/7/2019 - Add detection library Chinese document - Fixed an issue where R-CNN series model training was evaluated simultaneously - Add ResNext101-vd + Mask R-CNN + FPN models - Added YOLOv3 model based on VOC dataset ### 3/7/2019 - First release of PaddleDetection Detection library and Detection model library - models：Faster R-CNN, Mask R-CNN, Faster R-CNN+FPN, Mask R-CNN+FPN, Cascade-Faster-RCNN+FPN, RetinaNet, YOLOv3, 和SSD.

PaddleDetection/docs/CHANGELOG_en.md/0

简体中文 | [English](./detection_en.md) # 目标检测任务二次开发 在目标检测算法产业落地过程中，常常会出现需要额外训练以满足实际使用的要求，项目迭代过程中也会出先需要修改类别的情况。本文档详细介绍如何使用PaddleDetection进行目标检测算法二次开发，流程包括：数据准备、模型优化思路和修改类别开发流程。 ## 数据准备 二次开发首先需要进行数据集的准备，针对场景特点采集合适的数据从而提升模型效果和泛化性能。然后使用Labeme，LabelImg等标注工具标注目标检测框，并将标注结果转化为COCO或VOC数据格式。详细文档可以参考[数据准备文档](../../tutorials/data/README.md) ## 模型优化 ### 1. 使用自定义数据集训练 基于准备的数据在数据配置文件中修改对应路径，例如`configs/dataset/coco_detection.yml`: ``` metric: COCO num_classes: 80 TrainDataset: !COCODataSet image_dir: train2017 # 训练集的图片所在文件相对于dataset_dir的路径 anno_path: annotations/instances_train2017.json # 训练集的标注文件相对于dataset_dir的路径 dataset_dir: dataset/coco # 数据集所在路径，相对于PaddleDetection路径 data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd'] EvalDataset: !COCODataSet image_dir: val2017 # 验证集的图片所在文件相对于dataset_dir的路径 anno_path: annotations/instances_val2017.json # 验证集的标注文件相对于dataset_dir的路径 dataset_dir: dataset/coco # 数据集所在路径，相对于PaddleDetection路径 TestDataset: !ImageFolder anno_path: annotations/instances_val2017.json # also support txt (like VOC's label_list.txt) # 标注文件所在文件 相对于dataset_dir的路径 dataset_dir: dataset/coco # if set, anno_path will be 'dataset_dir/anno_path' # 数据集所在路径，相对于PaddleDetection路径 ``` 配置修改完成后，即可以启动训练评估，命令如下 ``` export CUDA_VISIBLE_DEVICES=0 python tools/train.py -c configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml --eval ``` 更详细的命令参考[30分钟快速上手PaddleDetection](../../tutorials/GETTING_STARTED_cn.md) ### 2. 加载COCO模型作为预训练 目前PaddleDetection提供的配置文件加载的预训练模型均为ImageNet数据集的权重，加载到检测算法的骨干网络中，实际使用时，建议加载COCO数据集训练好的权重，通常能够对模型精度有较大提升，使用方法如下： #### 1) 设置预训练权重路径 COCO数据集训练好的模型权重均在各算法配置文件夹下，例如`configs/ppyoloe`下提供了PP-YOLOE-l COCO数据集权重：[链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) 。配置文件中设置`pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams` #### 2) 修改超参数 加载COCO预训练权重后，需要修改学习率超参数，例如`configs/ppyoloe/_base_/optimizer_300e.yml`中: ``` epoch: 120 # 原始配置为300epoch，加载COCO权重后可以适当减少迭代轮数 LearningRate: base_lr: 0.005 # 原始配置为0.025，加载COCO权重后需要降低学习率 schedulers: - !CosineDecay max_epochs: 144 # 依据epoch数进行修改 - !LinearWarmup start_factor: 0. epochs: 5 ``` ## 修改类别 当实际使用场景类别发生变化时，需要修改数据配置文件，例如`configs/datasets/coco_detection.yml`中: ``` metric: COCO num_classes: 10 # 原始类别80 ``` 配置修改完成后，同样可以加载COCO预训练权重，PaddleDetection支持自动加载shape匹配的权重，对于shape不匹配的权重会自动忽略，因此无需其他修改。

PaddleDetection/docs/advanced_tutorials/customization/detection.md/0

# 将FairMOT模型为ONNX格式,并用OpenVINO做推理 ## 简介 PaddleDetection是一个充满活力的开源项目，拥有大量的贡献者和维护者。 PaddleDetection是PaddlePaddle下面一个人工智能框物体检测工具集，能够帮助开发人员快速的将人工智能集成到自己的项目和应用程序中。 Intel OpenVINO 是一个广泛使用的免费工具包。 它能帮助优化深度学习模型，并使用推理引擎将其部署到英特尔硬件上。 很显然，当我们可以协同上下游（PaddlePaddle, OpenVINO）一起工作,这将可以极大的简化工作流程, 并且帮助我们实现AI模型从开发到部署的流水线工作模式, 这也让我们的生活更轻松。 本文将向您展示如何在 PaddleDetection 中使用 Model Zoo 中的FairMOT模型 [FairMOT](../../../configs/mot/fairmot/README.md) 并用OpenVINO来实现推理过程。 ------------ ## 前提要求 为了专注于介绍如何在OpenVINO中使用飞桨的模型这一主题,本文将不是一片入门级文章,它不会帮助您设置好您的开发环境, 本文只会提供最核心的组件安装, 并且会为每个需要用到的组件提供相应的链接. 在开始之前 请确保您已经安装了 PaddlePaddle. ``` conda install paddlepaddle==2.2.2 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ ``` 为了运行演示程序, 您还需要下载已经转换好了的[ONNX格式的FairMOT模型](https://bj.bcebos.com/v1/paddledet/models/mot/fairmot_576_320_v3.onnx). ## 将FairMOT模型到ONNX格式 1. 下载[FairMOT推理模型](https://bj.bcebos.com/v1/paddledet/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.tar). 2. 使用Paddle2ONNX来转换FairMOT模型. 请确保您已经安装了[Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX). ``` paddle2onnx --model_dir . --model_filename model.pdmodel \ --params_filename model.pdiparams \ --input_shape_dict "{'image': [1, 3, 320, 576], 'scale_factor': [1, 2], 'im_shape': [1, 2]}" \ --save_file fairmot_576_320_v2.onnx \ --opset_version 12 \ --enable_onnx_checker True ``` 更多关于如何使用Paddle2ONNX的详细信息, 请参考: [ONNX模型导出](../../../deploy/EXPORT_ONNX_MODEL_en.md). ## 使用ONNX模型以及OpenVINO进行推理 当我们把Paddle模型转换成ONNX模型之后, 我们可以直接使用OpenVINO读取其模型 并且进行推理. *<sub>请确保您已经安装了OpenVINO, 这里是[OpenVINO的安装指南](https://docs.openvino.ai/cn/latest/openvino_docs_install_guides_installing_openvino_linux.html).<sub>* 1. ### 创建一个execution network 所以这里要做的第一件事是获得一个执行网络，以后可以使用它来进行推理。 代码如下: ``` def get_net(): ie = IECore() model_path = root_path / "PaddleDetection/FairMot/fairmot_576_320_v3.onnx" net = ie.read_network(model= str(model_path)) exec_net = ie.load_network(network=net, device_name="CPU") return net, exec_net ``` 2. ### 预处理 每个 AI 模型都有自己不同的预处理步骤，让我们看看 FairMOT 模型是如何做的： ``` def prepare_input(): transforms = [ T.Resize(target_size=(target_width, target_height)), T.Normalize(mean=(0,0,0), std=(1,1,1)) ] img_file = root_path / "images/street.jpeg" img = cv2.imread(str(img_file)) normalized_img, _ = T.Compose(transforms)(img) # add an new axis in front img_input = normalized_img[np.newaxis, :] # scale_factor is calculated as: im_shape / original_im_shape h_scale = target_height / img.shape[0] w_scale = target_width / img.shape[1] input = {"image": img_input, "im_shape": [target_height, target_width], "scale_factor": [h_scale, w_scale]} return input, img ``` 3. ### 预测 在我们完成了所有的负载网络和预处理之后，终于开始了预测阶段。 ``` def predict(exec_net, input): result = exec_net.infer(input) return result ``` 您可能会惊讶地看到, 最激动人心的步骤居然如此简单。 不过下一个阶段会更加复杂。 4. ### 后处理 相较于大多数其他类型的AI推理, MOT（Multi-Object Tracking）显然是特殊的. FairMOT 需要一个称为跟踪器的特殊对象来处理预测结果。 这个预测结果则包括预测检测和预测的行人特征向量。 幸运的是，PaddleDetection 为我们简化了这个过程，我们可以从`ppdet`导出JDETracker，然后用这个tracker挑选出来符合条件的检测框,而且我们不需要编写太多代码来处理它。 ``` def postprocess(pred_dets, pred_embs, threshold = 0.5): tracker = JDETracker() online_targets_dict = tracker.update(pred_dets, pred_embs) online_tlwhs = defaultdict(list) online_scores = defaultdict(list) online_ids = defaultdict(list) for cls_id in range(1): online_targets = online_targets_dict[cls_id] for t in online_targets: tlwh = t.tlwh tid = t.track_id tscore = t.score # make sure the tscore is no less then the threshold. if tscore < threshold: continue # make sure the target area is not less than the min_box_area. if tlwh[2] * tlwh[3] <= tracker.min_box_area: continue # make sure the vertical ratio of a found target is within the range (1.6 as default ratio). if tracker.vertical_ratio > 0 and tlwh[2] / tlwh[3] > tracker.vertical_ratio: continue online_tlwhs[cls_id].append(tlwh) online_ids[cls_id].append(tid) online_scores[cls_id].append(tscore) online_im = plot_tracking_dict( img, 1, online_tlwhs, online_ids, online_scores, frame_id=0) return online_im ``` 5. ### 画出检测框(可选) 这一步是可选的。出于演示目的，我只使用 `plot_tracking_dict()` 方法在图像上绘制所有边界框。 但是，如果您没有相同的要求，则不需要这样做。 ``` online_im = plot_tracking_dict( img, 1, online_tlwhs, online_ids, online_scores, frame_id=0) ``` 这些就是在您的硬件上运行 FairMOT 所需要遵循的所有步骤。 之后会有一篇详细解释此过程的配套文章将会发布，并且该文章的链接将很快在此处更新。 完整代码请查看 [Paddle OpenVINO 预测](./fairmot_onnx_openvino.py).

PaddleDetection/docs/advanced_tutorials/openvino_inference/README_cn.md/0

English | [简体中文](QUICK_STARTED_cn.md) # Quick Start In order to enable users to experience PaddleDetection and produce models in a short time, this tutorial introduces the pipeline to get a decent object detection model by finetuning on a small dataset in 10 minutes only. In practical applications, it is recommended that users select a suitable model configuration file for their specific demand. - **Set GPU** ```bash export CUDA_VISIBLE_DEVICES=0 ``` ## Inference Demo with Pre-trained Models ``` # predict an image using PP-YOLO python tools/infer.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml -o use_gpu=true weights=https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams --infer_img=demo/000000014439.jpg ``` the result：  ## Data preparation The Dataset is [Kaggle dataset](https://www.kaggle.com/andrewmvd/road-sign-detection) ，including 877 images and 4 data categories: crosswalk, speedlimit, stop, trafficlight. The dataset is divided into training set (701 images) and test set (176 images)，[download link](https://paddlemodels.bj.bcebos.com/object_detection/roadsign_voc.tar). ``` # Note: this command could skip and # the dataset will be dowloaded automatically at the stage of training. python dataset/roadsign_voc/download_roadsign_voc.py ``` ## Training & Evaluation & Inference ### 1、Training ``` # It will takes about 10 minutes on 1080Ti and 1 hour on CPU # -c set configuration file # -o overwrite the settings in the configuration file # --eval Evaluate while training, and a model named best_model.pdmodel with the most evaluation results will be automatically saved python tools/train.py -c configs/yolov3/yolov3_mobilenet_v1_roadsign.yml --eval -o use_gpu=true ``` If you want to observe the loss change curve in real time through VisualDL, add --use_vdl=true to the training command, and set the log save path through --vdl_log_dir. **Note: VisualDL need Python>=3.5** Please install [VisualDL](https://github.com/PaddlePaddle/VisualDL) first ``` python -m pip install visualdl -i https://mirror.baidu.com/pypi/simple ``` ``` python -u tools/train.py -c configs/yolov3/yolov3_mobilenet_v1_roadsign.yml \ --use_vdl=true \ --vdl_log_dir=vdl_dir/scalar \ --eval ``` View the change curve in real time through the visualdl command: ``` visualdl --logdir vdl_dir/scalar/ --host <host_IP> --port <port_num> ``` ### 2、Evaluation ``` # Evaluate best_model by default # -c set config file # -o overwrite the settings in the configuration file python tools/eval.py -c configs/yolov3/yolov3_mobilenet_v1_roadsign.yml -o use_gpu=true ``` The final mAP should be around 0.85. The dataset is small so the precision may vary a little after each training. ### 3、Inference ``` # -c set config file # -o overwrite the settings in the configuration file # --infer_img image path # After the prediction is over, an image of the same name with the prediction result will be generated in the output folder python tools/infer.py -c configs/yolov3/yolov3_mobilenet_v1_roadsign.yml -o use_gpu=true --infer_img=demo/road554.png ``` The result is as shown below： 

PaddleDetection/docs/tutorials/QUICK_STARTED.md/0

[简体中文](PrepareKeypointDataSet.md) | English # How to prepare dataset? ## Table of Contents - [COCO](#COCO) - [MPII](#MPII) - [Training for other dataset](#Training_for_other_dataset) ## COCO ### Preperation for COCO dataset We provide a one-click script to automatically complete the download and preparation of the COCO2017 dataset. Please refer to [COCO Download](https://github.com/PaddlePaddle/PaddleDetection/blob/f0a30f3ba6095ebfdc8fffb6d02766406afc438a/docs/tutorials/PrepareDetDataSet_en.md#COCO%E6%95%B0%E6%8D%AE). ### Description for COCO dataset（Keypoint): In COCO, the indexes and corresponding keypoint name are: ``` COCO keypoint indexes: 0: 'nose', 1: 'left_eye', 2: 'right_eye', 3: 'left_ear', 4: 'right_ear', 5: 'left_shoulder', 6: 'right_shoulder', 7: 'left_elbow', 8: 'right_elbow', 9: 'left_wrist', 10: 'right_wrist', 11: 'left_hip', 12: 'right_hip', 13: 'left_knee', 14: 'right_knee', 15: 'left_ankle', 16: 'right_ankle' ``` Being different from detection task, the annotation files for keyPoint task are `person_keypoints_train2017.json` and `person_keypoints_val2017.json`. In these two json files, the terms `info`、`licenses` and `images` are same with detection task. However, the `annotations` and `categories` are different. In `categories`, in addition to the category, there are also the names of the keypoints and the connectivity among them. In `annotations`, the ID and image of each instance are annotated, as well as segmentation information and keypoint information. Among them, terms related to the keypoints are: - `keypoints`: `[x1,y1,v1 ...]`, which is a `List` with length 17*3=51. Each combination represents the coordinates and visibility of one keypoint. `v=0, x=0, y=0` indicates this keypoint is not visible and unlabeled. `v=1` indicates this keypoint is labeled but not visible. `v=2` indicates this keypoint is labeled and visible. - `bbox`: `[x1,y1,w,h]`, the bounding box of this instance. - `num_keypoints`: the number of labeled keypoints of this instance. ## MPII ### Preperation for MPII dataset Please download MPII dataset images and corresponding annotation files from [MPII Human Pose Dataset](http://human-pose.mpi-inf.mpg.de/#download), and save them to `dataset/mpii`. You can use [mpii_annotations](https://download.openmmlab.com/mmpose/datasets/mpii_annotations.tar), which are already converted to `.json`. The directory structure will be shown as: ``` mpii |── annotations | |── mpii_gt_val.mat | |── mpii_test.json | |── mpii_train.json | |── mpii_trainval.json | `── mpii_val.json `── images |── 000001163.jpg |── 000003072.jpg ``` ### Description for MPII dataset In MPII, the indexes and corresponding keypoint name are: ``` MPII keypoint indexes: 0: 'right_ankle', 1: 'right_knee', 2: 'right_hip', 3: 'left_hip', 4: 'left_knee', 5: 'left_ankle', 6: 'pelvis', 7: 'thorax', 8: 'upper_neck', 9: 'head_top', 10: 'right_wrist', 11: 'right_elbow', 12: 'right_shoulder', 13: 'left_shoulder', 14: 'left_elbow', 15: 'left_wrist', ``` The following example takes a parsed annotation information to illustrate the content of the annotation, each annotation information represents a person instance: ``` { 'joints_vis': [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'gt_joints': [ [-1.0, -1.0], [-1.0, -1.0], [-1.0, -1.0], [-1.0, -1.0], [-1.0, -1.0], [-1.0, -1.0], [-1.0, -1.0], [1232.0, 288.0], [1236.1271, 311.7755], [1181.8729, -0.77553], [692.0, 464.0], [902.0, 417.0], [1059.0, 247.0], [1405.0, 329.0], [1498.0, 613.0], [1303.0, 562.0] ], 'image': '077096718.jpg', 'scale': 9.516749, 'center': [1257.0, 297.0] } ``` - `joints_vis`: indicates whether the 16 keypoints are labeled respectively, if it is 0, the corresponding coordinate will be `[-1.0, -1.0]`. - `joints`: the coordinates of 16 keypoints. - `image`: image file which this instance belongs to. - `center`: the coordinate of person instance center, which is used to locate instance in the image. - `scale`: scale of the instance, corresponding to 200px. ## Training for other dataset Here, we take `AI Challenger` dataset as example, to show how to align other datasets to `COCO` and add them into training of keypoint models. In `AI Challenger`, the indexes and corresponding keypoint name are: ``` AI Challenger Description: 0: 'Right Shoulder', 1: 'Right Elbow', 2: 'Right Wrist', 3: 'Left Shoulder', 4: 'Left Elbow', 5: 'Left Wrist', 6: 'Right Hip', 7: 'Right Knee', 8: 'Right Ankle', 9: 'Left Hip', 10: 'Left Knee', 11: 'Left Ankle', 12: 'Head top', 13: 'Neck' ``` 1. Align the indexes of the `AI Challenger` keypoint to be consistent with `COCO`. For example, the index of `Right Shoulder` should be adjusted from `0` to `13`. 2. Unify the flags whether the keypoint is labeled/visible. For example, `labeled and visible` in `AI Challenger` needs to be adjusted from `1` to `2`. 3. In this proprocess, we discard the unique keypoints in this dataset (like `Neck`). For keypoints not in this dataset but in `COCO` (like `left_eye`), we set `v=0, x=0, y=0` to indicate these keypoints are not labeled. 4. To avoid the problem of ID duplication in different datasets, the `image_id` and `annotation id` need to be rearranged. 5. Rewrite the image path `file_name`, to make sure images can be accessed correctly. We also provide an [annotation file](https://bj.bcebos.com/v1/paddledet/data/keypoint/aic_coco_train_cocoformat.json) combining `COCO` trainset and `AI Challenger` trainset.

PaddleDetection/docs/tutorials/data/PrepareKeypointDataSet_en.md/0

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import math import random import numpy as np from copy import deepcopy from typing import List, Tuple from collections import defaultdict from .chip_box_utils import nms, transform_chip_boxes2image_boxes from .chip_box_utils import find_chips_to_cover_overlaped_boxes from .chip_box_utils import transform_chip_box from .chip_box_utils import intersection_over_box class AnnoCropper(object): def __init__(self, image_target_sizes: List[int], valid_box_ratio_ranges: List[List[float]], chip_target_size: int, chip_target_stride: int, use_neg_chip: bool=False, max_neg_num_per_im: int=8, max_per_img: int=-1, nms_thresh: int=0.5): """ Generate chips by chip_target_size and chip_target_stride. These two parameters just like kernel_size and stride in cnn. Each image has its raw size. After resizing, then get its target size. The resizing scale = target_size / raw_size. So are chips of the image. box_ratio = box_raw_size / image_raw_size = box_target_size / image_target_size The 'size' above mentioned is the size of long-side of image, box or chip. :param image_target_sizes: [2000, 1000] :param valid_box_ratio_ranges: [[-1, 0.1],[0.08, -1]] :param chip_target_size: 500 :param chip_target_stride: 200 """ self.target_sizes = image_target_sizes self.valid_box_ratio_ranges = valid_box_ratio_ranges assert len(self.target_sizes) == len(self.valid_box_ratio_ranges) self.scale_num = len(self.target_sizes) self.chip_target_size = chip_target_size # is target size self.chip_target_stride = chip_target_stride # is target stride self.use_neg_chip = use_neg_chip self.max_neg_num_per_im = max_neg_num_per_im self.max_per_img = max_per_img self.nms_thresh = nms_thresh def crop_anno_records(self, records: List[dict]): """ The main logic: # foreach record(image): # foreach scale: # 1 generate chips by chip size and stride for each scale # 2 get pos chips # - validate boxes: current scale; h,w >= 1 # - find pos chips greedily by valid gt boxes in each scale # - for every valid gt box, find its corresponding pos chips in each scale # 3 get neg chips # - If given proposals, find neg boxes in them which are not in pos chips # - If got neg boxes in last step, we find neg chips and assign neg boxes to neg chips such as 2. # 4 sample neg chips if too much each image # transform this image-scale annotations to chips(pos chips&neg chips) annotations :param records, standard coco_record but with extra key `proposals`(Px4), which are predicted by stage1 model and maybe have neg boxes in them. :return: new_records, list of dict like { 'im_file': 'fake_image1.jpg', 'im_id': np.array([1]), # new _global_chip_id as im_id 'h': h, # chip height 'w': w, # chip width 'is_crowd': is_crowd, # Nx1 -> Mx1 'gt_class': gt_class, # Nx1 -> Mx1 'gt_bbox': gt_bbox, # Nx4 -> Mx4, 4 represents [x1,y1,x2,y2] 'gt_poly': gt_poly, # [None]xN -> [None]xM 'chip': [x1, y1, x2, y2] # added } Attention: ------------------------------>x | | (x1,y1)------ | | | | | | | | | | | | | | | | ---------- | (x2,y2) | ↓ y If we use [x1, y1, x2, y2] to represent boxes or chips, (x1,y1) is the left-top point which is in the box, but (x2,y2) is the right-bottom point which is not in the box. So x1 in [0, w-1], x2 in [1, w], y1 in [0, h-1], y2 in [1,h]. And you can use x2-x1 to get width, and you can use image[y1:y2, x1:x2] to get the box area. """ self.chip_records = [] self._global_chip_id = 1 for r in records: self._cur_im_pos_chips = [ ] # element: (chip, boxes_idx), chip is [x1, y1, x2, y2], boxes_ids is List[int] self._cur_im_neg_chips = [] # element: (chip, neg_box_num) for scale_i in range(self.scale_num): self._get_current_scale_parameters(scale_i, r) # Cx4 chips = self._create_chips(r['h'], r['w'], self._cur_scale) # # dict: chipid->[box_id, ...] pos_chip2boxes_idx = self._get_valid_boxes_and_pos_chips( r['gt_bbox'], chips) # dict: chipid->neg_box_num neg_chip2box_num = self._get_neg_boxes_and_chips( chips, list(pos_chip2boxes_idx.keys()), r.get('proposals', None)) self._add_to_cur_im_chips(chips, pos_chip2boxes_idx, neg_chip2box_num) cur_image_records = self._trans_all_chips2annotations(r) self.chip_records.extend(cur_image_records) return self.chip_records def _add_to_cur_im_chips(self, chips, pos_chip2boxes_idx, neg_chip2box_num): for pos_chipid, boxes_idx in pos_chip2boxes_idx.items(): chip = np.array(chips[pos_chipid]) # copy chips slice self._cur_im_pos_chips.append((chip, boxes_idx)) if neg_chip2box_num is None: return for neg_chipid, neg_box_num in neg_chip2box_num.items(): chip = np.array(chips[neg_chipid]) self._cur_im_neg_chips.append((chip, neg_box_num)) def _trans_all_chips2annotations(self, r): gt_bbox = r['gt_bbox'] im_file = r['im_file'] is_crowd = r['is_crowd'] gt_class = r['gt_class'] # gt_poly = r['gt_poly'] # [None]xN # remaining keys: im_id, h, w chip_records = self._trans_pos_chips2annotations(im_file, gt_bbox, is_crowd, gt_class) if not self.use_neg_chip: return chip_records sampled_neg_chips = self._sample_neg_chips() neg_chip_records = self._trans_neg_chips2annotations(im_file, sampled_neg_chips) chip_records.extend(neg_chip_records) return chip_records def _trans_pos_chips2annotations(self, im_file, gt_bbox, is_crowd, gt_class): chip_records = [] for chip, boxes_idx in self._cur_im_pos_chips: chip_bbox, final_boxes_idx = transform_chip_box(gt_bbox, boxes_idx, chip) x1, y1, x2, y2 = chip chip_h = y2 - y1 chip_w = x2 - x1 rec = { 'im_file': im_file, 'im_id': np.array([self._global_chip_id]), 'h': chip_h, 'w': chip_w, 'gt_bbox': chip_bbox, 'is_crowd': is_crowd[final_boxes_idx].copy(), 'gt_class': gt_class[final_boxes_idx].copy(), # 'gt_poly': [None] * len(final_boxes_idx), 'chip': chip } self._global_chip_id += 1 chip_records.append(rec) return chip_records def _sample_neg_chips(self): pos_num = len(self._cur_im_pos_chips) neg_num = len(self._cur_im_neg_chips) sample_num = min(pos_num + 2, self.max_neg_num_per_im) assert sample_num >= 1 if neg_num <= sample_num: return self._cur_im_neg_chips candidate_num = int(sample_num * 1.5) candidate_neg_chips = sorted( self._cur_im_neg_chips, key=lambda x: -x[1])[:candidate_num] random.shuffle(candidate_neg_chips) sampled_neg_chips = candidate_neg_chips[:sample_num] return sampled_neg_chips def _trans_neg_chips2annotations(self, im_file: str, sampled_neg_chips: List[Tuple]): chip_records = [] for chip, neg_box_num in sampled_neg_chips: x1, y1, x2, y2 = chip chip_h = y2 - y1 chip_w = x2 - x1 rec = { 'im_file': im_file, 'im_id': np.array([self._global_chip_id]), 'h': chip_h, 'w': chip_w, 'gt_bbox': np.zeros( (0, 4), dtype=np.float32), 'is_crowd': np.zeros( (0, 1), dtype=np.int32), 'gt_class': np.zeros( (0, 1), dtype=np.int32), # 'gt_poly': [], 'chip': chip } self._global_chip_id += 1 chip_records.append(rec) return chip_records def _get_current_scale_parameters(self, scale_i, r): im_size = max(r['h'], r['w']) im_target_size = self.target_sizes[scale_i] self._cur_im_size, self._cur_im_target_size = im_size, im_target_size self._cur_scale = self._get_current_scale(im_target_size, im_size) self._cur_valid_ratio_range = self.valid_box_ratio_ranges[scale_i] def _get_current_scale(self, im_target_size, im_size): return im_target_size / im_size def _create_chips(self, h: int, w: int, scale: float): """ Generate chips by chip_target_size and chip_target_stride. These two parameters just like kernel_size and stride in cnn. :return: chips, Cx4, xy in raw size dimension """ chip_size = self.chip_target_size # omit target for simplicity stride = self.chip_target_stride width = int(scale * w) height = int(scale * h) min_chip_location_diff = 20 # in target size assert chip_size >= stride chip_overlap = chip_size - stride if (width - chip_overlap ) % stride > min_chip_location_diff: # 不能被stride整除的部分比较大，则保留 w_steps = max(1, int(math.ceil((width - chip_overlap) / stride))) else: # 不能被stride整除的部分比较小，则丢弃 w_steps = max(1, int(math.floor((width - chip_overlap) / stride))) if (height - chip_overlap) % stride > min_chip_location_diff: h_steps = max(1, int(math.ceil((height - chip_overlap) / stride))) else: h_steps = max(1, int(math.floor((height - chip_overlap) / stride))) chips = list() for j in range(h_steps): for i in range(w_steps): x1 = i * stride y1 = j * stride x2 = min(x1 + chip_size, width) y2 = min(y1 + chip_size, height) chips.append([x1, y1, x2, y2]) # check chip size for item in chips: if item[2] - item[0] > chip_size * 1.1 or item[3] - item[ 1] > chip_size * 1.1: raise ValueError(item) chips = np.array(chips, dtype=np.float32) raw_size_chips = chips / scale return raw_size_chips def _get_valid_boxes_and_pos_chips(self, gt_bbox, chips): valid_ratio_range = self._cur_valid_ratio_range im_size = self._cur_im_size scale = self._cur_scale # Nx4 N valid_boxes, valid_boxes_idx = self._validate_boxes( valid_ratio_range, im_size, gt_bbox, scale) # dict: chipid->[box_id, ...] pos_chip2boxes_idx = self._find_pos_chips(chips, valid_boxes, valid_boxes_idx) return pos_chip2boxes_idx def _validate_boxes(self, valid_ratio_range: List[float], im_size: int, gt_boxes: 'np.array of Nx4', scale: float): """ :return: valid_boxes: Nx4, valid_boxes_idx: N """ ws = (gt_boxes[:, 2] - gt_boxes[:, 0]).astype(np.int32) hs = (gt_boxes[:, 3] - gt_boxes[:, 1]).astype(np.int32) maxs = np.maximum(ws, hs) box_ratio = maxs / im_size mins = np.minimum(ws, hs) target_mins = mins * scale low = valid_ratio_range[0] if valid_ratio_range[0] > 0 else 0 high = valid_ratio_range[1] if valid_ratio_range[1] > 0 else np.finfo( np.float32).max valid_boxes_idx = np.nonzero((low <= box_ratio) & (box_ratio < high) & ( target_mins >= 2))[0] valid_boxes = gt_boxes[valid_boxes_idx] return valid_boxes, valid_boxes_idx def _find_pos_chips(self, chips: 'Cx4', valid_boxes: 'Bx4', valid_boxes_idx: 'B'): """ :return: pos_chip2boxes_idx, dict: chipid->[box_id, ...] """ iob = intersection_over_box(chips, valid_boxes) # overlap, CxB iob_threshold_to_find_chips = 1. pos_chip_ids, _ = self._find_chips_to_cover_overlaped_boxes( iob, iob_threshold_to_find_chips) pos_chip_ids = set(pos_chip_ids) iob_threshold_to_assign_box = 0.5 pos_chip2boxes_idx = self._assign_boxes_to_pos_chips( iob, iob_threshold_to_assign_box, pos_chip_ids, valid_boxes_idx) return pos_chip2boxes_idx def _find_chips_to_cover_overlaped_boxes(self, iob, overlap_threshold): return find_chips_to_cover_overlaped_boxes(iob, overlap_threshold) def _assign_boxes_to_pos_chips(self, iob, overlap_threshold, pos_chip_ids, valid_boxes_idx): chip_ids, box_ids = np.nonzero(iob >= overlap_threshold) pos_chip2boxes_idx = defaultdict(list) for chip_id, box_id in zip(chip_ids, box_ids): if chip_id not in pos_chip_ids: continue raw_gt_box_idx = valid_boxes_idx[box_id] pos_chip2boxes_idx[chip_id].append(raw_gt_box_idx) return pos_chip2boxes_idx def _get_neg_boxes_and_chips(self, chips: 'Cx4', pos_chip_ids: 'D', proposals: 'Px4'): """ :param chips: :param pos_chip_ids: :param proposals: :return: neg_chip2box_num, None or dict: chipid->neg_box_num """ if not self.use_neg_chip: return None # train proposals maybe None if proposals is None or len(proposals) < 1: return None valid_ratio_range = self._cur_valid_ratio_range im_size = self._cur_im_size scale = self._cur_scale valid_props, _ = self._validate_boxes(valid_ratio_range, im_size, proposals, scale) neg_boxes = self._find_neg_boxes(chips, pos_chip_ids, valid_props) neg_chip2box_num = self._find_neg_chips(chips, pos_chip_ids, neg_boxes) return neg_chip2box_num def _find_neg_boxes(self, chips: 'Cx4', pos_chip_ids: 'D', valid_props: 'Px4'): """ :return: neg_boxes: Nx4 """ if len(pos_chip_ids) == 0: return valid_props pos_chips = chips[pos_chip_ids] iob = intersection_over_box(pos_chips, valid_props) overlap_per_prop = np.max(iob, axis=0) non_overlap_props_idx = overlap_per_prop < 0.5 neg_boxes = valid_props[non_overlap_props_idx] return neg_boxes def _find_neg_chips(self, chips: 'Cx4', pos_chip_ids: 'D', neg_boxes: 'Nx4'): """ :return: neg_chip2box_num, dict: chipid->neg_box_num """ neg_chip_ids = np.setdiff1d(np.arange(len(chips)), pos_chip_ids) neg_chips = chips[neg_chip_ids] iob = intersection_over_box(neg_chips, neg_boxes) iob_threshold_to_find_chips = 0.7 chosen_neg_chip_ids, chip_id2overlap_box_num = \ self._find_chips_to_cover_overlaped_boxes(iob, iob_threshold_to_find_chips) neg_chipid2box_num = {} for cid in chosen_neg_chip_ids: box_num = chip_id2overlap_box_num[cid] raw_chip_id = neg_chip_ids[cid] neg_chipid2box_num[raw_chip_id] = box_num return neg_chipid2box_num def crop_infer_anno_records(self, records: List[dict]): """ transform image record to chips record :param records: :return: new_records, list of dict like { 'im_file': 'fake_image1.jpg', 'im_id': np.array([1]), # new _global_chip_id as im_id 'h': h, # chip height 'w': w, # chip width 'chip': [x1, y1, x2, y2] # added 'ori_im_h': ori_im_h # added, origin image height 'ori_im_w': ori_im_w # added, origin image width 'scale_i': 0 # added, } """ self.chip_records = [] self._global_chip_id = 1 # im_id start from 1 self._global_chip_id2img_id = {} for r in records: for scale_i in range(self.scale_num): self._get_current_scale_parameters(scale_i, r) # Cx4 chips = self._create_chips(r['h'], r['w'], self._cur_scale) cur_img_chip_record = self._get_chips_records(r, chips, scale_i) self.chip_records.extend(cur_img_chip_record) return self.chip_records def _get_chips_records(self, rec, chips, scale_i): cur_img_chip_records = [] ori_im_h = rec["h"] ori_im_w = rec["w"] im_file = rec["im_file"] ori_im_id = rec["im_id"] for id, chip in enumerate(chips): chip_rec = {} x1, y1, x2, y2 = chip chip_h = y2 - y1 chip_w = x2 - x1 chip_rec["im_file"] = im_file chip_rec["im_id"] = self._global_chip_id chip_rec["h"] = chip_h chip_rec["w"] = chip_w chip_rec["chip"] = chip chip_rec["ori_im_h"] = ori_im_h chip_rec["ori_im_w"] = ori_im_w chip_rec["scale_i"] = scale_i self._global_chip_id2img_id[self._global_chip_id] = int(ori_im_id) self._global_chip_id += 1 cur_img_chip_records.append(chip_rec) return cur_img_chip_records def aggregate_chips_detections(self, results, records=None): """ # 1. transform chip dets to image dets # 2. nms boxes per image; # 3. format output results :param results: :param roidb: :return: """ results = deepcopy(results) records = records if records else self.chip_records img_id2bbox = self._transform_chip2image_bboxes(results, records) nms_img_id2bbox = self._nms_dets(img_id2bbox) aggregate_results = self._reformat_results(nms_img_id2bbox) return aggregate_results def _transform_chip2image_bboxes(self, results, records): # 1. Transform chip dets to image dets; # 2. Filter valid range; # 3. Reformat and Aggregate chip dets to Get scale_cls_dets img_id2bbox = defaultdict(list) for result in results: bbox_locs = result['bbox'] bbox_nums = result['bbox_num'] if len(bbox_locs) == 1 and bbox_locs[0][ 0] == -1: # current batch has no detections # bbox_locs = array([[-1.]], dtype=float32); bbox_nums = [[1]] # MultiClassNMS output: If there is no detected boxes for all images, lod will be set to {1} and Out only contains one value which is -1. continue im_ids = result['im_id'] # replace with range(len(bbox_nums)) last_bbox_num = 0 for idx, im_id in enumerate(im_ids): cur_bbox_len = bbox_nums[idx] bboxes = bbox_locs[last_bbox_num:last_bbox_num + cur_bbox_len] last_bbox_num += cur_bbox_len # box: [num_id, score, xmin, ymin, xmax, ymax] if len(bboxes) == 0: # current image has no detections continue chip_rec = records[int(im_id) - 1] # im_id starts from 1, type is np.int64 image_size = max(chip_rec["ori_im_h"], chip_rec["ori_im_w"]) bboxes = transform_chip_boxes2image_boxes( bboxes, chip_rec["chip"], chip_rec["ori_im_h"], chip_rec["ori_im_w"]) scale_i = chip_rec["scale_i"] cur_scale = self._get_current_scale(self.target_sizes[scale_i], image_size) _, valid_boxes_idx = self._validate_boxes( self.valid_box_ratio_ranges[scale_i], image_size, bboxes[:, 2:], cur_scale) ori_img_id = self._global_chip_id2img_id[int(im_id)] img_id2bbox[ori_img_id].append(bboxes[valid_boxes_idx]) return img_id2bbox def _nms_dets(self, img_id2bbox): # 1. NMS on each image-class # 2. Limit number of detections to MAX_PER_IMAGE if requested max_per_img = self.max_per_img nms_thresh = self.nms_thresh for img_id in img_id2bbox: box = img_id2bbox[ img_id] # list of np.array of shape [N, 6], 6 is [label, score, x1, y1, x2, y2] box = np.concatenate(box, axis=0) nms_dets = nms(box, nms_thresh) if max_per_img > 0: if len(nms_dets) > max_per_img: keep = np.argsort(-nms_dets[:, 1])[:max_per_img] nms_dets = nms_dets[keep] img_id2bbox[img_id] = nms_dets return img_id2bbox def _reformat_results(self, img_id2bbox): """reformat results""" im_ids = img_id2bbox.keys() results = [] for img_id in im_ids: # output by original im_id order if len(img_id2bbox[img_id]) == 0: bbox = np.array( [[-1., 0., 0., 0., 0., 0.]]) # edge case: no detections bbox_num = np.array([0]) else: # np.array of shape [N, 6], 6 is [label, score, x1, y1, x2, y2] bbox = img_id2bbox[img_id] bbox_num = np.array([len(bbox)]) res = dict(im_id=np.array([[img_id]]), bbox=bbox, bbox_num=bbox_num) results.append(res) return results

PaddleDetection/ppdet/data/crop_utils/annotation_cropper.py/0

# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import operators from . import batch_operators from . import keypoint_operators from . import mot_operators from . import rotated_operators from . import keypoints_3d_operators from . import culane_operators from .operators import * from .batch_operators import * from .keypoint_operators import * from .mot_operators import * from .rotated_operators import * from .keypoints_3d_operators import * from .culane_operators import * __all__ = [] __all__ += registered_ops __all__ += keypoint_operators.__all__ __all__ += mot_operators.__all__ __all__ += culane_operators.__all__

PaddleDetection/ppdet/data/transform/__init__.py/0

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import yaml from collections import OrderedDict import paddle from ppdet.data.source.category import get_categories from ppdet.utils.logger import setup_logger logger = setup_logger('ppdet.engine') # Global dictionary TRT_MIN_SUBGRAPH = { 'YOLO': 3, 'PPYOLOE': 3, 'SSD': 60, 'RCNN': 40, 'RetinaNet': 40, 'S2ANet': 80, 'EfficientDet': 40, 'Face': 3, 'TTFNet': 60, 'FCOS': 16, 'SOLOv2': 60, 'HigherHRNet': 3, 'HRNet': 3, 'DeepSORT': 3, 'ByteTrack': 10, 'CenterTrack': 5, 'JDE': 10, 'FairMOT': 5, 'GFL': 16, 'PicoDet': 3, 'CenterNet': 5, 'TOOD': 5, 'YOLOX': 8, 'YOLOF': 40, 'METRO_Body': 3, 'DETR': 3, 'CLRNet': 3 } KEYPOINT_ARCH = ['HigherHRNet', 'TopDownHRNet'] MOT_ARCH = ['JDE', 'FairMOT', 'DeepSORT', 'ByteTrack', 'CenterTrack'] LANE_ARCH = ['CLRNet'] TO_STATIC_SPEC = { 'yolov3_darknet53_270e_coco': [{ 'im_id': paddle.static.InputSpec( name='im_id', shape=[-1, 1], dtype='float32'), 'is_crowd': paddle.static.InputSpec( name='is_crowd', shape=[-1, 50], dtype='float32'), 'gt_bbox': paddle.static.InputSpec( name='gt_bbox', shape=[-1, 50, 4], dtype='float32'), 'curr_iter': paddle.static.InputSpec( name='curr_iter', shape=[-1], dtype='float32'), 'image': paddle.static.InputSpec( name='image', shape=[-1, 3, -1, -1], dtype='float32'), 'im_shape': paddle.static.InputSpec( name='im_shape', shape=[-1, 2], dtype='float32'), 'scale_factor': paddle.static.InputSpec( name='scale_factor', shape=[-1, 2], dtype='float32'), 'target0': paddle.static.InputSpec( name='target0', shape=[-1, 3, 86, -1, -1], dtype='float32'), 'target1': paddle.static.InputSpec( name='target1', shape=[-1, 3, 86, -1, -1], dtype='float32'), 'target2': paddle.static.InputSpec( name='target2', shape=[-1, 3, 86, -1, -1], dtype='float32'), }], 'tinypose_128x96': [{ 'center': paddle.static.InputSpec( name='center', shape=[-1, 2], dtype='float32'), 'scale': paddle.static.InputSpec( name='scale', shape=[-1, 2], dtype='float32'), 'im_id': paddle.static.InputSpec( name='im_id', shape=[-1, 1], dtype='float32'), 'image': paddle.static.InputSpec( name='image', shape=[-1, 3, 128, 96], dtype='float32'), 'score': paddle.static.InputSpec( name='score', shape=[-1], dtype='float32'), 'rotate': paddle.static.InputSpec( name='rotate', shape=[-1], dtype='float32'), 'target': paddle.static.InputSpec( name='target', shape=[-1, 17, 32, 24], dtype='float32'), 'target_weight': paddle.static.InputSpec( name='target_weight', shape=[-1, 17, 1], dtype='float32'), }], 'fcos_r50_fpn_1x_coco': [{ 'im_id': paddle.static.InputSpec( name='im_id', shape=[-1, 1], dtype='float32'), 'curr_iter': paddle.static.InputSpec( name='curr_iter', shape=[-1], dtype='float32'), 'image': paddle.static.InputSpec( name='image', shape=[-1, 3, -1, -1], dtype='float32'), 'im_shape': paddle.static.InputSpec( name='im_shape', shape=[-1, 2], dtype='float32'), 'scale_factor': paddle.static.InputSpec( name='scale_factor', shape=[-1, 2], dtype='float32'), 'reg_target0': paddle.static.InputSpec( name='reg_target0', shape=[-1, 160, 160, 4], dtype='float32'), 'labels0': paddle.static.InputSpec( name='labels0', shape=[-1, 160, 160, 1], dtype='int32'), 'centerness0': paddle.static.InputSpec( name='centerness0', shape=[-1, 160, 160, 1], dtype='float32'), 'reg_target1': paddle.static.InputSpec( name='reg_target1', shape=[-1, 80, 80, 4], dtype='float32'), 'labels1': paddle.static.InputSpec( name='labels1', shape=[-1, 80, 80, 1], dtype='int32'), 'centerness1': paddle.static.InputSpec( name='centerness1', shape=[-1, 80, 80, 1], dtype='float32'), 'reg_target2': paddle.static.InputSpec( name='reg_target2', shape=[-1, 40, 40, 4], dtype='float32'), 'labels2': paddle.static.InputSpec( name='labels2', shape=[-1, 40, 40, 1], dtype='int32'), 'centerness2': paddle.static.InputSpec( name='centerness2', shape=[-1, 40, 40, 1], dtype='float32'), 'reg_target3': paddle.static.InputSpec( name='reg_target3', shape=[-1, 20, 20, 4], dtype='float32'), 'labels3': paddle.static.InputSpec( name='labels3', shape=[-1, 20, 20, 1], dtype='int32'), 'centerness3': paddle.static.InputSpec( name='centerness3', shape=[-1, 20, 20, 1], dtype='float32'), 'reg_target4': paddle.static.InputSpec( name='reg_target4', shape=[-1, 10, 10, 4], dtype='float32'), 'labels4': paddle.static.InputSpec( name='labels4', shape=[-1, 10, 10, 1], dtype='int32'), 'centerness4': paddle.static.InputSpec( name='centerness4', shape=[-1, 10, 10, 1], dtype='float32'), }], 'picodet_s_320_coco_lcnet': [{ 'im_id': paddle.static.InputSpec( name='im_id', shape=[-1, 1], dtype='float32'), 'is_crowd': paddle.static.InputSpec( name='is_crowd', shape=[-1, -1, 1], dtype='float32'), 'gt_class': paddle.static.InputSpec( name='gt_class', shape=[-1, -1, 1], dtype='int32'), 'gt_bbox': paddle.static.InputSpec( name='gt_bbox', shape=[-1, -1, 4], dtype='float32'), 'curr_iter': paddle.static.InputSpec( name='curr_iter', shape=[-1], dtype='float32'), 'image': paddle.static.InputSpec( name='image', shape=[-1, 3, -1, -1], dtype='float32'), 'im_shape': paddle.static.InputSpec( name='im_shape', shape=[-1, 2], dtype='float32'), 'scale_factor': paddle.static.InputSpec( name='scale_factor', shape=[-1, 2], dtype='float32'), 'pad_gt_mask': paddle.static.InputSpec( name='pad_gt_mask', shape=[-1, -1, 1], dtype='float32'), }], 'ppyoloe_crn_s_300e_coco': [{ 'im_id': paddle.static.InputSpec( name='im_id', shape=[-1, 1], dtype='float32'), 'is_crowd': paddle.static.InputSpec( name='is_crowd', shape=[-1, -1, 1], dtype='float32'), 'gt_class': paddle.static.InputSpec( name='gt_class', shape=[-1, -1, 1], dtype='int32'), 'gt_bbox': paddle.static.InputSpec( name='gt_bbox', shape=[-1, -1, 4], dtype='float32'), 'curr_iter': paddle.static.InputSpec( name='curr_iter', shape=[-1], dtype='float32'), 'image': paddle.static.InputSpec( name='image', shape=[-1, 3, -1, -1], dtype='float32'), 'im_shape': paddle.static.InputSpec( name='im_shape', shape=[-1, 2], dtype='float32'), 'scale_factor': paddle.static.InputSpec( name='scale_factor', shape=[-1, 2], dtype='float32'), 'pad_gt_mask': paddle.static.InputSpec( name='pad_gt_mask', shape=[-1, -1, 1], dtype='float32'), }], } def apply_to_static(config, model): filename = config.get('filename', None) spec = TO_STATIC_SPEC.get(filename, None) model = paddle.jit.to_static(model, input_spec=spec) logger.info("Successfully to apply @to_static with specs: {}".format(spec)) return model def _prune_input_spec(input_spec, program, targets): # try to prune static program to figure out pruned input spec # so we perform following operations in static mode device = paddle.get_device() paddle.enable_static() paddle.set_device(device) pruned_input_spec = [{}] program = program.clone() program = program._prune(targets=targets) global_block = program.global_block() for name, spec in input_spec[0].items(): try: v = global_block.var(name) pruned_input_spec[0][name] = spec except Exception: pass paddle.disable_static(place=device) return pruned_input_spec def _parse_reader(reader_cfg, dataset_cfg, metric, arch, image_shape): preprocess_list = [] label_list = [] if arch != "lane_arch": anno_file = dataset_cfg.get_anno() clsid2catid, catid2name = get_categories(metric, anno_file, arch) label_list = [str(cat) for cat in catid2name.values()] fuse_normalize = reader_cfg.get('fuse_normalize', False) sample_transforms = reader_cfg['sample_transforms'] for st in sample_transforms[1:]: for key, value in st.items(): p = {'type': key} if key == 'Resize': if int(image_shape[1]) != -1: value['target_size'] = image_shape[1:] value['interp'] = value.get('interp', 1) # cv2.INTER_LINEAR if fuse_normalize and key == 'NormalizeImage': continue p.update(value) preprocess_list.append(p) batch_transforms = reader_cfg.get('batch_transforms', None) if batch_transforms: for bt in batch_transforms: for key, value in bt.items(): # for deploy/infer, use PadStride(stride) instead PadBatch(pad_to_stride) if key == 'PadBatch': preprocess_list.append({ 'type': 'PadStride', 'stride': value['pad_to_stride'] }) break elif key == "CULaneResize": # cut and resize p = {'type': key} p.update(value) p.update({"cut_height": dataset_cfg.cut_height}) preprocess_list.append(p) break return preprocess_list, label_list def _parse_tracker(tracker_cfg): tracker_params = {} for k, v in tracker_cfg.items(): tracker_params.update({k: v}) return tracker_params def _dump_infer_config(config, path, image_shape, model): arch_state = False from ppdet.core.config.yaml_helpers import setup_orderdict setup_orderdict() use_dynamic_shape = True if image_shape[2] == -1 else False infer_cfg = OrderedDict({ 'mode': 'paddle', 'draw_threshold': 0.5, 'metric': config['metric'], 'use_dynamic_shape': use_dynamic_shape }) export_onnx = config.get('export_onnx', False) export_eb = config.get('export_eb', False) infer_arch = config['architecture'] if 'RCNN' in infer_arch and export_onnx: logger.warning( "Exporting RCNN model to ONNX only support batch_size = 1") infer_cfg['export_onnx'] = True infer_cfg['export_eb'] = export_eb if infer_arch in MOT_ARCH: if infer_arch == 'DeepSORT': tracker_cfg = config['DeepSORTTracker'] elif infer_arch == 'CenterTrack': tracker_cfg = config['CenterTracker'] else: tracker_cfg = config['JDETracker'] infer_cfg['tracker'] = _parse_tracker(tracker_cfg) for arch, min_subgraph_size in TRT_MIN_SUBGRAPH.items(): if arch in infer_arch: infer_cfg['arch'] = arch infer_cfg['min_subgraph_size'] = min_subgraph_size arch_state = True break if infer_arch == 'PPYOLOEWithAuxHead': infer_arch = 'PPYOLOE' if infer_arch in ['PPYOLOE', 'YOLOX', 'YOLOF']: infer_cfg['arch'] = infer_arch infer_cfg['min_subgraph_size'] = TRT_MIN_SUBGRAPH[infer_arch] arch_state = True if not arch_state: logger.error( 'Architecture: {} is not supported for exporting model now.\n'. format(infer_arch) + 'Please set TRT_MIN_SUBGRAPH in ppdet/engine/export_utils.py') os._exit(0) if 'mask_head' in config[config['architecture']] and config[config[ 'architecture']]['mask_head']: infer_cfg['mask'] = True label_arch = 'detection_arch' if infer_arch in KEYPOINT_ARCH: label_arch = 'keypoint_arch' if infer_arch in LANE_ARCH: infer_cfg['arch'] = infer_arch infer_cfg['min_subgraph_size'] = TRT_MIN_SUBGRAPH[infer_arch] infer_cfg['img_w'] = config['img_w'] infer_cfg['ori_img_h'] = config['ori_img_h'] infer_cfg['cut_height'] = config['cut_height'] label_arch = 'lane_arch' head_name = "CLRHead" infer_cfg['conf_threshold'] = config[head_name]['conf_threshold'] infer_cfg['nms_thres'] = config[head_name]['nms_thres'] infer_cfg['max_lanes'] = config[head_name]['max_lanes'] infer_cfg['num_points'] = config[head_name]['num_points'] arch_state = True if infer_arch in MOT_ARCH: if config['metric'] in ['COCO', 'VOC']: # MOT model run as Detector reader_cfg = config['TestReader'] dataset_cfg = config['TestDataset'] else: # 'metric' in ['MOT', 'MCMOT', 'KITTI'] label_arch = 'mot_arch' reader_cfg = config['TestMOTReader'] dataset_cfg = config['TestMOTDataset'] else: reader_cfg = config['TestReader'] dataset_cfg = config['TestDataset'] infer_cfg['Preprocess'], infer_cfg['label_list'] = _parse_reader( reader_cfg, dataset_cfg, config['metric'], label_arch, image_shape[1:]) if infer_arch == 'PicoDet': if hasattr(config, 'export') and config['export'].get( 'post_process', False) and not config['export'].get('benchmark', False): infer_cfg['arch'] = 'GFL' head_name = 'PicoHeadV2' if config['PicoHeadV2'] else 'PicoHead' infer_cfg['NMS'] = config[head_name]['nms'] # In order to speed up the prediction, the threshold of nms # is adjusted here, which can be changed in infer_cfg.yml config[head_name]['nms']["score_threshold"] = 0.3 config[head_name]['nms']["nms_threshold"] = 0.5 infer_cfg['fpn_stride'] = config[head_name]['fpn_stride'] yaml.dump(infer_cfg, open(path, 'w')) logger.info("Export inference config file to {}".format(os.path.join(path)))

PaddleDetection/ppdet/engine/export_utils.py/0

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import paddle import ppdet import unittest # NOTE: weights downloading costs time, we choose # a small model for unittesting MODEL_NAME = 'ppyolo/ppyolo_tiny_650e_coco' class TestGetConfigFile(unittest.TestCase): def test_main(self): try: cfg_file = ppdet.model_zoo.get_config_file(MODEL_NAME) assert os.path.isfile(cfg_file) except: self.assertTrue(False) class TestGetModel(unittest.TestCase): def test_main(self): try: model = ppdet.model_zoo.get_model(MODEL_NAME) assert isinstance(model, paddle.nn.Layer) except: self.assertTrue(False) if __name__ == '__main__': unittest.main()

PaddleDetection/ppdet/model_zoo/tests/test_get_model.py/0

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import paddle from ppdet.core.workspace import register, create from .meta_arch import BaseArch __all__ = ['GFL'] @register class GFL(BaseArch): """ Generalized Focal Loss network, see https://arxiv.org/abs/2006.04388 Args: backbone (object): backbone instance neck (object): 'FPN' instance head (object): 'GFLHead' instance """ __category__ = 'architecture' def __init__(self, backbone, neck, head='GFLHead'): super(GFL, self).__init__() self.backbone = backbone self.neck = neck self.head = head @classmethod def from_config(cls, cfg, *args, **kwargs): backbone = create(cfg['backbone']) kwargs = {'input_shape': backbone.out_shape} neck = create(cfg['neck'], **kwargs) kwargs = {'input_shape': neck.out_shape} head = create(cfg['head'], **kwargs) return { 'backbone': backbone, 'neck': neck, "head": head, } def _forward(self): body_feats = self.backbone(self.inputs) fpn_feats = self.neck(body_feats) head_outs = self.head(fpn_feats) if not self.training: im_shape = self.inputs['im_shape'] scale_factor = self.inputs['scale_factor'] bboxes, bbox_num = self.head.post_process(head_outs, im_shape, scale_factor) return bboxes, bbox_num else: return head_outs def get_loss(self, ): loss = {} head_outs = self._forward() loss_gfl = self.head.get_loss(head_outs, self.inputs) loss.update(loss_gfl) total_loss = paddle.add_n(list(loss.values())) loss.update({'loss': total_loss}) return loss def get_pred(self): bbox_pred, bbox_num = self._forward() output = {'bbox': bbox_pred, 'bbox_num': bbox_num} return output

PaddleDetection/ppdet/modeling/architectures/gfl.py/0

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function from ppdet.core.workspace import register, create from .meta_arch import BaseArch __all__ = ["SparseRCNN"] @register class SparseRCNN(BaseArch): __category__ = 'architecture' __inject__ = ["postprocess"] def __init__(self, backbone, neck, head="SparsercnnHead", postprocess="SparsePostProcess"): super(SparseRCNN, self).__init__() self.backbone = backbone self.neck = neck self.head = head self.postprocess = postprocess @classmethod def from_config(cls, cfg, *args, **kwargs): backbone = create(cfg['backbone']) kwargs = {'input_shape': backbone.out_shape} neck = create(cfg['neck'], **kwargs) kwargs = {'roi_input_shape': neck.out_shape} head = create(cfg['head'], **kwargs) return { 'backbone': backbone, 'neck': neck, "head": head, } def _forward(self): body_feats = self.backbone(self.inputs) fpn_feats = self.neck(body_feats) head_outs = self.head(fpn_feats, self.inputs["img_whwh"]) if not self.training: bbox_pred, bbox_num = self.postprocess( head_outs["pred_logits"], head_outs["pred_boxes"], self.inputs["scale_factor_whwh"], self.inputs["ori_shape"]) return bbox_pred, bbox_num else: return head_outs def get_loss(self): batch_gt_class = self.inputs["gt_class"] batch_gt_box = self.inputs["gt_bbox"] batch_whwh = self.inputs["img_whwh"] targets = [] for i in range(len(batch_gt_class)): boxes = batch_gt_box[i] labels = batch_gt_class[i].squeeze(-1) img_whwh = batch_whwh[i] img_whwh_tgt = img_whwh.unsqueeze(0).tile([int(boxes.shape[0]), 1]) targets.append({ "boxes": boxes, "labels": labels, "img_whwh": img_whwh, "img_whwh_tgt": img_whwh_tgt }) outputs = self._forward() loss_dict = self.head.get_loss(outputs, targets) acc = loss_dict["acc"] loss_dict.pop("acc") total_loss = sum(loss_dict.values()) loss_dict.update({"loss": total_loss, "acc": acc}) return loss_dict def get_pred(self): bbox_pred, bbox_num = self._forward() output = {'bbox': bbox_pred, 'bbox_num': bbox_num} return output

PaddleDetection/ppdet/modeling/architectures/sparse_rcnn.py/0

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import paddle import paddle.nn as nn import paddle.nn.functional as F from ppdet.core.workspace import register from ..bbox_utils import batch_iou_similarity from .utils import (gather_topk_anchors, check_points_inside_bboxes, compute_max_iou_anchor) __all__ = ['TaskAlignedAssigner'] def is_close_gt(anchor, gt, stride_lst, max_dist=2.0, alpha=2.): """Calculate distance ratio of box1 and box2 in batch for larger stride anchors dist/stride to promote the survive of large distance match Args: anchor (Tensor): box with the shape [L, 2] gt (Tensor): box with the shape [N, M2, 4] Return: dist (Tensor): dist ratio between box1 and box2 with the shape [N, M1, M2] """ center1 = anchor.unsqueeze(0) center2 = (gt[..., :2] + gt[..., -2:]) / 2. center1 = center1.unsqueeze(1) # [N, M1, 2] -> [N, 1, M1, 2] center2 = center2.unsqueeze(2) # [N, M2, 2] -> [N, M2, 1, 2] stride = paddle.concat([ paddle.full([x], 32 / pow(2, idx)) for idx, x in enumerate(stride_lst) ]).unsqueeze(0).unsqueeze(0) dist = paddle.linalg.norm(center1 - center2, p=2, axis=-1) / stride dist_ratio = dist dist_ratio[dist < max_dist] = 1. dist_ratio[dist >= max_dist] = 0. return dist_ratio @register class TaskAlignedAssigner(nn.Layer): """TOOD: Task-aligned One-stage Object Detection """ def __init__(self, topk=13, alpha=1.0, beta=6.0, eps=1e-9, is_close_gt=False): super(TaskAlignedAssigner, self).__init__() self.topk = topk self.alpha = alpha self.beta = beta self.eps = eps self.is_close_gt = is_close_gt @paddle.no_grad() def forward(self, pred_scores, pred_bboxes, anchor_points, num_anchors_list, gt_labels, gt_bboxes, pad_gt_mask, bg_index, gt_scores=None): r"""This code is based on https://github.com/fcjian/TOOD/blob/master/mmdet/core/bbox/assigners/task_aligned_assigner.py The assignment is done in following steps 1. compute alignment metric between all bbox (bbox of all pyramid levels) and gt 2. select top-k bbox as candidates for each gt 3. limit the positive sample's center in gt (because the anchor-free detector only can predict positive distance) 4. if an anchor box is assigned to multiple gts, the one with the highest iou will be selected. Args: pred_scores (Tensor, float32): predicted class probability, shape(B, L, C) pred_bboxes (Tensor, float32): predicted bounding boxes, shape(B, L, 4) anchor_points (Tensor, float32): pre-defined anchors, shape(L, 2), "cxcy" format num_anchors_list (List): num of anchors in each level, shape(L) gt_labels (Tensor, int64|int32): Label of gt_bboxes, shape(B, n, 1) gt_bboxes (Tensor, float32): Ground truth bboxes, shape(B, n, 4) pad_gt_mask (Tensor, float32): 1 means bbox, 0 means no bbox, shape(B, n, 1) bg_index (int): background index gt_scores (Tensor|None, float32) Score of gt_bboxes, shape(B, n, 1) Returns: assigned_labels (Tensor): (B, L) assigned_bboxes (Tensor): (B, L, 4) assigned_scores (Tensor): (B, L, C) """ assert pred_scores.ndim == pred_bboxes.ndim assert gt_labels.ndim == gt_bboxes.ndim and \ gt_bboxes.ndim == 3 batch_size, num_anchors, num_classes = pred_scores.shape _, num_max_boxes, _ = gt_bboxes.shape # negative batch if num_max_boxes == 0: assigned_labels = paddle.full( [batch_size, num_anchors], bg_index, dtype='int32') assigned_bboxes = paddle.zeros([batch_size, num_anchors, 4]) assigned_scores = paddle.zeros( [batch_size, num_anchors, num_classes]) return assigned_labels, assigned_bboxes, assigned_scores # compute iou between gt and pred bbox, [B, n, L] ious = batch_iou_similarity(gt_bboxes, pred_bboxes) # gather pred bboxes class score pred_scores = pred_scores.transpose([0, 2, 1]) batch_ind = paddle.arange( end=batch_size, dtype=gt_labels.dtype).unsqueeze(-1) gt_labels_ind = paddle.stack( [batch_ind.tile([1, num_max_boxes]), gt_labels.squeeze(-1)], axis=-1) bbox_cls_scores = paddle.gather_nd(pred_scores, gt_labels_ind) # compute alignment metrics, [B, n, L] alignment_metrics = bbox_cls_scores.pow(self.alpha) * ious.pow( self.beta) # check the positive sample's center in gt, [B, n, L] if self.is_close_gt: is_in_gts = is_close_gt(anchor_points, gt_bboxes, num_anchors_list) else: is_in_gts = check_points_inside_bboxes(anchor_points, gt_bboxes) # select topk largest alignment metrics pred bbox as candidates # for each gt, [B, n, L] is_in_topk = gather_topk_anchors( alignment_metrics * is_in_gts, self.topk, topk_mask=pad_gt_mask) # select positive sample, [B, n, L] mask_positive = is_in_topk * is_in_gts * pad_gt_mask # if an anchor box is assigned to multiple gts, # the one with the highest iou will be selected, [B, n, L] mask_positive_sum = mask_positive.sum(axis=-2) if mask_positive_sum.max() > 1: mask_multiple_gts = (mask_positive_sum.unsqueeze(1) > 1).tile( [1, num_max_boxes, 1]) is_max_iou = compute_max_iou_anchor(ious) mask_positive = paddle.where(mask_multiple_gts, is_max_iou, mask_positive) mask_positive_sum = mask_positive.sum(axis=-2) assigned_gt_index = mask_positive.argmax(axis=-2) # assigned target assigned_gt_index = assigned_gt_index + batch_ind * num_max_boxes assigned_labels = paddle.gather( gt_labels.flatten(), assigned_gt_index.flatten(), axis=0) assigned_labels = assigned_labels.reshape([batch_size, num_anchors]) assigned_labels = paddle.where( mask_positive_sum > 0, assigned_labels, paddle.full_like(assigned_labels, bg_index)) assigned_bboxes = paddle.gather( gt_bboxes.reshape([-1, 4]), assigned_gt_index.flatten(), axis=0) assigned_bboxes = assigned_bboxes.reshape([batch_size, num_anchors, 4]) assigned_scores = F.one_hot(assigned_labels, num_classes + 1) ind = list(range(num_classes + 1)) ind.remove(bg_index) assigned_scores = paddle.index_select( assigned_scores, paddle.to_tensor(ind), axis=-1) # rescale alignment metrics alignment_metrics *= mask_positive max_metrics_per_instance = alignment_metrics.max(axis=-1, keepdim=True) max_ious_per_instance = (ious * mask_positive).max(axis=-1, keepdim=True) alignment_metrics = alignment_metrics / ( max_metrics_per_instance + self.eps) * max_ious_per_instance alignment_metrics = alignment_metrics.max(-2).unsqueeze(-1) assigned_scores = assigned_scores * alignment_metrics return assigned_labels, assigned_bboxes, assigned_scores

PaddleDetection/ppdet/modeling/assigners/task_aligned_assigner.py/0

# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import paddle import paddle.nn as nn import paddle.nn.functional as F from paddle.nn.initializer import KaimingNormal, Constant from paddle.nn import Conv2D, BatchNorm2D, ReLU, AdaptiveAvgPool2D, MaxPool2D from paddle.regularizer import L2Decay from paddle import ParamAttr import copy from ppdet.core.workspace import register, serializable from ..shape_spec import ShapeSpec __all__ = ['PPHGNetV2'] kaiming_normal_ = KaimingNormal() zeros_ = Constant(value=0.) ones_ = Constant(value=1.) class LearnableAffineBlock(nn.Layer): def __init__(self, scale_value=1.0, bias_value=0.0, lr_mult=1.0, lab_lr=0.01): super().__init__() self.scale = self.create_parameter( shape=[1, ], default_initializer=Constant(value=scale_value), attr=ParamAttr(learning_rate=lr_mult * lab_lr)) self.add_parameter("scale", self.scale) self.bias = self.create_parameter( shape=[1, ], default_initializer=Constant(value=bias_value), attr=ParamAttr(learning_rate=lr_mult * lab_lr)) self.add_parameter("bias", self.bias) def forward(self, x): return self.scale * x + self.bias class ConvBNAct(nn.Layer): def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, groups=1, use_act=True, use_lab=False, lr_mult=1.0): super().__init__() self.use_act = use_act self.use_lab = use_lab self.conv = Conv2D( in_channels, out_channels, kernel_size, stride, padding=padding if isinstance(padding, str) else (kernel_size - 1) // 2, groups=groups, weight_attr=ParamAttr(learning_rate=lr_mult), bias_attr=False) self.bn = BatchNorm2D( out_channels, weight_attr=ParamAttr( regularizer=L2Decay(0.0), learning_rate=lr_mult), bias_attr=ParamAttr( regularizer=L2Decay(0.0), learning_rate=lr_mult)) if self.use_act: self.act = ReLU() if self.use_lab: self.lab = LearnableAffineBlock(lr_mult=lr_mult) def forward(self, x): x = self.conv(x) x = self.bn(x) if self.use_act: x = self.act(x) if self.use_lab: x = self.lab(x) return x class LightConvBNAct(nn.Layer): def __init__(self, in_channels, out_channels, kernel_size, stride, groups=1, use_lab=False, lr_mult=1.0): super().__init__() self.conv1 = ConvBNAct( in_channels=in_channels, out_channels=out_channels, kernel_size=1, use_act=False, use_lab=use_lab, lr_mult=lr_mult) self.conv2 = ConvBNAct( in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_size, groups=out_channels, use_act=True, use_lab=use_lab, lr_mult=lr_mult) def forward(self, x): x = self.conv1(x) x = self.conv2(x) return x class StemBlock(nn.Layer): def __init__(self, in_channels, mid_channels, out_channels, use_lab=False, lr_mult=1.0): super().__init__() self.stem1 = ConvBNAct( in_channels=in_channels, out_channels=mid_channels, kernel_size=3, stride=2, use_lab=use_lab, lr_mult=lr_mult) self.stem2a = ConvBNAct( in_channels=mid_channels, out_channels=mid_channels // 2, kernel_size=2, stride=1, padding="SAME", use_lab=use_lab, lr_mult=lr_mult) self.stem2b = ConvBNAct( in_channels=mid_channels // 2, out_channels=mid_channels, kernel_size=2, stride=1, padding="SAME", use_lab=use_lab, lr_mult=lr_mult) self.stem3 = ConvBNAct( in_channels=mid_channels * 2, out_channels=mid_channels, kernel_size=3, stride=2, use_lab=use_lab, lr_mult=lr_mult) self.stem4 = ConvBNAct( in_channels=mid_channels, out_channels=out_channels, kernel_size=1, stride=1, use_lab=use_lab, lr_mult=lr_mult) self.pool = nn.MaxPool2D( kernel_size=2, stride=1, ceil_mode=True, padding="SAME") def forward(self, x): x = self.stem1(x) x2 = self.stem2a(x) x2 = self.stem2b(x2) x1 = self.pool(x) x = paddle.concat([x1, x2], 1) x = self.stem3(x) x = self.stem4(x) return x class HG_Block(nn.Layer): def __init__(self, in_channels, mid_channels, out_channels, kernel_size=3, layer_num=6, identity=False, light_block=True, use_lab=False, lr_mult=1.0): super().__init__() self.identity = identity self.layers = nn.LayerList() block_type = "LightConvBNAct" if light_block else "ConvBNAct" for i in range(layer_num): self.layers.append( eval(block_type)(in_channels=in_channels if i == 0 else mid_channels, out_channels=mid_channels, stride=1, kernel_size=kernel_size, use_lab=use_lab, lr_mult=lr_mult)) # feature aggregation total_channels = in_channels + layer_num * mid_channels self.aggregation_squeeze_conv = ConvBNAct( in_channels=total_channels, out_channels=out_channels // 2, kernel_size=1, stride=1, use_lab=use_lab, lr_mult=lr_mult) self.aggregation_excitation_conv = ConvBNAct( in_channels=out_channels // 2, out_channels=out_channels, kernel_size=1, stride=1, use_lab=use_lab, lr_mult=lr_mult) def forward(self, x): identity = x output = [] output.append(x) for layer in self.layers: x = layer(x) output.append(x) x = paddle.concat(output, axis=1) x = self.aggregation_squeeze_conv(x) x = self.aggregation_excitation_conv(x) if self.identity: x += identity return x class HG_Stage(nn.Layer): def __init__(self, in_channels, mid_channels, out_channels, block_num, layer_num=6, downsample=True, light_block=True, kernel_size=3, use_lab=False, lr_mult=1.0): super().__init__() self.downsample = downsample if downsample: self.downsample = ConvBNAct( in_channels=in_channels, out_channels=in_channels, kernel_size=3, stride=2, groups=in_channels, use_act=False, use_lab=use_lab, lr_mult=lr_mult) blocks_list = [] for i in range(block_num): blocks_list.append( HG_Block( in_channels=in_channels if i == 0 else out_channels, mid_channels=mid_channels, out_channels=out_channels, kernel_size=kernel_size, layer_num=layer_num, identity=False if i == 0 else True, light_block=light_block, use_lab=use_lab, lr_mult=lr_mult)) self.blocks = nn.Sequential(*blocks_list) def forward(self, x): if self.downsample: x = self.downsample(x) x = self.blocks(x) return x def _freeze_norm(m: nn.BatchNorm2D): param_attr = ParamAttr( learning_rate=0., regularizer=L2Decay(0.), trainable=False) bias_attr = ParamAttr( learning_rate=0., regularizer=L2Decay(0.), trainable=False) global_stats = True norm = nn.BatchNorm2D( m._num_features, weight_attr=param_attr, bias_attr=bias_attr, use_global_stats=global_stats) for param in norm.parameters(): param.stop_gradient = True return norm def reset_bn(model: nn.Layer, reset_func=_freeze_norm): if isinstance(model, nn.BatchNorm2D): model = reset_func(model) else: for name, child in model.named_children(): _child = reset_bn(child, reset_func) if _child is not child: setattr(model, name, _child) return model @register @serializable class PPHGNetV2(nn.Layer): """ PPHGNetV2 Args: stem_channels: list. Number of channels for the stem block. stage_type: str. The stage configuration of PPHGNet. such as the number of channels, stride, etc. use_lab: boolean. Whether to use LearnableAffineBlock in network. lr_mult_list: list. Control the learning rate of different stages. Returns: model: nn.Layer. Specific PPHGNetV2 model depends on args. """ arch_configs = { 'L': { 'stem_channels': [3, 32, 48], 'stage_config': { # in_channels, mid_channels, out_channels, num_blocks, downsample, light_block, kernel_size, layer_num "stage1": [48, 48, 128, 1, False, False, 3, 6], "stage2": [128, 96, 512, 1, True, False, 3, 6], "stage3": [512, 192, 1024, 3, True, True, 5, 6], "stage4": [1024, 384, 2048, 1, True, True, 5, 6], } }, 'X': { 'stem_channels': [3, 32, 64], 'stage_config': { # in_channels, mid_channels, out_channels, num_blocks, downsample, light_block, kernel_size, layer_num "stage1": [64, 64, 128, 1, False, False, 3, 6], "stage2": [128, 128, 512, 2, True, False, 3, 6], "stage3": [512, 256, 1024, 5, True, True, 5, 6], "stage4": [1024, 512, 2048, 2, True, True, 5, 6], } } } def __init__(self, arch, use_lab=False, lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0], return_idx=[1, 2, 3], freeze_stem_only=True, freeze_at=0, freeze_norm=True): super().__init__() self.use_lab = use_lab self.return_idx = return_idx stem_channels = self.arch_configs[arch]['stem_channels'] stage_config = self.arch_configs[arch]['stage_config'] self._out_strides = [4, 8, 16, 32] self._out_channels = [stage_config[k][2] for k in stage_config] # stem self.stem = StemBlock( in_channels=stem_channels[0], mid_channels=stem_channels[1], out_channels=stem_channels[2], use_lab=use_lab, lr_mult=lr_mult_list[0]) # stages self.stages = nn.LayerList() for i, k in enumerate(stage_config): in_channels, mid_channels, out_channels, block_num, downsample, light_block, kernel_size, layer_num = stage_config[ k] self.stages.append( HG_Stage( in_channels, mid_channels, out_channels, block_num, layer_num, downsample, light_block, kernel_size, use_lab, lr_mult=lr_mult_list[i + 1])) if freeze_at >= 0: self._freeze_parameters(self.stem) if not freeze_stem_only: for i in range(min(freeze_at + 1, len(self.stages))): self._freeze_parameters(self.stages[i]) if freeze_norm: reset_bn(self, reset_func=_freeze_norm) self._init_weights() def _freeze_parameters(self, m): for p in m.parameters(): p.stop_gradient = True def _init_weights(self): for m in self.sublayers(): if isinstance(m, nn.Conv2D): kaiming_normal_(m.weight) elif isinstance(m, (nn.BatchNorm2D)): ones_(m.weight) zeros_(m.bias) elif isinstance(m, nn.Linear): zeros_(m.bias) @property def out_shape(self): return [ ShapeSpec( channels=self._out_channels[i], stride=self._out_strides[i]) for i in self.return_idx ] def forward(self, inputs): x = inputs['image'] x = self.stem(x) outs = [] for idx, stage in enumerate(self.stages): x = stage(x) if idx in self.return_idx: outs.append(x) return outs

PaddleDetection/ppdet/modeling/backbones/hgnet_v2.py/0

# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import paddle import paddle.nn as nn import paddle.nn.functional as F import numpy as np from paddle.nn.initializer import Constant from ppdet.modeling.shape_spec import ShapeSpec from ppdet.core.workspace import register, serializable from .transformer_utils import zeros_, DropPath, Identity class Mlp(nn.Layer): def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class Attention(nn.Layer): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., window_size=None): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim**-0.5 self.qkv = nn.Linear(dim, dim * 3, bias_attr=False) if qkv_bias: self.q_bias = self.create_parameter( shape=([dim]), default_initializer=zeros_) self.v_bias = self.create_parameter( shape=([dim]), default_initializer=zeros_) else: self.q_bias = None self.v_bias = None if window_size: self.window_size = window_size self.num_relative_distance = (2 * window_size[0] - 1) * ( 2 * window_size[1] - 1) + 3 self.relative_position_bias_table = self.create_parameter( shape=(self.num_relative_distance, num_heads), default_initializer=zeros_) # 2*Wh-1 * 2*Ww-1, nH # cls to token & token 2 cls & cls to cls # get pair-wise relative position index for each token inside the window coords_h = paddle.arange(window_size[0]) coords_w = paddle.arange(window_size[1]) coords = paddle.stack(paddle.meshgrid( [coords_h, coords_w])) # 2, Wh, Ww coords_flatten = paddle.flatten(coords, 1) # 2, Wh*Ww coords_flatten_1 = paddle.unsqueeze(coords_flatten, 2) coords_flatten_2 = paddle.unsqueeze(coords_flatten, 1) relative_coords = coords_flatten_1.clone() - coords_flatten_2.clone( ) #relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Wh relative_coords = relative_coords.transpose( (1, 2, 0)) #.contiguous() # Wh*Ww, Wh*Ww, 2 relative_coords[:, :, 0] += window_size[ 0] - 1 # shift to start from 0 relative_coords[:, :, 1] += window_size[1] - 1 relative_coords[:, :, 0] *= 2 * window_size[1] - 1 relative_position_index = \ paddle.zeros(shape=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype) relative_position_index[1:, 1:] = relative_coords.sum( -1) # Wh*Ww, Wh*Ww relative_position_index[0, 0:] = self.num_relative_distance - 3 relative_position_index[0:, 0] = self.num_relative_distance - 2 relative_position_index[0, 0] = self.num_relative_distance - 1 self.register_buffer("relative_position_index", relative_position_index) # trunc_normal_(self.relative_position_bias_table, std=.0) else: self.window_size = None self.relative_position_bias_table = None self.relative_position_index = None self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, rel_pos_bias=None): x_shape = paddle.shape(x) N, C = x_shape[1], x_shape[2] qkv_bias = None if self.q_bias is not None: qkv_bias = paddle.concat( (self.q_bias, paddle.zeros_like(self.v_bias), self.v_bias)) qkv = F.linear(x, weight=self.qkv.weight, bias=qkv_bias) qkv = qkv.reshape((-1, N, 3, self.num_heads, C // self.num_heads)).transpose((2, 0, 3, 1, 4)) q, k, v = qkv[0], qkv[1], qkv[2] attn = (q.matmul(k.transpose((0, 1, 3, 2)))) * self.scale if self.relative_position_bias_table is not None: relative_position_bias = self.relative_position_bias_table[ self.relative_position_index.reshape([-1])].reshape([ self.window_size[0] * self.window_size[1] + 1, self.window_size[0] * self.window_size[1] + 1, -1 ]) # Wh*Ww,Wh*Ww,nH relative_position_bias = relative_position_bias.transpose( (2, 0, 1)) #.contiguous() # nH, Wh*Ww, Wh*Ww attn = attn + relative_position_bias.unsqueeze(0) if rel_pos_bias is not None: attn = attn + rel_pos_bias attn = nn.functional.softmax(attn, axis=-1) attn = self.attn_drop(attn) x = (attn.matmul(v)).transpose((0, 2, 1, 3)).reshape((-1, N, C)) x = self.proj(x) x = self.proj_drop(x) return x class Block(nn.Layer): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=0., window_size=None, init_values=None, act_layer=nn.GELU, norm_layer='nn.LayerNorm', epsilon=1e-5): super().__init__() self.norm1 = nn.LayerNorm(dim, epsilon=1e-6) self.attn = Attention( dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, window_size=window_size) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity() self.norm2 = eval(norm_layer)(dim, epsilon=epsilon) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) if init_values is not None: self.gamma_1 = self.create_parameter( shape=([dim]), default_initializer=Constant(value=init_values)) self.gamma_2 = self.create_parameter( shape=([dim]), default_initializer=Constant(value=init_values)) else: self.gamma_1, self.gamma_2 = None, None def forward(self, x, rel_pos_bias=None): if self.gamma_1 is None: x = x + self.drop_path( self.attn( self.norm1(x), rel_pos_bias=rel_pos_bias)) x = x + self.drop_path(self.mlp(self.norm2(x))) else: x = x + self.drop_path(self.gamma_1 * self.attn( self.norm1(x), rel_pos_bias=rel_pos_bias)) x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) return x class PatchEmbed(nn.Layer): """ Image to Patch Embedding """ def __init__(self, img_size=[224, 224], patch_size=16, in_chans=3, embed_dim=768): super().__init__() self.num_patches_w = img_size[0] // patch_size self.num_patches_h = img_size[1] // patch_size num_patches = self.num_patches_w * self.num_patches_h self.patch_shape = (img_size[0] // patch_size, img_size[1] // patch_size) self.img_size = img_size self.patch_size = patch_size self.num_patches = num_patches self.proj = nn.Conv2D( in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) @property def num_patches_in_h(self): return self.img_size[1] // self.patch_size @property def num_patches_in_w(self): return self.img_size[0] // self.patch_size def forward(self, x, mask=None): B, C, H, W = x.shape return self.proj(x) class RelativePositionBias(nn.Layer): def __init__(self, window_size, num_heads): super().__init__() self.window_size = window_size self.num_relative_distance = (2 * window_size[0] - 1) * ( 2 * window_size[1] - 1) + 3 self.relative_position_bias_table = self.create_parameter( shape=(self.num_relative_distance, num_heads), default_initialize=zeros_) # cls to token & token 2 cls & cls to cls # get pair-wise relative position index for each token inside the window coords_h = paddle.arange(window_size[0]) coords_w = paddle.arange(window_size[1]) coords = paddle.stack(paddle.meshgrid( [coords_h, coords_w])) # 2, Wh, Ww coords_flatten = coords.flatten(1) # 2, Wh*Ww relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww relative_coords = relative_coords.transpos( (1, 2, 0)) # Wh*Ww, Wh*Ww, 2 relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 relative_coords[:, :, 1] += window_size[1] - 1 relative_coords[:, :, 0] *= 2 * window_size[1] - 1 relative_position_index = \ paddle.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype) relative_position_index[1:, 1:] = relative_coords.sum( -1) # Wh*Ww, Wh*Ww relative_position_index[0, 0:] = self.num_relative_distance - 3 relative_position_index[0:, 0] = self.num_relative_distance - 2 relative_position_index[0, 0] = self.num_relative_distance - 1 self.register_buffer("relative_position_index", relative_position_index) def forward(self): relative_position_bias = \ self.relative_position_bias_table[self.relative_position_index.reshape([-1])].reshape([ self.window_size[0] * self.window_size[1] + 1, self.window_size[0] * self.window_size[1] + 1, -1]) # Wh*Ww,Wh*Ww,nH return relative_position_bias.transpose((2, 0, 1)) # nH, Wh*Ww, Wh*Ww def get_sinusoid_encoding_table(n_position, d_hid, token=False): ''' Sinusoid position encoding table ''' def get_position_angle_vec(position): return [ position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid) ] sinusoid_table = np.array( [get_position_angle_vec(pos_i) for pos_i in range(n_position)]) sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 if token: sinusoid_table = np.concatenate( [sinusoid_table, np.zeros([1, d_hid])], dim=0) return paddle.to_tensor(sinusoid_table, dtype=paddle.float32).unsqueeze(0) @register @serializable class VisionTransformer(nn.Layer): """ Vision Transformer with support for patch input """ def __init__(self, img_size=[672, 1092], patch_size=16, in_chans=3, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer='nn.LayerNorm', init_values=None, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, epsilon=1e-5, final_norm=False, pretrained=None, out_indices=[3, 5, 7, 11], use_abs_pos_emb=False, use_sincos_pos_emb=True, with_fpn=True, num_fpn_levels=4, use_checkpoint=False, **args): super().__init__() self.img_size = img_size self.embed_dim = embed_dim self.with_fpn = with_fpn self.use_checkpoint = use_checkpoint self.use_sincos_pos_emb = use_sincos_pos_emb self.use_rel_pos_bias = use_rel_pos_bias self.final_norm = final_norm self.out_indices = out_indices self.num_fpn_levels = num_fpn_levels if use_checkpoint: paddle.seed(0) self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) self.pos_w = self.patch_embed.num_patches_in_w self.pos_h = self.patch_embed.num_patches_in_h self.cls_token = self.create_parameter( shape=(1, 1, embed_dim), default_initializer=paddle.nn.initializer.Constant(value=0.)) if use_abs_pos_emb: self.pos_embed = self.create_parameter( shape=(1, self.pos_w * self.pos_h + 1, embed_dim), default_initializer=paddle.nn.initializer.TruncatedNormal( std=.02)) elif use_sincos_pos_emb: pos_embed = self.build_2d_sincos_position_embedding(embed_dim) self.pos_embed = pos_embed self.pos_embed = self.create_parameter(shape=pos_embed.shape) self.pos_embed.set_value(pos_embed.numpy()) self.pos_embed.stop_gradient = True else: self.pos_embed = None self.pos_drop = nn.Dropout(p=drop_rate) if use_shared_rel_pos_bias: self.rel_pos_bias = RelativePositionBias( window_size=self.patch_embed.patch_shape, num_heads=num_heads) else: self.rel_pos_bias = None dpr = np.linspace(0, drop_path_rate, depth) self.blocks = nn.LayerList([ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None, epsilon=epsilon) for i in range(depth) ]) self.pretrained = pretrained self.init_weight() assert len(out_indices) <= 4, '' self.out_indices = out_indices self.out_channels = [embed_dim for _ in range(num_fpn_levels)] self.out_strides = [4, 8, 16, 32][-num_fpn_levels:] if with_fpn else [ patch_size for _ in range(len(out_indices)) ] self.norm = Identity() if self.with_fpn: assert num_fpn_levels <= 4, '' self.init_fpn( embed_dim=embed_dim, patch_size=patch_size, ) def init_weight(self): pretrained = self.pretrained if pretrained: if 'http' in pretrained: #URL path = paddle.utils.download.get_weights_path_from_url( pretrained) else: #model in local path path = pretrained load_state_dict = paddle.load(path) model_state_dict = self.state_dict() pos_embed_name = "pos_embed" if pos_embed_name in load_state_dict.keys(): load_pos_embed = paddle.to_tensor( load_state_dict[pos_embed_name], dtype="float32") if self.pos_embed.shape != load_pos_embed.shape: pos_size = int(math.sqrt(load_pos_embed.shape[1] - 1)) model_state_dict[pos_embed_name] = self.resize_pos_embed( load_pos_embed, (pos_size, pos_size), (self.pos_h, self.pos_w)) # self.set_state_dict(model_state_dict) load_state_dict[pos_embed_name] = model_state_dict[ pos_embed_name] print("Load pos_embed and resize it from {} to {} .".format( load_pos_embed.shape, self.pos_embed.shape)) self.set_state_dict(load_state_dict) print("Load load_state_dict....") def init_fpn(self, embed_dim=768, patch_size=16, out_with_norm=False): if patch_size == 16: self.fpn1 = nn.Sequential( nn.Conv2DTranspose( embed_dim, embed_dim, kernel_size=2, stride=2), nn.BatchNorm2D(embed_dim), nn.GELU(), nn.Conv2DTranspose( embed_dim, embed_dim, kernel_size=2, stride=2), ) self.fpn2 = nn.Sequential( nn.Conv2DTranspose( embed_dim, embed_dim, kernel_size=2, stride=2), ) self.fpn3 = Identity() self.fpn4 = nn.MaxPool2D(kernel_size=2, stride=2) elif patch_size == 8: self.fpn1 = nn.Sequential( nn.Conv2DTranspose( embed_dim, embed_dim, kernel_size=2, stride=2), ) self.fpn2 = Identity() self.fpn3 = nn.Sequential(nn.MaxPool2D(kernel_size=2, stride=2), ) self.fpn4 = nn.Sequential(nn.MaxPool2D(kernel_size=4, stride=4), ) if not out_with_norm: self.norm = Identity() else: self.norm = nn.LayerNorm(embed_dim, epsilon=1e-6) def interpolate_pos_encoding(self, x, w, h): npatch = x.shape[1] - 1 N = self.pos_embed.shape[1] - 1 w0 = w // self.patch_embed.patch_size h0 = h // self.patch_embed.patch_size if npatch == N and w0 == self.patch_embed.num_patches_w and h0 == self.patch_embed.num_patches_h: return self.pos_embed class_pos_embed = self.pos_embed[:, 0] patch_pos_embed = self.pos_embed[:, 1:] dim = x.shape[-1] # we add a small number to avoid floating point error in the interpolation # see discussion at https://github.com/facebookresearch/dino/issues/8 # w0, h0 = w0 + 0.1, h0 + 0.1 # patch_pos_embed = nn.functional.interpolate( # patch_pos_embed.reshape([ # 1, self.patch_embed.num_patches_w, # self.patch_embed.num_patches_h, dim # ]).transpose((0, 3, 1, 2)), # scale_factor=(w0 / self.patch_embed.num_patches_w, # h0 / self.patch_embed.num_patches_h), # mode='bicubic', ) patch_pos_embed = nn.functional.interpolate( patch_pos_embed.reshape([ 1, self.patch_embed.num_patches_w, self.patch_embed.num_patches_h, dim ]).transpose((0, 3, 1, 2)), (w0, h0), mode='bicubic', ) assert int(w0) == patch_pos_embed.shape[-2] and int( h0) == patch_pos_embed.shape[-1] patch_pos_embed = patch_pos_embed.transpose( (0, 2, 3, 1)).reshape([1, -1, dim]) return paddle.concat( (class_pos_embed.unsqueeze(0), patch_pos_embed), axis=1) def resize_pos_embed(self, pos_embed, old_hw, new_hw): """ Resize pos_embed weight. Args: pos_embed (Tensor): the pos_embed weight old_hw (list[int]): the height and width of old pos_embed new_hw (list[int]): the height and width of new pos_embed Returns: Tensor: the resized pos_embed weight """ cls_pos_embed = pos_embed[:, :1, :] pos_embed = pos_embed[:, 1:, :] pos_embed = pos_embed.transpose([0, 2, 1]) pos_embed = pos_embed.reshape([1, -1, old_hw[0], old_hw[1]]) pos_embed = F.interpolate( pos_embed, new_hw, mode='bicubic', align_corners=False) pos_embed = pos_embed.flatten(2).transpose([0, 2, 1]) pos_embed = paddle.concat([cls_pos_embed, pos_embed], axis=1) return pos_embed def build_2d_sincos_position_embedding( self, embed_dim=768, temperature=10000., ): h, w = self.patch_embed.patch_shape grid_w = paddle.arange(w, dtype=paddle.float32) grid_h = paddle.arange(h, dtype=paddle.float32) grid_w, grid_h = paddle.meshgrid(grid_w, grid_h) assert embed_dim % 4 == 0, 'Embed dimension must be divisible by 4 for 2D sin-cos position embedding' pos_dim = embed_dim // 4 omega = paddle.arange(pos_dim, dtype=paddle.float32) / pos_dim omega = 1. / (temperature**omega) out_w = grid_w.flatten()[..., None] @omega[None] out_h = grid_h.flatten()[..., None] @omega[None] pos_emb = paddle.concat( [ paddle.sin(out_w), paddle.cos(out_w), paddle.sin(out_h), paddle.cos(out_h) ], axis=1)[None, :, :] pe_token = paddle.zeros([1, 1, embed_dim], dtype=paddle.float32) pos_embed = paddle.concat([pe_token, pos_emb], axis=1) # pos_embed.stop_gradient = True return pos_embed def forward(self, x): x = x['image'] if isinstance(x, dict) else x _, _, h, w = x.shape x = self.patch_embed(x) B, D, Hp, Wp = x.shape # b * c * h * w cls_tokens = self.cls_token.expand( (B, self.cls_token.shape[-2], self.cls_token.shape[-1])) x = x.flatten(2).transpose([0, 2, 1]) # b * hw * c x = paddle.concat([cls_tokens, x], axis=1) if self.pos_embed is not None: # x = x + self.interpolate_pos_encoding(x, w, h) x = x + self.interpolate_pos_encoding(x, h, w) x = self.pos_drop(x) rel_pos_bias = self.rel_pos_bias( ) if self.rel_pos_bias is not None else None feats = [] for idx, blk in enumerate(self.blocks): if self.use_checkpoint and self.training: x = paddle.distributed.fleet.utils.recompute( blk, x, rel_pos_bias, **{"preserve_rng_state": True}) else: x = blk(x, rel_pos_bias) if idx in self.out_indices: xp = paddle.reshape( paddle.transpose( self.norm(x[:, 1:, :]), perm=[0, 2, 1]), shape=[B, D, Hp, Wp]) feats.append(xp) if self.with_fpn: fpns = [self.fpn1, self.fpn2, self.fpn3, self.fpn4][ -self.num_fpn_levels:] assert len(fpns) == len(feats) or len(feats) == 1, '' outputs = [] for i, m in enumerate(fpns): outputs.append( m(feats[i] if len(feats) == len(fpns) else feats[-1])) return outputs return feats @property def num_layers(self): return len(self.blocks) @property def no_weight_decay(self): return {'pos_embed', 'cls_token'} @property def out_shape(self): return [ ShapeSpec( channels=c, stride=s) for c, s in zip(self.out_channels, self.out_strides) ]

PaddleDetection/ppdet/modeling/backbones/vision_transformer.py/0

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # The code is based on: # https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/gfl_head.py from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import numpy as np import paddle import paddle.nn as nn import paddle.nn.functional as F from paddle import ParamAttr from paddle.nn.initializer import Normal, Constant from ppdet.core.workspace import register from ppdet.modeling.bbox_utils import distance2bbox, bbox2distance, batch_distance2bbox from ppdet.data.transform.atss_assigner import bbox_overlaps __all__ = ['GFLHead', 'LDGFLHead'] class ScaleReg(nn.Layer): """ Parameter for scaling the regression outputs. """ def __init__(self): super(ScaleReg, self).__init__() self.scale_reg = self.create_parameter( shape=[1], attr=ParamAttr(initializer=Constant(value=1.)), dtype="float32") def forward(self, inputs): out = inputs * self.scale_reg return out class Integral(nn.Layer): """A fixed layer for calculating integral result from distribution. This layer calculates the target location by :math: `sum{P(y_i) * y_i}`, P(y_i) denotes the softmax vector that represents the discrete distribution y_i denotes the discrete set, usually {0, 1, 2, ..., reg_max} Args: reg_max (int): The maximal value of the discrete set. Default: 16. You may want to reset it according to your new dataset or related settings. """ def __init__(self, reg_max=16): super(Integral, self).__init__() self.reg_max = reg_max self.register_buffer('project', paddle.linspace(0, self.reg_max, self.reg_max + 1)) def forward(self, x): """Forward feature from the regression head to get integral result of bounding box location. Args: x (Tensor): Features of the regression head, shape (N, 4*(n+1)), n is self.reg_max. Returns: x (Tensor): Integral result of box locations, i.e., distance offsets from the box center in four directions, shape (N, 4). """ x = F.softmax(x.reshape([-1, self.reg_max + 1]), axis=1) x = F.linear(x, self.project) if self.training: x = x.reshape([-1, 4]) return x @register class DGQP(nn.Layer): """Distribution-Guided Quality Predictor of GFocal head Args: reg_topk (int): top-k statistics of distribution to guide LQE reg_channels (int): hidden layer unit to generate LQE add_mean (bool): Whether to calculate the mean of top-k statistics """ def __init__(self, reg_topk=4, reg_channels=64, add_mean=True): super(DGQP, self).__init__() self.reg_topk = reg_topk self.reg_channels = reg_channels self.add_mean = add_mean self.total_dim = reg_topk if add_mean: self.total_dim += 1 self.reg_conv1 = self.add_sublayer( 'dgqp_reg_conv1', nn.Conv2D( in_channels=4 * self.total_dim, out_channels=self.reg_channels, kernel_size=1, weight_attr=ParamAttr(initializer=Normal( mean=0., std=0.01)), bias_attr=ParamAttr(initializer=Constant(value=0)))) self.reg_conv2 = self.add_sublayer( 'dgqp_reg_conv2', nn.Conv2D( in_channels=self.reg_channels, out_channels=1, kernel_size=1, weight_attr=ParamAttr(initializer=Normal( mean=0., std=0.01)), bias_attr=ParamAttr(initializer=Constant(value=0)))) def forward(self, x): """Forward feature from the regression head to get integral result of bounding box location. Args: x (Tensor): Features of the regression head, shape (N, 4*(n+1)), n is self.reg_max. Returns: x (Tensor): Integral result of box locations, i.e., distance offsets from the box center in four directions, shape (N, 4). """ N, _, H, W = x.shape[:] prob = F.softmax(x.reshape([N, 4, -1, H, W]), axis=2) prob_topk, _ = prob.topk(self.reg_topk, axis=2) if self.add_mean: stat = paddle.concat( [prob_topk, prob_topk.mean( axis=2, keepdim=True)], axis=2) else: stat = prob_topk y = F.relu(self.reg_conv1(stat.reshape([N, 4 * self.total_dim, H, W]))) y = F.sigmoid(self.reg_conv2(y)) return y @register class GFLHead(nn.Layer): """ GFLHead Args: conv_feat (object): Instance of 'FCOSFeat' num_classes (int): Number of classes fpn_stride (list): The stride of each FPN Layer prior_prob (float): Used to set the bias init for the class prediction layer loss_class (object): Instance of QualityFocalLoss. loss_dfl (object): Instance of DistributionFocalLoss. loss_bbox (object): Instance of bbox loss. reg_max: Max value of integral set :math: `{0, ..., reg_max}` n QFL setting. Default: 16. """ __inject__ = [ 'conv_feat', 'dgqp_module', 'loss_class', 'loss_dfl', 'loss_bbox', 'nms' ] __shared__ = ['num_classes'] def __init__(self, conv_feat='FCOSFeat', dgqp_module=None, num_classes=80, fpn_stride=[8, 16, 32, 64, 128], prior_prob=0.01, loss_class='QualityFocalLoss', loss_dfl='DistributionFocalLoss', loss_bbox='GIoULoss', reg_max=16, feat_in_chan=256, nms=None, nms_pre=1000, cell_offset=0): super(GFLHead, self).__init__() self.conv_feat = conv_feat self.dgqp_module = dgqp_module self.num_classes = num_classes self.fpn_stride = fpn_stride self.prior_prob = prior_prob self.loss_qfl = loss_class self.loss_dfl = loss_dfl self.loss_bbox = loss_bbox self.reg_max = reg_max self.feat_in_chan = feat_in_chan self.nms = nms self.nms_pre = nms_pre self.cell_offset = cell_offset self.use_sigmoid = self.loss_qfl.use_sigmoid if self.use_sigmoid: self.cls_out_channels = self.num_classes else: self.cls_out_channels = self.num_classes + 1 conv_cls_name = "gfl_head_cls" bias_init_value = -math.log((1 - self.prior_prob) / self.prior_prob) self.gfl_head_cls = self.add_sublayer( conv_cls_name, nn.Conv2D( in_channels=self.feat_in_chan, out_channels=self.cls_out_channels, kernel_size=3, stride=1, padding=1, weight_attr=ParamAttr(initializer=Normal( mean=0., std=0.01)), bias_attr=ParamAttr( initializer=Constant(value=bias_init_value)))) conv_reg_name = "gfl_head_reg" self.gfl_head_reg = self.add_sublayer( conv_reg_name, nn.Conv2D( in_channels=self.feat_in_chan, out_channels=4 * (self.reg_max + 1), kernel_size=3, stride=1, padding=1, weight_attr=ParamAttr(initializer=Normal( mean=0., std=0.01)), bias_attr=ParamAttr(initializer=Constant(value=0)))) self.scales_regs = [] for i in range(len(self.fpn_stride)): lvl = int(math.log(int(self.fpn_stride[i]), 2)) feat_name = 'p{}_feat'.format(lvl) scale_reg = self.add_sublayer(feat_name, ScaleReg()) self.scales_regs.append(scale_reg) self.distribution_project = Integral(self.reg_max) def forward(self, fpn_feats): assert len(fpn_feats) == len( self.fpn_stride ), "The size of fpn_feats is not equal to size of fpn_stride" cls_logits_list = [] bboxes_reg_list = [] for stride, scale_reg, fpn_feat in zip(self.fpn_stride, self.scales_regs, fpn_feats): conv_cls_feat, conv_reg_feat = self.conv_feat(fpn_feat) cls_score = self.gfl_head_cls(conv_cls_feat) bbox_pred = scale_reg(self.gfl_head_reg(conv_reg_feat)) if self.dgqp_module: quality_score = self.dgqp_module(bbox_pred) cls_score = F.sigmoid(cls_score) * quality_score if not self.training: cls_score = F.sigmoid(cls_score.transpose([0, 2, 3, 1])) bbox_pred = bbox_pred.transpose([0, 2, 3, 1]) b, cell_h, cell_w, _ = paddle.shape(cls_score) y, x = self.get_single_level_center_point( [cell_h, cell_w], stride, cell_offset=self.cell_offset) center_points = paddle.stack([x, y], axis=-1) cls_score = cls_score.reshape([b, -1, self.cls_out_channels]) bbox_pred = self.distribution_project(bbox_pred) * stride bbox_pred = bbox_pred.reshape([-1, cell_h * cell_w, 4]) # NOTE: If keep_ratio=False and image shape value that # multiples of 32, distance2bbox not set max_shapes parameter # to speed up model prediction. If need to set max_shapes, # please use inputs['im_shape']. bbox_pred = batch_distance2bbox( center_points, bbox_pred, max_shapes=None) cls_logits_list.append(cls_score) bboxes_reg_list.append(bbox_pred) return (cls_logits_list, bboxes_reg_list) def _images_to_levels(self, target, num_level_anchors): """ Convert targets by image to targets by feature level. """ level_targets = [] start = 0 for n in num_level_anchors: end = start + n level_targets.append(target[:, start:end].squeeze(0)) start = end return level_targets def _grid_cells_to_center(self, grid_cells): """ Get center location of each gird cell Args: grid_cells: grid cells of a feature map Returns: center points """ cells_cx = (grid_cells[:, 2] + grid_cells[:, 0]) / 2 cells_cy = (grid_cells[:, 3] + grid_cells[:, 1]) / 2 return paddle.stack([cells_cx, cells_cy], axis=-1) def get_loss(self, gfl_head_outs, gt_meta): cls_logits, bboxes_reg = gfl_head_outs num_level_anchors = [ featmap.shape[-2] * featmap.shape[-1] for featmap in cls_logits ] grid_cells_list = self._images_to_levels(gt_meta['grid_cells'], num_level_anchors) labels_list = self._images_to_levels(gt_meta['labels'], num_level_anchors) label_weights_list = self._images_to_levels(gt_meta['label_weights'], num_level_anchors) bbox_targets_list = self._images_to_levels(gt_meta['bbox_targets'], num_level_anchors) num_total_pos = sum(gt_meta['pos_num']) try: paddle.distributed.all_reduce(num_total_pos) num_total_pos = paddle.clip( num_total_pos / paddle.distributed.get_world_size(), min=1) except: num_total_pos = max(num_total_pos, 1) loss_bbox_list, loss_dfl_list, loss_qfl_list, avg_factor = [], [], [], [] for cls_score, bbox_pred, grid_cells, labels, label_weights, bbox_targets, stride in zip( cls_logits, bboxes_reg, grid_cells_list, labels_list, label_weights_list, bbox_targets_list, self.fpn_stride): grid_cells = grid_cells.reshape([-1, 4]) cls_score = cls_score.transpose([0, 2, 3, 1]).reshape( [-1, self.cls_out_channels]) bbox_pred = bbox_pred.transpose([0, 2, 3, 1]).reshape( [-1, 4 * (self.reg_max + 1)]) bbox_targets = bbox_targets.reshape([-1, 4]) labels = labels.reshape([-1]) label_weights = label_weights.reshape([-1]) bg_class_ind = self.num_classes pos_inds = paddle.nonzero( paddle.logical_and((labels >= 0), (labels < bg_class_ind)), as_tuple=False).squeeze(1) score = np.zeros(labels.shape) if len(pos_inds) > 0: pos_bbox_targets = paddle.gather(bbox_targets, pos_inds, axis=0) pos_bbox_pred = paddle.gather(bbox_pred, pos_inds, axis=0) pos_grid_cells = paddle.gather(grid_cells, pos_inds, axis=0) pos_grid_cell_centers = self._grid_cells_to_center( pos_grid_cells) / stride weight_targets = F.sigmoid(cls_score.detach()) weight_targets = paddle.gather( weight_targets.max(axis=1, keepdim=True), pos_inds, axis=0) pos_bbox_pred_corners = self.distribution_project(pos_bbox_pred) pos_decode_bbox_pred = distance2bbox(pos_grid_cell_centers, pos_bbox_pred_corners) pos_decode_bbox_targets = pos_bbox_targets / stride bbox_iou = bbox_overlaps( pos_decode_bbox_pred.detach().numpy(), pos_decode_bbox_targets.detach().numpy(), is_aligned=True) score[pos_inds.numpy()] = bbox_iou pred_corners = pos_bbox_pred.reshape([-1, self.reg_max + 1]) target_corners = bbox2distance(pos_grid_cell_centers, pos_decode_bbox_targets, self.reg_max).reshape([-1]) # regression loss loss_bbox = paddle.sum( self.loss_bbox(pos_decode_bbox_pred, pos_decode_bbox_targets) * weight_targets) # dfl loss loss_dfl = self.loss_dfl( pred_corners, target_corners, weight=weight_targets.expand([-1, 4]).reshape([-1]), avg_factor=4.0) else: loss_bbox = bbox_pred.sum() * 0 loss_dfl = bbox_pred.sum() * 0 weight_targets = paddle.to_tensor([0], dtype='float32') # qfl loss score = paddle.to_tensor(score) loss_qfl = self.loss_qfl( cls_score, (labels, score), weight=label_weights, avg_factor=num_total_pos) loss_bbox_list.append(loss_bbox) loss_dfl_list.append(loss_dfl) loss_qfl_list.append(loss_qfl) avg_factor.append(weight_targets.sum()) avg_factor = sum(avg_factor) try: paddle.distributed.all_reduce(avg_factor) avg_factor = paddle.clip( avg_factor / paddle.distributed.get_world_size(), min=1) except: avg_factor = max(avg_factor.item(), 1) if avg_factor <= 0: loss_qfl = paddle.to_tensor(0, dtype='float32', stop_gradient=False) loss_bbox = paddle.to_tensor( 0, dtype='float32', stop_gradient=False) loss_dfl = paddle.to_tensor(0, dtype='float32', stop_gradient=False) else: losses_bbox = list(map(lambda x: x / avg_factor, loss_bbox_list)) losses_dfl = list(map(lambda x: x / avg_factor, loss_dfl_list)) loss_qfl = sum(loss_qfl_list) loss_bbox = sum(losses_bbox) loss_dfl = sum(losses_dfl) loss_states = dict( loss_qfl=loss_qfl, loss_bbox=loss_bbox, loss_dfl=loss_dfl) return loss_states def get_single_level_center_point(self, featmap_size, stride, cell_offset=0): """ Generate pixel centers of a single stage feature map. Args: featmap_size: height and width of the feature map stride: down sample stride of the feature map Returns: y and x of the center points """ h, w = featmap_size x_range = (paddle.arange(w, dtype='float32') + cell_offset) * stride y_range = (paddle.arange(h, dtype='float32') + cell_offset) * stride y, x = paddle.meshgrid(y_range, x_range) y = y.flatten() x = x.flatten() return y, x def post_process(self, gfl_head_outs, im_shape, scale_factor): cls_scores, bboxes_reg = gfl_head_outs bboxes = paddle.concat(bboxes_reg, axis=1) # rescale: [h_scale, w_scale] -> [w_scale, h_scale, w_scale, h_scale] im_scale = scale_factor.flip([1]).tile([1, 2]).unsqueeze(1) bboxes /= im_scale mlvl_scores = paddle.concat(cls_scores, axis=1) mlvl_scores = mlvl_scores.transpose([0, 2, 1]) bbox_pred, bbox_num, _ = self.nms(bboxes, mlvl_scores) return bbox_pred, bbox_num @register class LDGFLHead(GFLHead): """ GFLHead for LD distill Args: conv_feat (object): Instance of 'FCOSFeat' num_classes (int): Number of classes fpn_stride (list): The stride of each FPN Layer prior_prob (float): Used to set the bias init for the class prediction layer loss_class (object): Instance of QualityFocalLoss. loss_dfl (object): Instance of DistributionFocalLoss. loss_bbox (object): Instance of bbox loss. reg_max: Max value of integral set :math: `{0, ..., reg_max}` n QFL setting. Default: 16. """ __inject__ = [ 'conv_feat', 'dgqp_module', 'loss_class', 'loss_dfl', 'loss_bbox', 'loss_ld', 'loss_ld_vlr', 'loss_kd', 'nms' ] __shared__ = ['num_classes'] def __init__(self, conv_feat='FCOSFeat', dgqp_module=None, num_classes=80, fpn_stride=[8, 16, 32, 64, 128], prior_prob=0.01, loss_class='QualityFocalLoss', loss_dfl='DistributionFocalLoss', loss_bbox='GIoULoss', loss_ld='KnowledgeDistillationKLDivLoss', loss_ld_vlr='KnowledgeDistillationKLDivLoss', loss_kd='KnowledgeDistillationKLDivLoss', reg_max=16, feat_in_chan=256, nms=None, nms_pre=1000, cell_offset=0): super(LDGFLHead, self).__init__( conv_feat=conv_feat, dgqp_module=dgqp_module, num_classes=num_classes, fpn_stride=fpn_stride, prior_prob=prior_prob, loss_class=loss_class, loss_dfl=loss_dfl, loss_bbox=loss_bbox, reg_max=reg_max, feat_in_chan=feat_in_chan, nms=nms, nms_pre=nms_pre, cell_offset=cell_offset) self.loss_ld = loss_ld self.loss_kd = loss_kd self.loss_ld_vlr = loss_ld_vlr def forward(self, fpn_feats): assert len(fpn_feats) == len( self.fpn_stride ), "The size of fpn_feats is not equal to size of fpn_stride" cls_logits_list = [] bboxes_reg_list = [] for stride, scale_reg, fpn_feat in zip(self.fpn_stride, self.scales_regs, fpn_feats): conv_cls_feat, conv_reg_feat = self.conv_feat(fpn_feat) cls_score = self.gfl_head_cls(conv_cls_feat) bbox_pred = scale_reg(self.gfl_head_reg(conv_reg_feat)) if self.dgqp_module: quality_score = self.dgqp_module(bbox_pred) cls_score = F.sigmoid(cls_score) * quality_score if not self.training: cls_score = F.sigmoid(cls_score.transpose([0, 2, 3, 1])) bbox_pred = bbox_pred.transpose([0, 2, 3, 1]) b, cell_h, cell_w, _ = paddle.shape(cls_score) y, x = self.get_single_level_center_point( [cell_h, cell_w], stride, cell_offset=self.cell_offset) center_points = paddle.stack([x, y], axis=-1) cls_score = cls_score.reshape([b, -1, self.cls_out_channels]) bbox_pred = self.distribution_project(bbox_pred) * stride bbox_pred = bbox_pred.reshape([b, cell_h * cell_w, 4]) # NOTE: If keep_ratio=False and image shape value that # multiples of 32, distance2bbox not set max_shapes parameter # to speed up model prediction. If need to set max_shapes, # please use inputs['im_shape']. bbox_pred = batch_distance2bbox( center_points, bbox_pred, max_shapes=None) cls_logits_list.append(cls_score) bboxes_reg_list.append(bbox_pred) return (cls_logits_list, bboxes_reg_list) def get_loss(self, gfl_head_outs, gt_meta, soft_label_list, soft_targets_list): cls_logits, bboxes_reg = gfl_head_outs num_level_anchors = [ featmap.shape[-2] * featmap.shape[-1] for featmap in cls_logits ] grid_cells_list = self._images_to_levels(gt_meta['grid_cells'], num_level_anchors) labels_list = self._images_to_levels(gt_meta['labels'], num_level_anchors) label_weights_list = self._images_to_levels(gt_meta['label_weights'], num_level_anchors) bbox_targets_list = self._images_to_levels(gt_meta['bbox_targets'], num_level_anchors) # vlr regions vlr_regions_list = self._images_to_levels(gt_meta['vlr_regions'], num_level_anchors) num_total_pos = sum(gt_meta['pos_num']) try: paddle.distributed.all_reduce(num_total_pos) num_total_pos = paddle.clip( num_total_pos / paddle.distributed.get_world_size(), min=1.) except: num_total_pos = max(num_total_pos, 1) loss_bbox_list, loss_dfl_list, loss_qfl_list, loss_ld_list, avg_factor = [], [], [], [], [] loss_ld_vlr_list, loss_kd_list = [], [] for cls_score, bbox_pred, grid_cells, labels, label_weights, bbox_targets, stride, soft_targets,\ soft_label, vlr_region in zip( cls_logits, bboxes_reg, grid_cells_list, labels_list, label_weights_list, bbox_targets_list, self.fpn_stride, soft_targets_list, soft_label_list, vlr_regions_list): grid_cells = grid_cells.reshape([-1, 4]) cls_score = cls_score.transpose([0, 2, 3, 1]).reshape( [-1, self.cls_out_channels]) bbox_pred = bbox_pred.transpose([0, 2, 3, 1]).reshape( [-1, 4 * (self.reg_max + 1)]) soft_targets = soft_targets.transpose([0, 2, 3, 1]).reshape( [-1, 4 * (self.reg_max + 1)]) soft_label = soft_label.transpose([0, 2, 3, 1]).reshape( [-1, self.cls_out_channels]) # feture im # teacher_x = teacher_x.transpose([0, 2, 3, 1]).reshape([-1, 256]) # x = x.transpose([0, 2, 3, 1]).reshape([-1, 256]) bbox_targets = bbox_targets.reshape([-1, 4]) labels = labels.reshape([-1]) label_weights = label_weights.reshape([-1]) vlr_region = vlr_region.reshape([-1]) bg_class_ind = self.num_classes pos_inds = paddle.nonzero( paddle.logical_and((labels >= 0), (labels < bg_class_ind)), as_tuple=False).squeeze(1) score = np.zeros(labels.shape) remain_inds = (vlr_region > 0).nonzero() if len(pos_inds) > 0: pos_bbox_targets = paddle.gather(bbox_targets, pos_inds, axis=0) pos_bbox_pred = paddle.gather(bbox_pred, pos_inds, axis=0) pos_grid_cells = paddle.gather(grid_cells, pos_inds, axis=0) pos_grid_cell_centers = self._grid_cells_to_center( pos_grid_cells) / stride weight_targets = F.sigmoid(cls_score.detach()) weight_targets = paddle.gather( weight_targets.max(axis=1, keepdim=True), pos_inds, axis=0) pos_bbox_pred_corners = self.distribution_project(pos_bbox_pred) pos_decode_bbox_pred = distance2bbox(pos_grid_cell_centers, pos_bbox_pred_corners) pos_decode_bbox_targets = pos_bbox_targets / stride bbox_iou = bbox_overlaps( pos_decode_bbox_pred.detach().numpy(), pos_decode_bbox_targets.detach().numpy(), is_aligned=True) score[pos_inds.numpy()] = bbox_iou pred_corners = pos_bbox_pred.reshape([-1, self.reg_max + 1]) pos_soft_targets = paddle.gather(soft_targets, pos_inds, axis=0) soft_corners = pos_soft_targets.reshape([-1, self.reg_max + 1]) target_corners = bbox2distance(pos_grid_cell_centers, pos_decode_bbox_targets, self.reg_max).reshape([-1]) # regression loss loss_bbox = paddle.sum( self.loss_bbox(pos_decode_bbox_pred, pos_decode_bbox_targets) * weight_targets) # dfl loss loss_dfl = self.loss_dfl( pred_corners, target_corners, weight=weight_targets.expand([-1, 4]).reshape([-1]), avg_factor=4.0) # ld loss loss_ld = self.loss_ld( pred_corners, soft_corners, weight=weight_targets.expand([-1, 4]).reshape([-1]), avg_factor=4.0) loss_kd = self.loss_kd( paddle.gather( cls_score, pos_inds, axis=0), paddle.gather( soft_label, pos_inds, axis=0), weight=paddle.gather( label_weights, pos_inds, axis=0), avg_factor=pos_inds.shape[0]) else: loss_bbox = bbox_pred.sum() * 0 loss_dfl = bbox_pred.sum() * 0 loss_ld = bbox_pred.sum() * 0 loss_kd = bbox_pred.sum() * 0 weight_targets = paddle.to_tensor([0], dtype='float32') if len(remain_inds) > 0: neg_pred_corners = bbox_pred[remain_inds].reshape( [-1, self.reg_max + 1]) neg_soft_corners = soft_targets[remain_inds].reshape( [-1, self.reg_max + 1]) remain_targets = vlr_region[remain_inds] loss_ld_vlr = self.loss_ld_vlr( neg_pred_corners, neg_soft_corners, weight=remain_targets.expand([-1, 4]).reshape([-1]), avg_factor=16.0) else: loss_ld_vlr = bbox_pred.sum() * 0 # qfl loss score = paddle.to_tensor(score) loss_qfl = self.loss_qfl( cls_score, (labels, score), weight=label_weights, avg_factor=num_total_pos) loss_bbox_list.append(loss_bbox) loss_dfl_list.append(loss_dfl) loss_qfl_list.append(loss_qfl) loss_ld_list.append(loss_ld) loss_ld_vlr_list.append(loss_ld_vlr) loss_kd_list.append(loss_kd) avg_factor.append(weight_targets.sum()) avg_factor = sum(avg_factor) # + 1e-6 try: paddle.distributed.all_reduce(avg_factor) avg_factor = paddle.clip( avg_factor / paddle.distributed.get_world_size(), min=1) except: avg_factor = max(avg_factor.item(), 1) if avg_factor <= 0: loss_qfl = paddle.to_tensor(0, dtype='float32', stop_gradient=False) loss_bbox = paddle.to_tensor( 0, dtype='float32', stop_gradient=False) loss_dfl = paddle.to_tensor(0, dtype='float32', stop_gradient=False) loss_ld = paddle.to_tensor(0, dtype='float32', stop_gradient=False) loss_ld_vlr = paddle.to_tensor( 0, dtype='float32', stop_gradient=False) loss_kd = paddle.to_tensor(0, dtype='float32', stop_gradient=False) else: losses_bbox = list(map(lambda x: x / avg_factor, loss_bbox_list)) losses_dfl = list(map(lambda x: x / avg_factor, loss_dfl_list)) loss_qfl = sum(loss_qfl_list) loss_bbox = sum(losses_bbox) loss_dfl = sum(losses_dfl) loss_ld = sum(loss_ld_list) loss_ld_vlr = sum(loss_ld_vlr_list) loss_kd = sum(loss_kd_list) loss_states = dict( loss_qfl=loss_qfl, loss_bbox=loss_bbox, loss_dfl=loss_dfl, loss_ld=loss_ld, loss_ld_vlr=loss_ld_vlr, loss_kd=loss_kd) return loss_states

PaddleDetection/ppdet/modeling/heads/gfl_head.py/0

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import paddle import paddle.nn as nn import paddle.nn.functional as F from paddle import ParamAttr from paddle.nn.initializer import Constant from ppdet.core.workspace import register from ..initializer import normal_, constant_, bias_init_with_prob from ppdet.modeling.bbox_utils import bbox_center, batch_distance2bbox from ..losses import GIoULoss from ppdet.modeling.layers import ConvNormLayer from ppdet.modeling.ops import get_static_shape from ppdet.modeling.assigners.utils import generate_anchors_for_grid_cell class ScaleReg(nn.Layer): """ Parameter for scaling the regression outputs. """ def __init__(self, init_scale=1.): super(ScaleReg, self).__init__() self.scale_reg = self.create_parameter( shape=[1], attr=ParamAttr(initializer=Constant(value=init_scale)), dtype="float32") def forward(self, inputs): out = inputs * self.scale_reg return out class TaskDecomposition(nn.Layer): """This code is based on https://github.com/fcjian/TOOD/blob/master/mmdet/models/dense_heads/tood_head.py """ def __init__( self, feat_channels, stacked_convs, la_down_rate=8, norm_type='gn', norm_groups=32, ): super(TaskDecomposition, self).__init__() self.feat_channels = feat_channels self.stacked_convs = stacked_convs self.norm_type = norm_type self.norm_groups = norm_groups self.in_channels = self.feat_channels * self.stacked_convs self.la_conv1 = nn.Conv2D(self.in_channels, self.in_channels // la_down_rate, 1) self.la_conv2 = nn.Conv2D(self.in_channels // la_down_rate, self.stacked_convs, 1) self.reduction_conv = ConvNormLayer( self.in_channels, self.feat_channels, filter_size=1, stride=1, norm_type=self.norm_type, norm_groups=self.norm_groups) self._init_weights() def _init_weights(self): normal_(self.la_conv1.weight, std=0.001) normal_(self.la_conv2.weight, std=0.001) def forward(self, feat, avg_feat): feat_shape = get_static_shape(feat) b = feat_shape[0:1] h = feat_shape[2:3] w = feat_shape[3:4] weight = F.relu(self.la_conv1(avg_feat)) weight = F.sigmoid(self.la_conv2(weight)).unsqueeze(-1) feat = paddle.reshape( feat, [b, self.stacked_convs, self.feat_channels, h, w]) * weight feat = self.reduction_conv(feat.flatten(1, 2)) feat = F.relu(feat) return feat @register class TOODHead(nn.Layer): """This code is based on https://github.com/fcjian/TOOD/blob/master/mmdet/models/dense_heads/tood_head.py """ __inject__ = ['nms', 'static_assigner', 'assigner'] __shared__ = ['num_classes'] def __init__(self, num_classes=80, feat_channels=256, stacked_convs=6, fpn_strides=(8, 16, 32, 64, 128), grid_cell_scale=8, grid_cell_offset=0.5, norm_type='gn', norm_groups=32, static_assigner_epoch=4, use_align_head=True, loss_weight={ 'class': 1.0, 'bbox': 1.0, 'iou': 2.0, }, nms='MultiClassNMS', static_assigner='ATSSAssigner', assigner='TaskAlignedAssigner'): super(TOODHead, self).__init__() self.num_classes = num_classes self.feat_channels = feat_channels self.stacked_convs = stacked_convs self.fpn_strides = fpn_strides self.grid_cell_scale = grid_cell_scale self.grid_cell_offset = grid_cell_offset self.static_assigner_epoch = static_assigner_epoch self.use_align_head = use_align_head self.nms = nms self.static_assigner = static_assigner self.assigner = assigner self.loss_weight = loss_weight self.giou_loss = GIoULoss() self.inter_convs = nn.LayerList() for i in range(self.stacked_convs): self.inter_convs.append( ConvNormLayer( self.feat_channels, self.feat_channels, filter_size=3, stride=1, norm_type=norm_type, norm_groups=norm_groups)) self.cls_decomp = TaskDecomposition( self.feat_channels, self.stacked_convs, self.stacked_convs * 8, norm_type=norm_type, norm_groups=norm_groups) self.reg_decomp = TaskDecomposition( self.feat_channels, self.stacked_convs, self.stacked_convs * 8, norm_type=norm_type, norm_groups=norm_groups) self.tood_cls = nn.Conv2D( self.feat_channels, self.num_classes, 3, padding=1) self.tood_reg = nn.Conv2D(self.feat_channels, 4, 3, padding=1) if self.use_align_head: self.cls_prob_conv1 = nn.Conv2D(self.feat_channels * self.stacked_convs, self.feat_channels // 4, 1) self.cls_prob_conv2 = nn.Conv2D( self.feat_channels // 4, 1, 3, padding=1) self.reg_offset_conv1 = nn.Conv2D(self.feat_channels * self.stacked_convs, self.feat_channels // 4, 1) self.reg_offset_conv2 = nn.Conv2D( self.feat_channels // 4, 4 * 2, 3, padding=1) self.scales_regs = nn.LayerList([ScaleReg() for _ in self.fpn_strides]) self._init_weights() @classmethod def from_config(cls, cfg, input_shape): return { 'feat_channels': input_shape[0].channels, 'fpn_strides': [i.stride for i in input_shape], } def _init_weights(self): bias_cls = bias_init_with_prob(0.01) normal_(self.tood_cls.weight, std=0.01) constant_(self.tood_cls.bias, bias_cls) normal_(self.tood_reg.weight, std=0.01) if self.use_align_head: normal_(self.cls_prob_conv1.weight, std=0.01) normal_(self.cls_prob_conv2.weight, std=0.01) constant_(self.cls_prob_conv2.bias, bias_cls) normal_(self.reg_offset_conv1.weight, std=0.001) constant_(self.reg_offset_conv2.weight) constant_(self.reg_offset_conv2.bias) def _reg_grid_sample(self, feat, offset, anchor_points): feat_shape = get_static_shape(feat) b = feat_shape[0:1] h = feat_shape[2:3] w = feat_shape[3:4] feat = paddle.reshape(feat, [-1, 1, h, w]) offset = paddle.reshape(offset, [-1, 2, h, w]).transpose([0, 2, 3, 1]) grid_shape = paddle.concat([w, h]).astype('float32') grid = (offset + anchor_points) / grid_shape grid = 2 * grid.clip(0., 1.) - 1 feat = F.grid_sample(feat, grid) feat = paddle.reshape(feat, [b, -1, h, w]) return feat def forward(self, feats): assert len(feats) == len(self.fpn_strides), \ "The size of feats is not equal to size of fpn_strides" anchors, anchor_points, num_anchors_list, stride_tensor =\ generate_anchors_for_grid_cell( feats, self.fpn_strides, self.grid_cell_scale, self.grid_cell_offset) anchor_centers_split = paddle.split(anchor_points / stride_tensor, num_anchors_list) cls_score_list, bbox_pred_list = [], [] for feat, scale_reg, anchor_centers, stride in zip( feats, self.scales_regs, anchor_centers_split, self.fpn_strides): b, _, h, w = get_static_shape(feat) inter_feats = [] for inter_conv in self.inter_convs: feat = F.relu(inter_conv(feat)) inter_feats.append(feat) feat = paddle.concat(inter_feats, axis=1) # task decomposition avg_feat = F.adaptive_avg_pool2d(feat, (1, 1)) cls_feat = self.cls_decomp(feat, avg_feat) reg_feat = self.reg_decomp(feat, avg_feat) # cls prediction and alignment cls_logits = self.tood_cls(cls_feat) if self.use_align_head: cls_prob = F.relu(self.cls_prob_conv1(feat)) cls_prob = F.sigmoid(self.cls_prob_conv2(cls_prob)) cls_score = (F.sigmoid(cls_logits) * cls_prob).sqrt() else: cls_score = F.sigmoid(cls_logits) cls_score_list.append(cls_score.flatten(2).transpose([0, 2, 1])) # reg prediction and alignment reg_dist = scale_reg(self.tood_reg(reg_feat).exp()) reg_dist = reg_dist.flatten(2).transpose([0, 2, 1]) reg_bbox = batch_distance2bbox( anchor_centers.unsqueeze(0), reg_dist) if self.use_align_head: reg_offset = F.relu(self.reg_offset_conv1(feat)) reg_offset = self.reg_offset_conv2(reg_offset) reg_bbox = reg_bbox.transpose([0, 2, 1]).reshape([b, 4, h, w]) anchor_centers = anchor_centers.reshape([1, h, w, 2]) bbox_pred = self._reg_grid_sample(reg_bbox, reg_offset, anchor_centers) bbox_pred = bbox_pred.flatten(2).transpose([0, 2, 1]) else: bbox_pred = reg_bbox if not self.training: bbox_pred *= stride bbox_pred_list.append(bbox_pred) cls_score_list = paddle.concat(cls_score_list, axis=1) bbox_pred_list = paddle.concat(bbox_pred_list, axis=1) return cls_score_list, bbox_pred_list, anchors, num_anchors_list, stride_tensor @staticmethod def _focal_loss(score, label, alpha=0.25, gamma=2.0): weight = (score - label).pow(gamma) if alpha > 0: alpha_t = alpha * label + (1 - alpha) * (1 - label) weight *= alpha_t loss = F.binary_cross_entropy( score, label, weight=weight, reduction='sum') return loss def get_loss(self, head_outs, gt_meta): pred_scores, pred_bboxes, anchors, \ num_anchors_list, stride_tensor = head_outs gt_labels = gt_meta['gt_class'] gt_bboxes = gt_meta['gt_bbox'] pad_gt_mask = gt_meta['pad_gt_mask'] # label assignment if gt_meta['epoch_id'] < self.static_assigner_epoch: assigned_labels, assigned_bboxes, assigned_scores = self.static_assigner( anchors, num_anchors_list, gt_labels, gt_bboxes, pad_gt_mask, bg_index=self.num_classes) alpha_l = 0.25 else: assigned_labels, assigned_bboxes, assigned_scores = self.assigner( pred_scores.detach(), pred_bboxes.detach() * stride_tensor, bbox_center(anchors), num_anchors_list, gt_labels, gt_bboxes, pad_gt_mask, bg_index=self.num_classes) alpha_l = -1 # rescale bbox assigned_bboxes /= stride_tensor # classification loss loss_cls = self._focal_loss(pred_scores, assigned_scores, alpha=alpha_l) # select positive samples mask mask_positive = (assigned_labels != self.num_classes) num_pos = mask_positive.astype(paddle.float32).sum() # bbox regression loss if num_pos > 0: bbox_mask = mask_positive.unsqueeze(-1).tile([1, 1, 4]) pred_bboxes_pos = paddle.masked_select(pred_bboxes, bbox_mask).reshape([-1, 4]) assigned_bboxes_pos = paddle.masked_select( assigned_bboxes, bbox_mask).reshape([-1, 4]) bbox_weight = paddle.masked_select( assigned_scores.sum(-1), mask_positive).unsqueeze(-1) # iou loss loss_iou = self.giou_loss(pred_bboxes_pos, assigned_bboxes_pos) * bbox_weight loss_iou = loss_iou.sum() / bbox_weight.sum() # l1 loss loss_l1 = F.l1_loss(pred_bboxes_pos, assigned_bboxes_pos) else: loss_iou = paddle.zeros([1]) loss_l1 = paddle.zeros([1]) loss_cls /= assigned_scores.sum().clip(min=1) loss = self.loss_weight['class'] * loss_cls + self.loss_weight[ 'iou'] * loss_iou return { 'loss': loss, 'loss_class': loss_cls, 'loss_iou': loss_iou, 'loss_l1': loss_l1 } def post_process(self, head_outs, img_shape, scale_factor): pred_scores, pred_bboxes, _, _, _ = head_outs pred_scores = pred_scores.transpose([0, 2, 1]) for i in range(len(pred_bboxes)): pred_bboxes[i, :, 0] = pred_bboxes[i, :, 0].clip( min=0, max=img_shape[i, 1]) pred_bboxes[i, :, 1] = pred_bboxes[i, :, 1].clip( min=0, max=img_shape[i, 0]) pred_bboxes[i, :, 2] = pred_bboxes[i, :, 2].clip( min=0, max=img_shape[i, 1]) pred_bboxes[i, :, 3] = pred_bboxes[i, :, 3].clip( min=0, max=img_shape[i, 0]) # scale bbox to origin scale_factor = scale_factor.flip([1]).tile([1, 2]).unsqueeze(1) pred_bboxes /= scale_factor bbox_pred, bbox_num, _ = self.nms(pred_bboxes, pred_scores) return bbox_pred, bbox_num

PaddleDetection/ppdet/modeling/heads/tood_head.py/0

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import paddle import paddle.nn as nn import paddle.nn.functional as F from ppdet.core.workspace import register from ppdet.modeling import ops from functools import partial __all__ = ['FCOSLoss', 'FCOSLossMILC', 'FCOSLossCR'] def flatten_tensor(inputs, channel_first=False): """ Flatten a Tensor Args: inputs (Tensor): 4-D Tensor with shape [N, C, H, W] or [N, H, W, C] channel_first (bool): If true the dimension order of Tensor is [N, C, H, W], otherwise is [N, H, W, C] Return: output_channel_last (Tensor): The flattened Tensor in channel_last style """ if channel_first: input_channel_last = paddle.transpose(inputs, perm=[0, 2, 3, 1]) else: input_channel_last = inputs output_channel_last = paddle.flatten( input_channel_last, start_axis=0, stop_axis=2) return output_channel_last @register class FCOSLoss(nn.Layer): """ FCOSLoss Args: loss_alpha (float): alpha in focal loss loss_gamma (float): gamma in focal loss iou_loss_type (str): location loss type, IoU/GIoU/LINEAR_IoU reg_weights (float): weight for location loss quality (str): quality branch, centerness/iou """ def __init__(self, loss_alpha=0.25, loss_gamma=2.0, iou_loss_type="giou", reg_weights=1.0, quality='centerness'): super(FCOSLoss, self).__init__() self.loss_alpha = loss_alpha self.loss_gamma = loss_gamma self.iou_loss_type = iou_loss_type self.reg_weights = reg_weights self.quality = quality def _iou_loss(self, pred, targets, positive_mask, weights=None, return_iou=False): """ Calculate the loss for location prediction Args: pred (Tensor): bounding boxes prediction targets (Tensor): targets for positive samples positive_mask (Tensor): mask of positive samples weights (Tensor): weights for each positive samples Return: loss (Tensor): location loss """ plw = pred[:, 0] * positive_mask pth = pred[:, 1] * positive_mask prw = pred[:, 2] * positive_mask pbh = pred[:, 3] * positive_mask tlw = targets[:, 0] * positive_mask tth = targets[:, 1] * positive_mask trw = targets[:, 2] * positive_mask tbh = targets[:, 3] * positive_mask tlw.stop_gradient = True trw.stop_gradient = True tth.stop_gradient = True tbh.stop_gradient = True ilw = paddle.minimum(plw, tlw) irw = paddle.minimum(prw, trw) ith = paddle.minimum(pth, tth) ibh = paddle.minimum(pbh, tbh) clw = paddle.maximum(plw, tlw) crw = paddle.maximum(prw, trw) cth = paddle.maximum(pth, tth) cbh = paddle.maximum(pbh, tbh) area_predict = (plw + prw) * (pth + pbh) area_target = (tlw + trw) * (tth + tbh) area_inter = (ilw + irw) * (ith + ibh) ious = (area_inter + 1.0) / ( area_predict + area_target - area_inter + 1.0) ious = ious * positive_mask if return_iou: return ious if self.iou_loss_type.lower() == "linear_iou": loss = 1.0 - ious elif self.iou_loss_type.lower() == "giou": area_uniou = area_predict + area_target - area_inter area_circum = (clw + crw) * (cth + cbh) + 1e-7 giou = ious - (area_circum - area_uniou) / area_circum loss = 1.0 - giou elif self.iou_loss_type.lower() == "iou": loss = 0.0 - paddle.log(ious) else: raise KeyError if weights is not None: loss = loss * weights return loss def forward(self, cls_logits, bboxes_reg, centerness, tag_labels, tag_bboxes, tag_center): """ Calculate the loss for classification, location and centerness Args: cls_logits (list): list of Tensor, which is predicted score for all anchor points with shape [N, M, C] bboxes_reg (list): list of Tensor, which is predicted offsets for all anchor points with shape [N, M, 4] centerness (list): list of Tensor, which is predicted centerness for all anchor points with shape [N, M, 1] tag_labels (list): list of Tensor, which is category targets for each anchor point tag_bboxes (list): list of Tensor, which is bounding boxes targets for positive samples tag_center (list): list of Tensor, which is centerness targets for positive samples Return: loss (dict): loss composed by classification loss, bounding box """ cls_logits_flatten_list = [] bboxes_reg_flatten_list = [] centerness_flatten_list = [] tag_labels_flatten_list = [] tag_bboxes_flatten_list = [] tag_center_flatten_list = [] num_lvl = len(cls_logits) for lvl in range(num_lvl): cls_logits_flatten_list.append( flatten_tensor(cls_logits[lvl], True)) bboxes_reg_flatten_list.append( flatten_tensor(bboxes_reg[lvl], True)) centerness_flatten_list.append( flatten_tensor(centerness[lvl], True)) tag_labels_flatten_list.append( flatten_tensor(tag_labels[lvl], False)) tag_bboxes_flatten_list.append( flatten_tensor(tag_bboxes[lvl], False)) tag_center_flatten_list.append( flatten_tensor(tag_center[lvl], False)) cls_logits_flatten = paddle.concat(cls_logits_flatten_list, axis=0) bboxes_reg_flatten = paddle.concat(bboxes_reg_flatten_list, axis=0) centerness_flatten = paddle.concat(centerness_flatten_list, axis=0) tag_labels_flatten = paddle.concat(tag_labels_flatten_list, axis=0) tag_bboxes_flatten = paddle.concat(tag_bboxes_flatten_list, axis=0) tag_center_flatten = paddle.concat(tag_center_flatten_list, axis=0) tag_labels_flatten.stop_gradient = True tag_bboxes_flatten.stop_gradient = True tag_center_flatten.stop_gradient = True mask_positive_bool = tag_labels_flatten > 0 mask_positive_bool.stop_gradient = True mask_positive_float = paddle.cast(mask_positive_bool, dtype="float32") mask_positive_float.stop_gradient = True num_positive_fp32 = paddle.sum(mask_positive_float) num_positive_fp32.stop_gradient = True num_positive_int32 = paddle.cast(num_positive_fp32, dtype="int32") num_positive_int32 = num_positive_int32 * 0 + 1 num_positive_int32.stop_gradient = True normalize_sum = paddle.sum(tag_center_flatten * mask_positive_float) normalize_sum.stop_gradient = True # 1. cls_logits: sigmoid_focal_loss # expand onehot labels num_classes = cls_logits_flatten.shape[-1] tag_labels_flatten = paddle.squeeze(tag_labels_flatten, axis=-1) tag_labels_flatten_bin = F.one_hot( tag_labels_flatten, num_classes=1 + num_classes) tag_labels_flatten_bin = tag_labels_flatten_bin[:, 1:] # sigmoid_focal_loss cls_loss = F.sigmoid_focal_loss( cls_logits_flatten, tag_labels_flatten_bin) / num_positive_fp32 if self.quality == 'centerness': # 2. bboxes_reg: giou_loss mask_positive_float = paddle.squeeze(mask_positive_float, axis=-1) tag_center_flatten = paddle.squeeze(tag_center_flatten, axis=-1) reg_loss = self._iou_loss( bboxes_reg_flatten, tag_bboxes_flatten, mask_positive_float, weights=tag_center_flatten) reg_loss = reg_loss * mask_positive_float / normalize_sum # 3. centerness: sigmoid_cross_entropy_with_logits_loss centerness_flatten = paddle.squeeze(centerness_flatten, axis=-1) quality_loss = ops.sigmoid_cross_entropy_with_logits( centerness_flatten, tag_center_flatten) quality_loss = quality_loss * mask_positive_float / num_positive_fp32 elif self.quality == 'iou': # 2. bboxes_reg: giou_loss mask_positive_float = paddle.squeeze(mask_positive_float, axis=-1) tag_center_flatten = paddle.squeeze(tag_center_flatten, axis=-1) reg_loss = self._iou_loss( bboxes_reg_flatten, tag_bboxes_flatten, mask_positive_float, weights=None) reg_loss = reg_loss * mask_positive_float / num_positive_fp32 # num_positive_fp32 is num_foreground # 3. centerness: sigmoid_cross_entropy_with_logits_loss centerness_flatten = paddle.squeeze(centerness_flatten, axis=-1) gt_ious = self._iou_loss( bboxes_reg_flatten, tag_bboxes_flatten, mask_positive_float, weights=None, return_iou=True) quality_loss = ops.sigmoid_cross_entropy_with_logits( centerness_flatten, gt_ious) quality_loss = quality_loss * mask_positive_float / num_positive_fp32 else: raise Exception(f'Unknown quality type: {self.quality}') loss_all = { "loss_cls": paddle.sum(cls_loss), "loss_box": paddle.sum(reg_loss), "loss_quality": paddle.sum(quality_loss), } return loss_all @register class FCOSLossMILC(FCOSLoss): """ FCOSLossMILC for ARSL in semi-det(ssod) Args: loss_alpha (float): alpha in focal loss loss_gamma (float): gamma in focal loss iou_loss_type (str): location loss type, IoU/GIoU/LINEAR_IoU reg_weights (float): weight for location loss """ def __init__(self, loss_alpha=0.25, loss_gamma=2.0, iou_loss_type="giou", reg_weights=1.0): super(FCOSLossMILC, self).__init__() self.loss_alpha = loss_alpha self.loss_gamma = loss_gamma self.iou_loss_type = iou_loss_type self.reg_weights = reg_weights def iou_loss(self, pred, targets, weights=None, avg_factor=None): """ Calculate the loss for location prediction Args: pred (Tensor): bounding boxes prediction targets (Tensor): targets for positive samples weights (Tensor): weights for each positive samples Return: loss (Tensor): location loss """ plw = pred[:, 0] pth = pred[:, 1] prw = pred[:, 2] pbh = pred[:, 3] tlw = targets[:, 0] tth = targets[:, 1] trw = targets[:, 2] tbh = targets[:, 3] tlw.stop_gradient = True trw.stop_gradient = True tth.stop_gradient = True tbh.stop_gradient = True ilw = paddle.minimum(plw, tlw) irw = paddle.minimum(prw, trw) ith = paddle.minimum(pth, tth) ibh = paddle.minimum(pbh, tbh) clw = paddle.maximum(plw, tlw) crw = paddle.maximum(prw, trw) cth = paddle.maximum(pth, tth) cbh = paddle.maximum(pbh, tbh) area_predict = (plw + prw) * (pth + pbh) area_target = (tlw + trw) * (tth + tbh) area_inter = (ilw + irw) * (ith + ibh) ious = (area_inter + 1.0) / ( area_predict + area_target - area_inter + 1.0) ious = ious if self.iou_loss_type.lower() == "linear_iou": loss = 1.0 - ious elif self.iou_loss_type.lower() == "giou": area_uniou = area_predict + area_target - area_inter area_circum = (clw + crw) * (cth + cbh) + 1e-7 giou = ious - (area_circum - area_uniou) / area_circum loss = 1.0 - giou elif self.iou_loss_type.lower() == "iou": loss = 0.0 - paddle.log(ious) else: raise KeyError if weights is not None: loss = loss * weights loss = paddle.sum(loss) if avg_factor is not None: loss = loss / avg_factor return loss # temp function: calcualate iou between bbox and target def _bbox_overlap_align(self, pred, targets): assert pred.shape[0] == targets.shape[0], \ 'the pred should be aligned with target.' plw = pred[:, 0] pth = pred[:, 1] prw = pred[:, 2] pbh = pred[:, 3] tlw = targets[:, 0] tth = targets[:, 1] trw = targets[:, 2] tbh = targets[:, 3] ilw = paddle.minimum(plw, tlw) irw = paddle.minimum(prw, trw) ith = paddle.minimum(pth, tth) ibh = paddle.minimum(pbh, tbh) area_predict = (plw + prw) * (pth + pbh) area_target = (tlw + trw) * (tth + tbh) area_inter = (ilw + irw) * (ith + ibh) ious = (area_inter + 1.0) / ( area_predict + area_target - area_inter + 1.0) return ious def iou_based_soft_label_loss(self, pred, target, alpha=0.75, gamma=2.0, iou_weighted=False, implicit_iou=None, avg_factor=None): assert pred.shape == target.shape pred = F.sigmoid(pred) target = target.cast(pred.dtype) if implicit_iou is not None: pred = pred * implicit_iou if iou_weighted: focal_weight = (pred - target).abs().pow(gamma) * target * (target > 0.0).cast('float32') + \ alpha * (pred - target).abs().pow(gamma) * \ (target <= 0.0).cast('float32') else: focal_weight = (pred - target).abs().pow(gamma) * (target > 0.0).cast('float32') + \ alpha * (pred - target).abs().pow(gamma) * \ (target <= 0.0).cast('float32') # focal loss loss = F.binary_cross_entropy( pred, target, reduction='none') * focal_weight if avg_factor is not None: loss = loss / avg_factor return loss def forward(self, cls_logits, bboxes_reg, centerness, tag_labels, tag_bboxes, tag_center): """ Calculate the loss for classification, location and centerness Args: cls_logits (list): list of Tensor, which is predicted score for all anchor points with shape [N, M, C] bboxes_reg (list): list of Tensor, which is predicted offsets for all anchor points with shape [N, M, 4] centerness (list): list of Tensor, which is predicted centerness for all anchor points with shape [N, M, 1] tag_labels (list): list of Tensor, which is category targets for each anchor point tag_bboxes (list): list of Tensor, which is bounding boxes targets for positive samples tag_center (list): list of Tensor, which is centerness targets for positive samples Return: loss (dict): loss composed by classification loss, bounding box """ cls_logits_flatten_list = [] bboxes_reg_flatten_list = [] centerness_flatten_list = [] tag_labels_flatten_list = [] tag_bboxes_flatten_list = [] tag_center_flatten_list = [] num_lvl = len(cls_logits) for lvl in range(num_lvl): cls_logits_flatten_list.append( flatten_tensor(cls_logits[lvl], True)) bboxes_reg_flatten_list.append( flatten_tensor(bboxes_reg[lvl], True)) centerness_flatten_list.append( flatten_tensor(centerness[lvl], True)) tag_labels_flatten_list.append( flatten_tensor(tag_labels[lvl], False)) tag_bboxes_flatten_list.append( flatten_tensor(tag_bboxes[lvl], False)) tag_center_flatten_list.append( flatten_tensor(tag_center[lvl], False)) cls_logits_flatten = paddle.concat(cls_logits_flatten_list, axis=0) bboxes_reg_flatten = paddle.concat(bboxes_reg_flatten_list, axis=0) centerness_flatten = paddle.concat(centerness_flatten_list, axis=0) tag_labels_flatten = paddle.concat(tag_labels_flatten_list, axis=0) tag_bboxes_flatten = paddle.concat(tag_bboxes_flatten_list, axis=0) tag_center_flatten = paddle.concat(tag_center_flatten_list, axis=0) tag_labels_flatten.stop_gradient = True tag_bboxes_flatten.stop_gradient = True tag_center_flatten.stop_gradient = True # find positive index mask_positive_bool = tag_labels_flatten > 0 mask_positive_bool.stop_gradient = True mask_positive_float = paddle.cast(mask_positive_bool, dtype="float32") mask_positive_float.stop_gradient = True num_positive_fp32 = paddle.sum(mask_positive_float) num_positive_fp32.stop_gradient = True num_positive_int32 = paddle.cast(num_positive_fp32, dtype="int32") num_positive_int32 = num_positive_int32 * 0 + 1 num_positive_int32.stop_gradient = True # centerness target is used as reg weight normalize_sum = paddle.sum(tag_center_flatten * mask_positive_float) normalize_sum.stop_gradient = True # 1. IoU-Based soft label loss # calculate iou with paddle.no_grad(): pos_ind = paddle.nonzero( tag_labels_flatten.reshape([-1]) > 0).reshape([-1]) pos_pred = bboxes_reg_flatten[pos_ind] pos_target = tag_bboxes_flatten[pos_ind] bbox_iou = self._bbox_overlap_align(pos_pred, pos_target) # pos labels pos_labels = tag_labels_flatten[pos_ind].squeeze(1) cls_target = paddle.zeros(cls_logits_flatten.shape) cls_target[pos_ind, pos_labels - 1] = bbox_iou cls_loss = self.iou_based_soft_label_loss( cls_logits_flatten, cls_target, implicit_iou=F.sigmoid(centerness_flatten), avg_factor=num_positive_fp32) # 2. bboxes_reg: giou_loss mask_positive_float = paddle.squeeze(mask_positive_float, axis=-1) tag_center_flatten = paddle.squeeze(tag_center_flatten, axis=-1) reg_loss = self._iou_loss( bboxes_reg_flatten, tag_bboxes_flatten, mask_positive_float, weights=tag_center_flatten) reg_loss = reg_loss * mask_positive_float / normalize_sum # 3. iou loss pos_iou_pred = paddle.squeeze(centerness_flatten, axis=-1)[pos_ind] loss_iou = ops.sigmoid_cross_entropy_with_logits(pos_iou_pred, bbox_iou) loss_iou = loss_iou / num_positive_fp32 * 0.5 loss_all = { "loss_cls": paddle.sum(cls_loss), "loss_box": paddle.sum(reg_loss), 'loss_iou': paddle.sum(loss_iou), } return loss_all # Concat multi-level feature maps by image def levels_to_images(mlvl_tensor): batch_size = mlvl_tensor[0].shape[0] batch_list = [[] for _ in range(batch_size)] channels = mlvl_tensor[0].shape[1] for t in mlvl_tensor: t = t.transpose([0, 2, 3, 1]) t = t.reshape([batch_size, -1, channels]) for img in range(batch_size): batch_list[img].append(t[img]) return [paddle.concat(item, axis=0) for item in batch_list] def multi_apply(func, *args, **kwargs): """Apply function to a list of arguments. Note: This function applies the ``func`` to multiple inputs and map the multiple outputs of the ``func`` into different list. Each list contains the same type of outputs corresponding to different inputs. Args: func (Function): A function that will be applied to a list of arguments Returns: tuple(list): A tuple containing multiple list, each list contains \ a kind of returned results by the function """ pfunc = partial(func, **kwargs) if kwargs else func map_results = map(pfunc, *args) return tuple(map(list, zip(*map_results))) @register class FCOSLossCR(FCOSLossMILC): """ FCOSLoss of Consistency Regularization """ def __init__(self, iou_loss_type="giou", cls_weight=2.0, reg_weight=2.0, iou_weight=0.5, hard_neg_mining_flag=True): super(FCOSLossCR, self).__init__() self.iou_loss_type = iou_loss_type self.cls_weight = cls_weight self.reg_weight = reg_weight self.iou_weight = iou_weight self.hard_neg_mining_flag = hard_neg_mining_flag def iou_loss(self, pred, targets, weights=None, avg_factor=None): """ Calculate the loss for location prediction Args: pred (Tensor): bounding boxes prediction targets (Tensor): targets for positive samples weights (Tensor): weights for each positive samples Return: loss (Tensor): location loss """ plw = pred[:, 0] pth = pred[:, 1] prw = pred[:, 2] pbh = pred[:, 3] tlw = targets[:, 0] tth = targets[:, 1] trw = targets[:, 2] tbh = targets[:, 3] tlw.stop_gradient = True trw.stop_gradient = True tth.stop_gradient = True tbh.stop_gradient = True ilw = paddle.minimum(plw, tlw) irw = paddle.minimum(prw, trw) ith = paddle.minimum(pth, tth) ibh = paddle.minimum(pbh, tbh) clw = paddle.maximum(plw, tlw) crw = paddle.maximum(prw, trw) cth = paddle.maximum(pth, tth) cbh = paddle.maximum(pbh, tbh) area_predict = (plw + prw) * (pth + pbh) area_target = (tlw + trw) * (tth + tbh) area_inter = (ilw + irw) * (ith + ibh) ious = (area_inter + 1.0) / ( area_predict + area_target - area_inter + 1.0) ious = ious if self.iou_loss_type.lower() == "linear_iou": loss = 1.0 - ious elif self.iou_loss_type.lower() == "giou": area_uniou = area_predict + area_target - area_inter area_circum = (clw + crw) * (cth + cbh) + 1e-7 giou = ious - (area_circum - area_uniou) / area_circum loss = 1.0 - giou elif self.iou_loss_type.lower() == "iou": loss = 0.0 - paddle.log(ious) else: raise KeyError if weights is not None: loss = loss * weights loss = paddle.sum(loss) if avg_factor is not None: loss = loss / avg_factor return loss # calcualate iou between bbox and target def bbox_overlap_align(self, pred, targets): assert pred.shape[0] == targets.shape[0], \ 'the pred should be aligned with target.' plw = pred[:, 0] pth = pred[:, 1] prw = pred[:, 2] pbh = pred[:, 3] tlw = targets[:, 0] tth = targets[:, 1] trw = targets[:, 2] tbh = targets[:, 3] ilw = paddle.minimum(plw, tlw) irw = paddle.minimum(prw, trw) ith = paddle.minimum(pth, tth) ibh = paddle.minimum(pbh, tbh) area_predict = (plw + prw) * (pth + pbh) area_target = (tlw + trw) * (tth + tbh) area_inter = (ilw + irw) * (ith + ibh) ious = (area_inter + 1.0) / ( area_predict + area_target - area_inter + 1.0) return ious # cls loss: iou-based soft lable with joint iou def quality_focal_loss(self, stu_cls, targets, quality=None, weights=None, alpha=0.75, gamma=2.0, avg_factor='sum'): stu_cls = F.sigmoid(stu_cls) if quality is not None: stu_cls = stu_cls * F.sigmoid(quality) focal_weight = (stu_cls - targets).abs().pow(gamma) * (targets > 0.0).cast('float32') + \ alpha * (stu_cls - targets).abs().pow(gamma) * \ (targets <= 0.0).cast('float32') loss = F.binary_cross_entropy( stu_cls, targets, reduction='none') * focal_weight if weights is not None: loss = loss * weights.reshape([-1, 1]) loss = paddle.sum(loss) if avg_factor is not None: loss = loss / avg_factor return loss # generate points according to feature maps def compute_locations_by_level(self, fpn_stride, h, w): """ Compute locations of anchor points of each FPN layer Return: Anchor points locations of current FPN feature map """ shift_x = paddle.arange(0, w * fpn_stride, fpn_stride) shift_y = paddle.arange(0, h * fpn_stride, fpn_stride) shift_x = paddle.unsqueeze(shift_x, axis=0) shift_y = paddle.unsqueeze(shift_y, axis=1) shift_x = paddle.expand(shift_x, shape=[h, w]) shift_y = paddle.expand(shift_y, shape=[h, w]) shift_x = paddle.reshape(shift_x, shape=[-1]) shift_y = paddle.reshape(shift_y, shape=[-1]) location = paddle.stack( [shift_x, shift_y], axis=-1) + float(fpn_stride) / 2 return location # decode bbox from ltrb to x1y1x2y2 def decode_bbox(self, ltrb, points): assert ltrb.shape[0] == points.shape[0], \ "When decoding bbox in one image, the num of loc should be same with points." bbox_decoding = paddle.stack( [ points[:, 0] - ltrb[:, 0], points[:, 1] - ltrb[:, 1], points[:, 0] + ltrb[:, 2], points[:, 1] + ltrb[:, 3] ], axis=1) return bbox_decoding # encode bbox from x1y1x2y2 to ltrb def encode_bbox(self, bbox, points): assert bbox.shape[0] == points.shape[0], \ "When encoding bbox in one image, the num of bbox should be same with points." bbox_encoding = paddle.stack( [ points[:, 0] - bbox[:, 0], points[:, 1] - bbox[:, 1], bbox[:, 2] - points[:, 0], bbox[:, 3] - points[:, 1] ], axis=1) return bbox_encoding def calcualate_iou(self, gt_bbox, predict_bbox): # bbox area gt_area = (gt_bbox[:, 2] - gt_bbox[:, 0]) * \ (gt_bbox[:, 3] - gt_bbox[:, 1]) predict_area = (predict_bbox[:, 2] - predict_bbox[:, 0]) * \ (predict_bbox[:, 3] - predict_bbox[:, 1]) # overlop area lt = paddle.fmax(gt_bbox[:, None, :2], predict_bbox[None, :, :2]) rb = paddle.fmin(gt_bbox[:, None, 2:], predict_bbox[None, :, 2:]) wh = paddle.clip(rb - lt, min=0) overlap = wh[..., 0] * wh[..., 1] # iou iou = overlap / (gt_area[:, None] + predict_area[None, :] - overlap) return iou # select potential positives from hard negatives def hard_neg_mining(self, cls_score, loc_ltrb, quality, pos_ind, hard_neg_ind, loc_mask, loc_targets, iou_thresh=0.6): # get points locations and strides points_list = [] strides_list = [] scale_list = [] scale = [0, 1, 2, 3, 4] for fpn_scale, fpn_stride, HW in zip(scale, self.fpn_stride, self.lvl_hw): h, w = HW lvl_points = self.compute_locations_by_level(fpn_stride, h, w) points_list.append(lvl_points) lvl_strides = paddle.full([h * w, 1], fpn_stride) strides_list.append(lvl_strides) lvl_scales = paddle.full([h * w, 1], fpn_scale) scale_list.append(lvl_scales) points = paddle.concat(points_list, axis=0) strides = paddle.concat(strides_list, axis=0) scales = paddle.concat(scale_list, axis=0) # cls scores cls_vals = F.sigmoid(cls_score) * F.sigmoid(quality) max_vals = paddle.max(cls_vals, axis=-1) class_ind = paddle.argmax(cls_vals, axis=-1) ### calculate iou between positive and hard negative # decode pos bbox pos_cls = max_vals[pos_ind] pos_loc = loc_ltrb[pos_ind].reshape([-1, 4]) pos_strides = strides[pos_ind] pos_points = points[pos_ind].reshape([-1, 2]) pos_loc = pos_loc * pos_strides pos_bbox = self.decode_bbox(pos_loc, pos_points) pos_scales = scales[pos_ind] # decode hard negative bbox hard_neg_loc = loc_ltrb[hard_neg_ind].reshape([-1, 4]) hard_neg_strides = strides[hard_neg_ind] hard_neg_points = points[hard_neg_ind].reshape([-1, 2]) hard_neg_loc = hard_neg_loc * hard_neg_strides hard_neg_bbox = self.decode_bbox(hard_neg_loc, hard_neg_points) hard_neg_scales = scales[hard_neg_ind] # iou between pos bbox and hard negative bbox hard_neg_pos_iou = self.calcualate_iou(hard_neg_bbox, pos_bbox) ### select potential positives from hard negatives # scale flag scale_temp = paddle.abs( pos_scales.reshape([-1])[None, :] - hard_neg_scales.reshape([-1]) [:, None]) scale_flag = (scale_temp <= 1.) # iou flag iou_flag = (hard_neg_pos_iou >= iou_thresh) # same class flag pos_class = class_ind[pos_ind] hard_neg_class = class_ind[hard_neg_ind] class_flag = pos_class[None, :] - hard_neg_class[:, None] class_flag = (class_flag == 0) # hard negative point inside positive bbox flag ltrb_temp = paddle.stack( [ hard_neg_points[:, None, 0] - pos_bbox[None, :, 0], hard_neg_points[:, None, 1] - pos_bbox[None, :, 1], pos_bbox[None, :, 2] - hard_neg_points[:, None, 0], pos_bbox[None, :, 3] - hard_neg_points[:, None, 1] ], axis=-1) inside_flag = ltrb_temp.min(axis=-1) > 0 # reset iou valid_flag = (iou_flag & class_flag & inside_flag & scale_flag) invalid_iou = paddle.zeros_like(hard_neg_pos_iou) hard_neg_pos_iou = paddle.where(valid_flag, hard_neg_pos_iou, invalid_iou) pos_hard_neg_max_iou = hard_neg_pos_iou.max(axis=-1) # selece potential pos potential_pos_ind = (pos_hard_neg_max_iou > 0.) num_potential_pos = paddle.nonzero(potential_pos_ind).shape[0] if num_potential_pos == 0: return None ### calculate loc target：aggregate all matching bboxes as the bbox targets of potential pos # prepare data potential_points = hard_neg_points[potential_pos_ind].reshape([-1, 2]) potential_strides = hard_neg_strides[potential_pos_ind] potential_valid_flag = valid_flag[potential_pos_ind] potential_pos_ind = hard_neg_ind[potential_pos_ind] # get cls and box of matching positives pos_cls = max_vals[pos_ind] expand_pos_bbox = paddle.expand( pos_bbox, shape=[num_potential_pos, pos_bbox.shape[0], pos_bbox.shape[1]]) expand_pos_cls = paddle.expand( pos_cls, shape=[num_potential_pos, pos_cls.shape[0]]) invalid_cls = paddle.zeros_like(expand_pos_cls) expand_pos_cls = paddle.where(potential_valid_flag, expand_pos_cls, invalid_cls) expand_pos_cls = paddle.unsqueeze(expand_pos_cls, axis=-1) # aggregate box based on cls_score agg_bbox = (expand_pos_bbox * expand_pos_cls).sum(axis=1) \ / expand_pos_cls.sum(axis=1) agg_ltrb = self.encode_bbox(agg_bbox, potential_points) agg_ltrb = agg_ltrb / potential_strides # loc target for all pos loc_targets[potential_pos_ind] = agg_ltrb loc_mask[potential_pos_ind] = 1. return loc_mask, loc_targets # get training targets def get_targets_per_img(self, tea_cls, tea_loc, tea_iou, stu_cls, stu_loc, stu_iou): ### sample selection # prepare datas tea_cls_scores = F.sigmoid(tea_cls) * F.sigmoid(tea_iou) class_ind = paddle.argmax(tea_cls_scores, axis=-1) max_vals = paddle.max(tea_cls_scores, axis=-1) cls_mask = paddle.zeros_like( max_vals ) # set cls valid mask: pos is 1, hard_negative and negative are 0. num_pos, num_hard_neg = 0, 0 # mean-std selection # using nonzero to turn index from bool to int, because the index will be used to compose two-dim index in following. # using squeeze rather than reshape to avoid errors when no score is larger than thresh. candidate_ind = paddle.nonzero(max_vals >= 0.1).squeeze(axis=-1) num_candidate = candidate_ind.shape[0] if num_candidate > 0: # pos thresh = mean + std to select pos samples candidate_score = max_vals[candidate_ind] candidate_score_mean = candidate_score.mean() candidate_score_std = candidate_score.std() pos_thresh = (candidate_score_mean + candidate_score_std).clip( max=0.4) # select pos pos_ind = paddle.nonzero(max_vals >= pos_thresh).squeeze(axis=-1) num_pos = pos_ind.shape[0] # select hard negatives as potential pos hard_neg_ind = (max_vals >= 0.1) & (max_vals < pos_thresh) hard_neg_ind = paddle.nonzero(hard_neg_ind).squeeze(axis=-1) num_hard_neg = hard_neg_ind.shape[0] # if not positive, directly select top-10 as pos. if (num_pos == 0): num_pos = 10 _, pos_ind = paddle.topk(max_vals, k=num_pos) cls_mask[pos_ind] = 1. ### Consistency Regularization Training targets # cls targets pos_class_ind = class_ind[pos_ind] cls_targets = paddle.zeros_like(tea_cls) cls_targets[pos_ind, pos_class_ind] = tea_cls_scores[pos_ind, pos_class_ind] # hard negative cls target if num_hard_neg != 0: cls_targets[hard_neg_ind] = tea_cls_scores[hard_neg_ind] # loc targets loc_targets = paddle.zeros_like(tea_loc) loc_targets[pos_ind] = tea_loc[pos_ind] # iou targets iou_targets = paddle.zeros( shape=[tea_iou.shape[0]], dtype=tea_iou.dtype) iou_targets[pos_ind] = F.sigmoid( paddle.squeeze( tea_iou, axis=-1)[pos_ind]) loc_mask = cls_mask.clone() # select potential positive from hard negatives for loc_task training if (num_hard_neg > 0) and self.hard_neg_mining_flag: results = self.hard_neg_mining(tea_cls, tea_loc, tea_iou, pos_ind, hard_neg_ind, loc_mask, loc_targets) if results is not None: loc_mask, loc_targets = results loc_pos_ind = paddle.nonzero(loc_mask > 0.).squeeze(axis=-1) iou_targets[loc_pos_ind] = F.sigmoid( paddle.squeeze( tea_iou, axis=-1)[loc_pos_ind]) return cls_mask, loc_mask, \ cls_targets, loc_targets, iou_targets def forward(self, student_prediction, teacher_prediction): stu_cls_lvl, stu_loc_lvl, stu_iou_lvl = student_prediction tea_cls_lvl, tea_loc_lvl, tea_iou_lvl, self.fpn_stride = teacher_prediction # H and W of level (used for aggregating targets) self.lvl_hw = [] for t in tea_cls_lvl: _, _, H, W = t.shape self.lvl_hw.append([H, W]) # levels to images stu_cls_img = levels_to_images(stu_cls_lvl) stu_loc_img = levels_to_images(stu_loc_lvl) stu_iou_img = levels_to_images(stu_iou_lvl) tea_cls_img = levels_to_images(tea_cls_lvl) tea_loc_img = levels_to_images(tea_loc_lvl) tea_iou_img = levels_to_images(tea_iou_lvl) with paddle.no_grad(): cls_mask, loc_mask, \ cls_targets, loc_targets, iou_targets = multi_apply( self.get_targets_per_img, tea_cls_img, tea_loc_img, tea_iou_img, stu_cls_img, stu_loc_img, stu_iou_img ) # flatten preditction stu_cls = paddle.concat(stu_cls_img, axis=0) stu_loc = paddle.concat(stu_loc_img, axis=0) stu_iou = paddle.concat(stu_iou_img, axis=0) # flatten targets cls_mask = paddle.concat(cls_mask, axis=0) loc_mask = paddle.concat(loc_mask, axis=0) cls_targets = paddle.concat(cls_targets, axis=0) loc_targets = paddle.concat(loc_targets, axis=0) iou_targets = paddle.concat(iou_targets, axis=0) ### Training Weights and avg factor # find positives cls_pos_ind = paddle.nonzero(cls_mask > 0.).squeeze(axis=-1) loc_pos_ind = paddle.nonzero(loc_mask > 0.).squeeze(axis=-1) # cls weight cls_sample_weights = paddle.ones([cls_targets.shape[0]]) cls_avg_factor = paddle.max(cls_targets[cls_pos_ind], axis=-1).sum().item() # loc weight loc_sample_weights = paddle.max(cls_targets[loc_pos_ind], axis=-1) loc_avg_factor = loc_sample_weights.sum().item() # iou weight iou_sample_weights = paddle.ones([loc_pos_ind.shape[0]]) iou_avg_factor = loc_pos_ind.shape[0] ### unsupervised loss # cls loss loss_cls = self.quality_focal_loss( stu_cls, cls_targets, quality=stu_iou, weights=cls_sample_weights, avg_factor=cls_avg_factor) * self.cls_weight # iou loss pos_stu_iou = paddle.squeeze(stu_iou, axis=-1)[loc_pos_ind] pos_iou_targets = iou_targets[loc_pos_ind] loss_iou = F.binary_cross_entropy( F.sigmoid(pos_stu_iou), pos_iou_targets, reduction='none') * iou_sample_weights loss_iou = loss_iou.sum() / iou_avg_factor * self.iou_weight # box loss pos_stu_loc = stu_loc[loc_pos_ind] pos_loc_targets = loc_targets[loc_pos_ind] loss_box = self.iou_loss( pos_stu_loc, pos_loc_targets, weights=loc_sample_weights, avg_factor=loc_avg_factor) loss_box = loss_box * self.reg_weight loss_all = { "loss_cls": loss_cls, "loss_box": loss_box, "loss_iou": loss_iou, } return loss_all

PaddleDetection/ppdet/modeling/losses/fcos_loss.py/0

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import paddle import paddle.nn as nn import paddle.nn.functional as F from ppdet.core.workspace import register from ..bbox_utils import decode_yolo, xywh2xyxy, batch_iou_similarity __all__ = ['YOLOv3Loss'] def bbox_transform(pbox, anchor, downsample): pbox = decode_yolo(pbox, anchor, downsample) pbox = xywh2xyxy(pbox) return pbox @register class YOLOv3Loss(nn.Layer): __inject__ = ['iou_loss', 'iou_aware_loss'] __shared__ = ['num_classes'] def __init__(self, num_classes=80, ignore_thresh=0.7, label_smooth=False, downsample=[32, 16, 8], scale_x_y=1., iou_loss=None, iou_aware_loss=None): """ YOLOv3Loss layer Args: num_calsses (int): number of foreground classes ignore_thresh (float): threshold to ignore confidence loss label_smooth (bool): whether to use label smoothing downsample (list): downsample ratio for each detection block scale_x_y (float): scale_x_y factor iou_loss (object): IoULoss instance iou_aware_loss (object): IouAwareLoss instance """ super(YOLOv3Loss, self).__init__() self.num_classes = num_classes self.ignore_thresh = ignore_thresh self.label_smooth = label_smooth self.downsample = downsample self.scale_x_y = scale_x_y self.iou_loss = iou_loss self.iou_aware_loss = iou_aware_loss self.distill_pairs = [] def obj_loss(self, pbox, gbox, pobj, tobj, anchor, downsample): # pbox pbox = decode_yolo(pbox, anchor, downsample) pbox = xywh2xyxy(pbox) pbox = paddle.concat(pbox, axis=-1) b = pbox.shape[0] pbox = pbox.reshape((b, -1, 4)) # gbox gxy = gbox[:, :, 0:2] - gbox[:, :, 2:4] * 0.5 gwh = gbox[:, :, 0:2] + gbox[:, :, 2:4] * 0.5 gbox = paddle.concat([gxy, gwh], axis=-1) iou = batch_iou_similarity(pbox, gbox) iou.stop_gradient = True iou_max = iou.max(2) # [N, M1] iou_mask = paddle.cast(iou_max <= self.ignore_thresh, dtype=pbox.dtype) iou_mask.stop_gradient = True pobj = pobj.reshape((b, -1)) tobj = tobj.reshape((b, -1)) obj_mask = paddle.cast(tobj > 0, dtype=pbox.dtype) obj_mask.stop_gradient = True loss_obj = F.binary_cross_entropy_with_logits( pobj, obj_mask, reduction='none') loss_obj_pos = (loss_obj * tobj) loss_obj_neg = (loss_obj * (1 - obj_mask) * iou_mask) return loss_obj_pos + loss_obj_neg def cls_loss(self, pcls, tcls): if self.label_smooth: delta = min(1. / self.num_classes, 1. / 40) pos, neg = 1 - delta, delta # 1 for positive, 0 for negative tcls = pos * paddle.cast( tcls > 0., dtype=tcls.dtype) + neg * paddle.cast( tcls <= 0., dtype=tcls.dtype) loss_cls = F.binary_cross_entropy_with_logits( pcls, tcls, reduction='none') return loss_cls def yolov3_loss(self, p, t, gt_box, anchor, downsample, scale=1., eps=1e-10): na = len(anchor) b, c, h, w = p.shape if self.iou_aware_loss: ioup, p = p[:, 0:na, :, :], p[:, na:, :, :] ioup = ioup.unsqueeze(-1) p = p.reshape((b, na, -1, h, w)).transpose((0, 1, 3, 4, 2)) x, y = p[:, :, :, :, 0:1], p[:, :, :, :, 1:2] w, h = p[:, :, :, :, 2:3], p[:, :, :, :, 3:4] obj, pcls = p[:, :, :, :, 4:5], p[:, :, :, :, 5:] self.distill_pairs.append([x, y, w, h, obj, pcls]) t = t.transpose((0, 1, 3, 4, 2)) tx, ty = t[:, :, :, :, 0:1], t[:, :, :, :, 1:2] tw, th = t[:, :, :, :, 2:3], t[:, :, :, :, 3:4] tscale = t[:, :, :, :, 4:5] tobj, tcls = t[:, :, :, :, 5:6], t[:, :, :, :, 6:] tscale_obj = tscale * tobj loss = dict() x = scale * F.sigmoid(x) - 0.5 * (scale - 1.) y = scale * F.sigmoid(y) - 0.5 * (scale - 1.) if abs(scale - 1.) < eps: loss_x = F.binary_cross_entropy(x, tx, reduction='none') loss_y = F.binary_cross_entropy(y, ty, reduction='none') loss_xy = tscale_obj * (loss_x + loss_y) else: loss_x = paddle.abs(x - tx) loss_y = paddle.abs(y - ty) loss_xy = tscale_obj * (loss_x + loss_y) loss_xy = loss_xy.sum([1, 2, 3, 4]).mean() loss_w = paddle.abs(w - tw) loss_h = paddle.abs(h - th) loss_wh = tscale_obj * (loss_w + loss_h) loss_wh = loss_wh.sum([1, 2, 3, 4]).mean() loss['loss_xy'] = loss_xy loss['loss_wh'] = loss_wh if self.iou_loss is not None: # warn: do not modify x, y, w, h in place box, tbox = [x, y, w, h], [tx, ty, tw, th] pbox = bbox_transform(box, anchor, downsample) gbox = bbox_transform(tbox, anchor, downsample) loss_iou = self.iou_loss(pbox, gbox) loss_iou = loss_iou * tscale_obj loss_iou = loss_iou.sum([1, 2, 3, 4]).mean() loss['loss_iou'] = loss_iou if self.iou_aware_loss is not None: box, tbox = [x, y, w, h], [tx, ty, tw, th] pbox = bbox_transform(box, anchor, downsample) gbox = bbox_transform(tbox, anchor, downsample) loss_iou_aware = self.iou_aware_loss(ioup, pbox, gbox) loss_iou_aware = loss_iou_aware * tobj loss_iou_aware = loss_iou_aware.sum([1, 2, 3, 4]).mean() loss['loss_iou_aware'] = loss_iou_aware box = [x, y, w, h] loss_obj = self.obj_loss(box, gt_box, obj, tobj, anchor, downsample) loss_obj = loss_obj.sum(-1).mean() loss['loss_obj'] = loss_obj loss_cls = self.cls_loss(pcls, tcls) * tobj loss_cls = loss_cls.sum([1, 2, 3, 4]).mean() loss['loss_cls'] = loss_cls return loss def forward(self, inputs, targets, anchors): np = len(inputs) gt_targets = [targets['target{}'.format(i)] for i in range(np)] gt_box = targets['gt_bbox'] yolo_losses = dict() self.distill_pairs.clear() for x, t, anchor, downsample in zip(inputs, gt_targets, anchors, self.downsample): yolo_loss = self.yolov3_loss( x.astype('float32'), t, gt_box, anchor, downsample, self.scale_x_y) for k, v in yolo_loss.items(): if k in yolo_losses: yolo_losses[k] += v else: yolo_losses[k] = v loss = 0 for k, v in yolo_losses.items(): loss += v yolo_losses['loss'] = loss return yolo_losses

PaddleDetection/ppdet/modeling/losses/yolo_loss.py/0

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This code is based on https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/tracker/multitracker.py """ import numpy as np from collections import defaultdict from ..matching import jde_matching as matching from ..motion import KalmanFilter from .base_jde_tracker import TrackState, STrack from .base_jde_tracker import joint_stracks, sub_stracks, remove_duplicate_stracks from ppdet.core.workspace import register, serializable from ppdet.utils.logger import setup_logger logger = setup_logger(__name__) __all__ = ['JDETracker'] @register @serializable class JDETracker(object): __shared__ = ['num_classes'] """ JDE tracker, support single class and multi classes Args: use_byte (bool): Whether use ByteTracker, default False num_classes (int): the number of classes det_thresh (float): threshold of detection score track_buffer (int): buffer for tracker min_box_area (int): min box area to filter out low quality boxes vertical_ratio (float): w/h, the vertical ratio of the bbox to filter bad results. If set <= 0 means no need to filter bboxes，usually set 1.6 for pedestrian tracking. tracked_thresh (float): linear assignment threshold of tracked stracks and detections r_tracked_thresh (float): linear assignment threshold of tracked stracks and unmatched detections unconfirmed_thresh (float): linear assignment threshold of unconfirmed stracks and unmatched detections conf_thres (float): confidence threshold for tracking, also used in ByteTracker as higher confidence threshold match_thres (float): linear assignment threshold of tracked stracks and detections in ByteTracker low_conf_thres (float): lower confidence threshold for tracking in ByteTracker input_size (list): input feature map size to reid model, [h, w] format, [64, 192] as default. motion (str): motion model, KalmanFilter as default metric_type (str): either "euclidean" or "cosine", the distance metric used for measurement to track association. """ def __init__(self, use_byte=False, num_classes=1, det_thresh=0.3, track_buffer=30, min_box_area=0, vertical_ratio=0, tracked_thresh=0.7, r_tracked_thresh=0.5, unconfirmed_thresh=0.7, conf_thres=0, match_thres=0.8, low_conf_thres=0.2, input_size=[64, 192], motion='KalmanFilter', metric_type='euclidean'): self.use_byte = use_byte self.num_classes = num_classes self.det_thresh = det_thresh if not use_byte else conf_thres + 0.1 self.track_buffer = track_buffer self.min_box_area = min_box_area self.vertical_ratio = vertical_ratio self.tracked_thresh = tracked_thresh self.r_tracked_thresh = r_tracked_thresh self.unconfirmed_thresh = unconfirmed_thresh self.conf_thres = conf_thres self.match_thres = match_thres self.low_conf_thres = low_conf_thres self.input_size = input_size if motion == 'KalmanFilter': self.motion = KalmanFilter() self.metric_type = metric_type self.frame_id = 0 self.tracked_tracks_dict = defaultdict(list) # dict(list[STrack]) self.lost_tracks_dict = defaultdict(list) # dict(list[STrack]) self.removed_tracks_dict = defaultdict(list) # dict(list[STrack]) self.max_time_lost = 0 # max_time_lost will be calculated: int(frame_rate / 30.0 * track_buffer) def update(self, pred_dets, pred_embs=None): """ Processes the image frame and finds bounding box(detections). Associates the detection with corresponding tracklets and also handles lost, removed, refound and active tracklets. Args: pred_dets (np.array): Detection results of the image, the shape is [N, 6], means 'cls_id, score, x0, y0, x1, y1'. pred_embs (np.array): Embedding results of the image, the shape is [N, 128] or [N, 512]. Return: output_stracks_dict (dict(list)): The list contains information regarding the online_tracklets for the received image tensor. """ self.frame_id += 1 if self.frame_id == 1: STrack.init_count(self.num_classes) activated_tracks_dict = defaultdict(list) refined_tracks_dict = defaultdict(list) lost_tracks_dict = defaultdict(list) removed_tracks_dict = defaultdict(list) output_tracks_dict = defaultdict(list) pred_dets_dict = defaultdict(list) pred_embs_dict = defaultdict(list) # unify single and multi classes detection and embedding results for cls_id in range(self.num_classes): cls_idx = (pred_dets[:, 0:1] == cls_id).squeeze(-1) pred_dets_dict[cls_id] = pred_dets[cls_idx] if pred_embs is not None: pred_embs_dict[cls_id] = pred_embs[cls_idx] else: pred_embs_dict[cls_id] = None for cls_id in range(self.num_classes): """ Step 1: Get detections by class""" pred_dets_cls = pred_dets_dict[cls_id] pred_embs_cls = pred_embs_dict[cls_id] remain_inds = (pred_dets_cls[:, 1:2] > self.conf_thres).squeeze(-1) if remain_inds.sum() > 0: pred_dets_cls = pred_dets_cls[remain_inds] if pred_embs_cls is None: # in original ByteTrack detections = [ STrack( STrack.tlbr_to_tlwh(tlbrs[2:6]), tlbrs[1], cls_id, 30, temp_feat=None) for tlbrs in pred_dets_cls ] else: pred_embs_cls = pred_embs_cls[remain_inds] detections = [ STrack( STrack.tlbr_to_tlwh(tlbrs[2:6]), tlbrs[1], cls_id, 30, temp_feat) for (tlbrs, temp_feat) in zip(pred_dets_cls, pred_embs_cls) ] else: detections = [] ''' Add newly detected tracklets to tracked_stracks''' unconfirmed_dict = defaultdict(list) tracked_tracks_dict = defaultdict(list) for track in self.tracked_tracks_dict[cls_id]: if not track.is_activated: # previous tracks which are not active in the current frame are added in unconfirmed list unconfirmed_dict[cls_id].append(track) else: # Active tracks are added to the local list 'tracked_stracks' tracked_tracks_dict[cls_id].append(track) """ Step 2: First association, with embedding""" # building tracking pool for the current frame track_pool_dict = defaultdict(list) track_pool_dict[cls_id] = joint_stracks( tracked_tracks_dict[cls_id], self.lost_tracks_dict[cls_id]) # Predict the current location with KalmanFilter STrack.multi_predict(track_pool_dict[cls_id], self.motion) if pred_embs_cls is None: # in original ByteTrack dists = matching.iou_distance(track_pool_dict[cls_id], detections) matches, u_track, u_detection = matching.linear_assignment( dists, thresh=self.match_thres) # not self.tracked_thresh else: dists = matching.embedding_distance( track_pool_dict[cls_id], detections, metric=self.metric_type) dists = matching.fuse_motion( self.motion, dists, track_pool_dict[cls_id], detections) matches, u_track, u_detection = matching.linear_assignment( dists, thresh=self.tracked_thresh) for i_tracked, idet in matches: # i_tracked is the id of the track and idet is the detection track = track_pool_dict[cls_id][i_tracked] det = detections[idet] if track.state == TrackState.Tracked: # If the track is active, add the detection to the track track.update(detections[idet], self.frame_id) activated_tracks_dict[cls_id].append(track) else: # We have obtained a detection from a track which is not active, # hence put the track in refind_stracks list track.re_activate(det, self.frame_id, new_id=False) refined_tracks_dict[cls_id].append(track) # None of the steps below happen if there are no undetected tracks. """ Step 3: Second association, with IOU""" if self.use_byte: inds_low = pred_dets_dict[cls_id][:, 1:2] > self.low_conf_thres inds_high = pred_dets_dict[cls_id][:, 1:2] < self.conf_thres inds_second = np.logical_and(inds_low, inds_high).squeeze(-1) pred_dets_cls_second = pred_dets_dict[cls_id][inds_second] # association the untrack to the low score detections if len(pred_dets_cls_second) > 0: if pred_embs_dict[cls_id] is None: # in original ByteTrack detections_second = [ STrack( STrack.tlbr_to_tlwh(tlbrs[2:6]), tlbrs[1], cls_id, 30, temp_feat=None) for tlbrs in pred_dets_cls_second ] else: pred_embs_cls_second = pred_embs_dict[cls_id][ inds_second] detections_second = [ STrack( STrack.tlbr_to_tlwh(tlbrs[2:6]), tlbrs[1], cls_id, 30, temp_feat) for (tlbrs, temp_feat) in zip(pred_dets_cls_second, pred_embs_cls_second) ] else: detections_second = [] r_tracked_stracks = [ track_pool_dict[cls_id][i] for i in u_track if track_pool_dict[cls_id][i].state == TrackState.Tracked ] dists = matching.iou_distance(r_tracked_stracks, detections_second) matches, u_track, u_detection_second = matching.linear_assignment( dists, thresh=0.4) # not r_tracked_thresh else: detections = [detections[i] for i in u_detection] r_tracked_stracks = [] for i in u_track: if track_pool_dict[cls_id][i].state == TrackState.Tracked: r_tracked_stracks.append(track_pool_dict[cls_id][i]) dists = matching.iou_distance(r_tracked_stracks, detections) matches, u_track, u_detection = matching.linear_assignment( dists, thresh=self.r_tracked_thresh) for i_tracked, idet in matches: track = r_tracked_stracks[i_tracked] det = detections[ idet] if not self.use_byte else detections_second[idet] if track.state == TrackState.Tracked: track.update(det, self.frame_id) activated_tracks_dict[cls_id].append(track) else: track.re_activate(det, self.frame_id, new_id=False) refined_tracks_dict[cls_id].append(track) for it in u_track: track = r_tracked_stracks[it] if not track.state == TrackState.Lost: track.mark_lost() lost_tracks_dict[cls_id].append(track) '''Deal with unconfirmed tracks, usually tracks with only one beginning frame''' detections = [detections[i] for i in u_detection] dists = matching.iou_distance(unconfirmed_dict[cls_id], detections) matches, u_unconfirmed, u_detection = matching.linear_assignment( dists, thresh=self.unconfirmed_thresh) for i_tracked, idet in matches: unconfirmed_dict[cls_id][i_tracked].update(detections[idet], self.frame_id) activated_tracks_dict[cls_id].append(unconfirmed_dict[cls_id][ i_tracked]) for it in u_unconfirmed: track = unconfirmed_dict[cls_id][it] track.mark_removed() removed_tracks_dict[cls_id].append(track) """ Step 4: Init new stracks""" for inew in u_detection: track = detections[inew] if track.score < self.det_thresh: continue track.activate(self.motion, self.frame_id) activated_tracks_dict[cls_id].append(track) """ Step 5: Update state""" for track in self.lost_tracks_dict[cls_id]: if self.frame_id - track.end_frame > self.max_time_lost: track.mark_removed() removed_tracks_dict[cls_id].append(track) self.tracked_tracks_dict[cls_id] = [ t for t in self.tracked_tracks_dict[cls_id] if t.state == TrackState.Tracked ] self.tracked_tracks_dict[cls_id] = joint_stracks( self.tracked_tracks_dict[cls_id], activated_tracks_dict[cls_id]) self.tracked_tracks_dict[cls_id] = joint_stracks( self.tracked_tracks_dict[cls_id], refined_tracks_dict[cls_id]) self.lost_tracks_dict[cls_id] = sub_stracks( self.lost_tracks_dict[cls_id], self.tracked_tracks_dict[cls_id]) self.lost_tracks_dict[cls_id].extend(lost_tracks_dict[cls_id]) self.lost_tracks_dict[cls_id] = sub_stracks( self.lost_tracks_dict[cls_id], self.removed_tracks_dict[cls_id]) self.removed_tracks_dict[cls_id].extend(removed_tracks_dict[cls_id]) self.tracked_tracks_dict[cls_id], self.lost_tracks_dict[ cls_id] = remove_duplicate_stracks( self.tracked_tracks_dict[cls_id], self.lost_tracks_dict[cls_id]) # get scores of lost tracks output_tracks_dict[cls_id] = [ track for track in self.tracked_tracks_dict[cls_id] if track.is_activated ] logger.debug('===========Frame {}=========='.format(self.frame_id)) logger.debug('Activated: {}'.format( [track.track_id for track in activated_tracks_dict[cls_id]])) logger.debug('Refind: {}'.format( [track.track_id for track in refined_tracks_dict[cls_id]])) logger.debug('Lost: {}'.format( [track.track_id for track in lost_tracks_dict[cls_id]])) logger.debug('Removed: {}'.format( [track.track_id for track in removed_tracks_dict[cls_id]])) return output_tracks_dict

PaddleDetection/ppdet/modeling/mot/tracker/jde_tracker.py/0

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import paddle import paddle.nn as nn import paddle.nn.functional as F from paddle import ParamAttr from paddle.regularizer import L2Decay from ppdet.core.workspace import register, serializable from ..shape_spec import ShapeSpec from ..backbones.lcnet import DepthwiseSeparable from .csp_pan import ConvBNLayer, Channel_T, DPModule __all__ = ['LCPAN'] @register @serializable class LCPAN(nn.Layer): """Path Aggregation Network with LCNet module. Args: in_channels (List[int]): Number of input channels per scale. out_channels (int): Number of output channels (used at each scale) kernel_size (int): The conv2d kernel size of this Module. num_features (int): Number of output features of CSPPAN module. num_csp_blocks (int): Number of bottlenecks in CSPLayer. Default: 1 use_depthwise (bool): Whether to depthwise separable convolution in blocks. Default: True """ def __init__(self, in_channels, out_channels, kernel_size=5, num_features=3, use_depthwise=True, act='hard_swish', spatial_scales=[0.125, 0.0625, 0.03125]): super(LCPAN, self).__init__() self.conv_t = Channel_T(in_channels, out_channels, act=act) in_channels = [out_channels] * len(spatial_scales) self.in_channels = in_channels self.out_channels = out_channels self.spatial_scales = spatial_scales self.num_features = num_features conv_func = DPModule if use_depthwise else ConvBNLayer NET_CONFIG = { #k, in_c, out_c, stride, use_se "block1": [ [kernel_size, out_channels * 2, out_channels * 2, 1, False], [kernel_size, out_channels * 2, out_channels, 1, False], ], "block2": [ [kernel_size, out_channels * 2, out_channels * 2, 1, False], [kernel_size, out_channels * 2, out_channels, 1, False], ] } if self.num_features == 4: self.first_top_conv = conv_func( in_channels[0], in_channels[0], kernel_size, stride=2, act=act) self.second_top_conv = conv_func( in_channels[0], in_channels[0], kernel_size, stride=2, act=act) self.spatial_scales.append(self.spatial_scales[-1] / 2) # build top-down blocks self.upsample = nn.Upsample(scale_factor=2, mode='nearest') self.top_down_blocks = nn.LayerList() for idx in range(len(in_channels) - 1, 0, -1): self.top_down_blocks.append( nn.Sequential(* [ DepthwiseSeparable( num_channels=in_c, num_filters=out_c, dw_size=k, stride=s, use_se=se) for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG[ "block1"]) ])) # build bottom-up blocks self.downsamples = nn.LayerList() self.bottom_up_blocks = nn.LayerList() for idx in range(len(in_channels) - 1): self.downsamples.append( conv_func( in_channels[idx], in_channels[idx], kernel_size=kernel_size, stride=2, act=act)) self.bottom_up_blocks.append( nn.Sequential(* [ DepthwiseSeparable( num_channels=in_c, num_filters=out_c, dw_size=k, stride=s, use_se=se) for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG[ "block2"]) ])) def forward(self, inputs): """ Args: inputs (tuple[Tensor]): input features. Returns: tuple[Tensor]: CSPPAN features. """ assert len(inputs) == len(self.in_channels) inputs = self.conv_t(inputs) # top-down path inner_outs = [inputs[-1]] for idx in range(len(self.in_channels) - 1, 0, -1): feat_heigh = inner_outs[0] feat_low = inputs[idx - 1] upsample_feat = self.upsample(feat_heigh) inner_out = self.top_down_blocks[len(self.in_channels) - 1 - idx]( paddle.concat([upsample_feat, feat_low], 1)) inner_outs.insert(0, inner_out) # bottom-up path outs = [inner_outs[0]] for idx in range(len(self.in_channels) - 1): feat_low = outs[-1] feat_height = inner_outs[idx + 1] downsample_feat = self.downsamples[idx](feat_low) out = self.bottom_up_blocks[idx](paddle.concat( [downsample_feat, feat_height], 1)) outs.append(out) top_features = None if self.num_features == 4: top_features = self.first_top_conv(inputs[-1]) top_features = top_features + self.second_top_conv(outs[-1]) outs.append(top_features) return tuple(outs) @property def out_shape(self): return [ ShapeSpec( channels=self.out_channels, stride=1. / s) for s in self.spatial_scales ] @classmethod def from_config(cls, cfg, input_shape): return {'in_channels': [i.channels for i in input_shape], }

PaddleDetection/ppdet/modeling/necks/lc_pan.py/0

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import paddle import paddle.nn as nn import paddle.nn.functional as F from paddle.nn.initializer import Normal, Constant from paddle import ParamAttr from paddle.nn import AdaptiveAvgPool2D, BatchNorm2D, Conv2D, Linear from paddle.regularizer import L2Decay from paddle.nn.initializer import KaimingNormal, XavierNormal from ppdet.core.workspace import register __all__ = ['PPLCNetEmbedding'] # Each element(list) represents a depthwise block, which is composed of k, in_c, out_c, s, use_se. # k: kernel_size # in_c: input channel number in depthwise block # out_c: output channel number in depthwise block # s: stride in depthwise block # use_se: whether to use SE block NET_CONFIG = { "blocks2": #k, in_c, out_c, s, use_se [[3, 16, 32, 1, False]], "blocks3": [[3, 32, 64, 2, False], [3, 64, 64, 1, False]], "blocks4": [[3, 64, 128, 2, False], [3, 128, 128, 1, False]], "blocks5": [[3, 128, 256, 2, False], [5, 256, 256, 1, False], [5, 256, 256, 1, False], [5, 256, 256, 1, False], [5, 256, 256, 1, False], [5, 256, 256, 1, False]], "blocks6": [[5, 256, 512, 2, True], [5, 512, 512, 1, True]] } def make_divisible(v, divisor=8, min_value=None): if min_value is None: min_value = divisor new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) if new_v < 0.9 * v: new_v += divisor return new_v class ConvBNLayer(nn.Layer): def __init__(self, num_channels, filter_size, num_filters, stride, num_groups=1): super().__init__() self.conv = Conv2D( in_channels=num_channels, out_channels=num_filters, kernel_size=filter_size, stride=stride, padding=(filter_size - 1) // 2, groups=num_groups, weight_attr=ParamAttr(initializer=KaimingNormal()), bias_attr=False) self.bn = BatchNorm2D( num_filters, weight_attr=ParamAttr(regularizer=L2Decay(0.0)), bias_attr=ParamAttr(regularizer=L2Decay(0.0))) self.hardswish = nn.Hardswish() def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.hardswish(x) return x class DepthwiseSeparable(nn.Layer): def __init__(self, num_channels, num_filters, stride, dw_size=3, use_se=False): super().__init__() self.use_se = use_se self.dw_conv = ConvBNLayer( num_channels=num_channels, num_filters=num_channels, filter_size=dw_size, stride=stride, num_groups=num_channels) if use_se: self.se = SEModule(num_channels) self.pw_conv = ConvBNLayer( num_channels=num_channels, filter_size=1, num_filters=num_filters, stride=1) def forward(self, x): x = self.dw_conv(x) if self.use_se: x = self.se(x) x = self.pw_conv(x) return x class SEModule(nn.Layer): def __init__(self, channel, reduction=4): super().__init__() self.avg_pool = AdaptiveAvgPool2D(1) self.conv1 = Conv2D( in_channels=channel, out_channels=channel // reduction, kernel_size=1, stride=1, padding=0) self.relu = nn.ReLU() self.conv2 = Conv2D( in_channels=channel // reduction, out_channels=channel, kernel_size=1, stride=1, padding=0) self.hardsigmoid = nn.Hardsigmoid() def forward(self, x): identity = x x = self.avg_pool(x) x = self.conv1(x) x = self.relu(x) x = self.conv2(x) x = self.hardsigmoid(x) x = paddle.multiply(x=identity, y=x) return x class PPLCNet(nn.Layer): """ PP-LCNet, see https://arxiv.org/abs/2109.15099. This code is different from PPLCNet in ppdet/modeling/backbones/lcnet.py or in PaddleClas, because the output is the flatten feature of last_conv. Args: scale (float): Scale ratio of channels. class_expand (int): Number of channels of conv feature. """ def __init__(self, scale=1.0, class_expand=1280): super(PPLCNet, self).__init__() self.scale = scale self.class_expand = class_expand self.conv1 = ConvBNLayer( num_channels=3, filter_size=3, num_filters=make_divisible(16 * scale), stride=2) self.blocks2 = nn.Sequential(*[ DepthwiseSeparable( num_channels=make_divisible(in_c * scale), num_filters=make_divisible(out_c * scale), dw_size=k, stride=s, use_se=se) for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks2"]) ]) self.blocks3 = nn.Sequential(*[ DepthwiseSeparable( num_channels=make_divisible(in_c * scale), num_filters=make_divisible(out_c * scale), dw_size=k, stride=s, use_se=se) for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks3"]) ]) self.blocks4 = nn.Sequential(*[ DepthwiseSeparable( num_channels=make_divisible(in_c * scale), num_filters=make_divisible(out_c * scale), dw_size=k, stride=s, use_se=se) for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks4"]) ]) self.blocks5 = nn.Sequential(*[ DepthwiseSeparable( num_channels=make_divisible(in_c * scale), num_filters=make_divisible(out_c * scale), dw_size=k, stride=s, use_se=se) for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks5"]) ]) self.blocks6 = nn.Sequential(*[ DepthwiseSeparable( num_channels=make_divisible(in_c * scale), num_filters=make_divisible(out_c * scale), dw_size=k, stride=s, use_se=se) for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks6"]) ]) self.avg_pool = AdaptiveAvgPool2D(1) self.last_conv = Conv2D( in_channels=make_divisible(NET_CONFIG["blocks6"][-1][2] * scale), out_channels=self.class_expand, kernel_size=1, stride=1, padding=0, bias_attr=False) self.hardswish = nn.Hardswish() self.flatten = nn.Flatten(start_axis=1, stop_axis=-1) def forward(self, x): x = self.conv1(x) x = self.blocks2(x) x = self.blocks3(x) x = self.blocks4(x) x = self.blocks5(x) x = self.blocks6(x) x = self.avg_pool(x) x = self.last_conv(x) x = self.hardswish(x) x = self.flatten(x) return x class FC(nn.Layer): def __init__(self, input_ch, output_ch): super(FC, self).__init__() weight_attr = ParamAttr(initializer=XavierNormal()) self.fc = paddle.nn.Linear(input_ch, output_ch, weight_attr=weight_attr) def forward(self, x): out = self.fc(x) return out @register class PPLCNetEmbedding(nn.Layer): """ PPLCNet Embedding Args: input_ch (int): Number of channels of input conv feature. output_ch (int): Number of channels of output conv feature. """ def __init__(self, scale=2.5, input_ch=1280, output_ch=512): super(PPLCNetEmbedding, self).__init__() self.backbone = PPLCNet(scale=scale) self.neck = FC(input_ch, output_ch) def forward(self, x): feat = self.backbone(x) feat_out = self.neck(feat) return feat_out

PaddleDetection/ppdet/modeling/reid/pplcnet_embedding.py/0

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import detr_transformer from . import utils from . import matchers from . import position_encoding from . import deformable_transformer from . import dino_transformer from . import group_detr_transformer from . import mask_dino_transformer from . import rtdetr_transformer from . import hybrid_encoder from .detr_transformer import * from .utils import * from .matchers import * from .position_encoding import * from .deformable_transformer import * from .dino_transformer import * from .petr_transformer import * from .group_detr_transformer import * from .mask_dino_transformer import * from .rtdetr_transformer import * from .hybrid_encoder import *

PaddleDetection/ppdet/modeling/transformers/__init__.py/0

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Modified from DETR (https://github.com/facebookresearch/detr) # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved # Modified from detrex (https://github.com/IDEA-Research/detrex) # Copyright 2022 The IDEA Authors. All rights reserved. from __future__ import absolute_import from __future__ import division from __future__ import print_function import copy import math import paddle import paddle.nn as nn import paddle.nn.functional as F from ..bbox_utils import bbox_overlaps __all__ = [ '_get_clones', 'bbox_overlaps', 'bbox_cxcywh_to_xyxy', 'bbox_xyxy_to_cxcywh', 'sigmoid_focal_loss', 'inverse_sigmoid', 'deformable_attention_core_func', 'varifocal_loss_with_logits' ] def _get_clones(module, N): return nn.LayerList([copy.deepcopy(module) for _ in range(N)]) def bbox_cxcywh_to_xyxy(x): cxcy, wh = paddle.split(x, 2, axis=-1) return paddle.concat([cxcy - 0.5 * wh, cxcy + 0.5 * wh], axis=-1) def bbox_xyxy_to_cxcywh(x): x1, y1, x2, y2 = x.split(4, axis=-1) return paddle.concat( [(x1 + x2) / 2, (y1 + y2) / 2, (x2 - x1), (y2 - y1)], axis=-1) def sigmoid_focal_loss(logit, label, normalizer=1.0, alpha=0.25, gamma=2.0): prob = F.sigmoid(logit) ce_loss = F.binary_cross_entropy_with_logits(logit, label, reduction="none") p_t = prob * label + (1 - prob) * (1 - label) loss = ce_loss * ((1 - p_t)**gamma) if alpha >= 0: alpha_t = alpha * label + (1 - alpha) * (1 - label) loss = alpha_t * loss return loss.mean(1).sum() / normalizer def inverse_sigmoid(x, eps=1e-5): x = x.clip(min=0., max=1.) return paddle.log(x.clip(min=eps) / (1 - x).clip(min=eps)) def deformable_attention_core_func(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights): """ Args: value (Tensor): [bs, value_length, n_head, c] value_spatial_shapes (Tensor|List): [n_levels, 2] value_level_start_index (Tensor|List): [n_levels] sampling_locations (Tensor): [bs, query_length, n_head, n_levels, n_points, 2] attention_weights (Tensor): [bs, query_length, n_head, n_levels, n_points] Returns: output (Tensor): [bs, Length_{query}, C] """ bs, _, n_head, c = value.shape _, Len_q, _, n_levels, n_points, _ = sampling_locations.shape split_shape = [h * w for h, w in value_spatial_shapes] value_list = value.split(split_shape, axis=1) sampling_grids = 2 * sampling_locations - 1 sampling_value_list = [] for level, (h, w) in enumerate(value_spatial_shapes): # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_ value_l_ = value_list[level].flatten(2).transpose( [0, 2, 1]).reshape([bs * n_head, c, h, w]) # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2 sampling_grid_l_ = sampling_grids[:, :, :, level].transpose( [0, 2, 1, 3, 4]).flatten(0, 1) # N_*M_, D_, Lq_, P_ sampling_value_l_ = F.grid_sample( value_l_, sampling_grid_l_, mode='bilinear', padding_mode='zeros', align_corners=False) sampling_value_list.append(sampling_value_l_) # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_*M_, 1, Lq_, L_*P_) attention_weights = attention_weights.transpose([0, 2, 1, 3, 4]).reshape( [bs * n_head, 1, Len_q, n_levels * n_points]) output = (paddle.stack( sampling_value_list, axis=-2).flatten(-2) * attention_weights).sum(-1).reshape([bs, n_head * c, Len_q]) return output.transpose([0, 2, 1]) def get_valid_ratio(mask): _, H, W = paddle.shape(mask) valid_ratio_h = paddle.sum(mask[:, :, 0], 1) / H valid_ratio_w = paddle.sum(mask[:, 0, :], 1) / W # [b, 2] return paddle.stack([valid_ratio_w, valid_ratio_h], -1) def get_denoising_training_group(targets, num_classes, num_queries, class_embed, num_denoising=100, label_noise_ratio=0.5, box_noise_scale=1.0): if num_denoising <= 0: return None, None, None, None num_gts = [len(t) for t in targets["gt_class"]] max_gt_num = max(num_gts) if max_gt_num == 0: return None, None, None, None num_group = num_denoising // max_gt_num num_group = 1 if num_group == 0 else num_group # pad gt to max_num of a batch bs = len(targets["gt_class"]) input_query_class = paddle.full( [bs, max_gt_num], num_classes, dtype='int32') input_query_bbox = paddle.zeros([bs, max_gt_num, 4]) pad_gt_mask = paddle.zeros([bs, max_gt_num]) for i in range(bs): num_gt = num_gts[i] if num_gt > 0: input_query_class[i, :num_gt] = targets["gt_class"][i].squeeze(-1) input_query_bbox[i, :num_gt] = targets["gt_bbox"][i] pad_gt_mask[i, :num_gt] = 1 input_query_class = input_query_class.tile([1, num_group]) input_query_bbox = input_query_bbox.tile([1, num_group, 1]) pad_gt_mask = pad_gt_mask.tile([1, num_group]) dn_positive_idx = paddle.nonzero(pad_gt_mask)[:, 1] dn_positive_idx = paddle.split(dn_positive_idx, [n * num_group for n in num_gts]) # total denoising queries num_denoising = int(max_gt_num * num_group) if label_noise_ratio > 0: input_query_class = input_query_class.flatten() pad_gt_mask = pad_gt_mask.flatten() # half of bbox prob mask = paddle.rand(input_query_class.shape) < (label_noise_ratio * 0.5) chosen_idx = paddle.nonzero(mask * pad_gt_mask).squeeze(-1) # randomly put a new one here new_label = paddle.randint_like( chosen_idx, 0, num_classes, dtype=input_query_class.dtype) input_query_class.scatter_(chosen_idx, new_label) input_query_class.reshape_([bs, num_denoising]) pad_gt_mask.reshape_([bs, num_denoising]) if box_noise_scale > 0: diff = paddle.concat( [input_query_bbox[..., 2:] * 0.5, input_query_bbox[..., 2:]], axis=-1) * box_noise_scale diff *= (paddle.rand(input_query_bbox.shape) * 2.0 - 1.0) input_query_bbox += diff input_query_bbox = inverse_sigmoid(input_query_bbox) class_embed = paddle.concat( [class_embed, paddle.zeros([1, class_embed.shape[-1]])]) input_query_class = paddle.gather( class_embed, input_query_class.flatten(), axis=0).reshape([bs, num_denoising, -1]) tgt_size = num_denoising + num_queries attn_mask = paddle.ones([tgt_size, tgt_size]) < 0 # match query cannot see the reconstruction attn_mask[num_denoising:, :num_denoising] = True # reconstruct cannot see each other for i in range(num_group): if i == 0: attn_mask[max_gt_num * i:max_gt_num * (i + 1), max_gt_num * (i + 1): num_denoising] = True if i == num_group - 1: attn_mask[max_gt_num * i:max_gt_num * (i + 1), :max_gt_num * i] = True else: attn_mask[max_gt_num * i:max_gt_num * (i + 1), max_gt_num * (i + 1): num_denoising] = True attn_mask[max_gt_num * i:max_gt_num * (i + 1), :max_gt_num * i] = True attn_mask = ~attn_mask dn_meta = { "dn_positive_idx": dn_positive_idx, "dn_num_group": num_group, "dn_num_split": [num_denoising, num_queries] } return input_query_class, input_query_bbox, attn_mask, dn_meta def get_contrastive_denoising_training_group(targets, num_classes, num_queries, class_embed, num_denoising=100, label_noise_ratio=0.5, box_noise_scale=1.0): if num_denoising <= 0: return None, None, None, None num_gts = [len(t) for t in targets["gt_class"]] max_gt_num = max(num_gts) if max_gt_num == 0: return None, None, None, None num_group = num_denoising // max_gt_num num_group = 1 if num_group == 0 else num_group # pad gt to max_num of a batch bs = len(targets["gt_class"]) input_query_class = paddle.full( [bs, max_gt_num], num_classes, dtype='int32') input_query_bbox = paddle.zeros([bs, max_gt_num, 4]) pad_gt_mask = paddle.zeros([bs, max_gt_num]) for i in range(bs): num_gt = num_gts[i] if num_gt > 0: input_query_class[i, :num_gt] = targets["gt_class"][i].squeeze(-1) input_query_bbox[i, :num_gt] = targets["gt_bbox"][i] pad_gt_mask[i, :num_gt] = 1 # each group has positive and negative queries. input_query_class = input_query_class.tile([1, 2 * num_group]) input_query_bbox = input_query_bbox.tile([1, 2 * num_group, 1]) pad_gt_mask = pad_gt_mask.tile([1, 2 * num_group]) # positive and negative mask negative_gt_mask = paddle.zeros([bs, max_gt_num * 2, 1]) negative_gt_mask[:, max_gt_num:] = 1 negative_gt_mask = negative_gt_mask.tile([1, num_group, 1]) positive_gt_mask = 1 - negative_gt_mask # contrastive denoising training positive index positive_gt_mask = positive_gt_mask.squeeze(-1) * pad_gt_mask dn_positive_idx = paddle.nonzero(positive_gt_mask)[:, 1] dn_positive_idx = paddle.split(dn_positive_idx, [n * num_group for n in num_gts]) # total denoising queries num_denoising = int(max_gt_num * 2 * num_group) if label_noise_ratio > 0: input_query_class = input_query_class.flatten() pad_gt_mask = pad_gt_mask.flatten() # half of bbox prob mask = paddle.rand(input_query_class.shape) < (label_noise_ratio * 0.5) chosen_idx = paddle.nonzero(mask * pad_gt_mask).squeeze(-1) # randomly put a new one here new_label = paddle.randint_like( chosen_idx, 0, num_classes, dtype=input_query_class.dtype) input_query_class.scatter_(chosen_idx, new_label) input_query_class.reshape_([bs, num_denoising]) pad_gt_mask.reshape_([bs, num_denoising]) if box_noise_scale > 0: known_bbox = bbox_cxcywh_to_xyxy(input_query_bbox) diff = paddle.tile(input_query_bbox[..., 2:] * 0.5, [1, 1, 2]) * box_noise_scale rand_sign = paddle.randint_like(input_query_bbox, 0, 2) * 2.0 - 1.0 rand_part = paddle.rand(input_query_bbox.shape) rand_part = (rand_part + 1.0) * negative_gt_mask + rand_part * ( 1 - negative_gt_mask) rand_part *= rand_sign known_bbox += rand_part * diff known_bbox.clip_(min=0.0, max=1.0) input_query_bbox = bbox_xyxy_to_cxcywh(known_bbox) input_query_bbox = inverse_sigmoid(input_query_bbox) class_embed = paddle.concat( [class_embed, paddle.zeros([1, class_embed.shape[-1]])]) input_query_class = paddle.gather( class_embed, input_query_class.flatten(), axis=0).reshape([bs, num_denoising, -1]) tgt_size = num_denoising + num_queries attn_mask = paddle.ones([tgt_size, tgt_size]) < 0 # match query cannot see the reconstruction attn_mask[num_denoising:, :num_denoising] = True # reconstruct cannot see each other for i in range(num_group): if i == 0: attn_mask[max_gt_num * 2 * i:max_gt_num * 2 * (i + 1), max_gt_num * 2 * (i + 1):num_denoising] = True if i == num_group - 1: attn_mask[max_gt_num * 2 * i:max_gt_num * 2 * (i + 1), :max_gt_num * i * 2] = True else: attn_mask[max_gt_num * 2 * i:max_gt_num * 2 * (i + 1), max_gt_num * 2 * (i + 1):num_denoising] = True attn_mask[max_gt_num * 2 * i:max_gt_num * 2 * (i + 1), :max_gt_num * 2 * i] = True attn_mask = ~attn_mask dn_meta = { "dn_positive_idx": dn_positive_idx, "dn_num_group": num_group, "dn_num_split": [num_denoising, num_queries] } return input_query_class, input_query_bbox, attn_mask, dn_meta def get_sine_pos_embed(pos_tensor, num_pos_feats=128, temperature=10000, exchange_xy=True): """generate sine position embedding from a position tensor Args: pos_tensor (Tensor): Shape as `(None, n)`. num_pos_feats (int): projected shape for each float in the tensor. Default: 128 temperature (int): The temperature used for scaling the position embedding. Default: 10000. exchange_xy (bool, optional): exchange pos x and pos y. \ For example, input tensor is `[x, y]`, the results will # noqa be `[pos(y), pos(x)]`. Defaults: True. Returns: Tensor: Returned position embedding # noqa with shape `(None, n * num_pos_feats)`. """ scale = 2. * math.pi dim_t = 2. * paddle.floor_divide( paddle.arange(num_pos_feats), paddle.to_tensor(2)) dim_t = scale / temperature**(dim_t / num_pos_feats) def sine_func(x): x *= dim_t return paddle.stack( (x[:, :, 0::2].sin(), x[:, :, 1::2].cos()), axis=3).flatten(2) pos_res = [sine_func(x) for x in pos_tensor.split(pos_tensor.shape[-1], -1)] if exchange_xy: pos_res[0], pos_res[1] = pos_res[1], pos_res[0] pos_res = paddle.concat(pos_res, axis=2) return pos_res def mask_to_box_coordinate(mask, normalize=False, format="xyxy", dtype="float32"): """ Compute the bounding boxes around the provided mask. Args: mask (Tensor:bool): [b, c, h, w] Returns: bbox (Tensor): [b, c, 4] """ assert mask.ndim == 4 assert format in ["xyxy", "xywh"] if mask.sum() == 0: return paddle.zeros([mask.shape[0], mask.shape[1], 4], dtype=dtype) h, w = mask.shape[-2:] y, x = paddle.meshgrid( paddle.arange( end=h, dtype=dtype), paddle.arange( end=w, dtype=dtype)) x_mask = x * mask x_max = x_mask.flatten(-2).max(-1) + 1 x_min = paddle.where(mask, x_mask, paddle.to_tensor(1e8)).flatten(-2).min(-1) y_mask = y * mask y_max = y_mask.flatten(-2).max(-1) + 1 y_min = paddle.where(mask, y_mask, paddle.to_tensor(1e8)).flatten(-2).min(-1) out_bbox = paddle.stack([x_min, y_min, x_max, y_max], axis=-1) if normalize: out_bbox /= paddle.to_tensor([w, h, w, h]).astype(dtype) return out_bbox if format == "xyxy" else bbox_xyxy_to_cxcywh(out_bbox) def varifocal_loss_with_logits(pred_logits, gt_score, label, normalizer=1.0, alpha=0.75, gamma=2.0): pred_score = F.sigmoid(pred_logits) weight = alpha * pred_score.pow(gamma) * (1 - label) + gt_score * label loss = F.binary_cross_entropy_with_logits( pred_logits, gt_score, weight=weight, reduction='none') return loss.mean(1).sum() / normalizer

PaddleDetection/ppdet/modeling/transformers/utils.py/0

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import os import numpy as np import paddle import paddle.nn as nn from .download import get_weights_path from .logger import setup_logger logger = setup_logger(__name__) def is_url(path): """ Whether path is URL. Args: path (string): URL string or not. """ return path.startswith('http://') \ or path.startswith('https://') \ or path.startswith('ppdet://') def _strip_postfix(path): path, ext = os.path.splitext(path) assert ext in ['', '.pdparams', '.pdopt', '.pdmodel'], \ "Unknown postfix {} from weights".format(ext) return path def load_weight(model, weight, optimizer=None, ema=None, exchange=True): if is_url(weight): weight = get_weights_path(weight) path = _strip_postfix(weight) pdparam_path = path + '.pdparams' if not os.path.exists(pdparam_path): raise ValueError("Model pretrain path {} does not " "exists.".format(pdparam_path)) if ema is not None and os.path.exists(path + '.pdema'): if exchange: # Exchange model and ema_model to load logger.info('Exchange model and ema_model to load:') ema_state_dict = paddle.load(pdparam_path) logger.info('Loading ema_model weights from {}'.format(path + '.pdparams')) param_state_dict = paddle.load(path + '.pdema') logger.info('Loading model weights from {}'.format(path + '.pdema')) else: ema_state_dict = paddle.load(path + '.pdema') logger.info('Loading ema_model weights from {}'.format(path + '.pdema')) param_state_dict = paddle.load(pdparam_path) logger.info('Loading model weights from {}'.format(path + '.pdparams')) else: ema_state_dict = None param_state_dict = paddle.load(pdparam_path) if hasattr(model, 'modelTeacher') and hasattr(model, 'modelStudent'): print('Loading pretrain weights for Teacher-Student framework.') print('Loading pretrain weights for Student model.') student_model_dict = model.modelStudent.state_dict() student_param_state_dict = match_state_dict( student_model_dict, param_state_dict, mode='student') model.modelStudent.set_dict(student_param_state_dict) print('Loading pretrain weights for Teacher model.') teacher_model_dict = model.modelTeacher.state_dict() teacher_param_state_dict = match_state_dict( teacher_model_dict, param_state_dict, mode='teacher') model.modelTeacher.set_dict(teacher_param_state_dict) else: model_dict = model.state_dict() model_weight = {} incorrect_keys = 0 for key in model_dict.keys(): if key in param_state_dict.keys(): model_weight[key] = param_state_dict[key] else: logger.info('Unmatched key: {}'.format(key)) incorrect_keys += 1 assert incorrect_keys == 0, "Load weight {} incorrectly, \ {} keys unmatched, please check again.".format(weight, incorrect_keys) logger.info('Finish resuming model weights: {}'.format(pdparam_path)) model.set_dict(model_weight) last_epoch = 0 if optimizer is not None and os.path.exists(path + '.pdopt'): optim_state_dict = paddle.load(path + '.pdopt') # to solve resume bug, will it be fixed in paddle 2.0 for key in optimizer.state_dict().keys(): if not key in optim_state_dict.keys(): optim_state_dict[key] = optimizer.state_dict()[key] if 'last_epoch' in optim_state_dict: last_epoch = optim_state_dict.pop('last_epoch') optimizer.set_state_dict(optim_state_dict) if ema_state_dict is not None: ema.resume(ema_state_dict, optim_state_dict['LR_Scheduler']['last_epoch']) elif ema_state_dict is not None: ema.resume(ema_state_dict) return last_epoch def match_state_dict(model_state_dict, weight_state_dict, mode='default'): """ Match between the model state dict and pretrained weight state dict. Return the matched state dict. The method supposes that all the names in pretrained weight state dict are subclass of the names in models`, if the prefix 'backbone.' in pretrained weight keys is stripped. And we could get the candidates for each model key. Then we select the name with the longest matched size as the final match result. For example, the model state dict has the name of 'backbone.res2.res2a.branch2a.conv.weight' and the pretrained weight as name of 'res2.res2a.branch2a.conv.weight' and 'branch2a.conv.weight'. We match the 'res2.res2a.branch2a.conv.weight' to the model key. """ model_keys = sorted(model_state_dict.keys()) weight_keys = sorted(weight_state_dict.keys()) def teacher_match(a, b): # skip student params if b.startswith('modelStudent'): return False return a == b or a.endswith("." + b) or b.endswith("." + a) def student_match(a, b): # skip teacher params if b.startswith('modelTeacher'): return False return a == b or a.endswith("." + b) or b.endswith("." + a) def match(a, b): if b.startswith('backbone.res5'): b = b[9:] return a == b or a.endswith("." + b) if mode == 'student': match_op = student_match elif mode == 'teacher': match_op = teacher_match else: match_op = match match_matrix = np.zeros([len(model_keys), len(weight_keys)]) for i, m_k in enumerate(model_keys): for j, w_k in enumerate(weight_keys): if match_op(m_k, w_k): match_matrix[i, j] = len(w_k) max_id = match_matrix.argmax(1) max_len = match_matrix.max(1) max_id[max_len == 0] = -1 load_id = set(max_id) load_id.discard(-1) not_load_weight_name = [] if weight_keys[0].startswith('modelStudent') or weight_keys[0].startswith( 'modelTeacher'): for match_idx in range(len(max_id)): if max_id[match_idx] == -1: not_load_weight_name.append(model_keys[match_idx]) if len(not_load_weight_name) > 0: logger.info('{} in model is not matched with pretrained weights, ' 'and its will be trained from scratch'.format( not_load_weight_name)) else: for idx in range(len(weight_keys)): if idx not in load_id: not_load_weight_name.append(weight_keys[idx]) if len(not_load_weight_name) > 0: logger.info('{} in pretrained weight is not used in the model, ' 'and its will not be loaded'.format( not_load_weight_name)) matched_keys = {} result_state_dict = {} for model_id, weight_id in enumerate(max_id): if weight_id == -1: continue model_key = model_keys[model_id] weight_key = weight_keys[weight_id] weight_value = weight_state_dict[weight_key] model_value_shape = list(model_state_dict[model_key].shape) if list(weight_value.shape) != model_value_shape: logger.info( 'The shape {} in pretrained weight {} is unmatched with ' 'the shape {} in model {}. And the weight {} will not be ' 'loaded'.format(weight_value.shape, weight_key, model_value_shape, model_key, weight_key)) continue assert model_key not in result_state_dict result_state_dict[model_key] = weight_value if weight_key in matched_keys: raise ValueError('Ambiguity weight {} loaded, it matches at least ' '{} and {} in the model'.format( weight_key, model_key, matched_keys[ weight_key])) matched_keys[weight_key] = model_key return result_state_dict def load_pretrain_weight(model, pretrain_weight, ARSL_eval=False): if is_url(pretrain_weight): pretrain_weight = get_weights_path(pretrain_weight) path = _strip_postfix(pretrain_weight) if not (os.path.isdir(path) or os.path.isfile(path) or os.path.exists(path + '.pdparams')): raise ValueError("Model pretrain path `{}` does not exists. " "If you don't want to load pretrain model, " "please delete `pretrain_weights` field in " "config file.".format(path)) teacher_student_flag = False if not ARSL_eval: if hasattr(model, 'modelTeacher') and hasattr(model, 'modelStudent'): print('Loading pretrain weights for Teacher-Student framework.') print( 'Assert Teacher model has the same structure with Student model.' ) model_dict = model.modelStudent.state_dict() teacher_student_flag = True else: model_dict = model.state_dict() weights_path = path + '.pdparams' param_state_dict = paddle.load(weights_path) param_state_dict = match_state_dict(model_dict, param_state_dict) for k, v in param_state_dict.items(): if isinstance(v, np.ndarray): v = paddle.to_tensor(v) if model_dict[k].dtype != v.dtype: param_state_dict[k] = v.astype(model_dict[k].dtype) if teacher_student_flag: model.modelStudent.set_dict(param_state_dict) model.modelTeacher.set_dict(param_state_dict) else: model.set_dict(param_state_dict) logger.info('Finish loading model weights: {}'.format(weights_path)) else: weights_path = path + '.pdparams' param_state_dict = paddle.load(weights_path) student_model_dict = model.modelStudent.state_dict() student_param_state_dict = match_state_dict( student_model_dict, param_state_dict, mode='student') model.modelStudent.set_dict(student_param_state_dict) print('Loading pretrain weights for Teacher model.') teacher_model_dict = model.modelTeacher.state_dict() teacher_param_state_dict = match_state_dict( teacher_model_dict, param_state_dict, mode='teacher') model.modelTeacher.set_dict(teacher_param_state_dict) logger.info('Finish loading model weights: {}'.format(weights_path)) def save_model(model, optimizer, save_dir, save_name, last_epoch, ema_model=None): """ save model into disk. Args: model (dict): the model state_dict to save parameters. optimizer (paddle.optimizer.Optimizer): the Optimizer instance to save optimizer states. save_dir (str): the directory to be saved. save_name (str): the path to be saved. last_epoch (int): the epoch index. ema_model (dict|None): the ema_model state_dict to save parameters. """ if paddle.distributed.get_rank() != 0: return save_dir = os.path.normpath(save_dir) if not os.path.exists(save_dir): os.makedirs(save_dir) if save_name == "best_model": best_model_path = os.path.join(save_dir, 'best_model') if not os.path.exists(best_model_path): os.makedirs(best_model_path) save_path = os.path.join(save_dir, save_name) # save model if isinstance(model, nn.Layer): paddle.save(model.state_dict(), save_path + ".pdparams") best_model = model.state_dict() else: assert isinstance(model, dict), 'model is not a instance of nn.layer or dict' if ema_model is None: paddle.save(model, save_path + ".pdparams") best_model = model else: assert isinstance(ema_model, dict), ("ema_model is not a instance of dict, " "please call model.state_dict() to get.") # Exchange model and ema_model to save paddle.save(ema_model, save_path + ".pdparams") paddle.save(model, save_path + ".pdema") best_model = ema_model if save_name == 'best_model': best_model_path = os.path.join(best_model_path, 'model') paddle.save(best_model, best_model_path + ".pdparams") # save optimizer state_dict = optimizer.state_dict() state_dict['last_epoch'] = last_epoch paddle.save(state_dict, save_path + ".pdopt") logger.info("Save checkpoint: {}".format(save_dir)) def save_semi_model(teacher_model, student_model, optimizer, save_dir, save_name, last_epoch, last_iter): """ save teacher and student model into disk. Args: teacher_model (dict): the teacher_model state_dict to save parameters. student_model (dict): the student_model state_dict to save parameters. optimizer (paddle.optimizer.Optimizer): the Optimizer instance to save optimizer states. save_dir (str): the directory to be saved. save_name (str): the path to be saved. last_epoch (int): the epoch index. last_iter (int): the iter index. """ if paddle.distributed.get_rank() != 0: return assert isinstance(teacher_model, dict), ( "teacher_model is not a instance of dict, " "please call teacher_model.state_dict() to get.") assert isinstance(student_model, dict), ( "student_model is not a instance of dict, " "please call student_model.state_dict() to get.") if not os.path.exists(save_dir): os.makedirs(save_dir) save_path = os.path.join(save_dir, save_name) # save model paddle.save(teacher_model, save_path + str(last_epoch) + "epoch_t.pdparams") paddle.save(student_model, save_path + str(last_epoch) + "epoch_s.pdparams") # save optimizer state_dict = optimizer.state_dict() state_dict['last_epoch'] = last_epoch state_dict['last_iter'] = last_iter paddle.save(state_dict, save_path + str(last_epoch) + "epoch.pdopt") logger.info("Save checkpoint: {}".format(save_dir))

PaddleDetection/ppdet/utils/checkpoint.py/0

#!/bin/bash source test_tipc/utils_func.sh # set env python=python export model_branch=`git symbolic-ref HEAD 2>/dev/null | cut -d"/" -f 3` export model_commit=$(git log|head -n1|awk '{print $2}') export str_tmp=$(echo `pip list|grep paddlepaddle-gpu|awk -F ' ' '{print $2}'`) export frame_version=${str_tmp%%.post*} export frame_commit=$(echo `${python} -c "import paddle;print(paddle.version.commit)"`) # run benchmark sh # Usage: # bash run_benchmark_train.sh config.txt params # or # bash run_benchmark_train.sh config.txt function func_parser_params(){ strs=$1 IFS="=" array=(${strs}) tmp=${array[1]} echo ${tmp} } function set_dynamic_epoch(){ string=$1 num=$2 _str=${string:1:6} IFS="C" arr=(${_str}) M=${arr[0]} P=${arr[1]} ep=`expr $num \* $P` echo $ep } function func_sed_params(){ filename=$1 line=$2 param_value=$3 params=`sed -n "${line}p" $filename` IFS=":" array=(${params}) key=${array[0]} new_params="${key}:${param_value}" IFS=";" cmd="sed -i '${line}s/.*/${new_params}/' '${filename}'" eval $cmd } function set_gpu_id(){ string=$1 _str=${string:1:6} IFS="C" arr=(${_str}) M=${arr[0]} P=${arr[1]} gn=`expr $P - 1` gpu_num=`expr $gn / $M` seq=`seq -s "," 0 $gpu_num` echo $seq } function get_repo_name(){ IFS=";" cur_dir=$(pwd) IFS="/" arr=(${cur_dir}) echo ${arr[-1]} } FILENAME=$1 # copy FILENAME as new new_filename="./test_tipc/benchmark_train.txt" cmd=`yes|cp $FILENAME $new_filename` FILENAME=$new_filename # MODE must be one of ['benchmark_train'] MODE=$2 PARAMS=$3 # bash test_tipc/benchmark_train.sh test_tipc/configs/det_mv3_db_v2_0/train_benchmark.txt benchmark_train dynamic_bs8_null_DP_N1C1 IFS=$'\n' # parser params from train_benchmark.txt dataline=`cat $FILENAME` # parser params IFS=$'\n' lines=(${dataline}) model_name=$(func_parser_value "${lines[1]}") # 获取benchmark_params所在的行数 line_num=`grep -n -w "train_benchmark_params" $FILENAME | cut -d ":" -f 1` # for train log parser batch_size=$(func_parser_value "${lines[line_num]}") line_num=`expr $line_num + 1` fp_items=$(func_parser_value "${lines[line_num]}") line_num=`expr $line_num + 1` epoch=$(func_parser_value "${lines[line_num]}") line_num=`expr $line_num + 1` repeat=$(func_parser_value "${lines[line_num]}") line_num=`expr $line_num + 1` profile_option_key=$(func_parser_key "${lines[line_num]}") profile_option_params=$(func_parser_value "${lines[line_num]}") profile_option="${profile_option_key}:${profile_option_params}" line_num=`expr $line_num + 1` flags_value=$(func_parser_value "${lines[line_num]}") if [ ${flags_value} != "null" ];then # set flags IFS=";" flags_list=(${flags_value}) for _flag in ${flags_list[*]}; do cmd="export ${_flag}" eval $cmd done fi # set log_name repo_name=$(get_repo_name ) SAVE_LOG=${BENCHMARK_LOG_DIR:-$(pwd)} # */benchmark_log mkdir -p "${SAVE_LOG}/benchmark_log/" status_log="${SAVE_LOG}/benchmark_log/results.log" # get benchmark profiling params : PROFILING_TIMER_ONLY=no|True|False PROFILING_TIMER_ONLY=${PROFILING_TIMER_ONLY:-"True"} # The number of lines in which train params can be replaced. line_python=3 line_gpuid=4 line_precision=6 line_epoch=7 line_batchsize=9 line_profile=13 line_eval_py=24 line_export_py=30 func_sed_params "$FILENAME" "${line_eval_py}" "null" func_sed_params "$FILENAME" "${line_export_py}" "null" func_sed_params "$FILENAME" "${line_python}" "${python}" # if params if [ ! -n "$PARAMS" ] ;then # PARAMS input is not a word. IFS="|" batch_size_list=(${batch_size}) fp_items_list=(${fp_items}) device_num="N1C4" device_num_list=($device_num) run_mode="DP" elif [[ ${PARAMS} = "dynamicTostatic" ]] ;then IFS="|" model_type=$PARAMS batch_size_list=(${batch_size}) fp_items_list=(${fp_items}) device_num="N1C4" device_num_list=($device_num) run_mode="DP" else # parser params from input: modeltype_bs${bs_item}_${fp_item}_${run_mode}_${device_num} IFS="_" params_list=(${PARAMS}) model_type=${params_list[0]} batch_size=${params_list[1]} batch_size=`echo ${batch_size} | tr -cd "[0-9]" ` precision=${params_list[2]} run_mode=${params_list[3]} device_num=${params_list[4]} IFS=";" if [ ${precision} = "null" ];then precision="fp32" fi fp_items_list=($precision) batch_size_list=($batch_size) device_num_list=($device_num) fi # for log name to_static="" # parse "to_static" options and modify trainer into "to_static_trainer" if [[ ${model_type} = "dynamicTostatic" ]];then to_static="d2sT_" sed -i 's/trainer:norm_train/trainer:to_static_train/g' $FILENAME fi if [[ ${model_name} =~ "higherhrnet" ]] || [[ ${model_name} =~ "hrnet" ]] || [[ ${model_name} =~ "tinypose" ]] || [[ ${model_name} =~ "ppyoloe_r_crn_s_3x_spine_coco" ]] ;then echo "${model_name} run on full coco dataset" epoch=$(set_dynamic_epoch $device_num $epoch) else epoch=1 repeat=$(set_dynamic_epoch $device_num $repeat) eval "sed -i '10c\ repeat: ${repeat}' configs/datasets/coco_detection.yml" eval "sed -i '10c\ repeat: ${repeat}' configs/datasets/coco_instance.yml" eval "sed -i '10c\ repeat: ${repeat}' configs/datasets/mot.yml" fi IFS="|" for batch_size in ${batch_size_list[*]}; do for precision in ${fp_items_list[*]}; do for device_num in ${device_num_list[*]}; do # sed batchsize and precision func_sed_params "$FILENAME" "${line_precision}" "$precision" func_sed_params "$FILENAME" "${line_batchsize}" "$MODE=$batch_size" func_sed_params "$FILENAME" "${line_epoch}" "$MODE=$epoch" gpu_id=$(set_gpu_id $device_num) if [ ${#gpu_id} -le 1 ];then func_sed_params "$FILENAME" "${line_gpuid}" "0" # sed used gpu_id if [[ ${PROFILING_TIMER_ONLY} != "no" ]];then echo "run profile" # The default value of profile_option's timer_only parameter is True if [[ ${PROFILING_TIMER_ONLY} = "False" ]];then profile_option="${profile_option};timer_only=False" fi log_path="$SAVE_LOG/profiling_log" mkdir -p $log_path log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_${to_static}profiling" # set profile_option params tmp=`sed -i "${line_profile}s/.*/\"${profile_option}\"/" "${FILENAME}"` # run test_train_inference_python.sh cmd="timeout 5m bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 " echo $cmd eval ${cmd} eval "cat ${log_path}/${log_name}" fi echo "run without profile" # without profile log_path="$SAVE_LOG/train_log" speed_log_path="$SAVE_LOG/index" mkdir -p $log_path mkdir -p $speed_log_path log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_${to_static}log" speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_${to_static}speed" func_sed_params "$FILENAME" "${line_profile}" "null" # sed profile_id as null cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 " echo $cmd job_bt=`date '+%Y%m%d%H%M%S'` eval $cmd job_et=`date '+%Y%m%d%H%M%S'` export model_run_time=$((${job_et}-${job_bt})) eval "cat ${log_path}/${log_name}" # parser log _model_name="${model_name}_bs${batch_size}_${precision}_${run_mode}" cmd="${python} ${BENCHMARK_ROOT}/scripts/analysis.py --filename ${log_path}/${log_name} \ --speed_log_file '${speed_log_path}/${speed_log_name}' \ --model_name ${_model_name} \ --base_batch_size ${batch_size} \ --run_mode ${run_mode} \ --fp_item ${precision} \ --keyword ips: \ --skip_steps 4 \ --device_num ${device_num} \ --speed_unit images/s \ --convergence_key loss: " echo $cmd eval $cmd last_status=${PIPESTATUS[0]} status_check $last_status "${cmd}" "${status_log}" "${model_name}" else IFS=";" unset_env=`unset CUDA_VISIBLE_DEVICES` log_path="$SAVE_LOG/train_log" speed_log_path="$SAVE_LOG/index" mkdir -p $log_path mkdir -p $speed_log_path log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_${to_static}log" speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_${to_static}speed" func_sed_params "$FILENAME" "${line_gpuid}" "$gpu_id" # sed used gpu_id func_sed_params "$FILENAME" "${line_profile}" "null" # sed --profile_option as null cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 " echo $cmd job_bt=`date '+%Y%m%d%H%M%S'` eval $cmd job_et=`date '+%Y%m%d%H%M%S'` export model_run_time=$((${job_et}-${job_bt})) eval "cat ${log_path}/${log_name}" # parser log _model_name="${model_name}_bs${batch_size}_${precision}_${run_mode}" cmd="${python} ${BENCHMARK_ROOT}/scripts/analysis.py --filename ${log_path}/${log_name} \ --speed_log_file '${speed_log_path}/${speed_log_name}' \ --model_name ${_model_name} \ --base_batch_size ${batch_size} \ --run_mode ${run_mode} \ --fp_item ${precision} \ --keyword ips: \ --skip_steps 4 \ --device_num ${device_num} \ --speed_unit images/s \ --convergence_key loss: " echo $cmd eval $cmd last_status=${PIPESTATUS[0]} status_check $last_status "${cmd}" "${status_log}" "${model_name}" fi done done done

PaddleDetection/test_tipc/benchmark_train.sh/0

# Linux端基础训练预测功能测试 Linux端基础训练预测功能测试的主程序为`test_train_inference_python.sh`，可以测试基于Python的模型训练、评估、推理等基本功能，包括裁剪、量化、蒸馏。 - Mac端基础训练预测功能测试参考[链接](./) - Windows端基础训练预测功能测试参考[链接](./) ## 1. 测试结论汇总 - 训练相关： | 算法名称 | 模型名称 | 单机单卡 | 单机多卡 | 多机多卡 | 模型压缩（单机多卡） | | :---- | :---- | :---- | :---- | :---- | :---- | | PPYOLO | ppyolo_mbv3_large_coco | 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | 正常训练：FPGM裁剪、PACT量化 <br> 离线量化（无需训练） | | PPYOLO | ppyolo_r50vd_dcn_1x_coco | 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | 正常训练：FPGM裁剪、PACT量化 <br> 离线量化（无需训练） | - 预测相关：基于训练是否使用量化，可以将训练产出的模型可以分为`正常模型`和`量化模型`，这两类模型对应的预测功能汇总如下， | 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 | | ---- | ---- | ---- | :----: | :----: | :----: | | 正常模型 | GPU | 1/8 | fp32/fp16 | - | - | | 正常模型 | CPU | 1/8 | - | fp32/fp16 | 支持 | | 量化模型 | GPU | 1/8 | int8 | - | - | | 量化模型 | CPU | 1/8 | - | int8 | 支持 | ## 2. 测试流程 运行环境配置请参考[文档](./install.md)的内容配置TIPC的运行环境。 ### 2.1 安装依赖 - 安装PaddlePaddle >= 2.2 - 安装PaddleDetection依赖 ``` pip install -r ./requirements.txt pip install -r ./test_tipc/requirements.txt ``` - 安装autolog（规范化日志输出工具） ``` git clone https://github.com/LDOUBLEV/AutoLog cd AutoLog pip install -r ./requirements.txt python setup.py bdist_wheel pip install ./dist/auto_log-1.0.0-py3-none-any.whl ``` - 安装PaddleSlim (可选) ``` # 如果要测试量化、裁剪等功能，需要安装PaddleSlim pip install paddleslim ``` ### 2.2 功能测试 先运行`prepare.sh`准备数据和模型，然后运行`test_train_inference_python.sh`进行测试，最终在```test_tipc/output```目录下生成`python_infer_*.log`格式的日志文件， 以yolov3_darknet53_270e_coco为例。 `test_train_inference_python.sh`包含5种运行模式，每种模式的运行数据不同，分别用于测试速度和精度，分别是： - 模式1：lite_train_lite_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度； ```shell bash test_tipc/prepare.sh ./test_tipc/configs/yolov3/yolov3_darknet53_270e_coco_train_infer_python.txt 'lite_train_lite_infer' bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/yolov3/yolov3_darknet53_270e_coco_train_infer_python.txt 'lite_train_lite_infer' ``` - 模式2：lite_train_whole_infer，使用少量数据训练，一定量数据预测，用于验证训练后的模型执行预测，预测速度是否合理； ```shell bash test_tipc/prepare.sh ./test_tipc/configs/yolov3/yolov3_darknet53_270e_coco_train_infer_python.txt 'lite_train_whole_infer' bash test_tipc/test_train_inference_python.sh ../test_tipc/configs/yolov3/yolov3_darknet53_270e_coco_train_infer_python.txt 'lite_train_whole_infer' ``` - 模式3：whole_infer，不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度; ```shell bash test_tipc/prepare.sh ./test_tipc/configs/yolov3/yolov3_darknet53_270e_coco_train_infer_python.txt 'whole_infer' # 用法1: bash test_tipc/test_train_inference_python.sh ../test_tipc/configs/yolov3/yolov3_darknet53_270e_coco_train_infer_python.txt 'whole_infer' # 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号 bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/yolov3/yolov3_darknet53_270e_coco_train_infer_python.txt 'whole_infer' '1' ``` - 模式4：whole_train_whole_infer，CE： 全量数据训练，全量数据预测，验证模型训练精度，预测精度，预测速度； ```shell bash test_tipc/prepare.sh ./test_tipc/configs/yolov3/yolov3_darknet53_270e_coco_train_infer_python.txt 'whole_train_whole_infer' bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/yolov3/yolov3_darknet53_270e_coco_train_infer_python.txt 'whole_train_whole_infer' ``` - 模式5：klquant_whole_infer，测试离线量化； ```shell bash test_tipc/prepare.sh ./test_tipc/configs/yolov3/yolov3_darknet53_270e_coco_train_infer_python.txt 'klquant_whole_infer' bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/yolov3/yolov3_darknet53_270e_coco_train_infer_python.txt 'klquant_whole_infer' ``` 运行相应指令后，在`test_tipc/output`文件夹下自动会保存运行日志。如'lite_train_lite_infer'模式下，会运行训练+推理的链条，因此，在`test_tipc/output`文件夹有以下文件： ``` test_tipc/output/ |- results_python.log # 运行指令状态的日志 |- norm_train_gpus_0_autocast_null/ # GPU 0号卡上正常训练的训练日志和模型保存文件夹 |- pact_train_gpus_0_autocast_null/ # GPU 0号卡上量化训练的训练日志和模型保存文件夹 ...... |- python_infer_cpu_usemkldnn_True_threads_1_precision_fluid_batchsize_1.log # CPU上开启Mkldnn线程数设置为1，测试batch_size=1条件下的预测运行日志 |- python_infer_gpu_precision_trt_fp16_batchsize_1.log # GPU上开启TensorRT，测试batch_size=1的半精度预测日志 ...... ``` 其中`results_python.log`中包含了每条指令的运行状态，如果运行成功会输出： ``` Run successfully with command - python3.7 tools/train.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml -o use_gpu=True save_dir=./test_tipc/output/norm_train_gpus_0_autocast_null epoch=1 pretrain_weights=https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams TrainReader.batch_size=2 filename=yolov3_darknet53_270e_coco ! Run successfully with command - python3.7 tools/eval.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml -o weights=./test_tipc/output/norm_train_gpus_0_autocast_null/yolov3_darknet53_270e_coco/model_final.pdparams use_gpu=True ! ...... ``` 如果运行失败，会输出： ``` Run failed with command - python3.7 tools/train.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml -o use_gpu=True save_dir=./test_tipc/output/norm_train_gpus_0_autocast_null epoch=1 pretrain_weights=https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams TrainReader.batch_size=2 filename=yolov3_darknet53_270e_coco ! Run failed with command - python3.7 tools/eval.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml -o weights=./test_tipc/output/norm_train_gpus_0_autocast_null/yolov3_darknet53_270e_coco/model_final.pdparams use_gpu=True ! ...... ``` 可以很方便的根据`results_python.log`中的内容判定哪一个指令运行错误。 ### 2.3 精度测试 使用compare_results.py脚本比较模型预测的结果是否符合预期，主要步骤包括： - 提取日志中的预测坐标； - 从本地文件中提取保存好的坐标结果； - 比较上述两个结果是否符合精度预期，误差大于设置阈值时会报错。 #### 使用方式 运行命令： ```shell python3.7 test_tipc/compare_results.py --gt_file=./test_tipc/results/python_*.txt --log_file=./test_tipc/output/python_*.log --atol=1e-3 --rtol=1e-3 ``` 参数介绍： - gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在test_tipc/result/ 文件夹下 - log_file: 指向运行test_tipc/test_train_inference_python.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持python_infer_*.log格式传入 - atol: 设置的绝对误差 - rtol: 设置的相对误差 #### 运行结果 正常运行效果如下图： <img src="compare_right.png" width="1000"> 出现不一致结果时的运行输出： <img src="compare_wrong.png" width="1000"> ## 3. 更多教程 本文档为功能测试用，更丰富的训练预测使用教程请参考： [模型训练](../../docs/tutorials/GETTING_STARTED_cn.md) [PaddleDetection预测部署](../../deploy/README.md)

PaddleDetection/test_tipc/docs/test_train_inference_python.md/0

from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys # add python path of PaddleDetection to sys.path parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2))) sys.path.insert(0, parent_path) # ignore warning log import warnings warnings.filterwarnings('ignore') from ppdet.utils.cli import ArgsParser, merge_args from ppdet.core.workspace import load_config, merge_config from ppdet.utils.check import check_gpu, check_npu, check_xpu, check_version, check_config from ppdet.utils.cam_utils import BBoxCAM import paddle def parse_args(): parser = ArgsParser() parser.add_argument( "--infer_img", type=str, default='demo/000000014439.jpg', # hxw: 404x640 help="Image path, has higher priority over --infer_dir") parser.add_argument("--weights", type=str, default='output/faster_rcnn_r50_vd_fpn_2x_coco_paddlejob/best_model.pdparams' ) parser.add_argument("--cam_out", type=str, default='cam_faster_rcnn' ) parser.add_argument("--use_gpu", type=bool, default=True) parser.add_argument( "--infer_dir", type=str, default=None, help="Directory for images to perform inference on.") parser.add_argument( "--output_dir", type=str, default="output", help="Directory for storing the output visualization files.") parser.add_argument( "--draw_threshold", type=float, default=0.8, help="Threshold to reserve the result for visualization.") parser.add_argument( "--save_results", type=bool, default=False, help="Whether to save inference results to output_dir.") parser.add_argument( "--target_feature_layer_name", type=str, default='model.backbone', # define the featuremap to show grad cam, such as model.backbone, model.bbox_head.roi_extractor help="Whether to save inference results to output_dir.") args = parser.parse_args() return args def run(FLAGS, cfg): assert cfg.architecture in ['FasterRCNN', 'MaskRCNN', 'YOLOv3', 'PPYOLOE', 'PPYOLOEWithAuxHead', 'BlazeFace', 'SSD', 'RetinaNet'], \ 'Only supported cam for faster_rcnn based and yolov3 based architecture for now, ' \ 'the others are not supported temporarily!' bbox_cam = BBoxCAM(FLAGS, cfg) bbox_cam.get_bboxes_cams() print('finish') def main(): FLAGS = parse_args() cfg = load_config(FLAGS.config) merge_args(cfg, FLAGS) merge_config(FLAGS.opt) # disable npu in config by default if 'use_npu' not in cfg: cfg.use_npu = False # disable xpu in config by default if 'use_xpu' not in cfg: cfg.use_xpu = False if cfg.use_gpu: place = paddle.set_device('gpu') elif cfg.use_npu: place = paddle.set_device('npu') elif cfg.use_xpu: place = paddle.set_device('xpu') else: place = paddle.set_device('cpu') check_config(cfg) check_gpu(cfg.use_gpu) check_npu(cfg.use_npu) check_xpu(cfg.use_xpu) check_version() run(FLAGS, cfg) if __name__ == '__main__': main()

PaddleDetection/tools/cam_ppdet.py/0

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"urai": 16998, "ĠPrison": 16999, "Ġrust": 17000, "ĠSketch": 17001, "Ġbees": 17002, "ĠTheory": 17003, "Ġmerit": 17004, "Tex": 17005, "chat": 17006, "Ġmim": 17007, "Ġpaste": 17008, "ĠKoch": 17009, "Ġignorance": 17010, "ĠShoot": 17011, "Ġbasement": 17012, "United": 17013, "ĠAdvis": 17014, "height": 17015, "Ġfoster": 17016, "Ġdetain": 17017, "information": 17018, "Ġneural": 17019, "';": 17020, "Ġproves": 17021, "allery": 17022, "Ġinvitation": 17023, "umbers": 17024, "Ġcattle": 17025, "Ġbicycle": 17026, "zi": 17027, "Ġconsultant": 17028, "Ġapology": 17029, "ĠTiger": 17030, "Ġ123": 17031, "999": 17032, "Ġindividually": 17033, "rt": 17034, "igion": 17035, "ĠBrazilian": 17036, "Ġdisturb": 17037, "Ġentrepreneurs": 17038, "Ġforests": 17039, "cerpt": 17040, "plates": 17041, "pher": 17042, "clipse": 17043, "Ġtwitter": 17044, "Ġacids": 17045, "ographical": 17046, "hum": 17047, "ĠBald": 17048, "ifully": 17049, "Ġcompiler": 17050, "ĠDA": 17051, "Ġdonor": 17052, "asi": 17053, "Ġtribal": 17054, "lash": 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"uras": 17786, "Ġcryst": 17787, "Ġlowered": 17788, "Ġaerial": 17789, "Ġcombinations": 17790, "Ġhaun": 17791, "Ġcha": 17792, "Ġvine": 17793, "Ġquantities": 17794, "Ġlinking": 17795, "bank": 17796, "Ġsoy": 17797, "Bill": 17798, "ĠAngela": 17799, "Ġrecipient": 17800, "ĠProtest": 17801, "Ġsocket": 17802, "Ġsolidarity": 17803, "ĠâĨ": 17804, "mill": 17805, "Ġvaries": 17806, "ĠPakistani": 17807, "Dragon": 17808, "Ġune": 17809, "Ġhorizon": 17810, "³³³³³³³³": 17811, "Ġprovinces": 17812, "Ġfrankly": 17813, "Ġenacted": 17814, "notes": 17815, "['": 17816, "Ġ192": 17817, "ocracy": 17818, "Ġendorsement": 17819, "Ġovertime": 17820, "True": 17821, "Lab": 17822, "licted": 17823, "ĠDNC": 17824, "Ġbeats": 17825, "ĠJamie": 17826, "152": 17827, "ĠINT": 17828, "Contact": 17829, "Ġaccounted": 17830, "hash": 17831, "ĠPackers": 17832, "pires": 17833, "Ġlesbian": 17834, "Ġamendments": 17835, "Ġhopeful": 17836, "ĠFinland": 17837, "Ġspotlight": 17838, "Ġconfigured": 17839, "Ġtroubled": 17840, "Ġgaze": 17841, 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"ĠMol": 17958, "Brit": 17959, "ĠJong": 17960, "INAL": 17961, "ĠStarting": 17962, "Ġdice": 17963, "urtle": 17964, "Ġrelying": 17965, "closure": 17966, "Ġprofitable": 17967, "Ġslaughter": 17968, "ĠManual": 17969, "caster": 17970, "Ġ\"$": 17971, "Ġfeather": 17972, "ĠSimply": 17973, "ieves": 17974, "Ġdeterior": 17975, "ĠPCI": 17976, "Ġstamp": 17977, "Ġflaws": 17978, "Ġshade": 17979, "hammer": 17980, "Ġpassport": 17981, "Ġconting": 17982, "amel": 17983, "Ġobservers": 17984, "Ġneglect": 17985, "ĠRB": 17986, "ĠBrotherhood": 17987, "Ġskeptical": 17988, "family": 17989, "usk": 17990, "Ġemotionally": 17991, "âĻ": 17992, "ĠBeta": 17993, "asonable": 17994, "idity": 17995, "ĠMul": 17996, "Ġkicking": 17997, "ĠCarm": 17998, "ollah": 17999, "VERTIS": 18000, "ĠAthen": 18001, "Ġladder": 18002, "ĠBullet": 18003, "å£": 18004, "0001": 18005, "ĠWildlife": 18006, "ĠMask": 18007, "ĠNan": 18008, "Rev": 18009, "Ġunacceptable": 18010, "legal": 18011, "Ġcrowded": 18012, "agi": 18013, "ĠCox": 18014, "je": 18015, "Ġmorality": 18016, "Ġfuels": 18017, "Ġcables": 18018, "Ġmankind": 18019, "ĠCaribbean": 18020, "Ġanchor": 18021, "Ġbyte": 18022, "ĠOften": 18023, "ĠOz": 18024, "Ġcrafted": 18025, "Ġhistorian": 18026, "ĠWu": 18027, "Ġtowers": 18028, "ĠCitizens": 18029, "Ġhelm": 18030, "Ġcredentials": 18031, "Ġsingular": 18032, "ĠJesse": 18033, "Ġtackles": 18034, "Ġcontempt": 18035, "Ġafore": 18036, "ĠShadows": 18037, "Ġnil": 18038, "Ġurgent": 18039, "apple": 18040, "blood": 18041, "Ġvon": 18042, "Ġoffline": 18043, "Ġbreathe": 18044, "Ġjumps": 18045, "Ġirrelevant": 18046, "oxic": 18047, "omal": 18048, "important": 18049, "Jim": 18050, "Ġgloves": 18051, "arming": 18052, "depth": 18053, "Ġtalents": 18054, "ookie": 18055, "ĠSB": 18056, "Ġpalm": 18057, "uffs": 18058, "esta": 18059, "IGH": 18060, "Ġcanon": 18061, "ĠVerizon": 18062, "ĠPle": 18063, "Ġcoupled": 18064, "velt": 18065, "Ġfundraising": 18066, "ĠGetting": 18067, "ĠDLC": 18068, "Ġmathematical": 18069, "ĠHS": 18070, "ĠCardinals": 18071, "telling": 18072, "Ġsponsors": 18073, "ĠÏ": 18074, "ĠBulls": 18075, "option": 18076, "Ġpropose": 18077, "Ġmemorable": 18078, "Ġembraced": 18079, "Ġdeclining": 18080, "Health": 18081, "eda": 18082, "Ġ};": 18083, "Ġspam": 18084, "mile": 18085, "Ġpitcher": 18086, "ĠEight": 18087, "Ġcaring": 18088, "utic": 18089, "role": 18090, "Ġairline": 18091, "ernandez": 18092, "ĠAthlet": 18093, "Ġcertification": 18094, "uxe": 18095, "riger": 18096, "Ġempir": 18097, "Ġsensation": 18098, "Ġdism": 18099, "Ġbolt": 18100, "Ġevolve": 18101, "House": 18102, "Ġconsultation": 18103, "ĠDuty": 18104, "Ġtouches": 18105, "ĠNathan": 18106, "Ġfaint": 18107, "had": 18108, "\"(": 18109, "ĠConsumer": 18110, "ĠExtreme": 18111, "Ġ127": 18112, "ĠHerm": 18113, "ĠSacrament": 18114, "izoph": 18115, "Ġanxious": 18116, "ulously": 18117, "Ġsocially": 18118, "ĠUTC": 18119, "Ġsolving": 18120, "ĠLetter": 18121, "History": 18122, "educ": 18123, "Price": 18124, "));": 18125, "Ġreload": 18126, "amic": 18127, "Ġpork": 18128, "Ġdiscourse": 18129, "Ġtournaments": 18130, "airo": 18131, "ĠKur": 18132, "ĠCosta": 18133, "Ġviolating": 18134, "Ġinterfere": 18135, "Ġrecreational": 18136, "uffle": 18137, "Ġspeeches": 18138, "Ġneeding": 18139, "Ġremembers": 18140, "Ġcredited": 18141, "nia": 18142, "focused": 18143, "amera": 18144, "Ġbru": 18145, "umbs": 18146, "ĠCuban": 18147, "Ġpreceding": 18148, "Ġnonsense": 18149, "acial": 18150, "Ġsmartphones": 18151, "ĠStories": 18152, "Sports": 18153, "ĠEmergency": 18154, "ouncing": 18155, "efined": 18156, "Ġber": 18157, "Ġconsulting": 18158, "Ġmasters": 18159, "heastern": 18160, ".\"[": 18161, "ĠRunning": 18162, "Ġsuscept": 18163, "ĠFeng": 18164, "America": 18165, "prises": 18166, "stitial": 18167, "ĠWeekly": 18168, "ĠGreater": 18169, "modules": 18170, "ifter": 18171, "Graphics": 18172, "uler": 18173, "Ġwholly": 18174, "Ġsuppress": 18175, "Ġconcealed": 18176, "Ġhappily": 18177, "Ġaccepts": 18178, "ĠEnjoy": 18179, "Ġrivers": 18180, "ĠExcept": 18181, "225": 18182, "ĠNHS": 18183, "ĠMcConnell": 18184, "Ġpussy": 18185, "ferred": 18186, "utable": 18187, "Ġattain": 18188, "Ġ>=": 18189, "Ġdeposits": 18190, "rophic": 18191, "Ġnotorious": 18192, "ĠShaw": 18193, "ilitation": 18194, "Ġepidemic": 18195, "allic": 18196, "Ġsmallest": 18197, "ovich": 18198, "Ġaccessories": 18199, "perties": 18200, "Ġsurplus": 18201, "ĠMech": 18202, "Ġambig": 18203, "ĠImmigration": 18204, "Ġchim": 18205, "eval": 18206, "Ġpracticing": 18207, "ĠMystery": 18208, "Ġdomains": 18209, "ĠSilicon": 18210, "apps": 18211, "Ġkilometers": 18212, "ea": 18213, "ĠSmash": 18214, "Ġwarranty": 18215, "Ġnost": 18216, "sil": 18217, "rev": 18218, "Jon": 18219, "ĠDublin": 18220, "Ġtastes": 18221, "Ġbout": 18222, "great": 18223, "error": 18224, "Ġswitches": 18225, "ĠBapt": 18226, "DO": 18227, "oki": 18228, "Ġsourced": 18229, "produ": 18230, "Ġattachment": 18231, "ĠIssue": 18232, "ĠQuestion": 18233, "Join": 18234, "Ġfitted": 18235, "Ġunlawful": 18236, "^^": 18237, "erek": 18238, "Ġauthentication": 18239, "Ġstole": 18240, "Ġaccountability": 18241, "label": 18242, "Search": 18243, "Ġalbeit": 18244, "atican": 18245, "funded": 18246, "ĠAdding": 18247, "ĠIQ": 18248, "Ġsubmar": 18249, "lit": 18250, "aque": 18251, "ĠLearning": 18252, "Ġinteger": 18253, "Master": 18254, "ĠChrom": 18255, "Ġpremier": 18256, "Op": 18257, "ĠLiu": 18258, "Ġblessed": 18259, "ĠGlobe": 18260, "ĠResponse": 18261, "Ġlegitim": 18262, "ĠMerkel": 18263, "Ġdisposal": 18264, "´": 18265, "Ġgauge": 18266, "peat": 18267, "Ġinduced": 18268, "Ġquestionable": 18269, "arthy": 18270, "ĠVit": 18271, "ĠFeed": 18272, "Until": 18273, "Ut": 18274, "worthy": 18275, "RY": 18276, "ĠHerald": 18277, "ĠHammer": 18278, "Ġmedal": 18279, "ĠRivers": 18280, "ĠHack": 18281, "Ġclarify": 18282, "Ġtracked": 18283, "Ġautonomous": 18284, "Ġtenant": 18285, "ĠQatar": 18286, "erie": 18287, "Ġgrim": 18288, "ĠMonitor": 18289, "Ġresistant": 18290, "ĠSpec": 18291, "ĠWells": 18292, "NAS": 18293, "148": 18294, "Ġminers": 18295, "iotics": 18296, "Ġmisses": 18297, "116": 18298, "gian": 18299, "git": 18300, "ĠEyes": 18301, "pres": 18302, "Ġgraduated": 18303, "Ġangel": 18304, "Ġsynchron": 18305, "Ġefficiently": 18306, "Ġtransmitted": 18307, "Harry": 18308, "Ġglobally": 18309, "ENCE": 18310, "ĠMontana": 18311, "raged": 18312, "ĠPrevention": 18313, "Ġpiss": 18314, "ĠLl": 18315, "Ġshelf": 18316, "ĠBJP": 18317, "ĠTestament": 18318, "ĠLate": 18319, "iker": 18320, "ĠHapp": 18321, "ĠJulian": 18322, "hall": 18323, "Ġspont": 18324, "Ġshutdown": 18325, "Ġinconsistent": 18326, "Ġsubscribers": 18327, "Ġskeleton": 18328, "ĠNebraska": 18329, "Ġinspire": 18330, "ĠVoid": 18331, "Feed": 18332, "Ġangles": 18333, "ĠSprings": 18334, "Ġbenchmark": 18335, "Ġvaccines": 18336, "izophren": 18337, "sexual": 18338, "uffed": 18339, "Ġshine": 18340, "ĠKath": 18341, "Ġgesture": 18342, "inea": 18343, "Ġrip": 18344, "Ġoppression": 18345, "Ġconscience": 18346, "bt": 18347, "ĠLum": 18348, "Ġincidence": 18349, "ĠFa": 18350, "wr": 18351, "Ġmineral": 18352, "ĠSpurs": 18353, "alky": 18354, "Ġthunder": 18355, "Ġopio": 18356, "Being": 18357, "ĠPalm": 18358, "Ġwasted": 18359, "Ġlb": 18360, "iaries": 18361, "ĠInitiative": 18362, "Ġcurric": 18363, "Ġmarker": 18364, "ĠMcL": 18365, "Ġextensions": 18366, "ĠPv": 18367, "ĠArms": 18368, "Ġofferings": 18369, "Ġdefenses": 18370, "Ġvendor": 18371, "Ġcontradict": 18372, "ĠColin": 18373, "Ġreddit": 18374, "Ġperipher": 18375, "122": 18376, "Ġsins": 18377, "Edit": 18378, "ICT": 18379, "Soft": 18380, "ĠShah": 18381, "Ġadministrator": 18382, "ĠTrip": 18383, "Ġpornography": 18384, "Ġtuition": 18385, "inence": 18386, "ĠProgress": 18387, "Ġcatalog": 18388, "Ġsuite": 18389, "Ġhike": 18390, "Ġreproductive": 18391, "engine": 18392, "Ġdrought": 18393, "ĠNoah": 18394, "Ġ230": 18395, "Ġdude": 18396, "Ġrelaxed": 18397, "Ġpartition": 18398, "Ġparticipant": 18399, "Ġtelesc": 18400, "Ġfeas": 18401, "ĠFF": 18402, "owner": 18403, "Ġsweeping": 18404, "Ġlenses": 18405, "Ġmatchup": 18406, "ĠRepl": 18407, "ournals": 18408, "Ġcredible": 18409, "Ġgrandmother": 18410, "Ġthermal": 18411, "Ġsubscribing": 18412, "Ġidentities": 18413, "colm": 18414, "UCT": 18415, "Ġreluctant": 18416, "users": 18417, "ĠCort": 18418, "Ġassisted": 18419, "OSS": 18420, "ATIONS": 18421, "ISH": 18422, "Ġpharmaceutical": 18423, "icable": 18424, "adian": 18425, "ĠSonic": 18426, "ĠFury": 18427, "ĠMong": 18428, "AH": 18429, "ĠPsychology": 18430, "Ġphosph": 18431, "Ġtreats": 18432, "ŃĶ": 18433, "Ġsteadily": 18434, "ĠHello": 18435, "Ġrelates": 18436, "Ġclue": 18437, "Expl": 18438, "auth": 18439, "Ġrevision": 18440, "Ġeld": 18441, "osion": 18442, "Ġbron": 18443, "144": 18444, "rikes": 18445, "Ġmines": 18446, "Ġblanket": 18447, "ĠFail": 18448, "eled": 18449, "ĠImagine": 18450, "ĠPlanned": 18451, "aic": 18452, "Request": 18453, "Mad": 18454, "ĠHorse": 18455, "ĠEagle": 18456, "Ġcapac": 18457, "157": 18458, "Ġling": 18459, "ĠNice": 18460, "ĠParenthood": 18461, "minster": 18462, "ogs": 18463, "ensitive": 18464, "Nothing": 18465, "Ġcarn": 18466, "Fin": 18467, "ĠPE": 18468, "Ġrifles": 18469, "ĠLP": 18470, "Sand": 18471, "ĠguiActive": 18472, "Ġtourist": 18473, "CNN": 18474, "Ġunveiled": 18475, "Ġpredecessor": 18476, "}{": 18477, "uber": 18478, "Ġoffshore": 18479, "Ġoptical": 18480, "ĠRot": 18481, "ĠPearl": 18482, "eton": 18483, "Ġstared": 18484, "Ġfarther": 18485, "atility": 18486, "contin": 18487, "ĠGy": 18488, "ĠFoster": 18489, "ĠCoc": 18490, "rients": 18491, "Ġdesigning": 18492, "ĠEconomy": 18493, "ONG": 18494, "Women": 18495, "ĠNancy": 18496, "erver": 18497, "Ġmascul": 18498, "Ġcasualties": 18499, "Ġ225": 18500, "ĠSullivan": 18501, "ĠChoice": 18502, "Ġaster": 18503, "ws": 18504, "Ġhotels": 18505, "Ġconsiderations": 18506, "Ġcouch": 18507, "ĠStrip": 18508, "ĠGn": 18509, "Ġmanipulate": 18510, "lied": 18511, "Ġsynthetic": 18512, "Ġassaulted": 18513, "Ġoffenses": 18514, "ĠDrake": 18515, "Ġimpe": 18516, "October": 18517, "ĠHeritage": 18518, "hl": 18519, "ĠBlair": 18520, "Unlike": 18521, "Ġgrief": 18522, "Ġ450": 18523, "Ġopted": 18524, "Ġresignation": 18525, "ilo": 18526, "Ġverse": 18527, "ĠTomb": 18528, "Ġupt": 18529, "Ġaired": 18530, "ĠHook": 18531, "ĠMLB": 18532, "Ġassumes": 18533, "outed": 18534, "ĠVers": 18535, "Ġinferior": 18536, "Ġbundle": 18537, "ĠDNS": 18538, "ographer": 18539, "Ġmultip": 18540, "ĠSouls": 18541, "Ġillustrated": 18542, "Ġtactic": 18543, "Ġdressing": 18544, "Ġduo": 18545, "Conf": 18546, "Ġrelent": 18547, "Ġcant": 18548, "Ġscarce": 18549, "Ġcandy": 18550, "ĠCF": 18551, "Ġaffiliated": 18552, "Ġsprint": 18553, "ylan": 18554, "ĠGarcia": 18555, "Ġjunk": 18556, "Print": 18557, "exec": 18558, "Crit": 18559, "Ġportrait": 18560, "iries": 18561, "ĠOFF": 18562, "Ġdisputes": 18563, "WR": 18564, "Love": 18565, "ãģĦ": 18566, "ĠReyn": 18567, "Ġhipp": 18568, "opath": 18569, "Ġfloors": 18570, "ĠFeel": 18571, "Ġworries": 18572, "Ġsettlements": 18573, "ĠPos": 18574, "Ġmosque": 18575, "Ġfinals": 18576, "Ġcrushed": 18577, "ĠProbably": 18578, "ĠBot": 18579, "ĠMans": 18580, "ĠPeriod": 18581, "Ġsovereignty": 18582, "Ġseller": 18583, "Ġapost": 18584, "Ġamateur": 18585, "Ġdorm": 18586, "Ġconsuming": 18587, "Ġarmour": 18588, "ĠRoose": 18589, "Ġintensive": 18590, "Ġeliminating": 18591, "ĠSunni": 18592, "ĠAleppo": 18593, "jin": 18594, "Ġadvise": 18595, "pal": 18596, "ĠHalo": 18597, "Ġdescent": 18598, "Ġsimpler": 18599, "Ġbooth": 18600, "STR": 18601, "Later": 18602, "ĠCave": 18603, "===": 18604, "Ġmol": 18605, "Ġfist": 18606, "Ġshotgun": 18607, "supp": 18608, "Ġrobbery": 18609, "Effect": 18610, "Ġobscure": 18611, "ĠProfessional": 18612, "Ġembassy": 18613, "Ġmilitant": 18614, "Ġincarcer": 18615, "Ġgenerates": 18616, "Ġlaunches": 18617, "Ġadministrators": 18618, "Ġshaft": 18619, "Ġcircular": 18620, "Ġfreshman": 18621, "ĠWes": 18622, "ĠJoel": 18623, "ĠDrew": 18624, "ĠDuncan": 18625, "ĠApparently": 18626, "sight": 18627, "ĠInternal": 18628, "ĠIndividual": 18629, "ĠFE": 18630, "Ġbore": 18631, "ĠMt": 18632, "Ġbroadly": 18633, "ĠOptions": 18634, "ountain": 18635, "ipes": 18636, "ĠVideos": 18637, "204": 18638, "Ġhills": 18639, "Ġsimulation": 18640, "Ġdisappointment": 18641, "itan": 18642, "ĠLaboratory": 18643, "Ġupward": 18644, "Ġboundary": 18645, "Ġdarker": 18646, "hart": 18647, "Ġdominance": 18648, "Cong": 18649, "ĠOracle": 18650, "ĠLords": 18651, "Ġscholarship": 18652, "ĠVincent": 18653, "ede": 18654, "ĠRah": 18655, "Ġencourages": 18656, "rov": 18657, "Ġquo": 18658, "Ġpremise": 18659, "ĠCrisis": 18660, "ĠHolocaust": 18661, "Ġrhythm": 18662, "Ġmetric": 18663, "club": 18664, "Ġtransported": 18665, "Ġnod": 18666, "ĠPist": 18667, "Ġancestors": 18668, "ĠFreder": 18669, "thumbnails": 18670, "ĠCE": 18671, "OND": 18672, "Phil": 18673, "venge": 18674, "ĠProducts": 18675, "castle": 18676, "Ġqualifying": 18677, "ĠKaren": 18678, "VERTISEMENT": 18679, "Ġmighty": 18680, "Ġexplanations": 18681, "Ġfixing": 18682, "Di": 18683, "Ġdeclaring": 18684, "Ġanonymity": 18685, "Ġjuven": 18686, "ĠNord": 18687, "ĠDoom": 18688, "ĠActually": 18689, "Ok": 18690, "phis": 18691, "ĠDesert": 18692, "Ġ116": 18693, "IK": 18694, "ĠFM": 18695, "Ġincomes": 18696, "VEL": 18697, "okers": 18698, "Ġpecul": 18699, "Ġlightweight": 18700, "gue": 18701, "Ġaccent": 18702, "Ġincrement": 18703, "ĠChan": 18704, "Ġcomplaining": 18705, "ĠBaghd": 18706, "Ġmidfielder": 18707, "Ġoverhaul": 18708, "Process": 18709, "ĠHollow": 18710, "ĠTitans": 18711, "Small": 18712, "manuel": 18713, "ĠUnity": 18714, "ĠEvents": 18715, "Sty": 18716, "Ġdisproportion": 18717, "nesty": 18718, "enes": 18719, "ĠCod": 18720, "Ġdemonstrations": 18721, "ĠCrimson": 18722, "ĠOH": 18723, "Ġenrolled": 18724, "Ġcel": 18725, "ĠBrett": 18726, "Ġaide": 18727, "Ġheels": 18728, "Ġbroadband": 18729, "Ġmarking": 18730, "Ġwizard": 18731, "ĠNJ": 18732, "ĠChiefs": 18733, "Ġingredient": 18734, "Ġdug": 18735, "ĠShut": 18736, "urchase": 18737, "endor": 18738, "Ġfarmer": 18739, "ĠGoldman": 18740, "129": 18741, "155": 18742, "Order": 18743, "Ġlion": 18744, "iably": 18745, "Ġstain": 18746, "array": 18747, "ilitary": 18748, "ĠFAQ": 18749, "Ġexploded": 18750, "ĠMcCarthy": 18751, "ĠTweet": 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21479, "Ġuh": 21480, "Conclusion": 21481, "wage": 21482, "Ġrespir": 21483, "Ġreminis": 21484, "hetical": 21485, "Ġgy": 21486, "Ġutilized": 21487, "icidal": 21488, "Ġ1900": 21489, "Ġhunters": 21490, "ĠSwan": 21491, "ĠReact": 21492, "Ġvisitor": 21493, "ĠThanksgiving": 21494, "308": 21495, "Posts": 21496, "Ġhips": 21497, "1997": 21498, "omers": 21499, "Ġknocking": 21500, "ĠVehicle": 21501, "Ġtil": 21502, "Ġ138": 21503, "Ġmi": 21504, "ĠInvestigation": 21505, "ĠKenya": 21506, "Ġcasino": 21507, "Ġmotives": 21508, "Ġregain": 21509, "rex": 21510, "Ġweekends": 21511, "Ġstabbed": 21512, "boro": 21513, "Ġexploited": 21514, "ĠHAVE": 21515, "ĠTelevision": 21516, "cock": 21517, "Ġpreparations": 21518, "Ġendeav": 21519, "ĠRemote": 21520, "ĠMaker": 21521, "ĠProdu": 21522, "ĠEvan": 21523, "Ġinformational": 21524, "ĠLouisville": 21525, "154": 21526, "ĠDreams": 21527, "Ġplots": 21528, "ĠRunner": 21529, "Ġhurting": 21530, "Ġacademy": 21531, "ĠMontgomery": 21532, "nm": 21533, "ĠLanc": 21534, "ĠAlz": 21535, 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"osi": 21707, "anmar": 21708, "Ġ1947": 21709, "ĠFell": 21710, "estial": 21711, "itating": 21712, "GF": 21713, "ĠSr": 21714, "ifted": 21715, "Ġconnector": 21716, "ĠBone": 21717, "illes": 21718, "260": 21719, "hma": 21720, "Ġoverlap": 21721, "ĠGitHub": 21722, "Ġcleaner": 21723, "ĠBaptist": 21724, "ĠWAS": 21725, "Ġlungs": 21726, "Ñģ": 21727, "ĠBUT": 21728, "Ġcite": 21729, "Ġpitched": 21730, "reatment": 21731, "Ġtrophies": 21732, "ĠNu": 21733, "386": 21734, "ĠPride": 21735, "Ġattendees": 21736, "[]": 21737, "179": 21738, "Ġspatial": 21739, "Ġprizes": 21740, "ĠReligion": 21741, "Ġshowcase": 21742, "ĠCategory": 21743, "vidia": 21744, "Target": 21745, "Property": 21746, "?,": 21747, "Ġfusion": 21748, "pie": 21749, "ĠUCLA": 21750, "Ġsoundtrack": 21751, "Ġprincess": 21752, "ĠCaval": 21753, "should": 21754, "Ġlimbs": 21755, "Background": 21756, "Ġlonely": 21757, "Ġcores": 21758, "ĠTail": 21759, "sheet": 21760, "Ġ132": 21761, "Ra": 21762, "ãĤ«": 21763, "ĠBolt": 21764, "Ġbooked": 21765, "Ġadminister": 21766, "Ġequals": 21767, "wy": 21768, "Ġobserving": 21769, "ĠBaron": 21770, "ĠAdobe": 21771, "Ġvirgin": 21772, "ĠSocialist": 21773, "Move": 21774, "ghazi": 21775, "ĠLinda": 21776, "212": 21777, "Ġbrewing": 21778, "Ġmerchants": 21779, "burse": 21780, "Ġdivor": 21781, "Ġmetals": 21782, "ĠNer": 21783, "Ġsums": 21784, "ĠEnemy": 21785, "Ġenvision": 21786, "Ġgranting": 21787, "ĠHoney": 21788, "ĠSkyrim": 21789, "Ġsocio": 21790, "graded": 21791, "Ġselective": 21792, "WASHINGTON": 21793, "Ġ1948": 21794, "ĠSirius": 21795, "ĠGross": 21796, "activity": 21797, "ĠIvan": 21798, "Ġfurious": 21799, "BSD": 21800, "ĠPrevious": 21801, "Ġresponsive": 21802, "Ġcharitable": 21803, "Ġleaning": 21804, "ĠPew": 21805, "Ġviolates": 21806, "\\\\\\\\\\\\\\\\": 21807, "ĠComing": 21808, "wire": 21809, "Ġpoet": 21810, "Ġresolutions": 21811, "command": 21812, "ĠPortuguese": 21813, "Ġnickname": 21814, "Ġdeaf": 21815, "February": 21816, "Ġrecognise": 21817, "Ġentirety": 21818, "Ġseasonal": 21819, "placed": 21820, "ĠTelegraph": 21821, "Ġmicrophone": 21822, "ouring": 21823, "Ġgrains": 21824, "Ġgoverned": 21825, "Ġpostp": 21826, "ĠWaters": 21827, "inement": 21828, "Ġundocumented": 21829, "ĠComcast": 21830, "Ġfox": 21831, "Ġassaults": 21832, "reon": 21833, "many": 21834, "ĠJenkins": 21835, "ĠAnyway": 21836, "Ġassessments": 21837, "Ġdowns": 21838, "ĠMouse": 21839, "Ġsuperb": 21840, "kt": 21841, "ĠDow": 21842, "Ġtaxation": 21843, "401": 21844, "Ġsmiles": 21845, "Ġundertaken": 21846, "Ġexh": 21847, "Ġenthusiastic": 21848, "Ġtwent": 21849, "Ġgovernmental": 21850, "Ġautonomy": 21851, "ĠTechnologies": 21852, "ĠChain": 21853, "Ġprevalent": 21854, "fb": 21855, "Ġnicotine": 21856, "ogram": 21857, "job": 21858, "Ġawaiting": 21859, "ĠMenu": 21860, "Ġdeputies": 21861, "kov": 21862, "ishops": 21863, "Button": 21864, "ĠShanghai": 21865, "Ġdiesel": 21866, "ĠDuck": 21867, "Ryan": 21868, "ĠPCs": 21869, "NF": 21870, "jury": 21871, "ente": 21872, "Ġinaccurate": 21873, "eddy": 21874, "Whatever": 21875, "Ġshowc": 21876, "ĠNad": 21877, "odus": 21878, "etr": 21879, "Ġplaintiffs": 21880, "ĠWOR": 21881, "ĠAssange": 21882, "Ġprivat": 21883, "Ġpremiums": 21884, "Ġtam": 21885, "URL": 21886, "Ġelites": 21887, "ĠRanger": 21888, "ottenham": 21889, "ĠHoff": 21890, "ĠAthens": 21891, "Ġdefinite": 21892, "Ġsighed": 21893, "Ġevenly": 21894, "211": 21895, "ĠAmber": 21896, "akia": 21897, "Ġmailing": 21898, "Ġcrashing": 21899, "ĠConfederate": 21900, "rugged": 21901, "Wal": 21902, "ĠDepths": 21903, "Ġjuvenile": 21904, "Ġreactor": 21905, "Introduction": 21906, "ĠDeluxe": 21907, "1995": 21908, "ĠSanchez": 21909, "ĠMead": 21910, "ivable": 21911, ":-": 21912, "ĠPlanning": 21913, "ĠTrap": 21914, "quin": 21915, "ĠProtect": 21916, "vered": 21917, "Information": 21918, "Ġkidney": 21919, "innamon": 21920, "las": 21921, "Ġpolicing": 21922, "Ġtolerate": 21923, "ĠQi": 21924, "Ġbiased": 21925, "Fort": 21926, "ĠKi": 21927, "save": 21928, "Ġprivileged": 21929, "Ġbeasts": 21930, "ĠGlas": 21931, "ĠCinem": 21932, "Ġcomeback": 21933, "Sunday": 21934, "Ġextinction": 21935, "hops": 21936, "Ġtransmit": 21937, "Ġdoubles": 21938, "ĠFlat": 21939, "167": 21940, "Ġdisputed": 21941, "Ġinjustice": 21942, "foo": 21943, "Vict": 21944, "roleum": 21945, "ĠJulie": 21946, "Context": 21947, "ĠRarity": 21948, "issue": 21949, "Component": 21950, "Ġcounseling": 21951, "anne": 21952, "dark": 21953, "Ġobjections": 21954, "uilt": 21955, "Ġgast": 21956, "Ġplac": 21957, "Ġunused": 21958, "ãĥĩ": 21959, "ĠTrial": 21960, "ĠJas": 21961, "hedral": 21962, "obb": 21963, "Ġtemporal": 21964, "ĠPRO": 21965, "ĠNW": 21966, "ĠAnniversary": 21967, "Large": 21968, "Ġtherm": 21969, "Ġdavid": 21970, "Ġsystemic": 21971, "ĠShir": 21972, "mut": 21973, "ĠNept": 21974, "address": 21975, "Ġscanning": 21976, "Ġunderstandable": 21977, "Ġcanvas": 21978, "Cat": 21979, "ĠZoo": 21980, "Ġangels": 21981, "LO": 21982, "ĠStatement": 21983, "ĠSig": 21984, "ovable": 21985, "ĠAway": 21986, "sharing": 21987, "ocrats": 21988, "stated": 21989, "Ġweighing": 21990, "Nor": 21991, "wild": 21992, "Bey": 21993, "Ġastonishing": 21994, "ĠReynolds": 21995, "Ġopener": 21996, "Ġtrainer": 21997, "Ġsurgical": 21998, "pn": 21999, "Ġadjusting": 22000, "wheel": 22001, "Ġfrown": 22002, "ervative": 22003, "Ġsuspend": 22004, "Within": 22005, "tein": 22006, "Ġobstacle": 22007, "Ġliberties": 22008, "ymes": 22009, "Ġuranium": 22010, "ansom": 22011, "anol": 22012, "uba": 22013, "ĠLoss": 22014, "Ġarous": 22015, "ĠHenderson": 22016, "Wow": 22017, "spl": 22018, "cur": 22019, "ĠÂŃ": 22020, "Ġtheirs": 22021, "Damage": 22022, "Ġdownloading": 22023, "Ġdiscern": 22024, "ĠSto": 22025, "ĠFla": 22026, "Ġhath": 22027, "ĠAj": 22028, "Ġunpleasant": 22029, "European": 22030, "expensive": 22031, "Ġscreenshot": 22032, "ĠUV": 22033, "Ġallied": 22034, "ĠPersian": 22035, "Ġmonopoly": 22036, "Ġatom": 22037, "ĠRedskins": 22038, "\"><": 22039, "Ġcancell": 22040, "Ġcinema": 22041, "131": 22042, "fair": 22043, "ĠAlfred": 22044, "Ġduck": 22045, "args": 22046, "223": 22047, "ĠISI": 22048, "Ġsignaling": 22049, "inar": 22050, "Ġlaughs": 22051, "Ġforwards": 22052, "Ġreckless": 22053, "Ġlisteners": 22054, "ativity": 22055, "Ġvastly": 22056, "nant": 22057, "Less": 22058, "ĠHunting": 22059, "ĠScientific": 22060, "ITED": 22061, "Ġknight": 22062, "ĠHTC": 22063, "usa": 22064, "tmp": 22065, "Ġrude": 22066, "ĠLegendary": 22067, "Ġarises": 22068, "Bad": 22069, "ĠClaim": 22070, "peg": 22071, "Ġrealities": 22072, "Think": 22073, "Ġ°": 22074, "Ġrode": 22075, "Ġstrive": 22076, "Ġanecd": 22077, "Ġshorts": 22078, "Ġhypothes": 22079, "Ġcoordinated": 22080, "ĠGandhi": 22081, "ĠFPS": 22082, "RED": 22083, "Ġsusceptible": 22084, "Ġshrink": 22085, "ĠChart": 22086, "Help": 22087, "Ġion": 22088, "deep": 22089, "ribes": 22090, "ĠKai": 22091, "ĠCustomer": 22092, "Summary": 22093, "Ġcough": 22094, "wife": 22095, "Ġlend": 22096, "Ġpositioning": 22097, "Ġlottery": 22098, "ĠCanyon": 22099, "Ġfade": 22100, "Ġbronze": 22101, "ĠKenny": 22102, "Ġboasts": 22103, "ĠEnhanced": 22104, "record": 22105, "Ġemergence": 22106, "Ġakin": 22107, "ĠBert": 22108, "itous": 22109, "âĸij": 22110, "Ġstip": 22111, "Ġexchanged": 22112, "omore": 22113, "alsh": 22114, "Ġreservoir": 22115, "Ġstandpoint": 22116, "WM": 22117, "Ġinitiate": 22118, "Ġdecay": 22119, "Ġbrewery": 22120, "Ġterribly": 22121, "Ġmortal": 22122, "levard": 22123, "Ġrevis": 22124, "NI": 22125, "elo": 22126, "Ġconfess": 22127, "ĠMSNBC": 22128, "Ġsubmissions": 22129, "Controller": 22130, "Ġ202": 22131, "ĠRuth": 22132, "});": 22133, "ĠAzure": 22134, "Ġ.\"": 22135, "206": 22136, "ĠMarketing": 22137, "Ġlaund": 22138, "iencies": 22139, "Ġrenowned": 22140, "ĠTrou": 22141, "ĠNGO": 22142, "blems": 22143, "Ġterrified": 22144, "Ġwarns": 22145, "Ġpert": 22146, "Ġunsure": 22147, "480": 22148, "alez": 22149, "ultz": 22150, "ĠOutside": 22151, "Ġstyl": 22152, "ĠUnderground": 22153, "Ġpanc": 22154, "Ġdictionary": 22155, "Ġfoe": 22156, "riminal": 22157, "ĠNorwegian": 22158, "Ġjailed": 22159, "Ġmaternal": 22160, "ée": 22161, "ĠLucy": 22162, "cop": 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25855, "Ġviolently": 25856, "ipation": 25857, "Ġskepticism": 25858, "Ġ1918": 25859, "ĠAnnie": 25860, "ĠSI": 25861, "Ġgenetics": 25862, "Ġonboard": 25863, "atl": 25864, "ĠFriedman": 25865, "ĠBri": 25866, "ceptive": 25867, "Ġpirate": 25868, "ĠReporter": 25869, "278": 25870, "Ġmythology": 25871, "Ġeclipse": 25872, "Ġskins": 25873, "Ġglyph": 25874, "ingham": 25875, "Files": 25876, "Cour": 25877, "women": 25878, "Ġregimes": 25879, "Ġphotographed": 25880, "Kat": 25881, "ĠMAX": 25882, "Officials": 25883, "Ġunexpectedly": 25884, "Ġimpressions": 25885, "Front": 25886, ";;;;;;;;": 25887, "Ġsupremacy": 25888, "Ġsang": 25889, "Ġaggravated": 25890, "Ġabruptly": 25891, "ĠSector": 25892, "Ġexcuses": 25893, "Ġcosting": 25894, "idepress": 25895, "Stack": 25896, "ĠRNA": 25897, "obil": 25898, "Ġghosts": 25899, "ldon": 25900, "atibility": 25901, "Topics": 25902, "Ġreimburse": 25903, "ĠHM": 25904, "ĠDeg": 25905, "Ġthief": 25906, "yet": 25907, "ogenesis": 25908, "leaning": 25909, "ĠKol": 25910, 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"optional": 25968, "eworthy": 25969, ".</": 25970, "Ġundertaking": 25971, "Ġchickens": 25972, "Ġstimuli": 25973, "ĠElse": 25974, "igators": 25975, "ĠBeginning": 25976, "ctory": 25977, "Ġprepares": 25978, "Ġdelta": 25979, "Ġvicinity": 25980, "tool": 25981, "Ġworkshops": 25982, "MHz": 25983, "Ġaccusation": 25984, "Ġhistories": 25985, "ropolis": 25986, "ĠChurchill": 25987, "Ġneon": 25988, "Ġbaff": 25989, "dies": 25990, "maybe": 25991, "Ġè£ıè¦ļéĨĴ": 25992, "Ġsymptom": 25993, "ECH": 25994, "ĠManuel": 25995, "Ġbanana": 25996, "ĠHB": 25997, "Ġ****": 25998, "ĠKoreans": 25999, "coll": 26000, "FB": 26001, "Ġpraying": 26002, "ĠCannot": 26003, "ĠMile": 26004, "Ġembracing": 26005, "ĠSilk": 26006, "393": 26007, "oters": 26008, "FD": 26009, "Ġdaylight": 26010, "alias": 26011, "ĠBrigade": 26012, "ĠHannah": 26013, "Ġclergy": 26014, "Ġsoutheast": 26015, "Ġalcoholic": 26016, "Ġproposes": 26017, "livion": 26018, "Ġcalculating": 26019, "Ġstimulate": 26020, "Ġsplitting": 26021, "eight": 26022, "ĠIndy": 26023, "plays": 26024, "ĠPik": 26025, "Ġdomest": 26026, "Ġforgiveness": 26027, "ĠRings": 26028, "patient": 26029, "kinson": 26030, "Mont": 26031, "igible": 26032, ";\"": 26033, "Ġperiodically": 26034, "ammad": 26035, "ĠBritt": 26036, "pard": 26037, "Ġarbitration": 26038, "ĠSchneider": 26039, "ĠCorporate": 26040, "ĠMaya": 26041, "Ġsnakes": 26042, "aum": 26043, "Ġblasted": 26044, "Ġmysteries": 26045, "Ġrevive": 26046, "ocamp": 26047, "ĠDodge": 26048, "ĠOpera": 26049, "279": 26050, "Ġorphan": 26051, "Ġspecifies": 26052, "ĠMets": 26053, "Duration": 26054, "Hen": 26055, "Ġfireworks": 26056, "Ġprosecute": 26057, "ĠTillerson": 26058, "dp": 26059, "usage": 26060, "liness": 26061, "ĠDebian": 26062, "Ġ224": 26063, "rises": 26064, "ĠInfect": 26065, "atra": 26066, "ĠRR": 26067, "ĠLor": 26068, "diff": 26069, "ĠCharleston": 26070, "Ġacoustic": 26071, "Ġamuse": 26072, "330": 26073, "Ġcer": 26074, "ĠTac": 26075, "Ġ[+": 26076, "Ġcardiac": 26077, "ĠRestaurant": 26078, "ergy": 26079, "Ġfuzz": 26080, "Ġbites": 26081, "Ġhazardous": 26082, "Ġbrighter": 26083, "rans": 26084, "ĠStephanie": 26085, "extra": 26086, "RET": 26087, "ĠChristine": 26088, "ĠSue": 26089, "statement": 26090, "Ġbolster": 26091, "Ġantit": 26092, "Radio": 26093, "BIT": 26094, "ãĤ°": 26095, "Ġvisions": 26096, "ĠConcept": 26097, "Ġinline": 26098, "ĠPhilosophy": 26099, "isans": 26100, "ĠIrving": 26101, "ã": 26102, "taking": 26103, "Ġinconsist": 26104, "ĠKumar": 26105, "Ġlig": 26106, "ĠSchumer": 26107, "ĠRegulations": 26108, "ĠHz": 26109, "thro": 26110, "ĠVoldemort": 26111, "ĠMED": 26112, "ĠFrederick": 26113, "Pad": 26114, "221": 26115, "Ġalleging": 26116, "ĠCommunication": 26117, "Ġ167": 26118, "Ġforecasts": 26119, "Ġspiders": 26120, "Organ": 26121, "ĠParticipants": 26122, "ĠOps": 26123, "design": 26124, "Close": 26125, "Ġfacto": 26126, "Ġbombers": 26127, "resistant": 26128, "ategories": 26129, "School": 26130, "Ġhomework": 26131, "Ġcorro": 26132, "Tuesday": 26133, "ĠBrendan": 26134, "ĠMX": 26135, "ĠTS": 26136, "ĠStri": 26137, "Ġstakeholders": 26138, "ĠMillennium": 26139, "Ġtransferring": 26140, "Jud": 26141, "Ġtac": 26142, "Ġ1600": 26143, "ĠSDK": 26144, "rb": 26145, "Ġinterpretations": 26146, "ĠSG": 26147, "Ġupstairs": 26148, "ĠHarvest": 26149, "Ġvagina": 26150, "Ġingest": 26151, "xf": 26152, "ĠOrion": 26153, "ĠJoey": 26154, "Ġsandwic": 26155, "Ġimmortal": 26156, "Ġflipped": 26157, "ortex": 26158, "threatening": 26159, "Ġsniper": 26160, "Ġconverts": 26161, "Ġinstallations": 26162, "ĠBulgar": 26163, "orsche": 26164, "mails": 26165, "Ġlure": 26166, "Ġnarrowly": 26167, "Ġgrenade": 26168, "ĠGing": 26169, "Ġunderwear": 26170, "--------------": 26171, "Ġchased": 26172, "ĠVAL": 26173, "Ġparenting": 26174, "ĠHamb": 26175, "ĠBlaz": 26176, "Ġanarchist": 26177, "ĠMedian": 26178, "ĠPrograms": 26179, "ν": 26180, "Ġobj": 26181, "ĠNokia": 26182, "orman": 26183, "anqu": 26184, "atism": 26185, "opa": 26186, "Ġfulfilling": 26187, "Ġpuppy": 26188, "Ġentit": 26189, "ĠSebastian": 26190, "Ġshooters": 26191, "Ġricher": 26192, "è¡": 26193, "Ġtempted": 26194, "ĠATT": 26195, "ĠCV": 26196, "Ġtore": 26197, "Resource": 26198, "ĠDevils": 26199, "408": 26200, "inational": 26201, "Ġassurance": 26202, "ĠDarren": 26203, "Ġwhichever": 26204, "posure": 26205, "Ġfury": 26206, "Stock": 26207, "Ġuniversally": 26208, "response": 26209, "Ġoak": 26210, "Ġworkload": 26211, "ĠCorner": 26212, "eele": 26213, "\"...": 26214, "Ġdeprived": 26215, "kowski": 26216, "Ġcasts": 26217, "Ġaffiliation": 26218, "ĠAch": 26219, "ĠAsked": 26220, "athe": 26221, "Ġlact": 26222, "ĠThu": 26223, "rm": 26224, "Ġairlines": 26225, "Ġnotions": 26226, "Format": 26227, "ĠFAA": 26228, "ãĥĬ": 26229, "driver": 26230, "Ġtranscend": 26231, "Settings": 26232, "ĠProsecut": 26233, "Ġspinal": 26234, "Ġdefaults": 26235, "FK": 26236, "Ġprefers": 26237, "rendered": 26238, "thus": 26239, "film": 26240, "Ġtiger": 26241, "ĠSpicer": 26242, "recogn": 26243, "ĠRugby": 26244, "Network": 26245, "Ġpity": 26246, "Ġcompartment": 26247, "casters": 26248, "ĠMonroe": 26249, "Ġ720": 26250, "Ġcorrections": 26251, "Ġdopamine": 26252, "ĠAZ": 26253, "Cut": 26254, "Ġroomm": 26255, "Ġspeculate": 26256, "Hash": 26257, "Ġrestrictive": 26258, "1111": 26259, "redible": 26260, "onel": 26261, "Ġrampant": 26262, "reported": 26263, "ĠSuite": 26264, "ĠMinimum": 26265, "alys": 26266, "azard": 26267, "loop": 26268, "Ġlent": 26269, "sha": 26270, "Ġvandal": 26271, "menu": 26272, "ĠBoehner": 26273, "Ġnarratives": 26274, "Ġauthenticity": 26275, "269": 26276, "anic": 26277, "duty": 26278, "285": 26279, "Ġthanked": 26280, "Ġbetrayed": 26281, "lift": 26282, "Ġsouthwest": 26283, "ĠDexter": 26284, "ĠBod": 26285, "Ġkeywords": 26286, "Average": 26287, "DIS": 26288, "Ġethnicity": 26289, "!),": 26290, "ĠNationals": 26291, "á¹": 26292, "ĠTah": 26293, "ioxid": 26294, "Ġwidget": 26295, "Ġpasta": 26296, "Ġbilling": 26297, "Ġtrilogy": 26298, "ĠLines": 26299, "Ġsniff": 26300, "Ġnephew": 26301, "Late": 26302, "Ġprincip": 26303, "ĠLoop": 26304, "ĠMarxist": 26305, "Ġdissolved": 26306, "Ġcontexts": 26307, "ĠAmount": 26308, "ĠSpike": 26309, "Ġtotals": 26310, "Ġorganizer": 26311, "Ġuprising": 26312, "ships": 26313, "YY": 26314, "ĠNortheast": 26315, "money": 26316, "gradation": 26317, "Ġgoalkeeper": 26318, "ĠHear": 26319, "Ġsteak": 26320, "ĠBuzzFeed": 26321, "Ġsolemn": 26322, "ĠScand": 26323, "Ġpopping": 26324, "Ġadhere": 26325, "ĠAlleg": 26326, "byte": 26327, "ĠWolver": 26328, "Ġunin": 26329, "Ġrecol": 26330, "itud": 26331, "Ġmimic": 26332, "ibus": 26333, "Ġpredicts": 26334, "ĠKeeper": 26335, "iating": 26336, "Ġdeception": 26337, "Ġlearnt": 26338, "Ġdiary": 26339, "Ġconditional": 26340, "Ġrelic": 26341, "Ġinvoke": 26342, "ienced": 26343, "åĪ": 26344, "ĠPont": 26345, "Ġcellphone": 26346, "Ġspeeding": 26347, "Ġtackling": 26348, "Ġnude": 26349, "opened": 26350, "ĠManafort": 26351, "Ġ1952": 26352, "Ġmajors": 26353, "ĠSilence": 26354, "Ġlogistics": 26355, "Ġweighted": 26356, "ĠPsychiat": 26357, "\":[\"": 26358, "Ġsickness": 26359, "Ġdividends": 26360, "zon": 26361, "Release": 26362, "ĠKeys": 26363, "ĠIch": 26364, "Ġenz": 26365, "ĠFernand": 26366, "Ġα": 26367, "Ġmeanings": 26368, "Ġpenny": 26369, "Ġstern": 26370, "Ġlar": 26371, "ĠPublished": 26372, "Ġbackdrop": 26373, "Kim": 26374, "ĠSynt": 26375, "Ġdebuted": 26376, "wm": 26377, "ĠIsle": 26378, "Ġregulating": 26379, "otti": 26380, "ĠScholars": 26381, "icester": 26382, "ĠChef": 26383, "Ġpops": 26384, "ĠLauncher": 26385, "ĠVarious": 26386, "Ġcommenting": 26387, "oslav": 26388, "enzie": 26389, "Ġrivalry": 26390, "âĤ¬": 26391, "Really": 26392, "Ġorc": 26393, "Ġbean": 26394, "ĠJudy": 26395, "Notice": 26396, "ĠBike": 26397, "?]": 26398, "Ġrented": 26399, "sten": 26400, "Ġforefront": 26401, "ĠBaldwin": 26402, "Ġyielded": 26403, "tails": 26404, "Prime": 26405, "ĠSources": 26406, "icator": 26407, "Sean": 26408, "Ġmarching": 26409, "Output": 26410, "ĠJungle": 26411, "Ġreside": 26412, "zzle": 26413, "ĠAndrews": 26414, "Ġtorque": 26415, "Basic": 26416, "Actually": 26417, "strap": 26418, 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27103, "Ġcyt": 27104, "Ġribs": 27105, "oodle": 27106, "ĠSounds": 27107, "hair": 27108, "ĠSyndrome": 27109, "tf": 27110, "Ġproportional": 27111, "uid": 27112, "Ġpertaining": 27113, "ĠKindle": 27114, "ĠNegro": 27115, "Ġreiterated": 27116, "ĠTonight": 27117, "oths": 27118, "ĠCornell": 27119, "Ġowing": 27120, "Ġ208": 27121, "elfare": 27122, "ocating": 27123, "ĠBirds": 27124, "Subscribe": 27125, "Ġessays": 27126, "Ġburdens": 27127, "Ġillustrations": 27128, "arious": 27129, "ERAL": 27130, "ĠCalcul": 27131, "Ġxen": 27132, "ĠLinkedIn": 27133, "ĠJung": 27134, "Ġredesign": 27135, "Connor": 27136, "296": 27137, "Ġreversal": 27138, "ĠAdelaide": 27139, "ĠLL": 27140, "Ġsinking": 27141, "Ġgum": 27142, "USH": 27143, "capt": 27144, "ĠGrimm": 27145, "Ġfootsteps": 27146, "ĠCBD": 27147, "ispers": 27148, "Ġprose": 27149, "Wednesday": 27150, "ĠMovies": 27151, "edin": 27152, "Ġoverturned": 27153, "Ġcontentious": 27154, "USB": 27155, "~~~~~~~~~~~~~~~~": 27156, "ĠCopper": 27157, "Ġpointless": 27158, "NV": 27159, "values": 27160, "olphin": 27161, "dain": 27162, "Ġdeposited": 27163, "ĠGW": 27164, "Ġpreceded": 27165, "ĠCla": 27166, "ĠGolem": 27167, "ĠNim": 27168, "Ġβ": 27169, "ĠEngineers": 27170, "middle": 27171, "Ġflatt": 27172, "operative": 27173, "Ġcouncils": 27174, "imbabwe": 27175, "elin": 27176, "Ġstressful": 27177, "ĠLD": 27178, "Ġresh": 27179, "lake": 27180, "Ġwheelchair": 27181, "ĠAlternative": 27182, "Ġoptimize": 27183, "operation": 27184, "Ġpeek": 27185, "Ġoneself": 27186, "igil": 27187, "Ġtransitions": 27188, "opathy": 27189, "blank": 27190, "Ġ169": 27191, "171": 27192, "________________________________________________________________": 27193, "Ġlaundering": 27194, "Enc": 27195, "ĠDEC": 27196, "Ġworkouts": 27197, "Ġspikes": 27198, "Ġdinosaurs": 27199, "Ġdiscriminatory": 27200, "Pool": 27201, "Rather": 27202, "385": 27203, "RNA": 27204, "testers": 27205, "eto": 27206, "ĠIdentity": 27207, "Ġvein": 27208, "ĠBurton": 27209, "Ġarcade": 27210, "420": 27211, "Ultimately": 27212, "ĠSadly": 27213, "ð": 27214, "pill": 27215, "Ġcubic": 27216, "ĠSpectrum": 27217, "these": 27218, "states": 27219, "Ġunofficial": 27220, "hawks": 27221, "ĠEVERY": 27222, "Ġrainbow": 27223, "Ġincarceration": 27224, "anding": 27225, "Ġsyll": 27226, "ĠEverton": 27227, "Ġ179": 27228, "ĠSerbia": 27229, "Ġ189": 27230, "meter": 27231, "ĠMickey": 27232, "Ġantiqu": 27233, "Ġfactual": 27234, "neck": 27235, "ĠNare": 27236, "norm": 27237, "must": 27238, "Ġhighways": 27239, "Ġglam": 27240, "Ġdividing": 27241, "ĠSquadron": 27242, "ĠMartha": 27243, "Ġbirths": 27244, "Cover": 27245, "////////////////": 27246, "ĠWong": 27247, "Phot": 27248, "ĠALS": 27249, "rio": 27250, "ĠNonetheless": 27251, "ĠLemon": 27252, "Ġ206": 27253, "ĠEE": 27254, "Ġderivative": 27255, "ĠWWII": 27256, "vote": 27257, "Ġtherein": 27258, "Ġseparating": 27259, "446": 27260, "sync": 27261, "ĠStreets": 27262, "Ġratt": 27263, "Ġmunicipality": 27264, "ĠShortly": 27265, "Ġmonk": 27266, "),\"": 27267, "Ġscrub": 27268, "Ġoperatives": 27269, 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"Ġadvertisers": 27835, "ĠSpear": 27836, "Ġbids": 27837, "Ġdestroys": 27838, "utz": 27839, "Ġundersc": 27840, "ĠADD": 27841, "Ġants": 27842, "ĠCum": 27843, "ipples": 27844, "ĠFill": 27845, "Ġglanced": 27846, "Ġindicted": 27847, "ĠEff": 27848, "Ġmiscon": 27849, "ĠDesktop": 27850, "Ġabide": 27851, "ãĥĢ": 27852, "ĠIo": 27853, "ĠCoul": 27854, "Ġcapsule": 27855, "ĠChrys": 27856, "MON": 27857, "Ġundes": 27858, "ĠIRA": 27859, "Ġcitation": 27860, "Ġdictate": 27861, "ĠNetworks": 27862, "ĠConflict": 27863, "ĠStuff": 27864, "xa": 27865, "isec": 27866, "ĠChemistry": 27867, "Ġquarterly": 27868, "Williams": 27869, "anan": 27870, "Opt": 27871, "ĠAlexandria": 27872, "outheastern": 27873, "ĠSpringfield": 27874, "ĠBlacks": 27875, "Ġgeography": 27876, "242": 27877, "Ġutmost": 27878, "ĠExxon": 27879, "abouts": 27880, "EVA": 27881, "ĠEnable": 27882, "ĠBarr": 27883, "Ġdisagreed": 27884, "ĠCyprus": 27885, "Ġdementia": 27886, "Ġlabs": 27887, "Ġubiquitous": 27888, "ĠLOVE": 27889, "Ġconsolidated": 27890, "sr": 27891, "Ġcreamy": 27892, "ĠTimber": 27893, "Regardless": 27894, "ĠCertificate": 27895, "Ġ\"...": 27896, "ogenous": 27897, "Captain": 27898, "Ġinsulting": 27899, "ĠSoros": 27900, "ĠInstr": 27901, "ĠBulgaria": 27902, "better": 27903, "Ġsucking": 27904, "ĠDavidson": 27905, "atz": 27906, "Ġcollateral": 27907, "gif": 27908, "Ġplagued": 27909, "ĠCancel": 27910, "ĠGardner": 27911, "RB": 27912, "Ġsixteen": 27913, "Remove": 27914, "uristic": 27915, "cook": 27916, "Rod": 27917, "Ġcomprising": 27918, "fle": 27919, ")âĢĶ": 27920, "ĠViking": 27921, "growth": 27922, "agonal": 27923, "Ġsrf": 27924, "afety": 27925, "mot": 27926, "Nearly": 27927, "stown": 27928, "ĠFactor": 27929, "Ġautomobile": 27930, "Ġprocedural": 27931, "mask": 27932, "ampires": 27933, "Ġdisappears": 27934, "jab": 27935, "315": 27936, "Ġ1951": 27937, "needed": 27938, "Ġdaring": 27939, "leader": 27940, "Ġpodium": 27941, "Ġunhealthy": 27942, "Ġmund": 27943, "Ġpyramid": 27944, "ocre": 27945, "Ġkissed": 27946, "Ġdreamed": 27947, "ĠFantastic": 27948, "ĠGly": 27949, "åĬ": 27950, "Ġgreatness": 27951, "Ġspices": 27952, "Ġmetropolitan": 27953, "Ġcompuls": 27954, "iets": 27955, "1016": 27956, "ĠSham": 27957, "ĠPyr": 27958, "flies": 27959, "ĠMidnight": 27960, "Ġswallowed": 27961, "Ġgenres": 27962, "ĠLucky": 27963, "ĠRewards": 27964, "Ġdispatch": 27965, "ĠIPA": 27966, "ĠApply": 27967, "Ġaven": 27968, "alities": 27969, "312": 27970, "things": 27971, "Ġ().": 27972, "Ġmates": 27973, "ĠSz": 27974, "ĠCOP": 27975, "olate": 27976, "OFF": 27977, "Ġrecharge": 27978, "caps": 27979, "ĠYorker": 27980, "icone": 27981, "Ġgalaxies": 27982, "ileaks": 27983, "Dave": 27984, "ĠPuzz": 27985, "ĠCeltic": 27986, "ĠAFC": 27987, "276": 27988, "ĠSons": 27989, "Ġaffirmative": 27990, "Hor": 27991, "Ġtutorials": 27992, "ĠCITY": 27993, "ĠRosa": 27994, "ĠExtension": 27995, "Series": 27996, "Ġfats": 27997, "Ġrab": 27998, "lis": 27999, "Ġunic": 28000, "Ġeve": 28001, "ĠSpin": 28002, "Ġadulthood": 28003, "typ": 28004, "Ġsectarian": 28005, "Ġcheckout": 28006, "ĠCycl": 28007, "Single": 28008, "Ġmartyr": 28009, "Ġchilling": 28010, "888": 28011, "oufl": 28012, "Ġ];": 28013, "Ġcongestion": 28014, "mk": 28015, "ĠWhereas": 28016, "Ġ1938": 28017, "urrencies": 28018, "erion": 28019, "Ġboast": 28020, "ĠPatients": 28021, "Ġchap": 28022, "ĠBD": 28023, "realDonaldTrump": 28024, "Ġexamines": 28025, "hov": 28026, "Ġstartling": 28027, "ĠBabylon": 28028, "wid": 28029, "omew": 28030, "brance": 28031, "ĠOdyssey": 28032, "wig": 28033, "Ġtorch": 28034, "ĠVox": 28035, "ĠMoz": 28036, "ĠTroll": 28037, "ĠAns": 28038, "Similarly": 28039, "ĠFul": 28040, "006": 28041, "Unless": 28042, "ĠAlone": 28043, "stead": 28044, "ĠPublisher": 28045, "rights": 28046, "tu": 28047, "ĠDoesn": 28048, "Ġprofessionally": 28049, "Ġclo": 28050, "icz": 28051, "Ġsteals": 28052, "Ġá": 28053, "1986": 28054, "Ġsturdy": 28055, "ĠJohann": 28056, "Ġmedals": 28057, "Ġfilings": 28058, "ĠFraser": 28059, "done": 28060, "Ġmultinational": 28061, "Ġfeder": 28062, "Ġworthless": 28063, "Ġpest": 28064, "Yesterday": 28065, "ankind": 28066, "Ġgays": 28067, "Ġborne": 28068, "ĠPOS": 28069, "Picture": 28070, "Ġpercentages": 28071, "251": 28072, "rame": 28073, "Ġpotions": 28074, "AMD": 28075, "ĠLebanese": 28076, "Ġrang": 28077, "ĠLSU": 28078, "ongs": 28079, "Ġpeninsula": 28080, "ĠClause": 28081, "ALK": 28082, "oha": 28083, "ĠMacBook": 28084, "Ġunanimous": 28085, "Ġlenders": 28086, "Ġhangs": 28087, "Ġfranchises": 28088, "orers": 28089, "ĠUpdates": 28090, "Ġisolate": 28091, "andro": 28092, "Soon": 28093, "Ġdisruptive": 28094, "ĠSurve": 28095, "Ġstitches": 28096, "ĠScorp": 28097, "ĠDominion": 28098, "Ġsupplying": 28099, "Arg": 28100, "Ġturret": 28101, "ĠLuk": 28102, "Ġbrackets": 28103, "*)": 28104, "ĠRevolutionary": 28105, "ĠHonest": 28106, "Ġnoticing": 28107, "ĠShannon": 28108, "Ġafforded": 28109, "Ġtha": 28110, "ĠJanet": 28111, "!--": 28112, "ĠNarendra": 28113, "ĠPlot": 28114, "Hol": 28115, "sever": 28116, "eenth": 28117, "Ġobstruction": 28118, "Ġ1024": 28119, "staff": 28120, "jas": 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"ĠSoldiers": 28179, "ĠPLAY": 28180, "Ġoutgoing": 28181, "LAND": 28182, "Ġrewrite": 28183, "ĠMOV": 28184, "ĠImper": 28185, "ĠSolution": 28186, "Ġphenomenal": 28187, "Ġlongevity": 28188, "Ġimpat": 28189, "ĠNissan": 28190, "irie": 28191, "Ġodor": 28192, "ĠZar": 28193, "oks": 28194, "Ġmilitias": 28195, "ĠSPEC": 28196, "Ġtolerated": 28197, "arser": 28198, "ĠBradford": 28199, "+,": 28200, "Ġsurreal": 28201, "sf": 28202, "Canadian": 28203, "Ġresemblance": 28204, "Ġcarbohydrate": 28205, "VIEW": 28206, "Ġaccessory": 28207, "meal": 28208, "largest": 28209, "iegel": 28210, "Someone": 28211, "Ġtoughest": 28212, "oso": 28213, "Ġfunnel": 28214, "Ġcondemnation": 28215, "luent": 28216, "Ġwired": 28217, "ĠSunset": 28218, "Jesus": 28219, "ĠPST": 28220, "ĠPages": 28221, "ĠTycoon": 28222, "ĠPF": 28223, "Ġselections": 28224, "Ġà¤": 28225, "partisan": 28226, "Ġhighs": 28227, "ĠRune": 28228, "Ġcrafts": 28229, "lead": 28230, "ĠParents": 28231, "Ġreclaim": 28232, "eker": 28233, "ĠAllied": 28234, "aeper": 28235, "Ġlooming": 28236, "Ġbeneficiaries": 28237, "ĠHull": 28238, "Students": 28239, "Jewish": 28240, "dj": 28241, "Ġpact": 28242, "template": 28243, "ĠOfficials": 28244, "ĠBaylor": 28245, "Ġhemp": 28246, "Ġyouths": 28247, "ĠLevels": 28248, "ĠXiao": 28249, "ĠChes": 28250, "Ġendeavor": 28251, "ĠRemoved": 28252, "Ġhippocamp": 28253, "Hell": 28254, "ãĤĬ": 28255, "805": 28256, "Ġdinosaur": 28257, "ĠWrath": 28258, "ĠIndonesian": 28259, "Ġcalculator": 28260, "ĠDictionary": 28261, "Ġ420": 28262, "ĠMAG": 28263, "(_": 28264, "!,": 28265, "tarians": 28266, "Ġrestricting": 28267, "racuse": 28268, "Ġweekday": 28269, "OUNT": 28270, "Ġshrugged": 28271, "leground": 28272, "Ġbald": 28273, "ĠDoctors": 28274, "Ġtouted": 28275, "ĠMaxwell": 28276, "Ġ214": 28277, "Ġdiplomat": 28278, "Ġrepression": 28279, "Ġconstituency": 28280, "vice": 28281, "ranked": 28282, "ĠNapoleon": 28283, "gang": 28284, "ĠForever": 28285, "tun": 28286, "Ġbulb": 28287, "ĠPDT": 28288, "ĠCisco": 28289, "VEN": 28290, "Ġresumed": 28291, "Steven": 28292, "ĠManitoba": 28293, "Ġfabulous": 28294, "ĠAgents": 28295, "1984": 28296, "Ġamusing": 28297, "ĠMysteries": 28298, "Ġorthodox": 28299, "floor": 28300, "Ġquestionnaire": 28301, "Ġpenetrate": 28302, "Ġfilmmakers": 28303, "ĠUnc": 28304, "Ġstamped": 28305, "Ġthirteen": 28306, "Ġoutfield": 28307, "Ġforwarded": 28308, "Ġappra": 28309, "Ġaided": 28310, "try": 28311, "Ġunfocused": 28312, "ĠLiz": 28313, "ĠWendy": 28314, "ĠScene": 28315, "Charg": 28316, "Ġrejects": 28317, "Ġleftist": 28318, "ĠProvidence": 28319, "ĠBrid": 28320, "regn": 28321, "Ġprophecy": 28322, "ĠLIVE": 28323, "499": 28324, "Ġforge": 28325, "ĠFML": 28326, "Ġintrinsic": 28327, "ĠFrog": 28328, "Ġwont": 28329, "ĠHolt": 28330, "Ġfamed": 28331, "CLUS": 28332, "aepernick": 28333, "ĠHate": 28334, "ĠCay": 28335, "Ġregistering": 28336, "ortality": 28337, "ropy": 28338, "ocalyptic": 28339, "aan": 28340, "nav": 28341, "Ġfascist": 28342, "IFIED": 28343, "Ġimplicated": 28344, "ĠResort": 28345, "ĠChandler": 28346, "ĠBrick": 28347, "Pin": 28348, "ysc": 28349, "Usage": 28350, "ĠHelm": 28351, "usra": 28352, "âĺħâĺħ": 28353, "ĠAbbas": 28354, "Ġunanimously": 28355, "Ġkeeper": 28356, "Ġaddicted": 28357, "???": 28358, "Ġhelmets": 28359, "Ġantioxid": 28360, "apsed": 28361, "808": 28362, "giene": 28363, "Ġwaits": 28364, "Ġminion": 28365, "raved": 28366, "ĠPorsche": 28367, "Ġdreaming": 28368, "Ġ171": 28369, "ĠCain": 28370, "Ġunfor": 28371, "asso": 28372, "ĠConfiguration": 28373, "kun": 28374, "hardt": 28375, "Ġnested": 28376, "ĠLDS": 28377, "LES": 28378, "Ġtying": 28379, "enos": 28380, "Ġcue": 28381, "ĠMarqu": 28382, "skirts": 28383, "Ġclicked": 28384, "Ġexpiration": 28385, "ĠAccordingly": 28386, "ĠWC": 28387, "Ġblessings": 28388, "Ġaddictive": 28389, "ĠNarr": 28390, "yx": 28391, "ĠJaguars": 28392, "Ġrents": 28393, "ĠSiber": 28394, "Ġtipped": 28395, "ousse": 28396, "ĠFitzgerald": 28397, "Ġhierarch": 28398, "outine": 28399, "Ġwavelength": 28400, ">.": 28401, "chid": 28402, "ĠProcessing": 28403, "/+": 28404, "ranking": 28405, "Easy": 28406, "ĠConstruct": 28407, "Ġtet": 28408, "insured": 28409, "HUD": 28410, "Ġquoting": 28411, "Ġcommunicated": 28412, "inx": 28413, "Ġinmate": 28414, "Ġerected": 28415, "ĠAbsolutely": 28416, "ĠSurely": 28417, "Ġunim": 28418, "ĠThrone": 28419, "heid": 28420, "Ġclaws": 28421, "Ġsuperstar": 28422, "ĠLenn": 28423, "ĠWhis": 28424, "Uk": 28425, "abol": 28426, "Ġsket": 28427, "ĠNiet": 28428, "Ġperks": 28429, "Ġaffinity": 28430, "Ġopenings": 28431, "phasis": 28432, "Ġdiscriminate": 28433, "Tip": 28434, "vc": 28435, "Ġgrinding": 28436, "ĠJenny": 28437, "Ġasthma": 28438, "holes": 28439, "ĠHomer": 28440, "Ġregisters": 28441, "ĠGlad": 28442, "Ġcreations": 28443, "Ġlithium": 28444, "Ġapplause": 28445, "until": 28446, "Justice": 28447, "ĠTurks": 28448, "Ġscandals": 28449, "Ġbake": 28450, "tank": 28451, "Mech": 28452, "ĠMeans": 28453, "ĠMaid": 28454, "Republicans": 28455, "isal": 28456, "windows": 28457, "ĠSantos": 28458, "Ġvegetation": 28459, "338": 28460, "tri": 28461, "Ġflux": 28462, "insert": 28463, "Ġclarified": 28464, "Ġmortg": 28465, "ĠChim": 28466, "ĠTort": 28467, "Ġdisclaim": 28468, "metal": 28469, "ĠAside": 28470, "Ġinduction": 28471, "Ġinfl": 28472, "Ġatheists": 28473, "amph": 28474, "Ġether": 28475, "ĠVital": 28476, "ĠBuilt": 28477, "Mind": 28478, "Ġweaponry": 28479, "SET": 28480, "Ġ186": 28481, "admin": 28482, "gam": 28483, "contract": 28484, "afa": 28485, "Ġderivatives": 28486, "Ġsnacks": 28487, "Ġchurn": 28488, "Econom": 28489, "Ġcapped": 28490, "ĠUnderstanding": 28491, "ĠHers": 28492, "ĠIz": 28493, "Ġduct": 28494, "IENT": 28495, "aughty": 28496, "ĠâľĶ": 28497, "ĠNP": 28498, "Ġsailing": 28499, "Initialized": 28500, "Ġted": 28501, "Ġreactors": 28502, "ĠLomb": 28503, "Ġchoke": 28504, "ĠWorm": 28505, "Ġadmiration": 28506, "Ġswung": 28507, "ensibly": 28508, "Ġrash": 28509, "ĠGoals": 28510, "ĠImportant": 28511, "Shot": 28512, "ĠRas": 28513, "Ġtrainers": 28514, "ĠBun": 28515, "Working": 28516, "Ġharmed": 28517, "ĠPandora": 28518, "ĠLTE": 28519, "Ġmushroom": 28520, "ĠCHAR": 28521, "ĠFee": 28522, "ĠMoy": 28523, "Born": 28524, "oliberal": 28525, "ĠMartial": 28526, "Ġgentlemen": 28527, "Ġlingering": 28528, "Official": 28529, "Ġgraffiti": 28530, "ĠNames": 28531, "Der": 28532, "Ġquint": 28533, "istrate": 28534, "azeera": 28535, "ĠNOTICE": 28536, "ĠFlorence": 28537, "Ġpayable": 28538, "Ġdepicts": 28539, "ĠSpecies": 28540, "Heart": 28541, "âĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢ": 28542, "Ġenclosed": 28543, "Increases": 28544, "Daily": 28545, "ĠLis": 28546, "Ġenactment": 28547, "ĠBacon": 28548, "ĠSteele": 28549, "demand": 28550, "Ġ183": 28551, "Ġmouths": 28552, "Ġstranded": 28553, "Ġenhancement": 28554, "011": 28555, "ĠWhats": 28556, "Ġhealed": 28557, "eny": 28558, "ĠRab": 28559, "Ġ340": 28560, "ĠLabyrinth": 28561, "roach": 28562, "ĠYosh": 28563, "ĠClippers": 28564, "Ġconcerts": 28565, "Internet": 28566, "355": 28567, "Ġstickers": 28568, "Ġtermed": 28569, "ĠAxe": 28570, "Ġgrandparents": 28571, "France": 28572, "ĠClim": 28573, "ĠUh": 28574, "ulic": 28575, "Ġthrill": 28576, "centric": 28577, "ĠOverview": 28578, "ĠConduct": 28579, "Ġsubstantive": 28580, "Ġ182": 28581, "mur": 28582, "Ġstray": 28583, "ĠCoff": 28584, "Ġrepetitive": 28585, "ĠForgotten": 28586, "Ġqualification": 28587, "ewitness": 28588, "ĠZimbabwe": 28589, "Ġsimulated": 28590, "ĠJD": 28591, "253": 28592, "ĠWare": 28593, "Ġunsc": 28594, "Times": 28595, "Ġsummons": 28596, "Ġdisconnected": 28597, "Ġ184": 28598, "cius": 28599, "ĠGujar": 28600, "odka": 28601, "Ġerase": 28602, "ĠTobacco": 28603, "elected": 28604, "Ġuncont": 28605, "ĠShepard": 28606, "ĠLamp": 28607, "Ġalerted": 28608, "Ġoperative": 28609, "arna": 28610, "uint": 28611, "Ġnegligence": 28612, "acements": 28613, "Ġsupra": 28614, "Ġprevail": 28615, "ĠShark": 28616, "Ġbelts": 28617, "ãģ«": 28618, "Ġtighter": 28619, "Engineers": 28620, "Ġinactive": 28621, "Ġexponent": 28622, "ĠWillie": 28623, "aples": 28624, "Ġheir": 28625, "ĠHits": 28626, "iann": 28627, "ĠSays": 28628, "Ġcurrents": 28629, "ĠBengal": 28630, "Ġarist": 28631, "Buffer": 28632, "Ġbreeze": 28633, "ĠWesley": 28634, "Cola": 28635, "Ġpronoun": 28636, "Ġdeed": 28637, "ĠKling": 28638, "Ġoft": 28639, "Ġinflict": 28640, "Ġpunishing": 28641, "Ġnm": 28642, "iku": 28643, "ODUCT": 28644, "014": 28645, "Ġsubsidy": 28646, "ĠDEA": 28647, "ĠHerbert": 28648, "ĠJal": 28649, "Bank": 28650, "Ġdeferred": 28651, "Ġshipment": 28652, "Bott": 28653, "Ġalle": 28654, "bearing": 28655, "HTML": 28656, "Offline": 28657, "Ġ213": 28658, "Ġscrolling": 28659, "Ġscanned": 28660, "ĠLibyan": 28661, "ĠTOP": 28662, "chrom": 28663, "dt": 28664, "column": 28665, "PsyNetMessage": 28666, "Zero": 28667, "Ġtorso": 28668, "050": 28669, "âķIJ": 28670, "Ġimperson": 28671, "ĠSchwartz": 28672, "udic": 28673, "Ġpissed": 28674, "ĠSapp": 28675, "257": 28676, "ĠISPs": 28677, "ogl": 28678, "Ġsupervised": 28679, "Ġadolescent": 28680, "Ġattained": 28681, "ĠDelivery": 28682, "ĠBunny": 28683, "Ġ1937": 28684, "Ġminiature": 28685, "Ġos": 28686, "Ġ370": 28687, "608": 28688, "ĠMourinho": 28689, "Ġinnate": 28690, "Ġtempo": 28691, "ĠNM": 28692, "ĠFallen": 28693, "009": 28694, "Ġprovocative": 28695, "Streamer": 28696, "ĠBenedict": 28697, "ĠBolshe": 28698, "Ġturtle": 28699, "ĠPCB": 28700, "ĠEqual": 28701, "Director": 28702, "ĠRend": 28703, "Ġfluids": 28704, "Authorities": 28705, "Ġcousins": 28706, "requency": 28707, "ĠNeighbor": 28708, "sets": 28709, "shared": 28710, "Charles": 28711, "password": 28712, "Ġgears": 28713, "Ġ211": 28714, "ĠHardware": 28715, "rika": 28716, "Ġupstream": 28717, "Hom": 28718, "Ġdisproportionately": 28719, "ivities": 28720, "Ġundefined": 28721, "Ġelectrons": 28722, "Ġcommemor": 28723, "Eventually": 28724, "Ġ><": 28725, "Ġirresponsible": 28726, "218": 28727, "ĠReleased": 28728, "ĠOVER": 28729, "ĠIGN": 28730, "ĠBread": 28731, "stellar": 28732, "ĠSage": 28733, "tted": 28734, "damage": 28735, "edition": 28736, "ĠPrec": 28737, "Ġlime": 28738, "Ġconfinement": 28739, "Ġcalorie": 28740, "weapon": 28741, "Ġdiffering": 28742, "ĠSina": 28743, "mys": 28744, "amd": 28745, "Ġintricate": 28746, "kk": 28747, "ĠPAT": 28748, "ão": 28749, "stones": 28750, "links": 28751, "Ġranch": 28752, "Semitic": 28753, "Ġdifferentiate": 28754, "ĠSinger": 28755, "occupied": 28756, "Ġfortress": 28757, "cmd": 28758, "Ġinterception": 28759, "ĠAnkara": 28760, "Ġrept": 28761, "ĠSolitaire": 28762, "Ġremake": 28763, "pred": 28764, "Ġdared": 28765, "autions": 28766, "ĠBACK": 28767, "Running": 28768, "Ġdebugging": 28769, "Ġgraphs": 28770, "399": 28771, "ĠNigel": 28772, "Ġbun": 28773, "Ġpillow": 28774, "Ġprogressed": 28775, "fashioned": 28776, "Ġobedience": 28777, "ERN": 28778, "Ġrehears": 28779, "Cell": 28780, "tl": 28781, "Sher": 28782, "Ġherald": 28783, "ĠPayment": 28784, "ĠCory": 28785, "ĠDept": 28786, "Ġrepent": 28787, "ĠWeak": 28788, "uckland": 28789, "Ġpleasing": 28790, "Ġshortages": 28791, "Ġjurors": 28792, "ĠKab": 28793, "qqa": 28794, "Anti": 28795, "Ġwow": 28796, "ĠRCMP": 28797, "Ġtsun": 28798, "ĠSic": 28799, "Ġcomprises": 28800, "Ġspies": 28801, "Ġprecinct": 28802, "nu": 28803, "Ġurges": 28804, "Ġtimed": 28805, "Ġstripes": 28806, "ĠBoots": 28807, "Ġyen": 28808, "Advanced": 28809, "Ġdiscrete": 28810, "ĠArchangel": 28811, "employment": 28812, "Diff": 28813, "Ġmonuments": 28814, "Ġ209": 28815, "worker": 28816, "Ġ196": 28817, "ĠIg": 28818, "utterstock": 28819, "TPS": 28820, "Jac": 28821, "Ġhomelessness": 28822, "Ġcommentator": 28823, "Ġracially": 28824, "fing": 28825, "seed": 28826, "Ele": 28827, "ellation": 28828, "Ġethanol": 28829, "Ġparish": 28830, "ĠDong": 28831, "ĠAwakening": 28832, "Ġdeviation": 28833, "ĠBearing": 28834, "ĠTsuk": 28835, "Ġrecess": 28836, "Ġlymph": 28837, "ĠCannabis": 28838, "åľ": 28839, "ĠNEWS": 28840, "Ġdra": 28841, "ĠStefan": 28842, "ĠWrong": 28843, "ĠSAM": 28844, "Ġloosely": 28845, "Ġinterpreter": 28846, "ĠPlain": 28847, "Government": 28848, "Ġbigotry": 28849, "Ġgrenades": 28850, "avez": 28851, "pictured": 28852, "Ġmandated": 28853, "ĠMonk": 28854, "ĠPedro": 28855, "Ġlava": 28856, "274": 28857, "Ġcynical": 28858, "ĠScrolls": 28859, "locks": 28860, "Mp": 28861, "Ġcongregation": 28862, "ornings": 28863, "phil": 28864, "ĠIbid": 28865, "Ġferv": 28866, "Ġdisappearing": 28867, "Ġarrogant": 28868, "syn": 28869, "ĠMaver": 28870, "ĠSuit": 28871, "241": 28872, "Ġabbre": 28873, "ackers": 28874, "Pa": 28875, "ĠYel": 28876, "Whenever": 28877, "Ġ235": 28878, "ĠVine": 28879, "ĠAnat": 28880, "Ġextinct": 28881, "LET": 28882, "Ġexecutable": 28883, "VERS": 28884, "oxide": 28885, "DNA": 28886, "ĠPrel": 28887, "Ġresentment": 28888, "Ġcomprise": 28889, "ĠAviv": 28890, "Ġinterceptions": 28891, "Ġprolific": 28892, "INA": 28893, "ĠErin": 28894, "thought": 28895, "219": 28896, "ĠPsychiatry": 28897, "unky": 28898, "chemist": 28899, "Ho": 28900, "ĠMcCoy": 28901, "Ġbricks": 28902, "Los": 28903, "rily": 28904, "ĠUSSR": 28905, "Ġrud": 28906, "Ġlaud": 28907, "ĠWise": 28908, "ĠEmerald": 28909, "Ġrevived": 28910, "Ġdamned": 28911, "ĠRepair": 28912, "idem": 28913, "ctica": 28914, "Ġpatriarch": 28915, "ĠNurs": 28916, "meg": 28917, "Ġcheapest": 28918, "reements": 28919, "empty": 28920, "ĠCelebr": 28921, "Ġdeprivation": 28922, "chanted": 28923, "ĠThumbnails": 28924, "Energy": 28925, "ĠEthan": 28926, "ĠQing": 28927, "Ġopposes": 28928, "WIND": 28929, "vik": 28930, "ĠMau": 28931, "ĠSUB": 28932, "667": 28933, "GRE": 28934, "ĠVolunte": 28935, "nton": 28936, "Cook": 28937, "åIJ": 28938, "esque": 28939, "Ġplummet": 28940, "Ġsuing": 28941, "Ġpronounce": 28942, "Ġresisting": 28943, "ĠFishing": 28944, "ĠTrials": 28945, "Ġyell": 28946, "Ġ310": 28947, "Ġinduct": 28948, "Ġpersonalized": 28949, "often": 28950, "Reb": 28951, "EMBER": 28952, "Ġviewpoint": 28953, "Ġexistential": 28954, "())": 28955, "remove": 28956, "MENTS": 28957, "lasses": 28958, "Ġevapor": 28959, "Ġaisle": 28960, "meta": 28961, "Ġreflective": 28962, "Ġentitlement": 28963, "Ġdevised": 28964, "music": 28965, "ascade": 28966, "Ġwinding": 28967, "offset": 28968, "Ġaccessibility": 28969, "kered": 28970, "Better": 28971, "ĠJohnston": 28972, "thinking": 28973, 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"ĠTowers": 29031, "ĠDart": 29032, "akable": 29033, "Ġmp": 29034, "ĠHeavenly": 29035, "Ġripe": 29036, "ĠCaroline": 29037, "ryan": 29038, "Ġclassics": 29039, "Ġretiring": 29040, "Ġ228": 29041, "Ġah": 29042, "Ġdealings": 29043, "Ġpunching": 29044, "ĠChapman": 29045, "Options": 29046, "maxwell": 29047, "volume": 29048, "Ġstal": 29049, "Ġexported": 29050, "ĠQuite": 29051, "Ġnumerical": 29052, "Burn": 29053, "Fact": 29054, "ĠKeystone": 29055, "Ġtrending": 29056, "Ġaltering": 29057, "ĠAfricans": 29058, "478": 29059, "ĠMN": 29060, "ĠKnock": 29061, "Ġtemptation": 29062, "Ġprestige": 29063, "Overview": 29064, "ĠTraditional": 29065, "ĠBahrain": 29066, "Private": 29067, "ĠHOU": 29068, "Ġbarr": 29069, "ĠTat": 29070, "Cube": 29071, "USD": 29072, "ĠGrande": 29073, "ĠGat": 29074, "ĠFlo": 29075, "Ġresides": 29076, "Ġindec": 29077, "volent": 29078, "Ġperpetual": 29079, "ubes": 29080, "Ġworldview": 29081, "ĠQuantum": 29082, "Ġfiltered": 29083, "Ġensu": 29084, "orgetown": 29085, "ERSON": 29086, "ĠMild": 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29764, "ĠCapacity": 29765, "ĠKazakh": 29766, "WN": 29767, "Ġfinanced": 29768, "389": 29769, "Ġtid": 29770, "Ġcollusion": 29771, "ĠMyr": 29772, "îĢ": 29773, "Senator": 29774, "Ġpediatric": 29775, "Ġneatly": 29776, "Ġsandwiches": 29777, "ĠArchitecture": 29778, "Ġtucked": 29779, "Ġbalcony": 29780, "Ġearthquakes": 29781, "quire": 29782, "Future": 29783, "Ġhefty": 29784, "éĹ": 29785, "Ġspecializes": 29786, "Ġstresses": 29787, "Ġsender": 29788, "Ġmisunderstanding": 29789, "Ġepile": 29790, "Ġprovoke": 29791, "ĠColors": 29792, "Ġdismay": 29793, "uko": 29794, "[_": 29795, "586": 29796, "neutral": 29797, "Ġdonating": 29798, "ĠRandall": 29799, "Multi": 29800, "Ġconveniently": 29801, "ĠSung": 29802, "ĠCoca": 29803, "Ġtents": 29804, "ĠAcceler": 29805, "Ġpartnered": 29806, "272": 29807, "irming": 29808, "ĠBAS": 29809, "sometimes": 29810, "Ġobjected": 29811, "ubric": 29812, "posed": 29813, "LCS": 29814, "grass": 29815, "Ġattributable": 29816, "VIS": 29817, "Israeli": 29818, "Ġrepeats": 29819, "ĠRM": 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30551, "ĠRamsey": 30552, "sword": 30553, "ĠXI": 30554, "ourced": 30555, "Same": 30556, "346": 30557, "ĠRepe": 30558, "ĠKou": 30559, "cake": 30560, "emis": 30561, "Cache": 30562, "ĠMeaning": 30563, "ĠEnlight": 30564, "onomy": 30565, "Ġmanifestation": 30566, "sworth": 30567, "Jay": 30568, "Ġchore": 30569, "ör": 30570, "Dream": 30571, "Ġsanctioned": 30572, "Ġculturally": 30573, "ĠAra": 30574, "Nav": 30575, "Ġtheological": 30576, "Ġstrut": 30577, "ĠVO": 30578, "ĠHandbook": 30579, "Ġconstructing": 30580, "Ġ¶": 30581, "ĠBenefits": 30582, "ĠPsychological": 30583, "sac": 30584, "å¸": 30585, "policy": 30586, "ĠMatters": 30587, "ĠReported": 30588, "ĠByte": 30589, "Ġvitro": 30590, "ĠMaiden": 30591, "Ġlam": 30592, "ĠJennings": 30593, "Ġgarment": 30594, "ĠRutgers": 30595, "ĠStafford": 30596, "ĠWellington": 30597, "Ġintermitt": 30598, "Ġnpm": 30599, "Ġordeal": 30600, "Ġplugged": 30601, "ooming": 30602, "inished": 30603, "framework": 30604, "Ġtimber": 30605, "Ġcass": 30606, "Ġ850": 30607, "iless": 30608, "ĠRedux": 30609, "768": 30610, "Stre": 30611, "Ġsurpassed": 30612, "whel": 30613, "Ġparallels": 30614, "Ġveil": 30615, "ĠGI": 30616, "ĠREST": 30617, "Ġreadiness": 30618, "sort": 30619, "Ġmodifying": 30620, "ĠSlate": 30621, "ruff": 30622, "Ġmarble": 30623, "Ġinfrared": 30624, "Ġauditor": 30625, "ĠFANTASY": 30626, "ĠPoverty": 30627, "ĠSPD": 30628, "Ġ\"(": 30629, "Ky": 30630, "RAY": 30631, "Ġexecutions": 30632, "ĠBeverly": 30633, "ĠMarxism": 30634, "ĠBurst": 30635, "ĠKali": 30636, "estones": 30637, "Clearly": 30638, "Ell": 30639, "ãģ§": 30640, "ĠProceedings": 30641, "Token": 30642, "IFIC": 30643, "ña": 30644, "Central": 30645, "ĠHaley": 30646, "ĠDrama": 30647, "Ġformations": 30648, "ORN": 30649, "Books": 30650, "Ġdominating": 30651, "ĠFlyers": 30652, "ĠCompanion": 30653, "Ġdisciplined": 30654, "ĠYugoslav": 30655, "ĠSpells": 30656, "Ġvengeance": 30657, "Ġlandlords": 30658, "Len": 30659, "ĠOgre": 30660, "anoia": 30661, "Ġpiercing": 30662, "Ġcongreg": 30663, "Ġscorer": 30664, "obia": 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"Ġprowess": 30721, "ĠNorton": 30722, "Ġplainly": 30723, "Ġfreight": 30724, "Ġinhibition": 30725, "Ġclam": 30726, "Ġ303": 30727, "kef": 30728, "aleigh": 30729, "Luke": 30730, "Ġpsycho": 30731, "atorium": 30732, "MED": 30733, "Ġtreaties": 30734, "Ġindisc": 30735, "Ġdc": 30736, "OPS": 30737, "Ġresilient": 30738, "ĠInterstate": 30739, "Ġslack": 30740, "Ġmundane": 30741, "Ġestablishes": 30742, "359": 30743, "Ġstrained": 30744, "Ġnond": 30745, "Sus": 30746, "Ġcaste": 30747, "arate": 30748, "ieving": 30749, "Ġunfairly": 30750, "Ġparser": 30751, "onial": 30752, "ursive": 30753, "Via": 30754, "ĠOtto": 30755, "ĠAuthorities": 30756, "stroke": 30757, "KR": 30758, "ĠMercy": 30759, "Ġfurnished": 30760, "Ġoutset": 30761, "Ġmetic": 30762, "1982": 30763, "olithic": 30764, "ĠTent": 30765, "ogical": 30766, "ĠAircraft": 30767, "Ġhides": 30768, "ĠBecame": 30769, "Ġeducators": 30770, "reaching": 30771, "Ġvolatility": 30772, "Ġtoddler": 30773, "ĠNASCAR": 30774, "ĠTwelve": 30775, "ĠHighlights": 30776, "Ġgrape": 30777, "Ġsplits": 30778, "Ġpeasant": 30779, "Ġreneg": 30780, "ĠMSI": 30781, "Temp": 30782, "stars": 30783, "Ġtrek": 30784, "ĠHyde": 30785, "binding": 30786, "Ġrealism": 30787, "Ġoxide": 30788, "ĠHos": 30789, "Ġmounts": 30790, "Ġbiting": 30791, "Ġcollapsing": 30792, "Ġpostal": 30793, "Ġmuseums": 30794, "Ġdetached": 30795, "Ġrespecting": 30796, "Ġmonopol": 30797, "Ġworkflow": 30798, "ĠCake": 30799, "Template": 30800, "ĠOrganisation": 30801, "Ġpersistence": 30802, "369": 30803, "Coming": 30804, "Brad": 30805, "Ġredundant": 30806, "ĠGTA": 30807, "Ġbending": 30808, "Ġrevoked": 30809, "Ġoffending": 30810, "Ġframing": 30811, "Ġprintf": 30812, "Commun": 30813, "members": 30814, "Outside": 30815, "Ġconstrued": 30816, "Ġcoded": 30817, "FORE": 30818, "Ġchast": 30819, "Chat": 30820, "Indian": 30821, "ĠYard": 30822, "?!\"": 30823, "ĠPorts": 30824, "ĠXavier": 30825, "ĠRET": 30826, "'.\"": 30827, "ĠBoat": 30828, "ivated": 30829, "icht": 30830, "umerable": 30831, "Ds": 30832, "ĠDunn": 30833, "Ġcoffin": 30834, "Ġsecurely": 30835, "ĠRaptors": 30836, "ĠBes": 30837, "Installation": 30838, "Ġinception": 30839, "ĠHealthy": 30840, "endants": 30841, "Ġpsychologists": 30842, "ĠSheikh": 30843, "cultural": 30844, "ĠBlackBerry": 30845, "shift": 30846, "Fred": 30847, "oche": 30848, "Ġcakes": 30849, "ĠSEO": 30850, "ĠGian": 30851, "ĠAsians": 30852, "ogging": 30853, "element": 30854, "Ġpundits": 30855, "ĠVaugh": 30856, "ĠGavin": 30857, "Ġhitter": 30858, "Ġdrowned": 30859, "Ġchalk": 30860, "ĠZika": 30861, "Ġmeasles": 30862, "802": 30863, "âĢ¦..": 30864, "ĠAWS": 30865, "]\"": 30866, "Ġdistort": 30867, "ĠMast": 30868, "Ġantibodies": 30869, "ĠMash": 30870, "Memory": 30871, "ĠUganda": 30872, "ĠProb": 30873, "Ġvomiting": 30874, "ĠTurns": 30875, "Ġoccupying": 30876, "Ġevasion": 30877, "ĠTherapy": 30878, "Ġpromo": 30879, "Ġelectr": 30880, "Ġblueprint": 30881, "ĠDre": 30882, "priced": 30883, "ĠDepot": 30884, "Ġalleviate": 30885, "ĠSomali": 30886, "marg": 30887, "nine": 30888, "Ġnostalgia": 30889, "ĠShepherd": 30890, "Ġcavalry": 30891, "Ġtorped": 30892, "ĠBloody": 30893, "xb": 30894, "Ġsank": 30895, "Ġgoalt": 30896, "reportprint": 30897, "embedreportprint": 30898, "cloneembedreportprint": 30899, "ĠInitially": 30900, "ĠFischer": 30901, "Ġnoteworthy": 30902, "cern": 30903, "Ġinefficient": 30904, "rawdownload": 30905, "rawdownloadcloneembedreportprint": 30906, "cation": 30907, "ĠDynasty": 30908, "lag": 30909, "DES": 30910, "Ġdistinctly": 30911, "ĠEstonia": 30912, "Ġopenness": 30913, "Ġgossip": 30914, "ruck": 30915, "Width": 30916, "ĠIbrahim": 30917, "Ġpetroleum": 30918, "Ġavatar": 30919, "ĠHed": 30920, "atha": 30921, "ĠHogwarts": 30922, "Ġcaves": 30923, "678": 30924, "Ġsafeguard": 30925, "ĠMog": 30926, "isson": 30927, "ĠDurham": 30928, "slaught": 30929, "ĠGraduate": 30930, "Ġsubconscious": 30931, "ĠExcellent": 30932, "ĠDum": 30933, "-----": 30934, "Ġpiles": 30935, "ĠWORK": 30936, "ĠGarn": 30937, "ĠFol": 30938, "ĠATM": 30939, "Ġavoids": 30940, "ĠTul": 30941, "Ġbleak": 30942, "ELY": 30943, "ivist": 30944, "lightly": 30945, "Pers": 30946, "ĠDob": 30947, "ĠLS": 30948, "Ġinsanity": 30949, "ε": 30950, "atalie": 30951, "Enlarge": 30952, "Ġtwists": 30953, "Ġfaulty": 30954, "Ġpiracy": 30955, "Ġimpover": 30956, "Ġrugged": 30957, "ĠFashion": 30958, "Ġsands": 30959, "'?": 30960, "swick": 30961, "Ġnatives": 30962, "Ġhen": 30963, "ĠNoise": 30964, "ãĥĹ": 30965, "Ġgreens": 30966, "Ġfreezer": 30967, "Ġdynasty": 30968, "ĠFathers": 30969, "ĠNewark": 30970, "Ġarchaeological": 30971, "Ġot": 30972, "obar": 30973, "Ġblockade": 30974, "Ġallerg": 30975, "LV": 30976, "Ġdebit": 30977, "ĠRFC": 30978, "ĠMilton": 30979, "ĠPressure": 30980, "Ġwillingly": 30981, "Ġdisproportionate": 30982, "Ġoppressive": 30983, "Ġdiamonds": 30984, "Ġbelongings": 30985, "1970": 30986, "Ġbells": 30987, "Ġimperialism": 30988, "Ġ227": 30989, "Ġexploding": 30990, "ĠEclipse": 30991, "Ġ1919": 30992, "Ġrant": 30993, "Ġnominations": 30994, "347": 30995, "Ġpeacefully": 30996, "rica": 30997, "ĠFUCK": 30998, "Ġvibration": 30999, "malink": 31000, "Ġropes": 31001, "ĠIvanka": 31002, "ĠBrewery": 31003, "ĠBooker": 31004, "ĠOwens": 31005, "goers": 31006, "Services": 31007, "ĠSnape": 31008, "Ġ191": 31009, "395": 31010, "Ġ299": 31011, "justice": 31012, "Ġbri": 31013, "Ġdiscs": 31014, "Ġprominently": 31015, "Ġvulgar": 31016, "Ġskipping": 31017, "lves": 31018, "Ġtsunami": 31019, "374": 31020, "ĠUrug": 31021, "ĠEid": 31022, "recated": 31023, "phen": 31024, "Ġfaults": 31025, "ĠStarted": 31026, "950": 31027, "Ġpi": 31028, "Ġdetector": 31029, "Ġbastard": 31030, "Ġvalidated": 31031, "SpaceEngineers": 31032, "OURCE": 31033, "Ġ(~": 31034, "Ġunsur": 31035, "Ġaffirmed": 31036, "Ġfascism": 31037, "Ġresolving": 31038, "ĠChavez": 31039, "ĠCyn": 31040, "Ġdetract": 31041, "Lost": 31042, "Ġrigged": 31043, "Ġhomage": 31044, "ĠBruno": 31045, "555": 31046, "eca": 31047, "Ġpresses": 31048, "Ġhumour": 31049, "Ġspacing": 31050, "Ġ'/": 31051, "olkien": 31052, "Coun": 31053, "OPER": 31054, "Tre": 31055, "Son": 31056, "ĠCambodia": 31057, "ierre": 31058, "mong": 31059, "ozy": 31060, "Ġliquidity": 31061, "ĠSoviets": 31062, "ĠFernando": 31063, "Ġ229": 31064, "Ġslug": 31065, "ĠCatalan": 31066, "electric": 31067, "Ġscenery": 31068, "ĠHearth": 31069, "Ġconstrained": 31070, "Ġgoalie": 31071, "ĠGuidelines": 31072, "ĠAmmo": 31073, "ĠPearson": 31074, "Ġtaxed": 31075, "Ġfetus": 31076, "Response": 31077, "ĠAlexis": 31078, "thia": 31079, "Guy": 31080, "Ġreconstruct": 31081, "Ġextremes": 31082, "Ġconcluding": 31083, "ĠPeg": 31084, "ooks": 31085, "Ġdeductions": 31086, "Rose": 31087, "Ġgroundbreaking": 31088, "ĠTarg": 31089, "ãĥģ": 31090, "ĠReve": 31091, "resource": 31092, "Ġmoons": 31093, "Ġelectromagnetic": 31094, "Ġamidst": 31095, "ĠViktor": 31096, "NESS": 31097, "BACK": 31098, "Ġcommute": 31099, "ĠAnaheim": 31100, "Ġfluctuations": 31101, "640": 31102, "Ġnoodles": 31103, "ĠCopenhagen": 31104, "ĠTide": 31105, "ĠGrizz": 31106, "ĠSEE": 31107, "Ġpipelines": 31108, "Ġscars": 31109, "endo": 31110, "agus": 31111, "ĠETF": 31112, "/#": 31113, "ĠBecome": 31114, "448": 31115, "Ġvisc": 31116, "ĠRecommended": 31117, "Ġjumper": 31118, "Ġcognition": 31119, "Ġassassin": 31120, "Ġwitnessing": 31121, "ĠSetup": 31122, "Ġlac": 31123, "vim": 31124, "ISM": 31125, "pages": 31126, "SSL": 31127, "358": 31128, "Ġadject": 31129, "industrial": 31130, "lore": 31131, "chery": 31132, "Ġglitter": 31133, "Ġcalf": 31134, "Florida": 31135, "Ġspoilers": 31136, "Ġsucceeds": 31137, "Ġchanting": 31138, "Ġslogans": 31139, "ĠTracy": 31140, "Visit": 31141, "rology": 31142, "Ġmornings": 31143, "Ġlineage": 31144, "Ġsip": 31145, "Ġintensely": 31146, "Ġflourish": 31147, "ĠSleeping": 31148, "ĠFem": 31149, "orpor": 31150, "ĠKlan": 31151, "ĠDarth": 31152, "hack": 31153, "ĠNielsen": 31154, "Ġtumors": 31155, "Ġprocurement": 31156, "ĠYorkshire": 31157, "Ġraided": 31158, "KY": 31159, "Anna": 31160, "Ġ//[": 31161, "ĠDisorder": 31162, "ĠMustang": 31163, "ĠWen": 31164, "ĠTrying": 31165, "sq": 31166, "Ġdeliveries": 31167, "Ġshutter": 31168, "Ġcerebral": 31169, "Ġbipolar": 31170, "ĠCN": 31171, "lass": 31172, "jet": 31173, "Ġdebating": 31174, ">:": 31175, "Ġeagle": 31176, "grades": 31177, "ĠDixon": 31178, "UGC": 31179, "MAS": 31180, "ĠDraco": 31181, "ĠMachines": 31182, "affer": 31183, "Ġeman": 31184, "²": 31185, "pron": 31186, "ĠGym": 31187, "Ġcomparatively": 31188, "ĠTribunal": 31189, "PRO": 31190, "Ġlex": 31191, "Ġfertile": 31192, "Ġdepressing": 31193, "Ġsuperficial": 31194, "essential": 31195, "ĠHunters": 31196, "gp": 31197, "Ġprominence": 31198, "Liber": 31199, "ĠAncest": 31200, "otechnology": 31201, "Ġmocking": 31202, "ĠTraff": 31203, "ĸļ": 31204, "Medium": 31205, "Iraq": 31206, "Ġpsychiatrist": 31207, "Quantity": 31208, "ĠLect": 31209, "Ġnoisy": 31210, "520": 31211, "GY": 31212, "Ġslapped": 31213, "ĠMTV": 31214, "Ġpara": 31215, "pull": 31216, "Multiple": 31217, "asher": 31218, "Ġnour": 31219, "ĠSeg": 31220, "Spell": 31221, "vous": 31222, "ordial": 31223, "Senior": 31224, "ĠGoldberg": 31225, "ĠPlasma": 31226, "need": 31227, "Ġmessenger": 31228, "eret": 31229, "Ġteamed": 31230, "Ġliteracy": 31231, "ĠLeah": 31232, "ĠDoyle": 31233, "Ġemitted": 31234, "UX": 31235, "Ġevade": 31236, "Ġmaze": 31237, "Ġwrongly": 31238, "ĠLars": 31239, "Ġstereotype": 31240, "Ġpledges": 31241, "Ġaroma": 31242, "ĠMET": 31243, "Ġacre": 31244, "ĠOD": 31245, "Ġff": 31246, "Ġbreweries": 31247, "ĠHilton": 31248, "undle": 31249, "ĠKak": 31250, "ĠThankfully": 31251, "ĠCanucks": 31252, "inctions": 31253, "ĠAppears": 31254, "Ġcoer": 31255, "Ġundermined": 31256, "rovers": 31257, "Andre": 31258, "Ġblaze": 31259, "umers": 31260, "Ġfamine": 31261, "amphetamine": 31262, "ulkan": 31263, "Amount": 31264, "Ġdesperation": 31265, "wikipedia": 31266, "development": 31267, "ĠCorinth": 31268, "ussia": 31269, "Jackson": 31270, "LI": 31271, "Native": 31272, "Rs": 31273, "Ohio": 31274, "ĠKathleen": 31275, "Fortunately": 31276, "Ġattendant": 31277, "ĠPreferred": 31278, "ĠDidn": 31279, "ĠVs": 31280, "Mis": 31281, "Ġrespondent": 31282, "Ġboun": 31283, "stable": 31284, "Ġpaved": 31285, "Ġunexpl": 31286, "ĠCheney": 31287, "LM": 31288, "ĠCull": 31289, "blown": 31290, "Ġconfronting": 31291, "ocese": 31292, "serving": 31293, "Wi": 31294, "ĠLithuania": 31295, "anni": 31296, "Ġstalk": 31297, "hd": 31298, "Ġvener": 31299, "APH": 31300, "ynchronous": 31301, "URR": 31302, "umably": 31303, "historic": 31304, "Half": 31305, "Hay": 31306, "Ġresilience": 31307, "spection": 31308, "Ġabandoning": 31309, "Obs": 31310, "ĠDebbie": 31311, "Ġgradient": 31312, "ĠPlaint": 31313, "ĠCanal": 31314, "ARCH": 31315, "Ġexpansive": 31316, "Ġfung": 31317, "Ġbounced": 31318, "Und": 31319, "Ġprecautions": 31320, "Ġclarification": 31321, "Ġdagger": 31322, "Ġgrips": 31323, "Ġµ": 31324, "ĠRivera": 31325, "ĠUndead": 31326, "isites": 31327, "ĠFIRST": 31328, "ño": 31329, "audi": 31330, "Ġhostages": 31331, "Ġcompliant": 31332, "Ġalumni": 31333, "Seven": 31334, "Ġcybersecurity": 31335, "either": 31336, "Collect": 31337, "Ġinvariably": 31338, "ĠSoci": 31339, "Ġlawmaker": 31340, "Ġale": 31341, "ĠPersonally": 31342, "Nazi": 31343, "Ġcustomization": 31344, "ĠProc": 31345, "ĠSaskatchewan": 31346, "eaturing": 31347, "Ġspared": 31348, "Ġdiscontinued": 31349, "Ġcomputational": 31350, "ĠMotorola": 31351, "Ġsupremacist": 31352, "governmental": 31353, "Ġparadise": 31354, "ĠDowning": 31355, "ĠNikon": 31356, "Ġcatalyst": 31357, "berra": 31358, "Toronto": 31359, "875": 31360, "beta": 31361, "ĠMacron": 31362, "Ġunrealistic": 31363, "vector": 31364, "ĠVehicles": 31365, "itiveness": 31366, "ĠRV": 31367, "ĠColbert": 31368, "sin": 31369, "oji": 31370, "entin": 31371, "ĠKrish": 31372, "hello": 31373, "ffield": 31374, "oky": 31375, "ĠTate": 31376, "Ġmaple": 31377, "Ġaids": 31378, "chemical": 31379, "334": 31380, "nuts": 31381, "ĠWarp": 31382, "Ġxx": 31383, "ĠRobb": 31384, "umerous": 31385, "_-_": 31386, "ftime": 31387, "ĠVW": 31388, "Ġwinger": 31389, "ĠDome": 31390, "tools": 31391, "ĠPV": 31392, "ĠGeorgetown": 31393, "Ġgeared": 31394, "Ġjihadists": 31395, "Ġcp": 31396, "Ġsteroids": 31397, "Mother": 31398, "clerosis": 31399, "ĠDRM": 31400, "nesia": 31401, "Ġlinger": 31402, "Ġimmersive": 31403, "ĠCOUN": 31404, "Ġoutweigh": 31405, "ensual": 31406, "Band": 31407, "Ġtransforms": 31408, "matched": 31409, "psons": 31410, "ĠJudicial": 31411, "factor": 31412, "Ġreferral": 31413, "Ġoddly": 31414, "ĠWenger": 31415, "Bring": 31416, "ĠBows": 31417, "602": 31418, "ICLE": 31419, "Ġlions": 31420, "ĠAcademic": 31421, "ĠThorn": 31422, "ĠRaider": 31423, "kefeller": 31424, "Storage": 31425, "Lower": 31426, "ĠOrt": 31427, "ĠEquality": 31428, "ALT": 31429, "ĠSOC": 31430, "Types": 31431, "Ġlyn": 31432, "ĠAsset": 31433, "coat": 31434, "TPP": 31435, "CVE": 31436, "ĠPioneer": 31437, "application": 31438, "Modern": 31439, "ĠHK": 31440, "Environment": 31441, "Alright": 31442, "Rain": 31443, "IPP": 31444, "ĠShiite": 31445, "Ġmound": 31446, "ĠAbilities": 31447, "condition": 31448, "Staff": 31449, "Ġcompetence": 31450, "ĠMoor": 31451, "ĠDiablo": 31452, "Ġwithheld": 31453, "Ġostensibly": 31454, "ĠBrom": 31455, "Ġmsg": 31456, "Ġdenomin": 31457, "ĠReferences": 31458, "ĠFP": 31459, "Ġplunged": 31460, "Ġpamph": 31461, "moving": 31462, "central": 31463, "Ġdownright": 31464, "Ġfading": 31465, "Tal": 31466, "Typ": 31467, "ĠThy": 31468, "ukes": 31469, "ithe": 31470, "Ġove": 31471, "Ġbattled": 31472, "Ġseafood": 31473, "Ġfigur": 31474, "ĠRD": 31475, "crop": 31476, "Ġsquads": 31477, "{\\": 31478, "à¹": 31479, "ĠEh": 31480, "Ġinterviewing": 31481, "ĠQin": 31482, "Ġaspiring": 31483, "PLIC": 31484, "Ġclauses": 31485, "ĠGast": 31486, "ĠNir": 31487, "Ġluggage": 31488, "Ġhose": 31489, "Ġsystemd": 31490, "Ġdescending": 31491, "ĠRevised": 31492, "ĠRails": 31493, "align": 31494, "709": 31495, "337": 31496, "Ġfug": 31497, "charging": 31498, "tags": 31499, "Ġuter": 31500, "kish": 31501, "WARNING": 31502, "490": 31503, "profits": 31504, "Ġvoyage": 31505, "Ġace": 31506, "ĠVanguard": 31507, "ĠTanks": 31508, "ĠMuk": 31509, "Ġ226": 31510, "Safe": 31511, "Armor": 31512, 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"Choose": 31851, "Ġparach": 31852, "Ġbarric": 31853, "ãĢIJ": 31854, "Ġcompass": 31855, "Ġallergic": 31856, "âĢķ": 31857, "OTHER": 31858, "errilla": 31859, "Ġwagon": 31860, "Ġzinc": 31861, "Ġrubbed": 31862, "ĠFuller": 31863, "ĠLuxembourg": 31864, "ĠHoover": 31865, "Ġliar": 31866, "ĠEvening": 31867, "ĠCobb": 31868, "esteem": 31869, "Ġselector": 31870, "ĠBrawl": 31871, "isance": 31872, "ĠEk": 31873, "Ġtroop": 31874, "Ġguts": 31875, "ĠAppeal": 31876, "ĠTibetan": 31877, "Ġroutines": 31878, "ĠMent": 31879, "Ġsummarized": 31880, "steamapps": 31881, "Ġtranqu": 31882, "Ġ1929": 31883, "oran": 31884, "ĠAuthent": 31885, "Ġgmaxwell": 31886, "Ġapprehens": 31887, "Ġpoems": 31888, "Ġsausage": 31889, "ĠWebster": 31890, "urus": 31891, "Ġthemed": 31892, "Ġlounge": 31893, "Ġcharger": 31894, "Spoiler": 31895, "Ġspilled": 31896, "hog": 31897, "ĠSunder": 31898, "ĠAin": 31899, "ĠAngry": 31900, "Ġdisqual": 31901, "ĠFrequency": 31902, "ĠEthernet": 31903, "Ġhelper": 31904, "Percent": 31905, "Ġhorrifying": 31906, 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"çī": 31965, "answered": 31966, "Ġcompulsory": 31967, "ascist": 31968, "Ġpodcasts": 31969, "ĠFrankfurt": 31970, "bnb": 31971, "Ġneoliberal": 31972, "ĠKeyboard": 31973, "ĠBelle": 31974, "warm": 31975, "Ġtrusts": 31976, "Ġinsured": 31977, "ĠBucc": 31978, "usable": 31979, "607": 31980, "ĠPlains": 31981, "Ġ1890": 31982, "Ġsabotage": 31983, "Ġlodged": 31984, "felt": 31985, "Ġga": 31986, "ĠNarc": 31987, "ĠSalem": 31988, "Ġseventy": 31989, "ĠBlank": 31990, "pocket": 31991, "Ġwhisper": 31992, "Ġmating": 31993, "omics": 31994, "ĠSalman": 31995, "ĠKad": 31996, "Ġangered": 31997, "Ġcollisions": 31998, "Ġextraordinarily": 31999, "Ġcoercion": 32000, "Ghost": 32001, "birds": 32002, "èĢ": 32003, "kok": 32004, "Ġpermissible": 32005, "avorable": 32006, "Ġpointers": 32007, "Ġdissip": 32008, "aci": 32009, "Ġtheatrical": 32010, "ĠCosmic": 32011, "Ġforgetting": 32012, "Ġfinalized": 32013, "大": 32014, "yout": 32015, "library": 32016, "Ġbooming": 32017, "ĠBelieve": 32018, "ĠTeacher": 32019, "ĠLiv": 32020, "ĠGOODMAN": 32021, "ĠDominican": 32022, "ORED": 32023, "ĠParties": 32024, "Ġprecipitation": 32025, "ĠSlot": 32026, "Roy": 32027, "ĠCombined": 32028, "Ġintegrating": 32029, "Ġchrome": 32030, "Ġintestinal": 32031, "ĠRebell": 32032, "Ġmatchups": 32033, "Ġblockbuster": 32034, "ĠLoren": 32035, "ĠLevy": 32036, "Ġpreaching": 32037, "ĠSending": 32038, "ĠPurpose": 32039, "rax": 32040, "fif": 32041, "Ġauthoritative": 32042, "ĠPET": 32043, "astical": 32044, "Ġdishon": 32045, "Ġchatting": 32046, "Ġ\"$:/": 32047, "Connection": 32048, "Ġrecreate": 32049, "Ġdelinqu": 32050, "Ġbroth": 32051, "ĠDirty": 32052, "ĠAdmin": 32053, "zman": 32054, "Ġscholarships": 32055, "Ġ253": 32056, "contact": 32057, "alsa": 32058, "767": 32059, "creen": 32060, "abbage": 32061, "Ġ1915": 32062, "Ġblended": 32063, "Ġalarmed": 32064, "Language": 32065, "356": 32066, "Ġblends": 32067, "ĠChanged": 32068, "Wolf": 32069, "Ġhepat": 32070, "Creating": 32071, "Ġpersecut": 32072, "Ġsweetness": 32073, "arte": 32074, "Ġforfeiture": 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"Ġyogurt": 32132, "lbs": 32133, "ĠNorfolk": 32134, "OPE": 32135, "ĠMoody": 32136, "Ġdistributor": 32137, "Ġscrolls": 32138, "Ġextracts": 32139, "Stan": 32140, "Ġviability": 32141, "Ġexposes": 32142, "Ġstarvation": 32143, "ĠSteps": 32144, "ĠDodd": 32145, "few": 32146, "STD": 32147, "332": 32148, "Ġclosures": 32149, "Ġcomplementary": 32150, "ĠSasha": 32151, "umpy": 32152, "Ġmonet": 32153, "Ġarticulate": 32154, "ĠDoct": 32155, "killer": 32156, "Ġscrim": 32157, "Ġ264": 32158, "Ġprostitutes": 32159, "Ġsevered": 32160, "Ġattachments": 32161, "Ġcooled": 32162, "Lev": 32163, "ĠFalk": 32164, "fail": 32165, "Ġpoliceman": 32166, "ĠDag": 32167, "Ġprayed": 32168, "ĠKernel": 32169, "Ġclut": 32170, "Ġcath": 32171, "Ġanomaly": 32172, "Storm": 32173, "emaker": 32174, "ĠBreakfast": 32175, "uli": 32176, "oire": 32177, "JJ": 32178, "hz": 32179, "Operation": 32180, "ĠSick": 32181, "354": 32182, "ĠGuatemala": 32183, "Rate": 32184, "Ġexposures": 32185, "faces": 32186, "ĠArchae": 32187, "raf": 32188, "ĠMia": 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"Ġsegreg": 32301, "Ġrevisit": 32302, "ighthouse": 32303, "Li": 32304, "Ġsubstrate": 32305, "ĠSeas": 32306, "ĠReward": 32307, "ĠHep": 32308, "ĠBrass": 32309, "sbm": 32310, "Ġeliminates": 32311, "Ġstamina": 32312, "ĠVAT": 32313, "ĠLoan": 32314, "Ġconstraint": 32315, "Ġappropriated": 32316, "Ġpes": 32317, "ĠALE": 32318, "ranging": 32319, "Ġ404": 32320, "392": 32321, "Ġintellectuals": 32322, "achu": 32323, "Ġrestructuring": 32324, "ĠLevin": 32325, "Ġrunes": 32326, "Ġdelightful": 32327, "Ġcarbohydrates": 32328, "ĠModels": 32329, "ĠExpo": 32330, "Ġtransporting": 32331, "alloc": 32332, "Ġringing": 32333, "Samsung": 32334, "Ġscarcely": 32335, "ĠURLs": 32336, "ĠMAS": 32337, "Ġprototypes": 32338, "Ġnarrator": 32339, "ĠCPUs": 32340, "cdn": 32341, "ĠBarton": 32342, "Ġdecidedly": 32343, "ĠShu": 32344, "ixir": 32345, "ocious": 32346, "ĠMyst": 32347, "Nintendo": 32348, "Ġreuse": 32349, "Ġforgiven": 32350, "Few": 32351, "inical": 32352, "nat": 32353, "Ġseamless": 32354, "ĠEva": 32355, "ĠEVE": 32356, 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"usher": 34055, "Ġrepercussions": 34056, "ĠVintage": 34057, "ĠSuperintendent": 34058, "Officers": 34059, "Ġflagged": 34060, "Ġblames": 34061, "Ġinverse": 34062, "ographers": 34063, "Ġmakeshift": 34064, "Ġdevoid": 34065, "Ġfossils": 34066, "ĠAristotle": 34067, "ĠFunds": 34068, "Ġdepleted": 34069, "ĠFlu": 34070, "ĠYuan": 34071, "Ġwoes": 34072, "Ġlipid": 34073, "Ġsitu": 34074, "requisites": 34075, "Ġfurnish": 34076, "ĠSamar": 34077, "Ġshameful": 34078, "Ġadversely": 34079, "Ġadept": 34080, "Ġremorse": 34081, "Ġmurderous": 34082, "uckles": 34083, "ĠESL": 34084, "Ġ314": 34085, "sent": 34086, "Ġredef": 34087, "ĠCache": 34088, "ĠPurs": 34089, "igans": 34090, "Ġ460": 34091, "Ġprescriptions": 34092, "Ġfres": 34093, "Fuck": 34094, "ocrates": 34095, "Twenty": 34096, "ĠWeird": 34097, "ĠToggle": 34098, "ĠCalled": 34099, "itizens": 34100, "Ġpoultry": 34101, "Ġharvesting": 34102, "ãĤ¦ãĤ¹": 34103, "Bottom": 34104, "Ġcautioned": 34105, "tn": 34106, "396": 34107, "ĠNikki": 34108, "Ġevaluations": 34109, "Ġharassing": 34110, "Ġbindings": 34111, "ĠMonetary": 34112, "Ġhitters": 34113, "Ġadversary": 34114, "unts": 34115, "Ġsetback": 34116, "Ġencrypt": 34117, "ĠCait": 34118, "Ġlows": 34119, "enges": 34120, "ĠNorn": 34121, "Ġbulbs": 34122, "Ġbottled": 34123, "ĠVoyager": 34124, "317": 34125, "Ġspheres": 34126, "politics": 34127, "Ġsubtract": 34128, "Ġsensations": 34129, "Ġappalling": 34130, "Ġ316": 34131, "Ġenvironmentally": 34132, "ĠSTEM": 34133, "Ġpublishes": 34134, "560": 34135, "Ġdiligence": 34136, "484": 34137, "Ġadvises": 34138, "Ġpetrol": 34139, "Ġimagining": 34140, "Ġpatrols": 34141, "ĠInteger": 34142, "ĠAshes": 34143, "actus": 34144, "ĠRadiant": 34145, "ĠLT": 34146, "itability": 34147, "htaking": 34148, "Setting": 34149, "Ġnuanced": 34150, "ĠReef": 34151, "ĠDevelopers": 34152, "Ni": 34153, "pieces": 34154, "990": 34155, "License": 34156, "Ġlowers": 34157, "ĠOttoman": 34158, "327": 34159, "ooo": 34160, "Ġquitting": 34161, "markets": 34162, "Behind": 34163, "Ġbasin": 34164, "Ġdocs": 34165, "anie": 34166, "flash": 34167, "ctl": 34168, "Ġcivilized": 34169, "ĠFukushima": 34170, "\"],\"": 34171, "ĠKS": 34172, "ĠHonestly": 34173, "arat": 34174, "Ġconstructs": 34175, "ĠLans": 34176, "ĠDire": 34177, "ĠLIKE": 34178, "ĠTrouble": 34179, "Ġwithholding": 34180, "ĠOblivion": 34181, "Ġsanity": 34182, "anya": 34183, "Const": 34184, "Ġgrocer": 34185, "ĠCelsius": 34186, "Ġrecounted": 34187, "ĠWife": 34188, "Border": 34189, "atered": 34190, "happy": 34191, "Ġspoiler": 34192, "Ġlogically": 34193, "Hall": 34194, "Ġsucceeding": 34195, "Ġpolymorph": 34196, "Ġaxes": 34197, "ĠShotgun": 34198, "ĠSlim": 34199, "ĠPrinciples": 34200, "ĠLeth": 34201, "arta": 34202, "Ġscor": 34203, "Screenshot": 34204, "Ġrelaxation": 34205, "#$#$": 34206, "Ġdeterrent": 34207, "iddy": 34208, "Ġpowerless": 34209, "Ġlesbians": 34210, "Ġchords": 34211, "ĠEdited": 34212, "selected": 34213, "Ġseparatists": 34214, "0002": 34215, "Ġairspace": 34216, "Ġturnaround": 34217, "Ġcunning": 34218, "PATH": 34219, "Poly": 34220, "Ġbombed": 34221, "Ġtion": 34222, "xs": 34223, "Ġwithhold": 34224, "Ġwaged": 34225, "ĠLiberties": 34226, "Flag": 34227, "Ġcomforting": 34228, "454": 34229, "ĠIris": 34230, "arers": 34231, "Ġrag": 34232, "Ġrelocated": 34233, "ĠGuarant": 34234, "Ġstrategically": 34235, "Ġgamma": 34236, "uberty": 34237, "ĠLockheed": 34238, "gres": 34239, "Ġgrilled": 34240, "ĠLowe": 34241, "stats": 34242, "ĠRocks": 34243, "Ġsensing": 34244, "Ġrenting": 34245, "ĠGeological": 34246, "اØ": 34247, "otrop": 34248, "Ġsew": 34249, "Ġimproperly": 34250, "486": 34251, "Ġâĸł": 34252, "Ġstarving": 34253, "ĠBj": 34254, "Discussion": 34255, "328": 34256, "ĠCombo": 34257, "ĠFixes": 34258, "NAT": 34259, "Ġstriving": 34260, "thora": 34261, "Ġharvested": 34262, "ĠPing": 34263, "Ġplayful": 34264, "Ġavenues": 34265, "Ġoccupational": 34266, "Ġwakes": 34267, "ĠCourier": 34268, "Ġdrummer": 34269, "ĠBrowser": 34270, "ĠHouth": 34271, "itu": 34272, "Ġapparel": 34273, "paste": 34274, "Ġhunted": 34275, "ĠSecondly": 34276, "lain": 34277, "XY": 34278, "ĠPIN": 34279, "icons": 34280, "Ġcocktails": 34281, "Ġsizable": 34282, "Ġhurdles": 34283, "estinal": 34284, "ĠRecreation": 34285, "Ġeco": 34286, "648": 34287, "ĠDied": 34288, "mint": 34289, "Ġfingerprints": 34290, "Ġdispose": 34291, "ĠBosnia": 34292, "tsy": 34293, "2200": 34294, "Ġinspected": 34295, "ĠFou": 34296, "Ġfuss": 34297, "Ġambush": 34298, "ĠRak": 34299, "Ġmanifested": 34300, "Prosecut": 34301, "Ġsuffice": 34302, "rences": 34303, "Ġcompensated": 34304, "ĠCyrus": 34305, "Ġgenus": 34306, "ĠWolverine": 34307, "ĠTrends": 34308, "Ġhikes": 34309, "ĠSeen": 34310, "Ġenrol": 34311, "Cold": 34312, "Ġpolitely": 34313, "ĠSlav": 34314, "ĠRupert": 34315, "Ġeyewitness": 34316, "ĠAlto": 34317, "Ġuncomp": 34318, "Ġposterior": 34319, "Must": 34320, "ĠHerz": 34321, "Ġprogressively": 34322, "Ġ234": 34323, "Ġindifference": 34324, "ĠCunningham": 34325, "Ġacademia": 34326, "Ġsewer": 34327, "Ġastounding": 34328, "ĠAES": 34329, "rather": 34330, "Ġeldest": 34331, "Ġclimbs": 34332, "ĠAdds": 34333, "Ġoutcry": 34334, "Ġcontag": 34335, "ĠHouses": 34336, "Ġpept": 34337, "ĠMelania": 34338, "interested": 34339, "ĠUCH": 34340, "ĠRoots": 34341, "ĠHubbard": 34342, "ĠTBD": 34343, "ĠRomanian": 34344, "filename": 34345, "Stone": 34346, "ĠImpl": 34347, "Ġchromosome": 34348, "Cle": 34349, "dx": 34350, "Ġscrambled": 34351, "ĠPt": 34352, "Ġ242": 34353, "OPLE": 34354, "Ġtremendously": 34355, "Street": 34356, "Ġcraving": 34357, "Ġbundled": 34358, "ĠRG": 34359, "pipe": 34360, "Ġinjuring": 34361, "Ġarcane": 34362, "Particip": 34363, "ĠHeroic": 34364, "sty": 34365, "Ġtopping": 34366, "ĠTempest": 34367, "rentices": 34368, "bh": 34369, "Ġparanoia": 34370, "ĠUnicode": 34371, "Ġegregious": 34372, "Ġ\\'": 34373, "ĠOswald": 34374, "Ġgravel": 34375, "ĠSimpsons": 34376, "Ġbland": 34377, "ĠGuantanamo": 34378, "Writer": 34379, "liners": 34380, "ĠDice": 34381, "JC": 34382, "Ġparity": 34383, "Ġsided": 34384, "Ġ237": 34385, "ĠPyrrha": 34386, "atters": 34387, "dk": 34388, "Fine": 34389, "compan": 34390, "Ġformulated": 34391, "ĠIdol": 34392, "ilers": 34393, "hemoth": 34394, "ĠFav": 34395, "Ġintrusion": 34396, "Ġcarrots": 34397, "ĠLayer": 34398, "ĠHacker": 34399, "Ġ----------------": 34400, "Ġmoderation": 34401, "éģ": 34402, "ococ": 34403, "Ġcharacterize": 34404, "ĠTeresa": 34405, "Ġsocioeconomic": 34406, "Ġperk": 34407, "ĠParticipation": 34408, "training": 34409, "ĠPaulo": 34410, "phys": 34411, "Ġtrustworthy": 34412, "Ġembodied": 34413, "ĠMerch": 34414, "currency": 34415, "ĠPriority": 34416, "Ġteasing": 34417, "Ġabsorbing": 34418, "Ġunfinished": 34419, "ĠComparison": 34420, "Ġdisple": 34421, "writers": 34422, "Ġprofessions": 34423, "ĠPenguin": 34424, "Ġangrily": 34425, "ĠLINK": 34426, "688": 34427, "ĠCorrespond": 34428, "Ġprevailed": 34429, "Ġcartel": 34430, "lp": 34431, "asms": 34432, "ĠRedemption": 34433, "ĠIslamists": 34434, "effects": 34435, "dose": 34436, "ĠLatter": 34437, "ĠHalifax": 34438, "Ġvas": 34439, "ĠTopics": 34440, "ĠNamed": 34441, "advertising": 34442, "zza": 34443, "ICES": 34444, "Ġretarded": 34445, "achable": 34446, "ĠPuppet": 34447, "ĠItemLevel": 34448, "Ġretract": 34449, "Ġidentifiable": 34450, "Aaron": 34451, "ĠBuster": 34452, "sol": 34453, "helle": 34454, "assemb": 34455, "Hope": 34456, "ranged": 34457, "Ba": 34458, "ĠPurch": 34459, "éĢ": 34460, "ĠSiri": 34461, "Ġarrivals": 34462, "Ġ1912": 34463, "Ġshortened": 34464, "Ġ312": 34465, "Ġdiscrepancy": 34466, "ĠTemperature": 34467, "ĠWalton": 34468, "Ġkinderg": 34469, "polit": 34470, "Ġremix": 34471, "Ġconnectors": 34472, "ãĥĺãĥ©": 34473, "ĠKazakhstan": 34474, "dominated": 34475, "Ġsugars": 34476, "imble": 34477, "ĠPanic": 34478, "ĠDemand": 34479, "ĠColony": 34480, "onen": 34481, "ĠMER": 34482, "775": 34483, "uria": 34484, "azaar": 34485, "ĠDegree": 34486, "Pri": 34487, "Ġsunshine": 34488, "Ġ251": 34489, "Ġpsychedelic": 34490, "Ġdigitally": 34491, "ĠBraun": 34492, "Ġshimmer": 34493, "Ġshave": 34494, "ĠTelesc": 34495, "ĠAstral": 34496, "ĠVenezuelan": 34497, "ĠOG": 34498, "Ġcrawling": 34499, "Integ": 34500, "ĠFeather": 34501, "Ġunfolding": 34502, "Ġappropriation": 34503, "Ġè£ıè": 34504, "ĠMobility": 34505, "ĠNey": 34506, "-.": 34507, "bilt": 34508, "LIN": 34509, "ĠTube": 34510, "ĠConversely": 34511, "Ġkeyboards": 34512, "ĠCao": 34513, "Ġoverth": 34514, "Ġlaure": 34515, ">>\\": 34516, "ĠViper": 34517, "acha": 34518, "Offset": 34519, "ĠRaleigh": 34520, "ĠJae": 34521, "Jordan": 34522, "jp": 34523, "Ġtotalitarian": 34524, "Connector": 34525, "Ġobserves": 34526, "ĠSpartan": 34527, "ĠImmediately": 34528, "ĠScal": 34529, "Cool": 34530, "Ġtaps": 34531, "Ġroar": 34532, "Past": 34533, "Ġchars": 34534, "ĠBender": 34535, "ĠSheldon": 34536, "Ġpainter": 34537, "Ġbeacon": 34538, "ĠCreatures": 34539, "Ġdownturn": 34540, "Ġhinder": 34541, "ĠAndromeda": 34542, "ÃĽ": 34543, "ccoli": 34544, "ĠFitness": 34545, "etrical": 34546, "Ġutilizes": 34547, "Ġsenate": 34548, "Ġensemble": 34549, "Ġcheers": 34550, "TW": 34551, "Ġaffluent": 34552, "kil": 34553, "rylic": 34554, "ordering": 34555, "Computer": 34556, "Ġgruesome": 34557, "ostics": 34558, "ĠUbisoft": 34559, "ĠKelley": 34560, "Ġwrench": 34561, "Ġbourgeoisie": 34562, "IBLE": 34563, "ĠPreston": 34564, "worn": 34565, "arist": 34566, "reating": 34567, "Ġstained": 34568, "arine": 34569, "Ġslime": 34570, "ENN": 34571, "Ġchests": 34572, "Ġgroundwater": 34573, "annot": 34574, "ĠTray": 34575, "ĠLocke": 34576, "ĠCTR": 34577, "Ġdudes": 34578, "ĠExternal": 34579, "ĠDecoder": 34580, "Ġparamed": 34581, "ĠMedline": 34582, "809": 34583, "ĠDinner": 34584, "rupal": 34585, "gz": 34586, "ĠGum": 34587, "ĠDemo": 34588, "jee": 34589, "Ġdh": 34590, "berman": 34591, "archs": 34592, "Ġenqu": 34593, "ĠEpstein": 34594, "Ġdevastation": 34595, "Ġfriendships": 34596, "ĠArd": 34597, "Ġ231": 34598, "ĠRubin": 34599, "ĠDistance": 34600, "Ġspurred": 34601, "Ġdossier": 34602, "Ġoverlooking": 34603, "\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\": 34604, "Forest": 34605, "ĠComes": 34606, "\\\",": 34607, "ĠIranians": 34608, "Ġfixtures": 34609, "Laughs": 34610, "Ġcurry": 34611, "ĠKingston": 34612, "Ġsquash": 34613, "Ġcatalogue": 34614, "Ġabnormalities": 34615, "Ġdigestive": 34616, ".........": 34617, "Ġsubordinate": 34618, "ogly": 34619, "Ġ249": 34620, "Middle": 34621, "Ġmassac": 34622, "Ġburgers": 34623, "Ġdownstairs": 34624, "Ġ1931": 34625, "394": 34626, "ĠVG": 34627, "Ġlasers": 34628, "ĠSikh": 34629, "ĠAlexa": 34630, "derived": 34631, "Ġcyclist": 34632, "ãģ®éŃĶ": 34633, "oneliness": 34634, "!!!!!!!!": 34635, "Ġbuffs": 34636, "legate": 34637, "Ġraping": 34638, "Ġrecommending": 34639, "rored": 34640, "Ġmulticultural": 34641, "unique": 34642, "Ġbusinessmen": 34643, "Ġuneasy": 34644, "ĠMAP": 34645, "Ġdispersed": 34646, "cipline": 34647, "Jess": 34648, "ĠKerala": 34649, "å§": 34650, "Ġabstraction": 34651, "Surv": 34652, "Uh": 34653, "Ġprinters": 34654, "ija": 34655, "owder": 34656, "Ġanalogous": 34657, "ĠASP": 34658, "afer": 34659, "Ġunfolded": 34660, "Ġleveling": 34661, "Ġbreached": 34662, "ĠHearing": 34663, "Ġnat": 34664, "Ġtranslating": 34665, "critical": 34666, "Ġantagonist": 34667, "ĠYesterday": 34668, "Ġfuzzy": 34669, "wash": 34670, "mere": 34671, "Ġbewild": 34672, "ĠMae": 34673, "Virgin": 34674, "phrase": 34675, "Ġsignaled": 34676, "ĠHIGH": 34677, "Ġprotester": 34678, "Ġgarner": 34679, "unknown": 34680, "Ġkay": 34681, "Ġabducted": 34682, "Ġstalking": 34683, "amn": 34684, "Ġdeserving": 34685, "ĠRiv": 34686, "ĠJorge": 34687, "Ġscratching": 34688, "ĠSaving": 34689, "iping": 34690, "Ġtease": 34691, "Ġmissionary": 34692, "ĠMorrow": 34693, "TIME": 34694, "Present": 34695, "Ġchemotherapy": 34696, "terness": 34697, "ĠHomes": 34698, "ĠPurdue": 34699, "Ġstaunch": 34700, "ĠWhitney": 34701, "ĠTHERE": 34702, "μ": 34703, "iatus": 34704, "ĠErnest": 34705, "ĠDeploy": 34706, "Ġcoveted": 34707, "FML": 34708, "ĠDialogue": 34709, "Ġexited": 34710, "fruit": 34711, "Ġnerd": 34712, "\":\"\",\"": 34713, "Ġvivo": 34714, "ruly": 34715, "460": 34716, "ĠAmen": 34717, "rehensible": 34718, "Ġâĺ": 34719, "DIR": 34720, "Ġadherence": 34721, "Ġchew": 34722, "ĠCoke": 34723, "ĠSergei": 34724, "digital": 34725, "ĠNeck": 34726, "gently": 34727, "enthal": 34728, "/)": 34729, "Ġweary": 34730, "Ġguise": 34731, "ĠConcord": 34732, "ĠOnion": 34733, "atcher": 34734, "Ġbinge": 34735, "ĠDirective": 34736, "Ġmanned": 34737, "ansk": 34738, "Ġillusions": 34739, "Ġbillionaires": 34740, "383": 34741, "olyn": 34742, "odynamic": 34743, "ĠWheat": 34744, "ĠAlic": 34745, "Ġcoloured": 34746, "ĠNAFTA": 34747, "abo": 34748, "Ġmacros": 34749, "independent": 34750, "sweet": 34751, "Ġspac": 34752, "ĠKabul": 34753, "ĠÄ": 34754, "eme": 34755, "Ġdictated": 34756, "Ġshouts": 34757, "={": 34758, "Ġripping": 34759, "ĠShay": 34760, "ĠCricket": 34761, "directed": 34762, "Ġanalysed": 34763, "ĠWARRANT": 34764, "agons": 34765, "ĠBlazers": 34766, "Ġcheered": 34767, "Ġarithmetic": 34768, "ĠTanz": 34769, "373": 34770, "ĠFlags": 34771, "Ġ295": 34772, "Ġwitches": 34773, "ĠIncluded": 34774, "ĠGained": 34775, "ĠBlades": 34776, "Gam": 34777, "ĠSamantha": 34778, "ĠAtlantis": 34779, "ĠPratt": 34780, "Ġspoiled": 34781, "ĠIB": 34782, "ĠRamirez": 34783, "Probably": 34784, "rero": 34785, "ĠNg": 34786, "ĠWarlock": 34787, "tp": 34788, "Ġoverhe": 34789, "Ġadministrations": 34790, "Ġtint": 34791, "Ġregiment": 34792, "Ġpistols": 34793, "Ġblankets": 34794, "Ġepist": 34795, "Ġbowls": 34796, "Ġhydraulic": 34797, "Ġdean": 34798, "Ġjung": 34799, "Ġascend": 34800, "705": 34801, "ĠSantiago": 34802, "î": 34803, "Ġunavoid": 34804, "ĠShaman": 34805, "reb": 34806, "Ġstemming": 34807, "998": 34808, "ĠMG": 34809, "sticks": 34810, "esthesia": 34811, "ERO": 34812, "Ġmorbid": 34813, "ĠGrill": 34814, "ĠPoe": 34815, "anyl": 34816, "Ġdeleting": 34817, "ĠSurveillance": 34818, "Ġdirectives": 34819, "Ġiterations": 34820, "ĠRox": 34821, "ĠMilky": 34822, "Father": 34823, "Ġpatented": 34824, "447": 34825, "Ġprecursor": 34826, "Ġmaiden": 34827, "ĠPhen": 34828, "ĠVegan": 34829, "ĠPatent": 34830, "Kelly": 34831, "Redditor": 34832, "Ġnods": 34833, "Ġventilation": 34834, "ĠSchwarz": 34835, "Ġwizards": 34836, "Ġominous": 34837, "ĠHeads": 34838, "ĠBG": 34839, "Ġlumber": 34840, "ĠSpiel": 34841, "ĠisEnabled": 34842, "Ġancestral": 34843, "ĠShips": 34844, "Ġwrestler": 34845, "phi": 34846, "Ġyuan": 34847, "ĠRebellion": 34848, "Ġiceberg": 34849, "Ġmagically": 34850, "Ġdiversion": 34851, "arro": 34852, "ythm": 34853, "ĠRiders": 34854, "ĠRobbie": 34855, "ĠKara": 34856, "ĠMaintenance": 34857, "ĠHerb": 34858, "Ġharms": 34859, "packed": 34860, "ĠFeinstein": 34861, "Ġmarrying": 34862, "Ġblending": 34863, "ĠRates": 34864, "Ġ1880": 34865, "Ġwrink": 34866, "ĠUnch": 34867, "ĠTorch": 34868, "described": 34869, "Ġhumanoid": 34870, "ilitating": 34871, "ĠConv": 34872, "ĠFeld": 34873, "IGHTS": 34874, "Ġwhistleblower": 34875, "ortmund": 34876, "etsy": 34877, "arrett": 34878, "ĠMono": 34879, "ĠIke": 34880, "ĠCNBC": 34881, "ĠWAY": 34882, "ĠMDMA": 34883, "ĠIndividuals": 34884, "Ġsupplemental": 34885, "Ġpowerhouse": 34886, "ĠStru": 34887, "Focus": 34888, "aphael": 34889, "ĠColleg": 34890, "atti": 34891, "ZA": 34892, "Ġperenn": 34893, "ĠSignature": 34894, "ĠRodney": 34895, "Ġcubes": 34896, "iddled": 34897, "ĠDante": 34898, "ĠINV": 34899, "ilingual": 34900, "ĠCth": 34901, "Ġsofa": 34902, "Ġintimidate": 34903, "ĠRoe": 34904, "ĠDiplom": 34905, "ĠCountries": 34906, "ayson": 34907, "Ġextradition": 34908, "Ġdisabling": 34909, "ĠCardiff": 34910, "Ġmemorandum": 34911, "ĠTrace": 34912, "Ġ???": 34913, "sector": 34914, "ĠRouhani": 34915, "ĠYates": 34916, "ĠFreeze": 34917, "Ġbladder": 34918, "Motor": 34919, "ĠPromise": 34920, "antasy": 34921, "Ġforeseeable": 34922, "ĠCologne": 34923, "container": 34924, "ĠTrees": 34925, "ĠGors": 34926, "ĠSinclair": 34927, "Ġbarring": 34928, "keye": 34929, "Ġslashed": 34930, "ĠStatistical": 34931, "éĩ": 34932, "Ġâĸº": 34933, "Allows": 34934, "Ġhumility": 34935, "Ġdrilled": 34936, "ĠFurn": 34937, "443": 34938, "Ġsewage": 34939, "Ġhomepage": 34940, "Ġcourtyard": 34941, "Ġvile": 34942, "Ġsubsidiaries": 34943, "ajo": 34944, "directory": 34945, "Ġammon": 34946, "Vers": 34947, "charges": 34948, "Ġ}}": 34949, "ĠChains": 34950, "Ġ246": 34951, "nob": 34952, "Ġpercept": 34953, "Ġgrit": 34954, "Ġfishermen": 34955, "ĠIraqis": 34956, "ĠDISTR": 34957, "ĠFULL": 34958, "ĠEvaluation": 34959, "graph": 34960, "atial": 34961, "Ġcooperating": 34962, "Ġmelan": 34963, "Ġenlightened": 34964, "Ġali": 34965, "tailed": 34966, "Ġsalute": 34967, "Ġweakest": 34968, "ĠBulldogs": 34969, "UA": 34970, "ĠAlloy": 34971, "Ġsemen": 34972, "ocene": 34973, "ĠWilliamson": 34974, "spr": 34975, ",âĢĶ": 34976, "ĠGF": 34977, "ittens": 34978, "Beat": 34979, "ĠJunk": 34980, "iphate": 34981, "ĠFarmers": 34982, "ĠBitcoins": 34983, "igers": 34984, "dh": 34985, "ĠLoyal": 34986, "payer": 34987, "Ġentertained": 34988, "Ġpenned": 34989, "Ġcoupon": 34990, "Queue": 34991, "Ġweakening": 34992, "carry": 34993, "Ġunderestimate": 34994, "Ġshootout": 34995, "Ġcharismatic": 34996, "ĠProcedure": 34997, "Ġprudent": 34998, "inances": 34999, "Ġriches": 35000, "Ġcortical": 35001, "Ġstrides": 35002, "Ġdrib": 35003, "ĠOilers": 35004, "540": 35005, "ĠPerform": 35006, "ĠBangkok": 35007, "Ġeuth": 35008, "SER": 35009, "Ġsimplistic": 35010, "tops": 35011, "campaign": 35012, "Quality": 35013, "Ġimpoverished": 35014, "ĠEisenhower": 35015, "Ġaugment": 35016, "ĠHarden": 35017, "Ġintervened": 35018, "Ġlistens": 35019, "ĠKok": 35020, "Ġsage": 35021, "Ġrubbish": 35022, "ĠDed": 35023, "Ġmull": 35024, "pelling": 35025, "Ġvideot": 35026, "Production": 35027, "DJ": 35028, "miah": 35029, "Ġadaptations": 35030, "Ġmedically": 35031, "Ġboarded": 35032, "Ġarrogance": 35033, "Ġscrapped": 35034, "Ġoppress": 35035, "FORMATION": 35036, "Ġjunction": 35037, "415": 35038, "EEEE": 35039, "Skill": 35040, "Ġsubdu": 35041, "ĠSuggest": 35042, "ĠPett": 35043, "Ġlett": 35044, "ĠManip": 35045, "ĠCaf": 35046, "ĠCooperation": 35047, "Ther": 35048, "Ġregained": 35049, "¶æ": 35050, "reflect": 35051, "Ġthugs": 35052, "ĠShelby": 35053, "Ġdictates": 35054, "ĠWeiner": 35055, "ĠHale": 35056, "Ġbattleground": 35057, 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35113, "Feel": 35114, "Ġfermentation": 35115, "Ġyoutube": 35116, "Ġoffend": 35117, "ĠTerm": 35118, "resist": 35119, "Ġcessation": 35120, "Ġinsurgency": 35121, "Ġbir": 35122, "ĠRaise": 35123, "595": 35124, "Ġhypotheses": 35125, "502": 35126, "Ġplaque": 35127, "ocrat": 35128, "Ġjackets": 35129, "ĠHuffPost": 35130, "among": 35131, "Ġconfer": 35132, "487": 35133, "ĠLilly": 35134, "Ġadapting": 35135, "ĠFay": 35136, "Ġshoved": 35137, "vec": 35138, "Ġrefine": 35139, "Ġgon": 35140, "Ġgunmen": 35141, "zai": 35142, "ĠShuttle": 35143, "ĠIzan": 35144, "Ġ1913": 35145, "Ġplethora": 35146, "··": 35147, "Ġ510": 35148, "Ġpuberty": 35149, "Ġ241": 35150, "ĠWealth": 35151, "ĠAlma": 35152, "ĠMEM": 35153, "ĠAdults": 35154, "Cas": 35155, "prison": 35156, "Race": 35157, "Ġwaterproof": 35158, "Ġathleticism": 35159, "Ġcapitalize": 35160, "ĠJuice": 35161, "Ġilluminated": 35162, "ĠPascal": 35163, "Ġirritation": 35164, "ĠWitnesses": 35165, "adle": 35166, "ĠAstro": 35167, "Ġfax": 35168, "ĠElvis": 35169, "Primary": 35170, "ĠLich": 35171, "ĠElves": 35172, "Ġresiding": 35173, "Ġstumble": 35174, "319": 35175, "ĠPKK": 35176, "Ġadversaries": 35177, "DOS": 35178, "ĠRitual": 35179, "Ġsmear": 35180, "Ġarson": 35181, "idental": 35182, "Ġscant": 35183, "Ġmonarchy": 35184, "Ġhalftime": 35185, "Ġresidue": 35186, "Ġindign": 35187, "ĠShaun": 35188, "ĠElm": 35189, "auri": 35190, "Aff": 35191, "WATCH": 35192, "ĠLyon": 35193, "helps": 35194, "361": 35195, "Ġlobbyist": 35196, "Ġdiminishing": 35197, "Ġoutbreaks": 35198, "Ġgoats": 35199, "favorite": 35200, "ĠNah": 35201, "sonian": 35202, "ĠBooster": 35203, "Ġsandbox": 35204, "ĠFare": 35205, "ĠMalta": 35206, "ĠattRot": 35207, "ĠMOR": 35208, "lde": 35209, "Ġnavigating": 35210, "Touch": 35211, "Ġuntrue": 35212, "ĠDisaster": 35213, "Ġludicrous": 35214, "Password": 35215, "ĠJFK": 35216, "blogspot": 35217, "416": 35218, "ĠUNDER": 35219, "ernal": 35220, "Ġdelaying": 35221, "TOP": 35222, "Ġimplants": 35223, "ĠAVG": 35224, "ĠHuge": 35225, "attr": 35226, "Ġjournalistic": 35227, "ĠPeyton": 35228, "ĠIA": 35229, "Rap": 35230, "goal": 35231, "ĠProgramme": 35232, "Ġsmashing": 35233, "wives": 35234, "println": 35235, "ĠPlague": 35236, "inus": 35237, "EEP": 35238, "Ġcruiser": 35239, "ĠParish": 35240, "uminium": 35241, "Ġoccupants": 35242, "ĠJihad": 35243, "mop": 35244, "Ġpint": 35245, "Ġhect": 35246, "ĠMecca": 35247, "director": 35248, "ĠFunding": 35249, "ĠMixed": 35250, "Ġstag": 35251, "Tier": 35252, "Ġgust": 35253, "Ġbrightly": 35254, "orsi": 35255, "Ġuphill": 35256, "RD": 35257, "Ġlesions": 35258, "ĠBundy": 35259, "livious": 35260, "Ġbiologist": 35261, "ĠFaculty": 35262, "ĠAuthorization": 35263, "Ġ244": 35264, "Allow": 35265, "ï¸": 35266, "ĠGiul": 35267, "Ġpertinent": 35268, "otaur": 35269, "esse": 35270, "ĠRoof": 35271, "Ġunmanned": 35272, "351": 35273, "ĠShak": 35274, "ĠOrient": 35275, "Ġendanger": 35276, "Dir": 35277, "Ġreplen": 35278, "edient": 35279, "Ġtailor": 35280, "Ġgadgets": 35281, "Ġaudible": 35282, "âĺĨ": 35283, "Nice": 35284, "Ġbombard": 35285, "ĠRape": 35286, "Ġdefiance": 35287, "ĠTWO": 35288, "ĠFilipino": 35289, "Ġunaffected": 35290, "ervatives": 35291, "Ġsoared": 35292, "ĠBolton": 35293, "Ġcompromising": 35294, "ĠBrewers": 35295, "RAL": 35296, "ĠAHL": 35297, "icycle": 35298, "Ġvampires": 35299, "Ġdipped": 35300, "oyer": 35301, "ĠXIII": 35302, "Ġsideways": 35303, "ĠWaste": 35304, "ĠDiss": 35305, "ĠâĶľâĶĢâĶĢ": 35306, "$.": 35307, "Ġhabitats": 35308, "ĠBeef": 35309, "truth": 35310, "trained": 35311, "split": 35312, "Rus": 35313, "Andy": 35314, "ĠBram": 35315, "REP": 35316, "pid": 35317, "è£ħ": 35318, "ĠMutant": 35319, "Anim": 35320, "ĠMarina": 35321, "Ġfutile": 35322, "highest": 35323, "frequency": 35324, "Ġepilepsy": 35325, "Ġcoping": 35326, "Ġconcise": 35327, "Ġtracing": 35328, "ĠSUN": 35329, "panel": 35330, "ĠSophie": 35331, "ĠCrowley": 35332, "ĠAdolf": 35333, "ĠShooter": 35334, "Ġshaky": 35335, "ĠIG": 35336, "ĠLies": 35337, "ĠBarber": 35338, "pkg": 35339, "Ġuptake": 35340, "Ġpredatory": 35341, "ULTS": 35342, "/**": 35343, "Ġintoxicated": 35344, "ĠWestbrook": 35345, "odder": 35346, "hement": 35347, "Ġbaseman": 35348, "APD": 35349, "storage": 35350, "ĠFifty": 35351, "editor": 35352, "GEN": 35353, "UTION": 35354, "irting": 35355, "Ġsewing": 35356, "rift": 35357, "Ġagony": 35358, "ĠSands": 35359, "Ġ254": 35360, "Cash": 35361, "Ġlodge": 35362, "Ġpunt": 35363, "Natural": 35364, "ĠIdeas": 35365, "Ġerroneous": 35366, "ĠSensor": 35367, "ĠHannity": 35368, "Ġ1921": 35369, "Ġmould": 35370, "ĠGon": 35371, "kaya": 35372, "Ġanonymously": 35373, "ĠKEY": 35374, "Ġsimulator": 35375, "Winter": 35376, "Ġstreamed": 35377, "507": 35378, "?\",": 35379, "Ġteased": 35380, "Ġcoefficient": 35381, "Ġwartime": 35382, "ĠTHR": 35383, "''.": 35384, "ĠBanking": 35385, "mpire": 35386, "Ġfandom": 35387, "Ġlia": 35388, "Ga": 35389, "Ġdownhill": 35390, "Ġinterpreting": 35391, "Individual": 35392, "Norm": 35393, "Ġjealousy": 35394, "bitcoin": 35395, "Ġpleasures": 35396, "ĠToys": 35397, "ĠChevrolet": 35398, "ĠAdvisor": 35399, "IZE": 35400, "Ġreceptions": 35401, "706": 35402, "Cro": 35403, "Ġ262": 35404, "Ġcitrus": 35405, "iru": 35406, "Reviewer": 35407, "jected": 35408, "UES": 35409, "anz": 35410, "1981": 35411, "ĠWorker": 35412, "Ġcomplied": 35413, "orescent": 35414, "continental": 35415, "Ton": 35416, "ĠPrism": 35417, "ĠSheep": 35418, "Ġ288": 35419, "nox": 35420, "ĠVog": 35421, "Ord": 35422, "Ġrealms": 35423, "tek": 35424, "Ġirrigation": 35425, "Ġbicycles": 35426, "Ġelectronically": 35427, "poly": 35428, "tall": 35429, "());": 35430, "Ġaesthetics": 35431, "ĠIntegrated": 35432, "Explore": 35433, "Ġdunk": 35434, "476": 35435, "pain": 35436, "ĠJacques": 35437, "ĠDmit": 35438, "Frames": 35439, "Ġreunited": 35440, "Ġhumid": 35441, "Dro": 35442, "Political": 35443, "Ġyouthful": 35444, "Ġentails": 35445, "Ġmosquito": 35446, "363": 35447, "species": 35448, "Ġcoordinating": 35449, "ĠMayhem": 35450, "ĠMagnus": 35451, "Mount": 35452, "Improved": 35453, "ĠSTATE": 35454, "ATTLE": 35455, "Ġflowed": 35456, "Ġtackled": 35457, "Ġfashioned": 35458, "Ġreorgan": 35459, "ivari": 35460, "finger": 35461, "Ġreluctantly": 35462, "etting": 35463, "ĠVand": 35464, "young": 35465, "ĠGarland": 35466, "Ġpresumption": 35467, "Ġamenities": 35468, "ĠPleasant": 35469, "onential": 35470, "ĠOxy": 35471, "Ġmorals": 35472, "ĠYah": 35473, "Ready": 35474, "Simon": 35475, "Enh": 35476, "Demon": 35477, "Ġclich": 35478, "Monitor": 35479, "ĠDU": 35480, "Ġwelcomes": 35481, "Ġstandout": 35482, "Ġdreadful": 35483, "Ġbananas": 35484, "Ġballoons": 35485, "hooting": 35486, "basic": 35487, "Ġsuffix": 35488, "Ġduly": 35489, "cano": 35490, "Chain": 35491, "atos": 35492, "Ġgeopolitical": 35493, "Ġ(&": 35494, "ĠGemini": 35495, "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ": 35496, "Ġacquitted": 35497, "Luck": 35498, "protect": 35499, "1024": 35500, "Ġscarcity": 35501, "Ġmindfulness": 35502, "ecided": 35503, "DN": 35504, "prime": 35505, "ĠPresidents": 35506, "ĠVIDEO": 35507, "Ġ(âĪĴ": 35508, "addock": 35509, "NOR": 35510, "ĠPru": 35511, "pun": 35512, "ĠLOL": 35513, "))))": 35514, "ĠLiqu": 35515, "ĠSAS": 35516, "Ġstyling": 35517, "Ġpunishments": 35518, "Ġnumb": 35519, "Ġascertain": 35520, "ĠRockies": 35521, "flu": 35522, "Thumbnail": 35523, "Ġperpetrated": 35524, "ĠSemi": 35525, "Ġdisarm": 35526, "ĠOlder": 35527, "ĠException": 35528, "Ġexponentially": 35529, "ĠCommunities": 35530, "Ġabolish": 35531, "ĠPartner": 35532, "ptoms": 35533, "Ġ777": 35534, "ĠFoley": 35535, "ĠCases": 35536, "Ġgrease": 35537, "ĠRebirth": 35538, "Ground": 35539, "Ġ;)": 35540, "ĠDoctrine": 35541, "ikini": 35542, "Ye": 35543, "ĠBlossom": 35544, "Ġpersists": 35545, "bill": 35546, "Ġinfusion": 35547, "Ġbuddies": 35548, "911": 35549, "ĠPatient": 35550, "Ġdemos": 35551, "Ġacquaintance": 35552, "ĠPaw": 35553, "atari": 35554, "Ġxml": 35555, "Ġfascination": 35556, "ĠServe": 35557, "ÏĤ": 35558, "branded": 35559, "Ġaz": 35560, "Returns": 35561, "Ġovershadow": 35562, "Ġroam": 35563, "Ġspeedy": 35564, "numbered": 35565, "helial": 35566, "Ġdisciple": 35567, "Ġassurances": 35568, "given": 35569, "pecting": 35570, "ĠNatalie": 35571, "çĶ°": 35572, "Ġmosquitoes": 35573, "rotein": 35574, "Ġnumeric": 35575, "Ġindependents": 35576, "Ġtransitional": 35577, "Ġreactionary": 35578, "ĠMechdragon": 35579, "doctor": 35580, "Ġshortest": 35581, "Ġsequential": 35582, "ĠBac": 35583, "ĠAccounts": 35584, "ãģĮ": 35585, "achy": 35586, "ractive": 35587, "ĠRegiment": 35588, "Ġbreathtaking": 35589, "fficiency": 35590, "ĠBates": 35591, "Ġ311": 35592, "Ġwardrobe": 35593, "fts": 35594, "ĠBerk": 35595, "Simply": 35596, "ĠRiverside": 35597, "ivering": 35598, "idential": 35599, "lucent": 35600, "Ġenriched": 35601, "ĠConver": 35602, "ĠGiving": 35603, "ãĥĻ": 35604, "Ġlegalize": 35605, "ĠFTC": 35606, "Ġfreaking": 35607, "Mix": 35608, "Ġterrestrial": 35609, "esian": 35610, "cients": 35611, "Wing": 35612, "LOAD": 35613, "Ġledge": 35614, "ĠViolent": 35615, "ĠMetall": 35616, "Ġ308": 35617, "Ġsoutheastern": 35618, "hetto": 35619, "Meat": 35620, "Ġslowdown": 35621, "Ġretreated": 35622, "Jeremy": 35623, "endas": 35624, "*****": 35625, "eric": 35626, "Ġreins": 35627, "oppable": 35628, "ĠHumanity": 35629, "earances": 35630, "rigan": 35631, "Camera": 35632, "Ġwaivers": 35633, "soc": 35634, "Ġalteration": 35635, "transform": 35636, "ĠCemetery": 35637, "506": 35638, "Ġindefinite": 35639, "Ġstimulating": 35640, "yg": 35641, "603": 35642, "ĠSop": 35643, "Ġdescriptive": 35644, "Phase": 35645, "ĠEdmund": 35646, "Ġpneumonia": 35647, "ventus": 35648, "Amb": 35649, "Ġlaboratories": 35650, "ĠExclusive": 35651, "ugar": 35652, "Were": 35653, "Ġmalfunction": 35654, "Ġhomosexuals": 35655, "Ġ-------": 35656, "uni": 35657, "Ġturbines": 35658, "ĠEquity": 35659, "Du": 35660, "Ġminded": 35661, "ĠRH": 35662, "ĠBlackhawks": 35663, "Ġfeats": 35664, "Ġ1700": 35665, "repl": 35666, "362": 35667, "laden": 35668, "Ġindispensable": 35669, "lyss": 35670, "tti": 35671, "Ġreel": 35672, "Ġdiverted": 35673, "Ġlikeness": 35674, "Ġsubscriptions": 35675, "Ġfingert": 35676, "Ġfilthy": 35677, "destruct": 35678, "draft": 35679, "ĠBernardino": 35680, "launch": 35681, "Ġperplex": 35682, "ĠSUM": 35683, "carb": 35684, "Ġsweater": 35685, "ĠVenture": 35686, "ĠJag": 35687, "ĠCeleb": 35688, "ĠVoters": 35689, "Ġsteadfast": 35690, "Ġathletics": 35691, "ĠHanson": 35692, "ĠDrac": 35693, "Tracker": 35694, "Ġcommend": 35695, "ĠPresidency": 35696, "ĠDID": 35697, "informed": 35698, "Ġwebpage": 35699, "Pretty": 35700, "Ġforcefully": 35701, "ãĥĥãĤ¯": 35702, "Ġrelocation": 35703, "Ġsatire": 35704, "âī": 35705, "ĠSunderland": 35706, "æĦ": 35707, "Voice": 35708, "????????": 35709, "Ġinformant": 35710, "Ġbowel": 35711, "ĠUniform": 35712, "Ġ...\"": 35713, "Ġpurge": 35714, "Ġpicnic": 35715, "ĠUmb": 35716, "ĠUPDATE": 35717, "ĠSapphire": 35718, "ĠStall": 35719, "learn": 35720, "Ġobjectively": 35721, "Ġobliter": 35722, "Ġloophole": 35723, "Ġjourneys": 35724, "Ġomission": 35725, "Pros": 35726, "ĠSidney": 35727, "ploma": 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"Ġnicknamed": 35786, "Ġappe": 35787, "Ġphotographic": 35788, "Ġcorpus": 35789, "reference": 35790, "ĠTrog": 35791, "Unt": 35792, ")=(": 35793, "ĠLatvia": 35794, "Ġactivating": 35795, "Ġlicensee": 35796, "Ġdisparities": 35797, "ĠNewsletter": 35798, "ãĥĥãĥĪ": 35799, "Ġfreeing": 35800, "ĠJeep": 35801, "ĠPerception": 35802, "insk": 35803, "Ġsilicone": 35804, "ĠHayden": 35805, "Lean": 35806, "ĠSuzuki": 35807, "ibrarian": 35808, "668": 35809, "Ġspor": 35810, "Ġcorrelations": 35811, "aghetti": 35812, "Ġtuber": 35813, "ĠIPCC": 35814, "ilus": 35815, "ĠVu": 35816, "Ġwealthiest": 35817, "ĠCarbuncle": 35818, "anza": 35819, "Ġfooled": 35820, "ĠZur": 35821, "Ġdaddy": 35822, "rano": 35823, "ilian": 35824, "Ġknockout": 35825, "fman": 35826, "required": 35827, "ĠWikileaks": 35828, "ĠDuffy": 35829, "ONT": 35830, "Ġinsol": 35831, "ĠObjects": 35832, "Ġbou": 35833, "ĠNordic": 35834, "ĠInsert": 35835, "scan": 35836, "Ġdancers": 35837, "Ġidiots": 35838, "majority": 35839, "ĠNeville": 35840, "ĠFreeBSD": 35841, "Ġtart": 35842, "panic": 35843, "690": 35844, "Ġcocoa": 35845, "Ġsampled": 35846, "Ġlookup": 35847, "Indust": 35848, "Ġinjections": 35849, "genre": 35850, "Ġau": 35851, "Ġroadway": 35852, "Ġgenitals": 35853, "Kind": 35854, "ĠExaminer": 35855, "ĠYaz": 35856, "Fresh": 35857, "Ġparalysis": 35858, "ĠAluminum": 35859, "Ġreap": 35860, "oké": 35861, "Ġsloppy": 35862, "ĠTunnel": 35863, "posium": 35864, "nery": 35865, "enic": 35866, "Ġherbal": 35867, "ĠOuter": 35868, "ĠBuilder": 35869, "Ġincur": 35870, "Ġideologies": 35871, "Ġbackups": 35872, "consuming": 35873, "ĠDetect": 35874, "deck": 35875, "ĠKNOW": 35876, "ĠGret": 35877, "ĠMIC": 35878, "Ġtoughness": 35879, "ĠExhibit": 35880, "Ġhive": 35881, "Les": 35882, "ĠSCHOOL": 35883, "ĠAtari": 35884, "alde": 35885, "ĠNull": 35886, "andestine": 35887, "mouse": 35888, "Ġbrigade": 35889, "489": 35890, "Ġrevol": 35891, "ĠLawson": 35892, "ĠWah": 35893, "opoly": 35894, "ebted": 35895, "ĠSaunders": 35896, "Ġ313": 35897, "ĠWinc": 35898, "Ġtaboo": 35899, "ĠHelmet": 35900, "Ġwedge": 35901, "chip": 35902, "ĠTina": 35903, "bg": 35904, "Ġinfuri": 35905, "rn": 35906, "Ġanomalies": 35907, "ĠSync": 35908, "ĠExam": 35909, "ĠCommit": 35910, "ĠDiary": 35911, "ĠALSO": 35912, "ĠDebor": 35913, "omedical": 35914, "Ġcomprehension": 35915, "655": 35916, "Ġempowering": 35917, "Ġire": 35918, "Ġjuices": 35919, "ĠETH": 35920, "ĠBoxing": 35921, "=\"/": 35922, "Ġfacilitated": 35923, "poke": 35924, "ĠParsons": 35925, "ĠModer": 35926, "travel": 35927, "Ġcivilizations": 35928, "Ġlibertarians": 35929, "Ġrune": 35930, "ĠClarks": 35931, "athed": 35932, "Ġcampaigners": 35933, "ĠDispatch": 35934, "ĠFahrenheit": 35935, "ĠCapcom": 35936, "----------": 35937, "Ġlace": 35938, "Ġdraining": 35939, "Ġliner": 35940, "ĠArtificial": 35941, "én": 35942, "task": 35943, "]).": 35944, "ĠGMO": 35945, "ĠOperator": 35946, "ordinary": 35947, "ĠInfluence": 35948, "ĠUps": 35949, "Ġpotency": 35950, "ussen": 35951, "ospons": 35952, "ĠSwim": 35953, "ĠDeadline": 35954, "Unity": 35955, "Ġculinary": 35956, "Ġenlightenment": 35957, "Ġwearer": 35958, "Ġmined": 35959, "Ġply": 35960, "Ġincest": 35961, "ĠDVDs": 35962, "Walk": 35963, "BTC": 35964, "Trade": 35965, "Ġdeval": 35966, "iband": 35967, "ĠOversight": 35968, "Palestinian": 35969, "Ġdart": 35970, "Ġmul": 35971, "LR": 35972, "Ġremovable": 35973, "ĠRealms": 35974, "ìĿ": 35975, "Ġmiscar": 35976, "ĠVulkan": 35977, "685": 35978, "ère": 35979, "ĠSap": 35980, "Ġmerging": 35981, "ĠCarly": 35982, "chester": 35983, "Ġbrisk": 35984, "Ġluxurious": 35985, "ĠGenerator": 35986, "Ġbitterness": 35987, "Ġedible": 35988, "Ġ243": 35989, "TG": 35990, "Ġrectangle": 35991, "WithNo": 35992, "below": 35993, "Jenn": 35994, "Ġdarkest": 35995, "Ġhitch": 35996, "Ġdosage": 35997, "Ġscaven": 35998, "ĠKeller": 35999, "ĠIllustrated": 36000, "Certainly": 36001, "ĠMavericks": 36002, "Marginal": 36003, "Ġdiarrhea": 36004, "Ġenormously": 36005, "Ġ999": 36006, "shr": 36007, "quart": 36008, "Ġadamant": 36009, "ĠMew": 36010, "Ġrenovation": 36011, 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36068, "ĠRebels": 36069, "ĠQC": 36070, "ĠAuction": 36071, "xia": 36072, "ikk": 36073, "bred": 36074, "Ġinsertion": 36075, "Ġcoarse": 36076, "dB": 36077, "SEE": 36078, "ĠZap": 36079, "ĠFoo": 36080, "Ġcontempor": 36081, "ĠQuarterly": 36082, "otions": 36083, "ĠAlchemist": 36084, "ĠTrey": 36085, "ĠDuo": 36086, "Sweet": 36087, "804": 36088, "ĠGiov": 36089, "Ġfunn": 36090, "Nin": 36091, "hoff": 36092, "Ġramifications": 36093, "Ġ1922": 36094, "ĠExperts": 36095, "azes": 36096, "Ġgarments": 36097, "arial": 36098, "ĠNab": 36099, "Ġ257": 36100, "ĠVed": 36101, "Ġhumorous": 36102, "ĠPompe": 36103, "Ġnylon": 36104, "Ġlurking": 36105, "ĠSergey": 36106, "ĠMattis": 36107, "Ġmisogyny": 36108, "ĠComponents": 36109, "ĠWatching": 36110, "ĠFolk": 36111, "ractical": 36112, "Bush": 36113, "Ġtaped": 36114, "Ġgrouping": 36115, "Ġbeads": 36116, "Ġ2048": 36117, "Ġcondu": 36118, "querque": 36119, "Reading": 36120, "Ġgrievances": 36121, "Ultra": 36122, "Ġendpoint": 36123, "Hig": 36124, "ĠStatic": 36125, "ĠScarborough": 36126, "Lua": 36127, "ĠMessi": 36128, "aqu": 36129, "ĠPsyNet": 36130, "ĠRudd": 36131, "Ġavenue": 36132, "vp": 36133, "Jer": 36134, "Ġshady": 36135, "ĠResist": 36136, "ĠArtemis": 36137, "Ġcareless": 36138, "Ġbrokers": 36139, "Ġtemperament": 36140, "Ġ520": 36141, "Tags": 36142, "ĠTurning": 36143, "Ġuttered": 36144, "Ġpedd": 36145, "Ġimprovised": 36146, "Ġ:(": 36147, "Ġtabl": 36148, "Ġplains": 36149, "1600": 36150, "pressure": 36151, "ĠEssence": 36152, "margin": 36153, "friends": 36154, "ĠRestoration": 36155, "Ġpollut": 36156, "ĠPoker": 36157, "ĠAugustine": 36158, "ĠCIS": 36159, "ĠSEAL": 36160, "orama": 36161, "Ġthwart": 36162, "seek": 36163, "Ġpagan": 36164, "º": 36165, "cpu": 36166, "Ġgarn": 36167, "Ġassortment": 36168, "ĠILCS": 36169, "tower": 36170, "Recommended": 36171, "Ġunborn": 36172, "ĠRandomRedditor": 36173, "ĠRandomRedditorWithNo": 36174, "Ġparalyzed": 36175, "Ġeruption": 36176, "Ġintersect": 36177, "ĠStoke": 36178, "ĠSco": 36179, "Bind": 36180, "å¾": 36181, "ĠPNG": 36182, "ĠNegative": 36183, "ĠNOAA": 36184, "Leon": 36185, "Ġalloy": 36186, "ĠLama": 36187, "ĠDiversity": 36188, "575": 36189, "Ġunderestimated": 36190, "ĠScor": 36191, "Ġmural": 36192, "Ġbusted": 36193, "soon": 36194, "lif": 36195, "Ġnonex": 36196, "Ġallergy": 36197, "ĠUnderworld": 36198, "ĠRays": 36199, "ĠBlasio": 36200, "Ġhrs": 36201, "ĠDir": 36202, "Ġ327": 36203, "byter": 36204, "Ġreplacements": 36205, "Ġactivates": 36206, "rived": 36207, "MH": 36208, "Ġpans": 36209, "ĠHI": 36210, "Ġlongitudinal": 36211, "Ġnuisance": 36212, "aler": 36213, "Ġswell": 36214, "ĠSigned": 36215, "sci": 36216, "ĠIsles": 36217, "ĠAGA": 36218, "Ġdefiant": 36219, "Ġsonic": 36220, "ocon": 36221, "KC": 36222, "ĠAim": 36223, "tie": 36224, "ahah": 36225, "ĠmL": 36226, "DX": 36227, "Ġbisc": 36228, "ĠBillboard": 36229, "ĠSYSTEM": 36230, "NEY": 36231, "gaard": 36232, "Ġdistressed": 36233, "formerly": 36234, "Alan": 36235, "Ġchefs": 36236, "Ġoptics": 36237, "ĠComet": 36238, "ĠAMC": 36239, "Ġredesigned": 36240, "irmation": 36241, "Ġsightings": 36242, "382": 36243, "311": 36244, "ĠWB": 36245, "Ġcontraction": 36246, "ĠTOTAL": 36247, "Dual": 36248, "Ġstartled": 36249, "Ġunderstandably": 36250, "Ġsunglasses": 36251, "ETHOD": 36252, "Ġdocker": 36253, "Ġsurfing": 36254, "ĠHEL": 36255, "ĠSlack": 36256, "tones": 36257, "Ġshalt": 36258, "Visual": 36259, "498": 36260, "Department": 36261, "cussion": 36262, "Ġunrestricted": 36263, "Ġtad": 36264, "Ġrename": 36265, "employed": 36266, "Ġeducating": 36267, "Ġgrinned": 36268, "bedroom": 36269, "ĠActivities": 36270, "ĠVelvet": 36271, "ĠSWAT": 36272, "Ġshuffle": 36273, "igor": 36274, "Ġsaturation": 36275, "Finding": 36276, "cream": 36277, "icter": 36278, "Ġvodka": 36279, "tracking": 36280, "tec": 36281, "Ġforeground": 36282, "iesta": 36283, "Ġvehement": 36284, "ĠECB": 36285, "ĠTie": 36286, "Ey": 36287, "Ġturtles": 36288, "ĠRailroad": 36289, "ĠKatz": 36290, "ĠFrames": 36291, "Ġmenace": 36292, "ĠFellowship": 36293, "ĠEssential": 36294, "uggish": 36295, "Ġdrip": 36296, "chwitz": 36297, "ĠKyoto": 36298, "sb": 36299, "ĠNina": 36300, "Parameter": 36301, "Ġalarms": 36302, "ĠClaud": 36303, "Ġpioneering": 36304, "Ġchiefly": 36305, "ĠScream": 36306, "Collection": 36307, "Ġthankfully": 36308, "ĠRonaldo": 36309, "åŃIJ": 36310, "strip": 36311, "ĠDisneyland": 36312, "commercial": 36313, "Seeing": 36314, "Soul": 36315, "Ġevacuate": 36316, "Ġciv": 36317, "ĠAshe": 36318, "Ġdivides": 36319, "ĠDagger": 36320, "rehensive": 36321, "Ġberries": 36322, "ĠDF": 36323, "Ġsushi": 36324, "Ġplurality": 36325, "WI": 36326, "Ġdisadvantaged": 36327, "Ġbattalion": 36328, "obiles": 36329, "451": 36330, "Ġcling": 36331, "Ġundeniable": 36332, "ĠLounge": 36333, "Ġhaunt": 36334, "phe": 36335, "Ġquantify": 36336, "Ġdiffered": 36337, "Ġ[*]": 36338, "ĠViz": 36339, "cum": 36340, "slave": 36341, "Ġvideog": 36342, "Ġquar": 36343, "Ġbundles": 36344, "ĠAlonso": 36345, "tackle": 36346, "Ġneuronal": 36347, "Ġlandslide": 36348, "confirmed": 36349, "ĠDepth": 36350, "Ġrenewables": 36351, "Bear": 36352, "ĠMacedonia": 36353, "Ġjerseys": 36354, "Ġbunk": 36355, "ĠSpawn": 36356, "ĠControls": 36357, "ĠBuchanan": 36358, "Ġrobotics": 36359, "Ġemphasizing": 36360, "ĠTutorial": 36361, "hyp": 36362, "iston": 36363, "Ġmonumental": 36364, "æ°": 36365, "ĠCarry": 36366, "Ġtbsp": 36367, "enance": 36368, "Hill": 36369, "arthed": 36370, "Ġrotten": 36371, "Dean": 36372, "Ġtwisting": 36373, "Ġgoodwill": 36374, "Ġimmersion": 36375, "Living": 36376, "Ġbrushes": 36377, "ĠCGI": 36378, "ĠAtk": 36379, "traditional": 36380, "Ġphantom": 36381, "ĠStamina": 36382, "Ġexpansions": 36383, "ĠMarin": 36384, "Ġembarked": 36385, "ĠEg": 36386, "intestinal": 36387, "ĠPEOPLE": 36388, "ĠBooth": 36389, "ĠAppalach": 36390, "Ġrelegated": 36391, "VT": 36392, "MIT": 36393, "Ġmuster": 36394, "Ġwithdrawing": 36395, "Ġmicroscope": 36396, "ĠGathering": 36397, "ĠCrescent": 36398, "ĠArgentine": 36399, "ĠDecre": 36400, "ĠDominic": 36401, "Ġbuds": 36402, "antage": 36403, "ĠIon": 36404, "Ġwidened": 36405, "ONSORED": 36406, "ĠGloves": 36407, "iannopoulos": 36408, "razen": 36409, "feel": 36410, "Ġrepayment": 36411, "Ġhindsight": 36412, "ĠREALLY": 36413, "ĠPistol": 36414, "ĠBrah": 36415, "Ġwatts": 36416, "Ġsurvives": 36417, "Ġflurry": 36418, "issy": 36419, "Alert": 36420, "ĠUruguay": 36421, "Phoenix": 36422, "Slow": 36423, "ĠGrave": 36424, "ĠFir": 36425, "Ġmanageable": 36426, "Ġtariff": 36427, "ĠUDP": 36428, "ĠPistons": 36429, "ĠNigerian": 36430, "Ġstrikeouts": 36431, "Ġcosmetics": 36432, "whelming": 36433, "fab": 36434, "cape": 36435, "proxy": 36436, "Ġrethink": 36437, "Ġovercoming": 36438, "simple": 36439, "Ġwoo": 36440, "Ġdistracting": 36441, "ĠStanton": 36442, "ĠTulsa": 36443, "ĠDock": 36444, "659": 36445, "Ġdiscord": 36446, "ĠEmacs": 36447, "ĠVes": 36448, "ĠROB": 36449, "Ġreassuring": 36450, "Ġconsortium": 36451, "Muslims": 36452, "321": 36453, "Ġprompts": 36454, "sei": 36455, "ĠHitch": 36456, "imposed": 36457, "ĠFool": 36458, "Ġindiscrim": 36459, "wrong": 36460, "buquerque": 36461, "Davis": 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"Ġsupremacists": 37084, "ĠBillion": 37085, "Ġregimen": 37086, "innacle": 37087, "Ġrequisite": 37088, "angan": 37089, "ĠBurlington": 37090, "ainment": 37091, "ĠObjective": 37092, "omsky": 37093, "GV": 37094, "Ġunilateral": 37095, "Ġtc": 37096, "Ġhires": 37097, "mental": 37098, "Ġinvoluntary": 37099, "Ġtranspl": 37100, "ĠASCII": 37101, "¨": 37102, "Events": 37103, "Ġdoubted": 37104, "ĠKaplan": 37105, "ĠCourage": 37106, "igon": 37107, "ĠManaging": 37108, "ĠTart": 37109, "Ġfalsehood": 37110, "ĠViolet": 37111, "Ġairs": 37112, "Ġfertilizer": 37113, "Britain": 37114, "Ġaquatic": 37115, "ouf": 37116, "Words": 37117, "ĠHartford": 37118, "Ġevenings": 37119, "ĠVengeance": 37120, "quite": 37121, "Gall": 37122, "ĠPret": 37123, "Ġpdf": 37124, "ĠLM": 37125, "ĠSochi": 37126, "ĠIntercept": 37127, "920": 37128, "Ġprofitability": 37129, "ĠIdle": 37130, "ĠMacDonald": 37131, "ĠEstablishment": 37132, "umsy": 37133, "Ġgatherings": 37134, "ĠNaj": 37135, "Charlie": 37136, "Ġascent": 37137, "ĠProtector": 37138, 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"ĠCutler": 37194, "ĠAUTH": 37195, "Ġsplendid": 37196, "Ġpreventive": 37197, "ĠDudley": 37198, "ifacts": 37199, "uminati": 37200, "ĠYin": 37201, "Ġadmon": 37202, "ĠVag": 37203, "Ġinverted": 37204, "Ġhastily": 37205, "ĠHague": 37206, "Lyn": 37207, "Ġledger": 37208, "Ġastronomical": 37209, "getting": 37210, "Ġcirca": 37211, "ĠCic": 37212, "ĠTennis": 37213, "Limited": 37214, "Ġdru": 37215, "ĠBYU": 37216, "Ġtravellers": 37217, "Ġpane": 37218, "ĠIntro": 37219, "Ġpatiently": 37220, "Ġaiding": 37221, "Ġloos": 37222, "ĠTough": 37223, "Ġ293": 37224, "Ġconsumes": 37225, "SourceFile": 37226, "Ġ\"\"\"": 37227, "Ġbonding": 37228, "Ġtilted": 37229, "Ġmenstrual": 37230, "ĠCelestial": 37231, "ULAR": 37232, "Plugin": 37233, "Ġrisking": 37234, "Naz": 37235, "ĠRiyadh": 37236, "Ġaccredited": 37237, "Ġskirm": 37238, "éĽ": 37239, "Ġexaminer": 37240, "Ġmessing": 37241, "Ġnearing": 37242, "ĠChern": 37243, "ĠBeckham": 37244, "Ġswapped": 37245, "Ġgoose": 37246, "Kay": 37247, "Ġlofty": 37248, "ĠWallet": 37249, 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"ĠjQuery": 37420, "ĠBAT": 37421, "tesque": 37422, "Ġvertex": 37423, "pure": 37424, "frey": 37425, "ãĤº": 37426, "dos": 37427, "Ġtyph": 37428, "Ġcull": 37429, "Ġeq": 37430, "Ġdecon": 37431, "Ġtossing": 37432, "Ġdisparate": 37433, "ĠBrigham": 37434, "printf": 37435, "ledged": 37436, "Ġsund": 37437, "Ġcozy": 37438, "Ġhepatitis": 37439, "performing": 37440, "Ġaval": 37441, "ĠGG": 37442, "future": 37443, "Ġpetertodd": 37444, "ĠKosovo": 37445, "Ġmagnets": 37446, "Already": 37447, "ĠEdison": 37448, "ĠCeres": 37449, "ĠRAID": 37450, "Ġbrilliance": 37451, "576": 37452, "Ġderives": 37453, "Ġhypertension": 37454, "ĠÎĶ": 37455, "Ġlambda": 37456, "Ġflair": 37457, "Ġmissionaries": 37458, "Ġrapes": 37459, "ĠStarter": 37460, "ĠMonths": 37461, "Ġdefy": 37462, "Ġseismic": 37463, "ĠRaphael": 37464, "Ġeurozone": 37465, "656": 37466, "zsche": 37467, "Ġscratched": 37468, "Ġbows": 37469, "ĠLennon": 37470, "ĠGaia": 37471, "Ġdripping": 37472, "facts": 37473, "Ale": 37474, "Ġfrogs": 37475, "ĠBreast": 37476, "ogeneity": 37477, "ĠProsecutor": 37478, "Ġamplified": 37479, "ĠHodg": 37480, "ĠFn": 37481, "Thousands": 37482, "ĠNIH": 37483, "ĠMonitoring": 37484, "FTWARE": 37485, "ĠPriebus": 37486, "ĠGrowing": 37487, "hunter": 37488, "Ġdiagnose": 37489, "ĠMald": 37490, "ĠLR": 37491, "Ġcrowned": 37492, "Ġbursting": 37493, "Ġdissolution": 37494, "javascript": 37495, "Ġusefulness": 37496, "ĠExecution": 37497, ":(": 37498, "ĠIvory": 37499, "aah": 37500, "Ġpersecuted": 37501, "violence": 37502, "istas": 37503, "ĠCrate": 37504, "Ġimpulses": 37505, "ĠSpani": 37506, "edes": 37507, "Handle": 37508, "ĠZerg": 37509, "thinkable": 37510, "Lastly": 37511, "Ġspontaneously": 37512, "Ġinconvenient": 37513, "Ġdismissing": 37514, "Ġplotted": 37515, "Ġeighty": 37516, "Ġ737": 37517, "rish": 37518, "ĠThornton": 37519, "atham": 37520, "Ġsitcom": 37521, "Ven": 37522, "Recipe": 37523, "tel": 37524, "lund": 37525, "Ġclears": 37526, "ĠSasuke": 37527, "Ġ258": 37528, "Ġopting": 37529, "Ġenraged": 37530, "esthetic": 37531, "ĠAe": 37532, "uchs": 37533, "Prep": 37534, "Flow": 37535, "Ġrunoff": 37536, "ĠEating": 37537, "ĠGiles": 37538, "ĠActing": 37539, "resources": 37540, "ibaba": 37541, "Ġrpm": 37542, "Ġskewed": 37543, "ĠBlanc": 37544, "ĠSakuya": 37545, "Ġhotter": 37546, "Ġ1924": 37547, "opian": 37548, "cko": 37549, "Ġcrumbling": 37550, "Ġcaptains": 37551, "ĠAppropriations": 37552, "leaders": 37553, "dropping": 37554, "anuts": 37555, "Ġreversing": 37556, "ĠPose": 37557, "ĠSek": 37558, "Scot": 37559, "ĠIdea": 37560, "cise": 37561, "ĠSlovenia": 37562, "Ġ317": 37563, "Doctor": 37564, "Ġcrocod": 37565, "aldi": 37566, "Sea": 37567, "ĠFarrell": 37568, "Ġmercenaries": 37569, "ĠRNC": 37570, "ĠGuess": 37571, "Ġpacing": 37572, "Machine": 37573, "StreamerBot": 37574, "ĠCharity": 37575, "Ġ298": 37576, "Ġcannons": 37577, "ĠToby": 37578, "TPPStreamerBot": 37579, "ĠPassion": 37580, "cfg": 37581, "Thom": 37582, "Ġbadges": 37583, "ĠBernstein": 37584, ".âĢĵ": 37585, "ĠPOP": 37586, "ĠConj": 37587, "Ġinitialization": 37588, "Ġbiodiversity": 37589, "Dub": 37590, "Ġfeudal": 37591, "Ġdisclaimer": 37592, "Ġcrow": 37593, "Ġignition": 37594, "arf": 37595, "SHA": 37596, "ĠkHz": 37597, "hazard": 37598, "ĠArtists": 37599, "oeuv": 37600, "679": 37601, "ĠRudy": 37602, "Nine": 37603, "ĠRamadan": 37604, "å½": 37605, "itto": 37606, "Ġadrenaline": 37607, "Cert": 37608, "Ġsmelled": 37609, "Ġimpunity": 37610, "Ġagendas": 37611, "ĠReborn": 37612, "ĠConcent": 37613, "ĠSeems": 37614, "Ġomega": 37615, "ĠDustin": 37616, "Ġbacker": 37617, "ĠSauce": 37618, "ĠBoyle": 37619, "WIN": 37620, "Ġspins": 37621, "Ġpauses": 37622, "upt": 37623, "Ġshredded": 37624, "Ġstrapped": 37625, "ĠCorruption": 37626, "Ġscratches": 37627, "Ġni": 37628, "Ġattire": 37629, "ĠSAF": 37630, "FactoryReloaded": 37631, "ĠIPS": 37632, "Ġ(%": 37633, "Ġseminar": 37634, "focus": 37635, "civil": 37636, "Ġ1860": 37637, "intosh": 37638, "Ġcontinual": 37639, "Ġabbrevi": 37640, "ĠSok": 37641, "ocobo": 37642, "XM": 37643, "Ġfrantic": 37644, "Ġunavoidable": 37645, "Ġartery": 37646, "Ġannotations": 37647, "bath": 37648, "Climate": 37649, "Ġdors": 37650, "ĠSlide": 37651, "coord": 37652, "ĠReload": 37653, "ĠLDL": 37654, "ĠLovecraft": 37655, "Ġunimagin": 37656, "Ġresembled": 37657, "Ġbarracks": 37658, "np": 37659, "Ġsurrogate": 37660, "Ġcategorized": 37661, "ãĤ©": 37662, "Ġvaccinated": 37663, "Ġdrainage": 37664, "Ġindist": 37665, "ĠWhatsApp": 37666, "Ġ1870": 37667, "olerance": 37668, "invoke": 37669, "amorph": 37670, "Ġreconnect": 37671, "Ġemanc": 37672, "Ġblindness": 37673, "Ġ1280": 37674, "internet": 37675, "collar": 37676, "Ġaltru": 37677, "Ġabyss": 37678, "ĠTRI": 37679, "657": 37680, "Ġinfused": 37681, "HEAD": 37682, "Ġforestry": 37683, "ĠWoody": 37684, "ĠCi": 37685, "wi": 37686, "sam": 37687, "784": 37688, "holiday": 37689, "Ġmogul": 37690, "ĠFees": 37691, "ĠDEN": 37692, "Internal": 37693, "urbed": 37694, "fusc": 37695, "atom": 37696, "ĠIllusion": 37697, "Ġpolled": 37698, "Ġflap": 37699, "Ġcoax": 37700, "LGBT": 37701, "Analy": 37702, "ĠSections": 37703, "ĠCaliforn": 37704, "emn": 37705, "Ġhither": 37706, "ĠNIGHT": 37707, "Ġnailed": 37708, "ĠPipeline": 37709, "391": 37710, "oof": 37711, "ĠPrimal": 37712, "verend": 37713, "Ġslashing": 37714, "Ġretri": 37715, "aviour": 37716, "Ġdeparting": 37717, "gil": 37718, "ISC": 37719, "Ġmidway": 37720, "Ġultrasound": 37721, "Ġbehaving": 37722, "ĠTara": 37723, "classes": 37724, "Virtual": 37725, "ĠColonial": 37726, "Ġstripping": 37727, "Ġorchestrated": 37728, "ĠGraves": 37729, "452": 37730, "ĠIronically": 37731, "ĠWriters": 37732, "Ġlends": 37733, "ĠManz": 37734, "Ġraven": 37735, "Ġoxidative": 37736, "Ġ266": 37737, "ELF": 37738, "actually": 37739, "ascar": 37740, "Draft": 37741, "Ġfavourable": 37742, "Ġhumiliating": 37743, "Ġfidelity": 37744, "ĠHof": 37745, "ĠXuan": 37746, "496": 37747, "Ġlayered": 37748, "atis": 37749, "790": 37750, "Ġpaycheck": 37751, "iton": 37752, "Kar": 37753, "ĠVMware": 37754, "ĠFarmer": 37755, "Ġservic": 37756, "glomer": 37757, "Ġslump": 37758, "ĠFabric": 37759, "ĠDOC": 37760, "esting": 37761, "Ġreassure": 37762, "Ġphyl": 37763, "volt": 37764, "itory": 37765, "Rules": 37766, "Ġoxidation": 37767, "Ġprized": 37768, "Ġmistress": 37769, "ĠDjango": 37770, "WARN": 37771, "åij": 37772, "Ġencode": 37773, "ĠFeedback": 37774, "Ġstupidity": 37775, "Ian": 37776, "ĠYugoslavia": 37777, "ר": 37778, "acl": 37779, "UTE": 37780, "1977": 37781, "Ġqualifies": 37782, "Ġpulses": 37783, "pretty": 37784, "Ġfroze": 37785, "Ġss": 37786, "Iterator": 37787, "Ġurgently": 37788, "Ġmailed": 37789, "ĠCham": 37790, "Ġsustaining": 37791, "Ġbasil": 37792, "Ġpuppies": 37793, "ilant": 37794, "ĠPLEASE": 37795, "lap": 37796, "aceous": 37797, "Fear": 37798, "ĠMastery": 37799, "automatic": 37800, "ĠTAG": 37801, "Ġantim": 37802, "agles": 37803, "473": 37804, "frames": 37805, "Ġwhispers": 37806, "ĠWhoever": 37807, "Ġbravery": 37808, "ĠUKIP": 37809, "ractions": 37810, "\"\"\"": 37811, "Ġtame": 37812, "Ġparted": 37813, "everything": 37814, "CONT": 37815, "Ġindebted": 37816, "Ġaddr": 37817, "rek": 37818, "IRED": 37819, "Ġeminent": 37820, "clinton": 37821, "Ġousted": 37822, "Ġreviewer": 37823, "Ġmeltdown": 37824, "Ġrearr": 37825, "ĠYao": 37826, "thereal": 37827, "abyte": 37828, "Ġstumbling": 37829, "Ġbatches": 37830, "Ġ259": 37831, "Ġcontraceptive": 37832, "Ġprostitute": 37833, "ensis": 37834, "Decl": 37835, "ĠStrikes": 37836, "Military": 37837, "ĠOath": 37838, "vacc": 37839, "ppings": 37840, "052": 37841, "ĠpartName": 37842, "amping": 37843, "Reports": 37844, "KI": 37845, "CHR": 37846, "Ġsubtly": 37847, "swers": 37848, "Blake": 37849, "usual": 37850, "Ġcontestants": 37851, "Ġcartridges": 37852, "ĠGREAT": 37853, "Ġblush": 37854, "ĠâĢº": 37855, "472": 37856, "Ġreasoned": 37857, "ãĥ¤": 37858, "paralleled": 37859, "Ġdyn": 37860, "agate": 37861, "Ġnightly": 37862, "åĨ": 37863, "556": 37864, "Ġsemantic": 37865, "ĠAdvoc": 37866, "Ġ!!": 37867, "Ġdisagrees": 37868, "ĠBW": 37869, "Veh": 37870, "Ġharming": 37871, "Ġembraces": 37872, "Ġstrives": 37873, "Ġinland": 37874, "ĠKard": 37875, "Ġheats": 37876, "ĠGinny": 37877, "utan": 37878, "ernaut": 37879, "ylene": 37880, "ĠElev": 37881, "JD": 37882, "Ġhars": 37883, "ĠStarr": 37884, "Ġskysc": 37885, "Ġcollaborators": 37886, "Usually": 37887, "Ġrevolutions": 37888, "ĠSTATS": 37889, "Ġdismantle": 37890, "Ġconfidently": 37891, "Ġkinetic": 37892, "Ali": 37893, "Ġpercentile": 37894, "Ġextracting": 37895, "illian": 37896, "estead": 37897, "Ġphysicists": 37898, "ĠMarshal": 37899, "Ġfellowship": 37900, "Ġdashed": 37901, "ĠUR": 37902, "ĠSioux": 37903, "ĠCompact": 37904, "amide": 37905, "Python": 37906, "ĠLeigh": 37907, "ĠPharmac": 37908, "istrates": 37909, "herical": 37910, "Ġfue": 37911, "ĠEmin": 37912, "Ġ({": 37913, "ĠNeighborhood": 37914, "Ġdisrupting": 37915, "ĠDup": 37916, "Ġgland": 37917, "ĠSev": 37918, "ĠMarian": 37919, "argon": 37920, "ĠDund": 37921, "Ġ<!--": 37922, "Ġstrand": 37923, "Ġstadiums": 37924, "zos": 37925, "Ġpsychosis": 37926, "ĠRack": 37927, "Ġbrilliantly": 37928, "ï¸ı": 37929, "Ġsubmerged": 37930, "ĠInstit": 37931, "ĠChow": 37932, "Ġcages": 37933, "ĠHats": 37934, "ĠUrs": 37935, "Ġdiluted": 37936, "usat": 37937, "ienne": 37938, "ĠMembership": 37939, "ĠBurk": 37940, "Ġie": 37941, "Ġarchetype": 37942, "Drug": 37943, "ulton": 37944, "ĠSpock": 37945, "ĠMcKay": 37946, "ĠDepend": 37947, "Featured": 37948, "Soc": 37949, "1978": 37950, "ĠBere": 37951, "Ġrelentlessly": 37952, "Ġcrippling": 37953, "Ġarthritis": 37954, "çĶŁ": 37955, "ĠTropical": 37956, "ĠBulg": 37957, "ĠCheryl": 37958, "Ġadmirable": 37959, "Ġsubtitle": 37960, "Override": 37961, "Ġoriginating": 37962, "ĠCCP": 37963, "Ġswore": 37964, "ĠSole": 37965, "ĠDisorders": 37966, "329": 37967, "Ġprocession": 37968, "Ġrefurb": 37969, "Ġimmersed": 37970, "requently": 37971, "Ġskeptics": 37972, "Ġceramic": 37973, "mitter": 37974, "enstein": 37975, "belt": 37976, "ĠTIT": 37977, "bidden": 37978, "Ġfir": 37979, "mist": 37980, ">]": 37981, "Ġweave": 37982, "ĠParadox": 37983, "Ġentrusted": 37984, "ĠBarclays": 37985, "Ġnovelist": 37986, "ogie": 37987, "806": 37988, "Ġninety": 37989, "Ġdisagreements": 37990, "@@@@@@@@": 37991, "ĠAuschwitz": 37992, "cars": 37993, "ĠLET": 37994, "tub": 37995, "arantine": 37996, "POS": 37997, "Ġbackstory": 37998, "Ġcheerful": 37999, "ĠRag": 38000, "eka": 38001, "biased": 38002, "Ġinexperienced": 38003, "akra": 38004, "ĠWitt": 38005, "tan": 38006, "Ġrapist": 38007, "Ġplateau": 38008, "chal": 38009, "ĠInquis": 38010, "expression": 38011, "Ġcipher": 38012, "Ġshaving": 38013, "adden": 38014, "rely": 38015, "(\\": 38016, "isma": 38017, "ĠRegulatory": 38018, "CHAR": 38019, "ilyn": 38020, "NVIDIA": 38021, "GU": 38022, "Ġmurm": 38023, "laus": 38024, "Christopher": 38025, "Ġcontractual": 38026, "ĠProxy": 38027, "ĠJaime": 38028, "ĠMethodist": 38029, "Ġstewards": 38030, "sta": 38031, "peria": 38032, "Ġphysiology": 38033, "Ġbumped": 38034, "Ġfructose": 38035, "Australian": 38036, "ĠMetallic": 38037, "ĠMasquerade": 38038, "arb": 38039, "Ġpromul": 38040, "Ġdownfall": 38041, "Ġbutcher": 38042, "Ġbour": 38043, "ĠINFORMATION": 38044, "ĠBis": 38045, "pects": 38046, "adena": 38047, "Ġcontemplating": 38048, "aroo": 38049, "centered": 38050, "ĠPeaks": 38051, "Used": 38052, "Ġmodem": 38053, "Ġgenders": 38054, "Ġ8000": 38055, "371": 38056, "Ġmaternity": 38057, "ĠRaz": 38058, "Ġrocking": 38059, "Ġhandguns": 38060, "ĠDACA": 38061, "Autom": 38062, "ĠNile": 38063, "Ġtumult": 38064, "ĠBenefit": 38065, "ĠApproach": 38066, "workshop": 38067, "ĠLeaving": 38068, "Ger": 38069, "instead": 38070, "Ġvibrations": 38071, "Ġrepositories": 38072, "497": 38073, "ĠAunt": 38074, "ĠJub": 38075, "ĠExpedition": 38076, "Alpha": 38077, "Ġsans": 38078, "Ġoverdue": 38079, "Ġovercrowd": 38080, "Ġlegislatures": 38081, "Ġpaternal": 38082, "ĠLeonardo": 38083, "Ġexpressive": 38084, "Ġdistractions": 38085, "Ġsilenced": 38086, "trust": 38087, "Ġbiking": 38088, "Ġ560": 38089, "Ġpropriet": 38090, "Ġimposition": 38091, "Ġconglomer": 38092, "Ġ=================================================================": 38093, "ĠTeaching": 38094, "ĠYose": 38095, "intensive": 38096, "Town": 38097, "Ġtrolling": 38098, "ĠGrac": 38099, "ĠASUS": 38100, "Yo": 38101, "Ġspecials": 38102, "ĠNeph": 38103, "ĠGodzilla": 38104, "Database": 38105, "ĠHegel": 38106, "Ġ272": 38107, "1976": 38108, "ĠGloria": 38109, "Ġdisemb": 38110, "ĠInvestigations": 38111, "ĠBane": 38112, "agements": 38113, "Strange": 38114, "Ġtreasury": 38115, "ĠPlays": 38116, "Ġundesirable": 38117, "Ġwidening": 38118, "Ġverbally": 38119, "Ġinfancy": 38120, "Ġcutter": 38121, "fml": 38122, "Ġ2100": 38123, "prototype": 38124, "fine": 38125, "Ġdecriminal": 38126, "Ġdysfunctional": 38127, "Ġbesie": 38128, "ĠErnst": 38129, "zeb": 38130, "Ġnortheastern": 38131, "Ġaust": 38132, "porate": 38133, "ĠMarlins": 38134, "Ġsegregated": 38135, "eworld": 38136, "ĠMaher": 38137, "Ġtraverse": 38138, "Ġmonastery": 38139, "urgy": 38140, "Gear": 38141, "sand": 38142, "Compl": 38143, "ĠEMP": 38144, "Ġplent": 38145, "ĠMercer": 38146, "Ġ276": 38147, "TABLE": 38148, "Configuration": 38149, "Hundreds": 38150, "Ġpric": 38151, "Ġcollaborating": 38152, "ĠParamount": 38153, "ĠCummings": 38154, "Ġ(<": 38155, "Ġrecorder": 38156, "Ġflats": 38157, "Ġ416": 38158, "whose": 38159, "FontSize": 38160, "ĠOrbit": 38161, "YR": 38162, "Ġwrists": 38163, "Ġbakery": 38164, ")}": 38165, "ĠBounty": 38166, "ĠLancaster": 38167, "Ġendings": 38168, "according": 38169, "ĠSalam": 38170, "easy": 38171, "755": 38172, "ĠBurr": 38173, "ĠBarnett": 38174, "onomous": 38175, "Union": 38176, "Ġprecedence": 38177, "ĠScholarship": 38178, "ĠUX": 38179, "Ġrollout": 38180, "Ġboon": 38181, "alm": 38182, "ĠCanter": 38183, "æµ": 38184, "Ġrounding": 38185, "Ġclad": 38186, "Ġvap": 38187, "ĠFeatured": 38188, "isations": 38189, "Ġ540": 38190, "police": 38191, "Ġunsettling": 38192, "Ġdrifting": 38193, "ĠLumia": 38194, "ĠObamaCare": 38195, "ĠFavor": 38196, "Hyper": 38197, "ĠRothschild": 38198, "ĠMiliband": 38199, "analy": 38200, "ĠJuliet": 38201, "Hu": 38202, "Ġrecalling": 38203, "ahead": 38204, "696": 38205, "Ġunfavorable": 38206, "Ġdances": 38207, "Ox": 38208, "Ġlegality": 38209, "Ġ403": 38210, "romancer": 38211, "Ġinquire": 38212, "ĠMoves": 38213, "\\\">": 38214, "ĠVariant": 38215, "ĠMessiah": 38216, "ĠLCS": 38217, "ĠBahá": 38218, "756": 38219, "Ġeyebrow": 38220, "ĠÂ¥": 38221, "ĠMcF": 38222, "ĠForty": 38223, "Mas": 38224, "Ġpanicked": 38225, "Ġtransformations": 38226, "qq": 38227, "Ġrevolves": 38228, "ringe": 38229, "ĠAi": 38230, "axe": 38231, "Ġonward": 38232, "ĠCFR": 38233, "ĠBare": 38234, "login": 38235, "Ġliquids": 38236, "Ġdecomp": 38237, "secondary": 38238, "ilan": 38239, "ĠConvert": 38240, "amiya": 38241, "Ġprosecuting": 38242, "Ġâī¡": 38243, "ĠYorkers": 38244, "ĠByrne": 38245, "slow": 38246, "awei": 38247, "Jean": 38248, "Ġ269": 38249, "ĠSkydragon": 38250, "Ġé": 38251, "ĠNicaragua": 38252, "ĠHuckabee": 38253, "ĠHighly": 38254, "Ġamphib": 38255, "ĠPastor": 38256, "ĠLets": 38257, "Ġblurred": 38258, "Ġvisceral": 38259, "ĠCBO": 38260, "Ġcollaborated": 38261, "zig": 38262, "Legal": 38263, "Ġapartheid": 38264, "Ġbrid": 38265, "Ġpreset": 38266, "ĠDET": 38267, "ĠAMA": 38268, "×Ķ": 38269, "arching": 38270, "aucuses": 38271, "builder": 38272, "Ġpoetic": 38273, "Ġemulator": 38274, "ĠMolecular": 38275, "Ġhonoring": 38276, "iseum": 38277, "Ġtractor": 38278, "ĠCluster": 38279, "ĠCalm": 38280, "aredevil": 38281, "Ġsidewalks": 38282, "Ġviolin": 38283, "Ġgeneralized": 38284, "ĠAlec": 38285, "Ġembargo": 38286, "Ġfastball": 38287, "ĠHTTPS": 38288, "ĠLack": 38289, "ĠChill": 38290, "river": 38291, "Chel": 38292, "ĠSwarm": 38293, "ĠLevine": 38294, "roying": 38295, "Launch": 38296, "Ġkicker": 38297, "Ġadditive": 38298, "ĠDeals": 38299, "Widget": 38300, "containing": 38301, "Ġescalate": 38302, "ĠOPEN": 38303, "Ġtweaked": 38304, "Ġstash": 38305, "Ġsparks": 38306, "ĠEssex": 38307, "ĠEcc": 38308, "Ġconvict": 38309, "Ġblogging": 38310, "IER": 38311, "ĠHL": 38312, "Ġmurderers": 38313, "759": 38314, "ĠHib": 38315, "Ġdepl": 38316, "ĠJord": 38317, "Sac": 38318, "Ġdissect": 38319, "ĠHowe": 38320, "osher": 38321, "Ġcustomizable": 38322, "ĠFranz": 38323, "Ġatro": 38324, "Äĩ": 38325, "Ġ0004": 38326, "Ġoutpost": 38327, "Ross": 38328, "Ġglyphosate": 38329, "ĠHastings": 38330, "ĠBEFORE": 38331, "Ġshove": 38332, "opped": 38333, "ĠScala": 38334, "Ġamulet": 38335, "anian": 38336, "Ġexacerbated": 38337, "Ġeater": 38338, "471": 38339, "UME": 38340, "Ġpulp": 38341, "izontal": 38342, "ĠZam": 38343, "ĠATI": 38344, "immune": 38345, "abytes": 38346, "Ġunnecessarily": 38347, "ĠCAT": 38348, "ĠAxis": 38349, "Ġvisualize": 38350, "Ãī": 38351, "ĠRadical": 38352, "fm": 38353, "Documents": 38354, "ĠForrest": 38355, "Ġcontextual": 38356, "ĠSymbol": 38357, "Ġtentative": 38358, "ĠDOES": 38359, "ĠGoods": 38360, "Ġintermittent": 38361, "}:": 38362, "mediated": 38363, "Ġridicule": 38364, "Ġatheism": 38365, "Ġpathogens": 38366, "ĠMum": 38367, "Ġreintrodu": 38368, "Ġ307": 38369, "iHUD": 38370, "Ġflashlight": 38371, "Ġswearing": 38372, "Ġpengu": 38373, "Bu": 38374, "Ġrotated": 38375, "ĠCrane": 38376, "Ġ());": 38377, "Ġfashionable": 38378, "Ġendorsing": 38379, "463": 38380, ")[": 38381, "Ġingestion": 38382, "Ġcooks": 38383, "Ġ950": 38384, "otomy": 38385, "ĠImam": 38386, "Ġka": 38387, "Ġteaser": 38388, "ĠGhosts": 38389, "ĠãĤµ": 38390, "1969": 38391, "Ïĥ": 38392, "ubby": 38393, "Ġconverter": 38394, "zanne": 38395, "ende": 38396, "ĠPrepar": 38397, "ĠNickel": 38398, "ĠChimera": 38399, "him": 38400, "ĠTyrann": 38401, "ĠSabbath": 38402, "ĠNichols": 38403, "Ġrapt": 38404, "ihar": 38405, "Ġshelling": 38406, "Ġilluminate": 38407, "Ġdentist": 38408, "utor": 38409, "ĠIntegration": 38410, "Ġwhims": 38411, "ĠLiterary": 38412, "Beaut": 38413, "Ġparchment": 38414, "agara": 38415, "Brand": 38416, "Ġderog": 38417, "âĢ¦)": 38418, "ĠNorse": 38419, "Ġunwitting": 38420, "Ġcuc": 38421, "Ġborderline": 38422, "Ġupsetting": 38423, "Ġrecourse": 38424, "Ġdraped": 38425, "ĠRadar": 38426, "Ġcolder": 38427, "ĠPepsi": 38428, "iminary": 38429, "],[": 38430, "658": 38431, "Vi": 38432, "ĠFrem": 38433, "ĠPes": 38434, "Ġveterinary": 38435, "ĠTED": 38436, "ĠEpidem": 38437, "nova": 38438, "kid": 38439, "Ġdevout": 38440, "oct": 38441, "jad": 38442, "Moh": 38443, "ĠPAY": 38444, "Ġgeometric": 38445, "Ġ323": 38446, "Ġcircumference": 38447, "ichick": 38448, "1975": 38449, "ĠYuri": 38450, "ĠShall": 38451, "ĠHover": 38452, "unin": 38453, "Spr": 38454, "Ġgraft": 38455, "ĠHappiness": 38456, "Ġdisadvantages": 38457, "attacks": 38458, "Ġhubs": 38459, "ĠStarCraft": 38460, "éĸ": 38461, "Ġgalleries": 38462, "ĠKorra": 38463, "Ġgroceries": 38464, "ĠGorsuch": 38465, "Ġrapists": 38466, "Ġfungi": 38467, "ĠTyphoon": 38468, "Vector": 38469, "ĠEmpress": 38470, "battle": 38471, "468": 38472, "Ġparasite": 38473, "ĠBomber": 38474, "SG": 38475, "exist": 38476, "ĠPf": 38477, "Ġunse": 38478, "Ġsurgeons": 38479, "Birth": 38480, "ĠUnsure": 38481, "ĠPrinted": 38482, "ĠBehavioral": 38483, "ĠAster": 38484, "Pakistan": 38485, "Ġunethical": 38486, "Ġsv": 38487, "ĠIoT": 38488, "Ġlayouts": 38489, "Pain": 38490, "Ġconstants": 38491, "ĠLW": 38492, "ĠBake": 38493, "Ġtowels": 38494, "Ġdeterioration": 38495, "ĠBolivia": 38496, "Ġblinded": 38497, "ĠWarden": 38498, "ĠMistress": 38499, "Ġonstage": 38500, "Ġclans": 38501, "ĠBEST": 38502, "1960": 38503, "Ġantique": 38504, "Ġrhetorical": 38505, "ĠPercy": 38506, "ĠRwanda": 38507, ",.": 38508, "Bruce": 38509, "Ġtraumat": 38510, "ĠParliamentary": 38511, "Ġfootnote": 38512, "idia": 38513, "ĠLearned": 38514, "seeking": 38515, "genic": 38516, "Ġdimensional": 38517, "Hide": 38518, "èĢħ": 38519, "Ġintrigue": 38520, "inse": 38521, "Ġleases": 38522, "Ġapprentices": 38523, "washing": 38524, "Ġ1926": 38525, "VILLE": 38526, "Ġswoop": 38527, "scl": 38528, "Ġbedrooms": 38529, "onics": 38530, "ĠCrunch": 38531, "compatible": 38532, "Ġincapac": 38533, "ĠYemeni": 38534, "ashtra": 38535, "zhou": 38536, "danger": 38537, "Ġmanifestations": 38538, "ĠDemons": 38539, "AAF": 38540, "Secretary": 38541, "ACTED": 38542, "LOD": 38543, "Ġamy": 38544, "raper": 38545, "ethnic": 38546, "417": 38547, "Ġpositives": 38548, "Ġ273": 38549, "ĠRefugees": 38550, "Ġusb": 38551, "ĠVald": 38552, "oddy": 38553, "ĠMahmoud": 38554, "Asia": 38555, "Ġskulls": 38556, "ĠExodus": 38557, "ĠCompet": 38558, "ĠLIC": 38559, "ĠMansion": 38560, "ĠAme": 38561, "Ġconsolidate": 38562, "storms": 38563, "ontent": 38564, "996": 38565, "Ġclen": 38566, "Ġmummy": 38567, "flat": 38568, "758": 38569, "ĠVOL": 38570, "oteric": 38571, "nen": 38572, "ĠMinute": 38573, "Sov": 38574, "Ġfiner": 38575, "Rh": 38576, "lycer": 38577, "Ġreinforcements": 38578, "ĠJohannes": 38579, "ĠGallagher": 38580, "Ġgymn": 38581, "Suddenly": 38582, "Ġextortion": 38583, "kr": 38584, "iator": 38585, "Ta": 38586, "Ġhippocampus": 38587, "NPR": 38588, "ĠComputing": 38589, "Ġsquarely": 38590, "Ġmodelling": 38591, "ĠForums": 38592, "ĠLisp": 38593, "ĠKrishna": 38594, "Ġ324": 38595, "Ġrushes": 38596, "Ġensued": 38597, "Ġcreeping": 38598, "onte": 38599, "nai": 38600, "ilater": 38601, "ĠHornets": 38602, "Ġoblivious": 38603, "INST": 38604, "559": 38605, "Ġjeopardy": 38606, "Ġdistinguishing": 38607, "jured": 38608, "Ġbegs": 38609, "similar": 38610, "phot": 38611, "530": 38612, "ĠParkway": 38613, "Ġsinks": 38614, "ĠHearthstone": 38615, "ibur": 38616, "ĠBaton": 38617, "Avoid": 38618, "Ġdancer": 38619, "Ġmagistrate": 38620, "aryn": 38621, "Ġdisturbances": 38622, "ĠRomero": 38623, "Ġparaph": 38624, "Ġmischief": 38625, "âĸĵ": 38626, "ĠSharia": 38627, "Ġurinary": 38628, "route": 38629, "ivas": 38630, "fitted": 38631, "Ġejected": 38632, "ĠAlbuquerque": 38633, "Ġ470": 38634, "Ġirritated": 38635, "ĠZip": 38636, "ĠBiol": 38637, "Ãį": 38638, "Ġdenounce": 38639, "Ġbinaries": 38640, "ĠVerse": 38641, "Ġoppos": 38642, "ĠKendrick": 38643, "ĠGPL": 38644, "Ġspew": 38645, "ĠElijah": 38646, "ĠEas": 38647, "Ġdrifted": 38648, "sofar": 38649, "Ġannoyance": 38650, "ĠBET": 38651, "474": 38652, "ĠStrongh": 38653, "itates": 38654, "ĠCognitive": 38655, "ophone": 38656, "ĠIdentification": 38657, "ocrine": 38658, "connection": 38659, "Ġboxer": 38660, "ĠASD": 38661, "ĠAreas": 38662, "Yang": 38663, "tch": 38664, "ullah": 38665, "Ġdeceive": 38666, "Combat": 38667, "episode": 38668, "crete": 38669, "Witness": 38670, "Ġcondolences": 38671, "htar": 38672, "Ġheals": 38673, "Ġbuckets": 38674, "ĠLAW": 38675, "Blu": 38676, "Ġslab": 38677, "ĠORDER": 38678, "ocl": 38679, "atton": 38680, "ĠStevenson": 38681, "ĠGinger": 38682, "ĠFriendly": 38683, "ĠVanderbilt": 38684, "spirit": 38685, "igl": 38686, "ĠRegarding": 38687, "ĠPROG": 38688, "Ġsealing": 38689, "starting": 38690, "Ġcardinal": 38691, "ĠVec": 38692, "ĠBeir": 38693, "Ġmilliseconds": 38694, "weak": 38695, "perse": 38696, "Ġsterile": 38697, "ĠContemporary": 38698, "ĠPhant": 38699, "ĠClo": 38700, "Ġoutp": 38701, "Ġexiled": 38702, "Ġ277": 38703, "Ġselfie": 38704, "Ġmanic": 38705, "Ġnano": 38706, "terms": 38707, "Alexander": 38708, "Ġresolves": 38709, "Ġmillennia": 38710, "Ġexplodes": 38711, "Ġconstellation": 38712, "Ġadultery": 38713, "motion": 38714, "DOC": 38715, "Ġbroadcasters": 38716, "Ġkindergarten": 38717, "ĠMayweather": 38718, "ĠEco": 38719, "icho": 38720, "Ġ287": 38721, "laun": 38722, "Ġmute": 38723, "Ġdiscreet": 38724, "Ġpreschool": 38725, "Ġpreempt": 38726, "Delete": 38727, "ĠFreed": 38728, "Pi": 38729, "HK": 38730, "Ġblocker": 38731, "ĠCumber": 38732, "Ġwrought": 38733, "dating": 38734, "Ġinsurer": 38735, "Ġquotas": 38736, "Ġpreached": 38737, "Ġeviction": 38738, "ĠRegina": 38739, "ĠPens": 38740, "Ġseventeen": 38741, "ĠNass": 38742, "Dick": 38743, "Ġfolds": 38744, "Ġdotted": 38745, "ĠAad": 38746, "Universal": 38747, "Ġpizz": 38748, "ĠGuru": 38749, "Ġsoils": 38750, "Ġnovice": 38751, "ĠNeander": 38752, "Ġstool": 38753, "Ġdetonated": 38754, "ĠPikachu": 38755, "ĠMassive": 38756, "IVER": 38757, "ĠAbdel": 38758, "Ġsubdued": 38759, "Ġtallest": 38760, "Ġprecarious": 38761, "Ġay": 38762, "rification": 38763, "ĠObj": 38764, "cale": 38765, "Ġunquestion": 38766, "culosis": 38767, "adas": 38768, "igrated": 38769, "Days": 38770, "Ġqueens": 38771, "ĠGazette": 38772, "ĠColour": 38773, "ĠBowman": 38774, "ĠJJ": 38775, "ïve": 38776, "Ġdominates": 38777, "Student": 38778, "Ġmu": 38779, "Ġbacklog": 38780, "ĠElectro": 38781, "Truth": 38782, "483": 38783, "Ġcondensed": 38784, "rules": 38785, "ĠConspiracy": 38786, "Ġacronym": 38787, "handled": 38788, "ĠMatte": 38789, "jri": 38790, "ĠImpossible": 38791, "lude": 38792, "creation": 38793, "Ġwarmed": 38794, "ĠSlave": 38795, "Ġmisled": 38796, "Ġferment": 38797, "ĠKah": 38798, "inki": 38799, "keleton": 38800, "cyl": 38801, "ĠKarin": 38802, "Hunter": 38803, "Register": 38804, "ĠSurrey": 38805, "Ġstares": 38806, "ĠWidth": 38807, "ĠNay": 38808, "ĠSki": 38809, "Ġblacklist": 38810, "ucket": 38811, "Ġexpulsion": 38812, "imet": 38813, "Ġretweet": 38814, "vantage": 38815, "Feature": 38816, "Ġtroopers": 38817, "Ġhomers": 38818, "969": 38819, "Ġcontingency": 38820, "ĠWTC": 38821, "ĠBrewer": 38822, "foreign": 38823, "Ware": 38824, "Solar": 38825, "Ġundue": 38826, "REC": 38827, "ulnerable": 38828, "pathic": 38829, "ĠBoise": 38830, "Ġ322": 38831, "Ġaroused": 38832, "ĠYing": 38833, "ä¸į": 38834, "ueless": 38835, "Ġpas": 38836, "Ġmorp": 38837, "Ġfloral": 38838, "Express": 38839, "udging": 38840, "kB": 38841, "ĠGranted": 38842, "د": 38843, "ĠMicha": 38844, "ĠGothic": 38845, "ĠSPECIAL": 38846, "ĠRicardo": 38847, "Fran": 38848, "Ġadministering": 38849, "620": 38850, "pora": 38851, "Ġ®": 38852, "Ġcompromises": 38853, "Ġbitten": 38854, "Accept": 38855, "Thirty": 38856, "в": 38857, "Ġmaterially": 38858, "ĠTerr": 38859, "igmatic": 38860, "chains": 38861, "Ġdove": 38862, "stadt": 38863, "Marvel": 38864, "FAULT": 38865, "Ġwindshield": 38866, "Ġ336": 38867, "adier": 38868, "Ġswapping": 38869, "Ġflawless": 38870, "ĠPredator": 38871, "ĠMichele": 38872, "Ġpropulsion": 38873, "ĠPsychic": 38874, "Ġassigning": 38875, "Ġfabrication": 38876, "Ġbarley": 38877, "lust": 38878, "Ġtowering": 38879, "Ġaltercation": 38880, "ĠBentley": 38881, "Sphere": 38882, "Ġtuna": 38883, "ĠClasses": 38884, "Freedom": 38885, "uner": 38886, "Lady": 38887, "voice": 38888, "Ġcoolest": 38889, "orr": 38890, "Ġpalp": 38891, "${": 38892, "Ġhysteria": 38893, "ĠMetatron": 38894, "pants": 38895, "Ġspawning": 38896, "Experts": 38897, "ĠInvestors": 38898, "ĠAnarchy": 38899, "Ġshrunk": 38900, "ĠVictim": 38901, "Ġ289": 38902, "Ġecstasy": 38903, "ĠBinding": 38904, "585": 38905, "ĠMelody": 38906, "578": 38907, "otally": 38908, "ĠEtsy": 38909, "liga": 38910, "Ġapplauded": 38911, "Ġsweating": 38912, "Ġredistributed": 38913, "Ġpopcorn": 38914, "Ġseminal": 38915, "fur": 38916, "ĠNeuroscience": 38917, "Rand": 38918, "ĠOst": 38919, "ĠMadden": 38920, "ĠIncreasing": 38921, "ĠDawkins": 38922, "ĠSubway": 38923, "Ġarsen": 38924, "conserv": 38925, "BUR": 38926, "Ġspiked": 38927, "ĠLyft": 38928, "ĠImperium": 38929, "ĠDropbox": 38930, "Ġfavoured": 38931, "Ġencompasses": 38932, "ghost": 38933, "Ġinspires": 38934, "Ġburgeoning": 38935, "ĠYoshi": 38936, "ĠVertical": 38937, "ĠAuditor": 38938, "Ġintending": 38939, "Ġfilibuster": 38940, "Bloom": 38941, "fac": 38942, "ĠCavs": 38943, "igning": 38944, "Ġcoworkers": 38945, "ĠBarbarian": 38946, "remember": 38947, "FLAG": 38948, "Ġauditory": 38949, "asonry": 38950, "College": 38951, "Ġmuted": 38952, "gemony": 38953, "obin": 38954, "ĠPsycho": 38955, "968": 38956, "Ġlavish": 38957, "Ġhierarchical": 38958, "ĠDrone": 38959, "ouk": 38960, "Ġcrippled": 38961, "ĠMaxim": 38962, "Slot": 38963, "Ġquiz": 38964, "ĠVid": 38965, "ifling": 38966, "Ġarchaeologists": 38967, "Ġabandonment": 38968, "dial": 38969, "leon": 38970, "ĠFas": 38971, "Ted": 38972, "Ġraspberry": 38973, "Ġmaneuvers": 38974, "Ġbehaviours": 38975, "Ġinsure": 38976, "Ġremod": 38977, "Switch": 38978, "hoe": 38979, "Ġspaced": 38980, "Ġaffordability": 38981, "ĠFern": 38982, "notation": 38983, "ĠBalanced": 38984, "Ġoccupies": 38985, "environment": 38986, "Ġnecklace": 38987, "Ġsedan": 38988, "FU": 38989, "ĠBravo": 38990, "Ġabusers": 38991, "ĠAnita": 38992, "metadata": 38993, "ĠGithub": 38994, "aito": 38995, "ĠFaster": 38996, "ĠWasserman": 38997, "ĠFlesh": 38998, "Ġthorn": 38999, "rarily": 39000, "ĠMerry": 39001, "wine": 39002, "Ġpopulace": 39003, "ĠLann": 39004, "Ġrepairing": 39005, "Ġpsyche": 39006, "Ġmodulation": 39007, "awaru": 39008, "âĢĭâĢĭ": 39009, "arij": 39010, "Ġdecorations": 39011, "Ġapologise": 39012, "ĠGarg": 39013, "apply": 39014, "Ġgiveaway": 39015, "ĠFlan": 39016, "ĠWyatt": 39017, "Uber": 39018, "Ġauthorised": 39019, "ĠMoral": 39020, "HAHAHAHA": 39021, "activate": 39022, "Ġtorpedo": 39023, "ĠFAR": 39024, "Ġamassed": 39025, "ĠAram": 39026, "arkin": 39027, "ĠVictims": 39028, "stab": 39029, "Ġom": 39030, "ĠECO": 39031, "Ġopioids": 39032, "Ġpurposely": 39033, "ĠVest": 39034, "Ġerg": 39035, "atan": 39036, "ĠSurgery": 39037, "Ġcorrecting": 39038, "ĠOrtiz": 39039, "ĠBeet": 39040, "Ġrevoke": 39041, "Ġfreeway": 39042, "ĠHiggins": 39043, "Fail": 39044, "ĠFarms": 39045, "ĠATP": 39046, "hound": 39047, "Ġpoking": 39048, "ĠCommunists": 39049, "monster": 39050, "imentary": 39051, "Ġunlocking": 39052, "Ġunfit": 39053, "weed": 39054, "enario": 39055, "atical": 39056, "ĠEnlightenment": 39057, "ĠNG": 39058, "ĠCompensation": 39059, "deen": 39060, "ĠWidow": 39061, "ĠCindy": 39062, "ĠAfterwards": 39063, "Ġ6000": 39064, "ikhail": 39065, "agically": 39066, "Ġratified": 39067, "Ġcasualty": 39068, "HOME": 39069, "psey": 39070, "fee": 39071, "Ġsparkling": 39072, "Ġdé": 39073, "Ġconcerted": 39074, "Catal": 39075, "Ġcomplying": 39076, "ĠAres": 39077, "ĠDent": 39078, "Shut": 39079, "Ġskim": 39080, "administ": 39081, "Ġhostilities": 39082, "ĠGins": 39083, "Ġ608": 39084, "Ġmuddy": 39085, "ĠMcInt": 39086, "ĠDecay": 39087, "525": 39088, "Ġconspicuous": 39089, "ĠExposure": 39090, "Ġrescind": 39091, "Ġwearable": 39092, "Ġ328": 39093, "ourmet": 39094, "ahs": 39095, "ĠRobots": 39096, "Ġeclips": 39097, "instance": 39098, "ĠREPORT": 39099, "ĠAppl": 39100, "030": 39101, "ĠSkies": 39102, "0100": 39103, "Ġfallacy": 39104, "Socket": 39105, "ĠReceiver": 39106, 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39163, "Ġorderly": 39164, "catentry": 39165, "Ġ261": 39166, "Ġexchanging": 39167, "ĠIntent": 39168, "ĠAmendments": 39169, "DOM": 39170, "Ġstout": 39171, "³³³³³³³³³³³³³³³³": 39172, "ĠAirbus": 39173, "Ġ278": 39174, "hyde": 39175, "Poll": 39176, "ItemThumbnailImage": 39177, "Ġloopholes": 39178, "ĠPillar": 39179, "Ġexplor": 39180, "Stretch": 39181, "Apart": 39182, "Ġunmarried": 39183, "Limit": 39184, "ĠTransformers": 39185, "Ġintellectually": 39186, "uncture": 39187, "1800": 39188, "Ġdarn": 39189, "Brazil": 39190, "Ġleftover": 39191, "berus": 39192, "fred": 39193, "Minecraft": 39194, "326": 39195, "ĠForms": 39196, "Ġproofs": 39197, "ĠDesigned": 39198, "Ġindexes": 39199, "ĠSuppose": 39200, "EMS": 39201, "ĠLoving": 39202, "ĠBonnie": 39203, "imating": 39204, "OTUS": 39205, "Ġconductor": 39206, "Ġbehaved": 39207, "ĠFren": 39208, "Ġsynerg": 39209, "Ġmillennium": 39210, "Ġcatering": 39211, "ĠLauder": 39212, "Wr": 39213, "ĠYiannopoulos": 39214, "ĠATF": 39215, "Ġenslaved": 39216, "Ġawakened": 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41287, "ĠNF": 41288, "491": 41289, "642": 41290, "packing": 41291, "598": 41292, "texture": 41293, "Spider": 41294, "freedom": 41295, "cipled": 41296, "ĠTAMADRA": 41297, "âĻ¦": 41298, "authent": 41299, "ĠWANT": 41300, "rified": 41301, "Ġrites": 41302, "Ġuterus": 41303, "kiss": 41304, "Ġâī¤": 41305, "Ġskillet": 41306, "Ġdisenfranch": 41307, "ĠGaal": 41308, "Compan": 41309, "Ġageing": 41310, "guide": 41311, "Balt": 41312, "Ġiterator": 41313, "Ġdiscretionary": 41314, "tips": 41315, "Ġprimates": 41316, "ĠTechnique": 41317, "ĠPayments": 41318, "azel": 41319, "ĠROCK": 41320, "stantial": 41321, "060": 41322, "Ġdmg": 41323, "ĠJackets": 41324, "ĠPlayoff": 41325, "Ġnursery": 41326, "ĠSymb": 41327, "arton": 41328, "Ġannexation": 41329, "Colorado": 41330, "Ġcoils": 41331, "ĠShoes": 41332, "âĦ¢:": 41333, "ĠRoz": 41334, "COMPLE": 41335, "ĠEverest": 41336, "ĠTriumph": 41337, "Joy": 41338, "Grid": 41339, "à¼": 41340, "processor": 41341, "ĠProsper": 41342, "ĠSeverus": 41343, "ĠSelected": 41344, "rg": 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41677, "¶ħ": 41678, "Ġlikened": 41679, "Ġbesieged": 41680, "weeney": 41681, "ĠCreep": 41682, "Ġlinemen": 41683, "multi": 41684, "icably": 41685, "udder": 41686, "Ġvitality": 41687, "Ġshortfall": 41688, "ĠPants": 41689, "apist": 41690, "Hidden": 41691, "ĠDrops": 41692, "medical": 41693, "Ġpronunciation": 41694, "ĠNRL": 41695, "Ġinsightful": 41696, "JV": 41697, "ĠBeard": 41698, "ĠChou": 41699, "Ġcharms": 41700, "Ġbins": 41701, "Ġambassadors": 41702, "ĠSaturdays": 41703, "Ġinhibitor": 41704, "ĠFranch": 41705, "601": 41706, "','": 41707, "ĠConor": 41708, "artney": 41709, "ĠXperia": 41710, "grave": 41711, "bees": 41712, "ĠProtestants": 41713, "Ġsoaking": 41714, "ĠMandal": 41715, "Ġphased": 41716, "Ġ660": 41717, "Ġscams": 41718, "Ġbuzzing": 41719, "ĠItalians": 41720, "ĠLorenzo": 41721, "ĠJA": 41722, "Ġhesitated": 41723, "Ġcliffs": 41724, "ĠGOT": 41725, "inguishable": 41726, "Ġko": 41727, "Ġinterruption": 41728, "Zip": 41729, "Learning": 41730, "Ġunderscores": 41731, "ĠBlink": 41732, "Ku": 41733, "579": 41734, "ĠAutob": 41735, "IRE": 41736, "Ġwatering": 41737, "Ġpastry": 41738, "820": 41739, "Ġvisionary": 41740, "ĠTemplar": 41741, "awaited": 41742, "Ġpiston": 41743, "Ġantid": 41744, "currently": 41745, "Ġpard": 41746, "Ġwaging": 41747, "Ġnobility": 41748, "ĠYus": 41749, "Ġinjecting": 41750, "faith": 41751, "ĠPASS": 41752, "åº": 41753, "Ġretake": 41754, "ĠPROC": 41755, "Ġcathedral": 41756, "bash": 41757, "Ġwrestlers": 41758, "Ġpartnering": 41759, "Ġnoses": 41760, "Ġ358": 41761, "Transform": 41762, "amen": 41763, "Ġbouts": 41764, "ĠIdeal": 41765, "ĠConstantin": 41766, "Ġsep": 41767, "ĠMonarch": 41768, "atten": 41769, "ĠPeoples": 41770, "modified": 41771, "Ġmoratorium": 41772, "Ġpenchant": 41773, "Ġoffensively": 41774, "Ġproxies": 41775, "okane": 41776, "ĠTaiwanese": 41777, "ĠPoo": 41778, "ĠHOME": 41779, "usional": 41780, "Ġverbs": 41781, "ĠOman": 41782, "visory": 41783, "Ġpersuasion": 41784, "Ġmultit": 41785, "Ġscissors": 41786, "Gay": 41787, "oway": 41788, "ophysical": 41789, "lus": 41790, "gnu": 41791, "Ġapocalyptic": 41792, "Ġabsurdity": 41793, "Ġplaybook": 41794, "Ġautobiography": 41795, "IUM": 41796, "Ġsneaking": 41797, "ĠSimulation": 41798, "pps": 41799, "ellery": 41800, "Planet": 41801, "Ġrightfully": 41802, "Ġniece": 41803, "ĠNEC": 41804, "ĠIPO": 41805, "ĠDisclosure": 41806, "leanor": 41807, "ousy": 41808, "STER": 41809, "Ġ282": 41810, "Cruz": 41811, "Chall": 41812, "643": 41813, "ĠSurvive": 41814, "ĠFatal": 41815, "ĠAmid": 41816, "apo": 41817, "Weapons": 41818, "DEN": 41819, "770": 41820, "ĠGreenwald": 41821, "Ġlinen": 41822, "alos": 41823, "Ġpollutants": 41824, "ĠPCIe": 41825, "kat": 41826, "Ġpaw": 41827, "ĠKraft": 41828, "Chem": 41829, "ĠTerminator": 41830, "Ġreincarn": 41831, "Ġ][": 41832, "ĠSeeds": 41833, "Ġsilhouette": 41834, "ĠStores": 41835, "Ġgrooming": 41836, "ĠDirection": 41837, "ĠIsabel": 41838, "ĠBridges": 41839, "ðŁij": 41840, "EED": 41841, "ĠMorsi": 41842, "Ġvalves": 41843, "ĠRanked": 41844, "ĠPharma": 41845, "ĠOrganizations": 41846, "Ġpenetrated": 41847, "ĠRodham": 41848, "ĠProtoss": 41849, "Ġoverest": 41850, "Ġexasper": 41851, "ĠTJ": 41852, "Ġ000000": 41853, "Ġtrickle": 41854, "Ġbourbon": 41855, "WHO": 41856, "Ġwretched": 41857, "Ġmicroscopic": 41858, "Ġchecklist": 41859, "Ġadorned": 41860, "Royal": 41861, "Administ": 41862, "ĠRetirement": 41863, "ĠHighest": 41864, "Weather": 41865, "ilege": 41866, "Ġincrements": 41867, "ĠCosponsors": 41868, "Ġmasse": 41869, "ĠSinn": 41870, "rf": 41871, "Ġhordes": 41872, "assembly": 41873, "754": 41874, "ĠNatasha": 41875, "ĠTYPE": 41876, "ĠGENERAL": 41877, "Ġarranging": 41878, "Ġ407": 41879, "lator": 41880, "Ġglean": 41881, "Ġdiscredited": 41882, "Ġclinicians": 41883, "UNE": 41884, "Ġachieves": 41885, "ĠEmerson": 41886, "complex": 41887, "=[": 41888, "Ġprincipally": 41889, "Ġfrail": 41890, "picked": 41891, "Ġthanking": 41892, "Ġrecl": 41893, "ĠLAST": 41894, "Ġsuppressing": 41895, "ilic": 41896, "Ġantidepressant": 41897, "ĠLisbon": 41898, "Ġthor": 41899, "Ġspa": 41900, "Ġkingdoms": 41901, "ĠPearce": 41902, "emo": 41903, "Ġplung": 41904, "Ġdivest": 41905, "Ġ********************************": 41906, "bis": 41907, "ospels": 41908, "adr": 41909, "Spirit": 41910, "halla": 41911, "Pink": 41912, "endez": 41913, "Ġresurrected": 41914, "escape": 41915, "ĠRosenstein": 41916, "Ġgeological": 41917, "Ġnecessities": 41918, "Ġcarniv": 41919, "ĠElys": 41920, "ĠBarney": 41921, "Ġ296": 41922, "digy": 41923, "STON": 41924, "DOWN": 41925, "Ġmilestones": 41926, "Ġker": 41927, "Ġdismantling": 41928, "Ġreprim": 41929, "Ġcrossings": 41930, "1945": 41931, "Ġpatriarchy": 41932, "Ġblasphemy": 41933, "Ġ359": 41934, "metry": 41935, "ĠObesity": 41936, "ĠDifferences": 41937, "blocking": 41938, "ãĥķãĤ¡": 41939, "ichita": 41940, "ĠSabha": 41941, "phalt": 41942, "ĠColo": 41943, "uala": 41944, "efficients": 41945, "ĠMedina": 41946, "console": 41947, "557": 41948, "ĠHannibal": 41949, "ĠHabit": 41950, "ĠFever": 41951, "Ġthence": 41952, "Ġsynagogue": 41953, "Ġessentials": 41954, "Ġwink": 41955, "ĠTrader": 41956, "IDA": 41957, "ĠSpoiler": 41958, "ĠIcelandic": 41959, "ĠHayward": 41960, "Ġpeac": 41961, "Ġmalice": 41962, "Ġflashback": 41963, "Ġthw": 41964, "Ġlayoffs": 41965, "Liquid": 41966, "Ġtrooper": 41967, "Ġhinge": 41968, "ĠReaders": 41969, "Phill": 41970, "ĠBauer": 41971, "Created": 41972, "Ġaudits": 41973, "accompan": 41974, "Ġunsuspecting": 41975, "iera": 41976, "66666666": 41977, "Ġbroch": 41978, "Ġapprehended": 41979, "ĠMalk": 41980, "cerning": 41981, "ĠCodex": 41982, "OVER": 41983, "Marsh": 41984, "ĠDeng": 41985, "ĠExpression": 41986, "Ġdisrespectful": 41987, "Ġascending": 41988, "tests": 41989, "ĠPlaintiff": 41990, "stery": 41991, "ĠAlibaba": 41992, "dinand": 41993, "ĠDempsey": 41994, "Applications": 41995, "moral": 41996, "Ġthroughput": 41997, "Ġquarrel": 41998, "Ġmills": 41999, "Ġhemor": 42000, "ĠCASE": 42001, "terrorist": 42002, "stim": 42003, "ifestyle": 42004, "rozen": 42005, "CEPT": 42006, "Ark": 42007, "uci": 42008, "lectic": 42009, "Ġirritating": 42010, "sheets": 42011, "Ay": 42012, "Ġredeemed": 42013, "Ġhorny": 42014, "ĠTeach": 42015, "ĠSear": 42016, "democracy": 42017, "465": 42018, "ĠRestore": 42019, "Ġstandby": 42020, "ĠPis": 42021, "iffin": 42022, "Ġsleepy": 42023, "Ġextrater": 42024, "Ġcompliments": 42025, "Frameworks": 42026, "Ġinstalls": 42027, "Ġbanging": 42028, "surface": 42029, "foundland": 42030, "Ġmetaphysical": 42031, "Ġ283": 42032, "ouls": 42033, "devices": 42034, "Args": 42035, "ĠSacrifice": 42036, "ĠMcCorm": 42037, "eson": 42038, "Conservative": 42039, "ĠMikhail": 42040, "seeing": 42041, "isively": 42042, "ĠRooms": 42043, "ĠGeneric": 42044, "Ġenthusiastically": 42045, "Ġgripped": 42046, "Ġcomedic": 42047, "ĠElectricity": 42048, "Ġguerrilla": 42049, "Ġdecoration": 42050, "ĠPerspective": 42051, "Ġconsultations": 42052, "Ġunamb": 42053, "Ġplagiar": 42054, "Ġmagician": 42055, "Ġerection": 42056, "ĠTourism": 42057, "oried": 42058, "roxy": 42059, "1100": 42060, "Tam": 42061, "Īè": 42062, "γ": 42063, "ת": 42064, "ĠPredators": 42065, "Nitrome": 42066, "Ġtelescopes": 42067, "projects": 42068, "Ġunprotected": 42069, "Ġstocked": 42070, "ĠEntreprene": 42071, "nexpected": 42072, "Ġwastewater": 42073, "Vill": 42074, "Ġintimately": 42075, "ĠiCloud": 42076, "ĠConstable": 42077, "Ġspoof": 42078, "Ġnefarious": 42079, "Ġfins": 42080, "Ġcensor": 42081, "ĠModes": 42082, "ĠEsper": 42083, "arbon": 42084, "Ġintersections": 42085, "Ġlauded": 42086, "Ġphysi": 42087, "Ġgenerously": 42088, "ĠTheNitrome": 42089, "ĠTheNitromeFan": 42090, "Ġarisen": 42091, "ĠÙĪ": 42092, "Ġglands": 42093, "ĠPavilion": 42094, "ĠGupta": 42095, "Ġuniformly": 42096, "Ġramps": 42097, "riet": 42098, "ĠWHEN": 42099, "ĠVanessa": 42100, "Ġrouted": 42101, "Ġlimp": 42102, "ĠCPI": 42103, "pter": 42104, "intuitive": 42105, "Ġvaping": 42106, "Ġexperimented": 42107, "ĠOlympus": 42108, "ĠAmon": 42109, "Ġsighting": 42110, "Ġinfiltrate": 42111, "ĠGentleman": 42112, "Ġsignings": 42113, "ĠMeow": 42114, "ĠNavigation": 42115, "checks": 42116, "433": 42117, "Ġelapsed": 42118, "ĠBulgarian": 42119, "espie": 42120, "ĠSOM": 42121, "during": 42122, "Ġspills": 42123, "anca": 42124, "ĠPlymouth": 42125, "MAL": 42126, "Ġdomestically": 42127, "ĠWatergate": 42128, "ĠFAM": 42129, "killed": 42130, "edited": 42131, "ĠYourself": 42132, "Ġsynchronization": 42133, "ĠPractices": 42134, "STEP": 42135, "Ġgenomes": 42136, "ĠQR": 42137, "notice": 42138, "Ġlocating": 42139, "zin": 42140, "Ġ329": 42141, "alcohol": 42142, "Ġkitten": 42143, "Vo": 42144, "Ġrinse": 42145, "Ġgrapple": 42146, "ĠScrew": 42147, "ĠDul": 42148, "AIR": 42149, "Ġleasing": 42150, "ĠCafé": 42151, "Ġroses": 42152, "ĠRespect": 42153, "Ġmislead": 42154, "Ġperfected": 42155, "Ġnudity": 42156, "Ġnonpartisan": 42157, "ĠConsumption": 42158, "Reporting": 42159, "Ġnuances": 42160, "Ġdeductible": 42161, "ĠShots": 42162, "Ġ377": 42163, "Ġæľ": 42164, "anooga": 42165, "Benef": 42166, "ĠBam": 42167, "ĠSamp": 42168, "ifix": 42169, "Ġgalvan": 42170, "ĠMedals": 42171, "radius": 42172, "Ġnobles": 42173, "Ġeaves": 42174, "igrate": 42175, "KT": 42176, "ĠHarbour": 42177, "uers": 42178, "Ġrisked": 42179, "req": 42180, "Ġneurot": 42181, "gettable": 42182, "aina": 42183, "Romney": 42184, "Ġunderpin": 42185, "Ġloft": 42186, "ĠSubcommittee": 42187, "ĠMongol": 42188, "biz": 42189, "Ġmanifests": 42190, "assisted": 42191, "ĠGaga": 42192, "Ġsynergy": 42193, "Ġreligiously": 42194, "ĠPref": 42195, "ĠGerry": 42196, "TAG": 42197, "ĠChoi": 42198, "466": 42199, "behind": 42200, "ĠOu": 42201, "GoldMagikarp": 42202, "Ġhemorrh": 42203, "River": 42204, "Ġtendon": 42205, "Ġinjure": 42206, "ĠFiona": 42207, "Ġpag": 42208, "Ġagitation": 42209, "||||": 42210, "uran": 42211, "ĠESA": 42212, "Ġesteem": 42213, "Ġdodging": 42214, "Ġ412": 42215, "rss": 42216, "Ġceases": 42217, "excluding": 42218, "Ġintakes": 42219, "Ġinserts": 42220, "Ġembold": 42221, "ĠOral": 42222, "upuncture": 42223, "411": 42224, "ĠUnified": 42225, "ĠDele": 42226, "Ġfurnace": 42227, "ĠCoyotes": 42228, "ĠBrach": 42229, "Labor": 42230, "Ġhandshake": 42231, "Ġbruises": 42232, "Grade": 42233, "éĹĺ": 42234, "ĠGrammy": 42235, "ileen": 42236, "States": 42237, "ĠScandinavian": 42238, "ĠKardash": 42239, "866": 42240, "Ġeffortlessly": 42241, "ĠDIRECT": 42242, "ĠTHEN": 42243, "ĠMei": 42244, "ertation": 42245, "1968": 42246, "Ġgroin": 42247, "witch": 42248, "Requirements": 42249, "985": 42250, "Ġroofs": 42251, "Ġestates": 42252, "ĠHF": 42253, "Ġhaha": 42254, "Ġdensely": 42255, "ĠOCT": 42256, "Ġplastics": 42257, "Ġincidentally": 42258, "ĠTracks": 42259, "ĠTaxes": 42260, "Ġchanted": 42261, "Ġforceful": 42262, "ĠBieber": 42263, "ĠKahn": 42264, "Kent": 42265, "ĠCot": 42266, "licts": 42267, "Fed": 42268, "Ġhideous": 42269, "ĠVerd": 42270, "ĠSyndicate": 42271, "ĠIllegal": 42272, "Jet": 42273, "ĠDAV": 42274, "reasonable": 42275, "crew": 42276, "Ġfundamentalist": 42277, "Ġtruthful": 42278, "ĠJing": 42279, "Ġlil": 42280, "Ġdowned": 42281, "Ġenchanted": 42282, "ĠPolicies": 42283, "ĠMcMaster": 42284, "ĠHare": 42285, "ideshow": 42286, "Ġparams": 42287, "encers": 42288, "gorithm": 42289, "Ġallowances": 42290, "Ġturbulent": 42291, "Ġcomplexities": 42292, "ĠKT": 42293, "Ġ337": 42294, "ĠGenetic": 42295, "FUN": 42296, "Doug": 42297, "tick": 42298, "Ġgigs": 42299, "umenthal": 42300, "Ġpatriarchal": 42301, "Ġcalc": 42302, ",...": 42303, "Ġcout": 42304, "ĠGuan": 42305, "Ġpathological": 42306, "ĠRivals": 42307, "Ġunderrated": 42308, "Ġfluorescent": 42309, "ĠJiu": 42310, "arnaev": 42311, "ĠQuan": 42312, "Ġ429": 42313, "Ġà¨": 42314, "Mario": 42315, "Construct": 42316, "ĠCitation": 42317, "ĠRacial": 42318, "ĠRSA": 42319, "ĠFidel": 42320, "Ġ395": 42321, "Personally": 42322, "Cause": 42323, "û": 42324, "radical": 42325, "inen": 42326, "Ġvehemently": 42327, "ĠPapa": 42328, "Ġinternship": 42329, "Ġflakes": 42330, "ĠReck": 42331, "Luckily": 42332, "Bra": 42333, "2020": 42334, "ravings": 42335, "RN": 42336, "Wonder": 42337, "Seriously": 42338, "Ġreusable": 42339, "Ġpolluted": 42340, "ĠPeng": 42341, "leigh": 42342, "indle": 42343, "Ġcircuitry": 42344, "ĠMadonna": 42345, "ĠBART": 42346, "Residents": 42347, "attribute": 42348, "Philadelphia": 42349, "Club": 42350, "Ġplanner": 42351, "Ġfrantically": 42352, "Ġfaithfully": 42353, "ĠTerritories": 42354, "ĠLAT": 42355, "ĠAndersen": 42356, "anu": 42357, "ĠPARK": 42358, "ĠSora": 42359, "iage": 42360, "ĠPlayoffs": 42361, "ĠGCC": 42362, "427": 42363, "Ġabnorm": 42364, "ĠLever": 42365, "Ġdisobedience": 42366, "Async": 42367, "ĠShea": 42368, "Vert": 42369, "Ġskirts": 42370, "ĠSawyer": 42371, "xp": 42372, "Ġworsening": 42373, "Ġscapego": 42374, "ĠAngle": 42375, "othal": 42376, "Ġtrove": 42377, "ĠSty": 42378, "ĠNguyen": 42379, "marine": 42380, "ideon": 42381, "Depths": 42382, "Blog": 42383, "ĠIlluminati": 42384, "Ġtracts": 42385, "Ġorganise": 42386, "Ġostr": 42387, "Fs": 42388, "Ġleveraging": 42389, "ĠDaredevil": 42390, "asar": 42391, "Ġlang": 42392, "Ġextermin": 42393, "ursions": 42394, "ĠRomo": 42395, "ãĤ¤ãĥĪ": 42396, "Ġcontended": 42397, "Ġencountering": 42398, "ĠTablet": 42399, "ĠAlternate": 42400, "skill": 42401, "Ġsweets": 42402, "Ġcohesive": 42403, "capacity": 42404, "Ġrepud": 42405, "Ġlizard": 42406, "roo": 42407, "Ġpilgrims": 42408, "ĠRuff": 42409, "ĠInstrument": 42410, "ĠLogo": 42411, "uitous": 42412, "EH": 42413, "Ġsalesman": 42414, "Ġankles": 42415, "Led": 42416, "ĠPatty": 42417, "udos": 42418, "Owner": 42419, "Ġdiscrepancies": 42420, "kj": 42421, "MU": 42422, "Ġunconditional": 42423, "DragonMagazine": 42424, "iard": 42425, "Oak": 42426, "ĠConversation": 42427, "beer": 42428, "ĠOsaka": 42429, "Delta": 42430, "usky": 42431, "Ġsecretion": 42432, "Ġplaza": 42433, "Ġming": 42434, "Ġdepletion": 42435, "ĠMous": 42436, "ĠITS": 42437, "ĠHimal": 42438, "ĠFleming": 42439, "Ġcytok": 42440, "ĠHick": 42441, "Ġbatters": 42442, "ĠIntellectual": 42443, "675": 42444, "ér": 42445, "ISION": 42446, "ĠQuentin": 42447, "ĠChapters": 42448, "ihadi": 42449, "Ġcoaster": 42450, "WAYS": 42451, "ĠLizard": 42452, "ĠYor": 42453, "andering": 42454, "Skin": 42455, "haust": 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43417, "Ġfrivol": 43418, "ĠConsolid": 43419, "results": 43420, "MJ": 43421, "Ġphylogen": 43422, "Ġhauled": 43423, "ĠWiley": 43424, "ĠJessie": 43425, "ĠPrepare": 43426, "ĠEps": 43427, "Ġtreasurer": 43428, "IAS": 43429, "Ġcolonists": 43430, "Ġinund": 43431, "ĠWWF": 43432, "ĠConverted": 43433, "6000": 43434, "outside": 43435, "ĠAppearance": 43436, "ĠRelic": 43437, "ĠMister": 43438, "saw": 43439, "Ġresultant": 43440, "Ġadjective": 43441, "ĠLaurel": 43442, "ĠHindi": 43443, "bda": 43444, "Peace": 43445, "Ġrebirth": 43446, "Ġmembranes": 43447, "Ġforwarding": 43448, "Ġcollided": 43449, "ĠCarolyn": 43450, "Kansas": 43451, "599": 43452, "ĠSolidGoldMagikarp": 43453, "Beck": 43454, "Ġstressing": 43455, "ĠGoo": 43456, "ĠCooperative": 43457, "Ġfs": 43458, "ĠArchie": 43459, "Liter": 43460, "ĠKlopp": 43461, "Jerry": 43462, "Ġfootwear": 43463, "Warren": 43464, "Ġscree": 43465, "hare": 43466, "Understanding": 43467, "Ped": 43468, "Ġanthology": 43469, "ĠAnnounce": 43470, "Mega": 43471, "Ġfluent": 43472, "Ġbondage": 43473, "ĠDiscount": 43474, "ilial": 43475, "Cart": 43476, "ĠNightmares": 43477, "Sham": 43478, "ĠBoll": 43479, "ussie": 43480, "Http": 43481, "Atlanta": 43482, "Ġunrecogn": 43483, "ĠBid": 43484, "Ġundergrad": 43485, "Ġforgiving": 43486, "ĠGlover": 43487, "AAAAAAAA": 43488, "445": 43489, "VG": 43490, "paio": 43491, "killers": 43492, "Ġresponsibly": 43493, "Ġmobilize": 43494, "Ġeffected": 43495, "ĠLumin": 43496, "Ġkale": 43497, "Ġinfringing": 43498, "announced": 43499, "Ġfitt": 43500, "batch": 43501, "ĠTackle": 43502, "ĠLime": 43503, "ĠAPP": 43504, "ukemia": 43505, "Ġruby": 43506, "Ġexoner": 43507, "ĠCasual": 43508, "070": 43509, "Ġpelvic": 43510, "Ġautomate": 43511, "ĠKear": 43512, "ĠCoastal": 43513, "Ġcreed": 43514, "Ġboredom": 43515, "ĠStun": 43516, "riott": 43517, "Ĥİ": 43518, "Ġregenerate": 43519, "Ġcomedians": 43520, "ĠOPER": 43521, "Spons": 43522, "idium": 43523, "onis": 43524, "Located": 43525, "057": 43526, "Ġsuspense": 43527, "ĠDating": 43528, "Cass": 43529, "Ġneocons": 43530, "ĠShinzo": 43531, "Ġawoken": 43532, "christ": 43533, "ĠMessages": 43534, "attled": 43535, "ĠSpray": 43536, "ĠSpice": 43537, "CW": 43538, "Ġshielding": 43539, "ĠGaul": 43540, "Amid": 43541, "Ġparamilitary": 43542, "Ġmultif": 43543, "ĠTanner": 43544, "ilk": 43545, "Ġgoddamn": 43546, "gements": 43547, "Ġbefriend": 43548, "mobi": 43549, "Ġ388": 43550, "folder": 43551, "acca": 43552, "Ġinsin": 43553, "gap": 43554, "Nev": 43555, "fifth": 43556, "Ġpsychiatry": 43557, "banks": 43558, "THIS": 43559, "Ġharb": 43560, "acqu": 43561, "Ġfacade": 43562, "ĠPowerPoint": 43563, "803": 43564, "Ġbluff": 43565, "Shares": 43566, "Ġfavoring": 43567, "Elizabeth": 43568, "ÃįÃį": 43569, "Ġranger": 43570, "772": 43571, "ĠArche": 43572, "hak": 43573, "ĠGenetics": 43574, "ĠFEMA": 43575, "Ġevolves": 43576, "Ġeste": 43577, "ĠPets": 43578, "ĠMé": 43579, "ĠInteresting": 43580, "ĠCanterbury": 43581, "chapter": 43582, "ĠStarfleet": 43583, "Spanish": 43584, "Ġdrawback": 43585, "ĠNorwich": 43586, "970": 43587, "north": 43588, "aganda": 43589, "Ġtransformative": 43590, "ramids": 43591, "biology": 43592, "aday": 43593, "Ġpropagation": 43594, "ĠGamma": 43595, "ĠDenise": 43596, "ĠCalculator": 43597, "entimes": 43598, "ĠBett": 43599, "Ġappendix": 43600, "ĠHDD": 43601, "AKING": 43602, "Ġstigmat": 43603, "Ġholster": 43604, "Ġordinarily": 43605, "Chance": 43606, "ĠContrary": 43607, "Ġadhesive": 43608, "Ġgathers": 43609, "612": 43610, "reau": 43611, "onyms": 43612, "eways": 43613, "Ġinduces": 43614, "Ġinterchangeable": 43615, "sem": 43616, "Whit": 43617, "Ġtrance": 43618, "Ġincorporation": 43619, "ĠExtras": 43620, "Financial": 43621, "Ġawkwardly": 43622, "ĠSturgeon": 43623, "ĠHY": 43624, "Normally": 43625, "ĠEnding": 43626, "ĠAssist": 43627, "encrypted": 43628, "Ġsubjug": 43629, "Ġnos": 43630, "Ġfanatic": 43631, "Cub": 43632, "CU": 43633, "?\".": 43634, "Ġirreversible": 43635, "åĤ": 43636, "031": 43637, "ĠHAR": 43638, "spread": 43639, "ulia": 43640, "=$": 43641, "Scope": 43642, "Lots": 43643, "Ġlifestyles": 43644, "olon": 43645, "Ġfeds": 43646, "Ġcongratulate": 43647, "webkit": 43648, "Ġindistinguishable": 43649, "ĠSwing": 43650, "Ġcommandments": 43651, "quila": 43652, "abella": 43653, "methyl": 43654, "annabin": 43655, "Ġovere": 43656, "Ġlobster": 43657, "ĠQUEST": 43658, "ĠCONTIN": 43659, "bernatorial": 43660, "::::::::": 43661, "ĠTrave": 43662, "ĠSamoa": 43663, "ANI": 43664, "752": 43665, "д": 43666, "usercontent": 43667, "ĠModerate": 43668, "yeah": 43669, "ĠKitt": 43670, "Ġwee": 43671, "Ġstuffing": 43672, "ĠIntervention": 43673, "ĠDign": 43674, "Ġwarehouses": 43675, "ĠFiji": 43676, "Ġpellets": 43677, "Ġtakeaway": 43678, "ĠTABLE": 43679, "ĠClassical": 43680, "collection": 43681, "Ġlandfall": 43682, "ĠMuscle": 43683, "Ġsettles": 43684, "ĠADV": 43685, "Ġ344": 43686, "Laura": 43687, "Ġfared": 43688, "ĠPartial": 43689, "436": 43690, "ossibility": 43691, "ĠDaly": 43692, "ĠTarant": 43693, "ĠFuji": 43694, "aml": 43695, "cence": 43696, "551": 43697, "ĠProcedures": 43698, "ĠOCD": 43699, "ĠUD": 43700, "tin": 43701, "QUI": 43702, "acho": 43703, "438": 43704, "Ġglitches": 43705, "Ġenchantment": 43706, "Ġcalculates": 43707, "IRO": 43708, "ĠHua": 43709, "alyses": 43710, "ĠLift": 43711, "umo": 43712, "Ġleapt": 43713, "Ġhypothesized": 43714, "ĠGustav": 43715, "itans": 43716, "VERSION": 43717, "æł": 43718, "Roger": 43719, "Ġrand": 43720, "ĠAdapter": 43721, "Ġ331": 43722, "ĠPetition": 43723, "kies": 43724, "Mars": 43725, "Ġundercut": 43726, "zees": 43727, "ĠLyons": 43728, "ĠDHCP": 43729, "Missing": 43730, "Ġretirees": 43731, "Ġinsidious": 43732, "eli": 43733, ">)": 43734, ".ãĢį": 43735, "Ġfinalists": 43736, "ĠAure": 43737, "Ġaccuser": 43738, "Ġwastes": 43739, "ĠYs": 43740, "ĠLori": 43741, "Ġconstituencies": 43742, "Ġsupper": 43743, "Ġmayhem": 43744, "orange": 43745, "Ġmisplaced": 43746, "Ġmanagerial": 43747, "Ġexce": 43748, "ĠCLI": 43749, "Ġprimal": 43750, "ĠLent": 43751, "Crystal": 43752, "hover": 43753, "ĠNTS": 43754, "endum": 43755, "Ġdw": 43756, "ĠAlc": 43757, "nostic": 43758, "Ġpreserves": 43759, "ĠTsarnaev": 43760, "Ġtripled": 43761, "relative": 43762, "Arcade": 43763, "killing": 43764, "ĠWEEK": 43765, "ĠHanna": 43766, "Dust": 43767, "Completed": 43768, "ģ«": 43769, "Ġapproves": 43770, "ĠSurf": 43771, "ĠLutheran": 43772, "venants": 43773, "Ġrobberies": 43774, "weights": 43775, "software": 43776, "atana": 43777, "ugal": 43778, "Ġgravy": 43779, "ĠCance": 43780, "OLOGY": 43781, "lyak": 43782, "Tonight": 43783, "Ġunveil": 43784, "Ġ1904": 43785, "ĠMinion": 43786, "entious": 43787, "stice": 43788, "packages": 43789, "ĠGEAR": 43790, "Ġgol": 43791, "ĠHutchinson": 43792, "ĠProfession": 43793, "ĠGUN": 43794, "ĠDifference": 43795, "ĠTsukuyomi": 43796, "ĠLesbian": 43797, "670": 43798, "Ġfugitive": 43799, "ĠPlanetary": 43800, "--------------------------------------------------------": 43801, "Ġaccrued": 43802, "Ġchicks": 43803, "Ġstopp": 43804, "Ġblockers": 43805, "Cod": 43806, "Ġcommenters": 43807, "ĠSomewhere": 43808, "ĠPhotographer": 43809, "theme": 43810, "Ġmayoral": 43811, "wu": 43812, "Ġantennas": 43813, "Ġrevamped": 43814, "ĠSubjects": 43815, "ité": 43816, "imura": 43817, "Ġentrances": 43818, "literally": 43819, "Ġtenets": 43820, "ĠOMG": 43821, "ĠMPH": 43822, "ĠDonkey": 43823, "ĠOffense": 43824, "Ġ\"+": 43825, "Snap": 43826, "ĠAFB": 43827, "Ġanimate": 43828, "ĠSod": 43829, "Hispanic": 43830, "Ġinconsistency": 43831, "Db": 43832, "FY": 43833, "Export": 43834, "Ġape": 43835, "Ġpearl": 43836, "ibel": 43837, "ĠPACs": 43838, "Ġ{\\": 43839, "Ġactu": 43840, "ĠHSBC": 43841, "campus": 43842, "Ġpayoff": 43843, "Ġdeities": 43844, "ĠNato": 43845, "ouple": 43846, "Ġcensored": 43847, "ĠClojure": 43848, "Ġconfounding": 43849, "eni": 43850, "Ġreckon": 43851, "ophe": 43852, "Ġspotting": 43853, "Ġsignifies": 43854, "Ġpropel": 43855, "Ġfestive": 43856, "Suggest": 43857, "Ġpledging": 43858, "ĠBerman": 43859, "Ġrebellious": 43860, "Ġovershadowed": 43861, "Ġinfiltrated": 43862, "jobs": 43863, "672": 43864, "Ġscalable": 43865, "Ġdominion": 43866, "ĠNewfoundland": 43867, "ĠMeadow": 43868, "Ġpartitions": 43869, "AMI": 43870, "Ġsupplementary": 43871, "strument": 43872, "Ġhairy": 43873, "Ġperpetuate": 43874, "Ġnutshell": 43875, "ĠPotato": 43876, "ĠHobbit": 43877, "Ġcurses": 43878, "Float": 43879, "Ġquieter": 43880, "Ġfueling": 43881, "Ġcapsules": 43882, "ĠLust": 43883, "ĠHaunted": 43884, "Executive": 43885, "Ġchildbirth": 43886, "Gre": 43887, "Ġradiant": 43888, "åİ": 43889, "Ġmalls": 43890, "Ġinept": 43891, "ĠWarranty": 43892, "Ġspectator": 43893, "Eh": 43894, "thens": 43895, "Ġculminating": 43896, "æ©": 43897, "arya": 43898, "ãĤ®": 43899, "ilitarian": 43900, "ĠORIG": 43901, "ĠSpending": 43902, "ptives": 43903, "ĠSiren": 43904, "ĠRecording": 43905, "ayne": 43906, "Ġvim": 43907, "Ġsprang": 43908, "Tang": 43909, "ĠMFT": 43910, "morning": 43911, "ĠWeed": 43912, "mpeg": 43913, "cession": 43914, "ĠChung": 43915, "730": 43916, "warning": 43917, "562": 43918, "handedly": 43919, "Poor": 43920, "Politics": 43921, ":#": 43922, "Ġpian": 43923, "Ġfeces": 43924, "ĠDocumentation": 43925, "Ġbanished": 43926, "Ġ399": 43927, "ĠARC": 43928, "Ġheinous": 43929, "Jake": 43930, "ĠAmir": 43931, "wayne": 43932, "vre": 43933, "oshenko": 43934, "Ġnotebooks": 43935, "Ġfoundational": 43936, "Ġmarvelous": 43937, "ixtape": 43938, "Ġwithdrawals": 43939, "Ġhorde": 43940, "ĠDhabi": 43941, "isable": 43942, "ĠKD": 43943, "Ġcontagious": 43944, "ĠDip": 43945, "ĠArrows": 43946, "Ġpronouns": 43947, "Ġmorphine": 43948, "ĠBUS": 43949, "682": 43950, "Ġkosher": 43951, "finished": 43952, "ĠInstruments": 43953, "Ġfused": 43954, "yden": 43955, "ĠSalmon": 43956, "Fab": 43957, "affected": 43958, "KEN": 43959, "CENT": 43960, "Domain": 43961, "Ġpokemon": 43962, "ĠDrinking": 43963, "Growing": 43964, "ĠInvestigative": 43965, "ĠAether": 43966, "emi": 43967, "Ġtabloid": 43968, "Ġrepro": 43969, "ĠNotwithstanding": 43970, "ĠBerserker": 43971, "Ġdramas": 43972, "Ġcliché": 43973, "Ġbung": 43974, "ĠURI": 43975, "ĠDos": 43976, "044": 43977, "Ġpastors": 43978, "Ġls": 43979, "Ġacrylic": 43980, "aunts": 43981, "Edward": 43982, "Ġmajorities": 43983, "Bang": 43984, "Ġfielding": 43985, "ĠReplacement": 43986, "ĠAlchemy": 43987, "ppard": 43988, "ĠRomeo": 43989, "ĠSanct": 43990, "ĠLavrov": 43991, "ibble": 43992, "Instruct": 43993, "Ġimpractical": 43994, "ĠPlayboy": 43995, "cephal": 43996, "Ġswaps": 43997, "Ġkan": 43998, "ĠTheo": 43999, "Ġillustrating": 44000, "Ġdismantled": 44001, "ĠTransgender": 44002, "ĠGuth": 44003, "UGH": 44004, "Ġtriumphant": 44005, "Ġencompass": 44006, "Ġbookmark": 44007, "uddin": 44008, "jer": 44009, "Ġpredicate": 44010, "ESH": 44011, "Ġwhence": 44012, "ĠABE": 44013, "Ġnonprofits": 44014, "Sequ": 44015, "Ġdiabetic": 44016, "Ġpend": 44017, "Ġheartfelt": 44018, "shi": 44019, "Ġinteracts": 44020, "ĠTelecom": 44021, "Ġbombardment": 44022, "depending": 44023, "ĠLowry": 44024, "ĠAdmission": 44025, "ĠBlooming": 44026, "ustration": 44027, "enegger": 44028, "Brew": 44029, "Ġmolten": 44030, "ĠNerd": 44031, "PIN": 44032, "âĸĢ": 44033, "avement": 44034, "Ġtoured": 44035, "Ġcoefficients": 44036, "ĠTrayvon": 44037, "ansson": 44038, "Ġsandy": 44039, "told": 44040, "flows": 44041, "Ġpopulous": 44042, "ĠTinder": 44043, "ĠBliss": 44044, "Rachel": 44045, "Minimum": 44046, "Ġcontestant": 44047, "ĠReduce": 44048, "ĠMorse": 44049, "ĠGrassley": 44050, "ĠClicker": 44051, "Ġexpr": 44052, "Ġsincerity": 44053, "Ġmarqu": 44054, "Ġelicit": 44055, "ĠProposition": 44056, "ĠDemonic": 44057, "Ġtacos": 44058, "Greek": 44059, "Ġpostwar": 44060, "Ġinsofar": 44061, "ĠPork": 44062, "Ġ352": 44063, "doctoral": 44064, "walking": 44065, "Ġmidterm": 44066, "ĠSammy": 44067, "sighted": 44068, "ĠTRANS": 44069, "ici": 44070, "ALD": 44071, "ĠUSL": 44072, "ĠFISA": 44073, "ĠAmpl": 44074, "ĠAlexandra": 44075, "inelli": 44076, "Train": 44077, "Ġsignify": 44078, "ĠVersus": 44079, "Ġobfusc": 44080, "Ġkh": 44081, "Ġaggro": 44082, "ĠRenault": 44083, "Ġ348": 44084, "518": 44085, "oxicity": 44086, "022": 44087, "ĠTwist": 44088, "Ġgoofy": 44089, "Dynamic": 44090, "Ġbriefings": 44091, "might": 44092, "899": 44093, "Ġderogatory": 44094, "Tro": 44095, "Ġforging": 44096, "ĠKoran": 44097, "ĠMarried": 44098, "ĠBucs": 44099, "Ġpalate": 44100, "ĠConversion": 44101, "mable": 44102, "413": 44103, "Ġ(_": 44104, "Ġsiph": 44105, "ĠNEO": 44106, "college": 44107, "Ġmarginally": 44108, "Ġflirt": 44109, "ĠTraps": 44110, "ĠPace": 44111, "é»Ĵ": 44112, "Ġgoaltender": 44113, "Ġforbids": 44114, "Ġclerks": 44115, "ĠTant": 44116, "ĠRobbins": 44117, "ĠPrinting": 44118, "Ġpremiered": 44119, "Ġmagnification": 44120, "ĠTG": 44121, "ĠRouse": 44122, "ĠMock": 44123, "odynamics": 44124, "Ġpreclude": 44125, "ismo": 44126, "ĠPulitzer": 44127, "Ġavalanche": 44128, "ĠKodi": 44129, "ribune": 44130, "ĠLena": 44131, "Electric": 44132, "Ġrefinery": 44133, "Ġendowed": 44134, "Ġcounselors": 44135, "Ġdolphin": 44136, "ĠMith": 44137, "Ġarmoured": 44138, "hibited": 44139, "Begin": 44140, "ĠPW": 44141, "Oil": 44142, "ĠVor": 44143, "ĠSharif": 44144, "ĠFrazier": 44145, "estate": 44146, "Ġjams": 44147, "Proxy": 44148, "Ġbandits": 44149, "ĠPresbyterian": 44150, "ĠPremiere": 44151, "tiny": 44152, "ĠCruel": 44153, "Testing": 44154, "Ġhomer": 44155, "ĠVERS": 44156, "ĠProl": 44157, "ĠDeposit": 44158, "ĠCoffin": 44159, "Ġseminars": 44160, "Ġsql": 44161, "ĠDefendants": 44162, "Alternatively": 44163, "ĠRats": 44164, "ç«": 44165, "ethyst": 44166, "'>": 44167, "Ġissuer": 44168, "589": 44169, "Ġchaired": 44170, "ĠAccessories": 44171, "manent": 44172, "Ġmarrow": 44173, "ĠPrimordial": 44174, "CN": 44175, "Ġlimitless": 44176, "ĠCarnage": 44177, "Ġundrafted": 44178, "qv": 44179, "INESS": 44180, "onew": 44181, "Ġcohesion": 44182, "987": 44183, "Ġnecks": 44184, "Ġfootballer": 44185, "ĠGER": 44186, "Ġdetectable": 44187, "ĠSupporting": 44188, "ĠCSV": 44189, "ocally": 44190, "kHz": 44191, "Ġunde": 44192, "Ġshone": 44193, "Ġbudding": 44194, "trak": 44195, "Standing": 44196, "ĠStarcraft": 44197, "ĠKemp": 44198, "Bench": 44199, "Ġthwarted": 44200, "ĠGrounds": 44201, "athi": 44202, "Lisa": 44203, "Dialog": 44204, "ĠSX": 44205, "Vision": 44206, "Ġingenious": 44207, "ÙIJ": 44208, "Ġfostering": 44209, "ĠZa": 44210, "ĠIngram": 44211, "Ġ\"@": 44212, "Naturally": 44213, "616": 44214, "035": 44215, "ĠFAC": 44216, "Hmm": 44217, "554": 44218, "Ġaccelerator": 44219, "ĠVend": 44220, "Ġsunscreen": 44221, "Ġtuberculosis": 44222, "raviolet": 44223, "ĠFunctional": 44224, "ĠErrors": 44225, "edar": 44226, "1966": 44227, "ĠSpectre": 44228, "ĠRecipes": 44229, "885": 44230, "ĠMankind": 44231, "Liverpool": 44232, "Ġ|--": 44233, "Ġsubstitutes": 44234, "ĠXT": 44235, "wired": 44236, "Ġinco": 44237, "ĠAfgh": 44238, "Eva": 44239, "icc": 44240, "Song": 44241, "Knight": 44242, "Ġdiligently": 44243, "ĠBroadcast": 44244, "Aid": 44245, "Ġafar": 44246, "ĠHMS": 44247, "atonin": 44248, "ĠGrateful": 44249, "Ġfireplace": 44250, "ĠOmni": 44251, "euro": 44252, "ĠFRE": 44253, "ĠShib": 44254, "ĠDigest": 44255, "toggle": 44256, "Ġheadsets": 44257, "Ġdiffusion": 44258, "ĠSquirrel": 44259, "ĠFN": 44260, "Ġdarkened": 44261, "outher": 44262, "Ġsleeps": 44263, "ĠXer": 44264, "guns": 44265, "Ġsetups": 44266, "Ġparsed": 44267, "Ġmammoth": 44268, "ĠCurious": 44269, "gob": 44270, "ĠFitzpatrick": 44271, "ĠEmil": 44272, "imov": 44273, ".............": 44274, "ĠBenny": 44275, "Secondly": 44276, "Ġhearty": 44277, "Ġconson": 44278, "stained": 44279, "Ġgalactic": 44280, "clave": 44281, "Ġplummeted": 44282, "Ġpests": 44283, "Ġswat": 44284, "Ġreferrals": 44285, "ĠLionel": 44286, "holy": 44287, "Ġunderdog": 44288, "ĠSlater": 44289, "ĠProvide": 44290, "ĠAmar": 44291, "ressor": 44292, "åĮ": 44293, "onga": 44294, "Ġtimid": 44295, "Ġpiety": 44296, "ĠDek": 44297, "Ġsurging": 44298, "azo": 44299, "Ġ610": 44300, "Ġdesks": 44301, "ĠSpokane": 44302, "ĠAnfield": 44303, "Ġwarships": 44304, "ĠCobra": 44305, "Ġarming": 44306, "clusively": 44307, "ĠBadge": 44308, "agascar": 44309, "ĠPRESS": 44310, "ĠMcKenzie": 44311, "ĠFerdinand": 44312, "burning": 44313, "Afee": 44314, "Ġtyrann": 44315, "ĠIw": 44316, "ĠBoone": 44317, "1007": 44318, "ĠRept": 44319, "ĊÂł": 44320, "Ġcaravan": 44321, "ĠDill": 44322, "ĠBundesliga": 44323, "Chuck": 44324, "Ġhealer": 44325, "ãĥ¼ãĥĨ": 44326, "ĠHobby": 44327, "Ġnegate": 44328, "Ġcritiques": 44329, "sectional": 44330, "mopolitan": 44331, "Ġdx": 44332, "Ġoutsourcing": 44333, "ĠCipher": 44334, "tap": 44335, "Sharp": 44336, "Ġupbeat": 44337, "Ġhangar": 44338, "Ġcruising": 44339, "ĠNiagara": 44340, "Ġ342": 44341, "illus": 44342, "ĠSv": 44343, "Ġsubtitles": 44344, "Ġsquared": 44345, "Ġbookstore": 44346, "Ġrevolutionaries": 44347, "ĠCarlton": 44348, "abal": 44349, "Utah": 44350, "Ġdespise": 44351, "ĠUM": 44352, "consider": 44353, "aido": 44354, "Ġcarts": 44355, "ĠTurtles": 44356, "Training": 44357, "Ġhonorary": 44358, "¢": 44359, "Ġtriangles": 44360, "422": 44361, "Ġreprinted": 44362, "Ġgraceful": 44363, "ĠMongolia": 44364, "Ġdisruptions": 44365, "ĠBoh": 44366, "Ġ349": 44367, "Ġdrains": 44368, "Ġconsulate": 44369, "Ġbends": 44370, "Ġmafia": 44371, "uron": 44372, "ĠFulton": 44373, "misc": 44374, "Ġrenal": 44375, "Ġinaction": 44376, "cking": 44377, "Ġphotons": 44378, "Ġbruised": 44379, "ĠCodes": 44380, "ogi": 44381, "Ġnests": 44382, "ĠLovely": 44383, "ĠLibre": 44384, "ĠDaryl": 44385, "Ġ###": 44386, "Sys": 44387, ".,\"": 44388, "Ġfreezes": 44389, "establishment": 44390, "andowski": 44391, "Ġcumbers": 44392, "ĠStarg": 44393, "ĠBombs": 44394, "Ġlegions": 44395, "Ġhandwriting": 44396, "Ġgrun": 44397, "ĠCah": 44398, "sequent": 44399, "Ġmoth": 44400, "ĠMSM": 44401, "Insert": 44402, "Fif": 44403, "Ġmotel": 44404, "Ġdexter": 44405, "ĠBild": 44406, "heartedly": 44407, "Ġprope": 44408, "ĠTexture": 44409, "ĠJunction": 44410, "ynthesis": 44411, "ocard": 44412, "ĠVera": 44413, "ĠBarth": 44414, "Ġμg": 44415, "Ġlashed": 44416, "Ġ351": 44417, "ĠZamb": 44418, "ĠStaples": 44419, "ĠCortex": 44420, "ĠCorker": 44421, "Ġcontinuum": 44422, "ĠWRITE": 44423, "unta": 44424, "ridor": 44425, "Ġdeems": 44426, "033": 44427, "ĠGOLD": 44428, "pas": 44429, "Ġrepressive": 44430, "ãĥĨãĤ£": 44431, "Ġbaffled": 44432, "Scar": 44433, "Ġcrave": 44434, "Ġ______": 44435, "Ġentrepreneurship": 44436, "ĠDirectorate": 44437, "Ġ'[": 44438, "Ġvines": 44439, "Ġascended": 44440, "ĠGROUP": 44441, "ĠGoodbye": 44442, "Ġdogged": 44443, "ãĥ´ãĤ¡": 44444, "Manufact": 44445, "Ġunimaginable": 44446, "riots": 44447, "ierrez": 44448, "Ġrelativity": 44449, "ĠCrafting": 44450, "raught": 44451, "uden": 44452, "cookie": 44453, "Ġassassins": 44454, "Ġdissatisfied": 44455, "acci": 44456, "Ġconduit": 44457, "Spread": 44458, "ĠRican": 44459, "nice": 44460, "izzle": 44461, "Ġscares": 44462, "ĠWHY": 44463, "phans": 44464, "535": 44465, "Ġprotracted": 44466, "ĠKristen": 44467, "536": 44468, "ĠScrib": 44469, "ĠNeh": 44470, "Ġtwenties": 44471, "Ġpredicament": 44472, "Ġhandcuffs": 44473, "Ġfruitful": 44474, "ĠUL": 44475, "ĠLudwig": 44476, "Ġattest": 44477, "ĠBreaker": 44478, "Ġbiologically": 44479, "ĠDealer": 44480, "Ġrenovations": 44481, "fw": 44482, "essen": 44483, "Alice": 44484, "ĠHenri": 44485, "Ġunilaterally": 44486, "ĠSidd": 44487, "hai": 44488, "ĠStretch": 44489, "Sales": 44490, "Ġcumbersome": 44491, "ĠJavier": 44492, "Ġtrendy": 44493, "Ġrotting": 44494, "ĠChallenges": 44495, "Ġscraps": 44496, "Ġfacets": 44497, "ĠVeronica": 44498, "ĠVerge": 44499, "ĠSana": 44500, "Alien": 44501, "ĠRih": 44502, "Ġradial": 44503, "ectar": 44504, "Ġ630": 44505, "cli": 44506, "Marie": 44507, "Ġwildfire": 44508, "ĠCato": 44509, "hander": 44510, "Ġwaitress": 44511, "Ġchops": 44512, "ĠSECTION": 44513, "Ġbluntly": 44514, "ĠCatalog": 44515, "nian": 44516, "study": 44517, "Ġpatrolling": 44518, "ĠTenth": 44519, "nexus": 44520, "ĠNON": 44521, "opsy": 44522, "Ġscathing": 44523, "sie": 44524, "Ġdeteriorated": 44525, "VB": 44526, "Nazis": 44527, "Ġdepictions": 44528, "Ġauthenticated": 44529, "ĠConce": 44530, "krit": 44531, "Ġpromulg": 44532, "ĠLONG": 44533, "UFC": 44534, "ĠVisitors": 44535, "ĠRecall": 44536, "Ġrehabilit": 44537, "ĠSLI": 44538, "Ġglacier": 44539, "ĠBite": 44540, "Ġ503": 44541, "Ġvomit": 44542, "Ġfermented": 44543, "ĠKhalid": 44544, "Ġgraded": 44545, "ĠMagicka": 44546, "ĠIchigo": 44547, "powerful": 44548, "icators": 44549, "753": 44550, "Ġshrew": 44551, "Ġ356": 44552, "Ġlegalizing": 44553, "Ġallotted": 44554, "ĠArchdemon": 44555, "ithing": 44556, "iggurat": 44557, "VOL": 44558, "Leod": 44559, "Ġoily": 44560, "Ġinducing": 44561, "Ġamygdala": 44562, "Ġadmins": 44563, "ĠAcquisition": 44564, "CAN": 44565, "Ġschematic": 44566, "Ġmoan": 44567, "ĠCameroon": 44568, "Ġtink": 44569, "Ġmerry": 44570, "Ġbutterflies": 44571, "ĠGoff": 44572, "Ġworkspace": 44573, "ĠCorona": 44574, "Ġjavascript": 44575, "ĠDolphin": 44576, "ĠCantor": 44577, "464": 44578, "toe": 44579, "APS": 44580, "ĠAging": 44581, "Ġpadded": 44582, "ĠZheng": 44583, "ĠHeld": 44584, "Ġestranged": 44585, "Ġ770": 44586, ".}": 44587, "ĠDunham": 44588, "Ġsmokes": 44589, "Ġcapitals": 44590, "undai": 44591, "Shin": 44592, "ĠFounding": 44593, "Ġentitle": 44594, "Ġcenterpiece": 44595, "Discover": 44596, "Ġthereto": 44597, "alert": 44598, "ĠNou": 44599, "ĠAnalyst": 44600, "lc": 44601, "FH": 44602, "FIELD": 44603, "ĠPOV": 44604, "gray": 44605, "Ġarcs": 44606, "ĠHOT": 44607, "Ġrs": 44608, "Ġobligatory": 44609, "ĠArchitects": 44610, "ĠSven": 44611, "ĠFEC": 44612, "0200": 44613, "Christmas": 44614, "ĠAlbania": 44615, "ratom": 44616, "587": 44617, "Ġhardships": 44618, "Ġautos": 44619, "ĠCharges": 44620, "Ġapes": 44621, "Ġ376": 44622, "wallet": 44623, "Ġintoxication": 44624, "Ġgoblin": 44625, "Ġ570": 44626, "++++++++++++++++": 44627, "ĠYelp": 44628, "ĠMagnetic": 44629, "ĠBriggs": 44630, "Rail": 44631, "Ġspawns": 44632, "ĠWiggins": 44633, "Ġshowcased": 44634, "Ġresorted": 44635, "uben": 44636, "Ġwhipping": 44637, "Ġimitate": 44638, "Ġdigestion": 44639, "ĠUSPS": 44640, "ĠGest": 44641, "Ġyea": 44642, "ĠTight": 44643, "indal": 44644, "icas": 44645, "`.": 44646, "CAST": 44647, "'';": 44648, "ĠFet": 44649, "opathic": 44650, "Invalid": 44651, "Ġregretted": 44652, "Ġbroccoli": 44653, "ĠScores": 44654, "eve": 44655, 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"Ġ................": 44713, "calling": 44714, "akov": 44715, "Ġventured": 44716, "Ġ555": 44717, "auga": 44718, "Hart": 44719, "ĠAero": 44720, "MAC": 44721, "Ġthinly": 44722, "Ġarra": 44723, "STATE": 44724, "ilde": 44725, "ĠJacqu": 44726, "ĠFemales": 44727, "Ġtheorem": 44728, "Ġ346": 44729, "Ġsmartest": 44730, "ĠPUBLIC": 44731, "ĠKron": 44732, "ĠBits": 44733, "ĠVessel": 44734, "ĠTelephone": 44735, "Ġdecap": 44736, "Ġadjunct": 44737, "ĠSEN": 44738, "merga": 44739, "Ġredacted": 44740, "Ġprehistoric": 44741, "Ġexplanatory": 44742, "ĠRuns": 44743, "ĠUttar": 44744, "ĠManny": 44745, "ĠAUTHOR": 44746, "ĠUnleashed": 44747, "ĠBowling": 44748, "beans": 44749, "793": 44750, "Ġuniverses": 44751, "Ġsensit": 44752, "ĠKung": 44753, "repeat": 44754, "ctrl": 44755, "Ġpaced": 44756, "Ġfuller": 44757, "Clock": 44758, "Ġrecomb": 44759, "ĠFaul": 44760, "ĠBunker": 44761, "Ġpooled": 44762, "Ġana": 44763, "ĠMouth": 44764, "LLOW": 44765, "humane": 44766, "Ġbulldo": 44767, "ĠMichaels": 44768, "fam": 44769, 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44826, "Ġremembrance": 44827, "Ġeased": 44828, "Ġcovari": 44829, "ĠSind": 44830, "Effective": 44831, "Ġdissemination": 44832, "ĠMoose": 44833, "ĠClapper": 44834, "brates": 44835, "Apply": 44836, "Ġinvis": 44837, "Ġworsened": 44838, "âĢĶ-": 44839, "Ġlegislator": 44840, "ĠLol": 44841, "ĠRowe": 44842, "Ġdealership": 44843, "umar": 44844, "idences": 44845, "Ġinvestigates": 44846, "Ġcascade": 44847, "Ġbidder": 44848, "ĠBEN": 44849, "Ironically": 44850, "Ġpresiding": 44851, "Ġding": 44852, "Ġcontradicted": 44853, "Ġshuts": 44854, "ĠFIX": 44855, "Ġ366": 44856, "District": 44857, "Ġsinful": 44858, "ĠCharisma": 44859, "oops": 44860, "Ġtotality": 44861, "Ġrestitution": 44862, "ĠOptimus": 44863, "ĠDah": 44864, "Ġclueless": 44865, "urned": 44866, "Ġnutrit": 44867, "Ġlandowners": 44868, "Ġflushed": 44869, "Ġbroaden": 44870, "mie": 44871, "Ġprintln": 44872, "Ġnig": 44873, "ĠCorpus": 44874, "Jen": 44875, "Ġproto": 44876, "ĠWikimedia": 44877, "ĠPalo": 44878, "COR": 44879, "Ġstorylines": 44880, "Ġevangelicals": 44881, "ĠDarrell": 44882, "Ġrotor": 44883, "ĠHW": 44884, "skilled": 44885, "eryl": 44886, "Ġbegg": 44887, "ĠBlumenthal": 44888, "Ġweaving": 44889, "Ġdownwards": 44890, "ĠJacket": 44891, "ĠANGEL": 44892, "Technology": 44893, "Ġesoteric": 44894, "aldehyde": 44895, "Ġfuriously": 44896, "Ġforeigner": 44897, "Weak": 44898, "CHO": 44899, "ĠHound": 44900, "Experience": 44901, "ĠPlaystation": 44902, "ĠMIA": 44903, "ĠUng": 44904, "cloth": 44905, "agall": 44906, "Ġcalming": 44907, "izens": 44908, "Struct": 44909, "ĠWitches": 44910, "ĠCelebration": 44911, "Ġ..............": 44912, "ptroller": 44913, "ĠTCU": 44914, "Ġbunny": 44915, "ãĥį": 44916, "utorial": 44917, "Ġupscale": 44918, "ĠSta": 44919, "ĠColossus": 44920, "Ġchloride": 44921, "ĠZac": 44922, "ĠReasons": 44923, "ĠBrookings": 44924, "ĠWHITE": 44925, "][/": 44926, "ĠLose": 44927, "905": 44928, "Ġunderside": 44929, "ernels": 44930, "Ġvape": 44931, "dozen": 44932, "uppet": 44933, "ĠSTOP": 44934, "matical": 44935, 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"Ġairstrike": 46504, "kson": 46505, "SELECT": 46506, "Ġdeflation": 46507, "ĠHerrera": 46508, "Cole": 46509, "ritch": 46510, "Ġadvisable": 46511, "Fax": 46512, "Ġworkaround": 46513, "Ġpid": 46514, "mortem": 46515, "ersen": 46516, "Ġtypo": 46517, "Ġalum": 46518, "782": 46519, "ĠJamal": 46520, "scripts": 46521, "Ġcaptives": 46522, "ĠPresence": 46523, "ĠLieberman": 46524, "angelo": 46525, "Ġalcoholism": 46526, "assi": 46527, "Ġrecite": 46528, "Ġgaping": 46529, "Ġbaskets": 46530, "ĠGou": 46531, "Browser": 46532, "neau": 46533, "Ġcorrective": 46534, "unda": 46535, "scoring": 46536, "ĠXD": 46537, "Ġfilament": 46538, "Ġdeepening": 46539, "ĠStainless": 46540, "Integer": 46541, "Ġbuggy": 46542, "Ġtenancy": 46543, "ĠMubarak": 46544, "Ġtuple": 46545, "ĠDroid": 46546, "ĠSitting": 46547, "Ġforfeit": 46548, "ĠRasmussen": 46549, "ixties": 46550, "esi": 46551, "ĠKimmel": 46552, "Ġmeticulously": 46553, "Ġapopt": 46554, "ĠSeller": 46555, "088": 46556, "ecake": 46557, "hematically": 46558, "TN": 46559, 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"Ġreptiles": 46617, "Ġ413": 46618, "ĠGarr": 46619, "ĠChak": 46620, "Ġhashes": 46621, "Ġfailings": 46622, "Ġfolklore": 46623, "Ġabl": 46624, "ĠCena": 46625, "ĠMacArthur": 46626, "ĠCOURT": 46627, "Ġperiphery": 46628, "appers": 46629, "Ġreckoned": 46630, "ĠInflu": 46631, "ĠCET": 46632, "Ġ372": 46633, "ĠDefinitive": 46634, "assault": 46635, "421": 46636, "Ġreservoirs": 46637, "Ġdives": 46638, "ĠCoil": 46639, "DAQ": 46640, "Ġvividly": 46641, "ĠRJ": 46642, "ĠBellev": 46643, "Ġeclectic": 46644, "ĠShowdown": 46645, "ĠKM": 46646, "iped": 46647, "reetings": 46648, "ĠAsuka": 46649, "Liberal": 46650, "ĠÏĦ": 46651, "Ġbystanders": 46652, "ĠGoodwin": 46653, "ukong": 46654, "Sit": 46655, "ĠTrem": 46656, "Ġcriminally": 46657, "ĠCircus": 46658, "chrome": 46659, "887": 46660, "Ġnanop": 46661, "ĠObi": 46662, "ĠLOW": 46663, "ogh": 46664, "ĠAuthors": 46665, "obyl": 46666, "Urban": 46667, "Ġti": 46668, "ĠWeir": 46669, "trap": 46670, "agy": 46671, "Ġparentheses": 46672, "Ġoutnumbered": 46673, "Ġcounterproductive": 46674, "ĠTobias": 46675, "ubis": 46676, "Parser": 46677, "STAR": 46678, "Ġsynaptic": 46679, "ĠGears": 46680, "Ġhiber": 46681, "Ġdebunked": 46682, "Ġexalted": 46683, "awatts": 46684, "HOU": 46685, "Church": 46686, "ĠPixie": 46687, "ĠUri": 46688, "ĠFormation": 46689, "ĠPrediction": 46690, "CEO": 46691, "Ġthrott": 46692, "ĠBritann": 46693, "ĠMadagascar": 46694, "ëĭ": 46695, "Ġbillboards": 46696, "ĠRPGs": 46697, "ĠBees": 46698, "completely": 46699, "FIL": 46700, "Ġdoesnt": 46701, "ĠGreenberg": 46702, "reys": 46703, "Ġsling": 46704, "Ġemptied": 46705, "ĠPixar": 46706, "ĠDharma": 46707, "luck": 46708, "inguished": 46709, "Ġendot": 46710, "Ġbabys": 46711, "059": 46712, "chest": 46713, "rats": 46714, "Ġridden": 46715, "Ġbeetles": 46716, "Ġilluminating": 46717, "Ġfictitious": 46718, "ĠProvincial": 46719, "Ġ768": 46720, "Ġshepherd": 46721, "ĠRender": 46722, "Ġ1896": 46723, "Crew": 46724, "Ġmolded": 46725, "ĠXiaomi": 46726, "ĠSpiral": 46727, "Ġdelim": 46728, "Ġorganising": 46729, "Ġhoops": 46730, "ĠBei": 46731, "zhen": 46732, "Ġfuckin": 46733, "Ġdecad": 46734, "Ġunbiased": 46735, "ammy": 46736, "swing": 46737, "Ġsmuggled": 46738, "Ġkios": 46739, "ĠPERSON": 46740, "ĠInquisitor": 46741, "Ġsnowy": 46742, "Ġscraping": 46743, "ĠBurgess": 46744, "Ptr": 46745, "agame": 46746, "RW": 46747, "Ġdroid": 46748, "ĠLys": 46749, "ĠCassandra": 46750, "Jacob": 46751, "Ġ354": 46752, "Ġpasture": 46753, "Ġfranc": 46754, "ĠScotch": 46755, "ĠEnds": 46756, "ĠIGF": 46757, "definition": 46758, "Ġhysterical": 46759, "ĠBrowne": 46760, "771": 46761, "Ġmobilization": 46762, "æķ": 46763, "iqueness": 46764, "Thor": 46765, "Ġspearheaded": 46766, "Ġembroiled": 46767, "Ġconjecture": 46768, "judicial": 46769, "Choice": 46770, "Ġpaperback": 46771, "Pir": 46772, "Ġrecovers": 46773, "ĠSurge": 46774, "ĠShogun": 46775, "ĠPediatrics": 46776, "ãģł": 46777, "Ġsweeps": 46778, "ĠLaboratories": 46779, "ĠPacks": 46780, "alus": 46781, "addin": 46782, "Ġheadlights": 46783, "gra": 46784, "Evidence": 46785, "COLOR": 46786, "Admin": 46787, "Ĭ±": 46788, "Ġconcoct": 46789, "sufficient": 46790, "Ġunmarked": 46791, "Ġrichness": 46792, "Ġdissertation": 46793, "Ġseasoning": 46794, "Ġgib": 46795, "ĠMages": 46796, "unctions": 46797, "ĠNid": 46798, "cheat": 46799, "ĠTMZ": 46800, "citizens": 46801, "ĠCatholicism": 46802, "nb": 46803, "Ġdisembark": 46804, "ĠPROGRAM": 46805, "aques": 46806, "Tyler": 46807, "Org": 46808, "ĠSlay": 46809, "ĠNero": 46810, "ĠTownsend": 46811, "INTON": 46812, "tele": 46813, "Ġmesmer": 46814, "901": 46815, "Ġfireball": 46816, "evidence": 46817, "affiliated": 46818, "ĠFrenchman": 46819, "ĠAugusta": 46820, "021": 46821, "Ġsled": 46822, "Ġreused": 46823, "ĠImmunity": 46824, "Ġwrestle": 46825, "assembled": 46826, "Maria": 46827, "Ġgunshots": 46828, "ĠBarbie": 46829, "Ġcannabinoids": 46830, "ĠToast": 46831, "ĠKinder": 46832, "IRD": 46833, "Ġrejuven": 46834, "Ġgore": 46835, "Ġrupture": 46836, "Ġbreaching": 46837, "ĠCartoon": 46838, "Ġ455": 46839, "ĠPaleo": 46840, "614": 46841, "Ġspears": 46842, "ĠAmes": 46843, "abus": 46844, "Madison": 46845, "GROUP": 46846, "Ġaborted": 46847, "yah": 46848, "Ġfelon": 46849, "Ġcausation": 46850, "Ġprepaid": 46851, "Ġpitted": 46852, "oplan": 46853, "ĠShelley": 46854, "ĠRusso": 46855, "ĠPagan": 46856, "Ġwillfully": 46857, "ĠCanaver": 46858, "undrum": 46859, "ĠSalary": 46860, "ĠArpaio": 46861, "reader": 46862, "ĠRational": 46863, "ĠOverse": 46864, "ĠCauses": 46865, "Ġ*.": 46866, "Ġwob": 46867, "Keith": 46868, "ĠConsent": 46869, "manac": 46870, "773": 46871, "623": 46872, "Ġfateful": 46873, "etimes": 46874, "Ġspirited": 46875, "ĠDys": 46876, "Ġhegemony": 46877, "Ġboycot": 46878, "ĠEnrique": 46879, "emouth": 46880, "Ġtimelines": 46881, "ĠSahara": 46882, "ĠRelax": 46883, "ĠQuincy": 46884, "ĠLessons": 46885, "ĠEQU": 46886, "SEA": 46887, "NK": 46888, "ĠCostco": 46889, "Increase": 46890, "Ġmotivating": 46891, "ĠChong": 46892, "amaru": 46893, "ĠDivide": 46894, "Ġpedigree": 46895, "ĠTasmania": 46896, "ĠPrelude": 46897, "Las": 46898, "940": 46899, "574": 46900, "Ġchau": 46901, "ĠSpiegel": 46902, "unic": 46903, "-->": 46904, "ĠPhilips": 46905, "ĠKafka": 46906, "Ġupheaval": 46907, "Ġsentimental": 46908, "Ġsax": 46909, "ĠAkira": 46910, "serial": 46911, "Matrix": 46912, "Ġelecting": 46913, "Ġcommenter": 46914, "ĠNebula": 46915, "plets": 46916, "ĠNadu": 46917, "ĠAdren": 46918, "Ġenshr": 46919, "ĠRAND": 46920, "financial": 46921, "ĠClyde": 46922, "utherford": 46923, "Ġsignage": 46924, "Ġdeline": 46925, "Ġphosphate": 46926, "roversial": 46927, "fascist": 46928, "ĠVall": 46929, "ĠBethlehem": 46930, "Ġfors": 46931, "Ġenglish": 46932, "Solid": 46933, "Nature": 46934, "Ġva": 46935, "ĠGuests": 46936, "Ġtantal": 46937, "Ġautoimmune": 46938, ";;;;;;;;;;;;": 46939, "ĠTotally": 46940, "ĠOv": 46941, "Ġdefences": 46942, "ĠCoconut": 46943, "Ġtranquil": 46944, "Ġploy": 46945, "Ġflavours": 46946, "ĠFlask": 46947, "ãĤ¨ãĥ«": 46948, "ĠWeston": 46949, "ĠVolvo": 46950, "870": 46951, "Ġmicrophones": 46952, "verbal": 46953, "RPG": 46954, "Ġiii": 46955, ";}": 46956, "028": 46957, "Ġheadlined": 46958, "Ġprimed": 46959, "Ġhoard": 46960, "ĠShad": 46961, "ĠENTER": 46962, "Ġtriangular": 46963, "Ġcapit": 46964, "lik": 46965, "ĠAncients": 46966, "Ġlash": 46967, "Ġconvol": 46968, "Ġcolonel": 46969, "enemy": 46970, "Gra": 46971, "Ġpubs": 46972, "utters": 46973, "Ġassigns": 46974, "ĠPenet": 46975, "ĠMonstrous": 46976, "ĠBowen": 46977, "ilver": 46978, "Haunted": 46979, "ĠDing": 46980, "started": 46981, "plin": 46982, "Ġcontaminants": 46983, "ĠDOE": 46984, "ffen": 46985, "ĠTechnician": 46986, "Ry": 46987, "Ġrobbers": 46988, "Ġhotline": 46989, "ĠGuardiola": 46990, "ĠKaufman": 46991, "rower": 46992, "ĠDresden": 46993, "ĠAlpine": 46994, "Elf": 46995, "Ġfmt": 46996, "ĠSard": 46997, "urses": 46998, "gpu": 46999, "Unix": 47000, "Ġunequivocally": 47001, "ĠCitizenship": 47002, "quad": 47003, "mire": 47004, "ĠSweeney": 47005, "Battery": 47006, "615": 47007, "Ġpancakes": 47008, "Ġoats": 47009, "Maps": 47010, "ĠContrast": 47011, "mbudsman": 47012, "ĠEPS": 47013, "Ġsubcommittee": 47014, "Ġsourcing": 47015, "Ġsizing": 47016, "ĠBuffer": 47017, "ĠMandatory": 47018, "Ġmoderates": 47019, "ĠPatterns": 47020, "ĠChocobo": 47021, "ĠZan": 47022, "ĠSTATES": 47023, "ĠJudging": 47024, "ĠInher": 47025, "*:": 47026, "Ġbil": 47027, "ĠYen": 47028, "Ġexhilar": 47029, "ollower": 47030, "zers": 47031, "Ġsnug": 47032, "maximum": 47033, "Ġdespicable": 47034, "ĠPACK": 47035, "ĠAnnex": 47036, "Ġsarcastic": 47037, "Ġlatex": 47038, "Ġtamp": 47039, "ĠSao": 47040, "bah": 47041, "ĠReverend": 47042, "ĠChinatown": 47043, "ĠAUT": 47044, "documented": 47045, "ĠGABA": 47046, "ĠCanaan": 47047, "ĠÙħ": 47048, "Ġgoverns": 47049, "prev": 47050, "Esc": 47051, "ĠEstimates": 47052, "OSP": 47053, "Ġendeavour": 47054, "ĠClosing": 47055, "ometime": 47056, "everyone": 47057, "Ġworsen": 47058, "Ġscanners": 47059, "Ġdeviations": 47060, "ĠRobotics": 47061, "ĠCompton": 47062, "Ġsorcerer": 47063, "Ġendogenous": 47064, "Ġemulation": 47065, "ĠPiercing": 47066, "ĠAph": 47067, "ĠSocket": 47068, "Ġbould": 47069, "ĠOU": 47070, "ĠBorderlands": 47071, "Ġ1863": 47072, "Gordon": 47073, "ĠWTO": 47074, "Ġrestricts": 47075, "Ġmosaic": 47076, "Ġmelodies": 47077, "çĦ": 47078, "Tar": 47079, "Ġdisson": 47080, "ĠProvides": 47081, "Ġ......": 47082, "bek": 47083, "FIX": 47084, "Ġbroom": 47085, "anship": 47086, "Doctors": 47087, "Ġnerds": 47088, "ĠRegions": 47089, "naissance": 47090, "Ġmete": 47091, "Ġcrept": 47092, "plings": 47093, "Ġgirlfriends": 47094, "knit": 47095, "igent": 47096, "owe": 47097, "Ġushered": 47098, "ĠBaz": 47099, "Mobil": 47100, "434": 47101, "ĠPresents": 47102, "origin": 47103, "Ġinsomnia": 47104, "ĠAux": 47105, "439": 47106, "ĠChili": 47107, "irsch": 47108, "GAME": 47109, "Ġgestation": 47110, "algia": 47111, "romising": 47112, "$,": 47113, "crow": 47114, "ĠInspection": 47115, "atomic": 47116, "Relations": 47117, "JOHN": 47118, "roman": 47119, "ĠClockwork": 47120, "ĠBakr": 47121, "mone": 47122, "MET": 47123, "Ġthirsty": 47124, "Ġbc": 47125, "Ġfaculties": 47126, "Rum": 47127, "Ġnuance": 47128, "ĠDarius": 47129, "pleting": 47130, "fters": 47131, "etchup": 47132, "Registration": 47133, "ĠKE": 47134, "Rah": 47135, "Ġpreferential": 47136, "ĠLash": 47137, "ĠHH": 47138, "Valid": 47139, "ĠNAV": 47140, "Ġstarve": 47141, "ĠGong": 47142, "zynski": 47143, "ĠActress": 47144, "Ġwik": 47145, "Ġunaccompanied": 47146, "lvl": 47147, "Bride": 47148, "ADS": 47149, "ĠCommando": 47150, "ĠVaughn": 47151, "Wallet": 47152, "Ġhopping": 47153, "ĠVie": 47154, "Ġcaveats": 47155, "Ġalas": 47156, "ifled": 47157, "abuse": 47158, "661": 47159, "Ġibn": 47160, "Ġgul": 47161, "Ġrobbing": 47162, "til": 47163, "ILA": 47164, "Ġmitigating": 47165, "Ġaptly": 47166, "Ġtyrant": 47167, "Ġmidday": 47168, "ĠGilmore": 47169, "ĠDecker": 47170, "Ġ§§": 47171, "partial": 47172, "Exactly": 47173, "Ġphenotype": 47174, "Ġ[+]": 47175, "ĠPlex": 47176, "ĠIps": 47177, "versions": 47178, "Ġebook": 47179, "Ġchic": 47180, "gross": 47181, "\":\"\"},{\"": 47182, "ĠSurprisingly": 47183, "Morgan": 47184, "Ġresidues": 47185, "ĠConfederation": 47186, "infeld": 47187, "Ġlyr": 47188, "moderate": 47189, "Ġperpendicular": 47190, "VK": 47191, "Ġsynchronized": 47192, "Ġrefreshed": 47193, "Ġadore": 47194, "ĠTorment": 47195, "olina": 47196, "Ġ2600": 47197, "ItemTracker": 47198, "Ġpies": 47199, "ĠFAT": 47200, "ĠRHP": 47201, "048": 47202, "ĠRESP": 47203, "ĠBJ": 47204, "allows": 47205, "Pand": 47206, "Ġunwelcome": 47207, "ĠVoc": 47208, "ĠBastard": 47209, "ĠOW": 47210, "ĠLAR": 47211, "ĠHealer": 47212, "Environmental": 47213, "ĠKenyan": 47214, "ĠTrance": 47215, "ĠPats": 47216, "Ġaliases": 47217, "ĠGarfield": 47218, "Ġcampaigner": 47219, "Ġadvancements": 47220, "ĠOkinawa": 47221, "ĠCoh": 47222, "owsky": 47223, "Ġstarved": 47224, "Ġsizeable": 47225, "Ġ:-)": 47226, "ĠmRNA": 47227, "Ġsuspensions": 47228, "istar": 47229, "Scotland": 47230, "Prin": 47231, "------------------------------------------------": 47232, "Ġ502": 47233, "Ġteaspoons": 47234, "Ġ1050": 47235, "Ġcoercive": 47236, "ĠMasonic": 47237, "edded": 47238, "ĠPassenger": 47239, "Ġlatt": 47240, "Ġbraces": 47241, "ĠSteal": 47242, "ĠNYT": 47243, "ĠKats": 47244, "ĠCelest": 47245, "aez": 47246, "Tu": 47247, "ĠCoulter": 47248, "ðŁĺ": 47249, "Flickr": 47250, "ĠWilmington": 47251, "iths": 47252, "++;": 47253, "Ġvending": 47254, "Ġnegro": 47255, "ĠPhi": 47256, "ĠYellowstone": 47257, "Callback": 47258, "Ġshampoo": 47259, "ĠShades": 47260, "wat": 47261, "Ġsuperhuman": 47262, "Ġridiculed": 47263, "Ġholiest": 47264, "ombo": 47265, "Ġinterns": 47266, "Ġhone": 47267, "ĠParagu": 47268, "URI": 47269, "Ġdangling": 47270, "ãĤ»": 47271, "sov": 47272, "ictional": 47273, "availability": 47274, "Ġrevocation": 47275, "Ġdow": 47276, "inic": 47277, "ĠTHEIR": 47278, "Ġiso": 47279, "Ġoutings": 47280, "ĠLethal": 47281, "Ġ)))": 47282, "Ġinaccur": 47283, "Ġoutlandish": 47284, "Ġanus": 47285, "letico": 47286, "idon": 47287, "lol": 47288, "Ġunregulated": 47289, "Ġsuccumbed": 47290, "Ġcuff": 47291, "ĠWasteland": 47292, "letal": 47293, "Ġsubstr": 47294, "Ġcoffers": 47295, "Ġautomakers": 47296, "ovi": 47297, "ĠXue": 47298, "ĠDaytona": 47299, "Ġjarring": 47300, "Ġfumes": 47301, "Ġdisbanded": 47302, "zik": 47303, "itton": 47304, "Ġstrikingly": 47305, "Ġspores": 47306, "Adapter": 47307, ".):": 47308, "ĠLyndon": 47309, "ivalry": 47310, "Ġorally": 47311, "Ġtumultuous": 47312, "Ġdispleasure": 47313, "Ġcones": 47314, "orrect": 47315, "Ġappease": 47316, "Ġderby": 47317, "ĠTripoli": 47318, "ĠAless": 47319, "Ġpoked": 47320, "ĠGuilty": 47321, "vP": 47322, "Enough": 47323, "Ġoriginals": 47324, "699": 47325, "Ġrabbi": 47326, "Ġproverbial": 47327, "Ġpostpone": 47328, "elope": 47329, "ĠMisty": 47330, "Ġstaffed": 47331, "ĠUnemployment": 47332, "reditary": 47333, "Ġdiligent": 47334, "recomm": 47335, "measures": 47336, "asin": 47337, "825": 47338, "Ġponds": 47339, "Ġmmol": 47340, "ĠSAR": 47341, "ĠCARE": 47342, "Ġ371": 47343, "Ġclenched": 47344, "ĠCorsair": 47345, "Ġcaricature": 47346, "zn": 47347, "attach": 47348, "ĠSchro": 47349, "speak": 47350, "painted": 47351, "ĠSuc": 47352, "ĠENT": 47353, "Ġcellul": 47354, "ĠPaid": 47355, "diagn": 47356, "WHERE": 47357, "Ġtexted": 47358, "Barn": 47359, "Ġretracted": 47360, "ĠReferred": 47361, "Sav": 47362, "Ġupkeep": 47363, "Ġworkplaces": 47364, "ĠTokens": 47365, "Ġamplify": 47366, "clinical": 47367, "Ġmultic": 47368, "mberg": 47369, "Ġconvoluted": 47370, "Region": 47371, "565": 47372, "ĠTopic": 47373, "Ġsnail": 47374, "Ġsaline": 47375, "Ġinsurrection": 47376, "ĠPetr": 47377, "forts": 47378, "BAT": 47379, "ĠNavajo": 47380, "Ġrudimentary": 47381, "ĠLaksh": 47382, "ONDON": 47383, "Measure": 47384, "Ġtransformer": 47385, "ĠGoddard": 47386, "Ġcoincides": 47387, "irin": 47388, "Rex": 47389, "ĠBok": 47390, "quit": 47391, "Ġshotguns": 47392, "Ġproletarian": 47393, "Ġscorp": 47394, "ĠAda": 47395, "514": 47396, "Ġslander": 47397, "recorded": 47398, "Ġembell": 47399, "risome": 47400, "Ġapologizing": 47401, "ĠMulcair": 47402, "ĠGibraltar": 47403, "Cla": 47404, "Ġallot": 47405, "ĠAttention": 47406, "Ġ433": 47407, "leave": 47408, "Ġwhine": 47409, "ĠIssa": 47410, "ĠFaust": 47411, "ĠBarron": 47412, "heny": 47413, "Ġvictimized": 47414, "Jews": 47415, "Ġnurturing": 47416, "ettel": 47417, "Winged": 47418, "ĠSubtle": 47419, "Ġflavorful": 47420, "ĠReps": 47421, "enged": 47422, "callback": 47423, "Ġdirectional": 47424, "Ġclasp": 47425, "ĠDirections": 47426, "planet": 47427, "iculture": 47428, "Helper": 47429, "icion": 47430, "acia": 47431, "Ġç¥ŀ": 47432, "Ġsurges": 47433, "Ġcanoe": 47434, "ĠPremiership": 47435, "been": 47436, "Ġdefied": 47437, "ĠTrooper": 47438, "Ġtripod": 47439, "Ġgasp": 47440, "ĠEuph": 47441, "ĠAds": 47442, "vernight": 47443, "highly": 47444, "Role": 47445, "Ġentangled": 47446, "ĠZeit": 47447, "618": 47448, "ĠRusty": 47449, "Ġhavens": 47450, "ĠVaughan": 47451, "HAEL": 47452, "ĠSERVICE": 47453, "/,": 47454, "Ġstricken": 47455, "Ġdelusions": 47456, "Ġbis": 47457, "ĠHaf": 47458, "Ġgratification": 47459, "Ġenticing": 47460, "UNCH": 47461, "Adams": 47462, "ĠOLED": 47463, "ĠBeetle": 47464, "Ġ1899": 47465, "ĠSOFTWARE": 47466, "ategor": 47467, "VL": 47468, "ĠTotem": 47469, "ĠGators": 47470, "ATURES": 47471, "Ġimpedance": 47472, "Registered": 47473, "ĠCary": 47474, "ĠAerial": 47475, "onne": 47476, "enium": 47477, "Ġdred": 47478, "ĠBeg": 47479, "Ġconcurrently": 47480, "Ġsuperpower": 47481, "ĠXan": 47482, "jew": 47483, "imester": 47484, "ĠDickinson": 47485, "âĶģ": 47486, "Fla": 47487, "Ġpree": 47488, "ĠRollins": 47489, "©¶æ": 47490, "Ġdenomination": 47491, "ĠLana": 47492, "516": 47493, "Ġinciting": 47494, "scribed": 47495, "juries": 47496, "ĠWonders": 47497, "approximately": 47498, "Ġsuspending": 47499, "Ġmountainous": 47500, "ĠLaugh": 47501, "oidal": 47502, "Ns": 47503, "Detect": 47504, ")=": 47505, "ĠLuthor": 47506, "ĠSchwarzenegger": 47507, "ĠMuller": 47508, "ĠDevi": 47509, "ecycle": 47510, "Jar": 47511, "613": 47512, "ĠLongh": 47513, "Bah": 47514, "ĠSPORTS": 47515, "nw": 47516, "Ġrefinement": 47517, "Ġwaterways": 47518, "Ġdiner": 47519, "Blade": 47520, "683": 47521, "Fac": 47522, "Ġinitials": 47523, "Ġrog": 47524, "Ġparanormal": 47525, "BUT": 47526, "Ġ[(": 47527, "ĠSwanson": 47528, "ĠMesh": 47529, "âĸ¬": 47530, "Improve": 47531, "ĠRadiation": 47532, "ĠEsther": 47533, "ĠEsk": 47534, "ĠAly": 47535, "iky": 47536, "Ġirrad": 47537, "ĠBuckingham": 47538, "Ġrefill": 47539, "Ġ._": 47540, "Repe": 47541, "CONCLUS": 47542, "Ġdifferentiated": 47543, "Ġchirop": 47544, "ĠAtkins": 47545, "Pattern": 47546, "Ġexcise": 47547, "Ġcabal": 47548, "NSA": 47549, "ĠSTA": 47550, "ĠSIL": 47551, "ĠParaly": 47552, "Ġrye": 47553, "ĠHowell": 47554, "ĠCountdown": 47555, "nesses": 47556, "alysed": 47557, "Ġresize": 47558, "ãĤ½": 47559, "Ġbudgetary": 47560, "ĠStras": 47561, "wang": 47562, "Ġapiece": 47563, "Ġprecincts": 47564, "Ġpeach": 47565, "Ġskyline": 47566, "Ġ353": 47567, "popular": 47568, "Appearances": 47569, "ĠMechanics": 47570, "ĠDevOnline": 47571, "Sullivan": 47572, "Zen": 47573, "Ġpu": 47574, "opolis": 47575, "544": 47576, "Ġdeform": 47577, "Ġcounteract": 47578, "ĠLange": 47579, "Ġ417": 47580, "Console": 47581, "774": 47582, "Ġnodding": 47583, "Ġpopulism": 47584, "Ġhep": 47585, "Ġcounselling": 47586, "compliance": 47587, "UFF": 47588, "Ġundeniably": 47589, "Ġrailing": 47590, "ĠHorowitz": 47591, "ĠSimone": 47592, "ĠBungie": 47593, "Ġak": 47594, "ĠTalks": 47595, "xff": 47596, "flake": 47597, "Crash": 47598, "Ġsweaty": 47599, "Ġbanquet": 47600, "ĠOFFIC": 47601, "Ġinventive": 47602, "Ġastronomer": 47603, "ĠStamford": 47604, "ĠScare": 47605, "ĠGREEN": 47606, "olicited": 47607, "Ġrusher": 47608, "Ġcentrist": 47609, "ighting": 47610, "Ġsubclass": 47611, "Ġdisav": 47612, "Ġdefund": 47613, "ĠNanto": 47614, "ociate": 47615, "mast": 47616, "Ġpacif": 47617, "Ġmend": 47618, "eers": 47619, "immigration": 47620, "ESSION": 47621, "Ġnumbering": 47622, "Ġlaughable": 47623, "ĠEnded": 47624, "viation": 47625, "emark": 47626, "Pitt": 47627, "Ġmeticulous": 47628, "ĠLF": 47629, "Ġcongratulated": 47630, "ĠBirch": 47631, "Ġswayed": 47632, "Ġsemifinals": 47633, "Ġhumankind": 47634, "matter": 47635, "ĠEquip": 47636, "opausal": 47637, "Said": 47638, "ĠLayout": 47639, "Ġvoicing": 47640, "Ġthug": 47641, "Ġpornographic": 47642, "IPS": 47643, "Ġmoaning": 47644, "Ġgrievance": 47645, "Ġconfessions": 47646, "escal": 47647, "TEXTURE": 47648, "Authent": 47649, "osaurus": 47650, "Purchase": 47651, "Ġrelegation": 47652, "alter": 47653, "Ġ³³": 47654, "Ġriddled": 47655, "Ġogre": 47656, "ĠLowell": 47657, "Occup": 47658, "Eat": 47659, "ĠHyder": 47660, "ĠAdviser": 47661, "Commerce": 47662, "Hunt": 47663, "ĠOrth": 47664, "ĠCompetitive": 47665, "ĠCLA": 47666, "CDC": 47667, "Ġsalads": 47668, "Fle": 47669, "Ġindustrialized": 47670, "`,": 47671, "ĠOWN": 47672, "Ġbeck": 47673, "ĠParticularly": 47674, "oubt": 47675, "ĠmM": 47676, "ĠHussain": 47677, "ĠChennai": 47678, "Ġ920": 47679, "Ġappointing": 47680, "ĠCullen": 47681, ",,,,,,,,": 47682, "Ġpores": 47683, "verified": 47684, "Ġbiochemical": 47685, "emate": 47686, "Ġcowardly": 47687, "ĠHelsinki": 47688, "ĠEthiopian": 47689, "SOURCE": 47690, "ERC": 47691, "estro": 47692, "Ġbiotech": 47693, "ĠSour": 47694, "Ġbrewer": 47695, "Bloomberg": 47696, "Ġintensify": 47697, "Glass": 47698, "anco": 47699, "ĠFDR": 47700, "greSQL": 47701, "ĠFires": 47702, "©¶æ¥µ": 47703, "eco": 47704, "1001": 47705, "ĠHomeless": 47706, "Ġinstantaneous": 47707, "ĠHaste": 47708, "igel": 47709, "Diamond": 47710, "Ġpaving": 47711, "Ġlandfill": 47712, "Ġdads": 47713, "houn": 47714, ":]": 47715, "Ġincendiary": 47716, "ĠLivingston": 47717, "ĠHilbert": 47718, "ĠChecks": 47719, "styles": 47720, "inators": 47721, "ĠClive": 47722, "phrine": 47723, "Ġchimpanzees": 47724, "Ġpall": 47725, "ĠJM": 47726, "ĠAadhaar": 47727, "ðĿ": 47728, "Ġachievable": 47729, "disabled": 47730, "PET": 47731, "OOOOOOOO": 47732, "Mot": 47733, "Ġintangible": 47734, "Ġballet": 47735, "ĠWebs": 47736, "ĠEstimated": 47737, "Effects": 47738, "Ġbailed": 47739, "Joshua": 47740, "Ġturbulence": 47741, "Ġoccupant": 47742, "ĠDaylight": 47743, "Ġ361": 47744, "meet": 47745, "Ġstatically": 47746, "Ġonlook": 47747, "Ġki": 47748, "illegal": 47749, "Ġvelvet": 47750, "Ġdehydration": 47751, "Ġacquies": 47752, "ĠRez": 47753, "akura": 47754, "ĠUpton": 47755, "atro": 47756, "Ġincomprehensible": 47757, "Ġbackdoor": 47758, "ĠRhino": 47759, "727": 47760, "Ġmaths": 47761, ")+": 47762, "Ġheresy": 47763, "Ġdf": 47764, "ĠRoche": 47765, "ĠLydia": 47766, "Ġpancreat": 47767, "reply": 47768, "arrell": 47769, "Ġsolicitation": 47770, "Ġcircadian": 47771, "BIP": 47772, "Ġforay": 47773, "Ġcryptic": 47774, "izu": 47775, "imeo": 47776, "ĠTomato": 47777, "ĠHoms": 47778, "examination": 47779, "Ġquarry": 47780, "ĠValiant": 47781, "ĠJericho": 47782, "ĠINCLUD": 47783, "Ġ1840": 47784, "519": 47785, "Ġresists": 47786, "Ġsnapshots": 47787, "ĠSpur": 47788, "ĠAntiqu": 47789, "Login": 47790, "Ġbestselling": 47791, "Ġantic": 47792, "ĠSutherland": 47793, "ãĤ¢ãĥ«": 47794, "Ġ~/": 47795, "ĠParm": 47796, "èĥ": 47797, "Pages": 47798, "intensity": 47799, "Ġimmobil": 47800, "Ġ1865": 47801, "zzo": 47802, "Ġnifty": 47803, "Ġfentanyl": 47804, "ĠPreservation": 47805, "ophen": 47806, "Ġdarts": 47807, "ĠDinosaur": 47808, "pointers": 47809, "ĠRite": 47810, "suggest": 47811, "awareness": 47812, "ĠSheridan": 47813, "Ġstances": 47814, "Ġsorcery": 47815, "Ġperjury": 47816, "ĠNikola": 47817, "iever": 47818, "Ġfiance": 47819, "ĠJordanian": 47820, "ĠBalloon": 47821, "Ġnab": 47822, "Ġkb": 47823, "Ġhumanities": 47824, "ĠTanaka": 47825, "hillary": 47826, "Ġconsultancy": 47827, "ĠZub": 47828, "Ġremission": 47829, "Ġconfid": 47830, "CHQ": 47831, "ĠFug": 47832, "Ġimprovis": 47833, "Yep": 47834, "/_": 47835, "Ġunwillingness": 47836, "Ġportfolios": 47837, "055": 47838, "ĠInstructor": 47839, "aiman": 47840, "Ġclaimants": 47841, "Mbps": 47842, "ĠBye": 47843, "received": 47844, "Tweet": 47845, "Ġindemn": 47846, "riz": 47847, "amara": 47848, "Nat": 47849, "Ġevaluates": 47850, "ĠLur": 47851, "epad": 47852, "FOX": 47853, "ĠThro": 47854, "Ġrusty": 47855, "Ġbedrock": 47856, "ĠOprah": 47857, "JB": 47858, "Ġmanipulative": 47859, "Ġwillful": 47860, "Ġrelapse": 47861, "Ġextant": 47862, "Theme": 47863, "Sensor": 47864, "ĠStability": 47865, "govern": 47866, "Ġpoppy": 47867, "Ġknack": 47868, "Ġinsulated": 47869, "ĠTile": 47870, "ĠExtrem": 47871, "Ġuntold": 47872, "Ġconverge": 47873, "Ġrefuel": 47874, "igroup": 47875, "Ġdistortions": 47876, "Ġravaged": 47877, "Ġmechanically": 47878, "ĠReilly": 47879, "ĠNose": 47880, "ĠIncarnation": 47881, "ĠBecky": 47882, "abbling": 47883, "Ġtaco": 47884, "Ġrake": 47885, "Ġmelancholy": 47886, "Ġillustrious": 47887, "ĠDartmouth": 47888, "Guide": 47889, "ĠRazer": 47890, "ĠBenz": 47891, "Ultimate": 47892, "ĠSurprise": 47893, "Ġpageant": 47894, "offer": 47895, "Whoever": 47896, "Ġwiser": 47897, "Ġchemist": 47898, "ĠHELL": 47899, "ĠBulk": 47900, "Ġplutonium": 47901, "ĠCOVER": 47902, "Ö¼": 47903, "failed": 47904, "Ġtirelessly": 47905, "Ġinfertility": 47906, "ĠTrident": 47907, "ĠShowtime": 47908, "ĠCiv": 47909, "Vice": 47910, "requires": 47911, "ittance": 47912, "Ġuncontrolled": 47913, "interesting": 47914, "561": 47915, "Ġinnovate": 47916, "ategic": 47917, "Lie": 47918, "ĠSelling": 47919, "Ul": 47920, "Ġsavior": 47921, "ĠTosh": 47922, "Ġswast": 47923, "PASS": 47924, "Ġrink": 47925, "Ġcardio": 47926, "ĠIro": 47927, "udi": 47928, "Ġvantage": 47929, "Ġvans": 47930, "ĠNiño": 47931, "+=": 47932, "Ġpropagate": 47933, "<?": 47934, "Ġmethodological": 47935, "20439": 47936, "Ġtriglycer": 47937, "Ġingrained": 47938, "ĠAnnotations": 47939, "arranted": 47940, "617": 47941, "ĠSodium": 47942, "ĠAAC": 47943, "technical": 47944, "multipl": 47945, "Ġ373": 47946, "åĭ": 47947, "Ġdecisively": 47948, "Ġboosters": 47949, "Ġdesserts": 47950, "ĠGrenade": 47951, "Ġtestifying": 47952, "ĠScully": 47953, "IDs": 47954, "Ġlockdown": 47955, "ĠScher": 47956, "ĠRé": 47957, "ĠWhitman": 47958, "ĠRamsay": 47959, "remote": 47960, "Ġhikers": 47961, "ĠHyundai": 47962, "Ġconscientious": 47963, "Ġclerics": 47964, "ĠSiberian": 47965, "uti": 47966, "isbury": 47967, "Ġrelayed": 47968, "Ġquartz": 47969, "ĠCBI": 47970, "seekers": 47971, "ulla": 47972, "Ġwelding": 47973, "ĠShal": 47974, "bleacher": 47975, "Tai": 47976, "ĠSamson": 47977, "Ġtumble": 47978, "ĠInvestor": 47979, "Ġsubcontract": 47980, "ĠShinra": 47981, "owicz": 47982, "jandro": 47983, "dad": 47984, "Ġterminating": 47985, "ĠNeural": 47986, "代": 47987, "Ġleakage": 47988, "ĠMidlands": 47989, "ĠCaucasus": 47990, "íķ": 47991, "cit": 47992, "llan": 47993, "ivably": 47994, "ĠAlbion": 47995, "Ġ457": 47996, "Ġregistrations": 47997, "Ġcomrade": 47998, "Ġclipboard": 47999, "047": 48000, "Ġdiscouraging": 48001, "ĠOops": 48002, "Adapt": 48003, "Ġempath": 48004, "nv": 48005, "ĠPROT": 48006, "ĠDonn": 48007, "ĠPax": 48008, "ĠBayer": 48009, "tis": 48010, "Square": 48011, "Ġfootprints": 48012, "particip": 48013, "ĠChilean": 48014, "Brend": 48015, "inducing": 48016, "Magn": 48017, "Ġclubhouse": 48018, "ĠMagnum": 48019, "Ġencamp": 48020, "ĠEthnic": 48021, "ucha": 48022, "erey": 48023, "Ġwatered": 48024, "ĠCalais": 48025, "Ġcomplexion": 48026, "Ġsects": 48027, "Ġrenters": 48028, "Ġbras": 48029, "oÄŁan": 48030, "Timeout": 48031, "Management": 48032, "Ġinfographic": 48033, "Pokemon": 48034, "Clar": 48035, "Ġlocality": 48036, "Ġflora": 48037, "asel": 48038, "Pont": 48039, "Ġpopulate": 48040, "ĠOng": 48041, "Ġsubsistence": 48042, "Ġauctions": 48043, "ĠMcAuliffe": 48044, "ĠLOOK": 48045, "bringer": 48046, "Ġtitan": 48047, "Ġmanifold": 48048, "ĠâĹı": 48049, "Ġcalibrated": 48050, "Ġcaliphate": 48051, "ĠSHE": 48052, "ĠCommissioners": 48053, "ceivable": 48054, "jc": 48055, "Winner": 48056, "524": 48057, "Ġcondone": 48058, "Otherwise": 48059, "Ġpiling": 48060, "Ġembody": 48061, "ĠCrimean": 48062, "utics": 48063, "ĠExhibition": 48064, "Ġ426": 48065, "eering": 48066, "Ġvying": 48067, "ĠHUGE": 48068, "*=-": 48069, "Ġprincipled": 48070, "à¦": 48071, "Ġquirks": 48072, "ĠEditors": 48073, "puting": 48074, "GES": 48075, "ĠFTA": 48076, "ा": 48077, "addon": 48078, "ĠHAM": 48079, "ĠFrieza": 48080, "Woman": 48081, ".$": 48082, "Ġcrib": 48083, "ĠHerod": 48084, "Ġtimers": 48085, "ĠSpaces": 48086, "ĠMacintosh": 48087, "ataka": 48088, "Ġglide": 48089, "Ġsmelling": 48090, "ĠBAL": 48091, "Ġunsu": 48092, "Ġcondos": 48093, "Ġbicycl": 48094, "ĠRevival": 48095, "553": 48096, "Ġjuggling": 48097, "Hug": 48098, "ĠKardashian": 48099, "ĠBalkans": 48100, "multiple": 48101, "Ġnutritious": 48102, "ocry": 48103, "1900": 48104, "Ġintegrates": 48105, "Ġadjoining": 48106, "ĠFolder": 48107, "rollment": 48108, "venient": 48109, "Ġuber": 48110, "yi": 48111, "Ġwhiff": 48112, "ĠJuven": 48113, "ĠBorough": 48114, "nette": 48115, "Ġbilingual": 48116, "ĠSparks": 48117, "phthal": 48118, "manufact": 48119, "Ġtouting": 48120, "ĠPHI": 48121, "Keefe": 48122, "Reward": 48123, "Ġinfall": 48124, "ĠTemper": 48125, "typically": 48126, "ĠNikol": 48127, "Ġregulars": 48128, "Ġpseudonym": 48129, "Ġexhibitions": 48130, "Ġblaster": 48131, "Ġ409": 48132, "warming": 48133, "Ġreverber": 48134, "Ġreciprocal": 48135, "Ġ670": 48136, "ipient": 48137, "bett": 48138, "ĠBegins": 48139, "Ġitching": 48140, "ĠPhar": 48141, "Assuming": 48142, "Ġemitting": 48143, "ĠMLG": 48144, "Ġbirthplace": 48145, "Ġtaunt": 48146, "ĠLuffy": 48147, "ĠAmit": 48148, "Ġcircled": 48149, "ĠNost": 48150, "ennett": 48151, "Ġdeforestation": 48152, "ĠHistorically": 48153, "ĠEveryday": 48154, "Ġovertake": 48155, "792": 48156, "Ġnun": 48157, "ĠLucia": 48158, "Ġaccompanies": 48159, "ĠSeeking": 48160, "ĠTrash": 48161, "anism": 48162, "Rogue": 48163, "Ġnorthwestern": 48164, "ĠSupplemental": 48165, "ĠNYU": 48166, "ĠFRI": 48167, "ĠSatisf": 48168, "xes": 48169, "517": 48170, "Ġreassured": 48171, "Ġsporadic": 48172, "Ġ701": 48173, "Ġmedial": 48174, "Ġcannabinoid": 48175, "Ġbarbaric": 48176, "Ġepis": 48177, "ĠExplosive": 48178, "ĠDough": 48179, "Ġunsolved": 48180, "Supported": 48181, "Ġacknowledgment": 48182, "spawn": 48183, "Ġkitchens": 48184, "Ġ-=": 48185, "talking": 48186, "icist": 48187, "ĠPegasus": 48188, "ĠPSU": 48189, "Ġphoton": 48190, "ĠAuthentication": 48191, "RG": 48192, "@#&": 48193, "762": 48194, "ĠClair": 48195, "Ġdiaper": 48196, "Ġbrist": 48197, "ĠProsecutors": 48198, "ĠJem": 48199, "628": 48200, "ĠEverywhere": 48201, "ĠJeanne": 48202, "equality": 48203, "ãĥ©ãĥ³": 48204, "objects": 48205, "ĠPelicans": 48206, "Ġ392": 48207, "Ġblu": 48208, "bys": 48209, "ĠAgo": 48210, "Ġinstructional": 48211, "Ġdiscriminating": 48212, "ĠTRAN": 48213, "ĠCornel": 48214, "agos": 48215, "Ġtyre": 48216, "Ġaspiration": 48217, "ĠBridgewater": 48218, "\":-": 48219, "!\".": 48220, "ĠEns": 48221, "ĠCoco": 48222, "Pie": 48223, "Ġdetach": 48224, "ĠCouch": 48225, "Ġphysique": 48226, "ĠOccupations": 48227, "oscopic": 48228, "enough": 48229, "Buzz": 48230, "Appearance": 48231, "YP": 48232, "Ġracer": 48233, "Ġcomplicity": 48234, "rpm": 48235, "Toy": 48236, "Ġinterrupts": 48237, "ĠCatalyst": 48238, "Ġutilitarian": 48239, "impact": 48240, "Ġspaghetti": 48241, "Ġporous": 48242, "Ġesteemed": 48243, "Ġinciner": 48244, "ĠIOC": 48245, "748": 48246, "Ġespresso": 48247, "ĠSmile": 48248, "abilia": 48249, "635": 48250, "Ġmathematician": 48251, "Ġ424": 48252, "ĠKL": 48253, "ĠHIP": 48254, "Ġoverheard": 48255, "ĠTud": 48256, "ĠTec": 48257, "Ġquizz": 48258, "Ġflattering": 48259, "Ġconn": 48260, "âĢİ": 48261, "Ġattaches": 48262, "ĠROS": 48263, "ĠACS": 48264, "Ġtcp": 48265, "ĠShame": 48266, "skip": 48267, "respected": 48268, "ĠTrinidad": 48269, "grain": 48270, "Ġfoothold": 48271, "ĠUncharted": 48272, "ĠJulio": 48273, "zl": 48274, "avored": 48275, "ĠAnxiety": 48276, "errors": 48277, "ĠCentauri": 48278, "itsch": 48279, "Daddy": 48280, "Ġclutching": 48281, "ĠImplement": 48282, "ĠGutierrez": 48283, "Ġ760": 48284, "Ġteleportation": 48285, "endra": 48286, "Ġreversible": 48287, "stros": 48288, "Adventure": 48289, "083": 48290, "Ġliberating": 48291, "Ġasphalt": 48292, "ĠSpend": 48293, "ARDS": 48294, "imsy": 48295, "PRES": 48296, "ĠEmerging": 48297, "Ġwildfires": 48298, "Ġtechnologically": 48299, "Ġemits": 48300, "ĠARTICLE": 48301, "Ġirregularities": 48302, "Ġcherish": 48303, "çīĪ": 48304, "Ġstink": 48305, "ĠRost": 48306, "Economic": 48307, "Ġcoughing": 48308, "ĠMcCann": 48309, "properties": 48310, "ilantro": 48311, "Ġrenegoti": 48312, "Translation": 48313, "Ġinquest": 48314, "ĠGrape": 48315, "ooters": 48316, "gui": 48317, "ĠSwordsman": 48318, "aceae": 48319, "hitting": 48320, "Ġrc": 48321, "Ġexerted": 48322, "ĠSAP": 48323, "itent": 48324, "Ġperilous": 48325, "Ġobscurity": 48326, "Ġassassinate": 48327, "Ġaboriginal": 48328, "Ġrescuing": 48329, "ĠShattered": 48330, "locking": 48331, "allion": 48332, "Changing": 48333, "ĠHarrington": 48334, "ĠBord": 48335, "ĠAfghans": 48336, "Jamie": 48337, "aretz": 48338, "ĠAugustus": 48339, "Ġ386": 48340, "830": 48341, "Ġjog": 48342, "okingly": 48343, "Trigger": 48344, "ĠHOR": 48345, "Statistics": 48346, "Ġviewership": 48347, "Ġadditives": 48348, "hur": 48349, "Ġmaximizing": 48350, "ĠRove": 48351, "ĠLouie": 48352, "ĠBucket": 48353, "ĠCHRIST": 48354, "ousel": 48355, "Ġstreaks": 48356, "irted": 48357, "Ġtert": 48358, "Ġcolonialism": 48359, "Ġburying": 48360, "yk": 48361, "Condition": 48362, "ĠDPRK": 48363, "ById": 48364, "751": 48365, "âĹ¼": 48366, "Ġworrisome": 48367, "Ġvocational": 48368, "slice": 48369, "Ġsails": 48370, "ĠCorrectional": 48371, "954": 48372, "Ġtul": 48373, "Kid": 48374, "luster": 48375, "Ġfamilial": 48376, "ĠSpit": 48377, "ĠEpiscopal": 48378, "Specifically": 48379, "ĠVolcano": 48380, "runs": 48381, "qs": 48382, "Ġvetted": 48383, "Ġcrammed": 48384, "trop": 48385, "herer": 48386, "Thankfully": 48387, "Ġpercussion": 48388, "Ġoranges": 48389, "Ġroundup": 48390, "Ġ499": 48391, "xious": 48392, "Characters": 48393, "ĠZionism": 48394, "ĠRao": 48395, "ÃĽÃĽ": 48396, "WF": 48397, "Ġunintentional": 48398, "ONEY": 48399, "Grab": 48400, "Commercial": 48401, "Ġglutamate": 48402, "ĠMcKenna": 48403, "ruciating": 48404, "nington": 48405, "ihu": 48406, "Chan": 48407, "ĠSwap": 48408, "Ġleaflets": 48409, "Ġfunctionally": 48410, "erous": 48411, "Farm": 48412, "Ġcaloric": 48413, "ĠLiterally": 48414, "concert": 48415, "Ġshenan": 48416, "Ġrepaid": 48417, "eyes": 48418, "Ġbashing": 48419, "ĠGorge": 48420, "Ġcollaborations": 48421, "Ġunaccount": 48422, "itchie": 48423, "Ġteamwork": 48424, "ppelin": 48425, "Ġpiping": 48426, "Ġminced": 48427, "Ġdiam": 48428, "rieg": 48429, "Ġmascara": 48430, "Ġsucker": 48431, "ĠMoons": 48432, "Apps": 48433, "ĠPeck": 48434, "Ġperv": 48435, "ĠFloat": 48436, "oley": 48437, "ĠNish": 48438, "imize": 48439, "Ġaromatic": 48440, "uin": 48441, "endish": 48442, "!/": 48443, "ĠBicycle": 48444, "ĠASIC": 48445, "ileged": 48446, "ĠQuadro": 48447, "iosyn": 48448, "Ġlockout": 48449, "ĠWink": 48450, "SPEC": 48451, "Attempts": 48452, "Ġseeded": 48453, "redo": 48454, "iasis": 48455, "Ġsnag": 48456, "ãĥķãĤ©": 48457, "ãĤ¶": 48458, "Ġgrounding": 48459, "Ġreliever": 48460, "Ġfrivolous": 48461, "ĠGifts": 48462, "ĠFaces": 48463, "Especially": 48464, "Ġmicrobiome": 48465, "imag": 48466, "ĠSchl": 48467, "ĠPles": 48468, 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49090, "ĠSPI": 49091, "Ott": 49092, "ĠLazarus": 49093, "PLIED": 49094, "Flying": 49095, "blogs": 49096, "Wisconsin": 49097, "Raven": 49098, "Ġrebate": 49099, "Ġcreeps": 49100, "ĠSpan": 49101, "ĠPainter": 49102, "ĠKira": 49103, "ĠAmos": 49104, "ĠCorvette": 49105, "Consumer": 49106, "ĠRecover": 49107, "cki": 49108, "Ġpesky": 49109, "ĠInvention": 49110, "Companies": 49111, "Ġchallengers": 49112, "ademic": 49113, "ĠUkrainians": 49114, "ĠNeurolog": 49115, "ĠForsaken": 49116, "Ġentrants": 49117, "Ġembattled": 49118, "Ġdefunct": 49119, "ĠGlacier": 49120, "Ġpoisons": 49121, "ĠHorses": 49122, "makes": 49123, "ĠDirt": 49124, "Ġ423": 49125, "hhh": 49126, "ĠTransformation": 49127, "QUIRE": 49128, "..................": 49129, "Ġtraveller": 49130, "ĠSexy": 49131, "ĠKern": 49132, "ipolar": 49133, "Ġransomware": 49134, "oooooooooooooooo": 49135, "Ec": 49136, "ruby": 49137, "Professional": 49138, "ĠOutbreak": 49139, "argument": 49140, "Grey": 49141, "ĠFifa": 49142, "ĠCHO": 49143, "ĠFORM": 49144, "ĠAmtrak": 49145, "-[": 49146, "Ġcradle": 49147, "Ġantioxidants": 49148, "ãģ®å®": 49149, "736": 49150, "ĠNASL": 49151, "ĠContributions": 49152, "Indiana": 49153, "ĠSTEP": 49154, "CSS": 49155, "Ġsalient": 49156, "Ġallocations": 49157, "yrights": 49158, "Ġmashed": 49159, "ĠCutter": 49160, "Sexual": 49161, "Ġpounded": 49162, "Ġfanbase": 49163, "Ġcasc": 49164, "ĠTransparency": 49165, "Ġanalytic": 49166, "ĠSummoner": 49167, "×ŀ": 49168, "ĠADC": 49169, "detail": 49170, "Ġvanquished": 49171, "Ġcrabs": 49172, "arie": 49173, "Destroy": 49174, "ĠSack": 49175, "Ġtransistor": 49176, "Alabama": 49177, "ĠKoen": 49178, "ĠFisheries": 49179, "cone": 49180, "Ġannexed": 49181, "ĠMGM": 49182, "esa": 49183, "Ġfaked": 49184, "ĠCongratulations": 49185, "Ġhindered": 49186, "Ġcorrectional": 49187, "ĠITV": 49188, "leeve": 49189, "Ġinappropriately": 49190, "licks": 49191, "Ġtrespass": 49192, "Ġpaws": 49193, "Ġnegotiator": 49194, "ĠChristensen": 49195, "limits": 49196, "ĠDianne": 49197, "Ġelegance": 49198, 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"ĠCom m", "Ġwh y", "u red", "ur al", "Ġs chool", "b y", "ĠM ar", "Ġa ff", "Ġd ays", "Ġan n", "us h", "an e", "I f", "e g", "Ġpro f", "Ġhe alth", "ou th", "B ut", "ion al", ". ,", "Ġs ol", "Ġal ready", "Ġ3 0", "Ġchar act", "H e", "Ġf riend", "E S", "i ans", "ic le", "' d", "ĠO n", "Ġle ast", "Ġp rom", "Ġd r", "Ġh ist", "it her", "Ġ est", "i qu", "1 7", "s on", "Ġte ll", "Ġt alk", "oh n", "o int", "le ction", "A N", "Ġunt il", "au gh", "Ġl ater", "Ġ ve", "Ġv iew", "end ing", "iv ed", "Ġwor d", "w are", "Ġc ost", "Ġen ough", "Ġg ive", "ĠUn ited", "Ġte chn", "are nt", "O R", "Ġp ar", "ĠD r", "Ġ201 6", "r ist", "er ing", "Ġ Â", "Ġl arge", "s ide", "ac y", "cc ess", "Ġw in", "Ġimport ant", "Ġ19 9", "Ġdoes n", "Ġ1 7", "Ġbus iness", "Ġcle ar", "Ġre se", "\" ,", "ur y", "Ġe qu", "as ter", "al f", "ĠAmeric an", "n ect", "Ġex pect", "ivers ity", "Ġo cc", "ĠF l", "Ġk ind", "Ġme an", "Ġp ast", "Ġde v", "Ġb as", "le t", "ra ft", "Ġor gan", "Ġde l", "Ġper form", "Ġst ory", "Ġse ason", "ĠC ol", "Ġcl 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2", "ĠSt ates", "Ġg ames", "ra w", "ut ure", "Ġunder stand", "ur s", "ĠO b", "l ish", "s y", "Ġm akes", "Ġw on", "ag on", "Ġh tt", "Ġl ove", "ent ial", "Ġcomple te", "p ar", "ĠI m", "A L", "Ġacc ount", " ł", "ore d", "ver t", "Ġ ident", "Ġ201 5", "Ġother s", "ĠM in", "i ber", "ver age", "The re", "ition al", "d d", "Ġpro b", "Ġyou ng", "Ġal ong", "Ġacc ording", "Ġy et", "Ġmem bers", "ĠWh at", "o id", "ĠM an", "A nd", "Ġam ong", "a i", "Ġem ploy", "ĠR es", "Ġ >", "Ġinv ol", "Ġl ow", "a f", "ĠC ar", "Ġh ig", "ĠO ne", "ĠS ec", "in ation", "Ġlike ly", "Ġan t", "ag ed", "ĠR uss", "Ġb en", "Ġre le", "F or", "b ack", "ĠN ot", "Ġpres ident", "b all", "Ġacc ess", "ivid ual", "ĠD em", "ĠE uro", "6 0", "Ġkn own", "ir l", "ĠG r", "Ġear ly", "u se", "iet y", "âĢ ĵ", "Ġf ight", "Ġs ent", "Ġto day", "Ġmark et", "\" .", "Ġb ased", "Ġstr ong", "ur ther", "Ġde b", "m ber", "Ġproble m", "Ġde ath", "Ġsoc ial", "im ate", "A S", "ort un", "Ġcamp aign", "er y", "C h", "Ġe y", "i ally", "Ġm us", "w h", "p 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ed", "ri ed", "m ing", "Ġatt empt", "4 5", "f er", "Ġd ue", "ress ion", "# #", "Ġsh all", "Ġs ix", "o o", "Ġst ep", "Ġp ub", "Ġhim self", "Ġ2 3", "Ġc op", "Ġd est", "Ġst op", "A C", "ib ility", "Ġl ab", "ic ult", "Ġhour s", "Ġcre ate", "Ġf urther", "ĠAmeric a", "ĠC ity", "Ġd ou", "he ad", "S T", "ĠN orth", "c ing", "Ġn ational", "u le", "ĠIn st", "Ġt aking", "ĠQ u", "ir t", "Ġre d", "Ġrese arch", "v iron", "ĠG e", "Ġbre ak", "an a", "Ġsp ace", "ater ial", "Ġrec ent", "ĠA b", "Ġgener al", "Ġh it", "Ġper iod", "Ġevery thing", "ive ly", "Ġph ys", "Ġsay ing", "an ks", "Ġc ou", "Ġc ult", "ac ed", "e al", "u ation", "Ġc oun", "l u", "Ġinclud e", "Ġpos ition", "ĠA fter", "ĠCan ad", "ĠE m", "Ġim m", "ĠR ed", "Ġp ick", "Ġcom pl", "Ġm atter", "re g", "e xt", "ang u", "is c", "o le", "a ut", "Ġcomp et", "e ed", "f ect", "Ġ2 1", "ĠS en", "ĠThe se", "as ing", "Ġcan not", "Ġin it", "Ġrel ations", "ac hed", "Ġb ar", "Ġ4 0", "ĠT H", "Ġ201 2", "Ġv ol", "Ġg round", "Ġsec urity", "Ġup d", "il t", "3 5", 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"Ġin j", "id ing", "ĠS pe", "Ġch all", "ir m", "Ġ2 2", "itt ing", "st r", "Ġh y", "L E", "ke y", "Ġbe gan", "at ur", "ashing ton", "l am", "ĠD av", "b it", "Ġs ize", "ĠP ar", "3 8", "ourn al", "f ace", "Ġdec ision", "Ġl arg", "Ġj ud", "re ct", "Ġcontin ue", "ĠO ct", "ove red", "ĠI nt", "==== ====", "Ġp arent", "ĠW ill", "Ġeas y", "Ġd rug", "ang er", "Ġs ense", "Ġd i", "id ay", "Ġener gy", "ist ic", "Ġass oci", "ar ter", "ob al", "e ks", "ĠE l", "ur ch", "Ġg irl", "o e", "it le", "Ġ2 8", "ĠC he", "Ġrequ est", "Ġso on", "Ġh ost", "k y", "Ġst ates", "om es", "Ġm aterial", "le x", "Ġmom ent", "Ġan sw", "on se", "Ġes pecially", "Ġn orm", "Ġserv ices", "p ite", "r an", "Ġro le", "4 4", ") :", "Ġc red", "C l", "____ ____", "Ġm at", "Ġl og", "ĠCl inton", "O U", "Ġoff ice", "Ġ2 6", "Ġch arg", "Ġtr ack", "m a", "Ġhe art", "Ġb all", "Ġperson al", "Ġbuild ing", "n a", "s et", "b ody", "ĠBl ack", "Ġincre ase", "itt en", "Ġneed ed", "3 6", "3 2", "= \"", "Ġl ost", "Ġbec ame", "Ġgrou ps", "ĠM us", "Ġw rote", "ĠP e", "Ġpro p", "j oy", "à ©", "ĠWh ite", "Ġde ad", ". '", "Ġhtt p", "Ġwe bs", "O S", "Ġins ide", "Ġwr ong", "Ġstat ement", "Ġ ...", "y l", "Ġfil m", "Ġmus ic", "Ġsh are", "ific ation", "Ġre lease", "Ġfor ward", "Ġst ay", "Ġcomp ut", "it te", "s er", "Ġorig inal", "Ġc ard", "Ġc and", "Ġd iv", "at ural", "Ġfav or", "O M", "Ġc ases", "us es", "Ġse ction", "Ġle ave", "g ing", "ov ed", "ĠW ashington", "3 9", "ĠG l", "Ġrequ ired", "act ion", "ap an", "o or", "it er", "ĠK ing", "Ġcount ries", "ĠG erman", "ll ing", "Ġ2 7", "3 4", "Ġquest ions", "Ġpr im", "Ġc ell", "Ġsh oot", "Ġany one", "ĠW est", "Ġaff ect", "ep end", "Ġon line", "ĠIs rael", "ĠSept ember", "Ġab ility", "Ġcont ent", "is es", "Ġre ve", "Ġl aun", "Ġind ic", "Ġfor ce", "c ast", "Ġso ld", "av ing", "f l", "Ġso ft", "Ġcompan ies", "ce ed", "Ġart icle", "Ġa ud", "Ġre v", "Ġed uc", "Ġplay ing", "0 5", "Ġhe ld", "ct or", "Ġrele ased", "Ġf ederal", "3 7", "Ġad minist", "Ġinter view", "Ġinst all", "Ġrece ived", "Ġs ource", "u k", "P h", "Ġser ious", "Ġcre ated", "Ġc ause", "Ġim medi", "Ġdef in", "u el", "ĠDep artment", "ct ions", "ĠC our", "ĠN ow", "z e", "it es", "it ution", "Ġl ate", "Ġspe ak", "n ers", "Ġleg al", "ar i", "ĠC or", "Ġwe eks", "Ġmod el", "Ġp red", "Ġex act", "B C", "ĠB y", "IN G", "os ing", "Ġt akes", "Ġreg ard", "Ġopp ortun", "Ġpr ice", "Ġ19 8", "ĠA pr", "f ully", "Ġor d", "Ġproble ms", "ru ction", "h am", "ĠC ount", "le ge", "Ġlead ers", "E T", "le v", "Ġde ep", "olog ical", "es e", "h aps", "ĠS ome", "Ġp ers", "Ġcont ract", "Ġrelations hip", "s p", "ou d", "Ġb ase", "4 8", "m it", "A d", "anc ial", "Ġcons um", "Ġpot ential", "Ġl angu", "re m", "et h", "Ġrel ig", "ress ed", "6 6", "Ġl ink", "Ġl ower", "ay er", "ĠJ une", "Ġf em", "un t", "er c", "ur d", "Ġcont act", "Ġ ill", "Ġm other", "Ġest ab", "h tt", "ĠM arch", "ĠB ro", "ĠCh ina", "Ġ2 9", "Ġs qu", "Ġprov ided", "Ġa verage", "as ons", "Ġ201 1", "Ġex am", "l in", "5 5", "n ed", "Ġper fect", "Ġt ou", "al se", "u x", "Ġbu y", "Ġsh ot", "Ġcol lect", "Ġph ot", "Ġplay ed", "Ġsur pr", "Ġofficial s", "Ġsim ple", "av y", "Ġindust ry", "Ġhand s", "g round", "Ġp ull", "Ġr ound", "Ġus er", "Ġr ange", "u ary", "Ġpriv ate", "op s", "e es", "Ġw ays", "ĠM ich", "Ġve h", "Ġex cept", "Ġter ms", "im um", "pp er", "I ON", "ore s", "ĠDr agon", "ou l", "Ġd en", "Ġperform ance", "Ġb ill", "c il", "4 7", "Ġen vironment", "Ġex c", "ad d", "Ġwor th", "Ġp ict", "Ġch ance", "Ġ201 8", "b or", "Ġspe ed", "ict ion", "Ġal leg", "ĠJ apan", "at ory", "re et", "Ġm atch", "ĠI I", "Ġst ru", "ord er", "Ġst e", "Ġl iving", "Ġst ruct", "in o", "Ġse par", "her n", "Ġresp onse", "Ġen joy", "Ġv ia", "A D", "um ents", "ace book", "Ġmem ber", "ib r", "iz ing", "Ġto ol", "ĠM on", "ĠWh ile", "h ood", "ĠA ng", "ĠD ef", "Ġoff er", "T r", "a ur", "Ġturn ed", "ĠJ uly", "d own", "an ced", "Ġrec ently", "ĠE ar", "Ġc e", "ĠSt ar", "ĠC ong", "rough t", "Ġbl ood", "Ġhop e", "Ġcom ment", "ain t", "Ġar ri", "il es", "Ġpartic ip", "ough t", "ri ption", "0 8", "4 9", "Ġg ave", "Ġse lect", "Ġkill ed", "sy ch", "Ġgo es", "i j", "Ġc oll", "Ġimp act", "at ives", "ĠS er", "0 9", "ĠAug ust", "Ġb oy", "d e", "ĠD es", "Ġf elt", "U S", "Ġexpect ed", "Ġim age", "ĠM ark", "cc ording", "o ice", "E C", "ĠM ag", "en ed", "h old", "ĠP ost", "Ġpre vent", "N o", "Ġinvol ved", "Ġey es", "Ġquick ly", "A t", "un k", "Ġbeh av", "Ġ ur", "Ġl ed", "c ome", "e y", "Ġcand id", "Ġear lier", "Ġfoc us", "et y", "P ro", "led ge", "ix ed", "ill ed", "Ġpop ular", "A P", "Ġset t", "l ight", "Ġvar ious", "in ks", "Ġlevel s", "Ġro ad", "ell ig", "ab les", "he l", "itte e", "ĠG ener", "y pe", "Ġhe ard", "ic les", "Ġm is", "Ġus ers", "ĠS an", "Ġimpro ve", "Ġf ather", "Ġse arch", "The y", "v il", "Ġprof ess", "Ġkn ew", "Ġl oss", "Ġev ents", "6 5", "Ġb illion", "0 7", "0 2", "ĠNew s", "ĠA M", "Ġco ver", "w here", "ens ion", "Ġb ott", "Ġare as", "en ces", "op e", "ĠTw itter", "a el", "Ġget s", "ĠGo ogle", "Ġs n", "i ant", "Ġv ote", "Ġnear ly", "Ġinclud ed", "Ġrec ogn", "z z", "m m", "al ed", "Ġhappen ed", "0 4", "Ġh ot", "Ġwho se", "Ġc ivil", "Ġsu ff", "o es", "it iz", "ĠSy ri", "Ġresp ond", "Ġh on", "Ġfeat ures", "Ġeconom ic", "ĠApr il", "r im", "Ġtechn ology", "Ġo ption", "ag ing", "Ġpur ch", "R e", "Ġl at", "ch ie", "is l", "Ġrec omm", "u f", "Ġtr aining", "Ġeffect s", "Ġf ast", "Ġ201 0", "Ġocc ur", "Ġwebs ite", "Ġem ail", "Ġs ens", "e ch", "Ġo il", "Ġinf lu", "Ġcurrent ly", "ĠS ch", "ĠAd d", "Ġgo al", "Ġsc ient", "Ġcon v", "1 00", "em y", "Ġdec ided", "Ġtra vel", "Ġm ention", "L L", "0 3", "Ġe lection", "Ġph one", "Ġlook s", "Ġsit uation", "Ġc y", "Ġh or", "b ed", "ĠCour t", "a ily", "av es", "Ġqu ality", "ĠCom p", "w ise", "Ġt able", "Ġst aff", "ĠW ind", "et t", "Ġtri ed", "ide red", "Ġadd ition", "Ġb ox", "Ġl ack", "ar ily", "Ġw ide", "Ġm id", "Ġbo ard", "ys is", "Ġant i", "h a", "Ġd ig", "en ing", "Ġd ro", "C on", "6 8", "Ġsl ow", "b ased", "se qu", "Ġp ath", "E x", "ak er", "Ġwork ed", "Ġp en", "Ġeng ine", "Ġlook ed", "ĠSu per", "ĠS erv", "Ġvict im", "U n", "Ġproper ty", "Ġint rodu", "Ġexec ut", "ĠP M", "L e", "Ġcol or", "ĠM ore", "Ġ6 0", "Ġnet work", "Ġd ate", "c ul", "id ge", "Ġext ra", "3 1", "Ġs le", "6 7", "Ġw ond", "Ġreport s", "j ust", "ĠAust ral", "Ġcap ital", "Ġen s", "Ġcomm and", "Ġallow ed", "Ġpre p", "Ġca pt", "h ib", "Ġnum bers", "ch an", "Ġf air", "m p", "om s", "Ġre ach", "W ith", "t ain", "Ġbro ad", "Ġcou ple", "ec ause", "ly ing", "ĠF eb", "Ġsc reen", "Ġl ives", "Ġpri or", "ĠCong ress", "A r", "Ġappro ach", "Ġe mer", "ar ies", "ĠD is", "s erv", "ĠN e", "Ġbu ilt", "c ies", "Ġre pe", "Ġrul es", "for ce", "ĠP al", "Ġfin ancial", "Ġcons idered", "ĠCh ar", "n ces", "ĠI S", "Ġb rought", "Ġb i", "i ers", "ĠS im", "O P", "Ġproduct s", "Ġvis it", "Ġdoc ument", "Ġcon duct", "Ġcomplete ly", "in ing", "ĠCal if", "ib ly", "Ġwr itten", "ĠT V", "em ents", "Ġd raw", "O ne", "Ġpub lished", "Ġsec ret", "r ain", "he t", "ĠF acebook", "ond ay", "ĠU p", "Ġsex ual", "Ġth ous", "ĠP at", "Ġ ess", "Ġstand ard", "Ġar m", "g es", "ect ion", "Ġf ell", "Ġfore ign", "an i", "ĠFr iday", "Ġreg ular", "in ary", "Ġincre ased", "Ġus ually", "Ġdem on", "Ġd ark", "Ġadd itional", "ro l", "ĠO f", "Ġprodu ction", "! !", "und red", "Ġintern ational", "id ents", "ĠF ree", "rou p", "Ġr ace", "Ġm ach", "Ġh uge", "A ll", "le ar", "ove mber", "Ġto wn", "Ġatt ention", "ĠO ff", "y ond", "ĠThe n", "f ield", "Ġter ror", "ra z", "ĠB o", "Ġmeet ing", "ĠP ark", "Ġar rest", "Ġf ear", "Ġa w", "ĠV al", "or ing", "' ,", "Ġext reme", "ar r", "Ġwork ers", "A fter", "Ġ3 1", "n et", "am ent", "Ġdirect ly", "Ġpop ulation", "ub e", "ĠOct ober", "ĠI N", "ĠJan uary", "5 9", "ĠDav id", "Ġc ross", "ce mber", "ĠF irst", "Ġmess age", "ir it", "Ġn ation", "Ġp oll", "is ions", "Ġansw er", "n y", "is ode", "Ġcar ry", "ĠRuss ia", "Ġhe ar", "eng th", "ro y", "Ġn atural", "in ally", "Ġdo g", "m itted", "Ġtr ade", "Ġsub st", "Ġmult iple", "ĠAf ric", "Ġf ans", "Ġs ort", "Ġgl obal", "ic ation", "ĠW ed", "ar a", "Ġa chie", "Ġlangu age", "ve y", "Ġt al", "Ġnecess ary", "Ġdet ails", "Ġs en", "ĠS und", "ĠRe g", "ĠR ec", "0 6", "Ġs il", "ress ive", "Ġmed ical", "un ch", "orn ia", "Ġu nd", "f ort", "oc ks", "ĠM onday", "ues day", "c raft", "7 7", "ur t", "Ġ ver", "ĠH ill", "Ġrece ive", "Ġmor ning", "es tern", "Ġb ank", "Ġs at", "ir th", "ĠH igh", "Ġdev ice", "ĠTH E", "ĠCent er", "Ġsaf e", "Ġp le", "ĠCanad a", "Ġsystem s", "Ġass ist", "Ġsur v", "Ġb attle", "ĠS oc", "vert is", "S he", "Ġp aper", "Ġgrow th", "Ġc ast", "S c", "Ġpl ans", "ll ed", "Ġpart s", "Ġw all", "Ġmove ment", "Ġpract ice", "im ately", "Ġdis play", "Ġsomet imes", "om p", "ĠP aul", "ĠY es", "k ing", "5 8", "o ly", "Ġs on", "Ġav oid", "ok es", "ĠJ ew", "Ġto wards", "as c", "Ġ //", "ĠK ore", "Ġtalk ing", "Ġcor rect", "Ġsp ent", "ic ks", "i able", "e ared", "Ġter m", "Ġwant s", "om ing", "Ġ ut", "Ġdou b", "Ġfor ces", "Ġp lease", "6 9", "ĠN ovember", "at form", "ond on", "Ġon es", "Ġimmedi ately", "ĠRuss ian", "ĠM et", "Ġde g", "Ġparent s", "C H", "ĠAmeric ans", "al y", "ĠM od", "Ġsh own", "Ġcond itions", "Ġst uff", "Ġre b", "ĠY our", "Ġinclud es", "n own", "ĠS am", "Ġexper ien", "m ission", "ĠE ven", "augh t", "Ġannoun ced", "ĠRepublic an", "Ġdeter min", "Ġdescrib ed", "ĠCount y", "( )", "Ġdo or", "Ġchang ed", "Ġne igh", "ĠH ere", "Ġcle an", "Ġp an", "ĠDe cember", "ĠEurope an", "ir ing", "ap ter", "Ġcl ub", "ĠT uesday", "Ġp aid", "ĠN et", "Ġattack s", "Ġcharact ers", "Ġal one", "Ġdirect or", "d om", "Ġ3 5", "Ġl oad", "Ġr out", "ĠCalif ornia", "Ġfin ally", "Ġr ac", "Ġcont r", "Ġexact ly", "res h", "p ri", "ĠIs lam", "Ġn ature", "Ġcare er", "Ġlat est", "Ġcon vers", "ĠS l", "p ose", "ci ent", "ĠIn c", "iv ity", "8 8", "ĠA tt", "ĠM or", "nes day", "Ġwe ight", "k en", "Ġnot e", "Ġteam s", "Ġ \\", "air s", "ĠG reen", "Ġh undred", "on ent", "Ġstre ng", "Ġcons ist", "ic ated", "Ġreg ul", "Ġl ic", "ast ic", "Ġt en", "urs day", "ellig ence", "ous ly", "ĠU K", "B I", "Ġcost s", "Ġind epend", "ĠA P", "Ġnorm al", "Ġh om", "Ġob vious", "Ġs we", "Ġst ar", "Ġread y", "ac her", "Ġimp lement", "g est", "Ġs ong", "ĠG et", "ĠL ab", "Ġinterest ing", "us ing", "Ġg iving", "ĠSund ay", "Ġet c", "Ġm iddle", "Ġrem ember", "r ight", "os ition", "ut ions", "Ġm ax", "4 6", "Ġyour self", "Ġdem and", "Ġtreat ment", "Ġd anger", "ĠC ons", "Ġgu y", "ĠBrit ish", "Ġphys ical", "Ġrel ated", "Ġrem ain", "Ġcould n", "Ġref er", "Ġc itiz", "b ox", "EN T", "bo ard", "Ġin n", "I G", "er o", "ĠSt reet", "osp ital", "ren ch", "cher s", "Ġst ra", "O L", "ag er", "ĠA N", "Ġeas ily", "I A", "en ge", "in y", "Ġcl os", "ock ed", "Ġus es", "ĠC oun", "I m", "u ild", "? ?", "m ore", "Ġan g", "Ġwr ite", "ol ute", "5 7", "Ġlead er", "Ġread ing", "< /", "Ġaut om", "est s", "4 3", "Ġleg isl", "ĠG old", "Ġdesign ed", "ĠS T", "ĠLe g", "a res", "Ġbe aut", "ĠT ex", "Ġappear s", "Ġstru gg", "ĠR om", "Ġ 00", "Ġcho ice", "Ġparticular ly", "ĠF rom", "op er", "ĠL ondon", "ann ed", "Ġallow s", "ob ile", "Ġdiffere nce", "âĢ ¢", "ĠV iew", "ĠWed nesday", "Ġal though", "Ġrel ative", "Ġapplic ation", "ate ver", "Ġare n", "Ġmy self", "Ġim ag", "Ġdis e", "Ġsoc iety", "Ġfre qu", "ĠEng lish", "Ġpo or", "ĠD ay", "Ġwrit ing", "Ġse ven", "Ġstart ing", "Ġb ud", "Ġpr int", "ĠTr ans", "uf act", "ĠSt ud", "n ew", "Ġcr im", "Ġg ives", "Ġco ol", "a e", "i ance", "ĠGener al", "Ġthink ing", "Ġsa ve", "Ġlim ited", "ĠPart y", "Ġmean ing", "p en", "ow ers", "ĠJ ack", "E M", "Ġn ice", "ru pt", "Ġg as", "Ġe ight", "Ġfe et", "Ġeff ort", "Ġ ign", "ic it", "B l", "co in", "Ġop in", "Ġbr ain", "Wh ile", "he st", "ĠTh ursday", "Ġwould n", "augh ter", "Ġtou ch", "le ments", "Ġstud ies", "Ġcent er", "c ont", "or ge", "Ġcomput er", "Ġinvestig ation", "P l", "or ks", "Ġ200 8", "Ġincre asing", "Ġst ore", "Ġcom ments", "Ġb al", "m en", "Ġdo ll", "Ġl iber", "Ġw ife", "Ġlaw s", "atur day", "it ness", "Ġmod ern", "ĠS k", "Ġadminist ration", "Ġopportun ity", "Ġs al", "Ġpower ful", "M y", "Ġclaim s", "ĠEar th", "ord s", "Ġt itle", "Ġes c", "n ame", "N ot", "om en", "Ġbe yond", "Ġc amer", "Ġse ll", "it ute", "ear ch", "Ġapp l", "im ent", "4 2", "ĠAr t", "Ġun f", "Ġviol ence", "ur g", "ĠE ast", "Ġcomp ared", "Ġopt ions", "Ġthrough out", "Ġv s", "ig r", ". [", "ac hes", "7 8", "Ġfil es", "F L", "E L", "ar ian", "ĠJ ames", "ĠA ir", "an ch", "Ġdet ail", "Ġpie ce", "P S", "Ġn amed", "Ġeduc ation", "Ġdri ve", "Ġitem s", "Ġstud ent", "ic ed", ": :", "ic o", "Ġth row", "Ġsc ene", "Ġcomple x", "Ġ200 9", "Ġpre c", "ĠB re", "7 9", "Ġcon cept", "Ġstat us", "am ing", "Ġd ied", "Ġknow ledge", "Ġbegin ning", "O D", "ru ary", "Ġcertain ly", "Ġgu ys", "Ġsl ight", "in n", "ound s", "Ġf ine", "Ġf at", "ic ations", "Ġper haps", "ĠA nt", "Ġinc ome", "Ġhtt ps", "Ġmajor ity", "port s", "st on", "Ġgreat er", "Ġfe ed", "ent ially", "Ġsaf ety", "Ġun ique", "and om", "Ġg one", "Ġshow ed", "Ġhist or", "Ġcoun ter", "i us", "id a", "Ġlead ing", "i pe", "Ġs end", "ĠDon ald", "er ve", "Ġdef ense", "ines e", "Ġy es", "ĠF ire", "ĠMus lim", "ra q", "Ġcontin ued", "os h", "Ġprov ides", "Ġpr ison", "ĠP re", "Ġhapp y", "Ġeconom y", "Ġtr ust", "ag s", "ĠG ame", "Ġweap ons", "um an", "ĠC le", "it ation", "Ġanal ysis", "ĠT imes", "Ġsc ience", "- >", "Ġfig ure", "Ġdis app", "ent y", "Ġsoft ware", "Ġu lt", "Ġoffic ers", "N ew", "I s", "Ġrem ains", "ĠInd ia", "Ġp sych", "ri ef", "Ġc at", "es c", "Ġob serv", "Ġst age", "ĠD ark", "Ġent er", "ch ange", "Ġpass ed", "Ġdes pite", "ĠO ut", "Ġmov ie", "r s", "Ġv oice", "m ine", "ĠPl ay", "Ġto ward", "ĠT er", "Ġreg ion", "Ġval ues", "or ters", "Ġm ount", "Ġoffic er", "ĠO ther", "b an", "Ġh ous", "w ood", "ro om", "I V", "ĠS un", "se e", "ĠO ver", "ro g", "9 0", "Ġl ay", "ĠT ur", "a wn", "Ġpress ure", "ĠS ub", "Ġbook s", "ed om", "ĠS and", "A A", "ag o", "Ġre asons", "f ord", "Ġactiv ity", "U T", "N ow", "ĠSen ate", "ce ll", "n ight", "Ġcall s", "in ter", "Ġlet ter", "ĠR ob", "ĠJ e", "Ġcho ose", "ĠL aw", "G et", "B e", "Ġro b", "Ġtyp es", "Ġpl atform", "Ġqu arter", "R A", "ĠT ime", "Ġmay be", "ĠC r", "9 5", "p re", "Ġmov ing", "Ġl if", "Ġgo ld", "Ġs om", "Ġpat ients", "Ġtr uth", "ĠK e", "ur ance", "ant ly", "m ar", "Ġchar ge", "ĠG reat", "Ġce le", "---------------- ----------------", "Ġro ck", "ro id", "an cy", "Ġcred it", "a ud", "B y", "ĠE very", "Ġmov ed", "ing er", "rib ution", "Ġn ames", "Ġstra ight", "ĠHe alth", "ĠW ell", "Ġfe ature", "Ġr ule", "Ġsc he", "in ated", "ĠMich ael", "ber g", "4 1", "il ed", "b and", "Ġcl ick", "ĠAng el", "on ents", " Ń", "ĠI raq", "ĠS aturday", "Ġa ware", "p art", "Ġpat tern", "O W", "ĠL et", "Ġgr ad", "ign ed", "Ġassoci ated", "Ġst yle", "n o", "i ation", "a ith", "il ies", "Ġst ories", "ur ation", "Ġindividual s", "ĠâĢ ¦", "m iss", "ĠAss oci", "ish ing", "ab y", "Ġsum mer", "ĠB en", "Ġ3 2", "Ġar ch", "ut y", "ĠTex as", "h ol", "Ġfull y", "Ġm ill", "Ġfollow ed", "ĠB ill", "ĠInd ian", "ĠSec ret", "ĠB el", "ĠFeb ruary", "Ġjob s", "Ġseem ed", "ĠGo vern", "i pped", "Ġreal ity", "Ġl ines", "Ġp ark", "Ġmeas ure", "ĠO ur", "I M", "Ġbro ther", "Ġgrow ing", "Ġb an", "Ġest im", "Ġc ry", "ĠS chool", "Ġme chan", "ĠO F", "ĠWind ows", "Ġr ates", "ĠO h", "Ġpos itive", "Ġcult ure", "ist ics", "ic a", "Ġh ar", "y a", "ite ly", "i pp", "Ġm ap", "en cies", "ĠWill iam", "I I", "ak ers", "5 6", "ĠM art", "ĠR em", "Ġal tern", "it ude", "Ġco ach", "row d", "D on", "Ġk ids", "Ġj ournal", "Ġcor por", "Ġf alse", "Ġwe b", "Ġsle ep", "Ġcont ain", "Ġst o", "Ġb ed", "iver se", "ĠR ich", "ĠCh inese", "Ġp un", "Ġme ant", "k nown", "Ġnot ice", "Ġfavor ite", "a ven", "Ġcond ition", "Ġpur pose", ") )", "Ġorgan ization", "Ġchall eng", "Ġman ufact", "Ġsus p", "ĠA c", "Ġcrit ic", "un es", "uc lear", "Ġm er", "vent ion", "Ġ8 0", "Ġm ist", "ĠU s", "ĠT or", "htt p", "ol f", "Ġlarg er", "Ġadv ant", "Ġrese ar", "Ġact ions", "m l", "Ġke pt", "Ġa im", ", '", "c ol", "Ġbenef its", "if ying", "Ġact ual", "ĠIntern ational", "Ġveh icle", "Ġch ief", "Ġeff orts", "ĠLe ague", "ĠM ost", "Ġwa it", "Ġad ult", "Ġover all", "Ġspe ech", "Ġhigh ly", "Ġfem ale", "Ġer ror", "Ġeffect ive", "5 4", "Ġenc our", "w ell", "Ġfail ed", "Ġcons erv", "Ġprogram s", "Ġt rou", "Ġa head", "5 00", "vertis ement", "I P", "ĠF ound", "p ir", "Ġ %", "Ġcr ime", "and er", "Ġloc ation", "ĠI ran", "Ġbehav ior", "az ing", "Ġr are", "Ġem b", "Ġca used", "Ġsh ip", "Ġact ive", "Ġcont ribut", "Ġg reen", "Ġac qu", "Ġref lect", "ven ue", "Ġf irm", "Ġb irth", "] .", "Ġclear ly", "Ġem ot", "Ġag ency", "ri age", "Ġmem ory", "9 8", "S A", "ĠSe e", "ac ing", "C C", "Ġbig gest", "Ġr ap", "Ġbas ic", "Ġb and", "e at", "Ġsus pect", "ĠM ac", "Ġ9 0", "m ark", "ist an", "Ġsp read", "am s", "k i", "as y", "ra v", "ĠR ober", "Ġdemon str", "r ated", "Ġabs olute", "Ġpl aces", "Ġim pl", "ibr ary", "Ġc ards", "Ġdest roy", "Ġv irt", "ve re", "Ġapp eared", "y an", "p oint", "Ġbe g", "Ġtem per", "s pe", "ant ed", "ear s", "ĠD irect", "Ġl ength", "Ġbl og", "am b", "Ġint eg", "Ġres ources", "ac c", "if ul", "Ġsp ot", "Ġfor ced", "Ġthous ands", "ĠMin ister", "Ġqu al", "ĠF rench", "at ically", "Ġgener ally", "Ġdr ink", "Ġth us", "I L", "od es", "Ġappro pri", "ĠRe ad", "Ġwh om", "Ġey e", "Ġcol lege", "Ġ4 5", "ire ction", "Ġens ure", "Ġapp arent", "id ers", "Ġrelig ious", "Ġmin or", "ol ic", "Ġt ro", "ĠWh y", "rib ute", "m et", "Ġprim ary", "Ġdevelop ed", "Ġpe ace", "Ġsk in", "st e", "av a", "Ġbl ue", "Ġfam ilies", "Ġ ir", "Ġapp ly", "Ġin form", "ĠSm ith", "C T", "i i", "Ġlim it", "Ġres ist", "........ ........", "um n", "Ġconf lic", "Ġtw e", "ud d", "ĠT om", "Ġl iter", "qu e", "b on", "Ġha ir", "Ġevent ually", "Ġp us", "Ġhelp ed", "Ġag g", "or ney", "ĠApp le", "Ġf it", "ĠS ur", "Ġpre m", "Ġs ales", "Ġsecond s", "Ġstreng th", "Ġfeel ing", "¿ ½", "Ġt our", "Ġknow s", "o om", "Ġex erc", "Ġsom ew", "ï ¿½", "> >", "Ġsp okes", "Ġide as", "Ġreg ist", "so ft", "ĠD el", "ĠP C", "Ġpro pos", "Ġlaun ch", "Ġbott om", "T H", "ĠP lease", "v est", "it z", "ĠIn ter", "Ġsc ript", "Ġr at", "ar ning", "Ġ il", "ĠJ er", "ĠA re", "Ġwh atever", "ok en", "ci ence", "Ġmod e", "Ġag ree", "Ġs ources", "Ġinit ial", "Ġrest rict", "Ġwond er", "us ion", "## ##", "ĠS il", "vil le", "Ġb urn", "t w", "as ion", "Ġ £", "Ġn or", "u ing", "Ġre ached", "Ġs un", "Ġc ateg", "ig ration", "Ġc ook", "Ġprom ot", "Ġm ale", "Ġcl imate", "Ġf ix", "Ġalleg ed", "U R", "all ed", "Ġim ages", "C ont", "ot a", "Ġschool s", "i os", "Ġd rop", "Ġst ream", "ĠM o", "Ġprevious ly", "al ing", "Ġp et", "Ġdou ble", "Ġ( @", "ann el", "Ġdef ault", "t ies", "Ġr ank", "ĠD ec", "ĠCoun cil", "Ġweap on", "Ġst ock", "Ġanal y", "ĠSt r", "Ġpict ure", "ĠPol ice", "f erence", "Ġcent ury", "Ġcitiz ens", "Ġon to", "Ġexp and", "Ġhe ro", "ĠS ol", "Ġw ild", "Ġupd ate", "Ġcustom ers", "r ont", "d ef", "Ġl ik", "Ġcrim inal", "ĠChrist ian", "S P", "7 6", "Ġle aving", "Ġother wise", "ĠD ist", "Ġbas is", "5 2", "5 3", "ic ip", "ĠB er", "Ġrecomm end", "Ġfl oor", "Ġc rowd", "ol es", "Ġ7 0", "Ġcent ral", "ĠE v", "Ġd ream", "Ġdown load", "Ġconf ir", "ĠTh om", "Ġwind ow", "Ġhapp ens", "Ġun it", "Ġt end", "Ġs pl", "Ġbec omes", "Ġfight ing", "Ġpred ict", "ĠP ress", "ĠP ower", "Ġhe avy", "ak ed", "Ġf an", "or ter", "ate gy", "B A", "iz es", "Ġsp end", "H ere", "Ġ200 7", "Ġad op", "ĠH am", "Ġfoot ball", "ĠP ort", "od ay", "5 1", "amp ions", "Ġtrans fer", "h t", "Ġ3 8", "ter m", "ac ity", "Ġb ur", "] ,", "tern al", "r ig", "b ut", "Ġthere fore", "ĠB ecause", "res p", "re y", "Ġm ission", "S ome", "Ġnot ed", "Ġass um", "Ġdise ase", "Ġed it", "Ġprog ress", "r d", "ĠB rown", "oc al", "Ġadd ing", "Ġra ised", "ĠAn y", "Ġt ick", "Ġsee ing", "ĠPe ople", "Ġagre ement", "Ġser ver", "Ġw at", "Ġdeb ate", "Ġsupp osed", "il ing", "Ġlarg est", "Ġsuccess ful", "ĠP ri", "ĠDemocr atic", "Ġj ump", "ĠSyri a", "Ġown ers", "Ġoff ers", "Ġshoot ing", "Ġeff ic", "se y", "Ġha ven", "ver se", "te red", "ĠL ight", "im al", "ĠB ig", "Ġdef end", "Ġbe at", "Ġrecord s", "% )", "Ġsc en", "Ġemploy ees", "Ġdev ices", "he m", "Ġcom mer", "ĠM ex", "Ġbenef it", "ĠPro f", "Ġil leg", "Ġsur face", "ĠAl so", "Ġh arm", "ing ly", "w ide", "ĠA lex", "Ġsh ut", "ĠC ur", "Ġl ose", "p m", "Ġchall enge", "se mb", "Ġst ation", "Ġint elligence", "Ġacc ur", "ĠFl or", "Ġrequ ires", "ĠM al", "b um", "Ġh ospital", "Ġsp irit", "Ġoff ered", "Ġprodu ce", "ĠComm un", "Ġcreat ing", "Ġcr is", "s pect", "Ġend ed", "Ġd aily", "Ġvot ers", "land s", "i as", "i h", "on a", "Ġsm art", "ĠOff ice", "ĠL ord", "ri al", "ĠIntern et", "Ġcirc um", "Ġextreme ly", "' .", "Ġopin ion", "ĠM il", "Ġg ain", "B S", "ĠF in", "y p", "Ġuse ful", "Ġbud get", "Ġcom fort", "is f", "Ġback ground", "el ine", "Ġep isode", "Ġen emy", "Ġtri al", "Ġestab lish", "d ate", "ĠC ap", "Ġcontin ues", "Ġshow ing", "ĠUn ion", "w ith", "Ġpost ed", "ĠSy stem", "Ġe at", "ri an", "Ġr ise", "ĠGerman y", "il s", "Ġsign ed", "Ġv ill", "Ġgr and", "m or", "ĠEng land", "Ġproject s", "um ber", "Ġconf erence", "z a", "Ġrespons ible", "ĠAr ab", "Ġlearn ed", "âĢĶ âĢĶ", "i pping", "ĠGe orge", "O C", "Ġreturn ed", "ĠAustral ia", "Ġb rief", "Q u", "Ġbr and", "ill ing", "ab led", "Ġhig hest", "Ġtr ain", "ĠComm ission", "wh ile", "Ġn om", "cept ion", "Ġm ut", "ĠBl ue", "Ġinc ident", "v ant", "8 6", "ĠI D", "Ġn uclear", "7 4", "ĠL ike", "ĠR E", "ĠM icro", "l i", "m ail", "Ġcharg es", "8 9", "Ġad just", "ad o", "Ġear th", "N A", "Ġpr ices", "P A", "Ġd raft", "Ġrun s", "Ġcandid ate", "ens es", "Ġmanag ement", "ĠPh il", "ĠM iss", "Ġte ach", "g ram", "Ġunderstand ing", "a it", "ic ago", "A dd", "ĠE p", "sec ut", "Ġsepar ate", "Ġinst ance", "Ġe th", "Ġun less", "**** ****", "ĠF ore", "in ate", "Ġoper ations", "S p", "Ġf aith", "g ar", "ĠCh urch", "ron ic", "Ġconf ig", "os ure", "Ġactiv ities", "Ġtrad itional", "Ġ3 6", "Ġd irection", "Ġmach ine", "Ġsur round", "Ġp ush", "un ction", "ĠE U", "Ġeas ier", "Ġarg ument", "G B", "Ġm icro", "Ġsp ending", "iz ations", "Ġthe ory", "ad ow", "Ġcall ing", "ĠL ast", "Ġd er", "Ġinflu ence", "Ġcomm it", "Ġph oto", "Ġun c", "ist ry", "g n", "ast e", "ack s", "Ġdis p", "ad y", "d o", "ĠG ood", "Ġ `", "Ġw ish", "Ġreve aled", "Âł Âł", "l ig", "Ġen force", "ĠComm ittee", "Ġche m", "Ġmil es", "Ġinterest ed", "Ġsol ution", "ic y", "in ct", "Ġ- >", "ĠD et", "Ġrem oved", "Ġcomp ar", "e ah", "Ġpl ant", "ĠS ince", "Ġachie ve", "Ġadvant age", "Ġslight ly", "b ing", "Ġpl aced", "u nder", "201 5", "ĠM ad", "Ġt im", "os es", "Ġc ru", "ĠR ock", "Ġmost ly", "Ġneg ative", "Ġset ting", "Ġprodu ced", "Ġm ur", "Ġconnect ion", "ĠM er", "Ġdri ver", "Ġexecut ive", "Ġass ault", "Ġb orn", "ĠV er", "t ained", "Ġstruct ure", "Ġredu ce", "Ġdec ades", "Ġd ed", "u ke", "ĠM any", "idd en", "Ġle ague", "S e", "Ġjo in", "Ġdis co", "Ġd ie", "c ks", "act ions", "Ġass ess", "ag n", "Ġgo als", "our s", "I R", "Ġsen ior", "ill er", "m od", "ip ment", "oc ol", "u y", "ĠQ ue", "Ġpart ies", "ir gin", "Ġle arning", "it able", "Ġstre et", "Ġcamer a", "A pp", "Ġsk ills", "b re", "c ious", "Ġcele br", "ĠFr anc", "Ġexist ing", "Ġwill ing", "l or", "Ġ id", "ĠSp ace", "Ġcrit ical", "ĠL a", "ortun ately", "Ġser ve", "Ġc old", "Ġspec ies", "T S", "Ġanim als", "ĠB ay", "Ġold er", "ĠU nder", "est ic", "ĠT re", "Ġte acher", "Ġpre fer", "v is", "Ġth read", "ĠM att", "Ġmanag er", "ãĥ »", "Ġprofess ional", "ĠV ol", "Ġnot es", "The se", "ul a", "Ġf resh", "ent ed", "u zz", "ed y", "clus ion", "ĠR el", "Ġdoub t", "E O", "Ġopen ed", "ĠB it", "Ad vertisement", "Ġgu ess", "ĠU N", "Ġse qu", "Ġexpl ain", "ott en", "Ġatt ract", "ak s", "Ġstr ing", "Ġcont ext", "oss ible", "ĠRepublic ans", "Ġsol id", "Ġc ities", "Ġask ing", "Ġr andom", "u ps", "ur ies", "ar ant", "dd en", "g l", "ĠFlor ida", "Ġdep end", "ĠSc ott", "Ġ3 3", "Ġi T", "ic on", "Ġmention ed", "Ġ2 000", "Ġclaim ed", "Ġdefin itely", "ul f", "Ġc ore", "Ġopen ing", "ĠCon st", "wh ich", "ĠT ra", "A G", "7 2", "Ġbelie ved", "ad a", "Ġ4 8", "ĠSec urity", "yr ight", "ĠP et", "ĠL ou", "Ġhold ing", "======== ========", "Ġ ice", "Ġb row", "Ġauthor ities", "h ost", "w ord", "Ġsc ore", "ĠD iv", "Ġcell s", "Ġtrans l", "Ġneigh bor", "Ġrem ove", "u ct", "Ġdist rict", "ĠA ccording", "Ġwor se", "Ġconcern s", "Ġpresident ial", "Ġpolic ies", "ĠH all", "7 3", "Ġh us", "A Y", "Ġ200 6", "ĠJ ud", "Ġindepend ent", "ĠJust ice", "ili ar", "pr int", "igh ter", "Ġprotect ion", "z en", "Ġsu dden", "h ouse", "ĠJ es", "P R", "ĠIn f", "Ġb ul", "Ġ _", "ĠServ ice", "ĠP R", "Ġstr ategy", "ff ect", "Ġgirl s", "Ġmiss ing", "oy al", "ĠTe am", "ul ated", "Ġd at", "Ġpolit ics", "ab or", "A ccording", "Ġspe ll", "Ġg raph", "ort hern", "T C", "A b", "Ġlab or", "is her", "Ġk ick", "ĠiT unes", "Ġstep s", "pos es", "Ġsmall er", "E n", "ber t", "Ġro ll", "Ġresear chers", "Ġcl osed", "Ġtrans port", "Ġlaw y", "________ ________", "ĠCh icago", "Ġas pect", "Ġn one", "Ġmar riage", "9 6", "Ġe lements", "ĠF re", "ĠS al", "Ġd ram", "F C", "t op", "e qu", "Ġhe aring", "Ġsupport ed", "Ġtest ing", "co hol", "Ġmass ive", "Ġst ick", "Ġgu ard", "is co", "ph one", "F rom", "How ever", "Ġb order", "Ġcop y", "ograph y", "l ist", "7 1", "Ġown er", "cl ass", "ru it", "r ate", "ĠO nce", "Ġdig ital", "Ġt ask", "ER S", "Ġinc red", "t es", "+ +", "ĠFr ance", "Ġb reat", "ow l", "Ġiss ued", "ĠW estern", "Ġdet ect", "Ġpart ners", "Ġsh ared", "ĠC all", "Ġcan cer", "ac he", "rib e", "Ġexpl ained", "Ġhe at", "{ \"", "Ġinvest ment", "ĠB ook", "Ġw ood", "Ġtool s", "ĠAl though", "Ġbelie f", "Ġcris is", "Ġg e", "ĠM P", "Ġoper ation", "ty pe", "~ ~", "g a", "Ġcont ains", "ant a", "Ġexp ress", "ĠG roup", "ĠJ ournal", "k a", "Ġam b", "ĠUS A", "Ġfind ing", "Ġfund ing", "h ow", "Ġestab lished", "ide os", "Ġdeg ree", "Ġdanger ous", "ang ing", "Ġfre edom", "pp ort", "out hern", "Ġch urch", "Ġc atch", "ĠTw o", "Ġpres ence", "ĠGu ard", "U p", "Ġauthor ity", "ĠPro ject", "Ġbut ton", "Ġcon sequ", "Ġval id", "Ġwe ak", "Ġstart s", "Ġref erence", "ĠM em", "\" )", "U N", "or age", "ĠO pen", "Ġcol lection", "y m", "g ency", "Ġbeaut iful", "ro s", "Ġtell s", "Ġwa iting", "n el", "Ġprov iding", "ĠDemocr ats", "Ġd aughter", "Ġm aster", "Ġpur poses", "ĠJapan ese", "Ġequ al", "Ġturn s", "Ġdoc uments", "Ġwatch ing", "R es", "Ġr an", "201 4", "Ġre ject", "ĠKore a", "Ġvictim s", "Le vel", "ere nces", "Ġw itness", "Ġ3 4", "Ġre form", "com ing", "Ġocc up", "Ġc aught", "Ġtra ffic", "ad ing", "Ġmod els", "ar io", "Ġserv ed", "Ġb atter", "u ate", "ĠSecret ary", "Ġagre ed", "Ġtr uly", "yn am", "ĠR et", "Ġun its", "ĠRes earch", "h and", "az ine", "ĠM ike", "Ġvar iety", "ot al", "Ġam azing", "Ġconfir med", "Ġentire ly", "Ġpurch ase", "Ġe lement", "Ġc ash", "Ġdeter mine", "D e", "Ġc ars", "ĠW all", "â ĸ", "Ġview s", "Ġdrug s", "Ġdep artment", "ĠSt ep", "u it", "Ġ3 9", "as ure", "ĠCl ass", "Ġc overed", "ĠB ank", "Ġme re", "u ana", "Ġmult i", "Ġm ix", "Ġun like", "lev ision", "Ġsto pped", "Ġs em", "ĠG al", "ul es", "Ġwe l", "ĠJohn son", "l a", "Ġsk ill", "Ġbec oming", "ri e", "Ġappropri ate", "f e", "ell ow", "ĠPro t", "ul ate", "oc ation", "Ġweek end", "od ies", "Ġsit es", "Ġanim al", "ĠT im", "Ġsc ale", "Ġcharg ed", "Ġinst ruct", "ill a", "Ġmethod s", "Ġc ert", "Ġjud ge", "ĠH el", "Ġdoll ars", "Ġstand ing", "ĠS qu", "Ġdeb t", "l iam", "Ġdri ving", "ĠS um", "ĠEd ition", "Ġal bum", "and on", "I F", "ĠU k", "6 3", "ad er", "Ġcommer cial", "es h", "ĠGovern ment", "Ġdisc overed", "Ġout put", "ĠHill ary", "ĠCar ol", "Ġ200 5", "Ġab use", "anc ing", "Ġsw itch", "Ġann ual", "T w", "Ġst ated", "ag ement", "in ner", "Ġdem ocr", "Ġres idents", "Ġallow ing", "Ġfact ors", "od d", "Ġf uck", "em ies", "Ġoccur red", "ot i", "Ġn orth", "ĠP ublic", "Ġinj ury", "Ġins urance", "C L", "oll y", "ã Ģ", "Ġrepe ated", "Ġar ms", "ang ed", "Ġconst ruction", "Ġf le", "P U", "ic ians", "Ġfor ms", "ĠMc C", "ant ic", "Ġm ental", "p ire", "Ġequ ipment", "Ġf ant", "Ġdiscuss ion", "Ġregard ing", "k in", "ar p", "Ġch air", "og ue", "Ġpro ceed", "ĠI d", "O ur", "Ġmur der", "M an", "Ġ4 9", "as p", "Ġsupp ly", "Ġin put", "Ġwe alth", "liam ent", "Ġpro ced", "or ial", "ĠSt at", "ĠN FL", "hen s", "ĠInst itute", "Ġput ting", "ourn ament", "et ic", "Ġloc ated", "Ġk id", "er ia", "r un", "Ġpr inc", "Ġ !", "go ing", "ĠB et", "Ġcl ot", "Ġtell ing", "Ġprop osed", "i ot", "or ry", "Ġfund s", "g ment", "ĠL ife", "Ġb aby", "ĠB ack", "Ġsp oke", "Im age", "Ġear n", "ĠA T", "g u", "Ġex change", "ĠL in", "ov ing", "Ġp air", "M ore", "az on", "Ġarrest ed", "Ġkill ing", "c an", "ĠC ard", "y d", "Ġident ified", "Ġm obile", "Ġthan ks", "ony m", "ĠF orm", "Ġhundred s", "ĠCh ris", "ĠC at", "Ġtre nd", "h at", "ĠA v", "om an", "Ġelect ric", "ĠW il", "S E", "O f", "Ġrest aur", "ot ed", "Ġtr ig", "Ġn ine", "Ġb omb", "Wh y", " ¯", "Ġco verage", "Ġapp eal", "ĠRober t", "ĠS up", "Ġfin ished", "Ġfl ow", "Ġdel iver", "Ġcal cul", "Ġphot os", "Ġph il", "Ġpie ces", "Ġapp re", "k es", "Ġr ough", "D o", "Ġpart ner", "Ġconcern ed", "Ġ3 7", "ĠG en", "C ol", "ct ors", "Ġ= >", "st ate", "Ġsuggest ed", "ĠFor ce", "C E", "Ġher self", "ĠPl an", "w orks", "o oth", "ren cy", "Ġcor ner", "Ġhus band", "Ġintern et", "ĠA ut", "em s", "os en", "ĠAt l", "g en", "Ġbal ance", "6 2", "Ġsound s", "te xt", "Ġar r", "ov es", "Ġmill ions", "Ġrad io", "Ġsat isf", "ĠD am", "M r", "G o", "S pe", "Ġcomb at", "r ant", "ĠG ree", "Ġf uel", "Ġdist ance", "Ġtest s", "Ġdec re", "ĠE r", "Ġman aged", "D S", "Ġt it", "Ġmeas ures", "ĠL iber", "Ġatt end", "as hed", "ĠJ ose", "ĠN ight", "d it", "ĠN ov", "ĠE nd", "out s", "Ġgener ation", "Ġadv oc", "y th", "Ġconvers ation", "ĠS ky", "act ive", "ce l", "ri er", "ĠFr ank", "Ġg ender", "Ġcon cent", "Ġcar ried", "and a", "ĠV irgin", "Ġarri ved", "ic ide", "ad ed", "Ġfail ure", "Ġmin imum", "le ts", "Ġwor st", "Ġkeep ing", "Ġint ended", "Ġilleg al", "Ġsub sc", "Ġdetermin ed", "Ġtri p", "Y es", "Ġra ise", "Ġ ~", "Ġfeel s", "Ġpack age", "ĠJ o", "h i", "201 6", "re al", "Ġf ra", "Ġsy mb", "M e", "uck y", "p ret", "ĠK h", "ĠEd it", "ĠWe b", "em ic", "ĠCol or", "Ġjust ice", "I nt", "Ġfar m", "ck now", "\" >", "el ess", "Ġredu ced", "Ġ5 00", "x x", "ĠR ad", "ĠW ood", "Ġcl in", "Ġhy p", "il er", "ur a", "k ins", "8 5", "6 1", "ĠThe ir", "ĠM ary", "Ġs an", "Ġno vel", "ĠWh o", "Ġcap acity", "Ġimp ossible", "Ġpl ays", "Ġmin ister", "ij uana", "ic ate", "ĠS et", "Ġf ram", "Ġ ing", "Ġcommun ities", "ĠF BI", "it a", "Ġb on", "Ġstr ateg", "Ġinterest s", "l ock", "g ers", "m as", "ĠAN D", "Ġconflic t", "Ġrequire ments", "Ġs ac", "Ġoper ating", "in i", "rel ated", "Ġcomm itted", "Ġrelative ly", "Ġs outh", "¯ ¯", "Ġaff ord", "Ġident ity", "Ġdec isions", "Ġacc used", "pl ace", "Ġvict ory", "o ch", "i at", "N ame", "C om", "t ion", "ed s", "Ġsee k", "Ġt ight", "ĠIm ages", "Ġinit i", "Ġhum ans", "Ġfam iliar", "Ġaud ience", "Ġintern al", "vent ure", "Ġs ides", "ĠT O", "Ġd im", "Ġcon clud", "Ġapp oint", "Ġenforce ment", "ĠJ im", "ĠAssoci ation", "Ġcircum st", "ĠCanad ian", "Ġjo ined", "Ġdiffere nces", "ĠL os", "Ġprot est", "Ġtw ice", "w in", "Ġgl ass", "ars h", "ĠAr my", "Ġexp ression", "Ġdec ide", "Ġplan ning", "an ia", "Ġhand le", "ĠMicro soft", "ĠN or", "Ġmax imum", "ĠRe v", "Ġse a", "Ġev al", "Ġhel ps", "re f", "Ġb ound", "Ġm outh", "Ġstand ards", "Ġcl im", "ĠC amp", "ĠF ox", "cl es", "Ġar my", "ĠTe chn", "ack ing", "x y", "S S", "Ġ4 2", "Ġbu g", "ĠUk rain", "ĠM ax", "ĠJ ones", "ĠSh ow", "l o", "Ġplan et", "Ġ7 5", "Ġwin ning", "Ġf aster", "Ġspe ct", "Ġbro ken", "T R", "Ġdef ined", "Ġhealth y", "Ġcompet ition", "htt ps", "ĠIs land", "ĠF e", "Ġannoun ce", "ĠC up", "ĠInst ead", "Ġcl ient", "Ġposs ibly", "se ction", "ock et", "l ook", "Ġfin ish", "Ġcre w", "Ġres erv", "Ġed itor", "Ġh ate", "Ġs ale", "Ġcontro vers", "Ġp ages", "w ing", "Ġnum er", "Ġopp osition", "Ġ200 4", "Ġref uge", "Ġfl ight", "Ġap art", "ĠL at", "A meric", "ĠAfric a", "Ġapplic ations", "ĠPal est", "ĠB ur", "Ġg ar", "ĠSoc ial", "Ġup gr", "Ġsh ape", "Ġspe aking", "ans ion", "a o", "ĠS n", "Ġwor ry", "ĠBrit ain", "P lease", "rou d", "Ġh un", "Ġintrodu ced", "Ġd iet", "I nd", "ĠSec ond", "Ġfun ctions", "ut s", "ĠE ach", "ĠJe ff", "Ġst ress", "Ġaccount s", "Ġgu arant", "ĠAn n", "ed ia", "Ġhon est", "Ġt ree", "ĠAfric an", "ĠB ush", "} ,", "Ġs ch", "ĠOn ly", "Ġf if", "ig an", "Ġexerc ise", "ĠEx p", "Ġscient ists", "Ġlegisl ation", "ĠW ork", "ĠS pr", "à Ĥ", "ĠH uman", "Ġ è", "Ġsur vey", "Ġr ich", "ri p", "Ġmain tain", "Ġfl o", "Ġleaders hip", "st ream", "ĠIslam ic", "Ġ 01", "ĠCol lege", "Ġmag ic", "ĠPr ime", "Ġfig ures", "201 7", "ind er", "x ual", "ĠDe ad", "Ġabsolute ly", "Ġfour th", "Ġpresent ed", "resp ond", "rib le", "Ġal cohol", "at o", "ĠD E", "por ary", "Ġgr ab", "Ġvar i", "Ġqu ant", "ĠPh oto", "Ġpl us", "r ick", "ar ks", "Ġaltern ative", "Ġp il", "Ġappro x", "th at", "Ġobject s", "ĠR o", "ĠAnd roid", "Ġsignificant ly", "ĠR oad", "k ay", "R ead", "av or", "Ġa cknow", "ĠH D", "ĠS ing", "O r", "ĠM ont", "Ġun s", "pro f", "Ġneg oti", "ĠAr ch", "ik i", "Ġte levision", "ĠJew ish", "Ġcomm ittee", "Ġmot or", "Ġappear ance", "Ġs itting", "Ġstri ke", "ĠD own", "com p", "ĠH ist", "Ġf old", "ac ement", "ĠLou is", "Ġbel ong", "ĠâĢ ¢", "Ġm ort", "Ġprep ared", "Ġ6 4", "ĠM aster", "Ġind eed", "ĠD en", "Ġre nt", "T A", "our ney", "ar c", "S u", "9 7", "Ġadv ice", "Ġchang ing", "Ġlist ed", "Ġlaun ched", "is ation", "ĠP eter", "is hes", "Ġl ived", "ĠM el", "ĠSup reme", "ĠF ederal", "Ġ) ;", "ruct ure", "Ġset s", "Ġphil os", "u ous", "Ġ ł", "Ġappl ied", "ĠN OT", "Ġhous ing", "ĠM ount", "Ġo dd", "Ġsu st", "D A", "ffic ient", "Ġ ?", "ol ved", "Ġp owers", "Ġth r", "Ġrem aining", "ĠW ater", "L C", "Ġca uses", "ãģ ®", "Ġman ner", "ad s", "Ġsuggest s", "Ġend s", "stand ing", "f ig", "ĠD un", "id th", "Ġg ay", "Ġter min", "ĠAngel es", "M S", "Ġscient ific", "Ġco al", "ap ers", "b ar", "ĠThom as", "Ġsy m", "ĠR un", "th is", "P C", "igr ants", "Ġmin ute", "ĠDist rict", "cell ent", "Ġle aves", "Ġcomple ted", "am in", "Ġfoc used", "Ġmon itor", "Ġveh icles", "M A", "ĠM ass", "ĠGr and", "Ġaffect ed", "itution al", "Ġconst ruct", "Ġfollow s", "Ġt on", "re ens", "Ġh omes", "ĠE xt", "ĠLe vel", "r ast", "ĠI r", "Ġel im", "Ġlarge ly", "ĠJ oe", "Ġvot es", "all s", "Ġbusiness es", "ĠFound ation", "ĠCent ral", "Ġy ards", "Ġmaterial s", "ul ner", "Ġgu ide", "Ġclos er", "um s", "Ġsp orts", "ed er", "J ust", "Ġtax es", "8 4", "ĠO ld", "Ġdec ade", "ol a", "Ġv ir", "Ġdro pped", "Ġdel ay", "it ect", "Ġsec ure", "ste in", "le vel", "Ġtre ated", "Ġfil ed", "ain e", "Ġv an", "Ġm ir", "Ġcol umn", "ict ed", "e per", "Ġro t", "Ġcons ult", "Ġent ry", "Ġmar ijuana", "ĠD ou", "Ġapparent ly", "ok ing", "clus ive", "Ġincre ases", "an o", "Ġspecific ally", "Ġte le", "ens ions", "Ġrelig ion", "ab ilities", "Ġfr ame", "ĠN ote", "ĠLe e", "Ġhelp ing", "Ġed ge", "ost on", "Ġorgan izations", "à ĥ", "ĠB oth", "hip s", "Ġbig ger", "Ġbo ost", "ĠSt and", "Ġro w", "ul s", "ab ase", "Ġr id", "L et", "are n", "ra ve", "Ġst ret", "P D", "Ġv ision", "Ġwe aring", "Ġappre ci", "Ġa ward", "ĠU se", "Ġfact or", "w ar", "ul ations", ") (", "Ġg od", "Ġter rit", "Ġpar am", "ast s", "8 7", "Ġen emies", "ĠG ames", "F F", "Ġacc ident", "W ell", "ĠMart in", "T ER", "Ġat h", "ĠHe ll", "Ġfor g", "Ġve ter", "ĠMed ic", "f ree", "Ġst ars", "Ġexp ensive", "Ġac ad", "ra wn", "ĠW he", "Ġl ock", "Ġform at", "Ġsold iers", "s m", "Ġag ent", "Ġrespons ibility", "or a", "ĠS cience", "Ġrap id", "Ġt ough", "ĠJes us", "Ġbelie ves", "M L", "Ġwe ar", "le te", "Ãĥ ÃĤ", "ĠD ri", "Ġcomm ission", "ĠB ob", "O h", "ap ed", "Ġwar m", "ÃĥÃĤ ÃĥÃĤ", "Ġ200 3", "ort ion", "Ġhas n", "ust er", "Ġun ivers", "ĠI ll", "Ġk ing", "olog ies", "9 4", "ĠT em", "ĠM os", "Ġpat ient", "ĠMex ico", "ce an", "ĠDe ath", "ĠSand ers", "y ou", "ĠC ast", "ĠComp any", "pt y", "Ġhappen ing", "F P", "ĠB attle", "Ġb ought", "A m", "M od", "U s", "ut ers", "ĠC re", "ĠTh ose", "Ġ4 4", "is er", "Ġs oul", "ĠT op", "ĠHar ry", "ĠA w", "Ġse at", "ff ee", "Ġrev olution", "Ġ( \"", "ĠD uring", "et te", "Ġr ing", "Ġoff ensive", "Ġreturn s", "Ġv ideos", "Ġdis cl", "Ġfam ous", "en ced", "ĠS ign", "ĠR iver", "Ġ3 00", "P M", "ĠB us", "ĠC H", "Ġcandid ates", "ard en", "Ġpercent age", "Ġvis ual", "Ġthan k", "Ġtrou ble", "ner gy", "Ġ200 1", "Ġpro ve", "ash ion", "Ġen h", "ĠL ong", "U M", "Ġconnect ed", "Ġposs ibility", "O ver", "Ġexper t", "Ġl ibrary", "art s", "ĠDirect or", "Ġfell ow", "9 2", "ir ty", "Ġd ry", "Ġsign s", "ĠL ove", "Ġqu iet", "f oot", "Ġp ure", "ĠH un", "Ġf illed", "ph as", "ĠE lect", "end ment", "ĠEx pl", "Ġun able", "n s", "m o", "Ġv ast", "ob e", "Ġident ify", "app ing", "ĠCarol ina", "g ress", "Ġpro te", "Ġf ish", "Ġcircumst ances", "raz y", "ĠPh ot", "Ġb odies", "ĠM ur", "Ġdevelop ing", "ĠA R", "Ġexperien ced", "Ġsubst ant", "ĠBo ard", "es ome", "Ġdom estic", "Ġcomb ined", "ĠP ut", "Ġchem ical", "ĠCh ild", "Ġpo ol", "ĠC y", "Ġe gg", "c ons", "st ers", "Ġh urt", "Ġmark ets", "Ġconserv ative", "Ġsupp orters", "Ġag encies", "id el", "O b", "ur b", "Ġ4 3", "ĠDef ense", "y e", "ĠA p", "du le", "Ġtemper ature", "Ġconduct ed", "ĠCh ief", "Ġpull ed", "Ġf ol", "L ast", "ont o", "os is", "V ER", "D es", "ĠP an", "F irst", "Ġadv ance", "Ġlic ense", "r ors", "ĠJ on", "Ġimag ine", "Ġhe ll", "Ġf ixed", "Ġinc or", "os ite", "ĠL og", "ick en", "] :", "Ġsurpr ise", "h ab", "Ġc raft", "ol t", "ĠJ ul", "Ġd ial", "Ġrele vant", "Ġent ered", "Ġlead s", "ĠA D", "ĠCle an", "Ġpict ures", "ess or", "Ġal t", "Ġpay ing", "P er", "ĠMark et", "Ġupd ates", "am ily", "ĠT ype", "ĠH ome", "Ġ5 5", "semb ly", "rom e", "8 3", "Ġgreat est", "Ġhe ight", "Ġhe av", "ain ts", "Ġlist en", "as er", "ĠS H", "Ġcap able", "ac le", "Ġpers pect", "in ating", "Ġoff ering", "ry pt", "ĠDe velop", "ab in", "r c", "Ġbr ight", "al ty", "ar row", "Ġsupp l", "ind ing", "ack ed", "gy pt", "ĠAn other", "p g", "ĠVirgin ia", "ĠL u", "Ġpl anned", "Ġp it", "Ġswe et", "T ype", "ĠD i", "Ġtyp ically", "ĠFranc isco", "Ġpro spect", "ĠD an", "Ġte en", "re es", "Ġsc hed", "Ġh ol", "Ġsc r", "Ġlot s", "l ife", "Ġnews p", "Ġfor get", "ĠN one", "ĠM iddle", "ĠR yan", "ed d", "Ġse vere", "Ġsu it", "ll er", "9 3", "Ġcor respond", "Ġexpl os", "u ations", "Ġfl ag", "g ame", "r id", "Ġpr in", "ĠD ata", "Ġde ploy", "ĠEn ter", "su it", "gh an", "ĠM en", "Ġthough ts", "Ġmat ters", "Ġad apt", "ĠA ri", "Ġf ill", "Ġfor th", "Ġs am", "Ġ4 1", "Ġpay ment", "ĠH or", "Ġsp ring", "du c", "Ġl osing", "Ġbring ing", "F O", "al a", "Ġdist ribution", "he red", "b our", "ĠIsrael i", "om a", "Ġcomb ination", "Ġpl enty", "V E", "C an", "ĠH aw", "Ġper man", "ĠSpe cial", "Ġto w", "Ġsee king", "Ġexam ples", "Ġclass es", "c r", "Ġbe er", "Ġmov es", "ĠI P", "ĠK n", "Ġpan el", "E ven", "Ġproper ly", "Ġr is", "Ġpl ug", "Ġestim ated", "E very", "Ġdef ensive", "ag raph", "Ġpre gn", "Ġinst it", "ĠV ict", "Ġvol ume", "Ġpos itions", "Ġl inks", "ĠPro gram", "ĠWe ek", "ag ues", "Ġtrans form", "k er", "ĠC EO", "Ġc as", "Ġopp onent", "Ġtwe et", "ĠC ode", "Ġsh op", "Ġf ly", "Ġtal ks", "Ġb ag", "Ph one", "Ġa id", "Ġpl ants", "Ġ6 5", "Ġatt orney", "ar ters", "qu est", "ĠMag ic", "Ġbeg ins", "Ġmy ster", "Ġenvironment al", "Ġst orage", "N N", "Ġm arg", "Ġs ke", "Ġmet al", "ell y", "Ġord ered", "Ġrem ained", "Ġl oved", "Ġprom pt", "Ġupd ated", "Ġexper ts", "Ġwalk ing", "Ġan cient", "Ġperform ed", "AT E", "Ġne ither", "i ency", "Ġmanufact ure", "ĠP ak", "Ġselect ed", "Ġm ine", "Ġult imately", "Ġexpl an", "Ġlab el", "ĠServ ices", "ribut ed", "Tr ump", "Ġsy n", "ĠU lt", "S C", "Ġme at", "Ġg iant", "ĠW ars", "ĠO N", "Ġad m", "Ġinter pret", "Ġeven ing", "Ġev il", "ĠB oston", "ĠW ild", "Ġ Ã", "ĠBit coin", "ĠAm azon", "D r", "ĠIn formation", "Ġobvious ly", "Ġadv anced", "Ph oto", "ol ar", "Ġwe ather", "Ġsymb ol", "Ġso le", "Ġpot entially", "ost er", "Ġorig inally", "m un", "3 00", "az e", "ess ions", "Ġde ck", "Ġst ood", "Ġyou th", "ĠB ern", "R ep", "ĠT est", "Ġbas ically", "ot ic", "Ġinvol ve", "ol it", "ly n", "S ee", "Ġair craft", "Ġconf irm", "E W", "Ġmess ages", "ĠRich ard", "Ġk it", "Ġpro hib", "Ġv ulner", "is ters", "Ġexist ence", "Ġturn ing", "ĠS P", "Ġdes ire", "Ġfl at", "Ġm ent", "se ason", "ang es", "Ġneighbor hood", "ĠL ake", "AT ION", "Ġpoint ed", "b ur", "Ġinn ov", "uc ks", "U L", "Ġprofess or", "Ġexp ressed", "A B", "ic ious", "Ġ200 2", "ĠDe v", "Ġs ession", "Ġb are", "s en", "Ġdis s", "ĠC ath", "ĠP ass", "ĠP oint", "Ġdo ctor", "or row", "ail ed", "ĠR ub", "ĠD C", "ĠChar l", "p erson", "Ġwrit er", "igh ters", "ure au", "Ġob lig", "Ġrecord ed", "Ġbro ke", "Ġord ers", "il ty", "Ġmot ion", "in ity", "l aw", "ad ium", "Ġimm igration", "Ġcontr ast", "Ġb att", "Ġex cellent", "Ġtechn ical", "am i", "Ġt un", "Ġcl oud", "ĠY ear", "ge on", "Ġcre ation", "Ġstr ange", "Ġa uth", "Ġfor t", "b orn", "Ġext ent", "ĠT oday", "ĠCl ub", "Ġr ain", "Ġs ample", "Ġaccept ed", "Ġt act", "Ġf ired", "ĠS on", "Ġstand s", "Ġb oot", "Ġ4 7", "Ġstat ements", "Ġvers ions", "Ġse lling", "ound ed", "Ġ199 0", "Ġwere n", "ĠW atch", "Ġexper iment", "P ost", "Ġret ail", "ul ed", "In st", "un te", "ãĥ ¼", "Ġdep art", "Ġb ond", "i very", "om pl", "Ġre action", "ĠSyri an", "ĠP ac", "app ed", "ani el", "D P", "Ġres olution", "Ġre act", "Ġappro ved", "on om", "m ond", "ĠO ffic", "-- -", "Ġrepl ace", "Ġt ack", "Ġsp ort", "Ġch ain", "Ġemer gency", "r ad", "ĠPalest in", "Ġ4 6", "Ġautom atically", "Ġrout e", "Ġp al", "Ġb anks", "ĠPar is", "ĠMed ia", "ro ad", "ic ing", "i xt", "ist ed", "Ġg rew", "Ġco ord", "ĠW here", "om in", "Ġsub s", "� �", "Ġ ±", "Ġcorpor ate", "Ġse lection", "n oon", "ĠRep ort", "c s", "clud ing", "ord ers", "anc he", "ĠIt s", "Ġslow ly", "ĠE gypt", "ĠA cc", "Ġcol le", "iqu es", "E X", "Ġattempt s", "ur l", "ĠC ross", "Ġfind ings", "ĠS C", "ĠO R", "Ġind ex", "ens ity", "ĠW ay", "ĠL and", "Ġsh ock", "d is", "Ġd ynam", "Ġc art", "m osp", "S ince", "i est", "ĠB oy", "Ġst orm", "ĠCont in", "201 3", "he w", "il it", "Ġess ential", "iqu id", "O ther", "ive red", "Ġreason able", "A ct", "Ġsub sequ", "ĠP ack", "ĠF ort", "Ġconsider ing", "Ġun iversity", "l og", "Ġmar ried", "Ġill ust", "ĠTr ue", "£ ı", "Ġnumer ous", "rast ructure", "Ġserious ly", "Ġrefer red", "u a", "Ġconsist ent", "on na", "ĠRe al", "ru ption", "ci ples", "Ġfact s", "9 1", "ot es", "er g", "The n", "Ġacc ompl", "N ote", "Ġre venue", "Ġpass ing", "Ġm al", "e en", "ĠY et", "Ġg ather", "ter day", "ew ork", "ĠA uthor", "P e", "Ġopt im", "Ġr ub", "Ġè £ı", "Ġun known", "st one", "Ġun ion", "ol ve", "Ġopportun ities", "Ġbrow ser", "ĠW al", "ĠC ost", "Ġreport ing", "st s", "p et", "Ġs and", "Ġsudden ly", "Ġsurpr ising", "ĠV R", "Ġsomew hat", "ĠB as", "ult ure", "iz z", "ĠC D", "Ġchalleng es", "Ġsett ings", "Ġexperien ces", "ĠF ull", "Ġcan n", "Ġrece iving", "ES T", "Ġj oint", "Ġcult ural", "Ġa st", "8 2", "as tern", "ce ived", "ĠC ru", "Ġb ull", "p ired", "am m", "Ġfac ing", "p ower", "Ġb oss", "ĠH ol", "Ġinst r", "Ġincreasing ly", "Ġsh ift", "Ġstre ets", "ĠWilliam s", "ab b", "Ġl ie", "Ġl augh", "ĠC a", "P L", "Ġadult s", "Ġcustom er", "Ġob tained", "Ġsupport ing", "ht ml", "f ire", "Ġdetail ed", "Ġpick ed", "ĠR ight", "ld er", "E E", "st ood", "ĠK im", "Ġw ire", "Ġs ight", "Ġdevelop ers", "Ġpers ons", "Ġs ad", "Ġc up", "Ġwar ning", "Ġboy s", "l ong", "Ġb ird", "f o", "Ġw al", "Ġobserv ed", "Ġz one", "iven ess", "Ġch annel", "c ript", "Ġref used", "ĠAg ain", "Ġsu c", "Ġspokes man", "ĠRe f", "r ite", "ou ston", "ãĥ ³", "ĠS her", "Ġact s", "ĠN ame", "Ġstrugg le", "ar ry", "omet imes", "Ġdisc rim", "H T", "Ġcateg ory", "Ġreal ize", "Ġemploy ee", "ĠAf ghan", "en ger", "Ġgun s", "ĠSte ve", "ĠM ot", "ĠO l", "ok ed", "Ġth ick", "Ġfair ly", "ill y", "Ġsur ve", "ĠM at", "we ight", "â Ķ", "Ġtro ops", "Ġag ents", "Ġbatter y", "Ġmot iv", "à ¡", "S ec", "d en", "o very", "L S", "Ġfl u", "Ġconf ident", "ĠO per", "Ġem pty", "Ġp hen", "Ġse ctor", "Ġexc ited", "Ġrem ote", "ap h", "o en", "Ġdestroy ed", "Ġmor al", "ĠH P", "ĠR on", "Ġd ress", "ĠB at", "Ġl it", "ĠM S", "Ġa f", "H L", "r um", "is ms", "Ġshould n", "Ġsym pt", "ĠTor onto", "het ic", "Ġcar bon", "Ġinstall ed", "Ġviol ent", "Ġsol ar", "j a", "Ġpract ices", "Ġr ide", "ĠP enn", "Ġimpro ved", "Ġaud io", "Ġbehav i", "ĠP S", "Ġe ating", "D ata", "ĠRe view", "p ass", "cl aim", "u ated", "ang ers", "c hen", "Ġproper ties", "Ġany where", "An other", "Ġbl ow", "ĠJack son", "Ġp roud", "Ġplan e", "l ines", "Ġsqu are", "Ġpro of", "ans as", "Ġtalk ed", "m akers", "Ġs ister", "Ġhold s", "Ġres ident", "Ġ= =", "Ġresist ance", "Ġspl it", "Ġpro secut", "Ġconf idence", "res ents", "Ġcut s", "Ġexcept ion", "Ġz ero", "Get ty", "Ġcop yright", "Ġtot ally", "orm al", "ific ations", "ĠAustral ian", "Ġs ick", "Ġ1 50", "Ġhouse hold", "Ġfe es", "Ġdri vers", "og en", "ĠN Y", "Ġnecess arily", "Ġregul ations", "ear ing", "s l", "Ġperspect ive", "c are", "ic ial", "H is", "Ġesc ape", "Ġsurpr ised", "ĠV an", "ur rent", "Ġv ac", "8 1", "ĠTh us", "Ġem phas", "ĠCh ampions", "ĠI ce", "Ġn arr", "Ġhead s", "Ġca using", "b el", "f ortunately", "ĠM a", "Ġtarg ets", "ci pl", "Ġafter noon", "Ġadd s", "ĠMay be", "ĠF our", "ess ed", "ple te", "Ġus ual", "ch o", "ing u", "Ġwith d", "ĠE nergy", "ĠE conom", "O O", "Ġart icles", "Ġinj ured", "Ġman age", "Ġexpl ains", "Ġdi agn", "R ec", "at ures", "Ġlink ed", "Ġdiscuss ed", "Ġexpl o", "Ġocc asion", "ath an", "Ġopp osite", "Ġfac es", "Ġden ied", "ĠK night", "Ġn ut", "Ġapprox imately", "Ġdisapp oint", "onym ous", "ĠB est", "ĠL o", "ĠH y", "ĠA ff", "Ġvot ing", "an while", "ĠII I", "Ġinstit utions", "ag ram", "ĠD aily", "Ġdr ag", "Ġnear by", "Ġgu ilty", "Ġcon ver", "P re", "s hip", "Ġre ward", "Ġphilos oph", "ĠS S", "u gh", "Ġapp s", "f riend", "Ġu pper", "Ġad vert", "Ġs now", "Ġfr ust", "Ġour selves", "F r", "ĠD ie", "amp ion", "Ġdis miss", "Ġc ere", "Ġsign al", "f rom", "Ġ ).", "Ġ5 2", "Ġcr imes", "it ors", "est ival", "use um", "Ġcoun cil", "ĠS aud", "M ay", "ĠG un", "ic ian", "et her", "Ġsu fficient", "ĠH en", "so le", "Ġhistor ical", "ĠF ar", "ĠT urn", "Ġp in", "Ġsuc ceed", "m at", "ly mp", "Ġtrad ition", "ĠO k", "Ġc ro", "Ġdesc ription", "al le", "Ġsk y", "T e", "Ġwide ly", "Ġw ave", "Ġdefin ition", "ĠJew s", "Ġcy cle", "Ġref ere", "Ġbr ings", "us al", "Ġal ive", "Ġfrequ ently", "Ġint ention", "ĠCont rol", "l v", "y stem", "Ġpriv acy", "g ent", "ren ce", "ĠQu est", "ĠChrist mas", "Ġr ail", "Ġco oper", "Ġtest ed", "ĠC apt", "as ks", "Ġcomfort able", "Ġdel ivered", "sc ape", "Ġdep th", "ĠG OP", "Ġwrit es", "Ġass ets", "Ġsa v", "im ents", "Ġtrans ition", "Ġart ist", "ĠL ook", "Ġl ob", "Ġcomp onents", "ar ity", "Ġwalk ed", "Ġro ot", "Ġparticip ants", "Ġnot iced", "Ġres c", "Ġn av", "ĠAd minist", "d a", "ut ral", "pl ate", "Ġimport ance", "Ġass ert", "ious ly", "c ription", "Ġinj uries", "ĠChe ck", "Ġregist ered", "Ġint ent", "Ġmiss ed", "ograph ic", "Ġsent ence", "oun ter", "Ġassist ance", "ev in", "Ġdat abase", "Ġbuild ings", "Ġclass ic", "Ġth inks", "ĠOh io", "P r", "ug g", "Ġfe e", "p an", "Ġeffect ively", "Ġfac ility", "Ġbe ar", "Ġch apter", "Ġdog s", "ĠCol umb", "Ġl atter", "it ial", "Ġad mitted", "T V", "ĠGe org", "Ġpost s", "\\ \\", "Ġlawy er", "Ġequ ival", "Ġm and", "Ġcontro lled", "ĠW alk", "ĠAnd rew", "Ġmen u", "am ental", "Ġprotect ed", "v a", "Ġadminist r", "or al", "Ġre in", "ĠS ar", "Ġamount s", "Ġn ative", "ĠM oon", "Ġrep resents", "Ġab andon", "Ġcarry ing", "Ġt ank", "m ary", "Ġdecl ared", "T ube", "Ġh at", "Ġpun ish", "el lect", "m es", "Ġun iverse", "ĠR od", "ph y", "Ġinf rastructure", "Ġ5 1", "Ġopp osed", "ow nt", "c a", "ĠM ake", "Ġhard ware", "Ġco ffee", "R el", "b al", "w orld", "ĠS af", "ĠSe a", "in als", "Ġown ed", "Ġh all", "ers ion", "Ġdescrib e", "ĠP ot", "Ġport ion", "Ġat mosp", "Ġgovern ments", "Ġdep ending", "Ġoff ense", "Ġtr ick", "aw a", "ĠL ine", "ĠV is", "ĠH ard", "ĠOr ig", "ĠCl ick", "Ġdes k", "ĠVal ley", "ĠS ov", "Ġmov ies", "Ġrem ark", "Ġm ail", "Ġcons cious", "Ġrul ing", "ĠR ights", "Ġmed ic", "he nt", "ĠW omen", "> <", "Ġrepl aced", "ĠP rem", "ĠTh anks", "Ġre new", "ĠB all", "if orm", "Ġsh ots", "C omm", "Ġar med", "Ġconst ant", "Ġt aste", "Ġreal ized", "Ġbu ff", "Ġm o", "Ġeffic ient", "M ost", "or ation", "if ies", "Ġcommun ication", "Ġfl ood", "Ġconsequ ences", "Ġany way", "ig g", "ĠG M", "ĠTh ank", "Ġ iron", "Ġev olution", "ĠC op", "tw itter", "Ġ9 5", "Ġrelationship s", "ad el", "ĠYou ng", "Ġpropos al", "ay ers", "uild ing", "ĠH ot", "OR E", "c os", "Ġcoll abor", "P G", "ax y", "Ġknow ing", "Ġsupport s", "ow ed", "Ġcontrol s", "Ġmere ly", "um er", "Ġath let", "Ġf ashion", "p ath", "Ġg ift", "Ġer a", "AN D", "Ġkind s", "ĠKore an", "Ġleg it", "ul ous", "Ġess entially", "Ġthe rap", "n ic", "Ġsuff ered", "Ġh ur", "Ġprom ise", "Ġex cess", "Ġover w", "Ġpr ime", "ĠH ouston", "er ry", "ĠM s", "R S", "201 2", "Ġst ores", "ĠO lymp", "Ġj ourney", "Al though", "S ub", "ĠE duc", "ĠCh apter", "Ġrequest s", "Ġconsum ers", "Ġt iny", "Ġis ol", "ĠF air", "b a", "ĠY OU", "Ġcr ash", "ce ler", "Ġemot ional", "Ġgood s", "Ġelect ed", "Ġmod er", "ĠLin ux", "Ġbl ocks", "Ġis land", "ĠSoc iety", "Ġelect ions", "Ġbroad cast", "Ġche ap", "Ġn ations", "Ġse asons", "4 00", "Ġwas te", "ĠS at", "Ġfield s", "em ploy", "Ġprof ile", "Ġauth ors", "AL L", "ĠG ra", "w est", "ĠT y", "Ġdeath s", "Ġv acc", "Ġfor med", "Ġd u", "Ġon going", "ĠMuslim s", "el f", "ig ure", "Ġass ume", "ĠUkrain e", "w ater", "Ġco ast", "Ġvot ed", "g or", "ĠA S", "ĠMich igan", "az a", "ĠAr m", "i ro", "Ġf lex", "as ters", "' '", "Ġwel come", "ar l", "Ġloc ations", "ig ation", "ĠF il", "Ġbu ying", "Ġarch itect", "Ġhard er", "ĠC ub", "Ġinter face", "Ġrestaur ant", "Ġdisco ver", "Ġex ceed", "Ġfav our", "ger y", "Ġd uty", "Ġp itch", "ad or", "ĠM ach", "b oy", "Ġrespond ed", "Ġext ended", "her s", "M any", "ra id", "if er", "ĠIn s", "S er", "Ġmed ium", "s he", "ĠS ports", "Ġmag azine", "ut ation", "Ġlim its", "ĠG all", "Ġex ternal", "raz il", "Ġyoung er", "t le", "Ġrem ind", "ĠC ON", "Ġimmedi ate", "Ġh idden", "Ġvol unte", "Ġsim pl", "od cast", "Ġph ase", "d r", "Ġpl ot", "Ġexp osure", "R I", "og rap", "v in", "an ish", "ĠAc ad", "ĠEng ine", "Ġexp ansion", "ĠP ay", "Y our", "Ġpus hed", "ĠE ll", "ĠHe ad", "Ġmarket ing", "ĠA C", "k et", "Ġh its", "Ġg ro", "ĠA ge", "ĠSc ot", "] [", "Ġst im", "Ġi Phone", "Ī Ĵ", "Ġn arrow", "ĠGet ty", "ĠTur key", "Ġperfect ly", "Ġen able", "ut ch", "Ġprec ise", "Ġreg ime", "Ġsh if", "Ġcomp ens", "g un", "d iv", "Ġch osen", "ĠK en", "An y", "Ġtre es", "Ġrecomm ended", "ĠR en", "u able", "ĠH T", "F ollow", "E G", "ĠH and", "ĠK enn", "Ġarg uments", "Ġex ists", "Ġb ike", "ĠCons erv", "Ġbre aking", "ĠG ar", "Ġc razy", "Ġvirt ual", "ay lor", "ix el", "Ġ19 80", "Ġper mission", "ĠSer ies", "Ġconsum er", "Ġclose ly", "c alled", "Ġ5 4", "Ġhop es", "Ġar ray", "ĠW in", "ĠLab our", "Ġsp ons", "ĠI re", "Ġp ow", "Ġread ers", "Ġemploy ment", "Ġcreat ure", "Ġresult ing", "Ġaccur ate", "Ġmom ents", "Ġarg ued", "Ġp ed", "D uring", "Ġ5 3", "ĠT al", "Ġs ought", "Ġsuff ering", "Ġ icon", "le e", "Ġ( $", "al ian", " °", "Ġp ra", "Ġbon us", "( \"", "k o", "Ġact ing", "D E", "f all", "Ġcompar ison", "Ġsm ooth", "ĠN AS", "u pp", "ĠJose ph", "ep ing", "ĠT ake", "ĠM id", "Ġs ending", "f ast", "ĠF all", "Ġdeal ing", "us er", "ĠOr gan", "C o", "Ġatt ached", "Ġse es", "% .", "Ġtyp ical", "AR T", "Ġfind s", "ĠAs ia", "um in", "ĠC ore", "ĠE nt", "in ent", "u ce", "ĠBl ood", "ĠN ever", "Ġem ails", "Ġhigh light", "Ġconf ront", "at us", "ut ed", "Ġun us", "Ġtop ic", "ĠAd am", "Ġb le", "at i", "Ġunder stood", "S et", "st ruct", "T P", "Ġm ob", "a a", "ĠSt art", "pect ed", "se ll", "Ġded icated", "ĠC A", "u an", "Ġsong s", "esc ription", "Ġte ch", "Ġr ape", "Ġas ide", "Ġgr ant", "Ġ5 6", "s ub", "Ġarg ue", "Ġcont aining", "Ġsche dule", "Ġliber al", "Ġpublic ly", "Ġheav ily", "ĠU t", "in er", "ĠS ection", "ĠC are", "we et", "l s", "D is", "âĶ Ģ", "ĠF ollow", "B ack", "ĠI T", "Ġb es", "j i", "ĠH it", "est ed", "Ġevery body", "ĠSw ed", "Ġfem in", "Ġfac ilities", "Ġcon ven", "C omp", "ĠO S", "c ore", "Ġan x", "Ġdiv ision", "ĠC am", "ĠSt an", "m ates", "Ġexpl ore", "pl om", "Ġsh ares", "pl oad", "an es", "Ġide al", "et ers", "ĠB ase", "Ġpl astic", "Ġdist inct", "ĠNet work", "ĠSe attle", "Ġtrad ing", "ens us", "int end", "Ġex hib", "Ġinit ially", "ĠF ood", "Ġthous and", "ĠBus iness", "act er", "Ġpar agraph", "Ġrough ly", "Ġw ww", "Ġcreat ive", "ĠCon f", "Ġconsum ption", "Ġfil ms", "ag an", "Ġob tain", "Ġt all", "Ġt or", "Ġacknow led", "Ġg rown", "al o", "K E", "Ġ4 00", "end ers", "t aining", "U G", "Ġsu icide", "Ġwat ched", "ĠL ist", "al i", "re hens", "Ġsurround ing", "Ġp ip", "Ġf lying", "ĠJ ava", "ord an", "Ġserv ing", "in ations", "p ost", "Ġsh o", "A v", "Ġj ail", "z y", "Ġ199 9", "Ġ< /", "Ġliter ally", "ĠS ir", "Ġexp osed", "Ġl ies", "st ar", "Ġb at", "Ġear ned", "ĠD ig", "Ġspec ified", "ĠSe ason", "Ġdeg rees", "Don ald", "Ġcent re", "Ġsh aring", "Ġwin ter", "ĠC O", "C he", "Ġ Î", "M P", "Ġun w", "Ġfew er", "ĠM ir", "Ġsomew here", "ĠK ey", "Ġattack ed", "ĠK ir", "Ġdom ain", "Ġstrong er", "Ġ9 9", "Ġpen alty", "I d", "Sc ript", "Ġdecl ined", "Ġne ck", "Ġfra ud", "Ġcur rency", "Ġr ising", "R C", "âĢ¦ âĢ¦", "H z", "Ġt ab", "Ġtal ent", "n am", "ĠN BA", "Ġvill age", "Ġleg s", "ĠN ext", "E d", "Ġac id", "Ġhy d", "8 00", "Ġinvol ving", "ĠIm age", "ĠBe fore", "F l", "Ġyes terday", "S ource", "Ġterror ist", "Ġsu p", "Ġsy nt", "ĠSaud i", "Ġw est", "Ġr u", "b urg", "Ġvis ible", "Ġstru ck", "r ison", "Ġaw esome", "Ġd rawn", "Ġansw ers", "ĠG irl", "ĠR am", "Ġthreat s", "Ġdef eat", "os it", "Ġv ent", "atur ally", "Americ an", "end a", "ĠH oly", "Ġr um", "% ,", "c ase", "ĠHist ory", "ĠYou Tube", "Ġsit uations", "ĠD NA", "S te", "Ġsa ved", "It em", "Ġrec ip", "olog ist", "Ġfac ed", "Ġel ig", "O nce", "ĠL i", "u h", "Ġmist ake", "ĠDiv ision", "ĠB ell", "Ġsympt oms", " ®", "Ġdom in", "Ġfall ing", "Ġend ing", "as hes", "Ġmat ches", "ĠOn line", "Ġexplan ation", "D ef", "red it", "Ġany more", "ĠT otal", "ĠF OR", "us hed", "Ġlet ters", "Ġris ks", "ĠO K", "Ġreported ly", ": \\", "Ġpl ate", "Ġsubject s", "Ġattempt ed", "if ier", "ian a", "Ġunlike ly", "ĠTh ough", "um a", "ĠIn vest", "ĠPr in", "ic an", "ĠD ar", "ĠColor ado", "au g", "Ġve get", "a os", "ri a", "Ġshe l", "Ġmark ed", "Ġ( )", "Ġsp r", "p o", "ĠL ink", "Ġdef e", "ĠJ r", "Ġthem e", "Ġpass ion", "ĠP en", "Ġinf o", "iz er", "Ġsh it", "ĠC ivil", "ap se", "c re", "Ġpo ly", "Ġcomp onent", "ĠChar les", "ĠIre land", "ĠPro v", "Ġdo ctors", "Ġgr anted", "Ġpain t", "Ġhon or", "Ġsm oke", "Ġpay ments", "Ġprim arily", "ĠKing dom", "r ich", "ate ll", "Ġde als", "Ġsched uled", "Ġfund amental", "Ġprote in", "Ġnewsp aper", "Ġcl ients", "yth on", "ĠD ate", "h us", "Ġfeed back", "Ġstret ch", "Ġc ock", "Ġhot el", "ĠQue en", "Ġsu gar", "Ġj u", "Ġmil k", "Ġappro val", "ĠL ive", "Ġequival ent", "ef ully", "Ġins ert", "z ona", "Ġext ension", "d ri", "J ohn", "Ġacc omp", "S m", "ĠF und", "Ġconst antly", "Ġ` `", "Ġgener ated", "ĠA ction", "ĠP sych", "ĠT ri", "Ġrecogn ize", "Ġv ary", "ph a", "ĠR a", "d f", "et ch", "ĠSov iet", "Tw o", "Ġpattern s", "Ġprof ession", "an ing", "T ime", "ĠL im", "Ġcol ors", "ĠA z", "ĠT R", "Ġinf ect", "Ġphen omen", "Ġshe ll", "Al so", "Ġput s", "Ġdel ivery", "Ġbro wn", "Ġprocess ing", "Ġlight s", "ess age", "ĠBro ok", "ĠA ud", "l ation", "Ġindust rial", "L ike", "ĠB razil", "rou s", "ES S", "ĠL uc", "Ġsome how", "Ġ8 5", "Ġpro port", "Ġpolit icians", "Ġindic ate", "Ġh ole", "Ġtechn iques", "Ġcompet itive", "Ġph r", "Ġv o", "ist ent", "ĠD ream", "Ġcamp us", "Ġaspect s", "Ġhelp ful", "Ġsh ield", "or se", "Ġtrig ger", "m al", "Ġ5 8", "Ġt ort", "Ġperson ally", "Ġt ag", "Ġkeep s", "ĠV ideo", "Ġben ch", "Ġg ap", "a ire", "Ġe ast", "Ġrec overy", "per ial", "Ġprof it", "ĠM ic", "Ġ5 7", "Ġcol on", "Ġstrong ly", "st yle", "Ġalleg ations", "h an", "Ġrep orters", "j o", "r ine", "arg et", "and al", "Ġ0 3", "Ġfl ash", "tr ans", "Ġstr ict", "Ġpark ing", "ĠPak istan", "Ġl i", "Ġwe ird", "ĠE ric", "Ġreg ions", "ĠJ un", "Ġint ellect", "ĠW H", "od ing", "rib utes", "up id", "ĠT it", "Ġf inger", "or ia", "Ġe lev", "ĠF ield", "Ġcon clusion", "; ;", "Ġfeel ings", "Ġext ensive", "Ġm ixed", "Ġne uro", "v y", "Ġhar ass", "ĠC irc", "ou ch", "Ġterrit ory", "Ġsuccess fully", "M ar", "Ġing red", "Ġoverw hel", "Ġl ayer", "V iew", "Ġall ies", "ill ance", "ĠTh ree", "Ġb unch", "Ġnorm ally", "Ġnet works", "Ġsac r", "ĠC IA", "b les", "Ġch ose", "Ġopp onents", "Ġregard less", "Ġfr anch", "Ġpre f", "ĠP o", "Ġbr idge", "ann a", "ĠSil ver", "Ġw age", "p age", "ri or", "Ġrad ical", "ĠL ittle", "Ġman ip", "Ġsecret ary", "Ġg ang", "D R", "F A", "Ġdec ent", "ĠSp irit", "Ġun cle", "ĠDevelop ment", "Ġinvest ors", "Ġwall s", "Ġpub lish", "Ġgener ate", "iss ions", "c ar", "Ġprom ote", "Ġcut ting", "Ġche st", "Ġdrink ing", "Ġcollect ed", "Ġ7 2", "Ġhop ing", "Ġem br", "gor ith", "Ġwar ned", "Ġinstruct ions", "O G", "ĠD id", "ĠAg ency", "Ġg ear", "Ġcritic ism", "ĠF urther", "Ġut il", "ann y", "R ed", "Ġcoun sel", "ĠAs ian", "Ġredu ction", "p ool", "Ġteach ing", "Ġdeep ly", "i y", "Ġestim ates", "Ġcho ices", "Ġperman ent", "in em", "ke l", "Ġf asc", "p se", "f ile", "ĠL ow", "ĠP erson", "Ġt ournament", "st al", "Ġm el", "U ST", "ĠR ay", "az i", "V al", "Ġcont ained", "ĠH olly", "Ġw ake", "Ġreve al", "Ġprocess es", "ĠIS IS", "Ġ0 9", "Ġbl ind", "Ġste el", "ĠB ad", "Ġcare fully", "app y", "ro it", "Ġg aming", "Ġhous es", "ĠC oll", "Ġtr uck", "er m", "Ġsc ored", "Ġocc as", "ret urn", "b ound", "v ar", "Ġsh arp", "Ġaf raid", "ĠE X", "am ber", "c ific", "Ġsche me", "N C", "ĠPol it", "Ġdecl ine", "Ġ199 8", "Ġpus hing", "Ġposs ession", "Ġpriv ile", "Ġteacher s", "Ġy ield", "H A", "ĠDav is", "it led", "#### ####", "Ġr ig", "ĠD aniel", "ac on", "Ġh ide", "ut en", "Ġcolle agues", "Ġprin ciples", "Ġl oud", "Ġs in", "ĠDem on", "Ġst one", "Ġ0 2", "Ġt aught", "Ġter rible", "Ġst uck", "ĠPol icy", "te en", "Ġimplement ation", "ĠB BC", "ĠAP I", "Ġwhe el", "all as", "Ġch ampions", "ol ars", "play er", "Ġrepeated ly", "ĠSt ill", "Ġlik es", "ast y", "es ter", "ĠCath olic", "R L", "Ġb ath", "Ġno ise", "t itle", "Ġn orthern", "P art", "Ġmag n", "Ġf ab", "ĠAs h", "Ġdis pl", "Ġtick et", "Ġm urd", "Ġalong side", "ĠMus ic", "Ġr iver", "ĠSte el", "ĠC L", "ĠPl ayer", "ĠM ult", "ow ing", "re p", "s ize", "Ġt ur", "ĠGeorg ia", "isc al", "ra ction", "Ġc able", "Ġ5 9", "Ġw ins", "Ġup coming", "Ġsurv ive", "Ġins pired", "ĠEduc ation", "Ġstat istics", "ĠF oot", "iam i", "Ġy ellow", "ĠP age", ". -", "ĠH as", "Ġur ban", "Ġa x", "es sel", "\\ \"", "Ġquarter back", "Ġreg ister", "ĠLab or", "Ġab ilities", "ĠF amily", "Ġvar iable", "ĠPr ice", "Ġcont em", "Ġth in", "ĠE qu", "d ata", "Ġg otten", "Ġconst it", "Ġas ks", "Ġt ail", "Ġexc iting", "ĠE ffect", "ĠSp anish", "Ġencour age", "ins on", "ĠA h", "Ġcommit ment", "C S", "Ġr ally", "Ġ: :", "Ġsubs id", "Ġsp in", "Ġcapt ured", "201 8", "Ġinn oc", "Ġalleged ly", "ĠC ome", "Ġart ists", "ĠN umber", "Ġelect ronic", "Ġreg ional", "ap es", "Ġw ra", "Ġmy th", "pr ise", "ĠM iller", "ĠC reat", "ĠEp isode", "b ell", "Ġdirect ed", "Ġext ract", "Ġs orry", "Ġv ice", "ag ger", "ĠSu pport", "Ġ6 6", "ĠI ron", "Ġwonder ful", "Ġg ra", "N et", "ion e", "E ng", "Ġsh ips", "ik es", "ĠK evin", "it ar", "Ġactiv ists", "tr ue", "ĠAri zona", "ent h", "ĠDes pite", "ĠS E", "Ġha bit", "ern el", "Ġin qu", "Ġab ortion", "Ġv oid", "Ġexpl icit", "Ġeng aged", "Ġang ry", "Ġr ating", "Ġfr ag", "b ro", "ick ing", "d ev", "Ġwor ried", "Ġob ser", "Ġap artment", "ĠG T", "Ġest ate", "ĠConst itution", "em on", "ĠS now", "Ġcount y", "Ġdis ag", "ĠStep hen", "Ġimm igrants", "w ind", "ĠN ations", "Ġfol ks", "O ut", "Ġg all", "Ġtarget ed", "Ġst ead", "ĠB on", "ĠL ib", "Ġinform ed", "Ġ12 0", "ch ain", "idel ines", "or ough", "Ġdri ven", "Ġregular ly", "Ġbas ket", "Ġprinc iple", "oc ument", "Ġst un", "ib ilities", "ĠRom an", "ĠAb out", "Ġal ert", "Ġdemocr acy", "Ġrepresent ed", "H S", "c ers", "p arent", "Ar t", "p ack", "Ġdi plom", "re ts", "ĠN O", "Ġcapt ure", "ĠAd v", "Ħ ¢", "Ġannounce ment", "ĠL ear", "Ġh ook", "Ġpur s", "ĠS uch", "ĠC amer", "Ġrefuge es", "ĠV e", "P ol", "Ġrecogn ized", "l ib", "Ġhad n", "A ss", "Ġpil ot", "us hing", "Ġreturn ing", "Ġtra il", "ĠSt one", "Ġrout ine", "Ġcour ts", "Ġdes per", "Ġfriend ly", "ĠIt aly", "Ġpl ed", "Ġbreat h", "Ġstud io", "N S", "Ġimp ressive", "ĠAfghan istan", "Ġf ing", "Ġd ownt", "ink ing", "ĠR og", "i ary", "col or", "se x", "ar on", "Ġf ault", "ĠN ick", "D own", "ĠR ose", "ĠS outhern", "X X", "is odes", "L ist", "6 00", "Ġout come", "er r", "Ġelse where", "Ġret ire", "Ġp ounds", "ĠGl obal", "Pe ople", "Ġcommun ications", "Ġlo an", "Ġrat io", "ĠEm pire", "Ġg onna", "Ġinv ent", "D F", "Ġ19 70", "ĠComm on", "p at", "Ġprom ised", "Ġd inner", "ĠH om", "Ġcreat es", "Ġoper ate", "ver ty", "ĠJ ordan", "et ime", "Ġsust ain", "R eg", "Ġincred ible", "im a", "Ġwar rant", "Ġm m", "A tt", "Ġlaw suit", "Ġreview s", "it ure", "ĠS ource", "l ights", "ĠF ord", "Ġ6 3", "g roup", "st ore", "Ġfeat ured", "Ġfore ver", "Ġpo verty", "ĠP op", "ĠC NN", "az z", "ab is", "ach ing", "Ġl aid", "ĠSu pp", "Ġfil ter", "en a", "ĠCommun ity", "Ġcreat ures", "u ction", "ĠR oyal", "Ġassoci ation", "ĠCon nect", "ĠBr ad", "âĸ Ī", "l ers", "the re", "ĠG i", "Ġval uable", "AC K", "ĠT aylor", "Ġl iquid", "ĠAtt orney", "ĠCar l", "ĠF inal", "ag a", "ĠWil son", "B ecause", "ĠProf essor", "ak a", "Ġincred ibly", "r ance", "! )", "R ef", "s k", "Ġsol utions", "Ġatmosp here", "Ġbl ame", "um es", "ĠN ob", "C A", "um ps", "r ical", "ĠPut in", "ĠD est", "or ic", "ĠP A", "Ġrespect ively", "w an", "Ġfif th", "â Ħ¢", "ĠC ry", "Ġgovern or", "res ident", "Ġpurch ased", "Ġh ack", "Ġint ense", "ob s", "Ġorig in", "Ġdef ine", "Ġcare ful", "** *", "Ġshould er", "Cl ick", "Ġt ied", "Ġdest ruction", "ou red", "Ġno body", "Ġh o", "ĠEx per", "Ġt ip", "\" ;", "Ġtechn ique", "Ġj ur", "ĠP ok", "b ow", "Ġleg end", "Ġacc ord", "Ġbus y", "ĠInt el", "Ġh ang", "ak i", ". ]", "âĢĶâĢĶ âĢĶâĢĶ", "Ġsur gery", "Ġrep rodu", "Ġun iform", "Ġscen es", "c ode", "Ġ6 2", "l isher", "ĠH ave", "ph ia", "Ġcry pt", "Ġrec on", "Ġsc ream", "Ġadop ted", "Ġsc ores", "N e", "ĠIt alian", "in cluding", "B O", "Ġindic ated", "Ġent ertain", "G u", "T ext", "i el", "Ġtw enty", "Ġeng age", "off s", "ĠPac ific", "Ġsm ile", "Ġperson nel", "Ġto ler", "Ġdo ors", "Ġt one", "Ġmach ines", "Ġent ering", "ten ance", "C O", "ĠJer sey", "Ġfore st", "Ġhor se", "Ġcompl aint", "ĠSpr ing", "y o", "ĠPl us", "ed ing", "ĠRet urn", "qu arters", "ial s", "c ow", "Ġacad emic", "Ġf ruit", "Ġ199 6", "og ether", "Ġw ine", "Ġpur su", "ĠSte ven", "Ġlic ens", "Wh o", "Ġclot hes", "re ction", "Ġsqu ad", "Ġst able", "Ġr aw", "z ens", "St ar", "ut ies", "anc er", "Ġke ys", "ĠM u", "Ġcompl icated", "ig er", "ĠTe xt", "Ġabs or", "Ġ6 8", "Ġfun ny", "Ġrel ief", "ĠL ew", "ĠC ook", "Ġch art", "Ġdraw ing", "G E", "Ġmod ule", "ĠB ull", "I LL", "Ġs alt", "0000 0000", "il le", "Ġres ource", "aw ay", "adel phia", "ĠB ru", "Ġ6 7", "Ġsome body", "Ġparticip ate", "Ġro se", "we red", "Ġmus cle", "Ġcons ent", "Ġcontin uing", "ĠGuard ian", "ĠOr der", "reg on", "Ġre ar", "Ġprov ision", "Ġlik ed", "ri ent", "Ġb ra", "Tr ans", "Ġmeet ings", "Ġto x", "Ġcon vent", "Ġaut o", "Ġrec ording", "ĠSo ft", "00 1", "ĠR oll", "Ġprogram ming", "Ġp ic", "Ġprov ed", "Ġst ab", "ĠA st", "Ġca ption", "ul ating", "ĠAtt ack", "Ġnew ly", "Ġ199 7", "f r", "Ġdis cipl", "ĠGree k", "Ġed ition", "ĠDo es", "ĠB ox", "if le", "ack et", "Ġpass es", "Ġgu est", "Ġac celer", "it als", "U D", "Ġaut hent", "ĠR est", "ov al", "t a", "u ine", "Ġarm or", "ĠT own", "Ġcomp at", "Ġinc hes", "Des pite", "Ġass ign", "he rent", "Ġprep are", "ĠM eg", "oc key", "Ġdep ends", "Ġtrack s", "w atch", "Ġl ists", "ĠN orthern", "Ġal ter", "re c", "ĠE astern", "Ġcond em", "Ġevery where", "? '", "Ġaff ili", "Ġf ought", "\": {\"", "Ġm ac", "it arian", "Ġsc ope", "ĠA L", "aw s", "ar ms", "Ġqu e", "Ġenjoy ed", "nes ota", "Ġagg ressive", "ĠSt ory", "ĠI V", "Ġrec ipe", "Ġrare ly", "ĠMed ical", "val ue", "ang el", "ay ing", "omet hing", "Ġsub section", "Ġs outhern", "Ġfrequ ency", "re te", "roll ed", "ult s", "ĠN ic", "Ġbeh alf", "Ġsequ ence", "ab et", "Ġcontrovers ial", "Ġcomp rom", "Ġwork er", "Ġmain ly", "Ġal gorith", "ĠM ajor", "or ce", "g ender", "Ġorgan ized", "Ġf ake", "Ġconclud ed", "ĠE D", "ĠEx ec", "r age", "Ġch ances", "ber ry", "ĠTr ad", "Ġconfig uration", "Ġwithd raw", "Ġf ro", "ud es", "ĠBro ther", "ĠB rian", "Ġtri es", "Ġsam ples", "Ġb id", "ĠGold en", "Ġphot ograph", "if est", "ĠD O", "ĠPar liament", "******** ********", "R em", "Ġcont est", "Ġsign ing", "p x", "ĠZ eal", "âĶĢ âĶĢ", "E ar", "Ġex it", "Be fore", "ĠCor por", "n ull", "mon th", "Ġrac ial", "ott ed", "ĠV eg", "ĠRe uters", "Ġsw ord", "ps on", "ĠRom ney", "a ed", "Ġt rib", "Ġin ner", "Ġprot ocol", "ĠB i", "ĠM iami", "ever al", "p ress", "Ġsh ipping", "ĠAm endment", "ĠHow ard", "con nect", "ĠD isc", "ĠJ ac", "iam ond", "ĠThere fore", "s es", "ĠPrin cess", "ĠUS B", "ĠAn th", "Ġsurve illance", "Ġap olog", "Ġ6 1", "ow a", "Ġf ulf", "j s", "Ġl uck", "ust ed", "Ġ §", "n i", "Ġant icip", "em an", "Ġwin ner", "Ġsil ver", "ll a", "ic ity", "Ġunus ual", "Ġcr ack", "Ġt ies", "e z", "Ġpract ical", "Ġprov ince", "ĠPl ace", "Ġprior ity", "IC E", "Ġdescrib es", "Ġbr anch", "F orm", "ask a", "miss ions", "b i", "Ġp orn", "ĠTur k", "Ġent hus", "Ġf ighters", "Ġ0 8", "ĠDet roit", "Ġfound ation", "av id", "A re", "Ġjud gment", "cl ing", "Ġsol ve", "ĠDes ign", "W here", "hes is", "ĠT ro", "a fter", "Ġne utral", "ĠPalestin ian", "ĠHolly wood", "Ġadv is", "ĠN on", "y es", "ol is", "Ġrep utation", "Ġsm ell", "Ġb read", "ĠB ul", "ĠBe ach", "Ġclaim ing", "Ġgen etic", "Ġtechn ologies", "Ġupgr ade", "row s", "Ġdevelop er", "ĠJ osh", "ĠDis ney", "erv ed", "ip al", "Ġun ex", "Ġbare ly", "t hen", "ĠP ub", "Ġill ness", "et ary", "ĠB al", "Ġp atch", "Ġbut t", "Ġst upid", "ĠD og", "ĠD allas", "f ront", "ie ce", "Ġprot ests", "Ġch at", "oen ix", "Ġw ing", "Ġpar liament", "Ġ7 7", "ose xual", "Ġre nder", "pt ions", "ĠCo ast", "os a", "ĠG reg", "h op", "ĠMan agement", "Ġbit coin", "Ġrec over", "Ġincor por", "or ne", "ĠUs ing", "Ġpre ced", "Ġthreat ened", "Ġspirit ual", "ĠE vent", "ĠF red", "Ġadvert ising", "Ġimprove ments", "ĠC ustom", "Ġer rors", "Ġsens itive", "ĠN avy", "Ġcre am", "L ook", "Ġex clusive", "Ġcomp rehens", "Ġde leg", "Ġcon ce", "Ġrem em", "Ġstruct ures", "Ġst ored", "N D", "Ġ1 000", "U P", "ĠB udd", "A F", "w oman", "ĠAcad emy", "ð Ł", "se a", "Ġtem porary", "Ab out", "es ters", "Ġtick ets", "Ġposs ess", "in ch", "o z", "Ġl a", "Ġcontract s", "Ġun p", "Ġc ig", "ĠK at", "ult ural", "as m", "Ġmount ain", "ĠCapt ain", "St ep", "m aking", "ĠSp ain", "Ġequ ally", "Ġl ands", "at ers", "Ġreject ed", "er a", "im m", "ri x", "C D", "Ġtrans action", "g ener", "less ly", "Ġ| |", "Ġc os", "ĠHen ry", "Ġprov isions", "Ġg ained", "Ġdirect ory", "Ġra ising", "ĠS ep", "ol en", "ond er", "Ġcon sole", "in st", "Ġb om", "Ġunc ertain", "1 50", "ock ing", "Ġmeas ured", "Ġpl ain", "Ġse ats", "Ġd ict", "S L", "af e", "Ġest imate", "iz on", "at hered", "Ġcontribut ed", "Ġep isodes", "omm od", "G r", "AN T", "Ġ6 9", "G ener", "Ġ2 50", "vious ly", "rog en", "Ġterror ism", "Ġmove ments", "ent le", "oun ce", "ĠS oul", "Ġpre v", "ĠT able", "act s", "ri ors", "t ab", "Ġsuff er", "Ġn erv", "Ġmain stream", "ĠW olf", "Ġfranch ise", "b at", "Ġdem ands", "Ġag enda", "Ġdo zen", "Ġclin ical", "iz ard", "ĠO p", "t d", "Ġvis ited", "ĠPer haps", "Ġact or", "Ġde lic", "Ġcont ribute", "Ġin ject", "ĠE s", "ac co", "Ġlist ening", "Ġcon gress", "epend ent", "Ġprem ium", "Ġ7 6", "ĠIr ish", "Ġass igned", "ĠPh ys", "Ġworld wide", "Ġnarr ative", "ot ype", "m ont", "b ase", "ĠB owl", "ĠAdminist ration", "Ġrel ation", "ĠE V", "C P", "Ġco vers", "Ġ7 8", "Ġcert ific", "Ġgr ass", "Ġ0 4", "pir acy", "ir a", "Ġengine ering", "ĠM ars", "Ġun employ", "ĠFore ign", "st ract", "Ġv en", "Ġst eal", "Ġrepl ied", "Ġult imate", "Ġtit les", "d ated", "Ġj oy", "a us", "Ġhy per", "ak u", "Ġoffic ially", "ĠPro duct", "Ġdifficult y", "per or", "Ġresult ed", "rib ed", "l ink", "wh o", "~~ ~~", "ĠSpe ed", "ĠV iet", "W ind", "ĠBar ack", "Ġrestrict ions", "ĠSh are", "Ġ199 5", "ition ally", "Ġbeaut y", "op t", "Ġm aps", "ĠC R", "ĠN ation", "ĠCru z", "W ill", "Ġelectric ity", "Ġor g", "Ġb urd", "Ġviol ation", "Ġus age", "Ġper mit", "ĠCh ron", "ĠF ant", "Ġn aturally", "Ġ0 7", "Ġth rown", "ĠAw oken", "Ġal ien", "ĠHer o", "ĠK ent", "ĠR ick", "ri ke", "Ġp ace", "}, {\"", "G L", "Ġpo ison", "ĠT ower", "Ġform al", "al ysis", "Ġgen uine", "Ġk il", "a ver", "Ġproced ure", "ĠPro p", "intend o", "ĠM ain", "as ant", "Ġtr ained", "G ame", "ĠL oad", "ĠM A", "Ġcru cial", "Ġle ts", "ĠF R", "Ġch ampion", "1 01", "ĠCon ference", "Ġwrit ers", "Ġconnect ions", "Ġo kay", "ir ms", "ĠR and", "Ġenc ounter", "ĠB uff", "Ġachie ved", "Ġche cks", "isc ons", "Ġassist ant", "Ġwhen ever", "ĠA ccess", "ĠU r", "b in", "Ġcl ock", "is p", "op her", "Ġb orrow", "Ġm ad", "Ġperson ality", "on ly", "IS T", "ab ama", "Ġg ains", "Ġcommon ly", "Ġter r", "Ġhyp ot", "Ġre ly", "Ġt iss", "iscons in", "Ġrid ic", "f unction", "ĠO regon", "Ġun com", "r ating", "el and", "ĠN C", "Ġm oon", "ann on", "Ġvulner able", "ut ive", "³³ ³³", "ĠRad io", "Ġw estern", "se ct", "ĠT ony", "Ġocc urs", "ĠO s", "ĠH on", "à Ń", "Ġv essel", "ĠScot land", "Ġdiscrim ination", "Ġsubsequ ent", "st ring", "Ġfant asy", "ĠSh adow", "Ġtest im", "W E", "it i", "r as", "Ġbo at", "Ġmar ks", "Ġord inary", "Ġre n", "Ġrepresent ative", "Ġpet ition", "Ġ7 3", "Ġad venture", "Ġign ore", "ĠPhil adelphia", "ĠS av", "V P", "Ġfact ory", "Ġt asks", "Ġdep ression", "z ed", "................ ................", "ĠSt orm", "Ġc ogn", "Ġelig ible", "Ġredu cing", "v ia", "Ġ0 5", "Ġstri king", "Ġdoll ar", "h o", "O V", "Ġinstr ument", "Ġphilosoph y", "ĠMo ore", "ĠA venue", "Ġrul ed", "ĠFr ont", "IN E", "ĠM ah", "Ġscen ario", "ĠNAS A", "Ġen orm", "Ġdeb ut", "Ġte a", "T oday", "Ġabs ence", "S im", "Ġh am", "le ep", "Ġt ables", "ĠHe art", "M I", "K e", "re qu", "V D", "m ap", "Ġchair man", "Ġp ump", "Ġrapid ly", "v i", "Ġsubstant ial", "E P", "d es", "ch ant", "ili pp", "ĠS anta", "ri ers", "anche ster", "L oad", "ĠC ase", "Ġsa ving", "Ġ7 4", "ĠA FP", "er ning", "oun ced", "ĠMin nesota", "ĠW as", "Ġrec ru", "Ġassess ment", "ĠB ron", "U E", "Ġdynam ic", "Ġf urn", "ul ator", "Ġprop ag", "h igh", "Ġacc ommod", "Ġst ack", "ĠS us", "w rit", "Ġre ven", "ĠGod d", "ĠZeal and", "ab s", "Ġbr ut", "Ġper pet", "h ot", "Ġhard ly", "ĠB urn", "ãĤ ¹", "Ġst y", "Ġtrans actions", "Ġg ate", "Ġsc reens", "Ġsub mitted", "Ġ1 01", "Ġlangu ages", "ugh t", "em en", "Ġfall s", "Ġc oc", "Ĥ ¬", "Ġstri kes", "p a", "Ġdel iber", "ĠI M", "Ġrel ax", "ann els", "ĠSen ator", "Ġext rem", "Ġ} ,", "ĠDe b", "Ġbe ll", "Ġdis order", "c ut", "Ġi OS", "Ġl ocked", "Ġem issions", "Ġshort ly", "\" ]", "ĠJud ge", "ĠS ometimes", "Ġr ival", "Ġd ust", "Ġreach ing", "F ile", "¯¯ ¯¯", "ino is", "ĠJ ason", "Ġs atell", "are t", "Ġst ations", "Ġag ric", "ĠTechn ology", "com es", "ĠUn fortunately", "ĠChild ren", "Ġappl ies", "ast ed", "Ġan ger", "ail ability", "ĠDam age", "Ġcomp are", "ĠStand ard", "Ġaim ed", "ĠB a", "angu age", "Ġreg ulation", "Ġj ury", "Ġair port", "Ġse ctions", "ĠPr ince", "em ed", "Ġmedic ine", "Ġh itting", "Ġsp ark", "ol ves", "Ġad s", "St ate", "Ġfood s", "Ġrepl acement", "Ġch icken", "Ġlow est", "Ġmind s", "Ġinvol ves", "u i", "Ġarr ang", "Ġproced ures", "ĠWh ich", "ivers ary", "Ġb ills", "Ġimprove ment", "Ġin ev", "Ġexpect ations", "Ġintellect ual", "Ġsp aces", "Ġmechan ism", "2 50", "bre ak", "ĠZ e", "ĠT enn", "ĠB alt", "Ġbar rel", "Ġstat ic", "man n", "Pol ice", "Ġt ips", "Ġhand ling", "c us", "od ed", "il ton", "ir y", "Ġjournal ists", "our se", "Ġcom ic", "Ġnom ine", "IT Y", "Ġvers us", "Ġlo op", "Ġsur f", "ĠInd ust", "ĠHun ter", "Ġbelief s", "is an", "Ġset up", "Ġbre w", "im age", "Ġcomput ers", "f ol", "} ,\"", "ĠMed al", "Ġtax p", "Ġdisplay ed", "Ġg rav", "Ġf iscal", "M on", "ĠMos cow", "ĠK ong", "ĠCent re", "Ġcamer as", "ĠMr s", "ĠH ay", "Ġa ver", "ĠK elly", "p y", "Ġrequire ment", "Ġent itled", "omb ie", "Ġsh adow", "ag ic", "ĠA k", "Ġel ite", "Ġdiv ided", "Ġhead ing", "Ġcop ies", "Ġloss es", "Ġv it", "k ed", "ĠB ry", "Ġan s", "ĠSte am", "Ġrep orter", "he im", "ĠIt em", "Ġsuper ior", "d on", "ere nt", "à ¶", "Ġtherap y", "Ġpe ak", "ĠMod el", "Ġl ying", "Ġg am", "z er", "r itten", "Ġrespons es", "Ġconsider ation", "ĠB ible", "Ġl oyal", "Ġinst ant", "Ġp m", "ĠFore st", "à ¼", "Ġext end", "Ġconv icted", "Ġfound er", "Ġconv in", "ĠO ak", "che ck", "Ġsch olars", "p ed", "Ġover se", "T op", "c ount", "ĠAr k", " ·", "Ġ0 6", "ĠL A", "m d", "ĠLat in", "im ental", "ĠC PU", "Ġsubst ance", "Ġminor ity", "Ġmanufact uring", "E r", "ocol ate", "Ġatt ended", "ĠMan ager", "r ations", "Ġappreci ate", "om y", "GB T", "id ency", "B L", "Ġguarant ee", "pos ition", "Ġo cean", "clud e", "Ġhead ed", "Ġt ape", "Ġlo ose", "Ġlog ic", "Ġpro ven", "Ġsp ir", "Ġad mit", "is a", "Ġinvestig ate", "Ġ199 4", "sy lv", "ĠL ost", "c est", "Ġ7 1", "Ġrequest ed", "Ġwind ows", "ĠPok é", "ĠWith out", "M et", "Ġbehavi our", "Ġread er", "Ġh ung", "ĠKe ep", "Ġro les", "Ġimplement ed", "Ġbl ank", "Ġserv es", "ĠJ ay", "Ġc ited", "ĠF riend", "prof it", "ap on", "Ġrep air", "it em", "arr ass", "Ġcrit ics", "ad i", "ĠF ather", "Ġsh out", "Ġf ool", "Ġ8 8", "Ġprodu cing", "Ġl ib", "Ġround s", "Ġcirc le", "Ġpre par", "Ġsub mit", "Ġn ic", "mor row", "ãĥ «", "U nder", "Ġv ital", "ater n", "Ġpass word", "Ġpublic ation", "Ġprom inent", "Ġspeak s", "Ġb ars", "Ġde eper", "ĠM ill", "port ed", "Ġw id", "Ġbut ter", "Ġsm oking", "Ġindic ates", "K ey", "rop ri", "ĠF ile", "all ing", "ast ing", "ĠR us", "Ġad j", "Ġ7 9", "av al", "Ġpres um", "bur gh", "on ic", "Ġf ur", "Ġpoll s", "ik a", "Ġsecond ary", "Ġmon ster", "ig s", "ĠCur rent", "E vent", "Ġowners hip", "end ar", "Ġarri ve", "ĠT ax", "Ġn ull", "ĠPri v", "Ġth ro", "Ġk iss", "c at", "Ġup set", "ang le", "it ches", "ect or", "olog ists", "ĠGal axy", "Ġcor ruption", "Ġh int", "ent er", "ĠH ospital", "Ġgreat ly", "Ġbeg un", "es y", "Ġso il", "ĠAnt on", "Ġmain tenance", "ãĥ ©", "Ġdo zens", "Ġhuman ity", "ĠAl abama", "Ġr om", "w orth", "ap ing", "sylv ania", "l ah", "Ġg athered", "G A", "Ġattack ing", "f ound", "ĠSqu are", "Ġar bit", "ict ions", "ĠW isconsin", "Ġd ance", "ĠS aint", "arch y", "Ġbase ball", "Ġcontribut ions", "Ġliter ature", "Ġex ha", "per ty", "t est", "Ġb ab", "Ġcontain er", "let ter", "Ġfall en", "Ġwebs ites", "Ġbott le", "ĠS ac", "Ġbre ast", "ĠP L", "Ġveter an", "Ġinterview s", "ĠA le", "Ġb anned", "eng ers", "ĠRev olution", "in th", "Ġconc erning", "IV E", "Ġexp enses", "ĠMatt hew", "ĠColumb ia", "d s", "ist ance", "Ġent ity", ".. .\"", "Ġrel iable", "Ġpar alle", "ĠChrist ians", "Ġopin ions", "Ġin du", "l ow", "Ġcompet e", "Ġth orough", "Ġemploy ed", "Ġestablish ment", "ig en", "ĠC ro", "Ġlawy ers", "ĠSt ation", "T E", "ĠL ind", "ĠP ur", "it ary", "Ġeffic iency", "âĢ IJ", "ĠL y", "Ġm ask", "Ġdis aster", "Ġag es", "ER E", "es is", "ĠH old", "Ġcas ual", "b led", "Ġen abled", "ĠEn vironment", "ĠInt elligence", "i per", "ĠM ap", "ĠB E", "Ġemer ged", "is dom", "Ġc abin", "Ġregist ration", "Ġfing ers", "Ġro ster", "Ġfram ework", "ĠDo ctor", "et ts", "Ġtransport ation", "Ġaware ness", "H er", "Ġattempt ing", "O ff", "ĠSt ore", "ÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤ", "ĠK now", "Ġdef ence", "Ġsc an", "ĠT en", "ĠCh air", "ĠP H", "ĠAtl anta", "Ġfuck ing", "Ġans wered", "b n", "ĠK ar", "Ġcateg ories", "Ġr ational", "Ġc ust", "Ġrob ot", "Ġcorrect ly", "Ġg if", "Ġgraph ics", "m ic", "Ġground s", "ĠO pp", "i ate", "Ġdist ributed", "Ġsan ctions", "Ġchalleng ing", "ut o", "Ġingred ients", "Ġinv ited", "Ġfound ed", "ĠRe qu", "d ed", "Ġb owl", "Ġbrother s", "ĠH a", "I O", "Ġw ages", "im ore", "oc ial", "Ġse ed", "ative ly", "Ġaddress es", "ĠI owa", "ab eth", "Ġatt itude", "is d", "ch ild", "Ġm ole", "Ġdisco very", "y ard", "B r", "Ġ8 2", "Ġsuppl ies", "ell ing", "Ġdist ingu", "C R", "Ġre cept", "Ġ vert", "Ġsw im", "b ec", "d oor", "ĠY eah", "Ġg al", "Ġinter act", "ĠE SP", "ĠC S", "amp s", "Ġconvin ced", "Ġobject ive", "Ġdis h", "ĠPhot os", "l ad", "Ġdownt own", "o il", "in ction", "Ġto morrow", "ĠC OM", "Ġsurv ival", "sh ot", "Ġsett lement", "C ons", "ĠX box", "int erest", "ĠS M", "arg o", "en ess", "Ġeth nic", "b ered", "M in", "ĠT ok", "Ġinc ent", "ĠComm and", "Ġmain tained", "Ġbreak s", "br idge", "at ar", "ag g", "ĠF inally", "un icip", "ĠO nt", "le ft", "Ġrecogn ition", "Ġ* /", "ĠP ers", "Ġwe lf", "Ġaddress ed", "ĠK ansas", "Ġvir us", "Ġwhere as", "Ġp apers", "ram s", "ĠMin istry", "Ġple asure", "Ġacqu ired", "Ġd uration", "j pg", "Ġcal m", "ĠN HL", "Ġburn ing", "Ġfold er", "ick ed", "ĠP y", "ĠIll inois", "Cl ass", "ĠGodd ess", "Ġperform ing", "Ġwelf are", "j ar", "In ter", "Ġl in", "Ġenh ance", "Ġnot ion", "f are", "yp es", "ĠAre a", "Ġcann abis", "ĠDie go", "f s", "ĠM anchester", "com m", "in ite", "Ġcover ing", "ĠS ound", "Ġ19 60", "Ġ8 4", "e lect", "z ing", "Ġcitiz en", "Ġph ones", "Ġr aid", "Ġign ored", "ĠOb ject", "Ġu pload", "c ard", "Ġmod ified", "Ġroom s", "ia h", "r ange", "he ast", "ach us", "Ġsuggest ing", "âĢ ĭ", "gr ade", "E l", "Ġclot hing", "Ġr h", "ĠH an", "un ity", "en cing", "ĠAust in", "sec ution", "t ra", "d em", "ĠQ ual", "Ġhe aven", "Ġst ages", "Ġw edd", "pl us", "ific ial", "ĠIm m", "ĠH o", "iet ies", "Ġphr ase", "Ġbr ill", "act ory", "Ġprov iders", "Ġsil ence", "Ġa er", "ĠA I", "ĠAd venture", "Ġplatform s", "Ġdemonstr ated", "Ġinter f", "ing ton", "Ġr aces", "Ġgr ade", "ult ane", "ĠTh rough", "f alse", "Ġb ow", "ĠA B", "Ġfl avor", "Ġhistor ic", "g ov", "Ġcol our", "Ġview ed", "ĠEm ail", "el come", "Ġinter vention", "Ġd iversity", "Ġperiod s", "Ġre verse", "ĠV ery", "Ġqu ote", "ĠLe ft", "th rough", "Ġsc rew", "Ġland ing", "Ġp ill", "Ġw et", "Ġprot esters", "Ġrepe at", "av ed", "er k", "Ġsal ary", "ĠPenn sylvania", "St ill", "Ġmay or", "Ġkit chen", "Ġfeat uring", "ĠM useum", "ĠT ournament", "ĠF al", "Ġser vers", "U C", "Ġany body", "im g", "ĠTr ade", "ixt ure", "the less", "Ġfin ance", "Ġcl osing", "ĠPat ri", "i ac", "ab el", "Ġ> >", "or ous", "Ġf irms", "sc reen", "un a", "Ġemb arrass", "ul se", "Ġlet ting", "Ġth rew", "ile y", "Ġch annels", "l an", "ĠVeg as", "Ġse ar", "Ġfant astic", "ar re", "uzz le", "ĠD er", "Th ose", "Ġsw ing", "Ġshe et", "ind ex", "co ver", "og an", "Ġvari ables", "ĠTe ch", "Ġsp oken", "ac hel", "ĠD a", "ĠMount ain", "Ġload ed", "Ġfoot age", "vers ion", "Ġun l", "ĠPh oenix", "Ġthrow ing", "Ġf iring", "Ġtrack ing", "Ġw idth", "Ġstrugg ling", "ro oms", "ot ion", "Ġmonth ly", "ĠSer ver", "Ġegg s", "op en", "M C", "Ġ199 3", "Ġh ired", "Ġstay ed", "ĠAll en", "Ġst ro", "Ġ9 8", "st ep", "ĠTurk ish", "Ġfab ric", "ist ing", "ĠD om", "Ġd ates", "Ġpr on", "Ġbasket ball", "Ġl ucky", "ĠArab ia", "Ġassum ed", "est y", "Ġaff airs", "Ġgl ad", "ĠInd eed", "ĠF A", "ĠW ord", "Ġjo ining", "if ice", "p read", "ir ts", "ĠSe lect", "Ġpop ulations", "aw are", "Ġn ose", "Ġcompl aints", "st art", "Ġsc oring", "Th anks", "Ġmin ing", "Ġvisit ors", "S H", "Ġdam aged", "Ġcharacter istics", "ĠP ent", "D C", "Ġ8 3", "ĠS ix", "r ates", "Ġfl ags", "ĠB rew", "d og", "M ark", "// //", "Ġexec ution", "Ġj oke", "ph ones", "Ġtestim ony", "Ġob st", "Q L", "ĠC ut", "Ġstud ied", "ĠN intendo", "ick et", "ĠN BC", "Ġl ad", "ĠB ra", "ĠM oh", "Ġk ernel", "Ġoverwhel ming", "Ġag ed", "Ġapplic able", "ĠC ond", "Ġroad s", "ĠBl ock", "m ade", "od ge", "Ġcomm ands", "Ġoff ices", "vel and", "Ġt ut", "Ġrece iver", "ĠF ro", "Ġsho pping", "Ġi P", "ĠSt re", "ĠA BC", "Ġentertain ment", "ĠB ow", "ort ed", "M c", "Ġread s", "gr ad", "ĠCol lect", "Ġâ ĪĴ", "ĠCap ital", "eder ation", "Ġemploy er", "Ġinvolve ment", "Ġanx iety", "al ia", "Ġro of", "ĠAm ong", "ĠDemocr at", "Ġstat s", "ĠV ill", "Ġconst itutional", "Ġrefer ring", "itt y", "Ġtack le", "out ube", "Ġback ed", "ĠH ong", "ĠBro ad", "Ġe le", "ĠO tt", "Ġ199 2", "h our", "achus etts", "C al", "Ġdefe ated", "Ġ8 1", "es p", "Ġseem ingly", "w as", "ĠJ enn", "ĠK urd", "Ġg ene", "Ġdisc ount", "R et", "EC T", "( );", "Ġclub s", "Ġs id", "ĠM arsh", "Che ck", "Ġp p", "ĠE ag", "ides pread", "Ġbe ings", "F T", "Ġintrodu ction", "ĠCh ange", "AR D", "Ġ1 10", "ad ows", "ier ce", "Ġme al", "a uthor", "ĠB ang", "lah oma", "Ġr anks", "201 1", "?? ??", "m ax", "Ġcoll apse", "Ġop ens", "Ġe cho", "Ġs oph", "Ġrac ist", "Ġenorm ous", "Ġw aves", "Ġt ap", "Ġcomprehens ive", ". --", "ĠR oy", "Ġfarm ers", "Rel ated", "a ired", "ron es", "ĠC rim", "Ġproport ion", "Ġdesign s", "Ġnegoti ations", "Ġvirt ually", "ĠBat man", "Ġwar n", "Ġlegit imate", "m ate", "Ġcon vention", ", ,", "net ic", "ĠS D", "Ġconsist ently", "Ġcompens ation", "Ġpunish ment", "Ġy e", "Ġt ie", "ĠB ureau", "ir lf", "ĠB u", "ĠA ren", "ĠPh ilipp", "Ġkn ife", "Ġmem ories", "ĠR oss", "Ġang le", "Ġ8 6", "ĠTh under", "Ġre nd", "ĠT our", "Ġcount s", "s ung", "ĠIm p", "Ġeduc ational", "Ġaccess ible", "C OM", "Ġd rew", "y er", "G l", "am ine", "OR T", "O B", "I B", "m aster", "Ġtri als", "og y", "h ar", "ĠTr ust", "Ġprefer red", "irlf riend", "ĠN ev", "Ġb in", "Ġc ow", "P age", "Ġsign ature", "ĠB L", "7 00", "Ġret ired", "Ġby tes", "Ġneigh b", "ĠLeg end", "Ġdev ast", "Ġsuspect ed", "is ons", "ĠPoké mon", "sc ale", "Ġcap abilities", "Ġre vel", "Ġche ese", "d y", "igr ant", "Ġfail ing", "b its", "ĠHer oes", "ĠG host", "ĠS cient", "Ġappoint ed", "ur i", "Ġinst itution", "Ġexpand ed", "g reg", "Ġmonitor ing", "Ġp odcast", "Ġcoal ition", "Ġ9 6", "J o", "Ġst olen", "ĠS ab", "Ġstop s", "Ġhol iday", "Ġint r", "C ar", "Bl ack", "ĠL GBT", "Ġwar ming", "ĠAnd erson", "Ġ8 9", "Ġprodu cer", "M ed", "Ġaccur acy", "ĠMar vel", "iz abeth", "ĠPat rick", "m ony", "Ġmin i", "ac les", "Ġover t", "the y", "Ġmembers hip", "ĠV en", "Ġex ch", "Ġrem oval", "ĠD ave", "T Y", "m ad", "ĠF ind", "Ġad equ", "Ġe c", "Ġte eth", "Ġemot ion", "Ġper m", "Ġsole ly", "d b", "Ġextra ord", "IG HT", "c al", "Ġgu idelines", "Ġd ying", "Ġsusp ended", "ĠPrem ier", "ĠAnth ony", "el ve", "Ġd ad", "ĠE th", "ĠFoot ball", "Ġabandon ed", "Ġ< <", "Ġm arch", "Ġhor ror", "âĢ¦ \"", "Ġchild hood", "Ġcampaign s", "Ġl unch", "ĠAl bert", "bl ock", "âĸĪ âĸĪ", "ound ing", "Ġb one", "or gan", "ad ers", "ĠFl ash", "ĠDri ve", "Ġton ight", "Ġw ars", "ĠF L", "Ġform ation", "con st", "New s", "Ġcom pe", "or ious", "ĠSt aff", "Ġdiscuss ions", "ĠProt ection", "ĠJ am", "Ġcrit eria", "Ġinstall ation", "Ġaccompl ish", "iz za", "Ġpub lisher", "Ġresc ue", "ĠT ry", "U LL", "ĠS om", "ĠH op", "ore t", "th s", "ord on", "Ġp ocket", "ĠIn v", "Down load", "ĠCr ime", "Ġb ene", "ĠGu ide", "ĠAs sembly", "Ġparam eters", "I E", "ĠAlex ander", "Ġconc ert", "ĠSc he", "Ġsh oes", "Ġvis iting", "Ġrec all", "Ġb ub", "Ġr ural", "Ġconc rete", "ĠR os", "N ext", "R uss", "Ġlo ans", "ĠSh ield", "Ġtre m", "hem at", "k g", "ĠHar ris", "is ition", "ĠM ove", "ĠF C", "Ġf ate", "ĠCh o", "Ġt ired", "Ġprinc ipal", "h ist", "ien ces", "ath y", "Ġse vent", "Ġm ood", "Ġstrateg ic", "Ġdise ases", "Ġfor um", "Ġtem por", "Ġhead quarters", "P ar", "ig e", "fl ix", "Ġgu itar", "Ġ9 4", "On ly", "Ġrele ases", "ro ph", "================ ================", "Ġ6 00", "ĠContin ue", "ig ate", "ĠC rit", "sy stem", "Ġdis abled", "Ġunex pected", "ith ub", "Ġuncle ar", "ĠE st", "Ġcontr ad", "Ġstrateg ies", "vent ures", "Ġpass age", "AM E", "Ġimpro ving", "Ġreve als", "Ġdecre ase", "ov a", "Ġann oy", "ĠSh ort", "ĠL ibrary", "Ġcy ber", "n ell", "ĠH ur", "ĠC B", "Ġphot ograp", "U I", "Ġs ed", "G e", "Ġ8 7", "Ġd iverse", "Ġencour aged", "Ġcons piracy", "Ġbird s", "Ġoper ator", "Ġhand ful", "Ġclass ified", "? )", "Ġdram atic", "Ġinvestig ators", "it o", "Ġw idespread", "ĠR oom", "-------------------------------- --------------------------------", "Ġcollect ive", "Ġjournal ist", "St ring", "Ġtemper atures", "il a", "Ġgu id", "Ġins pect", "Ġmiss ile", "ĠMay or", "Ġman ual", "Ġsim ultane", "Ġrat ings", "Ġsu ck", "Ġ9 7", "Ġunivers al", "Ġph arm", "Ġdis rupt", "ian o", "A V", "Ġf t", "Ġstat ist", "old s", "ĠWalk er", "ph p", "Ġunder t", "ĠL as", "ish op", "nt il", "res hold", "ĠWhe ther", "M s", "Ġden y", "ĠCl oud", "Ġprov ider", "Ġsurv iv", "ĠUp date", "h as", "Ġmist akes", "ch arge", "pl ed", "r ity", "Ġn ode", "ĠMass achusetts", "ool s", "lic ation", "Ġf ails", "em ale", "or i", "back s", "Ġsh irt", "Ġ' '", "ĠN AT", "Ġwat ers", "els on", "Ġe ase", "Ġsc ar", "Ġcont ents", "m ind", "Ġcont ribution", "Ġsh r", "Ġhand ed", "Ġst ability", "Ġtra ve", "E m", "Ġmir ror", "12 3", "Ġwe igh", "Ġf iction", "ou ver", "ist ant", "r ition", "ĠF ed", "Ġphys ically", "Ġst ake", "ĠArt icle", "ĠAr c", "ĠLew is", "ĠM ind", "Ġdemonstr ate", "Ġprof its", "v ision", "om ic", "ol id", "Ġbatt les", "Ġdri ves", "Ġeas tern", "ĠS ony", "!! !", "ar ation", "v ard", "ĠG L", "port ation", "Ġ9 2", "Ġlaw makers", "Ġprotect ing", "ĠE PA", "Ġy eah", "Ġsh ame", "ol ph", "e ven", "x it", "Ġatt ach", "Ġrepresent ing", "Ġob s", "ĠUt ah", "iff s", "ĠFre edom", "à ³", "A K", "Ġinc idents", "it age", "Ġview ers", "c d", "Ġm ouse", "Ġcl ar", "Ġaccord ance", "Ġb ot", "c or", "ĠSum mer", "he ld", "Ġinnoc ent", "Ġiniti ative", "ol s", "________________ ________________", "Ġsp ots", "p ace", "Ġconvent ional", "Ġcorpor ations", "Ġblock ed", "H D", "at tered", "Ġref ers", "Ġbu ck", "ĠDig ital", "12 0", "Ġtop ics", "T F", "Ä ģ", "br id", "re ement", "Ġunder lying", "ĠM ember", "Ġinvestig ating", "Ġpregn ancy", "Ġtouch down", "ĠB and", "ĠCall er", "Ġinst ances", "P P", "w a", "G ood", "Ġ199 1", "ĠC old", "Ġfear s", "Ġrem arks", "Ĩ Ĵ", "at al", "Ġm it", "Ġexper iments", "i pt", "Col or", "ind u", "Up date", "Ġ9 3", "A g", "Ġ å", "anc ouver", "B oth", "Ġjud ges", "Ob ject", "Ġst ere", "umb n", "Ġparticip ation", "ĠSt ars", "ĠJ ere", "Ġweek ly", "ĠB an", "Ġconvers ations", "ĠP itt", "u z", "ĠIndian a", "ĠK ick", "Ġinf ection", "Ġhero es", "Ġsett led", "Ġstri p", "Ġh al", "Ġd ump", "ĠS ci", "Ġl es", "Ġref erences", "ĠU RL", "ĠBr idge", "Ġwant ing", "For ce", "Ġex clus", "Me anwhile", "m n", "Ġg entle", "m aker", "sen al", "ĠG ro", "ou ri", "ĠR ain", "ĠAll iance", "Ġl ift", "el a", "S D", "ĠCle veland", "Ġrank ed", "Ġst adium", "Ġdead ly", "ä ¸", "Ġr iding", "ar ia", "ĠAr mor", "Ġdocument ation", "ĠGree ce", "ree k", "Ġl ens", "ĠS a", "Ġg ross", "ĠE mer", "ag ers", "ĠD ub", "ĠR h", "ĠAM D", "Ġarri val", "Ġdes ert", "Ġsupp lement", "ĠRes p", "Ġkn ee", "Ġmarg in", "f ont", "og g", "201 0", "ĠP ir", "ĠP rom", "iv als", "Ġint ake", "Ġdifferent ly", "ug s", "Ġb its", "clud ed", "Ġsearch ing", "ĠD u", "um ble", "Ġfunction al", "ĠBalt imore", "ĠC ould", "Ġdes ired", "Ġcirc uit", "ĠL yn", "ĠG O", "ĠF alse", "re pre", "' :", "alt ies", "Ġmin im", "Ġdro ve", "ĠSh ould", "Ġh ip", "Ġpro s", "Ġut ility", "ĠN ature", "ĠM ode", "P resident", "o pp", "r at", "form ance", "Ġconcent ration", "Ġf ont", "ĠB ud", "Ġam id", "Ġre vers", "ĠM L", "B ar", "Ġinter action", "Ġjur isd", "Ġspell s", "d ep", "f il", "Ġcivil ians", "ut ter", "ĠCo oper", "ĠBel ow", "Ġent rance", "Ġcon vert", "Ġcontrovers y", "ow ered", "Ġcontr ary", "Ġar c", "ĠExec utive", "ĠOffic er", "Ġpack ages", "Ġprog ressive", "w idth", "Ġreserv ed", "v ol", "ĠSam sung", "Ġprint ed", "Ġcent ers", "Ġintrodu ce", "ĠKenn edy", "Ġodd s", "Ġsure ly", "Ġindepend ence", "Ġpass engers", "repre ne", "ĠBe h", "Ġl oves", "ĠESP N", "Ġfac ilit", "Ġident ical", "Ġdo ct", "Ġpartners hip", "con f", "ĠH ide", "Ġconf used", "ĠC ow", "M en", "Ġw rest", "ĠIraq i", "Ġh oles", "ĠStud ies", "Ġpregn ant", "h ard", "Ġsign als", "I X", "Ġpull ing", "Ġgrad uate", "Ġnomine e", "D ate", "Ġper mitted", "Ġâ Ĥ¬", "ĠOk lahoma", "St art", "Ġauthor ized", "Ġal arm", "ĠC os", "v an", "Ġgener ations", "c ular", "Ġdr agon", "ĠSoft ware", "ĠEd ward", "Ġcontro ller", "S en", "ge red", "ĠV ik", "Ġappro ached", "Th ank", "Ġcan ce", "Ġform ula", "ĠSm all", "Ġweak ness", "Ġr amp", "it udes", "j ud", "Ġbrill iant", "Ġacc us", "s ource", "Ġ8 00", "ĠE vil", "S w", "Ġhom eless", "we ek", "i ens", "r ics", "ĠTh ird", "T O", "Ġorgan ic", "Ġpresent ation", "ag h", "ĠDown load", "v ation", "Ġas sembly", "or able", "hold ers", "ĠBern ie", "ĠHel p", "Ġt ong", "ĠF ight", "Ġbe ach", "B ook", "ĠL ic", "Ġr ush", "ĠR ound", "ou p", "ĠMar x", "Ġcalcul ated", "ĠDe vil", "ĠSar ah", "Ġoccasion ally", "Ġbul let", "Av ailable", "g ate", "Ġ9 1", "Ġh osp", "Ġprom ises", "ĠH IV", "ĠSt adium", "ĠSt ock", "ĠCorpor ation", "g age", "N G", "ĠC redit", "Ġs ne", "ib l", "Ġacc um", "s uch", "Ġterror ists", "Ġconscious ness", "ĠZ h", "Ġdram a", "ool a", "pir ation", "Ġlab our", "ĠN in", "Ġut ter", "Ġdemocr atic", "Ġass ass", "il ation", "Ġg est", "Ġab road", "Ġmet ab", "Ġs orts", "Ġfl av", "U B", "Ġm g", "ĠNot hing", "ĠO d", "Ġmus ical", "200 9", "Ġdro ps", "oc ated", "ater al", "0000 00", "Ġg re", "Ġequ ality", "Ġburd en", "Ġv ig", "ĠLe ader", "-------- ----", "Ġcere mony", "Ġf ighter", "Ġact ors", "Ġ æ", "am an", "F i", "Ġal ign", "put er", "Ġe lder", "ĠN SA", "Ġrepresent ation", "ĠOnt ario", "IT H", "usal em", "Ġharass ment", "itz er", "Ġsy mp", "Ġbox es", "ĠD R", "Ġman ifest", "at re", "Ġ ^", "Ġd ies", "le ton", "Ġmiss ions", "et he", "Ġres olve", "Ġfollow ers", "Ġas c", "Ġk m", "l ord", "am med", "Ġsil ent", "ĠAssoci ated", "Ġtim ing", "Ġprison ers", "ĠK ings", "ĠF ive", "Ġtow er", "Ġappro aches", "Ġprecise ly", "Ġb ureau", "ĠM other", "ĠI ss", "Ġkey board", "it ual", "Ġfund ed", "Ġstay ing", "Ġpsych ological", "Ġm ile", "ĠLe on", "ĠBar b", "w ill", "Ġw ider", "ĠAtl antic", "Ġt ill", "ĠR ome", "ro t", "Ġaccomp an", "Ġfl our", "ac o", "W orld", "ĠExp ress", "ĠY u", "C or", "Ġple ased", "part y", "Ġpoint ing", "Ġinf lation", "Ġro y", "Ġ ),", "ain er", "Ġwedd ing", "orm on", "Ġrequ iring", "Ġqual ified", "Ġse gment", "EN D", "Ġs izes", "e als", "Ġcor rupt", "ass ador", "Ġcele b", "Ġdream s", "ĠM ess", "Ġcheck ing", "ĠV ersion", "Ġprep aring", "Ġact ively", "ĠD iff", "Ġl ux", "ĠW inter", "act eria", "ĠN E", "Ġdep uty", "Ġtrans gender", "Ġsum mary", "Ġin her", "er ies", "ch ar", "ĠY an", "Ġkn ock", "ĠP ath", "Ġl ip", "roll er", "Ġimp ression", "Ġcelebr ate", "Ġsl ide", "Ġgu ests", "Ġcl ip", "F S", "Ġsav ings", "Ġcapt ain", "Ġleg acy", "ĠDen ver", "Ġw ounded", "tab oola", "AC T", "Ġpurs ue", "Ġo xy", "Ġ q", "Ġsem i", "ĠN eed", "ĠAff airs", "Ġob sc", "Ġcheck ed", "Ġd ual", "C ode", "ĠM D", "le m", "ult y", "Ġ ©", "ĠEl izabeth", "Ġcent uries", "ard ed", "s rc", "Ġev ident", "enn is", "at in", "Ġunemploy ment", "ĠMar io", "Ġint im", "Ch rist", "Ġbi ological", "Ġsold ier", "ĠAdd ed", "Ġm ath", "ĠG il", "Ġbi as", "Ġd ating", "ĠO cean", "Ġm ice", "M us", "h ire", "ĠT es", "Ser ver", "lim ited", "S ize", "Ġmet ers", "Ġrock et", "es see", "Ġcertific ate", "ĠIran ian", "AS S", "Ġgr id", "D ec", "Ġro lling", "com mun", "ĠSwed en", "b ury", "Ġtiss ue", "Ġrac ism", "ĠL ocal", "Ġmyster y", "Ġexam ine", "Ġst em", "Ġs its", "Ġhop ed", "ot ing", "Ġdial ogue", "Ġpers u", "W atch", "l ay", "M AN", "Ġch ronic", "ĠPort land", "mark et", "ĠS EC", "Ġparalle l", "Ġsc andal", "Ġcar ries", "Ġphenomen on", "h uman", "ack er", "ĠO x", "Ġretire ment", "tain ment", "ov ie", "ĠG ear", "Ġd uties", "Ġdo se", "Ġsc roll", "M B", "in f", "Ġsa uce", "Ġland scape", "red dit", "ĠChampions hip", "ĠRed dit", "al id", "Ġco in", "Ġover s", "Ġpost ing", "ab out", "Ġf el", "and y", "Ġb old", "Ġfocus ing", "e ffect", "G R", "Ġde emed", "Ġrecommend ations", "Ġste pped", "Ġvot er", "ĠDe ep", "ĠInst agram", "Ġmoder ate", "ĠMary land", "Ġrestrict ed", "ĠM B", "ĠCh all", "Ġto b", "Ġc ir", "ĠO cc", "ĠE ver", "Ġcoll aps", "IN FO", "= -", "ĠP ict", "ĠAcc ount", "n c", "Ġo ught", "Ġex port", "Ġdr unk", "( '", "Ġw ise", "ĠM ort", "ne cess", "Ġan cest", "ĠInc re", "Ġfrequ ent", "m ir", "Ġinterpret ation", "Ġdepend ent", "Ġco ins", "ĠB ol", "V ideo", "ĠJust in", "Ġfat al", "Ġcook ing", "Ġconf usion", "ip her", "Ġcust ody", "ĠMor gan", "om ach", "ĠGovern or", "Ġrestaur ants", "el ing", "Ġacknowled ged", "Ġthe r", "Ġgen es", "ch ing", "He y", "Ġtact ics", "ĠMex ican", "Ġv end", "Ġhe s", "qu er", "Ġnot ing", "ĠCamer on", "Ġtarget ing", "ro ck", "Ġcred its", "Ġemot ions", "Ġrepresent atives", "new s", "Ġlegisl ative", "Ġrem oving", "Ġtweet ed", "ĠCar ter", "ĠF ixed", "Ġfor cing", "Ġspeak er", "Ġm ales", "ĠViet nam", "l ined", "Ġconcept s", "Ġvo ices", "o ir", "ĠT rib", "W he", "ĠJer usalem", "ĠS ant", "Ġc ul", "Ġl ady", "ĠHaw ai", "Ġar ts", "ĠIn n", "ĠMach ine", "ĠEm peror", "Ġsl ot", "g ly", "ĠPro cess", "II I", "Ġathlet es", "ĠTem ple", "ĠRep resent", "Ġpres c", "Ġt ons", "Ġgold en", "Ġp unch", "ĠG R", "iver pool", "Ġen act", "Ġlob by", "Ġm os", "Ġpick ing", "Ġlif etime", "Ġcogn itive", "E ach", "z o", "Ġd ub", "Ġcons ists", "ol n", "Ġf estival", "am ous", "Ġint ellig", "w ords", "ĠSm art", "Ġde le", "Ġl apt", "Ġmag ical", "ĠS in", "b us", "ur ities", "igh th", "ĠRub y", "ĠS ure", "ol ving", "Ġj un", "O ST", "Ġimp osed", "Ġast ron", "Ġcor rel", "ĠN S", "ĠK it", "ĠF uture", "b urn", "Ġimm une", "oc us", "Ġcour ses", "ĠSt ring", "Ġle an", "Ġg host", "Ġout comes", "Ġexp ense", "Ġevery day", "Ġaccept able", "A h", "Ġequ ipped", "Ġor ange", "F R", "ĠD utch", "Th ough", "ĠR ank", "Q U", "ĠRober ts", "wh at", "re nd", "Ġdisapp ear", "Ġsp awn", "ĠL am", "o is", "Ġdes erve", "Ġmin imal", "Ġnerv ous", "ĠW ould", "Ġro ok", "ĠV ancouver", "Ġres ign", "sh ire", "ĠW orks", "ĠB uild", "Ġafford able", "ĠG ary", "ĠAren a", "Ġh anging", "Ġimpl ications", "ĠS ong", "Ġmain taining", "Ġgu ards", "C ON", "Ġder ived", "Ġexecut ed", "Ġthe ories", "Ġqu oted", "ĠAnd re", "og a", "sel ess", "in fo", "ĠBel g", "Ġt ears", "ĠSur v", "Ġbirth day", "ig ious", "im mer", "Ġspect rum", "Ġarchitect ure", "Ġrec ruit", "arm a", "T able", "Ġmon sters", "ĠG ov", "Ġdest ination", "Ġattract ive", "Ġf oss", "ĠMore over", "Ġpres ents", "TH E", "Ġrep ly", "pt on", "Ġc um", "Ġdel ight", "Ġaffect s", "Ġdon ations", "ĠT oy", "ĠH im", "M ENT", "Ġover come", "it ched", "ĠFant asy", "ĠH at", "ĠBe ast", "b ott", "Ġinvestig ations", "R un", "Ġhun ting", "d i", "f und", "Ġs essions", "est yle", "Ġport ray", "oid s", "Y eah", "Ġcommun icate", "Ġcom edy", "ĠY ang", "Ġbel t", "ĠMar ine", "Ġpredict ed", "Pl ay", "Ġimportant ly", "Ġremark able", "Ġelim inate", "D avid", "Ġb ind", "V ID", "Ġadvoc ates", "ĠG aza", "im p", "D B", "ĠN a", "ĠSim ilar", "I ES", "Ġchar ity", "v as", "m ath", "Ġâ ĸ", "ok er", "nd um", "Ġcap s", "ĠH al", "2 000", "e an", "Ġfle et", "Ġrec re", "R ight", "Ġsleep ing", "ij ing", "k ind", "Ġdesign ated", "à ¤", "Ġanim ation", "ke e", "ĠInt rodu", "Ġ/ >", "Ġdelay ed", "Ġtrem end", "Ġcur ious", "U se", "Ġle ct", "d am", "Ġinnov ation", "ĠPoint s", "Ġload ing", "Ġdisp ute", "ct ic", "ird s", "ĠB Y", "Ġn urs", "ĠVal ue", "ION S", "ĠH um", "Ġtem plate", "m ers", "Ġappear ances", "ĠEnter tainment", "Ġtransl ation", "Ġsa ke", "Ġbene ath", "Ġin hib", "Ġe uro", "abet es", "Ġstud ying", "ĠM as", "Ġper ceived", "Ġexam ined", "Ġe ager", "Ġco aches", "Ġim per", "ch i", "Ġprodu ces", "\" ).", "ĠEvery one", "Ġm unicip", "Ġg irlfriend", "Ġh ire", "ĠV ice", "Ġsu itable", "op y", "Ġin equ", "ĠD uke", "f ish", "f irst", "ĠO bs", "Ġinter ior", "ĠBru ce", "ĠR y", "Ġanal ys", "Ġconsider able", "Ġfore cast", "Ġf ert", "ors hip", "ĠD rug", "ĠA LL", ": \"", "th ur", "ĠM ail", "Ġball ot", "Ġinst antly", "ĠCh annel", "Ġp icks", "Ġ198 9", "Ġt ent", "ol i", "Ġcivil ian", "b ling", "ell o", "b u", "Ġin ch", "Ġlog o", "Ġcooper ation", "Ġwal ks", "Ġinvest ments", "Ġimp rison", "ĠF estival", "ĠK y", "Ġleg ally", "Ġg ri", "ch arg", "S l", "Ġthreat ening", "du ction", "fl ow", "Ġdismiss ed", "ibr aries", "c ap", "e le", "ĠMc G", "ĠHar vard", "ĠConserv ative", "ĠC BS", "p ng", "Ġro ots", "ĠH aving", "umb led", "ĠF un", "\\ /", "ĠS earch", "ple x", "Ġdiscuss ing", "Ġcontin u", "ĠT ai", "ĠW ik", "F ree", "f it", "Ġref use", "Ġmanag ing", "Ġsy nd", "ip edia", "w alk", "Ġprofession als", "Ġguid ance", "Ġunivers ities", "Ġas semb", "unt u", "F inally", "AS E", "ĠAut o", "ĠH ad", "Ġann iversary", "L D", "ĠD ur", "ĠUlt imate", "ih ad", "pro duct", "Ġtrans it", "Ġrest ore", "Ġexpl aining", "Ġass et", "Ġtransfer red", "Ġbur st", "ap olis", "ĠMag azine", "ĠC ra", "ĠB R", "gg ed", "ĠH E", "M ich", "b et", "ĠL ady", "yl um", "erv es", "Ġme ets", "wh ite", "L og", "Ġcorrespond ing", "Ġins isted", "G G", "Ġsurround ed", "Ġt ens", "Ġl ane", "Ġco inc", "h ome", "Ġexist ed", "ect ed", "ĠDou ble", "lam m", "Ġske pt", "ex p", "Ġper ception", "ie v", "ĠBe ing", "o ft", "Ġadop t", ". :", "] ;", "Wind ows", "Ġsatell ite", "AS H", "Ġinf ant", "d escription", "ĠMe anwhile", "c m", "oc a", "ĠT reat", "act or", "Ġtob acco", "ĠN orm", "em ption", "Ġfl esh", "Ġj e", "o op", "ĠHe aven", "Ġbe ating", "an im", "Ġgather ing", "Ġcult iv", "G O", "ab e", "ĠJon athan", "ĠSaf ety", "Ġbad ly", "pro t", "Ġcho osing", "Ġcontact ed", "Ġqu it", "Ġdist ur", "Ġst ir", "Ġto ken", "D et", "ĠP a", "Ġfunction ality", "00 3", "s ome", "Ġlimit ations", "Ġmet h", "b uild", "con fig", "N T", "re ll", "ble m", "ĠM om", "Ġveter ans", "ĠH u", "Ġtrend s", "are r", "ĠG iven", "ĠCa ption", "m ay", "AS T", "Ġwond ering", "ĠCl ark", "n ormal", "Ġsepar ated", "Ġdes p", "st ic", "b rew", "Ġrel ating", "ĠN ik", "ĠF arm", "Ġenthus i", "g ood", "d eb", "Ġactiv ist", "Ġm art", "Ġexplos ion", "ĠEconom ic", "L ink", "Ġins ight", "Ġconven ient", "Ġcounter part", "su pport", "ĠV irt", "ag en", "ĠTenn essee", "ĠSim on", "ĠA ward", "OC K", "ĠF igure", "Ġoverse as", "Ġpr ide", "ĠC as", "n ote", "m g", "C urrent", "Ġdispl ays", "cont ent", "Ġtravel ing", "Ġhosp itals", "ĠFin ancial", "ĠP ast", "Ġdefend ant", "Ġstream ing", "m ble", "ĠBer lin", "uk i", "Ġdist ribut", "Ġant ib", "Ġch ocolate", "ĠCast le", "Ġinter rupt", "ĠR ow", "Ġconvers ion", "Ġbug s", "ĠR ather", "li est", "L Y", "ĠJe an", "com mon", "ak h", "Ġ1 30", "ot ton", "ĠDe an", "Ġam endment", "Ġgame play", "ĠWar ren", "od a", "Ġhigh lights", "Ġir re", "ĠNAT O", "Ġball s", "Ġdemand ing", "U RE", "ĠL uke", "F igure", "st op", "on ia", "z one", "iz ers", "ĠW R", "Ġaward ed", "Ġregul atory", "ĠH art", "ĠS N", "pl ing", "Ġs our", "ĠP ixel", "us ive", "Ġf et", "ĠS ent", "Ġautom atic", "Ġf er", "vern ment", "ĠKh an", "T ON", "f ather", "Ġextraord inary", "th rop", "ĠP ython", "ĠG PU", "Ġsex ually", "Ġdesk top", "it ivity", "ĠAnton io", "Ġo rient", "Ġe ars", "ob by", "ous es", "vertis ements", "Ġmanufacture rs", "ic ient", "min ute", "Ġconv iction", "Ġg arden", "p ublic", "Ġsatisf ied", "f old", "O K", "Ġin hab", "ĠTh ink", "Ġprogram me", "Ġst omach", "Ġcoord in", "Ġh oly", "Ġth reshold", "Ġr het", "Ġser ial", "Ġemploy ers", "ĠEvery thing", "ra h", "Ġb other", "Ġbr ands", "Val ue", "ĠT ed", "ĠPlan et", "Ġp ink", "ĠFurther more", "s a", "P E", "re ck", "ĠUS D", "ot te", "Ġ& &", "Ġland ed", "g ets", "Ġprodu cers", "Ġhealth care", "Ġdomin ant", "Ġdest ro", "Ġam ended", "ch ron", "Ġf its", "ĠSy d", "ĠAuthor ity", "AT CH", "Ġfight s", "ĠL LC", "Ġ-- -", "ĠCor p", "Ġtox ic", "spe cific", "ĠC orn", "ĠChe l", "Ġtele phone", "ĠP ant", "Ġmyster ious", "aun ch", "od ox", "med ia", "Ġwitness es", "ag u", "Ġquestion ed", "ĠBre xit", "ĠRem ember", "ene z", "Ġend orse", "iat ric", "ĠId ent", "Ġridic ulous", "1 10", "Ġpr ayer", "Ġscient ist", "Ġ19 50", "ĠA qu", "Ġunder ground", "ĠU FC", "m are", "ĠL ater", "w ich", "Ġsubsc rib", "Ġhost s", "Ġer r", "Ġgr ants", "ant om", "Ġsum mon", "ear ly", "ĠC lear", "ĠPr im", "Ġsusp ension", "Ġguarant eed", "app er", "Ġr ice", "ĠSe an", "ĠSh in", "Ġrefere ndum", "Ġfl ed", "r ust", "Ġ3 60", "ter y", "Ġsh ocked", "B R", "ĠO il", "ĠAll ah", "Ġpart ly", "Ġign or", "Ġtrans mission", "Ġhom osexual", "ivers al", "Ġhop efully", "ãĤ ¤", "Ġless on", "L eg", "Ġ ..", "Y et", "t able", "app ropri", "re tt", "Ġbo ards", "Ġincor rect", "Ġb acteria", "ar u", "am ac", "Ġsn ap", ".' \"", "Ġpar ad", "t em", "he art", "Ġav ailability", "Ġw isdom", "Ġ( +", "Ġpri est", "ĠÂł ĠÂł", "O pen", "Ġsp an", "Ġparam eter", "Ġconv ince", "Ġ( %)", "r ac", "Ġf o", "Ġsafe ly", "Ġconver ted", "ĠOlymp ic", "Ġres erve", "Ġhe aling", "ĠM ine", "M ax", "Ġin herent", "ĠGra ham", "Ġinteg rated", "D em", "Ġpip eline", "Ġapp lying", "Ġem bed", "ĠCharl ie", "Ġc ave", "200 8", "Ġcons ensus", "Ġre wards", "P al", "ĠHT ML", "Ġpopular ity", "look ing", "ĠSw ord", "ĠAr ts", "' )", "Ġelect ron", "clus ions", "Ġinteg rity", "Ġexclus ively", "Ġgr ace", "Ġtort ure", "Ġburn ed", "tw o", "Ġ18 0", "P rodu", "Ġent reprene", "raph ics", "Ġg ym", "ric ane", "ĠT am", "Ġadministr ative", "Ġmanufacture r", "Ġ vel", "ĠN i", "Ġisol ated", "ĠMedic ine", "Ġback up", "Ġpromot ing", "Ġcommand er", "Ġfle e", "ĠRus sell", "Ġforg otten", "ĠMiss ouri", "Ġres idence", "m ons", "Ġrese mb", "Ġw and", "Ġmeaning ful", "P T", "Ġb ol", "Ġhe lic", "Ġwealth y", "Ġr ifle", "str ong", "row ing", "pl an", "as ury", "âĢ¦ .", "Ġexpand ing", "ĠHam ilton", "Ġrece ives", "S I", "eat ures", "ĠAn im", "RE E", "P ut", "Ġbrief ly", "ri ve", "Ġstim ul", "Ġ`` (", "Ġ __", "Ġch ip", "Ġha z", "Ġpri ze", "ĠTh ings", "AC E", "ul in", "d ict", "ok u", "Ġassoci ate", "ock ets", "y outube", "St ory", "ateg ory", "Ġm ild", "ail ing", "ĠY e", "O rig", "ĠK a", "or ig", "Ġpropag anda", "Ġan onymous", "Ġstrugg led", "Ġout rage", "AT ED", "ĠBe ijing", "r ary", "Ġle ather", "Ġworld s", "Ġbroad er", "12 5", "id al", "ĠBet ter", "Ġt ear", "E xt", "Ġpropos als", "Ġit er", "ĠSqu ad", "Ġvol unt", "m i", "D id", "ĠP u", "p in", "Ġspeak ers", "Ġb orders", "Ġfig ured", "= '", "Ġsimultane ously", "aed a", "Ġcharg ing", "Ġur ged", "Ġcon j", "25 6", "ĠG ordon", "mer ce", "Ġdocument ary", "Sh are", "it ol", "ON E", "ĠG arden", "h att", "ĠThom pson", "ane ous", "ap ore", "Ġt anks", "Ġless ons", "tr ack", "Ġout standing", "Ġvolunte ers", "Ġsp ray", "Ġmanag ers", "l arge", "Ġcamp s", "Ġart ificial", "ĠR u", "Ġb ags", "th al", "Ġcompat ible", "ĠBl ade", "Ġf ed", "Ġarg ues", "F I", "Ġunf air", "Ġcor n", "Ġoff set", "Ġdirect ions", "Ġdisappoint ed", "ĠCon vention", "Ġview ing", "M E", "oc ity", "Ġtown s", "Ġlay ers", "Ġro lled", "Ġjump ed", "Ġatt ribute", "Ġun necess", "inc oln", "Ġsupp ose", "ĠNet her", "ch a", "Ġbur ied", "Ġsix th", "B en", "ress ing", "OU R", "Ġw ound", "Ġcy cl", "Ġmechan isms", "Ġcongress ional", "ĠE lement", "Ġagre ements", "Ġdec or", "Ġclos est", "ĠM it", "Go ogle", "} }", "Ġm ixture", "Ġflu id", "S ign", "ĠSch olar", "Ġp ist", "ask et", "ab ling", "Ġrac ing", "he ro", "ri el", "ass y", "Ġche aper", "b en", "Ġvert ical", "amac are", "ĠRead ing", "g ments", "Ġhelic op", "Ġsacr ifice", "ay a", "p aren", "V A", "ĠL es", "ĠStud io", "Ġviol ations", "ĠAn na", "ac er", "é ¾", "ĠR at", "ĠBe ck", "ĠD ick", "ĠA CT", "Ġcomp osition", "Ġtext ure", "ĠO wn", "Ġsmart phone", "ĠN A", "Ġfor b", "im port", "Ġdef ending", "il st", "re r", "Ġo h", "ĠJere my", "Ġbank ing", "cept ions", "Ġrespect ive", "/ .", "Ġdr inks", "ĠW i", "Ġb ands", "ĠL iverpool", "Ġg rip", "ĠB uy", "Ġopen ly", "Ġreview ed", "per t", "Ġver ify", "ĠCo le", "ĠW ales", "M O", "Ġun pre", "Ġshel ter", "ĠIm perial", "Ġgu i", "ĠD ak", "Ġsuggest ions", "Ġexplicit ly", "Ġsl ave", "Ġblock chain", "Ġcompet ing", "Ġprom ising", "S ON", "Ġsoc cer", "Ġconst itution", "4 29", "Ġdist ract", "ĠU ser", "es ides", "ĠMet hod", "ĠTok yo", "Ġaccompan ied", "Cl ient", "s ur", "al og", "Ġident ification", "Ġinv asion", "as ma", "Ġindust ries", "pp ers", "Ġsub tle", "ĠUn it", "n atural", "Ġsurv ived", "Ġfl aw", "ĺ ħ", "ĠH oll", "Ġdef icit", "Ġtut orial", "ĠCh ance", "Ġarg uing", "Ġcontem porary", "Ġinteg ration", "for ward", "Ġt um", "it is", "Ġh iding", "ĠD omin", "ĠT an", "ĠB uilding", "ĠV in", "Ġspokes person", "ĠNot es", "Ġemer ging", "Ġprepar ation", "Ġpro st", "Ġsuspect s", "Ġaut onom", "D escription", "Ġdeal t", "ĠP ear", "Ġstead y", "Ġdecre ased", "Ġso vere", "ĠCl in", "Ġgrad ually", "ors es", "ĠW AR", "S erv", "ãĤ ¢", "h r", "Ġd irty", "ĠB arn", "ĠB C", "Ġd il", "Ġcal endar", "Ġcompl iance", "Ġch amber", "b b", "Ġpass enger", "ate ful", "ĠT itle", "ĠSyd ney", "ĠG ot", "Ġdark ness", "Ġdef ect", "Ġpack ed", "ass ion", "Ġgod s", "Ġh arsh", "IC K", "le ans", "Ġalgorith m", "Ġoxy gen", "Ġvis its", "Ġbl ade", "Ġkil omet", "ĠKent ucky", "Ġkill er", "P ack", "enn y", "Ġdiv ine", "Ġnom ination", "be ing", "Ġeng ines", "Ġc ats", "Ġbuff er", "ĠPh ill", "Ġtra ff", "AG E", "Ġtong ue", "Ġrad iation", "ere r", "m em", "ĠExpl icit", "é¾ į", "Ġcou ples", "Ġphys ics", "ĠMc K", "Ġpolit ically", "aw ks", "ĠBl oom", "Ġwor ship", "e ger", "ut er", "ĠF O", "Ġmat hemat", "Ġsent enced", "Ġdis k", "ĠM arg", "Ġ/ *", "P I", "Ġoption al", "Ġbab ies", "Ġse eds", "ĠScott ish", "Ġth y", "] ]", "ĠHit ler", "P H", "ng th", "Ġrec overed", "ing e", "Ġpow der", "Ġl ips", "Ġdesign er", "Ġdis orders", "Ġcour age", "Ġch aos", "\" },{\"", "Ġcar rier", "b ably", "H igh", "ĠR T", "es ity", "l en", "Ġrout es", "u ating", "F il", "N OT", "w all", "s burgh", "Ġeng aging", "ĠJava Script", "ore r", "li hood", "Ġun ions", "ĠF ederation", "ĠTes la", "Ġcomple tion", "ĠT a", "Ġprivile ge", "ĠOr ange", "Ġne ur", "paren cy", "Ġb ones", "Ġtit led", "Ġprosecut ors", "ĠM E", "Ġengine er", "ĠUn iverse", "ĠH ig", "n ie", "o ard", "Ġheart s", "ĠG re", "uss ion", "Ġmin istry", "Ġpen et", "ĠN ut", "ĠO w", "ĠX P", "in stein", "Ġbul k", "S ystem", "ic ism", "ĠMarket able", "Ġpre val", "Ġpost er", "Ġatt ending", "ur able", "Ġlicens ed", "ĠG h", "et ry", "ĠTrad able", "Ġbl ast", "à ¤", "ĠTit an", "ell ed", "d ie", "H ave", "ĠFl ame", "Ġprof ound", "Ġparticip ating", "Ġan ime", "ĠE ss", "Ġspec ify", "Ġregard ed", "ĠSpe ll", "Ġs ons", "own ed", "Ġm erc", "Ġexper imental", "land o", "h s", "ĠDun geon", "in os", "Ġcomp ly", "ĠSystem s", "ar th", "Ġse ized", "l ocal", "ĠGirl s", "ud o", "on ed", "ĠF le", "Ġconstruct ed", "Ġhost ed", "Ġsc ared", "act ic", "ĠIs lands", "ĠM ORE", "Ġbl ess", "Ġblock ing", "Ġch ips", "Ġev ac", "P s", "Ġcorpor ation", "Ġo x", "Ġlight ing", "Ġneighb ors", "ĠU b", "ar o", "Ġbe ef", "ĠU ber", "F acebook", "ar med", "it ate", "ĠR ating", "ĠQu ick", "Ġoccup ied", "Ġaim s", "ĠAdd itionally", "ĠInt erest", "Ġdram atically", "Ġhe al", "Ġpain ting", "Ġengine ers", "M M", "ĠM ust", "Ġquant ity", "P aul", "Ġearn ings", "ĠPost s", "st ra", "ãĥ¼ ãĥ", "Ġst ance", "Ġdro pping", "sc ript", "Ġd ressed", "M ake", "Ġjust ify", "ĠL td", "Ġprompt ed", "Ġscr ut", "Ġspeed s", "ĠGi ants", "om er", "ĠEd itor", "Ġdescrib ing", "ĠL ie", "ment ed", "Ġnow here", "oc aly", "Ġinst ruction", "fort able", "Ġent ities", "Ġc m", "ĠN atural", "Ġinqu iry", "Ġpress ed", "iz ont", "for ced", "Ġra ises", "ĠNet flix", "ĠS ide", "Ġout er", "Ġamong st", "im s", "ows ki", "Ġclim b", "ne ver", "Ġcomb ine", "d ing", "Ġcomp r", "Ġsignific ance", "Ġremem bered", "ĠNev ada", "ĠT el", "ĠSc ar", "ĠWar riors", "ĠJ ane", "Ġcou p", "b as", "Ġtermin al", ", -", "O H", "Ġt ension", "Ġw ings", "ĠMy ster", "�� ��", "ĠUn like", "val id", "viron ments", "ĠAl i", "Ġn aked", "book s", "ĠM un", "ĠG ulf", "Ġd ensity", "Ġdim in", "Ġdesper ate", "Ġpres idency", "Ġ198 6", "h y", "IN D", "Ġun lock", "im ens", "Ġhand led", "ĠE b", "Ġdisapp eared", "Ġgen re", "Ġ198 8", "Ġdetermin ation", "St ream", "ik o", "ap ters", "Ġacknow ledge", "J an", "Ġcapital ism", "P at", "Ġ20 20", "Ġpain ful", "Ġcur ve", "Ġbom bs", "st orm", "ĠMet al", "en cer", "ĠF ig", "ĠA aron", "anc hes", "Ġins piration", "Ġexha ust", "t ains", "ash i", "Ġdesc ript", "Ġr itual", "ĠChel sea", "Ġpromot ion", "ĠH ung", "ĠW ard", "iv a", "ĠE T", "Ġto ss", "all ow", "ĠFranc is", "D ep", "Ġhapp iness", "ĠGl ass", "Ġbet a", "Ġstreng then", "N E", "o a", "Ġbutt ons", "ĠMur ray", "Ġkick ed", "Qu est", "ĠT alk", "ĠS everal", "ĠZ ero", "Ġdr one", "ul k", "Ġc am", "ĠM obile", "Ġprevent ing", "Ġret ro", "ĠA x", "Ġcru el", "Ġflo at", ". ),", "Ġfil ing", "ĠGr ant", "ĠB or", "Ġr ib", "Ġchampions hip", "ĠM erc", "Ġsty les", "Ġc ake", "Ġbuild s", "ĠS elf", "io x", "Ġep ic", "oy d", "B el", "ĠSt ew", ". (", "ah u", "ĠBe yond", "Ġout s", "Ġsol o", "ĠT ree", "Ġpres erve", "Ġt ub", "AR E", "ro c", "ĠIm pro", "ĠW right", "Ġbu nd", "Ġtr aged", "Ġoccas ional", "b ian", "Sec ond", "r ons", "Ġinter actions", "form ed", "s ing", "Ġown s", "Ġh ockey", "Gener al", "Ġlog ical", "Ġexp end", "Ġesc al", "ĠGr iff", "ĠC rown", "ĠRes erve", "Ġsto pping", "Ġexc use", "sec ond", "Ġoper ated", "Ġre aches", "ĠMal ays", "Ġpoll ution", "ĠBrook lyn", "Ġde lete", "Ġhas h", "Bl ock", "ah a", "âĢ ³", "Ġsh orter", "p iece", "> </", "Ġh orm", "ĠW at", "ĠBre ak", "Ġprohib ited", "Ġint ensity", "ĠAl an", "Ġli ability", "? !", "and ed", "Ġneigh bour", "ĠCol lection", "Ġf ires", "Ġrevolution ary", "f ly", "ĠOr leans", "Wh ite", "ĠW rit", "ĠD awn", "Ġsett le", "Ġexec ute", "B M", "Ġspokes woman", "Ġlif estyle", "Ġclick ing", "ĠK ill", "ĠLiber al", "ĠN azi", "Ġtra iler", "Ġmount ains", "Ġdam n", "z es", "p es", "Ġpress ing", "Ġb ail", "ĠOrgan ization", "Ġp ir", "Ġth irty", "Ġelect rical", "Ġ1 15", "ĠP oly", "ĠR ap", "ĠSt rike", "ĠC ann", "Ġdemand ed", "Ġback ing", "def ault", "spe ed", "ĠLeg isl", "Ġmother s", "ĠB ody", "Ġvar iation", "ced ented", "p owered", "le ading", "N ever", "Ġg rave", "ĠAnt i", "A W", "Ġinterview ed", "ĠG ab", "ĠF at", "Ġrook ie", "u u", "Ġdep os", "ix on", "Ġam pl", "ret ion", "ĠHe at", "Ġpeace ful", "S M", "ie ve", "Ġd iver", "ĠVict oria", "Ġm ic", "p df", "Ġst ating", "Ġl ung", "Ġcritic ized", "Ġvacc ine", "ĠLoad ing", "ur se", "T ake", "ĠFr an", "ĠS old", "ĠRob in", "Ġdetect ed", "ĠSc ript", "Ġadjust ed", "Ġsen ator", "Ġopp osing", "Er ror", "C ount", "Ġconflic ts", "Ġo w", "ĠAr gent", "Ġmatch ing", "h h", "ĠTre k", "st arter", "\" ),", "ĠA F", "od er", "xx xx", "ĠAl t", "ac re", "ĠP ick", "ĠSol ar", "ĠD al", "O ct", "ĠB att", "Ġs rc", "Ġeng agement", "Ġexecut ives", "Ġliber ty", "j ava", "Ġtal ented", "igen ous", "Ġcon secut", ".. ...", "In fo", "Ġhor rible", "Ġsurprising ly", "f eed", "ic ating", "ĠL ED", "Ġfem ales", "St ation", "ell er", "ĠOak land", "Ġmechan ical", "i ology", "ĠV ar", "Ġrob ust", "ett ings", "ott a", "Ġthe oret", "Ġret ain", "k ward", "Ġd a", "Ġdeploy ed", "d el", "ĠAnd y", "Ġsubsc ribe", "we b", "Ġn a", "ĠMic hel", "Ġpart ially", "ĠCome y", "Ġc rown", "ĠM aj", "ĠBl u", "r ator", "D ay", "IN T", "Ġdocument ed", "ĠG DP", "g i", "che ll", "Ġbrut al", "ĠB ab", "st ration", "Ġthe ft", "Ġt ube", "@ @", "Ġqu ery", "ĠL incoln", "Ġpublish ing", "Ġw ore", "or ical", "Ġr ic", "Ġnot able", "Ġsubsequ ently", "ne x", "Ġobser ve", "ĠB oe", "Ġc odes", "m ain", "W H", "ĠS L", "Ġresident ial", "av an", "Ġm as", "are st", "ade on", "OU T", "Ġsoph istic", "ant e", "Ġc ens", "Ġ **", "Ġmort ality", "Ġyour s", "Ġoccas ions", "Ġrec alled", "ĠDri ver", "Ġv ocal", "Ġbath room", "Ġsh ops", "Ġcollabor ation", "ĠOb amacare", "ĠC ell", "Ch ar", "Su per", "C re", "Ġt ends", "Ġt orn", "Ġeconom ics", "a very", "ĠR aid", "ĠS em", "Ġshould ers", "Ġexpect ing", "Ġexam ination", "en ame", "ĠU I", "i ability", "ol as", "ĠAm b", "ĠD ra", "Ġmid field", "ĠI C", "Ġlay out", "Ġflo ating", "f i", "it ative", "Ġtremend ous", "Ġ Ð", "Ġab und", "W ork", "ĠLight ning", "Ġsimilar ly", "Ġconserv atives", "Ġpr ay", "B E", "iz arre", "Ġt empt", "Ġemphas is", "ĠMet ro", "Ġf ishing", "Ġmar ry", "ne g", "ĠStud y", "Ġrec k", "Ġdis pos", "on ing", "bs ite", "Ġsusp ic", "Ġmer ch", "ĠG ib", "ĠDes cription", "ĠD VD", "w he", "ĠY emen", "Ġen vironments", "oot ing", "ĠMod ern", "e u", "Ġreflect s", "Ġh oney", "Ġanaly st", "Ġg ut", "d ec", "A ction", "Ġhousehold s", "Ġst er", "Ġtem ple", "Ġreform s", "Ġfavour ite", "Ġdead line", "ĠL E", "Th ree", "ĠWith in", "A ug", "Ġnight s", "elt a", "Ġinv alid", "ĠEx change", "ĠDel hi", "w hen", "inc ome", "Ġ ðŁ", "Ġwire less", "sc ribe", "ist a", "Ġhost ile", "Ġall y", "Ġg ig", "Ġout lets", "ĠD or", "EM ENT", "Ġas h", "Ġab stract", "OR D", "ĠMot or", "Ġadv iser", "ist le", "Ġb ases", "Ġcourt esy", "Ġcross ing", "Ġcle ared", "Ġrefuge e", "cos ystem", "Ġthrow s", "f un", "bour ne", "d ays", "Ġdisag ree", "ĠN ative", "Ġreflect ed", "ĠF ast", "ĠY ellow", "ĠSing apore", "ĠR aven", "Ġembr ace", "ĠK u", "ĠC hen", "ĠEar ly", "Ġappoint ment", "ĠMin i", "it ement", "Ġpl acing", "Ġb icy", "S R", "Ġwh is", "S U", "Ġinvestig ated", "Ġphotograph s", "g ithub", "ĠBe at", "ĠR ing", "ig hed", "i ar", "Ġev olved", "eral d", "Ġd un", "Ġh ub", "I AL", "Ġencour aging", "ĠPr int", "ĠD ays", "Ġpro secution", "Ġp ants", "az y", "l ive", "Ġfoss il", "ĠJ u", "Ġro cks", "ud ge", "ĠR ace", "Ġg reet", "b ie", "Ġf illing", "ĠL en", "Ġdi abetes", "Ġfire arms", "um ing", "enez uel", "ĠB B", "Ġaccept ing", "AT H", "Ġres ort", "Ġh unt", "ri k", "uck er", "am ents", "Ġsust ained", "Ġcross ed", "Ġbreak fast", "Ġatt ributes", "lect ed", "at ile", "Ġv ibr", "ĠK al", "ars on", "op les", "Ġtou ched", "Ġdam ages", "Ġimp ressed", "ru p", "Ġan ch", "ĠAd ams", "H el", "ĠVict or", "Ġmount ed", "ĠC C", "Ġdelic ious", "sp an", "ell a", "Ġel abor", "am ples", "Ġdef ic", "Ġconstit u", "u ates", "ĠM ission", "ĠT her", "ĠMon ster", "b es", "Re uters", "ĠInd ones", "h ill", "mun ition", "Ġconfirm ation", "ĠCons ider", "ac ent", "Ġj et", "ĠEm ploy", "ĠGT X", "n an", "ĠSp ider", "Ġprocess or", "Ġpat ri", "ĠPent agon", "ĠRob inson", "Ġreal istic", "à ±", "Ġappear ing", "Ġp ipe", "om ed", "Ġf ru", "Ġaw ful", "Ġeval uation", "Ġintellig ent", "ĠC itiz", "Ġfund ra", "od ium", "Ġtwe ets", "Ġwor n", "pr ing", "Ġkid n", "Ġreb els", "ĠK am", "ĠNether lands", "ĠS W", "Ġacqu isition", "ĠM ale", "ãĥ ª", "omb ies", "Ġtrad em", "ĠStat us", "B re", "ĠTH IS", "Ġad verse", "ĠN EW", "s ign", "Ġorgan isation", "en c", "ĠHar per", "ap or", "ĠMem bers", "ĠPe ace", "ĠAir port", "ĠOther s", "Ġscr atch", "ĠP il", "Ġsens or", "Ġadop tion", "ĠHot el", "ĠDr ag", "Ġhonest ly", "Ġy ard", "ĠFor ces", "Ġpat ent", "Ġb ass", "Ġquiet ly", "Ġbreat hing", "Ġp ose", "i ors", "ĠJ ess", "st atic", "IT E", "O ffic", "Ġj ew", "w cs", "Ġ14 0", "Ġpre view", "ipp i", "Ġunf ortunately", "oke mon", "Ġh orn", "Ġre ass", "Ġpe er", "ock er", "Ġunt o", "ĠGr ay", "Ġclean ing", "Ġattract ed", "200 7", "P oint", "k ill", "ĠAg reement", "ur ches", "Ġhor r", "ĠMiss iss", "Ġworth y", "Ġfl owers", "t own", "d ll", "Ġre actions", "Ġde ce", "Ġindic ating", "M D", "Ġpre ference", "ĠM VP", "ess ional", "ĠT arget", "g ence", "ĠInd ians", "Ġm isc", "Ġfree ly", "Ġmus cles", "Ġline up", "Ġimpact s", "ous ing", "om i", "ac ular", "Ġcontro lling", "ag ine", "c ery", "he ll", "Ġrank ing", "ĠN ich", "ĠA ve", "12 8", "Ġhigh way", "Ġinc ons", "Ġb inding", "Ġstrugg les", "ĠPitt sburgh", "Ġgr ay", "r in", "Ġcom ics", "ĠS port", "Ġrel atives", "Ġfr ight", "Ġpro be", "ĠPort ug", "Ġv oc", "Ġt u", "ĠCor ps", "Ġposs ibilities", "Ġqual ify", "wcs store", "Ġl ibraries", "Ġm igrants", "Ġent ries", "Ġconsecut ive", "v als", "ĠChair man", "Ġh ill", "IM E", "ĠG ard", "Ġinequ ality", "f ox", "ĠS ave", "Ġc ort", "claim ed", "Ġtra its", "Ġp our", "Ġmiss iles", "Ġess ence", "Ġs ends", "Ġall iance", "Ġw ishes", "ĠChrist opher", "B ig", "N Y", "ĠJac ob", "s an", "ur red", "ĠS O", "ll y", "Ġadvoc ate", "ĠB ond", "Ġ\" /", "Us ing", "Ġdistrict s", "ĠG ate", "ĠB ir", "r idge", "ĠN az", "ĠR s", "bo ards", "ĠG a", "ĠRe agan", "Ġinflu enced", "1 000", "ap y", "Ġchalleng ed", "Ġb arg", "Ġfac ulty", "ĠF if", "Ġacqu ire", "A c", "Ġin sect", "Ġinstr uments", "Ġle af", "th odox", "M essage", "Ġt ale", "Ġthere by", "Ġtra p", "Ġstrong est", "ĠMil itary", "is ible", "Ġ198 4", "ethe less", "Ġflex ible", "Ġkill s", "Ġfin ishing", "ĠS ize", "Ġredu ces", "Ġep id", "Ġorient ation", "f ull", "Ġtr ace", "Ġl aser", "Ġopp ose", "Ġed iting", "Ġmoment um", "ä º", "sh ow", "V I", "ĠL ad", "Ġ198 5", "Ġmurd ered", "9 00", "ut her", "Ġprob ability", "ĠP oll", "Ġrel uct", "ĠChe m", "ĠMont real", "Ġadequ ate", "ĠPol and", "ĠSher iff", "um ph", "Ġo k", "Ġ 000", "Ġ\" [", "Ġoper ators", "ĠF er", "Ġmod es", "ĠE ve", "Ġdiscipl ine", "N ET", "H and", "Ġor al", "ĠW E", "em ail", "J P", "ĠPalestin ians", "Ġhe nce", "ĠL ess", "Ġover l", "d ig", "Ġintim id", "ĠCo al", "Ġr anging", "th a", "Ġdist ant", "Ġf ib", "ĠInd ex", "ĠW onder", "ĠP el", "hatt an", "ĠH ug", "à Ĺ", "ra it", "Ġwra pped", "ĠR PG", "Ġchemical s", "ĠM oney", "Ġfro zen", "Ġind irect", "ĠAgain st", "E nd", "Ġuncom fortable", "ĠGall ery", "ĠPost ed", "Ø §", "ond uct", "Ġconsequ ence", "Ġbit ter", "Ġ198 7", "p op", "Ġcount less", "ĠAl aska", "ff ff", "Ġdepart ure", "Ġref und", "ĠI an", "i ated", "Ġsee ks", "Ġmechan ics", "Ġjurisd iction", "lyn n", "Ġal ike", "ĠH unt", "ath on", "Ġres olved", "Ġc ache", "Ġdist inction", "d irect", "Ġenc ount", "ou b", "be at", "ĠCount ry", "se arch", "Ġcontin uous", "Ġmod est", "ĠR ail", "th ood", "1 30", "B UG", "Ġcrim inals", "Ġindic ation", "Ġencount ered", "l ast", "ĠW y", "Ġide ology", "ĠP DF", "sec urity", "] )", "ĠJim my", "ĠE N", "Ġh iring", "T em", "Ġp ig", "aun t", "ĠCry stal", "Ġpen alties", "Ġcap ability", "Ġp y", "Ġproduct ive", "Ġbal anced", "ĠGe Force", "cl ick", "olit an", "od s", "Ġafter wards", "Ġplay offs", "ĠG ill", "U ser", "Ġback s", "p ub", "t ag", "Ġabs urd", "p iring", "Ġc iting", "Ġtr illion", "Ġoblig ation", "Ġmax im", "ah oo", "c f", "um i", "ĠAl pha", "ĠN elson", "Ġpursu ant", "in itely", "Ġf ract", "ent ry", "ber y", "ĠTh or", "Add ed", "ĠD J", "ĠG ene", "Ġaw kward", "St ud", "Ġwal let", "ĠDiv ine", "ari os", "Ġrele asing", "Ġed ited", "Ġaccompl ished", "B est", "Ġed ges", "Ġplan es", "Ġfeed ing", "\" },\"", "Ġdiscl osure", "Ġgr ain", "air y", "o ons", "ern and", "V R", "Ġreason ably", "Ġdr um", "Ġpart ial", "Ġgraph ic", "Ġunpre cedented", "Ġadv ised", "M icro", "ĠAss ad", "point s", "sc ar", "ĠZ one", "tt es", "Ġ7 00", "v o", "ĠH amp", "Ġfix es", "Ġca ution", "Ġstr ings", "Ġpan els", "Ġle ak", "Ġpr icing", "row th", "ĠEr ror", "ĠS aints", "f ix", "Ġobserv ations", "ĠA bs", "Ġsuggest ion", "ĠUkrain ian", "Ġbar rier", "Ġpain ted", "B et", "im ir", "ĠS pect", "p ot", "orne ys", "Ġcomp ound", "Ġbe ars", "ĠR ush", "Ġlux ury", "S um", "Ġor bit", "ĠMar c", "Ġex empt", "ĠTra il", "ĠM O", "ĠH ans", "ĠWe apon", "oc used", "umin um", "ĠJer ry", "Ġb ust", "ĠA G", "ĠW iki", "Ġend less", "ĠV lad", "ĠB ah", "ĠR adeon", "ke ys", "ĠSur vey", "ĠV iol", "def ine", "le an", "Ġcomm od", "Ġreven ues", "Å į", "Ġfurn iture", "Ġcast ing", "Ġdiplom atic", "ĠPlay ers", "ĠK illed", "Ġmod ify", "Ġinnov ative", "ĠAb u", "n or", "Ġbond s", "Ġcoach ing", "M er", "Ġmod ules", "ĠPatri ots", "Ġenh anced", "Ġproceed ings", "Ġteam mates", "Ġ12 8", "ard o", "Ġcomprom ise", "ĠM uch", "Ġfle w", "ĠEd ge", "Ġunnecess ary", "Ġdoct rine", "re port", "ĠOr lando", "ĠProf ile", "Ġplay off", "friend ly", "Ġcompl ain", "ĠM C", "ĠO pt", "ĠG B", "Ġbeat en", "Ġg olf", "Ġpl acement", "B it", "Ġnews letter", "Ġ201 9", "vis or", "raw l", "ĠiP ad", "Ġact ed", "Ġju ice", "Ġdec ks", "P N", "su ccess", "ĠH alf", "Ġdele ted", "Ġsec rets", "Ġas ylum", "M art", "ĠAct iv", "ĠGu y", "ĠT s", "Ġd ys", "Ġassum ing", "Ġman a", "Ġsub ur", "Ġ12 5", "M edia", "AR Y", "r ide", "c p", "Ġdifficult ies", "Ġcollect ing", "Ġbank rupt", "n on", "Ġcomp osed", "Ġvol t", "Ġmilit ants", "Ġ> >>", "ĠM ormon", "t or", "Ġpartic les", "ĠB art", "ry ption", "Ġad min", "Ġsqu ee", "VID IA", "Ġcreat or", "iam eter", "ic ular", "N BC", "Ġgrab bed", "Ġn odd", "Ġr ated", "Ġrot ation", "Ġgr asp", "Ġexcess ive", "ĠE C", "ĠWh it", "Ġinvent ory", "ault s", "ĠF B", "Ġe cosystem", "Ġbill ions", "Ġvent ure", "n amed", "Ġdef ender", "out e", "Inst ead", "ir able", "W ar", "Ġassum ption", "Ġb ite", "Ġearth qu", "t ail", "sp ace", "Ġgif ts", "boy s", "Ġinev itable", "Ġstruct ural", "Ġbenef icial", "Ġcompe lling", "h ole", "erv ation", "Ġco at", "o j", "inc arn", "ĠY ears", "Ġdetermin ing", "Ġrhet oric", "Ġbound aries", "Ġwh ites", "A nt", "add y", ") -", "ra ham", "eter min", "Ġhar vest", "ĠCon c", "Ġlapt op", "ĠM atch", "Ġenjoy ing", "cc a", "oll ar", "Ġtri ps", "Ġadd iction", "ĠS ak", "Ġpow ered", "Ġc ous", "ĠRuss ians", "ie re", "Ġret rie", "qu ality", "Ġdiff er", "Ġking dom", "ĠL aur", "ĠCap itol", "Ġcon clusions", "ĠAl tern", "ĠN av", "Ġtrans parent", "B ER", "G roup", "ĠCom plete", "Ġinf er", "Ġint rig", "Ġins ane", "R O", "oph ob", "is en", "qu al", "Mich ael", "Ġm useum", "ĠP ope", "Ġres et", "r ative", "f ive", "Ġagg reg", "itte es", "osit ory", "Ġcar b", "ĠRec ord", "Ġdec ides", "ĠF ix", "Ġexcept ions", "ĠCommission er", "un s", "ĠEnvironment al", "Ġlegend ary", "ist ence", "Ġtun nel", "k m", "Ġins ult", "Ġt roll", "Ġsh ake", "Ġdet ention", "qu es", "ĠCh rome", "ĠF iles", "Ġsub t", "Ġprospect s", "Ġpro l", "re nder", "pro of", "Ġperform ances", "St r", "Ġh ref", "ern ame", "Ġachieve ment", "Ġf ut", "F ull", "ĠLe ban", "go ogle", "ãĥ Ī", "amp a", "May be", "Ġproject ed", "ĠE mb", "Ġcol leg", "Ġa wards", "Ġâ Ķ", "G old", "ĠBl ake", "ĠR aj", "if ting", "Ġp ending", "Ġinst inct", "Ġdevelop ments", "Con nect", "ĠM and", "ĠW ITH", "ĠPhilipp ines", "prof ile", "Ġalt ogether", "ĠB und", "ĠT D", "oo oo", "amp ed", "ip h", "Ġste am", "Ġold est", "Ġdet ection", "ul pt", "Ġ ç", "ĠWay ne", "200 6", "f a", "Ġcir cles", "ĠF u", "Ġdon ors", "appropri ate", "ĠDak ota", "j amin", "Ġmotiv ated", "Ġpurch ases", "ĠLouis iana", "ĠS pl", "Ġgl obe", "Ġ10 5", "z ip", "c all", "Ġdepart ments", "Ġsustain able", "10 5", "ĠO P", "if iers", "Ġprevent ed", "Ġinc omp", "ĠComm ander", "Ġdom inated", "Ġ »", "Ġinvest ed", "Ġcomplex ity", "Ġin cl", "Ġens uring", "Ġreal m", "yn c", "ĠInd ependent", "r ained", "ĠJ en", "ĠFl ight", "Ġat he", "Ġspec ulation", "ĠT E", "oc ate", "t ic", "Ġpl aint", "her ry", "Ġto y", "Ġ1 11", "Ġpl ates", "st atus", "ĠIs a", "Ġdev oted", "C op", "ĠE S", "25 5", "ur rency", "M ain", "Ġsl aves", "Ġpe pper", "Ġqu otes", "Ġce iling", "ĠF ish", "Ġtrans formation", "Ġfra ction", "Ġadvant ages", "Ġto ile", "Ġstun ning", "Ġmo ist", "bre aking", "s i", "ĠL ocation", "ĠMed ium", "Ġtext s", "Ġu gly", "Ġb io", ". âĢĶ", "ĠB ased", "Ġtr ains", "ĠW ing", "ĠAn cient", "ĠRec ords", "ĠH ope", "Spe cial", "ades h", "ob i", "[ /", "Ġtempor arily", "V er", "h u", "os er", "Ġover night", "Ġm amm", "ĠTre asury", "ĠV enezuel", "ĠMeg a", "Ġt ar", "Ġexpect s", "bl ack", "or ph", "\\\\ \\\\", "Ġaccept ance", "Ġrad ar", "s is", "Ġjun ior", "Ġfram es", "Ġobserv ation", "ac ies", "P ower", "ĠAdv anced", "M ag", "olog ically", "ĠMe chan", "Ġsent ences", "Ġanaly sts", "augh ters", "force ment", "Ġv ague", "Ġcl ause", "Ġdirect ors", "Ġeval uate", "Ġcabin et", "M att", "ĠClass ic", "A ng", "Ġcl er", "ĠB uck", "Ġresear cher", "Ġ16 0", "Ġpoor ly", "Ġexperien cing", "ĠP ed", "ĠMan hattan", "Ġfre ed", "Ġthem es", "ad vant", "Ġn in", "Ġpra ise", "10 4", "ĠLib ya", "b est", "Ġtrust ed", "Ġce ase", "Ġd ign", "D irect", "Ġbomb ing", "Ġm igration", "ĠSci ences", "Ġmunicip al", "ĠA verage", "Ġgl ory", "Ġreve aling", "Ġare na", "Ġuncertain ty", "Ġbattle field", "ia o", "G od", "Ġc inem", "ra pe", "el le", "ap ons", "Ġlist ing", "Ġwa ited", "Ġsp otted", "ke ley", "ĠAud io", "e or", "ard ing", "idd ing", "ig ma", "ĠN eg", "Ġl one", "Ġ ----", "ex e", "d eg", "Ġtrans f", "Ġwas h", "Ġsl avery", "Ġexpl oring", "ĠW W", "ats on", "Ġen cl", "l ies", "ĠC reek", "Ġwood en", "Man ager", "ĠBr and", "um my", "ĠAr thur", "Ġbureau cr", "Ġbl end", "ar ians", "F urther", "Ġsupposed ly", "Ġwind s", "Ġ19 79", "Ġgrav ity", "Ġanalys es", "ĠTra vel", "ĠV eter", "Ġd umb", "Ġaltern ate", "g al", "Ġconsum ed", "Ġeffect iveness", ".' '", "Ġpath s", "ond a", "L A", "ĠStr ong", "Ġen ables", "Ġesc aped", "Ġ\" \"", "Ġ1 12", "Ġ198 3", "Ġsm iled", "Ġtend ency", "F ire", "Ġp ars", "ĠR oc", "Ġl ake", "Ġf itness", "ĠA th", "ĠH orn", "Ġh ier", "Ġimp ose", "m other", "Ġp ension", "ic ut", "bor ne", "ic iary", ". _", "ĠS U", "Ġpol ar", "is y", "eng u", "itial ized", "AT A", "w rite", "Ġexerc ises", "ĠD iamond", "ot ypes", "Ġharm ful", "on z", "Ġprint ing", "st ory", "Ġexpert ise", "ĠG er", "Ġtraged y", "ĠF ly", "Ġd ivid", "amp ire", "st ock", "M em", "Ġre ign", "Ġun ve", "Ġam end", "ĠProp het", "Ġmut ual", "ĠF ac", "Ġrepl acing", "H ar", "ĠCirc uit", "Ġthro at", "ĠSh ot", "Ġbatter ies", "Ġto ll", "Ġaddress ing", "ĠMedic aid", "Ġp upp", "ĠN ar", "ol k", "Ġequ ity", "M R", "ĠHis pan", "ĠL arge", "m id", "D ev", "Ġexp ed", "Ġdem o", "ĠMarsh all", "erg us", "Ġf iber", "Ġdiv orce", "ĠCre ate", "Ġsl ower", "ĠPark er", "ĠStud ent", "ĠTr aining", "Ret urn", "ĠT ru", "Ġc ub", "ĠRe ached", "Ġpan ic", "Ġqu arters", "Ġre ct", "Ġtreat ing", "Ġr ats", "ĠChristian ity", "ol er", "Ġsac red", "Ġdecl are", "ul ative", "et ing", "Ġdeliver ing", "est one", "Ġt el", "ĠL arry", "Ġmet a", "ac cept", "art z", "ĠRog er", "hand ed", "Ġhead er", "Ġtra pped", "ĠCent ury", "Ġkn ocked", "ĠOx ford", "Ġsurviv ors", "b ot", "Ġdemon stration", "Ġd irt", "Ġass ists", "OM E", "ĠD raft", "ortun ate", "fol io", "pe red", "ust ers", "g t", "ĠL ock", "Ġjud icial", "ver ted", "Ġsec ured", "out ing", "ĠBook s", "Ġhost ing", "Ġlif ted", "l ength", "Ġj er", "Ġwhe els", "ĠR ange", "umbn ails", "Ġdiagn osis", "te ch", "ĠStew art", "ĠP ract", "Ġnation wide", "Ġde ar", "Ġoblig ations", "Ġgrow s", "Ġmand atory", "Ġsusp icious", "! '", "A pr", "G reat", "Ġmort gage", "Ġprosecut or", "Ġeditor ial", "ĠK r", "Ġprocess ed", "ung le", "Ġflex ibility", "Ear lier", "ĠC art", "ĠS ug", "Ġfoc uses", "Ġstart up", "Ġbre ach", "ĠT ob", "cy cle", "ãĢ Į", "ro se", "Ġb izarre", "ãĢ į", "Ġveget ables", "$ $", "Ġret reat", "osh i", "ĠSh op", "ĠG round", "ĠSt op", "ĠHawai i", "ĠA y", "Per haps", "ĠBe aut", "uff er", "enn a", "Ġproduct ivity", "F ixed", "cont rol", "Ġabs ent", "ĠCamp aign", "G reen", "Ġident ifying", "Ġreg ret", "Ġpromot ed", "ĠSe ven", "Ġer u", "ne ath", "aug hed", "ĠP in", "ĠL iving", "C ost", "om atic", "me ga", "ĠN ig", "oc y", "Ġin box", "Ġem pire", "Ġhor izont", "Ġbr anches", "Ġmet aph", "Act ive", "ed i", "ĠFil m", "ĠS omething", "Ġmod s", "inc ial", "ĠOrig inal", "G en", "Ġspir its", "Ġear ning", "H ist", "Ġr iders", "Ġsacr ific", "M T", "ĠV A", "ĠS alt", "Ġoccup ation", "ĠM i", "Ġdis g", "lic t", "Ġn it", "Ġn odes", "e em", "ĠP ier", "Ġhat red", "ps y", "ãĥ ī", "Ġthe ater", "Ġsophistic ated", "Ġdef ended", "Ġbes ides", "Ġthorough ly", "ĠMedic are", "Ġbl amed", "arent ly", "Ġcry ing", "F OR", "pri v", "Ġsing ing", "ĠI l", "Ġc ute", "o ided", "olit ical", "ĠNe uro", "å ¤", "Ġdon ation", "ĠEag les", "ĠG ive", "T om", "Ġsubstant ially", "ĠLic ense", "ĠJ a", "Ġg rey", "ĠAn imal", "ĠE R", "ĠU nd", "Ġke en", "Ġconclud e", "ĠMississ ippi", "Eng ine", "ĠStud ios", "P ress", "o vers", "ll ers", "Ġ3 50", "ĠR angers", "Ġr ou", "ert o", "E p", "iss a", "iv an", "Ġse al", "ĠReg ist", "dis play", "Ġwe aken", "u um", "ĠComm ons", "ĠS ay", "Ġcult ures", "Ġl aughed", "Ġsl ip", "Ġtreat ments", "iz able", "m art", "ĠR ice", "Ġbe ast", "Ġob esity", "ĠLa ure", "ig a", "Wh ich", "hold er", "Ġelder ly", "Ġp ays", "Ġcompl ained", "Ġc rop", "Ġpro c", "Ġexplos ive", "ĠF an", "ĠAr senal", "A uthor", "ef ul", "Ġme als", "Ġ( -", "id ays", "Ġimag ination", "Ġann ually", "Ġm s", "as ures", "H ead", "ik h", "m atic", "Ġboy friend", "ĠCom puter", "Ġb ump", "Ġsur ge", "ĠCra ig", "ĠKir k", "D el", "medi ate", "Ġscen arios", "ĠM ut", "ĠSt ream", "Ġcompet itors", "Ù Ħ", "ĠStan ford", "ĠRes ources", "az ed", "b age", "Ġorgan is", "ĠRe lease", "Ġsepar ately", "Ġha bits", "Ġmeasure ments", "ĠCl ose", "Ġaccomp any", "Ġg ly", "Ġt ang", "ĠR ou", "Ġplug in", "Ġcon vey", "ĠChall enge", "oot s", "j an", "Ġcur s", "ĠRel ations", "ke eper", "Ġapproach ing", "p ing", "Spe aking", "Ġarrang ement", "ĠV I", "are ttes", "Ġaffect ing", "Ġperm its", "b ecause", "Ġu seless", "ĠH us", "!! !!", "Ġdestro ying", "Un fortunately", "Ġfasc inating", "S em", "Ġelect oral", "Ġtrans parency", "ĠCh aos", "Ġvolunte er", "Ġstatist ical", "Ġactiv ated", "ro x", "We b", "H E", "ĠHamp shire", "is ive", "M ap", "Ġtr ash", "ĠLaw rence", "st ick", "C r", "Ġr ings", "EX T", "Ġoper ational", "op es", "D oes", "ĠEv ans", "Ġwitness ed", "P ort", "Ġlaunch ing", "ec onom", "w ear", "ĠPart icip", "um m", "cul es", "ĠR AM", "ĠT un", "Ġass ured", "Ġb inary", "Ġbet ray", "Ġexpl oration", "ĠF el", "Ġad mission", "it ated", "S y", "Ġav oided", "ĠSim ulator", "Ġcelebr ated", "ĠElect ric", "¥ ŀ", "Ġcl uster", "itzer land", "he alth", "L ine", "ĠN ash", "at on", "Ġsp are", "Ġenter prise", "ĠD IS", "clud es", "Ġfl ights", "Ġreg ards", "Ġà Ĺ", "h alf", "Ġtr ucks", "Ġcontact s", "Ġunc ons", "ĠCl imate", "Ġimm ense", "N EW", "oc c", "ect ive", "Ġemb od", "Ġpat rol", "Ġbes ide", "Ġv iable", "Ġcre ep", "Ġtrig gered", "ver ning", "Ġcompar able", "q l", "Ġg aining", "ass es", "Ġ( );", "ĠG rey", "ĠM LS", "s ized", "Ġpros per", "\" ?", "Ġpoll ing", "Ġsh ar", "ĠR C", "Ġfire arm", "or ient", "Ġf ence", "Ġvari ations", "g iving", "ĠP i", "osp el", "Ġpled ge", "Ġc ure", "Ġsp y", "Ġviol ated", "Ġr ushed", "Ġstro ke", "ĠBl og", "sel s", "ĠE c", ",' '", "Ġp ale", "ĠColl ins", "ter ror", "ĠCanad ians", "Ġt une", "Ġlabor atory", "Ġn ons", "t arian", "Ġdis ability", "ĠG am", "Ġsing er", "al g", "ĠSen ior", "Ġtrad ed", "ĠWar rior", "Ġinf ring", "ĠFrank lin", "Ġstr ain", "ĠSwed ish", "Ġsevent h", "ĠB enn", "ĠT ell", "Ġsynd rome", "Ġwond ered", "id en", "++ ++", "ig o", "Ġpur ple", "Ġjournal ism", "Ġreb el", "Ġf u", "bl og", "Ġinv ite", "ren cies", "ĠCont act", "Is rael", "ĠCont ent", "Ġche er", "Ġbed room", "ĠEngine ering", "ĠQue ens", "Ġd well", "ĠPlay Station", "ĠD im", "ĠCol on", "l r", "Ġoper ates", "Ġmotiv ation", "US A", "ast ered", "C ore", "ĠTr uth", "ol o", "OS E", "ĠMem ory", "Ġpred ec", "Ġan arch", "Ġ19 20", "ĠY am", "à ¨", "b id", "Ġgr ateful", "Ġexc itement", "Ġtre asure", "Ġlong est", "ct ive", "Ġdes erves", "Ġreserv es", "Ġcop s", "ĠOtt awa", "ĠEgypt ian", "ank ed", "Ġart if", "Ġhypot hesis", ": /", "Ġpurch asing", "Ġlove ly", "H P", "Ġdiv ide", "Ġstrict ly", "Ġquestion ing", "Ġtaxp ayers", "ĠJ oy", "Ġroll s", "ĠHe avy", "Ġp orts", "Ġmag netic", "Ġinf lamm", "Ġbr ush", "t ics", "â ĪĴ", "Ġbott les", "pp y", "Ġp add", "ãĤ ¯", "m illion", "Ġdevast ating", "Ġcomp iled", "Ġmed ication", "Ġtw elve", "ĠPer ry", "Sp ace", "im b", "y our", "Ġle aked", "ĠT ar", "Ġun ity", "Ġinfect ed", "Ġtravel ed", "ID E", "ĠMc Donald", "t xt", "ĠPr inc", "Ġinter ven", "ĠTai wan", "ĠP ow", "Ġbe aring", "ĠTh read", "Ġz ones", "iz ards", "un ks", "Ch apter", "ll or", "Ġ ·", "Ġw ounds", "Ġdisc retion", "Ġsucceed ed", "ik ing", "Ġicon ic", "C all", "Ġscreen ing", "ĠM is", "ict s", "Ġmin isters", "Ġsepar ation", "Pl ayer", "Ġb ip", "Ġbel oved", "Ġcount ing", "ĠE ye", "ar ound", "ing ing", "Ġtable t", "Ġoff ence", "in ance", "h ave", "ĠInf o", "ĠNin ja", "Ġprotect ive", "ĠC ass", "M ac", "ĠQual ity", "N orth", "Ġ ic", "ĠCub a", "ĠChron icle", "ĠPro perty", "Ġfast est", "ot os", "ĠG erm", "OW N", "Ġbo om", "ĠStan ley", "ergus on", "Ġcle ver", "Ġent ers", "m ode", "ter ior", "ĠS ens", "Ġlin ear", "AR K", "Ġcomp aring", "Ġpure ly", "Ġsaf er", "ĠPot ter", "Ġc ups", "R T", "Ġgl uc", "Ġatt ributed", "Ġdu pl", "ĠP ap", "Ġprec ious", "Ġp a", "iction ary", "ĠT ig", "ĠTo o", "ol utions", "st an", "Ġrob ots", "Ġlob b", "Ġstat ute", "Ġprevent ion", "w estern", "16 0", "ĠAct ive", "ĠMar ia", "h al", "N one", "ell ar", "ĠK B", "ĠPart ners", "ĠSing le", "ĠFollow ing", "ang o", "ac ious", "Ġth ou", "Ġk g", "Ġinflu ential", "ĠFriend s", "S ur", "ain ted", "Ġfor ums", "Ġst arter", "Ġcitizens hip", "ĠE lection", "on ge", "ot ation", "os ph", ";; ;;", "ut ical", "p ur", "ere n", "Ġaccus ations", "bit ious", "ab bit", "ĠOr d", "Post ed", "ir k", "Ġsens itivity", "ic he", "ĠAm y", "ĠF ab", "Ġsum mit", "Ġped est", "Ġrub ber", "Ġagric ultural", "Ġcan cel", "A E", "Ġin aug", "Ġcont am", "Ġfirm ly", "i w", "st age", "ĠK an", "Ġt ier", "Ġinv ention", "Ġtransl ated", "ĠR ules", "B ox", "Tw itter", "ID S", "Ġp izza", "Ġdeb ug", "ĠD rop", "v s", "Ġh orses", "b ig", "Ġb oring", "Ġh ood", "ĠMcC ain", "at ched", "ĠBro s", "Ġsk ip", "Ġess ay", "st at", "ĠLeg ends", "Ġam munition", "au c", "Ġshoot er", "Ġun h", "Ġsuppl ied", "Ġgener ic", "ĠS K", "ib an", "yr ics", "Ġ25 5", "Ġclim bing", "Form er", "Ġfl ip", "Ġjump ing", "Ġfrust ration", "ĠTer ry", "Ġneighborhood s", "Ġmed ian", "be an", "Ġbr ains", "Follow ing", "Ġsh aped", "Ġdraw s", "Ġal tered", "J ack", "Ġrecip es", "Ġsk illed", "we alth", "ach i", "e lection", "Ġbehavi ors", "de als", "ĠU ntil", "F e", "Ġdecl aration", "mar ks", "ĠBet ween", "cel ona", "Ġres on", "Ġbub ble", "Am ong", "Ġim perial", "G S", "Ġfemin ist", "200 5", "ĠK yle", "Ġaccount ing", "ĠTe le", "ĠT yr", "Ġconnect ing", "Ġre hab", "ĠP red", "s im", "Ġmeant ime", "Ġphys ician", "M W", "ĠCamp bell", "ĠBr andon", "Ġcontribut ing", "ĠR ule", "ĠWe ight", "ĠN ap", "Ġinter active", "Ġv ag", "Ġhel met", "ĠCom b", "f our", "Ġsh ipped", "Ġcomple ting", "ĠP D", "PD ATE", "Ġspread ing", "Ġsc ary", "erv ing", "ĠG as", "Ġfr ank", "s chool", "Ġrom antic", "Ġstab il", "R ob", "Ġaccur ately", "Ġac ute", "ĠH ann", "Ġsymbol s", "Ġcivil ization", "ĠA W", "Ġlight ning", "Ġcons iders", "Ġven ue", "Ġ ×", "Ġo ven", "ĠS F", "h is", "Ġn u", "ĠLear n", "Ġpe oples", "Ġst d", "Ġsle e", "Ġs lic", "ĠStat istics", "Ġcor ners", "ĠB aker", "Ġ: )", "ment ation", "ol ver", "Ġlaugh ing", "ĠT odd", "ond e", "ĠH ills", "Ġn uts", "ĠW oman", "pl ane", "Ġl iver", "ĠIn side", "S orry", "Ġagre es", "Ġfund ament", "ĠF isher", "Ġa uction", "Ġthread s", "gl as", "ĠBas ic", "ĠN at", "Ġlack ing", "Ġceleb ration", "j u", "Ġs illy", "E uro", "Ġt att", "ight y", "cont rolled", "T est", "ĠSing h", "Ġr age", "Ġrh yth", "o ffic", "ĠPh antom", "Ġhead lines", "Ġrespond ing", "ĠMor ning", "Ġvit amin", "Ġboot s", "ĠS ite", "al in", "p i", "Ġvir al", "ĠU C", "D ER", "ĠSe x", "Ġst ocks", "c urrent", "Ġch urches", "ĠR are", "ĠMur phy", "Ġden ial", "ĠG aming", "Ġtou g", "Ġn ick", "Ġm akers", "ĠRon ald", "Ġgener ous", "ĠD oc", "ĠMor ris", "Ġtransform ed", "ĠN ormal", "Ġ10 4", "ĠKick starter", "ĠUp on", "On line", "ĠI RS", "Ġw rap", "Ġl oving", "Ġarri ves", "ĠD ue", "Ġhe ter", "ĠM ade", "Ġrent al", "Ġbelong s", "Ġatt orneys", "Ġcro ps", "Ġmat ched", "ul um", "ol ine", "10 9", "Ġdis par", "Ġbuy ers", "ĠCam bridge", "Ġeth ics", "rou ps", "Ġjust ified", "Ġmarg inal", "Ġrespect ed", "win ning", "Ġnodd ed", "ĠSer ge", "ĠForm er", "C raft", "######## ########", "ĠWar ner", "Ġd ash", "et e", "Ġent ert", "ĠE scape", "out heast", "Ġkn ees", "ĠB omb", "Ġr ug", "P ass", "Ġatt itudes", "go vernment", "ĠPri or", "Ġqual ities", "Ġnot ification", "ĠPh one", "l ie", "Ġanticip ated", "ĠCom bat", "ĠBar ry", "Ġ198 2", "Us ers", "on er", "Ġcomput ing", "ĠConnect icut", "Ġless er", "Ġpe ers", "ĠC u", "Ġtechn ically", "Ġsub mission", "ĠUn iversal", "Ġman ually", "our ge", "Ġrespond ents", "ĠB TC", "ĠH ost", "Ġf are", "ĠB ird", "Ġrece ipt", "al so", "Ġj ack", "Ġagric ulture", "Ġsk ull", "Ġ! =", "Ġpass ive", "ĠC I", "Ġsoc ieties", "Ġremind ed", "Ġinter ference", "B uy", "Ġâ ľ", "g on", "Ġscrut iny", "ĠW itch", "Ġconduct ing", "Ġ ãĥ", "Ġexch anges", "ĠMit chell", "Ġinhab it", "Ġtw ist", "B D", "Ġwhere ver", "group on", "Ġj okes", "ĠBen jamin", "ĠR andom", "fr ame", "ĠL ions", "Ġhighlight ed", "ĠArk ansas", "E nt", "Ġp ile", "Ġpre lim", "g s", "mind ed", "Ġfel ony", "ĠG A", "ĠL uck", "Ġpract ically", "ĠB os", "Ġact ress", "D am", "ĠB ou", "Ġvis a", "Ġembed ded", "Ġhy brid", "Ġear liest", "Ġsoon er", "s ocial", "ĠH A", "Ġste ep", "Ġdis advant", "Ġexplo it", "ĠE gg", "ĠUlt ra", "Ġnecess ity", "L ocal", "ie ge", "Ġd ated", "Ġmass es", "Ġsubsc ription", "pl ess", "Ġan onym", "Ġpresum ably", "Bl ue", "The ir", "asket ball", "ĠPhil ip", "Ġcom ed", "load ed", "r ane", "Ġref lection", "Ch ina", "Ġext ends", "Ġform ing", "Ġund ers", "200 1", "Ġgr at", "Ġconcent rations", "Ġins ulin", "Ġsec ular", "Ġwh ilst", "Ġwin ners", "Ad vertisements", "Ġdeliber ately", "ĠWork ing", "Ġs ink", "et ics", "d ale", "Ġmand ate", "Ġg ram", "Ġvac ation", "Ġwarn ings", "ri pp", "ĠTH AT", "Ġcomment ary", "Ġint u", "Ġa est", "Ġreason ing", "Ġbreak down", "ĠZ ombie", "Ġ-- >", "ĠPolit ical", "c ott", "Ġthr ust", "Ġtechn ological", "Ġdec iding", "Ġtraff icking", "L ong", "W elcome", "pr ising", "ĠCommun ications", "Ġend ors", "Ġsw ift", "Ġmetab ol", "co ins", "res a", "ĠHT TP", "Ġen roll", "ĠH appy", "us r", "int age", "Ġ[ \"", "u ably", "ĠM aterial", "Ġrepe al", "Se pt", "k h", "ĠMod i", "Ġunder neath", "ĠI L", "sh ore", "Ġdiagn osed", "ace utical", "Ġsh ower", "au x", "ĠSw itch", "ĠStre ngth", "Ġj ihad", "n ational", "Ġtra uma", "uss y", "on i", "Ġcons olid", "Ġcal ories", "ĠF lynn", "ag ged", "16 8", "ĠP ink", "Ġfulf ill", "Ġch ains", "Ġnot ably", "ĠA V", "L ife", "ĠCh uck", "m us", "ĠUr ban", "ĠH end", "Ġdep osit", "ĠS ad", "Ġaff air", "OR K", "ie val", "ĠF DA", "Ġt rop", "ĠOver all", "Ġvirt ue", "Ġsatisf action", "au nd", "Ġl un", "ĠSw itzerland", "ĠOper ation", "pro cess", "Ġsh ook", "Ġcount ies", "le ased", "ĠCharl otte", "1 12", "Ġtrans cript", "Ġre dd", "p ush", "ĠHe y", "ĠAn alysis", "[ \"", "Ġaltern atives", "ard less", "Ġele ph", "Ġpre jud", "ĠLe af", "H aving", "ĠH ub", "Ġexpress ions", "ĠVol ume", "Ġshock ing", "ĠRed s", "Ġread ily", "Ġplan ets", "ad ata", "Ġcollaps ed", "ĠMad rid", "Ġir rit", "i pper", "ĠEn c", "ĠW ire", "Ġbu zz", "ĠG P", "ash a", "Ġaccident ally", "ur u", "Ġfrust rated", "ĠS A", "Ġhung ry", "ĠH uff", "Ġlab els", "ant o", "ĠE P", "Ġbar riers", ") |", "ĠBer keley", "ĠJ ets", "Ġp airs", "ĠL an", "J ames", "ĠB ear", "Ġhum or", "ĠLiber ty", "Ġmagn itude", "Ġag ing", "ĠM ason", "Ġfriends hip", "umb ling", "Ġemer ge", "Ġnewsp apers", "Ġam bitious", "ĠRich ards", "atern al", "Ġ198 1", "Ġcook ies", "Ġsc ulpt", "Ġpur suit", "L ocation", "Ġscript s", "p c", "Ġarrang ements", "Ġd iameter", "Ġl oses", "am ation", "Ġl iqu", "ĠJ ake", "aret te", "Ġunderstand s", "ĠZ en", "v m", "Ġappro ve", "Ġw ip", "Ġult ra", "Ġint end", "ĠD I", "asc ular", "Ġst ays", "ĠK or", "ĠK l", "Ġinvest ing", "L a", "Ġbelie ving", "b ad", "m outh", "Ġtaxp ayer", "ãĥ ĥ", "ĠQue bec", "Ġl ap", "ĠSw iss", "d rop", "Ġdr ain", "ir i", "et c", "ft en", "ĠN ex", "Ġst raw", "Ġscream ing", "Ġcount ed", "Ġdam aging", "Ġamb assador", "cent ury", "Ġpro x", "Ġarrest s", "u v", "il ateral", "ĠCh arg", "Ġpresc ribed", "Ġindepend ently", "Ġf ierce", "ĠB aby", "Ġb rave", "Ġsu its", "= >", "Ġbas eline", "ĠR ate", "Ġis lands", "Ġ( (", "g reen", "ix els", "Ġname ly", "ĠVill age", "th an", "am y", "V ersion", "g mail", "ential s", "ĠS ud", "ĠMel bourne", "Ġarri ving", "Ġquant um", "e ff", "rop olitan", "T ri", "Ġfun eral", "ĠI R", "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ", "ĠC ob", "it ably", "Ġt urb", "Ġcomb o", "Re view", "Ġdeploy ment", "u ity", "ĠB ott", "Ġinv isible", "Ġrender ing", "Ġunl ocked", "Ġa qu", "ĠVlad imir", "Ġp ad", "ĠBr ain", "ĠLeg acy", "dr agon", "ĠKurd ish", "Ġsound ed", "Ġdet ained", "ĠD M", "g ary", "Ġd aughters", "Ġdistur bing", "uk a", "ĠPar ad", "Ġt ast", "Ġunf ortunate", "Ġu l", "em in", "Ġattend ance", "tr l", "Ġpar ks", "ĠMem orial", "ĠAl ice", "oth y", "gu ard", "ĠD ise", "ĠSh an", "ĠFor um", "R ich", "Ġshif ted", "ue z", "Ġl ighter", "ĠMag n", "Ġc od", "S ch", "ham mad", "P ub", "3 50", "ĠP okemon", "Ġprot otype", "Ġun re", "B ase", "ĠStud ents", "ĠRep ly", "ĠCommun ist", "Ġg au", "ĠTy ler", "I Z", "Ġparticip ated", "Ġsup rem", "ĠDet ails", "Ġvessel s", "ro d", "Ġt ribe", "ke ep", "Ġassum ptions", "Ġp ound", "Ġcr ude", "ĠAv ailable", "Ġswim ming", "Ġin clusion", "Ġadv ances", "c ulation", "Ġconserv ation", "Ġover d", "ĠBuff alo", "Art icle", "ed ge", "Ġaw a", "ĠMad ison", "Ġsid ew", "Ġcat ast", "ĠK rist", "uc le", "ĠHigh way", "ĠTer ror", "Ġactiv ation", "Ġuncons cious", "ĠSat an", "ĠSus an", "ill ery", "Ġarr anged", "i op", "Ġrum ors", "ur ring", "th ink", "ĠKe ith", "ĠK ind", "Ġavoid ing", "by n", "n ut", "ĠSpe aker", "r us", "n ames", "Ġgu ilt", "ĠOlymp ics", "Ġsa il", "ĠM es", "lev ant", "ĠColumb us", "a ft", "C ity", "S outh", "ĠHar vey", "ĠP un", "S everal", "Ġment ally", "Ġimp ress", "m ount", "ĠUb untu", "âĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶ", "ĠSuper man", "ĠMP s", "Ġintent ions", "ĠR acing", "Ġlike lihood", "Ġ2 40", "T otal", "Ġto ys", "ĠW atson", "Ġur ge", "L ear", "ĠP aper", "Ġoccur ring", "ĠB eng", "ĠC ert", "Ġst ones", "T im", "ĠTw in", "z b", "ĠD ynam", "Ġpolit ician", "k ens", "ĠEnter prise", "UT ERS", "Ġab ol", "Ġref resh", "Ġarbit rary", "pe ction", "Ġtrou bles", "Ġ} );", "t v", "Ġpil ots", "Ġdist ribute", "Ġaud it", "Ġp ause", "orig inal", "Ġr ivals", " £", "F ig", "T L", "ab il", "ry ing", "L in", "ion ed", "l on", "Ġf ancy", "Ġcr ashed", "Ġt ract", "Ġshe d", "Ġcons ume", "B ased", "down load", "in it", "Ġvolt age", "Int rodu", "Ġcondem ned", "ĠFin ance", "res pect", "Ġex cluded", "Ġestablish ing", "her ic", "Ġher itage", "Ġspect acular", "Ġun st", "ĠSnow den", "ĠL ane", "S an", "Ġprotect ions", "st ruction", "inc inn", "Ġmac ro", "C ustom", "ios ity", "Ġes p", "Ġfunction ing", "Ġm ush", "Ġp uzzle", "Ġeth ical", "M al", "Ġgo verning", "ĠF erguson", "Ġrest ored", "Ġst ressed", "ĠCoun ter", "ĠK as", "cl ip", "AN S", "Ġse iz", "U K", "by ss", "old own", "ap i", "Ġperman ently", "oun ters", "W est", "Th rough", "L ight", "at oes", "Ġne at", "Ġc ord", "ure r", "Ġsevere ly", "ĠA ven", "Ġinter rog", "Ġtri ple", "G iven", "N umber", "Ġar ise", "Ġs her", "pl ant", "Ġfl ower", "ĠC ou", "Ġat e", "Ġnew er", "b ul", "Ġmean while", "ĠL air", "Ġadjust ment", "ĠCop yright", "Ġd ivers", "i ological", "Ġgam ers", "o at", "Ġhistor ically", "Ġanal og", "Ġlong time", "Ġpres cription", "ĠM ist", "ĠHy per", "ĠM aine", "ĠDe ity", "Ġmulti pl", "ĠRe incarn", "ĠH yd", "ĠP ic", "S il", "r ants", "ĠC ris", ". ;", "( {", "epend ence", "Ġrec y", "ate ur", "Ġqu ad", "Ġgl ob", "Ġcon ced", "te am", "Ġcapital ist", "ĠL ot", "Ġroy al", "ĠCy ber", "Ġblack s", "met ic", "ri v", "ĠD anny", "Ġsp o", "ĠR O", "Ġanim ated", "rypt ed", "ĠDep uty", "Ġrend ered", "F E", "Ġstre ak", "Ġcloud s", "ĠDou g", "~~~~ ~~~~", "Ġdisc our", "ĠVe h", "Ġpsych ology", "ĠJ ourney", "Ġcry stal", "ĠFro st", "Ġsuspic ion", "Ġrel ate", "or us", "ĠC rypt", "ĠN VIDIA", "com ed", "ut ing", "incinn ati", "Ġvulner ability", "ost ic", "Ġisol ation", "Ġcool ing", "ĠCoal ition", "Ġ1 19", "F our", "ĠDe al", "Ġâ ī", "se mble", "ram ent", "ĠBar celona", "Ġ10 2", "Ġcoc aine", "ocaly pse", "F eb", "ogen ic", "Ġmut ation", "Ġcrypt oc", "ĠK el", "ĠG it", "a is", "Ġs isters", "AN K", "Ġactiv ate", "T er", "Ġd read", "yl on", "Ġprop ri", "A ust", "ĠDef ault", "Ġout door", "Ġshe er", "ce ive", "Ġg ently", "Ð ¾", "Pro gram", "Ġâ ĨĴ", "Ġve gan", "ĠCr us", "Ġrespons ibilities", "ĠH R", "OL D", "Ġprev ents", "Ġst iff", "ĠW ere", "Ġathlet ic", "ĠSc ore", "Ġ) :", "Ġcolumn s", "ĠL oc", "av ailable", "ĠF ram", "ĠS essions", "Ġcompan ion", "Ġpack s", "14 0", "ĠKn ights", "Ġf art", "Ġstream s", "Ġsh ore", "Ġapp eals", "ĠPer formance", "h aul", "ĠSt ra", "ĠN ag", "10 3", "ĠTrans portation", "B B", "E v", "z an", "P ublic", "Ġtw in", "uls ion", "M ult", "Ġelect ro", "Ġstat ue", "ation ally", "ĠN ort", "Ġins pection", "/ *", "ig ue", "Ġcomp assion", "ĠT ales", "ĠSte in", "ĠSc reen", "ĠB ug", "ĠL ion", "g irl", "Ġwithdraw al", "Ġobject ives", "Ġblood y", "Ġprelim inary", "Ġj acket", "Ġdim ensions", "ĠC ool", "ĠOcc up", "Ġw reck", "Ġdoub led", "ank ing", "Ġ19 75", "Ġglass es", "ĠW ang", "pro v", "P ath", "connect ed", "ĠMult i", "ĠNor way", "agon ist", "Ġfe ared", "Ġtouch ing", "Ġarg uably", "¯¯¯¯ ¯¯¯¯", "ĠNC AA", "che m", "Ġsp at", "ĠW WE", "ĠC el", "ig ger", "Ġattack er", "ĠJo in", "ob ject", "ett a", "Ġelim inated", "d et", "Ġdest ruct", "ĠLuc as", "ct uary", "18 0", "ĠBr ady", "ĠBl ues", "B ay", "au kee", "Ġtim eline", "Ġdeleg ates", "w ritten", "uff icient", "Ġsh apes", "Cop yright", "ou ble", "serv ice", "Ġp ione", "Ġcolleg es", "Ġrow s", "Ġsp ite", "Ġassess ed", "3 60", "Ġle ase", "Ġconfident ial", "ck er", "ĠMan ning", "ĠV oice", "Ġse aled", "Ġcalcul ate", "N O", "ĠAss istant", "Ġteen ager", "ul ent", "ather ine", "Ġm ock", "Ġd iamond", "Ġf est", "Ġsw itched", "Ġres ume", "ĠPu erto", "Ġl anes", "ir ation", "ĠSimilar ly", "Ġro d", "ĠS el", "ĠPal ace", "ĠLim ited", "e ous", "Ġvar iant", "Ġw ard", "Ġ) )", "Sh ow", "OO K", "A lex", "ĠN ep", "br is", "ĠWik ipedia", "Ġexcept ional", "Ġman ages", "ĠD raw", "Ag ain", "Ġco pper", "ut t", "Ġex ports", "Ġport folio", "Ġelev ated", "R ated", "ĠOther wise", "ĠT act", "ĠShe l", "ĠT X", "\" âĢĶ", "Ġres ur", "ĠW a", "ven ant", "Ġmon etary", "pe ople", "E mail", "Ġfif ty", "ĠS weet", "ĠMalays ia", "Ġconf using", "ĠR io", "ud a", "uten ant", "\" );", "Ġpra ised", "Ġvol umes", "t urn", "Ġm ature", "Ġnon profit", "Ġpassion ate", "ĠPriv ate", "Ġ10 3", "Ġdesc end", "ç ¥ŀ", "uff y", "head ed", "Whe ther", "ri en", "ze ch", "be it", "Ġch rom", "ĠMc M", "Ġd ancing", "Ġe leg", "ĠNot iced", "11 5", "Ġadvoc acy", "ENT S", "amb ling", "ĠMin or", "ĠF inn", "Ġprior ities", "Ġthere of", "ĠSt age", "ĠRog ers", "Ġsubst itute", "ĠJ ar", "ĠJeff erson", "Ġlight ly", "10 2", "ĠL isa", "u its", "ys ical", "Ġshif ts", "Ġd rones", "Ġwork place", "Ġres id", "ens ed", "ah n", "Ġpref erences", "ser ver", "Ġdeb ates", "d oc", "ĠGod s", "Ġhelicop ter", "Ġhon our", "Ġconsider ably", "ed ed", "ĠF emale", "ĠAn ne", "Ġre un", "ĠF ace", "ĠHall ow", "ĠBud get", "Ġcondem n", "Ġt ender", "Pro f", "ocr atic", "ĠTurn er", "ĠAg ric", "Ġ19 76", "Ġa pt", "d isc", "ĠF ighter", "ĠA ur", "Ġgar bage", "in put", "ĠK arl", "ĠOl iver", "ĠL anguage", "k n", "N on", "ĠCl ar", "Ġtrad itions", "Ġad vertisement", "ĠS or", "Ġarch ive", "Ġvill ages", "7 50", "Ġimplement ing", "w aukee", "Ġdiet ary", "Ġswitch ing", "Rep ublic", "Ġvel ocity", "Ġc it", "ĠA wards", "Ġfin ancing", "Ġlast ed", ") ]", "Ġrem inder", "P erson", "Ġprec ision", "Ġdesign ers", "ĠF ried", "ĠB order", "Ġtr agic", "Ġw ield", "Ġiniti atives", "ĠT ank", "w er", "Ġjo ins", "R o", "in ery", "Ġar row", "Ġgener ating", "found er", "Ġsear ches", "Ġrandom ly", "A ccess", "Ġb atch", "Ġp osed", "l at", "Ġpursu ing", "as a", "Ġtest ified", "form ing", "ĠSh ar", "w iki", "ĠE ither", "S ometimes", "Ġsen ators", "ĠJohn ny", "ĠTal iban", "ĠG PS", "\":\" /", "ãģ® å", "Ġanaly zed", "ĠRub io", "ĠMove ment", "op ard", "ii i", "St and", "f ight", "Ġign oring", "i ang", "ĠG N", "so ever", "ĠST AT", "Ġref using", "Ġswe at", "Ġb ay", "P ORT", "ir med", "ak y", "Ġdis pro", "Ġlabel ed", "Ġ10 8", "H ello", "Ġple asant", "ab a", "Ġtri umph", "Ġab oard", "Ġinc om", "ĠC row", "le tt", "Ġfol k", "Ġch ase", "` `", "ĠBr us", "Ġte ens", "c ue", "Ġter rain", "h yd", "il ight", "OR Y", "Su pport", "ew s", "ll i", "rain ts", "ĠC and", "Ġab used", "ach ment", "l arg", "B as", "ĠC ancer", "Ġ19 78", "Ġsupp orter", "ac cess", "ĠTer min", "ĠT ampa", "ĠAN Y", "Ġnew est", "ĠCrim inal", "ed u", "Ġ19 30", "Ġadm its", "Ġend e", "Ġfail ures", "ur ate", "ful ness", "cy cl", "ĠSub ject", "Ġinf inite", "th ree", "W A", "p it", "ĠInst all", "R ad", "ili ation", "G M", "Ġcontin ent", "Ġaccommod ate", "ĠCl ay", "Ġp up", "ĠF unction", "Ġham mer", "ĠAlbert a", "Ġrev ised", "Ġminor ities", "Ġmeasure ment", "Con nell", "Ġdis able", "ĠM ix", "In cre", "Ġfor k", "ĠR osen", "Ġimpl ies", "umb lr", "AN G", "Ġprote ins", "Ġagg ression", "Ġfacilit ate", "S N", "Ġilleg ally", "u er", "Ġacad em", "Ġp uzz", "ĠSh ift", "p ay", "oll o", "Ġaud iences", "B uild", "Ġno ble", "Ġsynt ax", "â ĺħ", "Ġbe am", "ĠB ed", "ĠA ld", "Ġorig ins", "v ideo", "Ġ19 77", "ĠAss ault", "Ġgar age", "Te am", "Ġver dict", "Ġd war", "ĠVirt ual", "e vent", "Ke ep", "Ġsent iment", "Ġwild life", "sh irt", "Ġb urg", "Ġrecommend ation", "rep resent", "Ġgall ery", "own ers", "Ġsch olar", "Ġconven ience", "ĠSw ift", "Ġconv inc", "C ap", "Ġwar fare", "ĠVis ual", "Ġconst itute", "Ġab ort", "ĠWe ather", "ĠLook ing", "ĠH em", "Ġmart ial", "Ġinc oming", "et ition", "Ġtoler ance", "ĠCre ated", "Ġfl ows", "ĠE lder", "Ġsoul s", "Ġf oul", "ĠP ain", "ĠC AN", "Ġ2 20", "b c", "he nd", "Ġgen ius", "R eal", "ĠW r", "omet er", "p ad", "Ġlim iting", "ĠS i", "ĠL ore", "ĠAd ventures", "Ġvar ied", "D isc", "f in", "ĠPerson al", "Ch ris", "Ġinv ented", "Ġd ive", "ĠR ise", "Ġo z", "ĠCom ics", "Ġexp ose", "ĠRe b", "let ters", "s ite", "im ated", "Ġh acking", "Ġeduc ated", "ĠNob ody", "Ġdep ri", "Ġincent ive", "ãĤ ·", "Ġovers ight", "Ġtrib es", "ĠBelg ium", "Ġlicens ing", "our t", "Produ ct", "ah l", "ĠG em", "Ġspecial ist", "Ġc ra", "ann ers", "ĠCor byn", "Ġ19 73", "RE AD", "Ġsum mar", "Ġover look", "ĠApp lication", "Ġin appropriate", "Ġdownload ed", "Q ue", "ĠB ears", "Ġth umb", "ĠChar acter", "ĠReincarn ated", "ĠS id", "Ġdemonstr ates", "s ky", "ĠBloom berg", "ĠAr ray", "ĠRes ults", "ĠFour th", "ĠED T", "ĠO scar", "c end", "Ġ10 6", "ĠN ULL", "ĠH ERE", "m atch", "ĠBr un", "Ġgluc ose", "ie g", "eg u", "Ġcert ified", "Ġrel ie", "Ġhuman itarian", "Ġpr ayers", "K ing", "Ġn an", "h ou", "10 8", "ul u", "Ġrenew able", "Ġdistingu ish", "Ġd ense", "ĠV ent", "ĠPack age", "ĠB oss", "Ġedit ors", "Ġm igr", "T ra", "ĠPet ers", "ĠAr ctic", "200 4", "ĠC ape", "Ġloc ally", "Ġlast ing", "Ġhand y", ". ).", "P an", "ĠR ES", "Ind ex", "Ġt ensions", "Ġformer ly", "Ġide ological", "Ġsens ors", "Ġdeal ers", "Ġdef ines", "S k", "Ġproceed s", "Ġpro xy", "az ines", "ĠB ash", "ĠP ad", "ĠC raft", "eal ous", "Ġshe ets", "omet ry", "J une", "cl ock", "T T", "ĠThe atre", "ĠB uzz", "Ġch apters", "Ġmill enn", "Ġd ough", "ĠCongress ional", "Ġimag ined", "av ior", "Ġclin ic", "Ġ19 45", "Ġhold er", "ro ot", "oles ter", "Ġrest art", "B N", "ĠHam as", "ĠJ ob", "Ġor b", "Ġr am", "Ġdiscl ose", "Ġtransl ate", "Ġimm igrant", "Ġannoy ing", "Ġtreat y", "an ium", "ĠTe a", "ĠLeg ion", "Ġcrowd s", "ĠB ec", "ĠA er", "oh yd", "B ro", "Look ing", "Ġl bs", "Ġagg ress", "Ġse am", "Ġinter cept", "ĠM I", "mer cial", "act iv", "ĠC it", "Ġdim ension", "Ġconsist ency", "Ġr ushing", "ĠDou glas", "Ġtr im", "Inst all", "ick er", "Ġsh y", "10 6", "Ġment ions", "pe lled", "ĠT ak", "c ost", "Ġclass room", "Ġfort une", "dri ven", "Ġun le", "ĠWhe el", "Ġinvest or", "ĠM asters", "k it", "Ġassoci ations", "ĠEv olution", "op ing", "us cript", "Ġprov incial", "ĠWal ter", "av i", "S O", "Ġun limited", "Eng lish", "ĠC ards", "ĠEb ola", "ne red", "Ġreven ge", "Ġout right", "um per", "Ġf itting", "ĠSol id", "Ġform ally", "Ġproblem atic", "Ġhaz ard", "Ġenc ryption", "Ġstraight forward", "ĠA K", "Ġp se", "ĠOr b", "ĠCh amber", "ĠM ak", "Cont ents", "Ġloyal ty", "Ġl yrics", "ĠSy m", "Ġwel comed", "Ġcook ed", "Ġmon op", "Ġn urse", "Ġmis leading", "Ġe ternal", "Ġshif ting", "Ġ+ =", "V is", "Ġinst itutional", "ill ary", "Ġp ant", "VER T", "ĠA CC", "ĠEn h", "Ġinc on", "ĠRE UTERS", "Ġdon ated", "âĢ¦âĢ¦ âĢ¦âĢ¦", "In tern", "Ġexhib it", "Ġt ire", "ĠR ic", "ĠCh ampion", "ĠMu hammad", "N ING", "ĠSoc cer", "Ġmob ility", "Ġvary ing", "ĠM ovie", "Ġl ord", "o ak", "F ield", "Ġve ctor", "us ions", "Ġsc rap", "Ġen abling", "m ake", "T or", ". *", "| |", "ĠWe bsite", "ĠN PC", "Ġsocial ist", "ĠBill y", "ĠAdd itional", "Ġc argo", "Ġfar ms", "ĠSo on", "ĠPri ze", "Ġmid night", "Ġ9 00", "se en", "ĠSp ot", "Ġshe ep", "Ġspons ored", "ĠH i", "ĠJ ump", "Ġ19 67", "Micro soft", "ĠAg ent", "Ġch arts", "d ir", "Ġadj acent", "Ġtr icks", "Ġman ga", "Ġex agger", "/ >", "foot ball", "ĠF CC", "G C", "ĠT ier", "and ra", "OU ND", "% ),", "Ġfru its", "V C", "ĠA A", "R ober", "Ġmid st", "â Ĺ", "ank a", "Ġlegisl ature", "ĠNe il", "Ġtour ists", "\" \"", "ĠWar ning", "ĠNever theless", "ĠOffic ial", "ĠWh atever", "Ġm old", "Ġdraft ed", "Ġsubst ances", "Ġbre ed", "Ġt ags", "ĠT ask", "Ġver b", "Ġmanufact ured", "com ments", "ĠPol ish", "Pro v", "Ġdetermin es", "Ob ama", "k ers", "Ġutter ly", "Ġse ct", "sc he", "ĠG ates", "ĠCh ap", "Ġal uminum", "Ġz ombie", "ĠT ouch", "ĠU P", "Ġsatisf y", "Ġpred omin", "asc ript", "Ġelabor ate", "Ġ19 68", "Ġmeas uring", "ĠV ari", "any ahu", "Ġs ir", "ul ates", "id ges", "ick ets", "ĠSp encer", "T M", "oub ted", "Ġpre y", "Ġinstall ing", "ĠC ab", "re ed", "re ated", "Su pp", "Ġwr ist", "ĠK erry", "10 7", "ĠK le", "ĠR achel", "Ġc otton", "ĠA RE", "ĠE le", "Cont rol", "Ġload s", "ĠD od", "an as", "b one", "Ġclass ical", "ĠReg ional", "ĠInt eg", "V M", "Ġdes ires", "Ġaut ism", "support ed", "ĠM essage", "Ġcomp act", "writ er", "Ġ10 9", "ĠHur ricane", "c ision", "Ġcy cles", "Ġdr ill", "Ġcolle ague", "Ġm aker", "G erman", "Ġmist aken", "S un", "ĠG ay", "Ġwhat soever", "Ġsell s", "ĠA irl", "l iv", "ĠO ption", "Ġsol ved", "Ġse ctors", "Ġhorizont al", "Ġequ ation", "ĠSk ill", "ĠB io", "g ement", "ĠSn ap", "ĠLeg al", "Ġtradem ark", "Ġmake up", "Ġassemb led", "Ġsa ves", "ĠHallow een", "ĠVer mont", "ĠFR OM", "Ġfar ming", "ĠP odcast", "accept able", "ĠHig her", "Ġas leep", "ull ivan", "Ġrefere n", "ĠLe v", "Ġbul lets", "ok o", "H C", "Ġst airs", "Ġmain tains", "ĠL ower", "ĠV i", "Ġmar ine", "Ġac res", "Ġcoordin ator", "ĠJ oh", "Ġcounterpart s", "ĠBrother s", "Ġind ict", "b ra", "Ġch unk", "Ġc ents", "H ome", "ĠMon th", "Ġaccording ly", "if les", "ĠGerm ans", "ĠSy n", "H ub", "Ġey eb", "âĶĢâĶĢ âĶĢâĶĢ", "Ġr anges", "ĠHoll and", "ĠRob ot", "f c", "M ike", "Ġpl asma", "Ġsw ap", "Ġath lete", "ĠR ams", ",' \"", "Ġinfect ions", "Ġcor rid", "Ġv ib", "Ġpat ches", "Ġtradition ally", "Ġrevel ation", "Ġswe ep", "Ġgl ance", "Ġin ex", "200 3", "ĠR aw", "work ing", "os ures", "ĠD at", "ĠLyn ch", "Ġle verage", "ĠRe id", "Ġcorrel ation", "ian ces", "av ascript", "Ġrep ository", "ret ty", "Ġ19 72", "24 0", "Ġo un", "p ol", "ĠRe ed", "Ġtact ical", "is ite", "App le", "ĠQu inn", "Ġrap ed", "ill o", "Euro pe", "Ġalgorith ms", "ĠRod rig", "i u", "Ġill um", "Ġf ame", "Ġintrodu cing", "Ġdel ays", "ĠRaid ers", "Ġwh istle", "Ġnovel s", "ĠRe ally", "Ġder iv", "Ġpublic ations", "ĠNe ither", "ĠCom merce", "Ġa ston", "l anguage", "Not es", "ĠR oth", "ĠF ear", "Ġm ate", "Ġpar ade", "ĠQ B", "Ġman eu", "ĠC incinnati", "m itting", "Ġwa ist", "ĠR ew", "Ġdisc ont", "Ð °", "Ġst aring", "Ġal ias", "Ġsec urities", "Ġtoile t", "ĠJ edi", "Ġun law", "v ised", "//// ////", "] (", "ĠWe iss", "Ġpre st", "ĠComp an", "Ġmem o", "ĠGr ace", "J uly", "ĠEl ite", "cent er", "ĠSt ay", "Ġgal axy", "Ġto oth", "ĠS ettings", "Ġsubject ed", "ãĤ ¦", "Ġline back", "Ġretail ers", "ĠW ant", "Ġd angers", "A ir", "Ġvolunt ary", "ew ay", "Ġinterpret ed", "ot ine", "à §", "Ġp el", "Serv ice", "ĠEvent ually", "Ġcare ers", "Ġthreat en", "Ġmem or", "ĠBrad ley", "anc ies", "s n", "ĠUn known", "N ational", "Ġsh adows", "ail and", "ĠD ash", "Every one", "izz ard", "M arch", "= (", "Ġpull s", "Ġstr anger", "Ġback wards", "ĠBern ard", "imens ional", "Ġch ron", "Ġtheoret ical", "k top", "Ġw are", "ĠInvest ig", "ĠIn iti", "ĠOper ations", "o ven", "oc ide", "* /", "Ġfl ames", "ĠC ash", "sh it", "Ġc ab", "ĠAn aly", "ĠSe ah", "Ġdefin ing", "Ġorder ing", "Ġimm un", "Ġpers istent", "AC H", "Russ ian", "m ans", "Ġh ind", "Ġphot ography", " ©", "Ġh ug", "Ġ10 7", "ĠH ence", "i ots", "ude au", "Ġsubsid ies", "Ġroutine ly", "ĠDev ice", "it ic", "Ġdisg ust", "land er", "Ġ19 40", "Ġassign ment", "ĠB esides", "w ick", "ĠD ust", "us c", "struct ed", "11 1", "de velop", "Ġf ond", "Ġinter section", "Ġdign ity", "Ġcommission er", "With out", "re ach", "Ġcart oon", "Ġsc ales", "ãĥ Ń", "F IG", "Ġsurve ys", "ĠIndones ia", "Ġart work", "Ġun ch", "Ġcy cling", "un ct", "au er", "or ate", "ĠOb viously", "Ġcharacter ized", "fe ld", "Ġaff irm", "Ġinn ings", "Ġ é", "Ġal iens", "Ġcl oth", "et ooth", "ĠC ertain", " §", "Ġdig est", "k now", "ĠX L", "Ġpredict ions", "Ġd in", "W AR", "Ġafter math", "Ex ample", "ĠSu ccess", "ĠTh r", "IG N", "Ġmin er", "B us", "Ġcl arity", "heim er", "ĠO UT", "ĠS end", "ĠCirc le", "ĠD iet", "Ġpron ounced", "Ġcreat ors", "Ġearthqu ake", "atter y", "ge ons", "Ġo d", "Ġlay ing", "or p", "U lt", "pro ject", "Ġunder min", "Ġsequ el", "S am", "ĠDark ness", "Ġre ception", "b ull", "Y S", "ĠV ir", "Ġsequ ences", "ĠCo in", "Ġout fit", "ĠW ait", "1 19", "Ġdel ivers", ".... ..", "Ġbl own", "ĠE sc", "ĠM ath", "per m", "ĠU l", "Ġgl im", "Ġfac ial", "Ġgreen house", "Ġto kens", "/ -", "ĠAnn ual", "ĠON E", "Ġteen age", "ĠPhys ical", "ĠL ang", "ĠC elt", "Ġsu ed", "ivid ually", "Ġpat ience", "ch air", "reg ular", "Ġa ug", "in v", "ex cept", "ĠL il", "Ġn est", "f d", "s um", "ĠCh ase", "Russ ia", "ĠJenn ifer", "Ġoff season", "Over all", "F ore", "Ġr iot", "A ud", "form er", "Ġdefend ers", "ĠC T", "iot ic", "rib ly", "Ġautom ated", "Ġpen is", "Ġins ist", "Ġdi agram", "ĠS QL", "ĠG arc", "Ġw itch", "cl ient", "ier ra", "am bers", "Ġrec ount", "f ar", "V ery", "oster one", "Ġappreci ated", "ĠPer fect", "S ection", "Ġd oses", "oca ust", "Ġcost ly", "Ġg rams", "ĠSh i", "Ġwrest ling", "Ġ19 71", "Ġtro phy", "Ġn erve", "ĠK az", "ĠExper ience", "Ġpled ged", "Ġplay back", "Ġcreat ivity", "by e", "Ġattack ers", "Ġhold ers", "ĠCo ach", "ĠPh D", "Ġtransf ers", "Ġcol ored", "ĠH indu", "Ġd rown", "Ġlist ened", "ĠW A", "ias m", "P O", "Ġappeal ing", "Ġdiscl osed", "ĠCh icken", "ag ging", "Ġple aded", "Ġnav igation", "ĠReturn s", "Ġ[ [", "R OR", "E A", "Ġphotograp her", "ĠR ider", "ipp ers", "Ġsl ice", "Ġe rect", "Ġhe d", "iss ance", "ĠVik ings", "ur ious", "Ġapp et", "oubted ly", "Ch ild", "Ġauthent ic", "o os", "ĠM aking", "Ġannoun cing", "Ġb od", "Ġmet er", "ĠN ine", "ĠR ogue", "Ġwork force", "Ġrenew ed", "Ġorganis ations", "ac s", "P LE", "Sh ort", "Ġcomp ounds", "ĠVis it", "Ġen velop", "ear th", "Ġsupport ive", "gg le", "ĠBrus sels", "ĠGu ild", "Cre ate", "RE L", "Ġaver aged", "Ġ19 69", "ri ages", "Ġlength y", "Ġforg ot", "O kay", "ĠE rd", "Ġdeal er", "Ġrec ession", "D D", "Ġdesper ately", "Ġhun ger", "Ġst icks", "Ġm ph", "ĠF aith", "Ġintention ally", "Ġdem ol", "ue ller", "ĠS ale", "Ġde bris", "s pring", "Ġle ap", ">> >>", "Ġcontain ers", "se lling", "rane an", "atter ing", "Ġcomment ed", "ĠC M", "on ut", "Ġwood s", "es pecially", "Ġorgan ize", "iv ic", "ĠWood s", "ang a", "s qu", "Ġm aj", "am on", "Ġax is", "Ġ19 74", "ĠDen mark", "Ġwar rior", "ĠP and", "Ġout lined", "ĠB O", "ins ula", "z illa", "eb ook", "Ġd are", "Ġsear ched", "Ġnav igate", "S n", "writ ing", "Ġun ited", "J apan", "ĠHe brew", "Ġfl ame", "Ġrel ies", "Ġcatch ing", "ĠSh o", "Ġimprison ment", "Ġp ockets", "Ġclos ure", "ĠF am", "t im", "ade qu", "Act ivity", "Ġrecru iting", "ĠW ATCH", "ĠArgent ina", "d est", "Ġapolog ize", "or o", "Ġlack s", "Ġtun ed", "ĠGriff in", "Ġinf amous", "Ġcelebr ity", "ss on", "Ġ ----------------------------------------------------------------", "ĠIs is", "ĠDis play", "Ġcred ibility", "Ġeconom ies", "Ġhead line", "ĠCow boys", "Ġind ef", "Ġl ately", "Ġincent ives", "but ton", "ĠM ob", "A ut", "Ġres igned", "ĠO m", "c amp", "Ġprof iles", "Ġsche mes", "olph ins", "ay ed", "Cl inton", "en h", "ĠY ahoo", "Ġab st", "Ġan k", "su its", "Ġw ished", "ĠMar co", "udd en", "Ġsp here", "ĠB ishop", "Ġincorpor ated", "ĠPl ant", "11 4", "Ġh ated", "p ic", "Ġdon ate", "Ġl ined", "Ġbe ans", "Ġsteal ing", "Ġcost ume", "Ġsher iff", "Ġfor ty", "Ġint act", "Ġadapt ed", "Ġtrave lling", "b art", "Ġnice ly", "Ġdri ed", "Ġsc al", "os ity", "NOT E", "ĠB h", "ĠBron cos", "ĠI gn", "Ġint imate", "Ġchem istry", "Ġopt imal", "D eb", "ĠGener ation", "Ġ] ,", "ich i", "ĠW ii", "ĠYOU R", "vent ions", "W rite", "Ġpop ul", "un ning", "ĠW or", "V ol", "Ġqu een", "head s", "K K", "Ġanaly ze", "op ic", "ear chers", "Ġd ot", "leg raph", "ast ically", "Ġupgr ades", "Ġca res", "Ġext ending", "Ġfree ze", "Ġin ability", "Ġorg ans", "Ġpret end", "Ġout let", "11 3", "ol an", "ĠM all", "ul ing", "t alk", "Ġexpress ing", "ĠAl ways", "ĠBe gin", "f iles", "Ġlic enses", "% %", "ĠM itt", "Ġfil ters", "ĠMil waukee", "G N", "Ġunf old", "M o", "Ġnut rition", "pp o", "B o", "Ġfound ing", "Ġunder mine", "Ġeas iest", "ĠC zech", "ĠM ack", "Ġsexual ity", "ĠN ixon", "W in", "ĠAr n", "ĠK in", "ãĤ £", "ic er", "Ġfort un", "Ġsurf aces", "agh d", "Ġcar riers", "ĠP ART", "ĠT ib", "Ġinter val", "Ġfrust rating", "ĠSh ip", "ĠAr med", "ff e", "Ġbo ats", "ĠAb raham", "in is", "Ġsu ited", "th read", "i ov", "ab ul", "ĠVenezuel a", "Ġto m", "su per", "Ġcast le", "alth ough", "iox ide", "ec hes", "Ġevolution ary", "Ġnegoti ate", "Ġconfront ed", "Rem ember", "Ġ17 0", "S uch", "Ġ9 11", "m ult", "ĠA byss", "ur ry", "ke es", "spe c", "ĠBarb ara", "Ġbelong ing", "Ġvill ain", "ist ani", "Ġaccount able", "Ġport ions", "ĠDe cl", "U r", "ĠK ate", "g re", "Ġmag azines", "UC K", "Ġregul ate", "om on", "ĠAl most", "Ġover view", "Ġsc ram", "Ġl oot", "ĠF itz", "Ġcharacter istic", "ĠSn ake", "s ay", "ĠR ico", "Ġtra it", "ĠJo ined", "au cus", "Ġadapt ation", "ĠAirl ines", "Ġarch ae", "ĠI de", "Ġb ikes", "Ġliter ary", "Ġinflu ences", "ĠUs ed", "C reat", "Ġple a", "ĠDef ence", "ĠAss ass", "Ġp ond", "UL T", ") \"", "Ġeval uated", "Ġob taining", "Ġdem ographic", "Ġvig il", "ale y", "Ġsp ouse", "ĠSeah awks", "resp ons", "ĠB elt", "um atic", "Ġr ises", "run ner", "ĠMichel le", "Ġpot ent", "r ace", "ĠP AC", "F ind", "olester ol", "IS S", "ĠIntrodu ced", "ress es", "ign ment", "O s", "ĠT u", "ĠDe x", "ic ides", "Ġspark ed", "ĠLaur a", "ĠBry ant", "Ġsm iling", "ĠNex us", "Ġdefend ants", "ĠCat al", "Ġdis hes", "sh aped", "Ġpro long", "m t", "( $", "ãĢ Ĥ", "Ġcalcul ations", "ĠS ame", "Ġp iv", "H H", "Ġcance lled", "Ġgr in", "Ġterrit ories", "ist ically", "C ome", "ĠP arent", "Pro ject", "Ġneg lig", "ĠPriv acy", "Ġam mo", "LE CT", "olute ly", "ĠEp ic", "Ġmis under", "w al", "Apr il", "m os", "path y", "ĠC arson", "Ġalbum s", "ĠE asy", "Ġpist ol", "< <", "Ġ\\ (", "t arget", "hel p", "Ġinter pre", "cons cious", "ĠH ousing", "ĠJ oint", "12 7", "Ġbe ers", "s cience", "ĠFire fox", "effect ive", "ĠC abin", "ĠO kay", "ĠApp lic", "Ġspace craft", "ĠS R", "ve t", "ĠStr ange", "S B", "Ġcor ps", "iber al", "e fficient", "Ġpreval ence", "Ġeconom ists", "11 8", "Th read", "ord able", "OD E", "ĠC ant", "=- =-", "if iable", "ĠA round", "Ġpo le", "Ġwilling ness", "CL A", "ĠK id", "Ġcomple ment", "Ġsc attered", "Ġin mates", "Ġble eding", "e very", "Ġque ue", "ĠTr ain", "Ġh ij", "Ġme lee", "ple ted", "Ġdig it", "Ġg em", "offic ial", "Ġlif ting", "Ð µ", "Re qu", "it utes", "Ġpack aging", "ĠWork ers", "h ran", "ĠLeban on", "ol esc", "Ġpun ished", "ĠJ uan", "Ġj am", "ĠD ocument", "Ġm apping", "ic ates", "Ġinev itably", "Ġvan illa", "ĠT on", "Ġwat ches", "Ġle agues", "Ġiniti ated", "deg ree", "port ion", "Ġrec alls", "Ġru in", "Ġm elt", "I AN", "Ġhe m", "Ex p", "Ġb aking", "ĠCol omb", "at ible", "Ġrad ius", "pl ug", "ĠI F", "et ically", "Ġf ict", "H ER", "ĠT ap", "atin um", "Ġin k", "Ġco h", "ĠW izard", "b oth", "te x", "Ġsp ends", "ĠCurrent ly", "ĠP it", "Ġneur ons", "ig nt", "Ġr all", "Ġbus es", "b uilding", "Ġadjust ments", "Ġc ried", "ibl ical", "att ed", "ĠZ ion", "ĠM atter", "Ġmed itation", "ĠD ennis", "Ġour s", "ĠT ab", "Ġrank ings", "ort al", "Ġad vers", "Ġsur render", "ĠG ob", "ci um", "om as", "im eter", "Ġmulti player", "Ġhero in", "Ġoptim istic", "Ġindic ator", "ĠBr ig", "Ġgro cery", "Ġapplic ant", "ĠRock et", "v id", "Ex ception", "p ent", "Ġorgan izing", "Ġenc ounters", "ĠT OD", "Ġjew el", "S ave", "ĠChrist ie", "Ġhe ating", "Ġl azy", "ĠC P", "Ġcous in", "Con fig", "Ġreg ener", "Ġne arest", "Ġachie ving", "EN S", "th row", "ĠRich mond", "ant le", "200 2", "Ġan ten", "b ird", "13 3", "Ġn arc", "r aint", "un ny", "ĠHispan ic", "ourn aments", "Ġprop he", "ĠTh ailand", "ĠT i", "Ġinject ion", "Ġinher it", "rav is", "Ġmed i", "Ġwho ever", "ĠDE BUG", "G P", "ĠH ud", "C ard", "p rom", "Ġp or", "Ġover head", "L aw", "Ġviol ate", "Ġhe ated", "Ġdescript ions", "Ġachieve ments", "ĠBe er", "ĠQu ant", "W as", "Ġe ighth", "ĠI v", "Ġspecial ized", "U PDATE", "ĠD elta", "P op", "J ul", "ĠAs k", "oph y", "Ġnews letters", "ĠT ool", "Ġg ard", "ĠConf eder", "ĠGM T", "ĠAb bott", "Ġimm unity", "ĠV M", "Is lam", "Ġimpl icit", "w d", "Ġ19 44", "rav ity", "omet ric", "Ġsurv iving", "ur ai", "ĠPr ison", "Ġr ust", "ĠSk etch", "Ġbe es", "ĠThe ory", "Ġmer it", "T ex", "ch at", "Ġm im", "Ġpast e", "ĠK och", "Ġignor ance", "ĠSh oot", "Ġbas ement", "Un ited", "ĠAd vis", "he ight", "Ġf oster", "Ġdet ain", "in formation", "Ġne ural", "' ;", "Ġprov es", "all ery", "Ġinv itation", "um bers", "Ġc attle", "Ġbicy cle", "z i", "Ġconsult ant", "Ġap ology", "ĠT iger", "Ġ12 3", "99 9", "Ġind ividually", "r t", "ig ion", "ĠBrazil ian", "Ġdist urb", "Ġentreprene urs", "Ġfore sts", "cer pt", "pl ates", "p her", "clip se", "Ġtw itter", "Ġac ids", "ograph ical", "h um", "ĠB ald", "if ully", "Ġcomp iler", "ĠD A", "Ġdon or", "as i", "Ġtrib al", "l ash", "ĠCon fig", "Ġapplic ants", "Ġsal aries", "13 5", "Put in", "ĠF ocus", "ir s", "Ġmisc onduct", "ĠH az", "Ġeat en", "M obile", "Mus lim", "ĠMar cus", "v iol", "Ġfavor able", "Ġst ub", "ad in", "ĠH ob", "Ġfaith ful", "Ġelectron ics", "Ġvac uum", "w ait", "back ed", "econom ic", "d ist", "Ġten ure", "Ġsince re", "ĠT ogether", "ĠW ave", "Ġprog ression", "Ġden ying", "Ġdist ress", "br aska", "th ird", "Ġmix ing", "Ġcolon ial", "Ġpriv ately", "Ġun rest", "atern ity", "Ġprem ises", "ant i", "greg ation", "Ġlic ence", "ĠH ind", "ĠSam uel", "Ġconvinc ing", "ĠA ce", "ĠR ust", "ĠNet anyahu", "Ġhand les", "ĠP atch", "orient ed", "ah o", "ĠG onz", "Ġhack ers", "claim er", "Ġcustom s", "ĠGr an", "f ighters", "Ġl uc", "Ġman uscript", "aren thood", "Ġdev il", "Ġwar riors", "Ġoff enders", "Will iam", "Ġhol idays", "Ġnight mare", "Ġle ver", "iff erent", "St at", "Ġexhib ition", "put ed", "ĠP ure", "Ġal pha", "Ġenthus iasm", "ĠRepresent atives", "E AR", "ĠT yp", "Ġwhe at", "ĠAl f", "Ġcor rection", "Ġev angel", "AT T", "M iss", "Ġs oup", "Ġimpl ied", "par am", "Ġsex y", "ĠL ux", "Ġrep ublic", "p atch", "ab lish", "Ġic ons", "Ġfather s", "ĠG ET", "ĠCar ib", "Ġregul ated", "ĠCo hen", "ĠBob by", "Ġn er", "Ġb ent", "vent ory", "ĠAl ong", "ĠE ST", "ĠWall ace", "Ġmurd ers", "r ise", "ke ll", "ĠCommon wealth", "Ġn asty", "et a", "ĠM IT", "Ġadminist ered", "Ġgenuine ly", "Ed itor", "n ick", "Ġhyd ro", "**************** ****************", "ĠB le", "Ġfin es", "Ġg orge", "aus ible", "r h", "Ġapp le", "ment ioned", "Ġro pe", "ot yp", "H R", "Ġdisappoint ing", "Ġc age", "n ik", "Ġdoub ts", "ĠF REE", "print s", "ĠM UST", "Ġvend ors", "ĠIn qu", "Ġliber als", "Ġcontract or", "Ġup side", "child ren", "Ġtrick y", "Ġregul ators", "charg ed", "l iter", "Ġ ***", "Ġreb ell", "l ang", "Ġloc als", "Ġphys icians", "Ġhe y", "ar se", "t m", "ĠLe x", "Ġbehavior al", "success ful", "F X", "Ġbr ick", "ov ic", "Ġcon form", "Ġreview ing", "Ġins ights", "Ġbi ology", "ĠRem ove", "ĠExt ra", "Ġcomm itting", "indu ced", "ignt y", "ig m", "Ġat omic", "Comm on", "ĠE M", "ĠP ere", "ĠIt ems", "e h", "Ġpres erved", "ĠH ood", "Ġprison er", "Ġbankrupt cy", "Ġg ren", "us hes", "Ġexplo itation", "Ġsign atures", "Ġfin an", "] ,\"", "ĠM R", "Ġme g", "rem lin", "Ġmusic ians", "Ġselect ing", "Ġexam ining", "IN K", "l ated", "H i", "Ġart ic", "Ġp ets", "Ġimp air", "ĠM AN", "Ġtable ts", "in clude", "R ange", "Ġca ut", "Ġlog s", "Ġmount ing", "Ġun aware", "Ġdynam ics", "ĠPalest ine", "ĠQu arter", "ĠPur ple", "Ġm a", "ĠIm port", "Ġcollect ions", "ci ation", "Ġsuccess or", "Ġcl one", "Ġaim ing", "Ġposs essed", "Ġstick ing", "Ġsh aking", "Ġloc ate", "ĠH ockey", "T urn", "17 0", "Ġfif teen", "ĠHar rison", "Ġcontinu ously", "ĠT C", "ĠVal ent", "ĠRes cue", "Ġby pass", "am ount", "Ġm ast", "Ġprotect s", "Ġart istic", "Ġsomet ime", "Ġsh oe", "Ġshout ed", "ific ant", "et itive", "ĠReg ister", "ĠJ in", "Ġconcent rated", "ling ton", "on ies", "Ġgener ator", "yr im", "ĠAr men", "Ġclear ing", "id o", "ĠT W", "al ph", "Ġlad ies", "H ard", "Ġdial og", "Ġinput s", "æ ľ", "Ġpos es", "Ġsl ots", "ĠPrem ium", "Ġle aks", "Ġboss es", "Ġ11 3", "c ourse", "A cc", "ĠNew ton", "ĠAust ria", "ĠM age", "Ġte aches", "ab ad", "Ġwe ars", "Ġc yl", "Ġcur se", "ĠS ales", "ĠW ings", "Ġp sy", "Ġg aps", "ĠIce land", "ĠP interest", "Ġland lord", "Ġdefin itions", "ĠK er", "Ġsufficient ly", "ĠP ence", "ĠArch itect", "Ġsur pass", "Ġ11 4", "Ġsuper hero", "ĠDise ase", "Ġpri ests", "ĠC ulture", "Ġdefin itive", "Ġsecret ly", "ĠD ance", "inst all", "ch ief", "ĠJess ica", "W ould", "Up dated", "Ġlock er", "ĠK ay", "Ġmem orial", "è ¦", "f at", "Ġdis gu", "Ġflav ors", "ĠBase ball", "ĠRes istance", "Ġk icks", "Ġen v", "Ġteen agers", "D ark", "ĠC AR", "Ġh alt", "ĠL G", "ĠGab riel", "Ġfe ver", "Ġs atur", "Ġm all", "Ġaffili ate", "ĠS leep", "ĠSpe cific", "ĠV el", "Ġj ar", "ĠSac red", "ĠEd wards", "ĠA CL", "Ġret ained", "ĠG iant", "Ġlim itation", "in ces", "Ġref usal", "ĠT ale", "ĠBut ler", "Ġacc idents", "ĠC SS", "Ġimport ed", "ĠCop y", "Î ±", "ER T", "z el", "Ġdiv isions", "h ots", "ĠAl b", "ĠD S", "Load er", "W ashington", "at isf", "ĠCreat ive", "\\ .", "ĠAut om", "red ict", "Ġrecept or", "ĠCarl os", "Met hod", "ok a", "Ġmal icious", "Ġste pping", ", [", "ĠD ad", "Ġatt raction", "ĠEffect s", "ĠPir ate", "ĠC er", "ĠIndust ry", "ĠR ud", "Ġchar ter", "Ġd ining", "Ġins ists", "Ġconfig ure", "Ġ( #", "ĠSim ple", "ĠSc roll", "UT C", "17 5", "ĠK on", "Ġmarket place", "Ġ ãĤ", "Ġref res", "Ġg ates", "er red", "ĠP od", "Ġbeh ave", "Fr ank", "n ode", "Ġendors ed", "he tt", "as ive", "ĠHom eland", "Ġr ides", "ĠLe ave", "er ness", "Ġflood ing", "A FP", "Ġris en", "Ġcontin ually", "Ġun anim", "ĠCont ract", "ĠP as", "Ġgu ided", "ĠCh ile", "b d", "Ġsu cc", "pt ic", "Ġcomm ittees", "ĠL uther", "ĠAny one", "Ġs ab", "12 4", "Ġp ixel", "ĠB ak", "ĠT ag", "ĠBenn ett", "En ter", "sm all", "ĠPresident ial", "Ġp ul", "Ġcontr ace", "arch ive", "Ġcoast al", "ĠK ids", "19 2", "âĢ ²", "ick y", "ING TON", "Ġw olf", "ĠSt alin", "T ur", "id get", "am as", "ĠUn less", "Ġspons or", "Ġmor ph", "ĠCho ose", "Ġrun ner", "Ġun bel", "Ġm ud", "ĠMan a", "Ġdub bed", "Ġg odd", "ure rs", "wind ow", "Ġrel ied", "Ġcelebr ating", "os c", "Ġ13 5", "Ġlobb ying", "Ġincom plete", "Ġrestrict ion", "Ġinc ap", "it us", "Ġexpect ation", "ĠAp ollo", "Ġint ens", "Ġsyn c", "G H", "Ġmanip ulation", "B Y", "Ġspe ar", "Ġbre asts", "Ġvol can", "il ia", "M aterial", "Ġform ats", "ĠB ast", "Ġparliament ary", "Ġsn ake", "Ġserv ants", "ĠTr udeau", "ĠGr im", "ĠArab ic", "ĠSC P", "ĠBoy s", "st ation", "Ġprospect ive", "ord e", "in itialized", "Ġb ored", "AB LE", "Ġaccess ed", "Ġtax i", "ĠShe ll", "aid en", "urs ed", "in ates", "ĠIns urance", "ĠPet e", "Sept ember", "6 50", "Ġad ventures", "ĠCo ver", "Ġt ribute", "Ġsk etch", "Ġem power", "Ġ Ø", "ĠGl enn", "ĠD aw", "= \\\"", "ĠPolit ics", "Ġgu ides", "Ġd ioxide", "ĠG ore", "ĠBr ight", "ĠS ierra", "Ġval ued", "c ond", "Ġpo inter", "Se lect", "Ġrisk y", "Ġabsor b", "im ages", "Ġref uses", "Ġbon uses", "__ _", "Ġh ilar", "ĠF eatures", "2 20", "ĠCollect or", "F oot", "Ġ19 64", "cul us", "Ġd awn", "Ġwork out", "ĠL O", "Ġphilosoph ical", "ĠSand y", "ĠYou th", "Ġl iable", "A f", "bl ue", "Ġovert urn", "less ness", "ĠTrib une", "ĠIn g", "Ġfact ories", "Ġcat ches", "Ġpr one", "Ġmat rix", "Ġlog in", "Ġin acc", "Ġex ert", "s ys", "Ġneed le", "ĠQ ur", "Ġnot ified", "ould er", "t x", "Ġremind s", "Ġpublisher s", "Ġn ort", "Ġg it", "Ġfl ies", "ĠEm ily", "Ġflow ing", "ĠAl ien", "ĠStr ateg", "Ġhard est", "Ġmod ification", "AP I", "ĠM Y", "Ġcr ashes", "st airs", "n umber", "Ġur ging", "ch annel", "ĠFal con", "Ġinhabit ants", "Ġterr ifying", "Ġutil ize", "Ġban ner", "Ġcig arettes", "Ġsens es", "ĠHol mes", "Ġpract ition", "ĠPhill ips", "ott o", "Ġcomp ile", "Mod el", "ĠK o", "Ġ[ ]", "Americ ans", "ĠTer ms", "Ġmed ications", "ĠAn a", "Ġfundament ally", "ĠNot ice", "Ġwe aker", "Ġ 0000", "Ġgar lic", "Ġout break", "Ġeconom ist", "ĠB irth", "Ġobst acles", "ar cer", "ĠOr thodox", "Ġplace bo", "ĠC rew", "asp berry", "ĠAng els", "Ġdis charge", "Ġdestruct ive", "11 7", "ĠR ising", "Ġd airy", "l ate", "Ġcoll ision", "ĠTig ers", "ean or", "ocument ed", "ĠIn valid", "Ġd ont", "ĠL iter", "ĠV a", "Ġhyd rogen", "Ġvari ants", "ĠBrown s", "Ġ19 65", "Ġind igenous", "Ġtrad es", "Ġremain der", "Ġswe pt", "ĠImp act", "Ġred ist", "Ġun int", "grad uate", "ãĥ ķ", "ĠW ILL", "ãģ® ç", "ĠCrit ical", "Ġf isher", "Ġv icious", "Ġrevers ed", "Y ear", "ĠS ox", "Ġshoot ings", "Ġfil ming", "Ġtouchdown s", "ai res", "m el", "Ġgrand father", "Ġaffect ion", "ing le", "Ġover ly", "Add itional", "Ġsup reme", "ĠGr ad", "Ġsport ing", "Ġmer cy", "ĠBrook s", "ount y", "Ġperform s", "Ġtight ly", "Ġdem ons", "Ġkill ings", "Ġfact ion", "ĠNov a", "aut s", "Ġund oubtedly", "ar in", "Ġunder way", "ra k", "Ġl iv", "ĠReg ion", "Ġbrief ing", "s ers", "cl oud", "ĠM ik", "us p", "Ġpred iction", "az or", "Ġport able", "ĠG and", "Ġpresent ing", "Ġ10 80", " »", "ush i", "ĠSp ark", "there um", "Ġjust ification", "ĠN y", "Ġcontract ors", "ming ham", "ĠSt yle", "å ħ", "ĠChron icles", "ĠPict ure", "Ġprov ing", "Ġw ives", "set t", "Ġmole cules", "ĠFair y", "Ġconsist ing", "Ġp ier", "al one", "in ition", "Ġn ucle", "j son", "Ġg otta", "Ġmob il", "Ġver bal", "ar ium", "Ġmon ument", "uck ed", "Ġ25 6", "T ech", "mine craft", "ĠTr ack", "Ġt ile", "Ġcompat ibility", "as is", "Ġs add", "Ġinstruct ed", "ĠM ueller", "Ġle thal", "Ġhorm one", "Ġor che", "el se", "Ġske let", "Ġentert aining", "Ġminim ize", "ag ain", "Ġunder go", "Ġconst raints", "Ġcig arette", "ĠIslam ist", "Ġtravel s", "ĠPant hers", "l ings", "C are", "Ġlaw suits", "ur as", "Ġcry st", "Ġlow ered", "Ġaer ial", "Ġcomb inations", "Ġha un", "Ġch a", "Ġv ine", "Ġquant ities", "Ġlink ing", "b ank", "Ġso y", "B ill", "ĠAngel a", "Ġrecip ient", "ĠProt est", "Ġs ocket", "Ġsolid arity", "Ġâ Ĩ", "m ill", "Ġvar ies", "ĠPak istani", "Dr agon", "Ġun e", "Ġhor izon", "³³³³ ³³³³", "Ġprov inces", "Ġfrank ly", "Ġenact ed", "not es", "[ '", "Ġ19 2", "ocr acy", "Ġendorse ment", "Ġover time", "Tr ue", "L ab", "lic ted", "ĠD NC", "Ġbe ats", "ĠJam ie", "15 2", "ĠIN T", "Cont act", "Ġaccount ed", "h ash", "ĠPack ers", "p ires", "Ġles bian", "Ġamend ments", "Ġhop eful", "ĠFin land", "Ġspot light", "Ġconfig ured", "Ġtrou bled", "Ġg aze", "ĠCal gary", "Ġrel iability", "Ġins urg", "sw er", "b uy", "ĠSk in", "Ġp ixels", "Ġhand gun", "Ġpar as", "Ġcateg or", "ĠE L", "ĠRe x", "Ind eed", "Ġkind a", "Ġconj unction", "ĠBry an", "ĠMan ufact", "y ang", "Pl us", "S QL", "ish ment", "Ġdom inate", "Ġn ail", "Ġo ath", "Ġeru pt", "ĠF ine", "it bart", "ĠCh ip", "ĠAb d", "ĠN am", "Ġbuy er", "Ġdiss ent", "Le aks", "Cont in", "Ġr ider", "ĠSome one", "Ġill usion", "c in", "ĠBoe ing", "Ġin adequ", "ov ation", "i ants", "Ġreb uild", "4 50", "ĠDest iny", "S W", "ĠT ill", "H it", "ia z", "ĠBang l", "acher s", "ĠRe form", "Ġse gments", "Ġsystem atic", "d c", "ĠConserv atives", "Ġport al", "h or", "ĠDragon bound", "Ġdrag ged", "om o", "Ġthe e", "ad vert", "ĠRep orts", "ĠE t", "Ġbarrel s", "Aug ust", "Ġcompar isons", "Ġhe x", "Ġan throp", "\" [", "bor ough", "ab i", "Ġpict ured", "play ing", "ĠAdd ress", "ĠMir ror", "Sm ith", "Ġt ires", "ĠN PR", "AA AA", "Ġclass ification", "ĠTh an", "ĠH arm", "ĠR A", "Ġreject ion", "min ation", "Ġr anged", "ĠF alls", "D I", "H ost", "ãĤ ´", "ĠEx ample", "list ed", "th irds", "Ġsaf egu", "br and", "Ġprob able", "Can ada", "IT ION", "ĠQ aeda", "Ġch ick", "Ġimport s", "h it", "l oc", "W W", "Ġble w", "Ġany time", "Ġwh oles", "ik ed", "Ġcal culation", "cre ate", "ĠO ri", "Ġupgr aded", "Ġapp ar", "ut ory", "ĠM ol", "B rit", "ĠJ ong", "IN AL", "ĠStart ing", "Ġd ice", "urt le", "Ġre lying", "cl osure", "Ġprof itable", "Ġsl aughter", "ĠMan ual", "c aster", "Ġ\" $", "Ġfe ather", "ĠSim ply", "ie ves", "Ġdeter ior", "ĠPC I", "Ġst amp", "Ġfl aws", "Ġsh ade", "ham mer", "Ġpass port", "Ġcont ing", "am el", "Ġobser vers", "Ġneg lect", "ĠR B", "ĠBrother hood", "Ġskept ical", "f amily", "us k", "Ġemotion ally", "â Ļ", "ĠBet a", "ason able", "id ity", "ĠM ul", "Ġkick ing", "ĠC arm", "oll ah", "VERT IS", "ĠAt hen", "Ġlad der", "ĠBul let", "å £", "00 01", "ĠWild life", "ĠM ask", "ĠN an", "R ev", "Ġun acceptable", "leg al", "Ġcrowd ed", "ag i", "ĠC ox", "j e", "Ġmor ality", "Ġfu els", "Ġc ables", "Ġman kind", "ĠCarib bean", "Ġanch or", "Ġby te", "ĠO ften", "ĠO z", "Ġcraft ed", "Ġhistor ian", "ĠW u", "Ġtow ers", "ĠCitiz ens", "Ġhel m", "Ġcred entials", "Ġsing ular", "ĠJes se", "Ġtack les", "Ġcont empt", "Ġa fore", "ĠSh adows", "Ġn il", "Ġur gent", "app le", "bl ood", "Ġv on", "Ġoff line", "Ġbreat he", "Ġj umps", "Ġirre levant", "ox ic", "om al", "import ant", "J im", "Ġgl oves", "arm ing", "dep th", "Ġtal ents", "ook ie", "ĠS B", "Ġpal m", "uff s", "est a", "IG H", "Ġcan on", "ĠVer izon", "ĠP le", "Ġcou pled", "vel t", "Ġfundra ising", "ĠGet ting", "ĠD LC", "Ġmathemat ical", "ĠH S", "ĠCard inals", "te lling", "Ġspons ors", "Ġ Ï", "ĠBull s", "op tion", "Ġprop ose", "Ġmem orable", "Ġembr aced", "Ġdecl ining", "He alth", "ed a", "Ġ} ;", "Ġsp am", "m ile", "Ġpit cher", "ĠE ight", "Ġcar ing", "ut ic", "ro le", "Ġair line", "ernand ez", "ĠAth let", "Ġcert ification", "ux e", "rig er", "Ġem pir", "Ġsens ation", "Ġdis m", "Ġb olt", "Ġev olve", "H ouse", "Ġconsult ation", "ĠD uty", "Ġtou ches", "ĠN athan", "Ġf aint", "h ad", "\" (", "ĠCons umer", "ĠExt reme", "Ġ12 7", "ĠHer m", "ĠSac rament", "iz oph", "Ġanx ious", "ul ously", "Ġsoc ially", "ĠU TC", "Ġsol ving", "ĠLet ter", "Hist ory", "ed uc", "Pr ice", ") );", "Ġrel oad", "am ic", "Ġp ork", "Ġdisc ourse", "Ġt ournaments", "ai ro", "ĠK ur", "ĠCost a", "Ġviol ating", "Ġinterf ere", "Ġrecre ational", "uff le", "Ġspe eches", "Ġneed ing", "Ġremem bers", "Ġcred ited", "n ia", "f ocused", "amer a", "Ġb ru", "um bs", "ĠCub an", "Ġpreced ing", "Ġnons ense", "ac ial", "Ġsmart phones", "ĠSt ories", "S ports", "ĠEmer gency", "oun cing", "ef ined", "Ġb er", "Ġconsult ing", "Ġm asters", "he astern", ".\" [", "ĠRun ning", "Ġsus cept", "ĠF eng", "Americ a", "pr ises", "st itial", "ĠWeek ly", "ĠGreat er", "mod ules", "if ter", "G raphics", "ul er", "Ġwho lly", "Ġsupp ress", "Ġconce aled", "Ġhapp ily", "Ġaccept s", "ĠEn joy", "Ġr ivers", "ĠEx cept", "2 25", "ĠN HS", "ĠMc Connell", "Ġp ussy", "fer red", "ut able", "Ġatt ain", "Ġ> =", "Ġdepos its", "roph ic", "Ġnot orious", "ĠSh aw", "il itation", "Ġepid emic", "all ic", "Ġsmall est", "ov ich", "Ġaccess ories", "per ties", "Ġsur plus", "ĠMe ch", "Ġamb ig", "ĠImm igration", "Ġch im", "ev al", "Ġpract icing", "ĠMyster y", "Ġdom ains", "ĠSil icon", "app s", "Ġkilomet ers", "e a", "ĠSm ash", "Ġwarrant y", "Ġn ost", "s il", "re v", "J on", "ĠDub lin", "Ġtast es", "Ġb out", "g reat", "er ror", "Ġsw itches", "ĠB apt", "D O", "ok i", "Ġsour ced", "pro du", "Ġattach ment", "ĠIss ue", "ĠQuest ion", "Jo in", "Ġf itted", "Ġunlaw ful", "^ ^", "ere k", "Ġauthent ication", "Ġst ole", "Ġaccount ability", "l abel", "S earch", "Ġal beit", "atic an", "fund ed", "ĠAdd ing", "ĠI Q", "Ġsub mar", "l it", "a que", "ĠLear ning", "Ġint eger", "M aster", "ĠCh rom", "Ġprem ier", "O p", "ĠLi u", "Ġbl essed", "ĠGl obe", "ĠResp onse", "Ġlegit im", "ĠMer kel", "Ġdispos al", " ´", "Ġgau ge", "pe at", "Ġindu ced", "Ġquestion able", "arth y", "ĠV it", "ĠF eed", "U ntil", "U t", "worth y", "R Y", "ĠH erald", "ĠHam mer", "Ġmed al", "ĠR ivers", "ĠH ack", "Ġclar ify", "Ġtrack ed", "Ġautonom ous", "Ġten ant", "ĠQ atar", "er ie", "Ġgr im", "ĠMon itor", "Ġresist ant", "ĠSpe c", "ĠWell s", "N AS", "14 8", "Ġmin ers", "iot ics", "Ġmiss es", "11 6", "g ian", "g it", "ĠE yes", "p res", "Ġgrad uated", "Ġang el", "Ġsyn chron", "Ġefficient ly", "Ġtrans mitted", "H arry", "Ġglob ally", "EN CE", "ĠMont ana", "r aged", "ĠPre vention", "Ġp iss", "ĠL l", "Ġshe lf", "ĠB JP", "ĠTest ament", "ĠL ate", "ik er", "ĠH app", "ĠJul ian", "h all", "Ġsp ont", "Ġshut down", "Ġincons istent", "Ġsubscrib ers", "Ġske leton", "ĠNe braska", "Ġins pire", "ĠV oid", "F eed", "Ġang les", "ĠSpr ings", "Ġbench mark", "Ġvacc ines", "izoph ren", "se xual", "uff ed", "Ġsh ine", "ĠK ath", "Ġgest ure", "ine a", "Ġr ip", "Ġopp ression", "Ġcons cience", "b t", "ĠL um", "Ġinc idence", "ĠF a", "w r", "Ġmin eral", "ĠSp urs", "alk y", "Ġth under", "Ġop io", "Be ing", "ĠPal m", "Ġwas ted", "Ġl b", "i aries", "ĠIniti ative", "Ġcur ric", "Ġmark er", "ĠMc L", "Ġext ensions", "ĠP v", "ĠAr ms", "Ġoffer ings", "Ġdef enses", "Ġvend or", "Ġcontrad ict", "ĠCol in", "Ġredd it", "Ġper ipher", "12 2", "Ġs ins", "E dit", "IC T", "So ft", "ĠSh ah", "Ġadministr ator", "ĠT rip", "Ġporn ography", "Ġtu ition", "in ence", "ĠPro gress", "Ġcat alog", "Ġsu ite", "Ġh ike", "Ġreprodu ctive", "eng ine", "Ġd rought", "ĠNo ah", "Ġ2 30", "Ġd ude", "Ġrelax ed", "Ġpart ition", "Ġparticip ant", "Ġtel esc", "Ġfe as", "ĠF F", "own er", "Ġswe eping", "Ġl enses", "Ġmatch up", "ĠRe pl", "ourn als", "Ġcred ible", "Ġgrand mother", "Ġther mal", "Ġsubscrib ing", "Ġident ities", "col m", "U CT", "Ġreluct ant", "us ers", "ĠC ort", "Ġassist ed", "OS S", "ATION S", "IS H", "Ġpharm aceutical", "ic able", "ad ian", "ĠSon ic", "ĠF ury", "ĠM ong", "A H", "ĠPsych ology", "Ġph osph", "Ġtreat s", "Ń Ķ", "Ġstead ily", "ĠHell o", "Ġrel ates", "Ġcl ue", "Ex pl", "a uth", "Ġrev ision", "Ġe ld", "os ion", "Ġbr on", "14 4", "ri kes", "Ġmin es", "Ġblank et", "ĠF ail", "el ed", "ĠIm agine", "ĠPl anned", "a ic", "Re quest", "M ad", "ĠHor se", "ĠEag le", "Ġcap ac", "15 7", "Ġl ing", "ĠN ice", "ĠP arenthood", "min ster", "og s", "ens itive", "Not hing", "Ġcar n", "F in", "ĠP E", "Ġr ifles", "ĠL P", "S and", "Ġgui Active", "Ġtour ist", "C NN", "Ġunve iled", "Ġpredec essor", "} {", "u ber", "Ġoff shore", "Ġopt ical", "ĠR ot", "ĠPear l", "et on", "Ġst ared", "Ġfart her", "at ility", "cont in", "ĠG y", "ĠF oster", "ĠC oc", "ri ents", "Ġdesign ing", "ĠEconom y", "ON G", "W omen", "ĠN ancy", "er ver", "Ġmas cul", "Ġcasual ties", "Ġ2 25", "ĠS ullivan", "ĠCh oice", "Ġa ster", "w s", "Ġhot els", "Ġconsider ations", "Ġcou ch", "ĠSt rip", "ĠG n", "Ġmanip ulate", "l ied", "Ġsynt hetic", "Ġassault ed", "Ġoff enses", "ĠDra ke", "Ġim pe", "Oct ober", "ĠHer itage", "h l", "ĠBl air", "Un like", "Ġg rief", "Ġ4 50", "Ġopt ed", "Ġresign ation", "il o", "Ġver se", "ĠT omb", "Ġu pt", "Ġa ired", "ĠH ook", "ĠML B", "Ġassum es", "out ed", "ĠV ers", "Ġinfer ior", "Ġbund le", "ĠD NS", "ograp her", "Ġmult ip", "ĠSoul s", "Ġillust rated", "Ġtact ic", "Ġdress ing", "Ġdu o", "Con f", "Ġrel ent", "Ġc ant", "Ġscar ce", "Ġcand y", "ĠC F", "Ġaffili ated", "Ġspr int", "yl an", "ĠGarc ia", "Ġj unk", "Pr int", "ex ec", "C rit", "Ġport rait", "ir ies", "ĠOF F", "Ġdisp utes", "W R", "L ove", "ãģ Ħ", "ĠRe yn", "Ġh ipp", "op ath", "Ġflo ors", "ĠFe el", "Ġwor ries", "Ġsett lements", "ĠP os", "Ġmos que", "Ġfin als", "Ġcr ushed", "ĠPro bably", "ĠB ot", "ĠM ans", "ĠPer iod", "Ġsovere ignty", "Ġsell er", "Ġap ost", "Ġam ateur", "Ġd orm", "Ġconsum ing", "Ġarm our", "ĠRo ose", "Ġint ensive", "Ġelim inating", "ĠSun ni", "ĠAle ppo", "j in", "Ġadv ise", "p al", "ĠH alo", "Ġdes cent", "Ġsimpl er", "Ġbo oth", "ST R", "L ater", "ĠC ave", "== =", "Ġm ol", "Ġf ist", "Ġshot gun", "su pp", "Ġrob bery", "E ffect", "Ġobsc ure", "ĠProf essional", "Ġemb assy", "Ġmilit ant", "Ġinc arcer", "Ġgener ates", "Ġlaun ches", "Ġadministr ators", "Ġsh aft", "Ġcirc ular", "Ġfresh man", "ĠW es", "ĠJo el", "ĠD rew", "ĠDun can", "ĠApp arently", "s ight", "ĠIntern al", "ĠInd ividual", "ĠF E", "Ġb ore", "ĠM t", "Ġbroad ly", "ĠO ptions", "ount ain", "ip es", "ĠV ideos", "20 4", "Ġh ills", "Ġsim ulation", "Ġdisappoint ment", "it an", "ĠLabor atory", "Ġup ward", "Ġbound ary", "Ġdark er", "h art", "Ġdomin ance", "C ong", "ĠOr acle", "ĠL ords", "Ġscholars hip", "ĠVin cent", "ed e", "ĠR ah", "Ġencour ages", "ro v", "Ġqu o", "Ġprem ise", "ĠCris is", "ĠHol ocaust", "Ġrhyth m", "Ġmet ric", "cl ub", "Ġtransport ed", "Ġn od", "ĠP ist", "Ġancest ors", "ĠFred er", "th umbnails", "ĠC E", "ON D", "Ph il", "ven ge", "ĠProduct s", "cast le", "Ġqual ifying", "ĠK aren", "VERTIS EMENT", "Ġmight y", "Ġexplan ations", "Ġfix ing", "D i", "Ġdecl aring", "Ġanonym ity", "Ġju ven", "ĠN ord", "ĠDo om", "ĠAct ually", "O k", "ph is", "ĠDes ert", "Ġ11 6", "I K", "ĠF M", "Ġinc omes", "V EL", "ok ers", "Ġpe cul", "Ġlight weight", "g ue", "Ġacc ent", "Ġincre ment", "ĠCh an", "Ġcompl aining", "ĠB aghd", "Ġmidfield er", "Ġover haul", "Pro cess", "ĠH ollow", "ĠTit ans", "Sm all", "man uel", "ĠUn ity", "ĠEv ents", "S ty", "Ġdispro portion", "n esty", "en es", "ĠC od", "Ġdemonstr ations", "ĠCrim son", "ĠO H", "Ġen rolled", "Ġc el", "ĠBre tt", "Ġa ide", "Ġhe els", "Ġbroad band", "Ġmark ing", "Ġw izard", "ĠN J", "ĠChief s", "Ġingred ient", "Ġd ug", "ĠSh ut", "urch ase", "end or", "Ġfar mer", "ĠGold man", "12 9", "15 5", "Or der", "Ġl ion", "i ably", "Ġst ain", "ar ray", "ilit ary", "ĠFA Q", "Ġexpl oded", "ĠMcC arthy", "ĠT weet", "ĠG reens", "ek ing", "l n", "ens en", "Ġmotor cycle", "Ġpartic le", "Ġch olesterol", "B ron", "Ġst air", "Ġox id", "Ġdes irable", "ib les", "Ġthe or", "for cing", "Ġpromot ional", "ov o", "b oot", "ĠBon us", "raw ling", "Ġshort age", "ĠP sy", "Ġrecru ited", "Ġinf ants", "Ġtest osterone", "Ġded uct", "Ġdistinct ive", "Ġfirm ware", "bu ilt", "14 5", "Ġexpl ored", "Ġfact ions", "Ġv ide", "Ġtatt oo", "Ġfinan cially", "Ġfat igue", "Ġproceed ing", "const itutional", "Ġmis er", "Ġch airs", "gg ing", "ipp le", "Ġd ent", "Ġdis reg", "ç Ķ", "st ant", "ll o", "b ps", "aken ing", "Ġab normal", "ĠE RA", "å£ «", "ĠH BO", "ĠM AR", "Ġcon cess", "Ġserv ant", "Ġas pir", "l av", "ĠPan el", "am o", "Ġprec ip", "Ġrecord ings", "Ġproceed ed", "Ġcol ony", "ĠT ang", "ab lo", "Ġstri pped", "Le ft", "to o", "Ġpot atoes", "Ġfin est", "% ).", "Ġc rap", "ĠZ ach", "ab ases", "ĠG oth", "Ġbillion aire", "w olf", "Ġsan ction", "S K", "Ġlog ged", "P o", "ey ed", "un al", "Ġcr icket", "Ġarm ies", "Ġunc overed", "Cl oud", "ó n", "Ġreb ounds", "Ġm es", "O per", "P ac", "Ġnation ally", "Ġinsert ed", "p ict", "Ġgovern ance", "Ð ¸", "Ġprivile ges", "G ET", "Ġfavor ites", "im ity", "Ġlo ver", "the m", "em pl", "Ġgorge ous", "An n", "Ġsl ipped", "Ġve to", "B ob", "Ġsl im", "u cc", "ĠF ame", "udden ly", "Ġden ies", "ĠM aur", "Ġdist ances", "Ġw anna", "t ar", "ĠS ER", "Ġâ Ī", "Ġle mon", "at hetic", "Ġlit eral", "Ġdistingu ished", "Ġansw ering", "G I", "Ġrelig ions", "ĠPhil os", "ĠL ay", "Ġcomp os", "ire ments", "ĠK os", "ine z", "roll ing", "Ġyoung est", "and ise", "ĠB orn", "Ġalt ar", "am ina", "ĠB oot", "v oc", "Ġdig ging", "Ġpress ures", "Ġl en", "26 4", "Ġassass ination", "ĠBir mingham", "ĠMy th", "Ġsovere ign", "ĠArt ist", "ĠPhot ograph", "Ġdep icted", "Ġdisp ens", "orth y", "Ġamb ul", "int eg", "ĠC ele", "ĠTib et", "Ġhier archy", "Ġc u", "Ġpre season", "ĠPet erson", "Ġcol ours", "Ġworry ing", "Ġback ers", "ĠPal mer", "ĠÎ ¼", "Ġcontribut or", "Ġhear ings", "Ġur ine", "Ġ Ù", "ourge ois", "Sim ilar", "ĠZ immer", "s omething", "ĠUS C", "Ġstrength s", "ĠF I", "Ġlog ging", "As ked", "ĠTh ai", "in qu", "ĠW alt", "Ġcrew s", "it ism", "3 01", "Ġshar ply", "um ed", "Ġred irect", "r ators", "In f", "ĠWe apons", "Ġte asp", "19 99", "L ive", "ĠEs pecially", "ĠS ter", "ĠVeter ans", "Ġint ro", "other apy", "Ġmal ware", "Ġbre eding", "Ġmole cular", "ĠR oute", "ĠCom ment", "oc hem", "Ġa in", "Se ason", "Ġlineback er", "Ä «", "ĠEconom ics", "es ar", "ĠL ives", "ĠEm ma", "Ġk in", "ĠTer rit", "Ġpl anted", "ot on", "ĠBut ter", "ĠSp ons", "P ER", "Ġdun geon", "Ġsymb olic", "Ġfil med", "Ġdi ets", "Ġconclud es", "Ġcertain ty", "ĠForm at", "Ġstr angers", "form at", "ĠPh ase", "Ġcop ied", "Ġmet res", "ld a", "ĠUs ers", "Ġdeliber ate", "Ġwas hed", "ĠL ance", "im ation", "Ġimpro per", "ĠGen esis", "ick r", "ĠK ush", "Ġreal ise", "Ġembarrass ing", "alk ing", "b ucks", "Ġver ified", "Ġout line", "year s", "ĠIn come", "20 2", "Ġz ombies", "F inal", "ĠMill enn", "Ġmod ifications", "ĠV ision", "ĠM oses", "ver b", "iter ranean", "ĠJ et", "Ġnav al", "ĠA gg", "Ġur l", "Ġvict ories", "Ġnon etheless", "Ġinj ust", "ĠF act", "ç ļ", "Ġins ufficient", "re view", "face book", "Ġnegoti ating", "Ġguarant ees", "im en", "uten berg", "Ġg ambling", "Ġcon gr", "Load ing", "Ġnever theless", "Ġpres idents", "ĠIndust rial", "Ġ11 8", "Ġp oured", "ĠT ory", "Ġ17 5", "Ġ: =", "Sc ott", "ange red", "T ok", "Ġorgan izers", "M at", "ĠG rowth", "Ġad ul", "Ġens ures", "Ġ11 7", "é¾į å", "Ġmass acre", "Ġgr ades", "be fore", "AD VERTISEMENT", "ĠSl ow", "ĠM MA", "âĢĶ \"", "ĠV atican", "Q aeda", "Ġo we", "66 66", "ĠS orry", "ĠGr ass", "Ġbackground s", "Ġexha usted", "Ġcl an", "Ġcomprom ised", "ĠE lf", "ĠIsa ac", "ens on", "In vest", "IF A", "Ġinterrupt ed", "ãĥī ãĥ©", "Ġtw isted", "ĠDrag ons", "M ode", "ĠK remlin", "Ġfert il", "he res", "ph an", "ĠN ode", "f ed", "ĠOr c", "Ġunw illing", "C ent", "Ġprior it", "Ġgrad uates", "Ġsubject ive", "Ġiss uing", "ĠL t", "Ġview er", "Ġw oke", "Th us", "bro ok", "Ġdep ressed", "Ġbr acket", "ĠG or", "ĠFight ing", "Ġstri ker", "Rep ort", "ĠPortug al", "Ġne o", "w ed", "19 9", "Ġflee ing", "sh adow", "ident ified", "US E", "Ste am", "Ġstret ched", "Ġrevel ations", "art ed", "ĠD w", "Ġalign ment", "est on", "ĠJ ared", "S ep", "Ġblog s", "up date", "g om", "r isk", "Ġcl ash", "ĠH our", "Ġrun time", "Ġunw anted", "Ġsc am", "Ġr ack", "Ġen light", "on est", "ĠF err", "Ġconv ictions", "Ġp iano", "Ġcirc ulation", "ĠW elcome", "Ġback lash", "ĠW ade", "Ġrece ivers", "ot ive", "J eff", "Ġnetwork ing", "ĠPre p", "ĠExpl orer", "Ġlect ure", "Ġupload ed", "ĠMe at", "B LE", "ĠNaz is", "ĠSy nd", "st ud", "ro ots", "ri ans", "Ġportray ed", "Ġ ??", "ĠBudd ha", "s un", "Rober t", "ĠCom plex", "Ġover see", "Ġste alth", "T itle", "ĠJ obs", "ĠK um", "Ġappreci ation", "ĠM OD", "Ġbas ics", "Ġcl ips", "Ġnurs ing", "Ġpropos ition", "Ġreal ised", "ĠNY C", "Ġall ocated", "ri um", "ar an", "ĠPro duction", "ĠV ote", "Ġsm ugg", "Ġhun ter", "az er", "ĠCh anges", "Ġfl uct", "y on", "Ar ray", "Ġk its", "W ater", "Ġuncom mon", "Ġrest ing", "ell s", "w ould", "Ġpurs ued", "Ġassert ion", "omet own", "ĠMos ul", "ĠPl atform", "io let", "Ġshare holders", "Ġtra ils", "P ay", "ĠEn forcement", "ty pes", "ĠAn onymous", "Ġsatisf ying", "il ogy", "Ġ( '", "w ave", "c ity", "Ste ve", "Ġconfront ation", "ĠE ld", "C apt", "ah an", "ht m", "ĠC trl", "ON S", "2 30", "if a", "hold ing", "Ġdelic ate", "Ġj aw", "ĠGo ing", "or um", "S al", "Ġd ull", "ĠB eth", "Ġpr isons", "Ġe go", "ĠEl sa", "avor ite", "ĠG ang", "ĠN uclear", "Ġsp ider", "ats u", "Ġsam pling", "Ġabsor bed", "ĠPh arm", "iet h", "Ġbuck et", "ĠRec omm", "O F", "ĠF actory", "AN CE", "Ġb acter", "H as", "ĠObs erv", "12 1", "Ġprem iere", "De velop", "Ġcur rencies", "C ast", "Ġaccompany ing", "ĠNash ville", "Ġfat ty", "ĠBre nd", "Ġloc ks", "Ġcent ered", "ĠU T", "augh s", "or ie", "ĠAff ordable", "v ance", "D L", "em et", "Ġthr one", "ĠBlu etooth", "Ġn aming", "if ts", "AD E", "Ġcorrect ed", "Ġprompt ly", "ĠST R", "Ġgen ome", "Ġcop e", "Ġval ley", "Ġround ed", "ĠK end", "al ion", "p ers", "Ġtour ism", "Ġst ark", "v l", "Ġblow ing", "ĠSche dule", "st d", "Ġunh appy", "Ġlit igation", "ced es", "Ġand roid", "Ġinteg ral", "ere rs", "ud ed", "t ax", "Ġre iter", "ĠMot ors", "oci ated", "Ġwond ers", "ĠAp ost", "uck ing", "ĠRoose velt", "f ram", "Ġyield s", "Ġconstit utes", "aw k", "Int erest", "Ġinter im", "Ġbreak through", "ĠC her", "Ġpro sec", "ĠD j", "ĠM T", "Res p", "ĠP T", "Ġs perm", "ed it", "B T", "Lin ux", "count ry", "le ague", "Ġd ick", "Ġo ct", "Ġinsert ing", "Ġsc ra", "ĠBrew ing", "Ġ19 66", "Ġrun ners", "Ġpl un", "id y", "ĠD ian", "Ġdys function", "Ġex clusion", "Ġdis gr", "Ġincorpor ate", "Ġrecon c", "Ġnom inated", "ĠAr cher", "d raw", "achel or", "Ġwrit ings", "Ġshall ow", "Ġh ast", "ĠB MW", "ĠR S", "Ġth igh", "Ġ19 63", "Ġl amb", "Ġfav ored", "ag le", "Ġcool er", "ĠH ours", "ĠG U", "ĠOrig in", "Ġglim pse", "---------------- ----", "L im", "Ġche ek", "Ġj ealous", "- '", "Ġhar ness", "ĠPo ison", "Ġdis abilities", "ne apolis", "Ġout look", "Ġnot ify", "ĠIndian apolis", "Ġab rupt", "ns ic", "Ġenc rypted", "Ġfor fe", "reat h", "Ġr abb", "Ġfound ations", "Ġcompl iment", "ĠInter view", "ĠS we", "Ġad olesc", "Ġmon itors", "ĠSacrament o", "Ġtime ly", "Ġcontem pl", "Ġposition ed", "Ġpost ers", "ph ies", "iov ascular", "v oid", "ĠFif th", "Ġinvestig ative", "OU N", "Ġinteg rate", "ĠIN C", "ish a", "ibl ings", "ĠRe quest", "ĠRodrig uez", "Ġsl ides", "ĠD X", "Ġfemin ism", "Ġdat as", "Ġb end", "ir us", "ĠNig eria", "F ox", "Ch ange", "Ġair plane", "ĠLad en", "Ġpublic ity", "ixt y", "Ġcommit ments", "Ġaggreg ate", "Ġdisplay ing", "ĠAr row", "Ġ12 2", "Ġrespect s", "and roid", "s ix", "ĠSh a", "Ġrest oration", ") \\", "W S", "oy s", "Ġillust rate", "with out", "12 6", "ĠâĶ Ĥ", "Ġpick up", "n els", "Ġ ....", "f ood", "ĠF en", ") ?", "Ġphenomen a", "Ġcompan ions", "ĠW rite", "Ġsp ill", "Ġbr idges", "ĠUp dated", "ĠF o", "Ġinsect s", "ASH INGTON", "Ġsc are", "il tr", "ĠZh ang", "Ġsever ity", "Ġind ul", "14 9", "ĠCo ffee", "Ġnorm s", "Ġp ulse", "ĠF T", "Ġhorr ific", "ĠDest roy", "ĠJ SON", "Ġo live", "Ġdiscuss es", "R est", "E lect", "ĠW inn", "ĠSurv iv", "ĠH ait", "S ure", "op ed", "Ġro oted", "ĠS ke", "ĠBron ze", "Ġl ol", "Def ault", "Ġcommod ity", "red ited", "Ġliber tarian", "Ġforb idden", "Ġgr an", "à ¨", "Ġl ag", "en z", "dri ve", "Ġmathemat ics", "Ġw ires", "Ġcrit ically", "Ġcarb ohyd", "ĠChance llor", "ĠEd die", "Ġban ning", "ĠF ri", "Ġcompl ications", "et ric", "ĠBangl adesh", "Ġband width", "St op", "ĠOrig inally", "Ġhalf way", "yn asty", "sh ine", "Ġt ales", "rit ies", "av ier", "Ġspin ning", "ĠWH O", "Ġneighbour hood", "b ach", "Ġcommer ce", "ĠS le", "B U", "Ġentreprene ur", "Ġpecul iar", "ĠCom ments", "f re", "3 20", "IC S", "Ġimag ery", "ĠCan on", "ĠElect ronic", "sh ort", "( (", "D ig", "Ġcomm em", "u ced", "Ġincl ined", "ĠSum mon", "Ġcl iff", "ĠMed iterranean", "Ġpo etry", "Ġprosper ity", "ĠRe ce", "Ġp ills", "m ember", "Ġfin ale", "un c", "ĠG ig", "ä ½", "Ġl od", "Ġback ward", "- +", "ĠFor ward", "Ġth ri", "s ure", "Ġso ap", "ĠF X", "R ES", "ĠSe xual", "oul os", "Ġfool ish", "Ġright eous", "Ġco ff", "terror ism", "ust ain", "ot er", "Ġab uses", "ne xt", "Ġab usive", "Ġthere after", "Ġprohib ition", "ĠS UP", "Ġd ip", "Ġr ipped", "Ġinher ited", "Ġb ats", "st ru", "G T", "Ġflaw ed", "ph abet", "Ġf og", "do ors", "Ġim aging", "Ġdig its", "ĠHung ary", "Ġar rog", "Ġteach ings", "Ġprotocol s", "ĠB anks", "à ¸", "p ound", "ĠC urt", ".\" )", ". /", "Ġex emption", "end ix", "ĠM ull", "Ġimpro ves", "ĠG amer", "d imensional", "I con", "ĠMarg aret", "St atus", "d ates", "Ġint ends", "Ġdep ict", "Ġpark ed", "J oe", "ĠMar ines", "chn ology", "! ).", "Ġjud ged", "Ġwe ights", "R ay", "Ġapart ments", "he ster", "Ġrein force", "Ġoff ender", "occ up", "Ġs ore", "e pt", "ĠPH P", "ĠB row", "Ġauthor ization", "ĠR isk", "ĠDel aware", "ĠQ U", "Ġnot ifications", "Ġsun light", "Ġex clude", "d at", "Ġm esh", "ĠSud an", "Ġbelong ed", "Ġsub way", "Ġno on", "ĠInter ior", "ol ics", "ĠL akers", "Ġc oding", "Dis claimer", "Cal if", "O ld", "Ġdis l", "???? ?", "Ġconfir ms", "Ġrecruit ment", "Ġhom icide", "Cons ider", "ĠJeff rey", "ft y", "} ;", "Ġobject ion", "do ing", "ĠLe o", "W ant", "Ġgl ow", "ĠClar ke", "ĠNorm an", "Ġver ification", "Ġpack et", "ĠForm ula", "Ġpl ag", "es ville", "Ġshout ing", "Ġo v", "ĠR EC", "ĠB ub", "Ġn inth", "Ġener g", "Ġvalid ity", "Ġup s", "j ack", "Ġneighbor ing", "ĠN ec", "ew orks", "ĠH ab", "are z", "Ġsp ine", "Ġevent ual", "ĠLe aders", "ĠC arn", "Ġprob ation", "Ġrom ance", "ms g", "ĠMechan ical", "ER Y", "R ock", "Ġpart isan", "N ode", "ass ets", "min ent", "Ġforeign ers", "Ġtest ify", "ĠUs ually", "l ords", "ĠG ren", "ĠPow ell", "BI L", "Ġs r", "Ġadd ict", "Ġshell s", "Ġs igh", "ĠY ale", "tern ity", "Ġ7 50", "E U", "ĠR ifle", "Ġpat ron", "em a", "ĠB annon", "an ity", "Ġtrop ical", "ĠV II", "c ross", "Every thing", "ĠIS O", "Ġhum ble", "ass ing", "ĠF IG", "Ġupd ating", "ys on", "Ġcal cium", "Ġcompet ent", "Ġste ering", "Pro t", "ĠS Y", "ĠFin als", "ĠR ug", "15 9", "13 7", "ĠG olf", "Ġ12 6", "Ġaccommod ation", "ĠHug hes", "Ġaest hetic", "art isan", "ĠTw ilight", "Ġpr ince", "ĠAgric ulture", "ĠDis co", "Ġpreced ent", "Ġtyp ing", "author ized", "O ption", "ĠA ub", "l ishes", "ach t", "m ag", "P eter", "ĠU FO", "mont on", "ĠL ith", "Ġa rom", "Ġsec uring", "Ġconf ined", "priv ate", "Ġsw ords", "Ġmark ers", "Ġmetab olic", "se lect", "ĠCur se", "ĠO t", "g ressive", "Ġinc umb", "ĠS aga", "Ġpr iced", "Ġclear ance", "Cont ent", "Ġdr illing", "Ġnot ices", "Ġb ourgeois", "Ġv est", "Ġcook ie", "ĠGuard ians", "ry s", "in yl", "Ġ12 4", "Ġpl ausible", "on gh", "ĠOd in", "Ġconcept ion", "ĠY uk", "ĠBaghd ad", "ĠFl ag", "Aust ral", "ĠI BM", "Ġintern ationally", "ĠWiki Leaks", "I ED", "Ġc yn", "Ġcho oses", "ĠP ill", "Ġcomb ining", "Ġrad i", "ĠMoh ammed", "def ense", "atch ing", "Sub ject", "ic iency", "Fr ame", "Ġ{ \"", "Ġche ss", "Ġtim er", "19 0", "Ġt in", "Ġord inance", "emet ery", "Ġacc using", "Ġnotice able", "Ġcent res", "Ġl id", "ĠM ills", "img ur", "Ġz oom", "erg ic", "Ġcomp ression", "pr im", "f ind", "Ġsur g", "Ġp and", "ĠK ee", "ĠCh ad", "cell ence", "oy le", "Ġsocial ism", "ĠT ravis", "ĠM Hz", "Ġgu ild", "ALL Y", "ĠSub scribe", "ĠRel ated", "Ġoccur rence", "itch ing", "Ġfict ional", "Ġcr ush", "ĠE A", "c od", "m ix", "ĠTri ple", "Ġretrie ve", "Ġstimul us", "Ġpsych iat", "ĠDo or", "Ġhomosexual ity", "Ġelement ary", "Ġcell ular", "id ian", "ĠL aun", "Ġintrig uing", "Ġfo am", "ĠB ass", "id i", "its u", "Ġass ure", "Ġcongr at", "Ġbusiness man", "ĠBo ost", "cl ose", "Ġl ied", "Ġsc iences", "ĠO mega", "ĠG raphics", "Ġ< =", "sp oken", "Ġconnect ivity", "S aturday", "ĠAven gers", "Ġto ggle", "Ġank le", "Ġnational ist", "mod el", "ĠP ool", "ophob ia", "V ar", "ĠM ons", "ator ies", "Ġaggress ively", "C lear", "For ge", "act ers", "Ġhed ge", "Ġpip es", "Ġbl unt", "Ġs q", "Ġremote ly", "W ed", "as ers", "Ġref riger", "Ġt iles", "Ġresc ued", "Ġcompr ised", "ins ky", "Ġman if", "avan augh", "Ġprol ifer", "Ġal igned", "x ml", "Ġtri v", "Ġcoord ination", "ĠP ER", "ĠQu ote", "13 4", "b f", "ĠS aw", "Ġtermin ation", "Ġ19 0", "Ġadd itions", "Ġtri o", "Ġproject ions", "Ġpositive ly", "Ġin clusive", "Ġmem br", "19 90", "old er", "Ġpract iced", "ink le", "Ar ch", "Ġstar ters", "ari us", "Ġinter mediate", "ĠBen ef", "ĠK iller", "Ġinter ventions", "ĠK il", "ĠF lying", "In v", "Ġprem ature", "Ġpsych iatric", "Ġind ie", "Ġcoll ar", "ĠRain bow", "af i", "Ġdis ruption", "ĠFO X", "cast ing", "Ġmis dem", "c ro", "Ġw ipe", "ard on", "Ġb ast", "ĠTom my", "ĠRepresent ative", "Ġbell y", "ĠP O", "ĠBre itbart", "13 2", "Ġmess aging", "Sh ould", "Ref erences", "ĠG RE", "ist ical", "L P", "ĠC av", "ĠC razy", "Ġintu itive", "ke eping", "ĠM oss", "Ġdiscont in", "ĠMod ule", "Ġun related", "ĠPract ice", "ĠTrans port", "Ġstatist ically", "orn s", "Ġs ized", "p u", "Ġca f", "ĠWorld s", "ĠRod gers", "ĠL un", "ĠCom ic", "l iving", "Ġc ared", "Ġclim bed", ") {", "Ġconsist ed", "Ġmed ieval", "fol k", "Ġh acked", "Ġd ire", "ĠHerm ione", "Ġt ended", "ce ans", "D aniel", "w ent", "Ġlegisl ators", "Ġred es", "g ames", "Ġg n", "am iliar", "Ġ+ +", "gg y", "th reat", "Ġmag net", "Ġper ceive", "Ġz ip", "Ġindict ment", "Ġcrit ique", "g ard", "ĠSaf e", "ĠC ream", "Ġad vent", "ob a", "Ġv owed", "ous ands", "Ġsk i", "Ġabort ions", "u art", "Ġstun ned", "Ġadv ancing", "Ġlack ed", "Ġ\\ \"", "Ġsch izophren", "Ġeleg ant", "Ġconf erences", "Ġcance led", "ĠHud son", "ĠHop efully", "Ġtr ump", "Ġfrequ encies", "Ġmet eor", "ĠJun ior", "ĠFle et", "ĠMal colm", "ĠT ools", "Ġ ........", "Ġh obby", "ĠEurope ans", "Ġ15 00", "ĠInt o", "Ġs way", "ĠApp ro", "ĠCom pl", "Comm unity", "Ġt ide", "ĠSum mit", "ä »", "Ġinter vals", "ĠE ther", "Ġhabit at", "ĠSteven s", "lish ing", "ĠDom ain", "Ġtrig gers", "Ġch asing", "Ġchar m", "ĠFl ower", "it ored", "Ġbless ing", "Ġtext ures", "F ive", "Ġliqu or", "R P", "F IN", "Ġ19 62", "C AR", "Un known", "Ġres il", "ĠL ily", "Ġabund ance", "Ġpredict able", "r ar", "Ġbull shit", "le en", "che t", "M or", "M uch", "ä ¹", "Ġemphas ized", "Ġcr ust", "Ġprim itive", "Ġenjoy able", "ĠPict ures", "Ġteam mate", "pl er", "ĠT ol", "ĠK ane", "Ġsummon ed", "th y", "ram a", "ĠH onda", "Ġreal izing", "Ġquick er", "Ġconcent rate", "cle ar", "Ġ2 10", "ĠErd ogan", "ar is", "Ġrespond s", "ĠB I", "Ġelig ibility", "Ġpus hes", "ĠId aho", "Ġagg rav", "Ġru ins", "ur ations", "Ġb ans", "Ġan at", "sh are", "Ġgr ind", "h in", "um en", "Ġut ilities", "ĠYan kees", "Ġdat abases", "ĠD D", "Ġdispl aced", "Ġdepend encies", "Ġstim ulation", "h un", "h ouses", "ĠP retty", "ĠRaven s", "ĠTOD AY", "Ġassoci ates", "Ġthe rape", "cl ed", "Ġde er", "Ġrep airs", "rent ice", "Ġrecept ors", "Ġrem ed", "ĠC e", "Ġmar riages", "Ġball ots", "ĠSold ier", "Ġhilar ious", "op l", "13 8", "Ġinherent ly", "Ġignor ant", "Ġb ounce", "ĠE aster", "REL ATED", "ĠCur rency", "E V", "ãĥ ŀ", "ĠLe ad", "Ġdece ased", "B rien", "ĠMus k", "J S", "Ġmer ge", "heart ed", "c reat", "m itt", "m und", "ĠâĢ ĭ", "ĠB ag", "Ġproject ion", "Ġj ava", "ĠStand ards", "ĠLeon ard", "Ġcoc onut", "ĠPop ulation", "Ġtra ject", "Ġimp ly", "Ġcur iosity", "ĠD B", "ĠF resh", "ĠP or", "Ġheav ier", "ne ys", "gom ery", "Ġdes erved", "Ġphr ases", "ĠG C", "Ġye ast", "d esc", "De ath", "Ġreb oot", "Ġmet adata", "IC AL", "Ġrep ay", "ĠInd ependence", "Ġsubur ban", "ical s", "Ġat op", "Ġall ocation", "gener ation", "ĠG ram", "Ġmoist ure", "Ġp ine", "ĠLiber als", "Ġa ides", "Ġund erest", "ĠBer ry", "Ġcere mon", "3 70", "ast rous", "ĠPir ates", "Ġt ense", "ĠIndust ries", "ĠApp eals", "ĠN ear", "Ġè£ı ç", "Ġlo vers", "ĠC AP", "ĠC raw", "Ġg iants", "Ġeffic acy", "E lement", "ĠBeh avior", "ĠToy ota", "Ġint est", "P riv", "A I", "Ġmaneu ver", "Ġperfect ion", "Ġb ang", "p aper", "r ill", "Ge orge", "b order", "in ters", "ĠS eth", "Ġcl ues", "ĠLe vi", "ĠRe venue", "14 7", "Ġv apor", "Ġfortun ate", "Ġthreat ens", "Ġve t", "Ġdepend ency", "ers ed", "art icle", "ĠBl izzard", "Ġch lor", "Ġmin us", "ĠB ills", "Ġcryptoc urrency", "Ġmetabol ism", "ter ing", "Ġp estic", "step s", "ĠTre asure", "ract ed", "ĠConst ant", "Ġtem p", "13 9", "ĠDet ective", "ur ally", "Ġrecover ing", "Ġcort ex", "Ġ14 4", "cl osed", "Ġprejud ice", "aun ted", "Ġstorm s", "ĠN OW", "Ġmach inery", "Add ress", "Ġcompe lled", "27 0", "Ġdesp air", "b ane", "Ġveget able", "Ġbed s", "Lear n", "Ġcolor ful", "Ġsp ike", "Ġmarg ins", "Ġsymp athy", "Ġworks hop", "ĠC BC", "S at", "Ġburn s", "ĠG ender", "Ġ12 9", "ĠC able", "Ġdeb ts", "ĠThe resa", "Ġreflect ing", "Ġa irst", "Ġr im", "ram id", "Ġweakness es", "W rit", "ogg le", "t i", "ĠCh arge", "Ġwe ighed", "Ġ( .", "Ġl aughter", "Ġrou ter", "ĠDemocr acy", "D ear", "Ġhas ht", "Ġd y", "Ġhint s", "run ning", "Ġfin ishes", "ar us", "M ass", "res ult", "asc us", "Ġv intage", "Ġcon qu", "Ġwild ly", "ac ist", "Ġl ingu", "Ġprot agonist", "st rom", "te enth", "ĠSol o", "m ac", "f illed", "Ġre nown", "it ives", "Ġmot ive", "ĠAnt ar", "ĠM ann", "ĠAd just", "Ġrock ets", "Ġtrou bling", "e i", "Ġorgan isms", "ass is", "Christ ian", "Ġ14 5", "ĠH ass", "Ġsw all", "Ġw ax", "ĠSurv ival", "V S", "ĠM urd", "v d", "stand ard", "Ġdrag ons", "Ġacceler ation", "r ational", "f inal", "Ġp aired", "ĠE thereum", "Ġinterf aces", "Ġres ent", "Ġartif acts", "Å «", "are l", "Ġcompet itor", "ĠNich olas", "ĠSur face", "c pp", "ĠT ot", "Ġeconom ically", "Ġorgan ised", "Ġen forced", "in ho", "Ġvar ieties", "Ġab dom", "ĠBa iley", "id av", "ĠSal v", "p aid", "Ġalt itude", "ess ert", "ĠG utenberg", "are a", "op oulos", "Ġprofess ors", "igg s", "ĠF ate", "he y", "Ġ3 000", "D ist", "Ġtw ins", "c ill", "ĠM aps", "Ġtra ps", "Ġwe ed", "ĠK iss", "Ġy oga", "Ġrecip ients", "ĠWest minster", "Ġpool s", "ĠWal mart", "18 8", "ĠSchool s", "att ack", "ĠAR M", "par agraph", "W arning", "j l", "Ġself ish", "anche z", "ĠHe ights", "F re", "ĠS oph", "Ġ --------------------------------", "t ml", "33 3", "Ġraid s", "Ġsatell ites", "KE Y", "Ġlast s", "Ñ Ĥ", "In s", "ĠD ame", "Ġunp redict", "// /", "gh ai", "Ġart illery", "Ġcru ise", "Ġg el", "ĠCabin et", "Ġbl ows", "ĠE sp", "Ġprox imity", "ot he", "ĠSk ills", "ĠU pper", "ob o", "ĠN DP", "Ġenjoy s", "Ġrepe ating", "ĠConst ruction", "ĠQuest ions", "H illary", "Ġu int", "Ġprocess ors", "ĠGib son", "ĠMult iple", "q a", "ĠB om", "ĠM iles", "vent ional", "Ġhur ts", "s kin", "ĠA IDS", "Ġadvis ers", "ĠR oot", "Ġmethod ology", "ĠD ale", "Ġdet on", "ĠKnow ledge", "sequ ently", "Ġ12 1", "Ġconnect s", "C y", "ĠD anger", "Ġcontribut ors", "ĠB ent", "Ġbr ass", "ĠGun s", "int o", "ĠFort une", "Ġbro ker", "bal ance", "Ġlength s", "Ġv ic", "Ġaver aging", "Ġappropri ately", "ĠCamer a", "Ġsand wich", "ĠCD C", "Ġcoord inate", "Ġnav ig", "Ġgood ness", "l aim", "Ġbra ke", "Ġextrem ist", "ĠW ake", "ĠM end", "ĠT iny", "ĠC OL", "ĠR F", "ĠD ual", "ĠW ine", "C ase", "Ġref ined", "Ġl amp", "L ead", "Ġb apt", "ĠCar b", "ĠS add", "ĠMin neapolis", "PD F", "Ear ly", "ĠH idden", "I ts", "ĠT IME", "Ġp ap", "Ġcommission ed", "ĠF ew", "ĠCol ts", "ĠB ren", "Ġbot hered", "Ġlike wise", "Ex per", "ĠSch w", "c ry", "n n", "ĠM itch", "im on", "M G", "b m", "UM P", "r ays", "Ġregist ry", "Ġ2 70", "ach ine", "re lla", "ant ing", "00 000", "Ġru ined", "sp ot", "Ġt a", "Ġmaxim ize", "Ġincon ven", "D ead", "H uman", "En abled", "ĠMar ie", "Ġch ill", "ĠParad ise", "Ġstar ring", "ĠLat ino", "ĠProt ocol", "ĠE VER", "Ġsuppl iers", "m essage", "ĠBro ck", "Ġser um", "âĸĪâĸĪ âĸĪâĸĪ", "Ġen comp", "Ġamb ition", "ues e", "Ġar rows", "And rew", "Ġanten na", "Ġ19 61", "ĠB ark", "Ġb ool", "ãĤ ª", "ĠSt orage", "Ġrail way", "Ġtoug her", "ĠC ad", "Ġwas hing", "P y", "' ]", "em bed", "ĠMem phis", "ack le", "Ġfam ously", "ĠF ortunately", "ov ies", "Ġmind set", "Ġsne ak", "ĠD h", "RA W", "ĠSim pson", "Ġliv est", "Ġland mark", "Ġc ement", "L ow", "Ġthr illed", "ĠCour se", "in el", "Ġch uck", "id ate", "gl obal", "Ġwh it", "Ġ �", "ad ays", "s ki", "ĠS V", "Ġvir uses", "30 6", "ĠResp ons", "Ġthe aters", "ĠBr anch", "ĠGene va", "ĠM K", "Ġunbel iev", "Ġcommun ist", "Orig inal", "ĠRe ceived", "ĠTrans fer", "ĠAr g", "In put", "ĠStr ategy", "Ġpal ace", "the ning", "D ri", "Ġsent encing", "umbn ail", "Ġp ins", "re cy", "Ġs iblings", "Get ting", "ĠB U", "ĠNorth west", "Ġprolong ed", "ĠSak ura", "C omb", "ĠB our", "Ġinadequ ate", "ĠK ash", "Ġus ername", "ĠImpro ve", "Ġbatt ling", "ĠM AC", "Ġcurric ulum", "Ġs oda", "ĠC annon", "Ġsens ible", "sp ons", "De cember", "Ġw icked", "ĠP engu", "Ġdict ators", "ĠHe arts", "og yn", "Ġsimilar ities", "ĠSt ats", "Ġh ollow", "it ations", "\": [", "Ġh over", "ĠList en", "s ch", "S und", "Ġc ad", "ĠPar ks", "Ġl ur", "Ġhy pe", "ĠL em", "N AME", "is ure", "Fr iday", "Ġshoot s", "Ġclos es", "Ġd b", "ĠR idge", "ĠDiff erent", "Ġrepl ies", "ĠBroad way", "op ers", "Ġint oler", "ĠZe us", "akes pe", "Ġpropri etary", "Ġrequest ing", "Ġcontro llers", "ĠM IN", "im edia", "be cca", "Ġexp ans", "Ġoil s", "B ot", "ĠCh and", "Ġpr inter", "Ġto pped", "ĠP OL", "ĠEar lier", "S ocial", "av in", "Ġdecre ases", "ĠSe b", "Ġspecific ations", "ĠBl ast", "ĠK urt", "Ġfre el", "B rown", "Ġdil ig", "ro e", "ĠPro blem", "ĠQu ad", "Ġdecent ral", "ĠV ector", "an ut", "Ġplug ins", "ĠGreg ory", "Ġfuck ed", "el ines", "ĠAmb assador", "t ake", "Ġcle ans", "ong yang", "An onymous", "st ro", "\" }", "al ine", "ĠO dd", "ĠE ug", "2 16", "Ġbo il", "ĠP owers", "Ġnurs es", "Ob viously", "ĠTechn ical", "Ġexceed ed", "OR S", "Ġextrem ists", "Ġtr aces", "ex pl", "Ġcom r", "ĠS ach", ") /", "Ġm asks", "Ġsc i", "B on", "Ġreg ression", "we gian", "Ġadvis or", "it ures", "ĠV o", "ex ample", "ĠInst ruct", "Ġs iege", "Ġredu ctions", "pt r", "Ġstat utory", "Ġrem oves", "Ġp uck", "red its", "Ġbe e", "Ġsal ad", "Ġpromot ions", "ĠJosh ua", "with standing", "ET H", "ĠCh a", "im us", "Ġexpend iture", "aun ting", "Ġdelight ed", "Ġ15 5", "be h", "Ġcar pet", "ĠSp art", "Ġj ungle", "l ists", "Ġbull ying", "ĠNob el", "ĠGl en", "Ġreferen ced", "Ġintrodu ces", "se in", "Ġcho pped", "gl ass", "ĠW rest", "Ġneutral ity", "Ġâ Ļ", "Ġinvestig ator", "Ġshel ves", "Ġun constitutional", "Ġreprodu ction", "Ġmer chant", "m ia", "Ġmet rics", "Ġexplos ives", "ĠSon ia", "Ġbod ily", "Ġthick ness", "Ġpredomin antly", "ĠAb ility", "Ġmon itored", "IC H", "Ġ] .", "ĠMart inez", "Ġvis ibility", "Ġqu eries", "Ġgen ocide", "ĠWar fare", "Qu ery", "Ġstud ios", "Ġemb ry", "Ġcorrid or", "Ġclean ed", "com plete", "ĠM H", "Ġenroll ment", "ING S", "Ġimpact ed", "Ġdis astrous", "ĠY un", "ĠCl aire", "ĠBas ically", "y t", "uster ity", "Ġindirect ly", "w ik", "Ġd od", "ĠCar r", "Ġam p", "Ġprohib it", "ĠIn itial", "ĠR d", "ij i", "Ġeduc ate", "c orn", "i ott", "ĠBeaut y", "Ġdetect ive", "ĠCon n", "s ince", "Ġst agger", "Ġob ese", "Ġb ree", "olog ic", "is se", "walk er", "Ġbl ades", "Ġlaw ful", "fun c", "ĠBeh ind", "Ġappet ite", "Ġ( *", "Ġt ennis", "Ġoff spring", "Ġj ets", "Ġstruct ured", "Ġafore mentioned", "N ov", "Ġsc aling", "f ill", "Ġst ew", "Ġcur b", "ĠStep han", "ed In", "S F", "ob ic", "é ŃĶ", "ou g", "ĠM M", "Ġgen etically", "ope z", "13 6", "Ġu mb", "anc ers", "Ġcoh ort", "Ġmerch andise", "Ġimp osing", "ĠLegisl ature", "ĠArch ive", "iv ia", "ĠN aval", "Ġoff ences", "Ġmir acle", "Ġsn apped", "Ġf oes", "Ġextensive ly", "ĠR af", "Ġc ater", "ed ience", "K it", "ĠB in", "Ġrecomm ends", "ĠC ities", "Ġrig id", "ĠRE AD", "ĠNob le", "ĠT ian", "Ġcertific ates", "ant is", "o iler", "ĠBudd hist", "d id", "Ġsurvey ed", "Ġdown ward", "Ġprint s", "ĠMot ion", "ron ics", "ĠS ans", "oss ibly", "u ctions", "Ġcolon ies", "ĠDan ish", "un it", "Ġsp oil", "Ġadvis ory", "ber ries", "Pl an", "Ġspecific ation", "op hers", "ĠRes ource", "Ġsh irts", "prising ly", "commun ications", "Ġtriv ial", "Ġmention ing", "ise xual", "Ġsupp lements", "Ġsuper vision", "B P", "v or", "Ġw it", "Ġco oldown", "Ġplaint iff", "ĠReview s", "ĠS ri", "ĠM int", "ĠSug ar", "Ġafter ward", "ĠPri est", "ĠInvest ment", "og ene", "ĠT aking", "Ġstretch ing", "Ġinflamm ation", "ĠTe hran", "Ġl ining", "Ġfree zing", "ĠEnt ity", "Ġins piring", "spe cial", "pr ice", "Ġsu e", "ĠP orter", "oun ge", "ET A", "ĠD erek", "ĠLu is", "u o", "ym ph", "Ġex terior", "ih il", "ĠAsh ley", "in ator", "Ġnut rients", "ĠTh rones", "Ġfin ances", "ĠIn spect", "Ġspe cially", "ĠRequ ired", "ĠP TS", "ĠViol ence", "oint ed", "sh ots", "Ġex cerpt", "co on", "IN S", "ĠG ri", "Ġrecogn ised", "We ek", "You ng", "Ġv om", "is le", "ĠCur ry", "ĠBudd h", "Ġnot ebook", "Ġd urable", "/ ?", "ĠG ad", "ĠP upp", "Ġforg ive", "p ark", "Ġpersonal ities", "an alysis", "cl amation", "Ġelev ator", "Ġware house", "ĠR ole", "un n", "Ġillust ration", "ĠSc an", "Ġatmosp heric", "Im port", "AN C", "rict ed", "f u", "01 0", "Ġar che", "Ġreward ed", "akespe are", "Ġintern ally", "ĠR BI", "alk er", "Ġeleph ant", "ow itz", "ĠP izza", "Ġbip artisan", "é s", "Ġslow ed", "ĠSt ark", "Ġover ride", "OU S", "Ġ3 20", "undred s", "ĠDe ck", "ĠC ensus", "be e", "14 6", "ot or", "Ġ ip", "Ġu b", "oc ations", "ĠBut ton", "r ice", "Ġc ripp", "ff f", "Ġorig inated", "Ġoverwhel med", "app a", "Ġfore most", "âĢ ij", "ĠL EG", "re lease", "eat ured", "at ches", "Ġre ps", "Ġl ending", "ĠRe ference", "ĠCl ient", "16 5", "vent h", "Com plete", "ĠPat rol", "Ġsw orn", "c am", "Ġshut tle", "ĠR alph", "Ġh ometown", "- ,", "on al", "ĠB P", "å ı", "Ġpersu ade", "ĠAlex and", "Ġcomb ines", "Ġv ivid", "ĠL ag", "Ġenc oding", "Ġsal vation", "w en", "ĠRec overy", "i ya", "Un iversity", "ĠB iden", "Ġbud gets", "ĠTex ans", "f its", "Ġhon ored", "Ġp ython", "T D", "## #", "cl one", "Ġbl ink", "ĠL iquid", "Ġunemploy ed", "Ġcl ashes", "ĠCoun sel", "Ġdirect ing", "Ġpun ct", "ĠFal cons", "Ġsh ark", "ĠDam ascus", "Ġje ans", "Ġemb ark", "Ġse ize", "Ġup wards", "2 80", "ĠE z", "ĠAny thing", "Ġex otic", "l ower", "ĠCreat or", "ĠU m", "Ġsubur bs", "ber ger", "ĠW end", "Ġm int", "ĠX X", "ĠD ro", "Ġsuff ers", "Ġher b", "t ree", "Ġfrag ile", "Ġflood ed", "ĠAl cohol", "ole an", "ny der", "ĠK O", "F ram", "Ġ13 6", "Ġow ed", "ĠMe lee", "ĠH ash", "Ġwh isk", "Ġsu do", "r r", "Qu ick", "app ro", "Ġi i", "ĠEx amples", "he e", "Ġpromot es", "per ature", "k ar", "ĠHon or", "Ġs odium", "ĠL if", "ros so", "intend ent", "Ġcorrespond ent", "F ound", "sec ret", "Ġident ifies", "ag ne", "Ġl ou", "ĠP P", "Ġcoinc idence", "m ove", "Ġmilit ia", "Ġinf iltr", "ĠPrim ary", "Ġpitch ing", "ĠI b", "ĠGO OD", "ãĤ ¸", "ĠW izards", "ir al", "ĠVen us", "R R", "ĠâĢ ķ", "ĠCase y", "Ġsad ly", "Ġadm ire", "Ġembarrass ed", "c b", "M el", "Ġtub es", "Ġbeaut ifully", "ĠQueens land", "Bel ow", "re z", "qu et", "ple asant", "Ġ «", "C amp", "Ġdec isive", "19 98", "ĠL amb", "ut ton", "h n", "ĠJ agu", "au nder", "ĠC ord", "Ġcl erk", "Ġca ffe", "Ġwip ed", "Ġre im", "ĠMount ains", "Ġimprison ed", "Ġdevelop s", "ĠP ra", "Ġmodel ing", "Any one", "ance l", "ĠS it", "Ġshield s", "Ġl awn", "Ġcard iovascular", "Ġdemonstr ating", "Ġpar se", "ĠIsrael is", "Ġeuro s", "14 3", "Ġgl orious", "ins ki", "ec d", "Ġcondition ing", "Ġhel pless", "Ġmicro sc", "ĠHar bor", "Ġst akes", "Ġ2 60", "Ġun equ", "ĠFl oyd", "Ġd amp", "Ġappar atus", "ĠLaw s", "Ġcoun ters", "Ġindu ce", "at able", "ĠAh med", "Ġsl am", "N ovember", "Ġpers ist", "Ġim minent", "á n", "Ġsh red", "Ġph ases", "ĠEd monton", "ĠArm strong", "ĠMe et", "ĠK itty", "Ñ Ģ", "c irc", "ĠAd ult", "Ġa rose", "ĠX en", "D an", "g ow", "Ġsuper f", "ĠAd mir", "Ġend ure", "Ġkey word", "yr us", "Ġy arn", "Ġpath way", "ĠHop kins", "mid t", "Ġcens orship", "d ependent", "Ġinstruct or", "S ources", "Ġto e", "Ġball oon", "N ob", "Ġsw ear", "ĠCast ro", "Ġgl oss", "ĠK avanaugh", "Ġremark ably", "Ph otos", "ĠN om", "ĠS outheast", "y ers", "Ġvalid ation", "Ġcann on", "ĠVict ory", "ĠPier re", "Ġcaut ious", "Aud io", "Ġf etch", "ĠG ift", "ĠH yp", "Ġrem edy", "Z E", "Ġsc ent", "Ġbe ard", "ĠR ut", "- \"", "Ġpat ents", "H y", "Ġun just", "Ġpot ato", "Ġforth coming", "Ġche f", "ĠR ift", "aff e", "ĠR OM", "ĠL aunch", "Ġp ads", "ĠNe o", "Ġon set", "Ġsquee ze", "s afe", "Ġpref ix", "ĠT M", "ĠN early", "ĠClin ical", "ĠM ental", "ot iation", "ĠUn ic", "ant ry", "ĠC ir", "Ġep it", "à ¦", "Ġextract ed", "verse ly", "ri ad", "Ġstr ains", "Ġto ps", "Ġpo em", "ĠRand y", "ĠMap le", "TH ER", "up iter", "ĠSS D", "ļ é", "Ġun con", "per ing", "Ġsle pt", "in ers", "Ġunder water", "ĠEv idence", "g one", "20 5", "Ġhistor ians", "Ġsynt hesis", "Ġf rog", "b asketball", "Ġvibr ant", "Ġsub ord", "Ġ3 65", "ĠD ial", "Ġcooper ate", "HA HA", "Ġgreet ed", "15 8", "Ġj azz", "Ġinto x", "ĠWalk ing", "Ġsuper visor", "ĠF usion", "ĠMer cedes", "s end", "H am", "s d", "n l", "Ġtour s", "ĠF IFA", "Ġcul p", "g d", "30 4", "Ġple as", "Ġillust rates", "ĠColomb ia", "Ġhighlight ing", "ĠSum mary", "Ġexp osing", "ĠD ru", "Ġir ony", "r itional", "ĠCar roll", "ĠEll is", "P ict", "ĠR apt", "Ġad apter", "Ġun m", "Ġcor pse", "Ġceleb rities", "D en", "at um", "ĠAp ocalypse", "ĠW ag", "lin ing", "Ġhorm ones", "R ub", "ĠX i", "ĠV aults", "20 8", "alky rie", "inos aur", "Ġfeed s", "v ity", "Ġdefe ating", "W ait", "Ġemphas ize", "ĠSteel ers", "yr inth", "le ys", "ĠWhe never", "Current ly", "ĠCl ock", "Ġcollect ively", "any on", "ĠJ P", "Ġment ality", "Ġdownload s", "Ġsurround ings", "ĠBarn es", "Ġflags hip", "Ġindic ators", "Ġgra pp", "Jan uary", "ĠElement al", "ĠAthen a", "ib al", "Ġs ights", "Ġcap ita", "ĠTreat y", "Ġvo iced", "ĠG az", "let te", "Ġy a", "Ġexp ired", "Leg end", "H ot", "n ature", "Ġunst able", "Ġ2 80", "à º", "Com ment", "AL E", "Ġquest s", "Ġhand ler", "n is", "Ġvers atile", "Ġconce al", "enge ance", "ĠInter active", "Ġobs essed", "ĠDog s", "Ġcr acked", "S ound", "s v", "ĠD ylan", "ro ads", "f x", "ĠCath olics", "ĠH ag", "Ġsl ammed", "Ġgl owing", "s ale", "Ġtiss ues", "ĠCh i", "ne e", "Ġc her", "s ic", "ur rection", "Ġb acon", "ul atory", ") .\"", "Ġir regular", "FOR M", "ass ed", "Ġintention al", "Ġcompens ate", "ĠSpe aking", "ĠS ets", "15 3", "Ġconvent ions", "b ands", "em ade", "Ġe cc", "ĠWin ston", "ĠAssass in", "ĠBelg ian", "Ġdepend ence", "Ġnic he", "Ġb ark", "ĠJ azz", "Ġdisadvant age", "Ġgas oline", "Ġ16 5", "çļ Ħ", "ess a", "mod ule", "ang ular", "O Y", "ĠTreat ment", "it as", "ol ation", "ĠArn old", "Ġfe ud", "ĠN est", "Ġthe atre", "ew ater", "Ġmin ors", "olic y", "ĠH aven", "div ision", "Ġtr unk", "F ar", "ĠP ull", "Ġcapt uring", "Ġ18 00", "ĠTe en", "Ġex empl", "Ġclin ics", "ĠB urg", "Ġsubst it", "Ġpay load", "ĠL av", "ĠT roy", "ĠW itness", "Ġfrag ments", "Ġpass words", "Ġg ospel", "ĠG in", "Ġten ants", "ol ith", "S ix", "Pre vious", "ĠAg es", "ĠDar win", "Ġbl at", "Ġem pathy", "sm ith", "b ag", "ĠE cho", "ĠC amb", "ĠM add", "ĠB oo", "Ġred e", "ĠBurn ing", "Ġsmooth ly", "ĠAd rian", "ĠV ampire", "ĠMon sters", "ste am", "Sty le", "M a", "re a", "ĠD war", "aly st", "urs or", "Ġelim ination", "Ġcrypt o", "ch t", "ĠE ternal", "âĢ¦ ]", "ĠS orce", "I ll", "N ER", "Ġu h", "Con clusion", "w age", "Ġresp ir", "Ġrem inis", "het ical", "Ġg y", "Ġutil ized", "ic idal", "Ġ19 00", "Ġhun ters", "ĠSw an", "ĠRe act", "Ġvis itor", "ĠThanks giving", "30 8", "Post s", "Ġh ips", "19 97", "om ers", "Ġkn ocking", "ĠVeh icle", "Ġt il", "Ġ13 8", "Ġm i", "ĠInvest igation", "ĠKen ya", "Ġcas ino", "Ġmot ives", "Ġreg ain", "re x", "Ġweek ends", "Ġstab bed", "bor o", "Ġexplo ited", "ĠHA VE", "ĠTe levision", "c ock", "Ġprepar ations", "Ġende av", "ĠRem ote", "ĠM aker", "ĠPro du", "ĠEv an", "Ġinform ational", "ĠLouis ville", "15 4", "ĠDream s", "Ġpl ots", "ĠRun ner", "Ġhur ting", "Ġacad emy", "ĠMont gomery", "n m", "ĠL anc", "ĠAl z", "2 10", "el ong", "Ġretail er", "Ġar ising", "Ġrebell ion", "Ġbl onde", "play ed", "Ġinstrument al", "C ross", "Ġret ention", "Ġtherape utic", "Ġse as", "Ġinfant ry", "ĠCl int", "Ġprompt ing", "Ġbit ch", "Ġst ems", "ĠK ra", "Ġthe sis", "ĠB og", "ru ed", "Ġk ings", "Ġcl ay", "ific ent", "ĠY ES", "ĠTh ing", "ĠCub s", "vey ard", "els h", "in arily", "ĠE y", "ĠRoll ing", "Ġev olving", "Ind ia", "Ġrecogn izes", "Ġgrad uation", "is ers", "Ġfert ility", "ĠMil an", "Comm and", "Ġbox ing", "Ġ19 43", "Ġgl uten", "ĠEm ir", "Ġid ol", "Ġcon ceived", "ĠCre ation", "Mer it", "udd y", "uss ions", "ĠLie utenant", "iet al", "Ġunch anged", "ĠSc ale", "ĠCrime a", "ball s", "ator ial", "Ġdepth s", "Ġempir ical", "Ġtrans m", "Ġuns afe", "miss ible", "com fort", "15 6", "Ġmechan ic", "00 2", "l ins", "Ġsm oked", "P os", "Ġslow ing", "Ġl av", "Tex as", "Ġche ating", "ĠMet ropolitan", "eth yl", "Ġdiscover ing", "as se", "Ġpen cil", "ĠPy ongyang", "Ġclos et", "ĠShe et", "ĠEnt ry", "ou stic", "Ġmy st", "er ate", "ari at", "Ġminer als", "Ġmusic ian", "ĠP ul", "ĠM az", "24 9", "Ġper missions", "Ġ iv", "en ary", "ick ers", "ĠB ing", "he a", "en able", "Ġgri ev", "Ġassert ed", "ĠColon el", "Ġaff idav", "w o", "Ġse ated", "ĠR ide", "Ġpaint ings", "ĠP ix", "Ġ13 7", "ish i", "umb ai", "g otten", "ĠEar l", "Ġin ning", "Ġc ensus", "Ġtrave lled", "ĠCons ult", "18 5", "b ind", "Ġsimpl icity", "Ġoverlook ed", "ĠHelp ful", "Ġmon key", "Ġoverwhelming ly", "Bl ood", "ĠFl int", "ĠJ ama", "ĠPres ent", "ĠR age", "ĠT A", "pt ive", "Ġturn out", "w ald", "ĠD olphins", "ĠV PN", "Ġon ion", "Ġcraft ing", "m ma", "ĠMerc ury", "Ġarr ange", "Ġalert s", "ĠO T", "zb ollah", "Ġg ases", "ĠRichards on", "s al", "l ar", "Ġfro st", "Ġlower ing", "Ġacc laim", "Ġstart ups", "ĠG ain", "ess ment", "Ġguard ian", "äº º", "ĠP ie", "ĠL inks", "Ġmer its", "Ġaw ake", "Ġparent al", "Ġexceed s", "Ġid le", "ĠPil ot", "Ġe Bay", "ĠAc cept", "ipe g", "C am", "ĠK ot", "Ġtrad ers", "olit ics", "unk er", "ĠP ale", "os i", "an mar", "Ġ19 47", "ĠF ell", "est ial", "it ating", "G F", "ĠS r", "if ted", "Ġconnect or", "ĠB one", "ill es", "2 60", "h ma", "Ġoverl ap", "ĠGit Hub", "Ġclean er", "ĠBapt ist", "ĠW AS", "Ġlung s", "Ñ ģ", "ĠB UT", "Ġc ite", "Ġpit ched", "reat ment", "Ġtro phies", "ĠN u", "38 6", "ĠPr ide", "Ġattend ees", "[ ]", "17 9", "Ġspat ial", "Ġpri zes", "ĠRel igion", "Ġshow case", "ĠC ategory", "vid ia", "T arget", "Pro perty", "? ,", "Ġf usion", "p ie", "ĠU CLA", "Ġsound track", "Ġprin cess", "ĠC aval", "sh ould", "Ġlim bs", "Back ground", "Ġlone ly", "Ġc ores", "ĠT ail", "she et", "Ġ13 2", "R a", "ãĤ «", "ĠB olt", "Ġbook ed", "Ġadmin ister", "Ġequ als", "w y", "Ġobserv ing", "ĠBar on", "ĠAd obe", "Ġv irgin", "ĠSocial ist", "M ove", "gh azi", "ĠLind a", "2 12", "Ġbre wing", "Ġmerch ants", "bur se", "Ġdiv or", "Ġmet als", "ĠN er", "Ġsum s", "ĠEn emy", "Ġen vision", "Ġgrant ing", "ĠH oney", "ĠSk yrim", "Ġsoc io", "gr aded", "Ġselect ive", "W ASHINGTON", "Ġ19 48", "ĠSir ius", "ĠG ross", "act ivity", "ĠI van", "Ġfur ious", "BS D", "ĠPre vious", "Ġrespons ive", "Ġchar itable", "Ġle aning", "ĠP ew", "Ġviol ates", "\\\\\\\\ \\\\\\\\", "ĠCom ing", "w ire", "Ġpo et", "Ġres olutions", "comm and", "ĠPortug uese", "Ġnick name", "Ġde af", "Feb ruary", "Ġrecogn ise", "Ġentire ty", "Ġseason al", "pl aced", "ĠTe legraph", "Ġmicro phone", "our ing", "Ġgr ains", "Ġgovern ed", "Ġpost p", "ĠW aters", "in ement", "Ġund ocumented", "ĠCom cast", "Ġf ox", "Ġassault s", "re on", "man y", "ĠJen kins", "ĠAny way", "Ġassess ments", "Ġdown s", "ĠM ouse", "Ġsuper b", "k t", "ĠD ow", "Ġtax ation", "4 01", "Ġsm iles", "Ġundert aken", "Ġex h", "Ġenthusi astic", "Ġtw ent", "Ġgovernment al", "Ġautonom y", "ĠTechn ologies", "ĠCh ain", "Ġpreval ent", "f b", "Ġnic otine", "og ram", "j ob", "Ġawa iting", "ĠMen u", "Ġdep uties", "k ov", "ish ops", "But ton", "ĠShan ghai", "Ġdies el", "ĠD uck", "R yan", "ĠPC s", "N F", "j ury", "ent e", "Ġinacc urate", "edd y", "Wh atever", "Ġshow c", "ĠN ad", "od us", "et r", "Ġplaint iffs", "ĠW OR", "ĠAss ange", "Ġpriv at", "Ġpremium s", "Ġt am", "UR L", "Ġel ites", "ĠR anger", "otten ham", "ĠH off", "ĠAt hens", "Ġdefin ite", "Ġs ighed", "Ġeven ly", "2 11", "ĠAm ber", "ak ia", "Ġmail ing", "Ġcr ashing", "ĠConfeder ate", "ru gged", "W al", "ĠDep ths", "Ġjuven ile", "Ġreact or", "Introdu ction", "ĠDel uxe", "19 95", "ĠS anchez", "ĠM ead", "iv able", ": -", "ĠPlan ning", "ĠT rap", "qu in", "ĠProt ect", "ve red", "In formation", "Ġkid ney", "inn amon", "l as", "Ġpolic ing", "Ġtoler ate", "ĠQ i", "Ġbi ased", "F ort", "ĠK i", "s ave", "Ġprivile ged", "Ġbe asts", "ĠGl as", "ĠC inem", "Ġcome back", "Sund ay", "Ġext inction", "h ops", "Ġtrans mit", "Ġdoub les", "ĠFl at", "16 7", "Ġdis puted", "Ġinjust ice", "f oo", "V ict", "role um", "ĠJul ie", "Con text", "ĠR arity", "iss ue", "Comp onent", "Ġcounsel ing", "an ne", "d ark", "Ġobject ions", "u ilt", "Ġg ast", "Ġpl ac", "Ġun used", "ãĥ ĩ", "ĠT rial", "ĠJ as", "hed ral", "ob b", "Ġtempor al", "ĠPR O", "ĠN W", "ĠAnn iversary", "L arge", "Ġther m", "Ġd avid", "Ġsystem ic", "ĠSh ir", "m ut", "ĠNe pt", "add ress", "Ġscan ning", "Ġunderstand able", "Ġcan vas", "C at", "ĠZ oo", "Ġang els", "L O", "ĠStat ement", "ĠS ig", "ov able", "ĠA way", "sh aring", "ocr ats", "st ated", "Ġweigh ing", "N or", "w ild", "B ey", "Ġaston ishing", "ĠReyn olds", "Ġop ener", "Ġtrain er", "Ġsurg ical", "p n", "Ġadjust ing", "whe el", "Ġf rown", "erv ative", "Ġsusp end", "With in", "te in", "Ġobst acle", "Ġliber ties", "ym es", "Ġur anium", "ans om", "an ol", "ub a", "ĠL oss", "Ġa rous", "ĠHend erson", "W ow", "s pl", "c ur", "Ġ Ń", "Ġtheir s", "Dam age", "Ġdownload ing", "Ġdisc ern", "ĠSt o", "ĠFl a", "Ġh ath", "ĠA j", "Ġun pleasant", "Europe an", "exp ensive", "Ġscreens hot", "ĠU V", "Ġall ied", "ĠPers ian", "Ġmonop oly", "Ġat om", "ĠReds kins", "\"> <", "Ġcan cell", "Ġcinem a", "13 1", "f air", "ĠAlf red", "Ġd uck", "arg s", "22 3", "ĠIS I", "Ġsign aling", "in ar", "Ġlaugh s", "Ġfor wards", "Ġreck less", "Ġlisten ers", "at ivity", "Ġvast ly", "n ant", "L ess", "ĠHun ting", "ĠScient ific", "IT ED", "Ġkn ight", "ĠH TC", "us a", "t mp", "Ġr ude", "ĠLegend ary", "Ġar ises", "B ad", "ĠCl aim", "pe g", "Ġreal ities", "Th ink", "Ġ °", "Ġro de", "Ġstri ve", "Ġan ecd", "Ġshort s", "Ġhypot hes", "Ġcoord inated", "ĠGand hi", "ĠF PS", "R ED", "Ġsuscept ible", "Ġshr ink", "ĠCh art", "Hel p", "Ġ ion", "de ep", "rib es", "ĠK ai", "ĠCustom er", "Sum mary", "Ġc ough", "w ife", "Ġl end", "Ġposition ing", "Ġlot tery", "ĠC anyon", "Ġf ade", "Ġbron ze", "ĠKenn y", "Ġbo asts", "ĠEnh anced", "rec ord", "Ġemer gence", "Ġa kin", "ĠB ert", "it ous", "âĸ ij", "Ġst ip", "Ġexch anged", "om ore", "als h", "Ġreserv oir", "Ġstand point", "W M", "Ġiniti ate", "Ġdec ay", "Ġbrew ery", "Ġter ribly", "Ġmort al", "lev ard", "Ġrev is", "N I", "el o", "Ġconf ess", "ĠMS NBC", "Ġsub missions", "Cont roller", "Ġ20 2", "ĠR uth", "} );", "ĠAz ure", "Ġ .\"", "20 6", "ĠMarket ing", "Ġl aund", "ien cies", "Ġrenown ed", "ĠT rou", "ĠN GO", "ble ms", "Ġterr ified", "Ġwar ns", "Ġper t", "Ġuns ure", "4 80", "ale z", "ult z", "ĠOut side", "Ġst yl", "ĠUnder ground", "Ġp anc", "Ġd ictionary", "Ġf oe", "rim inal", "ĠNor wegian", "Ġj ailed", "Ġm aternal", "é e", "ĠLu cy", "c op", "Ch o", "Ġuns igned", "ĠZe lda", "ĠIns ider", "ĠContin ued", "Ġ13 3", "ĠNar uto", "ĠMajor ity", "16 9", "ĠW o", "ãĤ ĵ", "Ġpast or", "Ġinform al", "Ð ½", "an throp", "jo in", "ãģ Ĺ", "it ational", "N P", "ĠWrit ing", "f n", "ĠB ever", "19 5", "Ġy elling", "Ġdr astically", "Ġe ject", "Ġne ut", "Ġth rive", "ĠFre qu", "ou x", "Ġpossess es", "ĠSen ators", "ĠD ES", "ĠSh akespeare", "ĠFran co", "ĠL B", "uch i", "Ġinc arn", "Ġfound ers", "F unction", "Ġbright ness", "ĠB T", "Ġwh ale", "ĠThe ater", "m ass", "ĠD oll", "S omething", "Ġecho ed", "ĠHe x", "c rit", "af ia", "Ġgodd ess", "Ġele ven", "ĠPre view", "ĠAur ora", "Ġ4 01", "uls ive", "ĠLog an", "in burgh", "ĠCent ers", "ĠON LY", "ĠA id", "Ġparad ox", "Ġh urd", "ĠL C", "D ue", "c ourt", "Ġoff ended", "Ġeval uating", "ĠMatthew s", "Ġto mb", "Ġpay roll", "Ġextra ction", "ĠH ands", "if i", "Ġsuper natural", "ĠCOM M", "] =", "dog s", "Ġ5 12", "ĠMe eting", "Rich ard", "ĠMax imum", "Ġide als", "Th ings", "m and", "ĠReg ardless", "Ġhum ili", "b uffer", "L ittle", "ĠD ani", "ĠN ak", "Ġliber ation", "ĠA be", "ĠO L", "Ġstuff ed", "ac a", "ind a", "raph ic", "Ġmos qu", "Ġcampaign ing", "Ġoccup y", "S qu", "r ina", "ĠW el", "ĠV S", "Ġphys ic", "Ġp uls", "r int", "oad ed", "ET F", "ĠArch ives", "Ġven ues", "h ner", "ĠTur bo", "Ġl ust", "Ġappeal ed", "que z", "il ib", "ĠTim othy", "Ġo mn", "d ro", "Ġobs ession", "ĠSav age", "19 96", "Gl obal", "J es", "2 14", "Ġsl iding", "Ġdisapp ro", "ĠMag ical", "Ġvolunt arily", "g b", "ane y", "Ġprop het", "ĠRe in", "ĠJul ia", "ĠW orth", "aur us", "Ġb ounds", "ie u", ")) )", "Ġcro re", "ĠCitiz en", "S ky", "Ġcolumn ist", "Ġseek ers", "ond o", "IS A", "ĠL ength", "Ġnost alg", "Ġnew com", "Ġdet rim", "ent ric", "3 75", "ĠG E", "Ġaut op", "Ġacadem ics", "App Data", "ĠS hen", "Ġid iot", "ĠTrans it", "Ġteasp oon", "W il", "K O", "ĠCom edy", "> ,", "Ġpop ulated", "W D", "Ġp igs", "ĠO culus", "Ġsymp athetic", "Ġmar athon", "19 8", "Ġseiz ure", "s ided", "Ġd op", "irt ual", "L and", "ĠFl oor", "osa urs", "... ]", "Ġl os", "Ġsubsid iary", "E Y", "ĠPart s", "ĠSt ef", "ĠJud iciary", "Ġ13 4", "Ġmir rors", "Ġk et", "t imes", "Ġneuro log", "Ġc av", "ĠGu est", "Ġtum or", "sc ill", "ĠLl oyd", "E st", "Ġcle arer", "Ġstere otypes", "Ġd ur", "not hing", "Red dit", "Ġnegoti ated", "---------------- --------", "23 5", "Ġfl own", "ĠSe oul", "ĠRes ident", "ĠS CH", "Ġdisappear ance", "ĠV ince", "g rown", "Ġgrab s", "r il", "ĠInf inite", "ĠTw enty", "Ġpedest rian", "Ġjer sey", "ĠF ur", "ĠInf inity", "ĠEll iott", "Ġment or", "Ġmor ally", "Ġob ey", "sec ure", "iff e", "Ġantib iotics", "ang led", "ĠFre eman", "ĠIntrodu ction", "J un", "Ġm arsh", "ic ans", "ĠEV ENTS", "och ond", "W all", "icult y", "Ġmisdem eanor", "Ġl y", "Th omas", "ĠRes olution", "Ġanim ations", "ĠD ry", "Ġinter course", "ĠNew castle", "ĠH og", "ĠEqu ipment", "17 7", "Ġterrit orial", "Ġarch ives", "20 3", "Fil ter", "ĠMun ich", "Ġcommand ed", "ĠW and", "Ġpit ches", "ĠCro at", "Ġrat ios", "ĠM its", "Ġaccum ulated", "ĠSpecific ally", "Ġgentle man", "acer b", "Ġp enn", "Ġa ka", "ĠF uk", "Ġinterven e", "ĠRef uge", "ĠAlz heimer", "Ġsuccess ion", "oh an", "d oes", "L ord", "Ġsepar at", "Ġcorrespond ence", "Ġsh iny", "P rior", "Ġs ulf", "Ġmiser able", "Ġded ication", "( ).", "Ġspecial ists", "Ġdefect s", "ĠC ult", "ĠX ia", "Ġje opard", "ĠO re", "Ab ility", "Ġle ar", "Ġamb itions", "ĠB MI", "ĠArab s", "Ġ19 42", "Ġpres ervation", "ific ate", "Ġash amed", "l oss", "ĠRest aur", "Ġrese mble", "Ġen rich", "ĠK N", "ĠCl an", "fl oat", "Ġplay able", "IT T", "Ġharm ony", "arr ison", "ĠWe instein", "w ere", "Ġpoison ing", "ĠCom put", "ĠWord Press", "m ajor", "ĠVal ve", "F an", "ĠTh row", "ĠRom ans", "ĠDep ression", "ad os", "Ġtort ured", "Ġbal ancing", "bott om", "Ġacqu iring", "ĠMon te", "ard i", "Ġa ura", "Ġ# #", "ĠStand ing", "ĠAtl as", "C F", "Ġintr ins", "ĠBen ghazi", "Ġcamp ing", "Ġt apped", "bl ade", "st rous", "ĠR abb", "ĠW ritten", "t ip", "ĠNe igh", "ster dam", "ĠAll ow", "ĠHe aling", "ĠR hod", "n um", "Ġcaffe ine", "ĠPer cent", "Ġbo o", "Ġapp les", "30 5", "Ġwel coming", "Ġappl aud", "Ġa usterity", " ±", "ĠRe ality", "ef e", "å ®", "Ġsu cks", "Ġtab s", "ĠPay Pal", "Ġback pack", "Ġgif ted", "abul ary", "ĠSc out", "ir teen", "Ġch in", "Ġo mitted", "Ġnegative ly", "Ġaccess ing", "ĠE arn", "Ġambul ance", "Ġhead phones", "Ġ20 5", "ĠRef resh", "p resident", "ĠKit chen", "ĠEnt ered", "ĠS nyder", "00 5", "om ical", "Ġborrow ed", "ĠN em", "Ġav iation", "Ġst all", "rim ination", "Ġuniform s", "it ime", "ĠSim mons", "ener gy", "ab lished", "y y", "qual ified", "Ġrall ies", "ĠSt uart", "fl ight", "Ġgang s", "r ag", "Ġv ault", "lu x", "ĠCom par", "Ġdesign ation", "20 9", "ĠJ os", "d ollar", "z ero", "Ġwell s", "30 3", "Ġconstitu ents", "Ġhe ck", "Ġc ows", "Ġcommand ers", "Ġdifferent ial", "ĠC atherine", "29 9", "Ġval ve", "Ġbr ace", "Ġperspect ives", "c ert", "f act", "icular ly", "ĠMc N", "pl anes", "Ġint ric", "Ġpe as", "ov an", "Ġtoss ed", "ret ch", "ĠL opez", "Ġunf amiliar", "de ath", "ĠA part", "ĠCh ang", "Ġrelie ved", "rop he", "Ġair ports", "Ġfre ak", "ut il", "M ill", "ĠCh in", "ĠOw en", "m ale", "ĠBro ken", "ĠWind s", "ro b", "r ising", "Ġfire fighters", "Ġauthor itarian", "Ġ14 8", "Bit coin", "ex ternal", "Ġbrow sers", "iche ver", "or ian", "Ġun b", "Ġpo ke", "ĠZ ot", "M id", "ĠPop ular", "Ġco vert", "Ġcont ributes", "Ġ6 50", "Ġcont ention", "G ate", "Ġcons oles", "Ġchrom os", "ĠI X", "Ġvis ually", "ĠE isen", "Ġjewel ry", "Ġdeleg ation", "Ġacceler ate", "ĠR iley", "Ġsl ope", "Ġind oor", "it ially", "Ġhuge ly", "Ġtun nels", "Ġfin ed", "Ġdirect ive", "Ġfore head", "ustom ed", "Ġsk ate", "Mus ic", "g as", "Ġrecogn izing", "am bo", "Ġover weight", "ĠGr ade", "Ù Ĭ", "Ġsound ing", "Ġlock ing", "ĠR EM", "St ore", "Ġexc av", "ĠLike wise", "ĠL ights", "Ġel bow", "ĠSupp ly", "w ic", "Ġhands ome", "19 94", "C oll", "Ġadequ ately", "ĠAssoci ate", "Ġstri ps", "Ġcrack down", "Ġmar vel", "ĠK un", "Ġpass ages", "@@ @@", "ĠT all", "Ġthought ful", "names e", "Ġprost itution", "bus iness", "Ġball istic", "person al", "c ig", "iz ational", "R ound", "ĠÂłĠÂł ĠÂłĠÂł", "ĠCole man", "Ġadm itting", "ĠPl ug", "Ġbit coins", "ĠSu z", "Ġfair ness", "Ġsupp lier", "Ġcatast rophic", "ĠHel en", "o qu", "M arc", "ĠArt icles", "g ie", "Ġend angered", "Ġdest iny", "ĠVol t", "ol ia", "ax is", "Ġche at", "Ġun ified", "IC O", "qu ote", "30 2", "ĠS ed", "Ġsupp ression", "Ġanaly zing", "Ġsqu at", "Ġfig uring", "Ġcoordin ates", "Ġch unks", "Ġ19 46", "Ġsub p", "Ġw iki", "ĠFor bes", "ĠJ upiter", "ĠE rik", "im er", "ĠCom mercial", "\\ )", "Ġlegitim acy", "Ġd ental", "ĠMe an", "Ġdefic its", "5 50", "Orig inally", "ĠHor ror", "Ġcontam ination", "ll ah", "Ġconf isc", "ĠCl are", "T B", "ĠF ailed", "an ed", "Ġrul er", "ĠCont roller", "Ġfemin ists", "F ix", "g ay", "20 7", "Ġr abbit", "Th ird", "ownt own", "Ġgl ue", "Ġvol atile", "Ġsh ining", "Ġf oll", "Ġimp aired", "Ġsup ers", "æ Ī", "Ġcl utch", "ļé ĨĴ", "Ġpro let", "Ġ( !", "Ġy elled", "ĠK iev", "ĠEr n", "ĠSh ock", "K B", "Ġsit uated", "qu ery", "ĠN as", "Ġan nex", "char acter", "ĠHol iday", "Ġautom ation", "ĠJ ill", "ĠRem astered", "Ġl inem", "Ġwild erness", "ĠHor izon", "ĠGu inea", "A Z", "Ġmain land", "Ġsec recy", "LE ASE", "Ġp unk", "ĠProv ince", "( ),", "Spe ed", "Ġhand ing", "ĠSeb ast", "S ir", "r ase", "Ġj ournals", "Ġcon gest", "ĠT ut", "ir rel", "Ġschizophren ia", "Ġmis ogyn", "health y", "I ron", "Ġreact ed", "- $", "25 2", "Ġpl ural", "Ġpl um", "Ġbarg ain", "Ġground ed", "f inder", "Ġdis se", "ĠL az", "O OD", "Ġat roc", "F actory", "Ġmin ions", "Ġo ri", "ĠB rave", "ĠP RE", "ĠMy anmar", "ĠH od", "Ġexped ition", "Ġexpl ode", "ĠCo ord", "Ġext r", "ĠB rief", "ĠAD HD", "Ġhard core", "feed ing", "Ġd ile", "ĠF ruit", "Ġvacc ination", "ĠM ao", "osp here", "Ġcont ests", "- |", "Ġf ren", "isp here", "R om", "ĠSh arp", "ĠTre nd", "Ġdis connect", "âĢ¢ âĢ¢", "Ġper secution", "Ear th", "Ġhealth ier", "38 4", "Ġc ob", "ĠTr inity", "OW S", "AN N", "Ġspecial ty", "Ġg ru", "Ġcooper ative", "wh y", "Start ing", "ĠIss ues", "st re", "ens or", "Ġ18 5", "Ad v", "! ?", "ĠRe vel", "em ia", "ĠH ulk", "Ġcelebr ations", "ĠS ou", "ra ud", "ĠKle in", "Ġun real", "con text", "Ġpartners hips", "Ġadop ting", "t ical", "Ġspl ash", "ĠHe zbollah", "c ategory", "cycl op", "xt on", "ĠD ot", "urd y", "t z", "Ġenvelop e", "ĠN L", "â ķ", "Ġwhere in", "Spe c", "18 4", "Ġte lev", "al iation", "Ġmyth s", "å °", "Ġrig orous", "Ġcommun icating", "Ġobser ver", "Ġre he", "ĠW ash", "Ġapolog ized", "ĠT in", "Ġexpend itures", "work ers", "d ocument", "Ġhes itate", "ĠLen in", "Ġunpredict able", "Ġrenew al", "cl er", "ok ia", "ĠCON T", "Ġpost season", "Tok ens", "Ġex acerb", "Ġbet ting", "Ġ14 7", "Ġelev ation", "W ood", "ĠSol omon", "19 4", "00 4", "out put", "Ġredu nd", "ĠM umbai", "Ġp H", "Ġreprodu ce", "ĠD uration", "MA X", "Ġb og", "C BS", "ĠBal ance", "ĠS gt", "ĠRec ent", "Ġc d", "Ġpo pped", "Ġincomp et", "pro p", "ay an", "g uy", "Pac ific", "Ġty r", "Ġ{ {", "ĠMy stic", "ĠD ana", "Ġmast urb", "Ġge ometry", "à ¢", "ĠCor rect", "Ġtraject ory", "Ġdistract ed", "Ġf oo", "ĠW elsh", "L uc", "m ith", "Ġrug by", "Ġrespir atory", "Ġtri angle", "Ġ2 15", "Ġunder graduate", "ĠSuper ior", "ch anging", "_ -", "Ġright ly", "Ġrefere e", "Ġluc rative", "Ġun authorized", "Ġresemb les", "ĠGN U", "ĠDer by", "Ġpath ways", "ĠL ed", "Ġend urance", "Ġst int", "Ġcollect or", "F ast", "Ġd ots", "Ġnational s", "ĠSec urities", "Ġwh ip", "Par am", "Ġlearn s", "M agic", "Ġdetail ing", "m oon", "Ġbroadcast ing", "Ġb aked", "26 5", "hol m", "ĠS ah", "ĠHus sein", "ĠCourt esy", "17 4", "Ġ14 6", "Ġge ographic", "pe ace", "Ġjud ging", "ĠS tern", "B ur", "Ġstory line", "G un", "ĠSt ick", "24 5", "30 7", "ãĤ´ ãĥ³", "ĠAdminist rator", "Ġbur nt", "Ġp ave", "ch oes", "Ex ec", "Ġcamp uses", "Res ult", "Ġmut ations", "ĠCh arter", "Ġcapt ures", "Ġcomp ares", "Ġbad ge", "S cient", "Ġer ad", "ier y", "o i", "ett es", "ĠE state", "Ġst rap", "Ġproud ly", "Ġf ried", "Ġwithd rawn", "ĠV oy", "ph ony", "It ems", "ĠP ierce", "b ard", "Ġann otation", "ant on", "ill on", "Im pro", "... )", "Ġhapp ier", "---- --", "ad just", "Ġstaff ers", "Ġactiv ism", "Ġper f", "Ġal right", "N eed", "Ġcomm ence", "Ġopio id", "ĠAm anda", "E s", "ĠP ars", "ĠK aw", "W orks", "24 8", "Ġind o", "t c", "end ant", "ĠM oto", "Ġlegal ization", "OT E", "Ġtask ed", "Ġt sp", "ĠACT IONS", "16 6", "Ġrefres hing", "ĠN R", "ĠPere z", "Ġinfring ement", "S Y", "List en", "in ning", "k u", "Ġrot ate", "pro gram", "ar ah", "Des ign", "Ġ( £", "Ġst oring", "Ġwar rants", "Ġjud gement", "ĠB rist", "us ually", "ph oto", "ĠR an", "ĠP ine", "Ġoutrage ous", "ĠValent ine", "lu ence", "ĠEvery body", "Al tern", "Ġrele vance", "Ġtermin ated", "Ġd essert", "Ġfulf illed", "Ġprosecut ed", "ĠW ords", "Ġm igrant", "Ġcultiv ation", "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ", "idel ity", "ĠV ern", "ĠLog in", "Ġmetaph or", "ĠT ip", "Ġrecru its", "ĠP ig", "rib ing", "Ġenthusi asts", "ex per", "Ġfright ening", "ĠH air", "ans on", "str ate", "Ġh i", "He ight", "Ġown ing", "n one", "Ġdis like", "Ġkn ives", "pher d", "Ġloud ly", "ĠAP Is", "Dis play", "ĠL ac", "ĠUS S", "ab l", "ver ages", "J ew", "Ġ17 2", "ĠHist orical", "at oon", "ĠPhys ics", "in tern", "Ġwarm th", "Ġto pp", "D M", "Ġgun man", "Ġem peror", "od i", "ãĥ £", "in atory", "ĠR ib", "Ġ13 1", "ĠSat urn", "ĠSh ining", "Ġw aking", "Qu otes", "Ġcomed ian", "en berg", " ½", "Ġbelie vers", "Ġpaper work", "c ustom", "Ġle v", "Ġl ament", "Ġpour ing", "22 2", "p olitical", "ĠSupp lement", "m aid", "Ġcruel ty", "Ġt read", "ys ics", "A w", "rit es", "Ġmod ifier", "ĠP osition", "Ad am", "l b", "ub s", "Ġimper fect", "Ġcl usters", "ĠEngine er", "ĠC herry", "Ġinaug uration", "ĠS au", "Ġembod iment", "ĠUn cle", "Ġover r", "Ġexplos ions", "c ule", "ĠPrinc eton", "ĠAndre a", "Ġincorrect ly", "Ġearn est", "Ġpil gr", "ĠS print", "Ġslee ve", "Ġhe ars", "ĠAm azing", "Ġbrow sing", "ag in", "Ġhom eland", "Ġha w", "Ġd iving", "ist ered", "17 8", "Ġbarg aining", "ĠArc ade", "Ġdeleg ate", "ters on", "................................ ................................", "ĠJackson ville", "27 5", "Ġst agn", "Ġad am", "ĠSher man", "C B", "Ġsub urb", "ĠFood s", "Ġconver ting", "ĠAr ist", "Ġch ambers", "l ove", "Ġam ino", "ĠG an", "Ġmad ness", "m c", "ĠUS E", "def ined", "Ġul tr", "ind ust", "Ġw olves", "l ance", "Add itionally", "Ġcr acks", "as ia", "ĠRe ason", "ĠP ump", "Ġaccident al", "ĠL aser", "ĠR id", "Ġinitial ized", "ell i", "Ġun named", "Ġn oun", "ĠPass ed", "Ġhost age", "ĠEth iop", "sh irts", "Ġun rel", "ĠEmb assy", "Ġ19 41", "Ġat oms", "Ġpur ported", "16 4", "ĠF i", "Ġgall ons", "ĠMon ica", "Ġp g", "en ment", "Ġsort ed", "ĠG ospel", "Ġhe ights", "Ġtr aced", "Ġunder going", "She ll", "Ġs acks", "Ġproport ions", "Ġhall uc", "F ont", "ac et", "Ġwar mer", "ĠIN TER", "Ġgrab bing", "Pl ug", "Ġreal ization", "ĠBur ke", "Ġen chant", "AT ER", "ĠSe ed", "Ġabund ant", "F M", "Ġc ivic", "V s", "is i", "Ġv ow", "Ġre per", "ĠPartners hip", "Ġpenet ration", "Ġax e", "Ġsh attered", "ĠZ ombies", "Ġv inyl", "ĠAl ert", "e on", "Ġoblig ed", "ĠIll ust", "ĠPl aza", "ĠFront ier", "Ġdavid jl", "ĠSer ial", "ĠH av", "ĠNut rition", "B i", "Ġâĸ Ī", "ĠJ ays", "lin ux", "Ġhur ry", "Ġv oy", "Ġhop eless", "ĠSte alth", "Ġ ãģ", "ess ors", "tt le", "b org", "ĠSaf ari", "f ell", "Ġw ary", "d ue", "ĠAb ove", "H a", "E LL", "Ġnot or", "ĠW on", "T oo", "Ġoccup ations", "Ġposs essions", "Ġinv iting", "Ġpred ators", "Ġacceler ated", "Ġ15 7", "uter te", "ĠC ube", "e ast", "acc ount", "G ive", "Ġtrans plant", "red ients", "id able", "Ġscreens hots", "ĠG und", "ĠF S", "Ġtravel ers", "Ġsens ory", "ĠF iat", "ĠRock ets", "İ ĭ", "_ {", "F riend", "Ġchar ming", "AL S", "Ġenjoy ment", "m ph", "Ġ5 000", "ĠRE G", "Ù Ĩ", "b ia", "Ġcomp ilation", "ro st", "ĠV P", "ĠSch ne", "201 9", "Ġcop ying", "M ORE", "ĠFl ore", "f alls", "2 15", "t otal", "Ġdis ciples", "d ouble", "Ġexceed ing", "Ġsm ashed", "Ġconcept ual", "ĠRom ania", "ĠB rent", "ĠI CE", "ĠT ou", "Ġg rap", "Ġn ails", "18 9", "ãĥ ĺ", "Ġproc ure", "e ur", "Ġconfir ming", "ĠC ec", "aw i", "ĠEd en", "Ġn g", "Ġengine ered", "at ics", "Ġhook ed", "Ġdisgust ing", "ĠMur der", "ãĤ ¿", "L ibrary", "Ġ16 8", "Al most", "hem atic", "Men u", "ĠNot re", "ĠJ ur", "Ġkidn apped", "Ġhack er", "ĠJ ade", "Ġcreep y", "Ġdraw ings", "ĠSpons or", "Ġcycl ists", "ĠGob lin", "Ġoptim ized", "Ġst aged", "ĠMc D", "bet ween", "A ge", "en o", "S ex", "ĠW ide", "n ings", "av is", "Ġincap able", "ĠK ob", "Ġreward ing", "ĠL one", "oles cent", "Ġcontract ed", "Ġstick y", "J ose", "B all", "f est", "ĠIn put", "ĠRec ently", "Ġto mat", "squ are", "App lication", "Ġnit rogen", "Ġdupl icate", "ĠRec on", "ĠD ear", "L ondon", "Ġint ra", "Ġd ock", "Ġout reach", "ĠM illion", "Ġmamm als", "am pton", "V AL", "Ġsn aps", "Ġd os", "ĠWh ole", "ĠRead y", "T ry", "ĠWinn ipeg", "ear ance", "Ġinc urred", "ren ched", "ĠNS W", "il ot", "rain e", "Ġc ube", "g ot", "Ġrun way", "etermin ed", "ĠHaw ks", "Ġsurviv or", "ĠW ish", "ĠD in", "ĠDE F", "ĠV ault", "18 7", "Ġmush rooms", "Ġcris p", "be y", "ĠDisco very", "Ġdevelopment al", "Ġparad igm", "Ġcha otic", "ĠT su", "Ġ3 33", "b ons", "Ġbacter ial", "Ġcomm its", "Ġcos mic", "Ġme ga", "oc ative", "ĠP aint", "ophob ic", "Ġv ain", "Ġcar ved", "ĠTh ief", "ĠG ul", "ows hip", "Ġc ites", "ĠEd inburgh", "Ġdimin ished", "Ġacknowled ges", "ĠK ills", "Ġmic row", "ĠHer a", "Ġsen iors", "Ġwhere by", "H op", "at ron", "Ġun available", "ĠN ate", "Ġ4 80", "Ġsl ated", "ĠRe becca", "ĠB attery", "Ġgram mar", "Ġhead set", "Ġcurs or", "Ġex cluding", "any e", "aunder ing", "eb in", "Ġfeas ible", "ĠPub lishing", "ĠLab s", "ĠCl iff", "ĠFerr ari", "Ġp ac", "vis ible", "mark ed", "pe ll", "Ġpol ite", "Ġstagger ing", "ĠGal actic", "Ġsuper st", "Ġpar an", "ĠOffic ers", "ãĢ ģ", "Ġspecific s", "ul us", "23 9", "ĠP aste", "AM P", "ĠPan ama", "ĠDe lete", "angu ard", "rest rial", "Ġhero ic", "ĠD y", "ا ÙĦ", "Ġincumb ent", "Ġcr unch", "t ro", "Ġsc oop", "Ġblog ger", "Ġsell ers", "ure n", "Ġmedic ines", "ĠC aps", "ĠAnim ation", "ox y", "Ġout ward", "Ġinqu iries", "22 9", "Ġpsych ologist", "ĠS ask", "ev il", "Ġcontam inated", "ãĤ ¨", "he rence", "Ġbrand ed", "ĠAbd ul", "z h", "Ġparagraph s", "Ġmin s", "Ġcor related", "er b", "Ġimp art", "Ġmil estone", "ĠSol utions", "ot le", "Ġunder cover", "Ġmar ched", "ĠCharg ers", "f ax", "ĠSec rets", "Ġr uth", "we ather", "Ġfemin ine", "Ġsh am", "Ġprest igious", "igg ins", "Ġs ung", "hist ory", "ett le", "gg ie", "Ġout dated", "ol and", "Ġper ceptions", "ĠS ession", "ĠDod gers", "u j", "ĠE ND", "D oc", "Ġdefic iency", "Gr and", "ĠJ oker", "Ġretro spect", "Ġdiagn ostic", "Ġharm less", "Ġro gue", "ĠA val", "E qu", "Ġtrans c", "ĠRoberts on", "ĠDep ending", "ĠBurn s", "iv o", "Ġhost ility", "F eatures", "ĵ ĺ", "Ġdis comfort", "ĠL CD", "spec ified", "ĠEx pect", "3 40", "Ġimper ative", "ĠReg ular", "Ch inese", "Ġstate wide", "Ġsy mm", "Ġlo ops", "Ġaut umn", "N ick", "Ġsh aping", "Ġqu ot", "Ġc herry", "ĠCross ref", "è¦ ļéĨĴ", "Stand ard", "he ed", "ĠD ell", "ĠViet namese", "Ġo st", "ĠV alkyrie", "O A", "Ass ad", "Ġreb ound", "ĠTra ffic", "pl aces", "æ ĺ", "ĠB uc", "17 2", "Ġshel ters", "Ġins isting", "ĠCertain ly", "ĠKenn eth", "ĠT CP", "Ġpen al", "ĠRe play", "he ard", "Ġdial ect", "iz a", "ĠF Y", "it cher", "ĠD L", "Ġspir al", "Ġquarterback s", "Ġh ull", "Ġgo ogle", "Ġto dd", "ĠSter ling", "ĠPl ate", "Ġsp ying", "mb ol", "ĠReal m", "ĠPro ced", "ĠCr ash", "Ġtermin ate", "Ġprotest ing", "C enter", "gu ided", "Ġun cover", "Ġboy cott", "Ġreal izes", "s ound", "Ġpret ending", "ĠV as", "19 80", "Ġfram ed", "Ġ13 9", "Ġdesc ended", "Ġrehab ilitation", "Ġborrow ing", "ĠB uch", "Ġbl ur", "R on", "ĠFro zen", "en za", "Ch ief", "ĠP oor", "Ġtransl ates", "M IN", "Ġ2 12", "J ECT", "Ġerupt ed", "Ġsuccess es", "S EC", "Ġpl ague", "Ġg ems", "d oms", "Ġstret ches", "ĠSp y", "Ġstory telling", "C redit", "ĠP ush", "Ġtra ction", "Ġin effective", "ĠL una", "Ġt apes", "Ġanaly tics", "erc ise", "Ġprogram mes", "ĠCar bon", "Ġbeh old", "he avy", "ĠConserv ation", "ĠF IR", "Ġs ack", "ter min", "ric ks", "Ġhous ed", "Ġunus ually", "I ce", "Ġexecut ing", "ĠMor oc", "ed ay", "Ġed itions", "Ġsm arter", "ĠB A", "Ġout law", "Ġvan ished", "ib a", "AL SE", "ĠSil va", "23 8", "C ould", "Ġphilos opher", "Ġevac uated", "Sec ret", "14 2", "Ġvis as", "ãĤ ¬", "ĠM alt", "ĠClear ly", "ĠN iger", "ĠC airo", "ĠF ist", "3 80", "ĠX ML", "aut o", "it ant", "Ġrein forced", "Rec ord", "ĠSurviv or", "G Hz", "Ġscrew s", "parent s", "Ġo ceans", "ma res", "Ġbra kes", "vas ive", "Ġhell o", "ĠS IM", "rim p", "Ġo re", "ĠArm our", "24 7", "Ġterr ific", "Ġt ones", "14 1", "ĠMin utes", "Ep isode", "Ġcur ves", "Ġinflamm atory", "Ġbat ting", "ĠBeaut iful", "L ay", "Ġunp op", "v able", "Ġr iots", "ĠTact ics", "b augh", "ĠC ock", "Ġorg asm", "ĠS as", "Ġconstruct or", "et z", "G ov", "Ġant agon", "Ġthe at", "Ġde eds", "ha o", "c uts", "ĠMc Cl", "Ġu m", "ĠScient ists", "Ġgrass roots", "ys sey", "\"] =>", "Ġsurf aced", "Ġsh ades", "Ġneighb ours", "Ġad vertis", "oy a", "Ġmer ged", "Up on", "Ġg ad", "Ġanticip ate", "Any way", "Ġsl ogan", "Ġdis respect", "I ran", "ĠT B", "act ed", "Ġsubp oen", "medi ately", "OO OO", "Ġwa iver", "Ġvulner abilities", "ott esville", "ĠHuff ington", "J osh", "ĠD H", "M onday", "ĠEll en", "K now", "x on", "it ems", "22 8", "Ġf ills", "ĠN ike", "Ġcum ulative", "and als", "I r", "Ġ ì", "Ġfr iction", "ig ator", "Ġsc ans", "ĠVi enna", "ld om", "Ġperform ers", "P rim", "Ġb idding", "M ur", "Ġlean ed", "ĠPri x", "al ks", "Ġ[ âĢ¦]", "ĠTw itch", "ĠDevelop er", "ĠG ir", "Ġcall back", "Ab stract", "Ġacc ustomed", "Ġfreed oms", "ĠP G", "ur acy", "Ġl ump", "is man", ",, ,,", "19 92", "ĠR ED", "Ġwor m", "M atch", "ĠPl atinum", "I J", "ĠOwn er", "Tri via", "com pl", "Ġnew born", "Ġfant as", "O wn", "Ġ19 59", "Ġsymp ath", "Ġub iqu", "Ġoutput s", "Ġal lev", "Ġpr ag", "K evin", "Ġfav ors", "Ġbur ial", "Ġn urt", "so lete", "c ache", "Ġ15 6", "Ġunl ocks", "te chn", "M aking", "Ġcon quer", "ad ic", "æ ĸ", "Ġel f", "Ġelect orate", "ĠKurd s", "ĠSt ack", "ĠSam urai", "Ġâ ĺħ", "Ġ{ }", "ĠS aid", "ĠFall out", "Ġkind ness", "ĠCustom s", "ĠBou levard", "Ġhelicop ters", "ot ics", "ĠVe get", "com ment", "Ġcritic ised", "Ġpol ished", "ĠRem ix", "ĠC ultural", "Ġrec ons", "Ġdo i", "at em", "Sc reen", "Ġbar red", "Com ments", "ĠGener ally", "Ġsl ap", "7 20", "V ari", "p ine", "Ġem pt", "Ġh ats", "ĠPlay ing", "l ab", "a verage", "form s", "ĠC otton", "Ġcan s", "ĠD ON", "ĠSom alia", "C rypt", "ĠIncre ases", "E ver", "mod ern", "Ġsur geon", "3 000", "Ġrandom ized", "================================ ================================", "B ern", "im pl", "ĠC OR", "Ġpro claim", "th ouse", "Ġto es", "Ġam ple", "Ġpres erving", "Ġdis bel", "gr and", "B esides", "Ġsil k", "ĠPat tern", "h m", "Ġenter prises", "Ġaffidav it", "ĠAdvis ory", "Ġadvert ised", "ĠRel igious", "se ctions", "psy ch", "ĠField s", "aw ays", "Ġhasht ag", "ĠNight mare", "Ġv ampire", "Ġfore nsic", "rosso ver", "n ar", "Ġn avy", "Ġvac ant", "ĠD uel", "Ġhall way", "Ġface book", "ident ally", "ĠN RA", "Ġm att", "Ġhur ricane", "ĠKir by", "ĠP uzzle", "Ġsk irt", "ou st", "du llah", "Ġanal ogy", "in ion", "Ġtomat oes", "ĠN V", "ĠPe ak", "ĠMe yer", "Ġappoint ments", "Ġm asc", "Ġal ley", "re hend", "Ġchar ities", "Ġund o", "Ġdest inations", "ĠTest ing", "\"> </", "Ġdest ined", "Ġimp lements", "ĠHar old", "RE CT", "Ġoptim ization", "Ġkilomet res", "Ġc md", "Ġimpair ment", "Ġun successful", "Ġswift ly", "ĠGlas gow", "art en", "ĠSh ares", "ĠAn swer", "ĠAl bum", "Ġnut ritional", "ãĥ ĸ", "ĠF ut", "Ġbl oc", "ĠN FC", "Ġwholes ale", "ĠC W", "Ġneg lected", "Ġlaun cher", "Ġannounce ments", "OU LD", "com b", "Ġrot ating", "Ġrest s", "ĠT icket", "ched el", "L ou", "ĠV ic", "Ġ\" '", "Ġtem plates", "Ġrepl aces", "Ar c", ":: ::", "ĠGil bert", "Ġillness es", "Ġsched ules", "Ġheter osexual", "L INE", "Ġhere in", "Ġco erc", "Ġdecre asing", "Ġde portation", "s udo", "ĠInd igenous", "Ġweigh s", "Al ong", "' );", "ĠBeng als", "70 7", "Ġjoint s", "ver ts", "Ġ14 9", "na ire", "Ġsimpl est", "Ġl ore", "10 80", "f iction", "ĠDat abase", "Ġreserv ation", "Ġs ou", "Ġsan ctuary", "aud io", "ap le", "Ġveget arian", "Ġanticip ation", "m icro", "Ġend uring", "Ġdepart ed", "Ġsidew alk", "Ġprohib its", "ĠF ont", "Ġcomp ute", "ĠS ect", "Ġ15 8", "B attle", "Ġbom ber", "Ġdist raction", "Ġend ured", "Ġpractition ers", "Ġdistur bed", "Ġdr ank", "ord ered", "Ġsurpr ises", "se at", "Sec urity", "ĠW isdom", "og o", "Ġsub paragraph", "ĠPen insula", "ĠOrig ins", "ire n", "ĠP av", "igg le", "Ġgrat itude", "ĠG ravity", "over ty", "im an", "ct r", "ĠCa esar", "c ould", "g em", "Ġsk ies", "Ġch amp", "Ġagree ing", "F amily", "D iv", "17 6", "Ġmess y", "um ption", "F ederal", "ern o", "ĠCh at", "Bey ond", "Ġdev ote", "ĠW alsh", "Ġdump ed", "Ġaccum ulation", "st ad", "hib ition", "Ġsm okers", "Ġinspect or", "F rench", "iss an", "ĠV ita", "Ġresearch ing", "R AM", "ĠCelt ics", "Ġcl oak", "ĠTer ra", "M ary", "so ld", "ĠD OM", "mod s", "Int el", "Ġmult itude", "ĠImpro ved", "Ġrel iance", "Ġartif act", "Ġalarm ing", "P rom", "h on", "T ION", "med ium", "Ġref lex", "ĠEx cel", "Ġweaken ed", "16 3", "2 24", "Ġcost umes", "Ġunique ly", "Ġs orrow", "Ġm ansion", "w p", "Ġsal v", "ĠGro ve", "bs p", "ĠSn iper", "ĠSh ipping", "ĠP OW", "Ġund is", "Ġbrand ing", "G irl", "ĠAh mad", "ĠL akes", "ĠCore y", "Ġinherit ance", "ener y", "Ġpack ing", "ĠP rest", "D est", "F W", "Ġregul ator", "l ocked", "Ġcont ested", "ĠMel issa", "ĠD uc", "Ġunpop ular", "Ġst acked", "Ġ19 17", "Ġyear ly", "Ġst are", "Ġassess ing", "à ¸", "Ġbe verages", "Ġcompet itions", "Ġstreng thening", "al ong", "ĠL ud", "Ġmel ted", "stan bul", "Ġb ounty", "EN C", "ĠL ands", "Ġdecl ares", "Ġcustom ize", "Ġcomp osite", "ãĥ ¬", "C M", "ograph ics", "ĠTem p", "Ġcont ender", "Ġins ign", "ĠL AN", "Ġdis asters", "ins pired", "Ġjud gments", "ustain able", "urs ion", "Ġvar iance", "ĠUlt imately", "Ġ --------", "u ador", "ĠR X", "Ġmel ting", "ĠExt ended", "ĠT we", "M ajor", "ĠB il", "Ġsy rup", "qu ick", "ĠHold er", "Ġinnoc ence", "U LE", "ĠM ight", "99 99", "Ġf al", "Ġcontinu ity", "Ġ19 53", "ĠB S", "st ill", "L at", "ĠAb use", "Ġun supported", "xxxx xxxx", "Ġinst itute", "Ġfrag ment", "ĠP ep", "W estern", "ĠC ause", "ĠFr ag", "ĠAr s", "à ¥", "ast ics", "Ġb ishop", "Ġcross es", "Ġ15 4", "ĠUp grade", "Ġmit igate", "ĠRay mond", "Mod s", "Ġtom ato", "Ġst umbled", "Ġdiff ers", "In itial", "ĠR aspberry", "Ġign ores", "Ġt ant", "à ł", "Ġrel ay", "Ġb isexual", "Ġconf ession", "Ġd ement", "in as", "ĠHe ather", "pl atform", "dri ving", "bour g", "ĠM ush", "Ġhy ster", "Det ails", "Ġdr ift", "ĠW ald", "ĠLuck ily", "or f", "Ġexp ire", "ĠP unch", "zy me", "g old", "Ġunp aid", "ĠT rent", "Ġun armed", "Ġill icit", "ĠT ottenham", "Ġsm ash", "Intern ational", "ink er", "Ġst ing", "ĠSadd am", "ĠAR T", "Ġtruth s", "b irth", "Ġso ber", "ĠN it", "Ġ ib", "Ġus able", "Ġst acks", "ĠSy lv", "Ġnort heast", "Ġdom ination", "ĠM our", "EN SE", "ĠMe asure", "Ġprogram mer", "Ġ< -", "18 2", "ĠCond ition", "Ġback yard", "ir ling", "ĠJ eb", "ĠCre ed", "ĠH ang", "ĠCOM P", "F ER", "ĠIs h", "Ġdetect ives", "------------ ---", "ĠMess enger", "Ġlo oph", "Ġgate way", "15 1", "ĠMaterial s", "ĠD T", "Ġdo omed", "od o", "Ġslic es", "Ġemail ed", "ĠPer l", "Ġren ov", "UT H", "ody nam", "ĠSouth west", "get ic", "ĠT PP", "Ġoptim ism", "ĠT ow", "ul ators", "prot ected", "y les", " «", "Ġex ile", "en v", "P rop", "ĠZimmer man", "Ù İ", "C a", "om aly", "ãĥ Ĩ", "Ġrail road", "L ee", "23 2", "Ġrepl icate", "Ġcomfort ably", "act ly", "Ġr av", "Ġtelesc ope", "Ġhonest y", "ĠPe pper", "ĠBr ing", "Ġric hest", "Ġout doors", "Ġh alls", "Ġcont end", "IS E", "Ġsub mitting", "Ġna ive", "ar ations", "Ġ14 3", "Ġpo ised", "respons ible", "Ġsoc ks", "ĠSk ull", "Quest ion", "Ġdiscover ies", "Jo ined", "ĠEn emies", "ĠWire less", "ĠRe venge", "Ġpuzz les", "Ġce ased", "29 0", "cript ions", "ĠCon sole", "Ġbo iling", "Ġdisc rep", "Ġded uction", "Ġar senal", "XX XX", "ĠAm sterdam", "rox imately", "ĠSh ane", "Ġpos ing", "ĠACL U", "ĠCompan ies", "Ġthe ology", "ĠU g", "qu arter", "ĠH ank", "Co in", "ĠL v", "Ġalleg ation", "ĠAv oid", "Ġindef initely", "Ġcommod ities", "Ġbr ig", "ĠMan it", "Ġt enth", "met hod", "ĠKn icks", "ĠâĢ İ", "Ġinv oked", "D ial", "AR A", "Ġc aucus", "22 7", "ĠJ ab", "Ġoun ces", "b ay", "Ġbud dy", "f an", "23 4", "ĠH il", "ad h", "ĠT Y", "ĠIN D", "Ġ19 39", "Ġiter ation", "ĠGonz alez", "ĠV ert", "ĠI O", "em b", "re ra", "en ch", "ĠRequ irements", "ĠW ins", "Ġlivest ock", "h ours", "\" âĢ¦", "b ral", "M arg", "ĠD one", "Ġwas ting", "ing ed", "g roups", "Ġw ishing", "ĠT umblr", "Ġt apping", "Ġnational ism", "ĠB yr", "Ġsqu ares", "ĠAct ions", "ãĥ ¥", "In side", "deb ug", "Ġapp end", "Ġstub born", "ĠC ind", "T ell", "Ġt earing", "ĠRe y", "or c", "ĠDay ton", "ĠN H", "ĠMad ness", "Ch arl", "ĠMor rison", "fil ter", "Ġacc use", "Ġ. /", "Ġtor rent", "Ġdecl ines", "g allery", "M ine", "Ġneg otiation", "ĠBash ar", "op ia", "19 93", "em ort", "ĠNo vel", "ĠF ang", "ers ive", "ĠInst ant", "Ġroll er", "A round", "ĠElect ions", "G ames", "Ġin expensive", "Ġwor s", "Ġv ul", "ĠH ole", "Ġunbeliev able", "Ġn ause", "Ġent r", "bo at", "ĠST E", "Ġbus h", "ĠHass an", "Ġw o", "Ġpa used", "ĠM ig", "l ived", "Ġsc out", "Ġl ith", "Pub lished", "du ino", "c ool", "Ġcirc ulating", "id as", "ĠP am", "viol ent", "ĠCraw ford", "udd le", "ĠLet ters", "Gu ard", "mor ph", "Ġwand ering", "Ġsoph omore", "Ġque er", "ĠBl ind", "r ue", "ĠMar riage", "D om", "Ġpadd ing", "Ġfold ers", "Ġmeaning less", "Ġcandid acy", "af ort", "Ġwhistle bl", "ĠIdent ified", "Ġcig ar", "Ġh id", "ĠDub ai", "Ġpost ure", "Ġh iking", "ĠTermin al", "Legend ary", "ĠT P", "ĠAT K", "ĠStar bucks", "ĠR iot", "19 91", "ĠBott om", "e ffic", "ĠEug ene", "ĠWy oming", "ĠRock y", "Ġsal mon", "Ġmet ro", "Ġb ilateral", "Ġcelebr ates", "L ength", "b illion", "B at", "Ġre leg", "Ġpse udo", "D T", "ĠRh ode", "P arent", "ple tion", "Ġatt ribut", "Ġtun ing", "ĠNOT E", "ĠRe bel", "ic us", "F und", "Ġcock tail", "Ġ5 01", "Ġsp oon", "Ġbrut ality", "Ġun ite", "Ġmicro bi", "ĠRe ich", "pos itive", "Ġam azed", "ĠN T", "D esc", "ECT ION", "Ġfalse ly", "ĠHigh lander", "ĠC rist", "ĠVictor ian", "Ġdistribut ions", "the ir", "ĠE instein", "Ġp od", "Ġepid em", "Ġhe ap", "ĠR anch", "Ġan them", "Ġre app", "ĠAub urn", "Ġconc urrent", "ĠThrough out", "ĠP OST", "â ĺ", "Ġhom emade", "k ick", "B eg", "Ġch assis", "c ounter", "Ġmer ger", "Ġl aps", "2 17", "un ion", "ĠTr igger", "Ġdeb ated", "Ġsil ently", "Ġrest raint", "B al", "0000 000", "Ġform idable", "ĠFil ip", "Ġsacrific es", "F ood", "Ġdwar f", "ĠSe qu", "in ian", "More over", "Ġtang ible", "ops is", "ĠMine craft", "ĠRegist ration", "o an", "Ġrepresent ations", "Ġth irst", "Ġcor p", "ire ment", "M ade", "l oe", "> \"", "c ats", "* .", "Ġgest ures", "gener al", "Le ague", "Ġpack ets", "ĠInspect or", "ĠBer g", "Ġfraud ulent", "Ġcritic ize", "F un", "Ġbl aming", "nd ra", "Ġsl ash", "ĠE ston", "Ġpropos ing", "Ġwh ales", "Ġtherap ist", "Ġsub set", "Ġle isure", "EL D", "ĠC VE", "ĠAct ivity", "Ġcul min", "sh op", "ĠD AY", "is cher", "ĠAdmir al", "ĠAtt acks", "Ġ19 58", "Ġmem oir", "Ġfold ed", "Ġsex ist", "Ġ15 3", "ĠL I", "Ġread ings", "Ġembarrass ment", "ĠEmploy ment", "w art", "ch in", "Ġcontin uation", "l ia", "Rec ently", "Ġd uel", "Ġevac uation", "ĠKash mir", "Ġdis position", "ĠR ig", "Ġbol ts", "Ġins urers", "4 67", "M ex", "Ġret aliation", "Ġmis ery", "Ġunre asonable", "r aining", "I mm", "ĠP U", "em er", "Ġgen ital", "ãĤ ³", "ĠC andy", "Ġon ions", "ĠP att", "lin er", "Ġconced ed", "Ġf a", "Ġfor c", "ĠH ernandez", "ĠGe off", "deb ian", "ĠTe ams", "Ġc ries", "Ġhome owners", "23 7", "A BC", "Ġst itch", "Ġstat istic", "Ġhead ers", "ĠBi ology", "Ġmot ors", "ĠG EN", "ĠL ip", "Ġh ates", "Ġhe el", "S elf", "i pl", "ED IT", "ort ing", "Ġann ot", "ĠSpe ech", "old emort", "ĠJ avascript", "ĠLe Bron", "Ġfoot print", "Ġf n", "Ġseiz ures", "n as", "h ide", "Ġ19 54", "ĠBe e", "ĠDecl aration", "ĠKat ie", "Ġreserv ations", "N R", "f emale", "Ġsatur ated", "Ġb iblical", "Ġtroll s", "Dev ice", "ph otos", "Ġdr ums", "ãĥīãĥ© ãĤ´ãĥ³", "N ight", "f ighter", "ĠH ak", "ri ber", "Ġc ush", "Ġdiscipl inary", "ba um", "ĠG H", "ĠSch midt", "ilib rium", "Ġs ixty", "ĠKush ner", "ro ts", "Ġp und", "ĠR ac", "Ġspr ings", "Ġcon ve", "Bus iness", "F all", "Ġqual ifications", "Ġvers es", "Ġnarc iss", "ĠK oh", "ĠW ow", "ĠCharl ottesville", "ed o", "Ġinterrog ation", "ĠW ool", "36 5", "B rian", "Ġâľ ĵ", "Ġalleg es", "ond s", "id ation", "ĠJack ie", "y u", "Ġl akes", "Ġworth while", "Ġcryst als", "ĠJud a", "Ġcomp rehend", "Ġfl ush", "Ġabsor ption", "ĠO C", "Ġfright ened", "ĠCh ocolate", "Mart in", "Ġbu ys", "Ġbu cks", "Ġapp ell", "ĠChampions hips", "Ġlist ener", "ĠDef ensive", "Ġc z", "ud s", "ĠM ate", "Ġre play", "Ġdecor ated", "Ġs unk", "ĠV IP", "ĠAn k", "Ġ19 5", "aa aa", "Nob ody", "ĠMil k", "ĠG ur", "ĠM k", "ĠS ara", "Ġse ating", "ĠW id", "Tr ack", "Ġemploy s", "Ġgig antic", "AP P", "ãĤ §", "in ventory", "Ġtow el", "at che", "l asting", "ĠT L", "Ġlat ency", "Ġkn e", "B er", "me aning", "Ġup held", "Ġplay ground", "Ġm ant", "S ide", "Ġstere o", "Ġnorth west", "Ġexception ally", "Ġr ays", "Ġrec urring", "D rive", "Ġup right", "Ġab duct", "ĠMar athon", "Ġgood bye", "Ġal phabet", "h p", "Ġcourt room", "ring ton", "ot hing", "T ag", "Ġdiplom ats", "Ġbar bar", "ĠAqu a", "18 3", "33 33", "Ġmat urity", "Ġinst ability", "ĠAp ache", "Ġ= ==", "Ġfast ing", "ĠGr id", "Mod Loader", "Ġ15 2", "A bs", "ĠOper ating", "ett i", "Ġacqu aint", "Don nell", "ĠK em", "ĠFor ge", "Ġarm ored", "M il", "Ġphilos ophers", "in vest", "Pl ayers", "â Ī", "Ġmy riad", "Ġcomr ades", "R ot", "Ġremember ing", "Ġcorrespond s", "Ġprogram mers", "ĠLyn n", "Ġo lig", "Ġco herent", "yn chron", "ĠChem ical", "Ġj ugg", "p air", "post s", "E ye", "ĠIn ner", "Ġsem ester", "ott est", "ĠEmir ates", "ric anes", "or ously", "m its", "ĠW is", "Ġd odge", "l ocation", "Ġf aded", "Am azon", "ĠPro ceed", "ĠIN FO", "j ournal", "ĠTru ck", "T en", "Ġ2 17", "Ġstat utes", "m obile", "ĠT ypes", "Rec omm", "b uster", "pe x", "Ġleg ends", "Ġhead ache", "f aced", "ĠWi Fi", "if ty", "ĠH ER", "Ġcirc uits", "ER ROR", "22 6", "ol in", "Ġcyl inder", "osp ace", "ik ers", "P rem", "Qu ant", "Ġconflic ting", "Ġslight est", "Ġfor ged", "ion age", "Step hen", "ĠK ub", "ĠOpp ortun", "ĠHe al", "Ġbl o", "Ġrul ers", "Ġh uh", "Ġsubmar ine", "f y", "ass er", "Ġallow ance", "ĠKas ich", "ĠT as", "ĠAustral ians", "Forge ModLoader", "ĠâĨ ij", "ĠMat rix", "am ins", "Ġ12 00", "ĠAc qu", "23 6", "D ocument", "ĠBre aking", "19 3", "ĠSub st", "ĠRoll er", "ĠPro perties", "ĠN I", "t ier", "Ġcr ushing", "Ġadvoc ating", "Further more", "keep ers", "Ġsex ism", "x d", "Ġcall er", "ĠS ense", "chie ve", "ĠT F", "Ġfuel ed", "Ġreminis cent", "Ġobs ess", "ur st", "Ġup hold", "ĠF ans", "het ics", "Ġâ Ĺ", "ĠB ath", "Ġbe verage", "Ġo scill", "25 4", "Ġpol es", "Ġgrad ual", "Ġex ting", "ĠS uff", "ĠS uddenly", "Ġlik ing", "Ġ19 49", "un ciation", "am ination", "ĠO mar", "ĠL V", "ĠCon sequently", "Ġsynt hes", "ĠG IF", "Ġp ains", "Ġinteract ing", "u ously", "inc re", "Ġrum or", "ĠScient ology", "19 7", "ĠZ ig", "Ġspe lling", "ĠA SS", "Ġexting u", "ms on", "Ġg h", "Ġremark ed", "ĠStrateg ic", "ĠM ON", "å ¥", "g ae", "ĠWH AT", "E ric", "ĠCamp us", "Ġmeth ane", "Ġimag in", "J UST", "ĠAl m", "X T", "i q", "ĠR SS", "Ġwrong doing", "att a", "Ġbig ot", "Ġdemonstr ators", "ĠCal vin", "ĠV illa", "Ġmembr ane", "ĠAw esome", "Ġbenef ic", "26 8", "Ġmagn ificent", "ĠL ots", "G reg", "ĠBor is", "Ġdetain ees", "ĠH erman", "Ġwhis pered", "Ġa we", "Prof essor", "fund ing", "Ġphys iological", "ĠDest ruction", "Ġlim b", "Ġmanip ulated", "Ġbub bles", "Ġpse ud", "Ġhyd ra", "ĠBrist ol", "Ġst ellar", "ĠExp ansion", "ĠK ell", "ĠInterest ingly", "Ġm ans", "Ġdrag ging", "Ġec ological", "ĠF it", "Ġg ent", "Ġbenef ited", "ĠHait i", "Ġpoly g", "ãĥ İ", "Ġ20 30", "Ġpro w", "Ġrecon struction", "Ġwas t", "Ġpsych ic", "ĠGree ks", "Hand ler", "16 2", "ĠP ulse", "Ġsol icit", "Ġsy s", "Ġinflu x", "ĠG entle", "per cent", "Ġprolifer ation", "Ġtax able", "Ġdisreg ard", "Ġesc aping", "Ġg inger", "Ġwith stand", "Ġdevast ated", "ĠD ew", "ser ies", "Ġinject ed", "ela ide", "Ġturn over", "he at", "Ļ Ĥ", "H appy", "ĠSil ent", "ãĤ Ń", "iv ism", "Ġir rational", "AM A", "Ġre ef", "r ub", "Ġ16 2", "Ġbank ers", "ĠEth ics", "v v", "Ġcritic isms", "K n", "18 6", "M ovie", "ĠT ories", "Ġno od", "Ġdist ortion", "F alse", "od ore", "Ġt asty", "Res earch", "ĠU ID", "- )", "Ġdivor ced", "ĠM U", "ĠHay es", "ĠIs n", "ian i", "ĠH Q", "Ġ\" #", "ign ant", "Ġtra umatic", "ĠL ing", "H un", "Ġsab ot", "on line", "r andom", "Ġren amed", "ra red", "K A", "d ead", "é t", "ĠAss istance", "Ġse af", "++++ ++++", "Ġse ldom", "ĠWeb b", "Ġbo olean", "u let", "Ġref rain", "ĠDI Y", "ru le", "Ġshut ting", "Ġutil izing", "load ing", "ĠPar am", "co al", "oot er", "Ġattract ing", "ĠD ol", "Ġher s", "ag netic", "ĠRe ach", "im o", "Ġdisc arded", "ĠP ip", "01 5", "ü r", "Ġm ug", "Im agine", "C OL", "Ġcurs ed", "ĠSh ows", "ĠCurt is", "ĠSach s", "spe aking", "ĠV ista", "ĠFram ework", "ong o", "Ġsub reddit", "Ġcr us", "ĠO val", "R ow", "g rowing", "Ġinstall ment", "Ġgl ac", "ĠAdv ance", "EC K", "ĠLGBT Q", "LE Y", "Ġac et", "Ġsuccess ive", "ĠNic ole", "Ġ19 57", "Qu ote", "Ġcircumst ance", "ack ets", "Ġ14 2", "ort ium", "Ġguess ed", "ĠFr ame", "Ġperpet rators", "ĠAv iation", "ĠBen ch", "Ġhand c", "A p", "Ġ19 56", "25 9", "r and", "Net Message", "d in", "urt les", "h ig", "ĠV III", "ff iti", "ĠSw ords", "b ial", "Ġkidn apping", "dev ice", "Ġb arn", "ĠEl i", "auc as", "S end", "Con structed", "Ġ ½", "Ġneed les", "Ġad vertisements", "Ġv ou", "Ġexhib ited", "ĠFort ress", "As k", "B erry", "TY PE", "Ġcan cers", "ump ing", "ĠTerrit ory", "Ġpr ud", "Ġn as", "Ġathe ist", "Ġbal ances", "ãģ Ł", "ĠSh awn", "& &", "Ġland sc", "ĠR GB", "Ġpet ty", "Ġex cellence", "Ġtransl ations", "Ġpar cel", "ĠChe v", "E ast", "ĠOut put", "im i", "Ġamb ient", "ĠTh reat", "Ġvill ains", "Ġ5 50", "IC A", "Ġtall er", "Ġle aking", "c up", "Ġpol ish", "Ġinfect ious", "ĠK C", "Ġ@ @", "back ground", "Ġbureaucr acy", "ĠS ai", "un less", "it ious", "ĠSky pe", "At l", "ID ENT", "00 8", "Ġhyp ocr", "Ġpit chers", "Ġguess ing", "ĠF INAL", "Bet ween", "Ġvill agers", "Ġ25 2", "f ashion", "ĠTun is", "Be h", "ĠEx c", "ĠM ID", "28 8", "ĠHas kell", "19 6", "ĠN OR", "Ġspec s", "Ġinv ari", "Ġgl ut", "ĠC ars", "Ġimp ulse", "Ġhon ors", "g el", "Ġjurisd ictions", "ĠBund le", "ul as", "Calif ornia", "ĠIncre ase", "Ġp ear", "Ġsing les", "Ġc ues", "Ġunder went", "ĠW S", "Ġexagger ated", "Ġdub ious", "Ġfl ashing", "L OG", ") ].", "J ournal", "t g", "V an", "ĠI stanbul", "ĠIn sp", "ĠFrank en", "D raw", "Ġsad ness", "Ġiron ic", "ĠF ry", "x c", "Ġ16 4", "is ch", "W ay", "ĠProtest ant", "h orn", "Ġun aff", "ĠV iv", "ill as", "ĠProduct ions", "ĠH ogan", "Ġper imeter", "ĠS isters", "Ġspont aneous", "Ġdown side", "Ġdescend ants", "Ġor n", "w orm", "Japan ese", "Ġ19 55", "Ġ15 1", "ĠDo ing", "els en", "umb les", "Ġrad ically", "ĠDr um", "ĠB ach", "Ġli abilities", "ĠO B", "ĠElement ary", "Ġmem e", "yn es", "Ġfinger print", "ĠGr ab", "Ġundert ake", "Mem bers", "ĠRead er", "ĠSim s", "g od", "Ġhypot hetical", "s cient", "ĠA J", "Ġchar ism", "Ġad missions", "ĠMiss ile", "tr ade", "Ġexerc ising", "ĠBack ground", "W ritten", "Ġvoc als", "whe ther", "Ġv i", "ĠW inner", "Ġl itter", "ĠSh ooting", "ST EM", "ãĤ ¡", "ĠA FL", "Ġvari ability", "Ġe ats", "ĠD PS", "b row", "Ġeleph ants", "Ġstr at", "Ġ Å", "Ġsett lers", "Matt hew", "Ġin advert", "H I", "ĠIM F", "ĠGo al", "Ġnerv es", "John son", "ey e", "ablish ment", "Th ursday", "BIL ITY", "H ad", "am oto", "het amine", "ep s", "Ġmit ochond", "Ġcomp ressed", "ĠTre vor", "ĠAnim als", "T ool", "L ock", "Ġtwe ak", "Ġpin ch", "Ġcancell ation", "P ot", "Ġfoc al", "ĠAst ron", "17 3", "ĠA SC", "ĠO THER", "umn i", "Ġdem ise", "d l", "Ù ħ", "Sem itism", "Ġcr acking", "Ġcollabor ative", "Ġexpl ores", "s ql", "Ġher bs", "Ġconfig urations", "m is", "ĠRes ult", "ace y", "ĠSm oke", "Ġsan ct", "el ia", "Ġdeg ener", "Ġdeep est", "Ġscream ed", "Ġn ap", "Soft ware", "ĠST AR", "E F", "ĠX in", "spons ored", "mans hip", "23 3", "Ġprim aries", "Ġfilter ing", "Ġas semble", "m il", "ĠMy ers", "b ows", "Ġpun ched", "M ic", "Ġinnov ations", "Ġfun c", "and o", "Ġfr acking", "ĠV ul", "о Ð", "osh op", "ĠIm mun", "Ġsett ling", "Ġadolesc ents", "Ġreb uilding", "Ġtransform ing", "Ġpar ole", "Ġhar bor", "Ġbook ing", "ot ional", "onge vity", "ĠY o", "b ug", "Ġemer ges", "ĠMethod s", "ĠCh u", "P res", "ĠDun geons", "Ġtra iling", "ĠR um", "ĠH ugh", "å¤ ©", "ĠE ra", "ĠBatt les", "Res ults", "ĠTr ading", "Ġvers a", "c ss", "ax ies", "he et", "Ġgre ed", "19 89", "Ġgard ens", "Ġconting ent", "P ark", "ĠLeaf s", "h ook", "ro be", "Ġdiplom acy", "ĠF uel", "ĠInv asion", "Ġupgr ading", "M ale", "Ġe lic", "Ġrelent less", "ĠCo venant", "ap esh", "ĠT rop", "T y", "pro duction", "art y", "Ġpun ches", "ak o", "cyclop edia", "ĠR abbit", "ĠHD MI", "Ġ14 1", "Ġf oil", "Item Image", "ĠF G", "Ġimplement ations", "ĠP om", "ixt ures", "Ġaw ait", "Ġ3 30", "am us", "Ġumb rella", "Ġfore see", "se par", "Ġcircum cision", "Ġperipher al", "S ay", "ĠExper t", "In c", "Ġwithd rew", "ĠAnd ers", "f ried", "Ġradio active", "ĠOp ening", "Ġboard ing", "ĠN D", "Ġover throw", "Act iv", "W P", "ĠAct s", "× Ļ", "Ġmot ions", "v ic", "ĠM ighty", "ĠDef ender", "a er", "Ġthank ful", "ĠK illing", "ĠBr is", "mo il", "Ġpredict ing", "26 6", "ch oice", "Ġkill ers", "Ġinc ub", "ĠChe st", "ather ing", "Ġpro claimed", "fl ower", "oss om", "umbled ore", "ĠCy cling", "ĠOccup y", "AG ES", "P en", "ĠY ug", "Ġpack aged", "Ġheight ened", "c ot", "st ack", "C ond", "Ġst amps", "m age", "Ġpersu aded", "Ġens l", "ĠCard inal", "Ġsol itary", "Ġpossess ing", "ĠC ork", "Ġev id", "ĠT ay", "Ġbl ues", "Ġextrem ism", "Ġlun ar", "Ġcl own", "Te chn", "Ġfest ivals", "ĠPv P", "ĠL ar", "Ġconsequ ently", "p resent", "Ġsom eday", "ç İĭ", "ĠMet eor", "Ġtour ing", "c ulture", "Ġbe aches", "S hip", "c ause", "ĠFl ood", "ãĥ ¯", "Ġpur ity", "th ose", "Ġem ission", "b olt", "Ġch ord", "ĠScript ure", "L u", "Ġ$ {", "cre ated", "Other s", "25 8", "Ġelement al", "Ġannoy ed", "ĠA E", "d an", "ĠS ag", "Res earchers", "Ġfair y", "âĢĵ âĢĵ", "======== ====", "Sm art", "GG GG", "Ġskelet ons", "Ġpup ils", "link ed", "Ġur gency", "en abled", "ĠF uck", "Ġcoun cill", "r ab", "U AL", "T I", "Ġlif es", "Ġconf essed", "B ug", "Ġharm on", "ĠCON FIG", "ĠNe utral", "D ouble", "Ġst aple", "ĠSH A", "Brit ish", "ĠSN P", "AT OR", "oc o", "Ġswing ing", "ge x", "ole on", "pl ain", "ĠMiss ing", "ĠTro phy", "v ari", "ran ch", "Ġ3 01", "4 40", "00000000 00000000", "Ġrest oring", "Ġha ul", "uc ing", "ner g", "Ġfut ures", "Ġstrateg ist", "quest ion", "Ġlater al", "ĠB ard", "Ġs or", "ĠRhod es", "ĠD owntown", "????? -", "ĠL it", "ĠB ened", "Ġco il", "st reet", "ĠPort al", "FI LE", "ĠG ru", "* ,", "23 1", "ne um", "Ġsuck ed", "Ġr apper", "Ġtend encies", "ĠLaure n", "cell aneous", "26 7", "Ġbrow se", "Ġover c", "head er", "o ise", "Ġbe et", "ĠG le", "St ay", "Ġm um", "Ġtyp ed", "Ġdiscount s", "T alk", "ĠO g", "ex isting", "ĠS ell", "u ph", "C I", "ĠAust rian", "ĠW arm", "Ġdismiss al", "Ġaver ages", "c amera", "Ġalleg iance", "L AN", "=\" #", "Ġcomment ators", "ĠSet ting", "ĠMid west", "Ġpharm ac", "ĠEX P", "Ġstain less", "Ch icago", "Ġt an", "24 4", "Ġcountry side", "ĠV ac", "29 5", "Ġpin ned", "Ġcr ises", "Ġstandard ized", "T ask", "ĠJ ail", "ĠD ocker", "col ored", "f orth", "\" },", "Ġpat rons", "Ġsp ice", "Ġm ourn", "ĠM ood", "Ġlaund ry", "Ġequ ip", "ĠM ole", "y ll", "ĠTH C", "n ation", "ĠSher lock", "Ġiss u", "ĠK re", "ĠAmeric as", "ĠA AA", "Ġsystem atically", "Ġcont ra", "ĠS ally", "Ġrational e", "Ġcar riage", "Ġpe aks", "Ġcontrad iction", "ens ation", "ĠFail ure", "Ġpro ps", "Ġnames pace", "Ġc ove", "field s", "ãĤ ĭ", "Ġw ool", "ĠC atch", "Ġpresum ed", "ĠD iana", "r agon", "ig i", "Ġh amm", "Ġst unt", "ĠG UI", "ĠObserv atory", "ĠSh ore", "Ġsmell s", "ann ah", "Ġcock pit", "ĠD uterte", "8 50", "Ġopp ressed", "bre aker", "ĠCont ribut", "ĠPer u", "ĠMons anto", "ĠAtt empt", "Ġcommand ing", "Ġfr idge", "ĠR in", "ĠChe ss", "ual ity", "Ġo l", "Republic an", "ĠGl ory", "ĠW IN", ".... ...", "ag ent", "read ing", "Ġin h", "J ones", "Ġcl icks", "al an", "Ġ[ ];", "ĠMaj esty", "ĠC ed", "op us", "ate l", "à ª", "AR C", "ĠEc uador", "ãĥ ł", "ĠK uro", "Ġritual s", "Ġcapt ive", "Ġoun ce", "Ġdisag reement", "Ġsl og", "f uel", "P et", "M ail", "Ġexerc ised", "Ġsol ic", "Ġrain fall", "Ġdev otion", "ĠAss essment", "Ġrob otic", "opt ions", "ĠR P", "ĠFam ilies", "ĠFl ames", "Ġassign ments", "00 7", "aked own", "Ġvoc abulary", "Re illy", "Ġc aval", "g ars", "Ġsupp ressed", "ĠS ET", "ĠJohn s", "Ġwar p", "bro ken", "Ġstat ues", "Ġadvoc ated", "Ġ2 75", "Ġper il", "om orph", "ĠF emin", "per fect", "Ġh atch", "L ib", "5 12", "Ġlif elong", "3 13", "Ġche eks", "Ġnum bered", "ĠM ug", "B ody", "ra vel", "We ight", "ĠJ ak", "ĠHe ath", "Ġkiss ing", "ĠJ UST", "Ġw aving", "u pload", "Ġins ider", "ĠPro gressive", "ĠFil ter", "tt a", "ĠBe am", "Ġviol ently", "ip ation", "Ġskept icism", "Ġ19 18", "ĠAnn ie", "ĠS I", "Ġgen etics", "Ġon board", "at l", "ĠFried man", "ĠB ri", "cept ive", "Ġpir ate", "ĠRep orter", "27 8", "Ġmyth ology", "Ġe clipse", "Ġsk ins", "Ġgly ph", "ing ham", "F iles", "C our", "w omen", "Ġreg imes", "Ġphotograp hed", "K at", "ĠMA X", "Offic ials", "Ġunexpected ly", "Ġimpress ions", "F ront", ";;;; ;;;;", "Ġsuprem acy", "Ġs ang", "Ġaggrav ated", "Ġabrupt ly", "ĠS ector", "Ġexc uses", "Ġcost ing", "ide press", "St ack", "ĠR NA", "ob il", "Ġghost s", "ld on", "at ibility", "Top ics", "Ġreim burse", "ĠH M", "ĠDe g", "Ġth ief", "y et", "ogen esis", "le aning", "ĠK ol", "ĠB asketball", "Ġf i", "ĠSee ing", "Ġrecy cling", "Ġ[ -", "Cong ress", "Ġlect ures", "P sy", "Ġne p", "Ġm aid", "Ġori ented", "A X", "Ġrespect ful", "re ne", "fl ush", "ĠUn loaded", "re quest", "gr id", "ĠAltern atively", "ĠHug o", "Ġdec ree", "ĠBuddh ism", "and um", "And roid", "ĠCong o", "ĠJoy ce", "Ġacknowled ging", "hes ive", "ĠTom orrow", "ĠH iro", "th ren", "ĠM aced", "Ġho ax", "ĠIncre ased", "ĠPr adesh", "W ild", "____ __", "16 1", "Ġa unt", "Ġdistribut ing", "ĠT ucker", "ĠSS L", "ĠW olves", "B uilding", "ou lt", "ĠLu o", "ĠY as", "ĠSp ir", "ĠSh ape", "ĠCamb od", "ĠIP v", "Ġm l", "Ġext rad", "39 0", "ĠPenn y", "d ream", "Ġstation ed", "opt ional", "ew orthy", ". </", "Ġundert aking", "Ġchick ens", "Ġstimul i", "ĠEl se", "ig ators", "ĠBegin ning", "ct ory", "Ġprep ares", "Ġdel ta", "Ġvic inity", "t ool", "Ġworks hops", "M Hz", "Ġaccus ation", "Ġhist ories", "rop olis", "ĠChurch ill", "Ġne on", "Ġb aff", "d ies", "may be", "Ġè£ı è¦ļéĨĴ", "Ġsympt om", "EC H", "ĠMan uel", "Ġban ana", "ĠH B", "Ġ ****", "ĠKore ans", "c oll", "F B", "Ġpr aying", "ĠCann ot", "ĠM ile", "Ġembr acing", "ĠSil k", "39 3", "ot ers", "F D", "Ġday light", "al ias", "ĠBrig ade", "ĠHann ah", "Ġcler gy", "Ġs outheast", "Ġalcohol ic", "Ġpropos es", "liv ion", "Ġcalcul ating", "Ġstim ulate", "Ġspl itting", "e ight", "ĠInd y", "pl ays", "ĠP ik", "Ġdom est", "Ġforg iveness", "ĠR ings", "pat ient", "kins on", "M ont", "ig ible", "; \"", "Ġperiod ically", "amm ad", "ĠBr itt", "p ard", "Ġarbit ration", "ĠSchne ider", "ĠCorpor ate", "ĠMay a", "Ġsn akes", "a um", "Ġbl asted", "Ġmyster ies", "Ġrev ive", "oc amp", "ĠD odge", "ĠOper a", "27 9", "Ġor phan", "Ġspec ifies", "ĠM ets", "D uration", "H en", "Ġfire works", "Ġprosec ute", "ĠTill erson", "d p", "us age", "l iness", "ĠDeb ian", "Ġ2 24", "ris es", "ĠIn fect", "at ra", "ĠR R", "ĠL or", "d iff", "ĠCharl eston", "Ġac oustic", "Ġam use", "3 30", "Ġc er", "ĠT ac", "Ġ[ +", "Ġcard iac", "ĠRestaur ant", "er gy", "Ġf uzz", "Ġbit es", "Ġhazard ous", "Ġbr ighter", "r ans", "ĠStephan ie", "ext ra", "RE T", "ĠChrist ine", "ĠS ue", "stat ement", "Ġbol ster", "Ġant it", "Rad io", "B IT", "ãĤ °", "Ġvis ions", "ĠCon cept", "Ġin line", "ĠPhilos ophy", "is ans", "ĠIr ving", "à £", "t aking", "Ġincons ist", "ĠKum ar", "Ġl ig", "ĠSch umer", "ĠReg ulations", "ĠH z", "th ro", "ĠV oldemort", "ĠM ED", "ĠFreder ick", "P ad", "22 1", "Ġalleg ing", "ĠCommun ication", "Ġ16 7", "Ġforecast s", "Ġsp iders", "Or gan", "ĠParticip ants", "ĠO ps", "des ign", "Cl ose", "Ġfact o", "Ġbom bers", "res istant", "ateg ories", "S chool", "Ġhom ework", "Ġcor ro", "T uesday", "ĠBrend an", "ĠM X", "ĠT S", "ĠSt ri", "Ġstake holders", "ĠMillenn ium", "Ġtransfer ring", "J ud", "Ġt ac", "Ġ16 00", "ĠSD K", "r b", "Ġinterpret ations", "ĠS G", "Ġup stairs", "ĠHar vest", "Ġvag ina", "Ġing est", "x f", "ĠOr ion", "ĠJoe y", "Ġsand wic", "Ġimm ortal", "Ġfl ipped", "ort ex", "threat ening", "Ġsn iper", "Ġconver ts", "Ġinstall ations", "ĠBul gar", "ors che", "m ails", "Ġl ure", "Ġnarrow ly", "Ġgren ade", "ĠG ing", "Ġunder wear", "------------ --", "Ġch ased", "ĠV AL", "Ġparent ing", "ĠH amb", "ĠBl az", "Ġanarch ist", "ĠMed ian", "ĠProgram s", "Î ½", "Ġob j", "ĠN okia", "orm an", "an qu", "at ism", "op a", "Ġfulf illing", "Ġpupp y", "Ġent it", "ĠSebast ian", "Ġshoot ers", "Ġric her", "è ¡", "Ġtempt ed", "ĠAT T", "ĠC V", "Ġto re", "Res ource", "ĠDevil s", "40 8", "in ational", "Ġass urance", "ĠDar ren", "Ġwh ichever", "pos ure", "Ġf ury", "St ock", "Ġunivers ally", "resp onse", "Ġo ak", "Ġwork load", "ĠCor ner", "ee le", "\" ...", "Ġdepri ved", "k owski", "Ġcast s", "Ġaffili ation", "ĠA ch", "ĠAs ked", "at he", "Ġl act", "ĠTh u", "r m", "Ġair lines", "Ġnot ions", "Form at", "ĠF AA", "ãĥ Ĭ", "dri ver", "Ġtrans cend", "S ettings", "ĠPro secut", "Ġsp inal", "Ġdefault s", "F K", "Ġpref ers", "rend ered", "th us", "fil m", "Ġt iger", "ĠSp icer", "rec ogn", "ĠRug by", "Net work", "Ġp ity", "Ġcomp artment", "c asters", "ĠMon roe", "Ġ7 20", "Ġcorrect ions", "Ġdop amine", "ĠA Z", "C ut", "Ġro omm", "Ġspec ulate", "H ash", "Ġrestrict ive", "11 11", "red ible", "on el", "Ġramp ant", "re ported", "ĠSu ite", "ĠMin imum", "al ys", "az ard", "lo op", "Ġl ent", "sh a", "Ġv andal", "men u", "ĠBoe hner", "Ġnarr atives", "Ġauthent icity", "26 9", "an ic", "d uty", "28 5", "Ġthank ed", "Ġbetray ed", "l ift", "Ġsouth west", "ĠDex ter", "ĠB od", "Ġkey words", "A verage", "D IS", "Ġethnic ity", "! ),", "ĠNational s", "á ¹", "ĠT ah", "iox id", "Ġwid get", "Ġpast a", "Ġbill ing", "Ġtr ilogy", "ĠL ines", "Ġsn iff", "Ġnep hew", "L ate", "Ġprinc ip", "ĠLo op", "ĠMarx ist", "Ġdiss olved", "Ġcontext s", "ĠAm ount", "ĠSp ike", "Ġtot als", "Ġorgan izer", "Ġup rising", "s hips", "Y Y", "ĠNort heast", "m oney", "grad ation", "Ġgoal keeper", "ĠH ear", "Ġste ak", "ĠBuzz Feed", "Ġsole mn", "ĠSc and", "Ġpo pping", "Ġad here", "ĠAl leg", "by te", "ĠW olver", "Ġun in", "Ġrec ol", "it ud", "Ġmim ic", "ib us", "Ġpredict s", "ĠKee per", "i ating", "Ġde ception", "Ġlear nt", "Ġdi ary", "Ġcond itional", "Ġre lic", "Ġinv oke", "ien ced", "å Ī", "ĠP ont", "Ġcell phone", "Ġspeed ing", "Ġtack ling", "Ġn ude", "op ened", "ĠMan afort", "Ġ19 52", "Ġmaj ors", "ĠSil ence", "Ġlog istics", "Ġweight ed", "ĠPsych iat", "\": [\"", "Ġsick ness", "Ġdivid ends", "z on", "Re lease", "ĠKe ys", "ĠI ch", "Ġen z", "ĠF ernand", "ĠÎ ±", "Ġmean ings", "Ġp enny", "Ġst ern", "Ġl ar", "ĠPub lished", "Ġback drop", "K im", "ĠSy nt", "Ġdeb uted", "w m", "ĠIs le", "Ġregul ating", "ott i", "ĠSch olars", "ices ter", "ĠChe f", "Ġpop s", "ĠLaun cher", "ĠVar ious", "Ġcomment ing", "os lav", "enz ie", "Ġrival ry", "â Ĥ¬", "Re ally", "Ġor c", "Ġbe an", "ĠJud y", "Not ice", "ĠB ike", "? ]", "Ġrent ed", "st en", "Ġfore front", "ĠBald win", "Ġyield ed", "t ails", "Pr ime", "ĠS ources", "ic ator", "Se an", "Ġmarch ing", "Out put", "ĠJ ungle", "Ġres ide", "zz le", "ĠAndrew s", "Ġtor que", "Bas ic", "Act ually", "st rap", "p enter", "Ġexam s", "ĠY a", "Ġ15 9", "ĠDec ision", "Ġr ansom", "ete enth", "ens ing", "2 13", "Ġsun set", "40 4", "ĠRap id", "ĠHe in", "ĠAb original", "Ġorgan ism", "ĠS ever", "Ġcl a", "aj i", "Sim ple", "ĠFl avor", "ĠE val", "pr us", "Ġch orus", "D AY", "Ġden ounced", "Ġbi ography", "ĠTurn bull", "Rec ent", "N ormal", "lect ions", "W ord", "Ġf erry", "ĠWag ner", "h om", "Un it", "Ġsuper market", "ĠS ith", "Ġnomine es", "Ġdictators hip", "idd ler", "Ġannoun ces", "ĠThe m", "ĠNept une", "Ġde ity", "ĠY i", "Ġmon arch", "AR R", "Ġinv aded", "ĠH ok", "unt ary", "C ertain", "eg a", "Ġk idding", "ĠReg ulation", "Ġtr ay", "Ġphotograp hers", "ĠArc ane", "Ġdis charged", "Ġevangel ical", "Ġinter change", "Ġfilm maker", "ĠEnd less", "Ġ29 0", "ĠSalv ador", "AS Y", "ĠSign al", "Ġwr ath", "â ľ", "l ot", "' /", "Ġproject ile", "Ġemploy ing", "ĠInter face", "19 1", "atell ite", "ĠR ath", "pack age", "Ġindic ations", "J ason", "Ġarg s", "ĠG Hz", "Ġt ilt", "n ants", "w on", "ãĤ µ", "red d", "res cent", "ĠCal endar", "Ġmod ular", "Ġassist ing", "Ġred eem", "ĠBe an", "Ġwor sh", "Ġdecentral ized", ") ...", "37 7", "Ġarr ays", "Ġaccomplish ments", "Î ¿", "d ot", "Ġmut ually", "Ġob struct", "Ġmis represent", "ore st", "ion ic", "ru ce", "% ;", "Ġknow ingly", "port ing", "in ently", "A ri", "ĠSch ultz", "D a", "ĠC ere", "Ġob solete", "ħ ĭ", "g ive", "Ġb ait", "Ġen larg", "Ne ill", "Ġ19 33", "Ġrecons ider", "ĠSerge ant", "ĠDian e", "ĠC ogn", "ĠI con", "P osition", "Ġf ost", "Ġstir ring", "se ven", "ĠSpace X", "ugg ets", "Ġmed d", "G al", "ĠS ister", "B oy", "Ġtrigger ing", "T aking", "Ġscream s", "Ġca usal", "Ġaw aken", "Ar m", "29 7", "Ġdisp atched", "ĠF ALSE", "Ġorgan izational", "ĠT ong", "Ġdile mma", "d emon", "S pl", "Ġhook s", "ud ing", "Ġvalid ate", "Ġpot ion", "Ġcl aw", "Ġburg l", "Ġqu ir", "AC A", "ĠBren nan", "Ġdur ability", "Ġbomb ings", "ĠWind ow", "Ġculp rit", "3 25", "There fore", "umb ered", "per formance", "w arts", "Ġen forcing", "ĠBl ow", "Ġre print", "if ax", "al pha", "Ġsin ister", "Ġbur ger", "fight ing", "Sc ore", "ĠSt ones", "i em", "40 5", "che my", "Ġvine gar", "n om", "Ġprev ailing", "ĠLat est", " ¶", "Ġb a", "ĠWrit er", "Ġ17 7", "ĠCon way", "Ġcollect s", "Ġquant itative", "Ġhor rors", "og ens", "ĠSl ov", "Ġl ays", "h aw", "ĠSl ash", "Ġnight club", "ĠDav ies", "Ġbr ide", "ĠScar let", "y mm", "ĠApplic ations", "vel ength", "Ġrev ival", "Ġsoft ly", "Ġz oo", "ita ire", "C ur", "Ġelect rom", "Ġplant ing", "OT O", "ĠE lements", "Ġsw allow", "por ter", "Ġlapt ops", "Ġpe anut", "Ġlobby ists", "Î ²", "Pan el", "ĠJo an", "im il", "t nc", "Ġresist ed", "Ġout we", "Ġret aining", "at ri", "Ġpo orer", "ĠSyri ans", "ĠHam mond", "Ġwe ld", "ud er", "top ic", "ĠT T", "ric ia", "Ġth ieves", "L ic", "ĠG ust", "ĠW ays", "are th", "24 3", "Ġbroad caster", "sh ield", "ass ium", "ub le", "Ġairst rikes", "on so", "Ġped al", "Ġcollect ors", "ĠV ander", "ĠMes a", "Ġdict ator", "Ġd ir", "ent on", "c art", "sc ore", "ad der", "C ry", "Ġs sh", "gg er", "Ġdrunk en", "ĠG S", "ĠSe at", "Ġcorner back", "Ġsk ipped", "ĠRes earchers", "ĠAud i", "Ref erence", "Ġhaun ted", "à «", "ĠClin ic", "c z", "Ġp s", "ĠPal adin", "ĠRec ipe", "Ġst igma", "opp y", "Ġmon keys", "ĠHaw k", "S ad", "\" />", "ĠWorks hop", "ĠRet ail", "ĠAv atar", "6 25", "N a", "ĠV C", "ĠSec ure", "M Y", "19 88", "oss ip", "Ġpro state", "Ġund en", "Ġg amer", "ĠCont ents", "ĠWar hammer", "ĠSent inel", "3 10", "Ġse gregation", "ĠF lex", "ĠM AY", "Ġdr ills", "ĠDrug s", "Islam ic", "Ġsp ur", "Ġca fe", "Ġimag inary", "Ġgu iding", "Ġsw ings", "ĠThe me", "ob y", "Ġn ud", "Ġbe gging", "Ġstr ongh", "Ġreject ing", "Ġpedest rians", "ĠPro spect", "R are", "s le", "Ġconcess ions", "ĠConst itutional", "Ġbe ams", "Ġfib ers", "p oon", "Ġinstinct s", "pro perty", "ĠB IG", "Sand ers", "im ates", "Ġco ating", "Ġcorps es", "ĠTR UE", "check ed", "Ġ16 6", "A sh", "ĠJ S", "ĠF iction", "Ġcommun al", "Ġener getic", "oooo oooo", "Ġnow adays", "IL D", "ib o", "ĠSU V", "R en", "Ġdwell ing", "Sil ver", "Ġt ally", "ĠM oving", "Ġcow ard", "Ġgener als", "Ġhorn s", "Ġcirc ulated", "Ġrob bed", "ĠUn limited", "Ġharass ed", "Ġinhib it", "Ġcomp oser", "ĠSpot ify", "Ġspread s", "3 64", "Ġsu icidal", "Ġno ises", "ĠSt ur", "Ġs aga", "ĠK ag", "is o", "Ġtheoret ically", "M oney", "Ġsimilar ity", "Ġslic ed", "ut ils", "ing es", "\" -", "Ġan th", "Ġimp ed", "Mod ule", "Through out", "Ġmen us", "comm ittee", "and i", "ob j", "in av", "f ired", "ĠAb dullah", "Ġund ead", "Ġfont s", "H old", "EN G", "Ġsustain ability", "Ġfl ick", "Ġr azor", "ĠF est", "ĠChar acters", "Ġword ing", "Ġpopul ist", "Ġcritic izing", "Ġm use", "v ine", "Ġcard board", "Ġkind ly", "Ġfr inge", "ĠThe ft", "icult ural", "Ġgovern ors", "Ġ ����", "Ġ16 3", "Ġtime out", "ĠA uth", "Child ren", "A U", "Ġred emption", "ĠAl ger", "Ġ19 14", "Ġw aved", "Ġastron auts", "og rams", "Ġsw amp", "ĠFinn ish", "Ġcand le", "Ġton nes", "ut m", "Ġr ay", "Ġsp un", "Ġfear ful", "art icles", "Ġca us", "or ically", "ĠRequ ires", "ĠG ol", "Ġpop e", "Ġinaug ural", "Ġg le", "AD A", "ĠIS IL", "ĠOff ensive", "Ġwatch dog", "Ġbal con", "ent ity", "ĠH oo", "Ġgall on", "AC C", "Ġdoub ling", "Ġimpl ication", "ĠS ight", "Ġdoct r", "---- ---", "Ġ\\ \\", "Ġm alt", "R oll", "Ġâī ¥", "Ġrec ap", "add ing", "u ces", "ĠB end", "fig ure", "Ġtur key", "Ġsoc ietal", "ĠT ickets", "Ġcommer cially", "Ġsp icy", "Ġ2 16", "ĠR amp", "Ġsuperior ity", "à ¯", "ĠTr acker", "C arl", "ĠC oy", "ĠPatri ot", "Ġconsult ed", "Ġlist ings", "Ġsle w", "reens hot", "ĠG one", "Ġ[ ...]", "30 9", "Ġh ottest", "Ø ±", "Ġrock y", "ĠD iaz", "Ġmass age", "Ġpar aly", "Ġp ony", "A z", "Ġcart ridge", "ĠN Z", "Ġsn ack", "ĠLam ar", "ple ment", "ĠLes lie", "Ġm ater", "Ġsn ipp", "24 6", "Ġjoint ly", "ĠBris bane", "ĠiP od", "Ġpump ing", "Ġgo at", "ĠSh aron", "eal ing", "Ġcor on", "Ġan omal", "rah im", "ĠConnect ion", "Ġsculpt ure", "Ġsched uling", "ĠD addy", "at hing", "Ġeyeb rows", "Ġcur ved", "Ġsent iments", "Ġdraft ing", "D rop", "( [", "Ġnom inal", "ĠLeaders hip", "ĠG row", "Ġ17 6", "Ġconstruct ive", "iv ation", "Ġcorrupt ed", "ger ald", "ĠC ros", "ĠChe ster", "ĠL ap", "ãģ ª", "OT H", "D ATA", "Ġal mond", "pro bably", "I mp", "Ġfe ast", "ĠWar craft", "F lor", "Ġcheck point", "Ġtrans cription", "Ġ20 4", "Ġtwe aks", "Ġrel ieve", "S cience", "Ġperform er", "Z one", "Ġtur moil", "ig ated", "hib it", "ĠC afe", "the med", "Ġflu or", "ben ch", "Ġde com", "ĠU nt", "ĠBar rett", "ĠF acts", "Ġt asting", "ĠPTS D", "ĠSe al", "ĠJuda ism", "ĠDynam ic", "ĠC ors", "V e", "ĠM ing", "ĠTrans form", "v on", "ĠDef enders", "ĠTact ical", "ĠV on", "ĠUn ivers", "Ġdist orted", "ĠB reath", "?' \"", "Ġag on", "ĠDead ly", "Ġl an", "ĠCy cle", "orn ed", "Ġrel iably", "Ġgl or", "ĠMon key", "ãĥ ¡", "Ġad ren", "Ġmicrow ave", "ĠAl ban", "irc raft", "dig it", "sm art", "ĠD read", "¯¯¯¯¯¯¯¯ ¯¯¯¯¯¯¯¯", "{ {", "ĠRoc hester", "Ġsimpl ified", "Ġinf licted", "Ġtake over", "Ġyour selves", "ad itional", "Ġmus cular", "K S", "Ġing en", "T ax", "ĠFe ature", "27 7", "Ġcru c", "Ġcr ate", "Ġun identified", "Ġacclaim ed", "ĠM anga", "ĠFr ances", "ĠNep al", "ĠG erald", "ĠKu wait", "Ġsl ain", "ĠHe b", "ĠG oku", "ãģ® æ", "28 6", "M rs", "ĠC ody", "ĠSan ctuary", "01 6", "Ġdism ant", "Ġdatas et", "ĠH ond", "b uck", "ĠPat terson", "Ġpal ette", "ĠG D", "ic ol", "ĠL odge", "Ġplanet ary", "ak in", "ĠRegist ered", "ab we", "ĠPeters burg", "Ġha iled", "ĠP iece", "S che", "ĠDO J", "Ġen umer", "18 1", "ĠObs erver", "ĠB old", "f ounded", "com merce", "Ġexplo its", "ĠF inding", "UR N", "ĠS ne", "ĠAc id", "ay ette", "ĠVal ues", "Ġdr astic", "Ġarchitect ural", "Ġ\" .", "× ķ", "ump ed", "Ġwra pping", "Ġwid ow", "ĠSl ayer", "l ace", "on ce", "German y", "av oid", "Ġtem ples", "P AR", "à ´", "ĠLuc ifer", "ĠFl ickr", "l ov", "for ces", "Ġsc outing", "Ġlou der", "tes y", "Ġbefore hand", "Ä ĵ", "ĠNe on", "ĠW ol", "ĠTyp ically", "ĠPolit ico", "-+ -+", "Ġbuild er", "Ġder ive", "K ill", "Ġp oker", "Ġambig uous", "Ġlif ts", "Ġcy t", "Ġrib s", "ood le", "ĠS ounds", "h air", "ĠSynd rome", "t f", "Ġproport ional", "u id", "Ġper taining", "ĠKind le", "ĠNeg ro", "Ġreiter ated", "ĠTon ight", "oth s", "ĠCorn ell", "Ġo wing", "Ġ20 8", "elf are", "oc ating", "ĠB irds", "Sub scribe", "Ġess ays", "Ġburd ens", "Ġillust rations", "ar ious", "ER AL", "ĠCal cul", "Ġx en", "ĠLink edIn", "ĠJ ung", "Ġredes ign", "Con nor", "29 6", "Ġrevers al", "ĠAd elaide", "ĠL L", "Ġs inking", "Ġg um", "US H", "c apt", "ĠGr imm", "Ġfoot steps", "ĠCB D", "isp ers", "Ġpro se", "Wed nesday", "ĠM ovies", "ed in", "Ġoverturn ed", "Ġcontent ious", "US B", "~~~~~~~~ ~~~~~~~~", "ĠCo pper", "Ġpoint less", "N V", "val ues", "olph in", "d ain", "Ġdepos ited", "ĠG W", "Ġpreced ed", "ĠCl a", "ĠGo lem", "ĠN im", "ĠÎ ²", "ĠEngine ers", "m iddle", "Ġfl att", "oper ative", "Ġcouncil s", "imb abwe", "el in", "Ġstress ful", "ĠL D", "Ġres h", "l ake", "Ġwheel chair", "ĠAltern ative", "Ġoptim ize", "oper ation", "Ġpe ek", "Ġones elf", "ig il", "Ġtrans itions", "op athy", "bl ank", "Ġ16 9", "17 1", "________________________________ ________________________________", "Ġl aundering", "En c", "ĠD EC", "Ġwork outs", "Ġsp ikes", "Ġdin osaurs", "Ġdiscrim inatory", "P ool", "R ather", "38 5", "R NA", "tes ters", "et o", "ĠIdent ity", "Ġve in", "ĠBur ton", "Ġarc ade", "4 20", "Ult imately", "ĠSad ly", "à °", "p ill", "Ġcub ic", "ĠSpect rum", "the se", "st ates", "Ġun official", "h awks", "ĠEVER Y", "Ġrain bow", "Ġincarcer ation", "and ing", "Ġsy ll", "ĠEver ton", "Ġ17 9", "ĠSer bia", "Ġ18 9", "m eter", "ĠMic key", "Ġant iqu", "Ġfact ual", "ne ck", "ĠN are", "n orm", "m ust", "Ġhigh ways", "Ġgl am", "Ġdivid ing", "ĠSquad ron", "ĠMar tha", "Ġbirth s", "C over", "//////// ////////", "ĠW ong", "Ph ot", "ĠA LS", "ri o", "ĠNon etheless", "ĠL emon", "Ġ20 6", "ĠE E", "Ġderiv ative", "ĠWW II", "v ote", "Ġthere in", "Ġsepar ating", "44 6", "sy nc", "ĠStre ets", "Ġr att", "Ġmunicip ality", "ĠShort ly", "Ġmon k", ") ,\"", "Ġscr ub", "Ġoper atives", "Ne ither", "Pl ace", "ĠLim it", "F emale", "ĠAct or", "Char acter", "Ġconstit uted", "35 7", "Ġprotest ed", "ĠSt raw", "ĠHe ight", "ild a", "ĠTy ph", "Ġflood s", "Ġcos metic", "W AY", "pert ure", "up on", "t ons", "ess ing", "ĠP ocket", "Ġro oft", "ĠC aucas", "Ġant idepress", "Ġincomp atible", "EC D", "Ġoper a", "ĠCont est", "Ġgener ators", "l ime", "Def ense", "19 87", "for um", "Ġsav age", "ĠHung arian", "n z", "Ġmet allic", "Ġex pelled", "Ġres idency", "Ġdress es", "66 6", "ĠC lement", "f ires", "C ategory", "Ġge ek", "al is", "Ġc emetery", "educ ated", "Ġc rawl", "ĠUn able", "ĠT yson", "ak is", "Ġp ardon", "ĠW ra", "Ġstrengthen ed", "ĠF ors", "33 5", "ĠH C", "ĠM ond", "Ġvisual s", "ĠBeat les", "ett lement", "Ġ ï", "g ro", "Ġb ash", "Ġpo orest", "Ġex cel", "Ġaspir ations", "ĠM unicip", "ens ible", "Ġceremon ies", "Ġintimid ation", "ĠCON TR", "be ck", "ĠK ap", "as u", "Ġtradem arks", "ĠS ew", "ĠComp etition", "net work", "ĠAr ri", "ĠT et", "Ro aming", "W C", "D at", "Ġso b", "Ġpair ing", "Ġoverd ose", "SA Y", "ab er", "Ġrev olt", "ĠF ah", "act ing", "e q", "est ation", "F ight", "ĠMar ks", "27 3", "Ġ17 8", "R aw", "ãģ ĭ", "34 9", "bl ocks", "Ġver ge", "est ine", "ĠPod esta", "Ġinv asive", "Ġprofound ly", "ĠA o", "e ach", "Ġl est", "inter pret", "Ġshr inking", "Ġerr one", "Ġche es", "ly s", "ĠI vy", "ĠDirect ory", "Ġhint ed", "V ICE", "Ġcontact ing", "ĠG ent", "he i", "Ġlabel ing", "Ġmerc ury", "ĠL ite", "Ġexp ires", "Ġdest abil", "rit is", "c u", "Ġfeather s", "Ġste er", "Ġprogram med", "ĠV ader", "Go ing", "ĠE lim", "Ġy o", "ĠMic he", "Ġ20 3", "Ġslee ves", "Ġb ully", "ĠHum ans", "36 8", "Ġcomp ress", "ĠBan ner", "AR S", "Ġa while", "Ġcal ib", "Ġspons orship", "ĠDiff iculty", "ĠP apers", "Ġident ifier", "} .", "Ġy og", "ĠSh ia", "Ġclean up", "Ġvib e", "int rodu", "im ming", "Austral ia", "Ġout lines", "ĠY outube", "tr ain", "ĠM akes", "Ġde ported", "Ġcent r", "ĠD ug", "ĠB oulder", "ĠBuff y", "Ġinj unction", "ĠHar ley", "ĠG roups", "ĠD umbledore", "ĠCl ara", "Ġ\" -", "Ġsacrific ed", "ep h", "Sh adow", "ib ling", "Ġfreel ance", "Ġevident ly", "ph al", "Ġret ains", "M ir", "Ġfin ite", "d ar", "ĠC ous", "Ġrep aired", "Ġperiod ic", "Ġchampions hips", "Ġaster oid", "bl ind", "Ġexpress ly", "ĠAst ros", "Ġsc aled", "Ġge ographical", "ĠRap ids", "En joy", "Ġel astic", "ĠMoh amed", "Mark et", "be gin", "Ġdisco vers", "Ġtele communications", "Ġscan ner", "Ġen large", "Ġsh arks", "Ġpsy chedel", "ĠRou ge", "Ġsnap shot", "is ine", "X P", "Ġpestic ides", "ĠL SD", "ĠDist ribution", "re ally", "Ġde gradation", "Ġdisgu ise", "Ġbi om", "ĠEX T", "Ġequ ations", "Ġhaz ards", "ĠComp ared", ") *", "Ġvirt ues", "Ġeld ers", "Ġenh ancing", "ĠAc ross", "er os", "ang ling", "Ġcomb ust", "ucc i", "Ġconc ussion", "Ġcontrace ption", "ĠK ang", "Ġexpress es", "Ġa ux", "ĠP ione", "Ġexhib its", "Deb ug", "OT AL", "ĠAl ready", "ĠWheel er", "Ġexp ands", "? :", "Ġreconc iliation", "Ġpir ates", "Ġpur se", "Ġdiscour age", "Ġspect acle", "R ank", "Ġwra ps", "ĠTh ought", "Ġimp ending", "O pp", "ĠAng lo", "ĠE UR", "Ġscrew ed", "ret ched", "Ġencour agement", "mod els", "Ġconf use", "mm m", "ĠVit amin", "âĸij âĸij", "C ru", "Ġkn ights", "Ġdisc ard", "Ġb ishops", "ĠW ear", "ĠGar rett", "k an", "ãĥ Ł", "Ġmascul ine", "cap ital", "ĠA us", "Ġfat ally", "th anks", "ĠA U", "ĠG ut", "12 00", "Ġ 00000000", "Ġsur rog", "ĠBI OS", "ra its", "ĠWat ts", "Ġresur rection", "ĠElect oral", "ĠT ips", "4 000", "Ġnut rient", "Ġdepict ing", "Ġspr ink", "Ġm uff", "ĠL IM", "ĠS ample", "ps c", "ib i", "gener ated", "Ġspec imens", "Ġdiss atisf", "Ġtail ored", "Ġhold ings", "ĠMonth ly", "ĠE at", "po ons", "Ġne c", "ĠC age", "ĠLot us", "ĠLan tern", "Ġfront ier", "Ġp ensions", "Ġj oked", "ĠHard y", "=-=- =-=-", "r ade", "U ID", "Ġr ails", "Ġem it", "Ġsl ate", "Ġsm ug", "Ġsp it", "ĠCall s", "ĠJac obs", "f eat", "ĠU E", "Ġrest ruct", "Ġregener ation", "Ġenerg ies", "ĠCon nor", "OH N", "ĠChe ese", "Ġg er", "Ġresur rect", "man agement", "N W", "Ġpres ently", "ĠBru ins", "M ember", "ĠM ang", "id an", "Ġboost ing", "w yn", "+ .", "requ isite", "ĠNY PD", "ĠMe gan", "ĠCond itions", "Ġp ics", "nes ium", "ĠR ash", "Ġ17 4", "ĠD ucks", "Ġemb ro", "z u", "on ian", "rel igious", "Ġc raz", "ĠAC A", "ĠZ ucker", "EM A", "ĠPro s", "We apon", "ĠKn ox", "ĠAr duino", "Ġst ove", "Ġheaven s", "ĠP urchase", "Ġher d", "Ġfundra iser", "Dig ital", "5 000", "Ġprop onents", "/ âĢĭ", "Ġj elly", "ĠVis a", "Ġmon ks", "Ġadvance ment", "ĠW er", "Ġ18 7", "e us", "ert ility", "Ġfet al", "Ġ19 36", "L o", "Ġout fits", "Ġstair case", "b omb", "Ġcustom ized", "cl air", "T ree", "Ġm apped", "ĠConsider ing", "ĠTor res", "Ġmeth yl", "Ġapprox imate", "Ġdo om", "ĠHans en", "Ġc rossover", "Ġstand alone", "ä ¼", "Ġinv ites", "Ġgra veyard", "Ġh p", "Donald Trump", "Ġesc ort", "G ar", "Ġpredec essors", "Ġh ay", "Ġen zyme", "ĠStra ight", "vis ors", "I ng", "ane ously", "ĠApp lied", "Ġf ec", "ĠDur ant", "Ġout spoken", "or b", "Ġz eal", "Ġdisgr ace", "' ).", "ĠChe ng", "28 9", "ĠRen a", "ĠSu icide", "29 4", "Ġout raged", "ĠNew man", "ĠN vidia", "ĠA ber", "ĠB ers", "Ġrecre ation", "Wind ow", "ĠD P", "x e", "Ġped oph", "Ġfall out", "ambo o", "Ġpresent ations", "ĠApp s", "Ġh tml", "3 45", "ĠX XX", "Ġrub bing", "ĠLe ather", "Ġhum idity", "se ys", "est ablished", "ĠUn its", "64 6", "Ġrespect able", "A uto", "Ġthri ving", "ĠInn ovation", "ang s", "Ext ra", "reg ulation", "29 8", "p ick", "Ex amples", "ĠC J", "Att ack", "Ġdr acon", "L T", "Ġstick er", "re rs", "Ġsun ny", "I ss", "reg ulated", "d im", "ĠAb stract", "Ġhus bands", "Off ice", "om ination", "it ars", "AN GE", "asc al", "ĠK ris", "ĠInf antry", "Ġm alf", "ĠA the", "ĠR ally", "bal anced", "................ ........", "OU P", "Ġmole cule", "met ics", "ĠSpl it", "ĠInstruct ions", "ĠN ights", "c ards", "Ġt ug", "Ġcon e", "å Ń", "Ġt x", "ĠDisc ussion", "Ġcatast rophe", "pp e", "g io", "Ġcommun ism", "Ġhal ted", "ĠGu ant", "cle an", "ĠSc hed", "ĠK anye", "Ġw ander", "ĠSer iously", "Ġ18 8", "enn ial", "f ollow", "product ive", "ĠFl ow", "ĠS ail", "Ġc raw", "Ġsim ulations", "or u", "ang les", "ĠN olan", "Ġmen stru", "4 70", "Ġ20 7", "aj a", "Ġcas ually", "board ing", "Ġ2 22", "ov y", "ĠN umbers", "um at", "O E", "28 7", "ĠCle mson", "Ġcert s", "Ġsl id", "ĠT ribe", "Ġto ast", "Ġfort unes", "Ġf als", "ĠComm ittees", "Ġg p", "Ġf iery", "ĠN ets", "ĠAn ime", "Pack age", "ĠComp are", "l aughter", "in fect", "Ġatroc ities", "Ġjust ices", "Ġins ults", "ĠVern on", "Ġsh aken", "Ġperson a", "est amp", "36 7", "br ain", "Ġexperiment ing", "K en", "ĠElect ronics", "Ġ16 1", "dom ain", "Ġgraph ical", "b ishop", "Ġwho pping", "ĠEv angel", "Ġadvertis ers", "ĠSpe ar", "Ġb ids", "Ġdestro ys", "ut z", "Ġunders c", "ĠAD D", "Ġan ts", "ĠC um", "ipp les", "ĠF ill", "Ġgl anced", "Ġind icted", "ĠE ff", "Ġmis con", "ĠDes ktop", "Ġab ide", "ãĥ Ģ", "ĠI o", "ĠC oul", "Ġcaps ule", "ĠCh rys", "M ON", "Ġund es", "ĠI RA", "Ġc itation", "Ġdict ate", "ĠNet works", "ĠConf lict", "ĠSt uff", "x a", "is ec", "ĠChem istry", "Ġquarter ly", "William s", "an an", "O pt", "ĠAlexand ria", "out heastern", "ĠSpring field", "ĠBlack s", "Ġge ography", "24 2", "Ġut most", "ĠEx xon", "ab outs", "E VA", "ĠEn able", "ĠBar r", "Ġdisag reed", "ĠCy prus", "Ġdement ia", "Ġlab s", "Ġubiqu itous", "ĠLO VE", "Ġconsolid ated", "s r", "Ġcream y", "ĠTim ber", "Reg ardless", "ĠCert ificate", "Ġ\" ...", "ogen ous", "Capt ain", "Ġinsult ing", "ĠSor os", "ĠInst r", "ĠBulgar ia", "bet ter", "Ġsuck ing", "ĠDavid son", "at z", "Ġcoll ateral", "g if", "Ġplag ued", "ĠC ancel", "ĠGard ner", "R B", "Ġsix teen", "Rem ove", "ur istic", "c ook", "R od", "Ġcompr ising", "f le", ") âĢĶ", "ĠVik ing", "g rowth", "agon al", "Ġsr f", "af ety", "m ot", "N early", "st own", "ĠF actor", "Ġautom obile", "Ġproced ural", "m ask", "amp ires", "Ġdisapp ears", "j ab", "3 15", "Ġ19 51", "ne eded", "Ġd aring", "le ader", "Ġp odium", "Ġun healthy", "Ġm und", "Ġpy ramid", "oc re", "Ġkiss ed", "Ġdream ed", "ĠFant astic", "ĠG ly", "å Ĭ", "Ġgreat ness", "Ġsp ices", "Ġmet ropolitan", "Ġcomp uls", "i ets", "101 6", "ĠSh am", "ĠP yr", "fl ies", "ĠMid night", "Ġswall owed", "Ġgen res", "ĠL ucky", "ĠRew ards", "Ġdisp atch", "ĠI PA", "ĠApp ly", "Ġa ven", "al ities", "3 12", "th ings", "Ġ( ).", "Ġm ates", "ĠS z", "ĠC OP", "ol ate", "O FF", "Ġre charge", "c aps", "ĠYork er", "ic one", "Ġgal axies", "ile aks", "D ave", "ĠP uzz", "ĠCelt ic", "ĠA FC", "27 6", "ĠS ons", "Ġaffirm ative", "H or", "Ġtutorial s", "ĠC ITY", "ĠR osa", "ĠExt ension", "Ser ies", "Ġf ats", "Ġr ab", "l is", "Ġun ic", "Ġe ve", "ĠSp in", "Ġadul thood", "ty p", "Ġsect arian", "Ġcheck out", "ĠCy cl", "S ingle", "Ġmart yr", "Ġch illing", "88 8", "ou fl", "Ġ] ;", "Ġcongest ion", "m k", "ĠWhere as", "Ġ19 38", "ur rencies", "er ion", "Ġbo ast", "ĠPat ients", "Ġch ap", "ĠB D", "real DonaldTrump", "Ġexam ines", "h ov", "Ġstart ling", "ĠBab ylon", "w id", "om ew", "br ance", "ĠOd yssey", "w ig", "Ġtor ch", "ĠV ox", "ĠMo z", "ĠT roll", "ĠAn s", "Similar ly", "ĠF ul", "00 6", "Un less", "ĠAl one", "st ead", "ĠPub lisher", "r ights", "t u", "ĠDoes n", "Ġprofession ally", "Ġcl o", "ic z", "Ġste als", "Ġ á", "19 86", "Ġst urdy", "ĠJoh ann", "Ġmed als", "Ġfil ings", "ĠFr aser", "d one", "Ġmult inational", "Ġf eder", "Ġworth less", "Ġp est", "Yes terday", "ank ind", "Ġg ays", "Ġb orne", "ĠP OS", "Pict ure", "Ġpercent ages", "25 1", "r ame", "Ġpot ions", "AM D", "ĠLeban ese", "Ġr ang", "ĠL SU", "ong s", "Ġpen insula", "ĠCl ause", "AL K", "oh a", "ĠMac Book", "Ġunanim ous", "Ġl enders", "Ġhang s", "Ġfranch ises", "ore rs", "ĠUp dates", "Ġisol ate", "and ro", "S oon", "Ġdisrupt ive", "ĠSur ve", "Ġst itches", "ĠSc orp", "ĠDomin ion", "Ġsupp lying", "Ar g", "Ġtur ret", "ĠL uk", "Ġbr ackets", "* )", "ĠRevolution ary", "ĠHon est", "Ġnot icing", "ĠSh annon", "Ġafford ed", "Ġth a", "ĠJan et", "! --", "ĠNare ndra", "ĠPl ot", "H ol", "se ver", "e enth", "Ġobst ruction", "Ġ10 24", "st aff", "j as", "or get", "sc enes", "l aughs", "ĠF argo", "cr ime", "Ġorche str", "Ġde let", "ili ary", "rie ved", "Ġmilit ar", "ĠGreen e", "âĹ ı", "ãģ ¦", "ĠGu ards", "Ġunle ashed", "ĠWe ber", "Ġadjust able", "Ġcal iber", "Ġmotiv ations", "Ġà ł", "m Ah", "ĠL anka", "hand le", "Ġp ent", "ĠR av", "ĠAng ular", "ĠK au", "umb ing", "Ġphil anthrop", "Ġde hyd", "Ġtox icity", "e er", "ĠY ORK", "w itz", "å ¼", "ĠI E", "commun ity", "ĠA H", "Ġret ali", "Ġmass ively", "ĠDani els", "ĠD EL", "Ġcar cin", "Ur l", "Ġrout ing", "ĠNPC s", "ĠR AF", "ry ce", "Ġwa ived", "ĠGu atem", "Every body", "Ġco venant", "Ġ17 3", "Ġrelax ing", "Ġqu art", "al most", "Ġguard ed", "ĠSold iers", "ĠPL AY", "Ġout going", "L AND", "Ġre write", "ĠM OV", "ĠIm per", "ĠS olution", "Ġphenomen al", "Ġl ongevity", "Ġimp at", "ĠN issan", "ir ie", "Ġod or", "ĠZ ar", "ok s", "Ġmilit ias", "ĠSP EC", "Ġtoler ated", "ars er", "ĠBrad ford", "+ ,", "Ġsur real", "s f", "Can adian", "Ġresemb lance", "Ġcarbohyd rate", "VI EW", "Ġaccess ory", "me al", "larg est", "ieg el", "Some one", "Ġtoug hest", "os o", "Ġfun nel", "Ġcondemn ation", "lu ent", "Ġw ired", "ĠSun set", "Jes us", "ĠP ST", "ĠP ages", "ĠTy coon", "ĠP F", "Ġselect ions", "Ġ à¤", "part isan", "Ġhigh s", "ĠR une", "Ġcraft s", "le ad", "ĠParent s", "Ġre claim", "ek er", "ĠAll ied", "ae per", "Ġlo oming", "Ġbenefic iaries", "ĠH ull", "Stud ents", "Jew ish", "d j", "Ġp act", "tem plate", "ĠOffic ials", "ĠBay lor", "Ġhe mp", "Ġyouth s", "ĠLevel s", "ĠX iao", "ĠC hes", "Ġende avor", "ĠRem oved", "Ġhipp ocamp", "H ell", "ãĤ Ĭ", "80 5", "Ġd inosaur", "ĠWr ath", "ĠIndones ian", "Ġcalcul ator", "ĠD ictionary", "Ġ4 20", "ĠM AG", "( _", "! ,", "t arians", "Ġrestrict ing", "rac use", "Ġweek day", "OU NT", "Ġsh rugged", "leg round", "Ġb ald", "ĠDo ctors", "Ġt outed", "ĠMax well", "Ġ2 14", "Ġdiplom at", "Ġrep ression", "Ġconstitu ency", "v ice", "r anked", "ĠNap oleon", "g ang", "ĠFore ver", "t un", "Ġbul b", "ĠPD T", "ĠC isco", "V EN", "Ġres umed", "Ste ven", "ĠManit oba", "Ġfab ulous", "ĠAg ents", "19 84", "Ġam using", "ĠMyster ies", "Ġor thodox", "fl oor", "Ġquestion naire", "Ġpenet rate", "Ġfilm makers", "ĠUn c", "Ġst amped", "Ġth irteen", "Ġout field", "Ġforward ed", "Ġapp ra", "Ġa ided", "t ry", "Ġunf ocused", "ĠL iz", "ĠWend y", "ĠSc ene", "Ch arg", "Ġreject s", "Ġleft ist", "ĠProv idence", "ĠBr id", "reg n", "Ġprophe cy", "ĠL IVE", "4 99", "Ġfor ge", "ĠF ML", "Ġintrins ic", "ĠF rog", "Ġw ont", "ĠH olt", "Ġfam ed", "CL US", "aeper nick", "ĠH ate", "ĠC ay", "Ġregister ing", "ort ality", "rop y", "ocaly ptic", "a an", "n av", "Ġfasc ist", "IF IED", "Ġimpl icated", "ĠRes ort", "ĠChand ler", "ĠBr ick", "P in", "ys c", "Us age", "ĠHel m", "us ra", "âĺħ âĺħ", "ĠAb bas", "Ġunanim ously", "Ġke eper", "Ġadd icted", "?? ?", "Ġhelm ets", "Ġant ioxid", "aps ed", "80 8", "gi ene", "Ġwa its", "Ġmin ion", "ra ved", "ĠP orsche", "Ġdream ing", "Ġ17 1", "ĠC ain", "Ġun for", "ass o", "ĠConfig uration", "k un", "hard t", "Ġn ested", "ĠL DS", "L ES", "Ġt ying", "en os", "Ġc ue", "ĠMar qu", "sk irts", "Ġclick ed", "Ġexp iration", "ĠAccording ly", "ĠW C", "Ġbless ings", "Ġaddict ive", "ĠN arr", "y x", "ĠJagu ars", "Ġrent s", "ĠS iber", "Ġt ipped", "ous se", "ĠFitz gerald", "Ġhier arch", "out ine", "Ġwa velength", "> .", "ch id", "ĠProcess ing", "/ +", "r anking", "E asy", "ĠConst ruct", "Ġt et", "ins ured", "H UD", "Ġqu oting", "Ġcommun icated", "in x", "Ġin mate", "Ġerect ed", "ĠAbs olutely", "ĠSure ly", "Ġun im", "ĠThr one", "he id", "Ġcl aws", "Ġsuper star", "ĠL enn", "ĠWh is", "U k", "ab ol", "Ġsk et", "ĠN iet", "Ġper ks", "Ġaff inity", "Ġopen ings", "phas is", "Ġdiscrim inate", "T ip", "v c", "Ġgr inding", "ĠJenn y", "Ġast hma", "hol es", "ĠHom er", "Ġreg isters", "ĠGl ad", "Ġcre ations", "Ġlith ium", "Ġappl ause", "unt il", "Just ice", "ĠTur ks", "Ġsc andals", "Ġb ake", "t ank", "M ech", "ĠMe ans", "ĠM aid", "Republic ans", "is al", "wind ows", "ĠSant os", "Ġveget ation", "33 8", "t ri", "Ġfl ux", "ins ert", "Ġclar ified", "Ġmort g", "ĠCh im", "ĠT ort", "Ġdiscl aim", "met al", "ĠAs ide", "Ġindu ction", "Ġinf l", "Ġathe ists", "amp h", "Ġe ther", "ĠV ital", "ĠBu ilt", "M ind", "Ġweapon ry", "S ET", "Ġ18 6", "ad min", "g am", "cont ract", "af a", "Ġderiv atives", "Ġsn acks", "Ġch urn", "E conom", "Ġca pped", "ĠUnder standing", "ĠH ers", "ĠI z", "Ġd uct", "I ENT", "augh ty", "Ġâľ Ķ", "ĠN P", "Ġsa iling", "In itialized", "Ġt ed", "Ġreact ors", "ĠL omb", "Ġcho ke", "ĠW orm", "Ġadm iration", "Ġsw ung", "ens ibly", "Ġr ash", "ĠGo als", "ĠImport ant", "Sh ot", "ĠR as", "Ġtrain ers", "ĠB un", "Work ing", "Ġhar med", "ĠPand ora", "ĠL TE", "Ġmush room", "ĠCH AR", "ĠF ee", "ĠM oy", "B orn", "ol iberal", "ĠMart ial", "Ġgentle men", "Ġling ering", "Offic ial", "Ġgra ffiti", "ĠN ames", "D er", "Ġqu int", "ist rate", "aze era", "ĠNOT ICE", "ĠFlore nce", "Ġpay able", "Ġdep icts", "ĠSpe cies", "He art", "âĶĢâĶĢâĶĢâĶĢ âĶĢâĶĢâĶĢâĶĢ", "Ġencl osed", "Incre ases", "D aily", "ĠL is", "Ġenact ment", "ĠB acon", "ĠSt eele", "dem and", "Ġ18 3", "Ġmouth s", "Ġstr anded", "Ġenhance ment", "01 1", "ĠWh ats", "Ġhe aled", "en y", "ĠR ab", "Ġ3 40", "ĠLab yrinth", "ro ach", "ĠY osh", "ĠCl ippers", "Ġconcert s", "Intern et", "35 5", "Ġstick ers", "Ġter med", "ĠAx e", "Ġgrand parents", "Fr ance", "ĠCl im", "ĠU h", "ul ic", "Ġthr ill", "cent ric", "ĠOver view", "ĠCond uct", "Ġsubstant ive", "Ġ18 2", "m ur", "Ġstr ay", "ĠCo ff", "Ġrep etitive", "ĠFor gotten", "Ġqual ification", "ew itness", "ĠZ imbabwe", "Ġsim ulated", "ĠJ D", "25 3", "ĠW are", "Ġun sc", "T imes", "Ġsum mons", "Ġdis connected", "Ġ18 4", "ci us", "ĠGu jar", "od ka", "Ġer ase", "ĠTob acco", "elect ed", "Ġun cont", "ĠShe pard", "ĠL amp", "Ġalert ed", "Ġoper ative", "arn a", "u int", "Ġneglig ence", "ac ements", "Ġsup ra", "Ġprev ail", "ĠSh ark", "Ġbel ts", "ãģ «", "Ġt ighter", "Engine ers", "Ġin active", "Ġexp onent", "ĠWill ie", "a ples", "Ġhe ir", "ĠH its", "ian n", "ĠS ays", "Ġcurrent s", "ĠBeng al", "Ġar ist", "B uffer", "Ġbree ze", "ĠWes ley", "Col a", "Ġpron oun", "Ġde ed", "ĠK ling", "Ġof t", "Ġinf lict", "Ġpun ishing", "Ġn m", "ik u", "OD UCT", "01 4", "Ġsubsid y", "ĠDE A", "ĠHer bert", "ĠJ al", "B ank", "Ġdef erred", "Ġship ment", "B ott", "Ġal le", "b earing", "HT ML", "Off line", "Ġ2 13", "Ġscroll ing", "Ġsc anned", "ĠLib yan", "ĠT OP", "ch rom", "d t", "col umn", "Psy NetMessage", "Z ero", "Ġtor so", "0 50", "âķ IJ", "Ġimp erson", "ĠSchw artz", "ud ic", "Ġpiss ed", "ĠS app", "25 7", "ĠIS Ps", "og l", "Ġsuper vised", "Ġad olescent", "Ġatt ained", "ĠDel ivery", "ĠB unny", "Ġ19 37", "Ġmini ature", "Ġo s", "Ġ3 70", "60 8", "ĠMour inho", "Ġinn ate", "Ġtem po", "ĠN M", "ĠFall en", "00 9", "Ġprov ocative", "Stream er", "ĠBened ict", "ĠBol she", "Ġt urtle", "ĠPC B", "ĠEqu al", "Direct or", "ĠR end", "Ġflu ids", "Author ities", "Ġcous ins", "requ ency", "ĠNeigh bor", "s ets", "sh ared", "Char les", "pass word", "Ġg ears", "Ġ2 11", "ĠHard ware", "ri ka", "Ġup stream", "H om", "Ġdisproportion ately", "iv ities", "Ġund efined", "Ġelect rons", "Ġcommem or", "Event ually", "Ġ> <", "Ġir responsible", "2 18", "ĠRe leased", "ĠO VER", "ĠI GN", "ĠB read", "st ellar", "ĠS age", "tt ed", "dam age", "ed ition", "ĠPre c", "Ġl ime", "Ġconf inement", "Ġcal orie", "we apon", "Ġdiff ering", "ĠS ina", "m ys", "am d", "Ġintric ate", "k k", "ĠP AT", "ã o", "st ones", "lin ks", "Ġr anch", "Sem itic", "Ġdifferent iate", "ĠS inger", "occup ied", "Ġfort ress", "c md", "Ġinter ception", "ĠAnk ara", "Ġre pt", "ĠSol itaire", "Ġrem ake", "p red", "Ġd ared", "aut ions", "ĠB ACK", "Run ning", "Ġdebug ging", "Ġgraph s", "3 99", "ĠNig el", "Ġb un", "Ġpill ow", "Ġprog ressed", "fashion ed", "Ġob edience", "ER N", "Ġrehe ars", "C ell", "t l", "S her", "Ġher ald", "ĠPay ment", "ĠC ory", "ĠDe pt", "Ġrep ent", "ĠWe ak", "uck land", "Ġple asing", "Ġshort ages", "Ġjur ors", "ĠK ab", "q qa", "Ant i", "Ġw ow", "ĠRC MP", "Ġt sun", "ĠS ic", "Ġcomp rises", "Ġsp ies", "Ġprec inct", "n u", "Ġur ges", "Ġtim ed", "Ġstrip es", "ĠB oots", "Ġy en", "Adv anced", "Ġdisc rete", "ĠArch angel", "employ ment", "D iff", "Ġmon uments", "Ġ20 9", "work er", "Ġ19 6", "ĠI g", "utter stock", "T PS", "J ac", "Ġhomeless ness", "Ġcomment ator", "Ġrac ially", "f ing", "se ed", "E le", "ell ation", "Ġeth anol", "Ġpar ish", "ĠD ong", "ĠAw akening", "Ġdev iation", "ĠB earing", "ĠTsu k", "Ġrec ess", "Ġl ymph", "ĠCann abis", "å ľ", "ĠNEW S", "Ġd ra", "ĠStef an", "ĠWr ong", "ĠS AM", "Ġloose ly", "Ġinterpre ter", "ĠPl ain", "Go vernment", "Ġbigot ry", "Ġgren ades", "ave z", "pict ured", "Ġmand ated", "ĠMon k", "ĠPed ro", "Ġl ava", "27 4", "Ġcyn ical", "ĠScroll s", "l ocks", "M p", "Ġcon gregation", "orn ings", "ph il", "ĠI bid", "Ġf erv", "Ġdisapp earing", "Ġarrog ant", "sy n", "ĠMa ver", "ĠSu it", "24 1", "Ġab bre", "ack ers", "P a", "ĠY el", "Whe never", "Ġ23 5", "ĠV ine", "ĠAn at", "Ġext inct", "LE T", "Ġexecut able", "V ERS", "ox ide", "D NA", "ĠP rel", "Ġresent ment", "Ġcompr ise", "ĠAv iv", "Ġinter ceptions", "Ġprol ific", "IN A", "ĠEr in", "though t", "2 19", "ĠPsychiat ry", "un ky", "chem ist", "H o", "ĠMcC oy", "Ġbr icks", "L os", "ri ly", "ĠUS SR", "Ġr ud", "Ġl aud", "ĠW ise", "ĠEmer ald", "Ġrev ived", "Ġdam ned", "ĠRep air", "id em", "ct ica", "Ġpatri arch", "ĠN urs", "me g", "Ġcheap est", "re ements", "empt y", "ĠCele br", "Ġdepri vation", "ch anted", "ĠTh umbnails", "E nergy", "ĠEth an", "ĠQ ing", "Ġopp oses", "W IND", "v ik", "ĠM au", "ĠS UB", "66 7", "G RE", "ĠVol unte", "nt on", "C ook", "å IJ", "es que", "Ġplum met", "Ġsu ing", "Ġpron ounce", "Ġresist ing", "ĠF ishing", "ĠTri als", "Ġy ell", "Ġ3 10", "Ġin duct", "Ġpersonal ized", "oft en", "R eb", "EM BER", "Ġview point", "Ġexist ential", "() )", "rem ove", "MENT S", "l asses", "Ġev apor", "Ġa isle", "met a", "Ġreflect ive", "Ġentit lement", "Ġdev ised", "mus ic", "asc ade", "Ġwind ing", "off set", "Ġaccess ibility", "ke red", "Bet ter", "ĠJohn ston", "th inking", "S now", "ĠCroat ia", "ĠAt omic", "27 1", "34 8", "Ġtext book", "ĠSix th", "Ġ اÙĦ", "Ġsl ider", "ĠBur ger", "b ol", "S ync", "Ġgrand children", "Ġc erv", "+ )", "Ġe ternity", "Ġtweet ing", "Ġspec ulative", "Ġpiv otal", "ĠW P", "ĠT ER", "ynam ic", "Ġu pl", "ĠC ats", "per haps", "Ġclass mates", "Ġblat ant", "' -", "Ġl akh", "ant ine", "ĠB org", "i om", "/ (", "ĠAthlet ic", "Ġs ar", "OT A", "ĠHoff man", "Never theless", "Ġad orable", "Ġspawn ed", "Ass ociated", "ĠDom estic", "Ġimpl ant", "ĠLux em", "ĠK ens", "Ġp umps", "ĠS AT", "Att ributes", "50 9", "av our", "Ġcentral ized", "ĠT N", "Ġfresh ly", "ĠA chieve", "Ġouts iders", "her ty", "ĠRe e", "ĠT owers", "ĠD art", "ak able", "Ġm p", "ĠHeaven ly", "Ġr ipe", "ĠCarol ine", "ry an", "Ġclass ics", "Ġret iring", "Ġ2 28", "Ġa h", "Ġdeal ings", "Ġpunch ing", "ĠChap man", "O ptions", "max well", "vol ume", "Ġst al", "Ġex ported", "ĠQu ite", "Ġnumer ical", "B urn", "F act", "ĠKey stone", "Ġtrend ing", "Ġalter ing", "ĠAfric ans", "47 8", "ĠM N", "ĠKn ock", "Ġtempt ation", "Ġprest ige", "Over view", "ĠTrad itional", "ĠBah rain", "Priv ate", "ĠH OU", "Ġbar r", "ĠT at", "C ube", "US D", "ĠGrand e", "ĠG at", "ĠFl o", "Ġres ides", "Ġind ec", "vol ent", "Ġperpet ual", "ub es", "Ġworld view", "ĠQuant um", "Ġfil tered", "Ġen su", "orget own", "ERS ON", "ĠM ild", "37 9", "OT T", "à ¥", "Ġvit amins", "Ġrib bon", "Ġsincere ly", "ĠH in", "Ġeight een", "Ġcontradict ory", "Ġgl aring", "Ġexpect ancy", "Ġcons pir", "Ġmon strous", "Ġ3 80", "re ci", "Ġhand ic", "Ġpump ed", "Ġindic ative", "Ġr app", "Ġav ail", "ĠLEG O", "ĠMar ijuana", "19 85", "ert on", "Ġtwent ieth", "################ ################", "ĠSw amp", "Ġval uation", "Ġaffili ates", "adjust ed", "ĠFac ility", "26 2", "Ġenz ymes", "itud inal", "Ġimp rint", "S ite", "Ġinstall er", "ĠT RA", "m ology", "lin ear", "ĠCollect ive", "ig ating", "ĠT oken", "Ġspec ulated", "K N", "ĠC ly", "or ity", "Ġdef er", "Ġinspect ors", "appro ved", "R M", "ĠSun s", "Ġinform ing", "ĠSy racuse", "ib li", "7 65", "Ġgl ove", "Ġauthor ize", "âĢ¦âĢ¦âĢ¦âĢ¦ âĢ¦âĢ¦âĢ¦âĢ¦", "ĠCru ise", "Ġcontract ing", "she ll", "IF E", "ĠJew el", "p ract", "ĠPhot oshop", "ĠKnow ing", "h arm", "Ġattract ions", "ad an", "et us", "01 8", "w agen", "Al t", "Ġmultip ly", "Ġequ ilibrium", ": {", "ĠF ighters", "ĠEd gar", "Ġfour teen", "Go vern", "Ġmis use", "Ġab using", "Ġancest ry", "ram er", "64 4", "Ġwor ms", "Ġthick er", "ĠComb ine", "Ġpeas ants", "Ġv ind", "Ġcon quest", "Ġm ocked", "Ġc innamon", "ĠC ald", "ĠGall up", "Ġavoid ance", "Ġincarn ation", "ĠStr at", "Ġt asted", "ent a", "ĠN eal", "p ared", "Ġtermin ology", "ject ion", "Scient ists", "ĠIN S", "ĠDe e", "Ġdirect ories", "R oad", "ĠSh ap", "br ight", "ĠDirect ors", "ĠCol umn", "Ġb ob", "Ġprefer ably", "Ġgl itch", "f urt", "Ġe g", "id is", "C BC", "Ġsur rendered", "Ġtest ament", "33 6", "ug gest", "ĠN il", "an other", "Ġpat hetic", "ĠDon na", "Ġ2 18", "ĠA very", "Ġwhis key", "Ġf ixture", "ĠCon quest", "Ġbet s", "O cc", "ĠLe icester", "] .\"", "Ġ) );", "Ġfl ashes", "45 6", "Ġmask ed", "ge bra", "Ġcomput ed", "che l", "aud er", "Ġdefe ats", "ĠLiber ation", "ĠOs ama", "ĠV ive", "Ch anges", "Ch annel", "Ġtar iffs", "Ġm age", "ĠS ax", "Ġinadvert ently", "ĠC RE", "ĠRe aper", "ink y", "gr ading", "Ġstere otyp", "Ġcur l", "ĠF ANT", "Ġfram eworks", "M om", "ĠAn ch", "Ġflav our", "car bon", "Ġperm itting", "let cher", "ĠMo zilla", "ĠPark ing", "ĠCh amp", "Sc roll", "Ġmurd erer", "Ġrest ed", "Ġow es", "ĠP oss", "AD D", "IF F", "res olution", "ĠMin ing", "Ġcompar ative", "D im", "Ġneighbour ing", "ĠA ST", "ĠT oxic", "Ġbi ases", "Ġgun fire", "ur ous", "ĠMom ent", "19 83", "Ġper vasive", "tt p", "ĠNorm ally", "r ir", "S arah", "ĠAlb any", "Ġun sett", "ĠS MS", "ip ers", "l ayer", "ĠWh ites", "up le", "Ġtur bo", "ĠLe eds", "Ġthat s", "ĠMin er", "M ER", "ĠRe ign", "Ġper me", "ĠBl itz", "Ġ19 34", "Ġintimid ating", "t ube", "Ġecc entric", "ab olic", "box es", "ĠAssoci ates", "v otes", "Ġsim ulate", "um bo", "aster y", "Ġship ments", "FF FF", "an th", "Ġseason ed", "Ġexperiment ation", "âĸ ł", "law s", "Me et", "idd les", "ant ics", "R ating", "IS IS", "h ift", "Ġfront s", "b uf", "01 7", "Ġun att", "ĠD il", "le ases", "ĠGard ens", "77 7", "t ouch", "ve ll", "45 8", "Ġ= ====", "s aving", "Ġer osion", "ĠQu in", "Ġearn s", "Ġaccomplish ment", "ĠWe i", "Ġ< [", "____ _", "Ġir rig", "ĠT eddy", "Ġconqu ered", "ĠArm ored", "Ġassert s", "Ġmanip ulating", "r é", "Ġtranscript s", "G allery", "Ġplot ting", "Ne il", "Ġbetray al", "load er", "ĠS ul", "Ġdispl acement", "Ġroy alty", "ĠW I", "he it", "ĠDev ices", "alle l", "Ġmunicipal ities", "Ġcan al", "St ars", "ĠU AE", "Ġ\" âĢ¦", "ĠC U", "ab ove", "Ġreson ance", "ĠguiActive Un", "add ed", "ĠBra ves", "ĠI bn", "Ġhere by", "ĠB RE", "Ġshare holder", "ĠH ir", "ĠJ i", "Ġstrange ly", "Ġadm ired", "Ġpl ight", "Ġb achelor", "ĠP ole", "cipl inary", "T ony", "ĠArmen ian", "Ġun man", "ĠZion ist", "St age", "isco ver", "Ġautom otive", "Ġs idelines", "Ġsl ick", "ĠRena issance", "ĠF UN", "Im ages", "ĠH aj", "Ġp ing", "Ġshort cut", "ĠBl vd", "ĠLook s", "Ġbur sts", "Ġcl amp", "Ġm ish", "Ġsort ing", "Ġpatri ot", "Ġcorrect ness", "ĠScand inav", "ĠCaval iers", "p ython", "az ar", "Ġ3 75", "ĠJa une", "40 9", "Ġdetrim ental", "Ġstab bing", "Ġpoison ed", "Ġf ountain", "oc ent", "or st", "ĠMar i", "Ġr ains", "ĠO vers", "ĠInst itution", "ud get", "AM Y", "t ale", "ĠK R", "ĠPr ices", "Ġhead aches", "Ġlands l", "ĠA ura", "Bon us", "ĠZ hao", "ĠH ip", "Ġhop s", "ĠKurd istan", "Ġexplo iting", "ry n", "Ġhypocr isy", "op ening", "Ġgun shot", "Ġw ed", "inter stitial", "Inter stitial", "Ġam en", "Bre aking", "Ġmarket ed", "W ire", "ĠC rowd", "Contin ue", "ĠK nown", "ĠEffect ive", "ore an", "iz ons", "Jose ph", "Ġescal ation", "us ername", "Ġcur tain", "AT ES", "ĠP AR", "ĠM iy", "Ġcounter fe", "l ene", "Ġcont enders", "d aily", "ĠAs c", "ĠPhill ip", "most ly", "Ġfil ename", "he ne", "Ġresemb ling", "Ġst aging", "ĠCh loe", "Ġw iring", "H on", "ĠRen ew", "ott age", "ĠHy brid", "m uch", "Ġstro kes", "Ġpolicy makers", "AP TER", "ĠArk ham", "pl ot", "Ġassist ants", "Ġde port", "ĠSe ga", "Ġinflu enza", "ĠC ursed", "ĠK obe", "Ġskin ny", "Prov ider", "ĠR ip", "Ġincrement al", "product s", "B F", "Ġd ome", "ĠC redits", "Ġlos ers", "int s", "ĠBet ty", "ĠTal ent", "ĠD AM", "L v", "E ss", "Ġd ens", "tem p", "J udge", "od ic", "Ġ' (", "UR ES", "ets k", "V O", "Ġretrie ved", "Ġarchitect s", "Ù ĩ", "Ġeth ic", "ĠSecond ary", "st ocks", "ad ia", "Ġ3 25", "ĠOp inion", "Ġsimultane ous", "Ġd izz", "ul p", "Ġsmugg ling", "ipp ery", "R andom", "f acing", "ĠD as", "Ġstock p", "Ġdiscl osures", "po inter", "Ġcor al", "ĠSe lection", "ĠP ike", "ival ent", "Ġruth less", "ĠR im", "Ġensu ing", "ĠExper iment", "Ġcongress man", "Ġbelie ver", "Ġun specified", "ĠM ord", "Ġknowledge able", "ĠV ERY", "T X", "Ġstra ps", "Ġtur f", "apesh ifter", "Ġmar ital", "Ġfl ock", "ãģ Ĩ", "26 3", "AM ES", "ĠOpp osition", "Ġtre asures", "ĠG OD", "Ġmodel ed", "ĠWOR LD", "Ġ( [", "ĠUs age", "H F", "Ġ$ (", "uss ed", "Ġpione er", "E ight", "par se", "b read", "rit z", "ĠMir anda", "ĠK ant", "++ )", "ore n", "Ġprov oked", "Ġbre eds", "ĠIn cludes", "ĠPast ebin", "ĠFl ip", "J ava", "Ġbr ink", "Ġrum ored", "Ġun seen", "Ġgar nered", "ĠDef in", "al ted", "Ġtatt oos", "Ġhes itation", "is itions", "ĠWe aver", "ĠReport ing", "Ġtherap ies", "Ġconsult ants", "Ġresid ual", "ĠMal i", "ĠRom a", "i ago", "ĠRes idents", "ub i", "Ġremed ies", "Ġadapt ive", "ĠAl ive", "ĠBar cl", "Ġwal lets", "c rypt", "etermin ation", "ĠPel osi", "Ġsl ipping", "oton in", "Ġall iances", "pat rick", "ir is", "Ġor th", "ĠPer kins", "ĠDe V", "ĠG ets", "Ġdry ing", "ge e", "fore st", "ĠFor get", "ore m", "33 9", "Ġvague ly", "ĠD ion", "ĠP orn", "ĠH OW", "Ġp neum", "Ġrub ble", "ĠT aste", "enc ia", "ĠG el", "Ġd st", "Ġ24 5", "ĠMoroc co", "inf lamm", "ĠTw ins", "Ġb ots", "d aughter", "ĠB alk", "Ġbre thren", "Ġlog os", "Ġgo bl", "f ps", "Ġsub division", "Ġp awn", "Ġsquee zed", "Ġmor ale", "ĠD W", "' \"", "Ġkn ot", "ook y", "Ġdiv isive", "Ġboost ed", "ch y", "ãĥ IJ", "if act", "Ġnewcom ers", "ĠWrest ling", "Ġsc outs", "w olves", "R at", "Ġnin eteenth", "ĠOs borne", "St ats", "Ġem powered", "Ġpsych opath", "ĠO EM", "ugg age", "ĠP K", "ĠMoh ammad", "P ak", "Ġanarch ists", "ĠExt ract", "est hes", "ĠStock holm", "l oo", "ĠG raph", "Ġdeploy ing", "ĠStr anger", "ĠM old", "Ġstaff er", "Ġdiscount ed", "uck le", "ple ase", "ĠLand ing", "ÃŃ a", "Ġ19 3", "Ġan te", "Ġrep etition", "Ġ+ /-", "Ġpar ody", "Ġlive ly", "AA A", "ĠHor us", "Ġp its", "ind ers", "L OC", "ĠVen ice", "40 6", "ĠDis cover", "â Ĩ", "ellect ual", "Ġp ens", "Ġey el", "ig uous", "Im pl", "Ġj oking", "Ġinv al", "ĠBel fast", "Ġcredit ors", "ĠSky walker", "ov sky", "Ġcease fire", "Ġse als", "is oft", ") ).", "ĠFel ix", "IT S", "Ġt resp", "ĠBlock chain", "ew are", "ĠSch war", "en ne", "mount ed", "ĠBe acon", "les h", "Ġimmense ly", "Ġche ering", "Em ploy", "sc ene", "ish ly", "atche wan", "ĠNic olas", "Ġdr ained", "ĠEx it", "ĠAz erb", "j un", "Ġflo ated", "u ania", "De ep", "Ġsuper v", "Ġmyst ical", "ĠD ollar", "ĠApost le", "ĠR EL", "ĠProv ided", "ĠB ucks", "ãĥ ´", "cut ting", "Ġenhance ments", "ĠPengu ins", "ĠIsa iah", "Ġj erk", "ĠW yn", "Ġst alled", "Ġcryptoc urrencies", "ĠR oland", "sing le", "Ġl umin", "ĠF ellow", "ĠCap acity", "ĠKaz akh", "W N", "Ġfin anced", "38 9", "Ġt id", "Ġcoll usion", "ĠMy r", "î Ģ", "Sen ator", "Ġped iatric", "Ġneat ly", "Ġsandwic hes", "ĠArchitect ure", "Ġt ucked", "Ġbalcon y", "Ġearthqu akes", "qu ire", "F uture", "Ġhe fty", "é Ĺ", "Ġspecial izes", "Ġstress es", "Ġs ender", "Ġmisunder standing", "Ġep ile", "Ġprov oke", "ĠCol ors", "Ġdis may", "uk o", "[ _", "58 6", "ne utral", "Ġdon ating", "ĠRand all", "Mult i", "Ġconvenient ly", "ĠS ung", "ĠC oca", "Ġt ents", "ĠAc celer", "Ġpart nered", "27 2", "ir ming", "ĠB AS", "s ometimes", "Ġobject ed", "ub ric", "p osed", "LC S", "gr ass", "Ġattribut able", "V IS", "Israel i", "Ġrepe ats", "ĠR M", "v ag", "ut a", "in ous", "Ġin ert", "ĠMig uel", "æ Ń", "ĠHawai ian", "B oard", "Ġart ific", "ĠAzerb ai", "as io", "ĠR ent", "A IN", "Ġappl iances", "Ġnational ity", "Ġass hole", "ĠN eb", "Ġnot ch", "h ani", "ĠBr ide", "Av ailability", "Ġintercept ed", "Ġcontin ental", "Ġsw elling", "ĠPers pect", "b ies", ". <", "ith metic", "ĠL ara", "Ġtempt ing", "add r", "Ġoversee ing", "cl ad", "ĠD V", "ĠGing rich", "Ġm un", "ĠApp ropri", "Ġalter ations", "ĠPat reon", "Ġha voc", "Ġdiscipl ines", "Ġnotor iously", "aku ya", "ier i", "? ).", "ĠW ent", "Ġsil icon", "Ġtre mb", "Cont ainer", "K nown", "Ġmort ar", "est e", "ick a", "Ar thur", "ĠPre viously", "ĠMart y", "Ġsp arse", "g ins", "Ġin ward", "ĠParticip ant", "C opy", "ĠM isc", "Ġantib iotic", "ĠRet ro", "Ġel usive", "Ġass ail", "ĠBatt alion", "ĠB ought", "Ġdimin ish", "ĠEuro pa", "s ession", "ĠDanger ous", "ies el", "Ġdisbel ief", "Ġbl asts", "ext reme", "ĠBoy d", "ĠProject s", "ĠGu ys", "Ġunder gone", "Ġgr ill", "ĠDw ight", "Ġ19 7", "US ER", "Ġfiles ystem", "Ġcl ocks", "T aylor", "Ġwra pper", "Ġfold ing", "ous and", "ĠPhilipp ine", "ATION AL", "ĠPer th", "Ġas hes", "Ġaccum ulate", "ĠGate way", "Sh op", "orks hire", "H an", "ĠBar rel", "ĠLe h", "ĠX V", "Ġwh im", "Ġrep o", "ĠC G", "ĠM am", "Ġincorpor ating", "Ġbail out", "Ġlingu istic", "Ġdis integ", "C LE", "Ġcinem atic", "ĠF iber", "S yn", "il ion", "ĠCom pos", "c hens", "Ġne oc", "Ġbo iled", "F INE", "on o", "un cle", "ik en", "ĠB M", "Î ¹", "Ġreceipt s", "Ġdisp osed", "ĠTh irty", "ĠR ough", "ĠA BS", "Ġnot withstanding", "oll en", "# $", "Ġunrel iable", "Ġbl oom", "Ġmedi ocre", "Ġtr am", "ĠTas man", "Ġsh akes", "Ġmanifest o", "ĠM W", "Ġsatisf actory", "Ġsh ores", "Ġcomput ation", "Ġassert ions", "orm ons", "ar ag", "ab it", "Dem ocrats", "ĠL oot", "ĠVol ks", "ha ired", "Ġgrav itational", "S ing", "ĠM iz", "Ġthro ttle", "Ġtyr anny", "ĠView s", "Ġrob ber", "ĠMinor ity", "Ġsh rine", "sc ope", "pur pose", "Ġnucle us", "our cing", "ĠUS DA", "ĠD HS", "w ra", "ĠBow ie", "Sc ale", "ĠB EL", "x i", "I ter", "Ġ( ),", "w right", "Ġsail ors", "ous ed", "NAS A", "ĠPro of", "ĠMin eral", "t oken", "ĠF D", "R ew", "Ġe ll", "6 30", "Ġchance llor", "ĠG os", "Ġamount ed", "ĠRec re", "ome z", "ĠOpt im", "ĠOl ive", "Ġtrack er", "ow ler", "ĠUn ique", "R oot", "Ġmar itime", "ĠQur an", "ĠAd apt", "Ġecosystem s", "ĠRe peat", "ĠS oy", "ĠI MP", "Ġgrad uating", "and em", "P ur", "ĠRes et", "ĠTr ick", "ĠPh illy", "ĠT ue", "ĠMalays ian", "Ġclim ax", "Ġb ury", "Ġcons pic", "ĠSouth ampton", "ĠFl owers", "Ġesc orted", "ĠEduc ational", "ĠI RC", "Ġbrut ally", "e ating", "Ġpill ar", "ĠS ang", "ĠJ ude", "ar ling", "ĠAm nesty", "Ġrem inding", "ĠAdminist rative", "hes da", "Ġfl ashed", "ĠP BS", "per ate", "fe ature", "Ġsw ipe", "Ġgra ves", "oult ry", "26 1", "bre aks", "ĠGu er", "Ġsh rimp", "ĠV oting", "qu ist", "Ġanaly tical", "Ġtables poons", "ĠS OU", "Ġresear ched", "Ġdisrupt ed", "Ġj our", "Ġrepl ica", "Ġcart oons", "b ians", "} )", "c opy", "G ot", "ou ched", "P UT", "Ġsw arm", "not ations", "s aid", "Ġreb uilt", "Ġcollabor ate", "Ġr aging", "Ġn ar", "Ġdem ographics", "ĠD DR", "Ġdist rust", "oss ier", "ĠK ro", "Ġpump kin", "Ġreg rets", "Ġfatal ities", "ĠL ens", "ĠO le", "p d", "Ġpupp et", "ĠOut look", "ĠSt am", "O l", "F air", "U U", "Ġre written", "Ä ±", "Ġfasc inated", "Ġve ctors", "Ġtrib unal", "u ay", "ĠM ats", "ĠCo ins", "[ [", "Ġ18 1", "Ġrend ers", "ĠK aepernick", "Ġesp ionage", "Ġsum m", "Ġd itch", "Acc ount", "Ġspread sheet", "Ġmut ant", "p ast", "40 7", "Ġd ye", "Ġinit iation", "Ġ4 000", "Ġpunish able", "Ġth inner", "ĠKh al", "Ġinter medi", "D un", "ĠGoth am", "Ġeager ly", "Ġvag inal", "p owers", "V W", "ĠWATCH ED", "Ġpred ator", "ams ung", "Ġdispar ity", "Ġ[ *", "Ġam ph", "Ġout skirts", "ĠSpir its", "Ġskelet al", "Ð »", "ĠR ear", "Ġissu ance", "ĠLog ic", "re leased", "Z Z", "ĠB ound", "Ent ry", "Ġex its", "is ol", "ĠFound er", "Ġw re", "ĠGreen land", "ĠM MO", "t aker", "IN C", "ãģ ¾", "Ġhour ly", "hen ko", "Ġfantas ies", "Ġdis ob", "Ġdemol ition", "ãĥ ĭ", "Ġen listed", "rat ulations", "Ġmis guided", "Ġens ured", "Ġdiscour aged", "m ort", "Ġfl ank", "Ġc ess", "Ġreact s", "ĠS ere", "s ensitive", "ĠSer pent", "ass ad", "Ġ24 7", "Ġcalm ly", "b usters", "Ġble ed", "ĠSt ro", "Ġamuse ment", "ĠAntar ctica", "Ġs cept", "ĠG aw", "a q", "ason ic", "Ġsp rawling", "n ative", "atur ated", "ĠBattle field", "IV ERS", "E B", "ĠG ems", "ĠNorth western", "ĠFil ms", "ĠAut omatic", "Ġappre hend", "ãģ ¨", "Ġgui Name", "Ġback end", "Ġevid enced", "ge ant", "01 2", "ĠS iege", "Ġexternal To", "Ġunfocused Range", "ĠguiActiveUn focused", "Ġgui Icon", "ĠexternalTo EVA", "ĠexternalToEVA Only", "F ri", "ch ard", "en aries", "Ġchief s", "Ġc f", "ĠH UD", "Ġcorro bor", "Ġd B", "ĠT aken", "ĠPat ricia", "ra il", "ĠCh arm", "ĠLiber tarian", "rie ve", "Person al", "ĠO UR", "ger ies", "Ġdump ing", "Ġneurolog ical", "it imate", "ĠClint ons", "raft ed", "ĠM olly", "Ġtermin als", "reg ister", "Ġfl are", "Ġenc oded", "Ġautop sy", "p el", "m achine", "Ġexempt ions", "ĠRoy als", "d istance", "Ġdraft s", "Ġl ame", "ĠC unning", "Ġsp ouses", "ĠMark ets", "ĠCar rier", "Ġimp lying", "ĠY ak", "s id", "Ġl oser", "Ġvigil ant", "Ġimpe achment", "Ġaug mented", "ĠEmploy ees", "Ġunint ended", "tern ally", "ĠW att", "Ġrecogn izable", "ess im", "æ Ŀ", "Ġco ated", "r ha", "Ġlie utenant", "ĠLegisl ation", "pub lished", "44 4", "01 3", "Ġide ally", "ĠPass word", "Ġsimpl ify", "ĠMet a", "ĠM RI", "Ġple ading", "organ ized", "hand ler", "Ġun ravel", "cor rect", "Ġ icy", "Ġparan oid", "Ġpass er", "Ġinspect ions", "of er", "ĠHealth care", "28 3", "ĠBr ut", "iol a", "for ge", "ĠMed ieval", "MS N", "ie vers", "ĠProgram ming", "å ī", "Ġ2 23", "m u", "ĠC LE", "ug a", "Ġsho ppers", "Ġinform ative", "ĠPl ans", "Ġsupplement ation", "ĠT ests", "ty ard", "ocy tes", "ĠVeg a", "ĠGujar at", "erman ent", "Ex cept", "ĠL OT", "all a", "ĠC umm", "ĠO sw", "Ġven om", "ĠDeb t", "ĠD OWN", "Ġreun ion", "Ġm uc", "ĠRel ief", "Ġge op", "ĠðŁ ĺ", "al ogue", "An th", "ech o", "Ġcor ros", "Ġrepl ication", "ĠBl azing", "ĠD aughter", "Ġinf lic", "ĠLind sey", "Ù Ī", "28 4", "Ex it", "Ġgl oom", "TA IN", "Ġundermin ing", "Ġadv ising", "h idden", "Ġover flow", "Ġg or", "urd ue", "Ġe choes", "enh agen", "Ġimp uls", "d rug", "c ash", "Ġas ync", "Ġmir ac", "at ts", "p unk", "Ġpiv ot", "ĠLegisl ative", "Ġblog gers", "ĠCl aw", "s burg", "d yl", "ĠRecomm end", "Ġver te", "Ġprohib iting", "ĠPant her", "Jon athan", "Ġo min", "Ġhate ful", "28 1", "ĠOr che", "ĠMurd och", "down s", "Ġas ymm", "G ER", "Al ways", "Ġinform s", "ĠW M", "ĠP ony", "ĠApp endix", "ĠAr lington", "J am", "Ġmedic inal", "ĠS lam", "IT IES", "Ġre aff", "ĠR i", "F G", "S pring", "b ool", "Ġthigh s", "Ġmark ings", "ĠRa qqa", "ĠL ak", "p oll", "ts ky", "ĠMort y", "ĠDef inition", "Ġdeb unk", "end ered", "ĠLe one", "a vers", "Ġmortg ages", "App arently", "N ic", "ha us", "ĠTh ousands", "au ld", "Ġm ash", "sh oot", "Ġdi arr", "Ġconscious ly", "H ero", "e as", "ĠN aturally", "ĠDestroy er", "Ġdash board", "serv ices", "R og", "Ġmillenn ials", "Ġinv ade", "- (", "Ġcomm issions", "ĠA uckland", "Ġbroadcast s", "Ġfront al", "Ġcr ank", "ĠHist oric", "Ġrum ours", "CT V", "Ġster il", "Ġboost er", "rock et", "ãĤ ¼", "ut sche", "ĠP I", "Ġ2 33", "ĠProdu cer", "ĠAnaly tics", "Ġinval uable", "Ġunint ention", "ĠC Y", "Ġscrut in", "Ġg igg", "Ġeng ulf", "Ġprolet ariat", "Ġh acks", "ĠH ew", "ar ak", "ĠSl ime", "ield ing", "ag her", "ĠEll iot", "Ġtele com", "Ġ2 19", "ult an", "ĠAr bor", "ĠSc outs", "B an", "Ġlifes pan", "Ġbl asp", "38 8", "Ġjud iciary", "ĠContin ental", "ask ing", "Mc C", "L ED", "Ġbag gage", "ĠSorce rer", "Ġrem nants", "ĠGriff ith", "ets u", "ĠSub aru", "ĠPerson ality", "des igned", "ush ima", "agn ar", "Ġrec oil", "Ġpass ions", "\\ \":", "Ġte e", "Ġabol ition", "ĠCreat ing", "j ac", "Ġ19 4", "01 9", "Ġpill ars", "ric hed", "/ \"", "t k", "Ġlive lihood", "Ġro asted", "ah on", "ĠH utch", "ass ert", "Ġdivid end", "Ġkn it", "Ġd aunting", "Ġdisturb ance", "Ġsh ale", "Ġcultiv ated", "Ġrefriger ator", "L B", "ĠN ET", "Ġcommercial s", "Ġthink ers", "45 5", "Ġch op", "B road", "Ġsuspic ions", "Ġtag ged", "l ifting", "Ġsty lish", "ĠShield s", "Short ly", "Ġt ails", "A uth", "ST E", "ĠG AME", "Ġse ism", "ĠK is", "olog ne", "Ġcow ork", "Ġforc ibly", "Ġthy roid", "ĠP B", "AN E", "mar ried", "h orse", "Ġpoly mer", "ĠCh al", "od or", "DE BUG", "ĠCon text", "Ġbl iss", "Ġpin point", "ĠMat hemat", "leg ram", "ĠWeek end", "Ġlab elled", "Ġb art", "it les", "Ġest rogen", "âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ", "\" '", "Ġvis ibly", "Ġouts ider", "aid a", "Are a", "Ġdisse min", "Ġdish onest", "ĠCl osed", "ĠBullet in", "ĠRam sey", "sw ord", "ĠX I", "our ced", "S ame", "34 6", "ĠRe pe", "ĠK ou", "c ake", "em is", "C ache", "ĠMe aning", "ĠEn light", "onom y", "Ġmanifest ation", "sw orth", "J ay", "Ġch ore", "ö r", "D ream", "Ġsanction ed", "Ġcult urally", "ĠA ra", "N av", "Ġthe ological", "Ġstr ut", "ĠV O", "ĠHand book", "Ġconstruct ing", "Ġ ¶", "ĠBenef its", "ĠPsych ological", "s ac", "å ¸", "p olicy", "ĠMat ters", "ĠReport ed", "ĠBy te", "Ġvit ro", "ĠM aiden", "Ġl am", "ĠJenn ings", "Ġgar ment", "ĠRut gers", "ĠStaff ord", "ĠWell ington", "Ġinter mitt", "Ġn pm", "Ġord eal", "Ġplug ged", "o oming", "in ished", "fram ework", "Ġtim ber", "Ġc ass", "Ġ8 50", "il ess", "ĠRed ux", "7 68", "St re", "Ġsurpass ed", "w hel", "Ġparalle ls", "Ġve il", "ĠG I", "ĠR EST", "Ġread iness", "s ort", "Ġmod ifying", "ĠSl ate", "ru ff", "Ġmar ble", "Ġinf rared", "Ġaud itor", "ĠFANT ASY", "ĠP overty", "ĠS PD", "Ġ\" (", "K y", "RA Y", "Ġexecut ions", "ĠBever ly", "ĠMarx ism", "ĠBur st", "ĠK ali", "est ones", "Clear ly", "E ll", "ãģ §", "ĠProceed ings", "T oken", "IF IC", "ñ a", "Cent ral", "ĠH aley", "ĠD rama", "Ġform ations", "OR N", "Book s", "Ġdom inating", "ĠFly ers", "ĠCompan ion", "Ġdiscipl ined", "ĠYug oslav", "ĠSpell s", "Ġv engeance", "Ġland lords", "L en", "ĠO gre", "ano ia", "Ġpier cing", "Ġcon greg", "Ġscore r", "ob ia", "Ġnic kel", "ĠLear ns", "Ġre jo", "Ġmaster piece", "Fl ash", "Ġinhab ited", "ĠOpen GL", "ĠD ud", "ĠI CO", "Ġar ter", "Ġpl ur", "Ġmaster y", "Ġlong standing", "st ed", "Ġw ines", "Ġtelev ised", "ĠSh rine", "ĠBay ern", "Ġâ ĵĺ", "Ġencl osure", "j ohn", "Ġprophe ts", "ĠRes urrection", "ĠOrd ers", "Ġun even", "r als", "Ġd wind", "ĠL ah", "ĠSl oven", "37 8", "Ġins istence", "aff le", "ĠCl one", "Ġhard ship", "ĠCongress man", "Ġple ad", "Ġreview ers", "Ġc ured", "Ġ19 35", "as ley", "f ake", "ĠTh inking", "yd ia", "P ART", "ĠD ota", "o it", "Ġwh ipped", "Ġb ouncing", "ĠHispan ics", "com ings", "Ġcann abin", "ĠCh ambers", "ĠZ ack", "Option al", "Ġco ats", "Ġprow ess", "ĠNort on", "Ġplain ly", "Ġfre ight", "Ġinhib ition", "Ġcl am", "Ġ30 3", "ke f", "ale igh", "L uke", "Ġpsych o", "ator ium", "M ED", "Ġtreat ies", "Ġind isc", "Ġd c", "OP S", "Ġresil ient", "ĠInter state", "Ġsl ack", "Ġmund ane", "Ġestab lishes", "35 9", "Ġstr ained", "Ġn ond", "S us", "Ġcast e", "ar ate", "ie ving", "Ġunfair ly", "Ġpars er", "on ial", "urs ive", "V ia", "ĠOtt o", "ĠAuthor ities", "stro ke", "K R", "ĠMer cy", "Ġfurn ished", "Ġout set", "Ġmet ic", "19 82", "olith ic", "ĠT ent", "og ical", "ĠA ircraft", "Ġh ides", "ĠBec ame", "Ġeduc ators", "re aching", "Ġvol atility", "Ġtodd ler", "ĠNAS CAR", "ĠTw elve", "ĠHigh lights", "Ġgra pe", "Ġspl its", "Ġpe asant", "Ġre neg", "ĠMS I", "Tem p", "st ars", "Ġtre k", "ĠHy de", "b inding", "Ġreal ism", "Ġox ide", "ĠH os", "Ġmount s", "Ġbit ing", "Ġcollaps ing", "Ġpost al", "Ġmuse ums", "Ġdet ached", "Ġrespect ing", "Ġmonop ol", "Ġwork flow", "ĠC ake", "Tem plate", "ĠOrgan isation", "Ġpers istence", "36 9", "C oming", "B rad", "Ġredund ant", "ĠG TA", "Ġb ending", "Ġrev oked", "Ġoff ending", "Ġfram ing", "Ġprint f", "Comm un", "mem bers", "Out side", "Ġconst rued", "Ġc oded", "F ORE", "Ġch ast", "Ch at", "Ind ian", "ĠY ard", "? !\"", "ĠP orts", "ĠX avier", "ĠR ET", "' .\"", "ĠBo at", "iv ated", "ich t", "umer able", "D s", "ĠDun n", "Ġcoff in", "Ġsecure ly", "ĠRapt ors", "ĠB es", "Install ation", "Ġin ception", "ĠHealth y", "end ants", "Ġpsych ologists", "ĠShe ikh", "c ultural", "ĠBlack Berry", "sh ift", "F red", "oc he", "Ġc akes", "ĠS EO", "ĠG ian", "ĠAs ians", "og ging", "e lement", "Ġpund its", "ĠV augh", "ĠG avin", "Ġh itter", "Ġdrown ed", "Ġch alk", "ĠZ ika", "Ġmeas les", "80 2", "âĢ¦ ..", "ĠAW S", "] \"", "Ġdist ort", "ĠM ast", "Ġantib odies", "ĠM ash", "Mem ory", "ĠUg anda", "ĠPro b", "Ġvom iting", "ĠTurn s", "Ġoccup ying", "Ġev asion", "ĠTher apy", "Ġprom o", "Ġelect r", "Ġblue print", "ĠD re", "pr iced", "ĠDep ot", "Ġallev iate", "ĠSom ali", "m arg", "n ine", "Ġnostalg ia", "ĠShe pherd", "Ġcaval ry", "Ġtor ped", "ĠBlood y", "x b", "Ġs ank", "Ġgo alt", "report print", "embed reportprint", "clone embedreportprint", "ĠIn itially", "ĠF ischer", "Ġnot eworthy", "c ern", "Ġin efficient", "raw download", "rawdownload cloneembedreportprint", "c ation", "ĠD ynasty", "l ag", "D ES", "Ġdistinct ly", "ĠEston ia", "Ġopen ness", "Ġg ossip", "ru ck", "W idth", "ĠIb rahim", "Ġpet roleum", "Ġav atar", "ĠH ed", "ath a", "ĠHog warts", "Ġc aves", "67 8", "Ġsafegu ard", "ĠM og", "iss on", "ĠDur ham", "sl aught", "ĠGrad uate", "Ġsub conscious", "ĠEx cellent", "ĠD um", "---- -", "Ġp iles", "ĠW ORK", "ĠG arn", "ĠF ol", "ĠAT M", "Ġavoid s", "ĠT ul", "Ġble ak", "EL Y", "iv ist", "light ly", "P ers", "ĠD ob", "ĠL S", "Ġins anity", "Î µ", "atal ie", "En large", "Ġtw ists", "Ġfault y", "Ġpir acy", "Ġimp over", "Ġrug ged", "ĠF ashion", "Ġs ands", "' ?", "sw ick", "Ġn atives", "Ġhe n", "ĠNo ise", "ãĥ Ĺ", "Ġg reens", "Ġfree zer", "Ġd ynasty", "ĠFather s", "ĠNew ark", "Ġarchae ological", "Ġo t", "ob ar", "Ġblock ade", "Ġall erg", "L V", "Ġdeb it", "ĠR FC", "ĠMil ton", "ĠPress ure", "Ġwill ingly", "Ġdisproportion ate", "Ġopp ressive", "Ġdiamond s", "Ġbelong ings", "19 70", "Ġbell s", "Ġimperial ism", "Ġ2 27", "Ġexpl oding", "ĠE clipse", "Ġ19 19", "Ġr ant", "Ġnom inations", "34 7", "Ġpeace fully", "ric a", "ĠF UCK", "Ġvib ration", "mal ink", "Ġro pes", "ĠIv anka", "ĠBrew ery", "ĠBook er", "ĠOw ens", "go ers", "Serv ices", "ĠSn ape", "Ġ19 1", "39 5", "Ġ2 99", "just ice", "Ġb ri", "Ġdisc s", "Ġprom inently", "Ġvul gar", "Ġsk ipping", "l ves", "Ġtsun ami", "37 4", "ĠU rug", "ĠE id", "rec ated", "p hen", "Ġfault s", "ĠStart ed", "9 50", "Ġp i", "Ġdetect or", "Ġbast ard", "Ġvalid ated", "Space Engineers", "OUR CE", "Ġ( ~", "Ġuns ur", "Ġaff irmed", "Ġfasc ism", "Ġres olving", "ĠCh avez", "ĠC yn", "Ġdet ract", "L ost", "Ġrig ged", "Ġhom age", "ĠBrun o", "55 5", "ec a", "Ġpress es", "Ġhum our", "Ġsp acing", "Ġ' /", "olk ien", "C oun", "OP ER", "T re", "S on", "ĠCambod ia", "ier re", "m ong", "o zy", "Ġliquid ity", "ĠSov iets", "ĠFernand o", "Ġ2 29", "Ġsl ug", "ĠCatal an", "elect ric", "Ġsc enery", "ĠH earth", "Ġconst rained", "Ġgoal ie", "ĠGu idelines", "ĠAm mo", "ĠPear son", "Ġtax ed", "Ġfet us", "Resp onse", "ĠAlex is", "th ia", "G uy", "Ġrecon struct", "Ġextrem es", "Ġconclud ing", "ĠP eg", "ook s", "Ġded uctions", "R ose", "Ġground breaking", "ĠT arg", "ãĥ ģ", "ĠRe ve", "res ource", "Ġmo ons", "Ġelectrom agnetic", "Ġamid st", "ĠVik tor", "N ESS", "B ACK", "Ġcomm ute", "ĠAna heim", "Ġfluct uations", "6 40", "Ġnood les", "ĠCop enhagen", "ĠT ide", "ĠGri zz", "ĠS EE", "Ġpip elines", "Ġsc ars", "end o", "ag us", "ĠE TF", "/ #", "ĠBec ome", "44 8", "Ġvis c", "ĠRecomm ended", "Ġj umper", "Ġcogn ition", "Ġassass in", "Ġwitness ing", "ĠSet up", "Ġl ac", "v im", "IS M", "p ages", "SS L", "35 8", "Ġad ject", "indust rial", "l ore", "cher y", "Ġgl itter", "Ġc alf", "Flor ida", "Ġspoil ers", "Ġsucceed s", "Ġch anting", "Ġslog ans", "ĠTr acy", "Vis it", "rol ogy", "Ġm ornings", "Ġline age", "Ġs ip", "Ġintense ly", "Ġflour ish", "ĠSle eping", "ĠF em", "or por", "ĠK lan", "ĠDar th", "h ack", "ĠNi elsen", "Ġtum ors", "Ġprocure ment", "ĠY orkshire", "Ġra ided", "K Y", "An na", "Ġ// [", "ĠDis order", "ĠMust ang", "ĠW en", "ĠTry ing", "s q", "Ġdeliver ies", "Ġshut ter", "Ġcere bral", "Ġbip olar", "ĠC N", "l ass", "j et", "Ġdeb ating", "> :", "Ġe agle", "gr ades", "ĠD ixon", "UG C", "M AS", "ĠDr aco", "ĠMach ines", "aff er", "Ġem an", " ²", "pr on", "ĠG ym", "Ġcompar atively", "ĠTrib unal", "PR O", "Ġle x", "Ġfert ile", "Ġdep ressing", "Ġsuperf icial", "ess ential", "ĠHun ters", "g p", "Ġprom inence", "L iber", "ĠAn cest", "ote chnology", "Ġm ocking", "ĠTra ff", "ĸ ļ", "Med ium", "I raq", "Ġpsychiat rist", "Quant ity", "ĠL ect", "Ġno isy", "5 20", "G Y", "Ġsl apped", "ĠM TV", "Ġpar a", "p ull", "Mult iple", "as her", "Ġn our", "ĠSe g", "Spe ll", "v ous", "ord ial", "Sen ior", "ĠGold berg", "ĠPl asma", "ne ed", "Ġmess enger", "ere t", "Ġteam ed", "Ġliter acy", "ĠLe ah", "ĠD oyle", "Ġem itted", "U X", "Ġev ade", "Ġm aze", "Ġwrong ly", "ĠL ars", "Ġstere otype", "Ġpled ges", "Ġarom a", "ĠM ET", "Ġac re", "ĠO D", "Ġf f", "Ġbrew eries", "ĠH ilton", "und le", "ĠK ak", "ĠThank fully", "ĠCan ucks", "in ctions", "ĠApp ears", "Ġco er", "Ġundermin ed", "ro vers", "And re", "Ġbl aze", "um ers", "Ġfam ine", "amp hetamine", "ulk an", "Am ount", "Ġdesper ation", "wik ipedia", "develop ment", "ĠCor inth", "uss ia", "Jack son", "L I", "N ative", "R s", "Oh io", "ĠKath leen", "F ortunately", "Ġattend ant", "ĠPre ferred", "ĠDid n", "ĠV s", "M is", "Ġrespond ent", "Ġb oun", "st able", "Ġp aved", "Ġunex pl", "ĠChe ney", "L M", "ĠC ull", "bl own", "Ġconfront ing", "oc ese", "serv ing", "W i", "ĠLith uania", "ann i", "Ġst alk", "h d", "Ġv ener", "AP H", "ynchron ous", "UR R", "um ably", "hist oric", "H alf", "H ay", "Ġresil ience", "spe ction", "Ġabandon ing", "O bs", "ĠDeb bie", "Ġgrad ient", "ĠPl aint", "ĠCan al", "AR CH", "Ġexpans ive", "Ġfun g", "Ġb ounced", "U nd", "Ġprec autions", "Ġclar ification", "Ġd agger", "Ġgri ps", "Ġ µ", "ĠRiver a", "ĠUnd ead", "is ites", "ĠFIR ST", "ñ o", "aud i", "Ġhost ages", "Ġcompl iant", "Ġal umni", "Se ven", "Ġcyber security", "e ither", "Col lect", "Ġinvari ably", "ĠS oci", "Ġlaw maker", "Ġa le", "ĠPerson ally", "N azi", "Ġcustom ization", "ĠPro c", "ĠSask atchewan", "eat uring", "Ġsp ared", "Ġdiscontin ued", "Ġcomput ational", "ĠMotor ola", "Ġsuprem acist", "government al", "Ġparad ise", "ĠDown ing", "ĠNik on", "Ġcat alyst", "ber ra", "Tor onto", "8 75", "bet a", "ĠMac ron", "Ġunreal istic", "ve ctor", "ĠVeh icles", "it iveness", "ĠR V", "ĠCol bert", "s in", "o ji", "ent in", "ĠKr ish", "hell o", "ff ield", "ok y", "ĠT ate", "Ġmap le", "Ġa ids", "chem ical", "33 4", "n uts", "ĠWar p", "Ġx x", "ĠRob b", "umer ous", "_- _", "ft ime", "ĠV W", "Ġw inger", "ĠD ome", "t ools", "ĠP V", "ĠGe orgetown", "Ġg eared", "Ġjihad ists", "Ġc p", "Ġster oids", "M other", "cler osis", "ĠDR M", "nes ia", "Ġl inger", "Ġimm ersive", "ĠC OUN", "Ġoutwe igh", "ens ual", "B and", "Ġtransform s", "mat ched", "ps ons", "ĠJud icial", "f actor", "Ġrefer ral", "Ġodd ly", "ĠW enger", "B ring", "ĠB ows", "60 2", "IC LE", "Ġl ions", "ĠAcad emic", "ĠTh orn", "ĠRa ider", "kef eller", "St orage", "L ower", "ĠOr t", "ĠEqu ality", "AL T", "ĠS OC", "T ypes", "Ġl yn", "ĠAss et", "co at", "TP P", "C VE", "ĠPione er", "app lication", "Mod ern", "ĠH K", "En vironment", "Al right", "R ain", "IP P", "ĠShi ite", "Ġm ound", "ĠAb ilities", "cond ition", "St aff", "Ġcompet ence", "ĠM oor", "ĠDi ablo", "Ġwith held", "Ġost ensibly", "ĠB rom", "Ġms g", "Ġden omin", "ĠRef erences", "ĠF P", "Ġplun ged", "Ġp amph", "m oving", "cent ral", "Ġdown right", "Ġf ading", "T al", "T yp", "ĠTh y", "uk es", "it he", "Ġo ve", "Ġbatt led", "Ġseaf ood", "Ġfig ur", "ĠR D", "c rop", "Ġsqu ads", "{ \\", "à ¹", "ĠE h", "Ġinterview ing", "ĠQ in", "Ġas piring", "PL IC", "Ġcla uses", "ĠG ast", "ĠN ir", "Ġl uggage", "Ġh ose", "Ġsystem d", "Ġdesc ending", "ĠRev ised", "ĠR ails", "al ign", "70 9", "33 7", "Ġf ug", "charg ing", "t ags", "Ġut er", "k ish", "WAR NING", "49 0", "prof its", "Ġvoy age", "Ġa ce", "ĠV anguard", "ĠT anks", "ĠM uk", "Ġ2 26", "S afe", "Ar mor", "Ġvolcan ic", "Ġwom b", "ĠM IL", "Ġbegin ner", "ĠRec ogn", "ĠA AP", "PL AY", ") !", "Ġdetect ing", "c n", "Ġbre aches", "Bas ically", "ĠP ag", "ĠMunicip al", "ĠInd ie", "ĠL af", "ĠDis able", "ĠOl son", "Ġrest rained", "Ġrul ings", "Ġhum ane", "ev ents", "ĠCinem a", "display Text", "ĠH atch", "action Date", "onna issance", "Ġassault ing", "ĠL ug", "CH AT", "Ġvig orous", "ĠPer se", "Ġintoler ance", "ĠSnap chat", "ĠSh arks", "Ġd ummy", "ĠDi agn", "ĠGu itar", "im eters", "40 3", "RE G", "A x", "Ġsepar ates", "ĠMah m", "Ġt v", "j ah", "O OL", "C irc", "ĠWinds or", "uss ian", "Ġintu ition", "Ġdis dain", "ĠDon ovan", "Ġ2 21", "E mb", "Ġcondem ning", "Ġgener osity", "zz y", "Ġpant ies", "ĠPre vent", "Action Code", "AN A", "34 2", "external ActionCode", "Ġspec ifying", "Ġcryst all", "J ere", "Ġru pt", "ĠApp rentice", "Ġprof iling", "Ð º", "St rike", "Ġsid eline", "Ġoblig ated", "Ġocc ult", "Ġbureaucr atic", "ant ically", "rupt ed", "neg ative", "ĠEthiop ia", "ĠC ivic", "Ġins iders", "el igible", "ĠTV s", "ĠB AR", "ĠT I", "i ologist", "ĠA IR", "Ġsubstit uted", "Ar ab", "ĠS aul", "ĠY og", "p rem", "Ġbuild ers", "Ġstation ary", "Ġdoubt ful", "Ġvig orously", "Ġthr illing", "Ph ysical", "ĠCare y", "ĠHyd ra", "geon ing", "ĠS ly", "y ton", "Ġborrow ers", "ĠPark inson", "Ġ ë", "ĠJama ica", "Ġsat ir", "Ġinsurg ents", "ĠF irm", "Ġis ot", "ĠK arn", "our ning", "ak ens", "doc s", "l ittle", "ĠMon aco", "CL ASS", "Tur key", "L y", "ĠCon an", "ass ic", "Ġstar red", "ĠPac ers", "et ies", "Ġt ipping", "M oon", "ĠR w", "s ame", "Ġcav ity", "Ġgo of", "ĠZ o", "Sh ock", "um mer", "Ġemphas izes", "Ġreg rett", "Ġnovel ty", "Ġen vy", "ĠPass ive", "r w", "50 5", "Ġind ifferent", "ĠR ica", "ĠHim self", "ĠFred die", "Ġad ip", "ä¸ Ģ", "Ġbreak out", "Ġhur ried", "ĠHu ang", "ĠD isk", "Ġro aming", "?????- ?????-", "U V", "ĠRick y", "ĠS igma", "Ġmarginal ized", "Ġed its", "Ġ30 4", "mem ory", "Ġspec imen", "29 3", "ãģ ¯", "Ġvert ically", "Ġaud ition", "ĠHe ck", "Ġc aster", "ĠHold ings", "ad al", "ĠC ron", "ĠL iam", "Ġdef lect", "P ick", "ĠDeb ug", "RE F", "Ġvers atility", "ot hes", "class ified", "ĠMah ar", "ĠH ort", "C ounter", "st asy", "not iced", "33 1", "ĠSh im", "f uck", "ĠB ie", "Ġair ing", "ĠPro tein", "ĠHold ing", "Ġspect ators", "ili ated", "ĠThat cher", "n osis", "ãĥ¼ ãĥ³", "Te le", "B oston", "ĠTem pl", "st ay", "Ġdecl arations", "47 9", "Vol ume", "ĠDesign er", "ĠOver watch", "id ae", "Ġon wards", "Ġn ets", "ĠMan ila", "part icularly", "Ġpolit ic", "o other", "Ġport raits", "Ġpave ment", "c ffff", "Ġs aints", "Ġbegin ners", "ES PN", "Ġshort comings", "âķIJ âķIJ", "Ġcom et", "ĠOrgan ic", "qu el", "Ġhospital ized", "Bre ak", "Ġpe el", "dyl ib", "asp x", "ur ances", "ĠT IM", "P g", "Ġread able", "ĠMal ik", "Ġm uzzle", "Ġbench marks", "d al", "ĠV acc", "ĠH icks", "60 9", "ĠB iblical", "he ng", "Ġover load", "ĠCivil ization", "Ġimm oral", "Ġf ries", "ãĤ Ĵ", "Ġreprodu ced", "Ġform ulation", "j ug", "ire z", "g ear", "Ġco ached", "Mp Server", "ĠS J", "ĠK w", "In it", "d eal", "ĠO ro", "ĠL oki", "ĠSong s", "Ġ23 2", "ĠLou ise", "asion ally", "Ġunc ond", "olly wood", "Ġprogress ives", "ĠEn ough", "ĠDo e", "Ġwreck age", "Ġbr ushed", "ĠBase Type", "Ġz oning", "ish able", "het ically", "ĠC aucus", "ĠH ue", "Ġk arma", "ĠSport ing", "Ġtrad er", "Ġseem ing", "ĠCapt ure", "4 30", "b ish", "Ġt unes", "Ġindo ors", "ĠSp here", "ĠD ancing", "TER N", "Ġno b", "ĠG ST", "m aps", "Ġpe ppers", "F it", "Ġoverse es", "ĠRabb i", "ĠR uler", "vert ising", "off ice", "xx x", "Ġra ft", "Ch anged", "Ġtext books", "L inks", "ĠO mn", "ãĢ ij", "Ġinconven ience", "ĠDon etsk", "= ~", "Ġimplicit ly", "Ġboost s", "ĠB ones", "ĠBo om", "Cour tesy", "Ġsens ational", "AN Y", "Ġgre edy", "ed en", "Ġinex per", "ĠL er", "ĠV ale", "Ġtight en", "ĠE AR", "ĠN um", "Ġancest or", "S ent", "ĠH orde", "urg ical", "all ah", "Ġsa p", "amb a", "ĠSp read", "tw itch", "Ġgrand son", "Ġfract ure", "Ġmoder ator", "ĠSe venth", "ĠRe verse", "Ġestim ation", "Cho ose", "Ġpar ach", "Ġbar ric", "ãĢ IJ", "Ġcomp ass", "Ġall ergic", "âĢ ķ", "OT HER", "err illa", "Ġw agon", "Ġz inc", "Ġrub bed", "ĠFull er", "ĠLuxem bourg", "ĠHoo ver", "Ġli ar", "ĠEven ing", "ĠCob b", "est eem", "Ġselect or", "ĠB rawl", "is ance", "ĠE k", "Ġtro op", "Ġg uts", "ĠApp eal", "ĠTibet an", "Ġrout ines", "ĠM ent", "Ġsummar ized", "steam apps", "Ġtr anqu", "Ġ19 29", "or an", "ĠAut hent", "Ġg maxwell", "Ġappre hens", "Ġpo ems", "Ġsa usage", "ĠWeb ster", "ur us", "Ġthem ed", "Ġl ounge", "Ġcharg er", "Sp oiler", "Ġsp illed", "h og", "ĠSu nder", "ĠA in", "ĠAng ry", "Ġdis qual", "ĠFrequ ency", "ĠEther net", "Ġhel per", "Per cent", "Ġhorr ifying", "Ġa il", "ĠAll an", "EE E", "ĠCross ing", "44 9", "Ġh olog", "ĠPuzz les", "ĠGo es", "eren n", "60 4", "ãģ ı", "ĠRaf ael", "Ġatt en", "ĠE manuel", "Ġup ro", "ĠSus p", "P sych", "ĠTr ainer", "ĠN ES", "ĠHun ts", "bec ue", "Ġcounsel or", "R ule", "Ġtox ins", "Ġb anners", "r ifice", "Ġgreet ing", "Ġfren zy", "Ġall ocate", "Ġ* )", "ex pr", "50 3", "ĠCh ick", "ĠT orn", "Ġconsolid ation", "ĠF letcher", "sw itch", "fr ac", "cl ips", "ĠMcK in", "ĠLun ar", "Mon th", "IT CH", "Ġscholar ly", "rap ed", "39 8", "Ġ19 10", "Ġe greg", "Ġin secure", "Ġvict orious", "cffff cc", "Ġsing led", "Ġel ves", "ĠW ond", "bur st", "Ġcam oufl", "ĠBL ACK", "Ġcondition ed", "ç ī", "ans wered", "Ġcompuls ory", "asc ist", "Ġpodcast s", "ĠFrank furt", "bn b", "Ġne oliberal", "ĠKey board", "ĠBel le", "w arm", "Ġtrust s", "Ġins ured", "ĠBu cc", "us able", "60 7", "ĠPl ains", "Ġ18 90", "Ġsabot age", "Ġlod ged", "f elt", "Ġg a", "ĠN arc", "ĠSal em", "Ġsevent y", "ĠBl ank", "p ocket", "Ġwhis per", "Ġm ating", "om ics", "ĠSal man", "ĠK ad", "Ġan gered", "Ġcoll isions", "Ġextraord inarily", "Ġcoerc ion", "G host", "b irds", "è Ģ", "k ok", "Ġper missible", "avor able", "Ġpo inters", "Ġdiss ip", "ac i", "Ġtheat rical", "ĠCos mic", "Ġforget ting", "Ġfinal ized", "å¤ §", "y out", "l ibrary", "Ġbo oming", "ĠBel ieve", "ĠTe acher", "ĠL iv", "ĠGOOD MAN", "ĠDomin ican", "OR ED", "ĠPart ies", "Ġprecip itation", "ĠSl ot", "R oy", "ĠComb ined", "Ġinteg rating", "Ġch rome", "Ġintest inal", "ĠRe bell", "Ġmatch ups", "Ġblock buster", "ĠLore n", "ĠLe vy", "Ġpre aching", "ĠS ending", "ĠPur pose", "ra x", "f if", "Ġauthor itative", "ĠP ET", "ast ical", "Ġdish on", "Ġchat ting", "Ġ\"$ :/", "Connect ion", "Ġrecre ate", "Ġdel inqu", "Ġbro th", "ĠD irty", "ĠAd min", "z man", "Ġscholars hips", "Ġ25 3", "cont act", "als a", "7 67", "c reen", "abb age", "Ġ19 15", "Ġbl ended", "Ġal armed", "L anguage", "35 6", "Ġbl ends", "ĠCh anged", "W olf", "Ġhe pat", "Creat ing", "Ġper secut", "Ġsweet ness", "art e", "Ġforfe iture", "ĠRober to", "im pro", "N FL", "ĠMag net", "Det ailed", "Ġinsign ificant", "ĠPOL IT", "ĠBB Q", "ĠC PS", "Ġse aw", "amin er", "m L", "end if", "f inals", "Ġ26 5", "u ish", "Ġ} )", "ĠPro blems", "Ġem blem", "Ġserious ness", "Ġpars ing", "Ġsubst itution", "Ġpress ured", "Ġrecy cled", "ale b", "Rub y", "Ġprof iciency", "Dri ver", "ĠW ester", ": '", "AF TA", "Ġm antle", "ĠClay ton", "fl ag", "Ġpractition er", "c overed", "ĠSt ruct", "add afi", "4 25", "ĠTown ship", "ĠHyd ro", "Lou is", "34 3", "Ġcond o", "ĠT ao", "Ġutil ization", "Ġnause a", "ĠDem s", "rid ges", "p ause", "Ġform ulas", "Ġchall enger", "37 6", "Ġdefect ive", "ĠRail way", "ĠPub Med", "Ġyog urt", "l bs", "ĠNor folk", "OP E", "ĠMood y", "Ġdistribut or", "Ġscroll s", "Ġextract s", "St an", "Ġv iability", "Ġexp oses", "Ġstar vation", "ĠStep s", "ĠD odd", "f ew", "ST D", "33 2", "Ġclos ures", "Ġcomplement ary", "ĠS asha", "ump y", "Ġmon et", "Ġartic ulate", "ĠDo ct", "k iller", "Ġsc rim", "Ġ2 64", "Ġprost itutes", "Ġse vered", "Ġattach ments", "Ġcool ed", "L ev", "ĠF alk", "f ail", "Ġpolic eman", "ĠD ag", "Ġpray ed", "ĠK ernel", "Ġcl ut", "Ġc ath", "Ġan omaly", "St orm", "em aker", "ĠBreak fast", "ul i", "o ire", "J J", "h z", "Oper ation", "ĠS ick", "35 4", "ĠGuatem ala", "R ate", "Ġexp osures", "f aces", "ĠArch ae", "ra f", "ĠM ia", "Ġ20 25", "Ġop aque", "Ġdisgu ised", "ĠHead quarters", "S ah", "Ġp ots", "9 78", "ĠM alf", "Ġfrown ed", "Ġpoison ous", "ĠCon vers", "ee ks", "Ġcr ab", ".\" \"", "Ġtre ason", "Ġr anc", "Ġescal ating", "Ġwar r", "Ġmob s", "Ġl amps", "ĠSun shine", "ĠBrun swick", "Ph ones", "Ġspe lled", "ĠSk ip", "Ġ20 50", "Ġ19 11", "ĠPl uto", "ĠAm end", "Ġme ats", "38 7", "Ġst omp", "ĠZh ou", "ĠLevi athan", "ĠHaz ard", "ad v", "ĠOr well", "Ġal oud", "Ġb umper", "ĠAn arch", "ub untu", "ĠSer ious", "f itting", "ĠOption al", "ĠCec il", "RE AM", "Ġser otonin", "Ġcultiv ate", "ag ogue", "} \\", "Ġmos ques", "ĠSun ny", "Ġre active", "rev olution", "ĠL up", "ĠFed ora", "Ġdefense man", "ĠV ID", "ist ine", "Ġdrown ing", "ĠBroad casting", "Ġthr iller", "ĠS cy", "Ġacceler ating", "Ġdirect s", "od ied", "b ike", "d uration", "Ġpain fully", "R edd", "Ġproduct ions", "Ġg ag", "Ġwh ist", "Ġs ock", "Ġinf initely", "ĠConc ern", "ĠCit adel", "Ġlie u", "Ġcand les", "ogene ous", "arg er", "Ġheaven ly", "inflamm atory", "Per formance", "C s", "ruct ose", "az aki", "Ġp essim", "Ġinf erence", "Ġpow d", "ĠZ oe", "Ġpain ts", "Ġd azz", "pt a", "-------- ---", "Ġins pir", "ĠExper imental", "ĠKn ife", "reg or", "b ors", "Ġshow ers", "rom eda", "Ġs aint", "Ġben ign", "ĠJ iang", "Ġenvision ed", "Ġsh roud", "IF T", "H O", "Ġsh uff", "ĠI CC", "Ġse greg", "Ġrevis it", "ighth ouse", "L i", "Ġsub strate", "ĠSe as", "ĠRew ard", "ĠH ep", "ĠBr ass", "s bm", "Ġelim inates", "Ġst amina", "ĠV AT", "ĠLo an", "Ġconst raint", "Ġappropri ated", "Ġp es", "ĠA LE", "r anging", "Ġ40 4", "39 2", "Ġintellectual s", "ach u", "Ġrestruct uring", "ĠLe vin", "Ġrun es", "Ġdelight ful", "Ġcarbohyd rates", "ĠMod els", "ĠExp o", "Ġtransport ing", "all oc", "Ġring ing", "S amsung", "Ġscarce ly", "ĠURL s", "ĠM AS", "Ġprot otypes", "Ġnarr ator", "ĠCPU s", "cd n", "ĠBart on", "Ġdecided ly", "ĠSh u", "ix ir", "oc ious", "ĠMy st", "N intendo", "Ġre use", "Ġforg iven", "F ew", "in ical", "n at", "Ġseam less", "ĠEv a", "ĠE VE", "ĠJ O", "land ers", "Ġso fter", "neg ie", "Ġtrans ient", "Ġorb ital", "Ġfulf il", "ĠK om", "Hop efully", "Ġdynam ically", "ĠHun ger", "å Ľ", "ĠArmen ia", "el man", "ber to", "Ġp ige", "ĠID s", "lim it", "Ġve ins", "Ġso aring", "p acks", "Gold en", "ĠCr ab", "ist or", "ĠR PM", "Ġ$ $", "g ression", "Ġjihad ist", "Ġgam ble", "Ġcare g", "Ġinf lated", "F ace", "ĠFire arms", "ĠEm manuel", "â Ŀ", "Ġsh ocks", "gr ab", "Ġspl end", "ĠHP V", "ab ortion", "Ab ove", "Ent ity", "play ers", "Ġcomm enced", "ul ence", "Ġfulfill ment", "Ġembod iments", "ĠW elfare", "Ġha il", "Ġ< @", "tt en", "Ġcat cher", "ĠJ azeera", "Ġvolcan o", "Ġstabil ize", "ĠHand ler", "Ġintens ified", "ĠAb rams", "Ġhum iliation", "p aced", "60 5", "ĠCent OS", "Spe cific", "Ġhe ed", "ĠC AM", "ĠGal ile", "D ie", "Ġabol ished", "ĠThom son", "ĠTe achers", "ĠW ass", "j ong", "ĠIS BN", "ĠAll ies", "sh ake", "å ·", "v ict", "How ard", "Ġde em", "Ġexceed ingly", "ĠSmart stocks", "ib e", "Ġdoor way", "Ġcompet ed", "ig mat", "Ġnational ists", "Ġg room", "ĠKe en", "Ġdispos able", "de cl", "ĠT olkien", "ĠSche me", "Ġb iod", "Ġav id", "ĠEl on", "ag ar", "ĠT SA", "R oman", "Ġartific ially", "Ġadvis ors", "X L", "ĠInf erno", "36 6", "Ġted ious", "ĠPhot ography", "ĠCar rie", "Ġtro pe", "ĠSand ra", "Ġdec imal", "Que en", "ĠGund am", "ĠO M", "ote ch", "N BA", "Ġ19 32", "Ġent renched", "ĠMar ion", "Ġfr aternity", "Lab our", "Hen ry", "Ġlat itude", "E ither", "Ġenh ances", "ĠPot ential", "Ġsh ines", "id ad", "Ġbread th", "Ġcapac ities", "ĠðŁ ĻĤ", "ĠBron x", "Ġsex es", "Ġdifferent iation", "Ġheavy weight", "ĠT aj", "d ra", "Ġmigr ate", "Ġexhaust ion", "ĠR UN", "els ius", "ĠCu omo", "Ġgu itars", "Ġcl ones", "ĠSom ew", "ĠP ry", "------------ -", "Ġwarr anted", "cy cles", "Ġsalv age", "Ġdis ks", "R ANT", "ĠNGO s", "ĠMart ian", "\":[ {\"", "Ġadd icts", "oj ure", "il let", "Ġamazing ly", "art ments", "p ixel", "ĠGPU s", "Lay out", "è £", "ĠTam il", "ĠBas il", "Ġimpart ial", "ĠSt ructure", "f ork", "b ryce", "Ġr idge", "ĠHamb urg", "ri ous", "Ġbl itz", "cig arettes", "Ġcan ned", "40 2", "Ġiron ically", "Ġcompassion ate", "ĠHaw kins", ". #", "ĠCat hedral", "Ġrall ied", "in ternal", "Ġqu ota", "st akes", "T EXT", "m om", "Ġcomple tes", "Ġ23 8", "Ġsh rug", "ãĥ ij", "ĠN inth", "Ġrev ise", "ĠProv ider", "Ġtre acher", "Ġqu asi", "ĠPR ES", "Ġdep osition", "Ġconfidential ity", "iss ors", "Ġim balance", "Ġspan ning", "Ġang ular", "ĠC ul", "commun ication", "ĠNor a", "ĠGen ius", "op ter", "Ġs acked", "Sp ot", "Ġfine ly", "ĠCH R", "28 2", "w aves", "Pal est", "ĠRo hing", "N L", "è ¿", "Ġsh itty", "ĠSc alia", "4 75", "Pro gress", "Ġreferen cing", "Ġclass rooms", "ab ee", "Ġs od", "hes ion", "70 8", "ĠZucker berg", "ĠFin ish", "ĠScot ia", "ĠSav ior", "ĠInstall ation", "an tha", "( -", "Ġ30 2", "ĠP unk", "Ġcr ater", "yout u", "Ġro ast", "Ġinflu encing", "Ġd up", "ĠJ R", "ĠG rav", "Ġstat ure", "Ġbath rooms", "A side", "W iki", "me an", "ĠZ ak", "ĠOn es", "ĠN ath", "Ġhyper t", "Ġcommence ment", "C ivil", "Ġmoder ately", "Ġdistribut ors", "Ġbreast feeding", "Ġ9 80", "ĠS ik", "ĠC ig", "ĠAM ER", "R IP", "ĠCare er", "ust ing", "Ġmess ed", "Ġe h", "ĠJ ensen", "/ $", "Ġblack mail", "Ġconvers ions", "Ġscientific ally", "Ġmant ra", "p aying", "Ġiv ory", "ĠCour ts", "OU GH", "aunt let", "Ser ial", "B row", "ĠH undreds", "3 23", "Ġpe e", "Ġlin ux", "Ġsub mer", "ĠPrinc ipal", "48 5", "ĠD SL", "ĠCous ins", "Ġdoctr ines", "ĠAthlet ics", "Ġ3 15", "ĠK arma", "Ġatt ent", "ur ger", "Ġpresc ribe", "Ġenc aps", "ĠC ame", "Ġsecret ive", "ĠCr imes", "d n", "C lean", "ĠEgypt ians", "ĠCar penter", "Ġ ll", "H um", "ĠMil o", "Ġcapital ists", "Ġbrief ed", "T we", "ĠBas in", "elve t", "M os", "Ġplun ge", "ĠKa iser", "ĠFu j", "ill in", "Ġsafegu ards", "Ġo ste", "ĠOpportun ity", "ĠM afia", "ĠCall ing", "ap a", "ur ban", "br ush", "ill ard", "c é", "int elligence", "ĠL ob", "ĠDru id", "Ġsm oother", "Ġfoot ing", "Ġmotor ists", "arc ity", "Ġmascul inity", "Ġm ism", "Ġabdom inal", "ĠTa vern", "ĠR oh", "Ġesc apes", "s igned", "Anth ony", "Ġsacrific ing", "Ġintim acy", "Ġan terior", "ĠK od", "Ġmot if", "Ġg raz", "Ġvisual ization", "Ġguitar ist", "ĠTro tsky", "m agic", "D ar", "ĠMor i", "Ġw ards", "Ġtoile ts", "l est", "Ġtele port", "ĠSund ays", "ĠPl at", "ET S", "Ġe Sports", "Pat rick", "ĠK atherine", "en ko", "Ġhas sle", "ĠM ick", "gg les", "Ġh ob", "aint ain", "Ġair borne", "Ġsp ans", "Ġch ili", "Ġa perture", "Ġvolunte ered", "ĠInc ident", "ĠF res", "ĠVeter an", "augh tered", "ing o", "Ġun insured", "CL OSE", "Ġf use", "Ġer otic", "Ġadvert ise", "ra ising", "Text ure", "Ġatt ends", "ĠRE AL", "udd led", "Ġsm oot", "Ġ30 5", "ĠWill is", "Ġbl ond", "An alysis", "ĠV T", "on ica", "Ġstrongh old", "R F", "N M", ". >>", "Ġprosper ous", "Ġbo asted", "29 2", "ĠManufact uring", "PR ESS", "g ren", "Ġpharm acy", "ĠRoc kefeller", "k ai", "Ġth umbs", "ĠH ut", "Ġmother board", "Ġguard ians", "ĠAl ter", "ll ular", "Ġsh ack", "Ġwise ly", "Ġback bone", "erv a", "Ġsu icides", "ĠMcG regor", "ij ah", "E mer", "ĠB rav", "Ġdesign ate", "P OST", "produ ced", "Ġcleans ing", "irl wind", "ex istent", "ĠHum ph", "ĠPay ne", "Ġv ested", "Å ¡", "Ġstring ent", "ion a", "Ġuns ub", "Ġsum med", "ĠHer cules", "sub ject", "ĠR agnar", "ĠN os", "Ġcharacter ization", "Ġsav vy", "ĠDaw son", "ĠCas ino", "Ġf ri", "ĠBar rier", "Ġmis information", "Ġins ulation", "Ġcorrid ors", "Ġair planes", "ĠNo ct", "ah i", "Ġ19 16", "k b", "arm ac", "Ġsh un", "Ġsche ma", "Ġhorr ified", "Ġ23 9", "aund ers", "N B", "i ates", "er ity", "ĠSh ard", "Ġr arity", "Ġgroup ed", "ĠGh ana", "again st", "ĠBi ological", "ĠA ware", "ow ell", "Ï Ħ", "ĠBe au", "sh aw", "H ack", "ĠJul ius", "US S", "ol son", "aun a", "c ru", "ĠMaur ice", "ĠI k", "Ġsequ encing", "Ġradical s", "Ġ( ?,", "v irtual", "Ġany ways", "Ġreper c", "Ġhand lers", "Ġhes itant", "é ĥ", "ĠM F", "ple mentation", "ass ociated", "Ġcampaign ed", "ĠY ue", "ut ations", "ĠY oga", "Ġsim mer", "Ġro ds", "Ġmel ody", "Ġconv oy", "v ideos", "Ġscreen ed", "N eg", "ochem ical", "Ġ( ))", "Ġultr as", "Ġant ip", "ĠIsland ers", "70 4", "Ġfet ish", "Ġridic ulously", "ĠK art", "Ġmitochond rial", "Ġinterf ering", "Build er", "Ġover fl", "Ġac ne", "ĠM ud", "ĠK err", "f lex", "ĠPost al", "ĠBalt ic", "47 7", "ĠPers ons", "our age", "H B", "ĠM use", "ĠImm ortal", "ĠDri ving", "Ġpet itions", "Ġsubsc ript", "Ġs orce", "ĠProcess or", "ut on", "S ony", "Ġph on", "Ġr aced", "ĠAnth rop", "Ġday time", "ĠEx ercise", "Add ing", "Ġeng ages", "ĠQual comm", "Ġmir acles", "Ġmem es", "ĠDr ink", "ĠOri oles", "Ġhair s", "ĠPol ar", "ath om", "Ġsl ippery", "ĠR emy", "Ġcar amel", "ĠY EAR", "Ġal k", "I gn", "a ution", "ĠMer lin", "ĠC ran", "Ġap ologies", "Ġ4 10", "Ġout ing", "ĠMem ories", "app ointed", "Ġcount ered", "u ld", "pos ing", "Ġfire wall", "ĠW ast", "ĠW et", "work ed", "se ller", "Ġrepe aled", "ere o", "ass uming", "BL IC", "m ite", "ĠCEO s", "ĠChap el", "ellig ent", "________________ ________", "D og", "Ġw art", "Ġsubsc riber", "s ports", "Ġbe gged", "ĠM V", "Ġsem if", "eth ical", "Ġpre ach", "Ġrev ital", "Ġpun itive", "Ġshort cuts", "Ġinstit uted", "ĠWars aw", "Ġabdom en", "ĠK ING", "Ġsuper intendent", "Ġf ry", "ĠGe o", "T OR", "Ġcontrad ictions", "apt ic", "Ġlandsc apes", "b ugs", "Ġcl ust", "Ġvol ley", "c ribed", "Ġt andem", "Ġrob es", "WH AT", "Ġpromot er", "Ġel oqu", "review ed", "ĠD K", "ĠPl ato", "Ġf ps", "T ank", "ĠDer rick", "Ġpriorit ize", "as per", "ĠHond uras", "ĠCom pleted", "ne c", "Ġm og", "n ir", "ĠMay o", "DE F", "st all", "in ness", "ĠVolks wagen", "Ġprec aution", "ĠM ell", "i ak", "ist ries", "Ġ24 8", "Ġoverl apping", "Sen ate", "ĠEnh ance", "res y", "rac ial", "OR TS", "ĠM ormons", "Str ong", "ĠCo ch", "Mex ico", "ĠMad uro", "Ġj ars", "Ġcan e", "W ik", "oll a", "iff erence", "Ġphysic ist", "ĠMag gie", "Ġ28 5", "Ġdep iction", "ĠMcL aren", "J u", "Ġsl ows", "Ġcommission ers", "ĠWill ow", "ĠExpl os", "hov ah", "Ġtechn ician", "Ġhom icides", "ĠFl av", "ĠTr uman", "Ġ100 00", "u ctor", "Ġsh ader", "News letter", "45 7", "Ġre ver", "Ġhard ened", "Ġwhere abouts", "Ġrede velop", "Ġcar bs", "Ġtra vers", "Ġsqu irrel", "Ġfoll ower", "Ġs ings", "50 8", "Ġrabb its", "emon ium", "Ġdocument ing", "Ġmisunder stood", ") '", "R ick", "gg ies", "Ġprem ie", "Ġsk ating", "Ġpass ports", "Ġf ists", "aged don", "H aw", "AC P", "0 80", "ĠThough ts", "ĠCarl son", "Ġpriest hood", "h ua", "Ġdun geons", "ĠLo ans", "Ġant is", "Ġfamiliar ity", "ĠS abb", "op al", "ĠIn k", "st rike", "Ġc ram", "Ġlegal ized", "Ġcu isine", "Ġfib re", "Tra vel", "ĠMon ument", "OD Y", "eth y", "Ġinter state", "ĠP UR", "em porary", "ĠArab ian", "develop ed", "Ġsadd le", "Ġg ithub", "ĠOff er", "ĠIS P", "ro let", "ĠSUP ER", "ĠDen is", "Ġmultipl ier", "Ġstir red", "Interest ingly", "Ġcustom ary", "Ġbill ed", "he x", "Ġmultipl ied", "Ġfl ipping", "ĠCros by", "Ġfundament als", "ia e", "ĠPlay ed", "ĠAt om", "am azon", "ĠFl am", "ee z", "activ ated", "Ġtables poon", "Ġliberal ism", "ĠPal in", "ĠP atel", "N um", "ĠT AM", "Ġs urn", "ĠRel oaded", "Ġco ined", "\" ],", "ĠCl ash", "ĠAg u", "Ġprag matic", "ĠActiv ate", "Ġ8 02", "Ġtrail ers", "Ġsil hou", "Ġprob es", "Ġcirc us", "ĠB ain", "ĠLind say", "ĠAb bey", "Del ivery", "Ġconcess ion", "Ġgast ro", "ĠSpr ite", "Ä Ł", "and el", "Ġg imm", "Ġaut obi", "ĠT urtle", "Ġwonder fully", "ĠHar am", "ĠWorld wide", "ĠHand le", "Ġtheor ists", "Ġsle ek", "ĠZh u", "ograph ically", "EG A", "ĠOwn ers", "ath s", "ĠAntar ctic", "n atal", "=\" \"", "fl ags", "`` ``", "Ġs ul", "K h", "Ġpot assium", "Ġlinem an", "Ġcere al", "ĠSe asons", "Ġ20 22", "Ġmat hematic", "Ġastron omers", "prof essional", "Ġf ares", "cknow led", "Ġch i", "Ġyoung sters", "Ġmistaken ly", "Ġhem isphere", "ĠDiv inity", "r one", "Ġ\" ,", "r ings", "Ġattract s", "v ana", "å ¹", "C AP", "Ġplay list", "Ġpor ch", "ãģ £", "Ġincorpor ates", "Ġso ak", "Ġassert ing", "ĠTerror ism", "ĠP ablo", "J a", "ces ter", "Ġfear ing", "ĠPr ayer", "Ġescal ated", "G W", "Ġro be", "ĠBright on", "ac ists", "ĠSym phony", "ĠDwar f", "ĠPar ade", "ĠLe go", "Ġinex pl", "Ġl ords", "le af", "RA G", "l iber", "Ġcig ars", "ĠJe hovah", "60 6", "WIND OWS", "ĠLiber ia", "eb us", "He avy", "Ġl ubric", "ĠR W", "angu ages", "Ġnarrow ed", "com puter", "ĠE mber", "Ġmurder ing", "Ġdown stream", "ĠT uls", "ĠT ables", "Top ic", "ĠAcc uracy", "= /", "l ost", "ĠRe i", "Ġprogress es", "b ear", "Ġestablish ments", "Just in", "ĠPe ach", "ĠG omez", "å ¿", "ĠTri angle", "Id ent", "ĠH ive", "Res ources", "Ġmix es", "ĠAss uming", "M u", "Ġhyp oc", "Ġs ane", "ĠW an", "id ious", "Su ccess", "Ġ io", "Ang el", "Ġdanger ously", "ĠCreat ure", "W ORK", ": [", "ĠKat rina", "List ener", "M iller", "ĠId lib", "h ang", "Ġcircum vent", "h ref", "Ġcel estial", "ĠWe eks", "ĠP ug", "ĠDal ton", "Ġsubpoen a", "uk u", "Ġpers isted", "pe i", "old ing", "ĠDoc uments", "ĠH ast", "ĠC ENT", "Ġprim er", "Ġsyn onymous", "Ġn ib", "om bs", "Ġnot ation", "ĠD ish", "ĠAt mosp", "Ġforb id", "ĠAN G", "pat tern", "l os", "Ġproject iles", "b rown", ".\" ,", "ĠVen om", "Ġfierce ly", "ub lished", "ĠU ran", "ĠNic arag", "4 10", "ĠC AL", "OT OS", "ĠMir acle", "ĠEn chant", "Ġguard ing", "app end", "Att ach", "Ġlevel ed", "Ġcond oms", "ih ilation", "64 9", "Ġnight mares", "ĠTHE Y", "ĠST ART", "ĠK inn", "Ġroomm ate", "Ġhy giene", "o pping", "J ob", "Ġl vl", "ĠV ER", "ĠKe eping", "ab etic", "Ġformat ting", "eral a", "Ġrev isions", "Ġres urg", "T el", "ĠGood man", "35 3", "p od", "Ġind isp", "ĠTrans lation", "Ġg own", "ĠM und", "Ġc is", "Ġby stand", "col lect", "ĠPun jab", "act ively", "ĠG amb", "te ll", "Ġimport ing", "g encies", "Ġloc om", "ĠBr ill", "H oly", "ĠBer ger", "Ġshow down", "Ġrespond ers", "IL Y", "Ġt akedown", "le ted", "Ġmat tered", "Ġpredict ive", "Ġover lay", "G PU", "ĠV ick", "Ġconvey ed", "T ab", "pe er", "Sc an", "Ġdefensive ly", "v ae", "Ġappro ving", "Ġt iers", "ĠV ia", "quer ade", "ĠSaud is", "Ġdemol ished", "ĠProp he", "Ġmon o", "Ġhospital ity", "H AM", "ĠAri el", "M OD", "ĠTor ah", "Ġbl ah", "ĠBel arus", "erent ial", "ĠT uc", "Ġbank er", "39 7", "Ġmosqu it", "ĠScient ist", "ĠMus ical", "Ġh ust", "Sh ift", "Ġtor ment", "Ġstand off", "E duc", "ĠF og", "Ġampl ifier", "Sh ape", "Inst ance", "ĠCrit ics", "Ġda emon", "H ouston", "Ġmatt ress", "ĠID F", "Ġobsc ene", "ĠA mer", "hett i", "Ġcomp iling", "35 2", "vere tt", "ĠRed uction", "ist ration", "ĠBl essed", "ĠB achelor", "3 16", "Ġpr ank", "ĠVul can", "dd ing", "Ġm ourning", "ĠQu int", "ĠBl aster", "test ing", "Ġsed iment", ">> >", "ĠE ternity", "ĠWH ERE", "ĠM aze", "Ġreact ing", "ĠAl v", "oms day", "ĠC RA", "Ġtransl ator", "Ġbog us", "at u", "We bsite", "oll s", "Ġbapt ism", "Ġs ibling", "ĠAut umn", "ve z", "ãģ® é", "gu ards", "Ge org", "assad ors", "ĠFre ud", "Ġcontin ents", "ĠReg istry", "Bern ie", "ĸļ 士", "Ġtoler ant", "ĠU W", "Ġhor ribly", "99 5", "ĠMID I", "Ġimpat ient", "oc ado", "er i", "ĠWor st", "ĠNor ris", "ĠTalk ing", "Ġdef ends", "ens able", "Ġ20 21", "Ġanat omy", "L ew", "Ġdraw er", "ĠCan berra", "Ġpatri otic", "é¾įå ĸļ士", "ĠAv g", "AR M", "Ġundis closed", "Ġfare well", "45 9", "b able", "ĠAll ison", "OL OG", "Ġcon co", "t ight", "ĠAC PI", "ĠM ines", "l ich", "ĠâĶ ľ", "represent ed", "200 000", "Ġenthusi ast", "OT S", "b il", "ĠIng redients", "Ġinvent or", "ĠMy SQL", "³³ Âł", "ĠAB OUT", "with in", "Ġm k", "B ul", "ĠF ake", "Ġdracon ian", "W a", "hel m", "ĠTer ran", "erv ille", "Ġcommon place", "SI ZE", "Ġ\" <", "re place", "ograph s", "ĠSE LECT", "inc ible", "ĠMost ly", "ĠShe ffield", "ĠID E", "ugg le", "Ġcit ations", "h urst", "ĠUn ix", "Ġunle ash", "ĠP iper", "ĠN ano", "Ġsucc umb", "Ġreluct ance", "Ġ25 00", "ĠMer chant", "Ġwire t", "Ġcomb os", "ĠBirth day", "Ġchar coal", "ĠU PS", "ĠFair fax", "Ġdrive way", "ĠT ek", "ĠP itch", "ove re", "Ġtechn icians", "ĠAct ual", "fl ation", "ĠF iscal", "ĠEm pty", "an amo", "Ġmag nesium", "Ġsl ut", "Ġgrow ers", "Invest igators", "( ):", "ĠS atellite", "ĠKe ynes", "miss ive", "l ane", "Ġb orough", "3 44", "ĠTE AM", "ĠBet hesda", "C V", "h ower", "ĠR AD", "Ġch ant", "ĠR iy", "Ġcompos itions", "Ġmild ly", "Ġmedd ling", "Ġag ility", "ane ers", "5 01", "Ġsyn th", "ling er", "29 1", "Ġex claimed", "Part y", "Ġcont amin", "ĠMan or", "ĠResp ond", "Ġpra ising", "Ġman ners", "fle et", "Sum mer", "ĠLy nd", "ĠDef initely", "gr im", "Ġbow ling", "st ri", "ç Ľ", "y nt", "Ġmand ates", "D IV", "Ġreconc ile", "view s", "ĠDam on", "vet te", "F lo", "ĠGreat est", "il on", "ic ia", "Ġportray al", "Ġcush ion", "50 4", "19 79", "oss al", "App lic", "sc ription", "Ġmit igation", "AT S", "p ac", "Ġer ased", "Ġdefic iencies", "ĠHolland e", "ĠX u", "Ġb red", "Ġpregn ancies", "f emin", "Ġem ph", "Ġpl anners", "Ġout per", "utter ing", "Ġperpet rator", "Ġm otto", "ĠEll ison", "ĠNE VER", "Ġadmitted ly", "AR I", "ĠAzerbai jan", "Ġmill isec", "Ġcombust ion", "ĠBott le", "ĠL und", "ĠP s", "ĠD ress", "Ġfabric ated", "Ġbat tered", "Ġs idel", "ĠNot ting", "Fore ign", "ĠJer ome", "0 20", "ĠAr bit", "Ġkn ots", "ĠR IGHT", "M oving", "ãģ Ļ", "Ġsur geries", "Ġcour thouse", "Ġm astered", "Ġhover ing", "ĠBr an", "ĠAl ison", "Ġsaf est", "m ilitary", "Ġbull ied", "Ġbar rage", "Read er", "ES E", "ĠGe ographic", "T ools", "3 14", "ĠGe ek", "ro th", "gl ers", "ĠF IN", "Ï ģ", "ĠA ston", "al tern", "48 8", "Ġveter in", "G amer", "Ġint el", "ren ches", "Sh ield", "Ġam nesty", "ĠB har", "Ġp iled", "Ġhonor able", "ĠInst itutes", "Ġso aked", "Ġcom a", "ĠE FF", "34 1", "by tes", "ĠG mail", "le in", "ĠCanad iens", "m aterial", "I l", "Ġinstruct ors", "ĠK Y", "Ġconce ive", "ub b", "ĠP ossible", "Ġeas ing", "ĠChrist ina", "Ġcar ic", "ĠHD R", "R OM", "Ġsho vel", "de lete", "Ġp uff", "ĠCh anging", "Ġseam lessly", "Att ribute", "Ġacqu isitions", "ak ery", "ĠE F", "Ġaut istic", "ĠT akes", "ĠPow der", "ĠSt ir", "5 10", "ĠBub ble", "sett ings", "ĠF owler", "Ġmust ard", "Ġmore over", "Ġcopyright ed", "ĠLED s", "15 00", "æ ī", "ĠH IS", "en f", "Ġcust od", "ĠH uck", "G i", "Ġim g", "An swer", "C t", "j ay", "ĠInf rastructure", "Ġfeder ally", "L oc", "Ġmicro bes", "Ġover run", "dd s", "ot ent", "adi ator", ">>>> >>>>", "Ġtorn ado", "Ġadj ud", "Ġintrig ued", "Ġs i", "ĠRevel ation", "pro gress", "Ġburgl ary", "ĠSai yan", "ĠK athy", "Ġser pent", "ĠAndre as", "Ġcomp el", "ess ler", "ĠPl astic", "ĠAd vent", "ĠPos itive", "ĠQ t", "ĠHind us", "reg istered", "ular ity", "Ġrighteous ness", "Ġdemon ic", "u itive", "ĠB DS", "ĠGre gg", "c ia", "ĠCrus ade", "ĠSina i", "W ARE", "+ (", "Ġme ll", "Ġder ail", "y ards", "A st", "Ġnotice ably", "ĠO ber", "R am", "Ġun noticed", "Ġse q", "av age", "T s", "Ġ6 40", "Ġconced e", "Ġ] )", "F ill", "Ġcapt ivity", "ĠImprove ment", "ĠCrus ader", "ara oh", "M AP", "æ Ĺ", "Ġstr ide", "al ways", "F ly", "N it", "Ġal gae", "ĠCook ing", "ĠDo ors", "Mal ley", "Ġpolic emen", "ãģ į", "Ġastron aut", "access ible", "49 5", "ĠR AW", "cl iffe", "udic rous", "Ġdep ended", "al ach", "Ġvent ures", "ra ke", "Ġt its", "ĠH ou", "Ġcond om", "ormon al", "Ġind ent", "Ġupload ing", "Foot note", "Import ant", "Ġ27 1", "Ġmind ful", "Ġcont ends", "C ra", "Ġcal ibr", "ĠO ECD", "plug in", "F at", "ĠIS S", "ĠDynam ics", "ans en", "68 6", "' ),", "Ġsp rite", "Ġhand held", "ĠH ipp", "=~ =~", "Tr ust", "Ġsem antics", "ĠBund es", "ĠRen o", "ĠLiter ature", "s ense", "G ary", "ĠA eg", "ĠTr in", "EE K", "Ġcler ic", "ĠSS H", "Ġch rist", "Ġinv ading", "ib u", "Ġen um", "aur a", "Ġal lege", "ĠInc redible", "B BC", "Ġth ru", "Ġsa iled", "Ġem ulate", "Ġin security", "Ġc rou", "Ġaccommod ations", "Ġincompet ent", "Ġsl ips", "ĠEarth qu", "s ama", "IL LE", "Ġi Phones", "as aki", "Ġby e", "Ġar d", "Ġext ras", "Ġsl aughtered", "Ġcrowd funding", "res so", "Ġfil ib", "ĠER ROR", "ĠT LS", "e gg", "ĠIt al", "Ġen list", "ĠCatal onia", "ĠSc ots", "Ġser geant", "Ġdiss olve", "N H", "Ġstand ings", "ri que", "I Q", "Ġbenef iciary", "Ġaqu arium", "You Tube", "ĠPower Shell", "Ġbright est", "ĠWar rant", "S old", "Writ ing", "Ġbegin nings", "ĠRes erved", "ĠLatin os", "head ing", "Ġ4 40", "Ġrooft op", "AT ING", "Ġ3 90", "VP N", "G s", "k ernel", "turn ed", "Ġprefer able", "Ġturn overs", "ĠH els", "S a", "ĠShin ji", "ve h", "ĠMOD ULE", "V iol", "Ġex iting", "Ġj ab", "ĠVan illa", "Ġac ron", "ĠG ap", "ber n", "A k", "ĠMc Gu", "Ġend lessly", "ĠFar age", "ĠNo el", "V a", "M K", "Ġbr ute", "ĠK ru", "ĠES V", "ĠOl ivia", "âĢ ł", "ĠK af", "Ġtrust ing", "Ġh ots", "3 24", "Ġmal aria", "Ġj son", "Ġp ounding", "ort ment", "Count ry", "Ġpostp oned", "Ġunequ iv", "? ),", "ĠRo oney", "udd ing", "ĠLe ap", "ur rence", "sh apeshifter", "ĠH AS", "os ate", "Ġca vern", "Ġconserv atism", "ĠB AD", "Ġmile age", "Ġarrest ing", "V aults", "Ġmix er", "Dem ocratic", "ĠB enson", "Ġauth ored", "8 000", "Ġpro active", "ĠSpirit ual", "t re", "Ġincarcer ated", "ĠS ort", "Ġpe aked", "Ġwield ing", "re ciation", "×Ļ ×", "P atch", "ĠEm my", "Ġex qu", "tt o", "ĠRat io", "ĠP icks", "ĠG ry", "ph ant", "Ġf ret", "Ġeth n", "Ġarch ived", "% -", "c ases", "ĠBl aze", "Ġim b", "c v", "y ss", "im ony", "Ġcount down", "Ġaw akening", "ĠTunis ia", "ĠRe fer", "ĠM J", "Ġun natural", "ĠCar negie", "iz en", "ĠN uggets", "he ss", "Ġev ils", "64 7", "Ġintrodu ctory", "l oving", "ĠMcM ahon", "Ġambig uity", "L abel", "ĠAlm ighty", "Ġcolor ing", "ĠCl aus", "set ting", "N ULL", "ĠF avorite", "ĠS IG", "> (", "ĠSh iva", "ĠMay er", "Ġstorm ed", "ĠCo verage", "we apons", "igh am", "Ġun answered", "Ġle ve", "Ġc oy", "c as", "b ags", "as ured", "Se attle", "ĠSant orum", "ser ious", "Ġcourage ous", "ĠS oup", "Ġconfisc ated", "Ġ// /", "Ġuncon ventional", "Ġmom s", "ĠRohing ya", "ĠOrche stra", "ĠPot ion", "Ġdisc redit", "ĠF IL", "f ixed", "ĠDe er", "do i", "ĠDim ension", "Ġbureaucr ats", "et een", "Ġaction Group", "oh m", "Ġb umps", "ĠUt ility", "Ġsubmar ines", "ren heit", "re search", "ĠShap iro", "Ġsket ches", "Ġde ceptive", "ĠV il", "es ame", "ĠEss entially", "Ġramp age", "isk y", "Ġmut tered", "th ritis", "Ġ23 6", "f et", "b ars", "Ġpup il", "ĠTh ou", "o S", "s ong", "Ġfract ured", "Ġre vert", "pict ure", "Ġcrit erion", "us her", "Ġreperc ussions", "ĠV intage", "ĠSuper intendent", "Offic ers", "Ġflag ged", "Ġbl ames", "Ġin verse", "ograp hers", "Ġmakes hift", "Ġdev oid", "Ġfoss ils", "ĠArist otle", "ĠFund s", "Ġde pleted", "ĠFl u", "ĠY uan", "Ġw oes", "Ġlip id", "Ġsit u", "requ isites", "Ġfurn ish", "ĠSam ar", "Ġshame ful", "Ġadverse ly", "Ġad ept", "Ġrem orse", "Ġmurder ous", "uck les", "ĠE SL", "Ġ3 14", "s ent", "Ġred ef", "ĠC ache", "ĠP urs", "ig ans", "Ġ4 60", "Ġpres criptions", "Ġf res", "F uck", "ocr ates", "Tw enty", "ĠWe ird", "ĠT oggle", "ĠC alled", "itiz ens", "Ġp oultry", "Ġharvest ing", "ãĤ¦ ãĤ¹", "Bott om", "Ġcaution ed", "t n", "39 6", "ĠNik ki", "Ġeval uations", "Ġharass ing", "Ġbind ings", "ĠMon etary", "Ġhit ters", "Ġadvers ary", "un ts", "Ġset back", "Ġenc rypt", "ĠC ait", "Ġl ows", "eng es", "ĠN orn", "Ġbul bs", "Ġbott led", "ĠVoy ager", "3 17", "Ġsp heres", "p olitics", "Ġsubt ract", "Ġsens ations", "Ġapp alling", "Ġ3 16", "Ġenvironment ally", "ĠST EM", "Ġpub lishes", "5 60", "Ġdilig ence", "48 4", "Ġadv ises", "Ġpet rol", "Ġimag ining", "Ġpatrol s", "ĠInt eger", "ĠAs hes", "act us", "ĠRad iant", "ĠL T", "it ability", "ht aking", "Set ting", "Ġnu anced", "ĠRe ef", "ĠDevelop ers", "N i", "pie ces", "99 0", "Lic ense", "Ġlow ers", "ĠOtt oman", "3 27", "oo o", "Ġqu itting", "mark ets", "Beh ind", "Ġbas in", "Ġdoc s", "an ie", "fl ash", "ct l", "Ġcivil ized", "ĠFuk ushima", "\"] ,\"", "ĠK S", "ĠHonest ly", "ar at", "Ġconstruct s", "ĠL ans", "ĠD ire", "ĠLI KE", "ĠTrou ble", "Ġwith holding", "ĠOb livion", "Ġsan ity", "any a", "Con st", "Ġgro cer", "ĠC elsius", "Ġrecount ed", "ĠW ife", "B order", "ate red", "h appy", "Ġspo iler", "Ġlog ically", "H all", "Ġsucceed ing", "Ġpoly morph", "Ġax es", "ĠShot gun", "ĠS lim", "ĠPrin ciples", "ĠL eth", "art a", "Ġsc or", "Sc reenshot", "Ġrelax ation", "#$ #$", "Ġdeter rent", "idd y", "Ġpower less", "Ġles bians", "Ġch ords", "ĠEd ited", "se lected", "Ġseparat ists", "000 2", "Ġair space", "Ġturn around", "Ġc unning", "P ATH", "P oly", "Ġbomb ed", "Ġt ion", "x s", "Ġwith hold", "Ġw aged", "ĠLiber ties", "Fl ag", "Ġcomfort ing", "45 4", "ĠI ris", "are rs", "Ġr ag", "Ġrel ocated", "ĠGu arant", "Ġstrateg ically", "Ġgam ma", "uber ty", "ĠLock heed", "g res", "Ġgr illed", "ĠLow e", "st ats", "ĠR ocks", "Ġsens ing", "Ġrent ing", "ĠGe ological", "ا Ø", "ot rop", "Ġse w", "Ġimproper ly", "48 6", "Ġâĸ ł", "Ġstar ving", "ĠB j", "Disc ussion", "3 28", "ĠCom bo", "ĠFix es", "N AT", "Ġstri ving", "th ora", "Ġharvest ed", "ĠP ing", "Ġplay ful", "Ġaven ues", "Ġoccup ational", "Ġw akes", "ĠCou rier", "Ġdrum mer", "ĠBrow ser", "ĠH outh", "it u", "Ġapp arel", "p aste", "Ġhun ted", "ĠSecond ly", "l ain", "X Y", "ĠP IN", "ic ons", "Ġcock tails", "Ġs izable", "Ġhurd les", "est inal", "ĠRecre ation", "Ġe co", "64 8", "ĠD ied", "m int", "Ġfinger prints", "Ġdis pose", "ĠBos nia", "ts y", "22 00", "Ġins pected", "ĠF ou", "Ġf uss", "Ġamb ush", "ĠR ak", "Ġmanif ested", "Pro secut", "Ġsuff ice", "ren ces", "Ġcompens ated", "ĠC yrus", "Ġgen us", "ĠWolver ine", "ĠTrend s", "Ġh ikes", "ĠSe en", "Ġen rol", "C old", "Ġpol itely", "ĠSl av", "ĠRu pert", "Ġey ewitness", "ĠAl to", "Ġun comp", "Ġposter ior", "M ust", "ĠHer z", "Ġprogress ively", "Ġ23 4", "Ġind ifference", "ĠCunning ham", "Ġacadem ia", "Ġse wer", "Ġast ounding", "ĠA ES", "r ather", "Ġeld est", "Ġclim bs", "ĠAdd s", "Ġout cry", "Ġcont ag", "ĠH ouses", "Ġpe pt", "ĠMel ania", "interest ed", "ĠU CH", "ĠR oots", "ĠHub bard", "ĠT BD", "ĠRoman ian", "fil ename", "St one", "ĠIm pl", "Ġchromos ome", "C le", "d x", "Ġscram bled", "ĠP t", "Ġ24 2", "OP LE", "Ġtremend ously", "St reet", "Ġcra ving", "Ġbund led", "ĠR G", "p ipe", "Ġinj uring", "Ġarc ane", "Part icip", "ĠHero ic", "st y", "Ġto pping", "ĠTemp est", "rent ices", "b h", "Ġpar anoia", "ĠUnic ode", "Ġegreg ious", "Ġ\\ '", "ĠOsw ald", "Ġgra vel", "ĠSim psons", "Ġbl and", "ĠGuant anamo", "Writ er", "lin ers", "ĠD ice", "J C", "Ġpar ity", "Ġs ided", "Ġ23 7", "ĠPyr rha", "at ters", "d k", "F ine", "comp an", "Ġform ulated", "ĠId ol", "il ers", "hem oth", "ĠF av", "Ġintr usion", "Ġcar rots", "ĠL ayer", "ĠH acker", "Ġ ----------------", "Ġmoder ation", "é ģ", "oc oc", "Ġcharacter ize", "ĠTe resa", "Ġsocio economic", "Ġper k", "ĠParticip ation", "tr aining", "ĠPaul o", "ph ys", "Ġtrust worthy", "Ġembod ied", "ĠMer ch", "c urrency", "ĠPrior ity", "Ġte asing", "Ġabsor bing", "Ġunf inished", "ĠCompar ison", "Ġdis ple", "writ ers", "Ġprofess ions", "ĠPengu in", "Ġang rily", "ĠL INK", "68 8", "ĠCor respond", "Ġprev ailed", "Ġcart el", "l p", "as ms", "ĠRed emption", "ĠIslam ists", "effect s", "d ose", "ĠL atter", "ĠHal ifax", "Ġv as", "ĠTop ics", "ĠN amed", "advert ising", "zz a", "IC ES", "Ġret arded", "ach able", "ĠPupp et", "ĠItem Level", "Ġret ract", "Ġident ifiable", "A aron", "ĠB uster", "s ol", "hel le", "as semb", "H ope", "r anged", "B a", "ĠP urch", "é Ģ", "ĠSir i", "Ġarri vals", "Ġ19 12", "Ġshort ened", "Ġ3 12", "Ġdiscrep ancy", "ĠTem perature", "ĠWal ton", "Ġkind erg", "p olit", "Ġrem ix", "Ġconnect ors", "ãĥĺ ãĥ©", "ĠKazakh stan", "dom inated", "Ġsu gars", "im ble", "ĠPan ic", "ĠDem and", "ĠCol ony", "on en", "ĠM ER", "7 75", "ur ia", "aza ar", "ĠDeg ree", "P ri", "Ġsun shine", "Ġ25 1", "Ġpsychedel ic", "Ġdigit ally", "ĠBra un", "Ġsh immer", "Ġsh ave", "ĠTel esc", "ĠAst ral", "ĠVenezuel an", "ĠO G", "Ġc rawling", "Int eg", "ĠFe ather", "Ġunfold ing", "Ġappropri ation", "Ġè£ı è", "ĠMob ility", "ĠN ey", "- .", "b ilt", "L IN", "ĠT ube", "ĠCon versely", "Ġkey boards", "ĠC ao", "Ġover th", "Ġla ure", ">> \\", "ĠV iper", "ach a", "Off set", "ĠR aleigh", "ĠJ ae", "J ordan", "j p", "Ġtotal itarian", "Connect or", "Ġobserv es", "ĠSpart an", "ĠIm mediately", "ĠSc al", "C ool", "Ġt aps", "Ġro ar", "P ast", "Ġch ars", "ĠB ender", "ĠShe ldon", "Ġpain ter", "Ġbe acon", "ĠCreat ures", "Ġdownt urn", "Ġh inder", "ĠAnd romeda", "à Ľ", "cc oli", "ĠF itness", "et rical", "Ġutil izes", "Ġsen ate", "Ġen semble", "Ġche ers", "T W", "Ġaff luent", "k il", "ry lic", "ord ering", "Com puter", "Ġgru esome", "ost ics", "ĠUb isoft", "ĠKel ley", "Ġw rench", "Ġbourgeois ie", "IB LE", "ĠPrest on", "w orn", "ar ist", "reat ing", "Ġst ained", "ar ine", "Ġsl ime", "EN N", "Ġche sts", "Ġground water", "ann ot", "ĠTr ay", "ĠLoc ke", "ĠC TR", "Ġd udes", "ĠEx ternal", "ĠDec oder", "Ġpar amed", "ĠMed line", "80 9", "ĠD inner", "rup al", "g z", "ĠG um", "ĠDem o", "j ee", "Ġd h", "ber man", "arch s", "Ġen qu", "ĠEp stein", "Ġdevast ation", "Ġfriends hips", "ĠAr d", "Ġ23 1", "ĠRub in", "ĠDist ance", "Ġsp urred", "Ġd ossier", "Ġover looking", "\\\\\\\\\\\\\\\\ \\\\\\\\\\\\\\\\", "Fore st", "ĠCom es", "\\ \",", "ĠIran ians", "Ġf ixtures", "L aughs", "Ġcur ry", "ĠKing ston", "Ġsqu ash", "Ġcat alogue", "Ġabnormal ities", "Ġdigest ive", ".... .....", "Ġsubord inate", "og ly", "Ġ24 9", "M iddle", "Ġmass ac", "Ġburg ers", "Ġdown stairs", "Ġ19 31", "39 4", "ĠV G", "Ġl asers", "ĠS ikh", "ĠAlex a", "der ived", "Ġcycl ist", "ãģ® éŃĶ", "onel iness", "!!!! !!!!", "Ġbuff s", "leg ate", "Ġrap ing", "Ġrecomm ending", "ro red", "Ġmult icultural", "un ique", "Ġbusiness men", "Ġune asy", "ĠM AP", "Ġdisp ersed", "cipl ine", "J ess", "ĠK erala", "å §", "Ġabst raction", "Sur v", "U h", "Ġprin ters", "ij a", "ow der", "Ġanalog ous", "ĠA SP", "af er", "Ġunfold ed", "Ġlevel ing", "Ġbre ached", "ĠH earing", "Ġn at", "Ġtransl ating", "crit ical", "Ġant agonist", "ĠYes terday", "Ġfuzz y", "w ash", "m ere", "Ġbe wild", "ĠM ae", "V irgin", "ph rase", "Ġsign aled", "ĠH IGH", "Ġprot ester", "Ġgar ner", "unk nown", "Ġk ay", "Ġabduct ed", "Ġst alking", "am n", "Ġdes erving", "ĠR iv", "ĠJ orge", "Ġscratch ing", "ĠS aving", "ip ing", "Ġte ase", "Ġmission ary", "ĠMor row", "T IME", "P resent", "Ġchem otherapy", "tern ess", "ĠH omes", "ĠP urdue", "Ġst aunch", "ĠWhit ney", "ĠTH ERE", "Î ¼", "iat us", "ĠErn est", "ĠDe ploy", "Ġcove ted", "F ML", "ĠDial ogue", "Ġex ited", "f ruit", "Ġner d", "\":\" \",\"", "Ġv ivo", "ru ly", "4 60", "ĠAm en", "rehens ible", "Ġâ ĺ", "D IR", "Ġad herence", "Ġche w", "ĠCo ke", "ĠSerge i", "dig ital", "ĠNe ck", "g ently", "enth al", "/ )", "Ġwe ary", "Ġgu ise", "ĠConc ord", "ĠOn ion", "at cher", "Ġb inge", "ĠDirect ive", "Ġman ned", "ans k", "Ġill usions", "Ġbillion aires", "38 3", "oly n", "odynam ic", "ĠWhe at", "ĠA lic", "Ġcol oured", "ĠN AFTA", "ab o", "Ġmac ros", "ind ependent", "s weet", "Ġsp ac", "ĠK abul", "Ġ Ä", "em e", "Ġdict ated", "Ġsh outs", "= {", "Ġr ipping", "ĠSh ay", "ĠCr icket", "direct ed", "Ġanalys ed", "ĠWAR RANT", "ag ons", "ĠBlaz ers", "Ġche ered", "Ġar ithmetic", "ĠTan z", "37 3", "ĠFl ags", "Ġ29 5", "Ġw itches", "ĠIn cluded", "ĠG ained", "ĠBl ades", "G am", "ĠSam antha", "ĠAtl antis", "ĠPr att", "Ġspo iled", "ĠI B", "ĠRam irez", "Pro bably", "re ro", "ĠN g", "ĠWar lock", "t p", "Ġover he", "Ġadministr ations", "Ġt int", "Ġreg iment", "Ġpist ols", "Ġblank ets", "Ġep ist", "Ġbowl s", "Ġhydra ulic", "Ġde an", "Ġj ung", "Ġasc end", "70 5", "ĠSant iago", "à ®", "Ġun avoid", "ĠSh aman", "re b", "Ġstem ming", "99 8", "ĠM G", "st icks", "esthes ia", "ER O", "Ġmor bid", "ĠGr ill", "ĠP oe", "any l", "Ġdele ting", "ĠSurve illance", "Ġdirect ives", "Ġiter ations", "ĠR ox", "ĠMil ky", "F ather", "Ġpat ented", "44 7", "Ġprec ursor", "Ġm aiden", "ĠP hen", "ĠVe gan", "ĠPat ent", "K elly", "Redd itor", "Ġn ods", "Ġvent ilation", "ĠSchwar z", "Ġw izards", "Ġomin ous", "ĠHe ads", "ĠB G", "Ġl umber", "ĠSp iel", "Ġis Enabled", "Ġancest ral", "ĠSh ips", "Ġwrest ler", "ph i", "Ġy uan", "ĠRebell ion", "Ġice berg", "Ġmag ically", "Ġdivers ion", "ar ro", "yth m", "ĠR iders", "ĠRob bie", "ĠK ara", "ĠMain tenance", "ĠHer b", "Ġhar ms", "p acked", "ĠFe instein", "Ġmarry ing", "Ġbl ending", "ĠR ates", "Ġ18 80", "Ġwr ink", "ĠUn ch", "ĠTor ch", "desc ribed", "Ġhuman oid", "ilit ating", "ĠCon v", "ĠFe ld", "IGH TS", "Ġwhistlebl ower", "ort mund", "ets y", "arre tt", "ĠMon o", "ĠI ke", "ĠC NBC", "ĠW AY", "ĠMD MA", "ĠIndividual s", "Ġsupplement al", "Ġpower house", "ĠSt ru", "F ocus", "aph ael", "ĠCol leg", "att i", "Z A", "Ġp erenn", "ĠSign ature", "ĠRod ney", "Ġcub es", "idd led", "ĠD ante", "ĠIN V", "iling ual", "ĠC th", "Ġso fa", "Ġintimid ate", "ĠR oe", "ĠDi plom", "ĠCount ries", "ays on", "Ġextrad ition", "Ġdis abling", "ĠCard iff", "Ġmemor andum", "ĠTr ace", "Ġ?? ?", "se ctor", "ĠRou hani", "ĠY ates", "ĠFree ze", "Ġbl adder", "M otor", "ĠProm ise", "ant asy", "Ġforesee able", "ĠC ologne", "cont ainer", "ĠTre es", "ĠG ors", "ĠSin clair", "Ġbar ring", "key e", "Ġsl ashed", "ĠStat istical", "é ĩ", "Ġâĸ º", "All ows", "Ġhum ility", "Ġdr illed", "ĠF urn", "44 3", "Ġse wage", "Ġhome page", "Ġcour tyard", "Ġv ile", "Ġsubsid iaries", "aj o", "direct ory", "Ġam mon", "V ers", "charg es", "Ġ} }", "ĠCh ains", "Ġ24 6", "n ob", "Ġper cept", "Ġg rit", "Ġfisher men", "ĠIraq is", "ĠDIS TR", "ĠF ULL", "ĠEval uation", "g raph", "at ial", "Ġcooper ating", "Ġmel an", "Ġenlight ened", "Ġal i", "t ailed", "Ġsal ute", "Ġweak est", "ĠBull dogs", "U A", "ĠAll oy", "Ġsem en", "oc ene", "ĠWilliam son", "s pr", ", âĢĶ", "ĠG F", "itt ens", "Be at", "ĠJ unk", "iph ate", "ĠFarm ers", "ĠBit coins", "ig ers", "d h", "ĠL oyal", "p ayer", "Ġentert ained", "Ġpenn ed", "Ġcoup on", "Que ue", "Ġweaken ing", "c arry", "Ġunderest imate", "Ġshoot out", "Ġcharism atic", "ĠProced ure", "Ġprud ent", "in ances", "Ġric hes", "Ġcort ical", "Ġstr ides", "Ġd rib", "ĠOil ers", "5 40", "ĠPer form", "ĠBang kok", "Ġe uth", "S ER", "Ġsimpl istic", "t ops", "camp aign", "Q uality", "Ġimpover ished", "ĠEisen hower", "Ġaug ment", "ĠH arden", "Ġinterven ed", "Ġlist ens", "ĠK ok", "Ġs age", "Ġrub bish", "ĠD ed", "Ġm ull", "pe lling", "Ġvide ot", "Produ ction", "D J", "m iah", "Ġadapt ations", "Ġmed ically", "Ġboard ed", "Ġarrog ance", "Ġscra pped", "Ġopp ress", "FORM ATION", "Ġj unction", "4 15", "EE EE", "S kill", "Ġsub du", "ĠSug gest", "ĠP ett", "Ġle tt", "ĠMan ip", "ĠC af", "ĠCooper ation", "T her", "Ġreg ained", "¶ æ", "ref lect", "Ġth ugs", "ĠShel by", "Ġdict ates", "ĠWe iner", "ĠH ale", "Ġbatt leground", "s child", "Ġcond ol", "h unt", "osit ories", "Ġacc uses", "Fil ename", "Ġsh ri", "Ġmotiv ate", "Ġreflect ions", "N ull", "ĠL obby", "¥ µ", "ĠS ATA", "ĠBack up", "Ñ ĥ", "n in", "ĠCor rection", "Ġju icy", "ut ra", "ĠP ric", "Ġrest raining", "ĠAir bnb", "ĠAr rest", "Ġappropri ations", "Ġsl opes", "Ġmans laughter", "Ġwork ings", "ĠH uss", "ĠF rey", "Le ave", "ĠHarm ony", "ĠF eder", "Ġ4 30", "Ġt rench", "Ġglad ly", "Ġbull pen", "ĠG au", "b ones", "Ġgro ove", "Ġpre text", "ã ħĭ", "Ġtransm itter", "ĠComp onent", "Ġunder age", "ĠEm pires", "T ile", "Ġo y", "ĠMar vin", "ĠC AS", "Ġbl oss", "Ġrepl icated", "ĠMar iners", "Marc us", "ĠBl ocks", "Ġliber ated", "Ġbutter fly", "Fe el", "Ġfer mentation", "Ġyou tube", "Ġoff end", "ĠTer m", "res ist", "Ġcess ation", "Ġinsurg ency", "Ġb ir", "ĠRa ise", "59 5", "Ġhypothes es", "50 2", "Ġpl aque", "ocr at", "Ġjack ets", "ĠHuff Post", "am ong", "Ġconf er", "48 7", "ĠL illy", "Ġadapt ing", "ĠF ay", "Ġsh oved", "ve c", "Ġref ine", "Ġg on", "Ġgun men", "z ai", "ĠShut tle", "ĠI zan", "Ġ19 13", "Ġple thora", "· ·", "Ġ5 10", "Ġp uberty", "Ġ24 1", "ĠWe alth", "ĠAl ma", "ĠM EM", "ĠAd ults", "C as", "pr ison", "R ace", "Ġwater proof", "Ġathlet icism", "Ġcapital ize", "ĠJu ice", "Ġillum inated", "ĠP ascal", "Ġirrit ation", "ĠWitness es", "ad le", "ĠAst ro", "Ġf ax", "ĠEl vis", "Prim ary", "ĠL ich", "ĠEl ves", "Ġres iding", "Ġst umble", "3 19", "ĠP KK", "Ġadvers aries", "D OS", "ĠR itual", "Ġsm ear", "Ġar son", "ident al", "Ġsc ant", "Ġmon archy", "Ġhal ftime", "Ġresid ue", "Ġind ign", "ĠSh aun", "ĠEl m", "aur i", "A ff", "W ATCH", "ĠLy on", "hel ps", "36 1", "Ġlobby ist", "Ġdimin ishing", "Ġout breaks", "Ġgo ats", "f avorite", "ĠN ah", "son ian", "ĠBo oster", "Ġsand box", "ĠF are", "ĠMalt a", "Ġatt Rot", "ĠM OR", "ld e", "Ġnavig ating", "T ouch", "Ġunt rue", "ĠDis aster", "Ġl udicrous", "Pass word", "ĠJ FK", "blog spot", "4 16", "ĠUN DER", "ern al", "Ġdelay ing", "T OP", "Ġimpl ants", "ĠAV G", "ĠH uge", "att r", "Ġjournal istic", "ĠPe yton", "ĠI A", "R ap", "go al", "ĠProgram me", "Ġsm ashing", "w ives", "print ln", "ĠPl ague", "in us", "EE P", "Ġcru iser", "ĠPar ish", "umin ium", "Ġoccup ants", "ĠJ ihad", "m op", "Ġp int", "Ġhe ct", "ĠMe cca", "direct or", "ĠFund ing", "ĠM ixed", "Ġst ag", "T ier", "Ġg ust", "Ġbright ly", "ors i", "Ġup hill", "R D", "Ġles ions", "ĠBund y", "liv ious", "Ġbi ologist", "ĠFac ulty", "ĠAuthor ization", "Ġ24 4", "All ow", "ï ¸", "ĠGi ul", "Ġpert inent", "ot aur", "es se", "ĠRo of", "Ġunman ned", "35 1", "ĠSh ak", "ĠO rient", "Ġend anger", "D ir", "Ġrepl en", "ed ient", "Ġtail or", "Ġgad gets", "Ġaud ible", "âĺ Ĩ", "N ice", "Ġbomb ard", "ĠR ape", "Ġdef iance", "ĠTW O", "ĠFilip ino", "Ġunaff ected", "erv atives", "Ġso ared", "ĠBol ton", "Ġcomprom ising", "ĠBrew ers", "R AL", "ĠA HL", "icy cle", "Ġv ampires", "Ġdi pped", "oy er", "ĠX III", "Ġsidew ays", "ĠW aste", "ĠD iss", "ĠâĶľ âĶĢâĶĢ", "$ .", "Ġhabit ats", "ĠBe ef", "tr uth", "tr ained", "spl it", "R us", "And y", "ĠB ram", "RE P", "p id", "è£ ħ", "ĠMut ant", "An im", "ĠMar ina", "Ġfut ile", "hig hest", "f requency", "Ġepile psy", "Ġcop ing", "Ġconc ise", "Ġtr acing", "ĠS UN", "pan el", "ĠSoph ie", "ĠCrow ley", "ĠAd olf", "ĠShoot er", "Ġsh aky", "ĠI G", "ĠL ies", "ĠBar ber", "p kg", "Ġupt ake", "Ġpred atory", "UL TS", "/ **", "Ġintox icated", "ĠWest brook", "od der", "he ment", "Ġbas eman", "AP D", "st orage", "ĠFif ty", "ed itor", "G EN", "UT ION", "ir ting", "Ġse wing", "r ift", "Ġag ony", "ĠS ands", "Ġ25 4", "C ash", "Ġl odge", "Ġp unt", "N atural", "ĠIde as", "Ġerrone ous", "ĠSens or", "ĠHann ity", "Ġ19 21", "Ġm ould", "ĠG on", "kay a", "Ġanonym ously", "ĠK EY", "Ġsim ulator", "W inter", "Ġstream ed", "50 7", "? \",", "Ġte ased", "Ġco efficient", "Ġwart ime", "ĠTH R", "' '.", "ĠBank ing", "mp ire", "Ġf andom", "Ġl ia", "G a", "Ġdown hill", "Ġinterpre ting", "Ind ividual", "N orm", "Ġjealous y", "bit coin", "Ġple asures", "ĠToy s", "ĠChev rolet", "ĠAd visor", "IZ E", "Ġrecept ions", "70 6", "C ro", "Ġ26 2", "Ġcit rus", "ir u", "Review er", "ject ed", "U ES", "an z", "19 81", "ĠWork er", "Ġcompl ied", "ores cent", "contin ental", "T on", "ĠPr ism", "ĠShe ep", "Ġ28 8", "n ox", "ĠV og", "O rd", "Ġreal ms", "te k", "Ġirrig ation", "Ġbicy cles", "Ġelectron ically", "p oly", "t all", "() );", "Ġaest hetics", "ĠInteg rated", "Expl ore", "Ġd unk", "47 6", "p ain", "ĠJac ques", "ĠD mit", "Fram es", "Ġreun ited", "Ġhum id", "D ro", "P olitical", "Ġyouth ful", "Ġent ails", "Ġmosqu ito", "36 3", "spe cies", "Ġcoord inating", "ĠMay hem", "ĠMagn us", "M ount", "Impro ved", "ĠST ATE", "ATT LE", "Ġflow ed", "Ġtack led", "Ġfashion ed", "Ġre organ", "iv ari", "f inger", "Ġreluct antly", "et ting", "ĠV and", "you ng", "ĠGar land", "Ġpresum ption", "Ġamen ities", "ĠPle asant", "on ential", "ĠO xy", "Ġmor als", "ĠY ah", "Read y", "Sim on", "En h", "D emon", "Ġcl ich", "Mon itor", "ĠD U", "Ġwel comes", "Ġstand out", "Ġdread ful", "Ġban anas", "Ġball oons", "h ooting", "bas ic", "Ġsuff ix", "Ġd uly", "can o", "Ch ain", "at os", "Ġgeop olitical", "Ġ( &", "ĠGem ini", "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ", "Ġacqu itted", "L uck", "prot ect", "10 24", "Ġsc arcity", "Ġmind fulness", "ec ided", "D N", "pr ime", "ĠPres idents", "ĠVID EO", "Ġ( âĪĴ", "add ock", "N OR", "ĠP ru", "p un", "ĠL OL", ")) ))", "ĠL iqu", "ĠS AS", "Ġsty ling", "Ġpunish ments", "Ġnum b", "Ġasc ertain", "ĠRock ies", "f lu", "Th umbnail", "Ġperpet rated", "ĠSem i", "Ġdis arm", "ĠOld er", "ĠEx ception", "Ġexponent ially", "ĠCommun ities", "Ġabol ish", "ĠPart ner", "pt oms", "Ġ7 77", "ĠFo ley", "ĠC ases", "Ġgre ase", "ĠReb irth", "G round", "Ġ; )", "ĠDoct rine", "ik ini", "Y e", "ĠBl ossom", "Ġpers ists", "b ill", "Ġinf usion", "Ġbud dies", "9 11", "ĠPat ient", "Ġdem os", "Ġacquaint ance", "ĠP aw", "at ari", "Ġx ml", "Ġfasc ination", "ĠSer ve", "Ï Ĥ", "br anded", "Ġa z", "Return s", "Ġover shadow", "Ġro am", "Ġspeed y", "n umbered", "hel ial", "Ġdisc iple", "Ġass urances", "g iven", "pect ing", "ĠN atalie", "çĶ °", "Ġmosquit oes", "rote in", "Ġnumer ic", "Ġindepend ents", "Ġtrans itional", "Ġreaction ary", "ĠMech dragon", "do ctor", "Ġshort est", "Ġsequ ential", "ĠB ac", "ĠAccount s", "ãģ Į", "ach y", "ract ive", "ĠReg iment", "Ġbreat htaking", "ffic iency", "ĠB ates", "Ġ3 11", "Ġward robe", "ft s", "ĠBer k", "Sim ply", "ĠRivers ide", "iver ing", "ident ial", "lu cent", "Ġen riched", "ĠCon ver", "ĠG iving", "ãĥ Ļ", "Ġlegal ize", "ĠF TC", "Ġfre aking", "M ix", "Ġter restrial", "es ian", "ci ents", "W ing", "LO AD", "Ġled ge", "ĠViol ent", "ĠMet all", "Ġ30 8", "Ġs outheastern", "hett o", "M eat", "Ġslow down", "Ġret reated", "Jere my", "end as", "**** *", "er ic", "Ġre ins", "opp able", "ĠHuman ity", "ear ances", "rig an", "C amera", "Ġwa ivers", "s oc", "Ġalter ation", "trans form", "ĠC emetery", "50 6", "Ġindef inite", "Ġstim ulating", "y g", "60 3", "ĠS op", "Ġdescript ive", "Ph ase", "ĠEd mund", "Ġpneum onia", "vent us", "A mb", "Ġlabor atories", "ĠEx clusive", "ug ar", "W ere", "Ġmalf unction", "Ġhomosexual s", "Ġ---- ---", "un i", "Ġturb ines", "ĠEqu ity", "D u", "Ġmind ed", "ĠR H", "ĠBlack hawks", "Ġfe ats", "Ġ17 00", "re pl", "36 2", "lad en", "Ġindisp ensable", "ly ss", "tt i", "Ġre el", "Ġdiver ted", "Ġlik eness", "Ġsubscript ions", "Ġfing ert", "Ġfil thy", "dest ruct", "d raft", "ĠBernard ino", "l aunch", "Ġper plex", "ĠS UM", "car b", "Ġswe ater", "ĠVent ure", "ĠJ ag", "ĠCele b", "ĠV oters", "Ġstead fast", "Ġathlet ics", "ĠHans on", "ĠDr ac", "Tr acker", "Ġcomm end", "ĠPres idency", "ĠD ID", "in formed", "Ġweb page", "P retty", "Ġforce fully", "ãĥĥ ãĤ¯", "Ġrel ocation", "Ġsat ire", "â ī", "ĠSunder land", "æ Ħ", "V oice", "???? ????", "Ġinform ant", "Ġbow el", "ĠUn iform", "Ġ ...\"", "Ġpur ge", "Ġpic nic", "ĠU mb", "ĠU PDATE", "ĠSapp hire", "ĠSt all", "le arn", "Ġobject ively", "Ġob liter", "Ġlooph ole", "Ġjour neys", "Ġo mission", "Pro s", "ĠSid ney", "pl oma", "Ġspray ed", "Ġg uru", "Ġtra itor", "Ġtim et", "Ġsn apping", "ĠSe vent", "urn al", "ĠUk ip", "Ġb owed", "por al", "l iberal", "R os", "Quest ions", "i OS", "Ġsummar ize", "ST AT", "Ġ18 50", "ap est", "Ġl ender", "ĠVari able", "br inging", "ĠL ORD", ", )", "Ġcollaps es", "x iety", "ĠN ed", "Y D", "ĠSch a", "Ġantib ody", "Ġdis band", "y re", "ill usion", "Ġro ver", "s hed", "ĠHiro sh", "cc i", "Ġcal am", "ĠMort on", "P interest", "Ġ19 28", "ĠE uras", "ord es", "Ġf ences", "ĠIn ventory", "ĠVal encia", "ĠU d", "ĠT iff", "Ġsqu e", "Ġqu otation", "Ġtroubles ome", "er ker", "QU EST", "ĠKing doms", "s outh", "Ġle vy", "Pr ince", "ĠSt ing", "Ġnick named", "Ġapp e", "Ġphot ographic", "Ġcorp us", "re ference", "ĠT rog", "U nt", ") =(", "ĠLat via", "Ġactiv ating", "Ġlicense e", "Ġdispar ities", "ĠNews letter", "ãĥĥ ãĥĪ", "Ġfree ing", "ĠJe ep", "ĠPer ception", "ins k", "Ġsil icone", "ĠHay den", "Le an", "ĠSuz uki", "ibr arian", "66 8", "Ġsp or", "Ġcorrel ations", "ag hetti", "Ġtu ber", "ĠIP CC", "il us", "ĠV u", "Ġwealth iest", "ĠCarb uncle", "an za", "Ġfool ed", "ĠZ ur", "Ġd addy", "ran o", "il ian", "Ġknock out", "f man", "requ ired", "ĠWik ileaks", "ĠD uffy", "ON T", "Ġins ol", "ĠObject s", "Ġb ou", "ĠNord ic", "ĠIns ert", "sc an", "Ġd ancers", "Ġid iots", "major ity", "ĠNev ille", "ĠFree BSD", "Ġt art", "pan ic", "69 0", "Ġcoc oa", "Ġsam pled", "Ġlook up", "Ind ust", "Ġinject ions", "gen re", "Ġa u", "Ġroad way", "Ġgen itals", "K ind", "ĠEx aminer", "ĠY az", "F resh", "Ġpar alysis", "ĠAl uminum", "Ġre ap", "ok é", "Ġsl oppy", "ĠTun nel", "pos ium", "ner y", "en ic", "Ġher bal", "ĠOut er", "ĠBuild er", "Ġinc ur", "Ġide ologies", "Ġback ups", "cons uming", "ĠDet ect", "de ck", "ĠKN OW", "ĠG ret", "ĠM IC", "Ġtough ness", "ĠEx hibit", "Ġh ive", "L es", "ĠSCH OOL", "ĠAt ari", "ald e", "ĠN ull", "and estine", "m ouse", "Ġbrig ade", "48 9", "Ġrev ol", "ĠLaw son", "ĠW ah", "op oly", "eb ted", "ĠS aunders", "Ġ3 13", "ĠW inc", "Ġtab oo", "ĠHel met", "Ġw edge", "ch ip", "ĠT ina", "b g", "Ġinf uri", "r n", "Ġanomal ies", "ĠSy nc", "ĠEx am", "ĠComm it", "ĠDi ary", "ĠALS O", "ĠDe bor", "omed ical", "Ġcomprehens ion", "6 55", "Ġempower ing", "Ġ ire", "Ġju ices", "ĠE TH", "ĠBox ing", "=\" /", "Ġfacilit ated", "p oke", "ĠPars ons", "ĠMod er", "tra vel", "Ġcivil izations", "Ġliber tarians", "Ġrun e", "ĠCl arks", "at hed", "Ġcampaign ers", "ĠDis patch", "ĠFah renheit", "ĠCap com", "-------- --", "Ġl ace", "Ġdr aining", "Ġl iner", "ĠArt ificial", "é n", "t ask", "] ).", "ĠGM O", "ĠOper ator", "ord inary", "ĠInf luence", "ĠU ps", "Ġpot ency", "uss en", "osp ons", "ĠSw im", "ĠDead line", "Un ity", "Ġcul inary", "Ġenlight enment", "Ġwe arer", "Ġmin ed", "Ġp ly", "Ġinc est", "ĠDVD s", "W alk", "B TC", "Tr ade", "Ġdev al", "ib and", "ĠOvers ight", "Palest inian", "Ġd art", "Ġm ul", "L R", "Ġrem ovable", "ĠReal ms", "ì Ŀ", "Ġmisc ar", "ĠV ulkan", "68 5", "è re", "ĠS ap", "Ġmer ging", "ĠCar ly", "che ster", "Ġbr isk", "Ġlux urious", "ĠGener ator", "Ġbit terness", "Ġed ible", "Ġ24 3", "T G", "Ġrect angle", "With No", "bel ow", "J enn", "Ġdark est", "Ġh itch", "Ġdos age", "Ġsc aven", "ĠK eller", "ĠIllust rated", "Certain ly", "ĠMaver icks", "Marg inal", "Ġdiarr hea", "Ġenorm ously", "Ġ9 99", "sh r", "qu art", "Ġadam ant", "ĠM ew", "Ġren ovation", "Ġcerv ical", "ĠPercent age", "en ers", "ĠKim ber", "Ġflo ats", "Ġde x", "ĠW itcher", "ĠSwan sea", "d m", "Ġsal ty", "y ellow", "Ġca pe", "ĠDr ain", "ĠPaul a", "ĠTol edo", "les i", "Mag azine", "ĠW ick", "ĠM n", "ĠA ck", "ĠR iding", "AS ON", "Ġhom ophobic", "AR P", "Ġwand ered", "C PU", "ood oo", "ĠP ipe", "Ġtight ening", "ĠBut t", "3 18", "Ġdesert ed", "S ession", "Ġfacilit ating", "J ump", "Ġemer gencies", "OW ER", "Ġexhaust ive", "ĠAF TER", "Ġheart beat", "ĠLab el", "ack y", "ĠCert ified", "ilt ration", "Z e", "ĠU tt", "Ġ13 00", "Ġpres ume", "ĠDis p", "Ġsur ged", "Ġdoll s", "Col umb", "Ġchim pan", "ĠR azor", "Ġt icks", "Ġcouncill or", "Ġpilgr image", "ĠReb els", "ĠQ C", "ĠA uction", "x ia", "ik k", "b red", "Ġinsert ion", "Ġco arse", "d B", "SE E", "ĠZ ap", "ĠF oo", "Ġcontem por", "ĠQuarter ly", "ot ions", "ĠAl chemist", "ĠT rey", "ĠDu o", "S weet", "80 4", "ĠGi ov", "Ġfun n", "N in", "h off", "Ġram ifications", "Ġ19 22", "ĠExper ts", "az es", "Ġgar ments", "ar ial", "ĠN ab", "Ġ25 7", "ĠV ed", "Ġhum orous", "ĠPom pe", "Ġn ylon", "Ġlur king", "ĠSerge y", "ĠMatt is", "Ġmisogyn y", "ĠComp onents", "ĠWatch ing", "ĠF olk", "ract ical", "B ush", "Ġt aped", "Ġgroup ing", "Ġbe ads", "Ġ20 48", "Ġcon du", "quer que", "Read ing", "Ġgriev ances", "Ult ra", "Ġend point", "H ig", "ĠSt atic", "ĠScar borough", "L ua", "ĠMess i", "a qu", "ĠPsy Net", "ĠR udd", "Ġa venue", "v p", "J er", "Ġsh ady", "ĠRes ist", "ĠArt emis", "Ġcare less", "Ġbro kers", "Ġtemper ament", "Ġ5 20", "T ags", "ĠTurn ing", "Ġut tered", "Ġp edd", "Ġimpro vised", "Ġ: (", "Ġtab l", "Ġpl ains", "16 00", "press ure", "ĠEss ence", "marg in", "friend s", "ĠRest oration", "Ġpoll ut", "ĠPok er", "ĠAugust ine", "ĠC IS", "ĠSE AL", "or ama", "Ġth wart", "se ek", "Ġp agan", " º", "cp u", "Ġg arn", "Ġass ortment", "ĠI LCS", "t ower", "Recomm ended", "Ġun born", "ĠRandom Redditor", "ĠRandomRedditor WithNo", "Ġparaly zed", "Ġeru ption", "Ġinter sect", "ĠSt oke", "ĠS co", "B ind", "å ¾", "ĠP NG", "ĠNeg ative", "ĠNO AA", "Le on", "Ġall oy", "ĠL ama", "ĠD iversity", "5 75", "Ġunderest imated", "ĠSc or", "Ġm ural", "Ġb usted", "so on", "l if", "Ġnone x", "Ġall ergy", "ĠUnder world", "ĠR ays", "ĠBl asio", "Ġh rs", "ĠD ir", "Ġ3 27", "by ter", "Ġrepl acements", "Ġactiv ates", "ri ved", "M H", "Ġp ans", "ĠH I", "Ġlong itudinal", "Ġnu isance", "al er", "Ġsw ell", "ĠS igned", "s ci", "ĠIs les", "ĠA GA", "Ġdef iant", "Ġson ic", "oc on", "K C", "ĠA im", "t ie", "ah ah", "Ġm L", "D X", "Ġb isc", "ĠBill board", "ĠSY STEM", "NE Y", "ga ard", "Ġdist ressed", "former ly", "Al an", "Ġche fs", "Ġopt ics", "ĠC omet", "ĠAM C", "Ġredes igned", "irm ation", "Ġsight ings", "38 2", "3 11", "ĠW B", "Ġcont raction", "ĠT OTAL", "D ual", "Ġstart led", "Ġunderstand ably", "Ġsung lasses", "ETH OD", "Ġd ocker", "Ġsurf ing", "ĠH EL", "ĠSl ack", "ton es", "Ġsh alt", "Vis ual", "49 8", "Dep artment", "c ussion", "Ġunrest ricted", "Ġt ad", "Ġre name", "employ ed", "Ġeduc ating", "Ġgrin ned", "bed room", "ĠActiv ities", "ĠV elvet", "ĠSW AT", "Ġsh uffle", "ig or", "Ġsatur ation", "F inding", "c ream", "ic ter", "Ġv odka", "tr acking", "te c", "Ġfore ground", "iest a", "Ġve hement", "ĠEC B", "ĠT ie", "E y", "Ġt urtles", "ĠRail road", "ĠKat z", "ĠFram es", "Ġmen ace", "ĠFell owship", "ĠEss ential", "ugg ish", "Ġdri p", "ch witz", "ĠKy oto", "s b", "ĠN ina", "Param eter", "Ġal arms", "ĠCl aud", "Ġpione ering", "Ġchief ly", "ĠSc ream", "Col lection", "Ġthank fully", "ĠRonald o", "åŃ IJ", "st rip", "ĠDisney land", "com mercial", "See ing", "S oul", "Ġevac uate", "Ġc iv", "ĠAs he", "Ġdiv ides", "ĠD agger", "rehens ive", "Ġber ries", "ĠD F", "Ġs ushi", "Ġplur ality", "W I", "Ġdisadvant aged", "Ġbatt alion", "ob iles", "45 1", "Ġcl ing", "Ġunden iable", "ĠL ounge", "Ġha unt", "p he", "Ġquant ify", "Ġdiff ered", "Ġ[* ]", "ĠV iz", "c um", "sl ave", "Ġvide og", "Ġqu ar", "Ġbund les", "ĠAl onso", "t ackle", "Ġneur onal", "Ġlandsl ide", "conf irmed", "ĠDep th", "Ġrenew ables", "B ear", "ĠMaced onia", "Ġjer seys", "Ġb unk", "ĠSp awn", "ĠControl s", "ĠBuch anan", "Ġrobot ics", "Ġemphas izing", "ĠTut orial", "h yp", "ist on", "Ġmonument al", "æ °", "ĠCar ry", "Ġt bsp", "en ance", "H ill", "art hed", "Ġro tten", "De an", "Ġtw isting", "Ġgood will", "Ġimm ersion", "L iving", "Ġbr ushes", "ĠC GI", "ĠAt k", "tr aditional", "Ġph antom", "ĠSt amina", "Ġexpans ions", "ĠMar in", "Ġembark ed", "ĠE g", "int estinal", "ĠPE OPLE", "ĠBo oth", "ĠApp alach", "Ġreleg ated", "V T", "M IT", "Ġmust er", "Ġwithdraw ing", "Ġmicrosc ope", "ĠG athering", "ĠC rescent", "ĠArgent ine", "ĠDec re", "ĠDomin ic", "Ġbud s", "ant age", "ĠI on", "Ġwid ened", "ONS ORED", "ĠGl oves", "iann opoulos", "raz en", "fe el", "Ġrepay ment", "Ġhind sight", "ĠRE ALLY", "ĠPist ol", "ĠBra h", "Ġwat ts", "Ġsurv ives", "Ġfl urry", "iss y", "Al ert", "ĠUrug uay", "Ph oenix", "S low", "ĠG rave", "ĠF ir", "Ġmanage able", "Ġtar iff", "ĠU DP", "ĠPist ons", "ĠNiger ian", "Ġstrike outs", "Ġcos metics", "whel ming", "f ab", "c ape", "pro xy", "Ġre think", "Ġover coming", "sim ple", "Ġw oo", "Ġdistract ing", "ĠSt anton", "ĠTuls a", "ĠD ock", "65 9", "Ġdisc ord", "ĠEm acs", "ĠV es", "ĠR OB", "Ġreass uring", "Ġcons ortium", "Muslim s", "3 21", "Ġprompt s", "se i", "ĠH itch", "imp osed", "ĠF ool", "Ġindisc rim", "wr ong", "bu querque", "D avis", "! ]", "Ġtim eless", "ĠNE ED", "Ġpestic ide", "Ġrally ing", "ĠCal der", "Ġå ¤", "Ġx p", "ĠUn le", "ĠEx port", "lu aj", "B uff", ") </", "B oot", "ĠChrys ler", "or ative", "M ess", "Ġneglig ible", "ert odd", "ĠMush room", "ĠG ale", "g c", "ĠCos by", "ĠR ural", "rit ical", "B ell", "Ġturb ine", "00 200000", "Ġlegit imately", "ĠAnim ated", "T ED", "ĠThe odore", "c onduct", "ĠH ier", "Ġcounterfe it", "ĠAlger ia", "Ġun beat", "cont roller", "Ġun res", "Ġscram bling", "ĠFall on", "T es", "Ġam ber", "Ġroy alties", "ĠShel ter", "ĠL ester", "Ġclass ify", "Rem ote", "Ġun heard", "Ġcontrovers ies", "Ġenrich ment", "ĠYan kee", "g amer", "Ġpl atinum", "Ġec ology", "ĠS ark", "Ġunt ouched", "Ġsuper visors", "Ġ\" %", "Ġf ooth", "Ġcomm ons", "Ġnarc otics", "Ġind ices", "ĠP ly", "Ġaddition ally", "ĠGaw ker", "ĠE Q", "Pl aying", "Ġcave at", "ĠAbs olute", "oss us", "B aby", "Ġr ation", "Ġres in", "Ġcalib ration", "ĠNew port", "Ġkn ocks", "v t", "Ġcomp ost", "Sc ene", "Ġsar cast", "Ġkiss es", "Ġn s", "all i", "ĠMar cel", "ĠP iet", "iat rics", "Ġsurround s", "ĠRep rodu", "ĠPhill ies", "Ġuncertain ties", "ĠE ur", "ĠRom ance", "ĠH ath", "ĠNeed s", "ĠCl oak", "Ġcre m", "que ue", "Ġ3 55", "Ġup front", "] );", "Ġrecip roc", "Ġ19 27", "Ġ11 00", "ut su", "Ġdep ressive", "ow ment", "F ans", "Ġme ch", "Ġann ihil", "Ġcounter terrorism", "ĠFig ures", "b old", "ĠMo ines", "ĠDri vers", "Ġmanuscript s", "ĠCrypt o", "Ġhyp not", "redd its", "Ġprosec utions", "Ġdiver t", "CR IP", "ĠB ene", "ĠRe ggie", "Ġtax ing", "ĠMor ales", "ent ing", "t ur", "sign ificant", "ĠPR OV", "Ġstr ands", "Ġp ouch", "ĠR ookie", "» Ĵ", "Ġnic er", "he my", "h w", "EC A", "Ġintimid ated", "Ġstr icter", "Ġmicro bial", "det ails", "Ġv ows", "Ġqu ake", "hh hh", "Ġrein vent", "U b", "Ġrel inqu", "ĠBuff ett", "lic ensed", "itte red", "ĠPic ard", "Ġche wing", "u cl", "organ ic", "Ġlocal ized", "ĠEconom ist", "Ġacqu ainted", "Def inition", "s ed", "Crit ics", "Ġc c", "45 3", "38 1", "Ġfell ows", "Ġcheck points", "0 25", "Ġre election", "Ġmed iated", "ĠK DE", "Ġhurd le", "Ġtext ing", "Per fect", "Ġtrust ees", "fect ure", "Ġd ich", "mon ary", "Ġdist inctions", "Ġ14 00", "Ġus her", "Ġparas ites", "ĠSh aring", "ĠV im", "Ġbar becue", "ĠMin isters", "ere lla", "Ġe b", "Ġm c", "ĠSome how", "ĠIn sect", "ch anges", "b road", "ĠBy z", "Ġgrap es", "66 9", "Ġ= ================", "Ġass imil", "Ġhaun ting", "Ġfire power", "Ġdef amation", "em phasis", "Ġcomp ose", "Ġallerg ies", "Ġstr ang", "roll ers", "b ang", "Ġbrew ers", "ron gh", "ri ot", "p oor", "c old", "S ample", "Ġbu oy", "0 40", "ĠCourt ney", "Ġ26 8", "ĠWed ding", "70 2", "Ġobsess ive", "Ġbra king", "ĠL al", "an ical", "å ¦", "at en", "Con struction", "Ġclin ically", "iers hip", "N ames", "ĠDisc uss", "ĠRam os", "Ġloc ale", "ĠAgric ultural", "En able", "Ġhorse power", "ent ure", "P ref", "C ourt", "Ġstaff ing", "Ġfut uristic", "dri vers", "ĠMarket place", "æĪ ¦", "Friend s", "Ġdam ning", "ĠCustom ers", "Ġwe eds", "ĠM ai", "Ġag ile", "ĠT att", "ic ent", "R anked", "cro ft", "ĠKat y", "Ext reme", "Ġcar ve", "ĠR over", "ĠBy ron", "37 2", "Ġconduct s", "r atch", "it ia", "ĠPump kin", "Sad ly", "Rel oaded", "P olicy", "Ġl ick", "pe ak", "is ks", "ĠCD s", "ĠEn cyclopedia", "in itial", "C os", "ĠAware ness", "ĠD ram", "$$ $$", "Ġr iff", "Ġscript ure", "run ners", "Ġbo iler", "ons on", "o in", "Ġham string", "Ġcat aly", "ĠArch bishop", "ch all", "Ġf aux", "ok in", "local host", "ĠN AME", "ad obe", "S AN", "am ate", "Ġscram ble", "Ġcar c", "ĠMan ifest", "ĠCed ar", "ĠSer gio", "l ater", "ff er", "Ġgrapp ling", "ĠDe utsche", "agon ists", "ĠNew sp", "Ġpret ended", "arch ment", "Ġcur ated", "Ġhead phone", "ĠUn common", "ĠS IGN", "A gent", "Ġdead lines", "Ġhorizont ally", "ĠM AT", "ĠSum mers", "Ġord ained", "ĠLast ly", "ĠKend all", "Ġfr ig", "ĠMach ina", "ĠWater loo", "ĠMex icans", "Ġprotect or", "Ġgl are", "} \"", "Prem ium", "Ġr ift", "ĠTelesc ope", "Met al", "Ġrec apt", "Ġ; ;", "Ġincl ination", "Ġimp oses", "ing en", "^ {", "Ġh aste", "Ġd olphins", "Ġcomm uters", "pl anned", "c ong", "m x", "ĠU pload", "Ġext rap", "ĠTuc son", "ĠExpl oration", "efe ated", "Ġsl ender", "70 3", "ĠB uk", "is el", "Ġcompet itiveness", "ch lor", "ĠP ermanent", "ĠE verett", "ĠSpecial ist", "ĠS OL", "Ġcy an", "ĠEx actly", "U F", "ĠL IFE", "ary l", "on et", "ĠEmploy ee", "aw ed", "ĠRat ings", "Ġextra vag", "ul hu", "ĠPl ane", "Ġelev ate", "ĠCoord inator", "ĠWat kins", "Ġex cludes", "Ġsent ient", "Ġep och", "Ġall oc", "Pre viously", "ĠSh y", "ĠSlov akia", "L OCK", "Ġmarked ly", "Ġkn ob", "Ġadventure rs", "ĠBe en", "ĠCost s", "amm ers", "Ġon slaught", "ĠSupport ed", "ĠT au", "ik arp", "ĠS overe", "ĠHam pton", "ãĤ ī", "Pre v", "ĠW orse", "Ġc ottage", "ĠH ades", "le z", "b owl", "Ġfrag rance", "ĠL ok", "EM OTE", "ĠPet ro", "Ġ19 25", "ĠP end", "produ cing", "Ġrel ocate", "v ati", "p ole", "Ġsem in", "ĠN UM", "Ġrock ed", "b uff", "b ly", "Rep ly", "ĠH ai", "Ġartic ulated", "ĠIslam abad", "66 5", "ĠClaim s", "Des ktop", "Ġtrust ee", "Ġscript ing", "ĠS ob", "ĠAs ylum", "STD OUT", "ĠCl own", "ĠD ortmund", "ĠDev on", "l ite", "ĠMar ble", "Ġb unker", "Ġcre st", "Ġarous al", "ĠS ears", "ĠBudd y", "ered ith", "ĠP olly", "Ġdec ode", "ĠV ish", "ĠRef lect", "an on", "Ġrefund s", "imm ers", "H M", "Ġwip ing", "Ġpuzz led", "Ġmat te", "un o", "P ierre", ") ),", "Ġt ainted", "Ġsymbol ism", "ĠF raz", "Ġprotest ors", "ethe us", "%% %%", "W ra", "Ġl ax", "ad em", "atur ation", "ãĥ ĵ", "ĠTra iler", "ĠE NG", "ĠBows er", "Ġatt m", "D ur", "80 7", "Ġsid x", "Ġc ider", "ĠA ffect", "Ġw oven", "ĠBark er", "ben ef", "Ġdst g", "ĠRy u", "> [", "Ġsq or", "S audi", "Ġis tg", "Ġindul ge", "pro c", "Ġdisg usted", "Ġcomp ounded", "Ġn em", "Ġschool ing", "ĠC ure", "process ing", "S ol", "Ġpro verb", "it ized", "ĠAlv arez", "Ġscar f", "Ġrect angular", "re ve", "Ġh ormonal", "ĠSt ress", "itiz en", "Ġ4 25", "girl s", "ĠNo ir", "ĠR app", "Ġmar ches", "ch urch", "ĠUs es", "Ġ40 5", "ĠBer m", "Ġord inances", "ĠJud gment", "Charg es", "ĠZ in", "Ġdust y", "Ġstraw berries", "Ġper ce", "ĠTh ur", "ĠDebor ah", "net flix", "ĠLam bert", "Ġam used", "ĠGu ang", "Y OU", "R GB", "ĠC CTV", "Ġf iat", "r ang", "Ġf ederation", "ĠM ant", "ĠB ust", "ĠM are", "respect ive", "ĠM igration", "ĠB IT", "59 0", "Ġpatriot ism", "Ġout lining", "reg ion", "ĠJos é", "Ġbl asting", "ĠEz ra", "B s", "Ġundermin es", "ĠSm ooth", "Ġcl ashed", "rad io", "Ġtransition ing", "ĠBucc aneers", "ĠOw l", "Ġplug s", "Ġh iatus", "ĠPin ball", "Ġm ig", "ĠNut r", "ĠWolf e", "Ġinteg ers", "Ġor bits", "ĠEd win", "ĠDirect X", "b ite", "Ġbl azing", "v r", "Ed ge", "ĠP ID", "ex it", "ĠCom ed", "ĠPath finder", "ĠGu id", "ĠSign s", "ĠZ er", "ĠAg enda", "Ġreimburse ment", "M esh", "i Phone", "ĠMar cos", "ĠS ites", "h ate", "en burg", "Ġs ockets", "p end", "Bat man", "v ir", "ĠSH OW", "Ġprovision al", "con n", "ĠDeath s", "AT IVE", "Pro file", "sy m", "J A", "Ġnin ja", "inst alled", "id ates", "eb ra", "ĠOm aha", "Ġse izing", "ĠBe asts", "Ġsal ts", "M ission", "Gener ally", "ĠTr ilogy", "he on", "leg ates", "Ġd ime", "Ġf aire", "par able", "G raph", "Ġtotal ing", "Ġdiagram s", "ĠYan uk", "ple t", "ĠMe h", "Ġmyth ical", "ĠStep hens", "aut ical", "ochem istry", "Ġkil ograms", "Ġel bows", "anc ock", "ĠB CE", "ĠPr ague", "Ġimpro v", "ĠDev in", "Ġ\" \\", "par alle", "Ġsuprem acists", "ĠB illion", "Ġreg imen", "inn acle", "Ġrequ isite", "ang an", "ĠBur lington", "ain ment", "ĠObject ive", "oms ky", "G V", "Ġun ilateral", "Ġt c", "Ġh ires", "ment al", "Ġinvol untary", "Ġtrans pl", "ĠASC II", " ¨", "Ev ents", "Ġdoub ted", "ĠKa plan", "ĠCour age", "ig on", "ĠMan aging", "ĠT art", "Ġfalse hood", "ĠV iolet", "Ġair s", "Ġfertil izer", "Brit ain", "Ġaqu atic", "ou f", "W ords", "ĠHart ford", "Ġeven ings", "ĠV engeance", "qu ite", "G all", "ĠP ret", "Ġp df", "ĠL M", "ĠSo chi", "ĠInter cept", "9 20", "Ġprofit ability", "ĠId le", "ĠMac Donald", "ĠEst ablishment", "um sy", "Ġgather ings", "ĠN aj", "Charl ie", "Ġas cent", "ĠProt ector", "Ġal gebra", "Ġbi os", "for ums", "EL S", "Introdu ced", "Ġ3 35", "Ġastron omy", "Cont ribut", "ĠPol ic", "Pl atform", "Ġcontain ment", "w rap", "Ġcoron ary", "ĠJ elly", "man ager", "Ġheart breaking", "c air", "ĠChe ro", "c gi", "Med ical", "ĠAccount ability", "! !\"", "oph ile", "Ġpsych otic", "ĠRest rict", "Ġequ itable", "iss ues", "Ġ19 05", "ĠN ek", "c ised", "ĠTr acking", "Ġo zone", "Ġcook er", "ros is", "Ġre open", "Ġinf inity", "ĠPharm aceutical", "ens ional", "Att empt", "ĠR ory", "Mar co", "Ġawa its", "H OW", "t reated", "Ġbol st", "Ġreve red", "Ġp ods", "opp ers", "00 10", "Ġampl itude", "ric an", "SP ONSORED", "Ġtrou sers", "Ġhal ves", "ĠK aine", "ĠCut ler", "ĠA UTH", "Ġsplend id", "Ġprevent ive", "ĠDud ley", "if acts", "umin ati", "ĠY in", "Ġad mon", "ĠV ag", "Ġin verted", "Ġhast ily", "ĠH ague", "L yn", "Ġled ger", "Ġastron omical", "get ting", "Ġcirc a", "ĠC ic", "ĠTenn is", "Lim ited", "Ġd ru", "ĠBY U", "Ġtrave llers", "Ġp ane", "ĠInt ro", "Ġpatient ly", "Ġa iding", "Ġlo os", "ĠT ough", "Ġ29 3", "Ġconsum es", "Source File", "Ġ\"\" \"", "Ġbond ing", "Ġtil ted", "Ġmenstru al", "ĠCel estial", "UL AR", "Plug in", "Ġrisk ing", "N az", "ĠRiy adh", "Ġacc redited", "Ġsk irm", "é Ľ", "Ġexam iner", "Ġmess ing", "Ġnear ing", "ĠC hern", "ĠBeck ham", "Ġsw apped", "Ġgo ose", "K ay", "Ġlo fty", "ĠWal let", "Ġ[ '", "Ġap ocalypse", "Ġb amboo", "ĠSP ACE", "ĠEl ena", "Ġ30 6", "ac ons", "Ġtight ened", "Ġadolesc ence", "Ġrain y", "Ġvandal ism", "ĠNew town", "Ġcon ject", "c akes", "Ġche ated", "Ġmoder ators", "par ams", "E FF", "Ġdece it", "ĠST L", "ĠTanz ania", "ĠR I", "Ġ19 23", "ĠEx ile", "the l", "Ġthe olog", "Ġquir ky", "ĠIr vine", "Ġneed y", "or is", "U m", "K a", "Ġmail box", "3 22", "Ġb os", "ĠPet ra", "K ING", "Ġenlarg ed", "O ften", "Ġbad ass", "Ġ3 43", "ĠPl aces", "ĠC AD", "Ġpr istine", "Ġinterven ing", "d irection", "Ġl az", "ĠD SM", "Ġproject ing", "ĠF unk", "ag og", "pay ment", "n ov", "Ġch atter", "AR B", "Ġexam inations", "ĠHouse hold", "ĠG us", "F ord", "4 14", "B oss", "Ġmy stic", "Ġle aps", "ĠB av", "ul z", "b udget", "Foot ball", "Ġsubsid ized", "Ġfirst hand", "Ġcoinc ide", "oc ular", "Con n", "ĠColl abor", "Ġfool s", "am ura", "ah ar", "r ists", "Ġsw ollen", "Ġexp ended", "ĠP au", "s up", "Ġsp ar", "Ġkey note", "s uff", "Ġunequ al", "Ġprogress ing", "str ings", "ĠGamer gate", "Dis ney", "ĠEle ven", "om nia", "Ġscript ed", "Ġear ners", "bro ther", "ĠEn abled", "æ ³", "Ġlar vae", "ĠL OC", "m ess", "Wil son", "ĠTem plate", "success fully", "Ġparam ount", "Ġcamoufl age", "Ġbind s", "ĠQu iet", "ĠSh utterstock", "r ush", "Ġmasc ot", "fort une", "ĠCol t", "ĠBe yon", "hab i", "Ġha irc", "Ġ26 7", "ĠDe us", "Ġtw itch", "Ġconcent rating", "Ġn ipples", "c ible", "Ġg ir", "N Z", "M ath", "n ih", "Requ ired", "Ġp onder", "ĠS AN", "Ġwedd ings", "Ġl oneliness", "N ES", "ĠMah jong", "69 5", "add le", "ĠGar ner", "ĠC OUR", "Br idge", "Ġsp ree", "ĠCald well", "Ġbri bery", "Ġ���� ����", "plug ins", "Ġr acket", "Ġchamp agne", "vers ible", "V ote", "Ġmod ifiers", "May or", "6 80", "Ġassemb lies", "ĠS ultan", "ĠN ing", "ĠLad ies", "Ġsulf ur", "Ġor bs", "Ġ---- -", "____ ___", "ĠJournal ism", "Ġes ports", "Ġl ush", "Ġh ue", "Ġspect ral", "H onest", "ãĥ ı", "Ġbus hes", "Ġrein forcement", "Ġre opened", "ĠWhe els", "ĠM org", "rie ving", "Ġaux iliary", "Ġj Query", "ĠB AT", "tes que", "Ġver tex", "p ure", "f rey", "ãĤ º", "d os", "Ġty ph", "Ġc ull", "Ġe q", "Ġdec on", "Ġtoss ing", "Ġdispar ate", "ĠBr igham", "print f", "led ged", "Ġsu nd", "Ġco zy", "Ġhepat itis", "per forming", "Ġav al", "ĠG G", "f uture", "Ġpet ertodd", "ĠKos ovo", "Ġmagn ets", "Al ready", "ĠEd ison", "ĠCe res", "ĠRA ID", "Ġbrill iance", "57 6", "Ġder ives", "Ġhypert ension", "ĠÎ Ķ", "Ġlamb da", "Ġfl air", "Ġmission aries", "Ġrap es", "ĠSt arter", "ĠMon ths", "Ġdef y", "Ġseism ic", "ĠR aphael", "Ġeuro zone", "65 6", "z sche", "Ġscr atched", "Ġb ows", "ĠLenn on", "ĠGa ia", "Ġdri pping", "f acts", "A le", "Ġfrog s", "ĠBre ast", "ogene ity", "ĠProsecut or", "Ġampl ified", "ĠHod g", "ĠF n", "Th ousands", "ĠNI H", "ĠMonitor ing", "FT WARE", "ĠPri ebus", "ĠG rowing", "hun ter", "Ġdiagn ose", "ĠM ald", "ĠL R", "Ġcrown ed", "Ġburst ing", "Ġdiss olution", "j avascript", "Ġuseful ness", "ĠExec ution", ": (", "ĠIv ory", "a ah", "Ġpersecut ed", "viol ence", "ist as", "ĠCr ate", "Ġimpuls es", "ĠSp ani", "ed es", "Hand le", "ĠZ erg", "think able", "Last ly", "Ġspont aneously", "Ġinconven ient", "Ġdismiss ing", "Ġpl otted", "Ġeight y", "Ġ7 37", "r ish", "ĠThor nton", "ath am", "Ġsit com", "V en", "Rec ipe", "t el", "l und", "Ġcle ars", "ĠSas uke", "Ġ25 8", "Ġopt ing", "Ġen raged", "est hetic", "ĠA e", "uch s", "Pre p", "Fl ow", "Ġrun off", "ĠE ating", "ĠG iles", "ĠAct ing", "res ources", "ib aba", "Ġr pm", "Ġske wed", "ĠBl anc", "ĠS akuya", "Ġhot ter", "Ġ19 24", "op ian", "ck o", "Ġcr umbling", "Ġcapt ains", "ĠAppropri ations", "le aders", "dro pping", "an uts", "Ġrevers ing", "ĠP ose", "ĠS ek", "Sc ot", "ĠIde a", "c ise", "ĠSloven ia", "Ġ3 17", "Do ctor", "Ġcro cod", "ald i", "Se a", "ĠFar rell", "Ġmerc enaries", "ĠR NC", "ĠGu ess", "Ġp acing", "M achine", "Streamer Bot", "ĠChar ity", "Ġ29 8", "Ġcann ons", "ĠTob y", "TPP StreamerBot", "ĠPass ion", "cf g", "Th om", "Ġbad ges", "ĠBern stein", ". âĢĵ", "ĠP OP", "ĠCon j", "Ġinitial ization", "Ġbiod iversity", "D ub", "Ġfeud al", "Ġdisclaim er", "Ġc row", "Ġign ition", "ar f", "S HA", "Ġk Hz", "h azard", "ĠArt ists", "oe uv", "67 9", "ĠRud y", "N ine", "ĠRam adan", "å ½", "itt o", "Ġadren aline", "C ert", "Ġsmell ed", "Ġimp unity", "Ġag endas", "ĠRe born", "ĠCon cent", "ĠSe ems", "Ġo mega", "ĠDust in", "Ġback er", "ĠSau ce", "ĠBoy le", "W IN", "Ġsp ins", "Ġpa uses", "u pt", "Ġshred ded", "Ġstra pped", "ĠCor ruption", "Ġscr atches", "Ġn i", "Ġatt ire", "ĠS AF", "Factory Reloaded", "ĠI PS", "Ġ( %", "Ġsem inar", "f ocus", "c ivil", "Ġ18 60", "int osh", "Ġcontin ual", "Ġabbre vi", "ĠS ok", "oc obo", "X M", "Ġfr antic", "Ġunavoid able", "Ġar tery", "Ġannot ations", "b ath", "Cl imate", "Ġd ors", "ĠSl ide", "co ord", "ĠRel oad", "ĠL DL", "ĠLove craft", "Ġunim agin", "Ġresemb led", "Ġbarr acks", "n p", "Ġsurrog ate", "Ġcategor ized", "ãĤ ©", "Ġvacc inated", "Ġdrain age", "Ġind ist", "ĠWhats App", "Ġ18 70", "oler ance", "inv oke", "am orph", "Ġrecon nect", "Ġem anc", "Ġblind ness", "Ġ12 80", "intern et", "c ollar", "Ġalt ru", "Ġab yss", "ĠT RI", "65 7", "Ġinf used", "HE AD", "Ġforest ry", "ĠWood y", "ĠC i", "w i", "s am", "78 4", "hol iday", "Ġmog ul", "ĠF ees", "ĠD EN", "In ternal", "ur bed", "f usc", "at om", "ĠIll usion", "Ġpoll ed", "Ġfl ap", "Ġco ax", "L GBT", "An aly", "ĠSect ions", "ĠCalif orn", "em n", "Ġh ither", "ĠN IGHT", "Ġn ailed", "ĠPip eline", "39 1", "o of", "ĠPr imal", "vere nd", "Ġsl ashing", "Ġret ri", "avi our", "Ġdepart ing", "g il", "IS C", "Ġmid way", "Ġultras ound", "Ġbeh aving", "ĠT ara", "class es", "V irtual", "ĠColon ial", "Ġstri pping", "Ġorchestr ated", "ĠGra ves", "45 2", "ĠIron ically", "ĠWrit ers", "Ġl ends", "ĠMan z", "Ġra ven", "Ġoxid ative", "Ġ26 6", "EL F", "act ually", "asc ar", "D raft", "Ġfavour able", "Ġhumili ating", "Ġf idelity", "ĠH of", "ĠX uan", "49 6", "Ġlay ered", "at is", "79 0", "Ġpay check", "it on", "K ar", "ĠVM ware", "ĠFar mer", "Ġserv ic", "gl omer", "Ġsl ump", "ĠFab ric", "ĠD OC", "est ing", "Ġreass ure", "Ġph yl", "v olt", "it ory", "R ules", "Ġoxid ation", "Ġpri zed", "Ġmist ress", "ĠDj ango", "WAR N", "å ij", "Ġenc ode", "ĠFeed back", "Ġstupid ity", "I an", "ĠYugoslav ia", "× ¨", "ac l", "UT E", "19 77", "Ġqual ifies", "Ġpuls es", "pret ty", "Ġfro ze", "Ġs s", "Iter ator", "Ġur gently", "Ġm ailed", "ĠCh am", "Ġsust aining", "Ġbas il", "Ġpupp ies", "il ant", "ĠP LEASE", "l ap", "ace ous", "F ear", "ĠMaster y", "aut omatic", "ĠT AG", "Ġant im", "ag les", "47 3", "fram es", "Ġwh ispers", "ĠWho ever", "Ġbra very", "ĠUK IP", "ract ions", "\"\" \"", "Ġt ame", "Ġpart ed", "every thing", "CON T", "Ġind ebted", "Ġadd r", "re k", "IR ED", "Ġem inent", "cl inton", "Ġo usted", "Ġreview er", "Ġmelt down", "Ġre arr", "ĠY ao", "the real", "aby te", "Ġst umbling", "Ġbat ches", "Ġ25 9", "Ġcontrace ptive", "Ġprost itute", "ens is", "De cl", "ĠSt rikes", "M ilitary", "ĠO ath", "v acc", "pp ings", "05 2", "Ġpart Name", "amp ing", "Rep orts", "K I", "CH R", "Ġsubt ly", "sw ers", "Bl ake", "us ual", "Ġcontest ants", "Ġcart ridges", "ĠGRE AT", "Ġbl ush", "ĠâĢ º", "47 2", "Ġreason ed", "ãĥ ¤", "paralle led", "Ġd yn", "ag ate", "Ġnight ly", "å Ĩ", "55 6", "Ġsem antic", "ĠAdv oc", "Ġ !!", "Ġdisag rees", "ĠB W", "V eh", "Ġharm ing", "Ġembr aces", "Ġstri ves", "Ġin land", "ĠK ard", "Ġhe ats", "ĠGin ny", "ut an", "ern aut", "yl ene", "ĠE lev", "J D", "Ġh ars", "ĠStar r", "Ġsk ysc", "Ġcollabor ators", "Us ually", "Ġrev olutions", "ĠSTAT S", "Ġdism antle", "Ġconfident ly", "Ġkin etic", "Al i", "Ġpercent ile", "Ġextract ing", "ill ian", "est ead", "Ġphysic ists", "ĠMarsh al", "Ġfell owship", "Ġd ashed", "ĠU R", "ĠSi oux", "ĠComp act", "am ide", "P ython", "ĠLe igh", "ĠPharm ac", "ist rates", "her ical", "Ġf ue", "ĠE min", "Ġ( {", "ĠNeighbor hood", "Ġdisrupt ing", "ĠD up", "Ġg land", "ĠSe v", "ĠMar ian", "arg on", "ĠD und", "Ġ< !--", "Ġstr and", "Ġstadium s", "z os", "Ġpsych osis", "ĠR ack", "Ġbrilliant ly", "ï¸ ı", "Ġsubmer ged", "ĠInst it", "ĠCh ow", "Ġc ages", "ĠH ats", "ĠU rs", "Ġdil uted", "us at", "ien ne", "ĠMembers hip", "ĠBur k", "Ġ ie", "Ġarche type", "D rug", "ult on", "ĠSp ock", "ĠMcK ay", "ĠDep end", "F eatured", "S oc", "19 78", "ĠB ere", "Ġrelent lessly", "Ġcripp ling", "Ġar thritis", "çĶ Ł", "ĠTrop ical", "ĠBul g", "ĠCher yl", "Ġadm irable", "Ġsub title", "Over ride", "Ġorig inating", "ĠC CP", "Ġsw ore", "ĠSo le", "ĠDis orders", "3 29", "Ġprocess ion", "Ġref urb", "Ġimm ersed", "requ ently", "Ġskept ics", "Ġcer amic", "m itter", "en stein", "b elt", "ĠT IT", "b idden", "Ġf ir", "m ist", "> ]", "Ġwe ave", "ĠParad ox", "Ġentr usted", "ĠBarcl ays", "Ġnovel ist", "og ie", "80 6", "Ġnin ety", "Ġdisag reements", "@@@@ @@@@", "ĠAus chwitz", "c ars", "ĠL ET", "t ub", "arant ine", "P OS", "Ġback story", "Ġcheer ful", "ĠR ag", "ek a", "bi ased", "Ġinexper ienced", "ak ra", "ĠW itt", "t an", "Ġrap ist", "Ġplate au", "ch al", "ĠInqu is", "exp ression", "Ġc ipher", "Ġsh aving", "add en", "re ly", "( \\", "ism a", "ĠReg ulatory", "CH AR", "ily n", "N VIDIA", "G U", "Ġmur m", "la us", "Christ opher", "Ġcontract ual", "ĠPro xy", "ĠJa ime", "ĠMethod ist", "Ġstew ards", "st a", "per ia", "Ġphys iology", "Ġbump ed", "Ġf ructose", "Austral ian", "ĠMet allic", "ĠMas querade", "ar b", "Ġprom ul", "Ġdown fall", "Ġbut cher", "Ġb our", "ĠIN FORMATION", "ĠB is", "pect s", "ad ena", "Ġcontempl ating", "ar oo", "cent ered", "ĠPe aks", "Us ed", "Ġmod em", "Ġg enders", "Ġ8 000", "37 1", "Ġm aternity", "ĠR az", "Ġrock ing", "Ġhandgun s", "ĠD ACA", "Aut om", "ĠN ile", "Ġtum ult", "ĠBenef it", "ĠAppro ach", "works hop", "ĠLe aving", "G er", "inst ead", "Ġvibr ations", "Ġrep ositories", "49 7", "ĠA unt", "ĠJ ub", "ĠExp edition", "Al pha", "Ġs ans", "Ġoverd ue", "Ġoverc rowd", "Ġlegisl atures", "Ġp aternal", "ĠLeon ardo", "Ġexp ressive", "Ġdistract ions", "Ġsil enced", "tr ust", "Ġb iking", "Ġ5 60", "Ġpropri et", "Ġimp osition", "Ġcon glomer", "Ġ= ================================================================", "ĠTe aching", "ĠY ose", "int ensive", "T own", "Ġtroll ing", "ĠGr ac", "ĠAS US", "Y o", "Ġspecial s", "ĠNep h", "ĠGod zilla", "Dat abase", "ĠHe gel", "Ġ27 2", "19 76", "ĠGl oria", "Ġdis emb", "ĠInvestig ations", "ĠB ane", "ag ements", "St range", "Ġtre asury", "ĠPl ays", "Ġundes irable", "Ġwid ening", "Ġverb ally", "Ġinf ancy", "Ġcut ter", "f ml", "Ġ21 00", "prot otype", "f ine", "Ġdec riminal", "Ġdysfunction al", "Ġbes ie", "ĠErn st", "z eb", "Ġnort heastern", "Ġa ust", "por ate", "ĠMar lins", "Ġsegreg ated", "ew orld", "ĠMa her", "Ġtra verse", "Ġmon astery", "ur gy", "G ear", "s and", "Com pl", "ĠE MP", "Ġpl ent", "ĠMer cer", "Ġ27 6", "TA BLE", "Config uration", "H undreds", "Ġpr ic", "Ġcollabor ating", "ĠPar amount", "ĠCumm ings", "Ġ( <", "Ġrecord er", "Ġfl ats", "Ġ4 16", "wh ose", "Font Size", "ĠOr bit", "Y R", "Ġwr ists", "Ġb akery", ") }", "ĠB ounty", "ĠLanc aster", "Ġend ings", "acc ording", "ĠSal am", "e asy", "75 5", "ĠBur r", "ĠBarn ett", "onom ous", "Un ion", "Ġpreced ence", "ĠScholars hip", "ĠU X", "Ġroll out", "Ġbo on", "al m", "ĠCan ter", "æ µ", "Ġround ing", "Ġcl ad", "Ġv ap", "ĠF eatured", "is ations", "Ġ5 40", "pol ice", "Ġunsett ling", "Ġdr ifting", "ĠLum ia", "ĠObama Care", "ĠF avor", "Hy per", "ĠRoth schild", "ĠMil iband", "an aly", "ĠJul iet", "H u", "Ġrec alling", "a head", "69 6", "Ġunf avorable", "Ġd ances", "O x", "Ġleg ality", "Ġ40 3", "rom ancer", "Ġinqu ire", "ĠM oves", "\\ \">", "ĠVari ant", "ĠMess iah", "ĠL CS", "ĠBah á", "75 6", "Ġeyeb row", "Ġ ¥", "ĠMc F", "ĠFort y", "M as", "Ġpan icked", "Ġtransform ations", "q q", "Ġrev olves", "ring e", "ĠA i", "ax e", "Ġon ward", "ĠC FR", "ĠB are", "log in", "Ġliqu ids", "Ġde comp", "second ary", "il an", "ĠCon vert", "ami ya", "Ġprosecut ing", "Ġâī ¡", "ĠYork ers", "ĠByr ne", "sl ow", "aw ei", "J ean", "Ġ26 9", "ĠSky dragon", "Ġ é", "ĠNicarag ua", "ĠHuck abee", "ĠHigh ly", "Ġamph ib", "ĠPast or", "ĠL ets", "Ġbl urred", "Ġvisc eral", "ĠC BO", "Ġcollabor ated", "z ig", "Leg al", "Ġapart heid", "Ġbr id", "Ġpres et", "ĠD ET", "ĠAM A", "× Ķ", "arch ing", "auc uses", "build er", "Ġpo etic", "Ġem ulator", "ĠMole cular", "Ġhon oring", "ise um", "Ġtract or", "ĠCl uster", "ĠCal m", "ared evil", "Ġsidew alks", "Ġviol in", "Ġgeneral ized", "ĠAle c", "Ġemb argo", "Ġfast ball", "ĠHT TPS", "ĠL ack", "ĠCh ill", "ri ver", "C hel", "ĠSw arm", "ĠLev ine", "ro ying", "L aunch", "Ġkick er", "Ġadd itive", "ĠDe als", "W idget", "cont aining", "Ġescal ate", "ĠOP EN", "Ġtwe aked", "Ġst ash", "Ġsp arks", "ĠEs sex", "ĠE cc", "Ġconv ict", "Ġblog ging", "I ER", "ĠH L", "Ġmurd erers", "75 9", "ĠH ib", "Ġde pl", "ĠJ ord", "S ac", "Ġdis sect", "ĠHow e", "os her", "Ġcustom izable", "ĠFran z", "Ġat ro", "Ä ĩ", "Ġ000 4", "Ġout post", "R oss", "Ġglyph osate", "ĠHast ings", "ĠBE FORE", "Ġsh ove", "o pped", "ĠSc ala", "Ġam ulet", "an ian", "Ġexacerb ated", "Ġe ater", "47 1", "UM E", "Ġpul p", "izont al", "ĠZ am", "ĠAT I", "imm une", "aby tes", "Ġunnecess arily", "ĠC AT", "ĠAx is", "Ġvisual ize", "à ī", "ĠRad ical", "f m", "Doc uments", "ĠFor rest", "Ġcontext ual", "ĠSy mbol", "Ġtent ative", "ĠDO ES", "ĠGood s", "Ġintermitt ent", "} :", "medi ated", "Ġridic ule", "Ġathe ism", "Ġpath ogens", "ĠM um", "Ġre introdu", "Ġ30 7", "i HUD", "Ġflash light", "Ġsw earing", "Ġp engu", "B u", "Ġrot ated", "ĠCr ane", "Ġ() );", "Ġfashion able", "Ġendors ing", "46 3", ") [", "Ġingest ion", "Ġcook s", "Ġ9 50", "ot omy", "ĠIm am", "Ġk a", "Ġte aser", "ĠGhost s", "ĠãĤ µ", "19 69", "Ï ĥ", "ub by", "Ġconver ter", "zan ne", "end e", "ĠPre par", "ĠNic kel", "ĠChim era", "h im", "ĠTyr ann", "ĠSabb ath", "ĠNich ols", "Ġra pt", "ih ar", "Ġshe lling", "Ġillum inate", "Ġdent ist", "ut or", "ĠInteg ration", "Ġwh ims", "ĠLiter ary", "Be aut", "Ġp archment", "ag ara", "Br and", "Ġder og", "âĢ¦ )", "ĠNor se", "Ġunw itting", "Ġc uc", "Ġborder line", "Ġupset ting", "Ġrec ourse", "Ġd raped", "ĠRad ar", "Ġcold er", "ĠPep si", "im inary", "], [", "65 8", "V i", "ĠF rem", "ĠP es", "Ġveter inary", "ĠT ED", "ĠEp idem", "n ova", "k id", "Ġdev out", "o ct", "j ad", "M oh", "ĠP AY", "Ġge ometric", "Ġ3 23", "Ġcircum ference", "ich ick", "19 75", "ĠY uri", "ĠSh all", "ĠH over", "un in", "S pr", "Ġg raft", "ĠHapp iness", "Ġdisadvant ages", "att acks", "Ġhub s", "ĠStar Craft", "é ĸ", "Ġgall eries", "ĠKor ra", "Ġgrocer ies", "ĠGors uch", "Ġrap ists", "Ġfun gi", "ĠTyph oon", "V ector", "ĠEm press", "b attle", "4 68", "Ġparas ite", "ĠBom ber", "S G", "ex ist", "ĠP f", "Ġun se", "Ġsurge ons", "B irth", "ĠUn sure", "ĠPrint ed", "ĠBehavior al", "ĠA ster", "Pak istan", "Ġun ethical", "Ġs v", "ĠIo T", "Ġlay outs", "P ain", "Ġconst ants", "ĠL W", "ĠB ake", "Ġtow els", "Ġdeterior ation", "ĠBol ivia", "Ġblind ed", "ĠW arden", "ĠMist ress", "Ġon stage", "Ġcl ans", "ĠB EST", "19 60", "Ġant ique", "Ġrhet orical", "ĠPer cy", "ĠRw anda", ", .", "B ruce", "Ġtra umat", "ĠParliament ary", "Ġfoot note", "id ia", "ĠLear ned", "se eking", "gen ic", "Ġdim ensional", "H ide", "èĢ ħ", "Ġintrig ue", "in se", "Ġle ases", "Ġapp rentices", "w ashing", "Ġ19 26", "V ILLE", "Ġsw oop", "s cl", "Ġbed rooms", "on ics", "ĠCr unch", "comp atible", "Ġincap ac", "ĠYemen i", "ash tra", "z hou", "d anger", "Ġmanifest ations", "ĠDem ons", "AA F", "Secret ary", "ACT ED", "L OD", "Ġam y", "ra per", "eth nic", "4 17", "Ġpos itives", "Ġ27 3", "ĠRefuge es", "Ġus b", "ĠV ald", "odd y", "ĠMahm oud", "As ia", "Ġskull s", "ĠEx odus", "ĠComp et", "ĠL IC", "ĠM ansion", "ĠA me", "Ġconsolid ate", "storm s", "ont ent", "99 6", "Ġcl en", "Ġm ummy", "fl at", "75 8", "ĠV OL", "oter ic", "n en", "ĠMin ute", "S ov", "Ġfin er", "R h", "ly cer", "Ġreinforce ments", "ĠJohann es", "ĠGall agher", "Ġgym n", "S uddenly", "Ġext ortion", "k r", "i ator", "T a", "Ġhippocamp us", "N PR", "ĠComput ing", "Ġsquare ly", "Ġmod elling", "ĠFor ums", "ĠL isp", "ĠKrish na", "Ġ3 24", "Ġr ushes", "Ġens ued", "Ġcre eping", "on te", "n ai", "il ater", "ĠHorn ets", "Ġob livious", "IN ST", "55 9", "Ġjeopard y", "Ġdistingu ishing", "j ured", "Ġbeg s", "sim ilar", "ph ot", "5 30", "ĠPark way", "Ġs inks", "ĠHearth stone", "ib ur", "ĠBat on", "Av oid", "Ġd ancer", "Ġmag istrate", "ary n", "Ġdisturb ances", "ĠRom ero", "Ġpar aph", "Ġmis chief", "âĸ ĵ", "ĠSh aria", "Ġur inary", "r oute", "iv as", "f itted", "Ġeject ed", "ĠAl buquerque", "Ġ4 70", "Ġirrit ated", "ĠZ ip", "ĠB iol", "à į", "Ġden ounce", "Ġbin aries", "ĠVer se", "Ġopp os", "ĠKend rick", "ĠG PL", "Ġsp ew", "ĠEl ijah", "ĠE as", "Ġdr ifted", "so far", "Ġannoy ance", "ĠB ET", "47 4", "ĠSt rongh", "it ates", "ĠCogn itive", "oph one", "ĠIdent ification", "ocr ine", "connect ion", "Ġbox er", "ĠAS D", "ĠAre as", "Y ang", "t ch", "ull ah", "Ġdece ive", "Comb at", "ep isode", "cre te", "W itness", "Ġcondol ences", "ht ar", "Ġhe als", "Ġbuck ets", "ĠLA W", "B lu", "Ġsl ab", "ĠOR DER", "oc l", "att on", "ĠSteven son", "ĠG inger", "ĠFriend ly", "ĠVander bilt", "sp irit", "ig l", "ĠReg arding", "ĠPR OG", "Ġse aling", "start ing", "Ġcard inal", "ĠV ec", "ĠBe ir", "Ġmillisec onds", "we ak", "per se", "Ġster ile", "ĠCont emporary", "ĠPh ant", "ĠCl o", "Ġout p", "Ġex iled", "Ġ27 7", "Ġself ie", "Ġman ic", "Ġn ano", "ter ms", "Alex ander", "Ġres olves", "Ġmillenn ia", "Ġexpl odes", "Ġconst ellation", "Ġadul tery", "m otion", "D OC", "Ġbroad casters", "Ġkinderg arten", "ĠMay weather", "ĠE co", "ich o", "Ġ28 7", "l aun", "Ġm ute", "Ġdisc reet", "Ġpres chool", "Ġpre empt", "De lete", "ĠFre ed", "P i", "H K", "Ġblock er", "ĠC umber", "Ġw rought", "d ating", "Ġins urer", "Ġquot as", "Ġpre ached", "Ġev iction", "ĠReg ina", "ĠP ens", "Ġsevent een", "ĠN ass", "D ick", "Ġfold s", "Ġd otted", "ĠA ad", "Un iversal", "Ġp izz", "ĠG uru", "Ġso ils", "Ġno vice", "ĠNe ander", "Ġst ool", "Ġdeton ated", "ĠPik achu", "ĠMass ive", "IV ER", "ĠAb del", "Ġsubdu ed", "Ġtall est", "Ġprec arious", "Ġa y", "r ification", "ĠOb j", "c ale", "Ġun question", "cul osis", "ad as", "igr ated", "D ays", "Ġque ens", "ĠGaz ette", "ĠCol our", "ĠBow man", "ĠJ J", "ï ve", "Ġdomin ates", "Stud ent", "Ġm u", "Ġback log", "ĠElect ro", "Tr uth", "48 3", "Ġcond ensed", "r ules", "ĠCons piracy", "Ġacron ym", "hand led", "ĠMat te", "j ri", "ĠImp ossible", "l ude", "cre ation", "Ġwar med", "ĠSl ave", "Ġmis led", "Ġfer ment", "ĠK ah", "ink i", "ke leton", "cy l", "ĠKar in", "Hun ter", "Reg ister", "ĠSur rey", "Ġst ares", "ĠW idth", "ĠN ay", "ĠSk i", "Ġblack list", "uck et", "Ġexp ulsion", "im et", "Ġret weet", "vant age", "Fe ature", "Ġtro opers", "Ġhom ers", "9 69", "Ġconting ency", "ĠW TC", "ĠBrew er", "fore ign", "W are", "S olar", "Ġund ue", "RE C", "ulner able", "path ic", "ĠBo ise", "Ġ3 22", "Ġarous ed", "ĠY ing", "ä¸ į", "uel ess", "Ġp as", "Ġmor p", "Ġfl oral", "Ex press", "ud ging", "k B", "ĠGr anted", "Ø ¯", "ĠMich a", "ĠGoth ic", "ĠSPEC IAL", "ĠRic ardo", "F ran", "Ġadminister ing", "6 20", "por a", "Ġ ®", "Ġcomprom ises", "Ġb itten", "Ac cept", "Th irty", "Ð ²", "Ġmater ially", "ĠTer r", "ig matic", "ch ains", "Ġdo ve", "stad t", "Mar vel", "FA ULT", "Ġwind shield", "Ġ3 36", "ad ier", "Ġsw apping", "Ġflaw less", "ĠPred ator", "ĠMiche le", "Ġprop ulsion", "ĠPsych ic", "Ġassign ing", "Ġfabric ation", "Ġbar ley", "l ust", "Ġtow ering", "Ġalter cation", "ĠBent ley", "Sp here", "Ġtun a", "ĠClass es", "Fre edom", "un er", "L ady", "v oice", "Ġcool est", "or r", "Ġpal p", "$ {", "Ġhyster ia", "ĠMet atron", "p ants", "Ġspawn ing", "Exper ts", "ĠInvest ors", "ĠAn archy", "Ġshr unk", "ĠVict im", "Ġ28 9", "Ġec stasy", "ĠB inding", "58 5", "ĠMel ody", "57 8", "ot ally", "ĠE tsy", "lig a", "Ġapplaud ed", "Ġswe ating", "Ġredist ributed", "Ġpop corn", "Ġsem inal", "f ur", "ĠNeuro science", "R and", "ĠO st", "ĠMadd en", "ĠIncre asing", "ĠDaw kins", "ĠSub way", "Ġar sen", "cons erv", "B UR", "Ġsp iked", "ĠLy ft", "ĠImper ium", "ĠDrop box", "Ġfav oured", "Ġencomp asses", "gh ost", "Ġins pires", "Ġbur geoning", "ĠY oshi", "ĠVert ical", "ĠAud itor", "Ġint ending", "Ġfilib uster", "Bl oom", "f ac", "ĠCav s", "ign ing", "Ġcowork ers", "ĠBarb arian", "rem ember", "FL AG", "Ġaudit ory", "ason ry", "Col lege", "Ġmut ed", "gem ony", "ob in", "ĠPsych o", "9 68", "Ġlav ish", "Ġhierarch ical", "ĠDr one", "ou k", "Ġcripp led", "ĠMax im", "Sl ot", "Ġqu iz", "ĠV id", "if ling", "Ġarchae ologists", "Ġabandon ment", "d ial", "le on", "ĠF as", "T ed", "Ġr aspberry", "Ġmaneu vers", "Ġbehavi ours", "Ġins ure", "Ġrem od", "Sw itch", "h oe", "Ġsp aced", "Ġafford ability", "ĠF ern", "not ation", "ĠBal anced", "Ġoccup ies", "en vironment", "Ġneck lace", "Ġsed an", "F U", "ĠBrav o", "Ġab users", "ĠAn ita", "met adata", "ĠG ithub", "ait o", "ĠF aster", "ĠWass erman", "ĠF lesh", "Ġth orn", "r arily", "ĠMer ry", "w ine", "Ġpopul ace", "ĠL ann", "Ġrepair ing", "Ġpsy che", "Ġmod ulation", "aw aru", "âĢĭ âĢĭ", "ari j", "Ġdecor ations", "Ġapolog ise", "ĠG arg", "app ly", "Ġgive away", "ĠFl an", "ĠWy att", "U ber", "Ġauthor ised", "ĠMor al", "HAHA HAHA", "activ ate", "Ġtorped o", "ĠF AR", "Ġam assed", "ĠA ram", "ark in", "ĠVict ims", "st ab", "Ġo m", "ĠE CO", "Ġopio ids", "Ġpurpose ly", "ĠV est", "Ġer g", "at an", "ĠSur gery", "Ġcorrect ing", "ĠOrt iz", "ĠBe et", "Ġrev oke", "Ġfre eway", "ĠH iggins", "F ail", "ĠFar ms", "ĠAT P", "h ound", "Ġp oking", "ĠCommun ists", "mon ster", "iment ary", "Ġunlock ing", "Ġunf it", "we ed", "en ario", "at ical", "ĠEnlight enment", "ĠN G", "ĠComp ensation", "de en", "ĠWid ow", "ĠCind y", "ĠAfter wards", "Ġ6 000", "ikh ail", "ag ically", "Ġrat ified", "Ġcasual ty", "H OME", "p sey", "f ee", "Ġspark ling", "Ġd é", "Ġconcert ed", "C atal", "Ġcomp lying", "ĠA res", "ĠD ent", "Sh ut", "Ġsk im", "ad minist", "Ġhost ilities", "ĠG ins", "Ġ6 08", "Ġm uddy", "ĠMc Int", "ĠDec ay", "5 25", "Ġconspic uous", "ĠEx posure", "Ġresc ind", "Ġwear able", "Ġ3 28", "our met", "ah s", "ĠRob ots", "Ġe clips", "inst ance", "ĠRE PORT", "ĠApp l", "0 30", "ĠSk ies", "01 00", "Ġfall acy", "S ocket", "ĠRece iver", "Ġsol ves", "ĠButter fly", "ĠSho pping", "ĠFI RE", "65 4", "Med ic", "Ġsing ers", "ĠNeed less", "'' ''", "isher s", "ĠD ive", "58 8", "Ġselect ively", "Ġcl umsy", "88 9", "Ġpurch aser", "ear ned", "ard y", "Ġbenef iting", "eng lish", "Ġyield ing", "ĠP our", "Ġspin ach", "Ġdel ve", "ĠC rom", "6 10", "Ġexport ing", "ĠMA KE", "Ġ26 3", "Ġg rop", "Ġenv oy", "ĠInqu iry", "ĠLu igi", "d ry", "ĠT uring", "Thumbnail Image", "ĠVar iety", "Ġfac et", "Ġfl uffy", "Ġexcerpt s", "Ġsh orth", "ĠOl sen", "CL UD", "Ġrel iant", "ĠUN C", "T our", "Ġbat hing", "Comp any", "Ġglobal ization", "P red", "ĠMalf oy", "Ġh oc", "j am", "craft ed", "ĠBond s", "ĠKiss inger", "Eng land", "Ġorder ly", "cat entry", "Ġ26 1", "Ġexch anging", "ĠInt ent", "ĠAmend ments", "D OM", "Ġst out", "³³³³³³³³ ³³³³³³³³", "ĠAir bus", "Ġ27 8", "hy de", "P oll", "Item ThumbnailImage", "Ġlooph oles", "ĠPill ar", "Ġexpl or", "St retch", "A part", "Ġun married", "Lim it", "ĠTransform ers", "Ġintellect ually", "unct ure", "18 00", "Ġd arn", "B razil", "Ġleft over", "ber us", "f red", "Mine craft", "3 26", "ĠForm s", "Ġproof s", "ĠDes igned", "Ġindex es", "ĠSupp ose", "EM S", "ĠL oving", "ĠBon nie", "im ating", "OT US", "Ġconduct or", "Ġbehav ed", "ĠF ren", "Ġsy nerg", "Ġmillenn ium", "Ġcater ing", "ĠL auder", "W r", "ĠY iannopoulos", "ĠAT F", "Ġensl aved", "Ġawaken ed", "D VD", "ĠED ITION", "ĠConc ert", "ĠChall enger", "ĠH aku", "umer ic", "Ġdep recated", "ĠSH AR", "4 12", "Ġdy stop", "Ġtremb ling", "Ġdread ed", "ĠSp ac", "p adding", "Re pl", "ĠG arrison", "M ini", "Ġun paralleled", "am ar", "URR ENT", "w reck", "c ertain", "t al", "ĠC LS", "app ings", "Ġsens ed", "Ġf encing", "ĠPas o", "ĠDes k", "Ġsc off", "Ġcontem plate", "ĠL iga", "l iquid", "75 7", "Ġapp rentice", "ĠUCH IJ", "5 70", "ĠTh ousand", "ĠIll um", "Ġchampion ed", "ãĤ Į", "Ġelect ors", "Ġ3 98", "ĠH ancock", "round ed", "ĠJ OHN", "Ġuns atisf", "Ġqual ifier", "ĠGad get", "EN E", "Ġdead liest", "ĠPl ants", "Ġ ions", "Ġacc ents", "Ġtwe aking", "Ġsh aved", "F REE", "ĠCh aser", "Again st", "9 60", "Ġmeth amphetamine", "Ġnormal ized", "Ġ$ \\", "ĠPre cision", "ĠGu am", "Ġch oked", "ĠX II", "ĠCast ing", "Tor rent", "Ġscal p", "ĠJagu ar", "w it", "Ġsem ic", "ix ie", "ĠG ould", "Ġconf ines", "N usra", "ĠL on", "ĠJ ugg", "y cle", "ĠCod ec", "E gypt", "Ġrest rain", "ĠAl iens", "Ġch oking", "ĠD unk", "ĠBell a", "ab c", "Ġsl ang", "Ġneuro trans", "s av", "Ġempower ment", "â ĨĴ", "Ġclim bers", "ĠM im", "ĠF ra", "ros se", "Cap ital", "ĠCth ulhu", "Inter face", "Ġprof icient", "ĠIN TO", "Ġ3 18", "ront al", "5 80", "ĠDes pair", "K enn", "Ġscrim mage", "ĠCo at", "as ions", "Ġwall paper", "ĠJ ol", "Ġresurg ence", "Ġant iv", "ĠB alls", "² ¾", "Ġbuff ers", "Ġsub system", "ĠSt ellar", "ĠL ung", "A IDS", "Ġerad icate", "Ġblat antly", "Ġbehav es", "ĠN un", "Ġant ics", "ex port", "DE V", "w b", "Ġph p", "ĠInteg rity", "Ġexplore r", "Ġrev olving", "auth ored", "g ans", "Ġbas k", "Ġas ynchronous", "å į", "TH ING", "69 8", "G ene", "ĠR acer", "ĠN ico", "iss ued", "Ġser mon", "p ossibly", "Ġsize of", "Ġentrepreneur ial", "ox in", "ĠMin erva", "Ġpl atoon", "n os", "ri ks", "A UT", "ĠAval anche", "ĠDes c", "ij 士", "ĠP oc", "Ġconf erred", "Î »", "Ġpat ched", "F BI", "66 2", "Ġfract ures", "Ġdetect s", "Ġded icate", "Ġconstitu ent", "Ġcos mos", "W T", "Ġswe ats", "Ġspr ung", "b ara", "s olid", "Ġuns us", "Ġbul ky", "ĠPhilipp e", "ĠFen rir", "Ġtherap ists", "ore al", "^^ ^^", "Ġtotal ed", "Ġboo ze", "ĠR PC", "Prosecut ors", "Ġdis eng", "ĠSh ared", "Ġmotor cycles", "Ġinvent ions", "Ġlett uce", "ĠMer ge", "ĠJ C", "Ġspiritual ity", "ĠWAR NING", "Ġunl ucky", "ĠT ess", "Ġtong ues", "ĠD UI", "T umblr", "Ġle ans", "Ġinv aders", "Ġcan opy", "ĠHur ricanes", "ĠB ret", "ĠAP PLIC", "id ine", "ick le", "Reg arding", "Ġve ggies", "Ġe jac", "ju ven", "F ish", "D EM", "ĠD ino", "Th row", "ĠCheck ing", "be ard", "( &", "Ġj ails", "Ġh r", "trans fer", "iv ating", "Ġfle ets", "ĠIm ag", "ĠMc Donnell", "Ġsnipp et", "Is a", "ĠCh att", "ĠSt ain", "ĠSet FontSize", "ĠO y", "ĠMathemat ics", "49 4", "Ġelectro ly", "ĠG ott", "ĠBr as", "B OOK", "ĠF inger", "d ump", "Ġmut ants", "Ġrent als", "Ġinter tw", "Ġc reek", "ail a", "Bro ther", "ĠDisc ord", "pe e", "raw ler", "Ġcar p", "Ġ27 9", "ãĤ· ãĥ£", "rel ations", "Ġcontr asts", "Col umn", "Ġrec onnaissance", "Ġun know", "Ġl ooting", "Ġregul ates", "Ġopt imum", "ĠChero kee", "ĠA ry", "Lat est", "Ġroad side", "Ġd anced", "ĠUnic orn", "A cknowled", "Ġuncont roll", "ĠM US", "at io", "ch ance", "ha ven", "VAL UE", "Ġfavour ites", "Ġceremon ial", "b inary", "pe ed", "wood s", "EM P", "Ġv ascular", "Ġcontempl ated", "Ġbar ren", "ĠL IST", "Y ellow", "ospons ors", "Ġwhisk y", "ĠM amm", "ĠDeV os", "min imum", "H ung", "44 2", "P ic", "ĠSnap dragon", "77 6", "Ġcar ving", "Ġund ecided", "Ġadvantage ous", "Ġpal ms", "ĠA Q", "Ġst arch", "L oop", "Ġpadd le", "Ġfl aming", "ĠHor izons", "An imation", "bo ost", "Ġprob abilities", "ĠM ish", "Ġex odus", "ĠEditor ial", "Ġfung us", "Ġdissent ing", "ĠDel icious", "rog ram", "ĠD yn", "d isk", "t om", "Ġfab rics", "ĠC ove", "ĠB ans", "Ġsoft en", "ĠCON S", "Ġin eligible", "Ġestim ating", "ĠLex ington", "pract ice", "of i", "Ġshe dding", "ĠN ope", "Ġbreat hed", "ĠCorinth ians", "y ne", "ek i", "B ull", "Ġatt aching", "reens hots", "Ġanaly se", "ĠK appa", "Ġuns ustainable", "Ġinter pol", "ank y", "he mer", "Ġprot agonists", "Ġform atted", "ĠBry ce", "ĠAch illes", "ĠAb edin", "sh ock", "Ġb um", "b os", "qu a", "ĠW arn", "q t", "ĠDi abetes", "8 64", "ĠIn visible", "Ġvan ish", "Ġtrans mitting", "Ġmur ky", "ĠFe i", "Ġawa ited", "ĠJur assic", "umm ies", "Ġmen acing", "g all", "C ath", "B uilt", "ild o", "ĠV otes", "Ġon t", "Ġmun itions", "ĠFre em", "ÃŃ n", "Ġdec ency", "lo pp", "ie ved", "ĠG ord", "Ġun thinkable", "ĠNews week", "Ġ3 21", "He at", "Ġpresent er", "ji ang", "Ġpl ank", "ĠAval on", "Ġben z", "ĠR out", "Ġslam ming", "ĠD ai", "ou ter", "ĠCook ie", "ĠAlic ia", "ge y", "Ġvan ity", "Ġow l", "á µ", "t ested", "ĠAw akens", "Ġcan v", "Ġblind ly", "ĠRid ley", "ĠEm ails", "Requ ires", "ĠSer bian", "ograp hed", "if rame", "eter ia", "Ġaltern ating", "qu iet", "Ġsoc iology", "ĠUn lock", "ĠCommun ism", "Ġo ps", "Ġatt ribution", "Ġab duction", "ĠAb ram", "Ġsidel ined", "ĠB OOK", "Ġref ining", "ĠFe eling", "ĠOs lo", "ĠPru itt", "r ack", "ang ible", "Ġcaut iously", "ĠM ARK", "eed s", "M ouse", "ĠStep h", "ĠP air", "S ab", "99 7", "ĠBa al", "B ec", "Ġcomm a", "ĠP all", "ĠG ael", "Ġmisunder stand", "ĠP esh", "Order able", "Ġdis mal", "ĠSh iny", "% \"", "Ġreal istically", "Ġpat io", "ĠG w", "ĠVirt ue", "Ġexhaust ing", "wh atever", "oph ys", "y ip", "4 18", "Ad just", "ĠWa iting", "ess on", "ĠMaz da", "ĠDo zens", "Ġstream lined", "Ġincompet ence", "ĠM eth", "Ġeth os", "ON ES", "Ġincent iv", "Ġgr itty", "ĠBut cher", "Head er", "Ġexp onential", "à Ł", "Ġcorrel ate", "Ġcons ensual", "s ounding", "R ing", "Orig in", "Ġcon clusive", "fe et", "ac ly", "ĠF ernandez", "Buy able", "Ġd ucks", "aunt lets", "Ġel ong", "Ġ28 6", "Ġsim ul", "G as", "ĠK irst", "Ġprot r", "ĠRob o", "ĠAo E", "op ol", "Ġpsych ologically", "sp in", "ilater ally", "ĠCon rad", "W ave", "44 1", "ĠAd vertisement", "ĠHarm on", "ĠOri ental", "is Special", "Ġpresum ptive", "Ġw il", "ĠK ier", "ne a", "Ġp pm", "Ġhar bour", "ĠW ired", "comp any", "Ġcor oner", "atur days", "ĠP roud", "ĠN EXT", "ĠFl ake", "val ued", "ce iver", "Ġfra ught", "Ġc asing", "Ġrun away", "Ġg in", "ĠLaure nt", "ĠHar lem", "ĠCur iosity", "qu ished", "Ġneuro science", "ĠH ulu", "Ġborrow er", "Ġpetition er", "ĠCo oldown", "W ARD", "Ġinv oking", "conf idence", "For ward", "Ġst s", "pop ulation", "Delivery Date", "Fil m", "ĠC ov", "quick Ship", "quickShip Available", "prim ary", "isSpecial Orderable", "inventory Quantity", "channel Availability", "BO X", "ĠMulti player", "ĠJen ner", "77 8", "ĠM d", "Ġ~ /.", "M N", "Ġchild ish", "Ġantioxid ant", "ĠChrom ebook", "Ġ27 4", "Ġscreen play", "Ġadvent urous", "ĠRelations hip", "respons ive", "ming ton", "Ġcorner stone", "ĠF ey", "F IR", "Ġrook ies", "ĠF eaturing", "Ġorig inate", "Ġelectro des", "ant es", "Ġscript ures", "Ġgl ued", "Ġdiscont ent", "Ġaff licted", "lay out", "B rave", "Ġm osa", "ĠQuant ity", "ĠH ik", "w inner", "H ours", "Ġent ail", "ĠCell s", "olog ue", "Ġv il", "Ġpre acher", "Ġdecor ative", "d ifferent", "Ġprejud ices", "ĠSm oking", "ĠNotting ham", "so Type", "Ġrhyth ms", "ĠAl ph", "bl ast", "Ste el", "ĠDaniel le", "Ġstr ife", "Ġrem atch", "so DeliveryDate", "ĠF ork", "t rip", "ol ulu", "hes es", "C G", "ĠPOLIT ICO", "ost a", "ĠDr ift", "é¾įå ¥", "é¾įå¥ ij士", "Ġvet ting", "ĠJin ping", "ĠRec ession", "Min or", "ĠF raud", "enf ranch", "Ġconven ed", "ĠNA ACP", "ĠMill ions", "ĠFarm ing", "ĠW oo", "ĠFl are", "rit o", "imm igrant", "Ġvac ancy", "ĠHE AD", "ĠV aj", "eg al", "ĠV igil", "Stud y", "Ġru ining", "Ġr acks", "Ġhe ater", "ĠRand olph", "ĠBr ush", "ĠT ir", "Ø ¨", "Ġc ov", "% ]", "Ġrecount s", "ĠO PT", "ĠM elt", "Ġtr uce", "Ġcas inos", "Ġcrus ade", "Ġcarn age", "Ġstri pe", "ĠK yl", "Text ures", "Ġ6 98", "Ġpro clamation", "Ġgood ies", "Ġ........ ..", "pro claimed", "P olit", "Ġtop ical", "Ġspecial ize", "ĠA min", "g m", "Ġanch ored", "Ġbear ings", "s ample", "ĠHigh land", "ĠAut ism", "Ġmerc enary", "Ġinterview er", "L ER", "ĠSom ers", "Ġembry o", "ĠAss y", "Ġ28 1", "ĠEd iting", "ĠCh osen", "6 60", "Ġp ci", "ĠThunder bolt", "BI LL", "Ġchuck led", "jri wal", "h of", "Ġearth ly", "() {", "ind ependence", "Ġdisp ers", "ĠV endor", "ĠG areth", "Ġp als", "P enn", "ĠSub mit", "ic um", "Th u", "Ġcl andestine", "Ġcann ibal", "ĠCl erk", "E Stream", "gal itarian", "âĻ ¥", "g ew", "Ġhor rend", "ĠL ov", "ĠRe action", "ocr in", "Class ic", "Ġecho ing", "Ġdiscl osing", "ĠIns ight", "og un", "ĠInc arn", "upload s", "pp erc", "guy en", "Ġ19 01", "ĠB ars", "68 7", "Ġb ribes", "ĠFres no", "ur at", "ĠRe ese", "Ġintr usive", "Ġgri pping", "ĠBlue print", "ĠR asm", "un ia", "man aged", "ĠHeb do", "Ġ3 45", "Ġdec oding", "Ġpo ets", "Ġj aws", "ĠF IGHT", "am eless", "ĠMead ows", "ĠHar baugh", "Inter view", "ĠH osp", "ĠB RA", "Ġdelet ion", "m ob", "W alker", "ĠMoon light", "ĠJ ed", "ĠSoph ia", "Ġus ur", "Ġfortun ately", "ĠPut ting", "ĠF old", "Ġsan itation", "Ġpart isans", "IS ON", "B ow", "ĠCON C", "ĠRed uced", "ĠS utton", "Ġtouch screen", "Ġembry os", "âĢ¢âĢ¢ âĢ¢âĢ¢", "ĠK rug", "com bat", "ĠPet roleum", "Ġam d", "ĠCos mos", "Ġpresc ribing", "Ġconform ity", "ours es", "Ġplent iful", "Ġdis illusion", "ĠEc ology", "itt al", "Ġf anc", "Ġassass inated", "regn ancy", "Ġperenn ial", "ĠBul lets", "Ġst ale", "Ġc ached", "ĠJud ith", "ĠDise ases", "All en", "Ġl as", "Ġsh ards", "ĠSu arez", "ĠFriend ship", "inter face", "ĠSupp orters", "add ons", "46 2", "ĠIm ran", "ĠW im", "Ġnew found", "ĠM b", "An imal", "Ġd arling", "and e", "Ġrh y", "ĠTw isted", "pos al", "yn ski", "Var ious", "× ľ", "ĠK iw", "uy omi", "Ġwell being", "ĠL au", "an os", "Ġunm ist", "Ġmac OS", "Ġrest room", "ĠOl iv", "ĠAir ways", "Ġtimet able", "9 80", "Ġrad ios", "v oy", "ias co", "Ġcloud y", "ĠDraw ing", "Any thing", "Sy ria", "ĠH ert", "st aking", "Ġun checked", "Ġb razen", "ĠN RS", "69 7", "onom ic", "est ablish", "Ġl eng", "Ġdi agonal", "ĠF ior", "L air", "ĠSt ard", "Ġdef icient", "jo ining", "be am", "Ġomn ip", "Ġbl ender", "Ġsun rise", "Mo ore", "ĠF ault", "ĠCost ume", "ĠM ub", "Fl ags", "an se", "Ġpay out", "ĠGovern ors", "ĠD illon", "ĠBan ana", "N ar", "Ġtra iled", "Ġimperial ist", "um ann", "ats uki", "4 35", "ĠRoad s", "Ġsl ur", "ĠIde ally", "Ġt renches", "C trl", "Ġmir rored", "ĠZ el", "ĠC rest", "Comp at", "ĠRoll s", "sc rib", "ĠTra ils", "omet ers", "w inter", "Ġimm ortality", "il ated", "Ġcontrad icts", "un iversal", "ill ions", "ĠM ama", "opt im", "AT URE", "Ġge o", "et ter", "ĠCar lo", "4 24", "Ġcanon ical", "ĠStrongh old", "n ear", "Ġperf ume", "Ġorche stra", "od iac", "Ġup he", "Ġreign ing", "vers ive", "Ġc aucuses", "ĠD EM", "Ġinsult ed", "Ġ---- --", "ĠCr ush", "Ġroot ing", "ĠWra ith", "Ġwh ore", "Ġto fu", "C md", "ĠB ree", "Ġ$ _", "Ġr ive", "ĠAd vertising", "Ġw att", "ĠH O", "Ġpersu asive", "ĠParam eters", "Ġobserv ational", "ĠN CT", "ĠMo j", "ĠSal on", "Ġtr unc", "Ġexqu isite", "ĠMar a", "Ġpo op", "ĠAN N", "Ex c", "ĠWonder ful", "ĠT aco", "Ġhome owner", "ĠSmith sonian", "orpor ated", "mm mm", "Ġlo af", "ĠYam ato", "ĠInd o", "Ġcl inging", "á s", "Ġimm utable", "h ub", "Or ange", "Ġfingert ips", "ĠWood en", "ĠK idd", "ĠJ PM", "ĠDam n", "C ow", "c odes", "48 2", "Ġiniti ating", "ĠEl k", "ĠCut ting", "Ġabsent ee", "ĠV ance", "ĠLil ith", "G UI", "Ġobsc ured", "Ġdwar ves", "ĠCh op", "ĠB oko", "Val ues", "Ġmult imedia", "Ġbrew ed", "Reg ular", "CRIP TION", "ĠMort al", "Ġa pex", "Ġtravel er", "Ġbo ils", "Ġspray ing", "Rep resent", "ĠStars hip", "4 28", "Ġdisappro val", "Ġshadow y", "Ġlament ed", "ĠRe place", "ĠFran ç", "67 7", "d or", "Ġunst oppable", "Ġcoh orts", "gy n", "ĠClass ics", "ĠAm ph", "Ġsl uggish", "ĠAdd iction", "ĠPad res", "Ġins cription", "Ġin human", "min us", "ĠJere miah", "at ars", "Ter ror", "ĠT os", "ĠSh arma", "ast a", "c atch", "Ġpl umbing", "ĠTim bers", "Sh ar", "H al", "ĠO sc", "Ġcou pling", "hum ans", "Ġsp onge", "Ġid ols", "ĠSp a", "ĠAdv ocate", "ĠBe ats", "lu a", "Ġtick ing", "Ġload er", "ĠG ron", "8 10", "Ġstim ulated", "Ġside bar", "ĠManufact urer", "ore And", "19 73", "Ġpra ises", "ĠFl ores", "dis able", "ĠElect rical", "ra ise", "E th", "Ġmigr ated", "Ġlect urer", "K ids", "ĠCa vern", "Ġk ettle", "Ġgly c", "ĠMand ela", "ĠF ully", "å§ «", "FIN EST", "Ġsquee zing", "ĠRy der", "amp oo", "oreAnd Online", "Inst oreAndOnline", "Buyable InstoreAndOnline", "Ġcommem orate", "ĠRamp age", "Aust in", "ĠSh roud", "ĠRu ins", "9 15", "ĠK H", "Ġwater front", "ĠE SC", "b aby", "ĠC out", "ĠEm blem", "Ġequival ents", "49 2", "Un ique", "ĠNiet zsche", "brow ser", "Ġim itation", "ĠWere wolf", "ĠKir in", "ac as", "' ,\"", "Ġà ¾", "Review ed", "Ġc unt", "Ġvo ic", "ĠLen ovo", "Ġbond ed", "48 1", "Ġinhib itors", "Ġendeav ors", "ĠHav ana", "ĠSt out", "ĠJ olly", "A ctor", "*/ (", "Ġoccur rences", "ĠT ens", "Incre ased", "ĠACT ION", "Ġ ãĢĮ", "ĠRank ings", "ĠB reat", "Ġ30 9", "D ou", "Ġimpact ing", "ĠDuc hess", "pre fix", "Q B", "Ġsummon ing", "Ġbest owed", "ĠKe pler", "ĠPOW ER", "c ube", "ĠK its", "ĠG rip", "Ġop ium", "Ġrep utable", "t oc", "ich ael", "ĠR ipple", "Ġcaf é", "ĠZ oom", "ĠBur ma", "Ġwa ive", "Ġst alls", "Ġdem eanor", "inc erity", "Ġfluor ide", "ĠSH OULD", "Par is", "Ġlong ing", "Ġpl at", "Ġgross ly", "Ġbull s", "Ġshowc asing", "ex pected", "ĠG addafi", "engine ering", "Re peat", "ĠK ut", "Ġconce ivable", "Ġtrim med", "osc ope", "ĠCand idate", "ĠT ears", "rol og", "Lew is", "S UP", "Ġroad map", "Ġsal iva", "Ġtrump et", "Jim my", "Ġmirac ulous", "Ġcolon ization", "Ġam put", "ĠGN OME", "ate ch", "D ifferent", "ĠE LE", "ĠGovern ments", "ĠA head", "ãħĭ ãħĭ", "word press", "L IB", "ĠIn clude", "ĠDor othy", "0 45", "ĠColomb ian", "Ġle ased", "88 4", "Ġde grading", "ĠDa isy", "i ations", "Ġbapt ized", "Ġsurn ame", "co x", "Ġblink ed", "ãĥ ¢", "Ġpoll en", "Ġder mat", "Ġre gex", "ĠNich olson", "ĠE ater", "ç ľ", "rad or", "Ġnarrow er", "Ġhur ricanes", "Ġhalluc inations", "r idden", "ISS ION", "ĠFire fly", "Ġattain ment", "Ġnom inate", "Ġav ocado", "ĠM eredith", "Ġt s", "Ġreve rence", "Ġe uph", "Ġcr ates", "ĠT EXT", "Ġ4 43", "Ġ3 19", "J SON", "iqu ette", "Ġshort stop", "ic key", "Ġpro pelled", "Ġap i", "ĠTh ieves", "77 9", "Ġovers aw", "Ġcol i", "ĠNic ola", "Ġover cl", "ik awa", "ĠC yr", "Ġ38 4", "78 9", "ĠAll ows", "10 27", "Det roit", "TR Y", "set up", "ĠSocial ism", "Sov iet", "s usp", "ĠAP R", "ĠShut down", "Ġal uminium", "zb ek", "ĠL over", "GGGG GGGG", "Ġdemocr acies", "Ġ19 08", "ĠMer rill", "ĠFranco is", "gd ala", "Ġtraff ickers", "ĠT il", "ĠGo at", "Ġsp ed", "ĠRes erv", "Ġpro d", "55 2", "Ġc ac", "ĠUn iv", "ĠSch we", "Ġsw irling", "ĠWild erness", "ĠEgg s", "Ġsadd ened", "Ġarch aic", "H yd", "Ġexcess ively", "B RE", "Ġaer ospace", "ĠVo ices", "Cra ig", "Ġign ited", "In itially", "ĠMc A", "Ġhand set", "Ġreform ing", "Ġfrust rations", "ĠDead pool", "ĠBel ichick", "ract or", "ĠRagnar ok", "ĠD rupal", "ĠApp roximately", "19 20", "ĠHub ble", "arm or", "ĠSar as", "ĠJon as", "Ġnostalg ic", "Ġfeas ibility", "Sah aran", "Ġorb iting", "Ġ9 70", "R u", "Ġsh in", "ĠInvestig ators", "Ġinconsist encies", "ĠP AN", "B G", "Ġgraz ing", "Ġdetect ors", "ĠStart up", "ĠFun ny", "ĠNa omi", "Consider ing", "Ġh og", "ut f", "ce mic", "Ġfort ified", "ĠFun ctions", "Ġcod ec", "nut rition", "H at", "\" !", "micro soft", "55 8", "ĠTh in", "ĠA CE", "Al ias", "ĠO PS", "p apers", "P K", "ãĢ İ", "Ġimpro bable", "N orthern", "equ al", "Ġlook out", "Ġty res", "ĠMod ified", "ĠK op", "Abs olutely", "Ġbuild up", "sil ver", "Ġaud i", "Ġgro tesque", "ĠSab er", "ĠPres byter", "ON Y", "Ġglac iers", "ĠSho als", "ĠK ass", "ĠH RC", "ĠNic ol", "ĠL unch", "ĠF oss", "âĸ Ĵ", "AD RA", "ĠOne Plus", "o ing", "ground s", "Ġincident al", "Ġdatas ets", "68 9", "ĠClarks on", "Ġassemb ling", "ĠCorrect ions", "Ġdrink ers", "Ġqual ifiers", "Ġle ash", "Ġunf ounded", "ĠH undred", "Ġkick off", "T i", "Ġrecon cil", "ĠGr ants", "ĠCompl iance", "ĠDexter ity", "Ġ19 06", "w arn", "D allas", "Max imum", "n ard", "av ia", "be aut", "ens itivity", "tr ace", "Ġpione ers", "ĠF ract", "ãĢ ı", "Ġpre cept", "Ġgloss y", "ĠI EEE", "Ac ross", "Ġ6 80", "S leep", "che on", "Ġsatir ical", "ĠMin otaur", "ĠCla ude", "Ġr é", "ape go", "Ġcar rot", "ĠSem in", "ino a", "Ġz o", "Ind ependent", "Ġdiagn oses", "ĠC ue", "M AR", "Ġrend ition", "ĠK ik", "Ġpath ology", "Ġselect s", "Link edIn", "Ġass ay", "ĠD res", "Ġtext ual", "post ed", "IT AL", "ĠM aul", "N eal", "Ġinter connected", "Ġerr atic", "ĠVir us", "Ġ5 30", "Ġenvironmental ists", "ĠP helps", "Ġeng agements", "ĠIN ST", "Ġeconom ical", "nox ious", "Ġg earing", "izz y", "Ġfavor ably", "ĠMcG ill", "T erm", "Ġh anged", "Ġball park", "ĠRe yes", "Ġbe ware", "ĠP sal", "ĠMass acre", "q i", "Ġin accessible", "acly sm", "Ġfr ay", "ill ac", "Ġbitter ly", "ĠCert ification", "Mich igan", "Ġir respective", "al ore", "Em pty", "Ġendorse ments", "Ġund et", "f g", "equ ipped", "Ġmerc iless", "ĠC ust", "Ġimm ature", "Ġvou cher", "ĠBlack well", "Ñ ı", "h awk", "dis ciplinary", "ile e", "ĠMak oto", "ĠD ude", "ãĥĩ ãĤ£", "Y ears", "Ġin ver", "Ġsh aman", "ĠY ong", "ip el", "ell en", "ĠCath y", "br ids", "Ġs arc", "65 1", "N ear", "Ġground work", "Ġam az", "Ġ4 15", "ĠHunting ton", "hew s", "ĠB ung", "Ġarbit rarily", "ĠW it", "ĠAl berto", "Ġdis qualified", "best os", "46 1", "Ġp c", "Ġ28 4", "ro bat", "Rob in", "Ġh ugs", "ĠTrans ition", "ĠOcc asionally", "Ġ3 26", "ĠWh ilst", "ĠLe y", "Ġspaces hip", "cs v", "Ġun successfully", "ĠA u", "le ck", "ĠWing ed", "ĠGrizz lies", ". �", "Ġne arer", "ĠSorce ress", "ĠInd igo", "El se", "8 40", "let es", "Co ach", "Ġup bringing", "ĠK es", "Ġseparat ist", "Ġrac ists", "Ġch ained", "Ġabst inence", "lear ning", "Ġrein stated", "Ġsymm etry", "Ġremind ers", "ĠChe vy", "Ġm ont", "Ġexempl ary", "ĠT OR", "Z X", "Ġqual itative", "ĠSt amp", "ĠSav annah", "ĠRoss i", "Ġp aed", "Ġdispens aries", "ĠWall s", "ĠCh ronic", "Ġcompliment ary", "ĠBeir ut", "Ġ+ ---", "igs list", "Ġcrypt ographic", "mas ters", "ĠCap itals", "Ġmax imal", "Ġent ropy", "Point s", "Ġcombat ants", "l ip", "ĠGl ob", "ĠB MC", "ph ase", "th ank", "HT TP", "Ġcomm uter", "Ġ\\( \\", ".. /", "ĠReg ener", "ĠDO I", "ĠActiv ision", "Ġsl it", "os al", "RE M", "Ġch ants", "Y u", "Ke ys", "Bre xit", "ĠFor ced", "Ari zona", "Ġsquad ron", "IS O", "ĠMal one", "Ġ3 38", "Ġcontrast ing", "Ġt idal", "Ġlib el", "Ġimpl anted", "Ġupro ar", "ĠC ater", "Ġpropos itions", "M anchester", "ĠEuro s", "it amin", "G il", "ĠEl ven", "ĠSe ek", "ĠB ai", "Ġredevelop ment", "ĠTown s", "ĠL ub", "! \",", "al on", "K rist", "Ġmeas urable", "Ġimagin able", "Ġapost les", "Y N", "7 60", "Ġster oid", "Ġspecific ity", "ĠL ocated", "ĠBeck er", "ĠE du", "ĠDiet ary", "uts ch", "ĠMar ilyn", "Ġbl ister", "ĠM EP", "ĠK oz", "ĠC MS", "y ahoo", "ĠCar ney", "Ġbo asting", "ĠC aleb", "By te", "read s", "ad en", "Pro blem", "ĠWood ward", "S we", "S up", "ĠK GB", "Set up", "Ġtac it", "Ġret ribution", "Ġd ues", "ĠM ü", ". ?", "ä¸ Ń", "p ots", "Ġcame o", "ĠP AL", "educ ation", "A my", "like ly", "g ling", "Ġconstitution ally", "ĠHam m", "ĠSpe ak", "Ġwid gets", "br ate", "Ġcra ppy", "ĠI ter", "Ġanticip ating", "ĠB out", "P ixel", "ĠY ep", "ĠLaur ie", "Ġh ut", "Ġbullet in", "ĠSal vation", "Ġch ats", "ear able", "Honest ly", "AL TH", "onse qu", "c ult", "isco very", "ovy ch", "Ġse lves", "ĠSat oshi", "S ounds", "Ġconver gence", "ĠRosen berg", "19 74", "Ġnas al", "Ġfull est", "Ġfer ocious", "x us", "ist e", "AM S", "Ġlobb ied", "Ġso othing", "ĠGun n", "t oday", "0 24", "Ġinspir ational", "ĠN BN", "p b", "g ewater", "or ah", "all owed", "ĠCol iseum", "Ġspecial izing", "Ġinsane ly", "ĠT ape", "del ay", "Ġt arn", "ĠP ound", "Ġmel anch", "Ġdeploy ments", "il and", "Ġless en", "Ġfur ry", "ĠUE FA", "Ġblood shed", "ĠMe ier", "ither ing", "Ġhe irs", "ĠJ aw", "ax ter", "ĠPublic ations", "Ġal ters", "int ention", "ĠWinc hester", "d etermination", "ĠLif etime", "th in", "Mon ster", "7 80", "Ġapprox imation", "Ġsuper markets", "ĠSecond s", "or os", "h uge", "Ġb ribe", "ĠLIM ITED", "un ed", "Ġmis interpret", "ĠIn jury", "Ġ3 67", "Ġthreshold s", "ĠCarn ival", "Ġgastro intestinal", "Ġguid eline", "Ġde ceived", "f eatures", "Ġpurported ly", "ĠRon nie", "ĠNew t", "Ġsp acious", "as us", "Ġsuperhero es", "ĠCyn thia", "le gged", "k amp", "ch io", "Ġth umbnail", "ĠShir ley", "ill ation", "Ġshe ds", "ĠZ y", "E PA", "Ġdam s", "Ġy awn", "n ah", "ĠPe ggy", "ĠE rie", "ĠJu ventus", "ĠF ountain", "r x", "don ald", "al bum", "ĠComp rehensive", "Ġc aching", "ĠU z", "ulner ability", "ĠPrinc iple", "ĠJ ian", "ing ers", "cast s", "ĠOs iris", "ch art", "t ile", "ĠTiff any", "ĠPatt on", "ĠWh ip", "Ġovers ized", "J e", "ĠCind erella", "ĠB orders", "ĠDa esh", "M ah", "Ġdog ma", "Ġcommun ists", "v u", "Coun cil", "Ġfresh water", "Ġw ounding", "Ġdeb acle", "Ġyoung ster", "Ġthread ed", "ĠB ots", "ĠSav ings", "ãģ Ĥ", "ol ing", "oh o", "Ġillum ination", "M RI", "Ġlo osen", "tr ump", "ag ency", "ur ion", "Ġmoment arily", "ĠCh un", "ĠBud apest", "ĠAl ley", "D isk", "Ġaston ished", "ĠCon quer", "ĠAccount ing", "h aving", "ĠWe in", "ĠAl right", "Ġrev olver", "Ġdel usion", "Ġrelic s", "Ġad herent", "qu ant", "Ġhand made", "or io", "Ġcomb ating", "c oded", "Ġquad ru", "re th", "N ik", "ĠTrib al", "ĠMyster ious", "Ġin hal", "ĠWin ning", "ĠClass ification", "ch anged", "Ġun ab", "Ġsc orn", "icip ated", "w l", "ond uctor", "Ġrein forcing", "ĠChild hood", "an ova", "Ġadventure r", "Ġdoctor al", "ĠStrateg ies", "Ġengulf ed", "ĠEnc ounter", "Ġl ashes", "Crit ical", "ric ular", "ĠU TF", "oci ation", "check ing", "ĠConsult ing", "Run time", "per iod", "ĠAs gard", "Ġdist illed", "ĠPas adena", "ĠD ying", "ĠCOUN TY", "Ġgran ite", "Ġsm ack", "Ġparach ute", "ĠS UR", "Virgin ia", "ĠF urious", "78 7", "ĠO kin", "Ġcam el", "ĠM bps", "19 72", "ĠCh ao", "ĠC yan", "j oice", "ef er", "ĠW rap", "ĠDeb ate", "S eg", "Ġfore arm", "ĠIgn ore", "Ġtim estamp", "Ġprob ing", "ĠNo on", "ĠGra il", "f en", "Ġdorm ant", "ĠFirst ly", "ĠE ighth", "ĠH UN", "ĠDes ire", "or as", "Girl s", "ĠDes mond", "z ar", "am ines", "O AD", "exec ute", "Ġbo obs", "ĠAT L", "_ (", "Chel sea", "Ġmasturb ation", "ĠCo C", "Ġdestroy er", "ĠCh omsky", "Ġsc atter", "ĠAss ets", "79 6", "ĠC argo", "Ġrecept ive", "ĠSc ope", "Ġmarket ers", "Ġlaun chers", "Ġax le", "ĠSE A", "se q", "ĠM off", "f inding", "ĠGib bs", "Georg ia", "extreme ly", "N J", "Ġlab orers", "st als", "Ġmed iation", "ĠH edge", "at own", "Ġi od", "des pite", "v ill", "J ane", "ex istence", "Ġcoinc ided", "ĠUt ilities", "ĠChe ap", "Ġlog istical", "Ġcul mination", "ĠNic otine", "p ak", "F older", "Ġrod ents", "st uff", "Ġlaw fully", "Ġreper to", "io ch", "j j", "Dial ogue", "HH HH", "lic tion", "Look s", "Ġ29 7", "Ġtur rets", "ĠAb andon", "Ġinc ess", "ĠTraff ord", "Ġcur led", "Ġprefer ring", "Ġprivat ization", "Ġir resist", "ĠP anda", "ĠSh ake", "ĠMc Gr", "ãĥ Ħ", "und ers", "Ġdiscrim inated", "Ġbart ender", "I LE", "Atl antic", "Ġprop ensity", "ĠW iz", "ĠG im", "con ference", "Ġrein forces", "G h", "w agon", "Ġe erie", "F al", "Ġhug ged", "rac ist", "R IC", "F u", "Ġf iller", "ĠSt ub", "Ġeng raved", "ĠWrest le", "Ġimagin ative", "ĠPe er", "ĠFact ors", "an us", "ĠDrac ula", "mon itor", "Ġrou ters", "ib ia", "ĠBoo lean", "end ale", "ĠSl aughter", "ĠSh ack", "R FC", "ĠSpiel berg", "S ax", "ĠPH OTO", "ĠCl over", "ĠR ae", "Dep ending", "ĠMem or", "ar am", "Ġpier ced", "Ġcur tains", "v ale", "ĠInqu isition", "ĠP oke", "Ġforecast ing", "Ġcompl ains", "S ense", "ĠHer mes", "isc overed", "Ġb ible", "ĠMor ph", "Ġg erm", "78 5", "D ON", "Ġcon gen", "Ġcr ane", "ĠD PR", "Ġrespect fully", "R oom", "ĠN aw", "ĠDal ai", "re ason", "ĠAng us", "Educ ation", "ĠTitan ic", "Ë ľ", "Ġo val", "un ited", "Ġthird s", "Ġmoist ur", "ĠC PC", "M iami", "Ġtent acles", "ĠPol aris", "ex c", "ex clusive", "ĠPra irie", "Ġcol ossal", "ĠBl end", "sur prisingly", "ÃŃ s", "Ġindo ctr", "Ġbas al", "ĠMP EG", "und o", "Spl it", "Develop ment", "Ġlan tern", "19 71", "Ġprov ocation", "Ġang uish", "ĠB ind", "ĠLe ia", "duc ers", "ipp y", "conserv ancy", "Ġinitial ize", "ĠTw ice", "ĠSu k", "Ġpred ic", "Ġdi ploma", "Ġsoc iop", "Ing redients", "Ġhamm ered", "ĠIr ma", "Q aida", "Ġglim ps", "ĠB ian", "Ġst acking", "Ġf end", "gov track", "Ġun n", "dem ocratic", "ig ree", "Ġ5 80", "Ġ29 4", "Ġstraw berry", "ID ER", "Ġcher ished", "ĠH ots", "Ġinfer red", "Ġ8 08", "ĠS ocrates", "O regon", "ĠR oses", "ĠFO IA", "Ġins ensitive", "Ġ40 8", "Recomm end", "ĠSh ine", "Ġpain staking", "UG E", "ĠHell er", "ĠEnter prises", "I OR", "ad j", "N RS", "L G", "Ġalien ated", "Ġacknowled gement", "ĠA UD", "ĠRen eg", "Ġvou chers", "Ġ9 60", "Ġm oot", "ĠDim ensions", "Ġc abbage", "B right", "g at", "ĠK lu", "Ġlat ent", "Ġz e", "ĠM eng", "Ġdis perse", "Ġpand emonium", "H Q", "Ġvirt uous", "ĠLoc ations", "ee per", "prov ided", "Ġse ams", "ĠW T", "iz o", "PR OV", "Ġtit anium", "Ġrecol lection", "Ġcr an", "Ġ7 80", "ĠN F", "49 1", "64 2", "p acking", "59 8", "text ure", "Sp ider", "fre edom", "cipl ed", "ĠTAM ADRA", "âĻ ¦", "aut hent", "ĠW ANT", "r ified", "Ġr ites", "Ġuter us", "k iss", "Ġâī ¤", "Ġsk illet", "Ġdis enfranch", "ĠGa al", "Comp an", "Ġage ing", "gu ide", "B alt", "Ġiter ator", "Ġdiscretion ary", "t ips", "Ġprim ates", "ĠTechn ique", "ĠPay ments", "az el", "ĠR OCK", "stant ial", "0 60", "Ġd mg", "ĠJack ets", "ĠPlay off", "Ġnurs ery", "ĠSy mb", "art on", "Ġannex ation", "Color ado", "Ġco ils", "ĠSh oes", "âĦ¢ :", "ĠRo z", "COM PLE", "ĠEve rest", "ĠTri umph", "J oy", "G rid", "à ¼", "process or", "ĠPros per", "ĠSever us", "ĠSelect ed", "r g", "ĠTay yip", "St ra", "Ġski ing", "Ġ? )", "Ġpe g", "Tes la", "Ġtime frame", "Ġmaster mind", "ĠN B", "scient ific", "ĠSh it", "gener ic", "IN TER", "N UM", "Ġst roll", "ĠEn ix", "ĠM MR", "ĠE MS", "m ovie", "Ĥ ª", "Ġminim izing", "idd ling", "Ġilleg itimate", "Ġprot otyp", "Ġpremature ly", "Ġmanual s", "obb ies", "ĠCass idy", "D EC", "des ktop", "Ġaer os", "Ġscreen ings", "Ġdeb ilitating", "ĠGr ind", "nature conservancy", "Ġf ades", "ter mination", "assets adobe", "F actor", "Ġdefinitive ly", "P oké", "ap ult", "ĠLaf ayette", "C orn", "ĠCor al", "Ġstagn ant", "T ue", "Ġdissatisf action", "G ender", "Ġkid neys", "ĠG ow", "ĠDef eat", "ĠAsh ton", "Ġcart els", "Ġfore closure", "ĠExpl ore", "stre ngth", "ot in", "Ġveterin arian", "Ġf umble", "Ġpar ap", "ĠSt rait", "r ils", "Ġpr ick", "ĠBerm uda", "ĠAm munition", "skin ned", "Ġab ound", "ĠB raz", "Ġshar per", "ĠAsc ension", "Ġ9 78", "Ġpreview s", "Ġcommun ion", "ĠX Y", "Ġph ony", "Ġnewcom er", "Ġ3 32", ".\" ,\"", "Ġredist ribution", "Prot ect", "ĠSo f", "K al", "Ġlip stick", "w orst", "Ġtang led", "Ġretrospect ive", "int eger", "Ġvolunte ering", "Ġ19 07", "Ġ --------------------", "ic hen", "Ġunve iling", "Ġsen seless", "Ġfisher ies", "\\ -", "Ġh inges", "Ġcalcul us", "My th", "Ġund efeated", "Ġoptim izations", "Ġdep ress", "Ġbill board", "ĠY ad", "ĠPy ramid", "Is n", "I de", "Ġleg ion", "ĠK ramer", "ent anyl", "Ġpenet rating", "ĠHaw th", "ĠPR ODUCT", "ĠGer ard", "ĠP act", "ĠIn cluding", "ĠEl ias", "ĠEl aine", "vis ual", "Ġhum ming", "Ġcond esc", "ĠF asc", "ä¸ Ĭ", "Ġe galitarian", "Ġdev s", "ĠD ahl", "O ps", "D H", "ĠB ounce", "id ated", "ald o", "Ġrepublic an", "Ġh amb", "ĠS ett", "ograph ies", "CH APTER", "Ġtrans sexual", "Ġsky rocket", "ans wer", "Ġmark up", "Ø ª", "Ġhero ine", "Comp are", "ĠT av", "Be ast", "Ġsuccess ors", "Ġna ïve", "ĠBuck ley", "st ress", "me at", "Ġdownload able", "Ġindex ed", "Ġsc aff", "ĠL ump", "ĠHom o", "Stud io", "In sp", "Ġr acked", "far ious", "ĠPet ty", "Ex ternal", "Ġ19 09", "W ars", "com mit", "put ers", "Ġun ob", "ĠEr r", "ĠE G", "ĠAl am", "ĠSiber ia", "ĠAtmosp heric", "IS TER", "ĠSatan ic", "trans lation", "ĠL oud", "tra umatic", "l ique", "Ġreson ate", "ĠWel ch", "Ġspark ing", "ĠT OM", "t one", "Ġout l", "Ġhandc uffed", "ĠSer ie", "8 01", "Ġland marks", "ĠRee ves", "Ġsoft ened", "Ġdazz ling", "ĠW anted", "month s", "Mag ikarp", "Ġunt reated", "ĠBed ford", "M i", "ĠDynam o", "O re", "79 5", "Ġwrong ful", "Ġl ured", "Ġcort isol", "Ġve x", "d rawn", "ile t", "Download ha", "ĠF action", "Ġlab yrinth", "Ġhij acked", "w aters", "er ick", "Ġsuper iors", "ĠRow ling", "ĠGu inness", "Ġt d", "99 2", "Ġune arthed", "Ġcentr if", "Ġsham eless", "P od", "ĠF ib", "Ġ icing", "Ġpredict or", "Ġ29 2", "fore station", "con struct", "C and", "@ #", "Ġag itated", "Ġre pr", "OV A", "Ġkn itting", "ĠLim a", "Ġf odder", "68 4", "ĠPerson a", "k l", "7 01", "Ġbreak up", "á ¸", "Ġapp alled", "Ġantidepress ants", "ĠSus sex", "Har ris", "ĠTher mal", "ee ee", "U pload", "Ġg ulf", "Ġdoor step", "ĠSh ank", "L U", "ĠM EN", "ĠP ond", "s orry", "Ġmis fortune", "n ance", "Ġb ona", "M ut", "Ġde graded", "ĠL OG", "ĠN ess", "an imal", "Ġa version", "und own", "Ġsupplement ed", "ĠC ups", "Ġ50 4", "Ġdep rive", "ĠSpark le", "Å Ĥ", "ĠMed itation", "auth ors", "ĠSab an", "ĠN aked", "air d", "ĠMand arin", "ĠScript ures", "ĠPerson nel", "ĠMahar ashtra", "Ġ19 03", "ĠP ai", "ĠMir age", "omb at", "Access ory", "Ġfrag mented", "T ogether", "Ġbelie vable", "ĠGl adiator", "al igned", "ĠSl ug", "M AT", "Ġconvert ible", "ĠBour bon", "amer on", "ĠRe hab", "nt ax", "Ġpowd ered", "pill ar", "Ġsm oker", "ĠMans on", "ĠB F", "5 11", "ĠGood ell", "ĠD AR", "m ud", "g art", "Ġob edient", "ĠTrans mission", "ĠDon ation", "8 80", "Ġbother ing", "Material s", "ãĤ ±", "dest roy", "Ġfore going", "Ġanarch ism", "ĠK ry", "ice ps", "Ġl ittered", "ĠSch iff", "Ġanecd otal", "un its", "Ġf ian", "ĠSt im", "ĠS OME", "ĠInv aders", "Ġbehaviour al", "ĠVent ures", "Ġsub lime", "Ġfru ition", "ĠPen alty", "Ġcorros ion", "¶ ħ", "Ġlik ened", "Ġbesie ged", "ween ey", "ĠCre ep", "Ġlinem en", "mult i", "ic ably", "ud der", "Ġvital ity", "Ġshort fall", "ĠP ants", "ap ist", "H idden", "ĠDro ps", "med ical", "Ġpron unciation", "ĠN RL", "Ġinsight ful", "J V", "ĠBe ard", "ĠCh ou", "Ġchar ms", "Ġb ins", "Ġamb assadors", "ĠS aturdays", "Ġinhib itor", "ĠFr anch", "6 01", "', '", "ĠCon or", "art ney", "ĠX peria", "g rave", "be es", "ĠProtest ants", "Ġso aking", "ĠM andal", "Ġph ased", "Ġ6 60", "Ġsc ams", "Ġbuzz ing", "ĠItal ians", "ĠLoren zo", "ĠJ A", "Ġhes itated", "Ġcl iffs", "ĠG OT", "ingu ishable", "Ġk o", "Ġinter ruption", "Z ip", "Lear ning", "Ġundersc ores", "ĠBl ink", "K u", "57 9", "ĠAut ob", "I RE", "Ġwater ing", "Ġpast ry", "8 20", "Ġvision ary", "ĠTempl ar", "awa ited", "Ġpist on", "Ġant id", "current ly", "Ġp ard", "Ġw aging", "Ġnob ility", "ĠY us", "Ġinject ing", "f aith", "ĠP ASS", "å º", "Ġret ake", "ĠPR OC", "Ġcat hedral", "b ash", "Ġwrest lers", "Ġpartner ing", "Ġn oses", "Ġ3 58", "Trans form", "am en", "Ġb outs", "ĠId eal", "ĠConstant in", "Ġse p", "ĠMon arch", "att en", "ĠPe oples", "mod ified", "Ġmor atorium", "Ġpen chant", "Ġoffensive ly", "Ġprox ies", "ok ane", "ĠTaiwan ese", "ĠP oo", "ĠH OME", "us ional", "Ġver bs", "ĠO man", "vis ory", "Ġpersu asion", "Ġmult it", "Ġsc issors", "G ay", "ow ay", "oph ysical", "l us", "gn u", "Ġap ocalyptic", "Ġabsurd ity", "Ġplay book", "Ġautobi ography", "I UM", "Ġsne aking", "ĠSim ulation", "pp s", "ell ery", "Plan et", "Ġright fully", "Ġn iece", "ĠN EC", "ĠIP O", "ĠDis closure", "lean or", "ous y", "ST ER", "Ġ28 2", "Cru z", "Ch all", "64 3", "ĠSurv ive", "ĠF atal", "ĠAm id", "ap o", "We apons", "D EN", "7 70", "ĠGreen wald", "Ġlin en", "al os", "Ġpollut ants", "ĠPCI e", "k at", "Ġp aw", "ĠK raft", "C hem", "ĠTermin ator", "Ġre incarn", "Ġ] [", "ĠSe eds", "Ġsilhou ette", "ĠSt ores", "Ġgro oming", "ĠD irection", "ĠIs abel", "ĠBr idges", "ðŁ ij", "E ED", "ĠM orsi", "Ġval ves", "ĠRank ed", "ĠPh arma", "ĠOrgan izations", "Ġpenet rated", "ĠRod ham", "ĠProt oss", "Ġove rest", "Ġex asper", "ĠT J", "Ġ 000000", "Ġtrick le", "Ġbour bon", "WH O", "Ġw retched", "Ġmicrosc opic", "Ġcheck list", "Ġad orned", "R oyal", "Ad minist", "ĠRet irement", "ĠHig hest", "We ather", "ile ge", "Ġincre ments", "ĠC osponsors", "Ġmas se", "ĠS inn", "r f", "Ġh ordes", "as sembly", "75 4", "ĠNat asha", "ĠTY PE", "ĠGEN ERAL", "Ġarr anging", "Ġ40 7", "l ator", "Ġg lean", "Ġdisc redited", "Ġclin icians", "UN E", "Ġachie ves", "ĠEm erson", "com plex", "= [", "Ġprincip ally", "Ġfra il", "p icked", "Ġthan king", "Ġre cl", "ĠL AST", "Ġsupp ressing", "il ic", "Ġantidepress ant", "ĠLis bon", "Ġth or", "Ġsp a", "Ġking doms", "ĠPear ce", "em o", "Ġpl ung", "Ġdiv est", "Ġ ********************************", "b is", "osp els", "ad r", "Sp irit", "hall a", "P ink", "end ez", "Ġresurrect ed", "esc ape", "ĠRosen stein", "Ġge ological", "Ġnecess ities", "Ġcarn iv", "ĠE lys", "ĠBar ney", "Ġ29 6", "dig y", "ST ON", "D OWN", "Ġmil estones", "Ġk er", "Ġdismant ling", "Ġre prim", "Ġcross ings", "19 45", "Ġpatri archy", "Ġblasp hemy", "Ġ3 59", "met ry", "ĠOb esity", "ĠDiff erences", "bl ocking", "ãĥķ ãĤ¡", "ich ita", "ĠSab ha", "ph alt", "ĠCol o", "ual a", "effic ients", "ĠMed ina", "con sole", "55 7", "ĠHann ibal", "ĠHab it", "ĠF ever", "Ġthen ce", "Ġsyn agogue", "Ġessential s", "Ġw ink", "ĠTr ader", "ID A", "ĠSp oiler", "ĠIceland ic", "ĠHay ward", "Ġpe ac", "Ġmal ice", "Ġflash back", "Ġth w", "Ġlay offs", "L iquid", "Ġtro oper", "Ġh inge", "ĠRead ers", "Ph ill", "ĠB auer", "Cre ated", "Ġaud its", "ac compan", "Ġunsus pecting", "ier a", "6666 6666", "Ġbro ch", "Ġapprehend ed", "ĠM alk", "cer ning", "ĠCod ex", "O VER", "M arsh", "ĠD eng", "ĠExp ression", "Ġdisrespect ful", "Ġasc ending", "t ests", "ĠPlaint iff", "ster y", "ĠAl ibaba", "din and", "ĠDem psey", "Applic ations", "mor al", "Ġthrough put", "Ġquar rel", "Ġm ills", "Ġhe mor", "ĠC ASE", "terror ist", "st im", "ifest yle", "ro zen", "CE PT", "Ar k", "u ci", "lect ic", "Ġirrit ating", "she ets", "A y", "Ġrede emed", "Ġhorn y", "ĠTe ach", "ĠS ear", "dem ocracy", "4 65", "ĠRest ore", "Ġstand by", "ĠP is", "iff in", "Ġsleep y", "Ġextr ater", "Ġcompl iments", "Fram eworks", "Ġinstall s", "Ġb anging", "sur face", "found land", "Ġmetaph ysical", "Ġ28 3", "oul s", "dev ices", "Ar gs", "ĠSac rifice", "ĠMcC orm", "es on", "Cons ervative", "ĠM ikhail", "see ing", "is ively", "ĠRo oms", "ĠGener ic", "Ġenthusi astically", "Ġgri pped", "Ġcomed ic", "ĠElectric ity", "Ġgu errilla", "Ġdec oration", "ĠPerspect ive", "Ġconsult ations", "Ġun amb", "Ġplag iar", "Ġmagic ian", "Ġe rection", "ĠTour ism", "or ied", "ro xy", "11 00", "T am", "Ī è", "Î ³", "× ª", "ĠPred ators", "Nit rome", "Ġtelesc opes", "project s", "Ġun protected", "Ġst ocked", "ĠEnt reprene", "nex pected", "Ġwast ewater", "V ill", "Ġint imately", "Ġi Cloud", "ĠConst able", "Ġspo of", "Ġne farious", "Ġfin s", "Ġcens or", "ĠMod es", "ĠEs per", "ar bon", "Ġinter sections", "Ġlaud ed", "Ġphys i", "Ġgener ously", "ĠThe Nitrome", "ĠTheNitrome Fan", "Ġar isen", "ĠÙ Ī", "Ġg lands", "ĠPav ilion", "ĠGu pta", "Ġuniform ly", "Ġr amps", "ri et", "ĠWH EN", "ĠVan essa", "Ġrout ed", "Ġlim p", "ĠC PI", "p ter", "int uitive", "Ġv aping", "Ġexperiment ed", "ĠOlymp us", "ĠAm on", "Ġsight ing", "Ġinfiltr ate", "ĠGentle man", "Ġsign ings", "ĠMe ow", "ĠNav igation", "che cks", "4 33", "Ġel apsed", "ĠBulg arian", "esp ie", "ĠS OM", "d uring", "Ġsp ills", "anc a", "ĠPly mouth", "M AL", "Ġdomest ically", "ĠWater gate", "ĠF AM", "k illed", "ed ited", "ĠYour self", "Ġsynchron ization", "ĠPract ices", "ST EP", "Ġgen omes", "ĠQ R", "not ice", "Ġloc ating", "z in", "Ġ3 29", "al cohol", "Ġk itten", "V o", "Ġr inse", "Ġgrapp le", "ĠSc rew", "ĠD ul", "A IR", "Ġle asing", "ĠCaf é", "Ġro ses", "ĠRes pect", "Ġmis lead", "Ġperfect ed", "Ġnud ity", "Ġnon partisan", "ĠCons umption", "Report ing", "Ġnu ances", "Ġdeduct ible", "ĠSh ots", "Ġ3 77", "Ġæ ľ", "ano oga", "Ben ef", "ĠB am", "ĠS amp", "if ix", "Ġgal van", "ĠMed als", "rad ius", "Ġno bles", "Ġe aves", "igr ate", "K T", "ĠHar bour", "u ers", "Ġrisk ed", "re q", "Ġneuro t", "get table", "ain a", "Rom ney", "Ġunder pin", "Ġlo ft", "ĠSub committee", "ĠMong ol", "b iz", "Ġmanif ests", "ass isted", "ĠG aga", "Ġsy nergy", "Ġreligious ly", "ĠPre f", "ĠG erry", "T AG", "ĠCho i", "4 66", "beh ind", "ĠO u", "Gold Magikarp", "Ġhemor rh", "R iver", "Ġtend on", "Ġinj ure", "ĠF iona", "Ġp ag", "Ġag itation", "|| ||", "ur an", "ĠE SA", "Ġest eem", "Ġdod ging", "Ġ4 12", "r ss", "Ġce ases", "ex cluding", "Ġint akes", "Ġinsert s", "Ġemb old", "ĠO ral", "up uncture", "4 11", "ĠUn ified", "ĠDe le", "Ġfurn ace", "ĠCoy otes", "ĠBr ach", "L abor", "Ġhand shake", "Ġbru ises", "Gr ade", "éĹ ĺ", "ĠGram my", "ile en", "St ates", "ĠScandinav ian", "ĠKard ash", "8 66", "Ġeffort lessly", "ĠDI RECT", "ĠTH EN", "ĠMe i", "ert ation", "19 68", "Ġgro in", "w itch", "Requ irements", "98 5", "Ġroof s", "Ġest ates", "ĠH F", "Ġha ha", "Ġdense ly", "ĠO CT", "Ġpl astics", "Ġincident ally", "ĠTr acks", "ĠTax es", "Ġch anted", "Ġforce ful", "ĠBie ber", "ĠK ahn", "K ent", "ĠC ot", "lic ts", "F ed", "Ġhide ous", "ĠVer d", "ĠSynd icate", "ĠIl legal", "J et", "ĠD AV", "re asonable", "c rew", "Ġfundamental ist", "Ġtruth ful", "ĠJ ing", "Ġl il", "Ġdown ed", "Ġen chanted", "ĠPolic ies", "ĠMcM aster", "ĠH are", "ides how", "Ġpar ams", "en cers", "gorith m", "Ġallow ances", "Ġturb ulent", "Ġcomplex ities", "ĠK T", "Ġ3 37", "ĠGen etic", "F UN", "D oug", "t ick", "Ġg igs", "ument hal", "Ġpatriarch al", "Ġcal c", ", ...", "Ġc out", "ĠGu an", "Ġpath ological", "ĠR ivals", "Ġunder rated", "Ġflu orescent", "ĠJ iu", "arna ev", "ĠQu an", "Ġ4 29", "Ġ à¨", "M ario", "Con struct", "ĠC itation", "ĠR acial", "ĠR SA", "ĠF idel", "Ġ3 95", "Person ally", "C ause", "à »", "rad ical", "in en", "Ġvehement ly", "ĠPap a", "Ġintern ship", "Ġfl akes", "ĠRe ck", "Luck ily", "B ra", "20 20", "rav ings", "R N", "W onder", "Ser iously", "Ġre usable", "Ġpoll uted", "ĠP eng", "le igh", "ind le", "Ġcircuit ry", "ĠMad onna", "ĠB ART", "Res idents", "att ribute", "Phil adelphia", "Cl ub", "Ġplan ner", "Ġfr antically", "Ġfaith fully", "ĠTerrit ories", "ĠL AT", "ĠAnders en", "an u", "ĠP ARK", "ĠS ora", "i age", "ĠPlay offs", "ĠG CC", "4 27", "Ġab norm", "ĠL ever", "Ġdisob edience", "As ync", "ĠShe a", "V ert", "Ġsk irts", "ĠSaw yer", "x p", "Ġwors ening", "Ġsc apego", "ĠAng le", "oth al", "Ġtro ve", "ĠSt y", "ĠN guyen", "mar ine", "ide on", "Dep ths", "Bl og", "ĠIll uminati", "Ġtract s", "Ġorgan ise", "Ġo str", "F s", "Ġlever aging", "ĠD aredevil", "as ar", "Ġl ang", "Ġex termin", "urs ions", "ĠRom o", "ãĤ¤ ãĥĪ", "Ġcont ended", "Ġencounter ing", "ĠTable t", "ĠAltern ate", "sk ill", "Ġswe ets", "Ġco hesive", "cap acity", "Ġrep ud", "Ġl izard", "ro o", "Ġpilgr ims", "ĠR uff", "ĠInstr ument", "ĠLog o", "uit ous", "E H", "Ġsales man", "Ġank les", "L ed", "ĠPat ty", "ud os", "Own er", "Ġdiscrep ancies", "k j", "M U", "Ġuncond itional", "Dragon Magazine", "i ard", "O ak", "ĠConvers ation", "be er", "ĠOs aka", "D elta", "us ky", "Ġsecret ion", "Ġpl aza", "Ġm ing", "Ġde pletion", "ĠM ous", "ĠI TS", "ĠH imal", "ĠFle ming", "Ġcyt ok", "ĠH ick", "Ġbat ters", "ĠInt ellectual", "6 75", "é r", "IS ION", "ĠQu entin", "ĠCh apters", "ih adi", "Ġco aster", "WAY S", "ĠL izard", "ĠY or", "and ering", "S kin", "ha ust", "ab by", "Ġportray ing", "Ġwield ed", "d ash", "Ġprop onent", "Ġr ipple", "Ġgrap hene", "Ġfly er", "Ġrec urrent", "Ġdev ils", "Ġwater fall", "æĺ ¯", "go o", "Text Color", "Ġtam pering", "IV ES", "TR UMP", "ĠAb el", "ĠS AL", "ĠHend ricks", "ĠLu cius", "b ots", "Ġ40 96", "IST ORY", "Gu est", "ĠN X", "in ant", "Ben z", "ĠLoad ed", "ĠCle ver", "t reatment", "Ġta vern", "Ġ3 39", "ĠT NT", "ific antly", "Tem perature", "F el", "Ġunder world", "ĠJud ges", "Ġ< +", "Ġst ump", "Ġoccup ancy", "Ġab er", "ĠF inder", ") \",", "ĠN unes", "res et", "in et", "ect omy", "Ġwell ness", "ĠP eb", "quart ered", "and an", "Ġneg atives", "ĠTh iel", "ĠCl ip", "ĠL TD", "Ġbl ight", "Ġreperto ire", "K yle", "Ġqu er", "ĠC es", "Ġha pl", "98 9", "ĠTh ames", "isc opal", "Des k", "ivari ate", "ĠEx cellence", "found ation", "Ġâ ĩ", "X i", "Ġmyster iously", "esty les", "Ġper ish", "ĠEng els", "ĠDE AD", "09 0", "}} }", "ĠUn real", "Ġrest less", "ID ES", "orth odox", "ĠInter mediate", "Ġdin ners", "ĠTr out", "ĠSe ym", "ĠHall s", "og ged", "Ġtraged ies", "Ġdid nt", "67 6", "Ġail ments", "Ġobserv able", "ĠV ide", "ad apt", "ĠD usk", "Ġprofessional ism", "ĠPres cott", "ĠInd ies", "p ox", "ĠMe hran", "W ide", "Ġend emic", "ĠPar an", "B ird", "Ġped als", "ĠI U", "ĠAdam ant", "ĠH urt", "Ġcorrel ates", "urd en", "Ġspons oring", "cl imate", "ĠUnivers ities", "ĠK not", "enn es", "ĠDam ian", "ĠAx el", "S port", "Ġbar b", "ĠS no", "sh own", "ste en", "ud ence", "Ġnon violent", "Ġhom ophobia", "Ġbiom ass", "ĠDet ail", "Ġsrf N", "ĠT une", "accompan ied", "I ENCE", "Al bert", "ĠMong o", "z x", "ĠCer berus", "or bit", "c ens", "Ġsl ay", "SH ARE", "H Y", "Ġb rawl", "ĠPro be", "Ġnonex istent", "ĠClare nce", "ĠBlack burn", "Ġport als", "ĠR ita", "ĠRem ain", "ĠLe vant", "Ġtrick ed", "ĠF erry", "aver ing", "ĠStraw berry", "ĠAn swers", "Ġhorrend ous", "ĠA man", "Supp lement", "ĠT oad", "Ġpe eled", "Ġman oeuv", "ĠU zbek", "mond s", "ĠH ector", "Ġ40 2", "pe es", "fix es", "Ġd j", "Ġres umes", "Ġaccount ant", "Ġadvers ity", "Ġham pered", "ĠL arson", "Ġd oping", "part s", "H ur", "Ġbe arded", "Ġy r", "ĠPlug in", "å¥ ³", "Ġ/ **", "rol ley", "Ġwaters hed", "ĠSub mission", "if lower", "AS C", "Ġcho ir", "Ġsculpt ures", "m A", "incre asing", "ai i", "Ġsne akers", "Ġconfront s", "ĠEle phant", "ĠEl ixir", "Ġrec al", "ĠT TL", "w idget", "ĠW ax", "ĠGr ayson", "Ġha irst", "Ġhumili ated", "ĠWAR N", "app iness", "ĠT TC", "F uel", "Ġpol io", "Ġcomplex es", "Ġbab e", "ĠX IV", "P F", "). [", "P arts", "Ġ4 35", "M eg", "ĠY ards", "ĠAL P", "Ġy ells", "Ġprin ces", "Ġbull ies", "ĠCapital ism", "ex empt", "FA Q", "ĠSp onge", "ĠAl a", "Ġpleas antly", "Ġbu f", "Ġden ote", "Ġunp ublished", "Ġkne eling", "asc a", "Ġl apse", "al ien", "99 4", "Ġrefere es", "ĠLaw yers", "S anta", "Ġpuzz ling", "ĠProm etheus", "ĠPh araoh", "ĠDel ay", "Ġfacilit ates", "ĠC ES", "Ġjew els", "Ġbook let", "ond ing", "Ġpolar ization", "ĠMor an", "ĠSal ad", "ĠS OS", "ĠAdv ice", "PH OTOS", "IC AN", "iat ures", "ex press", "ĠWonder land", "ĠC ODE", "ĠCL ASS", "9 75", "Ġg rep", "ĠD iesel", "ĠGl ac", "! ?\"", "Ġr m", "o ine", "disc rimination", "ĠN urse", "m allow", "Ġv ortex", "ĠCons ortium", "Ġlarge Download", "stra ight", "augh lin", "G rad", "Ġpublic ized", "ĠW aves", "ĠRed d", "Ġfest ivities", "ĠM ane", "ar ov", "Ġfleet ing", "ĠDr unk", "ug en", "C ele", "Ġchromos omes", "ĠD OT", "-+-+ -+-+", "Ġbus iest", "ĠBe aver", "Sy rian", "ĠK yr", "k as", "ĠCross Ref", "19 50", "76 01", "Ġrepe aling", "ĠWin ners", "ĠMac ro", "ĠD OD", "bl ance", "S ort", "64 1", "Ġmet re", "ĠD irk", "Ġgo ggles", "Ġdraw backs", "Ġcomplain ant", "Ġauthor izing", "Ġantit rust", "oper ated", "Ġm ah", "Ġexagger ation", "Am azing", "ĠSer aph", "Ġha ze", "w ow", "Ġextingu ished", "Ġcan yon", "ĠB osh", "Ġv ents", "Ġsc rape", "Cor rect", "4 26", "Ġav g", "Dem and", "ĠâĪ ¼", "Ġmicrobi ota", "\"} ],\"", "ĠSt ev", "B io", "ĠPlan es", "Ġsuggest ive", "Ġdec ipher", "ĠRefuge e", "ĠKe jriwal", "ĠGreen peace", "Ġdecl ass", "ĠSound ers", "Ġth o", "Ġdec rypt", "Ġbr ushing", "ĠJane iro", "ip op", "S i", "8 77", "ĠGeoff rey", "Ġc pu", "ĠHaz el", "Ġview points", "Ġcris py", "ĠNot ification", "Ġsold er", "ĠMod est", "ĠHem isphere", "Ġcass ette", "in cludes", "Ġident ifiers", "ĠC ALL", "in cent", "T odd", "ĠSwe ep", "Ġ3 34", "b oss", "Ġsm ir", "gin x", "Ġtown ship", "Ġg rieving", "ĠMos que", "Net flix", "AS ED", "ĠMillenn ials", "oc om", "19 67", "Ġbold ly", "s leep", "Ġes che", "arij uana", "Ġsw irl", "ĠPen al", "Ġneglig ent", "ĠStephen son", "K ER", "ĠZ oro", "ris is", "Ġlocal ization", "ĠSeym our", "ĠAng lic", "red itation", "prot ection", "ĠPa ige", "Ġo mit", "ĠR ousse", "ĠT ub", "Ġinv itations", "t ty", "Ġm oss", "ph ysical", "C redits", "Ġan archy", "Ġchild care", "Ġl ull", "ĠM ek", "ĠL anguages", "lat est", "ĠSan ford", "Ġus ability", "Ġdiff use", "ĠD ATA", "Ġsp rites", "ĠVeget a", "ĠProm otion", "ãĥ¼ ãĤ¯", "rict ing", "z ee", "Tur kish", "ĠTD s", "pro ven", "57 1", "Ġsmug glers", "707 10", "Ġreform ed", "ĠLo is", "Ġun fl", "ĠWITH OUT", "ĠReturn ing", "ann ie", "ĠTom as", "Fr anc", "ĠProf it", "ĠSER V", "ĠR umble", "ik uman", "es an", "Ġt esters", "Ġgad get", "Ġbrace let", "ĠF SA", "comp onent", "Ġparamed ics", "Ġj an", "ĠRem em", "ĠSk inner", "Ġl ov", "ĠQu ake", "rom a", "Ġfl ask", "Pr inc", "Ġover power", "Ġlod ging", "ĠK KK", "ret te", "Ġabsor bs", "w rote", "Ġ ,\"", "K ings", "ĠH ail", "ĠFall ing", "xt ap", "ĠHel ena", "ire ns", "L arry", "Ġpamph let", "ĠC PR", "G ro", "ĠHirosh ima", "Ġhol istic", "\". [", "Ġdet achment", "Ġas pire", "Ġcompl icit", "ĠGreen wood", "Ġresp awn", "ĠSt upid", "ĠFin ished", "f al", "b ass", "Ġab hor", "Ġmock ery", "ĠFe ast", "VID EO", "Ġcon sec", "ĠHung ry", "P ull", "ĠH ust", "it ance", "? ãĢį", ") --", "ĠPar allel", "con v", "4 69", "ha ar", "w ant", "P aper", "m ins", "ĠTor o", "ĠTR UMP", "ĠR ai", "D W", "ĠW icked", "ĠL ep", "Ġfun ky", "Ġdetrim ent", "ios is", "ache v", "Ġde grade", "im ilation", "Ġret ard", "Ġfrag mentation", "Ġcow boy", "ĠY PG", "ĠH AL", "Parent s", "ĠS ieg", "ĠStra uss", "ĠRub ber", "× IJ", "Fr ag", "Ġp t", "Ġoption ally", "ĠZ IP", "ĠTrans cript", "ĠD well", "88 2", "M erc", "ĠM OT", "ãĥ¯ ãĥ³", "Ġhun ts", "Ġexec utes", "In cludes", "Ġacid ic", "ĠRespons ibility", "ĠD umb", "we i", "And erson", "ĠJas per", "ight on", "abs olutely", "Ad ult", "Ġpl under", "Mor ning", "ĠT ours", "ĠD ane", "Î º", "ĠT EST", "ĠG ina", "Ġcan ine", "aw an", "Ġsocial ists", "ĠS oda", "Ġimp etus", "ĠSupplement ary", "oli ath", "ĠKinn ikuman", "mitted ly", "second s", "Ġorganis ers", "Ġdocument aries", "Vari able", "GRE EN", "Ġres orts", "Ġbr agging", "Ġ3 68", "Art ist", "w k", "bl ers", "Un common", "ĠRet rieved", "Ġhect ares", "Ġtox in", "r ank", "Ġfaith s", "ĠG raphic", "Ġve c", "ĠL IA", "Af rican", "Ġard ent", "end iary", "L ake", "ĠD OS", "cient ious", "ĠOk awaru", "ĠAll y", "ĠTim eline", "D ash", "ĠI c", "contin ue", "Ġt idy", "Ġinstinct ively", "ĠP ossibly", "ĠOut door", "ĠWould n", "Ġl ich", "ĠBr ay", "ĠA X", "Ġà ī", "Ġ+ #", "\\ '", "Direct ory", "ab iding", "Ġf eral", "ic ative", "but t", "Ġper verse", "S alt", "Ġwar ped", "Ġnin eteen", "Ġcabin ets", "Ġsrf Attach", "ĠSl oan", "Ġpower ing", "reg ation", "F light", "se vere", "Ġst ren", "Ġc og", "ap ache", "Ġâ Ŀ", "Ġcaf eteria", "p aces", "ĠGrim oire", "uton ium", "Ġr aining", "Ġcir cling", "Ġlineback ers", "c redit", "Ġrep atri", "ĠCam den", "lic ense", "Ġly ric", "Ġdescript or", "Ġval leys", "Ġre q", "Ġback stage", "ĠPro hibition", "ĠK et", "Op ening", "S ym", "æĸ ¹", "Ġserv ings", "Ġoverse en", "Ġaster oids", "ĠMod s", "ĠSpr inger", "ĠCont ainer", "è »", "ĠM ens", "Ġmult im", "Ġfire fighter", "pe c", "Ġchlor ine", "Ð ¼", "end i", "Ġsp aring", "Ġpolyg amy", "ĠR N", "ĠP ell", "Ġt igers", "Ġflash y", "ĠMad ame", "S word", "Ġpref rontal", "Ġpre requisite", "uc a", "Ġw ifi", "Ġmiscon ception", "Ġharsh ly", "ĠStream ing", "ot om", "ĠGiul iani", "foot ed", "Ġtub ing", "ind ividual", "z ek", "n uclear", "m ol", "Ġright ful", "49 3", "Ġspecial ization", "Ġpassion ately", "ĠVel ocity", "ĠAv ailability", "T enn", "Ġl atch", "ĠSome body", "Ġhel ium", "cl aw", "Ġdi pping", "XX X", "Ġinter personal", "7 10", "Ġsub ter", "Ġbi ologists", "ĠLight ing", "Ġopt ic", "Ġden im", "end on", "ĠC orm", "Ġ3 41", "ĠC oup", "Ġfear less", "Ġal ot", "ĠCliff ord", "ĠRun time", "ĠProv ision", "up dated", "lene ck", "Ġneur on", "Ġgrad ing", "ĠC t", "sequ ence", "in ia", "con cept", "Ġro aring", "ri val", "ĠCaucas ian", "Ġmon og", "key es", "Ġappell ate", "Ġlia ison", "EStream Frame", "ĠPl um", "! .", "Ġsp herical", "Ġper ished", "Ġbl ot", "Ġben ches", "Ġ4 11", "Ġpione ered", "Ġhur led", "Jenn ifer", "ĠYose mite", "Ch air", "Ġreef s", "Ġelect or", "ĠAnt hem", "65 2", "Ġun install", "Ġimp ede", "Ġbl inking", "Ġgot o", "Dec re", "A ren", "Ġstabil ization", "ĠDis abled", "ĠYanuk ovych", "Ġoutlaw ed", "ĠVent ura", "ten ess", "Ġplant ation", "Ġy acht", "ĠHu awei", "Ġsol vent", "Ġgr acious", "Ġcur iously", "Ġcapac itor", "Ġc x", "ĠRef lex", "Ph ys", "ĠC f", "pt in", "cons ervative", "Ġinv ocation", "c our", "F N", "ĠNew ly", "H our", "As ian", "ĠLe ading", "ĠAer ospace", "An ne", "Ġpre natal", "Ġdeterior ating", "H CR", "ĠNorm andy", "ol ini", "ĠAm bro", "9 10", "Ġset backs", "ĠT RE", "Ġs ig", "ĠSc ourge", "59 7", "79 8", "Game play", "Ġm sec", "M X", "Ġprice y", "ĠL LP", "aker u", "Ġover arching", "ĠB ale", "Ġworld ly", "Cl ark", "Ġscen ic", "Ġdisl iked", "ĠCont rolled", "T ickets", "ĠE W", "ab ies", "ĠPl enty", "Non etheless", "Ġart isan", "Trans fer", "ĠF amous", "Ġinf ield", "ble y", "Ġunres olved", "ĠML A", "ãĤ Ĥ", "Cor rection", "Ġdemocr at", "ĠMore no", "ro cal", "il ings", "Ġsail or", "Ġr ife", "h ung", "Ġtrop es", "Ġsn atched", "ĠL IN", "ĠB ib", "ES A", "ĠPre v", "ĠCam el", "run time", "Ġob noxious", "4 37", "Ġsum mers", "Ġunexpl ained", "ĠWal ters", "cal iber", "Ġg ull", "ĠEnd urance", "ä½ ľ", "Ġ3 47", "Ir ish", "Ġaer obic", "Ġcr amped", "ĠHon olulu", "à ©", "us erc", "ec ast", "AC Y", "ĠQu ery", "ãĤ¹ ãĥĪ", "Bet a", "Ġsuscept ibility", "ĠSh iv", "ĠLim baugh", "Ġà ĸ", "ĠN XT", "ĠM uss", "ĠBrit ons", "ES CO", "EG IN", "Ġ% %", "Ġsec ession", "ĠPat ron", "ĠLu a", "n aires", "ĠJPM organ", "us b", "ocy te", "Ġcouncill ors", "ĠLi ang", "f arm", "Ġnerv ously", "Ġattract iveness", "ĠK ov", "j ump", "Pl ot", "Ġst ains", "ĠStat ue", "ĠApost les", "he ter", "ĠSUP PORT", "Ġoverwhel m", "Y ES", "Ġ29 1", "d ensity", "Ġtra pping", "M it", "Ġf ide", "ĠPam ela", "atl antic", "Dam n", "Ġp ts", "OP A", "Ġserv icing", "Ġoverfl owing", "ul o", "ĠE rit", "t icket", "light ing", "ĠH mm", "ãĥ¼ ãĥ«", "im oto", "Ġchuck le", "4 23", "ãģ ķ", "sh ape", "Ġque ues", "Ġanch ors", "ãĤ¼ ãĤ¦ãĤ¹", "F er", "Ġaw oke", "Ġ6 66", "h ands", "Ġdiver gence", "Ġ50 5", "T ips", "Ġdep ot", "Ġske w", "ĠDel iver", "op ot", "Ġdiv ul", "ĠE B", "uns igned", "ĠUn i", "X box", "Ġfor ks", "Ġ7 02", "å ¯", "Ġpromot ers", "ĠV apor", "Ġlev ied", "sl ot", "Ġpig ment", "Ġcyl inders", "C RE", "Ġsn atch", "Ġperpet ually", "Ġl icking", "ĠFe et", "ĠKra ken", "ĠHold en", "ĠCLS ID", "m r", "Ġproject or", "Ġden otes", "Ġchap el", "ĠTor rent", "b ler", "R oute", "ĠDef endant", "ĠPublisher s", "ĠM ales", "ĠInn ov", "ĠAg ility", "rit er", "ty mology", "st ores", "L ind", "Ġf olly", "ĠZur ich", "B le", "Ġnurt ure", "Ġcoast line", "uch in", "D omin", "Ġfri vol", "ĠCons olid", "res ults", "M J", "Ġphyl ogen", "Ġha uled", "ĠW iley", "ĠJess ie", "ĠPrep are", "ĠE ps", "Ġtreasure r", "I AS", "Ġcolon ists", "Ġin und", "ĠWW F", "ĠCon verted", "6 000", "out side", "ĠApp earance", "ĠRel ic", "ĠM ister", "s aw", "Ġresult ant", "Ġadject ive", "ĠLaure l", "ĠHind i", "b da", "Pe ace", "Ġreb irth", "Ġmembr anes", "Ġforward ing", "Ġcoll ided", "ĠCar olyn", "K ansas", "5 99", "ĠSolid GoldMagikarp", "Be ck", "Ġstress ing", "ĠGo o", "ĠCooper ative", "Ġf s", "ĠAr chie", "L iter", "ĠK lopp", "J erry", "Ġfoot wear", "War ren", "Ġsc ree", "h are", "Under standing", "P ed", "Ġanth ology", "ĠAnn ounce", "M ega", "Ġflu ent", "Ġbond age", "ĠDisc ount", "il ial", "C art", "ĠNight mares", "Sh am", "ĠB oll", "uss ie", "H ttp", "Atl anta", "Ġun recogn", "ĠB id", "Ġunder grad", "Ġforg iving", "ĠGl over", "AAAA AAAA", "4 45", "V G", "pa io", "kill ers", "Ġrespons ibly", "Ġmobil ize", "Ġeffect ed", "ĠL umin", "Ġk ale", "Ġinfring ing", "ann ounced", "Ġf itt", "b atch", "ĠT ackle", "ĠL ime", "ĠAP P", "uke mia", "Ġrub y", "Ġex oner", "ĠCas ual", "0 70", "Ġpel vic", "Ġautom ate", "ĠK ear", "ĠCoast al", "Ġcre ed", "Ġbored om", "ĠSt un", "ri ott", "Ĥ İ", "Ġregener ate", "Ġcomed ians", "ĠOP ER", "Sp ons", "id ium", "on is", "L ocated", "05 7", "Ġsusp ense", "ĠD ating", "C ass", "Ġneoc ons", "ĠShin zo", "Ġaw oken", "ch rist", "ĠMess ages", "att led", "ĠSpr ay", "ĠSp ice", "C W", "Ġshield ing", "ĠG aul", "Am id", "Ġparam ilitary", "Ġmult if", "ĠTan ner", "il k", "Ġgodd amn", "g ements", "Ġbe friend", "m obi", "Ġ3 88", "fold er", "acc a", "Ġins in", "g ap", "N ev", "fif th", "Ġpsychiat ry", "b anks", "TH IS", "Ġhar b", "ac qu", "Ġfac ade", "ĠPower Point", "80 3", "Ġbl uff", "Sh ares", "Ġfavor ing", "El izabeth", "Ãį Ãį", "Ġr anger", "77 2", "ĠAr che", "h ak", "ĠGen etics", "ĠF EMA", "Ġev olves", "Ġest e", "ĠP ets", "ĠM é", "ĠInterest ing", "ĠCanter bury", "ch apter", "ĠStar fleet", "Sp anish", "Ġdraw back", "ĠNor wich", "9 70", "n orth", "ag anda", "Ġtransform ative", "ram ids", "bi ology", "ad ay", "Ġpropag ation", "ĠGam ma", "ĠDen ise", "ĠCalcul ator", "ent imes", "ĠB ett", "Ġapp endix", "ĠHD D", "AK ING", "Ġst igmat", "Ġhol ster", "Ġord inarily", "Ch ance", "ĠCont rary", "Ġad hesive", "Ġgather s", "6 12", "re au", "ony ms", "ew ays", "Ġindu ces", "Ġinterchange able", "se m", "Wh it", "Ġtr ance", "Ġincorpor ation", "ĠExt ras", "Fin ancial", "Ġawkward ly", "ĠStur geon", "ĠH Y", "Norm ally", "ĠEnd ing", "ĠAss ist", "enc rypted", "Ġsub jug", "Ġn os", "Ġfan atic", "C ub", "C U", "?\" .", "Ġirre versible", "å Ĥ", "03 1", "ĠH AR", "sp read", "ul ia", "= $", "Sc ope", "L ots", "Ġlif estyles", "ol on", "Ġf eds", "Ġcongrat ulate", "web kit", "Ġindist inguishable", "ĠSw ing", "Ġcommand ments", "qu ila", "ab ella", "m ethyl", "ann abin", "Ġo vere", "Ġlob ster", "ĠQU EST", "ĠCONT IN", "bern atorial", ":::: ::::", "ĠTra ve", "ĠSam oa", "AN I", "75 2", "Ð ´", "userc ontent", "ĠMod erate", "y eah", "ĠK itt", "Ġwe e", "Ġstuff ing", "ĠInter vention", "ĠD ign", "Ġware houses", "ĠF iji", "Ġpel lets", "Ġtake away", "ĠT ABLE", "ĠClass ical", "col lection", "Ġland fall", "ĠMus cle", "Ġsett les", "ĠAD V", "Ġ3 44", "L aura", "Ġf ared", "ĠPart ial", "4 36", "oss ibility", "ĠD aly", "ĠT arant", "ĠFu ji", "am l", "c ence", "55 1", "ĠProced ures", "ĠO CD", "ĠU D", "t in", "Q UI", "ach o", "4 38", "Ġgl itches", "Ġenchant ment", "Ġcalcul ates", "IR O", "ĠH ua", "alys es", "ĠL ift", "um o", "Ġle apt", "Ġhypothes ized", "ĠGust av", "it ans", "VERS ION", "æ ł", "Rog er", "Ġr and", "ĠAd apter", "Ġ3 31", "ĠPet ition", "k ies", "M ars", "Ġunder cut", "ze es", "ĠLy ons", "ĠDH CP", "Miss ing", "Ġretire es", "Ġins idious", "el i", "> )", ". ãĢį", "Ġfinal ists", "ĠA ure", "Ġacc user", "Ġwas tes", "ĠY s", "ĠL ori", "Ġconstitu encies", "Ġsupp er", "Ġmay hem", "or ange", "Ġmis placed", "Ġmanager ial", "Ġex ce", "ĠCL I", "Ġprim al", "ĠL ent", "Cry stal", "h over", "ĠN TS", "end um", "Ġd w", "ĠAl c", "n ostic", "Ġpres erves", "ĠTs arnaev", "Ġtri pled", "rel ative", "Arc ade", "k illing", "ĠW EEK", "ĠH anna", "D ust", "Com pleted", "ģ «", "Ġappro ves", "ĠSur f", "ĠLuther an", "ven ants", "Ġrobber ies", "we ights", "soft ware", "at ana", "ug al", "Ġgrav y", "ĠC ance", "OLOG Y", "ly ak", "Ton ight", "Ġunve il", "Ġ19 04", "ĠMin ion", "ent ious", "st ice", "pack ages", "ĠG EAR", "Ġg ol", "ĠHutch inson", "ĠProf ession", "ĠG UN", "ĠDiff erence", "ĠTsuk uyomi", "ĠLes bian", "6 70", "Ġfug itive", "ĠPlan etary", "-------------------------------- ------------------------", "Ġacc rued", "Ġch icks", "Ġsto pp", "Ġblock ers", "C od", "Ġcomment ers", "ĠSomew here", "ĠPhot ographer", "the me", "Ġmay oral", "w u", "Ġanten nas", "Ġrev amped", "ĠSubject s", "it é", "im ura", "Ġentr ances", "liter ally", "Ġten ets", "ĠO MG", "ĠMP H", "ĠDon key", "ĠOff ense", "Ġ\" +", "Sn ap", "ĠAF B", "Ġan imate", "ĠS od", "His panic", "Ġinconsist ency", "D b", "F Y", "Ex port", "Ġa pe", "Ġpear l", "ib el", "ĠPAC s", "Ġ{ \\", "Ġact u", "ĠHS BC", "camp us", "Ġpay off", "Ġde ities", "ĠN ato", "ou ple", "Ġcens ored", "ĠCl ojure", "Ġconf ounding", "en i", "Ġreck on", "op he", "Ġspot ting", "Ġsign ifies", "Ġprop el", "Ġfest ive", "S uggest", "Ġpled ging", "ĠB erman", "Ġrebell ious", "Ġovershadow ed", "Ġinfiltr ated", "j obs", "67 2", "Ġscal able", "Ġdomin ion", "ĠNew foundland", "ĠMead ow", "Ġpart itions", "AM I", "Ġsupplement ary", "str ument", "Ġhair y", "Ġperpet uate", "Ġnuts hell", "ĠPot ato", "ĠHob bit", "Ġcur ses", "Flo at", "Ġquiet er", "Ġfuel ing", "Ġcaps ules", "ĠL ust", "ĠH aunted", "Exec utive", "Ġchild birth", "G re", "Ġrad iant", "å İ", "Ġm alls", "Ġin ept", "ĠWarrant y", "Ġspect ator", "E h", "t hens", "Ġculmin ating", "æ ©", "ary a", "ãĤ ®", "ilit arian", "ĠOR IG", "ĠSp ending", "pt ives", "ĠS iren", "ĠRec ording", "ay ne", "Ġv im", "Ġspr ang", "T ang", "ĠM FT", "mor ning", "ĠWe ed", "m peg", "cess ion", "ĠCh ung", "7 30", "w arning", "56 2", "handed ly", "P oor", "P olitics", ": #", "Ġp ian", "Ġfec es", "ĠDocument ation", "Ġban ished", "Ġ3 99", "ĠAR C", "Ġhe inous", "J ake", "ĠAm ir", "way ne", "v re", "os henko", "Ġnotebook s", "Ġfound ational", "Ġmarvel ous", "ixt ape", "Ġwithdraw als", "Ġh orde", "ĠD habi", "is able", "ĠK D", "Ġcontag ious", "ĠD ip", "ĠAr rows", "Ġpronoun s", "Ġmorph ine", "ĠB US", "68 2", "Ġk osher", "fin ished", "ĠInstr uments", "Ġf used", "yd en", "ĠSal mon", "F ab", "aff ected", "K EN", "C ENT", "Dom ain", "Ġpoke mon", "ĠDr inking", "G rowing", "ĠInvestig ative", "ĠA ether", "em i", "Ġtabl oid", "Ġrep ro", "ĠNot withstanding", "ĠBers erker", "Ġdram as", "Ġclich é", "Ġb ung", "ĠU RI", "ĠD os", "0 44", "Ġpast ors", "Ġl s", "Ġac rylic", "aun ts", "Ed ward", "Ġmajor ities", "B ang", "Ġfield ing", "ĠRepl acement", "ĠAl chemy", "pp ard", "ĠRome o", "ĠSan ct", "ĠLav rov", "ib ble", "Inst ruct", "Ġimp ractical", "ĠPlay boy", "ce phal", "Ġsw aps", "Ġk an", "ĠThe o", "Ġillust rating", "Ġdismant led", "ĠTrans gender", "ĠG uth", "UG H", "Ġtriumph ant", "Ġencomp ass", "Ġbook mark", "udd in", "j er", "Ġpred icate", "ES H", "Ġwhen ce", "ĠAB E", "Ġnon profits", "Se qu", "Ġdi abetic", "Ġp end", "Ġheart felt", "sh i", "Ġinter acts", "ĠTele com", "Ġbombard ment", "dep ending", "ĠLow ry", "ĠAd mission", "ĠBl ooming", "ust ration", "ene gger", "B rew", "Ġmol ten", "ĠNer d", "P IN", "âĸ Ģ", "ave ment", "Ġtou red", "Ġco efficients", "ĠTray von", "ans son", "Ġsand y", "t old", "fl ows", "Ġpop ulous", "ĠT inder", "ĠBl iss", "R achel", "Min imum", "Ġcontest ant", "ĠRed uce", "ĠMor se", "ĠGrass ley", "ĠClick er", "Ġexp r", "Ġs incerity", "Ġmar qu", "Ġelic it", "ĠPro position", "ĠDemon ic", "Ġtac os", "G reek", "Ġpost war", "Ġin sofar", "ĠP ork", "Ġ35 2", "doctor al", "walk ing", "Ġmid term", "ĠSam my", "sight ed", "ĠTR ANS", "ic i", "AL D", "ĠUS L", "ĠF ISA", "ĠAm pl", "ĠAlex andra", "ine lli", "Tr ain", "Ġsign ify", "ĠVers us", "Ġob fusc", "Ġk h", "Ġagg ro", "ĠRen ault", "Ġ3 48", "5 18", "ox icity", "0 22", "ĠTw ist", "Ġgoof y", "D ynamic", "Ġbrief ings", "m ight", "8 99", "Ġderog atory", "T ro", "Ġfor ging", "ĠKor an", "ĠMar ried", "ĠBuc s", "Ġpal ate", "ĠCon version", "m able", "4 13", "Ġ( _", "Ġs iph", "ĠN EO", "col lege", "Ġmarg inally", "Ġfl irt", "ĠTra ps", "ĠP ace", "é »Ĵ", "Ġgoalt ender", "Ġforb ids", "Ġcler ks", "ĠT ant", "ĠRobb ins", "ĠPrint ing", "Ġpremie red", "Ġmagn ification", "ĠT G", "ĠR ouse", "ĠM ock", "odynam ics", "Ġpre clude", "ism o", "ĠPul itzer", "Ġaval anche", "ĠK odi", "rib une", "ĠL ena", "Elect ric", "Ġref inery", "Ġend owed", "Ġcounsel ors", "Ġd olphin", "ĠM ith", "Ġarm oured", "hib ited", "Beg in", "ĠP W", "O il", "ĠV or", "ĠShar if", "ĠFraz ier", "est ate", "Ġj ams", "Pro xy", "Ġband its", "ĠPresbyter ian", "ĠPrem iere", "t iny", "ĠCru el", "Test ing", "Ġhom er", "ĠV ERS", "ĠPro l", "ĠDep osit", "ĠCoff in", "Ġsemin ars", "Ġs ql", "ĠDef endants", "Altern atively", "ĠR ats", "ç «", "ethy st", "' >", "Ġiss uer", "58 9", "Ġch aired", "ĠAccess ories", "man ent", "Ġmar row", "ĠPrim ordial", "C N", "Ġlimit less", "ĠCarn age", "Ġund rafted", "q v", "IN ESS", "on ew", "Ġco hesion", "98 7", "Ġne cks", "Ġfootball er", "ĠG ER", "Ġdetect able", "ĠSupport ing", "ĠCS V", "oc ally", "k Hz", "Ġund e", "Ġsh one", "Ġbud ding", "tra k", "Stand ing", "ĠStar craft", "ĠKem p", "Ben ch", "Ġthw arted", "ĠGround s", "ath i", "L isa", "Dial og", "ĠS X", "V ision", "Ġingen ious", "Ù IJ", "Ġfost ering", "ĠZ a", "ĠIn gram", "Ġ\" @", "N aturally", "6 16", "0 35", "ĠF AC", "H mm", "55 4", "Ġacceler ator", "ĠV end", "Ġsun screen", "Ġtuber culosis", "rav iolet", "ĠFunction al", "ĠEr rors", "ed ar", "19 66", "ĠSpect re", "ĠRec ipes", "88 5", "ĠM ankind", "L iverpool", "Ġ| --", "Ġsubst itutes", "ĠX T", "w ired", "Ġinc o", "ĠAf gh", "E va", "ic c", "S ong", "K night", "Ġdilig ently", "ĠBroad cast", "A id", "Ġaf ar", "ĠH MS", "aton in", "ĠGr ateful", "Ġfire place", "ĠOm ni", "e uro", "ĠF RE", "ĠSh ib", "ĠDig est", "t oggle", "Ġheads ets", "Ġdiff usion", "ĠSqu irrel", "ĠF N", "Ġdark ened", "out her", "Ġsleep s", "ĠX er", "gun s", "Ġset ups", "Ġpars ed", "Ġmamm oth", "ĠCur ious", "g ob", "ĠFitz patrick", "ĠEm il", "im ov", "........ .....", "ĠB enny", "Second ly", "Ġheart y", "Ġcons on", "st ained", "Ġgal actic", "cl ave", "Ġplummet ed", "Ġp ests", "Ġsw at", "Ġrefer rals", "ĠLion el", "h oly", "Ġunder dog", "ĠSl ater", "ĠProv ide", "ĠAm ar", "ress or", "å Į", "ong a", "Ġtim id", "Ġp iety", "ĠD ek", "Ġsur ging", "az o", "Ġ6 10", "Ġdes ks", "ĠSp okane", "ĠAn field", "Ġwars hips", "ĠCob ra", "Ġar ming", "clus ively", "ĠBad ge", "ag ascar", "ĠPR ESS", "ĠMcK enzie", "ĠFer dinand", "burn ing", "Af ee", "Ġtyr ann", "ĠI w", "ĠBo one", "100 7", "ĠRe pt", "Ċ Âł", "Ġcar avan", "ĠD ill", "ĠBundes liga", "Ch uck", "Ġheal er", "ãĥ¼ãĥ Ĩ", "ĠH obby", "Ġneg ate", "Ġcrit iques", "section al", "mop olitan", "Ġd x", "Ġouts ourcing", "ĠC ipher", "t ap", "Sh arp", "Ġup beat", "Ġhang ar", "Ġcru ising", "ĠNi agara", "Ġ3 42", "ill us", "ĠS v", "Ġsubt itles", "Ġsqu ared", "Ġbook store", "Ġrevolution aries", "ĠCarl ton", "ab al", "Ut ah", "Ġdesp ise", "ĠU M", "cons ider", "aid o", "Ġc arts", "ĠT urtles", "Tr aining", "Ġhonor ary", " ¢", "Ġtri angles", "4 22", "Ġreprint ed", "Ġgrace ful", "ĠMong olia", "Ġdisrupt ions", "ĠB oh", "Ġ3 49", "Ġdr ains", "Ġcons ulate", "Ġb ends", "Ġm afia", "ur on", "ĠF ulton", "m isc", "Ġren al", "Ġin action", "ck ing", "Ġphot ons", "Ġbru ised", "ĠC odes", "og i", "Ġn ests", "ĠLove ly", "ĠLib re", "ĠD aryl", "Ġ# ##", "S ys", ". ,\"", "Ġfree zes", "est ablishment", "and owski", "Ġcum bers", "ĠSt arg", "ĠBom bs", "Ġleg ions", "Ġhand writing", "Ġgr un", "ĠC ah", "sequ ent", "Ġm oth", "ĠMS M", "Ins ert", "F if", "Ġmot el", "Ġdex ter", "ĠB ild", "hearted ly", "Ġpro pe", "ĠText ure", "ĠJ unction", "ynt hesis", "oc ard", "ĠVer a", "ĠBar th", "Ġμ g", "Ġl ashed", "Ġ35 1", "ĠZ amb", "ĠSt aples", "ĠCort ex", "ĠCork er", "Ġcontinu um", "ĠWR ITE", "unt a", "rid or", "Ġde ems", "0 33", "ĠG OLD", "p as", "Ġrep ressive", "ãĥĨ ãĤ£", "Ġbaff led", "Sc ar", "Ġc rave", "Ġ ______", "Ġentrepreneurs hip", "ĠDirector ate", "Ġ' [", "Ġv ines", "Ġasc ended", "ĠGR OUP", "ĠGood bye", "Ġdo gged", "ãĥ´ ãĤ¡", "Man ufact", "Ġunimagin able", "ri ots", "ier rez", "Ġrel ativity", "ĠCraft ing", "ra ught", "ud en", "c ookie", "Ġassass ins", "Ġdissatisf ied", "ac ci", "Ġcondu it", "Sp read", "ĠR ican", "n ice", "izz le", "Ġsc ares", "ĠWH Y", "ph ans", "5 35", "Ġprot racted", "ĠKrist en", "5 36", "ĠSc rib", "ĠNe h", "Ġtwent ies", "Ġpredic ament", "Ġhandc uffs", "Ġfruit ful", "ĠU L", "ĠLud wig", "Ġatt est", "ĠBre aker", "Ġbi ologically", "ĠDeal er", "Ġrenov ations", "f w", "ess en", "Al ice", "ĠHen ri", "Ġun ilaterally", "ĠS idd", "h ai", "ĠSt retch", "S ales", "Ġcumbers ome", "ĠJ avier", "Ġtrend y", "Ġrot ting", "ĠChall enges", "Ġscra ps", "Ġfac ets", "ĠVer onica", "ĠVer ge", "ĠS ana", "Al ien", "ĠR ih", "Ġrad ial", "ect ar", "Ġ6 30", "cl i", "Mar ie", "Ġwild fire", "ĠCat o", "h ander", "Ġwait ress", "Ġch ops", "ĠS ECTION", "Ġblunt ly", "ĠCat alog", "n ian", "stud y", "Ġpat rolling", "ĠT enth", "nex us", "ĠN ON", "op sy", "Ġsc athing", "s ie", "Ġdeterior ated", "V B", "Naz is", "Ġdep ictions", "Ġauthent icated", "ĠCon ce", "k rit", "Ġpromul g", "ĠL ONG", "U FC", "ĠVis itors", "ĠRec all", "Ġrehab ilit", "ĠSL I", "Ġglac ier", "ĠB ite", "Ġ50 3", "Ġvom it", "Ġfer mented", "ĠKh alid", "Ġgrad ed", "ĠMag icka", "ĠIch igo", "power ful", "ic ators", "75 3", "Ġsh rew", "Ġ35 6", "Ġlegal izing", "Ġall otted", "ĠArch demon", "ith ing", "igg urat", "V OL", "Le od", "Ġo ily", "Ġindu cing", "Ġamy gdala", "Ġadm ins", "ĠAcqu isition", "C AN", "Ġsche matic", "Ġmo an", "ĠCamer oon", "Ġt ink", "Ġmer ry", "Ġbutter flies", "ĠGo ff", "Ġworks pace", "ĠCor ona", "Ġj avascript", "ĠD olphin", "ĠCant or", "4 64", "to e", "AP S", "ĠAg ing", "Ġpadd ed", "ĠZ heng", "ĠHe ld", "Ġest ranged", "Ġ7 70", ". }", "ĠDun ham", "Ġsm okes", "Ġcap itals", "und ai", "Sh in", "ĠFound ing", "Ġent itle", "Ġcenter piece", "D iscover", "Ġthere to", "al ert", "ĠN ou", "ĠAnaly st", "l c", "F H", "FI ELD", "ĠP OV", "gr ay", "Ġar cs", "ĠH OT", "Ġr s", "Ġoblig atory", "ĠArchitect s", "ĠS ven", "ĠF EC", "0 200", "Christ mas", "ĠAlban ia", "rat om", "58 7", "Ġhard ships", "Ġaut os", "ĠCharg es", "Ġap es", "Ġ3 76", "wal let", "Ġintox ication", "Ġgobl in", "Ġ5 70", "++++++++ ++++++++", "ĠYel p", "ĠMag netic", "ĠBr iggs", "R ail", "Ġspawn s", "ĠW iggins", "Ġshowc ased", "Ġres orted", "ub en", "Ġwh ipping", "Ġim itate", "Ġdigest ion", "ĠUS PS", "ĠG est", "Ġye a", "ĠT ight", "ind al", "ic as", "` .", "C AST", "'' ;", "ĠF et", "opath ic", "In valid", "Ġregrett ed", "Ġbro ccoli", "ĠSc ores", "e ve", "Ġpost ings", "Ġaccum ulating", "Ġneed less", "elf th", "Ġmay ors", "Ġsc rib", "Ġanecd otes", "Ġbot ched", "ĠRib bon", "ĠConstant ine", "i uses", "ess es", "Ġdev ise", "Comp ared", "Ġp udding", "Ġg arg", "Ġev oke", "79 7", "Ġdet ox", "9 09", "ĠPie ces", "ĠMcC artney", "Ġmet ast", "ĠK rypt", "P OR", "Ġt ending", "ĠMerch ants", "Pro of", "ĠV arg", "ĠPort able", "ãĥ¼ãĥĨ ãĤ£", "B rain", "25 00", "Ġfol iage", "Ø ¹", "Ġment ors", "ĠA ires", "Ġminimal ist", "Ġing ested", "ĠTro jan", "ĠQ ian", "inv olved", "0 27", "Ġer oded", "RA FT", "Ġbl urry", "M ob", "Ġbuff et", "ĠFn atic", "ae a", "KN OWN", "ĠIn it", "s afety", "en um", "ACT ION", "ĠCrus her", "ĠD ates", "Ġ ................", "c alling", "ak ov", "Ġvent ured", "Ġ5 55", "au ga", "H art", "ĠA ero", "M AC", "Ġthin ly", "Ġar ra", "ST ATE", "ild e", "ĠJac qu", "ĠFem ales", "Ġthe orem", "Ġ3 46", "Ġsmart est", "ĠPU BLIC", "ĠK ron", "ĠB its", "ĠV essel", "ĠTele phone", "Ġdec ap", "Ġadj unct", "ĠS EN", "mer ga", "Ġred acted", "Ġpre historic", "Ġexplan atory", "ĠRun s", "ĠUtt ar", "ĠM anny", "ĠAUTH OR", "ĠUnle ashed", "ĠBow ling", "be ans", "79 3", "Ġunivers es", "Ġsens it", "ĠK ung", "re peat", "ctr l", "Ġp aced", "Ġfull er", "Cl ock", "Ġrec omb", "ĠF aul", "ĠB unker", "Ġpool ed", "Ġan a", "ĠM outh", "LL OW", "hum ane", "Ġbull do", "ĠMicha els", "f am", "Ġwreck ed", "Ġport rays", "ĠWh ale", "ĠH es", "Ġguess es", "ĠBrow se", "ĠL APD", "Ġconsequ ential", "ĠInn ocent", "ĠD RAG", "Ġtrans gress", "ĠO aks", "Ġtri via", "ĠRes on", "ĠA DS", "-- +", "ĠT oll", "Ġgrasp ing", "ĠTHE M", "ĠT ags", "ĠCon clusion", "Ġpract icable", "Ġho op", "Ġunintention ally", "Ġign ite", "ĠM ov", "ur ized", "le hem", "Ter min", "Ġcolour ful", "ĠLin ear", "ĠEll ie", "G y", "Ġman power", "Ġj s", "Ġem oji", "ĠSHAR ES", "_ .", "0000 7", "Ġsophistic ation", "Ġunders core", "Ġpract ise", "Ġbl ob", "op ens", "Uk raine", "Ke eping", "Y C", "J R", "ult imate", "Cl aim", "Ġautom obiles", "99 3", "ste el", "Ġpart ing", "ĠL ank", "... ?", "Ġ38 5", "Ġremem brance", "Ġe ased", "Ġcov ari", "ĠS ind", "Effect ive", "Ġdisse mination", "ĠMo ose", "ĠCl apper", "br ates", "App ly", "Ġinv is", "Ġwors ened", "âĢĶ -", "Ġlegisl ator", "ĠL ol", "ĠRow e", "Ġdealers hip", "um ar", "id ences", "Ġinvestig ates", "Ġc ascade", "Ġbid der", "ĠB EN", "Iron ically", "Ġpres iding", "Ġd ing", "Ġcontrad icted", "Ġshut s", "ĠF IX", "Ġ3 66", "Dist rict", "Ġsin ful", "ĠChar isma", "o ops", "Ġtot ality", "Ġrest itution", "ĠOpt imus", "ĠD ah", "Ġcl ueless", "urn ed", "Ġnut rit", "Ġland owners", "Ġfl ushed", "Ġbroad en", "m ie", "Ġprint ln", "Ġn ig", "ĠCorp us", "J en", "Ġprot o", "ĠWik imedia", "ĠPal o", "C OR", "Ġstory lines", "Ġevangel icals", "ĠDar rell", "Ġrot or", "ĠH W", "sk illed", "ery l", "Ġbe gg", "ĠBl umenthal", "Ġwe aving", "Ġdown wards", "ĠJack et", "ĠANG EL", "Te chnology", "Ġes oteric", "alde hyde", "Ġfur iously", "Ġforeign er", "We ak", "CH O", "ĠH ound", "Exper ience", "ĠPlay station", "ĠM IA", "ĠU ng", "cl oth", "ag all", "Ġcal ming", "iz ens", "St ruct", "ĠW itches", "ĠCeleb ration", "Ġ........ ......", "pt roller", "ĠTC U", "Ġb unny", "ãĥ į", "ut orial", "Ġup scale", "ĠSt a", "ĠCol ossus", "Ġchlor ide", "ĠZ ac", "ĠRe asons", "ĠBrook ings", "ĠWH ITE", "][ /", "ĠL ose", "9 05", "Ġunders ide", "ern els", "Ġv ape", "do zen", "upp et", "ĠST OP", "mat ical", "ĠStat ements", "hed dar", "P AC", "Custom er", "Ġmem os", "ĠP J", "end ars", "ĠLim its", "l augh", "Ġstabil ized", "ĠALE C", "Y A", "Up grade", "al am", "Ġtechn o", "Ġan ew", "fore seen", "Ġcolleg iate", "ĠPy ro", "ĠD ism", "Ġfront line", "Ġammon ia", "I U", "Qu ite", "John ny", "ass in", "G OP", "ĠSt yles", "ĠSovere ign", "acter ial", "5 49", "ĠR IP", "ĠL ists", "Ġ3 64", "ĠRece p", "s ocket", "ĠByr d", "ĠCand le", "An cient", "Ġappell ant", "en forcement", "ace a", "ans ki", "Ġold s", "88 6", "Ġsl urs", "Ġem pires", "Ġbuck le", "Ġalien ation", "ĠAber deen", "Ġunic orn", "Ġoverr iding", "ĠL X", "pp a", "Ġdesp ised", "ĠB ugs", "ĠB ST", "S outhern", "5 33", "Ġhall mark", "ĠPost er", "Ġstem med", "Ġprincip als", "ĠT ECH", "ĠSand wich", "It aly", "Ġche esy", "ĠSet TextColor", "ĠProt ective", "ĠC ohn", "J O", "apt op", "Re ason", "Lead er", "ĠUnder stand", "ĠFr idays", "ĠContin uous", "Ġcl ipping", "ĠR ye", "Ġber th", "tim er", "ann is", "re act", "Ġbuff alo", "ĠPar as", "Ġ6 55", "Ġpres ided", "ĠSun rise", "Ġve ts", "Ġcl oves", "ĠMcC ull", "Stre ngth", "G AN", "Ġill iter", "ĠPric ing", "l é", "Ġresist or", "Ġbr un", "ĠSuff olk", "Ñ ĭ", "ĠL iver", "Re leased", "Ġwhat s", "8 60", "ĠMe asures", "Ġden ouncing", "ĠRy zen", "Ġsou ven", "Ġcareg ivers", "ch ini", "ĠScar lett", "Ġt rough", "Cong ratulations", "Ġtax is", "ĠTrad ition", "j it", "Ġtable top", "Ġhither to", "Ġdis information", "off ensive", "h ra", "ĠDISTR ICT", "Ġcompl icate", "chen ko", "ĠRecon struction", "Ġpalp able", "Ġa usp", "Ġ4 28", "Ġshowc ases", "ĠPublic ation", "know ledge", "inn on", "4 19", "Ġretri eval", "and ers", "Ġref ute", "Ġinqu ired", "g ur", "Ġneg ativity", "Ġcons erve", "Ġafter life", "Ġpres upp", "ĠGill espie", "Ġm t", "ĠD N", "T ap", "Ġper pend", "ĠS my", "does n", "Ġsp illing", "Ġhyp ers", "K ate", "® ,", "ke pt", "ĠP owered", "Ġj a", "ĠK lux", "ard e", "ab an", "Ġ4 44", "Ġflatt ened", "ĠImprove ments", "urg a", "ĠK und", "Ġins cribed", "Ġfac ult", "Ġunpre pared", "ĠCons umers", "Ġsatisf ies", "Ġpul monary", "Ġinf iltration", "Ġex ternally", "Ġcongrat ulations", "ag han", "Ġair liner", "Ġfl ung", "Ġfly ers", "G D", "Ġsnipp ets", "Ġrec ursive", "Ġmaster ing", "L ex", "Ġovert ly", "v g", "Ġluck ily", "Ġenc ro", "ĠLanc et", "ĠAbyss al", "function al", "Ġs ow", "Ġsqu id", "Ġnar ration", "Ġn aughty", "ĠHon our", "ĠSpart ans", "Ġsh atter", "ĠTac oma", "ĠCal ories", "ĠR aces", "Sub mit", "Ġpurpose fully", "w av", "ĠY ok", "F est", "ĠG err", "Met ro", "Ġit iner", "f amous", "Ġ\" {", "in line", "was her", "Iss ue", "ĠCL IENT", "oz o", "Vers ions", "7 25", "ĠGl ock", "Ġshield ed", "ĠPC R", "ENC Y", "ĠWe ld", "ĠSim pl", "Ġredirect ed", "ĠK ham", "Ġ( >", "Ġlab ou", "Ġdi apers", "ss l", "Ġcell ar", "organ isms", "ore sc", "ĠBer ks", "did n", "Sh ipping", "C hest", "Ġund one", "Ġmillion aire", "Ġc ords", "ĠYoung er", "appropri ately", "Ġsequ els", "u ve", "ant icipated", "Ġle wd", "ĠSh irt", "ĠDmit ry", "V eter", "Ġsl aying", "ĠY ar", "Ġcompl ication", "I owa", "ĠEric a", "ĠBL M", "g irlfriend", "b odied", "6 26", "19 63", "Ġintermedi ary", "Ġcons olation", "M ask", "ĠSi em", "ow an", "Beg inning", "Ġfix me", "Ġculmin ated", "Ġcon duc", "ĠVolunte er", "Ġpos itional", "Ġgre ets", "ĠDefin itions", "Ġthink er", "Ġingen uity", "Ġfresh men", "ĠMom ents", "Ġ35 7", "ate urs", "ĠFed Ex", "s g", "69 4", "Ġdwind ling", "ĠBO X", "sel age", "Ġt mp", "Ġst en", "ĠS ut", "Ġneighbourhood s", "Ġclass mate", "f ledged", "Ġleft ists", "Ġclim ates", "ATH ER", "ĠScy the", "ul iffe", "Ġs ag", "Ġho pped", "ĠF t", "ĠE ck", "ĠC K", "ĠDo omsday", "k ids", "Ġgas ped", "Ġmon iker", "ĠL od", "ĠC FL", "t ions", "r ums", "fol ios", "Ġm d", "Ġunc anny", "Ġtrans ports", "ĠLab rador", "Ġrail ways", "Ġappl iance", "ĠCTR L", "æ Ģ", "Pop ulation", "ĠConfeder acy", "Ġunb earable", "Ġdors al", "ĠIn form", "op ted", "ĠK ILL", "Mar x", "Ġhypoc ritical", "q us", "ĠN umerous", "ĠGeorg ian", "ĠAmbro se", "ĠL och", "Ġgu bernatorial", "ĠX eon", "ĠSupp orts", "ens er", "ee ly", "ĠAven ger", "19 65", "Ar my", "Ġju xtap", "Ġcho pping", "ĠSpl ash", "ĠS ustainable", "ĠFin ch", "Ġ18 61", "ict ive", "at meal", "ĠG ohan", "Ġlights aber", "ĠG PA", "ug u", "ĠRE PL", "vari able", "Ġher pes", "Ġdesert s", "ac iously", "Ġsitu ational", "week ly", "ob l", "Ġtext ile", "ĠCorn wall", "Ġcontrace ptives", "ĠA ke", "] -", "ä¹ ĭ", ": ,", "ĠW em", "ĠB ihar", "Ġ' .", "Ġbe re", "Ġanal ogue", "ĠCook ies", "Ġtake off", "Whe el", "Ġmaj estic", "Ġcomm uting", "0 23", "ĠCor pse", "ass ment", "min i", "Ġgor illa", "ĠAl as", "ere e", "Ġacquaint ances", "ĠAd vantage", "Ġspirit ually", "Ġey ed", "pm wiki", "ĠE nder", "Ġtrans lucent", "Ġnight time", "ĠIM AGES", "5 45", "ĠK amp", "ĠFre ak", "Ġ ig", "Port land", "4 32", "ĠM ata", "Ġmar ines", "Ġh ors", "ater asu", "ĠAtt ribution", "Ġ-------- -", "Ġk ins", "ĠBEL OW", "++ +", "Ġre eling", "ol ed", "Ġcl utter", "ĠRel ative", "Ġ4 27", "B US", "Ġa vert", "ĠChe ong", "ĠA ble", "ĠPry or", "Develop er", "Ġen cyclopedia", "ĠUSA F", "ĠG arry", "Sp ain", "Bl ocks", "Ġexp osition", "ĠGamer Gate", "W OR", "Ġstockp ile", "Ġclot hed", "ĠT one", "ĠR ue", "t umblr", "Ġtreacher ous", "Ġf rying", "Ñ Į", "ĠS ph", "Ġrest raints", "Ġemb odies", "ĠG es", "S afety", "Ġnegoti ators", "min ing", "ĠAppalach ian", "L OS", "ĠJenn a", "Ġpass ers", "ç ĭ", "sn ap", "Ġshort en", "creat or", "Ġinn umerable", "uther land", "67 4", "ĠW OM", "ĠAs cend", "ĠArm ory", "ĠTrans action", "K ick", "Ġsuit case", "day Name", "Ġwaste ful", "mar riage", "ĠMcC abe", "ite ch", "ĠO ss", "Cl osure", "ĠTreasure r", "Ġindec ent", "ĠD ull", "Ġresid ences", "19 59", "ĠS ettlement", "Ham ilton", "Ġself ies", "ĠRank ing", "ĠBark ley", "ĠB ore", "ĠW CS", "ĠMar itime", "ĠH uh", "ĠForest ry", "Ġcultiv ating", "ĠBall ard", "Ġg arrison", "ĠSD L", "9 30", "Ġnas cent", "Ġirresist ible", "Ġaw fully", "\\/ \\/", "Ġequ ate", "Ġanthrop ology", "ĠSylv ia", "Ġintest ine", "Ġinnoc uous", "cess ive", "ag ra", "ĠMet roid", "G rant", "8 55", "ģ ĸ", "Ġ\" _", "ãĥĥ ãĥī", "Ġappra isal", "ĠFred dy", "04 6", "Ġ40 6", "Ġ18 30", "Ġd ocking", "St atic", "Ġp ont", "ĠVolt age", "ĠSt ead", "ĠMort gage", "ĠJon ah", "Y L", "CLASS IFIED", "Ġas bestos", "nik ov", "Ġcoll agen", "ĠOrb ital", "P ocket", "7 99", "Ġhy brids", "inc hes", "Ġinv oice", "und y", "Ġinequ alities", "T rend", "w ashed", "B ALL", "Ġluc id", "ĠComment ary", "Ġw itty", "Br andon", "Ġbru ising", "Ġ6 20", "es cent", "box ing", "P OL", "Ġ3 78", "R ect", "Ġlic ences", "ĠMcG ee", "p ressed", "D anny", "Ġj ammed", "ord inate", "Ġle th", "Ġdistingu ishes", "ĠYam aha", "IL S", "ĠH ume", "ĠC ategories", "Rober ts", "Ch art", "Ġbeet le", "ĠGra veyard", "Ġ($ )", "o ÄŁ", "Ġtw ilight", "are lla", "á ½", "Ġbooth s", "ĠH HS", "ĠFeld man", "Ġexcav ation", "Ġphilosoph ies", "at ography", "ĠGar age", "te chnology", "Ġunfor gettable", "Ġver ifying", "Ġsubord inates", "E ls", "Ġne b", "G aming", "EN A", "ĠAchieve ment", "it ters", "ĠG abe", "Ġd umps", "for cer", "Ġpo ignant", "ĠM BA", "ĠHe idi", "ime i", "Ġm ages", "Ġliber ate", "Ġcircum cised", "ĠMer maid", "ĠMat th", "t ogether", "ĠW ichita", "Ġstore front", "ĠAd in", "V II", "Four th", "Ġexplore rs", "W ER", "Not able", "Bro ok", "m ens", "F aith", "-------- -", "ĠJ ou", "¬ ¼", "Ġpine apple", "Ġam alg", "el n", "ark able", "ĠãĤµ ãĥ¼ãĥĨãĤ£", "ĠãĤµãĥ¼ãĥĨãĤ£ ãĥ¯ãĥ³", "Ġov arian", "ĠE choes", "Ġhairc ut", "Ġp av", "Ġch illed", "anas ia", "Ġsty led", "Ġd ab", "ni per", "Ġminister ial", "ĠD UP", "T an", "Ġsul ph", "ĠD eter", "ĠBo hem", "od an", "Ġeduc ator", "â ĵĺ", "sp ir", "Ch icken", "ĠE leanor", "Ġqu i", "Ġheav iest", "Ġgrasp ed", "U RA", "Ġcro oked", "Jess ica", "pro blem", "Ġpred etermined", "Ġman iac", "Ġbreath s", "ĠLauder dale", "Ġh obbies", "y z", "Cr ime", "Ġcharism a", "d L", "Ġle aping", "Ġk ittens", "Ang elo", "ĠJ ACK", "ĠSu zanne", "Ġhal ting", "ENT ION", "Ġswall owing", "ĠEarthqu ake", "Ġeight eenth", "ĠN IC", "ĠIN F", "ĠCons cious", "Ġparticular s", "circ le", "7 40", "Ġbene volent", "Ġ7 47", "Ġ4 90", "Ġr undown", "ĠVal erie", "ĠB UR", "Ġcivil isation", "ĠS chn", "W B", "ot ide", "intern ational", "Ġj ohn", "Ġ19 02", "Ġpe anuts", "Ġflav ored", "k us", "Ġro ared", "Ġcut off", "é £", "Ġorn ament", "Ġarchitect ures", "Ġ3 69", "ol or", "ĠWild e", "ĠC RC", "ĠAdjust ed", "Ġprov oking", "land ish", "Ġrational ity", "Ġjust ifies", "Ġdisp el", "Ġa meric", "ĠPol es", "Ø ©", "Ġen vis", "ĠD oodle", "ä½ ¿", "igs aw", "auld ron", "Techn ical", "T een", "up hem", "ĠX iang", "Ġdetract ors", "ĠZ i", "ĠJournal ists", "Ġconduc ive", "ĠVolunte ers", "Ġs d", "Know ing", "Ġtrans missions", "ĠPL AN", "ĠL IB", "Ġall uded", "Ġob e", "Ġd ope", "ĠGold stein", "Ġwavelength s", "ĠDest ination", "nd a", "ug i", "Ġattent ive", "ĠLe an", "ral tar", "Ġman g", "mb uds", "ak ings", "b ender", "Ġacc ol", "Ġcraw led", "N OW", "Min nesota", "Ġflour ished", "ĠZ up", "ĠSuper visor", "ĠOliv ier", "Ex cellent", "Ġwid en", "D one", "Ġw ig", "Ġmiscon ceptions", "Cor p", "W an", "Ġvener able", "ĠNot ably", "ĠKling on", "an imate", "Bo ost", "ĠS AY", "miss ing", "ibli ography", "mel on", "Ġpay day", "Ø ³", "bo le", "Ġve iled", "ĠAl phabet", "It alian", "Ġever lasting", "ĠR IS", "ĠC ree", "rom pt", "Ġh ating", "Ġgrin ning", "Ġge ographically", "OS H", "Ġwe eping", "ĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂł", "Ġimpe cc", "Let ter", "Ġblo ated", "PL A", "ĠFe in", "Ġper sever", "Th under", "Ġa ur", "ĠR L", "Ġpit falls", "âĸ º", "Ġpredomin ant", "Ġ5 25", "7 18", "AP E", "7 14", "Ġfarm land", "ĠQ iao", "Ġv iolet", "ĠBah amas", "Ġinflic ting", "ĠE fficiency", "Ġhome brew", "Ġundert ook", "Ġcur ly", "ĠHard ing", "man ia", "59 6", "Ġtem pered", "Ġhar rowing", "ĠP ledge", "ĠFranken stein", "è ª", "M otion", "Ġpredict ably", "ĠExpl osion", "oc using", "er d", "col o", "FF ER", "Ġback field", "ĠV IDE", "ue bl", "N arr", "ĠArg ument", "Ġgen omic", "Ġbout ique", "Ġbatt ed", "ĠB inary", "Ġg amb", "ĠRh ythm", "67 3", "Ġa float", "ĠOlymp ia", "Y ING", "Ġend if", "is in", "Ġwin ters", "Ġsc attering", "I v", "D istance", "Ġtr u", "ĠCom fort", "Ġne xus", "Ġair flow", "ĠByz antine", "p ayers", "con i", "ĠB etsy", "D eal", "ĠN ug", "ĠContin ent", "red ibly", "Ġoptim izing", "al beit", "Ġec static", "ĠPro to", "ç ·", "iv ot", "âĸ Ħ", "em p", "rou nder", "Ġcl out", "ĠI ST", "66 3", "ĠDoll ars", "ĠD AC", "Ġsubsc ribed", "Ġrehears al", "Ġam ps", "ĠSh ang", "es m", "Ġspr inkle", "Ġassail ant", "ĠO o", "ĠCoin base", "T act", "Ġret ina", "Ġn uns", "R ON", "att o", "Ġj ug", "ĠSV G", "Ġb ikini", "ĠFI LE", "ĠFound ers", "ep ort", "ĠK P", "Ġrest ores", "ĠTh ick", "Ġash ore", "Ġappro vals", "R ender", "M AG", "G raham", "ĠCort ana", "ãĥ³ ãĤ¸", "ss h", "or ians", "ars ity", "ĠInsp ired", "u pper", "Ġsign alling", "Ġreb uke", "Ġfl ares", "Ġdownt ime", "Stud ies", "Ġstagn ation", "ĠSequ ence", "Ġgr unt", "Ġass ures", "ĠPL A", "59 2", "Ġintra ven", "d epend", "Sus an", "ĠManz iel", "Man ia", "Cont ract", "Ġsl ams", "Ġcult ured", "Ġcred itor", "L IST", "ĠH UM", "ĠChatt anooga", "serv ed", "Ġclo aked", "ĠF TP", "p owder", "ĠSt ella", "uct ive", "Ġcheap ly", "ĠMU CH", "ĠGalile o", "Ġsu ites", "spe ech", "Ġdeliber ations", "ĠCh ips", "« ĺ", "Bal ance", "ĠWyn ne", "ĠAk ron", "Ass et", "Ġhon oured", "Ġed ged", "Like wise", "anim ous", "ĠW age", "ĠEz ek", "ad vertisement", "ĠRT X", "ĠM AD", "Ġmigr ating", "ĠS QU", "Ġ4 75", "Ed ited", "Ġshorth and", "ĠBas ics", "Ġcro tch", "ĠEV EN", "Ġv m", "effic iency", "Ġcal ves", "ĠF rie", "ĠBrill iant", "Ġstri kers", "Ġrepent ance", "Ġarter ies", "r l", "B ed", "h ap", "Ġcrypt ography", "ĠSab res", "Ġ4 14", "vi ks", "ih ara", "aps es", "T alking", "Ġintertw ined", "Ġdoc ks", "Ġalle le", "ĠArt ifact", "ĠH IM", "t orn", "ç ķ", "Ġop acity", "ĠE ly", "os uke", "Ġn ipple", "Ġhand written", "ĠV K", "ĠChamber lain", "ĠLa os", "ig raph", "g row", "Ġtr illions", "Ġdescend ant", "ĠSail or", "as uring", "Ġce ilings", "ĠWare house", "f lying", "ĠGl ow", "Ġn ont", "Ġmiscar riage", "Ġrig s", "Ġmin istries", "Ġelabor ated", "Ġdel usional", "ĠHum ane", "Ġ3 79", "n ets", "Ġblack out", "add ers", "Ġn p", "ĠT ire", "ro sc", "Ġsub div", "Ġlink age", "Ġchron ological", "ĠHER O", "Ġres ettlement", "ĠVin yl", "Ġpast oral", "ĠMob il", "ĠBar bar", "Co oldown", "ĠF ritz", "c riminal", "re pe", "Ġbell ig", "ĠBre ed", "Ġ4 18", "Ġsem blance", "ij k", "Ġcur tail", "Ġclin ch", "cont ained", "ĠProm pt", "ast on", "Ġw i", "Ġpursu its", "5 15", "ĠGl oss", "Ġfl ips", "Ġcoup ons", "Ġcl oning", "ĠLike ly", "Rem oved", "ĠQu artz", "r ices", "ĠSpe ars", "Ġp ious", "Ġdep reciation", "ĠD are", "oun ces", "am az", "O nt", "Ġp innacle", "d ocker", "0 26", "ĠW yr", "ĠPro per", "Ë Ī", "n il", "By tes", "Ġseek er", "t rial", "Ġunf olds", "ĠMar se", "Ġextravag ant", "ĠSurviv ors", "RED ACTED", "ĠSpeed way", "ĠCra igslist", "sub mit", "ĠGener ations", "Ġup holding", "Ġblood stream", "ĠMiss ions", "ĠL awn", "Ġlim bo", "ene i", "H uh", "ĠWild cats", "pre p", "ĠMark us", "ĠFor bidden", "rit ic", "IN O", "Ġexhib iting", "requ ent", "ch uk", "Ġhabit ual", "ĠComp atibility", "Dr ag", "RIP T", "uj ah", "GR OUND", "Ġdelinqu ent", "Ġburn er", "Ġcontempor aries", "Ġgimm ick", "load s", "Ġno zzle", "p odcast", "ĠW ak", "ĠStat en", "ĠK uh", "ãģ ĵ", "inter rupted", "Ġinv incible", "ĠBurn ett", "cig arette", "ĠPeb ble", "ĠTem porary", "ĠMar ino", "58 2", "Ġwast eland", "ident ly", "T x", "Ġr ite", "ĠPan asonic", "ĠM iddles", "ĠHort on", "ae us", "Ġc uring", "Ġm ats", "Ġadj ourn", "Ġfears ome", "pe z", "bo ats", "Ġpro pell", "Ġconflic ted", "ĠAng er", "Ġinsurg ent", "K arl", "Ġco ales", "Ġsouth western", "Ġdis su", "ĠO vert", "******** ****", "Ġbox ed", "ĠBr une", "aa a", "Ġgard ening", "ĠEng el", "tr acks", "Ġpur ified", "Ġplace holder", "ĠL ikes", "Ġd an", "G ab", "Ġe ct", "ĠF aw", "ĠEl iot", "Ġ' ,", "otrop ic", "ĠRu in", "hed on", "Ġca ul", "Ġa ft", "ĠCad illac", "gh a", "ass ian", "ud eb", "ĠT ick", "Ġadjust s", "AR GET", "5 37", "isc he", "ant y", "ĠFried rich", "ĠBl izz", "ĠA OL", "Camp aign", "Ġmamm al", "ĠVe il", "ĠK ev", "ĠMaur it", "ĠDam ien", "N ation", "E astern", "Ġ{ :", "Ġ= ================================", "Ġstereotyp ical", "Ġatt ic", "ĠCy borg", "requ ire", "Ġaward ing", "ĠPap ua", "bt n", "b ent", "B oo", "Ġ( =", "ĠX ander", "ĠSomers et", "Ġcatch y", "Ġcert ify", "STR UCT", "Ġit al", "Ġt ides", "ĠBr ands", "G ray", "comp etitive", "Ġcur ator", "ĠD G", "omin ium", "ĠGM Os", "ci ating", "ĠCarm en", "ow ard", "Balt imore", "Ġr gb", "C u", "Ġwip es", "spe ll", "IT NESS", "Ġsummar izes", "ĠRe vis", "Ġwhistlebl owers", "ĠBre ach", "Ġcro chet", "k os", "ews ki", "Ġrep et", "Ġcrim son", "ĠKar achi", "read able", "dim ension", "ĠI gor", "ild ed", "ĠZ ed", "ĠKe ane", "ĠCos metic", "DE P", "Ġretreat ing", "ĠU A", "ens ical", "Ġd usk", "ĠDick ens", "Ġaren as", "ĠPass age", "level s", "Ġcur v", "P ope", "Ġch ores", "ĠEl ise", "ĠComp ass", "b ub", "Ġmamm alian", "ĠSans krit", "ĠAN C", "ĠCr ack", "Q ual", "L aun", "amp unk", "Ġlearn ers", "Ġglam orous", "Ġfur the", "erm ott", "c and", "Gener ic", "Ġnarr ated", "Ġdisorder ly", "ĠTrans actions", "ĠDet ention", "ĠR oku", "Ä į", "Ġunder statement", "ĠS aur", "ĠRodrig o", "ĠAS AP", "S in", "Ġre joice", "Method s", "Ġelectro de", "Ġworsh ipped", "Ġid i", "ĠPhys icians", "Ġpop up", "Ġde ft", "ĠRem oval", "ĠBu enos", "ver bs", "Ġfun k", "ush a", "rict ion", "ore a", "ĠBang alore", "ĠKen obi", "zz i", "Ġnorm ative", "Ġgobl ins", "Ġcaf es", "ĠUN CLASSIFIED", "ĠF ired", "S IGN", "Ġs clerosis", "ĠV oter", "ĠSon ny", "ĠExt end", "ĠEV s", "Ar senal", "Ġp si", "Ġwid est", "ĠT us", "Ġlo oms", "Ġjust ifying", "ĠGr anger", "è ¯", "Ref er", "58 3", "Ġflour ishing", "ab re", "Ġr ave", "ĠCont ra", "Ġ18 98", "Add s", "Ġf ul", "ĠCo oke", "some one", "= #", "67 1", "Ġy ak", "Ġar te", "ĠMis cellaneous", "ĠDet ection", "ĠCl ancy", "â ģ", "ass ies", "Ġval iant", "ĠFemin ist", "cor ruption", "V el", "P ear", "Ġsucc inct", "Ġquick est", "k w", "Ġsp itting", "ĠL ibraries", "åħ ī", "ant z", "D ad", "ĠSpec ifications", "rup ulous", "and r", "RES ULTS", "Ġsnow ball", "Ġpred is", "ĠB axter", "ĠNurs ing", "ĠCh aff", "s we", "Ġout age", "Ġnest ing", "Ġnotor iety", "tr igger", "on ite", "j on", "Ġf ou", "ook ed", "ĠCelebr ity", "re ality", "Ġfat ig", "Ġhug ging", "Ġbother s", "ĠPan zer", "ĠCh andra", "fig ured", "Ġvol ts", "ĠCloud s", "Ġfee ble", "ĠCur ve", "ĠAs us", "78 6", "abs or", "ĠV ICE", "ĠH ess", "Ġmanufact ures", "Ġgri zz", "ĠPower ful", "ac id", "Ġsub sections", "ĠKrug man", "ĠAl ps", "is u", "Ġsequ est", "ĠUlt ron", "ĠT inker", "ĠGo ose", "Ġmism atch", "Att orney", "Ġmorph ology", "ĠSix ers", "ut tered", "ĠE LECT", "gr an", "Rus sell", "ĠG SL", "Ġfort night", "Ġ. )", "Ġapost le", "pr one", "el ist", "Unt itled", "ĠIm plementation", "ist ors", "Ġtank er", "Ġpl ush", "Ġattend ants", "ĠT ik", "ĠGreen wich", "ĠY on", "ĠSP L", "cell s", "unt led", "S olution", "ĠQu é", "Ġvac ated", "Ġupt ick", "ĠMer idian", "æ ĥ", "ĠDr ill", "9 25", "58 4", "Ġrenov ated", "ĠKub rick", "zy k", "Ġl ousy", "pp el", "ohyd rate", "ĠI zzy", "lesi astical", "CC C", "ĠAj ax", "Ġad apters", "ĠPetra eus", "Ġaffirm ation", "ĠST OR", "le ms", "ad oes", "ĠConstantin ople", "Ġp onies", "Ġl ighthouse", "Ġadherent s", "ĠBre es", "omorph ic", "Fight ing", "Ġpl aster", "ĠP VC", "ĠOb st", "Ġdear ly", "ĠTo oth", "icks on", "Ġsh aming", "P lex", "A gg", "ĠâĢ¦ \"", "Ġsub reddits", "Ġpige on", "ĠResident ial", "ĠPass ing", "Ġl um", "ĠP ension", "Ġpessim istic", "Ġ4 32", "z inski", "c ade", "0 75", "Ġapolog ised", "iy ah", "Put ting", "Ġgloom y", "ĠLy me", "=-=-=-=- =-=-=-=-", "ĠT ome", "ĠPsych iatric", "ĠH IT", "c ms", "ap olog", "Ġbreak er", "Ġdeep en", "Ġtheor ist", "ĠHigh lands", "Ġb aker", "Ġst aples", "Ġinterf ered", "ĠAb ortion", "jo ined", "ch u", "Ġform ulate", "Ġvacc inations", "Ġban ter", "phe us", "Ġoutfield er", "ĠM eter", "Ġ# ####", "Ġ18 95", "Ġnarrow ing", "ĠST ORY", "f p", "ĠC ST", "ign ore", "Ġproclaim ing", "ĠR U", "ĠB ALL", "yn a", "65 3", "Ġpos it", "P RE", "59 4", "ĠRegist rar", "ĠPil grim", "ic io", "Ġpre tt", "Ġlif eless", "Ġ__ _", "Ne igh", "ĠCh urches", "orn o", "Ġor cs", "Ġkind red", "ĠAud it", "Ġmillenn ial", "ĠPers ia", "g ravity", "ĠDis ability", "ĠD ARK", "W s", "od on", "Ġgrand daughter", "ĠBro oke", "ĠA DA", "ER A", "Ġpick ups", "ĠWil kinson", "ĠSh ards", "ĠN K", "Ġexp el", "ĠKis lyak", "Ġj argon", "Ġpolar ized", "ian e", "Pub lisher", "Ġreb utt", "Ġapprehens ion", "ĠK essler", "Ġpr ism", "F UL", "19 64", "ĠL oll", "ä ¿", "le thal", "Å Ł", "Ġg hetto", "Ġb oulder", "ĠSlow ly", "ĠOsc ars", "ĠInst ruction", "ĠUl tr", "ĠM oe", "N ich", "ĠP ATH", "( *", "ĠRE LEASE", "un ing", "rou se", "en eg", "Ġre imb", "ĠDet ected", "Do S", "Ġster ling", "Ġaggreg ation", "ĠLone ly", "ĠAtt end", "hig her", "Ġairst rike", "ks on", "SE LECT", "Ġdef lation", "ĠHer rera", "C ole", "rit ch", "Ġadvis able", "F ax", "Ġwork around", "Ġp id", "mort em", "ers en", "Ġtyp o", "Ġal um", "78 2", "ĠJam al", "script s", "Ġcapt ives", "ĠPres ence", "ĠLie berman", "angel o", "Ġalcohol ism", "ass i", "Ġrec ite", "Ġgap ing", "Ġbask ets", "ĠG ou", "Brow ser", "ne au", "Ġcorrect ive", "und a", "sc oring", "ĠX D", "Ġfil ament", "Ġdeep ening", "ĠStain less", "Int eger", "Ġbu ggy", "Ġten ancy", "ĠMub arak", "Ġt uple", "ĠD roid", "ĠS itting", "Ġforfe it", "ĠRasm ussen", "ixt ies", "es i", "ĠKim mel", "Ġmetic ulously", "Ġap opt", "ĠS eller", "08 8", "ec ake", "hem atically", "T N", "Ġmind less", "Ġdig s", "ĠAcc ord", "ons ense", "em ing", "br ace", "Ġe Book", "ĠDist ribut", "ĠInvest ments", "w t", "] ),", "beh avior", "56 3", "Ġbl inding", "ĠPro testers", "top ia", "Ġreb orn", "ĠKel vin", "ĠDo ver", "ĠD airy", "ĠOut s", "Ġ[ /", "Ï Ģ", "b p", "ĠVan ity", "ĠRec ap", "ĠHOU SE", "ĠF ACE", "Ġ4 22", "69 2", "ĠAnt ioch", "cook ed", "Ġcoll ide", "Ġa pr", "Ġsle eper", "ĠJar vis", "Ġalternative ly", "ĠLe aves", "ĠM aw", "Ġantiqu ity", "ĠAdin ida", "Ġab user", "Poké mon", "Ġass orted", "ĠRev ision", "ĠP iano", "ĠG ideon", "O cean", "Ġsal on", "Ġbust ling", "ogn itive", "ĠRah man", "Ġwa iter", "Ġpres ets", "ĠO sh", "ĠG HC", "oper ator", "Ġrept iles", "Ġ4 13", "ĠG arr", "ĠCh ak", "Ġhas hes", "Ġfail ings", "Ġfolk lore", "Ġab l", "ĠC ena", "ĠMac Arthur", "ĠCOUR T", "Ġperipher y", "app ers", "Ġreck oned", "ĠInf lu", "ĠC ET", "Ġ3 72", "ĠDefin itive", "ass ault", "4 21", "Ġreservoir s", "Ġd ives", "ĠCo il", "DA Q", "Ġvivid ly", "ĠR J", "ĠBel lev", "Ġec lectic", "ĠShow down", "ĠK M", "ip ed", "reet ings", "ĠAs uka", "L iberal", "ĠÏ Ħ", "Ġbystand ers", "ĠGood win", "uk ong", "S it", "ĠT rem", "Ġcrim inally", "ĠCirc us", "ch rome", "88 7", "Ġnan op", "ĠOb i", "ĠL OW", "o gh", "ĠAuth ors", "ob yl", "Ur ban", "Ġt i", "ĠWe ir", "t rap", "ag y", "Ġparent heses", "Ġout numbered", "Ġcounter productive", "ĠTob ias", "ub is", "P arser", "ST AR", "Ġsyn aptic", "ĠG ears", "Ġh iber", "Ġdebunk ed", "Ġex alted", "aw atts", "H OU", "Ch urch", "ĠPix ie", "ĠU ri", "ĠForm ation", "ĠPred iction", "C EO", "Ġthro tt", "ĠBrit ann", "ĠMad agascar", "ë ĭ", "Ġbill boards", "ĠRPG s", "ĠBe es", "complete ly", "F IL", "Ġdoes nt", "ĠGreen berg", "re ys", "Ġsl ing", "Ġempt ied", "ĠPix ar", "ĠDh arma", "l uck", "ingu ished", "Ġend ot", "Ġbab ys", "05 9", "che st", "r ats", "Ġr idden", "Ġbeet les", "Ġillum inating", "Ġfict itious", "ĠProv incial", "Ġ7 68", "Ġshe pherd", "ĠR ender", "Ġ18 96", "C rew", "Ġmold ed", "ĠXia omi", "ĠSp iral", "Ġdel im", "Ġorgan ising", "Ġho ops", "ĠBe i", "z hen", "Ġfuck in", "Ġdec ad", "Ġun biased", "am my", "sw ing", "Ġsmugg led", "Ġk ios", "ĠP ERSON", "ĠInquis itor", "Ġsnow y", "Ġscrap ing", "ĠBurg ess", "P tr", "ag ame", "R W", "Ġdro id", "ĠL ys", "ĠCass andra", "Jac ob", "Ġ35 4", "Ġpast ure", "Ġfr anc", "ĠScot ch", "ĠEnd s", "ĠI GF", "def inition", "Ġhyster ical", "ĠBrown e", "77 1", "Ġmobil ization", "æ ķ", "iqu eness", "Th or", "Ġspear headed", "Ġembro iled", "Ġconject ure", "jud icial", "Ch oice", "Ġpaper back", "P ir", "Ġrec overs", "ĠSur ge", "ĠSh ogun", "ĠPed iatrics", "ãģ ł", "Ġsweep s", "ĠLabor atories", "ĠP acks", "al us", "add in", "Ġhead lights", "g ra", "Ev idence", "COL OR", "Ad min", "Ĭ ±", "Ġconco ct", "s ufficient", "Ġun marked", "Ġrich ness", "Ġdiss ertation", "Ġseason ing", "Ġg ib", "ĠM ages", "un ctions", "ĠN id", "che at", "ĠTM Z", "c itizens", "ĠCatholic ism", "n b", "Ġdisemb ark", "ĠPROG RAM", "a ques", "Ty ler", "Or g", "ĠSl ay", "ĠN ero", "ĠTown send", "IN TON", "te le", "Ġmes mer", "9 01", "Ġfire ball", "ev idence", "aff iliated", "ĠFrench man", "ĠAugust a", "0 21", "Ġs led", "Ġre used", "ĠImmun ity", "Ġwrest le", "assemb led", "Mar ia", "Ġgun shots", "ĠBarb ie", "Ġcannabin oids", "ĠTo ast", "ĠK inder", "IR D", "Ġre juven", "Ġg ore", "Ġrupt ure", "Ġbre aching", "ĠCart oon", "Ġ4 55", "ĠPale o", "6 14", "Ġspe ars", "ĠAm es", "ab us", "Mad ison", "GR OUP", "Ġab orted", "y ah", "Ġfel on", "Ġcaus ation", "Ġprep aid", "Ġp itted", "op lan", "ĠShel ley", "ĠRus so", "ĠP agan", "Ġwill fully", "ĠCan aver", "und rum", "ĠSal ary", "ĠAr paio", "read er", "ĠR ational", "ĠOver se", "ĠCa uses", "Ġ* .", "Ġw ob", "Ke ith", "ĠCons ent", "man ac", "77 3", "6 23", "Ġfate ful", "et imes", "Ġspir ited", "ĠD ys", "Ġhe gemony", "Ġboy cot", "ĠEn rique", "em outh", "Ġtim elines", "ĠSah ara", "ĠRel ax", "ĠQuin cy", "ĠLess ons", "ĠE QU", "SE A", "N K", "ĠCost co", "Incre ase", "Ġmotiv ating", "ĠCh ong", "am aru", "ĠDiv ide", "Ġped igree", "ĠTasman ia", "ĠPrel ude", "L as", "9 40", "57 4", "Ġch au", "ĠSp iegel", "un ic", "-- >", "ĠPhil ips", "ĠKaf ka", "Ġuphe aval", "Ġsent imental", "Ġsa x", "ĠAk ira", "ser ial", "Mat rix", "Ġelect ing", "Ġcomment er", "ĠNeb ula", "ple ts", "ĠNad u", "ĠAd ren", "Ġen shr", "ĠR AND", "fin ancial", "ĠCly de", "uther ford", "Ġsign age", "Ġde line", "Ġphosph ate", "rovers ial", "f ascist", "ĠV all", "ĠBeth lehem", "Ġfor s", "Ġeng lish", "S olid", "N ature", "Ġv a", "ĠGu ests", "Ġtant al", "Ġauto immune", ";;;;;;;; ;;;;", "ĠTot ally", "ĠO v", "Ġdef ences", "ĠCoc onut", "Ġtranqu il", "Ġpl oy", "Ġflav ours", "ĠFl ask", "ãĤ¨ ãĥ«", "ĠWest on", "ĠVol vo", "8 70", "Ġmicro phones", "ver bal", "R PG", "Ġi ii", "; }", "0 28", "Ġhead lined", "Ġprim ed", "Ġho ard", "ĠSh ad", "ĠEN TER", "Ġtri angular", "Ġcap it", "l ik", "ĠAn cients", "Ġl ash", "Ġconv ol", "Ġcolon el", "en emy", "G ra", "Ġpub s", "ut ters", "Ġassign s", "ĠPen et", "ĠMon strous", "ĠBow en", "il ver", "H aunted", "ĠD ing", "start ed", "pl in", "Ġcontamin ants", "ĠDO E", "ff en", "ĠTechn ician", "R y", "Ġrob bers", "Ġhot line", "ĠGuard iola", "ĠKau fman", "row er", "ĠDres den", "ĠAl pine", "E lf", "Ġf mt", "ĠS ard", "urs es", "g pu", "Un ix", "Ġunequiv ocally", "ĠCitizens hip", "qu ad", "m ire", "ĠS weeney", "B attery", "6 15", "Ġpanc akes", "Ġo ats", "M aps", "ĠCont rast", "mbuds man", "ĠE PS", "Ġsub committee", "Ġsour cing", "Ġs izing", "ĠBuff er", "ĠMand atory", "Ġmoder ates", "ĠPattern s", "ĠCh ocobo", "ĠZ an", "ĠSTAT ES", "ĠJud ging", "ĠIn her", "* :", "Ġb il", "ĠY en", "Ġexh ilar", "oll ower", "z ers", "Ġsn ug", "max imum", "Ġdesp icable", "ĠP ACK", "ĠAn nex", "Ġsarcast ic", "Ġlate x", "Ġt amp", "ĠS ao", "b ah", "ĠRe verend", "ĠChin atown", "ĠA UT", "d ocumented", "ĠGA BA", "ĠCan aan", "ĠÙ ħ", "Ġgovern s", "pre v", "E sc", "ĠEst imates", "OS P", "Ġendeav our", "ĠCl osing", "omet ime", "every one", "Ġwor sen", "Ġsc anners", "Ġdev iations", "ĠRobot ics", "ĠCom pton", "Ġsorce rer", "Ġend ogenous", "Ġem ulation", "ĠPier cing", "ĠA ph", "ĠS ocket", "Ġb ould", "ĠO U", "ĠBorder lands", "Ġ18 63", "G ordon", "ĠW TO", "Ġrestrict s", "Ġmosa ic", "Ġmel odies", "ç Ħ", "T ar", "Ġdis son", "ĠProv ides", "Ġ ......", "b ek", "F IX", "Ġbro om", "ans hip", "Do ctors", "Ġner ds", "ĠReg ions", "na issance", "Ġmet e", "Ġcre pt", "pl ings", "Ġgirlfriend s", "kn it", "ig ent", "ow e", "Ġus hered", "ĠB az", "M obil", "4 34", "ĠPres ents", "orig in", "Ġins omnia", "ĠA ux", "4 39", "ĠCh ili", "irs ch", "G AME", "Ġgest ation", "alg ia", "rom ising", "$ ,", "c row", "ĠIn spection", "at omic", "Rel ations", "J OHN", "rom an", "ĠClock work", "ĠBak r", "m one", "M ET", "Ġthirst y", "Ġb c", "Ġfacult ies", "R um", "Ġnu ance", "ĠD arius", "ple ting", "fter s", "etch up", "Reg istration", "ĠK E", "R ah", "Ġpref erential", "ĠL ash", "ĠH H", "Val id", "ĠN AV", "Ġstar ve", "ĠG ong", "z ynski", "ĠAct ress", "Ġw ik", "Ġun accompanied", "lv l", "Br ide", "AD S", "ĠCommand o", "ĠVaugh n", "Wal let", "Ġho pping", "ĠV ie", "Ġcave ats", "Ġal as", "if led", "ab use", "66 1", "Ġib n", "Ġg ul", "Ġrob bing", "t il", "IL A", "Ġmit igating", "Ġapt ly", "Ġty rant", "Ġmid day", "ĠGil more", "ĠDe cker", "Ġ§ §", "part ial", "Ex actly", "Ġphen otype", "Ġ[+ ]", "ĠP lex", "ĠI ps", "vers ions", "Ġe book", "Ġch ic", "g ross", "\":\" \"},{\"", "ĠSur prisingly", "M organ", "Ġresid ues", "ĠConf ederation", "in feld", "Ġl yr", "mod erate", "Ġperpend icular", "V K", "Ġsynchron ized", "Ġrefres hed", "Ġad ore", "ĠTor ment", "ol ina", "Ġ26 00", "Item Tracker", "Ġp ies", "ĠF AT", "ĠR HP", "0 48", "ĠRES P", "ĠB J", "all ows", "P and", "Ġunw elcome", "ĠV oc", "ĠBast ard", "ĠO W", "ĠL AR", "ĠHeal er", "Environment al", "ĠKen yan", "ĠTr ance", "ĠP ats", "Ġali ases", "ĠGar field", "Ġcampaign er", "Ġadvance ments", "ĠOkin awa", "ĠC oh", "ows ky", "Ġstar ved", "Ġsize able", "Ġ: -)", "Ġm RNA", "Ġsusp ensions", "ist ar", "Scot land", "Pr in", "-------------------------------- ----------------", "Ġ50 2", "Ġteasp oons", "Ġ10 50", "Ġcoerc ive", "ĠMason ic", "edd ed", "ĠPass enger", "Ġl att", "Ġbr aces", "ĠSt eal", "ĠNY T", "ĠK ats", "ĠCel est", "ae z", "T u", "ĠCoul ter", "ðŁ ĺ", "Fl ickr", "ĠWil mington", "ith s", "++ ;", "Ġv ending", "Ġneg ro", "ĠPh i", "ĠYellow stone", "Call back", "Ġsh ampoo", "ĠSh ades", "w at", "Ġsuper human", "Ġridic uled", "Ġhol iest", "om bo", "Ġintern s", "Ġh one", "ĠPar agu", "UR I", "Ġd angling", "ãĤ »", "so v", "ict ional", "av ailability", "Ġrev ocation", "Ġd ow", "in ic", "ĠTHE IR", "Ġis o", "Ġout ings", "ĠLeth al", "Ġ) ))", "Ġinacc ur", "Ġout landish", "Ġan us", "let ico", "id on", "l ol", "Ġun regulated", "Ġsuccumb ed", "Ġc uff", "ĠWast eland", "let al", "Ġsub str", "Ġcoff ers", "Ġautom akers", "ov i", "ĠX ue", "ĠDayton a", "Ġjar ring", "Ġf umes", "Ġdisband ed", "z ik", "itt on", "Ġstriking ly", "Ġsp ores", "Ad apter", ".) :", "ĠLynd on", "ival ry", "Ġor ally", "Ġtumult uous", "Ġdisple asure", "Ġcon es", "or rect", "Ġappe ase", "Ġder by", "ĠTrip oli", "ĠAl ess", "Ġp oked", "ĠGu ilty", "v P", "En ough", "Ġorig inals", "6 99", "Ġrabb i", "Ġproverb ial", "Ġpostp one", "el ope", "ĠMist y", "Ġstaff ed", "ĠUn employment", "redit ary", "Ġdilig ent", "re comm", "me asures", "as in", "8 25", "Ġpond s", "Ġmm ol", "ĠS AR", "ĠC ARE", "Ġ3 71", "Ġclen ched", "ĠCors air", "Ġcaric ature", "z n", "att ach", "ĠSch ro", "spe ak", "p ainted", "ĠS uc", "ĠE NT", "Ġcell ul", "ĠP aid", "di agn", "WH ERE", "Ġtext ed", "B arn", "Ġret racted", "ĠRe ferred", "S av", "Ġup keep", "Ġwork places", "ĠTok ens", "Ġampl ify", "cl inical", "Ġmult ic", "mber g", "Ġconvol uted", "Reg ion", "5 65", "ĠTop ic", "Ġsn ail", "Ġsal ine", "Ġins urrection", "ĠPet r", "f orts", "B AT", "ĠNav ajo", "Ġrud imentary", "ĠLak sh", "OND ON", "Me asure", "Ġtransform er", "ĠGodd ard", "Ġcoinc ides", "ir in", "R ex", "ĠB ok", "qu it", "Ġshotgun s", "Ġprolet arian", "Ġsc orp", "ĠAd a", "5 14", "Ġsl ander", "record ed", "Ġemb ell", "ris ome", "Ġapolog izing", "ĠMul cair", "ĠGib raltar", "Cl a", "Ġall ot", "ĠAtt ention", "Ġ4 33", "le ave", "Ġwh ine", "ĠIss a", "ĠFa ust", "ĠBar ron", "hen y", "Ġvictim ized", "J ews", "Ġnurt uring", "ett el", "W inged", "ĠSub tle", "Ġflavor ful", "ĠRep s", "eng ed", "call back", "Ġdirection al", "Ġcl asp", "ĠDirect ions", "plan et", "icult ure", "Hel per", "ic ion", "ac ia", "Ġç ¥ŀ", "Ġsur ges", "Ġcan oe", "ĠPrem iership", "be en", "Ġdef ied", "ĠTro oper", "Ġtrip od", "Ġgas p", "ĠE uph", "ĠAd s", "vern ight", "high ly", "R ole", "Ġent angled", "ĠZe it", "6 18", "ĠRust y", "Ġhaven s", "ĠVaugh an", "HA EL", "ĠSER VICE", "/ ,", "Ġstr icken", "Ġdel usions", "Ġb is", "ĠH af", "Ġgrat ification", "Ġent icing", "UN CH", "Ad ams", "ĠOL ED", "ĠBeet le", "Ġ18 99", "ĠSO FTWARE", "ateg or", "V L", "ĠTot em", "ĠG ators", "AT URES", "Ġimped ance", "Reg istered", "ĠC ary", "ĠAer ial", "on ne", "en ium", "Ġd red", "ĠBe g", "Ġconcurrent ly", "Ġsuper power", "ĠX an", "j ew", "imes ter", "ĠDick inson", "âĶ ģ", "F la", "Ġp ree", "ĠRoll ins", "© ¶æ", "Ġden omination", "ĠL ana", "5 16", "Ġinc iting", "sc ribed", "j uries", "ĠWond ers", "app roximately", "Ġsusp ending", "Ġmountain ous", "ĠL augh", "oid al", "N s", "Det ect", ") =", "ĠL uthor", "ĠSchwarz enegger", "ĠMull er", "ĠDev i", "ec ycle", "J ar", "6 13", "ĠL ongh", "B ah", "ĠSP ORTS", "n w", "Ġref inement", "Ġwater ways", "Ġd iner", "Bl ade", "68 3", "F ac", "Ġinitial s", "Ġro g", "Ġparan ormal", "B UT", "Ġ[ (", "ĠSw anson", "ĠM esh", "âĸ ¬", "Impro ve", "ĠRad iation", "ĠEst her", "ĠE sk", "ĠA ly", "ik y", "Ġir rad", "ĠBuck ingham", "Ġref ill", "Ġ. _", "Re pe", "CON CLUS", "Ġdifferent iated", "Ġchi rop", "ĠAt kins", "Pat tern", "Ġexc ise", "Ġcab al", "N SA", "ĠST A", "ĠS IL", "ĠPar aly", "Ġr ye", "ĠHow ell", "ĠCount down", "ness es", "alys ed", "Ġres ize", "ãĤ ½", "Ġbudget ary", "ĠStr as", "w ang", "Ġap iece", "Ġprecinct s", "Ġpe ach", "Ġsky line", "Ġ35 3", "pop ular", "App earances", "ĠMechan ics", "ĠDev Online", "S ullivan", "Z en", "Ġp u", "op olis", "5 44", "Ġde form", "Ġcounter act", "ĠL ange", "Ġ4 17", "Con sole", "77 4", "Ġnodd ing", "Ġpopul ism", "Ġhe p", "Ġcoun selling", "compl iance", "U FF", "Ġunden iably", "Ġrail ing", "ĠHor owitz", "ĠSim one", "ĠBung ie", "Ġa k", "ĠTal ks", "x ff", "fl ake", "Cr ash", "Ġsweat y", "Ġban quet", "ĠOFF IC", "Ġinvent ive", "Ġastron omer", "ĠStam ford", "ĠSc are", "ĠGRE EN", "olic ited", "Ġr usher", "Ġcent rist", "ight ing", "Ġsub class", "Ġdis av", "Ġdef und", "ĠN anto", "oci ate", "m ast", "Ġpac if", "Ġm end", "e ers", "imm igration", "ESS ION", "Ġnumber ing", "Ġlaugh able", "ĠEnd ed", "v iation", "em ark", "P itt", "Ġmetic ulous", "ĠL F", "Ġcongrat ulated", "ĠBir ch", "Ġsway ed", "Ġsemif inals", "Ġhum ankind", "m atter", "ĠEqu ip", "opa usal", "S aid", "ĠLay out", "Ġvo icing", "Ġth ug", "Ġporn ographic", "I PS", "Ġmo aning", "Ġgriev ance", "Ġconf essions", "esc al", "TEXT URE", "Aut hent", "os aurus", "P urchase", "Ġreleg ation", "al ter", "ĠÂł Âł", "Ġr iddled", "Ġo gre", "ĠLow ell", "Occ up", "E at", "ĠHy der", "ĠAdvis er", "Com merce", "H unt", "ĠOr th", "ĠComp etitive", "ĠCL A", "CD C", "Ġsal ads", "F le", "Ġindustrial ized", "` ,", "ĠO WN", "Ġbec k", "ĠPart icularly", "oub t", "Ġm M", "ĠHuss ain", "ĠChen nai", "Ġ9 20", "Ġappoint ing", "ĠCull en", ",,,, ,,,,", "Ġp ores", "ver ified", "Ġbi ochemical", "em ate", "Ġcoward ly", "ĠHels inki", "ĠEthiop ian", "S OURCE", "ER C", "est ro", "Ġbi otech", "ĠS our", "Ġbrew er", "Bloom berg", "Ġintens ify", "Gl ass", "an co", "ĠF DR", "gre SQL", "ĠF ires", "©¶æ ¥µ", "ec o", "100 1", "ĠHom eless", "Ġinstant aneous", "ĠH aste", "ig el", "D iamond", "Ġp aving", "Ġland fill", "Ġd ads", "h oun", ": ]", "Ġinc endiary", "ĠLiving ston", "ĠHil bert", "ĠChe cks", "st yles", "in ators", "ĠCl ive", "ph rine", "Ġchimpan zees", "Ġp all", "ĠJ M", "ĠAad haar", "ð Ŀ", "Ġachie vable", "dis abled", "P ET", "OOOO OOOO", "M ot", "Ġint angible", "Ġbal let", "ĠWe bs", "ĠEst imated", "Effect s", "Ġb ailed", "Josh ua", "Ġturb ulence", "Ġoccup ant", "ĠDay light", "Ġ36 1", "me et", "Ġstat ically", "Ġon look", "Ġk i", "il legal", "Ġvel vet", "Ġdehyd ration", "Ġacqu ies", "ĠRe z", "ak ura", "ĠU pton", "at ro", "Ġincomp rehensible", "Ġback door", "ĠRh ino", "7 27", "Ġmath s", ") +", "Ġhe resy", "Ġd f", "ĠRoc he", "ĠL ydia", "Ġpanc reat", "re ply", "arre ll", "Ġsolicit ation", "Ġcirc adian", "BI P", "Ġfor ay", "Ġcrypt ic", "iz u", "ime o", "ĠTom ato", "ĠH oms", "ex amination", "Ġqu arry", "ĠVal iant", "ĠJer icho", "ĠIN CLUD", "Ġ18 40", "5 19", "Ġres ists", "Ġsnap shots", "ĠSp ur", "ĠAnt iqu", "Log in", "Ġbest selling", "Ġant ic", "ĠS utherland", "ãĤ¢ ãĥ«", "Ġ~ /", "ĠP arm", "è ĥ", "P ages", "int ensity", "Ġimm obil", "Ġ18 65", "zz o", "Ġn ifty", "Ġf entanyl", "ĠPres ervation", "op hen", "Ġd arts", "ĠD inosaur", "po inters", "ĠR ite", "s uggest", "aware ness", "ĠSher idan", "Ġst ances", "Ġsor cery", "Ġper jury", "ĠNik ola", "ie ver", "Ġf iance", "ĠJordan ian", "ĠBall oon", "Ġn ab", "Ġk b", "Ġhuman ities", "ĠTan aka", "hill ary", "Ġconsult ancy", "ĠZ ub", "Ġrem ission", "Ġconf id", "CH Q", "ĠF ug", "Ġimpro vis", "Y ep", "/ _", "Ġunwilling ness", "Ġport folios", "05 5", "ĠInstruct or", "aim an", "Ġclaim ants", "M bps", "ĠBy e", "re ceived", "T weet", "Ġind emn", "ri z", "am ara", "N at", "Ġeval uates", "ĠL ur", "ep ad", "FO X", "ĠTh ro", "Ġrust y", "Ġbed rock", "ĠOp rah", "J B", "Ġmanip ulative", "Ġwill ful", "Ġrel apse", "Ġext ant", "The me", "S ensor", "ĠSt ability", "go vern", "Ġpo ppy", "Ġkn ack", "Ġins ulated", "ĠT ile", "ĠExt rem", "Ġunt old", "Ġconver ge", "Ġref uel", "ig roup", "Ġdistort ions", "Ġrav aged", "Ġmechan ically", "ĠRe illy", "ĠN ose", "ĠIncarn ation", "ĠBeck y", "abb ling", "Ġt aco", "Ġr ake", "Ġmelanch oly", "Ġillust rious", "ĠDart mouth", "Gu ide", "ĠR azer", "ĠBen z", "Ult imate", "ĠSur prise", "Ġpage ant", "off er", "Who ever", "Ġw iser", "Ġchem ist", "ĠHE LL", "ĠBul k", "Ġpl utonium", "ĠCO VER", "Ö ¼", "f ailed", "Ġtire lessly", "Ġinf ertility", "ĠTr ident", "ĠShow time", "ĠC iv", "V ice", "requ ires", "itt ance", "Ġun controlled", "interest ing", "56 1", "Ġinnov ate", "ateg ic", "L ie", "ĠS elling", "U l", "Ġsav ior", "ĠT osh", "Ġsw ast", "P ASS", "Ġr ink", "Ġcard io", "ĠI ro", "ud i", "Ġv antage", "Ġv ans", "ĠNi ño", "+ =", "Ġpropag ate", "< ?", "Ġmethod ological", "204 39", "Ġtrig lycer", "Ġing rained", "ĠAn notations", "arr anted", "6 17", "ĠS odium", "ĠA AC", "techn ical", "mult ipl", "Ġ3 73", "å ĭ", "Ġdec isively", "Ġboost ers", "Ġdessert s", "ĠGren ade", "Ġtest ifying", "ĠSc ully", "ID s", "Ġlock down", "ĠSc her", "ĠR é", "ĠWhit man", "ĠRams ay", "rem ote", "Ġh ikers", "ĠHy undai", "Ġcons cientious", "Ġcler ics", "ĠSiber ian", "ut i", "is bury", "Ġrel ayed", "Ġqu artz", "ĠC BI", "seek ers", "ull a", "Ġweld ing", "ĠSh al", "ble acher", "T ai", "ĠSam son", "Ġt umble", "ĠInvest or", "Ġsub contract", "ĠShin ra", "ow icz", "j andro", "d ad", "Ġtermin ating", "ĠNe ural", "ä» £", "Ġleak age", "ĠMid lands", "ĠCaucas us", "í ķ", "c it", "ll an", "iv ably", "ĠAlb ion", "Ġ4 57", "Ġregist rations", "Ġcomr ade", "Ġclip board", "0 47", "Ġdiscour aging", "ĠO ops", "Ad apt", "Ġem path", "n v", "ĠPR OT", "ĠDon n", "ĠP ax", "ĠB ayer", "t is", "Squ are", "Ġfoot prints", "part icip", "ĠChile an", "B rend", "ind ucing", "M agn", "Ġclub house", "ĠMagn um", "Ġenc amp", "ĠEth nic", "uch a", "ere y", "Ġw atered", "ĠCal ais", "Ġcomplex ion", "Ġsect s", "Ġren ters", "Ġbr as", "oÄŁ an", "Time out", "Man agement", "Ġinf ographic", "P okemon", "Cl ar", "Ġloc ality", "Ġfl ora", "as el", "P ont", "Ġpop ulate", "ĠO ng", "Ġsubs istence", "Ġa uctions", "ĠMcA uliffe", "ĠL OOK", "br inger", "Ġtit an", "Ġmanif old", "ĠâĹ ı", "Ġcalibr ated", "Ġcal iphate", "ĠSH E", "ĠCommission ers", "ce ivable", "j c", "W inner", "5 24", "Ġcond one", "Other wise", "Ġp iling", "Ġem body", "ĠCrime an", "ut ics", "ĠEx hibition", "Ġ4 26", "e ering", "Ġv ying", "ĠH UGE", "* =-", "Ġprin cipled", "à ¦", "Ġquir ks", "ĠEdit ors", "put ing", "G ES", "ĠF TA", "ठ¾", "add on", "ĠH AM", "ĠFrie za", "W oman", ". $", "Ġc rib", "ĠHer od", "Ġtim ers", "ĠSp aces", "ĠMac intosh", "at aka", "Ġgl ide", "Ġsmell ing", "ĠB AL", "Ġun su", "Ġcond os", "Ġbicy cl", "ĠRev ival", "55 3", "Ġjugg ling", "H ug", "ĠKardash ian", "ĠBalk ans", "mult iple", "Ġnutrit ious", "oc ry", "19 00", "Ġinteg rates", "Ġad joining", "ĠF older", "roll ment", "ven ient", "Ġu ber", "y i", "Ġwh iff", "ĠJu ven", "ĠB orough", "net te", "Ġb ilingual", "ĠSp arks", "ph thal", "man ufact", "Ġt outing", "ĠPH I", "Ke efe", "Rew ard", "Ġinf all", "ĠTem per", "typ ically", "ĠNik ol", "Ġregular s", "Ġpseud onym", "Ġexhib itions", "Ġbl aster", "Ġ40 9", "w arming", "Ġrever ber", "Ġrecip rocal", "Ġ6 70", "ip ient", "b ett", "ĠBe gins", "Ġit ching", "ĠPh ar", "Ass uming", "Ġem itting", "ĠML G", "Ġbirth place", "Ġt aunt", "ĠL uffy", "ĠAm it", "Ġcir cled", "ĠN ost", "enn ett", "Ġde forestation", "ĠHist orically", "ĠEvery day", "Ġovert ake", "79 2", "Ġn un", "ĠLuc ia", "Ġaccompan ies", "ĠSe eking", "ĠTr ash", "an ism", "R ogue", "Ġnorth western", "ĠSupplement al", "ĠNY U", "ĠF RI", "ĠSat isf", "x es", "5 17", "Ġreass ured", "Ġspor adic", "Ġ7 01", "Ġmed ial", "Ġcannabin oid", "Ġbarbar ic", "Ġep is", "ĠExplos ive", "ĠD ough", "Ġuns olved", "Support ed", "Ġacknowled gment", "sp awn", "Ġkit chens", "Ġ- =", "talk ing", "ic ist", "ĠPeg asus", "ĠPS U", "Ġphot on", "ĠAuthent ication", "R G", "@# &", "76 2", "ĠCl air", "Ġdi aper", "Ġbr ist", "ĠProsecut ors", "ĠJ em", "6 28", "ĠEvery where", "ĠJean ne", "equ ality", "ãĥ© ãĥ³", "object s", "ĠPel icans", "Ġ39 2", "Ġbl u", "b ys", "ĠA go", "Ġinstruction al", "Ġdiscrim inating", "ĠTR AN", "ĠCorn el", "ag os", "Ġty re", "Ġas piration", "ĠBrid gewater", "\": -", "! \".", "ĠEn s", "ĠCoc o", "P ie", "Ġdet ach", "ĠC ouch", "Ġphys ique", "ĠOccup ations", "osc opic", "en ough", "B uzz", "App earance", "Y P", "Ġrac er", "Ġcompl icity", "r pm", "T oy", "Ġinterrupt s", "ĠCat alyst", "Ġut ilitarian", "imp act", "Ġsp aghetti", "Ġp orous", "Ġeste emed", "Ġinc iner", "ĠI OC", "7 48", "Ġesp resso", "ĠSm ile", "abil ia", "6 35", "Ġmathematic ian", "Ġ4 24", "ĠK L", "ĠH IP", "Ġover heard", "ĠT ud", "ĠT ec", "Ġqu izz", "Ġfl attering", "Ġcon n", "âĢ İ", "Ġatt aches", "ĠR OS", "ĠAC S", "Ġt cp", "ĠSh ame", "sk ip", "res pected", "ĠTrin idad", "gr ain", "Ġfooth old", "ĠUnch arted", "ĠJul io", "z l", "av ored", "ĠAn xiety", "er rors", "ĠCent auri", "its ch", "D addy", "Ġclutch ing", "ĠIm plement", "ĠGut ierrez", "Ġ7 60", "Ġtele portation", "end ra", "Ġrevers ible", "st ros", "Ad venture", "08 3", "Ġliber ating", "Ġas phalt", "ĠSp end", "AR DS", "im sy", "PR ES", "ĠEmer ging", "Ġwild fires", "Ġtechn ologically", "Ġem its", "ĠART ICLE", "Ġirregular ities", "Ġcher ish", "çī Ī", "Ġst ink", "ĠR ost", "Econom ic", "Ġcough ing", "ĠMcC ann", "pro perties", "ilant ro", "Ġreneg oti", "Trans lation", "Ġin quest", "ĠGra pe", "oot ers", "gu i", "ĠSwords man", "ace ae", "h itting", "Ġr c", "Ġexert ed", "ĠS AP", "it ent", "Ġperil ous", "Ġobsc urity", "Ġassass inate", "Ġab original", "Ġresc uing", "ĠSh attered", "lock ing", "all ion", "Ch anging", "ĠHar rington", "ĠB ord", "ĠAfgh ans", "Jam ie", "aret z", "ĠAugust us", "Ġ38 6", "8 30", "Ġj og", "ok ingly", "Tr igger", "ĠH OR", "Stat istics", "Ġviewers hip", "Ġadd itives", "h ur", "Ġmaxim izing", "ĠR ove", "ĠLou ie", "ĠBuck et", "ĠCHR IST", "ou sel", "Ġstre aks", "ir ted", "Ġt ert", "Ġcolonial ism", "Ġbur ying", "y k", "Cond ition", "ĠDPR K", "By Id", "75 1", "âĹ ¼", "Ġwor risome", "Ġvoc ational", "sl ice", "Ġsa ils", "ĠCorrection al", "95 4", "Ġt ul", "K id", "l uster", "Ġfam ilial", "ĠSp it", "ĠEp iscopal", "Specific ally", "ĠVol cano", "run s", "q s", "Ġve tted", "Ġcram med", "t rop", "here r", "Thank fully", "Ġper cussion", "Ġor anges", "Ġround up", "Ġ4 99", "x ious", "Char acters", "ĠZion ism", "ĠR ao", "ÃĽ ÃĽ", "W F", "Ġunintention al", "ONE Y", "Gr ab", "Com mercial", "Ġglut amate", "ĠMcK enna", "ru ciating", "ning ton", "ih u", "Ch an", "ĠSw ap", "Ġleaf lets", "Ġfunction ally", "er ous", "F arm", "Ġcal oric", "ĠLiter ally", "con cert", "Ġshe nan", "Ġrep aid", "ey es", "Ġbas hing", "ĠG orge", "Ġcollabor ations", "Ġun account", "itch ie", "Ġteam work", "pp elin", "Ġpip ing", "Ġmin ced", "Ġd iam", "ri eg", "Ġmasc ara", "Ġsuck er", "ĠMo ons", "App s", "ĠPe ck", "Ġper v", "ĠFl oat", "o ley", "ĠN ish", "im ize", "Ġarom atic", "u in", "end ish", "! /", "ĠB icycle", "ĠAS IC", "ile ged", "ĠQuad ro", "ios yn", "Ġlock out", "ĠW ink", "SP EC", "Attempt s", "Ġseed ed", "red o", "ias is", "Ġsn ag", "ãĥķ ãĤ©", "ãĤ ¶", "Ġground ing", "Ġrelie ver", "Ġfrivol ous", "ĠG ifts", "ĠF aces", "Es pecially", "Ġmicrobi ome", "im ag", "ĠSch l", "ĠP les", "ĠBle ach", "ĠIr win", "ĠE aton", "ĠDisc iple", "Ġmultipl ication", "Ġcoer ced", "Ġ4 19", "st h", "E vil", "B omb", "Ġex orc", "Ġstag gered", "L ESS", "Ġinert ia", "ĠED IT", "Ġgo b", "Tr aditional", "Ġclass y", "Lear y", "ĠP AGE", "yr s", "Ġtrans porter", "Ġmat ured", "Ġhij ab", "Ġbi ome", "Where as", "Ġex termination", "ĠT ues", "ĠT akeru", "ĠAud rey", "er ial", "ĠAd en", "aff les", "Ġnarciss istic", "ĠB aird", "UT F", "I re", "ĠCon nie", "Ch amp", "Ġwhis pering", "ĠH att", "D K", "Ġdis infect", "Ġdeduct ed", "Ġpart ake", "Ġdown grade", "ĠEs ports", "ĠContin uing", "Ġdemocr atically", "icro bial", "itt a", "Ġlim estone", "Ġexempt ed", "ĠFren zy", "H erm", "7 28", "Ġfled gling", "Met a", "765 61", "69 3", "% :", "w ake", "5 26", "ĠDis cipline", "Ġvirgin ity", "ĠLeg ions", "ĠFrank ie", "int ent", "Ġrest rooms", "ĠRou ter", "da q", "Ġobjection able", "âĨ ij", "w ark", "ĠRah ul", "g ain", "activ ation", "abs olute", "ĠAccess ed", "Ġ24 00", "ogg les", "Ġsecond ly", "ĠDEF ENSE", "Ġpost age", "wra pper", "sh arp", "7 29", "Ġcommun icates", "Ġadd on", "ĠMil itia", "H ong", "Ġsl umped", "ĠJP EG", "ĠI car", "ad ish", "68 1", "Ġmaj esty", "ĠWolf gang", "ĠEl astic", "u per", "Ġv iz", "Ġunconscious ly", "ĠST D", "ĠS ass", "Ġflower ing", "ĠHel ic", "ĠDra per", "ĠAm ateur", "Ġman ure", "Ġdis ingen", "ĠLe i", "br ing", "9 49", "Ġinhib ited", "Ġhead quartered", "Ġen igmatic", "�� �", "Ġred ress", "R H", "Ġratt led", "Ġd iction", "l io", "ĠT BA", "ĠSN AP", "C alling", "Ġfasc ists", "ĠD ove", "iew icz", "0 36", "Ġco asts", "ĠR ect", "Ġ) ]", "L ot", "6 29", "ĠS EM", "ĠPeters en", "ĠExpl ain", "ĠBo ards", "ĠBe zos", "ĠJ ournals", "Ġ20 24", "p arser", "Ġmist rust", "Ġgr ate", "ĠL ocked", "bo a", "S aint", "g aming", "Ġvow el", "in ately", "bl ow", "All ah", "Ġun matched", "Ġb ordering", "ĠExp end", "n r", "Or acle", "rou ch", "Ġcont iguous", "ac us", "Ġdist raught", "58 1", "Ġanat omical", "O X", "ap ixel", "8 33", "ĠPL US", "Ġres usc", "Ġab iding", "57 3", "Ġvac ancies", "Em ily", "Ġhyp othal", "ĠWer ner", "ĠWe e", "ĠDJ s", "5 13", "Ġwitch craft", "Ġac upuncture", "ent ary", "benef it", "Product s", "ĠP SP", "ĠMP G", "ĠJ inn", "ĠJ arrett", "Ġ4 45", "ĠIm aging", "ĠP yth", "Fin ish", "Ġte x", "Ġjuven iles", "Ġhero ism", "Ġdoubt less", "ĠA ki", "ĠT end", "ĠPatri arch", "Ġbit ters", "ĠTele communications", "it atively", "ag na", "Ġr g", "ĠS OLD", "Ġcomp ulsion", "ĠN asa", "ĠKath ryn", "Ġmillion aires", "Ġintrins ically", "Ġbolst ered", "time out", "fl o", "Ġtut or", "p our", "Stat ement", "Ġ{ *", "ĠRud olph", "ĠKimber ly", "rog ens", "adi q", "] +", "Ġindign ation", "Ġfract uring", "ĠRe leases", "ĠGr ain", "pro tein", "L ago", "Ġvac ations", "Ġboot ed", "ĠTH REE", "ĠH G", "oresc ence", "Ġt f", "Ġso ar", "iosyn cr", "Ġgl ances", "ĠSp oon", "ĠJ ury", "ĠCow boy", "Ġcreat ively", "Hig her", "Ġsolic itor", "Ġhaw k", "ac io", "89 6", "Ġsuperf lu", "Ġbombs hell", "ct ure", "Ġbroker age", "Ġraid ing", "Ġf rench", "Ġang led", "Trans action", "ĠGen ocide", "u pe", "ĠHait ian", "57 2", "! :", "Ġunwitting ly", "iter ator", "sc roll", "Ġtall ied", "Ġbi omedical", "ĠC ARD", "Ġe uphem", "Ġbrain storm", "a quin", "K o", "Mic helle", "ĠR unes", "ĠBall istic", "ud ers", "Ġmod esty", "ĠiP ads", "ĠEzek iel", "Y E", "Ġstars hip", "Ġpower fully", "Ġper l", "ĠSh ade", "ĠQu art", "ĠE EG", "Ġfisher man", "OS ED", "ĠTyp ical", "df x", "Ġmes hes", "Ġet ched", "worth iness", "Ġtopp led", "Ġ3 96", "or ius", "We iss", "Ġmy sql", "ĠVal halla", "Ù Ĵ", "le asing", "Ġrec omp", "rap nel", "S el", "04 3", "Ġder ailed", "ĠGu ides", "IR T", "Ġde human", "ĠBritt any", "\" ))", "Ġex claim", "Ġb alk", "Ġ8 40", "CLA IM", "int el", "L AB", "Ġpe gged", "Ġast roph", "sm oking", "Ġrig ging", "Ġfix ation", "Ġcat apult", "ins ide", "ĠC ascade", "ĠBolshe vik", "G aza", "Dep th", "Ġloud spe", "Ġalmond s", "me yer", "l eness", "j en", "f resh", "Ġunbeat en", "ĠSqu id", "ĠPres umably", "Tim er", "B W", "Ġro sters", "Ġell ipt", "ĠHar riet", "dat abase", "ĠMut ual", "ĠComm odore", "uk ed", "kn ife", "ĠCOMM UN", "h ya", "Ġmel ts", "arch ives", "Ġrat ification", "Ġmultip lying", "Ġinter oper", "Ġasc ert", "w ings", "ver ting", "ĠScorp ion", "ay e", "ĠPorts mouth", "ĠM TA", "n it", "iaz ep", "Ġqu arantine", "Ġslides how", "Ġcent imeters", "Ġsyn opsis", "Ġsp ate", "th irst", "Ġnom inating", "ĠMel vin", "Pre view", "Ġthro b", "Ġgener ational", "ĠRad ius", "rest ling", "put able", "aw ar", "N ECT", "Ġunlaw fully", "ĠRevel ations", "Wik ipedia", "sur v", "Ġeye ing", "ij n", "ĠF W", "Ġbr unt", "Ġinter stellar", "Ġcl itor", "ĠCroat ian", "ĠCh ic", "ev a", "ĠDis app", "ĠA kin", "iner ies", "d ust", "Interest ed", "Ġgen esis", "ĠE ucl", "ö n", "p icking", "Ġmut ated", "Ġdisappro ve", "ĠHD L", "Ġ6 25", "Ì ¶", "c ancer", "Ġsqu ats", "Ġle vers", "Disc uss", "= ]", "D ex", "ĠVIDE OS", "A UD", "Ġtrans act", "ĠKin ect", "ĠK uala", "ĠC yp", "7 47", "Ġsh attering", "Ġarsen ic", "ĠInt ake", "ĠAngel o", "ĠQu it", "ĠK he", "Ġ18 93", "M aker", "0 29", "ĠPain ting", "Dis able", "9 16", "Ġanal ges", "Ġtact ile", "Ġprop hes", "Ġd iced", "ĠTravel s", "ĠHe ader", "ĠClub s", "Ass istant", "Ġinc rim", "Ġd ips", "Ġcruc ifix", "ĠShan ahan", "ĠInter pret", "Ġ40 90", "al ogy", "abb a", "Ġsimul ac", "hus band", "S IM", "Ġrecy cle", "uc er", "ed ged", "Ġre naissance", "ĠBomb ay", "Cath olic", "ĠL INE", "ĠCl othing", "re ports", "Ġpl aus", "Ġd ag", "ĠM ace", "Z I", "Ġintr uder", "ĠVeter inary", "g ru", "Ġsne aky", "ĠS ie", "ĠC innamon", "P OSE", "Ġcou rier", "ĠC NS", "Ġemanc ipation", "s it", "Ġplay through", "ĠFac ilities", "v irt", "ĠG auntlet", "Thom pson", "Ġunbeliev ably", "Param eters", "Ġst itching", "ign e", "ĠTH ESE", "Priv acy", "Ġshenan igans", "Ġvit ri", "ĠVal id", "59 1", "Ń ·", "ĠProt otype", "ink a", "SC P", "ĠT id", "è Ī", "old ed", "Ġindividual ity", "Ġbark ing", "Ġm ars", "ĠW D", "Ġ8 20", "Ġt ir", "Ġsl apping", "Ġdisgr untled", "ĠAng ola", "ri us", "ĠTorn ado", "ĠTh urs", "Ġcapt cha", "Ġang st", "ĠP og", "ĠAssass ins", "ĠAd idas", "Ġjoy ful", "Ġwh ining", "Emer gency", "Ġphosph orus", "Ġatt rition", "oph on", "ĠTimber wolves", "ĠJ ah", "ĠBr inging", "ĠW ad", "ĠEn sure", "oh l", "ĠX ie", "omm el", "c mp", "Ġz ipper", "Ġrel at", "ĠCor ridor", "m ilo", "T ING", "Av g", "Ġcro pped", "] }", "Ġr aged", "ĠLump ur", "ĠGuer rero", "our ke", "N ut", "Ġoff sets", "og lu", "dr m", "Ġmort als", "lat able", "Ġdismiss ive", "ä¸ ī", "Ġthro ats", "Ġchips et", "ĠSpot light", "Catal og", "art ist", "G b", "Ġch illy", "Ġst oked", "Ġ3 74", "W ard", "L atin", "Ġf iasco", "Ġble ach", "Ġb rav", "Enh anced", "Ġin oc", "ĠFior ina", "_ >", "Ġle ukemia", "Ġel uc", "Ġannoun cer", "ĠLith uan", "ĠArm ageddon", "å ĩ", "Len in", "ĠR uk", "Ġpe pp", "ĠRom antic", "ĠP IT", "ĠInter stellar", "ĠAt kinson", "R aid", "J s", "Go al", "C ourse", "Ġvan ishing", "es ley", "ĠR ounds", "Els a", "59 3", "Ġredund ancy", "ĠST AND", "Ġprop hetic", "Ġhabit able", "ry u", "Ġfaint ly", "M ODE", "Ġfl anked", "IR C", "Aw esome", "Ġsp urious", "ĠZ ah", "ĠMS G", "Ġsh ading", "Ġmotiv ational", "ĠSant ana", "ĠS PR", "Ġexc ruciating", "om ial", "ĠM iko", "ĠLe opard", "A byss", "Ġ[ |", "d irty", "Ġbath s", "Ġdem oral", "and re", "P B", "Ġun ification", "Ġsac rament", "Ġ[ &", "Ġpric eless", "Ġgel atin", "Ġeman ating", "ĠAll aah", "98 6", "Ġout burst", "Ġer as", "ĠX VI", "ĠSP I", "O tt", "ĠLaz arus", "PL IED", "F lying", "blog s", "W isconsin", "R aven", "Ġreb ate", "Ġcreep s", "ĠSp an", "ĠPain ter", "ĠKir a", "ĠAm os", "ĠCor vette", "Cons umer", "ĠRec over", "ck i", "Ġpes ky", "ĠIn vention", "Compan ies", "Ġchalleng ers", "ad emic", "ĠUkrain ians", "ĠNeuro log", "ĠFors aken", "Ġent rants", "Ġemb attled", "Ġdef unct", "ĠGlac ier", "Ġpo isons", "ĠH orses", "m akes", "ĠD irt", "Ġ4 23", "hh h", "ĠTrans formation", "QUI RE", "................ ..", "Ġtrave ller", "ĠSe xy", "ĠK ern", "ip olar", "Ġransom ware", "oooooooo oooooooo", "E c", "rub y", "Prof essional", "ĠOut break", "arg ument", "G rey", "ĠFif a", "ĠCH O", "ĠFOR M", "ĠAm trak", "- [", "Ġcr adle", "Ġantioxid ants", "ãģ®å ®", "7 36", "ĠNAS L", "ĠContribut ions", "Ind iana", "ĠST EP", "C SS", "Ġsal ient", "Ġall ocations", "yr ights", "Ġm ashed", "ĠCut ter", "Sex ual", "Ġp ounded", "Ġfan base", "Ġc asc", "ĠTrans parency", "Ġanaly tic", "ĠSummon er", "× ŀ", "ĠAD C", "det ail", "Ġvan quished", "Ġcr abs", "ar ie", "Dest roy", "ĠS ack", "Ġtrans istor", "Al abama", "ĠK oen", "ĠFisher ies", "c one", "Ġannex ed", "ĠM GM", "es a", "Ġf aked", "ĠCong ratulations", "Ġhind ered", "Ġcorrection al", "ĠI TV", "lee ve", "Ġin appropriately", "lic ks", "Ġtresp ass", "Ġp aws", "Ġnegoti ator", "ĠChrist ensen", "lim its", "ĠDian ne", "Ġeleg ance", "ĠContract s", "an ke", "Ob j", "Ġvigil ance", "Ġcast les", "ĠN AD", "ĠHol o", "Ġemph atically", "ĠTit us", "ĠServ ing", "ĠRich ie", "ĠP igs", "5 68", "Ġanim osity", "ĠAtt ributes", "ĠU riel", "M Q", "my ra", "ĠApplic ant", "Ġpsychiat rists", "ĠV ij", "ĠAb by", "ag ree", "P ush", "Ġk Wh", "hib a", "Ġinc ite", "ĠWe asley", "ĠTax i", "minist ic", "hy per", "ĠF arn", "Ġ6 01", "ĠNation wide", "F ake", "95 2", "Ġma ize", "Ġinteract ed", "Ġtransition ed", "Ġparas itic", "Ġharm onic", "Ġdec aying", "Ġbas eless", "ns ics", "Ġtrans pired", "Ġabund antly", "ĠFore nsic", "Ġtread mill", "ĠJ av", "ab and", "Ġssh d", "Ġfront man", "ĠJak arta", "oll er", "dro ps", "ĠSERV ICES", "rompt u", "oph ical", "h ospital", "bled on", "6 45", "Ġmid range", "ĠEV ENT", "cul ated", "raw led", "Ġper ched", "Ġover board", "ĠPe el", "ĠP wr", "ĠCar th", "ĠCOM PLE", "co e", "sh all", "Ġdeter rence", "M ETHOD", "ĠAbs ent", "M EN", "Ġs ill", "ĠLE VEL", "Y ork", "Ġsin ners", "ĠOP EC", "ĠN ur", "ĠDesign s", "se lection", "Ġunw orthy", "CH A", "Ġstreng thens", "88 3", "ed ly", "Ġslic ing", "Ġmal nutrition", "Ġfilm making", "ĠPol k", "ur ated", "Ġ4 21", "bre akers", "!' \"", "Ġwet lands", "ĠDisc rimination", "Ġallow able", "Ġste ered", "ĠSic ily", "S AM", "Ġmust ache", "Ġm ids", "Ġcl ipped", "Ġcirc ulate", "Ġbr ittle", "ĠBuild ings", "ra ised", "ĠRound up", "Ġwealth ier", "Ġoverw rite", "Ġover powered", "ĠGerr ard", "s ites", "PD ATED", "Ġacute ly", "ĠGam ble", "Ġp im", "ĠK us", "Typ ically", "De ploy", "ĠMoroc can", "p otion", "com be", "Ġvigil ante", "Ġ36 3", "St ew", "ĠB agg", "Ġres ided", "ĠSp o", "Ġrem nant", "Ġempt iness", "br ainer", "Ġout patient", "pri ority", "Ġle ptin", "ĠPay ton", "ĠGle aming", "ĠS hed", "ĠPol o", "ĠMormon ism", "rest ricted", "arl ane", "w x", "Ġcreat ine", "ĠAn on", "ĠST UD", "ĠJ UL", "ĠT ee", "5 28", "08 9", "Ġhat ched", "Dis patch", "ĠCompos ite", "Ġ45 1", "p uff", "ĠX COM", "ĠOr n", "ĠTH ANK", "END ED", "ĠAshe ville", "Ġà ľ", "Ġman go", "ĠS lightly", "world ly", "ĠW ander", "ĠExp and", "ĠCh r", "M ist", "Ġorthodox y", "ĠUN ESCO", "reg ate", "Else where", "k ie", "ir led", "Ġtopp le", "Ġadopt ive", "ĠLeg s", "d ress", "ĠS agan", "b are", "ĠGl ou", "Cr unch", "Ġhelp ers", "Ġchron ically", "ĠH uma", "1 0000", "Ġaccommod ating", "äº Ķ", "Ġwrink les", "Ġdod ged", "four th", "Ġpre con", "Ġcompress or", "ĠK are", "Ġev ict", "ĠWar wick", "im ar", "Ġmodern ization", "Ġband wagon", "Ġref uted", "Ġnet ted", "ĠNa ples", "ĠGen ie", "per ors", "Ġfield ed", "Ġde re", "ĠPar ables", "le es", "Ġtr out", "asp ers", "Ġn ihil", "Ġhapp iest", "Ġflo ppy", "ĠLo ft", "ĠHe ard", "Ġun ison", "Ġl ug", "ĠRed mond", "class ic", "Supp orters", "SH IP", "G MT", "Ġfue lled", "ç IJ", "Ġd d", "ĠEmin em", "Ġ18 97", "NY SE", "Ġsecret aries", "ĠF IA", "ĠCanaver al", "F avorite", "Ġp omp", "Ġdetain ee", "ers hip", "aim on", "i our", "ĠA pex", "Ġplant ations", "am ia", "ac ion", "R ust", "Ġtow ed", "ĠTru ly", "5 77", "Ġshel tered", "r ider", "W o", "Ġl air", "ĠInt elligent", "impro ve", "m atically", "Ġet iquette", "ad ra", "all o", "ĠJun o", "any thing", "ĠStru ggle", "ĠPred ict", "ĠGr imes", "ĠAMER ICA", "ct x", "ĠSit uation", "W OOD", "Ġsol uble", "me ier", "Ġintoler able", "ang ering", "Ġun interrupted", "Ġtool tip", "Ġinterrog ated", "Ġgun ned", "ĠSne ak", "æŃ ¦", "Ġt ether", "Ġcr umble", "L ens", "Ġclust ered", "ĠSy l", "ĠHas an", "Ġdystop ian", "w ana", "Ġjoy stick", "ĠTh ib", "amm u", "Tom orrow", "5 46", "Ġoverc ame", "Ġminim ized", "cept or", "Run ner", "ENG TH", "ĠBrend a", "ĠAchieve ments", "Ġtor ches", "Ġrapp ort", "ĠInvestig ator", "ĠHand ling", "rel ation", "g rey", "8 15", "Ġk cal", "ĠComm ands", "d q", "Ġcur ls", "Ġbe arer", "Ġcyn icism", "it ri", "ĠUse ful", "B ee", "D CS", "Ġab ras", "P ract", "BIL ITIES", "7 12", "Ġdebug ger", "Ġdebt or", "ĠL ia", "ĠK ers", "Ġexacerb ate", "ĠSt acy", "ĠB land", "ĠSc enes", "Ġbranch ing", "âĸĪâĸĪâĸĪâĸĪ âĸĪâĸĪâĸĪâĸĪ", "ape ake", "Ġs alsa", "Ġmish and", "ĠKon ami", "ĠN ib", "Ġanecd ote", "Ġagree able", "Ï ī", "ĠNath aniel", "ĠHe isman", "ĠB eware", "Ġ18 86", "spect ive", "69 1", "5 22", "Ġinhib its", "Ġhas hing", "Ġ18 89", "å° Ĩ", "v ich", "P ure", "Ġsolid ly", "Ġaspir in", "im aru", "Ġstreet car", "ĠU CS", "ĠJ udd", "Ġflash backs", "p ins", "Ġ14 40", "ĠUN HCR", "ĠSym ptoms", "T IT", "5 38", "F ra", "% );", "Ġo oz", "Ġcur few", "Ġcal med", "Ġparticip ates", "Te X", "Ġnons ensical", "Ġfull back", "ĠDe L", "mon key", "h ari", "Ġmetabol ites", "Ġloot ed", "ĠAL WAYS", "ĠB CC", "L t", "oc het", "B one", "Ġveto ed", "Ġg cc", "ĠCL ICK", "Ġ18 88", "s af", "Ġstiff ness", "Ġlow ly", "ĠGe h", "vers on", "ors et", "Ġun foreseen", "Ġan esthesia", "ĠOpt ical", "Ġrecon structed", "ĠT up", "sh ows", "NEW S", "ĠNewsp aper", "ĠA SA", "ter a", "N umbers", "Ġinexpl icable", "× ij", "Ġhard ness", "unt arily", "ĠA cer", "grad ient", "ARD IS", "Ġwood land", "Ġmetaph ors", "ĠWem bley", "ĠPa vel", "phil is", "Ġre writing", "Ġpercept ual", "Ġ10 70", "worm s", "ĠDown s", "Ġunsur prisingly", "Ġtag ging", "fl ame", "Ġlit res", "Ġboun ces", "ĠB abe", "sh ut", "Ġoverd oses", "ĠShe ila", "ĠCh au", "ĠBl ess", "Capt ure", "ĠSign ificant", "ĠSc ion", "Ġ38 9", "ĠMc H", "ĠTitan ium", "ĠMe al", "amed a", "ag ents", "agg ressive", "B illy", "76 3", "ĠS aying", "DER R", "it one", "Coll ins", "B ound", "Ġbol ted", "ĠDM CA", "95 3", "Ġun iqueness", "Ġep igen", "un ci", "ant am", "Ġreck oning", "ch airs", "OG R", "ĠSen egal", "Ġ18 62", "re levant", "Ġ ¯", "Ġpharm acies", "ĠG eral", "v ier", "Y an", "OR PG", "Ġrab id", "b ending", "ĠUN ITED", "Ġ4 65", "As sembly", "Ġwe ep", "Ġbe hest", "ĠMother s", "ĠJ ace", "h id", "Ġwh irlwind", "ĠUN IVERS", "Ġut opian", "Ġkidn ap", "Ph ilipp", "K in", "89 3", "Ġlivest ream", "ĠM ISS", "Ġsub versive", "ĠTechn iques", "ĠJUST ICE", "ĠB ASE", "Ġ38 7", "Ġassail ants", "ĠHard core", "Ġsprink led", "ĠP se", "é ļ", "print ed", "ĠH au", "OR GE", "ĠT OUR", "Ġl aced", "Ġit ch", "G iving", "Ġport ed", "78 1", "//////////////// ////////////////", "bre eding", "Ġlog ger", "ĠH OL", "inn ie", "First ly", "Ġembry onic", "Ġdeleg ated", "p ai", "O IL", "Ġcentr ally", "ĠR x", "ĠSc outing", "D utch", "Ġhe reditary", "ĠCru iser", "s at", "5 29", "ĠMar riott", "other mal", "Ġprohib itions", "E arn", "ĠSt ab", "ĠColleg es", "ĠBel ief", "st retched", "ĠL H", "ĠEntity Item", "C IA", "Ġun rem", "Ġlaure ate", "Ġdenomin ations", "sum mary", "h ler", "S pect", "ĠK laus", "ĠBe ans", "Ġins ur", "ĠPA X", "Ġfield er", "ĠV et", "ĠSp arrow", "z ie", "ĠS Q", "ĠMond ays", "ĠOff line", "ĠLer ner", "ĠExt ensions", "Ire land", "Ġpatron age", "Ġcontrast ed", "ĠMan ia", "h irt", "Mos cow", "Ġcondem ns", "ĠAn ge", "Ġcomp osing", "ĠPe pe", "ĠP addock", "Ġheter ogeneity", "Ġide ologically", "Ġf ishes", "Ġcur sing", "ĠR utherford", "ĠFlo ating", "ĠAm elia", "Te a", "Syn opsis", "Ġstun ts", "Ġbe ad", "Ġstock ing", "ĠM ILL", "ob ook", "mass ive", "\\ <", "Ġh ump", "ĠPref erences", "Engine Debug", "ge ist", "ĠNiet o", "ome ver", "ish y", "eval uate", "col onial", "Altern ative", "ĠGo Pro", "ĠV ortex", "ĠNET WORK", "ans ky", "Sec ure", "ĠTh rust", "Sn ake", "Ġparcel s", "Ġsam urai", "Ġactress es", "N ap", "M F", "ifer ation", "Be er", "5 23", "ĠI ly", "oint ment", "P ing", "Ġstri ped", "ĠMell on", "oss ession", "Ġneut ron", "end ium", "Ġa ph", "ĠFlav oring", "Ġ38 3", "Ġrespons iveness", "ĠJ indal", "ĠHitch cock", "Den ver", "ĠDRAG ON", "sm anship", "ĠDu pl", "Ġs ly", "Ġweb cam", "ĠTw ain", "ĠDar ling", "ili ate", "cons umer", "D IT", "Ġnames ake", "Ġun orthodox", "Ġfun er", "ĠPL oS", "ĠCONTR OL", "ozy g", "ogl obin", "F ACE", "ER G", "ĠD ia", "ĠF iesta", "ce le", "0 34", "Ġencl ave", "âĸ¬ âĸ¬", "on ement", "al ist", "M and", "Ġhome grown", "ĠF ancy", "Ġconcept ions", "ĠCont ains", "ure en", "Ġreiter ate", "Ġme ager", "Ġinstall ments", "Sp awn", "6 27", "Ġphot oc", "ĠCab rera", "ĠRos enthal", "ĠLans ing", "is ner", "Ġinvest s", "ĠUFO s", "EX P", "Hard ware", "Ġtr agically", "Ġconced es", "ie ft", "ch am", "bor gh", "ĠSch r", "ĠMel anie", "ĠH oy", "Ġvisit ation", "Ġid iosyncr", "Ġfract ions", "Ġfore skin", "ob os", "Ġpo aching", "ĠVI EW", "Ġstimul ates", "ĠG ork", "can on", "M IC", "ĠNem esis", "ĠInd ra", "ĠDM V", "Ġ5 29", "Ġinspect ing", "Ġgrand ma", "ĠW hedon", "ĠSh ant", "ĠP urg", "ik an", "ĠT eg", "ĠCL R", "z ac", "Vict oria", "ĠVer ify", "ion ics", "Ġpart ying", "ĠM ou", "col our", "Ġtestim onies", "l ations", "Ġpress uring", "hi ro", "ac ers", "Ġf id", "ang ler", "ĠCS I", "Ġhere after", "Ġdiss idents", "report ing", "iph any", "che v", "Ġsol itude", "Ġl obe", "Ġind is", "Ġcred ential", "re cent", "ad ult", "ĠNir vana", "ĠFranch ise", "L ayer", "H yp", "ĠBerks hire", "Ġwill s", "t if", "Ġtot em", "ĠJud ah", "rep air", "Inst ant", "5 48", "Ġemb assies", "Ġbott leneck", "Ġb ount", "Ġtyp ew", "ĠAl vin", "j ing", "im ilar", "R ush", "Ġbr im", "ĠHEL P", "A im", "] '", "Ġpass ively", "Ġbound ed", "ĠR ated", "Ġcriminal ity", "Ġbiom ark", "Ġdisp atcher", "ĠTow ards", "Ġ+ ++", "right eous", "f rog", "ĠP anc", "C arter", "0 32", "æ© Ł", "Ġult raviolet", "ĠLic ensed", "ĠT ata", "ĠBl essing", "ĠG AM", "Ġchem ically", "ĠSe af", "ĠRE LE", "ĠMerc enary", "capital ist", "Ġform ulations", "Ġann ihilation", "ĠVer b", "ĠAr gon", "Ġun loaded", "Ġmorp hed", "Ġconqu ering", "back er", "I ELD", "Ġtheft s", "Ġfront runner", "ĠRoy ale", "ĠFund amental", "el ight", "C hip", "necess ary", "ay n", "ĠSl ip", "Ġ4 48", "cern ed", "P ause", "Ġshock ingly", "ĠAB V", "Ġcomp osure", "7 33", "ĠMotors port", "ah ime", "Mur ray", "M ach", "Ġgr ids", "Ġdeb ian", "Ġfurther more", "Ġdexter ity", "ĠCollect ions", "os lov", "il age", "b j", "ĠMont eneg", "Ġstrut Connector", "Ġmassac res", "Ġbrief s", "fet ched", "uv ian", "ol ition", "Fail ure", "emon ic", "Ġfl ared", "Ġclaim ant", "Ġc ures", "Ġgive aways", "ĠSubst ance", "al ions", "Ġcr inge", "ĠK ul", "Ġarist ocracy", "ĠUl ster", "ol ated", "h ousing", "ĠM IS", "Ġgl ared", "ĠWil helm", "ne eds", "lam bda", "build ers", "ĠV IS", "Ġradi ator", "ĠGhost busters", "Ġ4 36", "act ual", "Ġher ds", "ç a", "watch ing", "Ġcounter ing", "Ch arge", "Ġchar red", "Ġwar heads", "Ġiod ine", "ĠM acy", "04 1", "Ġdepart ures", "ĠS ins", "Ġdy ed", "ĠConcept s", "g ado", "7 13", "Ġquot ations", "Ġg ist", "ĠChrist y", "Ġant igen", "ĠHem p", "ĠD rawn", "ĠB arg", "ez vous", "Ġp aternity", "Ġar du", "ĠAnch orage", "ĠR ik", "Ġover loaded", "ĠUs ername", "ĠTam my", "ĠN au", "ĠCell ular", "Ġw aning", "Ġrod ent", "ĠWor cester", "il ts", "ĠT ad", "Ġdwell ings", "Ġbull ish", "4 31", "Ġretali ate", "Ġmig raine", "ĠChev ron", "CH ECK", "Ġdon key", "c rim", "SP A", "ĠAn alog", "Ġmarqu ee", "ĠHa as", "B ir", "ĠGD DR", "ĠDownload s", "Ġwill power", "ĠFor th", "ĠRecord ed", "Ġimp ossibility", "ĠLog ged", "ĠFr anks", "ĠR att", "in itions", "Ġclean ers", "Ġsore ly", "Ġflick ering", "ĠEx amination", "c atching", "allow een", "Ms g", "Ġdun no", "F a", "Ġdys ph", "c razy", ".' '.", "Ġmain line", "Ġc s", "Ġp tr", "ĠW ally", "ig un", "95 1", "ĠBig foot", "f ights", "Ġretrie ving", "J r", "Ġdupl ication", "ĠExpl an", "Ġrel ational", "Ġqu aint", "Ġbisc uits", "Ġad o", "Ġsh udder", "Ġantid ote", "blood ed", "ks h", "Ġsa uces", "Ġrein vest", "Ġdispens ary", "ĠD iver", "Ġ9 000", "stud ent", "Ġin separ", "esc ap", "Ġtodd lers", "ĠGP IO", "ĠAss ignment", "head ers", "Ġlack luster", "Ġab ack", "95 6", "Ġtool bar", "7 45", "Ġo ust", "Ġcontempl ation", "ĠPRES IDENT", "Ġ4 58", "==== ==", "Ġguarantee ing", "ĠHe ist", "ĠCann es", "Ļ ½", "Ġcollabor ator", "ĠAm p", "Ġg ou", "ĠSH ALL", "st ories", "78 3", "Ġmobil ized", "Ġbro od", "ĠL U", "ĠðŁ ij", "Ġref in", "ĠAnthrop ology", "v ind", "ill i", "Ġwarrant ies", "ĠB abel", "Ġsw ath", "Ġc aches", "Ġantagon ists", "art ifacts", "Ġhot ly", "ĠSt arts", "ĠG ö", "z ag", "!! !!!", "Ġsc ourge", "Ġcons piring", "ru its", "re verse", "ĠShe en", "ĠJes uit", "ĠGiov anni", "ad ies", "Ġbutt ocks", "ear cher", "ac an", "Ġvolley ball", "Ġshroud ed", "Ġscore board", "b ats", "ĠI PM", "Ġass es", "Ġde regulation", "ĠTe legram", "ĠReb oot", "Ġ7 000", "ĠCan ary", "Ġk ernels", "ĠFranç ois", "ĠD uff", "ĠP on", "ĠLe ica", "ĠGar min", "Ġor phans", "ĠClaud ia", "Ġcal endars", "ĠLe ilan", "ent o", "R ocket", "Ġbr unch", "ĠHaw king", "ain ers", "Ġsens ibilities", "Ġk W", "ĠK and", "Ġre claimed", "Ġinteresting ly", "× ©", "rom y", "J M", "ĠEnhance ment", "b ush", "Sk ip", "Ġrapp ers", "Ġg azing", "p edia", "ath lon", "Rev olution", "Ġsn ipers", "Ġre verted", "Ġconglomer ate", "T erry", "79 4", "Ġhars her", "Ġdes olate", "ĠHit man", "Comm ission", "Ġ( /", "âĢ¦ .\"", "Com par", "Ġampl ification", "om inated", "Ġreg ress", "ĠColl ider", "Ġinform ants", "Ġg azed" ] } }

fauxpilot/copilot_proxy/cgtok/tokenizer.json/0

NUM_GPUS=1 GPUS=0 API_EXTERNAL_PORT=5000 TRITON_HOST=triton TRITON_PORT=8001 MODEL=py-codegen-350M-mono MODEL_DIR=${HOME}/models/py-Salesforce-codegen-350M-mono HF_CACHE_DIR=${HOME}/.cache/huggingface

fauxpilot/tests/python_backend/runner.env/0

{ "pipeline": [ { "limit": -1, "progress": false, "text_key": "text", "id_key": "id", "adapter": "<bound method BaseReader._default_adapter of \ud83d\udcd6 - READER: \ud83d\udc3f Jsonl>", "_empty_warning": false, "default_metadata": null, "data_folder": "DataFolder(path='/home/ubuntu/wensimin-work/get-data/filtered_data', fs=<fsspec.implementations.local.LocalFileSystem object at 0x7efddeeb6d10>)", "recursive": true, "glob_pattern": null, "shuffle_files": false, "compression": "infer" }, { "output_folder": "DataFolder(path='/home/ubuntu/wensimin-work/get-data/signatures', fs=<fsspec.implementations.local.LocalFileSystem object at 0x7efddeeb6d10>)", "config": { "n_grams": 5, "num_buckets": 14, "hashes_per_bucket": 8, "use_64bit_hashes": true, "seed": 1, "norm_config": { "lowercase": true, "norm_whitespace": true, "remove_punctuation": true, "norm_unicode_diacritics": true, "norm_numbers": true, "norm_weekdays": false, "norm_monthnames": false } }, "num_hashes": 112, "_parameters": null, "_hash_func": "<function sha1_hash64 at 0x7efddee2db20>", "language": "english" } ], "logging_dir": "DataFolder(path='/home/ubuntu/wensimin-work/get-data/logs/2024-07-05_01-48-57_flgve', fs=<fsspec.implementations.local.LocalFileSystem object at 0x7efddeeb6d10>)", "skip_completed": true, "tasks": 16, "workers": 16, "start_method": "forkserver", "local_tasks": 16, "local_rank_offset": 0, "depends": null, "_launched": true, "world_size": 16 }

get-data/logs/2024-07-05_01-48-57_flgve/executor.json/0

2024-07-05 01:50:08.546 | INFO  | datatrove.utils.logging:add_task_logger:47 - Launching pipeline for rank=8 2024-07-05 01:50:08.546 | INFO  | datatrove.utils.logging:log_pipeline:76 -  --- 🛠️ PIPELINE 🛠 📖 - READER: 🐿 Jsonl 🔢 - TOKENIZER: 📊 Counter 🫂 - DEDUP: 🎯 MinHash stage 4 💽 - WRITER: 🐿 Jsonl 2024-07-05 01:50:08.547 | INFO  | datatrove.pipeline.readers.base:read_files_shard:193 - Reading input file 00008.jsonl.gz 2024-07-05 01:50:18.614 | ERROR  | datatrove.executor.base:_run_for_rank:95 - An error happened while trying to locate the file on the Hub and we cannot find the requested files in the local cache. Please check your connection and try again or make sure your Internet connection is on. Traceback (most recent call last): File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/urllib3/connection.py", line 196, in _new_conn sock = connection.create_connection( │ └ <function create_connection at 0x7ff2741523e0> └ <module 'urllib3.util.connection' from '/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/urllib3/util/connectio... File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/urllib3/util/connection.py", line 85, in create_connection raise err └ None File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/urllib3/util/connection.py", line 73, in create_connection sock.connect(sa) │ │ └ ('2a03:2880:f11b:83:face:b00c:0:25de', 443, 0, 0) │ └ <method 'connect' of '_socket.socket' objects> └ <socket.socket [closed] fd=-1, family=10, type=1, proto=6> OSError: [Errno 101] Network is unreachable The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/urllib3/connectionpool.py", line 789, in urlopen response = self._make_request( │ └ <function HTTPConnectionPool._make_request at 0x7ff273fea3e0> └ <urllib3.connectionpool.HTTPSConnectionPool object at 0x7ff221629d90> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/urllib3/connectionpool.py", line 490, in _make_request raise new_e └ NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7ff22162a610>: Failed to establish a new connection: [Err... File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/urllib3/connectionpool.py", line 466, in _make_request self._validate_conn(conn) │ │ └ <urllib3.connection.HTTPSConnection object at 0x7ff22162a610> │ └ <function HTTPSConnectionPool._validate_conn at 0x7ff273fea980> └ <urllib3.connectionpool.HTTPSConnectionPool object at 0x7ff221629d90> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/urllib3/connectionpool.py", line 1095, in _validate_conn conn.connect() │ └ <function HTTPSConnection.connect at 0x7ff273fc65c0> └ <urllib3.connection.HTTPSConnection object at 0x7ff22162a610> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/urllib3/connection.py", line 615, in connect self.sock = sock = self._new_conn() │ │ │ └ <function HTTPConnection._new_conn at 0x7ff273fc5c60> │ │ └ <urllib3.connection.HTTPSConnection object at 0x7ff22162a610> │ └ None └ <urllib3.connection.HTTPSConnection object at 0x7ff22162a610> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/urllib3/connection.py", line 211, in _new_conn raise NewConnectionError( └ <class 'urllib3.exceptions.NewConnectionError'> urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7ff22162a610>: Failed to establish a new connection: [Errno 101] Network is unreachable The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/requests/adapters.py", line 667, in send resp = conn.urlopen( │ └ <function HTTPConnectionPool.urlopen at 0x7ff273fea5c0> └ <urllib3.connectionpool.HTTPSConnectionPool object at 0x7ff221629d90> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/urllib3/connectionpool.py", line 843, in urlopen retries = retries.increment( │ └ <function Retry.increment at 0x7ff274180ae0> └ Retry(total=0, connect=None, read=False, redirect=None, status=None) File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/urllib3/util/retry.py", line 519, in increment raise MaxRetryError(_pool, url, reason) from reason # type: ignore[arg-type] │ │ │ │ └ NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7ff22162a610>: Failed to establish a new connection: [Err... │ │ │ └ NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7ff22162a610>: Failed to establish a new connection: [Err... │ │ └ '/gpt2/resolve/main/tokenizer.json' │ └ <urllib3.connectionpool.HTTPSConnectionPool object at 0x7ff221629d90> └ <class 'urllib3.exceptions.MaxRetryError'> urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /gpt2/resolve/main/tokenizer.json (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7ff22162a610>: Failed to establish a new connection: [Errno 101] Network is unreachable')) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 1722, in _get_metadata_or_catch_error metadata = get_hf_file_metadata(url=url, proxies=proxies, timeout=etag_timeout, headers=headers) │ │ │ │ └ {'user-agent': 'unknown/None; hf_hub/0.23.4; python/3.11.9'} │ │ │ └ 10 │ │ └ None │ └ 'https://huggingface.co/gpt2/resolve/main/tokenizer.json' └ <function get_hf_file_metadata at 0x7ff273af2660> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn return fn(*args, **kwargs) │ │ └ {'url': 'https://huggingface.co/gpt2/resolve/main/tokenizer.json', 'proxies': None, 'timeout': 10, 'headers': {'user-agent': ... │ └ () └ <function get_hf_file_metadata at 0x7ff273af2480> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 1645, in get_hf_file_metadata r = _request_wrapper( └ <function _request_wrapper at 0x7ff273af1b20> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 372, in _request_wrapper response = _request_wrapper( └ <function _request_wrapper at 0x7ff273af1b20> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 395, in _request_wrapper response = get_session().request(method=method, url=url, **params) │ │ │ └ {'headers': {'user-agent': 'unknown/None; hf_hub/0.23.4; python/3.11.9', 'Accept-Encoding': 'identity'}, 'allow_redirects': F... │ │ └ 'https://huggingface.co/gpt2/resolve/main/tokenizer.json' │ └ 'HEAD' └ <function get_session at 0x7ff273adb600> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/requests/sessions.py", line 589, in request resp = self.send(prep, **send_kwargs) │ │ │ └ {'timeout': 10, 'allow_redirects': False, 'proxies': OrderedDict(), 'stream': False, 'verify': True, 'cert': None} │ │ └ <PreparedRequest [HEAD]> │ └ <function Session.send at 0x7ff273df5260> └ <requests.sessions.Session object at 0x7ff2220b8250> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/requests/sessions.py", line 703, in send r = adapter.send(request, **kwargs) │ │ │ └ {'timeout': 10, 'proxies': OrderedDict(), 'stream': False, 'verify': True, 'cert': None} │ │ └ <PreparedRequest [HEAD]> │ └ <function UniqueRequestIdAdapter.send at 0x7ff273adb380> └ <huggingface_hub.utils._http.UniqueRequestIdAdapter object at 0x7ff221623690> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/huggingface_hub/utils/_http.py", line 66, in send return super().send(request, *args, **kwargs) │ │ └ {'timeout': 10, 'proxies': OrderedDict(), 'stream': False, 'verify': True, 'cert': None} │ └ () └ <PreparedRequest [HEAD]> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/requests/adapters.py", line 700, in send raise ConnectionError(e, request=request) │ └ <PreparedRequest [HEAD]> └ <class 'requests.exceptions.ConnectionError'> requests.exceptions.ConnectionError: (MaxRetryError("HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /gpt2/resolve/main/tokenizer.json (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7ff22162a610>: Failed to establish a new connection: [Errno 101] Network is unreachable'))"), '(Request ID: b3c579fb-f1ad-4723-a298-6e9f3b78b148)') The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<string>", line 1, in <module> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/multiprocess/forkserver.py", line 273, in main code = _serve_one(child_r, fds, │ │ └ [23, 24, 25, 26, 27, 28] │ └ 8 └ <function _serve_one at 0x7ff274b03d80> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/multiprocess/forkserver.py", line 312, in _serve_one code = spawn._main(child_r, parent_sentinel) │ │ │ └ 4 │ │ └ 8 │ └ <function _main at 0x7ff274b02f20> └ <module 'multiprocess.spawn' from '/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/multiprocess/spawn.py'> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/multiprocess/spawn.py", line 135, in _main return self._bootstrap(parent_sentinel) │ │ └ 4 │ └ <function BaseProcess._bootstrap at 0x7ff27524f880> └ <ForkServerProcess name='ForkServerPoolWorker-63' parent=338489 started daemon> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/multiprocess/process.py", line 314, in _bootstrap self.run() │ └ <function BaseProcess.run at 0x7ff27524ede0> └ <ForkServerProcess name='ForkServerPoolWorker-63' parent=338489 started daemon> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/multiprocess/process.py", line 108, in run self._target(*self._args, **self._kwargs) │ │ │ │ │ └ {} │ │ │ │ └ <ForkServerProcess name='ForkServerPoolWorker-63' parent=338489 started daemon> │ │ │ └ (<multiprocess.queues.SimpleQueue object at 0x7ff2220b8410>, <multiprocess.queues.SimpleQueue object at 0x7ff22216e950>, None... │ │ └ <ForkServerProcess name='ForkServerPoolWorker-63' parent=338489 started daemon> │ └ <function worker at 0x7ff222159e40> └ <ForkServerProcess name='ForkServerPoolWorker-63' parent=338489 started daemon> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/multiprocess/pool.py", line 125, in worker result = (True, func(*args, **kwds)) │ │ └ {} │ └ (8,) └ functools.partial(<bound method LocalPipelineExecutor._launch_run_for_rank of <datatrove.executor.local.LocalPipelineExecutor... File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/datatrove/executor/local.py", line 74, in _launch_run_for_rank return self._run_for_rank(rank, local_rank) │ │ │ └ 8 │ │ └ 8 │ └ <function PipelineExecutor._run_for_rank at 0x7ff27384a3e0> └ <datatrove.executor.local.LocalPipelineExecutor object at 0x7ff2221003d0> > File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/datatrove/executor/base.py", line 83, in _run_for_rank deque(pipelined_data, maxlen=0) │ └ <generator object DiskWriter.run at 0x7ff27452f010> └ <class 'collections.deque'> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/datatrove/pipeline/writers/disk_base.py", line 173, in run for document in data: └ <generator object MinhashDedupFilter.run at 0x7ff2220b31c0> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/datatrove/pipeline/dedup/minhash.py", line 536, in run for idx, doc in enumerate(data): └ <generator object TokensCounter.run at 0x7ff27452ef00> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/datatrove/pipeline/tokens/counter.py", line 55, in run count = len(self.tokenizer.encode(document.text).ids) │ │ │ └ '.vscode\n.git\n.github\n.venv\ncache\ndata\ndocker\nsaves\nhf_cache\noutput\n.dockerignore\n.gitattributes\n.gitignore\n' │ │ └ Document(text='.vscode\n.git\n.github\n.venv\ncache\ndata\ndocker\nsaves\nhf_cache\noutput\n.dockerignore\n.gitattributes\n.g... │ └ <property object at 0x7ff222890ef0> └ 🔢 - TOKENIZER: 📊 Counter File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/datatrove/utils/tokenization.py", line 37, in tokenizer self._tokenizer: "Tokenizer" = load_tokenizer(self.tokenizer_name_or_path) │ │ │ │ └ 'gpt2' │ │ │ └ 🔢 - TOKENIZER: 📊 Counter │ │ └ <function load_tokenizer at 0x7ff22287a660> │ └ None └ 🔢 - TOKENIZER: 📊 Counter File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/datatrove/utils/tokenization.py", line 19, in load_tokenizer return Tokenizer.from_pretrained(name_or_path) │ │ └ 'gpt2' │ └ <staticmethod(<built-in method from_pretrained of type object at 0x141a980>)> └ <class 'tokenizers.Tokenizer'> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn return fn(*args, **kwargs) │ │ └ {'repo_id': 'gpt2', 'filename': 'tokenizer.json', 'revision': 'main'} │ └ () └ <function hf_hub_download at 0x7ff273e389a0> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 1221, in hf_hub_download return _hf_hub_download_to_cache_dir( └ <function _hf_hub_download_to_cache_dir at 0x7ff273af2200> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 1325, in _hf_hub_download_to_cache_dir _raise_on_head_call_error(head_call_error, force_download, local_files_only) │ │ │ └ False │ │ └ False │ └ ConnectionError(MaxRetryError("HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /gpt2/res... └ <function _raise_on_head_call_error at 0x7ff273af2700> File "/home/ubuntu/.conda/envs/get-data/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 1826, in _raise_on_head_call_error raise LocalEntryNotFoundError( └ <class 'huggingface_hub.utils._errors.LocalEntryNotFoundError'> huggingface_hub.utils._errors.LocalEntryNotFoundError: An error happened while trying to locate the file on the Hub and we cannot find the requested files in the local cache. Please check your connection and try again or make sure your Internet connection is on.

get-data/logs/2024-07-05_01-48-57_umniw/logs/task_00008.log/0

# Contributor Covenant Code of Conduct ## Our Pledge We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community. ## Our Standards Examples of behavior that contributes to a positive environment for our community include: * Demonstrating empathy and kindness toward other people * Being respectful of differing opinions, viewpoints, and experiences * Giving and gracefully accepting constructive feedback * Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience * Focusing on what is best not just for us as individuals, but for the overall community Examples of unacceptable behavior include: * The use of sexualized language or imagery, and sexual attention or advances of any kind * Trolling, insulting or derogatory comments, and personal or political attacks * Public or private harassment * Publishing others' private information, such as a physical or email address, without their explicit permission * Other conduct which could reasonably be considered inappropriate in a professional setting ## Enforcement Responsibilities Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful. Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate. ## Scope This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at contact@insightface.ai. All complaints will be reviewed and investigated promptly and fairly. All community leaders are obligated to respect the privacy and security of the reporter of any incident. ## Enforcement Guidelines Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct: ### 1. Correction **Community Impact**: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community. **Consequence**: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested. ### 2. Warning **Community Impact**: A violation through a single incident or series of actions. **Consequence**: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban. ### 3. Temporary Ban **Community Impact**: A serious violation of community standards, including sustained inappropriate behavior. **Consequence**: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban. ### 4. Permanent Ban **Community Impact**: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals. **Consequence**: A permanent ban from any sort of public interaction within the community. ## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 2.0, available at https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. Community Impact Guidelines were inspired by [Mozilla's code of conduct enforcement ladder](https://github.com/mozilla/diversity). [homepage]: https://www.contributor-covenant.org For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq. Translations are available at https://www.contributor-covenant.org/translations.

insightface/CODE_OF_CONDUCT.md/0

# Introduction We provide training and testing tools on synthetics data. ## Dataset ### Training dataset Download `Face Synthetics dataset` from [https://github.com/microsoft/FaceSynthetics](https://github.com/microsoft/FaceSynthetics) and put it somewhere. <div align="left"> <img src="https://github.com/microsoft/FaceSynthetics/raw/main/docs/img/dataset_samples_2.jpg" width="640"/> </div> <br/> Then use [tools/prepare_synthetics.py](tools/prepare_synthetics.py) for training data preparation. ### Testing dataset [300-W](https://ibug.doc.ic.ac.uk/resources/300-W/) ## Pretrained Model [ResNet50d](https://drive.google.com/file/d/1kNP7qEl3AYNbaHFUg_ZiyRB1CtfDWXR4/view?usp=sharing) ## Train and Test ### Prerequisites - pytorch_lightning - timm - albumentations ### Training `` python -u trainer_synthetics.py `` which uses `resnet50d` as backbone by default, please check the [code](trainer_synthetics.py) for detail. ### Testing Please check [test_synthetics.py](test_synthetics.py) for detail. ## Result Visualization(3D 68 Keypoints) <div align="left"> <img src="https://github.com/nttstar/insightface-resources/blob/master/alignment/images/image_008_1.jpg?raw=true" width="320"/> </div> <div align="left"> <img src="https://github.com/nttstar/insightface-resources/blob/master/alignment/images/image_017_1.jpg?raw=true" width="320"/> </div> <div align="left"> <img src="https://github.com/nttstar/insightface-resources/blob/master/alignment/images/image_039.jpg?raw=true" width="320"/> </div>

insightface/alignment/synthetics/README.md/0

BATCH_SIZE: 512 DATA: NUM_FRAMES: 1 SCALE_MID_MEAN: 0.720643 SCALE_MID_STD: 0.058 USE_RANDOM_DIFF: true NETWORK: DIS_RES_BLOCKS: 2 DIS_TEMP_RES_BLOCKS: 2 DIS_USE_SPECTRAL_NORM: false SCALER_INPUT_SIZE: 34 TRAIN: BOUND_AZIM: 2.44346 BOUND_ELEV: 0.34906585 DIS_LR: 0.0002 LOSS_TYPE: ss_adv LOSS_WEIGHTS: - 1.0 - 1.0 - 1.0 - 1.0 MAINNET_CRITICS: 4 NUM_CRITICS: 3 NUM_CRITICS_TEMP: 3 POSE_LR: 0.0002 PRETRAIN_LIFTER: false SCALE_LOSS_WEIGHTS: - 0.001 - 1.0 SUBNET_CRITICS: 1 TEMP_LR: 0.0002 USE_CYCLE: false USE_NEW_ROT: false USE_NEW_TEMP: false USE_SCALER: false USE_GT: true

insightface/body/human_pose/ambiguity_aware/cfg/h36m_gt_adv.yaml/0

#!/usr/bin/env python3 # coding=utf-8 import torch import torch.nn as nn from lib.models.building_blocks import ResBlock class Scaler(nn.Module): # for input size, default value is: 17 * 2 + h + w + mean_hw = 37 def __init__(self, input_size=37, num_channels=1024, num_res_blocks=1, dropout=0.25, bn_track=True): super(Scaler, self).__init__() self.blocks = nn.ModuleList() self.pre = nn.Linear(input_size, num_channels) for _ in range(num_res_blocks): self.blocks.append(ResBlock(num_channels, input_size, dropout=dropout, use_bn=True, bn_track=bn_track)) self.out = nn.Linear(num_channels, 1) def forward(self, x): bs = x.size(0) x = x.view(bs, -1) out = self.pre(x) for block in self.blocks: out = block(out, x) out = self.out(out) # add sigmoid # out = 2 * torch.sigmoid(out) return out def get_scaler(cfg): input_size = cfg.NETWORK.SCALER_INPUT_SIZE num_res_blocks = cfg.NETWORK.SCALER_RES_BLOCKS num_channels = cfg.NETWORK.NUM_CHANNELS bn_track = cfg.NETWORK.BN_TRACK dropout = cfg.NETWORK.DROPOUT scaler = Scaler(input_size=input_size, num_channels=num_channels, num_res_blocks=num_res_blocks, dropout=dropout, bn_track=bn_track) return scaler

insightface/body/human_pose/ambiguity_aware/lib/models/scaler.py/0

import numpy as np import h5py import argparse np.random.seed(2019) parser = argparse.ArgumentParser(description="Generate the diff data") parser.add_argument("--valid", action="store_true") parser.add_argument("--use_random", action="store_true") # specify the interval parser.add_argument("--bound", default=1, type=int, required=False) parser.add_argument("--use_previous", action="store_true", help="Specify whether to use previous frames or not") parser.add_argument('--use_pre', action='store_true') args = parser.parse_args() # compute the difference of frames bound = args.bound is_train = not args.valid use_random = args.use_random use_previous = args.use_previous use_pre = args.use_pre # in_filename = "../data/kinetics_final.h5" suffix = str(bound) if bound > 1 else "" if use_random: suffix += "_rand" if use_pre: suffix += "_pre" in_filename = "../data/mpi_{}_pred3.h5".format("train" if is_train else "valid") out_filename = "../data/mpi_{}_diff{}.h5".format("train" if is_train else "valid", suffix) f = h5py.File(in_filename, "r") names = [name.decode() for name in f['imagename'][:]] joints_2d = np.array(f['joint_2d_gt' if not use_pre else "joint_2d_pre"]) f.close() print("Load from", in_filename) size = joints_2d.shape[0] splits = [name.split('/') for name in names] sequences = ['/'.join(split[:4]) for split in splits] indices_ref = [int(split[-1].split(".")[0].split('_')[1]) for split in splits] indices = [] i = 0 last_seqname = None for index, seqname in zip(indices_ref, sequences): if last_seqname is not None and seqname != last_seqname: i = 0 last_seqname = seqname indices.append(i) i += 1 # calculate the length of each sequence seq_lens = {} for split in splits: seq = '/'.join(split[:4]) if seq not in seq_lens: seq_lens[seq] = 0 seq_lens[seq] += 1 intervals = np.random.randint(1, bound + 1, (size, )) if not use_random: intervals.fill(bound) if use_previous: spec_indices = [i for i, index in enumerate(indices) if index < intervals[i]] diff_indices = np.arange(0, size, 1) - intervals diff_indices[spec_indices] += 2 * intervals[spec_indices] else: spec_indices = [i for i, index in enumerate(indices) if index >= seq_lens[sequences[i]] - intervals[i]] diff_indices = np.arange(0, size, 1) + intervals diff_indices[spec_indices] -= 2 * intervals[spec_indices] # before_joints = np.concatenate((joints_2d[:1].copy(), joints_2d[:-1].copy()), axis=0) # after_joints = np.concatenate((joints_2d[1:].copy(), joints_2d[-1:].copy()), axis=0) # print(before_joints.shape, after_joints.shape) # diff_before = joints_2d - before_joints # diff_after = joints_2d - after_joints # diff_before, diff_after = before_joints, after_joints # diff_before, diff_after = diff_before[:, np.newaxis], diff_after[:, np.newaxis] # finally process the special cases # diff_before[start_indices] = diff_after[start_indices] # diff_after[end_indices] = diff_before[end_indices] # diff = np.concatenate((diff_before, diff_after), axis=1) # print(diff.shape) # diff_types = np.ones((len(diff), ), dtype=np.uint8) # diff_types[start_indices] = 0 # diff_types[end_indices] = 2 diff = joints_2d[diff_indices] dist = np.linalg.norm((joints_2d - diff).reshape(size, -1), axis=1).mean() print("Mean distance bewteen diff and original: {:.3f}".format(dist)) f = h5py.File(out_filename, "w") f['gt_diff'] = diff # f['gt_diff_type'] = diff_types f.close() print("Saved to", out_filename)

insightface/body/human_pose/ambiguity_aware/scripts/mpi_get_diff.py/0

import numbers import os import queue as Queue import threading import mxnet as mx import numpy as np import torch from torch.utils.data import DataLoader, Dataset from torchvision import transforms import cv2 import albumentations as A from albumentations.pytorch import ToTensorV2 from insightface.app import MaskAugmentation class BackgroundGenerator(threading.Thread): def __init__(self, generator, local_rank, max_prefetch=6): super(BackgroundGenerator, self).__init__() self.queue = Queue.Queue(max_prefetch) self.generator = generator self.local_rank = local_rank self.daemon = True self.start() def run(self): torch.cuda.set_device(self.local_rank) for item in self.generator: self.queue.put(item) self.queue.put(None) def next(self): next_item = self.queue.get() if next_item is None: raise StopIteration return next_item def __next__(self): return self.next() def __iter__(self): return self class DataLoaderX(DataLoader): def __init__(self, local_rank, **kwargs): super(DataLoaderX, self).__init__(**kwargs) self.stream = torch.cuda.Stream(local_rank) self.local_rank = local_rank def __iter__(self): self.iter = super(DataLoaderX, self).__iter__() self.iter = BackgroundGenerator(self.iter, self.local_rank) self.preload() return self def preload(self): self.batch = next(self.iter, None) if self.batch is None: return None with torch.cuda.stream(self.stream): for k in range(len(self.batch)): self.batch[k] = self.batch[k].to(device=self.local_rank, non_blocking=True) def __next__(self): torch.cuda.current_stream().wait_stream(self.stream) batch = self.batch if batch is None: raise StopIteration self.preload() return batch class MXFaceDataset(Dataset): def __init__(self, root_dir, local_rank, aug_modes="brightness=0.1+mask=0.1"): super(MXFaceDataset, self).__init__() default_aug_probs = { 'brightness' : 0.2, 'blur': 0.1, 'mask': 0.1, } aug_mode_list = aug_modes.lower().split('+') aug_mode_map = {} for aug_mode_str in aug_mode_list: _aug = aug_mode_str.split('=') aug_key = _aug[0] if len(_aug)>1: aug_prob = float(_aug[1]) else: aug_prob = default_aug_probs[aug_key] aug_mode_map[aug_key] = aug_prob transform_list = [] self.mask_aug = False self.mask_prob = 0.0 key = 'mask' if key in aug_mode_map: self.mask_aug = True self.mask_prob = aug_mode_map[key] transform_list.append( MaskAugmentation(mask_names=['mask_white', 'mask_blue', 'mask_black', 'mask_green'], mask_probs=[0.4, 0.4, 0.1, 0.1], h_low=0.33, h_high=0.4, p=self.mask_prob) ) if local_rank==0: print('data_transform_list:', transform_list) print('mask:', self.mask_aug, self.mask_prob) key = 'brightness' if key in aug_mode_map: prob = aug_mode_map[key] transform_list.append( A.RandomBrightnessContrast(brightness_limit=0.125, contrast_limit=0.05, p=prob) ) key = 'blur' if key in aug_mode_map: prob = aug_mode_map[key] transform_list.append( A.ImageCompression(quality_lower=30, quality_upper=80, p=prob) ) transform_list.append( A.MedianBlur(blur_limit=(1,7), p=prob) ) transform_list.append( A.MotionBlur(blur_limit=(5,12), p=prob) ) transform_list += \ [ A.HorizontalFlip(p=0.5), A.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), ToTensorV2(), ] #here, the input for A transform is rgb cv2 img self.transform = A.Compose( transform_list ) self.root_dir = root_dir self.local_rank = local_rank path_imgrec = os.path.join(root_dir, 'train.rec') path_imgidx = os.path.join(root_dir, 'train.idx') self.imgrec = mx.recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r') s = self.imgrec.read_idx(0) header, _ = mx.recordio.unpack(s) #print(header) #print(len(self.imgrec.keys)) if header.flag > 0: if len(header.label)==2: self.imgidx = np.array(range(1, int(header.label[0]))) else: self.imgidx = np.array(list(self.imgrec.keys)) else: self.imgidx = np.array(list(self.imgrec.keys)) #print('imgidx len:', len(self.imgidx)) def __getitem__(self, index): idx = self.imgidx[index] s = self.imgrec.read_idx(idx) header, img = mx.recordio.unpack(s) hlabel = header.label #print('hlabel:', hlabel.__class__) sample = mx.image.imdecode(img).asnumpy() if not isinstance(hlabel, numbers.Number): idlabel = hlabel[0] else: idlabel = hlabel label = torch.tensor(idlabel, dtype=torch.long) if self.transform is not None: sample = self.transform(image=sample, hlabel=hlabel)['image'] return sample, label def __len__(self): return len(self.imgidx) if __name__ == "__main__": import argparse, cv2, copy parser = argparse.ArgumentParser(description='dataset test') parser.add_argument('--dataset', type=str, help='dataset path') parser.add_argument('--samples', type=int, default=256, help='') parser.add_argument('--cols', type=int, default=16, help='') args = parser.parse_args() assert args.samples%args.cols==0 assert args.cols%2==0 samples = args.samples cols = args.cols rows = args.samples // args.cols dataset = MXFaceDataset(root_dir=args.dataset, local_rank=0, aug_modes='mask=1.0') dataset.transform = A.Compose([t for t in dataset.transform if not isinstance(t, (A.Normalize, ToTensorV2))]) dataset_0 = copy.deepcopy(dataset) #dataset_0.transform = None dataset_1 = copy.deepcopy(dataset) #dataset_1.transform = A.Compose( # [ # A.RandomBrightnessContrast(brightness_limit=0.125, contrast_limit=0.05, p=1.0), # A.ImageCompression(quality_lower=30, quality_upper=80, p=1.0), # A.MedianBlur(blur_limit=(1,7), p=1.0), # A.MotionBlur(blur_limit=(5,12), p=1.0), # A.Affine(scale=(0.92, 1.08), translate_percent=(-0.06, 0.06), rotate=(-6, 6), shear=None, interpolation=cv2.INTER_LINEAR, p=1.0), # ] #) fig = np.zeros( (112*rows, 112*cols, 3), dtype=np.uint8 ) for idx in range(samples): if idx%2==0: image, _ = dataset_0[idx//2] else: image, _ = dataset_1[idx//2] row_idx = idx // cols col_idx = idx % cols fig[row_idx*112:(row_idx+1)*112, col_idx*112:(col_idx+1)*112,:] = image[:,:,::-1] # to bgr cv2.imwrite("./datasets.png", fig)

insightface/challenges/iccv21-mfr/dataset_mask.py/0

from __future__ import print_function import sys import mxnet as mx import numpy as np from distutils.util import strtobool from ..config import config, generate_config STAT = {0: 0} STEP = 28800 class RPNFPNOHEM3Operator(mx.operator.CustomOp): def __init__(self, stride=0, network='', dataset='', prefix=''): super(RPNFPNOHEM3Operator, self).__init__() self.stride = int(stride) self.prefix = prefix generate_config(network, dataset) self.mode = config.TRAIN.OHEM_MODE #0 for random 10:245, 1 for 10:246, 2 for 10:30, mode 1 for default global STAT for k in config.RPN_FEAT_STRIDE: STAT[k] = [0, 0, 0] def forward(self, is_train, req, in_data, out_data, aux): global STAT cls_score = in_data[0].asnumpy() #BS, 2, ANCHORS labels_raw = in_data[1].asnumpy() # BS, ANCHORS A = config.NUM_ANCHORS anchor_weight = np.zeros((labels_raw.shape[0], labels_raw.shape[1], 1), dtype=np.float32) valid_count = np.zeros((labels_raw.shape[0], 1), dtype=np.float32) #print('anchor_weight', anchor_weight.shape) #assert labels.shape[0]==1 #assert cls_score.shape[0]==1 #assert bbox_weight.shape[0]==1 #print('shape', cls_score.shape, labels.shape, file=sys.stderr) #print('bbox_weight 0', bbox_weight.shape, file=sys.stderr) #bbox_weight = np.zeros( (labels_raw.shape[0], labels_raw.shape[1], 4), dtype=np.float32) _stat = [0, 0, 0] for ibatch in range(labels_raw.shape[0]): _anchor_weight = np.zeros((labels_raw.shape[1], 1), dtype=np.float32) labels = labels_raw[ibatch] fg_score = cls_score[ibatch, 1, :] - cls_score[ibatch, 0, :] fg_inds = np.where(labels > 0)[0] num_fg = int(config.TRAIN.RPN_FG_FRACTION * config.TRAIN.RPN_BATCH_SIZE) origin_num_fg = len(fg_inds) #print(len(fg_inds), num_fg, file=sys.stderr) if len(fg_inds) > num_fg: if self.mode == 0: disable_inds = np.random.choice(fg_inds, size=(len(fg_inds) - num_fg), replace=False) labels[disable_inds] = -1 else: pos_ohem_scores = fg_score[fg_inds] order_pos_ohem_scores = pos_ohem_scores.ravel().argsort() sampled_inds = fg_inds[order_pos_ohem_scores[:num_fg]] labels[fg_inds] = -1 labels[sampled_inds] = 1 n_fg = np.sum(labels > 0) fg_inds = np.where(labels > 0)[0] num_bg = config.TRAIN.RPN_BATCH_SIZE - n_fg if self.mode == 2: num_bg = max( 48, n_fg * int(1.0 / config.TRAIN.RPN_FG_FRACTION - 1)) bg_inds = np.where(labels == 0)[0] origin_num_bg = len(bg_inds) if num_bg == 0: labels[bg_inds] = -1 elif len(bg_inds) > num_bg: # sort ohem scores if self.mode == 0: disable_inds = np.random.choice(bg_inds, size=(len(bg_inds) - num_bg), replace=False) labels[disable_inds] = -1 else: neg_ohem_scores = fg_score[bg_inds] order_neg_ohem_scores = neg_ohem_scores.ravel().argsort( )[::-1] sampled_inds = bg_inds[order_neg_ohem_scores[:num_bg]] #print('sampled_inds_bg', sampled_inds, file=sys.stderr) labels[bg_inds] = -1 labels[sampled_inds] = 0 if n_fg > 0: order0_labels = labels.reshape((1, A, -1)).transpose( (0, 2, 1)).reshape((-1, )) bbox_fg_inds = np.where(order0_labels > 0)[0] #print('bbox_fg_inds, order0 ', bbox_fg_inds, file=sys.stderr) _anchor_weight[bbox_fg_inds, :] = 1.0 anchor_weight[ibatch] = _anchor_weight valid_count[ibatch][0] = n_fg #if self.prefix=='face': # #print('fg-bg', self.stride, n_fg, num_bg) # STAT[0]+=1 # STAT[self.stride][0] += config.TRAIN.RPN_BATCH_SIZE # STAT[self.stride][1] += n_fg # STAT[self.stride][2] += np.sum(fg_score[fg_inds]>=0) # #_stat[0] += config.TRAIN.RPN_BATCH_SIZE # #_stat[1] += n_fg # #_stat[2] += np.sum(fg_score[fg_inds]>=0) # #print('stride num_fg', self.stride, n_fg, file=sys.stderr) # #ACC[self.stride] += np.sum(fg_score[fg_inds]>=0) # #x = float(labels_raw.shape[0]*len(config.RPN_FEAT_STRIDE)) # x = 1.0 # if STAT[0]%STEP==0: # _str = ['STAT'] # STAT[0] = 0 # for k in config.RPN_FEAT_STRIDE: # acc = float(STAT[k][2])/STAT[k][1] # acc0 = float(STAT[k][1])/STAT[k][0] # #_str.append("%d: all-fg(%d, %d, %.4f), fg-fgcorrect(%d, %d, %.4f)"%(k,STAT[k][0], STAT[k][1], acc0, STAT[k][1], STAT[k][2], acc)) # _str.append("%d: (%d, %d, %.4f)"%(k, STAT[k][1], STAT[k][2], acc)) # STAT[k] = [0,0,0] # _str = ' | '.join(_str) # print(_str, file=sys.stderr) #if self.stride==4 and num_fg>0: # print('_stat_', self.stride, num_fg, num_bg, file=sys.stderr) #labels_ohem = mx.nd.array(labels_raw) #anchor_weight = mx.nd.array(anchor_weight) #print('valid_count', self.stride, np.sum(valid_count)) #print('_stat', _stat, valid_count) for ind, val in enumerate([labels_raw, anchor_weight, valid_count]): val = mx.nd.array(val) self.assign(out_data[ind], req[ind], val) def backward(self, req, out_grad, in_data, out_data, in_grad, aux): for i in range(len(in_grad)): self.assign(in_grad[i], req[i], 0) @mx.operator.register('rpn_fpn_ohem3') class RPNFPNOHEM3Prop(mx.operator.CustomOpProp): def __init__(self, stride=0, network='', dataset='', prefix=''): super(RPNFPNOHEM3Prop, self).__init__(need_top_grad=False) self.stride = stride self.network = network self.dataset = dataset self.prefix = prefix def list_arguments(self): return ['cls_score', 'labels'] def list_outputs(self): return ['labels_ohem', 'anchor_weight', 'valid_count'] def infer_shape(self, in_shape): labels_shape = in_shape[1] #print('in_rpn_ohem', in_shape[0], in_shape[1], in_shape[2], file=sys.stderr) anchor_weight_shape = [labels_shape[0], labels_shape[1], 1] #print('in_rpn_ohem', labels_shape, anchor_weight_shape) return in_shape, \ [labels_shape, anchor_weight_shape, [labels_shape[0], 1]] def create_operator(self, ctx, shapes, dtypes): return RPNFPNOHEM3Operator(self.stride, self.network, self.dataset, self.prefix) def declare_backward_dependency(self, out_grad, in_data, out_data): return []

insightface/detection/retinaface/rcnn/PY_OP/rpn_fpn_ohem3.py/0

// ------------------------------------------------------------------ // Faster R-CNN // Copyright (c) 2015 Microsoft // Licensed under The MIT License [see fast-rcnn/LICENSE for details] // Written by Shaoqing Ren // ------------------------------------------------------------------ #include "gpu_nms.hpp" #include <vector> #include <iostream> #define CUDA_CHECK(condition) \ /* Code block avoids redefinition of cudaError_t error */ \ do { \ cudaError_t error = condition; \ if (error != cudaSuccess) { \ std::cout << cudaGetErrorString(error) << std::endl; \ } \ } while (0) #define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0)) int const threadsPerBlock = sizeof(unsigned long long) * 8; __device__ inline float devIoU(float const * const a, float const * const b) { float left = max(a[0], b[0]), right = min(a[2], b[2]); float top = max(a[1], b[1]), bottom = min(a[3], b[3]); float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f); float interS = width * height; float Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1); float Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1); return interS / (Sa + Sb - interS); } __global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh, const float *dev_boxes, unsigned long long *dev_mask) { const int row_start = blockIdx.y; const int col_start = blockIdx.x; // if (row_start > col_start) return; const int row_size = min(n_boxes - row_start * threadsPerBlock, threadsPerBlock); const int col_size = min(n_boxes - col_start * threadsPerBlock, threadsPerBlock); __shared__ float block_boxes[threadsPerBlock * 5]; if (threadIdx.x < col_size) { block_boxes[threadIdx.x * 5 + 0] = dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0]; block_boxes[threadIdx.x * 5 + 1] = dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1]; block_boxes[threadIdx.x * 5 + 2] = dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2]; block_boxes[threadIdx.x * 5 + 3] = dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3]; block_boxes[threadIdx.x * 5 + 4] = dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4]; } __syncthreads(); if (threadIdx.x < row_size) { const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; const float *cur_box = dev_boxes + cur_box_idx * 5; int i = 0; unsigned long long t = 0; int start = 0; if (row_start == col_start) { start = threadIdx.x + 1; } for (i = start; i < col_size; i++) { if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) { t |= 1ULL << i; } } const int col_blocks = DIVUP(n_boxes, threadsPerBlock); dev_mask[cur_box_idx * col_blocks + col_start] = t; } } void _set_device(int device_id) { int current_device; CUDA_CHECK(cudaGetDevice(&current_device)); if (current_device == device_id) { return; } // The call to cudaSetDevice must come before any calls to Get, which // may perform initialization using the GPU. CUDA_CHECK(cudaSetDevice(device_id)); } void _nms(int* keep_out, int* num_out, const float* boxes_host, int boxes_num, int boxes_dim, float nms_overlap_thresh, int device_id) { _set_device(device_id); float* boxes_dev = NULL; unsigned long long* mask_dev = NULL; const int col_blocks = DIVUP(boxes_num, threadsPerBlock); CUDA_CHECK(cudaMalloc(&boxes_dev, boxes_num * boxes_dim * sizeof(float))); CUDA_CHECK(cudaMemcpy(boxes_dev, boxes_host, boxes_num * boxes_dim * sizeof(float), cudaMemcpyHostToDevice)); CUDA_CHECK(cudaMalloc(&mask_dev, boxes_num * col_blocks * sizeof(unsigned long long))); dim3 blocks(DIVUP(boxes_num, threadsPerBlock), DIVUP(boxes_num, threadsPerBlock)); dim3 threads(threadsPerBlock); nms_kernel<<<blocks, threads>>>(boxes_num, nms_overlap_thresh, boxes_dev, mask_dev); std::vector<unsigned long long> mask_host(boxes_num * col_blocks); CUDA_CHECK(cudaMemcpy(&mask_host[0], mask_dev, sizeof(unsigned long long) * boxes_num * col_blocks, cudaMemcpyDeviceToHost)); std::vector<unsigned long long> remv(col_blocks); memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); int num_to_keep = 0; for (int i = 0; i < boxes_num; i++) { int nblock = i / threadsPerBlock; int inblock = i % threadsPerBlock; if (!(remv[nblock] & (1ULL << inblock))) { keep_out[num_to_keep++] = i; unsigned long long *p = &mask_host[0] + i * col_blocks; for (int j = nblock; j < col_blocks; j++) { remv[j] |= p[j]; } } } *num_out = num_to_keep; CUDA_CHECK(cudaFree(boxes_dev)); CUDA_CHECK(cudaFree(mask_dev)); }

insightface/detection/retinaface/rcnn/cython/nms_kernel.cu/0

import numpy as np from ..cython.cpu_nms import cpu_nms try: from ..cython.gpu_nms import gpu_nms except ImportError: gpu_nms = None def py_nms_wrapper(thresh): def _nms(dets): return nms(dets, thresh) return _nms def cpu_nms_wrapper(thresh): def _nms(dets): return cpu_nms(dets, thresh) return _nms def gpu_nms_wrapper(thresh, device_id): def _nms(dets): return gpu_nms(dets, thresh, device_id) if gpu_nms is not None: return _nms else: return cpu_nms_wrapper(thresh) def nms(dets, thresh): """ greedily select boxes with high confidence and overlap with current maximum <= thresh rule out overlap >= thresh :param dets: [[x1, y1, x2, y2 score]] :param thresh: retain overlap < thresh :return: indexes to keep """ x1 = dets[:, 0] y1 = dets[:, 1] x2 = dets[:, 2] y2 = dets[:, 3] scores = dets[:, 4] areas = (x2 - x1 + 1) * (y2 - y1 + 1) order = scores.argsort()[::-1] keep = [] while order.size > 0: i = order[0] keep.append(i) xx1 = np.maximum(x1[i], x1[order[1:]]) yy1 = np.maximum(y1[i], y1[order[1:]]) xx2 = np.minimum(x2[i], x2[order[1:]]) yy2 = np.minimum(y2[i], y2[order[1:]]) w = np.maximum(0.0, xx2 - xx1 + 1) h = np.maximum(0.0, yy2 - yy1 + 1) inter = w * h ovr = inter / (areas[i] + areas[order[1:]] - inter) inds = np.where(ovr <= thresh)[0] order = order[inds + 1] return keep

insightface/detection/retinaface/rcnn/processing/nms.py/0

import mxnet as mx import mxnet.ndarray as nd import mxnet.gluon as gluon import mxnet.gluon.nn as nn import mxnet.autograd as ag import numpy as np from rcnn.config import config from rcnn.PY_OP import rpn_fpn_ohem3 from rcnn.symbol.symbol_common import get_sym_train def conv_only(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \ stride=(1,1), bias_wd_mult=0.0, shared_weight=None, shared_bias = None): if shared_weight is None: weight = mx.symbol.Variable(name="{}_weight".format(name), init=mx.init.Normal(0.01), attr={'__lr_mult__': '1.0'}) bias = mx.symbol.Variable(name="{}_bias".format(name), init=mx.init.Constant(0.0), attr={ '__lr_mult__': '2.0', '__wd_mult__': str(bias_wd_mult) }) else: weight = shared_weight bias = shared_bias print('reuse shared var in', name) conv = mx.symbol.Convolution(data=from_layer, kernel=kernel, pad=pad, \ stride=stride, num_filter=num_filter, name="{}".format(name), weight = weight, bias=bias) return conv def conv_act_layer_dw(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \ stride=(1,1), act_type="relu", bias_wd_mult=0.0): assert kernel[0] == 3 weight = mx.symbol.Variable(name="{}_weight".format(name), init=mx.init.Normal(0.01), attr={'__lr_mult__': '1.0'}) bias = mx.symbol.Variable(name="{}_bias".format(name), init=mx.init.Constant(0.0), attr={ '__lr_mult__': '2.0', '__wd_mult__': str(bias_wd_mult) }) conv = mx.symbol.Convolution(data=from_layer, kernel=kernel, pad=pad, \ stride=stride, num_filter=num_filter, num_group=num_filter, name="{}".format(name), weight=weight, bias=bias) conv = mx.sym.BatchNorm(data=conv, fix_gamma=False, eps=2e-5, momentum=0.9, name=name + '_bn') if len(act_type) > 0: relu = mx.symbol.Activation(data=conv, act_type=act_type, \ name="{}_{}".format(name, act_type)) else: relu = conv return relu def conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \ stride=(1,1), act_type="relu", bias_wd_mult=0.0, separable=False, filter_in = -1): separable = False if separable: assert kernel[0] == 3 if not separable: weight = mx.symbol.Variable(name="{}_weight".format(name), init=mx.init.Normal(0.01), attr={'__lr_mult__': '1.0'}) bias = mx.symbol.Variable(name="{}_bias".format(name), init=mx.init.Constant(0.0), attr={ '__lr_mult__': '2.0', '__wd_mult__': str(bias_wd_mult) }) conv = mx.symbol.Convolution(data=from_layer, kernel=kernel, pad=pad, \ stride=stride, num_filter=num_filter, name="{}".format(name), weight=weight, bias=bias) conv = mx.sym.BatchNorm(data=conv, fix_gamma=False, eps=2e-5, momentum=0.9, name=name + '_bn') else: if filter_in < 0: filter_in = num_filter conv = mx.symbol.Convolution(data=from_layer, kernel=kernel, pad=pad, \ stride=stride, num_filter=filter_in, num_group=filter_in, name="{}_sep".format(name)) conv = mx.sym.BatchNorm(data=conv, fix_gamma=False, eps=2e-5, momentum=0.9, name=name + '_sep_bn') conv = mx.symbol.Activation(data=conv, act_type='relu', \ name="{}_sep_bn_relu".format(name)) conv = mx.symbol.Convolution(data=conv, kernel=(1,1), pad=(0,0), \ stride=(1,1), num_filter=num_filter, name="{}".format(name)) conv = mx.sym.BatchNorm(data=conv, fix_gamma=False, eps=2e-5, momentum=0.9, name=name + '_bn') if len(act_type) > 0: relu = mx.symbol.Activation(data=conv, act_type=act_type, \ name="{}_{}".format(name, act_type)) else: relu = conv return relu def ssh_context_module(body, num_filter, filter_in, name): conv_dimred = conv_act_layer(body, name + '_conv1', num_filter, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='relu', separable=True, filter_in=filter_in) conv5x5 = conv_act_layer(conv_dimred, name + '_conv2', num_filter, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='', separable=True) conv7x7_1 = conv_act_layer(conv_dimred, name + '_conv3_1', num_filter, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='relu', separable=True) conv7x7 = conv_act_layer(conv7x7_1, name + '_conv3_2', num_filter, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='', separable=True) return (conv5x5, conv7x7) def ssh_detection_module(body, num_filter, filter_in, name): conv3x3 = conv_act_layer(body, name + '_conv1', num_filter, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='', separable=True, filter_in=filter_in) conv5x5, conv7x7 = ssh_context_module(body, num_filter // 2, filter_in, name + '_context') ret = mx.sym.concat(*[conv3x3, conv5x5, conv7x7], dim=1, name=name + '_concat') ret = mx.symbol.Activation(data=ret, act_type='relu', name=name + '_concat_relu') return ret def upsampling(data, num_filter, name): #ret = mx.symbol.Deconvolution(data=data, num_filter=num_filter, kernel=(4,4), stride=(2, 2), pad=(1,1), # num_group = num_filter, no_bias = True, attr={'__lr_mult__': '0.0', '__wd_mult__': '0.0'}, # name=name) #ret = mx.symbol.Deconvolution(data=data, num_filter=num_filter, kernel=(2,2), stride=(2, 2), pad=(0,0), # num_group = num_filter, no_bias = True, attr={'__lr_mult__': '0.0', '__wd_mult__': '0.0'}, # name=name) ret = mx.symbol.UpSampling(data, scale=2, sample_type='nearest', workspace=512, name=name, num_args=1) return ret def get_mnet_conv(data, sym): mm = config.MULTIPLIER all_layers = sym.get_internals() #print(all_layers) ##c1 = all_layers['mobilenetv20_features_linearbottleneck6_relu60_relu6_output'] #96 #c1 = all_layers['mobilenetv20_features_linearbottleneck5_elemwise_add0_output'] # 16 ##c2 = all_layers['mobilenetv20_features_linearbottleneck13_relu60_relu6_output'] #c2 = all_layers['mobilenetv20_features_linearbottleneck12_elemwise_add0_output'] # 48 ##c3 = all_layers['mobilenetv20_features_linearbottleneck16_batchnorm2_fwd_output'] # 160 #c3 = all_layers['mobilenetv20_features_linearbottleneck13_batchnorm2_fwd_output'] # 80 #c1_filter = int(32*mm) #c2_filter = int(96*mm) #c3_filter = int(160*mm) #c1 = all_layers['mobilenet0_relu10_fwd_output'] #c2 = all_layers['mobilenet0_relu22_fwd_output'] #c3 = all_layers['mobilenet0_relu26_fwd_output'] #c1 = all_layers['conv_6_relu_output'] #c2 = all_layers['conv_12_relu_output'] #c3 = all_layers['conv_14_relu_output'] #c1_filter = int(256*mm) #c2_filter = int(512*mm) #c3_filter = int(1024*mm) isize = 640 _, out_shape, _ = all_layers.infer_shape(data=(1, 3, isize, isize)) last_entry = None c1 = None c2 = None c3 = None c1_name = None c2_name = None c3_name = None c1_filter = -1 c2_filter = -1 c3_filter = -1 #print(len(all_layers), len(out_shape)) #print(all_layers.__class__) outputs = all_layers.list_outputs() #print(outputs.__class__, len(outputs)) count = len(outputs) for i in range(count): name = outputs[i] shape = out_shape[i] if not name.endswith('_output'): continue if len(shape) != 4: continue #print(name, shape) if c1 is None and shape[2] == isize // 16: cname = last_entry[0] #print('c1', last_entry) c1 = all_layers[cname] c1_name = cname if c2 is None and shape[2] == isize // 32: cname = last_entry[0] #print('c2', last_entry) c2 = all_layers[cname] c2_name = cname if shape[2] == isize // 32: c3 = all_layers[name] #print('c3', name, shape) c3_name = name last_entry = (name, shape) print('cnames', c1_name, c2_name, c3_name) F1 = int(256 * mm) F2 = int(128 * mm) if config.SHARE_WEIGHT_BBOX or config.SHARE_WEIGHT_LANDMARK: F2 = F1 _bwm = 1.0 if config.NET_MODE == 0: c1_lateral = conv_act_layer(c1, 'ssh_m1_red_conv', F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) c2_lateral = conv_act_layer(c2, 'ssh_m2_red_conv', F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) #conv5_128_up = mx.symbol.Deconvolution(data=conv5_128, num_filter=F2, kernel=(4,4), stride=(2, 2), pad=(1,1), # num_group = F2, no_bias = True, attr={'__lr_mult__': '0.0', '__wd_mult__': '0.0'}, # name='ssh_m2_red_upsampling') #c2_up = mx.symbol.UpSampling(c2_lateral, scale=2, sample_type='nearest', workspace=512, name='ssh_m2_red_up', num_args=1) c2_up = upsampling(c2_lateral, F2, 'ssh_m2_red_upsampling') #conv4_128 = mx.symbol.Crop(*[conv4_128, conv5_128_up]) c2_up = mx.symbol.Crop(*[c2_up, c1_lateral]) c1 = c1_lateral + c2_up c1 = conv_act_layer(c1, 'ssh_m1_conv', F2, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) m1 = ssh_detection_module(c1, F2, F2, 'ssh_m1_det') m2 = ssh_detection_module(c2, F1, c2_filter, 'ssh_m2_det') m3 = ssh_detection_module(c3, F1, c3_filter, 'ssh_m3_det') elif config.NET_MODE == 1: c3_lateral = conv_act_layer(c3, 'ssh_c3_lateral', F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) #c3_up = mx.symbol.UpSampling(c3_lateral, scale=2, sample_type='nearest', workspace=512, name='ssh_c3_up', num_args=1) c3_up = upsampling(c3_lateral, F2, 'ssh_c3_upsampling') c2_lateral = conv_act_layer(c2, 'ssh_c2_lateral', F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) c3_up = mx.symbol.Crop(*[c3_up, c2_lateral]) c2 = c2_lateral + c3_up c2 = conv_act_layer(c2, 'ssh_c2_aggr', F2, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) c1_lateral = conv_act_layer(c1, 'ssh_m1_red_conv', F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) #c2_up = mx.symbol.UpSampling(c2, scale=2, sample_type='nearest', workspace=512, name='ssh_m2_red_up', num_args=1) c2_up = upsampling(c2, F2, 'ssh_c2_upsampling') #conv4_128 = mx.symbol.Crop(*[conv4_128, conv5_128_up]) c2_up = mx.symbol.Crop(*[c2_up, c1_lateral]) c1 = c1_lateral + c2_up c1 = conv_act_layer(c1, 'ssh_m1_conv', F2, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) m1 = ssh_detection_module(c1, F2, F2, 'ssh_m1_det') m2 = ssh_detection_module(c2, F1, c2_filter, 'ssh_m2_det') m3 = ssh_detection_module(c3, F1, c3_filter, 'ssh_m3_det') elif config.NET_MODE == 2: c3 = conv_act_layer(c3, 'ssh_c3_lateral', F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) #c3_up = mx.symbol.UpSampling(c3, scale=2, sample_type='nearest', workspace=512, name='ssh_c3_up', num_args=1) c3_up = upsampling(c3, F2, 'ssh_c3_upsampling') c2_lateral = conv_act_layer(c2, 'ssh_c2_lateral', F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) c3_up = mx.symbol.Crop(*[c3_up, c2_lateral]) c2 = c2_lateral + c3_up c2 = conv_act_layer(c2, 'ssh_c2_aggr', F2, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) c1_lateral = conv_act_layer(c1, 'ssh_m1_red_conv', F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) #c2_up = mx.symbol.UpSampling(c2, scale=2, sample_type='nearest', workspace=512, name='ssh_m2_red_up', num_args=1) c2_up = upsampling(c2, F2, 'ssh_c2_upsampling') #conv4_128 = mx.symbol.Crop(*[conv4_128, conv5_128_up]) c2_up = mx.symbol.Crop(*[c2_up, c1_lateral]) c1 = c1_lateral + c2_up c1 = conv_act_layer(c1, 'ssh_c1_aggr', F2, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) m1 = ssh_detection_module(c1, F2, F2, 'ssh_m1_det') m2 = ssh_detection_module(c2, F1, c2_filter, 'ssh_m2_det') m3 = ssh_detection_module(c3, F1, c3_filter, 'ssh_m3_det') elif config.NET_MODE == 3: #c3 = conv_act_layer(c3, 'ssh_c3_lateral', # F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) c3 = ssh_detection_module(c3, F2 // 2, c3_filter, 'ssh_c3_lateral') #c3_up = mx.symbol.UpSampling(c3, scale=2, sample_type='nearest', workspace=512, name='ssh_c3_up', num_args=1) c3_up = upsampling(c3, F2, 'ssh_c3_upsampling') #c2_lateral = conv_act_layer(c2, 'ssh_c2_lateral', # F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) c2_lateral = ssh_detection_module(c2, F2 // 2, c2_filter, 'ssh_c2_lateral') c3_up = mx.symbol.Crop(*[c3_up, c2_lateral]) c2 = c2_lateral + c3_up c2 = conv_act_layer(c2, 'ssh_c2_aggr', F2, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) #c1_lateral = conv_act_layer(c1, 'ssh_m1_red_conv', # F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) c1_lateral = ssh_detection_module(c1, F2 // 2, c1_filter, 'ssh_c1_lateral') #c2_up = mx.symbol.UpSampling(c2, scale=2, sample_type='nearest', workspace=512, name='ssh_m2_red_up', num_args=1) c2_up = upsampling(c2, F2, 'ssh_c2_upsampling') #conv4_128 = mx.symbol.Crop(*[conv4_128, conv5_128_up]) c2_up = mx.symbol.Crop(*[c2_up, c1_lateral]) c1 = c1_lateral + c2_up c1 = conv_act_layer(c1, 'ssh_c1_aggr', F2, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) m1 = ssh_detection_module(c1, F2, F2, 'ssh_m1_det') m2 = ssh_detection_module(c2, F1, c2_filter, 'ssh_m2_det') m3 = ssh_detection_module(c3, F1, c3_filter, 'ssh_m3_det') elif config.NET_MODE == 4: c3 = conv_act_layer(c3, 'ssh_c3_lateral', F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) #c3_up = mx.symbol.UpSampling(c3, scale=2, sample_type='nearest', workspace=512, name='ssh_c3_up', num_args=1) c3_up = upsampling(c3, F2, 'ssh_c3_upsampling') c2_lateral = conv_act_layer(c2, 'ssh_c2_lateral', F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) c3_up = mx.symbol.Crop(*[c3_up, c2_lateral]) c2 = c2_lateral + c3_up c2 = conv_act_layer(c2, 'ssh_c2_aggr', F2, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) c1_lateral = conv_act_layer(c1, 'ssh_m1_red_conv', F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) #c2_up = mx.symbol.UpSampling(c2, scale=2, sample_type='nearest', workspace=512, name='ssh_m2_red_up', num_args=1) c2_up = upsampling(c2, F2, 'ssh_c2_upsampling') #conv4_128 = mx.symbol.Crop(*[conv4_128, conv5_128_up]) c2_up = mx.symbol.Crop(*[c2_up, c1_lateral]) c1 = c1_lateral + c2_up c1 = conv_act_layer(c1, 'ssh_c1_aggr', F2, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) m1 = ssh_detection_module(c1, F2 // 2, F2, 'ssh_m1_det') m2 = ssh_detection_module(c2, F1 // 2, c2_filter, 'ssh_m2_det') m3 = ssh_detection_module(c3, F1 // 2, c3_filter, 'ssh_m3_det') elif config.NET_MODE == 5: c3 = conv_act_layer_dw(c3, 'ssh_c3_lateral_m', F2, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) c3 = conv_act_layer(c3, 'ssh_c3_lateral', F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) #c3_up = mx.symbol.UpSampling(c3, scale=2, sample_type='nearest', workspace=512, name='ssh_c3_up', num_args=1) c3_up = upsampling(c3, F2, 'ssh_c3_upsampling') c2 = conv_act_layer_dw(c2, 'ssh_c2_lateral_m', F2, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) c2_lateral = conv_act_layer(c2, 'ssh_c2_lateral', F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) c3_up = mx.symbol.Crop(*[c3_up, c2_lateral]) c2 = c2_lateral + c3_up c2 = conv_act_layer(c2, 'ssh_c2_aggr', F2, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) c1 = conv_act_layer_dw(c1, 'ssh_c1_lateral_m', F2, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) c1_lateral = conv_act_layer(c1, 'ssh_m1_red_conv', F2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) #c2_up = mx.symbol.UpSampling(c2, scale=2, sample_type='nearest', workspace=512, name='ssh_m2_red_up', num_args=1) c2_up = upsampling(c2, F2, 'ssh_c2_upsampling') #conv4_128 = mx.symbol.Crop(*[conv4_128, conv5_128_up]) c2_up = mx.symbol.Crop(*[c2_up, c1_lateral]) c1 = c1_lateral + c2_up c1 = conv_act_layer(c1, 'ssh_c1_aggr', F2, kernel=(3, 3), pad=(1, 1), stride=(1, 1), act_type='relu', bias_wd_mult=_bwm) m1 = ssh_detection_module(c1, F2, F2, 'ssh_m1_det') m2 = ssh_detection_module(c2, F1, c2_filter, 'ssh_m2_det') m3 = ssh_detection_module(c3, F1, c3_filter, 'ssh_m3_det') return {8: m1, 16: m2, 32: m3} def get_out(conv_fpn_feat, prefix, stride, landmark=False, lr_mult=1.0, shared_vars=None): A = config.NUM_ANCHORS bbox_pred_len = 4 landmark_pred_len = 10 if config.USE_BLUR: bbox_pred_len = 5 if config.USE_OCCLUSION: landmark_pred_len = 15 ret_group = [] num_anchors = config.RPN_ANCHOR_CFG[str(stride)]['NUM_ANCHORS'] label = mx.symbol.Variable(name='%s_label_stride%d' % (prefix, stride)) bbox_target = mx.symbol.Variable(name='%s_bbox_target_stride%d' % (prefix, stride)) bbox_weight = mx.symbol.Variable(name='%s_bbox_weight_stride%d' % (prefix, stride)) if landmark: landmark_target = mx.symbol.Variable( name='%s_landmark_target_stride%d' % (prefix, stride)) landmark_weight = mx.symbol.Variable( name='%s_landmark_weight_stride%d' % (prefix, stride)) rpn_relu = conv_fpn_feat[stride] maxout_stat = 0 if config.USE_MAXOUT >= 1 and stride == config.RPN_FEAT_STRIDE[-1]: maxout_stat = 1 if config.USE_MAXOUT >= 2 and stride != config.RPN_FEAT_STRIDE[-1]: maxout_stat = 2 if maxout_stat == 0: rpn_cls_score = conv_only(rpn_relu, '%s_rpn_cls_score_stride%d' % (prefix, stride), 2 * num_anchors, kernel=(1, 1), pad=(0, 0), stride=(1, 1), shared_weight=shared_vars[0][0], shared_bias=shared_vars[0][1]) elif maxout_stat == 1: cls_list = [] for a in range(num_anchors): rpn_cls_score_bg = conv_only( rpn_relu, '%s_rpn_cls_score_stride%d_anchor%d_bg' % (prefix, stride, a), 3, kernel=(1, 1), pad=(0, 0), stride=(1, 1)) rpn_cls_score_bg = mx.sym.max(rpn_cls_score_bg, axis=1, keepdims=True) cls_list.append(rpn_cls_score_bg) rpn_cls_score_fg = conv_only( rpn_relu, '%s_rpn_cls_score_stride%d_anchor%d_fg' % (prefix, stride, a), 1, kernel=(1, 1), pad=(0, 0), stride=(1, 1)) cls_list.append(rpn_cls_score_fg) rpn_cls_score = mx.sym.concat(*cls_list, dim=1, name='%s_rpn_cls_score_stride%d' % (prefix, stride)) else: cls_list = [] for a in range(num_anchors): rpn_cls_score_bg = conv_only( rpn_relu, '%s_rpn_cls_score_stride%d_anchor%d_bg' % (prefix, stride, a), 1, kernel=(1, 1), pad=(0, 0), stride=(1, 1)) cls_list.append(rpn_cls_score_bg) rpn_cls_score_fg = conv_only( rpn_relu, '%s_rpn_cls_score_stride%d_anchor%d_fg' % (prefix, stride, a), 3, kernel=(1, 1), pad=(0, 0), stride=(1, 1)) rpn_cls_score_fg = mx.sym.max(rpn_cls_score_fg, axis=1, keepdims=True) cls_list.append(rpn_cls_score_fg) rpn_cls_score = mx.sym.concat(*cls_list, dim=1, name='%s_rpn_cls_score_stride%d' % (prefix, stride)) rpn_bbox_pred = conv_only(rpn_relu, '%s_rpn_bbox_pred_stride%d' % (prefix, stride), bbox_pred_len * num_anchors, kernel=(1, 1), pad=(0, 0), stride=(1, 1), shared_weight=shared_vars[1][0], shared_bias=shared_vars[1][1]) # prepare rpn data if not config.FBN: rpn_cls_score_reshape = mx.symbol.Reshape( data=rpn_cls_score, shape=(0, 2, -1), name="%s_rpn_cls_score_reshape_stride%s" % (prefix, stride)) else: rpn_cls_score_reshape = mx.symbol.Reshape( data=rpn_cls_score, shape=(0, 2, -1), name="%s_rpn_cls_score_reshape_stride%s_pre" % (prefix, stride)) rpn_cls_score_reshape = mx.symbol.BatchNorm( rpn_cls_score_reshape, fix_gamma=True, eps=2e-5, name="%s_rpn_cls_score_reshape_stride%s" % (prefix, stride)) rpn_bbox_pred_reshape = mx.symbol.Reshape( data=rpn_bbox_pred, shape=(0, 0, -1), name="%s_rpn_bbox_pred_reshape_stride%s" % (prefix, stride)) if landmark: rpn_landmark_pred = conv_only(rpn_relu, '%s_rpn_landmark_pred_stride%d' % (prefix, stride), landmark_pred_len * num_anchors, kernel=(1, 1), pad=(0, 0), stride=(1, 1), shared_weight=shared_vars[2][0], shared_bias=shared_vars[2][1]) rpn_landmark_pred_reshape = mx.symbol.Reshape( data=rpn_landmark_pred, shape=(0, 0, -1), name="%s_rpn_landmark_pred_reshape_stride%s" % (prefix, stride)) if config.TRAIN.RPN_ENABLE_OHEM >= 2: label, anchor_weight = mx.sym.Custom(op_type='rpn_fpn_ohem3', stride=int(stride), network=config.network, dataset=config.dataset, prefix=prefix, cls_score=rpn_cls_score_reshape, labels=label) _bbox_weight = mx.sym.tile(anchor_weight, (1, 1, bbox_pred_len)) _bbox_weight = _bbox_weight.reshape( (0, -1, A * bbox_pred_len)).transpose((0, 2, 1)) bbox_weight = mx.sym.elemwise_mul(bbox_weight, _bbox_weight, name='%s_bbox_weight_mul_stride%s' % (prefix, stride)) if landmark: _landmark_weight = mx.sym.tile(anchor_weight, (1, 1, landmark_pred_len)) _landmark_weight = _landmark_weight.reshape( (0, -1, A * landmark_pred_len)).transpose((0, 2, 1)) landmark_weight = mx.sym.elemwise_mul( landmark_weight, _landmark_weight, name='%s_landmark_weight_mul_stride%s' % (prefix, stride)) #if not config.FACE_LANDMARK: # label, bbox_weight = mx.sym.Custom(op_type='rpn_fpn_ohem', stride=int(stride), cls_score=rpn_cls_score_reshape, bbox_weight = bbox_weight , labels = label) #else: # label, bbox_weight, landmark_weight = mx.sym.Custom(op_type='rpn_fpn_ohem2', stride=int(stride), cls_score=rpn_cls_score_reshape, bbox_weight = bbox_weight, landmark_weight=landmark_weight, labels = label) #cls loss rpn_cls_prob = mx.symbol.SoftmaxOutput(data=rpn_cls_score_reshape, label=label, multi_output=True, normalization='valid', use_ignore=True, ignore_label=-1, grad_scale=lr_mult, name='%s_rpn_cls_prob_stride%d' % (prefix, stride)) ret_group.append(rpn_cls_prob) ret_group.append(mx.sym.BlockGrad(label)) #bbox loss bbox_diff = rpn_bbox_pred_reshape - bbox_target bbox_diff = bbox_diff * bbox_weight rpn_bbox_loss_ = mx.symbol.smooth_l1(name='%s_rpn_bbox_loss_stride%d_' % (prefix, stride), scalar=3.0, data=bbox_diff) rpn_bbox_loss = mx.sym.MakeLoss( name='%s_rpn_bbox_loss_stride%d' % (prefix, stride), data=rpn_bbox_loss_, grad_scale=1.0 * lr_mult / (config.TRAIN.RPN_BATCH_SIZE)) ret_group.append(rpn_bbox_loss) ret_group.append(mx.sym.BlockGrad(bbox_weight)) #landmark loss if landmark: landmark_diff = rpn_landmark_pred_reshape - landmark_target landmark_diff = landmark_diff * landmark_weight rpn_landmark_loss_ = mx.symbol.smooth_l1( name='%s_rpn_landmark_loss_stride%d_' % (prefix, stride), scalar=3.0, data=landmark_diff) rpn_landmark_loss = mx.sym.MakeLoss( name='%s_rpn_landmark_loss_stride%d' % (prefix, stride), data=rpn_landmark_loss_, grad_scale=0.5 * lr_mult / (config.TRAIN.RPN_BATCH_SIZE)) ret_group.append(rpn_landmark_loss) ret_group.append(mx.sym.BlockGrad(landmark_weight)) return ret_group def get_mnet_train(sym): return get_sym_train(sym) #data = mx.symbol.Variable(name="data") ## shared convolutional layers #conv_fpn_feat = get_mnet_conv(data, sym) #ret_group = [] #shared_vars = [] #if config.SHARE_WEIGHT_BBOX: # assert config.USE_MAXOUT==0 # _name = 'face_rpn_cls_score_share' # shared_weight = mx.symbol.Variable(name="{}_weight".format(_name), # init=mx.init.Normal(0.01), attr={'__lr_mult__': '1.0'}) # shared_bias = mx.symbol.Variable(name="{}_bias".format(_name), # init=mx.init.Constant(0.0), attr={'__lr_mult__': '2.0', '__wd_mult__': str(0.0)}) # shared_vars.append( [shared_weight, shared_bias] ) # _name = 'face_rpn_bbox_pred_share' # shared_weight = mx.symbol.Variable(name="{}_weight".format(_name), # init=mx.init.Normal(0.01), attr={'__lr_mult__': '1.0'}) # shared_bias = mx.symbol.Variable(name="{}_bias".format(_name), # init=mx.init.Constant(0.0), attr={'__lr_mult__': '2.0', '__wd_mult__': str(0.0)}) # shared_vars.append( [shared_weight, shared_bias] ) #else: # shared_vars.append( [None, None] ) # shared_vars.append( [None, None] ) #if config.SHARE_WEIGHT_LANDMARK: # _name = 'face_rpn_landmark_pred_share' # shared_weight = mx.symbol.Variable(name="{}_weight".format(_name), # init=mx.init.Normal(0.01), attr={'__lr_mult__': '1.0'}) # shared_bias = mx.symbol.Variable(name="{}_bias".format(_name), # init=mx.init.Constant(0.0), attr={'__lr_mult__': '2.0', '__wd_mult__': str(0.0)}) # shared_vars.append( [shared_weight, shared_bias] ) #else: # shared_vars.append( [None, None] ) #for stride in config.RPN_FEAT_STRIDE: # ret = get_out(conv_fpn_feat, 'face', stride, config.FACE_LANDMARK, lr_mult=1.0, shared_vars = shared_vars) # ret_group += ret # if config.HEAD_BOX: # ret = get_out(conv_fpn_feat, 'head', stride, False, lr_mult=0.5) # ret_group += ret #return mx.sym.Group(ret_group)

insightface/detection/retinaface/rcnn/symbol/symbol_mnet.py/0